MNIST_GAN.ipynb

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      "source": [
        "# Generative Adversarial Network\n",
        "\n",
        "In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\n",
        "\n",
        "GANs were [first reported on](https://arxiv.org/abs/1406.2661) in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out:\n",
        "\n",
        "* [Pix2Pix](https://affinelayer.com/pixsrv/) \n",
        "* [CycleGAN & Pix2Pix in PyTorch, Jun-Yan Zhu](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)\n",
        "* [A list of generative models](https://github.com/wiseodd/generative-models)\n",
        "\n",
        "The idea behind GANs is that you have two networks, a generator $G$ and a discriminator $D$, competing against each other. The generator makes \"fake\" data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it's received is real or fake. \n",
        "> * The generator is trained to fool the discriminator, it wants to output data that looks _as close as possible_ to real, training data. \n",
        "* The discriminator is a classifier that is trained to figure out which data is real and which is fake. \n",
        "\n",
        "What ends up happening is that the generator learns to make data that is indistinguishable from real data to the discriminator.\n",
        "\n",
        "<img src='assets/gan_pipeline.png' width=70% />\n",
        "\n",
        "The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector that the generator uses to construct its fake images. This is often called a **latent vector** and that vector space is called **latent space**. As the generator trains, it figures out how to map latent vectors to recognizable images that can fool the discriminator.\n",
        "\n",
        "If you're interested in generating only new images, you can throw out the discriminator after training. In this notebook, I'll show you how to define and train these adversarial networks in PyTorch and generate new images!"
      ]
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      "metadata": {
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      "source": [
        "%matplotlib inline\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
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        "outputId": "ccea2df9-b0ea-4d0b-f31a-fbfb007e6317"
      },
      "source": [
        "from torchvision import datasets\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "# number of subprocesses to use for data loading\n",
        "num_workers = 0\n",
        "# how many samples per batch to load\n",
        "batch_size = 64\n",
        "\n",
        "# convert data to torch.FloatTensor\n",
        "transform = transforms.ToTensor()\n",
        "\n",
        "# get the training datasets\n",
        "train_data = datasets.MNIST(root='data', train=True,\n",
        "                                   download=True, transform=transform)\n",
        "\n",
        "# prepare data loader\n",
        "train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,\n",
        "                                           num_workers=num_workers)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Extracting data/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
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        },
        {
          "output_type": "stream",
          "text": [
            "Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
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        {
          "output_type": "stream",
          "text": [
            "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
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          "text": [
            "Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/pytorch/torch/csrc/utils/tensor_numpy.cpp:141: 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.\n"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZDVE5zNzY1b",
        "colab_type": "text"
      },
      "source": [
        "### Visualize the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZbx74CzzY1b",
        "colab_type": "code",
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        "outputId": "8451ac8c-0dc8-4809-ce0c-4eb1bb15d00e"
      },
      "source": [
        "# obtain one batch of training images\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "images = images.numpy()\n",
        "\n",
        "# get one image from the batch\n",
        "img = np.squeeze(images[0])\n",
        "\n",
        "fig = plt.figure(figsize = (3,3)) \n",
        "ax = fig.add_subplot(111)\n",
        "ax.imshow(img, cmap='gray')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5984d4b2b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
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          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cTQ3Giu9zY1d",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "# Define the Model\n",
        "\n",
        "A GAN is comprised of two adversarial networks, a discriminator and a generator."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZ30SIYVzY1e",
        "colab_type": "text"
      },
      "source": [
        "## Discriminator\n",
        "\n",
        "The discriminator network is going to be a pretty typical linear classifier. To make this network a universal function approximator, we'll need at least one hidden layer, and these hidden layers should have one key attribute:\n",
        "> All hidden layers will have a [Leaky ReLu](https://pytorch.org/docs/stable/nn.html#torch.nn.LeakyReLU) activation function applied to their outputs.\n",
        "\n",
        "<img src='assets/gan_network.png' width=70% />\n",
        "\n",
        "#### Leaky ReLu\n",
        "\n",
        "We should use a leaky ReLU to allow gradients to flow backwards through the layer unimpeded. A leaky ReLU is like a normal ReLU, except that there is a small non-zero output for negative input values.\n",
        "\n",
        "<img src='assets/leaky_relu.png' width=40% />\n",
        "\n",
        "#### Sigmoid Output\n",
        "\n",
        "We'll also take the approach of using a more numerically stable loss function on the outputs. Recall that we want the discriminator to output a value 0-1 indicating whether an image is _real or fake_. \n",
        "> We will ultimately use [BCEWithLogitsLoss](https://pytorch.org/docs/stable/nn.html#bcewithlogitsloss), which combines a `sigmoid` activation function **and** and binary cross entropy loss in one function. \n",
        "\n",
        "So, our final output layer should not have any activation function applied to it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OAMg1x3hzY1e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class Discriminator(nn.Module):\n",
        "\n",
        "    def __init__(self, input_size, hidden_dim, output_size):\n",
        "        super(Discriminator, self).__init__()\n",
        "        \n",
        "        # define hidden linear layers\n",
        "        self.fc1 = nn.Linear(input_size, hidden_dim*4)\n",
        "        self.fc2 = nn.Linear(hidden_dim*4, hidden_dim*2)\n",
        "        self.fc3 = nn.Linear(hidden_dim*2, hidden_dim)\n",
        "        \n",
        "        # final fully-connected layer\n",
        "        self.fc4 = nn.Linear(hidden_dim, output_size)\n",
        "        \n",
        "        # dropout layer \n",
        "        self.dropout = nn.Dropout(0.3)\n",
        "        \n",
        "        \n",
        "    def forward(self, x):\n",
        "        # flatten image\n",
        "        x = x.view(-1, 28*28)\n",
        "        # all hidden layers\n",
        "        x = F.leaky_relu(self.fc1(x), 0.2) # (input, negative_slope=0.2)\n",
        "        x = self.dropout(x)\n",
        "        x = F.leaky_relu(self.fc2(x), 0.2)\n",
        "        x = self.dropout(x)\n",
        "        x = F.leaky_relu(self.fc3(x), 0.2)\n",
        "        x = self.dropout(x)\n",
        "        # final layer\n",
        "        out = self.fc4(x)\n",
        "\n",
        "        return out\n",
        "\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjE00clozY1h",
        "colab_type": "text"
      },
      "source": [
        "## Generator\n",
        "\n",
        "The generator network will be almost exactly the same as the discriminator network, except that we're applying a [tanh activation function](https://pytorch.org/docs/stable/nn.html#tanh) to our output layer.\n",
        "\n",
        "#### tanh Output\n",
        "The generator has been found to perform the best with $tanh$ for the generator output, which scales the output to be between -1 and 1, instead of 0 and 1. \n",
        "\n",
        "<img src='assets/tanh_fn.png' width=40% />\n",
        "\n",
        "Recall that we also want these outputs to be comparable to the *real* input pixel values, which are read in as normalized values between 0 and 1. \n",
        "> So, we'll also have to **scale our real input images to have pixel values between -1 and 1** when we train the discriminator. \n",
        "\n",
        "I'll do this in the training loop, later on."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQ70Ia7GzY1h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Generator(nn.Module):\n",
        "\n",
        "    def __init__(self, input_size, hidden_dim, output_size):\n",
        "        super(Generator, self).__init__()\n",
        "        \n",
        "        # define hidden linear layers\n",
        "        self.fc1 = nn.Linear(input_size, hidden_dim)\n",
        "        self.fc2 = nn.Linear(hidden_dim, hidden_dim*2)\n",
        "        self.fc3 = nn.Linear(hidden_dim*2, hidden_dim*4)\n",
        "        \n",
        "        # final fully-connected layer\n",
        "        self.fc4 = nn.Linear(hidden_dim*4, output_size)\n",
        "        \n",
        "        # dropout layer \n",
        "        self.dropout = nn.Dropout(0.3)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # all hidden layers\n",
        "        x = F.leaky_relu(self.fc1(x), 0.2) # (input, negative_slope=0.2)\n",
        "        x = self.dropout(x)\n",
        "        x = F.leaky_relu(self.fc2(x), 0.2)\n",
        "        x = self.dropout(x)\n",
        "        x = F.leaky_relu(self.fc3(x), 0.2)\n",
        "        x = self.dropout(x)\n",
        "        # final layer with tanh applied\n",
        "        out = F.tanh(self.fc4(x))\n",
        "\n",
        "        return out"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eDK5gE8NzY1j",
        "colab_type": "text"
      },
      "source": [
        "## Model hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WKhcWm5zzY1k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Discriminator hyperparams\n",
        "\n",
        "# Size of input image to discriminator (28*28)\n",
        "input_size = 784\n",
        "# Size of discriminator output (real or fake)\n",
        "d_output_size = 1\n",
        "# Size of last hidden layer in the discriminator\n",
        "d_hidden_size = 32\n",
        "\n",
        "# Generator hyperparams\n",
        "\n",
        "# Size of latent vector to give to generator\n",
        "z_size = 100\n",
        "# Size of discriminator output (generated image)\n",
        "g_output_size = 784\n",
        "# Size of first hidden layer in the generator\n",
        "g_hidden_size = 32"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSN6sHEqzY1n",
        "colab_type": "text"
      },
      "source": [
        "## Build complete network\n",
        "\n",
        "Now we're instantiating the discriminator and generator from the classes defined above. Make sure you've passed in the correct input arguments."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3LJHbCuXzY1o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "94fe03e6-4325-455a-f45f-d132dc9b171c"
      },
      "source": [
        "# instantiate discriminator and generator\n",
        "D = Discriminator(input_size, d_hidden_size, d_output_size)\n",
        "G = Generator(z_size, g_hidden_size, g_output_size)\n",
        "\n",
        "# check that they are as you expect\n",
        "print(D)\n",
        "print()\n",
        "print(G)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Discriminator(\n",
            "  (fc1): Linear(in_features=784, out_features=128, bias=True)\n",
            "  (fc2): Linear(in_features=128, out_features=64, bias=True)\n",
            "  (fc3): Linear(in_features=64, out_features=32, bias=True)\n",
            "  (fc4): Linear(in_features=32, out_features=1, bias=True)\n",
            "  (dropout): Dropout(p=0.3, inplace=False)\n",
            ")\n",
            "\n",
            "Generator(\n",
            "  (fc1): Linear(in_features=100, out_features=32, bias=True)\n",
            "  (fc2): Linear(in_features=32, out_features=64, bias=True)\n",
            "  (fc3): Linear(in_features=64, out_features=128, bias=True)\n",
            "  (fc4): Linear(in_features=128, out_features=784, bias=True)\n",
            "  (dropout): Dropout(p=0.3, inplace=False)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHRNUyOkzY1q",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "## Discriminator and Generator Losses\n",
        "\n",
        "Now we need to calculate the losses. \n",
        "\n",
        "### Discriminator Losses\n",
        "\n",
        "> * For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_real_loss + d_fake_loss`. \n",
        "* Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.\n",
        "\n",
        "<img src='assets/gan_pipeline.png' width=70% />\n",
        "\n",
        "The losses will by binary cross entropy loss with logits, which we can get with [BCEWithLogitsLoss](https://pytorch.org/docs/stable/nn.html#bcewithlogitsloss). This combines a `sigmoid` activation function **and** and binary cross entropy loss in one function.\n",
        "\n",
        "For the real images, we want `D(real_images) = 1`. That is, we want the discriminator to classify the the real images with a label = 1, indicating that these are real. To help the discriminator generalize better, the labels are **reduced a bit from 1.0 to 0.9**. For this, we'll use the parameter `smooth`; if True, then we should smooth our labels. In PyTorch, this looks like `labels = torch.ones(size) * 0.9`\n",
        "\n",
        "The discriminator loss for the fake data is similar. We want `D(fake_images) = 0`, where the fake images are the _generator output_, `fake_images = G(z)`. \n",
        "\n",
        "### Generator Loss\n",
        "\n",
        "The generator loss will look similar only with flipped labels. The generator's goal is to get `D(fake_images) = 1`. In this case, the labels are **flipped** to represent that the generator is trying to fool the discriminator into thinking that the images it generates (fakes) are real!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2aoIADRVzY1q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Calculate losses\n",
        "def real_loss(D_out, smooth=False):\n",
        "    # compare logits to real labels\n",
        "    # smooth labels if smooth=True\n",
        "    batch_size = D_out.size(0)\n",
        "    if smooth:\n",
        "      labels = torch.ones(batch_size)*0.9\n",
        "    else:\n",
        "      labels = torch.ones(batch_size)\n",
        "\n",
        "    criterion = nn.BCEWithLogitsLoss()\n",
        "\n",
        "    loss =  criterion(D_out.squeeze(),labels)\n",
        "    return loss\n",
        "\n",
        "def fake_loss(D_out):\n",
        "    # compare logits to fake labels\n",
        "    batch_size = D_out.size(0)\n",
        "    labels = torch.zeros(batch_size)\n",
        "    criterion = nn.BCEWithLogitsLoss()\n",
        "\n",
        "    loss = criterion(D_out.squeeze(),labels)\n",
        "    return loss"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bztf1-wczY1s",
        "colab_type": "text"
      },
      "source": [
        "## Optimizers\n",
        "\n",
        "We want to update the generator and discriminator variables separately. So, we'll define two separate Adam optimizers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "zmHGYTB2zY1s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "# learning rate for optimizers\n",
        "lr = 0.002\n",
        "\n",
        "# Create optimizers for the discriminator and generator\n",
        "d_optimizer = optim.Adam(D.parameters(),lr)\n",
        "g_optimizer = optim.Adam(G.parameters(),lr)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UbTDvbz5zY1u",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "## Training\n",
        "\n",
        "Training will involve alternating between training the discriminator and the generator. We'll use our functions `real_loss` and `fake_loss` to help us calculate the discriminator losses in all of the following cases.\n",
        "\n",
        "### Discriminator training\n",
        "1. Compute the discriminator loss on real, training images        \n",
        "2. Generate fake images\n",
        "3. Compute the discriminator loss on fake, generated images     \n",
        "4. Add up real and fake loss\n",
        "5. Perform backpropagation + an optimization step to update the discriminator's weights\n",
        "\n",
        "### Generator training\n",
        "1. Generate fake images\n",
        "2. Compute the discriminator loss on fake images, using **flipped** labels!\n",
        "3. Perform backpropagation + an optimization step to update the generator's weights\n",
        "\n",
        "#### Saving Samples\n",
        "\n",
        "As we train, we'll also print out some loss statistics and save some generated \"fake\" samples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "_Z8RH5MUzY1v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "14ed9f24-e4eb-4380-bb5a-7078e7ecea4c"
      },
      "source": [
        "import pickle as pkl\n",
        "\n",
        "# training hyperparams\n",
        "num_epochs = 100\n",
        "\n",
        "# keep track of loss and generated, \"fake\" samples\n",
        "samples = []\n",
        "losses = []\n",
        "\n",
        "print_every = 400\n",
        "\n",
        "# Get some fixed data for sampling. These are images that are held\n",
        "# constant throughout training, and allow us to inspect the model's performance\n",
        "sample_size=16\n",
        "fixed_z = np.random.uniform(-1, 1, size=(sample_size, z_size))\n",
        "fixed_z = torch.from_numpy(fixed_z).float()\n",
        "\n",
        "# train the network\n",
        "D.train()\n",
        "G.train()\n",
        "for epoch in range(num_epochs):\n",
        "    \n",
        "    for batch_i, (real_images, _) in enumerate(train_loader):\n",
        "                \n",
        "        batch_size = real_images.size(0)\n",
        "        \n",
        "        ## Important rescaling step ## \n",
        "        real_images = real_images*2 - 1  # rescale input images from [0,1) to [-1, 1)\n",
        "        \n",
        "        # ============================================\n",
        "        #            TRAIN THE DISCRIMINATOR\n",
        "        # ============================================\n",
        "        \n",
        "        d_optimizer.zero_grad()\n",
        "        \n",
        "        # 1. Train with real images\n",
        "\n",
        "        # Compute the discriminator losses on real images \n",
        "        # smooth the real labels\n",
        "        D_real = D(real_images)\n",
        "        d_real_loss = real_loss(D_real, smooth=True)\n",
        "        \n",
        "        # 2. Train with fake images\n",
        "        \n",
        "        # Generate fake images\n",
        "        z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
        "        z = torch.from_numpy(z).float()\n",
        "        fake_images = G(z)\n",
        "        \n",
        "        # Compute the discriminator losses on fake images        \n",
        "        D_fake = D(fake_images)\n",
        "        d_fake_loss = fake_loss(D_fake)\n",
        "        \n",
        "        # add up loss and perform backprop\n",
        "        d_loss = d_real_loss + d_fake_loss\n",
        "        d_loss.backward()\n",
        "        d_optimizer.step()\n",
        "        \n",
        "        \n",
        "        # =========================================\n",
        "        #            TRAIN THE GENERATOR\n",
        "        # =========================================\n",
        "        g_optimizer.zero_grad()\n",
        "        \n",
        "        # 1. Train with fake images and flipped labels\n",
        "        \n",
        "        # Generate fake images\n",
        "        z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
        "        z = torch.from_numpy(z).float()\n",
        "        fake_images = G(z)\n",
        "        \n",
        "        # Compute the discriminator losses on fake images \n",
        "        # using flipped labels!\n",
        "        D_fake = D(fake_images)\n",
        "        g_loss = real_loss(D_fake) # use real loss to flip labels\n",
        "        \n",
        "        # perform backprop\n",
        "        g_loss.backward()\n",
        "        g_optimizer.step()\n",
        "\n",
        "        # Print some loss stats\n",
        "        if batch_i % print_every == 0:\n",
        "            # print discriminator and generator loss\n",
        "            print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(\n",
        "                    epoch+1, num_epochs, d_loss.item(), g_loss.item()))\n",
        "\n",
        "    \n",
        "    ## AFTER EACH EPOCH##\n",
        "    # append discriminator loss and generator loss\n",
        "    losses.append((d_loss.item(), g_loss.item()))\n",
        "    \n",
        "    # generate and save sample, fake images\n",
        "    G.eval() # eval mode for generating samples\n",
        "    samples_z = G(fixed_z)\n",
        "    samples.append(samples_z)\n",
        "    G.train() # back to train mode\n",
        "\n",
        "\n",
        "# Save training generator samples\n",
        "with open('train_samples.pkl', 'wb') as f:\n",
        "    pkl.dump(samples, f)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1558: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
            "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [    1/  100] | d_loss: 1.3949 | g_loss: 0.7273\n",
            "Epoch [    1/  100] | d_loss: 1.3235 | g_loss: 1.5585\n",
            "Epoch [    1/  100] | d_loss: 0.9211 | g_loss: 4.8972\n",
            "Epoch [    2/  100] | d_loss: 1.2950 | g_loss: 1.9431\n",
            "Epoch [    2/  100] | d_loss: 1.3038 | g_loss: 1.0960\n",
            "Epoch [    2/  100] | d_loss: 1.2702 | g_loss: 0.8513\n",
            "Epoch [    3/  100] | d_loss: 1.1464 | g_loss: 1.0716\n",
            "Epoch [    3/  100] | d_loss: 1.2310 | g_loss: 1.3339\n",
            "Epoch [    3/  100] | d_loss: 1.1491 | g_loss: 1.7523\n",
            "Epoch [    4/  100] | d_loss: 1.0870 | g_loss: 2.7222\n",
            "Epoch [    4/  100] | d_loss: 1.0653 | g_loss: 1.3724\n",
            "Epoch [    4/  100] | d_loss: 1.0944 | g_loss: 1.1279\n",
            "Epoch [    5/  100] | d_loss: 1.0868 | g_loss: 1.4026\n",
            "Epoch [    5/  100] | d_loss: 1.1548 | g_loss: 1.1083\n",
            "Epoch [    5/  100] | d_loss: 1.3512 | g_loss: 1.0869\n",
            "Epoch [    6/  100] | d_loss: 1.0785 | g_loss: 1.4616\n",
            "Epoch [    6/  100] | d_loss: 1.1972 | g_loss: 1.0337\n",
            "Epoch [    6/  100] | d_loss: 1.2837 | g_loss: 1.7251\n",
            "Epoch [    7/  100] | d_loss: 1.3802 | g_loss: 1.2799\n",
            "Epoch [    7/  100] | d_loss: 1.2086 | g_loss: 2.1602\n",
            "Epoch [    7/  100] | d_loss: 1.2291 | g_loss: 1.2946\n",
            "Epoch [    8/  100] | d_loss: 1.4153 | g_loss: 0.8519\n",
            "Epoch [    8/  100] | d_loss: 0.9963 | g_loss: 2.8977\n",
            "Epoch [    8/  100] | d_loss: 1.0465 | g_loss: 2.5175\n",
            "Epoch [    9/  100] | d_loss: 1.1419 | g_loss: 1.3073\n",
            "Epoch [    9/  100] | d_loss: 0.9665 | g_loss: 2.8675\n",
            "Epoch [    9/  100] | d_loss: 1.0811 | g_loss: 1.8922\n",
            "Epoch [   10/  100] | d_loss: 1.0767 | g_loss: 1.6518\n",
            "Epoch [   10/  100] | d_loss: 0.8769 | g_loss: 2.8096\n",
            "Epoch [   10/  100] | d_loss: 1.0611 | g_loss: 1.4687\n",
            "Epoch [   11/  100] | d_loss: 1.1465 | g_loss: 2.0716\n",
            "Epoch [   11/  100] | d_loss: 1.2800 | g_loss: 1.1595\n",
            "Epoch [   11/  100] | d_loss: 1.0185 | g_loss: 1.2447\n",
            "Epoch [   12/  100] | d_loss: 1.2118 | g_loss: 1.1695\n",
            "Epoch [   12/  100] | d_loss: 1.1769 | g_loss: 1.0526\n",
            "Epoch [   12/  100] | d_loss: 1.2141 | g_loss: 0.9988\n",
            "Epoch [   13/  100] | d_loss: 1.2917 | g_loss: 1.3057\n",
            "Epoch [   13/  100] | d_loss: 1.2527 | g_loss: 0.9286\n",
            "Epoch [   13/  100] | d_loss: 1.2217 | g_loss: 1.0300\n",
            "Epoch [   14/  100] | d_loss: 1.3022 | g_loss: 0.9561\n",
            "Epoch [   14/  100] | d_loss: 1.1993 | g_loss: 0.8013\n",
            "Epoch [   14/  100] | d_loss: 1.3638 | g_loss: 1.0310\n",
            "Epoch [   15/  100] | d_loss: 1.2983 | g_loss: 0.8441\n",
            "Epoch [   15/  100] | d_loss: 1.2145 | g_loss: 1.1667\n",
            "Epoch [   15/  100] | d_loss: 1.2736 | g_loss: 1.1837\n",
            "Epoch [   16/  100] | d_loss: 1.4060 | g_loss: 1.1079\n",
            "Epoch [   16/  100] | d_loss: 1.2008 | g_loss: 1.1121\n",
            "Epoch [   16/  100] | d_loss: 1.3853 | g_loss: 0.7787\n",
            "Epoch [   17/  100] | d_loss: 1.3433 | g_loss: 0.9519\n",
            "Epoch [   17/  100] | d_loss: 1.2593 | g_loss: 1.0981\n",
            "Epoch [   17/  100] | d_loss: 1.2357 | g_loss: 1.4534\n",
            "Epoch [   18/  100] | d_loss: 1.1993 | g_loss: 1.0221\n",
            "Epoch [   18/  100] | d_loss: 1.0986 | g_loss: 1.1083\n",
            "Epoch [   18/  100] | d_loss: 1.2200 | g_loss: 1.6208\n",
            "Epoch [   19/  100] | d_loss: 1.4727 | g_loss: 1.5219\n",
            "Epoch [   19/  100] | d_loss: 1.2540 | g_loss: 0.9549\n",
            "Epoch [   19/  100] | d_loss: 1.2189 | g_loss: 1.0532\n",
            "Epoch [   20/  100] | d_loss: 1.3374 | g_loss: 1.2694\n",
            "Epoch [   20/  100] | d_loss: 1.2047 | g_loss: 1.3303\n",
            "Epoch [   20/  100] | d_loss: 1.3089 | g_loss: 0.9928\n",
            "Epoch [   21/  100] | d_loss: 1.2692 | g_loss: 1.0258\n",
            "Epoch [   21/  100] | d_loss: 1.2785 | g_loss: 1.2765\n",
            "Epoch [   21/  100] | d_loss: 1.3998 | g_loss: 1.2144\n",
            "Epoch [   22/  100] | d_loss: 1.2902 | g_loss: 0.9752\n",
            "Epoch [   22/  100] | d_loss: 1.2837 | g_loss: 0.9584\n",
            "Epoch [   22/  100] | d_loss: 1.3777 | g_loss: 0.9248\n",
            "Epoch [   23/  100] | d_loss: 1.3357 | g_loss: 0.9497\n",
            "Epoch [   23/  100] | d_loss: 1.2460 | g_loss: 0.8634\n",
            "Epoch [   23/  100] | d_loss: 1.3951 | g_loss: 0.8508\n",
            "Epoch [   24/  100] | d_loss: 1.3445 | g_loss: 1.2984\n",
            "Epoch [   24/  100] | d_loss: 1.3465 | g_loss: 1.0824\n",
            "Epoch [   24/  100] | d_loss: 1.3678 | g_loss: 0.8169\n",
            "Epoch [   25/  100] | d_loss: 1.1917 | g_loss: 1.6117\n",
            "Epoch [   25/  100] | d_loss: 1.3182 | g_loss: 0.7459\n",
            "Epoch [   25/  100] | d_loss: 1.3924 | g_loss: 0.9548\n",
            "Epoch [   26/  100] | d_loss: 1.4451 | g_loss: 1.0369\n",
            "Epoch [   26/  100] | d_loss: 1.2678 | g_loss: 1.2225\n",
            "Epoch [   26/  100] | d_loss: 1.2538 | g_loss: 0.8791\n",
            "Epoch [   27/  100] | d_loss: 1.2689 | g_loss: 1.0823\n",
            "Epoch [   27/  100] | d_loss: 1.3047 | g_loss: 0.8940\n",
            "Epoch [   27/  100] | d_loss: 1.3453 | g_loss: 0.8852\n",
            "Epoch [   28/  100] | d_loss: 1.2757 | g_loss: 0.8292\n",
            "Epoch [   28/  100] | d_loss: 1.3032 | g_loss: 1.0396\n",
            "Epoch [   28/  100] | d_loss: 1.2726 | g_loss: 1.6692\n",
            "Epoch [   29/  100] | d_loss: 1.3243 | g_loss: 1.3777\n",
            "Epoch [   29/  100] | d_loss: 1.3249 | g_loss: 0.9522\n",
            "Epoch [   29/  100] | d_loss: 1.2374 | g_loss: 1.6554\n",
            "Epoch [   30/  100] | d_loss: 1.5329 | g_loss: 1.0908\n",
            "Epoch [   30/  100] | d_loss: 1.2434 | g_loss: 0.9175\n",
            "Epoch [   30/  100] | d_loss: 1.2418 | g_loss: 0.9743\n",
            "Epoch [   31/  100] | d_loss: 1.3691 | g_loss: 0.8752\n",
            "Epoch [   31/  100] | d_loss: 1.2376 | g_loss: 0.9762\n",
            "Epoch [   31/  100] | d_loss: 1.2952 | g_loss: 1.2224\n",
            "Epoch [   32/  100] | d_loss: 1.2996 | g_loss: 0.8737\n",
            "Epoch [   32/  100] | d_loss: 1.2802 | g_loss: 1.0234\n",
            "Epoch [   32/  100] | d_loss: 1.3655 | g_loss: 1.4222\n",
            "Epoch [   33/  100] | d_loss: 1.2713 | g_loss: 1.1376\n",
            "Epoch [   33/  100] | d_loss: 1.2583 | g_loss: 0.9098\n",
            "Epoch [   33/  100] | d_loss: 1.3313 | g_loss: 0.9553\n",
            "Epoch [   34/  100] | d_loss: 1.2445 | g_loss: 1.3317\n",
            "Epoch [   34/  100] | d_loss: 1.1827 | g_loss: 0.9789\n",
            "Epoch [   34/  100] | d_loss: 1.3265 | g_loss: 1.0775\n",
            "Epoch [   35/  100] | d_loss: 1.2675 | g_loss: 1.0540\n",
            "Epoch [   35/  100] | d_loss: 1.2469 | g_loss: 0.8902\n",
            "Epoch [   35/  100] | d_loss: 1.2938 | g_loss: 1.0032\n",
            "Epoch [   36/  100] | d_loss: 1.2988 | g_loss: 0.9567\n",
            "Epoch [   36/  100] | d_loss: 1.2735 | g_loss: 0.9980\n",
            "Epoch [   36/  100] | d_loss: 1.3402 | g_loss: 0.9996\n",
            "Epoch [   37/  100] | d_loss: 1.3304 | g_loss: 0.8019\n",
            "Epoch [   37/  100] | d_loss: 1.3254 | g_loss: 0.9645\n",
            "Epoch [   37/  100] | d_loss: 1.3154 | g_loss: 1.0732\n",
            "Epoch [   38/  100] | d_loss: 1.4079 | g_loss: 0.9436\n",
            "Epoch [   38/  100] | d_loss: 1.1889 | g_loss: 1.0842\n",
            "Epoch [   38/  100] | d_loss: 1.2667 | g_loss: 1.0772\n",
            "Epoch [   39/  100] | d_loss: 1.2788 | g_loss: 1.0064\n",
            "Epoch [   39/  100] | d_loss: 1.3423 | g_loss: 0.8935\n",
            "Epoch [   39/  100] | d_loss: 1.2540 | g_loss: 1.0350\n",
            "Epoch [   40/  100] | d_loss: 1.3892 | g_loss: 1.3915\n",
            "Epoch [   40/  100] | d_loss: 1.2479 | g_loss: 0.9560\n",
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            "Epoch [   41/  100] | d_loss: 1.4338 | g_loss: 1.0309\n",
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            "Epoch [   41/  100] | d_loss: 1.4479 | g_loss: 1.0082\n",
            "Epoch [   42/  100] | d_loss: 1.3176 | g_loss: 0.9026\n",
            "Epoch [   42/  100] | d_loss: 1.3176 | g_loss: 0.7201\n",
            "Epoch [   42/  100] | d_loss: 1.2526 | g_loss: 0.8474\n",
            "Epoch [   43/  100] | d_loss: 1.3503 | g_loss: 1.0856\n",
            "Epoch [   43/  100] | d_loss: 1.2700 | g_loss: 0.9467\n",
            "Epoch [   43/  100] | d_loss: 1.3977 | g_loss: 0.9305\n",
            "Epoch [   44/  100] | d_loss: 1.2950 | g_loss: 1.0395\n",
            "Epoch [   44/  100] | d_loss: 1.2536 | g_loss: 1.0896\n",
            "Epoch [   44/  100] | d_loss: 1.2554 | g_loss: 0.9727\n",
            "Epoch [   45/  100] | d_loss: 1.4644 | g_loss: 0.8542\n",
            "Epoch [   45/  100] | d_loss: 1.3432 | g_loss: 0.9719\n",
            "Epoch [   45/  100] | d_loss: 1.4382 | g_loss: 0.7587\n",
            "Epoch [   46/  100] | d_loss: 1.2612 | g_loss: 1.1634\n",
            "Epoch [   46/  100] | d_loss: 1.3589 | g_loss: 0.8247\n",
            "Epoch [   46/  100] | d_loss: 1.3299 | g_loss: 0.9306\n",
            "Epoch [   47/  100] | d_loss: 1.1646 | g_loss: 1.0686\n",
            "Epoch [   47/  100] | d_loss: 1.3883 | g_loss: 0.9342\n",
            "Epoch [   47/  100] | d_loss: 1.3898 | g_loss: 0.8316\n",
            "Epoch [   48/  100] | d_loss: 1.3847 | g_loss: 0.8615\n",
            "Epoch [   48/  100] | d_loss: 1.3230 | g_loss: 1.0441\n",
            "Epoch [   48/  100] | d_loss: 1.2526 | g_loss: 0.9506\n",
            "Epoch [   49/  100] | d_loss: 1.2776 | g_loss: 0.8412\n",
            "Epoch [   49/  100] | d_loss: 1.3006 | g_loss: 0.9455\n",
            "Epoch [   49/  100] | d_loss: 1.3144 | g_loss: 0.9405\n",
            "Epoch [   50/  100] | d_loss: 1.3399 | g_loss: 0.8384\n",
            "Epoch [   50/  100] | d_loss: 1.2238 | g_loss: 0.8860\n",
            "Epoch [   50/  100] | d_loss: 1.4045 | g_loss: 0.9314\n",
            "Epoch [   51/  100] | d_loss: 1.3065 | g_loss: 1.0422\n",
            "Epoch [   51/  100] | d_loss: 1.2952 | g_loss: 0.8562\n",
            "Epoch [   51/  100] | d_loss: 1.3283 | g_loss: 1.0368\n",
            "Epoch [   52/  100] | d_loss: 1.2989 | g_loss: 0.8560\n",
            "Epoch [   52/  100] | d_loss: 1.3228 | g_loss: 1.0237\n",
            "Epoch [   52/  100] | d_loss: 1.2752 | g_loss: 1.0204\n",
            "Epoch [   53/  100] | d_loss: 1.2493 | g_loss: 1.1006\n",
            "Epoch [   53/  100] | d_loss: 1.2111 | g_loss: 1.8502\n",
            "Epoch [   53/  100] | d_loss: 1.3822 | g_loss: 0.9125\n",
            "Epoch [   54/  100] | d_loss: 1.2981 | g_loss: 0.9742\n",
            "Epoch [   54/  100] | d_loss: 1.2474 | g_loss: 0.9710\n",
            "Epoch [   54/  100] | d_loss: 1.3506 | g_loss: 0.9058\n",
            "Epoch [   55/  100] | d_loss: 1.3158 | g_loss: 0.8432\n",
            "Epoch [   55/  100] | d_loss: 1.3949 | g_loss: 0.8182\n",
            "Epoch [   55/  100] | d_loss: 1.4832 | g_loss: 0.8669\n",
            "Epoch [   56/  100] | d_loss: 1.1589 | g_loss: 1.3763\n",
            "Epoch [   56/  100] | d_loss: 1.2768 | g_loss: 1.0114\n",
            "Epoch [   56/  100] | d_loss: 1.4063 | g_loss: 0.9720\n",
            "Epoch [   57/  100] | d_loss: 1.3757 | g_loss: 0.8044\n",
            "Epoch [   57/  100] | d_loss: 1.4278 | g_loss: 0.9591\n",
            "Epoch [   57/  100] | d_loss: 1.3418 | g_loss: 0.8976\n",
            "Epoch [   58/  100] | d_loss: 1.2745 | g_loss: 1.0150\n",
            "Epoch [   58/  100] | d_loss: 1.2480 | g_loss: 1.2268\n",
            "Epoch [   58/  100] | d_loss: 1.3245 | g_loss: 0.9821\n",
            "Epoch [   59/  100] | d_loss: 1.3929 | g_loss: 1.4921\n",
            "Epoch [   59/  100] | d_loss: 1.3367 | g_loss: 1.0317\n",
            "Epoch [   59/  100] | d_loss: 1.3098 | g_loss: 1.0166\n",
            "Epoch [   60/  100] | d_loss: 1.3257 | g_loss: 1.0447\n",
            "Epoch [   60/  100] | d_loss: 1.3618 | g_loss: 0.9417\n",
            "Epoch [   60/  100] | d_loss: 1.4555 | g_loss: 1.0584\n",
            "Epoch [   61/  100] | d_loss: 1.2115 | g_loss: 1.0295\n",
            "Epoch [   61/  100] | d_loss: 1.2115 | g_loss: 0.7703\n",
            "Epoch [   61/  100] | d_loss: 1.2837 | g_loss: 1.0640\n",
            "Epoch [   62/  100] | d_loss: 1.2985 | g_loss: 0.9306\n",
            "Epoch [   62/  100] | d_loss: 1.2980 | g_loss: 1.0502\n",
            "Epoch [   62/  100] | d_loss: 1.4073 | g_loss: 0.9996\n",
            "Epoch [   63/  100] | d_loss: 1.3539 | g_loss: 0.8855\n",
            "Epoch [   63/  100] | d_loss: 1.3217 | g_loss: 0.9516\n",
            "Epoch [   63/  100] | d_loss: 1.3334 | g_loss: 0.9144\n",
            "Epoch [   64/  100] | d_loss: 1.3444 | g_loss: 1.2235\n",
            "Epoch [   64/  100] | d_loss: 1.1699 | g_loss: 0.9757\n",
            "Epoch [   64/  100] | d_loss: 1.2754 | g_loss: 1.1847\n",
            "Epoch [   65/  100] | d_loss: 1.3704 | g_loss: 0.9175\n",
            "Epoch [   65/  100] | d_loss: 1.2502 | g_loss: 0.9538\n",
            "Epoch [   65/  100] | d_loss: 1.3526 | g_loss: 1.0581\n",
            "Epoch [   66/  100] | d_loss: 1.3135 | g_loss: 0.7844\n",
            "Epoch [   66/  100] | d_loss: 1.2451 | g_loss: 0.9070\n",
            "Epoch [   66/  100] | d_loss: 1.3141 | g_loss: 0.9478\n",
            "Epoch [   67/  100] | d_loss: 1.2648 | g_loss: 1.0048\n",
            "Epoch [   67/  100] | d_loss: 1.2816 | g_loss: 0.8707\n",
            "Epoch [   67/  100] | d_loss: 1.3271 | g_loss: 0.9614\n",
            "Epoch [   68/  100] | d_loss: 1.2946 | g_loss: 1.2189\n",
            "Epoch [   68/  100] | d_loss: 1.2714 | g_loss: 0.8513\n",
            "Epoch [   68/  100] | d_loss: 1.3092 | g_loss: 1.0923\n",
            "Epoch [   69/  100] | d_loss: 1.3157 | g_loss: 0.8434\n",
            "Epoch [   69/  100] | d_loss: 1.3121 | g_loss: 0.8530\n",
            "Epoch [   69/  100] | d_loss: 1.2838 | g_loss: 0.9839\n",
            "Epoch [   70/  100] | d_loss: 1.2892 | g_loss: 1.1863\n",
            "Epoch [   70/  100] | d_loss: 1.3134 | g_loss: 1.0853\n",
            "Epoch [   70/  100] | d_loss: 1.3750 | g_loss: 1.0068\n",
            "Epoch [   71/  100] | d_loss: 1.2661 | g_loss: 1.1887\n",
            "Epoch [   71/  100] | d_loss: 1.1752 | g_loss: 1.0475\n",
            "Epoch [   71/  100] | d_loss: 1.3430 | g_loss: 1.0308\n",
            "Epoch [   72/  100] | d_loss: 1.2486 | g_loss: 1.0780\n",
            "Epoch [   72/  100] | d_loss: 1.2082 | g_loss: 0.8845\n",
            "Epoch [   72/  100] | d_loss: 1.4526 | g_loss: 1.0406\n",
            "Epoch [   73/  100] | d_loss: 1.4459 | g_loss: 0.8142\n",
            "Epoch [   73/  100] | d_loss: 1.2741 | g_loss: 0.9313\n",
            "Epoch [   73/  100] | d_loss: 1.4530 | g_loss: 0.9001\n",
            "Epoch [   74/  100] | d_loss: 1.3158 | g_loss: 1.2429\n",
            "Epoch [   74/  100] | d_loss: 1.3294 | g_loss: 1.0314\n",
            "Epoch [   74/  100] | d_loss: 1.4001 | g_loss: 0.9060\n",
            "Epoch [   75/  100] | d_loss: 1.2336 | g_loss: 1.1510\n",
            "Epoch [   75/  100] | d_loss: 1.2891 | g_loss: 0.9046\n",
            "Epoch [   75/  100] | d_loss: 1.2318 | g_loss: 1.4325\n",
            "Epoch [   76/  100] | d_loss: 1.3136 | g_loss: 1.1622\n",
            "Epoch [   76/  100] | d_loss: 1.2947 | g_loss: 0.9308\n",
            "Epoch [   76/  100] | d_loss: 1.3430 | g_loss: 1.0450\n",
            "Epoch [   77/  100] | d_loss: 1.3429 | g_loss: 1.1044\n",
            "Epoch [   77/  100] | d_loss: 1.3029 | g_loss: 1.2302\n",
            "Epoch [   77/  100] | d_loss: 1.3515 | g_loss: 1.0062\n",
            "Epoch [   78/  100] | d_loss: 1.3504 | g_loss: 0.9678\n",
            "Epoch [   78/  100] | d_loss: 1.2577 | g_loss: 0.8918\n",
            "Epoch [   78/  100] | d_loss: 1.4322 | g_loss: 0.9877\n",
            "Epoch [   79/  100] | d_loss: 1.2253 | g_loss: 1.8224\n",
            "Epoch [   79/  100] | d_loss: 1.2994 | g_loss: 0.9052\n",
            "Epoch [   79/  100] | d_loss: 1.3528 | g_loss: 0.9433\n",
            "Epoch [   80/  100] | d_loss: 1.3187 | g_loss: 0.9218\n",
            "Epoch [   80/  100] | d_loss: 1.3133 | g_loss: 1.4481\n",
            "Epoch [   80/  100] | d_loss: 1.3220 | g_loss: 0.9521\n",
            "Epoch [   81/  100] | d_loss: 1.2580 | g_loss: 0.8118\n",
            "Epoch [   81/  100] | d_loss: 1.3280 | g_loss: 0.9419\n",
            "Epoch [   81/  100] | d_loss: 1.3471 | g_loss: 0.9484\n",
            "Epoch [   82/  100] | d_loss: 1.3220 | g_loss: 0.8440\n",
            "Epoch [   82/  100] | d_loss: 1.2610 | g_loss: 1.2260\n",
            "Epoch [   82/  100] | d_loss: 1.3742 | g_loss: 1.0659\n",
            "Epoch [   83/  100] | d_loss: 1.2628 | g_loss: 0.8360\n",
            "Epoch [   83/  100] | d_loss: 1.2824 | g_loss: 0.9786\n",
            "Epoch [   83/  100] | d_loss: 1.1793 | g_loss: 1.1045\n",
            "Epoch [   84/  100] | d_loss: 1.2679 | g_loss: 1.2751\n",
            "Epoch [   84/  100] | d_loss: 1.1621 | g_loss: 1.1793\n",
            "Epoch [   84/  100] | d_loss: 1.4046 | g_loss: 1.1018\n",
            "Epoch [   85/  100] | d_loss: 1.2515 | g_loss: 1.4398\n",
            "Epoch [   85/  100] | d_loss: 1.2235 | g_loss: 1.0724\n",
            "Epoch [   85/  100] | d_loss: 1.3471 | g_loss: 0.9406\n",
            "Epoch [   86/  100] | d_loss: 1.3146 | g_loss: 0.8160\n",
            "Epoch [   86/  100] | d_loss: 1.3002 | g_loss: 1.0071\n",
            "Epoch [   86/  100] | d_loss: 1.2917 | g_loss: 1.0081\n",
            "Epoch [   87/  100] | d_loss: 1.3288 | g_loss: 0.9843\n",
            "Epoch [   87/  100] | d_loss: 1.2345 | g_loss: 0.9499\n",
            "Epoch [   87/  100] | d_loss: 1.3496 | g_loss: 0.8966\n",
            "Epoch [   88/  100] | d_loss: 1.3292 | g_loss: 1.4020\n",
            "Epoch [   88/  100] | d_loss: 1.2608 | g_loss: 1.1255\n",
            "Epoch [   88/  100] | d_loss: 1.3444 | g_loss: 0.9411\n",
            "Epoch [   89/  100] | d_loss: 1.3111 | g_loss: 0.9688\n",
            "Epoch [   89/  100] | d_loss: 1.2101 | g_loss: 0.9172\n",
            "Epoch [   89/  100] | d_loss: 1.3729 | g_loss: 0.8954\n",
            "Epoch [   90/  100] | d_loss: 1.3681 | g_loss: 0.9862\n",
            "Epoch [   90/  100] | d_loss: 1.1749 | g_loss: 1.0332\n",
            "Epoch [   90/  100] | d_loss: 1.3262 | g_loss: 1.2414\n",
            "Epoch [   91/  100] | d_loss: 1.2289 | g_loss: 1.0111\n",
            "Epoch [   91/  100] | d_loss: 1.2684 | g_loss: 1.3362\n",
            "Epoch [   91/  100] | d_loss: 1.4239 | g_loss: 1.0538\n",
            "Epoch [   92/  100] | d_loss: 1.2868 | g_loss: 1.1910\n",
            "Epoch [   92/  100] | d_loss: 1.1914 | g_loss: 0.9653\n",
            "Epoch [   92/  100] | d_loss: 1.4224 | g_loss: 1.1393\n",
            "Epoch [   93/  100] | d_loss: 1.3118 | g_loss: 1.4842\n",
            "Epoch [   93/  100] | d_loss: 1.3057 | g_loss: 0.8936\n",
            "Epoch [   93/  100] | d_loss: 1.4266 | g_loss: 0.9897\n",
            "Epoch [   94/  100] | d_loss: 1.3153 | g_loss: 0.9770\n",
            "Epoch [   94/  100] | d_loss: 1.2557 | g_loss: 1.0017\n",
            "Epoch [   94/  100] | d_loss: 1.3110 | g_loss: 1.0721\n",
            "Epoch [   95/  100] | d_loss: 1.3234 | g_loss: 1.1081\n",
            "Epoch [   95/  100] | d_loss: 1.2628 | g_loss: 0.9117\n",
            "Epoch [   95/  100] | d_loss: 1.2953 | g_loss: 1.1227\n",
            "Epoch [   96/  100] | d_loss: 1.3625 | g_loss: 0.9801\n",
            "Epoch [   96/  100] | d_loss: 1.2871 | g_loss: 0.9395\n",
            "Epoch [   96/  100] | d_loss: 1.2778 | g_loss: 0.8824\n",
            "Epoch [   97/  100] | d_loss: 1.2422 | g_loss: 1.6962\n",
            "Epoch [   97/  100] | d_loss: 1.1878 | g_loss: 1.1007\n",
            "Epoch [   97/  100] | d_loss: 1.2311 | g_loss: 1.1845\n",
            "Epoch [   98/  100] | d_loss: 1.3522 | g_loss: 0.8865\n",
            "Epoch [   98/  100] | d_loss: 1.3159 | g_loss: 0.9081\n",
            "Epoch [   98/  100] | d_loss: 1.4096 | g_loss: 0.8644\n",
            "Epoch [   99/  100] | d_loss: 1.2878 | g_loss: 0.9611\n",
            "Epoch [   99/  100] | d_loss: 1.2683 | g_loss: 0.9408\n",
            "Epoch [   99/  100] | d_loss: 1.2773 | g_loss: 0.9908\n",
            "Epoch [  100/  100] | d_loss: 1.3032 | g_loss: 0.9955\n",
            "Epoch [  100/  100] | d_loss: 1.2607 | g_loss: 1.1070\n",
            "Epoch [  100/  100] | d_loss: 1.4392 | g_loss: 0.8209\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/storage.py:34: FutureWarning: pickle support for Storage will be removed in 1.5. Use `torch.save` instead\n",
            "  warnings.warn(\"pickle support for Storage will be removed in 1.5. Use `torch.save` instead\", FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qjw0w_g4zY1x",
        "colab_type": "text"
      },
      "source": [
        "## Training loss\n",
        "\n",
        "Here we'll plot the training losses for the generator and discriminator, recorded after each epoch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N2trtT3PzY1x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "9570fc72-4aba-460e-db66-430423c7ef5f"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "losses = np.array(losses)\n",
        "plt.plot(losses.T[0], label='Discriminator')\n",
        "plt.plot(losses.T[1], label='Generator')\n",
        "plt.title(\"Training Losses\")\n",
        "plt.legend()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f597bfb71d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "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": "k-YynT6uzY1z",
        "colab_type": "text"
      },
      "source": [
        "## Generator samples from training\n",
        "\n",
        "Here we can view samples of images from the generator. First we'll look at the images we saved during training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oRZRftWDzY10",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# helper function for viewing a list of passed in sample images\n",
        "def view_samples(epoch, samples):\n",
        "    fig, axes = plt.subplots(figsize=(7,7), nrows=4, ncols=4, sharey=True, sharex=True)\n",
        "    for ax, img in zip(axes.flatten(), samples[epoch]):\n",
        "        img = img.detach()\n",
        "        ax.xaxis.set_visible(False)\n",
        "        ax.yaxis.set_visible(False)\n",
        "        im = ax.imshow(img.reshape((28,28)), cmap='Greys_r')"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_qDS3Z_zY12",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load samples from generator, taken while training\n",
        "with open('train_samples.pkl', 'rb') as f:\n",
        "    samples = pkl.load(f)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DqemwcL2zY14",
        "colab_type": "text"
      },
      "source": [
        "These are samples from the final training epoch. You can see the generator is able to reproduce numbers like 1, 7, 3, 2. Since this is just a sample, it isn't representative of the full range of images this generator can make."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DA71uA8gzY14",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "d09ac90c-a12e-47a7-8e90-6432fef1aee8"
      },
      "source": [
        "# -1 indicates final epoch's samples (the last in the list)\n",
        "view_samples(-1, samples)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1EdKu-AzY16",
        "colab_type": "text"
      },
      "source": [
        "Below I'm showing the generated images as the network was training, every 10 epochs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0YpR06vEzY16",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 687
        },
        "outputId": "bbe75a61-4ab0-4c0e-bcb1-1d7b24a010de"
      },
      "source": [
        "rows = 10 # split epochs into 10, so 100/10 = every 10 epochs\n",
        "cols = 6\n",
        "fig, axes = plt.subplots(figsize=(7,12), nrows=rows, ncols=cols, sharex=True, sharey=True)\n",
        "\n",
        "for sample, ax_row in zip(samples[::int(len(samples)/rows)], axes):\n",
        "    for img, ax in zip(sample[::int(len(sample)/cols)], ax_row):\n",
        "        img = img.detach()\n",
        "        ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n",
        "        ax.xaxis.set_visible(False)\n",
        "        ax.yaxis.set_visible(False)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FxUUVJUgkEkgkEti7dy9s28YPfvADzM/P44ILLkClUsGxY8eUvGi1WigUCst4hPRCD4fr7RT43Holmk4XehVgX1+fOl/+fuONNwIAjh075vn+bW97GwDXU6Fnms1mPb//wz/8AwBg//79AIDnnnsOgEuP9Fj0PivmYuQ0g3bN8115LlLDA8u9AGkZMxkra9Gj0ain8qfRaMC2baTTafT09CAWiyEWi6G/vx+xWEyVFkYiEZimqe6RTqdh27ZSJnQ7JRFT4Mn/c826UuwUeM12XoCf1xCLxRRhAVB5FhlOazabChfEVV9fH6LRqKfxi3kW4qLVamFxcRHNZlNZ7PIZ9fixLjz199op6AxAI0THBf8vw4o8T4bXWJCRzWaRSqUULvr7+z20QCuW90mn02g0GigUCh5csGRdf0b9eddKFwGsDDKfAWCZvGAoTYbMZNUkvQ7yCCMhpAuGJGu1mqdtobe3dxmPNBqNtu9bGs5+ueQAVoaOcy6sXiDQAgRcF5ZMz/j25s2bEYlEVK19NptFPB7H0NCQslpYJdbf3483velNyqNhDLxer+Po0aNYXFzEwsICYrEY9u3bh1qthvvuuw/z8/OYmZlBuVzG5OQkWq2Wpxu52WyqEl1a61LRnP2uo0oYPifghgRlXJm4oEBl9VckElEWY39/P0zTRDqdVsoxHo9j27Zt6O/vxxvf+EbE43GPF1Kv13HkyBEsLi5iZmYGpmnijW98IxqNBr7yla9gfn4ehUIB5XIZJ0+e9OCCuRYqMVrxiiBCoa46sSncJS6kYuE7ppfC6q9wOKzCFb29vSpBy9yeaZrYuXMn+vr6lEdLpWMYhsozFQoFzMzMIBaL4dprr0WtVsO9996L+fl5LCwsoFKpYGJiAq1WS1lhZ59TeSz6NAOg834GVovJkCCvA7hFKPQQSD9XXnklAOD48eMAgE2bNimcnMUxANeTue222wC4FiQ9j6eeegoA8PzzzwMAfud3fgcA8Fd/9VcA3NAQLVVatuPj457rsbNfGhxnq7Q6xgWBdCFzcPRKZK5p586dMAxD5X8GBwcRj8eRSqVUqXo8Hsf27dsxMDCAq6++2sMjzWYT9Xodr7zyCorFIubm5mBZFq677jo0Gg3cc889ikcY8Wi1Wiqcxmo00gV5RuKiXC53zCNSXpDOSBc0OIkvGl2XXXYZAODUqVMA3Pc1MjLieT/XXXcdAOCmm24CAI+CBYDHH38cgEsXH/jABwAAf/u3fwsAOHnyJADgxIkTAIChoSEAwMTEBADXk6LnIpVrO9m5Js+FIF06aZ3yWBIFSwJ7enpgmqYnPBSNRlVMtV6vK0UlwxcsaywWi6pckWW8zWZTWaqspJKhKrrdfpbrWkDmVnR8yNgt10MlIksCZaiM1ntPTw+SyaQSGhTKfMnxeFyVJNNbC4fD6O3tVYnLer2uqs9YOaOHJNq907UAPTpJF3r4yzAMJJNJRCIRRbDJZNITQuU5qVQKPT09njEf0hNlfooeL3GRzWZVWIO5Gyb++T5WwkXgvawPyHeu0wXfFflI8ghDM8lkUhkmpAvmbJPJpDIKZFk94OYfWUBEfsxkMkp58PdGo+Eb2g4qCbuHrpsoZexUEo8Mi/GFbdy4UcXyDcPAtm3bYFmWigMWCgUlRCKRCMbHx5V722g0cObMGYTDYWzfvh3JZBJvectbUK/X8b3vfQ+NRgO33noryuUy7rnnHgBLVl+z2fS4v6FQSHks8hmAtTdFUSHoDXtkAiqFcDisvDgyxOjoKCzL8tTw04NpNpuYmppSjGXbNsbHxxEKhbBlyxZs2bIFN910E2q1Gh544AHU63W87W1vQ7VaxRe/+EXYto3R0VE0Gg0VV+Z6pPcm3X+pgDsFPaZOQSKVLMNYEhe835YtW5BIJFT+IJ/PqxBJq9XCzMyMUiy2bWNmZgaGYajzNm7ciFqthvvvvx+1Wg1vf/vbUSqVFF0ASxYYjRxai4zfS4NIel6dgsQfPX29SoyWJQXo3r171VoAYN++fQDcmPvMzAwA1+P52c9+BsCNhbNKjJbru9/9bgDApz71KQBuFdGf/umfAnD7IdjfQKuduR2uU5991Q3oRpasEpN0UavVEA6HsWXLFkSjUWVMbNq0CaZpore3F6FQSBmX4XAY9Xod09PTyrCwbRtnzpxBKBTC6OgoEokEhoeHUa/X8eMf/xiNRgO33347qtUq7r77bjSbTYyNjaHRaCCfzy8Lo8opElx/tzwiK9fa4VXPxV1wwQUAXI/hiiuuAABs3LgRAHDo0CEArqdx4MABz/WZk7vjjjsAAO9///sBAH/2Z38GwKUH/uX9SQesZtTpQoYo28G6zBaTTCkZU+ZUQqEQstmscoMTiYQi6FqtpgRSs9lEsVhUli1LiFn5Y5qmaiBcXFz0jMWwLAupVEoRL++vV7FJWC/rVPeIJC7k9/TKMpmMSjpy3VRCPI8KktVSLCFm7oG4YOUc8RyNRpFMJlGtVj19L3q4ar1xoF/LLwcjv282mzAMA319fep9ExfAkqClIqGCpNFBOmGZKnuBSBfValV5NYlEAul0Wil02eciceFnrQawfrBaHqE8oMFlWZaSFzRaJY9Q2ZD3K5WKCp2ZpqmaKckjNPr8eMRxHDXIUqeDtRqhv2zQcc5FdsXrgkTWcDPJTEuzv78ft99+O1KplNLgmzZtguM4OHHiBBqNhkpsy5g8sGRdxeNxXHnllYjFYiiXyygUCvjmN7+JSqWCkZERZcmUSiU8+uijSkE1Gg2Mj48rApLVHzKh3U1sXXaC69Vy9BDICJlMRnkkg4ODeOc734lUKoVKpYJwOIyxsTHVdd5sNlXFTLPZRDQaxdjYGEKhEKanpxGPx3H55Zer/EOhUMB9992HarWKTZs2KTwWi0U8+uijyOfzME0T9Xodx48fV9aSrJxaTQx1Jbrwm35LXOh0IWPnAwMDuOOOO5BKpZTiHB0dBbAUB2ZoT1rS/H1iYgKmaeKaa66BaZqo1WrI5/P47//+b5RKJVXSziq8Bx98ELlcDrFYDPV63UMXjuMswwWFVzc5F9KDXh3EmDq9J1qg/MwYOD2anTt3AnA78PV9POjxsGqIVUSsBvvc5z7nuQ9zOV/+8pcBuJYpO7m5Tp4vy8U7nQSs40Iv7pBzzAzDQG9vr5pYMTw8jHe/+91Ip9MqsrF161bYto2XXnoJzWYTPT09yviIRCLYvHkzDMPA7Ows4vE4LrvsMvWu8/k8vv71r6NSqWDz5s2qsGZxcREPP/wwCoUCTNNEo9HA0aNHPfvm+MmLTnFBHpEVk2e/B+Bt/AXcnArp4G/+5m8AuDPiSBecuCDbMgDX833iiScAAO9973sBuJ7q5z//eQBuFRk9pC9+8YsAXI+YVWdcJ8P50kBo1/PTleeiE4tebilL9ujusnmLHflM2NLtpOsJQFmopVLJ49EsLi4qYqlUKspqZ4KXbnGz2VQxdibxKdjWG3QLWA8V8v/ERaVS8YRlTNNU+GDoTOYDisWiUppy9DwbvVg6SSZmEpLeHePKxMv5xIXECeCfm6NHaRgGqtWqcvfp0VIBk5kpbOiRMEcjQyzEBa1WPjvxwxJ04oIeIv+dr/lqAXgNL/KI3jBKoGFJHiGtMA/H8/jumTvTeYQeMT1b0gl5RG69QDohLbByldfgMwTQOXQ1W4yxen3AIMsFSQi0UB3HQSKRwEUXXYS+vj5cffXVsCwL1WoVtVoNU1NTMAwDO3bsQK1Ww4MPPohGo4HNmzerZFyz2cT09DRM08SNN96IRCKBarWKer2Oqakp5PN5PPvss8jlcjh27BhqtZoiGEmokrhkbqCbWUFyzprslWA4hnjgvCwKVeKiv78f11xzDZLJJMrlsnoWwzCwdetWVCoVPPDAA2g0Gti+fbuqPGs0GpiamoJlWbj55ptVUpPx54WFBTz99NPI5XI4ceKE+o3FEPLZaSlJw6Abz4UKQdIFrXaZTGWxAgWJaZrYs2cP+vr6cMMNNyCZTKJYLCq6oMVaqVRUzHzbtm0qgd9oNJQ396Y3vUk1XtZqNdUPc+DAASwsLODIkSOKLlgFJEOGciwIDZpuJzeQLvSqHXZe08Kk5UigJ/Lrv/7rAFxLkzmX66+/HgDwT//0TwCAN7zhDQDcqjLmYj7+8Y8DcD0RWrj//u//DsCtDmNVmF8eSkK3dKHzCOCGhqXSCIfD6OnpUQokmUwquti/f78qbqnX65iYmIBhGNi+fTuq1aqiix07dqhKVeYrLcvCW9/6VpimqeTFxMQEcrkcnn76aeTzeRw9ehS1Wk0pFuYnyRMynApA5WO65REdF4DrKfB7fSbcpZdeCgD44Ac/CMB9f6wufMtb3gIA+PSnPw3ApSPmL9nf9Gu/9msAXLojXdx9990AgKNHj6pnBlzPSZ8pxnVRIa+b5wK07+qWViu9FMBtLKPXQaVjGO6YGMbMaZ1TAPEB+NKZe6EXMzk5qV54uVxWwkOuhUpEz7+sV826vKb0ZuixyDEYEhfVatVT08/qLypUXosM2Gg0UK/Xsbi4qKw35h2onIiLYrGorHX5vLzm+bDIpBfHZ/KrDiKjstCDCkHHRTgcVlU/vDa9XpacFgoFWJal8lCWZXnCXqVSCYuLi0p4SA+KnhxBbw4OYH3Bj0+A5T1g5BFJFzzOsizVtd+OR2q1muIRKWNIXxwRxKIfOayU92nHJwFtrA46Vi6sgpKVUbLqKhQKKcukt7dXlQ+zaoMzgEzTxL59+9DX14ehoSEldGq1murIv+yyyxAOh3HgwAH1fTabVRVWuVwOzWYTR48exfz8vHKhKUA4Y2hqagr1ep216YpYZAlvp8RCC53Ep+NCekShUEjV6jcaDVXZFI/H8cILLyAej+PSSy9FX1+fiokzIT04OAjDMHDFFVfAMAw8+eSTqFar6O3tRTabxcaNG2FZFhYWFlCtVnH06FEsLCwoy5Ahw4GBATiOg8nJSdRqNVXLT0FOXFBhdwrEY7uCAXqM8Xgc2WxWCYZEIoHR0VHEYjE8+eSTsCwLl19+ObLZLIaHh5XHU6/XMTAwgFAohP379yMUCuGxxx5DqVRCKpVCf38/duzYgUQigYWFBeRyORw9etQzg43vPZvNwnGWxsQ0Gg3V76KHbxhi6YYuCLpnyOvRImT1D8PGu3btAgB8+9vfBgC8733vAwBcfPHFANyYO+nk7W9/OwDgm9/8JgB3SjKn4rJ/4ZFHHgHgxvIPHz4MANi9ezcAt/+BvSUEubNlp3RBo4rvX87PktWJDI2PjIyofhWO+4nFYnj++edVnjGbzapnZ+J/YGAAkUgEV155JQzDwGOPPYZqtYp0Oo2+vj6MjIzANE01KfzIkSMKj3JS8fDwMBzHwcTEhFJOevc5eYTvtROQckbfJ4W45VpIB/y8Y8cOAMBXvvIVAG6fCvOPNOL5+dZbbwUAfO973wPgziLjZIepqSkAridLT4nr2rJlC4DlOR0C77fS5IaOlYssIfSrDFrpPMYx2RxZLpdhGIaKhXIMDMNH9DJocSaTSVU1xTLEWq2mXFoJUthJga9bTt0Cr0VG0QWqxIfMMwDuni20qlkRxxwMsCRsiB9acYwFt86OhpG4qNVqqFarKtTINfAfFaFch6yOWi8vRs81+eFD/yzpgmEJFkUASyEb5lnosQDuJk+sPqQnI+mCTKt7TcSFrCbkmqX3EsDaQI9k+I3W0XmJ35HeOXCWORW5xQL5hh4LgGU8Qn5g7rdSqSijjwJfrk9fB9ev03QAK0NXO1HKqi7Af6e2aDSK4eFhT4KNo/QHBgYQjUZVPmXjxo0IhUKYmZlRLurZ+ymL1zRNNUNITsGt1Wo4duwYSqUSFhYWUCqVcOLECdi2rUoQ5+fnUa/XPftASIhEIl3tiS3HUsi6dcdxd7/jQM4NGzYoJRGJRFRnPkdWWJaljguFQpifn18W0qKiNU0Tl1xyyTJcNBoNNS+pUCigUqmo4Z8cDjo3N+cJq0mlyHxZpx36obO7L7IahoaH3ywmjulg8UU4HMbAwABM08Tg4KAKCZqmiU2bNiEUCmF2dtbTh8Jp1tVqFZZlYe/evcoy5SiYWq2Gw4cPo1QqIZfLoVwu4/Tp06oq0RQyjC8AACAASURBVLZtFAoFj0dLHJP2SBedxNaJC5ZT63uW8zPvQw+Dsezh4WEAbvUO37/el8L3ps/J45Rc5l64Dnow7PSmRUpBze/Zwa97cfF4vOMO/VAo5JDPZShU31WT/EJPpV6vK7pIJBKe0UfceiEUCiGXyy0LdUp5cemllyIajWJmZkbJA1aDcaZYtVrFiRMnUK/XkUgkYNs2pqenPTyiy0gWlXSKCxrSvIbEhQz7Aa4HQo+WdMEcG+mFuRX2uzCXQk+DCvcd73gHAHcyA3M8pIuXX34ZgFt1yHWyQ5+5OT9crGuHPoHEIgW21OzSDY7FYmrsi2wKZD5FVvDoQol9Hhs3boRhGJiYmEClUlFCgrszEuEULhRwMubP9a53vkGvEpPfA+7eIuxRyWQyasQ8lY6OC3or0srWcTE+Pq72fiEuANcbkKWr8pmliy77X9ajckp6c36VY8w3MW6ezWZVDk0qJoZXiQvp+TmOo3pYxsbGYBgGzpw5o/oW5LwxKnQWo+ixdL3KEQgqhNYL/PDcLuoh+01isZgKqzMXw1A8S5elNy8hFot5eOT06dOqIZLTKhKJhPJQJV1IGqBhTGNGPkMA54aOlEu7EJBsSgPcXEapVEI8HseWLVswOjqKO++8E6ZpqrwBp9pSIMZiMTXRNxwO46qrrlJVYf39/Wria6lUwtTUFKanp+E4DrZt2wbDMFCpVFAoFNT8MRIU19XT06PGxMgS6G5cXZ4nrSbiAnDHzNMiIS6Gh4exceNG3HXXXTBNE0899RQqlYqqnKHFkEwmUalU8PLLLyMSieDqq69GIpFAqVTy4KJYLGJqakrFULdt26aS5IVCAYVCAfPz8yiXyx5PqKenB61WC/Pz8x7G6dbtl3Qhx3TodAEshTPi8Tg2btyILVu24IMf/CBM08Sjjz7qoQuGCE3TVF5YJBJRdFEqldDX16dwkcvlMDU1pea2cUZVvV5HLpdDoVDA3NwcSqWSp0vcsixV6ix7MeTfTvDAMKV8Zqn0AdcyZOczY+F/+Zd/CcCNrdMSpZXP3MsPf/hDAG5VGXMI73znOxWOAddS5Uwy9j+w/4UWKZ+bnd70ZChMu81LyvyszHFKQc5nW1xcRDwex9atW7Fp0yZ84AMfQCwWW8YjtLoTiQTK5TIOHz6MSCSiKi9LpRKy2Sz2798Px3FQKBQwMTGhKu527typjNt8Pq/oQvKI4ziqiXthYUHxs4xKdAqs9JTPTPrQK+qmp6cBuJ7Jv/zLvwBwcyTModCzYe6O+/aQLvj7e97zHgBuro8THcg7vA+vr3uwpEPikM+/El2cl2oxaREDS645O9HlPDEKIB7PkEg6nVbWPTuvM5mM8lCkFS8rs/jQMjYvK8fa5YjWaqXy/ronIEHigjO0ZBhJ4oJEHIlEFC56e3tVlUwqlVL1+xIX+vPImLIoGVwxV7bWPJTEB6+v456/cf8afRsCiQ/AbUrlc/f29iKRSCgaIS74LDou5Fpo8bIiUW+OC2B9QXqJ8h3xNwmSLjgdXd+WQcoLSRekBc6rI99QXsjKVZ0eSWvM2a000cPv/AD8oSPlQqRSizMppidxJcPGYjFs27YN6XQaDz/8MBzHUVNGy+UyYrGYGv9RKpVgWRbe9a53eaqWaK0wWTs+Po58Po9NmzahVqvh2Wef9Uwy5Q6D+XxenaN7F9LS1ssQO8EFn1WvCvJLlpumiR07diCdTuOhhx5SDWC0hhgiCoVCqhnwHe94h6oy4zUsy1LDGCcnJ7G4uIgtW7ag0WjgwIEDqFQqilFYms28k65g6GXQA2NoolPgOVJg64qLdENv9Q1veANSqRR+9KMfKXpwHEeN8xgcHEQotDRLinTBBkkm8i3LUol74mLr1q2o1+v42c9+pnpmGEPnDoPEhRR+MsFPhdwNXUj6p6VIC5U0p4d+ORX5C1/4AgA3l8JcDS1Vejr0cJiD0fdUZ06FHsvXvvY1z3vifRlz5/24XoYU5TRcPWKxGlxIPmAFpAxv6sYHp2CnUik8+OCDy/qRmLMLh8MqHHzHHXd4NoVjzo7vfWpqysMjzz//vKoQZCGMbduYn59XfVAyByd5hLjqlkeIQ9k8DHh38QVc/HPa8T//8z8DcN8HcySsKmO/Czv5z5w5A8DNrfC9ctYY6Y10wetyHfRQSBf0gOS0DcDtAfKDNXfo+wkRKhzZp0KLgAujIpJhq0QioSadyvh7PB5XJYosP5R7gZTLZRXqkBY6QQrP9bY4dGGiX1/30KgA9RgvBSbzHpZlIZlMKguOe5Ewf8WqMApqlgRWKhVPf4vsA5Br88PFelTCSNrg++G6iQvZjCYtReKC+RVeg5vEcYK0NBai0aiiC+ZVGBYslUqq0oxCw++5V/JqA1g76PkKP7pjDwojDuQRmXek4iddJBIJRRcs7qGRwBB7vV5XxUMr8chK0QauMYDOoOMOfVn7L2flAK4glRs7sTt9YGAAN998M2KxGM6cOaNKBVkpxR0oWUMeCoVUPwTH8HPva/aynD59GvPz83jmmWdQLBaxsLCgiIed+ZwNRIJ1HMcjnLqtCmL3sYyh+uEik8moSheWGg8MDODGG29Uu23atq1CXsTF/v37EY/HUa1WEQ4vjdLndbhXBePQtm3j5MmTWFhYwLPPPotisagqaTjaolAoqJ4OWSwgLQ+Ji07nJjGGDbieIZUIhQEnNtBw6OnpweDgIH7lV34FsVgMp06dUrhgQ2Q6nVb7dVCByOqher2Ol156SeGi2WwqXDzzzDPI5XKeKkTSA8u/WRIu6YK4CIXaz01qB+GzM+domep72ZNGWP3D/gIWo/zRH/0RANfC5O+s+mJfCzu02fPBXAn7Y+jpsDObORrmZmiJcoYYPR6ugx4TIR6Pd7WHieyPkaFPQji8tDUC5QAVQX9/P26++WaYpqnogrk4ThEnj7B9obe3F5FIREU5Dh8+rCrHbNvGqVOnPPIil8upsnWOEZJyg144B1kCa+MRNjoDbg5O/A7AzW3o+2b9+Z//OQB3VhjfNz0JTsGmJ3rhhRcCWNoLB3A9YlYhMhf34x//GICbY6M84/unx0O60Pt7OOHdj0c6DjbrYTAZz5beijyGFqScQArAY2XyBZLp9fH4juMgl8shl8upFyx3tATgqRzxE/S8jg7dejOSUdrhQia8bNtW3eIU+ry3HBku5xux30Vab8BSqaHEBStqZOWN3zhs6Tn4PUO3uFjJsmNYwY8uGLbScUEjhmNrdFzI+L3EhbSA6THyfL14Yy0FHQGcG/T3LXkEgIc/JI8Ui0UVuqTCD4VCKoJBo4jGE71T3ftYXFz08Aj/8V68vg46j/BZ+BwBrA46rhaT1Q2yIoquKENZdDlpabZaLRw4cADxeFwlZNk1fuzYMczMzGBubk6N+7AsC5dccokat83YKUt6TdPE2NgY+vr6cPjwYdi2raqEaNEyL8H10aVmoo+E201sXQoyiQsqO1luTfeetfvhcBgHDx5UXpllWZ7O6ZmZGTVXy7IsWJaFiy++GJZlIZ1Oq/wCvY5EIoGtW7dieHgYR48eRavVwtTUlMpLyNyLjJlKXDDspocUVwuSLmSHO0OYjK3TgAiHw4oufvaznyEejyOdTiORSKg5WSdPnsTMzAwmJyfVTLJEIoELL7wQyWQSvb29arYYw4SsThwcHFRx6JmZGRVeZf6JdEFcMMQGwBPC7UaYyKo/Wnzch0VuUw24VUHswKbnQY9kz549AIAf/ehHANyZYrzeLbfcAsDtrKflyme54YYbALgeDHM03JmUnfr8LDexA9xqMt3YWy2wB0zyCHmDE9bpUfKdDA4OIhwO47nnnlPbnCcSCWWtHzp0CNPT05icnFSecCKRwKWXXurhkfHxccXjlBcDAwM4cuSIRxExMsL+FjkAt9Vyh8ASB5I+VgtSdgKuh0B6oKcipzED7s6TnGLMacjsg/npT38KAPjXf/1XAMDAwAAA1zOiR8IcCq/LXA49GB5H3qNnzHWRDmQDK9e7rjkXCmqCDCX4eQj0MjiBVuYAKNzr9Trm5+c9DYaAOxaezYOhkNpiFH19fZ57s2OdMXuuR7eYeLxfJdN64YIWmUx0UxnJjns9D0JccPz80NCQagalV8exKexqb7VaaqQJn407N+oepV/+heesFRd+lWcy38LvZBkjO6vleBYCLVTu50M80EhgiI+0UiqV1PuXnl4ymVRKVJag67hfz8q5AFxYrbygwNd5hCANFHbb02AjTTA6YNu24hHmVzKZjIcme3p6FO1Jz9qvulRfQwDnho6rxdjkB7j9LWRoufWwDF1xs6bR0VFPwyA9DV6be13v3bsXGzZsUNYbu0b379+PWq2G7373uyiVSqrbfG5uDs1mE7t27YJt23j55ZdRq9XQ29uLWq2GhYUFtW5ZiiqJqVOQgkqWzdbrdeW50AXXcdHb24vR0VFEo1FVqTIzM+NJ6i8uLsKyLOzYsQNDQ0PYsGEDHGdp7xv2ANVqNXznO99BqVRS1WDExe7du2HbNg4dOqRmkVWrVczPz6t1A66lREEvG8a6pQvigo2ccj8WWS3GnMrGjRs9x+TzeY9SyufzSCQS2L59OzZs2ICxsTE4jqM8kyuvvFLtQFkqlVTn/czMDBqNBt7whjeg2Wzi5ZdfRqVSUUKFs+n4DmXvC4VIN7iQgxZlnxPgWn78nh4GY+PsNyBv0LJkmIYWJT0W8ggtWM6c+vu//3sAbtUYPRvOnPrJT34CAGpCAD0Yroc5GX6WNNMJLmSFIAU2Q+CUF+QfTrygvBgbG4PMU/AZKNzz+Twsy8KuXbs8PHLs2DHFI41GA9/85jfVBI9araY69vfs2QPbXtofxrZtZLNZ1Go1zM7OKroA3OowWdnVKV3Q4yFvyIkegOtpsEqMx9Fbu+SSSwC4njD7VLhG0slHPvIRAG5uhTPlPvzhDwMAPvOZzwBwPWbm4LiDKemCHtOJEycAtJ+KvJKi7Vi56OCnyRkOIch5TgxH0QPh+YZhqBlReniC84MYC2Xs3bIs5Q3xvnS7ZeLJr1NcWlPdxt1llZhu+cu8ki5YZSkmw4fFYlFdgxVS3N+EiWUmo2XCnJVy3ISNTMrwnJzGzHXplVHrYYm1s/zl/eSzA1C5ER7Lrv1SqbQMF/Q+JC44e43vj/uAkC5k6FIq+JU8uADWF1jMoXuJkielYQF45YUUyrLIwDAMxSMMx1NeVCoVRf+UM8z5MoJAemAoW+ZiSBe6x0W6CXJ0q4OuBldKASrDLozhc8oxrXFq4zNnzngE5ODgoIqXJpNJ3HDDDYjFYpifn8exY8fw8ssvq1g4LZpyuaymHA8PDyMej2Pz5s2oVqtqG1NWaJ0+fVrFBiXhMETCnEU3xCKtWokTWmH0jDgfa2JiAo7jwLIshEIhnD59Gq1WC/l8HqFQSOEilUohnU6rarL5+XkcP34czz//vAcXx48fR7lcxvT0NBqNBrZs2YJIJIItW7agUqmoajGOmTl58qTqI5HhCA7rY85lLQ2FumIlbXD8Culibm4OjuMoa+306dNwHEcNp+zv71fNcel0Gm9+85vVFOyjR4/imWeeUXH0WCym5l6Nj4+j2Wxi06ZNavwHd6dsNpuq4o5jYmSfBYWMzMV1K0QY3gTcclZ9/47LLrsMgLvTH0N7Tz/9NADXc9l6dsoxY+l/+Id/CAB46qmnPMfT82FuhR4OY/Ts4OZ+HZx6e//99wNY3m/B/gjmYKSB0gke6L3wM+8lh0SOjo4iHA7j1KlTKp9KHmGCPxQKqZl0kkdM08Tc3Bzy+TxeeOEFpczi8biaIUYPduvWrYhEIth6dn8gevkcM0OeIh/wLzcl5E6laxmRpOfydLyzyo/RGuL9mWeeAeDmPrZv3w7ArSJk3xM9lccffxyA6/nwfPbHcF8gTsVmLo7XZY5PXx9lOT1bv1YUQlc5l3YX03s1aJkyDMbPujCWteqGYajkLMtwe3p6PJv9pFIpVSkiXzSr0Rh7lUIU8JaDdhMKa4cHP+tfMpYc9smkJS0lej8yP0Bc8Jmq1aoqt0yn0x5cpNNpladhaCoUCilcULj7xbeB5d7EeoNUOKQL2ZRm27ZiXuKLTN3T06P6fGiB0jshLizLUt5fJpNRVXJy3hSbats1eEq6CGD9YCXlLD1HKhlZBCM9DDl6JRQKIZVKqXYHYDmPDAwMqDJ+nUdk1SArziT/SR5hJECXF+shO34ZoOM+F1Z4AK7AIHFw9g9jp9u3b0c4HMbCwgIsy8JVV12FaDSq4n1DQ0PIZDK4/PLL0Wq18Pjjj6vOdHar9/b24vLLL0ckElHW2NDQEMrlMr70pS9hYWFBla2eOnXKs9MgXerJyUklfAFvxQ418tlzOqpb586QgBt6I/Eymcjcwo4dOxAOhzE7O4tEIqFwwSqO/v5+pNNpXHXVVXAcB48++qhy79mV3Nvbi3379iEcDuPMmTNwHAcbNmxAsVjEf/3XfyGXy6lej9OnT6uEN99TrVbDmTNnPExFXLD3o1tcyJJw4oJ0wfEuVLIcNJnP55FMJnHdddchEolgcnISjuOoZ73mmmsALO0fz5BINBrFpk2bkM1mcdlllyESiahqsGw2i2KxiP/8z/9ELpdTRSQ6XdA7mZqa8pRs03tgRSJx4ThOR30ulmV5cAG4sWpamuShm2++GYDrwXAmFHMltCR53H/8x38AcKuF2Jn/rne9C4DryVx99dUAgN///d8H4FaXsX+GypceCj0erpP9NfRcALXFQcc9P7KoRucRdt2bpoldu3ap95lIJLBv3z5Eo1GVa+nr60Mmk8G1114Lx3Hw0EMPqZA5p7Bns1m199HJkyc9PHL33Xcjl8upwp+TJ08qIwxw82WkF4bjiCNZ7twNj+iyE1i+EyV/ZzUX39fv/d7vAXA9Es6Yu+222wC4Hfz0VJhLe9vb3gbAzcnQgyVdkD5JbwR6TPye69SrIIkLPx5Z01RkwN8L4AugoiHxMMnN8AOP6+3tVcKP1odUUslk0hNrl9aE/Edrg+4rSyBXWm+3OYfV5mloLfN52M/CcS+s26fFnslkFC6YV2LuwDRNJBIJhEIhFV+WRQp6pzFDTY7jKHdW4oHHyOc5XzkI+Y6komHntOzz4XHERbVaRalUUt6fxAW9QXok0iOScXtJF6Q/CfqzB1VB6wPn4hHybKvVUrJB8ggNTeltp1IpZThyOrrkEQpA8ghphjxCPgHcalTADflIT1bSQZBr6Qw6Vi50U+nSysQYABWu4IgS7hdPwUGmZ2f10NAQ3vnOd6oYO69t20v7L/T09KgE7dGjR5HL5fDSSy8ppcT942mBFotFHDp0yDMjiATG6bdyKwDZWd4N8PoU9DKBzw59y7IULvbs2eOpm2cp7osvvojh4WHVZczQn2wAZLLetm0cPnwYuVwOBw4cUFY7y4+bzSaGhoZQKpUwMzPjGTdO5UyGlfmF9cAF4M6MkiE3ObmBvQQXX3yxGufSaDQQjUZh2zaee+45bNy4EZlMRnVucztoGgytVksNrDx48CAWFhbU+jnAkKHTjRs3olAoKLogfTHcwrAchY5syu1WoLCghEKMgovXYzUOp1mziov7cNCL+v73vw/AtVz7+voAtI+9s2rsscceAwBcfvnlnuNpgT788MMA3Co2hpfZIc6puPo0505BKg+9yIU00tfXp+blAUt5AD4/55FVq1UcP34cIyMjeN/73qeGU8owOIEy6siRI8jlcioXQ3lBehseHlZ5S/KIDOvz2eXoJTn6vxvQc3F6pz49UebcSBfsQ2F1IHce/ehHPwrAff/EA3NwzNU98MADANxqMFafsQOf5zE3o1eF8fqkI4YkZVWdDmtK6POz/jtvJplVehH8P5vmKPA4kI7WCCcIk3hkCCMUcuvU5ewoGRKTxKIDCahbkPFiP1yQmfhypNclq2dY0cVmSuZJiIvFxUVEo1E1QZjMyu5i4pfKaGpqSuUY5JgX2TOg56FkYcZaQU/q6/8H4DEyZA6O3kw2m1U7SxqGoQabUgGRLqgQGMJgjD6VSqHZbKrCD+KB92nXNCsFYADrA3rhix9IHpEbi8mkN3lAVgL29/fDMAwsLi4qg6ynp8fDI3I7YdmA7McjfrJC5oYCz6Uz6GonSmnZAa77yLg7B0+m02nE43EMDg6ip6dH7ZXeai11ju/fv1/tIBiLxbBnzx44jqPixnv37lWJuXK5jKeeegr1el1dhxVp99xzj+pYZ4OVHhoisflVe4iQUsc7Uep5BokLzlWLxWLIZDIwTRPDw8OwLAubNm1SFlwikVC7bC4sLCgPx3EcZUnwd071/elPf4pGo4Fdu3apMMD09DS+8IUvYHx8XCkz6Zm0WktTmCmQ2+HibNdtx7svtqML4omz5DiAcmRkBKlUSu1AyLLjq666Su2yaZomdu/ejVarhaeffhqhUAhXXHEFYrGYKjF95JFHYNs2du7cqcKMU1NT+OIXv6gqyBhqJF2wxJ3hFa6XoCmijnFBi1QaQ4DrkdBD4GQGWpqcVkvLkfuzPPnkkwCAu+66CwBw3333AXBzMexXoKfDfVvoMf3mb/4mgOWWM98/z2eORQ8RCcNtTTxCXMiGYp1HRkZGVLUf+VzuOMrcInmEu25ecsklaoAl6YI9X8x3TE1N4fOf/7yiC1aBcV3kERlGlSOcqLi6wYUfj+h0wckIpAfSB98n6YLvk7PB7rzzTgDAV7/6VQCux8MqMM4kY/Ug6Y99UXw3Or3qM+fkdhZ8jna46MpzIchqKcmY9BooYFkOzM5ZJv6Z6KXVKRlPxsw5XI4IoNBijmZxcVGN8Ze5HBkvlRaIXPtaLFW/uL2OC1pEjB1ToHHDLApc5h7q9br6TOuMHg8Az9h8qdC5n/ji4qJydWXBBUs/5drb4WKtFpqkCwmyqZK4AJaSzbZtI5VKeeakkS6Ii76+PkUXANRMOmnhEhfVahWLi4tKcMicDz/reNCfIfBi1g7t5IX8XVYNcpqx4zhKXrB6ksNdORld0oUMZ+qT0VmlSh4pFosqHCg9KxYeyLXpHst6efi/DNCx5yK70ZlrkZqdgj+ZTOKaa66BYRh48cUX1dyndDqNffv2qQZBWiTpdFqFgpgXmZubU0RXqVTwxBNPoNFoYGhoCPV6Hc899xwKhYK6fiwWU+fpoQ9JRHKtAFT+o1NLRD67/lc296VSKdxwww0wDAPPPfecyh9kMhm1ex6r7Xbv3o10Oq32k2chA6cnp1Ip1Go1ZZWNjo6i0Wjg6aefRj6fx8GDBxWum82mmggsFQctcokDiYtKpdIVLqRyZWhDHKPo4tprr0UoFMLBgwc9uLjuuutgWZYa3XHllVcik8moqbkUGqSLZDKJarWKRx55BI1GAxs2bEC9Xsezzz6LXC6HF198USlr27bV1Gw9dCfj9ZK+6e10gwu9A18vqCANsjqMM8X4/Xvf+17PGln1Q4uW3ir7XRirZ98KLdT/+Z//AeDusU7LmJ9lHgRwc0V8d/yeE7XtDveN9+MNOR1Z8sib3/xmxSOsAmPVYDKZVM+9Eo+0Wi3FIw899BAajQY2b96MZrOJJ598Evl8XskLVlayalBXJKQVrlV6HZ1ORSYueA25+y6vKfHO6j96ojyPdEHl+KEPfQiAW91HumMHP98f+1Y2btwIAPj6178OwJ1FR3ri1Atdgeohddn/0o5H1lQt5pe3oAVBBcHOWfZ3yPi/3EMegErKcntRDl/ctGmTKhNks2S9Xsfs7Kwa6SCThrqX4gfrYY3Ic4gLqVgArxtNXLDEUSY7WR1DYuCctUwmoxiAM8XozVWrVeRyOdRqNczNzWFubk55eBIH0mL3e079u7XiQscHgeuhRyL7DogjeiYyeT8/P6/ootlsYnx8HI7jqHJL9kURFzMzM2o4pcRFu5g6IehzWX/woyW/PiMmyQ3D8OTQZO5UVlkCS+NgqICazaYywKiIJI9QXnBDMJmD0/On7SopV3qmAJZDx1OR5bgOxmFlKSAANZrjySefVFZsIpHAxRdfrPZDtywLt956K0zTxMTEBE6cOIHHH38cjuNg7969sG0bTzzxBEzTxJ133gnLsjA9PY18Pq9yCqdOnUKxWEShUFBCC/AOjSTRyuogrpfxxW66j+XzAlD3ALzWL7BkZTz88MMqbJNIJHD55ZeD+37E43Hcfvvtqtt4dnYWDz74IBzHwb59+9BqtfDII48gFoth+/btqhdgYWFBxY5PnDihxvnLPAKtRDKorKBjWI3rlwUI3eCCTOrnCfB9lEolRReGYcA0TVx22WUwTRPFYhGxWAzvec97YJomxsfHcfz4cbWDKXHx+OOPIx6PY9u2bapvKpfL4fTp06jX6zh9+rSaMeY4jqqMk82bMhyj5x/WShcyLs3QH4H34N+7774bwPL9Wtj/9MlPfhIAcODAAQDuVGTOgrr33nsBuDOjOAuKFik9FFYfMXZOS5Xvit/z2WVvB7A81r4akN4K+VP3binAi8UiHnroIUUXPT09ii6q1Sosy8Jb3/pWWJallMQPfvADOI6jesMeeughtZMle8gWFhYwOTmJRqOheCSfz3uqRhlaJj2QF2j4MPzG/GW3PCJL4Bmu1T0W0iJ3iCRdvPnNbwbg7kT6qU99CgDwjW98A4DriTDXws+kF3oy/MtcDPfz0aczc130ZBnp0Kd666N7JHTtuZBgmATT45XNZlPNfkokEsvqyrkHg20vDW2cnZ1FLpdTVUG2batGuKmpKYRC7uwoumK6x6L3r0gh4ufFrNVS1XMV0gKS1o5t26qrnGEJHsdwHnMx3Hogn8/DMAylDAqFAiKRiGpAZbc6K+j05lEqUvleVsLFelljpAf5jFwDP5dKJRUSJTAPJXExPT2N2dlZhQu+63w+r+iCuOBGT8zP8TnblY2uBhdBzmX9QOJUD08DS+9/cXER4XBYCVR+vH0KWQAAIABJREFUT3phXnV+fh4zMzNqdBLpvlAoqNJiwOWRcrmsoieyF0yW0VKe+RlI+jMEsDroqlqMFh3jlHoOhtekdZJOp5Uw4e8sDeSk4Gg0ir6+PmSzWdx6662oVqv4x3/8R0xPTyOTyXiS+wyXMWxCJQV4E4Ry/DqAZbFfWrRUVN1UwtAakVUWjCtLBQwsWUjs3ZDdugz5RKNRJJNJmKaJ/v5+9PX14a1vfSuq1So++9nPYmJiAul0Wlk59XodExMTKsRIz0CGgiiQ2YFPXNCj4T9Z099NzkWWj8pKGL3AQcav5Y6lxBXDHJHI0q6d8XgcAwMD6O/vx9vf/nZUq1V85jOfweTkJFKplCekxvxSNBpVQklP3MtqwnZ0Iem7W7qgBSgrkQA3xq6HKdn3oodm6FGwI59Tk3/rt34LgDvtlh7G1rOzyNjZzaog9s9Q0fIvq4H4mbkh0hiNoXw+3xUuZNib3jTpQr53fh+JRJDNZpdtBEgeicViSCaTarfKvr4+3HbbbajVavjsZz+L8fFx9PT0KLpuNps4c+YMms2m6i8rFovKAKSSIY/ICAStcp1HKpVK1zyi57b0HIYsTgHcviYpK+T5pBvu+/Oxj30MgFs9xuPYH8OOe75XFv9I419+T1xw3bw/DUMadc56dujrlUd+1p5kaJlEJiwsLKhGQ24IxPxBpVJRndmFQgGGsbSNKZUBiUNfk1+uQVegekK3W5CMoV9bv48UgsDypjRulEZcMFk9NzfnscDo3Q0MDKjEosx96TinJyNx4RfvXitOVsKFvKf0ZBh+0HfMzOVyyoJNJpMqQavTBXcZ5D4dsqJQAr/T4+xy7dID188LoDvQ6azd/+Vnhqv4Pnkdx3EwMzOj8pDsaanVapifn1f8USqVMDs7C8MwMDQ0BMdxVIOkHqmQEQ9ddvjxiL72AFaGrmaLyXlV6kIilyG/k8Qjx2DL4+VID2p327axuLiIRqOxLE4sywwlQeiVQFLAnl2/+k2ukeWNnVgi4XDYgwu9Q5jPpitA3puei27F6bhgEUS5XFYNo8CS5REKhZQVrjMHGZT305WMbimRoSKRSMeVc8bZ2WJSeEuQ8XpdeLPBVhZCSE+CeRmWnjL8QZwA8IzEkfeX71+nC/6ur0la0QzRdkoXyWRyWUyd12WyWTfGKEj5O9ckQ4cSl7QseTxj55whRuNF71vgfXRjQs8z6QqfudJOKqQ4W4y0KGlUlhL7FX2Qh4gHyVOkC85w4/QPygtZ4k4e8VMkchdF3RCT78CPnjutFjPOziJsh3++Z92j1fd7Id/SMyZIr4prBJbvICnnqMn7kF70cKDOI7qSZR/imqvFSAiSAPyUk/6bXCAFCmPo/E4m0igkGLbi9xQ6MmYqQQoReT8ZV5WI4W/dduqvFhcSGOeVI13kOin8dOuNuzCSOJgcbZdX8osZy/vIskrdml/P2LIeGpTVhHxfkq5knkjSj56AlUqCuCDhky50xdpuTe1yZ0H12NpBNzD5HeDvBcj3yjCr9Cwln+k8Isf4AEuKlDyi5950o4PrIm0C8OWRdnwewHLoKiwmmU+WFcpRCwRaHIC3kU1Wb9Fakh6RJEC+XGpxXbnoVT4kRuYBZGWUTACuFaQQlIrCDxc8hjFtvYudZZgyPi1xQXzI6h7JOPJ6UgnJHJBUHtKb0JmsW1zw/UmPTF5fWj/6+wCWV8wQF9I74/qlpSmFz2roQl+f7OVYK5AOZG5L3oOWJb8n3hkD12PxXBPphjkSnX75zLTa+Zk8o+8kSNyQN/Wpt7pF2w3oxqj0TKWxIKsJdR6R4axms6m8eeKGskEaRFwzeURWf/F+8pnJt1KZEWdsH6Ac8otGdAJ838Q/n0V/LwR6KFwrgX0u+k6n+n2kogVc+cHv9XyYvi4eLyeGy+9XgjVVi/n9X4IusPwUB+BNJulhLnmetFykNaT/bRfrXW/ws8akAF3p3n5WslS+8i+ViDyPhC5xKO+n46LdWnRhuB7g5xGs9E7kM1E5SmUhw1tUYsDy0f46XbTLC55r3YFluj6geywSSO/yvfjRhaRjaVQArjGqR0j86MnPOGlHhzqt6PQcwOqg422OaWHrwl16KPJY6eZKZSE9FsZEdTdUry6R5bWS2NoNItRzAGzq1N3fboDrk9a5XCMtRa6RLrye0Kcl5zeBVR6v4wJwq/F0q016U1J56LiQFny7stzV4oJhPqnwuAbiQk+s6zkxnivpQt6D50hc8HrSi5bhRZ4rj9eVEHEhj+kWWFKtW360CNk/wnvzGflXtyDp0TB2ruNG94R4Pq1/fpb7bwDLrXeCbkHrIcZOoB2PkCbldGQeK2len16u8wjv0U5e8D6cZEw+kPlIGSLWw2ZSXkgPuxvg9WQvINcKuB4jeYHPzr/69zxe70Pi7zpd8Ht9IoDureseK/FMutDpeiW66CiorFsS+sXbuYx+1RiAv5Uvj9PP0y0duS75129tftbsWmC1uNBx0g4XumWmr10/j9/reQH9ZfslJf1wuVZr/VznSyb2O6edJakn43VcyONWu66VLOQA1g/a8Yj0Nv3khU7jBBn+lMbQSnShyxr9mivdez3Cxb/M0GmfywyAE+dvOa8pbHEcZ3C1Bwe4cCHAhQsBLlwIcOHCLyMuOlIuAQQQQAABBLAaCGotAwgggAACWHcIlEsAAQQQQADrDoFyCSCAAAIIYN0hUC4BBBBAAAGsOwTKJYAAAggggHWHQLkEEEAAAQSw7hAolwACCCCAANYdAuUSQAABBBDAukOgXAIIIIAAAlh36HQ/l1/odn7HZ6vOdhDgwoUAFy4EuHAhwIULv4y46HrkfgD+W/uez3ut9L2cLAwsH8632gGVr+YzBXD+od2Q126uE9DFzx+8lttIvC6Vi76lJuH1tt9GN+voRMi3u6e+7QC/72a6r76e1wtuf9mgU9o+1/HtNkDTz1utMfJ6pItzKc7Xm7w4n6BPP283KZ5wLlytB+5elzmXYAT6yuCHG7kL5moh2PwogAACOF/wuvRc2imW16PCWauG99u1cTXXk16dYRhqEyBuvLYai/T1iM9fFFgvuuiWF9pt2ey3l00n9309wbnWuFqr/dWE9fZQCefap6odLvTf231eaX3tjn1dKpdfBtBfcqfhMobFYrGY51y5KZfcBZK7/AUQwC8b/DwpzF8k+IVWLuebqLjdcydrIejbnfJzOwtExlTD4TBSqRQsy8K2bdvgOA4OHjyIWq3m2cY0Fothx44daLVaeOWVV1Cr1dpaqoSAAdcOnSS/zxXr1ulktdfTtynWPWQ9r8m/r+f3fy5c+eUdaIC1Wi21vTFBx8H5lhfdFEW08zjklvB+0C4Hs9La5N/V7ti70nV/rpXLal28nwcgI+h7n1uWpcJejuOgWq0iFoth9+7d6Ovrw9jYGKrVKo4dO+bZGzwcDiMSiShPhv/abSFL+HmtCpLFDe2Exc/rs/2ygeRrfZtj/qVX3m77bMdxFA+FQiGEw2GEw2E4joNarbbsevJvQCPrA6+KcmlX/dUtkLgikQgMw1DX5f8pZM+l3dcKftVsq72nfm6z2UQ8Hse2bdsAAMePH0ckEsH111+PTCaDbDaLer2O//u//8OmTZvw13/91xgYGEA8Hsfs7CxOnTqFF154AblcDo7jwLIshEIhHD9+HI7jIBqNIhKJoNFoeHCk51468cZea2Bo0DAMhMNhFQIslUpwHAeRyBJ5c7/11Vpva33+Ts7XrW6dLkhHv/EbvwEAuOeeewC4HgmBnsq73/1uAMD73/9+AMBHPvIRz3EUrLlcDgCQTCYBAOVyecW1v1qKmXwNuHvY+3ki8XgcY2NjSCQS6OnpgWEYqNfrsG0btVoNtVoNp0+fVuHgWCyGLVu2oNVq4fDhw6jX6548JQ0y3vd8QDtjzu+3cxXb8HjSPD/39fUBABKJBAD3vZK+CoWC7/e657rait3XTc6lW+bVLRb+IzEQERQ27ZKZr3cIhUIwTVMxgmmaGBsbQzqdxoYNG5SQHBgYwIYNG5BKpdSz79q1C5FIBPPz82i1WojH42g0Gjh+/LjHgjNNUxGMbdvLhNTrDeR75vPzeyqWeDyOSCQC0zThOA5s20ar1TpvQiKAVw904RUKhZRC2bVrF5LJJFKpFEKhkFISrVYL5XIZ8/PzcBzHIw/C4TDS6bRSRDqN/LwYVj8PEOoEmWvpMpVCwk+gncsVDofDykIhUTSbTSVw6CYzdLRSxVQ7cLrouD1XHLjdZ/3Zw+EwTNPE7t27sXXrVtx1113o7e1Fs9mEaZrYtWsXotEoWq0WDMNAMplUnppt28jn87BtG5FIBI7jIJ/PY2ZmBp/5zGdw+PBhHDt2DACwbds2hMNhTE9Po1KpIJfLLRPaZ63F17z7mO90eHgYADA5OenJIdCC3bx5MxKJhLLC8/k8yuUyTpw44ckzdVshtx500cH5K563c+dOAMArr7wCAMhkMgCAVCoFAHjhhRc85//Jn/wJAOAv/uIvPL9/6EMfAuAaZnNzcwCWvD55vk8V0XmnC2lA6mtJJBLYv38/tm7dig9/+MPo6+vzyAWGlufm5vDHf/zHeOmll5DL5ZSsSKVSuOmmmwAAP/3pT1EoFJDP518zebEaXPidR++uv78fAHDXXXcBAB599FEA7vv+4Q9/CAB4/PHHAUDJgfHxcQBYFoanIm4XYiashi5ek5wLBdi5XD9appZlIR6PY8OGDQiHwygUCrBtG41GA7Zto1gs+rqVr4UVspp7yudmLsSyLCQSCfT39yOTyWBoaAjpdBqNRgOmaaKnp0flUPySe5lMRoUUGBaLxWLYvn27Urq2bWPDhg1otVqYm5tr24j5aoN8HioNwL8B0DAMFeLr7+9HKpXCnj17YJqmCgVUKhWUy2VUq1UsLi4q4cFrBB7N6x+ksRMOhxVtW5alvPt4PA7LspDJZGAYhsdojUQiCIfDnvAOC15ooPH6Qa7l/MC6ei6dvCTZm+FXJkti6unpwdVXX43h4WFcd911MAwDzz//PKrVKkKhEEqlEh566CEUCgWUSiU0m01PrHa1wv7VsNYpHHk/wzDQ09MD0zSxfft29Yz9/f0YGxtDT08Ptm7dqgQnE5KAVxHolh4A5dnNzc2hXC5jfHwcuVwOhw4dwtTUFO69917Mzc0pL0/H1avpuUjrLB6P44ILLgCwZGXVajXPc9GT2bBhA+644w6MjY1h//79SpkSx4uLi/ja176GY8eO4atf/SpyuZzKJ1WrVV/vuB2trCcuzhVj17+nEEyn0wCAxcVFz3Ef/OAHAQB/8Ad/AADIZrMAXIuWCle3SOfn5wEAH/3oRwEA9913n+e+enTh1eARP2MzHA4jk8nANE2MjIwgGo3CMAz09fXhxhtvxJYtW3D99dcjHo+jWCyiWCziRz/6EU6fPo37778fMzMzWFhYQLPZVDyXSCQ8SqdUKq06LyfhfOCiXaSDf0dHRz1/x8bGAAADAwOe4+i58D1///vfB+B6vE8//TQA4MknnwTg0olpmgDcnAxzc/SU/Ay+dnTxmlaL+VVryKQ4rdTh4WFs2rQJu3btQjgcRr1eR6VSgW3byOVySCQSKon7Wlnfa4FIJIJsNove3l6MjIwgnU6jWCwC8CY1qTAYUwaWiCEcDitFLRNykUgEmUwGlmWhUCig1WohmUyqvMv5KnRYC0jPhfTAdcZiMUQiEWXBMvfEwgZauMzD7N69G6FQCAMDA2g2m6hWqyp08vNGIwEsQavVQrVahWmaaDabqNfr6r3Ozs5iYWEBx44dw9TUlCpekDKh1WphcXERoVAIlmWp3wOaWH9YN+VC95Xhl5WO0+O5fLmZTAbRaBSWZanQjWVZ2L9/P3bv3o29e/ciFovhsssug23bWFhYwMTEBH7yk59gdnZ21R7LueKH5wN4Two3hmfIKKOjoxgdHcWFF14Ix3Hwne98R1U89fX1YfPmzQCWYqXT09P43Oc+B8dxcOeddyrFy2owYEkQG4ahFMljjz2GmZkZxONxVU1Tr9eVMH4tFI3+HkgbrGhhBRhLTqPR6DLjAljyZvi8BNM0cf3112PPnj0YHx/HsWPH8OMf/xiLi4uK3vS48qsBK3hHvt/zmfheBwcHAQAXXXQRAODee+8F4Ho2n/jEJwAsryIirviZgvV///d/AUDR14kTJwDAE1VYaX1rgXbemjSQHMdBLpdDKBTC/Py8omkA2LFjB4aGhnDkyBHMzs7iO9/5Dubm5jA+Pq4mVcRiMYTDYVXgQSMNcK3z10OPj84L9BSYR+T7n5iYAADs3r3bczw9mfe+970AgM997nMAXM/mkUceAeBWFd5yyy2e877xjW8AWMpXAsurxvR+KcJKOFtXz+VcAl2GhBj2kr/TKk2lUiqBPzY2htHRUfT19SEej6vQB5P6lUoFyWRSCRe6v69n0MJPqjS4Vqshn8+rCpdms4nTp0+jUqkohjp27BiOHz+OAwcOAAD27t2LarWKbDaLRCKhYs26AAWgFDKT/+2KKM43SCXA4oRwOOyp+mJxArBE2NFoFCMjI0gkEhgcHMTAwAB6e3thmqbnenwmCqFUKoW+vj7Mzs52PHstgPMP56I73eugXKAsAIBTp05henoaJ0+exPT0tMrBUqkAbpuCvK7+OYD1hXVTLn4eiy5EWDpr2zYMw0A6nfYonHK5jEgkgp07d2J4eBjvete7VOiDQoTJOJbVZjIZbN26FdVqFXNzc6sikvUipE5caT/rjMK/Xq/jqaeeQjwex/3334/+/n7cdNNNMAwD3/rWt1AulxGNRlGv1/HSSy+hUqmoWOqnP/1pJBIJ7NixA5s3b8bHP/5xFSZyHEdZHPv27cORI0fw6U9/GmfOnIFt2zBNU+VcXi2gMgGAarWKnp4e3HLLLejt7cXo6Cgcx8HRo0dRKBRgGAaKxSJmZmaQTqfx27/929ixYwf27NmjjAlZpqwrmUgkglgspkKO9Hy6ia93Ap0o7XaW4MjICABgdnYWgFvFxVDPAw88AACqku4rX/kKANcy/bu/+zsAwPPPPw9gqVQdcC3iffv2AQDuvPNOAMCXv/xlzzrosZxPaOexEHS6ZL7kiiuuwMaNG9FsNjExMYFvfetbOHPmDCYnJ1Gr1VAsFj0VkHr+ot39zjd0E3pjjo2epS5jP/7xjwNwPVFWf/3u7/4uAKjKOHq2zGfyvR86dAgAVBj+Bz/4AQD3/VN+6H0zq4FXJedCIcrqjVarpZL18Xgc8Xgctm1jamrKY23IihDp5tO9BaCS4r29vT+XlinDY0yclUolLCws4Morr4RhGDhz5oxKSNZqNUxMTHgYf2pqCtFoVMWdpUdCD0gmc0ulEkqlkmc8xKvNZDQOotEoTNNENptFNptFMplUTZ5SsMgqsXg8rvJG/G2l53CcpU5tWTTyWlfHBdA5sNgjkUhgYGAAmUwGxWIR5XIZk5OTmJ6eRr1eV94NAFW4QdnB68hJFcH7P39wXpWLVABUGlQOhmHgwgsvRDqdxuDgIJrNJg4ePIhCoYCDBw/iyJEjqFQq2L59Oz72sY8pF5glhbyWbdvYtm0bWq0WHnroIc/9X88lhlS4zCdMT08r3FQqFWVJUrEA3meX12g0GigU/p+9M4+O8yzP/m/2fdVIGu2WZVuyHCc2FMoJJAVyylbWlO0kLVsLobTQklJoS8uhpRunIeyhaUmgoZT2tHCgKSGBkKRQkkCwndiWja3NljTaRzOj2ffvD/d+9M54ZEu2llG+uc7xsa0Zzbzv8z7LvVz3dS+TyWRoampSHmGhUGB6eppYLMbJkyeZnZ2lra0Nne5C5b72szZaRWE1iDdlMpnw+XwEg0F27dqF2Wzm1KlTxGIxTpw4QTqdVgeC3W6nVCrx8MMPMzQ0pEgevb29inoNK1aWlhCSz+c5fvw4J0+eJJ1Oq81H69Vtxj2u970yV8WzEGOj2lKtjn3Pz88D8LznPQ+4QMOGFRbQNddcA6zkUIQtJBboiRMnAOjv7wfg6NGjFZ+/3WtHrH2LxUJ/fz8dHR0q9/rMM88QiURYWFhQxpXBYKCnp4discjY2JhSvtDpdOqwkfkkJCDtd8H23HMtwwdW6pcEsj5f85rXACusL8nBfeELXwBWPNXXve51wEpl/sGDBys+T+aZeEjaAmvt9VR7LGsZq033XKprGCRJ63K5cLvduFwujEYjpVIJu92uNhSRcchkMkpPS5ujkcSvUJCr6aVbgav5PvHi5ICQ6nJZJEtLS+h0OnUArEIXVn8XCgXy+fxFeZZqK11i1fVgtUmOTcJVmUyG5eVlEolExfOU2oSlpSUsFgsTExOUy2W8Xq8KAwhDDFAFcRIahBUPUcKR8jPY/g20gUtDnmVfXx+tra20tLSg1+sZHx+nXC6rDVhYYEJXFo+l+jk36py2Bpt6uMjmKTCZTDgcDg4dOoTH42FgYIBischTTz1FKpXC5/PR2tqKzWbD4XDwvOc9D7/fz9NPP43X6+XgwYMqlJJMJvnP//xPxsbGeOSRR4hEIur0FdTrpiGhPMk/2Ww2XvjCFwIXKmxTqZSyUOUQ0m621Yw7oRVLvNnpdCr3v729Hb/fTyKRUBuweJISotzKg0auS0ITUgwrkNybWJU6nU6pEeh0OuLxOA888IDyTsxmM11dXQQCAV7+8pdjNBo5c+YMVquV5z//+ZTLZa677jrsdjvDw8MV1motWZmthsTKM5kMsBIzXy22XX0wikdy+vRpYCUGv3v3bgD+5V/+BVipyJecjdRFyO9JDF48meq6mO1cSwaDgUAgwG//9m8TDAaxWCyk02llXHZ0dBCLxfjhD39IMplU9+jxeBRVWSr0q3X1arFXNwu1Pr96fKsPQMm1yf/lwPzv//5vYGXeiKf6gQ98AFjJuc3OzgKo/aW6QDkSiQAXq29fTltsLdjQw6VWwko7WOVyWSV0XS4XTqeTfD5/Ed1SWwhZLBaJx+PKstc+jPn5eWZnZ1lcXCQej69blnwrF4z2O7UbLFyYMFIk2dHRQTweJ5FIKHp2oVBQYaxaRVZGo5GWlhZaWlpUTkXCbbBSL+RwOCq0mLYTWu90aWkJg8FAMpkkkUiovItAckni1QnbLR6PY7fbFYMwFothNpvV5hEOh8nn8ySTyYrNsl6NjgYuhngtEj6X4lAxTgqFgoqGOBwOyuWy8oabmpoolUosLCyQSqWYm5ur0BPb7jXwbMeGHC4Siqm2BKprOhKJBGazWSXgW1paADh06BDhcJiHH36YWCxGJpNRSfyWlhZe8pKXKJqtxE7j8ThLS0ssLi6SSqUq8gdbjcttWOKqw4plIKqsgUAAt9tNsVjE4XAoNdvR0VEsFgu/9mu/RjQa5Q//8A8JhULailhFt2xpaeH9738/gUCAxx9/HI/Ho6qWU6kU+Xweo9FIIBDg2muvJRAIcOrUKRWfh809aKsXsRgPUgz69a9/nXK5TDqdVpRsoIJ2nkgkGBoaqhjLUqmE1+vFbrdjMpk4efIkfr+fw4cPk81m+dznPsfU1BRHjx4lmUySSqUqVJ83s75hrQaMWJ7yPvFYtDRs7efJvYvnIe+XegaxYG+66aaK7xG1XDlkhY0mns1b3vKWitcF230QG41GOjo62LNnD01NTXi9XoxGI7FYjKGhIaanp5VX29fXp0LLPp+Pt73tbdhsNs6cOcPExARf/OIXVY4Ktv/eLnUN1Z6pzBN5Pk8++SSw8hzn5uYA+Id/+AdgRXNOXv+lX/olYCU3Jxpk1R6O1iCF1dXd1zJ2m55z0bp7Yq1LoaToYTkcDuLxOJlMRrm1Uknr8XiUlyM3Lq6u2WyuqLLdDlzO+tHmQCQMZTKZ8Pv92O122tvbcTqdmM1mrFYr3d3dSqDSYrEQCATUa7LhiMUmny21IWazmWg0SjKZVMlrYUrJmAaDQQqFwkU1RtsRFpNwnoT8aqkeaw9TLT1SxjSfz2M2mzGbzeRyOfL5vGIfhsNhzp8/z9LSkvL6tMQSGcsG6hMyV8xmsyqm1eqMeb1e4vE4kUgEo9FIW1sbRqORXC6Hz+fD7/eruSFh4Orn38DmYUMOl7XE7EUPKBgM4vf7aW1tZffu3ZTLF7ooakNhYtUWi0UsFgsDAwP4fD5VgX7+/Hmi0SjXXnstfr+fI0eOqNjkWq93I7Aet1qsZIvFQlNTE7fccguBQACfz0e5XGZmZgaHw8H+/ftxOBw85znPURtkJBLB6XQqqXCRvZDPy+fzPPnkkzQ3N/OCF7wAj8fD/Pw8RqMRh8NBNpvlxIkTGAwGXvKSlxCLxbjvvvtYXFzckoOlmjXo9XpVGExo6dr3aVlf2oNIwmVy72KxNjc309zcrGjKImjq8/lob29ncnJSHeoyr7QkiY3GeuZFdQ5F6hHEe5OxkHCQxMYFUjP0e7/3ewD86q/+KgB//dd/Dax4JtUV+gsLCwDcfvvtwIqlut7Ol5sJbehYSC9Go5FMJoPBYOB973sf4XCYL37xixSLRV7/+tfj8/lwOp3AhXxFKBTiv/7rvzh//rwSeNUKmcLWGRhrWWvV5RQSXRBlBpkX4smI5yrKDC9+8YsB+Iu/+IuK16XeJRQKAXDgwIGK16u9+dUa7q0Hm+q51EpWaT0YQIU8UqmUes1sNuNwOAgGg7S2tqqQmPQfEUtc25NB6yHUCxtEa30Lg8VoNGKxWCpqO0qlEm63WzX40ul0SgInmUwqVWjJJ8DFk1DuXfItDocDnU5HIpEgnU4Tj8cxGo0qPLRdm4eMgbbORvuaNq8GK8yvam9Dp9Mpi1YYhQ6HoyLXBBfGxW63o9PpFBOxHphyDawNUmxts9mUaoU8f6fTqbzxXC6H0+nE6XTi8/kUDX9ycpKZmRkWFhZW3Re0dS8NbBw27XCpFWcXSzmbzRKPxzlz5gyxWIwnn3ySyclJisUiTqeTlpYWentc4XPMAAAgAElEQVR7ee9730swGFRyIMJnl9qGRx55hNHRURVL17YC3oqJIoeh/LsW5LqCwaDSxRLLSooijUYju3fvxmq1MjU1pdoLSJhMmoWFw2Hi8TilUqmiMZbFYuGFL3whVquV73//+zgcDj784Q9jMBj4+te/rsIGuVyOT3ziE4TDYebm5rZtQcnhIjkWCX9UdyCUA7BWpbbFYmHv3r04nU6eeuopmpqaeN3rXofb7cZkMpHNZgmHw8RiMQ4fPkwqleKJJ54gkUhselhkLZ8tuROJjYviglik4p3JXBEW2a/8yq8A8I//+I/AikUqHsdv/uZvAiuV+WL5Vhsl4hFJfw9BtdGx3bUfVquVF7zgBXR1dTExMaFytTJ/9Ho9b37zm1WuLpVKEQwGyefzfOMb32BsbIzZ2VlyuRzLy8sXjYNWnV0M3M28n7W+p9qjlfkhkPkjeWvRmBNFh+uvvx6AG2+8EYCPfvSjAFx77bUA/OhHP6r5PZe71vWMz5aqIovVIRXWc3NzRKNRlV9obm5WG3FLS4tKdovlKiENqVafm5tTgpVaz6UeGEHaaxErW+RqWltbVc5FLCptnxKdTqcmujDqhBGjnQRSJyJqytL3RTwgyVPIgZxKpRgZGakozNyOcammqMvP5XCRjVWMCO1hoM1fORwObDYb2WyWYrGIx+NRHlut5y9e7nbPjQbWDoPBoNopSKmB2+1WIXLtPBb2oejnxeNxUqmUktiXvQYqN8laZKQGrh6bdrhUPyQJDfl8Pm688UbMZjNf+cpXiMfjHDp0SMXLJWwkEg8mk0lRS8UqHRsbY3x8nNnZWZaXly9K1FZ/92ZWn9e6T+3fWgq23+/nRS96EV1dXbzsZS+jXC6zuLioKLg2m42BgQHK5TInTpwgGo2STqeJRCIUi0W8Xi8zMzOqPsZqtfIrv/IrdHd3MzAwgMvlor+/X/VxyWQydHV14XA4OHbsmJIkT6fTFeGFS93PZkCel7QLEPpoc3MzxWKRmZkZyuUyfr8fvV6v7l+uT/I0FosFp9OJ3++ns7OTAwcOKHJILpfD6/WyvLzM008/rQptaz2r7dhQZFOUCmmRszl8+DAAU1NTwIrFKrkV+Vv6c3zuc5+r+JxnnnkGWF3NuFqRQa6jt7cXgHPnztX8va2G1ghJpVJEIhGGhoaw2WxEo1GsVitNTU2k02m+973vUSgUOHz4MJlMhs9//vMkk0kCgQCHDh3CbDarvk+xWEzNf+lSqpXml7kJtTvmbsR9yXfVQrURJajew2Q9PP/5zwdW5ok890cffRS4IGwL8Ed/9EcVny+dR8fGxoAVjTGtIrn2e64EW97PRRKyMnkMBoOSAxHLXXpzaOs6hFWUTCaZmppiYWFBJXhXcyW1P9+sxbJaYZTcn91ux2q1Kl2ktrY2Ojs7VXK5paVFFT5KbkV6U0gP8FQqpVhf2u81GAz4fD7l8UmuKpfLqc0mEAiog91gMCi2zVZDFq0cjNokf7XnpqULy+9qn61Ysz09PbjdbqVPJvMmk8koerrUuaRSKfW79ZCsbmBtKBQKxGIx1bOoXC4zOjqK1WpVYbDFxUWKxSLz8/NkMhlmZmZIJpM4nU5l2El0Q0tc8Hg8KowuUZHtPlSfTdiyhH65fEFEcW5ujq985Su0trZy0003EY/Hufvuu4lEIlx77bWq1a/T6SSRSOByuWhpaSGTyfDwww8zPz/P8ePHlesrLCKdTqdCaNVsoK1M8GuT+E6nk5e+9KWq3iQQCPC85z0Pl8vF+fPnFXvLYDAo6YpyuUw8Hufxxx8nk8nwhje8gXQ6zb//+78TCoXUYhE68cDAAHv27FGbthwgLS0tlEoldu3aRSKRIBqNMj09zfLysqoN0ia4tde/0QtMFrVI/QhLTCxHITtI7NvpdJLL5YhEIuoQlUPBYDDQ1NTEgQMH+JM/+RNcLhejo6PodDpljQ4NDTE6OsozzzzDzMyM6uEi+Q3pWLqV7Rmq6wfk7/b2dgD27t0LwLFjx4AV9dqvfvWrwIpWlLCCXvKSlwAr2mKiGSXqtWJ5itqtfH8120zGvDqmv53QGpSxWIwf/OAHtLe388Y3vpFcLsc3v/lNlpaWVEnDrl27MBgM/Pd//zd6vZ7e3l6y2SyPPvoo6XRalStISD2VSuF0OpWiw9GjR4nFYkr8crOMj7UYddWGseTa5JpE6kZYhf/2b/8GgNfrBVZYYDJPRDNO+rWIAoN8nuTmxKuvbjpYzSJbj+G+parIcMFtm5iYUBXWmUyGWCymKrNFDiSZTDI+Pq5E5lKpFJOTk4TDYeXGausg1upubhVks/R4PMqCElFJk8mkFKC7urqUVSaFppFIhMXFRTKZDPPz84oAIRRMuReh9Xq93os2L7PZTKFQUElzEQcV+ZetGpfqBVUqldSmrnXxxVPVQsISUkwJqA1FqNnazpoCyUVJZX+t0OV2eG8NrB+lUonl5WVMJpMK5czMzCjDQ7xWg8HA5OQkJpMJj8ejWKiJRIJsNqvmBFDRgA5WKO1ixOyEnlA7AZuuiqyl4Yq1evLkSc6ePcszzzyjLNK2tjZ6enqUhEMymeT++++nVCrxnOc8R+UhTCYTN910E8Vike985ztEIhEVHpMmQdvlscg9SzjM7XYTDAbp6enhpS99qQp9RaNRvvvd76pYstx7LpfjBz/4AZOTkzz55JPE43GOHDlCqVRicXGxonBSmmH19fXR1tamqMpaKfpoNMq9995LsVjk1ltvZWBggAcffLBCYkWe0WbQuKspxZIfqy4qFdKBhEGrddUkNyPEiGAwiMPhIBqNKsad2Wymv78fk8nEL/3SL9Hd3c0Pf/hDMpmMSvxKq2d5TluJ6pi59GGRexVNsLe97W3ASgxcvC2psL/nnnsqfk+0pDo6OoAVtpDE4p9++umK9wvLSFhkcj3y+mo5xK0cL+28KZcvKDfMzs7yz//8zxXGUzabJZ1O85Of/ESxC41Go1IY1+qzSdjLbDbT09ODXq/nRz/6kepqChc8Zmmot1ney+XywVrDEVY8DPlbrlWe15vf/GZgRdVYWGDiEd93333ASufJD3/4wwC88pWvrPh+GXPxYC6Xp17LvNjSnItcsGwkwmKSBSRei3guCwsLFItFZmdnK8QbV7NItUnf7bI8tJtnsVgknU6TTCZV9bjNZkOv1yvefiQSUcynbDbL+fPnmZmZUVaXdKbUuqlyeNlsNjWWorEk3y//ls01nU6r92qF++QzN9OSl8/XJpPlGiUXVS6XFVW0eoHJtYpmlMvlwmw2K6kgCa8tLy+rSn2tWCGsbC6S32nUNewcSEg9HA4rGSMpaSgWixU1csViUSWlq5+v7B3afUhymTqdToWgGtgY6NazwHQ63ZreXH2qibspEi7pdLoilq6NnUo9RiaTYXp6Wm02RqORpqYm9Ho9sVhM8da1G4h2U5f/r+f+yuXymndYnU5XrrawZILK95pMJrq6urDZbLhcLnbv3s3HP/5xXC4X8/PzxGIxfvKTn7C0tMTk5CTxeJyhoSHi8bgSXay2aKQo8LrrrqO5uZn+/n6am5u58cYb8fl8tLW1qdBZOp3mBz/4AVNTU5w8eZJwOMzPfvYzxZzSfnb1M1vvWKzy84rPlMNCquU7Ojpoa2vjta99LblcjrvvvrtmsZuENKSj5A033IDD4VCN5vr6+iiVShw/flzJ3sTjcb7//e+ruiChZWvnw1rnxpWMRfW9S0hG5uYv//IvAyv9U973vvcBKG25L33pSwD8wR/8AbDSM10+V1hdUpn/wx/+EIDXv/71wIrGmIylaEfVmk/a/wtWs1w3Yl6s8XcraPyAqumSfJSIuUrOVbwybS5Re2iI1qFQm30+H8VikVAopA4YOYDWYnxc7X4h91l937Vel1yasAUlhCyacsL6ktc/9KEPASu5mFe84hXAipbY//7v/wIrqtiCag92tYjGWvaLTfFcanxxxcOSZLs2PCKHhXgoklgT7rocTnq9nng8rkJEWumQWg9sK7jrtT5f8gjazUXCQHBBal2bexDVglQqRTqdrnDLqxPuImMh3o5U3It6gYynqAHn83kmJiaYnZ1Vn139mavdx0aNi5apU4syLveUzWZZXl6uuH8ZM4fDgc/nUxXbEiJYXFwkm80yNTVFMplU8XZpONaoX9h5qA7XiqcrYraAMj7l+VbvNzLnJL+iZamK919NcRc05szVY1MPF4FsJtXdzuSAkYcop3EkElHei3gAcGEySW8HLa1VPBjp1SHv19ZybMZEqeV2a09+m83Gi1/8Ynbt2sWv//qvK6siHo8zOTnJ8vIy5XKZYDDIzTffTCKRYHJykrGxsYrckUBryXV0dKiN1uv1qsLJsbExEokEP/7xj4lEIszMzBCNRjl9+rSi5FYvolr3stEQjwtQPc6np6dZWFhgenoar9fLc5/7XAqFAg888ICaK1pixAtf+EICgQDXXHONqoman5/nnnvuYXFxUeUlJFwmBojcrxz2W1FIuVrIVg7Yj3zkIwDcf//9wIoG2Ne+9jVgpYOgsMjk98UDkcprCQHJc3zggQcAeM5zngPAqVOnKq5nNct4tevfjpyLoLrYVryN66+/XkUwZmdnmZ6eVhEM8WBkrZhMJtrb2zEYDErJw+PxqKLtTCbDyMjIltDTa43hajku2cO0bUiAi4R6pS7qzJkzwAqbTHInt956K7CSkxGFB9EYGx0dBS6ui6r++0qwpVRkbX/3WtAmF7XhLm2/Fzk8pE6kVCpdVLug9RC2GnKYGQwGpVbscrnwer1YrVblOUjTM5GL1+pt1bp2WWjS+yUYDNLU1ITL5cLj8agq5EQiwezsLJOTk8RiMZaWllheXiadTle0h94OtpQseJGuEa9qcXHxIjFK7ftlrFwul8q3aFsVLy0tEYvFVHvnS7VgaLDEdhaESWkymWhubqatrQ2Xy6XWfTVpRDwVLQFE1p4YpNp1Jvm6ajS8lqvHphwucojUorzKzyXfIpaptDGuDqFpNack9i4bqWgGSfhMPBRtbH27LC6Xy4XD4SCTyRCNRpmfn8fj8eD1enE6nTQ1NZHL5bDZbITDYR566CF1KEiCUrvJSjLb6/UyMDDAW97yFuXBSbV7JpPhscce4/z584yPjyurVsJDWorlVm+y5fKFYjWr1crAwAA6nY6ZmRlFNkin0/z85z+nXC4r1pgcLKKm7Xa7sdvtJJNJIpEIIyMjRKNRtXk4nU7S6bRSJxDPWMvdX2s8fbMgbL6Pf/zjADz88MPASidI8Tje9KY3ARe8MFixPP/nf/4HWLFoJTQopBixbN/5zncC8Pd///cVnyOoFbqGiwVRt9JI02762ue/a9cuurq6eM1rXoPT6WRpaYmFhQVlOGnXitFoVN0qDQaDUhpvampSDLqjR48yPz/PI488QjQaVeEy2F4yEFCRXwIq9j5Y6c9y/vx5AL7//e8DK+xD0ZqTvi0SLRD4fD5gxdMRD0mYdat5qtX7xVrGaMvYYlp3VawHqV/Q5iPE+jAajRWTTCip4g1Igl82k1o9Orbb+jCZTLjdbjweT4XVJPer0+nwer0qhGY2m7Hb7eowEAaMJCXtdjv79u2jr68Pv9+Py+Wq8JT0ej35fF4tFp3ugnSGdHDc7vEQIwBQ8XNYMTjkdanFEYHKpqYmVcsAqHuSDqSy0Yo1K8WaQk2XsahuhNVA/UI8XLvdjsPhUEoXJpOJaDRKNBpVBoTUOUkDPo/Hg9vtpqWlBbvdzq5du/B6vQQCgYpaOvHmZf00lBs2FpuqLaZ9WCaTiba2Nkwmk4qFS5Wp5Eykx4S04ZXQhtBs9+zZg91uJxAI4HQ6ufbaa0kmk3zpS19SFe7bBS2rovx/tNjW1lbe9KY30dHRgcfjUQeL1srq7e2lp6eHwcFBwuEwiUSCkZERfvKTn5BOp1UTsKamJvbt28dHP/pR2tvb8fv96vCQQ6hcviAK6vf76e/vJxKJ8MMf/lCNr9ZrqbbUtK9tVn5KJHzGxsZUeM/tdrN79241LwqFAoFAQFFGHQ4Hg4ODSjY/kUgwNzdHIpGoUGqQMJnP5+M1r3kNuVyO+++/n2QyWaGWvV3ClfKdYiFK5bSwwl796lcDK+yehx56CKAiXAjw53/+58CKJyMei+RmbrnlFgC+973vAdDZ2Qms1M2sphm1miej9Z4324vRSrRYrVb27NmjQsqlUokf//jHFAoFpbsXiUQwGAwMDg6i0+kIhUKYzWYOHDhAV1cXt956K263G4vFonoajY+P8/jjjxMKhZQkkLS9ECWH7UT1Hiaq2KIRJh6MdCIVDTrxYCWnIjk5ec7CmJPfE8WHkZGRiu9db13LpfaLLa1zkU3Q4XAoy1Wv19PV1UU+n1eJObkRrYtoNptpb29XXovkXbR/6qWyVjZSYXKJdaTNBYlHIhufhAT7+vowGo2qw570l9+9ezc9PT3qYNHmHMQrSSaTRKNRVc2v7XtTg1K6LYyYUqmkku0SB29qasJutyvx0dbWVsrlcoWGmAiYZrNZkskky8vLLC8vKxkbSdjabDZsNhtGoxGz2axIIauJmjZQX6iVDxSDTOrFRL5HnrPkN/1+P06nk2AwSEdHBy0tLdhsNuURS1v0SCSiWIWrsUy1/2/MmSvDFR0u6x10nU6niiEdDgfPf/7zMRgMjIyM0NTUxDve8Q7sdruKhT744INks1m6u7ux2+14vV7cbjeHDh3CYDBw/vx5UqmU0g6SvtqhUKiiCE97nVprfaOr0Gvx18vlMtPT03zwgx/k4MGDfPKTn8TlcilrSVo8y/slPvzOd75TUYrn5+f5yEc+Qjab5Xd/93fp7u6mpaVFhZNEQmd5eZlTp04RCoX4zne+w/z8vFIukP4lWlRTNmu9thmQ65Ciz2Qyicfj4bnPfa5SdpbDVq/Xq7oo8XieeeYZpqamOHLkCDMzM8RiMeXdlkolpZ3W1dWlqKblcpmlpSXV6XSroLX4tfcu4/uf//mfABw5cgRY0YQaHBwEVjwVydGIEKnUL0iltYT6Dh06BKz0Un/jG98IwNvf/vaKz7tcT3SJyUcikYqfb+a8qMVgy+fzDA8P43Q6lTBroVBAr9czMDCgQqa5XI6nnnoKk8nEy1/+ctrb23nFK16hDppyucy5c+eYnp7moYceIhQKKaq7GGZaOSmtuvp2HC5yLeJhSo5FPFSpaxE24V133QWs9GkR9pc8Z5kf4jHLvHnssccq3nc5j2W11y81NlviucgFSD5A+pP4/X4CgQAWiwWbzUZbWxtwwWMRiXlhCGkTU8JPn5+fVzUv8j1bbWXUsnbkGnK5HOPj41itVsLhsHpNXFvte2Viy33K+3p7e8nlcnR3d6tWp4VCQVnxs7OzLC4ucubMGebn5wmFQqqXRT1a69rDWGLfUmntcrmUgoGERgC1qUhHTa0qgdb6LBQKZDIZwuFwxSbR6OGyc6Dd0EUTLJlMVqwziXhYrVaVy7XZbPj9fpqbm1WBrcyJmZkZRkdHmZ6eViSgWsK21WSGBq4OV3S4XAkHXnvADA0NEQwGeetb34rNZuOJJ57AbDbzspe9jJ6eHo4cOcLy8jKDg4M4HA5cLhfZbJbHH38ck8nEi1/8YtLpNN/97neVKqyW6lzL2tqsTVb7XbJxahdCJpNhYmKCu+++m71793LrrbcqthtcXPCpLQp1uVxKO2rXrl3odLqKfMPU1BT3338/c3NzxGIx8vm8OlQkDKfNqdTKuVSP02ZCDg05QHK5HHNzc3zrW9+iv7+fa6+9FpvNpjYPuUYhKnz3u99lbGysQn5dErlyD8lkkp/+9KcASuFgOxK1lxtTOTjFw/jmN78JwMte9jJgJbYuFqdoholnIiFjiaFLf5fqjpbVJIbLrVnxkLbSaq9mqmnrkrLZLMeOHcNms7F7927V/lvCwgaDgeuvvx6Xy8X+/fvx+XwVdU6xWIx/+qd/4syZM6qiX9ZCtfGlLajUXsdmYrU88a5du4ALtX3aaxIP92c/+xmwkosRtthtt91W8TlSuS+5vWq2oaB67FcLEQrqii0GKweA6G3l83n0ej2Li4uYTCaWlpYoFotKvLJYLKpiykwmo7SjksmkyiVodbK220qvFS8uly/IsJw/f17lUoQtJhtode5D7kNyMlJxXiwWmZqaIhwOc/r0aWZnZxkeHlZyOFp3vrpeZLW48naNl7amaWZmBqfTqQwFYXuJZSpV+7Ozs8zOzuL1elWYq3qDkPFuYOehllFYKBRIJBJKeFQ2RZ1Op9pOeDweCoUC0WhUKYELNX9xcZHx8XGmp6dVnkU20lpGVwMbh03RFrvE7ytarbTn1eqLSR1DR0cHhUKBY8eOKekXq9VKV1cXOp1OdWicmppSApiy0VwNyhugm6TVz5JQl9FoxOVy0drayjve8Q727dvHi170IsUEEyaOuPGCVCrFt7/9bSYnJzl9+jSxWIxQKKQO2lwuRzweX5VmLCEDWClAvZza6UaORS3rV0st1+ok2Ww2brjhBrxeL52dnXi9Xp73vOeh0+k4efIkMzMz/Ou//qsyQLQeS7W6s1j98vOr9Vw2Yiyqx726r4uwe6Q+QSr45cD9jd/4DWDFM5mcnAQutrY11yzXU/PnV4qNGIv1QGsoaY0yeU1yldomg3a7Xa05UWGX1hPVhsjVYCP3i2p9RIE2hA5w0003ASt1S+IBS2X+xz72MQC+8IUvACgvXuaLsAw11yX3UvO618Eu3BptsdWgdT/L5TJzc3MYjUacTqdqmGW322ltbVXUwHQ6rZJ3LS0t6HQ6ZmdnVSK3Xq0N7eCL9WWxWJiZmcFkMtHb24vT6awIkZVKJSW8CBco2mNjY4RCIY4dO6aS9+LRXYmndrn3bySDbDVPrjrsIF7X2bNn8Xg8ZDIZZWjo9XqGh4dVgzPJ29XyVht5lWcftDkRMby0Hr/URwkTU1uhL11dJURWD9GN/5+wpZ7L/33GRSwumRDSqlYKC2dnZyssbom3RiIRVftSLpdrUm2vBFdjiWhDUNVJa7iwINxuNzfddBMmk4nR0VHy+Txer1fdc7FYZHh4uKKvSTgcVq5+rTDQGq5T7u2yP9f9X+Ha/yXYN91C1VpF4tFKca02oS/PVyvQWeueLnVvV4MrmRe1GFBVn1nxcxkL+b829wYrLKIa11bx++v10tY6VrJ5b7XnconPriB8aEPJ1WN9OUbUlWIjvftqNl8121D+Fg9Fci/imTz44IMAvOtd7wJWDmLJ1WlbpG8kxBjdds8FaifZ5aGLRS49TLT0UcnVVCdw69ESWc1iFx0tuFDUJuEg0T8qFotMTk5WTIQNCPXV/Pl21LhUQ3tv5fKK7ItspNrk8nrCO9t9Xw1sDbQecK3cSWMebC82xXPR5h3WY2Frq9dhxaLVqhzXyq1s1KTaSKus1rVorXEp7tO697BihW0GtN9TvbFr37OaJXKJz13TBV/u+VQz2Kqttu0Kf23GWKzGztmuDVHYZ5ezcLdjXmz0720UNmMsLueBVhtZ1bm76ueoTUVo/7+O617T79WF57IaqpPZWmhDTNoNph6s79VQ67oktFPrffWia1QP47naIVIP19ZA/aAxH+obm9qJUnC1k2A1znV1XuNqsV5LROpI/u93V3tf9XdcxRWuHevJtdTCdsXWt8JgWK/Fu96xWEsN0XZZ3avlgjZrLNZ7Xeu5lu3GZs4L+ftyHSCrIZ5PdQ6nep9aaxRhra/XGou6LEmtjrHXY15lp2CnjWU9X1sDm4fGc3/2YVPCYhvlqazGtKnVSXE7sBYq9Fqov9r3rZVldDlomXhyrbV+t5q5t57v2AxshedSD59/OY9yrR7GFXge67rWrfQorvTzV6sVqbcDaz3Xs1ourno+rObhVGvZrfe5r9VjudR6rUvPZT2otwm0Fqx2IFXTmNf7mRtx0G7nYd1AA1eCjZr7DWws1uu5LALnN+NCtFiNDbbJ6Fnn+xeB81dybdV0bO2/rzSJXet95fLlq9NX+f4rGot1/s5lr6dOsCVjcbnnvtq4bLE3sW3z4lKoJgLV835xuTet97lXv77ZxKBVrq/mWKwrod9AAw000EADa8GOD4s10EADDTRQf2gcLg000EADDWw4GodLAw000EADG47G4dJAAw000MCGo3G4NNBAAw00sOFoHC4NNNBAAw1sOBqHSwMNNNBAAxuOxuHSQAMNNNDAhqNxuDTQQAMNNLDhWJf8y2a2La0HbJfMfD2iMRYraIzFChpjsYLGWKxgx0juN9BAAw00sLPROFwaaKCBBhrYcDQOlwYaaKCBBjYcjcOlgWcNDAaDao6mhV6vr/nzBhpoYPOwKZ0oG2hgq2EwGPB6vZTLZaLRqOoJpNfrcbvdAMTj8U3vd7FVMBovLF3pwVPdA6mB1bFRXT3rEdX3oO1Eu9V41h0uOp0Ou90OQCqV2tETpYEL0C4YOUTMZjNOp1O1uNXpdJjNZorFIg6Hg1wuRzweV+2b19KWtYFnL6q7Vep0OgwGA2azmba2NorFItPT02Sz2W28yvXDZDJhNpsJBoNYLBZMJhNwoWlYNptlenqaXC7XOFyuFNoe2na7nRtvvJFyucyPfvQjUqnUNl/dlWGt1tWzwdq6FHQ6HRaLBbjQcTAQCPD2t7+dnp4ebrzxRqxWK6VSiWQyyc9+9jOSySQWi4Xl5WUeeughpqenmZycJJvNPqus++ruiw1ces3odDqsVqsyLkwmEx0dHQwMDPCnf/qn5PN53v72tzM2NkaxWNwR68lkMtHV1cXg4CCf+MQnaG5uxmw2Axf2wtnZWX7nd36Hs2fPVnjzW4Udd7hoLVer1YrRaMRut6PT6SgWi5jNZiwWC5lMZrsv9aoh1hWstC/VWl8GgwGXy4XBYMBisVAqlYhEIuTz+WdN+EdgMplobW2lo6ODrq4uent7aW9vx2w2U3qdH1kAACAASURBVCqVyGazLC8vk0qlsFqtRKNRTp48SbFYZHJy8ll1sDRwadQywMRLkTCRxWKhtbUVj8eDXq+vaDFeTwZbrTCXxWKhs7MTj8fD7t276enpobOzE7fbrcKlpVIJk8nEwYMHsdvtzM7OkkqlCIVC5PP5rbn29QxgPRQCNTU18e53v5vOzk4OHjyIy+WiubkZo9GIwWBgfn6e3/iN3+Ds2bOk0+l1TZB6K4oyGo20tLSg0+kIh8Pk83kV5jGbzXg8Hl71qlfR3NzMgQMHiMfj/OM//iOhUIhIJHJVB0w9jIVer0en06HX62lvb+cjH/kIXV1dNDU14Xa72bNnjwoDlMtlNT6lUolUKsX999/P2bNnueeeewiHw1e8WdTDWNQL6n0sdDqd2mALhQI6nQ6bzaYOE4vFgtFoxOv18vznP59iscjjjz/O3NwcExMT5HI5DAYD5XL5sh7MZo+FTqdT8zufz6PT6XC5XAwMDPDlL3+Z1tZWTCYTBoMBu92u1sv/XZvy6HO5HNFolKmpKd75zndy/vz5DT84a43FjvNcBGKNyOYjHozL5SKTyezYcBishIIsFgt+vx+TyYTH4yGfz7O8vEyxWMRoNOL3+2lra6OtrY3du3eTyWS47rrrCAaDzM/Pk0gklKViMpkol8tkMpm6sMjWAvHIuru72bdvH4ODg/h8PgqFglpIspgk7FEsFonH46RSKbU5CIvs2ebNARX3L9A+X/n5/2+em+wLsjeI8SnjYTQaCQQC5HI5pqammJ+fr7DoteO53dDr9dhsNqxWKwMDAwwODtLZ2YnL5ao4ULSQ/dHpdFIoFCgUCni9Xq655hoMBgOTk5PKGNss7LjDJRaL8bWvfQ2r1Up7ezsWiwW73U5TUxO33HILy8vLJJPJ7b7MK4ZskgMDAzQ1NbF7926am5t5xSteAcBDDz1ENBolHo/jdDq5/vrraWpqIhgMYjab+fSnPw1csNrm5+f5wAc+wMTEBL29veRyOZ566inS6fR23uKaoNfrcblc7N+/n7vuuotgMKgWytDQELlc7iL33mg0kkwm+dznPsf58+eZm5sjGo1SLpex2Wwkk8kdc7AKtGwfrUElh0W5XFabjxygYnWLkVIul4nH48/6A6aabm632zEYDFitVvR6PZFIBLiwNgwGA8997nPR6XQ89thj6HQ6pqam6iqkLN64zWbj8OHDDAwM8Cd/8icEAoEKMstqkHBxKpXi0UcfJZfL8Xd/93dEIhHe8573MD4+Tjab3bQ1seMOl2KxSDgcxmg0kslklAvc2trKqVOnyOVy5HK5bb7KK4PZbKanpwe/38/g4CBOp5PW1lb8fj8ul0t5LGKtmEwm/H4/Pp8Pu92OxWLB4XCg1+tVzPWaa67B5/NhMBhIJBJ1ZZFdCnJ/TqeTlpYW3G43mUyGZDLJ3NwcpVKJlpYWzGazChVaLBYikQijo6NMTEyQTqfJZDJ1s1lcKWQsTCYTTU1NGI1G5aUtLCyozdJsNtPV1QXA+fPnlYf3bD9U4MLBYrVaMRgM2Gw2lbiXELKWqr6wsACgohuFQkEd0vXGJpRrMRgMypM3mUyXXMfivcvBEovFOHv2LDqdjuuuu27L7m9Lci4bzXASC66pqQm44M3o9Xo6Ozspl8ucO3fuipJW2xlP1ul07N27l3//938nGAwqFlQikSCRSPDoo48yNTXFY489RiwWo1AocODAAT7/+c/T1NSkJpx2gcjvh8Nh7rjjDoaHh3n88cfXFDLc7ti6yWSis7OT6667js985jMYDAY+/elPEwqFmJubI5PJKAaYxMn9fj/lcplQKARAMBgkl8tx6tQpMpnMFW+yGzkWVzL3zWYzHR0dtLe387rXvY6Ojg4OHjxIKpXirrvuYnx8nFOnTtHV1cW9996LTqfjz//8zzlz5gzz8/PkcrkNC4duxbxYzxjJe61WK/39/QQCAfr7+ymVSjz11FMkk0lF1X3Tm96ETqfjgQceIBaL4fF4yGQyPP744ySTSeUJrJVZuBU5Fwnpud1ugsEgb33rWxkYGODlL385Npvtot8plUpEo1He+973MjQ0pEJiy8vL6PV6PB4PxWKRmZmZmhTljcxLbovnIu7rlVqUsnHKASKeytzc3FV97nbBZDLR1tbG4OAgHR0deL1eZXEKk8VkMimLNZPJYDabMRqNJBIJ7HZ7hUcDKzFnt9uNTqejra2NeDyuDqF6ss6qIZZ6W1sbfr+ffD5PNptlYmKC4eFhYrGYSlLm83llobvdbjUGNptNeXPaWPtOgBhPer1eeW/XXXcdbW1tHDhwgObmZlpbW0kkElitVkXVlpCq2Wymr68Pq9XKL37xC2VgFAqFHZVzuxRkfhsMBhwOBy6Xi76+PlwuFz6fj2w2q9aObKCyZmROTE5OkkgkSCaT5PP5irBaPa2RcrlMKpUiHo9fMmxXLBaJxWJMTExw5MgRxsbGKl4XYpA2HyWfvxn3uiWHi9Zj0ev1FUWOpVLpim5MTmj5N1yowNb+fydAuOqf+MQn2Lt3Lw6HQ01sGReHw8ErX/lKkskkHR0dqtI8m81y55130tzczB/8wR/gcrkwm80XFQ3abDbe8Y53cP78eX7+85+TTqc3PZl3NdDpdPh8Pm699Va6urpYWFggHA6zuLhINBpleXmZfD5f8ZxLpRLxeFwtGpPJxL59+1QsPRKJXDWDbiOw2phrjQKz2UxLSwsOh4Oenh56e3v50Ic+RFNTU0UOJZVKEYlEWFhYoFwuE4lE+MY3vsHu3bv5wz/8Q/R6PceOHWNycpJHH32UyclJnnnmmbrPuV1uXso+4vF48Hq9vPjFL6a5uZnrr78enU7H8ePHicVihEIhdQBnMhkeeeQRRV03mUzMz89XED+kgFIMlHqYK+J55HI5Ojo6eO1rX0tnZ6ci6Gi9vFgsxm233cbRo0eZnJys+ZlCcPB6veh0OlKpFPl8flMKzjf1cKmurJbYfy6Xu6oisOraD8FOOlQkRtzX18f+/fvp7+8nGAwqK1sba4ULyclSqaRokoVCgVQqxcjICNFolFAoRDAYJBAIVCT6xAr2er2qBsRoNG4Z1/1KoNfrMZvNNDc3q+sWAoPf76dQKKgFIZuA1kK1WCzYbDZFchgcHGRpaYnl5WUSiQQTExN1ef9a5mNbWxter5fm5mYCgYBK4up0OuWBSMhUNoZ8Pk8oFMLhcODxeJThYrFYVMx9aGiorqzyK4F4tl6vl46ODvbs2UNzczNut1vlGSTnkM/n1ViIhyL5qWKxSC6XU2NRj2OivbZSqaS8rnQ6rcgKYlhNTU1x9OjRizwWLcxmMzabjT179lAqlThz5oyiOe+Yw0VcdLgQw/T5fLzrXe+iXC7zla98hVgsdkWHgcFgIBAIAChXf6dBQjiDg4Pcc889BINBbDabStoJJDRSKBRYWFhgbm6OBx98kNHR0YpYusViYW5ujmuuuYa//du/VaEw7fc5HA6V/BdLrhZldbsXmLDEvF6v8uLGx8dJp9O89KUvJZPJMD4+zvLyMkePHiUej7O0tKSSsiaTiT179rB//37e9KY34XK5eNWrXgWA1+tlYWGBN77xjYyPj2/bPWop1NrNQ2LsTU1N3Hzzzfh8PqLRKM3NzRUhjHw+z/Hjxzl9+jTj4+OEw2G1SR49elRZuxaLhZaWFlwuF294wxsYGxvjiSeeqEvW3Fo3N9lXfD4fL3vZy9i9eze33norer2eRx99lLGxMf7nf/6HmZkZEokENpuN66+/HpvNRi6XQ6/XK4NFwq3V37vdHstqyOVyTE9Pk0wmOXHiBE6nk1e+8pVkMhl+7/d+j6effnpVjwVWoiR79uzhAx/4AOl0mt///d8nEolU7DUbhS3NucjpK3/WA2FSCXsom83y85//XIXWdgqMRiMOh4PBwUGuueYa2tvblbBiNYrFIqlUimw2SzKZVEyp+fl5FhYWFN00l8tx5swZ7HY7mUxGUTCrDxiz2Ux/fz+5XI6jR4/WZXjEYrGwd+9e+vr6cDqdWK1WvF4vdrsdp9NJLpfDaDSyuLjIyMgI+Xweu92uDlqBeAFi4QIqBl8vkGuUhDNwUUy8WCyqBS95xmQyyfj4OFNTU6pITqjKXq8Xp9NZwSgsFov4/X6SySQ2mw2z2bypFNT1Yj35MPHErVYru3btYu/evTidTqVOsbS0xNLSEslkUo2tw+HAbreztLREsVhULMJCoVC3B8lqEANUDuNCoUA6neb06dOMjIzUPBzkQHa5XAwODtLd3U1bWxvpdBqv18vi4mKFB7dR2LTDRQr25N+RSIR77rkHQOVK1gqj0ciePXv4t3/7N1paWiiVSszNzfHud7+b4eHhHcPhNxqNdHR0cM0113DHHXcQDAZxOByrvj+VSvHpT3+aaDTKoUOHSCQSLC8vV0wEyVmFw2HOnTvH+Pg4xWJRqRZo4XQ6+eQnP0koFOI1r3kNo6Oj6rV62GiE8XfnnXfS3t6OzWbDZDKxf//+ivBXoVBgcXGRUCjExMQEwWCQdDrN8PAwuVyOsbExMpkMP/rRj+jt7aWjo4NYLMbf/M3fcPr0aaamprb1PiWfZjAY8Hg87N27l0996lMUi0Vuu+02FhYWeOKJJ7BYLGSzWTo6OnjjG99ILpfj2LFjDA8P85WvfIVQKEQ0GlUbZEtLC+95z3vYt2+f0tGSAzYQCFAul9m7d68qHKwXr3+tc088PpPJRHd3N7fccgt+v59sNks4HObEiROcO3eOZDKJTqdTYVWn04nJZCKfzxOLxRRjUsauHub+WmC32+nr6yMQCNDX16eMz7m5ORKJxKp7oNVq5fDhw+zbt4/bb79dFWbHYjGuv/56mpub18wiXQ825XCpJfNcLBZZWlq6os/T6S4oHQeDQXw+H+l0mkQicdkionqD2Wymt7eXXbt2EQwGFbupGjJpFhYWGB4eZmlpCY/Ho+LJ1daWNgcTCoVUUWk1xOoTZlk9QXJQbrcbv9+vdJLEStXpdKTTaZV3MhqNioashSRmM5mMYpKZTCb0ej3nz58nFApt66ZabaVrNaO0BkMkEsFqtZJOp3G73crLjEQiLC4uMjc3pyxx+VydTofD4VBJfznASqWSGo+dZqlrIcWiAwMD9Pf343a71QEstR2irafX6+nu7lbrJp1OE4/HicVixGIx0un0jjpYtLVOkneVZ240Gjl48CAGg4Hx8fGLlAbkPWazmaamJpxOJ4uLiywtLan3bgZjbMMPF4mZAxviUchiFNphsVhkeHiYM2fOMD09vWO8FgCfz8d73vMe+vv7VUV1LaRSKe68807Onj3Lww8/TDKZ5MiRI5RKJSX/UmuTWFpa4r777mNgYICPfvSjSiFVkE6n+epXv8rp06dZXFzclHu8EsjB0t/fz+7du5mdnUWv17Nr1y5Fnc7lcpw+fZpkMkmxWGRqaoqf/vSnzM7OqoWRzWbVYrLZbOzfv5+uri4Vejx48CAAp0+f3pZCW1nk2vxCLBbj5MmTfOhDHwJgenoaWKHXh8NhLBYLQ0NDuFwuxRSUvIpA6KpHjhxRNRwul0vl8p566ilOnTrF6dOnmZubq8tD5lJ5F71ej9PpVLpaIjEvhcUGg4GZmRkmJydJpVK0trby7ne/G4A777yT6elpZWxcDUt1OyAFwuJ9ZbNZ5X2LgOW9995LKBTi5ptvZmRkRP2eGODj4+OqEDuZTHLXXXcxOjrKE088QSKR2JQQ+bZX6GsT9IuLi2rSm81mdu3apUIjAwMDygqbmJhgZGREWbI7ARL37Onpobm5uabXpa3fCYVCnDt3jng8TjqdVg//UhZGsVgkEomoA1dLVRRks9lNia9eDWQRiMBgtQ5UNpslm82qeo35+XlmZ2crFoWWem21WlUoRD5L6L2dnZ0qfLbdkOedTqcZGRlBp9MpNpOWIioV1h6Ph9nZWRYXF2t6X4VCgWg0SiQSUetCDDKRhBHZnHp6/oJLHSySYxFWpcvlUgdsLBYjEokoxWAAt9ut8k3xeJxEIqHWzk46WARy8KZSKeWZigcv9W/lcpk9e/aodWIwGOjp6VFjIuod2uLMTCazboHftWLDDxehxcm/L4dAIMDtt99OuVzm05/+NPPz8+h0Ovr6+vjmN79Ja2srgFL+jEajfPGLX+T48eNKK6jeIRueaIU1NTXVPFxk7MLhMHAhuS2WuGy0lxpTo9FIV1cXra2tFItFJVgpv2s2m3n961/P+Pi40ijbbsjBIoeJzWajv79fSaFnMhlOnDjB8vIy6XSahYUF7r77bqX8XL0oLBYL+/btY8+ePYpZVSqVMBqNvPrVr6a3t1cRQbYaEr6sRqlUYnFxsWJzPHPmjMqZRCIRpVIgBI9YLHbR50i+yWQyqSS2UJv7+/uVGOpO2lilLq63t5e//du/pbe3F5vNRrlcJpfLsbS0xBe+8AUWFhbo7u6mp6dHFSA/8MADyvjwer0qNFhvxtVaoNfricfjPPLIIwwMDPDc5z5XGWKyvm02G7fffjsnT57ks5/9LHa7nS996Uu4XC6efPJJ7HY7VqsVq9XKH/3RHzE9Pc3Q0BDDw8ObYqRviueyngvVhtFEG6u7u5vDhw/T3t6Ox+MBLljlUs8xNjbG/Pz8Zlz6pkE2DrPZrGS0BSIwl8/nK6zyZDK5LitLErfNzc1AbUvQZDJhsViwWq2YTCZF4d1OyNh0dXXR1taGzWbDaDSq4rGRkREWFhYUSywUCq0a1rNYLAwMDNDd3Y3T6VTaY5Kf2e76luqxrmU0yMYp1GqR6wAUM6xWWEsOL8lLaXOf8XicxcXFHePpayGaWoFAoCJPKfMjHo+TyWQIBoMVEvXxeJxcLkdrayvpdJpIJKLWWXURLtQHqeVSyOVyzMzMKMNLDpbqommj0YjH46GpqYnOzk6cTieDg4PqNekD1dLSwr59+1SudqNzkdseFhNrS7TCBgcH+exnP0traytOp1O9Lx6P8773vY9jx45tO9tnvRC3XtRZq5HNZnn00UcZHR3lxz/+MbOzs5w8eZJ0Oq2qhtcSI3e73bz2ta9VhXOAkpCRz5icnGRsbIyenp6r0mHbKMiEb29v54Mf/CCdnZ2qMCwSiRAKhfjSl77E2NiY2jwv5bEGg0H+7M/+TBVQAsrC/drXvsbQ0FBdebzanjXaOgMhLGhFFYFVk9CS8A0EAkprTkJi8Xicv/qrv+LYsWM7zigDlBeeTCZVfZZWpeDw4cOkUillkCwsLKDX69mzZ4/aaPP5PKdOnSIWi3H8+HFVaCnjLXOrXg8Yabfx05/+lKWlJW688caKWkK4kFP9h3/4B0ZHRxkcHFRRErvdzi//8i8DK/NNmIof/vCHOXXqFB/72MeUAbNR2LbDRU5PYS/o9Xp8Ph9NTU2qQ5wWxWKRsbEx1YdgJ0CKF202G7t27aK7u3tVdtgvfvELRkdH1eYnifv1QKRDZFMVi1XYdhLfl6R4vUB68TQ3N+PxeFQV+uzsLCMjI0xMTKjE/aUgiU+/368ME8lhlUolZmZmmJmZqat710JrSWu1n0SuQyjnxWLxovdKKEz6+1QXXU5OTjI3N1c39OO1wmAw0NzcrNpryAFcKpVYWFggGo0q5WvRDhMKvlYpOpfLce7cOdLp9I7SmYOVPGsul2N2dha3262o5G1tbRiNRvWcp6ammJub48CBA2ofEC8HKueYTqfD7/crQ2yjq/S3TbjS4/Hwhje8gV27dnHzzTerZKQUAFbDYDCwa9cuwuGw6rtQ73A4HLzqVa+is7OTm2++mZaWlouUTIV2/O1vf5sTJ04oqvGVbIDiBRqNRnw+H4lEgjvuuAOAD37wg5hMJhKJBPF4XCnmbsU41qKma19zuVwEAgFcLhcWi4VCoUA4HObjH/84zzzzzJoOFvksbcta8QhsNhsOh0MlfuspNCTXIn+LZSlinD6fj2AwyFve8haKxSL33XcfCwsLqghSNlARtXzXu96lOi5qP9fv9+PxeHZUaEyn0xEIBPid3/kdRT82Go3MzMywsLDAl7/8Zebm5giHwzidTl7wgheoXFMul2N+fl4VDhYKBR577DHC4TDhcLii+LpaqaIevRcp+Dx9+jSjo6M8/fTTHDp0iM9//vNK6VhC6ZlMRtGu4/G4EuvUrg8JDYq6uLSH38ji2m0Ni4krKvTKWpNeTmyZDDvJ6qi2KH0+X4VFKQdLOBxmenp6zQl2sdDl88XN9fv9qthQWCBS2zAzM4PJZGJycpL5+Xn12nZC1Ar6+vqUGJ9Op1M5qLGxsUvGgrVzwWKxsHv3bgYHBy8qHhXLLRgMMjMzU1dU3Fo5GG1Ng81mw+fzsXfvXoxGI895znOYmZlRZAZRfhYmnN/vV4l8+Tyggn13KVaWiMrWk0SMKHpI7iyTyRCLxZicnGRyclJ5p+FwmHQ6jdVqVUw7UQ4vFAokk0kSiYR69kJuEPZcvdxvLWi9FzEirVarUl0QIVvR3RNDSjrRtra2YjQaK9qCFwoFlpaWmJ2dVXtBtTTR1YzJthwuomj8zW9+E5PJxL/+67+qhbR///6K2KfUNwwPD3Pq1Kkd5dpns1lOnjyp4psul0sVQOXzeRKJBHfffTdnz55dV92JxWLh2muvxefzKSvV4XCoTchqtXL69Gl0Oh3vfe97icVifPKTn2RsbIyZmRkymcyWMsVqGQ1Go5HW1lb27dvHxz72MTo7O7Hb7ZTLZZLJJMvLy+p9l8ozyEHb39/P17/+ddra2pTAo/b7rVYr73vf+zh58iSf+tSn6ib3oF3Ikn+yWCxKoUCYUjfccANut5sbb7yRTCbD1NQU5XJZkTfm5+exWCy43W4V4gBU8ZzQdi9VOGi321XH0wcffJBEIrEFI7A6yuULjb0+97nP0d/fj8/nw+/3K+97bGyMpaUl+vr60Ov1fPvb3wagu7tbtQVeWFjgU5/6FNlsliNHjpBOpzGZTNjtdtrb2ymXy0xOTpLJZK6q5892IJFIMDQ0pEKGc3NzpFIpUqkUTz/9NKdOneInP/kJAwMD/OVf/iXBYFAZoJKHvOeee9S+msvlKvLCEka/0gNm2zyXUqmk6JTRaBSTyURLSwuRSIT5+XllqaRSKX7xi18wMjKi2B9ruVmTyUR7ezvAlldliwXodDrp6OioSLDCSj3K3NwcZ86cYWRkZF11F0ajkc7OTnw+H93d3WrzkNoOsVIlVg8XiqjOnj2rGGjbDUlGut1u1W1TJvVa1QOq63hqzQuxviQpXm+qDrXuQa/XEwwG8Xq9eL1eenp6cDgcqleLSAaVSiVcLpdaJ9XKCzIeYvGudQ3UkwUv3TbNZrMSMF1eXmZqakoRXoT0InIuQp4RzT4J/YjXIt6eKCPDis5bPUjtrxXFYpH5+XmVc5mamlJswmQySSqVUlX4osNXLBbR6/UkEglmZ2c5d+4cMzMzKsRa3RPqarDtbDFA0USnp6eJxWJ84AMfwGKxEA6HyWQyKowTjUbXvEDa29u5++67KZVK3HbbbZdUC91o2O12Xv7yl9Pd3c3b3vY2AoFARa4lmUzyx3/8x5w8eZLR0VFlNa0VTqeTG264gT179nDw4EEKhQL333+/Ci2azWb2799fQUcVafZ6OFgA1YLB6/WqQlm4MKG10vqrNbeS2LiEAM+cOcMtt9zCoUOH+MxnPqOUobWh1//4j/9gaGhIeUX1Au0hkM1mcbvdvPCFL6Svr4+XvOQluN1u1bpXQqF+v18xwVKpFGfPnsVms9HZ2akOUTlUksmkEnOUEGr1Biq9PR588EF1WNUDJBS2uLjIPffcg8lkUgeLiLeeOXMGvV6vQjuhUOgiKjZc2Ixl7Ewmk9Lpk86THo+HcrlMNBqtm3VSDe2Gn8lkOH78OCdPnuThhx9mcXFR9faRfVKn0xEKhbj99tsxmUyK8BMMBslmswwNDZHL5fD5fACKQSekiR0XFquG1rpMpVIqpCNS6lfaPW87K3GFmy/iedW5lqmpKVWBv9YDU1vJ3tHRoXp+FItFOjo6yOVyysIVimIkEiEcDq+5detWQVslrC2iFFc8FoutKbkoGy5ckM2pnivl8gUB1eXlZcUWq+ewqvbA1MrZyGEgCVnRT1tYWFDV/FpaKqAs2Gg0quLxl/recrm87aGwWpDNcmJiAoCFhQUl5SIHKKyESWVzFC9V5r3WINHpdKqocidW7AOqLq5UKnHu3LmKZ6e9n0KhoMo3pKBYwvBaI0JUUQqFApOTk+Ryuavy4urmcBFWj1Silkqlq9IACoVC3HbbbQAbzt++HKQdAFyompX7koldKBTw+/34/X5VQXy5DU+SvNIO+dChQ4p9ptPpePWrXw2sJG51Oh3xeJy77rpLFSHWywLSSs1L8yJJ5mcyGb7whS9w5swZFhYWVv0MmTPS/6Srq4vbbrtN6bbJRpLNZnniiScYGhri+PHjdZ+z0+l0xGIx7rnnHgKBACMjI/T09PDKV74Sj8eD2+0mk8lw7733Mjk5yczMDA6Hg7e+9a0Eg8EK7bJsNsuDDz7IqVOnFAGgHjbS1Zr9rQYheIju2qXuQQ5JaX+s0+lUKFg+Z2ZmpkJlW4wYSezXkxFWDW2Obnl5me9+97sqT1kLtZLyhUJBtYSXsGwsFmPXrl185jOfAeCP//iPGRsbuyovbtsPF9k0jUaj2oRFsfRqFoKcvtsBkdaX0IUo82qxFou8ujGa/J5WkC6VSqmFVK2sWy6XmZ6eXndOZysgNFvpQSMCi8VikXPnznH27NlV2WwyZ7TFon6/X3lysrkKy3B4eJhz586RSqXq+mARiIK4sIKy2SyDg4P4fD5aWlpIJBIMDw8zOTnJ1NQUgUCAubk57Ha7EnLUxs0ljl6vNNu1QDZ/LZupFkTtwWQy4Xa7leeq3UukaZgcLvJnJ+RaZP1rC0mvBIVCQa0jqcPbt28fXq9X0eCvFtt+MSlq/gAAIABJREFUuEivc7vdjslkIpPJcOrUqSsKhV2OarlVaGtr44477qClpYWZmRmWlpbo7e3FbDarEJDw7UUuXAutl/L+97+fUqnEnXfeyfz8PNPT01itVqampohGo3z/+9/H6XTyzne+UxVKyv0XCoW6KBysfi7lchmHw8ENN9xAW1sbTz75JIFAgBe84AXk83lOnDjBiRMnVKK2+rNMJhO7d+/mrrvuwmKx8I1vfENVcWvrhM6ePcvY2Bj33Xcf586dq7tcSy1o524ymeShhx7CbDbzve99T7ECC4UCoVBIScmL1PqhQ4e48847CQQCKjT6a7/2axw+fJjvfOc7ddO/5Ernohwskk+qtW5sNhuHDx/G7XarVgUPP/wwmUxGzUMt7Ra2f79YD9ZLD77UPUqB6jXXXMNnP/tZHA4HP/3pTxkeHmZiYuKqc0/berhI3F2q2IXNUA8L4Goh+k9ay0jqdHQ6HT6fD7fbrWp84GIvzu12K2tUJol0npuZmVFJb+13SpjNbDar/2+W6unVQOR+XC4X4+PjJBIJDhw4QDweJxqN1pQAlzBYc3MzXV1dahNtb29XhAhR/i2VSszOznL+/HlmZmYqmmrtFEgOREKc2jbYsk5ERTkajRIOhyuMC5EdcjqdF9X+7ERovZZq70UrztrX14fL5VIGanVX1npbC9sBmRuDg4MMDAyokGo+n1cGi/x/x1GRhYoq1dMAIyMjJJPJKw5d1MukmZqa4rd+67c4dOgQd9xxh0q6i86XwWDgDW94AwMDA9x9990quSYtfl0uF83NzVgsFp566inS6XRF4i0ajfKtb32L3t5e3vOe9+D3+7FareRyOR577DFKpRIvetGLSKfTiiW0nXHkWs/FZDLR2dmJ2Wzm6NGj5HI5VWFcqwZHr9fjdrtpamriLW95C21tbSQSCfL5PDfddJMqSJUwmDCfQqHQJbv07QRIQlt7OGotWKl7GhwcxG63V4TFtMyxnWq0yaGilTCR+9CG/eBCN873v//9WK1W7r33XqLRqCKOVGuH7cSxuBy0ChVQ+x61Tdc+85nP8P/YO/Poxs7y/n+kq6vVkizb8jqezZPxbCEJe1gD5CRsYSdpoYQABUpZSkuhcPiFJk2BtpScshVI2AqEJg2ENU2ZEiAkEBICJLNkdntsj/dFm7XrSr8/3OfVlcaesT22JU/u95w5Gmu5y3vf93227/M87e3tKvE0GAzS0tLCrl27OHXqlKIwLwc1Ey66rrNt2zaam5vZtm0b2WyWxx57rK56ey8X+Xyevr4+PB4PyWQSn89XoT1pmsaWLVsoFovs2bOHgYEBJiYmcLvd7N69m0AgoGiR0t9akuGE9RWJRAiHwxW5G8Vikf7+fjKZDJs3b1Zl6utxYxWOvrlzorSorfb5lkol1Uxs06ZNStMSIoDZHSjWobRCliZR631Onc0dInW1zBRc+Z2MzXobA7OVYhYiMt/NVHMpebR582ZaWlpUNrq5MOgTATJWsg4kKbejo0M1V9M0jUAgoHrjSGyqWCwyMzNDJBJZkSKeNRMu3d3dfO1rX1Ml1icmJnjooYfm7dGx3iAMlKGhIb761a+yc+dOXvOa1+B0OlWewpOf/GSe9KQncemllzIyMsJNN92E2+3mxhtvpKWlRblC7rjjDoaGhti2bRuTk5MMDQ2pQpTZbJa9e/eqDO58Ps8DDzzA448/zte+9rWKjnX1BLvdzuTkJDfeeCPbt2/ngx/8IIZhcP3119Pf368yhc3l07dv386tt95KR0eHKm8ii0gWQzAYVFTtyclJHn/8cQ4fPrxsKvt6QbFYZHx8nPb29oqNVOahUJXh9NpZ9RB3MF+vXJO4iEVRgDmlTNd1mpublds4l8tx7NgxAoEA73rXu9i5cyehUAjDMAgGg/h8PhXrlD4v59NcMLv7RPDKeOq6zubNm9m1axc33ngjTU1Nah643W4VkjC3MLj99tt57LHHFKHkXEpE1Uy4uFwulZkt7A0prheLxepS214KSqUS6XSagYEB1SVOFo24BA3DoK2tDV3X2b17t6pSIBV9HQ6H4p2LBnLBBReosiDSj0V6VKRSKSYnJxkbG6ub4G01RJuy2WyMjIzgdDpJpVKUSnMVf6Vqg/T7drlcqr1tV1cXgUBAWYHCIEqn0+RyOdVcLZfLqWOd74IFUK1rJWhtzveIRqNMTEysC4+AOWAvSphQ1nVdV62NdV3H7XbT0tJCJpOhv79fCZ1gMEgymSSbzarX9b6XLAQRwE6nk7a2NiVEYS4dwu12c9FFF9HT00NnZ6cig5jXIFBRry0Wi5FMJlfE41HTkvtut5tCocC3v/1tRkdHeeYzn0lvby933XXXvJ321hvS6TS/+MUvVFlwyb8w+8OlO9xNN90EUFF00Ov18pKXvISxsTHuuOMOHA4HX//61/H5fNx5552USiWe9axn4fP5OHXqFKOjo4rlUa+UW2Go2O12pqenGRgY4MMf/jAwF6sSKm1jYyNvfOMb2bRpE5dddhl+v79CsABKkNx9993KrZhOp3nwwQcZHx9XeRHnO4Q9Fg6HlRIDcxncX/jCFzhy5MiCxTrrQeCIC8flcimXjVTwFe27q6uLt771rdjtdn74wx+SzWbZvHkzyWSSgwcPqpa9g4OD3H///UQiER544AGmp6dVJn49KlvLhQiWDRs2sHPnTq6//npVCT2TyTAwMIDX662oFC3jKb8XZDIZ7rzzTk6ePElnZycOh4Pf/va385JqloI1Ey4yGOLOkJstFov09fUxPj5OS0vLOflGl5qctdooFosqM3x4eFj5O3Vdx+/3qwUk71VDAm9SiFG0DkmakvIf+Xyeqakp+vv7FRW33iFJbYVCgYGBAQA1PsFgkFAopDYcKZ9jFixmbevkyZOqcGMqlVIlXuott2e14HA4aG1tpbOzsyKDP5FIMDg4WJd5TmaYrS3JU5GcJ6fTSUNDAz6fT7nJZI+QsvES6JdS+kNDQ8RiMaanp1UFjPNFqAAq/83j8RAOh2lubqarq4tgMKjqrUmCcigUUn1doFKoSKJ6JBLh0KFDjI6Orr+S+zJhenp6VPG97du3K6rbb37zG44cOUIoFFIlK5YKc8Lh1NRUzTdYEaKZTIbDhw/zhje8AbfbjaZp7Nixg8985jOqXenZjuNwOAgEAhw9elTlvZw8eRKbzcYjjzyCzWZjYGBAucXqeSGZs4PlGcXjcTRNo6GhgcbGRi6//HI0TePuu+9m69atvOxlL6sw42FO2/rud7/LoUOHuOuuuxTDzDAMZdLX8zisJFpbW/nEJz6hqhPkcjn+8Ic/cOjQIR599FFGRkZOs2RlQ661hSsKhChNso4dDgfT09Nq75idneXrX/+6SjBNpVI88MAD5HI5NE2jWCzyrW99C13X2bhxo6onl06nlXA5H+aDzWYjHA7zvve9j4aGBvr6+mhpaVHxJalh2NraqhT6+VAqzZXa+spXvsKxY8f4yU9+Qjweryg/tND55fdnw6oKF7EkxFQXjb2rq4vW1lampqZIJpNMTExUJLgtx9d3pqzdWkEYGJlMhuPHjyutS4L9UqHV3JelmukjvmPp43DkyBHVYdJut3Ps2DFKpRITExM13ygWi+rrrM46lr/NPdOrLZZoNMqhQ4dU2fXlZiqfD5AKDW63W42j0NclXrdt2zYMw2BwcLDmfXzOBNncJKfH5XLR2NiIw+FgYmKCVCpFPB5XddWKxSI+n49Saa7gpKZpqhK4uUfU+SBYzJA8lQ0bNhAOh1VFbHP3UjOqKdiFQoFUKsXx48c5evSosvqqqcznglUTLpIkB3PmaqFQYHZ2lq6uLt7xjndQKpV473vfy9GjR9VGK3GWpQoX4fFLvkitrRY4/R6kdAXAkSNHeM1rXsOmTZu4+uqr6enp4cILL8Tr9eL1eit85g8++CAHDhxgYGCAaDRasQkbhqGsgPUiWBaCWKzZbJbvf//77Nixg3//939XgUhBJpPhP//zPzly5Aj/9V//pRbFExmJRIJ77rlHUbXN9da6u7vp7e3lH//xH8nlclxzzTX09/fXzXwRBcpMoR0eHsbtdhMOh9m8eTOvfvWrsdls3HbbbUxPTys3n7h7pDSSsDGlIoVUvzifBIsI0dtvv51du3Zxww03EA6HK1xf8/1GrDdpE33y5En6+vq49957GRwcVInWZ9s7lzKWaxZzkaJx0mcgl8tx4MCBivpf58pOqAehciaYaxtJUbiLL76YdDqtmomJq0yys/fv369yV+Ybn3rZJM4VYuUJ/VEUklwup5QOWViHDh2iv7+f6enpuqziu5YQa1cYhBKX8/l8NDU1EQgE0HVdjW89wrxhyT4BqLp8UqUA5u5XeriIli7rXu7PLFTOJ8EiKBQKjI2NEQqFiMfjFe0YZO+QGJso3lIEWHLlxGKJRCKrppzZllinZklPqjrALq1bW1paKJVKdVf+vFQqLdq3ttSxmA8SiJXgm8RXoLywJDC9ED37TP3pzwVrPRamY1EqlXA6napJlpldlslkGBkZUfkba7F51GosFgNd19m1axd33XUXzc3NzMzMYLfbCYfDJJNJbrzxRk6ePMmpU6dIJBKMjIyo8jjLwVqMhbnatZRD8vl87N69m3w+z969e1VvGrvdTjAYBFCxWnPpIyGOrAZquUaEbStJo3v27KGhoYG2tjbS6TR79+4lk8mwe/duisUi+/bto1AosHnzZgzD4ODBg6RSqRWLT883FqtquVRftLA7pC/DEx3SkRKo6LB5vmpci4F5DKTv+eTkJIVCgVgspnp1WCjHGaX0jdPpZGhoCKfTSTgcRtM08vk88XicY8eOkU6n6zI2WQ2py2fuIOn1etm4caMSFmaBIVRmsVbMCtf5uI7MQfexsTESiYQi/USjUVKpFI899phSIorFIgcPHqRQKKj9Zj6Sx0pjVS2X9YZaaCL1kCE9H2qtrW/atIkvfvGLALz3ve9laGioZmVcaj0WZ4KwqTZs2ICu6+TzeXp7e7n55psxDIM3vOENHD16dMXKAK3lWJgz98X1B5zWUXW+pmDyO/PrSqPW88Ln8/GCF7wAu93Ob37zG9LpNLquq6TaUqmkWlIIxXi1WIJrbrlYODvqTajUC8yFFkX7ssZqfoj7VFr9SjJtPp8nEonUbX25s8F8zbJhwulrptpDYi4jc75DqmNL0dZqVFOK19LqtywXE2qtidQTaj0WDoeDjo4ObDZbRZ0jy3I5HW1tbbznPe/B6/Vy4MABYrEYhw8fZnZ2VsU1zSzDc0G9j8X/nRdYfcWt1mNhs9mUNVfrthqW5WJh3UA6iZ4pEczCHMz5QRLIHhgYWFYysoX1A2GB1Sssy8WEWmsi9YR6Gotax6XqaSzmg6ZphEIhNE0jk8mQz+cXpK6fK+p9LNYS1liUMd9YWMLFBGuylGGNRRnWWJRhjUUZ1liUsRJusSlgYGUup+6waYnft8aiDGssyrDGogxrLMp4wo3FkiwXCxYsWLBgYTF4YvT+tGDBggULawpLuFiwYMGChRWHJVwsWLBgwcKKwxIuFixYsGBhxWEJFwsWLFiwsOKwhIsFCxYsWFhxWMLFggULFiysOCzhYsGCBQsWVhyWcLFgwYIFCyuOJZV/eSLWx1kI1liUYY1FGdZYlGGNRRmLHQuv1wtQ19WOzdA0TfotWSX31ytWojKwuXx9rXqj1BOkg+FK9BBfLfh8PoAFy+fXumK0hZXF2YSKef3WEuYW2wvBEi7rBNbmYcGChfUEq+S+CbU0+ddKA13seSz3RxlrORbLnQfrufvierW+ljoW0g10MbDb58Lh66U9tdWJsk5gs9lwuVwAZLPZBSec2Y1VKBRUt0GbzYbD4ah4fwlCY6Vuw4IFCxYWRF0Jl2ppLX+vRO/veoLb7WbHjh0AHD58mEwmo4SDoFQqoes6mzdvBmBoaIhsNgvM9Zfv6uoCYGxsjEKhoIRQLperaHsrqB7bpWhRFtYOy30m8rvFarz1ZC3UwzWsBZZyn/VmsSxnvtRMuNjt9opgpd1up7OzE5fLhcvlUr3AYW4zttlsFItFstksp06dqnlAa7moFiLynqZpOBwOgsEgpVKJSCSCpmlqs3A4HJRKJSVI3G43xWIRu92ufg/gcrkolUpKEMnnInwymcwTZjFbsGChdqiZcPH7/bz85S+nVCrx4x//mMbGRj7xiU+wdetWuru7KRaLHDhwAIAdO3ag6zq5XI6RkRGuu+46BgYG6prlsxDsdjv5fJ5Dhw6p99xuN36/n9bWVq655hqKxSLf/va3mZ6e5tSpU9hsNhobG7HZbORyOTRNw+PxVFghLpcLp9NJKBSiUCgwODiIYRgEAgGcTieNjY0UCgVOnjy57phi9aRlLxVrfe1n03jdbjcwZ+HC+hxTCyuHF7zgBQD87ne/A05nq8l8Ws48WVPhIgtNNsPu7m50XeeSSy6hqamJ3t5e2tvbCYfDlEoltm7dCkBbWxuapqn4gt/vR9d1isXiulscYonkcjlsNhtOp1NZF1B2AWqaplxXmqYRDAZxOBxks1k1fvl8HrfbTT6fP+33ArP1UywWsdls69YlZrbSxIpbj/dhwcITAWvGFrPZbMq9BdDT08Ott95Ke3s7brcbXdfx+XxqU4Uyh1o2k3w+z+TkJO985zvZt28fExMTFAqF5V7SaVgOE8Zuty86JmS32wkEAtjt9grhAnMag81mw+/3Y7PZlBAJBAIEg0EuueQSPB4PpVKJXC7H1NQU2WyWdDpNKpWir6+PbDaLYRgUi0UMw8But9PQ0ADMucPks1KpdFarr1ZsMbPbUMZUBKLH48HlctHS0oJhGAwODp6Te3Sx8Yl6Ys4t1RKS5y9uUvndYtdNdS5QPY1FNcwxWvPrfPFGM84hzlV3YyH3FgqFAPjv//5vALq7u4HyWIjiLsQg2Yf6+voACAaDADzzmc8E4KGHHgIWHqu6YosZhkEymSSTyeDz+bDZbMzOzqqN2uFwqI02n89TKBSIRqOMjY2tq7jBQpuBuCdKpRLFYpFCoUCxWFTBfXFzAepzEUj/lxGrYi52u10JFHF3yLFlExFWmgjDeoIID6fTic1mU9eYz+dxOByEw2GVCex0Otm0aRMwF18qFApKIbHb7RQKBWZmZsjlcszOzqpxsnB+Qp67KK8wt7eY5361pV5v8/98xapYLgtpEKIFlUolnE4nXV1dNDc3s2fPHkqlEvv37yebzeL1eunt7eWf//mf8Xq9HDp0iP7+fm699VZOnjzJ6OgouVxuxWMuK6GJmIWJmXIsLC673Y7L5WL79u2USiWOHTtGNput8G2K60c2XU3TcLlc2O12dF3H4XDQ2tpKqVRifHycbDarNtKFMmeXusGuRT6D3J/D4VBuUo/Hg9/vxzAMhoeHaW9v5/3vfz8ej4f77rsPwzDYsWMHTU1NbNmyBY/Hoyxeh8NBIpHgm9/8JgMDA/zqV78iFosRj8fPScCsZj7D2SCapRyvra0NgHg8DqBIL6JpioWyZ88eAC677DIAbr/9dgBOnToFLL8qwUrPCzMZZTleCPEGyJ6haVrFmhClTKx1IQqtxPOpB8vlWc96FgA//elPgXJFh/nW2nx/S4xF9qkTJ04A8JGPfASAu+66a1HXUXPLxbz55fN5RkZGiEQiOBwODMPgwIEDZLNZPB4P2WyWwcFBAoEAhw8fpq+vjwMHDjA5ObkuNA+xKMxxArPFYdauqhe6WDOCfD6v3GSapuF0OnG73djtdtLpNLlcTrm7qt1K9ThW5tibaJ7m+xJBKjG4Cy64AJfLpZSKCy64AL/fT1dXF263W42FuAF37tyJ3W7noYceUuewcP5CnrHX68XtduP1ekmlUmqjlLkRCATI5/NMTEyoNVWP6+N8wZpn6IvmrmkaPp9PWTmGYTA9PY1hGIo629XVha7rZLNZcrkck5OTKxpjqcZKaSJ2u51gMKg0MkGxWCSVSlEsFlUcaakT3Kztw+na3kqZ/6ullZmv3QyXy8XGjRsVlXrTpk184AMfoLOzk66uLmw2m6qvJcLE7A6TY8sYj46Ocs0113DkyJFzdqOu5Fgs5CY1J8tCOVbS1NQEwPXXXw/Mxc4APvzhDwNlS0UKHopbVFyqv/zlLwFobm4G4MlPfjIAMzMzQH1YtEtFtXdA13W8Xi+XXHIJnZ2dXHHFFWSzWb785S8zNTVFS0sLHR0dXHXVVRSLRX7yk59w6tQpNTeWi1qMhcz1QCAAwL59+wAIh8NA2d2+EKpzCAXf+MY3ALjmmmsqPj/b8QQ1t1yqIRp6NptVMQd5P5fL0d/fr5hS8r5ZE61XX7r5GqvNUHGZyKRe6qYn1ogEsqtdTMs55lrBbEFUu44k3qRpmmIMtrW1EQqF0HVdaabViaFm9puZsABzCst6pKtbWBrkmQt70OPxqHlhs9nw+XwEAgE6OzsV29RMLqr3dbNesebCRYSJzWZT/mKY34UjG8rznvc8YE4LSyaT+Hw+SqUSyWSy7gK2suGJL1M2t/k0hpWYzLIwquNc9YDqRTsfJVpQKBQYHR1lw4YNvP3tb2fz5s2EQiEcDociLjgcDnK5HCdOnMBms7Ft27YKjX9qaopEIsHvf/97+vr6FJuwlmOy0MZV/f6ll14KlPMN3v3udwOnWyxPf/rTgXKMRWIvs7OzQNkCEstHfOfCFhJL59WvfjVQ9qnX07w5G6oVKl3XlYs4Eomwb98+8vk8mUwGXddpb2+npaWFQCCAYRgVjFQzEaCeiUKNjY0A9Pf3A/CXf/mXABw9ehSADRs2AKc/R9l3Tp48CaAqe0iMRT6XPdY8FlCeVxdeeGHF+ReDmlguIkgW8yCdTicbN25E0zROnDhBPB7H4XBQKBSUu6watSz6Jlq0uDeqk5CqzdGVOme9F7o7G/1TLA+xyIrFIolEQpEdJIE0lUrR39+PrusV9MpCoUAmk2F2dpYTJ04wODioArkWzk9IDpfT6cTj8bBp0yaCwWAFayyfz5PL5Uin00xNTSmhc6ZE4npfS+sFaypclvPQmpqauPbaawkEAjz96U9nenqa0dFRJicnufPOO5UmBmXmifgjo9Homk+QajdMdfBa1/UK4bNcmIWV3W7H6/VSLBZJJpN1s6HOJ0CAClcWVM6HqakpvvnNb9LY2Ehraysej4dgMEgqleK3v/0tkUiEbDbLxo0bcbvdhMNh/H4/MMecikaj/OxnP+PEiRMkEom6GQtB9fVIPsJvf/tboGyJiNIkG6XMI7H2q92qsrbMwhngr/7qr4Ayq2h8fBwo5z/U2/gsFpqm4ff7cTqdBAIBtm/fzoc//GGamprI5/OMjo5yxx13MD4+zkMPPYTL5WL//v2USiVGRkZIp9MUCgWVUyaw2+0qn6yaVbaS7rOFYqYL4fLLLwfKFustt9yixgFQFT8GBwcBeP7znw+ULeGXvOQlALz4xS8G4Ac/+AFQjum94Q1vAOAzn/kMULZs/vd//xcoW8x1b7ksFkIvlU1ZNJPx8XHFtqpHmONEsVisYvNcKVeY2QKSIHm9jgfMT4mspqrD3GIbGhoiFouRTqdxOp14vV4ymQyPPvoo6XRa+csHBweVxVMqlZiamuLkyZOMjY2dNu4Wzi+IO0sUjHA4TGtrK4FAgOnpaWw2G4ZhUCgUmJ2dJZPJKIGdTqeV5VI9By1m4cphzanIi4WmaTQ3N+NwOPjBD37A5s2bueKKKwiFQtx4440cOXKEeDx+GjvKMAyi0eiSz7eSaGxs5Nprr6VUKvEf//EfRCIRNWmrM6UXC/Okt9vtygebSCSUFlcqlUgkEnW7qS7GFSqxk2g0yrFjx1QCqWEYzM7OKrpxNpvll7/8JaFQiB07dpBMJrntttsYGRlhamqq5rEWwXyEDihbGsLOed/73geUWV7yKhviRz/6UaBsuYgvXJ61WMzyu0gkApTzGETD7enpAWB6ehqAX//61ytzo6uI+cgxTqeT9vZ22trauOiii9i8ebOqv7d//34OHTpEPp9H0zQ1BjKWwtCcr3yU2WJZKEdkJbCQxfKqV70KKFsWwvZ7xjOeAZSfW0tLS8XvxAJ56UtfCsD27duBsuUhqM5bmZqaAuCzn/0sAJ/4xCeAcr7MO97xDqC8by1lLOrWcpGqyY2NjRX5D9lslkgkogRINWqd2zGfZm5+Xz5b6jHFjBYaswQlhUm1HuuszQehZ4vGKVUHZDydTiebN2/G7/cTjUZVXlQymaS/v19RbC2cvxC3qsvlwuv10tjYqJQtQO0VskaqBbCslfnWS633j/MJdStcXC4Xl1xyCVu3buW6664jEAhQLBbnDcTVE5XQZrMRj8f55je/qSwJiYsIzhRzqabrCkuqoaEBTdOUBiH5QGKxzMzMUCgU1HHXa1DSbHnKQjfXhGpra+P//b//h6Zp3HTTTQwMDHDvvfeSz+eJx+OnUZzrDea8Lig/H2F5VbtlJMO+uraTudqFGfL8xS17xRVXAGUfvxx/qTG/1RzXxTLq5D3zehIPRzAYJBaL4fF4eM5znsOOHTu48847VYxOWKrznedsWI37lryk6irEEnMTluCWLVuAcgxkobwTOc7AwEDF61Ih5xF87WtfA+bc+wCvfe1rF32suhUuUO6yKJWDJyYmGBkZmZchVk8oFovqYZjLW5wNsnCqYyq6rtPR0YGmaczMzGAYhkoklHiD0+lUhT3Xe26HCM7qhFGpNycJY1I/LBaLqd9YOP8wXz08sXBTqRTxeJx4PK7mvcvlUiWBVjreaWHxqFvhkk6nefDBBxkfH+fKK6/EZrPxr//6rxw7doyRkZGK79bTpCmV5uqH+f1+tSiExXW2nBwJUuq6TmNjI6VSiWg0SigU4g1veAMul4uHH36YQqHAxo0bKZVKDAwMoGkaF154IalUiltvvZXp6el1Z7FUQ9wemqbh9XpxOBxmvvCXAAAgAElEQVSEQiG2b9+u8l0SiYTS2MxtC2otXM+k5cs19vb2AuX8AslTkOcmloVosoJqy6daoMrn3/ve9wDweDxAWZP9wx/+AJQtoTPdA5xeH3A1sNCxZS2Jti6xEpvNRqFQ4Pjx44yMjBCNRunt7eWZz3ymajSYz+eJxWJLYk9WZ/5Xj/VKotpiETzwwAMA/O3f/i1QriVX/ZyFLSisMYnJnasXp9oiftGLXgTAs5/97CUfq26Fi2Shp9NpxQ47evQoIyMjq1oCZqVh1sAF85n/brdbNfvyeDyq5MnExAStra2qvtbAwADZbJZgMEixWMTlcqnYi7msSj25ChcLESibN2/G4/Hg8XjQNA1d13G5XDQ3N9Pe3o7L5cIwDDweD263Wy0IEeaZTKbukmstLB6LzYmSZzw2NkYoFGJychKYIzVMTEycFpg/1+uwsDTUrXBxOBxs2LCB1tZWDh8+TD6fx+l00tDQQD6fr7l2Wg3zRDQMQ/n/xT+s6zq6rs9bkdXtdnPhhRfS3t7OxRdfTHNzM7t370bXdVKpFF6vl4suuohisYjD4WBiYoKxsTHFnkqn09x7773E43HS6bSK0wCL7jpZa2Fks831a9m5cyff/va3VcM4wzBIJBLYbDZVr01iTBdffDHhcFhp9263m2w2y/Hjx0mlUspaXGucaQzlM9HGh4aGADh8+DBQrgEmAeqPf/zjQDlfRfIYqo8nLDFhG4mmKdq35EkMDw+f8z2sNkRZkMB7dU6P+dqKxSKRSITHHnuMD37wg6pwZTKZVDk9i8VCMZ+1iONJnomwsy6++GLg9BiIxCPF7f6d73wHKFtC53qd1QJV9tn7778fYNEufqhD4aJpGo2NjQQCAXbv3q2YIKlUalWy21cL5sz8UqmkNsX54HA46O7uprW1lW3bttHa2kpXV5eqRCBsOcMw8Hq9+Hw+lf/R0dFBNpvF5XLhcDgqOk+uJ2iaRjgcZsuWLbS3txMMBpVwkWC33++nWCwyPj5OLBbD5/MRDofp6upSbQny+Txer5epqSkOHz5c1yU9LJyO+Sz8hZJxzf+Xfk8221yB04Wqd1hYO9SdcGlsbORtb3sbPT09XH755fh8PtxuNxMTE9x5553n3JtjtbDQBlZdYn8+7cjv9/PCF76Q3t5e9uzZg8fjUcmjEkvQdZ1MJkM8HieVSrFlyxYCgQC9vb3ouq6ykq+77jqOHz9eUfrkTJpXvbDKGhsbue6669i9e7dy9QmkGrLP5yOTyfDlL3+Z8fFxWlpa2LJlC1dffTUNDQ2KdhqPxxkeHuYtb3kLJ06cqIucl+qNUiwVycy/9tprAWhvbwfKPnWJmUjeQUdHR8X3hHotls4NN9wAlNln8lz//u//vuJzqTVVLzC7jk1dL8/6Oyml397ezhVXXEGhUOC22247p1wnc3+ZtZo399xzDwBf/epXgYVZYcLWuu+++4CViwdJA76FkkmrY3x1n+dSfaFShn/Lli309vbS0tKienv4/X71//WExQREJXErmUxWdJsEKvJZkskkIyMjzM7OqthMMBhE13WVjRwMBvF4POTz+Yr+OesBxWJR1YESQZzL5RgZGVHjl0gkOHHiBBMTEyoO4/f7VfsG6VZZKBROcylYqE9Ur+mzkSHM0DQNt9tNQ0MDzc3NFAoFWlpayOVyFU34loNaKyTrHTUTLuJjhzktzW63Ew6H2b59Oy984Qtpa2vD6/UqLUI6VwodeT0F9c+EUqnE5OQkn/rUp2hqauJFL3oRuq4zODjI7Ows09PTOBwOtm/fTrFY5Pe//z2hUIj3vOc9qv2vUC51XWf79u3k83n279+/qF4VtbZYBJFIhFtuuYXOzk6mp6cJhULq/u+66y6i0Shut1tVTy4Wizz66KPs2rWL17/+9WqTkextj8ejSu7X0yYhG6mwfD72sY8B5Qxr2UBlbbzwhS8E4De/+Q1QrnYs36vu4ih/V38u9G3J3BcsJcdkNSAkDqnxJUwt8zWIkiTP19x0T9f1ivIvHo+Hq6++mtHRUb75zW8uK6m2FvFciRvO1+sIyh0if//73wMrf40Sw/njH/8IlPv+yDO4+eabgaXNh7pxi8nkCQQC+Hy+igxbmBv0jRs3kkgklMl7vqBQKDAxMUEymeTYsWM4HA4OHTpEKpUiGo3icDiUoJCieyMjI4RCIRX0lI20sbGR9vZ2Dh06dBo91zwxhFlVLxuvYRhMTU0pVmBDQ4MqRNnX16cmvxnSFGx0dBRd12lra6tgjm3atImZmRlVet9C/eJs81DTNEKhEJqmqfbF2WwWh8NBR0cHra2tNDc3q6ZzIrTEmrWw9ljzTpSmY6mNQJICL7nkEvbs2cPHP/5xgsGgijvAHOtpeHiYkydP8pa3vIWBgYEV3xhLNeyyJ6wyceVIzES0NnPfEulRcckll/CFL3wBv9/P+Pg48Xicxx9/nIGBAb70pS8xPT2Nx+NRcQgRMFL5FeY26AXKYNRkLKSqtWwKhULhjKwvn8/H8573PHbu3MnHPvYxfD4f2WyWTCbDgQMHOHToEP/wD//A6Ojosq9pJcdCLBczmw/gW9/6FlDu6yJ5L+bEQCjXFpMM7+q8mGpXoDzb97///QB87nOfq3h/qViNebFQro5Z+QmFQvzpn/4pTqeTP/7xj0xNTdHX10drayuf+tSn2Lx5M11dXdjtdhKJBCMjI7znPe+hv79fdX9daazkWEhFBbEsF8prkdibsAGFxbVUbN68GYCtW7cC5SrZ1TXtZPwlxjefkvd/36u/TpTyKv71TCZDLBZD13VV8gTKxerMwd56L/WxVBSLRdLptNK6AOU3lsllt9vJ5XKMj4/T39/P5OSkShqz2Wy0traSTqdpaGggmUwqC0UCpvO5P+ppDA3DOM11cybkcjmOHDmCz+cjmUwq+nYmk8HhcODxeJQFV0/3aaGM6vIu8lr9vMzuMDMbM5VKkc/nVc5TqVQiEAgo74eF2mBNhMt8/ltx5Qiy2SwHDhxgeHgYv9/Pjh07eMtb3qK6TuZyOX7zm99w8OBBkslkheUj2t96hlgU0g98z549lEol/vjHP1ZYF2LJZLNZhoaG+NznPsfWrVu5+uqrCQaDBINBAoEAu3btQtd1xRwTmrIIK9GA17vLoFAocOrUKRwOB/v27aO5uZnx8XEikQi///3vlbtxOcJlNQTSQpbGddddB8CVV14JlKslCwtM+nJ86EMfAsr9O0QQSxXdT33qUxXnEQ1ULLd6FrDVCcdmyyUWi3HHHXeouS+1wsbHx/nYxz7GU5/6VG655RbFLhVix3rBV77yFWDhxE15nlIVWfoALRcS4xGLWfC2t70NKNcUk1hPdaWIxaBuYi6SLJXJZCommQiWdDrN6Oio6tVgDnw5nU6V0V+PWMwmZa6jpes6TqdTBSydTqcKdkJ50WWzWQYHB3E6ncqyKxaLeL1eWlpaSCaTTE1NkU6nyeVyFcJ8vQsVgSgpUg5GSsXY7XYmJycZHh5WTaEs1DfMMcJqmAuamoWPYRik02nVgkCUTrPLvZ4F6vmMVRUuy1nQbW1tvPe976WjowO3200+n+f48eOMjo6yf/9+xsfHlU9+amoKTdPYuHEjMNclrd4EjJmQUG2tyeciGDRNo6GhAV3XOXXqFDDHIsrlcoyOjp4WlM5ms+zfvx8oWz4wx5G/7LLLmJqaYufOnUxPT7N3715isdhZBd16cx9JnoOUw3G73Vx88cVEo1G+/OUvc/DgQSWkRSgv9v7OpWzI2X4rwl3cnTJPJN/hwQcfBKhotQDwT//0T8DpVXWlVtknP/nJivPI5z//+c8rzrPUe9M0bVVZVNKnx2azLRgjme+9YDDIK1/5SrZv345hGCrIXywWlXt0Pcznq6++GijH1KpjZxIjFdbfcp+jsBKl0kN1hQexWKS/i+RHLWcM10S4LHZBi4+8sbFRBaILhYJiiEWjUSKRCJqm4XQ6Veb6zp07KZVKDA8P151wqaZWVv/fHA+x2+0qw75YLKq/HQ6HYr7I/Zk/gzmzVZIpC4WCyuSXa6gnZthiYdY+F4LT6WTTpk1s27aN5uZmPB4PhUJBWWp2u13lP5wPFaPPd5jn6mLnq67rKtZi/p1ltdQWqypcllJV1OFw0NraSnd3NzbbXIvSZDLJ7OwskUiEqakpHnvsMWZmZgiFQvh8Pvx+P1u3buUjH/kIuVyOffv2qWzueoJYLGYBIht/tWYlZUy6uroolUqq9tSWLVvI5XKcPHkSwzBoaGggEAjwvOc9D7/fz2233UZDQwN79uzB6XQyOTnJxMQEP/7xj5mcnCSVSqlif2e71nqA9OmAOT/zQnOotbWVj370o2zbto1t27aRy+X49re/zcDAAKFQiGc+85ns2rWLTCbDt771rSWRBZaL5YxhdbKt5GcspKFWs3bkbxknYRdKNeSF+r8sFqstlIvFYkV3zcVeZyaT4ZFHHiGdTle4P2XNrRf3r8TYHn30UaDcebIaMi+OHj0KwCte8Qqg3FFSxrAaspZ+9atfAeX5UJ0LJ+P++c9/Hjh7rMXn8ylrqxqrIlzMmjic3vRpPui6Tk9Pj6oTBags28nJSaanp0mn0xiGQXt7Ow0NDYRCIWUmLrZA42rjTOZqqVSqqP0lMRZzZzzJtDdTkUulEl6vVxW/lIoFErx3Op1MTU2peltOp1NZezMzM8sKxq0X6LpOd3c3bW1typUgAjwYDKq5uF42mScyzAywxcJms9HQ0KAqNIg34HzpzLqeseLCxWaz4fV61QYq+RvFYvGM/d3D4TAf+MAH6OnpwefzKVbIzMwMe/fu5cSJEySTSUKhEH/yJ3/CBRdcwJ49e5idneVv/uZvOHDggOrzcqby9qsJs0CtntwSE5HcndnZWRwOh/KBRqNRDMNgZmYGm82miAvm8halUknRs5/xjGcQCAQIhUIYhkEmk2F8fJz777+fdDpNNpsln88TjUbr1iV2JkFsGIZixsynNZsz2Tds2EBzc7MK5L/pTW8iFovxve99j5MnT/Ld736XSCSyIEe/nnG25ybzTXzl8veBAwcAOH78eMX79YzlzNFQKMS1115Lb2+visXm83kymUzduUDPFIu7/fbbgXJtMfledbUCUaD27NkDQF9fX8X377rrLqDc876rqwuA5z//+RXfE7bhy172MqCc5yL5Mwu1ka/GmTxFq+YWE+1RtEZN02hra6NYLKpMbCgLo+bmZrq7u1VJE7OWL1r8tm3b2LBhA729vbS2tioBdOTIkVVJqlwNVDO+qj+TWItALBmz+WrOuk8kEhiGQSwWY2ZmhrGxMTKZTEXhyvnyCNbDWC20OUiyaXd3tyr0ac6HcrlceDwecrkcyWSS6elpYrHYurhnC4uHw+HA6/WyceNGtW+IS92qyFB7rLhwkaQmXdeV1BwbG6OpqYn3v//9FItFPv3pTzMxMQHMaZ7Petaz2LlzJ1u2bKno4Chc7i1btrBx40b+7M/+jObmZpqbm0kmk9x0000cPnyY4eHh06yEWmAhNpj5NZvNVuToCJtHAvVerxe3283GjRtVKZRYLMb+/ftVW2NN03jooYeUcBXLRbj/Z3IJVNeiqiWqtbPFPDcRHr29vXzrW9+is7NTaaylUkkRQIaHh7nrrrs4evQo8Xj8vBUsEruTWlDyfKX3+ic+8Qlgbg2eT3A4HITDYXp6eti6dSvhcBhd11XCsZSIWQ4WQyRZDhYzB+V5PvWpTwXg17/+NVCOoQmq14wopK973evmPZcop4lEAihn6F911VUAXHjhhcDCKQrLUUpXxXIRDVwu1Nw1br4MceGlS6KfHEP85YVCQQXrxLTMZrP09fVx+PDhddW3oZrLL2W9qy0acyay+f4lRjM7O6sEuXlsZdyqGy5BfXXWs9vtik6bTqcrLLb5LC4ox6hCoRAbNmygo6ODxsbG09ygiUSCmZkZxsfHF23eW1hfkFqEUodQ3M2GYTAxMcGpU6fqjjn6RMOqucUKhQLDw8Nqo4vFYtx8882USiXlS4c5TX7fvn2qTDqUhVM6nWZqaopf/epXDA8Pc/ToUTo6OnjFK15BMpnk8OHDFS62WuJsfVHENSibYTKZVAlg5t8lEglmZ2dVfMDs3gKUkDEvnGpfrqZptLS0ACj6bb0llfl8Pq688kpKpRJ79+4lmUxW1DVaSLA0NTXxmte8ht7eXuV/NluHhUKB+++/n4MHDyot7XyGWPciqAXSD+RHP/rRml/TWkDYhJK2IEpXIpHghhtu4A9/+ANjY2PLmuu12E/kOQqb8ZFHHgHKMRZZGz/72c8AeM5znnPG40kspL+/H4CnPOUpwOnVsxfbO6fu8lzM1oZoFPPFGQzDIJVKEYvFVImSfD7P5OQkg4ODTE1NEYlEOHToEJFIhJ6eHvL5vKopVMvNcqFA6WKsBDODrlpbP1NzHrMlMh95oTrGstR8o7WE2XKVunG6ruNwOGhsbMThcFQ0TgsGg7jd7oqipmZlJBKJ0NfXx8jIiKW5rhMsp2Gdw+Ggs7OT5uZm9ftCoUA6nebo0aMMDQ3Vhev3iYxVL/9SKpXLssy3sUmFX03T+NrXvkaxWOSXv/wlkUiEfD5PNptlZGQEwzAYHh5mfHycEydOUCqVFMOqVjC7dsQ9JRteNWtMrAtx01QvpIU2/YXeP9NClPL1ou0Lg6aeBEsqleLee+9VbMKmpibC4bAid7S3t/PmN7+ZpqYmAoEAMJfLMT09zVe/+lVmZ2dV33GJs3z+85/nxIkT/OIXvyAajdZVzpNYEovpsXMmSNKsaLJvfetbgfI8EZaQfG/btm1AOQ9C+srXCzVb1pA5M/9M81SUjObmZq666ipVQ88wDKWMzs7O1oU3YylYKP9KYjAvfvGLAbj88suB8vN+5StfCZRrgkl+TGtrK7BwbHUtBO+a1BY702SRFsCzs7P09fWpZEhzMpD8XmIP57pAVwPzMbLm+85aLWqxGs15NfUGEX5SbaG9vZ1AIMCGDRtob2+nvb2dYDCI3+9X+QziPo3FYoyNjalupZlMhrGxMYaGhpienlZECQvrA4slc0ipJK/Xy4YNGwgGgypmd+rUKU6cOFFRh89C7VDzwpX5fJ6jR4+iaRr79u1TQer1MDmkfhFUsjbEDWh+v1b3U68anNfr5corr8Tj8TA7O4uu61xwwQV0dnZyxRVX4Ha7icfjJJNJFbD1+/2q2u3Ro0d53etex9atW7nhhhuUa01ypGoFEZjVLrmlkk6q72HHjh1AmfUlFvMVV1wBwOOPPw6UWT/CEpP3q+dh9fFrNT+r19DZrBap5LF161Z6enpwuVx8/etfZ3BwkJ///OdMT08rJur5ALG+JX+lutLCd7/73YrXekLNhYvQR8Vfut4wnyVSK4FSncRZi2tYCsSSEysrk8mQTCZVApwEZDOZDE6nE7/fTyQSIZlMkkgkiEQiOJ1O+vr6cLvdKgYn41Avrh8LZ8ZinpNY4V6vl+3bt9PV1aXKQx0/fpz+/n76+vrWjWL6REDNOlGajlkRt6jlxCjVsBPlEs4LoBJNzZaJmSU2PT2t/KrLqViw2mMhvnZN05RwEQslEAioHB5AFe202WyqUVpDQwOvf/3r0TSN++67j3g8ro41OTmp4nUrMZ9qMS+q2TzmihdQ9plv2rRJzgugqmlXW6wL1flbak5HrdaIMC17e3u5+eab0XWdL37xiwwMDPDYY4+RTCaVe2ytcC5jsRwSQz1jvrGoueViYemYr7JyNarZZfWU4wKVhQoF8rcEnQXzMeYkYa5YLDIyMkI8HldC6XxZsBbmhyQTDw0NcfToUUX+sVBfqLnl8n/HBWrvwlkPlotgIY1zpbKL630spI+LxFrMpXBW2iW4nLGontNrNcer+7ysNGo5L4SK/tKXvhSbzcb9999PPB4nFovVRKFYzlicbxaLoG4tl1oLlfMJ9RrAX2kUi0Wi0Sh2u121Ai4UCufdorVQhpR2OX78uEpEXk/VOZ5oqAvLpV5Qa63M7NJZ7HOpzqtZKeFS75aLGautDa7lWNS7ZlvreWGz2VRXxrPV0VttrMRY1PvzXizq1nI5n7FYd8i5xkTqLaaylljvC9PC4iHsQbCee71jqcJlChhYjQupA2xa4vcXNRaL1aqWa3GYE0xXEKsyFusUazoWdb5h1sW8qJMxWpGxqJN7OVfMOxZLcotZsGDBggULi0F91gWxYMGCBQvrGpZwsWDBggULKw5LuFiwYMGChRWHJVwsWLBgwcKKwxIuFixYsGBhxWEJFwsWLFiwsOKwhIsFCxYsWFhxWMLFggULFiysOCzhYsGCBQsWVhxLKv9S6wKFq41aF+WrJ1hjUcZyxmKlWh/UG2o5LxyOue3KMIy6qKRurZEy5huLZVku0qTKwulY72Oznq+9ntDQ0EBDQ8Oqn0f62SwVDodDbdbzod7msaZpBAIBgsEgDodDVRNeDFbyXmw2G263u67Gpl6xLOFSKpXqQnOoR6znsZGW00tZuBbmRywWIxaLnfb+Sm10chzDMJZkHYkwKhQKFcVO7XZ7xXOvnsdbtmzB6XSe83UvF4ZhEIvFiEajS+7bs5Jr0uVysXv3bnw+34oc72yoNyG/FFgl9y1YsLAusNIdRs/lOmp9DesBVrMwE56IPtT52vH+n6b3hBuLhXAuY7HUfj7V3xNrIhwOA+XWCqdOnQLgsssuA2BgYK6auzTSkr+XWtJd13WgHCuS6zG9rti8WO3Wz+d6/GqroVgsYrPZcLlc0qhs3YzFamPFYi4Wzl+s18lt4fzBcl1Bq+VCMh9TmpVZ6+TssNxiT3BYi2TlIdYfLH585Xutra0ATExMAKg4x5VXXgnA9PQ0AJOTkwB88pOfBODOO+8E4Bvf+Mair3G+68vn84v6/UpgobFZbMtuuYfqe7Hb7SsSZ5FjrEXMY6nXOp/HYTnHWS7MY7+QdVxz4WKz2VT/eKfTic1mw+FwqFeYG7BisUgikagbGuJKo3oCL3aBWbDwRIWmaei6TmNjIwCzs7MYhkEul1vR+Mz5uN+sBWoSc5GN1G63K4qh1+tl8+bN+Hw+QqEQLpeL1tZWHA4H2WyWaDTKj3/8Y2KxGIVCQQkcWPjhi5ksG/XZWgHXKs5gvsZSqaTGpFQqEYvFaiJgrJhLGbWMuQhkfvzwhz8E4EUvehEAyWQSgL179wLwpje9CQC32w1AKpWqOM5LXvISAB544AGgbKkI+0kso2p4PB4ymUzdxBk0TSMcDtPe3s7VV1+N3W7ngQceYHR0lOPHj5NOp8nn86sqGNZijYiCvcJtzE+z+qqtj+bmZgCi0ShwdiV3vrFYE8vFPIlsNhuapinXgcPhoKmpiVAoxAUXXEBDQwOhUAiPx0N7ezvFYpH+/n6y2Sytra04nU4ikQiFQkFNnmrqrPxd76yO6rEwv6fr+pqZ5PUC83OzcP5juWvTbrej6zput5tAIKD2hc7OThwOBydPnlTekHpe/+c71lS4yMYZCoWw2WwkEgkCgQAvetGL2LRpE694xSvw+/00NDQo3n0kEuH6668nEonw2te+lnw+z49+9COmpqaIRqOUSiWV1FQsFtXxS6USIyMj5HK5unGlVQccNU2r0BCKxSIejweHw4HL5aJYLKpxWM/5M4uBpmn4/X4A4vH4eSNglhpzEcg88Xq9ALzmNa8B4BnPeAYwl28BcOzYMQA++MEPAmUBXW2xyPHuueceoKwRt7S0AOUYz0LWRDqdXtR9rCbEwm9oaMDtdtPV1UVnZycNDQ00NTVx1VVXcerUKR599FGSyeSKr5eVjGssVpE6V6+FsP+q55OwD7/zne9UfL5//34AHnzwQQC++tWvLvvcaxZzsdls6LqOrus0NTXhcDgIBoM0NTXR3d1Nd3c3Xq8Xt9uNy+XCZrOpZKlcLkcul1OxGL/fTyqVIp/Pnzb4IsDM79fDplxthsLchlptdUmSW7FYPE0oVls4sPCmVA/3vNowuxLNrxbObwgdeNOmTbS1tdHU1KQqImSz2WVVLDBDFDqXy0WpVCKdTltzaxlYdeFidn+1trbS1NTEpZdeSlNTE7t27SIUCrFlyxYA+vr6sNlsdHd3UywWOXjwIKOjoyrY/4c//AGbzUZHRwetra1ks1lmZ2fZt2+f0tTEgimVSuTz+brTgMXV5XK50DSNbDar4kdi7sOcJSPZ19XuMTPZQeJP5uOa369316DAMAzi8TiwsDYnLkQZK7F0stlshZv0fIDcx7/9278B8La3vQ0oKw/Dw8MAXHXVVQCMjIws6ngSi2lqagLgaU97GgAvfOELAXj5y18OwJ49e4D6sFgEMs8LhQKhUIi3v/3tdHR0qIB+PB4nk8ko5Ww5sNvt+Hw+fD4fF110Efl8nt/+9rdqHFZqfi32+qrPZyY5wdktG3mO2WwWgHe9610AvPKVrwSgra0NKLMSn/KUpwBw2223VRxHLJ6ljMOqChfRLMVqaW1tpa2tDb/fj9/vJxAIqH+lUomJiQkMwyCfz5PL5RgaGmJiYoJsNqsEiaZpNDY2qglk1lIkySmXy1UE/NcSS7EczJu/bJzyTxaSjKHT6UTXdVpaWnC5XOphZzIZcrkc0WgUm81GMBjEMAzGxsbUhFovvmfz86p2IYow0XUdn8+HrusEAgFgjiWUTqcZGRlZUyqthbWHuMWam5tpaWlR5B/z/NY0DYfDoYLgi5374nar9ig8kbwBK4lVEy5marEwO17+8pcTCARIJBKkUikeeeQR2trauPDCC3G5XLjdbnK5HMlkkng8zuHDhxkZGeGxxx7DMAxaW1vxeDykUikMw2BwcJB0Oo2mabjdbtLpNMVikUwmA9TXZBDLQv5ls1k1aTVNU7EWXdex2Wz4fD6lpem6Tnt7O9u2beOv//qvCYfDNDQ0YLPZyGQyxGIxfvjDH1IoFLj00kuJRqNcf/31jIxfm4kAACAASURBVIyMKCFVT2NxJn+zzBe73Y7H41H1s3w+H89+9rNpb2/nla98JQ0NDeTzeQqFApOTk5w6dYqbb76Z8fHxmtO3V0KYi1X23Oc+Vx3TjB/84AcADA4OLum4sjY+97nPAfC+970PgFtvvRWAv//7vwfKmq6gHhQUYVG+4AUvoKenh6amJlWRIJ/Pq31DlNdisUihUDhrzFXWpLjjDcMgkUjw0EMPYRiGGrN6wEKsMVlTd911FwAve9nLABYsTlo9HrIWZe38z//8DwCjo6NAmZW4Y8eORV/rmrjFRJOQqqapVKri5szau6ZpZDIZNVGi0aiUWlAPWdd1CoUC6XRauZXkXGdK6qkXiMVivlbzJDALIqFpb9++nY0bN9Ld3a20NZvNRj6fx+fz0d3dTSaTUZaLWHT1sCmYYXZnJRKJ056VWLlut5utW7ficDiIxWK43W68Xi8ej0cpIg6Ho8JSkffFHbrS9E0LtYHsIX6/n2AwiNfrxev14nA4lAfDbrfjdDrx+/10d3ereGw2m1VKp8y1s1kish9JJn697yf1ilUVLrlcTgXHzL5/l8uFz+djx44d+Hw+RkZG0HVdCZaHHnqI4eFh+vv7mZ6exuVyKVcPoNwhyWSSfD6vJo/D4aBUKlUIHDhd61tl7vsZP6smGqTTaUWbNNOSc7kcXq+XSy+9lM7OTl796lcTDodV7o+maWriezweLrzwQmKxGI899hijo6NkMpnTCADVrgNYe7q2x+Phsssuo1Qqce+99yqNSK5R0zRaWlro7e3lhhtuwOfz8cgjjzA1NaUs2dtvvx2Xy0VTUxPJZJKf/vSnxONxgsEgwWCQZDJJNptlZGSk5gJmuS4VEcAXXHBBxe/l9f7771/W+UOhEAD/8i//AsA73/lOADZs2ADAxz72MaDsg6+1F0AsikAgwGWXXaZcooVCAZfLha7rau1v2bKFzs5O/uIv/oL+/n727t3LyMgIR44cIZPJnDEwL2uuep3UG1lELJTPfvazAGzduhUoV3BYqKK5XH+1gM3lckD5eZtjvgB/93d/B8BHP/pRADo7O4Gzx/hgjdhiYpHk83kymQyJRAJN04hGo0rL1HWdUChENptlZmaGSCRCPB5XE0JiMVAOKgmbrHqDrBYm9ewzFSvF6/VWCJdCoaD+FoZdMBg8zR8s9+/z+chkMkxMTDA2NnbG4PZa5s7YbDblukin0yr2ZrPZaGxsVK5AmHNtuFwuLrjgArZs2UI4HMbj8bB9+3aCwSAjIyPMzs6SyWSUuzCVSinyQ09PDw6Hg5GREaLRqFJGLKxfmPO+gsEgfr9fpR8IZJ7b7XYcDgehUIh0Ok0gECCZTCqWqVlwzLc26s19vN6x6gF9p9NJU1MTjY2NTE1NMTk5yX333Uc8HsflcuH3+9m1axdtbW289KUvxTAMhoeHGR4eZnR0lGQyqbR92YQki7i6v4RI4erSKdVB8nqaQLL5XnrppbhcLkZHR8lms8TjcUqlEgcPHlQalSRWikAyDIPJyUnlGkun09x///0cOXKEmZkZDMOYV/CebZGtJNxuN09/+tMpFov87ne/w+fz8ZznPAefz0c+nyebzdLd3U2pVGJgYICGhgbe/OY309jYSKFQIJvNctFFF5HL5dB1nampKWZnZ8nlcsTjcQzD4FnPehbBYJA3v/nN2O12fvzjHzM4OMjQ0FCF22ytnvt8mu9SccMNN8x7zKNHjwJwxx13LOl4siZmZ2cB+NGPfgSczhZ79atfPe/5awmn04nP56Orq4vm5mZcLhfhcFiVehEFVRRQiVmGw2FluZrjLtXPxPx+dbUMwWq5l8+WKV8NqSkXDAYBKghN80H2wJmZGQBe9apXAWXLtaenB4Dbb78dgGuvvRaAG2+8EUA1vHv3u98NLM5iEay65WK323G73coXXigUmJ6eVrRTt9tNOBzGbrczPT1NsVgkEokwMzNDNputoBNXt46VySDximq2lfhM6zHLXa7T6XTi9Xppbm7G6XQqxpz8k9I3sVgMj8eD0+nE4XDgdDorXF6yIGZmZlTJGHPsZiU2vOXCvGCEIq5pGlu3bsUwDNra2iiVSspn3tnZqYgbklDqcDhobGxUgtcwDGXdbdiwgZaWFpUQKO6yTZs2EYlESCaTFAqFeWM8Fuobkm/i9/sJhUI0NTXhdrtpaGggnU5jGIaK4VZTkCU/7kyCpRpm5c3CuWFZwmUxbiZz0Lq9vZ3m5mbl15R/drudfD6vaLN79+4lm81y3333EYvFSKVSFZNFtBK5hnw+ryipbrebzs5OnE6ncr8NDAwoX+tqbSrLdbmJL7m7u5sNGzYoTv3PfvYzJiYmlBUmCaG33347XV1dPPe5zyUUCrFx40bFwjMMg+npaVKpFKlUqoKCLIF/sWCq41GrjUwmwyOPPKL+H4/Hueeee+jp6eE973kPPp+PyclJSqUS4XBYtd8VWrVYbICinD766KOkUilCoRDt7e288Y1vpKGhQc2v3t5euru7ef7zn8/s7CwPP/ww4+Pj3H333SQSiTW79+VA5pNY5dU+dGF5LRbyrMUiecUrXgHAww8/DMDx48cBeMc73gHAhRdeWPG7c8W5avxOp5OtW7fS09PD0572NBobG1Wqwh//+EdmZ2cZGhrC7XbzvOc9T7nLMpkMDz/8MCdPnlTrf7HXsdjA/3JQzZRcrNCTPD65v4WEn+wbP/nJTwB4/etfX3E+ieWJRSIVQt761rcCcPPNNwNwyy23AOX8FmER/vKXvzzbLSqsKhVZWGJOpxO73U4mk1HML7MJKsHpSCSiqMgL9Uwwa+MSCPf7/Xi9XqXhSqDP6XRiGEZdJYJBeWzcbjc9PT20t7cTDAaZnZ1VriJzoC2VSqlk0mw2qzSxYrGoFloikSCRSKhYS7X2VSvWWKlUIpVKYbfb8Xq9NDQ0oGkaTqeTYDCI2+1WwlAm/MzMDMViUZUByuVyZLNZJiYmGB0dZWJigkQioaziRCKBw+FQDDJxw0rG9vT0tJoXs7OzdeUWtbAwJKk4HA7T2NiI0+lUicf5fJ6TJ08SjUYZHR3F4/EwMzODx+NhdnaWiYkJlVQp600SixeLeo7VrgcsS7gsZrDtdjvBYLAiH+P48eNKcABqs9m8eTO6rjM9Pc3s7CxOpxOPx6MC9mbXjrjBRLg0NDTw9Kc/Hbvdzv79+zEMgy1btqDrujq3bChSo2sl8yCWM/HsdjuBQICenh7e+9730tTURDQalaqzFWa88Pcffvhhdu/eTSAQULXZxNobHx8nmUwyOTmpairJP8mnqU5QXO61LwdCWHjOc55DV1cXL3/5y+nq6lJKgGhPdrudaDTK+9//fhKJBO9617tobm6mUCgwPj7Opz/9aYaGhojFYhSLRfbt24eu69x999086UlP4jOf+QzNzc2qbpK4SVpbWxkaGuKee+45o+JSD5CxuOaaa+b9/Etf+tKSjtfd3Q2UferiS3/+858PwIc+9CGg3AdGNGNRbs51nJb7e7G6Gxsbufjii+nq6iKVSpFOpzl16hRjY2N8//vfZ2JignQ6jc/nU3HciYkJ5QoNBAJkMhmlhC3l/GYiykrFapfqQam2ZM2ucDNEQZPYypvf/GbgdEtJYm7C+pKQwqWXXgrAkSNHAHjLW94CwFe+8hUAPvzhDy/pumENAvoiDIQVJAwoSRxsbGwkFApht9uVO8jpdFIsFhVNVYSJQI4hPnqh505NTZFOp8nlchQKBSWc6gnmagXd3d10dHTg8/mYmJhQyaHmiSzMsWQyyezsrKIhw9yEiUajzMzMMD09TSQSOS1D/UwkhrW0ZlwuF9u2baO7u1sFZs3zQ+4znU4rQsKhQ4dobm4ml8sxMTHBwMAAk5OTpykHkUgEt9uNYRhqfGWOFYtFAoGA0nwtX/r6guRFCUtMSCzj4+OMjo4SiUSU0ikbrOwt7e3tpNNpJiYmTovHWlh9rJpwKZVKJJNJNE2joaEBv9/P1q1bSaVSDA8Pk8/n2bNnD+FwmEsvvZRsNkt/fz+GYdDS0kI2myWTyWAYBoFAQAkY2SgdDgcdHR10d3dz5ZVXqszdU6dO8dnPfpaRkRG1WVdnn9YagUCA1772tWzbto3W1lYKhQIHDx5Ulp35OuXak8mkcgVJHKZYLBIOh4nFYtx9992Mjo5WuADnEypmlyJUsmhWCxJPed/73kdbW5vKfTIzXQzDYGpqiuHhYeXKuvnmm5W1WSgUVOXo+ZDL5YjFYvj9fuWOlfuVALAcZ7VhLj0iwuxszd9EM5X4lMSZBPJcz6YsyXmkdtjjjz9e8f5NN90EwMDAAFDOlxCIV0EysYWdtphzn+2alvJ7iZ/J+g+FQsoF+rvf/U5ZsIZh0NjYSEdHB7t27aKjo0Nd++OPP874+Dj33nsvw8PDzMzMLPr5iztX/g+1cZP9+Z//OVCZl2aGKJNS7VoqN8i1y/erY3eSxyJWvlRD/vSnPw3ARz7yEaB8z8sJLayq5eJwOFSsRdd1PB6PMk1lkwMUi0xedV3HMAz1e6mU7PV6lQ/e5XKxZcsW2traaGxsVM2O4vG4ysqVGmP14js1kxHE+kqlUuRyOSYnJ4lGo6f5hc3uv0AgUEGTFIaV5HuIW636nLW+b0DFvoQ2be63IXGm4eFh+vr6lOU5MTGx6Gs3DINIJEIgEFBBS6isFyVWn1V/bH1Anr3L5aooyJpMJonFYsCcUG5tbaWjo4OOjg42bNigWkVns1nV8yUSiSzZaq2HdbOesWrCxeFw0NnZiaZpqkaPBO6lLPaJEyeUf7RYLDIwMEA2m6WpqUmZwzabTQX0du/e/f/ZO/Mgyc+6/r/6vq/pnmtnZmcye2Q3m9sYEBNIRMTEigJiQEApLrU0FhYoqGBJ/SjwoERF8QQriyIqFCqHGo4cEiEhZHOQZM/Z2WPumZ7u6en7/P2xfJ5++jvdO8d2z/Ruvu+qrdnp+fb3eJ7n+3zu9weHw0E6ncbn83HXXXepzaRSqZBOpy+qnXSDkBFh+/jjj3Pq1CkmJiYoFos8+uijiqTTeHwwGOTVr361iiVJmrJU5wuRXzqdJpFINGS7GNmToc4UsJ3jcPbsWd7whjdw880388lPflKxLJRKJVWT8pGPfITTp0+TSCQ2LQRWVlb44he/yIEDB3jb296msuQkRVXqrYLBoNKIO4Vm517verJ5Go+TuUwkEkC93uDw4cMNx4ul8uY3vxmoZ/dIDEWUElHujD53XekDOHbs2EXvdzPYisUja9ftdtPX10cgECCXyymS0lqtphI3fuInfoLdu3erCn5R3GKxGOPj4zz++OPk83mOHj16ScrWdr4vYskKY4KMoXDJSTxI6k6k30+rmIxxDqSi/xvf+AZQr/T/whe+0HB9WTc6k8ZGsS0V+pLdIZu+mHgSfJaCv1wup/zu4g7RA/k6p1QwGCQajSqCx2KxyPT0NPPz82ssFp0qpRu0ESFbFAqLUqnE0tKSqsMwWjgej4fR0VGuuuoqlTEj2rjP5yMUCjWYvRvJ09/ucZC6I30Tk2y38+fP8/zzz3P8+HHm5+fXuITWgzBAGC0++albyd0w/yY2D8kc8/l89PX1YbPZyGaz9PT0MDQ0xO7duwkEAqr+SxdOQgvUisTRRGfQkdEWwbC4uIjD4SAcDlOpVJifn1e1KuVyWaUKr66uUi6XSaVSqvJaNkur1aoKpQYHBwmFQoyPj9Pf308kEsHpdJJMJllaWuLv//7vOX36tCrGlBRo6eq4urq6o5uLuLJKpRLT09OKsaBUKimhKM8sL4fP52N8fJw3velNDAwM4Ha71fg6HA4OHDhAJBJRRYK6QIZGFtWdDGYPDAzw0Y9+lKuvvhqPx6P6t8zOzvKRj3yEo0ePqmC9sQbgYpAWDOPj49xzzz0q81CHjOfi4iLxeHxbY28b9deLK0c4voyV29J3Q3zhYsFIMfL58+eBej2LKHDG68nvevC72d+7oeW0vPvxeJxgMMjY2BgjIyOEw2FWV1eZnJzE6XRyyy23KEJLSQ6RJoOVSoWbbroJj8fDN7/5TTVe3Q6JiUmvmltvvRWox0ZkDa83z60g60UsWJlnsYCef/55gE1l2BnRduGi15+IK0aK3/RNQyqsdeJJocfWaVtsNhvFYpFsNquypYRWRv6fy+VIJpPMz88rnqluhwSXJSgpTASS0STPKEkL4XAYj8fTsBmIsHK5XMRiMYLBoKpe14/phgwp4UCTYL3NZiOdThOPx5mammrY9HXNE5onHQjBpWwmwvRgtOJ0yPoycflAssOEcFN6Gkldi17XJvuOLsjFOt5pAtMXIzoiXFwul6q+F8Gg15fIAhDhMz8/36AxiVtLalPsdjuVSoWnn36agYEBxsfHKZVKygR+5plnmJiYwO/3Kw4z4R2SSn7Y2awP/bo2m02l4gqXmAiW4eFh1aWyt7eXu+++m71796riQ3mBAFUTY7PZeNWrXsVVV13F4cOHWV5ebpolJt/ZCczPz/P+979fCcJoNMrLXvYyKpUKgUCA/v5+lpaWVBGoPD+gKHGgPme9vb28973vxWq18ulPfxq73c7IyAg9PT0X5VvajgSHVsW/xvuAesWzxDgky0c0VuOalfkTtmTpt3HzzTcDazVZ4/fEqvvVX/1VoLVmutPvimB1dZWvfe1rnDx5kqGhIXp6elQfF0kQyWazKhapZ0LChey3hx56iImJCdWlcrvjjRvBj/7ojwL1ef35n/95oF6vMjk5CaydL31PhY3Pk3CTSaxPvn/fffcB8MEPfhBozbK8EXQ0FVl+tqoal/iCMYgt39M1D4vlQodJ6fWSTCZJp9NUq1VWVlZIp9NNB7pbFpGxWl4y31qlC0uGjNQIGWtf5HxiATbLEmt1HzsxJuVymbm5OaxWK8vLy/T09HDttdfidru5+uqrGRwc5OjRo4p3TJigrVYrfX19a55vcHCQsbExLBYL1113naIAauZXF6vYrHO4vCDzNjs7i8/nI5FIYLVaVTaoJO8sLi5SrVaV69D4/ZWVFZaWlhrimSY6j7YLl2q1qgLyHo9HWSLGzUFecImnwNoeI/JPKqplQz569Cjnz58nmUxit9tZXl4ml8sRj8dViqJOYqdvyrAz2ph+LavVSiwWw2azKc1T0m+XlpYU6+vS0hLf+MY3OHHiBNddd50K4ssmKYJ1dnaWr3zlKxw/fpxMJnPRALb+XR2d9rFL1psQdfb19dHX18fY2Bjvete7qFarfP3rXyeXy3HVVVcBFzJjfD4fd9xxh+I4EkjVPlzoM+92u1U6ug5JFJF41KX0V28HRPiJZTI9PQ3UqW+kUl40SIGwF3/7298G6n02JDtsaGio4Xjj/MtPyS564IEHANi/fz8AR48ebfi+8V2RnkybxaUIc9lLJicnSSaT/Od//icOh4Pvf//7ZLNZtb9861vfYmxsjA9/+MMNlez5fJ50Os3KygrJZFIpZaLUbXUddEJB+b//+z8AfuM3fgOAl73sZUB9XmWepAJfntPolVkPMp/PPvtsw+8Cec/a4UbsiOUiQkFMcKPQMC58+VxvKqYPlvjrRUMXbb5QKFAulxVnmR6v2cg97hRk4UvAUn9eMXulmC2RSNDT06MsFEnDFHff0tISs7OzzMzMkEgkWi6KVimK2w3dGl1ZWVGsAiJwK5UKY2NjiotMUlElbif3XywWlasgGAwqV6zxWpVKhYWFBc6fP9/UQjbRvZC1UigUSKfTShGTNhS9vb3KzeXz+dYIC0kOCQQCRKNR1UdK5+czrdjOoWPZYkJMKNXYxmwmWNunW4+1QONGKBlVNpuN22+/Xflf0+k0//7v/660WBE6+nWasZzu1KKq1S50n3ziiSewWCyq8FF3A4rrz+fz0d/fr9oVJJNJvvrVrxKPx1ldXaVYLCo6/rNnzyo3oVyn1fWbodPafLVaVZk6QvUzPT2Nw+HgT//0TxkcHOT1r389e/fuZffu3bhcLsbHxxvoXPT1kM/nFXPrxz72MZXcoaNWq5FKpXjve9/LU089xczMzLYVULayHEX4Ly0tAXWf+n/9138BcOeddwLw3ve+F6hnkYnSIdxj9957b8Pv0hhNfOjyuRFiIYmFv15/DqPls1lcyvfkX7lcJp1O8/Wvfx1A7SOJRAK73Y7f72dwcFDFcGUdiNJy5513cs011/DEE0+wsLDAuXPnyOVya5JfOv1MF4Pcs9QvSR3KwMAAUGevlnUlVrrUP20UUpkvdWYCGQfhoGsHOsqKLFxi0orUaE5uZtOv1WqKHVeyyBwOh6p9kRRd/RpiOYnQ0s+1kxBzX/5v/Jv8rNVq7Nq1S/U7yefzTE5OKsZXETjpdLqBbVpHs01up55fFrBkx+nkiMvLy6qdgMyvuADlGLFMs9ksc3NzynKZnp6mVqupzpa6kCmXy5w4cUKldpq4fCAKoggYESp6QL5SqaxxmQqkNkYYPnY6OeHFho4KF6HbsNvtyp2lZ/00C/Ib3TZG6yWVSvHkk08yPT3NjTfeiNPp5MYbb2RwcJAnn3xSsX7abDal9c3NzSmtZqcyxICmgvViiEaj3HffffT19ZHP55mbm+Phhx/mzJkzKnVZnks0cuNYGgVsN71Yuta4vLzM4cOHuf766/nhH/5hent7FU2MbCjpdJr5+Xk++MEP8sILL3D27FksFgvvete7OHToEP/v//0/otGo8sVDveZqu9HqmqJxyvyLBim+damUFreoEYODg0CdI0xiJ+KjlzVvvL7EVH7rt34LqFdcb8ZXv5VxvNSK+PW+K7E8USz0jEzJHDx79ixnzpzhueeeY2VlRVks3ZSWLvMmXG4Sg5HOk0aOsM3W60jMxmixiPdI+hz9+q//+qbvvRU6mi1WLpcVL5akJMPWaadlk0mlUoqnzOl0qg6F4XBYcWw1iy3s9Ma60RdNUpVHR0dV7YZUtycSCUU5r798zXL8LydUq1Wy2awirwRU6rXUQ83OznL27FmOHDnCuXPnqFQqWK1WTp48CVxQIqTuQVLdK5UK+/fvV4SpZr3DlQWbzUYgEFB0KMa/2Ww25WbO5XKKDLfb2NKvRHREuEgQbmFhgVAopPpzP/TQQ6yurqrgtNS/rJfFpW+c1WqV6elpJcE9Hg9erxev18uP/MiPMDMzw8MPP6z6m4iQ28kYy2aD6JFIhHe+850cOHCAnp4elYos42bM05cMGKG7qNVqKv6ip4Eb0cqq2gm4XC4OHTqE1+vl93//9+nt7VUdJl944QUWFxd5+OGHmZubY3p6Wmmdwrxw6tQp/uzP/owDBw7wnve8B6fTyczMDPl8no9+9KPMzMzwjne8Q3EzbReMa1osBklyEc1RigRbbXrCESZcUOJSlErqG264AahrtiJE5brvete7ADhy5EjD5xvFdsdcNgqHw8E111yjMgxF4RDXqtVqJZVKkUgkWF1d7WhX2kuBxMwkrvRXf/VXQJ21WP6+0XsXj4X0aXnkkUeaHidsykL2auSiu5SunB0l25GJFpp1r9dLoVBQxJXrbbq6Ji4uHpfLRTAYVAJF2JKr1Soul0txb+nFk/J92JlNdLMCRhifx8fHVeqqTsppFCzighQfs7HBWjPsdNaYESI8s9msIjR94YUXcLvdTExMEI/HOXr0qOrWqUPcg1L/IJqpuD7k3CZ2Fp1IdxfuPY/H03JNSyq6rpTttDL1YkBHYy4+n4/h4WF+8id/EovFwrPPPsvp06dVxbWecy7f0SHanWyesViMwcFBXvWqVzEyMsKePXsUK3AymSSVSik+M5fLpdJ2jR3ldhIXsxZkPCKRCC9/+csZGBhQbY6/+tWvcuLEiYaMKxEe0pbAZrPh9Xqp1S70otCtFuO1LiZ0dmKM8vm8yqCTep8/+7M/U0W2kobdinw0EAhw5513cvDgQVUwGQwGyeVyvO997+OFF15YNzOqExCN+vTp0w2fi7CT+Xz88ceBtdxeolxIdpj0KpG//9Ef/RFQt4AkW0xchZ/73OcAeOyxxzZ13+1WxiwWi6rNkGzOdpxT+kUFg0EcDofKLBSFrlqtcvr0aU6cONGWuGOrOrFLhex1YrH85V/+JVBnt37HO94BwFe/+tWm35cC0o997GMAvOUtb1H3C/UYnlhA3/nOdwD4zd/8TQCVbdtKCdvKmHWszkU2St3yCIfDDAwM4PV6yWazzMzMUCwWlYmv08PL73Iel8ul2E/37NlDf3+/WlByHfGtyobbDRlSAuPLKotU7l36soTDYfbs2YPf71f0NlIwODU1tWbyhZxTCixFY9/s/ew0JEUbUHGWzTQocjgc7N69W9U+yJja7XYmJiY4d+5c1zyrifZBPBryHggkyUVo+vWUfxPbg44G9KWC/Bvf+AaBQIBDhw5x8803c/DgQVZXVzl8+DDnzp1jfn5ecUxZLBbV30U2zEAgwK5du1QdxEte8hKlqQvbcjweZ2JigrNnz6oOlNCY9qunNm4ndK1NBKmkUgshpTA9//RP/zQjIyMEg0ElMOPxOE888QTHjx9X9CjValXxb0lhmDTYapUJoz+3CPJmbZF3GlvRCv1+PzfffDO9vb0NVp1eYLkTrjGjxSLQY4YAb3/72xv+Lum1Mk9//Md/3PB3yYqU80ss6X3vex9QzwqSbKPNKhPtXge1Wk1ZV+20hhwOB6FQSDEeCEqlEsePH+fkyZMsLi62zVrqVMalzLcwNHzyk58E6hbJl7/8ZaDOPXfPPfcA8Dd/8zdAPRvMCLFUJEvxFa94BVCP5UhhaieeqWOsyKJ9Sk1CoVBgeHiYSCTC0NAQhUKB/fv3E4lElHAJBoOq5kEIGcUdFo1GOXDggCoqFK1eOhDOzc2RyWQUzbaupRiDUtsJPRlBhIyMkdPpJBqNEg6HGR0dpb+/n7GxMVUAVyqViMfjzM7Osry8TDabXfMM+kYqTAZ6jGe92qErBTab2Wi0IwAAIABJREFUrWFd6J8LTF6xnUUnxr5SqSimB1GqZM51tvFuS8N/MWDTwmW9F9RqtSrGzVQqparRh4eHGR0dJRKJqFjCBz7wAeXGkaCsTkRnt9txOBxEo1GcTmdDf3SxSJaXl/nMZz7D5OSkYtWVgkLY2eJBq9WqajWKxaJKbpB6n2AwyO23305fXx8vf/nLVS9wm81GKpVieXmZP//zP2diYoK5ubmmBZe5XE5Ze1DXWCXlUrRFo6/4SkzJ1eum9HoHsRI65S83QixqHZK9IzERgRwnnFE33ngjUGfBHR0dBeoxFankf81rXgOspfWRn0YW6Z3CVjnJNoJqtUo8Hufw4cNce+213H777crtbrFY2L17t0ok6rYEFiOkzuSOO+4A4PbbbwfqWV7GyvwHH3xwQ+c9fvw4UGd+MK4HqauamJjY6q23RMdiLlDvw5FIJHC5XCwtLeF2u0kkEirby+l0NrR4Fc2jUqkon7n0MXE4HNRqFyg9CoUCqVRK0TmcOXOmoUp9p18qI4wp1263uyHbLRQKKeEprr7z589z/PhxVTSpQ++ZA42biXGjaXU/VwrEfaoLF73ocjOxGxOXF8RzMT09zfLyMj6fT7F1SCxGJ3u9ktZ9t2PTwmW9yREzVY6tVqvMzs6yuLjIuXPn8Hq97Nu3j3A4zIEDBxgZGeG1r32tEiAWi0X5H71eL5VKhdnZWWq1Gv39/aysrPDBD36QiYkJcrkcuVyOhYUFlRjQTUG7arXaUAktnFoej4c9e/YQCASU4BVkMhni8Ti/+7u/y7Fjx1T3Tj29WLQxQGVQifDR+z3omWLdMibthtVqJRAIKJeqPOfq6irvfve7efbZZ5mamlJrcTvQLLYj87Met9nTTz8NwNjYGFB36UqM5U1velPDNVrFUtoZ17iU823HmNdqNVZXV/mf//kfDh48yD333IPT6VTFt5JVtl28cq2wEeEm/X0ku0uyCG+55Ragzg0m60GyEQUy3rLvSJaZERJr6eSYdKyIUod0gisWizgcDkqlktLSpXJaelyLxlmr1fD5fJTLZc6fP6/8p8vLyzz33HPKUtHjK92Yw26M9zgcDvWcxWJRZbPIYikWi0xPT3Ps2DH13ECDa0eHMEUbBUi3uwE6AXF55XI5FhcXOX78OJOTk5fUqtXE5QF5b3w+n0roWVpaYn5+vm3BfBObg2Uzg26xWC55hvTKWY/Hg8PhYGhoCI/HQzgcplarkUwmFUmlnlrrdrtVTKZQKJDNZjfVb3091Gq1De/Imx0LqUG58cYbsdlsnD59mlqtRiwWIxaLceutt1Iul3n44YdZWFhgampqjVYh9T6iAUlCQztfHLvdLoWaHRuLdsNms3H99dfzwAMP4Ha7+bu/+zsmJiZ45JFHSCQSLC4uXlKMabNjoWuoUnm/UfZao6XQjOlZ/7zdGXDrWSrbuS42YzU5HA4GBgYYGxvj137t17DZbHz2s5/lzJkzHD9+XPWEaic6ORbrFZxKJf3f/u3fAvX6FmG9FiaG7VKsmo1FRyv0W9yEetHFZM3lcjgcDuXqyWQyDXUO4i6TOIME9LvJBbYR6O2NpagrnU5jtVqVMJmYmCCVSq27GXZrbGknUKlUyGQyzM3N4XQ6OXbsGGfPnlVFs+YYXdkQYS7vkyQEHT9+XLURN9fA9mPbhYsOvdiyUqmwvLysLBXdR96skr3Z590MSVSQHiWPPfYY+XxesbROTk6qF+Ri/mHZLI0sqe3ybV9OWWT6s09OTvLa174Wq9XK4uIixWJRxaq2u75FX5etLBZj0kWrNtXG7C9Bp57pcnqnBOIF2bt3LwMDAyQSCfL5PMvLy6yurrbt3djOfWe991tiMr/6q78K1GMskpW40/El2GHhIpDJkvhBq5bI633W7ZBGYFDPjBNKCmEW0PtOGGFq4a0hVp+JFy+kKn9paYlcLqfc6p1u4W2iObY95tLknA20Lzu5eXban2yz2RpqgKTKHlBcazo32EZehk5pU5dDzGW7NMlLiblc5Dg5N7CW+kigtwjXj+80pG+MZBVpcb6uXBc2m41gMKhon/TOp7q7fSvjd5GMvB0fi1gsBtQ7m+4UuiLm0gwvFo2iUqmoVEIje4BguzeRyxnmGJkQ6DRPeozFmBBhYvuw45aLdu4d3yx2ShPRrTeJN21W822FrboEukEr2ykYx7adY9Eq1tJqPpsRnjY7rtV1XkwWbatn7pRbrJvH4lIh/V2EOWA9dK3lAp3RQi/HwP+LxYoz8eLAdr6Dra5hvlM7g80KlyXgbCdupBPY5IIe3eTp2zYWEojc7Hc2gi2+WDs2Ft0Aw9i2dSxaFfpepK5kQ8et9702YdNjUavVrph1YcAV/Y5s1GL5AZqOxabcYiZMmDBhwsRGYEa7TJgwYcJE22EKFxMmTJgw0XaYwsWECRMmTLQdpnAxYcKECRNthylcTJgwYcJE22EKFxMmTJgw0XaYwsWECRMmTLQdpnAxYcKECRNthylcTJgwYcJE27Ep+pd2t+rsNlzJRHSbhTkWdZhjUYc5FnWYY1FHs7HoiOVisVgaGl4Zm18Z/76V85swYeLKwKXuBybq6Kax7Cgr8qVYLPogCQW9UWC14kW7HNmQTZgwcWm43DwlVzraIlya9L+46PEbEQoWiwWPxwPUm/+0Or9R6OjdHfXvmULHxJWGK2FNt+PerVZrQw+Sy03AtGsejU3S1usX1EnsSD+X9R7UKCyamXm6JbOR402Y2CiuhA37SsNm5sTo9TCxM9j2TpQWi0VJVaMlYmxJarVaVZfGWq1GsVhs+I4sIv04gFKppPpoX6yrYyc7DnYK22X6d/NY6MrDdjSZ6+ax2G5sx1gYx9/hcAAXPBHQeu3LPmC1WqnVaspz0S4Y371LGYutvsebUcz139u9X2xkLDpuuUhspFmAX9xXAqMJJwtFHkQWTbNryMICcDqd1Go1CoUCUBdEcr1yudzOR9xW6GNzsQXmcrlwOBwMDAxQqVSYmppSQld/AbvNfbCRl8e4lrrtGUy0D/p619e8uMFkHcuar9VqZDIZKpWKEkZbva7b7QYgn8+bFtAW0DHhom/48vLbbDa1QOx2O5FIBIfDgcVioVKpkEgkqFQq2O12JVSsVquyXGSCnU4ngLJk9A3J4XAwODhItVplZmaGYrFIpVLBarXS09NDrVYjHo9f0sJrBzZrtusC+WJCQV6KAwcOcODAAd7//vdTKBT4hV/4BU6fPt0Qz6rVamSz2a7anGu1WkPMTIdsMLI+ZCwKhYKuQbXlHi4XtFsD3kl3UrNnMcZMZf8IBAK85jWvwe/3k8vlcDqd7N27l2w2y7/927+xsLBAqVTasvXidru56aabqFarPPPMM+Ryuba+J1s9lzyL8R0R60462so8er1eALLZbMP3LzUOvZH776jlIjcuAiISiWCxWEgmk8qSEAGkWxcej0dtHvIgIjjkfPKzWaBfrudyubBYLOTz+QaNtxviMsbrNwvEWa1WPB4PDocDl8uF1WrF6XRSLpdZXFykXC43nWSXy8XVV1/N+Pg4PT09ZLNZvF4vTqeTUqmkBH+3CJWLCROj+1PGYCvPYPrhL0/IGnA4HNhsNgKBAH19fYyOjhIIBCiVSjidTvbv308ikcDtdq9xsW8F3fJ+XK7oqHCp1WpqM4jFYrzxjW+kVqvxr//6rySTSbLZrLJmAOx2Ow6Hg7179+J2u3G5XOTzeY4fP06lUiEUCgGQy+XWbEQiZMrlMul0GrvdzuDgIOVymdnZWUqlEul0Wt0T0Haf7Eaha99yD9VqVZn2cCFDzuv1cttttxGLxdi/fz8ejwen00k8Huf+++9ncXGRXC7X8AwWi4Xe3l7e8573EI1GmZyc5Pz587jdbmKxGOl0WrkMRGjvJGw2G5FIBEBZrpVKpUExcTqdWK1W/H4/dru9wV1RLpfJ5XLAxYWGHuvrhufeLGTty/qQMRgcHATgp37qpwD4h3/4B6AuSIeHhwH4xCc+AcCv/MqvAPDcc88BkMlkGq6zk+Ni3Mz1eIF4HoaHh3nLW97C8PAw4+PjeDwefD6fUkTm5+dxuVzK9b2V57FYLBSLRZ599lkA5V7vBDaq8LSKpYjCLZC4syCdTl/qLW4Z22K5AE1dObLB6ZaIw+EgGAzi9XqVxi4WiM/nAy7ETCwWC+VyuSHdWM5VLBaBxkVZLpfJZrMNG+t2wriIjEkNopU7HA7sdjv9/f2EQiH6+/uJRqOMj4/j9/txuVzMzs4qS0b3Q9vtdnp6ehgdHaWvr49AIMDCwgI2m41QKEQqlaJWq1EqlbYlXXMrrhd9zYhlK/GjWCymXKLlclm5PczsoCsbRteYzWbD5XIRCoXweDx4vV61HwQCAQYGBojH40qR2mTSUkP81sTW0Rbhom+U+kSKFC0UCiwsLPCZz3wGuCBNde1RNtVwOEw4HGZwcBCv14vNZqNUKlEoFCiVSgSDQSqVCtPT02QyGebn56lUKir2IgJmdXUVq9XKysoKkUiE173uddjtdp599lnm5+c5fvz4tmqves2OBAfFNyoWi8/nU6684eFh3v/+9xOJRDhx4gQul4sf+qEfwuv14na7WVxcJBKJsLCw0DD24XCYt7/97Rw8eJBIJILL5eLaa69laGiI7373u0QiEdLpNKlUiu9973uqfqhTEKtUf8Gb1SGtrKyoZ5C/22w23G63snpjsRg/8iM/gs1m4+TJkyQSCVKpFJVKBafTqdaBnKcZmsVlLpYYsZMYGBgA4Bd/8RcB+LEf+zEAbrrpJgCi0ShQT045evRow+9///d/D8A73/lOAPbt2wfAhz70IQB+9md/tuF63Sic9XupVCosLy+TzWY5fPgwhw4d4g/+4A8Ih8NqnxFl8o1vfCNHjx7lr//6r5X7eCMQoeJyufB4PBw4cIBKpaJiLpeKZmttvSSjVvNizKQTi/bQoUMATE5OAjA7O9twHeM7YFR2pVYolUoBsGvXLuCCVwHqsZuNoO2Wiz6AMtmSRhyPx4HGmIloCna7nXA4TDQaJRKJ4PV6VbA2EolQLpex2WxUq1W1oOLxOKVSqcH3rqcgl8tlisVig4beDl9sO2BMqRbYbDblBhKNvFqtkkqlKJfL5PN5kskkhUJBuQb1WFUkElGxLf1apVKJUqlEuVymXC5fNEW7HdBTw40L2hhL0zPYxPpyOp0EAgE8Hg/Dw8MEAgG10MXNabfblXtR3J1yvm7aJE00YiuCTNawxWJhfn6e4eFhpYQY13o7PBPm+rl0tLVCHxo3TdHOm22ksvnoFstLX/pSYrEYt956K263m9nZWXK5HLFYjFwux9TUFJVKhauvvppsNsvc3BzVahWHw0G1WlUbrmSHwQUJ/MUvfhGLxaIsIF3obcciqtVqDZqP8brValVlvEQiEZLJJB//+McJBALs2bOHWq3GZz/7WdLpNKVSiVwux7lz5ygWi0qw9Pb20tfXRz6fJ5VKkcvlKBaLPP/885w8eZIHHniAubk5ZS3k8/mOPrNogCIgK5WKWg96ZovEUkRBiEQi/PzP/zyxWIz+/n58Ph/9/f1KschkMsTjcbxeL/F4nEQioYSmXt+gQ1Kwm811t20iRp/5j//4jwNw2223AfVMSVnfEjP55V/+ZQCefPJJAMLhMAC/8zu/A8Djjz/e8H1jHKHbxkGHHnvRFRJxJcv6SqVSzMzMcP/99/PCCy+wtLS0qbID8SLk8/mGmIsotpeaYdpsjGW+5dytLJRWtT/yHoviJfMu3gDxmMj35B0cGhoCYM+ePUB93ch1JAQhx8v+tRnFoC3Cxah9659f7HjRVh0OBx6Ph0AgQDgcpre3F6vVSjwep1gsKt+7uukfaKyiuUhgXBaaZFHJ58vLy0DdqtqJLJD1JkN36+Xzec6cOYPb7SYYDFKtVjl+/DjZbJZCoaCsD6i7FEdGRhgeHiYajRIMBtU4zM7OMjMzw8rKikpo2C7o8TSgYc71bEGXy6XSioPBINFolMHBQUZHR5U1ZrFYyGazKtXa4XDgdruV60w+11OTZSPSX4hu3kRNrA+r1UosFiMUCjXMq7jDl5eXOXfu3KbcYTp0yzeXy3Wt2/RyQFuEi7H4sRmapf/abDbsdjtOpxOv10s0GmVoaIiRkRFqtRovvPACy8vLzMzMUCgUVGZUKpWiUCiogL9oq6LByMYi96Xnfutus25ZNHpRo7gORbOcn58HUM9uvG891rJ//3727dunsspSqRRPP/00ExMTZLPZbRcseiaSnkosioH8v7e3F5fLhc/no7e3F4/HQzAY5KqrrlIp1HBBg0ylUkrbCwaDWK1WNb+9vb1Uq1UWFxfJZrMkk0mKxWJXpV0bodcvQT0LLJlMAvC2t70NqMdK3vCGNwB1H/grXvEKAGZmZhrOKxqtaJzf+MY3ADh16hSws2m2m12HujIaCoV4/etfz8GDB7HZbMpiLRaLPPbYY8pi2Ujh48VqhMSt3mm08iAYE37EUpG4otybxFg+97nPARCLxQD4+Mc/DsDJkycB2L9/PwBzc3MA3H333QB8+ctfbvh9dXUVqMfwpqenga1V+m8Lt5guVIz/F43T6/WqzA+p5VhZWWF5eVltEvpElMtlZfLJBiIDr0PfkLtdg61WqyoeItZXs2cS2Gw2wuEww8PD7N27l8HBQWW1rKyssLCwwPz8vEp82G4YlQ6pUxDBotfwiJXm8/mIxWIq7uZ0OtXGW6vVlHuiUqmooH8kEsFutzMyMoLFYiEcDpNKpTh+/DiAcoV245yb2Dj0LLFQKNTgAi2Xy0xPTzM3N9dAE9VOdGPSQzfjkoSLHj8xpgu2Ok5+yj+Xy8X4+Dh9fX3s2rWLaDSKw+Egl8vx7W9/m2PHjrG6ukqtVsPtdmOz2VTWlEjjmZkZUqmUCnSLlqwHrvX7a+XH3CnoWpL8XE9DsFqtRCIR3vrWt7J//35uuOEGvF4vdrudVCrF7//+73P06FGOHTtGLpfreIylGYyJCuFwWAkHh8PB2NiYynJxOBz09fUxMjLCHXfcQSAQaGBvkGywbDbL0tISyWRSFdSNjIwQiUR45StfidvtJp/PMzc3x+HDhzlz5gyTk5NdW2wp15B5N9YliEXyb//2b0Ddkv2Xf/kXAM6ePdtwvFh1/f39APzWb/0WAO95z3uAy7cw0Gq1qtislBWk02mlgKTTaZ544gmeffZZVTS93vxtdk3oscJOrQ2jhSDzKetDasJEcZLsLnmP5L4+8IEPAHD+/HkAHnnkEaAeoxsfHwfgl37pl4C6Zby0tATULZY3v/nNAHz6059uuE/jfTVDRyj3m/1dL2KT6nuxWHw+n1oktVqNRCJBIpFgcXGR5eVlZdqLtiqJAJIp5Pf71WQY/fzGe+xWXGyx2mw2YrEYFouFXC6n/M7Dw8NcffXV7Nu3D7fbjd1ub4jZnDx5kpWVla7hUpN5l8CpXrPgdDqVBSMxFUAlApRKJZaWllhcXGRlZYVsNqvOFwqF6OnpoaenB5fLxfLyMm63G7/f31Bgt1GYfvbug7znYqVKgg5c8FysrKyQSCRYXV1d10rvFqXySseWhIs+OXrdQjOJrtM2yIteLpdxu91ce+21RCIRdu/eTTgcxul0srS0xGc+8xmWlpZUrCCTyaiMKrfbTX9/Px6Ph3w+r4LZuVyOiYkJSqVSU7NYv089eC6/dwuMeee1Wo1YLMa73/1uPB4PR44cwefz8da3vpVoNEosFsPhcOB0OqlWqypGFY/H286HtFnocTbdupXNYXp6mnA4zA//8A/j8XjUHIlAyWQyFAoFJVT+4R/+gZmZGWZmZqjVakSjUQYGBhgdHWV4eJhgMEixWOTRRx9lYWEBn8/H8PAwJ0+eVBxzG0E3afcSg3npS18K1DXW559/vuE4cRG/733vA+A3f/M3gXo9SyerzLcDbrebW2+9lcHBQa666ir8fr/iDpyYmGB2dpZz584prryLKbpbIbBtlv3aDhiVXlGq5BoSbxTccMMNQJ2xQf4ux0vykqwXyRoTy+Z1r3sdAOfOnQNgZGQEgI9+9KNAPQYjjA5f+MIXgLqlYrS0L4a2xlyaucN07TQWi6mHlDoGt9utAtDFYpFUKsX09DRTU1Mq60fSZyVY73a78fl8qh5Cii31ylo9xqLX08BaYrxugqRoR6NR9RIMDQ0xPj5OIBAgnU7j8/kYGhoiGAyqdF+p6ZmenmZyclIxw+708+nzIMqHHmCvVqtEo1ECgQDVapVQKKSSD1KpFPl8Xm0cJ06cYH5+nlKppNZUJpOhWCwqtgZAJYkY3bEmLl+IO9zv9yv2jtXVVTKZjCqoFTd4p7DT79Llhi0JF2O2khQ3wVqXgvjFe3t7ec1rXkNPTw99fX3kcjkee+wxarWaIqo8deoUyWRSUTcUCoWGgLZsHrFYjJGREd7ylrcAF/zRiURCucuk/kM2MrGYZLPVO1t204IRK6+/v593vetdRKNR7HY7gUCAG2+8Ea/Xq6rUpXhMYkvHjx9ncnKSj3/845w7d465ubmOv2zrQXd/VSoVRVjqdDoVzU84HOZVr3oVvb292Gw2lfWTy+UUTc2RI0dYWlpienpaxY7ERZhOp3nyySeZm5ujr68Pv9/P3XffTSKR4C//8i9ZWFhQdEHrubu6qU2uaKRSqf/bv/3bALz+9a8HGklhoW7hHDhwAKg/w8c+9jEArr/++u247TVol4tREkD8fj/BYBC73c6ZM2dIp9MEAgGKxSIul2tNnZARO1WK0ArG+K/sd36/H6hXxI+NjQHwN3/zN8DabDKpyBfLRGJzEsMTdmSxYO+8806gnh323e9+F6jH8CQL7amnngLWsi4LLja/batzgbUPLJtlLBZjYGBA+dTD4bDys0smjxRB6pQkUsOhm4rCiDo4OEgkEqFarTbwbLXSUnWXi5yrmwSLQGJRUrfidDpxu92qWt3v9ytLBVAb9/nz5zlx4gSnTp1iYWGhq55N5kRPF5d4idS4SJpytVplbm6O1dVVCoUCmUyGqakplpaWFNuCpDKLO1CCu7LwPR6PWkc6NVA3jYmJjUNS1nXLxW63KyEDF6zVAwcO4Pf7yWazDS5hfd5NK3b70BG3mPx0Op309PTwMz/zM/h8Ps6cOcPU1BSZTAar1aqYj6WaXFJNvV6vyoKQ/iwSUxgYGODNb36zcrFJBX86nW5oJqbHVQqFgtrItnNxbVZrkzGJRCJcffXVKntOkhh0f7Fes5PJZPjc5z7HM888o3ys3QTJ+pI5LpfLipzT7/fz/e9/n2AwSD6fJ5fLcerUKVKpFCdOnCCVSim6Fymo7enpIRwOc9NNN2G321WAXxSRYrGoLB9J6d7IPHSTRiu45557gHpdi1RU9/T0ACjr/Q//8A+BumYpysett966fTfbBJcq0MViCYfDHDhwgP379zM4OKhcx5JJWKlUuPPOO5mbm+P9738/ExMTqm+TrqzqNWXdNN8SM5PkJcnqEovjrrvuAuqWrCjJotAL95zEUgS6Sxpg7969Dec1VuI//PDDQH29GUshxELeCOt0R7jF5J/Qo/f09ODxeKhWq5RKJbLZrMpsqlaryl0CF4qKJPgv3xsZGcHhcFCpVIhGo/T29hIIBNRmGw6HCYVCagPWKRXE+tEbla03KDsNsficTqeKKRkz4ORZisUi6XSamZmZLVclbweM5r9YaFarlaWlJfUy5fN5VldXSafTLCwskE6nVZC2Wq0qi0WsYMkwDIVCygqqVCprxqGb59vExSHvdCgUUm0XRGGBOked1+vFarUyMjJCKpViZWWloX2DwFwL24O2CRe9UFHShIXGQ4REJBKhUCioPhwLCwtkMhnOnTun4jZOp5OhoSGGh4f58R//cXbt2sXevXux2+2q/4v450ulEg6Hg1tuuYXBwUEeeOABstksHo+HSqWi+roMDQ1htVpVHGK7CuqameMXy2KBC5trNptVAWrRtPRsKxnnUqnEU089xbFjx1haWmogsrzYtbYTEuMSiHXm8Xhwu92Uy2WefPJJ3G43V111leoguri4yNzcHPl8vuG5arWaEiKJRIKenh7uvvtu+vr66O/vx2KxMDc3RyaTUa6UVvEWXWB3m9tMtOrPf/7zAPzzP/8zUPepSyzlR3/0R4FGBnKAd7/73cDaBmyXGySRx2q1MjAwQE9Pj1K2jJlUcGFO77rrLkZHR/n0pz/dEKOT83WTxSIwvrPioRDL4cyZMwANCVFQZ3IwkgIbnzEYDAJwxx13NJxHxkd+FwtGPjc28ttMvVzbLRfjhiq+dYfDgd/vb7jZYrFIJpNRFfhwwewaGhpq0FSEfl8q8WVxSc2EXFc4x4QSQqcZ6Xboi0tv5CUau7F3ixwr/mXZrCVtW87ZDRumHtgXwShWl8ViUcVw8tyZTIZMJtPQrwfqzyNtGDKZzJo6Kai7P4y42Hh0y1iZWAuxRHO5XENMtlmM1W63Mzw8TDKZxOFwKM8FsKao2kRnsWnhcrGXUE8vrVQqqrCpWq3i8Xi45pprFKNvqVQimUyqv8tGKhtKsVhkamqKfD6vfOdPP/00wWCQ173udWozrdVqHDlyhFOnTlGpVHC5XKoRltROzM7OrqnJ2W6st5h1CwVQqcW5XE4Re8pxeuZbKBRi165d3HjjjQwMDPDoo4+SyWTUPG2Xm2y9zVm3uGSDOH36tHJZhkIh5Tufn58nmUyuaQQmmXHnz5/H4XCQSCTYvXs3c3NzKj3VarUSCoWUxaN36jTe30Y48XYawgUl9ygcYRJ7kX4dUq8gMRqphxGuKenPIZrn5WLRiDKxuLjIf/7nf3LgwAHe+ta3runAKJDAvsRtU6mUGhu9vKHbYOwIKvco+9Xv/d7vNfwu60GyyoSBQepT5Hyy5iXWct111zWcX/YHYdUWC6gdaKvlomul8lClUolUKqWC6VIMKRq3BHnFJSb+G970AAAgAElEQVSdJyVdtVarMTc3R6FQIJlMqu+LRZLL5YjH4ySTyYYAru5S0skt5VrdpKkaBUupVGJhYUHFJISDSzZXqJu/TqeTUCikGIQnJiZIJpOsrKysSRvsFsj8SNDd5XJRKBRIpVIqAUNfFwI9XbNWqykXol4gKQqExKyaJXEYz9kt68BEc4iVOzs7i8/no1AoqFikHuOVY+Xdv+aaaxTLeqFQYH5+/qJcfSbai7Zni8kkS6qgzWbj0Ucfxev10tvbS6lU4siRI6RSKRVbKJfLOBwO9uzZQzgcZvfu3cRiMa677joqlQqf//znyeVyjI6O4nQ6ee6553A4HKTTaZLJJBMTEywuLqoOlbKplkol/H6/qlZ94oknyGQyuFwuKpXKJXdi3ArbbjMtWeIQVqtVVaTff//9RCIRRkdHGRwc5DWveQ12u50TJ05gs9m47rrrsFqtiuzx0KFD5PN5Dh48yOnTpzl8+LCq1t0ObHQcxUItFoskEgkcDge9vb2srq7y9a9/XTEf66nWErzVq4NFo5UMM9FIRUgLc0O1WuXYsWOKbwpoOAbqxZ3dpM23SkiQz0+cOAHAwYMHgTpXlNQtiGUjMZnPfvazwOVjsRiRzWZ58MEHmZqa4t5772VgYIBAIKAKZkXZKpVKfPe736VQKPDHf/zHWCwWlaJ/4sQJZdXDxtfsdsQwW11D5vs73/kOUK9XknkUYSnzPjw8DNTrVST77C/+4i+Aer2K7Fs68We70dFUZNkQlpaW8Pv9OBwOyuWyalcqxYASK5Fe6WL6iWWzsrJCPp9XdC9CWhiPx0mlUqyurjYIKqjzWOkpyIFAQAX6d9o01s1bp9OpMuJyuZyin7dYLErbOn/+PHa7neeff76Bpl4aGUk2nfCtyc+dfs5mMPq9S6WSmhe90l5g1Ezlp9DECGO20f0lqcnNfPNSXAuotWMyJ3cvpF/LwsICZ86coVqt0t/fj8vlUrFcWe+yn/T19WGz2cjlclQqFXp7e8lkMqysrJhzvQ3YtHC5WEBUf4GFQFGCuVKLUC6XlZTt7e1tyAKTGEE2myWfz/OpT31KtfYFeOGFFwgEAkQiEdxuN/F4nOXlZc6cOaO6zkkQXxICfD4f6XQah8PB1VdfTTqd5pFHHmkLs+lWNu5araZI+ETwDQ8P8+53v5u+vj7lJhoeHiaVSvGnf/qnHD16lK985StqE7XZbPzVX/0Vo6OjvP3tbyccDnPy5ElmZ2f5whe+oBhNxefc7EXayWp0/V7K5TKLi4sq8KoXUwp0wSKWj7hKcrkcCwsLBINBNf8SAJYEATmnnkzi9Xq55ZZbsNlsPPvssyqxpNs3HGFJfuihh4B6Hw+p5JasMbFojD76yxWiTMzNzfHhD3+YwcFBXvKSl9Db28v111+v2mDb7XZuu+02VS9nsVhUbcyrX/1qTp48yde//nUVg9rotTsNuYaxM6UkqXz4wx8G6tle//Ef/wHUO0g++OCDQD1bUCyUffv2AXDTTTcB9fUg7//f/u3fAnSkPq6j/VyE70s2hFQq1RCkFctC8tNFwEh17fT0NMViUf1dNlYpppMAsGQX6Zuo5MZbrVay2SxOp1Nlku30BiL35vV6FRHf2NiYKoyz2WyqeLJQKLC4uMiZM2ca+NOWlpZYWVlhcnKSaDRKPB4nHo+zsLDA6upq19a76NBdPfrmLwtffpesQKPFIxuONJKT9SAZdhKDazXf3eYKM7E+SqUSMzMzZDIZBgYG1M9qtaq4CqWCX2KZkrYsSUCXu6C9XNDWOhc95VRSSqvVKuPj49hsNtVXo6enR236oqXLBlIulzl58iSFQqGhCEp88263W9FqSz2N9FIXoSTaK1zQAM6ePUupVGJlZYVisahSmo31E9sBEaqBQIDR0VHe8pa3MDY2xu7du1XDNKF3sVgsnD9/ntnZWbVJyj1Xq1VmZ2f5xCc+oZqsSSzDbreTTqcVXUozdIO7zOjukl48ohToLNq6JSxzJVmF8XicQCBAMpnE5/NRLBYpFApK4MqY6R1Js9ksjz32GIBaNzutdFwMUpkt9yg+daNwFI31iSeeAODlL3950+MuZ8j8PfTQQ/T39zMwMKCKbr1eL7t378bv97Nr1y61fgqFQoP7vNvmulWhp8yb1LNIDO3ee+8FYGFhAYBoNArU+/hIduntt9/e9PyCzVhwm0VHLRfdjQH1QKywIAstixwj9QtCWikBd7FwbDYbTqeTcDiM3+9naWmpodJf5w0Tf7xsSkKCKBvVdqFZoE4y2ILBIHv27GFgYECl0kqG3OrqKqlUSo2F8WUQt9D8/DwOh0N9X0hARYPvdugZhvpnsiaMbRKa1Sjocy3rQGIxEujXv6crP8Cav5vobujUI8IrJ6zqxWKRcDis5lg8J2LhZjKZpmvORPvR1mZh+kaqZ/nMzMyoyXS73Yz9oAOhbACTk5MUCgXl1pAaBRE+vb29+Hw++vr62L17N/fccw9Op5OHHnqITCajGmWJq0xiN3JdWWh6u9vtWFgWi0VVvuoFXMKP1d/fz6FDhwiFQg2+8Ww2y3/9139x9OhRVXnfTLjIc0par2Tc6XTzxvsxfm6s9djOF269einJdNHroPTv6EHcWq2mLNlsNqtifFLzBGvjTDrR6k5vNK00S+GA6u3tBeDYsWNAa0tENt4vfvGLQL0DodTHSLxzu9DOTVyExMrKCh6Ph4MHDxKJRJienmZlZYWxsTGq1SrPP/880WhU9X0SBXZ6epqJiQkldLpR+dKzJKHR2tZ/f+aZZwD4/ve/D8C1114L1F3JYuG8+tWvBhrZKKC+fg4fPtyBp7iAbbFcJEbicrlUkE23LjKZjMr2kQ2zFZeU1+tVLMgSa2nVt0QmQtd+9Z/bBXH1AIpCf3h4mL6+PkUTLotHNO+zZ88yNTXVMvFAPx7qjMOXY1MovZ5F1oC+eTZzWen1DbKeZA3Nzc0xNTXF6upqQxFlsw18p4WKic1DhENvby/hcLihc6m8P4VCoYHhQXeLmXO+PWh7ESWgTFHx+YtGuXfvXpxOp2r4tLi4SD6fVxlNslHoUlYqrqUYKhAIMDs7Sz6f54EHHuDUqVMsLi4q94d8fzPB2k4tNlnUUoHu9XrZu3cvw8PDvPGNb2RwcBC4kKsuBIyFQoFEIsG3vvUtnn/++QbXoZzTWHQJqOLDs2fPNrgEdO3M6HrajjHYKIwZZPpPHfoakaxAqVt69NFHyeVyfOUrX2Fubk6tCym61Lv2ifKin3cnx8AoAOV3yZSUzpOttG15Z8RS/sd//EegznIrGYTbjU6Mqc1mIxgM8tKXvpSBgQHFQiCsDoFAAL/fr9zhyWSSqakpzp49y/Ly8iXXt3UCcj9GLi/j33fv3g3Ac88913Dc4uJiw3Ey76997WsbPhfl82tf+xrQ2XXR0U6URneDVNRK4ZtUV4tmqvNniVtN5weSnibSgW5lZYVUKtWQFWRkCGj1/07DqImLBRaLxejp6SEWixEKhda4ZkqlErlcTvUDF4Gpx6ZkLHWXkJxDiqpabVbdjIvdo762dMHi8XiIRqMEg0FSqRTJZJJz586RSCTWJADo7lvjOjFx+UB/l/r7+wkGg0q5qtVqqgRBNupkMsn8/HzXNgm8UrFl4WLU9CwWi8pQkEnU6VdsNpuqGF9dXaVYLCqXj7i5AoEAtdqF9rYWi0WxKUsg/9ChQ/j9fk6cOKFiEULtYswiknuCjfUeaAd0i0J3/cnv0gRsZGSEYDCIx+NRmVFynzMzM6pNsZ6kIGObzWaxWq309PRgsViIx+MN2qxR49E1oU6/VK0E2VYFXKu4kbQiiMVi7N+/n5/92Z/F6XTypS99icXFxTXBfV3pEBdlM0FshN5crtNoNUbGtWw8TtaOxGbe9773AfXsItFMuzG+sBXIfhIKhTh48CCxWEzR/whN0tDQEC6XC4/HQy6X43vf+x6nT58GLrxHYsl2I4z3ZWzAODExAax9NyQ5Rdzv4XAYqHOJyfGf+tSnAPjd3/3dttzv/v37VeaiEVsSLpvNE5eKcdHAfT6f6kJZqVRUX45QKKTSaQHFMybCSeg+5ufnicfjZLPZhmwgWKuxd4PmLhui1+slFovR19eHx+Np4L3K5XLkcjnm5+cVwZ4IAxk3qGvtOt240d1lDNwLvUytVq8h6lboQsA4h4CqW3C5XIyNjTE0NKSyg/L5fEMMTtbFxdZAt24yVwKEAko4AdsBi8WC1+slFAo18A9aLPUOp7KfiKdkeXlZFVmb87192JJwaRYglY1L/i9+/1qtpgoGh4eH8Xg8imPsmWeeoVKpsG/fPpWeXC6XOXfunHJpOJ1ORXXy+OOPUy6XOXPmDIlEQnFGGes55L6M2UGtNpl2Z7MY70O0qT179nDbbbcxMDBAX1+fynDL5/P893//N5OTk3z/+99naWlJ+Y+NMRZJABCKeokvGZ9BfO+FQgG3281tt90GwP/+7/+qau52o9U4rje+RiFiDNbriokI1/7+ft75zncyPDyMz+cjlUpx9uxZTpw4ofoFyfgZe2PoY3axWEsnrRbRMMWqFtZasTT0dwnqYyPfk7oGeSbRRGXeQ6EQUGdNboVOKl99fX284Q1v4P7771edDS8FYv3feOONjI6Oqro4n89HqVTi/PnzeDwe9u3bp9zp1WqVo0ePcuTIkQardrshbv71iDONe5XMt8SV5ufnG46XPU7WqvAo3nzzzQ3fl3qYP/mTPwHalzUoHHfN0JGAvv5/2XB1rUE2if7+ftU5TvLUpTdJrVZTmR0iqJLJpGI3TaVSFAoF1WSsVfB6vZemk5qMrjXrm50U/8m1RaBOTU0xNTVFPB5vyWgs3Gu660eHMSFCv49u19qaCRfJMhRrRazgaDTK8PCwahxXq13IMpQGcoCy1iSLzOznsf3oxDjr75Awa+fzeebm5vD7/Q3u8Xw+z8rKiiK0vZKKSbsdlyRcjDEXY6aD1JYsLi4q7hqfz0culyMYDPKKV7yC3t5errnmGsrlMt/85jeVZC+Xy8zNzWG324lEIlitVqampshkMqpiXcxiuZd0Ot2wccgGvVFB027ImJTLZV544QUWFxfZt28fsVgMv99PNBrlJS95CYVCgdOnT3P69GlOnjzZkOgg910sFnE4HITDYWq1GgsLC2oz1bVbqfDXU7vz+byqRpcq+G7bXHXBIu5CSeiQ2NtVV12lMg+HhoYYHx9XVkutdqHORViiJTOvVCrx4IMPqrVRLpcbGqrtFMRiEY10aWkJgCNHjgDwwQ9+EKh3IBSuMMkeu++++wD4l3/5F6BewS91Lf/0T/8E1GNu+lrS0cl1sLCwwOHDh9vKW5XP53niiSeYnZ3llltuwePx8Nhjj7GyssLS0hIjIyO8+tWvxuFwsLi4yNTUFEtLSw0p6TsBPcanQ9zb8jd5DySGJrEUGUNjSCISiQD1vU76+OzatQuo92d55StfCdAyPtIJdKzORdeWdVroSqXC8vIyVqtVVdTmcjlFUKlbInoXSam0z2azaywh+btcV//ZDZB7T6fTLC0tqfGQRl9CVSIvgbHbntVqbeBLkqyyZjUwuptDMs1qtdqa5kHdBmNsRGe1li6msVgMt9uNzWYjFospqnVpna230hYLT0/m6NZnv1JRLpfb4g7TUa1WyWazJBIJpqencTgcTE5OsrKyQrlcpre3t8EjIHtMq1o4E51DW7nFdPoV+Sn/FwJGocp3uVxMTk6qYL3krkvdQjgcVsJFqOWLxaKimJesMqnqh9YZMUa/8nYF+UVTFuRyOY4ePar6spTLZe6//34qlcoaS0QEg9PpxOfz8dKXvhSLxcJTTz2lqOmNz1ur1fvV68JWhNF2PPNWoLs+AcWPJokfYr2+4hWvoKenh1KppHqC5/N5nn/+eRYWFjhw4ADDw8OcOHGCZDLJAw88oKr1u9UlJhaLsBtL7ZP0XzGSecralXkWzfcjH/kIUK/El/l2u93A2k6HlyN0S3x2dpZPfepTWCwXGNddLhc333wzkUikIV5ht9uV8tFN8y4wWjNyj8b5kt/1JoFQj6nIecRieeqpp4D6PiBsymLZbId7sKMV+gLdTSUvubh9JN3T4XAwMDCgtE+48AIJwaX4VeV7IlSacWjtlDBZD9VqlXg8jtVqJR6PUygUWFhY2FBmk6Q0y/O22iS3GlTfaehzJeMhAVuhCRLIZiFMyNPT08TjcdXzp1AoKKEiPvlm8bhuWRcmNgdR2qRwUKwSyRZcWFjA5/OxsLDA7OxsV6ceX8mwbGbQLRZL7Qc/gdYvpe4X1APMxhiNuC6EUl9v9CVFUVDX3sRdZEw1FcjmbMzEMWZUtEKtVttwjrWMxWYgLYuBDacEy/hJp0oh6tQ5szaDVhXARnR6LNY5n3pucQlGIhHuvvtunE4nU1NTeDwefuZnfgaAL33pS8zPz3PixAmVml6pVFRHS3lW47N3y7qAuib6sY99DICf+7mfa/hcFC6BsOJKX49AIADU+7p0qp3vTq6LJucH6m5UodwfGxtTzODpdJpz5851pOV3O8ei1Z4qe5m86+u987KmpROp9IF529veBtSzENuNZmPRdsvFKFgMN6B+ihYOKAtEjzXoLh7jwBuF1Ga1kp3SWHWBuVHBIMJUiif9fv+OB6O3E6Kl5vN5FhYWsNvtnD17lmAwyMLCgup5I354nS5ns3UNpiVzecGoWErdm5B7isXajt5GUl9Tq9V2PDngcsGmLZeLvYD631pZLLpLQlJLxcUji0AKo0TYGKV3s+td7KfxvsQPrRcq/uDvXaOVNbmesmCAjhPwbcdYNFM+xPK02WyqfYDEn2RdZDIZenp6ePOb34zf72d+fp5EIsF///d/q8wxsWzlvBu5F93iNazZrl0XTa4PtE9ANnnHunos9OLbdib3+Hw+VSv2rW99S96/TY3FVpiYjY3zjKzJxmeU4wQ/8RM/AcD//M//NByn3VfTz1vhrrvuAuCBBx4AGmK77bFcNnIjhk37oseJG0NILiXjQ2+S1c772yzDQDfhSrZams2LrCObzcauXbuoVqucOHGCbDbL8vIyxWJRaawy72Z/lhcvOj3vV/L7125sKebSlgtrmqKeZdbf3899991HrVbjL/7iL1TQzkhpIsdfLKjdTCrr2r/RvO1GrWwHM912ZCyaFYHCBQ6jf/3Xf6VarXLvvfdy/vx5hoeHsVgu8KvpjcGMaacbHTMjo4OgG9fFTuHFOhbN9o2dGItWa1QsFr/fD9TrYjbKUrJVSFhiW2Ium0Ezy0bPFGp2rD44rdxf611TH+jLTbu93O53s9DnVWeA1hM5xIUqNRTSDruZUNrstU2YaAaprzGxcWyL5dJKWjbTym02m2L0TCQSTbm6dGw1mK9bQpob5UWplTXDTmeLybxarVZ8Ph8ul4tIJEKlUuH8+fOUy+WLWRrtvB1zXWgwx6KOnX5HfnAPQJ1rTiryjTDGvy/1HRFmFK1+rjstFx2VSkUVlrU6fqsxE33TEj++8XMTOw/jXEilvbSoFRjp6M05NGGie7BZ4bIEbJqcZqMvfbuPW+97hvqG0U2ebktjcZmga8ZCCEsvhg4Lla4Ziy6AORZ17OhYGNd8K4tF0O5EBEPdUNOx2JRbzIQJEyZMmNgILi0CasKECRMmTDSBKVxMmDBhwkTbYQoXEyZMmDDRdpjCxYQJEyZMtB2mcDFhwoQJE22HKVxMmDBhwkTbYQoXEyZMmDDRdpjCxYQJEyZMtB2mcDFhwoQJE23HpuhfTCK6OsyxqMMcizrMsajDHIs6XoxjsaPElZc7mnW9M6IV2abxc/mpM/026+C5HrvpRkgcL9atzyTxbA863RGy09cz0d24HObbdItdIjbT+W6zbM768SIQrFYrVqt1ze9bvVYrobdZGNurmjBh4sUN03JpE6T/u/57s2PWs3BaWSIiRFwuF1arlWKxCIDdbqdWq6nueLpG08wKkXuQv4ugMlpKm4HD4SAajbK4uGhkmn5Rwmq1rhnLVr1nNopWHUkFm23tvd2dTZvdy6X0YoIXd8vhnbJYNjN3pnBpI4wCZj20ck8ZWzrrFooIA9msrFYr1Wp1jSC5WIO2i7lWutnMNmHC+F40g9GF/GIWQjsJU7hcAlpZJ7BxCW8UMHK8w+FoeHns9gtTVSwWsVqteDweAPL5/Jr+2BaLpeF4o0WjH9uOF69UKplWi4ZmY9qqh/lGu7Q6nU4AyuVy0+NdLhdQ77Mh1zPOSTdsuFtVYIxxyVaWTLVaxWq1EgwGAVhdXaVSqaz7zJdDHEOwU/NoHBun02ns7aJgCpdtxsUsm/WsHt2lZfye2+2mVqs19J2HC5uR7iLTLSP9XFvt7ikwBYuJdkPWqfxzOBw4nU5isRgulwuPx4PNZqNarVKpVMjlclQqFcrlMhaLhVAoRKlU4vTp002texOdxY4KF3HxCC63DUoWdjN3lPH3ZkF3qAsMEQZyLqM2YBwbeYHggtY6Pj6OzWZjaWlJHVupVEgmk1QqFRUHEEum1XnNF/DSYbPZWq7l9WIxsg5E6w6FQgCEw2EAfuiHfgiATCYD1Hunf/Ob3wRgZWUFgLm5OaBu8cr9yHWMn7e6P2Ov9M1C3LabhcViweVyYbfb8fl8OJ1OotEo4+Pj/Nqv/Rp9fX309vaqFtiFQoGpqSkKhQLFYpFqtUomk2F6epo///M/Z35+XlkvxvbYcn+X07rvFlefxH6bYUeEi2giTqcTm82Gz+dr2JStViuVSoVEIqEGUYLZANlstmsGd7OB1I2iWcxF/m+xWLDZbOpzl8tFJBLBbrc3vFylUgmPx6NcKRaLhWg0Sq1WI5PJUCqV1FheTi4BE1c+ZI3b7XZsNhtut5vx8XFGRkYYGRkhFosRDAaxWq2Uy2XK5TKVSkUpTyJcPB4Phw4dwuPxMDc3R6FQIJ/Pm+t8G7DtwkUWjdPppK+vj2g0ykte8hJ8Ph+BQACn04nf7yeRSPCZz3yG5eVlbDYbXq+XW265hXK5zDe/+U0ymcyGBUyn/JPNtH6jP7hZHYn81N1SehDSYrHg8Xga4jGi0VarVex2Oz09PTgcDjweD16vl9HRUSVo8vk82WyWWq1GqVRSfeh7enq49957sdlsPProo0xPT/P000+Tz+fVM3TTS2dMbIDNpX7vFPR1Ybx/Y52SHCvze8MNNwBw9OhRAO655x4AXv7ylwNw5513AtDX1wfAzMwMUF/b3/72t4G6xTE9Pd1wfbmuKBxiIV1//fUAHDlyBKhbRlu1WAT6O9cqm7LZHOuWS6VSIRgM8qY3vYnx8XGGhoZwOp0NSS2yR7jdbqWslstlRkdHGR8fZ3Z2lj/8wz/k1KlTzM3NqfeimYvZzEZrD7ZVuBh9p+FwmL6+Pq666ioikQjhcBiLxUIul6NUKjE0NITL5aJcLuP3++nr66NYLK4Jdm/m+p3amFrdjzHoaPxcd5fJ+Njt9oZ/ornBhQC+1WolGo3i9Xrp6elRwkWeL5fLMTc3R7FYxOl0UqlUcLlc6hxwwS2iv6A7CaM1JgHZcDiMw+FQG2AqlVI+9Wq1qiw00VZlbs1N4fKGMTElGo0yOjrK8PAwPT09ysUr6yCXy5HP50kkEuocDodD7TOhUIhCoUAwGMTj8eBwOKhWqy3Xiekabg86IlxkszBqLbJZhsNhQqEQ1113HaOjo7zhDW8gGAxis9lIpVJ88pOfJJlMctdddwEwNTWF3W7nhhtuYGVlha997Wusrq6uW5thrPnQg9ztfFYdxg3OZrNhtVrX+K7tdrtyDQIUCgXgwkshY+T1etm1axeBQIDdu3dTq9U4e/YsAOPj48rqi0QijI2NUa1Wefrpp5mYmODzn/88qVRKWVORSIRSqcQnP/lJarUaoVBIxW0kRlCr1bbsI9/MWOlzJtaWy+Wit7cXi8XCysoKwWCQ173udezevZs77riDWq3GI488wsrKCul0mmw2y7lz50gkEpw6dUptMJVKpavcHsb7EEtFhLpukUI9FnLLLbc0/C7rQxSEr3zlKwCcP38egL179wLw3ve+F4APfehDADz44INA3cIRSLahnPc73/lOw+dyX+2Mg26m2NhisVCpVAiHw9x3333s27eP/fv3Y7fbVRyxUqmwvLzMX//1X7O0tKQSW/bv309fXx8/9mM/htPpJJlMEo/H8Xg89Pf3k81myeVyJBKJhue7HJQTn88H1C3LbkbbhYueBmt0YdjtdlwuF36/H7/fr473er24XC7lD02n0+TzecLhME6nk0KhgN1uJxKJYLFY6Ovro1AoNASv18NObDbNcvJ1U14vYnS73Q2uAK/Xi9frJRAIKHMfLmwuNpuNvr4+FdQMBoP4fD4qlQo+nw+/36+ElwjgSqVCoVBgYWGBarWqNg+r1apcD50cB90y0deH1WolFArh8/m45pprcDgcrKysEA6H/z97Xxok11md/fS+r9PTs49mJFmStWAbsxkMOGBMsA1xQpYiJIGEKhIgCfmRpEgloSgg4aOKJEVVAgVhCcEVkmAwphIK2zI2sbwFL3iRbY2kWTR77+vtvfv7MTyn377qnkWanhmRPlWqkTS93Pve9z3Lc55zDo4ePYqJiQkMDw8DAI4ePYpEIoFEIoFcLodGowG/349Go4FsNotIJIJSqSSK50ojiPSkKdwvNpsNXq8XdrsduVwOABCPx1GpVFAul7GysoLTp09jaWlJIDGz2QxN05BMJuF2u1GtVmE0GhEMBlGpVDA/Py+frzIsKXvFMbnSZduNi9FoRCgUQqPRaKl9MJlM8Pv9cLvdOHHiBAwGA1566SWUSiVomoZKpYKvf/3rWFpaQiwWg8PhgMPhgM/nQzAYhNlshs/ng8fjwbvf/W4sLi7i3/7t35BMJteteu8mG62T8VBxW1XRqQqW0F69XofVasX+/fvhdDrh9/ulAp8G1Ww2Y8uI0EYAACAASURBVHl5GY1GAzabDX6/XyKWYDAIm82GWq2GSqWCTCaDbDYLv9+ParUq+HIqlRKcuV6vIxqNwmQyweFwwGazoVKpbKoW4FLWiBCFy+WCw+HA5OQkzGYzkskk7HY7XvGKV2BgYAC33347vF4vrFYrLBYLfD4fTCYTLBYLGo0Grr/+elQqFVEuhUIB1WoVmUwG8XgcJ0+eRCwWw09/+lPkcjnxcPeC0KDSueB1MVLg7wcHBwEA1113HQAgEokAAMbGxgAAv/d7vwcAePnllwE0vWx6tE888QQA4NprrwUAxGIxAGuQovq93J+M4nldmqZtw91uTvQOl5qXZNcHv9+PCxcuYGlpCc8//zwKhYKQVeLxOLLZLM6fP49SqSS5l0gkgqGhIQQCAYyOjuIVr3gFBgcH8a53vQszMzN44YUXkEwmxZFjpLubEYsenu7v7wfQfP5cG33kslGvwRMnTgAA3vve9wIAPv3pTwOAGGpKp8+9nDXpSuTSTtnTY7Xb7fB4PMJYisViWFpagtFoxJkzZ7C6uiqediQSQaVSwfj4uEQ85XIZfr8f2Wx2U/mCdgWE3Rb1O1Ujo6+w51oxGen1ehEMBiWqMJlM8Pl8QiGu1+sSmdhsNoHQiEHXajXk83kUi0VJ9pOmWS6XWxQL/0647nLrXNYT/XMiPMpn2t/fj+HhYYTDYaGd0mvlvdGwms1m2Qdms1nWxOVyIRQKoVqtSp6qJ3tfNtp3tVpNFOH09DTS6TTq9Tqq1SqSySTK5bLUtwBrey2bzcJmsyGfz0PTNJhMJnHqAAizTFWcvWhl+2VbjQuVVjQaBdDqJak1GQcPHkStVsOpU6fw3HPP4Xd+53cAQIwJsLZJHn/8cUxOTuKv/uqv0NfXh9HRUZTLZTz77LOoVCqb3hA7sXH0XHkaGHrdjAxIr6QCtFqtEs57vV5cc801CAQCUreSz+eRy+Xw3HPPoV6vY3h4GC6XC9lsFo1GA319fRLp5HI5zM7OIhqNwuv1otFoIJPJIJ/Pi7IlvMY8C3NX+qLL7RLeO2GMXC6HarUKq9UKr9eLQCCAw4cPY3x8XAyLyqTT52cajYYw6VKpFAwGAwYHB+Hz+XDkyBFYLBYxsrupMPSen0oHB5o5DUYKoVAIAHDTTTcBAN72trcBAGZmZgAA3/ve9wAAzz33XMv7GPm87nWvAwDcdtttAICTJ08CaDoPhw4darkOstH4e0ZOZA5eLktsM9KJUUlnKhKJoFwuS74oFoshnU4jnU5LpE6nisIEf7lchsPhgNvthsViQbFYxL333osLFy4gFou1lDPshRyLnqF24403AgDuueeelp9kC/IcMwdLA3z06FEAnevq/viP/xhA87lzv330ox8FAHz4wx8G0MzFZbPZS76nriT014MiqCDofWiaJt4I8wNqQs/j8Qh7SD0I7Wiqe0k6UZBVYQTDvIfFYoHb7UYgEIDP5xPmHBlSzJsYDAbJNzERXyqVUCgU5E+pVBKlTkXBNVOvRSVFdGM9VXo2nzENqgoh1ut18TjV36nvKxaLqFQqkpOLRqOwWq0YGBgQRh3/WK1WFAqFbb+fnmyPqPuwHb2c0FexWBQnQtM0lEolyat1Mgo8L8ViseW15XIZpVJJ9lMvcumubKtx0T8glQlDo1EqlXD+/HlREoRrVAXH3ITH45GELd9vNBrh8Xjg8XgEGtpKFNOt++VBoQfIe65UKlK3AkCulQaWsBjhLp/PJ2SHQqGAxx9/HJFIBGfPnkWhUIDFYhFm1dDQECYnJ1EqlSRXtbq6ing8jpdeegmpVAq5XA6VSkWMkpoo5fXSOHVjDU0mE4LBoLDAXC4X3vKWt8Bms2F6ehomkwmzs7NYWVnBgw8+CKfTiTe/+c1wOByiHM6cOYNoNIof//jHksw3mUwYGRnB6Ogo/uiP/ggOhwNmsxkejwfHjx9HMBjEM888s2sGRq/46GkycqADxsp7/pvXywjizjvvBND0UOlRqn2zgGauhV4+9yFZZMTUp6enW76Php/fp2etdcO7p+PAhLraoghonqtyuYxUKiWMN+bYVH3RTur1OjRNw9TUFDRNw9GjR2EymaQW7LHHHpPIdrfrpv71X/8VAPDbv/3bAJr74fz58wCa7L83vvGNAJqRKmViYgLA+k4s0HzeZKdSN586darl/X/+538OoJmruxzZ0ToXRiqpVArlclkSzBRuOLfbLfx0r9fbUtfC11gsFoRCIWlxQtipnexUzkW9RjVxq0/gq//PdhTVahWVSgXZbFbyJcViEfF4HIlEArFYTBQAvXuXyyXFksViUSC0bDaLVCrVkpxVDTS/X81nqPew3bCY+tmsqObPRqOBSCQirWucTifGxsYE+svn85iamkIsFsOzzz6LTCaDUqkEq9WKarUKm82GXC4Hg8EAj8eDQCCAQCAgDT57sndlvX2mOl80oPz3RuUHTqdTdAhrudqRb3rSXelanQvQWnVOhlIymRSlR2WpetQ+nw9vf/vb4XQ6Ua1WBSIiVZfRgMPhwO23345UKoUHHngAiUQC8Xi8bdU8vfRueefq/TKv4na7JZRnh1ZCewzRea3VahVTU1NwuVzi3e/fvx+VSgVnz55FLBbDwsKCFENybQqFAs6cOSNsOmCNRbSwsCBwGqM7YswqNVltl9GtA1er1RCLxaSepVAo4J577oHRaJRKauYRSPo4deoUjEajsAhZVMv8ENcvnU5jZWUFL7zwAvbv34/jx4+jWq0il8vh/PnzOHXq1K7XA3BdGXHQ4NEDZZT6v//7vwCA3/iN3wAAPPzwwwCaHuzCwgKAZgTBCIefz3oXRi7cW2SJ0RM9fPgwgGblP9d+dXW15X283m6IPhrqxHSiA7aRc6i+xul04nWvex2Gh4dxyy23YGhoCP39/SgUClhYWMD8/Dyq1WpLL7+NIqFuyvve9z4AwD/+4z8CgBSC8vrYMaGT6K+b//7P//zPls/50z/9UwDNXBsjaT3xKB6PX+qtXCRdi1xoCPjg1YpaTdNaihrp0RKuYW1HKpUS7JXvJYzEym16r8zD7LS0+05VWTOn0s7jUqMEMlq42VngRYWrvp+0bFawsyNsJpOBpmkSFbYzGvr/2wlYgJg3c2a5XK6lTQ5b+ZAlRqeDz5y4ueqssH3Q0NCQEAFIBuB99WRvy2ae0UZwD/UI90QgEMC+ffswPDyMkZER+Hy+ljwv87dqsXBvz3RHttW4qJxrPR1XLegrFAotNRx8DWthLBYLyuUynnnmGbjdbhw6dAgHDx5EMBgEAKmjIRzGhHg7YT6Bf99u0eeK+H+FQkHmrpDZRCVLr52b3OVy4ciRI/D7/Thy5AgajQZeeOEFVKtVHDt2TCCvbDYLl8uF8fFx/Nqv/RrC4TD8fj9yuRzuvPNOLC8vo16vS8GZHnpTMWbVsKv/360DxjWh1Ot16SDAQ14qlWCxWBAIBGC32+Hz+VCr1bC4uCgEBWCN6TI8PIwPfvCDOHToEK699lpYLBapd3nhhRcwMzPTVe97s6JXjh6PB0AzF0LMnNAPe4txTdhDjDmV2dlZAM2cCz9fP8eFBvonP/kJgGbvMLUTBICLmJ3swqyvr9gLomfg0SllWxeyDt///vcjFArJveRyOeTzebjdbni9XthsNunmy7NAJ7bb96u/B34fI1f+ns9Tv386zelhruTLX/4ygGZuJZVKtbzuS1/6EoAma0xvtPm5arupS5Wu51z0F28wrFWj22w2mctAaKBYLMJut0vH3lQqhWKxiNXVVTgcDhw4cEDqI4LBoLTc3r9/PxwOB6LR6La2drkc0beVoGHRkxMYsTBqGxwcRKPRwLlz51CpVKTWY3R0FJqmwev1IhwOy+GxWCwwGo2Sa2F+hzkJ/YHp5KXthBLRX4c+38ZrJxQ6MDAg7ykWiygUCjCZTOKVXn311RgcHITT6WxxYHK5nDAQe/LzI2p3C6DZ4YEdLfbv34/x8XGEw2Gp6mc+knqDHS5IGGo0GtA0TajNPdk+2VbjonoUaqGgSne12+145StfiVAohImJCfj9fhw/fhz1eh1PPvkkIpEI7rvvPmQyGRSLRVitVpw8eRIDAwOYmZnB0NAQ3vrWt2J8fBzXXXcdKpUKbrrpJpw/fx4f+9jHsLCwcJHSUvt6ddMzB1pn1LCOhPkGwluMFlhfUqvVpJ/WsWPHZLpbIpHA6uoqjEYjfuVXfgVutxvBYBB2ux2hUEjyEkxy12o12O12OWylUgkXLlyQCEZdE9Vz4//tpJeqRkncK36/H319ffiFX/gFhMNhHDt2DGazGfPz8ygUCtIW5vbbb4fP54PT6RQDXa/X4Xa7kc/nJRezVxwNVcjaYoTCyOUb3/gGgCZGzojF6XQCAMbHxwEAX/ziFwE057aw/x7rYRKJBICm58r8JlvofOpTnwIAfOQjHwHQrItgpPLqV78aAPDtb38bwN6BjOiU0kgAa3knnu+xsTF8+MMflpontWjSarXC5XJheHgYhUIBVqsVHo8Hr3nNa1Cv1/Hoo4+KY9bt++zk8CwvLwMAhoaGADRzZHz+jHhZz8LnzohWH8noWZI0yHzunWBGzgdiDu9yZEciFy4QoSL2yrJarZIrYaK+XC4jk8lIIhJoFleyet1qtcLhcMBut0vuhW1T2slOK01VOtWRqMlK1mjwnmw2mzzkfD4Pi8WCiYkJqURnASZrP9gSA4DkbdZL0ndiiO20gdEL14MKJBAIwGQyIZVKCf26v78f4XBYamGAZh6Jf0hX7cnPl6jdLYDW/eJwOKTHnn4fM7pxuVxwOp0IBAISzfT2Sfeka8aFnrnD4ZBZFOy8SqbT3NwcGo0GTp06JQwfFgEaDAYpGlxaWhIWFb0Qu90urR+WlpYEY28H93SaO74douZa1O+g58QIQZ0cqb7W5XIhGAzila98Jfbv3y/GY3BwEH6/H5OTk7Db7ZicnJRq/1qthng8jnw+j7m5OSwvL8vnM8GfSCQEX9fnVfS5sN32SrlX2EmAzLhrr70WtVoNZ8+ehdPpxHvf+154PB4xLBtd9164P33vp3/6p38CAPzLv/wLAOB//ud/ADR7iREiZv3B/fffD6DJGvrmN78JoOmBvvnNbwbQrJd45JFHADQjHbLNGCGxEpu9x1j5zbkvemWr74W2naLP0a4nKiGIz1TTNOlZ53Q6USgUkM1mxXEjBEYjwqjg9a9/PZLJJKamppBKpZDNZtdlku5E9MbI8plnngEAHDlyBABw9uxZAMDIyAiAZgTDSKeTdLpW5tj4fRT2qiMrcTukq2wxQhZ6uhsVZDqdRrlcxsLCAjRNQzwelxoIQkgApJqffbIymYxMq2Tjy/U2x05HLu049e2iCRoDh8Mh82yYfOfB4N8JqzFCYV5hdXVVeizVajWhOvP7uJbrQUSq8dlNITWaDS2j0SiMRiMymYwkXImN07hwn/HvavEtc3C7fV892R5Rk9kq+4v08+XlZZlhZLFYWjp/q0xTtkziufL7/SiVSm1LGVTpxPrsSXvpinEhNZDY6FNPPQUAQhuml/rSSy+hWCyKYmThm1ojAqwVDbpcLoTDYVSrVXzhC19AIBDAzTffLKN96dGo3upOJanb0XtZ70LsW6UGs4Gn3W5HX18fQqEQRkdH4fF4sLq6Kn23AIghVZvvFYtFnDx5EgsLC1heXoamaYhEIpL4Z00RAIGJ2hWZcn12aq3W67SqDv16+umn4XK5kMlkYLVaRWF87WtfQ39/P17/+tdLFMuWOaSn+/1+vOENb0A4HEY0GpUIbrcUgv5eydJ561vfCqDJzpqamgIAHDt2DADwhje8AUAz0mGFPteQdQx8H71yerhXXXUVAOCGG24AANxyyy0AmjmZT37ykwCa815GR0cBND1XPTupG6LP0a73jFTWp9pNoFarYXZ2FsvLy5ifn8fo6Ch++Zd/GePj4zh69CjMZrO0kZmbm0Mul8PVV1+NSqUCj8cj9WCpVAp33XXXRewqfjcjIRIEtms/6efmHD9+vOX3ZH+RvbVRxKIXVvwzMmEuj8Ic3Ac+8AEAzX2wJ3MuasSiJvT5O2Btc9DA8O9Ak/3B6lqXyyV1DnwfsJaHSKVSiEajcLvdLYZFD5fslFJpB8fpr0NV5hQqC7UfGFu8qAaLXZEBSPSWTqcRj8ehaZpgyPTmNmqf3y4ns9seGaEPNrecnZ2VIlB2gE4kEggGgzJMjbkZkhxMJhOGhoaQzWbh8/mkH1kPW9+7stl9pz8//EnndG5uDoVCAdFoFA6HQ5ARjv6OxWIolUoYGxuDxWKB3W6XRpfbeZ09WZOuGBcWs9VqNZjNZkxOTgJYY7RUKhVMTU1J3YOafKUXesMNN8Dv9yMUCiGfz+M73/kOUqkUHnvsMfEgqGA4AtVqtcpEQ4bA9IS7Je1yLfw/3pe+RxOAlmmJqVQKJpMJDz/8sNCMuX5utxvHjx+HwWDACy+8AABS10JcOZFIIJ/PS6Q0OzsrORc2rVSjK71Hqm+N0U3ZyAvmc2NPsdOnT7dAqmzl8Z3vfEc6S1utVoRCIRw8eBCf/OQn4fF4cOLECQwMDODcuXOYnZ3FE088IW1ydlvoEX7/+98H0JzbwcpsRij63mGM4nlWSJJZWVkB0Kz450/mOe+9914Azd5U+/btAwA5k+yyy9wOpZ0jtNPSLp+5HvRdq9WgaRqmp6cRjUbx3HPPwePx4A1veAM0TcMjjzyCer2Offv2wWazIZlMIhaL4dFHH0UymVy3AzD3Jf9+KUKYWxX9vxmBcv4KWYV/8zd/AwD4/d///S19J+nVZCnqz/pf/uVftvye97jZeS7rTa7dduOi99DV7qNsOUKFRzxcz1xSK/DVCnd6GMwtcLQt38/OwkzisYJ/YWGhaxz2Tsm+9R4ODwINULFYRDqdbhk7TGrtxMQEzGYzYrGYQEf5fF5aoqjGmVEe81P8fbtDuRvR3WaEewZoHjxeH58/I1W208lkMnC73bKvHA4HPB6PNANVZ+f05MoQ1bConSs6vZbC7to8S5qmYXZ2FsViEYlEAkajURSopmmCgnCExXrS2z9bk64YF7Z3AdYgnBdffBHAmnJol5/gQ2Ob7IWFBZTLZWlASMoyDVU+n5dZDV6vVz6PzJFDhw5h3759+PCHP4xCoYD3ve99Utm83aLSn9XohZ4lPWbmS3j/agVuvV5HOp1GNpvFuXPnWuDBbDbb0lE5lUqhUCjgwoULEsGo3H+1zTgP5EZYtvpzL0ina9G3SK/VarBarRgaGkJfX5/AihzToB+oxvftxL0yYtUbSLK8yPridRF7p/dMKj5rtMg65BqwZxqfO3/PCOhb3/oWAOAzn/kMgGYdDCMZesjE8vUR9k4wpPTfwX/riyTJoON+1ztsqkNaq9Vw+vRpuN1ujI+PQ9M03HnnncIsDQQC0j/r3LlzEvkROuumqMZRf++MTJkz+/rXvw6gWVF/qbAuP5drqJf/+I//AHBxJf5mn/t6kU1XEvoqDKNCQOrwMP7Ue9BkkVksFiSTSfHM2ylxl8sFn88nNSFerxf1eh2hUAihUAgjIyMoFAoXUVe388C0y12oXHz9/7H9ix4+o3LJZDJCfCBsRqPM6XvFYlGKTNU1bTSaQ8k6KdF2SmMvGZatitFoxPDwsORbgNbu2h6PB8FgUPaTupfUGqSe7B3pZNjUGi7VIKnnqdFoSK861n8tLS2hWq3C4/HIYD2z2YxQKCQICKdWAt2hXf9flK4WUaoRSbv/13ssxDWnpqYwMzMjTBi2ZyBJAFizxNdccw0mJiZw4MAB5PN5nDp1CvF4XHoMcTNxLLCag9kOpaKSFfT3R0+Sr2OEwnYt9KZzuRwsFgtGR0dhNBqxtLSESqUCr9cLs9mMTCaDRqOBxcVFVCoVuFwuaWypJqr5d7bOAVqjKhVGAnBRPupKhY28Xi/e8Y534MiRIwgGg9L2xmg04tWvfjUmJiYwNjaGaDSKu+++G5lMBg6HQ5QQodrtlk7Ub7J/KGT/kFWoz4nRk2XkzQhDTWYDzfoHsoP4/2QHMZfzwAMPAAB+9Vd/FQDwD//wDwCABx98EECz8n8nWue0g2pVxieFXS6cTqcgI2qExbPEqIbTV5mX5PvVAXyDg4P42Mc+hlKphK985StYWFjAc889h1wuJ07ITt47kQ698HnTgd5q1+JO4775/Yxc9AZ9O3TBjs5z0Yve4wAgVeec9dKOAUYl6fV6xXDoPdJSqdQyLKsb0i5fRNFHW+q1EyJjZb7b7ZaEGt/D/mDq/VBpqkZZrSNSP1/9d7tr7MQUu9KMjNlslgae9GyZiyN0Ssoy83c92dui35s0Hmo903q1ZNQbzLuoKEG1WpWKfv3e2Cliy/8V2TXjwmI3g8EgdSr0ptXEP7sGA63V7yw8ZPO5crmM8+fP4+zZs8hkMnA6nXjkkUegaRrOnz9/2UyPdtLus9RNSghMhQk5NIwUyRtuuAF9fX0YHByUhDW9LDbaazQaGB0dRbFYxIULFyTEV6E2VrcPDAygUqkIicHlcom3pybLSRrgfaie4JVmXAYHBxEIBMSwnD59GtlsFh6PB9VqFc8//7xU/TNaVDtF76QwoiEbjL2imAPh77nnWYHPCnp9N10KX886hT/4gz8AAHzlK18B0GQdsRcVOwKwcp/nQ99dfDebf5K4Mz4+Lm2ACGmxWh9orhlzNMyzORwOaQhbrVYRj8dhNpsxMTGBkZERLC8vI5PJYHFxEfPz80gkEi2tlPaC8N46RSz6Ohm9sK5JL3/yJ38CoNmhoRuyK8aF3gTppE6nU1qaqAeeSlllQ6mfQU81m80iFoshmUwil8thdXVVsNSNqve7cW+8dvWn/rqdTiecTid8Pp9AGgCkw6/D4ZAEpRrB0fDSUNF4qa9TvTt66mrHg58nIR2ZCqdcLiMejyOTySCbzSKRSCASiSAWiwlzbr2cVE92T9pFD5zTwudM6FytuleJAGr341AoBKvVKmcmk8kgHA7DZDKhWq1iZWUF0WgU2WxW2JftHI6dIDf8PMqOGJd21pV1C8FgEDfeeCMqlQq+973vIZVKXXTwi8Viy6bjTHhgLQH+1a9+FXNzczh37hzy+bwwabipdqM7rt6ToNLndMjrrrsOgUAAw8PDsNlsiEajqFQqOHjwIIxGI0KhEKrVKk6fPo18Pg+j0Sj3QSPDCM5qtWJgYAC1Wk16rKmjofl5JEvo8wzqWnfDa+vW4WSnA7ZXJ6U0EolgcXER9913H+LxOOLxuMCs+oTwTot+3gonQ77yla8EAHz3u98FANx4440AmjkZzn9hBMNKcubQKIxE2KPq05/+NADgO9/5DoDm5EP9jCMVOtpOWS8SVmFsdfYRoxK1P5/JZMLg4KAMwiN0zs/mAD2bzYahoSGEw2G86lWvkoiO86MASKuYp556ColEQia3qmUAqtHqRjTPayG771KlU8Ty13/91wDQ4rgCzef9hS984bK+dzOya5GL2WxGMBjE4OAgwuHwRQpRFX1rF0JmnLw4PT2NqakpFItFwVWBi2mg3boXVdqxsPTUa45z9vv98Pv9UjRJPBhYi2A4VbJUKgl7jKQGPc1Yn7jjWvGA8u8/L8LITc1LlctlaJqGWCyGaDSKhYUFqR/qeZ17V9SzreYjaSzGx8dleFy5XJaaL3XCKdvok4Ls9/sxODgodU7qGIqFhQXEYjHMzs7KwEHV8ejJ9siOGBd9xMKJk29/+9sxMDCA/fv3Ix6Pt0BBFDUfQDEajUgmk/je974HYK0TLJs37qRnqiYL23nn+qSjCgcODQ1haGgIr3jFK+B0OuH1emXqIj0remY8QOpo53w+L0aUkV2j0RByA7+T/HayyPRwXTsIYLvXbrs/j8oiGAwiGAxKd9xkMom5uTncfffdWFhYQDab3TW6cafksJ7lxToLdgx///vfDwD47//+bwDN3An/TdYXnRCyww4cOACg6akyKiC2zp5h9JTZ00ztnK3+bFdLcinruN57GB3QoHC2/cjICMbGxvDrv/7rMhDQ4XBgZGQE6XQan/rUpzA9PS3UYYfDIdNc+/r68KY3vQmBQAAul0saw5ZKJbz00ktYWlrCyZMnpRdZo9FAKBSS79eXS3RLl1xuxELR6x3WLbF3nF7e8573ANi+CHW9fbGjkQu9zbGxMezbtw8HDx7E4OAgQqHQ2sWsM6pY/QzCQisrK5KsVsP8nVQmavisXut6jBZ62QzxOZoAgNCn4/G4FIax7QkAabtPpcnDQChQrdpXiQS8Bhrv3UhmbyR6Jlc7iI7KiNNIqVyr1SrS6TRisRji8TjS6XQPO9/jwrPMGSxM1k9OTmJsbAyHDx8WI0gInfrDYDAgFouh0WhIy32PxwOv14vBwUF4PB6JanlOGNGurKwgHo9L7obXotcd7UolerJ52THjQsW2b98+/N3f/R0mJiYwPj4usxZWVlaE1bGecDMajUY8+eSTomRZYbvThkX9qTcojUajpfK50WgIRfqhhx5CKBRCIpHAyMgIbrvtNskfED+uVCoYGhqSGeBqexj1OyqVCqanpwUSZOEqsFYjwNwEqc9kkxHLVu9ht4R1DMS8SUlXIw/SrDme4MYbb8T4+LjU/jz00EOYm5uTDhH6e+rmbBK9bLSezAWo3rf6vr/4i78A0JytTkVJNhg9VHZRZoQaDAYBAD/4wQ8ANCcaqq1z1Pfrnz/Xhv/fiZ12uULD4na78drXvhZGoxHPPvss+vv78cEPfhD79u2TGUbcE5qmwWaz4Xd/93eRzWbx05/+FOVyWRpQplIp+Hw+DA8Pi3EplUp49NFHMTMzg/vuuw9LS0uYm5uTLhYqMajdfRoMBumCsB3EID4nFXW4FGlHfACadSudZKPfb1XWW48dMS5qxXQ4HMbExASGhoZaiqJyuZx0/u10waoHoSpQtXZkN5WkytJSr4MwDl/DqAvL7AAAIABJREFUuhUORisWi9IPje9zuVySa2HrCpUUoMJ/THCqYb2ai6hUKsK06XSI9oIwn0QmEP/NnBsnj7rdbgQCAQSDQfh8PjQaDZkDQ8pmtzoy9GT7hc+XThCfq9q6h8aIjhcpysViES6XSxAA6hT21svlcpiZmcH8/LzkWmhYgOZICqC3T7ZbumpcqCg4YvSGG27A4cOHMTY2BrfbjUajgVwuh89//vOYmprC8vLyul4ljUsul5NEHz0sFhnuhfbqeiNjMpkwPDwMg8GASCQCs9mMq666Cn6/Hx6PBz6fT7Dl2dlZaQseCoUwNTUlQ9IMBgNWVlZQKpXEiyIMRuPCnlqHDh1Co9HAuXPnUCqVBOOncWP+RV9gulv5CYPBIKwhEjtY48N2OFdffTXcbrdAIIcOHUIwGEQ8HkcymcSFCxdw4cIFwei5F9pR2XfjHoHm+rKHGOtbWO9CL5nzN3jN119/PYDmJMmbb74ZQLOinjmVz33ucwCa3ZdZ+c1eZextxkmYnWQnKtRJD37kkUdkhhGfm9ojj0J4eHV1FbVaDSdOnBBaf7VaRX9/vyTsc7kcTp48idXVVTz22GPIZrMyyoGfybEWkUhECALtrlPPrLsc4XO+XFGjeaCZUnjnO9/Z9vV61thOyI5FLlS0jEyo4HK5HObn5zEzMyOwD5UzPRe73d6SaGRLFDVBrf8uYGcUZbu6FjV5T9GztnhvZrO5BeYitZSKla+j8i2VSjJgjd+ldlhWr4V0b3r2qlHRY8udEtA7KSp5w2w2o7+/X+pX7HY7xsfHxbC4XC5J2NIA0Yh2gim5zrtBTe/JxcJ9SFakw+FoGX+u7n8O3qMjpf6O9U3FYlG6HCeTSUxPT2NhYQGrq6sXQebqud2ITdiLaC5NumpcVL54LpfDj3/8YywuLuI973kP+vr6EIlEsLS0JAOv1HnXNpsNBw8eRCgUwokTJ2C1WmW073333Yd8Po/p6ekWD1edCwOgrfHZTllPIatdcVlfoiYNGX0Ba8ouGo3C5XLJBEE2q3S5XMhms9Ksko0r2V+JdExCC5zcePbsWdhsNuzbtw+NRgMzMzMoFApiZNTDtVuGRY/3MwdksVgQDAbxvve9D+FwWCATGpWJiQnU63W8/PLLqNVqUolPogQr8NUiU5PJJMQRFlTupPAeuS/4/ZFIBEAzYvna174GoFnBT2GX49e//vUAmrPVWf/CynvmcphjIUzIuTF33303gOZ0WM5qJ8S8W8lrnoFUKoVPfOITsFgsQi0+duwYXC4XPB4P7HY7hoaG4PV64XK5UK1W8eCDDyIej2N+fl6cC03T8MwzzyCfz0sHEAqfAaN5FbK+0gzJW97yFgDNHnF64T5jro4R8U5I1yMXPjB6lvF4HOfPn5fxorVaTfr8cP47B39xQ3m9XlgsFvFa6MVXKhWJcHZD1svxEOKhsJUNhfUr+XwemUxGiuJYSFav1wXmq1Qq0m2A/2byXl/wRe+fnhoPlZ7HvxciFVV4rVwjwqmkkjKCtVqtcDgcqFaryGazKJVKGBgYAAChJrNNSK+P2JUl3NOLi4sCjzqdToHMXC4XHA4HarWadLYoFAqYnp5GIpHA8vKy7HtGMeqkW73oz+6VZlj2uhi2sqAGg+GSV5/KzGKxYHh4GEeOHMHHP/5xOBwOPPDAA1hYWMD3v/99FAoFjI2NwWazwel0wuVyYXx8HAaDAcvLy0gmk9IzjLCH6q2p2OlWN0uj0di0xjUYDA21Bbhe1DkdKuGAbSyGhobEKPb19eGWW27ByMgIrrnmGumjFI/HcffddyMSieDMmTMy3Ih1Lu1qEVR2mtrEUg8Xqa002s3w2OpabPa1+uvl91FYA8UpnH6/H/v37xcHxOPx4JprrkGxWMTXv/51VCoVvPnNb4bdbsfKygpisRi++93vStdoDo+iAVah2c3Kpe4L9d70kJw6s6TdmlAYwXBy5NGjRwE0cyqcUEoWEiMVGtzV1VUATe+c18FcjT7PoEb97WQn9oXyfmFrMX9rsVgQDocFEqtUKlheXm4hA/GPWvfW7rPb3NuWrm8n16KTsEfcww8/DAAX9WFkXRT3T7ek3VrsGBW50WhICxN6JktLS/D7/YK5Et7hHxoLHoRkMolkMtlCjyTbQ40idtoDaRfBqNeidhggfMdeRpzJwpkT5PJHo1HEYjGk02mk02mBtNQaFTW/Q1HXRu1WoI9Y9ioEoLLpWAhpt9vhcrkQCARaWvqo1dUkjeTz+Rb4UV2P3SZ69GRrwmeozrin4SaLkI4W/w/Y3JwefbTfk+2XrkYueu9MffhslW4ymaBpGqrVKnK5nCTqVOohawDIiFLnOaify++61M2yVU9ELfpsB0/97HUC3alVyG63u4V6q1IvVcMKrHmX2Wz2IsoxDxTXSh3nSzxZ39dNva52MJliFLvmlanXDFzsJfP3rHEgRHr99ddjYmICf/iHf4hyuYyPfOQjaDQa+H//7//Bbrfj/vvvx4ULF/Ctb30LyWSyo7PRTQ/VaDQ2COECG0921HvQnf7N96kU8/V+6j9H/5z1uZ9Oa7KT+2Kzoh9bTedN1RM7NI9myxFttxycz372swCAP/uzPwNw8TwgRrDdMqi7Grn87ALkZ6VSkSZ8euEDoMKkouw0D77Tgeq2qFDLRmwTfe6DHjcVLOEM5lGYTyLcoc63Udvl8/PbfVe73+1WdLcV0SvKQqGAUqmElZUVuN1uRCIR6S0HrLUVdzqdiEajSCQSF1FYKXstz9STS5O91BK/J51lx3IulypUCCr80y3rv1VPpJOyUr1yRhjqa9lG/Gff2TaMbxdxmM1maXeSTqfF4PAzgVZDqzckG3nNutfuuoeqfL5g72azuWUqn9FoxMjICIxGIyKRCMrlMjKZzLYqoO1cC33E0Ob9LT/1jDpi6jSsfB3rGFjx38UBeXtmX+hlo9km2y17YS34/M+cOQMAuOuuuwA020GxN934+HjL+37uIpdLEVUx7jX2TycYrNNr+XsV/9d76XyNakj5PXpDtZmk5EZwR6fr20uix97Vsbf1eh0XLlxoS07Ya/fRk+5KL6LZW7LnjYsqe3HztFNgqvFop+yZbFdFH+no2+MzumE3VX3jSf379dES0KpwO2GvbAq6l4VsN3pj09PT0rGAsCGwN4slN3tN7ZwO4OIKb/6e0HG7/FW7z9mqMIG+l0XPzNvr+3gr0uk58p4///nPA2jOA/qv//ovAM35PrvhaF1RxuVKkHZKHVh/QqXKJNtIOhmtdodfzybbKLq6koQwGdDKDupFKz3pyd6QreZcogDmunc5uyr7Go1G/2Zf3FuLpvTWoim9tWhKby2a8n9xLbZkXHrSk570pCc92YzsrQx5T3rSk5705OdCesalJz3pSU96su3SMy496UlPetKTbZeecelJT3rSk55su/SMS0960pOe9GTbpWdcetKTnvSkJ9suPePSk570pCc92XbpGZee9KQnPenJtkvPuPSkJz3pSU+2XbbUW2w3Wu7vpOyFFtp7RXpr0ZTeWjRlt9ZC7SXHJqWXKxuNP1jvWn7WHLa3L34m7daiF7lcpujHDJtMJphMpo7NKzkAbaufr/+erbxvM9et/v+lSLv3bXSvl3NPm3nfpayd+oz22oiH3ZTdbnZqNpsxPDyM4eFhqBNgL1VMJhO8Xi+8Xu+WzyOnym6nbPUsXAmy54eF7aRciifSqeNwu3U1Go3weDyo1+vI5/Mts0f4Pn1n407dkjcaY9tpMBj/Xz+Cus344y0PTmvXdXm9eTLqmABVOrUXbzcQTZVOXZ/1/69f6/UM8M86LnfdQ71S2sTvZuSiRhrbEbmYTKaLJrdu9lou9YxQNjqf/L9OemGja7tc2eo45CtyWNiVJpt5GJwBvtFG4AO2WCwIh8MA1qYvViqVi8YXb4fQK7vUKYaq8l7PILY7WBvdgxpRUBFzHbowVe/nzou80oWjwVXRO0iq87GV8RWXci2XK1tV3puVLQYLsFgsAJpzgGjA9fOiLkV6xmWbpJOCpCHhhuewJypL/n+76ZL01kZHR/GJT3wCRqMRn/3sZ7G4uIhCoYBarbYhXtxpE7fzhPhdS0tLW7t55f1A66FV74ujncvlcovHqI9I2kVhBoMBLpcLdrtdBiDNzMygVCqhVCqhXq9f5NF2MhDrRW2UnTYwOx2xbNcQsd36LqPRCIfDAYPBgFKpBJPJBLfbDWBtxG+1Wu3aqOfLEeoDvVPUad9utGbrDf1r93719RaLRc7S7OwsGo0GRkdH0Wg0sLi4iGq1elnPrmdcdlhqtVoLJKSKXpnydczjAGuhPDfoZrHinR6rsIVQ+iJj2u5zVHhANVJmsxnValVyXDTUlxIZXc59dFPWM3Bb9VK3+p4rRbg/zGYzrFYrhoaGUKvVsLy8jHK5jEKhsKemaG4n0tDu70AT8uMfva4w/GxiK1/H/9tOh6qXc1GkW3hyO6xVb0gYnqoK0mAwwOl0wuFwYGxsTObFl8tl8dIJDenzB5uBmfSv4wGtVquo1+uXnX/Sf996EYpqTLkWHN2rbnqLxYLBwUGYTCaJ3srlMur1ukQwjIzUPEa7a9ooR3Sp2PpmX7uJzxKGFCMbdYSzeq38vd7x0Bvby42Q9hJDSo0CTCYTgsEgDh8+jL//+7+HyWTCl770JczOzuLxxx9HLpfb9rHXu7kv+FNvDPjsXS4XGo0GMpkMAMDj8QBonimr1Qqj0Qir1YparYZUKoVGowGHwwEA0DQN9Xp90/rkisu5XClJzo2kHcSyEeyi/r5SqWBhYQEARJHqpd0ma+fBr7dZ2uHaW5H1NqCq5NbLyaiK0Wg0iuLgewwGQ1sCQCdosZ3s9XyKyWRCIBCAyWRCpVIRSNBsNsPlcqFWqyEWi6FSqaBSqYhhNRgM8Pl8MJvNMJvNaDQayGazLQb4501MJhMGBgbgcDgwPj6OgwcPYmRkBCaTCRMTE6hWqwLXbleyezdEdRoYpRMaNJlMsNlsAru3QwRsNhuMRiPMZjMMBoMYkXw+35K7pPHZDlh4TxoXhmx+vx8AkEqltiXB1A3ZyLKvh4mqm11VmOprmUugYuA6MFrha+12O4BmDYD+e7kx6QmreQ/1+5gH2g7RXwM9Ib3HpRIc6E3xfTQuPEBmsxkOhwONRgPpdFrWjQdD/Q41n6WutQqj8br4GbtteEwmE0KhED7wgQ/AYrHg4YcfhtVqxa233orBwUFMTk5C0zT8+7//O6LRKBYWFpDL5bCwsACHw4HbbrsNfr8fPp8PhUIBP/7xjxGJRDA3N7ftnvtuCvfJ8PAwPv7xj+PAgQM4ePAgHA4H7HY7arUa7rjjDszOzuLkyZNIJpMd2YmUnYAOLwVZYCRisVjg9XoBANlsFlarFddee61EbOVyGffccw9isRgymYzcJ9fJ6XTCbrfDarUiGAyiVCrhRz/6EQqFglwPnRkVRtPnJoHN5dF2xLjoPUoumNPphNFoRKlUklyEyWSCy+WCw+HA8PAwAGBlZQWlUgmJRAK1Wu2KimQ6wU96Baty5/WKluuiKml6pvl8HkAzzHU4HKjX6xLWqnAKlTi/Y6eNdbvoij/10JhKdmDeyW63i/feaDSgaVoLLFgsFtFoNDpSVmk8GBG3y8/slvDZmEwmyR3wDJjNZni9Xng8Hvh8PlitVrhcLuTzeQQCATgcDnnN6OgoAoEAfD4f0ul0i6G+0oX7g47GxMQEDh06hMOHD2NoaAh9fX0wm82y530+HwKBgMCsaoS72+ux2WvgubVYLLBYLKLUuU8cDgesVqvA2RSefUYrXq8XgUAA4XAYFotlXUejXeRzKevWdeNCHJ8bAljzrt1uN26++WaYzWY8//zz0DQNABAIBHDzzTcjFArhxIkTMBqNuHDhApaXl3HnnXciEokgmUzuGQOz1U1Kr1n1okg1tlgscDqdMJvNAotwE9hsNlSrVSwuLgIAhoaGUK1Wcfr0adRqNfT19cHr9eLEiROo1Wo4deoUMpkMCoUCAMDpdAKA5CmA9vDQdkIneoOqhzkZoqshPw8SDSWwZiSYa3G73QiHwzAajZicnESlUkE+n4emaTh37hwqlYoo1FqtJkanXq8LlEDDrI/a9E7QTu0x1j+ZTCZYLBaYzWY88sgjGBgYwNve9jZYLBb89Kc/xcsvvyy5A03TYDAYMDY2Bo/Hgze+8Y3wer3ijADA8vIyEokEYrHYnjkvlyoGg0GcC7/fj6uuugp/+7d/i8HBQQSDQdEx3GcGgwFutxuBQAB2u132lJqfpLTzzLstm0E6TCYTPB4PzGazMOGYG+F9RqNRpFIpvPjii9A0DfPz8yiVSiiXy7IGPp8P+/fvx759+/Bbv/VbqNfr+MpXvoJ0Oi1Gi4iH6nip16OvCdqMntiRyIWeWF9fHwAgkUjITXHh7HY7/H4/gsEgDh06hMHBQYyNjcFoNMJut8PhcMDv9yOVSu06dHGponrnapUxPQuXy4X+/n44HA7xwgjzGI1GVCoVMTIOhwOlUgl2u10ogxaLRTB5p9Mp+ZONvI5OCe1LvcdOn9Xuual1K/ooj0qChAe32y1RrYo7l8tlWK1Wif74PkZx7WjK7aQdhNZtUeFKm82GcDgMp9MJn88Hu90Op9MJg8Eg0GgqlUK1WkWhUJD7JmxC2MNgMIiRZV7mSheV0DE8PIzx8XEMDQ2JYVFhZj7vTCaDRCLRcg74WbsVuWz2e1U9wb1NxEKFxnO5HIxGI+LxuNDyude5t0wmk0TCLpdLaNqdWHQ8W3o0oRMbs5N01bjwoPt8PgwMDOD2228HAPzgBz9AJpPBiy++CLvdjr6+PgSDQfziL/4iRkZGcOTIEcEGG40GBgYGMDAwgOHhYSSTSbHe+hqR3dgw/O5OBVz8u5pXYBRHT8rlcuHo0aPo7+/H61//egwMDGByclKUC7AW7RWLRbz00ktYWlrCQw89hGq1iqGhIWiahkQiIUrHbDZjfHxcYKNisYiZmRnxZpgoVjcWPZPL8XAZedBwMq/TjpWlblqXywVgjaHC3InZbJY1slgscDgcCIfDcDgc8Hg8ktQuFApIp9OoVCoIBAKyBlarFceOHQMAvPzyy4IrV6tVWStVVO91p5QQlYfdbsfQ0BBGR0fxrne9C+FwGMFgENVqFS+88AIKhQL8fj8MBgMymQxyuRzOnTuHXC6HcDiMRCKBe++9F4ODg3jnO98Ju90umHylUpHnsNlr4jrsFeHZKpfLCIVCeO9734sjR46I4VWvuVarIZvNIh6P44tf/CJmZmawvLzcUnisV5a7KZ1gc7PZLM4ijQuJHtVqVWjWc3Nzogv1KQN+Do1vuVzG4uIiNE3DCy+8IHtI1QV64+F0OgWCI6tssyyyrhkXeo4Wi0UWh9gnva1MJoNyuYz+/n5YrVYMDg4iFArB6XRKVMOF43vUz+dPlUHBG97NTdNOKamHQMXWbTabYOmBQADDw8MIBAIIBoOyZvwswkPcKMVisW2C32AwwOPxwGKxIJ1Oy+tqtZpAUbwO/c/LUaidIsp25IJ2hob3ynup1+uSa3E6nbBarbBareKZFotFaJqGQqHQojzMZjOcTif6+vqEspzNZhGLxVAsFvdU5KtCcPyj0swjkQjy+bxg78ViEfl8HrlcDoVCAblcDgaDAblcDpqmiadrs9mESbTZeigVrt0LihdohVPVXK3NZpMcG19nNBpRrVYRj8exsrKC8+fP4+zZs0JRV+EkdU/uFotuM/tQJfRYLBZJLQBNEks7WI8Qq9PphN/vFwde/52q08szSIeOa0VnmBA+r2sj6ZpxcTgcuO6662C1WrGysoJisYjnnnsOAORhJ5NJCXMbjQbsdrvgozQsaphLRUKhp+x2u8W48HU7cTA6Jbr0f2doyoPLh+jxeHDttdfC5/Ph+PHj6O/vx8DAANxut2DnmUwG+XweTz31FFZWVvD8888jmUzi6aefFoUCAG63Wzw5q9WKffv2QdM0TE1NIZ1OC4SkV/Bq+4fLUSh6465fB6DJFqMnxdewwjoQCAAAMpmMhPxOpxNXX301XC4X3G43Go0GlpeXoWkaLly4gGKxiFwuJ4QQq9WKyclJjIyM4MYbb4Tf70epVEIsFsNdd92FxcVFiYh4MPXKl/tup5RrtVrF3NwclpeXMTs7C5fLhbGxMYlcqERNJhP6+vrEMWs0GhKFHTx4EE6nEx6PB06nEy6XCwaDAcFgECsrK6hUKhdFpe0UjVr1zvO0m8J8FA2H0WjEzMwMstks5ubmUKlUcObMGWiaJhEsnYlnnnmmhWqr/zwSiVS21HpyuVGdqic6sa34e+YLV1dX5fWET3nWarWa3BsNMM+5x+OBx+PBa17zGoyOjuI3f/M3hQySSqVw7NgxuN1u0SO5XA4mkwmDg4Pwer246qqrUK/X8dRTT6FarQq5ymAwoFgsCoN3Pdl246ImZlUFUq1WkUwmAUAeKrFQFgWq+DsXqlarIZPJIJlMtrCD9N42v0+1wjulHNb7nnbeicGwxjt3Op2ScAsEAi3JRyr6fD6PVCqFmZkZrK6uSpI2m82iWCyiUCjAZDKJx5vP5+V6isWiJPf0Cl/PCNlO6bQe7aI5wmh0KACIY2G1WmWNmNyvVCrIZrPIZrMoFApyf2TG0IgzGRoIBGC1WuHz+TA2NiZePj9LNbYmk6mFYbcTQiiH56FWq8FqtYrCi8ViQrHmujAXRw/cZrPB7/fD7/dLYpuKKBQKwefzSaPUzZ6J7WJWbcc51O8TYM0gx2IxaJqG2dlZpNNpRCIRgQFLpZJAPvrPUvXFXqv90aMJqnFX9RvQ6gTRqFitVtjtdhw4cAB9fX3Yt28fxsbGEAgEhGVJGLZcLsPr9UoERDSFiEqtVmthqQIQ5GAzsOK2Ghc+NDJfTp8+Ld6xxWKRXEkymUSpVJLDvbKygkAgIAeDB65QKCCVSuFb3/oWZmdnBSPUH37eLAuJisUiqtXqtnVP3Yy0+x41pK9Wq/Jvu92O4eFh9Pf3IxQKIRQK4eDBg0JoAIBIJAJN0zA9PY2lpSU8+OCDWFpawurqKkqlEvL5vHg3xEJphNW2DrlcriWhrd8Q7arlL2XNNmtg+WwJddpsNoyNjcFisSAej6Ner8Pj8cBut+Pw4cNwu93Yt28fGo0GLly4gFwuh/PnzyOfz0uBIA8glW0mk4GmaTCZTHA4HJicnASwRk2dm5vDl7/8ZfF6K5VKSwTMpPlOKB06Xeo61Wo1aJqGbDYr0Bijq1qthmQyKdGf0+nE4cOHMT4+jltvvRXhcFg8W+aybrjhBvT19eGee+5pqQvi96sOYL1eR6FQkLOkshUvRfg5Kny11fcbDAaB+0KhkNT6OJ1OvPjii4jFYvjJT36CbDYr90Fno12kxjVnUnsrz3k7dUmnjtxqIh9o5i31r1Nn2vB3ZrMZY2NjOHDgAD760Y8iFArB7/fDZrMhEAjI/vL5fHjXu96F1dVVZLNZLC4uYmFhAeVyGdlsFqVSSV7vdDpRKpUQiUQAQKJ+7jN9E11Vtj1yoYdhNBqlBoMLRq+ahoW4Oj0NUkoJfaXTacTjcczNzeHChQvQNE02Ox8AlbXVakUgEJBIZ7fDeb20g4hUnJ0Kl4c8kUggnU7j/PnziEQiWFpaQjwel+hNX1TKzyAG38m70UcunSCsboh6Tar3qFbg83f0ujlzg8ogn8+jWCyKA6FfB75XVbKkJjP/x7oARgJqrmGnITE9hKp6rPproGLkPanUWhpKeuqMXkKhEOLx+IYzSPhdemdjt4XOiFr/Q1gonU4jmUwim82KM8E1Wq/lDyHUnc63dFrTdg5dp+vSn2UaYLIpT5w4gbGxMQwNDUm3BpPJJGQefpfL5RKas3ouGSVnMhmBjpnfVIkQm5FtNS76DaA+wGKxKNg/D47qOaVSKfzoRz9CIBAQGu3S0hKSySQefPBBJJNJ6XZKw8LkPwvHDh8+DE3T8I1vfKMFHuqWrLdZ+LPda1ivkkqlYLVakUgk4HQ6EQ6HZSPcc889WFxcxGOPPYZMJiNeOj9PP0WPilJd207XqE+ob7cy1XtU/KnmNlgsazKZEI/HxQsCIJTrG264Af39/cIMS6VSWFpaQj6fF8olP5/RmtPpxPHjxzE0NCRFusAaRHjy5ElcuHBBqrWZwyEThrTV3YRKOkFyKiXdYrEIVf3cuXNYWlqC0WjE8PAwbr/9dgSDQYTDYdRqNRw6dAiNRqMlEaz/XFWofJnLu5x9oRa2Xur7VcNPh2F1dRWVSgUPPfQQIpFIS0TWjjyi/35CQNw3u0nV1p8VvXOj/mSbH6D53Oho9Pf348SJE/jMZz6DYDAoDvvc3Bw0TcPq6irsdjuOHz8u/cR4rubn5yUvx0iebWAYORF2pkPDyGnHYDF1ERg6MTqp1WqSJKTwYskqi0ajyGazUm3Kf6dSKTEs9O4YsbhcLvh8PjidTjidzhaa3F6QdovPaM1kMiGfz0uSzWQyCWS4vLwsMBgjQIpK99V7X502J0W/Nt02wCrpQf0/9dCrnHrCOV6vFz6fDy6XS6DQQqEgRaDqdTNSZlRit9ths9nEu+fei8ViUg+gV+Kqx7sXRd33ak0HC0QXFxfRaKw1KmSuivdNmGgrz3q79sV27i86UNFoFOVyGfF4/CKnS/XmO323Gg3thQitHezVKepSRXU2JiYmMD4+jlAoBIfDgXw+j3w+j/n5eaRSKSQSCVitVoTDYdjtdtTrdWkTUywWW/rUcV8Bre2mGPHxWjZau201LoR2EokEbDYb9u/fj3q9junpaTkEXEgye9xuN66++mqYzWb85Cc/EdosP69UKiGdTrd0yCUlb3BwEH6/X5K2Kysruw6JqZtEVfrc7Ly3SqUCs9kMTdNgsViwuroqMKCmaXj22WcRjUbFqKpev9FohM/nE9ZQtVqVcFZvaPSdybiLAAAgAElEQVSiQk+Xy37ZaB3aeWMGg0EK/fL5PCwWi+TbyKA7ceIEwuEwXC4X6vW1TtArKyuIRqNyv6qyZWW63W6XBCUASWJGIhHx0Obm5pBOp2VfqYlyFSrcSULIVoSYdyqVAgCBxF588UWkUilMT08jk8ng/PnzyOVyePrppxGJRKSh4V6WTvuRz8PlcqFcLuOuu+6CpmnSP0v1/tU8p/6z+Pk8I9QTO7ku691ju7/r36u/V4PBgP7+fnzoQx/CVVddJUXWTz/9NM6ePYsf/vCHyOfzmJychMViwenTp6U9VDabxcsvvyw6Rv1+wmgkF+lh2s1Q1rfVuPBL2ImUirRdZaeescHiNk3TWvphqTdGj4TQW39/P9xutyTBE4nEjsBhm5H1rkHFTRm+slq2UqnIwaFBpqjePbFSrtFmNmen6+iWtPPIVKhMT8/mfXk8Hml3wUprsuP0kCojFjKm2FeLiUyDwYBUKoVIJCJePHNWembYXtg364mqULi2NDaEBfm7bDYrBI9kMrmj5JbtFjUPValUkEgkhE3XTtnq82cqhAS05pX2ypps5jrUe+W+Hx0dxZEjRzA+Pi40dcKauVwOy8vLyOfzCAaDsNlswqZLpVLCtmwXrauowlavk9IVWAxYYzNMT0/L39WFoVKxWq2oVqt48skn0Wg0Wkb48uBQmM8hrh4Oh/HqV79a2CPpdBpf/epXcf78+Zbv22lZLxTX/5vVxI1GQxh2pFCStUGDyjVjfyX+P/+tGqJ2Xrc+/7FThkWNkkgjJX47MjICi8Ui/bKq1aoUffl8PikYnJqawsrKCtLpNAqFgnhZhFMJoV199dUYGBjArbfeCr/fD6/XC03ThGlXLpeFvVQuly9q8rlenmq3hIpEHRjHQkK73Y7JyUnpBBwMBqUz8Pz8PPL5vHQw2Ev31Ena7Vk6k43GGluQXnW7/cszQlieMDkhHhbpbiZf0C251O9UnTRCx2NjY/j4xz+OiYkJHDx4sKVJKanpBoNBKvPV86dpWothUQkC6r/ZJZl/VFRpI+laEaWafNJ7F/qcAWm1hHaokFQ2EV+rcrl5qPh9ZJvtVdycoip6MpsACBWUszlIpQUg7R8cDoesiZqr2Mz37aS0+05ea7tuxazbACCFcPF4HLlcDplMBqlUqgUTVveR3W6XeiG/3y/V/NVqFaVSCdFoFPF4XCr5GbWsd517RRmr0TpJEKzb8Xg8GB0dlYFygUCghQHE/bTXJjFuVfiMmLPtdC969iHPEJ0xQqYsPN0rz3gj0ecrLRYLgsEgxsbGMD4+joGBAVit1pY6LZ4N9nMkuUVN1G+UPmgHKW5FumJceFH6Pjd88KyopzHRQxTMxwSDQekpVavVMDs7i3q9jmAwCJ/Ph1QqhXK5jJdffhnpdBorKyttq227mVvY6neoD8lms+H48eOw2+0X9YAaGBhoieDGx8flkFQqFUxNTUlhnJ4hpv8eVfSGXs/I2Q7D3I7WqzoJDN1XV1dFEVitVgwMDEj9Ag9KqVTC7OwscrmcRBxUGhaLBXa7XeqFDh48iFAohHK5LFHO6uoqXn75ZczPz8uALR4wHi56tPqiyt1UyCr0QXKM2+3Ga1/7WoGCA4EA3vSmN0mxKA1toVDAmTNnkMvlMDU1JQnwK0WZqkJlajAYJHmvfy6q08qSBABSr+R0OuH1enHTTTehXq/j3nvvlbZIu7UmakX9etegNyzsZPHud78b+/fvx/DwMLxerzio7D1HotBb3/pW6S9YLpexvLyMdDqNhYUF6WSifke76F2FFvXXtd617/g8F7V6X8VEVSXHViZsaWG1WlEulxGLxSS5TeyVC5nL5a6YA6RCXR6PBw6HQ9p5sOU+YTKXywWbzYahoSGYzWak02lomiZjS/P5fEvYCrTv4cWfGxmPbnjtKryhfj6hQYbb9L4ymYwYlkqlglwu1zLzR1UmXEdGdYS9AGB1dRUrKyvSh0st3AWaLDX1Oim7vY94LfRAyQALhUIIBAIYGRlBX18fBgcHpe0L38PojPm83YSJt0vUWjC9cG95PB5BNQAILEYnBLjYG9/pZH6779usY0oiTzgcxsTEBPbv3w+73S6sNzpI1JEOhwOjo6NyhlgfRjidkX23ZMeMi6oM9Kwxvoat59/ylrdgeHgYd9xxBwwGA+69914kEgnY7XbB4OPxOM6ePSv9lgAIR1tNegJ7I2Lha+nBkyk3Pj4Op9OJ+fl5VCoVqV8IBoPweDy45ppr4HK5EAwGoWkavv3tbyORSODo0aPScyybzQqRQTXS9PrU7gWs2NfnGLajeI5roSchMMrwer0wGo2ywZ1OJxqNhniYnDTJLgSkR6bT6ZZxtSrUAawZWDLGAODs2bNIp9N45plnkEqlkM1mW5wZtaKfopJO9kKil+tGskJ/fz9GRkZw/fXXY3JyEidOnGiZU8K1IMVd0zSpB1qvinovC/flejU3zEN6PB7cdNNNsFgsmJmZQaPRwNGjRyXi0TQNDzzwgPQgo6Oyky1g1OtvRzLYyNi53W7ccsstmJycxDve8Q4hrQDNccXMSfb19bU0q6RxmZubQzQaRaOx1qPvueeek9rBdmQIvX5TdfhG0lXjwgtVB2SpCpaJunK5DKPRiEAggP7+fhw4cACjo6MYGRkBAGkPQg9WDfPI86b1Vj3bvXigqAjY6ZfrQA+ErXKYP2AND3MIrC5nNKeuLRWo+j1qMljtCbTToj4Xdboer0l1OvSRBXn5vB8eKGLLpGIzr8LuBqurq0gmk8Lj1xsN9bsJ1e1FMZnWRh/39/fLDBO32y1wkQpxknGYTqeFKHEl51vWc3wY1VFvjI+Pw2KxSD6BvbSy2ay0NmlXJ7UbooejNhKeG7WujyNJ6vW6IDcs22DHArWrMbBGz2cZhFqdT92xnXqza8aFYVm7hDyhnn379sFsNqNQKKCvrw/vfOc7MTY2JrMa6NnecccdiEaj+NznPod4PC71CaOjozCbzchms22TU/qH140NpUJQ632H6hnYbDYcOXIEwWBQIolQKCTwECEeQkRk/bBzqdPpbKlSbzTWKrAtFstFeQNeF5UMf7Z7zeWuUbu14Obn4WY9i2r8mIPjZDzOCS8WizCbzRgcHJRIj5TjYrGIU6dOIZvNCuy1uroKm82Gvr4+6TTMYjKy0QC0TKqk8SKkoBqc3TY2jLJsNhve9KY3YXJyEseOHZOkPa+Tr2WXgeXlZTz++OM4c+bMnqHmX460M4500AKBAO644w6MjY3h1ltvhcViwZkzZxCNRvH8888jHo/jpZdeEkOrttvfi0a3HV2aOUk2d6UjwTrAQqGAs2fPYnV1FU8++SQ0TRMCw2233SYdTxqNBnw+H8rlMiKRCObn5wGgpafjRvR81UHbaF91HRZTjYva04fGx2w2iyVlop4t59n9Va28ZgEejRQLEXkQ1/NK9ko0oyoF1bskyYGKjd4Hi+bYSlwtNOWmY2TDDaJ6te02DeVSWCBbET3sqU7g5DXy3ml4vF6vNE202+0YGRmRSaQcHJbNZuF0OqX2hbgyZ1ioXYbVPnZAKxym7ge1UHWvCM8PC4epCIDW5Csjec4yWV1dRSKR2BNe+uXKeueZc6DGx8cRDAZhMpkwNDQEq9WKl156CY1GQwbt7ZXzT+kEv3a6Rjpj1A+MVHK5HObm5hCJRLCwsIBcLger1Yp8Po+ZmRkZgc738cwQiiaqAaBlKNh6ztVm1rGrVORisQiTydSC+6lt4Ympz8zMIJlM4uzZs2g0Gti3b5+0kefvE4kEDhw4ALfbjeXlZRQKBQwODgq0ls1mZX60/kDtxIba6DtUqIesjVQqhWg02lIsGovF0Gg04PV6pfOt6qmwVmNsbEyiP03TEI1GpabBaDSKt8/DNT093bI2+kT/RqyVjUTNaVDUkNtut+Oqq66CyWTCzMwM6vW6jBjgaxnRjoyMCI7u9Xrxqle9ShrtVatVLCwsYGlpCeFwGNVqFUtLSy05G9633W6XA6IqYv34ZzUHpNa97KYyIgxCZ2pmZgbVahUnTpyAwdCsAaNhOXnyJJaWlnD+/Hnx1ne7W0W3xWAwwOv14oYbbsDo6Kjk9CYnJ+H3+/GjH/1IEtyq47lZtGEnZKtFioxcMpkM0uk0fvCDH2B1dRXnzp1DMpnE7OyspA5MJhOefPJJTExM4EMf+hDcbjeeeuopxGIxmM1mDAwMiP6kQTp06BDK5TJ++MMfCsnoUqWrkYv+gNKDJkbOuRps8728vAyLxYKrrrpK5qWXSiUsLi7KSNtarSZ4sxoR0RDp5x/w93tF1HAWaBaYsnV8MpmUh01qpbqGhHH6+/tFkRLKUau0Vb6/XsG2k+06YPpISA+58T6Jl7POR2VFMR/l8/kQDAbR19cn00l5aNTKfn62CgECEEaMmmdSk5adsPy95N0Ca/eTy+WEKehwOERRVioVZDIZLC4uyh9O3FQbe+61e9pO4b5XO35QH9CRYG5ur6zDpRg2NbdGksvi4qJ0TScNmV0oAMjPlZUVeDweZDIZlEolacdPhIB1Zmp0pN83KoS+q5ELL4DFjUBTkZLV8NRTTwGA0Iq/+c1vwul04uTJk/D5fDh48CDq9TpOnz6NdDqNWCwGi8WC/fv3w2AwCBWZkwkJf6jUVCrqbsMD7eCldt9H5cpWJAw/GTmwBoPzKfSfS+ioXq/D5XJhZGSkpa0Dvfbl5WUpotIXDHYjF9XuM9TvKZVKeP755+WQExJ1Op0SqaZSKdnQzCP5fD6EQiGYzWZEo1HkcjmhGKfTaeTzeWHC8aAsLS3BYrFgbGwMpVJJErl0YliYqvar4zXqvdvdEhpiDryamppCqVTCuXPnUCgU5Dm/+OKLWF1dxfnz5xGLxfDEE09IAluFOy43Mt2LYjCsTcw8e/asdHxoNBo4c+YMpqensby8LMgA4fe98GzJ4gSaA+vWO5OMYuks5vN53H///cjlcnjiiSeQTCYFFWIels9b0zQsLS3ha1/7GgKBAEZHR+H3+/FLv/RLsNvt0s/w/vvvRzQaxaOPPir0dfWaLgU+73rOhYeEf1fZH2qra0JCLBrjsBoA0soik8kIHGA0GlEoFFqMisp4UBXpTrCBNmPR1QiOTDcqNG4GVRm08xz4b85toccfDocl19JoNGTwltopodM1b4eo6868ir5bLeEntZKY8JUaZdJQlstlYXrV63Vks1mk02ksLS0hGo1C0zRZR6AJGTCH5/f7UavVpEhVjXZUz0xVwntFeFa4HrlcDqlUCisrKzAajQIRnj17VpysYrEITdNQLBZbmJrrVbXvZdnoTKnRvJovU/fQbo9QaCc8A0CrDtxI6Jiz92Aul5NpnOx8rSfs8D0cUzA2NiZ5THaVZ+6K6YVO/ca2KjtiXFTltp7nQLhoZWUF8XgcS0tLLZRlGpLFxUUYDAYsLCyI0lDbwNAbIOymdr3dTs+FyklVqJupKVBHPutn1+vfq4fE+Id0S4fDgWAwiFtuuQWBQADj4+NoNBqYnp7G/Pw8/vmf/xnRaFSML79DDX0Zal+uNBprs1FIIV9cXBRYSoUlqPA43Y7XoDZY5IHhyAGj0YiFhQWkUimcOnVK6MaNxlrhKVli/5+9L4ut67rO/s6d55nzJFIDJUseGttybDl23Mxo3LTp8NAGaRE0aYOiDYoCBYqgw1PRt7QPTZsWKP4ESJsBQZNmsDLUtRMnlqU4jhwNlkRSnO4leS/vPI/nf2C+pX2P7qU4k264AILkHc7Ze5+91/ittYg68/l8eOyxx8SKY0ayWs1ADWKq8aGDEKdQ90Sr1ZJk0BdeeEFqqjEe2Ww2EQwGxRJTEXlWqxXN5loHS5VhHDSGaySVAXfyOui6LrlPp0+fxtDQkHxueHgYZrMZ3/3ud1Gv19s08YMCOaeSvNE6Z2w5sby8DLPZjBs3biCXy4nQMCqnJKIKySMHBwdx5MgRqaCcSCSkPQUhzSpgiLQVnrknSZSbGRglLTVbuk6AO/EKxiPoU1WtFNXvehBMYJU4PiMzV11BRtcVX+s0D7PZLG6jiYkJeL1euN1uga9aLBbJ0r7XOuyFX94Yf6GAVAWc+lMsFlGr1TA7OwsAWF1dlV7phJ+r2clEvlCzVxUNlVSrULVs92NNNkK0YCqVCmKxmBTcpKLFPUXBzVwpY+4T0K4QHYS5dSKjBdwtNsbPOJ1OiU0CawVNqZF34gHd5r6XAf5OY+r2uuqhYHySEHxjrx71+1Qu2J5kYGBAkiuJ1CwWi1IqyehF6TZuo7us22d3Vbio2iBwb23JmGzJ7zBmw40Sj8dhMrW34ATuaKD0wavggZ22WjgejlM1bzstOAPshARSgNKqonXHz9ESU9v5qtdnN8Inn3wSY2NjOHXqFJrNJr72ta9hYWEB09PTSKVSSCaTUkNJtYBIO5GZr46LaC5em2vRarVkToQIc148BER3+f1+sTiq1SquXbuGRqMhteSYsU3GybbYLpcLHo8HIyMjaDabeOGFF5BKpTA9Pd1WR8noiqAyYgSE7DQD3gzzUpkKz0+9XsfCwoK81mw2BdHD3jjs7glAFDFjWSSjkN/qGHeDKDAsFoskQQJoOwO0vL1er2Sps3SQyWRCKBSSz6glg3gtVeCqZ4LrttuBf7p4VUHfiUeqa+HxeNDf348nn3wSrVYLFy5caKvm3ek5UvkcGhrCb/7mb2J0dBSTk5NSfy6Xy+Ff//VfMTs7i9dee02y/DdCqou5myW4J5YLSV1M4+tG7VFdLCNjJbNSc2hUS4fBMuaM7GeJbZU0TZPqpDQ9uZF5CJjjQSg2tVG1ZAkZscfjaauES1fTysqKNMgyBjG7BfV3koyuSo7BGPNwOBxSaYDPWH2mDLzH43HpT2GsL8VrqbkgTqdTcgBSqZRoZGrFAuBO3tVeuEm6BUfvtSdVVybjZ6r1S2ZAl5iKtqObuVNS3F6eBTV4TVrPemBCMHv6qNXVyT8cDgeO/Lz7Iq10tdqC+jfvz9+srsw1U8fRSQjvxlpR8TL+qGcGuJPgSMVb5RvdLAzO22azYXBwEOPj4zh27Bj6+vpkTVOpFOLxOObn53H79m1RQu4F/NhMLtiuo8VUMg6MC+p0OuWQ05faycdKBuV2u8Xsp0ZDLLuu6wKxczqdEszabeFi1EA6+SytViuOHj0Kh8OBTCaDSqUiPWyonbzzne+EzWbD/Pw88vm8lAdXBaTT6cTx48cxODgommo2m0Uul8OtW7cwPz+P2dlZ0WzWc/mozGYn18dms+Ho0aPQdR1TU1NtLjDOdXJyEk6nE7dv35bvEWLu8Xjw8MMPo1Ao4Etf+hLy+bwoEBQw3AN+v7+t3D4L9DFgSSJCjZZRIpFos3zVGAWwvbiEkTEZ/zeCHrqtPQUgmRGv1cmNSmu2p6cHDz74IFqtFn74wx9ieXlZXIzGe91rnNslq9UqMbjFxUU0Go2OwoZksVgwMDAAv9+Po0ePotFo4OLFiwJg4TkaGxvDJz/5SYyPj6O/v1+y15lfV61WhQeEw2HY7XY88MADAIAbN24gk8kgGo22CRi1MyWFHICO67ZTpFaLcLlcePjhh2E2mzE1NSVnnspTKpXCpz/9aanEsJ5gcTqdGBgYwIc//GGcOHECjz32GKxWq7Sy+Od//mfMzMzg1VdfFRBIJyXU6GZjiIK8ZT3as5hLJ41Z7bugBtv4P/2C/D6z0ands3SKy+USBsxWuZqm7Ukfi61uOAIV1DlSS7FarcIo2baU2paurwWwBwcH0dvbKxYOuw6y/0m5XBbLTdUSjRbibkC0uzEo1dpU3Z7UxkOhkFQaIFpMzVMwZqZz/QKBgFSJbjQabegZQtMpiNSGUp0siP10B6m/jc+La6Y2fFIVLha4DAaD8Pl8bbE3NR6x3vx205rdDNEqJ4x+cHAQuVxOXIBMzGYba9V1qLriPR4PAoGAtIS+1/yMQlet5MBrArsHhiCPY6FSCj4AchZYdUHlk/wux2iz2RCJRDAyMiJl+ZmM22g0UC6XsbCwgJmZGTkr6l5S12I7e2JPhEsnE9BkMkkbWqOlwmS6vr4+WK1WVKtVuN1uPPTQQ7Db7dKt8Xvf+x5arRYefvhh2O12ZDIZWK1WDA8Po1arYWpqCvl8fk+qwqqHt9u9arUabt68KVoWWzVTi242m3jllVcQDofx9NNPC4NQhTPX4ty5c2JCZzIZ/PSnP5VMXSbRNZtNYapqNz5aD2ppFNUfvdXDo46T68/nq2laW6HRZrOJW7duiQunp6cHjz32GEwmEy5cuIDFxUVcvHhRkD4s/QOgLbHU5XLhxIkTMJlMiEajEjep1WpYXl5Go9FAb2+vxLXq9ToymUxb4hivZexIuZ2YSyfB2u19Nc6o+v1p1RNWzWfJOBNhqWazGf39/ejp6cFDDz0Eh8MhlbLVs6eS0QW9W6VvmHcE3LHm1/Prky+Ew2E8/PDD8Hq9OHPmjNSOY96Sy+WS2F5PT0+bK4zN1J544gn09PRIvyfWJGStOXUcnbwsVFh4dvg/reitkNE9qgqQarWK+fl59Pb24oknnpDWI/V6HbOzs8jn83jttddQLpfvQnRRAbHb7QiFQvjgBz+I8fFxPPLII1K7r9FoYHZ2Fjdv3sTU1BSWl5fbwD70GACQKioqZJrVK4CNCdg9i7kYrRdq7qrPm5+h7zwYDEqCoMfjQU9PD6xWqzARSlyasMFgUNBDZDC7GZwzMh8y7fVce3yYnLtqpbGeGPMyuC6shqppa8Fot9stcNREIoFMJoOVlRWsrq7eFUTvBHOmmwVo18a2s06dNBweXs5RDT7qut7W+IuaGQVhsVhELBYTuLUaW1GVEvqjAUhchnEGIgkDgYDEoxinUNeGLqq91tyNmqdavZbW1uDgIFwul5Q64ri5/1dXV2E2m3H06FH4fD45I3QFqzlgPBvqXDs98508L0Z3073uTUuWtdJarbXmgKzMQBcOs9NNJpO4u/h9gj6IgCJiknteRVh1cy1xfLvJO4C7zx/Xqq+vT6wzPsN0Oo35+XlkMhnY7Xa0Wi0Bs7DdeTAYRH9/P4aHhzE2Nib1F1W0YSwWk+D9ZubYSUlZj3ZEuKgB0vWI2gXdW16vVxIJuVAmkwmBQAChUAgPPfSQlJa22+04fvw4crkcvv71ryMWi6FcLsNisSAajSIcDuP06dNoNpu4dOmSlFrfzc1hsVhkM1AjZwdJMsZu3+vr65Oqx61WS7rnMV7w/PPPSyVgn8+Ht7zlLdJITNfXkFTFYhHPPfcc8vm8bBJ2prt69apUQuDGVYUOGT8rAhNNtR2NzOjKIaM0Wo50VxGIkMvlUCqVcP78eXF30m+u1qJrNpui1TJjv1qtYmFhAc1mE9evX5dcFu4jv9+P++67T3JCcrmc3J9rQObLsipGC2YnSGVY3AP8n+9zX3Dd+vv78aEPfQiDg4MIBAJi4QJAJBKR6hVmsxmnT5+W3BeWSWI+VSaTgdfrlYxsNQ6nlojhWgA76wIyAjCMOR4q1et13LhxQ6os9PT04IknnkAoFMLExARSqRReeOEFRKNR/OAHP8Dp06fx9re/Xapx1Go1vPDCC7hx4wa+/OUvY3l5Gaurq20ll7oJDzJPPptkMin7DrhTNWM7a8LnT2XC6/VC13Vks1kAa/kvkUgE586dQ19fH3p7ewEAjz/+uMwjk8kgGAyi1WohFovBZDJhcHBQ4pjBYBDj4+Pwer2y1hQqX/nKVzAzM4NUKtVWoYJKi9EyUfeDyXSnVTR74qxHe4oWM5IxoEyy2WxtPcGp0fl8PtFKyHDIxJjjwPjDdkzX7c4J6K4F8yGq9ZCMjKfZbCKfz4vQZr4KNc9ms4lEIoF8Po/V1VWk0+m72huQgRqZJP9WLRyVtuMKUq/RidR8DJW58zVq4WSM6ppx3vQ1c44ARMtVtVpeh5osgR+dhAbHYxz3TqxFt/UwAi2MgVNCcfv6+hCJRBCJRABAXIPBYFAEIasQML+FFjI1d/YBMlov+xVfWo8oIIrFIhYXF6Hraz3veZa53zOZDKrVKkKhkLiIaNlPT0/j9u3biEajkjza6fkCnZOUOQ5jPHK3+QnPBAvQssMo81s8Ho+45lgMmACY4eFhKZlFSL7FYpHzEIvFMDMzg8XFRSwvL9919ozKRac9stnzsCPCpZsEM5q/qunXarVEWqsPkVaN3+/HmTNnEAgEkE6nxR/IukrU0oiwcLvdSCaTyGQyUg5jNw+PasJybtVqFbFYTB4CM9HV+VMrLpVK4u5otVpS9ZZMgsU52dclHo9L7xtes1qtSjVkWh7FYhGVSuWu/AYjQ6ZgVzUValNbgeaqh5f3VCGk1FjpzqpUKlhYWIDT6ZQmT6zAkMvlAEB6gzMOxDbGalyCygRLxdBapSVcrVbx3HPPyZjcbnebAFIhqeqzpdKyE21gOwkzuuxUKH2lUpGeNH19fW1taEOhEKxWq2iywNqzZH26F198EWazGU8//TQsFgtef/11zM7OSpfPSCQia8X8L6M1ud6Yd4q4b+91D8bsotEostksNE3D/Pw8KpWKWF+6riOdTmNubg6NRgPBYBCpVArf+973cOXKFUFiGq0xNcZAIaJpWltu2W66xLg/dV0XsAH/n5+fx8rKCubn5xEMBnHy5En09vbife97H8xmM4aGhlCpVODz+SQepQoTKhd0EZ8/fx6xWAyvvPIK4vE4pqen21qGGy1Y4z5QgQONRqONZwP7lERpRDGQVNcLg5ZMJgyFQnA6nRgaGpKqnZTUqi+V11X7g/DAqOW1d9uHrkp8LrCaj0JSx2HUwgmnpsB1uVxwOp0SpKSGUSqVpPieyjj5PboWif4wMg8VaaUKHJrNO+1C7GQREenEMdOPzjVjwJ1aPQUJYy5ms1l6++j6WjDf2Cde9WVzzsViUaCeHAvv24mJqNfZLep0T2rtwNrzLpVKWJBBMtgAACAASURBVF1dlTp7tHRVBY0191hzjNXDM5kM0um0KBjGRMrdZJ5GUgWoet/13Om6rotFEo1G0Ww2pdwTQRh8hoVCQVxWq6uriMViyGQybWCVbs9TjXuq9zZakhz/dsjIiFVeqAJhGo0G5ufnxQWaz+cxNzcnLSE4HiJn3W63oMFo9WcyGSSTSdy8eRPLy8u4efMmCoXChiDERtqq9bYrwkV1fwDtjI2/VaQOAITDYXz0ox9FT08P3G433G43BgcHpXQBffKsEcWmUBaLBZcuXRINmIxnrw5Pt3twQ6vaEhksqwtomtZWcsHpdOL+++9HKBTC5OQkGo0GXnnlFRQKBUSjUdjtdgwPD6PVaiEajUpQjsgoNYZgFCx+vx8ARJMF1vI+zp49C13XcfHiRXGxbGXd1OdtdDOQEagdJlkXiggZakYmk0kgpkNDQ3C73RgbG4Pdbpd2x0wODYfDKBQKuHjxIorFoli0dAMxv4LBXWrAVFgodEhqHISWxF5Zv7wPXXzVahXZbBbhcBi3bt3C6dOn27oQ5nI5JBIJ/Nu//ZvUX2s2m3j55ZelTUWlUhFLJZ/PbwoRtl7QfTOkoq7U/iBmsxk9PT0AIEhJ49owGM/yP8aEY2rpy8vLKJfLuHr1KlZWVqTQrTF5VE3U5Wsqn+iUVMm8OgCCvtsqdXJPq/+rChiVi1dffRVXr17FzMyM8AWHwyGIN/IYKt/Xrl3DwsICvvnNb2J5eRkzMzOSja8qnPxtXHcqL93GZ9wX6+2PPYm5dBqQkfFQiLDroNlsFlippq01x4nH40gmk22+c6IoSqUSAoHAXRraXtG9DiPnysCYil7hbzUTV7XCqtUqcrmc5HRQq6Omr+aEqFBiFaFlPFDdxrudtTP6r41kvDatTVZLZoUFdR3UPaJeh8+eh8oIGlB95lx31VLuNPZ7jXc3qNP6c9zlchnz8/NwOp2IxWIIhUKS0xWNRhGLxXD79m1xxdLtRctdRcWpCaxvFlKfs/F1/q7X61hdXUWxWMTs7CwSiUTHwov8fCfrQb3XemPZDerGrHluma+2sLCAXC6HSCQifa40TZPAPFGWb7zxBhYXF3H9+nUkEgnp+6Iia+81rx2LNW7mIpqmbfqORr8yiZnV3OxjY2P4q7/6KwwNDSEUCqFareJb3/oWYrEYXnvtNUkMZJxBNXnV+lT0N25Fw9B1fcO+EE3TdDUYxnkY81L4mwyT2rsaO6Evndq6pmnSx4OZ1SzUyLUjaoOuDyKEjIeFsRCjP5nWJeuwqcJuq2uhuqQAtGmKmqZJNnI3mDbHMz4+LsFMQtZ5PU3TJP9ncXERxWJRqimrrjHek/dtNptSRVktA6IKXmMwc6trsdHP3ouIYHM4HNJpcWRkBLqu44033pCquNwXqttJ3X+0kNZTKjZKW1kLo2JD2ijKdJ3rt2W1X716VTT+9RSIzc59HW1+02thHEM3UIfx/oQaEz3b09MDr9eL0dFRQVgWi0VZAyoZ3fa0MYBvdAtuljqtxZ6U3DdKTJXxUTBUq1Wsrq4KpLBUKmF+fh5LS0u4fv06yuWyMEK18KGq2XQya3d7bqRu0t6oLamJZCrWvl6vSzkYXdelTD41bbqsqKWQ4VJrN+L2jWNRBZP6Gcawdps4R+NrnawZlkk3anF0m/X29qLZbArcnPPnGrDSA4P2xgOsBiPVw74fFu96RNcckV82m032B1t6d8vjMlp8G3Fj7BZ1U/S2W9NN19diSdFoFAAkzrLTc9xNlFgn3gjczezprWH5HOb88Fwnk0mUSiUsLy/fVa5ms/HDN43lonz3rkkatcvx8XFYLBbR2nO5nDBdFY3ULQdhuwuyVQ1V9ZUaN3enOatjVS0fFZZsBAYYg6GqRdQJ/dNhvNA0TYpB3su6285aGBPDjL5+4xyYca+6Dvk5oxZus9kk7rS4uCh++fXce0bhwmuqBU7XEy77Zbmoe4drwy6k3cAI3a6xUwx3v9ZiPVKBInspPLezFsbn0s1dxb2r8gbWx9M0TXgj97CqcG90b3R6bQtB/723XJSb37WgqklGGC8Aaf3L99RrdKLNSubdoG5mp/F947xUy8X4efrRjZ8H2qG/ne5/r/HtNnVjfOprnCMRMPyba2Gcu1r6BIDE5LqtEWmza3VQSD0zxvjcXguVnSaz2YxgMAhd19vO+1ZoM90c95uMSrZRyTRSJ75JDwZwBxxjTHYksEmNtaiKWzf3verq3+7e2dPoXrfAHCd2rxLY3Wg/DxDHrrql1ovBdHNPGNeim0/UuDadDmW3zau6wXZjze5VSqaTNkTBuhEFgWAFxk6M1+vUuZBxHODurn9qxrZ6mA4SY96sEKGQ5rMwMt6DMrdgMIg//MM/hK7r+MxnPoNkMrmv49kLYuyTShQVqfVclp0UJaL/+D8/R8vWbrfjyJEj0PU7FcmZYBsKhdBqrWX2q0VhGQ9l7NV4JrZCBwY60o1Rvllotw/rOi6btv/vxaRVhryT2vy9rrURy6qbgFUFrabdKfZnFGJGTWw9S7eT67LT6/vNhP8v00bdN/tNOyWQu3kz7kXq2TB6QDp9ttPv9e65WWV+o7RnMZcNXh/A/mlVm/Whrsd8jAikTj7WjWjLG1kLNXCrZuB3uy//5/c6CfbNroURKGAcX7e5GMeu63c3FVMTPYG70WjqGhtjPsp8Ot6703vqWv38YO/Yvtgr2ugYNnvmthJn6IQKM5vNAqtXc6/UMW1mXLtF68VTN7MWJpNJN1bs+Pk1tjyu9d7r5jkxnjUjX+h0/U6f28gZOTCWC7D/G2mztN4B3qg7Yz3ajGazkWt2e38nY1absVqMlkm3zxgFl/rT6T7G63WCWx6EON1u0kE6S53OSbPZ3JAr7CAI6p1qR6Aw4h25HtD9vHUqaQR0Pgvq+1TQjEroVsa8WcslAWBu03d5c9CYrus9G/3w4VrcocO1uEOHa3GHDtfiDv0irsWmhMshHdIhHdIhHdJGaHfazx3SIR3SIR3SLzQdCpdDOqRDOqRD2nE6FC6HdEiHdEiHtON0KFwO6ZAO6ZAOacfpULgc0iEd0iEd0o7ToXA5pEM6pEM6pB2nQ+FySId0SId0SDtOh8LlkA7pkA7pkHacDoXLIR3SIR3SIe04baq22G4Xrtxv2kpRvv8rpBbnAw7XQtd1adj2i7wWRvpFXosOxWF/YdfCSHteuHK/qxwfFFIr9XYrlb2ZctzrVWrdKu1mK1cjdapavB0yduvcLu3kWmz3DGz1+2+2s9ep7856nwXuPbed3mdvlrU8KHSgqiL/X6Wd2pQUKmSmJpMJwWBQ+slvpx/Om7GXjtomW9M0uFwuAECxWNwWQ3kzroVKLGcPYNtdHt9sxDNiMpnQaDSgaRrcbjeA7e+LvVTA/i/QrgqXnWSqO3m9/aB7bUy150I3stvtOHbsGABgZmYGPp8PH/3oR6HrOj796U8jmUx27J1x0Gi7z5GtXL1eLwAgl8vB6XTive99L3Rdx/nz51EoFA7EvtnuvTfak0XV0gOBAD784Q8DAD73uc8hnU7Lte7VP2i/SNd16Qy60TF1moN6Rm7fvg273Y53vetd0HUdzz33HAqFwj07ph7SztC2hMt+9Vo4aAdjI7ReTxOTydTWEIufNZlMcDqd0DQNzWYTNptNPketXb2O8f+Duj5bcWeYzWZ4PB5omoZisXhXs7Wd6rnxZqFOiohqxXFtzGZz1z42B21/bNQVzAZYfJ0Wa7VabTsXVEI6XYvvd+sNdEjbp33pREnt2tjA5uf3uOvz29FkNkO7FaDrxujZMc7tduOhhx5Cq9XCq6++inK5DF3X4XK58La3vQ12ux3z8/Oo1+uwWq1oNBqIx+NoNpuw2+3SeIkBaF4b2Lopvx/BSgpap9MJACiXyzJ+v9+PX/3VXwUAnD9/Hvl8vm1NNU2DzWZDq9W6y/2xXd/7fgVujU2b1N9qnIn/8/NWqxU+nw+apiGfz6PZbMreYOdRxjU2q4TsxVqoXRQBiPI1MjICAFhcXEStVkOr1YLL5cK5c+egaRp+/OMfo1KpwGaz3SVEm80mCoWCrJe6z0ql0pb2xmFA/w4diE6UavdBY4dBHg6z2Sw/wNomazabYsaqWovZbIbf74fZbEa1WkWj0ZDNclAtHFVzstlssFgsMhdaKrVaDU6nE729vXA4HCJUTCYTyuUy8vk8SqUSUqmUtCwl4wAO3pw3QiozNZvN8Hq90HUdpVIJNpsN4XAYFosFExMTyGazqNVqaDQayOVyqNfrwjzejHM30ka6c/Jvau9ms7ktvqDrOrxeL8xmMywWC1qtFgqFAhqNBur1eltXwoO4ZsY5Anf2Ra1WQ7VahdvtRn9/P8xmMyYmJmQP1Ot1FItF1Go15PP5NqFivOYh7Q5tS7hsViPk5yk4VA2VwsVmsyESicDj8WB4eBitVgtzc3Mol8vIZDKo1+vSS91qtSISieB3f/d34XQ6cevWLayuruKll15CqVRqQ2gdJL87kTFkmJqm4fLly7BarThz5gxMJhNisRj8fj/OnDmDnp4e9PX1weFwwOVyIZVK4VOf+hRmZmZQrVbRbDbbXAatVguVSmVLAna/GQ2Fidfrxbve9S5omoYf/OAHcLlcGBoawsDAAN7xjnfAZDIhn89jZWUFn/vc5xCLxbC8vAxd17vuy/2e20bI2PpZVaTUv202G0wmEywWCxwOB06dOoVIJIKTJ0+i2WziRz/6EWq1Gk6dOgW/349Tp06hUqngG9/4BhYXF2XvGC3cg6CcNJvNNpcwlYhYLAav14u3ve1tMJvNmJ6eRiAQwLlz5xAOh/Frv/ZraLVayGazWFlZwb//+78jHo8DuHPmVIFCAasqumrs8yDulU5j497YjtdnN2jPLBdj/EDVHMgMHA4H7HY7enp64Pf7MTg4KCZ8NptFqVQCAGEgPp8Pvb298Pl8sFqtsFgsbe6Bbgd1P8g4BovFIuMF1g433VxOpxM+nw9+vx/9/f3o7e0V4eJwOGCz2RAKhbC6uopsNitWD6/TrU+2ev+DTDabDQ6HA263G1arFadOnUIgEEB/f7/80ILzer0YHh5GtVpFMplsE6jA3Yewk4A5KGuiWiRAZ6WEn7FarbDb7QgGgwgEAjh58iSCwSBOnDiBer2ORCKBRqOBiYkJeL1eDA4OIpfLwefzwePxwG63CzM9KIKFc1fPiMovuP+pmE1MTMDn82F0dBSBQAAOhwO6rqNQKMBut8Pn8yGTycBsNqPVakkMhtatauUa98ZB2RMkNZRA3mcymQQEwc/Y7XZYLBb4fD5xL9IlWK1Wsbq6uiGwz07wzG0Jl3tZLOoAHQ4H3vKWt0DTNFy9ehXVahW1Wq1N+7rvvvsQDAYxNDSEQCCARx99FABw6dIlJBIJ5PN5FItF9PX1IRAI4IknnoDD4cDS0hKKxSJ++tOfCvTSZDLJptpvYmwFgLi2PB4PLBaLoFeGh4dhtVqRz+cRCoXwZ3/2ZwiFQrI+fr+/7aBNTk7C6XSiXC6jWCyK67BWq8n8eW/1QDEY2m3z7DdAw26348SJEzL33t5e/Omf/qkoEGazWQK4tVoNoVAIzzzzDI4cOYJUKoVMJgPgjpBVkyGNwW1+hpbtfu8VNddDBXYAd9zGqsXb39+P9773vRgfH8cv//Ivw+l0ol6vo9ls4plnnhFGVKvVcPPmTRQKBXEjZTIZlMtlFAoFicm0Wq175pjsFqnov3w+D03T4HQ65Ry7XC48+eSTsNlsSCaT8Pv9+NCHPoTBwUEcPXpU3H602oPBICYnJ2EymXDz5k2Zl+piV+9NaxC4k2djdMPvF5nNZoTDYQBAOp2G3+/H7/zO78DhcGB2dhbValUUsmPHjol1T1dppVLBpUuXcOvWLfzjP/4j4vH4uu5jFWBUq9W2fDb2Jc+FbjHVTUYtzOPxwOVyiWalauFut1sYs9/vh9frhdVqbfO1qwt2kNwinawo+oZpwZjNZmF0TqcTTqdTNC4V/NBqtWC1WsVa4/v8rjrH9dAyB5WosdZqNdTrdTgcDjidTtkz3Z4j3zcCRFRBa/xtMpkOLGzb6OqgksKz0tPTg5GREYyMjCAQCMBqtaJcLqPZbIpixSTbSqUibjCeN36O8TreY78s/fU8DaplUSwWxYK32Wxt8VkSBavqyeCZ4zkxxnJUBVBdm/10q5vNZjnrDBn09/djcnISDocDjUYDtVoNdrsdbrcb9913H/r7+9HX1we73Q6TyYR6vY5jx47BYrHI70QiIXtD5Rkqws5kMsHlcsmab3YN9iTPRdM0VKtVvP7668IAyDwtFgusViu8Xi96e3vhcrlgs9lQKpXw1a9+FZVKBYlEAq1WC0ePHpVDQy3f6XRiYGAAoVAIlUoFq6urEptRyne0bab90szUe2cyGXFvtFotZDIZcXeVy2W89NJLGBgYwNjYGKxWKxKJhFwrn8+L5klhk8/nRWslaZomjJbC1wjDPAgBcHWfNBoNLC0tIZ1Oo1KpoFAo4Gtf+xqGh4fx1re+FXa7HblcDo1GA6lUColEAt///vexsLCAer0Os9ksDFYFSgAQjZVrQmairgXHoY5rr6jVaqFcLsv/RjeZ1WpFIBBAIBDAgw8+iMHBQdx///0Ih8PCCCuVCsrlsljzt2/fRrVaRblcRqlUEtCLw+FAq9VCOp0WV6Kq8BldZbtNuq5L6SGuPy0YKgA//OEPoWka6vU6gsEgnn/+eYyNjcHlcgmQoVarYXZ2FouLi1hZWRHAS6vVEi1cZaQARLmjtt5oNESIt1ot5HK5fVFAzGYzIpEIbDYb3G43jh07hr/5m79BX18ffD4fms0mTp8+jVqtBr/fD6fTif7+fhFEKnpwcnISY2Nj8Hg8eOONN/AP//APWF5elvNAxYNegVKpBIfDgUceeQS6ruOVV15p25sboS0Jl60cPl3XUS6XYbFY4HK5hLHyIfLBNptNlEol1Go1LC8vo1gsIp/Pw2azob+/Xz5LiQ5ANFweEMYzOmnyHP9+MA6VUdC9ZbfbBTrKeIumaUgmkwCAQCAAs9ks1pmmachms8jlcm1Q3W5zO6g+5E7EZ+p0OuHxeOD1emGz2VAoFJBKpZBOp0W4VKtVrKysYHl5GUtLS1hdXRWXn1FgGn3p/JvPoNPn9otUQUtSYy1kNH6/X/zujEsCa26TQqGAxcVFZLNZLCwsyHu1Wk2C47yXMYitWsl7TcbnQRcyqVgsyv+lUgnpdBoul0uUDYJ9bt++jaWlJWQyGVQqlTY+0GlufN0oQPZ7L5CsViuOHDmCsbExDA4OimBpNBqwWq3i0uMPXyOR1wLAyMiIuNUtFgvi8bjsC7PZLHuK16J1tBVvx6aFS7eEv3tRp4fLpECv1wuLxYJYLIZWq4VUKtUWoGUuiNVqRTAYxOOPPw673Y7l5WWkUin86Ec/QjqdlkUiU0omk23+VVUoGtE3u7GRjMgUEtfQ6XTi5MmT6OnpwfHjx2GxWBCNRtFoNDA3N4elpSXMzc2h2Wxibm4OlUpFIJhLS0uC9afLkIdLPUydfKXGg3QQmKrZbEYoFEIoFMLb3/52+P1+9PX1ySav1Wr44he/iEKhgKmpKeTzeSSTSQF78LDRHdKNSap7QM0LMr6/HeoGKNjO9ex2O1wuF/r6+sSiDYfDWF5exurqKqanp5HNZvHiiy8il8uhWCyiXq+LFc/9R208n8+jUqmg0WiIpr5fMSeuV61WA9DuDuTzBNDmAm61WlhcXESlUsFLL70Ek8mEK1euIJPJYGpqCoVCASsrK6J4qp4L1ZoH7gg1Y4UAauocw16vD59TOBzGJz7xCYyOjsLtdqNYLOJLX/oSstks+vv7xUqJRCJixQJ3u4StVitGR0fR19eHv/iLv8D09DQ+85nPIBqNotVqwel04sknn4Tb7YbNZkO1WsW1a9eQSqXa+NdGaU9iLkbfsRGhQQFQrVZRr9dlMpVKRRaFpqvVapV8h+XlZTQaDRQKBdHsCcvVdV1+70dQzpjk1s1/7na74XQ6EQwGYTKZkMvlxLVRLpdRqVRQr9cxPz8vrzGXB4AEIVU0SbPZFAGjZjOr69BJQ95PIgN1u93o7e0V37LJZBKkSyKRQCaTwa1bt1AqlZDL5doOvDonCnCjr12lThZtt/e2M6+tXovzoLXvcrkQDAbh8/kQDofh8/lEQKTTaWQyGczPzyOTyYiFwmRTaq8ej0fOERWRarUqiuJ+FDDtZF0b96oxpkoXYKlUQjweh67rWFhYwOrqKhYXF8UVuNF4SaezYYzL7DXRkujr60N/fz+CwaC4Luv1OqrVKtLptHhqmERMhdMopHlNq9WKUCgkHiFjHFj9IYhqK3t408KFaAx1wBslBtd40C0WC3Rdx+LioggJNSvX6XTCZrOhr68Pfr8fbrdbgvmtVgszMzNYXV2Fz+eD2WwWv7vX64XD4RBEmpEJdXMd7RQRdABAErjUYKX6Q/QPYZbj4+MoFAr48Y9/jGw2i3K5jFqtJtYcr0ftnG5ABjZ9Ph8ASJJhqVQSl4GaiLpbc98qcc2Ghobw+OOPo7e3F16vF/V6Ha+//jqSySQSiQRisZi4O1TLWV1TwpgffvhhWCwWLC4uIp/PIxqNrus736m12ClrhQLS5XJhcnIS4XAY9913H/r6+vBLv/RLMJlMuHz5MpaWlvDyyy8jkUhgZmZGUJi6ficB9fTp0wgGgxgcHITH48HRo0eh6zpu3ryJ1dVVPPfcc4KS2os9wecE4K6cLBJf43yKxaIwR8Jw0+k0Xn/9ddRqNUxPT6NUKom1ZnT5qcKik9DoBAraD8WU5PP58Nu//ds4evQogsEgnE6nQI0/8IEPIB6P4wtf+AKKxSLGxsbgdrvbzrjRxUgeSMW9VCpJMjY9IufPn2/j03S9byXmtCXLZauLzc1CXyrNULp7VKSLmm3udrvh9XolcOdyudBsNuF2u1EqlQSiyoUl2oO0n4F8dROTYVAb5RxVgc0HyaB9qVRCvV5HuVxGvV6XoCSvQQ2DWfxer1fmWq1WUa1W9x1iuxEi3NTtdiMUCiEQCMDpdMr4mYGvwq7VQ8Q1YdyNcSxah9xznbTQnS7Nvl1SYyyMPQ0MDMDv94uSReaZTCaRTCaxvLyMRCIhFrwKYtA0DcFgEH6/XxKUuSbch2qF6YOicADtJaJ4ptVYKwA5F0TEkZF2m4dRuByk+apktVoxODiI0dFRiTNzf3s8HvFkFAoF5HI5uN1uZLNZcXUCaINXk1dQWaXCqkLxC4WC3H+767JnUGSiQcgY6vU65ubmANyN4uHvarUKu92OwcFB9PT04P7778fg4KC4S5599lnE43F8/vOfl6BepVJBKpVqY0DG0g+7DS2kOQm0l7lRXXw+nw8ulwulUgmapuHGjRuChCsWi7h+/XobxFrVxIA7mkg+n4fD4cDIyAjcbrdYccPDw6hUKrhw4YKsuxp74sHab4aqaugTExMYHx/H+Pi4CMlyuYxcLodEIoHFxUU5EGq8RGVAqnuN8NxcLgdg7aCpsRneW82vOAiwZAZo3W43zp49i0gkgsceewxer1fyVr7+9a8jm83iZz/7GZLJpLiCaH3Qog2FQujp6cGZM2fQ19eHM2fOoNls4vvf/z6SySRu3ryJbDYLm80Gn8+HbDa7J65kXdfvQsZ1i5GpAAy698xmM9LptCDJqJB162+kCmwKJ47DiCIzKiD7JXycTifOnj2L0dFR+P1+sdiIlrVYLMjn81hYWEA8HofP50Mul4PVasXq6iparVZbvgqVWJ6BYrEoccvdiD/vaZ6LygSMftROrhpqo36/H6FQSLLxqf3TimHchtdUpfF+bQwybWPFAKLjPB6PZEoTDQZA3HjlcrntoZPpGf2jnKfX60UgEEBvby9MJhNKpdJdpvFBJGLpA4EAhoeHMTIyIqY/AIGe0myne6/Tc+V8ub7BYBAWiwUrKyuCoiEqRlU6+F3ebz81We4Tm80Gl8uFSCQiAXyn0ynxFFos0WhU9gsVCApZTdMQCoXaqjx4vV5UKhVks1mkUikkk0kp9Gk8i7sRg1LJeJ9ue1UVDOrnVAWDCkO358h9QTQiAFkzdSwHhSgE1Vw2NUbF2CrPhsPhEFBCtVpFNBpFtVptayrYbDZRLpeFp1YqlbZ0CCrjKuhhO7SvzcIYczFqGyoj9nq9uP/++zExMYHjx4/DZrNJEJPxCJXxqNbReqS6AHZyY3U6IOrGDgQC8Pl8OHPmDFwuF7xeL6rVKn784x8jnU4jmUyKi5DCVSWjEGZux7FjxzA2Nob3v//9aDab+MIXvtAWpCU023gI95KMbgj63R955BFMTk7iT/7kTxAOhwXRREBCJpNBNptFsVhEqVRaV3GwWq0YGxvDkSNH8J73vAcAkEgkUK1WJcufTIXxK1o2exVrMK6B+h5hoKFQCGNjY3j66adx5MgRnD59Gq1WC5cvX0YikUAikcDS0hJisdhdTJLX9Hg8eOaZZzAyMoJz585JLki5XMbNmzcFYUa3Kys8qNfg79206LoFzrn/GXRmjBFYEybMzWAytarEGZ+lzWbDkSNHEAgEcPToUTQaDVy4cAG5XE7mrN53P4UNrWmXy4V0Oo1gMAigfe/kcjmBnZvNZpw9exa9vb146KGHkMvlcPHiRSwsLLShR4G18+H3+3Hy5ElomiZAkEwmIzyVQmi7tKfCZSNaNH2KNpsNfr8fPT09CIVCstgWi0U2Q6lUQjableCU6gZbT2vfD42ecSSr1QqHwwGfzyeum0KhIME1umaMyJVOB0ZlAExGtdls0HUdoVBIyn2wJAxwt0twP/zO6tqrwiAUCklVAsZZ+Izp9rxXSQ4KLMagTCYTwuEw6vW6BDzVcjAA2tyFwP5kpqvPmb72oaEhAbNYLBYBveRyOflNZUr9PuNXgUAAAwMDGB4eFsFK33suh6IypAAAIABJREFUl0M+nxcrn0UcjUHw/SQKCrWAJYC2vWxcOyNwhn/T9crMfhbOZdXke40D2DvlQx0rrRbj8+CZ7+3tFQ8Ik4aB9hCApmno7e2Va7HElqZpWF5eloRaxqx2SpHYVeFifCjGTUvstKq5sI5WMBjEO97xDgwNDYnGwRIgdrsdxWIRr732Gqanp3H58mWk0+m7oIfrbYbd0k6MWpga7yFk0OVyYWBgAJFIBMePHxfUB4vKqQfceD3juMlAi8UiEokEXnnlFQQCAbzzne9EPp/H6uoqbt++jZs3b4rFp5q9e3lweA81gB4MBvF7v/d7kmltMpkECba4uIi5uTl85zvfwcLCAgqFwrrjVGMoFosFU1NTCIfDePbZZ5HL5ZDL5TA3N4fl5WVomoZAICAMu9VqiWDeTP7WTpDxXn6/Hx/84AcxMTGBiYkJ2Gw2xONxpNNpvPjii1haWsK1a9dkPdiOAVjT0D0eD86ePYuRkRE89dRTCIfDCAQC0PW1Mh7VarUtF4YgmE6KzF5p8Sogg8zNZDJJxWcAuHLlingrgDXrhYooGTJbb6hzoUvUZFprfZxMJmEymTA2NoZcLid5Up1c9BTWvN9ukQqosNvt6Ovrw9jYGIaGhqTOHs8q21jbbDb88R//MZLJJL797W9LZYtms4lIJAKHw4FCoYC+vj58/OMfh9vtxtLSEhwOB06fPo1ms4mBgQHEYjFcuXIF8XgcFy9evMuS2yrti1usU3yFGhurIg8ODuLIkSMYGhoSaLG6+Wq1GuLxOBKJBIrFojDO/Q5QA50ZNTdpOBxGMBgUXyiZnjFzWv2e8W8yURb8dDqdUjanXC6L5t5qrbW8jUQiUgZkv2MKJMbMenp60NPTg0AgIMCHSqUi0OHZ2VmkUqm2Rk/rBWzVig90o/Agjo6OStkgos66XW+naLPCm3OIRCIIhUISH8rn88hms8hkMkilUpL/xO/QNx8IBEQzJcKMGnq9XhdkmVqQsJulvJlx7wQZXXF8fiqqkrEGjo0xAtV9TBcwv+90OqVdhd1ub0sINNYeOwjEOeq63jHjHlibI/PjACAcDsu+BoD+/n6phhyJRDA8PAy32y3WkM/nQ6PRQCAQQC6Xk5iOsajndmjPhQs3hEp0F0UiEfT29uJ973sfRkdH8e53vxsej0eqo2raGow5Go0iFovhpZdewq1btwTXvtf1kNYjo99a0zT4fD48/fTTEmeZnZ3Ff/3XfyGRSCCVSnWMO1EIqe/RuqMgHhoawtmzZxEKheQ7FFiTk5PweDy4evWq5BOoMM39WCtd1+F2u/Ge97wHJ06cwMDAgLQwrtfruHHjBubm5vDtb38b0WgU2Wy244ZXfeycN63CoaEhHD16FF6vF16vF8FgEH/+53+OVCqFv/u7v8ONGzdw/fp1WRPgTsWJnVoTCgrg7oCzkXi4mdfC58u9PTU1JXDjdDrdhnhjflMoFMK73vUu9Pb24i1veYtAj5vNJn72s59hdnYW58+fl7pjdru9DareSenbS+J8CI/m74WFBYm/GcdFRskqDqrCQFfx4OCgtGYAgGg0KuVj+My7ZeCrwmw3SfUkVKtVzM3NSeV0Iw+gRWc2m6Vo5R/8wR+gVCrhs5/9LHRdx+///u9LaRybzYahoSGYzWYRRhaLReZeqVSwsLCAaDQqqLKdoH23XFRTLxKJYGhoSFxhPGAqZp8Z/NFoFPF4/C5f/EEi1aXFRElmWjMOEo/HpS5WJ79qp1ImjNvQr05klMfjEWbJ/BhVy9vvWAuJTJRxBfrA6a6Lx+OIRqOiRHRzRxiFCxkTgRLs/cEDygqvvb29yOfzeOONN9rmv99WL/cJKwsw76BcLiMejwtSjMyQ87JarRgaGkJ/fz9GR0fR39+PcDgMh8MhbrCZmRnMz89jamoKqVRK9p96bg6K9k5FktaYWnECaLdwGF9iLxeeI13X5fkHg0G4XC44nU6pSFAqleSM3GvOe+0iVS1KoHOsmnxBTW0gfB2AzJn8U82r4zUZfwkEApLxv5NnYE+qIt9zED/XxJ955hmMjY3hySefFHNe1fwInzx//jzm5uYQjUbvCoAfNOIhYXC2UqmIqd5oNODz+aRrptpEjcyDbWqZDEU/c09Pj2S09/T0SPXU5eVlybgtFAq4evUqksmkFHvkmPYq58dIrLwwMDCA97///RgdHRU0kK6vod8uXbqE6elpXLt2Dfl8Xhgq9wLJWEOJ8biBgQGMjIzA5/NJAJcaoa7r+MAHPoCbN2/i8uXLAk3Wdb3NV78TRGZHuhfztlgsGBgYQH9/P4rFIjRNw9TUFHK5HKanpwVG6vV6pYjjsWPHEAqF8MQTT6Cvrw8nTpyQunXFYhH/7//9P0SjUbzxxhtIp9MCUSXDNgru/RQwapY+3aZqgqfKUPm83W43JiYmJPm21WphZWUFzWZTYrRkwOx/tLi4KJWOVcHVSXhxXHtJZPqRSATA3co4x2oymTAwMAAAUurq7Nmz0DRN6oORb/C8q4qqzWbD2bNnMT4+jv/4j//Y8VjjvkKRgTvaBzXZ0dFReL1e6d3CGEuz2USxWEQ2m8Xy8jKWl5fb8lkOKqnoD2oRPCRqkJ8VBrgJmChGzVvt4qn2eGD5GMacCNdVM5bJNNXg5F4FajutB9FyRAEa4da0UFXLi2vFa3S6LjU0Mh6VIakCOxwOY2RkBJFIROJ1u+X6MDKsTqQyM+4H5ikxJsd+8D6fT7Rym82G8fFxhEIhHDt2TBCIjNNUq1UsLi5ifn4ei4uLkjulQtKNY1CBJPtBnZ6jzWZDo9EQC5fCxWq1SoVoFjqlQGefJHUvsOwJ0xe6nQEjgGY/iMpOMpmUNAYVDcZxqWeCApnvq56TTp4L8iWPx4NwOAyPxyOAgJ2gTQuXndx4nCA18KeeegoDAwMIBAJtLU6LxSIKhQKuXbuG6elpXLp0SfD9B12w0M1BQTAwMACXy4Xl5WUJLNKSIeqF2H4GoSlgbDaboH6i0Sh0XRdT/xvf+IZci0Kp0WhIy2QiqMh46MvnOEm7Hdy22WwYGxvDxMQEIpGIJMWq7tGRkRFUKhWp8EqoKK04uoxotVL7ZlJtPp+XKg3MZuaasj6Tx+PBO97xDszNzeGFF15APp8Xv/turMG9rqkytEqlgtnZWVitVqysrIgLh91cQ6EQRkdH5dwwTgPcKYUyNzeH+fl5XL16FQsLCxKn6aalU6M1jnOvmKwqWNXnbDab0d/fDwDS4IznhFWiz549i/7+fgwNDaHVauHGjRvI5XJS7dftdks/KQKAjK50df3VrHa+t5d8ptlsIp1O48qVK/j7v/97jI+P4+Mf/7ggZlUl0agU+P1+1Ot1ZLNZ2edEybH0C3kuz53NZsMzzzyDgYEBfP7zn0c6ne46383sh21ZLltBwvDzFCzMmPX7/W3Be36O/V0KhQLi8bi0sqVg2c5D3+1NozJMaukUGqzt5Pf7peIvTV21J43ahY6aGoWK2r2SrUupuar+ZfZ9UJEg+xFfoGtrfHwcAwMDbQhA47pxf6iaLCGZ1OJVE5++d6fTiUKhgHQ6LUUbeU01IKpahRyb8XM7QeudkU4xIwrOUqkEi8XSVuqGnQhZdp9VktV8HRYrXV5eRiwWa8ve7xQQJ603570QLKpwUWvvsVKBpmlS3kW1aFg7zuv1wuPxSEzNZrNJgiiVVOZLqbEmFYW2nnW518RSVtPT07BYLEilUm3WuLonqDiRX1K4MF6lVqSoVquCMuW50rS1PJhEItGGTNvuc9+0cFEPIQ+mUQNebwOrjINNoU6fPo3x8XFhNrVaTZhKtVrF9evXEYvF2jQPNZP4IAgY49zJGGu1GqxWK5544gn09/fLoXjsscdkDrFYDLFYTNAurdZabwXVt0wBQ+E7MDAg8ZxKpYI33ngDuVxOBA4DnCy5zs0UjUbbEu92g3Gstw9CoRA+9rGPSRVXMkZ+T9f1Noh5vV6Hx+NBIBDAu9/9brRaLXzrW99CvV5vK43hcrlw6tQpOBwOXL58Gbdv34bb7Za+J5qmtWWhZ7NZzM3NYXp6GoVCQbTUvSaOn5WuKfCy2ayggVqtlqzB6dOnBaZM5aPVakkZl6mpKcTjcVy4cAHJZFIslnspFXxWxme2Fxq7rq/VQvP7/W37IRAIwOv1IhKJQNd1JBIJSTamwGCSMHkQk3LZkZEtGlKpFGKxmFQaJ9MF7iRl8v+dyvPYDpG3rq6uwmw248UXX8TQ0BBOnTqFer2O73znO2g0Gnj00UfR09OD++67D7quSwO9W7duSfUOs9ksngvC1R9//HF4vV7xHAwNDUmNOdVNrQKC1N8bmsNOLMRmGbTq73O5XFL7iFKU2gRbsy4vL2NlZQXZbBb5fH5Xk5l2g1SNgpuaB56WjBo8pPbKwnNkOKpFQzcI14slcAgKIAqGgWFWCOZYSHtt8qsWiVFTpLBjPxtV06aVq+u6aKxqfTAi6Og64dwZuyBYgBo8/e+77Vrd7LVZ1qRarYrGyedHZKBafYDPM5/PI5fLYXFxEaurq1hdXcXKykrH0kr7zTi7EZ8Tzz8RU9wHPDNEhKnxROBOvIZKRzgclmupLSc6uT6pqPHzHI/6/l6vG8fN4pSVSgU+n0/SGFqtlsCrM5kMWq21BmpUzhhf4v6hte7z+TA6OopwOAy32y2KDXkLz9Z2lfdttTk2MvmNDIIBXcYfJiYm8Bu/8RsYGBiQID7jLC+88AJWVlYwOzuLXC6HW7dutfWz3urEd8OP3E3js1gsKJfLOH/+PCKRCCYnJ2G323H9+nUpucAAvHqIGGfo6emBy+US1xaTJb1er7gMW60Went7YbVaJRbj9/sBrG06xm5arRaWlpYA3Omypwrz3VoLkqattWj+4he/iMnJSXzkIx9pC0CScaRSKWm/SoEAAHNzc7BarVLyIh6Piw+eVaZ9Ph8eeOABETIsQa5pmvS2oXuAgAeObS/h7NyDanvuWq2GaDQqHVjVeNm5c+cwNDSESCQiLlFdXyuBVCwW8frrryMWi+HVV19FOp3G7du320rQq9poNwa6n9RsNpFKpdpcNisrK9LeWtO0tlbfuq5L22s+XyYPO51O6LouMORLly6JO03tZqm6rWk5AXeqY3NvcK33CjzEYD4VqnQ6jS984QtwuVy4//77pUeRy+VCb28vYrEYXn75ZZRKJVy/fh3VahXhcBiNRgNXrlwRFBjjKw6HA88//zyOHTuGv/3bvxVkYalUgsfjEQRrp/5Pm6FtWS5bvSl9hDTvw+Fwm0lMHPrc3JwkGLJpjTEQeVA1MTWuRHikrus4cuSIaKQAJHtaRYkBd9BRah9ruruombHQHACpRcSijypChBoQYzp0sbVaa+X997LMvMoQjaY2NVV2mVQrXVODs9vtbYKbybdkwl6vF36/X3zQav5DMpmUSrCpVEoCnlyL/XKNqfuXyEgKHLqfQ6GQaJ/q5xhHWF1dRSaTQaFQkJph6nzUvXjQiGfZmJNFZYOvcc7GqtbcB8Z8DjVniNfg/ShQ+LpqRfMsEplHJOZekjr/ZrOJSqWCYrGImZkZqcTRbDbFHVosFlEul7GwsCDKBEsbqWhIrk+5XJb4JHmFun50y6kpC5ulHW0WZkSeGDcyN7jL5UIoFMIjjzyCI0eOoL+/Xyqb1ut13Lp1CzMzM3j++eexsrIicNve3l74/X4kk0nRRFRGei/ay4CdGqClVgZA/J9HjhyR5lhLS0t47rnnUCwWEQ6HhfkHAgH4/X64XC6J1QwPD4ubrFwuI5/PS1DTarUiGAyiVqthaWmprcHYT37yE1kn5kU0m018/etfv2fhvp0ki8WCwcFBDA8PC7QUgFgT2WwWb7zxBmZmZtriQpqmYX5+vq2Px8DAAMLhMB599FGBoprNZtTrdTgcDhw7dkyKVaZSKfzTP/0T4vE4AoEA7HY7jh8/Ln1v2OlytwWtEXYN3IlfqvEyogTZ04iAELr1WBPr+eefRyaTkXwgu90u/WlUZYIWm3o2jW4ffqfT/7tJqqIIQJgbwT6nTp1Cq9XCxYsXJQbH9aJiyqC+6i5U2//SFV2v1wVxRTeYsTq2pmnweDx46qmn0Gq1cP78+R3PgdoI6bre5mGo1+uYnZ2FxWJBMBhEvV7H//7v/8LhcODEiRNoNBpYXV1FuVxGIpEAACn5xDYDajOwWCyGq1evoqenR6olE7VKS557h20ZNkM7nudyLwbOg+R2uwU+SDOYEnphYQHz8/NYWVlBJpOB3+9vK3mgooiA/c+s7kRGFBQ3plrxWcXth0IhNJtNTExMCFyWtYNo6tMdRuGjahoEAHATUkvnJuEmU8eyHxYfrQgjFJTZ6Ol0Wpigyog1TROrQ02go1AJBoOCpKOfmWtSq9VQLBYRi8UwNzfXZumo1YB3e02oXPE+RuauoqPcbjecTqdUzmbFAcYgGFeJx+OifQJ34LpqORlahLz/RgToXgoV42v8obBVx8/nTjAQe9SQN6iAAGNsT0WlcQ+piZtqTpUa/9kt4MtGiONT3agkNU5LUIrafoR8UV0TvkavRyKREL7icDgkx5Du1O1Y89uKuRi1HL6mPgz1PT7YQCCAI0eO4JlnnkFfXx98Ph9arZZk3X/5y1/G7du3xZXkdruhaZrEGVhHiAKJcQd1DJ1otxkH70H/L9FixJWzDzZjAwBw7do1ZDIZPPXUU7Db7XjrW98qDxiAZPT39vYCgDBfZhvfvn1bMnrJPJgXZDKZxH/MUvYsAvnNb35T3GJ7SZVKBa+++irK5TJ+5Vd+RTTUSqWCr371q5idnRWkz9LSUhuqp1KpSOKgz+dDX18fIpEIHnzwQTidTiQSCQGB8Jnouo7FxUVEo1Hpf0Hf/SuvvCK9K+hy2SnqxDi5DwAIik9VlBwOB8bGxtDT0yNVs0+fPo1gMIiJiQmx3hKJBL761a9KORhm9hO6XCwWsbi4iGKxKAUK5+bmhOkY3UDA/pUBAtob61F5ZMtuk8mE1157TSwRn8+H+++/H4FAAGfOnEFvb6/UkKOAoWXGa6lnUtd1cT1TUJN5qy7YWq2Gb3/72wIL32/i86FLnOjB48ePo9Fo4Pvf/z7y+Xxb91oA4jlRUboApLba9evXkc/nce7cOYRCIRw/fhzBYBDZbBbZbBaXL1+WkASvp45nPdqVDP1ugkV1i1ErU33iiUQCs7Oz0rZT7RqoIkgYj+iG299Lk74TqQ+R/xPto2bWk7nY7XZpU9zf399WK4nJfWoshURrTtVKNU1rS5ZjPIGYd4vFIvGL/arHRnemMd+A9a4YtGbtJ/p/y+WyuP5otQQCAamhxIq3ZChECLF0PyHsFCRcA9JuKx9Ghs5nxvwlVokOBAJS3TgSicDlcsmYk8kklpaWkEgkkMvlpAo0zwSZCfOcyGx5Xoz3388zYlwP1SuhaZogItl6mWg5xlK4TqorTLVA1bwuTdPagv/kOZ3AQeQ1VAD2M6Zr5KF0FQaDQaknRmStsaOoSuQdKmKU1j3zC3m2WGpLRddthXYk5tJJmBj/58NlVnE4HJagExMjv/KVr2Bubg6zs7Nt2gK1y1gsBgDiM1V9pJ02q+py2W1S18AoXJhZHYlEpBNlIBCA2WzGmTNnAEDWg8RNQqGSSqVQr9elPEMul0Or1cLg4CBqtRpWV1eFmbJQYaFQwOjoKEwmk/hgjxw5gnq9jpmZGUHD7CU5HA489NBDOHr0qDBCKgxnz57FiRMnkEgkkMlkpCUv1yCdTsNqtWJ0dBQ+n0/qarlcLnEtOp1OORhEXv3Lv/wLFhYW7qqeTQG/0wX7gLthrNybqlVlNq9VM/Z4PHjwwQcRCARw6tQpBINBnDp1Sqyzer2O7373u4hGo7h06RJyuZwUn6zVavB6vejr65PcMWCt5Homk5FaYvSjb8V3vtPU6bxS6aQiwUoNTI41m80YHR0V5YjJokRams1mlEolcTPzjGSzWVHCWH8vlUqhWq0KfJdgAKPbkgx4P4ULrS4qogMDA+jp6cHZs2dhNptx+/Ztyf/ppGzzf1q4TETO5XLo6+vDs88+Kz1j6vU6Hn30Udy8eRMvvviiQNlVIMhm1mJPaoupPlSHw4HBwUEMDAxIYJp1pDKZDFZXV0W7VB86cAf6bLPZRAs3+iR5P/Wh7CdRi2DVUSYqcVMziYm/1V7XjUZDIIHs35HJZNBsNoUxUhvn5wCIS0CFUK5XS2mv16JaraJQKCCRSKDRaKCvr0/cnowRMfBfqVSEWdBXHg6H4fP55DeFFFEzLNFvs9mQTqexvLyMZDIpgkz13+/VvI1/GxUQxtFCoZAAV6il67ou8GwKXjIStXYaNVO19zqh3Gqsyug22e8zQjIydJZNotBR21SrQWfGFtUcDZ4hVvcolUoSR+A+4BoaC6Iax3QQiIrD0aNH0dvbi/Hxcei6jqWlpbY8OZLqveEcKMAdDgcGBgZw9OhRiT+qOWQmkwnlcnnbOWA7Kly6IU1osjNj+iMf+QgGBwclc5oxFDJhmrlEQDHAzYn7fD5xgRBWpy7CfmpmfJgqo89ms/jv//5v2O129Pb24sSJEzh58iT6+/vFV66WISEaKJVK4eWXX5ayFzwQ1N5rtRouXryIarUKt9stqKJGo4EzZ84gk8ng2rVrKJVKgnVnmRm1j8leUiaTwX/+53/C5XLhf/7nfzA5OYm//uu/hsfjQSQSgcfjQbVahcPhwG/91m9JIL9arWJqagomkwknTpwQkIOmrWXeZ7NZfOlLX0IqlZI6XCdPnmzL8E8kEm0xBwYr9zq/RQ08s1r1wsICLBYLjh49KkAXChzgTiFT/vCcsNIB24AHAgGUy2URxLRu2CSKLjRj8uF+MFH1vkRrqQKSVa2pYKgNr5iXomkabt26BYvFgp6eHlmbRqOBTCaDpaUlXLhwQbLzqbhQMKv5LAcliG8kk8kEv9+PiYkJfOITn8Dw8DAGBwfFIp2ensaVK1faCr2qIAXOxWw2S12/P/qjP5JGfXSnEmW6vLyMQqEge2Sr67Drlgv9hETBBINB0ThVE5ifUXM1/H4/HA6H9HVhEJyBSSMKRI07UFDtV0xGZdw00WmF+Xw+5PN56b6oWmf0cbLMRSwWE+HCB82YTb1elyB2T09PGyKM60pklupf3m/0SyaTQbFYxK1bt+BwOJBKpdpMf8abgsGgABTK5bI8fyofdGeVSiXE4/G2atmEuxN+Sm3W6Jffq7XoZL2oRMgsNUu1tI3JZJIcHqfT2VYvjrEmxgn4eTW7nb9p5atnpNt4dpM63ZfjB+4EoenKUWuOqbX3qGytrKwAgKAJCcFnAdNkMol8Pt+1pLzRfX6QBAtwJ/Hc5/MhGAy2tSMZHh5GoVCQzH0G3405PIxRjoyMYGRkBIODg2KxGEEPLNy5XeVj14QLmT0zqr1eLyYnJzE+Pi7CQmUodrsd4+PjwjQA4P7774fX65WaXPV6HYVCAT/96U/bsq3V/AC6FhqNBmZmZu6C0nFMADbUKGgrxACbKvxUl1AymcRPfvIT5PN59Pb23qW95fN5XLp0CYuLi3jxxReRTCbb/Kmq1sYWpcSvZ7NZAGsJYAzo2mw2FAqFNpeDalXy3ntJDJhOTU3hs5/9LEZGRvDkk09KTI7wajIdCtVyuYybN2+KllUqlbCysiL7gmXldV3H1atX26w0dQ33moGQsaugDLop/H4/HnjgAYyNjSEUCknNNTIIs9mMBx98EIODg9KNcmVlReIRFotFCnWGQiHoui6Mle5Y5jKpZds5LpX2CkVGC129LxkcsAbeuHr1KgKBgBTpTKVSoqQRMVUul3Hp0iXk83lx+VG40OJdWVlpcwszgVedazdFw/iZ/SB6KgYGBqSiB12dDzzwAAYGBnD9+nXMzs7iypUrqNVq0hgvl8vB7Xbj3e9+N4aGhvDrv/7rEvulYFHv8+ijjyISieBzn/scksnk3iZRrifFOvn91HgL8fuqpg7cyTZmYUbCav1+v2hrnCRRL2qtoL3ynW+HVIQKg4nsQsn5UQtjUUUyEjVbnetJLY3mLk39XC7X9nwogGklqszE+Lz2kmhZlkolyZZnkJrVGlgrLZlMolwuS1taYvljsRiKxSKi0SgKhYJ0XWSxR7W0C+9pHMNez1l1V6jng0KgUqm0CVV+T00GZMKbmvvF8kH5fF6QQ6yAy/jDveIL+03qc6KiRZg91458gApVqVSSpoH8HrV38gjuB1WJ47nh/e4lWPabOD4VvUW0l8/nw8DAAJrNpljuVMhLpRJ8Ph9OnDiBkZERSf2gJQis7UVm8hMEsRPK5qZ2GoNKKu6Zk+T7XAj+r2kaMpkMdH2tTDyzh/k9Mo9kMolarQabzYbJyUnB7muaJpnTzMtYXl4WTU3NOmZZkVQqJQ+BuTDqOJmItJPMRT0YRMYBdyqsqrkaLDCYzWbx2muvAVgDKVSrVVy7dg25XA6JRKItUG8cKxPnqH1QC2ShRrqMWACRB3R1dVUYG7B/8Sk+SzZQK5VK+PKXvwxd16XczcLCgtRYUn3xjMtcuHBBgvgsDNop9sb9sVe0ntuVwkQtXJrP5/Hd735XKlVMTEzgsccea2sQx9wlat0PPvigBLwbjQbi8TjK5TIuXLiAfD6P6elp6LouNeWmpqYk58roHlTJGDfdrXXh8wTQBk4AIAKBLZ6j0ajEZnnOeb4ZM1EFtxGO260GorHi70FRQoxUq9XkedJqoaAlJPv9738/EokEhoaGAADnzp2D3+9HOByWWCwVFGOCcqFQwF/+5V9iZmYG2WwWxWIR6XRaeOeBiLl0ejjUIAgfLBaLAgGkFbKysoLV1VXkcjkJ3pFh6rouZi5r5RD5QWaiblJuOOCOC8AYsN3LzWK8F90z6XRatPFWa636cblcxtTUFEql0l0CvNN1ud5MnlPnSktJ3RydEHX7SdwbjCnF43HTYEfbAAAgAElEQVTU63Upe3Ljxg2JLVCo2O12hMNhlMtl6etjLM3RzYLer7we4zhUTZl7NZPJQNM0LC4uQtM0jI+PC1qMMTsGvFkBmh1IeTbq9TpyuRwymUxXmPlGlImDsDeoIJlMJlQqFQCQ9gIULmqrDxX9Z4TN3svTYkwQPGhE124ul8PS0hI8Hg96enpgt9vFYvX7/bBYLDh+/DhMJhPGxsYEyMJioMaqIbx2s9lELBbDjRs3UCgU2jLzt7MPtM18WdM0vVPxOz5cIlvUfgiUsA6HA5OTkwgGgxgfH4fb7UZvby8ajQZu3LghFonZbMbw8HCbb5ioF3aYW1lZkcNk1Fi4IDQZAWwYGaXr+oZ3l6Zp6y6c0YpT7iEWoHEt6SpaTxgaBbjRamRSXk9PD3R9DarYaDTEbbbRStY7uRbrEVGBBB/8/HoA7mi0agFHEuHZqjKhjIdzaMv9oDW3WWttJ9aiWyyDz43W7pEjRxAMBnH8+HH4fD4cPXpUYioMzmvaGmy7XC7j8uXL4vrQ9TXIcj6fx+3bt6XGGvOkWq2WuNy6KS/3isHt1L5QXVzd/Pl87p0si24uLDWmqtamM35H3U9bjb3u1RnhnFwuF06fPo2BgQE89dRTUnfQ7/fjvvvua+sBpQIgVPerYfyo1WpIp9P45Cc/iWvXruFnP/uZnKvNrEmntdi05bLewTS+p/o4qa3TJ+5yuSQhkNp6qVRqKytP889ut6NSqSCVSrVlWataunq/g0DrmdbUvtajjc5JXWMjAsgYe1G/c1C0NAb2gbsLGBrjE5zDvRqdHaT53YtUrZl5THRlqug4tfq1GriOx+NtAV41n4P1o1QLqVsAfz/iT/e6ZyehY7S+1et1+lv9fzddfrtJjLXUajUsLi6iVCphYGAAwWAQQ0NDoohbLBZRWo1nSF1vKpnN5lo75Xg83pYDtFNlkDZtuXR5fSPfbetypvpQaemw4J6KJCMxfqBqqvcau9F62ACj3jENdT1ro9trG72ekdQ1I/Og4FWBAHx9I7RXWtkGr9/2e6PPU/2+UfBuhnZrLYznxmw2SwY13RJMkGNRUoI3gsEgms2muBJVcAvr7anFQY2KmOqeM45lvfXZjbXYaQFn1NK7WTlb3Q/KdffsjNDSIwSZ/bA+9rGPYWJiAidPnoTL5YLX621DGnZy/bG6cjQaxac+9SnMzs62VS3YinDZEculy4Xb/u9kflGIqJ+nNmZkusbJ8fubLUGgLuhBsGq2wuA28jmjEDbGWt7stJ4VuNHvHzRrptNYqFFqmiY5GYSYEynGPA9acew1wrgT4xLqPuC11d/dxrHfMamduL/xeXezWN5M54Pzcblc0DRN2mAnEgnJAWRRVLVfFOeogpqq1Sqmp6cxNTWF119/HfF4XNzmO7kmO2K5dPic/L2Z66sQZdV3yoOz08imDhrMrmllpI2ux2byT1T/tXFTbfVZ7ORaHES35WZoN9aiwz3kOWraWjIbAImNUBtnpj7jm2wEReahNhPrdGY6gR2AuyHq3eIbB2lfbPb7O70P99q69/v9ePbZZ6HrOr7xjW+gVCohHA5L8m0oFMJTTz0Fs9mMq1evCghELUDJv+k+ZVkk4z7YbP7brlku69xwW99Zz4/6ZqG98PMa/anrjeOQDi6prmHj86SAoRAhUoquLyPiaT0t1GghbMQ1e7h/Dgapbk5WkqdSkkgk/n97Xxbb2Hme/XDfV4naRxppxp4Z76nbOIgTZxKjdp0mLtoiNzVaBG2vil73IkCBAAESFOhFb9qiF71qUXRBiwRpEfcP4s7ieEndGce1x7N4pNFIpCSKFPedh+e/mD6vPh6RkiiRIsc5DzCQRqLIc77zfe/6vO+LmZkZGVNA6rpKPe+WqB/E8z2S57IfE+rYF9SB1dCrx9LNQjH+vIO137Ml0k9ryJhbUK/tuO85KnmGhxFHWYuDCvQOeI89HqdxzxqViPH3Bxkbh9kXnTyYYZ8R9f2IYSm+kz4jZFbq+m6tj5HgwDEFRsaXWteyXxSon/Ji5Mp1B6GwTJh4WNApAavioNzJYT2SwwiRUctTdcIvknxQmZWE8XmrY4y7ve6kcKT2L8Oq6j4suh0c4//7cR/9fGi9Jvs7oRMThjRFNcE7DIxK7mXQ19GJlHLYvzOi0x5Wc2zGsGu3z+2W3D7I0xm1M9KP9ztsZOPThB5yJ337TOvBLxkePo0PedDoFFZUfzdM7Hdtv+joZV0GFV562HHc/WXuz/6iV88lpev66kCuRMFxD88Rra2FHl+fAjDwtegVne5d1/dW5h+AgazFqHi8Pe6vntfiKGek1z3fq2dkfP8jerAjfUYOu4bdXtfj/hzptThhdFyLnhL6JkyYMGHCxGEw0mExEyZMmDDxcMJULiZMmDBhou8wlYsJEyZMmOg7TOViwoQJEyb6DlO5mDBhwoSJvsNULiZMmDBhou8wlYsJEyZMmOg7TOViwoQJEyb6DlO5mDBhwoSJvqOn9i9ma/VdmGuxC3MtdmGuxS7MtdjFL+JajFzL/YcJ6hClw3RUtVqtMm+B8xhOGp/mzq/9hjkkaxfmWpjoFaZyecjQaaAYsP/kwU6vN2HChIlBwlQux4A6TOkwsyE4u5rfd0Knv1MVitPphNVqhdPpFGtS13WZtc5ut8br4dyOQc1zGaRle1RvS50nfxQM01If9Hz5Xt//YfRaTC99uDCVS59xkIegKhWLxQKbzdY2xKvbfOv9ZrQcZga6iV8MYaOOV1a/flphHMWsflVf02kdjmt8jDKOstf7bSD21HL/FzEp1Q3GteAmt9sf6Otms7lns6sTBDVNg81mQzgcBgBks9l9N7lxVjZhtVrbZmJ381gOOz5a8YaOvBY8tOoMb/W6ByXweK+dvEL12TQajZ6uZxCJ20HMlbfZbAiFQgCAUqmEZrMJTdP2GCfcq/vNUu+GUUhi8znzPLlcLgBAvV5Hq9WS2UXqPuTf0dO3Wq17zl6vymbYa6EqVjWKof6e2C9szr0DQPZLrzAT+icI40M+rhDpNhv9sBaqkXigXlsv73PSMJIgKBiMihrYFSaNRuNQ3pzL5UKr1TrU60cFh1FKFDrqv07P3/i7hwGqkQA8eOZ87na7XQwa7hP1vlUlpApU/v5hgfrMeAaMRqZqVPLfST9r03NR0A9LpFeL9KiuudEqOSp4UI0WyyCssv08C8P78RoQCATw8ssvAwB+8pOfoFKpwO/3w+FwIBwOQ9d17OzsQNM0+Hw+NJtNbG9vi/XazXNyOBxYXFwEAKysrIg30wnDtFDVNTN6xobPBYA2wQvs7q96vd62Fur7HkQGUTFsa91utyMWi8FqtaJWq7XlGR0OB4AHxoUaZq5Wq3A6nTh79iysVivW19fRbDZhs9nQarVQKpVk//dyloa9FlarFW63GzabTRQn97Hb7Yau6yiVSm0GVLcIh5EoZDyjB8m1kfdc+iUwh4lBjqtVLVHVau9kxY/q+qnXyg2sblw11DE7O4uJiQmcPXsWrVYLN27cQKFQgNPplN/ruo5GoyHCgp4Of6YKT9XaoxAe1XUywjjfXRUGFosFDocDNpsNXq8XFotF9gGteVLfKWSM3szDsg4En6Gu67Db7bBYLPB6vdB1HcViETabDRaLRZSP2+3G/Pw8bDYbyuUyyuUyqtVq2xqMyhjuw4DP3el0inJRQ4SqV85nrirQbhGLfnpwI6NcaJXxIatJ7k8TevVsjPFln88Hm80Gp9MpBwkA/H4/dF1HLpeTfM9hYurNZnNP3HZQ4IFwuVzQdR21Wq1NONA69/v9OHfuHP76r/8aExMTcDgcKJfLmJubw9bWFuLxOOx2O5566ilomoZr164hk8ng1q1bAIALFy4AADY3N1Gv11GtVtFoNFCtVgFAwmz379+Hpml7vICThBqa6uRl8asxUa8qFbfbjYWFBUSjUTz55JOw2+1IpVKyD5rNJjY2NlAoFLC+vo5arSaCtZuCGUXyA89CoVCAw+FAMBiE0+nE1NQU3G43otEoms0mbty4gVqtBp/PJ2diYmICr776KjweD959912kUim89dZbyOfzaLVaaLVa8v7AwdT+YYPPPhKJwO12w+VyweVyYW5uDjabDfV6HZVKBbdv30atVkO9XoemaSgWix29F37PM2hUMkdZi5FQLoybBgIBOUTNZhOFQkEefCdt+zCjU7LRKOSNAsZut8PtdsPhcMDhcIjVDkAEtsPhgMViaUtaHyYfM+iYMw8DP8dqtSIYDIrSpBXmcDgQjUZx5swZTExMSOjLYrFgYWEBbrcbwIO1mJ+fh6ZpSCQScLlcKJfLaDabmJycRKvVQrFYRKlUQq1Wg9VqbYuxt1otSQAPE92IGp1yI+r/uQcikQiCwSAee+wxhMNhnD17Fna7HeFwWDy3RqOBYDAo61EoFA7MM43aGWMIyO1249SpU3C73QiHw3A4HPD5fHI2ms0mYrEYarUaAIhHy98HAgHMzs7C6XQiHA5D0zRUKpWRu9/9oEYvHA4HnE6nePOBQAAOhwOapqFWqyGVSqFUKiGbzaLVau0xUg7zWSp6WaehKxcehEAggGeffRYejwdutxuVSgU//elPkc/nRckA7RbFsAXDUaDrehurp1KpAHhgTdvtdhG0fKh0ZTVNg9PpxPz8vAjYRqMBn88nvwMexFprtRq2trZQr9f3JPr2u65+QzUG7HY7pqamAACJRAJerxevvPIKPB4PcrkcnE6nCMiFhQWEQiFUq1WkUik0Gg20Wi08+uijOHPmDJ555hnYbDaMj49D13WMj4+jUqmIh5LP55HJZFAsFrG5uYmdnR00Gg1ZC8ahh71/1BCh+jOgs4eihkLm5uYwMzODl156CXNzc3j66afF8FCT1QwLVatV7Ozs4G/+5m9w//59/M///A/K5bLkLfhZoyZkeS9utxtnz57FuXPn8Cd/8icIh8Oo1Wool8v48MMPkclksL6+jlarhYWFBdRqNfk5n/mtW7cwOzuLl156SfZDPB7HpUuXUCgUpFZM/Wxg9BQtADEu6LEAgNPpxPj4OILBIMbGxtBsNhGNRpHJZHDlyhXUajUJHdOr7ZTLU+WFqsjoBR92PYamXHhDtETGx8cxNzcnsUK6+7VaTcIWdHPz+bwk7YyCUxXMwxYenUAL2u12w263S8x0dnYWdrsdmqZB0zSxwhm+YOGkx+OB0+kUN57hMN6z2+1GtVpFOp0+Mq1wELDZbIhEItB1HVtbW22/o/Xl8/kQiUTEMuVaUADyeVKIMiFLxezxeNBsNlGpVEQgM+SmrtEoKBYV3YQ6BavqwbpcLni9XszNzWFqagrnzp3D1NQUYrGYJLRV5cLz4fP54HA4cObMGdhsNgkdPQxQFer09DQmJiYQCAT25FboifKMMeRJ46xYLKJcLsPn8wEAZmZm0Gw24Xa7USqVhnyXh4Mq7CnwNU1ryyHqug632y1no1qtwm63w+FwiLFhtVrbQoFG1lm3HEwvCvdIyuW4xUdMxk1NTeHUqVP45je/iZmZGYyNjaFWq+Hy5cuoVCoIBAKwWCyYmJhANBrFl770JbRaLfz7v/87tre3RclQAHPhyZQwWiKDwGEWW03EOxwOeDweLC4uIhgMIhaLIRKJ4OWXX4bb7cb9+/exs7OD9957Dzs7O7h9+zYsFgseeeQRuFwuyVOoa2ixWFCtVmG1WjE+Po5qtYpisYhCoXBg/cwgoa6J2+3GZz7zGQCQ+P+PfvQjuN1unD59GuFwGDdv3kSpVMJXvvIVeDwerK2toVqtyv288847sFqtuHjxIjweD/L5vORumLTO5XK4du0astksms0mXC4X7HY7ms2mCJ9RUbjA7v7hMzIm6j0eDywWC+r1Omw2G6anpzEzM4MXX3wRS0tL+OxnPwuPx9Pm9QJ72WAMm/zhH/4hNjY2cOvWLdy8eXNP7mUUwejGr/3ar+Hs2bOwWCwol8tYX19HMpnEpUuXkE6nkUgkYLVaMTc3B03TsLq6KiFRAEgmk9LZwuv14itf+QrW19fx9ttvS5JfxaitCZWKapzS42IoOB6PI5fLIRgMimHlcDgwMTEBp9MpXryaW1E9aCog4744Sl7yRD0XWlTBYBA+nw+PP/44Zmdn8eijjyISicDn86Fer2N6ehpWqxVLS0uoVqvweDwIhUJ45JFHUK/XMTs7CwAiMGjJM89Ai/aw1Nfj3hOxX15IZUF5PB5EIhGEw2HMz88jGo0iGo0K24dJerUAzul0wm63i0dDa8VofXi9XmGPlMtlWYthQ9f1tvoCq9UqdSsAJC5OS5Svr1QqSCaT2NnZQTweh8PhQDqdRjAYRLValefebDZRLBaRz+eRSqVQKBQk7KOyxUZNYHRKqqqejJoroufq8Xjg9/vh9/vF8zUy7tTQscoIMq4zMYohMWA3wsH7BB4k9HVdRzweRyKRQDKZxNbWFjKZjORmALRFPRgNYBjauMaqYh5FqEaHGjrnnqaXVq/X2xL49Xpd5KIxBMufqe/ZCZ1k2cA8l6PUZNCjCIfD+OpXv4qFhQX89m//NkKhEMLhsNycruuYmZlBrVZDPB6HpmmihSkwd3Z2kEgk8N5776FarWJmZgaapuHOnTsol8vI5/MAAI/HM9CEHQ8+H64akjN+HgX+zMwMxsfHce7cOUxPT+NXf/VX4XQ6EY/HkUql8LOf/QzpdBrXr19HsVhErVYTC8Vut2NzcxO1Wg2lUglWqxWZTEYSesFgEC6XSz7L4XC0HZxhCo9CoYAf/OAH4rlFIhE0m014PB6cPn0aY2Nj+KVf+iVMTEwgk8kAeJCbicfj4qkWi0X4/X5hy6XTaTQaDTlMuVwOtVoNyWRSDpmmaW11DMBwLNJOuRQAe2LbqhAh0cXlcmF+fh5Op1NCgNlsFqlUSqxttWiOwkKlINMa/eCDD7C8vIxEIrGHiqte3yjAZrNJPZPD4cD169dx584d+P1+VCoVvP/++0ilUlhbWxPWI8NhJLUwrFqr1fDJJ58IEaZer+PSpUtYW1tDpVJpI3sQJ7EmvUY+eJ75c553ygEy6Bh6jsfjyOfzyOVyKJfLomzoubBOBthVwCp937g/e1mPE/NcmB+IRqOYnZ3FwsICJicnJYdgvHCbzYZoNCrxYtXiWlhYgM1mw8bGBur1OsbHx1Gv1xGPx4Ux1Wg0kMvlTiQsZrFY9ijcTg+CVlUwGMT09DTm5+cRiUQAAJlMBltbW9je3kYqlZL4MDcBlUm5XJZQoMVikdAfN4ixen1U0Gq1REGSfAC0U3FrtRoKhQISiQRarRbu37+PZDKJ1dVVZDIZySlkMhk5ULVaDblcTijH9XodxWIRzWZThMtRCuROCp1i20ZhYrPZhOji8/ngcrlEae7s7KBcLnfsVqAWCAJAtVrF+vo6UqmUeHTqZx9FgAwazDXa7XYxHmq1GiqVClZXV5HP5+WcUEbwbFA40vjjueFe2tjYQDKZRKVSaTMOO1nqKk5ybYyfTa9f3SNUpLquS36FBnWlUkGlUkG9Xu+YjDcanuqadboO9etB63BiysXtduOzn/0s5ubm8MILL2BychJer7etu69qYXGxdF1HuVwWiqrT6cTnP/95lEolzM3NoVQqweFwYGdnBysrK/B4PHj++efRaDTwD//wDwNP1FHg8cGpBxTYDW01m000m014vV5MTk7iN37jNxCNRuXQvPnmm7h37x6uX7+OUqnUFvOs1Wq4c+dO28FnLJkHaWpqSorI1IR1t01lhDG81y+oyXSGYlKplIT6Go0G4vE4dnZ2cPfuXWF7kcxQq9WQzWYBQBTx2tqaxJMbjQa2trag6zqi0SgcDgcymQzq9bpUpQ87LNjpIKr/N64RFQrZQKxjCAQCWFxchMfjAQCsr6/jz//8z9vCIAwZUdB6vd42uq7VakWxWGxLfqvXwpj7sFviULE6HA5MTk7C5XJhZWUFLpcL58+fR6vVEoucz9lqtcpzpyHHfc2Qcz6fx9/+7d+i1Wrh+vXryGaz2N7eltCR3W7fE0bis+FZVxXzcXHQ+6gGgFrnphpLjM4AD7pNeL1eMUpTqVQbk5JnQTVIAbT9jl6v+tmqUlNZY/tFsU5EuRhdOrvdvqdCWhXAxWIRjUYD9XodAIQZxPdgwn52dlYSvnytaqUO2lo97Htzg9vtdoyNjSEajSIUCgm7SdM0ZDIZbG5uSihM07Q2oaiGO4DdYif+3OPxSJUuf68WUx50fUYleRR0sno7CVX1n8qMq1QqaDQayGQyEi/mfaj7hQeB4Q0KF5U92MvzH3Ru7jDXoOYjmUtTPRaC3nupVEKpVMK9e/eQz+elUWWpVGojfASDQXg8HkxNTcHj8WB8fBxOpxPRaFTum1XchOr1DtOTYViQCpIEHv7rZGGrAo8CEYCwEe12OxKJBGq1GnZ2dqQOiut90H0O21BRBbwq5NXcEmWKzWZr8+C7JeXVs3JQ3qmXfTAQ5WIUgKwm//nPf45MJoPPfe5zsFgsbdWlrVYLm5ubKBQKuH37NiqVisSYSZ+k4KAb/Mgjj6DZbGJlZQWZTAb5fB5bW1v4l3/5FzSbTaTT6WMJy8OASs0oNI2WKRkvX/rSl/D444/D7/fDbrcjk8lI3HhjY0NaWrCSlu/DmDL/0Qq1Wq3weDyYnp5GIBCA3+9Hq9VCLpeT6uODYLfbMT09DQASkuoVjP8CnS07HgAaBxR+NpsNOzs7kqxmlXG9XkcikUC9XpfcEd/T7/dD0zQsLy+jWq2iVCq1eYxqb6mDoCaASRDoB7qFDTqFWGgcBINBvPjii3A6ndjY2ECj0RBDqlAoCDOoUqng7t27yOfzUnFfqVTEOAMgxlg2m4Xb7YbX64XH48H58+elFUo2m8Xly5exvb0tn6e2D2JMnmtzUgpGNSJ9Pp9ELCgs4/G4eOxkTdEIIWw2G8bGxoS2TlYiOzrk83nxkI2CmjDuoW59uY57rwe9pxoe5TXRkEqn0yInmM8MhUIYGxuDruuSU+L+MEYo+DNGO5xOp4QQjUZgr3nLEwuLMf6bzWblXy6XQ7Vahcvlkk2TyWSwsrKCer0uB44VtVxY4yagdmZRFQ/ZSdR5HLThVK+NngtbfWuahmw2i2QyiXw+Lx5LJ8tf9VKAXTq3z+dDKBTC+Pg4AoGACOxewkH7eRr9RielQyNkYmJCisJonTLpz2Qlre96vY7l5WXouo5QKCRhJFbmH+Sy74d+WOu9erX0HqPRKFwuF/L5vChW/p75BE3TkEqlkM/nxdsztpvn+1J4VKtV+Z6V/QAkNN3rdQ8aaiiG9Tgqc441cgzhqMQdkiC8Xi/cbjf8fr8IXXpo/JtOozG6Ydhr06k+yygLSe5hzo3ni5EQGmkqO0z1+o11LoRxjYamXCgs6MGQgqrrOlKpFD7++GMkEgl89NFHolRKpZJoWR6qWCyGsbExBAIBoSfbbDZ5TTweR61Ww/3797G2toZ0Ot2WxB+0C6sKsG5sIDX55nQ6MTs7i1AohEqlglKphL/7u7/DysoKVldXJaTBv+9Uvc3QF+moTz31FGZmZvDKK6/A6XTixo0be/j6ByXfNE1DPB6X74+6FhQC+yla4IFnw+9dLhei0Simp6fx6quvIhaLwePxoF6v45NPPoHNZpPODTw0Ho8H29vbuH//PjKZDF588UUphCsUCrh8+bKEUA4T5mC8Ws17AEfj9h8Eo0erEkKsVqt0YGCND72aZ555Bpqm4fr169je3sa9e/ckuc33VNsk8R8Nre3tbQAPYvKs4GbtCz3DTtfKfmwnKVj5WfSa3n33XTgcDszPz0uLG+BBUTXXqFarYWVlBZqmiZHCkof5+Xn4/X7Mzs6K10ev7iTJHgfl3rqB10cDnQXDRhIIczJquJ1KxWh4qCFDnl0jbb2T0Tn0sBgvQhW4dMHq9TrS6bQwgyhE8vk8Njc3JVZIK9RmsyGVSgkl0el0Cp0un8+jWCwimUwKc2iUqtIJu92OaDSKqakpifsWCgVkMhlsbGxIDFgVZkZWhuoB0c0PBoOYmpqSkJjVahWLn4qYlh2wb7vsvgjSw667GgoiLXlsbEw6NbA3kq4/KP47deqUUC55/zabDY8++iiKxSLOnDkDj8cjazoxMQFdf9Dyo9FoHKhcT9JzM0L1WCYmJiRM7PF42owjWtm5XA7ZbFZyTcZhYJ1i5sxT0aBh23q1ir0bhnmWSCwoFoviXZFtaLVaJW9JEhBLD9xuNzweD8bGxuDxeBCNRoUEQaHba16uH+iHJ6zmFNVnzkgGPX81lNWN/cVwGN9zvwLjo1z7QMNixrYsZLO8+eabcLlcCIfDkmthXQIAsaaY2L127Rqi0SgajQZCoRCCwSA0TcPm5ia2t7dx5coV8X6GeRiMlgQRCoXw2muv4cyZM9Im/kc/+hHW19dx584dbG5uSrzbyDZTLUqGwRgzZ60QCzCLxSLu3LmDRCIhygd4cKC6eRUnmbBVPQO/34/nnnsO4XAY09PTmJqawmOPPSa90iwWC5aWltqsNNWr83g8+M53viPGCA9YLpeDpmm4d+8efvzjH4sH00tl/iA7GqjrTcUSi8Xw2muvYXx8XITghQsXJGFfLpfxxhtvoFQqIR6PtxXG8T15b2oRqurRkDXJPBY7RyeTSemgzOsaBTD0B+wym9g3i+GdhYUFNBoNbG9vw+l04syZM2g0GtLi5fnnn8fY2BgikQhKpRIuX76Mra0tGd1gnHEzqlCfL6NCBFmTzE/RayM1v1QqSTsklfHF8JdaB9XvtTixnAsvnPx8tXK6XC5LWIBei6qF+ffUsHT1mbthc8Jhtk4njLxxWlrBYFCaVdLSUhNt/FvSJ9XiJYLUUuah2DVYFcikHjMJqhYZDsMqV6F+PoVFJBLB5OSktKdgiIYKhIwho/dhtVqlY7JKobTZbDIUanp6GjabDel0GsDescvdcNNLo+YAAB7CSURBVFIhEn7lvXq9XiG3cA1oGGxvb4ugMLIAjaG2TuC5IS01l8u17b1RhBrGYd4AgHgm4XAY9XpdGtuq3g3PHWUI61tY13IcOjF7AJ40VG+Fe4T/SNpgQ0tg76ynTntjkO2QTrxxJV101ZpmjmVubk7YH16vF6dPn0YwGMS5c+cQiUSwuLgIm80m1ck3btxAMpnE2toaisXisRK4/Vhgi8UibeTJYKJSYVFnMpmUkI7b7cZ//dd/odFoSC3CY489JpX4bBcOQNz9CxcuYHx8HF/+8pcxMTEhFds+n0+Svj6fDxcvXkS1WsVPf/pTbG9vi4I5iC7cz7VQY/lq7QTDXefPn8fc3JyEtSj8uAfIiFOppcDeQlHVo3E4HPjGN76BfD6PUCiE5eVlfP/735ewqfF+TwpGj0VVHuVyGZ988glKpRIuXrwIXdfx/vvvI51OY2VlBfl8Hul0GvV6va2CWn1v1ePl98b8CwChLH//+9+XBH8gEEChUNhzncOE8fObzSbW1tZgs9nw8ssvY2ZmBnNzc6jVavjXf/1XJJNJaSvPThXr6+vI5/NYWlpCJpPBz3/+c4lwHDV8brfbMTk5KTmsk4C6v/lcG42GyEk2/w0EAhJqDoVCsq9owKvJf2NY8KjPW2WwGXGiyoWbXW2aplr4oVAIgUAA4XBYFiwYDGJubg7BYFA6AKsuHVlWpASfNLpZivwdXVHmQvizYDCISCSCQCCAXC4nyuWRRx6B0+nE2NiYMIBIDXS5XJiYmMDY2BgmJyeFJUVhxUR/rVYT5pjaAkal8w5TeHAzq3kVp9PZtvnV/lfq5jWut7rGaogA2B0axvbsjEEPewhdp7VX6aRskV8ul1EoFCR3xPksxnCzatF2e396eKwfopLhWpDOO+w6jm7QdV26VrD9D3urkezTau12RqZ3UalUsLW1hWw2K+y6Ub3HXkGSENtqkTlKli17GfIcca/Q61EVzCAwFM+FFhMTcyyUevTRRzE5OYkvfOELEi5xuVxSlUohW6lUpMqYyuU49SxH/TuVGskHlcvlxINQrYxcLif5Ba/Xi1wuB6/XiyeeeAJzc3OIRCKIxWL4xje+Aa/XK6771taWhDGAB/kbMl8olNUw2vnz57GzsyP9xzjXRB0itl+C/zgbzUjgMM6lV4VgpVLBtWvXsLW1hfn5eVgsFmnDkclk0Gq14Pf72wr91tbWUC6XkUqlJNfi8/nw7LPPIhQKYXFxURhlDocDFy9exNNPP42PP/4Yt2/fxu3bt1Eul2Wey0nnm1Tw8xm2mZqawuTkJGw2G6rVKrLZLLa2tpBOp6W6upO1bYzFM7ysJmlV0HsLBoOo1+tYXV1Fo9GQkFyhUBi68u2ERqOBnZ0duFwubGxsSJ2Y1+vF17/+9bbxDLdv30Ymk8Hq6iqy2SwuXbqE7e1t8eCPo1w0TcPW1tbQwolqrs7lcgmj9vTp04hGo3j22Wfh9XpRKpWwvb0tXdIpP1hrZiw2PyqGXqFvhNF6plZlnJQT5wKBgLBnAMjB8fv9CAQCkk8YBYZYp4QbrWiv19vWQJL36na7hUE2MTGByclJYbb4fD7xdKrVKgKBAHRdF2+ELB8qMYIKh1ZarVZrUyjDWJNuv+Mcc9Uqr9frWF9fR6PRgMfjkYNQq9WEqq4qF4YFJicnpYiUjVC5h06dOoVGo4F0Oi1U9lHYL2qvOTLm2LaFjDFjuOsw3qdqPBhZRqQWsw6CQlmlpo4iVMZbsViUzuE2m03OBgDp8GC327G1tQWr1YrNzU0JKx73udM4HiXQU3O73RgfH5eclK7r8Pv9YpwD7WyzQZ+BoXguTDRxBgmTmNS27777rjR2ZNM6VVsz+ZnP57G9vT3UeSVGlo0q6F0uF5588kmp4wiFQtJ8jwnIr33ta7BYHnQrYEcCq9UqfcIYUmPMlJ1Nb9y4AYfDIda60+mUugeyxtLptDQ2VCuvO11vPzZat/cwfga/BoNB8dLYrXZnZwdvvvkm8vk8bDZbWz0T2wLRw6FAfP311xEOh/HCCy9gaWkJf/AHfyD9lZxOJ37nd34H29vbiMViiMfjeP3112UWzLBAxfLEE09gcXERFy9eRCgUgqY9mCr65JNPIhaLSQcHVpOroWTuv/3CbFx7hsM2NjbaBLJKZx4FI20/MHl/584dAMBLL73U1oofeGCZs8UNa96KxaKwCEf5/g4DGht8bolEAqVSCYuLi4jFYjh16hT8fj8AIJfL4d1334XVasXKygoAtOU91fccxLoMbRKlUdDoui6df7e2tsSyUus7CCoaY8uHYaDTQ1EVDD0M5oeSySTcbrcwW1i3waFWbLpnbAFOAkSpVEK5XEYikYDT6ZRhYWSIMAleKpWQy+WEWWekpw4L/HyyBguFAtbX1yUEprL/GMZTqer0blTBydYod+/elTV0uVwSLlSZQ8zlqAy0QbcIUqHuZ5IW6I2yAr/ZbCIUCqFWq8Hn84mHxwJSriNbIXWqVTEKD2NFtrEf26hDjWzwWdK4UNlQuq5LvQtfww4eD8N9HhZ8/jwX6jnn/vb7/ZiamkKpVILH45GC05OKYgx1zLG64avVKv73f/9XwgLnzp2T/ANzM0bGDb8/7qY5qubu9Ddq6KtareKdd96By+WSXMvVq1fh9XqxuLgoY31DoRAuXLgAm82GZDIpjBf2CqrVavjv//5vmRPOYrhAIID5+XkAQCAQkIR/uVxGOp0Wi7/VaklokQnPbvfQbyuGmx1obypaLpdx9epVOBwO/PjHP4bT6UQgEBBrjPkaVRAaY8TcB2x2+rOf/QylUgnpdFr2V7VaxfXr1xGPx7G8vCzTCEOhEHw+H5rNZtcY+iDWgh0WqEw2NzfhdruxtbWFSqUiVeYcRzE9Pd2mGM+ePQuHwyF1DDdu3JDW+2o9D58xlShrx6LRKAAgm822kUyYu9uPoDIs8PqmpqawtLSEl19+GYuLizKLRL1mm82GWCyGQCCAmzdvSq7q05LE57PlmeIYAbbW2t7ehq7rmJiYgN/vx6//+q8jHo9jdXUVn3zyyR5CwyAiGMTQlIsKeiDFYlGskGQyic3NTTgcDni9XgDth50aeNSsEQo8WoWchkgLNZPJyDxzv98v3kgkEoHNZsO9e/ekToUtLMrlsiQnNzc3pUZG13fbNhhZY8y3GA/VqKwXw6AWiwWFQkFCNQAk4d7JuzVC9YTK5TIymQxyuZzQmSuVinQNpuXv9Xpl/IFKzz5J8P4YylSLaJlzqdVqYmypbfcdDoeElFWPXu3GAOx6SSQ4sDkoAEnwAtijtAcVJjkKuB4ejwcXLlzA/Pw8Tp06JV2dOwlF1Rs0top/mGE0pNX8idqLT/VG2SbKODzwoLxdPzD0sBiwy2Ch2+ZwOHD37l1873vfw4ULF/Dtb39bBI9KPeW8j34k6foFKhW1CNJisSCZTLYd9GQyCY/Hg5mZGTgcDly5ckXCHLquS9iMSoLWPC1NejQM+/h8vrYGfrlcTpKeFotF1vgghdzPtaDgM+Z7jMKcr2Pn3V4tTb5fs9lEJpPB1atXMTs7i/HxcWlD73Q68fTTT6NcLiMQCMjI20KhgHg8vofZpl5vv0Blogp9t9stSsTn8+Ezn/kMdF2Xrr0UGEzUqqEeYDckaKyypiChUInFYnC73VJzxWJd4xyYURPAHBp4/vx5fPvb38b09LR0pGAHcaNypDIJh8PixbCj9CgapL3A6LXToMhkMgiHwxL2pbfPMg2eJ0YwKCsG+cyH7rmoG4OHjsKF1eZA53qSg/oiDRqdrKFuD0odQKRpGvL5vMTR1c2g3i/DR81mE/l8XthyzMmor+WBIrXVWM9hPICdwDoZNnLsBUar2QhjDoA/U5/rcVgsqre4vb0tgpPv63A4pAaA/ag0TROKdr9AS7uTsgLQttdpQfL5BgIB6WjNHmK0RKks+H9W2rMVjFFxc58wT8F7p7emXsug2n/0Ayyu5uRaEl+MTRdV+cH8I9mC4+Pj4g1TWY/ivR4FPHOUmcZmlYxyqD3oDjqr/cJQlYux3oJfmXN47bXXcOHCBdG2fA0PxHHrW44DWp58aGq9iwq1EJDCj/U6VqtVOhh3SjiqykZtccKkPb/SimO/JR4ydX0OE/LweDy4ePEi3nrrrZ7WghYyPQ9j7F4t4OKa8P64JkcNW/C91Xu+efMm1tfXpd/S+fPnEQqFcOrUKZRKJdy4caNNgVLBG/fjUa6JRX3r6+v7Gj5qqCafz+Pq1avS1LTVagmrMJ1Oy5q2Wi2ZZbK6uipFluoUVDIruT9JzX788cdFqRYKBXzwwQfS1v+kFEuvcX2yKH//938fjz/+OMbGxvYMxKvVavjoo49Qr9elY8X4+DgsFot8feGFF5BIJHDt2jXkcjmZPHkYg6ufOG5ozrjXgV0Dm5GOdDotbNNms4k7d+7g3r17whoFIEbKoJ/7UJVLtxujBb6wsIBYLNYWV2Z8cVTqW/aDmsw2UpZpXTBc1W2uhNHiN1qoqodisezW1RgF+CiA68FZIqTXqjiqgiFIPeWeodU+NjYmLXIYKmKX7pNeH1X4cw9Q0PNnpF3rut4mTJk/Uint6vWrni1zN36/H5OTk3A6ncK8I7vooHvvNt9j0KCiVKvPjTkW7n2O4GAbKSpadr1YXFyEy+XC1tYWHA6H7I+TVKzHxUHetdoJRDUoySZTPdxPPVusGywWC7xeL2KxGM6ePYuJiYk2Qcn5LysrK5KfGAZUrwLYbRKn/l510Y0sLYI5FP7tQZtdFR7AA9ZPsVgUGqLX60U0GpX+Skbhud97l8tlXLp0qeewGN1vVTkavTleWyQSwUsvvYRWq4Uf/vCHyOfze3IzR2Wu8ODQqwsGg5icnMRzzz0ndR3FYhGnTp0CAHz44YdSXKeu+3GYMywC3c9roRHAccX0VEifVfdWKBSS66nVatKDjPkDCmG+L3vNseJ+dnYWc3Nz+PrXv456vY4//dM/lfc4qEsBFTHQn0mUvXgsdrsdU1NTiMVi2NraQjgcxmOPPSbvw/wkW+0zFwXsNnXkz3/zN38TxWIRwWAQ6+vrePvtt5FOp5FMJoV1dxJyZL/P4HPo9jzU35MhSfnCnCuLjtVWSKFQCF6vV9rfqHLIGGHo9xqMlHLhZj59+jSWlpbkoDAkUK1WUa1Wcf/+fdy+fVus/mGD8zEA7CnqPMwD6xRK6gajS8wDpobNVDZIL9B1XVqzH+VveX1erxcWi0UOrTG3wjxHNBptq+0AuleXdwItWTVvwr5KgUBAuh5Q8JAcwc9im4x+5lyMRsd+YFycBY5qxwXmFXw+HwCIda6GRdSuD7r+oHsDKekczTA2NiajF1SBbCw8Po5CHQTo2XGiJCvOqVg0TZNJtoxgNBqNtlA134dU7nA4jGKxiPHxcbRaLWnpP8ow7k31/1arVTqaUJHSo+PeUufXdMppDpJFN1LKhW3Sv/vd72JxcVEGYAEPLKf/+I//wK1bt/Bv//Zv2NraGpnNEYvF8K1vfQutVgvf+973sLm5CWD3MHcKKxhzEqpy6OS6qqEUi8UiQ5Tu378Pp9OJZ599tq2/FBseUrDz89UQSyccN9nHjsy6ruPy5cuirNS1uHbtGmKxGL761a9C0zRcvXoV2Wy2jfWkHgQmbNW1YRsg1sfQghsfH8ev/Mqv4NSpU3juueckF7Szs4P3338fyWQSH374ITRNw3PPPYdyuYx//ud/RjabPVCZHQWdhDbXQk2u1mo1VCoVyV/Nzs4iHA7j85//PBqNBl5//XXYbDacP38e5XJZZgBxnUit/uIXvwiPxyONTs+ePQu73Y7/9//+n9xjIBBAsVhss2DpYZOIwOtmu5iThJoLCQQC+MIXvoBYLAZd18XAzOfz+MEPfoBSqYQzZ86IocDWSXa7XYonM5kMKpUKPB4PYrEYLly4gFgshuXl5SMRgvohkI37wvi1myfD80zFe/r0aUxOTuLMmTMIh8PSHieXy6HRaGBlZQWJREKecSfDwvi5RuWj/r4XjIxyYZ1DLBbD3NycTMrjzTabTdy9exd3797FvXv32sYZjwK6MW72u8ZuVsl+1grBz/F6vfB6vW2dAFKpVJs3c9LY76C0Wi1ks1m43W5EIhERICqrhclJo8WlFoLSy3W5XIhEIvB4PDLfZnFxEbOzs0JZJcuOOZZcLod6vY5oNDo0tqHafYIeKIscKSA5n4OKlNfv8/lkhg8tVQ7IImW5WCwKyYMDtXK5nHQUNlr43TBMT4bn3jh1s1KpIJvNYmNjA9VqFZFIRHIKpKPbbDbxUtlnr1gsigc4jIT+cdBJ6NOYIoOOXgyfLdvlsAN2J4P1U5vQJ8hqef7553Hu3DnMzs6K10J3N5fL4cqVK/jggw/Ewh0VJJNJfPe73wUAGUy1H9REPzd3p9kk6lf+nMKw0WjA7/fjlVdewcTEBGq1GpLJJP7yL/8Sy8vL2NzcbPOIiIPW7bjrWiqVcOnSJQC7dUtkY7G2IpVKwWazYWxsDA6HQ8YLMP/C2h9anslkElarFUtLS7BYLDIOm8nqc+fOYXJyEr/1W7+F8fFxTE9Piyej6w9qhiKRiPRY+vu//3vE43G89957ku8Y1H466PAy3Lu8vIyJiQl8+ctfFsKDrut455135N41TcP9+/dht9vx3HPPwefzweVyyayTRqMhY8CXl5dRKpXw/vvvy95oNpuSGGdzR3o/RlbhMME9X6/XkUgk8I//+I8yaI9MqEqlIvdcr9elm4HVasXVq1fFW7XZbDh9+jR0Xcfy8jKy2Szef/99lMtl8R57RT/W6DBK3ZhnodfOUF8wGMRTTz0lk3kbjQbm5uaEuFKv13H37l2sra1J93iGUdXnrUY3jNd1nHsdunKh8PF6vVhaWsLS0pJYVrTqWMWeTqeRz+dHSrEAu224gfacyHEeTDcvhmD9gt/vl15KACRsQPbISbNh1LyNmh9giMZqtWJ1dRXNZhOFQkHyA9FoFJlMBgCk5QnzEOzqSuExPj4ulpnH48Hk5CSmp6fFc1FnWLC2oVqtolgsihVbqVSEmnnSjU8pIIHdRDkZPQ6HQ5QwQzrqGk5NTck9szWSOoqC71Mul1EsFpFOp6VzQavVkqFsKkbRgqfXUiqVsL6+jnQ6LQWCpNnyXpl7YX6KrXSSySQcDocM8Mtms0in0zKFUw0LDvvejTKjW6LdmG+jZ09DnHuInjpbwxi9VP6tMTzf6TOPiqEqFyqWcDiM8+fP44//+I+lpxIVS71ex8cff4xbt24hlUp1LBgb9sYA9jIv+LNu1sB+A7CAXWqhWvxEWK0PRvwyMQk8GIjl9/vxzW9+U9YqHo+LMh6GQuZ9a5qGiYkJ/MVf/AWsViu+9a1vYW1tDT/84Q8RiUTwzDPP4MyZMxIKfeKJJ+B0OlEoFGC32/Hoo4/CYrHg7t27cLvd+OIXvwir1YqPPvoIzWYT8/Pz8Pv9GBsbg91uR7lcFgFer9fxxhtv4O7du7h69So2NzdRKpXgdruHxjb0er148cUXAQBXrlyR/Ee9Xkc2mxVFrOu7Ywnu37+PcDiMV199ta3QlUPWqIwSiQRSqZTMMOHzZ06F4REKHD6nflqsxwXPB8NYb7zxBlwuF2ZmZiSXYrE8mPKq6zpWVlZgtVqFYswx6vSQGWbd3NyUuiAA4tnSAFO9g5OEGsnYL/fK19GwbDQaePfddzE+Po5f/uVfhqZp+M///E94PB48++yzsnfYAUIt1ub7smhXfd7qmhwHQ/dcSBnkECy236c1ViqVEI/HkUgkpLblYcNRLCOGydTiPv5fHQGsWl9U1KxpUJljRgV2UsLDyPBRn+3m5qYkstUhX2xz0mw2JafCg+H1ejExMQGr1SrtUSKRiFj7rNC3Wq2YnJxEtVrFnTt3sLy8jFu3bkmnaK7ZsIRoN4Mjn8/D5XIhlUq1jZqggmCHAeYguJYsqEylUkilUuLlk3LMnmIMgw269Uc/QEWojlpgnQrDqVarVWjmNDw5loL3yDUolUqSi6TVPsr3T3Q7v81mE6lUSvKYJDq43W6k02nJORm9lk60+26fcxwMtSsyGwkuLS0hFouhUCiItc6Jcpubm7h8+TI2NzelBQr/fpQ2Rq8HVWVvqa6u0VKh0FRjraqVw7wEDxAbfbJIjs37ePCMHYpPwkrTdR1ra2v4vd/7PVgsFmxtbUHTNHg8HuRyOaRSKbhcLpnIuba2JiEfXdexsbGBsbExfO5znxMCAwAsLi5Ksh6AzJr/sz/7MzSbTfzu7/4u6vU6/umf/knizmrhIPfgSSf1y+UyfvKTnwBorx/J5/N44403ZADa2NgYXnjhBZlSGggEUKlU2mjbyWQSuVwO7733Hra3t/HWW2/J9FGSPJgEpwIbdaWiFk0DEO+DebNMJgOr1YpYLIZQKITZ2Vk0m018+OGHsp/YP81ms4lyojFjzDMRwyTAHDQygT/j82O+iASeRCIhIUCuk67ryGQybSO++eyNEREjCacfGIhy6SVcRcHaarWws7Mjf8PipnK5jGw2i1QqtafP2CgfkIPQzUJQZ1OoikdN6jFGz0ptejG0fLe2tkaqgwFjwaurq/IzVkiTDeR0OuHz+aDrOlKpVBt3n/PQFxYW5GAYQzu89/X1dXz88ceo1Wq4ceMGms0mNjY2sLOzs8frNc7MOSnw2oH2fdBsNqXhqMVikVb6wIPaIJI4XC6XhEUSiQTS6bRMW0wmkygWi3sUCa3VUctX7gc1F0S6NtlvwAPF7HK52roBM7+m9vLjHlMnnj5sa0Fwv7Bom3u6Xq/LOdF1XfYNvTWVeQmcTOcOSy8fYLFYDvXig2opVEHpcrnwyCOPIBaL4YknnkA0GsXTTz8tLTuy2Sy+853v4JNPPkE8Hm+jqvajI7IKXdcP7RMa1+KwCk9tYaG+Xp1LQY+OSlc9YLS6rFYrQqEQvva1r8HlcuHmzZuoVqtSlHjr1q223mJqXPkw13mctTgsuBZqW3SGgdQCsfPnzws1V10/KmAOQvrwww+xs7OD9fV1aJqGcDgMAG2K5Sj75STW4v/+VkJhrLLmVEVW3M/PzyMQCODJJ5+Epml4++23kUwmZVaHWnl/lOd+EE5qLZT3EIIGqegsnuZYBbLmQqEQms0mVlZW2opl2cGA1en9MroGtRadkux8phyCp1Kp1cgHvd6lpSW0Wi3cvn1b8i7q+/Xb8Oy0FkMJixmZUGSzJBIJadNA1y6dTqNQKJxYi4Ze0Gt8Uk3675cDUa0Ktc5DdeVZ07K+vg6LxYKbN2+iVqtJKIkxZ6OCH+QaHmRUGMEDQ6/LGKZgb6y1tbW29h48UByB7fP5JLdCxpSu60gmk/2/yQFCJXqordQpWMkMi0QiCIfD0DQN9+7dQzqdRiaTkZkwQPcivYcRRqubSoP1TqVSSRQP9z49Ff49fzfqIcHDQL0PVcEAkI7XNKjYOVtN4J8UBuK5HOJ92rQwBQwpp6xxYegkkUi0bRZaIv3uiNyrJcJrBNA1htmJQUbrHNhNrhlDNnxf3quxazI9HSZqGVPnZ6uJfvXrYS2WXteC+SGG7NTPOEjp8B47eRdcK7Xzs8qoobWv63obM26/e+zVij+KhXocT0ENiTqdTimu5DrR+tY0DZlMpq0B56AF50l7Lv/3Pp2uY8/e5j44qbzSINeimxxRw+ZGuaEar1wLtWGuykozyoHjerYj47kYQaVBS5ZtS6g8jI0FR8Xy4EhV4EEhZbd+TZ3A0JYK9b6MMdVuHgjXqpsyUw/hSeAon7Xf8+TzV0NGxkOhUkpHJY5+nMNq9FzVZCy911KpJMPjPi0WeTd0uy+jPKDV/mnAQfdszMPydwetxUnK0KEqF1WrqlqWUK1tFWpSapiIRqP4oz/6I+i6jr/6q7/ao2A6XZ8qNDr93PizbpvBuCa6rreNCmZC39j2Qc3x8DqMwvqoArFbI9GDBP5hNry6P4yCWx3MdUiv7MDXHAeqhXhUoc81Y/0Lxxoz38gw8bBICb2C3hhw8kWrnwbQSNR1veM+MKKbcWP8/yDJUSMxz4UHsFPvm/3+btjgNR9FgPQQkmkTVgd9HjefsTPyQWGifqzpoK0io3c2KvtgkFDPhiqguQ+M3u+o4hfhWQ0aqoI5ikF44lGMYeRcDvE5AE5+Q/YaQ1UHX3EkbQ9/z89s+7lxMBoVC3srZbPZPS3FjX/PzrrAA7JEr80z/09wDZw5d1T0ShwYRDx5n8/Sle/7vgbdYvEndVaGkXMZFg5a22GuxbD3gREjm3N5WKFp2pHb/nfbDOrPVcGvDoTaT2gdt1/SSVo2n3YM4qCbHoAJ4OHYB716LtsAVg984cOJBV3XY4d9sbkWuzDXYhfmWuzCXItd/CKuRU/KxYQJEyZMmDgMHo5soAkTJkyYeKhgKhcTJkyYMNF3mMrFhAkTJkz0HaZyMWHChAkTfYepXEyYMGHCRN9hKhcTJkyYMNF3mMrFhAkTJkz0HaZyMWHChAkTfYepXEyYMGHCRN/x/wEwmOTzLcnXfgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 504x864 with 60 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7hNMOeFzY18",
        "colab_type": "text"
      },
      "source": [
        "It starts out as all noise. Then it learns to make only the center white and the rest black. You can start to see some number like structures appear out of the noise like 1s and 9s."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LhrJqcf2zY18",
        "colab_type": "text"
      },
      "source": [
        "## Sampling from the generator\n",
        "\n",
        "We can also get completely new images from the generator by using the checkpoint we saved after training. **We just need to pass in a new latent vector $z$ and we'll get new samples**!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFBu1UP5zY19",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "89260785-7ecd-43e9-d826-eab140328b33"
      },
      "source": [
        "# randomly generated, new latent vectors\n",
        "sample_size=16\n",
        "rand_z = np.random.uniform(-1, 1, size=(sample_size, z_size))\n",
        "rand_z = torch.from_numpy(rand_z).float()\n",
        "\n",
        "G.eval() # eval mode\n",
        "# generated samples\n",
        "rand_images = G(rand_z)\n",
        "\n",
        "# 0 indicates the first set of samples in the passed in list\n",
        "# and we only have one batch of samples, here\n",
        "view_samples(0, [rand_images])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1558: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
            "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tfDPy_BOSJIl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}