GNN/07_ARGA_&_ARVGA.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ramsi-K/3D-Vision-Playground/blob/main/GNN/07_ARGA_%26_ARVGA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pt8PBZzGwL0S"
      },
      "source": [
        "# Tutorial 7: ARGA & ARVGA  \n",
        "\n",
        "Paper:\n",
        "* [Adversarially Regularized Graph Autoencoder for Graph Embedding](https://www.ijcai.org/Proceedings/2018/0362.pdf)  \n",
        "\n",
        "Code:\n",
        " * [ARGA & ARVGA](https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/nn/models/autoencoder.html)\n",
        " * [Example on clustering](https://github.com/rusty1s/pytorch_geometric/blob/master/examples/argva_node_clustering.py)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "icgU76iUwL0U",
        "outputId": "dc28be8d-d8f0-4c5e-fe60-eb082754681e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.2.1+cu121\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import torch\n",
        "os.environ['TORCH'] = torch.__version__\n",
        "print(torch.__version__)\n",
        "\n",
        "!pip install -q torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
        "!pip install -q torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
        "!pip install -q git+https://github.com/pyg-team/pytorch_geometric.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLdvo1AawL0V"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "06xWGZxuwL0W"
      },
      "outputs": [],
      "source": [
        "import os.path as osp\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics.cluster import (v_measure_score, homogeneity_score, completeness_score)\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "import torch_geometric.transforms as T\n",
        "from torch_geometric.datasets import Planetoid\n",
        "from torch_geometric.nn import GCNConv\n",
        "from torch_geometric.nn.models.autoencoder import ARGVA\n",
        "from torch_geometric.utils import train_test_split_edges"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "YHgdREAPwL0W"
      },
      "outputs": [],
      "source": [
        "use_cuda = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oArn6viEwL0X"
      },
      "source": [
        "## Define the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Teg5dqobwL0X"
      },
      "source": [
        "Download the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "qUT0s9HRwL0X"
      },
      "outputs": [],
      "source": [
        "dataset = 'Cora'\n",
        "path = osp.join('.', 'data', dataset)\n",
        "dataset = Planetoid(path, dataset, transform=T.NormalizeFeatures())\n",
        "data = dataset.get(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N3Ih1jexwL0Y"
      },
      "source": [
        "Get the number of nodes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "1n8yQZNuwL0Y"
      },
      "outputs": [],
      "source": [
        "num_nodes = data.x.shape[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vl637NWDwL0Y"
      },
      "source": [
        "Create the train/val/test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "PYpySCHlwL0Z",
        "outputId": "3bce2adb-2cef-4eae-8a62-d468a5855976",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch_geometric/deprecation.py:26: UserWarning: 'train_test_split_edges' is deprecated, use 'transforms.RandomLinkSplit' instead\n",
            "  warnings.warn(out)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data(x=[2708, 1433], y=[2708], val_pos_edge_index=[2, 263], test_pos_edge_index=[2, 527], train_pos_edge_index=[2, 8976], train_neg_adj_mask=[2708, 2708], val_neg_edge_index=[2, 263], test_neg_edge_index=[2, 527])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "data.train_mask = data.val_mask = data.test_mask = None\n",
        "data = train_test_split_edges(data)\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yoVXMm2DwL0Z"
      },
      "source": [
        "## Define the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-BfJqZh5wL0Z"
      },
      "source": [
        "Define the encoder classes (the same as in `Tutorial 6`)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "2IyB0HJOwL0a"
      },
      "outputs": [],
      "source": [
        "class VEncoder(torch.nn.Module):\n",
        "    def __init__(self, in_channels, out_channels):\n",
        "        super(VEncoder, self).__init__()\n",
        "        self.conv1 = GCNConv(in_channels, 2 * out_channels, cached=True)\n",
        "        self.conv_mu = GCNConv(2 * out_channels, out_channels, cached=True)\n",
        "        self.conv_logstd = GCNConv(2 * out_channels, out_channels, cached=True)\n",
        "\n",
        "    def forward(self, x, edge_index):\n",
        "        x = F.relu(self.conv1(x, edge_index))\n",
        "        return self.conv_mu(x, edge_index), self.conv_logstd(x, edge_index)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F8MYXEhTwL0a"
      },
      "source": [
        "Define the discriminator class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "tlW8dj_ewL0a"
      },
      "outputs": [],
      "source": [
        "class Discriminator(torch.nn.Module):\n",
        "    def __init__(self, in_channels, hidden_channels, out_channels):\n",
        "        super(Discriminator, self).__init__()\n",
        "        self.lin1 = torch.nn.Linear(in_channels, hidden_channels)\n",
        "        self.lin2 = torch.nn.Linear(hidden_channels, hidden_channels)\n",
        "        self.lin3 = torch.nn.Linear(hidden_channels, out_channels)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.lin1(x))\n",
        "        x = F.relu(self.lin2(x))\n",
        "        x = self.lin3(x)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kzoy37ajwL0b"
      },
      "source": [
        "Define the training algorithm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "n_txHaHfwL0b"
      },
      "outputs": [],
      "source": [
        "def train():\n",
        "    model.train()\n",
        "    encoder_optimizer.zero_grad()\n",
        "\n",
        "    z = model.encode(data.x, data.train_pos_edge_index)\n",
        "\n",
        "    for i in range(5):\n",
        "        idx = range(num_nodes)\n",
        "        discriminator.train()\n",
        "        discriminator_optimizer.zero_grad()\n",
        "        discriminator_loss = model.discriminator_loss(z[idx])\n",
        "        discriminator_loss.backward(retain_graph=True)\n",
        "        discriminator_optimizer.step()\n",
        "\n",
        "    loss = 0\n",
        "    loss = loss + model.reg_loss(z)\n",
        "\n",
        "    loss = loss + model.recon_loss(z, data.train_pos_edge_index)\n",
        "    loss = loss + (1 / data.num_nodes) * model.kl_loss()\n",
        "    loss.backward()\n",
        "\n",
        "    encoder_optimizer.step()\n",
        "\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PyZi2ztNwL0c"
      },
      "source": [
        "Define a training test method"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "212yXAFtwL0c"
      },
      "outputs": [],
      "source": [
        "@torch.no_grad()\n",
        "def test():\n",
        "    model.eval()\n",
        "    z = model.encode(data.x, data.train_pos_edge_index)\n",
        "\n",
        "    # Cluster embedded values using k-means.\n",
        "    kmeans_input = z.cpu().numpy()\n",
        "    kmeans = KMeans(n_clusters=7, random_state=0, n_init=10).fit(kmeans_input)\n",
        "    pred = kmeans.predict(kmeans_input)\n",
        "\n",
        "    labels = data.y.cpu().numpy()\n",
        "    completeness = completeness_score(labels, pred)\n",
        "    hm = homogeneity_score(labels, pred)\n",
        "    nmi = v_measure_score(labels, pred)\n",
        "\n",
        "    auc, ap = model.test(z, data.test_pos_edge_index, data.test_neg_edge_index)\n",
        "\n",
        "    return auc, ap, completeness, hm, nmi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1pRaUPowL0c"
      },
      "source": [
        "## Initialize the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nb13dWyfwL0c"
      },
      "source": [
        "Initialize an encoder and a discriminator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "NKIOLqZEwL0d"
      },
      "outputs": [],
      "source": [
        "latent_size = 32\n",
        "encoder = VEncoder(data.num_features, out_channels=latent_size)\n",
        "\n",
        "discriminator = Discriminator(in_channels=latent_size, hidden_channels=64,\n",
        "                              out_channels=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTdwyW9lwL0d"
      },
      "source": [
        "Initialize the model and move everything to the GPU"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "IGVBh5V-wL0d"
      },
      "outputs": [],
      "source": [
        "model = ARGVA(encoder, discriminator)\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() and use_cuda else 'cpu')\n",
        "model, data = model.to(device), data.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "asXQjKfCwL0e"
      },
      "source": [
        "Define the optimizers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "o_khbbOMwL0e"
      },
      "outputs": [],
      "source": [
        "discriminator_optimizer = torch.optim.Adam(discriminator.parameters(), lr=0.001)\n",
        "encoder_optimizer = torch.optim.Adam(encoder.parameters(), lr=0.005)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpzkUjsUwL0e"
      },
      "source": [
        "### Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "YBMvXNtrwL0e",
        "outputId": "76b567dc-9a05-4805-92f5-0c39ca9a49ce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 001, Loss: 5.611, AUC: 0.775, AP: 0.795, Completeness: 0.157, Homogeneity: 0.159, NMI: 0.158\n",
            "Epoch: 002, Loss: 4.971, AUC: 0.747, AP: 0.777, Completeness: 0.119, Homogeneity: 0.118, NMI: 0.118\n",
            "Epoch: 003, Loss: 4.524, AUC: 0.734, AP: 0.768, Completeness: 0.102, Homogeneity: 0.100, NMI: 0.101\n",
            "Epoch: 004, Loss: 4.172, AUC: 0.732, AP: 0.768, Completeness: 0.111, Homogeneity: 0.108, NMI: 0.109\n",
            "Epoch: 005, Loss: 3.614, AUC: 0.739, AP: 0.773, Completeness: 0.148, Homogeneity: 0.146, NMI: 0.147\n",
            "Epoch: 006, Loss: 3.337, AUC: 0.755, AP: 0.785, Completeness: 0.150, Homogeneity: 0.149, NMI: 0.149\n",
            "Epoch: 007, Loss: 2.935, AUC: 0.769, AP: 0.795, Completeness: 0.187, Homogeneity: 0.190, NMI: 0.188\n",
            "Epoch: 008, Loss: 2.615, AUC: 0.754, AP: 0.778, Completeness: 0.170, Homogeneity: 0.171, NMI: 0.170\n",
            "Epoch: 009, Loss: 2.454, AUC: 0.723, AP: 0.749, Completeness: 0.195, Homogeneity: 0.199, NMI: 0.197\n",
            "Epoch: 010, Loss: 2.281, AUC: 0.714, AP: 0.740, Completeness: 0.225, Homogeneity: 0.230, NMI: 0.227\n",
            "Epoch: 011, Loss: 2.289, AUC: 0.739, AP: 0.758, Completeness: 0.250, Homogeneity: 0.256, NMI: 0.253\n",
            "Epoch: 012, Loss: 2.221, AUC: 0.784, AP: 0.802, Completeness: 0.291, Homogeneity: 0.297, NMI: 0.294\n",
            "Epoch: 013, Loss: 2.157, AUC: 0.782, AP: 0.800, Completeness: 0.278, Homogeneity: 0.283, NMI: 0.280\n",
            "Epoch: 014, Loss: 2.163, AUC: 0.759, AP: 0.780, Completeness: 0.262, Homogeneity: 0.256, NMI: 0.259\n",
            "Epoch: 015, Loss: 2.264, AUC: 0.761, AP: 0.784, Completeness: 0.277, Homogeneity: 0.270, NMI: 0.273\n",
            "Epoch: 016, Loss: 2.375, AUC: 0.787, AP: 0.808, Completeness: 0.307, Homogeneity: 0.308, NMI: 0.307\n",
            "Epoch: 017, Loss: 2.449, AUC: 0.812, AP: 0.835, Completeness: 0.334, Homogeneity: 0.332, NMI: 0.333\n",
            "Epoch: 018, Loss: 2.472, AUC: 0.805, AP: 0.825, Completeness: 0.343, Homogeneity: 0.345, NMI: 0.344\n",
            "Epoch: 019, Loss: 2.566, AUC: 0.784, AP: 0.800, Completeness: 0.366, Homogeneity: 0.368, NMI: 0.367\n",
            "Epoch: 020, Loss: 2.694, AUC: 0.778, AP: 0.792, Completeness: 0.381, Homogeneity: 0.381, NMI: 0.381\n",
            "Epoch: 021, Loss: 2.754, AUC: 0.794, AP: 0.807, Completeness: 0.386, Homogeneity: 0.389, NMI: 0.387\n",
            "Epoch: 022, Loss: 2.783, AUC: 0.819, AP: 0.835, Completeness: 0.384, Homogeneity: 0.388, NMI: 0.386\n",
            "Epoch: 023, Loss: 2.834, AUC: 0.832, AP: 0.850, Completeness: 0.395, Homogeneity: 0.391, NMI: 0.393\n",
            "Epoch: 024, Loss: 2.944, AUC: 0.829, AP: 0.848, Completeness: 0.393, Homogeneity: 0.389, NMI: 0.391\n",
            "Epoch: 025, Loss: 3.087, AUC: 0.823, AP: 0.842, Completeness: 0.385, Homogeneity: 0.386, NMI: 0.385\n",
            "Epoch: 026, Loss: 3.285, AUC: 0.822, AP: 0.842, Completeness: 0.368, Homogeneity: 0.373, NMI: 0.371\n",
            "Epoch: 027, Loss: 3.440, AUC: 0.827, AP: 0.848, Completeness: 0.380, Homogeneity: 0.385, NMI: 0.383\n",
            "Epoch: 028, Loss: 3.546, AUC: 0.832, AP: 0.852, Completeness: 0.386, Homogeneity: 0.390, NMI: 0.388\n",
            "Epoch: 029, Loss: 3.614, AUC: 0.833, AP: 0.852, Completeness: 0.390, Homogeneity: 0.388, NMI: 0.389\n",
            "Epoch: 030, Loss: 3.730, AUC: 0.832, AP: 0.850, Completeness: 0.397, Homogeneity: 0.394, NMI: 0.395\n",
            "Epoch: 031, Loss: 3.859, AUC: 0.835, AP: 0.851, Completeness: 0.402, Homogeneity: 0.399, NMI: 0.400\n",
            "Epoch: 032, Loss: 3.954, AUC: 0.840, AP: 0.854, Completeness: 0.431, Homogeneity: 0.417, NMI: 0.424\n",
            "Epoch: 033, Loss: 4.046, AUC: 0.843, AP: 0.855, Completeness: 0.434, Homogeneity: 0.418, NMI: 0.426\n",
            "Epoch: 034, Loss: 4.055, AUC: 0.839, AP: 0.850, Completeness: 0.419, Homogeneity: 0.394, NMI: 0.406\n",
            "Epoch: 035, Loss: 4.187, AUC: 0.833, AP: 0.844, Completeness: 0.421, Homogeneity: 0.394, NMI: 0.407\n",
            "Epoch: 036, Loss: 4.391, AUC: 0.834, AP: 0.844, Completeness: 0.400, Homogeneity: 0.388, NMI: 0.393\n",
            "Epoch: 037, Loss: 4.488, AUC: 0.840, AP: 0.849, Completeness: 0.402, Homogeneity: 0.392, NMI: 0.397\n",
            "Epoch: 038, Loss: 4.580, AUC: 0.848, AP: 0.856, Completeness: 0.410, Homogeneity: 0.400, NMI: 0.405\n",
            "Epoch: 039, Loss: 4.578, AUC: 0.857, AP: 0.864, Completeness: 0.415, Homogeneity: 0.406, NMI: 0.411\n",
            "Epoch: 040, Loss: 4.643, AUC: 0.865, AP: 0.871, Completeness: 0.417, Homogeneity: 0.405, NMI: 0.411\n",
            "Epoch: 041, Loss: 4.551, AUC: 0.872, AP: 0.877, Completeness: 0.434, Homogeneity: 0.424, NMI: 0.429\n",
            "Epoch: 042, Loss: 4.543, AUC: 0.877, AP: 0.881, Completeness: 0.437, Homogeneity: 0.420, NMI: 0.428\n",
            "Epoch: 043, Loss: 4.538, AUC: 0.879, AP: 0.882, Completeness: 0.436, Homogeneity: 0.417, NMI: 0.426\n",
            "Epoch: 044, Loss: 4.552, AUC: 0.879, AP: 0.883, Completeness: 0.438, Homogeneity: 0.420, NMI: 0.429\n",
            "Epoch: 045, Loss: 4.574, AUC: 0.877, AP: 0.881, Completeness: 0.440, Homogeneity: 0.421, NMI: 0.430\n",
            "Epoch: 046, Loss: 4.639, AUC: 0.875, AP: 0.878, Completeness: 0.424, Homogeneity: 0.419, NMI: 0.422\n",
            "Epoch: 047, Loss: 4.613, AUC: 0.871, AP: 0.875, Completeness: 0.426, Homogeneity: 0.424, NMI: 0.425\n",
            "Epoch: 048, Loss: 4.546, AUC: 0.867, AP: 0.872, Completeness: 0.433, Homogeneity: 0.425, NMI: 0.429\n",
            "Epoch: 049, Loss: 4.656, AUC: 0.864, AP: 0.869, Completeness: 0.434, Homogeneity: 0.428, NMI: 0.431\n",
            "Epoch: 050, Loss: 4.617, AUC: 0.862, AP: 0.868, Completeness: 0.431, Homogeneity: 0.424, NMI: 0.428\n",
            "Epoch: 051, Loss: 4.396, AUC: 0.860, AP: 0.866, Completeness: 0.423, Homogeneity: 0.412, NMI: 0.417\n",
            "Epoch: 052, Loss: 4.459, AUC: 0.861, AP: 0.866, Completeness: 0.420, Homogeneity: 0.421, NMI: 0.420\n",
            "Epoch: 053, Loss: 4.360, AUC: 0.862, AP: 0.867, Completeness: 0.428, Homogeneity: 0.416, NMI: 0.422\n",
            "Epoch: 054, Loss: 4.418, AUC: 0.865, AP: 0.869, Completeness: 0.431, Homogeneity: 0.426, NMI: 0.428\n",
            "Epoch: 055, Loss: 4.322, AUC: 0.868, AP: 0.871, Completeness: 0.431, Homogeneity: 0.429, NMI: 0.430\n",
            "Epoch: 056, Loss: 4.255, AUC: 0.871, AP: 0.873, Completeness: 0.425, Homogeneity: 0.427, NMI: 0.426\n",
            "Epoch: 057, Loss: 4.227, AUC: 0.873, AP: 0.875, Completeness: 0.431, Homogeneity: 0.432, NMI: 0.432\n",
            "Epoch: 058, Loss: 4.086, AUC: 0.875, AP: 0.877, Completeness: 0.431, Homogeneity: 0.433, NMI: 0.432\n",
            "Epoch: 059, Loss: 4.009, AUC: 0.876, AP: 0.877, Completeness: 0.420, Homogeneity: 0.414, NMI: 0.417\n",
            "Epoch: 060, Loss: 3.967, AUC: 0.878, AP: 0.878, Completeness: 0.422, Homogeneity: 0.418, NMI: 0.420\n",
            "Epoch: 061, Loss: 3.882, AUC: 0.878, AP: 0.878, Completeness: 0.435, Homogeneity: 0.437, NMI: 0.436\n",
            "Epoch: 062, Loss: 3.879, AUC: 0.879, AP: 0.879, Completeness: 0.436, Homogeneity: 0.438, NMI: 0.437\n",
            "Epoch: 063, Loss: 3.845, AUC: 0.879, AP: 0.879, Completeness: 0.429, Homogeneity: 0.432, NMI: 0.430\n",
            "Epoch: 064, Loss: 3.828, AUC: 0.880, AP: 0.880, Completeness: 0.432, Homogeneity: 0.435, NMI: 0.433\n",
            "Epoch: 065, Loss: 3.792, AUC: 0.880, AP: 0.881, Completeness: 0.429, Homogeneity: 0.430, NMI: 0.430\n",
            "Epoch: 066, Loss: 3.893, AUC: 0.880, AP: 0.881, Completeness: 0.428, Homogeneity: 0.429, NMI: 0.429\n",
            "Epoch: 067, Loss: 3.864, AUC: 0.881, AP: 0.882, Completeness: 0.438, Homogeneity: 0.441, NMI: 0.440\n",
            "Epoch: 068, Loss: 3.885, AUC: 0.880, AP: 0.883, Completeness: 0.438, Homogeneity: 0.441, NMI: 0.440\n",
            "Epoch: 069, Loss: 3.979, AUC: 0.881, AP: 0.883, Completeness: 0.411, Homogeneity: 0.418, NMI: 0.414\n",
            "Epoch: 070, Loss: 4.009, AUC: 0.880, AP: 0.883, Completeness: 0.413, Homogeneity: 0.419, NMI: 0.416\n",
            "Epoch: 071, Loss: 4.037, AUC: 0.880, AP: 0.883, Completeness: 0.410, Homogeneity: 0.416, NMI: 0.413\n",
            "Epoch: 072, Loss: 4.016, AUC: 0.879, AP: 0.883, Completeness: 0.446, Homogeneity: 0.443, NMI: 0.444\n",
            "Epoch: 073, Loss: 4.155, AUC: 0.879, AP: 0.883, Completeness: 0.416, Homogeneity: 0.423, NMI: 0.419\n",
            "Epoch: 074, Loss: 4.026, AUC: 0.879, AP: 0.883, Completeness: 0.414, Homogeneity: 0.418, NMI: 0.416\n",
            "Epoch: 075, Loss: 4.105, AUC: 0.879, AP: 0.883, Completeness: 0.419, Homogeneity: 0.404, NMI: 0.411\n",
            "Epoch: 076, Loss: 4.196, AUC: 0.878, AP: 0.883, Completeness: 0.418, Homogeneity: 0.402, NMI: 0.410\n",
            "Epoch: 077, Loss: 4.200, AUC: 0.879, AP: 0.884, Completeness: 0.434, Homogeneity: 0.410, NMI: 0.422\n",
            "Epoch: 078, Loss: 4.140, AUC: 0.880, AP: 0.884, Completeness: 0.425, Homogeneity: 0.405, NMI: 0.415\n",
            "Epoch: 079, Loss: 4.195, AUC: 0.881, AP: 0.886, Completeness: 0.436, Homogeneity: 0.412, NMI: 0.423\n",
            "Epoch: 080, Loss: 4.222, AUC: 0.883, AP: 0.887, Completeness: 0.438, Homogeneity: 0.396, NMI: 0.416\n",
            "Epoch: 081, Loss: 4.177, AUC: 0.884, AP: 0.889, Completeness: 0.433, Homogeneity: 0.411, NMI: 0.422\n",
            "Epoch: 082, Loss: 4.173, AUC: 0.885, AP: 0.890, Completeness: 0.448, Homogeneity: 0.407, NMI: 0.427\n",
            "Epoch: 083, Loss: 4.152, AUC: 0.885, AP: 0.890, Completeness: 0.458, Homogeneity: 0.442, NMI: 0.450\n",
            "Epoch: 084, Loss: 4.158, AUC: 0.886, AP: 0.890, Completeness: 0.452, Homogeneity: 0.412, NMI: 0.431\n",
            "Epoch: 085, Loss: 4.153, AUC: 0.886, AP: 0.891, Completeness: 0.445, Homogeneity: 0.424, NMI: 0.434\n",
            "Epoch: 086, Loss: 4.079, AUC: 0.887, AP: 0.890, Completeness: 0.473, Homogeneity: 0.458, NMI: 0.465\n",
            "Epoch: 087, Loss: 4.061, AUC: 0.888, AP: 0.891, Completeness: 0.466, Homogeneity: 0.465, NMI: 0.465\n",
            "Epoch: 088, Loss: 4.003, AUC: 0.888, AP: 0.891, Completeness: 0.474, Homogeneity: 0.472, NMI: 0.473\n",
            "Epoch: 089, Loss: 4.036, AUC: 0.889, AP: 0.892, Completeness: 0.479, Homogeneity: 0.478, NMI: 0.478\n",
            "Epoch: 090, Loss: 3.984, AUC: 0.890, AP: 0.892, Completeness: 0.480, Homogeneity: 0.478, NMI: 0.479\n",
            "Epoch: 091, Loss: 3.979, AUC: 0.891, AP: 0.893, Completeness: 0.480, Homogeneity: 0.479, NMI: 0.480\n",
            "Epoch: 092, Loss: 3.939, AUC: 0.891, AP: 0.894, Completeness: 0.478, Homogeneity: 0.478, NMI: 0.478\n",
            "Epoch: 093, Loss: 3.931, AUC: 0.893, AP: 0.895, Completeness: 0.480, Homogeneity: 0.480, NMI: 0.480\n",
            "Epoch: 094, Loss: 3.915, AUC: 0.894, AP: 0.896, Completeness: 0.485, Homogeneity: 0.485, NMI: 0.485\n",
            "Epoch: 095, Loss: 3.913, AUC: 0.895, AP: 0.897, Completeness: 0.487, Homogeneity: 0.487, NMI: 0.487\n",
            "Epoch: 096, Loss: 3.945, AUC: 0.896, AP: 0.898, Completeness: 0.490, Homogeneity: 0.490, NMI: 0.490\n",
            "Epoch: 097, Loss: 3.813, AUC: 0.896, AP: 0.898, Completeness: 0.490, Homogeneity: 0.491, NMI: 0.491\n",
            "Epoch: 098, Loss: 3.869, AUC: 0.897, AP: 0.898, Completeness: 0.492, Homogeneity: 0.492, NMI: 0.492\n",
            "Epoch: 099, Loss: 3.812, AUC: 0.897, AP: 0.898, Completeness: 0.494, Homogeneity: 0.495, NMI: 0.495\n",
            "Epoch: 100, Loss: 3.869, AUC: 0.896, AP: 0.898, Completeness: 0.492, Homogeneity: 0.492, NMI: 0.492\n",
            "Epoch: 101, Loss: 3.862, AUC: 0.896, AP: 0.898, Completeness: 0.489, Homogeneity: 0.490, NMI: 0.490\n",
            "Epoch: 102, Loss: 3.839, AUC: 0.896, AP: 0.898, Completeness: 0.490, Homogeneity: 0.490, NMI: 0.490\n",
            "Epoch: 103, Loss: 3.807, AUC: 0.897, AP: 0.898, Completeness: 0.492, Homogeneity: 0.493, NMI: 0.493\n",
            "Epoch: 104, Loss: 3.854, AUC: 0.897, AP: 0.899, Completeness: 0.493, Homogeneity: 0.494, NMI: 0.494\n",
            "Epoch: 105, Loss: 3.753, AUC: 0.897, AP: 0.899, Completeness: 0.456, Homogeneity: 0.460, NMI: 0.458\n",
            "Epoch: 106, Loss: 3.906, AUC: 0.898, AP: 0.900, Completeness: 0.458, Homogeneity: 0.465, NMI: 0.461\n",
            "Epoch: 107, Loss: 3.834, AUC: 0.898, AP: 0.900, Completeness: 0.464, Homogeneity: 0.468, NMI: 0.466\n",
            "Epoch: 108, Loss: 3.861, AUC: 0.899, AP: 0.901, Completeness: 0.442, Homogeneity: 0.440, NMI: 0.441\n",
            "Epoch: 109, Loss: 3.837, AUC: 0.899, AP: 0.902, Completeness: 0.452, Homogeneity: 0.452, NMI: 0.452\n",
            "Epoch: 110, Loss: 3.806, AUC: 0.899, AP: 0.902, Completeness: 0.450, Homogeneity: 0.450, NMI: 0.450\n",
            "Epoch: 111, Loss: 3.900, AUC: 0.899, AP: 0.903, Completeness: 0.458, Homogeneity: 0.460, NMI: 0.459\n",
            "Epoch: 112, Loss: 3.753, AUC: 0.899, AP: 0.903, Completeness: 0.452, Homogeneity: 0.452, NMI: 0.452\n",
            "Epoch: 113, Loss: 3.769, AUC: 0.899, AP: 0.903, Completeness: 0.457, Homogeneity: 0.461, NMI: 0.459\n",
            "Epoch: 114, Loss: 3.793, AUC: 0.900, AP: 0.904, Completeness: 0.450, Homogeneity: 0.453, NMI: 0.452\n",
            "Epoch: 115, Loss: 3.870, AUC: 0.900, AP: 0.904, Completeness: 0.435, Homogeneity: 0.430, NMI: 0.432\n",
            "Epoch: 116, Loss: 3.831, AUC: 0.901, AP: 0.905, Completeness: 0.432, Homogeneity: 0.429, NMI: 0.431\n",
            "Epoch: 117, Loss: 3.833, AUC: 0.901, AP: 0.905, Completeness: 0.439, Homogeneity: 0.438, NMI: 0.438\n",
            "Epoch: 118, Loss: 3.816, AUC: 0.901, AP: 0.905, Completeness: 0.438, Homogeneity: 0.438, NMI: 0.438\n",
            "Epoch: 119, Loss: 3.831, AUC: 0.902, AP: 0.905, Completeness: 0.440, Homogeneity: 0.439, NMI: 0.439\n",
            "Epoch: 120, Loss: 3.808, AUC: 0.902, AP: 0.905, Completeness: 0.442, Homogeneity: 0.439, NMI: 0.440\n",
            "Epoch: 121, Loss: 3.791, AUC: 0.901, AP: 0.905, Completeness: 0.454, Homogeneity: 0.456, NMI: 0.455\n",
            "Epoch: 122, Loss: 3.794, AUC: 0.901, AP: 0.906, Completeness: 0.453, Homogeneity: 0.456, NMI: 0.455\n",
            "Epoch: 123, Loss: 3.727, AUC: 0.902, AP: 0.906, Completeness: 0.475, Homogeneity: 0.490, NMI: 0.482\n",
            "Epoch: 124, Loss: 3.782, AUC: 0.903, AP: 0.907, Completeness: 0.457, Homogeneity: 0.459, NMI: 0.458\n",
            "Epoch: 125, Loss: 3.691, AUC: 0.903, AP: 0.907, Completeness: 0.472, Homogeneity: 0.483, NMI: 0.477\n",
            "Epoch: 126, Loss: 3.700, AUC: 0.904, AP: 0.908, Completeness: 0.477, Homogeneity: 0.489, NMI: 0.483\n",
            "Epoch: 127, Loss: 3.636, AUC: 0.905, AP: 0.909, Completeness: 0.476, Homogeneity: 0.488, NMI: 0.482\n",
            "Epoch: 128, Loss: 3.711, AUC: 0.906, AP: 0.910, Completeness: 0.496, Homogeneity: 0.499, NMI: 0.498\n",
            "Epoch: 129, Loss: 3.707, AUC: 0.907, AP: 0.911, Completeness: 0.488, Homogeneity: 0.499, NMI: 0.493\n",
            "Epoch: 130, Loss: 3.747, AUC: 0.907, AP: 0.912, Completeness: 0.468, Homogeneity: 0.464, NMI: 0.466\n",
            "Epoch: 131, Loss: 3.716, AUC: 0.908, AP: 0.912, Completeness: 0.473, Homogeneity: 0.484, NMI: 0.478\n",
            "Epoch: 132, Loss: 3.663, AUC: 0.908, AP: 0.911, Completeness: 0.468, Homogeneity: 0.481, NMI: 0.474\n",
            "Epoch: 133, Loss: 3.737, AUC: 0.907, AP: 0.910, Completeness: 0.479, Homogeneity: 0.480, NMI: 0.479\n",
            "Epoch: 134, Loss: 3.686, AUC: 0.906, AP: 0.909, Completeness: 0.478, Homogeneity: 0.479, NMI: 0.478\n",
            "Epoch: 135, Loss: 3.734, AUC: 0.905, AP: 0.907, Completeness: 0.463, Homogeneity: 0.465, NMI: 0.464\n",
            "Epoch: 136, Loss: 3.763, AUC: 0.904, AP: 0.906, Completeness: 0.480, Homogeneity: 0.487, NMI: 0.483\n",
            "Epoch: 137, Loss: 3.762, AUC: 0.904, AP: 0.905, Completeness: 0.407, Homogeneity: 0.407, NMI: 0.407\n",
            "Epoch: 138, Loss: 3.755, AUC: 0.903, AP: 0.905, Completeness: 0.407, Homogeneity: 0.407, NMI: 0.407\n",
            "Epoch: 139, Loss: 3.724, AUC: 0.902, AP: 0.905, Completeness: 0.454, Homogeneity: 0.465, NMI: 0.460\n",
            "Epoch: 140, Loss: 3.716, AUC: 0.902, AP: 0.905, Completeness: 0.415, Homogeneity: 0.420, NMI: 0.418\n",
            "Epoch: 141, Loss: 3.766, AUC: 0.903, AP: 0.906, Completeness: 0.461, Homogeneity: 0.471, NMI: 0.466\n",
            "Epoch: 142, Loss: 3.725, AUC: 0.903, AP: 0.907, Completeness: 0.447, Homogeneity: 0.450, NMI: 0.449\n",
            "Epoch: 143, Loss: 3.652, AUC: 0.904, AP: 0.908, Completeness: 0.492, Homogeneity: 0.499, NMI: 0.495\n",
            "Epoch: 144, Loss: 3.802, AUC: 0.905, AP: 0.908, Completeness: 0.465, Homogeneity: 0.474, NMI: 0.469\n",
            "Epoch: 145, Loss: 3.665, AUC: 0.905, AP: 0.909, Completeness: 0.465, Homogeneity: 0.475, NMI: 0.470\n",
            "Epoch: 146, Loss: 3.745, AUC: 0.905, AP: 0.909, Completeness: 0.489, Homogeneity: 0.496, NMI: 0.492\n",
            "Epoch: 147, Loss: 3.648, AUC: 0.905, AP: 0.908, Completeness: 0.490, Homogeneity: 0.498, NMI: 0.494\n",
            "Epoch: 148, Loss: 3.581, AUC: 0.905, AP: 0.908, Completeness: 0.488, Homogeneity: 0.497, NMI: 0.492\n",
            "Epoch: 149, Loss: 3.627, AUC: 0.905, AP: 0.908, Completeness: 0.447, Homogeneity: 0.454, NMI: 0.450\n",
            "Epoch: 150, Loss: 3.609, AUC: 0.905, AP: 0.908, Completeness: 0.460, Homogeneity: 0.472, NMI: 0.466\n",
            "Epoch: 151, Loss: 3.610, AUC: 0.905, AP: 0.909, Completeness: 0.420, Homogeneity: 0.431, NMI: 0.425\n",
            "Epoch: 152, Loss: 3.633, AUC: 0.905, AP: 0.909, Completeness: 0.391, Homogeneity: 0.400, NMI: 0.395\n",
            "Epoch: 153, Loss: 3.538, AUC: 0.905, AP: 0.909, Completeness: 0.488, Homogeneity: 0.498, NMI: 0.493\n",
            "Epoch: 154, Loss: 3.659, AUC: 0.905, AP: 0.910, Completeness: 0.480, Homogeneity: 0.488, NMI: 0.484\n",
            "Epoch: 155, Loss: 3.614, AUC: 0.904, AP: 0.910, Completeness: 0.461, Homogeneity: 0.478, NMI: 0.469\n",
            "Epoch: 156, Loss: 3.590, AUC: 0.904, AP: 0.910, Completeness: 0.498, Homogeneity: 0.512, NMI: 0.505\n",
            "Epoch: 157, Loss: 3.662, AUC: 0.904, AP: 0.909, Completeness: 0.488, Homogeneity: 0.496, NMI: 0.492\n",
            "Epoch: 158, Loss: 3.641, AUC: 0.904, AP: 0.910, Completeness: 0.473, Homogeneity: 0.485, NMI: 0.479\n",
            "Epoch: 159, Loss: 3.635, AUC: 0.904, AP: 0.910, Completeness: 0.489, Homogeneity: 0.499, NMI: 0.494\n",
            "Epoch: 160, Loss: 3.645, AUC: 0.904, AP: 0.910, Completeness: 0.489, Homogeneity: 0.499, NMI: 0.494\n",
            "Epoch: 161, Loss: 3.630, AUC: 0.904, AP: 0.911, Completeness: 0.469, Homogeneity: 0.481, NMI: 0.475\n",
            "Epoch: 162, Loss: 3.675, AUC: 0.904, AP: 0.910, Completeness: 0.414, Homogeneity: 0.427, NMI: 0.420\n",
            "Epoch: 163, Loss: 3.630, AUC: 0.904, AP: 0.910, Completeness: 0.453, Homogeneity: 0.470, NMI: 0.461\n",
            "Epoch: 164, Loss: 3.664, AUC: 0.905, AP: 0.910, Completeness: 0.433, Homogeneity: 0.436, NMI: 0.434\n",
            "Epoch: 165, Loss: 3.558, AUC: 0.905, AP: 0.911, Completeness: 0.428, Homogeneity: 0.442, NMI: 0.435\n",
            "Epoch: 166, Loss: 3.601, AUC: 0.906, AP: 0.911, Completeness: 0.450, Homogeneity: 0.464, NMI: 0.457\n",
            "Epoch: 167, Loss: 3.524, AUC: 0.906, AP: 0.912, Completeness: 0.435, Homogeneity: 0.446, NMI: 0.441\n",
            "Epoch: 168, Loss: 3.590, AUC: 0.907, AP: 0.912, Completeness: 0.484, Homogeneity: 0.494, NMI: 0.489\n",
            "Epoch: 169, Loss: 3.639, AUC: 0.907, AP: 0.912, Completeness: 0.481, Homogeneity: 0.496, NMI: 0.488\n",
            "Epoch: 170, Loss: 3.604, AUC: 0.907, AP: 0.913, Completeness: 0.452, Homogeneity: 0.457, NMI: 0.454\n",
            "Epoch: 171, Loss: 3.507, AUC: 0.907, AP: 0.913, Completeness: 0.473, Homogeneity: 0.485, NMI: 0.479\n",
            "Epoch: 172, Loss: 3.579, AUC: 0.907, AP: 0.912, Completeness: 0.459, Homogeneity: 0.462, NMI: 0.460\n",
            "Epoch: 173, Loss: 3.573, AUC: 0.906, AP: 0.911, Completeness: 0.426, Homogeneity: 0.427, NMI: 0.426\n",
            "Epoch: 174, Loss: 3.590, AUC: 0.906, AP: 0.911, Completeness: 0.425, Homogeneity: 0.426, NMI: 0.426\n",
            "Epoch: 175, Loss: 3.486, AUC: 0.906, AP: 0.910, Completeness: 0.427, Homogeneity: 0.428, NMI: 0.428\n",
            "Epoch: 176, Loss: 3.592, AUC: 0.906, AP: 0.910, Completeness: 0.450, Homogeneity: 0.444, NMI: 0.447\n",
            "Epoch: 177, Loss: 3.569, AUC: 0.906, AP: 0.910, Completeness: 0.424, Homogeneity: 0.417, NMI: 0.420\n",
            "Epoch: 178, Loss: 3.551, AUC: 0.906, AP: 0.911, Completeness: 0.426, Homogeneity: 0.428, NMI: 0.427\n",
            "Epoch: 179, Loss: 3.555, AUC: 0.906, AP: 0.911, Completeness: 0.456, Homogeneity: 0.461, NMI: 0.459\n",
            "Epoch: 180, Loss: 3.549, AUC: 0.906, AP: 0.911, Completeness: 0.402, Homogeneity: 0.405, NMI: 0.404\n",
            "Epoch: 181, Loss: 3.541, AUC: 0.906, AP: 0.912, Completeness: 0.404, Homogeneity: 0.421, NMI: 0.412\n",
            "Epoch: 182, Loss: 3.623, AUC: 0.906, AP: 0.912, Completeness: 0.420, Homogeneity: 0.428, NMI: 0.424\n",
            "Epoch: 183, Loss: 3.511, AUC: 0.906, AP: 0.912, Completeness: 0.422, Homogeneity: 0.431, NMI: 0.427\n",
            "Epoch: 184, Loss: 3.525, AUC: 0.907, AP: 0.913, Completeness: 0.425, Homogeneity: 0.420, NMI: 0.423\n",
            "Epoch: 185, Loss: 3.610, AUC: 0.907, AP: 0.913, Completeness: 0.382, Homogeneity: 0.382, NMI: 0.382\n",
            "Epoch: 186, Loss: 3.569, AUC: 0.907, AP: 0.913, Completeness: 0.387, Homogeneity: 0.399, NMI: 0.393\n",
            "Epoch: 187, Loss: 3.547, AUC: 0.907, AP: 0.912, Completeness: 0.337, Homogeneity: 0.340, NMI: 0.338\n",
            "Epoch: 188, Loss: 3.628, AUC: 0.905, AP: 0.911, Completeness: 0.379, Homogeneity: 0.392, NMI: 0.385\n",
            "Epoch: 189, Loss: 3.489, AUC: 0.905, AP: 0.911, Completeness: 0.398, Homogeneity: 0.382, NMI: 0.389\n",
            "Epoch: 190, Loss: 3.504, AUC: 0.906, AP: 0.912, Completeness: 0.421, Homogeneity: 0.424, NMI: 0.423\n",
            "Epoch: 191, Loss: 3.595, AUC: 0.907, AP: 0.913, Completeness: 0.380, Homogeneity: 0.384, NMI: 0.382\n",
            "Epoch: 192, Loss: 3.515, AUC: 0.908, AP: 0.914, Completeness: 0.381, Homogeneity: 0.392, NMI: 0.386\n",
            "Epoch: 193, Loss: 3.486, AUC: 0.908, AP: 0.915, Completeness: 0.386, Homogeneity: 0.397, NMI: 0.391\n",
            "Epoch: 194, Loss: 3.470, AUC: 0.908, AP: 0.915, Completeness: 0.383, Homogeneity: 0.394, NMI: 0.388\n",
            "Epoch: 195, Loss: 3.533, AUC: 0.908, AP: 0.915, Completeness: 0.340, Homogeneity: 0.352, NMI: 0.346\n",
            "Epoch: 196, Loss: 3.472, AUC: 0.908, AP: 0.915, Completeness: 0.346, Homogeneity: 0.360, NMI: 0.353\n",
            "Epoch: 197, Loss: 3.513, AUC: 0.908, AP: 0.915, Completeness: 0.345, Homogeneity: 0.358, NMI: 0.351\n",
            "Epoch: 198, Loss: 3.425, AUC: 0.907, AP: 0.915, Completeness: 0.386, Homogeneity: 0.404, NMI: 0.395\n",
            "Epoch: 199, Loss: 3.440, AUC: 0.907, AP: 0.915, Completeness: 0.345, Homogeneity: 0.358, NMI: 0.351\n",
            "Epoch: 200, Loss: 3.511, AUC: 0.907, AP: 0.915, Completeness: 0.348, Homogeneity: 0.361, NMI: 0.355\n"
          ]
        }
      ],
      "source": [
        "for epoch in range(1, 201):\n",
        "    loss = train()\n",
        "    auc, ap, completeness, hm, nmi = test()\n",
        "    print((f'Epoch: {epoch:03d}, Loss: {loss:.3f}, AUC: {auc:.3f}, '\n",
        "           f'AP: {ap:.3f}, Completeness: {completeness:.3f}, '\n",
        "           f'Homogeneity: {hm:.3f}, NMI: {nmi:.3f}'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "--FNypm1wL0f"
      },
      "outputs": [],
      "source": [
        "@torch.no_grad()\n",
        "def plot_points(colors):\n",
        "    model.eval()\n",
        "    z = model.encode(data.x, data.train_pos_edge_index)\n",
        "    z = TSNE(n_components=2).fit_transform(z.cpu().numpy())\n",
        "    y = data.y.cpu().numpy()\n",
        "\n",
        "    fig = plt.figure(1, figsize=(8, 8))\n",
        "    fig.clf()\n",
        "    for i in range(dataset.num_classes):\n",
        "        plt.scatter(z[y == i, 0], z[y == i, 1], s=20, color=colors[i])\n",
        "    plt.axis('off')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "CUF8xa3nwL0f",
        "outputId": "2d1639ae-14e9-4124-edf2-ee67d08fcace",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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IZQq3jFVvuIi04j8hIcy52cA87WbRa5h6hE31FThf+B60ilv9hmsAdelAjIh6AZ4+wS6OnBDSlVAZGEJIj+LnFYZefqOtwRXTcq016psrBJaZnVfbWIzjV1YgvfgNhzqF3NeXwtAsnvXM1CM8X/getHIN65xWrsH5wvdcGzAhpMuhAJAQ0uNxtVxzRkOz1o2jaaE3XOCsU2hPSskbtW8C/LzCUFtZYJn5k9nt/pGZoVXkobaq0MXREkK6EloCJoT0ePYt12SQQ6P7VvL9QT5xbTY22zqFfMRK3gR4xWBE6AoAgKGuRPBaQ20x7QckpAegGUBCCLmFabnWN2Cc5HuCfYagl18Cgn2GAI4N5NzC0FQueN7Pqx/Uvgng+pUe7DME4wa8Y00qUfqGCz5L6Rfh8jgJIV0HBYCEEGJHKKCy5SkPwPC+y5BRsuZWdrFzRRX8vQZIus62lzCfEaErrCVpGGrfBIzq9wLrmF9QNNTGgYDZLlg1y6A2DqTZP0J6CCoDQwghHMRqCAb7DMGofi/cuka82DQXD7kfhvV5EhfKP4YJDZzXOFu/zz7LmYtDFjAAtZGygAnpSSgAJIQQAbYBFQBWcFXbWIrjV55r1fNlUMAME/hmD12tCyhFbVUhDLXFVAeQkB6IloAJIUQAu2wMO0iTkn0rxgyjw3Nt6Q3ZkjKBXeEXGIVeYRMp+COkB6IsYEIIEcEsBxcbylEv7w0f03VEKMMQG/JQO7y6SVImMCGEOIMCQEIIEXG6/O84aZyKCt9467E8Yy4adf+F2jfB5T2AzmC6eBBCiDtQAEgIIQJqG0tx0jgeFYqhrONVijjsNYViaUgjALi1Ewg32rFDCHEfCgAJIURAab0OFR7xnOea5QH4b7UBvw1/xZosknPja9Q2FbfBSFyfYaxtLIWh6ZpgZjAhpGehAJAQQgTUy3sLni8zKnGtoRohHgG4fOOLNgr+pNUCtMdVykbtm4ARoSvaJKuYENJ10JoCIYQICPPpJXrN6Rv/sakH6BqVMp6n+LTc2sfXWVxj0tadb7OsYkJI10EBICGECOjloUCEp/A1zfVpt2bZWpMIYubp5jHC2sfXGbWNpTxjaskqJoT0XLQETAghtxTl5KM0/yrCYwcgIi7aevzX6kD8rVyLBthFgmYjgo3ZUJqvt/q19YaLqKzXINFmP2Fr9uyJ1SikrGJCejYKAAkhPV6VrgJv/PpJnNmbaj2WNHsyVm1ahwBVEJRyGR70PY//Vvmj2mOQ9ZpgYzYGN2xw2zjOlK6x7tFrbXCm9Owrct75PYWEkO6DWsERQnoEvkzYJmMNnp19O3KPFcNsbLlerlAgcUYK3v1lo/X+41eeg0HWB/XyPvAxXXfLzB8XlTIeSRF/avVzMkrWcNQolEPtO8Kp/sKEkO6HZgAJId2aWCbs/uNvIOewY+auyWhE+u7DKM4tQERcNPy8+kGlHA4YLkJpZAd+fp7hqG0qafVYdYX10F+th6p/Oob2cez84Ww5lxGhKzi+dtf2FBJCuhcKAAkh3ZpQJuzg3o+hIOes4P0lmivW/YAJYb9HZtlfoTdcZF1jaNI5NSa5zBsmc0PL/RXN+PGPGhQcq7IeOz59KVZ/uxlyv1pUNxSiqGInKuovWc9LKefiqfB3255CQkj3QgEgIaTLS9t9CNmnMjEseTSSZk2yHm/JhLVnyYTVG7Kh6u8j+Ozw2AHW//ZU+EMu84ClgELLsqoJBqfGaxv8AcCPf9Sg8GQV61jWoWy8cP9duO/v3AEbE8RKWcr18wqjwI8QwkIBICGkyyrRXMHylHmo0uqtxwJCgvDpyZ8QFh0pmglb3XAVIVE+iE4JROHJKtYeQJkCGDl1NHz6VaC20bIcyx9Quk5XWM+a+WOYjCbkHC6GrlCFkCiuILWlnAsFd4QQZ1EdQEJIl2Uf/AFAta4STyTPQpOxRjQTtrjyfwCAe/8Si6hxgaxzMeNDMG01cKb0bRy/sgIZJWtQ3VDo1vEbKprxwx/yBK/RX60XfkZTuTuHRAjpIWgGkBDSJaXtPuQQ/DHq9A3Y+u8/YsHDn0LtmyA6a+cT5IGHNgy2JmH0jlIjsL8JQEuRBG3deRhNDfwPccGPf9Tg2iXh5WNmibrkZhPKtE0IU3sivFdLPUIq50IIcQUFgISQLin7lHBQd+HwWdTeV4YBwXdLXrYNifLBmMTHoNFt4ThrQkX9JQR6x6GqIdeFEbPxLf0yZHIganwgPPt44k//KkdGbstMYGKcD154qC8GqEfR8i8hxCW0BEwI6ZKGjk0QPF9wrBKGpnLkajcDsARcmsMV0BU6LqmW3GxC+uU6lNxsQm2jY0kYW/YJHAwPuZ/EkVuILe32GeKLe/8Si/e/v4Gzeexrz+bV48OtdVTOhRDiMpoBJIR0SWNmT4HcQwFTs5Hz/LUsA8o05bjekOtQYiU6JRD3/iUWTZ4yvP/9Ddbs2qThqXh6nhH+SgXnc2sar3IebzbVYmifJ5F9/e+Sxi+WfXzvX2KhbTKzxsYwmYHUSzdQeF2HuDD+MjCEEMKHZgAJIV1SUU4+b/DHKM8v5SyxUniyCj/8QcM5u3Y8Ow8fbq2H469HOQK8oyHEx0MFtW8Cx72OmOxjmV2cKVNYAtSQKB+UaZsEn6EpF56tJIQQPhQAEkK6pNJ8y0ycMro/VFNT4BPV3+EapVcICo6xy7sAgNkIZGXWIiO3Hia7ZphGkwmpl26grpYd7Kl9R2Bo76WCY1J6hmJE6AoE+wwSvI7BlX0cNc4yOwkAYWpPrtusYkMjJL0OIYTYoyVgQkjX09QMtUmG4Rs/QcjUFOth3cFjuPzMi2iuqkZ8yhh4ylW8jzD4cS/xMrzNd2PCgGiHDhqWrGLu/rrMNdEh83Cm9G2Bp8sAmB2yj1X9fVg1/8J7eSIxzgdn89iBqkIux/QRYxEX5hj0EkKIFDQDSAjperLzcSIuDqpJ41iHVROTMfijdxCoVmH19g3oF8MfIClrhZePY0Mj4OcVhl5+o1mZtiNCV0DtO4J1rWN/XZngs+2XkkOifBA7OZiz4PMLD/XGqIHs49NHjMXXK1YLvgYhhAihGUBCSNdSV4+bNQZoevV1CLNkHh4ImZqCP1w8gABVEAJUQUiaPRkZ+47BZGwJ+OQKBSZNSEFIQhD2nz8Fo6llNk9sdo2rvy5gRmV9rs1MoZnzXsZA9UNQeoZCX3EW2ZVfCl7rr1TgjUdDUXKzCb64D6OjptDMHyGk1SgAJIR0LYYG6D2F98Y1+Pla/3vVpnVYvehZpO8+bD2WOCMFqzatQ7OnDIvXrsKezJPWc1Jn1/y8wuClCMD58rWsOoNq3wTEhjwkeC8TKBr8yoFK0ZcCYFkOnjBgJtX9I4S4BQWAhJCuRekNVZNwdmyIR8vulgBVEN79ZSOKcwtQormC8NgBiIhrWYL9+ZV1yC27Ck15MWJDI5yaXbMEf+dZx5g/S9krKNaqzpZKOZyCP0KI21AASAjpWnx90MtfidjaWuT7+sIsa1kIlpnNiPHxhNrDMcEjIi6aFfjZigvr7/Syam1jKXKzUjmSN0zQ1mVibIQlCYQ9O8jeK+jn1Y8nUOQivK+QEEKcITObzcKbVQghpLNpaobhUgG2+vhB49fSgSPWU4771f5Qyts2WKrSVeDPCx9D5r6z1mNMcWmfIMvn6tH9XkIvv9GsvYJ+XmE4W5aFSzevYmjvKCSEDkGTscZhGZnPhAFraRaQEOIWFAASQrquunpoaxug8/JAiJ8P58xfW1h55xJk7DsKk7Fl1k6msNTwe2jDYACOwdq1mhuY9916nLCp3Tw+AvjvQ0+jj38v1DaWobz6GPJ13/O+LhNUEkJIa1EZGEJI1+XrA3XvIMQF+bVb8FeUk4/03YdZwR9gKS5dcKwKusJGqH0THGbq5n23Hqkl7M/bqSVm/Oq7TwBYkkpCAyYIvrYl45gQQlqPAkBCCHEC04GEj/FaqF1NQOBsWRZOFAMmM3tp2mSW4UQxkFl+CUDLnkCuNnRcQSUhhLiKAkBCCHGCUHFpAJicvBKeCn/WsUs3hYPG7BuF1v+WVmiaEEJah7KACSFEotrGUijDKzF6ZjIyD6Q7FJdOnJHCmWk8pFd/AKm8zx3aO8r631yFpmnmjxDibhQAEkKICPtM3SlvNqN2ZRhyDrdkdDDFpbmMChuG8RGWPX+2y8BymRnJ4TIkhA5xuMfPK4wCP0JIm6EsYEIIEZFe/Dr0hosOx43lkVDV3u1QXJrL9Zqb+NV3n/BmARNCSHuiAJAQQgTUNpbi+JXneM87W5svs/wSsm8UWusAEkJIR6AlYEIIEaA3ZImedyYATAgdQoEfIaTDURYwIYQQQkgPQwEgIYQICPCKEjyvUg5rn4EQQogbUQBICCECNLrveM+plPGUqUsI6ZIoACSEEB61jaXW0i9c4tS/bsfREEKI+1ASCCGE8DA0XRM832SqaqeREOJ+OVotNDodBoaEIE6t7ujhkHZGASAhhPBQevYVOR/aTiMhhN+uvDyklpRgfEQEZsXGil6vMxiwcNs27NJorMfmxMZiy/z5UCmVbTlU0olQHUBCCBGQUbIG2rrzAEw2R+VQ+45AYvgrHTUsQqDR6ZD82WfQGgzWY2qlEmlLlyJapeK9b+6mTdibnw+jzdu/HMCEyEgc+c1v2nLIpBOhPYCEECJgROgKqH1HsI6pfUdgROiKDhoRIRb2wR8AaA0GjNmwgfeeHK0WuzQaVvAHWD7eHC0qwuQvv4Te7pmke6IlYEIIEeCp8Mfg3o9Bb8gGYCn7Qpm/pC04sydvV16eQ/DH0BoM2KPRYFZsLHK0Why6cgUysxlToqKg0ekEn3usqAgLtm3DzkWLXP46SNdAASAhhPBoMtZgz9FVuJqXBVV/H4RE+UDtm4ARoSvgqfDv6OGRbsKVPXmpJSWCz9x68SLeOXYM+wsKWMfHRUQI3mcym7FLo0GuVkuJId0cBYCEEMJBd6MELz54DzRHW2ZMolMCce9fjADW0v4/4jYLt23D3vx81rG9+fmCM3HJ4eGCz/znmTOcx08WF8NDJkOzyPb/PJ2OAsBujvYAEkIIh5cfegD5J9jLZYUnq/DDH/KgrctEbWNZB42MdAVpuw/hq9XrkL7niOB1W7OyOPfkGW1m4rjMGTgQKh8fl8YmFvwBwMCQEJeeTboOmgEkhBA7ORdPIveIY4BnNgIFx6qgK6yHoV857QUkDko0V7A8ZR6qtHrrsUC1CutP/ICw6EjrMa5lXy7MTBzX/sARffrg8NWrbv8apkdF0exfD0ABICGE2Mm9dELwvP5qPZRTqAYgcWQf/AFAlVaPZePvxfbyDOsxrmVfLr18fTF30yaH/YFvTpvWJsGfDABkMrc/l3Q+tARMCCF2VJHCS2thseE0+0ccpO0+5BD8Maq0eutyMF8pFnuJYWF49cABzv2BT/78s3sGbccMYH9BAe/SM+k+KAAkhBA7/QfHITolEDIF+7hMYUkEGZXwQMcMjHRq2af4+0YDQFaqJTFDrBQL4+WJE3n3B2aUl7s2SInyJI6RdF0UABJCiB2Vchju/UssosYFso5HjQvEvX+JRS+/0R00MtKZDR2bIHh+WLLl5yZWJMFCBssyr6+np+B1iWFhULTRci0lgXR/tAeQEELs+Hn1Q1joKDy0wQO6wnror9Zb6wCqlPG0/Esc5Gi1uBndD36qINTqKx3OB6pVSJo1CQAwSK3GnNhYh3ZsjJTISGyZPx836uoEX/ORkSOxobkZF2/ccM8XAUAhk2FmTAwlgfQA1AuYEEI4NBlrcL58LbR1Lct6VASa2LPP5vXRV2PCxh1ATUuXDq4sYL3BgAUCWcATIyPxTHIyPkpNxYniYtH9gu4iVoCadB8UABJCiIDaxjIYmsqh9AylmT/iYO6mTQ4zeQqZDOOrGzG1WYGkSWNx73138t6fq9Xi/n//G+euXeO9JtjbGxUNDdY/y2BJ1nCnxNBQfHv//TTz14PQEjAhhAjw8wqjwI9wYrJ57RnNZhz198RRADifjokV1/DjggWcs2pmQDD4A8AK/ph73K2tk0pI50NJIIQQQogLpGbzHi0qQszatfj+4kWH8io7cnLaYmguoczfnoVmAAkhhMPN2rOorM9DkE8cevkJZ3eSnkksm9dWRUMDHtq6FQB7n90Gnp69HYEyf3sWCgAJIcRGXWM5ThW9giZTtfWYpzwAYyPfgq9X3w4cGelsxLJ5+ezRaLBg2zasu/12ZLkxg5cQZ9ASMCGE2LAP/gCgyVSNU0Uvd9CISGe2Zf58zIyJceoeE4BdGg3mfftt2wzKRYcKCzt6CKQdUQBICCG33Kw96xD8MZpM1bhZK9zpgfQ8KqUS626/3aV7s2/edPoehUyGObGxyFm+HImhoW59E1/688+Yu2kT9AaD+MWky6MAkBBCbqmszxM5n9tOIyFdidRkEHuuZPPOjInBlvnzYYYlc9fk5P23iQSNe/PzsWDbNhdGRroaCgAJIeSWIJ+BIufj2mkkpCtxJhmktSpvlYRxNeh8euxYzIqN5T1vNJuxS6NxyFYm3Q8FgIQQcksvv1HwlAdwnvOUB1A2MOE0SK2GRxv15LV3srgYC7Ztczno/OLMGexctAgb7r5b8LqHt26lpeBujgJAQgixMTbyLYcgkMkCJj2ErhIoLLX8v9ilBgOS/vlPNLdjU61dGg1ksHTvcNbRoiLkarWYPGCA4HVnystpKbibozIwhBBiw9erL6bGfo6btZmorM+lOoA9SV09cCYbaDa2HPNQAIlDAaUP5y0Lt21DRllZOw2wRZ5Oh7/fdRfGfvaZS/feHheHObGxvL2IzYB1KZjaw3VPNANICCEcevklIFZ9PwV/PYl98Od5AvD6O3DuS87LmVZw7Tf316KkqgrBPj6YExsLhZPLzx5yy1v/m9OmiV5L3UG6L5oBJIQQQnSVLcGfvAgIeQxQ2CwBX14FRKcDXtHWQ2clzPwFe3s79PJ15jyfpT//DABI6NMHY8LDcbK4WPK9zSZL7vDNujrRa6k7SPdFM4CEEEJIVW3Lf4c8Bsjt9v+ZdUDhGNahj9LSBB8Z7O2NjN/+lnOWjqnnl79iBSb17+/ysDOvX8fJ4mJM6t8fL6akSLpnYEgIdAYD3jpyRPC6pLAwWv7txigAJIQQQgL9LP/vecIy88e1qmrSArV7AFiWf49evcr7uPjevaF/8UVEq1Sc3UKYen4qpRKf3XNPq4d/vKgIR4uKBK+RwdKH2Axg1tdf44TIrGF6WRkVhu7GaAmYEEIICQmyJHx4XhC+ru4E4DdLtA7fq1OmWP9bpVRi56JFyNVqkafTYWBICGtmzdWafraMZjOOXr2KiZGROFFczNmbeFp0NJqMRgz++GPJz92j0WDmV1/h2/vvp9nAboZmAAkhhBDAku1rGil8je94AOLFn0dzlGiJU6txe1ycQyDlzkLSzyQnO8w2JoaFIe3xx+Epl+PQlStOPc8ES8eRQR9/TLOB3YzMbG7H4kWEEEJIZ3dZbdnzZ0+uBga19O+du2kT9ubns2bbFDIZZsbEYOeiRU69JNezXJGzfDni1GqH2cYcrdapmT8urn5tpHOiGUBCCCHEVnS6JdizJVcDUeykD6G9fc7iepb9NkSFTAa1Usl5P5NUwswuMrONZgD/y83F4cJCp8dkj9rEdS80A0gIIYRwqd1j2fPnOx7wm8V7Gd/ePlcwz+rt64tVBw6wCjVPjIzEM8nJ8JDJsOboUVYB6jmxsdakkhytFmfLy/FRaqpoYogttVIJvcEAk8h1OxYuxO1x1Be7q6MAkBBCCHFGYw7QqAG8BgJebRsI5Wq1OFNWho/T0nDEJut4TmwsVk+bhht1ddbAU2cwYOG2bbzdPWSAQ9FqOYBRoaH49v770cvXFwsE7mcwy8yka6MAkBBCCJHCqAOK7wUMR1uO+c0B+m0BFKo2e1mpew1d2UdoO3PIyNVq8fC2bcgsL3fL/kbSOVEASAghhIgx6gDNIEstQBYF4DcTiNzZJi8rlrzBzMY5k+Sx4e67ER4QgIEhITADOFRYCJlMhikDBlhn9vQGg8NsIFewSLouqgNICCGEiCm+lyP4AwAjULsLaMxtk+VgsRqBeTod4tRqp2oJygEcvnIFL+3di8zr11nnpkdHY+sDD4jWLiRdHwWAhBBCiJDGHPayL+c1eS0BoBv3CIrVCGR69UqpJaiQySCXyfB/P/3Ee83+ggJW4Wfmf6T7oTIwhBBCiJBG4aQIAJZgz6gDrkwC8gcDxXcA+YOAormAUe/ySw9SqwV7CTPBGd91tmQAmkxiOb5U+LmnoACQENJjpO0+hK9Wr0P6niMdPRTSlXjFCp9XTgIUakAT5zhTWLsLKLm/VS8vtd4g13W2mp3c8r83Px8Ltm1z6h7SdVASCCGk2yvRXMGycfegpqLKeixQrcL6Ez8gLDqyA0dGuoyiuZa6gPZV8uRqIDYXKL5HeJk4JqfVy8Fc+/FytFpobI7tysvD3M2bW/U69qjsS/dEewAJId1ala4Cj42YCWNTM/u4Vo9l4+/F9vKMDhoZ6VL6bQFKF1hm9BjKiUDEj4DxhvgewbpDrQ4AbffjcdX8mxMby9mDuLWYRBPSvVAASEgbSdt9CNmnMjEseTSSZk1y+v6inHwc2voLKm7oMO6O6S49gwAv3L7YIfhjVGn1SN9zRNL3tignH6X5VxEeOwARcdHuHibp7BQqS6mXxtxbCR82CR6Gk+L3u3mtbeG2bdibn886tjc/Hzfr6tz7QmhJNCHdCwWAhLhZieYKlqfMQ5W2ZeO3M8uNVboKPDf1QVzJzrUe2/7xvxAQEoxPT/5IS5Z2hAKzopx85GZcELw/K/WMYABYpavAmsUrkL77sPVY0uzJWLVpHQJUQa0bPOl6vOIcZ/LE9ggCgN8Utw0hR6vl7NZhNJtxuqwMwd7eqGhoaPXrMIWfafave6IkEELczD74A1qWG8WUaK5gfngSK/hjVOsqJD2jp6jSVWDlnUvw6PAZePnux7Bk2HSsvHMJqvWV1mtK868KPMFiWPJowfNrFq9Axr5jrGMZ+45h9aJnXRs46X68Blk6gvDxne7WGoFiNf/+mJLiltfhSjQh3QcFgIS4UdruQ9bgTxndH6qpKfCJ6g+gZbmRT5WuAo/Fz4Cp2ch/jcgzehJLYMbed2UfmPWL6S/4DP/gQMHZv6KcfKTvPgyTkf13YjIakb77MIpzC1wYOemW+m2xBHr2fKcD4Vvd+lJiNf/MEkq9iEkKC8PORYuo60c3RgEgIW6UfSoTHkGBGL7xEyQd/BHxGz/BmEM/YvjGT+ARGICs1DO8975w+2IYBYI/htAzeorstMxbgRn7jc4+MIscFIOk2ZMBjtpoCk8P/P3Uz5zPL8rJR+rOgzh35JTgOEo0V1z8Cki3o1AB/fdZsn1DNwB9N1j+u/8+t/cJ5qv5JwegViqx6uBB0WcEe3tjXEQE7/n0sjLkark6n5DugvYAEuJGQ8cmYMj69xGcMpZ1XDVlAoZ8+hcMU3F/mpayV40htmQpRWsTVDra2qdXCZ4v0Vyx7gdctWkdVi98Bul7W2ZO4+Ji8P4/30NARBjrPq79fkKCe7uwOb6uHjA0AEpvy5+rL+LG9XSUXPNDrwEplnG7sZMEaWdcewR5tObf4Zb581m9epXaSvStbcBNfx8gJFD0/qqGBlSIJIxQ9m/3RnUACXGjm81GfHy9hvf8M338ofZQOBxP3XkQL9/9mOjzA9WqVpUtaW2CSmdQlJOPR4fPELxmY9Z+dkLIuRwUn7+MktJyhPcLRQQT+KkCgZGDrJetvHMJMvYdc1jy5TM0eTQ+PvofaQNvagay8wH9rVqEsko0+b0ET7806yUZJ70REKJC3KDylvv85liWF908i0Q6jjv/HZ7Ny8dHj69E4bF06zFtTD9cnDcFzcyHDBdR/b/ujZaACXGjKw3c5UYYhTznxfaqAUBASDDWn/jBpXExWpOg0lmIJXbEjY5nB3919YC+ChERYUgeO7ol+AMswVhdPQDLbAzXfj8h2alnpO8DtA3+ACBoFeQ+6axLRic3YODAcvZ9tXst9edIt+HOf4dbVryOqyfZ20JUBWUYvv1Qq8ZIuj8KAAnpBJi9anKF4z9JmUyGlV/+Ff+9dqZVs3S2CSr2ukpySZWuAt+884ngNc+vX8M+YBAuh1FVeh0r71yCF+981KUxZR5JFb/oVhBqpbgCeJ+EQsFegJHJAJnDj4DRUny40TEznHQ97vx3yJekJDeboc4vhVJXxXOnNHki2caka6MAkBA3GuAtvK02SuD8qk3rkDhjIuvYoMQR2H7tDGYvuq/VY8s+lSl4viskl7zx8DJcOJbOeU4mlyNp9mQMThrJPiGyDLZm+SsOZV6cImUTjX0Qqih2/nUa85y/h3Q67vx3KDYb3toAkApAd2+UBEKIG/XyUCDKS4HCRsdlxCgvBef+P0aAKgjv/rIRxbkFKNFccXvHiaFjEwTPuyO5pC0V5eTjzIETvOeHj78Nqzatczzh62PZ66d3fDMsqqpC+j6RFl4iBiYME7/IPgg18mdf8vIa6Pw9pNNx579Dsa0jBgnJIHzUSiV6+fq6fD/p/GgGkBA3eyjED7F2M32x3h54KMRP0v0RcdFInjvV7e3GxsyegkA1dyJBoFrV6bOBz4kstc5efB9/Z46hMZYg0JYqEKWy1ufAffHaX8UvYoJQhnEA0DAORiO7jIfZDJgdSrgpLIkglA3cLbjz32HL1hH2B0u5Qo6olCSsW/Jrl8epNxiwYNs2l+8nnR8FgIS4mVIuw2K1H57p449fh/jimT7+WKz2g1LuWIuuva0/8YPDmw+Tfdj5iXz/hE57eliyfcfEA/Fxlv8fOQj9BrU+yJZcENo+CK1cA3N9EuuSM6neyMsLZd/nN9OSBUy6DXf+O7RsHWF3/kicMRF/2/4ZJg8Y4PIYTQB2aTRUC7AbozIwhHRiRTn5KLh8BqoBPogZkgg/rzDxmyRI33MEWalnulQdQLHyL7alX4T6A9tztvQLl7d++hLJc6dKe22HOoBZuHkjHcXlvjZ1AHMte/6oDmC35s5/h3xbR+Zu2oS9+fkw2rzVK2QyBPv4QG8wQKxnyI6FC3F7HP0MdkcUABLSClILuRbl5FuWMGUyJExKFg1KqnQVWL1oOU7vaUlOiE4JxKPr70Xy4BfgqfB329fQVez48jt8sep96K87zkiMmjYef939DWch56TZk7Fq0zre5eFqfSVWL3pWcvFnLhuz9iNQrXL6tQlpa8xSLlMwGgDmxMbi0zvvxFO//MI6zoVqAXZfFAAS4gKphVyrdBV44+FlDskLMSOG4g8b3sHg2+wyVm9ZeecSnN53BGZjyz9PmQKIGheEZZsfQGL4K27+ijqvw9t34s0Fy3ln6GyDLK7ZPLlCgcQZKXj3l42c9zvb/cOW7bO5X1uOxBkTeV+bkPaSq9UiT6fDwJAQVkCXq9Xi4W3bkFlezpolZMyJjcWW+fOpJ3A3RAEgIS6YF5rIWcvLLzgQP95oKfOw8s4lgoEF1wyR2FLnEztG4K4p/3DbcnBnJRaYyRUKfHl+D2vZV+oSsa3WLAHHp4zB6u0bUHFDK/ja60/+wBvsE9LRuGYJGXKZDCmRkTj8mHinItK1UBIIIU4SKuRaW1GFp5LvQbW+0lqkVUjGvqNYvehZ1jGx2l76q/UwNJULXtMdrFm8QrAorsloZNUEFPu+lWiuOBzjK6Qr1cIXlyFAFYS8zCzB61YvfFZ6xxBC2plKqcS622/nPGcym3Hk6lVM+uIL6A2Gdh4ZaUsUABLiJLFCrrlnLuCPcxbh3JFTos8yGU0OWaRitb1U/X2g9AwVvKarswbPIgsUuzdvt37vxL5v4bGOGZFiQaMY5pn//UR4ibc0/wqWDJuOlXcuQbW+slWvSUhb0Ih0/TheVERlYboZCgAJcZJYIVez2YzcMxfwwZMvSX6m7ewUU9tLpmDXNZEpgOiUIMQNS+72y79SA7PMgyesgVVwbzVPTTQFkmZPRlnBVXy1eh1rVlFKD2YucoUco2daknmKcvJ5u5PYy9h3zGHGl5DOIFak6weVhel+KAAkxAnZp85i3TOvuf259rNTqzatQ+L0CaxjUeMC8ej6ezAidIXbX7+zcTYwO733KFbNW8pZE234+ERcTsvEi3c+io2vf4iVdzyCeaGJKCso4i2ka0vp7+fQnWHAOH9MebMRaUV/QlHeZcnjNBmN0usGEtLGcrRa/C83F7laLQap1ZgTGwu5TLjeJvUH7j4oCYQQCap0FXj94WU4K9CKzFVxo+Px91M/cZ4rzi1A/qUzUA3wdmsdwK7AleQMJimj8qbOWhPtmUnzOfdsBqpV2F6eIVoGRiaX47aZEzH9pb4ouJwJVX9vhET5WM9XXVFg/e0nnfrabOsG2svRaqHhyNYkxF10BgNmf/01TpeVWY8xpWEe+e9/cfQq/ww8lYXpPigAJEQCsWze1vjz959i0ry5bfLsrowrMJMr5DAZBUrXymRImjXJWnYlbfchvHjno7yXv/bdp5h8n+V7f+g/O/DGQ0/zXvvEjhGswM/WtidLkHe0HGaTWFldC66MZJ3BgIUc9dqoBAdxJ41Oh6GffIImu59VhUyGmTEx2LloESZ98QWOFxWxikTbnifdAy0BEyJCSjZva3gpuYMKdyrKycemtz7Cx8+/LphZ25kEqILwytdrEZ8yxnpMMPgDALMZ6bsP43L6OQDiCTsbXn7H+t9b3vlU8Nof/qBBfWUz57k73+2LUdPHcJ6zxexHrAsJxP9yc/FFRgbeOHQIezQaLNy2DXvz81nX783Pp433xK2S/vlPh+APAIxms3WP348LFmBWbCzr/MyYGGyZP7+9hknagYf4JYT0bK3NFBXDlZ3qLlW6Crw6fykuHG1JUtj+8b8QEBKMT0/+yCpa7W7OtGPjs2bxCmSdzHD6vg+XvYK/n/pJNGGnVHMFxbkF1sQdIdcv1eGHP2jw0IbBDud8gjzw4taV0GYr8OGyV3ifNWLqOJy9ZyIGf/yxpK/D9k2Zlt1Ia+3Ky0NFQ4PgNXk6HeLUauxctIi3eDTpHigAJESEq5miUiTNnuxycCTFmsUrWMEfo1pXgWXj78X2cueDKzGutGPj0pqZ19wzF1CcW4Axs6dAGeAPQ3UN77Ulmiu4WSJeV9FsAgqOVUFXWM+5FKz0DMXgpDD8/dRPKD5/CSXZGoQPjQV8vK37ER9PPYZ9drN8UjBvyoS0RmpJieg1A22ygePUatGfu115eUgtKcH4iAiHWUPSudESMCEimExRdxs1bTxWbVrn9ucyxAKoKq2+TZaD1yxegdN7j7KOuVL+pLUzr6t//Syq9ZV4Y+s/BK/76s11TpXs0V+ttzsih9o3wZKg09QMnMtBhK4GyX37IkJXgwhDE5JnTERdSCB2aTSc7bbEDBQp0UGIFMnh4YLnk8LCJH/Q0Oh06PXee5i7eTNeO3gQszdtQq/33kOBnrtIPul8KAAkRIJVm9Zh1LTxrX7OvOWP4nf/eBsbs/bjr7u/cWgBl7rzoGOJkLp6QFtp+X8nSAmgslLPOPVMMdmnziJ992GHZAhXyp+0duZVcy4Lqxc9i8TpExCfkgQZR3kLhacHLp1y7ntw6vNK1l5Ate+IltI82fmAvgpFxaVIPXUGxcVlgL4KyM4XLbTLJ9jHh2b/iFvMGTgQap6EIk+5HLsXL5b8rOTPPoPWrjOI1mDAmA0bWjVG0n5oCZgQCQJUQfjr7m/wy+ffOjVbZO9Xyx6B2WxG5uGTyDySioRJyQhUqxyWTOMS45E0PQWjBvRH0tBBLQ9QBQJDYwBP8X+6UgIo+/p2rbV2+auC50s0VyQveTMzr6f3HpWcXWuL6bLyfwlzUJiVw3mNsYk7qUPIlbTr2PdiJFb98BKUnqEtpXnq6lF1pQRr3lqL9PSW5JOkpASseuU5xI4axPNEYRX19bQHkLhN2tKlGLNhAyt4C/b2RsZvfys523xXXp5D8MfQGgzYo9HQcnAXQGVgCHFCUU4+Hh0+w/kbZTIMGTMKSj8fnLGrJaj094Wh1sDb9iwwMADrP3kbYWF9LQdUgcBIacGEUPkapg6eu0j53nCVPxFSra/EqnmPS+600Z4ee/33mPrAnS1fj7YSK+9agoyM8zDZBKxyuRyJiSPw7s8bMfd/P2Fvfr7Ty8A7Fi7E7XFx7hw+6eH2aDQ4UVzs0t69Nw4dwmsHD/Kef33qVPxpypRWjpC0NVoCJsQJUjpHxI4cisjBMeyDZjMunTrjEPwBgKGmTrDnbVVVNZY9bTPrqK+SvBy8atM6xE90LE8SEBKM9Sd+YB3jXYKWSGzJOW50vNMJLwGqILz5nw3wCw50aUxt6cvX/oolw6bjD7N/jWp9JYpKSpGenskK/gDAZDIhPT0TxaVl2DJ/PmbGxPA8kR/tASTuNis2Fn+aMsWlmTqxvYTjIyJcHRZpR7QETIiTVm1a51Cg2D84EDUVVQAAzblst79mVVU10tMzkZR0q6yJoQHwFa8fGKAKwtoD36M4twAHt/6CiutajLtjOpJmTWp5totZu/ZlXsSWnJ//dI3oeLmsWbwCdVX8Wbwd7cyB4/jzQ0+htqJa8LqSknIkjxjCKq9xraYGV6uqEN+7N574+WeHZTWm+C4t/5LOhNlLyLUMrFYqafm3i6AlYEJcVJxbgBLNFXzzznpkncxwqmWZK5YseQiPLL7f8ocx8ZICQHtcbca4Wq7JFXIkzpiId3/Z6BDo8QWMj/35d/jb06uQl5nF2rPHtFJjunM4w+Uld4lkcjn8gwJQra9kHR82bjT6RPbDwX//4tSzhPYqii1/6w0G3Pvttzhi04aLOoGQzqpAr3fYS6hWKpG2dCmiVaoOHBmRimYACXFRRFw0zGYzLhxLa5fXGzb01h6wQH+ngz+dwYAFn/wD6ZkXYQgJhCEkEHNiY/HXkbdx7hFkEiiemXQfsk62ZMkmzZ6M5qYmnDt8inV9+u7DvHsNzSYTmpuaUK2vdKoOIND2RbgTpiRDBhnOHDhuPcb0Ez78n/85FQDyBX9MACy2/K1SKnH4sceo+C7pEqJVKtx84YVW7SUkHYtmAAlphdSdB/Hy3Y+1+esEBgZg+3++ADwUwNgRkrKAGVW6Ctw34wGYL+RZj2lj+uHSfVMx2aiA8cOvJT9LtBcv730KJM5IcXoWsC1mAONTxmDe8iUYmDAMHz33Z47ZT8tYl3/4Gh5Kng2DnwLKGiOUta7N8MaNjsf7uzY5HfwSQkhbogCQkFZwZ4AiV8jRf8hA3Cwpt+4nBGyygAfHAPFxTgV/APDMrIW4cOgk5Db/1E0yGSrDe0MX0w+xh8+6ZfxScC2DirWM+/3shTjLkTzjDL/gQCx7fxXiU5KsryH0d9fkKYPX8zNwJLelnEvwtQYMSauGR5NzvzKdzXwmhJD2QEvAhLQCkxVsP4vkimHjbsNT77+CSq0e+vIbyM3MgkoVhKm3T0fY8EEu7fkryslH1sETDun+crMZquLrUBVfb9WYnVWiuQLVAG8Ymq6hudoXH/7fW6LJJzI4FnCWql/MADzxzouYNG+u9Vja7kPIPpUJuZy/CMLlMQGoyjvHOlbR2wuXxgQg/ngV63iv8FBEDopB5uFUzplECv4IIZ0RzQAS0krV+kqHrGBXxI2OR+6ZC5znXOmlC7TdErVYwgOfFw48AHnfQgDAN49ewtVTjpmzo6aNx193fwOgdTOs7+74ipXtXKK5guUp81ClFW5VZfBX4PQs/rIrt+3WsZaDH3v997j3qcUOPwNcf2dis52EENJeaAaQkFYKUAXh3V82WrOC9eXX8f7SlU4/R3Mui/cc00vX2T10rW2nZk+uUCBhcjIUnh5OB7yDJkdA3teS1KErrOcM/gDg7IETKM4tQERcdKuSQIx2M7JSgj8AaAjwFDxv8FewAsCpD9zp8DNgH+C5WmqHEELaCgWAhLhJRFy09U1/7zc/sDJLhcjkMphNZsHkCtteus7MHLVmiXrUtPEOGbKJM1KwatM6VNzQ4tyRU4AMGJgwDF/86a+iAeH45QEALF/j1bQqwWszj6RKqi0oJDx2AADLrNuhrb9ICv4A4LaRCbiAfN7zypqW7+OIiWNhiIzAwap6RHopEGvzM2DrjYeXORQBT999GK8/tAx/2b1Z0rgIIcSdKAAkpA08/tZKPD3+XknXenl7o8EgrbOHM710Gas2rcPrDz3F2YWEy8KXnsacxfOtr2M7qxWoVvEudR7+z/8E+yTX6Wz77ors67u1McWVAJapYRioVgm2wrM1b/mjGDNninXmrnTNs9h//hSMtvUMzUDQ9Qbr7F/SwnlQvf0aNuvqrNcoZcATvQOg8mjZX1iUk8/7vT9z4LjTQT0hhLgDtYIjpA1U3tRJvraxoUHytcyslq2inHz88vkW/PLFt5xt3AJUQVB4Ci9r2vrm7U+w+tfP4nK6JQkiIi4ayXOnIiIuGmsWr0DGvmOs65nl6ZGTxgo+V9W/JYml/5gAwWsTJidb/qOuHqvWrUbi1PGSxx87chhWbVrHOVY+4+6Ybv0aAeDrFasxfQT765k5ahz2f7QJb/30JTZm7UfIO6/BPmw3mIF/3mAvbZ87kir42pmHhc8TQkhboBlAQtyI2eSvcCK1ymySdnF8yhiHfWVcS4ujpo3Hn7/71Lq3LDst0+n9erlnLmDZ+HtZ+9SKcvJ5ikZblqdlMhnnbB2TDRs3bBC0decBmBAS5YMByQG4kuq4D3D0tAmIiIoEzuUA+ioEAHj3pRU4NHsq3li5WnTsHl6eyNh/TPLXHKhWsZJFAEDlH4ifX1mH3LKr0JQXIzY0AnFht5ajRwK59U0w2Mz82TKYAU19E2J9mKBbZLbT5jQliRBC2gtlARPiBlyb/AMDA1BdXQN3/BMLVKvwVfYBVsKA0PKmbSbtk2Pv5s0uFmPbxk0so/itn77EsOTRvEvEPoEKnC9fC22dpbZefWUzdqy8gZzDxQ7XBhRdA/TsfYIrX1yN9PRMuJNvoD/+mb4DYdGRTt13sKoeB2v4Z26n+ntjaqBlxlMsk3lj1n4EqlUOPz9MRxJKEiGEtAUKAAlxA65+uu7CFQhIKY/yu7+/jT79++HFO5a0egwbs/bDbDaLBjJc+wbtZ7JqG8tgaCqH0jMUfl5hjtfW1QNp7IC1qLgUjz66otVfB5dAtQrrT/zgVBCYW9/E2vtnb3GIr80MIH8xayZQXzH1AVw4fhqw+3XMFfgTQog7UABISCu5u12ZXKHAsHGJWPjiMt6lwPZqQcd466cvkTx3Kmeg62qbN17aSuBCrvWPVVXVeGHlm8jl2N/oLoFqFbaXZzh1z7tllTBw/PZUyoCVYeyAjatWZNLsyVjx0Zt49ze/x4Vj6byvE58yBmsPfu/U2AghRAztASSklfIy+ev3uYIptSI06+Pu+n5imOSTVZvWOQQyzHjdRunN+uOat9YiL6/Qfc/nUKXVI33PEYe9gAyuvXlP9A7AP29Us4JAJgvYHl+dwD/MXigY/AHAhWNplClMCHE7CgAJaaX/ftL6ma/f/eNt9OoXKnnzf+SgGMSnJIkGD+6QNHuydUxiBY/dwtcHUAUC+ioUFZe6fd8fn6zUMw4B4MFtO/D5y++hNP+K9RizT1GlCsLKsCBo6ptQ1Gi01AH0Ec62tq0VKVQexp4r5X8I6UpytFpodDoMDAlBnFrd0cPpESgAJKQVinLy3ROEmeF0MPX0B6/hqeS7W//aIn7z+u8djkXwFDx2m6ExQHY+Sk+dabvXsDMsebT1v4Xaxtl3ZYn18RQN/LiIlYexxVX+h5DuYGtWFl7cuxcafcu/tTmxsdgyfz5USmUHjqz7owCQkFZoTasyW0wBZWfag6284xG3vLaYCidqGrqNpwcwchD6wQi83PYvZ18KRqhtnKtdWRyJlIdBy/5Kmv0jXVFtYykuXNOgtMYLfl69YDSZrDN8Gp0OyZ99Bq3B4HDf3vx8LNi2DTsXLeqAUfccFAAS0gru3ot3eu9RrJq3VHTTf9ruQ5Jbm8kVCvgHB6KmosqlLOUOnX3y8Ra/xknM94LBZAEzpH5vpS7L8tX2EyucDbTB/kpC2kGTsQZHC/+GF/ZVIL3McU/snNhYpJWWQscR/AGA0WzGLo0GuVotLQe3IeoEQkgrMK3K5AoF67hcoUDS7MnYmLUfv/v7W5KfZzaZcOFYGlZMfRDV+kqk7T6Er1avQ/qeI6zrsk9J3xeXOCMF60/8gMQZKazjgWqV4H3M19CRs0+uzrAqPDk+28qB6JRAvHb2Qbz1yz+w5LXn8e6Or7C9PINVAkbq91YsMK7SVWDlnUvw6PAZePnux7Bk2HSsvHMJqvWVACw/O6OmcXc4iRgcg41Z+/HuLxupBAzpcs6Xr8UL+yqRUe7PeX6XRsMb/NnK03XA6kMPQmVgCGmlan0lVs17nLUX0H4p15U6gQpPDxibWvrn2tarS9t9CC/e+SjvvY+v/iOampoxLHk0a2nTPnmD+XNw7xB88ae/chZwdlcAYj8TJqXrhSsldvrFDkBZQRHMNn18AUAZrMBv/zcSPkFeUPuOQGL4K5z3i31vASBm5FBsOL1D8Bqx2n8Ad3kYhru//4S0h9rGUnx//gX85ue4Vj8rZ/lymgFsQxQAEuKiopx8pO48gB2ff4crWS1167gKNwu90TvDtl7dvNBEzqVK+8DRmUCiLbJ7s0+dxdrlr7K6kQSqVayx24+RCQ6Deqnw0l2PSV7uFvPEjhEIibJ06JgwYC38vMI4r7u3dwJrmdje0ORR+Pjodt7zYoHr+pM/YPBtI61/XjH1QVw8cZoVtLq9viIh7SD3Ziru+GY78vStS+BIDAvD6SeecNOoCBcKAAlxUpWuAi/f8xtkp3JnqAq9cdsGWB8992ec3nvUYaZKzLs7vkLSrEkoKyjCsvH3soIjhacHTEZTpwgkuNrj8WHG+MrXayXf44oH/h6H2MnBAIDR/V5CL7/RnNeVFRRhybBpMDbzz9jadj6x98vn31oTe7gMGBqHL87tBiCtVVxPTgKh/shdy6yvPsf+wiKYzOJJTkLSHn8cSeHhbhoV4UJ7AAlxQpWuAkuGTecN/oCWLNFfvvgWxXbdKyLiopE8dyoi4qKxatM6DB+f6PQYsm69dlh0JLaXZ+DdHV9hyWvP448b3oWxqdkhoLTNWm0PzL7FF25/BKf3HpV0DzPG56Y9KPkeV6j6+1j/W+kZyntdWHQkXv56reCzSjRXBM4Kf66+kp1r/fsQ2+co/Drdl9geStL55Gi12FtQ3Orgb05sLAV/7YACQEKc8Op9SyUvR37w25ewZNh0a0KHvQBVENYe/DfiU8ZAJpf+T9G2Xh0AJM2ahEdWPQtVaB/B+9o6kCjRXMG80ES8eOej2Pj6h8jNOO/07OaVrFyn75HkVgKIZflXDrVvAu/yLyN25FDB80JJICMnJYsOifn7EMsk76k1AN94eJnDTHD67sN4/aFlHTQiIkbjhqQNpgYgaXsUABIikatFny8cS8MjQ6fxzlys3r4Bt82cyDrGmcUKx3p1tjo6kBCqndeW5AoF4lOSBK+JGOWHe/8SCwBQ+47AiNAVos+NHBTDu28yQBUkuBwZOSgGQ5O5l5cZzN+HWCZ5T1z2FOqScubA8XabzSbOiQ0JadX9X95zD3YuWkQFoNsJBYCESNSaos9VWj1WzVvKeY5pr7Yxaz/e+ulLbMzaj40X9zuUabGvV2evIwMJZ+oSulvC5GSs3v4Z59cuk8sRnzIG64/swfihr2LCgLVIDH8Fngru8hS2inLyeYP2an2laBDy9k9f8pbaCVSrENSr5c1y1aZ1DmV6enINQLEuKZmHpXdRIe1nkFqNObGxUMhcWwK+WsWfeEXcjwpBEyJRa4s+XziWJtg9wr692vbyDKTvOYKs1DMO5Vz4rNq0ziHbuD0CCWfqEgKOWcCtoTmXjZK8Qtz+2IOorzXgwrE067nh42/D6u0b4OcVJLrkay9150HB85lHUgWD6gBVEL7KPoBfD5qMWruM4pqKKlY7uXbpsdyF6K/dbNV50nG2zJ+PBdu2YZdGYz2mVio5O37Y6x8Y2JZDI3YoACREImaGjS9zN3JwDIou5ws+Q2r3CEbSrEmSAj9GRwUSQ8cmSLoubnQ8nv90DQbfNhLFuQXIPJwqmC0rRZVWj6cn/Mr6Z7/gQGvAdeFYGlYvetahDE6OVouL1/PQL6AR8X0GcgaHOz7/VviFJdRPqLihdQj+AP52cm3eY7kLKMrJh/6G8F4yVWivdhpN98SVWZ2j1UKj01lbtblKpVRi56JFyNVqkWfzvFytFlsuXMBrBw/y3tvXX3xmnrgPBYCEOIFrhm3A0Dis/OIvGJw0EiumPiC4T7C9NvS3dyAxZvYUwVm9uNHxWLV5HWewc/g//3O6SLYQ+4ArY98x62ybzmDAgq3fY3d+ofV8UtgPeH9mMFIGPGddGi7KyWfVduSSMFk80UNKhm9PD/gYzpQNSpCQZEMccX2PE2ak4OK8KdhdXmo9xiRiCO3FEwsY49Rq1vE4tRoPx8cLBoADW7mHkDiH9gAS4gSu/XpfnNuNwUmWor6rt3/Gue9LJpdL3odnKTB9sMttdH/wd/xFW22LQNvj2v/mTrazbQu3bcPegkLW+Yxyf/xxbyXOl7eUfREL3GJGDpX0d9nRiTldRVFOPp6b9qBDy0Muo6aNp6DZRWsWr0DGvmOsY2cPHMe1D/7FOrY3Px8Ltm3jfIbOYMDcTZsw+OOPccc332DQxx9j7qZN0EtY4uXbI6iQyTAnNpa6frQzmgEkxAV8M2zMvq9V85ay9qLdNnOi6D48rk/nXaEdWInmiqQMYPvZrqKcfJw7cgoA8Mzf/my9JjfjPL587QO3j3PrgXewq6yfw3GTWYb0Mn+cK8/G4N5l8PMKEw3c/vDPdyS9JrNtwH6Gkyl83ZaBTFcooFylq8AbDy/jzfi1F58yBn/+7tM2HlX3dORUBufsqsxkhjq/FEpdFQwhlj14RrMZuzQa5Gq1DkHZwm3bsDefvdWFCRh3LlokOg6uPYIzY2Ko9EsHoACQEDez1Pf73ul9eFyfzm2XLzsrqeVfmNmuKl0FXn94mUOf3NHTJuC179bjZkl5m4zzhl8VAMcAkFFS4wVDUzn8vMIEAjc5EmdMZLVxE9PeiTlcHyS42hN2BmsWr5Ac/AHAvOVLOt3X0NnpDAYs3LYNabsOYZTAdbYBICNPp2MFgDlaLStwYwgFjPb49giS9kdLwIS0EduuH2KKcvKRvvuwwz649u7i4Swp5V/sy9CsWbzCIfgDLPXdVi96FiMnjXXrGGUKSxHogUMUgteF+zeyuoNwl2YRn8m1x7Vt4N1fNrZZIPOn+U84zPRcOJaGRYMmd6ouGszPvDO+ff8fbTSa7ouZsTOoAgSvsw/+AMDDpkB9jlaLj0+dEnxGnhOFoOPUatweF0fBXweiGUBCOoGumiwgpfyL7WyX2Jt++u7DOHf0FIYmjxZstyfEN8gPdZW11j9HjQvE5GcjUHf2OsYq/JBuCmO1qpLLzEgMrcXI0KGsbGB3Z1S3R2JOUU4+zh9N4zxXU1GFVfOWYu3B79t0DFK5UlczN+O8YCklwmY7Y2dQB0Eb0w+qgjLIzS0p7CaZDProMM4AsNlkgs5gwAP//jf2F4h/CKUkjq6FAkBCOoGumiwgVv7ljxvew9xHH7D+Wcqb/ge/vVUWxl8J1IhvLLenUHjgiR0joL9aD6XKA0fWlWDjQ1mWRyIXU4dH4/DccWhWegMAEkNr8P7MYN7uIFICt6Jz2Si9lI/wobGIGDHE6TG7y6GtvwieF6tF2R6YvYlyBf8ClDK6P3wGRMJQWIT6QvbPTGf9MNQZ2bdmuzhvCoZvPwR1fkvGrz46DBfnTeG8f2BICBZu24YDIsGfQibDzJiYDp/N+zwjAwcLCzEjOhqPjhbuxEMoACSkU3A2WSBt9yFkn8qUXCC6rQiVfwlUq1jBH+BcMW1zbT0qIvvgSspINPr6YOChMwjRlIjeV62rhEkbjtjJeny3NBuFJ9llYeSXruLJ3qGYvm6FYB1AKaqu3cSaB55E+onT1mNJ42/Dqq3/QECf9n8zFKufB3RcAFWlq8Cr9y0VLJPkERSIweveRsjUlqV33cFjuPzMi2iuqgbQeT8MdUb2rdmald7IXDgbSl2Vdc8f18yfHMCo0FAU6PWce/7sdXQSx+nSUoz//HM03arPuun8eTzx88849fjjGBXm2r/tnoD2ABLSSUhpB1aiuYJ5oYl48c5HsfH1D7HyjkcwLzQRZQVF7T1cq/UnfpDcto4JdKWQmc1QFV2HISQQNf164dKcZDR5e0m6tyE3Fqby/ig4VgWzXXlBk9GIrIMnMMajD5IjJrkc/AHAmgeeRIbdUnVG6hmsvv+3Lj+zNWJHis8+dkQAVaWrwJJh00V7aQ9e9zZUE9k1/lQTkzH4I0vW9ehpE2j2zwmD1GoM793b4bghJBC6gRGcwR8AmABklJdjzubNoq+x4a67Orx/r23wx2gymTD2s886aERdA80AEtJJSNlzxpVxW6XVY9n4e7G9PKM9h2sVFh3pVNu6VZvW4c8PPcWZCMKFmalI+vIXeDQ0SronflwyjLVGAP/mvaa1M2FF57JZM38Mk8mE9BOnUXz+UrsvB5tNwu1JBgyN65AA6tX7loomCwVPGsea+WPIPDwQMjUFPlH9YZbSfoWwDOvdGxdv3OA9f8+gQXgyKQkecjle3LcPmeXlMJqlf58HBAe7YZSu+zwjwyH4YzSZTPjXmTO0HMyDZgAJ6WTMPL98hTJuq7R6SUV021LSrEl4ZNWzokvSAaogPPfxavSNjpT0XENIIFSaYngZGiClxXygWoWkWZNc21dZVw9oKwFdpeX/6+p57y+9JNL2L1t86cz9hN+471q6oJ3G0aIoJ1902Xf4xk8wYtPfBZ+jjIrE2QMnOm1GfGc1JzZW8Py8IUNwe1wcBgQHI6OszKngDwBSS0rwxqFD2GOzVLwrL8/hWFs5WFgoeH6fhOSVnopmAAnpJMQKQYtl3GalnunQ/YBSONPuyywDdNH9YAgJRPipLEnPt116dmpfZVMzcCEPNw0N0Ht6IqSpCeqmJss5VSAwNAbwZP+67DckRnAs4UOF33jbwkiRFmlj505tn4HYEEv84Vr25WIotGxzoCQQbrWNpTA0XYPSM5S1reH/EhPx1C+/cM6SKWQypPS3fFCyTxiR6tUDB6z/HeztDchkqKhv+eCkViqRtnQpolWOHZLcYWpUFDadP897fkY0/azwoRlAQjqJV+9bitN7j7KOMYWgAfGM22HJnX+Zg6vYNR/ZwAG4PHccEr7Zjf7pl0Svj0scge3lGQizmVmUsq8STc2oS7+Ir/0D8XFUFDaHh+OjqCh83a8fDHI5oK8Csh1n+yJHDkXS+Nsgl7N/jcrlciSNv61DsoEjB8Vg9LQJnOc6av+c0EysMro/QqamQObBPxdhbm6G7uAxazYwJYGwNRlrkFGyBsevPIczpW/j+JUVyChZgyZjjfWaU48/Dk+549u90Wy2tnLr5evb6rFUNDSwgj8A0BoMGLNhQ6ufzef/EhM5vzYA8JTLaflXAAWAhHQA236/VboKrJj6AC4cS4fZ7lO6bSFoJuOWC7Ps2ZnxFbvm0i82CvuyDuLxrDL0uiKtMwhTI86WpCLMF3KxrVdv5Nu9Aeb7+mJr6K3C0PoqzuXgVVv/gUS7wDsxeTRWbe24gsWvfbfeIdEmafZkvPbd+g4ZT+SgGN6fW58B4tsA9EdTcfmZFx0KihOL8+Vroa1jz4Bp686zeluPCgtD46uv4tM7J6OPr2Mi1d78fLx64ABnn1530BoM+E+WtFl8V3AFuJ5yOU49/nibvWZ3QEvAhLQjriXQQLUKVboKwfuYZa/1J37AsvH3svYC8mXcdjbOFP5dtXktinLykXVQepswoOX7ZF8mh7eWX109bhoaoenr53DKLJNB4+cHraenZTnY0AD4+rCuCeijxruHt6L4/CWUZGs6vA4g4FoB67bsG1yUk8+7d7X+inD2+rmFv0XlsVQALdshSIvaxlJo67i2hpigrctEbaOlt3WTsQbny9fCW5aN63VxDlczrdzSHn8cxVVVgkkjzyUnw2Q2Y51IVxB7L+zdi/uGDXPqHqmYAPdfZ85gX0EB1QGUiAJAQtoR1xKolD66CoWljZmzGbedidQagKOnTcDg20YidedBp1/Dw9MD80ITOQPkMK6kk1t7/oTomADwVuFoLhEjhnR44GdPSgHr9ugbLBT4GwquQnfwGFQTk1nLwObmZuiPplqDv3d3fNVlfs7bk6HpGuvPxVVeKK3xQrh/I8IDG3HhWh509TVoaPoOfp4XUFojvMx7o64Oo0NDBQPAfL0ey8aMcToA1Oj1knoFt8ajo0dT4OcECgAJaSeu9D5lrLzjEVZCSNKsSV3uDZEvKcOW7SyPM0WjmcSO1b9+1rkyOUpvqJhkDx4hTU2WRBC72T9XteVsm7PWLF7hsO/0wrE0PDJ0Gr7KPuCWIFAuF15SvPzMixj80TusEjDMsq9MLsdtMyd2uZ/19qL07AsAqGpQ4J3j4Ugva+n366Mwot643/rnpLAILBlxXfB5A0NCRJMqfszJQYPRiBAfH+jq+bPkuZwpL++QbiE5Wi00Oh0GhoR0eLeSzkRm5qs5QQhxq9SdB/Hy3Y+5fD8T5Lz7y0Y3jqp9VesrsXrRsw4zTvOWL8HAhGEOAdHKO5dwZvH6BweyAr2k2ZNxx28ewhsPP8372ryzSOdy8LXSH/m+vjDb7H+Smc2IqavDYkMNZxaws7hn25Iwb/mjnF97WyvKycejw2fwno9PGdOqvsHOZHwDgE9Ufyij2O3fbD/0EG4ZJWuw5IdKXLzhC7NAoSRLz2tLYkhGuT+rHzbTym3nokUAAK833+StrcdcPyEyElk3bkBraGnXGOjtjaqGBt77JvXvj8OPuf470Fk6gwELt21jdTOZExuLLfPnd2jh6s6CAkBC2onYG65UG7P2d/jMUWtJ3Z/GFTAyQUHlTR3rGV+tXoeNr3/I+6wlrz2PR1Y963iiqRmGSwXY6uMHjV/LXsBYmHF/oDeU/u55o+AKZm3FpyRh8vw74OOnRMKk5Db/O5bygaQ1P2tiX6+QuNHxeH79GgxOGunSa/cUOoMB92zZjGNF4i0SGR/NycPGc31Zs4X2QdHZsjKM/ewzwSAQAHKWL0dhRQVOFBdjfEQEZsXGYtIXX+BoEf/ezpzly9ttFm7upk3Ym5/Pqm0oBzArNtYa7PZktARMSDuRsgQqxepfP4v3d23q0rMiUvanAcIJDQGqINYzXC6T4+kB5Yg4LK6rh7a2ATovD4T4+UDtoZD+BYmQsvx/4Vg6q2DyqGnj8efvPm2zv2cpS+yu1twT+3plcjkr412uUGDYuEQsfHFZp1ga7wp25eXh6R07oNGL7yG2VdngibemXUVJlTeqmwZhbtwyxKnVyNFqcbK4GANDQqxJFc/v3Im/pabyPitPp8PtcXGYZVNs+pnkZMEAME+nc2sAyLe8m6PVcvYxNgHYpdEgvaQESeHhbhtHV0QBICHtaNWmdQ4zWs7Ky8zC6kXPdumlYGdJCRiZMjlcSTWSyuT4+kDt64O2mJtwJgOacfbAiTb9e44cFIO4xHjkZlzgvYZJPnKW2Nc7MGEYcs+0vC5Tm7Erf6hpLxqdDsmffcZaenWG4la3mOF9ojGq3/OoblRg7qZNnMukT40ZIxgADgwJcTgW5M2fLAUAHjw1+4RwBXliy7tiha1/+8svOP3EE06PpTuhOoCEtCNmRuudHa6/qZtNJmttQMK2/sQPDjXnOkOZHGcSWmy19d/zrEXzBM9fc2Jp0ZbY17tq8zrh2oyEF1fwp9RWIiSvGEpdlej9NU2Wt/2K+ks4X74WC7Z+j7357ELne/PzsWDbNgxSqzlrAypkMsyJjeWcyTOJ7CprFllWtqUzGDDpyy8x+OOPccc331iLVutvBX984waAWI7g1FZGWRlytVrWsRytFv/LzXU43l3RDCAhHWDMrMkYPW0Czhw47vIzLmfl0FKZnc5aJqc1y/9tueTv4yuyv9GJHeK22c1S2/DRz69zduXlsYI/D0MDhm8/BHV+qfWYNqYfLs6bgmaeskU/5IRgyoBqAEBm+WXszm92uIapC5ir1WLL/PlYYDfTNjMmBlvmz+ecmRMLvLhmDbnoDAYM+ugjh2B3b34+7vn2Wxy96jjLbDvuQWo1EkNDkVHOX0ieWY7uqckiNANISAfh6tjgjPdyxduj9VRJsybhkVXPdorgj8HVlk6K3DMXsOq+pW0wIvHewQmT2edtO9gwqnQVWHnnEjw6fAZevvsxLBk2HSvvXILnPl4t3oaPOCW1hD0jO3z7IagKyljHVAVlGL79EO8zLtzwx2WtJTgsrRGeA9qRmwuVUomdixYhZ/ly7Fi4EDnLl+Ob+fNxz7ffcs7MuTJryOXeLVs4l7mNZjNn8Gcr79by79/vukvwOiYYFZtN7K4oACSkgzDLwe/t/BpyF/ZaHSm62mOWKroD27Z0r275GPEpYyTfe+FoWpssBUvpHVyUk48D//4ZK6Y+6BDkXUrLxAtzFyNjn2MP678tXyXeho84JdkmaUGprYQ6vxRyuyVXudkMdX6p4HLwulP9AAD9/BsFX++5XbusgV2cWo3b4+Kg9vXFoI8+cgjCbAOmLfPnY2ZMDOs8M2soRY5WK5hIIoYJ7MaEh4sGo0yyiNHu+2g7m9hd0RIwIR1s9a+f5VwWlMvlMAnsl1HqqtyeUUfaHpPQMvX+O1GcW4C8zCxs+9vnyEo9I3jfwa2/YNFLy90+nte+W++QmBQ3Oh4LVy7DyjuX8CYspe8+zHvOtoe11IxvIm7OwIFQK5XQGgxQ6qsFr1XqqmAICeQ8l6v3RUmVFyICGxHfuwYXbvjzPmePRoMF27ZZy6bM/vpr3pk5JmCKU6uxc9Ei5Gq1yHOhALNYAgcATIyMxIniYlbgxtQztH0tviXsN6dNw/cXL+K1AwcEX6c7/46lGUBCOlDa7kO8reCEgj8AMIQESt5P012k7T6Er1avQ/qeI07dx7V02RkwgeBHR/8jmpBRcb1tZiKYmclPTvwXcaPjAViWnf84d1GrstUBSxkZ4l5pS5dCrVTCoAoQvI4v+GOU1HgBkGPRCOHrmLIpaSUlmPTFFzhdViZ4fZ5N8MbMGjobQIntI5zUvz9+XLBA0iyj/RL2qccfBwCM/ewzPLR1Ky6JzPB159+xNANISAfKPsXVyL1Fv5gBKC28CphaPuWaZDJUxPTD5DGju+0nU3slmitYnjJPeo/fW6p0FXj1vqWs+nqdtbvEjAX3Ys+m7bznx90xXdJz0nYfQvapTKcTYL587QNozmVLvl6K8NgBbn0eAaJVKtx84QXs0Wjwj7RcVJzJgtnY8mHRJJNBHx0mGgBuPt8LKZGBuGvwI3hx/5eir/vkzz/jrEBCBcMdAdMgtRrToqJwoLDQ4VyIjw9+ePhha2AndZYxTq1GnFptLQ4txaT+/bv171iaASSkA4kVL37inRcxym6PVmVEb/R5fonk/TTdgX3wB7T0+OVTpavAkmHTWcEfAJzeexSrF3F0BOlgY2ZPQUBIMOe5gJBg0WCuRHMF80IT8eKdj2Lj6x9i5R2PYF5oIsoKxPdSMYWbW1Og3JZcIUfS7Mm09NuGZsXG4ssdm3DbjIms4/roMFycN0X0/mytH945HoXhffpz7pOzl1FeDrECLs4GTHxlV3K0Wuh46hyOCgtjZeY6M8vIt9+Pz/KxYyVd11VRAEhIBwqN4p+9AoDo+MF47Rt2woCq6DpG/XgUHvXCG7i7C6Fl8iqtnnc5+NX7lnLe15nrKH568kfOOoafnvwRgPASuCtBMsOVQtVCYkcOo2zfdmCbWKR4fjFOLbsPmQtnW0vAyEWCOttSL/bLqc5SK5X44eGHJV2rMxgwd9MmhyzifL3eejzz2jXOe/cXFLiUmKEzGLBg61an7hkdGur063QlFAAS0oHE3nhLNFewZvEKZJ3MYB3P2HesU85itQWxZXKu5IminHyHmT97nXF/GlPH8N0dX2HJa8/j3R1fYXt5Bkwmk+DsnjNBMtd+SFcLVfNZtbnzLbF3ZxFx0fju9ZeQMobd7nCUhAAmT6ezLqemPf44El0IepLCwpD7zDOSa+bxlV0Zu2GDpOXZPAlJIlyvKWUJm+FMyZquivYAEtKBxN545Qo550Z8+yxLgF2ItzstvbnS41fKjFZn3p+WNGsSa8lXaHZve3mGpCB50G0jsGbxCtbPE7Mf0l19qgFgwLC4bvXz11Vw7YkzAxj88ceC99nu2UsKD8fp3/4Wu/PyMGfzZtHXlAOYEBmJz++919pHWCxo4uvRazSbJbe3k7LP0LZItRngfE0+4yIiesQWGwoACelAYh0TTEbhXTclmisIVKt439i7wyyMKz1+xQLr+JQxXSZIkTK7JyVIfvW+pbh4gnsm+d1fNrqlTzUA3Pl/C1p1P2kdJtmBMSc2ljP4kQGYzTPLJXWP3JSoKMhkMlaQKdZBQ0qJF6mYIE8hk8FoNmNgSAjUvr4OXT2cndUU62fcXdASMCEdjKtDBNMxQSyQCY8dgDWLVyBj3zHW8e62ROxsj18msOYqsB2oVmH19g1tMs62IGV2jwmSuQSEBOHrNetw4Vg6zHalhWxnkgNUQVj+4WutHm/y7VNb/QziPlvmz8f0qCiH49Oio3lnucTKsADA6mnT4KVQ4JBdpi5XBw3bZA8pzxZzpryctYdwzubN1r2Egz76CHvsAl5nln75vobuiGYACelgzEbu4twClGiusJZwA1RBgjOEZrNZ8hJxW2rr5WdXevxyzWjFp4zB6u0butTMqNQl8PUnfsCy8fc6lMoZMDQOF44L74dkiky7Ixnkw6dfwZ+/+7RLfY+7M5VSiX1LliBXq8VuTSYajVWYHj0ECaFDAAC1jaUwNF2D0jMUfl5hACxlWIK9vVHR0MD73DH9+mEVRxFl24LQXLNxajf01v0oNRUnios5z3EtIzMfe5iZQjH2Ra27K5nZLHGulxDSIar1lQ6BDLPEm5V6Bi/f/RjvvW/99CWS505ts7F1hTp7XIF1V3Nv7wTUVDi29gpUq7C9nL2saxsk9x0QjkeHz5D0GoFqFV7d/BH+OHdRq8ebNHsy3v1lY6ufQ9yjyViD8+Vroa1rmU2uahyO4iogyCsX4YGWigJq3wSMCF2BgooGwb2Do/r2xVszZuCOb77hvWbHwoVYm5qKvfn5kpeUxShkMoyPjBTtBcwnoW9f3uxiLjsWLsTtcXEuvVZXQDOAhHRyQjOEUpaI2wpTZ89+f5rtvjI+O778DmcPnkTitAmY++gDbTZGAF26FVmVrgJrFq/gDf64lsBtE0hSdx6U/lpaPd789TNImj0Zp/cedVgudkZ7zj4TcZllH0BvuAAAqGpQ4J3j4Ui3NvSIQ1JYNV5KKQZwHufL1+Ja3YOCzzt77RreOiLcjUchkzmVeMFleO/euHjjhvXPM2Ni8JvRo10OAAO9vSEHROsZMrpzFxCAAkBCugyuQEYsiaQt34D56uwJLT9fPn0ez0y6D8amZgDAvm/+iw+WvYz1x3/AwFHD2mysXRXX/k7IZIgaOhD3PfsbZOw/hoTJybx/z86Wd6nS6nHX4wtQeUOH3DMXXB02AEuCEgWAHa+2sdQa/AHAO8fDkVHO7v2bUe6Pt49F4K1pV6Gty0SY/32izz1WVIQQpRKV9fWc/XjdMeu3/aGHAIDV6eNUSYlLzwrw9MQRiYEjV0/h7oiSQAhxk6KcfPzy+bf45fNv27XIsFASSVtxtc6ebfDHMDY1Y9kE8WLFPQ1vdw6zGYVZufjgyZfwwZMvYcmw6fjD7F+jWl/p8AzmA4JMLv1X/aX0c/D29Wnt8Dt1mZ2eRG/Isv53cZUX0ssCYDKzC0SbzDKklwWgpMoLABARWC/aHcQMS3HlCZHsYvZMP97WJHsoZDJrHT77Th9/4th3KEV1U5Pka7l6CndHNANISCtV6Srw+sPLcPbACdbx0dMm4LXv1vPuhXNX4oTQEnFbcaXO3o4vv3MI/hjGpmbs/Ne/23w5uCtxJiHjzIHjnMvuVboKGJuanVrOTdt1CAUXLku+ngu1gescahtLUd3Q0gqwtMZL8PqSGi+EBzZC6RmKLfOHYIFdAgeXRxIS8Pk99zj041UplZgTG+vSHkC+AIyvhqA77F60CM0mk6Raht0FBYCEtNKaxSscgj9A+E25Ler2tddet8unz+PVeY8LXsNVZ+/swZOC92QcOE4BoA1nl2/Tdx/Glr0Hkespw/iICMyKjcWaxSuQeTjVqedoMrPELxIQmzCU2sB1MK6kDwDo5y/cPjLcvwlq3wT4eYXBD8DORYuw+tAhvHrwIP9NZrND7UHGlvnzMW3jRsmJF0xh6Z2LuBORzpaVcR5n3DNoEH7MyZH0WvavOSs21qn7ugNaAiakFZhlOj5cPWdfvW8pTu89yjrWler2LZ84D8Zm/m4RfHX2Rk0dJ/jcxGkTWj227kSoliGf5Rs347WDBzF70yb0e+Fl7iXkNiSTy/HP9B0IUAVxtpwj7cMS/J13OB4R2IiksGrIZewZObnMjKSwaowMHYIRoSusx3UGA3aJtGabwlFjkKFSKhHgRFFlE4CjRUW8vX7fO35c8P57Bw+W/Fr2rzl30yboRTqR7MrLwxuHDjnUGeyqKAAkRAK+NzMpy3SZh1ORuvMgDm3bgd8kzBYtyOvqWNrDji+/g0kg+OsXG4Wvsg9wzmTe8dhDUHhyLzooPD1o9o8D1/5OIYaQQOt/N5TdELiybZhNJlxKy8TKO5fg0eEz8PLdj2HJsOlYeecSzj2KxP1qG0tvzfxxL/u/lFKMxNAa1rEpA/pg+0PLkRj+CjwVLQkiC7dtw4miIvtHWE2PihJcLs3Ral3K2LXv9aszGDDpyy9xWmAGcGJkJH6TmOhynUGh4s8anQ693nsPcze3fMDq9d57KNBzd+jpKmgJmPR4QnvxxJZr+bov2PrgyZckj0Uoc5JrLO1d2Pj4j3sFzw8YOlBwLOuP/4BlE+5l7QVUeHpg/XHujh49nf3+zm/eWY8Lx9IcrjMDqIjsA6XOUi7GEBIIr+pal19XJpe7XAbmD7N/DUMN+7XTdx/G6w8tw192i/eXJa1jaBJebk0IvR8HH01BabWXw749W2L77SZGRmLrg8LlYlxt+2ZffmXhtm04LhCIAsD9wyxVBP4yaxaW7dgBQzP3fmM+QsWfkz/7zKHAtNZgwJgNG3DzhRecep3OhAJA0iMV5eQjLzML//3kK9Ybqn1AJdRm7ZWv1woWYXaFUObkmsUrHJaOLxxLw8LYifjL7s0YnDTSrWPhEhYdKXhebN/awFHDsLsuFzv/9W9kHDjeLnUApWrrbiatwezvHJY8Gn9+6CmHPadNPl5QFV2H6ltLgF7TOxgVEX0En9kvZgDKrxQ7lA8anDQClTf1KOXI4pbCPvhjnDlwnGoDtgOlZ1/B86EBKfDzCkOcGoKzd2LB28uTJvH2+2U4mwnMVX5lV16epMSP53btwh/37EFTK+pXApbZR/vX5+ouAliCwD0aTZfdP0gBIOkRbjYboa+th9dNPdY//RLS9x3lvO7CsTQ8EDkWr3y9FlHDBwm2WXvh9sWcdfBcIZPLcdvMibxvjkJ7Deuqa7Bs/L3t0oHjnicX4T8ffSl4Xoq5jz7QaQK/tkrKaQsBqiD8dfc3KM4tQOaRVMAMfL9hM4oyLrKu879RAf8bFYLPeuKdF/HzZ1tYX7d/cCCyU8+2wcgtMg+nUgDYxvy8+kHtm3BrD6BtMCSH2neEtd2bGLHgTUqR5EFqtVOZwLbZvzqDwaGNnJjWBn8A0NvXl/XnVJG6gyeKi7tsAEh7AEm3Vmcy4esb1fj4eg021zbjS2UA6pcshEdgAO89TQ2N+PODT+Gp5HsEn52b0bpCubaGj79NMHNSyl7D03uPtnkiSeSgGAwbl8h5bti4xC755i40y9tZRcRF487fPIyRk8ai+PQFyHjeXPnecgPVKkyaNxfv/rIRG7P2462fvkRcYrykfXoxI4di/ckfEDc63vmB85eVI240InQF1L4jWMfUviNYCR5imODNvhagbY0+KbbMn4+ZMTG854O9vfHFPfcgZ/ly7Fy0CDfq6vC/3Fzc+c03bVbyhY8McOhvnBweLnjP+IiINhxR26IAkHRr2/QG5DeykxaCU8Zi8EfviN7Lt5TlTjK5DPEpY7D24PeCs01SSoKYTSbJiSSt8daPXyBp9mTWsaTZk/HWj1+06eu2Bb5iy84k5bjbji+/w1tLnsfOf/1b9FqxDwZc8ZZfcCCrhVygWoVv3vkEuRkXJO37C1QHY/BtI/HKprWi19pLmJTs9D3EeZ4KfySGv4IJA9ZidL+XMGHAWocEDym4gjdniySrlErsXLQIOcuXIzE01CHoqG5sxHcXL0Lt64u5mzZh8Mcf445vvsHJ4mKnxuoOZsC6D5AxZ+BA3sQStVLZZWf/AFoCJt3YzWYjNA3NgN0nWJmHB0KmpsAnqj/qC53PUJMrFBg2brRoJwxAfDP9bTMnSaqZFjkoBvEpSZJes61bcHVE4em2IhZAtWc7M1fa5EmtFRg+MAoleYUAgNqKKvxt+SrrEveaxStw8cRpyeM8e+AEinMLeNsQ8hk1bXyX/Tnpqvy8wiQv+XJhgrdcrdYhYSRHq8WhK1cgM5sxRSQbGLAEVxnl5Q7HmeSLX337rWiiR3ux3weYtnQpxmzYwNoLqFYqkbZ0aUcMz21kZrMbGvYR0gnl1jdhs66O9/yFJU9Df/AY73mGl7c3GhsarH9m9oetXvQsTu89KhjgxaeM4czajBsdj+c/XYPBt0lP3KjWV+KRodNE9x1uzNpPb7QSFeXk49HhM3jPt+f3crZvHGenFIWnB3bX5fLet2LqA6IfDOw/iDC9oh/98++wfMKvnB7rWz99ieS5U1Gtr8TqRc+y9hGOnjYBZphZiSqddU8lcZ7OYMAD//439hewZ8enR0Vh64MP8iaG/C83F3d8841bxjBQpUJeG5ZgyVm+nDOg3aPR4ERxsbXQeldHM4DEbTpbFqXKQ3iHg6FQ/NOmMro/fAZEIsZPiQcW/QoDE4ZZvzYmCORKzmDeYG1nyhQKBYxGo8vfnwBVEL7KPoBV85ZyBpXMa3aG731XkX8+W/B8eWFxu3w/W9Mmb/X2z3BfWCJMRv4PInx1J13N9GWy1YVmg7vDDDFxtHDbNhwocNwasb+wEAu2bePt4tGa3sD2CioqOI97yuWsRBCVjw9G9OmDwxJrEXJlIduaFRvbLQI/BgWApNU6Q306Lr08FIj19kB+fRPMNsvAZqMR+iMnBZd/PYICMXjd2wiZ2lKEd9+FLCTHRln/zLz5XU4/hw+XvYLcMy1JIYkzUqxLu+5s0RagCsLag9/j8ulz+PAp/tck4qp0FXj3sT8IXpOVegZJsya1+Vha0yav4oZWMPgT4mwAyPchg+tnvL1aE5L2I1YbkK+OHsCfEayQyZAUFobU0lLJ4+DLKP554UKklZTgem0t7ho0CAOCg6HR6bBKLseLe/fibHk5T3lsC2f3N3Z1tARMWu33sxeylnuYWTO5Xo/P93zToUGgwWTGVm0NNE0t/+xja2sxIS8PN41NCB8cgw+ffsWhrtrwjZ9ANTEZMo+Wz0jm5mZEwoTH+/fmfK2OmPGgWRbXSVk6fXfHV+0SAO748jv89YkXec//ccN7GD7hNpTmX4VCIYfRaLL+nafuPOj2epQAEDk4Fn0HhHeJ8jikfUhZxt2xcCFuj4vjPKc3GLDArrTLnNhYNJlMDkvKXPw8PVHb1CRprGqlkrd+n70Nd9+NKQMGSM5s7i4oACStYruHimvWrCEjE3d6yxAVFdGhAYq22QhdbT1CGpuh9vMGfH2s5y6lZeJpm31Qyuj+SDr4I++znunjD7WH9P6sbamzLbt3FWJ7/wBAGeCPn3WO/VTbCu8eQA8PjJ4+gXOrQdLsyXjsz79j/fy6y/oTP2Bw0khJHzLSdh9C9qlMDEse3S4BM+kYOVotBn/8sfA1PPvnbNkmlZgB0We2NaGgtTujJWDSKueOnLL+9+B1b0M1kV3mwWvkcHx/NBUX75jeobMHag8F1EF+DserdBX429OrWMd8Bgh3u9A1m1wOAN0VsHWl4sWdkZS6im9u+0c7jKQFX5u8QbeNcKhTyGCOJ82ezFso3GkyGZJmTbJ2lhFayi3RXMHylHmsxKRAtQrrT/wg2jWGdD3MMu5ujYazxqRtfcCc0ivIv1aC2NAIxIWxs9Xj1GrrdRsyMpwagwz89S1d5SHvmRXxaAaQtMovn2/BB0++LDprljblHjQWlVgTIzqLlXcuQca+o6w9VGJfy9ScLFw9mubUbId9wKaM7o/Bt8/Aij+tQH+18wGb/bI7Y9S08fjrbumZdj11BlFsBpCpzdgRbNvkDZ9wm+hMJQC89v2neP3Bp9zy+s7s350XmsiZlR6oVmF7uXNv7KRr0BsMuF8gC9hsbMQja1/FnsyWfa2zEsbh6xWrofIPtB5zpdNHW5oTG4st8+eLtrfrTmgGkLTKyFuFXcVmzXrfMwc3ftxlLa7bGYINvvZqhoKr0B085rAHUGY2o/JEGt5c8IT1mNTZjlfvW4oLx0/DIygQwzZ8iKDk2wAAXzQAUeWVeKhPIJRyaW0SinLyOYM/oKVGm9j3t6fPIArVsAtUq7B6+4YOGhm7TV7qzoOS7im8mCN4Xq5QiNfqk8kQPyFJcuCbtvsQb0miKq0e6XuO0HJwJ5Kj1UJjV8vPFSqlEvseeQS5t+oAwq4O4F1rVmL/+VOse/afP4XFa1fh51daktQWbtuGvfn5Lo/D3fbm5wtmMXdHPXPek7hN5KAYjJ42AfVXhEuqRP3+aYw59COGb/wEp50oOtuWhJYBLz/zIurPsvd/VZ5Iw8Xf/p51rEqrx7Lx9/I+JzstE79JmI0Lx9LhERiA2w78YA3+GAVNRmzV89crtGe77M4l80iq6DO6Yvszd1u1aR0SZ6SwjsWnjMFX2Qc6TRAstdDz0ORRgudNRqPoh5S4UcOdCnyzT2UKns9KPSP5WaTtnCopwW3/+Ie1w8agjz/GpC++gJ4nQaIoJx+pOw+KdsGJU6vxeGIiHr/tNsSp1cjRavHZicPYk3kSRruyQ0aTCXsyTyK3zPI7l8kmltIfuL0wBaltu4B0dzQDSFrtte/W46W7H+OcNTObzZDZlGBRTUzGvqOp2DPxPrz905cd+kYbGBLMe665qhrPDB0AZR9/6JpNKDmZzpr5s8U128E1wzZ0w4fwUqsc7pcpFNA0NEPbbJS4t1Dkl6bIab6ZT9v2Z51hhratdYWOJmLdNpiyLGNmTUbc6HhWWSB79Y2Ngq+1arNzs79DxyYInh+WPFrys4j7CS2xHi0qQtxHHyH3mWegUiqRo9XiQn4hDrz8V2QddK6At+3ryKqvw1NgTJryYsSF9YdGp2vNl9am7LuAdGc0A0haLUAVhI+P/gfyzd+j4ih79knG04ZNFzUAj89eJKn5vDvcbDYit74J2uZbb6J19fjXc6/zXp80ezIi4qKh9lAgzscTV486Fl62ZT/bYZlhO2r9szK6P4LtZv7s6Zql1XIbKdJPNWGy8Hkp7c96koi4aCTPndrpgj8G10wlw7b243OfrBZ8jr7kGudxuUJh/Xl3xpjZUxDI8YEGsCyj0/JvxxJbYtUaDJi+caO1/+4bi57FhUPsepQZ+46Krgrc//331iDT7OUreG1saITl/91YFNrdBnbisbkbzQASt3lz01o8MnQa8gIC0PueOYj6/dO81w56788AgNVHzuCJoVGIjYtqkzHVmUzYpjdYegLfEuvtgfEHjiA97Szvfb9Z/ADQ1Ax4Wv6JODPbwTXDJrZHEgBCRDqXMJhl9zMHjjucGz1tgugbudiyItPlgXQO9jOVfB1lhoxJEJ0F5NKaAuLrT/yAZePv5cwCJh1HrGAz4+y1a8C1a1BqK6HOdyzEbDKakL77MLLOZ2PYiKGcr3OgsLDlgLc/TH69Iau9CZnNUoRCLsf0EWOt2cCD1GpMi4pi39vBxLqAdEc0A0jcpuKGFlVaPeoLr+LmT7sk3eMzagTWpmVh5Z1L2mQ2cJvegPwGdm21/IZm7IyKEryvoqgMyG759CxptqOuHtBWojQ7z+EasT2S/b0UTpWWee279UiaPZl1LGn2ZLz23XrRe5llRbmC/XquzgSR9sHMVCbNmsQ7Yyk2C2jrd39/Gxuz9uPdXza6vBUjLDoS28sz8O6Or7Dktefx7o6vsL08g0rAdDBnl1iV+mrB8899vYXz+CGOAK45cjTMfr1Yx6aPGIuvV7B/Nu1XhzpaT+sCAtAMIHEj26VFvkxae8yS8Ok33sfqRc+6tUTMzWYja+aPYQZws18YfKL687aDU8jlSN11COEyEyJGDAEgMNtxeBtwLgfQV6Gqqhrf/PlDh+cJfT98ACwIaalRKKU0S2v3r3H1MaZWcl0H38/IkDEJlpqAe48AJuHNoPV1BrcF+0mzJtGSbyfi7BKrQRUgeP5oQy13izeuIE7hieaosUBDLZ4bHY9lE6ey6gDmaLXYkZMjqfNHe9m9aFG36vErFQWAxG3slxYvP/MiBn/0DqszCB/vyHC3JyDoRfbUjZw1Bemfb4bJJmNNJpMhIMAfK1+89Wn1ZSA+JQnzlj+KgQnDsL08A+l7jiAr9UxLHcBbwR8ArHlrLbKyuEtycH0/Ij0VWKj2g1IuQ5WuAi/cvhi5GS1LeGKbsF3tt2obQGYetuzbTJicbH2d3PomlDQaEemlQKyP0LZu0p6qdBWWkkI2Lezsf0ZWbVqHlx98irWZn8sPn3yF+c+41kKOOn90bnx9d/kY1EHQxvSDqqAMcpvrTTIZ9NFhMIQEciZH9A8MtH9UC28/TBqeZA3+OlvdP1vNJtd6aXd1VAiauJWlsDI7Y9Enqj+Ckm/DoPde470vbco9qC+8ird++hLJc6e6ZSw3m434+HoN7/nfXMzCX194AxcuXLIeCwwMQE1NLSsotOUQkNXVA2mWgK2ouBSPPrqC9/UGDIvDys//gl6jhkPXbEKIhxxqDwWKcvJx/lga/rbsFRib2ZmeMrkct82c2CbFszlrAS6cB9Xbr6He5jqlDHiidwBUEvcokrZRpavAkmHTHWrv8f2MzHrwcZi27xN85sas/U59gKDOH10HV99dIR6GBgzffoi1F1Ab0w8X501Bs9Kbs8WbWG/gxLAwnH7CUj1h7qZNkgPS9ialfV13RAEgcatqfaXD0iJj+MZPHMvENDdDfzQVF5dYEkacfUMS87W2FvkNzazKKDIA/c1GXHrg/1iJIHFxMcjNFS5MKpPLMTBhGFZtXmcZp7YSuJALAEg9dQYvv/wW7732wS1XAMbH3d8XgDtYH3fmADxUwQ77c5QyYGVY56iN15MU5eTj3JFUQCbDT//YzJodtmf/M3LxXDaeve0Owec7+4GLOn90PbZ9d//vxx9xrKgIJoG3faWuCkpdFQwhgTCEBFqTI7gKJEvtDdwZ+v3yCfb2hv7FFzt6GB2CPtITt2KWFjdm7ceS155nnbv8zIvQ25WJ0R9NxeVnLP/44kbHuz3IuV/lixhv9k6HGG8PXHruZWRksAs9iwV/AGA2mZB75gKWDJtuSVypb5kr69evr+C9ttm1RTn5eGHuYpzee1TgjhbuLs3CZCrbBn/Bk8bBM0TFuTnbYAY09U1uHUNPI7XALmD5cPCH2Qvx6PAZ+ODJl/HBb18SDP4Ax5+R4SOHYqhILT5nMr6ldP4gnU+cWo3b4+IQp1bjh4cfxqyYGNb56VFRGBcRYf2zISQQuoERMIRYlneFkiMGqdVIDA0VfP08na5D6v4lhoVBISHRpKKhoUcVf7ZFewBJm4iIi8a0B+/CxtdbEiKaq6pxccnT8InqD2VUJAyFRawkjOc/XeP2cSjlMixW+0HbbLQuu9blX8G//mvJUlZG94fPAMexSJGx7xjWvPo+/u+PyxGir0RkRD8kJSUgI+M8awmZKdYbERft1KyfLXeUZrFNHOCqBRgweoTw/Y1G2g/oAq6/c6F+u2m7D2Hds6+h1Mmgn+tn5O2fvsQjQ6c5BG62P5NSSen8QfsBOzeVUomdixaxZgWZpU/bYwAczvP5+113Yexnn/GeHxgSIlabvk14KRSYEhUlKdmkJxV/tkUBIGkzkYNiEJ+SxNqwDgD1hVdZwZZcIUfijIkYfNvINhuL2qOlzEpe/lV4BAVi8Lq3WQkZuoPHcPmZF9FcJVwSAYD1fp+pKdgMAEEqRGh1+MOffo+/vPFXpKe3vFky2bVFOflYs2gFNOeynBp7XOKIVs2McgcgSQ7XVZ8573DMVqSX9DI1pAVX270Lx9LwyNBprLZzJZorWDbuHtRUVDn9GvEpYzh/RgJUQfgq+wBWzVuKC8daipm7kvFNnT+6jzi12iHgsT8mNSAaEx7OmXBiX1cvVqWCRs89g9wW0kpKMDMmBhMjI3GsqEgwCO1JxZ9t0R5A0qaq9ZWcMxC2pLQbcqeinHy8n35JdD+iEL79jKasy3hhRCwq6+qs5VkC1SqXZv0AQOHpgW0l6a363qy8cwlO7z0Ks92spH9wIGoqqmgPYBsqysnHo8Nn8J6PTxmDtQe/R5WuAvdHjIGxybFskZhAtUpS/2J3tLyjPYCEC1fCyZzYWGyZPx9moNNm/wrtb+wJKAAkba5aX+kwAxGfMgbzli/BwIRhbVp4+GazEXqbjFvmmFB2MJORzEcZ3R9JB3/kPV//5vt459OWoqdcyRZS+AcH4u+nfm5VdmX2qbNYnjKP93x8yhjW30vSgl9B9c6fKQvYTVJ3HsTLdwuXWtmYtR+rFz0rusePy4BhcVh78N/t9uGprKCIt/MHZQETrqXltsz+VchkiA4ORp6LM4uJYWHYu3gxVEqlm0fWNdASMGlzAaogrD34vVtmIKTiawF3v8pXtD6gMipSMAAUa+vWdO8d1nqGXG3hhMQljkDSrIkYNWW8W/ZTrV3+quD5hS8uQ3jsAIe/F019E4qoDmCribXdA4DMw6kuBX8A8MbWf7Rb8Ae0dP5wqIVJerwcrRYau+BPaks6VxnNZpeDPwD4dv78Hhv8ARQAknbkatFiV/C1gPtOVwuxkp+GQnbbNvtEEbG2bgEjhyPnUhYi4qI5ky3syRVyxI60KS3jJkU5+aJ9YZmgz/51Y308KfBzA759sO7QFlnzUlHnD8LgKvDMLP92RPYvg1neBSC6P7GnogCQdHm2tdISJiXDJ7o/bwu4wkYj+AoDMHsAmdk/oUSRmku58B8SxzsmZZRlllDKDFDijIltsgdSLPjsyACiJ1m9/TPOfbBMAeeRk8a69Nzn17s/a54QZy3ctg1789kltPbm52PBtm1Yd/vtHTQqdvka+/2JPbHvLxcKAJ1A7bHanu2ePTPgsH/PVpWuAm88vAxnDrBbXt327OPw/f1y3tfg24mivKnFyWdaCoIOXvc2VBOTWdeoJibjtoM/wkutEvw6BvUPA2CZAUqaPdlhD6BMLsPAhOFun/WzxQSffKVu7MvuSOlBTJzHl4l728yWwD9p9mSc3nMEkrZky2RImjUJg5PaLmueECn4lniNZjN2aTSQAU61pHOXewYNwl9mz7Yu7/KVvunpKAlEAm2zEZ/dqIHB5jtFG+Pdi2vPnq0wDznuClYi3KvlM8vKO5dw7q8TS9LgU/3u33B2/b9a9Qyz0YiwpkY8GROKnNIryL9WglBlEDY9u8YhUB2SPAoPPPe4NRHG3QFYncmENbtOwDMh3npMd/AYcp57GQljEqytw7jKxMSNjsfz69dQkOFmfPtghTro2GvvrHlC+Ii1gtuxcCHGRUQ41ZLOnebExuLNadNws66uXQK/HK0W60+dwvnr1zGyb18sGzOmUwebFABK8G5ZJSv4Y1BpDPfhatnGhUnkuJlXIFheY/jGT6CeMgFmCZXgGbbZv6qpKYjf+Inkexm6g8dw4fkXUZyixhWPWuvxAc1+CNtdBI8G7kzgQLWKtUTo6ps8M4Oqz8rBf0q18E6Ih0zRMntqbm6G6VIOXhgzzPrsFVMfwMUTGawyMa0dB3HN5dPn8OFTr7D2bibNnozfvPF7VNzQ0ews6VROlZQgWaAItG2PXWYGrq6pCff/+9/tNUQWZm+ilMQPrqQWvuvOlpfjrSNHkHntmsP5UaGheGniRIwODe10wSAFgOAuFcLIrW/CZl0d772LQ3xpObiVxMqy2Iv19sCgtDTB8hoegQG4+9jPuBkYxLrPZDajsNHo0Bs4uKYGR55eaV0mdWUGMOeFP+Pad//FxckhqOjlwVrOk8lkCL7ZjOGHpW2KZjo1MLN0YsRmUO09UFeByJAgvHrfUuEEBZkM8ROSsPbg95KeS9yjPTPmCXHV9I0bcaCwkPPcnNhYzvp6GzIy8MRPP7XxyLhJqfsnlNRiGzhyXSfGmQC0PfToAFCoVIhSLsPNZiMOVdXjfD3/m2qKnxdmBXWOv8yuSizI5nJ/rR4rhk0TvGZj1n4oo/tbW8CpPRQwmMzYqq9j/Z0rZWDN8DKJHoM/eseh2LOQtCn3QH+zBKdn8VeVv223DsralllAg78CBj8FlDVG1nHbr0FKACB1BpVxYcnTMJ7PQpWuAjCbRVviCbUuI4T0PDlaLQZ//DHv+bTHH0dSeLj1z6dKSvCb7zcjq+QKzF5+gLefy689LiICz48bh9GhoTh69Sq+PneONxDlHLvNzOSuvDyklpRgfEQEZsXG8ga106OisG/JEuufXalv2NkKT/foJJB/6+pQ0Mh+09U0NGOLrhaeMpmk2ZT8Rucr9xM2V/ZRekeGI2n2ZN49U0mzJ1sDJ9tZ3VqTCeP8vDDBzwsmAIerG1DcxP4ZUE1MxuCP3rEGgbZZwObqGsBX6bCsymQPm0aGA2jkHXfomt+j9k+fwmCoweUxAajo6209F3ytAUPSquHR1PILpURzRTQAvNlslDzzxzAUFqFeq5fcEu/iidNYvehZyTOShJDu7ZBIwPX1uXMI8vGB2tcXD2zZjMNHtkNeewPMepnJrzeaI0cDCudW0OQAgry9MTMmxuUOI3k6HeQyGZI/+wxag8F6PNjbGxUNDZz37C8sRK5Wizi12uX6hkxyDPOcjtZjA8CbzUaH4I9xlec4l7ImEzT1TbQM3Aq9PBSI9fZwagYrxEOOVZvW4fWHluHMgeOsc6OmjWf1OU3bfQgXzmaj7t47cSMg0Ho80lOBoibHv2uZhwdCpqbAI0SFi0uehk9UfwQlJwIA6i7lIn7Ny/AYMdx6vf5oKi7fyh4O7tUPQCHvuMPHT4TsowH47h8voaK3F+tcRW8vXBoTgPjjLb1gw2MHiH4vxApb26s+d9E6y8eX6Tz4o3dYLfHMJhPSdx+2FrgmhPQstY2lMDRdg9IzFH5eYYDI/up1p05h3alTUCuVqMw+BFntTdZ5We1NeBSdQXOUc2WQTAB2aTT41bff4niRcE1WPgNDQjBmwwbo6+tZx/mCP8ahwkLEqdWtrm+Yp9NRANiRrjg5YyLka10da+mYOO9+la/D0iwXGYAYbw/LrJ4qCH/ZvRnFuQXIPJwKyICEScnWAKVEcwXLU+ahSqu39O5V+rJqAHIFf7aUUZHwCPTHwDWvIGBkS8BXfe4i5uquY+eGLSg4lw25hwIeISo0V1XDo+Aaoh9OQmEOO6lCJpcjalAi1KH9oZXLWTN/VnIZKvp6w+CngF89kDgjRVKw5ewMau5LljZ1yuj+rJk/61hvBcA+Uf0dloOlzEgSQrqPJmMNzpevRWZ5NkprvBDu34iRoUMxIWKxpPu1FTfgVXvD4bgMZshqbwANtS4tBx+5Kl5kXwZ22S9mCTZfr3cI/qS4VmtJ7IsN4d/mI8XAVt7vLj02AHS3/IZmbNXXYbHa9X0NPZlSLsNitR+0zUbrnr16kxk/VRhQbjPDFXMr0LbF12GECf74Ah0x/X/3FAJtSqgwAkYOxzEAfV5cgQCOvYNTYmYAZjMKLp+2nosalIh7H3kZAFBxs1TwdQ3+CkxKGceaxRTCN4NqNpshs/mUzixV117IBiDe0o6rJZ6UGUlCSPdxtPBveGFfJdLLWgrfJ4VV4P2ZX2F6dDQOFBQIrtzIGoX3d8saa2FuxX5ALq9PnYo7Bg7EqgMHOAtAv3bwoEvP7etnGecgtdql+oadrQNJjw0AAxXurd9nhmX/oLbZyFm0mEij9lCwvn9P9glgBYVSv7dpuw9Zy6qIBTp8AuKHCZ63Lw2knjIBdx/7GbcrTBh75xxkll6B/kYpVL37IaR3hPU6yzIxv3X//hKTxk9waqxcM6hNugpWweqKY6esS9UARFva2bfEi08ZQ7N/PVDa7kPIPpVJfX97oNrGUrywrwIZ5f6s4xnlfvjj3gpsf2gRHv9JLrgfzuzly3vOct654E8hk2GIWo2LN2/yXtM/MBBJ4eG8BaD7+AqPic+UqCjrf2+ZP9+hvuGk/v0R36sXcnQ6DFKrceHGDdZMZWfrQNJjA8C2cr6uCSN8QUGgG9kHhVJkn8q0/rdYoMNH5uSHBLNMhpuBQQju44//81DgC9kAFPWOcCg5MzYyCgUJ47D//CkYbZaJFXI5po8Y63TwBzjOoDYWl6KmpBD+Shm8IvrBu7YOn2z6lpXYYSi4Ct3BYw6ZzvYt8Rjzli8B6Tlst1AwAtUqrD/xA8KiXftQRbqWC9c0SC8LcDhuMsuQXhaA7Bv5HHfZ8faHya83ZLU3IbP5bWiGDGa/XqLLv2qlkpWoMTMmBqNDQwUDwKtVLfuo49Rqhxm3B+Pj8aqLs4AMlVIpqcNIZ+5A0mPLwDhbe85ZtCewY6XtPoTXl/8JgWMtyRt959+NwNsSWIGODICPDKg387eHc8WvQ3wR5+PJWXKG+bmor6vG4rWrsCfzpPXcrIRx+HrFaqj8A7ke6xbFuQVY/etnkZeZBbPJBI/AAIdMZ64sYEB6SZquJOfiSVzJvYioQSMQN8y1nrzd1bzQRIf+xYAlCNxentEBIyLtbXt2Ku77fifv+VF91Th/XSe+DGpsgkfRGcht9gJKyQLecPfdeDwx0SGI2pWXh7mbN/Pet3vRIsyKjRUc0pQvv8RhCfsIbe1YuBC3x/H3gO9qemwACABvllZCer6vc5hkBdoT2P7qTCbOEj+NFZXwCmYXhr4rSImfK9m1IPmyg6V6po8/a8ZSaAk7t+wqNOXFiA2NQFxYf5df0xlcbcd8ovpDGcVdB9DZotRdge5GCV55+GHkHC62Hhs0OQJrvvsWIb3CBe50TVfrs5y2+xBevPNR3vPv7viKloPbmNROFG09BqFaf05rqLXs+ZNYB/DZsWNx16BBnMFcr/feY80MMtRKJW6+8ILos/UGg9Mt6mzrB3YHPTYAbOsZQIZ9MEDa3tfaWt5s4ubKKsz19cDQ8D5QeyisXWDksJQXYII0ZwsrA+4P+tv6DYDpNhHcKwRfvPZXwZqK3a0d3FMzUpB7rBRmmzhfpgDiUvrh033H3PY6XH2Wu8L386vV67Dx9Q95zy957Xk8surZdhxRzyG1E0V7mfP1RuwtKITJ5pehXAYk9O2DM+XX22UMaqUSaUuXIlrVsqe5QK/HmA0bHOr4fTBnDib27y/6O5MpAO0ll+Ol/fsFr+1sBZzdpccGgIeqDDhQw1+w112Y5UDSPqQE9v29FHg4xFewCwzX8q03ANsqUfYdRNy17N9RbwC27ccAdNtWZDkXT+KpUQt4z/898zu3LQevvHMJMvYdg8nYEml2hRlVmgHsOLO++hwHCktYy6pMALLu9tvbfVaQa6ZsTmws3pw2DWMF+gC7G9/M3h6NBvsKCrAvPx/pZWWsMXL9ztTodA4FoMV0thZu7tJjA8AdFXU4VdfU5q9DM4DtS2pbud4ectywK6DMNYNnv3xr/+e8+iYUNxoR6aVwWzFwrhZD3fUTaEf4ZduH+OBh/hI7v/t2Be6c/1yrX6coJx+PDp/Be76z76mc7RsHY5PjTLrC0wO763I7YETdW5OxBj9f/hvu+7e07SftEZTYrkIAcEhm4GuHZl9/Tyqx+/j29s346iuHcjQyANOio7HvkUdY1/ItHQvpbku/DPfWQulC2npWTgbLjBAFf+1LalFk++APYJfyYag9FIjz8bT+PTJ/Vspl+Fpbi026OhysacDXujp8ra2FwST9197NZiNy65tYr8e0GLL/hWrbQoi0Tt+o3sLno/u45XVK84U3mJdorrjldVyVtvsQvlq9Dul7jjicK8rJ5wz+AMDY1Izi3IK2Hl6Pc758LbJuSP++7s3Px4Jt29pkLDqDAXM3bcLgjz/GHd98g0Eff4xn/vc/jIuIYAVCW+bPx8yYGNa906OiMC3atQ82Yr89TxQXOxzL0Wqxn6MWoRnA/oIC1u/MXXl5Tgd/gCXw7Y56bBmYOB9PhyU8d+IqWEzaHlMU2dneuLZ0zSbRwH2b3oB8u9eQWgy8zmTCt7o6VstBZvlYrMVQZ2kh1JUNHTEd0Snvo/BklcMewKhxgRgaP80tr9MvRjipp6OKaksp75KXmSX6jM48e9nZMW3VYN19LIe2LhP9/L1E7mzhjr6ynPuM6+qx8LvvsLeI/QGGCThtVyGESqHkarV4eOtWnCkvd1uVhfEREQ7HDl0R/iB16MoV65hSS0pcet3O0rnD3XpsAAgAT/QOwD9vVIsGgTIACgDuax5HXHWz2Wht4xfFM8N6v8oXW3S1TvV0tmU/h2ifwXmz2cgZYEopBn7R0IjteoPDzxITPCaL/KLprr+I2pOfVz888sld+Orpn1FwrKVeWNS4QDzyyV2WPqduEDkoBqOnjceZAycczo2eNqHDAij74A8AqrR6LBt/r7W8y38/Ed6fSB1hXMO0VdPWZXKejwhsRKB3M6oaFACk7SV25UPhqZISPPXLL8iw3TMXE4MtQ0fjxo2b2HXVMagSCji5au3FqdXY+8gjmPX11zht8zquUiuV3KVdxHax2ZxPDhfO8OdrHdddP3T36ABQ5SHHyrAgZNQ24MdK/r6AZjgf/FFrOPeqM5nwva4OhXZBXbSXAg+G+LESL5RyGTxFGpULYRaHmQzOi7mF8BlgKZESPygKv94o3KKNawZR22zEZzdqeD9sMMHjHX2COVsMKWQyTK814sSnm1FJHRlabfyQl+C/2Re5WanQX62Hqr8P4oYlY0ToCre+Dt9bk9mtlSels+2QY69Kq0f6niPoOyAcF46l8z6DOsK4zhL8nec9X1zlhaoG596WezvR1YIrwYyxt6AAC/TVWBERJfgMZwJOlVKJN6dNwx3ffCPpeoVMhgmRkci6cYO1VMtkAXOx7c4hdn7OwIEOhaWtY/XxwdjwcM7Wcd1Vjw4AGYl+3thTVe/W5WBqDede2/QGh+APAAoajQ6BNt8MnVQht/YRrvntS6hf9DCSprR05tAfOo5v/vQ+fFb9kff+z596Ca988CqrzIdQ8GdL12xyaDHko6vChK/+B2ONAcy8DHVkaB1PhT8Sw1/B4N5lMDSVQ+kZCj+vMLe2PSvKycdZjtk/ADh74ASKcwvaPZCy7ZDDJSv1DIxG4Zlz6gjjmtrGUt6ZP0ZpjfQlYMaqAwckJ4ct3LYNe3jq3hnNZuzS38DvIqMEn+HsKkSsE9czAZdKqcQejQY/5eSgr68vHoyPZ5WAsTVIrcb0qCjsLyx0ODc9KsohWE1butShfIxtmZnO3LnD3SgAhCVgaKu9gFL2kxFhYgGdfaCt50jwkILJAlZ7KFCUkw/DvLuhSmGXAwlOGQu92YyBzY0o8fBizeMwLdSyt/wXq6/fsJb5yK1vkvzzFeIhh8qDva/m05QHUFvD/sRqv2RHXOPnFQY/rzCUaK5gUYql84Uyuj98Dp2C4uX38Lfv17scZEtJAmnvAHDo2ATB88OSR6PvAOFlstrK6lYFr0U5+SjNzkN4v1BEDB8E+Pq49JyuxrLnT1g/f+dLk+3SaJB1owjDerf8nHLt7WMSzMQYzcAcVW/s1d+EEexVCFeWQwep1byrGjNjYvDR7bc7BFw6gwF/PXHCOt5VBw8KZj1vffBBzlI1XLN30SoVbr7wAvZoNDhRXIzxERGspWWu5ezuigJAuB4wSBEiMSuV8JPy9+OOQDvCU2FN3MktvsZqj8aQeXggZGoKInMuwRwTgxKPlk/s+qOpuPzMizAZjUjffdj6JlkicS9ify92z+M4tRoVpy+gVl/JeT2zZEfLwa23PGUe6pqNGL7xE9bf+/vHT2HNgAhJtR3t94p2xiSQMbOnIFCt4m3xxvwsJc2e7FC/kPHBky8BAOIS4/HwH5/EwIRhkoLBquJyvPrgU7iQdtZ6bNjoeLz1wZ8RkJwAeHbvtyOlZ1/RayICGzEtqg8OX7kh3l7Nxj7NvzCs96vQGQy499tvcdSmxRkTCIklmDEGKv2wZdhoLMg6g136ltZtrVkOtV/VsH2eSql0CLgWbtuGvfnsPsNcSSgMqX15bc2KjRVtF9fdde9/cRJJLR3irN4K0OyfG0j5+6kymlq93D45wNv6Ru8zwDHbzFZdVH/E5uXhv799kbeFGjPDE+4lPialDFgQ4rhfVMqSHQWAzrFf5mX2xQ3f+AlUE5NZ1waMTcQXucV4ejD/LKBQtw+uQIopBN1R++jWn/gBy8bfy5kFzFi1aZ1Du0B7uRkX8OaC5QBEups0NaPq5Fks+dVvUGXXX/rCmQuY9+Qf8O9/fQRVUnwrv7LOzc+rHxQyXxjN/HVKg32GYNuDjzrdoizA8zLOX9uJqRvPQmdoYJ1jAqd1t98u+AwFZJip6oU4X8vvoZ0JY5FbV4s8hRkDbxvRqlkxZwI0vplKKVnPPWn2zh0oAERL6RBnW3+J0bVVo+EeRkppl59uJfHEentgWoC3S69jO1s7aEA/7BboKJLp5QsMGYbY11fi8jMvotnujQ1omeERKzkU6anAQrUf5yyTlCU7Ig1f+ZNZv54HZXR/3hnfGwGBgh8u1ixegQy79nEZ+45h9aJnOQOpxBkpWLVJOJGoLYVFR2J7eQbS9xxBVuoZzv2OAaogvPvLRhTnFiDzcKp11o8P8/VydjfJzserz77qEPwBlox78+VCPPzDD9g1bGC3Xg6ubSwVDP4AIFg5lBUs/eq775B144bAHWYEeBkRHtiIhdv2QWfwd7iCCZxkAOdSLGNmTDS2DB0N1LYkRMaFhyFuaEyrZ2dtl6Rvj4sTvFZspvJgYSEOFhZCJpNhyoABFPC1AgWAt9yv8nVo/RXlpYAMlkQDVxgBaOqbnOoQwfSmZTpNEIv7Vb74TlfLmQhiK7+hGU1mM8I85CiTuLRvu/eP0ctDgXAZUCLyiUA1aRyGfPwuLjyyzHqMa4aHq+SQN4CHQnwRI/DzIXXJjojjK3+y48vv4HPbKMF7+bYYFOXkc86SMdsAKm/qrIFUZ2utlzRrkujPT0RctKSC1fbbHqzq6lF0/hIuXLgkeH96YSFyy8oRFxslZehdkt4gXFsRAAr121FSuRdjI9+CGR4iwR8AyFDd6IHTpX64cMMx+LOVp9NxLsUmhobiH3fdhSSmREpdPWBoAJTerQ7IXWlrKZY08sTPP7P+PD06GlsfeKDbtWlrDxQA3qKUy7BY7efQ6guwlPAoazTiVF0jq7ZcpKcCRU3CAUlRo1FSAFhnMnH2pp0W4A2DydzjA0KlXIZHe/lD22xEYUMzaoxmHKhpcLjODDhd/4+vaPf4ICW2VghXjZcpFFBNmQCfqP7WJWCuGR6m5JCmvglFTraOk7JkR4QJlT8xVNfCRys868C3l1dqokddSCB0CEdISKC0AXciYnsZbTkkthgaUFoqnvxgCAlEnqEWwnNDPUOTqRp781bh9aNjHE821EDWWAezlx/g3bJlJLVUOPgDLNm7kpZifX3cNhN7//ff44Bddu4ujQb3f/899i3hziYfpPTDnP4DsLfoqqR9kAcKCnj3BhJhFADaUXsoHAIt5li8r5dDgLj+WhWuG/l/SCMl7P8CuDtLaBqaHQLC+1W+kjakd1fM38XpWsfgzxk+MmC+ylcwsA6V+HcHAKt2bYY8Jw/+A6PhFdEPjTwBQ6yPp9M9g6Us2RFhYnspp00bD83xUwgYmwiZR8uvRZnZjBibVoC2dAYD/nThrOBzz5ka8YcvvsDRoiLrsa7WWD5yUAziU5IEawMyHBJblN7o1084+UEf2QeGkEAMDAttzTA7PZVymORr3zwahLPlNsWTmxvhUXwW8tqWGUGTX280R44GFJ44Xhwg+LxJ/fuzAr322CuXo9U6BH+M/YWFjnv5mpqB7HxAX4UtkYOxoLqOlYTCxwy0uiNKT0Upqk6y7w37WO8A3m+iUgZJb/ZMmROxzzqahmb880Y1Ttc2QNts5Owl293VmUz4Wltr3fPnCqUM+G3vANbfY2tFRoQiZ8wY/Ns3GJt1dfjoeo3TvYHFJM2ahEdWPUvBnwvE9lImz52KNffNRB8De49WjI8nd0vHunos/PY77KurgjamH0z2hcflMmhj+uGJtBOs4A9o2x6ubWX19s8QqOauwwZYtj0kzZ7suLzt64PIEUOQlJQAudzxN2Wj0htZD83EnG7cbYHh59UPKqV4oktxlRfSywJg+6vDo/gsZLU3WdfJam/Co+gMAOB6nRcCvZsh43gXUSv/n70zD4+qvtv+Z2ay75MJS0LCkhCQJSIxLIooi4BVq6+KdamA2sc+dUF8qtYNaxdQsa0VXKutG7ZqK7W2apVNkEWWEJZAwIQEshIgmawz2WZ5/ziZyZyZc87MJJP9fK7LS3LO7yyTmczc813ubzif3Xpr127eT/Krq3ll3z7FNdvdxWG7+APQBwfz1ZTp5E+fw5cZ03hrXIbXaw7Ueb3diRoB7CLhWg3Lh0bzxvkGWlz+9sI1Qt2XL/hjQ1NjtUuKn8ESHZSKlPrC3KgQ7Gj8Sr36+rzodRq+aWjp9GzgwU5BcxvlfqbF/UWpllIXHMT4rAsJ12q4f3yKZBmIk/YoRX55hXNc1rEbrmDSp9sxFFU4l1WPTuTYDVdI3ksgZrj2NNH6WN4//g0rb7iHo7v2e+x3lD3s37idvf/dRtwQA3NuvkYQhBNSWfm7p1n16G/Jzu6IxNakDCX3R/OZP2nCgJ624MqUxJ8rjoIrqw9hW7FbmUBLoyjy50CDHY3pPLSYIDSS+pYgJg9pFNUCXpaSwr9vu63Hos3GpiZu+cff2HyqzPti1y9N5man+HMlPSKS9IhI8s3yDXkO1DGZ/qOx2/0wG1JRpDP1XSBEAF9R6Dj1BUcjw0AWG135PS2Jj/BbXPh6vda//p2QH/9Idv/yoVGDun5TDqnxeI4vTt1hzXTmVCnLJs3D2iYW6lqdlsz5l7F4xd3ep4Acyac09wT/PvE9j9ZX0uRS06cvKiem7Dz1yUOpSU3yej9f3n67147IvoijoUWn02G1WhmRNgqNVsv9l15Pg1HsWZlx2XR++883BXsYczNlx/Ipr6ikLWkIDXFRg2LaghSmVmECTW7lOiw2E/UtOp7fPYLsM55BA03DOYJLPEW3g7aR07BHDwVg1Zxi4sLGcN48k1kjU3vU567N2sgV7/6BveV2bHbvgYj8Bx7oeO6r6+BogeL6qw7vk0wJa4CFaWlqDWAnUCOAAaQz9V0gdJyODNH53bzgymAYPdcVw+5aq//HerMHstvttBlrKdi8g0kKAlCdBiON1Hi8Jju8eb6BxxIl/OS6iKWtzUP8AdisNrI3fivq5pUatVdfVsnq//2FM4p1CVCdmsT3V81k/Fd7RBHAmpShlE2bQONwg0gkutJfIxbJ6WM8Ur03DM/0EH8AuTv3ddjDRISRPO1CkrnQr+tVmQ5R13yS2LB0EiKVU/n9BccEmhkpz7Ov9Eme360np1K6kcMeojzr1x7S8aV/w4mLOVhZBewGdvdovenn37/Ed2UgSDJ5NMDcMWPEwj/cu3XXhxOnsvjoAbbWVYu2z40z8OG8Bf7fsIoqAPsK0yNCKGlV7jj1hb8bzdyZEDUgU8HdZdjtwH2SA0jbAzloM9Zy6Lrb0QYri351GoyYowWnOVzfRFOidJSsye6/fZIveOvYdUVq1N7qux4mJydXtC7+1Bmy3vmCoGbxCC996Tn0pecAQSQeu+EKLO0fcp0dqRUoAjnv2HE+uQ5rQNoexgfMrZXsK32KNluHf2CwNprpKc8SEeJ9qkZ/ICJkGElxL5B95hX5RaFR2CKHoDFViWr87GiwRyaIuoEF8deB0vSMQGJqrSDvfBHgfbrNQqkRbRFhoI+RTAMTpAOLFX1wMFumzqTAbGJ7rSACr4gzCMbVXQieDGZUAdhH8KfjVImzFlu/rzuTqwnrimH36FD5l7rSJIdofazTHui7vYf54OV3CRsxnPoDR6jbtde53rhtF4YrLsXuUtci5S/oL1KitL9ytrqWdfuOETxlMkTKNxSA7/ZJ/uCPnQmIR+2V5heRvW23xxqN3U5Ik3JHuv7UGSZ9up3Dty8EIC4sjNevucavewkEckbY7pFOf/HWYe24tr+vX3fxB4JFyr7SJ5mT9he/ztWX8WVEmyVlKkGlB4Wav3bskQlCF7AC7vWmX588yd7yco/5t12lqe2s1znGb117LVeMHi3/xWdCqqgRBBBE4YRUqKqBfKHm1lEXKCKm/37e9SaqAOwjBCIN7KC/poJ9qQlTisjJMTrE09rHFaVJDo7JBoYgHVOGxPHHDf+RPMf3yx/n5oNbKHOZDSznL+gL9cZanr7xHpH1huK4rT5OvbGWNd/sJ3p6pk/rfbVP8oeUcamKM26lcIza8yd66I7WbsdQVEG4sZ6m+Bhqm5u594sverxmSc4I2z3SKYfclxFvHdbg/9zjKtMhD/HnoM3WQJXp8IBJB3szPgZAF4xl9HRmDzPQ0GDkSE0jtlDfRc+O4mIu+ctfqG7qyDIZwsPZf889jNErfxnzhfDgYSTHtJKV2EBOZZSoBlCrsTNvdAr/c/HFyicJDoILx0kbUScOgaIykHK8CNJBfP97T+wLqLmpPoDD2iQQ4s+BsQv1cr3Fm+fka8IcOAy7lw+N4sfxESyJj2CYQoo1LTSIWyRm7DpwTHJwFwSukw0cOASEVicWJ1qdjotmTuV/Rg5x3tfyoVEskRnv5o16Yy3LJs7z8F07sHknq+540O/z9QWeuW8lMZdOF3nsyeGrfVJnWPnBOtKmTPB5vWPUnr/RQynCjUJkwzUq01MopWkdkU456o21rJhzM3dOms+TP7yLZRPn8dg1y2ioEWr+HB3Wckjaw3ihrvmkl/3KDQP9iXEGA4vS0tC5WQnpNBoWpaWR/8ADfHn77eQ/8ADf/uwBtj7wKBeN8i969/DGjSLxB1Dd1MS0t97q8v2DYHFjiJjCE7MqyBwubpybMULD32++3feTRYSBIdbTjDpzgiD2XAnSCdtVOoUqAPsAnbU2UaI/1Z2ZbTb+dK4BuUSaoybMFYcfY1pYMHcmRJHmluJNDNJyT0KkVxHmyyQHV1Z+sI7M+eKZsa6TP9x9IjvDk9fdLflhbbfZPERpf6A0v4jS6lqf1vpjn+Qv5YXFLJ0wl4Kco85tUXExRMVJN2lEREcxfHQyALkS1if+4t4M0pO+Zd7StHl7D0pul/sy4oiQO3jtu8+IlojCZFw2vVNzj2PDxnrZL909bWqtoMp0EFPrGcn9vU1pfhF7v9pGQd5e0X1+eNNNXJmaKlp7ZWoqH950E+kGAz9IT3emTvXh4Xy4eLFP19NpNFycmEhti/S7a3VTE5tcxrR1hYzhKxgdN4ln55bwzrUFrJpTzKc369h+58OBaUIJD4NZUyEjHUYlCf+fNVXYrtIp1BRwL+MwgQ4kaV2sO+tJqixWNhjNXuf2KtWEKY3x84a3yI576ipaH9uts11L84s4LvNh7KAz9VS9SUVRCc3FpV7XXRgWxI0K0dquIpUCbaytJyouRtIj0NzQyLKJ89AFB0l2D/uKTaOhZkyihwDsyS5gb2laR6TTnadvvEfyy4gjQu6okUwck8K/zh4ie9MO9vx3K3EJLj6AnSAh8iKCtdGSaeBgbbRH+rfN2ujhr2eImELG8BUE67yPSetupOqMx8yK4frfpzEi8WIyhq/wPqLNBUfUcHNRkeK4tCtTU5k6fDgHzsgL4u/KygJSDxisiyJzxFNOi5vw4OFEhiR2+bwexMeqKd8AoQrAXqYr1iZSjAnRdbruLJBUWawUtwvb0RKCVGr2sRLhPmRSpcb4eSNlXCpZ8y8jZ9t3ojSwVqcjc/4s2Q8wKSuMQHBkx16va/ytp+ptklJH0nSqhPrDR4mZIj8JYUpEiOy+rqKUAm2srWfNl+8DsPaBp6k4VQIuH6r+iD8bYAkNJqSlI2JdOyaJYzdc7vy5N7qAlYywYwx6yW7g0vwir+PfHrt6KVPnXsozH79GtD6WrAWzAzapZnrKs+wrfVKyC9idw2depKbpqGhbtfkwh8+8SFbyLwNyP11Bqs749J56PnukkFveCiG3ci2ZI57ya0TbhzfdxG0bNvC1SwRvUVoaq+bO5bzZ7BSRX588yfO7dsme55Lk5M49KBkcFjcqfR9VAHqhu6cUBMLaZIhOw8WRIQEdbdZZzDYb/zCaOeVWzzg6RMct8ZGYbDZqLDZ2NLZQ6kfN48GmNmZEBzjU3z7VYeUDP2FVXYNoSoFrWtcXqixWavyMPkqjrHRHTUjvV9E/6KidPLT0fmbs34w2RPx3ZLfbCcPebXV/4FsKdO6PrqWiqFhxnSvJo9pISmmjvCSY8hLh3rVASEsbB29fiMZm4/lpM9hAK4dcDGwdqb2e5rXvPuO+S66X7AKWwtfGl4Pf7BY1TAWKiJBhzEn7C1Wmw9Q1F8j6AJpaK6hpOorxdDM1Jc3oR4YRP1p4r6hpOoqp9UyvChJHnbE7diuc2lWP8bQZRh/2+z714eE+RQ0XjR2LITzcowYQhEaQnjSLVulbqAJQhp6aUtAVa5PhQVpujo/osugLnHgR6hndxR/A6VYr687WezR5+EqlxRb4zuZjhVDXQHR0FGueX0lZ2RnKKyoZMT6N5Ovm+XQKqUhmV8byXTh7uuL+x97+vd/n7Aus/GAdq+54kOy51zH1i48IjutI4bQZazm+9F44+N9uu74vKVBfBU90jJUn15xn+mUdIxn37Qxj9WNDaKwXXp8amw3j2GTmpKdzz4hECkYYfErtdSeJY1L4tDKH7E07yNt70KsPoD+NL67p4ECTEDlFseP3dOlOPr7ne07t6rAPcaRXw2KDqGnK61UB6O11VVPSTPzoMJraKjt1n75EDfffcw/T3npLsgtYZfCijoKTYc2ZOkmxEq4h4FMKmmx2v61NuiIyHIKv1W5nR0MLlS5p6K6et6sj7ZT4cXwE6YGKEpmbYf9R+f3TJnt2oUnwVsl5KnTBkv5/nfFirDfWckf65ZjqPWufLpp7CX/Y+De/z9lX2L9xO49fcydNUTpCFl5K0sXTseXkc77dWmfNl+93i4BwcMPwTNkU6KeVOZTmF3HnpPkAhI8ZSdioFJpOl9J8WvwB/tzrlVw8sxmdy9dnqwUO7AnjiXuHA7DvvhuZlZbGV3OuFHzMgnv+u3Z+dTWFnRCdptYKmtrOcnz7ad78v7VUnCrG12+nvWFV9L/zplO4+zx2l++dGh2MnhnDLW+NZ8LQ/yU5dn6P3Y87rq8rKX76ZQbxo8O4dNTabheqmwoL+a6sLOA+gCr9EzUCKEFBc5tspKo7phQ4mhhONrfxgdEsu25JfAQ26HSkzpe6u8IWCx8bTdyZ4H/hdKDrGd0JaGdzrbTHmGi/ggCsN9by7COrCFv1tMe+zo7lc3RcSom/GIOeX338us/n6otk797HiUVJVEW0gf0EZJ9gzAVZzP3gZYrue9LpudddeEuBpoxLZdzlMwj+yVLi53R0ehu37eL75Y9jqW8gbVYC0y877XFuXRBMv6yZxFEWjupGMuuiDD68/v9BfFy3PR45jE1N3C5RG+Y6EkzK08/RSHHyxF7evzWPplr/banc/TOlCKS5ef6xPZzc4TkftiO92ox+1MQuXaOryPlPOkRq/OgIDBEZPRKlXJCWpgo/FSeqAJSg3EttWndMKQAYGxYsmQ52RJS6ek1f7WZOt1o7lW7trlFtgZioEWhWL1lBkcXOJIU1ueY2MiLw+b7lOi5B8GqrqzL2SxNoEL58vN5aQHWUVeiUaOd0fg7fADdu+zdjz1XIHh8IfEmBjly1kuYR4g9i/WUzuOD132O3WJg+ywxNN8te4+JFo3jk5+u5KK336jRv37CBzUVFom2OkWB/v/paj27U9MzJZC2YTeTkUyRcXCMv/jR4jQS6+me6iztvE3c6Q8GJ7xT3m8sj+0RDgrMEwuWxj54ppKkNERlkDF/Ri3enMlhRBaAEI7xMIeiOKQUOpCZddGWihAN/7WZOt1j8FlyOesau2toMC9Jy1iWaGIjH70GcF685hf2Oou7wMco1UtsaW9jW2EJaaBCXVJ+jukjeNsaXjsuTh/P6XQOIg1dP5PP9yUMe2+02G6dOZNNoM3H8wgu5tAfuRa5TtcpipWVUikcbjiYoCP1lM7BbLNjt2xXP/X8vPAORvfcc5VdXiyJ/Dhzm00/dch8nduwT7SvIOer0RgyJ1NJqkonk+1EsJGVV5MvEHX8ZNnqI4v5Jkxd16ryBxt0+Kn5kBPpRwd1nlaKi4gOqAJQgPSyYcA2yNYDd2a3YFU87Jbo7PetgsT6CvxtNko0gvhKq1bB8aFRAH78HEWGCyJNKBcdFK6Z/HUXdTadKMG7bhf6yGaIJF3a7HY1LTeBJcwv7s49zbNn9gHTUw3FOpdqzT195jzmLe36GbFepslg5UqHsA1hzvoLSIcm9OsLQ29+IJigITZuXvyN7YD09/UVprmx4dR1525QjZrLiz43Y5BDqyuRnvzqsihzpXp1OK9kJqxQxdFBWt4Uacx76iMkkx84V7ZuQMY8xs37H6T31kjWAky7sGwLQQXfZR/VHPty0jT2Hcrk080JumX9Fb9/OoEQVgDL8dEg0b55vkOwC7gk642mnhL/p2dGhnXtphGs1LEuIotpi5bSLD+A/a8yUe/vwbMcxEi9gDR9yTEyTHz6ugGt35PfLH2f8y8+LasY0biOdNDod8XNmETZ6JM2nSySjHobUUUx671XZ2jOAo7v2K35Q9lVqLDbiEpIU1+iHCPuNFluvCMD8Y3s4UVoMF16luK5G6+V3H6I8waK7UZorG17jpe7VD+rKWknOjKTicBM2a8fftcM/M8ag57FrlkmKPimkIoZ1TYXsL1uJHeH9oLJxByfOvcn05GeJCRfWRoYksfTVa3n//s9FXcCjZ8aw9NVr1ehaH+RI4Sl+8sNlxBQIXwqPAr9PT+GdL9YzeUz/8jjt76gCUAZ9kJbHEmMpbG6jtBt9AHsKf9KzY0K6Lj7dBexN+kheOdeAr3HIHhECSsPHFXAt6rbUN3Bs2f2EjR7JkOsWMfrh+2WPCx+dQvPpEsmox3eGoehni+ug9JfNYPzLzzsjh9D/poCA8LdkGJrMmAuyOJ2fg93W8SrQaLWMHpdJ/BDBjLanRxgaz5fz1K23kv9tGQCT3otGP3smGtd5z3YbaIT7qtaN5aRuPqnWbWjpCDnZ7VqqbNM4VNiEJaig1+xe5CZE6DQasqZMwvLR5oBdK/OHGQxPCBGJvIkzp/KDu37E0zf+lLw9OT6fS8rc3FX8ObBjZV/Zk1yZ/qFz2yUXPEHUXyMoyNvr9AFMnzhDravro/zkh8uIOlkm2hZ1soy7rlnC/jzfvjCoBIb+MzC2l0gLC2ZOTFi/Fn8OFusjSAxWfsoTg7X8qBvGcX1R1+Sz+IMeFgJyw8cVcJ8J3Hy6hKr/fK14TNNpcRp068f/oazglLM+UyQ6EFKOjsihg/42BQQ6vnz8v6VPMnpcpmjf6HGZXL/0SUCIsPd09O+pW2+lYFfHh9H3yx+nduce0Zo4ax7BVUewW4QvT59E/IUi3RzRmo0no5i8ppVrn/kfrnv2Icat/SOLfv8SWz77usdnN8vNlf3o/v8la+HlaHWB+R2nDr+KNV+8x3t5W1n5t5eZPGsaR3dl89vbHuDorv2ijld/Kavb4iH+HNixUlb3jfNnxwiya6/4E4tvXcO1V/yJzBFP9YkRcCpiPty0jZiCUrRu7nNau52YglI+3qJcY6sSWFQfwEHIc2fqaJF41kM18ESAPQ5BsNX5q4K9jStd8dDrDRxF3X97/jXy9uQw4e11njWBFgs1O/eKInmuTP/5/xK64l7Zaxxddj91O/aQOX9WwKct9BSuXpfG82XUnK9APyTJGflzsHxoVI+JwPxje7j3otsk94WNHsmN71/OkCE1hNvPcb5Ey56zYnsY3Xd/5/i2NRxujeBkTceXBzsQVa9h6pZzzm294Y8nNSGioabOoxvVnej4WFKnjuHIN4ex2+Q/HtZ8+R5Wq40RaaN4+aFfedic+MPTH74iqm/NPfMylY07ZNcPj5pNRuLyTl1LpfdY8buXOfrki7L7Jz/7c9Y+qj6vPYUqAAchNRabbH1jIK1c/J33C10zou5NHB+sh/Yc9KgJNOWf5MxfP6Fm226Pxg6AiLFjuHjLp7Ln3n/FdUweN7rHBUR3cMDUwn/qmmX3d8Xse//G7Rzfd9jrhAsHm/71F56/eZXs/pvfSCft8jjnzx/f8z2VlXGEpQhNOvXNdey7RH5+8cUbjYSbBEGk0WqZdEkmtz9+f0D877pKWcEpcndl88ajq2is7aidc/giRsXFKArFaH0sDTV1AbufybOmsXbb3zvur24Lx8/9SXb9hKH3ejSEqPR9Pty0jT9ffZfs/p9+9a7aENKDqAJwENPd9Y1vVzU6GzqU6KzBdSANZX3F29g8R0QweGwqe2P0IjsbgJqdezhx76POxg4Hk957FcMVl4onitjtGBrq+X86a68LhkDhbVpMZyKA5Ue/54F5t1JfU+vc5hAyiWNSZI9TigBCx4QGB811Fj57pNDZbBD9wkP897u/yh4/cXcd8WelO2V7IyIoh5Iv4vcHjvDHe5+i4GDH1JwYg57G2voupXileC9vq+h1vrngNsk0sAadqAZQpX8xbeLlRJ0sE6WBbRoNjWOT1RrAHkYVgCrdgi9j4Tqb7nU1lHXYpoxNHs5TLz4d8A9Uh+AL12r4pqHF55m/66tNkpFPu81GzbffeaSDg2Ki+eGuz6mK6bj//hoN9cb6apOs2blfr4U2Cxwv4oa5i6mXmZ7yaaVyI8K982dTsKtMdoyYFKPbHqG00sqn8TG8+ax8NMM1AuiOo1u2v6T09xSWUlhnQt9s4g9XLO6Wazz7n3eYcdUc58/1TafYV/akSARq0Im6gDtLQXMb5QOgua8/cvRUMXdds8TZBQxQr3YB9wpqF7BKt3DU3OZ1TWcNnlcvWcGR7FwP25QX9ufx5JUzAyKYfB2b97dqEz8ZIi42VzLd1mi1IksYB5b6Bv6fzkp4d/sf9gGkzM4N9XVcqrOBPwLweBH7N30rKf5AmJ6SvWmHYjp49ccf8dQtHV3AAOmzkrlt3WxaKEI0tgQthogM0kdMh9Q2DEazbGdzQksY4SbPEWUOfPG/6wtUW6z8+XwjTeExEB4DwIycbzh03e20lJ0J6LVam8SlATHhY7gy/UPK6r6hxnxU0gfQX5yPp5vLX1TkmTxmFPvzvuXjLdvZnXNE9QHsRdRXvEq3YLYp9/xeGCZEe/wVa44pHOl/XIX+shmifdoLxvHXsiq/71UKX8fmlbZZebuqkSaXYnlfTLfDR3ekJrU6HVkLLyc5fQyGIB3pYcEDVvxBh9n5XSF2Wta+zv4rruPTjCv46cS5PHbNMt9qy8zNUFPP8eMnFZfl7T2ouD8+YQSvb9nBG4c/5vF/rOSNwx/z+pYdXHLBkxgiMkRrXUd2OcTC9TKdzROP+9bzXl5Y7NO63sJdLAEEx8dx0b//FvBr/epH90o+/8mxc8lIXB6Qmj+px9NkhzfPB84jUcU3bpl/BWsfXa6Kv15EjQCqdAvpYcHsU4gCTomQL56Xo95Yy+o7VhA+ZqQo8udAExREGXR5moS/Y/NKW618UmN2pi99iSS4WsJkzp/Fyg/W+X+j/ZzXlq3w6Bz1eTRYUwsAEyYoGy9PnDHVp3tJnzid9InTnT87rEVMrWdoaqv0GNnlsLYpIppbf/acs7M5PiGJkNIqcp77r0/X7cu2PgXNbZLTkDQaDSEGPbGzZlC3a29Ar5mzZWeXRsMpIfd4QBCBhc1tajpYZVChCkCVbiE9LJgwQKrfM4zOjdNbvWQFhUfyGLL4OsV1XTWR9ndsnh0hHewQnt5Mt9NCg/ifL9+lvFB+NvBAJRCjwQDBtBuYlnURMTHRsjWAvnQDKxEZkig7TcI1lR0/JJn4IclYj+ax/ycPKY70g44awL783Jd7aeCKufhCnwSgNkgHNjs2L1kBAJvV1m2pcW+Pp7TVqgpAlUGFKgBVuo3/HRq4cXql+UUc2nuICW+/LBn9E63dnU1BcSlV5ZU+W4K40tlaIFfhKTcTeXSITmjsMAzsmaAOoafVabFZbcQa9Lzzqxe7NBpMRESYMLavpp7XXn2O++5/QiQCHV3AUvcUKNHtPre7pbSch2//GePXPac40g/6R9R3RIjyl6jW4/k+ncdmsaINCgIfBKCD7ph44+3xpHjZr6Iy0FC7gFW6nUDYzez9aht/NZo9TJZdsdvt1Gzf7dFh64sliCtVFiufGM1U+hkJlLIwqbZYySk5Q935GiYYYpg0tu+m/Dzwc0QeiDu0u4K7JYgk7V3AjlnO2dmHyTtdwsRr5pLl2k1qrOXpG+/h6K5s57busGGRe406jMAXNdWTMGJ4v4r6rjlT55E2tdvttBlr2ZvZfT58Pj3/nUDq8YDwxfSxbjDBV1Hpy6gCUKVfcLTgNJ9E6r2uy7nmNkxHj3ts98USpDPG1Q5Ghui4O0HcDSwlhvqS/5ssbsIKEKJtE1KF+ckKPLzwdg59812nL90pexQFoVpvrGXZxHnUV9d4XGfizKkBNWb29hq92Vzbv74AIJRD/OlcPc10NGu1Vtd0SxewK931d9JTJvgqKv0BVQCq9At8HSd3dNn91GzbJblvzZfvK6aD5fzp4nQaro0NZ7epVVIchmvgwWExHh3Nj12zzKPJoV/4vx3JF4s/B/oYSrFScaKIEcnDsbe0UVFVzYiLJpKcPobszTt47AdLu3TpQH/wr5hzsyjy5/N1W/OhtRBCxkJIuk/X8vYa7cqUk65Sml/E9k++oPa8kZlXz/OrLOKxa5ZR2NxK1EUZ1B84EtDGj7DICJpN0r+zjMum89I3H3f63Eopf0dWwpx7jKpvdnWqVERFpb+j1gCq9At8/Xbu2l3rTt7eg7Jv8gdMLdLGzUCN1c56o5nRITrGhOhEdX0pwTpul7CzcdjVuNPn/d/a7VXcqa9vYPXjq8jOPix5WNb8y8j97kCnLrnmy/exWq0BT42W5hf5JP4Asjd+y69vuY/f//dlqLgdTF937IxcBEkfgk45Au3tNRrfCxGmemMtv7zpp+Tu3O/c9ukr7xKtj+X1vf/xWhbh+jqu+XaPbxfVaMDHuIKc+API3bmvU38nvkTew8orWDvrBlFk2N9SERWV/o4a81bpFzg6a5VcA1ura7AYa2T3OyxBqixWCprbqLZYqbZYWXOmTnE+rYPiVitajYblQ6P4cXwEy4dG8ZMhUZJehhVFnp2frvRZ/7d2exV3Vj+7lpycXNnDDmzdRYu5ya9LOfwPsxbMZsZVcwIuiL09B+4c/GY3zQU3gGmzeIdpM1TIj4xzIPca1SB0fveGt+PqJStE4s9BQ00d911yvdfjK4pKaIrSYRwWQlOk9/uPjI0OaCRt2z++8PuYp2+6x+PLV/bGb1l54z3Onx9wE38gGIf78jtRURkoqAJQpd+wWB9Baqh80Do4NprxLz8vuS/GoGfi/FmsrzbxyrlG/mo08/K5Rl4552kMK4fD7gXwataclDpS8Vx91v+t3V7FldKyCrKzDyvaeHSmkqS7O2G9PQfuJI9qI4xvwWP+rFWICLYWeD2H1Gu0sxNvuopcFNqBY1KKHMbGOp769q8cWBBP3qWxHFgYz9FLY7AEy38Nu/XRn7Hmi/d4L28rWl3XBW9tVbVf60vzizi6Uzrqe3TnfsoKTrF/43YP8efA2+9ERWUgoQpAlX6Dw3bjjnjpD1NNUJBzzJorjtSO1HSPzhTAGk3NUF0npEvbKWhuY1t9M4XNgvl1yrhUshZe7vEh6Dr1o0/isFdxoaLibEBOPXnWNF7b8xnP/ucd3svbypov3utUrV9pfhF7v9pGWcEpyZ8dOJ4Djda3t7mkFC/jC1uVp45Ax2vUNUrcmYk3gcCXCKjSpJSla59md1Eu6YZmFqXXMja+mdohIZyYJm/jFJsQD0DR+XIueXIpLbFdq3mc+YN5fq3f/olyxHDbJ19wfJ90GYMDb9NjVFQGCmoNoEq/w5tou/fdPxJUUMi5sjPO4u4qi5XCc40Bub72+9PQJKQ7qw1x/NkwRLKrcOUH61h1x4OiKEx/8H9jQqqoCzgpaViXTzlxxlTWbvt7l84hVdsVY9CLojnutV5Sz4EcFaVexEqI8tQRVwxBul4f5+dLBFRuUkp+RTHZ+Tv59MeFLErvqAn9uiCGpf9Io+mwjnCTp7FywriRjLtxLsVBJmHDvDgSG4O4LSaDjIum8Jenf+fz/UfHx/qdTi7NL1LcX5Z/ivm3KRvJ+zo9RkWlv6N2Aav0K8w2Gx8ZzZQouPrfkxDJiBDxdxtfu4h9Jc1kYnFlJetGjaJJpxMK311w9RUrKzjVP6d+uNirPHbVHeTsPSibBtZqtUyalcXpo997zHLNmD2d3254s8udvVJd1R73IdNl7XgOdDotj129TPb4L3JHEsYuxGlgHUReCSlfden+e4PHrlkmK36VrJG+OrgbTdkPmJ9Wj2vvisUGWwpjePze4cSfbRUdM3Xupfw7qoJibSO4RjxtdkbZosj/5zftz+FObFZlj83ONmR88OzLvPPMi7L77/r1w9zx5APcMDxTMg3si12UispAQU0Bq/R5XJs2PjKaKfUy0mlrg2cjQ6A9vooiIng/KYmmoCAP8Qcds0UBktPHdEuTQ7cTEQaGWIgIY+UnfyJTITKSOfdSfrvhTf517hBrvnyfGx64k7t+/TDv5W3lpa0fd1n8OerZlMQfiLusXXE8B1kLLmfq3Eslj50691LC0v8liD1XIq8UuoD7ISs/WEfG7Oke26P1sR6TUlwZn9DConSx+AMI0sKi9HquuVJsjZO18HKufHipEPlzT3drNRQHmdjx3W5WfrCOzPmXSV5To9WQlDqKNV++z6eVOZ3qxr1i8TWK++fcLOx/7bvPiDGIu7qlpseoqAxk1AigSp+lK8bMUlM51lebOnWuzjInKpQ5Mb5N0OgvlOWeoPx4ISOSh0OrhfLzVU4fwO5k71fbePKHd/m8/tn/vMMMl2kgrjTU1HmkhT19AAuEmj8/fAD7MmUFp9j2jy+oPV/tmw9g43+h7Gr5/clfUnbmAlFke+1bb/CLTW/LHvLCgrtZcc/PAPj+wBH+eO9TFBw86twfKA/Ih+bdQu6OfR7bM2ZP56WtYl/B7E07yNt7UPUBVBmUqAJQpc8iZczsK1Kmu002Ox8aTYrp40CyJD5CHS4fAPZv3M7e/27j01fe9fmY9777jOSsCxXX9NvUfHfTZoHjWyDkKvk1qfkewvjb3TtZ8NLPZQ/Z/H8vMfsScfS1O54DnwS+ioqKKgBV+iZVFiuvdKFpY/nQKMHE2WIjPkjrjAYGuhYw1GqlRatVrAFU6RzlhcWSfm1KaLVaMjMzWPP8SgjSQeYECB9YUdhuxzEJJm45hOwDjWu9nnI95Lgb5yrWAPYI7bWrZRVnKC+vVAW+iooMag2gSp+kxqJcJC6HBhgTouPLumaR39/6ahNNNnunagGDbTY0bt+TNHY7aSYTPyspIdytLs3RBazSNXwRf+51XJmZGax86iHhB4sVcjznQqso4DoJpm41tLrVD4bNU6yH3LJuPaNs4pnYo2xRbFm3PtB36kmbRRCv+4/C0QKSjY3MSEoiebQ62UNFRQrVBkalT9LZpo3U0CCsdruH319hi4X3qhq5OT6CtNAgv1LLmREhVNWbKAzpMElONZtZXFlJuM3GY6dOURgeTqk+lpSUYWraNwAomfWC0M055+ZrSE4fQ1n2Ecq/2cuIpOEkJyeKF1qsYKyDeDUa6xOuk2DsMVD7MuhKQFcK1hSYMB90nr/L/Ru3c3zfYSbOmEr+P79hx3e7yTl6hMzJF3qkfbsNF+siJzX1wvYLx/XMPaio9CNUAajSJ0kI0pEWpKGozYZdosvWneFBWm6Oj8AOsqnjSouNl881MiZEx6gQHad9rAUcFxHCD+IjqW4wY2xqIz5Eh6GqEVwsUdLCgkkbPRyC1T+pQODNrNdmsznTeskJCSRPV/BuqzepAtBXJCbBYB0p/CexXypN7+imDZTwM7VW0NR2lvDg4USGJEovkplhDQjbzc1CV7uKiooT9dNKpc+yOCGav5VUURoSorgurX3UVrhWQ0Gzl2kOwKlWKynBOpYPjSLX3Ma2Run5twAh4IzoGaIjMDgyu3HjRD556odLYJkwfYrifpFZb0yk8sm87e8uWvPBtF2oD424on90EzsmwUiJKX2Mx+tcaaZuV/302qyN5Faupdrc8WXAEDGFjOErCNaJ08xyM6xF+9W/URUVEWoNoEqfJVyr4fIE5Vq6H8aGscQQiclmo6C5DV8HbpW2CZ6CQ7ykmm+VGTsHiHzyVALLtIVXeNT3OYgx6MWWHfGxQsOHFEG6no/+Ne2DgmQoGg9nfwqV90DROCiZD1bfG1p6jQmpHuMA0ccI213o7pm6gvjLFW2rNueSW/KHjjGMrfmCZU1QqfLJpCKbKiqDHDUCqNKn0YcFQ738t/vhwToPf79wDaLRbHKct9j4R22T7Pq00CBS1Xq+XuO17z7jvkuul0wvepA5QWj4sLik9R1dwD2F1QjF86H1kPR+81YoXwwjt/TcPXWG4CChZs5LhNuXmbr+eOuV5hdRUVTCiLRR6EeFiiJ/HdiothzDdPBzIvVrQbuzY9eQy6Dq10LtoisSkUsav4amvRBxCUQu8PkeVVQGEqoNjEqfR8oPUIPQ8AFI7gvzUQQ6cBeByZZWZibEUmWxkRKiUxs7ehG/zHqNdULNX0xkz0b+rEYoHAe2au9rJTz0+iP7N27n8WvulN2/5sv3fRKAUjOep8y/iLmrNITFSscormjOJyQo182iRgu2qXD+jY5Njsiloza3tRBOzxA/T1oDjN4PIapVjMrgQhWAKn2eJpudT2rMoihfWmgQ86JDeavKJHvckvgINtc3c8ZHS5mWF1+l4HAetpZWLnj1BUJcUpAOa5dAj5RTIWC1lK4RpB73fSueDU07va8DGP4WxP1P995PDxGImbpSM561Oi2jZkZxy1vjPdZH2BuYZVOYyxxyKUT/DSKHeb6e8hOkRbrWAOOqfLpfFZWBgpoCVunzmGw2ZkaGcGlkCDZwGjt7a/iwAUsTovhbtYnSNu8dvydzT1CzbRczcr4hOD5OtK/JDm+eb1DNnQNJfSMUFENjU8c294iNL6dpjyDt2L2Lpkgd4Y1WZs+a1XOTH1rzfRd/QKdG2/RR/ErTS+CY8eyOzWrj1K56jKdbiR/t0gRm1zCsNUrxk8vespuW1qWEDdku3tH4tXyE1lYNpk1qOlhlUKEKQJU+i9Qs4JRgHbcbhK5Ob9G4+CAt4VoNPxkSxdtVjZS2WhU/e81FxcTNnimK/LnSZIfC5jY1HdxV2izSnm3QKd+2p5c8wMemo9QsiHduKzp3mOY77mftFx8E4o6VaS30b33kFd1zH71A4pgUPq3M6fRM3YqiEsX91rPDMVJBTUkz+pFhpA+fwKj6KWD4RPYYjQbC+JYXlt3AvS+92/EloGmv8s2Yv1MFoMqgQhWAKn0Wd/EHQvfuurP1PDgsRvAKlDB1dtQHOsa/VVmsTAgLosFqo8aqHH6JnpqhuL+01aoKwK5gbhYEXqPCOD4/fNtK84v4qDGX2iFiq6CahGA+Op/LowWnuj8dHJLm+9qIeQOi/s+drAWz/RJ+DrRa5b79r1/Mpiy3I2o37pJzrH5sHvFxUaBTHhVZW7aHVXc8yJov3hM2hM9QvpmIS3y6ZxWVgYJa0KTSJ6myWD3En4MmO3xoFGr/FusjnM0gDlLbfQHNNhvvVTXyyrlGvq5v8Sr+wken0HAwV3FNSoiM3YiKMq5jupTEnwNvvm7t7D14gNphoeLZswBaDbXDQtmTk92Jm/WTkHEQuQivb6eRi2CEfORqMGKzKf9Nlh0Tp2wL9p1l7Tu/8Cr+AMpO68je+C1lBaeEDVGLhFo/KbQGNfqnMuhQI4AqfRJvs4BLWq1UW6wYgnQsMURSbbFitNic9YEA66vNnPJx2gdAS2k55sLTtFbXEBwfh8ZtAkm4BjX611nkUr5y+Ojb1hypLLqaI3tIsCd9CBW3genrjm3aBIi7V4gshYwdkJG/rpKUOlJ5gdvbgN0KrdWVXs97PDeYESOFGuHywmIhCmxuhtitUDdPugtYRWWQoQpAlT6JL922RosNQ5COKouVGjfxpxRBdEcDjNJCU9pIsgtPc+i627no33+T7AJW6QRKY7qkkPJtk2F6Zha8L79/xsVZvl+3K+j0kPIVtBZA60lV8LVjaq2gqbGUcIueyKiRHs9ryrhUshZe7tEFrNFqsLtEB5NHtZGU0kZ5STAVpcpfwtraYEJGG8+9fg6AZvsv4cgfoMbxhe4r0OfCsGKImqVG/lQGLaoAVOmTJATpSAnWKXbvRmg1HibQiUFaro0Lx+wlteRKapCGxQnRhH/xHmUFpygvLGZEmJ2W+AhKW62qD2BX8TGdC0hOnFBiXNIo5k3M4ptj2dhdArYaO8ydlEV6opcIU6AJSVeFH+1j3Cpeorr5iHNb0tlYxhNCkEELsdc5hdfKD9ax6o4HRd3AE2ZmkLf7CNExVp5cc57plzU79+3bGYa5bSoRwYdxDxG2tYHW7btjGLvB/iDwcsfGmgxgFgzzvdlIRWWgofoAqvRZmmx21p2t9zB0VjKBduBNPFafK2P8yTxmhMcyLSq+U/YjKj5ibhZq/5SIioD0UZ2a21vTWM+StSvZdHiPc9uCKTNZv2IV+qgYhSNVuouc8tVUm46Axk6QvZUptp3E42bBoo2H0dlOA2bnl692H8d758/m7tuzyZzRjM7lz9JqBV3kTNDFilLuFaVRJKUo1AZWbQDrSNAVQ3AOoIGxMyFUp0ZsVQYlqgBU6Rak0rKdoclm50OjiRKXWj5fTKABQgH32FOTqZ7P1j/HqRMdzQELUiayfuHd6BOH+2U/ouIHR/Kl08BR4TAhLSDzlAvOlFBYWUba8OSej/ypODG1VrC7+CHnz1Ot32LgrPScbgUD5tqzO4iruVz+QmEzhf8375FfIzrhsxD+TwiVaQyKXCTUcuqkbaBUVAYaqgBUCShK3n3hXiwflHBv8ihobuOvRu/dpKEaaHF5hX/0xhOczs/BbutIHek0WmaPnMDX194P0yYHRIyouCHl/adGXQckVaaDHKx4DvBhagdAykaPOjxjYx1rP1rCry/7T+BurHUKBB9GWokC6CDySqGWU0VlEKDawKgElH8YzZLefX+srKe81bemDCkMQTrSw4Kd0URfR7K12oW6QBDSvqdOZIvEH4DVbmNb8TF+HwJNZj/q1VR8JzhIiK5OmwyT04X/XzhOFX8DkPDgYR3/xrtdC+bvPDYtfuER/plTHJgbsuug9SIIURJ/AFYhpdxaEJjrqqj0cVQBqBIwqixWWduVVuCtKhPrq000+dGgIYfDBNpbTNEOzlnAtVUVimuPmIx8Yut8lFLFByLCwBCrRlkHMJEhSRgipoBdQxNR3g9oN2DOryjmH7s3MfXnt7LrxGHyq0L5uiAGq2+jvBXOPx80y3xf33qyixdUUekfqAJQJWAU+2C7Uthi4ZMaH4yAfUDKBFqJuIQk5f1DRlBosVNt8d07UEVFxZOM4SswhGdg1kRTxTD5EYxaA0b7dK5d/SAZD93MHS89RV5ZkXP30n+ksbvE/8ag9pND+GUw6msYf7Xvh4WM7eT1VFT6F6oAVOlxClssPousKouV7fVNfFlrprC5TbQvXKthiSGSexIiSdApR+5SgnUkDE1mzAVZaNx8IjRaLWMuyCJ+SDIg+AuqqKh0nmBdFJkpK7l01Fo0CeuxaiX8GLXxMHo/S9c+zdbcfZLnqW0O4sq3J7LzdFQnIoE2aNoJNe8I85rDL/OyXic0gqjdwCqDBLUJRCVgVFmsvHLOh5of4MfxEaQreOuZbTY+qjZR0iZ+1w/TwP8OiRbVAJa1Wvi8tolKCeHmsIxZrI/gkxozR2tq+Oz9Z0VdwGMuyOL6pU8SHiEYPS8fGtWlzmUVFRUJWgug7u9gOwdR10LkAvIrisl46Gavh8aFWXj/5kIWpfthKC6FRg/2Gul9ahewyiBDFYAqAeXdqkZO+zB+zZvIcjd4diVcA48lxkp2HLuT1i7+HB3Ijm7iDUWF5FWUEjckyRn5c4jFJYbOppxUVFT84auDu7n+uYd8Xn/g2V8zmYfBcryTV9RB+KUQs1T4gw8eBW2nwa6ByCvU6J/KoEJtwVMJKLfER/Kx0SQrAh0iS0n8eRvj1mSHwuY2dptaKfJSdzgxLAizzUa4VrieIUiHIUjHHWPH8kXCCGeDCHREClVUVHqG1GEj/FpfdqiOyUNfAcONoKvrxBWt0LQDEv8COgNU3C6e36xGAVUGEaoAVAko4VoNdyZEUW2xkt/URk5TG+f9FFk1PtTgfd/c5tOs3//UCSOkHJFAO3aPqKFep2GxPoIRIeqfQ1+jNL+IiqIS53QIlYHFuKRRLJgyk625+7DavP/dpwXHgD0Gqv8J+nsguMjrMZK0noSa5WDaLN5u2gwVt6legCqDAjUFrNLtuJs4e8OXWsK5USF809jq8z24jo+TEo5aYPnQaJ/9BQcN5maobQBzE6UFp6g4c5YR49NInjqpW61c6o21rF6yQjQfdtTEdK75n9uYcdUcVQwOIKRG+bmj02iZl3wBn1/3gMvGYkhY3LmLpnwNpYvk9w9/B+Lu7Ny5VVT6CaoAVOmTeKsB/MmQKJ8bTnwlVANPJMYG9Jz9ljYLHCuEugbq6xtY/exasrMPO3dnZU1h5QsriZ55UbeYOT92zTJytuzCZpUuJbho7iX86uPXidarz9dAwTHKr6mlhTWfvsPBUyec+5zjGsPcsgdxyyFkjxeDZ1c0EDEX4h+BMi/WMFoDjN7vnFWsojLQUAWgSp+kyWbnw+pGxS7g9dUmilos8h5jnWBJfARpCt3JgwaX2b2PPb6KnJxcbC4pOq1WS2ZmBmv+9ELA5yeX5hdx56T5XtddNPcS/rDxbwG9tkov0pov2LWEjIWQ9I7ZzmGxpJ9rci7Lrz1LUV0VabFDSNeHgf4+CP7e9+tEzIPhr0PReOV1doQ6QZlZxSoq/R216CnArP72W7acOsXC1FQenz27t2+n3xKu1XCdPoLiFgvn26zYgPFhwSJx5rB28aUW0Fe+b27rVgFYZbFS40c6vFcwNzvFX2lZhSjy58Bms5GdfZiy3O9JHjsyoOngiqISn9Yd+uY7ygpOqeng/o7VKNmMkZ70IemGBGg4DtVWjCYDSze+w6bSPOeyZZNH8MerbyIy+Fnfr2feCmiEhg/TZkDGtUAD2KrBtMljVrGKykBAFYABYmtREVeuX++MRn1z+jRPbt3KtmXLuHz06N68tX6DQxyFazV809AiEnYjQ3TMdWvScBhBO2oMtYAN2NHYQmmrVRQZdNQAGi1WaqzyMcNIbfeMgpOyrHG3qOkzNHXMQ66oOKu4tLyikuSmloAKwKTUkT6vPbxjr6IALM0vYu/BAzRH6phxcRbpib6fWyVw5FcUsy8nm/zCQlrCNCyYeTlXTpkh7Ky4XaIZYxMUpgsCDCABTjUlkVOVCGjRh1t4b3Ehi9L3A//y/4ZaTwrdvqdvhrYtymvN36kCUGVAogrAAOEq/hzYgTnvvYftmWd645b6Db74+ZW0Wll3tp4Hh8WIBJNURC05JMgjMujoPq62WPlzlUn2OpMjQgLwiDz5u9HsYY1T2GLhY6OJOxN8mJfak4SHOv+ZlDRMcemIpOGi9YEgZVwqWQsvJ3vTDvBSoVJ8rEBye72xlqeXPMBHjbnUDmu/v/dh3sQs/vbI8+ijYgJ6zyrSGBvruP33T/BNXrZo+0tb/kF8ZAz7Vj9Dimvkz4mtQ/y1M2VEBe8sbuS69eN5b3Eh89O6YAqtCRKsXhI/hyPPQtxv5de2zypWURloDNiWxyqLlYLmth6Z67r6229l69DswPM7dnT7PfRnNtQ0efXzA8H/72/Vgngz22ysrzbxyrlG/mo08/K5RtZXmyhrtVDWauHq2DCWD43ix/ERLB8axRJDJOFaDckhQYwMln7ZjwzunrRslcUq64t4utXa92YPR4SBXhBIKclJZGVNQes2Pk+r1ZKVNYXkjPHd0g288oN1TL70Yq/rbDLWIauXrOBj01Fqh4gF/TfHslmydmVA7tFvqv8DxU9B9Re9c/1eYOnap9l2LFtyn7GxnsffftDncwVpYVF6PfPT6liUXk+XGvbt7e83EWEQcQdYY5H8Bq81qNE/lQHLgBOAcsKgydbx1+0qDqssVg6YWjhgaun0B/GWU6cU928s6qRX1SDAYfrsayNHaZsgmD4ymj1EY2GLhT9XmZzP+5d1zSSHeJpO32aIIi1UHPxOCw3iNkP3ROKKvYjb0wGsYQwYE1IhVhiNt/Kph8jMzBDtzszMYOULK4V13UC0Ppa12/7BCC/1fWOnTPTYVppfxI7du6gZGgJu6XW7BjYd3kPBGd/qDAOC6Xs4rofz10HTs3D+WuFnk3T0cqCQX1HMpsN7sMtVOGjgcLn/77nTkwPQ/R8ytuPfE1LBugFsbh3luvYuYBWVAcqASwFLRZOKWix8UmPmJn2411TjmBAdP4qP9Ksua/6YMXxz+rTs/oWp3fMhORDwxfTZnY+qTZxXqONz4Hje3Ue7udcO9nZDxh5TKxPDQ/pWLWBwEFw0HszNRNc2sGb9OspOnqa8orJHfAAdDEkeTnmB/Bes393zC775x+es/GCd0xKmoqiEpkjl57Owsqzn6gFLZgK1bhtroWQ6TJCZSzsAKDpb7nVNQXUYhQ2TSIs+gWwzhhv7yrr4RS1ykXjkW3AQXDgfzJVQ+xWQA7Gz1cifyoBnQEUA5aJJdoTo0IfVnlEjd061WvmkxuzXdZ+6/HJZGyoNqN3ACnTGeNkX8Qcdz3thc5vkfkOQjvSw4G4Xf6NClb9nnbfY+NgoX5fYq0SEQdIQGDuS5KsuZ8bdPyJ51sU9Iv5K84s49M13XtflbNnFqjs6UolJqSMJNymLibThyV2+P5+o/g9oaj196jQI2wdwOtjXMW+lIX+EyCvFG62xYBe/N9jRcaByJFsKYzGaO/nRFTZTaP6QIiIMkv4fJP1GFX8qg4IBJQC9RZNK26w+pRoLWyx+p4O3LVsm+R6/bdkyv84z2EgI0nmkYwPNeqOZv5xvFJUB9CQJQTrGhCiLzNOtVlmhOljx1Q7GZrWSvfFbytojhSnjUpl96Sz051rB7TnX2GHBlJk9F/1rlJ9uIezf3TP30Qs4xrxpFAqkDdGxzJmyQBi9pj8ANS9B1QZh1FvrdNFyTfAcLp59iO9ffIH4CP8zBwAk/FKd86ui0s6AEoCBHONlbBeTvjaTXD56NLZnnuG5efOYO3o0z82bh+2ZZ1QLGB+YGx3YLlIpStuELuLeEoE/io9kiE45xbveaPaoVx3M+GMHA1BeWOz898oP1nFL5GTizovHBc6dlMX6FasCcn8+ETXTy/5Le+Y+eon1K1Yxd1KW5L74qBh2Pftux4boidA6C6wjhXm/tS8LYtAhChM/B+yMbnu48zfkWvunojLIGXCTQOSmQ8RqNdT58cH6I304B8xtHl500yNCSAzR9V0T335IQXMbfzX6l3bvLCNDdNzdS7Yr2+qb2dbYorjG4VfoXrc4WPE2Es6V9/K2engClhWcYk9Odu/6AB7XA7XiNLAdIG5A1wC6UnCmhL0HsikoLKQ5DLEPoCsuE2hE6GOEiTOlVymbNzuwa0HjGiXUCWnmlK+68jBUVAYUA0oAVlmsVLZa2WdupUTGdsNXEoO0VFpssinjPmvi2w+pslgDPtdXieVDo/wW8FUWq7Obd3SoZ2exL2yvb+KbxlbvCzt5jwORhpo6Vt3xINkbv1VcF2PQ82llTg/dlZ+YCoSGD01txzZ7HIzcB5HpckcNKkrzi6goKmHEqGSS22xiEaiPETp17UW+jW9rzQKCINQl/R65SKj9U9O/KipOBkQXsJSRsAZPWyd/OOOlnlCuw1TFfxx1gIEc6aaE0WLzWVyZbTZJE+fOdItH6XwvUfDnHgcy0fpY1nzxHmUFpzj87R5e/NmTkuvqq2vI3rSDrAWzPWbK9jqR6UKkr/oLoeYv6lIwXNPbd9UnqDfW8vSN93B0V4dXYNbCy1n55gtEh4UJJuOOhqPGQu8ntMVA3RohhawrgdhqGD+nb7wOVFT6GAOiBlDK+qWz4k8DDPehltDRYdrnTHz7KYv1EQFrBknwUmvnz4t+Q02TpIlzZ7rFvXUDuxIfwHrWgUBy+hgSRiQqrll164/5/l8jhChR2dVQ1J4ytHZzmtXcDNV1wv+VMFwDo1YPPPHn6+N3o95Yy7KJ80TiD9q7un/6CzDEirvNQ9K8n1RXD9pa4d8xk+GCu1Txp6IiQ7+PADqsXwJFQpCW0cE6Kn30p1MjNV3HMc5tbnQoxjYrNV5qNeWiu476uakRwXxS0yR7vK/9g95eW44vAL48/2abjf/Wef+AdDwG9TXlibemkCfXnGfsWLffsWkzVNzWPbVfbRY4XiSdrgzu92+t3uni43/6xnuor/YU565d3aKazpBxQirXtAnFv+JUG8RN7hGrIhWV/ky/DzN0xkhYifMWG3uafLfjUCM1ncd9asufq0xexR/IR3fDNEIkcXiwsnjy9Tnz5bVl9PH15+u4O8fMYhVPHDOCNVrP5y95VBvTL2tG56E7rGD6Glq7YeqGu/gB4efjfWfyT2l+EXu/2ua0yAkoXXj8pflFHpE/d1y7up0kfQjhXjqnE6ZAUAk0/rd7nncVlQFCv/+aGkjrF39JUyM1XcJXUeQrTXZBVDpqCt27wf2Nrvny2vJFTHqLJC6Jj8DWfi719aTMipd/y52T52N1mwGclOLlS1vrycCmAs3N0t2qIGw3N/dqBKreWMvqJStEzTNZCy8XTUzpEr48foCmFnEdX3t9ZnVJtddLjEgbJd7gqO1MfBvO3A1NuxFHAnUQcTmcXS6IfgdqA4iKiiT9MnzlqzdfdzI8SKtGarqAvzOAfcURkVusjyDVrebO3+iaN5NqX78AeIsk2qBHJpIMBNbc/TDWNk8xXVEarHxgoP3fmpTtfLzu72aevvEeDmzeKdrmPjGlS3h7fMcLYf9ROFog/P/IPihe5KzPvCh5Cc+9XklUjPR7+ORZ0zrSv1ajUMvpWtupCYGIOeKDIq8ENO02MS44ygBUVFRE9NkIoJTthlS3b0gv3d/N8aoFTFcIdOregSMiF6h5v4v1EXxsNEl2AfsqJr1FEtUyAt9QShuWFQezb2cYF890TwO3+78FuhEg3It5ubf93YCptYLz507y/v/9hghNLknJwZSXdAhjR23dH//5b669YhbpBkPnL+bt8TW61eDaV4B5v8gL8eJLWlj5QhWP/2yYaGmMQc+qT9/q2FBxu6eoM28XntfU/Pbo7lhoLYKyqyRuxqUMQG0IUVFx0ucEoJLtBuCx3TdXtcCSFhqEHcHAWE3bdY7uSt3XujXldDXCGK7VcGdCFNUWK6c76QMYqJT0YOfk4TzF/asfG8Lzb7YyYdLZjo2RV8rPfu0KEWFCw4OcaXEPpn/brI3kVq6lzrSfIblb+OVvOzw19+0M451X4ojT2ygvEQThqk//w89zDzJvzBg+uflm9OHh/l9U6fG7oysWe/I5NuvsTJvVxIiRbU6hOnnWNFZ9+lZHmro1X5zOdWLt2B4+o10kSq1zIdBlACoq/Zw+ZwS9vtrUY35wnWFksBatRiMSoqopdOeQm9rSVdJCg7g0MoRN9c2ibu7efJ6abHY+qTGLXtvq68Y/Vsy52WvjwHt5W0keZemICnXnB34vdgE7jZPTRnEu4m9Um3OZ3LiVocHnRRFQuw00Lt+19u0M4/99fzdnooaiARampfHVHXd07iakHn9UBDS62SOF7AL9Q7KnOdrwO7YciiJz8oXMvsStwaPxv0LaV47QTNDFCRFBb9NBUvNVAaii4kKfEoBlrRb+XGXq7dvwYGZEMEOCdYwODeLLumbZSI5qCu0fUqIoINjtoPEUVRogJUTH7KjQXovcdjUlPVgpzS/izknzva579j/vMOOqOd1/Q66Ymz2bHbqJ4/sPs/b+lRQcPOrcNmZWDLf/YSjzIjcrHClgscLmc2n84Nslzm35DzzQtXSw6+MHoebPFV0xJCyWPXzSSxmcNAq/twVTZrJ+xSr0UTHt0b/tcPanChf3xfJfHQOnoiJFn0oBf1Er793Wm6SFBZMeFizbzelqCq1+qPuOVJ1ejcXGB12dCywh/kB4nkparc65w70RgTMEqXOkO0NFUYlP6zw6R3uCiLBuF35SXb0OTu+p58jb9cxb7v08QTq4KrGQsVHVnGwURN9Jo7FrAtD98bunhq2joGUmhOwHTUeUzmrTsKUoxin+ALbm7uPe1x7ho9tKvKd0AZ+KPLqrDGAg0Pg1NO2FiEsgckFv341KD9Nnqs+rLFav49d6C0eRvrfGBV894VTEGIJ0pIcFY0d4Ox8Z0jMCyTHOz0Ff6C7vN5ibqTpfR0GdqUd+X95MoLU6LVkLLxcbBw8gVi9ZQc6WnZL77FY4+LXv3qUAY6OMHf+Oj+/SvXkwIVUQga5o1kGEOIK7uTCaJX9PFW2z2mzcNeFj7O5NH50lZaMQ+VMtYMS0FkJ+gtA0U/0MlC4Ufm7tBr9IlT5Ln4kAdldXaFdwL9JXuzm7B6nu7p7AEbktb7WwtaFFrc/zhTYL5hOn2BAWSWFkJNXlp6mtqmBKYjL3T7igW35fjnq3ybOmkbcnB5vVU3Bmzr+MlR+sE60fkTZqQAjC0vwiycifK2XFwZTUGEiOM6LVeI+KWWwafjC8gOGxF3Yt+idFcBBcOE4iNd7eidt6kv2FJ3h1z+skRFjIGmFiWnIje0ujKK0LZVG6D40lHugQ1wC2p33VqJY0p2eAzc2L0VYNp6fBuKreuSeVHqfPCMDeNHSWw903Tu3m7B4CbQjtL5/XNnmM/nNEB9W6TjeOF7EhPIpjdgv/euMJTp0QmjL+Dvxt4jS2PPKcUL8VAKTSnjEGvWh8WPrUyfzf66sZf/GF1BtreeyaZd1nftxL+Jr+zjZlEaE/RAIdXdB2RM4r2O3QZtOwae4H7Vv+Cnn/gdR/QtiQgN0zIJ0at9bA+ZVMC83h30s8D6lr7sTnQPB4CBoCTS4RUjXtK0/j157iz4GtWhi1pwrnQUGfUV0OceUeP9AAoV0MKiQFaTvlF3h1bJhHRCMQBsMAHzz/Cj9fcDsfrnmtE3c2cOguQ2h/OGOxeVzfta4TunmkVn/B3ExVYxOFkZH864PnOZ2fI9qdd+IAt770lMdhnf3dCWnPXaJtjbX1TJ4+lZ+v+zU/f/k33PrMzyloaOLYyWLJ9QE1P+4l5NLfwvg7M8mjrWQtvJxFl78DKf/FnLIThr8J4Pl+qoEQndurXbMLim7shjt3wWHmXDwDWnJkl8WEdiIT1Pa9IP7CZ0PSx0K3r5r2ladpr/J+83c9cx8qvU6fiQCCIK7cu0JTQ4OYFRnK+8bOdQenBOv4yZAoAN44W0+l1XepYXTzlIOuGwznbN3No1f92Fm7fHjbd/z56d/x4uaPmHL5DJ/PM1Doi6l/V4qMDbxwVzeO1OpPNLVQExxM9bkyZ+TPFbvNxrYjeyk4U0J64sgujSOTS3varFaO7jvIie8LGb/uOeKnZgKQDTT9+Ba0ew5iq28Qrc/e+C1lBaf6bTo4ZVwqWVfOJmfrLmw2G9ExVp5cc57plzU711hCygnShRFpmdA+pcOPNJ7GDpqdUHcUYicH/gFAu0/fJu+30q5YbXbwu5qgaTdoIyBG7fZVJNzL50zEJT1zHyq9Tp+JAEKHuFo+NIofx0ewfGgUSwyRWH2MD7m/X6SFBnG7Swpv2ZBop6G0LyjV9DkaF/xN+7qKPyd2+PmVt/p1noGCP6n/8F4ox/t3XuGAjCp1ivBQ9G1t1FZVKC47fCofgKdvusdDxGVv/JaVN94jeZxrpNBb2nP8uufQXyb+INNfNoPxLz8vub68sFjxfJ3G3AwV54X/zM3e13eSlU+tIDMzA4An15zn4pniawW1boPvF8DhP8HxLXC8EyUVphMBuFMJnGbOvn/Z61wpqcvEDxV5ohaBVqbuU2tQ07+DiD4VAXTgbpXhq0iwA2EauEkf4YzMVVmslLV2ROq0MhYh7mjb7yOQfPD8K/KuBXb4cM1r3PbYfQG9Zl/HW13l1bFhokirI/KqRfg4idBqPBo45JgbFcI3jf7NjtGNG0tIygiaT3cIkoEQVeoUEWEkRIWTVKs8B/aFT99hRkIqR3dKmzYf3blf9LuTihROnpUle/7wMSOJnzPLY7smKIj4ObMIGz1S9HxBN9jDtFkgrxBqG8TbY6NhUlpgjaDNzURb7ax5fiWV57MZPvleiUVW0B4A/QHhx9aLoG0sBBWBxkfhFXlBoO5YTGth95xX9nrqxA+vjN4vNHy41gJqDcJ2P+mOpquNh75j/8ljzEjP4Mopgy8z1lP0SQHojpxIkKLZLoi3cK3GY6rIyBAdJa2+WVbYgMLmNtLCvAyZ94OcLbsV9+/fvHPQCUCQT/07unBdhbj7lwOzzUarzbcIcZSucwHv8NEpHoIChKjSoBKAABNSmVV4mJcUlhwuLuDvf1UuwN/2yRfc8cQDgHStX96eg8QY9DTW1nt0/YaNSlE8t+vzpdXpyLx8BsnBoWCsg/gApe1zC6BBoiylrkGYjnHhuMBcB6CphfzasxTVVZGRdM63Y0IO+XcN28Xdl/4NSfNruYyPewcJv4WqpxWuN9av6w1KQsYI3b6mTULNXyd8AKW/uHWM8uuMMCysLGX2U3dT3VDn3GaIjmXXs+8yZtgIv+5PxTv9QgCCtEiQo7TVym5Tq0dnqa/iz/U8gRSAmfMv5fA2+QLbaVdeFrBr9Sc6W1dpttl4+WwDTV70nyOaOCq0cy/3IIO0T1qvmA73NsFBXDBjOnhpsDx4RjnqU3tOiDwo1frVV9cwedY0ju4SRyWai0sVz910umN/ZmYGKx+8B4rb09ZBOsicAOGdNG5us8BRGfHnoKZeSAcHwBza2FjH0lefYtPRfQCkG5o5usLfs2gh9CLQRgp1cu4j0+xxkPrfLt+rLCHjhJFtCs0frjjEn9UGrt/ZrBbYvz+ck0MmcuP4aURYDyBOK7dbv6jRP9+JXNDplK/UF7eju/az5IIrGDtlEge/6Qh4eMx4lsFd/AFUN9Qx68k7qfiL9xpSFf/oUzWASjhEwh3x3rttw7QEpLM0JcCGxHc8/oBnoaIDDYMy+ueKv3WVHxnNXsUfdEQT5TrNvRGenCT6OWLsGKb//H8JH6NsTjxQGZc0ionJqYprxkxWTifOvHoe4N3i5PbH7+O9vK08++KveO/ddWRlTaGluAzjtl3YLeIveHaLBeO2Xc7o35rf/5I1zz1FdHRUxyKLFXKOK15TkeNFUO9DQ1qTcprcV5aufZqteR2p9ILqMDYWxGK1+fMqtgnia+iLgkByJXQWjCsKvAWMO8Pf8PuQplbxY9x+Iople8YyxH4XEdb9eNQUqtYvPYbji5uUJ2eDsU4k/kAQhksnzKWhps5jvYONh77zEH8Oqhvq2HzYS/eyit/0GwHoYGxYMGkKkZxwDRwy++eKL3eeQEb/HLy4+SMJb4b27So+U2Wxeo3ozokKdTYSOex8pGx8Er3UmA5v/6AJio1h0nuvcvGWTwldcS8vn2tkfbWJJh9T0AOJN+9TSMEBS2++lej4OMl90fFxZC2YDXif8DGizUayqZUZF04iOTmRlU89RGZmBt8vf5yaneIPhJqde/l++ePOn62tMtkCi1VIB/uLuVk84kwJx1zcLpBfUcymw3uw2sRCZ8k/UtlcGO3/Ca3nBXuU1HxI/lL4/5idPWOXEj4Nwv3LcESF2Xn0nmE8ce9QZj49nh98dAHrri1mfpr7c6AVzq1av3QrX588yW+2b2dTYaHP3pSu1FfXsPIG6QYwgP0njykev7cg1+9rqijTb1LArizWR/C36kZK28RvjOEaYd/6Ls6SDdfAT4d04g3WB6ZcPoMtraf4cM1r7N+8k2lXXjboI3+dwRf7mIwIz2iiXLp5zZk6yWhiuMbOnHsLuWhRBoVDf05rwoWi/YPVMHra2EnMmZTFtmOejR5zJmWRnjiS1/f8m/suuV5k3Bxj0PPad585f04Zl0rWwsvJ2bJLFE3QarVkZmaQHBMj1NW1Ex0dxZrnV1JWdobDh4/w2jNrCB+dQtPpUs/Gj6Th8g+goERIBfvTrOFrVC8mKiDp36Kz5ZLba5uDuG79eDY9+TCXj42B6uekU7vuOGrjQtJ7J02a/G8oTJc3IZYgKMjO9kPRHFwQQ3pCs8yUEJvgA9haoKZ/u4FCo5EZf/4z1U1Nzm2JphYmdOJcR3ftl22emzZ2kuKxM9IzOnFFFSX6pQAM12r4yZBoqi1WjppbMdnsjA8LJi0smILmrkX/ro8NY2pk17+9e+O2x+5ThV8X8NYZnhKsU0wluzeT/HRING+eF9cThmvgQvMq2mwNhI8ZRWvEVI/zuBpGD7ZJMB89/DxL1q5k0+E9zm0Lpsxk/YpVACSOSeHTyhyyN+0gb+9BJs6Y6oz8ubLyg3Wsuu0Bsl1m3WZmZrDyqYdkr52cnEhyciLffruHnG+/w+YSJXM2fiQnyt98c4v/zRq+RPV0Wpgs3YTgb1F8qpei98Rhl0DUSAifCRW3tVutSN5U36iN0+khrQDKroemHT4dUl4STFOk8HeVGu/FZkft/u0W3MUfQG1zM01D9YSfq5E5Sh655rmFF12CITpWMg1siI5Vu4G7AY3dbh9Q+asqi5VXzjX6fZyjUWCwRXL6M+urTZKd4eEaeHBYTKfm0hY2t1HaaiUlREes9RgHK54FoEY3mbww+er7H8dHkN4NJQP9gYIzJRRWlpE2PJn0xE7WRVbXUfbVt5RXVDIiabiyeHOhQadh1eq1ZG/uEBROs+m8IiHdq8S0yf5F647ky6eBYyJhcrpHVLErhtjXrn6Qrbn7RGlgnVbLvIzpfP7UOvHi1gJoPgg1L7uNRVsk1MZJpUdb8wWblpCxPSueGjdC0x4InQyVP5WICgqitaz5dfbkZPPjDb9rb4BRSAOm5qsCMMB8ffIkV/31r86fg5pamPTpdgxFyl6gSryXt1X2C9Cps+XMevJOtQu4hxhwAhDkhYGDcA0khQSJOorTXGxHVPoHTTa7R2f4yBAdt8VHBuR5LKz+hCLj34VraYaRE7FKdu3yoVGDLgIYUMzNsP+of8dkpDttXcoKTlFeWCyOrjU1w4E8oZ1UjsnpYPDDGqbNIkQOXUVgVDikjxYEoASPXbPMM8Wt05E5fxZrvnhP8XI1jfWyUVbFmcutBe0RMRlhZzW2T+foiBqagq4gcsynXaujMzcLqfLwUGlhLXFdIuaBzQzNHY/RXbQ6hPCnPz7O/LR6xAmA9ghnijoBJND8Zvt2ntm2zfnzRR98hf50pWIj3UVzL6Hw8HEajLWi7b6+5gE2H97L3oJc1QewmxmQAlBKGDhw1Pfpg7SdHuem0jscMLVwusVCamiQKE3fXc9jlemQMwIIcCx0BbW6CaDpuIYaOQ4MpflFVGz5jhExMb5F//QxvqVvvQlLfyKAruIGlIVOO6X5Rdw5ab7sfqVoiCsBibKKbuwq7KbNaFzqBi02OHxuJKlZucriUgopYRwTCSOGQVREx++o9CowbUZcr9huUzP0ebBbJEWrQwjv/34n799cKK4FVIpwqnQJ1whgeHUdl7z+qezan//pOabMnkFy+hgaaupYecM9IgunQTtCsw/TL2sAveFe6N9otVFntZMSohN19rrXgan0TcpbLfylyuQ0fchttvCfumbuSYgiMUTXbc9jQuRFtOlSaSSCMNs5xre8xfeh91Ab1GGY67CYUekckunRrCmsfOohwb4lLlootHRpBEEfAxOUbWicRIQJ66XStvoY38SflLhx3INLuje/opiis+UikeatW9JXM/H0xJGBEX7gHM3mHsUJ0sLFw0v46V8e4M0V7/t3TvffDwh2OaZvQFcGkRMgLVmmTrHdpqZ0YYeYc0MfFcPnT61zCuHTUW2MjjP3fOp6oNH4NTTtlTWCXjR2LIbwcKqbmogrrlQ+lx3nazlaH8vabX+Xjsyr9BkGpAB0oAq8gYGr+HNgA96qauSXSfLfJqsazNQ0tREfHowh2j+RZrbZ2FDTRGHYE85tcZajjG95C7tlGCOHPkJiWLz6+uoiUmayOQePsmrtW6z57C8dAs09tWhuFgSGlwgcIAg1OQHnC1Lipqbe2URibKxj6dqnPdK0ry19lL89/6riqXvFTNzLaLbKc3soOFPiu+CUssfR1EHsSgh1SesWT/RuPGbaLDS0yKRzAyqEBzOthXB6RkftZTWgiYHYuyH6apEY3H/PPUx76y3v55TICyenj1GFXx9mQAtAlf7PAVOL7Ah5G3DQ1OLRtW1uaWPDmRoKQ9q3N7SRVn2OxYl6wkN9a9TYUNPkMUmmLmgiZ8Ke4+6hvjUoqLTTLt6qQoKpCQ4mPkiLHcgvruBo/mkPM1mb1Ur2tt2UlZ8hbMxIakzNxJvMGECIuLk3YkhE40QEBwnpYm/1aXL3Ltf00T7xY+nap9mau0+0a2vuPq5csZRRe6SnljjqoXrlw9HLaLbC6jDyKwp8F1pS9jixKyFE/Duxa477YMJuFaKEqqWLXzy/4W22Ht3PlRfO4Bc33On9AFfx58BeD7UvCf855gKHjGGMXk/VL37Bx5u38+aX8pOspsxWa/X6G6oAVOnTnPYy+q+oxeIhADecqaEoOES8LjiET87UsGT0UK/XrLJYJetH7WgpsUQMSsuXTtGeOjXXNbJh+HAKIyMBF5um8Bimbf83xm27+H7541jqO9K8QbEx/Muio8rZ0R9CmsnE4sLvCXczRnaNxikSEea/P58X77/804WiyJ8Dq81GcZCJhDAIlxgcMnFmJis/WOe5oycIGYdJdzmhbd+KmiksNthSGMNJYxgW3WbAs3bR1FpBU9tZwoOHExnS/kXI3R5HVyyO/LWj0Qjl5u4j3iRRLV184pvcffzgtw84Gx63HzvALz98jY3PvMHlkzKlD2r82rsXo60aTk8T5gW384PMKXyoj5Wc5nHR3EvUSF8/pN9NAlEZXIz2Mr/XfapHVYOZwpBQ7G7T5O0aDYUhoVQ3eDcJ92YybfTBhFoFZ+p0w/DhFEbIp+D1l81g/MvPi7aNX/cc1THiRoSiiAg+GS5j7uyYv9tFSvOL2PvVNsoKTgkbvHj/FdWfV9zfFCX9ReH2x+/r1WL4yNR/cehcsmjblsIY7vwkjYmjQskry+VIyQHnvjZrIznlq9ld/BAHK55jd/EKcspX02Zt7KizdKArU7x2Y6sPHzsh0l6KKmJcxZ8DO7Dw1z+TP6jJx5FqtmowdczfXb1kBY2utbjtxBj0/Orj18UbW/Oh8b9CJFelz6IKQJU+zcWRobIvUi14RP9qmpSNwI1e9oN3k+l4L/tVcKZOq4KDhcifRj75pwkKIn7OLMJGCynHiLTRxM+ZJS3iIyOpDpZJ43dh/m69sZbHrlnGnZPm8+QP72LZxHk8ds0yGlpaxOLGjdQm5aRmeKO0D2Gv1P65otMTN/FDbv5XFtetT2fSSxlct348Fk0IecUt/Or9c0x75F6uXf0gNY315Faupdos9uCrNueSW7lW+GFCasfvyZqMErFhNn7w7jgOlEdg9/jr1gmNIGr0zyvPb3hb1urMDrzw6bvSO8P9SNWahZSvY/av3T36jjDira7KKPxgNQqd3kXjoexqKBon/Gz13zBapftRP8lU+jz3JER5vFC17dvd0Ycr1/jFe9kPkBCkIy00SGpkM2mhQWr61xfaxViNnFiTIHx0CgCT53lOC3HFKHfOLszflWxG2bKLVXc8KBY3bozTRrIgNQOdVvwK1Wo0DLWGonXLdWp1OrIWXt4n0mWJcan89KYx3HjtJO64diQTR4XQ2CT+gN+au4/b//go1ebDINGKVW0+jKn1TEed5bTJMG4uWCcqXjtYZ+fq98ZTZZ0m3hF5pWQXsIonW4/uV9y/+YhMpC9qkVDj5wsRlwC+dbMLC29vt/lxwdHYo9LnUAWgD3z5zsesvPEeXn34tx2pIZUeIzFExy+TYrk+NoyMsCCujw3jl0mxJIZ4CrGE6AjSWlvQuNlbaux20lpbfO4GzowIJs7NTFrJ8mX/xu28v2od2Zt8G3E14GkXY/o230cz3nfTNbz37joevuMmxXXxUuf01dJFAkd0Q7IZZeO3lJ0uhbHyDRHr5y5j3sQs8bF2O+d0LeyfH8fBOXE0xAqlCpnzZ/Ve7Z8bkSFJGCKmMCIhlERDMHnFrdjcQkpWm42tuQcpr5J/HpvaXOxBIsJgaDwkval47cLqMGqbg6iN/7swwSP5S+H/KV+pfn4+Mm/yNMX9V16oEOkbvd+7CNQanN3ASanKDUEj0kY57YU8Z1K7NPa4pobVNHGvozaBKPD9gVyWX3YjVktHQ8A/173NxJmZPPvvt1VDyx5mamSoT3OaFyfq+cS1CxhIbWtlcaL3D5Zqi5U/n28UzQQOBW6JjyBVYtRbeWExD8y6gfrqjhRHjEHPa999RuKYFK/XG7BEhEFMFAn1jaSYzZQq1ABit5NmNjNvQnvar62NNJOJoogIURpYY7eTajZjcBeA/li6uNLeFVxx/KTisvLCYpLj42X36yPP8/n/zOG07WZuffkvHCnOF41uM+mDOTxPz+z0KTz+xB+I9tdk2U/yq6spNBoZGx9PukH5Qz5j+ApyK9eSXS3f3QlwprqNEQnSkdfwYLe6TKsRGn8ruda10cQQHUtCdByExKgp307w+E1386uP35BMA2tAuRs4ZIzQ4GHaBI1fQu1fwO5S3+foAm4nZVwqWQsv95hokzLGyuU/HEfyKItXeyHKbxX8HqUInw3Jn6niv4cZkJNAAsXCiHSsbdJdqFkLL/dppI1K71HdYMbopw/gmjN1IvHnIFwDjyV6Cv4bhmeKxJ+DGIOeTytl3uwGC20W2JdLk83OulGjaAqS/r6ZZjKxuLJS1N3bpNXyyYgRFIaFea6LiYQxycL5/bF0cb0vF2+/0rIK7rxTfs7zezn/JcxgoKb8PPFtbR0CVMLr7uuCGJb+I43aZs/HqtVomH/hDM8ZvgHCWHGW27/4nK8rOpowFqWl8eFNN6EPD1c89nBxNtMfvU92/9v/dzlDE8oQp4G1GCIyyBzxlHix5LQPAdffT3f/PgYD3x7LYeGvfyYSgRpQ7gKWw7RJqPmTMYU2Vf6TXR/+hk3/rKQgL4Qn15xn+mWujVdBgLJrgyJaA6T8F6xVqsF3D6EKQBm+fOdj/vDTxxXX+DrGSaV/UNDcxl+N8l3CS+IjRJNk9m/czuPX3Cm7fs2X75O1QLmebcDTZoG9R2iyw4eJiZS4RAITm5u59tw5RrTIN29UBwdjjIkiPi4ag1YjTAbpZKrXibuPIPDY46vIycnF5iJCtTodmXNnMXndc+0WNgIdQvR+wetO03GMI8J13frxspc/uvYTwWOvNV+ImnT1w87cDAePc9WB79hcU4XVRQ7oNBquTE3lqzvu8Hqaa1c/KGlpA3DlhdP47V1J7bWAAoaIKWQMX0GwzqUWtzVfaACQYdJLGZw0ip8/5+9DpdO88Om7bD6y13cfQDekptgAwvNp3gnnHgZbrXOzza5Fo7GjkW1DCQDqiL9uR00By3Bom/QboSu+jnFS6R+Ut0p3bDoobbWKBODxfYcVVkPe3oOqAGyzgNVGOHB3ebkg6IKDOyJpMZHgKgC1WnARYYa2NgzVNVBXD9MzIDhImBtcVOIxXsqn7SMSJY2dVz71EKtWv0R2dsdzmjn9VifVWAAAVnVJREFUIia8+FuK3NLXRRERfD3MxP+zeb5HBGlhUXo9Y+ObPYSOg5LKPNItPxWPRevKh11OHvkN9Xxd42lJY7Xb+bqwkILqaq/p4F/d8jNZAbj5yH5e0n7C+FF309RWKfYBdMVLGjDN4Pl7KawsUwVgF/nFDXd2SvjJTbH5YPnPCa1YSjjSNc1ajWc3cMDxMhVGpeuoAlCGi+bMZMvf/qW4ptetHFQCygiJphJXUtz2T5g+RXH9xBlTu3xP/R43axaDawoVYGSSkMZtagENlP53OxUVZxmRNJzkZBeBYbFSvyuH1b97VTw3eOHlrHj5t6xd/rRv2+dcysoV9whzhl2Ijo5izfMrKYsIobzayIgRwwlraOGVWM+0v12joTH0LDTJP2wpoePgkqjfgsmt5s60EcoXw8gt8ieVwlgHVhuFTcr+lieNRq8CsKqhVnG/INQulRZ+DnyYMuJO2nBl2xiV7kNuis2RbZnMSq3pZYWgToXpblQBKMPVd93CS/evdNYAho8ZSdioFJpOl9J8uqTPWDmoBI70sGDCNcjWAKa5NYFMW3gFMQa9bA3goI/+gXdrlvYavvrmZlbfcj/Z23Y7d2VlTWHlUw85xdrqJ54l5+BR0eE5W3Zx38zrPKYTZG/8lvtmXoepvlG8fsdeVjWaWPP8SsnbSZ40juSIMKiuo6CoXHKNwXqSGFuF4sOSEjo6rZY7LkknwvqBxBF2MG/1/8OuWnjcaeHKNa5jFZpYHKQOG6G43yehpksQarncJk1YrB3NH86lWi3zMqar0b9eIr+iWDLim6o3c/m4PuTb13xQFYDdhGoDo8Bruz8j1BDPpPdeJWvbv5n83qtM2/5vZmz5J4+sVwuXByI/HRJNuJsBYLhG2C7Fa999RoxBnLZzdAGr4DklwhUX65bVS1aQs0PsW5aTk8uq1S8BQqNGdvZhSasWqdFUAA01ddLWLtmHKSs7o3g/hId6WNiE22u4w3QTy01ZXNciNI1IVVDb0PPX5a8wdYy4Fm5exnT+eMt0yXt1Yt4u+jG/upr/FhRQUC0zuiskiHxzI4VNZi6L0aNzc6/UaTQsSkvzGv0DGJc0igVTZnp4Guq0WhZMmembUKu4HWye4qGxXsvy18UCcl7GdNavWOX9nCrdQtFZ6S84qfFdn6gTUGpe6e07GLCoEUA3CprbKG+1EqPTEH1BOtcc2cb5NvGHSMjYVF5vhuUWm9epESr9C32QlscSYylsbqO01UpKiM4j8udK4pgUPq3MIXvTDvL2HmTijKlq5M+dCalwvIjS3BMd6d2M8ZQGaaj4ahs6nVaUpnVgs9mcYq2i4mxAb6m8vp5kOlKZVQY9NaNHEO+Y8xwRRkJUuMiO5ibz/5Bq3eb13HZrDRdZHmPPj9+jQB9MYdWZjuL6mre8HCz8z9jUxO0bNvB1YUdNnXtHr7Gpidu3bebr0x3epIagYKotHcL1ylGj+fAmZV9FV9avWMWStStFkSGfhVrTPnFdowtxehsz8+tIqG6hKUrHun+8w+xLLvX5vlQCj1zEt0imdKHXaNqhpoG7CbULuB0p/zdvhGngcQlrEBUVlQ7qjbWsXrJCJPLkUudSPPvskyQlDVO0avGX9/K2kjwiEbO5hQ02DYWWjj/8tHbD7/CWFpoOfc8nCUOoDTvDclOWwhklqNoAMZOFCRkOvHTJkpoPIelc9cEHbC4qwmqX7+iVXIOGS2PieGLUWMYOGUL6bGWzYDkKzpRQWFnm2RUqhaOb+dyj0HpMdtnvf2ng63/HsfCWDB599ad9x+ojUN3Y/ZBrVz/I1tx9It9KbHb+vSyf+Wn19Jn4RvKXEPWD3r6LAYcqANt57kwdLZ34Tbhbg6ioqIh5bNEd5Gz7TmSx4g/vvbuO5ORESasWjVYrOZ9Ubr9WpyNz/iynh+f6ahNFLRYPH7XU0CCWlJc7O4brI/YSE/2Afzde85IwF3dCEERP7BAXJfOFej93IubByC3kV1cz/pWOtFd6VBVpUTWcbIznZKOB/AcewA6iNe7kL7iW9GlThBFt3YXV2D76SzrqJ0VjYyhRUS6NQb1p9SFx/ybd5XzX+AR2bRwl5ytptdVycdpwJqVMUW5+6Q+05kPd38F6HqKvhcgF1DTWe0R8h1pDaY008f7NhSxK7+iYzykPIy7cRmp8a8/fe/sXI5XAMuhTwGabjXeqGjsl/sDTGkRFRaWdNgulX31L9tZd3tdKoNXpyJx5sbMbWMqq5eLLptNit5G7Y5/H8RmzpxMaHiaKPLqOYquyWCls8TSutQOFLRaqG5swAOiKvTZ9SBL5LoQcgvMI/0UuAv07EP422O6B5k0uaxc5Z+AWGo0A6EPM/G3mBq5K7EgDf3UmjdPnr8KiU27qODkkhvTuFH8gPffVCyLxB71r9SFx/6Ft32ItO+Th43hhaii/+8k8ZqU+KvY97EFkvfq8YTVC2f8TUqkOateBRo9+zAE+f2qdKOLbWNfAzF/exXXrxzM2vpk0QzOF1WHOBp7vX/wdo2POQs3L0LTT43J2O2g0HpudNDIJu7WBaJ3yfGEBnTAfWhV/3cKgFoBmm411Zxto7kIM1N0aREVFpZ3jRVSc8DIeSoHMOZewctXjYBL8VpxWLWVnKK+oFGoJx42mYexIVt3xoIcNzMp3XkRbkUfRnTOoKWkmdXg6yRnjIUowda6xKEcka4NNGCIfFk36cP9ws9sBO2jcUmVWmw5t8BFxS0bjZjDeCLUvA8+C/hEY2QoR40UfcGntHbt/m7mBK4cVic575bAiWpoepDzx34r37kvXb5dwzn3tKr1k9SFz/3I+jkeKWnji3W28el+I5+STbkbOq2/9ilXofRkrWHG7WPw5sNfA6Wkwror0xJEdojJROP/WI/s4aewQfo6u7dHJVwjrYn7EildvYdmEz8lM6rAhqjLrGBLZUTd/9GwYn+XFUVIXxs7T0e3ni2BsfCyf3FHOeEOtrK9glTWLQ1UPMSqoRO0W7wYGrQA022ysPdvQ6cgfSFuDqKioIEynqKknKWmYX4eteX4l1hFDGREVRXJ0tFP8uZKcnNjhEWhuITo0lDVfvEdZwSnKC4sZkTaKYSl6ck8+R3XkKbgIuAjONZ9lWI2e4OPAheO8NnClhD4KwZ6RRVcO7g0lbXwrsXrxG4lOK2EqrrEKYlJXAtaRUBMPxMCFYuEzzmDg7gvCRZE/B0FaO0Hh+xhXeZBFaWmydYK+dP06aZ+J7NdYPW9zX/2l9WT3CEC5+r5OGFbnFDRxpCSb8UPO9Gg6WM6rb8nald7H6HkT6rZqYQSc2+g3X5qB8iuKeWP7Kd7YPskjUigVOXTnpDGMOW+O4v2bLaJU847Tkby2ZxhHKiM5abQBvwT8FL0qPjFoBeCH1eYuiz85axAVlUFPuwF0SnISWVlTPGr33NFqtWRmZpCVpWyuLXutiDCS08c4vTlzjj9Jdchp0bLq0NPk6v9DpvFmMDeTEBFGWmiQZw2g3c4UUy4hIbtxxxH9M55+gJKiYSQNTSBWf69/96srFQQgCDWG5mYP4fXSzGRFo2mavufDq5dx25efizqFr0xN9b3r120mMiBY4UxI9V476MXw2W9Cxgb2fA0VUHkHWL/p2OZab9gJw2qAM9VtNLVVigSgIzWr02qx2mySKVpTawVNbWflJ6jIIOfVZ7XZ2HR4DwVnvETGfBHq5u88BKA+KsYjNWy329lbcNT5+FxtZFwjhVI/y1HbHCSbanbHZ9Gr4jODUgBWWayUtkl8Q/dCMHBxRAjpYUFq5E9FRQkXA2ip2r2YmGjq6xucP2dmZrDyqYe6fC0AU90pqoNOeq7T2KkOO4VJZySyXTQu1kfwSY1ZVAuYajbzg5pDoNDgH68fT/ykmRDSifpGa4r45/Z7cSU6LkNZAFpT0Ns1fHXHHRRUV3PSaGRsfLx/kT938QfCz8eLxJ3LUoSMEwSVaTPgx3upHcR58QDXeLVZIK8QuLN9TrPLPtd6w5BxELYAmrZIznKWEyGJhmDCg4cD0qlZBwumzOSqzEvZn3+EtJQqLsmode6TnKEsQWl+Ef/dvlFxjdcxer4I9YhLZHelJ47EEB0rmYJ+5pb/9X5uH/FFMPoselV8ZlAKQG+1P+7ogNvVbl8VFd9xGEDX1EvX7iUnevzsD6VlFYKn4ISxwuQOF5rM0ga3zv1BtUS2i8ZwrYYlhkiqG8wYT5zumFGsS1K+gbYk0GmFLl9fsWuhdXpH9M+B1LSU2AwomwXa70QCRXSO9uPSDQb/hB84U/SSyEQlPUj6UBBUSilGd8FniwWdi3F35JXO5peAcLwIGo5CgtRMY7d6w8i3oO5WUY3nlsIYlv5DWjRlpodz4cgsZwRPKjXrYNPhPSLBpPs7PH7bEIJ1GhINOcBa2VrCg8eP8txjz1D27WHhd7dAvp7T63QWp1CXeY60Bo/onzu3vfgE245mi7ZtOryHNqtFqBV0s5HRajToo2KobpA2aO8q6uzowDEoBaA/5s1a4IGh0arh8yCiNL+IiqISRqSNUsf9dYV2A2iH0BDV7kn87Av19Q2sfnatKJqYtfByVn6wjmh9LJibCbfGKZ4jPCzJQ9wYWtswmF3m6VpHQcvM9iiSjADDBpEXgHYOduu3aNwiSTVN4mJ4WqdD3WrxzbhOH3En7VPIvx5CXOYGO86hdJwvuM1oltzv7fw6vRBNa9wIZYuk12gA48tC/aM1Rfi96Upggk5sjRMIHKI2pEx5naPeMHKY0JCjK6HBepKHt+3hvWPSx16YGspzd84hY7jgRSmXmpXDaoPVfz3v/HniqLM8ct04po+d4RQzoohiGLAwnrizLcSca6E+IQS0HUrarzF6SR+2dwG7ma1r9DB6v+Kh+RXFHuLPwbaj2Xz88PMAot/F/AtnsH7FKorOlvGTV37D8fIiyeM7izo7OnAMSgGYEKQjLTRI0gLCleFBWpYlRBGuVehpVxkwSBkWi8SFin8EBwmpREeTQXAQnC6Xjzz5wOpn15KTkyvalrNlF6tuX86aNSuhpp5IwBA/hurQ06Bxqe6zazC0jiZy0kzPE0tF4epWQ+xTogiRh4gzNYH5aTRu6xyRpKGRNm4cl8Cvp93nGflz1NvJETYEJnwLxzZD0/cdAsrbcb7gy4xmn/GSAtZYoXVWx88xk8HgJcXcGRyi1ltU1lFv6IxSjySakbw5Zx6PXnSOwrrzBMVEURKto8Vax8Vpwzx8AOXGqPlKXnErd7/8O6CjuWHp2qfZekQcUawdEkJsVRtx51upHdbxnPg1Rk+nh1Hbhchn/d/Bcs7pA+iNHXkHFfff8ofHMURLvzdenDaRc/UyIww7ydQx49XoXwAZtEbQTTa7R+1PSrCOq2LDMNvsxAdphZFQKv2PznQ1Ao9ds4ycLbtE82PdjYNVAoC5GRrNUH4O6hudm6uCgykODwe7ndHNzUIq1oXSsgrFaSAOw2iANk0zufr/UB3mMibNMpaMMb8gOCxO+gRH8j3EaWlZBdV1R0hOsZCgv9BTxLlQZ8nnxSNv88n355z1TAtSJrJ+4d3owyI6FkZFdAg4X1+nnXxNKyLxeAFBFHmrAXTF23STqg0dvzdfm0w6g7kZ9h8V/h23XDp6Gz4fRrvU1XWyESa/opiMh24OyG3rtFpmjstg14nDsmsu3ih4Q97yu0e4+tqre0wE/WXzv7jvzWf9OsYRnXzwmtv44bOBm94DsPu5d7k4bWJAzzmYGZQRQHCp/bFYMVpsquAbCLRZhA+1RpdUXkwUTB7r9QOnNL9Ieh6t1Ur2xm8pKzilpoMDRUSY8N/QeDA3Yz5exD9i9ZyKiBAtG20ycUtlJeHt9UXe5gGXV1Q6BWCwPYxM482YdEaaRoYQHjeayJjRyvflkrKWTDVnTWHlUw8RHS0U7zvqENvio2nQxzA2fAS/znyeO1KFKFJa7BDS44aKr6GPgbEj4WSJf6LD8TsLJG4petF9+INsQ0h7g8fIqwMvXqVwqTuVjN7aL4WUj8XHuEepfbzHcUmjWDBlJluO7MXWxRiK1WZTFH8ATVE64s+2ctOcRST3YAQsJcE/GyfoaNYYlzRKcd3wk2biz7WhsdkxRes4lx6FOUI62+YQlar4CyyDVgA6MATpVOE3EHD99t+Os1Hg+0KS510iRF5AeKPXIBSot7/hVxQpu9KXFxarArA7iAhjw8gUTrV6phFPR0TwyfDhLKkQpnB48xQckTTcY1ukNZ7IyHSI8SGF7yIGVl//E3IOil9POTm5rFr9Ek89ucJDHFanJnHshiuYn5TMhxOnioXfuFEQEtIhLqQib7523waSToofSaQaQhwNHrpuEK9yTEgVuoBrcdb3oSuFiAtg4nzQBU5gS3nldReRTXayFl7e7e9B7tNGuiJuE6LjFPcbKlvRnxei/PrzbSQXNfPAh3/AmhTDq199zG4XUexXylvFZwa9AFQZIOTkOf/pS/RGhD6GpJEjFE8/Ik3526xK56iyWClss0nPjtJoKIyMpDo4GENbm6ynoMNDULahxEs92/6N2zm+7zATZ0wla8FsSssqyN7m6QFos9nIzj7M0798gby8fNE+/akzTPp0O5tvX8RteQf5asp0YUeQDhKHdCwMRPdtoAlEdNHRENJa0N5kMbZ3xncFB8GU8cLvsbYBGAVx0d3yO3V45WUX5vHAm89z8NQJ5z7H98suY7MTbWxj1sxLnCMMuwO5aSO/uuVnnT7nzbMW8Mp/P/LsBrbbCWq1OcWfKw0nyli6+EEWX3qlyINQrfvrHlQBqNL/MdYJbXbtSDYKtEdv1jy/0uPw+uJyXvndq5KndtQAyn3zVjuGu4YvlkzGdgEI0p6Cih6CCt2y5YXFPDDrBuqra5zbYgx6fvr8E4r3c/ToCdHPyaPaSEppo7ykmHxjHV9jp8BsIj0mBjIniA8ORPdtXyYkvW/Mbe2OlLkMWWkT2bPmfQ/B8t43/+Gbo/uZkT6ZLw7s7FykUKuhISGEY1NisQR3XzOi3LQRQNLqRQnXDuVdz77LrCfvFInAoFYbF22rlTx24oypzn+LxtOpdAuDtglEZQBxugKKhTShP40CDh57fJXspAq5LmC1YzgwVFmsvHKuUXHN8tOnPRpCPDwEY6MFmww/6upuGJ4pEn8OouJiaKz13qkcHWPlyTXnmX5Zs3PblsJkFh/+MX+76lZ+kHmR50ESpQoipk3u3wJQRZaCMyUcPpXPa1/9nV0nDvl1rFajYf6FM7plCoa3hpZdz73Lrz56Q1bAGqJjRQJv1gUXseEXvxeNbNt8eC97C3KZkZ7Bq4vulfy7izHo+bQypwuPRMVf1AigSv8nJtL5T38aBUAQjK7RJHdufuh/JAXd6iUryNkingKRs2UXq+54UO0Y9oX2urOE8FAia89wpKIU/ZAk4oe4WHjY7aSZzR7iD9w8BMNCYVKaIPR8rGfbv3G75IcQQGNtPemZGRQezhN1hLvz5JrzXDyzWbTtijHlfBjxCWmjHpU+yLVRwZ2uevt1hpq/gHkbRM6HuDt79tqDDEdEy5HevPl3j3K87JT3AwGb3d5tUzC8WdpU1dd6jIUDnP82RMdy0wuPOGv2dp04xE0vPCISgVdOmcGVU2YAMOm7z7jvkus9Iu+vffdZQB+XindUAajS/4mPFWqtLFa/GwVOnlR+A37s6qUekT21Y7gLuNhuGJtNLN34DptKO+o3x1yQxfVLnyQ8IprRZjOLKyu9nzN9ZEeUzzX1pyAGj+9T7rrMWnAZsQl6yecZhLSva+TPQZDWzlWJhRBtBGSmcwSq+9YfWvOFubCO2rymA1B8CdAurhs+gMqfwqh9EH5R992HCgB2u91n8edKd0zBSB2mXP/sEHzuKVnHv69d/SB788UlN7tOHGLSips4tnaDKBIIkDgmhU8rc8jetIO8vQedtbcqPY863kJlYJA5AYJ0zkYBrVb80tZqtWRlTfFI/37yyedeT+2I7DnwpWNYRQYX4bN04ztsLRPX0xV/n8N3bz3D8tOnubOiwmkBI0uQTvgC4IrDDmj/UThaIPz/SL6wvZ0J06convaiKy7hgT8+I7s/KcUzKimiVWIWsQNH9+20yTA5Xfj/heO6xxvPaoTSqwSfvrKroWgcFM+G4uk4xZ+TtvbtKt1NZ42ku2MKhsPSRuf2nqnTalkwZaai4HRMRJGqD6xuqOPGFx6WPTZrwWyWrnxQFX+9iCoAVQYG4WEwayqMG8XKpx4iMzNDtFuqUaC0rIITJxQ+qNtxjewBJKUqfwNXO4ZlcOmAza89y6bSPKx28QeHzW5j76lcjOfdPiBjogSx50qQzrPJAgQbECmblbxC54/TFl5BjEEveZsxBj1ZC2YrCv2KUi9zwdvKhY5YJSLCwBAb2LRvaz40/rfj2hW3t/vzudC0E5AT1m1Q+27g7kdFEm9RNykuvWBKtzVFrF+xinkZYvHvi/WKNyG7+8RhCs4of2FW6T3UFLDKwCJxCNEjk1jz/ErPRgEQhMTIRNDAkUNH/Dq1wwswZVwqWQsvl50aoqZ/ZXDpgC2qq1JcWlh3XvDSG5UkGEY7RJKxDupNQt2ne+QPXOw/JKhtENmsvKZQi1RvrOVvz0t3hgOUFQfTXDeVsJjD4mkTDs7eI/w/clG7F5602AwYVmO72Ovw4TOHzCKidZfCQTKYtqj1gN2Mv0bS0eGR/PMXf+i2+3FY2nizXnH3CfRFyHZH2lolMKgCUGXgMSEVjhWSDOKUb1w0TBSaBeqNtWx4+yO/Tusa2bvrVz+n7ryRAhez4Mz5s7rVq6u/UmWxUmOxER8S7KyKS41NUDwmLbbdO89V/IEg+qSEHwgp3qNeom61Dc7zKdUiPXbNMvL2SM9BdfgOhrU8CK3u0yYQTOAcmDYLBskpXynflze8NbdIRPrCWr/r3LUi53fuOBW/WL9iFT/47f0cPPW917Vf/fJVj1q67kDOekXKJ9DR7TvrgimKk0y6I22tEhhUGxiVgYtrNMjNDFaY+7sTm9VLjRniecBS9i/pUyfzf6+vZvzFFwb8IfRnzDYbG2qaRPO201pbWFxaRrjNxrX/foWtZSdEaWCdRsu85Av4/LoH/J9JKzfb1pX0UZA0RHFJaX4Rd06SF0GTL5rEqmce6TAVDykH7T6IVZiZmprfKX+8/MqzFB45zlirhvSI9m53d3sbb7N4/SIYLmgN0LlUvOHNgkUDXDllZrfYv/jDtasflPQCNETHSnr9Qfda16gEBrUGUGXgEhEmfNgnDRGJP0cXry/iD8SRPSn7l8Ijx3n7l92Xnumv/MNoFok/gMKQUP6eIkQE1i+8m3nJF4j2z0u+gPUL7/a/K1ZpwoYrcdFel3hr8rn9t48QPW+m0MCRkQ6tI8A2VPEYmqWjiXIYm5q46oMPGP+nN7j6u+2M27eNqw7vo6atrWNsnIPWQvkTATaf3+aDhS5glR5jXNIoDNHyvqFXTpnZ6yPQNh76TrHR4+5Xf8WxtRu49AJxY9X8C2f0+r2rKKOmgFUGHd4+4F1Z8+X7zrSgav/iO1UWq+R8X4BTIaFUZ07E0NrG57PfoKDunFB3FDuE9NihnZtJ623CBghm0T6c16cmH4fdTHV71MPqJc1V8wrE/KjjZ3MzNByDoDKInugRHbx9wwY2FxWJtm2uqeoYM+c6Ni4kTfHSpdoZjLLJpIPtgHYYDHve97q/QMwOVgGECKDHqDQX/nj3Iz2S+pVCKu0rxe4Th6lqqOWb37yljm/rZ6gCUGXQ4e0D3hWrS5PHycN5Cis7mkRUoNgt8ufOaa0OgyECgPSIAIx88jLvF51OMIv2gZRxqWRdcjE5ew96zhyeMbXjOW6zQIkwgQbrKGidAiEytVBNO4TOXM0YOJ4D9hUdtYPngfCFkPwR6PTkV1fzdaFnVM+Kna9rzgtj5iIiodEsCDBNKtguw675Do2m4/VqQ0eRbg4fRG4g3lrIXZVbiA79q/geW2dC2j8hSmaOsiuOGst6U8c2L9NWVJTx1kXbmw0UUuPh5HDcpzq+rX+hpoBVBhX1xlpe+b9f+7ze0fhRb6zlxf99XHGtTqdT3D+YaPSSXi9usVBtEcRKlcVKQXMbJ5vbKGhuc273i4gw0UQYD6xWkQ+gIuZmVv7iAWkroV88IHQig5CGdRVD5luUz9t6UjjGvgJC3D5YzVuEZhGg0GhUPM3JpvZrlp/ruI+qX9NcL65BLdLN4ZOIv2C3WDi5o5K6/Aug5s9QtQFqXhL+r3nPd/G3L1f8eMEzHa3iF76aMPc0Sv5+UgRp1fe+/oj6tU1lUCFVwyfHRXMvcUZ7nr7xHswNJsX1VoWxYYONKJ3yd8sjzRaONDcSroEmiTa0tNAg5kaH0mSzEx+kxeDuASjFiGFQryBGmloUU5ZOi4uwWNKjo+SthHILBDuhercZxhYvDSvWFKjPhQSJlJrGKli4tBaQFh+veJqx4e1Ct75REKM19UAMR769lzc++D3TV/+YtinXYtQJEc+anbv4fvnjlK96jGQSwTpS+M+fOsujJ0FOmLumo1X8wmEH495godNqmZcxvdeiaf4aVVts6ntff0QVgCqDBrkaPjk07X4e+zdu5+iubK/rR4wY7nXNYGFUqG9vLVLiD6CwxSLuHg4NYrE+gnCtRvoAgKgIxWtVhQRT09zmISilap0WpExk/cK7nTOH82vP8lXxMaFOMW6op/gDIQ3cMlOI7om8AXUQeSVYUkC3RfEeaT3JOMMPWJSWxuaiIqwuJg06NFypT+joBgY41mFknpQ0jJK8VkpueIew0VsIH51C0+lSmk8LNa8jrrwURiT6X79nbpZ+vK54Edcq8qxfsYola1eKXn++mDD7irt3ny/4a1StWr30T1QBqDJo8Kf5A+DgN7tZMedHHN2137cDjhSAXQujRwgpsy4Uye/fuJ3j+w6rczLbKWqx8EmNmSUGhTRvRJgQ1XLrBjZrtWxISaawoQ3H+DNXQbn0pZVszRU/x1vLTrBk49u8v/Auj3nFDnGoD5MQnHWrIdbNGzDySsEMuiXUe7NI+wSRD2+6ids2bBDVAl6pT+DDiVPJrz1LUV0VFyS0MlpvFiKL1pHOMYg5Obk0ny5xCj8Pg/LuaLDxVoOpIouvJsz+suG7zTy2fh2lVR3ztBe0dxV7aywZlzTKq78f9H6kUqVrqD6AKoMGb/5uXeXZZ59kxvSp4o1+FsmXFxbzwKwbJKdTJI5JCeTtdisFzW381WgO+HmXD41STge3WUTzhgHWjxpJUUgorm90GiA1NIgZLVWKPmyXDk9j79lT8l6FUuhj4AKNUPMXMlbc4XskH+zLJKKEbkQugiHrKdhxgpMNDYwNj8SgFeYnZ1cd4b3FhSxKdxG6LTOhbjUN9VpWrX6J7OyOD+6shZez8oN1ROvl7UYUMTcL85TliI6UHsmn0isUVpYy64m7qDF52iJpNVqmjB7H+odWeRVt+08e47In71JcExMeyb4XPmBMJ0bbqfQ+qgBUGVQ8ds0yDmzeid3H4mZ/eO/ddeLJIw78MDS+YXimSPw5iDHo+bQyp6u32GNUWay8cs5L2rAT/Dg+gvQwL3N4wWlVUhUSzCsNbbLLJp3O4Y6XHuvUvRz98a9IHznGv67YNgscPwj2+yBUqaxAR6vtUopNbxDf1oahrc1pnP3pHceZn1ZPkGuZpV0LrdOh9mUAyoxGyoN1jBifGpjO9F0H5WsAY6PhokAZUat0laSfLFC0lnFw6QVT+Ocv/iAZDTQ21nH1b5dz8NQJr+cJ1gWR+9I/VBHYD1G7gFUGFSs/WMfIC3yzA/EVrVZLVtYUafEHHUXyXti/cbuk+AOor64he9OOrtxmj5IQpCMtNAiFir1OER/k41tWRBgYYqkJVhaLcdFeDJwVKLSaYOoEmDZZMIWeNlkQ+krR3uAguHAaRId4ObuVEO0O/ju8mZdHj+Z3IXY2leaRGm9mUbqb+AMhmhi6h28rt1BQe47kRZejzxzL0cYzFJzxr/TBA3OzvPgDqGvw6fWt0v1sPPSdT+IPBP++SStuoqbRM1K4dO3THCnO9+k8bVYLMx9f6td9qvQNVAGoMqiI1sdy04N3B/ScmZkZrHzqIeVFPtRRHd+nXG+Tt9e/aRK9zWJ9BKk+NoN4QwOMDNFhtNj8sonRexGMmdYgFqRMRKcRr9NptIwdpZzWrIjSCeKqXWz6XFvXmg+tu31aGm8TuppzTcIXg9R4ZaH1+yN/YfJff0XS8h+S8dDNXP/cQ0xesZhrVz8o+UHvE9W13tf4Uieo0u3sP3nMr/XVDXXc9MIjom3+WsAA1Joa2Hx4r1/XVul91CYQlUHHhbOnd/kca55fidVmE9uDKOFDkfyE6VMU90+cMVVxf18jXKthiSGSaosVo8VGhFbD1oYWUXfvmBAdbXY7ZW0dHzYjgrWEaDSiSSJhGihptTrrCn3qCqYjElnUYvGsATSbMdQ1sH7h3SzZ+Lao0WPMyPH88Ker+Nf65zidnyNZMnDvn58HvBTWO6ZmBJWArkyoCfQyus0Vo1awaYlNENJrRUZlkVlYLex3jwJtzd3HkrUrOzeXteK89zVqE0ifYNrYSX4fs+vEIQrOlGC329mRd5BdJw516tp7C3K5csqMTh2r0juoNYAqg5KHF97OoW9kxmN5YeKEdF5++Vn/DvKxGWSg1AAq4RCErnYsStu+bWihrNWCXdMh9hxNHIpdwe002ex8UiOeS5zW0szisnLC24WdsdnE4i/fYNeZDnE25oIsFt38IF//Yx2nTsjX6zk6IUXiytGMUlsKsSvFXcHhl0HTTsV7dp3i4eCjN56g5PscyRpAiw22FMZw3XrlWryjaz/xr2PTWwMICJHPaZN9P6dKt+JrDaArGaPSyS0u6NJ1v3jqZVUA9jNUAagyKGmoqWPphLmyNXdyxOjjeP/tPxIdHeX/RX1oBjlzqpT7Lrm+97qA+9ic1+KqWt5plY/yee0KdsEpMtvaMOSIx/o5Gixcu301Wi2jx2Vy68+ew3i+jOKTR/jq4z/Knl8kro7kC7Wfccs9On7t6DA1BhEe1oJO5vvASd18Pon4C82aOOe2JnMDh/70NIfOHub9m8VdwF8XxLD0H2nUNit/wfjsiZe4auqlimtEVNcJ49+UmDpBeQqLSo9y6mw5lzy+TLILuLswRMdS8ZdNPXY9lcCgpoBVBiW156v9Fn+TZ01j1V9fJvpkqfyiyAgwydif+DAxIXFMCp9W5pC9aQd5ew/2nA+ghH1KX5jz+sbv3iB0xb2y+40Wm88C0BCkE9a6PT/5tWdF6V8HdpuNUyeyMZ4vI35IMjXnKxTP75zbam4Wfo+6YnHkrx0NVqKirBzODmFKVqtze0mxnm83T+T0JXfSlnUDGk3H791utTIZHWuuvZ+C2nMU1pzn9PlggpKbWbTmJU56SQ078Nuw11tqNzpCFX99jDHDRlD5zmY2H97Llzk7GRqrZ8GUmfzw2RUekUGtRovN3jVHhPCQUHY9+26XzqHSO6gCUGVQ4o8pdER0FL/Z8Cemzm2PnFTXeZgNA8J4sJGJyhETHycmZC2Y3bMG0HmFUNsg3lZTL2yf0vMWH/kVxezLyWbPF19whYIA9LUruMpipcaRYnYTNUV1VYrH1pyvIH5IMvqEJMV1abFDhNdGS7uo05Uprt/07yj+8Ew4I0a2UV4STHlJMFBO0NvrGP9yEvFzZnXcw449XKrTwIJLSbenk94eoc2vKOak8Q3F60AXDHtlzLUBCNJBhm/2Rio9z5VTZohSssfWbuCmFx4R1fhNGT3OJ6sXJT559PeqBUw/RRWAKoOSpFQvH4QaDUmpI1nx8m89hdiEVPloWZsFRfpisby52VP8Oaht6NE5rx5j2SZCybM/Y/EDzxEeo3eus1ssDGlowBAXKggRGcw2GxtqmjzHysXHEm4UoiGpsQmK96QfIgi/acOSOTl5Otvysj3nto6eRHppHZS6RFi8TP145DdG9u0MY/VjQ2is73gMlvoGji27n7DRI0Xj3KqefZI0O0LHcTu+zmzt0mgxqdd7TBRMHtur0WEV/9BHxbD1N2+KJo7Y7XZFI3RvGKJj1bq/foz616syKEkZl0rWwsvJ2bILm9XTViRrwWz56QnBQUItn6twiosWtgcHyUdM9DF9oq7OAznx57q/h+576dqn2Zq7T7StrPIkn7zyBEue7Ih01ezcy1KdBqqqFFPVG2qaKGoRi/KiFgufDB/OErsdauoZFzeMBSkTZWsAJ0Yn8MOSEka0tHDtjB+xpL6RTSUdKeN5yRewfu4yzwcjOxu4g4tnNvPUmvM8ca/nHGnXcW4AI5KGe3yB8DazdULyGP7x6O9kI3/5FcXsyDuIRqNh9sSp0utcX+99qD5UpXOkJ44UPc8LpswUzSH2FX1kjJr67eeoTSAqg5aGmjpW3fEg2Ru/dW5LnzqZ/3ttNeOzLlQ+WKpmLiZSMASGPllPJ0vFeSgolt+fPgqShnT7beRXFCuPZcsLxZA4ipaSMibGx7Hm+ZUdOyUabLxNI1k+NApDaxs0tVBja2XJm78VfRCOuSCL65c+SUR4FKlmM0sqOmoAC2rPUVh3nrTYIaTHKZhJa+o9ZwNLcOcPUxiWkAlATk4uNpcIo1arJTMzgzV/ekGyieja1Q+yNXefh2+bITqWY2s3oI+KIb+imKKz5cL9xg7FaG3htjd+xbaj4u7mOZOy+Ojh573OilXpOzif207OEK5prOfWFx/3eC2AEDWU8o/UajTMv3BG52yFVPoMqgBUGfSUFZyivLCYEWmjfB+b5ejydCdIB9MzBKHXXyIm3qw+MtIhvpNzZP3gq4O7uf65h2T3T9xdR/zZVrKyprDyqYc8O7GnTRb9nr3NI3YfK1dlsfKbYyeoOV+BfkgS8UPEKdzlp09jaJMfK+cgv/YsRXVVYnEY9i+IXS17zNsvzuTm654D8JzlmzWFlb97mugZUyS/QNQ01rNk7UqReJ11wUVs+MXvsWMXp9SBBSkTabNZ2Vb+veS9LJgyU/1g7wd4lEvgxZPSCwVnStiRl8O5uhqGxuqZPTHTa4rYb1shlT6FKgBVVPzFm2CKiYKpF/Tc/QSCQ98LI73k6IEIZn5JERmP3Cq7/68ZP2Jm6nh54+3J6aL6OH8igISHUqDVKQvG8nLSzfL7jc0mlm58R9RRvCBlIusX3o0+8jwkLJY9lqoNYO34IC0rO0O50ciImVNJnjRO9guEa/QHcNZ2OT6UpaKDWjTYUH7bVz/Y+z5Sz62kJ2UX8PalzG9bIZU+RR/MR6mo9HG8jb2qb+zRxoku02bB69Demnohre3Fx7ArjKu1SNbi6TRa5iVfwOLL5yifwK0+TnEKSLAWQ26B8Fy1ozfoIV6+ISTeS/Rv6cZ32Fom7qjcUnacm758na03K4g/CZKTEzFfOIqjlkaa6s6RHiEWY75EfxwjvdzxJv7AxdJGpU8i99xabTY2Hd5DwZmSgDx/3mpM/bYVUulTqLOAVVT8xZdO3v40G/V4kfdGEOjwMewO2r3z1i+8m3nJ4ujpvOQLWL/Qy/zmmEhJwS01jzg1WMvik0Ui8QeQUF1DWnMzGrekiMZuJ81kUkz/bizJY1Npnki4AtjsdnadKeSxPV4iMsEHIGQX6EowNpu49t+vkPHb/5Gd5SvVLOMY9+bA1w5hKdQP9r6Nt+e2sFLZgshXxiWNYsGUmei0brOytVoWTJmpfkno56gRQBUVf4kIEwRHvUl+TS/YvZTmF3Fkx17QaJgye4Zv9YwO02Jf8dHH0G/aBbM+LILPr3vA9yYLAJ22o/nGDfd5xPFBWgy5+dAqLeYWl5fzyfDhFEZ2mBsnGWsYs3U7ZQkGj/SzsdnE0q3vsemU8ri0L4tqWKO0ILZjtOCppiRyqhJx/X6+NXcfN77wMI/dcBeVNecVoz8HCvO4OG2i1+iNHOoHe9+nJyNz61es8qgx7ZKtkEqfQa0BVFHpDPWNcFDBQNWtIaFbb8VYy29uvY+DbrONL5p7Cb/6+HVpKxsHvoz6cmViKmh1gW9s8WXmrBwTUmFofOCuExVOdYuFsjYLH659k/3/2ejc5d6Acu2Xr7O1+JhHB64UZY/lkBBp9cy22xGl4H2d6yvH1DHj2bNmPQBzf3kPu08c9nJEB2oXcP8hEDWA/nQQu/oHql8QBgZqBFBFpTPUyTcXAD3qnbd6yQoP8Qdw6JvvWHXHg6z54j35g/2NVOYVdfw7kI0hShMnvBEV4ftaX1Lz6aMxnC7nhf/9BTk5uaJdOTm5rFr9EmueXymMkDuVK3MSt1MamhkS6ek3CXjUXwZpYVF6PWPjm30e8ebKwVPfO2vA7r/qFkUB+PqcHwv/GJPE7ItmqB/s/Yj/++GP2ZOfS0NTRyYiLjKal//nMa/HdqaD2N0/UKX/o9YAqqh0hkrl8WE9RWl+kcjH0J3sjd9SVnBK/gQO4dUZaurhWGHnjpViQqpgqO1ObLTwnxT+mmt7E7wxURATSWlYENnZh0V+fAA2m43s7MOUlZ3xOkLOldR4/2sn0wydr7d01IBdOFo6Ne5g9oh07r7sB9z9g5vVD/d+grGxjnm/vIerVy0XiT+AWlMDt734BAVnlEdd3vbiEx5lBJsO7+HWFx8P+P2q9F1UAaii4i/mZu/NEFJCphvwZaZxeZ6XFO+E1M6LwLqGjt9HdV3XmkSCg4S5w9MmC+bT6aOEf180Hialed6jIwLpD0qCN0gnjDfD+++1vKLS6wg5V07Xhvu81kFhdecjyI4aMNkifo2WBSkTSR8z1v/foUqvYWysY/KKxeySiepabTYOnjoh2TjkIL+iWNL0GWDb0Wyv4lFl4KAKQBUVf+lDHb5eZxoDIxpbBONqpTnFXSkFPnZSqKs7WiD839u1vBERJkweSRrSEd1zjCObNllo+Jg2Wfi5M+lnKcEbE9lh4I333+uIpOHCCLnUDMkOSUO0uO5yZNIVtIVc4tPt2dFxoHJkp9K/Ut2Z61esYl7GdNG6eRMyWf/47zr/O1TpFW564RGqG+q8L8SzK9zBjryDisftyMvp1L2p9D/Uv3wVFX/x1QamB2oA40eHMWX+RRzeckh2zcuv/EVoXABpHz9fbWDkcI/6dadnYERY13+vPsy2lZsV7RjLlpycCPoYnrlzBVXv/J6DpzoaghwdklUNtRRWlnHBkFZGx5wD436fbk8TeSUXz/6QWVsf47vvj2CzKzSY2O2g6SgilOrO1EfF8PlT69Qi/n5OfkWxXw09Up6A+RXFHCvxVrbhzRRUZaCgCkAVFX+JCBNqxeoVGkG62QamzdpIbuVayioOUNOo/Ibu2rjgYVDtrw2Mrzg8A/uyGbYXMbnyg3Ues6Iz517KylefxRgdytK3VonqqKaOGc+rP32Ci9MmAqAPt5Bu+Q00fg1KPUORiyDqaWg6A5EXQOxk8iuK2XXikNeHoD/fxqKocdz224e8Cju1iL9/01lfx8LKMgzRsR5NH3LMnji1U9dR6X+oAlBFpTMkD4U8hU/15u6NAOZWrqXanMu/Hy2kNFs5eufauJA8OV18X92Zzm40920B6IVofSxrvnhPcla0w4LDlSPFBTyz/hU+/8WLwuOuuB1Mm71e58yx2ynJzWNE0nCSk5tBn09Ry1nFY1LyTAwta2H2rFmsXL9O2epHZUDQWV/HtOHJksbhUsyZlEV64kje2fIZ2/MOMHfyNJbN/WGnrqvS91EFoIpKZzB5aXaoN0F893wom1orqDYfxni6mVO7fI/elVdUkuxe79Wdkcrys7778/VhktPHiEy1Fcdw5WVTsGkr6WN0EPK1T+dft+ZR9u0UrGwcPoOpkcppuOef/hUzM7N8M/tWGRA4Gnq2HNmLzYeaXa1Gy8zxGfxj9yafIn8Lpszk4euWEHXbpbRZhRreD3d8xf1vPsfOZ9/hojGd86VU6buoTSAqKp0hJrJr+7tAU5sQHaop8a/jdkTScDjtlkbqig2MN+pN3Tc6rhfxOoar7jyYFEzC3SgvCXb+25GuH6eNZMHk6bIjuBbfcnOnxF9+RTFfHdytdnr2U9avWMX8C2f4tDY6PILdJw7z64//pLjulz/6KUfXfsLnT63jh8+ucIo/B21WC7OeuFN93QxA1AigikpniI8VbEMsEua+Qbpui/4BhAcPA0A/0rf0qqhxQao2b0Iq5BV6NoLERgv14F1pEOmhZpiexOsYrtghYI3yeh6rBQ7sCRMJQNd0/fo7H2fJu8+z6WhH6q6zI7g6Y/yr0vdwb+h54dN32ZN/RDQNRKvREBMRRa3Jt7/bH81a6Ez7uos/BxableufewhQXzcDCTUCqKLSWTInCGLPlSCdsF2JLnrmRYYkYYiYQvzoCMbMikGjU16fmZnByqce6tjgXvfXZoHk4ZCR7um/N1HCf88femEmcnfj1VsvbihYR0HLTED+yTmwJ4zVjw2R3FdeUYm+rIrPr7iToz/+FZ9dez9H7/s9n//ixU598N72B2nj3+ueW+E9stOaD43/hVbBT1KNIvY+6YkjuWrqpWz4xe89LH5mjr/QJ/Hnbhm0Pe+AT9eWs5dR6X+os4BVVLqKsU5Id8ZEKkf+2iyCPYpr120nx6k5uoDLzxzgs0cKRbWAWVlTuPuu26itq29vLEgUH+yYU+zP/Zib4cAxsPnxdqGP6R4rmD5ATWM9S9auFEfUUiayfuHd6MPaR9Np6mHk89C8qePA8Nmgf4AzZwzcMfl/ZM//3rvrPJ836NTvNL+imIyHbva6ziOyYzW2N7J01DIeqBzJ1W8bqG0Okj5GpVdwRAR1Wi37Co7ym7+/6fUY9+funS2f8bM/rfb5mkfXfqJ2lfdzVAGootJTHMmXtlzpglAytZ6hqa2SmuI2jCVmRrRYSY7zkn52iDx38Sd3P20WOJwPJrPvNxYXLUQPB7LJsLmZgk1bKaw7T1rsECHy50pMFEy9QIictZ6EkLEQ0j6arc3CY/NvJWfvQdG4OUe6fs3zChEWh4D3kXVffMij7/3R6zqtRsOU0eNY/9Bq4YO99Kr2LuaOMgeLDbYUxnDdeqEhQKfVMi9jOp8/tc7n+1EJPFIpfiW+eOplrpziWUvo2gDijc+eeImrpl7q132q9C3UFLCKSmcx1sHpCuH/3lDy23PU5XWCyJBEEiKnkj5xOjOumkPy1Vd4pqWlrne0wPf7OV7kn/jLSBdGug1k8QfQ1EJ63FCuGjXJU/wBjGjfFpIOUT+AkHRK84vY+9U2yr7ewcpfPEBmZoboEI90vcx1/eHtLf/yaZ3Nbufgqe+ZvGIxP127pD3yJ65xDdLCovR6xrbPNk7Vm9Gav+J02Xa/7kklsPhq8wIQGRqOxWb1SOHnVxTzwtKH0Gp8M4J2jBtU6b8M8HdoFZVuwNwMB4+LG0B0Wrh4IoTLRGa8fWgHqlkiOEgYaXb0/7d35+FR13cewN8zk4RckxACQsIRSJhwJURiEApqVUSwgt0tuLW2gK5tfbRCqHVxEehjKQFl226DB7XiKoKrVnBbj5YzigrI0UBIQJxJAiHJEI7cM7kn2T9+TDLH75qZzAQy79fz+MT8bmTG+cz3+/18PiZhWlqK3D5ASPyIDPeuUHSwzCkorW+Mjuz+14aaOuQsynYqKm0wjMEvl/8cS596DJXmKvHpem/u68BoLsM3FedUH29XdfmI7P6MBCv+cH8Z5hiuvTYsdwLlc4DEdwFdnMf3I+9JlSWSYm1t7k7omDk+A4vvnI/X9uxAfqm6zHX7qC+nf298DACJPOUa/AGArRM4WgTMuFl85EvpQ/uCWVhD2BujZqEhwJQJgPkKYCrz7hqmMuBqLTBssOfnBksnKXsJHalpdIeAPmdRNvL3H3Q6xGQ6hyd/sbK79p9er5w5jJgoj74oeNs9olShD/GT0y5j+iiXQujWfYD5R8DIXV7d83plrK5GSU0Nxg4aBEN8fF8/jhtv/44B4ODZAhz0oL0c4H0mOl1/OAVM5ImaevHSL3YFRvHtSvX2GqzAaaUenR4aqJffH6MQcNQ2AJWXPb9vsIwAAsJaSte/V/say2vKjaU4vucLp57Cjuy1/xwZ6y5hV9lpmOoc/vuH6IA0g0eP5233CFN1OHabYtDh0oa4oxP48nwUbhttQYjbp4dNmDa+li18o6tpbsbc7dsxbu0KzH/xP5C69lnM3b4dtc3Nff1oTrz9OyZiAEjkCaWpU2uT9Hq+CcnyQVd9Y+8WTpYLOuNigLSxyiVeGiyeF7Xuh6VfJIWGCAkzU9OE4GxqmvC7w0iuuVS+XIpj7b+aFivmffQy0t/5Db7/yStIe+d5zPvoZdRqbcLUvocjxFIla9RY/EEK9pc4vz4KLo/Cq18PlT+xrdjje12P5m15FXkfvYSwi4XQNZgRdvEU8j56CfPe2NzXj+YkNTEJM8ZnBOx+LAPTfzAAJPKEmmBIar1faAgwVGEKyZeiy2LkRqhCQ4Cxo4BE5wSG8gozjhw9gYqKi8KG4UPdryGVaOIy9Rk0IsOB+FjRP3tisrq1UpXmKize8ybyKpzXYuVVnMWi/Vu9Xh6wLXudW604NepaQvDAtnGY9Md0fNWYAyQbccudZdjw0zfkTwwb69VzXk+M1dU4/uVf4D6c3YVjX7wPU3V1XzyWpF/M/WHA7mXr7MTegq9ZB7If4BpAIk8MihUSPmyd0sdcTyNg9hGqphYhMI0Y0FMD0KUsTUNDI3LW5+L48Z41QVlZGVi94zXoXa8RGiJdQ5CcjExNRta9d+Cf+75CV6f066YtJhR7j51x227rutZj+OIFrxbe27tHbNj5Bp5XaAsmprgmHGs+PofPpgrTz23aMdhtisGslAanaWB7iZjk2AEwqMhluZ79ac+H0IisZRCWt3bhtb3/h989JF3HMdAmj1ZeGhA5IBwtba2q+girUVJVwUSQGxxHAIk8dctE6X1KI2AO6/KsuhpcHVAKq65GdH+vch2hEqkBmLM+F/n5hU7b8vMLse7nK9yvoWLqk3qs3r4Jt9xzm+g+rVaLrKwMWCPks2dKqip8eoabYr1PYDh0tqB7xKf0UqXo9PD+khgs/iAFX5454dNzXg/KKuWnsc9VXF/rHNVM9Te1tvRa8AewDEx/wACQyFMR4UK2b1Sk83Y1I2CR4WgfqEP+oA9waOgWnIjfgUNDtyB/0AdoHxjSe9Oncu3mREq7lFeYcfx4gVNRYuDa+rQ9X6DCJFFKRGbqk3ro42Lx4qdb8ernf4HB4Pwasdf+S46Vz7j29QP39olTfDrfHoAmDx3uND38wDbDtZ/jUNcSgidey8G8nGWotXhYPug6Mk+hwPEDt4gH81L2nDyMnB1bsK9AvryOL7Zlr8P01HTF43RaLSYnGTAlebxX93FtIUc3LnYCIfKF69SqCvnl61DdXAhoHN56XRrER6Qjc6SPi6vVtHcTKQ9z5OgJPPfcesnLrv/4TUybe6dvz0aCU0ZUFH7rXvsvLgbzPn4ZeYVHYXMIxHuz28actU/i86LjXp3r2PprXs4yt+d0pNVoMGvytBu6Q0jUj76DDpHM7RCdDtZ3DwMAdhcX40hlJb4zYgRmp6S4HVtSVY7bV/07qht7isXH62NxcP1bGNNL2btGcxlKL1UiZdgIlFRVdNf4U/Lpqpew5t1XVNf/s2P7v/6DASBRAFnbzDhUtlxy/4ykXESF+bCASk27ua/y3dYwlleY8cgj2ZKX3XomDyMMY7x/LuohE6TXtja59xjuxQ9csR7GaswcfzPy1vb0l1V7nYMb3kJWisySievYyXPf4rbnHnVqjRaqC8FX69+EPnYIpm3ZgmqHkjDxERE49rOfYUxcTyHsxMdmOwV/3cfqY2F+Y6/bdk+ItX+bMT4Dhzyo65eeNBbGyjK0drQrHvv7JU9jTuYMjvz1IwwAiQLoqvUETpg3SO6fkrgSg6O8nKpragGOFUnvn5oGtLQCheLrl579z3XIzy907k2r0yFz1ky8+OlW756JpMmMHpsuXkBJVQVSho3wywfu/+z/K554TXrE19U7y9dj4Yx7PL5O1IAIlGz++LocLSo3lsJcegHDU5Jkv9xs/exjfFZ0DHelTcWSu+YDAAZv3OgU/NnFR0Tg6gphzeyek4cxf730lyqpfrxqiY3C6rRa6CMiUWe1yJzpGfZ77r+4BpAogCJC5WuoRYQOU76IWA/i9g7gjEIh6eZW2TqGq1ctR+aMLKdtmbNmYvV2/o/fL2TWTxoSRmHuFP+Nttw2wbMvGRljUr26jrW1GeOX/st1tR6woaYOz96/BI9MmoXn5j+KJRPvxrP3L0FjrXhP7yV3zcdbS9d2B3+7i4tFgz8AqG5uxt4S4X14rPi07HMcMRXK7pdjb//mOgVv6+z0OfgbGOWciMbOH/0XU/aIAigqLBHx4ZMl1wDKTv+K9SAO0QGZEwDTBcCq0KGgtQ0YECq5W6+Pxos7/4yK6hpUlpQpjozQjcueNSq3jg9Q7vs66KYRmDh6HM6c/1byGnVWCxZsfMZpCtnOcf1aoKYWxdry5e8/iHU/WaZqpPtIpXzrtcMVFZidkoKpYyfJHjfNoJywIcWX9m9K6qyNeP9XLyA8LDygfy8UeBwBJAqw9Nr5iG8d7bQtvnU00mvny58o1oO4wwb88xvxdX+uTGWAUaY3cIgOGBSLEYYxmDb3TgZ//ZxYgeh4fazT71KjP02dndhWbcXLly2Y+oOlivc6ePakU+HgGks95uUsQ/ryB/H9DcuRlr0Qd/36Z34fKZRqy9dps0lmuxurq/EPk6m7+PO04fLJG98ZIWRr33vzd9z+e9rF62N9mv5Vav82c/zNXnV/sXvhwzf9OgJN1weOABIFUlMLQmvbkYkHYdXVoDmkDhEdAxFlGwSgXRjlE8smlutBLNFj1iP2kUQKGvYC0a7rDdWsP9xZ24zSViE5InHUOIxITkNFqcz6UzgXDl6cuwb7TzmXRDl0tgCTshfgdO5Ov60ZVGrLV1lS1v3Fp6a5GQ/v3IndJT1LK+akpODdBQsQHxEhuQbQMRv44Pq3MPO5R0SzgH0Z/ZQawbWP2G7LXueWpBOvj0WtpUFVLcAT5771uvA43TiYBEIUSNX1QJFMEdk0g7AuzNV5M1Bm7v3nCR8AGEYJHU6IVLjaYcPLl53XmTU3NWLzbxejtVl6/Zm9jIzRXIb05Q9KHueacdyb08TlxlI8MmmW5H7HbPe527djX2kpbA4fkTqNBvckJ2Pz/fdj6uuvo7q5GRHV9YiobcSAhCE4uPI/nLKA7fYVHMERUyGmGdKRmTIeCzY+45St602mt1gmtut1HIP5wfqBHmWA/23lHzFXoR4i3dgYABIFkppMXakRQInsXQBAVITyGkApUybI9zi+eEXoUTxQDyQM8e4e1G+YWtrxTk2T2/baq2b8ad0S0XNCdSGwvHsIALDrxCHFWnVFuTsQr491K3PSGyVxnr1/CfL3H3SaBnbMdi83luLYySL85OABNA+KAVot0LQ1oSssChggvE+MTz2FoRodnl7wM5z7qqeuYta9d2D19k3Qx4l/oaqx1CMte6FbaRhf6iZ6mjFuungBh74pwNNv/R6WFve/RzvHuo/UP3ENIFEgRYYDeolga6Beupj0oFhhmlZMiA5IHS0Egd4wnRff3mABDhwX1g1erhF+HjgONEpnElP/Fxfi/rFRfbkC729eKXlOu60D+45+CUB5/RogTBcvzl2DvMKjTtvzCo9iUa5vxdJXb9+EzFkznbZlzpqJ7Jd+250d/MqPs5H1+v8h7vDfEVZ8AKEXjiGs+HOEnD8K2NpRXFODnEXZKDvs3PbOnkwiZeHGZ0TrAnZ2dWFvwddO6yTV8jRj3JAwCkvuno/qtz8X7QbCTh/BgwEgUaC0dwiFmqUCKPtYvFiZF0BYo+caBIbogMgIIUHEPgKole8p68bSLN4y7oREh4D8bzy7PvUrg0N0SBkgLB9vtjbgvT+txJ/XP4raajMM8S2YY6jD2EHur6cjX3wGnDIidchwzBifIXsPnVYrWebE20DJzt6Wb+uZPKz/+E1sPZOHFz/dityla5yyg7+dqoc12vn+GutVhJSfgL62UTKZ5MtDB7H9kw/dnnHPycM4qFCk2dd+z576x+qXMTtjutM2ln0JHkwCIQoU1+4PruobgS/zAccPPXtyRkS4ECCOTwba2oDWdmHatuKS+zU7u4TzpJJGxDS3Oo8+Xrwif/zFK5wODmIL4yLxv9VW/Ne2DThvzEdcRAe2LizBHEPPa3G3KQaLP0hBXYvwMTNt6BjhtXq6BB+u+D0mZS9wGw2zJzHIlaYBnBNKvDXCMKZ7vZ89O9iuOVqHuqED3M7RoAsa6xW8u/9DVCWFI/ZqOyKswvusPVSDb6fqUTd0AB57+wXgbWHKetNjK7DsjY2q1t7Z+z0HqjyOVCIQBQcGgESB0NSirlSL6wdfh00YcdNHOZ8fEy1MC0tds8MGREcCFuk1Pk4iXD7s6hrlj69rZAAYxCK0GtzeXo0nzwrr37YuLMGsFOfX4qyUBrz9YAke2DYOA8MicM+oa1nm9Y2I0ybhdO5OLNj4DA6ePdl9jn306UpDrez97YGSmzYj0FYChI0Fwgyq/zyu2cHNURLLLa7ZcmIPkCkUTI693IYJRxuE4G9ImNNxeYVHcduqR1FnVXg/QWjjFq+PxbycZX5rBSjFkDCKgV8QYgBIFAjNrd6f22FzD/QaLMAp6eK7ANQHf2JrDwfqhXV/cudQULMXIxamfd2/iIRogTmGBoyPb8Nf71vrvLOuEXGJQ5C39s+io09x0TGyZU7cghVbDWB+GLDu7tkWNQdIfBfQuWflukpMdr6efVRPjfohoSicGQtrnHuRdVtnp+iaP1fx+lgsmD4LU57+ES7XVzvts697ZCs26m1cA0gUCK4jbL3BJj9NpppYHQCl0T2O/gU9ezJHssh6P0dfPfQwxsQOltwvlcQgVqhacn2a+WHAus95m3UfYP6R7LPZjUxNRta9d0CrE0b+Iiw2xF5uA9QUydBoRIM/tTJGG1BvteBXb/0BVXVX3er09ca6RyIxDACJAiEyHIjz3xSOT+obxZNApApDs2A0oacY8fk6+exzvW6s+0YVI8j29WlFuTvwt5V/RFHuDnyyapP7VGib8drIn+uonU3Y3iZTPsmBa3ZwF7oAeJhQ5aFPV72EM+Xn0NGpPOIY6AQR6v8YABIFyoRk9yAwOkLY3tfEpqj1UcB3s4DUJOCmQcLP72ZJl7GhoLMtex1GJX4XeSV6dLoOlnUBaM0CbC7TtXLljkQoljlpKxHf3r2/WNV9LjbX4a51j+P5L97BE9s3ouGmAX6L/+ylVsqvVqHd1qHqHMl1j0Re4hpAokAJDQEmpwqjbc2twrRwZLj46JsjNcf4Sm6KOmEIp3xJlH2Uzmr6JzQdh5x3aiC85p1OiOn9LzxhKQr7RUYgHdRY6t0KTk8c4d8vZXen34pfzv8xfvzfqxSPlVz3SOQjdgIh6ktNLUKyhVybtwnJgKnMs7IunoiJErqBEHmjzQiUjpPen1AIdIzs+cLjD+Vzr60BdHyP6ICoe4CRu2RPnbf2KeSdPg5bVy+tqZWx+fHnMHm0Ac+/95rqlmyByAKm4MQpYKK+YC8KfaxIucdvdCSQrr6khceGD/Xftan/U5qC1ZUL/a39FfwBQrZv1D3O26LuQUXTBhzZ9TkqTOfcz2nvgPGzA9hbdDQgwR8AJA66CaveeUV18Lf58efE1z0S9QJOARP1BaWi0HYhOuGDs1q5lITXoiP9d23q/3ycgu0Vujhg5C6cK/8c5VePoyNsIvb8Og/Htv+g+xC3Pr3flKK0/Lz/n83xMbVafF50XPnAa26fmOnHp6FgxxFAokBTWxQaEKZ9m1r8l4wYHSmsR/T3GkPqv8JShZp7cC2erBO2e1CQ2Vs1lnrcl7MM43+1ArM3/AX3/eZ5/HVkE8a+/juExAgZx059eq+9B5NlytPY/X7J00hNSPLp+exJHxeuVKk+J14fy3V/5FcMAIkCzdOi0M2tQEubf57F0gQUmYSp6FNGYWqayFMSU7BIfDcgt1+cuwYV5gNOfYjPG/NxoGQfxr30AgChT+/xPV8I08HX3oOpA4diZoL8COaczBn4r0d+6dPzedNft7qxnrX/yK8YABIFmqdFof1RRFon8tavbRCmpok8dW0KFslGYMTfhZ8jd6nqwuGr4soCLJ28FYVLT+GjRSacXl6IjxZ9i9iwNpz79p/ApCSEj+4ZSassKXN6T+383hOIH+Be2kir0WB2xnQYEkZ1F732lFajwYzxGd3r+G6fOMWj81n7j/yJASBRoEWGA9Ee1NIzlglrAX01OhFIMwgJJVJdRGobOB1M3gszANH3BWTa1y627nHJPsQAUHvFjIjRI7v3DU9JcirMHhceia8eXIGYUOcklc6uLjS1tqDW0oDUxCTMHJ/h8bN1dnXh0NmC7pG81MQk3Do2TfX5IdpeeN8TSWAASNQXPMk6rG/snZG50FAhG1Op8JMvfYuJAqnNiCG6Iwhx+SSz9yEeO6gFWp0WzefLodXpkHXvHRhhGCMc5FCYfdmB99HQ7v7F5+DZk1iUuxoA8OTcH3r9mCVVFaix1GNezjIcLS5SfZ6aDiFE3mIASBRoTS2AtTnw9w0PE34qTSn7Y8qZyB8UStCkxLeg/mQRWs5fQOasmVi9fVPPzmuF2Y0jY7G3/IzkNex9eCeP9n5UM2XYCCzOXYO8wqMen0fkLwwAiQLNXyNs0fI9WbtH/uT6EsfF+LdeG1FvUihBU1IdjodGjsTWM3l48dOtPSVgHJTWX1G8TUlVRXfvY63GPSVfp5X/KH34Dyuxt+Br2DrVjfzbs4aZBUz+xACQKNBC/bSuR+FDyGlkT6wvsT/adBH5U1gqED4btk7noKyjE9hjisWYqEw89L3v9Uz7ilCT4GEfiduWvQ46kXV5SoHdqTKT7P7RQxKdfvcma5jIUywETRRo5xU6f3irwSoki4i1jHMd2ZPqS0x0o4l6HZ21/wZdRM/06v6SGPzPofuw7d5/F17fMq9t+8ieVHeOlKE9CSTHik+j3db7pZIS44fgk9WbUFJVgZRhIzjyRwHBXsBEgdTUItTc86eYaKDB0vO7fWQvlN/3qB+yv6d0F1DZUoSS6gFICE+HYeBNwv6paYpfbmotDXjoD/8p26UjM3kCxgxNxM7D+3vz6bttfvw53D4xk8EfBQwDQKJAqq4XCi/7U1IiEBPVUzx6oJ6je9S/nTKKd9eJixFGulUyXbyAaSsWwdraB0la18wcfzN2rvgd+/+S33ENIFEgBSLDtswMFJoAU5nwD7t8UH/XS2taz12q7NPgDxBKz0zKXoBai8p2kUReYgBIFEhyGbj+xC4f1J/Z17ROTROKnU9NE373cNnDseLTfnm8uKgY3JmWpfr46sZ6/GDjr/zyLER2DACJAk1stCIQ2OWD+rvIcKHYuYdLHozmMuw6cQjDB93kl8eqtTZg/Y+f8igIdOwgQuQPXBVOFGiOGbiWJqD8EmCxBubeChmRRDe0MrPwRScuRlgLq6DGUo/FuWucMoBDdSF+yfT96Strkbf2z7jaWIcvz+TjUl0Nnn//T7LnlFRVMCmE/IYjgER9JTIcqLoauOAPYJcP6p9q6oEDx4USS/UW4eeB4+KJIQ7EunPYOm0I0fV+rc4zFaWYlL0Ag/UDcduEKRibMBL6CPme4OwEQv7EEUCivtLUovgBJUqq1p8Sdvmg/qpQIrP+lBH4rvi0q9FcJlr7r7OrC502G3770JM4UlyEqLBwvH9oj+zt4/WxqG6sV3zM6sZ6GH7xABqbm2SP02o0mDV5Gkf/yK8YABL1FW9awtkzG9s7ego4F19QDiTZ5YP6qzKFwuplZtHp4NJLlbKnrXnv1e5/j9fHotbSiM6uno4fGmgwdNAQrP1JNpbcNhumixew58RhPLP1v52Oc6UU/AGAPiIKL/30WcXjiHzBKWCivuLpdKwhqSez0XGxu1hSSUwUMGWCTxmRRDcEpS8/EvvVtIDrvoSlAXHReqdtXehCVc1l/HzTKiT8YiFCQsORs3OLbPCnVn2TBUu3vOjzdYjksBA0UV+SKmArRqmjAdu6UTAqM8u3VxydKJkQMi9nGfIKjyr28rWbkjweJ0rPum3vggb6mMGwNFxRdR21inJ3cBqY/IYjgER9SW1JGDXr97wsgUF0Q1PK9pXZvy17Haalpqu+1UmR4A8ANOjq9eAPELKAifyFASBRX3ItYDtlQq90NCAKKlLt3hTawMVFx+DZf31U9W0CPV3GLGDyJy4KIroeRIb3jNzZawRyOpdInbgYIdvXwzqAgGdrAZXowyPR2KKc5KHG7IzpnP4lv+IaQCIiCmqJj81WVcZFSheA+CGjMHFwPL765oTPz3NnWhbee/oFxEX3QccgChqcAiYioqBlNJcpBn93TsrC7Izp0GnFPzLjh4zCO0+u8jn4G5eYhIMb3sLuX7/K4I/8jgEgEREFLaV6gABQa6nHtux1uDv9VqftyYmj8d6zubj4yg60tTX79BwaAKOGJCArZaJP1yFSi2sAiYgoaKlZA1hQZsLVxjp8smoTTBcvoKSqAjqtFrbOTqQMG67qOq89sRqPb14nub8LwN6Cr2G6eIFr/yggOAJIRERBKzUxCVOSxyseZy/JEq+Pxct/fw/zcpbh+xuWIy17IeblLMOQmDjRaWKdVovZGdPxyF0PYHbGdGg18h+7BeeM3v9hiDzAAJCIiILaP1a/jBCtTvaYlGEjYDSX4Xu/XYq8wqNO+/IKj2JR7mrRaeK702/Ftmxh5G9b9jpkjJEvTfO7j9724k9A5DlmARMRUdA7d6kSadkL0dFpc9qu1Whwx6RbEKoLwd6Cr2WvYe/cYZ8mThk2wm06d9eJQ/j+huWqrkPkTxwBJCKioDdm6HBUbNmNzOQJTttnTZ4GdMFt1E+MfZrYkDAKc6fMEA3i1Kw5ZAcQCgQmgRAREUHoDHL4ha1OI3hdXV1IX/6gqvN7q3MHO4BQIDAAJCIicmBIGNU9erfrxCHF43VaLe5Ov1XVtK1S2ZkpY8Zx+pcCglPAREREEtRM2Tomevh6vVd+vlLVdYh8xQCQiIhIQmpikmh5F61GgyljxqEodwc+WbVJdecOqevZy8XcwkLQFCAMAImIiGSIlXeZNXka/rHmFa+ma5XKxRAFAsvAEBERqSBX3uV6uB6RJxgAEhEREQUZTgETERERBRkGgERERERBhgEgERERUZBhAEhEREQUZBgAEhEREQUZBoBEREREQYYBIBEREVGQYQBIREREFGQYABIREREFGQaAREREREGGASARERFRkGEASERERBRkGAASERERBRkGgERERERBhgEgERERUZBhAEhEREQUZBgAEhEREQUZBoBEREREQYYBIBEREVGQYQBIREREFGQYABIREREFGQaAREREREGGASARERFRkGEASERERBRkGAASERERBRkGgERERERBhgEgERERUZBhAEhEREQUZBgAEhEREQUZBoBEREREQYYBIBEREVGQYQBIREREFGT+H/qYFmgbEwsFAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#%%\n",
        "colors = [\n",
        "    '#ffc0cb', '#bada55', '#008080', '#420420', '#7fe5f0', '#065535', '#ffd700'\n",
        "]\n",
        "plot_points(colors)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}