RESNET_18_accuracy_91_07.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "RESNET_18_100_SGD_decay 91.07.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJZZ9hNj7Ys1",
        "colab_type": "code",
        "outputId": "88e6967c-7aac-4165-d18f-b406a11345d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# ---------------------------------------------------------------------------- #\n",
        "# An implementation of https://arxiv.org/pdf/1512.03385.pdf                    #\n",
        "# See section 4.2 for the model architecture on CIFAR-10                       #\n",
        "# Some part of the code was referenced from below                              #\n",
        "# https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py   #\n",
        "# ---------------------------------------------------------------------------- #\n",
        "!pip install ipython-autotime\n",
        "%load_ext autotime\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "\n",
        "# Device configuration\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: ipython-autotime in /usr/local/lib/python3.6/dist-packages (0.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ixieggsE7eQR",
        "colab_type": "code",
        "outputId": "4991185c-7210-4e27-8159-c076080a9ebb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Hyper-parameters\n",
        "num_epochs = 100\n",
        "learning_rate = 0.01"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 2.21 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RJoy91C7eTd",
        "colab_type": "code",
        "outputId": "e94dabf4-d2a7-4d3b-9ea4-b1b896f51e84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Image preprocessing modules\n",
        "transform_train = transforms.Compose([\n",
        "    transforms.RandomCrop(32, padding=4),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
        "])\n",
        "\n",
        "transform_test = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 7.48 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjCnhcUluLFw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AKqgCvcN3G86",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#transform_train = transforms.Compose([\n",
        "#    transforms.RandomCrop(32, padding=4),\n",
        "#    transforms.RandomHorizontalFlip(),\n",
        "#    transforms.ToTensor(),\n",
        "#    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
        "#])\n",
        "\n",
        "#transform_test = transforms.Compose([\n",
        "#    transforms.ToTensor(),\n",
        "#    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
        "#])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vFQ-c_D7eWp",
        "colab_type": "code",
        "outputId": "6bce0cf3-4fbb-4d73-e271-4141115066e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# CIFAR-10 dataset\n",
        "train_dataset = torchvision.datasets.CIFAR10(root='../../data/',\n",
        "                                             train=True, \n",
        "                                             transform=transform_train,\n",
        "                                             download=True)\n",
        "\n",
        "test_dataset = torchvision.datasets.CIFAR10(root='../../data/',\n",
        "                                            train=False, \n",
        "                                            transform=transform_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "time: 1.39 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78eYeI8H7eZi",
        "colab_type": "code",
        "outputId": "7b4791cc-a687-4717-bfac-271a6bb581cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Data loader\n",
        "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2)\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=100, shuffle=False, num_workers=2)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 3.55 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hW9WNjPii86",
        "colab_type": "code",
        "outputId": "cd8a90d8-37f8-4e11-8782-90b6fc166b81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "# functions to show an image\n",
        "\n",
        "\n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# get some random training images\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# show images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "# print labels\n",
        "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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f7ae/NQ+aGWE40HEg0DEwU9QoOSCPoJx8/FxEMEDJYDE7eeu9DTy/6lkufmkS\nX/zb66mua8AlF5AsZkrGNWLxlYMtwIdZTucVqSwmqPXDsSTEFRgUkLDZiFq9hMJJSoaGiXd1ESgt\nxZyPQRoMXUNTwxjJXoSvCgic9kSD9sMRzM4QTlceLZ/DOG4DlZbW8trqTew/cpC3175C1dhZdB/e\nhmy2sOzSFbQc2E19QxNOpwMlnyPT0krv64/xeMcBRq1WRlNF56kErF63huvuuBmT+ezhrK0xUxGs\nJBQuYziq4fOXc++9X+PF1zbgcDYTHm7j4J7N1E+eRKDCx7o/PFusqMsoOYGQnaTSaWLxDJqSo7Kk\ngsKoj52rDzDxykYOvnoUs0tBywkMkQUyyMiox+WHLGDBLSpweS3nGHkBQsejH6PaluLGS1dg1rL8\n6tGn+NTSWbj8PoZHRrnm01dTXuYG++lHRc7GeUUqTYeRVNEElARgghWfvpfOUI7vPPpL7E++wEA2\nzIH+PqrKm7AINwVN5bdvrSbYXoUoC548uHkCe3cMMpLdznO/iaAbebxyBQAzpjSzYsUK2rsX8+aq\nV/n2dx/kRw99i0uvuJL/86Uv8Lk772L+4iXIsoRCMahMKsWFspOXU6FTDUgmLrv6KmTTae2eZsyX\nlsDUqaBhYigkMzBisPyKTxPJ1bN941Y2rV1NsPkKrrr7Mlq3PHOyXm40RbwjQkFPY7LmCAW82Bx2\n3utrJ9MrsJsttO7fS0JR0dIaLslb9CkR4WzLqYBhG0azjvDjH//4ZGlb23FHlcUGJbU4A4Iv/30V\nuzYcpNTnQnZOo3n6BHB5i07RXB6iGUiL/2/kqMtmExarlZbRPE1OHY9iENYE7sZmZl0whu8/9zSj\nezpQTQaqGar9EpJNQtV1fvzSsx/dwPFjajfeOAtZlo+f/dOpKCvhxls+w6zJjfhLSqmuLMMiQSKZ\nZNLERkyShK7rtOsFnh3pYAoWrLKMdJw4JpNEtc/Lhl8/SePk8WRHR+necQAtm0cIwcEejdV70tQF\nc1wwXWLRNA+jMYNyn4PwcJIJF1zHtGWLCXph1GkgyzJCCOROGe2QSlgOg2EwIoq7UF0zirFOWcYw\n9JP81X3AYs44QX06UvkhfvSTB88qX7hwIamslb19Y3F4HVhMadw1VqqrTLhdGXRVg6FhKORA18BQ\n0ROx4utz4LzIUqisrDSuu/EmSnxelLyCy1ocGEWHMdVVaEDfwAADAyPY7HYy6SQOhwOTLBOLxbAI\nsIjiUiQLiGtgEuA+Xq5S/ItoUFFRQTgcprq6Go/bhU03MCRBicdFe2cv/vJieVtHFx6HjM/jQjc5\n6Onrob+vF/vxZ50I9gghqP9BDBgAACAASURBVAoEyMaTOO02stksZiEzrORwud00jp9CQbeTTGTw\nOk1UlTvQdZXh4RShcJpAZTUgcNhBycYZ6u9EkiRGRkZQ1XPs1j4AZrOZQCDA0NDQWZ8JwGE5Hmc0\niq4m1QDNgBKfD5PZgs/rw2UtbkgEOpJhUMgrKJpOqV2loGjECzKSDCV2hW89/Az9/QPnb5aCqmn0\njeZZsvwCmieMp7mpilQ6TyKZIljqQyB49e2NPPWNb/PQt7/GA1+6j5KSEmpdLsyHd6NbbHRkssxB\nJycMXs3CVBkeqIK5fsGqbliXM3g+B9dddx27jx7Au61AwqExu22IsVYLd/zb3zBcGmSoYKUy2MjF\nC5dxeOXzSB07GfJdSLA0yLz58z62TG1tbbz44ovccccdbNm4hXe3vMF/fP2rKKkYa156lsrqKiY2\n1zNhcj1mTxkVtTXIskxLSwsPP/wwa9as+ZPSc3w+H4sWLeLll18+6zMTsDAIvWHo0qHMDEkV8gYs\nmz+foWSSOvkwX/8bicB4NybvHI6+vYX2RC3f/0MnV0wqMJqAtQeLJ67/806BMFzn7Mt5QSoMePu9\njcxesIBLLpzP4GiMw22D+D1OQok87+7ax0PffpDx1aVcuGA2hUIBRVHIOErpqXuA8trxHBnYiZ5N\n0NuziYBymO0FncdjsDYqkc1LbEqp5CmmLUdzo2QDHsbPb2bELvE5yUAWFnLOeqx5je639nHk5U30\nDu7msrFWNE3H7S9l/uXXYhMCTVFIa+B3mhGI4nk7qSiIouo4LBI7t2/nueeeY6irn9efepqDx1r4\n7be/SUdLC16zlXCpn126itPjQfeUsvSGW7j6ppswmUzous7999/PjTfeeHKIVFVFVw0kWT6+mzeQ\nTrPjUqkUjzzyyMlcNChqbq8FCgpoyKQ1Db0AdgliajE7QlZVFEWhIpCnzG8hYMrimFTD0bfMZDUr\nAX+AZ7f1gaFxTa2Mv6IEry32Ia7P84RUQpKQTBI/+O6DrHr3LfKpAh5ZYuK4RobSCYYKBp76cUxo\nHo/5tIGsvfYedg5fRvv6brR9/8GovBTnsqfw93yT/KFXeHYUQMeMTvC09oyUxlB/P/G+JOM8fnqD\nJmbXr6Bat1Joe4XhMV4e/PcfcfuchfRGcrze8wa17itYvzaFPZNj59OPcvn1l9O0cCGpvIFsN6iU\ndWwuGE6rjK+z0k3Rlt36+ko8JgtTKutxO+yU14xBiaUJVtcxHI+SKmjEOjp44gffJzKcYMr86QCU\nl5czadIkDMMgE4pycO9ewuEkvopSrJpKRamPYFU9JrcTSZaIRCJn2VM6xeV/ol8ilZaJKxoCMGuQ\nVYvh08SJ7woIBq0MD+ThzWfJZbL4Gu7jjy/fwg3XXMG+Iz283KEyRWhMr7sVpLXnnM/zglQ2q4Xf\nPfEYL770GluPHWEoPMKBA7vYua2UuRfOomXbLkprp7PvwGZ+/ZiTVCqN3++nRPWw1CPRVxWgZ+oD\npMw+SheWYbr6Bzh/U4ra+Rb5SB8qGsnT2pv6qWmMv3YyPt1JlaecGW0OhNWFbWAtanI9ci5IsLSG\nuU0O2nra2LCrk5tmXUQ4HCU70Mahdb/kqunjSE6ejMfhItM7QMvOdZglM/J1N3A4Loged44raop3\nDu0hXVDo6uvA4XJjswrSrfs4MhDBwExCKVDusLHm2d9R0G452U/DMNi6dg1DbQcZHooy84JF5IY7\nSKTCiGQ5LrcXiyFj89nPObbxPHTqBh6rRqkEPRqkCsWJd1JMnAbISQKttJxkazdHd8dJaLX81T/e\nR6JzNzYjh6AYGkzg4IoV3+Bb39twzjbPC1IJIbh40VwuWTSHvtAo//Ljn7I6kcLhDrJ721Eyg4P0\njESJewXbK12oanHnMWvoEM35LHOvnsraXYtYb7WzIxIkFy+DRT+n4roIsXceJb7uuyRP22iPegTm\nagj8sQWPuZTgtClkOzaQ79pA58EOTHUmbr397xk+/DRy5hiVNhO6qpKNp7CYLUjCTLSzh6mFBJJL\np9RtotZkp7GhiZWjEsOKQWIUMCCdV/A67OTyBQZzORo8Pm7+zGdoa9mPxx+lry8MuQLTJjRBMs3B\n3XvRLMXFJR+LkB9pZ+Obz1DIWVHzOcocZsbNn4vVakcTeWLt+wmMnXLOsZUEXH/DbGZOb+KV59fT\nt3cEcTyBIc+pDUcuY7DvjQ5CUZ20YePKO7+J3VVC2u7jX7/2ZR5/+ikOtfZT6pZxOc59kgbOE1IB\nmKXifQpui5UD7+0mF8th6Cluv/0OWltaONY7TJXHxM8e+TkzN84C4PWd6+nr2EH+vecotVs4eHg7\n19z/U1ZvaSR0qIdo4Bjp/l6KAdZTuso0rCKP5lgqVzPd3YC+8zFymoEpn6VhxhKCMyZi8yzkte3P\nsKR5IhlHkqgQaLrOaF8fSi7Kmwf3UN56BVcstRHb9AalSLjtVqwuGV0u3h4D0NoXwm02o5pkGiZO\nxq7nGRgZoKq+npd37ac9HKOiNIhhyAwPxIF+yptrABjtbqPr2FFMhouBwRaExUKmZiz7nnkKV6Ca\nysp6Jtda8QbLwO07YzxtFLXQ3OmVXHVxNfWT6rDZ5jOS3cLBo6MoenH5O2EbJQowlNLpHIXn1unM\nuqGMSlkm2DSdy5umc8WtdxOPxsnkMjje19b7cd6QCmAomuCJ1zZQNmMmznHjsKazPPiV+/nvx19j\nfmkQqeMwJtMpr3BnaoT9A1uQzfOoMsxMm76YYztXsXjmnWzo6WZmMM1Iroz9o14gxQmPXX5DAquj\nk/4KB42jbYRyObA7qJ26AqfThSneS6DWy/IrP09Ndj16IMIrukJsoA1PIoHJWkJMUxgt5NESGosD\nDtwL5rA/rDAUz+HwOk/6j27+zO089otfEQpF+eKnrmPb1o289Mrr5AoFPJIJq8POhQsXYFNht97C\nwqZGCqKoVdO6if5QjHBKZyia5q1jm2jw7KWywovF3kq5F1rLJpKWy5m6fPkZY2k1Q0qFJUuaKfW7\n2L9nH2+/s49Jk8qorvJzZHMrkRyUmopXGcWzcGQIQgVwehSyLWvoS0Z4c9MmLr32Qqqn3YCvvAEf\nBvqH+KjgPCLVobYufvT4qxw42sbll17E8oUz+M9vf59ELMHUcVX84oln+Nd/vP+MrEvDmEkqU8Lb\nLQeRxCC1Uz/NSM866gY0sskMG9/7A6qSo3jvmg4MABDNRFn/u1aW3LyEpDfM3u4o0UKMCfEAV8xr\nwiy58ZeWoFcEsfZasEtOLCZBqRbn+vFeqm6+D3tVOUg5rFUBdPssMrLEkZEoWVMQSUAsVmTVvEXz\nmLtoEfd87n7+61e/wKRrfP0L9yHZzTz38qsow6NEE3GOHDiMp7KSqVOnsfvYbqCY0dt2qBW3Oc+0\nCVU0NRq8ta2DAnmWNua4eMZsWqISGzfuYNyChSfHxe0CWQehQ93YKgaGh5h1wUJkq+DAgR5WvdWN\nkgcvxZ0gQDID4SRggsmTwBFN8l9PPEj5HB+DPRGqp68AMkAYJXGEU/ncZ+O8IFVBUXngwZ9QUzWG\nH/3T3zJvxkRMEjzp92BgcO1lF1BXW83EhmrQT4+0m4DJQDO6MUz3ge1gDHN0789BSBhanGIKSCkQ\n5ASpXGaVcbXNrN6vsuPIRnJ6AEUF87oNLF63gXsWVLBk9nWU1ZZiSwdJeZsoHI2TPxYmWOInlQxT\n6ZvIvImVGMOd7BtK0Riwc2QgQTuD1AsL2ax6MpIxZ/Z0Hvvtz7nrzrvx23Suuv021q1bz+GeHgq6\nwUgqwfiFi7nj9juoralgT1vxKFhVXSO33H4bw0dbkJ12Zk30cde17QRqS6koK+NABzg6Uoxrno3J\ndGoqM5mi89cwBEK20davYumSmbtgLulUGK9PpiNe/JlFjg+n0woVpdAbgY5Rg2+9+0cKHoVMvorL\nym8BoZKPPU3LgT4S8kROmfhn4yNJJYT4DXA1MGIYxpTjZX7gWaAe6AJuMQwjKoph+oeBqyjS+k7D\nMHZ/VBtmk8zPHvwqVRXlyLJACEEimWTv7i28smo8/+fv72H+tHGAIB6PnVbzEEV7qRkYB8ZYQMLQ\nM0CIYqqHTJFYXSdrLZgwlnCnjdf3DRWHQDKAAophsKFVMBI9zOp/KeY8iTI/o7sPk49JFNq66K6c\nxlAyh+ngQa6Y1UDJrveIrjtEZPEy+jOlJPQkx451oIcinFhuhRDMnD2FJ575PatXvoDV72Pjzj3k\nNMGsmbOZNnsGX7jv76irLudIS8vJflqtNi5edimFG25DCQ3gLg9QGGrFXl0BjgC1vgHsgTYmLlrC\n6VeoaSftJYOOjn72HgzhKOul3FdPWfkkPK5eDDIoQOY4880CLCpUuMEiQzqrMJCBSCyJw1VN6PCr\n/ObnT2Cz+7nn6/cD3zjnfH4cTfU74KfA46eVPQCsMwzjoeOX7z8AfAW4sji7jKN4I/HP+IibiaE4\n6KV+L6pa4ERkwmwy8cBX/oWdO/aROy3RPp/Pn5Z9OQrso0iuxUAD4KBIohKKpMpRDKycssUO9Q/T\nMagCYWAsEmZ0YxS0fgwa6c1a6YumCERC6LEhav0FTL0yQ6GjvL2lwOBIBwWLk7w7RuMCL64JNg7E\nw2zrjpLylmFz+XHlzjzBK4Rg8uTxKOllbF61lgXN0/m7L30Ji8WCv9RPeVnpWQ7FbCpGoqBhjg+T\ntnoxVEFHWz9TamdgNZfgsQ3hnD4bRyBA7rQjZ6fj8d+vI6tIXHvlbH756LM8t+YYyXQBwZnnsC0A\nGnhMIKwQMEGpF9ImM4HKBXhdPu6820Jg7GJkW9kHtnUCH0kqwzDeFULUv6/4WmDZ8de/B96hSKpr\ngceN4qxvFUL4hBCVhmGcfUr0NORyOWbPnn3Wub98vkA+X2Dly0+dLNM0jVAoRCBwIsUlQ5FUnRQJ\nJXPifoTjElBU9Kc8zV2hEeL5+PHPDqDrLad9PkI6ZeIzt63Ago6k53C47MyfdxnX1zew2J0ilU+j\n2hQmhHfx299m2TSaZrB0gH7PFMrKahgaHMSdGKL0fXJmMjl6RxL8z++f4uFHHqZ5avPJ67I/yEOd\nzysMDfUwbdly/LIFQzeYeMHl6GrRJ+Cqn4Bstpzzym0D6A0XAPjmt19g/ux6srkCJsM4qcPNx7/b\nPQrr9oDbDgUNErmixnK5c/zPo79ENnnJdqzGXrMTLGV/kVtfyk8jyhBQfvz1B92hXg2cRar3X3nt\n8XjOIpUsSzgctrMCq3V1dZSXl+NwOGhqOjMn/MNRhsvloq5uDH5/8iO/rSOj40QzOzHMOqPmDKrN\ny/zlF1Gw6Pxufyc7RpLETRYULUuplMafG6a6OoBqMmMPBgmFQrS1tWEYBm+/s51XV67mr++6C7Pd\nTHt7+1ltDg8P43A4yOfzhLMFXA0T6Og+63JniMTOeJtMJrHb7R86Hke68oyva0Si+FMUgCsQoNxu\npyubpSX0AZVGYGf7C6cVrEQI8aHB7o+VpXBcU608zaaKGYbhO+3zqGEYJUKIlcBDhmFsPF6+DviK\nYRg7P+CxJ1FdW2N897f/Q6vSSiSZoW3PVnTdgTFYADmN8AuC+VIi4Sy2aVPQe4dwRdJcfNll1NfV\nIX+MA54AkUiEjZs2cizSiSmjYAiNVFpBMpvw1QYRso6W1dFlL8HAWJLREKM9rTgsNuoryzl46OBH\nN3IaKisrARgc/FBFfRamTp+Kp6yAlLGR1ATTFowjayRIDISJqjGcko1Uhw2LvQQ1E8fQFSKJGGa7\njZaWFm6+5RYmjB8PQN7IoxoaDslejFO+D319fbyxbjWDfSPH3SCCEp+DRDKDphmYzTI1XgezGoP0\nJHLsODKAEIJDhw4xNDT0Z81SGD6xrAkhKoGR4+Uf6w7190OWJPqC+3jx6B8wmRwMWI6iGILksIoU\nLKAfA2/aS8GwwIvbyPXGmBwcx2Wfupr68RNorKn+WMRqa2vjtZWvsWX3JgJ4+Mztn6dvNIkvUM6c\nxXN4e8ubDIx2kFKgzq+jmFQObWtHS6o4LbNYv379nzRIs2fPBmDXrl1AMcBrAwLuMiQ9h6GkcKo6\nNQLkEgnK7AjJiYhXY64zqJbcROwlTFk8hcF8JyNHNLLWLuJvlTOSt2B1RJA1QTaUprO7k/KqAOvX\nr6f58mnceeHn0A2N3xx6igM9h/jOxd8gYPcX+yFO0WvTpk1En+vl7bc3AyCECSE0dN3AZLETrPIT\ndWocaYH23gShRAYDcZr5cTb+t6R6Ffhr4KHj/185rfyLQohnKBro8Y+ypwByao6fvPJD/GMEHVvS\n1Hhc1EkG+z0FkgGwtMDiLyylfNzFDD2zidX7n4MgHD3SimS2UnPdCmw224dGzk/CAIYNrr/nDv71\nn792xiGJefWTiCbi+ANuErF2Dvd00bFviKMb3kScQ6MLs7mYovwRDkEJ8AHVkoWlk5ZhSfXgTbaw\nYDTBJTfMRbr7NoxyH8nn1/Bst5muIAy0Jti5tZWMS2HKJSWMnVLH5uf3YiSzlDfB5Ck+4gM29mxM\nYDKZT7a1qWUH//nmE4xmO1l56EUivQmWjVmGXigjmU7y6QVLcFhM5NWivZWNn1rKDHSEkBgzYxZ/\n/aWvUjOuhse+9002r3oDWYa6Gg+Dox+e5/VxXApPUzTKS4UQfcC/USTTH4UQdwHdwIko6GqK7oQ2\nisv25z7q+QCGbpBMZBjTNZ3FtomM9O3mphmNlLgO8UZfN5UFmDN3P5v2FrjwguWs/t1zAIyEwuT3\nHqSpsYGZkyfhdNhP9PlD23NaXFxz5dUno/qGYdDS2sanP30HPb09XLhsOV/+0pe48aJbGFezkFv/\nqvUDs2fLa5q4/3sPUe910NfRxg//41tEIx98b5MVqBAy5YaB2rELl8jickGZSyJ+2e20bo3T/sKv\nGNnfSurWW4kUNPpMEdaseROlXjDu2nl0jkZYNH8uvllZUnEL697aQ019BVmLSuG0e6Tatx3lv3of\nIhLvxuQ3MPnhP178J2xGNb17RrF87UFUyxCbWt9jsXQxodNupSktH8NVd3+Oiz57BzaTBT0aobqm\nmc/cNwanyULPsf2UDKbp6e055/h+nN3fp8/x0cUf8F0DuP+jnvl+CEkiWN3IcvkGxldOQFt4IbLo\nY+bYfsKhbrIV0NXZTdbfz9pYB8HFPhiGyGiYWDJFPJnkpVWvM2PGNBrr6nBYzKQyWVwO+wcSbMGy\ni5gxY/LJ99lMmv+HuvcOs6ss9/4/q+y1e51eMjOZmcwkmfQCCWlAqKFXBUREREQ5iA3Fo8KxYDmW\n4+s5vioiAiJFUDqEUNJI7z2ZZHqf3ftee5XfH2vSJEHPe7h+V859XbmyZ2attZ/1rHs95b6/9/e7\nacMG9u7eiqFrvPriM6j5NI89+gjTW+u4/NbPMLLTIkdzuNwYYzIk8dgo2Tw0LFvEhZddQnlZGV/4\nzB0Ucn9PvG8FNjpMnSno1I4ewQakwrDSBM/nvkY+W0DEKtvImwaHDvRSMrmCqbe24RlvIEcdBIQg\nM1vmMXBkG2u276dEhKvOaeWHq1eQLRzfjaUPRFAHcnidPhgETdXp7R6haA+jJQvce98d2Lw6qpmh\nSz6+OnE4nXz83i/grKtn1VvLmdkYYs/yF3nrz2/gCXj40v1fY+vmDcSH+z70eZ4REXVRlJi1aB4T\nXOMZP1TOkbV7CZUriLHpXLbIT/58L5lslGRmJ+68gmtbMwPDeWbPnUHf4DAdRzoIlYT44Y9+wtVX\nX8VVyy4BSSKaTBH0ehAE4Zhz2e0O7r//G6SyRTL5JB63Qiw2RCQ6crzaWNfZtnUTw+EwpWWltE5s\nI92xh4e+9zALFy1m8679rF29mkN7dnF41bu8OtJF85yFqFIJbdPnsm3DGv6+MsAERJcXw+VkdzzK\nOkmiICm4tSLXFXUWuOzkswXsgCwL2OplkhmT+RdMZOnMebQEJ5COS+zas40Wn8IdF0+jf7SNN9/Y\nz4ZNB7E7bTSdgBprmTKTL375G8xqbaRQVNHkIv1ZFT2fIRtLMToc5a33XqTYZ+0ivX4fSy9dzNZV\nLyEJbs5ZOBdxKEFf+24yyTgeu8ijv/gZxXyMSpdJUnWc9nmeEU4lAJm9h4i0NDNdLKXMK+EoJGjb\n66RbqGPHzA425Y5QG5xKg0OmX4gCEi6Xi0mTJ7F+/Xouv+IKCrksL7/0Ms1NjUyZ2IoiSezYd5C2\nlmbsirXmEAQBU9V5f91aNF2nrr6SdGaEt997G06YQpLxGOs2bsYTqmHj6rWMr63mX+65m4Dfz+JF\n87nvc7eTTCRxu5w4HAoFzSCbybNw6m/50xNP8odHTuZvsgkiX/zCl7jx3IWMJpLc2zAeVdUoZBI4\nCjkak2He/eJ9xBMp8rpJXtfx2OIsbKnnrOp6Kh3jkUvqyPlqMJODtA8P09dfYHuPCx03mVjkpO87\nuG0bB3bt47wZc3BXKGh5lWYthZTPMlTQqJk7j3nj63j44R8h+Fx88wtLOe+CeaTNKdQ2TqY8GOAv\nLzzN6lUdlJYG+dFDd7H6neW0TZlFLjLIL5/9YDjkqJ0RTmUaOkfeOkRIn8Blc68ggo/O9mFiXYcY\nfq6d1ltrWdQwlXywAm+rAsXtgI7X6+PQkQ7y+Twzpk/j7LlzKORz3P/1b3DLTTfR1Die+ro6JEk6\npvFnmgadnfs4vH8n7kA1TRPG8fozb3PoYCeNTU10dXZiGAb14+t5+s9PEdFDbN6+m5olLfznI79h\nQtt0RJcdn9uFz+Nh1Zvv4HW4uedztyL73IQCrfzoB9+jpqaap57447HYm8fh4fKrbqB5diuTRJAl\nCcMwLM1AQUASBKYPD/Hq1/+VfFEnvSPFzMWT8Rg1lCjTEY0StEIShg/y+Avv8OzKnVw2rZnaqmZm\nzp9P5+6NJ/epWuTN3/2G7O691DU2oh0e4rKdaygJD+AqqIw4FYL19VwdCKBe2sTnvjgbxBpMZtI7\nmOCOz3+WN158hUIuz/zZTQTQmdxQxqymKt7rP1VA67idGU5lmngr3XRt3Upy7gApyWRHwokUmEXz\ntACNFwUY9DzHSCHI+30yG45000IbB/bvJ6/puFwuDuzfT3lFBYIoUl5WxtvvvkcimWTOjOkWmcfY\nbCQIAsFSGVUdQg0XUeT5+P0Bbr/9Nq655gr+8NgTrH9/HZ/9wp08/oenWLtuDUP7NyIsbuHxR//I\nwqXnc875ixgeLJCMJ/jzn/5IVfV4Pn3bx3E6FIYGh0imMzQ21qM4FAo5K1Jv6BrReJYNOzrBVHn1\nhb/Q39dPLpfFYVe46cabKHVXMKIouESRSb5Krp5zOaVFP/1b20nkj7DindfYsXsnL69YS115JXUX\nzeeF1WvxNk+lor7ypD4tsXu5whS57uB6nlu7gktyBmI6zrBQpEf28x95jbLdu5hxzjwWnTOXRx7r\n4sknfolWdHD1BTO4oNGJ74qFBP02Pn7dZTQ2TOOshW2sfPGvHDl4+kU6nCFOJUoyIW8TR/62lsfr\nfsXkyplo8U0EZnupvXUuiYosf3hIxzg3TNFrooxNZVs2baRvcJQbb7qZTVu20tzcTFV1FQ888A2e\nf/4FFp0zn/c3bWXq5IkEfF7AymjUj2umsspLV/sohVyCz999D3ZFwe/x8tUvf5Gua6/BNAU+8em7\neWPTe4hSge7eI0huO6tXrmIoPkwoGMSjODEFBYc3wMr1G+no6uKJR//Ivp3bMCVoa5t07B7Tao4V\n76xg3kWXki/mefSR/0M6eTwqXl1azsyqag7KNiYgEFJC7Hh2NSvWHWDv/g6iukI6PopZSAM6vUND\nPPnuBrq6u9BHI5SVlR8jehEQCOcTvGFobBvNsNkQeF7QkSXI2SAfiqGUCCTrbPgniqx46Ck2rN9O\nY6mLWy6dzBSfwZSlU7j9a3dgGjKG5gQ1yaYVb7Fjx24ml/p4iw9uRo7aGeFUpmlSmU7xtetu52eZ\nNcSNQSSPyiF5gGXjbualg4/SeMNsGlrL8cdsrAv2kyWB0+lGkhNs3bKDOXOnH5Ofdbk9nHf++eRV\nlQe/821uvPlmbrr+OgzTygnaFSeCHKevZy9D/XsJlZShO1zEosMM9naSSMTp6uqmpGYCC2dOJr33\nAKjWzm/gyH4G+3sQTRHDMKkfP5FUIsa993wetWBN5UXDQLG5MU8I5+iY/PXFF3jlzVe45IZPYFM8\ngOVUsmxjoP0QG956A6fNSV4z2Le5k3EXnU2qUKB7YAAEGVA5imPK5XOs3LCZolpAN/rIjmugcs4M\nAGpLA8hBjV4xzaDPxFcJnioIjYdQjYAzBJ6gDY/Hi+2wzPa3VWoq/DSUi/gcOoIeYfv6lRRTecxI\nP+1dSTZ1D2Nk0mhFEU+VBxg+7fM8M5zK0PCkIyxcsoQefzVro7243QmmN5ZRrtu50baMqfVzcBRk\nREkmIf6CzSRQC3lcbi+lZWVEo1FqaquRZJl8UaW+oYGhvj7OOvtsJk+ezJfv/yYfv94qeZJFJ9df\n8yVmTFtHNDFM1+Ft9PV0k81k0DSd4ZFhclqMQEMF1y9eSqNQzcurX2Skr5dCNo1WPO4tkeEhMtkM\n/b2HLPxIwQQNimIBlIaxPLUIyDjtdu79ypdpmTiRrWvX0HVoN7l0jNJQNTPOWsj5i5ey44WncSsu\n9u3sY/fuUYxsHCiC+fcJXIFsJnPsp3TyeHzMfkmM8UvBXQVOp4DTLqGY4LTZwdTQRQ1JgkwyijOr\nsuDcKsqb60ht7MeWN4gPj9JxuIie9hAUY4T7IvT3jKBlFYLn2Fk38L9gTaVIdr74laexldVzg7mQ\nBSP9HPDuJDx6mO6/biXk0nnzjUc5uK+DEilDx64w4EdRFMbVVNLZ0cnCRWdTWVFBVUUFiUSSQkHF\n5nLyre98m9179iIKBm730W2wSMDfwpxZzejGIKOxYUZGttB1ZBd51QBngMuuO5d94a10dUoE/eNo\nbKzirjvPJ5vQkWWBW3EHfgAAIABJREFUQsYgk88QTxYYHU0wrr6FvJlHQibep+ItF3ArCpmCirWr\n1OjqOMBD3/gX3B4/0egopglaUaWvdz8/+PlDKLrODElmclsTpiGRSWQYY5YYa/dR4s2jyIsT7ISI\nf1mFQMhnIhRBMWWUog1dhtG8pX5lR8KuKOiqiK5JzFk8jYwjzvLHt3H2DQvZs/kA7d0J6iYLROIG\nwZoKplWUUUyZTL5mGdEOk3ff/GAJ/VE7I8rea2trzR//+MeoxRj5fJrRSJRoIks+k8HptFNd6UMw\n3eg5g2IuyuBokgI23B4vSCK5XAan04HX5ycQ8COK4hg7sIDX68HQdUvCVdfo7eqhuXkCDocDu/2o\nwmaGghqmUEhT1HQyOQiGyhmNDmAYTlxmiEw+RmmFC1kUkSU7ICCIlspCsWgABoYpWWLZpoEg6Rxp\nHyIaiY3RawvINida8fRVxwLgA0rq6hkIJynkc9gkAWwSIuZRUZBTnqvrOuXl5fT29lI6QUT2mQi6\nBXFxKRLIAgY6hiFi6AaKJGOaAlLBR6bfJJlKIhgGTruMoZsYRR2b2w2CgKkXUfOqBSiSbAiYvLli\nBYODH21C+SM10xTYtyvIuPoqLlw2idUrV5DM9iA6NBZcchmuUCl5EQaH+tjw2juseO1vNNTX4vF4\niJVvx10pIGjQe8BAEcEogKwIRMNjD8KweL38PlhQdynP/uA5Zs2axSOPPPJPtS+dTvPTn/6MyobF\njBvXgGEqIEq4HBKpZI60LuB1ObE7BOwKIFgDh69kD//1s2+xZ89/D91w3XUfp2LWvSx/9ucsENLM\n+NQNzLl8MVV2G6eq8Mvn89x00014FIGta9+FlSYVfhiNgipaWHdc4HTB9FY/omYw0G1QP7WVnsFh\n0uk0/T3dfP9jU7lgSZDCsBv5iITna99Hz2bZ9usf8cxjr5DVLNZGR1BAMP4HcOL/v6yuqoWgPUtQ\ncWLkU6x48Tn2HNjH839+jECwnOlzzmFgoIdMqkg8HidTGrRyd0aWkVSeAiAEIJMFQwM1IeBxQeSw\niaME3A5w2GQQiyQSCQqFAsFg8J/iRZckCcPQqawI0dw8DlMT0TQTScoRt9kgLiHLLjw+Adlmcdjb\nZAGn24XHH8BfUotWNDBFCaesYAgisiRgmtYiXR9TAU1lMqRSMQzTYP5Z1axvX4jjpiWse+IxDi5f\nzszrruLWG5chSCc/0EwmQyaToawigDcoMnA4zIwp5fT1R0jmdUwbuHUYGRaYWWNjcssMvv7l63jt\n9VUc7Ogik8mQSiSwD6R49a0hRjpyfP7KK/DJJmt+cj/7Xnud7ZEcVZicVw5v9wuYno8epfDRmmly\ncPvLtNRXQdLBhjVr2bN/L4ZpMDrUw+hQD+37tyBIMss+/nkO7Vt37NSqhgCGK8VgMoPTCQ4Nxo9z\nIBgKNkmiuEQincmh6zJur5tAwvYhDflws7lFnH4RMS+gy4DqQNOLCIKOKumIuoSkCyiytdszTBAE\nHy7XOGSbSZmYIavZCZaVYYRHGFac2G3W0DY6MoKqZtFNG5IIE0Iic9samTWjnv7sFbzx/R9Qt3EN\nf9m/kwvv/BSB2poPvBCK10V1XR3ZkSSuuqk0RbsZ7B4mome46opmzpnaALrBgoWLeOWpZ3jt+feh\ncQ4AuaLO66sOszoS5Za5FRzetIr6lnn816N/xVQsieCkBnoC3KbFgH06OyPYiYtFlV//+Zv85Nf3\n0N91kI2bt2OcQvvE7nChagXsnuOMIx39UcSARknQRsgpocuQNQrkTA25s4K7Jt/C1IZJlDaWUPQW\nj9XUnWjmCf8+tJ2qjq6DYcPiLFIk3G471WVOqksFXDaTTAFyKiiGgQiIgogkgl204TSclFbXMXn6\nbJw2O27FQTGfJpVMks/n0TUNwbSm6sO7U4y8/y6Db7zLf37+LsT4AP12GzticW69+kZefeJpMvHE\nSQv0xHAP6a49TJw5jQtv+Tlf/+kzfPaem7n25nlMnX8BOw6lmTJjJrHMASbPqEdLqsfOVYsqTw2H\nGdJNAv5KGj/5DQqeKjab8GrKoE8zqXcLdBSgoH54X50ZIxUmpbLIRLHAke0bGAlHTnmUmsux+qVn\nKeQT1JaOqWSZRdqPmEyokBgt6JS4YOc+kwmJEDefdS5TZ1/P395YTzQ2QiSawV042VlNEyKxBPsO\ndNE2cTyhoPe0U6LfJkFawMTE7zEZGFGprLXjtouASMJehFwamyBjUwHVxDANNBO6R1V2hws4Bo6w\n9eAh8gUTu13AZ9ORFRGbzYbD4URRRAzTZGAkgxyq5+d3381IuINhYPWubVSHSvjGgz/k8Mgoy++9\nn7tvu5WGs634VC4rUOayccPHzmdiMMvogb/hFMP0duXZ/fZTRPUCi6+ewZJ59ezduY5eAepOuD+v\n20tbbSVzll3HIbWElnHVXNAyiYGOgzROGMen55fwwJNbOJT9cKc6I0YqRZb4t3MnMa+1nr+u3Ikp\nHc+AuxU78hj5ud0RRHFWWZp2YxbySigCjGR1HDZIJyDQUcXnFl7J9GktqMkYfrcLNZulttxByP/B\n6W/V6g1cc+XVfPL2L7Fy7U40zUDXDQzTRDcsmAuAywlVFSYV5eB0FyiVUoiGNcYJgjXiakIBU88T\nL8oUdRPJ7qJy0vUUlUnUzVxMQS0w1LmFxNABRoeGGEkLVI+fQYnXjc/txG63Yxgm/X1drF3xIo7y\nVkorGkCSkUWRyVNmYIuncdhDfPMnP7B2tWNCRE3Nddz1yTZa7YdZ/dd/Y927m3n6+U30dw1S3+im\nbVY5w0eGiB4J43cU+ca/Ljnezy4P//7NB3jh999nQ0eYW+74LEnTwb3f/i6zZs/l7n+5C93mQc1b\nRQkfZmfMSBWVBHaIzYRzAbSi1UluWWZpSytvHzyAUNRQHGVkTRnTOP4uaEUNxQHZBEgI1EbGc8uV\nl7DkynNxlp/F7v69bO7aSzRtMJLO4RGsIGIkmmTL9nZKSkpoP9xNPD7EqpXv4fEF8PnvIRyOYbPZ\nSCQzFPJZNE3HZkYQhQSIfsBBoMEBpEATQLYjOQWcXjdqLEekIJDLaQiCQCIZJVQRpCgq1E67BY/7\nTgLBEEUD6pvqmDi5lu1rlpNY/TjFdAJd1xkc6ufcpbcw6YoFlEgDDOzdx6aVy5nZ2sqk8y9g3fpD\nFGwltF50LkbOCoL29w5R2TCL115/m2fe7MKmCvjtEu7aEuRx5Vx77vlcfd1i4kf+xsIlLWzcneJo\nga3DBm01XvyhFtpmFVmS8FFSVUqix8d5l59P7bSpvPLykwScInLmwxcKZ4RTFQ2BZ/rGU1TAriYs\ngnkGuGDSJK6cv4gDAyMciQ6TzubRTftJY6+hgpGBEslBTbKZBWdNQavtYN9AhL6Otfzm9WcYyIyi\nFyEQgtxYYFpVi0TiKSTZwYEDHbg91aRTo7z95l/Zs2sHsmxnclsbjY3jqa4utdAG5onyXViBpUIU\nyGDmA3iVSooeiCYKuLUsmiYQHjjCnl3voBVFnMEmalvOppAN0jx3Ki67SSTeT3xEoaSklkuv/gTP\n/P7niJJIbWMN6aKErcbLhLazmHv+WVx487Xc+8l72X/wN1x3zfV4XFZF49FXLJYosKZTYsPOJB5F\npKJEZjSep8KWpV6Aq67/MsObHiTctwVZkYkdNrEIhcAsZhh497vU5q5nbt0imu69HbtdRstFKIx2\nQLqRgaEo586po/v9bj4MpndGOJVpQipfQCgIaJKE3R2CqMiq9iOEMyYZo4iOCYXs2IM9vi7a9TZQ\nEJg1qZZ2Oc3L7/0FT5VO6zgnumySk/LUVHgZ6cugFEXkMSR7eXmIxQtnYpcFli1bytLzF7F9+w56\n+/qY0NJGJBrljk9dz6TWBjStyP6dq1FHEmC6gRSYGRArwVYDxiCpgyN0aQrYFSTJhmJXcdpFBNmD\nXkggSuVkhjZwaGQDwfJmmm5ZwlVL6/jTE5sJKeC09XHBhUt484UAiiLx6U/PoyzoY59N4AgCAwLs\nO3CIkb6DPPL8C7TWlWF3WvdyNFlTFlQYGRlFLpmBz94LQg6PUGByVS0Tm+dj5rooLXWi5N1opg3l\nhKRw0RToKyq0DrUT7h3g8OizRJfdjseAYO0kEA38PonxTY3Ub+sfG99ObWeEU4EVwJNlmWJRQxRt\niKKdeD7L+x0nBA7NLFYt7fHCUKEA7oBJ51AHpmlQUyFSAGLJPIJdYjAKibSKTzRJhUEcC69EY2ne\nW7UbKLJ5yx7mzJnJkvPOo6u7H6fLzVXXXMn4ulJkmwO1aO2SCqYTI59FkCIY+QyCpwJB1BHSHbir\ny6hUg3QNRPG43QSdbrwOgWIhBegIQtyq7DUhFYuw/t0XmVixgJlTatm+YzuxZIz/+Pl6smmL+lBH\nJFwUONCjkm+2kxWh40gfDTW11NeVY3N8cG3ocFdx2823E032sW7TJgb7dzEhoHDPnReRzUSJ7vwl\nZmyQpJ5nOJeioBwPpRqGQT6dpqunB9VfjzxuBklT4KxLrkRIDNG/8klCjZMRHQKa8cGd+Yl2hjiV\niZGNU5RkiqIDExNJqcDI93Nc+kLAytKrcAKB2cRFAs3TnahZkFSVbN4kpstEwxptZQqVLgf7uzI4\nSiUCJdIxAn2bTaG6pgaHXaahvhrFJqFrGuNqyhFFAY/HQTZXQC3qpNJ5DCDprCauehFND5lIDiWt\n4/cryLaZGIaE3SNid/vJqBoxVSKti+jFFLoaR1etsnTZXo4puHnn1UdIRA5yYM8mcpkkmUyGcXVN\n5LIJCrrJU9s0yhuhT5OxJ000CdrmL+L8htmYNhkB81g0wQLDQFYM8JdNfVSlX+S6pRcST1TzxM9+\nSGdVF23XXIzsqyKj6OTDEuV2k817BmAMgpwrwH+9leCWhXW0nT+JQP1sKsdPQlQUDK1AZCRBWU0j\nib4Ostr/gpCCTYIHzssTy1k0Pz0jGgeAvrCXlJqxpr5jXAhZTrylphI3ds0kZxZIaSbRvE5dWYAS\nd5qsliWeF6lplNBzJv1JjfqxEJckiQgCKIpIqd97gtSK7wPtE3TD4sp0y2h2G7oO9koFw4DRgokg\n+rApEEtBpKggSAqm3VJStTu9BEqq0I0ixUIOXUujFRNk1SLr3v0rgmkgSyI2TMLDXdgcXtR8lqd+\ncCuqoWFIliAoArhFUArwBzu4ThioUrrO8NAQ4axGXy7DxgNFOkc3c9/nbqa7qPD1P3Zya/sztEwP\n4qooQbDL+AIOmusbYN0YPzswVIQXO0ZZNfI6hrQGUXHg9XvQMkmykWF0TSeTTDCoGh/qOGeEU2m6\nye+3HgesaZpBXtJxByRsumdsBXV0OeoATLxeL263m7RQgttuQ1TTuA0NwW6nqbKcYjZOLJFifvNU\nIrk0Az39xNICdslOMBik4/B+PnXTxQiigPQPAisOh4PW1lb+/ORT+AOlCIbVGhNrPWiaIIhWekY3\nLWk8QQRFLBIMBKipqcYwra2/YRiImDhlcCiKdRXDIF/Q0EyRVK6ITRIpDu4lk06f1I7MqRo3ZoFA\nAEk0yWk5DE8t8Uw3wz3dLGry01RTRcOEamyVDhBN3A4Fe0Ul0ugobreFZztaHBpOFgknY8CpCT/A\nhq3k71kiTrYzAqVQWVlpLlywAFG0cNsIWH19UlRdQBBETNPafQUCAWw2Gy7BybwpY8QyxWNHWmgB\nwUQSRAxMTN0EA3pyvWwd3Iaqjamb6mPnHe2Go6gSDUs6wzSwyTI1NTV0dXX9t+4rFAohCALR09QC\nnspMoKG+nv7+/jESDKsz3N4SfCVVZPIG2dgQWuHka9rtdmprxzEasRQiBExkyZrSJQkEQRxjARkj\nLxEEspksbo+PYtHKp/6zJggC77///kde9v6RmmkY9O5r54LLr2L5S8+imio2OUBf5yFUVaUgK+iG\niE20kykmAZ0pU9rwer2ME8fxsa/cBEETY1seIQ0EFYQG0cIO7+Ik53m8+3HeOvAWsWzMKsZLYg0B\nKtYMq2LNsHjw1E/DiPUiFxMsu2wZf3v5bwg2S/5Glq3RStOOi6BLEugaSLKlIDtrxmzQYffe7Zz/\nsTLsHoWCkMdu6uhCAZvDjiRa969pKiCjmxqqdC2rNx4krzSjaAkSA1sZN76VC6+dwt/+9DtKq8+m\n/9BGCrnje7BgMMjDDz/MPff847LLZDyLXDD52kP309nVg9frpafnw3HnAOlcDpfDwcjw8DHh8VPZ\nGeFUABWhevqHVXJOFwf3dtLU2srMJfPZEW4i1NREZMtraNG9eG1V5FK7j59ogpk00Muz0DSMVFAw\n5TgESzC63YiGF0ESj+PbwHIaFWsTmeYY05Aggkuw0dhcSqhhMetXvcOElhqyaRPRA0or2CvA7gKP\nB5SxKEc+Z/mv5LQ+Ox2gxSw9ayMNNjvMu1LBXeZCtQn4sGEqWYqCQVY1kSQbdruPXAYcNpPkGgcT\nl90PFVNZevE4Up2HiXYOEevayDWTJrP+0C6mL72H7W//imL+OKuA0+mkvr7+5I41T/hgginA/kSv\nVQkdyYMMP/zhD5k8efKHPp++4SG+/YuH+NYXv8XPvvcwL7/88mmPPTOcSoAt+1aT2bUazZSom/Fx\npl+6jKrZM9jdW0I468Lnc3JWRzWr17xJSkthra0ACfJ7w3Q/9xKxyQbjz5pOKFTG3q8+SGRngPO/\n8CBCuceazo6qtBaxnCk79r9owVvGKTIXTysj4DApm1BASzaTTMbwOk1MAew14AlC0A+KzRLqtDks\n59FVi/DeFC1+J0Ecg5VjTT2BYAiPT0CUXehFFdwCdrsdPwpiJEDIDtHACBg5MhLsePEXmGWzaK7/\nLPMunslgQ5KzfbNpcyi07zlAlybSc34N7QcP8Nrzf8I0jq+/jq5ojqYwTcOEkSiMxBkq9ZDrOULh\n3dewKRJFA2RZRlFOlm3LZfJs3z9Ie3cf6Codvb2Mdveyc/+mDx2l4AxxKovVTSCdh9B5d3LF/Z9h\n8/Mr6Hz5PbJbX0Z1+LDXT8V11ZV8vHMDj0c7jp8sgr3aj7jezczzL0SpDiHkkuQiaWqnXIVQ5jrO\ng3aUCy2HNUopWKNUXqTcJXBds0xtCbgdIvH+bdx65WWs6wzzyjPLmTwPsjlw2MFdCYJpTYH5EQGf\nU2AoYlDUQIuDHgA1jkXsh/VQC3mRcocHu8OBrutoQpJM2sSUFLIJiYKZIhnR8JfY0IoaerqL9GgP\nT923nM5r7+Gimz5G2+xaqhSB8sYFNMRMQu552GSTN65cxn333GV9l2liApqmE40kiacyCOi8++46\nZLudbe+voZgN8I37v4D/+98kO2g5Y7JQIJXJMDoyzPCB/dhTft5evYVkwEXA58Ame6kNzqJUD/5D\nOMcZ4VSyJHHbQ//JrwdrmDB9GpnUCNmcQizaj+QpRSnoxDq7WH6uzFWhCnxZ7/GTBRD8CpUzz0HM\nmIiiiOl2M/ezsxGP6GAzLafSOb6BzI39bFh0n421XlqcEpneOD1RnbYFtdg9TgZ2vM2gVoFuWE7k\nFaAqA9Ig6DIECgL1gUrKZNiXDbPuYBEtAkLMitNKTdbXyDaZqRNbcIdcGIKAaqYp6H5EJY2WljFM\nCU+oQCaqoQpOdBNAw1XWiMftZc3T30PQ8wzuaKQ55OATH19Gex8UYjEuWVTJpRedw5vX3QCAqplk\ns3l2795He/t+fvGbV/nMLRfy698/jbc0yJ7tW2k+7yb6RAlJsoay0XSegzt7mVHtYe+BQ+zetp3s\ngJcSn48pFaUkCxqTF8wiGmnnLz/+Kd2xU6NIjj3Pj9Q7/h9NMyV+c2QemcUNHBoC5dwQld9vRZYM\na3pxmdwuCsQ0kV//aTbZkecIllukXgggSALelmrIdAABBCTk+UtQN9yPkdVxzLgcQVSOj1QuEMIC\nDXVNtJ1lZ9K4EOODDYgD+9i3vp3uvjgTWl3kJBvxve0AmDnw6BAohbAKPh1sHSJZycvCS87B593F\n5j3b0DVwCpAtcizwbxowMmrg0RLY7TaC7mpEMYuRkdj5lyhuXzkNMz14/F7UnEFcdgI6mcEdOGqn\nMeWcj3N40wbee/rHCJj0df0rZ89ewtZtBzi77XrcPhchv4dwNMnKNbtQdY1Nq5ZTViLhdGioqkF1\nTQ2ldeMYyRg4Sz0MZY+naF57dQPTlEY858zg8ubFXOCfSd+zK0mt3MK4WD/FYgLXrnZK332EPelR\n3i4WIXSGIz91zcT827dg/w1ErrqWVTsE8AvgEC1dab/Jn8wk+aREZijI0SQocKzYRPBIIIStlagA\ngs2NzTVAfvSrJNYM4J/zGQTZqrYUkgI1JXXUTQqyYftB1q3q4hsXV7H0/Ck0TqzAWRrA5i3Dnghz\n+IXnACcYkMpBXoDxskCLp4JEeSXnLTqXt9es59VNu/D5ZTweDV9IJi1o1NdLtO+1CMRGuooYaTsl\nZTJ//s/3mbdoJiN7g7zz3EEuujxIMeJFknQ8oo50bEg1iPTtIDF6BEwFWfBT1OL88be/QrsqzK+f\n+h19nRupr5/C66++wacuOZvK5W8wXN/Ant17ufaai3nxmd9iFAxu+cQNuN12Vm7cz+6eEVpLQ8e6\n8DLDxbzBPM5HV0C8B38uSmUyCYYLFl2Aufwv0N2HIx5mpaRSKBc4hfzpMTsjnEqgyMcWh1jf+Q67\n366nYsZ47PVOBlQbBUOA3YfJrf8tOQo4sg20TmpAPxqUOpEzNpUE1Wapk9uGEYI+nO4BMhsewhwn\nQOkd1nGVEsPOYfq2dUMWSnQ/V3ziPlrOHYeReo9MMcTbuwb47i9/QixSxOdzWjm7gxBwSdy6dApy\nYCrvGzm2HgyzqT1COKkhpK1pMpLRMA3oOWzF1ERBoLZEoNQn47HbKNMc7H1qmNU7O9javo+JM2XG\n9zagF+yISopi7uTcmlaw+EkNyY0huAnHYuzcsQXT0Nm/ewf50QJaPo3qdLKrrI7GYgF7Ks3EiW24\nHXYkj4jH48LQdfa88jqrDnWzqOr4EuLx4X28kFZoS6SoTUeoiCaoKA5Sm9WQ9h0iHl7DrthWZL3A\nK27Q3IIlE3Ea+2dIz8Zh0V1XjD3C35mm+cuPkkvdJsE0bSOO0iLT4msZeM3OSKGM0lwJowU7WmKE\nT31pKX2DEV568gk8YieJkkbr5JyB0a2CqmFWlyPabBRHRWRJQmxrg3YovbsNbe1TpHfkMCr9mJJG\n0chbFOwi+EQ3drcX0VmD6Lgc7cB6nnzkN+zdMaYxfFRYPgtlcgWzlPEkQk3YL6wmpfl56s3lFmb7\nKCrmqKDQ2PQnmBBIKZQ6HWRjBrPPK+Olxw8yEj/MLZ+YyfW3T0P3FwkoDlKmjSPSqR+LXXGjY6Lm\nVAaiQ1x98XWks2l2d+9mONzLUDTBO6ue5o7rroeCSvad1XztO99h7qLF3HnOAqiqQE3F2fr+a+Rv\nvvLYdROZLKMOEXtdDb9f24W/xsmSRVdwfcrG5J89yEFM7tNSXARsyQJDJt7TC3f9UyOVBnzFNM1t\ngiB4ga2CIKwAPsVHxKUuyxKfeeBjCE31oEgYRoRCeJiBI70M7D7A/g2d5AY9RA8MMUPaSzrLMVF3\nfTSO+tJ+dF1Dn5/AM2M+G7/0IOU+g5aHbsSUNyLOvRMp+wNif/0vCrNusJxkbHQTbAKLL1yEwza2\nTRb8hBor+cH9DzKt/gmeX7uH/i4rFiQAuXCGQ29sZsLnz6aj7yC9ST/2sWn1dCYYApVmBfX2akbS\nEUrLvNz8ST9ut8Ed983F7lRIZzNoRXAgYztBKuVEK6hJFHsJYNDZ2U1bXqYY8jI5GSdiCIxvamDv\nrn388sk/0NTQyu83bmX1jv0UHRVMypTgHvkria4+zp69APUE5QzZptHR3s3o/r3oOriVAEsuupLd\nf3yUCZjkWqbQ3d/Do7kkBQ0EHU5ZK3b0eh/aG8AYZ+fg2OeUIAj7sWisPzoudROMIxq24HQEt4Tk\ndWEridPi3kZLfR1LpuwinR3HxTVJeipr+PXuzDHxQ0G0Y/eEoCKPeXErRq/K1JJleM63Q8kkjME8\nohmEcQvwTtuHaWas12Qsci4KIg2+OnrDQ4T3HF0oFChs2M61iy/BVT2DHz787wBUeF0Ey+rIT5vN\nkO4h5BzGUxXi+muv5v/+/rcUTsctLkBUSyMmuxCQ0UcFvLJMS2UVzoILuyGQw0SSimTVAoLoPeVl\nDF0ln+0fa2GRg5kkZaKbmF6GXVHwet18/ov3EB6N0D8UwTRF2nv62LRvK3sGh5notxNqaeHfPvkx\nautreer15wBo37ed+omz8DhKWbf8dZR0Hz/9VoTWI7uY4A3wbk0JLi1BoSqJtNMCRX6Y/bfWVGPU\n1zOBjfwPudRP5FEP+n0c7ExS6QoTmlSH6C1DSDgxI83oERkt68XuqaV0QjU5Wxll+V66OiyCCF3S\nSXhUS1rz3RjkDSgtIR23waYUdFXBYASiZyNV1WDLboYOjuVLdXS+f+jnPPyL/ziZId+wIpgm4HK5\nAPA6XYxrmI5ZO4Pf/uktbJFOJk7qZPqEaVwxdzZvbtpMQbMwFSeuY3XToGOwnzW7UgwPw7YdnZSX\nSHzy3IXEBhTsgTAOm0RUy0JBtODJpzTjpGvuSvRDwnKyYMiqg7z66mWWkr1pks2qTJ3UyJ+eeZp5\niy5i355dLLrwPM6aP+skPn+Ht8BbLz3OuIYJRFJRIskhBrODjJZ7ec8epLWmklIjglHeix6F+IHT\nNO/os/1nE8qCIHiAVcAPTNP860fJpV5bW2v+/OtfJJ/Q8ZeUYa+vtv4Qj1IMD1JIhbH5ysDpRiop\n4f1N2+jp6UFRFGKjMUpcQcshjnXU0ZDy8Y+YVo6tpLaMQx2HT5Im+UemKArjx4+no/0wFV4/sigR\nzmXIFXKEFBtlAS9IdjpGRlF17VgLysstuY1IZIS6OjujiQIed4CRkTjBkMKMCdV4nE7cLgOv6CUt\n5ikCA4MKmzZgkPizAAAgAElEQVTtQVXVU7bnVM/M4XBw7rnnMmnSpA/8TVU1BFGkUCjgsNuRLcFn\nXn/9dVKpFL4qlZ4jYbKnyCkLgoDb40GzZVB8Brk4FOOwc+fO0yaU/1lyfhvwKrDcNM2fj/3uIHDu\nCVzqK03TbBUE4bdjn5/+++NOd/3y8nLT7XafsrNOZ+PHj+e8z32VXGkbZUETd1CwRH7M476kF7Gi\n3Lol6hjv72f05SfYsn4dyVTqWCmWKErIsg1RFBEs+BuyLI3BWkwkSWT2nNmUlpbyubvuJJ9P43IH\n/2EbDx06xE9/+lMOHTr0T98XwBVXXMFtd99NyOX6QLlTqgj/8qV/pWvP+yf93uv1MmfOHOJDMdxO\nJ6ZeJJNOoRc0nB4/8XiE/tEB8noRWRRxO5y4nXbqm5qIlIbpDHcyuUXmkwuuoKq6jZFEhrWb/8aq\n3i6iKVATWEHjThByAplMhpGRkf83lMLYbu5RYP9Rhxqzj4xL3TRNuru78QY9OHwuBNPENMZyV4Ll\nEJIkHIO0aLqBamrYy4L4ZzWw7qmNVJYZzLpsLoZLQkLAMC0EgVwEVYWiCmZGI5fPEx8cZDASodJl\nx+mwMaltPh+76fN43C4MJLLJFNt2biUwbiK7t6+mmImRy+XQNJWAeYgde99j0dXfw6Z8yGoVCIfD\nZHM5XD7fsYFU/btOP5HH5ejLICkKZbW1lPp8OLFeFMaO2zmU53B7D/3d3Sd9VzAYZOqUKcyrrKas\nqhGHIjEyMkjf4R529vZyZKCDPAWyuoGWz3HXsgtZvmULc2dNp0s4wKDZzWyjjAubm3BVTQBXExPL\nG0m/cDerGSQiG8T2gTkEQvx/Ts6/ALgV2C0Iwo6x332Tj5hLHWDCwjpmXjsbQ8sjmwJ1HjvRTIHu\npIjHZSCn7Bg5yDh0ymxBRMEKSU29uIW3H32VTa8t59I7bmXarDr6VBnFDgiCBZoTLD1hWZS49rwr\neXvLSkJ2EWcmhkvIsHb9FioCJTi9XlKxUd5Z8QaHo0+RywwS8Hu54OKlFPMpDi3/BT5vEMwPJ+M/\nahMnT+bhX/4ShygiYMkySQa4TPCZkJcgrIPbImahCKx+/vmjpHioJvQMJHEVU1TVV3OoL87o8GlE\nNEzIJaPsGohSXVqGzetEddmI2XQ85aVMahhHxhDY/P4qOiIpJE0gmsigClDsh03747wr/oWF5w3g\nbWyiueUi7rrxAYx1P2Fz9wCZlGaFST5UFOaf2/2t5dQiT/ARcqkDRNOD7Dn8LuNKHDgVG2tWiZjJ\nLJGQSC4i4o5lKIwqaEqO5hnN1M+8CVGE2sYgt/3bLax6dgO/+NR9zF+2iPO/eB9rt40wdYaPYKXP\nQneKYFNsfOWBbzPz5dcYGj3Mbx97knHhGJsPPkcg2ETt5BoiB9chyhJFdQTNyKPqVgZfcXhpnj6b\nrJins3MfE1rn/kOCD0kQ8Nts2CSJVEYjtX4IV7LI6yveYmeii5KJ47ny07cyrtKBJAuopolgQmc4\nQ7Pbi0MUOJDXmKpGyFGNo9SHK1CFmuj8wHcJokDbzGkMdHZj+EvIFYtI4yqQBw4x2DdEXWsTXfv2\nkFPzvLF1K1KxgKe/n4IPHArkfEX+488b6esOc86CNqbeUsPUpvN4wOvivY2v8X/Cq9l2SlWkk+2M\niKgftTKfnapSUKQC9hzUuauJduvs3dXLSHee+govWl7l+usuQHZbZe+iaI1AoiJywU3z2fbaLN76\nw28IR3LUzrucnk4PLh9IY5huQ9fZu2Unm1a+xZrtuxnNOhg83EuwzEs6soHR9R5EyUVP/0H0v+dz\nECRK5l5E19MP8M1H3+DzX3uYSy+5Arty+jiVgNXJArB9+xEe//zPOK9tCivffoweHPS+8AK9nUW+\n8+1baBvvxw4Ucxl++MAXmHPBFVxxw8eYNz6Iq+Aln9awOxR83gCnwmkausGzz79EuFDgglvuIBDy\n839/+B36+7sxTZP4u++ga0VMXaeQOx4XEP0gBiDkFzmQKvCHt/tI5A0KtueZumQhIbtEdscoYqx4\nvA7lQ+yMKHs/anZ0ptd4uKBhCo1DJWj72zl8aAg9otBSX43LZkc3M6xYuZKN6/YcI7M4ur7PZTVS\no3kMNcn2Nx5HHdzIrLO8FI7qkAEmAit3xXhx7VZ2Dx5GNSKYNpl0NofdLjFhUimBCsWCcY5BmE80\ns+ihVM4gZju4/bbbePC7D5DLn55U9agJgKHqDAoOktU+IjXj6dL7Sag9vPTUQ3z3dy+RU8e4YHWD\n1Sve45ff/BJP/PElOvKwcmMn+/cMssArcN2S2QTrl4JwcpmWZui8uHMj/akEl16yBGwmTp//2AYo\nm0lTKBQ+0DYtA5l2OLzDAJuNCW11rD8SpefgYWKdnfRuXcVftm5lW3f89HPWCXZGjVTlIZN5TRVM\nqbiMl95/ncbmAHVNMqYrhD1Yw9N/eoLaKh8eh0Rt2RivnGHR2ogmuLwyX33iQR67300kkad3MM76\nP7zOWZ++DAMRQbQccNOOfYyqeTweLz/62X/SOKmN/oEhotEM2WyUQjrFnAUxXn/hBcodXjpGDx9r\no+BqwhUM0lIps25/lt/+6lcES8r5yr1fRT5NeuWoeZwKObdJyszjrK9H7V4DqMiSyHBeZSSRw1vm\nxETA0BWS8TC733iVm668gqrGMr77nR9z9tSpVDc0MWNmlpW9qzBPyH0KQJ3Lh9vlYjgcIzocZXz1\neAKGxNYD2zFOA4QSFMgfBp/LxsS6euKRMAsnBvGGghgOHyPdwwxL6nGU7D+wM8qptEKWdG8/X/rx\nE1w0fyF3Xj6bgH+EwZ1H6IjoHKx1Eys4iI8ME1aOF5+Z5lgIAQF/tYtP/PtX0AsGokfmuQf/iPDs\nO8y6YekY7t+gVhxkek0d119zAbfddA0ur4ecYdIRL9DfHSXcP0j74b34AyUE1TwB23FUpCAE8DVf\nSEnlYbKFYcyCxr//+N9ZsngpZ8+a86FrLL9dZPHECvrDw8h97Uxvm8nIgYNMdpWzKNPDzrfeQFx8\nHqmcjiz7EYQwu3bsYP1727n8xvlcc80NPPf6Fvq2/4rWxUtYfHUjtS0+3n+1k0S/gU2S+MyFS9ib\nh7PmzsQuOVnx/LMcOnLwtA4FYKZAdglMa23mtmsvodIewe30s6N7kN2vrmDVwV0MJIt4yiC7+8N0\n3i07o5xKcngIpwVee3ULL76wi9//oY6rLp3MsgU1zD7PQ9uEG3jksV1EszWs3xW20ju6FUw3x9ZW\nhgmhSjeYoBbh8vtu4b0n1+Bc006oUkISBW68cAZ7ymBKQ5DV6zZzyUXnUtQMstE0iXCY3r4eXnnu\naToPbWXU0BHdrhNbSUf2LAZyjyHbBDTNJDIc5bHHH+fsWXM+cE+maVLIF5Ekg+RojM73X6bXhOHR\nHoIN49GKWcKZXpLRIxzammagd5RIcpDmqQIufzn+cjvFsr/RnRxm1iIHdZNUBO1SPEEPJd4vkC3m\n+NyWP5LoH0YQBSY0V3DVjXfyu0efosRn58rrr+fwj9tJFjMfcCsBCPn8JHNJZjdPo7W1jbUbd5Hr\nP0BvrEjCzJIqFumiQPlEELwQ1CR6hv5XVChbNpTIsEt1UzPDzeENKTbuPkLX8AiV1TfzhYf/hNem\ncNs1C/nja1sxZBHzqFOJVhWLeRRNMPYqmQYEy31cetclZIZUEiPWVry+zEPbJbNwTVlIrryBZDrL\n9kMdvP7KW0g2mWgySm9fJyAR1QuclIkTwBssw6kY1I3z0tmTBB3Wr15DKp3G5z05b9ff2c83P/kA\nhqyQSucZHY2wJzeMiUqsQ0U1CoQqJpMsFjDCSbb8f9S9Z2Ac13X+/btTtjcAi94JgGAFOymKFlUo\nUqJ6F2W5xSWRaxI7thM7iR0njp3E/luOLTdZ7pLVexdFsXeCBSRBNJLoWOwC2N6mvR8Wokh1Je8H\n5nxZYGdn752Zs7ec85zn6dhGeWsRX/vB9Xh8Mjk1TYmrFEl0oUsq7uIsibgLzdTpCw8RjY2TyRXI\naYWQmHHBZcTUIp578EFKdJ3aijpi2eQZh3K7PKQzabBMijw+1i9fys+3DdJ/eoTKMjeTY/10Hhlm\nIgXCK8BvoVbAVBL8KuSjBR35d7PzyqnsNpW2OouKr/rYuTFAx5ZRzFSao8dPEmhawKmTp/iP37zK\nSDjD/GWtvK7daExHDcX0q8UbC3gLEKqMp9LJ1HjhPYfdpKaliee27+Rnz32fkZGTeIqLmYrHiMei\nxCIhdM0goxUW4G/+XVaWlfGxa1bzofVLuesLX2NiQqOn+wQ7dmzm0ksvR5ZlhJALHFcizeH+Vzh+\nIoFwBInrE2QFVBXVMJkMo9htDDNOq6san9+gRrWhulUmMhlsfhepjEzA4WBSm8RKx2gKdBDVa9Dt\ndUTzo0iWA0sUEBaWadJ9uJuuI8NcOWcmQ6dOMDzWRU7PU+7zE03EaauvpqqihJb6Wp7auLuwMXDA\nmBLm2S1hTANsPvAWCUoWS0wkTNwui7gCsQjk35lc+YydV06lWIJQRpCz2Zl3icHc1aVU2FV6NiWp\nKhnk2mts/PQHE5TOqKCsUSM2Mszk4fYzwU0h3ohKW9OFDoZZiKxbOsSGBhFYqJZAH43R4pYwR09x\n+NB7RPOmzZrW+ZsaPILT7qTYVsKCWTOYSOmsuXQxzzz3G/Ye3kRdYyt2TzldR3spD1aAo4j2ru3E\nJw4hCRmfO4ghFFTVSWvrLBrLMpQE7QyOD1JSXo5QBN3b+5GX1DCVjCLXS2giQTo1ziyPgdutsOXg\nAVR3lBp3Ddb00CypKsVtiyh7dSO/eP4JZhdVEMkmsdtsrJ21mEcObKOmspTvffkuUokpHn7uNU4N\njyKiIGsUwgUmGBnIKxbR4wZaDkY0EC4QqQLz83ttAM8bp3I6nYRjFod7ddwOCbdH4MWLlnUxNang\ndyfRmcFFF1ViBlRUh8qu3/2M/fsPfIBWLG6+4Qa6Tw0y5bIhSSaXz25l64l3p6R2OBzIsszTTz/N\nkSNHMHIxkvEIGW0zWHkcniIOHYpgmDrHj4YpJB4ElRUVeFUb81sbGR4NEYmVICwYHB9FmHnKfQEy\nySkS7mL6h1WOdCaYyvSx/uqr2PTMcTY93kk2n0VWLRRJYJoav3ep5OlmKp7GpkgoUi+jA0kcDhcC\nQaxvkFBolJFclsxUmAta56G7fbx4rB3JppKMx3nh2Y10Hz1BU2UDNrcbNWfDFj4r5TQNscgkpp8N\nnFMFL5zv7lbnRdl7ZWWltWbNGmSbQLIJJFGom5MsgbAkclkTmx0kqXBMNy2cDjeGJk0T33+QtqrQ\n0g4cSoqGmiBTiQmOnRwDnIVGrQxeu52mWa2MhSKMTeRQrCnKysoYGXk3Vqa3mt/vR1EUhGGi6RqG\nYWJXJfwuG4k0+D0+nG47kk0BU0KSBZZlkMlr9J06ia6/u1bx2aaqKsFgkEwiiWkYJDJphBAokowk\nSWSn6ZDsqoIqyeiahiEkfD4feV0jkUh8oGvbtGkTo6Oj52/Zu67rdJ/s5qpr10/X08lYml7Qgcll\nwS2Qp9co2WycXDRKSs7ReWqQfLqLsnIJ5ByGqaIAGSWOHhXY8ZOMSRR5iqioqyOfyWLli7n/YZMi\nZQe/v/sv2du7gz8/uBVELYo1hZMUq/w2PvXnP/O9n/6SJ7dp+NjC+vXrefjhhz8QkuLNau8CWDen\nkpuvvpBvPrSVpa0LuOsvb6dmRjORkSQ11QGciszjO7byyqFncQZSxJQ8Xh0G9wClAsthYfVTIKfx\nAxEgUeBtuOyyy3j00Uc/0L2/8MILicfjH0hAQJIkioreGaVxXjgVgNvjpa6pGUVWcDjdYFpksymM\nXL7ATOdwksvlySRiRMJhMok0x08OUF+fYd2qWmwz4kgZONaepGGlgTxewuZXc6yfO58Z9fXMWXEx\njQsWc/9jOzDNXkZjI/T29/LDX+3HMjN46CeFTL3QmLRkhkKTpNNZ1GwXlr3gSOZ7kH292V53wNdf\n3RLMqVLR7SrjU+M8v/sV/BUyNQ1zcKtBLnctxelwoksm6z/r5Ya1Du7vHOPUNpPT+0BqAnPKQm4U\nmEELJQvaGNPEMQVByg8681hWQRVDBTxOO7NmNFJSXIxm6IWRWUgE/D4OdXaRzuURgO1t6MjPtvPG\nqVTVTrCsBofDAwi0XBaHwwdmjoA/gCWpJJJZ9EAVnuJ6JsfHgM1U+meyaNFctp54huZaG5lwCZsf\nSIEVZU55K/3947Q0tVBe4Wf88E6ysTggY5o5ZlTOoMhVQ6bEg1NK0FBWynplnHllpVx1w5V4IicI\nb3uR3rNLwv4XVuaVmLWgkZTlprmljaUL53P7uou5/9mXefalX9Ldexkz6uejFtu48aNzGZA3Mm+l\nRGPQgWuqEXtLI9HRFFZ9GPQUNV44/nCaRIdGzngf+ZN3sG//82pqqq/CJgVwe/2oNjumlUDL+bDw\no6iC2OQWsr27OPJyD796Js670XmcN07lcLooK6tCllTi0XHcXhuWoeBx+fD5PUxOJXHZ3YTGIjhd\nTjy+AntZx8FRIrdJjI6kmFftYcf+MImkhw2XN7PhhpvIR0IEAm6SodMcPnCc8EQJICEki7KFK3n2\nhSsY6h2maX4jiqqSfuIfkUInUWwKi5bNo75Yove9U3vvy/wulWJ/E46qtcxZVMzQ8BF++odnwHLT\n1rKYIqfM7x68l0vWr6Nn62mKZwV5dd8INy1fxSP//Ufc9jIiuSSfv+fzdB46SLoR/KsWYA4cwYie\nvf6SKMyNKqAUKJGkN6V5rel4DB6aWmuYP68FK3kYjCjW5DjGVBilfCEYdeC2FcST0haJmITyHnXv\n541TybIdSXIQi0WQzQRts5Zhkwv6wrqmU11WiWRZHCXHsb4JzOnYTJlPYWh3nqJUGZLTx+8f+xh+\n28VEe7ejigRhXcduQYlNpWbmbPbvnkAwxqIZtVRWVfLYfT9lxyv7uPmq9az9/Gfwz7sIFl6KZHcy\nKDXQna0Hxv/X1yeEQHcWkypZjuKsIejvZeeWLrzeefh8PhoaK0mnRrBZCnZV4bK2+eSkSbSaei4t\n/RbFjhoEgv7EEHvv30XUFUJ22gjUlOO5uhjp6chZrQWBT4CtElwl4PKB1z29+5HA1LFJGtpkFGyj\nWCcjWI0JzCkTkRhG5CZJ7e4lObYDkbCj5Ivw1ldjGzfgdOIMaPCd7LxxqlQmTVdvFz3H9mFHJzXS\nz8rlC1GlAmGF3SE4+toLdMXsyNXLcE8Xnk2KOA9uC1Hs8KLMddPse5ErluhUN5SRy2RR+oPkQwam\nZmEaBUK1C9qcfPcf/h1PSTGZjkPoh7fwYscelt90FcXLbwcEFhJ7Bww6U5V4XP87pwoGy/jsXXdx\n643XguLkj398mOMdG8mnImA/RbFHRTfCWD4H1193Fd6yMtZX1JNXQ8wPuMnKNTzadZTZ5eVsObCd\n5MgkOhbSGChXaBgNWWTP2dOfAsIPih8cfnB6we4Gmw0hBD5Z4647K3j6+VNgxMgfDJOdUcaBZzez\nfGaG/Ml+Tp1MT0eW4/iq7FiyjjKVw3yH8jHObf38sHQmzbGOg7z69OOc6DhBQ1mAf/nsHSypb8JT\nU0X/sS1s3rSPl0dkvvvzW9FyhSKtAwcSzJ+pYgTz/OfX+zAEfOM7aT786WZ83lb8c0+T9fqJnXDj\nKQqS08b5xx99mwULKxFCsHZxFXPUNoIrb8ZbUlKYKgBNNzh8YCum3vW/ui6v18NnPv15Fs6dyXNP\nvkBsfAhPeQkbPrqBeS3/Qk1lBb6AH0uysNmcmIbFq5teIp6t5MiYRI1tPguqaynRwjy05Sn+9Muf\nkklkcFUUYbc7Ce2cQI0ouHNnDx+vE26ZCFkHkcebHSXlnEOtFuK6FSrf/qslZCbCtO+2iEbTWC93\nsGh1G2qDSmT/CSZOZxgaBMMmkPuGuPDWKpJanJxlpwCKfmc7f5wqHuX0ib2kRruQcmm6Tqf52X/8\nmv/42j9TUeMhEx6FrMGyhUtZNLuOY9Nb4GgGOk8a+PqzqLqEZhhs2t7N4nVFXNriQxcjeGtsJIfc\n9LR3kcwYtNY7EEJgGCbumz5LVd1mHLPXgGOaqN6yeOSp1/jjvT+kMPW9lVz2/VpJcQn79u1g7/ZX\nufCCC/nc1/6ee351H7/74X9z329+weDYECMHDpBKpTh06AjHj3dxzbXrce3388/fvY+2Jcv5/tec\nfPZjn6C9q49EKAEGJE9GSMpAsQ0JN1bubM4oCyQTpBxN1j5cKRm3HGeovJT5Sj/FUYvdr1gERQi3\ncxgRG8C0NGQjQKYjj+eKlYiO7YRCOSYyFjnJoLtvN/NKZbpO6Zji3WF4541ThUND5GIDxEMpVARu\n1U5pXSP9UpQWX47A6mu4kZ0cKqoilc2fSRX47DYyOYOEYZAzoKzMiU1WOXaiF4+8i7l1HoQ0g47u\nvZQ7q2isLlSV5TWNe+79Mz+756c0eRoYST7Cb//0R5YsqiOVyfOzn9xNMv5eKizvbYlkEsu0WHnB\nBbQtnMf9D/yGBx96gnA4yg3X3YiFiZYrwCll2YbD4WJWaxMP/Pl+Oju7KA142fzEbwifOkU+nn8D\ndzKdj/IoHvS+DKbjbCSFBWYG0pOEk3/EZ8szZnMhnzxOl+plYFLm6ROvMTg+Qkt9PQkSZOQE1mCc\n08dGafrmLcz4hJNXD79IrUMnZ4P6+TbCAwa6/hZs4FvsvHGq0cExhk2TgAyLFjeT7gnh8yrc/fOf\nYoY3U+eZRX1uistuvB6XTTnT8SUz6jkxOspotEDepRt55i60s+OxCV6+ewuf+W4VlTadqWGF2llF\nWBGTPYfC1FRN8e/f+QfaZvgZivfS3X2cgVNDLFlYRzqVYmS4/507+wFsYnKS9gPtbN22GbfTiVAF\npRWzqapxMXdONQ0z6sjn8sSicfbsOoQk2ZAkmfB4CC2f49UXNrLj1dfQLUFpbSWjqcE3CNwQbPjE\nJ6isLOKxX/75rFbNAveR4SCh1KA4Be7ihWjOVpJqGWPZBPMamjFSR4EJusY7EIcG8Jk6O8ah+HP3\nc/Xn5yK8Klpep2U+BCokunbnkF8v/XkXO2/gxOY0A5zdITN32UJss2fy/KETHB2M8Y17dvCFex4g\nfOEF+GubsJ+1Pd7S2UMolkKVCwQJsmZjTmAefjNI0qWTTxbz5M8H2bSzn0TcJBZN8dxLBwkWF1PT\nvIZJ2yWEjCZc9kV0dvQXhAEswaLla7D7LwFlwf/quoRQkGQnmq4RTcRJp/NEwmNEwiF6+wbZ+PIW\nXnppG6+8upeBgUGEWkSgpIxrrrkGKBSy6qagrKqa//rZL/D4/AigtbWG2z98FQ6vg9s+ei1Ox9nl\nYjoFhtw0pr6aiegaBgarGA25GRnLE49K7D02xuSoBoaEkdaxRXQaayRqTejtznDwqQ6isSwdOYm8\n3c3unTmGsrDdeG/w53kzUjlUhRKfnYaaInweF7XNDRzpOEpeN1E8KsuW1WPYVBRXxTnoypaWOTRV\n+AsFm7JMjU+iVq3grz++kEjRJvq2uujrnsSw+5jK2ImnonQUB/nOEycINdzBaESBepDqMrw6JbNk\n914mJtLUrLoZOb4Oho9D37/+j69LVRTKKxpIp1MYho4kJISl43C4SKUsDAO0vIVlynh8ZWTSSfKa\nxlXXX8ufH3yQ2z58E1lTY8eOw6TdKkK18BQLRHGKjHeck5FuxtODb1ArAQXc71YK5F5OwA66CnHl\nTLxKTymQiQPVYBaq7du7TE7nYX6rH5ua41jWoj0GKyZUxodSnDIg9z4C9ueFU0mSxJp1lzN33iyK\nigJ4vD5UX4xFS8cw0xmCPhsNC2YxqRVzoP04IBgeHqa2thab4iChpZnXtoD1Fy6lp/sIBztlPOE8\neV8NRr6GNbfNZO/eI7R39ZPKC9jyXfan01QBVWfdpPhO+KddnMFkzbYA2cLZ1kYgEDiTy3u/Vl9f\nj9PpoWVWUSGFYhaYiBW1oMksSyqGYZLPaxhGHgsTRVFwOe2oSpprbryQriMnMEyoKi7m0KZnuOmO\nFZTW2KibW0N5yTw8UgvmaQ8z6pspLi5+hz7meEPP5+yQgEVNDSj5GRildjIpKKuHySoH7rkOSpIx\nLs1aDKoCxxKLpimwC8GgTWKg5/Q7Xvd5gVIoLy+3amtrz8lbCSGmSSTEmf/PtqqqKmw2O30nT/J2\nds6np88VWCxatIhIJMxXv/plbDbXOefo2QyHdm6mpcSJUzJJRiZ48JXdnMwYzJs/j9Od+7AsBUky\ncNhVwI6GiiQVdJBT6RSRWOzMkqO+vp6PXLWc2iIJyaWALoOlEddHsFt+silBPpXGIUlops6evjGe\neO0Yy5Yto6Ong2wuW+B6n/5ChwxX1hZT7bfz0MlJIok3KmPcbjezZ82i/cCBt+KdRAFrbU4jF89+\n5E1NTcyaswijZzudfSEm7T4aZrYxb/Y8fF4fuq5jWoUYn5bXMAyJffs3sXXLS4RCofMXpWCaJu3t\n7ThViepiD0k9xeU3X8TqNRdTEWwjnY0RDLbiREbT8sSTCfqHT/LsXo2wuAJFUUEU4B9CCBRFwW6z\nIySBJMvYnHZqa50sDo5y+OnHGRzsZ8GCObhcr1MUCjRNY3RwgLqbbsXV+RKnjh1n954j7N57hCnF\nwaI5M5jly1NVE0BTgwTd5eRRCRtOklMwNjjM2OgI4fFh8oZJNBNHCEFdyYUsrlWQXIBpw0Qm56hA\nm4B8ymByNI3dZsPCpLMnRnt7OzNnzeToqQ7ipxNndnmyEyo9gusWLaW8opkut52fPPgE+YSFqUHA\nX8SN5eXcfPQ4brsdWyI27VxVcPvfYnxlNU9vfY5djz9E7/4eBvOFlZHD4WDVitWsWtGMe+wUOweG\nOJ7SmDtnMfNnL0ESEvl8jnw+j6bpZHMWL7zw+Lsm188LpwJQVYnVlzbSVlfF8ZE+1lz1IVZddg12\ndSabX/ij5OYAACAASURBVNqBqzjIg488wGQkTnzSJFjkxOZbhMvegM1uwzbtUJIsIUsyNrsdWZJQ\nbSoOp4O5yzxc5jnJ0WefpLA/eV1SV2BZFjtfe43NmzZz9fXX0XMwxLY9/URSOpUz6qgwZQK+AFXB\nFhx2D5PWMkbjWZyuNEJ3YpMFRd48C5ZezJJMlNDAMLtPFeJoVi6D8ASx8hFAQ0h2HPixB23k9DAp\nm4ppc2CaEi7ndFGqCdZEoZeLa31cs2YRfaNxntx4mB0DCVbMn8ltt63jiRMvoEgZBvYVTisGFmh5\npnSdVqZHayFjxmMQsNEa8HO4tIiXSx38Yjh9BndXHghQEyznk1+5hZJHX+M/Hz1MV/teLl6wgoaG\nBsAiEU+SyWaxOZ2UFPvf9VmeN06lqApldeXINbU4rRSyuxwpk2OoewdPPbmJOwNBdhxIU5Rvo6lm\nAcXFEyS1JDZLxbIsDNNEsRXkps6M7tNTqMtm4XEVuAresMIvLT4Z4eknnsLrK+a2DXdQVBbkH7Yc\nJtB2A/MWzSfWtYfevRtJZfMc7jwNkpfa2W2EJ0O4kip+/yRZm49gQzml/jJk0yDdeJyIGSYGpPUs\no8MhNm0/RH1lMQsXt+BRXAiHheJVsdkMXtp1jLZZzZy9V5dUuGSGjwe+sJJAsIJDzxzEXQT/8OIJ\nvlw3yprFI9hyGr2dkIuCLQBgI+pwcNpjoyUcRVLtiFsuY1TaT/D/7cPfvJRZdXU8eaCTZcC0L+Is\nrSFev4K9zz/E9x5rZziR5pHH/wC5PF/84pdpaZqB11OEYRpEkxoe/7sTk5w3TiWEIBCwc2jCwHRU\nks0aBHJRTkUTqHYVVBWX18bHP1yB3TkIiRTdOxyYloSRniI9dBBXzRzcpQ0FLnXTBFku8Cv5VSqK\nBFL87BYLdYObnnqIL3/z39nw0buoa2pjfGKK/e27Se87yJaaFlpmtNA8ezk2R5y8s4JszkM0FaOu\nvo62uUvxejVkDUTcRHhNQsksHQcl7KoFGqS0LNvbJ/nL7z6LrMgsbK3miovaWDCnhjWrZ6Ll8ry8\n9ShzZ884I1Yp5IK6xAI9T98ftjA0oiHbTRa7LR4BIpte5Gi5nb5OCyPDGV/UEGx3SFDtJj4Rw3Xd\naqS/WU9Fsgcp78UQRTy8awcvhRI08AbDor+4iGBjM4vWXk/TffcykR4mnk7wu8d+w7a9W9hw3Qbu\n+tJnqWgoB6f6f4OcHwpQYa9qZ77TRFZUGnMThHs9PPbybvbsOMBQYojjO9v5+chWFq2azezGOvLZ\nGaQmxtH2/JZc38tkK+ajXPm3uCpbsOSCYKTPb+fiRQ6uDArCZ5zKYqinG800SJomlQ1zMPNwqOMY\nqy6YTX11OWMTcULHtxI+voWjPj+f+Yu/QPXOxlbio3b+PBZfuIa9+/o4/dwW5gZllnz8TuxWnnVV\nfpavbOb4UBfj3QNMhuNEhoYxTItMJs/2Q6fYfugUdlVh7bJm4hNRDvSNs6CtG4f8BhGbz6mw63CC\nWLYwppbLkBZwERDqH0Q9uIUl8+0If54Dm14Ps+cIOmxo16wic2GGyOJGyhO9ZOLD+IpreOaFbfx6\nx14GLPMctj+/VyLgFSiNNQR8QWZ5YxyYimGYBt393fzHPd+lf/A0P7nvHjxFbtT38Jrzxqly2TwP\nP7Idb5mHFatmE7E7ON4dpnd4EIeiI2kp5s2sQ9MS5KZUittqyeUyGKaFf/ZFeBgiNNpH6uG/RZl3\nKbReiKNlEbOaglxeLfAJizPgENNg19YtBOrnct3Nn6CofCGSsLF3/3Za66upr6rH7UlgF6BreVS7\nHVWW8HgE1bMX4K2ay5PPHeDYnu049z6Kv66UKxbNwitpmL0yruY6jGkF8N89cZijh9vRjHMXtjlN\n57mdJ2goD3DbulYGI2PMrC7o6DmdMt//yXIe+t4BHnwiQqlD4vIymXRUoz0OHqfEVbctZf38WmQp\nxzcnDtF11EAK2GleUER31wjPRia5+fIP4WlqQQwZWFaQ3d39DE+myejQM92PJkCZLsT1BHysWLEc\nWyZBbyqD3+umf2IKmyzzzAuPUPQFG2tuuonxsXdnfjlvnErTTIQa5F/+9Yc018+iobaWXDbJDdfd\njNPhxeH0YFfhVOgwqVyW9i0HMKwgeSBbMotZc5aQGTpKLB7Dc/hxbL0vcwQXDSMbSJV9GJqbz7Rl\nGRqPPfowrqp5vPTMS+w7cBBhd3C6r5Pf/fZeFAWaZzRQVVFOIpMmkUhQGizjIzfeht8fZKKzn7rh\nAyzdcz8Btxuv30H6sd/Qc/wEp0ZG2FwS4KXhUermtzGz1ManvnQlvcMTDIejPLrjFNFUDsOCimI3\n//1312BzGYxEJujoLjwsWRK0tHq45JZSnnwmgm2Wk6KVPsTmKUobJeYs8nPxbQlsNic6Npzegk7K\nlKpxMnGK/97VRywrkfI/yXrrw1jZJEVFJQSKSjEscFGIVLy5rCKbStLTe4p1t32Kzj//ATM9hYzg\nurZ5jIfCdHQepuunw/QOvpXG6Gx7P0x6DgrhWfv05x+1LOtbQohG4EEK5NMHgI9alpUXQtgp8K4v\nASaA2y3LOv2e7UiCj370dtrb9/D3f/PP/Ohfv8jlV7XgsWeQlDkgVEaHdvFXH/8mwgEXXbwCVa3G\n6fSi2mWcLQu4LL6AroPdVBS5aS5zo4wmmFWsU99Qj3RWnCuRSNMbj+PXfWzq6WRiMozd4yGXSBKZ\nECiqyvjEJJlMHCEsHDYHxmiMvi/9CHwy7uZGTt7/I/akp2i0irC/dpx4LodCgdSs6toNNO57DQMo\nrfAyu9XJ5VdcjplJc+M1Y2ze1cPLu/uoLHaybf8A6y5tom1GHa9rTZpAIq8SnFFMIKgSDMIjr0xR\nVKQwPJLnMzfWYimCPAIXKgoFhEI6bTGWVBjN5tFzJk8/s4cXDvZSRJKP3HkdgViYK/xwJA1j6Tfx\n61sGSqKfL37jb5i18lKWXX819/z4JzTv20Z8fJLbP/xpbvvaF1EcDr7411/iqaee/J87FYVQ7GWW\nZSWnuT+3CyFeAL4M/MiyrAeFEL8APkWBM/1TwJRlWc1CiA3AfwC3v1cjqipRX5rnhWd3MXt+OXff\n81NSicVEYlEU4cHj9bF58zFS+Rz5eJLx4U4s6SJMw8CyBINiNmuvvo4634tMTsTpjcW47Kar2PDp\nv0JRz5UdS+d1coZJfUUVTbKd+N7NNDQ2Y3cHmQiHuOOO29mxdTtHO/aSTcXxFQUYiQzwzPanWPcX\nn8MYP8iJdIR98xfwaucJbsnliE471KAMsxMDuLwyCcARyDNp9DE1kGE0MgZ5N/NmKdjMGq65dCHJ\nvMnIWBRfqZtcvIBblpFotpVSO9vB9Z8a4ut/vYr77j1BzhI88cAJlrfVUCzZAQ0Z88x+1+G1sfza\ni8n0bmI0Z1BkmDSTZv21lbTUyySeHuFHTTXszYT53mCefVNnr7glbFWzaauaD0Iwd9Fc7rnvZ0zt\n2kT/VJ556y5HtRdAftJ7APXeD5OeRSGZBAXQs0ph/X8Z8OHp938PfJuCU10//TfAo8BPhRDCeo/Q\nvdfjZs4M6KqPs+voMJlkmCceOU0saZLMa5iyRCSUR3V68Xny+F2VGIkc2XgMoUmM614epYkvfOFr\n1OROMp6y07T2Birdnre0lU6nmYpGee35x1m0dAW3bPgYrzz/NIEKieGTR/njfRFUIdNYW4HX3wpC\nJiXB/kyS537+X8yyLAbtdjbcdAcPPfB7HjjRQVGZnzmzK7l8ZhGzZ/r59fMKXRPgrdQ5ET7BQPgU\nlqLi1AOUCjvN5Q4Od50go5qkkyr1xXPPkveVcFKLW43hlV3UBuax4bp5/OkPe6kqCdFSWYsi4gX5\nQktBnh6pZEXl8S0nGEkb6IBiB3s8S/euKFpPB8VzVlOyuJmrerYxbD/G4KmzGJqFmH60r/8rELJE\nyao1lLxe/v0+7X2tqYQQMoUprhm4B+gDopZlvT4tv86VDmfxqFuWpQshYhSmyMibvvMMj7rH40G1\nKfTm6ll2y1o+9vUK9PwJPHYbsWwExczQ35XC53OQk4o52nOacKgSc8pACAvT0DHTcQZ0hf960eQz\n80spmTefmOqlRry1TLuQhzPJZTOcPNmDrptEo5Oksykqq0tpaqqlrrQSU0BsYoJMPk9WQLssIZkm\nAwJMTPZt38qcapWll1/Bwnmzqan04/OqGDI4tvbDhEZoMo03p2PTVcbDBsJmoVRYFJVWoJsmJyPD\npL0jDAiLkXx4un8moVAIu0Nm6bwWwiEJKx/iyouDXLDkUqbCBpZkR8aNhUY2W7jCXCbLvo6haWk3\n2JeDWM5gsn2SdINEW6WDzqAd+8zFLFx/NcF/+wUAL730Eq+99tr7cQUAjh079q7H35dTWQU17IVC\niADwBDDrfffgnb/zV8CvAEpLS614MsGP7nuKhfMu4MQpH/s6JomnQ8Tzk1iSgRlVIG6RivUwOhSm\nsSLL3PU+1JpSipwyq+YXUeGVCXqdLCgHZ1ExQjKwjLMkY6ZTC5IkIU1n6ydCY6QScUpKi2huasHj\ncWJqJvFogryukYwnGYtNsmLJIv7mjovJagY+p0RNTQWNDVVUVy7AhYpmyaQtg0zawqXaME0FIXRU\nt4Tk9+KzHPiCbuLjBqNTaRLSODbJRlITRNJpMunTDIQzSJJEMqmxdu2fSJ5Re9/IG/zFZ5PDFyyZ\nzOPx+DEF6IqEJBVgRF7gCmCFEFzwyS/gvflaXGUlqN4ycrpO9W83Eo+m+MMf/vCejnK2CSHetZj0\nAyeUhRD/TIFR++tAxfRotBL4tmVZVwghXpr+e5cQQqGgBlH6btNfZWWltebyy1E9dtAtykormIxO\nkEknsN6u8tsq8IY3zZmHJxDAIYGKQDdN9Fzhwch2B+lUGqfdjjodWIlEIgwPDxMOh88hvj87cf12\npigKtTVVWEYKSQhURUIWEthksCwkRUHYJExdL6jKyxKjoUkMXUHoFrFYIUD2Vnd4x/vB+Pg4siRw\nu11nfgxCFLDzqVTmTPJdlgr60F6Pm2Kfh/GxEO7iAJIk4QAaNB0JGbVhBlJZkNf3fJYlc7qvm3RO\nw7LEByqx1zSNhx566H9e9i6EKAU0y7KiQggnsJbC4vs14BYKO8A386h/HNg1fXzTe62nLMtibGys\nQNUiYPB0/1mSa6+TdRaO6brBwfZ2mpubueOOO1i2bBmGYTKRSPHajsMYoUMsXd5CcMGlfPUb36W1\ntoK/+9JdyJLE97//fQYGBtiyZQvR6NtRsb69eb1e1q5by+PbHy/sZ2XwlAeoumIOo9FTNK+8CH9V\nOZwaZMfu/WjhBChJFlYshG6DQ4cOvWcbZ9tNN93EK6+8wpIFLXz9ix9lYiKKoqogCw4d6eaH9z+J\nZhTGX4dNJVjs5S/uvIHO/jF2bX6F53/xJcZKArgczfQe72JgXGftZUsIllTQe/h5plIZmutauW7V\nzew4dpyyytb3FOY+29rb23nggQfe8fj7mf4qgd9Pr6sk4GHLsp4VQhwHHhRC/BtwkAKBP9OvfxRC\n9AKTwIb3akBIgsrZTSh+B7JLRdhBC2eRJRnhsmGaJrloEktVqA5WcfT4cfL5PKqqcOTYUb72j3cz\neOoI42MhGivqMdWPcezle+jpOM5Ljz3IdTddzeLWwoxtGAa5XI6Kigquu+4NebLnnnuRvr5uiorL\nWXvNVUzFR3G5bJT4Khgbn0AIiaycLcyleWjyV/OHr/2OU6l+FJcTf9zJQ7t+xZbjA2RzBkQMtFlZ\nMO1k8wZItmmGNp13VWCkME1ns1lU2aS63InHbmC3OzBkma5uBU3Lk80XgquGrrF4QQtV1ZUcOzXF\nyPgkt3/xx+TcRcxYNJvOIyE87hDjiZXccPMGLFclqupjaGAIb9EsOo/0UVM/H6fz3fN5Z5uqqu9a\nXv9+dn9HKIgcvfn9k8Dyt3k/C9z6vnsI6JrO/ff8CktQUBlFYBnm9Gh1FpMZ0LZsJYb+Bvq/ff8R\ntr/6BJ6K+VQHy0gmRvjSV7+BaRpIEhimxtMvbGThzNZz2pw7dy533303AIZuomXt/LzvBxSXVPLl\nr32ZgbG9KMY4JIOcOt7PkZ6BQswAwA7x+BSjh8dZs3o5NrvMbx99kmBRE00blnG8rwfz+QnQoWjF\npykLXE9w/ipENo3IxjHzE2j5NPlMhkwyRi4awYiNo8W6yEXekByx22WKS2x4/UU4PT4iUwkcdp3P\n3Hkd2/ceQlJlrrryQm64bj3hUAwhOsnkdTqHZVpX3EjnmIvB4ReZO8dJOifx+98+zbwZ82luqeQn\n9z5LauqXXLn+UiwLend2kIuGCBQXUdbUguy2I+wFpOh7ccW/2c6PiLoAp1dGS5sI3cSjKsRlC0u2\nMHTrnB+2eBNCOjSZ4ILLbuHYoUNcvWIOq++4lX//zx+xf99uXof83HvPT7nj1lvesfmp8QguVOY0\nrcVyVvOrX++ioSJBUNVYNL+EbHmejt7BNzQ/NKhqrERylHB6IAMCHMX1XFw/H2/LXH678Uf0aIdh\nAMpmzWfhysW4HBKKbKEUlFAwzYI8iqZDLg+5nMXI9j/R87uPn+mXJKsU+fxoQmXj5j289OJWLFOl\nurqar/7tR2id3UAqlcTrUQiHdcDCbrcxo7mcE+0HMBwzMZLlHNsTpNhZT1WVl/akTH9UYenV38Ir\nxqnxj4Blcf8jj7Pz7u+w1OZkdk0dNd5yHK0zmbFhA8F1q5Bc9vdFdw3niVMpqsSd321h5/3j1Pi9\nzLXVMWpFUJfnmIhm0GIW2WwOXdNZu7KNX3zn9Jlz//4rnyaTu5eOQ0fYO6FhvrCFS+fUcmB/O5ZV\nWIyPnOxj/969b9u2EIKSiiC33no5uuTk6Rf3cGznLoKrqnHYYxzZu409+49jeEuRqsCaECxfvY5v\n/dM3uHhuCX2HdzMcNrl+dRP2okYu9M5goKaP00o/pMDlgJKAhN1W0NeZFljHzJsYCHRDTDuVIOqS\nz+2cqmI5i3nh4efYtrMdQ5eZnIjg9fmJx9Ls3tkBlkW6xUK12wFBVWUlf7z/Pu789PdIpKcIJVPo\nupctm0dx2UdxV85E1OZYt74NPVPBqVN9jEvd9E2MMoKFyKfJnewmTg/m4W30PPUQPRddwdjsVuat\nW4/ulDh5svct9/Gc5/k/dYT/P01IEGh1MfdmP57OEmp1D7OaWqkMuDju72O8epi8M4VQVJY3LOLe\nf3vuzLk2m5vKoJ/bbriE1tEDXDfPxT893IFluYAyYIjyYJDug4cL0+u0TcZyjIxkqKpyYVkQqG6g\npPQgc1p8BAMSE5Fx/JXFHDhympGIRqkX1JjgyjU38J+/+g3NFX6MnEbL8lU0ohAbj+OdshAm1HVC\n/rWTUDkLpx1kDzhUUFRQhEU+m2dg66sUtS3DFQiSVwQ5CVy2t9wZOo7389CTr9Da2srxPfuJJZI4\n3H68Pj8SZgHa43Lh8toL9JSSTElJCcGgAzNehByQMSfHQd9CxvCQHWmjdYaN6HAv257bzLzqAbZv\n+hXdR4+xnAJ0MYPJBJAC7Lk4+sZHCG0UdP3sRwwaGkdlBYpcb+7sGTsvnMoSFsmiFCfH45x8bpi+\n8hIud0K6M8exsRhDZRG0i0O4gxKLc+fyGsiSxBc+dQsvPWsj9Ooo24ZNdvSlcfuLSMXGkRWV1atW\nMDU+iml/46nt37OHF/74Jz75d58kFUuya9smGmYEKAus5NE//ZqBqSjRxiY8TgeJyQjBugqWemv4\n1j9+j2TKh27Avi1HmIhNkExMsXnjFj5x5ydxu+L84Ilvo0VTUA52G6guGO8eInqqByEEp3e+RP/+\n52hd8ymW3/ZRfCXF5AHXm57G2MAIX/jid+gbGGU4lqfnRD+pVBZT2LCA+FQYWbGx/uo1mOKNappg\nUZBHfn83rx3o4me/eo39Ww2uXnMDVbUVPL7NYt1FZZTa+jghDlNcZMfubiUb0oiOd7HRMvBQcIxp\nzhjcQAaLtJEiBsRNjUJa+u3tvHAqACObo7rBi32dTE2r4MVtO0mcMslFVU5vmmJm1k3TnQFs1lu5\noiSHh2gyw6bxNIde6eavv/gpmprn85ef+zRrb7iNb/3dV/jxd/4d3fvGjdC0BP/0/a8THuvhL770\neebOX0bPtleY6jpKLqVxQduHqC3Okw730VLtQlJkvvmZT5KRq9j3SgflN85hZDTKY7/7T3pO9zEV\nz9J28Wqo6mE8P4KtWaDUunAqhXSJraoYZ6YOZJnDIydoWXUj81evQzUl3MLCroBdOndHtXPfMQzT\nQlVkBnsHiMWzZPIGew71ks6byMAlFy6kttrN2GS8IF1HQcK3sizIHVeWsGppG394ci9+KcHKC+bz\n0EP/wI8fuAImxzBCIcorPdzyla/zuHkv2587jgTYKLhMmgKaYVorisKqrbC0CvDOdn44lQVTUxJ1\ndTZqyxQSjhy1c2ycejlDz+8nKVskoyUFPsBhvlVxR5Ikln7oIiKhXhorvaxcWU8kEsbpCnLnho8w\nNDROzfwFJBMTxGOF+JTbb2fu6pk4ygNowmLe7Fqc8ZkMn+5m5fwGkCep9tsIxXQa5y+iIwyB5Ws5\nfXKUnsM7GVpSTnh8nBMnejkaGgAhcbh7mJETh7FGofzKKq5YcS02CVwyuL0y3iY/qViOyz7yt5RW\n11M7o4JEHPRkHhWwm+fqxujT+Za8ZjAcmqTE58LtlIjEEgyPjqNrJv/2rctxez3YEtZbFtJCCOpL\nvfz9Jy/l5HCEwYFhJHsae1MxWrILm/sEwdJV9AyNcXr41Jn9UJ43kr1vZ/8n2ImFJfDoBuMiRDST\nx9IVVAwmxwxKl0vUrobxzixhu4Sw3irYI4TA7S5i7tK1TNSd5FhIZcvGTWQ0k5qqKhbNa2Xtukv4\n9re/feacJSuW8vuHf065fSYyhWRq45L5rDYyHNu/D9OI47NbeIuLOXRyCkPyk3L4SJyOMNx9hJM9\n8ygOBAiU1OKfCBMz8ljkGO0fxhqDyO44R0sOsHzBpbh0C7vThs1TglFiMquuHNOwyOVyJOIRyGbI\n5/PER/rOuS6XvUDXnTemUyOBIlKZHMQS0yRwNiLRKA/8+RV27DxGPv/2JAeqLDGjOsjg6UHu+fl3\niMrF5CY+zsy6jyGyIX65o4+UboBQwNKRAUkoBTFPwELGxFaYYi2N9ypsPy+cCgTebBX2vIJqmUzk\nYyTHkzgCChVtCs4ZGqVlOlNpQUq8vTaY06nSefQgG26/idKqJjyGQXVZOfNmNb1tnMWrBqiyz0MI\nQTarkUin8FkJWusUzIhMJKyTzlv0nhwillCwfH7iaZN4Ks3I2AAnOo4yu7GO8vJy4tE6olqa6On9\nhPuOY5dAPZ1AOdiHmJlGRONIagFdqWIitDz5bIrJkSFS4+NkMxkGTg8R6Tt+Th8DgSLqa8oYjcTQ\n8nkSmQSGJXA77aQyObJ5jY0bN3HV+ktYdeFCXt72zorZqixx+eo3Ck0tCnG7nTsmGB8aYmL4JJJa\nhOIowkiPYbdMTMtCVYvJmDZ03UTxlqFnJpBUwTkc2G+y88SpLOQMqLhQbBN47BpGtYy7OEdGE8Ti\nJkKCZDjHUOUk1ttU8+uGht/jIafZiccSjJzs4dYbrkJV3vsSLQTdfRFKCNN74BB7dx9CWCb+siD7\n+mIsWriA8SxkMgYT4XHGxkfYsuVFnNZaIuFhhib6qZ/RxIVlsL5qOVtrO+naN0agykM2dJp4Ok9W\nkZGFiWXo6JpGbCpGb89JJiamGBoaJ523SKfOgc1hc9hpaW2iYYZBIpEgk0mjaQYDgyFSg+OYlsW+\n9k5S2RwOhwNTKiIajXL33Xdjs71lK/m2ls/nifYPYmpOIINpGqieFnQ0MLNospNA3RImIyPYXeVY\nmRgk+vk/4FQSFZ456FIOtzpJMpejTCjk5TypWAbFKaNpRqE4dMpJVVUVPq+fF154gWefffbMt+i6\nTufJe8hmc4yHIkxmXuDpZ14+c7yjo4OSkhJqamrwer2cPn0aKIgkHW4/yJIFNYznnZjuSgZHQ+QT\nSZatXE48oyArOSbDI2iZcWoqy0gmI/QP94LNRvPcWuqbZnDZx76COdTBhP9pnMJL07I5dB/oQlbH\nUaZREYZhkdMNIpMxhoZDZOJT5DIWQnXjkCUaGhpwuVzU1dWRSqXYuvPc0QsA2TVdjwc5Aw52DOLz\n+Vi0qAqfz8e99977vu/83LlzKbFX0FAbgJyfAj+RH1dpFW5fAGSFZDZDedMchN1HciICWQ+JzqF3\n/M7zouy9trbWuu+++95+Bfj6dmO6m7lcjt/+9rc4HA5sNhsrPvQhSkrLGR0ex0Sjrq6aZCzO0OAp\nDENDUu2gOLArCoZukIknaD+w723FFN/JbDYbF198MQsWvDsDjJhOKb1+S0OhEC+//DKhUOhNH3Rg\nWdnpazpLsI8Cx2lbWxsfWrYMWZIwTBNLgKbrmIZRYMexLCzLPHNjhoZH2LlnL3V1dXR1fTDmv+rq\navL5POHw2cUM4pyXc7o4/eeRI0cYGxs7v8ved+xpJ51OkEsbOFSFhcuWkdNVrESUfUf2ISnwl5/5\nDPW1Vdx1110UFxfj8/m4+c6PcOWay+g61cneTQ/j0PKotizdxx8iF0kie7x4mhdgb7wAU/GTSWXY\nt2/fOytFWGBhnZE7NU0Dj8fLtddey9q1axFCoOk6nYPjJNMa8akk46PDrLt4CeWlxees3/bv38+P\nf/xjDhw4gFDsIKlY+SRCcmOZqbdvH2hsbOTSSy7GbXdgaRqWgHw+RzaVQlBAami6hiUVEJmbtm5j\n+/btrFmzho0bN36ge/86OX8sFqOlpeUDnftOdl44VT6f51+/9c3pXx984a7Psf6KS7DbXaSjUzz7\n7B948vmXmT17FvV3nAt3zxs6Ejrju/4fyo6HODCkkU2DNpwnGwV/CVhDxwn3t+NcsQG7w8nTTz+N\n3dMczwAAIABJREFUx1OAGVu6iZXLo2ezJFIpEvEEuWwWhyyRjCbofv7X7Bp4A7RqWRDOGVQU+ymq\nd2GaFi9vTfLDX/yJf/v7z2G3vf0OzDIM/P4KYhMpLDP7tp8524TTheL1Fgg1ANUycOfSZ3TnTC1f\nUJxXVQLBdwbMvV+79dZb+cEPfvC+PmtZFo2Nje94/LxwKoRAVmUMTFRsLGpbRMDvw8jnsCLDqJlk\nYch/GyCZTVXJpCaxx/pZsXwZs5dIdPcnGXphP6mMydw5dnwNHsYiPeRGN6KVXUNzU4F2ByARCtF7\n5CCjIyNMxRLEMjq1NVU0NdQTjcSwqoN0R98YVbKmRZHLhoNCEQCS4OLlbTzw6HP8+dHn+Pgd158z\nWglJRladGFg4VBtNs5f9f9S9d5hdZbn3/3nWWrv3qXtqJpmZTHojEEoSEiCIVLGA4BELir3r8cVL\n5Xd8j4oHy4v62qgKIirSBAIhQCAhlfQ+mUym99m9r/L8/lg7CQGinKPnvXLu65prZs/stfdez/rO\ns57nvr/f782Bo4colOwmAXaF+jRLkDI33O6oKmyX4fJzVUxsHo4GjrdPW/lb8XbZCH9vyXRmgAq7\nVa3D6eQrX/o6H/jITWSGx+jdvg1HIsarO21PKsV8c35EmEXypSSzlr0Lj5EimcqRV7ejtgmUuQ7C\nEY3G9no8boOMP0tXZpB8poissOk0o33dPPrEI5iGybwZ0+k62E3Q5ydZmSOeLWI5BFK1MKWkbzxD\nwYT2qB8hbL6EAHKFHCWjyJ+eW0+kqYGzZk2nPlI2n5UqDqcHb2UNXqebd1/7XrKrX6C79wB6fAQ0\nJ+ind//XdZ1NL73Mlq1b8JgWoelzuXTVCmoqQ4gT5pv/xW4PAjxBD6nUST+AoilxKKAIcULFfJxI\n93bjjACVIgRRv4urrruIr3/tq7hcDoqH+nC8uhOzYwphf4SxeALHG6RBEuibGETsHWd6yMvUaQsJ\nWAK9qxNPNIIW8BHMjyFGh2iaPZ+kr4m+HoVMKsZIT5FCIc/eHfs4fLCXufMa6e0+wNo1G/B5ogjd\nQ7aYID82Tj5fwMrm2bD3MEfjFl6HgjsUIBRQ8ZglPF4fH/jcp+moCROLTfDA8zuIWHlmNQaRskQh\nG6dULOANV+A20gQ8UFldheYJkU2OkpYFLEvFsk6didNjE/z1uWfYNjjMA3f8HzpSk8SdXv5w1ko+\nec0lnHfBYiJzZv+Xx93jczFr7nRGB+yNhJQwnDTxqTrxyUlik3GGJ5OUDKgKelkwr4OK0Olrfsfj\njACVE4W7lDAepYFgWVIVaK0lFwxQmCjxu7ZV9I88xvl1pxLtBNBU08Ti+bMYPrqGku7BSA7QPHM+\nRxJFMskBKnyCeMkiqFXS3dVLIuVk24ZXMPQ8xVKJmvo6vvnv32T8yB4e/MOj7B/tI/PU46xYMBth\nZQjm8yD9jO47wu3fv5dBxxQbzYYXHCqIAnjDaJrCeYvqWTqrmps/ej3jIwPs3HsyQx7yeSnoRV58\n9iV+/pkP0D+eYaRrL+tee431XTFKUlAUfvKFDBgmpYef5thLz9HzynqqnQ4+n0szFUllKUtx01Oo\nm58lFW7A+uZXobXplHFxuV34A16WLz+fA7u2c+TYKJZV1gZ6VSprArROizJ3+VLaWmfz0ur1AOiW\npHPCwpvp5+abP8Ng9x6yKXtDozh8fPXW7/CNr95MwOfmb8UZASpFUbjUW4e44XpI5ZC6RLgC1Cxa\nwfhrm1kwcpRFGKC4eCOzvGgaRII1pNQw/fsPMT40SrihkvCmJPHsJENLmmmet4Jtu1/gtec3457X\nRKk0wOGDBwgGAlxyRS0un5eqqQvJaC+xYsU7sXImR0aHKaZjZONjFISGb36RUlMj6tGdyHwayxDY\nwycBNwYqG46k2B/xs3VPF0tnaBzqz+NV3ahCwWvkcalB9vWNs+bBP3Hheefw23VbOZweJRptYHRy\nHEt3kAeKuSxrvv5ZliRiXF9+hxy2jKkCmAtY0qAq3ovn4AGY2nDKmJx3/jm899rlXPqOdzEwcYi7\n7n6CP9z7CP6wi0suW8j0RXNZeullBLxRioMnb72pnMlo2qRKOpg1dwFnn70Yy1KojrhI5nR6R+NM\nxtJ4PX87sXpGgAoArxcqK+n513+lNJRh+rduJ7unE6Ovk+TRjYRREMc7bpdDApZuUCpmGRo+jDpp\n0NvVg2/CzezntiOzGfYMZBi5wcvag10MJCULLIudL69h58FDdExtY9/hvYTDURyuWlKlEtt3bkJT\nBKpuInNJ/E4Xqj9IqVBgYvcaWtrmEK1fwKaXn3nTKfh8FXz4I5/h5z+7g01Pm8ydP5cKl5OmSIS2\nsAOPr4JNPZPcuz1FzqnQk8/TG0tQbVoUhUWhZP/LlBSV+/whFiVi+LH9BsaBn3nDvKeY5YemThyw\nNJVs52HkJStOHZd0HyNHd7BjW4iaploawhqaQ2XFpUu46SufI1EwqGxoY17FDNYNrDtx3OjwGOc0\nV+DwNfONH95G1KFRyuRpqQ9x/++eYN2OY3z93+/hoze+829eyjMDVFLApBM+ejuhfWuwgn5kpclI\nk05xxzD9pRyLVQ9q0XzToaYQjI3twx9x0DhtDvXNLQzccycH8hkUAW59kqPbX2D53HZCy2bSHw/x\n6po+vFaBwe79FBG4PF2cde5y2hobKBRm8/GbP8TeF55gwwuv4FQFaU0wd+58lq68mE2b91Iq5tkE\niLJ+UCAQQuD0BKioqUAoBtV1dVx0ySrGD+7Do7eSGB9BZIcxSin6iiZrd2UYTRVsSolpEpS2Xi9m\n2cKC6mkz+O3oAKaU5A2DvKIwbdEy1u3ZzMdzCfpMg4VOjYv1EqJwaluPGTNbmHvWfLL5PqpqZrJ4\nSTsdzzdx06c+RXPLfGZ6XQRdAYSU9A33nDgu4FVJxcaYXtVEs8dnK6AjbqSULFu5lNvvvJe+Y4fo\nP3rgxO30reLMAJWmwKrLEalJKupvRHo0yJqExiwmJwzqll9O1hckOK3uDQdKxmMTxFIKIV8loSlT\nMb2jZHwqExrkdDjkd7JqXpDKaAXHjKm4Q4386BufxyUs/OEwQmgkR7oZjKX533c/ga9yCgtnLOLS\nua3ccvO/IEyT+/74BBWRELfc/BGk/CM//tEduFxePvPFrzB33gJ8Hi9en4+9+zt5/rln0FTBXXfd\nSyTg5oEjOxmc7CdTAL+SpsKpkSuZ5FQvhlLkgrYOknqGGrL0pSAWy2NKne2BLqyPrUKR8MhTz3Lu\nxUtJj44hZ0ZY9vFv8Lv7/sBv9+3nilu/wp7OUwvJunCSLeZpnzOTcRIYAQ83fOLdLJy1hJbKFkws\nhJSsW/80O146WcZqaqjh2TWvMqWhmgOdx2hprKW6wkcub+JxGMT6N1NKTbBtU4qKwOn3g2cGqFQB\nv/rMqdUBVaF65vupvuU6+7EiwOGA1Mmiq0DQVttAbSTKxOYnKBFnLCtJZQPU6YIqKQl6PRhWiO6Y\nQrxlHnpBZ/XTT1HMpFGEgmmCmctQkiCLBfLpUboPbePxjc+xZOFMcvkCz659mU+duxIJTJ8+jfe9\n770E/UHmz19IKBjCsCS6XqS2KsyMjnbOXryI9tYOJsZ6EfkxGis0KqorGRxJsrAuRKth0G2UqDDc\nzGlqYnBykFRK4nRqQN7ugRwb4fEXuwhWCjSfxbbtGymVTBRN8I37vklprESxaPCBm28ilyzi850k\nL86fP4dkYpLq6tkorlpkbRXt7Q5GYkO0VregorBvYC+f/eRnqa1oPjmeQnDRinNxOFSG+weoifgp\nGYd4blOeojOCp3oBDoeBgRus02sZzwhQ6brO7T/6j7f13EKhQDqdPpG8TEzEmEhXsqd/ggN7/sio\new4lPUSH4mTAIehpbEN3zMNRMYv0mEBkc6zfuo/JePyE0wiADuSEYFZtBS+/+BSl7DhmqZHfPvAI\nY6kcvb293P797wMQ9ABmkt071r3p8zXU2vmphx68C8uyODapExsvoHoTxHUoyBxGsUhGLyBKTp7c\nsYVEJothmChlRoU0IbMPinnJ+MDxT2inGywkKU4mYxMFu6H260E1ERvl0ktXUB1pIpWDxx55gh/c\n9h8IVWM0M8nQcC8Rb4SSbuL0aHZziHK4XBpSSurrauk51kvzigvoWJTkR/evI6v78Lt0prS1Mnxg\nz2mv0RlRUI5Go3Lp0qVomoaqqn//AGzVMAiymRyqpmEYBSxTB6EiLIHLkkhFxXKomJaFUO3G3vXR\nGhJDg5RKJ5smGXAiPy0UQcjrI+DRaGuZws69BymYkur6ekZGht+QdX5DpfWUEIRCIXRdJ3bceOpt\nxpQpUxgaGkLXdYRqv83faVuM0+kkGo3S19eHz+dgaks14WAl3mAtW7dv45zF5+D3BhiLTzA01M/M\n9lkcProfS5fEJpJUV1czd+7cE6+Xzxfo7u6ho6Odw91DDI1nKBmWPRtPreY7//YtBgcG3vLkzwhQ\n1dTUSCklt912GzfccMPbOqazs5Pbbvs9h3cXaKqdQtfwi+jWIQQWleEVvPe9n6WpsYlCPsaa3/6C\nSxaezfqREZo6TJ5a/SDJZPLvv0k5/H4/l1xyMTfc9A6mtbUhpBOsAVA9CFGNIA/SQpaz7FIKpHRz\n5NAA997zW2acffYJMJrYhRmLsmBZ2vo/KU8+9uTj/Pn3vyWTyVB7jkqVJji40TjZ54iTzj5lNwBC\n4TDnnXceq1evftvnBbBo0SIymYzdhuVthlIWmI6NjZ25LAUpJZOTkxhSEI5E7P4t4iQzxCorlJWy\nt4IQ9oXOZkYYGJvEQGPlNR9k3epfk5s8ysqLljH9snNJ6hByVxL/VZ4p2zfR+pGP0lXax/z58ykU\n3rqoK+WbrZicTie6IfnCvX5cjRWoXg9SjSAiQRQVhKYjLLDyJma8gJEpYpUcTJUmVaEQt3zhC/ZM\niQ0qAziukdUt0MugMgwwdMmWB39DPB4nnU6TewWi0wUTMVuIWh+EWAZwaWQLBjgEsiixpE1P1lMx\n/McNhMtjWG70gLDsL4fHBrBhAPkcmUyGpUuX8uEPf/h0V4jXu85IKfn85z9/2ut5RoDqePzx2WeQ\nddW01EVpqm8mmc6QtQxGUgK3brJ0VhM14Up8HnvnUeVuoNaTQ0u9xLXZVhZH2gjFBlm+4WWa6tpQ\n/F6yyREK4xt4rZSgcfQyXDUO7rnnnlOscExsspueyXLnj3/NBcuXcN7ys8mpCs4jXRRjKb51/285\nkJ4CWostNVHtepiqCCwDpC6xMhB1QzwHRQc4rQlqsK2lj89Qx13jDFEGmAK6Yv/NcIDuONEcFTw2\n8DbuKdNwJKRL9neXIok4wFEpkYWTdklTfDArApawMzVS2KAyiiDyoBpQ2+oiny1imirBIIwkoa2t\njWuvvfYtroqFlLspFsbJ5QSB4CJUNcyXvvSl017HMwpUw/EBHl79B6rCLhoC9YxJuxt5Q3ULVy5d\niBuFvuEEsXiKwYEE2dIEi91V3NpwLk1/+RWmr4KaxZ9BHN6Nfu/3KVR08Pzgn+m2EjymergxoRMM\nG1hIJJKYmSeTnWBnxsfGdJBFxx7nmTu+w19+EWTeJRfRvuoSrjlvAZGKSpu7VA9KVKCFwOeGUBBc\nUpLqN4hvTWEe7OPHP27D2+jnju2C0iFQ07ZuroQtfYJT1PNYwva/tMrPKTlAVUDzClwKiAoo7bcB\n7AC0IqgSShmTQAASeYGiS1zlyokhoVSyX1fRsA+0QBon5fZHDxQp6mBKk+bo374mNresimK+ns59\nI8yYW8LKnJ71CWcYqEZGc0yaI1x72VncdNnV7C0GUfKCedEAL2zez18GDmGVTMJBga/aR62V4xZV\npb77z+gySc2cW3BfdAPJns2kJ/rZkN3BXWToBBIizGsv72duwM/zo+toUJt4ae0LPNE/B2cmxHRF\ncN9rL9NZSFFVSLH/j79j22OPsn7JhXzmy19EUQXt06FxNnj9kpHuIkNPxhneMEqxexySeTzeNDVm\nMxc1CWbVwHcl0GuD6Xi17PiayKK8OZCQKy+MikJQlDYOnC6Fyll+ioUSk2oJv2LPeHkLTAGRiCAQ\nkkwOSbJ5W/0MNmgQoKknQSTKXidVlW4qKwPsPzCBbkiMt7GclrLA4EQ/ijmXkYxJVUyn0qvZBiqn\niTMKVHPnns+iS9/B5U0RvO56isNeYgf3sqD+RbatXUf/gJv61gVM5p3Ma5zOMkpEJp7DUqM0Tv8W\nYtyF8fvH8TnPZ6PYzk9khtcoV+eMImYyiZ7X+I87vk2wykeTr57JteMEz1vM2tV/wB8fIaCoDFgm\nPkAtZejZ8DTeaXmqXdNYVWcwsnOCLY+NMLJzGDOZA1nAnntcIFRMy6T36BGKps7iWthXPjfLtDAt\ni0LJIJcvEQl5cagKuWyONS++ypxZHfjCEYKVQTQglzCJd2W55IM1HDs4RkCReIG0BaEaqF/kZP1L\nRaa3QaYA8deZXyqK/WVJENIG2LlnT+WKlRdSPa2BP/zibh7fNIqRe2NDSIl9u+tnaHAXvYOD1FSl\n+OEPnyfkr8fj9NISnUZDvYJQ31zdOB5vG1Rlf6rXgEEp5ZX/bMtrgGUrz2X51Zfx4Hfv5Oi224lZ\nXjLxGE84xolrUVxN7TTOqGXDzqO0tOaZi+3zXSVWoR3NIksDSCboFLu5Xw6yEwWJQMNHWHRQKOYw\nrSBtXx2mJLPE7/Hhyh+m56nt5I/tY9TtILAogtaVJpko2kMsYGIwSaiuxAMfWYsenw6y9LqhO26Z\naGKYFrf/+jmG9v2ZxbNruPHLn2AfsPqlLTz3xKOMT4wzMTlGPKMyZ1YbC+ZMZ/WaNfQc6cSpOfDV\ntfPjn99RJuVBqUey45UY4xOSRY1QXQGhEDjaIHqOwv2fmYk/P8RPfppk9Yv2p1EUTnD6FWH7VEyp\ndXP9pRfQtHQZpWyMho4ZaNtHcUs7n3wyDOAQQiSob+ilviGFZWX4zm05IpUpSrkDeIOPI2Uau0Hl\nW8d/Zqb6AnCQk63Pf8A/0fIa7Ck8EvHjq/Szc8dGjnszTiJAmUQbN3k6V6J+ViP1zX4O4yVEiDoj\nRZ7DqJQYZYTb5T6eBFzeMGFXBE88SEegDQsvxZKBWQkjD6g0PRWjYvIoR5ITCLOALDlIHjJQinZq\nQFHAOUsjV5nGkhZ6bABoxv6PPk5dc5Yf6xhWga37usiO+VArfaw81k0yneHRZ9Zy3//9aVmIaQ/7\njk2b+b0KppnGvjlK1K5hfnHn3cxuLbeMM2DkRR0k9MRhxtmw5Co3K+d00Np2EcFAimL6BQqvqzKY\n5V2dUwOnA3wewSUzG3AkR1EaZ+Kpmk+gU8f12MtoDnC6jh9pMTGyF68/BZobj2sPpZKJQ3ucmmgS\nKUH1gVGCZFpgmv8gqIQQjcAVwHeBLws76fJPtbyGMmPW6+TK667h4Z/+gGJ68vhfgAyBYJoVF7dy\nwcpz8TsqeV6JcidJpvEMHhQCwDAGu9CxAKeioKkevEIh4tLImRqa6mB4SKHrZYvXju0nKgQ1TjeJ\ngkLJKtkcE6niUF3o7iLqp0zizaPIx0zgMPZyWsUG0/GcvBdNzeAQL0CvScgdxp/YwfZHtxHPRZkw\nB6mfMQ8pQXM40Bwabo8PxeFGWgLLMkFx46sIE3f4GI29LsVdHrWJNLir3Hz42sVU+C2E2Gn7dnYK\nNr528ulWOWfh9UHAA601tcSHnYxGw9T5WhGKh6ZZ86AsbhUncs1jjA79G95AM41ToiTj67jxxlE+\ndnOW9gbo3AWZmA3WrqRK8dQa9inxdmeq/wP8K7bhLdi3vH/I8vqtQrXAISWVFT6izRFiw0mcTiee\nsIspc6aw6PzzmLVgOjUVreTTWcasBJNYTPLWOadsJk6kGKXW56eiWlCj+lCFRTxlod1i4Fuq4dEE\nMuNhfEPG3igNgkia6AUTWiGXlSTS9m7R61yPbjyPWa7QexAUBGiK4CMrg8yfpRC0NCocIxjOFMOF\nIAPDPmZdeT0dK67ANC0saWEYBkJVMSwNXddPyMUMQyJNBavUdcIUwyrfwqQJuzcU2b9llKktKqpU\nGR5w89gDg6cw3FUVQm6o9cPUukrCrhpQi7R96D0Ir335xgc6yZjgEidEQ+SzXcQTB6io0NFUi93b\ndDr7ZrD+xX7mfyLL4vOhqsa+s379O7Vo6j+wUBdCXAmMSSm3CyFW/L3nv914o486gNRNSpkc+WQ3\nl1/fQKlgoKpegqEo3mA9NfUthCO1uLyCfNogRwZ7tnizhk9B0CwamBFqpT4SxaG4IZfCqXipawBZ\nb6LWmTh8kBMFPBfak4JZsLftZhZMF0gfODN2zfuTZ0FLk5dk1mIip9OfhF19FsmcycOvxPjrNsGM\nGhdfW6XiKploDoEpJZkSlHBhKRamaWKioFgKpmWiGwaGaf/eMAzbYc+yTiygFRV8YUhPQF+35MYb\njiBU8DsEdTWCBdMtAj6YLJcDnZrdwbQ+pODz1FHIZ3jPB5fiK43D2F5kzQK69u+zhTgquMtkA11v\nYfWzKeZPP8J7Pmiy74jGqmuuo0o8xNSpXejlpJpe8qIXq1G1088Rb2emugC4WghxOfbNPwjcCYSF\nEFp5tmoEBsvPHwSagIGy5XUIe8F+SrzRRz2bzSJ1k2ImQXK0h5rKACZz0VwBgv4qNHcUR7gJy+Gl\npOSRZr5srPHWolAHYMk8k8YYWkbHyEq0Ekw1WshkQQShKmr7R6kGOBXIZGFSASUEEQ9YGiRTtrO1\nEBB25lhQ4aeuLYxGnrzpYDxeIGaojE/qiJKFu0JS0GrQdZ2cCGNZFvl8Ft0wMfQCSAvDMBEITMuk\nVCyilwwM08CwLJAmUrXXXhaAAcdXAaYF6ZRt2FHwS+Ysl+zugrEYqOW1UV2FmxUzAgxnkhQyeWqa\nfPT2xyl2v0D7Uo1SOseRV57jvHo3sVgBbxlUwXADH/jw5/nMZy7DX3sN6UyJ9GgS2TCbA0dX8tD9\nm3B6oiy79AZe3vyT1/mu/hdAJaW8FbgVoDxTfVVK+QEhxJ/5J1leCyGoqKhg/9ZNFLNx0vFeMuks\nUvEilCIuxxiKEkO4+1CdHjTNgU9xo3jVE2yFt4oMJhkOc/R1uAuJCNZQGD0hUFwQCtvbbmcegirU\nBsFUwNBBz4HHhKDhx6U5WOaKMLUXRF8eRbXbcocUFUsBKRwoQUHmwmW4VnwKhMahzmGcv1uNLMRQ\nLInDMsDU0aRdH7QsHWHpaBhYisSUBkKx0BRJOBzGcerWDKcDNAlFAxxOOLgfrCJobgiFQjidThbP\nnsnU6T7Sw4P0p9yEM3E6D4G3qgp/7076nn8Mr1Vi1rQ6ur1JEk4Pfr+FoqhIUUsiOYfv/WCMQrFA\nsdDN9o0OnlyTJhxpp3lKKy+9kueKy6/moT/cf/rr+Z8pKL8OVFcKIaZhA6oC2/L6X6SUxXLXrQew\nHY1jwPvLTsanjWg0KpctW4bk+E3+73+mcDiMpmlMTPzdpdop0dTcxHh8lJKhn1rnEyd1b9KyTumI\n7tAcNEfrsTJDKCULt9sFTltzKPImxaJBJlMi2uRn0rKIpSwUE1RHAF03SKaPuz3JN52afItHjQ0N\nTIzHqfd6CQVcZDNZWqa34wgG7fuhxF48vS70fIGDRzrJJNNEa2uoqanCHwig5JMkYmkUlwdTU7F0\ng0RiBIfDIBQJUCh5ONjZQzabRdM0isXiG5puC7vZgaqiaeqJMXr++ef/6+b8p5yylOuAdeWf/2mW\n16Zp8vhzB3E0vovC8DpkYit2EeP0MWfOHAKBANu3b+fmz36WilAIFTgdcUYef0WjxHNPPM9QQUXP\nGeTSybKts4rQHGgujVLcpqpUhh24nCqZgpMbP3A1o53rKAwmiTYE8DX5aawNkzua5tBrPezJmnz0\nQzOYzKUZGSlQmswzYXRw1VVX0fCfsIweGxtjZHiYl9du4KoZZxEOmujdB5jq11h+9YdRI42Q1xGV\nlXZK3uUERSE2OckrX/4y/V39zOno4OKLL6U+4GLk2UfwNFciNIWUZTBRMNmVc9HSMY1ZC5vpPKrz\nu9//ifbZMVKpJCMjReae3YKquYlNxNi2cZTBHoOF5/tYcm4zyViereuHMYzAac/hjMmoV6z8GU0L\nlzHcH2N8z5PI7ruZGszQ1XMAWTaueKtZ1ely8akvfpGpTU2o2CKB17frkK/7ygGP//4BprV7CLkm\nEIU5bHlhO5ZhoKoCRS9SypcVwE4H77p8Hl0Hj7HzqIFuCCazDrJJE90VpzbkosZjMifkZb/TRzqW\nZCKVR3M6qa1wYGk+JgfhiiuuYPHixW97HPbu3csdd9xBwdLYoV6DMfI0V89oppQ4jFz/PUaOFWDa\n1VS0zUQrFVCijdDWcULNvGBhCzfedDG5SYMNd9/LlJkdTFtxEQx3ET62nZF1z7Dygktpv+oynN4w\nI5MHEQpc9u5aAloER86Hf6qHe+85Qo1HJZ8wEBIGe4psNYdBGtTV+0j+jYYZZwyofCE/VWGN2qpq\ndrg+QqH1nbSl76CYz1AydOprGtjTubNM5XgzuI7nNo6rhk0pyafzjB8+SsPcmUi3ZgNNkZz3cYvS\nggJ7NuxgXpNF0Kcyf5qbGu9cul/K4jFMrEo3jVVTWL/RvnPruknv2CQirSNqNeREgaurw9Tow1y+\n8kIcqzfhzioMZA0ShSIir76tPjSni0hdDdfedDlTH3uZA4PdHDqSoujz0eFT2HHnN5jyjnfROm0G\njuEutPFhSk6/3Yzcb1EQWR76wY+Ier3MbLgUpaoJwwRVt3DW7GH86BFmaDUIxQcoWKbkgR8forHR\nQ2W1wshTgkDhbHwpqM6M4ncp9A3mGRu0kSSEoLKy8rSf/YwBlddnorihpkpQn1QYVqMEqlbw0fYg\n+VyWgeEB0DNo/mr2Hj6VynqcuQnlvaCUjPTH2PbAXVTLDIHp30Rza/ZuSoPq5SaOkAfvKqiUfKZv\nAAAgAElEQVSdZ5teOJ1ZumO7WdU8m6tnr6IwkaF7y26e9BYYiTkwTIt4IodSAm9RMlVobNs5SHvI\npCWUJqxkiAy7CNZ6OJbNYKX/DlXz742HS9Jh7scfdnKkq0A0Usc3/7CPiyIFFrjyMLmNyk9+GFHU\n6Vu7hiM9aSzDwOtsRNWDeH1udm/dTO3MObg8Aba9uIaOlgomJnNYeMmMJwk3HZfmQ2+nh8RYFT3j\nPVSYbr7zhSXcff9vcLgCrGybTsxlsnrXQdLFHH9vzXvGgKo40YXDv4TxtCBSKVBcKoo4n8VVgnw2\nQSyd4lBfN6bspqiXgBbAPr08NpgclIlvRYvH73oA9+BuFl53PUrAdQJ4lpRYlmDbnV6GB1y0vcOH\n1ZIk2CxJ+vJ89Z6dbByeIPD8JFLopHUDu0cXGGU+kjPnZUEih2mAN1pDKBRlXlsj3vgE109vQWRy\n9AqD0X9gqkoPDrHzF7dRLVN0TKnCHajl2n/9PHr/M/hee4z6y1bhaDoPKfN4dzzO6OajSBlGU7xU\n1bRwybvfw0f//Fdif32avXv2sHHzZj7wznM5sG8/YdXJ6K5XCTVed2IMeycn0RIZNCTXXPEORndv\nY2VjkA3DFo2VLm49q5WpHh+/3HwEhM7f6q9zxoDKG9+HP2BilDR8IXD6oZgPo1klasQ4rXUVOJ0u\nEpkU/qoFeGuXQc6uT+jY6yUF+4SODRepnD0Xo60Jx3mXkBMn+UqGKZjcIDFrBHvGR9j1kMZ7Z7RR\n9X6TLYOdKBl46M89rHSHWL6gCW2nzR2SEgIulWLR5PBAiRdSDj545fnMWnoxYYeDmYaLZOdaSkMD\nXK6rvKbrPP260dV1i1SmQEXY+7aafBbzWfqHB0ipJoYqGege42Pvu5xpc6soGtPxejMg01ixfpy1\nEbbsOYDRfi46aQwly7GuvRxK5BjYtZvYyBF2Hh4jHRtlIFnk6loPm9feT9Ml03m9D7Fplbh24WLO\nb53CkYF+otM6qE0f5Eh/L7k2i5vOCvFKZzP43XTFTi98+M+Yefy3Rnzf0ziHDrN4rmTODMm0Wpjm\niOHPdeNM9TC3UnDF8osRikq0cTqqPnTi2BI2qLLYs5YzaOBzTuC3kozteJFiIkYe2xPcQtDSU8W2\nn6UYedCkptvJ3T84wm9uTHLo627OLaqcc65G9LISWZFDKHauSFGgra2BioiLiuoA+3UXP1q7jxu/\n+394cfsutmzdhuavI2mpGKpCtPpU9lvnwS5+csfPObltKLuSy+JbqhpURUFB5azGKVw7YxY+E468\nsoUj3RnueyZB1vAhY+uRic0E5r2DjppGFCnpSh/gLwd+xp/+eC+BKhV3i4vKa5upvaKZESf05wxG\nY3l2dR7mWHwnhp4++aYS9vQO8vi2Q7x06BDffmI1j3d2sv5IN/lskpYWlXnaML5C4UQTzreKMwZU\npdQQz3zndh7+9xfp3TxKvTnCezzrqVbi1LUvpjYS4pqVy1EQdO36M+nYSaK+gU12MyTkJUhhUUyP\nMDF8ACsxSCqdJMtxUIEIBlm2pA1jAm75xBV0XFDD7m0jOJKSr9xWwaovSbT5Jnt6NRwu2/vJ6dCI\nVIepnxJiRqvKFe3vxDVZycTYJEN9h9nTO4LpqaNhzhKmnnM+ztDrjMikpD7g5Jabrik/tpB6D9Iw\nkPu3wboXYMc22LUN+jpBSsJuN5++cAlph4O1vePkijoD+/fy1E83kBt1cXDdMWTPc6jVjYzvy6KP\npxBC0Nnfx5pXtzDk0mieU4evUcE128FFn2yh4kIXOpB2V+LP5/jLL//C+KidPlGFzfLsnBziTxue\n4oW9u5jIF8CyeE+Fi5pKL56QxSXhEUrDO+xNwWnijLn9JQppZOkgydUPsW+1ycXnTOWy91dz1Grj\nwvmX44gcJH9oDw2VTfSNH+P4YlFig0mXdhkDgU2V1SWxRIZCSWJlirjKQgMUhcppM7hlWYAp7T7q\nz+mjZovJkgsjfPD9FRRq4/QMaBxcF0LtlTjLs4jf58Pd0kyF309QryDSO50n1ad477kLmdo+n65d\nBzg2OEpjazu19fWMbtoHaOT37kbfv4ewyBMe6YE5c7EmJimNHqY0ZOJ75nnkwBhq2AlFi7FKD/Ks\ni1EExDMFJot5ojUBuow80VyedCJLVdjPgW0DzHrfTLpf7ubFb99NSjhACJLxGIlxB8E6nVCtly3r\n44x19rJl9xhmQeIJqXQ74vRtM3nfcgeqp4CmKtz+rzegO11IhwKqQCDwOp24TRP/I4/iyo2BWcfy\nhc0cHumi8x/p9/f/KlTVi5MePKkecib447PY81oLj27u4dCBHhLxCdz6KIXCqQZhAvAKCJXL+haQ\nFpJCtkTJcpAumLRpLlzYf3NKidMnqJjazmXXjnDHH55l46MZbrguSmWVg9V/rAVTo+vRfq5cOp1N\nLx4BwOf14/M205XKUzPZjF8W+eanbmTx9FbQKpkxfSsHt2+jPlrB+MQYulEA/Gy76y4iO3bQKCRu\nTHSHSt6yKEpJIS8Zl+Vmp7k8FT6Nw21nYQlBUQg2T8ZpqWxm/3AfVU4vE7E0QZ+HimgFVdNmkOnR\n+M2/fZ8Lmqfgs9x0AfHxGD0H8kTyeQaTJj6zCrdowDNqUVVwICvGaLrAx8ZHk+w6NoizoQehqFz2\nLx9nzuwZHCccImx9gNQFD27fR2zXNsIeFcUVZMm0an7d809gfv53h5QKDkWj0hT4NXD5KtnUOcmx\ngX56+h8hVyrg9CpMZsZPOU5gV7gDnJQ/ZQ2TTDqN2+2z/+M0jcry4tglYfBgH0d69lIatWiIN/OV\nTwk8XokR93F2uJq9yQRich/J4b3ohn2gw6GRLiUwHBJPXKVxlhvvohrSRhG3M83cS8/H63VTKFqI\nZBzF4YQ8jCx7Bw/OOJea5Dju4jgz2ps5MK4zkshQsiCWkWilPGHNSUNVgKor34nyynOE6ut4z/du\nY+ufn8MYPAoOyd50lvZQmF17erhOjTL80Dj+lBMhVTwOeyUT9GvU1gpix7xE6koUYnF2dedpnlFJ\ns6eGY2vGeHVXAqci2bBzP+Fpx72tSiAynGTU+wEHWDGWt8H4HsngzkGYHmDqwnMRfZtPey3PGFBp\n0kSVOnE3ZPJF/rrxBQzLQNd1TGlhYp3WD/74wvBE4rNkYVmScDiCx+PFoTnwc5KvmUjkUZJFFFOn\nvcVHqihAc1EVqcA/J8Avv/8adXWglrLk0wBe+gdGeHHdWgzdYFvyEO4JN6WdJUCgqSoONMwsyGIW\naZZI5/NUts8ksvg8FE8V2ViS0WIWR20VIu+kpihwKBotUsXh1FErq/F7XcTzA/DKc0igZu4sVkZr\nyOkTrHthLe3VYQ70jbKooYr1h19jr64R0DR6hmJ4Q0HwVZJL6ZjCT8e5UTBNihGTkVgCxS8xGizm\nvL+K8HiOhDfP/v059u893pXMC0SwIaEBbqxCP5Ri1FZX4bqgg7EjRwmU8tS3Vv/Na3lGKJSj0aic\nM2duWU309j5PdXU1TqeTsfFx3vXxjxMIBE7IHUuxDEOHduP1B6jwuwlUN+AJ+NCBowf2U8ruwDIL\nqAg0pwNdqlgIXEIhppeYGEmi5/P43IKhYcjmNVpbWzl48OB/9rxoX7wcnF5M3cA0TRwuFVVoqDjQ\ndRNVKKhOgdPtR1UFxVKSob1b6Dl2jMrKSoQQGPk8k5MxvA6VXFFHU2xBrWmBS7Gpzy63i4rGJg53\nHcLpUeziL4rNVVcFiOM+njaX3ihaFLMWDQ0NGIbBVVetoKqqFnuWUgAdaU4iinESe/bjVyVj3SO4\nNJVIVQU33/M0/UP/hILyf1dYUhJY/GWC0enlx7b2TdVAdYAoN2BUFDtfpGngSvewtDb31l3KpwJn\nzT3lV5s2beK2225j1apV3PKJLxE3fNy+wWLgYBIrmcY4chRfRyMVMxupjujc9pkQHofgu9/9Loc6\nt9HU1MStt95Ki/DA0xuQF56D7GhA0U6/gT5y5AirV6/ms5/9LCVFsrp4jDW/e5LuJ1/i7Hdcytc+\n/jFCrjc7Cz+dHefFF15g3759b/Gqbx2BQICFkQq6jxx728cAeDwe8vkc3//hD5ixzEdsJEu8oBLf\na7wpca4oCgvOXsLO59dR/Ef4VP8vQloW7mgVFR2tCGxJkVBsMDm95Y6eDnA6wTDt7/qxHJWeNK2t\nrfaLZGWZNl6mr5Rn4ONUjX379tHd3U0+n8dR0cgvHyiy43CW2NZxGI9BVkf0HMW5e5Koz0nT164l\nnY4zPDJBX18fCxcupCpUwdYf/RL/w/dy9gONbHj/p5lz8TKmTKvA1TENxaGdopmPx+MUi0WmTZuG\noigMxRWMj9/Apg2b+NOG55l+1Vl8a8WNaMpJboWUErfLRWxslHQqgeZUUIWgaFq2ZVFex+PSTlQH\nkCamCf5ghJlzFzGadyFlWZlcZtocvxlZlp0Ss44bflgm0UQGT8UkvopjTDsKvozFhpSgMCDfxH8U\nikJDWxvd3d3/M2p/tVFoarZP1ixJijkD6bQIhDXClQqaU+DQ7AFRFEgl4BRqusEJGa+UktJYFi3s\nRnW9+RTr/Ar//uFq1u0wWOcx2bi+h9HecUrJCorxEbSFVew6cJAH7v89mUyZpyst0vueZosjQW91\nNeeM97Lm57fy//1S5T8qqlj5uW/j+fxNEPCe1gjmvGAzIyS47Dtf4qk/P8fDB19i/ow2rq0791Q3\nGSHwaCpun8r8GV5aIhG2DKfo7EtjFnSWtPkQlS7i6RIeJcN4wmJwEkLtyzhn6S8xAanYPg2FcgNw\n04B8DgoF+3suJymlszDxNWpb0rz6okWLFyYcKu4Ji4IOTmGP9+sLMm/VbOqNccaAqioA9RHJnq39\nrHlwM4N7t5FPHsRd08Cciz/EB7+4hKZmFUXYi+1RB6eASmoSJvLQ6MXMG7zyv3/FgutXUb10/ikX\n2ZLQWYSaWifvvmoKN1zZzGRsFTsPTPLCq328/MIhcLj49q/H+fiNn6f32BdPHKsUk8yodbLbyJIC\n7pQWuwyLb02OIX/4fbz7dnLWr35EMPxm+ZIQAo+qMd9TxzPTDlG7sJVQyc9PNv6J2Zc2MT3YePJj\nSomSyRFLF9i8u4g2zSQ7WUTgpqgaFKRKhQJKoAqPUo/XZzA4OQ5CwXK4cDhsFbNlYVsqWaBbAukA\n4QIcNrtVNwFF4eBrJSYG4WCDYL7XIumXpL3w3mrojMHDEvpSZcms5nrTub0xzhhQ5ScS1Dgkz//m\nAbI926gPL2T3sWFSkxt5Yfxshg+EufM305nTpKKWjeNPuatrFrJ/krWHh3jk17/imWfu5/czG7lw\n6alNikq65Bs/G0dt1mho8hNwCuqqPUxpb+TaxY2853PnsnHtKEamyCs7JxmasOVSUkoyI30srPKx\naUaUr27NcrtpsQS43TTZmB7kh2sfZUPiGwSD7te5bJwMVShUmz48mFQudDL+UppotpZvbPst9y7/\nEkGHt/w8QVvQx7ZCjljG4LF9SaorQ3zza1+hrjbK4T072LBlK+0zZvK5z3+e/Ue62Pgx24nQUu2y\nlQWggJlKUpwYwdPegdSEPYNJ0AxwFBUQAne9JLcdnhiS3LcQxnbDhc1w/lSYyEHuMPyiLFIV8jTT\n8OsvxX/h+v+3xH3f+Q25ybNJJBoJRltZccEi3nn9h6itKtHd6+OhR+J8+tPdrDxfx2ltxWeluWrV\n4vLayTYPKFR5efGnj3L/o/ex0BehccnZb7oVSUtn6KU7GVU62O6agQy3YrlDVIeKNEfzfOGDrfzL\n+ZX8+uFucgMjlHL58nEWm15+gQvOPotPXjafH5dKfHB7L/8XOBuosEyezmX5w//6NJ/79gfx1LdA\ndpA3RtQX4prcLB4f+CNWTRhS9fQPDPO5l+9hVWQFsxuqMITg1l/+ii07d/Ltf/8exVKJ4fEk3/y3\n22kM+RHJOIezJbZs2M7o0R6u++SHAHs5pylQKsu6NIdkomcvip7B6+6wrZgM0J1QXwNpDeiGvAFS\nheE85Gvgk7Mh0gbuGqgWMH4ElGrwVCp4/qeY8wPUh+fT+ewreJI9eNQQ115WzbFukyVLXOQXjnHP\nTz5AcbiKu547iupyM6s9jCzlmDPrLGSqiJlIE3EEWRYvsnnmeRijJrnRdHkRdhJZiqKx4qLLGO/d\nxMGDu4j350iUqihFZxO9eAW10uQ3dz/Gy5sTeB2glGccRQjO72ihpcKLGvJw+Vm1/HQsyU39CX6G\nvZy7Lejn4PZOfvL1n3Pd9XPIFFTyuSK5bIFA8KR94gwamR+YwssTm8hN6oQ0J8+md7F/f5FMaoyL\nLTc3XH8d51yyil07d7HusccpApPZLKFSlsubndzdDbM0iAz3U8jbO1CXBivbIeiQ9PdJsm6TA1ue\nprJ1BiENFMOeyeq9Ra5pHOIvst7O640DOdA98NcBP5fMzqJNkXaLlHHoStrqI6dX4vr7mDpzQNXe\novOJjzdy74/vR7cMHr5/nMlsDXt3t1AdNSlkBmhuDJGNZQlH3olQcgwd6yOXyhEOepk8Mow8NsCf\n/vorbliwlBWlElO+9SOKjd+DjihFvSx7MnW6d6/h/de/ky9+qoUdx3Qef6aPkUkXO/aM8KNfpNj0\n19+iKM24nBbRmgAj/YCAmqZKWloCqNU13NBeyXlLZnP77zfwLy8fxVQVqsYneCSWpOtYH7du3cmS\ndy3gSKfg0aee56Yb3gXYuaXmUC3fX/A53vns0yScB8l1LsQ9mGfE3MfC1jk0FWzTIZfbyZWXXkLx\n2SeZwOLVPOg6OAtQKit+jk1mmTlha7hCXljZAkEBgRbB0HiSTdoIgYyDj9UbpAsq3eMFconXWPv8\nAL2+9zDtdRkRKw9r9ud5VUom0jBvCsSOwLgF0gF1y1R8vr8hTS7HGQOqI71rmLng01z6zvPo69rP\nbx6/l4lEmultS8EMUFNdz9hIH6oEM/UKTXNmsmD6HDRDxyxkKCaG2DHSx7OiwPyKKKFFs5Ebd6M/\n8Bzb5tQyMt5XfidJ595X+PTa+/D5QlRGFxFovJAKTy2D/WMcLrpYsOzdnNvmYe3aR/G4y/txAWqb\nA63OBdk87go/HYtq+UXDZdzdvhu8Tfzyoac4OppkGfBKusSe3iIlQ/Dggw+gyCKhUJR0usCCs+aS\nTyt4MiESU7rJJfLkXp2gtraaW66/ieyBYfR4H4YZYOnK5fw2WkdsYBALSLo0StEGLj+rkfV/XU+2\nWOS8pA2qiTR87yU7JRPxQnt1BVfd/AmObX+RA4P7mNI6g0ojR/+gysCEieky7FYsS6B3TJA6Ioln\nTLYmYE8J/nQIPuCBSQGWF6yW01c1Xh9nDKgqhcmnP/VpXIoTr6oQ9vuZPaWWNVvW2h06pYXbkgSF\nQJTSFLIRDm/czMfufgDF40abnKTPkyNTSnHfEz/lLy4XfiGY1VXLIYdKj2IvuN1uF0/+9SG27TzE\nIw//iW3b93P00IuAiabNIPKOT/DRj1zFu86r4j3vPpfbvvW/yp9QQGMEpjkgkQczg/CF8KqC666a\ny8PPHsalqTylKTxvWPTVBTi/pZ6+fJLE6CDf//4PiOciJEaOUjt1IanEIMngEMpFbtQjgkuXv59P\nve9DnNXQwXO7+jDyKaTboqEpyhXX3cTmLRu45cqrmdtaw/xZTQwPD/Pa+t10xVP0HbZb4pZMmMyD\n5hSUXFDMgiMf4JlNabbED9F25TyKBRdp17kk61qwTHurk+z14jY7SHEYKSTPTcD2FBRLsGA+CI+d\ncA54JMWh/0EL9XjKZOPqJ5kTkVA0CU09iwXz5rN68x4Mw6RC2Nvg8bKX4kgiScviCHHdwlmdIBzx\ncu2SxbjuGmH95leYO9vNmKHibfr/2zvv8LiqM/9/zi3TZzSj3i3Jki1X3DvGxjbGQGhJTMmPECAJ\npG1IyC5kyWYDSTYJaUsKSdgkVNNLWAjFFIPBDfcuy5Jt9a4ZzYym33t+f9yRMYSWDVkr+/j7PGPd\nGZ+Zee+c997znrd83xiDQwoTJ/ho+K9OhBBUVpRy2tRJfPryC2g+2s6eXbtY+8JaNm/ZQSLehm6E\n0LQiJtWPRT0xGS2ZsAgzS1XL8ZMeBrebopIA19XP4FPXzGXDxhZ2bG7ic5csIZ1byYbvPcf0SVUk\nUkm60gFe+3Mzk6bPYu3jr2MODiHuVrDZfLSH+vjVoXtwezRqxwQYOLQHn9dJ3dQpOCvGENu9mWmT\nKshkVLYdGcJM6yiFuchgmNZe64IxpZX2I1RIZyCRgnTZROZ847v0HWqiv7kTvbycjKpgLy4lNZi2\n0rFbXAz1JFEdgowCm8ICSgRaBP67DpyF4IqoVLTY8Rv/QC4FH8eYVmjHqZp0GhmUWJRNjR1IaRmM\nLt0K2wzF3npPVd1Y1EQh1XO8lFXkQlJj2jgveSUVqHoH1dPGgj5MfONRqsoqgAZSqRQ/+MEP3lEw\nCYGAC79fZ++2u/jTfYOsf9ryNe3evRuwXApNB3uRJCxhNBVIgxkDfcDKEjQUxp4mGDtzJrhctB4Z\nIJNJE44NYxoZzNB+ZkypYqB5HSWFblQ9D48vF4/Hi6qkMM0QUnpIJOP87N67KAo4OevshYypGYPH\nkaLQ0cGeHQdpbOmipaUXVVh3mo0bXsfpyWGkS6+Ulr2VSAlsusDmUSiYWM9wKEMmSz4qpMDMJqGu\nOncuS1cKTPN0HHZJPKNit6noKOheSdLQQHVT5FY5eqD7A+dyVCiVAJbOqqUiR2F4OEYommZL8wAv\nbWnFbreujH5AN6DAKcj12qkuzMHnPYtt++7ktFVfwpNbhK5nOPerBrrDQV9LGw/ddTdjpywkk7ER\nGarDbn8aKSX333//e/dQBh5cc8/bnvt8PkwpuOJftp5Q0fwuy4Ac+ccK4E6dehqGIXn9jfVvH3ZC\nEL+ns31k+PFf4+MXX0x+fgkTJtbh85bSsLeR4YhBX0Qw7bwLObsoH2nA73/5CA23/ZaiHDsxaUdI\nEy2ZsFhisnn52AHFqlWwaRYJipq2nquZFKqqsPbZV2hoeHsrkveCaZo4HO/fmm1UZCmUlJTIZcuW\n/VXv8Xg8lI8ZSzQyjD8vF5tuIxmPI4CB0CDRaIRENIzT7UKaklQqQzIxTHFxMQMDA6TT718BfSI0\nTaOgoID4cJICw24RGagCTWQIer0Ip4aiWKnMGbCckAY4zAyprg6GIxHGeXQ0M/NWProC2FUrcm5K\nK6hpSEwJCX8JvQNDjCkvp7ikiEw6Qd9gEJtdR1VVfD4vCEkiFqc/GCIUjOBx+EgqglAc6sbVAcKK\nPmiWozidzhAOx+nqbMfjdgACI5XBKWKEI2Gi0eh7nf674uWXX/5oyt7/XshkMjz9zJ/fHv8a0XVh\nXdnvVP768ePJyR9HOJnH1JrJ5Lg9tB9qJ5mOs2bd83S1HsXr0cDmwKa76e8JM9R7kIsvvpDXXl3H\nQGIQpyIsHnSbpLJyGhPHT0YVVnryYGKIpiPb6Gjtxq14OXvp2bjCTs4X07BV1OMMNWNqLfRPm0vp\npYvRdIMB06RF6rwmVNoMcO7awb6vfJHWhv38aOUECga7QAxb9I5mGkpsUJlveSq7+5D9CUy74Pfa\nan7x2HP88FtfZ/acSkKpLuoUne74EE6bToXNhp4yyRiQyrjYvr2L+pqp3PXY02zctInbtm8nFZfY\nnTbcHjtSSnp7j3DfHU/QtuElPnXtFYyvr2ZoYJDG5g7uevllUvEQPq8bt9uJw6ahCtBtOna7A9OU\nDMdjxOIZcvNy0FBOmKC/xKhQKoCf3n4XFZVjMQwD07SIwaSUOJ1OIpEIkUjkuB0khCAW7efRFxp4\ns9Hk/hfbLRtBlSBUkonxXHTxhSyaUc7P73uTYx2DmPY0hjiKIiUVpkk6aeLK5iINJyUTnAGWnbaE\neQvmYqsJ8PSmuwg9+iqtIQNTs7435XBy7Nqz8d35EM+9cgdnFNtYPh16diYonD6NUrWDyaqX6Uo9\nd+oKIc0q1TdNUPwlKKoCxhAkhiE9BD0xEANQXUhCc7EnGEc4IVUmME2ToroUmreZRFcHXp8LpzFE\nPJOhBxvV5aUUuHPRDTcFOUXYc3V4XBKNRAkOhhhTVXn8t8qkEmx4fSvGsJ0brvouudMKEH44MFRL\nKLEOVQjmTZ/AxHFjqKkpo7KsGLdukQfkFxYQi0YZHIrS1jXE9Km1CGky72Nffc+5HDVK5XC68fr8\npFNpTGmRgGmaht1uR7c50G0OjIzVSkNRFDTFIONwkfQopNDBkEinSl2ejX/61HRmjs9l98EWuqIq\nKT0f1BQIFRPoMjWcKYUIJglgWbHKdxe3IeI/wXsgl4GmWjIv72baoJMWkSCZTVRo96v0Li7myW0u\nXnmxl6WXrea6Rx5my/d+wm/+7dNU1pZTOqmOovx8zrf5eXTk5FQBYwsINQ6S6EsTHEgxlDBIJiAv\nnGCwo5sDXWnChsSvSRI1lhIHhzpwaz4mlAbw5BYwsCdI53AKCnyE0wqOJITDYfIKZxBNHgMkbpeN\ngoBEEMUiUHdiGmkeenAHOw72s6k+jvHgfsZOmsizO4+xenkNiqoRijpo6wzidTrxeb24i/IxZYpQ\nJILX40FPG9jtCVxOF6lMkn+IO5UQAsMwSGfSqNnQiJHJkARUVcXj8RCNRACBoirYbDY0IXFpEk1J\nY6QS+D2Sa86ZzqTqAlKZJB49jGEqYKYsdtUsd5DuVOgLSRIjv0vCYPcrjfxsn8Cuaixdlua2Z1pA\nxElKgTtLE9XduJ2WrnaKFyzizJULeOqV13lwcxNL68bz/Z8/yr7eEKtm1LL8tEmMW3khwx0nhLw9\nOtu6+1m37hg+OxQWgF2BYBykKqksFig2jd09GbqzO9y8lJ/CSCFDpsmzrzTy4st7aOzot2w6JDke\nBw7dgc3+AjX1uRZtpBCI4xTFdkBFqApuNUUyGSTc0051cDezwiEOpDqRwQTxZJJnX00kAu4AAB30\nSURBVHyFhVMrSSZixM0MyVSaooIAXpcTu7sYLZbBSPdi0x0IdYSR+d0xapSqtMiD1+vGMAyOHTtG\nNBJB1/Xs7k+iKCqpVAp/IEDAF8DMRKkpMigtz+Gzn1iOW5O4nRqVZQEGg2F6OuIYCQOEgsPjobK0\nhsZjoGkKn/viItKhPooLaujtj5DY8gYtmSAH4wo+1c3FFYVMmO/k2NEmMpHQ8XSIyEAX3VsbOHP5\nQlZ/60aO/f4nrNvcRkNbG+2xOKrw8tjmI+gJGxtafk0mt9R6o2lC61EckUGCSehOg90FPgUcPomS\nMNg7JNnaJ5kqBBXzLd+Yy1PNvvUt3HDHI7x+8ICVXPceG6u8/HyWLllikYNmUiDDSKEQ6ezmhaef\nY87caorddgYHe9lyrIvLKhfw0H/cwqGj+/nzG7tIJYcI94XRytIY4STpSAqbXxIKD5PjjiNTKnk5\nxTh8eQy1Nr2nHPDh2YmPARGyBStSyllCiFzgYSxSg2PAaillMMtcfDtwDlbh8GeklDs+6Dsiw5KS\nCic+H8RjMfbu3oXT7eGxh+5hKNhh8UcJlRu/9UNWrDybRGyQOeNzuPDiVQQClXQN9GOaGVTdQU1V\nATVVNZRVFvOJ7VEO9xnMnjmO5hc1pCJI1JYyTq1A3xdmc6vKGBHn4qUegt0x1g5EGF+UYOGEUuaM\nyUWoSTbttbbbuquQhq2vIdt7seuNPLSxkfZ0koRhYsOFpigUO8vY1dLFxTX1tMhsnMw0oLMZ93Ds\neCuRUMJiEN58DKLJDJqE84AlTngubS1/ir+QySs+Rt7DmzDM/e/7+414x4eHImy4749MnzOZzt4e\n7v7VGg51hfnyT39JiVrObT/7Nu2hCEfjDpaXT0R09gCQkSa729pRfT7qT1+IYculMyyxp1KUlHgR\nUiMeDzHQ1U1fX+hvV6oslkopT6Stuwl4WUr5QyHETdnnNwKrgLrsYy4WDfbcD/x0oeDzufD5HKw6\n5yxWnLUC0zTJ8bn43R0/ITTYjddXRE1tLbm5OYQGNSaMr6UovxBV1akpKT1+oqaZQAiNivIK7rvt\nau5fu5n7nmsGJJm0wR9/8ixXnr8KZUs3T699iS8vSDImDdcYcESDccW7WXaGQupwiqRZw++KZrK7\nGcL9XXTdfSf7HVW8mGinq7+XEcrXYpuOS9UYiPURjCdYuHIRcsDF1gNtmFIy3BMmPpDABKImFKYg\nFYExhpW1GwaK7VBeYTl5rdnJQXfZKKvwoqsqGcNAKAqmNI+T72uqhqZq1FdXo6oqqqqg2xLY7WF0\nt41n9jThyKS57vPX4IsK5hpl3PTl77P4gpWIVIITs9ISmTR1k+cRcFbi1HzMPXMhqUwMm1TYvObX\nPP/mG9SMKaCrq/t9XTJ/y/J3AbAke3wPFsPejdnX783yfG4WQviFECVSyq73+zC7XUHTBDabht+n\nEolZLp0vfOnznH/h2TQ2HOK+ex9i8pRJ5OQ4QAoamjpxenYxaeIMPG73CS4JHbLNslVNId/tpKu9\nxeINF4LCyiJeP3SQdFs3MR3cXjvYDPJqMlSH8sl0J/j9swatHRBT2xCTPEAxupogr2wM6aiLjo5e\nRppwaAjqVB/lhaW80tdIXyJD495eTr/sGh5/+DFLAVIwSUrO0mBHxor8n2OHGgMOGtCpwPwqBa3c\nZdEGAxAGNc7Nt36B6TOK2bN/F7u7h4i5emnfGGHl9Mms+sQKikpmUlxWxK23/QaH183sSy7A5bNT\nGU4RyPUyHEwSH4rROhSkVeuloMFBbVkUd+2VvNVrwcLGV9fx6pOPM3PyVHa/9gJTZ83A7rPxmzX3\n0BUeZuMOhdycvyzWOBEfVqkksFYIIYHfZZmFi05QlG6gKHt8nEc9ixGO9fdVKo8TfB5BIm2RaNj0\nEVpLjfr6GsaNq2L+ovm4nDmkUqDpKo/+6Q2+/rVvMHf+fK6/4QZWLV+KXdcR4u1Zl5PHV/HDG6/g\nC9vvQld1vrryWmIm7HNuYs+RZtZszxBK2vjO5TZublc4sjfJf6710jSsMK1MZXattf2rrJqIt3wh\ne9c9+7bNTwbJxngnBa0DJDBJI3hqTxOfvdjKdxYC7KpJTjmc51VZFpL0JEz8JnjzFE7PsyN1DaFh\nRW/FyLS4QPOTU9TBJddMYXZzGb/85Vqqp8wgWZHg8k98nLGzaxFaLoNB03LyCx3hmIjQfDh8wyxZ\nOIOF06fy2NqtPP7SK8ybMp59Q2EeW/ss37jofMhyPI/gSOcRnEKlYe8bZHr2kOzey5yFc/jB167B\npoDf78PlsnP6l374nnP5YZVqkZSyQwhRCLwohHibT19KKbMK96HxTh51m2rRTit2Kwfdqb1jf6Eq\nOAr9KMJK6Pe4VQwU/JWT6IvEefip50mkbRTk56PbbOTn+3DaNPqDcXr6wuzbd5hoNIqqKJwxcwY5\ngTwOlOZx9/13czji4N5dgo9Pz1CW7uVfX3IiSmooSyagyG9lSAJ2pQC3t4B0tA9N1dE0SSLb+iCO\nQaeMA4KKfC8XXFTPlp1PWueKQPF4EI40+By4ypxUp0zLgNd18LgQqlXVITXVqksDEFYNnqpoKGYt\nVfkaX7q0gMHwMLG8AconT0fonmzAb4RVXDDS4kRRnYydNYGaBStx727gy/90CecsXUmwpweP14V3\nzAzo2IhfE8cpLQ2ZYVhmUD1+NF+AgXianmAYVyzDhLoxlI4dg6e8HKH+jd3epZQd2b+9QognsQhk\ne0aWNSFECTBS6jrCoz6CEznWT/zMt/Gog3VFj5A8v3PDKgFtpLrDtMYazlzMivk487y0R1V+98gb\nBPuD+Pw+3E6VWCTBwcMDhFISIzKMGUmQTCX59BeuIR5LIgzIGBnOWraE2vH1rL7jdjQTWjICZ7IF\nl9dJZHiQjGIVJdQeaaG0YBxvCklJ9TiKcjVefXPncRkVFc6ur+Q73/82+4zNpBqtEn1TtRGcshLV\nm2XQkgqoNlBsVuTXNC1CWJsORop0tuWyGTqCGaxG2HJQyBAf7MGrORk/Zw4v/ukJBlpClE32gvCA\nSJzwS8ls1UuUuqpqqmoqmHLaBOwuHbvPSeu+fs6auRxEEkhz+pRqXIk+Nh7pI5KybCwhTfLzPNTX\nljNrfAUVNVW4/bkIvwep/41MekIIN6BIKSPZ47OAW3mLL/2H/CWP+peFEA9hGehDH2RPARimpKWl\nA7fbRX6e/4TV5e3qpSCPd4qSyQzxsIOjQyFUDYRMEwkN4RsI4nRopBNp4qYDU5poeQFSvZaHu7X3\nGN2dfQhNRbEJunt6SaVStKQUDMMEYvg9GotXl1NR6aL9UNxil8kxOG3/enwiTWlxAQ63E1Xdg5Hd\neV0wcyxfumYZsqyaji3bGOq3HE7BWIzFf3jqeGryB+Gss84CJL/67T08+eQzoOsI1YaBghGOoBsP\n0NM7gO+1bbhKywDLQE+lLIa7ocR23th/kENH+nj5+deYengHve1Bcn05rFq1mG3bEvgCbqAHiDCl\nfiw+M0x3MMq+nigG0BSMktl/lEOdg7T3D3NafS/VFaVUTajGW1v1vvJ/mDtVEfBk1gjWgAeklM8L\nIbYCjwghrgFagNXZ8c9iuROasFwKV33QFyiKwoGGJm778S8oKy/j/PMvRFEU7HYbNpt1IauqdZdP\npyGVMeloO0aps5tYqvkEPmusqm1DwrBVTVkwQg0gBEyqIRDwc+aK2SSHY3S0DhEOGRio9IciTJky\n+bhMOUV2PPYcXEoufkcSMyeHrZgcVKN4KkoZHA7CcPBtHdKb04Jv3LkVxDZMU1I5ppLy8vLjSvdh\n4XQ6mThxEi19MVr6Yu89MNIGRy3z1e12M2HCBMaOraP1cAq/OoEx/mIu+Vg14UQHoKHEbTQ3tVCc\nX8bR5g56uhN0doaICAdqXiUL5mv42geIZLtL+nSF0jw/hidAe9wg1DnI0XAK7/52zMx71/+NiiyF\noqIiWVVV9b6+D4AJ46dw7XWfIxQaYs39D5AxExw9ehS7v4BVdW4ml4fIm3g2eskiAGQqRHLz7zi0\nvo27uy29mzN7NgvnLSKvIIcYUXqCnbgDHnJ9FUSikGN3E3DY3pbZsmHDBhobG9m+ffv7yicApwKp\nLAFbZWUll6pQ2dYB0qTHkOyaMpPKJaez9pUXqK+vY8UZy9/GSvfEE08wNDTE/gP739Y8/MT79YkZ\nNiMvuFwuPn7BDObWd8FgGI6FMYcM0oMS4bajlthQcnyYvmLU4jjxWJzbbz+CLb8KuzuPcDRuuSd0\nxdogWQzaFv2A8pallpGWCfL800+N7iwF0zTZunXrcaXSVN0KJEffalHm8+Ryy3d+xIxpM4iEY9x3\n1wO0dbaxc9duFo+tICfeS9f+FLahNCtu+SzSFMT2PsDGbS+SezhC7yFokjBu3Dj8/kKK/bnYXG5q\nqibSnziEofRSXzuZYu9Y8h1eBAKZ9QXt3LmTRChEeu9Wwilok+DweHE6HEjTJBMNMy2TxhmA6kJ4\ntgWODlvnVVXoZWZHByIcY1togO1jT+PIoXYuXHkRj6x9GsW2m/NWLGNcbQVOh52HHnqIoViY4LQM\nGc3AqzrB42ZMJs7/iyk0HW7g4ZIydhxuRjoNDEWDTILAPjdX6eOY496BjKfI5EL7lj7cSob8cX7M\ncT42tlejeVYRyDRx+NAuXlj/JtNmqyxf/RlySqYQyPVTWGgnx2/1AVQ1cDnBa3uLXGjIhL645OUX\nnnvP+RwVSnUihFD48nX/zCdXX8Qll11Ge2cTACtXnsOKFYtJJzPYdZUlcxZxz58O4zQyeJuP8niT\nyX4JZ4goy0zYsf41Hvm3G2nvilGesnYLTVgTve9wE7PmX0IyluThxx6lJL+Q4vI8uo1mQs42xhRP\npNhXweCwhldXLAoiBQYz4DDhzNoivnf/41SMqUVisP73t3PTv/+Eln4T2f+OUOsgiPYeMNNIm4Ow\n1Ln9B//K7NlziWcyzJk+i4ryQtq6+qmrssI6QlEwSnNQczwUFpQwGO5iYtpgTiofJdhPSamNHH8R\nGTNCNBbDMCQcBKMjwq6DAxzYFWUskpqJAXKr7XTHPWzYXMC6Dj+uAzuor5vLKxvbiaUtSR0uO263\nC6fbidMjcHoErmxeukMFt2JFEe2AX0LwXThKT8So4fwcgc+bw9WfvZJ5i2Zy0UUXAyCEyqKFS9B0\nFafHjjfXw/zlVgcT1TRxqSaqhGJFMHXRKlS7l554ht/tCPPnLoOtQeiXWZeoUFBtTm7+7s957tXd\n7DnYx623/gd/euhFGt5MkSPKaW1rZmvjqwwkDtExPERPOGUl/xnQCExaspzpM6ZRVByguLiE5Vd/\nnrGzpqCId4vdxy1SAgGq20XltMm4PR4ONR3GVG20Hz2Cpqoo0iSeSBOLp5BS4tV1zgwOcnpzE1dP\nGM9VKy4nZ9llFFRMZ0nNJBx2G4qqoaDCsAGm5M0Wg6vvGkSNpKgtTjEQkzzQUMy9PXM5nJ5OZ/sg\noaEYZ507g70HtgBWs2/V5sCpO3HaJA6bwJ3tamrTrB33iFkK1qkUfMCtaNTdqSaNn0l1TQWKIvjs\nZ69iwqRqdN3F+R879/gYoQhEljnOIawybx343hULKb/gfL51y/e56tKLuXVlLf+9/whtLQbdvGXL\nt7e3s+auO3jhmXVMnzUHf8VEtuw7RDClcub8WTjjGexeGGpvQXG0IrU+oqaV2XnunIl85YabUFRn\ndrmWuL02bvzcuajdjaxri5M6UbOWngFXXg5d/SQ3buaSy89j+qIZXHzR+TQfbuZIw0EONTaz+PQz\nmHradKLDFgfnuaad1Es7GLd4AReetgTR0YAcdx7TL2jnjeYtRMJRTCmwiwCZrAFU7pCcWy2p80FX\n1MkzsTL8C5dz6eWf5alHHyegHmD17Gq2vvIiTUcsyqEMoOs2XGxnnNKKFilCEbW4PKXoqOjZ31ZD\nHDfh/OIvXT4nYtQp1YwZ03B7rRzoqdPqmTqt/j3HCqzCyRyHwrBdxVhyBRnFw8vPv8zXv/xFrrvl\n33n1yq8AQUb2UFJK9u3eQSwyQGdiFz09DdhsXmom1NNw6BBdAwnK84sJBwfRZAAbdtKRt/rvSX8h\nRXlett71U2Zffi2JZIqvXXctOZE9dIcT/NMnZ5Jw53DsaIiuCAw0tRNujeCpqAAth0Qswfy5M/nK\n9TcQjKR4+O47ePO1tbTs2sz1//xNCvN9BAd6iK/dxcbtPVx6w0TSKRO9sITug2+gCJ2mngEM5zCZ\ntI4qXEjDATJFqVtw5kSNYxE7Dd4JhN3jWbz0fIYNJ/PnzOPC8YJAUZz1r25EmAagkEag21IU2x9F\ndL3Jj39+hAkhndkXLqZo4RkUjKtAL5yFUPORI13GPmAOR5VSCSFYunjp29OK3wcSwdj6Wj51wxcJ\nmQqGI0BdxRh+/eM7yc0L0Hiomq0tCQZPfI+U5BWW4vL4EUJlzulns3HdE+zZ3ILNkcsDax7l+q9f\nS05BPseONOKMmaRPaGm8b99BgsMxZlz+RbA7UdKDTC6zowzYSBqw6+ggt9x8IdI3g6/d+B1+frSR\n7+3ey8f8hfh8XmrcLnRN4+Ybb8AwTa5aNouuJx+nv6+bQGyIBtMkgyQ1VmdZ3IYSauexXz/O6uv/\njcAYjVhkKwfDQRLxCKah4nYHsBf4EUo/qh1sXkEifwqvHkgTyldo6ehD7UywfH4Vf/6X71DsTeJM\nhJldXM6GXhNwkg7HKPM5KZm9ms/fuJtdj63nV//5ELOfeojLvl1EzrJPEA9egMM/B6F5+CAHyahS\nKl2zM6au5EOPr5swkxuu/yJTJo8HKenaupnI7teZOGM+kcF+1j/zCpGMJM/vJx4KEcJSqs7WFoyM\ngdPjYcpp09i24c8k48MkY33s3fsqwcgVHDvahdMhOHB0P9GI1U1dAH09fRzZsZvSiycgMylIx3Bp\nKRqbQ8Rikk1bj7LqsluYM30y0lQZO2c+v9m5k82hHjzRfjZ9+1auuOIyli1dQmtLC9//0S8QA4Ns\nP3qIOdJFbnERphB0FOcyR8sgwl2cc9ElGIrBkV6dqrwxlAVs5KQqGO4Pkoy24QjkI1TIJCSRvGkk\np3wST89alEAeuzZuZHpVATuffJU1j72KmTbQgF4EXpxAmubOMI9t9DD1zjUsvm4qp/34S8z79H6G\nNm0inRegr3E/Xp+BtB0kqc7HVN579YBRplR1tfXUjqv7wHFSShKRFFd9+pOWQgEYGfximHShm2DD\nm3T2dtK1ewPf/cY3WH3lp/n3q6/lD2+sQ1EUFi9ZisvjZe/urbz07BPEh9+qJIlE4hxt7+HOO35C\nNJzk8k9dxkAoQoEN5tnBZZN0dPcjTZPW/7qD9WufZU/4MA3bQ1RlL+GcwgDnnncea554nmXLl5E2\nNQqKinjqiUe49+7f09HaxPy5s4nHh4mQwFZeyOz6SvLLy0iEY7hVjX+dOI/uhq24vB5syUF279zP\ntf98N0/cczMFfge2YIyMoRBptcI6dlNBsyv09bazb8PrnLvidPQxs/CU5PG9G7/JeC1Fsc9DNDSE\nYsIEKflYsZ1un4fBngEa40U80z6ZtVeu4zOnb2LitfPh85fR1dvKvi2HSMpGopFmXLEncVZf8r7z\nMyqUSiCoLp3INVd+EZ/X/YHjU8kMzz6zlhXnnfFWH8C0gXPWmbiEgg8oJc20M8/i2OGd5JYUcsX1\nX+OBja8jpSQWHWbR6UuYMX06+/fsJBLqo6uzhUw6SVPDLr5x7WoU1UEgr4K9+/ZQUFTCcH8bMVVB\ncXupKXKSTgzxHw8+xDOvb2FeuUBDkgTyVZhZFmDanEU88OQLVFaW89s7fooQgquvvJxLL/kUjQ3H\n2LZ9Nwvmz+H+Bx/kjt/ezpr7H8Hb3MaECfU4Vcng4YOEegYwlASmr4iC+qnIaCvxeJSphXU8duwo\n0htDLfSj22woIgkeBwUeB7sef5md2w7w8U/FKPUvxOP3IaKd/NuPvkDP688j+lswhuKUlaZprLWz\n1uXBYJhjET9PRaewZWMnl6x7ijOnOCk6bywLZgUwqotJKTZMtQS7Zxnw/fecn1GhVHaHgzc2rSO/\nKBehfLA9FewPs33XTlacdwZSSsL9AyiDvXjKAuAKIBSLLk7zFpKOBrn7Bzczuf4MbIoGSCLhIMFQ\nkIDfx/wFC5g2aw579u5h7443aW9vJjjQzZzFH6O0ooZd2zewYOEiJkwcy1eu+APldacRKB9HKtJG\naYlKbUDnYIfVFzkOlHvBNAZJRd4Kd454zGuqK7jjN7/i3HPOZfUnP87Zq85FR/DYnx5laCiMzx8i\nnZHMmlzDrjc20dmdZuaiYvoauuhufIMrzrkGvynwYyM1KHA5/DjH5GHG09nSLxWXEz6zwME1D7bw\n/K2/YHrtE1TkBThzjEZN6xpqpthAKwJ3LoRa6CSJy+ahvnAcY+sq6W0/QldrE/c2FnHftoPkr9vK\nEp/KRXOmkFNlx/6ZqTjmVr7v/IwKpVIUQUFRAE3/cAHX3IIc5sxYQDQapfXoMW69/nr8mShf+fbN\nuKrrLAZaMkASf/VY0rFHuPf275GW6ez3AYZBKBQiGo3idruZN2c2s2fOoLGxiW3b36S/q5VoOERt\n3RTS6QyeQD7jFpyL3eFGtzmJdhtcevVlrFg5j899/Vd0DKXwAIquUD+1DiWbExWNRunt7T0ue2VF\nMfetuZ9oJMxrr73G3Xf9kUgkTEFhCf9y4000HNgD6RglFV462rrxaDq2UDPD7ol8/LpLyLX1kNMS\nwak6cTr9xNKQ0r1kzAw9UZ3LH4vwRvMgcd1Ojk/lye0N5Ns0LrmgBHPyNETTi2TiGvrYNKK6DPqd\n5PlysPuK0Milrq4WxBKGhgbobWtncKCNo2mD9fNmsvL06eTkehDv2VTYwqhQqkQiya3f+jHTp88i\nGomi22yYpoErx0FxiR9Ve/vdq2HfMdZvfon1bz7Lzh07iQwFsek692+7NFuOPrLptf4a6RTpeJJk\nlnVPKApSZlAUBUURJBIJ2tva0HUb48bVMXHSZNpaW9m/fw85/jxisUF279zNOb+/g9KKMeSUVGMR\njmqkY1F6TYWwEGSAxmFBoE2w8e7HkVJyyy23cODAgXc9bykl8fgwQgiGhgb58Y++z/DwMJdcfD5p\nI0OBx6C/P0ZVnYv6WZL9619ncr2dcR4XVYX5hBSNUGcvHlc5UVXHVlTCygVzef3Yy6SlSjSaQgjB\nBVNLUTxxtu14EyNkgk1Hb4+Bq4r2tA9XgUJpWQC7Q5Ix4qTTGQIBP9U1Y7E7TbxeFzkeHdNnZ0gI\ncj4gRjsqAsolJSXy/PPPh+OpYicwC7wbZYGUOBwOVFX9q8u1c3Nzcbo8VrBWWkvTcReGEEhpoms6\nTpeTaHQYIRSSiThCmAwODr7/h78DLpdFOxeLvU+mwbsgPy8Xd3oIM20gNAWP14nL7ycaNdAdGrpq\ncDgSIW6mUYQOUsMcGMbn9hIKBi0q7BMg3tNZKXB73OQWlGNzuNE0gcjGWOQIV7YAXRNvRQuk5WW/\n9ZZb6OjoeNePHRVKJYSIAIdOthzvg3wsjpDRipMh3xgp5bv2ExkVyx9wSEr54Vui/y9DCLHtlHwf\nHqMuoHwK//g4pVSn8JFjtCjVnSdbgA/AKfn+CowKQ/0U/m9htNypTuH/EE66UgkhzhZCHBJCNGU5\nGU6GDH8UQvQKIfad8FquEOJFIcTh7N9A9nUhhPhFVt49QogZf2fZKoQQ64QQB4QQ+4UQXx1N8r0r\nRqgPT8YDq0CjGajB6ta3G5h4EuRYDMwA9p3w2m3ATdnjm4AfZY/PAZ7D8ifOA7b8nWUrAWZkj71Y\nGc0TR4t87yrzSVaq+cALJzz/JvDNkyRL1TuU6hBQcsLEHsoe/w647N3G/S/J+RSwYrTKJ6U86cvf\ne5F5jAb8tQQkf3cIIaqA6cCW0SjfCE62Uv1DQFqX/EndJgshPMDjwPVSyvCJ/zca5DsRJ1upPhSZ\nx0lCT5Z4hP8JAclHCSGEjqVQa6SUT4w2+d6Jk61UW4E6IUS1EMIGXIpF8DEaMEJAAn9JQPLp7C5r\nHh+SgOR/iizd5R+Ag1LKn402+d4VJ8MofofheQ7WjqYZuPkkyfAgFilbGssGuQbIA14GDgMvAbnZ\nsQL4dVbevcCsv7Nsi7CWtj3AruzjnNEi37s9TnnUT+Ejx8le/k7h/yBOKdUpfOQ4pVSn8JHjlFKd\nwkeOU0p1Ch85TinVKXzkOKVUp/CR45RSncJHjv8PXHspBMrL39cAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "plane  frog  deer  frog\n",
            "time: 317 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YJAT2jCimio",
        "colab_type": "code",
        "outputId": "5daae9dd-5596-476a-bd46-efb4e3f349d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# 3x3 convolution\n",
        "def conv3x3(in_channels, out_channels, stride=1):\n",
        "    return nn.Conv2d(in_channels, out_channels, kernel_size=3, \n",
        "                     stride=stride, padding=1, bias=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 2.24 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xd6Th-OF7emh",
        "colab_type": "code",
        "outputId": "3a44ea38-c4f3-4f2a-db6f-3218b0220be5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "class BasicBlock(nn.Module):\n",
        "    expansion = 1\n",
        "\n",
        "    def __init__(self, in_planes, planes, stride=1):\n",
        "        super(BasicBlock, self).__init__()\n",
        "        #dropout=0.25\n",
        "        #dropout = 0 if dropout is None else dropout\n",
        "        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(planes)\n",
        "        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn2 = nn.BatchNorm2d(planes)\n",
        "        #self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "        self.shortcut = nn.Sequential()\n",
        "        if stride != 1 or in_planes != self.expansion*planes:\n",
        "            self.shortcut = nn.Sequential(\n",
        "                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(self.expansion*planes)\n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.bn1(self.conv1(x)))\n",
        "        out = self.bn2(self.conv2(out))\n",
        "        #out = self.dropout(out)\n",
        "        out += self.shortcut(x)\n",
        "        out = F.relu(out)\n",
        "        \n",
        "        return out"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 21.3 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0_toKu87e3j",
        "colab_type": "code",
        "outputId": "15913503-4079-451c-836a-9e151fa0fa3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "class Bottleneck(nn.Module):\n",
        "    expansion = 4\n",
        "\n",
        "    def __init__(self, in_planes, planes, stride=1):\n",
        "        super(Bottleneck, self).__init__()\n",
        "        dropout=0.30\n",
        "        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(planes)\n",
        "        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n",
        "        self.bn2 = nn.BatchNorm2d(planes)\n",
        "        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)\n",
        "        self.bn3 = nn.BatchNorm2d(self.expansion*planes)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "        self.shortcut = nn.Sequential()\n",
        "        if stride != 1 or in_planes != self.expansion*planes:\n",
        "            self.shortcut = nn.Sequential(\n",
        "                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(self.expansion*planes)\n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.bn1(self.conv1(x)))\n",
        "        out = F.relu(self.bn2(self.conv2(out)))\n",
        "        out = self.bn3(self.conv3(out))\n",
        "        out = self.dropout(out)\n",
        "        out += self.shortcut(x)\n",
        "        out = F.relu(out)\n",
        "        return out"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 20 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HpFNWO6-7ejB",
        "colab_type": "code",
        "outputId": "4ba27e41-eaf1-4622-93db-fbace7286744",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "class ResNet(nn.Module):\n",
        "    def __init__(self, block, num_blocks, num_classes=10):\n",
        "        super(ResNet, self).__init__()\n",
        "        self.in_planes = 16\n",
        "\n",
        "        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(16)\n",
        "        self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)\n",
        "        self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)\n",
        "        self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)\n",
        "        self.layer4 = self._make_layer(block, 128, num_blocks[3], stride=2)\n",
        "        self.linear = nn.Linear(128*block.expansion, num_classes)\n",
        "\n",
        "    def _make_layer(self, block, planes, num_blocks, stride):\n",
        "        strides = [stride] + [1]*(num_blocks-1)\n",
        "        layers = []\n",
        "        for stride in strides:\n",
        "            layers.append(block(self.in_planes, planes, stride))\n",
        "            self.in_planes = planes * block.expansion\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.bn1(self.conv1(x)))\n",
        "        out = self.layer1(out)\n",
        "        out = self.layer2(out)\n",
        "        out = self.layer3(out)\n",
        "        out = self.layer4(out)\n",
        "        out = F.avg_pool2d(out, 4)\n",
        "        out = out.view(out.size(0), -1)\n",
        "        out = self.linear(out)\n",
        "        return out"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 26.3 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VivXxrw08DW7",
        "colab_type": "code",
        "outputId": "7c516060-96d9-49e9-a090-2cb398cddbf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#RESNET-18\n",
        "model = ResNet(BasicBlock, [2,2,2,2]).to(device)\n",
        "#RESNET-34\n",
        "#model = ResNet(BasicBlock, [3,4,6,3]).to(device)\n",
        "#RESNET-50\n",
        "#model = ResNet(Bottleneck, [3,4,6,3]).to(device)\n",
        "#RESNET-101\n",
        "#model = ResNet(Bottleneck, [3,4,23,3]).to(device)\n",
        "#RESNET-152\n",
        "#model = ResNet(Bottleneck, [3,8,36,3]).to(device)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 3.03 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EiyEBpKI8Dge",
        "colab_type": "code",
        "outputId": "ecda5f90-b19f-4486-a60c-8b9e53e07857",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "#optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "#optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate,weight_decay=1e-5)\n",
        "#optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4)\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4, nesterov= True)\n",
        "#optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "\n",
        "# For updating learning rate\n",
        "def update_lr(optimizer, lr):    \n",
        "    for param_group in optimizer.param_groups:\n",
        "        param_group['lr'] = lr"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 5.03 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dRNxNEUy8DeB",
        "colab_type": "code",
        "outputId": "81d7e388-87ab-402b-e6ce-364aba48d525",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "curr_lr = learning_rate\n",
        "for epoch in range(num_epochs):\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for i,(images, labels) in enumerate(train_loader):\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "        #if (i+1) % 100 == 0:\n",
        "          \n",
        "    print (\"Epoch [{}/{}], Train Loss: {:.4f} Train Accuracy: {} %\"\n",
        "                   .format(epoch+1, num_epochs,  loss.item(),round((100 * correct / total),2)))\n",
        "\n",
        "    # Decay learning rate\n",
        "    if (epoch+1) % 20 == 0:\n",
        "        curr_lr /= 3\n",
        "        update_lr(optimizer, curr_lr)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [1/100], Train Loss: 1.3038 Train Accuracy: 44.76 %\n",
            "Epoch [2/100], Train Loss: 1.0167 Train Accuracy: 62.35 %\n",
            "Epoch [3/100], Train Loss: 0.7680 Train Accuracy: 69.6 %\n",
            "Epoch [4/100], Train Loss: 0.6391 Train Accuracy: 74.4 %\n",
            "Epoch [5/100], Train Loss: 0.5944 Train Accuracy: 76.94 %\n",
            "Epoch [6/100], Train Loss: 0.5448 Train Accuracy: 79.19 %\n",
            "Epoch [7/100], Train Loss: 0.3866 Train Accuracy: 80.53 %\n",
            "Epoch [8/100], Train Loss: 0.6196 Train Accuracy: 81.71 %\n",
            "Epoch [9/100], Train Loss: 0.5252 Train Accuracy: 82.66 %\n",
            "Epoch [10/100], Train Loss: 0.4534 Train Accuracy: 83.62 %\n",
            "Epoch [11/100], Train Loss: 0.3665 Train Accuracy: 84.48 %\n",
            "Epoch [12/100], Train Loss: 0.3823 Train Accuracy: 84.78 %\n",
            "Epoch [13/100], Train Loss: 0.3996 Train Accuracy: 85.56 %\n",
            "Epoch [14/100], Train Loss: 0.5393 Train Accuracy: 86.3 %\n",
            "Epoch [15/100], Train Loss: 0.5232 Train Accuracy: 86.41 %\n",
            "Epoch [16/100], Train Loss: 0.6153 Train Accuracy: 86.94 %\n",
            "Epoch [17/100], Train Loss: 0.3531 Train Accuracy: 87.18 %\n",
            "Epoch [18/100], Train Loss: 0.3917 Train Accuracy: 87.59 %\n",
            "Epoch [19/100], Train Loss: 0.3832 Train Accuracy: 88.04 %\n",
            "Epoch [20/100], Train Loss: 0.2809 Train Accuracy: 88.3 %\n",
            "Epoch [21/100], Train Loss: 0.1773 Train Accuracy: 90.7 %\n",
            "Epoch [22/100], Train Loss: 0.2881 Train Accuracy: 91.53 %\n",
            "Epoch [23/100], Train Loss: 0.2972 Train Accuracy: 91.77 %\n",
            "Epoch [24/100], Train Loss: 0.1451 Train Accuracy: 91.92 %\n",
            "Epoch [25/100], Train Loss: 0.2502 Train Accuracy: 92.23 %\n",
            "Epoch [26/100], Train Loss: 0.4220 Train Accuracy: 92.34 %\n",
            "Epoch [27/100], Train Loss: 0.1995 Train Accuracy: 92.5 %\n",
            "Epoch [28/100], Train Loss: 0.2371 Train Accuracy: 92.64 %\n",
            "Epoch [29/100], Train Loss: 0.1723 Train Accuracy: 92.78 %\n",
            "Epoch [30/100], Train Loss: 0.2708 Train Accuracy: 92.98 %\n",
            "Epoch [31/100], Train Loss: 0.1676 Train Accuracy: 93.02 %\n",
            "Epoch [32/100], Train Loss: 0.2033 Train Accuracy: 92.99 %\n",
            "Epoch [33/100], Train Loss: 0.2543 Train Accuracy: 93.17 %\n",
            "Epoch [34/100], Train Loss: 0.1584 Train Accuracy: 93.39 %\n",
            "Epoch [35/100], Train Loss: 0.1674 Train Accuracy: 93.32 %\n",
            "Epoch [36/100], Train Loss: 0.1933 Train Accuracy: 93.52 %\n",
            "Epoch [37/100], Train Loss: 0.1101 Train Accuracy: 93.68 %\n",
            "Epoch [38/100], Train Loss: 0.1358 Train Accuracy: 93.74 %\n",
            "Epoch [39/100], Train Loss: 0.2544 Train Accuracy: 93.65 %\n",
            "Epoch [40/100], Train Loss: 0.1976 Train Accuracy: 93.91 %\n",
            "Epoch [41/100], Train Loss: 0.1504 Train Accuracy: 95.2 %\n",
            "Epoch [42/100], Train Loss: 0.1222 Train Accuracy: 95.72 %\n",
            "Epoch [43/100], Train Loss: 0.1166 Train Accuracy: 95.78 %\n",
            "Epoch [44/100], Train Loss: 0.0591 Train Accuracy: 95.81 %\n",
            "Epoch [45/100], Train Loss: 0.0964 Train Accuracy: 95.86 %\n",
            "Epoch [46/100], Train Loss: 0.1175 Train Accuracy: 96.02 %\n",
            "Epoch [47/100], Train Loss: 0.1415 Train Accuracy: 95.99 %\n",
            "Epoch [48/100], Train Loss: 0.1925 Train Accuracy: 96.26 %\n",
            "Epoch [49/100], Train Loss: 0.1773 Train Accuracy: 96.24 %\n",
            "Epoch [50/100], Train Loss: 0.1167 Train Accuracy: 96.27 %\n",
            "Epoch [51/100], Train Loss: 0.1718 Train Accuracy: 96.39 %\n",
            "Epoch [52/100], Train Loss: 0.1503 Train Accuracy: 96.58 %\n",
            "Epoch [53/100], Train Loss: 0.1524 Train Accuracy: 96.33 %\n",
            "Epoch [54/100], Train Loss: 0.1283 Train Accuracy: 96.41 %\n",
            "Epoch [55/100], Train Loss: 0.0404 Train Accuracy: 96.49 %\n",
            "Epoch [56/100], Train Loss: 0.1730 Train Accuracy: 96.64 %\n",
            "Epoch [57/100], Train Loss: 0.0632 Train Accuracy: 96.58 %\n",
            "Epoch [58/100], Train Loss: 0.1426 Train Accuracy: 96.58 %\n",
            "Epoch [59/100], Train Loss: 0.0829 Train Accuracy: 96.68 %\n",
            "Epoch [60/100], Train Loss: 0.1345 Train Accuracy: 96.79 %\n",
            "Epoch [61/100], Train Loss: 0.1314 Train Accuracy: 97.17 %\n",
            "Epoch [62/100], Train Loss: 0.0869 Train Accuracy: 97.36 %\n",
            "Epoch [63/100], Train Loss: 0.0879 Train Accuracy: 97.48 %\n",
            "Epoch [64/100], Train Loss: 0.1618 Train Accuracy: 97.5 %\n",
            "Epoch [65/100], Train Loss: 0.1159 Train Accuracy: 97.49 %\n",
            "Epoch [66/100], Train Loss: 0.0237 Train Accuracy: 97.56 %\n",
            "Epoch [67/100], Train Loss: 0.1473 Train Accuracy: 97.61 %\n",
            "Epoch [68/100], Train Loss: 0.0348 Train Accuracy: 97.7 %\n",
            "Epoch [69/100], Train Loss: 0.0731 Train Accuracy: 97.69 %\n",
            "Epoch [70/100], Train Loss: 0.0332 Train Accuracy: 97.68 %\n",
            "Epoch [71/100], Train Loss: 0.1165 Train Accuracy: 97.71 %\n",
            "Epoch [72/100], Train Loss: 0.0879 Train Accuracy: 97.75 %\n",
            "Epoch [73/100], Train Loss: 0.0731 Train Accuracy: 97.74 %\n",
            "Epoch [74/100], Train Loss: 0.1388 Train Accuracy: 97.71 %\n",
            "Epoch [75/100], Train Loss: 0.1246 Train Accuracy: 97.84 %\n",
            "Epoch [76/100], Train Loss: 0.0682 Train Accuracy: 97.81 %\n",
            "Epoch [77/100], Train Loss: 0.0473 Train Accuracy: 97.77 %\n",
            "Epoch [78/100], Train Loss: 0.0969 Train Accuracy: 97.81 %\n",
            "Epoch [79/100], Train Loss: 0.1018 Train Accuracy: 97.93 %\n",
            "Epoch [80/100], Train Loss: 0.0700 Train Accuracy: 97.89 %\n",
            "Epoch [81/100], Train Loss: 0.1057 Train Accuracy: 98.07 %\n",
            "Epoch [82/100], Train Loss: 0.0888 Train Accuracy: 98.0 %\n",
            "Epoch [83/100], Train Loss: 0.0365 Train Accuracy: 98.1 %\n",
            "Epoch [84/100], Train Loss: 0.0475 Train Accuracy: 98.18 %\n",
            "Epoch [85/100], Train Loss: 0.0466 Train Accuracy: 98.16 %\n",
            "Epoch [86/100], Train Loss: 0.0971 Train Accuracy: 98.08 %\n",
            "Epoch [87/100], Train Loss: 0.0491 Train Accuracy: 98.07 %\n",
            "Epoch [88/100], Train Loss: 0.0233 Train Accuracy: 98.09 %\n",
            "Epoch [89/100], Train Loss: 0.0606 Train Accuracy: 98.1 %\n",
            "Epoch [91/100], Train Loss: 0.0102 Train Accuracy: 98.11 %\n",
            "Epoch [92/100], Train Loss: 0.0333 Train Accuracy: 98.24 %\n",
            "Epoch [93/100], Train Loss: 0.0610 Train Accuracy: 98.17 %\n",
            "Epoch [94/100], Train Loss: 0.0490 Train Accuracy: 98.31 %\n",
            "Epoch [95/100], Train Loss: 0.0495 Train Accuracy: 98.14 %\n",
            "Epoch [96/100], Train Loss: 0.0283 Train Accuracy: 98.18 %\n",
            "Epoch [97/100], Train Loss: 0.0786 Train Accuracy: 98.27 %\n",
            "Epoch [98/100], Train Loss: 0.0276 Train Accuracy: 98.28 %\n",
            "Epoch [99/100], Train Loss: 0.0850 Train Accuracy: 98.2 %\n",
            "Epoch [100/100], Train Loss: 0.0568 Train Accuracy: 98.21 %\n",
            "time: 22min 51s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2-vn6CLxSBq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5057ac7b-ca04-48c2-d440-e2cd6322fd08"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "def convert_to_imshow_format(image):\n",
        "    # first convert back to [0,1] range from [-1,1] range\n",
        "    image = image / 2 + 0.5\n",
        "    image = image.numpy()\n",
        "    # convert from CHW to HWC\n",
        "    # from 3x32x32 to 32x32x3\n",
        "    return image.transpose(1,2,0)\n",
        "\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "fig, axes = plt.subplots(1, len(images), figsize=(12,2.5))\n",
        "for idx, image in enumerate(images):\n",
        "    axes[idx].imshow(convert_to_imshow_format(image))\n",
        "    axes[idx].set_title(classes[labels[idx]])\n",
        "    axes[idx].set_xticks([])\n",
        "    axes[idx].set_yticks([])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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QPkKNM8fbn+qgbmBWMlFPmXDOsXlGP8mmwNv5pG7+V+0OFI1tOiKgH+upVUOI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\n",
            "text/plain": [
              "<Figure size 864x180 with 128 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "time: 3.84 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ytAhp0JWxCuV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bcca556d-1c33-4cbd-85ff-190594a19f88"
      },
      "source": [
        "dataiter = iter(test_loader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "fig, axes = plt.subplots(1, len(images), figsize=(12,2.5))\n",
        "for idx, image in enumerate(images):\n",
        "    axes[idx].imshow(convert_to_imshow_format(image))\n",
        "    axes[idx].set_title(classes[labels[idx]])\n",
        "    axes[idx].set_xticks([])\n",
        "    axes[idx].set_yticks([])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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F2udrIgcM19na/z3WAFq3a5e9lr8+zNnKxjLV5yb0WY1+LJ3Wh74MrD2qWjc6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\nbwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x180 with 100 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "time: 2.98 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Bo-eyWBxn9T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "08d78909-2c23-4d66-b796-7036c782d7d5"
      },
      "source": [
        "images = images.to(device)\n",
        "labels = labels.to(device)\n",
        "outputs = model(images)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 19.8 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wv_wo9I6yIMW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f8e98f12-b108-463c-9772-f06d6006c1d4"
      },
      "source": [
        "sm = nn.Softmax(dim=1)\n",
        "sm_outputs = sm(outputs)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 2.16 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfhHhpi2yO9q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9d13ee09-3452-4fc7-8006-f8563e5a9c4b"
      },
      "source": [
        "probs, index = torch.max(sm_outputs, dim=1)\n",
        "\n",
        "for p, i in zip(probs, index):\n",
        "    print('{0} - {1:.4f}'.format(classes[i], p))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cat - 1.0000\n",
            "ship - 0.9999\n",
            "ship - 0.9798\n",
            "plane - 0.8543\n",
            "frog - 0.9999\n",
            "frog - 0.9743\n",
            "car - 0.9389\n",
            "dog - 0.5598\n",
            "cat - 0.9930\n",
            "car - 0.6998\n",
            "plane - 0.9997\n",
            "truck - 1.0000\n",
            "dog - 0.8858\n",
            "horse - 1.0000\n",
            "truck - 1.0000\n",
            "ship - 0.9919\n",
            "dog - 0.9920\n",
            "horse - 1.0000\n",
            "ship - 0.9940\n",
            "frog - 1.0000\n",
            "horse - 0.7189\n",
            "plane - 0.9997\n",
            "deer - 0.9966\n",
            "truck - 1.0000\n",
            "dog - 0.9534\n",
            "bird - 0.9981\n",
            "deer - 0.9947\n",
            "plane - 1.0000\n",
            "truck - 1.0000\n",
            "frog - 0.9999\n",
            "dog - 0.5133\n",
            "dog - 1.0000\n",
            "ship - 0.9558\n",
            "dog - 0.9798\n",
            "truck - 0.9985\n",
            "cat - 0.6619\n",
            "deer - 1.0000\n",
            "car - 0.9212\n",
            "truck - 0.9999\n",
            "dog - 1.0000\n",
            "deer - 0.9999\n",
            "frog - 0.9969\n",
            "cat - 0.3787\n",
            "frog - 0.9924\n",
            "plane - 1.0000\n",
            "truck - 0.9998\n",
            "cat - 0.9802\n",
            "car - 0.3603\n",
            "horse - 0.9998\n",
            "frog - 0.4995\n",
            "truck - 1.0000\n",
            "ship - 0.9997\n",
            "bird - 0.8477\n",
            "cat - 0.9943\n",
            "ship - 0.9998\n",
            "ship - 1.0000\n",
            "horse - 0.9986\n",
            "cat - 0.5342\n",
            "horse - 0.8579\n",
            "cat - 0.9971\n",
            "horse - 1.0000\n",
            "dog - 0.5657\n",
            "frog - 0.9803\n",
            "cat - 0.9067\n",
            "frog - 0.9988\n",
            "bird - 0.9999\n",
            "car - 0.9979\n",
            "bird - 0.9763\n",
            "cat - 0.9790\n",
            "horse - 0.9727\n",
            "bird - 0.9958\n",
            "frog - 0.9999\n",
            "ship - 0.9963\n",
            "ship - 0.9995\n",
            "plane - 0.9974\n",
            "bird - 1.0000\n",
            "truck - 0.9992\n",
            "cat - 1.0000\n",
            "dog - 0.9606\n",
            "ship - 0.9998\n",
            "ship - 1.0000\n",
            "cat - 0.5574\n",
            "car - 1.0000\n",
            "horse - 1.0000\n",
            "dog - 0.5816\n",
            "dog - 0.9659\n",
            "bird - 0.9997\n",
            "horse - 0.6589\n",
            "ship - 0.9997\n",
            "truck - 0.9978\n",
            "plane - 1.0000\n",
            "frog - 0.7978\n",
            "ship - 0.9937\n",
            "frog - 1.0000\n",
            "deer - 1.0000\n",
            "frog - 0.6788\n",
            "frog - 0.9324\n",
            "plane - 0.9931\n",
            "plane - 0.9987\n",
            "horse - 1.0000\n",
            "time: 43.9 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RiZftaxHyShZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "63112811-8a14-4ea5-d8e6-cf9d9895d307"
      },
      "source": [
        "total_correct = 0\n",
        "total_images = 0\n",
        "confusion_matrix = np.zeros([10,10], int)\n",
        "with torch.no_grad():\n",
        "    for data in test_loader:\n",
        "        images, labels = data\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "        outputs = model(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total_images += labels.size(0)\n",
        "        total_correct += (predicted == labels).sum().item()\n",
        "        for i, l in enumerate(labels):\n",
        "            confusion_matrix[l.item(), predicted[i].item()] += 1 \n",
        "\n",
        "model_accuracy = total_correct / total_images * 100\n",
        "print('Model accuracy on {0} test images: {1:.2f}%'.format(total_images, model_accuracy))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model accuracy on 10000 test images: 91.07%\n",
            "time: 2.32 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6v5gH5D6y0sZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "f06aca81-7928-4297-a293-980044bc5010"
      },
      "source": [
        "print('{0:10s} - {1}'.format('Category','Accuracy'))\n",
        "for i, r in enumerate(confusion_matrix):\n",
        "    print('{0:10s} - {1:.1f}'.format(classes[i], r[i]/np.sum(r)*100))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Category   - Accuracy\n",
            "plane      - 88.3\n",
            "car        - 94.6\n",
            "bird       - 81.1\n",
            "cat        - 75.1\n",
            "deer       - 86.4\n",
            "dog        - 80.5\n",
            "frog       - 88.9\n",
            "horse      - 90.5\n",
            "ship       - 93.8\n",
            "truck      - 91.5\n",
            "time: 4.66 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHszLmdNy5v_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "outputId": "de6fbf2e-a3f1-4dc2-fac4-93b616d19911"
      },
      "source": [
        "fig, ax = plt.subplots(1,1,figsize=(10,8))\n",
        "ax.matshow(confusion_matrix, aspect='auto', vmin=0, vmax=1000, cmap=plt.get_cmap('Blues'),)\n",
        "for (i, j), z in np.ndenumerate(confusion_matrix):\n",
        "    ax.text(j, i, '{:0.1f}'.format(z), ha='center', va='center',\n",
        "            bbox=dict(boxstyle='round', facecolor='white', edgecolor='0.3'))\n",
        "plt.ylabel('Actual Category')\n",
        "plt.yticks(range(10), classes)\n",
        "plt.xlabel('Predicted Category')\n",
        "plt.xticks(range(10), classes)\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u3IjVn67gvXnva1JmfRQVFfHl6pUsWrQIf39/i66rc+fONGrUiK8+W8mbs+dadF1lfjsW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QkMspygviZsDdoA9ifv49DPB8jPy7M5jWvx8fWhUeNIevftZ/MFpMZBG4BmJrz4KMEqD1/l3O+C\nl5i9uKiCyPFrWau5Om0J2qA8cPt26xbOnM7E6KBhCvUCg4iJ7UhMh1ib07AlaAMoKMjnu23fkJ11\nBqNR/7o0GAyE1a/P7T17Y+snq/QI2gDOns1hx3fbybGzH/X18aFxZCR9f9/fLQK2K06dPMmuxB85\nd+4s9tw8rVu3Lq3btKXHbb/TJV/2Bm3Jl2cgqLxsn6ZpnXTJnYPpFbS5Kz2CNndma9DmCewJ2jyB\nPUGbJ6hV0OaBbA3aPIWtQZsn0CtoE65xraCtJjXrpSo9n1BKeQHuEyoLIYQQQvwPqMmrRBuBz5VS\n71/++8+XlwkhhBBCCCepSdA2FXgMmHj57y3ABw7LkRBCCCGEsFCTCePNmqYt1DRtuKZpw4FDgMe+\nTSqEEEII4Ylq9KVFpVQnYBRwP5AGrHZkpoQQQgghRFXVBm1KqTaUB2qjgLPA55S/bdrHSXkTQggh\nhBCXXetO2y/ADmCIpmlHAJRSk52SKyGEEEIIUcW1xrQNBU4D3yqlPlBK3UX5N0WFEEIIIYSTVRu0\naZr2taZpIymfe/Rb4CkgQin1nlKqv7MyKIQQQgghavb2aIGmaf/SNO0eoAmwj/LPgAghhBBCCCep\n1VwXmqblapq2SNO0uxyVISGEEEIIYUkmKBNCCCGE8AA1+k6bp1Py+oRwQ3Xr3NjNz2C4sRvesXeH\nuzoLDlV/2EJXZ8Ghzq+e4OosCBtpmubqLLiM3GkTQgghhPAAErQJIYQQQngACdqEEEIIITyABG1C\nCCGEEB5AgjYhhBBCCA8gQZsQQgghhAeQoE0IIYQQwgNI0CaEEEII4QEkaBNCCCGE8AAStAkhhBBC\neAAJ2oQQQgghPIAEbUIIIYQQHuDGnrFaJ8ayMj7+aAmHDh6goCDfrrRuqlePjh078eDDYzF4eemU\nQ/sUFhTwwfvvcvToEYqLimxPSCnCwsIYMPBueve9S78M2mDP7iTWrF7FubM5mB0wubBCERIaQu++\n/eg3YJDu6V/Pti2b2L51Mxdyc9HQv3xeBi/CGzTgvpEP0uaWdrqnfy1Go5FlHy/h54P7yc+3r71V\nx8fbh8aRkYwd/xiNGkc65BjVKSws5MNF73Hs6BGKiu1ob9cQEBBA69Zt+dOjE/Dz83PIMSoL5AK3\nBhzD11wAmtnhx6tM8/IjxxhMcmkbNCffhzh+PI1PPlpC1pnTGI1Gm9MxGAzUDw9n2H0jievUWccc\n2md/8j5Wfr6cnJxszGbb69XHx5cmTZsy/rEJREQ00DGH9tm8cQPbtm4h9/x5sKMf9fcPoHXbtox/\ndAJ16tTRL4NWKM0BFzR30vnWLtrOxJ9s3t9oNDL3rddRaDz00EPUq1cPpZRNaWmaxsWLF/nwww+5\nKTCIJ558yu7Azda8XFFYUMArM6bTOqoV99xzD3Xr1rU5LbPZTGZmJnPmzGHo8Pv5ff+BduUNwGiq\nfUfxU1IiHy56j0mTJtG8eXO8vfX/bWI2m8nKymLu3Lnc1X8Qd9/zh1qnYTTb1vY2rf83Wzb+h8lP\nPUXDhg0xGPS/UJWVlXH48GE++GAxk56ZalPg5uNV+3wZjUb+OedNzGYjDz/0EIGBgXaf49aUlJSw\na9cuvv76a55/6RUaNWpc6zRs6TsLCwuZPTOBli2aEx8fz0033VTrNGoiLy+PVatWkX32HFOee8Gm\nwC18+Ps12i6QC/zOL4Un//IYMTEx+Pj41PpYttI0jby8PN5f/BH/PXyGXSUdahy4nV89wa5jnzh+\nnFdfns7o0aPp2LGjXcGx0Wjk6NGjzJ+/gL88+Vc6db7Vrrzp0WaS9+1l/jtzePwvfyEqKsqufrS4\nuJgfftjJpk0bmT7jVcIjIuzKmx5xy7q1a9iyaT1PPfUUDRo0sKsfzcvLY/Xq1ZzJymbqP160O3AL\n8DXs0TSti7V1ErRdx+6fdvHVlyuZ/dprunVGJSUlPP3004wd/xgxHWLtSsvexrnu32tI/eUwzz03\nVbeLY2ZmJpMmTeLjT1fYHTDZErQ9/udxPDd1Ku3aOf4OUU5ODo8//jjz3/+QgICAWu1rS9BWUlzM\n44+O4d133yXCzo6vJnbs2MHqr9cw/ZXZtd7XlqBt757dfPn5v3jjjdedcvH/4osvSDt+kieeeqbW\n+9rSd25cv45fDx3gH//4h0OC0cpMJhMvvfQS3W/vSe8+tb/zXdOgrU/AXqZMeIA+ffrU+hh6KSsr\n4/G/Ps3GU2HkULM7OfYGba/NnEG3rl24++677UqnspSUFBa8+y7z5i+0Kx09zq2nnpzIY48+SufO\n+t35W7p0KRfzCnhswl/sSsfeuKWwsJCJj/6JBQsWEB4ebldaV5jNZhISEuja/Xfc1a+/XWldK2iT\nMW3XkXXmDLe0bavrBcTPz482bdqQlXVGtzRtlXXmNDEx0bpeQBo3bkxgYCDnz53VLc2aKi0pIe/S\nJdq2beuU44WHhxMeHs7ZnGynHC8nJ5v64eFOCdgAYmJiyHbieZp95jS33KJve7sWZ5cv68wZYmJi\nHB6wAXh5edG+fXuyzzi2fL7mAmJiYhx6jOvx8fGhQ3R7/Cl02jGzss7QoUMHXdNs3749Odn2PYrU\ny5VzVU8xMTFknTmta5q2yMnOJjwiQreADcofcbdv397h5ZOg7TqMJpPVC0jz5s3p0KEDcXFxdOli\nGRBrmsakSZOIiooiNjaWvXv3Vlnv6+tr1xgIvRiNRovyFRcX061bNzp27Eh0dDTTp0+32K+kpIQR\nI0YQFRVF9+7dOX78eJX1Pr6+GE0mR2bdKpPZjLe3t8Wt7nHjxhEREVGlExoxYgRxcXHExcXRvHlz\n4uLirKa5ceNG2rZtS1RUFK+99prFeh8f59WlyWTC18e3yrLq6mvbtm107tyZmJgYxowZU20ely5d\nSuvWrWndujVLly6tss7HxweTE89Ta+3NWt299NJLREZGVtTf+vXrraZ3vbrzdfJ5ajJZtjdr5UtO\nTqZHjx4V/UtSUpLV9K5Vd3ClnynTtxBX0yzrbM6cOURHRxMTE8OoUaMoLi5m69atdO7cmbi4OO64\n4w6OHDkCwIkTJ7jrrruIjY2ld+/epKenWz3Mnj176NChA1FRUUyaNMnibksdP1+UA8Z3VsdkrFru\nX3/9teJ8jIuLIzAwkLlz51bZ53rXBR8fn/JyucETMJPJhK/v//c1elwXfH19MbngunA1k8mIj7f1\nH4Ymk4lOnToxZMgQi3U1KZ+j+xOXBW1KqUlKqcNKqc9clQd7ffvttyQnJ7N7926LdRs2bCA1NZXU\n1FQWLVrExIkTXZBD2/j5+bFt2zZSUlJITk5m48aNJCYmVtlmyZIlhISEcOTIESZPnszUqVNdlNua\nGTt2LBs3bqyy7PPPPyc5OZnk5GSGDRvG0KFDLfYzmUw8/vjjbNiwgUOHDrF8+XIOHTrkrGzXiLX6\n+vHHHxkzZgwrVqzg4MGD3HzzzVYv6ufPnychIYFdu3aRlJREQkICubm5LihF9azVHcDkyZMr6m/w\n4MEW6z2h7sB6+f72t78xffp0kpOTmTFjBn/7298s9nPXusvIyOCdd95h9+7dHDx4EJPJxIoVK5g4\ncSKfffYZycnJPPDAA7zyyisAPPvsszz88MPs37+fadOm8fe//91quhMnTuSDDz6o6FetnROu1LZt\n24rzcc+ePQQEBHDvvfdW2UauC+5v3rx51Q6tcYfyufJO21+Afpqmjb6yQCl1w7zNumbNGh5++GGU\nUvTo0YMLFy5w+rTrbwvXhFKqYoB0WVkZZWVlFo9z1qxZw5gxYwAYPnw4W7du1WVwqKP07NmT0NBQ\nq+s0TeOLL75g1KhRFuuSkpKIioqiZcuW+Pr6MnLkSNasWePo7NaKtfry8vLC19eXNm3aANCvXz++\n/PJLi303bdpEv379CA0NJSQkhH79+rndxfBadXctnlB3YL18SikuXboEwMWLF2nc2PJFCXeuO6PR\nSFFREUajkcLCQho3blxtmQ4dOkTfvn0B6NOnj9U6On36NJcuXaJHjx4opXj44Yf5+uuvnVegWtq6\ndSutWrXi5ptvrrJcrgvuLT09nf/85z888sgjVte7Q/lcErQppRYCLYENSqmLSqllSqmdwDKlVHOl\n1Dal1H6l1FalVLPL+7RSSiUqpQ4opV5RSjnmWwA1LwP9+/fn1ltvZdGiRRbrMzIyaNq0acXfTZo0\nISMjw5lZtIvJZCIuLo6IiAj69etH9+7dq6yvXD5vb2+CgoI4d+6cK7Jqtx07dtCgQQNat25tsc5T\n6vHq+urWrRtGo7HiLvCqVas4deqUxX6eUj5r5s+fT2xsLOPGjbN6h8mTyzZ37lymTJlC06ZNefbZ\nZ5k1a5bFNu5avsjISJ599lmaNWtGo0aNCAoKon///ixevJjBgwfTpEkTli1bxnPPPQdAx44dWb16\nNQBfffUVeXl5Fn1JRkYGTZo0qfjbXcpanRUrVlj9EeiudVZTN/p14amnnuL111+v9k1SdyifS4I2\nTdMmAJlAH2AO0B74vaZpo4B/Aks1TYsFPgPeubzbPGCepmkdAOuDHpzohx9+YO/evWzYsIEFCxbw\n/fffuzpLuvLy8iI5OZn09HSSkpI4ePCgq7PkMMuXL7fawXqSq+vr559/ZsWKFUyePJlu3bpRr149\nvNzku4B6mDhxIkePHiU5OZlGjRrxzDO1f/vTnb333nvMmTOHU6dOMWfOHMaPH+/qLNVYbm4ua9as\nIS0tjczMTAoKCvj000+ZM2cO69evJz09nT/96U88/fTTALz55pt89913dOrUie+++47IyEiPPldL\nS0tZu3Yt9913n6uzorsb+bqwbt06IiIiuPVW+z634mju8iLCWk3Trnxl8jbgX5f/vQy4o9LylZf/\n/S+uQSn1mFJqt1Jq99mzObpnFsp/TQJERERw7733WgwUjoyMrHJnIz09vWIfTxIcHEyfPn0sHrtU\nLp/RaOTixYuEhYW5Iot2MRqNrF69mhEjRlhd72n1WLm+brvtNnbs2EFSUhI9e/aseFRamaeV74oG\nDRrg5eWFwWDg0UcftTpQ31PLBuUvGFwZY3nfffd5VPm++eYbWrRoQXh4OD4+PgwdOpSdO3eSkpJS\ncWdmxIgR/Pjjj0D52+arV69m3759zJw5Eyg/jyuLjIys8oKCu5TVmg0bNtC5c2caNLD89Ii71llt\n3YjXhZ07d7J27VqaN2/OyJEj2bZtGw8++GCVbdyhfO4StBXomZimaYs0TeuiaVqX+vX1e6X3ioKC\nAvLy8ir+vXnzZotXo+Pj4/nkk0/QNI3ExESCgoJo1KiR7nlxhJycHC5cuABAUVERW7Zs4ZZbbqmy\nTXx8fMXA9lWrVtG3b1+nfMZAb9988w233HJLlUcvlXXt2pXU1FTS0tIoLS1lxYoVxMfHOzmX11Zd\nfWVnl3+GpKSkhNmzZzNhguV3qQYMGMDmzZvJzc0lNzeXzZs3M2DAAKfm3xaVxwF99dVXVj9N4Al1\nV53GjRvz3XffAeVvAVt7dO+uddesWTMSExMpLCxE0zS2bt1K+/btuXjxIr/99hsAW7ZsqRjsffbs\n2YpPXMyaNYtx48ZZpNmoUSMCAwNJTExE0zQ++eQT/vCH2n/Q2hmudedergvua9asWaSnp3P8+HFW\nrFhB3759+fTTT6ts4w7lc8eB/z8CIym/yzYa2HF5eSIwDPj88nqXycrKqngryGg08sADDzBw4EAW\nLiz/IOKECRMYPHgw69evJyoqioCAAD766CNXZrlWTp8+zZgxYzCZTJjNZu6//36GDBnCtGnT6NKl\nC/Hx8YwfP56HHnqIqKgoQkNDWbFihauzfU2jRo1i+/btnD17liZNmpCQkMD48eOtjj3JzMzkkUce\nYf369Xh7ezN//nwGDBiAyWRi3LhxREdHu6gU1lVXX1OmTGHdunWYzWYmTpxYMdh79+7dLFy4kMWL\nFxMaGsqLL75I165dAZg2bZpNg/4dyVrdbd++neTkZJRSNG/enPffL/8QrKfVHVgv3wcffMBf//pX\njEYjderUqRg36wl11717d4YPH07nzp3x9vamU6dOPPbYYzRp0oRhw4ZhMBgICQnhww8/BGD79u38\nESBq+AAAIABJREFU/e9/RylFz549WbBgQUVacXFxJCcnA/Duu+8yduxYioqKGDRoEIMGOX/6uOsp\nKChgy5YtFecjINcFD+du5XPHoO1J4COl1BQgB/jT5eVPAZ8qpZ4HNgIXXZQ/WrZsSUpKisXyyncy\nlFJVOh9PEhsby759+yyWz5gxo+LfderUYeXKlRbbuKvly5dbXf7xxx9bLGvcuHGV734NHjzY6icl\n3EV19fXGG2/wxhtvWCzv0qULixcvrvh73LhxVu9uuAtrdVfdGC9Pqzuo/tzcs2ePxTJPqbuEhAQS\nEhKqLLv33nstPoEB5W/hDR8+3Go6VwI2KC+7u4+hqlu3rsXAdLkueJ7evXvTu3dvwP3K57KgTdO0\n5pf/+dJVy08Afa3skgH00DRNU0qNBJzyyXuDUg75GKDJZMLgBreNlTI45OvbZheVTymF2WxG0zSn\n3bY2mU0Omf/TGuWg87E6ZrMZ5aSygePaW3Wc3Q6VMji9fEo5uv6cW2fVKTM6Nw/KoH+53eG/4xVX\n+ho9XwopPx/d5bqn/39rs9ns8P7EXca01cStQLJSaj/l33hzyutiYfXrc+rUKV2/xaJpGidPnsQR\n4+1qq379+pw4cVLXNAsKCjh//jyhoc4fgFqnTh18fH3JyspyyvGKi4vJyc4mNKy+U44XFlafnJxs\nioqKrr+xDk6cOEGYk8oGjmlv13LixAmn1R2Ul+/kSX3b27WcOHmS+uGOLZ/Zu65Ty2SNpmkcPXac\nYvyddsywMP3rMj09neDgEAxu8PasI87VEydOEFbfee2tOmH165OdnU1xcbGu6TqjfB4TtGmatkPT\ntI6apsVqmtZT07QjzjhuXFxnzudeYOnSpbr8CjIajbz//vuYTGbaRbt2vj6AO3v25qefkli/foMu\nd9wuXbrEtGnT6TdgEL5+fjrksPbi/ziMhIQEcnIc8+bwFQUFBSQkzKB7j98RGBjo0GNdUfemm7jt\n9p4kJCRQUKDr+zsWTp48yVtvvcWge5w34LtDx05cvJTHxx9/7NC7DpqmsX//fj75ZBkD777HYce5\n2u9uv5M9e/byn//8x6HzS5pMJr788ktSf0ulW/ffOew4APsLI5n1+pukpaU59DjVMZlMfLDkI35J\ny+QczgsIhsT/kffee4/ffvtNlx8ZZ86c4ZVXZhJ/r+XMLK7whz8O49VXX+WMDnPXaprG7t27Wbly\nJQMGun64QmBgIN1v+x0JM2bo0o+aTCa++uorDv/yCz1+d7sOOaye8qSvFdui861dtJ2JP9mVxqVL\nl3jztZkcO3aUgIAAm2/vappGYWEhbW9pxzNTniOgbl278gXocqv5zOlMXp81k+zsLAICAmxOx2w2\nU1xcTP8Bg3ho7Dhd8mY02XZhW/v1ar5a9Tk+Pr54e+v/q9VsNlNUVESv3ncx9pHHbHo8ajTb1vbM\nZjPLPvqA77dvo06dOg55NGssK0PTNEaPHc+dvayNVrg+Hy/b8pWfl8dbr7/K0SOpdrW3aykpKcHX\n15cn/voMMbEdbUrD1r4zK+sMb71mf3u7lvJZCCJ55rnnbb5TGj78/etvdFlDlUms9y94GRReXs4b\ndaMBZSVFlPqEsrM4ljJ8r7vPFedXW75NXVs/Je3i/Xf/aTFPZ20ZjUaMRiMjRj3I4CH2/4jQq81s\nXL+Of326DG9vL7y9ba/X4uISAgL8eeqZv3FLu/Z250uPuMVsNvPhB++z/dut+Pv729WPFhYW0qhx\nJH/7+/O6PEEL8DXs0TTNclJzJGirldLSUgrtjMrr3nST1QnobaXnBa24uJhiOx67KaWoFxioaxBh\na9AG5Q0779Ilh9zRUEpRr149ux5j2Bq0XWE2m8i7lIfmgEmyvby8uOmmenadX7YGbVfo0d6q4+Pr\nQ926N9mVhr19p73t7Vr8AwLws/NOd22CtnIa3pRhcOKk7QBGvDFT+3aoR9AG5edBQUEBxrIym9Mw\neHlRr5597a0yPa8LmqaRl5eH2Y47376+vrrcpKicJ72YTSYu5eWBHWnq0d4qu1bQ5o5vj7otX19f\nu35Nubs6depQp04dV2dDN0opAoOCXJ0NhzEYvAi66iOkNxJpb55GYazFna4bReU5OW9ESimnDf9w\nBYOXl8XHnN2Zx4xpE0IIIYT4XyZBmxBCCCGEB5CgTQghhBDCA0jQJoQQQgjhASRoE0IIIYTwABK0\nCSGEEEJ4AAnahBBCCCE8gARtQgghhBAeQII2IYQQQggPIEGbEEIIIYQHkKBNCCGEEMID/E/MParj\n3LJu6IYuHAYdJz52N943+E8mPSd1Fs6X+9VEV2fBoULumevqLDhM7r+fcnUWHErdwNeF67nBLxtC\nCCGEEDcGCdqEEEIIITyABG1CCCGEEB5AgjYhhBBCCA8gQZsQQgghhAeQoE0IIYQQwgNI0CaEEEII\n4QEkaBNCCCGE8AAStAkhhBBCeAAJ2oQQQgghPIAEbUIIIYQQHkCCNiGEEEIID/A/MWG8rdKOHePE\niTSKi4sdkn6Avz/NW7Sk2c3NHZJ+bZw8cZy0tGMUFRbZnIYyKMLCwmjfPoaAunV1zJ19NE3jl8OH\nOJ2ZQWlZmc3peHt5ER7RgOiYDnh7u7bpXLp4kV9+OUTu+fMOSd/Ly4vw8Ajax3TAx8fHIce4lrM5\nOaT+9iuXLl10SPp+fn5ENmlK6zZtHZL+9Zw6eeJyeyt0SPp169alRctWRDZp6pD0a+PkieOkHTtG\nUZHtZVXKUN63RLumb/GmjBB1AT9VgrOnKi/RfLmoBVGCn5OPDGVlZfx8YD/ZOdmYTSab0/H19aVJ\n02a0btPWrSZ7v3jhgi79aB1/f1q0aMnNzVvolLPqKU3THH4QV+p8axfth//+VOv9Nm/awFerPie2\nY0fqBgQ4IGdQUFBAcnIyox/+Ez179bEpDT3O/++3f8unn3xEXFwcde3oEM1mMxmZmVy4cJFpL71M\nYFCQ3Xmz9/TUNI1PP/mIPT/tIjo6Gj8/2zu+srIyjhw5Qv3wCP46eQredgYzZhsLl52VxcyEF2h2\n882Eh4djcEAnWGY0knbsGAF1b+LpKX/H147/brV19Egqb772Cu3btyc4ONghnXxRcTEHDhygd5/f\nM/S+Ebqnfy0/7PiOz5Z+SMeOHbnpppsccoy8vHxSUlIY/+eJdO3Ww6Y0vL3sfxDz/fZvWbb0Q137\nlukJr+jSt4TcM7dG2/lSQs+6B2nVvDFNm0TiZfCy+9g1pWkaWdk5/PLrr/xQEEMBNTtfcv/9lN3H\nLi0p4Y3Zr1KQn0fLli3t+qFaXFzCgQMH6NWnL/eNGOUWgduZM6eZMf0Fml/uR+3JU0FBASkpKTzw\n4Bh6973L7rz5+6g9mqZ1sbZO7rRZsTtpF2u//pLZs2fTsGFDhx7r1KlTvPjii4SEhNIhtqNDj2XN\ngf0pLP/sE1599VWaNrX/V7mmaXz22We8+koCr73xtg45tM+6tV9z+OeDvP3227pcIMvKypg9ezYf\nLlnEYxMe1yGHtVNaWsrMGS9y7733MmTIEIcey2Qy8fbbb/P+e/N58qlnHHqsK86fO8ebr73Ck08+\nSbdu3Rx6rNzcXJ5//nlCQkPpc1c/hx7rip8PHmD5so+ZOXMmzZo1c+ixjh49yksvvURISChRrds4\n9FjWHNifwr8+Xap73zLz5ZeY/eYcHXJYo6NyZ92fuf+PA3nowdFOOqalb7/9Fm3B+2wruBUjzrnz\n/d6CdwgKrMdL06fh5WV/oHrhwoXy9hYSSr8BA3XIoe1KS0qYMf0Fhg8bzuDBg3RJMz09vfxaHhpK\nx7hOuqRpjYxps+LAgRT+EB/v8IANoGnTpgwYMICfD+53+LGsOXhgPwMHDtSlUwVQSjF69Giyzpwm\nPy9PlzTtcWB/Mg8+OFq3Oxo+Pj6MGzeOA/uTdUmvts6czsTXx9fhARuUPyJ95JFHOOjEsh49mkpU\n69YOD9gAQkJCuP/++51alz8f3E///v0dHrABtGrVil69enH40M8OP5Y1B/aneHzf4kcpdQxlLg3Y\nAPr06UOjhg2pp/KddsyU5GTGjx+vS8AGEBwczIgRI9if4pq+s7LMzAz8/f11C9gAmjRpwsCBgziw\nP0W3NK2RoM2K/Lw8gq66/T5u3DgiIiKIiYmpWLZy5Uqio6MxGAzs3r272vQ2btxI27ZtiYqK4rXX\nXrNYHxISwqVLl/QrQC3kXbpEcHCwxXKTyUSnTp2sBgclJSWMGDGCqKgounfvzvHjx6usV0oRFBTE\npTzXlKmyPCt1OWfOHKKjo4mJiWHUqFEWYxavV77g4GDyXFRf+fl5BAVXLc+pU6fo06cP7du3Jzo6\nmnnz5gEwZcoUbrnlFmJjY7n33nu5cOGC1TSvdX4GBweT58TgOz8vj+CgmpXv/Pnz9OvXj9atW9Ov\nXz9yc3Otprl06VJat25N69atWbp0aZV1zq7L/Lw8i/ZWXflefPFFYmNjiYuLo3///mRmZlpN81rl\nCwkJIc9B4wKvJy/Psm8pLi6mW7dudOzYkejoaKZPn26x37Xan7P7Fh/KCLipnsXyefPmERMTQ3R0\nNHPnlj9mre583L59O0FBQcTFxREXF8eMGTOsHistLY3u3bsTFRXFiBEjKC0trbI+ODgYH2wfk1tb\n+flVz1V76w6u9CfucV24up+xdo2vTNM0Jk2aRFRUFLGxsezdu9dim5AQx5fPYUGbUqq5Uuqgo9J3\ntKufb48dO5aNGzdWWRYTE8Pq1avp2bNntemYTCYef/xxNmzYwKFDh1i+fDmHDh2yPJ4+2dbNvHnz\naNeundV1S5YsISQkhCNHjjB58mSmTp1quZEbjFm4onJdZmRk8M4777B7924OHjyIyWRixYoVVbav\nUflcSF11tnh7e/PWW29x6NAhEhMTWbBgAYcOHaJfv34cPHiQ/fv306ZNG2bNmmWR1vXOT5eMPVE1\nK99rr73GXXfdRWpqKnfddZfVH0Tnz58nISGBXbt2kZSUREJCQpXgzhXlu/qY1ZVvypQp7N+/n+Tk\nZIYMGWL1Yn+98rkbPz8/tm3bRkpKCsnJyWzcuJHExMQq21y3/Tm5zq4+2sGDB/nggw9ISkoiJSWF\ndevWceTIkWuej3feeSfJyckkJyczbdo0q8eZOnUqkydP5siRI4SEhLBkyZKr8uHac1WPunOHsWwV\nanCNr2zDhg2kpqaSmprKokWLmDhxoqNzaJVb3mlTSrndWLuePXsSGhpaZVm7du1o2/bab58lJSUR\nFRVFy5Yt8fX1ZeTIkaxZs8aRWbVbeno6//nPf3jkkUesrl+zZg1jxowBYPjw4WzduhVPeqHFaDRS\nVFSE0WiksLCQxo0bV1nvaeVr1KgRnTt3BqBevXq0a9eOjIwM+vfvXzF4uEePHqSnp1vs6wnnZ3Xl\nq1xPY8aM4euvv7bYd9OmTfTr14/Q0FBCQkLo16/fNTtmV6iufIGBgRXbFBQUWL3geUL5KlNKVQxV\nKCsro6yszKJc7t7+Dh8+TPfu3QkICMDb25tevXqxevXqGp2P1dE0jW3btjF8+HCb9neGG6HursXa\nNb6yNWvW8PDDD6OUokePHly4cIHTp087MYflHB20eSmlPlBK/ayU2qyU8ldKxSmlEpVS+5VSXyml\nQgCUUtuVUnOVUruBvyql7lNKHVRKpSilvr+8jZdS6g2l1E+X9/+zg/Nvt4yMjCpjOpo0aUJGRoYL\nc3R9Tz31FK+//joGg/XTo3KZvL29CQoK4ty5c87Mos0iIyN59tlnadasGY0aNSIoKIj+/ftX2caT\ny3f8+HH27dtH9+7dqyz/8MMPGTTIcvyGp52flcuXlZVFo0aNAGjYsCFZWVkW23ty+QCef/55mjZt\nymeffWb1TpunlQ/K7+7GxcURERFBv379LM5Vd29/MTEx7Nixg3PnzlFYWMj69es5derUNc/H//73\nv3Ts2JFBgwbx88+WYwzPnTtHcHBwxY8sd61HT687e7hLW3N00NYaWKBpWjRwARgGfAJM1TQtFjgA\nVH4w7qtpWhdN094CpgEDNE3rCMRfXj8euKhpWlegK/CoUsrxH0b5H7Ju3ToiIiK49dZbXZ0Vh8jN\nzWXNmjWkpaWRmZlJQUEBn376qauzpYv8/HyGDRvG3Llzq9ylmTlzJt7e3owe7drB1PaqrnxQfhfA\nrR692MBa+WbOnMmpU6cYPXo08+fPd3EO9eHl5UVycjLp6ekkJSVx8KBnjaJp164dU6dOpX///gwc\nOJC4uDiLwfqVz8fOnTtz4sQJUlJSePLJJ/njH//oimzrwtPr7kbg6KAtTdO0K6+K7AFaAcGapn13\nedlSoPKAsM8r/Xsn8LFS6lHgSovoDzyslEoGdgFhlAeGVSilHlNK7VZK7T57Nke/0tggMjKSU6dO\nVfydnp5OZGSkC3N0bTt37mTt2rU0b96ckSNHsm3bNh588MEq21Quk9Fo5OLFi4SFhbkiu7X2zTff\n0KJFC8LDw/Hx8WHo0KH8+OOPVbbxxPKVlZUxbNgwRo8ezdChQyuWf/zxx6xbt47PPvvMalDjKeen\ntfI1aNCg4vHE6dOniYiIsNjPk8tX2ejRo/nyyy8tlntK+awJDg6mT58+Fo9zPaH9jR8/nj179vD9\n998TEhJCmzZtqj0fAwMDKx4rDh48mLKyMs6ePVslvbCwMC5cuIDRaATcvx49ue5s5S5tzdFBW0ml\nf5sAy9cUqyq48g9N0yYALwBNgT1KqTDKx4Q+qWla3OX/tdA0bfPViWiatujyHbsu9euH218KO3Tt\n2pXU1FTS0tIoLS1lxYoVxMfHX39HF5k1axbp6ekcP36cFStW0LdvX4s7UfHx8RVvqa1atYq+fft6\nzF2OZs2akZiYSGFhIZqmsXXrVosXLjytfJqmMX78eNq1a8fTTz9dsXzjxo28/vrrrF27loBqPhDt\nCedndeWrXE9Lly7lD3/4g8W+AwYMYPPmzeTm5pKbm8vmzZsZMGCA0/JeE9WVLzU1teLfa9as4ZZb\nbrHY1xPKV1lOTk7FW8xFRUVs2bLFolye0P6ys7MBOHnyJKtXr+aBBx6o9nw8c+ZMxbiupKQkzGaz\nRSCjlKJPnz6sWrXKYn93caPUna3i4+P55JNP0DSNxMREgoKCKh6HO5OzB/xfBHKVUndqmrYDeAj4\nztqGSqlWmqbtAnYppQZRHrxtAiYqpbZpmlamlGoDZGiaVmAtDT2NGjWK7du3c/bsWZo0aUJCQgKh\noaE8+eST5OTkcPfddxMXF8emTZvIzMzkkUceYf369Xh7ezN//nwGDBiAyWRi3LhxREdHOzq7ups2\nbRpdunQhPj6e8ePH89BDDxEVFUVoaKjF25furHv37gwfPpzOnTvj7e1Np06deOyxxzy6fDt37mTZ\nsmV06NCBuLg4AF599VUmTZpESUkJ/fqVfzi2R48eLFy40OPOz+rK99xzz3H//fezZMkSbr75Zr74\n4gsAdu/ezcKFC1m8eDGhoaG8+OKLdO3aFSg/j6812NgVqivfkiVL+PXXXzEYDNx8880sXLgQ8Lzy\nVXb69GnGjBmDyWTCbDZz//33M2TIEI9rf8OGDePcuXP4+PiwYMECgoODqz0fV61axXvvvYe3tzf+\n/v6sWLGiIpAZPHgwixcvpnHjxsyePZuRI0fywgsv0KlTJ8aPH+/KIlq4UequOtau8WWXpz2cMGEC\ngwcPZv369URFRREQEMBHH33kkny64i3NMcBCpVQAcAz4UzXbvaGUak353bWtQAqwH2gO7FXlZ30O\n4JQBAsuXL7e6/N5777VY1rhxY9avX1/x9+DBgxk8eLDD8uYovXv3pnfv3gBVBkHXqVOHlStXuihX\n9ktISCAhIaHKMk8u3x133GH1Da3qzjlPOz+rKx/A1q1bLZZ16dKFxYsXV/w9btw4xo0b57D82au2\n9edp5assNjaWffv2WSz3tPa3Y8cOi2VhYWFWz8cnnniCJ554wmo6ldthy5YtSUpK0i+TOrtR6q46\n1V3jr1BKsWDBAiflpnoOC9o0TTsOxFT6+81Kqy0mw9M0rfdVf1sO7AAN+Mfl/wkhhBBC/M9wy++0\nuZq/vz/5+c6bLiQ/P586/o6ZlP56/AMCKCjQ/+lyQX4+AS4qU2UBDqjLgoIC/KsZI+Zo/v4B5Bc4\n79zMy8vD39/facfzDwhwatvLy8urdryfI/j7Bzh1hom8vDyXnque3rcY8aKoqNApx7qevPw8jE58\nOOaI62B5f+L664K/fwAFDuhn8vPzHV4+CdqsaBnVmi1bvnHKxePixYts27aNqNYWL8E6RVRUa7Zt\n28bFi/pNdbNt2zZ8fH0JsjI9lrO1jGrNv//974qxCfbSNI2vv/6aVq1cU1+NGjXm/Llz7Nmzx+HH\nulLWlq2iHH6sK25u3oLDhw5x9OhRhx+rpKSEDRs20MKJ5WsVFcX2776rdkoxPZ09e5adO3fSKspF\nfUvrNg7oW77F14l9SzF1KCk18t13VodeO82hQ4fIOHWSfK2u047ZKqo1X32l3wd+S0pKWL9+A62i\nnNfeqtM4MpKcnByrj3ttdenSpfJruYPbm/KUrxXbqvOtXbQf/vtTrfbRNI1PPl7C4Z8Pcvvtv6Nu\nXcc0lIKCAr777ju6dv8d9498wKa3bOx9MUfTNL5Y8S+SEn+kV69edpXVbDaTkZHBrl27eH76DJo0\nsX+iaHtPT2NZGXPfep3i4kJuvfVW/Pz8bE6rrKyMX375hTNZ2Tw/bYbdk9CbbSzcb7/+wtuvz6Jv\n3z6Eh4c75O0so9HIkSNHOHYsjedfepmgIOcF4EmJP/LxkkX069ePkJAQhxyjuLiYXbt2UT+iIRMf\nn4RBp0mxa2LVF8vZ9eMP9OrVy+5zqDp5eXls2/Ytd/UfyJB424b9envZ95te0zQ+X/4ZSYn/pVev\nnjr0LZns2pXICy+9rEvfEnLP3BptV5d87qh7kH59e9G0SWS1Hx13lOycs6xbv5FdBa05r9Xs8xm5\n/37K7uNevHCBhOnP07JFS6KiWlV8+NcWxcXFJCUlEVo/nCf/+rRuk9Db45fDh3hz9iz69u1LeHh9\nu/rRgoICvv/+e27t2p1Rox+yu0/291F7NE3rYm2dBG3V0DSNH3fu4MTxNIsJxfXi7+9Py1ZRdOt+\nm82VrMf1WtM0khL/y9GjRyguKrI9IaUIq1+f7j1uo2FDfV6F1uP0NJaVsX37Nk5nZth1x83Ly4uI\niAb07N2HunXtv9jaGrQBnDxxnL17fiL3vGPmmTQYDIRHRHDHnb0IvGpiZWf49ZfDHDyQwqWLjpl8\n2dfXl8imTenZs7dTA7Yrknb9l2NHjlBkT3u7hoCAAFq3bUvnW7vanIa9QRuU9y27Ev/LsaOpdpVV\ncblvue13uvUtNQ3aAAIoJMKQQ6CP0enTKueVGjirhXFJC7z+xpfpEbQBXLp4ke+/305OdjZms8nm\ndHx9/Yhs0oRevfu6RcB2xYnjaezZ/RO5ueftSsff358WLVvR47bbdfkRLUGbDUGbp7hBPoFTrRv5\n9LQnaBPC0fQI2txZbYI2T6NX0CZc41pB243dKoUQQgghbhAStAkhhBBCeAAJ2oQQQgghPIAEbUII\nIYQQHkCCNiGEEEIIDyBBmxBCCCGEB5CgTQghhBDCA0jQJoQQQgjhASRoE0IIIYTwABK0CSGEEEJ4\nAAnahBBCCCE8gARtQgghhBAewNvVGXAGk/nGnZjby3BjzxhvuJHLZ3Z1Bhzrhq474fFu5EnVQ37/\nsquz4FDnNr/g6iy4jNxpE0IIIYTwABK0CSGEEEJ4AAnahBBCCCE8gARtQgghhBAeQII2IYQQQggP\nIEGbEEIIIYQHkKBNCCGEEMIDSNAmhBBCCOEBJGgTQgghhPAAErQJIYQQQngACdqEEEIIITyABG1C\nCCGEEB7gf2LCeFtcvHiBxQvnc+J4GsXFxQ45hr9/AC1ateLRPz9B3ZtucsgxaiI/P59F783n2NEj\nFBUV2pyOUgbCwsLoP/Bu7urXX8cc2ufY0aN8tGQRZ05nYjQabU7Hy8uL+uER3DdiFLd26apjDu2z\nb+9uVn2+nJycbEwmk83p+Pr6EtmkKY/8+S80bNhIxxzaZ/26tWz7Zgvnz59D0zSb0wkIqEubtm15\nbOLj+PsH6JhD25lMJpZ+tISUfXu5dOmiXWkFBQXTpVs3HnhwDAaDe/wev5Cby8J3/8mJ48cpLi6y\nOR2lFCEhofT5fT+G3PMHHXNon/T0Uyx+/z0yMzMoKy21OR0vL28iGjTggQcfJqZDrI45rAmN9jdl\n0dT/Epgcc62rllKYvfxJOR9KtjHU4YczlpXx4ZJF/HxwP/l5eQ45ho+PL40jIxn/6AQimzTVPX1l\nTyfoCTrf2kX7bmdSrfa5ePECrya8SI8e3RkwYAB169Z1SN7y8/NZu3YtP/98iL+/OMOmwM3LoOzO\nw8wZ04hu3474+HhusiN4NJvNZGRk8OabbzEk/o8MGHS3XXkDMNhZvmNHjzLrlZcYO3YscXFx+Pn5\n2ZyW0WjkyJEjzJs3jz//5Um6dO1mV97MZvvb3t49u1m44B3++tdJREVF4ePjY3NaxcXF/PDDD6xd\n+29eTHiFBg0a2pU3e+sO4OvVX/Ltti088/TTNGzY0K5gJC8vj1WrvuTkqVM8/+JL1PH3tzt/9jCb\nTLwz722KCvJ55JFHCA4ORinb/ptpmkZubi7vvvsuDRtF8tjEx10euF24kMuMaS9w2223MWBAfwIC\nbA+UzWYzWVlZvP3229zZqw9Dh9+vY05tk5GezssvvcB9991Pjx7d7epbysrKOHz4MO+++y5PPfM3\nuwO3kN+/XMMtNWLrZdD55jpMfXYyYWFhNp+DtjAajZw4cYJXZs3mp7MRZBnDarTfuc0v1P5YZWXM\neWs2Pt5ejBkzhsDAQIeUtaSkhMTERL5YuZIXpr9MZGSTWqdR18+wR9O0LtbWSdBmxZdfLOc2RhGC\nAAAgAElEQVTShXNMmjTJQbn6f5qmMfv112nRqg2Dh9T+F6S9Qdu6tV+TdvQ3/va3v+l2Ap85c4Yn\nnniCDz9Zjre3fTdz7b3wv/ryS/zuth4MHDjQrnQqO3DgAP/85z95591FdqWjR9A2+cmJ/OUvE+nY\nsaPdaV2xbNkyzl+4xKN//otd6dhbd4WFhUx4ZCzvvfceYWE168yvx2w2M+Pll+nStTu/76/fOWGL\nnw8e4MMPFjJnzhx8fX11SbOoqIhJkyYx+dnnaNmqlS5p2mrFvz4l/9IFnnjiCd3SPH/+PBMmTOTd\nRUvs+oGph3lvv0Grli0ZPnyYbmkmJiay4vMveO2Nt+1Kp6ZBW11DEXc1OMGHixc57OZETaSmpjLl\nuefZdK5Djba3JWjbszuJr1Z9zuzZs+36cVtTq1at4uix4zz51DO13vdaQZt73EN3M9nZWbRv394p\nx1JKERMdTfaZM0453tWys84QHR2t6y+Ohg0bEhgUxPlz53RL01bZWfrXZbt27cjJycFsNuuari2y\nss7oXr7o6Giys1xzPlaWnZVFeESEbgEbgMFgoH27dmRlZemWpq2yss4QFRWlW8AG4O/vT4uWLcnK\nOq1bmrbKzsoiOjpa1zRDQ0Np2LABOdnuUH9ZREfr2/bat2/v1LbnbyihcWRTlwZsAFFRUWA2YsD2\n4R3Xk5WVRZs2bZwSsIHj+lEJ2qwwm0wWFXvq1Cn69OlD+/btiY6OZt68eQC89NJLREZGEhcXR1xc\nHOvXr7ea5saNG2nbti1RUVG89tprVdZ5e3tjNNk+1soeJpPJ6t2wOXPmEB0dTUxMDKNGjbIY11dS\nUsKIESOIioqie/fuHD9+vMp6H28fl5WpMtNVdVldPVamaRqTJpU/boyNjWXv3r1V1nt7e5ePrXKD\nu9Rms9niXLW77nx87Bobpxez2YSPd9WyFRcX061bNzp27Eh0dDTTp0+32K9G5bNjbKNerPUz+pXP\n9fVnMhlr3I9Wdt325y7np5X6u1Y/D9evO29vb4xOrDuFho+vZRAzb948YmJiiI6OZu7cuQC8+OKL\nxMbGEhcXR//+/cnMzATg4sWL3HPPPRXn7EcffWT1WHv27KFDhw5ERUUxadKkKuNTlVIYDF4YcFyf\naq2+xo0bR0REBDExMRXLVq5cSXR0NAaDgd27d1eb3vXq2sfHB6MDzlO3CdqUUseVUvWtLI9XSj3n\nijxV5u3tzVtvvcWhQ4dITExkwYIFHDp0CIDJkyeTnJxMcnIygwcPttjXZDLx+OOPs2HDBg4dOsTy\n5csr9nVHGRkZvPPOO+zevZuDBw9iMplYsWJFlW2WLFlCSEgIR44cYfLkyUydOtVFua2da9XjFRs2\nbCA1NZXU1FQWLVrExIkTXZTb2ruR6w7Az8+Pbdu2kZKSQnJyMhs3biQxMbHKNlI+93Ujt7+a9POe\nUHcHDx7kgw8+ICkpiZSUFNatW8eRI0eYMmUK+/fvJzk5mSFDhjBjxgwAFixYQPv27UlJSWH79u08\n88wzlFp5KWPixIl88MEHFXW7ceNGZxfNwtixYy3yERMTw+rVq+nZs2e1+7nymu42QVt1NE1bq2ma\nZRjrZI0aNaJz584A1KtXj3bt2pGRkVGjfZOSkoiKiqJly5b4+voycuRI1qxZ48js2s1oNFJUVITR\naKSwsJDGjRtXWb9mzRrGjBkDwPDhw9m6datdb/Y5S03qcc2aNTz88MMopejRowcXLlzg9GnXP26q\nqRu17qD8F/mVsUxlZWWUlZVZPNqX8rmvG7n91aSf94S6O3z4MN27dycgIABvb2969erF6tWrCQwM\nrNimoKCg4rxUSpGXl4emaeTn5xMaGmrx9Ob06dNcunSJHj16oJTi4Ycf5uuvv3Zquazp2bMnoaFV\n31pt164dbdu2veZ+rrymuyRoU0rVVUr9RymVopQ6qJQacXnVk0qpvUqpA0qpWy5vO1YpNf/yvz9W\nSi1USu1WSv2mlBriivwfP36cffv20b17dwDmz59PbGws48aNIzc312L7jIwMmjb9/1d/mzRpUuOA\nzxUiIyN59tlnadasGY0aNSIoKIj+/at+wqNymby9vQkKCuKcG4xhq42r6/EKT6uvyv4X6s5kMhEX\nF0dERAT9+vW7Zv1J+dzXjdb+apJvT6i7mJgYduzYwblz5ygsLGT9+vWcOnUKgOeff56mTZvy2Wef\nVdxpe+KJJzh8+DCNGzemQ4cOzJs3z+LN5YyMDJo0+f+3KD2lTqvjynPUVXfaBgKZmqZ11DQtBrhy\nf/KspmmdgfeAZ6vZtznQDbgbWKiUquPozFaWn5/PsGHDmDt3LoGBgUycOJGjR4+SnJxMo0aNeOaZ\n2r8p4m5yc3NZs2YNaWlpZGZmUlBQwKeffurqbOnq6nq8Ufwv1J2XlxfJycmkp6eTlJTEwYMHXZ0l\nXd3o5YMbt/3dCNq1a8fUqVPp378/AwcOJC4uDi8vLwBmzpzJqVOnGD16NPPnzwdg06ZNxMXFkZmZ\nSXJyMk888QSXLl1yZRFuaK4K2g4A/ZRSs5VSd2qaduWrkqsv//8eyoMza77QNM2saVoqcAy45eoN\nlFKPXb4bt/tsTo5umS4rK2PYsGGMHj2aoUOHAtCgQQO8vLwwGAw8+uijJCVZfl4kMjKy4pcKQHp6\nOpGRkbrlS2/ffPMNLVq0IDw8HB8fH4YOHcqPP/5YZZvKZTIajVy8eFHXt/wcyVo9VuZp9VXZjV53\nlQUHB9OnTx+LMSlSPvd2o7a/muTbU+pu/Pjx7Nmzh++//56QkBDatGlTZf3o0aP58ssvAfjoo48Y\nOnQoSimioqJo0aIFv/zyS5XtIyMjSU9Pr/jbU+q0Oq48R10StGma9hvQmfLg7RWl1LTLq0ou/7+J\n6mdruHoAgMWAAE3TFmma1kXTtC71w8P1yDKapjF+/HjatWvH008/XbG88liLr776qspbKFd07dqV\n1NRU0tLSKC0tZcWKFcTHx+uSL0do1qwZiYmJFBYWomkaW7dupV27dlW2iY+PZ+nSpUD592j69u3r\n1I8y2qq6eqwsPj6eTz75BE3TSExMJCgoiEaN3GeGgGu5kesOICcnhwsXLgDl3yTbsmULt9xS9Xeb\nlM993cjtryb9vKfUXXZ2NgAnT55k9erVPPDAA6SmplasX7NmTcV52axZM7Zu3QqUf1bj119/pWXL\nllXSa9SoEYGBgSQmJqJpGp988gl/+IP7zGxRW668prtkGiulVGPgvKZpnyqlLgCP1GL3+5RSS4EW\nQEvgV0fk8Wo7d+5k2bJldOjQgbi4OABeffVVli9fTnJyMkopmjdvzvvvvw9AZmYmjzzyCOvXr8fb\n25v58+czYMAATCYT48aN0/37RXrq3r07w4cPp3Pnznh7e9OpUycee+wxpk2bRpcuXYiPj2f8+PE8\n9NBDREVFERoaavGGoruqrh5PnjwJwIQJExg8eDDr168nKiqKgICAal9hd0c3ct1B+Y+kMWPGYDKZ\nMJvN3H///QwZMkTK5yFu5PZXXT/viXU3bNgwzp07h4+PDwsWLCA4OJjx48fz66+/YjAYuPnmm1m4\ncCFQ/imQsWPH0qFDh/KPxc+eTf365R+CiIuLIzk5GYB3332XsWPHUlRUxKBBgxg0aJDLynfFqFGj\n2L59O2fPnqVJkyYkJCQQGhrKk08+SU5ODnfffTdxcXFs2rTJba7prpp7tAPwhlLKDJQBE4FVNdz3\nJJAEBAITNE1zymRpd9xxh9W3fKx94gOgcePGVb7ZNnjw4Gq3dUcJCQkkJCRUWXZl4ClAnTp1WLly\npbOzZbfq6rEypRQLFixwUo70d6PWHUBsbCz79u2zWC7l8ww3evuz1s97Yt3t2LHDYtmVx6FXa9y4\nMZs3b7a67krABtClSxe3G5+5fPlyq8vvvfdei2Xuck13SdCmadomYNNVi5tXWr8b6H353x8DH1fa\n7htN0yY4Mn/KYHDqxxtNJhMGg5fTjleZMhgc8mV/s9mEQbn+izLKoHSvy4r03OCxhlLl5bsyUFgP\nRqPRLR7ZKKUwm/VvhyaTCeUGE6or5Zh+xmQ0unzeUSiffcIR5TObTO5xfjqgbzGbzbrM2VsbJpPr\nZ3bRNA2z2YyG48p+pa90FqPRiMEB56nrW7YbCgurb/Glakc6fvw4YfUtvivsFPXrh5OWlqZrmvn5\n+Zw/f55QNxhgW79+uO51efLkSUJCQt3iwuiI8p04cYL69fUZC2qP+uHhZGdnU1hYqGu6x4+7T/lO\nnjyl648mk8nEqVOn0Gssrz3C6oeTlnZc1zSLioo4cybLPerPAW3v+PHjTi1bkdmPM5kZlJWVOe2Y\n1mRlZYFSmBwYktQPD+fEiZNO+y7eiRMnCHNAXbr+qlMLmqaN1TStpo9Rbdar7+/5/vsdbN682aGR\nuclkYu3atezdu5c77uzlsONcyx09e7F37z7Wrl2rS1lzc3N54YUXGTh4iK5zKtoq/o9DWfj++/z8\n8892N1ZN00hPT+eVV2Zy7/D7dMqhff449D5mvvoq6enpdpfPbDaTmJjIqlVfMmDQ3Trl0Hb16gVy\n+509SUhI0OUTAkajkZUrV5Kamsptt9+uQw7t065de7x9vJk/f4HVL8jXVklJCW+//TahYfVp2SpK\nhxza567f92f79m/ZsuUbXfqWvLw8EhISuO322wkKDtYhh/a5+54/sGzZMvbu3Wt34K1pGmlpabz5\n5pv8cehwnXJ4fflmf07nG0h45VWKioqcdtzKzpw5w5TnnudIUUNw4J222I5xFBYWsnjxYl3aW3XM\nZjN79+5l2bJlDB6i/8sJyt2+xqy3zrd20b7bafkZjuvJzMxg7puzyM7KcljwUVpaSuPGkUye8nfC\nIxrYlIaXDrfSs7OzeOv1WWRmpNtVVrPZjKZpDBh0NyMfeEiXRxh6PCrYu/sn3n9vAQUF+XZNFnxl\nntaRDzxI/4H2j2Uwm/Vpe99s3siKfy2jrKzM6jyyNVVaWkpwSAiTnnqW1m2u/UXwmtCj7sxmM0s/\nWsK2bzZjMBjsurtZWlpKs5ubM+W5fxAa6vq7wADFRUXMeesNDh5IwcfH1+Yn7poGRmMZnTrfypNP\nPeMWP5gA0tNP8dbsWWRn29ePapqGyWSid9+7+NP4x9ziLjfAoZ8P8s+5b5OXd8muvsVoNOLn58fD\nfxpPz1597M5XyO9frvG2CjPdgtMJ4ixeXt5OHfahmU1omuJIcUOOFtX8Gnhu8ws2Ha+gIJ85b87m\nl8OH8PX1c0hRy8rKqFevHo9Pepr20ZZfk6iJun6GPZqmdbG2ToK26ygtLaXMQVG5r5+fXQ0d9Ana\nrigrK6OkpOT6G1ZDKQgIqKvreBM9x3cUFxdjtGOicIPBQEBAgG750Stou6KwsNCuX/y+Pj74+vnp\nlh89607TNAoLC7Cnu6rj54e3ne3NUUwmk913OgL8/THoOLZRT6WlpXbd3XBE36KnkpISux4xenkZ\n8PfXr2+pTdD2/zS8cd6YryuMeFHbO2y2Bm1X6NHequPj44Ofnf3otYI2V7096jF8fX3d5lero/n4\n+NgdRLqzOnWcOnmG0+kZULobpRR1697k6mw4jJeXV8WcozeiG70f9fPzs/tC7XoK4/9ISODJ7c09\n7jELIYQQQohrkqBNCCGEEMIDSNAmhBBCCOEBJGgTQgghhPAAErQJIYQQQngACdqEEEIIITyABG1C\nCCGEEB5AgjYhhBBCCA8gQZsQQgghhAeQoE0IIYQQwgNI0CaEEEII4QH+JyYa03NSdXdTarJ9gnBP\n4Kfkd4UQQtRGzqbnXZ0Fhwob8pars+AyckUUQgghhPAAErQJIYQQQngACdqEEEIIITyABG1CCCGE\nEB5AgjYhhBBCCA8gQZsQQgghhAeQoE0IIYQQwgNI0CaEEEII4QEkaBNCCCGE8AAStAkhhBBCeAAJ\n2oQQQgghPIAEbUIIIYQQHuB/YsJ4W6UdO0basSMUFRU5JP2AgLq0ioqi2c3NHZL+tWiaxuGfD5CZ\nkUFZWalDjhEUFEybdu2pXz/cIenXlNlkYv/+FM6cPo3RaLQ5HS8vA/XDI+jYMQ5fPz8dc2if0pIS\n9u9PJic7G7PZbHM6Pr6+NGnSlHbto1FK6ZhD++SeP8+hQwfJPX/ernTq+PvTsmUrWraK0iln+sjM\nSOe3X38lPz/PrnTq1Quk7S230LBRY51ypo9jR4+SduwoRUWFNqehlCIsrD4xsR256aabdMydfTRN\n4+CB/Xb3o15e3v/X3r3HRVXmDxz/fBEVUAEvYCrdFFAEFZCi8rbWkmbl5qXy8nNNtNZ2t3y17W6X\n9bJYuW6ZpWVZ2cXtomVqtmVqoYZWhqCgSBYQrhcsL6AyqMAMz++PGXgNMAjMhZnx9bz/gnN5zvOd\nc84z33nOOc8htGtXBgyIxbd1ayfWsPnOlJQ45XxrSKtWrQgJCaVf/wEt1o4GSikdfAy0wtQi26um\nEC4oP4qrOlLlpD4yUUo5pSBPFT8wQe38bnez19u+LZXVH7xLfFycyxqJUoOBPZmZTJv+B264aZBd\nZVSYmv8lrZTig5VvsT97DzH9+uHnghNHKUVxcTE5Bw7w6ONzuObannaV09bXsQO9ymTi1Vde4n+F\nPxMVFUWbNm3sLstkMlFQUIBPK18ee3IOfn5+DtXNGadeeXk5z/7rKYyVFfTq1QtfX/t/h5WXl5Od\nnU3ijYOYMGmKw4mbj4/jiV9R0TGemjeHiIhwQkJCELH/eCgrM7Bnz17unjCR3yaNcLhuzpCdtZeX\nXlxMXHwcgR0C7f7MlVKcPXuWrKwsHv3740T1jXZyTe2zNfVLVn/wHgPj42nXzv52tKqqiqKiY5w4\ncZK5KU8T3LGjE2tpH6UUb694nf37soiJ6UdbB9rRyspK8vPz6BAYxKN/f4LWDiZuRju+FwB+OX6c\nZ+bPISK8+nxz/o83o9FIQUEBrVq34W+Pz7brcwu5c3GTl7269XH6B/5CfFw8gYEtm/BXVBr58cef\nyD9u4NvSSKpo1aT1Ln75t0ylVIKtebqnzYb9+7L5aPX7LHjmGcLCwly6rcLCQubNm0fnLl2IiOzt\n0m1V++KzDeTnHWTRokUu/9X6zTff8PzCp1jw3BI6BAa6dFu2rF71PiWnT7Fo0SKHGtVqVVVVLF26\nlGVLX+DRvz/hhBo65pWXXiSkS2dmzZpFq1ZNaxAu5dy5c8yePZtOnToz4rbbnVBD+5WXl/PUvDlM\nmjSRpKQkp5RZVFTE7Nmz6dy5C3HxA51Spr2OHjnMS0sW8+STT9C3b1+nlJmVlcWiZxey4NlFhIZ2\ndUqZ9srO2sua1av414IF9OjRwyllrlq1mmfmz+PZxUvc3hu8Yf06CvLzWLRoEe3atXO4PKPRyHPP\nLeKN117hj3+e5YQaNk9FRQXPzJ/DhHvvZcQI1/6oMZlMLFmyhOUvL2HWo3932XZCfE4R3/EULzy/\nmK5d3XM+mEwm/vXv56jMLOT7Usd7+fU9bTbkHtjPiFtvdXnCBnDttdcybNgwcg/kuHxb1XJz9jFp\n4sQWucwwaNAgwnqE8b9DhS7fli05+7KZMmWKUxI2AB8fH5KTk8nZvw9P6KU+kLOP5ORkpyRsAIGB\ngdx9993k5OxzSnmOKDp2lPbt2zktYQPo3r07I0eOJGe/++P78eBBEgYmOC1hA4iNjaVvdF/yf/rJ\naWXa60DOPkaOHOG0hA1gwoR7OXnyJKWljl1Kdob9+7KYNGmiUxI2AF9fX+67b6rbjs1fjhfh7+fn\n8oQNzJdIp02b5vJ25oo2pdwzfozbEjYwx/rgH+4nGOdcbtZJmw0Gg4HAOr1CycnJhIaGEhMTUzOt\nuLiYpKQkIiIiSEpKoqSkxGZ5K1euJCIigoiICFauXFlvfnBwsMP3szSHobS0VnwXL17k+uuvZ8CA\nAURHRzNv3jzA3P3/j3/8g8jISKKioli6dKnN8hqLLzAokLIyg2uCaYStfXnmzBnGjx9Pnz59iIqK\n4rvvvqs1XynFww8/THh4OP3792fPnj215nfo0IELFy6gHLh/zFkMBgNBQUG1pr3wwgtER0cTExPD\nxIkTuXjxYq355eXl3HvvvYSHh5OYmMihQ4dqzQ8KCqLM4J79Za2p56G1xvYdtPz51hCDobRefEeO\nHGH48OH07duX6OholixZUm+9xmIMDgryiKSmtLS03rEJsGnTJnr37k14eDgLFy6sN/9Sx6eIEBQU\n6BH7r6zO8dlQO2qtKeeeodRdbWUpgXX2l63zLTs7mxtvvJF+/fpx5513cu7cOZvlNbafg4ODXd7O\nBLSuqneOLVmyhJiYGKKjo3nxxRcBWLNmDdHR0fj4+JCRkVGz7KFDh/D39yc2NpbY2FhmzpxpczuN\n5QLBwcFUVZY7JSaXJ20iEiwif3RSWb8Rkc+cUVYTtlXr//vuu49NmzbVmrZw4UJuueUW8vLyuOWW\nW2wemMXFxaSkpPD999+Tnp5OSkpKg8ldS7KOr23btmzdupXs7GyysrLYtGkTu3bt4p133uHIkSMc\nPHiQH374gQkTJtQrpynxCe69jFF3X86aNYuRI0dy8OBBsrOziYqKqjX/iy++IC8vj7y8PF5//XUe\nfPDBeuW5+9KMNeu6HDt2jKVLl5KRkUFOTg4mk4nVq1fXWv7NN9+kY8eO5Ofn88gjj/DYY481WJ7b\nNeE8tNbYvvM0dT9qX19fnn/+eXJzc9m1axfLli0jNze31jJNOT49lclk4k9/+hNffPEFubm5rFq1\nql58jR2f9T40N2pKO2qt0djcrG5bbet8mzFjBgsXLmT//v2MGTOG5557rl45TdnPLXKcSu3t5OTk\n8MYbb5Cenk52djafffYZ+fn5xMTEsG7dOoYOHVqviF69epGVlUVWVhbLly+3uZmm5ALO0hI9bcFA\nvaRNRLzqfrqhQ4fSqVOnWtM2bNjA1KlTAZg6dSqffPJJvfU2b95MUlISnTp1omPHjiQlJV3yS8cd\nRKTmUmllZSWVlZWICK+++ipz587Fx8d8mISGhtZb1xvis3b27FnS0tKYPn06AG3atCE4OLjWMhs2\nbOD3v/89IsINN9zAmTNnOH78uDuqaxej0ciFCxcwGo2cP3+e7t1rP01ofdyOHz+e1NRUj7jU2xS2\nzkNr3r7vunXrRnx8PGDu0Y2KiuLYsWO1lvHmGNPT0wkPD6dnz560adOGCRMmsGHDhlrLeOvx2VA7\nas3bYrN1vv300081yU1SUhJr166tt15T9rM7/PDDDyQmJhIQEICvry/Dhg1j3bp1REVF0bu3/feU\nNyUXcJaWSNoWAr1EJEtEdovIDhH5FMgVkWtEpOZmLhH5q4j80/J3uIh8JSLZIrJHRHpZFyoi14nI\n3rrTW9Kvv/5Kt27dALjiiiv49ddf6y1z7Ngxrrzyypr/w8LC6jXCnsBkMhEbG0toaChJSUkkJiZS\nUFDAhx9+SEJCArfddht5eXn11vOW+KoVFhYSEhLCtGnTiIuLY8aMGZSVldVaxttistajRw/++te/\nctVVV9GtWzeCgoK49dZbay1jHZ+vry9BQUGcPn3aHdV1Om/ed3UdOnSIvXv3kpiYWGu6N8fYlLp7\n8/Fpqx215s2xVYuOjq5JwNasWcORI0fqLeOpx2hMTAw7duzg9OnTnD9/no0bN9qsv7XCwkLi4uIY\nNmwYO3bssLlMU3IBZ2mJpO1xoEApFQv8DYgHZimlIhtZ731gmVJqAHATUPNTUkRuApYDv1NKFbim\n2s3jaZfMmqtVq1ZkZWVx9OhR0tPTycnJoby8HD8/PzIyMrj//vtJTk52dzUdZjQa2bNnDw8++CB7\n9+6lXbt2Lu3KbmklJSVs2LCBwsJCioqKKCsr47333nN3tbRmMhgMjBs3jhdffLHePTma57LVjl5u\n3nrrLV555RUGDhxIaWmpQ8MotbSoqCgee+wxbr31VkaOHElsbOwlH+Lq1q0bhw8fZu/evSxevJhJ\nkyY1eA9fNVfnAu54ECFdKXXJRwlFpAPQQym1HkApdVEpVT0yYxTwOnCnUupwA+s/ICIZIpJx6tRJ\nZ9a9lq5du9Zcljh+/LjNy4c9evSolckfPXrUqU9TOVtwcDDDhw9n06ZNhIWFMXbsWADGjBnDvn31\nn/TxtvjCwsIICwur+QU8fvz4ejdye1tM1r766iuuvfZaQkJCaN26NWPHjuXbb7+ttYx1fEajkbNn\nz9K5c2d3VNfpvHnfVausrGTcuHFMnjy55vyz5s0xNqXul8Pxad2OWrscYuvTpw9btmwhMzOTiRMn\n0qtX/YtdnnyMTp8+nczMTNLS0ujYsSORkQ33H7Vt27Zm/wwcOJBevXrxk40ns5uSCziLO5I262tR\nxjp1aMpopceBi0BcQwsopV5XSiUopRJcORr/6NGja56WXLlyJb/73e/qLTNixAi2bNlCSUkJJSUl\nbNmypUUeqW6OkydPcubMGQAuXLjAl19+SZ8+fbjrrrvYtm0bAF9//bXNg9sb4rN2xRVXcOWVV/Lj\njz8CkJqaWm/IhdGjR/Of//wHpRS7du0iKCiopuvb01111VXs2rWL8+fPo5QiNTW13oMW1sftxx9/\nzM033+zVvcTWvHnfgfnJ0OnTpxMVFcVf/vIXm8t4c4zXXXcdeXl5FBYWUlFRwerVqxk9enStZbz1\n+GyoHbXmrbFZO3HiBGAes/Lpp5+2+URlU/azu1TX//Dhw6xbt45JkyY1uOzJkycxmcxvUfj555/J\ny8ujZ8/6A8U3JRdwlpZ4GKAU6NDAvF+BUBHpDBiAO4BNSqlSETkqIncppT4RkbZQM5TwGWA68KWI\nlCmltru4/gBMnDiR7du3c+rUKcLCwkhJSeHxxx/nnnvu4c033+Tqq6/mo48+AiAjI4Ply5ezYsUK\nOnXqxJw5c7juuusAmDt37iVvpHaH48ePM3XqVEwmE1VVVdxzzz3ccccdDB48mMmTJyelpNUAAAxV\nSURBVPPCCy/Qvn17VqxYAXhffHW99NJLTJ48mYqKCnr27Mnbb79d81TQzJkzGTVqFBs3biQ8PJyA\ngADefvttN9e46RITExk/fjzx8fH4+voSFxfHAw88wNy5c0lISGD06NFMnz6dKVOmEB4eTqdOneo9\nXerJbJ2HlZWVgPfvOzAPRv3uu+/Sr18/YmNjAViwYAGHD5svKnh7jL6+vrz88suMGDECk8lEcnIy\n0dHRl8Xx2VA76s2x2TrfDAYDy5YtA2Ds2LFMmzYNMA9cPWPGDDZu3NjgfvYE48aN4/Tp07Ru3Zpl\ny5YRHBzM+vXreeihhzh58iS33347sbGxbN68mbS0NObOnUvr1q3x8fFh+fLlNd9vM2bMYObMmSQk\nJDSYC7iCy5M2pdRpEfnG8sDBBcyJWvW8ShGZD6QDx4CDVqtOAV6zzK8E7rZa71cRuQP4QkSSlVLf\nuzqOVatW2Zyemppab1pCQkJNggPmsW48+X6w/v37s3fv3nrTg4OD+fzzz+tN97b46oqNja01Fg9Q\n69eiiNQ0St4oJSWFlJSUWtPmz59f87efnx9r1qxp6Wo5RUPnYTVv33eDBw9u9GlCb49x1KhRjBo1\nqta0y+H4bKgd9ebYGjrfZs2q/8aG7t27s3Hjxpr/be1nT2DrYYIxY8YwZsyYetPHjRvHuHHjbJZj\n/R3YuXNnm7mAK7TIsBtKqQb7H5VSS4F6o7YqpfKAm+tM/hnYbpl/GPCM1F3TNE3TNM3F9BsRbPD3\n92/REcVLS0sJCAhose0FBLRr0fgMhlL8/f1bbHvW/AP8MTh51O2ysjLatGmD+Lj/9HHFsVpa6r79\nZc3fPwCDC45Tc3wtd741xD8ggFIXjH5/7lzLticNCQho5/RzD8xvdAnwmP3n3OPTYDAQEOCmttI/\noEXfNHHu3DmXtzMXjT4e83YQH1/nPGXr/m8dDxQR2Zut27ZRXOycd4VdyokTJ9ixYyfhEY2NgOI8\nPSMi+fTTT6moqHD5tg4cOEBhYSFXXnWNy7dlS0REb9atW1dzM6mjlFKsXbuW8IhIj7iBOCKyN2vX\nrnXaAJ3l5eX897PPCI+wf6BJZ+kR1oPTp0+ze/dup5V55swZvkpNJTLS/fGFh0eQkZnR6DhRzVFQ\nUMD+nP307OX4i6kdFRHZm9TUVKe+AWbLli34+fkTVGdAbHeIiIxkw6ef1txT6Shz27KOCDcdm926\n96C4pIT09HSXb0spxbp161z+vXfiYgAb/rux0WE6XEkpxaoP11Dm45xjVjx5NGZniB+YoHZ+1/xG\nf/3aj0jbvpUhQ4a47MXqpaWlbN/+NaPuGM1tt99pVxkVpua//9JkMvHq0sWcO1NMfHw8fn5NeWi3\neZRSFBcXs23bdmY+9Agx/QfYVU5bX8d+V1RUVLDo3wtQVSb69evn0IvjTSYT+fn5HD5ylLn/fKre\ne/qayxmn3rlz53gmZQ49enQnPDwcX1/773i4cOECGRkZXNGtBw/+6WF8HHwJvY+P40lt3k8/8u8F\nTzN06FBCQro4lCgbDGV8881Obhw0hHsmNPzEWEtK276ND95byfDhwwkMDLQ7PqUUZ8+eZevWrSTf\nP5MbbrzJyTW1z8cfrWZH2naGDB5C+/b2v1i9qqqKY8eK2LNnD3NSnqJ7d/cPH2E0GlmyeBHnzp0h\nPi7OobalsrKSgwd/pOTMGWbPTSHAwZfQG+34XgAoyM9j0cKnGTx4MKGhoS75YWo0GsnLy+NY0XGe\nnDvfrnEIQ+5c3MQlFVF+h+nX5SK/vXkYHVz0Xd4Qo9HIvpwfyMzJI+1cFEZaN2m9i1/+LVMplWBr\nnk7aLiEzYzc/F+Rx4cIFJ9fKLCCgHRGRvRkQ2+DoJY2yJ2kDcwLy7c6v+aWoiIpK1/S4BQUFE9Nv\nANf0tP+lFY4mbWBO3Hamfc0vx4swGo12l+Pj40NIaCg3DR5Chw6OD3jqrFPPUFrKNzvTOHnyBFUO\nvMS+TZs29Ai7kkGDhjicsIFzkjaAo0cOsyczk5ISx3q+/f39ubZnT667/gan1MtZcg/k8EPuAYcv\nJXbo0IGYfv2J7N2n8YVbUMbudH4uyHeoHRWgc5cuXJ94IyEuHAOruYxGIzvTvqao6CiVlfa3La1a\n+RDa9QoGDxnmlEvb9iZtAMeOHmHvnkxKXHSlqaYdHTSE9h0aGlji0pqetAEoQnxOE9TKgJ/jzVqz\nGBUYjG34xRTS5IQNdNJmd9LmDexN2ryFM5I2T3WZn3pOS9o0TWseR5I2b9C8pM37XCppu3y/ETVN\n0zRN0y4jOmnTNE3TNE3zAjpp0zRN0zRN8wI6adM0TdM0TfMCOmnTNE3TNE3zAjpp0zRN0zRN8wI6\nadM0TdM0TfMCOmnTNE3TNE3zAjpp0zRN0zRN8wI6adM0TdM0TfMCOmnTNE3TNE3zAjpp0zRN0zRN\n8wKX/QvjReQk8L8W3GQX4FQLbq+l6fi81+UcG+j4vJ2Oz3tdzrFBy8d3tVIqxNaMyz5pa2kikqGU\nSnB3PVxFx+e9LufYQMfn7XR83utyjg08Kz59eVTTNE3TNM0L6KRN0zRN0zTNC+ikzfled3cFXEzH\n5708LjYRMYlIlojkiMgaEQlwoLgKERlvKXeFiPS9xHZ/IyI32VHfQyLSxcb09iLymogUiEimiGwX\nkcRGynqymZv3uP3nZDo+73U5xwYeFJ++p03TNLcREYNSqr3l7/eBTKXUYqv5vkopYxPLegf4TCn1\ncROW/SdgUEotamZ9DwEJSqlTdaavBgqBfyilqkTkWqCvUurzS5RVE7urNOfz0zTN8+meNk3TPMUO\nINzSC7ZDRD4FckWklYg8JyK7RWSfiPwBQMxeFpEfReQrILS6IEtPV4Ll75EiskdEskUkVUSuAWYC\nj1h6+YaISIiIrLVsY7eIDLKs21lEtojIARFZAUjdSotILyARmK2UqgJQShVWJ2wi8oml9+2AiDxg\nmbYQ8Lds/33LtP8TkXTLtNdEpJVl+nQR+cky7w0Redky/RoR2Wr5TFJF5CrL9HdEZLmIfA88KyJ5\nIhJimecjIvnV/2ua5l183V0BTdM0EfEFbgM2WSbFAzFKqUJLonNWKXWdiLQFvhGRLUAc0BvoC3QF\ncoG36pQbArwBDLWU1UkpVSwiy7HqaRORD4AXlFI7LcnPZiAKmAfsVErNF5Hbgek2qh8NZCmlTA2E\nl2zZpj+wW0TWKqUeF5E/K6ViLduPAu4FBimlKkXkFWCyJRmdY/k8SoGtQLal3JeAlUqplSKSDCwF\n7rLMCwNuUkqZROQsMBl4EfgtkK2UOtngztA0zWPppE3TNHfyF5Esy987gDeBm4B0pVShZfqtQP/q\n+9WAICACGAqssiRLRSKy1Ub5NwBp1WUppYobqMdvgb4iNR1pgSLS3rKNsZZ1PxeREjtifFhExlj+\nvtJS99N1lrkFGIg5qQPwB04A1wNfV9dbRNYAkZZ1bqyuG/Au8KxVeWusksi3gA2Yk7Zk4G07YtA0\nzQPopE3TNHe6UN3bVM2StJRZTwIeUkptrrPcKCfWwwe4QSl10UZdGnMAGCAirer2tonIbzAnhDcq\npc6LyHbAz0YZgrnX7Ik6699lY9mmqPn8lFJHRORXEbkZcxI42c4yNU1zM31Pm6Zpnm4z8KCItAYQ\nkUgRaQekAfda7nnrBgy3se4uYKjlwQBEpJNleinQwWq5LcBD1f+ISHUimQZMsky7DehYdwNKqQIg\nA0gRS5Znud/sdsy9giWWhK0P5p6/apXVMQGpwHgRCa2up4hcDewGholIR8sl5HFW638LTLD8PRlz\nT2VDVgDvUbsHTtM0L6OTNk3TPN0KzPer7RGRHOA1zFcJ1gN5lnn/Ab6ru6Ll3q0HgHUikg18aJn1\nX2BM9YMIwMNAguWm/lzMDyoApGBO+g5gvhR5uIE6zsB8X12+pY7vYL68uQnwFZEfgIWYk8hqrwP7\nROR9pVQuMBvYIiL7gC+BbkqpY8ACIB34BjgEnLWs/xAwzbL8FGDWJT7DT4H26EujmubV9JAfmqZp\nHkxE2iulDJaetvXAW0qp9c0sIwHzgxZDXFJJTdNahO5p0zRN82z/tDyskYN5LLhPmrOyiDwOrAWe\naGxZTdM8m+5p0zRN0zRN8wK6p03TNE3TNM0L6KRN0zRN0zTNC+ikTdM0TdM0zQvopE3TNE3TNM0L\n6KRN0zRN0zTNC+ikTdM0TdM0zQv8P7CESccJfrvAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "time: 468 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZ5U1tkZ7ecu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3a5724f5-fc32-40e9-f826-2623d25676ad"
      },
      "source": [
        "# Save the model checkpoint\n",
        "torch.save(model.state_dict(), 'resnet.ckpt')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 15 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2x-t3dD2-H5A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "48de1129-0ba3-486d-e488-b769a7c8491a"
      },
      "source": [
        "pytorch_total_params = sum(p.numel() for p in model.parameters())\n",
        "pytorch_total_params"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "701466"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "stream",
          "text": [
            "time: 19 ms\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}