FINAL_FASHION_MNIST_WITH_DROPOUT_ADAM_THREEHIDDENLAYERS.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FINAL_FASHION_MNIST_WITH_DROPOUT_MODIFIED3",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "rZFoBK4AIAyi",
        "colab_type": "code",
        "outputId": "cdd8680e-7d0c-40f7-f7b7-4aae496eb93c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "# http://pytorch.org/\n",
        "from os.path import exists\n",
        "from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag\n",
        "platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())\n",
        "cuda_output = !ldconfig -p|grep cudart.so|sed -e 's/.*\\.\\([0-9]*\\)\\.\\([0-9]*\\)$/cu\\1\\2/'\n",
        "accelerator = cuda_output[0] if exists('/dev/nvidia0') else 'cpu'\n",
        "\n",
        "!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.4.1-{platform}-linux_x86_64.whl torchvision\n",
        "import torch"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tcmalloc: large alloc 1073750016 bytes == 0x58ece000 @  0x7fbf5dcf22a4 0x591a07 0x5b5d56 0x502e9a 0x506859 0x502209 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x507641 0x502209 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x507641 0x504c28 0x502540 0x502f3d 0x507641\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HlflMzNEIJQK",
        "colab_type": "code",
        "outputId": "fdb84880-f45e-4f44-9cb2-6d85bc888357",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torchvision import datasets, transforms\n",
        "# Define a transform to normalize the data\n",
        "transform = transforms.Compose([transforms.ToTensor(),\n",
        "                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "# Download and load the training data\n",
        "trainset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
        "\n",
        "# Download and load the test data\n",
        "testset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=False, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "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-labels-idx1-ubyte.gz\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-labels-idx1-ubyte.gz\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "t6sN5E4eILaU",
        "colab_type": "code",
        "outputId": "04c478b1-8645-4fea-cf55-731931b6cb06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        }
      },
      "cell_type": "code",
      "source": [
        "!wget -c https://raw.githubusercontent.com/udacity/deep-learning-v2-pytorch/master/intro-to-pytorch/helper.py"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-12-27 13:22:44--  https://raw.githubusercontent.com/udacity/deep-learning-v2-pytorch/master/intro-to-pytorch/helper.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2813 (2.7K) [text/plain]\n",
            "Saving to: ‘helper.py’\n",
            "\n",
            "\rhelper.py             0%[                    ]       0  --.-KB/s               \rhelper.py           100%[===================>]   2.75K  --.-KB/s    in 0s      \n",
            "\n",
            "2018-12-27 13:22:44 (43.7 MB/s) - ‘helper.py’ saved [2813/2813]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "a5FFCtGfIPZS",
        "colab_type": "code",
        "outputId": "ab034c9b-c390-4e52-9fa2-ac86fb0393eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "cell_type": "code",
      "source": [
        "import helper\n",
        "images, labels = next(iter(trainloader))\n",
        "helper.imshow(images[0,:]);"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAc8AAAHPCAYAAAA1eFErAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fddc8246ba8>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 231,
              "height": 231
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hses2CPXIQ0L",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "from torch import optim\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "luq5s2rjITql",
        "colab_type": "code",
        "outputId": "b32da9f9-dfe4-4c9e-98ff-38f5ad3a48e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "cell_type": "code",
      "source": [
        "#three hidden layers and adam optimizer\n",
        "model = nn.Sequential(nn.Linear(784,256),nn.ReLU(),nn.Dropout(0.2),nn.Linear(256,128),nn.ReLU(),nn.Dropout(0.2),nn.Linear(128,64),nn.ReLU(),nn.Dropout(0.2),nn.Linear(64,10),nn.LogSoftmax(dim=1))\n",
        "\n",
        "criterion = nn.NLLLoss()\n",
        "\n",
        "optimizer = optim.Adam(model.parameters(),lr=0.003)\n",
        "\n",
        "epochs = 30\n",
        "steps = 0\n",
        "train_losses, test_losses = [], []\n",
        "\n",
        "\n",
        "for e in range(epochs):\n",
        "  running_loss = 0\n",
        "  for images , labels in trainloader:\n",
        "    optimizer.zero_grad()\n",
        "  \n",
        "    images = images.flatten(start_dim=1)\n",
        "  \n",
        "    output = model(images)\n",
        "  \n",
        "    loss = criterion(output,labels)\n",
        "  \n",
        "    loss.backward()\n",
        "  \n",
        "    optimizer.step()\n",
        "    \n",
        "    running_loss += loss.item()\n",
        "  else:\n",
        "    test_loss = 0\n",
        "    accuracy = 0\n",
        "    with torch.no_grad():\n",
        "      model.eval()\n",
        "      for images , labels in testloader:\n",
        "        images = images.flatten(start_dim=1)\n",
        "        log_ps = model(images)\n",
        "        test_loss += criterion(log_ps,labels)\n",
        "        \n",
        "        \n",
        "        ps = torch.exp(log_ps)\n",
        "        top_p , top_class = ps.topk(1,dim=1)\n",
        "        equals = top_class == labels.view(*top_class.shape)\n",
        "        accuracy += torch.mean(equals.type(torch.FloatTensor))    \n",
        "    model.train()\n",
        "    train_losses.append(running_loss/len(trainloader))\n",
        "    test_losses.append(test_loss/len(testloader))    \n",
        "    print(\"Epoch: {}/{}.. \".format(e+1, epochs),\"Training Loss: {:.3f}.. \".format(running_loss/len(trainloader)),\"Test Loss: {:.3f}.. \".format(test_loss/len(testloader)),\"Test Accuracy: {:.3f}\".format(accuracy/len(testloader)))\n",
        "    "
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1/30..  Training Loss: 0.597..  Test Loss: 0.492..  Test Accuracy: 0.822\n",
            "Epoch: 2/30..  Training Loss: 0.482..  Test Loss: 0.448..  Test Accuracy: 0.838\n",
            "Epoch: 3/30..  Training Loss: 0.453..  Test Loss: 0.419..  Test Accuracy: 0.849\n",
            "Epoch: 4/30..  Training Loss: 0.433..  Test Loss: 0.425..  Test Accuracy: 0.852\n",
            "Epoch: 5/30..  Training Loss: 0.421..  Test Loss: 0.423..  Test Accuracy: 0.853\n",
            "Epoch: 6/30..  Training Loss: 0.414..  Test Loss: 0.398..  Test Accuracy: 0.858\n",
            "Epoch: 7/30..  Training Loss: 0.405..  Test Loss: 0.394..  Test Accuracy: 0.861\n",
            "Epoch: 8/30..  Training Loss: 0.399..  Test Loss: 0.380..  Test Accuracy: 0.864\n",
            "Epoch: 9/30..  Training Loss: 0.393..  Test Loss: 0.398..  Test Accuracy: 0.858\n",
            "Epoch: 10/30..  Training Loss: 0.394..  Test Loss: 0.387..  Test Accuracy: 0.862\n",
            "Epoch: 11/30..  Training Loss: 0.387..  Test Loss: 0.382..  Test Accuracy: 0.866\n",
            "Epoch: 12/30..  Training Loss: 0.376..  Test Loss: 0.390..  Test Accuracy: 0.866\n",
            "Epoch: 13/30..  Training Loss: 0.381..  Test Loss: 0.421..  Test Accuracy: 0.856\n",
            "Epoch: 14/30..  Training Loss: 0.381..  Test Loss: 0.392..  Test Accuracy: 0.863\n",
            "Epoch: 15/30..  Training Loss: 0.374..  Test Loss: 0.382..  Test Accuracy: 0.866\n",
            "Epoch: 16/30..  Training Loss: 0.367..  Test Loss: 0.401..  Test Accuracy: 0.864\n",
            "Epoch: 17/30..  Training Loss: 0.368..  Test Loss: 0.380..  Test Accuracy: 0.871\n",
            "Epoch: 18/30..  Training Loss: 0.365..  Test Loss: 0.394..  Test Accuracy: 0.865\n",
            "Epoch: 19/30..  Training Loss: 0.361..  Test Loss: 0.395..  Test Accuracy: 0.867\n",
            "Epoch: 20/30..  Training Loss: 0.365..  Test Loss: 0.395..  Test Accuracy: 0.864\n",
            "Epoch: 21/30..  Training Loss: 0.353..  Test Loss: 0.404..  Test Accuracy: 0.864\n",
            "Epoch: 22/30..  Training Loss: 0.367..  Test Loss: 0.410..  Test Accuracy: 0.863\n",
            "Epoch: 23/30..  Training Loss: 0.365..  Test Loss: 0.375..  Test Accuracy: 0.869\n",
            "Epoch: 24/30..  Training Loss: 0.361..  Test Loss: 0.388..  Test Accuracy: 0.872\n",
            "Epoch: 25/30..  Training Loss: 0.367..  Test Loss: 0.390..  Test Accuracy: 0.870\n",
            "Epoch: 26/30..  Training Loss: 0.350..  Test Loss: 0.369..  Test Accuracy: 0.877\n",
            "Epoch: 27/30..  Training Loss: 0.348..  Test Loss: 0.366..  Test Accuracy: 0.872\n",
            "Epoch: 28/30..  Training Loss: 0.347..  Test Loss: 0.368..  Test Accuracy: 0.873\n",
            "Epoch: 29/30..  Training Loss: 0.348..  Test Loss: 0.368..  Test Accuracy: 0.874\n",
            "Epoch: 30/30..  Training Loss: 0.345..  Test Loss: 0.370..  Test Accuracy: 0.875\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JM33_3p58ZhP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "b0ImkWz3-N2u",
        "colab_type": "code",
        "outputId": "888932d6-6249-4c78-a4ae-e5f49bbdb16e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(train_losses, label='Training loss')\n",
        "plt.plot(test_losses, label='Validation loss')\n",
        "plt.legend(frameon=False)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fddc818ceb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvEAAAHwCAYAAAAvjDDZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFxERERFJMkriRURERESSzP8HEhhmCSshnF4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fddc818c630>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 376,
              "height": 248
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "imz4F0fkHS56",
        "colab_type": "code",
        "outputId": "ed88d3a9-502c-4009-e653-2b86d765c235",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        }
      },
      "cell_type": "code",
      "source": [
        "print(model)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sequential(\n",
            "  (0): Linear(in_features=784, out_features=256, bias=True)\n",
            "  (1): ReLU()\n",
            "  (2): Dropout(p=0.2)\n",
            "  (3): Linear(in_features=256, out_features=128, bias=True)\n",
            "  (4): ReLU()\n",
            "  (5): Dropout(p=0.2)\n",
            "  (6): Linear(in_features=128, out_features=64, bias=True)\n",
            "  (7): ReLU()\n",
            "  (8): Dropout(p=0.2)\n",
            "  (9): Linear(in_features=64, out_features=10, bias=True)\n",
            "  (10): LogSoftmax()\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bPUUAxCcHYky",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fc7d2a00-47dc-4a02-c20f-f94936c40c57"
      },
      "cell_type": "code",
      "source": [
        "print(model.state_dict().keys())"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "odict_keys(['0.weight', '0.bias', '3.weight', '3.bias', '6.weight', '6.bias', '9.weight', '9.bias'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "osdCchj5I9H_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "outputId": "ff98882a-f611-4389-9551-c2b7cfd80ad4"
      },
      "cell_type": "code",
      "source": [
        "# Import helper module (should be in the repo)\n",
        "import helper\n",
        "\n",
        "# Test out your network!\n",
        "\n",
        "model.eval()\n",
        "\n",
        "dataiter = iter(testloader)\n",
        "images, labels = dataiter.next()\n",
        "img = images[0]\n",
        "# Convert 2D image to 1D vector\n",
        "img = img.view(1, 784)\n",
        "\n",
        "# Calculate the class probabilities (softmax) for img\n",
        "with torch.no_grad():\n",
        "    output = model.forward(img)\n",
        "\n",
        "ps = torch.exp(output)\n",
        "\n",
        "# Plot the image and probabilities\n",
        "helper.view_classify(img.view(1, 28, 28), ps, version='Fashion')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1EAAAGkCAYAAADQRa8pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAWJQAAFiUBSVIk8AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3XmYXVWVsPG3CAmBEAYxMsqg6AJE\nRAaZIgTFFqT5QJAGEZug0C0KAmKLCCoIyCAogiDOUTAyidA4DwxCM4+i4pIQwAgBAiRAwhBI6vvj\nnAuXa1WqdlK3pry/56nn1D1nnX323Smou2rts09HZ2cnkiRJkqTeWWKgOyBJkiRJQ4lJlCRJkiQV\nMImSJEmSpAImUZIkSZJUwCRKkiRJkgqYREmSJElSAZMoSZIkSSpgEiVJkiRJBUyiJEmSJKmASZQk\nSZIkFTCJkiRJkqQCJlGSJEmSVMAkSpIkSZIKLDnQHZAkSRpsImIr4MPAdsBqwGhgOvAP4GfA+Zn5\nRMs5awP3A2RmR3/2ty9FxCRgv24OvwQ8AdwG/CAzL+nnPh2Xmce2+VoPAGsB22fm1b0851jgi8AP\nM3NivW9tuvh5iIirqX6u9s/MSX3UbfUzkyhJkqRaRCwLfB/Ys971J+AK4BlgdeBdVB+Aj4+I/TLz\nZwPS0f5xN3Bly74VgADeB7wvIi4GPpiZ8/q7c4PMjcDXgZt7EXsJcCfw1+adEXES8NmhnIAvTkyi\nJEmSgIhYCvgD8A6qD7j7Z+bNLTGvAY4FDgF+GhF7ZebF/d3XfnJ9Zh7W1YGI2A74OVWyeRtwSn92\nbLDJzF8Dv+5l7De6ObR53/VI7eY9UZIkSZUvUSVQ9wPbtSZQAJn5ZGZ+EjgD6ADOjIix/dvNgZeZ\n1wDH1S8PGsi+DAcR0QFsNtD9UO9ZiZIkSYu9iFgR+ET98rDMfLyHUz5PNcXv4sx8phftvw44AtgJ\neCMwCpgB/B9wambe0sU5GwOfAcYDKwPPAw9RVYDOyMyHW+InAIcCWwDjgKeBacClwJmZOaunfha6\nut6uFRErNNqPiM56/8rA0cA+wJOZGU19fS3VeOwCrA2MAB6mqgSelpl/7+6iEbEGcALwHuC1wGPA\n/wJfaL1PrY7fFTiQKklZCXgBuBe4EPhaZr6wgGttAxxDlVyPAaYCP6jPe6kp7lha7olaQJtX03RP\nVOs9aE3j9zHgXGA+sGZmPtRNe/cC6wL7ZuaPF3Rt9R0rUZIkSdU9PmOoPsj/vKfgzJydmV/IzLt7\nio2I1YDbqRKi5ag+vH+fapGKDwDXR8S/tZzzTqr7bPam+uA+Cfgp1We3/wFujIg1m+L3prp/aWeq\n+7i+C/wCeA1VxeiaiFiup74Wak7KRndx/BPAh4DLgcua+vom4A7gs8CydT8vBOZQJTt3RMS7urnm\nSsBNVFPffg5cACwNfJzqPS7THBwRX6iv/d76mt+pz1sbOAn4dUR093l4W+AqYKm6f7+kSoBPBb7d\nzTkL47dUiVnD1+uvPwDXUf2b/2dXJ0bEplQJ1NNUybL6iZUoSZIk2KreXpuZ8/u47UOpFqW4Bdg2\nM59vHIiI46kqHacDb20652iqD+8fy8xvNcV3UH3APgQ4DPhUfehYqumFu2Tmb5riR1IlAO8HJgJn\n9uH7WrfezgG6qtx9GNg0Mx9s2f9DYA3gIuBDLRWdE4HPAedFxBu6qBL9N1V15tDM7KzPWZnqvqy3\nUCVuX6n3L081jgB7ZebLSUZ9zl+ACVRj89Mu+n80sE/zCoQR0Uis9o+Ib3ZVQSyVmZMj4npg//r1\ny/ehRcT3qCqRE6mSvlZ719sLM/O5Re2Les9KlCRJUpXkQL0kdR/7FXAwcERzAlVrJEgbRsQKTfvX\nqbfXNwfXicPRwNbAyb2If5FqWtg7gPMW9g10ozH98RfNiVCTP7QmUHXlZCvgReATXZx3LNU0x9WA\nf++izTnAkY0ECiAzHwXOql/+R1PsksB/AZ+mWpaelnMa1bF3dvXmgKtbl3DPzD9SVY5ar9UuF1NN\nG31zPbXwZXVC3ejDD1pPVHtZiZIkSaqmlUH1Ib1P1c8aurqbw833uSzPK1PkEngzcFZEHJiZ9za1\n9wxwQ+tlqCpZ34uIw5vvn8nMx6juG1pkETGKqgJ1KFWS8zRVJa0rV3Wxb9t6e3NX951l5osRcQ3V\nNMet+NcK0dXdVFwayeNbI2JEZs6r74/64QLeTmOMlu/m+C+72X89sCOw8QLa7hOZOSciLqCa5rg/\n1T10DVsCa1Zh2frzoDYziZIkSaqSAajuWepz9ZLgHwc2pVr0oavrND8f6PA6djvg7xHxJ6p7ZH4N\nXFVXmJodAPyOasnxPSLilqb4/1vIKYr/HRH/vYDj9wAfbk7wWnSVuDUqZguq+D1Qb9fs4tjUbs5p\nJERLAStSTy+s7wP7JFXCtxbVQhStn3+7ey5TT9datZvjfe17VEnUf0TEJzPz2Xp/YyrfpH7qh5qY\nREmSJEFj2tn6fd1wRBxMdS9SB9UH8F9RfchvTGU7tPWczLwvIjaiSqY+CGxUfx0OzIiI4zPzrKb4\nmyNiQ6pFJ/agWqFvC6r7ix6MiCMz88LCrnf1sN2XgCeoFr24pofkbHYX+xoLPyzo/p3GlMeluzjW\nXaWweZrkaHj5mV7XUz0c+CWqKs4vqabHQVXJ2WIB/ejpWl0tptHnMvOmiPgL1T1fHwB+VC+GsScw\nD/hRf/RDr2YSJUmSVH3YPhx4Z0Qs3Zub9CNiVGbO7SFmHNWiER1US2Af33w/T/1h+F+SKIB6Otox\nwDERsS7wb8BeVFPizqynrZ3RFD+NquryyTqh2pFqefG3AxdExAuZeVnrdRag24ftLoJGYrLMAmIa\nx7pKwrpLXJr3Nyo1n6dKoB6keu5X6/1ZX2LBSVRP13q2m+Pt8D3gq8C+VEnTO6kqYb9uXepe/cOF\nJSRJkqrq0Cyq+2Mm9hQcESOAmyJickQsaFrXVlTPhHoKOLE5gaq9oTedy8wpmXlOZm5HtUIdVItV\ndBf/58w8LTM34ZVV3bqN70eNKXILet+NY11N+etqih/A6+vtHGBm/f129fasLlYI7KkPvbnWP3s4\nvy+dB8wFtq+fOfaher8LSgwQkyhJkrTYy8w5wGn1yy9HxJt7OOXLVAsLjKfriklDY9bP05k5r4vj\nBzV93wHVg38jYs+I2LGbNifX29Xr+FUjYt+I2Lo38QPs6nq7eb3M+KtExGiqZcebY5ttXy/b3mp8\nvb2jKVFtjP3M1uCIWAXYrX7Z3T1R7+1mf+Nat3VzfJHUq+69Sr0Ix+VU72lfqml9M+t9GgAmUZIk\nSZWTqe4BWgH4Y0Ts2hoQEeMi4ltUD86dQ7WwwjOtcU3uqbdrRMSrVnOLiI9RTbmbUe9qVDhWpnq2\n048iYr0u2tyr3t5ZbzeiqlT8oJuqWGv8gMnMu6jGeEmqlQdHNI7VycOpVOP/F+D3XTSxAnB88476\nYcaNKtvkpkONsd+5JX4N4H+pHoAMr4x7qx0j4n0t574beDcwn+pBv32l+WdorW5ivldvv0i1eMZP\nuniOlvqJ90RJkiQBmTkvInYGvgnsB1wWEfdTLSc+i6qSswMwBpgG7JaZt3fXXt3mPRFxBbALVWJ2\nOfACsA2wSt3el4D3Ad+NiD9k5kERcRLVohB/ioirqaa2LUG18MXWVAncEfU1fhMRP6aa4jUlIq6k\nmmo2iup+qLdTJWpfWPRR6hMfAa6hWhhhk4i4gaoatBnVPUyPAR/sZtGKs4APR8ROVP8uy1AlSa8B\nbga+2xT7FaqKze4RcRNV0rQa8B7gkrqtm4EJETEZmJyZP286/1jgZxHxR+Dv9bk71309IzP/sojj\n8LLMfCIiHgDWpvo5+TNwefODlqlWX5zGK0nfpL66vspZiZIkSapl5vOZuT+wOfANqlXkdqZ6aOvW\nVB/cPw5ETwlUk/+keqjuHKqq0HuoVrd7R2beBhxFtRLeWtT38WTm0cDuVEuUb1C38WHgdcC3gbdn\nZvODdT9MlZzcQJWMfIRqCexRVAsSvG0BS5H3q/r+pE2pKn8vUK0muDvQSbUIx9sy8+5uTn+UajGI\nu4Bdqd7jbKr3+J7mpd8z81aqKXs3UVXr9qFajOEwYL/MvAX4GtX9ajtTjW2zK3ml6vRBYCeq53Ed\nQvUA3772n8BfqSqRm1A9kPhldVLZuAfqL3X/NUA6Ojtb72+UJEmSNNhExKlUy9gfnJlnD3R/Fmcm\nUZIkSdIgFxErUU3rnAesUS+GogHidD5JkiRpEIuIpYEfA2OB002gBp4LS0iSJEmDUET8J9WS6u+h\nWnTi/4BTBrJPqliJkiRJkganrYADgWWBs4GdmhfP0MDxnihJkiRJKmAlSpIkSZIKmERJkiRJUoHh\ntrCEcxMlqUzHQHdAkqShxkqUJEmSJBUwiZIkSZKkAiZRkiRJklRguN0TJUnS4q5zxoxnBroPg964\ncWMBcKx6x/Eq43iVGcjxGjdu7ELdG2wlSpIkSZIKmERJkiRJUgGTKEmSJEkqYBIlSZIkSQVMoiRJ\nkiSpgEmUJEmSJBUwiZIkSZKkAiZRkiRJklTAJEqSJEmSCphESZIkSVIBkyhJkiRJKmASJUmSJEkF\nTKIkSZIkqYBJlCRJkiQVMImSJEmSpAImUZIkSZJUwCRKkiRJkgqYREmSJElSAZMoSZIkSSpgEiVJ\nkiRJBZYc6A5Ikoa/iPgCcBxwUWbutZBtHAt8EVg6M5/v4vjawP3AQZl57iL0dQJwVcvup4EHgfOB\nr2XmiwvbviRp6LMSJUlqq4joAPYH/gTsGhGvGeAu9dY+wKr11xbAecBJwFfacbGIWCciOtvRtiSp\nb1mJkiS12w7A2sBmwHXAh4CzBrJDvTQzMx+pv38E+FtEvAv4f8Bhbbje1m1oU5LUBiZRkqR2OwC4\nITNvi4hLgY/QkkRFxNXALKrpcl8C1gHuA/4nM3/VXcMRcTpwILA98EQXx99MVT3aAlgJ+AtwXGZe\nsQjv58mWa6wPnAJsCyxd9/ubmXlWU8xSwLHAB4HVgJnAr4DPZOZjTVMVqatRP8zMiYvQR0lSGzmd\nT5LUNhGxErAb8L161/eBjSPi7V2Eb0g17e9DwDuAOcD5EbFMN20fBnwC2C0zb+vm2n8E3gDsDWwC\nXAtcFhHbF76PpSJiH6qq2leb9r+uvsZKwPvq93Ae8PWI+GRTE98BPg58AVgfmEiV+P2ynu54GvCN\nOnZV4NCS/kmS+peVKElSO30YmAtcWL++kmrxh48Ah7TErgFsmZmPA0TE2cAPgXWp7qd6WUR8ADgV\n2Dszr+zm2gcArwO2ycz76n2H1wtHHMW/Lh7R6mcRMa/+fhngJaoq2QVNMR8FXgN8IDOn1/tOioit\ngU8CZ0bE6sC+wGcz80d1zH0R8Sngkrp/10XEHICmKYQLbdy4sYvaxGLDsSrjeJVxvMoMpfGyEiVJ\naqePUq3INxsgMzuBHwD71FPcmk1pJFC1GfV2xZa4d1JN+zskMy9dwLW3AO5rSqAarqSqSvXk48DG\n9dfbqKpH+wC/j4hRdczmdb+nt5x7PfDGiFgO2BTooKqCtcbQy75IkgYRK1GSpLaIiC2pprdtGBEf\n6SJkN16pUAHMbjneWKmuo2X/JcBIqmlvC7Ic8IaIaG13JDAqIkZl5twFnD89M6c0vb47Im4G7qVK\nDr9ZX+OpLs59ut6OrWPoIq45pk/NmPFMXzc57DT+4u1Y9Y7jVcbxKjOQ47Ww1S+TKElSu3wU+DvQ\n1XOhvkY1pe/CLo715EhgDHBKRFyZmX/sJm4mMBXYqZvjL5VeODOnRMRTwFvrXbOA13cRuny9faqO\nad7XGjMLSdKQ4nQ+SVKfi4gxVMnTTzLzztYvqsUXdoiIrhKQnkyiWtzhD8DkegGJrtxIleA8nZlT\nGl9UydOjmTl/Id7XalSVo4fqXTdRTdtbrSV0PHBPPY3xVmA+1ep9rTEAt7Rco7XyJkkaZKxESZLa\nYW+qZOOCbo7/DDiX6j6j40sbz8zOiNgPuItqxb9duwj7AXAEcElEHEWV+LyDahW884FP9XCZFSNi\nlfr7kcCbgS8D/wC+3XSNTwMX1gtFPEW1uuBOwH51Xx+JiEnAURHxD6rEa0OqatxVmXlz3dbMertb\nRPw1M7M3YyFJ6n9WoiRJ7fBR4K7M/FtXBzNzJvBbYOLCVl7qVewmAru0LCfeOP4k1SIUjwE/p7qX\n6STgDOB/enGJycD0+us+qmXabwe2y8wZ9TUeByZQJU+/B+6mutfrP5tW4gM4iCppPAXI+vvL6tiG\nH9fnX1T3U5I0SHV0dnb2HDV0DKs3I0n9wKljw0+nN7P3zBv/yzheZRyvMgO8sMRC/R60EiVJkiRJ\nBUyiJEmSJKmASZQkSZIkFTCJkiRJkqQCJlGSJEmSVMAkSpIkSZIKmERJkiRJUgGTKEmSJEkqYBIl\nSZIkSQVMoiRJkiSpwJID3QFJGsrmzJlTFD9mzJg29USSJPUXK1GSJEmSVMAkSpIkSZIKmERJkiRJ\nUgGTKEmSJEkqYBIlSZIkSQVMoiRJkiSpgEmUJEmSJBXwOVGSJPUgIiYB+zXtmg/MAG4HTsvMKwei\nX5KkgWElSpKk3pkBrFp/rQnsVu//XURsOmC9kiT1OytRkiT1zvzMfKTp9UMRsT/wCLAzcNvAdEuS\n1N9MoiRJWnRPAkTEksAXgH2B1wNPANcBn87MBxrBEbEbcBKwDjAF+AxwKLBUZk7oz45LksqZREmD\nUGdnZ1vb7+joaGv7pf1vZ38efvjhovgbbrihKH78+PFF8csss0xRfLv/rbTwImJl4GvAQ8AF9e7P\nAZ8F9gFuAlYBzgF+Cmxan/cW4GLgN8BewErAGcCKwNS+6Nu4cWP7opnFgmNVxvEq43iVGUrjZRIl\nSVLvvC4iZtffjwBGAw8Ae2Xm4/X+c4ALMzPr19Mi4nvANyNiXGbOoEqwAD6cmTMBImJf4E/0URIl\nSWovkyhJknrnCWCrptevBf6NamGJIzLzm8DzwL71dL3VgVG88rt2JarFKd4ITGkkUACZeXdE/KOv\nOjpjxjN91dSw1fiLt2PVO45XGcerzECO18JWv0yiJEnqnXmZOaXp9RTgxogYBXw1Ii4AJgHvBY4E\nrgKeBXYHTmk6byWgq08KM9rRaUlS3zOJkiRp0dxKNbXvrcAuwCmZ+fXGwYgY0RL/AtDVzXErAbO7\n2C9JGmR8TpQkSYtmvXr7INBBU0WpTqD2rV82Vgm5F3hTRKzYFLcpsHbbeypJ6hNWoiRJ6p0lImKV\nptdjge2BzwNnZ+aDEXEvMDEifke1+MSJwLXABsC2EfFP4CLgMODsiDgBGAecRpWESZKGACtRkiT1\nzjhgetPXbcBBVPc/HVrHfAiYC9wMXAJcCnwS+D/gTGDPzLwBOBDYum7jNOBTVBWs5/vpvUiSFoGV\nKEmSepCZE4GJvYi7Bdisi0OtDxS7HPhhZr4ILz+kdx2q5EuSNMiZREmS1I8iYj3gbuD8iDgV6AQO\np3rY7vcGsm+SpN5xOp8kSf0oM/8G/DvwZuAm4Eaqlf12zszbB7JvkqTesRIlSVI/y8zfAL8Z6H5I\nkhaOSZQ0CHV0dPQcNIi1s/+PPfZYUfypp55aFD9y5Mii+Pe+971F8UP931aSJDmdT5IkSZKKmERJ\nkiRJUgGTKEmSJEkqYBIlSZIkSQVMoiRJkiSpgEmUJEmSJBUwiZIkSZKkAiZRkiRJklTAJEqSJEmS\nCphESZIkSVKBJQe6A1o8dXZ2FsV3dHS0qSeLp7lz5xbFP//880XxDz/8cFH8euut1+vYq666qqjt\n448/vij+xRdfLIpfdtlli+JL+d+KJEmDj5UoSZIkSSpgEiVJkiRJBZzOJ0kaEiJiS+AwYCtgFWAu\ncA/w3cz89gD1qRM4JTM/OxDXlyQNDCtRkqRBLyImANcB84A9gXWBCcBNwLci4lMD1jlJ0mLHSpQk\naSg4CHgI2DczG6ttTANui4ilgU0GrGeSpMWOSZQkaShYGhgBjKSaxveyzDyg8X1EPABcBvwd+DSw\nMvAX4ODMvLkpbkfgKOCtVL8LrwOOyMx7WmKOAd4OdNZtnpCZl3bXyYh4I3A98Dvgw5nZ2dO1ImIi\n8APgfcDZwBOZuXnJ4EiS+pdJlCRpKPgVsAtwbUScCvw+M5/qJnZH4DV1/BhgMvAjYD2AiNgO+AVw\nEVWFaxngFOCaiNggMx+vk6H/rc/bH5gPHABcFBGbZ+YdrReNiHHAr4FbgYl1AtXjtZqa+BzwEeBv\nCzNAzcaNG7uoTSw2HKsyjlcZx6vMUBovkyhJ0lBwLvA64EjgEmB+RNwB/BaYlJl/b4pdDjggM+cC\nRMSPgOMiYrnMfBr4LPAA1dTAeXXMPsCDwH8BX6aaOrgRMC0z59Qxx9Xn7gC8KomKiGWAK4BHgT0z\n86X6UG+u1XBhZl698EMkSeovJlGSpEGvvg/quIg4A9gJ2I5qYYmjgCMj4pDMPKcOv72RQNVm1NsV\ngaeBLYCfNpKauv1HI+Iv1PdWZebzEfEW4OyIWB8YCzSeZLxSS/dGAD+hSt62ycxnm471eK0mt/Zu\nNHo2Y8YzfdXUsNX4i7dj1TuOVxnHq8xAjtfCVr9MoiRJQ0Y9he+C+ouI2AQ4H/haRFxSh81uOa2x\nEEUjCVoO2C8iPtgSNxp4vm73/VQVr4uB/6CqMHUC93bRrf8ClqVK1ka1HOvxWk1mddG2JGkQcolz\nSdKgFxGjI2J06/7MvJ2qGjWK+p6nXphJdY/Sxi1f6wF71zH7Uk3p2zszr8vMe4E53bQ3FdiQasGL\n8yKio+lYb64lSRpirERJQGdnZ89Bi+D551v/4LxgM2fOLIp/6qnu7q/v2osvvlgUf+eddxbFr7XW\nWkXxK63UOjuqe3vuuWdR23PmdPe5t2vz5s3rOWgRlP6slcZ3dHT0HDTERMSqwP3AV4DPdxGyTr19\nqJdN3ghskJlTWq6zHtX9S1AlZU9m5vymkIn1tnWQf5OZ90TEh4CrgM9QLR7R22tJkoYYkyhJ0qCW\nmdMj4mzg6IgYQ1XZmQ4sT7US37HAeZl5X0T0pslTgKsj4hyqBSueA3YHTqy3/wvcALwvIvYCbgb2\noLq/aRqwSUSsmpnTW/p5bUScCJwQEVdn5k29vJYkaYhxOp8kadDLzCOopti9DbgUmAJcC+wGHEG1\nDHlv27qOKvnaiKpSdDdVkrRXZjaSmq8DP6ZKfG6jesbTh4EzgG3qY135ElXS9ZOIWL6X15IkDTEd\n7Z7G1M+G1ZsZzgbbFCWn8y1Yu6fzbbDBBr2OLZn6B+XT+Ur/rcaNG1cU3+7pfEssUfy3seE3/0+d\nrgjWM1dPK+N4lXG8ygzw6nwL9XvQSpQkSZIkFTCJkiRJkqQCJlGSJEmSVMAkSpIkSZIKmERJkiRJ\nUgGTKEmSJEkqYBIlSZIkSQVMoiRJkiSpwJID3QEtngbbw3NL46dNm1YUX2r06NFF8a997WuL4keM\nGFEUv+GGGxbFP/zww0Xx48eP73Xs+uuvX9T2xRdfXBS/7LLLFsW320I8PFeSJLWZv50lSZIkqYBJ\nlCRJkiQVMImSJEmSpAImUZIkSZJUwCRKkiRJkgqYREmSJElSAZMoSZIkSSpgEiVJkiRJBXzYriRJ\nXYiILYHDgK2AVYC5wD3AdzPz23XMA8DVmTlxAe1MAnbMzFXa22NJUn+xEiVJUouImABcB8wD9gTW\nBSYANwHfiohPFTR3KPDWXlzzmoiYWNpXSVL/sxIlSdK/Ogh4CNg3MzvrfdOA2yJiaWCT3jaUmU/1\nFBMRSwKbAT9YiL5KkvqZSZQGRGdnZ89BTTo6OtoaX2qNNdYoih8xYkSbejI4XXPNNUXxTzzxRK9j\n3/jGNxa1PXLkyKL4div92Sz9b+WFF14oih89enRR/GJkaWAEMJJqGt/LMvOA1uCI2Bc4FlgTuBc4\nMDOvr49Nomk6Xz0F8HJgLLA3cCRwZt3UDyLiB5nZ3v+JSZIWiUmUJEn/6lfALsC1EXEq8PsFVJS2\noEq4dgOWBX4ITI6IN2Tm/G7O2Rn4BdU0v0eAO4Brqe7BunBROz9u3NhFbWKx4ViVcbzKOF5lhtJ4\nmURJkvSvzgVeR1UlugSYHxF3AL8FJmXm35tixwIfzcy5ABHxPeAUqsUoHu6m/bHA4Y0kKyIer/c/\nlZmP9PWbkST1LZMoSZJa1PdBHRcRZwA7AdtRLSxxFHBkRBySmefU4bc3EqjajHq7oD+p3rGAKtUi\nmzHjmXY1PWw0/uLtWPWO41XG8SozkOO1sNUvkyhJkrpRT+G7oP4iIjYBzge+FhGX1GHPtpzWuJFt\nQfc1zerLfkqS+pdLnEuS1CIiRkfEv6y6kZm3U1WjRgHr9XvHJEmDgkmUJElNImJVqkrR0d2ErFNv\nH2rD5V2VT5KGAKfzSZLUJDOnR8TZwNERMQa4CJgOLA/sSLWU+XmZeV9E9NVlZ9bbCRFxO/D3zHyu\nrxqXJPUtK1GSJLXIzCOAfYG3AZcCU6iWIN8NOALYv4+v9yhwDvAfwJXAa/uyfUlS37ISJUlSFzJz\nMjC5h5i1u9g3CZjU9HpiT+fU+z8BfKK0n5Kk/mclSpIkSZIKmERJkiRJUgGn82lAdHSULUDV2dnZ\nc9AitF8aP2LEiKL4dmv3+JS67LLLiuLnzp3bc1Btyy23LO1OW82fX/a81Mcff7wo/uGHHy6Kf/HF\nF4viN99886J4SZJkJUqSJEmSiphESZIkSVIBkyhJkiRJKmASJUmSJEkFTKIkSZIkqYBJlCRJkiQV\nMImSJEmSpAImUZIkSZJUwIftSpI0jOxyxOUD3QVJKnbF6bsOdBeKWImSJEmSpAImUZIkSZJUwOl8\nGhCdnZ1F8R0dHW3qyfAw2MbnnHPOKYr/4x//2OvY8ePHF7Vd+rM2derUovi//vWvRfFrr712Ufzq\nq69eFL/MMssUxUuSpHJWoiRJkiSpgEmUJEmSJBUwiZIkSZKkAt4TJUkaViJiS+AwYCtgFWAucA/w\n3cz8dj/14QJgy8xcu+CcY4EvAktn5vNt6pokqQ9YiZIkDRsRMQG4DpgH7AmsC0wAbgK+FRGfGrDO\nSZKGDStRkqTh5CDgIWDfzGxGMDU3AAAgAElEQVQszTgNuC0ilgY2GbCeSZKGDZMoSdJwsjQwAhhJ\nNY3vZZl5QOP7iFgW+DKwB/A64FHgN8BnMvOJOuZYqmmB7wTOoUrAngDOzsxTmtraBjgL2AB4BDit\ntVO9uZ4kaegwiZIkDSe/AnYBro2IU4HfZ+ZTXcSdCewK7A38DVgHmAR8myrRaRhJlSB9CZgKHAmc\nHBHXZOaNEfEa4OdU91xtDcwHjgbGAy8sxPUkabE1btzYge5Cr5lESZKGk3OpKj1HApcA8yPiDuC3\nwKTM/HsddzRwQmY2nq48LSIuAg6OiI6mqYDLAKdl5u8AIuIE4EDgHcCNwO7ACsBHM/OeOuZDwMO8\nOonq7fUkSUOASZQkadiok5HjIuIMYCdgO6qFJY4CjoyIQzLzHKqK0SERsRPVCn5LAqOoKk9LAc2r\n493Y9P2Mertivd0QeLaRQNV9mBsRtwLrNZ1Xcj1JWizNmPFMv19zYatfJlGSpGGnnsJ3Qf1FRGwC\nnA98LSJ+SnU/0prAp4BbqZKYTwKHdNHc7KbvGxWjjno7FpjTxTkvfxKIiI7C60mSBjmTKA2Ijo6O\nnoM0aHR2ls00Gj16dFH8Hnv0/paQl156qajt2267rSi+1DbbbFMUP3Zs2V+85s2bVxQ/atSoovjh\nJiJGA7Q+Zykzb4+Io4DLqCpEbwM+lpmTms5daiEuOYdqyl+rFZq+37APrydJGgR8TpQkaViIiFWB\nWVT3H3VlnXrbqCw1puYREctR3d8Er1SZeuMeYExEvKWpraWBzZpiGpltX1xPkjQIWImSJA0LmTk9\nIs4Gjo6IMcBFwHRgeWBH4FjgPKrV8WYCn4iIPwOvAb5KVaU6ANg+Iq7u5WUvBb4CfCMiDq/3fZFX\nTwHsy+tJkgYBK1GSpGEjM48A9qWaPncpMAW4FtgNOALYPzPnAPsAqwF3Ad8BTqZafOKvVKv69Wqe\nZmY+Wre9EnAzcDlwJfCLppg+u54kaXDoKL3XYZAbVm9GGixK/z8xe/bsnoOalNwnVHpP1J133lkU\nX+oNb3hDUfxguydqiSWWcCrZMLPLEZf7u1DSkHPF6bsO1Op8C/V70EqUJEmSJBUwiZIkSZKkAiZR\nkiRJklTAJEqSJEmSCrjEuSRJw8hA3Zw91IwbVy3y4lj1juNVxvEq0xivocRKlCRJkiQVMImSJEmS\npAJO5xtESp/F09ExdB/v0u7nkw3lsYHB97NQ2v6IESOK4ufPn9/r2CeeeKKo7Tlz5hTFr7vuukXx\npc9xKnmvAM8++2xR/OjRo4viJUlSOStRkiRJklTAJEqSJEmSCphESZIkSVIBkyhJkiRJKmASJUmS\nJEkFTKIkSZIkqYBJlCRJkiQVMImSJEmSpAImUZIkSZJUYMmB7oAkSYNRRPwbcAiwBbA88ChwI3Bm\nZl43kH2TJA0sK1GSJLWIiOOBXwL3ATsDAXwUWBa4JiL+qw3X/GFEHNvX7UqS+p6VqEGko6NjoLsw\naC1uYzPY3u+TTz5ZFD9y5Mii+GnTpvU6durUqUVtjx8/vii+tO9Tpkwpil9mmWWK4kv7o0UXETsB\nxwAHZ+bZTYceiIjfAxcBp0TExZk5sw8vvTVwfx+2J0lqE5MoSZJe7dPA34FzWg9kZmdE/DcwPzNn\nRURHHX8AsA4wG7gKODIzX86wI+JDwBHABsALwN3A0Zl5TX28sw79YkR8EVgnMx9o0/uTJC0ikyhJ\nkmoRsSSwDfDNzOzsKiYzm0uzxwFHAp+hmv63CvAN4MqI2CAzZ0fEtsD5wAnAHsAo4LPAzyMiMvNh\nYG3gAeB04DRgxqK8j3Hjxi7K6YsVx6qM41XG8SozlMbLJEqSpFe8FliKKqFZoIgYBRwGfC8zv17v\nvjciPgLcCuxGlTzdBmwIZGa+VJ97CjCRKmG7mGrRCoDZmflIX70ZSVJ7mERJkvSKRvWpNzcmrgeM\nBa5t2X8H8DywCXB+Zs6JiK2Ab0fEusCYpvZXWvQu/6sZM55pR7PDSuMv3o5V7zheZRyvMgM5Xgtb\n/XJ1PkmSXvE48Czwpl7ELldvn2remZnzqe6NGgsQEYcD3wHuAnYBNgbe10f9lSQNAJMoSZJqmTkP\nuAb4f/X9Uf8iIlaMiAOBWfWu5VuOL0GVQDWO7wvcmJkfz8yb6wUn5rblDUiS+oVJlCRJr3YasAbw\n+dYD9Wp83wC+SlWBegrYtiVsM6r7qm6pX4/iXxeKmFhvW6cNDq7nG0iSuuQ9UZIkNcnMK+tlxo+L\niLWBbwEPUS1h/hlge+CDmTktIk4HPh8RdwO/plpl7xvA34DL6yZvAPaJiB2oFqw4kOqPmPOALSLi\nYuAJ4Dlgq4jYCPhHZjYqWZKkQcZKlCRJLTLzS8AOwIpUyVAC36daRW/TzLysDj0BOBo4vI65kGo1\nvndl5gt1zDHAlcClwPXASODjwFnA3sBX6uXUjwe2olqoYv02v0VJ0iLo6Ozs8jEYQ9WwejPDWenP\nXUeHM1wG0pNPPtlzUJORI0e2rf2pU6cWtT1+/Pii+NK+T5kypeegJquuumpR/Pz584vix44tXmXI\n/7iGn05XBOuZq6eVcbzKOF5lBnh1voX6PWglSpIkSZIKeE/UIFJanSmNX2KJwZMzD/XK0lCvpM2Z\nM6co/rHHHiuKf/DBB4vi11577V7Hbr/99kVtt1tp5Wru3LJF2ZZaaqmieEmS1H6D51O1JEmSJA0B\nJlGSJEmSVMAkSpIkSZIKmERJkiRJUgGTKEmSJEkqYBIlSZIkSQVMoiRJkiSpgEmUJEmSJBUwiZIk\nSZKkAiZRkiRJklTAJEqSJEmSCiw50B3QKzo6Otoa39nZ2evY2bNnF7X9xBNPFMWPGzeuKH7MmDFF\n8aVKxqY/2i/9t33hhReK4mfOnFkU/9hjjxXFb7nllkXxyy+/fFF8iXaP/ciRI4viS/tT2r4kSWo/\nK1GSJEmSVMAkSpIkSZIKmERJkiRJUgHviZIkLVYiYhKwX9OuucCjwA3AtzLzyoHolyRp6LASJUla\nHM0AVq2/3gxMBJ4BfhcRpw9gvyRJQ4CVKEnS4mh+Zj7S9PpB4MqI+AMwOSL+kpnfH6C+SZIGOZMo\nSZJqmfmTiPgocBTw/Yh4ALgcGAvsDeyVmVdExCrAqcA7qapZ9wGnNydeEfF+4HNA1LvuBr6Ymb+v\nj28EnAxsDiwL3A98MzPPavf7lCQtGpMoSZJe7XLgzIhYs369M/AL4K3AIxExCrgSGA0cRJVA7QF8\nNyJeyswfRcSbgYuAz9fbUcDHgV9GxBszcxrwc+B6YAIwB3gPcHZEPJaZFy7KGxg3buyinL5YcazK\nOF5lHK8yQ2m8TKIkSXq1f9TbVevtWODwzJwPEBF7AesD78rMq+qYkyNiS+AY4EfAxlS/Y3+QmY/W\n5x0O/ASYGRGvA14P/Cwz/1K38Z2IuBWY3tZ3J0laZCZRkiS92sh6+1K9vaORQNW2oFrR75qW8/4A\n7BoRy1JVmGYA10TEt4HfZ+afqFYAJCLm1N9/MyLeBvwGuD4z7+iLNzBjxjN90cyw1viLt2PVO45X\nGcerzECO18JWv1ydT5KkV3sT0Em12ATArJbjy1FNz3s6ImY3voCv1MdXzcx/Au+gSqwOB+6KiAfq\n+63IzE7g34DTqaYLXg3MiIjT6umCkqRBzErUEPboo48Wxc+a1fo5oHtrrbVWUdsrrrhiUXxmFsWv\nssoqRfGrrbZaUXxHR0dRfGdn56Bq/7HHHiuKnzp1alH8ZpttVhQ/ZsyYovihbPTo0UXxd911V1H8\nhAkTiuLVJz4A3JaZj0dEV8dnAs9RTdnryj8AMvMB4BPAJyLiLcAhVPdN3Z+ZV2bmbOBE4MSIWBX4\nEHA88CzwhT58P5KkPmYlSpKkWkQcAmwCfHkBYTcCSwNjMnNK44sq+ZmZmXMjYuOIeFfjhMz8S2Z+\nDHga2DgiVq/vrWocn56ZpwG/pfvkTJI0SFiJkiQtjpaolymH6g+KawH7AwcAJ2XmzxZw7hXAn4Ef\nR8RhwL3ABsBZwJ+A3YEtgdPqpKxx79T7qZYyvw5Ygep5VBsD51E96HdTYDwLTuAkSYOASZQkaXE0\njldWwesEngRuAnbMzN8u6MS60vRu4BRgMvAa4BGqlfe+WMecGxFjgM8AZ1MtRPFXYM/MvBkgInah\nWs3vE1S/jx8ATgO+1mfvUpLUFiZRkqTFSmZOBCb2MnbtbvY/RlW5WtC5p1MtHNHd8V8Cv+xNPyRJ\ng4v3REmSJElSAZMoSZIkSSpgEiVJkiRJBUyiJEmSJKmASZQkSZIkFTCJkiRJkqQCJlGSJEmSVMDn\nRA1hTz/9dFH8HXfc0evYJZYoy6/f9KY3FcXfeuutRfHnnntuUfykSZOK4jfaaKOi+I6OjqL4UtOn\nT+85qMnUqVOL4ldeeeWi+DFjxhTFl+rs7Ox1bLvHvtS8efOK4k899dSi+O23374oXpIktZ+VKEmS\nJEkqYBIlSZIkSQVMoiRJkiSpgEmUJEmSJBUwiZIkSZKkAiZRkiRJklTAJEqSJEmSCphESZIkSVIB\nkyhJkiRJKmASJUmSJEkFlhzoDkiS1BsRMQnYr2X3i8BU4MfAKZk5t5dtHQt8EVg6M5+PiKuB0Zm5\nZZ91WJI0bA2rJKqzs7Ot7Xd0dLS1/fnz5xfFr7DCCkXxe+yxR69jJ0+eXNT28ssvXxT/5JNPFsU/\n9NBDRfGbbbZZUfwhhxxSFH/iiScWxT/66KNF8aXvd+WVVy6KX2+99Yri263kv612/3deauTIkUXx\nyy67bJt6stiYAWzU9HoFYAfgFGAD4IMD0SlJ0uJlWCVRkqRhb35mPtL0+hHgbxExDvhCRPxPZv5z\ngPomSVpMmERJkoaDu+rtmhFxPi1T8yJiAnAVsFNm/rqnxiJiKeBYqsrWasBM4FfAZzLzsYg4Hvgf\nYFxmPtN03pbADcDumfmziFgFOBV4J7AqcB9wemZ+v+mcTuAoYDxVVW3zzLx7oUZBktQvTKIkScPB\nm+rttD5q7zvArsAhwP8BbwbOBX4ZEZtT3YN1DLAL0Dz/eS/gSeAXETEKuBIYDRxElUDtAXw3Il7K\nzB81nXcA8F3gYGD6onZ+3Lixi9rEYsOxKuN4lXG8ygyl8TKJkiQNWRExEtge+DRwaWZOi4hFbXN1\nYF/gs02Jzn0R8SngEmCbzLwuIm4H9qROoiKio359UWbOjYi9gPWBd2XmVXU7J9fVqmOA5iRqVmae\nvEgdlyT1G5MoSdJQ8rqImN30ejTVCn3nAZ/qo2tsCnQA17bsv77ebgJcR5U8nRARy2bmbKrpeKvX\nfQHYApgLXNPSzh+AXZvOA7i1j/oOwIwZz/QctJhr/MXbseodx6uM41VmIMdrYatfJlGSpKHkCWCr\nptcvAtN7u7R5Ly1Xb59q2f90vW38xv0J1f1O/w5cQDWV777MbCRbywGjgKdbqmON372rAvfW38/q\nk55LkvqFSZQkaSiZl5lTeojppKokNStZW76R0LQ+u2H55uOZ+XD9fKk9I+Ji4ANU9001zASeAzbu\n5jr/KOiTJGkQWWKgOyBJUh+bBYxr2VfyEN1bgfnAti37x9fbW5r2TQZ2BN4DrAyc33TsRmBpYExm\nTml8Ac8CM/u4eiZJ6kdWoiRJw83NwG4RcSDV/UfbAjv39uTMfCQiJgFHRcQ/gJuADYGvAVdl5s1N\n4T8Fzga+DNzQUiW7Avgz8OOIOIxq6t4GwFnAn4DdF+7tSZIGmpUoSdJwcybVEuSnAHdS3bN0SGEb\nB1FNzTsFyPr7y4DdmoMycxbwC+DtvLoKRV1pejdV5Woy1RLn3wF+RrX6nyRpiOro7Owc6D70pWH1\nZnoyc+bMovhnn32217GjRo0qavuee+4pit9229ZZMgt2wgknFMV//vOfL4pfccUVi+InT57cc1CT\nHXbYoSj+qquu6jmoybvf/e6i+CWWaO/fT4by/1c6OlpvpVmwGTNmFMWfeOKJRfFnnHFGUfxCKHvD\nGgo6XRGsZ66eVsbxKuN4lRng1fkW6veglShJkiRJKmASJUmSJEkFTKIkSZIkqYBJlCRJkiQVMImS\nJEmSpAImUZIkSZJUwCRKkiRJkgqYREmSJElSAZMoSZIkSSpgEiVJkiRJBUyiJEmSJKnAkgPdgb40\na9asovhRo0YVxS+5ZNlwlbZ/8803F8WfeOKJRfHnnHNOr2NPOumkorb33HPPovgXXnihKP6YY44p\nip82bVpR/NNPP10Uv+OOOxbFP/TQQ0Xx22yzTVH8EkssPn8P6ejoKIrv7OxsU08qpT/LpT9rkiRp\n8Fl8PnlJkiRJUh8wiZIkSZKkAiZRkiRJklTAJEqSJEmSCphESZIkSVKBYbU6nyRp+IuIScB+PYRd\nk5kT2t8bSdLiyCRKkjTUHAp8tun1ucCmwOZN++b2a48kSYsVkyhJ0pCSmU8BTzVeR8TzwLzMfGTg\neiVJWpyYREmShqWIuBqYBdwNHAYclZnfiIjlgZOBXYHXAo8ClwKfy8w5TeeOzswtm9qbAFwF7JSZ\nv46IFYGvADvV7TwG/Aw4MjOfq89ZBTgVeCewKnAfcHpmfr+p3U7gKGA8sAOweWbe3YYhkST1EZMo\nSdJw9lbgeWAzoFGpugJ4I/Bx4E/1sXOB1wO7F7R9JrAx8AHgn8BbgG8DI4GDImIUcCUwGjiIKoHa\nA/huRLyUmT9qausA4LvAwcD04nfZYty4sYvaxGLDsSrjeJVxvMoMpfEyiZIkDWevBzbLzJkAEbEV\nVVVor8y8rI6ZGhGvB06LiNdn5rRetr0p1QIWN9Svp0XE9ryy8u37gfWBd2XmVfW+kyNiS+AYoDmJ\nmpWZJy/MG5Qk9b9hlUQ99NBDRfHPPfdcUfwyyyxTFD9ixIii+FInnHBC29reZ599iuLXXHPNovjH\nH3+8KP7RRx8tij/44IOL4kv/bWfMmFEUv9xyyxXFl/ZHA6f0/yPTpy9ykUFlpjYSqFpj8YlrW+Ku\nr7dvB3qbRF0OfCYilgL+F7gqM+9rOr4F1QIX17Sc9wdg14hYNjNn1/tu7eU1e2XGjGf6srlhqfEX\nb8eqdxyvMo5XmYEcr4Wtfg2rJEqSpBazWl43/qLxVMv+p+ttyW/TzwF/BT4KXAQQEVcAn8zMh+pr\njQKejojm8xq/e1cF7u2mn5KkQcwkSpK0OGkkK8sDzzbtX77leCfQ0XLuss0vMrMTOA84LyKWBd5H\ntdDET4BtgZnAc1T3TXXlHwvRf0nSIGASJUlanNxUb7cFLmzaPx6YD9xev54FrNVybvNKfcsA/w/4\ndWbOqqflXRQRqwPH1WE3Ap8GxmTmXU3nrga8kJk+y0qShiiTKEnSYiMzb4mIK4HTI2I2cA+wNXA0\n8MPMbNy0djOwW0QcSHUP07bAzk1NvUhVdfpQRBxPtaLe64F9eeUeqCuAPwM/jojDqKbubQCcRbUq\nYMlKgJKkQWSJnkMkSRpW3g/8HPgekMAJVInNfzfFnAn8GDgFuBP4d+CQxsHMfBF4N1X16pfAFKpp\nfLcAE+uYuXXMLcBkqiXOv0P1LKl92/TeJEn9wEqUJGlIy8y9u9k/oZv9TwMfq7+6a3MOXSc6HU0x\nf6d6YO+C+vYYsH8PMa33XkmSBjkrUZIkSZJUwCRKkiRJkgqYREmSJElSAZMoSZIkSSpgEiVJkiRJ\nBYbV6nxvectb2tr+Cy+8UBQ/d27ZcxTXWqv1uY596/777+917BprrFHU9ksvvVQU//jjjxfFr7zy\nykXxY8eOLYrv6ChbHKu0/Xbr7Oxsa/ul41Ma3+7+t9Ob3vSmovif/OQnbeqJJEnqL1aiJEmSJKmA\nSZQkSZIkFTCJkiRJkqQCJlGSJEmSVMAkSpIkSZIKmERJkiRJUgGTKEmSJEkqYBIlSZIkSQVMoiRJ\nkiSpgEmUJEmSJBUwiZIkSZKkAksOdAeGkqWWWqqt8e22/vrrD3QXXrbaaqsNdBeGlY6OjoHuwiJp\nZ/8H29issMIKA92FARURk4D9egi7JjMnLGT7ncBxmXnsAmKuBkZn5pYLeY1LgFmZecDCnC9JGvqs\nREmS+tOhwKpNX5cD/2zZt3ub+7A78L6egiLiwYiY0LJvBPBu4Df16x9GxLFt6KMkaRCzEiVJ6jeZ\n+RTwVON1RDwPzMvMR/qxD0/2FBMRqwNrdnFoC2A54Pf1662B+/uud5KkocAkSpI0JEREB3AUMBF4\nPTAbuBb4dP7/9u492K6qPuD4N+LwsDzEEtGKEOjIL0Zh5FECAjWlGNCxtlawZaAtL0vVUiQIaltL\nETq1bdBCU6QWELFSxypEhwLtKARqBQSplvL4iYWQODgaMGlIQlD09o+1D25Ozjm5+56Te87J/X5m\n7ux79lr7tWbdfffv7LV/O/ORtrrnAGcDs4H/Ak7JzAersmXUhvNVQwA/CBwBHA2cC1xarerWiHgs\nM+dUn48F7s7M1dVyAOdHxPnA3pm5PCIOAy4CDqH8n30Q+OvM/Gy1vTmUwOtU4FDgeGA74N+BMzLz\nB303liRpizKIkiSNi1OBPwZOpARGuwN/BdwAzKvVOx64mRIQ7QF8BvgEcGSPdZ8OXAH8IfA94Ang\nWuDtlECt5Zhq3QBzgOXAxcBiYFVEzANuoQREbwCeBv4A+OeIeDozv1hb1wWUYG0+MBf4VPXzps03\nRW+zZ+/U7ypmDNuqGdurGdurmXFqL4MoSdK4OAhYUQtEVkTEO4C9IuIFmfnTav76zDyn+v2hiPgC\ncNpm1r0mMz/S+hARq6tff5iZq6p5LwEOBhZVZd+vputawxEj4ixK4PRbmbmxKj8rIo4CzqQ8A9Zy\nf2Yurn5/OCKWAH8aET+fmU9urjEkScNjECVJGhc3AL8fEV8GPg3ckpkrKXeN6u5s+7wK2DYitsvM\nZ7qs+55JbH8h8BRwV486v0QZ7rexbf7XKHfI6v6j7fO9wCxgL6CvIGrVqqf6WXxGaH3jbVtNju3V\njO3VzDDba6p3v8zOJ0kaKRGxZ0Ssq/3cD5CZNwILgDXAJZQ7UXdGxOFtq9jQ9rn17FKvfPdrJrFr\nxwBfycxne9TZmVrijJq1QPt/6vZtrqumMzsPviSNAYMoSdKoeRx4Xe3nuXTkmfnVzDwO2A34FWAj\ncFNETEfgsZDyrFMva4BdOszfhU2DpvagqvV5NZKkkeZwPknSSKnu9HynfX5EHAOszMwHqjrLIuJs\nyjC4farpoM2qtr0/8AtU74fqVKdyF3BCRGzfGtJXZRU8HLi7bbkFlMQYLQcBPwIeQZI00gyiJEnj\n4hTggIg4E3iIMnRuEfADShrxQWrdDVoYEWsomf6+nZnLa3WeoSSROKwKslZQsu2dAlxbpT3/CeUF\nw3Mpmf/q9ouI9wPXVeXvAZZW79KSJI0wh/NJksbFO4GvAFcCD1e/7wwszMynB7ytuymZ9BZRUpq/\nmbahfJk5AVwIHEZJEvHqzEzgV4FdKQkuvgHsD7wlM29t28bFwN6Uu1efBZZRAilJ0oibNTExsfla\n42OrOhhJmga9ki1oC6i9bPddmXn5FtjEhBnBNs/sac3YXs3YXs0MOTvflP4PeidKkiRJkhowiJIk\nSZKkBkwsIUnSNKqSUziMUpLGmHeiJEmSJKkBgyhJkiRJasAgSpIkSZIaMIiSJEmSpAYMoiRJkiSp\nAYMoSZIkSWrAIEqSJEmSGjCIkiRJkqQGDKIkSZIkqQGDKEmSJElqwCBKkiRJkhowiJIkSZKkBgyi\nJEmSJKkBgyhJkiRJasAgSpIkSZIaMIiSJEmSpAZmTUxMDHsfBmmrOhhJmgazhr0DkiSNG+9ESZIk\nSVIDBlGSJEmS1IBBlCRJkiQ18MJh78CAObZfkiRJ0hblnShJkiRJasAgSpIkSZIaMIiSJEmSpAYM\noiRJkiSpAYMoSZIkSWrAIEqSJEmSGjCIkiRJkqQGDKIkSZIkqQGDKEmSJElqwCBKkiRJkhowiJIk\nSZKkBl447B2QJElFRLwEOB/4DeDlwBPAjcCHMvN7k1j+9cCHgEOBHYBvA/8ILMnMiba684APA28A\ndgYeA/4J+Ehm/mhQx7QlDaC9jqiWPwTYHlgJfAG4MDPX1eotB/bqsaoDMvObUzuK6dFPW0XERK9y\nYNfMXFOrP2P7VkScDHxyM6u/LTMXVPWXM+Z9qyUitgUuAt4H3N46xkkuO3bnLoMoSZJGQETsACwD\n5gJLgHuAV1EuSI6KiIMyc3WP5Y8CbqIEAn8O/BD4deBS4BeB99bqvgb4GvA0sBj4LrCgWu5AyoXj\nSBtAe51IufBKysXyWuAtwHnAkRFxRGb+tLbIKuDdXVb3aH9Hs2X121aVByjt1Mn62rZmet+6FTi+\nS9kewMeA+9vmj23faomIAK4F9gVmNVx2LM9dBlGSJI2G9wL7Ae/JzMtaMyPiW8D1lG9pF/VY/jJg\nI3Bk7ZvyT0fEUuCPIuKTmfmtav5HgR2BIzLzvmreZyJiPXBWRLw1M780sCPbMqbcXhGxHfBxykXb\n/Mz8v6roqoi4nnIhdizlzkPLhsz8/MCPYnr027cAVk3y+Gd038rMxyh3RjZR/S0+CfxZW9E49y0i\nYlfgXuBh4GDgoYarGMtzl89ESZI0Gn6X8o3+lW3zv0j5tvWkiOj4DW9EzAcC+FyHoUZLKN8Mn1TV\nfTnwRuCW2kVIvS7A70z1IKbRlNsLeBlwHfCXtQCqpRU47T+oHR0B/bTVpNm3uouIt1Hurrw/M58c\nyF6Ojm2Ba4BDMzObLDjO5y6DKEmShiwidqYMHbo3M5+pl1XPA3wdmA3s3WUVh1TTOzqU3VVN51fT\ngykXJpvUzczvUIbSzG8vGyX9tldmPpaZJ2fmxzsU71JN1/bY/osGEXRMhwH0rfb1zYqIn+tSPOP7\nVpd1bgdcUi171Wbqjk3fasnM72fmuzJz4xQWH9tzl0GUJEnD13qw/LtdyldU0326lM/ptnxmPgWs\nqS3btW5tW6+MiFEe8nBr9NUAAATaSURBVN9ve3VUPRh/KrABWNpWvENEXBoRqyl3KTZExNKImNtk\nG0MwqLbaLSKuAZ4C1kXE2oi4JiJeUaszZxLbmol9653AK4Hz2pMkVMa1bw3CnGo6ducugyhJkoZv\np2q6oUv5+rZ6U1l+pwZ1e21rFAz8GCLiBZRsYK+mZGB7vK3KSykXcWcAbwM+QUlEcWdE7DvZ7QzB\noNpqXjU9iZI44XrK0Kk7ImK3AW9rmAZ6DNVdqA9QstXd1qXauPatQRjbc9cofxMgSZK0xVXZ2K6l\nJJT4+8z8aFuV3wN+kplfrc1bGhH3UQKvC4ATpmVnh+NNlMQS36jN+3xErAT+BDgH+OBQ9mz0nQy8\nAjizS/lM71tjyyBKkqThaz1/0+1Zkx3b6k1l+bUN6kIZtjWq+m2v50TEbOBLlPfTXJiZ7ZnT6HEH\n4Srg74CjN7edIeq7rTLz5i5Fl1GCqKMpQZR9a1OnUzLy3dCpcMz71iCM7bnL4XySJA3fo8AE5T0y\nnbSe03i4S/kj1XST5SNiF0qyhIc3V7e2rUcz89leOzxk/bYXABGxO/CflAfWT+kUQPVSvUfqCcoL\nP0fVQNqqi1XVulvHb9+qiYg5lL71r5n54yY7MSZ9axDG9txlECVJ0pBl5nrgv4EDI2L7ellEbAO8\nHliZmSs6LU95+STA4R3KjqymreFCXwee7VQ3Il4LvLhWdyQNoL1aWdhuBvYE3pqZV3ept09EnFa1\nTXvZjpShWl23M2z9tlVE7BcRZ0TEnh2KX0XJltZa1r71fMdU01s6FY573xqQsT13GURJkjQargRe\nRHm4vO4kyoPnV7RmRMTciHguxXJmfpPyssvjI2KPWr1ZwNnAj4FPVXWfoAxfWxARB7Rt65xqegWj\nb8rtVbkEeB1wQmbe1GM7u1fr+liH1NMfoAQR1zXf/WnVT1u9FricTV8QCz97Duo6sG91cFA1/Z8u\n5VtD32pkazp3+UyUJEmj4XLgRGBxROwF3AO8BlgE3AcsrtV9EEjK+2xa3g3cCtweEX9LSQ3828BR\nlGxz/1urey7wy8C/RcRi4HHg2Gr7V2bm7YM/vIGbcntFxP6UB/ofALaJiOM6rH9VZt6WmXdExNWU\nBAHLIuJzwDOUuwzHVdv6i4Ef3WD107f+hZL2/bQqC9+NwDbAb1Ke1/kyJQFCy4zuW21amfWWd9rI\nVtK3iIh5/Cx7Y8vstr+rGzNzA1vRucs7UZIkjYDqmYmFlIfJ3w5cTbnQvwJYUF2A9Fr+LsrFxUPA\nh4F/AF4GnJqZF7XVfYQyLGkZcB7lm/cDgfex6bfvI6nP9jqQ8i3/PEqQ0Onnglr906ufnYC/AZZQ\n7tBcBByemZNNMjAU/bRV9XzJr1EuXvcFLgUuprxw9lzgzfVnUOxbz7NrNe2V6GCs+1blHTz/bwc2\n/dt6abeFx/XcNWtiotM7vyRJkiRJnXgnSpIkSZIaMIiSJEmSpAYMoiRJkiSpAYMoSZIkSWrAIEqS\nJEmSGjCIkiRJkqQGDKIkSZIkqQGDKEmSJElqwCBKkiRJkhowiJIkSZKkBgyiJEmSJKkBgyhJkiRJ\nasAgSpIkSZIaMIiSJEmSpAYMoiRJkiSpAYMoSZIkSWrAIEqSJEmSGjCIkiRJkqQGDKIkSZIkqQGD\nKEmSJElq4P8Bi/hTFZ1Jyi0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fddc82223c8>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 424,
              "height": 210
            }
          }
        }
      ]
    }
  ]
}