Image_Classifier_Project_—_VGG19.ipynb

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      "name": "Image Classifier Project — VGG19.ipynb",
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      "metadata": {
        "id": "4hB_Ae3jD3TE",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "# Developing an AI application\n",
        "\n",
        "Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. \n",
        "\n",
        "In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using [this dataset](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) of 102 flower categories, you can see a few examples below. \n",
        "\n",
        "<img src='https://github.com/udacity/pytorch_challenge/blob/master/assets/Flowers.png?raw=1' width=500px>\n",
        "\n",
        "The project is broken down into multiple steps:\n",
        "\n",
        "* Load and preprocess the image dataset\n",
        "* Train the image classifier on your dataset\n",
        "* Use the trained classifier to predict image content\n",
        "\n",
        "We'll lead you through each part which you'll implement in Python.\n",
        "\n",
        "When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.\n",
        "\n",
        "First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here."
      ]
    },
    {
      "metadata": {
        "id": "a6DGkFfJFF8W",
        "colab_type": "code",
        "outputId": "a5a5961b-39d2-40fd-b5c8-2b2d01c5dbf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "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 == 0x58de6000 @  0x7f3bf42602a4 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"
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          "name": "stdout"
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      ]
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    {
      "metadata": {
        "id": "HsDmGQCVD3Te",
        "colab_type": "code",
        "outputId": "cd1787c2-af22-476c-cc53-50046fc492c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "cell_type": "code",
      "source": [
        "# Imports here\n",
        "\n",
        "import torch\n",
        "from torch import nn, optim\n",
        "from torch.optim import lr_scheduler\n",
        "import numpy as np\n",
        "from torchvision import datasets\n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets, transforms, models\n",
        "from collections import OrderedDict\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# The Pillow version of 4.1.1 is the right package for running the model\n",
        "!pip install pillow==4.1.1\n",
        "%reload_ext autoreload\n",
        "%autoreload # reload all modules so no kernel restart is required.\n",
        "\n",
        "from PIL import Image"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pillow==4.1.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/36/e5/88b3d60924a3f8476fa74ec086f5fbaba56dd6cee0d82845f883b6b6dd18/Pillow-4.1.1-cp36-cp36m-manylinux1_x86_64.whl (5.7MB)\n",
            "\u001b[K    100% |████████████████████████████████| 5.7MB 7.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow==4.1.1) (0.46)\n",
            "Installing collected packages: pillow\n",
            "  Found existing installation: Pillow 5.4.1\n",
            "    Uninstalling Pillow-5.4.1:\n",
            "      Successfully uninstalled Pillow-5.4.1\n",
            "Successfully installed pillow-4.1.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "E3oed1Y0I4Qy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Download the data (for Colab)\n",
        "The following code downloads the required dataset, and additional resourses to Colab."
      ]
    },
    {
      "metadata": {
        "id": "BCrJ3AfhJH3G",
        "colab_type": "code",
        "cellView": "both",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# download notebook resources\n",
        "!wget -c https://github.com/udacity/pytorch_challenge/raw/master/cat_to_name.json\n",
        "!wget -P ./assets -c https://github.com/udacity/pytorch_challenge/raw/master/assets/inference_example.png\n",
        "!wget -P ./assets -c https://github.com/udacity/pytorch_challenge/blob/master/assets/Flowers.png"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mJa01r_mlhVu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# delete existing unzipped dataset\n",
        "!rm -rf 'flower_data'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ht_PZy26lnq7",
        "colab_type": "code",
        "outputId": "7299f4bf-b854-4c2e-e302-5dce36c9f48b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "cell_type": "code",
      "source": [
        "# download dataset\n",
        "!wget -c https://s3.amazonaws.com/content.udacity-data.com/courses/nd188/flower_data.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-01-09 08:50:25--  https://s3.amazonaws.com/content.udacity-data.com/courses/nd188/flower_data.zip\n",
            "Resolving s3.amazonaws.com (s3.amazonaws.com)... 54.231.82.84\n",
            "Connecting to s3.amazonaws.com (s3.amazonaws.com)|54.231.82.84|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 311442766 (297M) [application/zip]\n",
            "Saving to: ‘flower_data.zip’\n",
            "\n",
            "flower_data.zip     100%[===================>] 297.01M  16.2MB/s    in 20s     \n",
            "\n",
            "2019-01-09 08:50:45 (15.0 MB/s) - ‘flower_data.zip’ saved [311442766/311442766]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8Fs9MYmeK-DL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# unzip the dataset\n",
        "!unzip 'flower_data.zip' # -d Cat_Dog_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fxdyia-CD3T-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Load the data\n",
        "\n",
        "Here you'll use `torchvision` to load the data ([documentation](http://pytorch.org/docs/0.3.0/torchvision/index.html)). You can [download the data here](https://s3.amazonaws.com/content.udacity-data.com/courses/nd188/flower_data.zip). The dataset is split into two parts, training and validation. For the training, you'll want to apply transformations such as random scaling, cropping, and flipping. This will help the network generalize leading to better performance. If you use a pre-trained network, you'll also need to make sure the input data is resized to 224x224 pixels as required by the networks.\n",
        "\n",
        "The validation set is used to measure the model's performance on data it hasn't seen yet. For this you don't want any scaling or rotation transformations, but you'll need to resize then crop the images to the appropriate size.\n",
        "\n",
        "The pre-trained networks available from `torchvision` were trained on the ImageNet dataset where each color channel was normalized separately. For both sets you'll need to normalize the means and standard deviations of the images to what the network expects. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`, calculated from the ImageNet images.  These values will shift each color channel to be centered at 0 and range from -1 to 1."
      ]
    },
    {
      "metadata": {
        "id": "8JYyqYLvD3UG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_dir = 'flower_data'\n",
        "train_dir = data_dir + '/train'\n",
        "valid_dir = data_dir + '/valid'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JOEeBBHAD3Ud",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# TODO: Define your transforms for the training and validation sets\n",
        "train_transforms = transforms.Compose([transforms.RandomRotation(30),\n",
        "                                       transforms.RandomResizedCrop(224),\n",
        "                                       transforms.RandomHorizontalFlip(),\n",
        "                                       transforms.ToTensor(),\n",
        "                                       transforms.Normalize([0.485, 0.456, 0.406], \n",
        "                                                            [0.229, 0.224, 0.225])])\n",
        "\n",
        "valid_transforms = transforms.Compose([transforms.Resize(255),\n",
        "                                       transforms.CenterCrop(224),\n",
        "                                       transforms.ToTensor(),\n",
        "                                       transforms.Normalize([0.485, 0.456, 0.406], \n",
        "                                                            [0.229, 0.224, 0.225])])\n",
        "\n",
        "# TODO: Load the datasets with ImageFolder\n",
        "train_data = datasets.ImageFolder(train_dir, transform=train_transforms)\n",
        "valid_data = datasets.ImageFolder(valid_dir, transform=valid_transforms)\n",
        "\n",
        "# percentage of training set to use as validation\n",
        "valid_size = 0.2\n",
        "\n",
        "# obtain training indices that will be used for validation\n",
        "num_train = len(train_data)\n",
        "indices = list(range(num_train))\n",
        "np.random.shuffle(indices)\n",
        "split = int(np.floor(valid_size * num_train))\n",
        "train_idx, valid_idx = indices[split:], indices[:split]\n",
        "\n",
        "# define samplers for obtaining training and validation batches\n",
        "train_sampler = SubsetRandomSampler(train_idx)\n",
        "valid_sampler = SubsetRandomSampler(valid_idx)\n",
        "\n",
        "# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
        "trainloader = torch.utils.data.DataLoader(train_data, batch_size=32, sampler=train_sampler)\n",
        "validloader = torch.utils.data.DataLoader(train_data, batch_size=32, sampler=valid_sampler)\n",
        "testloader = torch.utils.data.DataLoader(valid_data, batch_size=32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hF1mzpGED3U8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Label mapping\n",
        "\n",
        "You'll also need to load in a mapping from category label to category name. You can find this in the file `cat_to_name.json`. It's a JSON object which you can read in with the [`json` module](https://docs.python.org/2/library/json.html). This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers."
      ]
    },
    {
      "metadata": {
        "id": "Pn__1pEQD3WV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "with open('cat_to_name.json', 'r') as f:\n",
        "    cat_to_name = json.load(f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XL5Zopr31Ziz",
        "colab_type": "code",
        "outputId": "825a760d-027e-4459-8b86-f768577ceddd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "# check the contents of 'cat_to_name'\n",
        "print(cat_to_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'21': 'fire lily', '3': 'canterbury bells', '45': 'bolero deep blue', '1': 'pink primrose', '34': 'mexican aster', '27': 'prince of wales feathers', '7': 'moon orchid', '16': 'globe-flower', '25': 'grape hyacinth', '26': 'corn poppy', '79': 'toad lily', '39': 'siam tulip', '24': 'red ginger', '67': 'spring crocus', '35': 'alpine sea holly', '32': 'garden phlox', '10': 'globe thistle', '6': 'tiger lily', '93': 'ball moss', '33': 'love in the mist', '9': 'monkshood', '102': 'blackberry lily', '14': 'spear thistle', '19': 'balloon flower', '100': 'blanket flower', '13': 'king protea', '49': 'oxeye daisy', '15': 'yellow iris', '61': 'cautleya spicata', '31': 'carnation', '64': 'silverbush', '68': 'bearded iris', '63': 'black-eyed susan', '69': 'windflower', '62': 'japanese anemone', '20': 'giant white arum lily', '38': 'great masterwort', '4': 'sweet pea', '86': 'tree mallow', '101': 'trumpet creeper', '42': 'daffodil', '22': 'pincushion flower', '2': 'hard-leaved pocket orchid', '54': 'sunflower', '66': 'osteospermum', '70': 'tree poppy', '85': 'desert-rose', '99': 'bromelia', '87': 'magnolia', '5': 'english marigold', '92': 'bee balm', '28': 'stemless gentian', '97': 'mallow', '57': 'gaura', '40': 'lenten rose', '47': 'marigold', '59': 'orange dahlia', '48': 'buttercup', '55': 'pelargonium', '36': 'ruby-lipped cattleya', '91': 'hippeastrum', '29': 'artichoke', '71': 'gazania', '90': 'canna lily', '18': 'peruvian lily', '98': 'mexican petunia', '8': 'bird of paradise', '30': 'sweet william', '17': 'purple coneflower', '52': 'wild pansy', '84': 'columbine', '12': \"colt's foot\", '11': 'snapdragon', '96': 'camellia', '23': 'fritillary', '50': 'common dandelion', '44': 'poinsettia', '53': 'primula', '72': 'azalea', '65': 'californian poppy', '80': 'anthurium', '76': 'morning glory', '37': 'cape flower', '56': 'bishop of llandaff', '60': 'pink-yellow dahlia', '82': 'clematis', '58': 'geranium', '75': 'thorn apple', '41': 'barbeton daisy', '95': 'bougainvillea', '43': 'sword lily', '83': 'hibiscus', '78': 'lotus lotus', '88': 'cyclamen', '94': 'foxglove', '81': 'frangipani', '74': 'rose', '89': 'watercress', '73': 'water lily', '46': 'wallflower', '77': 'passion flower', '51': 'petunia'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zflxltoVD3W8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Building and training the classifier\n",
        "\n",
        "Now that the data is ready, it's time to build and train the classifier. As usual, you should use one of the pretrained models from `torchvision.models` to get the image features. Build and train a new feed-forward classifier using those features.\n",
        "\n",
        "We're going to leave this part up to you. If you want to talk through it with someone, chat with your fellow students! You can also ask questions on the forums or join the instructors in office hours.\n",
        "\n",
        "Refer to [the rubric](https://review.udacity.com/#!/rubrics/1663/view) for guidance on successfully completing this section. Things you'll need to do:\n",
        "\n",
        "* Load a [pre-trained network](http://pytorch.org/docs/master/torchvision/models.html) (If you need a starting point, the VGG networks work great and are straightforward to use)\n",
        "* Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout\n",
        "* Train the classifier layers using backpropagation using the pre-trained network to get the features\n",
        "* Track the loss and accuracy on the validation set to determine the best hyperparameters\n",
        "\n",
        "We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!\n",
        "\n",
        "When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right. Make sure to try different hyperparameters (learning rate, units in the classifier, epochs, etc) to find the best model. Save those hyperparameters to use as default values in the next part of the project."
      ]
    },
    {
      "metadata": {
        "id": "j0TYoyCdD3XG",
        "colab_type": "code",
        "outputId": "97b68161-8820-428b-9c6e-aaebf6ea081e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "# TODO: Build and train your network\n",
        "\n",
        "# Use GPU if it's available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Load VGG-19\tpre-trained model\n",
        "model = models.vgg19(pretrained=True)\n",
        "\n",
        "# Freeze parameters so we don't backprop through them\n",
        "for param in model.parameters():\n",
        "    param.requires_grad = False\n",
        " \n",
        "# Define the model\n",
        "classifier = nn.Sequential(OrderedDict([\n",
        "                          ('fc1', nn.Linear(25088, 4096)),\n",
        "                          ('relu', nn.ReLU()),\n",
        "                          ('dropout', nn.Dropout(p=0.3)),\n",
        "                          ('fc2', nn.Linear(4096, 102)),\n",
        "                          ('output', nn.LogSoftmax(dim=1))\n",
        "                          ]))    \n",
        "\n",
        "model.classifier = classifier\n",
        "\n",
        "# Define Loss\n",
        "criterion = nn.NLLLoss()\n",
        "\n",
        "# Only train the classifier parameters, feature parameters are frozen\n",
        "optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)\n",
        "\n",
        "# Decay the learning rate by 0.1 after every 5 steps\n",
        "scheduler = lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)\n",
        "\n",
        "# move the model to the available device\n",
        "model.to(device);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.torch/models/vgg19-dcbb9e9d.pth\n",
            "100%|██████████| 574673361/574673361 [00:20<00:00, 28315524.84it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "cOy11sZuDbL5",
        "colab_type": "code",
        "outputId": "cc9a9570-f4ee-45bb-e4f8-61eb953d7410",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        }
      },
      "cell_type": "code",
      "source": [
        "# train the model\n",
        "\n",
        "epochs = 25\n",
        "\n",
        "valid_loss_min = np.Inf # track change in validation loss\n",
        "\n",
        "for e in range(1, epochs + 1):\n",
        "    train_loss = 0.0\n",
        "    valid_loss = 0.0\n",
        "    accuracy = 0.0\n",
        "    \n",
        "    scheduler.step()\n",
        "    model.train()\n",
        "    for inputs, labels in trainloader:\n",
        "        \n",
        "        # move inputs and labels to GPU\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "        \n",
        "        # clear the gradients\n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        log_ps = model.forward(inputs)\n",
        "        loss = criterion(log_ps, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        train_loss += loss.item() # *inputs.size(0)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        model.eval()\n",
        "        for inputs, labels in validloader:\n",
        "\n",
        "            # move inputs and labels to GPU\n",
        "            inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "            log_ps = model.forward(inputs)\n",
        "            loss = criterion(log_ps, labels)\n",
        "\n",
        "            valid_loss += loss.item() # *inputs.size(0)\n",
        "            \n",
        "            # calculate accuracy\n",
        "            ps = torch.exp(log_ps)\n",
        "            top_p, top_classes = ps.topk(1, dim = 1)\n",
        "            equals = top_classes == labels.view(*top_classes.shape)\n",
        "            accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
        "        \n",
        "    # calculate average losses\n",
        "    train_loss = train_loss/len(trainloader)\n",
        "    valid_loss = valid_loss/len(validloader)\n",
        "    accuracy = accuracy/len(validloader)\n",
        "\n",
        "    # print training/validation statistics \n",
        "    print('Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f} \\tAccuracy: {}'.format(\n",
        "    e, train_loss, valid_loss, accuracy))\n",
        "    \n",
        "    model.train()\n",
        "    \n",
        "    # save model if validation loss has decreased\n",
        "    if valid_loss <= valid_loss_min:\n",
        "        print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format(\n",
        "        valid_loss_min,\n",
        "        valid_loss))\n",
        "        torch.save(model.state_dict(), 'best_model.pt')\n",
        "        valid_loss_min = valid_loss"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1 \tTraining Loss: 3.193117 \tValidation Loss: 1.553994 \tAccuracy: 0.6023373981801475\n",
            "Validation loss decreased (inf --> 1.553994).  Saving model ...\n",
            "Epoch: 2 \tTraining Loss: 1.510479 \tValidation Loss: 1.328150 \tAccuracy: 0.6495934957411231\n",
            "Validation loss decreased (1.553994 --> 1.328150).  Saving model ...\n",
            "Epoch: 3 \tTraining Loss: 1.296803 \tValidation Loss: 1.122336 \tAccuracy: 0.6945121957034599\n",
            "Validation loss decreased (1.328150 --> 1.122336).  Saving model ...\n",
            "Epoch: 4 \tTraining Loss: 1.174917 \tValidation Loss: 1.160091 \tAccuracy: 0.6856707319980715\n",
            "Epoch: 5 \tTraining Loss: 1.135433 \tValidation Loss: 1.121749 \tAccuracy: 0.7028963420449234\n",
            "Validation loss decreased (1.122336 --> 1.121749).  Saving model ...\n",
            "Epoch: 6 \tTraining Loss: 0.895979 \tValidation Loss: 0.927586 \tAccuracy: 0.7456300811069768\n",
            "Validation loss decreased (1.121749 --> 0.927586).  Saving model ...\n",
            "Epoch: 7 \tTraining Loss: 0.760211 \tValidation Loss: 0.805626 \tAccuracy: 0.7775914639961429\n",
            "Validation loss decreased (0.927586 --> 0.805626).  Saving model ...\n",
            "Epoch: 8 \tTraining Loss: 0.715655 \tValidation Loss: 0.846300 \tAccuracy: 0.7732215451031197\n",
            "Epoch: 9 \tTraining Loss: 0.752023 \tValidation Loss: 0.804163 \tAccuracy: 0.777896341754169\n",
            "Validation loss decreased (0.805626 --> 0.804163).  Saving model ...\n",
            "Epoch: 10 \tTraining Loss: 0.659112 \tValidation Loss: 0.829776 \tAccuracy: 0.7733739832552468\n",
            "Epoch: 11 \tTraining Loss: 0.637925 \tValidation Loss: 0.780128 \tAccuracy: 0.7940548774672718\n",
            "Validation loss decreased (0.804163 --> 0.780128).  Saving model ...\n",
            "Epoch: 12 \tTraining Loss: 0.635422 \tValidation Loss: 0.765104 \tAccuracy: 0.78785569057232\n",
            "Validation loss decreased (0.780128 --> 0.765104).  Saving model ...\n",
            "Epoch: 13 \tTraining Loss: 0.628327 \tValidation Loss: 0.804840 \tAccuracy: 0.7899390240994896\n",
            "Epoch: 14 \tTraining Loss: 0.595870 \tValidation Loss: 0.812037 \tAccuracy: 0.788465447542144\n",
            "Epoch: 15 \tTraining Loss: 0.648683 \tValidation Loss: 0.820729 \tAccuracy: 0.7799288615947817\n",
            "Epoch: 16 \tTraining Loss: 0.637620 \tValidation Loss: 0.753646 \tAccuracy: 0.8059451216604652\n",
            "Validation loss decreased (0.765104 --> 0.753646).  Saving model ...\n",
            "Epoch: 17 \tTraining Loss: 0.611064 \tValidation Loss: 0.772884 \tAccuracy: 0.7871443091369257\n",
            "Epoch: 18 \tTraining Loss: 0.633500 \tValidation Loss: 0.798873 \tAccuracy: 0.788313007936245\n",
            "Epoch: 19 \tTraining Loss: 0.614726 \tValidation Loss: 0.774095 \tAccuracy: 0.7898882118667044\n",
            "Epoch: 20 \tTraining Loss: 0.594370 \tValidation Loss: 0.712686 \tAccuracy: 0.8084857725515598\n",
            "Validation loss decreased (0.753646 --> 0.712686).  Saving model ...\n",
            "Epoch: 21 \tTraining Loss: 0.624884 \tValidation Loss: 0.764818 \tAccuracy: 0.7946646344370958\n",
            "Epoch: 22 \tTraining Loss: 0.636327 \tValidation Loss: 0.745806 \tAccuracy: 0.8038617881332956\n",
            "Epoch: 23 \tTraining Loss: 0.607038 \tValidation Loss: 0.761572 \tAccuracy: 0.8022357719700511\n",
            "Epoch: 24 \tTraining Loss: 0.610457 \tValidation Loss: 0.718184 \tAccuracy: 0.8014735768480998\n",
            "Epoch: 25 \tTraining Loss: 0.623251 \tValidation Loss: 0.747037 \tAccuracy: 0.8060467475798072\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "eKMtarYw0eWl",
        "colab_type": "code",
        "outputId": "1b4bf3f6-1622-4bbb-ef81-41f11f49ccac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# TODO: Do validation on the test set\n",
        "def perform_testing():\n",
        "    loss_fn = nn.NLLLoss()\n",
        "    model.eval()\n",
        "\n",
        "    accuracy = 0\n",
        "    test_loss = 0\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        for ii, (images, labels) in enumerate(testloader):\n",
        "            images, labels = images.to(device), labels.to(device)\n",
        "\n",
        "            output = model.forward(images)\n",
        "            test_loss += loss_fn(output, labels).item()\n",
        "\n",
        "            ## Calculating the accuracy \n",
        "            # Model's output is log-softmax, take exponential to get the probabilities\n",
        "            ps = torch.exp(output).data\n",
        "            # Class with highest probability is our predicted class, compare with true label\n",
        "            equality = (labels.data == ps.max(1)[1])\n",
        "            # Accuracy is number of correct predictions divided by all predictions, just take the mean\n",
        "            accuracy += equality.type_as(torch.FloatTensor()).mean()\n",
        "\n",
        "    print(\"Batch: {} \".format(ii+1),\n",
        "          \"Test Loss: {:.3f}.. \".format(test_loss/len(testloader)),\n",
        "          \"Test Accuracy: {:.3f}\".format(accuracy/len(testloader)))\n",
        "\n",
        "perform_testing()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Batch: 26  Test Loss: 0.396..  Test Accuracy: 0.891\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8HxLyoYoD3Xv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Save the checkpoint\n",
        "\n",
        "Now that your network is trained, save the model so you can load it later for making predictions. You probably want to save other things such as the mapping of classes to indices which you get from one of the image datasets: `image_datasets['train'].class_to_idx`. You can attach this to the model as an attribute which makes inference easier later on.\n",
        "\n",
        "```model.class_to_idx = image_datasets['train'].class_to_idx```\n",
        "\n",
        "Remember that you'll want to completely rebuild the model later so you can use it for inference. Make sure to include any information you need in the checkpoint. If you want to load the model and keep training, you'll want to save the number of epochs as well as the optimizer state, `optimizer.state_dict`. You'll likely want to use this trained model in the next part of the project, so best to save it now."
      ]
    },
    {
      "metadata": {
        "id": "FBsnyHETD3X3",
        "colab_type": "code",
        "outputId": "57ec0b7c-0202-4358-eff6-fba35e9a761d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        }
      },
      "cell_type": "code",
      "source": [
        "# TODO: Save the checkpoint \n",
        "\n",
        "model.class_to_idx = trainloader.dataset.class_to_idx\n",
        "model.cpu()\n",
        "torch.save({'arch': 'vgg19',\n",
        "            'state_dict': model.state_dict(), \n",
        "            'class_to_idx': model.class_to_idx}, \n",
        "            'classifier.pth')\n",
        "\n",
        "# put it back to gpu\n",
        "model.cuda()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VGG(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU(inplace)\n",
              "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU(inplace)\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (6): ReLU(inplace)\n",
              "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (8): ReLU(inplace)\n",
              "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (11): ReLU(inplace)\n",
              "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (13): ReLU(inplace)\n",
              "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (15): ReLU(inplace)\n",
              "    (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (17): ReLU(inplace)\n",
              "    (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (20): ReLU(inplace)\n",
              "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (22): ReLU(inplace)\n",
              "    (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (24): ReLU(inplace)\n",
              "    (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (26): ReLU(inplace)\n",
              "    (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (29): ReLU(inplace)\n",
              "    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (31): ReLU(inplace)\n",
              "    (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (33): ReLU(inplace)\n",
              "    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (35): ReLU(inplace)\n",
              "    (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (fc1): Linear(in_features=25088, out_features=4096, bias=True)\n",
              "    (relu): ReLU()\n",
              "    (dropout): Dropout(p=0.3)\n",
              "    (fc2): Linear(in_features=4096, out_features=102, bias=True)\n",
              "    (output): LogSoftmax()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "GGh7bcnmIJRO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Save the checkpoint to Google Drive"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "9HiOIqNyg6Cl",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# mount drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oDnvu2sRIgEQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model_save_name = 'classifier.pt'\n",
        "path = F\"/content/gdrive/My Drive/Colab Notebooks/{model_save_name}\" \n",
        "torch.save(model.state_dict(), path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZCqrw4_yD3YP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Loading the checkpoint\n",
        "\n",
        "At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network."
      ]
    },
    {
      "metadata": {
        "id": "rJ5IJJYwD3Yx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# TODO: Write a function that loads a checkpoint and rebuilds the model\n",
        "\n",
        "def load_model(checkpoint_path):\n",
        "    chpt = torch.load(checkpoint_path)\n",
        "    \n",
        "    if chpt['arch'] == 'vgg19':\n",
        "        model = models.vgg19(pretrained=True)\n",
        "        for param in model.parameters():\n",
        "            param.requires_grad = False\n",
        "    else:\n",
        "        print(\"Sorry, invalid architecture\")\n",
        "    \n",
        "    model.class_to_idx = chpt['class_to_idx']\n",
        "    \n",
        "    # Create the classifier\n",
        "    classifier = nn.Sequential(OrderedDict([\n",
        "                          ('fc1', nn.Linear(25088, 4096)),\n",
        "                          ('relu', nn.ReLU()),\n",
        "                          ('fc2', nn.Linear(4096, 102)),\n",
        "                          ('output', nn.LogSoftmax(dim=1))\n",
        "                          ]))\n",
        "    # Put the classifier on the pretrained network\n",
        "    model.classifier = classifier\n",
        "    \n",
        "    model.load_state_dict(chpt['state_dict'])\n",
        "    \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gUc9BFkqsF7X",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let us load the model and pass it throught the testing loop."
      ]
    },
    {
      "metadata": {
        "id": "PUxrrwussEm7",
        "colab_type": "code",
        "outputId": "343700cc-6a7b-422b-8bf1-d8f5cb2ced00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1345
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('classifier.pth')\n",
        "model = model.cuda()\n",
        "perform_testing()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "VGG(\n",
            "  (features): Sequential(\n",
            "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): ReLU(inplace)\n",
            "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (3): ReLU(inplace)\n",
            "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (6): ReLU(inplace)\n",
            "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (8): ReLU(inplace)\n",
            "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (11): ReLU(inplace)\n",
            "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (13): ReLU(inplace)\n",
            "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (15): ReLU(inplace)\n",
            "    (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (17): ReLU(inplace)\n",
            "    (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (20): ReLU(inplace)\n",
            "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (22): ReLU(inplace)\n",
            "    (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (24): ReLU(inplace)\n",
            "    (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (26): ReLU(inplace)\n",
            "    (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (29): ReLU(inplace)\n",
            "    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (31): ReLU(inplace)\n",
            "    (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (33): ReLU(inplace)\n",
            "    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (35): ReLU(inplace)\n",
            "    (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (classifier): Sequential(\n",
            "    (fc1): Linear(in_features=25088, out_features=4096, bias=True)\n",
            "    (relu): ReLU()\n",
            "    (fc2): Linear(in_features=4096, out_features=102, bias=True)\n",
            "    (output): LogSoftmax()\n",
            "  )\n",
            ")\n",
            "Batch: 1  Test Loss: 0.011..  Test Accuracy: 0.035\n",
            "Batch: 2  Test Loss: 0.019..  Test Accuracy: 0.070\n",
            "Batch: 3  Test Loss: 0.024..  Test Accuracy: 0.107\n",
            "Batch: 4  Test Loss: 0.051..  Test Accuracy: 0.139\n",
            "Batch: 5  Test Loss: 0.086..  Test Accuracy: 0.168\n",
            "Batch: 6  Test Loss: 0.091..  Test Accuracy: 0.206\n",
            "Batch: 7  Test Loss: 0.128..  Test Accuracy: 0.234\n",
            "Batch: 8  Test Loss: 0.156..  Test Accuracy: 0.266\n",
            "Batch: 9  Test Loss: 0.161..  Test Accuracy: 0.303\n",
            "Batch: 10  Test Loss: 0.179..  Test Accuracy: 0.335\n",
            "Batch: 11  Test Loss: 0.199..  Test Accuracy: 0.367\n",
            "Batch: 12  Test Loss: 0.210..  Test Accuracy: 0.401\n",
            "Batch: 13  Test Loss: 0.214..  Test Accuracy: 0.439\n",
            "Batch: 14  Test Loss: 0.214..  Test Accuracy: 0.477\n",
            "Batch: 15  Test Loss: 0.216..  Test Accuracy: 0.516\n",
            "Batch: 16  Test Loss: 0.227..  Test Accuracy: 0.552\n",
            "Batch: 17  Test Loss: 0.233..  Test Accuracy: 0.589\n",
            "Batch: 18  Test Loss: 0.246..  Test Accuracy: 0.625\n",
            "Batch: 19  Test Loss: 0.246..  Test Accuracy: 0.663\n",
            "Batch: 20  Test Loss: 0.248..  Test Accuracy: 0.701\n",
            "Batch: 21  Test Loss: 0.274..  Test Accuracy: 0.732\n",
            "Batch: 22  Test Loss: 0.303..  Test Accuracy: 0.760\n",
            "Batch: 23  Test Loss: 0.309..  Test Accuracy: 0.797\n",
            "Batch: 24  Test Loss: 0.365..  Test Accuracy: 0.826\n",
            "Batch: 25  Test Loss: 0.386..  Test Accuracy: 0.859\n",
            "Batch: 26  Test Loss: 0.388..  Test Accuracy: 0.898\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MXu9djetD3Zo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Inference for classification\n",
        "\n",
        "Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called `predict` that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like \n",
        "\n",
        "```python\n",
        "probs, classes = predict(image_path, model)\n",
        "print(probs)\n",
        "print(classes)\n",
        "> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n",
        "> ['70', '3', '45', '62', '55']\n",
        "```\n",
        "\n",
        "First you'll need to handle processing the input image such that it can be used in your network. \n",
        "\n",
        "## Image Preprocessing\n",
        "\n",
        "You'll want to use `PIL` to load the image ([documentation](https://pillow.readthedocs.io/en/latest/reference/Image.html)). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training. \n",
        "\n",
        "First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the [`thumbnail`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) or [`resize`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) methods. Then you'll need to crop out the center 224x224 portion of the image.\n",
        "\n",
        "Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so `np_image = np.array(pil_image)`.\n",
        "\n",
        "As before, the network expects the images to be normalized in a specific way. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`. You'll want to subtract the means from each color channel, then divide by the standard deviation. \n",
        "\n",
        "And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using [`ndarray.transpose`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.transpose.html). The color channel needs to be first and retain the order of the other two dimensions."
      ]
    },
    {
      "metadata": {
        "id": "wBosMNkoD3Zu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def process_image(image):\n",
        "    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n",
        "        returns an Numpy array\n",
        "    '''\n",
        "    \n",
        "    # TODO: Process a PIL image for use in a PyTorch model\n",
        "    \n",
        "    # Resize the image by choosing the smaller side\n",
        "    if image.size[0] > image.size[1]:\n",
        "        image.thumbnail((10000, 256))\n",
        "    else:\n",
        "        image.thumbnail((256, 10000))\n",
        "        \n",
        "    # Crop \n",
        "    left_margin = (image.width-224)/2\n",
        "    bottom_margin = (image.height-224)/2\n",
        "    right_margin = left_margin + 224\n",
        "    top_margin = bottom_margin + 224\n",
        "    image = image.crop((left_margin, bottom_margin, right_margin,   \n",
        "                      top_margin))\n",
        "    \n",
        "    # Normalize\n",
        "    image = np.array(image)/255\n",
        "    mean = np.array([0.485, 0.456, 0.406]) #provided mean\n",
        "    std = np.array([0.229, 0.224, 0.225])  #provided std\n",
        "    image = (image - mean)/std\n",
        "    \n",
        "    # Move color channels to first dimension as expected by PyTorch\n",
        "    image = image.transpose((2, 0, 1))\n",
        "    \n",
        "    return image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "j0xBmkQOD3Z_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your `process_image` function works, running the output through this function should return the original image (except for the cropped out portions)."
      ]
    },
    {
      "metadata": {
        "id": "Gbpww5MOD3aF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def imshow(image, ax=None, title=None):\n",
        "    \"\"\"Imshow for Tensor.\"\"\"\n",
        "    if ax is None:\n",
        "        fig, ax = plt.subplots()\n",
        "    \n",
        "    # PyTorch tensors assume the color channel is the first dimension\n",
        "    # but matplotlib assumes is the third dimension\n",
        "    image = image.transpose((1, 2, 0))\n",
        "    \n",
        "    # Undo preprocessing\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    image = std * image + mean\n",
        "    \n",
        "    # Image needs to be clipped between 0 and 1 or it looks like noise when displayed\n",
        "    image = np.clip(image, 0, 1)\n",
        "    \n",
        "    ax.imshow(image)\n",
        "    \n",
        "    return ax"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fKfYeNHQwGol",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Test the `process_image` function:"
      ]
    },
    {
      "metadata": {
        "id": "IGEeQK7LwFA-",
        "colab_type": "code",
        "outputId": "a46beba9-5661-4f70-bb16-0dacd0ad9274",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "cell_type": "code",
      "source": [
        "# Open the image\n",
        "image = Image.open('flower_data/valid/95/image_07513.jpg')\n",
        "image = process_image(image)\n",
        "imshow(image)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3a96bf5f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vjq92Mj9I3NZEioQYSZusTGGTKyBI8KiMgesESf3FlmIgcF8YEjzhSuMGJs9m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\nmB+P99F7QStjdjC6UmmbjErfB5rc+hZcJO4Kt0rveu/obnWcOrIb74mLb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BUAACAASURB\nVDfdgvmCZ1gsdW4EVyu9MY0cHu2xszhDk7MYLQ94WQUkkUwOJf8oNOOy9Gkw7G042jtiyOIyTltE\nA6WyJM/d4vZfXaG5kQUw+h26cIaYedJp6/CidMtcBGwWqHO1Gh58wKWEZB0BnxT1wlB4nQje+bqI\nppSymEhd7YhxoDTmeA0E8eg43ZuqvkbCpvChpLyJRIn5utrn+ZgQCWgOBWNKuDTpKtiG6+u2nFTw\n0c9UmMpx5UhaG061Vqk/254hEI51+6mLBHQCRcUj4skNW4RWCCHU6rxlg64WkiAxxFR1LjRp1u74\n5iLRE0z0ZJyMk3EyvoVxdyPRglGVDd3ifMBWd6/UiqOG3OqW39uo4AZYZ9rC97/le3n/g9/N7qFF\nVu0WGnwNyXsdkYWvBUhNkeBcbRsMtLgUJgxUMe+ZfIziFBcFqRipI2mRWzN5LZP4ss+P6EQzIqc0\nCrHYhYqYPuZsHwuuw+cQyGWl9YJ0pBz9FAHy1MPRtUMOXzb9zdXr7gHfV2CoplI1ErW/T4VnK/E4\nJYqAFF+g3JPva88NOaJ0uIxTiXqO8T4TFhUWqTo8ngZP9rByDWic1OgUGCLOZem78QZIoGOJrC+i\nN26w99w1bjxlFCd/daA79EhW+hfnSBIZM2Yc00Dj1DigwGE8AD+QlRN5+fkXOXz5FgCnF2es37r0\njgNIwtUOI0XGCA0MN6/zwle+yr2vfz3nHnigHnpk6miJ2xE/psqs0KPI+KJy8/J1AG49f5PNtUNC\n7vDx3ltFO0dcy5cCw5WetjfoYdHsEkehz/m6qKdpG8hMEcWDCzV7G8aIxBFfBEPFVMMKkdK1wTDZ\nsTxolkWpGp48jmqsk/xyaAPBNfSbia7Wpy2+UBFjwidfoRTLUyZdBJygyBTpCaSYKBffg3FJKfUB\nU2ybw3pepUrnCR1r7LqfX55h53DFNi1ITZ5/F/FC5a2qcxCksi+C84TgK9whTsAlxkypiqMyxKml\n2/QytGYW8TUi0ru6iJbH20dAk9EeZs98kwTJIfjWjSQXafIhrzRwwa35yLdbm+A/u/+fcPr6LmHf\nPqAdAjEmRmcTZZhIIhWH4hhotaHgMJ0ukFGmXvikwIh3dRVD1OFTvlBq5YNqA5uN2MqDkSRzHst9\nERU3uJoiedeQoqte5gDeLyl4RRy3OHH4nNIQItHHyigOLBhuCgdftFbB9X27cEmI+e2jIfm0TKtu\n0iMGt8lzbxhUWWRlxvEUhUYCZPO7fAKIOopeaMYCKgdNfczmcDm9xiOyC8WILikMY9WgPLh5i/Hq\nZS7fsEXy2uHLjIz4beD9P/Rf86nf/ZfEFxLDFbse7fUtZ8YOzRqTSRYMKeX+fXAu4nysdcIgt+m5\nhebrs31xj7/8Pz4NwDu/Z8G5h+8nNoVeZelyWUTpe4aXX6Z54H6e+bd/wX/81Kd47v5LPPLd7wLg\ngUcewS+WxLxoylEiXj3k6DmzWrn9zDU2TytHV+3zTusF7pVL1pMNaDPCzsjRaFBDeg700BOy4EZC\niWkzPQtBiMFNaxQm/lJkFFUj+MkCObp4rJwwpghxknF0MlgxLe8wKg0+LKr5pHdiqWyYFtHR7eOa\nAieMiG+oWsWhAe+RivkuLKWut4r16nMMMw1G1sYoraYkWe7VDHBmylRKDp+x+7PtaS7sn2cz3uaw\nsU1RW9u8fdUmCMRAvZ6NNLSumUS1ZTRxICmLsLk1x7E0jhi9qS7pr8Fxurs80YxpFDVyJB0DGEbx\nE1dNA6vUspPVXN5+8VH++Qc+ymMXHrY/fdaxGTacyhhfHKJ1EOVFrWtbq17qbFFkApedC4x9rIUc\nCS4rkBeC8pgX1jm5daxcQa36nrmwIlZx1Px5MVkxJGQcK8aUMcD5FUp199McyRbMMYtxozkSbsZA\nOlCe/cuv2Vz5kfMfeAPtpUyGd1ssXjKyocZbRDYT+E9AmDaR+X87V3rFxRT/7dDAu6qcb33ORioH\n8IwkSfQVV7TotWC8ogMiPTdesuP99Kf+lCsvfJm9wfRDY5eIGnFbz/uBz37h03S3OrqNPTyrTSCk\nli4vJG1UGvWk4gYbUq6J2/G1LIjxkKN92zQODq9z/epnbO5Sy3v0vSwvGTHfdx3Ekf6mFXZuvfQi\nl7/yVd7xY0/wlT//CnrTc/PmTZ6+agLSt++/QbNe1XurUSHe2qe/Zg912htpDs6xyjxOL9lNNWOI\nS+lIe0K6mTHKI2HpViwk9/IPDq9SRJAyGdxXcrpzDhGrstvP1i8m9d5WK4pVyq4ypinS1CRodFUv\n07lACIFQBK2xRpBSTwBY7+wwHVBnLJeCQTpvIi55lSr3dOXOitb9wA4AUK3PjtUGmDjORb6qRqaO\nJtjcrFen6DZLezZy/KEuZ0qFLOAC3gsur+JNE+oc2vl6FJmWgpSIkSrwnfKx1eN7Dd3ZE0z0ZJyM\nk3EyvoVxdyPRUp0TQ+vcTIY/ATEIXT7EbvCcGZe85z7rlf6JD/4zHmwusf2aRTLtYUNHW9syA2Kd\nZxn3GntlVCUkw+hSFBgdKRaKjnm2FEzNNSDjJFcmrtCSMm4mCtU1stBEZpqQqKm9F4pUzJhiib5V\ns77ovEMr1oqvfdfkyyNqkUaZoRAdbuO4fdkizef/7GnkwR1ed8moIF6OiOwTuZWn+hamO1A+oQO6\n2rEEHU5K91KLtTv6qRcdw3yHUpEl0pG4+YKl4/HmTVZn1qSLxittmwWKGDYMeN2S+hs8+4ypMH31\na3/Obb3BJlmkeNhvabqWhdj12a4OkBTps9Qe/VkWvbLIauzLIeBTSwmU+0YZRjW+ItBIIqZN9cAK\nYUEbDFd7+T88zef3Iw88/Ab7Yx+4ffMWLzz3PAD712+SNgPv+DFoXm65191POzTsPGNZzvZvDzhq\n9thqce/saTSyzFH+uttFkqddlOrxlt71+ArVJPqrG9qjopHZ0bi2UqLSYPdGeR6sA0gqfk9S4jjU\ne0e8s3sxFk6uYYSVRwk4VWK+ll4aXBsIbnr8Y1T63M3WhIALDfM8NsWJTeMkw0El1HXmMjAFnopx\niqfqt7EZyqepdfaV4xefX7M3RCxjLDoXvmnY6ezarRdnWC3WuK2r+p+jNw6qT4WtYPNVe+l9wLlQ\nI1MnBhyUSHQcU56bqWX67xKJ3t1FtKS2uaMuGd4L5MKSg0W+L87EBT/47g/zQ+/8JwDcs9lleHqP\nCym3KqY1cYg1JCc4fDuR2/vtgCZHk9P9URWNERdLoSgb5RU5Lk2goRLQRYLdM+XGTNHA7zzBCbNP\nLjfamMz+oWpIFk+mcmGiUY6OCVIzE8HIo6TzTkCd1kVW4kAztCwzvNFsWpapM3AHcO6IQW4R1Yob\nQbb5Zih2KB0iHVQ5uK7is2YJ7GoKD+YkkUoPeP6Uw8tX+MIf/DEAm6cv87r77+Psu8x+ZXH/60zT\nMq/RW73N157+DJ975i8AOPQvc1WvcZBB6sEnvAS6bAlxY+cWO23EL0sha82wB2FTMPEFCxo042rS\nK+InUWznAd1OgjOxpd3m9LqHm0++wPYrJjg9DpHtdqiFkXXoWHgTWrk/3s94sGUn7bDI2rQb3SJL\nYXClSNeDj9Vee9hXaDa1cOHWnmbdcJTtP45u3yAMDV3MGKMu0XGslKYg3qCjKaawQmfxNIoJxRGK\nXqfkRancuynSH42VkwtCHNSkHoGuWRB8V/HjpCPBK00ovfl5cd8WLVYbdRH35qdU6lgaBcKMopTr\nla48zEK5gfPxlQBhdn5JbfEEkkScaC08pzRWWb91e4rd9jTtdkXKHlOD31irR8FYJaHJm8UKTO22\npREkFS7o9N0pTeI8KR3n/cr8QnydcXfJ9qUa6oxf6Udw+anr8IQ+cm9r/c7//MM/yA+89YMsXsr6\nnM/f4FRa0mWM0Kuzlt+iUdiCtK4GUmkUiN6wISBtE3E7opq13RUQRYtyexwJLOouJIgRtItmpJad\ntlyY8djPLm8MpZofgsdFmar9+XP9sSswzv7elHBK14mGEQ0jY47cvHQgQ1UKWoYGPwzQ26Kkfsvg\nDqvxmymL+yxWDI6ldYUUHFRCVso3pJN8Ew35Tt+KlZnafP5Hz1/lyd/7Yw6etEj04sbBi9e49owt\nJFfOfpHtBYHX2Q14efsMz770OWKyyLhfHXG0OWI/F1cGl8A1+Fytv9JcZ7cdaLNjpWdN6ka8WTLR\npAa2gUXxyEqezocq3dO2Dpf6ugkFtyKM2a9qFPT2hj4e5Hl3LHzLamU82QVrfN6c/JWAHzyN7CCD\nbdjrbpfQCymTdnu2+FZIWdxlGzccHh6wvzV91riJ0Cptnutm9KzbdeU++G2LqmRBFivyOd/VxpKU\nRsZ+yB16BfODQuyMKaIM+CJbJFZ0ElcWVSBCk3vdQwg452p1PCaxhas2TnhIser9CjlarC1ABrhq\njUSDiSBXcn8kuDRhol5yMFo4x/Y81ZprjASVyvkVb8FJ+f5iXAewak5xrr3ELmfQzMwYOEIZa0eU\ny26idVPwPkeh5dm1GkWaNdLAFACVf8szGl9DlPkEEz0ZJ+NknIxvYdxdilPRChVFEnR0NKOlXIvU\n8LC7wA99zz8F4Hvf9F0sLitNjkR22gu45KaK4yahOhKWmeK0Dgwk4ph3v9Gh0dHnSI5BcTpjSebQ\nsUSqKlaRn3Ado++UNkYngeCUbUz1Z/FKLJFktn/wte3TNH2kKsvkdL60tUDlsUGORL1DpaguJQa/\nJba5g8oPoIExZMqQHhAvN+zctB07eZBli+T0WDQhalVUm/vcNTOz8yi8Q8HYBhrH2urYqaNRkGuG\nwT77h5/h4K8uc3Frae/uJiBbOLxm87F3+ho3Lt3k2lWzF7mxepltc5t2WShJjvPNLi5bROzFDRsG\nJNNajmQfdZFlm+GasGBcrUhtjnbwjNpwus8RSlrTxq7eU+qNBlWij8atacRwtTDAsA3QD/naB9rF\nDsvB5spvG8aD/NpeS1BrwWxbi1QbHHo0ohkKaiUgjRDzdy/YofVLFjEzA4YDDtM+Q5hkErf/N3vv\nFmtZct73/b66rLX3Puf0dPdwZsgZ3kYSLcmmeBECR4wDGGIkiA8WgVhirAjDRIETGLBhWbEB+RI7\noCMggnOBEQtKABOBHqQgCOQ8RA8B7CSGYCSQZUOEFUmOObQokRxeZqZnuvtc9t5rrar68vBV1dpN\niTOCHWMcowsYTO+zz9mXdan66v/9L/5ACZV+Nyshjmh1qs/LQiypY5w5TVbNtXyqUvXvbVelpu3v\nlZhTNDvy3HwdMO24a1AOhqHWazlrse1tS2ot1SqvVYJUXPa0EvUR1zhOIYCD0nc9mVQMIgCqBn7d\nFa475VY5A873nPfFJcqceoSzuECsSsLdcMGzu/fw1MPnONZKP4YNJUxrpe4dEhz+RNbqTz5/weLN\n2/fLyaLOV2gtV+erdVf4RuMtnkTrSUMIEhiIuLp/fP5t38R/9MEf4jve8W0AjF8qbPeebeNpFmV2\nqdtxbZK1MaSC+YtPzMtMrvEhkje4NKB5qu+peAn9QGtZKCWfcMmUoku3thMt5lPYDqyEaphin6dw\nJARlqLSQVGBeymr6kAouuxNepsN70BO8RVzpuJHx5lZpWtFM8hNpY9v1SW/IFA5iN+p0mHjba/Be\nDJOMwznqLInJ3q+JGNr7Byw/fEWm1jZANu1+9MQqo5zv3efeF77Ka7/6TwG4/I2XuHU9Ikdb9KY8\nwjL0iWTaLBzzzB5b9Y7c45CuuLxvx//O7hbv3DzJs+fWCLt2M/f2D7l3Y7/v0pESi3EegXvbzNlw\nmydCo/mcE10mvFYjMVJhR6TU31+cx2no+mnHFsn2WUsGlozOVVY4bhk4Y762z75cHxlaQ7NsidmT\nFu3b81EC58OWsxon4VTIU09UIpdEyVvO6iTyxLBw5IbJG3yQ/B7VmX22czm4xOB2DNUP1KTEYbV5\nzBMU6Y2/JrFs22fvbHPcfAXSMuOWDb7HecPgh5pzZdtVaYY/YGYv4eu8F7I8cm08YgUoznihvpHb\nxQx3Wlx1aBBA1SQ7k9V2qzt1cJIT75sw5YQSZX2rJjOl+/pGv+Ud5+/k2e07ubc3KGnKD0khP5JX\nVkg8Erd+Or5uO7+mlq/b+xbZDqwwyTcYby0m2rWynrA4wtHzwXd/AIB/5w//AB8p38nNl6377g4B\nXzarykQKbDarv0Zc7CKpONKyLOhJdTX4iJORuXZUFaXkRG7ouoPoY/fnmFKm5NIVRAKga1Z4zgUQ\nYmiTlGVtNy1+oWLpFayW7BGVfqFqnk37fAKoaH+jVqVnSq00Zz9xo5fcJOMyJp1Z0kJx7UZy5HKS\nDVQcosPaQdWMk3LSbG/4bev+1w6pA3fMgOf65pL7/88XAfjC//WrzK8+ZLy0A7Q7ROQQkFJNpOWM\nY9Lu5J/E8WC64qsPXwbg1f1LHPWqY6pySGxHYXen4pDFwWFGjk1wPls1VL9fGZOlVp5ZNbm9neAa\nuN94qQLe43qaqVbCv93oA9uOtyu1CVUVLzFu0ewpdcHYbc44jzahBRnRlAhhIFbt+s4NthE42Hv7\nMNKy5AGibBg4XzG3pCz+yE02ddmRh0x62TG/mQXV49oF9lrnkWb4MdsOphUEhPW81lG0rHh+ychS\n+iTloqVddgMV78A7XGOaWElBm0RKdcIPw8oTJYZOlqcexe41IM74z/GEJ1pyv1ftc/j1WldX+6Mr\nT5Sy9iMUM+5Z/TTWzr4vnifD23hm8w7ODrYLutLXSfnYX8/UgaEzW2xSlK52bF6hrZKXWnW2x537\n3Q3b31ix9HvCRF988UW+53u+h5/7uZ8D4C/8hb/A93//9/PJT36ST37yk/ziL/4iAL/wC7/AD/zA\nD/CJT3yCn//5n/+9vPTj8Xg8Ho/H/6/Hm1ai+/2en/iJn+AjH/nIIz//s3/2z/Ld3/3dj/zeT//0\nT/O3/tbfIsbID/7gD/K93/u93L59+xu+dvat87rjYtrxb//+j/Ef/Bs/BMCtywH/pQf4ygv0eFx0\nffs7uIGoobodQfKXFDcYaQwY/Bmo67nwTh1BEmVXMUMKy3EmpcQ5sIjiikOPFVPNG5YApcIFHo9O\niaXSTJx4BqcUmn3WloMOCMf6fgcied2iqOCKX7l7egAdWLVzsGyk2sOByDXJTbx6Zmmf1/4h1/GS\nS7FKVIKyi+fcLrYa3yp3OF7vmF6z1XN7dwNxNvoNkJwyFkesW5wblwjiCRWHc1cD93/jq9z5Q0/y\nuf/hH8BeOdwc2H/BZKVPXBdUd8Z1ApgFnbTbuXk3E4rjWHHAy/0D9tuHfMVZ9fXKOciw5Vb1/3z9\n+j6vpWuGZJVionCYbii+cStBnetu43evPdN24Xqw17tw5+z257z9YKqjqCMPtg42FTNOM5t8xlgj\nJnyJLRmGZVkMs9u2uIjI8eaIb/hvHJgqXnpMic12wzjs8LWqzSkzlUyork6TJE6LFclqmGVjbhBR\nDZCtuo1FiLrhwlUoY5vBLeTKiV30NcQpscpAl82rqAZi3e47GVG/QevnScVBFlxqkuCBqKvVniNQ\nCKYAtIvLrseeUhBwxdXdlSH1BIffNg4xcOZNkw6oywasugZguEft4rLtl0s99s5J3yK3kXLqlCmP\ngEudSTGkkaZqshdYbQg3ecOzy9v4luFbeHH4TQBezq8g/iEBO/chjVB2qww12Ls034mlRZe3+G4y\nTh25T4epOsvZo98tA+50vOkkOgwDn/70p/n0pz/9hr/3q7/6q3zHd3wHFxe2PfvO7/xOPvOZz/DR\nj370G/7Nk7Up8Ox+xx/5wPfyI3/oh9h+rV6YX7vhXDb40OnllFzWYDYU79eY1yw17KpPWpDTKss0\nwwVWJ7iibIZt115LNWhoJgjBe8bRrea6S8EpJ1sMqaTc+nZuQtye2JpDyRkBtmnfCTgZuxZf4kgO\nSor7fjz2u5dJ3iaZzEzWmbnU7XuZ2fot51ubNIdhRyxbwlzDtnRDedlx+Y9s0t3s7hCfOyPsKkbr\nMtmVvsXZasEtHr1nk8Urv/JPeOkf/FPu/KH38/AffhFuEi4Luwqqq3rKXNZYogV0kX58BMWpEGqj\nKLH0fCOwRUjQLh0sFEpJHKr+vJCsMdEubFeApd/YixwpKoQqpmCx5kl76BxI9L0JkLNR0hreI7pG\nt6TFOJSb0SapeV5wOMZq0jsdj90jYbfdMoSRII5SKUX7ww1pzp2nOYaR4H3nZYYoho3XSSI5T9KC\nr3huLg7NASq5fauF4AUXqmQ5H5iXIzrVi/km4t3APtgk68NCDJlQfQQ4CD554lKt9FKEIMhQJ9EY\ncNH1izXPanBHMwjPiXnJHRoLY7St/Gbdzmtw3RdCfLZM9wa1OIv+aDn0TcTZDFoa4TVra3SZn2k3\naW6SzTbHFqUUwbWgPR/6e+ED0W1528UzPDVZdM32ekdKQ6c3JgdOUud3Ovw6L9S3OyXQm7esdKjO\nnhKa5vzNHPHedBI1Te3v/LWf+7mf42d+5md48skn+St/5a9w79497t6925+/e/cur9Yc7W80nt7b\nBPDvvv9j/NAf/KPsvuxYXrIb7wluQdTeTS5ZyFnNNxLDOLKeKHyCgHNo64aLexSDXApLyj1YTVUp\nUtYKohKde0dQxMD8FUipj1eTAhVZyfC6x/sry4YBSopoirjcft+Adg21I7qZkG1iHq/68Xi4/RJ7\ntRulOAhx4Lzqqc/dbYJuiZOt7nE5x8mmxdCgKsSXJ27+oRHI9/Iyuw88i7yz/v6FwOgg1Ur3Rpl/\n8xW+8isvAnD52Vc5f2jH7umHZ8g+o1PmUI13j6PiS6giASNYO5wxJICSQCVTWiU4KovL/fB6nLED\nerBghpDWRdFlvIdc7O9z2eP9hrGGs+UyGduhaSnmgCwRrY0z9Y4iacWnsuDFE1q1VWCpjZcxjmyH\ncTUpPia8eOaatEqC7VirqJqXnsvC8WAL3rIcGeLI2HiXzhgmrnubCjEESl1Qc7b8rkb+Hv3WiMz1\n2opOkZwouX33iFs2HcPbzBd4v+HSG9NhKUfSMuHLZT0WkZAucHmdROeYLTcJkJDttToG6vEqlGN7\nP0F8xFdjAh8tvfMU7Cte1pz4mGyBatWnOFJZXb3afKe+MV2qAqtzrC114UQgZHhyex6p7Jj25ork\nxkEVXNlwK9zh2Z25at1++CT79Cq6q/eWy2SX8D2x1RnjgBXrNEw099c/LXwtyO8kuPBNKlHR0xr7\nDcZP/dRPcefOHV544QV+6Zd+idu3b/Pt3/7t/M2/+Tf52te+xoc//GF+7dd+jb/0l/4SAH/9r/91\nnn32Wf7YH/tj3/A1f/Mff5Zv/v3f+nt5+8fj8Xg8Ho9/Kcc/U3f+FB/96Ec/yqc+9Sm+7/u+j3uV\nEwjwyiuv8KEPfegNX+e//cR/yn/9G/8Tn/nk/8jb908xPtgy7K0CUPUc4562XDlnHclOy/AQwro6\nlrFWhr0S9UiRrtDRKUFSctv/KZSU8OJ44hc/zNUf/kewaM9EQkFcXkv8Emwr21UXxfws6+dZwhV5\nvIEq5aNsQX1/PZUj3s+U0aqd+7cuKbJndqbg+YP/y1/l733iR6HixGG8YIg7ttngkTFt8VeRsXpO\nuhzwQyRX6Vt2mbAIS+PmPbMlvOeC3fOGScenNvhzz1QxS750yev/+CUevmSy0M1hYDdveO5//kFe\n+6O/wHLMHPdH9sGqlbQpbMKmJwuURQnqyXULl+aM+MTNhV0Dn7/4Ar9x65/w5VvWnd+PExIWQuW9\nRp8IYaKD2n4Gl8gk/vv/WPkP/5uIuLFvkb1kPBfc2r8XgGe/+j7e/fn38sxDi9IdxnMO5wVfIzLL\ntDD6MzbeuvnMjuXGvsvZuMMLPHxw2b8LrFZqgxsZw4bn/94f4csf/TuUrCzHI7lm2IcQiGHoNofe\nBbxbd2xBqhqo8RJFKAi+YnRjGAzcqNXVPB9ZloklVbWZLqgaP/HJX/q3ePBv/p94NzCfVYqUHMj5\nmmWxx9EFNu4WUcy3IJQz0gAyNlXcQFGHa7ut4vFZ+i4CP8Bmi2zrdDA68lA6XdD9l7dZ/pP7eN84\n0nvz72r8SQngRkLDINWRUoFqNei9xzn3SNdbVTuOKk4gr91z8xalb8GdZJxPyJ8fWf7zS3xOzOEB\n/3j5vwH437/4C/za1T/gcGG7sGVzRAYYot0rMW6QOJDrLnNR25Uuzdu3WFB3i7AuOZmPRR3Owf/2\nX3yVbzT+mSbRP/2n/zQ//uM/zrve9S5++Zd/mfe973188IMf5C//5b/M5eUl3ns+85nP9Kr0G41/\n/3v+PQCe5Z3ES8+4nBNCNaBYFiSdcMnCSFHtJstFF/COsUu7AlPK5NT0vtmy4E8MPLSayQKUrLiy\nPu8IqC59kvTek0vqhiJe1Ywh6qQu3qzb8J1MRmGDqzxRM5o9oGPbnj8ku2sOgz3+6nCFpiOjX7GZ\nu5u3I1Vs4I5nyLQlRGs+bPKOkB2h4mQuJfJ0RKq5gAsTId3B1cbP8SuJy3uvcPlPzOMynDv8puDr\nohNuCv7Bwq2b+n5LRCt+uz86FoTrOPTG3DDbNrB5AUgpLHOi1InAS6CExHU0eOJBuOYqLjRYT3D4\nMvTI6OwXnFtwjYPnwXKMqOdTQXNv9hCdeQVMlWa0P2OYL7rps5lcLyw1TiQSrBnYIrCzsGnE/bjh\neH1juUfYYqpl9Wq1n9l5n45HliWRjkfjM2K8zLzMKy9RC2ZDvFJ2vHN9X+u90Yu6WrwkDofE8dqu\nhTwv+Ga9CF1aHKt/51i2UIThoUmgiz8ju5FUF9yj7jnInnlj7zduM1pClxwXEuKiBQNC9eeMHeMk\nOKMnNWu7UZCNR+O6SfWxQGna/cVEJZUA74doE2kz6ymW95Tbgh5MaHIqDsBJD5brWGun99mhe/T5\ntiCECkfseJu8DYDn4rv5p/NnuT4YjzeFhPgF30Ing9rM7FcTaD1JiFQtNhWFjgAAIABJREFUVZX6\nKKWpTfppeWPZ55tOor/+67/OX/trf40vf/nLhBD423/7b/PCCy/wYz/2Y2y3W3a7HT/5kz/JZrPh\nz/25P8cf/+N/HBHhT/2pP9WbTN9o3H5oTRJ3LzKkM4SBpelVg8eXDaHlorjAUlJvNDmnuKCdLpdy\nqYqFhtEpNGMGQBLIkvE1Wx0RMqm7fetc9fsN/9XqdH2C00hYSevqCuJSn0RlBI3C3FyffTb3Hqza\nWcpDlnzJsXbz3TBwcfY2zmo4HMDF8Z24pfIuj8JmcwG+TqJuxA0FbSoWPbAc10jGYWdVW1NoyQT+\nSnEPK/fOzxCUsyfs/ULccJwCrnahnQ7k2rlP2TG5zIHSuYwxCUm0G+eWbCYN7TZTlOQzD+ui8Xrc\nczksLBVn626ltaObPeBi5/W6YAtm43l6VzvIdWJLahP9pk76m+sd2+UWsU+iC16W7mnpNZiJdCsv\nsqD12EzLkbSs5jO+OHKmT4pDiH2xtLayTYSxvnZzP2qVkwIuuG52UlJhkaUbiIguII5Sr7Vln0mH\n3D+Pd4IWoWir3Bzex25+05Jww2ILdMBTguvO7zFE/C7hLmoT8GxGpgxLPZ8k+wzNWd8FMtLJ98Vn\n1C3dBzRXZ3g3rAu8c/PJxBPwIaAtuA6HSOgLqgAurKGORUt1pTqpPFmbO4pSraHq87Xn0DKr3Po3\nbvBoKYQcOSs2v7xNnma7PMF8bdVi2QREjqSax+XCgPkJtRQFS3d9xPxH9aQRtgoR7HP+c5Lt3//+\n9/OzP/uzv+Pn3/d93/c7fvaxj32Mj33sY2/2ko/H4/F4PB7/yoy3VLE0fqV2K682hDKC86SqvxVX\n8G5Yc1OwhMKxbnHUO5ZyYKk0muAHcA7XaBwUSKUrkEKxpG/tGUoC2ZFbCV8sprVTmETxIfTqQIri\nRCjV3qwwo0PCV9wpjzOMN7iadjnnaw7pmmOV9mmEeHGLi8EqqSfZEOYdY1qr9Yvje9BK6ZnSHpkh\nRMPh4saj40rxWnKipAFf4zeYIrOfOyVIkxp0URpNBItgnuuWK0ZkiLi52Z5pP1ZDMfzqLAtz/ftM\nQZe0ulqpVmu8tmILs1u4HAyTfDAcuR6VVLfvMcmJJsZs9VLx+B5jLOZwflKNWLlaqycfGXXLbrbj\ndXa4YJzP8Fr15jojZer6cF88eSmkyY6fpMCuRhDnlMlT7vEeFGFwAd9t87R/UnN4D/gYiT1TyrD1\n7pzuR+NiplXNljV1Pp162xGlyjH26vESVievQUDMtQtsO1tI665HktGjYtsFOLSMnRMd3Q5NE2lv\nx/4wXSE+dzgghI0xVeq58Nnbue5d9ELxpceBlOCQ4L9udpjWe8NtTfLZZJ/NI/IEusHRK89Sisk4\nv64C7UON3dE/kDPbqRZn4v2qdlIpyOBhiZxV3u1T26d5avcOfvPqt+34Hw8G5DVJ99fJOEvJJ1JP\nqhVefgQHVdXVhcp93ef9uvGWTqJn13aSh9l0u8UrPQs8mv/hauFvW6ZHjFJP/C2NB6h9EhKt3MR6\n4NKc0GzGFcDqMdj3owIrKxQfBEdcY2ZZKJpXH9qgpJBItTGS4g3z+BqpNgeOXJGjsr1VTS/GWyA7\n/GQ3/e3rDZK2hHSrH48436G4ipPFzDFdwaGS+eOCOk+uBhxsHKQRrVxb5g1sXkOaiYQTw6laxpDC\nvswsuW3PsxkzVLikTKkHkW1VGPFsdOCmUZzcYvZpzT+1KFpS18ormcktXFee6zFm5kAN2KvNBNZI\nBkUQFzsM6VxAfMDVGzW4QDm5EYSBUEZiqlvaZWDQDaFnVAleSm/WlKxoypS5ErgldjradFxA9SR3\n3K6DIbRJJ3RRheGdRnOaKt6q2bBxPzT6XWHJaTVwUduetyC3vEwouW+fnQhDYPXtDMXggGZ7mGSl\nAWHXoveOHGpTUAMu7RiLTSKFTJoP/dx4p0zbSya1xtO0zIzjGUMTMkgBL6sZjfcwOlxtLLkNMCid\nFAwVD215HJ6S18+nUrXxLe6aRycdizNZf9bOUf+Z8Ih3qmnlrUFnX0g6YFpqJr3D/AAAnr54muee\nfDeb/a8DcJgfEIfVAL2UXP1DWwFlXas2l+ScSCWfXKvVgGStp95wvLWmzNvq2jLYyqPioYV5Va56\nq3SKM6Z8a0S4YAt9wz3icgbFd1wKbGVX3zC4IwtLj0X3InhHV/SInw3QbgdOwB+3pn7ATCOW4Ybj\nxhon0+aSOV6Tavf6JlzxsFwSamV49/xZbm+ewk+1YzsFJClScSo/3TKTjxPnpiI3XcUxcoYkjxwr\nphkG3Haw3CgAnRmC4ivO5pcJDrFX2lbJawfzXVaCCO6m6ZuPxM1AqNXKPPquDppcRBwUlZ5gGUsh\njivup4syFdcrz4fbS772xMt8sXbnr/0NPmdK7SzNMqInXEqRzFg2vdEnpUA2+iSA+oB6yJWQHsqM\nTJ5Q01y3xws2ZSQ0z0wttbqrk6wMUARfJ0pfRnqXq3jjRjbmhtRFsQkFyt5MPwDKAZdhzorUnJ/o\nB1NAVc5wzkLxhSQt5LCwSSNxU/HnYYMfpOP73nm8eMNsAT9srGHXfByW2gCthdE27IyAXjnEaDJl\nWzPoSMKQtlB5oklvsT1c9KYm40zSIynYucrjGfP4JFoNVOao5O2B7aY1tDJCgqXNHs9BGdHaSNLo\n7PMurWmnduO0ys9b9Z1qsqzgqthiXWQekaOLVdvt752AnHjD4oyo74E5KEM1R2nO/zt9gmfH53lH\ned6+//GGMr7cXZoo4LzvKRSqlhLbfC1UU5XnV05yLibe6Mwg3nD8nrTzj8fj8Xg8Ho/H7z7e0krU\nV1mZG4NFCFelApjKIZ/krpvVirbobcQLTlx3186y2ELW3LVTZkmZ0DqqG/MTlVq5ohNZExLqahoN\n88z9A3jS5mbVM7uJNBxJVZEzxQN79msGkPM8c+t5zqsT/1jOcdNAaGmaWfG6dnazH+CRZBpqJEmt\nzNThiD2C4bhPROdpcR1eHCG6zrAqOdsWlnX1VaE76aMYZFKrhzwtaBBc7bCKaPeTnPOMV2fpm279\nPFYEtzeUtfON5eJMLJ19UKTgnKxejOrWDwU1WkV7TpAr1tHt2e/iTrgW4FPAL3HdzhNNylsx9OLM\ntci1UjYbTheqy9aGDcelOngt9tlaLrmlcJZOj1vS3LH2PRPeewYXOatWdN4bzUYqBUhHRc6k2+4t\nUtAhIWfVpyEUJpc4+lapWpXT1FTOHQ2zbNVZVkY34GvqwvRtB1P7HOr7TUrQQp5qysEccSWgNS/M\nqMNrXDVlQsoBrfh8mgpZbzqeP4xbJARCg07yUl2VmkMZIOdQ1XMa/CPXgjgxlsopXc3pul3XWkm2\n73cCVdj3BYnrlt1C5IXTcrWxNEKISArgM82G8jzuePbiad5+ZpSnr+4DR1YmDXkB58gtH6wk2+Kf\nVKpFT4KF7SN13PTNJsm3dBJttmlxZ1ZipeRq52bAuh8E1wjLBZaU1vtWM0vJK2AxWD5RqX6kKVvc\nQeMZmlVdQSo5nZQMpK56ZsJMEtdliYpn3r5OGo2MXpwwMzNVWeLiEnG75eLcyOzB7xg4Zxet8eGz\nAf/SjWxtkl5NF5LxEx85IitOI6oEceR646acKbNSeyMMIeK9oMkmkcS8TqB1OBF8ay5oYM4Tc8P1\nQkJH35sfPghxaJxcRcuCdxDaqpULSzp2Lp3LHudl5XFi2FNq8IuAOllxOnV4pRO+JddtX7Mvg9Wd\nD8PLPKt4YnM8ZzOfMVT/0pAjKc2kSplKOlGydr10KY4ooWe9l0UplVwdxVkYW8X8sibLMKrbacv2\nqRhc8LhtZJQd5zXPi1EsO6r2BKfdkYfykKtKZzuEieMTRi0COOqR2Se7XrGtqpaVf+grjarhv8EL\nUoSI4+P8Qf7u5u8SXOCp7VMAbMs5O3fBuBi9a5fOGacRbirGd5hhCrj6eX3e4PMtXLWBzGlPTjN5\nMl8GPV4S9gNy1r7fYAR8t1kvpuEcrddCCYKUQq7CCe8VN+ST7bdR40I1IFn9C3jk8Qk/rjae1nNf\nZ+76gxU/9bhKJB2gTvKD7HjH9i7vvmVa+s8eRi7V4SsU5EuhFDvPYJNj0RNNfDZvg+ajUbTYbVEf\nJ3njDftbHFRXgehgBgEt/QcMrC4ys1ReZFEqGN4Iu8nMg5seNhezMKwdy41GSPRmiQHVsXcMCQ5C\npsSKk+xm1Cnasrd9RLfXTNWFKCfTGJ9t7c45v7iN9+MadrUIMQ/4qpX36pESV6ec2AxJ6qThhZwK\n+cTdwLuwNidEzPG/FZK6mLn+VBtHoRlq1EouClJO8BtxiD9JiKzd4B4utsyk/cI81u61i/i4NjKW\nqVjOfXNTrzYFrTnivUecWLIi5meKmMYaDMM2Ll7FufA4lc7NFO/Iwhq2VjuyK6Id7KTVSXpbztnO\n52yOdmOGyVNIZFerS1dwGmsFCmRPkKE3EPM098JmGwecKIfU/CsdQTxSH8cUGCu/9XzYMmwGzs/P\ne3Lp69N9Xs+vcXkwtdkVlxwvjvBErfLPlMvt3O+uIwuzW5hyS0rFcs5rNz9QzCynCQPUkh6kwMeB\nvz/8Q1DlrDI5ztwFcRl5Qs2r4pnhGW7729yK1sTcPDFw5+YO4VB3QcUjZdPxbV92SJjRauid9xN6\nlSm76nh2PsD5gJyvHGY20vO6nBQjqLeLU0zssgaGKUVyb+q6Ew6oXRychMHZac6Ufu4danjxaVe8\nKQVTISc7X+3eIjnO05Z3jlaJ3vW3eTUPlFrwWG5pXjOgNFejqbZLqjzdzmu1/aHr1fL/B36ij8fj\n8Xg8Ho/H7z7e0ko0DHWlrPiHD76rAyxZY92+i3d4F3oluSwzXuiV0HbZkifwdTvvs6Cz9i2DeIEg\nvdJUnylxoowzI6byKH6iDI2bl5nlpmNsZ2fn7IYniNIyizYwR3Kl0GieEJm7okewuIKWuEhxkDLL\n3EpXRbynpJVGknMm0LZ05pTe1kBHQU80vSklSimMleYRNxuKzixzUzQVnKx2YzFGXHS9+lnSAZ2U\n1DKe4sqjW8qCamFJpWOopW2tu7WgdVh7RIVIfb+6k6jePM0H0vBaoTTeagKc656XuSScxk7RMn6w\n1Hwe84jcpjM2U+14LwGComOTWjpiCuSl7QQGhrBBWlxpwTBlrDu7pLknmSYJhLmwref6yVtnDDWq\n+fZZ4Hp5yG/e/xIvedNmH90Rvatgu0emOxPHs2Ov6mdZzMGqFjqzJGZJNPAmFSFnug8BucbQnIYQ\n5dz3m69e3Ee08Ooti06R7Ihp7D4Ku8OO82XH3WLQ0t3xCb6V53l6eDsAY7nNcBB8VcO5dAELiGt0\nuBn8TKm7hGV2kByx2TEBlOveP/CyUHCE2KYPU7t5bdE9xlPOjfMtxgFu96LJqct6LTkHkik940tr\ntHg7d0ZdFHyVlNbuftt2ZWEsG957Ya5O77v7zXx5/1VuKq4tOuHErRHOxT5jd3HSgDjXFVkGq2g/\nHe6NC9G3OGOp0nVyTqS0IKmgDX12Sozb1fouK6hx/wC8FobgidXkIOYNUhbTBwIkISCtT0MJRihe\nqr5YxmJeH2N9/fNEliM3ybbvEpTz3dNsgklTx7BD0kA5VtpDGpE8ECtladErCnPnu1kA2+rRiBO7\nqeoRj7KpBN91yxKrbyVAcL42MOzzzUthXmaWatc2bCJj2ND1yt4z7iKFGkF8PFJmelBecSYkGCuo\n6nVHKUvXVwuBWClnu7Mzkkss+7l/PrPQqM0hrDFiu7T2fJ1I26Ran1n1yI6i2rX3BY/TlXZC5eZp\ns9pL1rzpxsaL4I6BeGw8UY/G0m/s4CLhsCFUGW0MGwYiqR6vvJR6Y1YKTXB9EQ9hZIOwqwt0iAtX\n5TVuA789fZbXl9d49eKKV95uk+Tu7g69DTc17+owHpiHxBxW85q4uA49ZackKf27gOD1JDJYPI94\nv6naotOC1AZ7zbmGFKaSEd0js8EJfnTsjiNfq96yQw68kr/I8+N7AHg6votnzt7NrXOb9YerLW4K\nUOl7luq3YZ31QS8LUzVcGYHy8gPceZ20NgUXNn37btZ1aw59LkZjajJaqcekG8Y58wJuFCXxjpq2\nY48bON4PEF00Yt6gdi81BpZzDp8Hnrtlk+iH4wf47S99ic/V41PGBXJrIEIpM1npptaqdqz1dF/e\nPnT/9zceby0mqi2oDsYQLGGyZeR4ARdWo1VdoKSTIDmBspBme36abwBlqFhW2JjKoWUqLX7B7QJy\nXjE0vzAxkdyRM+CGaxYSuzuGq9y6e5uoT1IqrqQJRGMHx0t2sAjSnfc3hHirTwolF0Rcd45BnDXS\nGltATi6qOrabDXlZ/x5N3cg3YtVLarxQAkPYcKy5QFqEIQihVtohBLQosfIkXRUjNFUOOrDMitaJ\nYzgfiaPdVDGO+DwQ8shcwfjjPNUqsuGmznYF9c5ZpomyS9Ykwbrv7us6nuocqRNxLQ2167HVOvet\nUi05Glm69FUIVwJDDV8b/UgOc89gcsUzTDuoN0bAo7lY5js2ya5JoAsuRDbdP7PgNXNZGy2vXX+V\ne+MrvItP8tm7L3LzxA2HJzNXTxiG+MBlppCYqzesOkHVo0vjvEqv6uz97Pu3JpwvVU1W79rkB8pJ\n+qZqtgSHWikOcUBL6eqsmYWFhbmmhZZtZhlmDovxQN2sPAyXfEHNQevu/CLv4V2875bZTr7j4l1c\npCdxTahx5U0E0j6f20BxpOupf4f8FcHtaoGzy3AhcNbUbw6J2hszwYsp5JqXr1olqSdNx+Blber6\n5qC1Vp5AF85QJ2V7M4FsTaJDrXR9dOzKwKCGCb9zfAffMr6bl64tVPFq2aOkXhlnFlRdLxCKQlbp\ni2x/0/7PN2bbP8ZEH4/H4/F4PP45xltaiTbEb4jB+A2OrpfNJUNZI419jFBmqCoITYmcjuTK+0zD\nhjD6XnkvaSJLIleMs2wLaSxk+Yr9frmmiDMbL2C4NXK+fZpxVzOhXCTNW/r+3Du8870SValdvkbR\nKQ5Ju565hGQIkVgjJ5ABp874jIAcZ6B0Cg+AE4+rMs08J3LK+NDcyNUoRW17njJLUpxbc3ZyWfD1\n74chknPpiZPOe7zza/UrA3NZujM+6jo9yNZWg09ad9u5BPlE0KfSfsVeTsQgiGYlGCxVoDPS6tnW\nk8q8aFjhDA0oemJNOOBwKxwi9nqhqoRGF1lCqYlWhsENeTSLNoz3qgli6+A6pVRKjgRP3EZ85W1O\n0+tcTdc8KIZ5vra5x/2nbSv40nNf42u3XmPaHmnXXhbAO7TJHFMkLFtTRQFaHJNfv0sLqxRpeG0k\nqPYKxsIyPKsroiBu5RBHH1BXiDfGTJjLwhQmcFZ5Tu7ATOrqLgfcP8u8nO35O9f3uNy/yr2HXwTg\nufwu3rl5nrvbdwBwNzzL+eVtjoe6M0yBIOcMulKcwtVd9MaOdjmbkKND6i5Qd4m8XfCbCg0Fc0jS\npcadlGLeds2xy4nxnXsJp4axdOgHKLnLQ7WcUOkE+z3J5HqtOz9YqmtNir3jbvNNd97DP1rsXr7i\nPpTV8c05j2juNo84O/6PZCmdVKVvbIT3Fk+iWieYZYjE4C2npXU3qo1dN75FcZ6OMyWfyGPqmOeo\npoJLlbZRmMl+psRKoJaJw3Ig1Bvh1q0nGDYX+Mplu3X+HpzsKFXb7mRAltAJ5eIcOA+x8TgzLido\n9lnFQS41Mxx0UBCLbba/DzgN66QZC95JxU7r8YjFzJ8BJ5E8zZTZMFrnPWMYOsfOIidybcqB02Sf\n4RTcX4rFMkPn2jW4YJkmVBPaDEbyGuUhIUAoEJR8rDScyROyZ67SwMsBQl7YVThjOwSKKEvd0i26\nUGRZKVuGotK2SYWCuIVQ1khjFpjqRLe4zJA9vhqknN0IYb7NXI2Hp82CC1dsmzhh2VLcQnBN/x4p\nJa12dI4eghe2I4srvHYwDvDV/h4pFB6e27Xz8lPXfP7WbwHwue3nuA5XaFoYpAkTBCZv/wHeDWSZ\n+oIj4sjJd06sq6bNdHK/bX1d83HQiDsVmqgDF1YapduSi5K3leeYzUAj1HNtDcjSyeQEIbJHK4f2\n5izw23HmazXk73PHr3BXP8szg/FOvym+j+f5Nm6d2+OLG8HtA6TK88Sab1TbwXB9jmriqHa8lrzg\ngF0TbiSx+6Zq970kMku3jcQF8CNU2WmLVm5WgbkUXBa7noFSjJzvoOJChomfp7oo+Qx+j25s0YgF\n3qtP8vvKewF4/Xifq1v3OVbfiZQcYdo0vxWWYWFmIZaGkebaGG10RN5wvLWY6ImfYFZFpPTVyYvv\nUmLAvA4l90ZF8kp2ghsrphcyceNJdRI+zEeQhWN1/5aQefKpc868NYq836DFk1qLNu9ARny7UTRY\nI+U0mO4UYRa1aqo+dGo/ahYmJWfIiq+8ReuEft1K53TFhYCkmdDEASHAEjpY37TJqq3StWCtFnyH\nQJoLKTXwXHHeryYRRe3m1fXlSPRuPtPU0x19CPgRUhZSg8VcQBbhUF9gKhlSYaBxHR3iAyFWDLaZ\nEtdJ1TW2QK0eNAjuJMXRYSTnrkCqvL6eYLkX3DQgLYvdCSEIqWKPOZlK6mzTmiViE3q9ccPoOn56\nNT/kwfVDpkqO10HQTSRVA47DmHjozCPhWq+Y856syXB5wOdgZhttApcJYV0gRRxZh366gw/EMLK4\n6jjmIt6HLmQIFDNg6Y7UrvppNkzReJa57kqKK2jS3hQMYpOo6zsJIea5Y5DZCctQeiW+uMxxOvJg\nMp+DB+l1Xsv3eOf4PADvHn8fdzfP4a8MY4w8Dcw9JUJsRcLPVYgxOcQpWhfUIhkR6X6i+EwcV/Vh\nSZmkrvs2GIwt/fsYZVgo+YRg3zr7EyaayKUrzkQrqbYVFDJye7jLe3b2fT539RWO840ZT2Nd+Uz/\n9eq0n0n1XjUP6DW47jFP9PF4PB6Px+Nf4HhrtfMN76Ryw+TEnksqjNJdjgrKmuWdBTJq7k5A8a9z\neZh7fMgwDsRh4Mnb5gw/xAhOcBUn0dlb7gwtw2eLyID21dxXnLZVy2C8x/q0C0iQVaSREyra/UdL\nxjLaW6XorMpqugyLg/Wc+myVk0rWoYgXpObEpJwN16nVi5eCc4o2DJZCKdorwXEYKCl37bzmQilr\nlraTiJPVyabMmdw7/0IcBpbsWFpq7lDIy7rlIln8Bh0OKNZNPWGFeOd6Ie9FCGLVIxjqoLLGsRSH\nib5dq6QXNMWuehnnLWMa+pZxBHL2HJfGBvC4Ya3+8mL5WE0fntyBy0p5eX1/nxwyrspcXRxZQibV\nyumgE1M9rlOZmcqC5plSmR5Oh+pb2i6GYOivrJhy4dgpSl4C0Y/dei/4SPRDr0S9TIQQu5Uc4hF3\nghezILpeSqiAuJNc9MapXHdKwQ19O7w4+2djjixDQhw0ivIkN7wWXuaL7vMAfFF+i3eO38RzwSq5\n9/A04eyGVNO9NQzGw628U/bRIqwrdJbc0eCg6oXrghrmruu9rSipVtahH8pWiTrbqOUV2utEuyWB\nGjukx5s4QCJSOdxBCufn8M3lDwDwheM9rq/vc+NqfEjck8PcUzRSMQWT1F1sabr/5nX7Jlmeb+0k\n2qzBxlCvgbLmwjtwIZk8E8Cr2XNVPXJKE4dlb9k4ADqxu9hye2uY2TheGNjcqCaLkudCnmyLkheP\nBNcbM1LOEHd6OBwndh5m3nxyIXsXYAg9QhmdKYWqu8ZOQCodk9Ncqqysa+GqUWzjVYIbNt0DsSGI\nTqtn5HTsJHgwuCCVspL5gRi3/UYWYBh8D+ZbykTOpxC5I4SBTYs/Ed9jacmKBPDRd8/M7Ctpusk4\ns0XgdnuzQu8P1K+Hd9oXmagQJSOhyni9iROaf2xxBfETubaKUp7Ji0drlvpmumCcR8a2hSajxRHq\npDqEC0SUZV/xBwU/KhN249zfv8r9ycjqZUjE80gurek0cnCJZajHKiSW6rEwyZGkc12wbBSZKXpi\njqOnE6phmqrSJ8WskNKBIdiCuLjA7IJJZQFPJISRWM1SnIt1ErW/X5aDfeMTiXApZe0f0GhEJ+e3\nyGpA7iwPrFRoI8eJg85IbeI6rwSUl5M1XV/Kv83b0m/w7vTNALyH7+ZrF5/l4twKkpCfoMyJUCW4\nMg0QI1IpYy4oOU6ERg1aioUBVnydEM0Csh6yWReTKDfT6CB1PjihPKkCniDOLnFPZ8GXIuDiKtNM\nMISBZyvU96GzPfu8ZzrUJjRfZh+OpHoCl4w1lvq+vNj82aJqeOPxFmvn61I4VA24rAoVfEbi1BMD\nCzNzvmE/202Rw8z5rR27anrswwC47vEobCA5tDYmyqzkVJDaMTSzStcxOWFbV/e2vBeKlm46XJfP\nlbcoziqCXrgOVjm21Sxlcp6JDRMc7E+yNIGB1t+pXDdMdXR64XjoWnAHhCQcp9VLIIZwknUOubh+\nY4lzKy4JJOe6AYd9P0HC0D9+Ltr9IdOSLHiurBi1umLNqXalZVe1+vXCl1BTHe01xsGRB3r2+EAm\nUFBX++nuSJZDZwQomcSRrK3fPuPY4utEEKcdY9owNBcoKUxFKM0wxjmUqatoSins0yWvHYwr+TC/\nTh4rprkNTK5Af29lcYVD9QPdy5G5fs7ikyXKKqthBYpqWh+XhO2l/MnzxhcF7JpWIVUttycSfOxN\nN0cguECok6z3wRqb1QHsuL+uBj326mbQsZrVGFn8xIehurhLncSSJGadWRpPUhdSPq7O8QuEpN1Q\n+6E+5GX9Kl9ZrJv/AvC/Xv08z58bz/S92w9ye/skW6xg2Rwychzp00kRvJxU1h5Q6QotCRHxkeJ6\nKY2G9dJXMQptW9PdCZNBELsmT2a2TEFV+iIk4pFl5HZtMv6BW8G8C67t+02HibS9x1IXbBFwrCkX\nXanU2iWPeaKPx+PxeDwe/+LGW0tx8pNV9HECAXW+hW+aS5O7IVfj7uDSAAAgAElEQVTgpvgMURnO\nrJLcXTxtK12NcdB8Cy2uU3YoA1KixS5TeZzQKUGqtqK1OIpuqd5coYzY17dctvJz4iajBmd2+CEg\nQ+iZQSUlXF49C0Vtp92YkwklZcWN7X2BOKyvXxaK03WLIx4ldJcoV6xLuokt4iJzOOa+aooIOee+\nyfTeU8LKEy1J61LfKEiJUulPuiSSLORcmNPcz8fgHb4dz1xJS62jqg7vhKF+nWWEJWZK3W14zThd\n0Mpt1HAEyasShUzxB4pURRkLTi3bHmBIO0YGQsN7yoLhwJUXu41cDIX5pjrtHx5yme5zqXULv0nI\nrnrPerGqrmJuS56YY6I0f9AhUyr/RYIarlv6oaLDZa3bLPaJV55jMSegvt1PiPrOS/QkYljokt0a\n7S2VnmeV6bZXotdXl72ahcpz/HpepRq7AawylaxrRxtl0USqNpOLLqS88iSHKcAcWZrfqZug7Lmf\nrmjjF4//By+WzwHwLelLvO/Jb+Xbnnm/PbmfcZcjLtdUiuyJOsJUr83gIJxguFmQuEbTeOdRWYyd\nA+C8RXU0p3t8/y44aU2U1fgxmmPZCscJaCRWps2TLvL7t/8ax2zH9+rhFcf9kWPoMRfkxNqNVzVp\ncD9/vOF4a7XzzWkrzmQsgiG37WFwMG6JdRJxG2//bqStReAIVAoMZWPk7LplKJlHtkTinAHE3RSh\namVPc16gT2KCUES6KYKF1NGPrBOjSvRGijjbQldt+hAL6hxIy+UpkJeG9VPEEcaI52QSFddxGMXC\nzrrVnytIEFydRLW6xvaIC/WoHim5bdmMghUa7cY5QgirCbJzj5Dbk0i3YksVMkils3jwg0Nv1htT\nsLAzaVnlmHVfC+rzfiHEtE6iZcGTuv0a4WjBgG0HSgI3Uer5QTJCwddJMsqO6CK+4nqOzDhEvDN7\nuF2IpOM9Hu4rbWe+5IYbdFvFCluPdq23B/X4ut3Lko1/3HPSizVDAB8M0qiQXD3W9b8GdWCE8H6v\ntef7kTG4ou22c0nkFFYbRazp0s6lIxAkdbz9+vLaJtpTm0R3kjdGo+m07WjV3Z98oJK154WlnMlZ\naeiOLsIyNSkBqIjRo9q5Ar7iXueqGnrc2z/gK/7z3OMlAN63+Xaee+q9nB2saSuHkXRculkPyRmf\nqF0rsZLtu6wzo3Ls96r6kSLSj29SIaQT1DkbXNO8ZBmdEfzb8U7YgtMwZiJv5718eGff/3J5wP2b\n17isYoRlyKRlWn0vet+iFSS84XhLJ9El74nAogeyF9y4YRhtZnVxQP0FEpvrklrIWs3J8WnAzYFm\nxSOlgf31ccM3W+NFi+XWN+d04wP0ScoMM1mPmLPuX8c7Kg7VueuO2nY6Vfl4aP6hMoAkSl39c0rW\nExsq4XsYcESknIA73q+rrxSCKK7pqQsWbFa142k20Ci1K1/EnGjqw1IU7+FUn/9IWFixida1L5S1\nV/15SQb8+0Bojb3o8d7jc8PxPJKLiRDq8RSpxGes4xrCiom6nPEsaA+5Wh5ZlIpOlo++CsTw4roL\nleBJufTj6UMhSen+psfrPXL9Godi+vfFz4QRtHI/Z196I0FKQNh2Xqd3M8E76L4HiaZTCc7jQiIj\nFGkuUHVSPZmktO1UTn78iJ+F+F5pFVWS5tVRq9TDUBtdogFywrdJ9OqmNxrBFD89eRPWZmPbxaiy\nVC4kWNKtZIdWJkNKSlkKS/dTzaSl9OejeGOBnDBHLg+JvTNFV94dOOaHXL76KgAPdi9zvfkgz7tv\nA+BJ/w58CmhafSf0JBMQARdKPx6LLmSu8GM1x8EmwFJOj6RNsdqwXl/67CUB8Nq9B5wqUkpPvRAZ\niPNtnuHdAHz7+fv5LfciX374JQAepkuC+DV3XpUGvdrf84bjMSb6eDwej8fj8c8x3tJKdNo4W10u\nblnOi9v0kp5iWm5t1nYLuKJGlQCWqSDJEh4B3BIxvl7jYVaVQ9sTSDZ5bl7LBSl+5fYlqwIb79Tc\nl7QvQ8E7U/m0HYhX8uC6c42UQkwZak6PloTm0rO+xWfcqHDWtu/n6KIUPVHslJNMolpJd625h5Kn\nNdMpKOqEkluH2VksefPvFOMGJm3bY0XcmmlUfEa19OezLpT65VJecDLgvOCaU3zZULaFoWUoHTJF\nj+zrlm/OR2IubKu/6RIvYbiEqtKRYhhhqs5DUzwyZxir81HAoy51Nzac4hkYl+rWfrPFJ2Wu1cbO\nnzGUwv5omOfV/oZSMtNotJvsPDkcup48zdKTQKOPoAnfKsGgXG0OvHZhPNLLi9fIo+GBYcyohxC0\nt4tTaRuGBoirWfhJf/RI91jVtse9ma+Qsnb2HdU6tHnLalJ0Tmi1qnv4YEEYaNWxd9V3t1v7Sc9O\nb6MY6diOdTEOb6t8y1LIZb0XknrmpB3fHqOvPN8Tr9tDgMG+4GvDDcuQ0Yobq5+5nB9yf7Bj9i1n\n38FTF+/k1uv1VM4eOeygNBmpI4Xc8e+FPUwZP7ZdYrKUhb5JlEoj8xSHqf+CrqkIrZXfKtdcKFLQ\nBmWNDlcSQ921vUPfzjfp+/hcehGAG7dnHg5dJG+7WXdi3vTGiqXf0yT64osv8if/5J/kR37kR3jh\nhRf40R/9Ue7ft4v3wYMHfOhDH+JP/Ik/wfd///fz/vcb2Hznzh3+xt/4G2/4uru7ptUNF3fsIsm+\nS8ekWLBcO5KNkdgMLhadmPPMXA1IxhQJblgzjTKPZEdTeYut+C7JDCr6tigFOxpN32vHsXPPTukX\n9nqCi47cwOyc0DR12ojzDmEgVRzJFTNVlqZtB/NhPNluq+r6BiIm5GtxG2U22WEF/3EYB7bFf2TL\nHWrb88mEk0hs8AJIzv14pjQxzQfyYq/ng8dVOpndkAXV3GWXmh2LSGfODmRmKSyhRV4UYhZCa374\nBOHG/g8MJUCm04iOYr4HzSsgFEG9sFQsEq94icRUjYfLlkgwIxqgMDLvL5nmun3XmWUIpCpOKF4o\nMnerPdOnV1NiDSAzvnFOB8d+e+TBzu76m+19GEwu7AazwRZPh4IQu636hC90OTJg0R6ywieC3d+P\nbAvl5HERctETClUml9B9Dg7HDDn37a2QcLKau3hxlTd6UiA0XLCez5Tz2nQVXznNrTFVc+TbpaTg\nXO6XFlh50nwJoh8gFo6j0Q0fhETUwlivvewOXMZX+NYL297vpnPi9YI26CmcE3To1r9hmRAZCNWL\nQCbjDLs6aauvBkV4K0S09jcqtKeTQJZO78vZA6EXIOIUt00NaeNufoJ3T+/hGW8GLF/U3yKzSpBN\nzi2Na7/e899gvOkkut/v+Ymf+Ak+8pGP9J+dTo5/8S/+RT7xiU8A8Pzzz/OzP/uzb/aSfbjqEuNk\nrMBGrf4AshkwN+6bo3bKG/c+eKKGjgd57xGV3qFMS17NXYFA1amPq5Y9zabP9mBmJ+VEYIR7xKTV\nLtCTlmAF7vucikPDgDS3fsxBqfuTzAV3UEKdVMqwR9QjJyWLyFpJqNqd2CatnDOp5N5ccN6h4nDV\nPxPvrEQqK48SMfd8e2wejP34uYRzvjfOSlp198f9BCEQhF692CyQkBZGJs74jrUxFhnxGju3Lngx\nJUr7esmOX3ve7rew5uQ4EO+QjqlGog/dtUrUQSloVTQdp2tubl4nOavMNRSQ3LmFpSQ6JQIzCem5\n4lgqaKpquHmcKeOBxdfgNn/dRQL2K4Nhkr51z+vEuJ450+X3WVHrJLpeQKKc4N0gktYmI56oiaVX\nQuCDrnlYWPqoLKsvgIOTBbayQBpHWARLwjzBcIun9LyrOrk0/FkKcaPdAN15iEHxJ9f/uM29yTvK\nQJSF6CvvdTig/h7X9dh/tTzgmL9ArAvsM+PbuTM+Q92EWCDgfkOony/mC8owrWyDXCg3GTmrn3NU\nJBYgIrKHtIHlJLl2KWb23bX3lrJQIVk0ZDs3sTZ9wxl3z+5w64HxXOM04oP0tFsjqvp1Z3Fq4vG7\njDfFRIdh4NOf/jRPP/3073ju85//PFdXV3zgAx94s5d5PB6Px+Px+FdyiH69vfo3GD/1Uz/FnTt3\neOGFF/rPPvWpT/Gxj32M7/qu7+Kll17ih3/4h/nQhz7EK6+8wg//8A/z8Y9//I1f9BWFp9+kVn48\nHo/H4/H4l3j8MzeW5nnmV37lV/jUpz4FwO3bt/kzf+bP8PGPf5yrqys+8YlP8F3f9V2/awXbhv60\nIn9V0P/M6ArFl66qdE6Q7NfUVOVR8t3pzwAQmAq5k92c7bd6LGul84ztDWxrrhPIT0L5cdN7N0qU\nlswiuWv3QzCrso5EKZDopsc5G/+7mSbEIjAdWA4Gtpd8wPlE2NVDfl6t09rH/6+egx/7ct8jasF4\noN2qL7GkG0qe6utlUE/oZFtPSEOXdh6niWWeuh2ar+FbjWCtMqEld7hBk1K08I5f/h5e+tf/DjIq\nfvQ9M6nkgkTpZP98DdN+5jga1+5y85CHz32Fz3/47wPw4Omv8crFbzOJ4WbjPOKS4+hrLpE/4suW\nIY/9dMzDkeIT/90PLPz5/5e9d4+5Lrvr+z6/ddn7nOf2Xua9zIztsY0xmMSGhkBUUqUSkZJCVQko\nF1chSv8gCpGiSpGiWlVKpND8gTESok1TIUXlIkVNnFgRohGSKb2k0AAiEALmauNg47m+816e6zln\n77XWr3/81lr7vMb2oPiPIdKzRqOZ85zn2Wefvdde63f5Xv6353nLa1/OnRffBsC7f+M/Yj3fIG9r\nCjtP+LBlVz2lJikQ1+yqRmUh49yuNxpV14ivIsFBSWy4XNu5P779Kq8882959favAXB58CpFlB/7\n68p//b+sKKxBwZVqMaxacZZ2baeUyWmRGcy5ZfaN2NH+XciLWrSXotLsrQ7dcLpbJW0h7Ty/+k8S\nf+KbItM247d2/jpbQt5MG4PIAumzL4947cSS/SoZ7KH4Gu7UZbzPHWwuLjNGh1TpvP/rI1f853/5\niDFYfnzoBsK6MFTcZRwNChZqeS4U87o6mG4BcJdneF5f4L68G4A7vJub09u4cWk9Ebc9BDfuNTAU\ndYVS/bPKaoLDRPzgDcrfO4VVqJCxPbGgxELJLrXs1tJ5r6ibkdnmnuyuuMgv8X+//n8A8BOv/u/8\njv8tToP1ebRgNdFGRPGF3/xHn7+59O+9iP7SL/3SU2n80dER3/qt3wrA7du3ee9738snP/nJL7yI\nVgCtZqvPFBVSx+cFAnsFezU5kF6D9LYANR8W0WLA8E7gsSaSfPai27vzS9cbrPlXYI/7nkz0pOld\nSu32l71jsdd4ctbM6J8ntjGEav42T6l23u14aQvBWcd8GYVFgaV9ROWCa9V32mtseQkdnWB6mtoX\n/bWMeClsN5t2+eocXWqsojDUbrriukBJIFDmmSJpKQHnjLjQfey9d/hQcM2NMwuuSOe6O6240dDQ\nBw5HxLu2qBf72R4Lx/vQ9VwPyw2Otzc5ujD910EdedoxVzX14IUkmdREnp3ViHv5yllNsqEri+zp\nCMhEdhNXK9vgzg4esD14RKqNEokQahMqhpU9k6oLW0ytptk9erBNetnPDXP82UmeX6Trzee8FUVx\npDR3QW9ptfbaFBtWxqDpUy8YPraZGDpxVsfcS+ok5N7Ug4r06DU/+5C26Hpx1qhqny8Qwkz0y/nH\nMdHMPYMvDKMnhLZKOXIOFK0CK7pCCOyCER9Ur0h6xUVVUXrEY54LTyjj2wE4nm7A9g55VxlM84qY\nV7i0IGfSXAWgX4dy8wp/GBhi05YthlSpc8mU1LRjmgEmodfXY/aM04p7zhbxO+4en0z/DtdKzio4\n8b2Rl98gWf/3xon++q//Ou95z3v661/4hV/g+77v+wBrRv32b/8273znO/99D389rsf1uB7/QYw3\njEQ/9rGP8f3f//28+OKLhBD46Ec/yt//+3+fBw8e8MILL/Tf+5qv+Rp+4id+gve///3knPlrf+2v\ncf/+/S94bJPuqvQv9XgvXdlFvSwcZcxHXVX3YCGl7qj2UqI3Oa2wpDRMdHsPC/ULvQXqDEdWxPrj\nxVM70Pa2HwIxLimSplzpdU36rcKlWniQFV/ouNFSJsQvNrnBR0S1M6qYPEkWDyULEhcz8qKWiIce\niVYWRePKzzPifMeVppwpLneNVieKbHOnsCmFnEvXW00lESR0KUCjYFVpNg2mMzqlnmK5YD7w/fw7\nk2gvh50LkpuSjzNPoRqJajHKbvs8Sx/p0n9OAt6tuofSYTrh6Oomh6fWQWVn/PMWTahTZjLacDhu\nIGvsHW/vHd6FHrhrKSSq5bBsyG7Dbm3p2/b4IfPheaeImqx/Q45E8BtUM1I9lARDfjRIb8CDLG6d\nFmzvMZgMz9YjPVVBJXftWK1Iil4pcsZYbN35MGDvNcvf5HCS99L3jAGpa1YQoql79ciyIjWa/bja\ne10WojiCjP11KQnnlLDHMhoGGCrkyAfBDX6PrRbQ7HvWolqgTGRn1/tcEhp27AYrnzxxTzh3jzjP\nLwJwa77JiXs7Q3WdGK5uM25us5Kq0FYsiwGIu4H5aianmbnKNMa1Q6Lv7LYi1cWhR/ZCDrKHQQ+E\nbeBGsbl1U24x5BWL8awa0mMPMvaFxhsuou9973s/J2zp7/ydv/P0gULggx/84Bsd7qkhe9JdIoZT\n6/YRrqKI2lMggviFhtl0Mhvg1vjNuoDzi6U4C2DXsI+uXinJDgkOqTlQCIbbRJvHEbXW0KeG1bE6\n7qQmc09BntRqFBh43TmlSN8FADqVz6cDEhsaOc8BOU00xK+ImXO1ieq9J6unrdoFtXplS8mCp+Sy\nmG2VgvPCULGfKRdSnju1zTszvWvUR7enRebU43HG6W5mbG5FInXLZsmYiHNs5ZQMOXUa6+BXhDh0\nzysRj/Ox1+GCz+Byt09xYjXiWKqv/DQSLgbGi1rzTRknaiULIGUD5jeygpnejcvC4bMh0moNOOWJ\nuVvLbMDP6IGdmx5dktcbUmiL2oCrD7BzwSyWAd88jdTZdWv6nbXs0SBNpqng+r1QtVJQ6Xqfsswf\nbJ/ac9PuuNK2SDufcQVKFcywBUKWAEJNoKb5dakrCL4vPIb0U9rcUjXpPtfzUAXmXj93TvFBl4AE\nCFEIlbIscTRadKuFFcMft3p9SoWSIaiB67dxpqyumGL1QFpfcCmnXCaTKbwx3eCF+SE33VvsNW9H\nslAqr8S+R4U4TRPhIhhRoS6iOjtk7ToO1EWDQnpp5RYle/raMCSQLQzVo2lMa1yKPS+3vcr16/VG\n480VIGknrQVKNVprWHdVyL6rSvvKzW51phC8RUFt+0hCmpUytcjIMcTQP6OI1TZKj3xMYKRj4dxT\ngW2LEXqkJVIFH/oiLTwl9SKgwS04TQVxhdI8jzBvnUYiduKRWemyVdiitHguqTG0cuWaO8MBdv3R\n4E3FyjezroBPmTJVFkiZyJJILXqRgvOe2HCXmMBy7jXlpeArrlRJ6vJ0nSw7yq42mnYZLXOvszlx\neA3EVkuMQvSRuS5GwQWC185nLnKM6LTHjVcGDRxOFo0cnj3D+vUTTs5u9utZVJboLTjTpGzce/GY\ntkFd9Eshy64D1ouOaI2K51Ui3Txle8O44OngEqLDV0YTziEtQwgQ/GBRdT+2Q9PcH54qq9A3GC3l\nqZqnSumOB3ZnQfd1GYpFsm0qFawp1PS+w2DztNldSTNPbItYVmvE9gNWkZ39D9z/b3W2bIGWqKsZ\nSnu2qlvnHsrcyeLXJdqaL03tRyllZq6Mq3meSXOh8SZSgXV03Vl2LJe4GDgd66YWz1CfeLg1ssNd\nf8492XBSF9X19hg3D8ARejnDPBDHYclKrxJz2lHWda4S0MF1HLCqw12BnNXXVxGVwGXVd93MF0iZ\nFsR27Uu1+/tGYPtr7vz1uB7X43p8EeNNVrZvvPKKwciF2GqiYlzrLm1X6UF7JBCURf7LpYiftadv\nxtLQ7sPiRgEfuoNgPWhvhhtbaR/G4NC41FWcOCTLIt2Wax2rKclEseijcsGdq7Wout07PM6vSY1G\nKTM+JaQZ3YBZXdSUSVPGadmjmVYaZotOQmX71Mi3ZDFvmlo2KFTPoLrblwmkBGI74eyhLJF9lkKJ\n9WLHYqk5GW33o8zIzuEuaySYZ5zLnUEEDl/WjM5qiTuZiU6IGMzFk/HutG/rJR/hdA2V9eIkcZgO\nuP34DgC3X7rPyYN7HFzY308hU2SiSQuaFsC4aEDWmmFDU2QSypYWJ3jWpJq/zgePOHvmIec3LZ2c\nVhc4PzBWWb2d24JvuWTC5xuICLNrqkfO0CFtMuZ2jRqX3Eov2rKAWitvkWWrd/dmfrKosrHDVAsi\nntDvdZ3/fa4IZNf7Bb66NPQszlX0wL5Su7b6FGhx5LI8SB4h655/GAKaFnhh/Q7dGUELuEhp6bIG\n5uLZNS2CDHOacXpWzy/AdER7WFZE3IEyV3jcxTBxepQJG+vmvxIfcF9e4jneAcB9/w5uzc8RuYtz\nMzkP5ulUFb3I9qyUdr0RVD2plprcHDl8uEJOW74fOB0nPn5lNdk/2H2KyV0gLenDaMwdIfYGq+Sb\nuogm700KzwWcignstnXMGRgo1TQo18ZKa5yIQGERABmpKWrDvhW1BaZdCDGIiW++bmpCwWaJEWFO\nVlZoM9sHkl8smqKAeMiVlqiS8YMs/N4kSMYsOQCKJ0+73qwJwXj9oWHPZkFdRJo0HLWJ0Y3SraDb\n9DpVM947XDU7kxAhFXJNSUpJUHJ/bcIieU+oNyNOOkRJvKekpbZsdbxWxAAQqzdXTcasZsvb0mlE\nbMEq+7mOW66/9zhfzeyw/dJr6IuoxwEXlIZ11BscXdzh9qPbABw/GFhpZLeqH5eNrtgRaqlQ3OLV\nLlUebiO2+CXZELQwNL63+m71ErwQY1xqfEERX7ruQNjzN/IummShE2I915wzSemNmpbmLlJ0QFlK\nD1Iboq3JJ2I/26u2W0+gNTG9M/uY1qREKUJv9CSnKLlzxVPxiLrexHQCqThr5gGo0Yd7C6Lf6vYs\nKV4U12QLxRpzT0UsbklrW/+1yYGmVNhOmavqbzVNmTmVDrkao7KNqQuabOctflKkwutCWOPDjBzY\n51/q67zKxHmyxt/D8oB78R38Cb6cl2/9LseXb2NdTroeLPkIP3t8E2i/3KE+s6olGZl3MG3RCnHb\nhjM+cfUb/Nbj3wLgtfKEq0FJizSCNdbqsxf/OC+irR7mnbMzX/oqNCFc6ZEcNsnbIivWdet3VhVx\nhZBbDazgitJVhRW7662b64VSCllnIpFZJnwMVvsCVKo2Z4vsClAMJWCfbw9G4/bPOyVO2kWD825m\n3s7EKvUuYbACWyvZtlpi6/ZjHWwpVaAkF1Mnr4usOLGIo+mj2lNJ2z6LTqCFUqV1nctkFfLcwhWL\nXmNT9p+FkhK57uaiy/7hxFF8oOjc4REuevwQSLsqODJnw2B2kYbq5tj0XFWsTiqtY+wIGruSvelz\n7ihY9Dfu7nDy6Dme+bQtos+8dpNhGthUrOOqA0Dr8b1DyxIpt82qG+WpoCX0yLT4iXJgD3m+ccF0\n85JUX0tMILk7yxYXe2fcuRXBr1CUVK7qpfeEsHTfrSu/eCCVAiWAS0skKpS+wViteelZZkPH9zVL\nnaBeu9+Yr5laW9K8wszi/WMe9BZNtiukczT1I+y+Fl2enUXAuS2iiTBoXwx8Ma3d/bxMpZB7U9CZ\niHqNPLfbmd2kXG3s9TS7Gn23erqw2SZCVfTyHuIgjPV8Ew5fHKVy87c3LtmMj/BVTOa1hy/zB6ef\n4k/w7fxy+X95j/wpnh3fwWG0rEU0whS6H5mAEQWqShR5Q3E7HmE18E9s/x2/eP7L/ObVxwB4IK9z\nxY4FeCLEwTMOLQD6/EB7uK6JXo/rcT2uxxc13lzL5DAD0ayRi0UPpBZZWMHSd6l+wcLTuu47Gi6o\nHsw8d6SpOolak7LnIFh3e6gWxN5RNJFKIgLJK0TfITi51ktXPdKt6KU9FoSfhVwjpIhJ7TVpOVEY\nVuuF1REiqnRVSQmuSist16OkWmuDWrPMNF95cc6KM00f1MKbrvxUSBY7dCUoNW9uqfkwQhAPPQUs\npOpoau/Kwr7ynkAk59KjG4cyhND1Vi2xF5oenHPesJ613OBDxPu46L0WJRB7uOvUMD2r6r56dH6H\nGy/f55lXzZb37uVdrmTgYqjnO2WKzuY6ikU5RUqXOyuAijDMjVU0MEnhwhstNK9PmZ4xxszpcy/z\n4N6LTONZvVRWoWylmOB8jxK9G0AdIkpsMnylVFvkBbmgqni/OI2WWj6xa2Pz4Q85ALchVm5poag6\nqZlSfTtUlnIDomQrj6bqZDttxNh/7V6pt+u7P3f3gimlMuta6SVkU5PfD+p9YN9B3MdhcUHwgZxh\nV5+9q13i6iqzubITnLJliNk3yJiizJ2dJ76wWsNcn6WQIO5Wnc2WVzO7ODGFShHmiots9+53yq9y\nfvE6b1t9Cc8NRva5FV4g7g4ZK/piGBT0klzxDOf5jM9sX+Rj578JwK+d/xofn36PV50p86cDk8lr\n7qTBOYboGOujOzaq+OcZb+oi+spr/5Zn+RoeP/kdYhgMStJ8ZlytIbUSo4JmZdrZInWxueDi6oqr\njT0kZ1ePSCkx1BrejcMbHK7Wne8bfWQ1jPidpY9eAs5R0/8TwvYJMa3RlhIUh8Q1y0wyuFOnwokH\nNaqjvU7gpu4RZFi7wFJXsu/SsWxZe6WhjVLowrgiHsMaVuO2GTtWAzib01pPx4vOCNq5BZoLwQ+s\nqhFemq2G1tGJXghxrw6XFx9zK1EoznlCTWlcDLaBuIaF1FpjtS8UZSDIsFgou4BzS43Ue6uDNrKC\nK5GYTzjeWUr2zKs3OfnMDdaPrZHkpmPTFG1mYtEmgVbcTHHZ6uE1BU9quFDZtrpiIZ1kLo5soZzv\nnDK91aTuTt/6Cg9PXsLXRtB6jjhClx30Kv2+GxU12P1rxpJGM0EAACAASURBVHLibaHcE0E2qiT1\n+lmjI/R02ei+7fq66kTTcbnRU7Iu9Wvv0UJv2olre9/e46qO3Btaguaw+MyzWIHbAQr4hZbqXKVA\n1kU+DIEwKENNp4e1EEYY1svnDesVoUnvZZjmRbpvlzPbXNi1DTpRMb/2/nZO4AQ/2Q+GUdhOM0PT\nso2OI3zHiBOF4D2lNkX1ONN8cKbjJ/z+9JhPX/0uh6/9KgDH5XmO5A53j20u3RrWbLdPePncRLY/\nefoKv3/xSV6RlwB4GF/hPJyS6twKzuElE+tcXo2eYSg0D8nV8FkP6meNN3UR/cF/8SE+9D/9U/7n\nj/4QR4dHSKYbk40h4txSnN+ViW2a2FQc5NnVGVe7DVNVkp/CTEmZWBfNo9UB6zAy1kV1FQZW44pn\ns9Xc7h7f5uhgzdH6kK/kr/Dg0a9xcnCLMRiuMcZD5ngEoTYmSrGVrNMaojFb2iXUDH7GN3D3vKOU\nQFxVsLgPpsWZGtc7GTh6/w7ogiv1BEwKt3LbtS6YtZrvPVTLT2AJ1LtnEdX4TBo33jCdDZ0Yo0XD\nc/Oan+YuRmImaN7qgO18fGIMQwd02+kuXuIWjtHPR8T16LSdnyP2brpn5GA+4fbDKlLx6SNuPTzG\nF8OJ7oY1IjvWDcunM8VlNrvaOIoZooMa+RZx5lt0aA/bfDRzeX/D5r49SNO9B2xvWuRxefCIEufu\nXKrFW029+3NJX4S8t3qmwlN+WIL0yFVLNo+l3sRMqJvNpZJa+1elQX4br79Fprt5xntPasiC2Tao\nrqvga8G64aqdRXOLvZfhWhtQwYstCh2876RqmLZfr0D7erwQDYs6jPaD9ToQA09FouJD7yHOJZPV\nLWTAdqxGfmvl+vqBuSTmBKkKcO+mxGYnxOr2GWJkKzPrSrRwZWDlQscA50FIR/Xi3d5yOVxx9uiK\nzcNP2udfjch2jbxWtW1dwAk8rmiGh2nDNl6yXVk0uxs2EHP3DxNxDM6zqovmOHjikBlq36r99/ON\n65ro9bge1+N6fBHjTY1Ef76Y9NhPX/wc8kSRXFjV7U/nAhLMOhnrdiYpixq4UzgW5qa6xFQtMOx9\nlwtu4wj9D0w16KhGZusHAysfOFkf8z/yV/hff+1HuXN0hzsnzwJw+/gOx8fPcqOmCMcxssKxrnUx\nn6NxqdvuScTLiKvpfMnJYEY13YyDAxaVKu8UgoLfL47lhZFEABmRaNQ5yTOkRaVINRkVsXXrG0e9\noQkEZC9SzjlVtaxah8IoorrXg23vOR/IOVtK2QlaVlhr0n+lKAVd1P+zIGUpD7hKK23ceHGK67EZ\nOAbCdIfjhxb5P/Ny5MbVik2N9k6jI+bCuLUI5HKcmFzqHk2zy2Z/0mvmpgNwfmwd2Pn5mbO3bLh4\n1rCHVwd/gIZaAy1wY3e713c1zEiR5Vao67V1U0/KsB959ntQ/96ZrJ22brozAFc/XFGc0u1XyE1Q\nsabHvkkittCwdfIrBCgG49o3DDRU1aWKe3UTmgOhyR76jCuxZx0+GG241TS9M0q0b3C8AC4qoc6h\n1Wjog7inOiV7deKCoG7es9/wxJX2ekbOFUpaWmlMK1qkZpXzzGbnGIZqHTMkzsZzcrVYPuKQUWO/\ngnPMlMMaid7aMoUdxc+kapp0/vgR23NlqnYhqQSyBkpoGGhILrOr+EYNGTe4BcrhPaMX1vX9IRis\nKbTyxvCFl8k3tyZ6aKnWy+snOIVBBKnppVuJYfVaShI9eGGuEyVjqfBS50lWvG8pTbbiue/8XiWI\n8GSoIGqBKIGVMyzav9Lf4PDygFXVbIwPR26XY24FEyk48IGbwyHPrI2GeGu8xa2jexyvLR099jc5\ncbfxdRNwI/i5UHKVopsFLeMixcdsK/5ezTq4JWUqXsmuWDPIfmKNgtwwiUDZs84tnuCkN17wDh+G\n7l3uxeDnTSaiiFaL6Vbsd8upOKHkSpNtuM5iWEPX9FVjxKt0s7IpZMQv1z97Q5cNdROLOERiX/QC\ngcPdDdanVu5YPzkgXkU2tZA2+y2uzAbzApIoU86d5qoYtrPEqlUwzIyHUN5VvcTfumF744LzA0vh\nJ3/OUHGXcRpxuwOmwR6aOSTcnmyisFwLS+e1Yz/rnWhytP166V7zxnth9CtypV82/GajGBe0N/gA\nhnE0vG7/+0hKiw5CCNbYalx9L6BeibW0UkaD97TSiqA4dQQ31OM5QpAuS+i9M52KNnfcjHjXA5bg\nAzGEjpO034mLyaFztqnWXUdiMXhYI56UgmYYUtthq0OaNDB+Ypomtjt7fzVGdL6kaeGMKRBkZKiL\nsjql1HPxh8pKRrIXdtJMELdkn9hWIsjV1pHlAF+vl2giCd0qxwXHGKMZ3gExONYrz0F7vQrEIRJX\ndROKf4xros3nmskEFba6gLkVxZM6ltTpXuOZVv7zXWSgaTt0sLsXkiqp3nh1tgiMZemY4hStOMTT\nYcMmTMTq8uiD51HRvUUgMqRAeGivj9yKtVtzWDUNnx2e5UtXX8r9Oy8AcDzcZB1HxraolmP89Ay+\n4UDDmT2MexUVmWZKXQRTmMnR48pxfTOBZnytUUopVSC6RsIameNl11/1zpt8ZlnQCqqZPDdWTbII\nqoH5i5JqlLkjVQKMo0HkwuTQkCih1UgVZkeq1zP5HZkrw+YCl2NmDhMnlxZJr/KaC5+67/zRHLjx\nyBNqI2nFlzKwYo1trCOvkh1cDfb3szhSdn0TklUgH27Y3rLosrxlg94PvP7Of1c//5SSYVUXojHf\n6FzvLAU9uOr6nU6NtCFdkaP0BUPEokjda3Ka15P2a+3IeC1oBZNHT2Uh2cjqqi5Di/QrfHmP3KBe\nFp0GtUxlrJHUGEyha1U3xBIcaS40uU3NAdXQm7JCwMuqd5sbEaE1BZ13uLAsDE4SQeYuRmNRq1+c\nZoEkK+bWn5BM8h7WtQYZBphTR8p4LBPzTReiYCiO0rKwiHpIdZOZ8sR4FRbPxbhjDtoVv5x4otpn\nHfgjdJjNuK8qhs2zJ+m2y1qkrOR5x7bHF4oXackaMRbWYSLWACAOyuHa4+qiHAdHjCtca3LrFy6K\nXtdEr8f1uB7X44sYb66KU4eALNbBe0ll7SY2xlDqvwuYrBoLg2ivMWyv1Xaftr11jngL8Z3h4qa6\nW+aUmUTRqgLlciH5mV2jfeJxuA5juSgB2RX0wv7+9/In+C35GCevWWT13N37vOddL/DWe4Z7PBpu\nEMoN2FadyjMPQnfLPOJdzO4Mqjr4Kh2ADIvFQ4honpl39Tpo7e7vUwPd3C2FvTjbraea7koiSSdt\n4iTWyKeBEQ2WBeCDaU0qDle3d3G5WiYs11uVHo2VrIatbfdOBae+10yLZjQs3ua+eCRr94Xf6hUe\nx9xSvqrYVGqK5ZMjDMKFWKR6eXSGvGVG32rd+/BC4erwnBzn/v3FaVcqKmjXPXBVu7PVPUul++5z\ndJp6mFRvD63US7BSiGLlEICs2XCzeyGJlrykuxSDq7Vr13Ci++wqoWvpOgXE93sffEQl4sdVPbaQ\nI+TUusvR2Fk9Eo0IYYl8nUOc63YijeXV03tXoExLDbdiWvfdSlNmcQt1VjOVzmAaCGHqmGlQckpI\no2GWYrX60rQHzOm0JaK7UliVxK7CmEJy+Mye/bYjVpTHej0YLVZnUrXzTEcrVA0RYzfIk+dg0TGQ\nva/YTzvGEOu/lbI9DtaBb7KPIXiDZHc0whdmLL2pi2hbAJNa1Wh/AUWkCil0nIa9+5QP/GLBi3d9\nUbZfX2h5YI0OqT+vh6uana3OVZBUmBoYPoGOE9QULWDSew3i4yq1uPglvX6pvESozYV3Hz3LwVte\nZ/wSa0yloxsM4YSDZIvqyevPsrssXL5uxzsCzm6+wvF8tx5vDbOjg0kdpmPZvMSp3OpWFxMFl/Cx\nPTgFTfPCly4G4ZFeIzVYT6ZN9HlhxHolSa4lk1qTzLbQpLqouhJwPiDVsllcBHF9UZUiZiNdV65Z\nthSX8HXiD7uR4WKArf3+lGYCi+VxVsdcsbdgdcvL4Zyr24b1zO+4Ir9jQ3nOHpztwTm7sF3sbUvl\nl3e+eul2z9L0a5fOj533nl9Xm1dCk4hbuO7s/QwMfqaaewlAteBC7t/dpmXpG7g1rvrB0OwRcYvg\nBaaN2bjvq+EIFFypi6gGg5e1xg3RwP+lPc6u2nEv6bsPvj87Wa1c0TDDJnbul/KCFpIu5AOAVGIn\nYzhJiF8Esp3LOAn9WVCF7OZO9tdcKDlXEXYLAIpLfcPcqTJpZqqY55A9UReRlKDSSy3BC6vBU0pE\na5qtmtACFe1IcDDPgkyVOOOE4KRz4IcxEIMutM4I4ic01u8vmSxhCRD+OC+iXbS2FHDOJDq7vqTt\nBL323eo6PfIs4GXx3kb6Agksv7s8F7YQ98/MpKJ7vjc1UtiLbDWD1t2pUH/QRJPRqsxjr+ecKT5x\ncGgnfHHvkCd3X+L1u7UjfOuYg9UtfGVh5HcU/Bzwj5eJOv3J1zh72Rojq0dPGDkm6HP2edlDnoxZ\nAqDBcIutkURmlmVRdJXS1WpfHijO9+3VZQ9FKY0BRe7sp7hy+OS5uLzAVVbJGEdySYuqlBdI0h/E\nJvRfNSVsy0mFOVdzN/FkEuNsC8Hq7ID1q0f4U3stKVoHuD74Gdi6ibk2f87vvkq5N+Peaa/Tc6ec\n33zMReu4h4x4x1j1TH31xGqbQGGJPJtwSNPddJKq5mfTRt3Dg0p3PuyRppKrZ2KdC6GgKfdsqWgi\n67SA56Xegc7zz3WatbkerEnXBUQi4hfB7FU8BvxSm9NgF1t9//t9X3nU2QK6hyIoaNd5EPFGBKmj\nlAbDWCJn1cX33g4SlwCkCVu0RRqH+ZEtmwh4qItqcQou9/6DaDIsbUWyJO/ZquLq9XN5JmbH0DtV\n2rOk4Oy7jEOwRi2Q54mcEtq0fGVmt8sd91rqPW3aCMMoxOgJsWWllmW05pVlWdkEkYA5LevK5xrX\nNdHrcT2ux/X4IsabWxPds/YAqtBS3XFiRdr196y73NN7h9WM3F5K4twelk/xspjENO1R6apORm1s\nKVYulZbZur+uwByogR2FjKrvVHfNyUoAjTYZRmYZGdQil+wLZZXYBGNJbKLgh5Hz0dS754MLVAL5\nyM73DnD6ZR+nVHqEhIGDq5vcurBPHPJAnujUtMGtjP3UPY2ANeSuhA9IqJYjlgLiXI8uFMNxtpjL\nus+te1q42l2R5sTR2tABIazQNHUHSec9JeUOqSoUiEoe7fMziZJzl59LMpOTcri1UHV8fc3Jy7fR\nyxYpe3ZM7KKl51fDOQ/DA4Y79v7Ve19C7mfOj6wmerZ+3D177PqsCPOK0mBFgqWvja+tBdfhHTWd\n1Wa3kYzf3fynenQOqex6VtRVk0rF2LZIDzXFqyaN5zLkuUN6VGspqSmE7RdEgSARCZ9V09ShW8ms\nwm2c82hFdgi+lrfq8YrJSLZIrFGkO4610nhbeSGbiEN/VryrNNZeI7Zr5/abDPil5inmZ9Qww84N\niOaFFloKRuavkaqAOovu7P0ZJ/PCfvOF2WWmGupPpTArzBWJMsnca6JOhOA8BEVr/f9gXNl3r5bW\nqBI9HW2iVaKx4T5D9DintIRDfKaIkFrWmYW5KKkynvJ+RP45xpubzrd8Q8zvSJVugWypdlkEwKSl\nTk3n0XBn3fnY+T2ISq2lyl6KX4z73l/WGlTeEzRpC7X9Qn3++qLjKvyqplBqjbFucVIinkJuNrWz\nZdrNIsGLM/3Dmu9u40QpV+zGTT/n1258jPRctVh2kfJk5NZsNcBht+ZofYtnRjP/O9qeEHPA1boP\nsxqus95RPyhI6hPBJnVcYCziCE663UkqqdvSnp0/ISfh4OCYYbBFdAgjuB1aFzk3WTNE66KFJGTM\nbIMtbJPuzKKkXr8sGaeR4dI2iYPHJ6wfH5Lqgzn5LZuw5ZF/na8GXjr6JPKuifBO+0IXb32ZUx6z\nC3aOZUwUVUKtE0pWfFbKns+Q0Svr6e39P0ptqbUanpEO+uuy1D+zZETtnf4sNYxt+52SyOQuLlNU\nKwC/flzDlPbXYjTgprWqK7O/dtZUdAxIHpFamhjDLcB3MoRRbBdqak5WWGhFClHj4TdPJ/vs0n/f\nCldLOq5qc2fZFFo5bR8nutex0Pal2vVzoPvGfG0Bdv3Xy54UoA9mEFlqwDHnYul2fRa3SRmmwqo+\nPIN0nRsoVLGUwOjt3ucRIzr0ErQQw848wNo1EdfFgFx0IKVz9UUgiWfWBpkycYP2/n5553ONN3UR\nbQIO+4Iz7DV+Cvv7dR39obCb2nGRXurNr3/vqzhGq4nVSd3U660xIks1mgrgbzO9WOd4n52iHTZd\n53GBNlFEMy7tGGokNpBYe2VdGz1jGBj8yEBVkZoPKOWMnC7650/jS5we2TV5vC082c0MK8M9rvWE\n++EF3pK+BID75W3cSDcZ58YX9ngdu+hFxKEkptxEmwsShKFOJI8imilV4GQ3XZJzxcnFwI3jm9bA\nmOvEcyNDdM0iCnxBY6mNFxCXICZ2VTVppxumtOsPhvqCL5F4VQVhTlfI7JmdLYrn4ZTH4RGPj1+z\ne/G+M3Zf9pAXb7wCwCacMqepb5R5lxENfRGfdUsh43uzxWrovYEjy621ZlPu9eOkqdYxde9362bO\nBG0R3MMwV75W/f2M8/uLcqZWoetcMWCouKXx4yR2HGTkAE/EVVcArytERjy2qEZ/QimyCHSgddFq\nEYQiouTUECgFkdL7C6rZfr8LzGgVNatRutSMqvcfPFr0qYaKNZ8aGqAdtz5rrdnZTZscTpypstWr\nVmQhD6hiGMyWJThPcZDq3J2LMs2FbUWihOIYahCVpgzOm/hNjU4HP6IRytAjHmJ0lPZsl2zOuK0/\nEE2hqjUaCxNzEfxsm5Yrk8kV1N8vn60U9FnjuiZ6Pa7H9bgeX8R4cyPR/TW8gtNa5Oyc1BpNj/3+\n8AGETsXL1YKhdeBCbPWnGpIj5m3TQn4H6qTLl7Y9V/f+q5IWuTrNlL1AVYvlhF15PyekbE3LEDhY\nwTCUzpJwzSKxdruH6cA642WJREPMpNEiue1x4mLekLb2fn7gOducMtVucXIT2T/PjVDdMd0Bkk/w\nTe80G2Or+7RrpqSZqXHrXSZNWzZbqzF6rxwfWep+dHyC04GSArnu9ilhKV07ngfnZYl2pIDPTFVe\nLJdESWm5ng5iAq5qZHoOU5rYruz7nq+fMN294OaXVxztV2x5cOv3ef3Q5MtWZytT9u9uruYJVCoE\nbXIZ3JZVxdl6HSp/famZt8jR6tuL/XLxxpRrNUMtCxsp9XLFMjfa77bjVSeXJeqtc6N7EtfST0sL\nWxQaqr3Fyh0gZaCxz3wZcRzgK5d8iIfktKS/WrvnPRJVAV1qoIDRRBc7XZt7XdfAkCv7c72U/BQk\n0KT+9iPRBRK14EeXZ1N1uWamULVoEZRqm9DLA0KtXS/Pf1GW0g6FncCuzq0RR2r3eU7EIIgLiwyj\nij1aw3K+oex5VhVwLuBDc5lwpirZ7FaK4rLgGrKDYJ36PW3hLzTe1EU01AnvJKJcorLruMNMJIgY\nYBPMA8e5LqWmBYQVvupvZdnYJGnF+2pdsdTETOC5AaazCkkL2hpREkHDggUULJ1vZlh79gr1A6A0\nqAokd4AOkeGmHWF1aw0DaGhCvh4j9ln6OocdpWxIey5YWo4Z66p9kM5YbQqP68dfhpndyatc1L9/\n6fITvHV4nncM7wbg/vwCz1zew4lx/ad8yMRBr5OobvFF8RXhvPFPYIB1axy5SJSaPoY7kCK+KM1i\nYdIN7IS2q6SY2bnE1Gih6YApw0U1Jzs+O0LHc05P7HUugeOzW4wPTXugnDs2MfPk2LQLTr/kM8if\nPuPR/Zft+939N6juODk12udUiknd9XtQG4+tN4EnuEBqotEymWZsT0HrTaVCgmRP+k6zAcGfAti3\n1DDZIuj9AmlSS9FzLUeJmO1LF6AOmblsTWQGawR5EVxtDAU5wnGMrxCdUdb2RDcIk1sjZUBzndu5\nlqoaZVmd6Y2XhmO0zaLXZHF4Kf21E6P35m40Vx+VtgbjrT7b02072r5RnejQAxxlsoCnw9uSLZB7\nRdMi0hdBLWp+WLpf3lgt6X0uhDyhvpVQPFdO+4bpfMHVstTlfMUgB6y8s8AE8Dox+tQbEF4HZg1M\nDWKFERra0+0Ve9ZTS9cHQk6EuokOQUzEpC7y5Qtn83+0RfRDH/oQv/zLv0xKie/+7u/mfe97Hx/4\nwAfIOXP37l1+4Ad+gGEY+Mmf/El+/Md/HOcc3/Ed38G3f/u3v8GR6yTLgBOCWy0TSQPePR1/Gj6t\nThRnGL7GxRZvIgctOsi5ILjFV566nrTdmBa91tfB6qetW790UFs0UR/aPUAyxS/nV8kBodddDOTc\nzkcxHGLDZTrx1szJDYdoP9tV0emriw27zZY42+evvOCGwk4sMn2ol+zmSx7vrJFz27/Oc+5d3PHm\n1X3X3edoXnUHQ4ojqaN4W5TiOsJYCHXT0mLVRQ/kUBCXya7gaqge58A8p0XFKc8UTf36OgpcKfHc\nFuI8PGEbr5jrCYy7I4azNfnSru/GX3B2e0N+q0Wi668YObszcTFUzceY0LRkK6qzNc76Iio4/B5L\nzdkip0tN1ARSG9ZwcbMsBbQUQra5FjSQS9l7yF2PsO+Mz1X8qjKnWl8GIxrUSEZVmOfCXHGOWTPJ\nr7oPvSBEWeO9KVZ5jpGyghr5iBxhVeq64RJM66HWAWc/oaKEaS8r21fgcnW+Nsxw9ZRvXvf2/4q2\nDcUp7JMHOtFgiU33qQX2R7psyJUWs/QLaiLpltUm570mbQtkei21PBX1Khbt9ZK0CNkJU920NpK6\nhsNmO6ESUeiLXnTFBNabf5gImoTS5rYqJO1g/yxWx+2CKmLfWPYibbe39uj+IvI5xhsuor/wC7/A\nxz/+cT784Q/z+PFjvuVbvoWv+7qv4y/9pb/EN37jN/KDP/iDfOQjH+Gbv/mb+Qf/4B/wkY98hBgj\n3/Zt38Zf+At/gZs3b77RR1yP63E9rsd/sOMNF9Gv/dqv5Su/8isBODk5YbPZ8Iu/+It87/d+LwBf\n//Vfz4/8yI/wzne+k/e9730cH1t6+NVf/dX8yq/8Cn/+z//5z3tsqd1R7xLilTQlXI0kQhhxLtEx\nOww4fPeWVmaSm5Ga7pNHvCy7YdHM0xtIw/A93VEse9up+eTssVUSdDWvZnHb0zyHoN3COTsoMneu\ntnNKHKS7f1oZonRWS87Z6IF7sKzNdsvmqkKEJmNgNBtYXywq29Tu9+Vh4mq44NG6YiXPP8Mz0xOe\nL9bdfnd5G+/Uu9yidnyJ5DSwa9DcFHAoLlbl+72a36wZXCYPSmxupFsLNzq/PkK5mpjVPr+kHcNF\n4Ph10w54dOsh56vzrn5+6/wmR49vsruo5YGDLZu3zhy/q0K+nn3M4+EB26HhbBUtrnPntV6vJXpy\nFhjtwXJg6PzsJVdtHlVh4XojFpXV9JrZHoT2J83aBOBQnoFSI5WKtJBBGIaBWBW8Si7Mc2aaqrRe\nnrlMG/PtAjQrooFQa5yOA5wMaIU65HJELtIptbkU1DdIHcwuVQ+tBoHyGGCvzuHqeNW5+a5AjuT+\nfrZjdQ+nJoPYcJTLP2BaAvv11XYM6VkBLHAaqu7sQs00WcCyF8rx9LFKi4RbWlHIZYEbqtiz2PRe\nfSlIfW4ut4XEJasSGRtaITii891ePKh9x9So9MmcYTvwpszkNC39ECdWM61awzQUTtMy8J/1BT5r\niH721foC48Mf/jD/+l//a37u536On//5nwfg05/+NB/4wAf4zu/8Tn7913+dv/23/zYAP/RDP8Rz\nzz3H+9///s97vN/53d/iy7/sK/6oH389rsf1uB5/7MYfubH0Mz/zM3zkIx/hR37kR/iLf/Ev9p9/\nvjX4j7I2/xf/5dfy8Y9d8F9915fzn379l3FwCA8eGKPnlZce89KLj3jpResenz6G3XaNqkW6BYcP\nsfvMSDlnjCs6GD7GSu+t5+GBPRyh28O8/eL/+Rt87dd/xVPnbDxjoYWWJaVacG9fsDUYWiQ64dan\nvOdPWrTxp/7MCe96z4o7d+318cFNBn8b7+y1qCAukep2+d67/4h/9q++lsePq/He6YYnj7dMj+33\n5xLQ4ExLEQjDFifbzqjKW8fVhePiST29q2NOym2eFxNAead7nrem53hWrGZ6f34XR3qLKLUGnYPV\nff/Zu+FbfxdIKMmiUmBWww02RtQ879hNl5TKEsnzlquy4eHbrTH0if/45/nkl/wbxmzogec++RW4\nT6xY3am348s2vPq2T1CqwIjGGWJiTjMf+LpX+P6fvUve0n3uN3Jlakm9Zu1R9b2Z44M1kQYa1s8E\nN1pgOk1LpLjZzOy2O3aNaKAYu6oeu2Un//y/U77l+6R6Erl+r703HnYz5QvBE0LoddQhREaJhEqs\nGONI8CtW0bKC4AecuF4/j7NjnugixdNusKxhEv6Hv/5P+O9/+DvJWSnNbwvLqBaGUWUb9aZpIad5\n6R/U+mmqTdJU5qeuJRjOtDeqaiTarsdH/u4v8e3f+2eWerQ6vHe9hmnXWHgaSSO0gvzS6d/n1rOo\nYOWEqFucZz1AItSO/MrD6Bz/6O99gr/6ve9htZ44Wg8crezeH46OVQjE2IwEhTkp82TH320ndpsd\nu11t6qZEFmGqnzeTETfuZb3mlNsYTi4IP/DffJzPN/5IONGf/dmf5Yd/+If5h//wH3J8fMzBwQHb\nrZ3Qq6++yr1797h37x6vv/56/5vXXnuNe/fu/VEOfz2ux/W4Hv/BjjeMRM/Pz/nQhz7Ej/3Yj/Um\n0Z/9s3+Wj370o3zTN30TP/3TP82f+3N/jq/6qq/ie77nezg7O8N7z6/8yq/01P7zjdt3bGd9759+\nC+H4CZ968HucnlmNzR2veO5dkXsvmOfR44eJV17cQO8CRgAAIABJREFU8uiBQWZ2mwHNA6Sm3m1d\nxxZhFmynWyJHRSV3uxDd68wD1eFx6dhZu5Gu6eidR9Nip7GPEQUTNRLvezQSY2Qc1z1aMf60IzR3\nypJIaWa7W/jfpZROVZvmxHbasamRVUrgU2RVb9lAIPpxUXUKEI42bOvu/+hwx+v5jM9MnwHgpfL7\nvH1zn+eSbWzvDmfcDy9wwxkk6rjcJMxrPKCyRedMTjO5wU6CMpPItUZZYvVgqjAR50dCVtxp9bB6\n5Qa3D96CVum7q1d3DBHCPfv7i5PX2HFKrHW0iKA7h691wrGMTGRKrUc5rbTgLqUkGPWxFbZMf3Rb\nYTxptzVv9no9d9tsdD5g2iXyrGzqd5kdT9l9KEun+NImEiLLXPI+mW1IPZVQAj4v7p3RO4bke7fX\nu1BpnZULPwTiEFhV756jqkQfo92LVTzBsSZVttj9GyM5w1yPP6VkrKM6/7Iq05wWbnspODd3SFYu\nJvXXlB8Um/vql/5BLmlBkvSa6D6mbw8kTWMzST+e2/dgylo/w15b/X85XinmRNoi0nbNG5HWOv+6\n4DxVybUTfzkn1CvOp87t91R0SJf+E0MktCwRy6S29XiXm8Q2Jbb1WZ/U0Ciu4XS9EKLSwAZx+CK7\n8z/1Uz/F48eP+Zt/82/2n33wgx/ke77ne/jwhz/M888/zzd/8zcTY+Rv/a2/xXd913chIvyNv/E3\nepPp841n7ldjqlsjr529yO+/+jIPH9U38ykHIXB4aL9zeDvyrhuRt15YSvTkQebxaxPnp5b+pjxQ\npgntoozGX17wFU1YV+sXf/rCLAZrvZOEiPQL6dRTnPTGkBY1Y7b628kL4zBycmIbzeHRCcH7DojW\n4uzf+jqliXmemPdop2UPzS/qcOLJapCfKQmxHELlC+MFcXt8ah8ZAhzXRWjyiWl3wa5y+V/hlMvw\nhE+lVwH41HTG8/4tPD+8DYC3+XdwL9/jPl/F+e1z9FLJu9QbcUULidxxtragLQ+OlIiP4Ge7YIev\n3uRufDvn1WPq8nxG7hTOblh55jy8DCl3kQ1NofJ828IQmUNhbgLCaQDNHfZT1OiJHbc6W4Nne2UZ\n0tXVlmnKpLnhihf9zZygFKGEJX1LpfRFaF8+ctrV9Fl75QgdQMJi/JYyHeMIBr+bSQs3XgyrGasf\n1DQBU+oC36esGMKKVS3VrPyOg2FitTY42smdK4LzzHXRTKmA831D2U6F3RbmukjMc2G3S8xVNyHl\nQk7aMdYiBn9qAcGs2SxTmsCK5sp92S/J5QWyhKtztaXPdqzSywF146to9iVYWYKWkvdos66VHJZy\nStaEb4u4Llqsm2mHcx6R3Ct1Qcw7vi3SUYwS2+7ILFb12lZ43tWcOd8WLmvTdsa49KE2qUMshLS3\niC6P6Occb7iIvv/97/+czaEf/dEf/UM/+4Zv+Aa+4Ru+4Y0O2cfX/Sd/BoB7z93nD37zN3n9CVzW\njppnRUmJXbKHLng4HD1HN2xRvXl8k/t3Dnj9FYvkXnvtkPOLbb+R+GxCEu2pd5BFuyAI8BQItRTr\nlLdFVMRVoP9yY8VVBRmwJ6ss0awIhKA90jSmkOsdTe8jzg1LJEWhlNz1A8AUahaGjUWr1O63I4J4\nckMVAJPr3AKKwCBHHI110Skz46xcVVzpzm15bVB21cf9M1en3Mt/wPPlRXs9vcrb8tv5Rv4zPp4+\nyVBGnHPcrDXcNQMuLA+IFmdudDU8Emde543eER+uOfK3uGoP1FDQG7mrWG3DGSEdNUlPVMwLtHXj\ndypsPezq5V7nwL7stgM0u77wTdOO3W5ms7XP32yUvbIgztkmBhWTWITSILpi6l6urZ4l93nkZlsE\nnLge9cvOmv59anlXXWgb80BIg6fkdi8TTjMxNVWjRNZdB7M7rwwhc1DZbqO/5Gh1ykGN4l988qsI\niVCaVupACGMTbqcUj3Mr1ivbYGMsDDFQ6u9PU2aelakvskpK2hXbm3Oo7AuI7iEf7JotLgGQKGXR\nD3W4p1k94oAlq9POploiTTteEzRX1LPor9Yab643qBTF1UV0O80Iycgr1dMpaDGEbfu8MlhdtNVc\nRVEn5CbYQmTKyq5u+JOYi67UyRhSZsgwNsi6/8LL5DV3/npcj+txPb6I8abSPr/jL1uX/3T7En/w\nyoucbRb42ZRnkl+a624WLubMoytz4xzDxPHRETe/tNYIb17w8osXPH5cud7TCSqH3TJYyKATpXLP\nZ/vhEtuEbESQvq+I1Uja7lhZHh2paJJQ/buIi4yD4Ktatos7kEjwVtJwsqqRb+NiS6WWLrv/nKe+\n+05lYpd2lNRYJzOZU1ylYYpfEVzYs1SZmeNCzRuCYy2BuaomzcWsRVz9/BwveRi2nBWzFP5991uc\npJt8I/8t/yL8Y+4c3ucZ/wz3azf/7nyfG9MRh1eWYrILTCWxC3Y+s7sgTTsONmZ/UpInpUA6MR/4\n6f4FF8cXbCtDy12e4GJh7iXOjJOFySJbx6hjt1vxuXZx+7VK7FJiUxucm2nHdlKmKimZUmUmtds1\n565iZPdYlppirZsupRzX1amM715QkRptWmSkeZF2M8po6RhjV6zc0/U8rbzO1dTyQjXud+vuS2aj\nGy6rzN96NbLNA66yuz796GWQgm/d48oQaoFjUcNVD7X7LyK4MNAVxsThB88w2PsHboTsyFPT94SU\nHVPz46papHlPSPOZkMitlEVhTmWhRWpgk7W/FufIOTM0hlJFenQ9U5fJUbteqDhvDuIdp+vQeXEf\nJc+dYjtroiQhy4KxHrLD7bak2j8YxCE+dumC4BI72RKqr7yTiei1ax2UqZB1cR5wsiLNidhrefu8\nyT883tRF9B//8x/n7/7V7+b/+Zc/y/kmIwVyq1sVQXzqTDZRJSWYXQMkz+RyxjBaMfjk5JDn5YjV\n2r7wgwcTmytBtdYQs4kQL72kbPQuWdLRp0mmwL7rk8BThXZt9cv21CtHRwfcvFlplVEs3Wm2vFJL\nBU30QvMCTWlnpMpUa6SqiRAGtjRR5YJKWYzdJhgYCdUXJoSAOF3MzZww5MhQqY1pyuRU+sRRyez8\n3MkEE1u2k9WXPzV+nCfT67xaTnjVG3j/fnyOe3qLZwYD04+cGCSkpcC7HbmkDlh2ZWDeKVSAuh4o\nl3rJblv1SIsjhplYN50cBvyeYWGeM4IQKhWSomiGVFO43axcbLZc1VVzytRGUr2W9f/3qYkL1zAD\n0lN9xFLO9lAry3PTxT5yYs7LsaxmuCwyqiZCA+BcRnd7pQ/d++g6nKMTFyJWe2+v05zZbbedLHJ2\nuUNL7uUI1VIB7vXSFBC5eoq4gfOk+n08nsFH1tHA+ut4xCquGEPl7o+RtXe4qmfaRteiBZ597qB/\nXtSAiuNqZ3Pxapu42mWutrXmm3YkXUSupQrzNnicUSxDJ9aYsHihtPeL9QTy3NL55RlJSchYk9hp\n+34JKWHhVwj4ACEsEC4nrnuIBb/8C+BTLce3xmC1yQ71B+ELr6Fv7iL6L/+/n4e/Cq892FJUKMWT\na2MkhEiazpYvVte4RTRB2E5TV6/2mlkf3uDZtU2MOM689tol2409tCmNUMbu/okz986le++tgdB5\nvgBlr9tv3b+lXF259LQiuhJHWOjDxRxJmxCtd7Uu1zqUqdZEn1Y1ac2IUhzznPZEJmyBbA+GiliB\nfQ8NICy7s3OBg/Uy8cXPlO2uEXjYykTxqUeCzjlSjSrPjh+z2V7yKI28rKbn+Uk95K6/yd1oQM+T\nfIf1dIvjnXWUT3SFmxy5Fp1TKMhRwB/ZonqVJ86nDclXzdJs1eJWs05ph4gQa015bsLoTRQEz25O\n/UG92Fwy57TUBeutWGrUWjvA/dLuXWYB3FMLrGrpjKGiywOWs9W9i7ruUdQi2M8ur/clR7VHi/b7\ndZFtc8s15lxFDqDEsGzhW8l4D7V5z/nZ7qmouiFAeiMs22fsA0ucXwRSHKYj63RbXz8mutC1ZZ1X\nvFdCXJwgSsnkvULnS08+RXQV95pHvA/dp+lgdBysAnrDFuF5ntlNictq/L6bJnbz1L9AzkIpmdj9\n3D2zzHsK8orzQupc/eopBUzFm4stinTju4InL0Z/mhjH0k0PxSneOcb6/YYxE+ZMaIIluaBp2hMo\nEYKDoTehv/Aqel0TvR7X43pcjy9ivLmWyTVqmKdgcAil7wakncV+TR6zpfXtb7MpCM01hfIk8Mpq\nZbvhM88ODCvHw9fs/SePQHPoMBdUyLroj2ppVh8ds4OXJVYVXMW1tUgP2NfelxnlqoMHXRhwInuR\noUM170Uz+Q9153MuzDWFSfOMliXqKSXjXVh8XxqrpMuxgawCoq0D7Ksl7Kp9XaZs2E+AbFes+wnN\nZaKJBD3xTwgrTyDinFGgXBEezJF1suOt8w2Owz3uRXMjvR/ucMwR49YYSmWdSCeZy2gQp7NywaZs\ne11tnqwevSQGtdRS0QXbSc12o97fUkxFqmWYcwrMeUnfC4BqV5lSFUQWO5jMHnRJeSpKzblQ8qIQ\n5R29zFOK4R6z5g5P+2wHXakKUS0yzHnxTLdzMahd+5Hz3mg5vXucrL7ZI02I3uBVAOfV0HRBAxhS\npDRkg1Zf+L1yA3ulhhAcc8q93h6k4ppbpAy1nNAvT6/jtvHSo9f6+fnZIwqxhuvDEBlj5PjQVKqG\nGDk+GTm+2coDIykdsKtK9ZeXM2enO6bdrl4vT/Fzh1A51yBUXSKsl15yNr/6XErn1jtg9JkgNdLW\nQBRFainI7J1Dc3AmBrNMbjVS763AHGvpbHSB1Ri5cVL1Xsc/xvYgVxW/kmUk50wMqdf45lzBze1G\na0cVAYv5Qguld4CmLaXqDq7HFTfvHtO+oiicP7limy0lCXGN6r4JlWvFGPv9qsfYhHQ70q2n++Wp\n9N9HJY6ZEO1uhyAgpS9y0zyZD3uXWwMlk/ZwotO0I82Lb41zAVc1EWMcrKTQU1A7dqmCnjEG5nlL\nWFXaYy19rIYGQD4ACYRqxyGXM9O0SNklRwfSq2bjMAg08zUUctxwKaf1fjzA717m5Z2B+e+tbnM/\nPsu9y3fY8dfK5eEp52tbAaZhy46ZUps4g/NMueAqOSCOEIexw3B2c2az3fz/7X1bjGVF9fevLnvv\nc053zw2HUfN9qDFeCOCFoAGNN1SMGlBQEOJISCBgkJvBDIMhME8qoA+iJirxDiZGHswkkmCUF2Ng\nYiBBIFFCePgT4wczzNCXc9u7qtb3sFZV7dM08Nee7vZhr0Scc/qcs2vXrlq11m+t9VtomqhEi5ld\nTaoAKZ36BiF4OO/S84wtf+Nrpr+LH6WUCwmIIlbIc+Gj4svf4YvHvEggeJcOdmPkei14wDlqNXpT\n8D6T1bCW8qmdCIrYNyiPvXFIz3rK9J2px1MsLMnnr4LzlMbK92HT2mWoLB9AXooLUsoSAVRnJcpr\nc1aJDicZyPLwnDcrgSnd1DAAeiM+cCtrUViTMNiqKlFVPZQ93nuDuTls31ZgZZkHtLQ4gqsdKB6Y\n3sPDpBG0wgYgEl7UAukUHCPAThP8jkI5FBopsGXB8GBM5i+MQlWqpBw5TxaYF0KZHTu3Y35hgAi3\nrAyX8Uqytd0+Jf8qkIcpAoJ2mLRPU+JTBIAw3WegW2lm4iFZmA15eCI4IXKd+DEq3WBuxy4AQGEH\n8H4R9Uqs4vDQqgclgZCEHSJfn5ONo1LlRdYqWAJaHITGBswtaGyXwFKvb6Q5Ws4j1QoJZyKwlRUt\nT4A3eiGsSmVJGI8n0LE3N7mEi/L4ifPrxCziMlyfPq+8RWEtqpIXcq+y0DBpI6vGYuxryD5AQwpO\nrPTCWSkDaVneZEG6RiOYZmMBV9UYN5yHOnRHMJpbRDMUjLrUmFYjLA94Y02rCcgG6GgpN5xDm54v\nSihdoBElOxw6TBqfErYdMBPXC0GzBZmIgRWoxS+qEDM9cvM5n5SoeDbxdYx2R0tPZ88n8lxSPr8E\nm7epe2i0QNv4KwGpZ5O1FoVt/T1hdxLNNsinMdjr0C1OU6UVKy0Tg64eoeHcY/48V+xEJcHZBgQf\nmzs6PhhTjq0CGo+EGfKYLdq59asDYeNpCZJD1hk3Y+DEQoRxTJPVDhoOhRg0xgJVCfQkCNzvzWHQ\n24bBgnAJVCWqscZQivdGYwcXaoym0Wtj/lYe1xQgBwoZg64BTOAxEm3GyfcO2kbLlkm446FWlQXq\nQOjLTZqqgDHz2LWNc6JJAS8cfQErMqDQXnhrSIeJdtJJJ52sQ7bYEhWrzDTcclawHUBYqkOO3jL7\ndOYsDGID5qSVEpocXOojz/1xqOC0nWqgsef/FsBz/P1jxyaAQsvl8ZJiI2MDW3khWnaKy8raLXjz\n+0BZWswvKPT6VsZPsMZkSxRg1zg34kEIbqZ+2DuHyUQsvZo5Dis5vZ1zqN0UbSfLe5c4GcfjAG0c\nbEqbqdArLHRsaQGN0mrMlfJ7CwTSDpjI/LrswlovldKBcjmjctBBQTex/QoQSsBJKePILuOFwsOV\nUvWhCsAGjCt2habVBEoDhbS8sM5CGYKNfW+URdMQJoKTTRoHF7ihCgA0xJ0KEmdmIDTBpzQaHyil\nxgA5DSg939hnSMQawMVqKqUQPGXLStlcTQSbuTpbppo2irHNNSSWVcY8S2M0oFTCv1N+aSqnktzT\nkPM2rQkpU4Or3FSynA0ZfpmY8zUsDHScqyCeT8uaDC0MuHaQfmTyN1gEsgkvIHBaUXtt1lNKJhfF\nnlLxfqPlLWt9Svwrqbep4dLJQro22JUapV1GvyfVcFUfC4Mets2x1zStCwxHUyyN+PPD0RRTgXU0\n1bDaw7vMRtoEhVFA8sJ0UDCqhBE6fKscQxixhNiIZyZ7q4aGcwFHV4RBbXkR42k9o0teSbZUiUKA\n3AAv7pVKVHGBDIwawguQQwQm2UiBAs9FyoLWe9IwyqKIpASKSQ8azxhgYRSKAbB9J7v30waYjByC\njAEqgCiTRlBgcouMhcZE8OyzKSmXAxgDrXoG2kZHP0Bp23LJ4rfEJWq4J3xY5TflFCgtuYCy0aUp\nXPy8LUs0rmk1B3MYjZwQMXBgIniddoo1Bfq9EqXgPr4CfK+BWxaXZkQIgscqQ1CaXcLG5BYPtrEo\nvPQRIgtNXFIHAKVWaEqH53r/w699D1YbeFGqzkxhCDDSJ76n+9BlQFXyeEgpDOsJxpLCNJk6kAKc\nuIQo9EwyPgUvcyoucWCMMaX9OA/vkTDTGDwCeFMohUQI4gNv9HhAMp6cE9VTkj5lJQfVzsHmf8Qg\nIgEIaJIWc+ThMxLFTRIzUiTGQtapSnFpZmrC13hoxbm/8QJEuqWUDZMah6hUeVZSKpaA9zG5vwl+\nxpX3waEJbgbKavW1kzHUabxFS9ny/zOPQzywiBQIhNrGOWxgjIc1EZMEFBzKMePrZTHGDgXMzfHa\nqHoDbF8oMRjw2hhO5zCeMHa+Y3sf48kEK5M65Vh7sqg9MJLAVaktKg9Uonh71qIgZBpF7xGCw0jS\nH18cTrA0ajCJBDWOIZ6kPF/FX99SJRqTl/mBKsn1i1UTDk75HB03nLAeF47W0jMnLhTNHVlU6hcO\n9IoSdTzd3QTWGJSC0e3aBRzzGn4kVQ5UwFNIC1fZGqAy5aqBPFeCxIgfAUADL4EWXRLKah7Wxmh8\n7AEUsRwOKkXrpAkNgvKgnF0I13gEwSVDo+CnAahjR8OKe3NTJEAJ8F7DxY2nLRRqxDhV4xya4GEo\nZi9wjXPE0bYZjh4YsfStLjAWvkVtLXMFGLbDePwMvsc8WKU9bABKicYUWiOgQZCIp7eANZnmpWgM\nNBmUVqpmenMYLOxI1sTKcISm9miknrlpGDcmUUTkdPI+AEgTOpXmTxOgQ8A0RA5LVgSpF3zISs/I\nSUxSzVbXAc77dOA55xFE6y0u1gCkC23mphFFmwFYDwIEM4QGaPXGa72W5Z6VEK1SooEVbcwCaAIr\nqiImpwdCoDwbCqwYYhBNK+FIbSlKpSg1XFNgS9vHtUJSrZfcOprJFgAAmppkiLug+Pv5RIPRmYWJ\nA5KAjw2+iJgPVeagkayVRqjlV8Yey9qjGEt0vByiKkpUcuBXRQ8nbOd//5/d86C6B1e7VHjROAcK\nLlnqGgGFIhTidamyRA2DlTFnihxbWcHSeIKV2M+s5kBeuv0QCxjS7b2idJhoJ5100sk6ZGvd+VTe\nY0AU2D0TS9BotlBD67gm71suF0GHHDczsZ+NvHYBGDd1Ym3yllM05iqOuO3YNYD2JV6M9bs0hVYF\n6ng8G4fK9HJEWEn/JYoRUoAwRSkpTXMLJfr9Hqy4z0oHdmsSpstdcXLZCbs87aoQIuTeTw7wjYLy\nkYmfUFifJqQhtpyUHO9NQ/BTSqVwWjsYXSOklskFSqthxXLs2xJ2sA0DSUMZ9CZYkv5Oc9U8VqYT\nOA9uH8x3hKClQg9coWV0ruxRysNonSxvoxiri21+C8213dvnuWx0YdsOaMylFCaigOCnMGLNWVMx\nZhkjzMRVK5nZQAHBpwwsRdwXtIhAH3lhwIz13Cp1v/RNwLRxCWebTAje+ZTi0wbbG8dRcUYlcx5j\noVQuDSXuQx95FVpBehkbcoEbxN2ldj9NA6iQ55rk+bcwTFKUcmajpGg7AhBeHrtTcj8kH9DKQCmT\nc5SJ8zIboQ20WsEoM1P2SU1Ic6kV92pPf5P25BHuIGHZjxitpMnmfmUgaOUTPyoIGLb4WQvlUeox\nCsOeUWVW0JPqqKNHjqFvFXpFiYG4/9ayl5dStLRg0DKfR5bHWF4ZYUloEodNg5o0JJsOk5qfs03P\nk7dZwsX/m935qBGNMfBt5Bt8Ewo6rRQtW4Jaf2/7G1zqhhYmqgUnY3GemJJM3hgUAdtO0OnB2/4y\nhiMNSGlbgQUEpxM9loKHg0JMkFZKw2iV6MD4PjSsjYGlAGPKFAiLeFR0GSkwWW17oXpPKSF5PAK8\ns4BggkXB7Z8jvZeCh9FFAgPctAZ5JLo2bbh0LeJo1jhoXQvWCVgqURiTmq0VhYGRsW6bmwNIYVI3\nmEQ4RBmmotN55fOhJxguEYzhxr/yBXgfYLS0JTYDbN92AuYHnIyvyGI4mmIoaSS1a2BUgcL25fM9\nBO9TCpPWRSpY4PlrQMGBKOPqRB5e6OeMrmCNSlhk01Ar2dthMkHihm271vFZxY1TlvJvjdRyGBDF\nGZsfKs2E4PK3IP9p51nymyr/O7RTnjxiAUD8VGjHwQLDA0lJ0oxOBjSXdkb+z9Xup0CiSamE4AGi\npERjbf/MGbKKtJzLjyXQIgHg1C5bGwFh82cJaBkgeAl8EVQLcyXAGDszPVMgNZIbgxAbUv6/56dQ\ntuH9JSe4Lbi9enzNqWABXghV3GSK0ZQy9wEkJdLn6xnk+Y14dWyy+F+NiUYJwqZAEoEFGANqDz5n\nLIpoSIKdYHZewoUykYm9Wx5drK2Op1+jGvT6E/TZMEJ/xxCTRsH7HfJ7JRQclI2BG0gScsQIAVBI\nC9H5Bt43mVCENJTWGWwHB0NcisASmmaVEm2ISTsAqegw6EtgRpkCxrYWrjLwQaeqldJYTGsDMSbh\nPcEqhVKqNGzhYCwlW04HD40ChWCUtl+ikFy81+7ahbmyh8WVIY4NecIm5Jg0I06/ZswyEaxYtkBM\nYg4qYU0f/YqV5txgOwa9HYmVamU4wcrKBNNaIrZlKYtfWLnsANCKAzQAFIzghDnPd+omjIWBnwsF\nQiA+BH1NmE4cJqI4pxOukgJalT1yL8raZGW2nzUgzpLitRVzdBVhhntTqcAKJ84FeEOmbrOk4Ily\nVB58H8nwVIyDoqUkZ6LfYMw380bwPVBbK7U0dsowaSmp2ZgoCdv87NdTTT43nVpVMd4ih3GQvuz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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa3ac00beb8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "kcMTb_ecD3aY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Class Prediction\n",
        "\n",
        "Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.\n",
        "\n",
        "To get the top $K$ largest values in a tensor use [`x.topk(k)`](http://pytorch.org/docs/master/torch.html#torch.topk). This method returns both the highest `k` probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using `class_to_idx` which hopefully you added to the model or from an `ImageFolder` you used to load the data ([see here](#Save-the-checkpoint)). Make sure to invert the dictionary so you get a mapping from index to class as well.\n",
        "\n",
        "Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.\n",
        "\n",
        "```python\n",
        "probs, classes = predict(image_path, model)\n",
        "print(probs)\n",
        "print(classes)\n",
        "> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n",
        "> ['70', '3', '45', '62', '55']\n",
        "```"
      ]
    },
    {
      "metadata": {
        "id": "wdzx8mOUD3ae",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def predict(image_path, model, top_k=5):\n",
        "    ''' Predict the class (or classes) of an image using a trained deep learning model.\n",
        "    '''\n",
        "    \n",
        "    # TODO: Implement the code to predict the class from an image file\n",
        "    # Process image\n",
        "    img = process_image(Image.open(image_path))\n",
        "    \n",
        "    # Convert to tensor\n",
        "    image_tensor = torch.from_numpy(img).type(torch.FloatTensor)\n",
        "    # Add batch of size 1 to image\n",
        "    model_input = image_tensor.unsqueeze(0)\n",
        "    \n",
        "    # Get the probabilities\n",
        "    model = model.cuda()\n",
        "    model_input = model_input.cuda()\n",
        "    ps = torch.exp(model.forward(model_input))\n",
        "    \n",
        "    # Get the top k probabilities\n",
        "    top_p, top_l = ps.topk(top_k)\n",
        "    top_p, top_l = top_p.cpu(), top_l.cpu()\n",
        "    top_p = top_p.detach().numpy().tolist()[0] \n",
        "    top_l = top_l.detach().numpy().tolist()[0]\n",
        "    \n",
        "    # Convert indices to classes\n",
        "    idx_to_class = {val: key for key, val in    \n",
        "                                      model.class_to_idx.items()}\n",
        "    top_labels = [idx_to_class[lab] for lab in top_l]\n",
        "    top_flowers = [cat_to_name[idx_to_class[lab]] for lab in top_l]\n",
        "    return top_p, top_labels, top_flowers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OH3yyaOZD3bD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Sanity Checking\n",
        "\n",
        "Now that you can use a trained model for predictions, check to make sure it makes sense. Even if the validation accuracy is high, it's always good to check that there aren't obvious bugs. Use `matplotlib` to plot the probabilities for the top 5 classes as a bar graph, along with the input image. It should look like this:\n",
        "\n",
        "<img src='https://github.com/udacity/pytorch_challenge/blob/master/assets/inference_example.png?raw=1' width=300px>\n",
        "\n",
        "You can convert from the class integer encoding to actual flower names with the `cat_to_name.json` file (should have been loaded earlier in the notebook). To show a PyTorch tensor as an image, use the `imshow` function defined above."
      ]
    },
    {
      "metadata": {
        "id": "8E9Dqd_WD3bI",
        "colab_type": "code",
        "outputId": "ef212e89-27c8-4a97-8b4e-95d718cf9073",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        }
      },
      "cell_type": "code",
      "source": [
        "# TODO: Display an image along with the top 5 classes\n",
        "\n",
        "image_path = 'flower_data/valid/95/image_07513.jpg'\n",
        "\n",
        "plt.figure(figsize = (6,10))\n",
        "ax = plt.subplot(2,1,1)\n",
        "\n",
        "# Set up title\n",
        "flower_num = image_path.split('/')[2]\n",
        "print(flower_num)\n",
        "title_ = cat_to_name[flower_num]\n",
        "print(title_)\n",
        "\n",
        "# Plot flower\n",
        "img = process_image(Image.open(image_path))\n",
        "imshow(img, ax, title = title_);\n",
        "\n",
        "# Make prediction\n",
        "probs, labs, flowers = predict(image_path, model) \n",
        "\n",
        "# Plot bar chart\n",
        "plt.subplot(2,1,2)\n",
        "sns.barplot(x=probs, y=flowers, color=sns.color_palette()[0]);\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "95\n",
            "bougainvillea\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TlvF2WOjqd8HuzpWcDQDNMeGOQ6a4cZx6m9+ysNhCHmed4FSclGHo2U8LWC0Y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\nV0oNO4eqBSTUu3PWQGcRTXNwalu6KZsyNUVd2U0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N2ODxTCHvnBdcGtI2UAWf3uPZ7g/4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9m4ShtXdfYPhoeix0SJGo18yollm1ivbOOqhroOVDHSlgKo5cHLc7ZRQHJijAa5IMRytjqSU7ex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V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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa3a960ab38>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}