convnet_tutorial1_Lawrence_Moruye.ipynb

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  "cells": [
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      "cell_type": "markdown",
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        "id": "mCt-frpFH8oN"
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      "source": [
        "The tutorials use PyTorch. You will need to load the following dependencies."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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      },
      "source": [
        "# This specific version of torchvision is needed to download the mnist set\n",
        "#!pip install torchvision==0.9.1  torch==1.8.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torchvision==0.9.1 in /usr/local/lib/python3.7/dist-packages (0.9.1+cu101)\n",
            "Collecting torch==1.8.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/94/99/5861239a6e1ffe66e120f114a4d67e96e5c4b17c1a785dfc6ca6769585fc/torch-1.8.0-cp37-cp37m-manylinux1_x86_64.whl (735.5MB)\n",
            "\u001b[K     |████████████████████████████████| 735.5MB 24kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision==0.9.1) (1.19.5)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.9.1) (7.1.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.8.0) (3.7.4.3)\n",
            "\u001b[31mERROR: torchvision 0.9.1+cu101 has requirement torch==1.8.1, but you'll have torch 1.8.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: torchtext 0.9.1 has requirement torch==1.8.1, but you'll have torch 1.8.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: torch\n",
            "  Found existing installation: torch 1.8.1+cu101\n",
            "    Uninstalling torch-1.8.1+cu101:\n",
            "      Successfully uninstalled torch-1.8.1+cu101\n",
            "Successfully installed torch-1.8.0\n"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hqnl0AKVXIA4"
      },
      "source": [
        "import random\n",
        "import PIL\n",
        "import imageio\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import skimage.transform\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.utils.data\n",
        "import torchvision\n",
        "from torchvision import datasets, transforms\n",
        "from IPython import display\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CKeYuM-cIXxs"
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      "source": [
        "The code below may be helpful in visualizing PyTorch tensors as images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZd_rI8edYIB"
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "def show(img):\n",
        "    \"\"\"Show PyTorch tensor img as an image in matplotlib.\"\"\"\n",
        "    npimg = img.cpu().detach().numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)), interpolation='nearest')\n",
        "    plt.grid(False)\n",
        "    plt.gca().axis('off')\n",
        "\n",
        "def display_thumb(img):\n",
        "  display.display(transforms.Resize(128)(img))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dzfEE578uSNp"
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      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
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      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "71LRkRxndajG"
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      "source": [
        "## First tutorial:\n",
        "\n",
        "In the first tutorial, we are going to train a logistic regressor on the MNIST dataset of handwritten digits. Next, we will turn this logistic regressor into a non-linear convolutional network.\n",
        "\n",
        "The following code will load the MNIST dataset. Run it and inspect some of the images and their labels to confirm they are correct.\n",
        "\n"
      ]
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        "mnist_train = datasets.MNIST('/tmp/mnist', train=True, download=True)\n",
        "mnist_test = datasets.MNIST('/tmp/mnist', train=False, download=True)"
      ],
      "execution_count": null,
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          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to /tmp/mnist/MNIST/raw/train-images-idx3-ubyte.gz\n"
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            "Extracting /tmp/mnist/MNIST/raw/train-images-idx3-ubyte.gz to /tmp/mnist/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to /tmp/mnist/MNIST/raw/train-labels-idx1-ubyte.gz\n"
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            "Extracting /tmp/mnist/MNIST/raw/train-labels-idx1-ubyte.gz to /tmp/mnist/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to /tmp/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
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            "\n",
            "Extracting /tmp/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz to /tmp/mnist/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
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            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to /tmp/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4542.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting /tmp/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz to /tmp/mnist/MNIST/raw\n",
            "\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 63
        },
        "id": "E8dy7-kBHwco",
        "outputId": "e3998f54-18b2-40d3-fbca-72a6fce8fded"
      },
      "source": [
        "# Show a random image and the corresponding target.\n",
        "img, target = mnist_train[0]\n",
        "print('Label of image:', mnist_train.classes[target])\n",
        "img"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label of image: 5 - five\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABAElEQVR4nGNgGMyAWUhIqK5jvdSy/9/rGRgYGFhgEnJsVjYCwQwMDAxPJgV+vniQgYGBgREqZ7iXH8r6l/SV4dn7m8gmCt3++/fv37/Htn3/iMW+gDnZf/+e5WbQnoXNNXyMs/5GoQoxwVmf/n9kSGFiwAW49/11wynJoPzx4YIcRlyygR/+/i2XxCWru+vv32nSuGQFYv/83Y3b4p9/fzpAmSyoMnohpiwM1w5h06Q+5enfv39/bcMiJVF09+/fv39P+mFKiTtd/fv3799jgZiBJLT69t+/f/8eDuDEkDJf8+jv379/v7Ryo4qzMDAwMAQGMjBc3/y35wM2V1IfAABFF16Aa0wAOwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=28x28 at 0x7F76384C4450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K7zCczoJpu3W",
        "outputId": "b7147784-1a6a-4190-fff5-c128d47425b5"
      },
      "source": [
        "img.size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKbXgidhWr6L"
      },
      "source": [
        "Next, we create a PyTorch dataloader for the MNIST dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ay0iiI1kWyMC"
      },
      "source": [
        "# This ensures the MNIST dataset produces PyTorch tensors.\n",
        "mnist_train.transform = transforms.ToTensor()\n",
        "mnist_test.transform = transforms.ToTensor()\n",
        "\n",
        "# Size of the batches the data loader will produce.\n",
        "batch_size = 64\n",
        "\n",
        "# This creates the dataloaders.\n",
        "train_loader = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYk87rLBp-3E"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "liekFZzvYX9E"
      },
      "source": [
        "Next, implement a logistic regression model in PyTorch. Note that a logistic regressor uses a linear transformation of the input.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zEw5YorSYkWF"
      },
      "source": [
        "class LogisticRegression(nn.Module):\n",
        "  \"\"\"Linear logistic regression model.\"\"\"\n",
        "  \n",
        "  def __init__(self, input_size, num_classes):\n",
        "    super().__init__()\n",
        "    ###########################################################################\n",
        "    # TODO: Instantiate the layer here.                                       #\n",
        "\n",
        "    ###########################################################################\n",
        "    self.linear=nn.Linear(input_size,num_classes)\n",
        "    \n",
        "    \n",
        "  def forward(self, x):\n",
        "    ###########################################################################\n",
        "    # TODO: Apply the layer to the input.                                     #\n",
        "    ###########################################################################\n",
        "    x=x.view(-1,28*28)\n",
        "    x=self.linear(x)\n",
        "    return x\n",
        "    \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HCerRXDHZFAS"
      },
      "source": [
        "We will use the following generic training loop for a PyTorch model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVyEKl3OZLJw"
      },
      "source": [
        "def train(model, criterion, data_loader, optimizer, num_epochs):\n",
        "    \"\"\"Simple training loop for a PyTorch model.\"\"\"\n",
        "    \n",
        "    # Make sure model is in training mode.\n",
        "    model.train()\n",
        "    \n",
        "    # Move model to the device.\n",
        "    model.to(device)\n",
        "    \n",
        "    # Exponential moving average of the loss.\n",
        "    ema_loss = None\n",
        "    \n",
        "    # Loop over epochs.\n",
        "    for epoch in range(num_epochs):\n",
        "\n",
        "      \n",
        "      # Loop over data.\n",
        "      for batch_idx, (data, target) in enumerate(data_loader):\n",
        "          \n",
        "          data = data.to(device)\n",
        "          target = target.to(device)\n",
        "        \n",
        "          # Forward pass.\n",
        "          output = model(data)\n",
        "          loss = criterion(output, target)\n",
        "          \n",
        "          # Backward pass.\n",
        "          optimizer.zero_grad()\n",
        "          loss.backward()\n",
        "          optimizer.step()\n",
        "          \n",
        "          # NOTE: It is important to call .item() on the loss before summing.\n",
        "          if ema_loss is None:\n",
        "            ema_loss = loss.item()\n",
        "          else:\n",
        "            ema_loss += (loss.item() - ema_loss) * 0.01 \n",
        "          \n",
        "          # Print out progress.\n",
        "          if batch_idx % 500 == 0:\n",
        "              print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "                    epoch, batch_idx * len(data), \n",
        "                    len(data_loader.dataset),\n",
        "                    100. * batch_idx / len(data_loader), ema_loss),\n",
        "              )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oN8qTuGkZ-3X"
      },
      "source": [
        "**Question:** For the model you are currently using, is there any difference between using the model in `train` mode or using it in `eval` mode? \n",
        "\n",
        "Create an SGD optimizer and us it to train the logistic regressor on the MNIST training data for a few epochs. What loss function do you need to use?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SqvmBRvBo7vk"
      },
      "source": [
        "Answer:Yes there is a difference.Model.train() sets the modules in the network in training mode and therefore  the model keeps some layers, like dropout, batch-normalization while  in model.eval() we deactivate such layers so that the model outputs its inference as is expected."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UFtgBECDoFii"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kC3z31pvadeP",
        "outputId": "63943af7-a921-4004-de8a-3a07d50b900b"
      },
      "source": [
        "# Create model, criterion, and optimizer.\n",
        "model = LogisticRegression(28 * 28, 10)\n",
        "###########################################################################\n",
        "# TODO: Create criterion and optimize here.                               #\n",
        "###########################################################################\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(),lr=0.01)\n",
        "\n",
        "\n",
        "# Train the model. If everything is correct, the loss should go below 0.45.\n",
        "train(model, criterion, train_loader, optimizer, num_epochs=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Epoch: 0 [0/60000 (0%)]\tLoss: 2.367172\n",
            "Train Epoch: 0 [32000/60000 (53%)]\tLoss: 0.920298\n",
            "Train Epoch: 1 [0/60000 (0%)]\tLoss: 0.652321\n",
            "Train Epoch: 1 [32000/60000 (53%)]\tLoss: 0.563355\n",
            "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.518461\n",
            "Train Epoch: 2 [32000/60000 (53%)]\tLoss: 0.463791\n",
            "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.457175\n",
            "Train Epoch: 3 [32000/60000 (53%)]\tLoss: 0.431599\n",
            "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.430602\n",
            "Train Epoch: 4 [32000/60000 (53%)]\tLoss: 0.405921\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0QN7Mhiar7d"
      },
      "source": [
        "Visualize the weights of the trained model. What do you see? Why?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "id": "gxletH44a4MX",
        "outputId": "11c23b4f-bb73-461a-f568-a358def7a478"
      },
      "source": [
        "assert model.linear.weight.shape == (10, 28 * 28)\n",
        "show(torchvision.utils.make_grid(\n",
        "    model.linear.weight.view(10, 1, 28, 28),\n",
        "    normalize=True,\n",
        "    nrow=5,\n",
        "))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lxf5NxQ6a5cT"
      },
      "source": [
        "Use the following function to measure the test accuracy of your trained model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8UYWbqZYa9Qr"
      },
      "source": [
        "def test(model, data_loader):\n",
        "    \"\"\"Measures the accuracy of a model on a data set.\"\"\"\n",
        "   \n",
        "    # Make sure the model is in evaluation mode.\n",
        "    model.eval()\n",
        "    correct = 0\n",
        "\n",
        "    # We do not need to maintain intermediate activations while testing.\n",
        "    with torch.no_grad():\n",
        "      \n",
        "        # Loop over test data.\n",
        "        for data, target in data_loader:\n",
        "          \n",
        "            # Forward pass.\n",
        "            output = model(data.to(device))\n",
        "            \n",
        "            # Get the label corresponding to the highest predicted probability.\n",
        "            pred = output.argmax(dim=1, keepdim=True)\n",
        "            \n",
        "            # Count number of correct predictions.\n",
        "            correct += pred.cpu().eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "    # Print test accuracy.\n",
        "    print('Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
        "          correct, \n",
        "          len(data_loader.dataset),\n",
        "          100. * correct / len(data_loader.dataset)),\n",
        "    )  \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MrEY_dy5AKQV",
        "outputId": "3e287da2-2a0f-4134-ce75-ce9563e50a58"
      },
      "source": [
        "# Accuracy should be around 90%.\n",
        "test(model, test_loader)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 9004/10000 (90%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1DV-MS2bxYq"
      },
      "source": [
        "**Question:** To have the logistic regressor output probabilities, they need to be processed through a softmax layer. Implement a softmax layer yourself. What numerical issues may arise in this layer? How can you solve them? Use the testing code to confirm you implemented it correctly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "82F9ucdVuJjc"
      },
      "source": [
        "Answer:Exponential function grows very fast, and so does the magnitude  and therefore can lead to any numerical errors\n",
        "\n",
        "we subtract the maxmum value to improve the numerical stability."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SDdCNkjuHDr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dj4X2PnOfK9W",
        "outputId": "aa4b1c62-8d91-471f-b238-3e538a2eb1f4"
      },
      "source": [
        "def bad_softmax(logits):\n",
        "  \"\"\"Computes softmax in a naive manner.\"\"\"\n",
        "  probs = logits.exp()\n",
        "  probs /= probs.sum(-1, keepdim=True)\n",
        "  return probs\n",
        "\n",
        "def good_softmax(logits):\n",
        "  \"\"\"Computes softmax in a numerically safe manner.\"\"\"\n",
        "  ###########################################################################\n",
        "  # TODO: Implement a more stable way to compute softmax                    #\n",
        "  ########################################################################### \n",
        "  probs=(logits-logits.max()).exp()\n",
        "  probs /=probs.sum(-1,keepdim=True)\n",
        "  \n",
        "  return probs\n",
        "\n",
        "\n",
        "# Test the new softmax layer.\n",
        "logits = torch.rand((1, 20)) + 100\n",
        "print(bad_softmax(logits).sum(), \n",
        "      good_softmax(logits).sum())  # by definition, the correct value is 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(nan) tensor(1.0000)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4C_J5S0RScXJ"
      },
      "source": [
        "Because of numerical issues like the one you just experiences, PyTorch code typically uses a `LogSoftmax` layer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lgStX-ctjIms"
      },
      "source": [
        "**Question [optional]:** PyTorch automatically computes the backpropagation gradient of a module for you. However, it can be instructive to derive and implement your own backward function. Try and implement the backward function for your softmax module and confirm that it is correct."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P0xTuXo3dyWH"
      },
      "source": [
        "## Convolutions and images\n",
        "The spatial dimensions of the ouput image (width and height) depend on the spatial dimensions of the input image, kernel_size, padding, and striding. In order to build efficient convolutional networks, it's important to understand what the sizes are after after each convolutional layer.\n",
        "\n",
        "In this exersise you will derive the dependency between input and output image sizes. For the sake of simplicity we assume that the input tensor is _square_, i.e., width = height = image_size.\n",
        "\n",
        "We will use the nn.Conv2d layer here. We have not yet discussed what a convolutional layer is yet, but if you set the first two parameters (input channels and output channels) to 1, then this defines a basic convolution.\n",
        "\n",
        "If your code is correct, you should see 'OK'."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vg4CO_WDeLqh",
        "outputId": "fe1d8c26-79f9-43fb-f336-47e18438f9a6"
      },
      "source": [
        "def compute_conv_output_size(image_size, kernel_size, padding, stride):\n",
        "  ###########################################################################\n",
        "  # Add code that computes the size of the image after a conv layer.        #\n",
        "  ###########################################################################\n",
        "  output_size =((image_size +2*padding -kernel_size)//stride) +1\n",
        "  \n",
        "    \n",
        "  return output_size\n",
        "\n",
        "\n",
        "# Compare the size of the output of nn.Conv2d with compute_convnet_output_size.\n",
        "for image_size in range(5, 21, 1):\n",
        "  # Shape: batch x channels x height x width.\n",
        "  input_tensor = torch.zeros((1, 1, image_size, image_size))\n",
        "  for kernel_size in 2, 3, 5, 7:\n",
        "    for padding in 0, 5:\n",
        "      for stride in 1, 2, 3, 4:\n",
        "        if kernel_size >= image_size:\n",
        "          continue\n",
        "        output_tensor = nn.Conv2d(1, 1, kernel_size, stride, padding)(input_tensor)\n",
        "        output_size = output_tensor.size(2)\n",
        "        predicted_output_size = compute_conv_output_size(\n",
        "            image_size, kernel_size, padding, stride)\n",
        "        assert output_size == predicted_output_size, (\n",
        "            f'ERROR: the real size is {output_size},'\n",
        "            f' but got {predicted_output_size}.'\n",
        "            f'\\nimage_size={image_size}'\n",
        "            f' kernel_size={kernel_size}'\n",
        "            f' padding={padding}'\n",
        "            f' stride={stride}'\n",
        "        )\n",
        "\n",
        "print('OK')\n",
        "        "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "OK\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jkld36NRxEIH",
        "outputId": "ed25c275-c45e-4030-ef9f-4825a0b36683"
      },
      "source": [
        "compute_conv_output_size(1,1,1,1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hcvvkpu9oEns"
      },
      "source": [
        "You can now use the function you just implemented to compute the size of the output of a convolution."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EgLLXSScfwqK"
      },
      "source": [
        "compute_conv_output_size(1, 1, 1, 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncHkdY3df6R7"
      },
      "source": [
        "**Question [optional]:** Implement your own convolution operator **without** using any of PyTorch's (or numpy's) pre-defined convolutional functions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RV30kha3vSsC"
      },
      "source": [
        "def conv_naive(x, w, b, conv_param):\n",
        "    \"\"\"\n",
        "    A naive Python implementation of a convolution.\n",
        "    The input consists of an image tensor with height H and\n",
        "    width W. We convolve each input with a filter F, where the filter\n",
        "    has height HH and width WW.\n",
        "    Input:\n",
        "    - x: Input data of shape (H, W)\n",
        "    - w: Filter weights of shape (HH, WW)\n",
        "    - b: Bias for filter\n",
        "    - conv_param: A dictionary with the following keys:\n",
        "      - 'stride': The number of pixels between adjacent receptive fields in the\n",
        "        horizontal and vertical directions.\n",
        "      - 'pad': The number of pixels that will be used to zero-pad the input. \n",
        "        \n",
        "    During padding, 'pad' zeros should be placed symmetrically (i.e equally on both sides)\n",
        "    along the height and width axes of the input. Be careful not to modfiy the original\n",
        "    input x directly.\n",
        "    Returns an array.\n",
        "    - out: Output data, of shape (H', W') where H' and W' are given by\n",
        "      H' = 1 + (H + 2 * pad - HH) / stride\n",
        "      W' = 1 + (W + 2 * pad - WW) / stride\n",
        "    \"\"\"\n",
        "    out = None\n",
        "\n",
        "    H, W = x.shape\n",
        "    filter_height, filter_width = w.shape\n",
        "    stride, pad = conv_param['stride'], conv_param['pad']\n",
        "\n",
        "    # Check dimensions.\n",
        "    assert (W + 2 * pad - filter_width) % stride == 0, 'width does not work'\n",
        "    assert (H + 2 * pad - filter_height) % stride == 0, 'height does not work'\n",
        "\n",
        "    ###########################################################################\n",
        "    # TODO: Implement the convolutional forward pass.                         #\n",
        "    # Hint: you can use the function torch.nn.functional.pad for padding.     #\n",
        "    ###########################################################################\n",
        "    pad_x =torch.nn.functional.pad(x,(pad,pad,pad,pad),'constant',0)\n",
        "    h_out = compute_conv_output_size(H, filter_height, pad, stride)\n",
        "    w_out = compute_conv_output_size(W, filter_width, pad, stride)\n",
        "    out=np.zeros((h_out,w_out))\n",
        "    for i in range(h_out):\n",
        "      for j in range(w_out):\n",
        "        out[i,j]=(w*pad_x[stride*i:stride*i+filter_height,j*stride:j*stride+filter_width]).sum()+b\n",
        "    return torch.from_numpy(out)\n",
        "\n",
        "    \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6dDNQP8VvVVj"
      },
      "source": [
        "You can test your implementation by running the following:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Leww6XRuC_0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2c940a1-be69-4b90-81a9-801f617d8746"
      },
      "source": [
        "# Make convolution module.\n",
        "w_shape = (4, 4)\n",
        "w = torch.linspace(-0.2, 0.3, steps=torch.prod(torch.tensor(w_shape))).reshape(w_shape)\n",
        "b = torch.linspace(-0.1, 0.2, steps=1)\n",
        "\n",
        "# Compute output of module and compare against reference values.\n",
        "x_shape = (4, 4)\n",
        "x = torch.linspace(-0.1, 0.5, steps=torch.prod(torch.tensor(x_shape))).reshape(x_shape)\n",
        "out = conv_naive(x, w, b, {'stride': 2, 'pad': 1})\n",
        "\n",
        "correct_out = torch.tensor([[0.156, 0.162],\n",
        "                            [0.036, -0.054]])\n",
        "\n",
        "# Compare your output to ours; difference should be around e-8\n",
        "print('Testing conv_forward_naive')\n",
        "rel_error = ((out - correct_out) / (out + correct_out + 1e-6)).mean()\n",
        "print('difference: ', rel_error)\n",
        "if abs(rel_error) < 1e-6:\n",
        "    print('Nice work! Your implementation of a convolution layer works correctly.')\n",
        "else:\n",
        "    print('Something is wrong. The output was expected to be {} but it was {}'.format(correct_out, out))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing conv_forward_naive\n",
            "difference:  tensor(1.1941e-08, dtype=torch.float64)\n",
            "Nice work! Your implementation of a convolution layer works correctly.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4c_Whyy2ysdF"
      },
      "source": [
        "**Aside: Image processing via convolutions:**\n",
        "\n",
        "As fun way to gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SygERdfdv6U-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "outputId": "f2d36d14-86ea-4960-a7ab-967d3c0f0a49"
      },
      "source": [
        "# Load image of a kitten and a puppy.\n",
        "kitten_uri = \"https://upload.wikimedia.org/wikipedia/commons/thumb/1/1b/Persian_Cat_%28kitten%29.jpg/256px-Persian_Cat_%28kitten%29.jpg\"\n",
        "puppy_uri = \"https://upload.wikimedia.org/wikipedia/commons/thumb/6/6e/Golde33443.jpg/256px-Golde33443.jpg\"\n",
        "kitten, puppy = imageio.imread(kitten_uri), imageio.imread(puppy_uri)\n",
        "\n",
        "img_size = 200   # Make this smaller if it runs too slow\n",
        "x = np.zeros((2, 3, img_size, img_size))\n",
        "x[0, :, :, :] = skimage.transform.resize(puppy, (img_size, img_size)).transpose((2, 0, 1))\n",
        "x[1, :, :, :] = skimage.transform.resize(kitten, (img_size, img_size)).transpose((2, 0, 1))\n",
        "x = torch.FloatTensor(x)\n",
        "\n",
        "# Set up a convolutional weights holding 2 filters, each 3x3\n",
        "w = torch.zeros((2, 3, 3, 3), dtype=torch.float)\n",
        "\n",
        "# The first filter converts the image to grayscale.\n",
        "# Set up the red, green, and blue channels of the filter.\n",
        "w[0, 0, :, :] = torch.tensor([[0, 0, 0], [0, 0.3, 0], [0, 0, 0]])\n",
        "w[0, 1, :, :] = torch.tensor([[0, 0, 0], [0, 0.6, 0], [0, 0, 0]])\n",
        "w[0, 2, :, :] = torch.tensor([[0, 0, 0], [0, 0.1, 0], [0, 0, 0]])\n",
        "\n",
        "# Second filter detects horizontal edges in the blue channel.\n",
        "w[1, 2, :, :] = torch.tensor([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])\n",
        "\n",
        "# Vector of biases. We don't need any bias for the grayscale\n",
        "# filter, but for the edge detection filter we want to add 128\n",
        "# to each output so that nothing is negative.\n",
        "b = torch.tensor([0, 128], dtype=torch.float)\n",
        "\n",
        "# Compute the result of convolving each input in x with each filter in w,\n",
        "# offsetting by b, and storing the results in out.\n",
        "out = nn.functional.conv2d(x, w, b, stride=1, padding=1).numpy()\n",
        "\n",
        "def imshow_noax(img, normalize=True):\n",
        "    \"\"\"Tiny helper to show images as uint8 and remove axis labels.\"\"\"\n",
        "    if normalize:\n",
        "        img_max, img_min = np.max(img), np.min(img)\n",
        "        img = 255.0 * (img - img_min) / (img_max - img_min)\n",
        "    plt.imshow(img.astype('uint8'))\n",
        "    plt.gca().axis('off')\n",
        "\n",
        "# Show the original images and the results of the conv operation\n",
        "plt.subplot(2, 3, 1)\n",
        "imshow_noax(puppy, normalize=False)\n",
        "plt.title('Original image')\n",
        "plt.subplot(2, 3, 2)\n",
        "imshow_noax(out[0, 0])\n",
        "plt.title('Grayscale')\n",
        "plt.subplot(2, 3, 3)\n",
        "imshow_noax(out[0, 1])\n",
        "plt.title('Edges')\n",
        "plt.subplot(2, 3, 4)\n",
        "imshow_noax(kitten, normalize=False)\n",
        "plt.subplot(2, 3, 5)\n",
        "imshow_noax(out[1, 0])\n",
        "plt.subplot(2, 3, 6)\n",
        "imshow_noax(out[1, 1])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nuDNk5YV3cmVtmwN3nmLhqKcaVJz91BnK3ohNU3A4bjLMS67gILHYqEE6dpx43SswizHlYw9zYCws5gmrkSEfj2h1W2xub7DjN0jLhJe0l/jSA8f40LKh6I8o1wek8ynj0ZDKOSIyqtUJuTMsYlk5tsT59T7NdgvX72OsYbuakEURstThzJULHPAJV3xBGQuZRGhlyIs8PLwWnJYYLIkYOnHCYqfNlhlSxJ7CebJmys6Vgnych5FQQFRwpUMUXOlDe013PseQJLmKBIU9i0zKimitF6zR5YzmxTHDYw3ciYx0ZwFTeLKHzkJVBeIBtNOCzRJ1HklipN1CJzky12b1y44RjZTOmQk+tSDgW2mw4gdj3MEuZqvEbPWCAyCKYLFF70SKP53SvlCR9h3RoMR1G5hJRdwbUy22iNb7sLYRrGbvcCeWMaVH8hw5chC2e2AtOhojO338wUWqdkJ6bgvtdpDBKAwWgEQWnEd3ejWhhn7Rsgpkai0SReHe5tpIXqCRRcoKjMEsLuDWroSBBlCvyLSMoghWb1HsXu92YNaa2rXOaiurOJkjkccXFp9VeCfIlZR4WJNbEshCCsFlimspVHJVgaYMpGdzwaWQHyqJ1mNMEa7jE6i6NR+4QMi6mYJRorHBTqBqKuWCCxazhBHApYqfq6AwyMRgxibsqu9BXLBMEZCqtiQlWL2qYeBwqaKRYicGH9fHulCGcYKPwkChs69dPQCp4eoyheCN8uF6V1m6M993remphf8sxsubItiqqphdsF6BshL6Z9bxDcOZP/sUPoLRhQ0sluFgQnNlHqXBzqRk2/dwFLTiiALPkTtX6C636ByBcw99nKjTZDjMaZeeSztbRJHB9wfIRGmT0TrQhsWY3xg9Qf/CNgeSJZ6MPe2sweb6Kp3lFlqVLK0s0dseYYCB5ox9SSaAKK2FNqXzlHlJHilHbQMnyqXU0XRQSkXWalBVJeKFyjvUe6w1OKMM8IwG20hkKRMPsSGWCFSIjMU5h5UgB4R+MQgSBjsxeF89TcveXmhRXNVvUBOCKO7cxUCSSUJ7Eqy77nnQbhvWNhFr0AOLyGAUSLQ/QvMCxGBaTdR53LEVJgcyRssR4qD7+IiimxAPKqqGxfQC4RHHROsDJC/DtL7bxi22KTsxzSsV6foEn1gYCD42uEZEttYL8oHzaCtDjh5ENncAMIVDiqoe0SSYW3NtZH4ukGnl8YlBWxkaW2xZQVkFa1cVbWaI91BVyNQCdQ7iaHcg1MrB+iZqgpxCHKNpgkzy0KZRhFbVrtyi5adLJkFamTW3blG/mqd/6cWBbMRIJYhVXBxhKnAdR7lYD5IjS7xj8XGwIqUmV6lkd6os0yl/BJNjBRQG1/BUc4o4QUoh2qkHLAWXBFKzucHHkB8pQQUzsNixEO/EYepvwWzUEkOimIlgiloWiMCne5a2r8uc1s9UtQRga3KNpuZlqHOQNMBM3Rz1e4/W92UAE8hVzN4gNb2+Tz2mlF1iflpcOwm9AdHe3H8eSJSkjMlxmESITYIvS3SxhZ3LYGMH1055YbfNqHRsdDPihQ7baztkaRPXMkTFmC8ZtPitYo3NXp+3pccoP1zxsMZkg4IJJeaeZQ5NGuycWSeL26xv9mm3I0Za8EQFJ3cc7xwd58jyEX57acyf5xt0F5eYjCe4keImBYWOaaUtLl5cJ1LwVYW3Br8zwDUiOstdnvQ7nHaObSnpKhySBmcZU0YQNxLKosJ4wZiYiIhikhMlMT71FN5RFRBhmUwKxBocHiMGVY+g9YgpeK8YY1AF9c994EaQCHyY0noPjQbGmmCtxREcWA4W2miCtJuUh+bxmSWpHK6TUbUTkqKELMVncSAfVapmQnxuHbM1gOWMqgHzT5SIU6KRww4LivkW/Rcu0VhrE63u4Baa2JogEcHkFdEgnDM52KBsGoqOkPSVbKvCLbYBiNb7aBwhw3HQURsNTG8MVzahkQWCtAZGk6CbLs3jGzHJdshWlqJCjUAjhcgio0kgxGYWyLjZQIsSaTYgsmhvAEmMGIN6CcSbBzKX4Rid5ME6dS5YuyKom3nzxIRBzLKn6d5ieciU02sRpvOVYKow1d+11KLptFZIrkR4u6e5ukwxlWDzYMVpNGPdGfBznnJJkYnF9COkEnzDY8YGkwfC8nFNiKlHCgOVUHbDBaKNGDVgg2qATzSQWKLYsQmkRqivawYrVxxoHKzYXatWg4ygVpHSgFU09mhlwAc5wVTB4YwJFm8gcg3ELLVjOgn3Z6q6DeqpvsqeXBG017pdp1LALGalgWdhxd4UwUq3STFSKD02jWjNzUHkyT3IZMzK4S7loQ5PiMed63HKdmiVQEswcy3S9QHD4Yg/zRwLh49gteLPysv4RkJjEnH5/Bp33H8aF8Hw0jp5mXNxZ0BmLfFylyQGzUvaNmX59FE+xDqPDDcZFA4ZQ35lQmchpkw9C3ELM5+QlgVfvnOMj7LD1sE2cX8CVcXO9pCJVSpVijSiJS12qpJYDGM8mRFihEEkWImYDHOssXgRKqe0shaToqAqHN45xM+sXylAZPC+dn4oeFWMmCDOP8cwc52gC04mwVpttdB2M0xpvSKTHE1iqiOLVK0IUYi3J4FEOwkqgu82qToJ4pX44g7EEVFvQnlyBR8borGjtRZ0UB8ZkidWKe84QNG2tC7n2P4EmRTYxy+Bd7DQxS0EZ5C4YOWNli0uhdaqJ+5XRMMKnGK3B8FB1c6QMoZGiptrIJXH3XcCU3nMzghajeBw2u4hwzF2NNnVUzWO9ixTVTSamV83MijKIA0Ygw5HaFVhWs0gjTiHZMGxpb0+JHGQKZxDVQO5aq1xwy6Raq29QnAoanWL9feaRCGQq48VlxH0SQlaoh0JtrbOqlYgnem5CLhIcWlwEGlcW6aVkG4YzKphsqy4hRJGNui1u5KB4ttul1QlD4RpSkFGYb9reqQUyjnQxCMNh+ZBEHXir5IEzDBsNw5MKdjxlCgJ1mkRZAONCNerLKao79MqLqvvTQnllFNLOvgBfVy/C1Uoz8eA1UDOpeBNsKalkkDyM0QqMxMPtVztOHsGe+mmCLbplFGZI4sNXkKTbALRXJuH4wJppyRtS6k5ujbEquVTo00yFSS2zK0PKfoTXJQgRxc4lDYZuJIn3Iio6ak2dzh98jgbT1wE4wBLPCiYizO0k9FwBudzXAsuNpX3Xfk4ZTemX5WMNwZ0snmyuRaTyRB8xNhYFuZaLLTniB/1zNkG/bZBGilycYulMmJhYlic7/LYeJPNKA/rq48KOlETWyqVCqb0+ChETMRxjDcwyXNMpXigKksMYMUEQ0I9UZLgUWJjcHmOMYbIWvK8nHHZPocwBvIcybJgoR5ZwExKXCPGjkskjajmMlwzQiol3plgRgV4T3J+G00TsELUL1AjyKTAzTWoOin5QoxaaKwW2NwjCrafM77/COnFAd3+BKmJRcsSyVI0jdFmGqpWOKpuRJVZFh4d41KLqZ2L0ZUe2m7gOw3MRomMCygrhIhqLsWllqphaFye4JfaSBjJsKrQG6ALc1A5qgNz2J0JZjAKRKuKTIrw3ZhAjhA0ZanA+UCWcYSOx+hwBEUZfldV0FbjOESdEJxcYm1wsBUFEkdBTgGIY0QEPxqFOekthK/fXnHhscrWDT6CsqNEY2AslHNKuVJB5KE0wQJ0gXzxYNy0TkI0NLtT7fHhCmlX6CjCbsX4pkfyQLzjYxXScMhOvHt9mxvUQNUOfW0mBjMJVm7VqoLVObKIlz05otZMg3MpkKK4WkNe8PjMB8Irg4RgKsFLPQj4IEnoVCIw7JGjCZEJruWD9uuDta+G3QgDcYCTEO3Q8JjCIKXsWf113WQ20gCuslh3Nd4buFVuimAnWtK2ls5ci9ccvYuORDwsfY4dmKO33mNt8wL54+vYyFCMQUaePE8ojMMlEaMYmlHCpXOXwcZcHo3onjiCugLpJuRzwujMgINVRjdOeDyN8StzlDsjimrIHacOs1P0qMRyZbyD9kakJsZUEf1qzDifMN+c58TCYRrHD9DOLFe2LvLHc1sMNyYUhYC1tLRBMpcyieBjxRVGTU9SCtW4gjjGbY/wKlSRIVXLuApDWj7OsUmEwZBPyrphFUWpnMNGcQjfqhxiDF4VEUNVuWDBWtmzcp5LVFWw0uba5Efn2XhRhhrINj0+ahBNlMaVknR1iNnso1mCjHO0LME5TKMRrL5WAwDNEsy4JN3oE2+3Mb1AXEV3kbhXUM1nVE1Do56u+24LGRdIluJWuphhjtkaUNx9kKppwyCWGOwgJ7oSiJ0kDpEN4yLot40MbWUM71smnzMMDxvaFz3xyIdBYjgJA0kdp62dFr6ZgAjR5hAqhzbSIA1AKL8/2p3iIxKml1WYymtZQa+/q8X6PId8ZvG3SY46h8nCQKF1RAKqQSowtQRDiRoTIhFucbeaEuy4Jp5IGd5REW9b4n4gVgj78Ra1dpe8AMyk9qJPSUoUnwpY3f3oMMgCruPC1DlRJvdOYBBh6uk/BBLycdA5zcTUkQFhyu2atf45tNg8XLN+bVAbNN64D401DQMkULYEHxmqlqHoaAjtSva0YpfVzCczpGoJDtUoVEicYPJ6um+oteZaV48UaglCytpDUuvYMmudsjdD2NW2d7VeUNmzeK+HmyLY7olFTh84waBV8kHd4G2jBb4w6vDQU+fwI0O+OaHqg23EMHYYb5HcY3yFwWPFM151qApP2ZIj9x7ESMnw8oDxJCddzaEQLmnJ+nALbcS4YcEdp+5gyw84d/kCxw6vcOaRc7hexR333cVokBM7y3y2wL3HTnP6BXfx8i94CYsrCywcWKK3sc3Df/4Il89fZHV1lcub6zx5/ixP9c7RbMZEpFBMcB5KL7jemNJGVOqIKojSGMoSQXBlbd14RRHECEagKgqEkJigKEYNvnKkWUYhFrHg1SMI2TSG8jlEdfIA40MZRctgHLQuOwZHLMNDhu5TjtbZET6xmI0eOh4Hq6wmV1ShLNE86Mx4DV7y0TgM5htbuLtO4FoxSb/Erm1jRYivJDAaQxQhF6+EiqQpZphTHmhT3LVAslXQ3BzTu7vD3CM7yKUN3KlDFAspca9E2inFQkrVWmL9RZbJkYp777nAyUaf13Qf5xOjI6zlHT740F1Egy7JtpBsQ3Pd03ligEwqfDtBkwhxHqo6gDkv0HKERFEgwzqOVppZsFZ9COHS8SREJdSWcbBq46C/qg9xxUWxG0Ew3W46nTqONkgHFAWm2Qwxs7cQdhym6z4J+iIK7viEsjRQ1nJUEaxW8YCpddtp+G8SiMZUMhtGilQhPCkaCWqUqsGus4mNBFM7waZEhQaijvpBjpgcL4JuOzFow2OHhmQzPHtVU4n7gp1A96mK7MqEqhUzWQqEXaVC+6LDFJ5o5Ih2xqi1+GZM0Y3pH48YHTa4rNZzI3bJT009SajJ08dhu5lapjVZmlyuIs5dTDXXqdOw3j+1ekMIWz1IOIBgSd8yC/Zk9xDZwQYvXPdsr4748fwC7fYidjSkshbxHnGCHzuctVSRBh2vEna8EEWGRjdmMqqYtymNRpM4gcv9dSo1TIY5SRmC8k0rhHEVo4KdnS3W1zeoRhPmDtyJ6wvNxkHuPngf9nDEq1/7xdx/3wu5+9RpFg7OI1bI4hhvBHu34YGXvxIxQjHJmYxGrK2v8973/wL/8df+E+fWniJqWuJWQrHWRzXCeSFKEgqtsBbaPkKTirLTxDtH2auCP7j2XBsJumXlgx7nnQZLtqzAV7sB/nESP32c3m1GMZ8yWgkWjAO6Txa0Hx9QzdfWV2yJBkW4H6/oeFy7WBWM3YsZzeuwo6X58Ex5RSJL3g1SQfvxnUAyAP1B0CDLMjjZ0iRMqQdjojRGKiV69AKSpVQv6WI2+2AN48MN+scifJzQf1nOiSPrfMXhj/PX5j5CKpCI0JYYKwLdp3CqjE6UZGLZ9BVXXMKFap4fPfsGLvzaCQ7/4RCzuglRhJ/vhGiEsgwhWlRIvJc0gQjSakLqgjMwidHBMJDtbGibNUFPFYNW5a5DC4JUoONxbcXOvHlxFPThWwifQnGwxDQqfG6hMvheDIkPUoCn1lXZI9V4L/IgGpirdDiEbLkAACAASURBVEZTCmVLg2XnoZjzwfrL/O70GSFooKUECUAD+apAeajExQ7ZTnaTAOzIYCrBNZVoU1j5qCceOMQpg6MJmy9o4+I62SAOZLYlFnGWaJSANrATiMaQ9JXO2Yr2BWHzvoh8mVpPD/G+00gBpo9uHXHgI8UWe5ZusLZra3QafcGeeqfTkK1Q5F6khsjVTi72tN3r4aYIdv5Szkee+BPOxcKwhLmlQ/RtzlJucL0hD0jGp5qOy0VJUgqkhuWjS7QOd2lKxP3SYfzEKn9wYEDqLJs7W8FB1CuJ1eIiIcoaNFsZScsw2B7jipL+9g5uUBEVhiSPWUjnOXLsNF/15V/Jyx94BUeOHSJrNUAUMxOOFNWprFEanoxGnJG1U7rL8/yj7/ouvvSVr+Ynf+In+dBHPkTvci/oO17xAtlEWcwyokJ5jSzTiCy/5te4IDmdLGU8KUK/qEfEoN4HCWA6tVCPU4c3QhTF+KIin1RUz4OTy+aOA38Y4j19M8GMCiZH50I4S+mJNof4LMEdnMdaE0jFB2tP2k3Kw/NMVlJ8JJhKGRyxHPo9oepmmNITb44wZYZZ28L3B+E8ayFNkU4L386CDjYYoYMh0m4Qr26i3tVWH2inies2WH3AcurVZ/imo3/Iq7JzHIlqrZYG5hpTwaNEIqT1vwc7ahOOWnhZMuAN9/4Sf3oq47te/zW4/3AnB39/E7O+Fch1GvCvwRqXdgtEgqNvuY1PLcnlPrIdJAJpNmAw3M3sAuoByIf7vEZbVQ1Ou2n8sfeK29oJERu3EFW3gtjjRxFSGuItQ9ILCQHFQphWx9tmlxR2Ewlc0DvVgh3VemhticXD4KCqmoq2KvASYlXzPQetKcM501hUqQQ7NOg4OBNNKbsB+cmO0LysZDseNTBeMmzeZynm6iwp4/cSJWCPvOIQ5RAadG/qbicR7XNw8MMTqsyyfVfM6KjuxvXuEmYtH2DCAODSUE+NNYRyub1416lsojMRFsBeqNaMRSuzUXjCM7pUbqrHH9q6QDFnOUHGnS86zWPHMxarAQ/95mO8jDnuPTzHlaMxdmOH1sQx3+qyZCM+vtBDvEXaBxgvHMBdHOK8xZzPGU4K4vk5fOlpeOXgwRUubq4z7I1JGw0OHjzAxsYmxsQkScyBlWO85ovfyJve+uW89GUvIM2S4A2X6X+gnA7JV8frhtanfvmFdqvBW97yRk6fuoNf+dX/zE/89P/JWm+VSseo9yRRTDNOWe1t8AEpmLMNinxCwwLWILHFVw71tXwXh6BE2U09McHR1YoxGEpfYbzFyO0LPL8e0ifX0ciiWcrgVJtJ11A1w2h/8AM7SH9EfqJL2bZwR4to6IkHFWU7ouja4OWvve+jFUvVFDa+cJG5pya42BDlJfGlbRDBrCyFaba1VKcPE22NMGtboSJZirv7WMjGAiSO0XaToiM8+s3LzN27yb964S/wmqxPKjGG8G+b/HViYa4l3Fk0JOHVqeN3XvpzvP/0Mf7Zm7+MpZ+/g7mPru45rcqgTWszQyYFbPWIx3nQanuDQMYQZI4kCUkFxgRnmK0X2K8zviSK8HkeSNW5QMrWwliQOtJgNjvsVkAKQ7QZ1U4moWwrg9NVIEIFM7IUS46ob0l2ggzgGgQvehosufJQSJSQQbQXZG8g7gk6CrGqJg+xsEqIUMATQrFqj/rUQvaZD84oB8m2kG4pnXMlLjP0jlsmBwLRqdHrT6unXX0N6U5JuGopO/fA4HhK+ywsfjJn7qxl+66I0WG/p596cA2/N7hkwRqX3CDFXgjYbszrNDbWX329XSvWzdTF7F3j0+p6DW5uLYLFhM5SRqPZZL24yGZfGV8paSYNHpOK82mPA82TjBs5cy3L+uoOxVnH8mbOfKPFJw5vs7qzxVwUs3DHKVYn5zjc7XDusXOICKfvuxMXG/LzF7Ejx+Lhedb725Tekc13+JKXP8jf+Y7v4N4X3EOz3cHa2URl6uyyaUftpWmHMJpr7sUEDfWu++7iW45/C3fedy/f8w+/kwv9dRLnmKhytuqTJBFntaCKPZEIsVp8HNFspZTjApeXGEJIlvc+kIGCc560kRI3LGVREDcgNha9tbHmzwq+3cR1M6pWRNEyNNcdk3lLtuOQ0uGXuzSe2kbvXGB0IMJHwng5omzB0icn4UEyUMzFRGNH2jM0L+XkSzGjZcvCY1Ae6yBOaf75RVjoMrxvmfGiZfmPR8GqVcXdeYhofYD2B0i7hVvqcOYdXb72q3+Xd3Y/zF2Rqa3R5Kr6zxKpR29IrNeel0rEuzoX+IpX/QS/8qJTfP/738npf3MBkqROk3XIYBwcVEbQ0QimmVimDqLPUmg2kPEkJFnUWV5a1Gm3U0eZc2hRYDrtYBWXVbDo0jTIBe7Wdn68E7RIYihWKiT1IcW1MCQbFqmEYlGpDhT4ox4/jrDbUUhHjQJZRheTEId6sMCmFeUgQUYW11TiHUM0DA4ztUq6YfBJsFzt2OBTxdRT76rld8O0or7QvBQ02837Yopu2P+0mWfwaWR6Q9SEVzWV3mlhdDil85Qyd8YhzlLMs6vP4mXX4tTS7umr1KQ5rccMoU6dZbPX2q2rl70wS//pZPx0uCmCvf+BF/HEmTN8Ekckhp3Ht3BXKhaWDrB+YYP1j+esP7LJPS+9GzGOuZUFTNZE13e4tLXBUw89SjLXwkU5rccu8OAdd/O75z5J0kn5oi99GU9cOUu00ME0DXONDo2ViNZ8SlcXuOv4PXzt17yT++9/AVlzulqEBPJEkV2XJkxzzWVvvv7pmUx14rIYT6sV8+Y3fQn6ff8bP/vzv8Af/vHvU+RDmnHKWEr8xJGWEgLNNbSubXYwKgzGE5yG6bRTJYpiVIU0iYiiCC1KfA5xkmJEyKvP5N+Q/cWw+toFOhcc0dDROV/gMsPCJwfYnTHVcjtkRF3q0fzTHq0oYvyCw4wOxdhC2LkjwxZK2nPYsSM7twPWUHUbZGs5cS9i/f4GnYsV7Ud3KO46yNoXNOg+WQV9rpVi4gh/6hjFfIJPusjBOXxsuPxFKW95xx/zrQsfpGsS4mdp3c9atM+GbCMsHZPw1e0zHPn6n+Z/OPqNHPidmOU/uAT9IXiPtpvQGwT5wGvQaKsCiaLgFOt2QgLCcBSOn6bIOheOASRJQsyx1uePxoGcp4vGXLswzl8UNTm4pseMLMnFmLITrLayrWirxG5HmH6CT5TmpqHoBq88TqgWggQAYLZjfJkQlyHUyrU9runBKGZkSTcMZSekvYoTJBfsRDAFFPMhnCoaGEwFrYtK2RaGxxSX+mdOLb02FOrZQIJuW6TK5hy0z1iWP5YzWYrZuseQLweLlNnFWphxzgl7s89S9oi1tkxn9Vhxgi2C3upqxpw6C31cO76ug5si2K2iz8aj6ySx5a5X3IUbKq9YabO9ucF4pc09D7yCzdWLNE8t8tgjjxD3ld6TW3S7LUqrzNkmRQyawMb6Fr/76AepDmTc9eI7ODu8SG5z3MDRnos5dOgwG8NtSoUDy0u8/oEHefC1ryVrZEHykrqV92SaabuHv5+W83+1uGJmrFqDkkbCW9/2Bu69726++3v/Mb/9G79OLMGLKipE3mBUmODAKk1r8YTcO7HgFCKbIni8r+guLFKVjt5mD+cdph0xznOonnsNNh5C69FNZDimOHWA8UpMNIoZHWsSjTzJpmP8shPkCxHZesn2XQndJ0vifkm0PYaNbXRlEXEO306R3OEziyk8cb/gyH/eQBspo5NdRgcj2hd9re8GJ6e75wTFYkayVWAqz/hgg+FBywve9ijfvvw7LNqUiM9MOnk2Fu2UkGMsr0x3+K9v+kHe94oH+OX513Hk5x9G+3nI2Jo9ydQhdc7tLvJy1QIwcRTCsJKkzuIKROsPLWEub4R1CsoKU2RhRa3e4DO6vxuhOBIWXIm3LS5T8iVH3At19F2P3YpJN0PbVC0Y3Z1jYo+OwnxfxpZoPBOs33G4yCNjG6ILOiV+GFJux8cqTLtENlI0c6gIprQUC762YoWq7WleNBRzwuSA1uTDVRlRu9bezRDqrBU5e96UBAUGJz35Qkr7nNJYU1wmQUeOZghwGpY1TbcV6nUZfJANZp1c7FmzKkplp79DeT6pnWozWu7T4eYI9smLNLptlo4ssDXYwDaaPLS6yrbrcfjE/TSXEpJkjjMffpztM1tYlAMHOywsp2zlBRvr26RbljJSoqrBznjMEWlz4cmz9CYDsnYL4xxLyx1WN9fozq1wrLXEi+95MX/lbX+FxYUFxJpaXr122NNr/j7drr2e1npIVR96KKzjCkdPHuYdb387j3/yYS6tXkB8RWQM2o4oxONHFZI7xoMheT/HiN1dUSvJUio3JkkiRvmQ8ahAUktiYpxV0k4THZafXr/bjLmnQuzn+IWHUYF0q6Jq2mCBbo6oug127owxJVRZwuIjOenDF1HvkSjCnThIvpgSTRzxpR6oEl8hLO0YmZDdZA3ilflHR9hRSTmfkfQU107w1pBdGjA+0qbsWIq2EL3zCt97/Fc4Fn3m5DrFjTRaj+LxOA1/ATpGeNf8n/Crb72f0eOnaX3kHNrvM12SUbI0hKaVYaUthqNAov0+eB90Vq1Tj5uNkAVWh2CZ1U18f7C7BCTWgrGYxXl0NP4L3ee1kGFoNx8rphSqhqdYqSBWpB+02fHRsHymKQQZRWhVewl8INVy3iN5SD81k71YJjMxyMCGaJhOMCp0O0ETjxlbzETwWZiGR0OhcVlorXp87Nm+y4SVum6Vu+F6r7rs7VOCJb3dFNpnhdZ5ZXhMKOt/vr6bEFDHz+6eF/tAwLXlupupNdVdtc4oazlkYrHj6UxZ9rTZG0jrN7ealrUUfgfb6VKN4dzD5+j0HQ8euRPdMixUY9YSz5+vbyJasnh0iUGZM9lxNBebNDsN8u2QMZV1IuYPtlF19LfHtBc6SCXENqGscpppk+PLR/m6r/p63vzGL2NpZQlrp2ElM/oqisySq9Ze3BmEpfjqoVQl2DymbkcNjehVUC/ENuaNr38d5594gp9533uZVCManSaFCoNhjniDeMNoMML4YAVXkwobR2ikRHGCSMj2ipoxmiXhukZJ4vS2rq50Paidxugo5XxEtlGSfmINLQrcqUMMTjZoXfbs3GnJ1jVkb3mPtJqUh7pEWyOyoqJYalIttYlXd2CSgzH4VhNz7CBqhWx1BEA5nyGVDwu3CET9gsmhFoMjEb03jfiOl/4Wf7X9SZbtp0cG3AymhOlUsbVEM4tY7O4xViTM/OtnpSnKe+799/zd/+brOSLHaX7oDNLI8O1msFbXNkPqs3PBct/cYjeJoaqC1trIEGOg2dirU6+P6bSD1VsnLwBolu6upXyrILXG6LNgLEQ9G9JTxyGEKpoIxhm8BdfxuxlNdhycPFUTKKSO7Zx62EPZWqeRaqLYYVgeEGp9MwLXDlZfum1ItyHb9hQdw+C4hAiB6AZm3XVv6Oqfs9rmVeGN17Fop4u5TJagdRGy9ToNNgkH+7ie0gd1MUQNTGOEpU6OqEKm2VRGwIewNTOyu9ETOtuNdZTC9XBzTq4kYenIAhuX1yh3KqRXUaZNnihGfOfyK/nzyRV+v3eezvFDLEhEq5Hy+GOPkSQRplcy2BjjS0d3uUOyGJFMYrYu9GgtLhKJsnZpjSxuItZz9/FTvP1N7+Ctb3ori0tLmCiEQOluABszrVx/93532u+dw1gbXgpf1YJL8NYIhrC6UaDmqYMqBBkIy4sLPPjgl/Cp80/ygUc/gjcee36Lo0mbrWrMtiuwURC8vasQY+m0WozyEc6VxHFMlZfMrXTpHlrh8oXztNpNGknGlSsXbqbJbwl8Ypgc72JLT/vMCDMq0NEIabeQ3FGlwtY9htYFpblWMTm5QNxrMbijRevCBBkHMo0GBXZriA5HSJJQnlimalh8Ymie6UHlmJycx5Se5OI2moZ0UtdJGR6OGbxlwE++/Od4aTKgbW6OXKdWqsdTqtvVa6eW6dMttVrObCtxlOp3haJEhNPxFt/+kt/hh17/Ng6np2hcDpZ+tNZDWo2QeLC1E6zY6cDoPZQlJk2RTifEDBfl3sLbzqGLXcrFZmgDa/DdQNrm0tpN992NYCeyu5qURkq1WCLDiGQr5ODnC/XdLhSwlZDs1FP5plItlVCE8CvXdLsedlOFcquGhrTXqbdcg+Y51VOlCuRl82DF7dwZ9F21/tOI8mkxc8xuksO1JDrDqtd1JNVkK7Wxg1HKDowOC+kG2Bx8AuVcWCwm6tt6kRrdyyojlGHy8MsnGrLZPBDV165kNzbWNYPmbCchpO2WObk2L2wiZY6oYbCdY71wzytOM98Ufub8H3CmGJMuzrO4oox2Ch756JNUOXROHmYw3mbl6EHGxYioKfSv7JBNwE+U4daY+TRleWGRrStjOu0ur3nF63nH27+axQPLiNGaXGdXK9prmLBMlcNXLqxcZS1mmicOoBVep+uZCiIW1UCulVcq57E2Qur1BCJrePGLX8QrH3iAhy8/yVNnHkfHY1rpHGoVa6WOea0t5gp2trd3tfzJeAIo+c6I1d4FRv0ekntyHcL4uQ8jSFeHyCgPIWp5EVaKUs/wRYcYHoiIx8rhD5RE/ZLJwZSiG+MTw9wjO/DEeTRNcAe6mFGJNlOqI11sr0AjIV0f16RTIcMxyVYD14woD88TXwokmy+lbL91yA9/4ft5dZZjSJ81uXqUUh1lHegfYzEzczKPxxHWiphap3a37LBvokqpYAVi2LV2Y4EHW4/w+OtX+M3VB4iGMY3HN5DhOCQZxFEdzxrvEex0sZiiRK+sh23GhKwvDckHZqtHsrmDW9/ANJtYt4j2h7uOsVuF8kAZspbGBjMy9exKKBZ8PXVV7FKO20qJ6nUJ/FyJGMVsx0SDOovLSL0iFRTzSrEcnHFSGSQP1pybc5ihJRqGkC07EVrngvNzeMhevZDMs4TWJLq7qPW1U/8p88rstpkCZo+bLTdSylZwXomrkwF8GBBcO/xHBynNrqNqKgtoXaYo4b6viW6oFkPcsQwifOrx8y4MUsPrz0pvbj3Y0ZhEg0W0dGSRzvIKJsu5J13m7OIYsYaJKymvFJz72Hka0uDoqUOs3LMMzYNkccLlSxc5/6kLuK2SOG3BpKA/2qGRhf/ndWD5MH/rXf8t3/Cud3HwwHKImZuRBECvCrnaNWSdUpYl1ghg0DrJQKeygavwfoIqWJOBRDgVKq8gpl4Jy+PU470jii1f9MpX8cEPf4iLT5ylzCpGmWdSOFKb4r2S52OiVoLxjkY7RdRS5gWFK1APo94IMFgxjDYHiAjJ8/B/Js3OMHi10xhtZvjFOSaHmxRti3EwOBoC1CcHUlBoXCnJPnEBPxhi5ruUx5fo3dnAxSHG0VuYO2tIz2yGdWLbzZAhNZlgnrwIp47gE4tbaLH6qi4n//rj/Mwd/w8noibchN461U9HWjJRpWPClH822sChlOopVSkJVmssSry7P2zz9ZUdweotFUoEi/KGuU/w/33pnRRPLdAQgTRBkxipHBpFSJahZYn2B5juXFi0Jk0gy4IEUFWoG4ZFVJzDXVmvlyq0YZGXs6NdffdWwuxEu7nwxoWp//9P3Js1SZqd932/s71bbrVX9TLds2JmsBAYwiQ2wiBIWrQkUzIVYlAO2xG+sIIKO+wI+8L3/gjWhT+CL3jjJSiHLG7gDpEAOdhnn+6Z7unuWnN917P44ryZVT0AIYzdhE5ER3RVZmVlZb75nOf8n/9illAdRA8AVUrEIkemAXutIXQSsVJRhWUFdhBPg6oUdEOPHzhkqTBnesP/dHlvrtK7aXUjSM8kO69FyfjFxxTNrn9cerrGTK8e5bn8/6YxlfQb1ofu1983Kq/E5Q/0ner6/o8NpQQbr1eI5IhuCNmZID0TNDuxa9VzuZn+XzqRsTH53tSTdVd8BeqQpSIIFQuwFdDGB/hxWPNHKrBmkrCTDTl8/oiBFnzs6Fn+9Lvf4VvVQ85lRb2oWXx/SkgSikHC9uGY4+Ujpt+e8rGXrvP2g4e0peDWwTMshnOSsaF+4yGHyYS2DSixzT/7jf+K/+w3foO93e3HhlmXOHcsmAJB2NAJAkIqtFZRGSYVQUgCIWZiBYnzDmeb2MRqDULhhSSIKAUN3vdHTYdzFqU1zzx1i//0V/4T3nntLd5bPkDtDigyQXu+ZMsJqt0RwkJnLOWyRkqJFJCmhqbuCEEAceIKghA83b8HrayfDMAFFi9NaEYSl0F5Dfa+7RFVYPuNgE8E2UmLuXsCvZWfuHUdO0rRx3O25w3lM2MAivcWBKOontslmY1oJwnZ/WVMCdAqOnGR8P7fm/Af/9Ov8z/s/zH7Kv13P88PMQLi/yWZUPj+iF/IrL+v32CuXQjUAcqg6IKkkBZHQInI7liv+kpL0gVJHRRGeJ425/x3L3yN//nv/yPyky2SkxXdXgEukPQG2iJJEEcHvZuYIjQtfj6PqQdZuhEYhLq5jIa56v8afA9LPbm1zqeSLhYjnwRWN9iQ5sPNGtszAsTMoLqItUbFU8zBksMOuy0iswD6HCqB731h1x6zkdUoSM8F4zueakewvCVw2ZVz9oeHUVcPnFeboqv3/9Dtj7EMfsRga8Nf5bKwbYq7CBvaGYBLwWZQPOqHgIOID29eH9UP/9bwx7pg615lFsIGNlgrxB77+H54w/gR6yMV2GfGhnu2JtOOF6aCo+qYTz97yDvf+YD33nqAM5JskFMUOauyY7qa4VpHed7ywEzRRYZt5iwfPYAkob67ZGAKFrblxv4Rv/kP/ym//o//PlvbQ4TwBHrFxYc0aULI/p3QPVvLAh5rWxoLeQJKyoi9OodzHc52tHWFcx4yjdJqs/N5H4PtPAEffJ+bZSmKhC9+8Qu88dbb/O6f/R7vnb7HQCl0rjlKU951Ne20o+pcFBAIixcBnaQEfG+DJxA+Qhz04p+f9mr2C5KLGtUEZA5WRFOU2TOK4mFg8t3TSLY3mlBkhDxB2Dx6qoaAaDo4PWfgHOUzWwQpkWVL1lp8ZmJxbTv8OEedLylfPODeVzW/+kvf5L/c+QsGP6FN37q4qv7+ri9QXfBMPWQiMMJvIII1rrourmc+p/QpW6FkIhsS/OblboOkQ+L7T3CLxAeJQzASHV/M3+V//Nzv8i9P/gF7ryZM3lrhtSSMB9GLdpBFx675Er9YRnGB60MsnYs2jB8y1Q6O6E3Qd7NP2knNpZEG1Q2JhteFRciAX5rYh5ykCAF61bv563j6sEMHgmicvcgiFdFG+awdBOzEIdq+kkmweUC0guKhJDsNzJ+W1LshWv319WxjevLhAdTfUjg3aq717evu/sOVWETcN4hIl8T1RkuPPWZstx+jTPXDp2YvAJLBg0ATYvRN0D10oHsPhDW0sTGOEbAWKKy70w8X0SuQxY+Ty36kAvuCzjkvYPmDR3z/xhHfnp1x/IN3MT4HYTjYmeBGgq0iY/XGFHtqEUEy3Mmo2o5rewfgHTJRLOc12WQQt9/zhs99/JP80i9/iYPrE6QkxpnYFilV/7f0Rap/DYWIOT4hdHhXU5XzGD6Hoi57wr93ESD1Dm876sUidj0iIVMJhIjFtV2LbRvSvIi+AU7ibEuQLds7Q37zn/0mo4Ntfv8vfp97775GUIZ796e4ytH4gHHQCo8IEqkkPjhMomn7whvreDT/yEfFR3nJn8iqdw1IQXLRklzA/Nkc88gzentB0P2HvusoP3ENXVnakSF/sEKe1tG0RcfUA0T0ImgOC7KHK+rDgvS0wo1SREiQtcWPco5/1vDZX3iN/2L3z9lT3UYeXIUWIx7HUOGHxQIuREVcGVpOnWPhDYW0nDsDNBgeP/q3QbLwCe93u6x8ykqlGHOKEt3GoqUMmqnPUQT21QqDp0NSB02JpsDyq8PvY37N8i+f+yrln44Z3XOYSUJ6WqEeTWN3ulhsQiA3a/21Usg8x/euWptUAykQRiMn4yf6vka3/kAYWkSlyN7MaHY8jCxyrtFVDDb0Blxvji086IXaJKZGJy5/qbVvBekjveGwBgVex0l8Owl0A7HxICD+ibEbvPKWXh0cPUaJ0oGQeFhP6GFzzI4WXcQOu3esWuOvm+L64S64L77raX8ssldul9FlrN0O6FqQn3jMQrC6LjY+B8Kx6Xo3kTJXNoqNRNaLxzHmdff6JN20viE99qSlm5WUh/vcf/8RVAmf/MorVH/z14wGmrvf/gB5bYzuAqPJhEXTMFstUfM5y9MzfKq5+cxT/MxzT1FXC+5ePCLRAqcyXDJEuqjOcs6Bd4ReJSWExPc8ViHjcdvaFdPpMeVyynIx4/jBQw729xhPdkjyIUmSIoRAhkCznFOtlkilsFmHtR1eeFoXqOsGvEUaTaqHfUBaoG0qjAnsbE/45a/+Cpn13Jl73qrfYn7QIGtBezJDuoAWEucCSimEEigVX1wfQIaA0RqnAo2tP8pL/kRWftpFUcDDGaFIyc4TBm9fIJqO5ccPECEnf1+hlx3me3dRT19HLkoYDSBNELMl4WIO3pGdTSM1Kc8I1wZMXx5BgOEHLaJxyMbiE5iYihZFTEwJ1Fh8f6SXP9QOxMGTROLxPHINf90ccWzHXNgB35zdAuBnxve5lZxypGdsqUgJS/CsguG+3eZeuxO/JyyrkGyOfKuQsPAZZ3bIlirZkhVFnxtdB1j4jE50FLLjZ/M7/E+f/Df8r/lXOP/6PoN7EtmmECbIqou+F1UNJkTWSu/9ilKI4KNvwZXudu0XK3Q0736Sa53Gqi40rvC4Ty7xywQ50xuMNcgQu1srCakHJwg+Ho2DB5f5Da4pmmh/aIceM4tDoG4Sj9R6FYc+duQvTVFEhBE2ElJBtC+k71BDD0eoviCnHtEXqcGk4qmtKUPTcGe2w/lsgFv2yPkak12bYwcRHbx6aGMjR3uTUwAAIABJREFUV72cZV5CA4/BCnEDcrlneVvQbAvSC0F2Cu1E0E4ivqzrGAIZeb2PY7ubjaNPS1ibbK8x6nUywt+2PlKBXbxxRhjlqETz6M67yHlHEIJ3v/069qThQbOgE2DPKn5+sMvdXY/A0N23BC05ev46ezsDvhB2yXTGC8++wv/+/b/kdc4ZbU8wviHpc8m7rkbJgOsc2iQoaei6KpK2haPrGhbz+3zwwQNef+Nt/o//83d47733+dJnP8kv/MKX+MxnXsElBUk+AO+pywrXeswgw3aWuq4JUtM6R1muUAiMSZF5ICiFcwFCh1SCznq0Elz7+PPcW93n/G8eMT5esgwGzmaEJIku/NLhVSz+oYuTXIVAKokTIdoY1k/Wsu4nWdk7pyAEfpQTtGTw+mk0N1GSwbszsA7Rdpi6AylQxxe4o22CkrQ7GZkQyLMpwYJ75ohmN6UdKVaH8RSxuhFoxynju4pkqmm3PNYrxqJBCUEdPKWHQsAieDIhKISiC37ztQmCVfAsvOQPypd5dXGLV09u0P7+Hgd/VVJez/jm51/g1qce8E+uv8qOXrKlVoxkzbkb8kG3zcJl7JkFLkjO3ZBSdEg8ZUiZuoJH3QSPZCAbRnKGEo4zFG1QOGLXNPcZmej49N4H/OnPZMzyIelC45MC2XmyRYkvK0SaRieu9dHfuR4iCJvuVqwjav4uZLLAVXK86CTdSR5nRknA9qmtZqoIZR8eKARmIfBJT0UKoEt5OeTpC1eyktQHEUYwU4ly644S1DqmRYdNosK64IkuwgrBhNh19sUoaI8sLGnecThZ8Pw4si/eW27z3nyb+SrDt71XwLpgQz+8Fpc4qQ5s4mGveLTSF90QBHQiphOs0xIE0IchNnueZj/+XLRbjPdrd/xG4bUWFlzSv/qnsn5KVxkH9JLZx9J4H18fqcAmwwGrRaCpA52KiqSgLf7iAr2dMtjKabsaUzompWXu57RGM9mboIaGcjXlvqs5VymPzF3+bPUaUy3Y3d7h0y+9xFOHNxEmI7gW72qC9WhlwAeq+oLGliid4jpPuTzn3ttv8Nu//Tv87h/+FWfzkrrrGJl7fOqlktXZBcXYorVB9EcMrQusU0gfoGtpugW1bXn06ISLkwt29w45unmD8dYeQhqEr+m6CucsuJZqOuWdt95FtJqOjNVqxfDahG5WU60qcOCDR0kNyiNCoJOOg2LEeVsSlKJ9woYfP8ny4wI5L5Gnsxjsp2RMQtU6KpS0wu2NcYUmsdH31ScxcytrY/F113fxmSFoQXZco+oEmyWkc8/gAag2IFvPxYsFk2fP+dWd7zCSHS5AGcAhmHpBJjxSwLm3LLxiFVJGoqMJimM34Vv1LX77zmdx/2qXg28sUcfvQ9OS3M9YXrvBydND3q73KZOEzigy0+H6NqOQLV1Q1MHQBhXhAp+ydBlvVQd86+w6AJ/cecDPj97lueQYIyxKeFyQTEOx+TkbJM2jgu274IzAhICZ1rjDLWSf+LDO5Fqvx+wIvUfu7UaGQd0bd1dP9vSilnIzkIlk+F4mawL4fuq/41BLierjuZtr9rL96gShkQgDLndRuNAJ2pstQgZ04mgLg5pq0rP42LYI+CxckuvXzWQfLuhT37t5RY+QyF0UhFlCe5xyz464H673AYORo+qSgOozxa4u4fvCL9jwfYXvB1I6/jNFy7BoqFtDvUoIWuBVH9RoASs2HCTpo3mNqvqNQEXP23UkzZrStebkBnUFTl4beltILmTEs5M+C+xJQQTL1tEtYyj69lZB2EmZn81oFw2r6RLpYXd3D6dq/q9qhihbUukpG0+aD9nKNddu3eAvpzPCdE6wko8980mePXqOFz/2IkWRgWvxriM4R+cDOhlgncX5EmsrhJAooUh1wlM3P8Fv/dZLvPLKq/zR1/6E2eyC7dGQ/e3rZMUWxWhIPshjR2EdLSIOaExCsC2r2Rn3Hj7kX//r3+ODeyd8/otf5OeMoLEdO9sHyD6223pwtuOFZ5/n1//hP+HP/+rPuPjGHyPCEiE9ppA4aWhnHukdwXc4BdlWRiEU//nRJ7mTt3zt0TsszxYf5SV/IksuqijTbGISqnv6CAB1Oo+WfdM58uQM//Gncbsj5LyKMSun59GJPwRU28HuCDdMWN4usKkgP3WYpUW4wOpGyuKGZvai59dvvMktfU4qYBEELggWIR7/RrKJlKoAC5/Qokj6s3wiHLeTU/7Fc3/Md/7rm/zOlz9J8Y2nKB7F29stuDWZsWeW3E5OecqcMZItRjgcghMxRuEpZEMiHCd2zJ16j29f3OCtb9/k4C+hPJT80RcH+NuSB/kWrxR3UATaoOiCpguKmSt4oTim+1nF13eeJv1OQbKQPeQQYkYZRL+L5QqAECy0FjEaRqzVOmafuxlTVbvA6NWH0dz7CS436NsrwcZqUDiQQfTG2rEr9XkgtNH7VdW67wTXOGYvte0ztXzuwUqCE9ilQTbRxCUI+s73inLJx7Oy7NVgQUVq2HoJJzZWhqqMR/PhQ4eqPC6TNGOJzcEOJC4Fn8RK5VU00V4/PxFAOnHJU3XRyStIR5517A5KzMjxKB1y/nASu08dfZ0j/NFj1TqmM8gmwgI2i0wMW4TIj9URSln7EojuUtElrMCs4iZQH7o+FZdNsf7b1kcqsGmq0FoQRCBTFpcYUpVQrkoSr2nOKlbnDdloQNCG1Alq4RnpFF85Llhi330HaTLEB3OaScr7b7/HZ5/+NNeODjCJpGtLpBK0XUcxGGOMic5XJHQukKfbSDRaZYx2cw51zrVnnuHnPv8K9+68xeLinJ/9uVcYbG+RFYNI2fI1wQmE1OgkQyYJvm2pVh22fsg3vv4d7t59xPe++xZ7e9u88AnNIB8ggsP7Fo+iqUucl0yGY4529hnqlPaiwi4t9apDGoMPHqMkHsuNZ2+wc32XVVfxB9O7TBc1o1GCTLc/ykv+RFbQKvqZ+oDoc7VcoZFFBu8/iN1sYjDvPiJ4H7FXIjUpPkCIHqmdQ09rhvMWN05iKsFFSdCSofUEmbPcr/mPJt9jX1WcOkMqHItgKH3K82bOloyXnAqOVeiQwXOoOpQQFH7BbT1nS0p+c3SH39r9Y/7gMy/y/5x8gnuzCftpy39/+/c4UnP2VUshYuzzSCzxQZKJjrGsyURHJixGWE67EY3T7HxbsPOH77ADlK/d4i//xS3skeTAzNlSKxYuxyFReGYuZ2lTBrphf3vB6STHZQK5KBHW4U/OYkcKkSmSxGBDMRlTv3yDbqSQbcCsfEzGBbrr26jyyfpQqEpuBi9B8BiJHvpi28kYeW0CtueJyjaacm8wzR5WCDJs1Fwxvjp2qj4NqKYv1knflfZDKNldFtdNQeyL09UIGp8GlrdheVtilor0AlQdE23XsS8bL4ArBWsd6MgaHRAgagllIMwlq9mEd8MEnwSSoxIzauhmaXwdEI97z4aIWzcHgcZdmrvIDkIQiEZdymZNbGNlG7tbBDT7DtkIVC2j6ECFGMnzpCCC7b1d7pzei1hFJ2k/WOG6Dp0phJMIrRDWIUUgdwEbHF1wrM5nmC5DjzX5ZEQ5Kxke7NIksDPa4he/8EW2t0dYXxF8R+cEShpMkoAC1zY460iSHJkUaJkiU4PH4YUnGRqeeuYaRWpp6iMOb92EJI90Lh/wnWAwMgxGvVRSaeRAIFTKbFHy6U9+hkTdZXdrxNmDE46uHzAejTFSYrsaqQxtUzJfljw8PuXhyTF5NmaQjjk7OYlRxlohtUcXhnw8pgkNb772Ovs3D1hNJKulxR2XqO7Jutr/JKt5aovs9YcxtjtNkNMV6qyXdeYZou2J80pFVZYQCOuimmkdAfPwFDkeErIUtzugHRnSi5Zuf4CwgSAFs2cl/+2nv8Z1PaMOihbJyhsUgZGsGUmFEYrSdyx8wAfBvmwYSY0RimH/iVwLWo8U/NrwBzydnPC10ct8afQmv5BdUIgEiBtFEzqkdDxv5sB8I2MwQnCoTslEx9xm/Kunr5N/7jayDayuaeYPR9wZ7HAjm0IKF3ZAFxSFbLnoCj6oJpzVAx4dTyimApsK3M4Q9c4HEAK+aaLvawjROHxnCzcpSM5K0vdquhtbtOMY3CjLDtFFqOWJLt8PgADVQWguj9nr7pWeggU8TprvU1avprEKLzdFVXQ9XYmASwIyAzvq1Vprg6S1hHbNG10Pe6xg48ccRKwyMvRhgTFcsd2Kj69LkG1Mwg2KDbuBEAdx6xU0m0IZaWXxeeePBMl0XeiHLJ8S2H1PSPwPOWnF+OdAyDzSOHylSY7j59EO+99vYlFVtdz8nMtD7OyJMefIgFypzXN4YmYvo+uC54obZGmKSDTNYsl8tqCctlRVi1UdmUmpF3Nkpti7tkezqnBlh88NiRQsz6fQgQgaFQTnj06YFAUCh7Mtrm4IQiO1RkqFaxts12Bti0wTpNEEIRBBI1pP3dYoqdDKcHB4A5VmkOQEKQGHEB1SKqSKskfvPUIqvIB8OGRvb49f+qWv8OWvaG5eu85kK2HRTGnKJTJLCdbR1B2r5YL7H3zAG2+/w527d2ksWOcIwoGJ1DNpEpy0dMHSVo5EKeYXU8RWjpQamQrmzU+fRVDtG2x+A7iBLSSq8agmYBYdsuxQF5fpqYyKmLzatPijXYLZQnQOOS/jkExKghRkp3Uk3dv4GTLHCxA5+zpCIHVQnLgRI1kz9Rn7aoFCsPAtZQisgqZFYgR9eoFAC7A4lj4anhuitPUFc8bTO3/OdRUoxDpCRvTCBEkmoBASJQRdcEjkRg77gpnxc8N3+b1Pv8ijlwzjQc0L2yd8IV1y3g447wZkskMSqL1hZnOOmxFvnO2zOB1QvJ0wuB/QdYwTou0iYwDikT8xMavMaOR0Fb+WEn1WgY+qR9kIAgq5WD3R91WE/ohK5MTGTvBKyqm4pBatQ/pkdwklADEvK2FzFPca1Epu0lOFFZilwObr4tfHragQud/hMh2AK0kB6wK/+f19h7reEISLwYfCRu6tN+Gye10jEP0g7jGfgvXwSYhoh3ml3e0GURRh5gI7kH0eWX+NajZYrEpdlLnnlvBcS1eamOjgeligH7T7pMd6Q+QMr2EVaQEvIt6cX5l4/Yj1kQrs87dvsDwqOatbuvM5ZViyN96hG1hc6CjnC4rJCFrJeHfIbHnBYDtn++AW0/KcctlysHdIU65ISMhmHYfpDkVhaKspF2dTJsMJQoNOs36nDBiZ4EhRagBCRZpxkChjSIkvuMkGhCwnSBV5h8Q333uFDDYWBqFily3iB9SkOTu7+9x+9mnGu7skeUrX1VBKlPDYrkWEgEkShFI8PD7hu9/+Hm+99Q5nF+e44DGJIcgUaz3pOMHIDt+VCK/RqWGoU9zKUdcN3oIyP303rfntqFNPp/FDOHwQX9dmO8EdpWTnKS6RpKcV9UGBWVqqwwTVF5UgBdXeBFMGhAtIF+hyuTkOmpXHJxOq65ZMdLxvt/h+fYNDPcMpSSIcu7LBofBAJgSZcPggIkMBgRISFzwaxVAYTM/0LqRh0g+SIoe2x+l6Ge3ai2DdRBihNvaHUgR2pOZT6Qd8+cY77CZLbiVnZLJj6gpOzQiPQBKYuRwjHEPVIIkT6fF3ErbethT3ljE4MQQYFNC2iGQQaVeDPAYatl0swGWN3xricoOqOnAB2XQxufYJO6kFFU1M8KI/1vdFd21aYgKiilVB1bFrtUVvfKJj8Qz+cublkrBxjPJJvFaSWc+lzT2hcJGG1Ulkw6aLXRfC+KTi9z/MjYXLifz6dpsDhX+ce7q585W/88oR/yrJNgRBN4BmO24MQfe0tZ5u5n0cWoWefiUbgVhprASROkKj8E4gNjBAD7dICHk0dBG9i1hQRIvGNETs2xOlyP6SffGj1kcqsG4+Z1yk3Ny7wXk+4vPPTJjOFxzXc+689z4vPneTl599kSIf86d//afs7GgGoxEizTDjPQQeF2dkKNtRqYq961toWk7u30VlA3SR4tHoLMVjkdJD8JhEobREBr/JNwpBoIxc6zpAROtCZ13PtYx2cyF4RIjZSGsC+Pq2LE156tYtyqahLEusswyGYySOrl5h25aqijSuG0c3mE9XzGdLXOexzkWJuexDQ+sFZiKYbO+yOF5C5zm/qGKctw+MB0Ma+2S5kD/JUnW80Kr9OPzohhpbgF6BrkE1MZP+7NNj5s9BN5LsvipJhKcbqPgBS0RPMo/pnkRWGgDJPFDvasx2xZkb8v3FdbZ1yUG2YEctWfiMiRR0wW/gu0wElOzIxA9fgqnQG14ssPlkRiety4t5reRSCLzwKFQvJLlkajgCI+n49Z1vcL/bZuYGnLsBnddcS6ZkomXmBngEpUs4swNskFwfz7k32IqCi4vFJokWKTZRMqQpos/wcnsxMkedzhFNhy6byDQIISrk8jR2v09wuUEk5+tlLHS6jM77zU7MvvK5BxXQ5xpVi42LlLBXzKVVX5htNEfxJj6usLHo2Lx/nwXQyNjdOrHR/XvDJQE/RCxzw8/iykS+f4j1YOzDRXXNm40/dOWPXBevcInprot3jNHuv6djtyy7XlbfD7hUvxGsO2RpIX/P0I10lPle2SQ2mPH66a8HXT384YY+YrALFc1e8kAQ/slhsN284f3vPeTXXtnm+YOXuHP2AKsttexoFZyt5ihjGI8Lbj19RGtqkjRDphNmyylaabyV2DyjXVUU2RY/8+lXkEHR1Y5iMkD2KaIqETRtiXcNWkjqpqLQGV4nsd8XCikFUmoIPgoTQkASED6S4IQI4G0scF3kAUplEColBI93Hc47sjyhti3CSgbDMaPRENdVyODIEkMIgbZ1HOzsceP6dX7wg9dpygadKESIbvZKymhm7A3zaUM5axiYhHQ0RPqO2fSCbnmB+vfgRaDrwOR7LfOnE+bPQ33NUdyNhh5egewi7qqagDeCkHvmz0iKh4pmi03Mhqqi2Uu9Ez9gZhU/mMtrmupQ8KWn3+VBu8XKpjyXnZCJDkVgV5YoBA8ddEhGwjLzhjpoJrJGso7iuexS112oxW2O/cDGVUsh8CHQhUBJwHmPEpZMCFIkCtHLaD1dgJGsOdALFj5HhcBhcsYtcw7AQ+EwwiJN4E69x0BH16s3nm1xwwR5Z0Wo6t5UW0UBzBrbcw7hPKJzUe3VtNEkZjwgKAn3H0VP2STpJdhPbslS4nOPHXn0XGKHgeapGr8wyEqSnqhYJIeB8nZ0gpIz3Q+w+gcRAT+IgyC97CfjdTQ0kQ0k8yjF9Vdkvpda/ctJ/6aCrmGEtdQ0cIkBr3mucjPY3/zc1diVzWCqL6Ibj4F+oLZuuYOM3bjoeboxIjw+3mZwdmXwFlVpMYF23XU+VlSvpE6tTbjdKA4OZSs3sIHo+s0oRMbGj1sfqcCObz/N4d4+YveAU3HB26sHDEcjRumEV64dMJA59+sLLh5NCQND1TTY1qH8krPjR1w7eorhaEyTSEajEdfHt/j5z3yOIAwyyUAqvLU439FWK6QOSCnwDhbzGQFJESTSDNBG97Ew0ZMV75BKxIvfRcWNs46ubehsQ9tUeGcpBhOy4TbBBpzt8MFRVRVSCNI0YzDeIklTuuDReYESnqZu8XaBEYGtyRDnGvI8wYdA27Zoo2ibyC+155bOVeAEy3pFKEsSmZAIHQU/5se/IX8XqzwUdMOUejfy9syFIjsPtGOBHcLppxK8iRzH5EJgFgZvosxQOpA12EyQTgOyhfkLEfvriHudHUD20gX/wfgOF3bAQDeY/hN25gYo4dkJs970OvS+AQVvNkcU8m12ZMtASAppHoMLflRx7YLHCEkdHAsfH2vhE+Y+o0NxpOYcqvh4q+BpQpTSTl2xERHsJ3Nu6XMmsqEMmpVKGKkKReDCDrhT7lLahGK7AvqU2EFkVoS6jtedD71PrATnYyR518bvNw0sFtFUSEXIKibTPlmalq7APFK4DKqbFlFY5IMM03M23RVBQTSRjsVRr2Kn6tddrggIK3FpgIxYFSV9mGEUJkQ8M/5ebwLB9AO0foq+kaiuB1X9cG3jD7C+7MWV4srV7nfdvYbL+61vW8tnZV+w3RVqVM+TlT1m7Nc2Jb3Mdl18BZFy5bNLVsHVTn5dhNd/wwby8P3wr98EfBIZGbK+tDt8Yhjsy7t7iMMj7szu0zxasDdKSIcpugn44KjtElLNtKnJBmOMmUBjaeqSJDhcuaBVgmU1JzGG5fycSVHgpYdUkOaSanlBVVYQFNkwi14ESnF2fspyueTGzYQiGfQJshE5D643e2ktwXmUkrGgekdV18xmZ3RNSZ6mKK1RJu2pXoquu8xlz9IcrQyBuDvhPWmiKIocLcB3DVuTgjSNPEGlTUQbEBgjCMKR5FFft39wQJJrHj44ZnWxQqUpKlGI/y9O7/8/V7vtqZ9y6HNNci5RDbQTaEfhMvCNiF0F1U+YlaDeC6TnERbQVQ/uy8hplF38OnjQQZBqx0A2PPBbTHTFkZlx1iusSp9wffQtOiQDYXEIjHD83tnLFLLhs9n7DFTYFNK1F0EdeopTsNTB97ABnDtHHSRlMNzp9rjb7tEFxUSVkIARU3wvhU0FrILEIyl9yq5eUoiGQnbxNBGg9gkD2VDIhkMzI1VHNLUm0Y5uqEnyPF5r2ZV0WKWQgyIKCUYDRGcJgx3cIMEOE9L7MzifQaLiMEwrnnRsty0CdhAxQeEETE0sIoLe5zXiqsFEsxa1MgTZT8x1TIYlyIg/SrBjF/EbHRB1JNMHyWWuVm/fF9L+DL0udI/hryCyCAaHSseB9odyq64e9ddFLqhepCCI1TLxm7lrcL2Zdp+8gI2CCFjDBJdfb2hk/eOjI4wQRQtc4sNX5L6b59I/f728DEEUpUAvI9vBJ2ALGVkFa2gl/DDWfHV9pAL7YHFBnQTGDCmE4tguMJVl3xrePD8hubHNwXCAGo6ZlhcI35ClOXVtefGp25ycntOVS2QIqOCwdoVOAolIcT7QVg3l/BwREpqqIstTgne4ECjLmg/eeZ3dyYSkmCB1AlIivSd4G+kyCEgynKuZzs+Yryreef8DTo4f4ZuGVGm2d7a5duMaB/v7DLJtvBdY39JhmIy3sd7iugbb1fGoZxKk0kgpSKSi6xry3RxvoWtajm7tA5aD7SEtDZ2KXLpre7u0tkFnu8yGGpkkOALmh8IY/+6XmUtcE4/3QQdCJ5BNf8xJA/kxeCNoJzGNFEF/VOwHHEl0hl/diBdaehGPjbKLgwrVghCB6/qCe3KHVHbMXcYb9TVU3xa8bycc6QV1X819kLz63lP4INi6VlKIY1Lh+tjuNd56mUCQCYkRknNv+VZ7RO0Nfzx/kb85vcGqSWhbjdae/dGSj02O+cL4bZ5NjtmSdW9NaKi9YWQqntJz6qBYeMMqJNTBkBFVYJnoyFVHrju08qja4w93Is2qagjX9rCjCGNVBwnSBYIQqNbTjBXNRKArsIMdkukIVXW4gYlUNv1k33s3cnGAtVCo3svbFlFY4FTEX0UjoU8fCCreho+FzRV92F+IhRgRkI1Czh+PjxHu0r0q6NjtXvUGCCoQeiw2ymUDJrO0XhB8lEOtIYBNd9l3NUHH3xN0QA46hApo7QhB4F0/Z8nj1N97gfcStzKRwdL1QybRH93X/ghriKEf/q2lt0FfYraiFZu0gyADZhVb67UEWMBmg2p3HaQuDveWKqbp9oXVmyfIIriwC9LhgA6LH4CVknKgkMuW3cMtLsoSNZpEPBPNwWjMvffvkxU5F9Iyky3byYjEGUajLTKXk6Y52mmMSWnbBmMyQlDRHMU1gCMEjRSCV7/xV2xt7fFivoPSKSZNCd4SXIf38UWt2gX1asE3v/k3/OD1u0yXjtrWGCXp2oY8zxi9cZeXXnyaT7z0cYZFjvcB27VYb/HBYW2khWU9gdxojZcCUaSorZS9Z3ehayh0hkg0VbmgKBSFTTmdL9Eo5u8fMxgXbE0S8nyH2sUJfNr+9KWyso3emN704H/SXxz98bEbxR3brKAbxetfV4J2K2DmvTnGKOJXsonHwXV34/JYsLezikx2HJg5jTecuyGZ7DbyVSU8Cx+FC4pAJjrCccqrp8/xvynHPz/6I4yYcagiBtuFSNXyxIZkFQKLIHmzvc7/8u4v8/79XeRUo9rL7sg1gnvZmLujI/5N8XF+5eOv8eXJ69wwF0g8C5+x8ildkHRBsgoJ526IwpOJDiMcI1UhCSTSkmrL6pqh3jXo0uPS2LFJG/BKRLxaCdLzjqAl6dxhKkE7kDQTic1ThE96wn9P83qCS0+j65XPPb4AHKhaRptEL9FLEbutJNKgEIAVG5aBLRyyVj2uKDClji5TMm6+oe9OXR7icTiskw8uj/0ujfABsJGc+mlCOLRI4/GNeqxLXP/nMf/X/vd4KxmNSlbL3pvAxi7ZCQOJJx/XTAYrZklONcti19nI2C0bLo34ZSyaaxObNf/3yuxz0zULC6qTMWss8Zv7rZ+rqmN6bjQ06Dttczng/SGXrQ+/Rx/lDU23h1SrBaGRKKERheR4ccb10S6q6siEQQ8LRD2lnM4YaklRKNJRipSKvckOtJ7t7QNqBCYb0LYduU7JspS2CyRpitIJCEHblATX4Jxib2+f85Nj3vzB97j9sVfY3pPgHSL4Tf5W11lwgq7zzJZLHhw/5MHDGbOyYzgasj7DnF+c8dob30drxVPXr2GUoW07nGuw3tGUCxbTcwYH+yhjsG0V4YC6gVXN7iAjzQeIOuCCoLxwWNWhlWJra0QmDb6Jmm8faophzjgfsprOEd2TjQ35SVY3DkgLet4bfZgAPQWnGwWa3QgFdIOIy6112WYRHZlUHeWEXoPL+yJsod6Lk1cnAgPT8EG3za5ashIJpU8ZmZqRqshEx4kds6XKvsAp9vUccdAw/Kucv5w8w5e33+TTyRzgSv5WLK4EQBC3AAAgAElEQVRrHLUOiqkrWNQp6fsJ6RTS89gB2XwNrAnMSmJWmj88/RkuPp/zmck9tvWK027EdTPtu9eMMzfku9VNDs2MZ5NjFIHzMGSgG7Qc0DqFttCMBfW2QjXxg6ebwOBBy+ooISiwA0U7ivzgyMiIT77LBS6NxSS78HGi/QTXY8GCLnaaLuutB33AjuPteq4Qjs3U3KWxYKYPDXYtt4U46KouO01BdM/amK+s0wLWAzJ5aVsYTAATCFaRnksalSMmbTzq92bem8NbuPx9wguwESOlkSzqEeluRTqs6ayKJ0yr8FZSrxK6VmMSi+iHaT750BS/74aRIXrHysdx1iCJqjXh0UuFbKCbeGTdR8oMPKHP24odaoTCvKY3sem5x/3FKULshv+29dHMXkyOGiuaeUXbOnzVspdNqOuGuqoo8gG+bWlWM2xnaX0gH6WsyhWFGCMbzdbONmXXUowndMuai4szdm9MaNoOpRSBQFlVKJWQZlAulyTJiNF4m62tbS7Ozjg5OeHa0y8ihMB6jzJJ7EI9uM4SvEMqybe+/Srf+947NG3ApBnSSPIsgxA4e5jwy1/5PEd7Wwgj8d7ifUe9WnF8/31cU7E7GpOZDKV0JBZXHaZ1HA7HqEJzfP6Inf0DbGMxSpJvjSjbFdcO9phNp6SjAc1qQaozqqZFDjJk8tNXcnkTcHl/pgmXPMluGC9wM4+7tkvjIEM2sZut87iDt1v9YCQFs4zuWWuzDjsM6FLw+skB9YFhS5UsfEYmIyWp9CknfrzpDu+2e6Syo5ANX37uLb75zU8hzwx/cvECvzl6bSMgqIMju5IU+8gNkcLjkFgvGd0J7H3zAvHoHKF1jHnRipAnBC0RncMlWzz81JjFMEMSlWMrn1IHzXvdDl9fPM/DasSnJoIyTRn1MlvrJaVNKJuEgRE0W7HDH9/xlAcS6WB1lOASQTcU2ExQ74oNBzVq3GOXLxsQPlBvy81k/YmtEDX6m/woGTFxYSP0I8refnDg+6IDCI8sFW6wphmF/qgdcKYvlD2UtHa2El08ShP6dAPdD7lE7Fo3EEBfdLwOqKXEufRHyvQ3R3n637Oe/Xkgc3StplklCBWQKrKBTGrxTmIbhW0UotTxNNWjDWvDG5865MDiSx1fDxMjpq4qumQtUFXEUu0o/l438OilJHvY54vJSG8MZh2b0/NofYRhQu4QtYyd/o+ZXX40u8K6xglH6S07W1vICprGUbmO7e1t5LLjwffepTErqi7lXLcMxyp6XicZKyxNucAoQZiesyW3GI0KrI2BfM5Gz9CmaUgSRSFThJcoYXAOtrZ3sS6QJCl+PQwUccJng8ULgfdzFssTfLAImWCtpiun0URGeFYzjxQC47dJlWKQZYDB+0gpOjt+xGvf+w7PPHWT0Fmq2QIvAp21OC1iF7/sSL1nuF3gZcf4YIjtLAHBYFTw6OwBQgsWy4qhGnJ6/5jBcIgNHSr56UcayDZ+CGUr+kC4aEAcj67xPvVuQFeC4T1wicAsA+2od7kf9llcIe7kuoz4lUtBzuKFuzsscUGy8hGfbEO8tC7sABckO3qJwcWBl7lg4XM+PvyAf5t/CgLsJGUfrB763C1JFzyOqAobyZoTN+Lt+gCjHO1EIOoOmqa3D+wNWCDSprKUZDnGaMuhmWOEowl6k2jwR9OX+KO3X+D63pRymHDmhhy7EQDn3YBERg52sxWNQbphpKepNrC6Lkgv1gOgQLMd7yPbnu7k45Q+mQVUE7BZP4B5siSCyIPt8UbRSnCx84rmJPHFcHk/5CosoVbomcYnMV11LZddJyPEcMDY4claxmGmjQVlvXyyxjeJla13vFpLcoOORi1Xoda1ogq/Hr5dPt7aF4F+6JZ8kBJUjAe3gz5tIAhc/zPSXrFcXBuxrAu2CojM4RuFLNWmsw46bhqXG0HPjOgZD7KLhdJlMUl37QzmBiGKF/q0hCB8fF0aAbXG5X4DM/xt6yMV2Kp9xPKiYlBMqJop6ShBJUMK3XHy8JwwDeRbEx4dXzAa5qxWS3Z2b1GpFQ+r90EG2rOOw+EOk/2neW7vOYbZFlkxQmvFw0cPqOsGGSQi9aRJyhKBUg6hPKO9XR7efZ/p6UNW1YLRcITQMZ5FKY0WAeNHpGZBXuS88PIL3H1wwcWxw7YlSgS0TBlP9vn5z32cna0JnQuoVJDKFBUcb/7g+xhlGBQDrK3xoqPxAYvHuoaPvfA8b528Tj1/hB4WqK5i0dakSUKpOqrTGRrQqaIRjmys2L+xg6uhru0lf/KnuHQdh1PCRbxVtQGbxaOsT2KXmp1Gkno7EgweeIKKX6sGmt2IvSZT8Cnkx/Go5LcjxGAnlq8evYERlqfNKV1QfL16jpktKH2CEY4X9YKBbEhlhyMaszxlzukmEZJ4c77Pd3bGfCGtMEL1AgRLZLU5pj6wJUsy2THOGj44CjQ3t0i1Qjzq0137qJlw+5AQAve/Ivl0UrFwGRNdsqeXmB6I+9qbL4CAo8GcQrW83+2wpUrO7RAXBMsu5XC05O2PjRm9rUjPAy6LxSCZ9oMZFTOximOP1xEe6IYC3QU6Jaj2BbqKUs0NdPAEl+i5oXHjpDdjoXfWilJO4QT5BxphdYR5UjZ8zlhgwBcOkXik8bilRk810hIZJAm4jA3mKjsujWL6yXsciAXCsFd6IS+lpILYCVcSEa5YEoYrjCwXN4igIlwVJDEvrLk0UpGNxMzjdYyA8uiSiyu8QPRwQLASfWKQtp87ZB7RSYLxkctK/BuyM0F12Cu9dNhki8krHgSRzxtPfaKLQQA+8zE40oNZyMip/THMoI92XnWGncMt6sbSBIloJSqRzJcL8nHG/o09vLaY7ZewtcU7i7U1bbvArRbMTpdMkh0Oxrs8ld3kH3z110gFSKXpWkuRj7F2hlIgdGCxmjFbXjBfzRnuHOFdYDqdUZcrzk9PyNMc76GpG7quBRzlfMa8rGg7xyArKBLDI+twISaIguPwxiFf+ep/SJpnSK0RMolgPoJlVXHr+iHj8QhjooXb6fEJAkGeFQy3nmF3uIOlwlmPaB1CpSRZgQkdSmmW0wWDdIc8S6k7jzQWckiVJsyfMBD3EyzhokTSm0trOdnFD5uq41mtG8PiwCJbiR3KaPYRegyqu1R85acRz612+yPW2PHffOkP+KXBD3izPcQIz5kbcN1c4IOkUA2+H0ff6fY57Ua8sTridn7GreQU2UIyF8zqjL9YvcCz+hvsKEUTPGdOUAaNQ7PwGa8115nZnNpqgoTlzYTs7SpCes6Djrg8wL2/t821jz9kNy0pVEMmYmGPjlkB3yhu3zplYmqeSU9YuByJ59XFTQAK3ZIoh/rEPe6/d5t2Kx7DzRJcLjavqbCx49d1QLWBuj+hqLZnDfSb2FrK+kTf1/7Iuna+kl3fDfZFTK96Q+mJxxWx+9KV2EzOfXHp3SqmBj0XpFU8taTTWDTrp/suNImYp15JVAWIiMW6PEQqVCegvTSLQcQuOKhASOLPr3Ou1sXrKsUrdrhxs0gvYqBp6B8/xP2CbhRiUkPeF+VeDBBk6EMZQR+b/nqOSQ2qitaGa6xCthHu6UYRvvE6bjKykj2dS2y6bnOu+oEZG9tF0akoGzaxs5X12sbyR6+PVGD1qqOzc9JRTl02eCvItw07+YBqVdKFJe+9d5ecAalK6OqStNjCLh5husBevsPLtz7Dr/3iP+Jzn/1FVKJYzo+x3rFaLRmPtxmOJ1jbsFotsc4x2dqlriqW8zlVVbGzvU1blTy8d5dBPiDLC7x3+OBp24baBVovqHpD5JuHe4wHI+azKYkWmDTlxY89x2A4RCUpymSYfICrBKuqZrQ1oRgUrMolea5YzivapkXplEQnNF3HM7ef57vfeo/MBRwRTLfOIVvLwWSLGzv7LKoGHwx0LUoLHs1P2BlOGAz/3emqT3rFDgqarcvp6dqA2eVxiIWPu/fwvf7CTiL9yhbxotNlHIy4JOKNs5ct//jn/5p/vvsnHCrPPat5ypxx4ga83+3yQvqQW/o8+rS68cbI+mZywUjVnHZDam+QnaDZCdAavru4zv+tX+bLxZsYBE2fNDB1BWWIxtmt18zLDEKkiJUvH2ELSf6wxhYaaQPz2ymr245d6dkyJSNZs68XrHxCFzRvtIdkk4a9/P/l7c1iLcvO+77fmvZ0pjvWXNXd7G52s8kmm0NzEEmHlK0RsuQoiWQncQwjQYLEQJAAAmIEyUsQP+TBeUkeggCOH5IABoJIkRxEJjXRkiXSkihSHLrZc3XNded7hj2uIQ9r73Nvi6SUCspaQKHq1r1nuHuf/e1v/b//sKTxinvtJleSY/5w8TSlTciUpdAdtdNkyrJ6wjF5M06R2ynkBwGb951pC+1UUF6MkdnriXmPUZtVrCDd6PHvXAaane+7rxAiTjgkDdhx7x8A0Xg6DbQjh+h6yetKriEfEeL7TU4hnXvqTUl5pacs+bgdF13sQNfqpb6jpZVrPqjPYnHScxV3OROLMLEChUr3Dl2subFrHmw/fPJFoC6iSk3VkB0MNKwYvljv+PWwangPA//WZ8MTDbLfsFYhyuqcGbmANolzhcF0Bvpi3ifpCjtEn8cbjit6PLo/xsFGQ/MgQD6uApsUKf74AHV8iBhNkVd3KZsTWDpODo9o2gWboynLgxOE0qSJIZGSnckGIz1Di20+/Ykf5eVPfI40G2GDI81HrE4PyYuEpl6Rj8YEFwg+pouuyprlYgFKk2UJe6sFb7z2XZ40htlsg90LF2i6juUqDsbqxrJc1pSrhuAcs3FOvVry5NWL2LbDOostl5gkp2o8uqyxpSXXkuboEN+UuGaEylPqqmVv7wCd5vgQfQewniIfsZKSzd0N3r57EykMmxtjxLKirla0oUNnCUJqpnnO0f09wmGJv5Qieq/Vv8zlMpCLuC0KIjIB2mlANfGDnpwMOJakG4MuY8fKYEdnIs4YRIQI2pnn3/nMV/l7W19lIjWgmMkOPOy7KVfNMft2yrPJHkeuwAVJ7Q2ZaNlzU94qdwE4agu6qSc5llRvznhVxEHUtlpy3Rxy6MY8sBskwsYi6xPulTPqVYIOUV1Wbyl0HVg8mceInrZ3vuoEG2nFQTumUG0fFyO4YOb86t5LeC84bXMmpsEj+ObqBq/NL7CbLQHwQZBIR+1MzLTyiupCIN/rmQEO7BhCBdlRWJtFux7bIxck80B27HFJnEi308dbZO12h9ABmThcrdEHph8U+ch9rWIRHWTOEbeMRcXlfl0IfRJZJMk8FoyT9wu6cU9Z8rG4DUR+l3tcztoTVgQRwfnBG8DGm7UrzqpOqHvagY7Qk0gcwUpErfC9QctgmzgUVuHjcK6cxHO5TjLwsTnwahAVxIIZdEBPW5xNWXsfdAI/imGPg13hefzXjuLn26ZRPCErFalaKXHwF6DbcJFP3EnWCZq974FoZPRjyH/4OXqkAtu8e8S8OaJLLZcuXkQ7RRk6tmZbbF24xGlzhOoC46tjXAik6YjVvGVz5xl29AZf/MxP8tEPf5YiyXEhZv50dpC4Wqq6pKoXlKsFAdBKsJjvU61WCJMxnx8xGme07Yrl/JCT432WyyP2Dg8ZTTbZ2rrAallydHBMVdVkecZkNubgdJ9lfcL1qzdAKCazGT4oTDLCOmiDxTY1f/IHv8fOdMLy5ITcGBbLJW1jQTl0onGu5eBgj7ZpWDaO+/MT/GaGbDzHq1Mm4xlCW1aLU4KtCGiKvECXjivtlGSlKKvDRznkj2XlD0FXcZDhMji9SK/EihfD4uneSLifRkd2QXys6LuMZjNyKW+8eJ//+Imv8DPFPpBwltsKddAUosEjOXRj6sbwen0JSVh7rb5V7nJvNcMox0E5QjaRCiYdzI8L9kdj/sX8/XgEr55cZGRaPr31DguXcbvc5N2TTUIdI9dtAfWWZHrTIhWUF85FsecWj2Cia+p+utR4zbdPr/DdbzxJdn3B4aqgsZqr2Ql/vH+D3MSudWwall2OFJ4393agi/hqcjpMkeNNyMyH9zC4VcVoImGjkXTEGQOI+Lj84PGCsMlDs46fFrIvQCFCB/QKLOHBaXDbDnOq0GUcwKlaYvYEZgFmGR3SXAqra/1QK/ohxc/A+kbbb6ETj0g8wQuCi1xVVIiyVR1A+2j/10VHq1C4SPVSgaRokTJgO4UznugCTnws4EYOt+0RMsIMoVWouUIvJPIkfla6SY9ny7Dm4JpZg+3UekgHfcHvZNzGyx6iUP17nFjCXEfIIAhkGZN2B9xX1QNEptZCCFfEeBm1kOha4HXAa7F+Dz9oPRpEcH1G1m5COGFvtcfl9Ap2XnIsS4QsKMyYk3fvsrU5ZnNzg/Kw5Mr4Ki9+4DN89IWP88SVJ8iyAhECyoJEkmczVs5Sr45xrqM8PWG1nNP5wGg8YrGY07Y19WJJ8D5GeuN4eOst8jRhVS84XcwZTbY4uL/B0eEeJ0fHPHy4z6qsuHPvPm1d4WxLXY946ukPsH3hOtY5jhcrpCqRoWW+f5uH995lI3uaBsvpicAUI9LxiDwfobVmuZqzakru7z3g2UvXWR7vMQ9gbYsykq6ruLhxgZUsWNmShycHaFWwfXWGuZ7QhMBIX3y0q+gxrGYzDreEj6KD/CHUFwJyES+e0buKdjP0w65AeUmst1KqFNSXHReeOuRnr32bn5t+k2eMRqPjxL83MNlSio4ah+B2t00XFK9WV3h7tcPSpox1w0665K3THaQIPFhMWC6zqB7tGzu9l3A73eRoVbAqU9zSoMYd7xxuEYKgOiiQlaTYk6gWRvc9ugqoNg5zTCGZv0/QbnjUtOWwKpAEtHS0XtNYzetvXkbXgqYx8TmbhP3NMQG4mC8Y6YaprqmdoXWadq9ANYLVVSL26M8NuYozLLbdCGT7sXgVD2MxajcEJ4U+c456zKubxYm3KnuoYGYRq2jw4lXA7sT47uRU4I2KeCnxnAofb7KmjOe5uiCot8LaVSoMMtKBmiXjDST4uD8Xpeo7e9FP6GU0etEhJgM4IrOgL8JqbEnSDqMdq1VGCKATixk5sqSj6Qxto+kqg1ipGMnSRZzXzhxuZhG1WnNcQy8iQAbMJHLLxWHSv76MNwEvYoRN4IzL20/rwkITdCDZUzGGXBGHZDrgJg634yJ/t7+5QD/osjHCvFuLF3rmxg9Zj1Rgbap5+PA+Jk3I8ylVu2A02ub48Ig8UxTjERuXbyCkY3nUcW3nGb7wuZ/iIy9+kiKbIKWA3kMgeIf3kThnTIE1FZ0sybKMarVES8+4yDk+8KwWC8qeayqylNWqRLiO7379K6yqJaPJhDKdYpIRx8sD6rJCWMcolTx1Y4OuGyFFgGA5evgaihLhF9gLlyiKnJOD+7z5yjfJ0oy2rRllCScnx1yaTDFGo/twQB8C4/GIIs3YKi5xZ7Hi4nREVZeUzYrF8QOWe6esqpj/NdvYpRBT7q0eEEYtbVeT/Tkn41/ZEnFoITx0RW9tt4ouS2YeKVj4aHy8yuI2SlVxGFC/v+Y/+ujv8bdm32BHJhiRrD1ZIdoBDpLWQgQ2ZMNClZy4Iiq7sgUn87iHupTMMcrx4HRC0xj8ShPyYSATuy75Vk4TcvK5oN0MuFNNrTKEh6x3e5J9ON3qUvSk1Su5JvTn+yA7SeMy7tWa5VZKnnTMy4zm9pjxXUl5pVcYJZauU7x2cpHtvCSVNvrBikAiHadNjtpqcAcpza4nv6upLkbMEsAsQLqo6tI1qNZDgGZDrmla0sJaAaQe77k3pxI7CtiZx5xI9F5CdcVFjL2UsJK4iafdDuvU1NAPdaZvx4JZXorx1b43bwHO1FX9sMcPqQc+Ap7DNF5Vai1I8Cbg+k7uvPWgsCBLhc8jv7VepgQvEMrjpaScG8pq1BtjR6+JIRHWK3rvC4nLIYwsUvuzgZUMiB4A7VYJYvCllXHA5guHT8R7zV96CpZexKFWddXGRmIZP0uyFYi5wqWReSCCWA+Gk+MouOm2PH6gxzmBfVxKLpONMIVkWuxw8cJ17t5/BTBsbl7EOYtzNfsPH7C5cZFUbPMzP/7v8sHnXkIr1efp9D6s69tj5KQmSUrXZASpMGnG9s42EEgSRWoUpyfHLFdV9ANIMqRSHB0dsFoe4fHUy8D86BStU1QmkK6jMFGiN8oUziuECHRtQ5ootD2mXQQOumO8c1SLOb4+pWwqup0dyqokyXKWqxU5Euc8Ukq0UpTlCmsb3j68i0pBNha/aBhLhUhz0s0t0tZyOl8wm+XUVYerWnIbKKZjTt1fvpKrnQWywzhosSNotjzFXYmcC9pp9B8QXiCa6DPQbsTPo3v/iv/1U/+YjyQtRqRI5HuKq/8z49NMCJCOfdEhRWBHL5AENrfKtWx2O1tx8/424TSJJO0+C8pqgT6V6H6oANF0I7EA8X2Ljnjh9YF4oZf/DjxT6eJF6U0vpphrFtWURT/AS0/jzUSvopy1Xqako5aD5QijHPuMqZyJN2PAI7CVRlpB9jBSc4SMTILoRkVUGo2gc5GSpZpAsxlZA+aASN7XEfuU9vFCBN0kqrb0aZTFtrMzAUDooieCmEvs1OMTj24Uo7uC4kHk9y5vxMHRe6SeQ3HtB0fDCnoANmMxFQMPVoLrnbZEF7vYoYhFFyxABMRegpdxIOQNhMSfgUs9NnqWvtBztPuIcLxALxWhZwSExEe+K+DrBFGqnvAfzvi5AtRCRTx88Cbo/0T8vFe89cV1oHzZaVR16UoQGrWmpbncU96Ix0DP1doW8bwz2A9aj1RgH9w/pGstkwsFrlmRKodSYOsFuxd2OXpwj3E+48rOM/zkj/08H37hE0gG25n1qcIHh/ddDIqTcYshtUZqRVk1JEog8dTtCqHABYfWGqTES4FKYDydYnFUdUXVWdquw4SAsJ6urvHWUzcNm9tbSGkwWUaW5CgZfVDbckVXlygBwndkiaKzgbYuEdMRymjyPCdJDFpruq4jiMB4MmbnwjavHL9GoST1yQlplpEEzaKU3Hv7IdefvEo+keRSUouSyy++j8XRnOlok1ny5yDi/4pWvidQdYju71t+TUa3ecQSq4vRvzM7ihQYmwU+8tk3+O9u/F88qQt8L7U5X1wBuuBoehNt03/PEyhkw0g2vNPsIgnMdBn9CWxv+edkvKDSsw9+UJHkjZCYxdkWVrWcxTrZCHEA6DLQTQQu66fX+oxyg4gXvF4JghQMxPugz5JERSuRkw7nBLvTkpFuyXRHqiyVM/gQkw6yaUMtwNxK6MZnxXXghuoSJu96Vlck7YQYCqpBz2HxRDwmqu41+/LxdrCy37J2Ww7/ZItrFGKlCcbjEo8bR9xTzRXpkYqUuNPA4oak3umL8Q8oDlEV1tO3+u6bcNZZBtOHrwzFLvV4K2Khq+QZHW0YfHW90ZDocdwQoBNR3i/DWg0WTKR8DWbaQYl+on8mThCtQFhFqFQfbBhxYeEjiyIOy84occITuaxieF7weeTG4sPa+nDgsuqFjN141rMziIwG2Q/WgoohkMGc0bfk45LKTiebLE5PeeU7X+fG1QtU5RwlLInc4V5V41rLyy98kS98+qd54fkPxfZdeAIB0aPmoeeiBm/jBM4LXAClFWmWsFg4DucLqvkR85N9VuUKnRjSLAWp4xCmqcgIzASEEFh2cwQdbRtjvaWA4FuCq+maFUUxjR200gSpUUmCFAmCQNussF1N27VASlWV1E1DUozxwWM7S5ZltG1LXVccHhywd/8h83lNfmmHVi6Z+waakt2dLZgfsbh/hC9bvnf7IamSHF+YcvXKRQ7nd5hcu/Qoh/yxLG+gnQkmdzzpSaRZ6T4eSoRYqEZ3oN6G+oWKn3r+Ff7+xd/iooo3gz9bWM+vNgQUEeIaZK0jYdlVc+6ILW7XW5x0OQf1iL3lmLJOYgEsXMToZLzIfacISKzwhL7I6o51BtKAA0ZqUOjx5Dhk8Clr6s5QbIdAP0IsyrKLnbGIHz9UKbG5Ro5bOi+xQdI6xVZS0jjNSZtzXOc0q4T0dkJyGqi3oz9DxC0jda3eEqQnguww4njFviM5tQQp6CaKxVWNzaG4H1hdf7wF1mcBvYgFyJV9W++iOUn0eaUPIIxMkXTumT8haTd9X+h+8PNG2pSIWCfxGA6cU3zEHL3ub5BBEGqFcALZJ9IOsEHwZ4/zveeL8KyL69psgr5I9urCgdM6dMzrzrZ/viEHbnDwiom2kR3hEw+DzWLv+OVzv36tkDtoZMSPMwcLHRkPMooKuostVAq9jOayfuzw0+hxgomfVzltsZVGLDW+cLg/57Q+UoHNqkOe273GvQOHdacUepd6fw7ZEmRHES7yIx/5Ah9+4SNIPYS29KThIBDxK5QIODq61iKVjmIPBMbkTCfbSC+wTUWSFbggSDOBcxB8zOHRUmGSFJlkCGVQWrNcnGJtg+scQniSPCHNUopigskKhNQ4QIR+u68VSgq6Brquix6awrNcnrDTzcC2pFlO4+DhwT5tt+StO29yd/8et+/ew4wNd1+7iV3UuE1NkUk6Jxin25weLyiMYntjm1u3HpB2x2xlU/LckJR/+ZExso3GLNIOHMFekbXvCUKQHkbhgcvgFz/0df6L3X/JWBR/8fP2V0cZgBAwAroARnhGsuGJ5AAjHG+zy0mbk2iHTSzGRDs653puoo9DGW9ETLwwCp9I/EKiSwj2TNIbRIQEBoWRS/oLTZwxH4C1DV50v+qL6znTD7MU2A2BswojPRtJxf1yymmb01jN/YMZfmEobmlG9wLtTDB9O5CdOFwSp8zdKBaCdipIFgFVBmwqEWNNcXuJOZU0kwmE6FkgHnOorFrE45c96DHDng3lk4A+VdiZQ9aS0V2BLWB1Q+DOOUadL7DncVegH3SJ936/91AVDpQV0HtanFdlrVkccniSc3lh6+177E5j9EuEGYbzdxbYyKU5io4AACAASURBVFrKev5rQn9efexUGQp/X4xFz2QQiF7cICKzYOSQiUOqQLrRUVcJ4TBFr6Lj2OAzIE4SbBE9dtN9hTyQ2DxeG6o9u4Er0ceZL9Qab/5B65EK7PuuXWV+WpGbqzg7wbsF83HLk9cuc//dY37yiz/Hiy++hFT9UR5O1nAWiONJ7+JeQ2tFCOCdi94BAtI0wecFbTvBOYsxI3zwdN7RVg1da1ECdJqi05Q0y1FakaQZq+UC76LdoJICrTSJyREqwTkfcW4RZXwBTwhQFAU+WNquwaPI8ozlck6ajzg+3EOkGe88fJc799/iwTtvsXlxl3y7wKQSqyTpxYw0UyzaA27v3+TGxSfINgW4FruEJ2Y3aF3NSlhcZWmOH7fjx1+8Vk84VClZXQvoZQTqzULQjgWrazGbK2jBJ3/8O/xnO1+lENlf+JyeQBMsHjAi+nWUIbpeeaJqbkstaYOiyxQrlyBFoEoN1scbbm01ZWuwNmLkurec85nEZpo20bi8x2X7LLcBZx06VJf11KTzLvdi6Jz6/VL/M6oR607G5gG1VDgVOKkyXNiic4qDgwmcJMzekNgM2s2Y/NBOosii3VDU24FkHrmtxYPA6rLAZbFTNMtAs6GoN2ZRkpxDsoyE/SFQ8HEtn0SrQptL0mOJWka8XfR4cHqk4yDOBsorZ8yQYa2LKn/m/4jFTg6xLwMLQvSvmfaYvYtd4jDIG459/Ht4rbjLEH3HKgg9fzb+2Pqxciiow+u9t71eJybIc8U+nDumPR4q2zOBASrgi8hjlYmLhjFeUO6P0KeRlmUnvvewje/F5mdddrsRsevBfyCIPv8sxJ+TbWQUuNkPv6YfqcB+b+8m3i2YhGcoZE6r5wjtCa3mr33mZ/nxH/0ZTN77NNLTPeJh6Y9BIPgO56I1vhA9GC0AZ3G+ZVWt6KwlyUZsJQV1WeG8ZdWuCEiEttEIWwhs7ZEmYbK5RZASaeKEu+taqnJFWzekmUYgUVLinUdJjdYJITg6GxMQhNSYVILUmCzHZBkm0QRbc3Cyz1sP3uKtd7/HVaGRXc3swgbz+/skRU5Zecyx43D/lJ3ZNtOwwcIumEx2aXyF3vY431AUGxzPVxzN//J5sNkDherBegJrFUxQsXCVT1je/+w9/tur/w8zmf65kAAMxbWj68/rIKpJRexeV17Soth3U47cmC4onswPuZyectiNsF5ROcNhPUKIgNWKxiqUCHRO0TqJTBxh5rHK4LVcd5+yEzEbrDuDBOg76BjGF9bZS8JFY+RhFufW/MizQiHT2E2XTcLJgwnmSFPcEySngWYWX6u8EsgfimgyvorYbnIaSE8D7ThSs9KTwOpqPKYEqLdAOhEHiFaQnkS+6eNcYbuFTiJqSX3JoqYtbmmQpaIpfDzvbWQKdBP//Y3Wny2u5wMGBwOV/vie7/5BrHcFMY11KNZRIITsOdac1d21CUxv+EKvdENw9gEa8F4R1pJV+jrCucI/qMZix35mJNO/tWgo0+928ZJQOHylaecJohXoTsTuvsdWB5pVN+79OdKwxpJVpVCNwCURl+22I1wgBhNzAebwh5fRR/MiqE7Ymd5AnFYc3n0Xs1WwWWxzZed5PvOpn2Br+yJBnNOwhUCI4elIIQmuw9qarmtJVAJS4L0DIUEoTFIw2zQoJEJIjg4PmUyhqlewiLeuru1Ig6DpWkLwlFWJUAlbu5do24ambWmqBp2MaPshVp7mdG2HdPGMSikQSkeZq5AIFT0HhE7J8gJlEiwaoQwBaMqazdGYK5MN7h7vcf/tY7bTKVmaUxQ5eRnYcBW165C5YjydcDo/4fT0mIPv7nHt8gymjlwbrm/PHumQP46lq8jVtKPA+N3IFPAJNFvxg/+ZF9/gP738m1xW+Q8srraf90pkZG0Ey8I7mgCFACPEOu1VAonwbMiaxOzxbPIAICYY1Dd4f/6Ahcu51WwhRcAoR+Oiy1XnFKvWEAK0jQEVMJs1bqJwnYRO4ptIyVK9Z61L+9ft4kUFrO3r4tUCCBFpkIKzAtwnrkoJzknyJO7f9SoqnhY34t/F/UA4ilvsIONAcMAkbSFQVXy+eit2M7KD9Dgwvt+xuGYwiz400ESu6eNcci+Nnr7T6CsR9rJo7FMLsr2Iua6uSNqNc8V1KFbniiKc/R9wplg6h4MOXFLZO7ANDxu6xTUFbOh0Bxy1L7jCnqUAnC/aw00R4tfKxt1UZCrENxl6nH7opAdZ73kLwuFXwJ9hyKH3mVVHKu5iADMX1Bf8OvhRNiJGnxPfI06QHMZ8soGi1u12EYP1AtFIVC3XseYi9J65P2Q9mtBAbFI3KdrOSYuEMJqylV/nr//03+app55HSIc4d6Z8cATn8M7iQsD5Du8bQggxDVaC87HoaZ3F/yP6Nzrr2NjcxYeGzjdobVBSY7IEFwImScnzjCTLaJqaEAJJVpCHQL2qaauatlrSVAuUEphRxmK5QiqFMRKkRMneIQdASaTOyPMxaTYiyUbItODSlQ0+phQ33/wWXllaGdgwGVmuWZ08xHWCd+qKC9ubZGoTn0puPngbowPFZsLTkyfZyFISEuanS9r6MQNx/x9WDLuLAxrf04ZcEjvaz//Et/gHV77Mtvx+dsNgHViHaCMZOa+BDqh78xwjBEbEwjsMuWLRtWzIIVMrWg6+lN3CIXirvcCmzjiSIwrdUugW6yU2KDayilWXsGhSmk7HLb5zqLGnbTVdo/GtwvcKHWHjsMeZaEQSJHEarXryeE8TOtuGhjjQyB3aOJLUUqQthel44ql9bo82cTczXBrdpLwZTMqjfDg9iXh1NB+JRt927BndkhE2UFBvC5qtBJud4YrCgXnM8LtZCKorFhEijcmlIfJGKxA+cPq0jJ2reu/jvm8LPnSHPV65/ta5rnG9ZM+gIB5LEYiKrSKsp+qiL8TrpzlXsIfOeDClWWPB/Z+1xaEG37NB1qqyYejF0MWG96TRrn+/czeLIOLrZ/vRp7e64taSXDd2kbniBcJ4hPG4UuNziVrGeHKfeuRCoxoRPw8K6l0fmQqiv7H/OTFQj1Rgn9x5hlf/8JtcuXad1rWM2zGf/fxPcOni9R4W6GkdeLx3ONvibAf+rH83KgPpESIqgaSSccIfBMFLvPexMNMSREO5WuA9KCnJ84LgJVobOufjUMsJlDKcnh5hXYcxEZc1OiMfFZyeKMqTQ0Z5xqjIqeoWhKKtK4zRKG3QSiOMwSQ5o/EUk+QxVUEpTJ5x/cp1Rli+/p0/Ih1NaWzHG6+9yfb2FquqQc9yrBDkckRYar744Z+iXay4OtvlzoMjDh7coph2qNQhw9ajHPLHsspLns1XBN1I4PKIuXYjmH5in39/93eZyeT7HjPAAGVwtOeiWyDirZkEF8CIGJFtEEgRh5WOgAuBJl7v1L1pi0Nw125S9p6xE1OTq47Ga9K+GK9cQqY6nJdk2nJaZdRlQj6KEss073BJjM3uUo08NZGIPvLYwqMGuzkfC0Y0++g5jzKA8ajEk+UtibZo5aNrlvRspiVcgP18hPrmrIcSYlHYfN1R7cTAyMG4RfhIv9Irweq6J1xskPcyxs8fc3J3SnFb4/oiW4/DOi3gca36ko3FrIqOTmYet7tmAfWuoN3066L2HnOUQb+/7hwHOhTnQNiw/vH1jkD2xajf2g8S0jCOHCzRChAiMoX67nRgBcBZYRcuclZ91oc1OqJEtRe8yCEWu/dFku05Cp7s3bqceE/hHgrvOvBQng05653Q+1TEcMSgAmgwJwqXncEEwsd5QoQqAt7A1p9KzDJgM9C1R3goHgiW1+MsQ7asDWx+0HqkAnvr229w/Zmn2asXXN+6ypMXX+TlT3yWYpT3nDQ5vDustdi2xtoS61qUTFCyQJoUpQLIKD6QfZKA7SKoJoQkeEddrTg9PYippSrBe4FUBp0mCCQ+tAQlMVqjlKKqWqxrkUKhdYrOEqTIUFrjRwW+a0iSlLJsaF3EZ6ztUEZFWEBrTJKipCDREq1j4Q/WU1Yt9w+POT5dMbcLLmxv8PkPfYSVc5QE9GjEeDrj0uQCN4qrXLr4DA8WFR/75Mu0pw/42le+xB9/87dJpxPS9MqjHPLHsja+J2g24zZXtlBeCbQ7lv/+uV/jo6lfswHOryZ0LLxlEQSlN7RIMuEweEYyMmOH4iqRvVIO6uAjq4D4We8QdEFy18242e7SeMO4ty/yQZCrlqmukCIwt1ksuE6TKosQgQfHE3ytabVHKo8xDq0dAkhSS5O6CB8EgUwctlFQqb7r6bOfgiCYOEUWKpAkFiU9iXYkymGUQ4pA7QyLJqHcH1FYGN2LF+nyumD/pX7Q0R8qW/QpEZst+ajhQxcexue4YfhvbvwqX3v2ffyTOy/z4KtXYrfjo0nO41zCC8yJxKXRCSqZR6OUdhat/TiPo55f/QBQrPlUrBMRYvEM61TV4Tkive3c1PocvCAaecYF7VkDw88NnNb+W0h75gmr52r97zBADS7eqIWlVxPG/5e2L6BOnMWOm16cMMRsBxGpZX/GR2EYhNlRNBbqpkSBSyMY35RUlwPtpkNVcm2AZBZRNu4yqHbjTmn+Pmi3XW+BGFMibIDRu48Jg1XbU5yybI8Mz+48wTMvfJLJbDMSfM8VcUdNoCa4hq6O3UgyNhhjkDolCIf10SI/eI/3PmIlSuJdwLv49WQ8xXaWrrNszHbQOvJEnLM0rWKxaFBKspqvKPIpnW0RwhO8QwiFUprZdAOfJbi2xTlLLlNE68jGU6SU2K7FO0dqFJlJSNM0mnfr2N0m6QgXAisX+PDLL/PNr/8BRTC89IGP8Ctf/nWS6Qa7W0/y4jOf5NLmU1zY2uXilau8QOBrv/t7vPvgLS5vbzOfW+7dvMPf+Lkfe5RD/lhWuxG3Rz7pu4f3lXz2iXf5WHoEfH/3anF0wfdQgOLE5zgEI7VkJD2FEGQi7ju74AH/HvhAEgeXLoALgjpoDI6nkz3mPmPhciaq5to4DvyUCNTesDAZB92EpU0wynFYFSSJQ85qvJNIGXBOonUsiEVeM84aqtbgvMQ5iZQe23dochLhptCdVRito/Y9NRYjPbnuUNKTqY5ZUtM5xXG3SfOhiqDy/iILZC+coH5jkyBhdT1gNywXrx3z5OyIL2y+xvPpfT6arvhSeYm//87Pc/Ngi2cuHOCTwOxNUF1g8Yvzx3pe0wNFO/O9T0B0SEu9oJuFtdv/+bX2YIWeGtWboQxsgb67HbbYITakcQ3DpXPbb9kJRBVbx2GIJRvOOspBMCBYK8yciUM/verhCN9nX/Xvdy3NVeLMpUsG0GJN9Rp+35igwNqecV3w7Xt/cW/COtnAJZA9lOtsuvJKfExyrMgOexFJEv+cPrs+ctipJ2SO7E5CchJhknrX40ae1XM/3OP5kQpsNwtUx4eYjRF7oebnP/ISptfpR4ikL5hOEKzHt0tOju5i8hSTbiKFxKORWqIk4DxSCiS9FZ7t8N6jZKRwESRdsCSJJE1yhICqLjk9OYxDpPkcY3KK0ZgsL2jbFusanOsILl6EwVq0MXjn0EKiE4VOQSUJzlpc1yKCj7QunaL7QZfWaQw6lAHvK7rQMZru8OEPvURbOd4+8nz+x36Rp598mt3JDof7p+TaIINnPj8kyQo+/PGP8+nRZ9m7d5PJV/6Aa4Vjf//uoxzyx7IGxZbrt3dJavmly1+iEOr75K8WRx0sdfDUQXCz2+LV+irXkiOMcBixilirdJg+SrvrDV9M7zMhxVlQIUAmLE42feChxgjL9WTJRFa4IHlgZ7xVX+CwG/H2YofGanLdsZ2XTJOG1qtosh0ErVWRC6081imU9ORJx6pJ+uLp6ZIY8yxFwLrIqw1916a1RytPpi2TpCFTHYl0pNKuJbLCCopRjX2p43ivABXoDkeYz63YmJb8e098ne8ur/DWfIdEWu60W1xPDvkXdUIiHP/1k/+US0+X3LQz/oM3/y7LawnSCpYHj9eqsn6iiQVwEZn1Zin69ODvL64DdS2YXn2VOJLMkqYdApgfjRCV6n1sz03lB1oUsSCfdYtEcv/AU+0HT7435l4LBAaJLUSD/n46v6ZaRUIRyvf0qjU+exZmOLiCDTSxNYYLDFE1wkahweD8dT61VrQCvZJkh3EIqRqPdFBvxA5+esvitWBxXcfimgfabQ8jSz6po9vWd6ckp9Gysv5Qh1hptr8pSJaCwxe/v0kZ1qOlyjY1VWNRjHjx+Y8zKWZ41+NbCEKIBXa1us8r3/p9tJtzeHSPV958E6Om/M2/+fcopgajMkSP2Vnb4n3XwwT9rVQECA5wZJmmq2vKRYUQURQgvSNPErq8oChmCBGn/dZarPV07Yq2WSBCwDtL13QYJXHBkiQJQgasbXFNQ9u0LBZLrl69ymg8Ix9NGI3GmCRFm5QQDPn4Os8/90Vms43eo3ZOPhuxWlU0yzknd9+iPb5La0+YHxve3Tvg6Q9/jN0LV/nKl76EQ/LSp/4qn/rkJ/m9f/Z/PsohfywrmrtEvLCbBH7hfd/ioupwqN5PoCf84+mC46Hz/MOHP8Z+PeaoHnGwHFHenqAqwd/56d/hZ6ffxAQbEyIEfZGNMdsuRFig9BqHwAhP2ycJAGyrJXOfceIKHnQzjHCcuoLGa3LVMTE1Iy3ZTlfI3h+2C5LWa0qbUFlD2UUpawiCsjVkxmKUw3pJaiKu2liFdZKu1dHG7tQgtxsm+YrtvGQrXXExXTBWDZnsSGVHFxTXsmN+6se/wxeK16iDRgrPvpvwpD7GIXiluczKp9xabeKD4KgZcVCP+Z0Hz1I2Cb/0/G8wkYb/8I1/m1WbkM0a/rWf/w4fH9/kH3z5bzzW82oeJuvpuq7i1ryZhrNJfd9yxmggz86NE75w5Q1WLmWvHvd+uDWJcnzLXqEuR2dDox/ENAgiJsD2ney6o+1/fF1ceyGAbCVi1ZtUD0KR4b3BevA4PPXw9fkgw0HBN3ghhMGxa+A9e9YpyOd50MPzDZSubnImblBNxKkH8cLBhwzdOMJmyVaNbRVhYRBLTTUfE8aOjZeOMNqRBsHx97bY/lYceC1uKNqtHx5V8WhuWm0gYJi1BS898Wk6HVBCgu8gWLw1HJ/s8b//8n/JO29/k2myQbmoWJxA23pefvmLvPSx6+BsPGj9ndC5GNQslCSEDkGHUuC7QFM34B0aj+0ciqjkSk3GdJaSZSNsb6JhrcZaixQWQUdblijAuYDWhqAknbWUyxX1cklA4IPkwoVLjCcbJEnGaDQiy3KSNCVJ47g08466OeLe3QOsE4xHU7Z3tpnujHjYWu4sVzhbsjxyqJ0NdnYnzO+8yt0//V2WdcOnv/gzXHziw5we7DPfe/Aoh/yxrCETKuioW//Z2Z+g+r1fFxxd6JBC0ATP1+pdfumP/y2SPx2tQ/rMEiYh2gP+46uf4YuffYUNaekIyBDwxPTXLpwJDYAYzRIEmXD4IKkxnLgC1xO+HJLSpUjhyVXH3GbcGB1jvWI3WbB0af8eFa3XaOHRwiEJzNv4PS09TacjzcpC5yRVk9DUBl9p0HEwo7YbtmYrNrOKqam5nM3Z0ivGqmZDlWS9zKoO8T3+fvUMr66u8IHRPQA+lsTcr/cle/z6/CPcP52SaMtxmXNlOueFzYfcLWf8j29/gdNVTr1M+Tsf/Sp/d/MPmUnFPzr5IGbxeIdc3YZDdAK9lJEONjkLMFx7CIgYHJhs1kyzmn/27geoyhTfS2iZxxKgd2tC4nt+FQxUtKjCku+d4vepsqGfog9FPnJfxRnu22/5wzmK1XmIQXjWEeCi75yFH6DUfhjlBUJEyCMMBtv0N5VT1XftoQ8xDGdYbO89AP39wQuabY+fWnYvnrKVlxyWI45PR/H9nSQkBwp9e0x31WG2arpaQ6WQc81JGEfO9GlCmDiOPhjtDtMn58jmh6dZPlKBfXh3nyK5xKf+6k+weWEzgmy+4XjxkAcP79O5Qw6P3uHug3vcfxi4352QmsBkYtjdGPP2ze/x/As/QpplCKVRKkGbDJ3mCCmj9t/W2GaFcx0mUWit6JoS2zbIAE3bRPaA93gPdRU7sK7zGJPQdiUhOIxOMCOJ7TqUUAipCV5wfHRA19RUiyV10/G+515gY3uHPIs0JS0lWWpQSqElKGVIMCRJAFHzjW++wttvv8snP/ExurpkUkxIizHHJ4Kjh7cxBw+5fHmbNDWItGDDZLzxnT8lScf889/+Mg/vv/0oh/yxrMltj2rheBb4W5/6GhuyZeEDTjgeOsOvzF9mU69Yuox/vv8s4XZBehTQVUwIaCdxSHb0goT9lC8vXuTfmH2dkbCkAkZCUEhFARgUSoi+o3WUIXDiNUY4NlTJSDac+IKVT/FIFJ5TV1A5Q+USjHRUztA1kS982uVcSBc0TlNagw+SVFu2e/5spxWrNiFRDh8Eh8djCOBXpregUyRPLLm8MWczLcmURQqPEY6ZKilkgxGWiaxQwuOCZEOtWLiclUv4n1//HIs7U/7pCx/mqCr46O4dXj2+xDPbB7y+v0t1UPD6Sc7tyQYXpkusU0yLGqMdv/zOR3gme8iddov/6fe/yMa9x1tghRXIJkqdAZpdR0gdqrDcuHDEJ7ZvYb1krBvu1Rv81mvPIQ6SuO3PPLKU60TYrjTocYf1SZ8gcDalX8tYexggxlXHAu7N2bT+PDeUnkY1CAkGylbo42OG7nNIGwiSSIc/95rrYdzgytUCofcRsLGwd6OwjuwG1uKDNSwx3BiInFd931B/Z5eHNTTb4C85xLRlfG3O6OmWB/c3SW8nhKMCLljEOEaAqyODLhOCCrSXHBsfPGS7WHHraBNx+4cbOD1Sgd2Zjblx/Qqf/MSnkMrym7/xq9x6/Wt847vf5tgdsr014ujuEavGcu3SlGoZePPVfS48U1DZjoO93+eppz7N+597P+NijJcGEIjepFJIRZKOSZJt8B3e13TtKVYIgotsrxAaBJ6uXhG8puqWrOoK32NCo8kmzka3Lq3ACU+QgiRLyEbj6C07X3D//n1mm9sURUEAWttRpBneOnwXeyy8RmqBUAolJNOx5rM/8ik+97nPsTEZ8d0/+S32H/4ek+k1Pvihv8J3w9dYrF7l3XeXPP/sy6RZSdN6br3xXd5+/dsII/nYyy88yiF/LKvZkNQ78OSLd/jbm1/j7W6L//wbv0B7d8T4HcnkrmN+Q2GWAV3DxdJT7kiW1wU733KM77ScPJvHqGol+N/4PE/99D7PJg+4pEoSETABZN8VSySpkOQiYRO4rELslLEsfINxji5oOuFYETvRAQ44aEbUzlBZQ9XFzuDtk2228pKkhwGcl3HyT6Dzktx0TNOajSzQWcXJ3SmqkhT3JcsnHdOiZmRajpuCy8WcVDoaryl9Gr1fg8P3SbeZiPHtE1nzb+78ET+z9adkH2p5nz7if9j/Ub5670lGacvFfM5fef4N/tFrn6FaplSLlFurhGev7WGDJFGOO/e3+K9+9+ejHj71LD9XPtbzmh4oZBMHLtU1y+TSgn/9qW/xgewevzt/ji/fej5CKcDypICljh2nAFkrXOHx41jRhApMxhWnXuDnZi1dFbafzKtzxSoZusIoXR64r2qgoYkBBxZrVZ03RDzWijNeq+qjWspo0B0pdayVdxDOFHy1wE79ekdiTuU6Nibo8F7J7OAE5s9BGD523E7FIWVQAVXK6ONwlLHcTKhKSdZLYeudPhKmlcieEjYYlotacbrMONibktwz6MflprUzvcbHXvwCb938E/6X3/wKv/27v8006di6UWBby+vfbTHWMdvS2FWHdBUvfnDK/dOaP/mjJdK3XNr9HS5duoAWDqUkSqeIaBIAIVoZIqIQQcoxqS5Ish3CrKZrK1xTsTg95OGDexwdHtC0DYFA09R0LkZTZHkWPWiloBinBBvhBeccRkmOD6NL14de+ig6iQB1lueIAEZrvHcIEpRS/bAEpDKI1LCqKvaOHqDzp7l45WWCS5gf3eHma/+Sw9vvEICs0EhjSfNtWM3JDKAEk40JwdaPcsgfy6q3ob7e8tLmHX72D/4TRl8ruPy9Fm8cyyuCeiaZ3nKM7lQ02yndSKEamL7jKXcV5YWc7NiTP6xRq5bpzRH/8Km/xt9/4UvAPkaUSAHmHJYb19n0PhUag6JQsKs8z5l9muDZd5IuSG7abb5RPsF355c5qWNyrJKeZR0/H7eqTS7OFqTKrmlVm1nJLK2oXfQ3sF6iVYx7Tk6i+Ul+ZUluOnwQzJIID3ReMVYNDoEPAiMckmFQ50jwZFi8kJyEEd9ZXePSdM5PbnyLzise1hP+ZO86v7N4P91JGrsxFUg3apT0XMlPOWxG3PYCrEBNO0ajmqr64cOQ/z+rm3iUEbgna37hhW9gveSffO/j2HufjwOnPKwxyuFMDIomEWJsTGhknNwHmC9ztHG0RkcVlBfvoXiFtQIhdqbCxUgY0RuvyN6bID63OBs2hcgZdVlUu615uSJu+30e474HLuwQhAixgLpsMKgR4ATpYVRmDekLspJnAYRtxCAG9sLwMQyDF4I/Yxm4wuPGsbOVtcBuWDZeOGW7WPH6rUuw0FHM4qJ8NkgIuUNkDvegwNRxqNhtPCYMVhe3+YM/+jWKVHDz3VssFjC6vs28anj4bqDQkulkzHQz4/bNI65eLlCZZdqOuHIlYzFXvPnWa9y5dZMif4bRaErwDQEHWISQCClB6HPuBRIhE4Q2JDLHmxaTTxlvXqS4f4s333yDajmPih3nOTl4SJbl5KMJ4+kMJbMY+qZhfnyI7Roklufe/yzGGAJhnViQ5Rk6TUjzHGsdzseQZ+9DH1UDxihSZbh1+9v80bd/haPTt0iEJA0j8tlFju50XClmNE3NwfF9Xv3OK9x86zU++olPsHOhwNu/fLMXrwPFWwm/dvgpdCnIDj2qcngTT//0dofsMhPzDgAAIABJREFUPPVuRjeSrC5LNt60tGO5DsqrlCSojGRukDbQfXvGb1/+AD+xZdlVFZmAOsSxmURgeuulgaXg45ns/5ZkQmMIpNrRBctF9YAXkofcHG3wf5+8xB/uPUHZxIJkraKpDPfcDGMc46xhKy/RwuMRjHWDDZLbiw0aG7shlwWqJzvy/irTwpMpixaOrXTFRNXs6Dld0Kx8wkRGmVUXFEl/dWeiYyQblPD8yunH+eU3P0J1ULwHj0QGZBWzoGZX43P8wbtP0T3M2f26ZHVF0L7YMs4a2u7RlOl/0XITx+Zzp+Sm4//4/U+RHKrYJRY9d7SKBTKS6/tiNyS8ytiRiS7CDObU4E419nIMoxKtijiuCbGwDsUqsE4fOMNkY8fqAOHiAOp8hytbgVkKkvmgfOsTFBDQQkgACT6NPq1iLYSI3XHQPWfXgS6jjNX1XsKxfvf2lS7aKA4rnMN34z/OcOk4FBNsPHXE56+8zZ1yg7vLGasm4a0Hu8j9hOwgpmp0Uwe5w+Qdk1FNWac0GwJ3HCPC5eOKjDFpQulus2gtz34wY7JbcNI1+Dbh+Q9LTu4KUp1x78GK2VXHvUNHtYLy8BA9gtHkAnuHd3nltde58cQVqtUJwTuc9QglSEcj8mILISOQE0SIvFZCdMHSGqkEEoMwKdeeypltXeTdt9/k7TdfiwfQrnCdom1qytKgOo+SinI552j/LuXiCCECIQScczjr0KZ/filBKgKCYjQGIaK/rIxnSkqJFpJxkvDG66/y5p0/YiX2ECR0VcX7L/wIn/78X+fOW2/y2utfpyo7vv2dP0UpyLMMIxJev3vyKIf8saxBTSSb+GHb/wSsLueYZbQInF83eANZHysze8dSbSlsDtuv1rQTQ3rc4hNJN9aoxjN9C75650l+ZPYm+y5n38Gij9csZMMltWJXiti59pzZocjGf4MRCoUgE9FLdoJnQ57w9O7v8O3ZJX79+EX+8P4TtK1GqsiB9T3XVcmMziskcch2WmccHo/xS4N2MZKcTuK9oHWKlU2YJlHQEOlmERa4ZM7OR4JDiqGT9dQBNmTJ/8vbm/3Ylt33fZ817PlMNdetO3azu9kDh+YkkqJkWzIiK3bkB0lJgAhwngw4QPKeP8DvBhIgRvKWIAiMwInhRIkA0ZFEUpxJsdnNnvvOt27NVWfc81orD2ufU7cpt6BrX3k1qvt0TefU3mf/9m99f99hO5jyv9z/Gs3tPoHp+J+2E21cb/nKVz7gZ4+ucfR4xJFbIzjV7LzlkK2jHkEQtpSNpjx9tmbraqq4mK1z1m2Tq12vmVe5F0VYBSgvJRbGhx8+SX2Sq+h2SzuyiNafnSBraBrppbCR8VQwK/xQulLez/WJYdXHzGB4YtgkBFaCVT5QURc+2jwaex+HpteFBlaX7AAXWB8j1MV6M2pRgd/r2/MQE/oB25PqqaUZjFNuZTKz8iRYWh6KSxx4BSc4x/TdDf7ftzdoU4tb8sQTQ3B9Qb4V4AqF7PsBqJSO86MB+iwgrKAZXA7cPmk9VYGtVcGiblDFgDffO0engt52huoFTM5zwpEgFBoKGN9LkUnBaDvh1s1t2rzk4cEZOo44PHmAdBUXR/dYzOakyZD+WkZFS6j6qCAArRHC+5eJjirinEMIjXMOFYRoHRPIlOHnN7l+4znufPQeB/t3vDjBGpp8RtuOScOE8ekht99/i7apGI3WaNvG2yRai7UOrUKCwH9EUYKUCiU1UkqkUgghUFISqBSspd/fIJXrlPUZk0mBrS2P8zv8RfUvODi+wFQRD37xgKpxDIZDiumMDycf8tKXv/E0h/yZLFX74xcsBMEUeg+9O1CbCMptR3rgo07KNYGqIJoIopklnjiaVLPYVcyvJugC0qMGE0niieHkIkEJyw/zF9ivRlQ24IX0iOvBOY1TxMEEH+UJSgg0l6J4jcKu3LgkCEtAQCQCesIwjI/48u4h728M+ePpZ/nWwQvMy4i6VhgjmZURF/MUKR1KWqanGfGDEJ37OO2m57sz5wSmgw+iju+aqoq+KlhX8xV7IBYNUlhCLIGwGARbqqB2FYvgnN1sykG0jV4IgsVSzgk81PzIvYgsBYNHkjaBzTdbbCCohj5DrDhLKEh46aXHz/S8mrTLjCoErhUrAr5TUK8ZZLf9Xw2RuoA+j3U6TNod/0ZApbGRhdMIVwkYGmTqz50tlU9/tfhOUj5RAJdrSbPqHq/ctWDlLdtoR5v5LXkw914PbeZVcVZ6vqqzHd0z8m2nq5UHb9wT6a2d09aS87p8vHJR64r9yrh7OaDrjoPtxA6y9diqDbpj0NkPqnNNU0nWrk5wTrAoQoSAOg99cR0L6pHz5t5A+vAZKbkWpaUsF7jasXFLc3zkOH7vnOdeSLlyTXN4v+BiDJ96LUbbmJOjjEXVcnh4wvZmwouvDFjkko2Nhl/8/E1slZMmGVvbHryOrCJJaoIo8+Y91uAQSNFZyAjRGcVInHN+MxpESGAw3ODWp16ksi0Hjx8DFtsYyiJncn5COcu5c+eAUDmyLCXJ/GDLex9YdOC9aY1pqeuKLOtd0uqEQGuNtQ5jDWEYs7N1HVulFBeKnuqh4xIrz/je3cdUJISThKoS/No3vsEXvv4Nzo4uOHl8xPjk+GkO+TNZeuFtCW3gFUWLK4L00DE4NTitaGNvUJI9cqjKMb2pqIfe6zSaOHTpCHJHkwqmt4IVvUaEFf/D7b9N1W19b65drPike0HASBb0dbuihP2yK5fs/rl87CEEJQQpAdDwYjChP/oxAD85u8HjiyGu48DWVdA5+ftubnjHokvHxYsKkXpM37QK0/nPWidY1wsaqyltwJiUTFYE3R5S4YiEIewKbOl8h7yrpnxldJ+fhs8TPtKeEiR9/AxA77bysejGkT32ReD4y5KdLx+irOTw/gYox8ni2QoNVOFdnZ4stL9MkVK5vJSUCi6LanXpYuU/4QdFTkL00pSdtOB8nlJMY2+J2Hh7PqvxsS72soF9srD+pf9fUrSkp2sJ5wtcm/mipudeltom/rWtcNNlvlcj/XO1cmWNuFrLLnop5209pct1Ve1jUS5iiRtzqTCzPqpedW5zshG4wkughRGMH45wyqHHCr0QKOcpi8HMEZ9DsaWph47ytU928XmqAjs+LygLh0KTN3O2bkQMRylKKqZn0F8LSQaOBw9brj9vqYM5cRgyqzQzmxC0NU6HfPeNH/LHxz9hZ7TFr37li2TDjJFaIwoCrK0wbeU7WBWghPQnplOL4ZyX1goBUnR2aAoVhAyG67z48mcJ4x6P9x9ycnTIYj6nriZU84bDowmDXsRsUaDjkswYqqYhcBaEIIpC4jhGKS9XCUKN1gEI75EAoKRPl93eusZz117l8OAukhAVhJBJ1kPJ+bHlD37/v+LnP/6AxewQnGC4fQVLxGLyHx4iCKf4bdzCA/U6h3xXUG4pnxjQYVP1SFA7Qf+hYRwpqpHwX5cgA1apALrwPqjrfx6hFiFRILh4FR6+7LiWjunLkkxWNE6xcDXStQQoAqGeUI1dFlRYDsbk6isGrxRLsWzIin84/Ateig/5o+gz3D7fZDJJ4SL0MGgDwUySPfaSRV0q3FT4OJlWUDaaUPtuTArLTjBhpHIC4T+XiZpUtGRdQqnnyTsCDEpA6Gq+mt7mX936PLOH25jYEU6WEk9/cQZzKLYEye8ecfy9XUQLszLixY0T2muK0+OBf83PcNnQoedyVZRM3/o4nLKL1e66NRwr4xu6jKx2aHxHO5edhNrzWNuepViEJGHDei/nQjjySYITEqN9oqzrIrlX5bnjzH4Mm4bLAvhLne2qw1S+qMpaII3ANay6wuVAC/zUHvD+AaH11K7l26UVCMmq4AsrEPUl5PCxLnv5mui6We29BsKpx4xN4lbQiay9abnsUgzW3zM0meDkyzB4bszkIkOeBchGYE6iTzxHTxcZowPWB2ss7JRpETCbOLRoufNBS5W3rG8lpAPLaBPeeXvBcGB5fH9Gtj5g3kwZZQmT+QzbKE5Pprx/+zFWQm89JUz8SVRaooIQKTwBXajQQwUInLUdTLDEUIyHC6RECY0zmkhHbKxtcXZywfnZnNlsxmxxQVu2tCgaNLfvHfOCTrHyhEYExGmGdY4o9haIQshVt+yfzxd4KUEKhcQSBppXXvoK3/329zk8zulnQ/K24tqNhNEQ6os5v/X3f593f/onfO9b/x+vvf4rXL/1HO/89NHTHPJnskyE7yS0P27h2A8NTAzJqUU2Dl1apjf822F+TZGcOKKpQzaOYGFY7ASEc88/bFJBemqRrSM+qVGzkuywx0M94mfhVbQwNIkmjhpi0wINsbDEWCJxGaC4tEM0zq26XLhkISw5tVYaFq7manDB68NHnBY9xvsD4jM/eQxmHQwCmFjSf2BoMomJBa0ImbeSYMtTs1JZs6WnjGTe2S0ar0jD07qXydoBgOiScjva1n924y/4H3t/j8FHnvZjNejKMb8qKba8Lv6Lmw85/ntjfnznJuG31nnzawFr/ZxP3zrg/fevPtPzKuuOutR2fNEumtsGDhd7ChRGoGfeVEW2PkG16TlvtKK9z4KPEOo6YSMQxxHj/dhToDZrVGQwwkGhLodedEXwCfwTKz4eRPCEOfaysD3pS7v05jVhB210x9ylre9gA4tQDnkUdb6rLSJX3fd0QaqlROaXet6ln8LyOf6SZHj5pY6fa2KHXXjopOn51BNZ+b+ruNKC8hhE+VpDcCdGL/DFNTSINuzYGs/IDzaUjrvvH/L8Z/a4d/+IW9d7VI3g+Vc0J49z4qxhsdDMTha89vp17n0wRpqK6fEJ2Uaft753TpKFzPOSje0+rml4/+GHvPhoh7W1dTY2tkC2OIzXs1cFgoowTBBo7/3QDZysNd7M21iQAmdaBI4w8OkFpjWcnl3w85+/gdBw/959nr9xnbPpFD1XxNkp2wZOLxbs7l0jS1NvRiP9HVEKv90QAhy+a5ZS+aKLIwwDrl15nlBt4toLFpOCwVqP4+OCtYHi57/4Po1Mub9/wPz0goOH91FJn+l88TSH/Jksq73d3uQ5v5VNzi2za4pg6pjelPQeWeZ7ftCVHVjCmeHkdY2qQGpIjhr6jaXYCll7LwcBsmpxSlJtxgRaEO/PSY7WOeyt8yCd83xyAnh+a9x5xo5tS+NaRtJ7GBictzpcGcf4rbqX7NqVz4FxjliYThRgKFtNMFXs/KTxcSnjlujRhPnL66jS0v9oSnGtR30QUA8EnIRMwpSr18f0ZUFflis61tJ/QInLIovwknvJZZDjSNZ8Nf2I//7KbxL9TGGV366WI0E4dV3IIfzh97/I2q0L9P0YVQHv9Th7SVClmmD8S8as/57LLXHLrgio4glsshbIiedwLg1hnO7iXlqxkrS60PpI8byjOi3pTUs/g4sQ02tXVDSRS+STGCieReDNXLqu1l7StT4mixVPFNyliTb+sU3olGS+IgaDCmslplKYrOPqzrSnSYUWSuV9CBrp/55OWbbyvn2yyD4R5bDEbYFVSq5Jnbd6LIS/0WQWLOiZ3wWYvh/01RsWNZPIk9C/plGLLJTP5fqE9VfMv/7yOnjUkDeaP/+zfYI2xTQRe9dHHBzN2b66hgoG9LYUO59KKW1DOGoZ7A5xdUoWCn79Vz7NTtZjby/m5o0eSVJz5caAx0cTDk9yKqNobEvb1mAlkRIo0dJWOXWRo6VCSrmk4fmbk7AYU1GWc+q6IF+cY6k5mZxwOj2gZczZ+DG9jZipnbFwFaeLKe/ee8AHtx9SFy2j0bp34AKqpqKuc6pqQVUW1HUJtFhrsM6bvRsrcM7Q7/X4zKufQQYFRpacnU3pBRvowFG0p+xsbvGFr3yDKg+Z5y1bV3foj648zSF/Jqv/0FKOJHvfmTG802CV717Hr1p0AYurkqbnt/7FpmR2TeE01H3vIdv2AmRpkI3j7HMps5sJzShmcSNlflXjhCB/boCeQ3QY8OHpJnkncz03KTPrsF36rBLe0vDItDTOd7S/bJdouiugdIYT6zi3inMTc6/eJBCG80nmDZQTSXpQEoxLmu0+snYEUz+0Sh7NWX+3YvSRQecwGBQMVYHtnmtsUsY24cRk3UfCzGks3ky8we9CDZ0rGJ4v+42XP6JY952zsPhOvvYwgVUeF70+mHDj64+wGuJTQRw3vLB+ukpfeFZrVSyEt/ZrhgaTuC5rytAMLeUV3xqqQqCncuWj6gSI1hcRWfigQL3wsttl0oAwoHPhu8lKejpaV8RsajEDg+lZH2+9HCZ1xivwb5+ue3VXV9yXktsOthCVRA98qmt7nCAeJeij8NIda4nJNhKZNdAKdO6Log3871xNs7pCvWQmPCmxXWV/KT+XaAaWas2t8sB8BytWPFs1U5hSkezOMak3ivG/zO8YgsUzomkNNiWNkaztJMzmOafjBhlMCbD84E8fcvPFjMFGAiIkDMdsZAHzpiQfSeYXivnJbV77zC3uPb5AuYpf+foad9+veOv+u3zxpV+nnFSU0tAGNVU4w7mWKElJexvIJALhgwr9W9+CbXG2xZoG0zaYtqJazMA4kkRy7/H7NMGCwZbCIlgUc0xkMc5wOquoypqsNyQIAsqioE5TnBAEWqGUIi9K73pvDIGOwDiE9Cm4xhjqpkJpgXWGOIyRqqHJjxFW4Sz883/2z1hUIf1RS/voA/Y/fJ6951/8qw/y38Aq1yXCOBbXUmbXFP19QzCD5EARTh3iAgZ3Cw6/5jHCYE631QSnBPO9gN4jRzhtiU8Mp59PKdciegcGVcLZazGydWy+VbA4i5h8Bh5XI1J1hUAYjk2fTFYYJ9lQc/ZUzrqUREKv4IBlp2ixq/QEL7ONUDgOzZCDZs37FiQ1vYcJ0bilHoU4GSGcQ1YWGytkh9FFB1PC8wAnB0RpwcQkUMOWnjIzyQqHLV3AfrPGtp7RlwWZaCiFI8D6BGQnaJCMTephq+U0WgFCoCtHW14yMI7eeA6AzFrKkaB6Y42LX5/Te37yTM+r1b4Lk7XA9K3/u/EQgZ77UD+L9BzT2K0irq0QvhjVnm6mutiUesegJspDA6FF1MJTvGqB67dY51kELjGeqrU0hrG+cAo8lWuZGrU0hFk+Xj2QHf/WdMmyujNwaQT2MEZ0bAGT2hWuG0yfwJStxBqvNmsz67cbpnsNZmlr6JMGlskHv5zqAHRd2mWiLHhzGaxPrbCxo426LzTeDnPnxVNOL/owCxCxwdbBytP237aezq7waJOofoxra8zUkKwpqCKuX49IeobeQHJ2YgnSBRenLbqV7FxTjK4K5ucBxwfweHJKb01yclbxwQcF5RRe+WzKm+9+hxee28SYgP5wm2HYR0UxToaUlUW2C2QQI3XgcTJnaJsG09Y403iTXqlIwohyPqPfixht9hmXC0xdI4RGWsHaaMTkfAq2pZwVnJ+ecPD4EfrqVYxZI4wToijGWEOvHyGVQAjQKsAJb5UnhaRpGnq9jK9+9St87zvfQjoYDfpYN+Ngf0ovSximIcZVPDyeks9S7r5zl8//5vbTHPJnslThiCc+OqR3YBDWswVkI5jd8MOPup/ilC+u9VAQXUCbQXzqmLwIxWaENBCfOfqPWqqBotiQxGPL8E6FzBvqjZg2EZi3B7w/2mZhQjbDOVlaMZAlG2oGwMxpcuPoy5pYCCIhCTpnr8ZZxtaSdxN81X2sqzmbesajep3N3oJJf4STXvTgI1x851T3FG2sCGYNwhjEvCAapzw4WicLar62fpfShoxUzrqaU7qAQBh2oykKb3GYiZZAXMbixNJxYjS7esZ/uf1d/vFnXmX3+z7ZwMQCYR06d0QzRxsJ6r4gvnDEpw3hTBLOFLef3+LFq8+WQRKfSE/a1w5ZdDji0F7im7IrNGoZDOg9BlSHMfohj6Ud+JA/OdbYxKIWEjHvbrDaD8Bsrtl5/pRFFTI76K+GUEtfVtlN5010Wcw+th1f/Qs/pJKdb0FniO1vFl1ESyVWoYdL9kObWe+aZek8JvznxFqNrRRioTtjGv8kK7pWV9k/lurQNbmiM7xxyqcdhBeQX6HDhgRq2ZkKP1BsHmccO88EEdLDIjsvnPmC+wnrqQrsSzs3KPIB4Ch6ArX5IYWVVI1iMks4vii4dkOilaRUmqxnmZSGfj+jnZ5z9eUBWRYRiIKrnx5wcKhwVQIy57j5Bf/zv7zgN77697lypeGFeMQwzQiTAUqm3j9WBQjlO0icJIwlzgaYtsa2JbNJTlk1tI1Dq4jT0wnpZo/aFggibFsxPpmjheTlqzuMwh5CRtz+6EM2NzdwzqKkpGkNQngPAutatFQ0TQ0yQOuQQIdIqalNya1bz/H661/kR9//IefGomRIMthkviioqhlCSyKhmI1r/uRP/5w7J/f5nd/+R09z2P+9V7EtKLYVwRxk66hGlxeEcBCdQXpi/Llc89hiMPcdWpsIwglUG474VNCkMLvp8dmm55i8IOk98p3v0qMzOYbT//M6H72+y9bemMV2xG+P3iQQhnVZEwu6LC+1YhcAWHw6wpbyBt6Vc2Su5mGb8rhZ49xk5DakH1S0x4a6L1fuTD4SxBLMW5wWOC05+8IaqvZk9uB2yMPeiC+tqW673zKzcTfoagmExXYX46yzEUulL7SpEOyomtwJRrLg9379h3zzo68TjR3SuBWbwGpIzlvsbFnABKqw9HJD9L9rDj59A37j2Z1XEzviE1+MrIJqs/NG7XT9ywm+LL1oQLVe6WQiB9ohKoHOJbZ2l8WvFbR946fxjd+CmxBkCYd3N9h97oz4xgXjaeox0mmALLzQeJUYu4r7vvy9S6bBaghlBcJcFuNlpPqSKrayJVwqxjrZrqy6G4R/AloZroQDNnCrirbkt4K7NJ/psOqlR4EL3KrDblOfcRaNBfl1429KXQjiqgvuhAiiE5qwUByfDIiz+hPP0VMV2I3RENHfpDUlRxenfPiBY9xWDPfA6JKNKwkHJxWpVhgsJraMT1OquiEMJU1bIGTI2UNHzZR0M2J4zXL/rqBaKN794A6bg3fQAqIgJQ4DQpWiEoWUAickzrgVXcsbfHs7QucEKghRQUwQOPrZCClCppOSKm9oqpKAEFsYrl69wnOb65hFhQhDNtfXsUBjWow1RFGEE1A3NVJ0ibjdhNlaS2u626GTRFHC3/pbv8Gf/emfc3Ixoyxa4hh6vQCrNdJYhiGofsjFeMGjD/7Dx3aXO74rqNZ9QU0OYXDf+OIgBMWmpO5J6pEgOXa0qd8iqcBHVadHbpXCaZLOHF1B9shfyG0CxbZDVYL0wBHOHP1HFaqOOf3GgJ/JqySy5jeH76CYEKiGvrjcsz053Frir42zK3/ZWLTEsiGVNet6wU4y5TiTJKctJpJE53U3dHHIxtLEIfO9kHLTyzNNIKjXDKOwITchuY0YyJK+LIi7Qjuzngcb0658bP3rgBK3clfoy4bfG/2E/+PLX+Dav9Ie9521mMQPvuqhIpjbzmlf0Iw0wcygC8PaB8/2vJrEsUidxyG1395KKWh7xvuqmm541Tn6G30pY1UzSZs43wV2RcwlFllIdKVWN9+m7zvHZfd39OEmLnSEJ4qwFlRrFpt21KrmEt8FPuYbC35Y3D1Y8W5FzaqgCue77KVblqyXBdkXTF34QVTT89t3LOippE39DePSawBfSDuZ7XItizeX9bmLD/ec4HKzyzWbS+89EHTKMulvUmrs4ZYnZma4cUg5fkaG21ncpykalOqxtekJ5x8e32F6nHOyaCjyCFBkWxH5TDG7Yxn1ShazkH6WUbuGyXzB/XsFGzsp07stB3cKmjbk4d05aRTTyoZXPvMa6xu7zMZjTOtY2wlQIkIRIqT2qQdKYZzFmo66hSQIYlRQoGWNDDRBHFHkC5+oEApkaeipiJd3rpIO+lyYY4wTHB6d0BuO6Pf6hHFKlgUIIb2RhRSY1hBGEmcblNJdOq4AoTCmYjAaEkUZ5XzOIJO0RnAxruj1IrJU00tiBsMU4RrOL55xtOhfYzntELnnPuqpotx0/jwd+guj/6Ch6Svise9Ym76ABgb3PN2pST1UsOxu0yPfBfceGxa7imTuSI/9li89bgknDTi/tRytz/lPr/+MVFb8ZPE8J9EpX4wfEApvwR0Lz3ttMMRCd/6yllnnL+u/x7ClpoxNSm5DBrrsvEgtJvLyXdl6Q2VVGWbXQ+bXfUekczzroREc393grbigp6pOzjslld44fOoiYlGzcAEj6Y1glvSt3AmMEyjhVkbieztjcJsI42hTtepW43NHm0raSGL6gnogkGuC7NAQzD7ZFOTfZcnKY6k2vCTQx6eCptC4Tp5qEz8RX+nlrd+GN0NviuPcpWQWI7CJXXWOsvTDL9d1nsFUeYFJCfVu+5fUXMtYmGXXB5dMgxVlSlzunFYc3WXd7dgFwopV970y3RZdErJyCEDP/PfUew1hVlOPI0QtLwuye8KvFg8ZrHDYJV9X4bm1zr/RTOrfTy50Hs9tBdJ4loKNLCa73CHgfOyNU16J9knr6YZca0OCNYWV8OG92wwGGZvjAXKe8Wj/mEbDxpWAgAKNpVq01E3A+XlJMRyiepL5hSXqKVoT0B9G3P1wTKQs13ZHnM1yTvNz/vR73+ZTz7/EZpaxKy2LWUSUjYiiFC0ipBR+qGVb38k623FkrU+J1RFRmNDUhrJo0DJierYgbCxRnNCamiQOGN26yXTW8K0//Q7rG1vcvNnS1jV1XaN1QNTRtoyRtI1BSoVwDiUdFgPOslicU+QX9FMIa8U8rwgISYKUfFoz64fkeUVVNGwPMrZ6nzxx/Jtacq3GrkEgLW2sCR6FVGuOcOovkF5uSB7NmL48wgQCVTqkARMJgoVnGmSPK6bPxZfDgBqqoUTVUK4L1t9rqEaaYNGy2IvI9kucgvk76/xP5Te4tXnOIPQ46oKPAAAgAElEQVT0qHU1p3Fj1mUN0njIAEXp2lXnGgpB7qB2YjXB78uShYzoqQqnQM8bnPDR2sF5iXAOk4Wo2tOmTOaYDUHWjt3ve4Xa5PmYxinGJmOsEqTIKZ1mQxbEwjCzjoXThNgu6NESCEcJNE4ydRHvlFdprcQNFDKVRGMDFkzinZeCqUFkoCuIZrDYVix2FGL72dK0upxJX0wT33E163gMdek50Blie8gCXGwRlbjcigc+AXjpfgVeNWm17xJlLf1WX0Lbd9AK3KBhsJaznuWcL1KKMsC0ClsqHPJSSut/3V9Or12mDyzr0pPwwfJTyq3wYhv6P9Qm9hL3xQ/gVNzSnCb+71Vdxxl2v7MVGLXEW/GvqcN+VzBF56tglYXEYCWEZ8oLCMInmAWNuExxUGAjg40FauGd2z5pPV0H29/hznvvktc508UcW9VQJ2yOYG/3hLDXMDmvOD50iCjkbL8hvjLk5PEReZ0TTELi1BtwfPTemCCzJHFKkY9pG83atR77549441+8z3p/g9du3uSrX/oCv/ab/xFhkuFMg3Gu48BajHUoKVCdoa91gqpuUEFIoAOkUEQ6JCglu7u3qMuSYj7h9tkB8SghJmA6szx4cEhdt8wmFygpmc1ysiyjl8XEUYyTGqUj2rrCNHOU1jgV01Q1B48ecfujDzGmojdKifo9pJbkZUl91mArSNMBRhgIQ4b9TwbE/6bWcLDg4u4aopBe7jdb3oW9hVy5GVJu+sIULizh3P9cODPoRUs9CtAXOWmiPOdYCqqRIj5viR7PcUlAmwXEFy16XNJfNJQ73kxm71uG4I80s+1rHFxV/PjV5/mdL77B7679lFS0xM4DA7VrqboLTgK5g1g4mm7IdW5SGqfIZEWlAtpYUA9DwnHF9FM9qlGfILdE5zXpUUO5FlFuiI5yBIO3z5nvbTHNY96Z7jJOUu7pTTaDOet6zp6+YEN5jnImWhonOTK9zoy7RuEL78+Lm/zh4Wc5erjGloSmJxBW0Sa+YIVzv2UXrcOEEtcpF9pMrKS1z2rVax3ZvitospCr+GnPAPGF04ae/4r00ADQcT0FwUTR9nx3tiykPkzQnwy1kJjMP15Kce1cM7UZrZFcG00QA8eD8zWKXCOW8dvyicL6JHVLO7xyx12SRFsf9S3MEzCDgnDsjb2t8p16PfTR6GYReO6pA3sRdWwHi0hMN0y7pC64xnu6Oskq/8vTtrr/t8sYb895J21pEs/7dUZAK/1QrutaXedpq2eq83Ow2PgZ0bTeevsjAiR1azm/mLI1WqO/rsndI159vY8JHUencPf2BZGGsAeHZ8dkGyFRz3HycMzW9hARZdTthNhpwkByVjm2d/tU7YKj2yWB6/Hu7QecH5xz/frzTGYFg1GFEhrPSW1RWqKVpm0bTNOwWCxomobGtpiqoapLolCTF4KBDHl+5yqbW1d45fVX+Nd/9C/5cH+f3/vt3+GnP36XMI6xtuX9d9/kyt41kv46cRQjTMXNm7cYrO/g8IkLxfQUHUSoaJ2qWHB6csSDux9x7+495lVDWQqCGHqjPmdHUwLnEPoUHSt6F5IvffFLT3PIn8mavrOBgi6byxtt1ANo+n6LM99TNH28sKCC4b0GBJhQEowN0Ymh2cqwgSSYNeh5jRMZVglEVYOE6npK9mCBk5JiN0VYR+9hiZ6W2Dhg9IMT+ttDyo0BDxbr7PfX6MsSa71xduU0Ujj6nXzVp9kqzk2P47ZPbiMCYThoRpgOEFSlodzyDlX5jiTfE9jrAncScO3ftDipKX5tjvpFD1HWVCOoH/Z5YxZzd23BKC3ohxWtlcSq4Q92f8iWnmLwXNoz0+Ok7XM9PCMWDQsb8W5+hTtHm2R3AkYfLBDGohY1LlCUOynCQbw/WxVWF2rSLCDfDhm/+Iw7WCsQtVgVKptYbNJNxy3IyuOTwkAwlggrqDeMN6gufWdqInxntyyo8+WW2NOXXOD/qwqvVDNdNykWinLS56OHfe8F2whktHTE4gk4wMMGrvs537m6VdeKu5THem6x/5yeS4qrLWqmGH4I0cQna0xMjF1rPZUsNqi5P6bp9oKmUVijMJXyMEVkSNYKimnspcOR85irEzjpcfIVRtu5hQkHTIPLcEZ1ibmaUetvZtLRZgIZG5/d9azsCpFe/l81OU623H94hyRKEJFgftyysFOytQGfurbOw+Mjhr0MWzasb2xwdHFKmOScngiSzYgrn5JMHrVgBes7G1TW4oxmd3fI8WHOcH3I7tYOG3s7WNkBK5ZOVeWwdU3R5rRNSxAGnYeAxpQLatMynlywWBRAQ5D2aWzLres3URW8fP0lvveTH/D//Js/4eGdYz7z2Vf57ne+w+ZawGw+YWfvOYaDIScHD4CWqyiiZIBWjqPDR/T6fcIeTC5OOT9/zP37H/Dqa7fYvzhjfGExtBR1g1MJoyu7BJEhSQua+QWPH9x9qkP+TJbzNJNg5k2ow5lD5/7NVA/8QCvb91PweiiY3tRkR5Zw0mLSgDZVVENFOLd+0tdakv0Z5V6f/KUN4sOc5KCkuJISjhtk61Cl8RzVQKLnDWZ3jfmNlHqvoRdUKCyNU6thkun85I5sQukCSheQSo+VXg1aDtsR466LfXt6xcs+K0OxGxEsLFZL2t0aLS1ms2J6M2F4r0W2GcM7NeVzmyDh1v/VcPjViOlWwHQ7odcraY0kP8nYTWa8kh4wVAs29JyHzTrfPnuR39j4gFRWPKg3uDdfR9xPCKeOs8+mJOeW5ETTJppgWtOmGuqGxaub6MJy8VLI5lsFVguGtz9ZUvnvvJac08ChZspjqIG3HlwOn2zoqBN8h9tRnDyroOPNaofoTKtt4sA59FR68xXtiI4V9brFZgZRdsYvHWXKJyR4vFO3wjM4nhhyiS4k0W/LWRmzLCGCJX3KBp2CqrODbBMv5xUGpi94xkQwEYRjgdMKt13hFgHxiaTcstSVDz61jVwN0VwjKSZ+2+C5u7IzEXc+HqfQXkSxZDs4gcsVeiFph8Z75zYSkRicFQzeChneaZld0xTb3Q4isbD1jGK7F8UFKolJ44TNtS0OGsP0fMZwr6IsC05PDU1TEocZz13bpZWWk6M5Us7p64wXvh7x+KRBaEvQpLi1iijsk2Qhj/fv0VtPyauKne0NJiclWkbcuPoC1298iizto5RGa2/GLYQ3XQEwTUPbVsSxIIrXuOAh1cOWorCIgWPjyohb157j85/7EkJYJpM5L1459sVZhXz5Cy/yw+99nx/99DaEMRLB/DQhz2eMz7fR4QEb25CmMY8PjmD/MVeul4xnpzw+OuT9j/Z5+GDM0dkclYITMXHa8E/+8T/hN//ur/Hf/fN/yqP9Q+rCcHHxbC3r/jpLF4IWD8brAhZ7gvjUEU4sNvDbp6CwqNIRFJI2EhTrkvmViOTMexWYSGArwcWLMfaVmMH9lnxb+y1jkNF795zDX83Y+UlDMK2p10J0YWhTRZso4pMSGwj2rp7z+1s/4dXwiLST0cbC2837YVcN1FjnKJ0ld7747qkZR6YHwI/bmyRnBhP5yf34NUVxxfK1F++QqIb3x9ucbSZe8NGRzR/+VogqIZjVXPmB4+grMYWNmMUhovY+E2+e7XFep3ym/5ims2T6+V98ivLzAbd65zzKR9w/X6P3ADZ/nqNP53B8CkISjga4vEDtrPPuf7vOH3zp+/zr/+3XSY4donUkJw26eLZDLpwvrLIQBFPpmQSNxAZdce06QdH6c+9C1xm2eM5sm3ncNjz3Novgh2Gisz5UhcAkUF5vkDNFcKaxgccwTWZXRTI6U52ZdpeYEHRFU+Alt91EX6w62O5DcjnVdx7PtKFbGW3LmtVgStaCestgZopgJrFFjImgWnOYnkGcxL6IGgGxQYUW2wpcrjtZbncjCL0arC01MjQ+9ktbpHII4TBaYnsNzAPkXPsfKyRBIQgnjukNTX7V0Ww2qInGWYWYfvLO5KkKbC8LKeoGKVOaEjay58n1Pc5mH5EXITevXiefzZCUTC9y9m6uI/IesSq58fxVPnz4DtujK+xey5nc2+LatuP+w0ccnbTUTnN0XBCHmllxirIJm+s7vPTia/SyIWGklvA7pm0R8tIIO4i8IXNTVjhRozQMsz79MGW2WDA5uOCEA/74D/9v+v2I4+Nj1kdDqrohio5o6hnKVVSlw5qY/f1Dhr2EKHJMJo9J+n3KhzWD/oCzkyPG43OCrM/9/QP+5Ns/4xcfHlGXDdm65st/Z43K1FSl4nNf/DTXrrzE6699lfOTQ2RVsffc3lNdQ89itYkfWll8dyAMVOsCEymisUXVDhMIyjWJLpYOQw5Vwey6JDuwWAXnn1YM7nnaSr6jyXcE8ZmjWFeYz22w9+0ZJtbIqiXZb2h7oR8+GYMLNfM9yX998wd8Pjzs/Ag6I3MkuWugc9BSCJTozLpdQ+4cCyeQwtKXBdezC36R3CA6c+jKkh0IEJKffOdlmpEhuFAofDcOni7lgPgYqo2Y+GCOVTHJsc918nEmcHQ+IFCGi/IF9rIJkzommArevbtHc1Px4HQN/UaP3W8eIMoaN58j+n3GX79GNRBEU4ewjn/wuTf4td4H/NGvv4L+X9ep1kNkbTl/9dkabi/5rU53Ovm5RC86fqn2OwITOVzqvNppOVQSHUfUeSZCve6ZBsHEd3htZpGNHzDphaA2yym+J+/7JFffGQNUm4Y29ZCD00/QmFY4q6dQrdaT06xOSCC65wovpDcBTx31hu8i17ZntFaymMW40t8MnGQlWZW58hP/QiNrqNcFdtH9vZFFxIalJ4GzApHWuErhlnLaXGOTFldr5MJ3zcETabkIz/k+/yyEE8HoPSjXQ8pth8i7eJxPWE9VYMtFReMETZPjREsUSNosQS9SEA3j8oKiXiBdwNrWkLMzTZ1PkEpRhGdcu5Ygqh7n74fsbUXISNMPX+DdR0ectSXWFIxShUkTqgrK5oTGnONcn6byllBCeM9WpTXOSEznIWCM8TxKm6O14taN63zjS1/lvQ/fQtiC/mDA0ekBZR2R9mIWVYXFsrO7Ra8X07Y1WZbQGw45enyXxaKgrXPyqma6aNhYv8H58RG3b7+P1vD2mz/jYl7zzi9us5hXWAu760NmC0M20qxthJxdPKatJb/zH/8XfPTe24jtnFh+srXZ39QSnbI4yH0M8xJXqkbgpCSYu04yiXfMMhITCmzouwhd+AtJ5552VA+8n2x86ig3/Vax7iuarEf/QU2zkxLMWqr1ADZDgqlB1oZw4qhsQINg4Sw+58df+3l3LWbSm7s03WtfRoE3SEobMFI53xh8yDf/9ufY/FFKNLWYyDuGJYcClWt/gca+a5ENLHY07U6NPYqQtaXaybARZHcdTSbo7UM1FFSLlHvTED2o2Y+HLM4TBucCEwbcnl1FGNj7WQOn59jav0K7OUTnlrqnyLclsnG8eb7H1wcf8d+8+Gf803/4D1j/Zky+45VGz3Itp+pIhywFJrOY3QZXaJ8Yu4Q9jeeb2vAyHLAdeXhOtILgQiJbQbnTEkwU4UTSZo6m7/mowVRiEi9QsN2HrMUTqilLO/S0LdF0HKwnBAZA5xPQfUraTlnm35fCiNXrChYAXjwha4FTislszWO4BlDOD+S0g7n0clrtkAuvsqx2W+8ulhjvArYIVtivMwKh7aWUt5GotEXEeNplrlY0rzZxK1xaT/3wsB0aysTihCY5gXAsqEaO+uozEhrMZg1xFBIIy2AnZv/oDo8mp5xPK6RIkMqS7CacnRoW53PmF1Ps3BG1gt/9z3+LN975NhE5z926CSaiFhWiLdleGzA9nVKUC0ajHd65c0wQB9gdQaBTlIjQSmFMg7MWa1ts3RAEGoGjLCtAYJylaRqiMEVJwxdf/yyTi1OMy6mdxQQwbwrOp2OqqsXityM/f/tdjIpwVHx0+wOiOOB73/0po96I44ucW9OGa1cbbNtw78F9cJat7YY33rqDaw3DLMXSEGU187kDJRkOIh7tv8fZSx9wZftFvvKF3+CtN77F+eHJ0xzyZ7Kic99xCAv1mkM0ni8pnJfOtpFXdplQkG8pkjOLsNAi2P6gQFa+Vbj4dEY0FqRHjnzHbx+d9IXZKY/ntpmf7qqiAREQjluie6eYtT7CJZROo/A0LON8jpP/EMysRolm9brPjVdU1Uhy629MG2pOKAxfe/0DfnH7Fao1xdJ4pU0871XVnQnHvJt+G+i9ExGOHSaRhOOG/r0Aq2Hn+xPUZEF5a4NiK2A+1RTbiiKKyQ4lvX2DrBUmEfT2LenbBzAaQlEi4ohqGCFbR3JumCWapid4dLDODzee5z8ZvcHfffE9/uzu62T7Hvd+lkt26QRLuadoBW4aQOCwsfcmkLVYmcyoUq66TtHIzokLmnWLk14u2w48ni2M725t6KhH/jqRLX7iLy9v0sJAeNH5tarOn6GzzVp+38cKbiuQrpvcd98KftjUBlC5y+7aanzKguvMwYOu6DUCPdaYnqdWyXEAAtqNBhFYDwuAL67a/7xQFrlMSbD4hIbQYjqowI1DVHUpz5VtxyIAmnVPxL166xQtLffFFhB03g4QPXxGQoM4S7GLkjgQTMZjDiZjcjslzRxNZRltCaZzb44R65giL9CtoCzhBz/+E6S01POAIhZkA4sMI/r9lrZ27Gz3mA5Cjs+mrA8zWin41AvPk6Rxh7kqtBa0psY1jrLMyV2DkgodRrSmxVhLoEJMK6iqgq1tHw2Dc9RlhTOW2WKOrQxV03L9uVv89I2fIbXj9HhGVWh+8dZtZCxZNI5yOuH4Yszp2QxrHW1bcXQ6Jghj3r//FmfnJXllEcJiXcv0wrCeKILQURZTfvSj7zKejPk7X/1HHB3OefTgmFH2bJNF/zqrzXzhMbG/aKIxxOeOILfUPUk4925bbeat93RuIfUDMVFbROWVQRtvO4TxP9N/aJnekpjEE8Cd8IT3cqTQpSW/lhKf+Dt7s7eGDRWTF2AvGBN0F1Uq1QprNU4wtglnNmVsMrb0lIGoWLhg9XeEwrCwEWemx5V4whspPkpcej9RVdKZgjtU6Yvu7Jag9/OWcCaIxg16ViOnBRvzChtp1PkUl5fEH7ZED0Oy/QEXryQUm5L191tMKBk8bNELQ3i88LzryQzCALfI0ZMKiCg2QlTl+bfb3wz45r2v8Oavev/XwR1P36r6z5YD7fmhfitvIofLOlPxuUaVXSzQwHi/1NX3e9xW1AJV+y6yGXkFl2yF7/KM/x7bwQqiFUjr8VVVCkziJ/BLvFY2gnrQCRSeNN6mEwmoLs9LeROZFUfW+sBFPwTzXWm91qkGlwYqztPD1FT5OXfgB2feftFTzWxikbkkOAk8dzW1qPOgM5LxwgFifPSNw2O0c4nTEjYq7Czo/GZ9zI5oPWdYtIAANVc4BQcnQ0ajBVvXxpzGfYRyKGWpm2eEwZrZgs3tIUU94/GjY9qFZn17hw/vnIAUFCU0lSAahcyLinpRcCXbYLM/4NraJmeTA0ygyKIeNjcYU1AXOXEYc/joLkSaeVVSVZp+2qMxgrpuwViM8vlZznVJmUqgZURd19iqIi9LAq0JVEBroKhaJrMFQlrm5wuoLXtXdtFb27z59ge0teXxwZS7d8/I0pC1rV2m0wNG6xucTA69a5INyXPDvQdHfOlXvsCbb94hL3OuXh9x58EDpNAUdUUcBqS9GOta5nkEs5qmDZic5MjeHW48+jH7j0/54V98wNbu4GkO+TNZuoB66IcT4VQQjn0g32JXkR0aVO3oPW4xsd/iNj3fwcRnLXpaUu30aPrK2/RJiC4sJhS0KatCpmrfcVQjwfCnpZ+mOy9dFUUDgwinYGEjFlYSK0fj7MoSEKAvS8911TVnNsMKyX67Rl8WjFRObiNqpxiblP1ytPLzTM4spZWUW35rlxwJhncbZlc12b6j984ZLgyo9nqYLEC0lrYfEe5fgJQIJT116OiU4PQC+5mXGdy39N88ZvbZbWTjiN5+iAhD7GSKq2vUYBuEQBiDyhviscJNBMWmove4xuqIB+s7BDsFz705Qx2Naa+uP9PzqnKJbOmGVZbgOEC2Hi5pU4sqJXKsOp/XbqJvu/yuwK0KZnysaWMvVlh1m7JTf3XKL90V6bZvkKVE5qIj3DuqnRqm+mNBhCslVtflCkBU8mP+BFbjrQ5/WXRgllxcOg9biw0FrtciuqgXHbcwCVFjtXreZuifSCwlsXS0r0p4H1sjMOsNYq5XNycuQvyM1UfuLLO9lmKIj4kSTiMuLkJcv13BD66Uf6Xp61P5wa5tDkFbwlTx2ddeZpBEnO+PySJHawqK2uBiyfF+TTXRXL+yhQhq4r0QF2jaRlGXBU21oCkXbG1seLPrtYyNrT5FVZEOhkgdIBR8//s/5Pj4hKZpMAack94VS0boMMFaUEpjnaOXZV4+azxMMJvNWCxK2taS9oe01nH/wQPu3b3P/fsPePTgAT/+wQ+RWAb9mNnsFGNL7t2/j0ASx5rFYkplSkbb6/8/e2/WZFd23fn91t77DHfIEQmgANTIGljFoUhRlEyJrZai1W2r3Q8dbTscjnD4xd/AD/arHQ4/9FO/+SPYEY5whx86OqxZaoqDKEpisUkWx5oBFIBEjnc6wx78sM8599zMBAhQAElH3IVI5M17pn2m/17rvybeevvHvHfzLpOZ8M579/BAZSsGWYpWkGWGbDhAB6GeBo7v10wXFq1SvvW33+Dr3/xLdq8MSZ9sW6ZHkmojREfOIL5oB58PuFQY3nOkJxYzb3pSVVEzye/X5Pslygbmz2/icsX0msaO4jNpytBpicrFh/D0U3UM89qG4nKGrj0+07hMI1WsSXrjLy3fOHmZSUiY+UAdQlemUEtgQ9U4YubWSCocwovJPiNVMvF5B67vLi5ze7oVscDHXmGqDow/DIw/illl2d05l74/48rX7kMI2EsDdOnQsxp1NCH53vsEo3Hb49jf7fAoFlO/cZXxHcfuX90E5xh9MGXwYSwzGOZzJEtR43gTw2iAzxN8qlGlx8wdgwOHmdb4BIa3NFf+rwHqeIa7uo1PnmwcrBt76h3fxLnqyBMOG460Fuy2xW67qG1Wgk+i99+nodE+o0lfbfmIqS1H7AVpzOXhHSE70Nihj40eS7VsMDjw+NSjEk/6zLxr0SKu+WlrAzQA6HK/Uo+1pSFUU7ylrZng0+gsU2XMtApJiB0MSgUnCZQKO4lFZsRG7d1uuo7XXWkprkKX+KCejcXi9Vxh5irWPHDxXHUZOeW2AWI7RlXHGr+q05oDJnPsbcdsHFVKl7xxkTwWwM5qh8MgkrMx3Oba3nW2khsM1YD54ZTTOzNUabl8RZMp2P9wwqWNS3zui9d4/TPPcu36VYajDBVpMzbGO3zpd34PqzzOVmQmAzGIchweHqETzXe++118UIholEoQErxXSNB433AqIXSJBj7AYj7j+OCAO7c/ZmtrN8bNacWLn/gEx8cnWOvY3toiMYrRICFRlvEQBIcSxWJRoZVhMMjIBwnHJ6d88MGHJAn88//0n1DMFvjgydIUoxUboxzvLNPZgkQ5EutJVY6zio/evc/dux/j9QyVVexdebJ9mR5FVC1dO+NqyxOGjmJPcJkwuxY93INbE/TCoxeeatuACMlJyeCjCdNrhqPPO6YvW05fdcz3YoZSaEzIasdz47kDik8tMLP44nijUKUjGIWUFXpWgoL/8M1Pc+yGJELXJqaFnTooRlJz7GPd1ToYbtkd7tht6mCY+4wPqz1+Mr3Czbs7EGB+rfGW58Lxa2BmoXmxPeWlyNuGYYbLYhlDlxvwMdNQJjPUvETyDNkYI8MB6njCxnfugrUsPnmVW7+/DXfvg3NInnc/YTyEENDHc8QHXK7RC4tLBbxnfMuy/VOHLjykCW6QUOw9WXpIz3rtUhSUlzxmpsgOFMlEkd4z6FnMz7cbTVeAxiR3I08yUQw/FkY3FdmhkO8rkpNlNlh2pLDDqPnquepAL+hG83SCmWrUBznlwaALvYpZZMuffl+wvmYb0piO6/PGaaUCMrSYqWLwcSy4YqbC6AODLFTkjX2MnlCz+NS4kQcd4rUo43jaFGCpBV2oDmjtfo4+MjGT0RKTNKDJfGuKay8iBYJvaa8YnxskRguoQmGPUw6+d5nkvonJF+MHxzc/XsuYbExZVaQm5d13bjEabHP5asmwLhkNR7zzo1v4e57sek6+lTGoDQmKK8knuHRlj2t717n78T4ff3CCc57N3atkG1t88tOf5+++/0PykHB4OsMYQ7aVUdeBo+MZZe2Qco7RzQPa1B/I85yyLLHWopTC2tgNoSznHB3d5+23v8enP/MmH936gGquODo54eN7B7iguXnnHqiEq5evkbCgciVGqSaJQTiYT9ja3qSeLVBBUVc1r778DEf775CEmswkaBGUUeSDlNIvuHp9E2unZOOcsqwZZoZqaqkzz6tv7HH/cJ/JfPr4b9I/UNwwdO2cVSWkHyZUWwFxcX4NOmd41zD4aILbyvFpyulLOYN9g3IhOh0ksHv9hM285PSljIMfXupqyvo0cGU44fpLJ3z79mvs/jCmiSannqAVbm8TN05JppZ8P+G23eEZM2kKWkNCU9Q6KDJxXNYT5j5j5jNScdTBcOyGzHzGnXKLW6ebjN/KKXcD1WXL+EMT6Y8TYfPDEm8EsZ7BBxMQYf78BvVIkZ44fKpBK8LCIsYQbt9FtjaRQU5YFPGCFSVhPGRyI2H37To24FQqpngCYTumOwejKJ/dRGw0e+vNJF6vzJCe1qASFpcMi71LjG9W6PLJJhq0ZnCkCTwhDdgxtEVSzDx64t3AIXUEzmQaK2/5JFBctZQvWzhNSE4UgztRi1s8E6MHir04GZuDGAERmnsNLGNpB4FwtSLNLdVJBk3R72DoIgQiLdD2C6Mpa0h0mDKQMCgAACAASURBVLXOsqaVuDKBes9SX4qUQv1cTcgs4SSLTj0FmEC2rxl+HCh2FdVW2xlhWQc2EKmNVpsVu8x4Wzxfo0cWuRcn4HZMbhBTaFvOWkLk1PUshq65bFkExmWxepyqBP+Q2/p4HKyLoUxXrlzi/v49JrMTqD0n+5bB9hi9IRwcnKLuel769HMMnttitn/MV//2W7z52V9HG8ebb36OW7e+ghoanHK8/eOfcHC/gMIwFE2Rptw7mpAnA96/e5u/+Muv8Qf/2b9ge9tgkgyjDUoLZbEgzxRlWcbkgxAoyxLRwnR2wsH9O3z88U1Ka1FSMx4M+ODmXQ6PT5jXmuEg58rVa7z2+hu89Y2/YjqZxidWFGXpGSYZJydzxAT0IEEQDienZGpAolJSH4vNODxHk4qNnQHOF5ixptI1QVWM8ozJ3FMsSqaJcOPZLe7d+cX35BIX4yHdlsWXmvRUd2Ex+UEEh2pLk55kmPtTxA2ph0Pufy6GuAzvBna+bThe7HKkA9de3Sf9zD2Ov3E1trAeO96+8wyDrGZ4R2KaaAA7Skjuz5HaohY1cjLlBlf5w3/+GfK9mqEqyaVmUxXkYjnww66F9rGPLbUVntOQUwVDHTR3ig2ODsfceN/hbgunLyToKr702+940oMFMl1AmuBHKcWVAcefMOx9tyQ9XID1hMSA8/jFFEkM4TQ6rcQYSJNoN4uwcasmOS6RQR57v1kLWiOHJ5Cl2Gs7iA34tMnvbzhsNzAkJyWq1JgyOouOX0nZuPlk47RantBueMxE4Uu6algAVRa1PTPRuKHH7jrsFY/ZT0mPYw0FN4u9rernSqormmQ/QoJPoyfdl5FjtMOmIIxtahvkYQlIhynVSHfFVmi7uVrVOb6CCg01oaIp3xQED0Lcrik36O9nSNPxID1VyD3N4rkY+uUz32nt5Z4j/7UT6kWKPckQq7pEBWgzxJroioaqwC1rFvuDNMbRhmWaMCHytz7zXdaZqiK4QlOLgWZSaJI2zESttgc/I48FsNeuPk9tpxwf7bO3t8XsdM7+wRE+TJieTtkdDzm5YymqOXdvfsQrr73K5z/3JXYGI56/doN3P/wpH936iLq2bA13uPPRR3iv+cSLz3N1e5PjomB+sIgxrmK58swut+/e5fs/epdXXrmCtzXDLGM0HmCM4vD+hK2tHZQ2VHWNKM10MmM6mzIrZ3zmc29y/cbLzE6POD454MO//Q5lcOT5gOnJMZkKfO3j99kcDTEmkGJw9QyNYTqryBONUYrUpMzrgvncclgeIs5TV56QBSRRoIXJooBJzkB5zDDBq4RKDGFzQToaonTBfKYw+hdPwlaXY3dMfWxwGzEoXJVCPotcajrzVCNFtZVi9ieIC4xuFVQbA+bPCPe/4AmZZ/f6CUeHY26/t0d2aYG2UG0G1LFB/yTFfPk+k53AfE+TzgKDOzFHX0JAFiX185fRpxXf/M4rvPBbh/ggKAmxxmtywoYqGq7V8HoWM97qJpVn6nJuldvcmW3CNGH/c5pq2xMSix1p9AJGNxdIZSFLkdkCXVty4Pr7NW4zww0SzPGckKVQVaAUfhbLRyodW6yQJcgipj7mHxwTBmm0mKoKfIDNMRyfQmIwpwUSMsJCWFzL0UUMb/NGcKMk0gaVx25qspOAyx+LkfuZ0vaMklqwG02lqVJQUxXTRhvT26umtGFh8JnH7ljsWBjeNCRTqEeQvJNTXLPUezbGlOqA33Ikg5rkmmV+OCQkqnNQqfky3jUISKUwlwrq4wxpGihiAjiJXWgzjyx01y67yRnqRGyMoR1/qFAVzJ4NFM9VpOMKDvLYMFIRC9MMPVIrTt7fRhex2BMqdK1b2g4OLffaavQtYHrVi6pQy6LeMQlHugpabSeDZUxuWLbpaRyAtmmV/iB5LIC9u3+L4SDj+eee4wdvfxdtNM9efQEVAvePj1B4treE4e6AK5c3cMUcI4Gd8YhhKrz4/HWcFi5d2uBH3/shA7PJcHyZD/JDDmuYThbsjDfw5YJgNXfvHbK9Gfj+D/6eZ575HTYGGYSaxazmdHLKaJizsbFNWRVYW2Prmvl8RlkUHNy/z43rzzI9mfJ//h//N145nn3hOV5+bZu3v/sjLl+6xPM3rnPzw/c4PJ3ifKDwFQFPVRWkWYKogJfAfDGPqbkOjt0ULZpkKF3Foo2tMbUt8RLwTkhNwuS0YjBy6JFwOK8YLFIubdQM9ZN1dDyKJIcmmjgbDjW0qCohGJhdEwb3QDmFKeJD7y6NKXczyi3dNL2DkHmSrZJEe65eOeF4OmR7POdgPGbzPfA68nOHP95FhoHRnVjMu96MvbKSoxjcmNw+xO1tsvNdzVuffJZXN/fZNAvqoLlZ7TJxOR/Nd/ivr3yrowiOXeSs79abHNYjTouM9EBRXrWQei5/JWHvq3fwG0MWz444/NSQ4X3H+AcOuzti8tKArZ/MMAcz0Ap8QE0meCAsFogxSN4kf/iAzIvGnHWwKMANYqTBcNBQUwH2drCbOaqwiAu4sUGXsZZovl9SbyW4TMfU2NBoUgI2e8JhWk06bHKkSfZVjFfVoaupkh4p6g3f8IpxmZkpXBMGNX8+atRSR2dSdtfErqppiMW3E4e/PSD/sXDpjuPeryuqS35Z5JroBEI3ziQVyC4tKI9zZKHBRtNdxhYmSYyrzULH1Xb1CKzgh47kcsniGmyNCy5px72DTarDnGzfRF4/C10thRb87FZ0bgWJBbtjt4El10vjkAMQBT5ZFpZRVS9bqy2Y0xaz8URnX5PG69rW3D5SDSEQowvssp/XRfJYACtmwXxuKeaOvUvX+PDmjxlnhq2NHWalMMiF+/MPCFpz+9aUUSrsbh7hTktcWTGdL9h9Zo+rz+wxn5V8++/e4b//l/8dv/k7X+bW0X2+8uf/nspaTuclW9sjxjsZw0HGD95+myvbY1575XkGmWE+n1GUNa+/8TqT6YTEpBTFgtPJKcoE3nv/ff7kj/6CxbSmWFgcUcu88/E97t7dR6nA8eEpp8dTZsUClFA5h3WxW0GSRxfnYCOnosZJIPUKV1tCalB5gq8qRuMhp/Mps/mMdJDgVI31Kc7V5EnG7HjCaJQTdEoljoOJ58rmgwtDPC0JCsxM4QvFzvUTTgc5ZibUGwGfCJNnhe13HEevJVz9mwplA8nCs/F+ycmrQ6ptg9xKuPeKIViFaE+Va+z1ilOT8sxfOz7+beG/+L1v8qXxO/zr7/+37PxwjjiPuT/BD3N8niJZgs8TBgeBd//2Od5/cZfPXr+ND8Lcptw83uafvvAjflRc44X0PlXQTHzOu4vLsbMEgcWPtnnxjxeoyqGnJTIvCMMcfTJjGAL5vkEaGiB5/y47+wOYzKL2CYSiiGGa1iKDASICxsDOFjJp6BvnwTZOIR8IwxyZF/jxEHEOqjr2a1Iq5rL72BfM5iAukJxafKoQH/A6FuIGcOkTrqblolZVX7IEbRDb1AIwEfjsJc/4A40uYH49LrMbMSxKqsZDbwKyXcUsqGHMAEtPFWpfUW8YRreF2XU4/E98bAPedK/Vi1hUJRgiyJ9o/GQUHT6pj3UBmvKHHKWR42w0z+ap7Nqy+NyT7xTUlWEwLJmXCfODLaRQkASql2LzUecUfpJErTPzhLnuqoJJo0pL06HAN2a/shInxeY9kEZLFR8Bu21Tg8Qoh7bdeAzP8s019qgmSUNabbWJKOjaxzxAHgtgy2LB7PiE7cEWSa6ovWVqayQb8NrrL/Av/sv/iv/pf/kfuXn7Qy4PN9kZ7ZImI5w3vPXWD3n+xZeZnzpm1YIrV27w6me2uPLcM/zv/+Zf842/+A/MvWfhC0aDnHsfH2N0wvW9lOee2+X4+JjT0y1KA9ok7O1dpqprtEmxznF8dERRzpkuTvnzP/sK9/cnzKYLsiSnrD0q0ajEMJ/PEe8xoqiso7AelKasHD54BoOcQZ5S2gIvEERIdUI5KTAqAaWYlCVaFPO5Q6mENI+xdMONIZOTGdoImRkjLqc4qEl3hALB68CRT372hX7Cogu63kpHp0PCMMQ2zrPYEsYUcOtf1fzua2/z7k/fYPTuMdXVMT6PFex9CqIDgx/kMWPrU3OOjsaEUmHHjuk1w3/+e9/iDz94gz/74y+x96M5ynqwHr85RGqH+vg+JAlutMf8iiJoT71IeOdwj8TEp/bLz77LnWKTTw1vs283GaqSn86vcqeITqUf7l/lxX+3IL15SJjOkKS5licTyDPEaMzxFMoqcqlaI9M5oaoheEJt8YsCSQxqPEKGQ0JRxjhY6yCJ24QiWiyIEPIUOY4crZoXkb8ViRqs9dhRgplU4CFzAZ9pzKxGLwLVdoaZW3ymqccG0xa8fVLSdk8dePLXTpjd3iA5UtQbAWWFZBqTRYq9SCv7JGZrRUdXoN7xsFnjCxOrZIXI4RYbDoxn/HZGsQsv/s4HXBue8lfvvozfzyPYNOAkjaOq3on3MNkqcVYhCtzcYPZNrOmagHjVxZUG0yYUROvI1hp/nDLfzyLoNfVkVamgSHE0zrEmvMsXuunNtwwDayW2n4nUhRv4WFQbllljSWwU2XY1cG2pxkLFQuPNBIXIknZoiuR0bcatdLVr/UPasT8WwNoioIPnpz/6LldvXOXNz7zJN//u78n0kGJ6zB/+P/8WY+c4H3BieOX1l7h97yPu3z5gezPndLFgNBoxHAvv/vQOl6+8wZ/8+3/Ld374NWbuiDQbMjmdUddzrl7ZpijmnB4f8vWv/DVpmvPRhy/wa5/7FNevP4e1ntlsTl05tNbs7+9jjPCDt9/mnXfeo6wc2WADHwKhLvE+cHRwitKRV9W54nQ+w6FQgBaFEBDvY0iOF4qixhhNXda4oEnSBO8rjIn9wVxw4D3KWJxY6nJAPhRqB94XTMuKNIXi1DIYDghWqPPJ41zyJyJmHmdZM4f0W0MmrzjK1xcM34rFR7yG5IOMb/7ks1xKHEEpxAbm1zKmzwmjm7Bx01IPFcWuIvvqkNlzMYRm6z3P4hL8u7c+x8YPUnZ+XKLnsdCLth75+D5c2sY9dwUpa/SsYvsnhr3vOk6fz5k+m3H66QVbm3Mmdc5A1/zH6XNkqmZsSt46uEFmLHdPNwjf2iK9eRNqi+Q5GB09/0oIRRlBdzSMQOk8YWNIuHkHvI80QG1RaRI1Gh+gjmm5wVqYR7qA+QKqOnKtgEzmjePLR8CuLW5r1HCfLr6UA4NLFGYWCzXLosaPU7L7C3xuYrnGTC+D6Z+QtM6VYA3TahyzrJqUaDv01NuB0ZUZW3nJ3Vs76JNY3r/aacBlsyZMDapS+Nyz+cwErTyT6QBbGJLfOeC3n/mQP/nhG/xodmMlBlbqmMHlk6jVqZmKMbZeMRhVzE5ypFTL9NVWc22dR7nDT2Pra60DxWGKXkjnEAvCskh3GsGc0NSlVUASC9J0fAhN6FfDubYmfxsaFnRTh8E32mvLFLhlcXHXOLNwUalol7ccbkhDk07bdOr1kefmDMD35fEoAnHkwxSPIxnmfPDRLTbGY8p5hastH98+oDydsTUYY43l43sfUZYFha34wue/zP7hHb7x9b/ly7/1BUpX8fWv/wnXXrjB3eIeNYKazUm8x+Q5pwcF1tW88MyYYZ5xd/+EygtHkzkH3/sOO9s7XHnmOkYZtAl859t/z7Wrz/LHf/JVjo+niCiKskSZGHUgDSdPCGRJzsbmmMPjGZvjDcqiYDBKsdbFjgkKfOXwTijqOmonWU65qNnYHDCdTVGJkAxSnPec7s9RRuGHBdmmi8kQKJRVpHnGoi45uluwfSkwyB6vBO+TEJdHbcDMmxYalWA+iOXezAIQyA8EXQbSU4vdziEEzMJjCsXGRxZxcPBZwWWeZ/4att5z3Pti9OBffmvB9juGatOjSheBZ1ajKosM8hgT+95tZDxi8doVym2DuWUZHFpmNxLCQcbpxznfGG2RX16QJpbd0ZzCGg7eugIvzlE/HvHin06ipilCKIoIiD5EEx8QJdH01xp3bRc1j6m6amebUFaojZhUINbG7ZIE5qegNVhLWBRxeZpEkM1S8I4wWSCDAWGYxEni8BQ1TZpMrubl3BucueZxbObOMX57jC4c9d6TtV7a8CKIQOIG0TxXpyYW454L85tj5noc41dNE8xvmor+Rykigey+IijFpNwCFcjvaWQUOJ5t86cnQ/BCeqjxSeQodRkBxrXNDq00bcM14UhT6hzJAnKpJMssWnsWsxRfa6iFdLvEO4VsVqSppZhmnQkes6aW4NrSGRBBrqU41EK6wuDdGELU0tvIhbZPWde+XBFTdZtsL6BLjmgrerVOsdZ5169bKz7SCF0sb2ha4DwkOOSx3natPKI0s7Lk9t27sbiKLcEKeWJYlAuUyrHzOd7UHBzt8+yzzzAcjCnmBQcHd7AuZmW98ekX2b20y9s/+YDP//Zn2b+54O6HNznycHK6YLGwDIaa8XjMxniED57DoyO++rWbjHNhb3eXg8NjhoMhBwe3qcqCb3/7bd5664cYLSgCg9xQVBYlYG2sVTAajpnPppxOJ3gveGvxzpKNRiRJoHSOoi7I04TaByrncd5RFRXBO+YqkCQpde0oqAlYRsMRBNvE0ZaIaBCFXTgmxQQZp+TDQF05yvkvHmDxoFx0tAwOAt4oTJO3362SwHDfIy6gC4tPNNlhhZkaZs/Euquqig9wMnWo2sessE9r+NSAF/7fCacvjDn47IBrX0+azCaLsQ61fxy1xBCoNjXVhuBeGpAfOfL9wMaHkfNymWby4pjpMHA6HJPdSUhKMH8z4vqfHyF3DpZgmueEukaMJtSx3gS1RTY3CHWNeucWuGhhYG2kCILA7hZyFEE1LBYwyBGlmigBC84RKgiLAkkTZDSKVERdx6LU0xmyubGMiW06F+jSE1KFnlvUvCC9WYLWuMtbMQ+gdKj6yd57tVcislSL80FFWSQwqmNKOcTq/R78LImca7u+p0sBVV84QSlPsR+19vDmnPpgEAt4H2bRDNZRCwxJwDZcZcySihpgveNjYRUnkATyjRKtPc4pRALPXT0i05aBqbkz2+BoMmRrvGAyzwmFhiYxIJ4YK7UIIAJkn1rwaSA0HXFbLtrr5YRDaBMclhxpV2DGL2OIuxCtNuOsdXrpJgKi5ZGDdE0dpY4ZYyGLXa3DQ+ZNaQngtaxlLWtZy5OVJxuYt5a1rGUta+lkDbBrWcta1vKUZA2wa1nLWtbylGQNsGtZy1rW8pRkDbBrWcta1vKUZA2wa1nLWtbylGQNsGtZy1rW8pRkDbBrWcta1vKUZA2wa1nLWtbylGQNsGtZy1rW8pRkDbBrWcta1vKUZA2wa1nLWtbylGQNsGtZy1rW8pRkDbBrWcta1vKUZA2wa1nLWtbylGQNsGtZy1rW8pRkDbBrWcta1vKUZA2wa1nLWtbylGQNsGtZy1rW8pTkkbqwmTR2IOv374oNF2XZifjBnWvjYpHYsVOaVrsIo9EG+XC87NwoTaM2WW141kro/x/CcnE3rkAIHu99PE47zm53Hu8dzjm8ddR1zdmeZO3fWhu2xjkbqWFT55TWcVJXzMuSaVUgotBK46xDNeeW5TllVXbnmaYpdV134zEivLR1iY005avv//RnXLEnJ//M/DfLkwx+daE83hwr6uJhizGoS7uxE2t7KCWID4Qz24h/SB+4/v042y+ubbcdQtd08Ny6IcQGi87FTrFVTXBu5fnr7rkPiFao7S3CeAhpgh8kqEmBlBVhXhAmTZt1pWI3W2uXz9ZoGJskFmVcR2skzwhVHY8PsfNsnuGfvwZa+OO/+Z+f2H1/7X/7N7G/NSwfcqD77gmIBFAV595FCatdV5cLHryv/rC6d/JB++i1475ov+GiZaH3d9MVVlz8QcDlsYOshKaJ4tlj9LrJxg7Uq8fovm/E69gQUS+EH/6v/8OFZ/5LaHEKEBACPlisq/A+no10L7ujVa6DD0jzQgSRlWvYdZxs+6I3/2AJlGcBNgTfA1HdgZ/3HqVU99l7x+l0jhvk1JlweDrB+rhfQRF8wHqLIN12rnmRWynLcnnGIWB94Mcn97mUZk/uUv6KSAghgkptu6bz/UnuzMrLxvQQwasV78+uvbqdSATR9v623V37YOzcEnC1jscXic9R81uMiZ1kAX86QaxFRkP0vMDvHxDa47WHbu6tiMQ230rFc+2PPwTCbL78rjmXMJsjP/0Qtbnx4HP7OST0H+6+XPTdz3sM1bR9fxBw9g4lZ7/rL3vYkB51uO37Hjg3IOkdM7bqDgQNZHHVoBqwDUR4CcQutWfnp9B0km2PIaHX+vvMnC+xG7MdPfiZfTSAfZQL0EO+s1qhnNU2msnDuZq6KhutguYBbne2HPTy5kjzgi21WUFWTjouDquTX19rgZWXUykVQdIIxiQopVgs5iil8CEwnZfU1lE5342x/d2Kcy4eWCJohxDw3i819uaYQRTKw0GxeIQL+gTlrNb6KOu0k13/+4dpuz4QygqcByVR01Ry/t5fuG3vGD9nl+PuHvsLnj1jlvc8S5fnYW13/DCdQVXHsTu3ou12wNqKc3EdpRBjIlg3oN7d87MTRV3jDw5/rnP7ZYmE5vZLo6j0T6nVMvta4Nlb96DvLzqOotMuL5KLAF56xxcbx+eTCK4rQKqb5XY5pqDDyvE6TVpaBa1naQVQlgvAGFQJqnzwM/5IAHtWi37UZcDKw7l8SONv5zzGeIRGywgRMM/qPGFl27CyuEcUIEGak28RuL0iy02jBgo04NqOy5iE4XBEUSwwxlA32okPnrLVVAgrL1oHns2TFvwSuM++mO3nEMLFT8uvkjwmbdBKsBbRCnyzvQ/L/vNnDyFy3syPCx5tiI+yntagm/WNiYB6eRecR2YLqJOleQ9Ro+1r00ohLVA2FMGK9u193Ebr7m9pP589n7Na+6+APKqiu7JeC6h9E/oBmuojK6Y9iuDcq3GGPjg7ZvEROJMp+BS8AeUEr0M3QbTbnDP7eweUBjNCD6KCXj1P8fLoF62RRwLYsPKhnTL6Y+1rh+cf/ge9DN77aFbrB7/QqyDGzwSn9v6LiuZ/HFNoONvQraMawFNaY9KMra0dxoMRt27dBDRKLamEPlgqpTpaof38IHO1Tzn8ysg5dd+fB9T+d71louSh4BuqKt5L03usfB+wlprtubH8LOkDldYRrHr3Jx6rVblWtxFjIscKFDc2MXOLOZnE/SRJ1Eh7oIlScRLWegmqWscJ0rkV878bU/u3SFzecrCPe56/IDmHExdxuRdu2K5/8eJOE3zIOg8S5aQx51tE6x3znGYXwAlio3ZZj1uADZi5QCIE04DsRYO84DxXLkE4f0mCCsux9PW9h5znY3GwfXztjtEcsGMIzmhs9L5vga7/t/duqQHIee0VwPcU0fNLz5iEzf/GGKqGY2uvWDeTIaAV4iHPcsbjLTbGYwKQpIo02+Tw4GBlnP3zaH+01ud41z64nuV3Q9+E/P+JnAPVvsOqBTTpaazOI6bb+IH7PXtNH0vSBHqa59l71Hy5fK4GOWE8wG5l+ERFp9vGKK5zZ785r/YczkyGDZiKMfFcbVh+39doz/zdcrZnNeJfpvR5xv7fZ7ncczqMJ7pEzmqu/X032l3oAc+DB9L73Ncu6YEr59fpD1B8BFZTRCecHQE16EKwo4Z/bee8BiyX2ukSC/pjlg5o+lZxPF7QDbh6VgD4Ij64L48GsNL/2GppEKQx6aUx7M9QARe9OGe/897jg+/M9bjveIJJkkR+MyynjAc5j9tlciFIh+5HGu1bBU2SJWzv7PBrn/0MG8MRk+mCqig4OjlEG4O9IMrA9TzS1lpCCOjmRe5rqn1tVim1Arj6rBn5tOUsn3qhaf4QTfUBkQNntwOgrqMGay5+tCRNOo5zdcEFx+hPyO06IgTdgN6DzqMBfRGBLMXtbXL/c2MWV6JmMLpt2LEefX+CpAlh0dM2+yDYcutE+gPv40+SLLXZs+PvO/ja+9xzuD0teSzLtVnZX4D38gCNrG/FdhEEZ9d5RM21z+X299OCa5/jvGjFNrIhPYHhfY+ZeyQE6qFiel3jEyFols6us2NVq8oi0Hf5rByum3zC8tzPricPOeHHcnK1GqvQOKPOAOmDQp5aOavptdqdc24FYDvttvH4q4doOH1Nua8ddw6sZpwh0MwKcSZSGi7tbfEH/+g3+Y0vvEGaZxRWMKnwZ1/5K4zRuMYJ0te8+8fRjdnY51f7tEL/XPrf/8IpgwvM5hYYu7CrPlD2138YuF6wLIQA1kaNr6UEWvFnTPqfJSJLTvMMF9o6r7pVVzjOxsufJtTPbHP7H4+ovzBla7wgN5bbB1sEPeLyX02RhiIIVb16nPMnFgGytUCURCfKhddFLSmMPvD+Ii2XC0z+C0ObVrZ5oPXc49561mpPAw5mCUKPM76+JkkQVP/yd8uWm7Tgmh8GBvc9i0uK4mWDquN3ugyUu9KEYjUbyyrIiusNoQeaZ7XnTjvtbR+EqM33zvVBfgb4OcK0QjvmHrieNXsf1wx2zmGMWdV8JSASUOrRKawHadARL9pZQhAlDIYbfPk3Psebn3qF04UjDTDc2OD6jRskKkWJwhgTY2Z7EQF9wGx/+lQAgGlefN/TZtpJ5FeJHrgQXP+h4sN5LVb176sgShEed5I5a173tcPWRO+vZzRub5Nbvzei/twUAhxPBmxvLLi6e8rx1eHSrNcaSSF0zkyWy2CpyTYabIwYUCvHljRdxt+22/cnhadstaxwn/Gb7teKk4clsDx0X731IYJV0CxpgjPrPUqgyupY+1rbcszh7Hd+qTFLAKmF7CiQzALHr2iqnYDUAV0Js2vS4ml3PaLy2YvYbxzhreYZhKUzq6+Bh966qxH/S0PwZ9AD8JhRBCF0l2AljOXnAY6+1um9x1pLYtJu/0qrnqb38H1fpF22pRsaxAAAIABJREFUJnn7nVIqzr7Bk5iEL3zqk3zxjRc5mJck6RAlCdZ5lIrabWvWt9LyqEqpjiZogbevlbbn1fKzLffavz7qF83F9SmC7lp5gmuXuagptoDr1cM114eJkqUW2zovW1s0NcvQqEeV9hloxy2COL/i5OrWa0FNa/z2mI9/e4PF6wVhniLaYzJLCELtdHypjI6TQHvvgNA4qFoNPLgecrTg2YRpddJ3pjm3dMLBysRwLsLgSUsLEn1g7H1+3OCVc4kBLRerms/9Y/W0wcc6zhktVTwXhmy1yQHpCegCTl9Q2HFAL+KGdhgjVlTdoyCC9GiLRmk7SztIWAHx/vGCivsUKyuzyco1bcK9HiQ/X6JBcyMfxLWe9ao/TFqQds6htUeJXgbzx2nm3ENzfjD9/UUQlQ7Ylt78mH0l3Lh+nd968yWmhccpweQpZeWwdg7BY7Tq4lltjyZQSq3wrWcdeS2YtgDcUgiueRmXjr1foagCGk1W9cLIOqBtru3PA7Z1DWcjClhOzCvPxs96TlpTuwXU1ly3ls5r3+4jTQiJYf+L25y+bmFh4kuRxDfodJajVAAFPjVorZAkWcWJBqQxBpGolQZrzydE9BxboaqQNF0CczvO9vxCWOVsn7CsBNr3zfz2jzaEUc5zko8Cwiv8arv/PgC6VY60/XDh/uTM797Y437CCjC2CQJ6AWYRmF8TXB7QhSz3E6Lpb+bSi4ftHYOoiXaacFg99kVZZm32FiZEkD1LNYTehPAAeWyAVb2r09de+4B0kVf3QWDb5yZDCL1UzCXhE03LhwzqAtJImwSta5QEAtHcj2a/5nd//dPkRnNaQj5KUCpBUFRVxdZ4k6tXrlKUFd7PV7Tz/vm2Dq5+mFb/nKy13TXpf/9LDduSVvVoPy+/bz3eqtHCwuNqmq00YVghhBioLT1tuO+YehSLpwXW/jZtymqeIgXL7xMTowNEKF7a5fDzPr6sTghDF0FBBYIXqloT9hyLGyNGlUVmC8Q5gjG0DlsA0YqgEnDxqQ8P0Fy7jK2qWtVS2/vcD/d6StI31+UMyIqPIEWAYKJH/CJN82GaZ2sOL83vCDZdbGgboP8oc3E3rotPQnxzD3oAJhaSWaDaEuwgoOql8tUmFrTZVuIAswS+PkXi2wSDZkIQJ3E7zvCs7fGbpNKg+7PQ+fUeJD93qmzrPGqdSVrrLvd+sVg8MmXQbm+MwTQP5zkN5yHSagp9ZxOAUm2WlidNMxJj8AE+8+qzvPH8DjcPSsx4C2WSDjDLosQkhq2tLZI7d6iqOJ4WWFvNNElixldd1yscbbvuWT76LH/7C6cIusH41c+iltSAUtHDn6SoLCXM5vjpbLl+P371YaKiY0rSZNUJpdT5+NBHlfZ69aMIlIomfgiEPCNkBqkddnfErd9NCKnFHBvc2IMOHbjaWhNqBQNPuaUZ5AZVGcRFT3TLHYdmApU0jccQhdTVqmba/9x3FvgzvHC73i+Af78wnLUBReWAulVYwrltHu0ADYCfYTseZx/nzPT+n+2t9g0AN7+zowiM5XaIANxQFi4LhPYxCzHRoL3MXfZVzwJWQfBJIEhAWemORV+Ja9JjO5rC9fZz5hy6SeYB8nM5uUTAh4BWCiWKJMnIkoQ8z1iohKqyOFctB/MzogAQhdZJ1Pia4iBLoFwFo/MmZmjGtAT7EAJKa5RWJDpBacXG5pjxIOf3v/RZpguPysdkeU6aZdR1Ff0VjXm8ubXBYDimtpayXOBDQDXmQlXXOO/Rakkj9EGz/dynAtoxa607Df9XSUQESRNkNIqa4DCPykFVRe/6w4qzXLAvGg+/JEn8sjXpWznLRZ7lK9vr09dgQ+gANRLlQkhM40BS2M0c8YFbvzukfq5AHaa4PBAyh84t3ir6vk6Mp9o02I2MpI6hgGJdt38BQl3HWhhaRW02mFi8pkk2EJGunsE5GqBNSNA6blvVT52D7WulnSnsQJegK8BDMGAF3GOWw2i1VvGy9M73jaCzlMSFO1hdt68Jw1Ibjr/BpQFdCskk8q6z64KykWcNKtIANNqtquN4gqEr8OLTqHl2JnzD47YUgk3CMtqgoTwuChE7GznQ1+ZR4eLY3UYeD2BFmudoCSKBwCDP+LVPvsKzl3cprOWvv/s9bt87wDt/oTl8PoTJE5RBkhxs1Z3luWiAM+C6/L0EN9szbYMPoAM+BNJ8wD/+jU8zSAzHCzBpRpKk1NaTJAlZllFWCb6asbWxSZqmOOfxPaBUSkUtvaq7GNmzzq0+PXJR2Fjr/PqlSC9GNP4pEDwhCGIM9YtXOH51QLklJNNL7L21jXr3dtTmqupn7DxKCE1xHolhUvgQuVKjwS7jSldM5jMa6jkOuF3WAmB/WRN65zPN4RsZi9dKmCTRQZEGJPF4q1A6kKTx2agKQ/BCuS24XJNaH+mMvkPLGETrWI2rKGISRQhdREE3SSoVkwouOqd2Pa1B+6fKwXaXqdGmWgcNGswcBvc94iGdOmwe40XrccNVah4MEj3tL5rjrJjufW2vs+sv2tUK0dv8ajG39cE28abBhEjp2EhtZCeB2bWYQGAWkTYMOu5SzSPQ+QbJvI40o8tjskGnCfeH4QXtl9Ww2jH1kyWWoVvNePuRA8317SIN/qFxsKFL34oaReu8aQFmNMj5xMs3uH5lm0wbXnnxGn/619/h+z/5gMVijvf2wtCt1glF8NQuoNCIStCyTF2MIE5zEn0dPY5LVnfYra+1boYcSI3h9ecu8/KzlzmcVOgsJx2M8CjSNCfPEpy1eGupigpCYD6bNxE8eoU3bSkCC1hru8iCs2B71tHXOrtEpAvj+oXJmRgaUdLLnw/xRxST53OOPwlu4AkmcPLqJnvf2WD3m3cIh0f4RbHc5mF0gfcRVJ2Pjq62+lT7pF/EQfcqUp0N+u6+70KgGiBuIgl8njK9kXL8+QqsIFYIA4ekcUfa+A5cba0JTkWaAEgmMf41KEFMz/uPxHKGWRq53bIEF7qU2Tik5h6fTSg4QxFJm9WVPL373nrZYalEBiIQVJtCtaUiWCjF4G5g633L7IqmuCzYwfKUz2dNrf7pkwApqKpxPjXaZujxlOdSTDvTu/d3D7RbzbA12VsuVVVxcphdF3wK6Wlc7vVye5cHQhL/lka3qjcC4kGXzdhkOZjWb44H5Rutt9WkexEBnQXgH3A+KgbHiA0o9+B34TFqEcSZwRizkoPf8pdIymnhSLRHS+Bf/tMvMRqP+Zu3vstsOu1A5zzINgBuK+pyQZqkBEkweYIvS/AWh0cR0CJR21K+ewclNA96aDRWIlholZCmKRKEG1f2ePPVFwkqIckVkgxJsiEhaFQIOGtxtUUBeTairj15NqBYLDpnllLdnUB0ozV53/200gfUfl3afiRB0prOv2hpOFdJ4/GjZ7x96j2qMa30QgiNs+fuPwpMn73Os3+Uoz78OILs2ZoCfWkLvDQarwwHIBJBKk+RSiLwXgBG56iB9nebQdX8HbSOpiEGnGf+woiDN6O2GmpFGFkk8aiknfAC3kv8CYIYDyqgywSfKoJRSNU7bis6jkFIopbqo9WyQhG06/aTC1ppJoVQ1w1VsKyV+6SkA9Y+YPWiBoIK1JsBVdNpmbNnodw2bL7v4T4sLgsul9Vcey6w9BvNMWhwg/jORQqiAVsXM6hQyzF02VlnU2lldb+dkyostc7B/bh+vRm9/y5bas6t2a+sgF2CcvwyNBRB6MLJVrLHVO8+951zF5x3LwhjhWroHGAS6x88SB4tDrZn3rZhSiGEztlTWce9oyPGG8+xqGI+uqPmn/zmG2hxfPVb/5GiKFb2d+7iugpbeXBVfKfcIAKCycDXhBBjNcVHWiIxGlfX8YY045EG+FNtMEaRppphNuCLb77OeDxkXtQEydAmAQSjNUobtFHgA97XVGXJdDrhdDaltJbAMvyqP+gA57TUfmzwRdll7e9HzmR6UiIKURIdNq0GphoA0zqGFmnNYL/CzHPqcUDXQBCMV8xveN7/V7s8/0cp6vvvNVWnHjBrt987t0IrSMtJikS6oA+yTfosQEgMUtbN2NTSbG8BLEsJiQajCNrjNlPu/rrG7dZQK7AKUo9qHnolAaU9SeKwNiao29oQKk0yDZhJhZQ2xtZe5Om/gC+X3jvQydniL/3oh047f7LRI8qtAmtn5je5+mIh6GguexOdPtHLHs3tw08L2z+GjQ89sxsKO5AuvbTZzXlpzeXGdA4GqjxGbOgijqkLzu9lUq1ENrQxqe3EEFrTnc6sT0+gHgmLK3EFXUlzPksg9g2/2oKzb3hlr+O56nKp0cdr1B6IVY22maSkAWPfjxjoX+JefG27j/MJHqvyaB0NjEFr3aSsNjSBUvgASZKSphmn0wV17aMDyBgKryknC7705icREb729z9gMZ/G4i6hX/e1SWsLHoVDUChl8LbCWkc2GKNEQXAoCfjmd6I0ShxJoqlqG7nR4EmUYpwNGY+HFNpz/fIVhqnm+OQElY0ZbmyQ5kO8A2M02mh0YvDWQggkqcEBeZ5T1SWxynbzHHQalqCURzUVu5Ra0iV9Z1c/sqGlCH4ZcbAqzzpnSycSa5mGxnMuoyGqcJh5LLActZH4YiSnQj0OfPgHY57Z+iSDtz4kTKYP5hR9iNofROBsNeaqjgkIo2EETw+ExqlZNxqx1mB8jBQQQawjGI04H51aRlNdHiA2cr0nL2YEDerYxJdvbFGpI0ktzgnGeJQKpMYiEnA2RVSAJHKSdpx2Tq4u4aDP/TZAL01kQaxl0VzHXkH1FWBuwsm6ybS9To/hLHwkafG05UJ73KFPAjSl+3QFLgW1kC4jS1Wx2tTRGzC6qdh8z7HYUxR7Ep1HsAoeDTapaok4rZcfEVwWotnMMhDfmwjAqm7XXwKeqnt0gGuAPxqGKBuLtxR7Hp8F9EIRbCCkjZLSgLKupOOPxUnsvKBANxqwT1hq8x3YNxNIOwmFuE1QoTvv/rU9p9X2Zp0nFqbVprFG6yOaj0EJvrZ4F3DOc3j/mPKlEKtShaZKFgnzqubXP/0ygzTlL7/1PY5PTlEECG1sIoBCdIYyGVorTJqTJgZbzfDOYTQkqWZ7c8zR0TFGG/LUUONItGaYJhilmZUlu7tbjIZjksywk29zZWeD6WxOogekxhLwlGXJIB9hEo1SQlksIHiMMlTOUtQ14MC7Jj5OECKrHnlfwUiCtQ7nq3OxsGe12DaC4JcV/9pSAucKuASByjZ8qUPPSgb7A4q9+MJ0weQheqJdGrj9jxN2L32C3a/fxn18N55rp2U2Tp1EN+mnMVRLjCEMsgiYbVSCD4QsicVYNjLMnWPCIIvfqYYGAPyWQayHRUV9ZYNghGLXkMw81VhRbUYvMyqOLzRakXNCkjhS4xAJFFWCcwqlPc4qgo/AIyFEbhU6rTN0ZFtjU2sVQbeumy4GTbGafrLB2WveOLcEHj8t+BGli80Msgxdakzs4KLm6toqUESTWZWCKltgi6BUXAaX6Zjffy9QXJJojhOg1exa091FAIzHX4KMLgQ3aPjQpqpVC3LiWFIHvgkXI4JvUKB8q5HShVx5A2amUMdx3DEsLHSOr6DAjgIuC6hKUDbEqAGJ9IUEUEWMOogWBcvBdhoo3bXpKIJWhzxDE6xowfSomQclUzTyyBRB/KAI6Ag4JkFR4ZzFeaFynpPJjMFghNYGYxJqhDokaLG8dP0K8puf5et//10OjyY4TDTPtQaTIjrFmASDoFUgUcJolBFqS1mXDExGZgzj4YA8z9AK5r5mNBgiBlwNiXOMhjnZMGN7vEmxmIMStE4YjUd4pSJY9DKsZrMZAiSiKeYLgjhOTk6YLxbLeq4hatZBwHdmXuSjbR3JrYsyu4BOY+0v/2VQBMB5sz7L4vNkbQxREiGZB5Kpotpq8rSTWGynzabxGk5eVtTDG1z+1gh571aXUSWJgSSNmrLW0anTmsdag1b4YRYBs6pBKfwwxRuF3x5jxymIYIBgFG6QELSg5zXKG+zIUI8U1Ujwpik72ITm2KGn5R9DYyGJQO00VWnwXmESi60NvjDghOzEoU+rJv5VL+kIrVa1zXa5Mcs2MWfrv56hCLqkBBV576d2z8P5l/zskcQBScCOmlPLogYpNV1Ik8thsSdkJ4HhnRjQ7xOhX54PIg/atpAJuuE5G21SL6QDraDBDQNm2nj5m4F1ZQSFLvnB5dGhJQFUGY/RcqrtRC8ugq7PQixHqOIx0+P4TvssjkM5MEcq7rs5TpfR9QCNsytFGFaXPSgLLagGi92Z1NsL5JEBNnjB+YB3VdTidEXwjkCgrhbM5wvu3r3PzvYmw9EYneUkkiHe4uqCbDPhRhB+/3e+yE8/us/H+8csioD1DpQmeIvgSMWT4NA6RQJsjAdolZOkOZV1DAYZm+MNrHd4D8YkJHmCS6H2HlvWbO7kgGCSJL5keA4nJVtbhtnkhDxoykVBZjK0aJLEoJRGaTCJYTTIyQdDnLXUdYUJBhC6cLkmS8namoDvinefy0q74PtfSpJB65SqbdfaBIig13KfZYksKrLjmsE9hR1GszJosGl0pbamXj2C+TPCrd/fYePmFsPbBea4gLJqTH4VARuiqW904/yK2p7byPDJsKnNCmZusVsZ9dig6oAdjUgPC+zIxEnNCKkPqDqSf0GDS1vVA/CQTBT1OCALTbCKOkA1S1GpixioXeRha4NkjlAr7MDgRgkh00jpIk/cly40p7FiihIfQlMbtmf6t3I2bhciF92C8NMQidqdj49oHEZrMjeOrY4SaFuI1bLKozbg41Mod4T8fmDjw2hhFtsqhrMN4joujYH9XRa7A7GCNwFR0UGqbDseiRppc1wzAzukq2YVxw8uidpn6yhDAnYQgU/ZOFY38oiVhtqI2mk8t1aLj+AaVORQYyOoHrjSowaaQ/c5YfHtheBCrXQlPrYjZRug/YdSBNEa8lRVnPKMMSiI4CgCKuDxTOZzZkVFmsUy42miQRQmGUBwJAPL5TTlys428yrw0e0D3n3vFsYoynKOD55UQKHQozHHh4fUFWzubmDSHOYFolLG21scHh6wtb2Nr2vG4zG1C5SVo7KWqraMhxsgcDqZ4pzFWkddV2zkA8bDTQofCEoTBLJ0yHwxAxzeQdV0hvWNiehhWdiluSASQCtNkiaEXrzv2RCts7TALwVkfQQDX5aE2q5EEiw1sTgmVTiyU898ofEmpiQ6RQyoNk0utwq4HHwiHG4oDt8Ykh0NGX3sGH+0wCcaVVpUaQlGERoT2w0SkoNZvC7DhHojhtIpG3C5ptjWDO5bym2N/v+Ie7Nmy67sOu9b3d77tLfPm5lI9EBVgSCLVWWKpEVaDAaDbhRhv9gh2w9+c9j/xj/C4Tc77LAdssSgLNEh0RQ7sSiSxSoABRSQQDa3P/3u1lp+mGvvc+4FUEqaKHBFADcz72l3M9ecY44xZuVoRprgFKbWmNKj64DdBKLWgik2kM3kJmwbha4UfhhpR4GYaUj0mRigGDW0rdn2mYJCNykD2RmsKRnbLkSw8zPPkhghJIml/nzg7LLZL2qYfcWrCx5RpbK9zzal8xNTdt91uoNLgTZFBN2Qjr80kEgVyvJlxeqhQTfgVsJDbWuBDXStbmnvo+kyYMHsQ96/fd9lF3oYVAciHOie07lYdXjsroBAN0CremWWu9G9m1d+rnseLBGacexhi66899kOD7bdBsHP+cMG2SBUlPfqZLcvIiXuKFx/a4hARl2nHcoYMchQnau/JURLIMernEXZsL9H6sqnbA8lmWIxoNoE6rpE0XBylDEdvcVqtaCq12zKith6Gh8gG1KXFblR7B8c4lxONqiomwanNNPJlLqqmEwmFMMBflXi8gyjHJkrBOZD4dG0Xia61gHaGFmuZ0wOjsmtYzScENqIUi2DgaOpW4aDId572rbdGWioUUpuxIiUoNY58pBTJjjh7rrLHOj+/rVjsZ23QIjozIlhdGp4Rb+DIRrdBzy7hnackrLA7d09dabNhm2GkcHiZUN5OMKu5TWypRD4o1K4ZZsy1AJCpDqwlPs6wQ5bTKw8tJg60g4Mm2ONW8nN00ycVA0DTTQSHHUaYqd8TAETgpcg4DOFHrVY5ynyhty1XK1zjPPEoGkbTT3WjHxAV63AFiD9he687R5Dq4nRyflfrgRv7pp3HbTQVSx3j38Kuj+rcCu4aNpMzBZA1K1CryRIgqIdQr0X+0yxD4JdKd2poFqIDqpppDyW4J1fssUoYYtVJujIraSqMDU9H1VFaAvBSu1KYZJxi91AM5FGnNkoXCXBtp52G15iPHiFDdCMtnLY4CLtJGG8VXLRcpGgBVu2G7WdcAC3TLd7EkHYsSzsgmni8XYsi65xuDvl4C6v9wspZ3fWC2awouV2etdgOmK0gQjWWIKbsKgCZxc33DuYMhyMGAyHEkxUxKhItYm0TU0FVFUksw5TRFBD4tzTqAYyQ64tddQU+YjgS3xUTEcjstGI1WpFs1kzHoxZKsXJ8THLsqZpVhwf7VHkA6zN8HWDcYayATecEFAslmsGxZB6tmA4nBBNDtYwu76gXd8wnA44Odzn9Vde5sOfPIao8KHqoUsVQSkJQoJvK6zNUcl1RGvdK8m+yKbwb+qT+1WtmEpXVeRbilan5uos9rSSBhNg14HheaDe0/giEAZdi1f1PwMaXcuNkS2S9FJBOxTuo90oQiW1p+B1luAUvrCYdSL3W6hHkgnqBmwZaQcKnyvaQlHvKewGdBvYHFt8Id9HN3KlByc3oi+kXrVryarlxQ0+CwQdyaxnthzg5xneBgb7JZPxhuUrR+x9KM00vUyUsm6fDBDNzh0UEIx5V43WQSx3fQd2s1jY/v6r3li7xlO4nVV2QSGYSLsH1eHu7+hx1x7bdIJ1Ar14IOqIrsAtpVlV70mQs8tt86p7/7aQSidqySZREgC760MFCYQ2qa6aCbSDJAaoBX9txoKpErafzecSUHUj8EVwUq0EA34aZHPO4pbZsJtNRnn8LjNrexBSFhvVVgZLF1glYPeHeEd8cItqdvs0fOl6YWnJXX9UcCjladqGsiyJ6zV5nqN1kbq1RgxcrKUs132n3hgHWKyNnJzsc311w9X8BjcYMDY5m9USYzTlZsNwmOOjZrkuiWrO3t4edVVRDAqc1UynYtYCJU3TsD+5h80cTYxoW5A5jXGWsirJxiNiG7i4PGM8KJhdn9PU0qRbrWc4ZHRNWa853B/25b0xelvqdRnoDgRgdjxi74oO+hPwdxRYby0tevrPMQk8kuGuN+ibJYQh7VAui65kQ0VU4aVDXxlUs73JTCUXvy/kJsqvFdmNPG9zoMmWglf7XGOqQL1nMbnG1JHiOqJaeb5bR5qRXPDBgY0Kn4EKEV1Hqn1R89h1TGXhVpduykiYqD6TimtFvQfxxtEODNdR4Vc2Uc8U3muqxlIdBqJVqLUHu3OeoZ9Gu5W+IrpBa3rKFt5L9rqDxcYvmmbwM4SEJEDc6cx0mWYXDDw9Xhq1KJ10kzryUYJmVgk9axtABDboyP26UYQkWshuVI+ndl38bEESAyiavUi9HyTItmDWKjUiJSOMVqACItRTZDhhKxWIW3afETp8WaCOtKlmnYuXwB6qUdiVsCjagbAKslmCIbpM9UtlwKk3stP8vQUN7D5Nbf/6NzG2eUEll0pMh1Q6aUNE6DRat/jQ0lRr5gsBfFabJd+Lr3GwV7J3sM8wz3EKmqZis9lQ14Eiy6irGt8GlIe6WjEYTTB2n6urc8bjEacPH/H8/DnOWK6vbiirFmMiShsGWcbae9ZlRTEcMRzXuDzDtw1ZXjDY3+PoYJ+ry+csFkvOn5+RO80gz1h5z3hYYKolbQxoY7FuwGYtXYG9yQRnDNYaQuMlKMXY49vi0pVkujuy4f5c3BEefJmK7WtdIQgE0K3uz8ZsZbNlhXKW7NJgmkC2cFy+a6kOFb7SqL0GCk9UUqYbrxOmJzu9KVVfHpoq4nNFeajI5pAtA81QM39NM/4sEgxkq8j4SUt1YPqOcjtSmEqaHNkc2kJRHohBtm6EfI6G8kikjfmVxq4UgzPBHH0mN7lbJT6n0gSfCYZceLmpZzlxWhH2G4LThMyi6zaVjvF2oL0bHK3ZyUzjNjvtAmsXbHcx2J8RJHRrjHRnPpLKfRCIIHqRkwYTiRmSrZkoMs+UXVZH29J4N8B0iiqfydvoxDooTwLZXAvdy0j14HdEamYjjlXdOW2mEbdUEqRd7M9l9/rtUBgNpt5m18UllMeKdhjSRrq9toJLPN+ocAuNqVLmHMGtBIYwIX1vfUeQwfb4pMO28+8/ZWjjnSz21nn42za5lE4GJT6ilAW19c1U2oryqt3QlBkb5cAHnj2/5PRgwmq5wDtHVVXUTc16vRbFVfTUjWaxXBNCxDcts6srJntH7O8f0dQ1ZV2RW8f+ZMRqtSEqzXgyYHGzYpQNhYfqaw5GY7I84+DwgPlsicdw/8EpB/tTisLy+OPH3Fxe0tQ1w4FDGcVyvSHGQIZhPM5ZbzZE35LlQ4zJmIxHlHWNte5zB1sMTdKYmJ2W6BexBb5I3fV1rw4X7BpcPQe2+336t+gDqqz7GWiZj0x/ojk7kgwhzhzRRnSlUa0EMXkBen6iXctNYxrBYU0F9VShoiYYGffRFsIMiFYwM+8UYQiDy4DPBZdVDaCilP9AvS+OSiCZUHhYgoLNIGPwqaG4FujA5x1EIDdbGyG04MdRxjxvDNEFgjcoEykPHKN1K424RL/5wubUHUXe59YuDBAjX2jN+DPKZAUeiNsMK1Gcejs+D524p/NW9cOAXWiBWPKE4zaqV0iZVHb7PI3BRkr5kEcoFfV+wM00+Y2U95v7oadMuWVidUzkemgmkWYk0IFuJABv31OMtP0gEV0m8n66kefqWgu/VYsajdRA07V8Vl9E8R7wki1MWtakAAAgAElEQVS3g4jKkay2TYKmlADIwfmS83cnuN7aaL4kqH4Oi/2C9UIBVif+qCiRaiFOK6mijDLSBPEN0W+oygBhwGfPNN/55iNCG6iCRykxVnGuJHOGtinZVA1BGUZ7R/j5HF9W3MxvOHrwMsPWs1gumYzHDEY5r776iKdPzxkOhzRVw7pu2DQVh9MJTVWT24xBnjHXijoorm9mTEZD9vb2eKKfkOc5RkXqFuq2QSmHUZGgDDpKo8I5R9VC1ZQoLfBA4wG2QoIeg6YbqrjNUL9MIvs38bf9mayO8L4rX93hp/afLQZRWjUGvRQT6tFnCveNAfWhBNZAkBvapQs+CvZmynThezEXiUYwVbeOBKdY35Ob2a4izViCa3WoaAeW0VPP4mWDWytMJXSbdiBNE1tK0GwHchPl11JWqmcF6qUNcb+GTweCAQdpUJlKDDgawJLMTBaGmLKqYBW+NCKtVRCthjaINeGtTEd98Z9BaGe72Src5sZ+0XN+BquTm0Ybt8T/LnE2O3Z8KbiSGj661LTTgFmlRqODkAVUED5r91rRJXWWvBtuIec7bESMErXQulSr+yZZhwf7XK4Ru0k0rp3PErLY82abSZRscxDFqwLZsH2H027S/ZNw1eDYSmWVYLdds04p2TCaqcBPIjTYPY93s6VtFdAHTS23QodFbx3E5LFfFHC/bL2YkkubvvLwUeSqMSiCUigNqvPPbGvQYstWbjKu53MODw9Zr9esNyVoS5Y52rqCAE1V4UPN8maBV5psMkZrw2hYcHR8ymeffkpmDcY6hk6xt7fHciV2glfX12Q2x9oMtMF6w+HRAVc3cz79+CmOhlcePEiVWmS1rpiMC/Isp1yvmM3nRKbYeoVWYmBTli22yHj2fMambNBa4yxEbs/3koDZnTBQSqbXygwuc+dxafffcdz62sd2pzEmSqt+5tStYGDMFpuNEdrk/tR4dOMZnEfqQ0BH7FL3+JkfRjFwjnIhS+cXiutAMHIjbg41vlCUxxGfw+iJSoE53QReOv52E1mfaEbPA81AEYxgrmoJfgz+tMaUOdlcGA433wTrPHVlE/YaaQsJ3BEJsvkN1Huqv1nblGnrCN4qimeWbNGkDEdL8IlsaVo7KyqBD6JRyTxcQb31fL2Fu3Yy2W4T2zH6+SrXrqy+C0y3Gj29THTbqCGkazc9pxu7Egc7xtWk4GURyMEAHpq9IEMGg5Ls9UqyRjl/CU91CUNfQXGhqA6E7aEi/fysaMUnQDeymZqNnGuisA10pQh5yihNwow7GpZK3rYRoQ0i1LIktOzFMWaVOLMpc1VBfXFQ7CTGodtCYk/l23o7pGN9R478IuuFAqyzNs2qUymSh0RVSv9p1UsC20aMOjblEq0VB/sFvqlZLlrZGYwihpamKVmvVwJbVTVV3bKurxnv7/HuO++QFQX3Hzzggw8+JPicV186ZX9/n/c/+ABjDVmRMSpytFZkzqKVwlnNo/snPP7kCTrCydEeaFhulminWG1K1us1o0FO5hyr1TqJDDx5nuNMzmw252Y2SzO9QCvhwe4avnQm4TFGTGrkddnsl0EBuxnu163kUpmMPYlKoVTolUr9Z00zufogm5gQKka0M+h6RJi20Ch0Zbfd4SQ80JUEVreUG1k34NYBUwaiVszesNTHHtUoVg81B38N1b6Yg2weedqxZvSpKIyaoTS3SM2udiR/1y5QnbY0T41QgkaBVw5mPJtNcMsBwaTMtem64tK8cItIsElK2kpm5gF7bcmv0rlR6eZTCYPsa+3uAG7/HDuFWodd73gY7K7+HO+wCb7y8941s9Jn/EI8sY/CKXtMMEh0ETrBgZIssy3k5dph7OWsfaNMgV0o+S5KHiOBr4sJUq7r1AC1K0W2kOqlOoi4uRjMRCMMA8Hb5fn5pbAVmomRKQQaiArtJcuNWjZzXSlsUnq1wwTHpQDafY4wChJ8VcKBw86x2D10O4FyayJO//26Y7j7+P7PHesg3P77F60XCrDGWjkprafzcA3J1Uqur0hUQf49/X21WbNa1xgUhRPj5dVqgdUjYttS1yWtb1ksS1ABZaQBNcwd69UCH+Dy4oL5coZRIwZ5xvT4iPVqxdXVJePxABVaFJHJeMRqtWYymXA4mfDJ42dMpxOODid8+NFnlFWkGEwYFDkq1GgibV3TNDVKRarS0NQbnKpZNjc8u5zRNol5TTre6X99gExH3GibpMEmyWG3o2XgNi4L3Jp2+7WtzMnN1BPnU6mza0KS2s2xM+QOAdVqtFJkyyg2+E5KufxaYVZQT1Rv5mEq+Wk3sZfUaq1oRnL12bkh6khxoXBraWw2E4Wa1jRjRbkpsGvJOHVNH2CbsdxQxaAmm6yZLw6Z/hgGD5acDJZ8+MF9ilqED81Q0088BUwd5bWi0MY6Tb1bChQxPAtiGnPXhKVrUN1peIhEXIvjl0sOYOm5seNLw+fx2LsQwle0Pndj7waSW8F2G1B6A+rkVdCOBFvVXmE3imYqAYqwNX3pgm1H7I9KuKyq1b3ZSrQJWy0i2VzjC1jnifmxVH2AN+k82FIamNEIA8WuwS3kGqr3JICOPpXKxlQkDq88NmSJywtyXZLgiEFANaqnjPVerjsbZPc9bjUI+wMq/3X3u8x4u3tt3H6teOfvd9eLsQi6E2QUOkQRHaSMjBQwBKaNqKgxVtPWDRdXF9TVA+qmYjAeUjYNi/mc4Ctm8xlNI2YqWimqZLJStA3OGvanOfN5RmY0TiuKQU6RD7i5ueHk4Jiri0uMy5hM9jm9d5+z58/IrGM8HvGdb/8cpw/ucXR8yuV1yT/6z/8RN8sZ1+fPOH/2hHKz4ezZU7xvWW8k4OID11VLHcT8pW0bjBa+r8QlkQqjEqOi+0lkMCiIsUWpqqdq7WarX4TNfu1LKZF4KpUGGm4DvxJ8I51kKZXlLpKR1qNnFarMiIlz2Ezk924tL2PXsRcEgNz4towiWCgDKmhCFglDT7vSeCfmHM009sR/t4TyJOIWgqc1Y2imAT/2ZBeWzLY8nM7xb2tu7g34b976Pk0w/OD+KZP/YsNsU7BeFfB4gN0ozEaaLf3NjMARXdDVPmKqiKlCj8OquC0P7940XfkZtIHxQCxPrRWv10TZAm6zCTr4BbbB9itcnxsZfbdw6m7+3Uw30s/TkhJcssyQSSAbPNM0Y2jHYYvlukTTylJQ7zJhEzBr1XNlfcFWhps2yJBoXNFIVhsy2Xx9ovl1vNx8FhietSweOXyhePivPPllTTN1LB9YgpVNMSR7CxUE142afnqBLgVTV43qZbMdO+IuFHBLRJCOS8dsENFB+metenVXF0zVnWD9t25ydTZ7u/8RU9mbsjbprItzUVtv0G7A1eUNZVkTUWzWa2ymIWbcXC1pGnG18r7BGicQwVraxMvFmpcfPeKdb7xCtVqwWqwYD0f4piHPc1rfcnQ0SlMHSmJsGI8naG05OX7Am2++STYasFqWvP3Wqzz+9GMef/xDAPKhZVMHmUFVBzIjyqrlfM56tSEah9IGoyOtl8mwAQmUTndtZoVPREOjFZkdk+eO1WrFer0hxkidmkk/zWXra1ut72/62NGQYugdoSIIbhgAHRBuC+JPEAJmXmOXhdx0Skq8btSIWwqH1TRIsEJgIF1H7KrFriSzXD9QxPsVftywaIeMPoWoIoOiYb3Ok0WiNLeaiWBxwomDZhKwJvDS8Ib/+sEfAVCohsfNIf/Za3/J75+/wc2TKXqjUUay2a5Rs1s6u5Woy0wZCdkWU9VtkJLSaLEyjKQsducYJj+CoBXBafTAYtYFer4WD4YQt5N4vyyQftXnffflukDKF9z0kduOVsjvdSOcVN0mN6w8sn6YNP/JRFsn8n2fzVYknFuCXdgP6MYwOI+MP4uUB+JjESyYFvIrxea+MAOClS4/yDXiEpc2OMFx5686Fq+Kcc/632v4D978MQ/yGT9ZH/FX5/eZX46wl66/NjrLRUqppIJNbmGdkKL7+jvy1y8r+3uGRcryuydLwN1iuf3z/h2Za7deTGgQth9Qo0XLnLIcgQqC7PxKSPhaa4IvOb+45PJyjsscCijXa6pKskOtlEwoUJqm8WgUzok44eZmRkDx8oOHPDp9yPPnZ5zef0AdIm+9+SY/+usfcv/kkKpqaRrRVo+mI1yRc3B0wP7BHhcX57z/3l/xR//6+/z1D9/H1xXLqkEpxem9ffG4tQanDUSPD4GqqWW6aJaLnWkat2yUQikjcEiSRepUL2mtBSawGqVkyOLGlZRVSV3XNGl2F2xx2K993S1NO+mv1dugmz6f4EopmzWGGAJ6sWby8ZTFa7q/qNwKdB2TGEGutmgUyoswgAgh0/hco714ytbAa6eXuAdnvP/0HnvjDa/s3bAY53z4umP0oaM6SB3rKJmHyjzRBkZZzXfHn/Cd/FMWIeN/vPw1fu/jt+DPp4wfRx6Uki3rKlLtG6o95Hq0cjMn/pVkrbV0y6OR/0IPqkFIpHO50bpgSy+hFdK7lkkITqo1VbboPENVNbEsiWXKVr8En/2qVk+/6oJr91m1VFYpF6Afi8IWN+woTn7qCZmmHSt0KaT9PhD75GblE26agopZSjkPYgLTcY91G9n/oGZzYqn2dG9Ao7x4A+hWXj+k1wxGXicqWL2kqKeR4s057x6fcV0N+b3vv8PwE4nm9V7Eqh3xS/ouIVkeKg+294elFyPc3iR3pjYobk1wUAGUgehVzz7YNcsJLgXxHXOZF1kv3OTalryS0XovOv02toQYiDGk8ytjXazSLFYrPnn6lOOjQ8r1BoPCas2yLKnrhrquMUbOwmiYczK+x+lLj3j51Vc5PT1lujdlPMh59bWXQOdsyprZzZzReExZt5gsE3WRckwPjiiGE/LBgLqu+P6f/hn/7P/+Ay7blibP+cU3HpAb+KufPOfBvZfIbOTDx49ZbJZc3sxZrVZkVjPKNL4saZuWqK0EIiVnM4RI2yacUif6mrZERM+fZRmZcxTDAU3TsFwumc/nfZD9O6Nq5VnKqmSjk5lZyZ+gVagdl63+ulEKfJBrsWo4eK+iOigEOUjQQLQKs47YUkrtPksC2qGmPDJsTrQwCF5f8+rxDa+MrjnOl/zG8fvkyZ7rg/Upn0wO8bl8Pl2pxG0EZSL5sOFksOSN7Iwmav7J4tv8s9/9Lvm1YvhMPvHsDU07VAyeK9wysnxVgvroScRWgfzG4xYNygd8YYlKo+uAbrc3mbx5atrE2OOzXQOMxFDoVrCaMM1R4wxd52JwM9+gbhYyKPFnPOSwU2LtNuMiKfBqJayILrimjx374NJ9CSmpTSUwQVDS3Y82NaHq1MBOgcVspDnp1rJZ1V7hjxT1PlycKLJZhl1us+W2gGwmjcp2FLFthzfKZ6j3JFFrhxFe2uC95i9//y3BazPJeLO5YvhU4QsYPhN/hOVrnjhOc7lrjd5oybpT9i7TG7aZZ+wCKtsk9lYAjl1gltFYu9NpdSvMBR9Boz5vGPNT1gsF2DzPdzBET+vFvahpIAS/Uxql5CgomjowW8Bf/ug9vvXNt1Fti0xmCbRty3K9AaXJjKbIM46Pj7l3esrrb77BYnHDBz/6AdVLD9nfm3D/wQMOj4/Ics+DBw949uwZy+WKtgnkuaJtG0bDISFGfAzcnF3yx3/0ZwyHij/88ZLGG8r6gt98+5CXHx4zHk843h/x5z/4IWdnl6w2pbiFWU1urPgsGEMTAjHSm2X7vvMutcTncFUtG5DNHNYJs6BpGnluanz9XWSwschul6chQCN8Vyols8y+yHoP0tjuFfmH5xzuPWB9ZG6VULqNuKUXw2OjQClCpqimmvJIsXyzhTygzwo+unrA2Utjjscr3t1/xq9P32PfrBnqmvdPT3hv/hCz1OJ+FOQmVzoyHlSEqGgw/Lg54X/6i19m8kyyydFTgWKyleXi5w2rlyLjTxXtwxI/yDj8YWRwVmEWZW/oonxO1CI9ilqhfaD3HkiZq/I7zS+lth4FsG1sdYFLKfzAEnKDcQbrAwRP7Cbm3G12fdVL3Q6i/WeDnht6a/WNHEXUBn2/xAdFmDt0qZMEOklQY3oxFXsTbRTM34D4zTW/8PAJR/mKWTPgJ7NDrmYjVucFbqaxaxGWhEy4yG6R8PskdlAdPJ1FkcBWBv3xAF9EijPN4U8C19/S1PuJTWBh9hYMn8PkA0M7NOJP0TMm6GOQUMxin53GlEkD/XfqoZS7LAMVE/YKytJ7XihB1hCR1YsF2RfjwWZuS/T1EkSiifggPNht1zXlsEpMrq0xzOcrHn/0GZmFYZFhLXjfUDiDy8RrIBsOGe/vg438L//b/4rftAyHBY02HB8d8s6br/Ef/ye/xcHhKffu3cM5x3Q8JHeOqlmSGcV6PsPagrZq+MEPfkiMDYWJ2HzCr/7ab/L93/vHtDFHsWZTlUwO7/POOz9Hkb3HJ4+fC1jeNBJEQ0sNKGvR1gjelEq+jo5lrN7S06LQuVTKfqQajWRZxnA4pKqqzzW7vs4VcrdjaBEla+2myVorTZpd5dGOeYkyWh4DjD6cY9djQDLUYDt3eTFo8bl8/2YowbU6ihz+mWH6ScTngbbQwJSbo33++eFL/OmvPeK/fe33GemKv3f0Me99+IBQRPw4YG+MmDl7xWKdc5GNWYWcf3L1bbL3BmxOI4PniurA8vxXFY/+uUd5k6zwIrHRqKOKq28NGI8HTD8C3Qb5/oD2wiAgRqIR6W/Ugst2PNhOox6t/O5zVJ/ODCZ2MUjubD0ZoKu6Zxn0/gQ/69Vl4T34KIFgy4LYPrSbhOoWChZDtI69oQo6mbNsFKqXr6b738LqjYbTl695/myfH/zTb4gT1mEgTFruP7ymOL7hJ49PiM8d0SqakfgDaC8MDqCnbPUTDwYeNgY3V7i5NMMuf1GwfF0Ljxkka68ORP0Fwl7pegLNeMdXNmWwvRfBl2Cm2w2Ez4HXPVyrSTzdO8f2q8JgB06miUUi3qTOAZEQQ4ILvGRBOx9Oa5MqLk3rPSoEFnUFscFlFuty6rLC5AVt2/L48WMuLm94eDTmrW/d5/uPZzy7jOwdRv76r35I23r+o//wtxlMpuR5jneGfGC5vljjlOLB/fssa09T16xWJcsKHh6OePt4wx///u/w1vEQ42BzteGdb7yO1o7YtrSN5/jwgGxQ8OTsjJtyw9CK0bPZnasEoBRa6WR8IydFobZuW6QgC7eECdtjIq/3dfvBhsH2NKsQUY2XgJs2CBmbErajTXY/351ZXroOwo9NP4PVYpxtkvFLYggMz+DgfY9beC6+ndMOYP8DT3HV4nMxUy//z1P+h9/6LX77tR9xzy3AROLQo50X74MEEXzj9Jx5VXDVjnmy2sOW0BgRL5hKc/8PAusTSzsSzXtwMNgvqUrpzGgfaUeW8tCSzz3FWYmdVYTC9lMRgk03I+m8amlqoZDM/G6A3dkog4akwpF/uOVlIPg90G9UX9WKZgcdiMBOydvf/Epxa7Jr91y1fWLIpEHUBw1PP2QwWIjFdrpFtBE7syw/usf+HMpjGdGS3Wh86bj5+JTy9YqD4wU3eoz6LKO4EqvEgHw+X4jKKlsovIN6PzL4xImN4WibabaTwPCxSd4DkF0nN65xpJnI49xczlF37aWTuPUfSBX17lTZLa6+c/Ci6sUIPWsgRdOoZdPZKro6eEF9Hue9s17ojLdJKtqZvRhjiFhsKvdNZ80HfQDpsrWmqVmvV1gVsSqikaMXoiYqTWgCeybj9ddfY10GXnmwz3Lt+cllS3H6Gp/MZvzGWyM+/Ogj/uRf/zG/8g/+PpPJhKpck6EIzpCFEl/OODp8wHK1xFhH3HvIrLnk3dOCN46GDDPHzfU1zo05eXjIxXzDzdUVvg0cHe6xf3SI9y3Pnp7RhoArbAqEEjS10mBUr9NXZivf0ykI746F6Ry26rq+BQ/8XQTYDlMEhMupdY+VEhIf1uwYliRq0a0hiSGilyXWpSemqkUVt70aooa4Z1m+pPHOYKaGeh+GTyPDs5r1aY5bB9b3ZPR29i/2+J3feIf/6ht/yvhgzWpRYDNPPW1QNxIgq9by9t45Py7vMXIV9UTcmHwO89dh9ZIYcNtSUVzC7O3AQV5TPhuRXwtWWB5Z1vc0m2PNYRXIbirJLp0h2OQbsVPyh50AGq2+E1zpoYStLDb9aAOqbARa2V1a3T6eX8HaHXUSYRtEvqR2veUqtYPFRuh9fXdxBjHPFhxXt4rsXDN6IiyNah82p/J7u1KMPpPPsnoJRj/Muf6mBLtgk9dBSy9xRUkGWx978nMRF/hBpB0jUI0V6fXwsaEZyYiYwXNNcRFZP1C9aMXd6O1GEgUnjVboW8HELawTdjJVoHcf2zET2IULejGGui2d7oNp91h9W/32RevFeLC9ofBORrZLP+Lz6XKMsQ8sPrRoo6maFqMV5bpm06zwPjAaDjk+OuKt11/h7PwGFT25yxkOCux4wsRWFEZhjx7yw0+f8J31AmM0zmpM4xnnHlhx9vwjTrAoTlAGVmcfowvL/mBMU1dcrNYcTA74+Xffphjv8xd/9C+4Wi6Y7I+ZHu2xvz/lfnnCbDZjtlyhgxdzkJ2AqFO3XKfRJ23b4px4vMn3lYy+aeTm2s1iu+Drd7r2X9fSjXAypRLaee/PkQvSb3uivMA+sW17xZfyUlZ32ZoOYK/XQgUDwt6QzYlj9ZqnHRkmH8vjfK5ohpY2VwQrWWZHMK8/HfHk1T20imRFQ/Aak3uCsRAUHzw9gQdQDQ11sGQz4URWB9KYUUEcu4KD2S9VvPLwksvVkMkHhmwhLl71nqI8ERxx9TDDbjx63aCcZOMyrUHdwjG7rDUaBE5QqnfhB8Gfo4rYJvTHs8dtY+ixWmnHI1XeV73ubG63/rFD7fiCmNtlusl7tadvpSxNJgsIq0TXqp9EsHokmW29l6Z6lHIOLn69gVaRP3OYCh78juX5r4AfBHSbvEw668NuwoALNK83xJtMIIgg1LHxJ4r8JtAWUJxDPo/YsmX+iqU89ahaUZyZXpodLIRBErtUQLJE7G8z3YWrLVzST7hNNoix4/bq7UYVsu0UiM7p6y79TV7sy0/PC040SM0BpfrA6VPjpvv3u7Onuud1zIOu0VMFKOuaNijGg5z96YSD/T3apuaVewd89uQxb70y5HuPRvz1xY9542RMNBn37r/GT977Pk+enhFDpF7N2awusW3Fpmwx68Cnix9x+kbk448fQ1PzrV94i9/+zd+QHbNtCSFyOVvyB3/4Z4RNzYPje/jYsrd3iPcRoy2j0Qgf07TuL/g+WuvEjZXjYLSl2lRUdU2ei8j+5uYG5xxFUdzyJPg7M31J+LHqsPIAKsE2hBQITBomH3fwwg776ChHWkxRQJ5PiOh1msXlLGE6pDrMKQ81qg5U9zz77wmmVu/BzVuO4Vln5C00mOXLkdFjzZ9fvETrNU1tCUsHOqKDIl7mYCLvLR8ye6Xg/HLCdC2O9dn3rvnv3/4D1j5n5ge0QTNvB/ybs5fwf7KPVrC5pyFAMxVakE3uUfXU4aySjaKjpyl6HBadHMgSNqt9l75o7KZFV55oND43mGWNXlaEcU7I7Db5uDtkMt7Z0b7K9UWNrJ2s7RYHNDWtvijcd3PXQjLX7sa5dIKAdix+sMNnwm+NWgQE+qOMjtK0uRdYf7sirCyq1aKycoARTD0mi0s9t4QpMPKwMphKk18r8utAPg9MPm6IRlHvWy7ftazeqkFB9tRtM0kjo2iiTnhslQLnrt3HjtmNilsIArZBM7oEk5DEEkqy9m6keP+aPUSgenXcl/rN8jeYaLD7546srrXGWtvPpOoCaoc1Anjf0rQ10RpI+n7QjPIcg0qTGRU3NzPe+carLDY1n57PeHCyx+F4wtprrsuIWv4166szPvjwY44P9rg+e0pOhW1LeY/GENYrFj9qGQ2muMxxcv8lossYHx5T1w2zq2t+9MFfsNksaGOgqRv29qcsZ0vW5QalIMtyIuuUhSpsanJ12Wpn1BKDyGSbumY2v8FojbUD1pu1+BekDaWu6z4w9yf16w6y3X2tU3BNQbbvbDsrlab3KNqdslf3vrHRB1TrUU279ZLVSoJxnhGdXEpd9z2baarXKy6+m7H3vnAgq0Oo9zVmIyVgcLD3gRhmP//kEHKPvnbYVm27voruf5y/d0zcb2hHIkb4xsE1Tnl+afghizDgSXPA//zp97h5/5DMyOtGpQSva2H0mWRhwYAvtCi+amHARKvBaVQjnz9kuvc67YJVcBq7bGTAI+CnOW5WoqquOSjVgiprqfpUMoUJMXk9fMXnPd7+8253vPNiBW5xQ3uScfejwxahd8JSQQKVqbpjJTitHwYZz2INe+9JkFm9lOwC0zVWXGiG7yuWm4KQQ7vf4lNloMvUOA2gYhoNvzHEcYuuhIpV78HZr0TiIIJ3qEqneWMRd+Fwsx0ZrBb8FiXNOlOnYJnT46y731EFtveCiltbR69oc3kdlYJrcGLmbcpEy+qQMdJ7dPQv+Fz1vrv+RgG2C55aa5wzRCJt06CVTkMR6z7Qdo9r25aqAVSGjoo2RCbjCfdOjrm5uma5XvPp8ydsmoaXH73Er/zSt3jy2SWfPDvDEnn97Tf45e/9Ah8//oRnlws+e/KcejNjc3PBEM9Ae4aDnE1ZUbUVi1nD8BgePnrIhx8/J+ic6fScvb0p0XtOTk5ZLFe4LMMaQ103XF7PMK7AqMhkMmWx2tC0spH4EGiDKLq66bEhRNqmxVrLbLmkbWsZCV5GVqslVlvyIv+cAg4kC7ZfcbPj37V6/BWVSOdaZkwh2ZZqPKpsRNm0WhO78aNti4qaTj4b6xpljWyW3bXhLNUrB0QF2WWJm9eMnxrs2lDvOezrSy4Pc7JnjvxKJZwucP/nznh9esUf/d47HP1lZPCppR0asrnqg0FIdgPklXkAACAASURBVHemUn1XuF1krB8G7FrxFz98mbPVmP1iw6vjK4yKHBZrHh80VMbJ+BonAwrzK6H5EMEPFOW+xtRGMFOfGiMpUAartwYwAXRIstEYsYsKVTdgNGbdoFal8IgzmdclBrRhh/ZmtjLkn8F53w2q3AmstwQG6fc9ftj9F7eBtRMF9JmaB1cCKDGHiaSpEJHqSFOcR07/WJzTbBkYPN0QMsP6gUw+XN9X+NwQc/EI6DyDuw3AlOKkFitHyKRxlc3Fj4JaoxoZy21KYQ24dcQnRZkvRE7djftuR5F2mHwmSumRRCfjbKKNPQsieef3ajTtBQogCIa/HbqoUoYct/h6V8TcLUz+thDBdsCh6X9qI/zT4EM/NqXji+6W1sYYggLfevK8oKlrYgwUecbh8T7LmznVquS8PucP//xH/P1f+g7feveQX/rl73B87z6HRyc0dc3j5xcMhwV1U/H0yRzVbiA3DEcOG2FoHXVVUdct5fUN3/3ud2haTV2XFMUJDx8+7IUBs0XJ5eUZ52dnrFYrQlSsNjW+3ojOWWnatqFpWqq6QmlFnmeQxR4iEdGBpSxLnHOEEKjqGmstuct7dkXXGOyye6CHVr6uFTsep9Zp99WSsWlQTUAHUDaIW7+1qF38MEQBWjFi09eKSDgOC9Sm6ilO1Z4BPcCuWswmMGwi6vuWczckP10zfTDjcLDmW3vP+XvjjyhUw/8z/6Y0N3K5KfIroY/5gTg7dZmXzyOuVthSso3mMJC9Nsevc9a147W9K35t+j4vuWv+IH+bxcs5Hz05xlsnTZQ6ZU2tTEgN/TgYMJVH1S2qboVJkTlUYYVtEY2IDXwQ1VYZUHUKpiGgNkkOPchFaly326oAuf5vGep0fPGvaN1lBai4DZTdqJ8vstdTWyjy9u/izk8lvr1SdstxtwuDH0b86xvs2yWXn+wx+sQwOJM08eI7Y5YvQ33kMWthdNiVwkcRAaBII36k+94F8pjmgBEgu4kcXyjxDDYiUIg2GXK7ZLKdCW1K5rhJszPY2HOnW7f9cnajeqlr5zGQiugt1pzQMZK1o24Fd9WNQCW+iD13t4dXuuOtuM2RvrNezE1rx5S5m7PlQyvNmxhuNXM+DydAqMVQwodIllkmkxEH+3u4QcH14AJftwyGBfPr5/yLf/kveeedd3j7rTdpL264uFny2ZMnvP/jj8WmUMP59TUZFYXXjI9GHEz2xBVOr6hWgZVSvPryK7z65lu4YpC4qpHFYoHScHRyRCAy3T9ksVhwcX5O07asQ6Csa5TNGI0n+LZlFMfp+0XatmVTythpaxR1XYkpjDHEGLHapBlhSEOjacm1obG2D7C7G9DXtrouuJaLLWQyLruTgkrm5bfYYTpePZTRN7t8j08SArHICAPH5shSHmrWJ4bhhQSnNpeL9fhPNOv7E87fznGPPD9ZHvFks8cHV8dcP58yuNByASvI5+IJoBsZfthMAjGTUi2mxqJuwR6W/Hff+H3eyJ8z0SUm1X2ftQcA7GUb9vdXLOyAZl/h5w53Y8hR1F5hmojdCAG+PsjRtRNoow09bqoaj1sLBqjagB9mmEW19coFsIZounGnoJpWxnobDXmOamp6C8gd562vat0Kjh3OmAxQ5AH0WfiXUYnuWu8RZd6W3Wx/55by5OpAERuF/cshy+mAeNyw+vmGZVCY3DMaVgxVJG4ywmqUbCxVL2ntBir23sEtyVxdssXBhagClRfD9M2RSsYzsQ/MphKZtppLFuuFQSpeB1mkGaceQSvvq7TwaO1GKpLghLEQQ0rgNT3OGo1AWboCdDKRSbhtTMG4N/DuySMRfkq+9MIBFiDLsh5L7KCAXfeobu1SlQDZ3Y0has10POT46Jh8NGQyGnG4v89ms+Hs+TlEMdO4ns/49PkVk3VLWW6o1jM2yzmnRweYGLm6vKCtwU0G2MEhcf8ljMvw2SWrzXPeeON1vvFzv8Bkf5/Wt9RlKU2uKBjrYJCzNx1xfXWNs4pBYTEmYA1s2pr1aklZrsXf1rcYY+kMlE3mBAppkvdt+r7OOZwTQUZZlhCjNPN821sU/l01uoLVcrM5TUijuaXcCygftkqjEOm6371PLPSeDMpocI6YOaIx+L2C9f2cZiw8R19AdSAzkorLiGliP+1Azy1Pz/a5yMf41uBLQ/7EER1sThXBRAbnYk/XDpMB87TFuIBSEa9zmBmqw8g//MZf8V9Of4BDcRMCi2gpo6EMDh81R/masKf5OCoWywG4QDvU2KUiDWHA1JDNWsxKzqGuvWT6VvcCgpgGIao2YFaV4M9dduoMYSCbqWoDeiM+yFS1ULS0SkbcaUN6UW3l32BFLaVvV+L3OGtXxoZtNdtb9H3uRejx2/41tPBR/VAyteoAsrkim0XiUlHtC2zjPs4EQkmwRDkZ9LSlfK5wSzFzISoZW7Zjyq5aySZXp5F2IuNnohXIoJmGpCYT5zO7StQrK8+p9yLtUKh6ZiPBGSPBvOPFdmO4Oy/Zej9loV6k2DZNU/BF3AoplNC7NAmSahXYnaTD0A883cXmf9q5fTEllzXkmchlu4y174PsBAxjzC0ObBdkjTH44InKMBoUKCI319csr2cMJiPyLOfRo1eovSdqGE6mHB4ecHiwzwfvv8fTZ88onOH1Vx4xv7rg/r1Dnjw555N5xfMfn3E0y1HO8dlnH9POb/jOu+/SVjecf3bFT37yGS+/+gqD8RQiNE2DsQYCrBZL6qbGWMPp6T1urmdsVmsm4yFGhYSngm/rZGqjaJqWvCjIBgNKwLdtT8NyzqG1pqpKnLOUqXwwWuGBGANaq68dg42Zxrtt4JDG4p0H7Ta2MrcNuN3vdoYjqrqR7M2KKbJdR0wppZuUdrC5JyCfiqJHjzYynm54tDfjh4/vY8+dGG7fD6jDCi5y1vc1408j2SIR0We5TIdVsP9xJFt4zv6eJtctj1vH/zH/Lh+tj/iHh3/By+6Ska5Z+hytAjaZmPqFkxtFReqDQMgFjw2LSDOyBKNwyyY1tBSqapJfg2Sk0VnaqcwLt23XTo7gRU4ZcoMuveDYXXTRiWusDYQEC1grdpFf4eo6/MGx7WR3Aba7+XdW2METb/kvsA3OuoWQqb4J1Ox5cJH6kehNzZWluNBkl/L4dgjNSFgC0UVCIWNfjFVJxgr5lUyJXT+Uc50PGtpWE4LGl5b8U8fgmaI6gurUo/drnPXUVwXu2hC70TfJG8DWyTQm2R12EFA7Ss2plDX32WkXLNN3DlamM5hSyQRaJcmBqADpWQY9EyGq/j26Y9WPwOn8IL5kvdAZHw0HKUsNAhLfLXMVfUOra+zsjvnWPjAY5kwGQ4ajIXmesVjWmCyjWlSoWDGeBFyWs3dwyGC4x3q5oVxvmK9WzJYbRtbSNJ6H9w9549EB/++/eZ/3fvBjzp9+ysXlDbl1rJcLfuMXf55f/OarDF2gbjcUuuT8ycc8eO2bOJtGuwSFHY4ZHx0TQku9qTg7e0ZEkQ0K4nKJbwNGG2KASMRaQxs83kDVbGhjZJAPCKalrDZYa3EuE6jAGnwMDAY5LuQ4a1mYDXXbMCgGX3sG24zszm4bE591hzLUfR6Tut516P0lhNqVICBAOUcsCsK4oJ46mqHYHGZLj881tpTA04yE++oL6fjHIrBe53ywOYGZk2ZEnUq7Y8heXmJe99z82332PoDp44aoBdtVEdzSc/lzjle+9ynfHj5mFTOO7ZK/ah/wu9fv8p8efZ9C14xNxbwtaIMhdy3ZQYlvkxPadQYruSabocKWqi9/xdA4ysQCQKkgPOimxSwg5oZ2b4AuW3TVSAabGYJRaK2ILk2bHQ0gRkKeoeoGtVhB5oh59tMEP/+/Vm92vRs02WardxOr3mGrO5nsUJE696iU/YVMssb8wvQ8ULOR1/a52Ep2UteQSbZoNor8Uq6Hal+aTqPPFG4dKW4ik09gfTpg9SjHHzVi8TCzfQMyuIida8z5AJ9FVLLH7Bpr7SiKlFcJXKDTYMYOyshugKiSKbfgxl3Hv29a2Sic1o0mmkh5JMmBXcsGG9LnEO7z9j26DF8nn4xgtkH8p4kNXphFoBLVomvQdFlYCIHoA771t9z6jTEURYF1VlymsgFRadabltZXLNdrTFMzNlMePXzIvZN75HnOoCjY25uS5Y6yrJhOD/jmW0Ni29D4hunBKW8+PODR0T3ef+s1fvcPfsAHH/+YdYwYpbj36AF7RxNCW1PXDfuDjFBMBTeOEW0z6nbNzfUVl8+espzdYDLLeDAiyx4w3tvHuByjHZeX5zR+gzFGsvAmYI1hmA/wQVGVFcPRkGKQUyYYom0lw40aLBq/LhkUA0zCYdGKsm2+9Fj/TNbdJodPMlCnBbqLCB80BDGR7oQFIBSuwUCChDWEwhEzmzxfA26FNH+iXHg+k+5xeRQJmWCocehxwxpjIk1jYNqwKULCBhVsDG89esq39z7jveN7vP+LJ9z82wPu/5GnuGxS80KxOY380tEnFKphHXI8itdHl4xNxVBVABS6wanATTXgajaiPS+wa+HCttNAfeBphxq3kA53cIrh84hZJ7pWngJsKSqvmDtCZjCLUhz8xhl+5DBli/IRW4uIww8k6zdKocuaOHDEgZNxPR3UUH+1TS6Rt7INrImfuSXTdyecfgxLd576LM2KlSCFNHtiN8wypWXSfZessDPW7gKQKWUWl6mktM/mMHweyBaB4BSr+zo1KSV4uzIy/swTMkNdidmOW8gm1w6SB22CEdxcoc4N5XGkHUhQtCslnwdoJ5HqfiNZdZoT5xuF7awUU1baDiD6Hcy0yziTgKKDKmRMzhaKQAt+2zFPVPLFbUeJ1ZJGE0X7001fXsxwO25pKkYbjNJ4H5KGO1CbQKBGKX2r/N1sNtjGivl0XJHlOa1vyPIcZzW5zRjnlr1RTmFhuZhRVTWj8YRpMaRF8fLhMccHe4yHQ4yKqFBih5Z7432mx4fMa81sOWNxc8PPv/su3/n7v4ad7NOoTLrirkbbjNnNDVXTcnN1yfxmhjGKo/unRGOIIdL4yKbcsJovsTbn+P49lNPcXF9Rlxu0NuRazDMGeS7TZg34IEbhXUYvFoxyfGKMBK1o6jXjLMfXovZqdrPHr2Ft7dliAvq1NLg8YDUhREyjt5lsmnzQNWhiWUJZoqxFbZw0yzJH3g4lsGiEjmNkDEg7FOqLrhVmqagVmElkf7xmsSkY7tW8sX/JYbbmUX7NzA8wBE7dnEeHV/zq/kf849HP82z9iIP3FHYTuPi243v//o/45dGHFLrGoxjqmqGpObQrPqzvsQ45761OeX92wrLKcFlLOK5or/O+EaGbFBRquUHWJxrvcoori1u3qEbmiMVB+p5W4Z2GkIsHQ+N7vNUkilt/3PzWKEYvS8Iwk+DaBhF1NF81i6ALnPTmLR0Fq9cU6J0LIP1eAgP0tK7USYeETy6lg27qnffynTl2mgxRSpbYjARjd8micHOiKQ91j59uTkn0KY1dw+Ai8OBfrVi8OmBzojn8YU15YNmcqK3ZTL2lSwUj15IfRWIWBBNtNPlzy9GfWFQro4eakQTUzYNAcBGz1mQzCfoyw2sLAXQChZDH/hhFA22+HUXT+Vt303W1igIzhCjZdcKDv8xEplsvRtNK5ZPVtqcpGa2JaHztaYOUHq33tE3d45EdfSnUgTzPyTLHZLrP0fERRZZhtGYymdC0sCprDo5OGI/3GI0mEDXDwYi8GDCcThkNRxR5TlMvadczsuEQPVT8+j/4VV568DL/9H//v/j17/4KodGcn63RpsRkBjXYo/HQ1g3X8yU38yWL5Yr55QWtDwzHU6b7B7Rty/Pzc84vLjl//oSqqsiLnHv37rFcLphfz3A2QytF65MvbBpo12Gwm80mYdCS6XfsixAiVesp64qmafDh66ZppZ+uYwUgF4bZ2Xrb1OjyHqoq2RQGcA6VZ2K+nQZaqkEBmaMdZ2zuZdSjVN3kcrHLTPvUrHhUoxM+6INmf7hh5P4/4t5jWZIkS9P7lJmZk8siMysruwoy3QsMBthhgSfABrOACARPiyfAbgAIIA0BBNI90yQrKzOD3OvEmDIsjqqa3yjSUVNR0SYSEhGXuJsbOXb0Pz9Z+dXwzK/6Z/5N9xajEj+Fe17ijl579nrhf/jl3/If/+ef+F/+7/+Gp/+1J/93Lzy4mf9w/Wt6HXAqcmdmvrYX5mx5iTt+uzzwd6eveXfZM50HuFqRqB4i/d2CMYnpPBCycHL3PyXsIqGIy5NheTTs3gXsNZB6I6qulDE+oUOSJIM1tkKqfKFpOdPUcdmZ4qpV+MUhoaZFiutnZo8IZrotk7POm7GJ2r7WfAduOl09VfxW8G67bt1v7XBVEIqWSjJ4VBFMNVGB0tFl1kc2alOhN2UtE387SfcXd2LSMn2juH534PCbhDtnfvpvO9anMlyqxU9nspPQwmzrcEphXixmlg+edebya+lG9VoyvS7gF40/Sue5FpjCjsKMqIwDymykPZxWeS+zyOvGjpaKkA1NjJB1KbKls33FMf4D26dBBEBnLakotsTsBfzkWX0mFYgABBqw1hajF/8Ki1VKs66BZfE8PnzFt99+y36/Rxvh1p4vI/MsRtWPD0/Yoccpwxo813liDQvOaAI9558uLMtKnhcO2vA//ff/nvt+QH+4sD7scMc96xKJWTxi3z+fOJ/PTNcLYV346ptvWENEW9c8W5/u7+j+6/+K3WHPP/393zHPE7l83l98+wvm8YJRihgDalbsdjtO53NjWUhsty54NTjn8OuK0Q4fEyFHQk5bx/MFt9sBF8iSOyewa0IvQaSzPjQFkipR3ygt/M0QtnwpEDrVGiUm5miYfqGYv07tBo3HEkD3/UAcMvOuXh+JtTc8+z1PbuTv12/Y6xVX1qz/3/QtpzDw43RPQvH112eWfz/xNCz8PB+ZouNXu2cG7bmsD/hsSFnxH69f8cP1ntM4sMwdpovonSd4gzWJlBQ5G5RN5G8Xzl1H2BsZql1SW04vDwZ/NJhFKGMqiZl4PrpyM8ZSUBNqCrJKSkiHqgSLVTFLxlf1bQjxL2K+3dyfXFnOh0qJKkvbepmVgqlvXKWyya14oGTwA5siSzkA6VSh0JuGCh/IH71C976scpwMu2KfXvFx9bKFWFZf1uUxo6Lmm/9jJnU989eyP6k8MOrQLlvxiQUwV42dZUlvR/nsslKS5f16L/s5vFfc/5086AWmAm/kc7lLOV6lezezPDTCIW9daBa+dVNvpW2YiK5zDG5i0v/4Q/PTEg2MIaZERnisKcFlilyXFb+MJL9sEStGty4XKFzYKE+mBM513H/1hje//CXadZzPV3IOPD4+8NWbN/TDwLC/IwHLeOXH84muN7x5fIS+p0BCYKQjXr3iTed4+Jt7fPSc55HpOvL+emY4HDldR+Z1ZVkWINN1PcOwJ8TM7tjjvWdZFpRSnE4v/OM//QPjNPPVN9/yw2/+kXUZMaZnWWceHx+ZxwmlFLunHS8vF6zWGK2ZinothIBCsR/2aKXxaSWqFasNUWmS0nxpHmwrrilTrfm0z9gpYi8reg6oxQs9y5rig+p+lxxfPWSLZ2ocLPOjYfxWsT4Ih9DMdbCgiHeR/JiwfeCwX1i8ZV0N1+cH/i/AZ823/ZkHK+aetVjujOepH3m3HPjrh/f82+NPvPcHfpqPXHzP367fsbcrnY78MN4zB8vsLSFKMe2OE7G0F/eHmRA10+Jk2BU0+ueOw2+l2MxvxKauO0tlSVY6tOmNxc4JN2aWJ4tZMv37ldQZESHMWeK7UxIxgdbEQ1e4xblo3NVm9/hKHP+Zzqu+KaxR8MA6uc+w0ZJKlPYt7go0Hb5KpfhWB/9VOtKwg9N/KTi6ey40t1SggySvHwahTaU+C95usshbV9Wcs7KTbliuQekE10f4T/9jh16h/yBGO3EveCsKci2sZ5E0V65qzW277bKxZfDUZS5/E7n8u4T7rWP3s2K9lwJsZoU/Speq120wFg4CebS4cHUzOEybbWPYy70Tu+IaGHIbfv2x7ZN5I3XQ433kfL4yz5G0TJBWrDEYrTFmIMbAuq5t2SwDModyHcenN3z93XfEBD/+8Fu+evOGb7/9Fmc1u2HH3f0dzjkZFq0r4zRyfjmxpMB3v5h4vH+g68RGUKVEWD3GKMajI8RJtOT393QRcopM68rqA/M8SwaXUZJGgGawDpAudJomTqcTruv47ttf8sP33/PTu7c8vHni5YMiTAt3dwfWZeXNmzd473l+/iAyfucIMeKs2ywJURyPR56fn8UUJgV0oT39q6Qa1Mmwk6vKjhKfoqeAXgP4QDYatIOSfqB8gNWTcxL8MifIEq+e+471mwPjLx2pQywBV8XylFm+iSIv7BLdYeW4n/HR4KMhBI2/dugXy8/rEy+XHX/15oWvhiuDCbhCrTr7nk5HBuMZjOf7+ZFOB/7m8I4pynDk/brn4nuua8e0OqyJdDbS2UDKCq0yTkvRdCYy9Y63pwNxNaRdYv5Gs/tRMbyXAUsyht27iDKFYbBIgN/yoHHXjDtHYm/E9MUn6WKBrKxAA50lHDu6t9cCtUjcDqZoM5XaTHQ+06ZvluvJFV/Xrjhg+WqWrRqpH2hdmcpF01+0s41FgCyt/YOEH3YfJPG3LollkFk6x11Jcy2x34wK/5hQXy2yYhgtqvj66sKplnhuJXlcSb9SQ9mrwow1s6t0xoXPipIVV9qVYVR5uJhJla5XPGndP8tgbf4mc/6byPCToX+vWJ+yvHb5HHYW/LhCLLGT1xWermo4bdYQS4erV/F+brlc8EcHXPDJPFhb5KOR0/nK9XQmpYjJYLXFOEPfD+Qcmee5TaB98OQM+8Mdj199DSR++8//wN39A7/+1X9B13XMi2fYP7C/uyfEREwrRkHnLH3XEw4iCHj/9h1Pj4883N9zf3/P3XFP33XshkEyw5LIUlORxdlsyT6UrrvbDkgWAxpV8rWUgq7vWZaZy8uJ9+/fsnpPWhd++/Zndoc9u/sDMSeO93cs60Lf9xhncV1HyiuDtbjOkdiw10zmeHckeM+6zoK7miwKr6ru+UJbcqrdHHaM9O9m9LhuB8RoGfbtHMnqbZBjBUPO3pcYak36xRPhYQfA8Z8W/NFy/aVleSp4lVfkIdIfBfMc5x5jRCzgXCR2kXRU6KshPR/4hx93/Ke7wHBceXN35ZvdFasjb7qRg10wJGLRIjoVS9gkLMly8gNGJ3bdxspQCNa7RE20kTVIIdx1QgvianHPusWDk+HwgyjU5ifT8MPlbrsJJRpaU9u27HSZ2G9DrVQMcMLTTuTHa0TNhepmDBBBf94utgqKks2CL+rclr01rrpO5fMN5lgHWyAdpS5WfP4Oll8EGSQFsfyb/yqxeIU966aWqhhlfa9spcgCmMngLwPhrlzvfUKNcrzMWHjRu/repUA1j4hidG0gq7xRyLJwq6tkNRSqVi4JwnoRCGN9E5hcQi0GPWpUUMy/jJirpntWpGKXGHeZuCtBjqXLNqt6zX/NMrwTyETw5PqzyUmhfjVI/APbJ3sRpATjtDKOCwqFUwaloe8dw3Hf8qeqV0Emo61hGHYY1/P8/h3LNHE8HllWUTd1hz1P3/wClTLLdWQYevphwBhD8CvHwx6jFb3TXK9X1nVlmhe6fmW/G4gxMXtPKBJdYywGxTiNjNNKKJzcruua8CHGRAgz0YtvQEqJ8/mEUpnLNBJzZllntNWQE8vlAvuB472APK7viDkWRkRCaYVRAg1My8rhcGAYBoZhT9/37biM4wjcKEG+4FZ102ZNEvzno0hPQcB+Zwh3PakT3DCv4lWg50JZsobsrHA5jaH7/gOME/nhDjPv0L5D5Y7nO0iD4LDr7LBdoOsid7uZNRhS0rBfWTSkPhLPRTS+GFZrubiewQa+Gq6cQs+SLL2WItVrDxqe/Y4fp3vWZBh9hwJ2TgpspyNrMoQkn21aHb0LxKS4zD2p6N31qopjVG44pfaZ/jmx3m1pqGEnhSc5oSHpoAhdmScEwWaTVoSjIfSa2IkuvpqmuPEG0/xLeMFWTXxUdC80m71aTOsyuiqgqhP/Ntyh8Vr9QyIeEnoUylMtxN1ZivT8tQg1tJchli7Up9QLxzR1tC54eKtIz7YZdqeuuHI9ihuXioVfWht6DXFIzR/AnrUM0+QjtpQBUzipNelWR03YZeJOYsG7D1LORDSQ0asW2KSH5euEvUihd6cSsli7zwIHVIy5xpX7u03Sm4tfgUh2M66q0kpH/4e2Ty6wWmsO+x3DzhGDLIHICW1o6q0KI+ScOdq7Ztk3Xk7M44g1Dsqy+q9+9Wu++8U3PP/0A35/R7q7wzhLnme6rsNqQzaJ/X6PcwbnHGPBP8dxxK8r2hisNdzf3zeSP4jngPehFLqhddQ5g/cbx3NZFqyxrOPEdZowTsxbLuczp9OzQCLLLJQe32P3R5Q2zNOEUmLJeDgcGC9XlmVBG4v3voVE1mPRdV2zOkwf+TV8ia3eULHTzN/06KeudG+lY9BqgwiNInYas2gsoOxergEv03Pz8zN5moTKlSUyZn2wPP9bWZ4OP1j8XSIeEwGwNvFy3bHrV7ROWKNQu5UYNd5m4mzAS9Eax57vV8tv1APOBfad53GYOLqFUHhEb6cDk7fkrHjaT+zdilaZlFWDBlJWGJ0JSYqs1YlpdviXXlbqXUafhH85PBeJbMpon3AThMG0IiVTdMFmw6BJTtGdy3CvFuclo7rSeZntmC/3quF68FFsyec4rx/RsbKWzvI2bjoO2/mvwyyBEEpn3omUlKToft48+SRiW+hN86+9wApBi4H2LNMelWSJHnuh5AFNXdVEEGWZXR+8KpSfK0qoSivTxZbQLOUhWCCNGmejCkdV9k3eJ/SC+aYuszpQd5m0S2VfFWbWaC8F0ywCIygDqZfhcaOJywAAIABJREFUW3JStHXltNaHUC5cX7V9nu2gF1ihHKePTes/3j6NRZBkNKx1wqBE7ZLFkFgr2h5UOenxzhKCYLGXy4UcoxQYrUkp8PT0yPnlhf/9P/xvHA4HHh/fMK0zi184Hg483N+TlcFZh+l1ERhVrlFinkamTKF+dbzEF1znRBliFIrE/d1BumdjpCjOc+si/bqKy1VOzNOFy/mF53dvWVbPh/fv+PDhLd4HUhLXr67rICWR2TpF1w8wJb56eMIHz6ivGGeISa7iij3HGFFay4Og7L8GvjSLQN/Y0WWjiDdhffB6iSOSxAxosu0gZewYUFZjfzrJzwwDaE1OibizzI+aN3+biZ1Mds2iCaNifVRMo8Xer0hSb8KZSO8CIWqUygSdCKshJ0XwhhAdqo94bwjBsHjLe7sXHqLKaJU5lMIrg66AUZkPy56XdcBHQ0yKeXU11YbJO/y5x36wdCdZEh9+SAwfAton9BwJBxFPhF5j1kwNPAyDKJHCoIQMH2R4lVz1Mi7HLcq5r8veZMv6vQ56/gKoUHWGkugaiBaKKri5U31srad9eWA42bdsha9qSkdaMUUzwfXXie67K5x61CylQkdF2iV5/UW1cMRsc7uum3w1FLmsydir5lZ1lSrzoeCeZpJluMpF8FD/1PlBsRRsGPNah1UiPhB+LpizAS2YaR1U2VUeAvU19Srvb2axNIylyJuxNGIOOTiKVw8BFW9ksfUWMn980PVpmVwxbEbT5apRSnKqmheBEkMHazrWdUVrg/eh0ba894SYeHO85/zyzLuffuL+8QFtFPpsSVm0/19/9TV+XXm8f8TaHTEG+r5nGAZ2ux0xRk6nEzllcorixZoC2hzoe8d+vyvKsYGu60gpMS++Dd2u16ss58dRpKzWCBthnjmfTqIdj5F5FAWXOxyIITDGSJcyd9bSD6LFi0GicIbdHtf1XMdJHjI3HrAyAFQYVfBMvnwHW2/+1vHcFtZ6U9UuH8DIslj7jPaZmC3ueSYPnWCOq4cQyF/dEwfN1//nhXB0zF85VK74pBI87iETfM94sPTHBTsIhc2axNNxJCbNGmRZfz0PZFs6HK+JRnMNHXeHmc5F7vuZ+25mZzwPbqLXgSVZzl68AmLSvEwD62rFUGbVuCFgXUTvZNxsr6U7UnJc7KUY9iSRh9olkQuE4A8i4fRHhTsXsxEN/qDFvm9M6JBvjjNgaRSpitN9Cl/yP++8bv++LeCZMhgych5ugwxyWbbXjrDyZitnVgcpdNe/DqAz6e+OdEE+k4rSUZoX05RMUD6nAmxZxpfNzuJ8lbUqtn/QyPtLoXCFDRPOGpK+uS41r9zBcuWqFjxWpeKwdZEHO4C7ygVdh3LJ5TaMq36ukvmlBNOljESyks+QKbQ7+X91elNeTmALxbw5r392ogHQ8qQ2Hb3cqZlielD2LpfKv5l0a7Q2aJ14fPOID4HTjz/wcH/PcT9gjWouXY8Pj1IIYyRET849zlmcta1bNVpzfXoipYj3K0pJNtbDw8NHkIAs0UNM2Jjx3nO9XpnnhWm8ojVcLgsK+PDumRgCl8sLziqGricO4qQ1jyNq19EPA1Yr5vFCZw0+BowVVdtBGaZ5xtnYjG3meRYMVik625FCkBPzr8EigDbkut2qL6jcgLkV21eFoBQitBbc1miUNcSvn0id5fD//Eze9aSnnjDUrC3pnsJdFrwsyARYPAEU1gScThy7hb1d2VuZkLx9OJKy4rL26NKt+qT5N3cfuHMzX7krTkdi1jgVcSpyoScYw5oMz9PAMjv85Fr35q8Orxz6ZLGzws7VmEYRe43e24KnSpItpYBmA3bOxF5ScquyCA2rUdhFlorZSMaYWWS5GrttqVyX6c3t6jOf09/r5Qo3sETBQG+62FZkurwNdQqdy04CC1x/nTFXwWLFFFswThn+SFGuWVY60pbJLeeqvF4ygKF4TsifxrlVaiucZutak6NhyyCvretxTFKozVrEARbWNxFsRl+M8GYHwXdvTVtAGAogHXDqhJ5lrwVrLkXVLPX9677cHCNDMX0vPGO2Y/znK7kwpAha3fbDebtjldy9lWivtdAyuq4Tm8AYOdzd45zl5x9/xBhNPwwsy8LD4yM5BlLwhHUhG0seesjicZATGCNZWX3XiVR16Mk5E6JwTsVYRTpXa20xuxaXqxgC3i/lPXdcriMxBJSFvhtYF8+6TDx/eMcyT1wvZ66nC4ufsdZhjGW/34PRxJTonENrxdP9A9M8l5iZjmke6Z3ldL5gO4dzIrTY7XZyUSlaLPYXj4y5La4fv/XNRZI/+rlcQh5Tp4nBYEJCJYf/9oHkNP1//FkKr5FiZafMcl/itkuhtmPpFAqkFJImJs1gA4/dxHfDC7bQqb7tz6SsWGprVLZvujMPdmRQHp8tc7b4ZPHZMKaOZy9j6fthYZ46WfMFBS6hbCYXjmf3QW6i/pywY8KdVvQiFLW0s6BgvTNtGJSMwADrvXR12ksBzRrskolO4ZaEXRNxEM9d4+UBU41BagyXQAuf+by+cn2m3ZuvMcNi+X5TtFRGolHS9uu1IJ3+nYQK2qsUKjl3UpBSKZaZ3IY+uRRRObm5uValjjZoSx1bIav7V2CKLKzJG6vEDbfNduNWq0leR9ncyP92hP7ZlljvLA87Q1OEVbxWJdU4rGaV37uVzdah2PLVBj9kI3CK9vVBUNgirn4I/mhhrdsnD7nKP17Bh5tCS7WYmFubQq012mj63UDvOr7//p8xRksnamRwtcwz0SX6rmddA8OgiGklxpUQF3amo+tce21XvA5qJygpBaoZgaeUiulKxPvE5TJyvpxZVzF/2Q0DyziSUqTrHM5pvv76a8ZxZFomRqWxzhGSL12y5zqNDF1Pv99zOBwEe3YWlzvGaWK6XAjeczlfUEq3Trqa5NyGQ35sSv6ltxYXcrNsBARMz1sXK18TLFGoOBqsxh8PqJTp/9/fgLXkzhDuB/SamB9dMbIWrJciGZVIC0WOihi1UKusZ2dkee5UZNAeTcaoRMwaoxJaJQYV2OsFpwIJXShYjnMcOIeB34wPvJ0OLMEyrQ5jE6GLZG8hKsw+oFTGPyjmWZgAdlIYBakvooGYUT5h5oR1JfV20GIUYmUIEocCeyzFnnHNuEvCrImwMzIAK3dTNWm+dWFSfH6IYLMo/F37yVZM2bKnXkEFBUIAKSLGw/irtBH70/Y9EkQjptdt+bzm1qnWz1ypU5WFoSquW94/12OiALMVV2E5yLX3seFMtptnqxnVVphNJurinTCBKgowU453KtBC6iG51OCErLOc11KUdQlJVJlmyp31RjtLXb7pXsuNk2unntv5/UPbJ0pls+RRUZf9W6Fo5Hr1OlkWtXWx/dDz/T/+E6YUZOec4JPO4pcVY23LvDJGty4xZ1nP1IFRdrbYAdqm81dKld8XD4A6YFoWz8vLmefnF55fPpCTMAhCWHFWsyye63hFKVj8yuIXdCngc3nPlFJRgIHegzWWUzpxd3dHKkVTZ0QmCQz7gcvlyunlhWkS5Vfdrxqn86+SaFDOR9vy9v/WAJV9qsOdyjCQr0nBXN70qAzD378TFoFWoIUsnjqNjrkMUFSbVudyw2AqdKTIZeLvs8Znww4ptIP29Nq3hIJBewblm4z2nAZS1szJ8f30yPfXB95eDlxPA7kUcUKpbhqU14RJor/xEj1dB0LyACjwR0jolNA+ASKhlcm18GCTlRu73VvlEMWdmOa4ayiYnmF+YzYmgaV1ialigJ/7nN7i6XW49ZG5i8AC2wWg8nbeKw1p+jYJJupp8tTYlQJaXqMmEjT8c6kFKW84rrndp3xDDVOvMNC6/8nkxsmtXWvcFcqWKquPUCLVCx3LLKrktcmf9UlgCz1v+Gjj/6YK5WyMBOmKwZ20dNfIfqgMxhfl1iE3KpdZVHPTkuOcG7Z96172+7ZP7GAz6qMI4ttiWjvWW6qWUh6tFfv9wNuf30o6a7bt99Z15Xq5crc/4Kwjp8g8XRkGy7p4OudFiFBoW10ngwtz00JXz4M6gAtR7AK99yzryrv37/nhhx/48OEdh2GPtQZjFBFD5zqW4nswz2fWdWGZJuZxJMWEM5aA+NtSkg0u1wt3d3fiDoYmxkjfdZAj1mgW77lcL/jVs9vvMWbzJajHph6nL7m1AL96Q36EtTaq1s0pzkZty0AtEIBKmf33Izjbptd5cJjzjO1NwTVVK0B2RExCQkatmmQ10SquS0dnIqPr2BnPFJ10rihS1hzMglMRQ2rFdc6Oa5J47p/XI++XPf/88xPxQ4971kVdlKEORarVnNfYk0EvJVlhpXShERWzSF8VKC+VUMckpPOU8UdDWqTDcleBBFKnJNewdPZmTug1QafxTm2FtHY2RchVFVSfc6su/HLCyl8fDVw+dty/HXLWounv8isrwKro0l44p/Je8ruxhAgSFf6YXrMVFO3fv2OV2DppVa633FgCbaBlBGKwZ1M6RxnG2XHzZU0uF/WZvFbqMu6km2GLupH8yqArbw+fCNbLw9/fi+oQBfbZbF176ZC1VyLhrd1wsSVsK4HyhPp9mWe32ydHxtx2qa9O4C0cUIpH7TIre6Dve5ZlYV3XNtmPKeKnhdU5cgp8eH6Hs508tXIu4YOq8V6ttRKuqLZEgNuCJZxboYadTmfevXtPzuLqNQw9yzpi7I5huEMpjfeRdR0JfuZ6upBDJATPbt/TdZZxmsirpMpep4mYM/f391wuF0II5HxmGIbGb+26jpgT1jpSDK3zzTlhjPud4/klt9ddRfmiurFfVtvSqKWLCm2R6IQdkoxkZsWDQ63FdHroIGdy7zBTwA5lEJYlXgTKDRU1a84kLD5LdT/rhNGJwQRCMmiV6VUADS45otIFNkj4bKW4hj2/WR75+/PXvL0cUDqVwVKhouXSEXXChSQo9KQbYd4spWPLGX9vUMFgx1hXyuglYE4ZtXesDw53iWiv6a7y/flBE3dSRCmNV3KKVMQHZk6opAt+S8MSa8H9CFr+87dyZ98+GH9fQW1drKKFA9aBVBxyUzHVYiKm0+WYVpi3YqR2w1mhFPQS10KiDb9yOfeUotVeo0aw5NedtLwWpGPCXDT9O92GTmFfjLbzTYEuclXlhSqmdrmYcMtrx6F0qqsM6VJXP39Z4MwKvZjWocsQU5IZVBbaWCpQRR2CKbb3r1vW+fUJ+Gj70wy360mrnM6Ci27FFXLKhYRP8UWN7fvGGEIQFdcyjgzGYs09xihyjmiT8X7lehmxRh7/OetS0CQi/P7uHufkSq3Ksc38O5cOduV6vfL23TtA0Xc993d39J1DkZnnmXVZWZeRabyyrgvX65lh6PBeKF2dtQxdx+V65TJOXKeZ2XsO+wOm69DaEHNm9Z5YwhC1MQxDTwyZzrqCB0c612NN9Vjldx5SX2J7hRO1jrYBdTdDBkWNsa4db+wKudvKUi07UXaRs/ieKiVLq3tXCo5Cr2IDqIIMRtxF4dGkDD7BKWh8NMzB8tVuxOrI3qyQYERiX3w2rYNdkmPJIo+9rB0vHw7kojRLu0R0CWxGuUT2WmKfoywn7ShBdnaUnDC/0xif0WWAlzqN9gYXE2qJmHNkGD3x2JGNIykpqqYsn80CbpLOKOw0dioKOFtNVTK4QhXSglA0BdVnP7HqVUfVCurNfV8n38ArLmc14d6WvgodhTPaUl+BGrENkFfV8E2lVWOhvKJU3eCYtYgWksq2mi4ra1lmyzesV6RVutJwRPDvVbwLQJJl/V1qXsNQqF6TarLg7ORzt/01NLOb1MmqpsIHt4GMdZgnaq0KHynqgqAmKcjy5TVGnf5IFf2TlFy3HextgF/tcFOM6KK37qwh5fTqNay1jOPIMAwlAUHRdx2dNQgklkgxEPzKPM2gNNO88PRYluVffc1+t39lC1i3VNy+6pDLOUPnjHgDOIszBmM01hpeTi9MV1FfjeNIzIlu6Ane472XrhTFsix01tF1kcWXPI0itz3uj+z3e3JOhOAhQ/CheR2sq4Qf7vZ7EWqURJHqH/slt1pMbzsFOSk33YOpk+/86mcqL7UuPbOWY4A1qOskEdazJw9OiPqFqiXtAlBuNG1lgg8addWEB811NSyzw+nEm94xJ0dvAj4bdIELdBWxFOx1LYou00XCKjtZMV5lE7YLhOTQoxZZ5yrUo6rWCYMSIUEQZVvWxY4wIMmxaRVMvXx+FYT64w+6OfNLsqgUBh1k6WjWhHZa2AWDIn50itvg6y+13Qxe6r9vu1b5/y0GVAqe3gpzhTXa4IqCSaYb3DRtqqisIBeT7LbyCVLItFdNStpeLNd74AYa4KbIKimmKgk8VQdcjXZW2AOVP/txYcuFGmfWCjnQlv0gXanNAnvEHgRLLR1tUb+hCiyiaH6wtddohjn1gQStk/9D258c2337tzH2BlPMGGvRhVCvjWqR1l3fsytxKlpr1nXluN9hnfy+NRYtbF+GoSMEz3i98OH5ma+++YbTKXM8Hhp+WxNaQbrrEEKDHVISFyNjFPd3RxEjrJ6UEzkGspZB1jmExq0NOWJKYGHfdczzgldzO4J7lel6R0aJ6GCcCMtKuH9g2PWSU5Zl2NaVYxNKJxxD3GLNgVTMYL7klrX6qIOVnam2gvV7dRgB2w2Q7EYxip1CDxqVOlxIKK1RQYaPqbfttbIqHMhCw6kdsLso+vfi1pSvgsnmnVy1oXgIGCvsgZQlziYmh88GnzePgZg0u/3CYhIpalJUMuQCojcokxoWJ7xHuSFJddmnSqGVop+VyILF1rEjGyWCiiiWjton7GRY78VvYFsel66+KOPMEgUiiNtAB7YCkj7zaa9T+na+yj/qkrZh7elmf27ayGxKcY3bMrgt93UpKKXgNH+D+mDxwoUVrwaapWHWQs3TvgyWanGvD5eKR9dZgKZBCCTESSvcfgbV5LCqFG1hFajm71qLYX2fOLDF6ZSv6RsYRKSuAgfUVAMVVKNlqWLski3kmNv+tkJfj2P5PH82Bltx07oZY1BowRtL55jRxZJMlSQD0fw754gx0sUeO89Cc8qZfuiZ15WXy5lEZL/fsxsGck70veV6uWCNY3AGaxQP93d0nexu7QC1Fgiidq3ruuK9dJN+DayLUH9ylvSBFCPvPrwnRphHSR8Y+h5UJqWOTCasK2SY5pHVB2KIGBSd60pHPWONIpK5LhNZw9D1YluYYuHtakIKpBDJObH4GbSY0YAII77kFnv1ymwkV5J3xaTSVgBuaSeNLK/KUCdThl1gOgN3O1RIpJ0jW427BPpBMNewU+UCLhe9g+5clmOFI5kP4hPb20BC4bMhlq7VqciYOmLWXGLPNfZcQsccHTkr5qkjhtLBVoJ7UuSrRS/F1OOiRGtfzTjqsi+CXoCcsZNQrlTI6JBJTstnzGAvXhgEWpUiK++TSry0SqJrTE61Tr8eS+2L3HaVY0CuHfxn3GqRqlBAxQrTVgzaUEvRhka3/77tMFsECtKlxX3pIksRMvPWPWZdiqzJqFHhftTF0UuOrcAjtcLdFNW6j3rb76r3r8Y0qUuNFqWSFHozKTHGRmGvmtRnYmM23Fyzt+9bV2FlcEU18jbShWtfr//tAXSLN4vZuLhwxS5vx7o+aLz8buPG/p7tkwvsbVosgLUdKaYGH9z+bErS3dTfq0XQWss0jqzryjKPdF3Hfrfj/rDHarE8VFoiVna7HfcPTyilORwO8rrltbz3WGtf8Uy996zryjiOMsn3gdPphRwjfVdbB0XX9VwuZ+k8GwQpCPjqVxY/MY8zy5rwZalosDhnGacVlGJwlpAVwzCIsQvy72VZyDHxeH/P6XwR7wOtePPmDQCHg0P9Kxhux57mcVk+cOley8Wptpsrl+9HJ11cJYqr0k1s01uDnj1qWjDTgnaWvOtY7638rN6MRuqgxx9leputmDO7g4yrfZTudIodL3HHPq/szULMmiVZrrHned3xftnzftozLo7gDep9R3aZbEuVyApcRk1y4/njDZZcJY4RmMFGCdITmpJwYcU/QDpSew2Nfpa1HAu/L4U139yYU8YfNGBx54g7BVQyzIV9n5xqxetWWfQ5NpWBUP5W9XNKcau0IzG4phHoUbRpO2yFqXViWYpJG9BF6F4E44xDbskDtRPUhbifOlrXWwtS5f+2LfOqiNV9ruYqyso+mkk1qlhTgy3QlQ5Z+wJTlAdnaxbKe+q1PgS27lTlbbkv7l+5mcC0TlqX67/sYzhuTIdaRGsnrG96pFt58MfbJxXYoe/bG+dcKFsZglIbOyNl+r6TmzUknHKknFBo+l46zGEYmKepGHIrXNc3RVc1vj7e3zMcBhQZ6xwpZ6x1TNMMWaO1LRCEbvuyrivzPDONI9M0cT1fGKcRpw1rjIzXsdxoCmVUMYXRKAQn9csqrIYY8SnISSpgkes7Outat2wizItnfzhgjZyxrutISlyzgg/4sLDMM6qYcFdI45bO9iW3tqy9WaZVa7gaIdOYA5pWRCj8QSlMdV0pA61wsJjZko0Wc24tyarJihw17OTiDbuyTC/Dh2pXl10kBs00O1JpZdrfTpFQjLHj2e/5sO54WXecl57zOLAuljxa8pBQq0Kvpg0r6nCnUnMoA49qclIt5yqett3gWtRY5WbVQZId7BQJe0PY6XaMbrOd5PMmtM+YORCHIoQpNb+JDxR/NN75P2drDIX2hbJbEczNpL+e6zopvx2Eta3QyWrRqQW2/lDV5Dev1GUrKo24X8QLVcWVza1do2Dy5Rndvl/3o64sBM8Ge5EutRbROAhuquPN8acUTbNhphIlI/leqi75a6cbEZjRAKhyLm9MYIovQnOWS1sBri5g+YYJIlhs5pUW+aPt0wy3zfZjbdClFYITZxlIpISxhhAjSllSzBhliWFpUMFuv2OZ5xbTMo4Tl8uIuTvQdQO73Z6u60kpczjIcKi3PeN1wpiVZZakBO+93NBFPbZMM5fLhfPpxLrMTNNU7OpWus6AOzCOI8syo1RmnWYitKV6Lid9niYupzN+9TitMLsebS19oWJlxOLwrh/wIRKK4xY50w09y7qijcJ1juPxQFIGa7vGohCYRX1xDPbVMOC209HlCzdYVVOy3MSJwPZ31hB6hUoGs3Y4H+GaUYvHnBfctcMfNdmIEqrZ5SmI+7SZjHhNChKJ7a9yF13XjnSnCVlzMCtLsvw8H/l5OvAy7lhmR1hKS2ETrFpiSsjoWbebXi9sfMh086fEobhLxo25DajCTlRYyapmTl6LSOrk/9pncWorxjmpqwMv6VJjr1kfO+EMd7pRuerx1lWb/zk3dVMMbotlLqe4rpSLNWG2peCU81z3rUpl60MItS2rqzZf1ek6iOa/DLl0oWeRRQnl72ohzltHWK+jUsQbVADNXFsFSCjcVa6PcCcuX/YirAZTc7FkkdGuabMCWbVOU0FTbZliJN5YMuUj60X+Efa5NRBV5VWPX3XJyra4bpltmHcrs/0sQ67KIICbAqtUEx9Us5dMRpfikRLklLHWkPKGsaYQW8hglbbGkHl+PhFzxjjXcFqJ9+5YlrVZH1orVohd30t6beHaXi4XzucXllksCa22dLs94/lMylGiZlQvQ6Y+M04ThsyyrqwhNkZBCIGUE4f9nmG3wzqHQgrr/nCg3+2YxpGUMus0cby/w/Zi65eTiBxiFLXavAplbFkswyBWQh9DKl9k09sN1YYhH/+/fq1sscY6104sCQ1GZfBZEaYM2aF8woUk6RHOSIpq1PQvUkzXR4U/0jw9cydLeuVlGKQnSzYwDT2LdvQ2ELJmcTMhi5vW+/OBZXTkyYoiLCpwCbqEGot1pssbjz+LQUuFRaonqA7QnQV31UE6Ob/bmA9ZK1GjRViPGrUHN4ljlvYZSzHk7rZj09y0FI2eZpaEtVpoW5HG0/2XbsY/dat0qjYkKp8dbtCgbdazneN6Xttn2L5Xh5RQH0plMLiTBNnshCKlfE2PzaS+MgvK31YqT3sosxWsV8OuXJbcSTVmRiqvn8ib8sq/HtKlWvQqs6UUP7LQ8WoeWDKArQ+JovZapTiHQ24PXUo3rYNwZqGyCspQra+rsO01XnFy/8h5/aQCezpfmnDAGLuptcqjSStTzo8s2YXuKUVEqZ6MdJ0KxeHuyDhPrYs9nc/CLnCO+6dHfIgsa4CU2fWDeMESWUpEcgor2RqWaWpL9LWEGk7jjLNyJn3w+HlCa4VfAqbskdKaqDURQItXrM6REAIxJSmqRjhVKWehajknnfW84FNkWT3GWoZebPJCCCKAKBLglDwR6XjWdabrXPFRkHWyUZ95rfgvbO6ci2PRxs28NYf+nUJ7+7UKLdRnQr0hlWJdNWZ16JAwLwk9LnQazNIBHf5Q8KoyUc4JQnGuz1a6Tr3Kki57DV3munRolbEqEbLmsnYCCUz25g5VqNm86tASJVa5FIR6U7al7k1RyVocs6r9IBRqT9HRt44uZuyUmpItOVUGWFunH/tSaNJmXq7XjLGZ9VhbqvLzn7nA1iFlK9xJ7sFWcDNw62FatoZ/IkWukuvlC+X8lmOlqVCEal1qdb9KFdstCQipuzGUKRStXE9bdd1S2/vYq0yL9CrXZSX6h12SIkk93UUAcKNca6YsFvDQP6tWgOvwKhxKSGFtEpzEeG8de0nNVZIvFvbSNW/m4Rl/oElv3ax/h8v8x6wK4ZPtCoX7GeMKrKWjFdlp11kxxja22BJWvmx+pezCwjAkiYxZFsJaBhzeMynF88szMUV++ctf0rkO3Q8YK7EsaIM1kt66LIs4ZhVJbEZ8CoZh4P7+HqMz8zwxTRMh1KHYSm8sRgs31zlHPwxFZTbQGYfptq8dDgeA8hqBaZo4ny5MJX1Wa83d3T0pi4PYukr8TGeFdhZTbOIIOTam4a7/Wl4Ekmmfwd90s5pit0eLFWlqrptOhttdWl0UAAAgAElEQVSLqqwI4wDrUaGDQYWOPiRUzfJK0DmNDoZpkdRZmQ5n6UBroavDA5PJsyHpzBoMMSte1gGrEyEadvuVCdA6E1dD9jQ3qMq3vCW5C7WINlSKhcmQnODLpsSVhL38rJ1Ar9K5dieJ8TbLhg+ud3pjDUSR2ianiA7I8nVzLflcCYlErw+xG2z7c3sRVEMS2M5b1mw4YX0g3cAlzV0KWkFqeHVRK1W5r0qID2rxN0idEP3F50EYBRXLrt4NqtDElC+d5a3aTG3HQQqkDIjk5ygUsU3OWuEFisxYZVClOw27TO43NkD1TVAZ/L4cgySvH/ssA7pC9TKzhCs2ilnxxTVzaUQaj7cCxPJZYsGU9W0qQ1RNVfb7tk8qsHVIU6WpgiUmYoRp8ixKputaaaxxYhvoNM4ptK6CBE0G7o4y+bfG8PzhmZeXF9Z15fn5GYDD4ShJBLYjvZyKYbc4VBmtsE7wuhA80zThosg1lZKEWEVinqW4nk4nUhINZWcMVktnqpQYylzOZ2IIGKXJWfDc4/HYzGXET1aW+fM0kYHdYY9SUkQrq0Jr3bK2XIEUtFL4GIlJCrpzHUppcWvKn/lO+xe2yhR4dTNRO7qMiZUPqtrFJBzGUnjtTbdbbuKogCPMWhOdIw6a7iXgPkzoxdO9jeh1ILmeMMgkXrSlVoYbBrJL0lX3iSqZCcFwmgYGF+is5GkZnRh2K0rBGAq0MItCayPTS/eYy/Q8l5RVd5LhCZQloyq0rVL4Gi2nDDncmLFX+YV4LBHm5WFggrArAHHgMpLBVZfYWSlMTCQlQ7NkN+PnvwSL4NZ4pmHlEVTNs1SqPShr4Yg7StGthW7T7quoZDAUXgsqhEWiiCmTlXSdWUM4pK1IFwmtvWjMXJNhpZglS3PIuqUBxn0mlA4xmfKQNEhiwiKuXijplE0sGOgAVcCighRaNUihrZladV9u1Vp6UeiLwBGovJl2F7xVHrCKXB4INZ6mDc/yls5bz2lTfP25UtmWZlBI/mJNCCDFiobLgo8rYfaoecNurbVFs6/Z7/Z0TtJg94cD3dDz4d17YQFMMx8+fCCEyPPzC/f39zw8PHB3d+AaAofdrkSvKNbVs67ic0CZ8E/jSAqeD+/f89Pbt5xPJ5zRPNw/SGS3FuhiWSQS3DlL3zmiXzA4YkrM09Q+I1qz+JXT5Uo/dCLz9L4JL7z37Pd7rJUsLx88VMZA6ZR3+yP7/QGtLNroBhd8ya11UpWMfeMg/wqjq9zJxuXclsJbx0tbWsde+K1hr/BHS/dg2O0Mw1tJrTVXT/cikhx3laGXv1P4o8LfJXKSyXyuBU9lYjBMUeO9aR4OIRj8bMlJoV8c7kXTv5diGHaK9Y4m1ZQPLAbLepFOMtzwLGMnLvtSUEp3pYtf7EXw03AskFeQfUpVApsKh9fIUlkXXLctr5UwIPzBsN5tS+lsNi7p59x0MWepjlaNAnV7eVWc1StUrsT8bVhTmQV05YdT+VxrGRQtpaucSzEOhchfKFG5SwJDlI62YpVm3orVtr83K6RSuLPJ7bypIOfLnM0rnqlKkgR764tQ87vMIkU2lq7TRMh+u05x0nW6a42YkZVLGmSwRpZBWhMhqBIvU6AGM6mmEKvDr9/pxv/Ig/NPMnup3ettDDbwioJU/w+ys7UbFE9U2cuc5QMMw8Dd3R1+2dgBP//8lnGceHp6Evhgmric7zjeHem6AYcmUgUGsQ02QghM88S6LFynEaW10MaCHMVp9GgjctYQItoZ/LxitMYozelyFU196VrXGPDjijGGh/t7fAyM14llXnHOYdd1M/k2huA9qSjK+r7HdR3jLHBKfcAoXqvivtTWpte1SJaBzu9sN0OOV5Pm+u1y0d9yANt7WFmamXuDjj1OgfKJ7t2EPRu6u475jZUwxSt0Lxp/J1zVNAjQm7MiR9kRnxQ+gzKZuBhYjHRIV9WoNH6/dbBmBgmtv5ls63KTlhvDrNtDoy5HtQfjpTiFXpGVeMS6Ufxhs6miAgkxFChFkbz4GmRVvveoRRm2JsEnrWpBelX2+bm3RnHKUpzqSqXxu2+YH+1vlRtLog5yXhXeWnS7m4dCvoEMYoUbRPGkJ7m44j6Rvg7k2aAnvbEslo2DmmwBdctKQkdZ8ou2X17XTtuDPOwlMbalyBZox03SOVY7wepBIEt7ytCq7PrN8MofS3frVfM3qA8jETFs3F4oHXWxnGzH74bypm6K7h/a/iQWQc6Zoe/bcrguj+tWoYRqMI3WslxuhVeKbwiRZZl5fn7m7dufOZ/PLY5GOsyF8/nE+/fv2O/2fPfdr8lZ+KaHw0Em+0o4rCHmQr+CjMJ1Hf1wICsLShP9QgJW71kus2RoDTuGvmPXD+Ib4BfxN00JozV91zMvC957Xj4803c9ISVSFObD/rBnv9tL0CKgkAHXuMyNcRAz9MOupOI6hhIz3ncDmddP9r/0VpfnOd3c6PXeqQKDW6ywFLAqlbwttM3ntZz2iuvZa2Z4TuzerpjTKim0gCh7MsYZ+g+w/ykRe8PlOxn4CYFdwxDRVmTOMWhRZfnCrUnbBR/2WRhaDwp35VWHZtZtOVyXiwzb98WGjg1SUGKgrVKmf5G7qGaRiapIFQ5sYRrUJS5SgNwk3E9hGchQzO80/iBhf7Gjkf6bK9Nf4vzewj9lWk4d8NUHZHN4UVRfZ6E1ZRlWlQeqCgqdts42DiWpVUkxNMWdTK/bMY0H+b5+56QLrZ6u6K2zThWSKrtRcNXadVeVW10d5SLZtVfxJ6idbzOQidC/V1unumzHwMw0vnKy5RosAy2BCCpXt8QZmQ1KACnUtXiGg3S6dipmMjervE2e/IfPzafxYItqCqRAmkLTurUKvC3Ct5CCuiHW57xhp+/ev+fHH3/kfD6TSTgn49AUAylnpulK13UcDwfWdeY3vznT947DYc+yLKRE8S9YWdYF56wYr3iPcT0OjbYWv84QE67v2B/3hFi6XmsZ+p6YIi7vcf2e8+kk+5Mz98c7dKFVWds1SGCaJlKM+OBxnQghQooYW9yyijdCQLO3jv1+zzAMdK4j6koy/JSj/vm2LZKbJvWrHV4rlNVYuWBq7cms5Gfbz5V9r1lMds50p8zuXaD/8Yo+T5CzuG0ZDUsErTExY58zqbOEX+xQCYb3mfwM670iWisR3vX94ZUFnkqyDJUbH6DEltSJbyfSzrakKbhg6suyN2TiTpJh3XnLhwoHBWOhZSVappZZM3ZJqCQd0CarBJUyyShRS5UbzV0TqZM0hLATbDbupLjk4s7/+WkEFRrYXlfdPIw2nf6N129hWqib46yTPAQqNzTtEnmIqC6Ro4LZNPOcW8y6Fk1z0QWfFOw1HFIj5sdehktZZ/Sqy7JcbbxYyrU15IZpVqPsWx/dKs+tuGl1z1JJul4Q1kDY5+KRUJzc5m3F0wprmQEkC5TuuBZnvUoxvaVrtdiYG25zpWzdGob/vu2T7Qph62RbR6o3O8KqWKoF9jbSJZTC9PLyzM8/v+V0Oonfas6kFFEaUopoZVrU9rr4IrlVvHv/sxzA6NtQTKE5HI5FWhtIxfBRHPMplDJ5WscQyUqm06qkvCYUl3FiN+zJIWBdh+s69vs91+uVaZrEULvviTE3NkHOiXn22BJjk3LGB6GB9a5jHK9Ya9HatTTcw0HG1dVW8Ut7EbziX9ahh9vwrDYoMhtfs+rCUdvvgFxw7prpT5nhrce9zOiXUdRcsdwVSqEKhp2tIQ9Ovr5ElDNko3BjonsJjL902FGjkm4Wc3FXOIdFN9+03mXukJ0UzWiQju0GA7ulIJEV5qpIw1ZsBCNUxJ1EU5s1izOTK/jfWoaVoxRXFXJzCSsvWbpgCUR0Yy6SWkUYNHEQjE88TOWGFfXTNnH/fOe1tp03T+xc7Afrl9PGF63/b8cyb0UrDpl0DJhdxKgsq4jJoCcjRcrX7k/Oyxapsh3vZAQLNWeDnYrCq5hVUzs+i2DfVcKLwDi5ripMJodt8AaQ+u38iT1gJtzJC+oIyw3c07+X5q8ONOuyPg75Jv5Ffq92pY2mluXaCya3gl4LdD1mt/TGBqX9uR3sK8Ptm6l5LaBtea90KSIZrQUqOF/OfPjwnh9/+1uen5/x69qMY5QS6pUq9K67uzv2+z3LNOOspXMGP08kEm/evGGZR0IQUUHX9fiwkFPCdWLo3XUiTNBaM04T1hqsG+gHcfAKKRJDFBaB1UzjzDwurMXlS5NlaAYo61hTlqIaUzHyfpFB3X7PbjdgjMIkOPZ7Xk4vrH5FW8fkI/uhK8X1UDr9iDEOYxV8YR7srdHGq6EVbEu2gok1A45uwy6F7iIKqN37yO63M+bdBTWvkJL8ATAGrDA10Jq0HwhPEkho5kDuRbmlQ6Z/DvijIfSy1HcXhbvKQGl9EIpM7NkGRY7Nh7R6fxbjGLTgaqrQjEwJ61NRBhi5FIh6LLIuN7+BtEi77i5SKHXImDnTnT1ZK8Ig+xgGxC2sMCu6kwgWoiuJslo1O8Swv4Fe7IY95s/dwf6+4lq7/3pedW5L2to5ZpMbPiydf4IhykPrxWFGjS1FrmVwletHpU12XKlwtUOMd9Ltd8/13BVK16pK4gCkIaFn3QZgehXePJnCPKDxmStMUVddWW1WgjpIEQ074dHWTjMO9VjIMYg7wXHFpEbBunXescuokl5wa61ZZd21c20D4rZ6Kqsr+dfnGXLdBvcprak5VJXnWgtwtSM0RjOOI+/evuM3P/yz4Kxhe43bFARtHMMwcDwcSSliBhkc6Zw4Xy+8efNI1zmWZWZ6OeG95+nJ8dNPPxbLQYnqfnx4ou8G5nl+tf8xJpZF5Bfer+x3ezHKThmjFaQAaIyzxOAbJW0YBq7XK76ovCoksiwL1/FK13cc9wf8LNzccZpIGbquZ7fbcTweW9JtFWmAwpgvS9NqRPu4PdHbvVkwRf3xLpUBEchF179kDr/19L+9oC8z+LA9uo0ROEBiKKRr3XWE+0HMrJeIvyuDUQXutOKPTuhbRoQQMoDKLI+K/oMQvv2xLv9Fghm7wnv120NDATmpohnfrO4yVUZJgw10hNDVTo4Nr+0Fn+tPER2EplXpWWZNuFERe8N6T+tKkwNKB5zLUMvvi2qt5EU1R6lKS/oXSOl/8nk1N+kEuT44N3y1Fdaa06Xy1k2n3Gh4KinUi8NMqg2+YLs+6nmrzlbVoNqOEjiYDEQj/gGq4JYUvmguggBZJWXUbMqgtQyuWpcqr12x4+BqioFqmL/28vCs/FOlsxjAlADD5Iqhdtz2W1IOVJO51hVa84qtiwDNxkSY5cGDonTu5Xfq6gVaN9uO8R/YPqnA+tVjrKTAZipkIKGEKSWMsQ0iAOj7nhDE2ep0OrEsa6NriXYfrDEYa1HGsN8d2O13dH3Pw+MD98c7ruOVlw/vJa6keLwuyyI+Ac4S1pUQPH3Xcb2csNbBwyPX64V5Weh7x7LIRKPG1YBAEs2JSytCDOx2faFmxeItK5HgMQSs1uAs1lmWZWY3DBhr+f/bO7ceOZIkO39+i1tmVmaR3ezenRUEQS8L6f//D70IggBBuw8zWM32dBfJqrxEhLubHszdI6sxPaBGbD6VA40mi1VZcTU3O3bsnOPDA9M44qxlpXB7Q8BYR9ePHA6HpgI2DEPZVFwzZvyWq86iNytl4S4joT1kr5pdZtNxdbPQf0745xVzK0+YdwUkE4UCggfvEGtJh540etJgWXeW+diRA4QXGD6mkpHoSy5WM1iTtRS3URqdxiZ1IVAamL7M7lqySKRxN+2ibgNiNiFmMxZ8tjbtPKyDNCETd9cssasOCcTB4hbBXdUAMU7Kg10OluVgmshI686XwBqdaRS0uNMyOZeJNb0Bwt8SBPl71707QVPcvwuKNWvVP7/ObrPX62s/G2yqNAPa/5tYdmsi6saiAUUU9xRh3RlkrMFPs1orJQhGcKs2xVKzdCmZaZFwlJqFFr3emgDU8zGpUKYKhSuO29dNVnU2dyvlmN2OuSYRmc36W6sXwRXZSTfr96VON0y3FEwVQ/dpG4mtVcv9avAAG2Tw19YXBdhxmJQdIJlMzYcNKVWNU12hDAFUGlLfaybX9wMGS5ZMFzoOhwPWaIB99/49j8cj3TDQDz1kpXWpBoDh8tIjcUZywhjhdBgJzjHHxDR2fDg98On5M4jh+vKZKJnQ9zpuKzqNtRRYolqFX69XEFiXhb7v+eV65uHhgdtyJcdEjBkraikyTSNPH2/0XcBbzd6n3cRu2jP0g2b2zpGXhXG353A8MU0T+2nPbqfiNXW8uDYEvzUGG8d2y7bd9i7ANknB+zLIbHCCOFhHSzc4ZOph0exVOk889Eiw2vTpLZfvPOvelPntjSLmlsohdYSLxV9zUaIqn783OmYatXxdjqVrH/Uz/EWDRBP5qE2MpXSWa9fcVVbD5sGVBnBnbaZVTQK7bhuNGIijwYvgb0IaVRNWnIq/LAfFbLPbmi2IwgHr3jTMNY1Zy+1QSuMmowiSbJnO+HqrlupiwZbueFWoqqsJ+Ji7e21oQalxo+vnRIWCXBGdBqWxVUeI1KnG7vW7Uo6Lbm73DgPZ65CHmzcM2i6aGboF4lDvpRD3GlxrAISyZ6+boaCqnxVsucD5qdfAKUaDd63OTKJR8FysE3V6PP5sMNHo71wN0dE2QbHK7TFZ5Rfb5xWea3ViqNjuq+v7/4vBbjbZkCQp51PKiZRyv1K0agMsBG3yPD4+QvGuevf+PY+nE8YYhmHEu0A/9PSh47YuGGvxZRxWTB09TFyviZfnC+9OB7V+AfZTx3enE9Y4ns+G3nvi8kI2lhVD13ct0Fdxmd1u1xpNy6y+XfM8E9NSRGcinfeEzhHnRJLM0/MnDcqL6te+nM8cHx8xzpOw4CxLvGBDz/5wYJymBluAp2oyVE+yezeGb7WqktLW+Cil0n1iVfBC2MpvKOVgb1gewOSOblRpwNs7x3w0bVoph/q9NM+kV/hfcVWYi3WNGLVXsVGYjxrMQAPdulctTv+sala5L+V+jVdSRiOhlPFFoOVoNnFtSrA4b8FinDftgNSXgFnKv/5TVe03IKKTaZ8jtykUL7LtOjUe7sGorXcx1fu18LIxgnFFlNuJduS/4mqOr8KrTLVijI1SVDrfLTDczfbXIQI766BFuGRSULijsk7WvSXuSnCpHNNcgosvgxwVizVb00invQq2XZTO5kct4/1ZMVF3x3ttEAU0tohY0aGOVe+NCsDo+VWh7jRlZYrUwYRasYy5QAvKn96y3w3+qve1arpWQSPdTGg+XjXZuK/8qtCO/Rv39Yt5sNW1tdYkztpGnm/+XNY2JoExnnGccM7z8PDA6XhkGidi0kwyJ6Xv6DEKY+GJptJwCt4TnWOcJi6XM0lgXRNGbnjnOI0dFrjcXpiGgLceJCI54p3hdHpHTBHvA5INWSIfvvue3W7ifH7m+eXM7XrmdrviLHx6emJZI13XYw0Ea/j06YngdCBhWRLPzxcOD3sOx6PivtaBgcPDCecc027XlMBAnRRuc2oUr8qsqI20b7VqwLR32FTrqtcnwN59vdK40IcxlQZP6gznf7AsDxsFSUv7u7K5PGupl1cPY+0Sm6STX34W7EUhAn0xpAXz3GkZV0W6K45qy/EsJw0QZJ3QMaIv1fworCelCLmbft0uqk9bV3hRB4N10gMVD8OTCrq4pahmXRPh88p6CMxHswUXFGuUB4UjUlWYutuQTDYwO8QIebGqvVAFSrqvCw3ZQr8SpzSoV9lUPR6hjcC2ST0xr2GArBze1OvGWTPT2gjTD6LxRtXaWrFlU5thd5ikvxXM867bnoOqbtXOPOhx18CWnTRfrPrchOXue2sCYGjSiXHSk+yebNvg0iDkPuPOTgPrRTPjuJf2TIfLNrll1u25rBxb5QfrxpPXTdi7VXV11NtQpBN/O4X94nZ2E4umMgAAyeRCy/LeFzFbW8phdGY/JbrgGMdRm1xL3ESnjSGUjFey4GvgBZzVn7lcLmBUi/W2zPTBYpwhi+HzywXvPH1wLMvMOPT0IkRjcM7S9TtyEqad8P79iXenI95agtMNIi4zcV0wCNM40HdCysLz58+8e9QNAWCdF+K64J2lHwamac/Qjxin+gp+UhaEOhyUNmax8L73DgPN7BUP/varPjj3jYvtH2mkeECzF6cPXGIrmbKHPAhVSKMKg9zL3LWPbJiYKeOQWlb6W81QlNRvoxC9aQ+wP5st+JfGQhr0OGqDK3ca4NMKTIblJKRTxA6RvDp1IjD6FthC4Ul9hSn0pRs+Zm5HqxzYpC+Du2bsklWAe7QKATSIRV+sWDan1P8Ka4VXARVQWKCch8x/A6z7O1bLYAtP9K/xbGsmmUdpEEAVf8kOXBk+WR40+7+nRL1iHtSNc9DfWTvvjc9q9dlx8+Z/Fnd6jyock8qwRXaidK3SNDMZ6AzrvmDk5bmRquplN9ufKuhSNX9T4c/6Cy2Ypl7pck03wGpDrv589uW820CDfnZlCDTtDbn7r13Quz9byPZXX/vV+qIAe7vdCoZpsU6bVZX/GkJoZXfKGWsqXKCShtM4EoIrE146kBBjRBD1saqi13dmhl2nPl63m/pm/cOPP3K7vvDx5594eblwPB1JMRNzIjrBB9V7HYIaE77MWvLbki3erjPjMDJNO3JcGIcRY8AZWJcbz88vOt4aOrrgmYaOl5dncs6sq24Iu12PDzo4sJsOeB90kstaDMIwjMR7Q8PsSuaciUmlGZs4zDcW3K7CwzWjaJJ8tTtaqUSlSdL4sbX5VXmEnhJAwJRmReN23pejsD2Ydzu+RXDlQZ9PltujZo/hrAr080MRZlm3Bz/7Wq7VYKLNh7gXzKLZhbtBnjJhrymIsRqMowXxFpssw1+kOTWcH2uJaQu2S9N8zb1hOQZSb1mnonNQaU2WMvSwbTI6+imvX8ZstGSd7VZKlwbL116VCWEcmFiO4354pERGWzLNej9rh14Cr75ukk5yVdEUsmzUpJL1gp6LvTNdzH4byFB1MsCY1o1vCl2JNtvfZCzLxl6fx0r8z70mYfesgNbIa7hybX5tvNvG4S65TmUC1ECvx6vXrbECoOH31dnhtQaHtN5E9psEoi0Z/W+tLxs0MBmsEHPEGQdJ70hc9ShEVAAlOI8rWVt1izXGoIJWCYMlBKVmxeoAW26QiOg0VhZEMkY8QzewdgvDMHA6HeiC5/nTR0RgzSqLV6fIOtNjcmZJQoxC6DqGYeQiF8Zx4PTwwGG/Y5kddIqRprLTBh/AWua4cp1nLucL2Vq18+4cISgGHTrP8Xig69Qp1zlPP/Qsy8wwTqQ14QpsIGTWGPn4+SPTOBTxGEfwPUv+xoMGtVSvL5Io9li5r9kWm5ASaKtAyX1J1LK0X3WuTXkp739Pxftq1lNfqIqrpVIqxlHxvfEvgr/UD9WHPpvtIa+jnCZpdrSWl1+6jPnkSZPAYeV4uBKTZU2O1Fni6ojFLKqWuybSRmz9lbIpFPHtSWfinVd+6/xQzPU6yL1mqhJULFymqAz3vlzUeu7R4j85dW+oJPYVcpc14H7F1dxgKYGmbH61U1/dWBtbxJWAIIVhUQOVv/PQynXDMZhqC1ODUAk2QBNuaZBhVb7yWm2EZyG8aGYcd2xcYL89IhUTrXBFhQNMUpZBeKlwBC3wVlpXtSJ6da41K6bIINptbLhKE9afqRn6KzzZFm7vrwKmkZKpQnFZsG3kWgwbhPBX1v8T473KFVprMTpIirWmBbmUM050UmmapibMrZqsd/bVJZNT0W4wRnm0zrqmpbquiwawfuTdu0eGIfDudOKXn37iennRLIIbzkbWGV4kIeJI1tHvjnRBdQuulyuHw0G1Yp1jGiecMYxr5OnpF2LOjH1HzInOe27Lyjj2zPONbC3GWIxRYZplWXEuEJNw6If2eRilrHV9wBpXxGwyS4yEYge+2+1YU1Zdg/nb0rRgezFaI6v8PZeOeOP5xYKfUR/0MolTM9TVbOOOdwmZ4oB3WS+0F1IdBQrWFnSSanlQ/BIrzO8s/S9Wm1EGKPuPTcC8lao1W0qTQJ8wVsA61oOwf7gyhpXkTUtyrkvg41OvsnbDnXV5Oe5UuJd1DLa67YottK8i9CFOVFIxCMbK9jl9woasrraixygrbXopC8iYybOFIEi6u2BfYTULl9p8KY9V9qWxtBqo0oUFJ6yi2K+8pyjBq31e5aC+vl61gaS/vFZApZM/aDPKn/W6rXtDeBGGn4V01gZknLahgVzHhw0NK7VRoYA06D2JY8kerRRtVsr0XsnAawVWBhOslOoq66irsmL02c29/h5XxcHDXSA15e9xC+ZtVcRsvjv3sikpLrwZSP619UUBtu8HcpnWkvobQAn5Xp/Ce9+pSklaFuW/phJYndPsLmdRcLi4zVqjmKgYaUHcGMPLy5nHx0c+fPiAQW23j/sHJK38+d/+yNPPanZoiiPBZU6M+wf+w3/+RxXNFuFwOHA+X9Ru209lQ4BkhBQznTFYU445Z4I1rElUhSsLz9ezag7kzGH/wGF/LNQzzYxiTPRhQCj82lycd61hHFTpWXVsAzIvCPLK4+xbrNS9fpiAV3Dd1vWVrVtaHl4suJtVWkzp0upLUkRCSjlHbaZkVJLhLhDlAMZpuecvirWtP65KY8qGtIfLo4FoCb84hp+1u1zpQZSSM06G8x8EOa24LpEXR3pI2LPjdgtcfaL3sZ1mygUrKOdRKWhNhtGoxiuAv0prtIFqEywHfTHzLmkgCPo7Ta9TTzaUfoTbIlHKRomMQZW4sIKEDEltbb76uiup68BBqwrvKFuVR2pSoTf5LYCKq1WCtGzSFF0AuNuc3Wu4oE55aTSnBNp6LDT8WrNTinuFUtoM24ZWtQZygQsqlJA7YS2Uqiqx2NgQpWLIpk59SeOlpirWUqCJHB1IA5kAAAwXSURBVErpbyBcuJNc3KAS0OtWG2x1k2qJRPmdtaGbu7vA+jdu65dBBElaxio544Mn1zupMlalyaVXuS9+WVWqUIxmrTHFZoBoRM0UjdeJDKBQpVKTLhTJTGPP2AV8CHhrWW43luuZDx9+5Hh65E9/+iM//fRnOilCx5QBhgJTTJMyF/78739G8ndM49iUuD59/MyyzkxTj7eeVdSFIC4rYixCYr/fcV1WhnHHd99/4MMPP2Cc4shDPyEkutCxLIs60XrTGlu35cZuHFmWiHEeYyKh67Y34But++Da+JB/hTi9Sbzp96RBSmlfPZCgOoOapJut4nQleOTCUyynV4n8QKMEIZo5uiHifcL7jDHC7doRZ8f6DuKDIXy07P6k/NeN4C+avVpBssH3kdwZlUD4eeQpOQ77K9bqpn29dnQfNTNutCwgC40jW7O/OG5shnVnuf5guP2YNoy1KHwZn3E+kxaHqeyAu1Q+JY8Zk1KyQlYguFybv9UM+XtWKpqm5bXTF76Kv9Rjq+IvTr+uUAKNyytOIQyXTBN80WxOWpNTg65pVKv6jNQMrjpLSFB94KoLgJSMdBT8WSf0+icV2Jkfy+cWv7Q0FpcCuzUy3U0DdNpn8mIahKCMBMFdLK40RGuQrYMlNRuvwuFVGF0sxD1NTYtyDrY66ZYAfu+3VfsT9TpX9oaRusH89j36skmuIjJdRz7htQh333WFmqWSguuqoE1KCWNtK/3vyfYGQ1rXZrWSUmpMhHmeCUG3yup+MPa9Nqys5fL8icPDgUf/yPF04r//z8DlcuZxd+D03fckycV8UDPkaRx5Xmeenp7acMGn50/8/PQLSVSbQGGJuAm8iLDrOjVndI794cDj6ciyLPTjgLXahTHi2pQahb1Qr4uIkHKmyhP2vV6bX+vp/t6rdd7DhkXpP9B232rnvHEMNy5rbUaYTLPdzkVXU0nkQLEiEafqSVRMsI60JtNkBMUJzgghJPbDTLCZJ+DlMmF3Ky4k5L3h42Hk8L8tGC0Xl5Oa7kkyyGJhgLw47C6SnwPp7Pl429MfVPtu/dTz8GQ0WEzaQKuDBhroDf4qzTss9noO86OyEtpLC9ApFGB9VvjE6caAEawtWrb3peJqsfuVvFqMz8jFY6avi73fi8fcwzVVsrBNG9VzMNJ4qq3sb0pS0vBZ5YEW88GS6Va8/deYZbW2rgMdWgVVCcDy7yUwzo/a7e8/aWWy7kzDMuN0tyFbyjBBkST8rDBQm1bLhvBRy/3Ubc0n5UTTJBNtaYLGSVkEFQJIgzbo6qYvBWPOYRteaFCX2RTT6qrXs43frr8dYb+MB+tMeZA0vOswgeC8oytMgFA0WkUSzhc9Au80I7WmGRRa79XGBaEfOp2EEos1hpyFlCPzsvJwOPB47JmvNyRlUlFqGvue4/GEMYL3nt2047/883/lX/70R8ZxYrfb40MgpYi1tYxfeDgeicuCD4E1JWLKjIeJrjekZSZFdUgw1uKK/1coo71+HPnw4QN9P4J1DP1IzoIIhODVFqbrsHYTJQeKOLlTq42ciWtspc23XK8yljveqxgNmJWSUoH8+lKJu9PLrA2bGlxLJ92sppRLkIYiU/crDMtdS5A8JAgZ++LJYsjZkLJl8JFdv7A+6DCLc5nOR+J/XHj2D4Qnq6Id+8pkN1rurxZjhVyDWbT4PpGTpqUmGdZJA2mlBKlCvyBeKWaVnytBHWXng2F+r+OuYkCKCIrtov4eJ+RkcCHhnLTsNdeXMKMBteokR4PECk5/3fvagtx9KVsbbvXfK3whWybbtBwqC6J8jpi7LFtojAmTDcTys7Np/mfZK+fVFZxcm4FodDRbJtm0GywsHYjXbDacVZEsTjQ+rh5jab7eleHdkw6dtImzpWbJBT4oXFYxlE292H0vhu5Z3WYpTazqyZY7/d7sCiOivptmC6710jXBG3O3YUGDL35rfeGggSkNKwWyrLV0vW6D95hro2uliHPqc1UfLINaSMQUi46rsgX6viPFymqHl5cz0zTx7vF9w2Gv843gPBICYiEMA8t8VaK/dbx7/x0RMEXm0BoLVjHgvhtJeUVEGMcJoWTfYng4HDHsuX3+xNPHn4v9Daq74D191zGMI9P+wOH4CNbjQwBj6Hrls4YusCwrgm4CFR6IUd12Y9TMfZ5nhn7gernghuGvXOXfbzUstBjDtRekZDKNXF1ggWrk1nC7EphtZBMjNorTSbFVri+DBGljjVXhys2wnDLhNCNiyM+edA6sNjM7z+Ajxghjv3JbAtZqdjh1K/KPz7xMI9ZndTXwWYW4S9lOlyDpfbM+E7rIunjNKo1oJiqKAYbnLfiAnlPqNVive0iDYd0JecoabCzYLpFXh7XKAA9dZL5pCbCuDu9TQ3zS6sCJBlQn5EX/zq+adF/vxlbhm7tAkLYMNPuNH9tE1EtXXjvpoqTeu4y3ls1Aay7ea55WVoI45aK6K41KF3eyOfqW78miwyT3tK7lqFWDvylsUzPf+jM20sZr6yafRj3HNLCpZ5Xss1ZHdfLL5FJVoToXdtFAnnraeaSxZKaYVyV/3Km6mL2ZNk4sAcyi7BqTCrWtjilXit5vrC+e5LJWyf7OKobqrGUtylMA4zi+4rS2wQTZGle3ZSb0Hcu6sht6NQZcV7wP3G5XUmkQnU4nJeSvK3/59Av7/YDYM6vJ9F1QS+6YFQ9Lids6F4WsiHM6669c2oVlXeg63/in87ywLldizDye3mFM5iklTtZwPp/JaaXveqZxxzCM/PDjj5jQk7HcloXOB+Z5UY0B77HGYq1hvt34l3/9V/7pn/7Aw+HA7TpjrSMnwdkCl5Rr2RwfvtG6F0iu45yV19n0XynTWUYzmWaz7LbgWzvLOSgtJ/f68OvUjpQMxyJBsFeNzP6i3WP7fsG6TIqO7qNlDkLsPJdsicnS+cQSHXHdsM0leubZ002rft0LMlvsFMk3Dz4jc2k6Ab5LLItHkiG+KHCbjlEx24++ZF2lk4xmXLVTPX+f4EG7XyIoBStk8tVjhkTOBtcnRFQNLUVH979GlmPG/nDDWP1eQhGptgKLbeyL30vwpQXX+0574QxXqpKW79u8fg3KrRwuVueNSbLeBd0SGCXTME6xGly75yIZuJfWhe8uutmlvjQMPdgkmJXmdYUB64SIPkNVd8AuRkfwoWTB5fu9TpmJ1wAestWJv7w10Frztrgk16y6qqW1is1uWb+JmhjYm1FYS9RZNg3FpQGrE4UVijG1qqO4OchG9fqN9WVyhcYTRegFfPD4oIHKWmFdEvvDTiEB61gWPRoXtCnQdZ6ctWnljcVmmEKPSERMj3WOJUbWJbHElfePj+yGkfl8499/+Ynvv/+erh8wNuCNZZkVDx73e+Iadbw2C953agljHV1nyRm6MLCuCwbl7KZ0Zp4Xnp4+Epyn7/YE73n3/Q9cLy+M+wsmq9Dy/uGBmA1+mJimHTEbhDPGOQavo7mpSL057/m3Pz8R9hOXnNiJPiDWqzmj7ZziUgU6+NYBFluzTz3eNOpoZVWWavSUgoHpAMF9p2p7OKFgarWzXOk8dsO+zKp4p7taFUE5RbwR5nOHfQrM75Mq5hvwIZGS5ZYtzmX2uxtrcsSoT621hXFyczpq6hQLRFDKlKHBBOIy6eJhtfhnVzQCIsbrMaRo1Z5GNqfZuM9KxRoToY+EkFhXxyoG43SEth9X5kug66OKuYdE/GVQGMELcXZ0U96OaXaYMSLJ6P8vZTNYvy4PVv75ha7T7N8AzmY6r6JIS3Q4q6yGzidStrhig16DmLNCTBZXSt7OR65LoPOJLIbg9P/OCEkM1ghrcogYlujoQ+TH/TMPQeVB/9v/+QNjt/J+OpPE4kp3dc2OlK3CejZjKxfJCEksWQxrcgSXWJPDFe1MawRvMr2P7XvOa4c1QnB6Tp9vPUv0rKsjpXJvXSpwnbAbFh6GWzv2NVtc2cCDS/pZdnsf92Hmf/z0A+vqeP+fzhz7G3N6HSZzuRb1z9c18FvL1BHOt/W23tbbeltfd33dLfVtva239bbeVltvAfZtva239bZ+p/UWYN/W23pbb+t3Wm8B9m29rbf1tn6n9RZg39bbeltv63dabwH2bb2tt/W2fqf1fwF/h57Sph8YSAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6MWWsIwPoEnv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}