05_pytorch_computer_vision.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ArmaanMistry/pytorch-learning/blob/main/05_pytorch_computer_vision.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s4fzMDTfLxT1"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "import torchvision\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = datasets.FashionMNIST(\n",
        "    root = 'data',\n",
        "    train = True,\n",
        "    download = True,\n",
        "    transform = ToTensor(),\n",
        "    target_transform = None\n",
        ")\n",
        "\n",
        "test_data = datasets.FashionMNIST(\n",
        "    root = 'data',\n",
        "    train = False,\n",
        "    download = True,\n",
        "    transform = ToTensor(),\n",
        "    target_transform = None\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PHWAqnVzO1me",
        "outputId": "14f01534-dea9-4531-b228-d03bbac23c85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 26421880/26421880 [00:01<00:00, 18032714.10it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29515/29515 [00:00<00:00, 305361.77it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4422102/4422102 [00:00<00:00, 5417900.36it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5148/5148 [00:00<00:00, 4551491.78it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "image, label = train_data[0]\n",
        "image, label"
      ],
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        },
        "id": "GlMeIb-ORCsE",
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              " 9)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image.shape # --> ([color_channel, height, weight])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MYI1WvpvSSx2",
        "outputId": "2e6cb65e-d4a0-4516-9825-06440c2ecc88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_data.data), len(train_data.targets), len(test_data.data), len(test_data.targets)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cla7AS65SiC8",
        "outputId": "4f5f7231-0642-438d-fd1f-1f862e5563ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 60000, 10000, 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = train_data.classes\n",
        "class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1qsOf4vFTGbE",
        "outputId": "b2174356-107c-447e-d525-e029e7ecacd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['T-shirt/top',\n",
              " 'Trouser',\n",
              " 'Pullover',\n",
              " 'Dress',\n",
              " 'Coat',\n",
              " 'Sandal',\n",
              " 'Shirt',\n",
              " 'Sneaker',\n",
              " 'Bag',\n",
              " 'Ankle boot']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(image.squeeze())\n",
        "plt.title(label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "0Tl1x4WeTdbk",
        "outputId": "e99f8fa7-313d-4de4-95e0-f1667102a313"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '9')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(image.squeeze(), cmap = 'gray')\n",
        "plt.title(label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "-VfPE_gQUjEG",
        "outputId": "475ad3ed-8b0a-44be-8f8f-7bf601346055"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '9')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "fig = plt.figure(figsize = (7, 7))\n",
        "rows, cols = 4, 4\n",
        "for i in range(1, rows * cols + 1):\n",
        "  random_idx = torch.randint(0, len(train_data), size = [1]).item()\n",
        "  img, label = train_data[random_idx]\n",
        "  fig.add_subplot(rows, cols, i)\n",
        "  plt.imshow(img.squeeze(), cmap = 'gray')\n",
        "  plt.title(class_names[label])\n",
        "  plt.axis(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "DW1Y-VMKVwbv",
        "outputId": "bece91ef-c8cb-4ebd-a162-0e66d3d272f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 16 Axes>"
            ],
            "image/png": 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c3HHHHXq9VohsKJwkhBARxoIFC/Doo4/ihx9+wMGDB1GzZk3ceuutePDBB62CkkKc62gRI4QQQohAIk+MEEIIIQKJFjFCCCGECCRaxAghhBAikITtEFOWyOJJQVuiInXccL/4OnD1VwDo3bu3pQ8ePGhprl6blJRk6QMHDnjanDlzpn9nI5CCHDeROmbE2XGuzjXi7PAbN3oSI4QQQohAokWMEEIIIQKJEg6IYofrsTI/kvR7RNm1a1fPtgoVKlj6dBG103D46IILLrB0w4YNPW0WRDjJL1QmhBDFBT2JEUIIIUQg0SJGCCGEEIFEixghhBBCBJKwyw4U59fX6tevb+nExERLc0VZ9kIAwPHjx3Pc58SJE5Y+efJkjtrV5nnnnZejdt2jMmXKWPqbb76xNL8mnN9E6rgpW7aspa+//npLt2jRwnPMkiVLLP2///u/lmYPzM8//2zpf/7zn542+VXuTZs2WXrBggWW3rdvn6eNokCvWIvcolesC49wfIGFQXJysqXZNwh4f/tWrFhhab1iLYQQQohiiRYxQgghhAgkWsQIIYQQIpBoESOEEEKIQFLsjb1sfgW8RqLHH3/c0lWrVrX0sWPHLO1KWsZG3tKlS1uaTbqcOM3F0aNHLV2ypJ2bcOvWrZZ23Ur+rM8995ylZ8+e7duPs6Eoxs2FF17o2XbRRRdZmq//77//bumaNWt62uB7zJ8tLS3N0vPnz7f0unXrPG3WqVMnx3OwMXvXrl2WZuMvAKxfv96zLb8pSJMgf2fDOZffOFPCv6LnXDX25iX5JB/Dmud1ns9q1arlaXPNmjWWPnTokG8/spOSkuLZVqVKFUu7as5lp1y5cp5t/JLFm2++aWm/fupJjBBCCCECiRYxQgghhAgkWsQIIYQQIpAU+wKQ4cQf2fPCXhROdrd27VpPG7GxsZYuUaKEpX/99VdLV6pUydIu7050dHSObfJn++233zxtxMTEWHrDhg2efYLODTfcYOlGjRp59snMzLT0nj17LM33x3UtOZ7LcekPPvjA0uyzOf/88z1tcrJBPi8nzCtfvryl+/bt62nzrbfesjQnjwo6/D0AvMkkGR4TfG9csI+Nz8H+Jdd3mPvKvra8+Dj8iplyP8L5rDyW4+LicmyD/w4Aq1evtnRBJ9IszuTWP+SXsBUADh8+bGm+XwwnAHXB3k8eJzxfucYE/87x768fehIjhBBCiECiRYwQQgghAokWMUIIIYQIJMXeExMOfrFvjtmxBryxbd6nWrVqluYYNMfKAW88nWPd3IYrJs9x+9zGGyMRLiLGeXtWrVrlOYbvD19bjvG7rhP7aNjXtH//fktzXNrlTeCxxuflPDH8959++snTZrNmzSwdNE+Mnx/Az/8CAF26dLF0165dLb1x40ZL796929MGF/TkeD575VzzAvvteByGU8SV8WvDr9isy8/CeUb4+vBn5fxGALB48WJLv/322559zkXyIz8Ot8FjgOcW9kICQEJCgqUrV65s6QEDBlia55aKFSt62uTfLf4OsA/HNfZ4vOb2eulJjBBCCCECiRYxQgghhAgkWsQIIYQQIpDIEwOvz4D9EuxLcMUG2XvCcT323fjFsV394DZYcx8AIDk52dIcowwiDRo0sPTevXst7fKecI6Wffv2WdrvfgHe68vn5ZwIfE9dvif2K3C+IR4nBw4csDSPTVc/8lK7JWg88cQTlmYvCvtZOJcO3wfA+11p06aNpTmnjyt/Dd8fHhM8zrgfrn4xfvME+xT42gDeujp8ffg79ec//9nTRnp6uqU//PDDM/RYnC08l7CnyVVviMcie2K4Fh/nxXK1yWONvZ98TDi/c7lFT2KEEEIIEUi0iBFCCCFEINEiRgghhBCBRJ4YAPHx8Zbmd+w5jse+BMAbk2Qfgl9OCVdckOPQvA/Htl2eGI5JuvJhBA3X58yO61py7JZrfvD9ceWJ4XHBngi+p+xncHkRGP5s3C/+bC6PE8ey2Q+0a9cu334UJXwN+DovXLjQc8zzzz9vafYWcR2YO++809JVq1b1tMl1q/j+XXXVVZZ25SfiMeOX64nnGpeHwM/TxPlA2D/BOWAAICUlxdLt2rXL8ZyufvH4r1u3bo79FGfGz8fG3pNatWpZ2vUd5zmN81hx/TjOx+XyxPDc8ssvv1iax4Qrlxn3y+UdzAk9iRFCCCFEINEiRgghhBCBRIsYIYQQQgQSLWKEEEIIEUhk7IU3CRCb7dhUxYmzAK/Bkg1PbPArW7aspV0J2ngbG6DYrMoGSNcxxQE2Lm7fvt3SbHIDgE2bNlnar7imy1zmZ8xlMx0XiHSZ2ngbG1LZRM5/Z1M64B0XbNqMdGMvj+P27dtbukqVKp5jZs6caelp06ZZmu9nWlqapV3XMTU11dKjRo2yNH+HL7vsMk8bnETOz8jL84SrICSPGe47Fyr98ccfLe2aJxo1amRpNmTyXOSar9gs7zqPyB94DmQzv+s7zmOLXxpg+HeRzcRnOk92+LfTNSZ4rPFLMn7oSYwQQgghAokWMUIIIYQIJFrECCGEECKQFHtPTDjF7jguzX4J9iG4Ej0x3AbHAlm7CvlxzJ0LDnIs3OV/cbUbdNhrsmHDBks3bdrUcwzHbtlrwgnWXF4Ehu8xx6X9khMCXh8B+zA+/fRTS1944YW+bfI4cSVyCxL9+/e39NChQ32PqVmzpqV37NiR4/6uBJZcSHPAgAGW/uc//2lp1/ePi5XyXMLwvOAah5s3b7Z0VlaWpffv329p9sjwGAO8Y2TdunWWZj+Fa16tV6+epYtDsdmiwi/ZHftIeOzxXAR45wX+Hatdu7alOdmda3xzMWS+564EeYxfcks/9CRGCCGEEIFEixghhBBCBBItYoQQQggRSIqdJ8YvluiC48HsZdi3b5+lOQ8H4PWvfPbZZ5bmPBXcpitOzdvi4uIszcUck5OTPW188803nm1Bh30k7AFw5YnhXBmcryMc+BgeJ3yP2a/iyj3DceYVK1ZYmvNB7Ny5M8c+AN54uSsHSiTDeSJ69Ohh6b59+/q2wd8Vngf4XriuIxdKbN68uaX79Olj6ZUrV3ra+PDDDy19ySWXWPqnn36yNM9FXFAPALp3727pxYsXW5rnDfY+cL8B4IMPPrA0jxmeV13fH/ZccD9F/sHzxtatWy3NuaEAry+M5wnOf8Z+Sv5OAcDq1ast7Vc82eUnDWefnNCTGCGEEEIEEi1ihBBCCBFItIgRQgghRCCRJwZeTwXHCtkD42qTvQtci4TZtm2bpV21SDh3BXtB2HfTrVs3Txtff/11jv0IApzzwK/OkSt2y3FWbiOcvDB8DLfJcWoeRy44R4JffJjHYmJioqdN9ia4rkck88c//tHSc+bM8T2G7x/H5v1wfaf5fnM+oiuuuMLSrlw0nNOI89csX77c0q+++qqlXf4f9kXxmOA6Ybfffrul2XcF+Oev4e+YKwcMj9UmTZrk2KY4M36/W1u2bLE0e2B4DgC8XkH2s7AvbMmSJZZ2+aD4d4vnGvZWhVM7KbfzlZ7ECCGEECKQaBEjhBBCiECiRYwQQgghAokWMUIIIYQIJEVq7M2LCbcgznvkyBFLc5IfTr7lSh7GBidOfsfmpSpVqvi2ybBZq1WrVr7HcCG3IMIJwPh+sVmMr70LNqnx/XMZrf3MwGwQr1y5co7ndLXJBkruB7fhMrBy0iouZBjpsAFx0qRJvsfw3MGmRb8iry7Y7MrXnr+PAwcO9LSxYMECS69du9bSPNc88MADlnbNC3x/MzIyLM0J9T7//HNLu4qG+pnQ+bOzIR3wmn1dSUHPRfx+51wvFfgdw+OXXyxxtcnzJM8d/DvnuscMFxflgpDVqlWz9Pr16z1tcJFI18sKOaEnMUIIIYQIJFrECCGEECKQaBEjhBBCiEBSpJ6YgvDAhJO0jGPdHE/kpHJ+CckAr7eB2+REaBw/5rigixo1auR4DheupFRBw6+oGPtGXAmV2IPEicvYE+Aq1sheAh5rXICTvQs8rgCv14DHEf+d/S1JSUmeNr/99ltL8/Xj8ey6XkUJe4nWrFnjewx7BMKZB7Ljmov8ih6y54OL8AFA27ZtLc2JvNhHxYVKXQnj+PpwIdiPPvrI0uEUu/Qbd3wtXNeX2y0sj2Ok43cdwhl7TO3atS3NcxMnCAW8XkKeN/mc4fjI2L/C/eLEi66CptyGy4+YE3oSI4QQQohAokWMEEIIIQKJFjFCCCGECCTFrgBkOHHYunXrWppjfexP4VihK6cCx5RzmyMhnLjo5s2bLc3eBlfMknOsBBHOlcGxWr4O27dv97TBXgS/ceLK6cLXl+85x6V5nLjaZLhf7Gdg/wp7rQDv9fCLl7Onoqjhz8yxfBfsR+J75fd9DOf7x/vwveDxAHiLNfqNIZ6b2DMDeH0GPM74/ofjD2IfAvcrnNwmfp6hc5W85EPzy8tTvXp1S3OxYFeuFc7hwjnEeDxzH1y/JezJ++GHHyzNPjH2cwFen19mZqZnn5zQkxghhBBCBBItYoQQQggRSLSIEUIIIUQgCbwnxi+/gYvOnTtbmuOJXM+E49iuc3D8l+OLnCOEc7y4auDwMewVYD+EK9dMs2bNLP3uu+969ol0/LwlfO1ceQb8ch5w3NrlL+Lzsub7wV6ecOox+Wn2t7ji63xe/mycJynS4OsYjqeDPU8MX6e8eDz86lqFU4/Jzw/B/h9X/Ro+j58Xi+emcHK88PclHF8HbzsXPDGuaxnO2Mppf9cxXA+L7zn/nrhymX3//fc5tsHeuMaNG1va9Rv1zTffeLZlp06dOpZ2efi4Bllux42exAghhBAikGgRI4QQQohAokWMEEIIIQJJvnliwolb50ctDY5Lu2J/2bnuuus82/i9dG7DL++C65x+Hhf2IbDm+CTgjbn71V9y5angzxpEeGzxdfGrpQR4fU5+9ZZcXgQ//xX7Mnh/V34hjv+yN8HPq+Aaizy2+LO6YtuRBHvUwqntxPeLr71fbiHXdfTzgfjl43G1wfeGPTB+uWgA/7pGft8Pl6fPz4vFuL5jfh6i4khecr4wrmtdr149S/PcwTWIUlNTLc0+E8A7l9SqVcvSnHuG9+d6c659uJ979uyxtGte5fkoHG9ZdvQkRgghhBCBRIsYIYQQQgQSLWKEEEIIEUi0iBFCCCFEIAnbeeVncnMZnHJrFuM2XIYol6EsOx06dLD0hRde6NmHi1JxQrgKFSpYmg20bBIFvIY9NuSx0YoNT67rx8nu2BTF/XCZq7noVxDxu7Z+RfUA77jhNv0S2bm2sXGR+8HjxmWo5XvGn43Pwfu77jkXQzx48GCO/Yw0+Pt5xx13WPpf//qX5xi+v35mSja7usYMX1s/g6zf/AZ47wV/h8NJRJiVlWVpP3NpXhL78Wf1K0QK+M/NBU1eCi3mJRFdbilbtqyl+fvJplzXeTmRaf369S3Nvw1sjge8LxH4JQDlNnjsAv7fEX7h5ddff/W0wQV7w0lYmx09iRFCCCFEINEiRgghhBCBRIsYIYQQQgSSfPPEuMhtbCscOKFYt27dLM0Fp1wxuPPPP9/SHG/0+2z79+/3bOPkQ/zZ/TxErngyxzBr1Khhab94JOCNvwYRP/8Kx+vDGZuclIkTCYbj8WKPy44dOyzNcWpXsjve5ld4j8eEayzyPWdvTkF8L/OTb7/91tLjx4+3tMsTw9chJSXF0lz8juP/rqJzfokHGdeYYe9I5cqVLc3J7nbu3Onbr6SkJEuz3469DOEkIvWDx6UrsR9/ftc+hUleilb6wXMAF1sFgNq1a1uaE47y/WFPiOsYngP5NyucwrD8va9SpYqlef7i30ked65+MT/99JOlXUWKp0+fbum0tLQc22T0JEYIIYQQgUSLGCGEEEIEEi1ihBBCCBFIwvbEcDytUqVKlm7YsKG3cZ/cAhyzZA8Bx+wAb5yOj+G4HsegAa//ZPXq1ZZmf0vVqlVzPB7wenX8igHytXC1ycewvyccz0U4xfMiHb42PK44n86WLVs8bbAvxC/3TDixcj6Gc36E4yPIbTFGHhOumLRf/JyvV6Tx/vvvW/q7776z9KWXXuo5ZsmSJZbmMcLflXByp/gV0gwHPobnzZtvvtnSK1as8G3zb3/7m6UzMjIszb6acIpdugrz5YTLH8S+MpdfqyDx+8667jF7TzivVmJioqX5++rKDcT78NzDhRbZFwUAu3btsnS5cuUsvW/fPkuzd4qPB4CWLVtamucr/m3l+cr1W8L3nH+jwskVx2Mvt98zPYkRQgghRCDRIkYIIYQQgUSLGCGEEEIEkrA9MUzdunUtzXkZAG/sPbe+kd27d3va5Pgh13PguKer3oNfvJzzLnAs0OVjYP+OXz84hsm+G8Bbc+Pnn3+2NF9fl+cit7HuSMQv/wTX52APBeD1InCMme85jxHAe0/ZJ8bXOpwaRTy2+DvglyfG5YPifrEnJmhjYvjw4Za+5557PPuwJ+a9996zNNdQ49i9y1vEsfm85Fvxyy3E/i0edy5/AHtNuE32vPBnC2ds89jlfoSTR+nTTz/17FOYcA08nicA7+fka7d582ZLu/L2MHwd+Bw8b7vGFX+v+XePxwD/frRu3drTJv/eVqtWzdL8e5OZmWlp9vYA3rmGPyvnlnHNzZ9//rmleW3hh57ECCGEECKQaBEjhBBCiECiRYwQQgghAknYnhjOt9KuXTtLZ2VleY7hWB/H5rmGBNeh4Hf4AW/81y+/hysOynFPjgVyDJnjoK64KMcLXV6cnM7B8VvA23eOk3KM03VOjntyLoAgwPeU47B87T7++GNPG82bN7c0e2T8YruufXjs+dUkcnkR/OLnfl4FjtkD3vph3K/c5qYpbPgzc90j9i8BwKxZsyzN33H+zPw9CCdPE19HPkc4+Vc2bdpk6S5dulj6xx9/9O0Xz5Ncn4Y/q1+tMRc8tvlzuOrosIfxs88+8z1PQdKoUSNLu+rI8edk7wnPuTz3u2oB8T58v3ic8DwOeO+RX14rns9cv1Gcn4bPu2rVKktzLhrOKwN4vYU8V3M/a9as6Wnj+eeftzT72/zQkxghhBBCBBItYoQQQggRSLSIEUIIIUQg0SJGCCGEEIEkbGNvq1atLN2tWzdL//DDD55jOCEPm43YDLxt2zZLsxEJ8JpX2TjEZlhXUiY2a7Hxzc9w7OoXG5zCMVrl1CfAa+pjAyv329UGm8hcBTEjHf5cbHJjIygX8AS8hlg2bbrMdQyPNe4HX+twim/6GdU5sR+PATbjAUCbNm0szeOEi7ZFGn6FY7/88kvPMZdccomlN27caGm+jmzsdZmd+TvrV8zO9f3jsclzy5AhQyzN5klXcUA2k7LmFxVcRfcYv+8Hv7jwySefeNp49NFHfc9TmPBnchWk5O89f1f8XtBwFSnmOXbPnj059sv129CiRQtLc4I8TnzK49dlYub5ae3atZbm7x1fG1c/eTxzGzyOXOZ3NvuGMxdb58jV3kIIIYQQEYIWMUIIIYQIJFrECCGEECKQhO2Jeffddy3N8d9rr73Wc0y9evUszR4Pjs3v2LHD0hxvA7wxSvY/cFIm9rO4tnFSPY5zcmInV2yVP8vkyZMtfcMNN1iavTuupFauonTZYb+PK0Ebx1JdCQQjHb7HPK44VuvyxHBslscJx2pd/ga/Nviebt++3dKuZFEM30PWHPvmRHCA11vGfq1169b59qMocfnYsuO6v5zwjeF5g++FKykm7+NXzDEcLxaPs8WLF1ua5wHXHOhXWJTvN/tqXF4tHqucdI8L97nmQCYvBTPPBv4+cqJP19y3a9cuS3MhY/ag8bXnhHCAd9zwHOw3r7vgRIIVK1a0NN9j/i0FvH6fhg0bWprvKY+9cJLy8XeX52bXmPD7jvihJzFCCCGECCRaxAghhBAikGgRI4QQQohAErYnhnnrrbdy1ADQoEEDS/fp08fStWvXtnTdunUtzbFEwBs/4/gux9xcMTjexgXlOPfGQw89ZGmOY4fDs88+m+M5XfkI/GKFfgUJAW+MvUaNGr59jTT88py4fAOMX04PPy+KC45tc5t+xUkB//Hql5vElfOF49TcJn/vXDmeihKXPyw7H3zwgWfbhx9+aGn2NvC94VwqLu8czz9+xTn9+g0AW7ZssbTL0xQEwsk941cQNb+pX7++pdkn4iqW6vdbwPfL5ZVj+DvpGlvZYS8P4PWncD857xFfax7fgP9vQTh5YRj+TvBn5zkvHE9MOPm1sqMnMUIIIYQIJFrECCGEECKQaBEjhBBCiECSZ08Mx/FccazVq1db+uGHH86xTY6XcYwTAKpWrWpproHCfgDX+/Jcd2LNmjU59is/+NOf/mRprhvFeQAAb5yT46Ac83XFMDm2unPnTktPnTr1DD2OHNhbxfkfXPk5mBUrVli6WbNmluZx47qWfD/8aqCwZ8bls/Grz8M1uDhm74LPw3VUwmkjaHDsnWsnifylsP0u4cBz2/XXX29p/o4D3jmVfSN+PkSXN4j38ctB5fJ+sj+F93HV+sqpD4B3ruF6WPwb7vfZAW/dLu5XXnLA+OWJYvQkRgghhBCBRIsYIYQQQgQSLWKEEEIIEUi0iBFCCCFEIIkyYbpoCruYlygccmuiyi35MW7YCMeF3Nhw5jJJM507d7b0FVdcYWkuouhqlwuDcj+2bt1qaVeRQTYGcvE4Nt+9+uqrlg4nIRXfg/y45wU5bjTXFE8Ke65hYz0b3AFvgUdOEsdmVzbcuooisnnV7yUYV5JE/l5zQk+ea/izuxLosbHZtU9u4c/KLyLwZ3PNV3v27LH00qVLLe03bvQkRgghhBCBRIsYIYQQQgQSLWKEEEIIEUjkiTnHCYInpjDgWG6TJk08+1SsWNHSHD/3SyblikFzfJwTL3LCyEhBnhiRWzTXiLwgT4wQQgghiiVaxAghhBAikGgRI4QQQohAErYnRgghhBAiktCTGCGEEEIEEi1ihCgkXnnlFURFRWHjxo25PrZfv35ITU3N9z4JIYof59JcE7GLmKioqLD+++ijj4q6qyKC+e6779CzZ0+kpKQgNjYW1apVQ+fOnTF27Nii7pooBmzYsAGDBg1C7dq1ERsbi7Jly+Kyyy7DmDFjcOTIkQI55+uvv47Ro0cXSNsi72iuKRpK+u9SNEyePNnSr776KhYsWODZ3rBhw8LslggQS5YsQYcOHVCzZk0MHDgQSUlJ2LJlC7744guMGTMGQ4cOLeouigDz/vvvo1evXoiJicEf//hHNGnSBMePH8dnn32G//mf/8GqVaswceLEfD/v66+/ju+//x7Dhg3L97ZF3tBcU3RE7CLmlltusfQXX3yBBQsWeLYzhw8fRunSpQuyawXCoUOHEB8fX9TdKFY8/vjjKFeuHL766itP0cidO3cWTadEsSAzMxO9e/dGSkoKPvzwQ1StWjX0t8GDB2P9+vV4//33i7CHojDRXFN0RGw4KRzat2+PJk2aYPny5bj88stRunRpPPDAAwBODZwBAwagSpUqiI2NRdOmTTFp0iTr+I8++sgZktq4cSOioqLwyiuvhLbt2LED/fv3R/Xq1RETE4OqVavimmuu8cQc58yZg3bt2iE+Ph5lypRB165dsWrVKmuffv36ISEhARs2bMDVV1+NMmXK4Oabb8636yJOsWHDBjRu3NgzqQBA5cqVQ///5ZdfRseOHVG5cmXExMSgUaNGePHFFz3HpKamolu3bvjss89wySWXIDY2FrVr1/ZUlgaAVatWoWPHjoiLi0P16tXx2GOPeSrPAsA777yDrl27Ijk5GTExMUhLS8M///lPT3VYEVmMHDkSBw8exEsvvWQtYE5Tp04d3H333QBOVfL95z//ibS0NMTExCA1NRUPPPAAjh07Zh0Tzlho37493n//fWzatCkUUg+Sf6G4ormm6IjYJzHh8ssvv+Cqq65C7969ccstt6BKlSo4cuQI2rdvj/Xr12PIkCGoVasW3njjDfTr1w979+4NTS654frrr8eqVaswdOhQpKamYufOnViwYAE2b94cmkQmT56Mvn37IiMjAyNGjMDhw4fx4osvom3btvjmm2+syeb3339HRkYG2rZti6effjqQT48inZSUFHz++ef4/vvvnWUETvPiiy+icePG6NGjB0qWLIl3330Xf/7zn3Hy5EkMHjzY2nf9+vXo2bMnBgwYgL59++I///kP+vXrh+bNm6Nx48YATi14O3TogN9//x333Xcf4uPjMXHiRMTFxXnO/corryAhIQF/+ctfkJCQgA8//BB///vfsX//fjz11FP5e0FEvvHuu++idu3auPTSS333vf322zFp0iT07NkT9957L7788kv861//wo8//oiZM2eG9gtnLDz44IPYt28ftm7dimeffRYAkJCQUDAfUoSN5poixASEwYMHG+5uenq6AWDGjx9vbR89erQBYF577bXQtuPHj5s2bdqYhIQEs3//fmOMMYsWLTIAzKJFi6zjMzMzDQDz8ssvG2OM2bNnjwFgnnrqqTP278CBA6Z8+fJm4MCB1vYdO3aYcuXKWdv79u1rAJj77rsv7M8vcs/8+fNNiRIlTIkSJUybNm3M8OHDzbx588zx48et/Q4fPuw5NiMjw9SuXdvalpKSYgCYTz75JLRt586dJiYmxtx7772hbcOGDTMAzJdffmntV65cOQPAZGZm5njuQYMGmdKlS5ujR4+GtvXt29ekpKSE/dlFwbFv3z4DwFxzzTW++65YscIAMLfffru1/a9//asBYD788MPQtnDHQteuXTUWIgzNNUVHoMNJABATE4P+/ftb22bPno2kpCT06dMntK1UqVK46667cPDgQXz88ce5OkdcXByio6Px0UcfYc+ePc59FixYgL1796JPnz7YvXt36L8SJUqgVatWWLRokeeYO++8M1f9ELmjc+fO+Pzzz9GjRw+sXLkSI0eOREZGBqpVq4ZZs2aF9sv+r5Z9+/Zh9+7dSE9Px08//YR9+/ZZbTZq1Ajt2rUL6cTERNSvXx8//fRTaNvs2bPRunVrXHLJJdZ+rpBh9nMfOHAAu3fvRrt27XD48OGILf54rrN//34AQJkyZXz3nT17NgDgL3/5i7X93nvvBQDLN6OxEFw01xQdgV/EVKtWDdHR0da2TZs2oW7dujjvPPvjnX6TadOmTbk6R0xMDEaMGIE5c+agSpUquPzyyzFy5Ejs2LEjtM+6desAAB07dkRiYqL13/z58z3mrpIlS6J69eq56ofIPS1btsSMGTOwZ88eLF26FPfffz8OHDiAnj174ocffgAALF68GJ06dUJ8fDzKly+PxMTEkLeKJ5aaNWt6zlGhQgVrcXt6/DH169f3bFu1ahWuu+46lCtXDmXLlkViYmLIvM7nFpHB6erlBw4c8N1306ZNOO+881CnTh1re1JSEsqXL2/NRRoLwUZzTdEQeE+MK/YXLmcq3e4yOg0bNgzdu3fH22+/jXnz5uGhhx7Cv/71L3z44Ye46KKLQkaqyZMnIykpyXN8yZL2pY6JifEsskTBER0djZYtW6Jly5aoV68e+vfvjzfeeAO33HILrrjiCjRo0ACjRo1CjRo1EB0djdmzZ+PZZ5/1GORKlCjhbN/koXrH3r17kZ6ejrJly+If//gH0tLSEBsbi6+//hr/+7//6zTniaKnbNmySE5Oxvfffx/2MWeaa06jsVB80FxTuAR+EeMiJSUF3377LU6ePGktFE4/MktJSQFwalULnLrB2TnTk5q0tDTce++9uPfee7Fu3To0a9YMzzzzDF577TWkpaUBOOVE79SpU35/JJGPtGjRAgCwfft2vPvuuzh27BhmzZpl/cvHFf4Ll5SUlNCTueysWbPG0h999BF++eUXzJgxA5dffnloe2ZmZp7PLQqHbt26YeLEifj888/Rpk2bM+6XkpKCkydPYt26dVZOq6ysLOzduzc0F+VmLPgtiETkoLmm4CmWjwKuvvpq7NixA9OmTQtt+/333zF27FgkJCQgPT0dwKkBUKJECXzyySfW8S+88IKlDx8+jKNHj1rb0tLSUKZMmdBrkhkZGShbtiyeeOIJ/Pbbb54+7dq1K18+mwifRYsWOf/VctqnUL9+/dC/drLvt2/fPrz88st5Pu/VV1+NL774AkuXLg1t27VrF6ZMmWLt5zr38ePHPeNPRB7Dhw9HfHw8br/9dmRlZXn+vmHDBowZMwZXX301AHgy7I4aNQoA0LVrVwC5Gwvx8fGBfvxfHNFcU3QUyycxd9xxByZMmIB+/fph+fLlSE1NxZtvvonFixdj9OjRIUNeuXLl0KtXL4wdOxZRUVFIS0vDe++95/GvrF27FldccQVuuOEGNGrUCCVLlsTMmTORlZWF3r17Azj1iPnFF1/Erbfeiosvvhi9e/dGYmIiNm/ejPfffx+XXXYZxo0bV+jX4lxm6NChOHz4MK677jo0aNAAx48fx5IlSzBt2jSkpqaif//+yMrKQnR0NLp3745Bgwbh4MGD+Pe//43KlStj+/bteTrv8OHDMXnyZFx55ZW4++67Q689nn5CeJpLL70UFSpUQN++fXHXXXchKioKkydPztPjYlG4pKWl4fXXX8eNN96Ihg0bWhl7lyxZEkrpcPfdd6Nv376YOHFi6JH+0qVLMWnSJFx77bXo0KEDgNyNhebNm2PatGn4y1/+gpYtWyIhIQHdu3cv7EsgsqG5pggpmpeics+ZXrFu3Lixc/+srCzTv39/c/7555vo6GhzwQUXhF6Zzs6uXbvM9ddfb0qXLm0qVKhgBg0aZL7//nvrFevdu3ebwYMHmwYNGpj4+HhTrlw506pVKzN9+nRPe4sWLTIZGRmmXLlyJjY21qSlpZl+/fqZZcuWhfbp27eviY+Pz/vFEGExZ84cc9ttt5kGDRqYhIQEEx0dberUqWOGDh1qsrKyQvvNmjXLXHjhhSY2NtakpqaaESNGmP/85z+eVxRTUlJM165dPedJT0836enp1rZvv/3WpKenm9jYWFOtWjXzz3/+07z00kueNhcvXmxat25t4uLiTHJycujVTNCr/0F77fFcYe3atWbgwIEmNTXVREdHmzJlypjLLrvMjB07NvTa6m+//WYeffRRU6tWLVOqVClTo0YNc//991uvtRoT/lg4ePCguemmm0z58uUNAI2LCEBzTdERZUxxWIoJIYQQ4lyjWHpihBBCCFH80SJGCCGEEIFEixghhBBCBBItYoQQQggRSLSIEUIIIUQg0SJGCCGEEIFEixghhBBCBJKwM/YGpV7HL7/8Yundu3d79uFiVwkJCZZeu3atpU/XWDpNqVKlPG0ePHjQ0hUrVrT0ihUrLH3jjTd62igKCjpNUFDGjcgdBTlugjpmstekOU3Hjh0tXbp0aUvHxsZamssJbN682dPmSy+9ZOmgFO/TXCPygt+40ZMYIYQQQgQSLWKEEEIIEUjCLjtQFI/qXOfk7tavX9/Sq1evtvTWrVs9bZyu6HmamJgYS/PjWS7Oxfu7th04cMDSx48ft3Tz5s09bRQFesQr8kJxCyeFM9cw27ZtszTPK4B3LjnvPPvfjfHx8ZbmcLirzerVq1u6bdu2ll68ePEZely0aK4ReUHhJCGEEEIUS7SIEUIIIUQg0SJGCCGEEIEk7Fesi4JwYqj/+c9/LP3zzz9besuWLZ5jOHbKr1hHR0db+vDhw5Z2xanZ88J95zaFEAUHp0H47bffctzf9f08duyYpfv162dp9sGxdw7wel64zU2bNlma5yJ+BRsAMjMzLf3RRx9Z2pUCIjvsywGC85q2EIyexAghhBAikGgRI4QQQohAokWMEEIIIQKJFjFCCCGECCQRbewNh0svvdTS69evtzTXMHLhMrplh01/LuPc77//nqPmBFVCiIKDjbxs5mfjPRtuXaSkpFia6xyVL1/ec0yZMmUsXa5cuRz7ceTIEUvzPOLa9t1337k7fAZk4hXFCT2JEUIIIUQg0SJGCCGEEIFEixghhBBCBJLAeWK4cCIXTON4sasoGCer41j2iRMnctQueJ+SJe1Ly+fgJFiHDh3yPYcQIm/4Jc7k7yMAjB071tLdu3e3NCfSTE5O9rQRFxdn6ddff93S7Kvp1auXpV2evp9++snSnHTv448/tvQDDzxg6XAKRPp5iISIFPQkRgghhBCBRIsYIYQQQgQSLWKEEEIIEUgC54m55JJLLM05WzjfQ4UKFTxtcN4X9rNw3piyZcv69ov74crvkB2OY8sTI0T+wb43/o6z18TlE0lMTLQ0F3jk7+zOnTt921i9erWlv/32W0v36dPH0nv27PG0efToUUvz3FOtWjVLz5o1y9L9+/f3tMn7cJtc4FaISEFPYoQQQggRSLSIEUIIIUQg0SJGCCGEEIEkcJ6Ya665xtJ+eWH279/vaYNzN5QuXTrHc3LOF1ftEc6j4FePydUvIUT+4JfbacCAAZaOjY317JOVlZVjG+xrY68K4PXAXHnllZZu3769pXke2bhxo6dN9quw/4f9K7/++qulBw4c6GmTPTHywIigoCcxQgghhAgkWsQIIYQQIpBoESOEEEKIQBI4T0yNGjUs/dtvv1naz4sCeGPKnEuGY91+9ZkAr2+GvTkcX/fLIyPOjN89dtV5KYzaLxdffLGl2Wv12Wef+bbhqvWVHf4cPO4A/7FVpkwZSx84cMC3X8UNzsdy5MgRzz7sq+HryprzTwHe+YlrNNWtW9fSPE+4xgPPV9xPzl/Df09KSvK06Qd/51y+QCGKAj2JEUIIIUQg0SJGCCGEEIFEixghhBBCBBItYoQQQggRSAJn7E1NTbX0vn37LM1mSjbWAd7EVpzYafTo0Za+7777LL1lyxZPm2zA434sW7bMc4zIGwVhKuT75zICsynztttuszQbJjdv3mzpCy64wNPmSy+9ZGm/JGNs5HWZeLkA4HPPPWfpvXv3WnrdunWeNt58801Lr1+/Psd+RTp+95cTxgH+hlmGXwgAgIMHD+a4z6ZNm3LsFxeQdPWDjdkug3F2ypUr59nGSfc++uijHNsQxY9w5kA/PvjgA0tPmjTJ0q+++mruO+aDnsQIIYQQIpBoESOEEEKIQKJFjBBCCCECSUR7YlyJnthrsnPnzhzbcMX1KleubOk///nPlp4wYYKl2RMTToIxjrGvWrUqx36KM+MXq82PWG44xxw+fNjS7K3iJIpceK9SpUqeNseMGWPpxx57zNLbtm2zNI+zBg0a+LZZpUoVS0+dOtXSFStW9LRx2WWXWTronpj69etbmovAuu4/e0v4GP6Ou7xa7KPiNnkMHTt2zNIujxQXj+W+czJD9uXwOAWAtm3bWlqemOJPOP667FxxxRWebTNnzrT07t27Lc2+wRkzZnja4PHp8qflhJ7ECCGEECKQaBEjhBBCiECiRYwQQgghAklEe2KaN2/uuw/ngeG4dK1atTzHcLz3xRdfzH3nCD+fxnfffXfW5zhX8fOrFEZxRwDo2LGjpXv06GFp9qLccMMNlv7kk088bXJc+vHHH7c0j9UVK1ZY+q677vK0yflpuM169epZmvPKAG6vTZDhHD2cr4W9KIDb+5Ydnntc45DnAS40yX4AxuUP4GKMrLlf3AeXdyc9Pd3S7M1Swcfih18B07/97W+Wvv322z1tLF682NKct61z586WHjFihKeNwYMHWzqcIs7W/rnaWwghhBAiQtAiRgghhBCBRIsYIYQQQgSSiPbEtGjRwncfjutxvRNXHZGMjIwc2+RcNIwrZsdx56NHj1r6888/z7HNcwVX7h+/fdibwLkzOAdI+fLlPW2yN4r9C9OmTfPtF8eIuV99+/a1NMd/Xd4T9jzs2rXL0pdccomlW7VqZenZs2d72uTcI9dee62leXy7vB+8Tzj3LZJp2bKlpdnj4fpO89zB+VVYu+q0sQeGrzWfg8clz2eAf34i/ix+3x/A65MSp/DzH7ngsVUYfqK85Mpizx7XOfr+++8tzXW+AG/uJJ57uTbc8OHDffvll6+G0ZMYIYQQQgQSLWKEEEIIEUi0iBFCCCFEINEiRgghhBCBJKKNvampqZ5tbJJi41tCQoKlP/30U08bLmNbdrjQH+MyOfK2888/39KrV6/Osc3iCl+XcAxobHZkkzSPizZt2lj6wIEDnja5jcaNG1u6SZMmlq5du7anDe7rk08+aWkuJHr//fdb2pV4sWHDhpZmI9yGDRssnZSUZOkuXbp42mSzHZt09+zZY2mXIZWP4aKCQaNq1aqWZvOgy3zJBTs5IR6PB5chkY3bbAx1GXdz2t91Xh7bXOB27969lnZ9VjaUFwfCMaPzteT7xfenIEy64dxjNmeHk2iRmTVrlqU5AST/Rh06dMjSrpdkOEniqFGjLB2Okfds0ZMYIYQQQgQSLWKEEEIIEUi0iBFCCCFEIIloTwwnqAKAX375xdIco+REUBMnTjzrfnAsPJxYq19ht3OVcGK3HONnOM66atUqS3/11VeeYzjWzd6SXr16WdoVp37qqacsnZiYaOmffvrJ0l27drX0e++952lz2LBhlmYvDie343O4vDvcd/YIcfFDl9+Fk7TltihbpJGcnGxp9sW5Pt/27dstnZWVZWn2OLm8WOxlYN8MJ6rzKyQLeH1/O3futPS2bdsszePU1U8eA1WqVLE0f/YgkJfCsH4eJf5OX3/99Z59+P4888wzlv7yyy8tHY7PxuVby84999xjafamAN7kdTxuypUrZ2n+LeXPBQB/+MMfLD1z5swc+xkOub1vwZ6ZhBBCCHHOokWMEEIIIQKJFjFCCCGECCQR7YmpWbOmZxu/u87xffY65EeMbt++fb77cOx7x44dZ33e4gDH9Dn+6/JjcMz+uuuuszQXUuT7869//cvTZoUKFSz90UcfWZrj1j169PDtF3sN/vKXv1j6oYcesnT79u09bXIM/ueff7Y0Xy/Ob+Mq3sjH1KlTx9Ls9eDCbwDwzjvv5Nhm0OC5hL11devW9RzDcwfn17nooossvWXLFk8bPP7Ze+PnrwunACTneFm2bJmlH374YUt/++23njY5Bwjn1QmCJ8bPt+X6O3uUatSoYekXXnjB0uyP5O8r4PX0/f3vf7f0mjVrLM33B/AWUuzZs6el77rrLkvzHHjrrbd62mTfTEpKiqX5O9GgQQNLcz4uAFi6dKlnW2GjJzFCCCGECCRaxAghhBAikGgRI4QQQohAEtGeGFeulYoVK1qaPTEco/SrgxQOHON01ZDgeOuPP/541uctDvh5KVw5Kxj2gXBMn/PEuO45x4w//vjjHP/u8ipwrRHO9/DAAw9YunXr1pZ2XQs/rwH7HzivCMfoAe934oknnrA0+11c+Hk5ggZ7othH4sqB8euvv1qax6pfXSTA7VnKCb9aPuEc88knn1ia72U4NZ74O7dixQrffhQ1fB1Yh+PrYh/bggULLP3cc89Zum3btp42OHcM13pjj9qgQYM8bfD4zMzMtDTnP9u6daulXfPCokWLLM2+p0svvdTS69evtzTXcQO8Nee45toVV1xh6erVq3vaYB9S//79PfvkRLBnJiGEEEKcs2gRI4QQQohAokWMEEIIIQJJRHtiXDE4jp9xjRdXvPds4ffnuQ+AN+68efPmfO9HEOGYKefj+OCDDzzH7N+/39IrV660NMeU165da+mpU6f69ovrhDRv3tzSnMsB8Ma64+PjLc2+mlmzZlmavSqAN38J527g8c0+MVedEa4d5eeBcXk52D+Qlzo0kURcXFyOf3ddg927d1uac1BxDheXj8ovT5Kfr8bl4+A6Ouxt8KuzE05uoWbNmll6ypQpObYZCfAY5TxO/H0FgI0bN1r6s88+s/Ttt99u6c6dO1u6RYsWnjY5R9ibb75pafa7sM8N8Hq0OJ8Wz6tXXnmlb5vffPNNjtr1e5sd9gAC3rmZ502u/eY6B/t96tevn2M/GD2JEUIIIUQg0SJGCCGEEIFEixghhBBCBBItYoQQQggRSCLa2Pv55597trVr187SbObyK6iWF9gg5jIPc1IyV/Ks7HA/g26cPBMdO3a0NBvhevfu7TmGDZV8bdlA+z//8z+WfvTRRz1tNmzY0NJsbuTkUFxkEgB++uknS/slj+Iiki5jIfeDTc1s5OUCqC6qVKliaTYvrl692tLbtm3ztMHF3/7xj3/4njeSSE5OtjSPIf7+cUJLwHu/2IS9d+9eS7sS053t99plOOYkfGw4ZmMkv5gQTj9r1aqVq35GAmlpaZYeMmSIpV3FCjl5Kl87LvrJpuk5c+Z42mSjKpuBea7h7yvgHZ9s7GWDLPfTZQhnIzpfL37xgMeZ67PybyEb6D/99NMc+wAA9erVszTPV37oSYwQQgghAokWMUIIIYQIJFrECCGEECKQRLQnxpUwjmOFrMMpmJZbOOFY2bJlfY85fvx4vvcjiHDBNKZLly6ebZxo67rrrrM0+xc4Lvv444972uQ49fnnn29p9sCw7wYAateubem7777b0hzr5mJorsKhXMySfRbsTXAVRWW4DU5UtmzZMkvv3LnT0wZ7BfwSYUUaXGiOY/HsNeEEZADwxz/+0dI8Jti/lB+eGO4ne3kAr9+BfVLsBWEfh6tP/B1yecIiHU5gyWPAlQSQPZY8zvn7yXOT67eAE+RxMeAJEybk2Cbg9amx14SLSrIvij0ygPfz83h2FY3Mjstv6pe8kefqiy66yNMGF/DlIs5+6EmMEEIIIQKJFjFCCCGECCRaxAghhBAikES0J4aLcQHeuB3Hd12x7bMlHA8Mx9j5HftzlaZNm1qa8x18/PHHnmPmz59v6ZEjR+Z4Dr725cuX9+zDuQg4pwvnSOB+h3NeHpsc13a1yTk8vvvuuxz//v3331t606ZNnjZz+x1wxbqDnreIczvx5+FYvSt/BXsq2GvEfgBXgU8+ryt/R3Z4TLnuDRf3Y58Ue0M4F5GrTf78nBMnCHz99deWvuOOOyzt8t9x4de2bdtamn0k7BXj4rOAt+Aq+1datmxpaVcuoGuuuSbHfXiuYX+Ly3/HvqdKlSpZmscAj1VXP7kf/B3ggr/sIwOAxx57zLMtN+hJjBBCCCECiRYxQgghhAgkWsQIIYQQIpBEtCdm+/btnm0cp2ZPgKs+zdnCuRpcdVY4fuiKsZ+LsMejQ4cOluacFoDXT8Q5d5YvX27pNWvW5Hg8AHzxxRf+nc3G1KlTc7V/kHH5XwqiBllhwnmA/GqVuTwE7DPg7zi34frOs4/AL4+VXz4bwOtD4L6z94H7HY5fwlXPJ2hwrpRp06Z59nFty07lypUtzTW5XDWmmjRpYmn2qLFnjz1LADBr1ixL+/mgeCxyjioX3A+eZ9kzw/48wHs9eLy+/fbblh4/frxvv3I79+hJjBBCCCECiRYxQgghhAgkWsQIIYQQIpBEtCfGxfr16y3N8USOBbpiu1lZWbk6Zzg5M3KbD+Jcga/DBx98kKMGvL4Bzq/SsGFDS995552WdtU9OnDggKXZ18RjwnXPd+/ebWmuL8Ntcr0TV6yX/Qm8D+f8YH+W67Pyeblf/B3h/QFgy5YtlvbzDkQa/Bk53wePMdf9Dsc3kx2Xf8W1LTdtuMYM952PYX8La1c+G/ZD8Dk4V5Yr30dxhOuKsV6xYoXnmJkzZxZkl4o9uc1RpScxQgghhAgkWsQIIYQQIpBoESOEEEKIQKJFjBBCCCECSeCMvWxSY7MdazZGArk39oZjAmQDHhswRfhwwi8u7MZaRjrBJCQkWNrPaB/O95VfIuC5KJxCmjy2XSZbP/g8fgn0uGihy5TrZ9Rt1qyZpT/55JMc9xeisNCTGCGEEEIEEi1ihBBCCBFItIgRQgghRCAJnCeGC0Du3bvX0hwv5sJZeYFj4a74ul/CKSFE4cHfWf5+lilTxtKu7zT7ZFyF+rLj8qbk1r/CuPx33AYnM0xJSbH0l19+aem0tDRPm+wlZN8gF0IUIlLQkxghhBBCBBItYoQQQggRSLSIEUIIIUQgCZwnhj0wfgXT8iNfy7p16yzNeRdc5+WCakKIwqNChQqW3rZtm6U5f9T777/vaYO9JkOGDLE0F/9zeWb8PHl5KRzLuWb8ijV26tTJ0kuWLPG0mZSUZGmeVytVquTbLyGKAj2JEUIIIUQg0SJGCCGEEIFEixghhBBCBJLAeWL27NljaY59c36WqlWrnvU5XTVR/OCYsl+brnwQQoi8UbduXUvz9zEuLs7S7H8BgKFDh1qaPTE1atSw9JEjRzxtsDeO5yfuF/tbXHllSpcubeny5ctb+pVXXsnxnN99952nzdTUVM+2nM4pRKSgJzFCCCGECCRaxAghhBAikGgRI4QQQohAokWMEEIIIQJJ4Iy9bIjlxHNcyOyCCy7wtPHee+/l6pxsrnMltfIrOCeEKDzYdMsJ4n777TdLf/31175tsrl13Lhxlr788ss9x7BhduPGjZb2mye43wCwY8cOS997772Wnjp1ao5tjh071rPtyiuvtDQbnxs1apRjm0IUFfqlFUIIIUQg0SJGCCGEEIFEixghhBBCBJLAeWJef/11S1900UWW3r17t6UXLFhw1ufct2+fpV1x7AMHDlj6+++/z7FNJbcTouBo0aKFpdlLFxMTY2lXsjuGk9kNGDAgj737P7hAZJkyZSzNcw/g9snkBi5cCXiTA3KBzO3bt5/VOYUoKPQkRgghhBCBRIsYIYQQQgQSLWKEEEIIEUiijMwZQgghhAgg58STmKioKDzyyCMh/corryAqKsqTeEqIgmDjxo2IiorC008/XdRdEQWM5hpxtkRFRXkqprvQ2DpFRC5iTt+c0//FxsaiXr16GDJkCLKysoq6eyIC+e6779CzZ0+kpKQgNjYW1apVQ+fOnZ3ZSYU4jeYaUZgU5Tz1xBNP4O233y7w8xQ2Ef2K9T/+8Q/UqlULR48exWeffYYXX3wRs2fPxvfff+9JAS7OXZYsWYIOHTqgZs2aGDhwIJKSkrBlyxZ88cUXGDNmDIYOHVrUXRQRjuYaUdDk9zx16623onfv3p50AWfiiSeeQM+ePXHttdfmofeRS0QvYq666qpQvofbb78dlSpVwqhRo/DOO++gT58+Rdy7guPQoUOIj48v6m4EhscffxzlypXDV199hfLly1t/27lzZ9F0qpA5fPiwfmzPAs01oqDJ73mqRIkSnrp+jDEGR48e9dTCKk5EZDjpTHTs2BEAkJmZifbt26N9+/aeffr16+cpuhYuL7zwAho3boyYmBgkJydj8ODB2Lt3b+jvQ4YMQUJCAg4fPuw5tk+fPkhKSrISUc2ZMwft2rVDfHw8ypQpg65du2LVqlWe/iYkJGDDhg24+uqrUaZMGdx888156v+5yoYNG9C4cWPPxAAAlStXDv3/07Hmt99+G02aNEFMTAwaN26MuXPneo7btm0bbrvtNlSpUiW033/+8x9rn+PHj+Pvf/87mjdvjnLlyiE+Ph7t2rXDokWLfPtsjMEdd9yB6OhozJgxI7T9tddeQ/PmzREXF4eKFSuid+/e2LJli3Vs+/bt0aRJEyxfvhyXX345SpcujQceeMD3nCJ8NNeI/Cbceeo0fvOUyxOTmpqKbt26Yd68eWjRogXi4uIwYcIEREVF4dChQ5g0aVIodNqvX798/oRFQ6AWMRs2bAAAVKpUKd/bfuSRRzB48GAkJyfjmWeewfXXX48JEyagS5cuoYq3N954Iw4dOoT333/fOvbw4cN499130bNnz9DKePLkyejatSsSEhIwYsQIPPTQQ/jhhx/Qtm1bjxHr999/R0ZGBipXroynn34a119/fb5/vuJMSkoKli9f7pslGQA+++wz/PnPf0bv3r0xcuRIHD16FNdffz1++eWX0D5ZWVlo3bo1Fi5ciCFDhmDMmDGoU6cOBgwYgNGjR4f2279/P/7f//t/aN++PUaMGIFHHnkEu3btQkZGhjMr6mlOnDiBfv364dVXX8XMmTPxhz/8AcCpf6n98Y9/RN26dTFq1CgMGzYMH3zwAS6//HLrBw4AfvnlF1x11VVo1qwZRo8ejQ4dOuTqmomc0Vwj8pv8nqfOxJo1a9CnTx907twZY8aMQbNmzTB58mTExMSgXbt2mDx5MiZPnoxBgwblx8cqekwE8vLLLxsAZuHChWbXrl1my5YtZurUqaZSpUomLi7ObN261aSnp5v09HTPsX379jUpKSnWNgDm4Ycf9rSfmZlpjDFm586dJjo62nTp0sWcOHEitN+4ceMMAPOf//zHGGPMyZMnTbVq1cz1119vtT99+nQDwHzyySfGGGMOHDhgypcvbwYOHGjtt2PHDlOuXDlre9++fQ0Ac9999+X2Mon/n/nz55sSJUqYEiVKmDZt2pjhw4ebefPmmePHj1v7ATDR0dFm/fr1oW0rV640AMzYsWND2wYMGGCqVq1qdu/ebR3fu3dvU65cOXP48GFjjDG///67OXbsmLXPnj17TJUqVcxtt90W2paZmWkAmKeeesr89ttv5sYbbzRxcXFm3rx5oX02btxoSpQoYR5//HGrve+++86ULFnS2p6enm4AmPHjx+f2UglCc40oLPJ7nuKxZYwxKSkpBoCZO3eu5/zx8fGmb9+++f65ipqIfhLTqVMnJCYmokaNGujduzcSEhIwc+ZMVKtWLV/Ps3DhQhw/fhzDhg2z6iINHDgQZcuWDf1rKCoqCr169cLs2bNx8ODB0H7Tpk1DtWrV0LZtWwCn6jXt3bsXffr0we7du0P/lShRAq1atXKGG+688858/UznEp07d8bnn3+OHj16YOXKlRg5ciQyMjJQrVo1zJo1y9q3U6dOSEtLC+kLL7wQZcuWxU8//QTgVJjnrbfeQvfu3WGMse5fRkYG9u3bh6+//hrAqZh0dHQ0AODkyZP49ddf8fvvv6NFixahfbJz/Phx9OrVC++99x5mz56NLl26hP42Y8YMnDx5EjfccIN1zqSkJNStW9czZmJiYtC/f//8uYBCc40ocPJznsqJWrVqISMjI9/7H6lEtLH3+eefR7169VCyZElUqVIF9evXdxZfPFs2bdoEAKhfv761PTo6GrVr1w79HTj1mHf06NGYNWsWbrrpJhw8eBCzZ8/GoEGDQkXm1q1bB+D/4upM2bJlLV2yZElUr1493z7PuUjLli0xY8YMHD9+HCtXrsTMmTPx7LPPomfPnlixYgUaNWoEAKhZs6bn2AoVKmDPnj0AgF27dmHv3r2YOHEiJk6c6DxXdhPepEmT8Mwzz2D16tWhUABwaiJh/vWvf+HgwYOYM2eOx2Oxbt06GGM8hfhOw4UCq1WrFlpAibNHc40oDPJrnsoJ19xTnInoRcwll1ziqUZ7mqioKGcl6LOt8OpH69atkZqaiunTp+Omm27Cu+++iyNHjuDGG28M7XPy5EkAp2LVSUlJnjZKlrQve0xMTIFMmOci0dHRaNmyJVq2bIl69eqhf//+eOONN/Dwww8DwBnd/KfH0ul7d8stt6Bv377OfS+88EIAp0y4/fr1w7XXXov/+Z//QeXKlVGiRAn861//CnkqspORkYG5c+di5MiRaN++PWJjY0N/O3nyJKKiojBnzhxnHxMSEixdnN82KAo014jC5GznqZw41+aGiF7E5ESFChWcj9ay/0smXFJSUgCcMkTVrl07tP348ePIzMxEp06drP1vuOEGjBkzBvv378e0adOQmpqK1q1bh/5++jFg5cqVPceKwuP0j9L27dvDPiYxMRFlypTBiRMnfO/dm2++idq1a2PGjBmhfxkDCE1ETOvWrfGnP/0J3bp1Q69evTBz5szQj0xaWhqMMahVqxbq1asXdn9FwaO5RhQkeZmn8kL2Oao4EdgleVpaGlavXo1du3aFtq1cuRKLFy/OdVudOnVCdHQ0nnvuOWul+9JLL2Hfvn3o2rWrtf+NN96IY8eOYdKkSZg7dy5uuOEG6+8ZGRkoW7YsnnjiCSvEcJrsfRZnz6JFi5z/Qpk9ezYA76P7nChRogSuv/56vPXWW863CLLfu9P/Wsp+7i+//BKff/75Gdvv1KkTpk6dirlz5+LWW28N/Uv6D3/4A0qUKIFHH33U81mMMWG9lSAKBs01Ij/Iz3kqL8THx3veciwOBPZJzG233YZRo0YhIyMDAwYMwM6dOzF+/Hg0btwY+/fvz1VbiYmJuP/++/Hoo4/iyiuvRI8ePbBmzRq88MILaNmyJW655RZr/4svvhh16tTBgw8+iGPHjlmPd4FTcegXX3wRt956Ky6++GL07t0biYmJ2Lx5M95//31cdtllGDdu3FlfA3GKoUOH4vDhw7juuuvQoEEDHD9+HEuWLAn9yzW3Btgnn3wSixYtQqtWrTBw4EA0atQIv/76K77++mssXLgQv/76KwCgW7dumDFjBq677jp07doVmZmZGD9+PBo1amSZMZlrr70WL7/8Mv74xz+ibNmymDBhAtLS0vDYY4/h/vvvx8aNG3HttdeiTJkyyMzMxMyZM3HHHXfgr3/961ldJ5E3NNeI/CC/56nc0rx5cyxcuBCjRo1CcnIyatWqhVatWhXoOQuFIngjypfTr4599dVXOe732muvmdq1a5vo6GjTrFkzM2/evDy99niacePGmQYNGphSpUqZKlWqmDvvvNPs2bPHee4HH3zQADB16tQ5Y/8WLVpkMjIyTLly5UxsbKxJS0sz/fr1M8uWLQvt07dvXxMfH5/j5xQ5M2fOHHPbbbeZBg0amISEBBMdHW3q1Kljhg4darKyskL7ATCDBw/2HJ+SkuJ59TArK8sMHjzY1KhRw5QqVcokJSWZK664wkycODG0z8mTJ80TTzxhUlJSTExMjLnooovMe++95xmD2V+xzs4LL7xgAJi//vWvoW1vvfWWadu2rYmPjzfx8fGmQYMGZvDgwWbNmjWhfdLT003jxo3zerlENjTXiMIiv+epM71i3bVrV+f5V69ebS6//HITFxdnABSb162jjAnDKSSEEEIIEWEE1hMjhBBCiHMbLWKEEEIIEUi0iBFCCCFEINEiRgghhBCBRIsYIYQQQgQSLWKEEEIIEUi0iBFCCCFEIAk7Y2+k1F1ITEy09DXXXGPpffv2WXrLli2+bW7dutXSXDSNqwVzMT4ASE9Pt/THH39s6a+//tq3H0VBQacJipRxI/KXghw3QRkzXGl427Ztnn3Otkjk9ddf79n21ltvnVWbebm++XG/NdeET5cuXSxdo0YNS3OJiQsuuMDS//73vz1trl271tJ8vSI1ZZxfv/QkRgghhBCBRIsYIYQQQgSSsMsOFMajuiZNmliaK7oC3lAPh3ZYn640nJ09e/ZY+tixY5Y+fPiwpcuVK5djH1xwAcBSpUpZes2aNZb+73//69tmQaBHvCIvRHo4KbePyps1a+bZduTIEUsnJydbetq0aZbmUDcAPPXUU5bmqtJpaWmWvvnmmy193nnef2fOmDHD0q+//rqlBw0aZOlrr73W0wbD8yRfr9PV1s8GzTVu2IoAwFO08+eff7Y0/5506NDB0itWrPC0edFFF+WqXzz28mMM5AWFk4QQQghRLNEiRgghhBCBRIsYIYQQQgSSiPLE3H///ZbmmDQAbNq0ydIcp05JSbE0+18Ab3yxXr16Of6d/S21atXytMm+mlWrVlk6Pj7e0lWqVLE0e2QAYM6cOZYuiBil4tQiL0S6Jya335Xdu3d7tq1bty7HNvkY9re4tvn1i+e3b7/91tNmxYoVLV26dOkcz8FzC/tuXBTE67eaa9yMHTvWs619+/aWZo8Lj73evXtb+sMPP/S0OX/+fEtPmjQpF70sOuSJEUIIIUSxRIsYIYQQQgQSLWKEEEIIEUi0iBFCCCFEIClSY2/16tUtPWzYMEtzTSPAWzOCTbfcZoUKFTxtrF69Osc2mKSkJEtzzRQAWLlypaX9EujVrl3b0mXLlvW0+Y9//CPHfuUHMtuJvBDpxl4/2EzpSjjmV1ON4UR2gNe4Gxsba2meFxhXAj1OdMYmT6711rhxY0u//PLLnjZHjBhhaf6sv//+e479DIdzZa7p37+/pS+++GJL80sdrlp8XHPr/PPP9z0mOzt27PBsi4mJsfTevXst/euvv1p6+PDhlt65c6enzcJIiCdjrxBCCCGKJVrECCGEECKQaBEjhBBCiEBSpJ6Ypk2bWvree++19Pr16z3HcCK6ffv2WZoLmVWtWtXTBhd0zMzMtDTHGzmB3o8//uhp089Xw/3iBHsu5IkRkUrQPTHLli2ztKtQ7IEDByzN/pVw+slekkOHDlm6TJkyOfbD5THgNuLi4izNnhlOtOmaE10JPLOTH8nviuNcc9lll3m2PfLII5Y+fvy4pdkvyckKAa9/hT0x7K1au3atpV1eKv6N4uvFv3tLly619ODBgz1tFgbyxAghhBCiWKJFjBBCCCECiRYxQgghhAgkOSc+KGA4HhxOoUV+V51jh/x+/caNGz1t8Hv6DRo0yLEfXISNz+HqB8et69SpY2mOWXLhNyFE/sHfPy6iyN46wPs99/OFuPwrfAx/7znvFXto2N8CeP0RWVlZlq5bt26O53B5MPzyfRREQcjiQM+ePT3btm3bZmn2r/C1dOUK+uWXXyzt58/i8ewqaOp3jznPUWpqqqXbtm3rafOzzz6zdFGMEz2JEUIIIUQg0SJGCCGEEIFEixghhBBCBJIi9cRwvgKu9+B6B//SSy+19H//+19Lc/yR44CAN97INSQYjim7Yt9ca4Rjluzv4T4IIQoOzvXEsXv25wHAkSNHLM1eOdauejaco+Xo0aOW5vmJPTD79+/3tMl5rvw8feztcdWk4/lpw4YNlpYH5hTsrWJ/JeD2V2WHf6N4nAHemn/bt2+3NOeeSU5OtrTLZ8M+Tf7N4rHI/qybb77Z0yZ7YopinOhJjBBCCCECiRYxQgghhAgkWsQIIYQQIpAUqSeG434cx1u9erXnmPbt21t64sSJlubaI66cLhx35mM45wvXJnHFMLn+EsfYOZbK9Zc4zg34v9cvhAiPiy++OMe/s+8NACpXrmxp9pqwp8A1L/DcwnMJ+w5Yu3Jl8T48x/E52FcTHR3taTMtLc3S7IlRnphTdO7c2dLsbwG894M9l36/N4D3t7B8+fKW5vpLe/bssbRrPPPvB48D9tnw5yhbtqynzUhAT2KEEEIIEUi0iBFCCCFEINEiRgghhBCBRIsYIYQQQgSSIjX2ciEyNjhxsUcAuPDCCy197bXXWpqTyLEZD/CaoFxF1nL6O5t0ASApKcnSbJriZFtsAmQTISBjb9DhRGdcUA0ALrjgAktPnTo1xzbzMiZkygQaNWpkab4GrqSYbMDkwos8P4Vjzuc5juci/rtr/qpUqZKluXAfm4G5GCB/LsCbMG/+/PmWPhfHjIv09HRLu8zcbIDlJHNs9HUlWuTEiWzm5nHlV0gU8M4V3Hc2C7NBnF/EAbzjxvUyTkGjJzFCCCGECCRaxAghhBAikGgRI4QQQohAUqSeGIZjdq54//fff29pjtNxDNnVBsf+2PPC8UaOIbuS/nAbHNPkuDX3yxWn5th3VlaWZx8ROdx3332W5oJpW7Zs8RzTuHFjS/M9XrRokaXD8cCEk/AxOy6/Q6dOnSz9wQcf+J43kuECefyZOf7v2ocTjrEH5ueff/a0wd449i5wgjFOnsZeO8DrceG+s3dn8+bNOZ4TAJo1a+bZlh15Yk7Bcz/P64DX6+lKPJcd9swA3nHj56upW7eupQ8cOOBp05VULzt+fjuXb6xFixaWlidGCCGEECJMtIgRQgghRCDRIkYIIYQQgaRIPTHs+dixY4elXYXKFixYYGmOGbtilAzHKP08MlzojYujuY7hAl6fffaZpdnLE06hSpF38iNXSmJioqUXL15saY5L/+1vf7N0zZo1PW2yJ2bhwoWWnjVrlqXvueceS2/cuNHTpp8Hhr9DHF8HgJYtW1o66J4YLnDIeTT43gLeeYE9Tew9qV27tqcNziXDcwnnj+KcIa5cWTx22QPFvho/Xw7gzfchTsE+Ev5uuXwivI3n8XDmHj4P55zi+Yz353Hmws9fx+d0zSvsiXnttdd8z5vf6EmMEEIIIQKJFjFCCCGECCRaxAghhBAikBSpJ4ZjbhyDbtWqleeYJ5980tJ33nmnpTlu58q/wu/xs3/FL6bpqmfCtY/4vNu2bbM0x7655pOrza1bt3r2ORfw87OE43fxi0Nz/odHH33Us0///v0t/cwzz1j6jjvusPSf/vSnHM8JeMce32PO15KZmWnpDz/80NMm11/q06ePpatWrZrjOQGgY8eOlubvXdBgDxrn43Hl6vj1118tzfMA++9ctWV4rnHlaMkOe2BcvgWen7hNHuvsPXTNNewZEqfg68LX1nV//HKA8Vhz/UbxefxyvPjVVnLtw74Znkf9fJ6A2wdW2OhJjBBCCCECiRYxQgghhAgkWsQIIYQQIpBoESOEEEKIQFKkxl5OysRJmFzJdTgxF5uiWLsSxrkMStlhgzGb81xGLDZScQEuLlx56623WvrHH3/0tFm9enVLf/3112focXBxGdDYGOdnyg0neVRqaqqlX375ZUu3b9/e0mwYB4ALLrjA0tu3b7c0G3/ZQLtp0yZPm2zw5uvBhnAei2zAdW1jIzq3yQZ7AGjatKlnW5C4+OKLLc1jhOeF9evXe9rg68QJAI8cOWJpNgID3mvP/WBzJWvX2PZLZsj94DHj6ifPaZyEzzV2zwUqVqxoaTbHhjP3HD161NJsoHXNgZygcM+ePTnqevXqWdplVOd7zL+DbEznsehqs0aNGp5thY2exAghhBAikGgRI4QQQohAokWMEEIIIQJJoXpiOPbOHhiO0bmKOdavX9/SCQkJObbpKiLph19CKhcc++bkaRzDXLVqlaVdCag4Lh1E/BLR+RUhywt33323Zxt7XLhf69ats/RHH33kaeORRx6x9G233WbppUuXWjo5OdnSVapU8bTJ44KTWrHm+Po333zjaZNj1+y7iYuLs7RrvNeqVcvS/L2LdDhWzz4SLt74/vvve9rg63j55ZdbmscujynA7Z/LCb4Xrjb9CjrymOJ5hL0+gNffFY6f61yAvzt+iexc+zD82+BKnsrjgP0p3C/2cbr6xfvwd4LHWjjeRB5L/Nn279/vOSa/0ZMYIYQQQgQSLWKEEEIIEUi0iBFCCCFEIClUTwznheFYIMf/XXlR2FfAPhqOD/N7/S78cs0wHJ8EvLE/jslzfJELvbHXAfAv+hUEOI5ap04dS/fq1ctzDOfYueiii3I8B+c3qFu3rmefhx56yNLXXnutpblIoitvD4/X0aNHW/qee+6x9OTJky19yy23eNrcsWOHpf08Q375TgB33pfs+BUMdJFbb0dRw/k9+Dryd9g113AeGM7nwXONC55/eA7kax/O/eXPwnlg2L/Cf2cNeP0QLVq0sPQXX3zhOeZcgO+Xn68E8F5L9qD5eU8A79hiD4zfOHL53Pg8rPft25fjOVzwvMBFi+WJEUIIIYQ4A1rECCGEECKQaBEjhBBCiEBSqJ6YSpUq5fh3jjfu3r3bsw/XdGGfQrly5SztillyLNBVuyI7HJ/k3DQA8PPPP+d4Xn5/nvvgij8mJibm2K8gMnPmTEu7am88//zzlua6U5deeqmlOR/L1q1bPW1269bN0s2aNbN0VlaWpV11QtjP06BBA0uzF4HHlSsfRPny5S3NMWb2buTFl8F+K46fu74jfAxfn0iH5wG+Tnyd2YcFeOu0+eU8Cscf4cr7ktPfXX4J3sbz6uLFiy3NtZLatGnjaZPHJo/1cxW/PGSue86/BTz381hz3WMenzwf8W8le8BcPjduk39z2AfIvhqeqwDvfJSUlGRpV02y/EZPYoQQQggRSLSIEUIIIUQg0SJGCCGEEIGkUD0x/L48xxc5/u96192vDY5RuuKNnH+Fj+HcDhwv5j4A3ngjH8Ntcq2k6tWre9osiLpChQ37W9LS0iy9YsUKzzG9e/e2NMel+X5xXpRwal9xfhaOOXM/AWDDhg2WZi8C+zA4z4grZwKPC/ZhMOGMCW6Tc43wWHXFz8PJhxHJuPKrZIe9J5yHA/DmG+Jrz+dwXUeea3gf7gfPgXwfXP3gOZD7xTmpuG4U4M0Rwj6OcxX2jfB3y/VbwHMLe/Z47nd5Mnkb50NjnxPf03A8MXyPN27cmOP+rVu39rTJY9H1PSpo9CRGCCGEEIFEixghhBBCBBItYoQQQggRSLSIEUIIIUQgKVRjr19iLjYS7dmzx7MPm9bYQMtmSlexRj/TH5sY2SjK5wC8iYB27dqV4zldydQYNnOx2SsIxt8ZM2ZYukePHpbmpHOAN7Eamxv9inxGR0d72mSjGxvh+B6vXr3a0waPpe3bt1t6zZo1lvYr3ugit4ZUv0SNrmMYl2mXiwRykslIx+/a83XbvHmzpw0ugsjfex4zrnvBc5pf8jvWrvHA94vnEk5Ux2M5nHnDbxyeK/B3PpwXR9gkzcnt+H65kqfyHMfzFb/MwHOk63ePxysXa+RzskHZVZCYP4srIV5BoycxQgghhAgkWsQIIYQQIpBoESOEEEKIQFKonhhOHORXlI29DoA3uRAnDvJLhgd4fTQc/2cdTqIvTnzGnhgu0BVOQT1Xoqug8cEHH1iaCz4OHz7cc8wtt9xi6QsuuOCs++HnA+B77PI3cEyZ487F2Udw5ZVXFnUXcoVfkVf2yLiSltWqVcvSPG+EM2b8Egv6+ZVc3gb+bDxPcMIx/v5wojTAO3aD4LcrDNhfx79JruRu33zzTY77hJNIkK8/zz089vj+hfOb5fK4ZGfdunWWrlKliu8+RVG0WE9ihBBCCBFItIgRQgghRCDRIkYIIYQQgaRQPTHsG+FYrl/cD/D6VX7++WdLcyyRi/IB3oJoHMvm+CL/3fVeP38WPobjoHPnzrW0Kw8HfxaON4bjq4l0Ro4cGda27HB+g0aNGlma83sAQNWqVS3tl8/AVUSS/Ql+cWvOK+LyOPG2o0ePWpo9E3xOl3eBxx73iz0RnA/C1e6iRYssfd9993mOiSS4/+xN4WvSvXt3Txv8feN74zcewjkv31/2zLja5PmJNZ+jWrVqnjYYvzFzrsK+Eb6fLh8cF3r18xu55gX2j7LHknOIXXjhhZbevXu3p02Gx15SUpKlOXeSy7/F49WVo6ug0ZMYIYQQQgQSLWKEEEIIEUi0iBFCCCFEICnS2kkcX9y7d2+Ofwe8cer169fneE5X/SU+D8e6uR4E98PVL46dsvaLg7piq3y9/N7rP1dgTxPrjz76qBB7IyIR/n5xPJ/HTOfOnT1trF271tJ+OUP8cr4A/rmG2JviapM/G88L7CXkXFrsKQO88xPntTpXYW+Ka+5n+J761c9yzf18TIUKFSzN94dzGLlyFvE2/p2rX7++pZcsWWJpl3fHryZZYaAnMUIIIYQIJFrECCGEECKQaBEjhBBCiEBSqJ4Yjv1xLJe9KS6+++47S3MskPN/JCcne9rg+j3cD47rcb4PVz0ml/cmO1ybhXPN8Odw7cM5coQQbjhWz5r9LK78FtWrV7c0+xD4O8/nALyeCj6vX00nF+x54RxU3CbPV678W+yJCcffcy7A1yWc+mg8lngeZ19UOF5Hvod+tZJc3h3exp4YzuMWDjzW5IkRQgghhAgTLWKEEEIIEUi0iBFCCCFEINEiRgghhBCBpFCNvWxqY7MrG2hdJqHevXtbeuvWrZbetm2bpV0F1Lh4Fie/8ysIyQn3AKBOnTqWZoMxJ5h69tlnczwn4DXsuT6LEMILvyTAhlk2OboSeS1evNjS8fHxOR7jMsP6FcTjfvolw3Odh+cnLhZ42WWXWbpmzZqeNvnFAn754Vxl3759lmaTLhdTBYALLrggxzb5d9Bl5vZ76YUTGNarV8/SbNp1wWZgfnGkdu3aluYEkYD3e8RG9sJAT2KEEEIIEUi0iBFCCCFEINEiRgghhBCBpFA9MRzfZQ8M+0a++OILTxsDBgywNHtJkpKScmwT8MY1/RIFZWVlWZrj1oA3Nsqx1DVr1niOyY4r4dH+/fstzf0WQrhh3wj7EPj79tJLL3naePLJJ/O/Y0UAz5kjRozw7MNzHHv6zlV2795tafZBuXyKbdu2tTT/FvBvliv5IPs2y5QpY2n2r4STKNbvd47PefXVV1valeSVfWJFgZ7ECCGEECKQaBEjhBBCiECiRYwQQgghAkmhemI474lfITMXy5Yty9c+RQrsDwK8+Wi4mOXXX39doH0SIqiwh4CLNYZTKJbJS7FGP9i7E06bfAz3i/0/qampvufwyz1zrsI5xPi6cK4zAJg4caKlb7rpJktzoUVXjjD22pQrV87SnAeG87O47jF7YPizsK9m9uzZlk5PT/e0yR6hL7/80rNPQaMnMUIIIYQIJFrECCGEECKQaBEjhBBCiEBSqJ4YV82h7HAsNxw490xe2igIOM7pV/conLioECI8/HJx8HcrnHwXBfF9zIuvJrfHcC0lV14SzgGyZcsWS7Mng3OfFFc2bdpk6XCu/XvvvZejbtasmaUvvPBCTxsVKlSwdNWqVS3Nv3ucu4zzyADecfDBBx9Y2pWXLTutW7f2bGMvDp+jMNCTGCGEEEIEEi1ihBBCCBFItIgRQgghRCDRIkYIIYQQgaRQjb0Mm4JcRRD9iBQjL5NbE6Drs7N5y5VYSQjhpVWrVpZmoy8n/nKZK4MCJ6pj2BTqMn2ysZkToXXq1MnSb731Vm66GFjS0tIsXbNmTUtv3rzZcwybcjnR4ooVK3LUkYorASKPrYoVKxZWd0LoSYwQQgghAokWMUIIIYQIJFrECCGEECKQFKonJisry9IcY3PFF88V1q5d69lWq1YtS3MxMiGEm8WLF1uaPR/79++3dJCLqfp5YsaPH29pV2I/9t9t2LDB0u+8804eexds5s2bZ+n69etbeseOHZ5j2APD5DYRan7B44S1Xz8WLVrk2bZu3TpLf/rpp3nsXd7RkxghhBBCBBItYoQQQggRSLSIEUIIIUQgiTJ5qUAmhBBCCFHE6EmMEEIIIQLJObGIiYqKwiOPPBLSr7zyCqKiorBx48Yi65MoOvr164eEhATf/dq3b4/27dvn23nbt2+PJk2a5Ft7ovDQmBGFRVRUFIYMGeK7n37HThGRi5jTN+f0f7GxsahXrx6GDBnieU1bnBu88MILiIqK8qSTF+HxxBNP4O233y7qbhQqGjNnx7k4Zgqa7777Dj179kRKSgpiY2NRrVo1dO7cGWPHji3wcxfX+xmRi5jT/OMf/8DkyZMxbtw4XHrppXjxxRfRpk0bHD58uKi7JgqZKVOmIDU1FUuXLsX69euLujuBo7hOYDmhMXN2nItjpiBZsmQJWrRogZUrV2LgwIEYN24cbr/9dpx33nkYM2ZMrtu79dZbceTIEaSkpIS1f3G9n0VaANKPq666Ci1atAAA3H777ahUqRJGjRqFd955B3369Cni3hUchw4dciakOlfJzMzEkiVLMGPGDAwaNAhTpkzBww8/XNTdEhGMxoyINB5//HGUK1cOX331FcqXL2/9befOnblur0SJEp5CpowxBkePHi3WxYMj+kkM07FjRwCnJqgzxZ779euH1NTUPLX/wgsvoHHjxoiJiUFycjIGDx5sZckdMmQIEhISnE+C+vTpg6SkJJw4cSK0bc6cOWjXrh3i4+NRpkwZdO3aFatWrfL0NyEhARs2bMDVV1+NMmXK4Oabb85T/4srU6ZMQYUKFdC1a1f07NkTU6ZM8eyzceNGREVF4emnn8bEiRORlpaGmJgYtGzZEl999ZXvOVasWIHExES0b98eBw8ePON+x44dw8MPP4w6deogJiYGNWrUwPDhw3Hs2LGwP8/y5ctx6aWXIi4uDrVq1fJkVAVOTWoDBgxAlSpVEBsbi6ZNm2LSpEme/Q4dOoR7770XNWrUQExMDOrXr4+nn34a2V86jIqKwqFDhzBp0qRQiLZfv35h9zeIaMxozEQaGzZsQOPGjT0LGACoXLmyZ9vbb7+NJk2aICYmBo0bN8bcuXOtv7s8MampqejWrRvmzZuHFi1aIC4uDhMmTCje99NEIC+//LIBYL766itr+5gxYwwAM378eJOenm7S09M9x/bt29ekpKRY2wCYhx9+2NN+ZmZmaNvDDz9sAJhOnTqZsWPHmiFDhpgSJUqYli1bmuPHjxtjjPnkk08MADN9+nSr/UOHDpn4+HgzePDg0LZXX33VREVFmSuvvNKMHTvWjBgxwqSmppry5ctb5+3bt6+JiYkxaWlppm/fvmb8+PHm1Vdfzd0FK+Y0aNDADBgwwBjzf/dg6dKl1j6ZmZkGgLnoootMnTp1zIgRI8zIkSPN+eefb6pXrx66h8acuubx8fEhvXTpUlOhQgXTuXNnc/jw4dB2HmMnTpwwXbp0MaVLlzbDhg0zEyZMMEOGDDElS5Y011xzje/nSE9PN8nJyaZy5cpmyJAh5rnnnjNt27Y1AMxLL70U2u/w4cOmYcOGplSpUuaee+4xzz33nGnXrp0BYEaPHh3a7+TJk6Zjx44mKirK3H777WbcuHGme/fuBoAZNmxYaL/JkyebmJgY065dOzN58mQzefJks2TJEv8LH2A0ZjRmIo0uXbqYMmXKmO+++y7H/QCYpk2bmqpVq5p//vOfZvTo0aZ27dqmdOnSZvfu3aH9XL9jKSkppk6dOqZChQrmvvvuM+PHjzeLFi0q1vczohcxCxcuNLt27TJbtmwxU6dONZUqVTJxcXFm69at+bqI2blzp4mOjjZdunQxJ06cCO03btw4A8D85z//McacmgCqVatmrr/+eqv96dOnGwDmk08+McYYc+DAAVO+fHkzcOBAa78dO3aYcuXKWdv79u1rAJj77rsvt5fpnGDZsmUGgFmwYIEx5tQ9qF69urn77rut/U7/IFWqVMn8+uuvoe3vvPOOAWDefffd0LbsP0ifffaZKVu2rOnatas5evSo1SaPscmTJ5vzzjvPfPrpp9Z+48ePNwDM4sWLc/ws6enpBoB55plnQtuOHTtmmjVrZipXrhz60Rw9erQBYF577bXQfsePHzdt2rQxCQkJZv/+/cYYY95++20DwDz22GPWeXr27GmioqLM+vXrQ9vi4+NN3759c+xfcUFj5hQaM5HF/PnzTYkSJUyJEiVMmzZtzPDhw828efOsxbIxp36voqOjrXuxcuVKA8CMHTs2tO1MixgAZu7cuZ7zF9f7GdHhpE6dOiExMRE1atRA7969kZCQgJkzZ6JatWr5ep6FCxfi+PHjGDZsmFWca+DAgShbtizef/99AKcesfbq1QuzZ8+2Hh9PmzYN1apVQ9u2bQEACxYswN69e9GnTx/s3r079F+JEiXQqlUrZyGtO++8M18/U3FhypQpqFKlCjp06ADg1D248cYbMXXqVCt0d5obb7wRFSpUCOl27doBAH766SfPvosWLUJGRgauuOIKzJgxAzExMTn25Y033kDDhg3RoEED676eDnO67itTsmRJDBo0KKSjo6MxaNAg7Ny5E8uXLwcAzJ49G0lJSZbvq1SpUrjrrrtw8OBBfPzxx6H9SpQogbvuuss6x7333gtjDObMmePbn+KIxswpNGYii86dO+Pzzz9Hjx49sHLlSowcORIZGRmoVq0aZs2aZe3bqVMnpKWlhfSFF16IsmXLOsckU6tWLWRkZOR7/yOViF7EPP/881iwYAEWLVqEH374AT/99FOB3JxNmzYB8FYojY6ORu3atUN/B05NeEeOHAkNuoMHD2L27Nno1atXqCro6cqeHTt2RGJiovXf/PnzPSaukiVLonr16vn+uYLOiRMnMHXqVHTo0AGZmZlYv3491q9fj1atWiErKwsffPCB55iaNWta+vSPE1eWPXr0KLp27YqLLroI06dPR3R0tG9/1q1bh1WrVnnuab169QCEZ85LTk72mLZPH386tr1p0ybUrVvXU+22YcOGob+f/t/k5GSUKVMmx/3OJTRmNGYimZYtW2LGjBnYs2cPli5divvvvx8HDhxAz5498cMPP4T24zEJnBqXfhWygVOLmHOJiH476ZJLLgm9ncRERUVZRrTTuP6llZ+0bt0aqampmD59Om666Sa8++67OHLkCG688cbQPqdLmk+ePBlJSUmeNrjsfUxMjGfyEcCHH36I7du3Y+rUqZg6darn71OmTEGXLl2sbWdy6/NYiYmJwdVXX4133nkHc+fORbdu3Xz7c/LkSVxwwQUYNWqU8+81atTwbUMULBozIghER0ejZcuWaNmyJerVq4f+/fvjjTfeCL1BF+6YdFGc30RyEdGLmJyoUKGC89FaXv4lcfo9+zVr1qB27dqh7cePH0dmZiY6depk7X/DDTdgzJgx2L9/P6ZNm4bU1FS0bt069PfTjwErV67sOVaEz5QpU1C5cmU8//zznr/NmDEDM2fOxPjx4/P0pY2KisKUKVNwzTXXoFevXpgzZ45vptW0tDSsXLkSV1xxReipW275+eefPa/Qr127FgBCb9WlpKTg22+/xcmTJ63F7erVq0N/P/2/CxcuxIEDB6x/WfN+pz/vuYDGjMZM0Dj9D/Xt27cX6HmK6/0M7D//09LSsHr1auzatSu0beXKlVi8eHGu2+rUqROio6Px3HPPWSvdl156Cfv27UPXrl2t/W+88UYcO3YMkyZNwty5c3HDDTdYf8/IyEDZsmXxxBNP4LfffvOcL3ufhZsjR45gxowZ6NatG3r27On5b8iQIThw4IAnlpwboqOjMWPGDLRs2RLdu3fH0qVLc9z/hhtuwLZt2/Dvf//b2d9Dhw75nvP333/HhAkTQvr48eOYMGECEhMT0bx5cwDA1VdfjR07dmDatGnWcWPHjkVCQgLS09ND+504cQLjxo2zzvHss88iKioKV111VWhbfHy8lS6gOKIxozETySxatMj5JGX27NkAvHaG/Ka43s/APom57bbbMGrUKGRkZGDAgAHYuXMnxo8fj8aNG2P//v25aisxMRH3338/Hn30UVx55ZXo0aMH1qxZgxdeeAEtW7bELbfcYu1/8cUXo06dOnjwwQdx7NgxK5QEAGXLlsWLL76IW2+9FRdffDF69+6NxMREbN68Ge+//z4uu+wyzyQibGbNmoUDBw6gR48ezr+3bt0aiYmJmDJliuf654a4uDi899576NixI6666ip8/PHHZ6xVc+utt2L69On405/+hEWLFuGyyy7DiRMnsHr1akyfPj2UmyEnkpOTMWLECGzcuBH16tXDtGnTsGLFCkycOBGlSpUCANxxxx2YMGEC+vXrh+XLlyM1NRVvvvkmFi9ejNGjR4f+Bd29e3d06NABDz74IDZu3IimTZti/vz5eOeddzBs2DDLGNi8eXMsXLgQo0aNQnJyMmrVqlXs0vFrzGjMRDJDhw7F4cOHcd1116FBgwY4fvw4lixZEnqa379//wI9f7G9n0X3YtSZOVOeGOa1114ztWvXNtHR0aZZs2Zm3rx5ec4TY8ypV6obNGhgSpUqZapUqWLuvPNOs2fPHue5H3zwQQPA1KlT54z9W7RokcnIyDDlypUzsbGxJi0tzfTr188sW7YstA/nnxCn6N69u4mNjTWHDh064z79+vUzpUqVMrt37w69LvvUU0959uP777rmu3fvNo0aNTJJSUlm3bp1xhjv67LGnHptdcSIEaZx48YmJibGVKhQwTRv3tw8+uijZt++fTl+pvT0dNO4cWOzbNky06ZNGxMbG2tSUlLMuHHjPPtmZWWZ/v37m/PPP99ER0ebCy64wLz88sue/Q4cOGDuuecek5ycbEqVKmXq1q1rnnrqKXPy5Elrv9WrV5vLL7/cxMXFGQDF8lVLjRmNmUhmzpw55rbbbjMNGjQwCQkJJjo62tSpU8cMHTrUZGVlhfYDYOUcO01KSop1D870inXXrl2d5y+u9zPKmDCcQkIIIYQQEUZgPTFCCCGEOLfRIkYIIYQQgUSLGCGEEEIEEi1ihBBCCBFItIgRQgghRCDRIkYIIYQQgUSLGCGEEEIEkrAz9hbXugvnOgWdJihSxw0XWMtL4VAu5Hm6svBpuLifq1I5pxqvWrWqpbl6Mf999+7dnjY//vhjS7/wwguWPnz4sOeY3FKQ4yZSx4w4O87VuYbh73yfPn08+2SvaA0Al156qaXXrFlj6c2bN1u6ZcuWnjYXLlxo6c8++8y/sxGA37jRkxghhBBCBBItYoQQQggRSMIuO1AUj+rCOadf9zlsAABvvPGGpflR3Omiaqc5cuSIpTt16uRpkytZr127Nsd+nXeevX50fY7CqAhxrjzi5et98uRJS8fGxlr6vvvu87TRtGlTSzdr1szSFStWtHTZsmVz200P27dvtzSP53379nmO4W1bt2619HXXXWdp1z3yGxcKJ4ncUhznGlfxzpSUFEu3bt3a0jzXuK7LTz/9ZOmEhARLf/fdd5YuXbq0b1+rVatm6XLlyln6k08+sfRXX31l6aKqgK1wkhBCCCGKJVrECCGEECKQaBEjhBBCiEASUZ4YP99COLAvgV8vBYDo6GhL82crU6aMpX///XdLHz161NPmgQMHLP3oo49aev369WfocdFSHOPULmJiYix97NgxS/fu3dvSkydP9rTB95DHAXtReJxVqFDB0yaPLfbR8FjkcbZjxw5Pm0lJSZb+5ZdfLH3xxRd7jskt8sQULzhdAI/L/KCo55q8eL9uvvlmS/N3i1MgAN7v5IYNGyzNnhhXegdul+cSvj/82fgcgNenyW3wq98897j8d7/++qul582b59nnbJEnRgghhBDFEi1ihBBCCBFItIgRQgghRCDRIkYIIYQQgSTs2kmFQThGXk4u1KtXL0snJydb2mW8YiMV159hk9uePXty/DvgNRSPGDHC0myqmjJliqW///57T5si//jtt99y/Dsb4Q4ePOjZh021PA5WrlxpaTbj8RgBgEqVKuV4Dja18XeEE/AB3s/C/WTz8P79+z1tiILBL8llXsyvV111laXZuH3llVdampOeAd458P7777f0jz/+aOmff/451/0sasK5trfeequlu3TpYuk333zT0hs3bvS0ERcXl+M52CDr+t3jxHL8u8amXP4O8zgDvJ+f+7lu3boc+8UJ9wCgffv2luaXCJYtW+Y5Jr/RkxghhBBCBBItYoQQQggRSLSIEUIIIUQgiahkdwwnjAOAevXqWfr48eOWPnTokKVd/WavAifsqVu3rqW3bNliaVfMk5OpsQ+B/86F/H744QdPmxyXLgiKOgFVYeGXSHHMmDGW7tmzp6eNVatWWbp69eqW5qJs559/vqVdXqqqVataetu2bZbmwm5VqlSxNCekArw+MB5rDz30kKWffPJJTxt+KNld3shtQk8u1gkAzz33nKV5HLK3ge+/65w8ZrgILo9l13zVqlUrS7OvLBLnGvakXX311ZauXLmypdesWWNp/r0BvL4QV+K57OQlsSD7OjnxpuucfP15buFCx+wj5N9NwDun8Xm5kGVevFRKdieEEEKIYokWMUIIIYQIJFrECCGEECKQRJQn5pZbbrG0Kx6clZV11ufh2B77Vfide5eXgeE2OM7JcWmOjbM3AgBWrFhh6eHDh/v2I7dEYpy6IOB+8OeeOnWqpTkfEeCN7zZs2NDSfp4YV54F7gfn6+AYc6NGjSzNeWUAb6FJv5h8Xu6RPDF5g/107EO44447LO3yBXLeKp5LeH5jfwvnIAG8uYP4/rL3g70kAFCuXDlL82eNxLnm0ksvtXRaWpql+dry/dq8ebOnTf694OvPXhOXJ4a3cT/YC8q4vvPcJveTPUx+fhfA66XatGmTpdlTtGTJkjP0+MzIEyOEEEKIYokWMUIIIYQIJFrECCGEECKQRFTtJM4z4Ir3++HnfQC8eRKOHTtmaY7lcgzT5ZHhNlnzMdwvzi0AeGugiLzjF1flvAsc+wb8a6Jw/JfzMHBsHAAOHz5safYzcBu8f/ny5T1tPvDAA5Z+9tlnLf3FF19Yul+/fp42XnnlFc82kXv4e8/jqnXr1pb+29/+ZmmXf4XHIecK4jHCfjz27wHescmeC6635KoZxPPokCFDPPtEGjVq1LA0+z44hxh7h9j3BnhztrB2zQN+sJ+F++nnd3Htw/h5YHh+A7x5X/i30s+7kx/oSYwQQgghAokWMUIIIYQIJFrECCGEECKQRJQnhuONHEsEvHE7v/f4XfFHP38Ex6E5Bu3KR8DbWHOskPvtilfmJXYq3PfH757XrFnT0i7fgGtbdjgPDJ/TNZ75vvM9Z28Vx5hdPh2uncNwfZj333/fs488MYUD53xhX4lrzPEcyB4YHiOcy8M1Zvx8gQz7bAAgMTExxzYjEc7PtW/fPktzPhy+H67aSfwbxNef77kLvof8++HnYQqnTR5HnOeHfYKsAa8fi8/Bf+cxAgC7du06Q4/DQ09ihBBCCBFItIgRQgghRCDRIkYIIYQQgUSLGCGEEEIEkogy9nJyHTYzAV6jEBfMC6fIGJtq2QjKBib+O5stXeflc7Cpkw1Orn6z6ZML+4VjEDsXcV1LvySIbG69+eabPW2w2ZHHnl/hNx4Trn7wuGDTm1/CKgDIyMiw9HvvvWdpTuD16quv+rYp8obLDJmdNWvWWJoNs+EU8uMxxOfkeSKcMcTnZcOmy3DM5/33v/9t6YkTJ/qetyCpUqWKZxt/Z/0+JyfHc/1GceJS1nz9XfeD++X3UoHf741rH9acwLBWrVqWZoMy4P1s3G9OWMsFbAHg448/9mzLDXoSI4QQQohAokWMEEIIIQKJFjFCCCGECCQR5YlhD4EraVlSUpKld+7cmeMxLv8Kx505lstJ97jNcLwNfA6OtbK/Zf/+/Z42Ob6YkpJiaXli8o8nn3wyRw14/QtcfJH9K5yEzAWPE77nixYtsnSnTp0s7RoDXNBx6NChOfbhxRdf9OumyCPhePSywz4rTsIIeBOyueaj7PAc6PI2+HHw4EFLuzwaPBdHGm3atPFsS09Pt/Trr79u6dq1a1u6a9euln766ac9bbK3hK93OInp/O4p/509TK4CtuyF47HGxWfZq9OhQwdPm8uXL7f03LlzLd28eXNLuwrWyhMjhBBCiHMSLWKEEEIIEUi0iBFCCCFEIClSTwwXzGN/gCuezH4VbsPlLWH4PBwzDscD4wf3nc9RuXJlS7sKiXE/+BgRPrn1JrjgPDFcMI013z+OQQPecbF06VJLs3eKY8ouT0xqaqqlu3XrZmnOG+Mq5ueX30QUDOvXr7f0RRdd5Nln+/btli5durSl/fKQuLxaPA7PP/98S/sVRgSAH374wbMtknj77bc929i/0rdvX0sPGzbM0l999ZWlXfM253ryy53i8ii52s0O369wiifzOHH5RbPD4yQtLc2zzx//+EdLP/DAA5b+8ssvLe0qNnu26EmMEEIIIQKJFjFCCCGECCRaxAghhBAikBSpJ4Y9Buw9CcfHwPFGjv/yu++A9z19zk/jV4vE5Rfwq0vBvgOOOW/evNnTJtfl4JimyDvsV+H746pnwnkw2I/FY5H/vnfvXk+bnFuGx0WdOnUszePKla+D4+FcG6lixYqWlv8lcvj6668t3atXL88+fL/8/BOcQ4THKeCtgcPz5rFjxyzNvg/Am0cpCKxYsSJHzfPyhg0bLH3TTTd52pw0aZKl/eoeueYa/k1ifwp7YHj+cv3uMX65Zthbx34XALjxxhstPWbMGN/z5jd6EiOEEEKIQKJFjBBCCCECiRYxQgghhAgkWsQIIYQQIpAUqbGXk/yEY1pkc+SWLVssXaVKFUu7TG9+SfXYaBVOEj6GPwubLdkkGk5RNjaKirzjd89d/Pzzz5auVauWpbnAGif/WrVqladNTpDHCQ05qRib71zjm02A3AYXkVy4cKGnDVEw8LzACcfYYOsyXfslVWT4/rsSobHZm+cnV0FB5scff/TdpyhxJS31S/j27LPP5vj3nj17erbVq1fP0vybxdfS1QdOiMdGX76HvH/VqlU9bXIbfAzPgdWqVbP09OnTPW0uW7bMsy07fmMTOPtEpHoSI4QQQohAokWMEEIIIQKJFjFCCCGECCQR5YnhmJzLE8PxM06wVLduXUu7CkKyr4Djzhw75FhqOMnuONlQVlaWpb///ntL169f39MmeyzyUohS5B9+SQ9Z8xho0qSJp8358+dbevHixZZ+8MEHLc3xc06ICHjHN8elOUGVPDGFh58Hg+fEcJJ18v3lZHb8d5f/y6+NcOA5LtLIjyKwjCuBpd8+fP84kSDgvUecGJY9Mby/q9gse3G4TU6m6mojt7CftCASa+pXUQghhBCBRIsYIYQQQgQSLWKEEEIIEUiK1BPDeU84XsaxfQDYt2+fpbdt22Zp9tG4YnB+sVE+huPY4cT1OMbMsW2ON7Zq1crTBucl4fiiyDt++Tpc15o9LRzL5jY4nwfnbwG8hfQaNmxoab/8Ha5+sp+BY/IqJBq5pKenW9rlueBxV758eUuXKVPG0uybcnliwily68eePXtyfUzQcX1mnuv5e+9XDBjw+iEZnmv4nrs8Tfx7yr4anhNdXh0/+HeP+1kQviQ9iRFCCCFEINEiRgghhBCBRIsYIYQQQgSSIvXEcIyO35/nOkgAsGHDBktzXI89Ma7YoF/dHL88C+HE9Xgf1ly3gt/ZB7zXgz0WIu/45esYMWKEZxvXNeK8GJyTiMeNK6dL48aNLd20aVNL8zjhNlz+lq1bt1qaY9t5yQEiCgbOC9O+fXtLs+cPAMqWLWtpnkfZk8Hj0uV1YJ8Gj91wfFTs14o0CsKP4fKu8DzNv0n8m+WaF/h+8HeWxwDfY5ef1K8+HP8Gsf80L/jNs/mBnsQIIYQQIpBoESOEEEKIQKJFjBBCCCECSUQFxzlW64rDcn4VrnMUTtyT44uumGR2OD7M5wS8MUyOg/Jn4ViqK98H17rgvDqi4OjWrZtnG8eIOa8C3+MVK1ZY+pdffvG02aBBA0tzXhCOfTPsmwK8Y4/rco0cOTLHNoUbP28c/921D/OPf/zD0uHMTZwXhvOQcI4XP8+fqw2GfRwuLr30Ukt//fXXvscEHZcnhq+v37Vzzf1+eXr8aie5zsk+GR5bPFZdv3ORiJ7ECCGEECKQaBEjhBBCiECiRYwQQgghAokWMUIIIYQIJEVq7OXkOuEYaNnYy4Wv2KDmSrbDJijWfAwbtcIpxMimKU5AtWPHDktXrVrVt58q3Jd3/EyZ1157raWrVavmaYOTyPH45LE4d+5cS69du9bT5sCBAy3dunVrS/M4YjOxywDIRSUzMzMtPX36dM8xQScvCSnPFr8ioi6GDh1q6b/85S+W/uabbyxdqVIlTxt+iTR5DmTNCfYA7zjyMy1zcVoAuOKKKyw9btw4zz7nAjwu+Fryb4FrrLIxl025/Jvk95vlwu93MCjoSYwQQgghAokWMUIIIYQIJFrECCGEECKQFKknJikpydJ+8X8AWL9+vaXZh8CxQU785ToPxyj9YtuuBFTcBichYz8L+yNc5+TP7yrqJcLDzyMxc+ZMS3/77beeffh+VK9ePcc22BNz8cUXe9rkNthv5Upmlx1XgjWOp2/evDnHNiId/ozheNL8PB6Ma67h8+a2TQDo2rWrpe+++25LX3PNNZZ+6aWXLL1nzx5Pm+xx4THCf2ePlOv68efnhHo8v3FhUsCbuPFcgAsvAt7CxcnJyTn+nRNcAl5PC/9++CXDa9KkiW9f2dPHXqm8+MoKw4vG6EmMEEIIIQKJFjFCCCGECCRaxAghhBAikESUJ4bjaY0bN/Yc8+mnn1q6V69eOZ4jnPgvxx/5fflw8kFwfJGPqVixoqU5343rHX2OQ3NeHXGKvOTr4MJtnJ/jv//9r+eYJ5980tJLly61NBfs7N+/v6XT09M9bXLsmtvgscmf1TVueJ958+Z59slpfyC8a1hY8LxQEPks8uPz9unTx7Ptvvvus3TTpk0t/de//tXSXOR1w4YNnjZ53mRPDP+dvT0uTx97C1lzcV7X9apQoYJnW3GDr+Utt9zi2WfBggWW5u8wt3Ho0CFPG+zlrFmzpqW5mOzBgwct7fJSsa+GxwH7bNjDN2fOHE+bkTBP6EmMEEIIIQKJFjFCCCGECCRaxAghhBAikERU7SSOfXO+C8AbCyxbtmyO5wgn/4rf39lX4zreFWfODseL9+3bZ2lX3gXOC8Nx6eIKx4z96oT45UwAgNGjR1ua7wd7lhYtWuRpg+/7c889Z2mOS992222WduX54c/C45lzvLBPypUnhvOEzJ8/37NPkDn//PM92/j7x9+v/IDrWt1///2WrlWrlueYZ5991tKPP/64pTlvzK5duyxdt25dT5vsCeL8HjzOeFy6vHXsl+C5mNvkMQZ4x2YQ8avB5fK1MVyr7KqrrrL0li1bLL1z505PG1y7jfvF45vnDde94N8P/n1lnybnsHLluVq2bJlnW2GjJzFCCCGECCRaxAghhBAikGgRI4QQQohAUqSeGI7lcq6Vbdu2+bZRuXJlS3O82OWXCMdDkR32Lbh8Nhw75Rg9x5zZA7N161ZPmxwH5etTXPG7lgx7JFz5OgYNGmRpznnw0EMPWfqCCy7wtMG1R+644w5L79ixw9JcE8Xl3+K6Npzfgb0H/J3hvDKAd2y56kBlJxJyPeQE59u56aabPPv89NNPluZ8K/ydr1GjhqU5LwoApKSkWJrvH3uNONcQADzyyCM5ntf1vffrF29jrxbff/676/u0fft2S7s8L34Uh9pufrV/6tSpY2m+boD3+laqVMnS/Lvm8limpqZamnMB8fh2+UcZHs/sv+K5hH03Ln+WPDFCCCGEEHlEixghhBBCBBItYoQQQggRSLSIEUIIIUQgKVJjLxtV2Ri2du1a3zY4SRmbk1wFINkwy6YpNjpyP12JhLhNboOP4b+vXr3a02b58uUtzcbSc4Xu3btbuly5cpZms50rIdXXX39t6UaNGlm6U6dOls7KyvK08eOPP1qaTX1sHr3uuuss7TI+cvE3TkzGBSI5YRUb2wFg+vTpnm1B5q677rI0m2MB7zzABmku+Mnjga8z4L2OnCCRk9+1bNnS0wYbZNnUySZdNm7zeACA9evXW5qTpbE5ldt0Jc1kYyibUXle5b8DkW8Qzw+Sk5Mt7UpUxy+X8IsHXAT0888/97RRtWpVS7NZm+cn/n3hew4ArVq1sjT/vvILAFxIlOdMwL94cmGgJzFCCCGECCRaxAghhBAikGgRI4QQQohAUqSeGPYIsH9l5cqVvm1wUjKOfbuSgXFyIb8ER+x3cRXd86N9+/aW5uR37LcAvDH3cyXZ3apVqyzNRT/Zm8BeFI4nA96id64kYtnhAmyAt0Baw4YNLc3JpHg8u/wNfE+5iCB7Dzp27GjpCy+80NMmJ1hjIiGOnRu++OILS7uSBvK9YC8KX/sGDRpYmq8JAHTp0sXS7DvwOyfg/Z7znMdzCc8DrsKwPEZ4LPM49esD4PUUcaJGLlro8r889thjnm2RhGve9pv7+drxPLJixQrPMX6/a2lpaZZmTyYAxMTEWNrP68n3w1XkkxPk8XfCz/vJYwLwznkbNmzw7JOdvNwDP/QkRgghhBCBRIsYIYQQQgQSLWKEEEIIEUiK1BPDsTCO63EuBxdcyI/jeK5iZ+x38IsvhhOz45whHEt97bXXLM3xcy5gBwDt2rXLsV/FgWbNmnm2cfEzvjZ8DBcqcxU8ZG8Bx/iZiy++2LOtfv36lubcGn7j2VV4lHMBvfPOO5Zmj9ebb76Zow6HSPfAMHfeeael+bsFeH1Af/rTnyzNPhpuw5U7hT0vPLfwdXT5anhMsK+A/XmuwqMMjxEeV9zvNWvWWJpznQBejxB7ML788ktLs08NACZMmHCGHkcGrnnczx/GuVL4urjyNPG8wNeqZs2aObYJeHMhsea8Yjw2XbnMuNgsj3nOZ8O4PH3sP2RPDHtgztb/4kJPYoQQQggRSLSIEUIIIUQg0SJGCCGEEIGkSD0x7FPgmF1mZqZvG3//+9/ztU9Fhev9eo7Pcqy1OPC3v/3Ns429JOyB8fMsuepQrVu3ztLsg6pevbqlXV4qjiFzLgb2JnA/XfVmuAbK7bff7tknO341uADv9XB5cSIZvzxMrnwsw4cPz1H37t3b0n/9618t3bx5c99+8XXNS74ohj1PzzzzjKVHjhzpOYbr9QwaNMjSf/jDHyxdpUoVS3NOGAB4++23Lc2+Ga4tNXbsWE8bQcTPH8Z5mPg3y+UT4bwvfI/Zo+Sqc8Q50njs8fzF3h5Xmxs3brQ0+zg5Nw1/VpefxZVPy++Y/EZPYoQQQggRSLSIEUIIIUQg0SJGCCGEEIFEixghhBBCBJIiNfay4ZATf23dutW3DU4WFakJ4fyS/rDxFPAa8MK5HkFj8eLFnm0ZGRmWZpMaX0tOBDVkyBDf8/L1379/v6VdCajYVMv3h9tg0+CePXs8bV5xxRWW5uJ9jKtYXHEjtwVZwzlm6tSpOWoXXLSVzb8ugyzDSRU/+ugjS7tM6LmFk8xxwUw2aLqM0X7FAXfs2HE2XYwI8jJu+J5zUkDX95GLeHJyOy6a6EqY17hxY0vzbyPPLfx3l+GW56/LL788xza5kKVrvLvmtMJGT2KEEEIIEUi0iBFCCCFEINEiRgghhBCBJMqEmY0mPxI7Mffcc4+lGzVqZOmBAwf6tlFcPDEuJk2aZGn2xDz44INn3a+CTkaUl3HDMePWrVtbuk6dOjnqChUqeNpkX40rmV12XLHuQ4cOWZpj35ywkP0+mzdvzvGc4VAYBdXCoSDPWxBzjSh6InGuyS1cnJa9RADw/fffW5oTafbt29fSTzzxhKcNvlacdG/Tpk2WbtOmTY7ndPWLk91xUj72wPD8B/h7+PIDv3GjJzFCCCGECCRaxAghhBAikGgRI4QQQohAErYnRgghhBAiktCTGCGEEEIEEi1ihBBCCBFItIgRQgghRCDRIkYIIYQQgUSLGCGEEEIEEi1ihBBCCBFItIgRQgghRCDRIkYIIYQQgUSLGCGEEEIEkv8PGUzaZhffc0sAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "train_dataloader = DataLoader(train_data, batch_size = BATCH_SIZE, shuffle = True)\n",
        "test_dataloader = DataLoader(test_data, batch_size = BATCH_SIZE, shuffle = False)"
      ],
      "metadata": {
        "id": "jbfirCUDYB_9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Length of train dataloader: {len(train_dataloader)} batches of {BATCH_SIZE}\")\n",
        "print(f\"Length of test dataloader: {len(test_dataloader)} batches of {BATCH_SIZE}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eu9kme9yicew",
        "outputId": "7e8ee7b5-4cf2-4a4e-cb83-7669e2c743ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of train dataloader: 1875 batches of 32\n",
            "Length of test dataloader: 313 batches of 32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_features_batch, train_labels_batch = next(iter(train_dataloader))\n",
        "train_features_batch.shape, train_labels_batch.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JKz90_g4i7GY",
        "outputId": "47bd4c61-e6e0-4cf5-a880-ad93b958bba9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([32, 1, 28, 28]), torch.Size([32]))"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "random_idx = torch.randint(0, len(train_features_batch), size = [1]).item()\n",
        "img, label = train_features_batch[random_idx], train_labels_batch[random_idx]\n",
        "plt.imshow(img.squeeze(), cmap = 'gray')\n",
        "plt.title(class_names[label])\n",
        "plt.axis(\"Off\")\n",
        "print(f\"Image Size: {img.shape}\")\n",
        "print(f\"Label: {label}, Label Size: {label.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "wUIrh5Sxk5Tp",
        "outputId": "6c6959eb-0a80-44fa-b240-5521e165c3bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image Size: torch.Size([1, 28, 28])\n",
            "Label: 6, Label Size: torch.Size([])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a flattern layer\n",
        "\n",
        "flattern_model = nn.Flatten()\n",
        "\n",
        "x = train_features_batch[0]\n",
        "\n",
        "output = flattern_model(x)\n",
        "\n",
        "print(f\"Shape before flatterning: {x.shape}\")\n",
        "print(f\"Shape after flatterning: {output.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yR5Qf_YSzfyi",
        "outputId": "f5625870-7bae-4a6d-f71a-191866eb7a7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape before flatterning: torch.Size([1, 28, 28])\n",
            "Shape after flatterning: torch.Size([1, 784])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import nn\n",
        "class FashionMNISTModelV0(nn.Module):\n",
        "  def __init__(self, input_shape: int, hidden_units: int, output_shape: int):\n",
        "    super().__init__()\n",
        "    self.layer_stack = nn.Sequential(\n",
        "        nn.Flatten(),\n",
        "        nn.Linear(in_features = input_shape, out_features = hidden_units),\n",
        "        nn.Linear(in_features = hidden_units, out_features = output_shape)\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.layer_stack(x)"
      ],
      "metadata": {
        "id": "n3ttJqhwKlVT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# input_shape = 28*28\n",
        "# hidden_units = 10\n",
        "# output_shape = len(class_names)"
      ],
      "metadata": {
        "id": "oObzy_cgSzPN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "model_0 = FashionMNISTModelV0(input_shape = 28*28, hidden_units = 10, output_shape = len(class_names)).to('cpu')"
      ],
      "metadata": {
        "id": "BkAFo9KMTVBg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wecOX_FuT0vW",
        "outputId": "c03bce97-6721-4212-8491-3d9b26997a4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FashionMNISTModelV0(\n",
              "  (layer_stack): Sequential(\n",
              "    (0): Flatten(start_dim=1, end_dim=-1)\n",
              "    (1): Linear(in_features=784, out_features=10, bias=True)\n",
              "    (2): Linear(in_features=10, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params = model_0.parameters(), lr = 0.1)"
      ],
      "metadata": {
        "id": "TOe0egRhUf7J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from pathlib import Path\n",
        "\n",
        "if Path(\"helper_functions.py\").is_file():\n",
        "  print(\"helper_functions.py already exists, skipping download\")\n",
        "else:\n",
        "  print(\"Downloading helper_functions.py\")\n",
        "  request = requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/helper_functions.py\")\n",
        "  with open(\"helper_functions.py\", \"wb\") as f:\n",
        "    f.write(request.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m2voanClWLmO",
        "outputId": "61b0c5ad-2847-4cd4-f097-970c13f31fd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading helper_functions.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from helper_functions import accuracy_fn"
      ],
      "metadata": {
        "id": "0u5_9T1GW0AN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from timeit import default_timer as timer\n",
        "def print_train_time(start: float, end: float, device: torch.device = None):\n",
        "  total_time = end - start\n",
        "  print(f\"Train time on {device}: {total_time:.3f} seconds\")\n",
        "  return total_time"
      ],
      "metadata": {
        "id": "yp0_AUTzW4XL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0o9td8-Omfsa"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}