02-node-classification-solution.ipynb

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      "source": [
        "# Node Classification\n",
        "\n",
        "Build a GNN to predict the subject area for each paper in a citation graph of scientific papers. This is an example of a GNN that does node classification.\n",
        "\n",
        "Most scientific research is usually based heavily on previous work, and it is common for authors to give credit for such work in their papers in the form of citations. This forms a citation graph. In this graph each node represents a scientific paper, and each edge represents a citation from one paper to another. In addition, each node has a feature vector associated with it.\n",
        "\n",
        "For this example, use the [CORA Dataset](https://graphsandnetworks.com/the-cora-dataset/) available from the `torch_geometric.datasets.Planetoid` package.\n",
        "\n",
        "## Setup\n",
        "\n",
        "The following two cells import Pytorch Geometric (PyG) and a couple of supporting Pytorch packages that are customized against the torch version."
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "2347763d-10c6-45b6-eb99-e2bdeb4a37fd"
      },
      "source": [
        "import torch\n",
        "torch.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "'1.9.0+cu111'"
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          "execution_count": 1
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      "source": [
        "!pip install -q torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cu111.html\n",
        "!pip install -q torch-sparse -f https://pytorch-geometric.com/whl/torch-1.9.0+cu111.html\n",
        "!pip install -q torch-geometric"
      ],
      "execution_count": null,
      "outputs": [
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          "text": [
            "\u001b[K     |████████████████████████████████| 10.4 MB 4.8 MB/s \n",
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            "\u001b[K     |████████████████████████████████| 325 kB 5.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 407 kB 38.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 45 kB 3.8 MB/s \n",
            "\u001b[?25h  Building wheel for torch-geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
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      "source": [
        "## Dataset\n",
        "\n",
        "The website for the CORA dataset provides the following information:\n",
        "\n",
        "> The CORA dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words.\n",
        "\n",
        "* Load the dataset from the `torch_geometric.datasets.Planetoid` package using the defaults.\n",
        "* The CORA dataset is a single graph consisting of multiple nodes. Verify that the number of graphs (given by the `torch_geometric.data.Data` object) is 1.\n",
        "* Each node is associated with a feature vector that is basically a word vector indicating presence or absence of a word from a fixed size dictionary. Find and report the size of each node's feature vector.\n",
        "* Find the number of subject areas the nodes are classified into, i.e. the number of classes.\n",
        "* Inspect the underlying Data object representing the graph.\n",
        "* Count and report the number of nodes in the training, validation, and test sets (Hint: count the number of `True` entries in each mask in the `Data` object)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tmGpFBkQLHSE"
      },
      "source": [
        "import torch_geometric"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "tuv3r2J4LT4j",
        "outputId": "41135588-e5dd-4e74-8290-41c74c8294c1"
      },
      "source": [
        "from torch_geometric.datasets import Planetoid\n",
        "\n",
        "dataset = Planetoid(root=\"cora\", name=\"Cora\")\n",
        "dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.x\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.tx\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.allx\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.y\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ty\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ally\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.graph\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.test.index\n",
            "Processing...\n",
            "Done!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cora()"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "b11c8725-06a3-4bbc-f8c6-8f5d8eecc0b4"
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      "source": [
        "dataset.num_features"
      ],
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1433"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "7-vIr2Llv9GZ",
        "outputId": "0deb311f-82c6-4830-fdf2-6a9500144d2b"
      },
      "source": [
        "dataset.num_classes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
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          "metadata": {},
          "execution_count": 6
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      "cell_type": "code",
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        "id": "dLsYzog3L5WY",
        "outputId": "83307c4d-2a1e-44d8-9934-168d7fa5b6aa"
      },
      "source": [
        "dataset[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708])"
            ]
          },
          "metadata": {},
          "execution_count": 7
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    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "XXLS9jmKMYKY",
        "outputId": "a95ac9e2-3158-4044-e29b-14c57b9095b6"
      },
      "source": [
        "dataset[0].train_mask.count_nonzero()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(140)"
            ]
          },
          "metadata": {},
          "execution_count": 8
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "ERAU5l-zudc6",
        "outputId": "3bebe5f6-76f3-4770-fb8d-c4f0fe0900e4"
      },
      "source": [
        "dataset[0].val_mask.count_nonzero()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(500)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
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      "cell_type": "code",
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      "source": [
        "dataset[0].test_mask.count_nonzero()"
      ],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(1000)"
            ]
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          "metadata": {},
          "execution_count": 10
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ewbxj6N_Txg"
      },
      "source": [
        "## DataLoader\n",
        "\n",
        "Create the train, validation and test DataLoaders (`torch_geometric.loader.DataLoader`). You will need them when building your training and evaluation steps later.\n",
        "\n",
        "Set the `batch_size` attribute of your `DataLoader` objects to `BATCH_SIZE`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TkCzTvXwAS5r"
      },
      "source": [
        "BATCH_SIZE = 32"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-QgH_zo_2X8"
      },
      "source": [
        "from torch_geometric.loader import DataLoader\n",
        "\n",
        "train_loader = DataLoader(dataset, batch_size=BATCH_SIZE)\n",
        "val_loader = DataLoader(dataset, batch_size=BATCH_SIZE)\n",
        "test_loader = DataLoader(dataset, batch_size=BATCH_SIZE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "orqEh3YTI_VE"
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      "source": [
        "## Model\n",
        "\n",
        "Now define the GNN network -- like any Pytorch network, it should subclass `torch.nn.Module`.\n",
        "\n",
        "As shown in the image below, the GNN should consist of 3 GCN ( `torch_geometric.nn.GCNConv`) layers, followed by a classifier head consisting of 2 Linear (`torch.nn.Linear`) layers. Notice the similarity to a CNN.\n",
        "\n",
        "Each GCNConv layer is followed by a ReLU activation and a Dropout function. The dropout percentage is provided via the constructor.\n",
        "\n",
        "The two linear layers have a Dropout layer in between them. The linear layer is followed by a `log_softmax` activation function.\n",
        "\n",
        "Your constructor should accept the `input_dim` (the number of node features), `hidden_dim`, `output_dim` (the number of classes) and the `dropout_pct`. You can use the same `dropout_pct` value for all Dropout layers in the network.\n",
        "\n",
        "Note that in addition to the node feature vectors (the `Data.x` values) in each batch, the graph layers takes the `edge_index` as well (the `Data.edge_index`).\n",
        "\n",
        "The diagram below shows the input and output dimensionality for each layer.\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TZp7XmNzAT39"
      },
      "source": [
        "![fig-02-01.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9YxGIbjAZVh"
      },
      "source": [
        "---\n",
        "Finally, sanity test your model by running a batch of training data through it and verify the output dimensionality."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86q9G2rf5jzH"
      },
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from torch_geometric.nn import GCNConv\n",
        "\n",
        "\n",
        "class NodeClassifier(nn.Module):\n",
        "  def __init__(self, input_dim, num_graph_layers, hidden_dim, output_dim, dropout_pct):\n",
        "\n",
        "    super(NodeClassifier, self).__init__()\n",
        "\n",
        "    self.input_dim = input_dim\n",
        "    self.hidden_dim = hidden_dim\n",
        "    self.num_graph_layers = num_graph_layers\n",
        "\n",
        "    self.convs = nn.ModuleList()\n",
        "    self.convs.append(GCNConv(input_dim, hidden_dim))\n",
        "    for i in range(num_graph_layers - 1):\n",
        "      self.convs.append(GCNConv(hidden_dim, hidden_dim))\n",
        "\n",
        "    self.dropout_pct = dropout_pct\n",
        "\n",
        "    self.clf_head = nn.Sequential(\n",
        "      nn.Linear(hidden_dim, hidden_dim),\n",
        "      nn.Dropout(dropout_pct),\n",
        "      nn.Linear(hidden_dim, output_dim)\n",
        "    )\n",
        "\n",
        "  def forward(self, data):\n",
        "    x, edge_index, batch = data.x, data.edge_index, data.batch\n",
        "    \n",
        "    for i in range(self.num_graph_layers):\n",
        "      x = self.convs[i](x, edge_index)\n",
        "      x = F.relu(x)\n",
        "      x = F.dropout(x, p=self.dropout_pct)\n",
        "    \n",
        "    x = self.clf_head(x)\n",
        "    return F.log_softmax(x, dim=1)\n",
        "\n",
        "  def loss(self, pred, label):\n",
        "    return F.nll_loss(pred, label)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h9HsaTyu_0cv",
        "outputId": "599c7931-227d-44ba-b99d-476bc864d54b"
      },
      "source": [
        "model_test = NodeClassifier(dataset.num_features, 3, 256, dataset.num_classes, 0.5)\n",
        "for batch in train_loader:\n",
        "  print(\"batch:\", batch)\n",
        "  pred = model_test(batch)\n",
        "  break\n",
        "\n",
        "print(\"pred:\", pred.size())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "batch: DataBatch(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708], batch=[2708], ptr=[2])\n",
            "pred: torch.Size([2708, 7])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tCfJmvEJCf3Y"
      },
      "source": [
        "## Training Loop\n",
        "\n",
        "Now define the training loop. One way to do so is to define the training loop as follows#\n",
        "\n",
        "```python\n",
        "for epoch in epochs:\n",
        "    train_step()\n",
        "    eval_step()\n",
        "```\n",
        "\n",
        "where the `train_step()` trains the model in batches for one epoch, i.e. the batches run through the data once. The function is defined as follows:\n",
        "\n",
        "```python\n",
        "def train_step(model: torch.nn.Module,\n",
        "               optimizer: torch.optim.Optimizer,\n",
        "               train_loader: torch_geometric.loaders.DataLoader,\n",
        "               device: torch.Device\n",
        "               ) -> \n",
        "               train_loss: Float, \n",
        "               train_accuracy: Float\n",
        "```\n",
        "\n",
        "Compute the loss and accuracy for each batch, then return the loss and accuracy over the entire epoch.\n",
        "\n",
        "Similarly, the `eval_step()` evaluates the model in batches for one epoch, i.e. for the entire evaluation dataset. The function can be defined as shown below. As with the `train_step()` function, compute and return the eval loas and accuracy over each epoch.\n",
        "\n",
        "Note that you can reuse the same function to report validation and test loss and accuracy, by appropriately setting the `eval_loader` and setting the `is_validation` flag.\n",
        "\n",
        "```python\n",
        "def eval_step(model: torch.nn.Module,\n",
        "              eval_loader: torch_geometric.loaders.DataLoader,\n",
        "              device: torch.Device,\n",
        "              is_validation=False: bool\n",
        "             ) ->\n",
        "             val_or_test_loss: Float,\n",
        "             val_or_test_acc: Float \n",
        "```\n",
        "\n",
        "In the `train_loop()` method, accumulate the training and validation accuracies and loss and return them as a list of tuples. Run the loop for `num_epochs` epochs, and report the losses and accuracies every N epochs. A suggested signature for the `train_loop()` method is as follows.\n",
        "\n",
        "```python\n",
        "def train_loop(model: torch.nn.Module, \n",
        "               optimizer: torch.optim.Optimizer, \n",
        "               train_loader: torch_gemetric.loaders.DataLoader,\n",
        "               val_loader: torch_gemetric.loaders.DataLoader,\n",
        "               device: torch.Device, \n",
        "               num_epochs: Long, \n",
        "               log_every=50: Long\n",
        "              ) ->\n",
        "              List[(Float, Float, Float, Float)] \n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxxjVgoNJQjA"
      },
      "source": [
        "def train_step(model, optimizer, train_loader, device):\n",
        "  model.train()\n",
        "  total_rows, total_loss, total_correct = 0, 0, 0\n",
        "  for batch in train_loader:\n",
        "    batch = batch.to(device)\n",
        "    optimizer.zero_grad()\n",
        "    pred = model(batch)[batch.train_mask]\n",
        "    label = batch.y[batch.train_mask]\n",
        "    loss = model.loss(pred, label)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    # update values for reporting\n",
        "    total_loss += loss.item()\n",
        "    total_correct += pred.argmax(dim=1).eq(label).sum().item()\n",
        "    total_rows += torch.sum(batch.train_mask).item()\n",
        "  return total_loss / total_rows, total_correct / total_rows\n",
        "\n",
        "\n",
        "def eval_step(model, eval_loader, device, is_validation=False):\n",
        "  model.eval()\n",
        "  total_rows, total_loss, total_correct = 0, 0, 0\n",
        "  for batch in eval_loader:\n",
        "    batch = batch.to(device)\n",
        "    mask = batch.val_mask if is_validation else batch.test_mask\n",
        "    with torch.no_grad():\n",
        "      pred = model(batch)[mask]\n",
        "      label = batch.y[mask]\n",
        "      loss = model.loss(pred, label)\n",
        "      total_loss += loss.item()\n",
        "      total_correct += pred.argmax(dim=1).eq(label).sum().item()\n",
        "      total_rows += torch.sum(mask).item()\n",
        "  return total_loss / total_rows, total_correct / total_rows\n",
        "\n",
        "\n",
        "def train_loop(model, optimizer, train_loader, val_loader, device, \n",
        "               num_epochs, log_every=50):\n",
        "  history = []\n",
        "  for epoch in range(num_epochs):\n",
        "    train_loss, train_acc = train_step(model, optimizer, train_loader, device)\n",
        "    val_loss, val_acc = eval_step(model, val_loader, device, is_validation=True)\n",
        "    history.append((train_loss, train_acc, val_loss, val_acc))\n",
        "    if epoch == 0 or (epoch + 1) % log_every == 0:\n",
        "      print(\"EPOCH {:3d}, TRAIN loss: {:.5f}, acc: {:.5f}, VAL loss: {:.5f}, acc: {:.5f}\"\n",
        "        .format(epoch + 1, train_loss, train_acc, val_loss, val_acc))\n",
        "  return history"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zk0sJtuTJN14"
      },
      "source": [
        "## Training\n",
        "\n",
        "Now train the network using the hyperparameters provided in the cell below.\n",
        "\n",
        "1. At this point, if your code runs without errors, you can switch from using CPU to GPU. To do so, go to Runtime :: Change Runtime Type and setting Hardware Accelerator to GPU. You will need to rerun the notebook from the beginning once you switch from CPU to GPU.\n",
        "2. Set the device to `torch.device(\"cuda\")` if GPU is available otherwise set it to `torch.device(\"cpu\")`.\n",
        "3. Instantiate the GNN model and set the model to the appropriate device using `model.to(device)`.\n",
        "4. Instantiate the `torch.optim.AdamW` optimizer with model parameters and the appropriate `LEARNING_RATE` and `WEIGHT_DECAY` parameters.\n",
        "5. Run the training loop for `NUM_EPOCHS` epochs and capture the training and validation losses and accuracies returned.\n",
        "6. Use the training and validation losses and accuracies tuple to plot the loss and accuracy curves for the model training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNdFYNw2Ni3G"
      },
      "source": [
        "# model parameters\n",
        "INPUT_DIM = dataset.num_features\n",
        "HIDDEN_DIM = 64\n",
        "OUTPUT_DIM = dataset.num_classes\n",
        "NUM_GCN_LAYERS = 3\n",
        "DROPOUT_PCT = 0.5\n",
        "\n",
        "# optimizer\n",
        "LEARNING_RATE = 5e-4\n",
        "WEIGHT_DECAY = 5e-3\n",
        "\n",
        "NUM_EPOCHS = 500"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gl6NPvDy_JLE",
        "outputId": "0ff909e5-7b1a-48ac-b462-5f1606171eac"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "device"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fL81g4ZdH32t"
      },
      "source": [
        "model = NodeClassifier(INPUT_DIM, NUM_GCN_LAYERS, HIDDEN_DIM, OUTPUT_DIM, DROPOUT_PCT)\n",
        "model = model.to(device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBbii3VYNNuS",
        "outputId": "b5ce29ea-76c9-473f-a870-54bc3915d382"
      },
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "optimizer = optim.AdamW(model.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY)\n",
        "optimizer"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdamW (\n",
              "Parameter Group 0\n",
              "    amsgrad: False\n",
              "    betas: (0.9, 0.999)\n",
              "    eps: 1e-08\n",
              "    lr: 0.0005\n",
              "    weight_decay: 0.005\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h3U6qxddIl6M",
        "outputId": "69af484d-be20-4349-cf0e-ccfb39656bf0"
      },
      "source": [
        "history = train_loop(model, optimizer, train_loader, val_loader, device, NUM_EPOCHS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPOCH   1, TRAIN loss: 0.01397, acc: 0.15000, VAL loss: 0.00384, acc: 0.30200\n",
            "EPOCH  50, TRAIN loss: 0.01209, acc: 0.45714, VAL loss: 0.00358, acc: 0.40400\n",
            "EPOCH 100, TRAIN loss: 0.00466, acc: 0.82143, VAL loss: 0.00227, acc: 0.66400\n",
            "EPOCH 150, TRAIN loss: 0.00156, acc: 0.94286, VAL loss: 0.00256, acc: 0.70200\n",
            "EPOCH 200, TRAIN loss: 0.00072, acc: 0.97857, VAL loss: 0.00294, acc: 0.70000\n",
            "EPOCH 250, TRAIN loss: 0.00039, acc: 0.97857, VAL loss: 0.00329, acc: 0.70400\n",
            "EPOCH 300, TRAIN loss: 0.00048, acc: 0.97857, VAL loss: 0.00316, acc: 0.72600\n",
            "EPOCH 350, TRAIN loss: 0.00026, acc: 0.98571, VAL loss: 0.00342, acc: 0.74800\n",
            "EPOCH 400, TRAIN loss: 0.00015, acc: 0.98571, VAL loss: 0.00384, acc: 0.73600\n",
            "EPOCH 450, TRAIN loss: 0.00011, acc: 1.00000, VAL loss: 0.00408, acc: 0.73600\n",
            "EPOCH 500, TRAIN loss: 0.00006, acc: 1.00000, VAL loss: 0.00410, acc: 0.72400\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "8w1Ad7AwOJq1",
        "outputId": "5cd44884-7e92-4398-e0b9-6374d3978354"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "def display_training_plots(history):\n",
        "  train_losses, train_accs, val_losses, val_accs = [], [], [], []\n",
        "  for train_loss, train_acc, val_loss, val_acc in history:\n",
        "    train_losses.append(train_loss)\n",
        "    train_accs.append(train_acc)\n",
        "    val_losses.append(val_loss)\n",
        "    val_accs.append(val_acc)\n",
        "\n",
        "  xs = np.arange(len(train_losses))\n",
        "\n",
        "  plt.figure(figsize=(10, 5))\n",
        "\n",
        "  plt.subplot(2, 1, 1)\n",
        "  plt.plot(xs, train_losses, label=\"train\")\n",
        "  plt.plot(xs, val_losses, label=\"validation\")\n",
        "  plt.xlabel(\"iterations\")\n",
        "  plt.ylabel(\"loss\")\n",
        "  plt.legend(loc=\"best\")\n",
        "\n",
        "  plt.subplot(2, 1, 2)\n",
        "  plt.plot(xs, train_accs, label=\"train\")\n",
        "  plt.plot(xs, val_accs, label=\"validation\")\n",
        "  plt.xlabel(\"iterations\")\n",
        "  plt.ylabel(\"accuracy\")\n",
        "  plt.legend(loc=\"best\")\n",
        "\n",
        "  _ = plt.show()\n",
        "\n",
        "\n",
        "display_training_plots(history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AkAhbCUORQul"
      },
      "source": [
        "## Evaluation\n",
        "\n",
        "Now evaluate your trained model on the held out test set. You can reuse the `eval_step()` function that you used for evaluating the model with the validation set during the training.\n",
        "\n",
        "Report the accuracy value on the test set to 5 decimal places."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qapm9mDnQR_J",
        "outputId": "f5810711-191b-44ac-a68e-30a8720a33a1"
      },
      "source": [
        "_, test_acc = eval_step(model, test_loader, device)\n",
        "print(\"Accuracy on test set: {:.5f}\".format(test_acc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test set: 0.73800\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j5AsqR1aRi2N"
      },
      "source": [
        "## Repeat classification with GAT\n",
        "\n",
        "Repeat the classification exercise using a Graph Attention layer (`torch_geometric.nn.GATConv`) instead of the GCN layer. Here are the steps.\n",
        "\n",
        "1. Either copy-paste the previous network and make the necessary changes, or refactor the previous network so it can be subclassed for the new convolution layer.\n",
        "2. Instantiate the model and set it to the correct `device`.\n",
        "3. Instantiate the `AdamW` optimizer with the new model parameters.\n",
        "4. Run the training loop for `NUM_EPOCHS` epochs, capturing the training and validation loss and accuracies.\n",
        "5. Use the list of tuples to plot the training curves for training and validation loss and accuracies.\n",
        "6. Compute and report the accuracy of the GAT based GNN against the held-out test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fvrt-FwXRc7-"
      },
      "source": [
        "from torch_geometric.nn import GATConv\n",
        "\n",
        "class NodeClassifierGAT(nn.Module):\n",
        "  def __init__(self, input_dim, num_graph_layers, hidden_dim, output_dim, dropout_pct):\n",
        "\n",
        "    super(NodeClassifierGAT, self).__init__()\n",
        "\n",
        "    self.input_dim = input_dim\n",
        "    self.hidden_dim = hidden_dim\n",
        "    self.num_graph_layers = num_graph_layers\n",
        "\n",
        "    self.convs = nn.ModuleList()\n",
        "    self.convs.append(GATConv(input_dim, hidden_dim))\n",
        "    for i in range(num_graph_layers - 1):\n",
        "      self.convs.append(GATConv(hidden_dim, hidden_dim))\n",
        "\n",
        "    self.dropout_pct = dropout_pct\n",
        "\n",
        "    self.clf_head = nn.Sequential(\n",
        "      nn.Linear(hidden_dim, hidden_dim),\n",
        "      nn.Dropout(dropout_pct),\n",
        "      nn.Linear(hidden_dim, output_dim)\n",
        "    )\n",
        "\n",
        "  def forward(self, data):\n",
        "    x, edge_index, batch = data.x, data.edge_index, data.batch\n",
        "    \n",
        "    for i in range(self.num_graph_layers):\n",
        "      x = self.convs[i](x, edge_index)\n",
        "      x = F.relu(x)\n",
        "      x = F.dropout(x, p=self.dropout_pct)\n",
        "    \n",
        "    x = self.clf_head(x)\n",
        "    return F.log_softmax(x, dim=1)\n",
        "\n",
        "  def loss(self, pred, label):\n",
        "    return F.nll_loss(pred, label)\n",
        "\n",
        "\n",
        "model_gat = NodeClassifierGAT(INPUT_DIM, NUM_GCN_LAYERS, HIDDEN_DIM, OUTPUT_DIM, DROPOUT_PCT)\n",
        "model_gat = model_gat.to(device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DYa1w2IaScQH",
        "outputId": "c8a47dcb-e488-4412-a8c4-c5860ec4f70f"
      },
      "source": [
        "optimizer_gat = optim.AdamW(model_gat.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY)\n",
        "history_gat = train_loop(model_gat, optimizer_gat, train_loader, val_loader, device, NUM_EPOCHS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPOCH   1, TRAIN loss: 0.01391, acc: 0.17857, VAL loss: 0.00396, acc: 0.10600\n",
            "EPOCH  50, TRAIN loss: 0.01116, acc: 0.48571, VAL loss: 0.00338, acc: 0.39400\n",
            "EPOCH 100, TRAIN loss: 0.00351, acc: 0.85714, VAL loss: 0.00230, acc: 0.62200\n",
            "EPOCH 150, TRAIN loss: 0.00122, acc: 0.96429, VAL loss: 0.00234, acc: 0.72600\n",
            "EPOCH 200, TRAIN loss: 0.00095, acc: 0.96429, VAL loss: 0.00255, acc: 0.67600\n",
            "EPOCH 250, TRAIN loss: 0.00069, acc: 0.96429, VAL loss: 0.00313, acc: 0.69200\n",
            "EPOCH 300, TRAIN loss: 0.00030, acc: 0.99286, VAL loss: 0.00335, acc: 0.71800\n",
            "EPOCH 350, TRAIN loss: 0.00010, acc: 1.00000, VAL loss: 0.00365, acc: 0.73000\n",
            "EPOCH 400, TRAIN loss: 0.00013, acc: 1.00000, VAL loss: 0.00380, acc: 0.70200\n",
            "EPOCH 450, TRAIN loss: 0.00014, acc: 0.99286, VAL loss: 0.00356, acc: 0.71600\n",
            "EPOCH 500, TRAIN loss: 0.00010, acc: 0.99286, VAL loss: 0.00335, acc: 0.72000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "giurxX5-St-V",
        "outputId": "fb6fdcfb-16b7-408d-ea19-7dffb5433e91"
      },
      "source": [
        "display_training_plots(history_gat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W7-nS1GPjjyZ",
        "outputId": "6b220e22-ac6b-4c30-8cd6-a787cdc9bb94"
      },
      "source": [
        "_, test_acc = eval_step(model_gat, test_loader, device)\n",
        "print(\"Accuracy on test set: {:.5f}\".format(test_acc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test set: 0.74800\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cd6Sq8dXTOtk"
      },
      "source": [
        "## Repeat Classification with GraphSAGE\n",
        "\n",
        "Repeat the classification exercise using a GraphSAGE layer (`torch_geometric.nn.SAGEConv`) instead of the GCN layer. As before, steps are as follows.\n",
        "\n",
        "1. Either copy-paste the previous network and make the necessary changes, or refactor the previous network so it can be subclassed for the new convolution layer.\n",
        "2. Instantiate the model and set it to the correct `device`.\n",
        "3. Instantiate the `AdamW` optimizer with the new model parameters.\n",
        "4. Run the training loop for `NUM_EPOCHS` epochs, capturing the training and validation loss and accuracies.\n",
        "5. Use the list of tuples to plot the training curves for training and validation loss and accuracies.\n",
        "6. Compute and report the accuracy of the GAT based GNN against the held-out test set.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95weFkBcS3gM"
      },
      "source": [
        "from torch_geometric.nn import SAGEConv\n",
        "\n",
        "class NodeClassifierSAGE(nn.Module):\n",
        "  def __init__(self, input_dim, num_graph_layers, hidden_dim, output_dim, dropout_pct):\n",
        "\n",
        "    super(NodeClassifierSAGE, self).__init__()\n",
        "\n",
        "    self.input_dim = input_dim\n",
        "    self.hidden_dim = hidden_dim\n",
        "    self.num_graph_layers = num_graph_layers\n",
        "\n",
        "    self.convs = nn.ModuleList()\n",
        "    self.convs.append(SAGEConv(input_dim, hidden_dim))\n",
        "    for i in range(num_graph_layers - 1):\n",
        "      self.convs.append(SAGEConv(hidden_dim, hidden_dim))\n",
        "\n",
        "    self.dropout_pct = dropout_pct\n",
        "\n",
        "    self.clf_head = nn.Sequential(\n",
        "      nn.Linear(hidden_dim, hidden_dim),\n",
        "      nn.Dropout(dropout_pct),\n",
        "      nn.Linear(hidden_dim, output_dim)\n",
        "    )\n",
        "\n",
        "  def forward(self, data):\n",
        "    x, edge_index, batch = data.x, data.edge_index, data.batch\n",
        "    \n",
        "    for i in range(self.num_graph_layers):\n",
        "      x = self.convs[i](x, edge_index)\n",
        "      x = F.relu(x)\n",
        "      x = F.dropout(x, p=self.dropout_pct)\n",
        "    \n",
        "    x = self.clf_head(x)\n",
        "    return F.log_softmax(x, dim=1)\n",
        "\n",
        "  def loss(self, pred, label):\n",
        "    return F.nll_loss(pred, label)\n",
        "\n",
        "\n",
        "model_sage = NodeClassifierSAGE(INPUT_DIM, NUM_GCN_LAYERS, HIDDEN_DIM, OUTPUT_DIM, DROPOUT_PCT)\n",
        "model_sage = model_sage.to(device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hdDZHipvXEhM",
        "outputId": "63ddf0e6-e72d-4c78-eeae-995b9181b8a5"
      },
      "source": [
        "optimizer_sage = optim.AdamW(model_sage.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY)\n",
        "history_sage = train_loop(model_sage, optimizer_sage, train_loader, val_loader, device, NUM_EPOCHS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPOCH   1, TRAIN loss: 0.01395, acc: 0.13571, VAL loss: 0.00396, acc: 0.05200\n",
            "EPOCH  50, TRAIN loss: 0.01147, acc: 0.45714, VAL loss: 0.00363, acc: 0.27600\n",
            "EPOCH 100, TRAIN loss: 0.00286, acc: 0.87143, VAL loss: 0.00244, acc: 0.63800\n",
            "EPOCH 150, TRAIN loss: 0.00044, acc: 0.99286, VAL loss: 0.00284, acc: 0.69000\n",
            "EPOCH 200, TRAIN loss: 0.00052, acc: 0.97143, VAL loss: 0.00315, acc: 0.71800\n",
            "EPOCH 250, TRAIN loss: 0.00024, acc: 1.00000, VAL loss: 0.00327, acc: 0.72800\n",
            "EPOCH 300, TRAIN loss: 0.00014, acc: 1.00000, VAL loss: 0.00391, acc: 0.72200\n",
            "EPOCH 350, TRAIN loss: 0.00005, acc: 1.00000, VAL loss: 0.00403, acc: 0.71200\n",
            "EPOCH 400, TRAIN loss: 0.00003, acc: 1.00000, VAL loss: 0.00400, acc: 0.72400\n",
            "EPOCH 450, TRAIN loss: 0.00007, acc: 1.00000, VAL loss: 0.00437, acc: 0.72800\n",
            "EPOCH 500, TRAIN loss: 0.00009, acc: 0.99286, VAL loss: 0.00399, acc: 0.72400\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "X_yf2ySxXMB6",
        "outputId": "b5617289-7a40-4ea2-8c89-bdcbc004e7b9"
      },
      "source": [
        "display_training_plots(history_sage)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iXlyQxCsLd7V2k5RSSqnTjoY21SyuHNCWtXsLeW7+Tv79ww5ddFcppZRqZhraVLO4aXgcVw9u1/B3akFFK7ZGKaWUOv00KbQZY+4xxvgb8bYxZp0xZmxLN06dOowx/H1CT76ddhYAG9KKWrlFSiml1OmlqZW2my3LKgHGAkHAdcCzLdYqdcrqEu6Lt7sL61OLqKixU6hrtymllFLNoqnrtJm6n+OBDyzL2mqMMUe7gTozubrY6B0TyPxt2cxdm06IrzuLHhrV2s1SSimlTnlNrbStNcb8iIS2H4wxfoCz5ZqlTmUPnt+F3NJqyqrt7M2voKhCq21KKaXUb9XU0HYLMB0YaFlWBeAG3NRirVKntP6xwfzvpoFcNyQW0PFtSimlVHNoamgbCuywLKvIGHMt8GeguOWapU51wzqF8vAF3bAZWJ+qoU0ppZT6rZoa2l4DKowxvYEHgN3A+y3WKnVa8PVwpUu4H+tSC1u7KUoppdQpr6mhzW7JaqmXAi9blvUK4NdyzVKni6EdQ1iZXKDj2pRSSqnfqKmhrdQY8wiy1Mc3xhgbMq5NqaOa1L8tNXYnn63LaO2mKKWUUqe0poa2yUA1sl5bFhAD/LvFWqVOG92j/OkdE8AHK/ZSY9cJx0oppdSJalJoqwtqM4EAY8xFQJVlWTqmTTXJPWM6k5xXzozFu1u7KUoppdQpq6mnsboSWAVMAq4EVhpjrmjJhqnTx7ndwhkTH86MxXtwOPVE8koppdSJaGr36GPIGm03WJZ1PTAI+EvLNUudbi7uHUlJlZ1tmSXUOpyUVtW2dpOUUkqpU0pTQ5vNsqyc/f7OP47bKsXQDiEALN+Tx3PzdzLuhSXIhGSllFJKNUVTzz36vTHmB2B23d+TgW9bpknqdNTG35OOYT4s251PZlElGUWVpBdW0jbYu7WbppRSSp0SmhTaLMt6yBhzOTC87qIZlmXNa7lmqdPRyC5hzFyRSo1DZpFuySjW0KaUUko1UZO7OC3L+tSyrPvr/mlgU8ftmsGxDYENYHOGnglNKaWUaqqjVtqMMaXA4QYeGcCyLMu/RVqlTkud2vgyqmsYq1MKiQr0ZEtmSWs3SSmllDplHDW0WZalp6pSzepfV/Qmu6SK95en8N2WLArLa3BztZGcW07PmIDWbp5SSin1u9WiM0CNMeOMMTuMMbuMMdMPc72HMebjuutXGmPi9rvukbrLdxhjzt/v8hRjzGZjzAZjzJqWbL9qfmF+HiREB3D90Diqa508NHcTf/tmOxNf+5WyantrN08ppZT63Wqx0GaMcQFeAS4AugNXGWO6H7TZLUChZVmdgOeBf9bdtjswBegBjANerdtfvVGWZfWxLGtAS7VftayE6AAeOr8rP23PZs6aNGodFpvSi1q7WUoppdTvVktW2gYBuyzL2mNZVg3wEXDpQdtcCrxX9/tcYLQxxtRd/pFlWdWWZSUDu+r2p04jNwyLIy7Eu+EsCRvSNLQppZRSR9KSoS0aSNvv7/S6yw67jWVZdqAYCDnGbS3gR2PMWmPM7S3QbnWSuLva+NcVvfnD2R2IC/FmQ6qGNqWUUupITsWzGoywLKsf0u061Rgz8nAbGWNuN8asMcasyc3NPbktVE02qH0wj1wQT992QaxPK9KzJCillDo8pxPmPwE5iYdet+VTyNt18tt0krVkaMsA2u73d0zdZYfdxhjjCgQgp8g64m0ty6r/mQPM4wjdppZlzbAsa4BlWQPCwsJ+88GoltWvXSC5pdWkFVS2dlOUUkr9Vjnb4fOpUFNxfLcrSoP/i4e9yw69LnM9/PoCrHz9wMurS+HTW2Hp8yfWVqcTTpGCQUuGttVAZ2NMe2OMOzKx4MuDtvkSuKHu9yuAny0ptXwJTKmbXdoe6AysMsb4GGP8AIwxPsBYYEsLHoM6SQbXnZt0RXI+JVW1LNyRc4xbKKXUGSZjLdhrWrkN62D1W8febtUM2PAh/Prfw19fVQxr3oWKAvk7fze8dzEseApKM2HlG4feJulH+bl7gYSsL6fBV/dC4jdgOSF784Hbf30frHv/0P188yDMf1x+dzrho6vglUGHr+DVs1dDadbRj/kkaOq5R4+bZVl2Y8zdwA+AC/COZVlbjTFPA2ssy/oSeBv4wBizCyhAgh11280BtgF2YKplWQ5jTDgwT+Yq4ArMsizr+5Y6BnXydG7jS7CPOyv3FPDO0mQSs0pZ9eho2vh7tnbTlFLNIWUpLP43XD0HXD1auzUnJmk+5O2EoVOPva3DDsmLpArU/VKQz60Tt+M7mD0FzvsrDJ92/LevqYCtn0F4AkT1kcssS/7ZbI2VpmO1c8HTsGchdBoDQXFyO3s1uO33Xu10SntBKmPh3aEwBQJioPtlUJ4H710kj+X8x8E/Gpx2yE/a73i/hY0fQ/xF4O4DWz+HRc8CBopSJaitq5vHuP4D+ZmTCI5acHGTgLvmHQiLh37Xg9Mh91uWDavfBJsbdL1QulV3fg9uPjBzEvxxTePrM30N5O+C3lMkAO78Hh7YCS4tFp2OqUXv2bKsbznoxPKWZT2+3+9VwKQj3PZvwN8OumwP0Lv5W6pamzGGQXHB/JyYTWFFLQA7sks1tCl1uvjfhfIzLwkiElq3LSdq5RuQsgQG/eHYH9yrZsAPj8jv130OHUcduk3hXvjhURj/H/CPPPK+HLWNlaHM9XW3TYGAtmBssOhf0P4sqCySQOYfBdu/hjbxENIR0lbB3JuhOE1C2x1LJZy9fyns2wBnT4eszVCeA/GXQG0FDLmz8f6dTnBUQ22lHD/Aiteg91USmDbMhnMfk2CYsw16XgGl+2DMk1JNm3N94746zZaAV5gCl74KqcuhIBn2LoXOY6Wa1u96WP8hzLsdNp0L5/4ZPqnrlOt7rdznT0/K390ugsSv6x6natg0BzqNhmUvyWW52+Vx/vxO2PurXObqCfYqeGes/J1wOfS5Gj68XPY98FYJ2x9fJyHPLxI2zpZqXs5WiGy9GNJ6cVGpg4zvFcmP2xrLz0nZZZzVWccjKnXKK05v/L1g94mFttIseGMkXPaafCgfiaMWbK4HVox2/QTBHSG4PXx8LbQbKvsLbAeDbmt6G/KT5MM+P0kCETRWqPJ3SaAZcLP8nbJEKlFOp3T5tR8pQSUoDtJWQtvBsOZtCRyOWrj64wPbXJYLX90DA26SsJS3Uy7P2gTl+fDyQAkzIZ3gl7/DLwawZP+D74TvH5b7uOFrqdC5+0oYWf2WdHE66yqBvuESgBw1cvtdP8n9BMZCt/Gw5xdpR+FeGs5q6R8j48rqx5b5RUn4BHnst34GHgHQ7wYJRBs/kmpV0o/w/SNyXyPuh77XyD+QwOnuC5vnQI8JMPIh2PkDfPsg5O6UcDp5poTf/N2QukyOdfTj8hhG9ZVA+8Vd4OEP1SUQfzFs/wq+mCqBbejdUJEP0f3lOSjOgAmvQ1CsPI/thspEh8BYqaqW7pO2zb5KjstRA6krNbQpBXBJ7yjGJ0TgsCyG/H0BSTmlrd0kpU5vFQXS9VTfHZSTKH8HtoXkJfJBGTf8+Pa5dxls+wLGPiPdVADb9hvOnH8cM/xqyqU7LncHxA6XqsfPz0D7s6GyEHxCDw1nn9wEZz8Mw+6uu7/d0u0V0BYm/U8+xHMSJUC5ekhVyCvo0PvO3QHLX5Yqy6hHobaqLrgA+zY2hrafn5HjbTdYqkMRvSVApC6HbhdKcPryjxIWd3wHfa6RsV6RfeR4PPwh6Qep5Gz8SELJ0D9KV+COb6Sb0D9aglH/GyWg7ZovAWLjx1LxC4qDiF4SSle9JYENpHq28zsJKhPekLZsmAW/Pg/VZXLcV38MM86R57rbxVJNKkqFz26HK9+Dz++S18TIByVE2Vzhouelq9ArWMafjXqs8fEs2SfBa9gfwTtY/p39J2nPwFshdgRs+1yu359XoPzsc7X8rA/USfPl8Yk7S0IkwFkPwMxl8joI6woX/xdiBsJrwxr31fk8mDADXhkoATqil3Qt2+qG8h8c1o2BK96FWZNg1mSwHDD4Dnn97F4g1/3wqDyvg1tvtTFzJiyxMGDAAGvNmhY849XCf0BJhjzpxib/MPKG5eoJbl77ffMzjT9tLnK9q6e82F3c9/t5uMs8wMUDXN3B1atV+9Vb2pWvL8dpWcy9c1hrN0WpU0PWFgjtIu8PTVFVDC8PAu8QuOFLyNwAH10tH/x3/AovJMj7zj0bjz3OKWe7hLw+V0s31PYv5QPvgn/K9Z/9QSo2IFWyy149/H5qyqUSNPA2+aBNWwVL/kPD+6mjbhC+sUm4iB0Ol78FG2bKGKt3xkklzC8S7t0st/nqHgkqTjt4BkJlwYH3efbDEso21HXb9ZgA2Vvh3Qsk2FgOuOpjCRGvDZXbDJkq1ZZtX0h4sRxSJaopk9ufPR1eHQyXvgIJV8Bz8Qfeb3AHqSxVFsAlL8H3j8qx1Ve7BtwsA+h7T5FxWDu/h5F/gthh8MFlUinKWNu4v4lvQq8r5ffSbKk4FadJlcozUB6H+xPlM2PxvyVoAlzwbwkgX9wtj+klL8rlJZnSnV2wR/6++UcJpa0ha7OEygufg/51XaSWJWPlul4IYV0at01eLI+tX1RjOCvNlupkeA/pNj6W6lJ5DZdmy/+LmnIo2iuP+dybYe9yuH/bbx+jeAzGmLWHO+vT6fupfzKlr5JvbpYTsOSn5ZSBqPbKxjea5uYVDG7eEuY8fMHdDzz85HcPP3kT8fCXsRL+0TII1D8a3L1bpj3NqHO4LzNXpvLkl1t5dHw87q6n4pKCqtXVlMv/kRZ+g21R1WXyf/po8pLg9REw7lkYcseB1zkd8PZYaDsIzv+7vDd9+6B0kZVlQ1WRVBbykuR9IzcR5v+lsWsoN1G6i7Ck4lKvokAqOKGdYcFfpSq06g25zMNfus4iekn3V/ZW6RKtrTqw0pa/WwaLn/0wePpLuJr/uFR06scfdR0vH6QpS6RK03aQfAl2cYdF/5TKWtoKWP22BLYR98nSD9u+kAC6/kPppguKk+Oq7zozLhIglz4v3Wg/PCqVu8g+8MEEed3c+jPMuU6qTj0uk/a4+0koXfWGBB2vIKjIk8DmFST361+3Fny7oRIE+11fNyA/AbK3NAaw7V9CrymQukKCZ48JEtLWvCP7Gv0keAbAlrky1sxySrsy1koXZVmWDOzvud/QcL9w6DpOKpHfPijP79kPN37JH/GAVMN82zRWmy59+cDXjH8U3LpAKo0uHq0X2AAiesJ9W8GnTeNlxsjzfLD2h1m21S8c/M5r+v15+MHkDxv/dvOS1wXIa9Hd59CJFyeRVtpOBqdDvuVZFhLq6h5zp13eZOxVMo3bUS0vBnt13e/7XeaoOfBnbQWU58qbYG2FfDhVl0JNqbzJV5fKm4i96tD2eAXXhTcfeQOM6iPfUj0DTurDcjSfrEnjobmbAHji4u70jA7gvwuSeO3a/vh66HcN1QTVZfBcdzjvycZxRr/V+pkyJuvsh1tmBmRRqowvuuh5+f+480epfo15Urrk/CIkvIz/P4jpLx/MH14hgWf3z9BlnIzd8guXoFCUClUlMlMPZPyPvVrCjkeABKqIXvD5HRKC7lgq45/qKyz1t0ldAemrJWDEDJDqzWe3yv13v0y6jCry5T0NZID5po+kUjZ1Jbw0AIbeJdWlde9JyGjTXYJK/i7ocy1c9gq8d4mMs4K6LsFICRR7FkmX39nTYdQjjW2bNVmqUPW8gmR23xsjpZJoc5Wgc+dSafPqt6Q77YOJENlLZrK+MriuW69uGdHwnlCSDjd9D226yWM4+yoJWyDjopa/LGHi+i8lRL02TF4XE2bImCqnHaL6wW0/S8CoLpPZs23iYcWrMOapAz/09y6X5S5u/Eb+/nCiVL0SLj/0NTL/cVlGo/9NMos1MPbI1dW1/5MQ0mPiqf3F5Qx0pEqbhrbTnb1aSt0lGTIYuDhdfi/JlDe19NXyBuMXKW/abeKhy/ngGyFvagExrdJsp9Mip7Sa++dsYGtmCcWVMqN01q2D6RDmS0SAzipVx7B3Obw7TqozN33TPPt8OhSctVKZufIDmU24+2f5cO97nQy0Tl0Bl799+A9SR62M/QntDGU5MsYpup8EAP8cODsAACAASURBVMuSrquFf4OLXpDQ9dpwmf0GUhmyHFL5qK+eB3doXO5ANqJhsHh9d527r3ypix1Wt85XtXRj1ndRWpaMGwvpKDPzCvbAdw9Ld1LyYhmwX5IhgaiySPbv6iUD4KP7NS67MP4/UiHK2Qb3b5djfam/bJO2Eia+JdW7+X9pnL3n7itjuLZ/JUH1mwelylOcJvurrwSVZsnMvglvHDiJYcNsCZw+YfIltuck6S5NXwtvj5HqzFWzpGtrf4v/DSGdpXq2/JXGQfT1zv2LjOGqV5YL/+kkvz9ZLF+WXT0ag9DX98vz8HCKrBu2eY6Evtihx3xJNaipaOwFsVcf+UtBeZ50A4/7h4zbUqclDW1namg7lupSGf/wy7PyjSxjnUz7rtflAhmoG91PPiBOsl05pTwwZyMb04sBOKtzKEt35TH/vrPp1OYYXUbq96e2UlY8338cSlM5nY3jVJpi5Rvw3Z8k7Dy0SwZEH05NuVSXYgZKVamevUa6v+q7lYpS4YWeUlmprZQq1foP5f9FUWrjsAiADqOkkhTZS/6PRfaW/X92G2z+BDqcI8scFNUNbO85SapJHn5SsYk7SwLUvD/AOY9Kde3cv0g4TF0pg6XrA5pflHTPtRsqVSrvUBlc/ss/pMqWvkpCy8Q3paoU2gVu/alpwySSfoKZl8vjcO8WCW7vjJUvfX9YIuHr/7pKkL1jqYS6Pb/A6L/I7b95UNbEArhzuXwJ3P6VVJAc1fJF0eYKb42RpSeMTdqWuUGO/1jVzKpiGe805inpahx4W2NXXuYGmXzgE3L0fVSXwfM95ItrfpJ8iZ22/tD3u8K9MgYtqu+h+6gokK7etgMl4GWslS5KpU6QhjYNbU1jWfIttyxHZmL9+iLUlst1bQfLwNiu46Wb5qQ2y6L/Mz9RUC7jA/95eU8mD2x3UtugmsE3D8gA6/u3N44TqZe8WAasD74dhk2TiTr1kn6SwcFXf3Rg1eRoQe6LqVKJsRwyY2/UYxAQLa/xsmzplvMOhZ//2rjO06Wvyofymndg08cyRubyt2S9pl3zZZtJ/4NPbpTfe18lS1CU58Hif8lP3zaw9j3AOnB4gleQdCd2Pl/u23LI4OpF/5RKVD2PABlz5Rsu+7p90aHHWJQGmetk/avz/y4Bp7II/ttLHruxf5XtyvPh5QGyGOuI+6SL1T/m2EFmf988KG29qO4UQTUVMiSj/vn7+Dp57v6058DnDCTc/vKsVN+mzD7y5Kn83bDw7zDo9tYZP5W2SsYuffsnOdZbfjz5bVBqPxraNLSdmKoSCXFJ82VKem7daT58I+TDLSBGxtF0HtPiTZkyYzkr9sgMrMkD2jKySxjjEiJwselYjd+spkKqJsdTyTpYXpJ8mB9pDaOyXKloOKolrAy85cDrP58qSyGAvKYm/U8+SCsK4NUhErS6XABXzZaKks1NuszaDYbLXm9cnNTplBD2zQONM8Y2fiRVnbPul8U39195HQPnPXXg4HcXDxn7lLVJKmTpqxs3f7wQ3hwlwWvaeglWB7Ms6ZosTpeB78mLZNxXcMe6QLrf41yeL8s6ZG+RwfsT35JxT5nr4NrPjr4mWeoKCbH1S2ukLJWxVPtX0ZrjuT2aigJ5buqXwDiVlecB5vhCrVItQEObhrbfzrLkQ2zvculS3bdBPpRqK2SgdI8J0l3h6gWhnZr97p/4YgvvLd97wGVPXdKDG4bFNft9tarUlbKS+vh/ywdySaZUPEc/Lo/vD49I6DlSd9/xytsl1ZgR98linYUpUkl194G01dJl16abVF+NiwSE5EUSQKqKJBQUp8K3D0k4uGejLCNRtFeuaxMvY39+/IusUu4bLrP6Js6QbqS4s2T75+JlYkzcWbKvsK5SmQqMlddb/MWyaOeQqbDiFelK8wmTx6T7ZbJW0/KX5PW5e4Ec2/B74Lyn5Zi+fUjGnHkFyey90C5QmCxfPmIGSHfo6jfldd77KqkKPZ8g4eui56V7zM1L1pzKSZRqWNtBzfMcgHxB2jpPxsYZU7cS+8mtaCulfh80tGloaxnVZTIeqP7UJiAf7AmXyweh0yFdJoP/8JvvaubKvTw2bwsJ0f5sySgBINDbjYUPnEOQTxPXpjoVfHKThBNXT7hlviwtsP5DuORlmaH30xNSheox4fC3z9osg6wPNyU9b5dUR908JZws+lfjMg0+YRJk9v4qldTLXoVPb5Gq0/VfyAxE71AZZ7Rvg1yOhaw56CrhLHMdhNUFvIo8uU/fcBnjteVT6DVZxrPN32/smLuvDLz/7Fa4+EVZi2nVmxLy/MIlcJ37Zxmv9PIAGXDuHyPB5pKXZCLAspckQKWtlFA28DYJfIP/IIP+QSpwG2bKdmFdm/ZcJC+R+4kb0bTtlVKqGWho09DWcixLTrGya4HMmMtYL+PhyhpPSUW3i2TMzTnT5UP8BAail1bVMndtOmF+Htw9az33jenCKwt3Mah9MFcObEtJZS1X9I/B4bT409xNdAzz4baRHfDzdGvGg21mToeEEL8IqVpu+VRm8nUaI+OPbK4y485pl4VEjU0C8siHJMgcbPtXsvJ6UJwsC/HzX2Um2vh/S8Vo9lUyi/CauTIOafZk6DhagtiOutME979JritO228xU5fGdQgBxv5NqkK1lRKq7JVw10qpVG3+RLo3YwbKfnctkGBlc5FtvEMklOZslzFjn90urxUXd5i2QcadgYQse6UMbO98vlS+Ns+FT2+Vldq7XyrbVRTIuKrszTJY/3hOS6SUUr9DGto0tJ18+zbJzLl5f5AAUD/N37jAiHulItNjonSvevo3ebcOp8Wq5AKGdAhm9qo0Hp23ueG6O87uyMC4IG55T57v0d3a8Ob1A7Cd6Li3g0/z09y+e1gG5k9dCTOvbFze4dpPZRHPb+6Xx6fDKDlPYb2weJkgEneWhB+fMAl51SWycntVsYQ9Vy/wDZNwVVMu46+KUmHALdKFaHOFO5dJEPxgAmDgwZ0ydvG9iyVgX/Iy7Fkoywv8+LiMjbrtF6lAWU4Z71ieI0tUHInDLmPZ9l+ctV7aalj5mnR7xvQ/9PqDleXKMSml1GlKQ5uGttaTtUWqMeHdZRr+mncau1M96hb0vfgFmUHmHyVdd90nSGWoCWEuvbCCihoHr/2ym683ZXJhz0i+3JjJg+d35V/f7+CWEe3584XxmIMXl6wslLFJkb0kTGSsk9DTbqiEtPzdshSBXySMfRqiBzSeHy8nUULLwd1sid/KWKRuF8kJjb2CpWvNUSOVpG2fywB2D38ZX7X7Zwk+QXESpkY9JkHxvKcaB5fXt/W9i6XrM7iDrKVVfyqfyD4S0iJ7y2N23tMSjj+8QsbBRfSCt8+T7urJH0rX5MbZst9rPpVJJNVl8Gw7mSF883dy3XfTpSK6/8K0VbL0yu9pIWallDrdaGjT0Pb7YVkyS2vTxxJa9m2QMVX784+RMVGXvy0hL2szXDu3cWZiTYV0sbUbKouCAtklVVz97Af4W+WMDMzh3j6GmTkdyElcxvXRmYQmnAtB7aXrccNM6Ya0V0HnsXJi56/ukX1H9JRuy9xECSe1VdJNF9JJzqcY0gm+nCbdmpPelTDm7ivrYf14UJelzU26I/N2ys8tnzZeF9BWBvnXn9uu1xSY+MaRHzenE4pSYMtn0u3Z9ULpJnQ5Qvfv/sthFGdIlc3FDXJ3wiuD5LivmdO4/co35Nhj9XyvSinVmjS0aWj7/UpbJefrGzpVwtjmT6TLsLZcKjteQTLovX7hzfJcWcV88xzASLdcyhLpbq0flwUNlSgnhjz3trSpSZXLvYJlkcyu4+U2S58DwGp/NqbvtbKiuW+YnOi55xVShUv5Vdb9ql+R3lEtga6+8uQdIkEvZoAslJq/G+KGy3pelYUS3py1MPxeSJgIuTtk/zYbLPyHrNA+dWXjoPmj2fWTLHVx5fuN47qOV8qvMnGguWagKqWUajYa2jS0nXo2fiwzJa+aLaHn7bESmuoXLB16t3QT7vhWZjuWZbG73ST+tiuWi0afw8QRfWDH9zyyyoPlhX78cltHGWf3xV2y2Or5fwfAuXIGf/05i5CBk7j7vO5HHseWNF/C1o+Pyar4922VVdgL9si5AAGu+1xOy1MvebHcZ3Q/Wd5i6N2HngPQXi1do00JbCCVyuTFMohfzyeolFKnHQ1tGtpOTZbVGEx2/gDzn5BFWQPbSTen5YTdC+W0QOW5VHi24V8/7OTuczsR6iuh660le3jmm+10buPLtNGdKa6oYmxCFMHe7sxenUaQtxt3z1qPu6uNhQ+eQ3Sg19Hb9MXdMvB/zBONbaw/QfW0DS23iKlSSqkzgoY2DW1nrC0ZxVz00tIDLpvUP4ac0moW7czFzcVQ67Bwd7Fxw7BYHruwOwA5JVX4eLji43GEU+/sryRTTpAdFNsSh6CUUuoMcqTQ1oRPI6VObT2i/PnvlD7ER/rz7q8p7Mwu5ZO16QD0jA5gc0Yx3SL8CPR2Y8WeApYm5REb4s2UGSsY1jGEf086wmmZ9ucf1cJHoZRS6kyn/TjqtGeM4dI+0XQJ9+MfE3vy8LhuAPRtF8iM6/vj5mIY2SWMQe1D2JpZzI3vrmLqrHVkFFWyIDEHh/P0r0YrpZT6/dNKmzrjDIwL4r4xXRjfM4LIAC++/uNZRAd5sSG1CKcFTstiU7rMCi0or2FTehF92wX95vuduzad7zbv4+0bB/7mfSmllDrzaKVNnXGMMdwzpjOdw/0A6Brhh6+HK33bBeJqM3i7uwDg6WbDZmBhYg4AZdV2KmscR9238yhVuR+3ZrEgMeeY+1BKKaUOR0ObUnV8PFx55Zp+vH/zIAD6xwYxrGMoH69JY0NaESP/tZCps9Yd8faPzdvMVW+u4EiTe3bllgGQUVTR/I1XSil12tPQptR+zu8RwYC4YG47qz3XDYnlD2d3ILukmgmv/kpxZS0/J+awM7sUy7L4fH0GN7yzipcWJAGwOCmXlckFrEstatjfU19tZdLry6i2O9ibL2EtrbCyVY5NKaXUqU3HtCl1GPXLfliWxZj4cGwG7h3ThQmv/sq7v6bg7mJ4b/levN1dWJmczxUDYkgrkDD22LzNTB7YlkBvN979NQWAH7ZmN0xoSNfQppRS6gRoaFPqKIwxvHVD41I5l/WJ5rN16dQ6nEwZ2JbL+kYzZcYKXqyrto2Jb0NiVilPfbUNgO6R/mzPKmmoxoGc4L5eVa2D9MIKOrXxO0lHdPy2Zhbj7e5K+1Cf1m6KUkqd0bR7VKnjcNOIOKrtTowxTBvdmUFxwUQGeDJ7VRoA/7y8F0sfPpdXru7HY+PjmTd1GANjg0nKkfFskQGerNhTwOfrM3A6LV5duItxLyw5IMgt353PJS8vJb+suuEyp9Nib375Ie2psTv5YMVe0gsr+Pu328ktrWbFnnxqHc5DtnU6LR75bBNr9xY2+XgdToub3l3NI59tavJtlFJKtQyttCl1HLpF+HNpnygCvdyIqjvd1QNju/LgJxsBCKk7ddaFvSIbbnP/2C68uXgPMUFeJOWUsWx3Pvd+vIHvt2SxM6cUu9Ni5spUzusezser0tidW8am9GJe+nkXj4zvRq3D4o+z1rFwRy43DY/jsfHxuLrI962vN2Xyl8+3NNzXvuIqvtqYyd8n9OTqwe0ACV7vLUshxNed2avSqKp10j9WljCpqnXg4WrDHOEcpmv3FpJTWk1RZS3Vdgceri6H3e6jVams2JPP9AviiQjwBCCvrJrC8pqGWbpKKaV+Gz2NlVLN4MuNmViWxaV9oo+63RWvLWPN3kLO6x7O/G3ZAHi7u+BqM3i4uZBbKtW1IG83Citq8XC1MbZHBF9tzGR0tzYsSMyhY5gPnm4uXN4vhgWJ2axJKaTaLpU1mwGnBRf1iuTlq/thWRZvL03mmW+2N1wX5ufBkA4h9Iz25/VFe2gb5MWLV/UlNsSHrzZmEhXoSffIAIyBv369jZkrUwH45I6h/Lorj9zSaqaN7ky4v2fDcQ35+wKySqroEObDT/edzdbMEi5+WU4dlvjXcXi6HT7snSyWZfHEl1sZlxDBsI6hrdqWw3E4LbZmFtMrJrC1m3JKqLE7+df3iVw/NI52Id6t3Rylmp2ee1RDm/od2JBWxOKdufzx3E5c9eYKVuwp4INbBvG3b7aTkl/OtYNj+WVnLq9f258ftmbxxqLdlFTZGRMfzls3DOCrjZm8tTQZa78FgO88pyNXD2rHc/N3Mm99BgDBPu5MGhDD7JWplFTZCfR2o6guBNYHPAAfdxdcXWxEB3rxwpQ+jHthMa42G24uhiq7E4fTYmSXMBbvzMXLzYXKWlljrnMbX2beNphfEnPxcLNxz0cb6Brux47sUv5300Ce/mobe/KkO/fN6wdwXvdw3lmazKxVqYzu1oaHzu/KyuQCkvPKuXpQO2y2w1f6DpZTUsWzdR/Wfdo2PeBsTi/m4peXEuHvyU8PnI2bi2Hiq8u4uHcUd5zdscn7OR7l1Xb2FVdSWeNkQWI294zufMSK5gcr9vKXz7fw1d0j6BkTcNT9FlfUcv+cDTx2YTwlVXYSovwbKq9nii83ZjJt9nr+MLIDj4yPP+H9lFXbcbWZVv9ScSazLOuI/y9+q22ZJQT7uDdU/08lrRLajDHjgP8CLsBblmU9e9D1HsD7QH8gH5hsWVZK3XWPALcADmCaZVk/NGWfh6OhTf0e5ZZWszmjiHO7heN0WpTV2PH3dDtgm7eXJvPXr7fx6Z3DGro0632/ZR/vLdvLv67oRdtgb2atTOXReZvx93SlpMoOSDdtv3ZBXNgzkmmz13NJnyj+/PkW+scGERvszbiECJyWxR0friPEx53yGjsD44Lx93IjNtibyEAvrugXw6Q3lrElo4SzOocyaUBbps1ef0CIA5h122Du+GAtxhiKK2t58/oB3D9nA642w6hubfhsXQYdQn3Yk1dOdKAXGUUyi3bqqI70jgnkq037yCquJK2gkvdvGYTdYdGhrqpY7+5Z6/h60z5cbYZxCRF4u7tw5zmdDpgkkVFUSYS/Jy77BcF/fZ/I64t2YwF3ndORIG93nvlmO2F+Hiyffi6uLjacTosZS/ZgMzB5QDtsNkgtqKC4spaSSjvxkX7EhjTez6yVqWxKL+L6oXF0j/I/4LlxOi2uenMF61IL6RDqy47sUubdNeyIZ9aY9PoyVqcUcsfZHZl+QbeGy1clF/CnuRt5bnIf+rULwuG0eH95Ck99tY0RnUJZuiuPh8d1w8fDhXO7tSEmSKpOxRW1FFXW0C7Yu8kfiBvSigj0ciMu1Ies4ipsNnA64YsNGUQHeXFRLzm/bnJeOQXlNYe8HvfXlA/iGrsTd9cTC5uT31jOyuQCukX48f29I09oH8UVtVz40hJ6tw3klav7Hddtc0qryCmpJiH66AH7cMqq7fycmMOFPSMPeI2eqMoaB7e+v5obh7XnvO7hh1xffy7ltsFHrkhW1TpwOC18PJp3xJTTaVFYUUOIrwcOp3XI8W7JKGbyG8t5/5bBR309nYiyajtD/76APu0C+eCWwc2675PhpJ8w3hjjArwCnAekA6uNMV9alrVtv81uAQoty+pkjJkC/BOYbIzpDkwBegBRwE/GmC51tznWPpU6JYT5eXBuN3mTtdnMIYEN4ObhcZwXH37YLqBxCZGMS2gcOze8UwiuNsO9Y7rw9NfbuLh3FC9d1bfh+jl3DAXA1WYY2jHkgADy4NgufLgilTvP7sQ9Yzofcl/v3DiQ4opaOob5AvD2kj1kFFXx/OQ+TPtoPS7GMDAumJtHtGfO6jSuHxrLed3DOS8+nM/WZ7Bgew5XDojhr5cl8I9vE/lodSpPXtydjenFvLJwd8PjERXoRWWtg4teXEqNw4nNyKLHl/eLITmvnEU7c7l1RHtqHE6+3rSPiho7K5MLuLBnJHanRWWNgw9W7KVfu0BGdA5jYFwQWzNL+HRdOsM6huLn6coHy/cC0MbPg5zSau75eAOxwd5sySxh8c5cAJ6fn4TDsqjZryrp5ebCTcPjyCurJr+shgWJOdgMfL1pH89clsCO7FIGtQ+mf2wQ//g2kZXJBRgDO7JLAXhvWQp+nq7M35bDp+vSsTuchPp6cP/YLqxOKcRmJIjfPDwOYwyfrE1j5opUMooq+eOs9Xi7u7A3vwJvDwmxS3flAfB/P+7A7rR4/Zfd9G0XxJAOwfzju0QqahxMGdiWtMIKhncK5c6zO2KMIb+smn3FVfy4NQsXm41xCRG8+HMS32zah6ebjScv7sF/FyRhWdLdnllcBUjXet+2gVzx2jIKK2p48pIeXD2oHa4uNsqq7RRV1BDm58Gf521hdUoBn08dzsrkAuasTuM/k3pTWmXHZoOYIG/mrE7j6a+38dHtQw4IPlsyinl03mYu6xPNnrwy7h7VuaFK4nRa2GyGLRnFrEwuIDrQi8SsUnJKqmjjf/hKypw1adiM4azOoQd051uWxaPzNpNeWEl2SRXFFbX8b1kK2/YV0zbImwFxQWQUVVFRbWfqqE7YbAan0yKntJpgH3ce/GQTq5LzWTZ9NME+7oe978MF14LyGp7+aiufb8gkObece8Z05setWTz55VZemNKXQe2DqXU4cduvclpV6+CNRXsY0iEYHw9XQn092JJRjIvN0DXCj49Wp/HrrnwAXGywM7uMMfHhdGrjy8o9+Vz3zkoGxgXz8e1DmLs2nZggb4Z2DGnYf15ZNRNfXUaAlxtfTB1+2Kq3ZVn8sDWLFXsKeOzC+APadyTJeeVMnbmOpJxS/jCyI7NWpfLx7UMOGOP68eo0ymscPD9/Jx/e+tuDlWVZ1DosdmaXMn9bNqXVdn7dlUdWcdUJVdvqi1otVQk8ES1WaTPGDAWetCzr/Lq/HwGwLOsf+23zQ902y40xrkAWEAZM33/b+u3qbnbUfR6OVtrUmaK4opYAbzf25pfTNsi7yd2Ox6usWip5vh6uzF6VSkF5DVNHdTpku/yyahKzShncPrihC8+yLKrtTjzdXHA4LX7dlUdZtZ3zuofj5mJj/rZsnvxyKzcMi6W0ys7O7FJ+2JpNTJAXk/q35c5zOjZUaNbuLeDW99bUBQJDjd3J6G5t2JBWRH55TUM7Qn09+NuEBEJ93bn8teX4e7oyb+pwbnhnFTml1TicFkHe7tw+sj0jOoXx4cq9uLvYGNJBqo4eri4888021qcWEeLjToC3G72iA7j73E5c+OLSA7qc/TxdKau2c9Ow9ni42fh4dRpDO4bwzaZ9DdsM6RBMmJ8ni3fmUlxZi7uLjVvPas+rv0iAdXexUeNwEujtxk3D2vPiz0n0bxeEm6vh1135dG7jS1JOGWF+HuSWVtOnbSAllbXkl9dQXFlLqK8HwzuF8MWGzIb7HN4phGAfD77fso9ah4XNgAXUfwRMG92ZlXvyWZlcAEi4txnDezcP4vmfdrI+tZAQHw8qax3ER/qxYk8Bfh6uxIZ6k15YSVFFbUP11WZgROcw1qQUUFHjwNvdhYq607dN6h/DgsQcCspr6NzGl3O6hhEZ4MXqlAK27yshJb9xJnWXcF/G9YjA7rR4a0ky3SL9qHVYZBVX8tq1/ZkyYwW92wZSa3cyvFMIkwa0xdPVhZvfW02YrwfL9+Q37Cs60Isu4b4UVdbi7e7Cr7vyubRPFF9syGz4GezjTnm1/YDnM8Lfk87hvpRU2dmYVkSEvydZJRJkR3UNw+608HJzoU+7QNbtLaJvu0D25ssXjBen9MXulBCxfV8Jc9akN+wzu7SKIe1DWJ9WSFWtBPhuEX6s2JPPvWM6c8uIDuwrruS5+Tv5er/XzuF4usmwh/rn0sPVxo3D4pi3PoO8smqcFkzsG81n6zPwcnNh9u1DcK17b/jz51vYmF6EZUnVO9TXgxBfD/w8XSmuqG3YR0aRPMcX947i4l6RpOSXM6prGzzdXMgtq2ZnVimhvh58uHIvWcVVWBZkl1bhcFqU1lX+R3drw9RzO7FyTwGfrUsnq+4LQWm1ndev7dfwJXRXThlfb8pkV04Z2SVVXNgzktHx4eSXS/U42MedxKwSps5cxyW9o7lyYAyp+RU8NHcTqQWNr5+2wV6kFVRy8/D2XNgrknV7CwnycWd4pxBC66p/G9OK8PV0JT7Cn0/WppFdUo3NQEmVnaziKlanFPCncV3pERVAYlYpxRU1XDc07qjPR3M46d2jxpgrgHGWZd1a9/d1wGDLsu7eb5stdduk1/29GxiMBLQVlmV9WHf528B3dTc76j4PR0ObUqe2qlrHEccd1Vc0LMuivMaBb10XT0WNnYWJuXSL9GuoEAK89stuBsQFMTAumKpaBzZjMAZcjDlqyLUsC7vTOqTKMG99OhtSi3jg/K68unA3mzOKeHhcN3rFBOJ0WlTZHVTWOJi/LRsvdxfaBnvTr66bdEtGMTNXpnLtkHZ0CfdjYWIOqQUVJOeVc9PwuIb1++qPv9bh5JcduXSL8OOil5by3yl92LavhIl9Y4gI8KSwvIa/fbudyQPbkhAVwAOfbOCChEhySquZsXg3lgVje4QzrGMovWICcHexMWPxHuIj/bm8fwwVNXb+8MFaogK8GN8rEgOM7BJGcWUtk99YTl5ZDe/eOJAeUf78tD2bxUm5pBVU4uXmQq+2AWQVVzG2ewRr9xby/E87iQ704taz2vPdliwu7hVJcl4FH67Yi8OyuOucjryxaA9AQ0Atqazl9Wv7y+MNPPTJRkqr7VgWnNU5lNxS+RLw9KU9uG5ILO/+msJHq1Px83RjXWphQ2jxdpfwOLxjKNMv6May3XlsyShhZ3YpPh6ubM0sZkBsMO/dPIjx/13CjuxS4kK8+f7ekbjaDG8tTQakurpiTz5Lk/Kodji5e1Qn3vk1mcoaB71jAlmVUkCXcF/2FVdRWmUnMsCTfXVBJNjHnYL9vjgAXNE/hpggtIqLcgAAIABJREFUL24a1p43Fu9m4Y5cOoT6cOXAtvznhx1U1ToIPShsAkw7txOxITJUIKukivhIP9xdbGzNLMHFZugW4ccVry8nNsSb928exD++TeT7rVm0C/bm2Yk9ueHdVdQ6LCYPaMvipNyGNgIYA69e3Y/nf9rJzuyyQ173Xm4uOCyrbm3Kdny0OpWjxYb6oQkZRZW8dFVfMooq+ef3iYzq2oaf687jDI2V7teu6ccbi/ewNbOYcH9PauxOckolOLUN9sbLzYXErNID7qN+CEj9l5t64f4eXD0olrhQb4oraxnUPpiXFuzim81HD71Her6AA4ZygHyZ2fHMBc3StX00Z1xoM8bcDtwO0K5du/579+5tkeNUSqkzQbXdgd3R9HFP5dV2PN1cDvlwq6p1UFRRS0SAZ0PVNbWggs5tfCmptBPgfeAwgRq7k6ziKtoGyxI7WSVVRPh7HtJllVZQwarkAnZkl3JJ7yi83V2ICvQ6bNgvrarFw9UFd1cbBeU1bM0splMbXyIDvA57LFnFVZTX2OkY5ktaQQV5ZdW0C/YmJb+Cfu0CySqpYldOGSM6hZJfXkNFtQNPNxs/bMsm0t+Tjm3kdmd1Dj1mV5tlWSzamcv61CKig7zoGR1AfKT/MW/z4oJdjOoW1jADeXduGeH+nvh6uPLD1ix83F0Z0TmU/LJq5qxJx9vdhdKqWtoGe3Npn2hyS6vJLqkiKtCLtIIKKmochPq6E+jtzp7cMjKKKpnYL4Z1qYXszCqlf2wQmzOKqXVIha9rhB8ZRZWc1z2cimoHmzKKGdlZZmrvK64ixNedn7bl4O3hQmSAJx3DfNmWWULvtoEUltfw3wVJlFT9P3vnHV5Vkf7xz6T3kF4pAQIJELoUKdKlKSKoCPaCa1m7rvrTXV3LuoplXV0LdgUUQQQFVECqCNIhkFBDSSGd9Hbvnd8f702DUJSEBJjP8+TJvXPOmTNn5twz3/POO+9UoFB0jvRlVFwowd5yj2w8lMv+jEKaebiQlFVEWl4JgV6uTOgRybzNybi7OBHo5ULfNgEEe7udUDer92ZRXG6he0s/juSUkHg0n8yCMhyVIjrEm/2ZhRzKLqJf20C6Nm9GSYWVnMJyjuaXMq5rBL/tzyY1r4S4CF9a+HvUu+9fXTSGaDPDowaDwWAwGAx/kJOJtoacJ74BiFZKRSmlXJCJBQuO22cBcLP980TgFy0qcgEwSSnlqpSKAqKB388wT4PBYDAYDIYLjgaz8WmtLUqp+4CfkPAcH2utdyql/gls1FovAD4CvlBK7QNyEBGGfb/ZwC7AAtyrtbYC1JVnQ12DwWAwGAwGQ1PBBNc1GAwGg8FgaEI0xvCowWAwGAwGg6GeuCgsbUqpTKChp48GAlkNfA7DH8e0S9PDtEnTxLRL08S0S9PjXLRJS6110PGJF4VoOxcopTbWZco0NC6mXZoepk2aJqZdmiamXZoejdkmZnjUYDAYDAaD4TzAiDaDwWAwGAyG8wAj2uqPDxq7AIY6Me3S9DBt0jQx7dI0Me3S9Gi0NjE+bQaDwWAwGAznAcbSZjAYDAaDwXAeYETbWaKUGqmU2q2U2qeUeqKxy3MxoZT6WCmVoZSKr5Hmr5RaopTaa//vZ09XSqm37O20XSnVvfFKfmGjlGqulFqulNqllNqplHrAnm7appFQSrkppX5XSm2zt8lz9vQopdR6e91/bV8eEPsSgl/b09crpVo1ZvkvdJRSjkqpLUqpH+zfTbs0Mkqpg0qpHUqprUqpjfa0Rn+GGdF2FiilHIF3gFFAB+B6pVSHxi3VRcWnwMjj0p4Almmto4Fl9u8gbRRt/5sKvHuOyngxYgEe0Vp3APoA99p/F6ZtGo8yYIjWugvQFRiplOoD/Bt4Q2vdFsgFbrfvfzuQa09/w76foeF4AEio8d20S9NgsNa6a43wHo3+DDOi7ezoBezTWh/QWpcDXwHjGrlMFw1a61XImrU1GQd8Zv/8GXBVjfTPtbAOaKaUCjs3Jb240Fqnaa032z8XIJ1RBKZtGg173Rbavzrb/zQwBJhjTz++TSrbag4wVCmlzlFxLyqUUpHAGOBD+3eFaZemSqM/w4xoOzsigCM1vifb0wyNR4jWOs3++SgQYv9s2qoRsA/fdAPWY9qmUbEPwW0FMoAlwH7gmNbaYt+lZr1XtYl9ex4QcG5LfNHwJvA4YLN/D8C0S1NAAz8rpTYppaba0xr9GebUEJkaDE0BrbVWSpnp0Y2EUsoLmAs8qLXOr2kQMG1z7tFaW4GuSqlmwDwgppGLdNGjlBoLZGitNymlBjV2eQy16K+1TlFKBQNLlFKJNTc21jPMWNrOjhSgeY3vkfY0Q+ORXmmWtv/PsKebtjqHKKWcEcE2Q2v9rT3ZtE0TQGt9DFgO9EWGcSpf3mvWe1Wb2Lf7AtnnuKgXA/2AK5VSBxH3miHAfzDt0uhorVPs/zOQl5xeNIFnmBFtZ8cGINo+08cFmAQsaOQyXewsAG62f74ZmF8j/Sb7LJ8+QF4NM7ehHrH72HwEJGitX6+xybRNI6GUCrJb2FBKuQPDEV/D5cBE+27Ht0llW00EftEmqGe9o7V+UmsdqbVuhfQfv2itp2DapVFRSnkqpbwrPwMjgHiawDPMBNc9S5RSoxGfBEfgY631i41cpIsGpdQsYBAQCKQD/wC+A2YDLYBDwLVa6xy7kHgbmW1aDNyqtd7YGOW+0FFK9QdWAzuo9tN5CvFrM23TCCilOiOO047Iy/psrfU/lVKtEQuPP7AFuEFrXaaUcgO+QPwRc4BJWusDjVP6iwP78OijWuuxpl0aF3v9z7N/dQJmaq1fVEoF0MjPMCPaDAaDwWAwGM4DzPCowWAwGAwGw3mAEW0Gg8FgMBgM5wFGtBkMBoPBYDCcBxjRZjAYDAaDwXAeYESbwWAwGAwGw3mAEW0Gg+GCRim11v6/lVJqcj3n/VRd5zIYDIaGwIT8MBgMFwU142D9gWOcaqwBWdf2Qq21V32Uz2AwGE6HsbQZDIYLGqVUof3jy8AApdRWpdRD9gXUX1VKbVBKbVdK3WXff5BSarVSagGwy572nX3h6J2Vi0crpV4G3O35zah5Lntk9FeVUvFKqR1Kqetq5L1CKTVHKZWolJphD8yJUuplpdQue1mmncs6MhgM5wcXhaUtMDBQt2rVqrGLYTAYDAaDwXBaNm3alKW1Djo+3amunS80WrVqxcaNZlUcg8FgMBgMTR+l1KG60pvU8KhS6mOlVIZSKv4k25VS6i2l1D77EEL3c11Gg8FgMBgMhsagSYk24FNkwdWTMQqItv9NBd49B2UyGAwGg8FgaHSalGjTWq8Cck6xyzjgcy2sA5oppcLOTekMBoPBYDAYGo/zzactAjhS43uyPS3tj2ZUUVFBcnIypaWl9VW2ixo3NzciIyNxdnZu7KJccGitsU8wrCI5t5j7Zm7hlYmdaRfifcZ5WW2aOz/fyI19WjI4Jvi05zmTMh1/XF35lFZYcXZ0wNGh7vzLLFYclcLJ0aFWHuUWGzb7ZKm1+7N4d8V+pl3ThZYBngCkHivhga+2cEOflozrGlF1nMVqY29GIc98F8+ouDBu69cKpRRHcop5fM52erf25/4h0Tw0eyshPm7sTS+gdZAXT4yKobjMyt/mbsfP04UnR8fg7epU61rLLDae+34XuUXl/HtCZ77dkszWI8d47ZouvLZkDwezinh7cnccFLXqobTCysz1h1m0I43nr+pEdLAXTo4Oteqr3GJjaUI676/cz+MjY+gV5Y/zcftorckrqeC2Tzdw12Vt+GhNEhO7R3Jl13AOZBbx7IKd3HVZa1oHefG3udvp1cqfDQdzSM4tAWB8twjuHdwWdxdHHvhqCxsP5jIsNpjHRsbg6eLIM/PjCfRy5YGh0VVlV0rx5tI97E0v5KXxcfh6OFNaYcWmNf9ZtpeEtAJevKoTczcnE5+Sx9jO4by+ZA8tAzz44MaeODiA1jB3czIfrDqAj5szj13enmfmx2OxVk+G8/d04e3J3Wjh70FSVhH3f7WF3KIKXJ0ceHhEO0J93HhpUQJPjY6lZyt/EtLy+b95OxjYLogVuzPJLCjD0UHxyIh2/LA9jbGdwxjXNYL0/FIe/WYbBzKLuLxjKEdyi+nZ0o+pA1vz9/k7+SUxg2t7Nuf+oW0ps9gAcHVyQClFmcXKCz8k8EtiBn3bBBAT6s2OlDwGRAexcHsq/xzXiW83p7DuQDY9Wvrx3dYUQnzc6N82kHlbUvDzcGZobAi/7svi3Rt64O/pAsDC7Wl8sGo/T46Oxc/DhSe/3c7Q2BC+25JCcbm1qgwPDm/HiA4hKAWuTo5YrDasWuPq5Mi+jAKemhfPlN4tWLA1lQk9IhkdF0ZaXgmPz9lO50hfHh7enifmbqeZhzOPj4zh7/PjWbUnq6rO+7YJ4PlxnXhz2R4y8st4clQMbyzdy6o9mQDEhnnTMsCT/JIKXp7QmdeX7GbbkTziIn35flsqNecyRgV68v6NPfh2SwoLt6fy3g092H20gFd+2s2Dw6J55rt4KqwaL1cnruwazs87j/LIiPa08PfgvlmbyS2qqPVccHZUTOwRyY87j56wzclR8ciI9szbnMyedJkgPqF7BPcMboubsyOVkyyPFVfw+NztBHu7kltczrYjebXy6dnKDzcnR9bsq66T2DAfmvu7k11YTu/W/vxv+X4AWgZ48Oo1XYho5k5j0eRmjyqlWgE/aK071bHtB+BlrfUa+/dlwN+01ifMMrBPy58K0KJFix6HDtX26UtKSsLb25uAgIAz7qgMdaO1Jjs7m4KCAqKiohq7OE2SCquNojILzTxcqtKsNk1hqQVfj2qhq7Ums7CMYG83AFbtyeSvs7Yw+66+tA/1JiO/lGAfN/63Yh+v/LibS1r5MfuuvlX3cEZBKUFeriilSDlWQlGZhbZBXpRUWBnxxirGdg7j/VUHGNM5jHcmV7uElpRbmfjeWga2C+JvI2M4VlyOm7Mjbs6OJ1yL1pq7v9xMal4J/5vSndd+3kNGQSkz7ujDv39MZPXeTL69ux8uTg4Ul1t4fM52ftieRqSfO0sfvozicmtVx2Wx2nhxUQKfrj2Il4sT067tgtbw5LfbGRwTzIKtqVhstZ9Rt/ZrxS2XtqKwzMKbS/eyZFc6IGLkl8QMBrcP4pfEDPJLLbg4OlButXFtz0ieGduB4a+vIqeonHKrjYHtgqo6JmdHRYVVM6F7JHvSC0hIy686b4+Wfjx2eXte/Wk33m5OuDo58NPOdJwcFDFh3qTklpBbXEGbIE/2ZxYBEBPqTYXVxrd39yM1r4RPfk1i9sZkgKoy+bg5MSQmmOW7M/n72A4cyinmneX7sNp01T4ujg68PCGOFxcmMHVgazxdnXh9yR6GxgTzzabkk95vgV4uVFg1JeXWqnMN7xBKdlEZK3bLNY/sGMqPO4/SKcKH+JR8ANqHeLM7vQA3Zwf6tA7AzckRpeBAZhF7MgrQGloHehLh587qvdWdXGV5a9IqwIOD2cW0C/Gq6lQB4iJ82ZGSh7erEyi4vGNo1bYf448S5O1KVkEZAV4uZBeVc3nHUBLS8tmZml+1X5sgT6bf1JMr3/6VcouNcquNQC9XBrUPYsvh3Kp28HZ14tVrOvOPBTspKLXQt3UAyxIzAHByUPx1SDRvLN1DVKAnSVlFtcraMdyHp8d04PUlu9lwMJdeUf78nlQ9EOTt6kRBWXUIv8o66Nc2gIS0AnKKyukV5c/BrCIyCsqq7tG/jYxhb0YBN370Oy6ODti0JryZO4dzigFoG+xF1+bNAGpdt4OCcV0jWJqQTmGZhSs6h7M8MaNWGXzdnfn3hDiemb+TvOKKE+7zynt0UPsgAr1cKamwsmhHGlGBnhyw11klozqF4ubsyMIdaZTbhWxlXpXXOiA6kBAfeVZZrDa+25pa63cwsUckmw7lkpRVhLerExoY2SmUzYdyOZBVhIujAxabjRb+HmQXlnN5p9BaZdibUci2I8cI93Xj0raBtbZtPpxbVeYruoSTX1LBSvt1Do0J5kBWEQOiA9lwMJd9GQVUWDXOjorRcWE4218Oyyw2Fu1Iw6Y1ozuF4e7iiNWma10zQKcIH9qH+PDzzqO4Ojuy5KGB+Hm60JAopTZprXuekH6eibb3gRVa61n277uBQVrrU1raevbsqY+fPZqQkEBMTIwRbPWE1prExERiY2Mbuyj1TlJWEV6uTgR5uwJwOLsYV2eHqocVQFpeCVabJtLPA4BjxeXklVRUWYTun7WFTYdyWXh/f37dl41Va975ZR8px0r45ZHL2HgoF39PF35PyuHtX/ax+MEB5BaV89Yv+1i1J5NeUf5c0yOSx+ZsZ85f+vLy4kR2pORRZrEx/aaeDO8Qwm/7s5ny4TremdydQznF/PvHRLSGITHBjO0cxsOzt+GgwKYhyNuV358aWnX/v/bzbv77yz6UgkdHtOe9lfsJ8nbl/iHR9G7tT3G5lV2p+XSJbEZ8ah73zNiMk4PCw8WR/FLpNL6/rz+3f7aBjIIyHh/ZnnsGteX+WVv4YXsqo+PC+GF7Gu1DvDmQVcjDw9vTo6Ufq/Zk8vbyfUzsEVn1gPZ2daKkworFphnVKZTOkdKBebo6snpvFssTM2oJufuHRpOeV8rXG48Q5utGWl4pnSJ8GNclgjGdw5j1+2H++8s+2gZ7sS+jkM9u68X321KZsykZL1cnXp4QR3SwN99uSeb9lQcAeHtyN/w9XFiXlMP0VQcoqbDi5KCqzvv4yPY09/Pgr7O2ABDs7UpOUTnPjevI4h1HWbs/C5sGHzenqvq5oU8LerT0o3dUAAu3p7FgWyo7UvKqygwwJi6Mvm0CGNkplO+3pfLKj7upsNpOEK4gVpgyi42pA1vTJsiTnKIKnBwULQI8+MuXm2gb5MWHN/ckPiWfThE+Vffij/FHmbMpmaUJ6bg4OfD7U0PZlpzHhqQcpq8+gI+7M5l2kVGJk4PCz9OFl6+O49FvtlFUZuX2AVH4uDkTHexFq0BPliak09Lfg3lbUvglMYMfHxzAO8v3M29LCtf3ak4Lf08CvVy4unsk495ZQ3xKPnf0j+LpsR2qzvPRmiSe/2FXVZ08d2VHbr60FaUVVuZsSqbMYsPPw5mHZ2/D280Jq03z4wMD2XJERFWYrzuJR/MZ89YaerT0Y+vhY5RbbUQ0c+fDm3sSG+bDL4npuDk5cs/MzRwrriAm1Juvp/bl8jdXUVRm4fYBUSgUH645QEGpBVcnB6Zd04UxcWGMf3ctBzIKqbDZKK2w8fjI9jgoRQt/DzqGi/gdHRdKal4pGw/mcEXncLIKy/h1fxbxKfl8tCYJJwdFywAPCkotLLx/AA/P3srqvVn844oOeLo6MSYuDE9XGQQrs8h155dYSDyaz/ytqXSJ9KVdiDffbEomJtSbh4a34+4vN3FJK382HcrFYtM093fno5sv4fPfDvLlusO4OTvwt5ExvLQogfah3sy/t3+V1XvJrnQe/GoLzTxceHViZ7Yl59El0rdKJO1KzWd/ZiGPzdlGaYWNG/q04LZ+UexJL+DyjqG1+tDH52xj9sZk7hrYmtIKK5/9JsaSIG9XMgvKuOXSVjx7ZUfySyv4Kf4og2OCeWLuDpYmpPPsFR24pV/tl/4Kq43vt6UysJ2IzJrsSs3nirfXMLJTKO9M7o7Wmh/jj7L5cC4f/3oQBVW/m/du6IGnqyO+7s5Vz5NKNh3Kpdxio2+bgFp5p+eX8r8V+9iZms+yRy4jzNedvekFrNidyZ0DW5/we6xvLhTRNga4DxgN9Abe0lr3Ol2eJxNtF6LAaEwu1Dod+MpyOoT58N6NPVi0I417ZmwmLsKX7//aH4DVezO58aPfaeHvwarHB2Ox2rjy7V85nFPML49cxp70Qm74aD0AE7pHMndzbQtJpZgAqkRVoJcLWYXlgFhtEo8WVG0b2C6INXszuWdQW2ZvPEJ0iBeD2wczY/1hkrKKqt50x8SF0THCh9d+3oOjUidYQh4YGs0VXcL5aedRpv28mxEdQtiRnEdqXintQrzIKiwnp6ic7i2akZ5fRsqxEjxcHNFahkHenNSVqZ9vxMFBkXqshKhALxLS8gn0ciWnqIyrukXw7eYUHhgazQNDo7ls2nKO5JTg7+lCTlF51fVe1TWC16/rSmmFDEsu2pHGjDv64OSo6Na8Wa1OYePBHK59/zcm927BgOggvFyd6Ns6AKVg65FjdAj3ISGtgJhQ71pWwme+i+eLdYdoGeDB8kcGcaykguGvr+SKLuE8e2VHAIrLLYz6z+oqsVN53iM5xexKy6dDmA+PzN5GQZmFBff1w8lBcdunGzicU8zcuy8lp6ic1kFeFJZZSDtWwqdrD/Lt5hSeHB1Dt+Z+xEX61qr/0goriUcLiA3zZu2+bNxdHOkd5V/reh/7ZhvfbErm2p6RjOsagcWmWb0nkw/XJDGldwtuvrQVbYO8cDhu2HlPegERzdyrOv/jyS+tYMTrq7isXRD/nti5Kv1ITjFuzo785ctNVcPSFqvm/Rt7oIGIZu5kFJRSWGqhdVDdC0GUVlhJyyslKtCT0gorBzKL6BDuU2ufOZuSeerbHfz44IBa+Wit2ZGSR4cwH3al5RMX4Vvni/XHa5J4YeEunhodyx0DTuxAD2QWEuHnzpGcEg5kFtIryr+WlRsg5VgJ8Sl59IkKwNfDmbS8EhSKUF95GUvLK2F7ch7tQ7xpFSiCt6C0grySCt5dsZ8VuzNZ8digKqvN6bDaNGv2ZfHk3O2k5pVy3+C2PHp5eyxWGwlpBXSK8DmlEUFrzbbkvKp7e3vyMdoGe+Hh4sT+zEKa+3lwOKeIpKxiekX54+vuTJ79Ph8aG8K/ro7jYFYRvu7OJ1iJUo+V4KCqr70unvx2B8sS0lny8GX4utftBlNmsbI3vZBOEb5YrDbW7MvC39OFpKwiHv1mG4vuH0D0ce4cNpsmPjXvpG19Kirb2dWp9ojAnvQC3J0duX76OjqG+/D+jSdonzOisMxCblE5zf09/tTxZ8N5IdqUUrOAQUAgkA78A3AG0Fq/Z48c/jYyw7QYuLWuodHjaYqi7dixY8ycOZN77rnnDx03evRoZs6cSbNmzU6/8zmmseu0Pvn8t4N0CPOhXag3nZ/9mWYezmx6eji9X1pGVqFYITY9PQw/DxcGvLKclGPiL/T25G589fsR1uzLQikY0SGEvRmFHMkppsKq8XZ1orm/B29d3xVfdxeGvb6SvJIKhsWG0CbIk0XxaYR4u7HxUC4xod4UlVuYfVdfftiWxkdrkogO8WL13iycHBSLHhjA99tS+e8v+wDwcHGkhb8HiUcL8HRxZP3/DcPL1YkXF+5i+uokmvtLJ1ZzOMzRQWG1aa7oEs6rEztjtWnS8kpoGeBJmcXG+yv3V+X/9JhYFscfxdFB8fbkbgR7u4kVyKp5aVECX6yTt+rVjw/m5cWJLNyRRpdIX76+qy9uzo58se4Qn/yaxLx7+pFdWMaM9YdZmpDOnL9cWmXF1FpTUGbBx+3kvpEFpRV4n2J7XeQVVzD+f79y+4AopvRuCYhwcXd2rNXplpRbcXE6ue9dhdWG1aarBGGZxYrFqusURzabpqjc8ofLWpOdqXnc8skGPru1V5XwOZJTzMT31vLO5O70bOX/p/MuLJPhYxenE0VHSbkVpcBBKTT6hE7xbKn0yzteSP0R8ksrTnmfNCQVVhtlFhteJxHFp2LprnSemreDeff2Oye+UQWlFbg6OdbZzn+EcouNMov1T9/Px4rLz6q9/wwl5VZcnRxOeKk5HzgvRFtD0RRF28GDBxk7dizx8bVD0lksFpyczrf5IUJj12l98PR3O4j08+DlxYl4uzrx6W2XMOHd3wB4ZUJnHp+7nTsHRDF9dRI+bk5c0sqfZYkZXNU1nO+2puLm7ICTgwPju0UQ4efOy4sTAXj2ig48+/0uAK7tGckrE7sA8MBXW5i/NZUZd/Smn304YvPhXKb9tJt3Jnc/4Y34UHYRT83bwX2Do+nbJqDKwfrWfq0Y3D6Yn3elc9cXm5jcuwUvjY8DpHO+Z8Zm7hrYms/WHuSmvq34ZtMR2gZ5semwDCvdfVmbOt9yc4rK6fPSMnzcnfntySE4Oag697NYbUz7eQ8VVhvP2Ie7Kp8txgXBYDAY/hgnE23npzq4AHjiiSfYv38/Xbt2xdnZGTc3N/z8/EhMTGTPnj1cddVVHDlyhNLSUh544AGmTp0KVK/uUFhYyKhRo+jfvz9r164lIiKC+fPn4+7eeLNazgfKLFZcHB3qFBKFZRa+XHe46ruzkwO7j1Y7UL/y024cHRT3Dm7L9NVJ5JdaWJaYQYCnC4+MaM93W1MprbDx9Jj2VUM2IT6upB4r5YY+LXlhYQIWm6412/POAa0J9XGjb+tqf4ruLfyYeWefOsvfMsCTGXdUbwvxceOL23tXfb+sXRA39mnJ1Bo+F16uTnx+m3gRVArD/tG1nXpPhr+nC8+MjcXP0+WUw0BOjg48MSqmVpoRawaDwVC/GNHWSLz88svEx8ezdetWVqxYwZgxY4iPj6+affnxxx/j7+9PSUkJl1xyCRMmTCAgIKBWHnv37mXWrFlMnz6da6+9lrlz53LDDTc0xuU0KQ5kFhIV6FlLNBzILMTfU4Yyn72iIxN6RAIyqSDYxxU3Z0f2pBfUyqek3Eri0Xw8XRzxdnPmaH4pfVqLb8ysO/tQYZVwFK5OjkT6uRPo5UpWYRnDYkOq8hjfLbLqc4sADw5kFtE+tFq0dYrwpVNEbV+ns8HN2ZHnrzrBHfSsuLFvq3rNz2AwGAx/DiPagOe+38muGtPJ64MO4T7844qOZ7x/r169aoXLeOutt5g3bx4AR44cYe/evSeItqioKLp27QpAjx49OHjw4NkX/DwnIS2fUf9Zzae3XsKg9hKHbO2+LCYDzp2JAAAgAElEQVR/uJ5hscEUlFqYv03iGWUUlDLsjZVMHdCaRy9vz56jItr+M6kr+zOLeGvZXlbvzSI6xJvnx3ViZ2pe1QyjmjONKukV5UdSVnGV0/LxtA70EtH2B+KqGQwGg8FQSZNaEeFixtOzuqNfsWIFS5cu5bfffmPbtm1069atziDArq7VU6AdHR2xWCwn7HMhkV9awYerD2CtI/xBJZsO5QKw2y7ArDbNc3ZfsqUJEp9p1Z5M3liyh2k/7abcYuOnnUflGPuMoys6h9OntTh4J2UV0SnCh7hIXyb1alEVNqEu/j2hMzPv6H3S7b2i/Ggd5FnldG8wGAwGwx/BWNrgD1nE6gtvb28KCgrq3JaXl4efnx8eHh4kJiaybt26c1y6psnC7Wm8sDCBSD93lidm8vcrOlBusVVFSPfzdCE+RaJdVwaq3JZ8jN01hj0rZ0v+Z9leQOJP7c0o5HB2MXvSC2gXIuETWgdWhyG4unskZ8LpZlVNHdiGOwe0Nr5eBoPBYPhTGNHWSAQEBNCvXz86deqEu7s7ISHVflAjR47kvffeIzY2lvbt29OnT91O6RcbBzJlUsCbS/eSeLSAobHB5BSV882mZDpF+HLzpa3YnlxbtFVGMH94eDteX7KHITHBBHq50DmyGb8n5TAgOpCHZ29j/tYUEtIKGGJf2inEp9oa1q15/YVXMYLNYDAYDH8WI9oakZkzZ9aZ7urqyuLFi+vcVum3FhgYWCtcyKOPPlrv5WtqJGXJkiWJ9qHP+JQ8ku3x0ZYmpDMgOrBqMsERu2jbkJRD6yBPRseF8vqSPXRr0Yx7BrUF4PpeLQD4dnMKry3ZA8CYzmGAiKtXJ3Y+YUKDwWAwGAyNhfFpM5w3HL823o6UvCpL2uq9WQx5bSUWm6ZtsBfJuSVUWG1sOJhD7yh/2gZ78+6U7tzQp+UJ+T57ZUecHRXDYkMY3L56EfVrejY/q+ClBoPBYDDUJ8bSZmjSHCsuZ2lCBuO6hlcNeVay3L7w9biu4czfmsq1PSMZ0zmcIznFPP1dPAu3p5FfaqGPPQbaqLiwOs/RNtiLnx+6jLBTLOFiMBgMBkNjY0SboUmSeqyEmesPsyg+jQOZRbg6OWCx6ao1Cfu2DuC3A9mALBj+4vi4qiVlft2XBcBrS3bj7erE8A4hJz1PJVEnCdNhMBgMBkNTwQyPGpokX6w7xNvL91UNic76XVYquGNAFP3aBvDM2A70bxvI11P70CbIq9YagJ0ifAn2duVITglXdYvAw8W8mxgMBoPh/Mf0ZoYmSWWw41subcXCHWms3Z+Nt6sTozqFVYXg+PIkMdF83Z357t5+vLtiP38Z1OacldlgMBgMhobEWNoMTYYyi5UjOcVorYlPyeOaHpE8e2VHBtjXybyqWwTuLo5nlFd4M3eev6oTEc3MWqwGg8FguDAwou08wctLgr2mpqYyceLEOvcZNGgQGzduPGU+b775JsXF1Q79o0eP5tixY/VX0LNgxrrDDH9jJXvSC8kuKicuUtbkHBAdiIOCyb1bNHIJDQaDwWBoPIxoO88IDw9nzpw5f/r440XbokWLaNas/oLHng0HsgoprbDx1QbxX4uzL6Q+rksEKx8bTGyYT2MWz2AwGAyGRqVJiTal1Eil1G6l1D6l1BN1bG+hlFqulNqilNqulBrdGOWsD5544gneeeedqu/PPvssL7zwAkOHDqV79+7ExcUxf/78E447ePAgnTp1AqCkpIRJkyYRGxvL+PHjKSkpqdrv7rvvpmfPnnTs2JF//OMfgCxCn5qayuDBgxk8eDAArVq1IitLZlu+/vrrdOrUiU6dOvHmm29WnS82NpY777yTjh07MmLEiFrnqU+O5sn6qt9sTMbVyaFKpDk4KJr7ezTIOQ0Gg8FgOF9oMqJNKeUIvAOMAjoA1yulOhy329PAbK11N2AS8L9zW8r647rrrmP27NlV32fPns3NN9/MvHnz2Lx5M8uXL+eRRx5B65Mvjv7uu+/i4eFBQkICzz33HJs2bara9uKLL7Jx40a2b9/OypUr2b59O/fffz/h4eEsX76c5cuX18pr06ZNfPLJJ6xfv55169Yxffp0tmzZAsDevXu599572blzJ82aNWPu3Ln1XBtCml20FZZZ6N82EDfnM/NfMxgMBoPhYqApzR7tBezTWh8AUEp9BYwDdtXYRwOVY2S+QGq9nHnxE3B0R71kVUVoHIx6+aSbu3XrRkZGBqmpqWRmZuLn50doaCgPPfQQq1atwsHBgZSUFNLT0wkNDa0zj1WrVnH//fcD0LlzZzp37ly1bfbs2XzwwQdYLBbS0tLYtWtXre3Hs2bNGsaPH4+np8Qru/rqq1m9ejVXXnklUVFRdO3aFYAePXpULaVV31Ra2gCGxp4+tprBYDAYDBcTTUm0RQBHanxPBo6P6fAs8LNS6q+AJzDs3BStYbjmmmuYM2cOR48e5brrrmPGjBlkZmayadMmnJ2dadWqFaWlpafP6DiSkpKYNm0aGzZswM/Pj1tuueVP5VOJq2v14umOjo71OjxaUFpBcm4JUYGeZBeV4+fhTH6ppWrhdoPBYDAYDEJTEm1nwvXAp1rr15RSfYEvlFKdtNa243dUSk0FpgK0aHGaWYensIg1JNdddx133nknWVlZrFy5ktmzZxMcHIyzszPLly/n0KFDpzx+4MCBzJw5kyFDhhAfH8/27dsByM/Px9PTE19fX9LT01m8eDGDBg0CwNvbm4KCAgIDA2vlNWDAAG655RaeeOIJtNbMmzePL774okGuuyav/byHrzYcZuH9AwB4eHg7ekUFEGqWlDIYDAaDoRZNSbSlAM1rfI+0p9XkdmAkgNb6N6WUGxAIZByfmdb6A+ADgJ49e57cMawR6dixIwUFBURERBAWFsaUKVO44ooriIuLo2fPnsTExJzy+Lvvvptbb72V2NhYYmNj6dGjBwBdunShW7duxMTE0Lx5c/r161d1zNSpUxk5cmSVb1sl3bt355ZbbqFXr14A3HHHHXTr1q3BhkIBtNb8vPMopRU2NtgXfm8V6En7UO8GO6fBYDAYDOcr6lSO7ucSpZQTsAcYioi1DcBkrfXOGvssBr7WWn+qlIoFlgER+jQX0bNnT318/LKEhARiY2Pr+Soubv5one5KzWf0W6sBuLJLOAu2pbL04YG0DTaizWAwGAwXL0qpTVrrnsenN5nZo1prC3Af8BOQgMwS3amU+qdS6kr7bo8AdyqltgGzgFtOJ9gMTYsv1x3ijs82ALBwR/U8krX7JexIiI8ZFjUYDAbDOSJ9F6RsbuxSnDFNRrQBaK0Xaa3baa3baK1ftKf9XWu9wP55l9a6n9a6i9a6q9b658YtseGPsnB7GksTMtiRnMeHq5MYHRdKgKcLWYXltA32wtvNubGLaDAYGpKa79nZ+2HeX6C86NTHlBfBd/fAsSOn3u9Cw2aDzZ9DWeGfOz4v+c8fe77yR+047/WH6YOh5CQrA2XugX1Lz75c9USTEm2GCxubTdYUBXjg6y0oBU+P6UBUoIQZGRprZoyeEZYy+O1/YClv7JIY/gz5abDx41N3LhUlcGTDH++AmiLJG+HbqVCaB7sXwytRkJMk2358ErbNgj0/Qmk+rHtXrnvmJCjMENECcGAlbJ0hAuZ4bNY/X7bs/ZDwg7RJY2OzisWnZpsf+hUW/FXqpXKfmhRmSt2mbOIELOXw/kBY+Ejt9BX/hk2fnr48KZthzzmwi2gt90hd93pl+6duAWvF6fMqSId3+8EaCQ5Pwvdy7PHns9kgL0VeArS9Tn/9j/wvzISsffI5Jwk+GQUzr4P8+okwdrZc1KLNjKzWH6eqy02Hcnl2wU7+t2IfBWUWAA5kFnFZuyDCm7nTOkhE2zATm+3M2P8L/PQk7FncsOepKD35gzL3oHR4lRz8FXbOq3vf9F3w61siRCo5sAJmTQar5fTlsJTL/jarPHCLc87wAmqgNWz4UN6aQYTvqX7/lnK5xvoSxlqLaAFY/y788NCJnUklqVvhzc7w0TARM8fnc6bXr7WI+4O/VqcV50hHXJh56mMTfqi7w85IgMzd1d+3zoIF91d/37UA5txW3ebWClj+Imz/Gr65RcRCSS58eyfMvhn2/iTHJS6CFf+CH5+wX/diWDUNXm0t5ziyXvbbvei48iTCtHawZYaIDEtZ7e3lRXDscHVbp22Tzvq7e2HDR/DFVfD1FClPJbsXQ+LCE6+9ouT0FsHM3XLdWp+4b1mBvQzb4fNxkHMAVrxs/51ZRHxNHwxr3qg+5qD4/LLlcxEiL0XAtq/g8Dqpx0/HSN2ue6/GNRfL+Q8sh+Js+V2W5Mo2SzmseV1ikxYclbSd30kbJvxQnUdxDsy4Ruom9xRRDLSW81XVTzHsmCN5FqSf/Liaz4L4ufDhUDmuJrkH4eXmcm0fDIKfn6593hX/hg+HVZc75wDMmAAZO2Htf6EoC+bcDov/Zi/TPHmOvNUVvrlZ2n764Oo8178v98ano+GdXiLiFj4i97DNKvfx4fUnv6ZzRJOZiNCQ1DURISkpCW9vbwICAlBKNVLJLgy01mRnZ1NQUEBUVNQJ2ye+u5aNh3Krvrs4OlButfHqxM5c07M5P8an8enag8y4ow+ODhdhW9hsUHoMPPzPbP/1H8Dix+DS+2HE89XppXng5lv3MXuXQPNetbcX54CrDzg6yUNwzm3Qdhh0myLf3x8IQe1hwocn5vfeAOkg7/vd/r2/PPAePwDH/55mXAN7f5aA07f9BC6e8MkYOLQGJs+WB3i7y8HZvfqY8iKxRmUmQs5B2bfdSBE0hUdh0iywWaD1IHD1FmvNkfXQZgjEXAEOx72PJq2Gz8ZCUCyMfEkExCV3Sv5RA0Hb4Mjv0GUStBkKHw6RDj56BEz5BrZ8CS5e0PEqyW/XAji4BrpOhoA20qEUZ8PET6SD2DUfJkyXsuWliCg49CtE9oKKYkiPhx63yLlD4sArCDZ9Btl7JV9rhbRBQBuY+LG07Te3QH6KdMDDn4dOE8DNRzr+2CshrAusngZdrpfjdv8Is64DFIx4QYbKNn4E1nLZZ/x71El5EbwWI+0xxd6Rxs8VcVCQBl6h8NBOuW/e6y+Bye9YBpE95Z5J2wZuzeSebjVArieovdS1kxtY7DEjfSKg7VA5356fpFwRPWR76hYpNxocnMHBsfq4rlOg/8Pg6gVfXC2dtKsvlOVB3/vkWvf8BD7hMGMiFKZDn3vlHvvyanBwkrycPaQt/FuLReWR3dJer8dKJ91tCigH6H4TLH1Ohsi8QuCK/0BuklyjezNpq8wESN4kZdr+NQR3ELFz8wKplw0fwk9Py+/lh4dh3xIIbAdZe2DcO1IHix+XtJwDMHUleAWLsE3eADb7y5Ozp9RJRTH4Noc8+3BxUAzcu15+0+/0gmYt5d44+CtYy6DFpdD3Xsnzo+FyTGA7eYb89BSU5YNnEDycCIk/iIDO2it11X6U3IPL/ikWvUtuF5FdlAHKEVI2SjsnrYbwrpC0UvJ38YK718rzYP370Oce8I2Q39L3D8LV70OH8fBeP8jYBWFdodsNUie97pLf9Oppcs0VRdIWt/4ILXqLkP/uL+DiLc+0/g/Ckn/IPdntRvjtbYi+vPqlwMFJnhcATu5gOS7W6ISPYO7t0r6FGdCqf7VgHvasPBt2L5IyTJoF7UfW/dupR042EeGiFW0VFRUkJyefVdBZQzVubm5ERkbi7FzbJy2nqJyeLyzh1n5RfLRGhkRGdAhhSUI6G/5vGIFernVld/5RUSIixr3ZHz92/fuw7Hl4eJc8aE/Hz0/Lm2TLfnCr3fKw8hV5E7xhrgivmqRsljfKS+6EMdMkrTgH/mN/SI58STra9wdC68Fw03eQvhPevRRQcP9m6dgAirLlDfjDIfL9vk3g5Apvynq4PLQTfCOrz20ph1daiwAoypQO8NL74W0JT1P1QHZ0gYC2cNN88AiAWdfLA9fdTzq0Fn0gaZWInqIMGaqwlkOzFtBxvLwVV3bCUQNh4OOw81u55uBYsWwc+b32w9rBWTpD5ShDJK6+cnzn62Drl3bBsVry3zlPOofrv5LOeOEjUm6vEBGK274CtFzbjm9E3LQdLqL6y4kiYDpNgM2f1d2mLt5QXiBCoLxI2uDI77DsOdnuFSJCo+PVUv8H7OF6PAKhOEs6k8B2IoyiL4drPxfrhLVMhOpuu+Wo241iecpMFJGibVLXllIpa1gXGYJc8Fd7HdkFvbbKdXoEwo7ZImy1VSwXIKKg/UhY8vfqa4q9UoSOpQzu/hWmD5H6Hf683HeVLykHVkj7hMTBlNkitubeIfUY3l3KW5wF7UbJdVvLRThWFEu5e94O6+zrOLv7Q/Pediu0kvK3GQz7lsn94RMuv1UnF8jeJ/vcNB8+vxLGvCb3xPc1LIcgYsZmkftiy5dQfgofMQcn+xCmlrIoBSNfFgticTa0H32itTC0s9zPIR2k3f7TVZ4Dxw7L9j73yL3XvJeI3k9GVbd75CUiRn95QYR4QZoMJbv5iNDvOkXKc2CFiNeANnLdl78k7ZyZKOfo9yD8+iaMfQN+fAqaNYcBj4go/OUF8A6TvCsFj4uXlKU4W555JbnVwqj/Q/Ky89mV0ONmGXrevVCuoc898lu1lsvvbsAjsOIlaNlfXsxA0t39pA6LsyXNOxwcneX7sGdFVPq3gSFPS9uB/O6veg+8Q+HtSyBnvzxLirOlbEP/LqK8/WjZrhzkd+nkDk8my4tV0krJs8tksf7lJcODO+Q+S9kkL0jZ++HB7Wf+kv0nOZloa0px2s4pzs7OdVqFDPXL8sQMbBrGdQ3n8o6h7M8spFO4L5e1Dzr/BVthBqDk7foDu5n97rUnWnlOR+JC6bBTt0DryyTNZpU33WB7rD6txaIR1lkeJGD387DA4d9EsIEMabUdJm/9ZfnQ8lL4/QPZtnUmxIyRvPctEcvExo/kIbvdvg5u2jY5187v5KGmHMWfpt8DYkFa/ZrkW8nGj6QOKtn0mTy4w7qIUPvpKbm28e/JG/maN8R/xcEJAqLFQtFupAi29e+JRaOiSATbmNflrd5aIWVJ3ihWi8SFMPtG6DxJhNyv/4EWfeHmH2DLF/DT/4lVDaQz9AmXh/Hl/xKBCdLRzLlNOlabVep50izpALZ+KR3/pBnwRpwItrbDRIB8MhrQ8qAf9JR0ONtnS+eoFKx9S/LvNBHi54jIcPeDWxeLpfHIeukoe98t+Y76t4iwlI1iPYroLsNlzm5iebBZxPKw4xvpUHveJqLjt7dFEP/+AYx4USxwm7+A8G5Sd19cJXV7/ddyT33/oHRq3abIfgvuk6HOQ7/K/q4+Mlx973oZNgyKEcfssnwRFU6uMPkbKc+en2D/suo2rxQih9dKO13xHzj0G4x7W/IoSBPh3H601EnLS2t3eK0HwdOZIqQqaTVArjluonT28++FLteJqDm6XSxmLfrC8H+KWA2Okfv2+/vFqjvwMdj2NfS8BbrdBG91k/vgpu/AM1hEw7RoCOko9RIUA6teExEfGgchnaTcyZvkZeOWRdCqnwj4tO0QO1aG30rzpI6OHRFLVEURjH1TLIbOHvD1DSIGlIPc47sXiYUxergMFYd0kutxdJVrcfcTIbPkGSlTYYaI/cgafffda+VafnxSXkoq7MOw22bJ/y6TYfSr8lsO6SB5VpRIWSod6vveK7+f9/qJxW7wU7DpE7ECOnvADd+KcNNavh9YCYOegJixcj+GdRExnp8iZT+0BvxayW/hsifk/u16PWz8RMR9z9ulPCv+JVbIqz8QUbfiJWm/KbPh9+nyYuDsAfPvEat650mw/Supr0FPiG/ZokdFtF/1PwiMhiveElEZPbzayn/bj/IsaTtEyhDcQZ5hlUz8RETg8pfkWeToJNY2qH5+37pYxKibD+AjFsfwbvLcbWDBdiouWkuboX5JPSYWjPBm7rXSH/56K6v2ZvL7U8NwuNCGPj8eKQ/j8G7SiQJMmQvRdktXQbp00BE9RNiBWLBWviKd36An5A3y9VixiAz9u7wtV1p15t8nD+iQDjJU+MNDMGkmrH7d7nis4a5Vkt/h36RTqCiB0dPg4xFyPr9WIvKa96l+k60koodY4Vr0keGJ8mLptB7cAV+MlwdhsxZSHlcfGZZscakIj+a9xUcn3b5mb0Bbu+XCjoMz+IRVWwueTJaH+8cj5KE3epp0nEufhXt/B/8oub6NH8v+w5+HfsdZPGqScwD8osQSsPwlGPAoBLaVbUVZ0jke+V1EHIglYdiz1Q91m00sFrFXSKfo3kxEps0qYsYnXB7MB3+Vtmo1QHxhirNh5L+kXoI7wjT7OSfNFAvXokelHm5aACtfFkfoa7+oLlv8t9JeU5eLpeJMXDO0ljwD2p64v9bVaZV+c+8PEJ+1oc9I53w8x46IZTT6crHAtOgjnfbHl4sFbf8yaZ+I7rLac2SP2udZ9k+pWwdHGVZ8YJt04EfWixUqZkzd13E0XkTmmNelkzwVxTkiSka8IEPHGYkyxFrzWo+vC5tNXhLaDBbrU8198pJFvLh4Vu+/f7lY0UI7yQvRF+PlHr3pO6lrkGHhwgzoe8+pywvw7V0yNProXikziABP3iDWn/xUuT8mfiIWnxX/EqG2a768uDSzx5a3lIu4jRl7Zpb38iJ4KVzE5+hpcm86u5+4X1khfDlBRPLgJyWtJFeeYW6+IiIPrhFrbOXL49lQnCMveYXpIuQdXeV3G9BWhNHWWfDd3fLbiRl94vFaS/k+Gi5COGqA1M22mSLump1mpaMzoSgb0OAZeNpdzzVmeNSItgblhg/XY7HZ+Gpq31rp173/G1rD7L/0PcmR5yGVTr2vtpUHi5uvvPWnbBTryJTZ0vlPHyxvl14h8uYaGC1DjkVZclxZXnWeykGGf9z9xNITEieCaODjMpT02VgRQIHtRThE9JAJCW2HiaWk34MyxLjy39IJZ+6GUa/Ab/8Vy8Hw52Woz8lNypu1V/yJ9i4RR13/1vImOm8qXHKH+OCM/0COfbcvoGQotuWldnFnEcF0NF6O9Y+CV9vItQx+2m7502I1aDNEfERAOsCUzTKUZrWIFcMnTLaV5olAjOgh5z1b8tPEv6fjVfI2fra+q5m7xeoX2qk67YNBIvIeTwIXj7PL/1zy1RTxXQrvJi8angHinL7+XRmyfiTx9IKhvFiGKJtgh/enKMkVy/KZCKW6KEiX3+zx7gnngvxUec44OJ77c58NRVkXzv1Tz5xT0aaU+hb4CFhc17qg5xoj2hqeIdNWkFtczpa/j6iVPnjaCjqE+/DO5O6NVLJ6QmuxfO1ZLKLJJxLyk6u3j/8AsnbL8N/DiTJsuPLfcNnfxA+mNE86yIOrxe8sKFaGKrbNEitKiz7iyFtRIla3uug6RcIeAAx6UnwrdswWoXfPehkO+3ycbB/8NFz22JldW3GOvPnbLDI7TVvFAvHQThkWW/iI+NBUvp2fjO/uFT+yJ5PFT+rQr9L5uzbiChdlhdVWzobgwAqZaNBtSsOdo6EoypZ7p3I4yGqBhQ+L31PNoSSDwXDOOdc+bf8DbgXeUkp9A3yitd59mmMM5zFZhWXkl1rILSrHz7PaNyU9v5TB7Ztw/LW6hllA/CFS7VGyI3rKMOPWL8Uahq4OxVDpeNx2qH323mt2B9Yj4jQ+6EnofrP4aBzdAcOeq34T73Gz/FkrxMKU8D2gpCMtyZXhv9wk6HGr+AO1HVot2nybi4/FgRUy9OAdIk63g/9PhvY6TzrzOqj0z3B0lhmNh9dB77uq/b/GvHZm+Vz5X/G7cnAUf5PCjMYVbNCwgg1kqOl8xTOg9ndHJ7jyrcYpi8FgOCMaRLRprZcCS5VSvsD19s9HgOnAl1rrM4iSZzhfKLfYyC+V6dQ3fryeLpHNeHF8HIVlForLrYT4nOMJB6lbRUDVJcasFvj+AZk23uV6mUXUZZL4lwEsekwc5Ne8LrOT3HxFiKFFuF37ufhyvdFBHIp9I8U3yzNQ/kI7i2Px8Ofh0r9KGXwjZIbayXB0hs7XirWt5JhY6TZ9Kv4pPuHi01ZJz9vE78srWK7xkd3VlhJHJ7js8bOru7Gv//ljHRzAwS7YK+vDYDAYDPVGg80eVUoFADcANwJbgBlAf+BmYFBDnddw7skpqg5AGp+ST3xKPi+OjyM9X8KpBJ9L0ZaySUILDHkGBj5anV5RCrNvEgfbrV/K35HfxZK14l/id+XWrHqmJUh8roA24mydtFJ8rZxcwClAnNGdPaHdiNpBWq+eLn5aUQP+eNmbtZC/uGskKnzzXif614x8Wab5t7GH3PijM1UNBoPBcN7SIKJNKTUPaA98AVyhta5cI+RrpZRxLrvAyCo80QfLatNk5Et6iPc5WAS+MrBs5dqEW76sFm1F2ZC2RRz2K6e8e4VWzywECTcRFFP9PSROBBuIk73/ceFhet5WdzmCY4CYuredKa36wz1r697m5CoBXQ0Gg8Fw0dFQlra3tNbL69pQl2Od4fxieWIG/p4udGkugWSza1jaKknPLyWjoNLS1kCirThHwg90GCdxqS65E/xayrbcJHFCX/6SBN5sZk/XVpkQ0GaoRNv2by1BanfNl5mQzXvLvtHDG6bMBoPBYDD8SRpqbKWDUqoqNLxSyk8pdQaBbgznA7d+uoFx71SvZ5htt7SN7RxGG/s6oqnHSup3eNRqkaWD1r5dvb5gwvf2gJAPyvcN08XCVsl7/USweQTAsUMyJR4k6n97e1ygFpfK57J8mf3Z8zZZfqjztWdfZoPBYDAY6pGGEm13aq2PVX7RWucCd55ifwCUUiOVUruVUvuUUk+cZJ9rlVK7lFI7lVIz67HMhjOgqOzEBb6zC8XS9tLVcbx/oyxPlHKshIz8MtydHfF2rQeD7s55EgX/5/+DeXfB3qXVi0jnHqxeZikzUcJp9DNswiYAAB1mSURBVH9Y4kiNngbj35dtvabKMid97xVrW6+pItJaD5IljGKvkKVqDAaDwWBogjTU8KijUkppexA4pZQj4HKqA+z7vAMMB5KBDUqpBVrrXTX2iQaeBPpprXOVUk04lsSFSVJW0QlpWUVluDg64O3qhJN9RYT4lDzW7Msi1NcN9WeDmmpdHXwxfYcEj+3/sESa3zlPvlfSfjTsXizrzfm1hGH/kL/KfK75zD7ZoIZj/+hXqz/fu05ik51tAFaDwWAwGBqIhrK0/YhMOhiqlBoKzLKnnYpewD6t9QGtdTnwFTDuuH3uBN6xW+7QWmfUc7kNp+FAHaItu7CcAC8XlFJ4uDjRzMOZ6auTOJBVxOOXt//zJ9s2S5YJerefrDsZEC0BXh/YJjHMrOXigwYy5BnWRT77Nq+dj1ISFf9Ukc597AsSGwwGg8HQRGko0fY3YDlwt/1vGXC6AFIRwJEa35PtaTVpB7RTSv2qlFqnlBpZT+U1nCFJmSLaahqksgvLCPCqtnodK5YwfC+M68SouLA/f7JdC2RZmYydEl0/yC4A/VrJIucg8dBuWVQd3BbqZ006g8FgMBiaGA0VXNcGvGv/q0+cgGgkzlsksEopFVfTf64SpdRUYCpAixamE68vDmQVAjLiOGdTMu7OjuxIyad3lH/VPnf0j2LDoVwm9oj88yeylEHSKll3M36uLERdMyRH95tEpIV3rU4L7yb/K2eQGgwGg8FwAdFQcdqigX8BHYCqeA9a69anOCwFqDmuFWlPq0kysN6+okKSUmoPIuI2HJ+Z1voD4AOQtUf/xGUYjqOozML25OpFzh/9ZlvV5wk9qo2iT4/twB9GaxnurFw66dBaqCgSX7WCoxJjLbiGaFOqtmADaDVAJhq0G/XHz28wGAwGQxOnoYZHP0GsbBZgMPA58OUpjxDhFa2UilJKuQCTgAXH7fMd9tUUlFKByHDpgfortuFU3DNjM4eyixjeIaRWepivG5e1O4s5IYWZ8NEIeKMjlNt95rZ9BS7esrJA26GSFtzx1Pk4OEDX62XVAoPBYDAYLjAaSrS5a62XAUprfUhr/Sww5lQHaK0twH3AT0ACMFtrvVMp9U+l1JX23X4CspVSuxCfuce01tkNdA2GGmit2XAwhym9WzK5d/Vw84DoQN64riuODmcx63LtW5D8uyz/tP8XKEiXIdGuk8HFUxZMv/l7CGxbD1diMBgMBsP5SUOF/ChTSjkAe5VS9yHDnF6nO0hrvQhYdFza32t81sDD9j/DOaTAvvh7c393fN2rZ1lOuqQFfVoHnF3madtk2ai8wxK2Y99SsFkkjhqI5Sxq4Nmdw2AwGAyG85yGEm0PAB7A/cDzyBDpzQ10LsM54GierG4Q6ltbtIX6nuUSVVpDejzEjBGftR1zwFoGl95vLGsGg8FgMNSg3kWbPUjudVrrR4FC4Nb6Pofh3PDm0j30ivLn0jaBpNlFW5ivWy3RFvZnRdvv02W9z04ToDhbLG3NL4GKEvCNhCHP1MclGAwGg8FwwVDvok1rbVVK9a/vfA3nlpRjJby5dC9jO4dxaZtAjuaVABDqUy3aHBQEef+JdUV3/wiLHgNnDzi4WtJC4yRkx6QZ9XUJBoPBYDBcUDTU8OgWpdQC4BugKoS+1vrbBjqfoZ75JVEWm9iTXkBeSQWHsosBCPFxw9nRAQ8XR7zdnHB2/INzWbSGX56HwHZw6yJ4tY2kh5xmZqjBYDAYDBc5DSXa3IBsYEiNNA0Y0Xae8P/t3Xt4VdWZx/HvS0IukJAA4RbCTe7InYB3UUErtsV6GbVatVUfrTN2nLa2Y+vTap1pHy2Otc60M0PrpTqKVizWohURUVCRi9wFA+FOhEASEnKBXNf8sXbCCSIQck7OCfl9nifP2XvtffZeJ+vh5GWttde7YGM+AFv3l3PFbxeTV3yIjJREEuJ9kJae3J5unU5haHTHh34O2/T/9DlF71nhU1QdL8WUiIiIRCwjguaxtWJb9pfxYW4BvdOTySs+RF6xHxrtkBDXcM6Abh3p07lD0y5cWw3zH4TkLjDqH3xZxmD/IyIiIscVqYwIz+B71hpxzt0WiftJeP3ibxtIah/HI9eM4uanlmHmRzV3FlU0nPPUrRMb5R89KR//HvJWwLXPQPvk8FZaRETkNBep4dG5IdtJwFXA5xG6l4RRSUU1izbt594pg5k0oAtx7YwJ/TpzRkZHJvY/kl80qX3cca7yJba+558SHXl1+CosIiLSRkRqePTV0H0zmwV8EIl7SXity/O5RbP7dyYxPo77LhvKmKw0zh2UceoXzf8USvfAvo1wxkVhqaeIiEhbE6metqMNBpqRnFIibUdhOZnpyQ1B28jMNADuvmjgqV/UOZ/Z4G/3wt71UHMIug8PR3VFRETanEjNaSul8Zy2vcC/RuJe0nyHq2uZPOM9xvVNJzMtmazOyXTuGIak63P/BTbOhYqCI2XdRzT/uiIiIm1QpIZHUyNxXYmM+mwHq3YWk5tYxgVDmjEUWq+uDj55NtgxaBfne93U0yYiInJKmrgy6skxs6vMLC1kP93MvhGJe8nJq66t44E569i6v6xRed6BQw3bcXHGV87s2fyb5a/zrz1GwlnfhX7nQWIn6NS7+dcWERFpgyI1p+1B59yc+h3nXLGZPQi8FqH7yUlYn1fCC0t38l7Ofj68/8i6x3nFfimPxT++mD5dmrj22rGseMYPjQLcNBs69YK966B4F01fJ0REREQgckHbsXrwWuqhBwlxoLyqYX7a5n2+hy2v+BAlFdUktm/HZb9ZRIeEONoZ9DzV5O+hKkvh7SDZe+Y4H7CBzy3ac1Tzry8iItJGRWR4FFhhZo+b2cDg53HgkxO9ycwuN7McM8s1s/uPc941ZubMLDustT7N7CqqIPuX77BkSyEAm/aWNhx7Y90ecveVsbOogs/2ljbkFG2Wdx6CP06FqlK4+TW45a/Nu56IiIg0iFTQ9j2gCngZeAk4DPzT8d5gZnHA74BpwAjgm2b2hUcNzSwVuBdYGuY6n3Z2FFZQW+fYuOcgADn5pZyZ2YluqYks3VZITkgQ1zu9mRkK9q6DD56Aoq3Q/wIYeDEkpZ34fSIiInJSIvX0aDnwpT1lX2ISkOuc2wpgZi8BVwIbjjrv34BHgR81t56nu8LySoCG3KGb8ks5b1AG/TM6smxbET1CEr5nNidom3M3rJnlk75/b5WCNRERkQiI1NOj880sPWS/s5nNO8HbegO7QvZ3B2Wh1x0P9HHOvRG2yp7GCsqqAP906IHyKvIPVjK0RyqT+ndhT8lh3v1sH6lJPm7v3fkUg7aqclj/qn869KZXoWNXiNP0RRERkXCL1F/XDOdccf2Oc+6AmTUrI4KZtQMeB759kuffCdwJ0Ldv3+bcutUqLDvS0zZ33R4Azj6jKwnxPlbP3VfG9DGZZKYnM31M5slfuLbav8a1h22LobYSLrwP+kwMa/1FRETkiEgFbXVm1tc5txPAzPrTOEPCseQBfUL2s4KyeqnASOA988tG9AReN7PpzrkVR1/MOTcTmAmQnZ19onuflgrre9qKD/Hi0p2M6NWJ0Vl+6PLqcb35y6o8hvVK5R8vGnTyF900D16+GcbcAAkd4ePfAwb9zo3AJxAREZF6kQraHgA+MLP3AQMuIOj1Oo7lwGAzG4AP1m4Abqw/6JwrARqW6jez94D7jhWwiVc/p62ovIqi8ir+/RsjCQJe/uO6MVwzIYsJ/Tqf/AVL98Ir3/Y9a6v+D1ytLx99PcQnhrn2IiIiEioic9qcc28B2UAOMAv4IXDoBO+pAe4B5gEbgT875z41s4fNbHok6nm6q5/TBtAhIY4rxx4ZAjUzzhuUQVL7uJO/4KIZUFsF5//gSMB202y46n/CVWURERH5EpFKGH8HflmOLGA1cDawBLjkeO9zzr0JvHlU2c+/5NyLwlHX01lheSW905PJKz7E9DGZpCa1P/WL1VbDmpdg1HUw4Vb44HGIS/DDospyICIiEnGRGh69F5gIfOycu9jMhgG/itC95Cgf5RbwxDub2VV0iBvP6ktJRTV3TR7YvIvuWQNVZTD4Ukjv53869/Pz2kRERCTiIhW0HXbOHTYzzCzROfeZmQ2N0L3atIKySnYVVXDbs8uZffe5FFdUccvTy6ip889eZHVO5ldXNTN9VEURbJ7vt/tf4HvWbnwZ2ochT6mIiIiclEgFbbuDddpeA+ab2QFgR4Tu1WYt2JjPHc+tYFC3FA5UVPO3NZ8z79N8uqUmkpGSyLq8EpKbMmftWJyD/50MJTshrQ+kdPPl3Yc3/wOIiIjISYtURoSrgs2HzGwhkAa8FYl7tWVvrNuDc0cSwf/+vS1U1dTx+5vGk5oUz81PLWNw99RTv8H+TT4BfMlOv599WxhqLSIiIqci4kvXO+fej/Q92qLaOsd7OfsZ0iOFwrIqLhzSjTmr8rhgcAbTRvbEzFjz88tI63CKDx/UVMIfp+BXbAHuWQEZg8NWfxEREWka5RtqhXL2lvKj2WsoKq/iF9PP5OtjMtlVVMHuAxU8fOWRtdhOOWAD2LUMKn2ieTp2g65NWIBXREREwk5BWys0c9FWNueXceXYTKYM99nB+nTpwCvfDUNWgpy/Q8/RsHUhWBzgoO/ZWtZDREQkyhS0tTIlFdXMXfs510zIav5ToUcr2AyzboBx34J9GyErGybdCd1HhPc+IiIi0mQK2lqB0sPVvLF2D+kdEiivrKGypo4bJvY58RubYvlTvpcNYONcOFwCFz8Ao64N731ERETklChoawWeW7KDGfNyAJg6vAeJ8e04MzMtfDco2gpv/MBvd8iAigLAfFJ4ERERiQkRyT0q4bV0W1HDlLKFOfsY3COFuHZhnGOWu8C/Xv4ofHuu3x54CaSHuTdPRERETpl62mJcbZ1j5Y4DXJ/dh7+syqOqpo4hPZqx9tqxbHnXp6U66y7/wMHXn4Q+k8J7DxEREWkW9bTFmJraOg6UVzXsb9xzkLLKGs4Z2JXhvToBMDScQVtNFWxbBIOmHHlCdMKtynggIiISYxS0xYDSw9Vszi8F4Ml3cznv0XfZVVQBwIrtRQBM7N+F0b39PLaw9rTtXuYTwQ+8JHzXFBERkbBT0BYD/mthLpf+ZhFPLtjMi0t3UlFVy8NzNwCQk19Kl44J9EpL4pyBXUmIb8eZvTs17QbOQc5bPsvB0XIX+PXYBlwYhk8iIiIikRJTQZuZXW5mOWaWa2b3H+P4D8xsg5mtNbMFZtYvGvUMt9z8MtoZPD5/EwVllZw7sCvzN+STs7eUnL2lDO6egpkxbWRPlv90Kt1Tk5p2gx0fwazrYeVzXzy2ZYGfv5YUxqdRRUREJOxiJmgzszjgd8A0YATwTTM7elXXVUC2c240MBv4dcvWMjJ2FlUwZXgPfjJtGNNG9uTJb44jIa4dLy7dwab8Mob29MOhZnZqqak+neNfty+G8gJ4YhS8dBN8vhr2rIGBU8L4aURERCQSYunp0UlArnNuK4CZvQRcCWyoP8E5tzDk/I+Bb7VoDcPo+SXbmbt2D8/ffhY7iyqYPKQbd00e2HB82qie/GnJDqCZc9jq6mDj6357+wew+kUo3gmle/0+wJlXnfr1RUREpEXETE8b0BvYFbK/Oyj7MrcDf49ojZqoqLyKkorqL5SXHKqmoKzxfLIZ83JYuq2Ix97OobKmjn5dOzQ6fteFRwK4+p62JjkcJHvPnQ9l+TBoKlQUwuLHIGsSTLwDDhdD5njIUDJ4ERGRWBdLQdtJM7NvAdnAjOOcc6eZrTCzFfv372+Ret3xp+X88JU1Xyj/4Z/X8J1nljcqS++QAPjk7+ATvocakdmJKcN8Mvgm97RteB0e7Qdv/wwWzYC0PvDV/4C4BJ+eauIdcM49kJAKE77dtGuLiIhIVMTS8GgeELoEf1ZQ1oiZTQUeACY7547xOKTnnJsJzATIzs524a3qFx08XM3qXcX07NT4IYHyyhoWbdpPnXNU1dSREN+Oyppadh+o4JwzurJkayEAfY8K2gBm3pJN3oFDpCWf5Dy2ulrY8FeY+30fkH30pC+/4jHo3B/u2wy1VZDig0F+tBnim/hQg4iIiERFLAVty4HBZjYAH6zdANwYeoKZjQP+F7jcObev5av45T7ZcYA6B5+XHOaFpTtIbh/H1eOzWLy5gKraOgC2FZQztGcqu4oqqHNw3cSshqAtq/MXg7a4dkbfrl8sp64O8tdBz9FHFsQt2gb/dw0UbYGug+HGl/2waM1hGDDZn5Oc3vg67ZPD9vlFREQksmImaHPO1ZjZPcA8IA542jn3qZk9DKxwzr2OHw5NAV4xH6zsdM5Nj1qlQyzfVtSw/cCc9QBcMaoXLyzdQTuDOufXXBvaM5Ut+8sBOCMjhT/eks2SrYUkxDdhpHrDHJh9G3QbDsmd4dqnfcL38v1w3fMw7KvQLg66DjzxtURERKRViJmgDcA59ybw5lFlPw/ZntrilTpJH24ppFNSPAcP1zSU/eJvn7J4cwE/mTaMGfNyWLe7mCE9UhqyHAzo1pExfdKZOqJH026Wt9K/ulqf0eC56VCwCab9GkbERAwrIiIiYdYqH0SINZvyS1mzq5jvnDegUfmSLYVMGdaduyYPpGNiPH9YvI3Ln1jMHxZvo1daEp2STmHNNYD9n0HPUXDPcv9QQcEmGDLNb4uIiMhpKaZ62lqrWct2khDXjlvO6cfLy3ex9+BhALYX+kVzwQ+Vzlq2k19fM5rUpHgGdU9p+o0ObPfJ3fM3wIALfNnFP/UPGYy72Q+JioiIyGlJQVszOedYsqWQr4zsSdeURCYP6caSrYXsDBK+1z8V+tD0ETw0fQSJ8ScZWK19Bd55EP7xY79Ux0dPwoe/9cndAboP969JaXD23eH+WCIiIhJjFLQ1k5kx93vnUxrMZXv02tHk7itj6uPvA0eCtpMO1vZthNx3YO96OJgH61/12Qvef8QPgW4K1hPuNjzsn0VERERil4K2MIiPa0fnjgkN+z3Tjqx9dvSiuSf0zkOw6S3o0NXvf/IMlBf6/KA3vgRLZ8LffwSZY8NQcxEREWkt9CBCBKQkxpOa5OPhrM4nWAtt2R/g2a+Bc3Dwc9j8ti+vKIQuZ/iE7gd3w/hbfPlZd8ID+ZDaM4KfQERERGKNetoiJDMtmZKEapLaH2dY1Dn46D+heIcfFs15A1ydn6d2uAQu/TeoKodt78PQK468r72yGIiIiLQ1CtoiZGTvNCqqahoXHtzjg7GMwVBb7XvRinf4Y5vfhpXPQ/8LIL0vrH4Beo2B9D4w5vqW/wAiIiISUxS0Rchj/zDab1SVQ/Uh6JjhsxgU5PiMBds/gD5n+dyfnXr7J0MPFcElP4MeIyC9H6RlRfdDiIiISMxQ0BZOu5b7tdJ6j8fM4JNn4e2fQeVByBwPnweZDFY+51+LtkL27X5+2sJfQkoPGP51P/zZ48yofQwRERGJPQrawqVgs08nVVsN3Yb6ZOx5K6HfuX7Ic/Fj0L4DxCfCoQM+QCsvgPP+2feqTfgOJKZovpqIiIgck4K25qqrg6X/DR8+6QOyzPFwuBgKc/3w5g0v+AcLBk2FqlLY+bH/+cqvfE9b5/7+OindovoxREREJLYpaGuudu0g5+/QdRBc+jBkTfDlh4oB5wM2OFJ+xkVH3ttjRAtWVERERFozBW3hcOPLkNCxcVlyenTqIiIiIqclLa4bDkcHbCIiIiJhpqBNREREpBVQ0CYiIiLSCihoExEREWkFzDkX7TpEnJntB3ZE+DYZQEGE7yFNp3aJPWqT2KR2iU1ql9jTEm3Szzn3hbXA2kTQ1hLMbIVzLjva9ZDG1C6xR20Sm9QusUntEnui2SYaHhURERFpBRS0iYiIiLQCCtrCZ2a0KyDHpHaJPWqT2KR2iU1ql9gTtTbRnDYRERGRVkA9bSIiIiKtgIK2ZjKzy80sx8xyzez+aNenLTGzp81sn5mtDynrYmbzzWxz8No5KDczezJop7VmNj56NT+9mVkfM1toZhvM7FMzuzcoV9tEiZklmdkyM1sTtMkvgvIBZrY0+N2/bGYJQXlisJ8bHO8fzfqf7swszsxWmdncYF/tEmVmtt3M1pnZajNbEZRF/TtMQVszmFkc8DtgGjAC+KaZjYhurdqUZ4HLjyq7H1jgnBsMLAj2wbfR4ODnTuC/W6iObVEN8EPn3AjgbOCfgn8XapvoqQQucc6NAcYCl5vZ2cCjwG+cc4OAA8Dtwfm3AweC8t8E50nk3AtsDNlXu8SGi51zY0OW94j6d5iCtuaZBOQ657Y656qAl4Aro1ynNsM5twgoOqr4SuBPwfafgG+ElD/nvI+BdDPr1TI1bVucc3uccyuD7VL8H6PeqG2iJvjdlgW77YMfB1wCzA7Kj26T+raaDUwxM2uh6rYpZpYFfBX4Y7BvqF1iVdS/wxS0NU9vYFfI/u6gTKKnh3NuT7C9F+gRbKutoiAYvhkHLEVtE1XBENxqYB8wH9gCFDvnaoJTQn/vDW0SHC8BurZsjduMJ4AfA3XBflfULrHAAW+b2SdmdmdQFvXvsPhIXFQkFjjnnJnp8egoMbMU4FXgX5xzB0M7BNQ2Lc85VwuMNbN0YA4wLMpVavPM7GvAPufcJ2Z2UbTrI42c75zLM7PuwHwz+yz0YLS+w9TT1jx5QJ+Q/aygTKInv75bOnjdF5SrrVqQmbXHB2wvOOf+EhSrbWKAc64YWAicgx/Gqf/Pe+jvvaFNguNpQGELV7UtOA+Ybmbb8dNrLgF+i9ol6pxzecHrPvx/ciYRA99hCtqaZzkwOHjSJwG4AXg9ynVq614Hbg22bwX+GlJ+S/CUz9lASUg3t4RRMMfmKWCjc+7xkENqmygxs25BDxtmlgxcip9ruBC4Njjt6Dapb6trgXedFvUMO+fcT5xzWc65/vi/H+86525C7RJVZtbRzFLrt4HLgPXEwHeYFtdtJjO7Aj8nIQ542jn3yyhXqc0ws1nARUAGkA88CLwG/BnoC+wArnPOFQWBxH/hnzatAL7jnFsRjXqf7szsfGAxsI4j83R+ip/XpraJAjMbjZ84HYf/z/qfnXMPm9kZ+B6eLsAq4FvOuUozSwKex89HLAJucM5tjU7t24ZgePQ+59zX1C7RFfz+5wS78cCLzrlfmllXovwdpqBNREREpBXQ8KiIiIhIK6CgTURERKQVUNAmIiIi0gooaBMRERFpBRS0iYiIiLQCCtpE5LRmZh8Fr/3N7MYwX/unx7qXiEgkaMkPEWkTQtfBasJ74kNyQB7reJlzLiUc9RMRORH1tInIac3MyoLNR4ALzGy1mX0/SKA+w8yWm9laM7srOP8iM1tsZq8DG4Ky14LE0Z/WJ482s0eA5OB6L4TeK1gZfYaZrTezdWZ2fci13zOz2Wb2mZm9ECzMiZk9YmYbgro81pK/IxFpHZQwXkTaivsJ6WkLgq8S59xEM0sEPjSzt4NzxwMjnXPbgv3bgpXPk4HlZvaqc+5+M7vHOTf2GPe6GhgLjMFn7FhuZouCY+OAM4HPgQ+B88xsI3AVMCxIRJ0e9k8vIq2eetpEpK26DJ8vcDU+xVZXYHBwbFlIwAbwz2a2BvgYnxh6MMd3PjDLOVfrnMsH3gcmhlx7t3OuDlgN9AdKgMPAU2Z2NT4VjohIIwraRKStMuB7zrmxwc8A51x9T1t5w0l+LtxU4Bzn3Bh8LsikZty3MmS7FqifNzcJmA18DXirGdcXkdOUgjYRaStKgdSQ/XnA3WbWHsDMhphZx2O8Lw044JyrMLNhwNkhx6rr33+UxcD1wby5bsCFwLIvq5iZpQBpzrk3ge/jh1VFRBrRnDYRaSvWArXBMOezwG/xQ5Mrg4cB9gPfOMb73gK+G8w7y8EPkdabCaw1s5XOuZtCyucA5wBrAAf82Dm3Nwj6jiUV+KuZJeF7AH9wah9RRE5nWvJDREREpBXQ8KiIiIhIK6CgTURERKQVUNAmIiIi0gooaBMRERFpBRS0iYiIiLQCCtpEREREWgEFbSIiIiKtgII2ERERkVbg/wG0MThCu42jmAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MpSUFTzfXteT",
        "outputId": "310c3fab-15d2-4860-ed90-e4e3088d1197"
      },
      "source": [
        "_, test_acc = eval_step(model_sage, test_loader, device)\n",
        "print(\"Accuracy on test set: {:.5f}\".format(test_acc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test set: 0.74400\n"
          ]
        }
      ]
    }
  ]
}