LSTM.ipynb

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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Symbol</th>\n",
              "      <th>BTC-USD</th>\n",
              "      <th>ETH-USD</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>2017-11-09 00:00:00+00:00</th>\n",
              "      <td>7143.580078</td>\n",
              "      <td>320.884003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-10 00:00:00+00:00</th>\n",
              "      <td>6618.140137</td>\n",
              "      <td>299.252991</td>\n",
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              "    <tr>\n",
              "      <th>2017-11-11 00:00:00+00:00</th>\n",
              "      <td>6357.600098</td>\n",
              "      <td>314.681000</td>\n",
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              "    <tr>\n",
              "      <th>2017-11-12 00:00:00+00:00</th>\n",
              "      <td>5950.069824</td>\n",
              "      <td>307.907990</td>\n",
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              "    <tr>\n",
              "      <th>2017-11-13 00:00:00+00:00</th>\n",
              "      <td>6559.490234</td>\n",
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              "      <th>2023-04-14 00:00:00+00:00</th>\n",
              "      <td>30485.699219</td>\n",
              "      <td>2101.635498</td>\n",
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              "    <tr>\n",
              "      <th>2023-04-15 00:00:00+00:00</th>\n",
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              "    <tr>\n",
              "      <th>2023-04-16 00:00:00+00:00</th>\n",
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              "      <th>2023-04-17 00:00:00+00:00</th>\n",
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              "      <th>2023-04-18 00:00:00+00:00</th>\n",
              "      <td>29556.863281</td>\n",
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            "text/plain": [
              "Symbol                          BTC-USD      ETH-USD\n",
              "Date                                                \n",
              "2017-11-09 00:00:00+00:00   7143.580078   320.884003\n",
              "2017-11-10 00:00:00+00:00   6618.140137   299.252991\n",
              "2017-11-11 00:00:00+00:00   6357.600098   314.681000\n",
              "2017-11-12 00:00:00+00:00   5950.069824   307.907990\n",
              "2017-11-13 00:00:00+00:00   6559.490234   316.716003\n",
              "...                                 ...          ...\n",
              "2023-04-14 00:00:00+00:00  30485.699219  2101.635498\n",
              "2023-04-15 00:00:00+00:00  30318.496094  2092.466797\n",
              "2023-04-16 00:00:00+00:00  30315.355469  2120.005859\n",
              "2023-04-17 00:00:00+00:00  29445.044922  2076.242920\n",
              "2023-04-18 00:00:00+00:00  29556.863281  2090.977539\n",
              "\n",
              "[1987 rows x 2 columns]"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.optim import Adam\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "\n",
        "import pandas_datareader as pdr\n",
        "import yfinance as yf\n",
        "from statsmodels.tsa.stattools import coint\n",
        "import requests\n",
        "import statsmodels.tsa.stattools as ts \n",
        "from statsmodels.tsa.vector_ar.vecm import coint_johansen\n",
        "import datetime as dt\n",
        "import pandas_datareader as web\n",
        "import statsmodels.api as sm\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "# %matplotlib inline\n",
        "\n",
        "start = dt.datetime(2017, 1, 1)\n",
        "end = dt.datetime.now()\n",
        "\n",
        "crypto_assets = ['BTC-USD', 'ETH-USD', 'USDT-USD', 'BNB-USD', 'USDC-USD', 'XRP-USD', \n",
        "                 'SOL1-USD', 'LUNA1-USD', 'DOGE-USD', 'SHIB-USD', 'ADA-USD', 'LINK-USD', \n",
        "                 'AVAX-USD', 'MATIC-USD', 'SUSHI-USD', 'ALGO-USD']\n",
        "\n",
        "currency_assets = ['EURUSD=X', 'GBPUSD=X', 'JPY=X', 'AUDUSD=X', 'CAD=X', 'CHF=X', 'CNY=X']\n",
        "\n",
        "assetsDF = pd.DataFrame()\n",
        "\n",
        "for asset in crypto_assets + currency_assets:\n",
        "    ticker = yf.Ticker(asset)\n",
        "    data = ticker.history(start=start, end=end)\n",
        "    data['Symbol'] = asset\n",
        "    assetsDF = pd.concat([assetsDF, data])\n",
        "\n",
        "assetsDF = assetsDF.dropna()\n",
        "assetsDF\n",
        "assetsDF = assetsDF.reset_index()\n",
        "assetsDF\n",
        "\n",
        "data = assetsDF[['Date', 'Symbol', 'Close']].pivot(index='Date', columns='Symbol', values='Close')\n",
        "data = data[['BTC-USD', 'ETH-USD']]\n",
        "data = data.dropna()\n",
        "data"
      ]
    },
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          "data": {
            "text/plain": [
              "array([[0.06073083, 0.0500395 ],\n",
              "       [0.05256296, 0.0454642 ],\n",
              "       [0.0485129 , 0.04872747],\n",
              "       ...,\n",
              "       [0.42093216, 0.43058221],\n",
              "       [0.40740333, 0.42132565],\n",
              "       [0.40914153, 0.42444226]])"
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      ],
      "source": [
        "target_variable = 'Close'\n",
        "time_steps = 100  # Adjust this\n",
        "# data.dropna()\n",
        "# Scale your data using MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "data_scaled = scaler.fit_transform(data)\n",
        "\n",
        "\n",
        "data_scaled\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
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      "outputs": [
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [1/100], Loss: 0.0081\n",
            "Epoch [2/100], Loss: 0.0013\n",
            "Epoch [3/100], Loss: 0.0015\n",
            "Epoch [4/100], Loss: 0.0015\n",
            "Epoch [5/100], Loss: 0.0046\n",
            "Epoch [6/100], Loss: 0.0014\n",
            "Epoch [7/100], Loss: 0.0004\n",
            "Epoch [8/100], Loss: 0.0015\n",
            "Epoch [9/100], Loss: 0.0016\n",
            "Epoch [10/100], Loss: 0.0003\n",
            "Epoch [11/100], Loss: 0.0023\n",
            "Epoch [12/100], Loss: 0.0036\n",
            "Epoch [13/100], Loss: 0.0001\n",
            "Epoch [14/100], Loss: 0.0012\n",
            "Epoch [15/100], Loss: 0.0005\n",
            "Epoch [16/100], Loss: 0.0007\n",
            "Epoch [17/100], Loss: 0.0012\n",
            "Epoch [18/100], Loss: 0.0005\n",
            "Epoch [19/100], Loss: 0.0002\n",
            "Epoch [20/100], Loss: 0.0010\n",
            "Epoch [21/100], Loss: 0.0023\n",
            "Epoch [22/100], Loss: 0.0008\n",
            "Epoch [23/100], Loss: 0.0002\n",
            "Epoch [24/100], Loss: 0.0003\n",
            "Epoch [25/100], Loss: 0.0004\n",
            "Epoch [26/100], Loss: 0.0012\n",
            "Epoch [27/100], Loss: 0.0001\n",
            "Epoch [28/100], Loss: 0.0005\n",
            "Epoch [29/100], Loss: 0.0006\n",
            "Epoch [30/100], Loss: 0.0007\n",
            "Epoch [31/100], Loss: 0.0013\n",
            "Epoch [32/100], Loss: 0.0002\n",
            "Epoch [33/100], Loss: 0.0009\n",
            "Epoch [34/100], Loss: 0.0013\n",
            "Epoch [35/100], Loss: 0.0003\n",
            "Epoch [36/100], Loss: 0.0002\n",
            "Epoch [37/100], Loss: 0.0006\n",
            "Epoch [38/100], Loss: 0.0002\n",
            "Epoch [39/100], Loss: 0.0005\n",
            "Epoch [40/100], Loss: 0.0006\n",
            "Epoch [41/100], Loss: 0.0015\n",
            "Epoch [42/100], Loss: 0.0016\n",
            "Epoch [43/100], Loss: 0.0004\n",
            "Epoch [44/100], Loss: 0.0009\n",
            "Epoch [45/100], Loss: 0.0029\n",
            "Epoch [46/100], Loss: 0.0004\n",
            "Epoch [47/100], Loss: 0.0024\n",
            "Epoch [48/100], Loss: 0.0004\n",
            "Epoch [49/100], Loss: 0.0010\n",
            "Epoch [50/100], Loss: 0.0002\n",
            "Epoch [51/100], Loss: 0.0011\n",
            "Epoch [52/100], Loss: 0.0038\n",
            "Epoch [53/100], Loss: 0.0004\n",
            "Epoch [54/100], Loss: 0.0001\n",
            "Epoch [55/100], Loss: 0.0002\n",
            "Epoch [56/100], Loss: 0.0002\n",
            "Epoch [57/100], Loss: 0.0004\n",
            "Epoch [58/100], Loss: 0.0002\n",
            "Epoch [59/100], Loss: 0.0012\n",
            "Epoch [60/100], Loss: 0.0001\n",
            "Epoch [61/100], Loss: 0.0003\n",
            "Epoch [62/100], Loss: 0.0005\n",
            "Epoch [63/100], Loss: 0.0002\n",
            "Epoch [64/100], Loss: 0.0002\n",
            "Epoch [65/100], Loss: 0.0019\n",
            "Epoch [66/100], Loss: 0.0001\n",
            "Epoch [67/100], Loss: 0.0002\n",
            "Epoch [68/100], Loss: 0.0004\n",
            "Epoch [69/100], Loss: 0.0003\n",
            "Epoch [70/100], Loss: 0.0001\n",
            "Epoch [71/100], Loss: 0.0000\n",
            "Epoch [72/100], Loss: 0.0015\n",
            "Epoch [73/100], Loss: 0.0003\n",
            "Epoch [74/100], Loss: 0.0001\n",
            "Epoch [75/100], Loss: 0.0003\n",
            "Epoch [76/100], Loss: 0.0001\n",
            "Epoch [77/100], Loss: 0.0004\n",
            "Epoch [78/100], Loss: 0.0004\n",
            "Epoch [79/100], Loss: 0.0001\n",
            "Epoch [80/100], Loss: 0.0007\n",
            "Epoch [81/100], Loss: 0.0002\n",
            "Epoch [82/100], Loss: 0.0002\n",
            "Epoch [83/100], Loss: 0.0004\n",
            "Epoch [84/100], Loss: 0.0007\n",
            "Epoch [85/100], Loss: 0.0001\n",
            "Epoch [86/100], Loss: 0.0006\n",
            "Epoch [87/100], Loss: 0.0001\n",
            "Epoch [88/100], Loss: 0.0002\n",
            "Epoch [89/100], Loss: 0.0001\n",
            "Epoch [90/100], Loss: 0.0001\n",
            "Epoch [91/100], Loss: 0.0008\n",
            "Epoch [92/100], Loss: 0.0009\n",
            "Epoch [93/100], Loss: 0.0001\n",
            "Epoch [94/100], Loss: 0.0013\n",
            "Epoch [95/100], Loss: 0.0004\n",
            "Epoch [96/100], Loss: 0.0018\n",
            "Epoch [97/100], Loss: 0.0003\n",
            "Epoch [98/100], Loss: 0.0001\n",
            "Epoch [99/100], Loss: 0.0001\n",
            "Epoch [100/100], Loss: 0.0001\n"
          ]
        }
      ],
      "source": [
        "# Create train and test sets\n",
        "X_train, y_train = [], []\n",
        "X_test, y_test = [], []\n",
        "\n",
        "train_size = int(len(data_scaled) * 0.7)\n",
        "\n",
        "train_data = data_scaled[:train_size]\n",
        "test_data = data_scaled[train_size - time_steps:]\n",
        "\n",
        "# Create input and output sequences for training and testing\n",
        "for i in range(time_steps, len(train_data)):\n",
        "    X_train.append(train_data[i - time_steps:i])\n",
        "    y_train.append(train_data[i, 0])\n",
        "for i in range(time_steps, len(test_data)):\n",
        "    X_test.append(test_data[i - time_steps:i])\n",
        "    y_test.append(test_data[i, 0])\n",
        "\n",
        "# Convert lists to numpy arrays and then to tensors\n",
        "X_train = torch.tensor(X_train, dtype=torch.float32)\n",
        "y_train = torch.tensor(y_train, dtype=torch.float32).unsqueeze(1)\n",
        "X_test = torch.tensor(X_test, dtype=torch.float32)\n",
        "y_test = torch.tensor(y_test, dtype=torch.float32).unsqueeze(1)\n",
        "\n",
        "# Create PyTorch Dataset and DataLoader\n",
        "train_dataset = TensorDataset(X_train, y_train)\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "\n",
        "# Define LSTM model\n",
        "class LSTMModel(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, num_layers, output_size):\n",
        "        super(LSTMModel, self).__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "        self.num_layers = num_layers\n",
        "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n",
        "        self.dropout = nn.Dropout(0.2)\n",
        "        self.fc = nn.Linear(hidden_size, output_size)\n",
        "\n",
        "    def forward(self, x):\n",
        "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).requires_grad_()\n",
        "        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).requires_grad_()\n",
        "        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))\n",
        "        out = self.dropout(out[:, -1, :])\n",
        "        out = self.fc(out)\n",
        "        return out\n",
        "\n",
        "#Parameters for the model\n",
        "input_size = X_train.shape[2]\n",
        "hidden_size = 50\n",
        "num_layers = 2\n",
        "output_size = 1\n",
        "\n",
        "model = LSTMModel(input_size, hidden_size, num_layers, output_size)\n",
        "\n",
        "# Mean Squared Error Loss Function and optimizer\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "\n",
        "# Train the model\n",
        "num_epochs = 100\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    for inputs, targets in train_dataloader:\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs, targets)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "p036CZp07OWD"
      },
      "outputs": [],
      "source": [
        "# Testing the model\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    test_outputs = model(X_test)\n",
        "\n",
        "target_variable = 'BTC-USD'\n",
        "feature_variable = 'ETH-USD'\n",
        "\n",
        "# Scale the data [0-1]\n",
        "target_scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "feature_scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "\n",
        "target_data = target_scaler.fit_transform(data[target_variable].values.reshape(-1, 1))\n",
        "feature_data = feature_scaler.fit_transform(data[feature_variable].values.reshape(-1, 1))\n",
        "\n",
        "# Combine the target variable and features into a single scaled dataset\n",
        "data_scaled = np.hstack((target_data, feature_data))\n",
        "\n",
        "# Convert test_outputs back to the original scale\n",
        "test_outputs = test_outputs.numpy()\n",
        "test_outputs_unscaled = target_scaler.inverse_transform(test_outputs)\n",
        "\n",
        "# Convert y_test back to the original scale\n",
        "y_test = y_test.numpy()\n",
        "y_test_unscaled = target_scaler.inverse_transform(y_test)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "xUNLhnQ-7VqC",
        "outputId": "bc3633ab-3736-485a-c424-aae650f5c934"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualize the predictions\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(14, 6))\n",
        "plt.plot(y_test[:, 0], label='Actual Prices', color='blue')\n",
        "plt.plot(test_outputs[:, 0], label='Predicted Prices', color='red')\n",
        "plt.xlabel('Time [days]')\n",
        "plt.ylabel('Price')\n",
        "plt.title('Combined BTC/ETH Price Prediction')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mh3m4yYM-TLN",
        "outputId": "dda62f0b-9aac-483c-d0f1-340763cd6a69"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test loss: 0.0005\n",
            "RMSE: 1477.9862\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-48-3dcc32503967>:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  y_test = torch.tensor(y_test, dtype=torch.float32).unsqueeze(1)\n"
          ]
        }
      ],
      "source": [
        "# Test the model\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    test_outputs = model(X_test)\n",
        "y_test = torch.tensor(y_test, dtype=torch.float32).unsqueeze(1)\n",
        "\n",
        "# Reshape y_test to match the dimensions of test_outputs\n",
        "y_test = y_test.view(-1, 1)\n",
        "\n",
        "# Calculate the mean squared error\n",
        "test_loss = criterion(test_outputs, y_test)\n",
        "print(f'Test loss: {test_loss.item():.4f}')\n",
        "\n",
        "# Convert test_outputs back to the original scale\n",
        "test_outputs = test_outputs.numpy()\n",
        "test_outputs_unscaled = target_scaler.inverse_transform(test_outputs)\n",
        "\n",
        "# Convert y_test back to the original scale\n",
        "y_test = y_test.numpy()\n",
        "y_test_unscaled = target_scaler.inverse_transform(y_test.reshape(-1, 1))\n",
        "\n",
        "# Calculate the root mean squared error (RMSE)\n",
        "rmse = np.sqrt(np.mean((test_outputs_unscaled - y_test_unscaled)**2))\n",
        "print(f'RMSE: {rmse:.4f}')\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "s_XT4O5P_vVe",
        "outputId": "350f1c9d-2ae4-48fd-db0c-61789f988493"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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vHrt37/b2cCpkxIgRpKSksGHDBu6++24ee+wxnn/++VKvzcvLA8yqXCEhIbU5zDpBgZCIiIiIiIhIFdjtkJ3tnR+7vWJjzcrKYvLkydx8882cddZZTJo0qcQ13333HX379iUoKIjGjRtz/vnnAzB48GC2bdvGnXfe6azUAXjsscfo0aOHxz1efvllWrZs6Xz8559/ctppp9G4cWMiIyMZNGgQf/31V8UGDwQGBpKQkECLFi24+eabGTZsGNOmTQNMBdF5553HU089RVJSEh06dABKVjWlp6dz4403Eh8fT1BQEF27duX77793nl+wYAEDBw4kODiYZs2acdttt5Gdne08/+abb9KuXTuCgoKIj4/nwgsvrPD7OB54NRCaN28e55xzDklJSeWeKzh37lx69epFYGAgbdu2LfXDKyIiIiIiIlJbDh+GsDDv/Bw+XLGxfvHFF3Ts2JEOHTpw+eWX8/7772N3S5WmT5/O+eefz5lnnsmyZcuYPXs2J5xwAgBTpkyhadOmPPHEE6SkpJCSklLu1z106BBXXXUVCxYs4Pfff6ddu3aceeaZHDp0qGJvoJjg4GBnJRDA7NmzWbduHbNmzfIIeSxFRUWcccYZLFy4kE8++YTVq1fzzDPP4OvrC8CmTZsYMWIEI0eOZOXKlUyePJkFCxYwevRoAJYsWcJtt93GE088wbp165gxYwannHJKld6Dt/h588Wzs7NJTk7m2muv5YILLjjm9Vu2bOGss87ipptu4tNPP2X27Nlcf/31JCYmMnz48FoYsYiIiIiIiEjdNXHiRC6//HLATL/KyMjg119/ZfDgwQA89dRT/Pvf/+bxxx93Pic5ORmA6OhofH19CQ8PJyEhoUKvO3ToUI/H77zzDlFRUfz666+cffbZFX4fdrud2bNnM3PmTG699Vbn8dDQUN577z0CAgJKfd7PP//M4sWLWbNmDe3btwegdevWzvPjxo3jsssu44477gCgXbt2vPrqqwwaNIi33nqL7du3Exoaytlnn014eDgtWrSgZ8+eFR7/8cCrgdAZZ5zBGWecUe7rJ0yYQKtWrRg/fjwAnTp1YsGCBbz00ksKhKTOys2FVaugVy9wVFyKiIiIiEgdEhICWVnee+3yWrduHYsXL+abb74BwM/Pj1GjRjFx4kRnILR8+XJuuOGGah9namoqDz/8MHPnzmXv3r0UFhZy+PBhtm/fXqH7fP/994SFhZGfn09RURGXXnopjz32mPN8t27dygyDwLy/pk2bOsOg4lasWMHKlSv59NNPncfsdjtFRUVs2bKF0047jRYtWtC6dWtGjBjBiBEjOP/88+tkjyKvBkIVtWjRIoYNG+ZxbPjw4c7krjS5ubnk5uY6H2dmZtbU8EQqZfx4eOgh8/N//+ft0YiIiIiISEXZbBAa6u1RHNvEiRMpKCggKSnJecxutxMYGMjrr79OZGQkwcHBFb6vj4+Px7QzgPz8fI/HV111Ffv37+eVV16hRYsWBAYG0r9/f4/pXuUxZMgQ3nrrLQICAkhKSsLPzzPWCD3GH8Sx3l9WVhY33ngjt912W4lzzZs3JyAggL/++ou5c+fy008/MXbsWB577DH+/PNPoqKiKvRevK1ONZXes2cP8fHxHsfi4+PJzMzkyJEjpT5n3LhxREZGOn+aNWtWG0MVKbeHHjLbp57y7jhERERERKT+Kigo4KOPPmL8+PEsX77c+bNixQqSkpL47LPPAOjevTuzZ88u8z4BAQEUFhZ6HIuNjWXPnj0eodDy5cs9rlm4cCG33XYbZ555Jl26dCEwMJC0tLQKv4/Q0FDatm1L8+bNS4RB5dG9e3d27tzJ+vXrSz3fq1cvVq9eTdu2bUv8WJVHfn5+DBs2jOeee46VK1eydetW5syZU+GxeFudCoQqY8yYMWRkZDh/duzY4e0hiXgYMMC1v3at98YhIiIiIiL11/fff8/Bgwe57rrr6Nq1q8fPyJEjmThxIgCPPvoon332GY8++ihr1qzh77//5tlnn3Xep2XLlsybN49du3Y5A53Bgwezb98+nnvuOTZt2sQbb7zBjz/+6PH67dq14+OPP2bNmjX88ccfXHbZZZWqRqqqQYMGccoppzBy5EhmzZrFli1b+PHHH5kxYwYA999/P7/99hujR49m+fLlbNiwgW+//dbZVPr777/n1VdfZfny5Wzbto2PPvqIoqIi54pmdUmdCoQSEhJITU31OJaamkpERESZH6TAwEAiIiI8fkSOJ4GBrv3Jk703DhERERERqb8mTpzIsGHDiIyMLHFu5MiRLFmyhJUrVzJ48GC+/PJLpk2bRo8ePRg6dCiLFy92XvvEE0+wdetW2rRpQ2xsLGD6+7755pu88cYbJCcns3jxYu65554Sr3/w4EF69erFFVdcwW233UZcXFzNvukyfP311/Tt25dLLrmEzp07c9999zmrnrp3786vv/7K+vXrGThwID179mTs2LHOaXZRUVFMmTKFoUOH0qlTJyZMmMBnn31Gly5dvPJeqsJmLz7Rz0tsNhvffPMN5513XpnX3H///fzwww/8/fffzmOXXnopBw4ccKZ5x5KZmUlkZCQZGRkKh+S40K8fWH+/duwIq1erubSIiIiIyPEsJyeHLVu20KpVK4KCgrw9HGlgjvb5q0jm4dUKoaysLOe8RTDLyi9fvtzZZXzMmDFceeWVzutvuukmNm/ezH333cfatWt58803+eKLL7jzzju9MXyRauG+GsHatbB1q9eGIiIiIiIiIg2EVwOhJUuW0LNnT3r27AnAXXfd5SzHAkhJSfFYgq5Vq1ZMnz6dWbNmkZyczPjx43nvvfe05LzUacWXp6zgqosiIiIiIiIiFebVZecHDx5cYmk6d5MmTSr1OcuWLavBUYnUrkOHzDYuDvbuhd27vTseERERERERqf/qVFNpkfrIqhCymtLv2uW9sYiIiIiIiEjDoEBIxIvy8iA/3+xbgZAqhERERERERKSmKRAS8SJruhhA+/Zmq0BIREREREREapoCIREvsqaLBQZCixZmX1PGREREREREpKYpEBIBpk41P7XNCoTCwyEpyeyrQkhERERERERqmldXGRM5Hhw6BBdfDDYbpKdDcHDtvjZAWBg0aWL2d+0Cu92MR0RERERERKQmqEJIGrxt20xj57w8yMio3de2KoTCwiAx0ezn5sLBg7U7DhERERERkep09dVXc9555zkfDx48mDvuuKPWxzF37lxsNhvp6enVet+tW7dis9lYvnx5td63NikQkgZv+3bXfmZm7b62+5SxoCCIiTGP1UdIRERERESq29VXX43NZsNmsxEQEEDbtm154oknKCgoqPHXnjJlCk8++WS5rq2pEKcsLVu2dP5eQkND6dWrF19++eVRn9OsWTNSUlLo2rVrrYyxJigQkgbPm4GQ+5QxOHofoZwcWL7cTCcTERERERGpjBEjRpCSksKGDRu4++67eeyxx3j++edLvTYvL6/aXjc6Oprw8PBqu191e+KJJ0hJSWHZsmX07duXUaNG8dtvv5V6bV5eHr6+viQkJODnV3c78SgQkgbveKgQKk8gdM890LOnd5pfi4iIiIjIUdjtkJ3tnZ8K/otxYGAgCQkJtGjRgptvvplhw4Yxbdo0wDXN66mnniIpKYkOHToAsGPHDi6++GKioqKIjo7m3HPPZevWrc57FhYWctdddxEVFUVMTAz33Xcf9mLjKj5lLDc3l/vvv59mzZoRGBhI27ZtmThxIlu3bmXIkCEANGrUCJvNxtVXXw1AUVER48aNo1WrVgQHB5OcnMxXX33l8To//PAD7du3Jzg4mCFDhniM82jCw8NJSEigffv2vPHGGwQHB/Pdd98BpoLoySef5MorryQiIoL//Oc/pU4Z++effzj77LOJiIggPDycgQMHsmnTJuf59957j06dOhEUFETHjh158803nefy8vIYPXo0iYmJBAUF0aJFC8aNG1eusVdW3Y2yRKrJ8RAIWUG5e2Pp4mbNMtsVK+D882t+bCIiIiIiUk6HD7v+lbe2ZWVBaGilnx4cHMz+/fudj2fPnk1ERASzHF9A8vPzGT58OP3792f+/Pn4+fnxf//3f4wYMYKVK1cSEBDA+PHjmTRpEu+//z6dOnVi/PjxfPPNNwwdOrTM173yyitZtGgRr776KsnJyWzZsoW0tDSaNWvG119/zciRI1m3bh0REREEO1b+GTduHJ988gkTJkygXbt2zJs3j8svv5zY2FgGDRrEjh07uOCCC7jlllv4z3/+w5IlS7j77rsr/Dvx8/PD39/fo0LqhRdeYOzYsTz66KOlPmfXrl2ccsopDB48mDlz5hAREcHChQud0/E+/fRTxo4dy+uvv07Pnj1ZtmwZN9xwA6GhoVx11VW8+uqrTJs2jS+++ILmzZuzY8cOduzYUeGxV+h91ujdReqAbdtc+9YUrtpS3gqhjAxYv97s79xZO2MTEREREZH6y263M3v2bGbOnMmtt97qPB4aGsp7771HQEAAAJ988glFRUW899572BxLIX/wwQdERUUxd+5cTj/9dF5++WXGjBnDBRdcAMCECROYOXNmma+9fv16vvjiC2bNmsWwYcMAaN26tfN8dHQ0AHFxcURFRQGmoujpp5/m559/pn///s7nLFiwgLfffptBgwbx1ltv0aZNG8aPHw9Ahw4d+Pvvv3n22WfL/XvJy8tj/PjxZGRkeARaQ4cO9QiXilcevfHGG0RGRvL555/j7+8PQPv27Z3nH330UcaPH+/8HbVq1YrVq1fz9ttvc9VVV7F9+3batWvHgAEDsNlstGjRotxjriwFQtLgHU89hKwKoVWrYM8eSEgwj//6y/UcNZwWERERETnOhIS4/rXXG69dAd9//z1hYWHk5+dTVFTEpZdeymOPPeY8361bN2cYBLBixQo2btxYov9PTk4OmzZtIiMjg5SUFPr16+c85+fnR58+fUpMG7MsX74cX19fBg0aVO5xb9y4kcOHD3Paaad5HM/Ly6Nnz54ArFmzxmMcgDM8Opb777+fhx9+mJycHMLCwnjmmWc466yznOf79Olz1OcvX76cgQMHOsMgd9nZ2WzatInrrruOG264wXm8oKCAyMhIwEzXO+200+jQoQMjRozg7LPP5vTTTy/X2CtLgZA0aAUFngGLt6eMNW9utvPnm3Do++/hjDNgyRLXcxQIiYiIiIgcZ2y2Kk3bqk1DhgzhrbfeIiAggKSkpBJNkUOLvY+srCx69+7Np59+WuJesbGxlRqDNQWsIrIcX56mT59OE+tf0h0CAwMrNQ539957L1dffTVhYWHEx8c7q6EsxX8vxR3tPVljf/fdd0sEVr6+vgD06tWLLVu28OOPP/Lzzz9z8cUXM2zYsBI9kqqTAiFp0FJSoLDQ9djbTaWHDTPNo7/+GrZsgcmTTSD055+u52jKmIiIiIiIVFZoaCht27Yt9/W9evVi8uTJxMXFERERUeo1iYmJ/PHHH5xyyimAqXxZunQpvXr1KvX6bt26UVRUxK+//uqcMubOqlAqdPuy1rlzZwIDA9m+fXuZlUWdOnVyNsi2/P7778d+k0Djxo0r9Hsprnv37nz44Yfk5+eXqBKKj48nKSmJzZs3c9lll5V5j4iICEaNGsWoUaO48MILGTFiBAcOHHBOoatuWmVMGjT36WLg/SljAQHw/PPw9tvm8Zw5ZtEA9wqhgwfhyJHaHaeIiIiIiDRMl112GY0bN+bcc89l/vz5bNmyhblz53Lbbbex0/Gv1bfffjvPPPMMU6dOZe3atfz3v/8lPT29zHu2bNmSq666imuvvZapU6c67/nFF18A0KJFC2w2G99//z379u0jKyuL8PBw7rnnHu68804+/PBDNm3axF9//cVrr73Ghx9+CMBNN93Ehg0buPfee1m3bh3/+9//mDRpUk3/igAYPXo0mZmZ/Pvf/2bJkiVs2LCBjz/+mHXr1gHw+OOPM27cOF599VXWr1/P33//zQcffMCLL74IwIsvvshnn33G2rVrWb9+PV9++SUJCQnOHko1QYGQNGjFAyFvNZUuNh2Xk08Gf3/YsQMWLzbVQmACI9C0MRERERERqR0hISHMmzeP5s2bc8EFF9CpUyeuu+46cnJynBVDd999N1dccQVXXXUV/fv3Jzw8nPOPsTTyW2+9xYUXXsh///tfOnbsyA033EB2djYATZo04fHHH+eBBx4gPj6e0aNHA/Dkk0/yyCOPMG7cODp16sSIESOYPn06rVq1AqB58+Z8/fXXTJ06leTkZCZMmMDTTz9dg78dl5iYGObMmUNWVhaDBg2id+/evPvuu85qoeuvv5733nuPDz74gG7dujFo0CAmTZrkHHt4eDjPPfccffr0oW/fvmzdupUffvgBH5+ai21s9rK6PNVTmZmZREZGkpGRUWa5mzQczzwDY8aYKb92O5x7LkydWnuv37evqf75/ns46yzMHLYhQ+CKKzhl5kPMn2+mkf38M7RrZ8a5fj388gsMHlx74xQREREREZecnBy2bNlCq1atCAoK8vZwpIE52uevIpmHKoSkQbMqhNq0MVtvTxnju+9g3TqYNAlrhcOffzbbiy5yrUKmCiERERERERGpCgVC0qDt2GG2XbqYrbdXGXOuL799O0MHFzmva9cOHn4YmjY1jxUIiYiIiIiISFUoEJIGbc8es+3QwWy9vcoYS5eabV4e/VrsoVEj8/C99yA42FUhpJXGREREREREpCq07Lw0aKmpZtuundnWZlNpu73YlLG8PFi50nk+cM82fvkliaws02QaNGVMREREREREqocCIWmw7PaSgVBtVgjl5ECRY1ZYeDjwzz8mFLJs3UryJf09nqMpYyIiIiIix48GtkaTHCeq63OnKWPSYKWnu/KXtm3Bh0KGHP6egtT9tfL61nQxgJAQXNPFLNu2lXiOpoyJiIiIiHifr68vAHnu/6ArUkusz531OawsVQhJg2VVB0VFQWwsnMu3TGEkubdfgd/nH9X461vTxUJCwNcXVyDk5wcFBbB1a4nnWBVCe/ZAYaHjeSIiIiIiUqv8/PwICQlh3759+Pv74+OjWgupHUVFRezbt4+QkBD8/KoW6SgQkgbLaigdHw8BAdDVby0UAKtX18rrl2goba0wNnQo/PRTqRVCcXFgs5kwKC3NjF1ERERERGqXzWYjMTGRLVu2sK2U/98uUpN8fHxo3rw5NputSvdRICQNllUhZIUqzf1SoAB8d+2ottfIyYGgoNLPlVhyfs0asz3nnDIDIV9fc31mJmRkKBASEREREfGWgIAA2rVrp2ljUusCAgKqpSpNgZA0WFYglJBgtk18TcmQ34G9kJsLgYFVuv+XX8Ill8CTT8KYMSXP79tnto0aYaaIWXPI+vQx261bTefrYqlvVJQrEBIREREREe/x8fEhqKx/ARY5zmmiozRY7lPGABJIcZ0stozXkiXw1FOQn1/++3/1lZna9eCDMG1ayfM7HIVIzZrhubxZ164mBDpyxMwLKyYy0mwVCImIiIiIiEhlKRCSBqv4lLG4wj2ukzs8p43ddRc8/DDMnFn++7uHR5dfXjLbKTUQCgoyTYUSE83jUhpLW4FQenr5xyIiIiIiIiLiToGQNFhWhVBCAmC3E5PvViFUbF13q53P7t0Vvz+Y2WB//ul53iMQssp9rLSnZUvPF3YTFWW2qhASERERERGRylIgJA2WR4VQVhZBhYddJ90qhOx2V7izf3/F7x8ba7abNnmeLzUQiogwWysQKv4kNGVMREREREREqk6BkDRYHoFQSornSbcKoYMHwVo4oJSWPmWyQqSTTjLbzZs9zx+1QqhzZ7NdtarEfTVlTERERERERKpKgZA0SHZ7sVXG3Od3gUeFkHtWVN4KoawsOOwoOLICIfdin8JCV99qjx5CVtrTvbvZ/v13iXtrypiIiIiIiIhUlQIhaZAOHnQ1fY6Lo2Qg5FYh5B4IlbdCyLpdSIgr23GvEEpNNSvN+/g4+kcXrxDq1s1sV68usbSZKoRERERERESkqhQISYNkVQdFRUFgIM7UZy0dzAm3CiH3rKh4hdCRI9CzJ5x3HhQVlbx/QgK0aWP2N282lUnut09KAj8/SvYQatECwsNNGLR+vcdrqoeQiIiIiIiIVJUCIWmQPFYYczvwJ33N4337ICcHOHqF0IoVsHw5fPstvP9+yfvHx5tsx8fHTCGzgiKP/kFQcsqYzeaqElq50uM1NWVMREREREREqkqBkDRIVtGNMxBypD6r6cwRW7A55mjyc7QeQlu2uPYfeMB13r1CKCDAFfxY08ZKBELFp4xBmX2ENGVMREREREREqkqBkDQ4djtMmGD2R4xwHHSU9KSQyA57U3PMkdq4B0IHD5reP5atW137+/fDM8943M6sYAa0bm22VmPpcgVCqhASERERERGRGqJASBqcBQvMNK/gYLjhBsdBR+pzMDCRnTgCoVIqhMCEQharQqhnT7P95BMTGHmsYIYrEDpmhZDVQwiOWSGkQEhEREREREQqS4GQ1Ht798Ipp8A775jHr7xitldcAdHRjoscJT0BzRPYS5w55mgYVHwBMvc+QlaF0E03mXvt2QNz5pSsELIaS5dZIVS8hxBA165mu327x/ww90DIalItIiIiIiIiUhFeD4TeeOMNWrZsSVBQEP369WPx4sVlXpufn88TTzxBmzZtCAoKIjk5mRkzZtTiaKUu+uQTmD8fbrzRhDlTp5rjt97quKCgwDSRBkLbJJBGYwCyt6Wxf7+rQsjX12zd+whZFULt28OoUWb/00/LrhDatMmsTGb1MGre3HGj0qaMRUVBq1Zmf+lSsy0qIv6igSyjB76FuWRnV/S3ISIiIiIiIuLlQGjy5MncddddPProo/z1118kJyczfPhw9u7dW+r1Dz/8MG+//TavvfYaq1ev5qabbuL8889n2bJltTxyqUuCg137//d/UFgIycmuAhy2bzelNoGBxHaOdQZCX76VRvv2ruKdDo4V6a0KoaIi2LbN7LdqBZdfbvanTHFNDbMqhKzX+uMPE0QdPGjCIGtWWKmBEMAJJ5itFZSuXYvvbwvowQrO4Ec1lhYREREREZFK8Wog9OKLL3LDDTdwzTXX0LlzZyZMmEBISAjvu6/f7ebjjz/mwQcf5Mwzz6R169bcfPPNnHnmmYwfP76WRy51SV6ea//118323HPdLti40WzbtKFVGx9nIBSak8aBA+ZUcLCrWMeqENq9G/LzTeVQkybQv7+pBMrKchYcOQOhLl3gwgtNGDVxojn2yCNmBTKg9B5C4AqE/vzTbBcscJ66hM/UR0hEREREREQqxWuBUF5eHkuXLmXYsGGuwfj4MGzYMBYtWlTqc3JzcwkKCvI4FhwczAK3L8mlPSczM9PjRxqWrCzXfmGh2f7rX24XWI192raldWucgVBjXM2C4uOhsTnsrBCy+gc1bw5+fmCzwXPPeb62FQiB6V0UHm72W7WCq65ynLDbS+8hBNC3r9laFUJun/Vz+I5DKVmIiIiIiIiIVJTXAqG0tDQKCwuJd//GDMTHx7OneBdfh+HDh/Piiy+yYcMGioqKmDVrFlOmTCGl+DJQbsaNG0dkZKTzp5mzi680FFnFMpOmTaFXL7cDbhVCbdqUHght3QoxMWbfqhCy+gdZlUMAI0dC796ux6Ghrv2kJHjzTWjUCF56Cfz9HSeys838MygZCPXqBT4+ZsWzXbucgVC+zZ8QjhA089tj/wJEREREREREivF6U+mKeOWVV2jXrh0dO3YkICCA0aNHc8011+DjU/bbGDNmDBkZGc6fHdbyTtJgFA+E/vUvU83jZAVCbdvSogUcsJlAKNaWxmmnmVNDh5asELICoZYtPe//3XemN9B//1tyLJdfDgcOFJuyZs378vWFkBDPJ4SGmvlmAN9+a17Ux4dvm44GIHreN2W+bxEREREREZGyeC0Qaty4Mb6+vqRayzE5pKamkmAtzVRMbGwsU6dOJTs7m23btrF27VrCwsJobS3hVIrAwEAiIiI8fqRhOXTIbPv2hT594Pbbi13gFggFBEBgkikFimE/U7+xM24cvP12yQoha8qYe4UQQGIirFgBb7zhdrCoCMaPh9mzSw7Qmi4WEVEsqXKw+gi9/LLZJiezIWkQAIF7d5b1tkVERERERETK5LVAKCAggN69ezPb7QtyUVERs2fPpn///kd9blBQEE2aNKGgoICvv/6acz3KLUQ8WRVCV11lejO3b+92sqjItSRY27YAJHUzyY+/PZ+QwkM88IA5VVYPoeIVQqWaMgXuucetcZCbslYYs1h9hDZsMNuTT8Ye7RjjoQPleHERERERERERT16dMnbXXXfx7rvv8uGHH7JmzRpuvvlmsrOzueaaawC48sorGTNmjPP6P/74gylTprB582bmz5/PiBEjKCoq4r777vPWW5A6wAqErIbOHnbvhpwc0xW6eXMAnns9hPwAx9StNFcfIatCyDq001Gc43ja0VlLi+3aBcWq4o4ZCF14IZx+OoSFmWllo0bh0zgagKDs/eV4cRERERERERFPft588VGjRrFv3z7Gjh3Lnj176NGjBzNmzHA2mt6+fbtHf6CcnBwefvhhNm/eTFhYGGeeeSYff/wxUVFRXnoHUhdYgVBYWCknreliLVuaUAho0wZIaAzbt5v0xzEl0aoQsqaMWb3MExOPMYAdO2DmTNfjFStMwGM5ViAUE2OeX1QEeXkQFITvVBMqBeUcJLlbEd9868NRZk6KiIiIiIiIePBqIAQwevRoRo8eXeq5uXPnejweNGgQq1evroVRSX1i9RAqNRByW3LeQ2O3QMghLs5s9++HgwddQVMZLa9cPvzQLC1vWb7cMxAqa8n54nx8ICgIgID4RuYQdnasSmfmzGhuvvkY4xARERERERFxqFOrjIlURqkVQkVFpsHzjz+ax6UFQuARCDVuDMP85tKTv1i2zBwLDS02FW3hQnj1Vc8A6LPPzLZbN7NdvtzztawKoQo0PI9oHMAhzBuK5oAzUxIREREREREpD69XCInUtBI9hOx2syb822+7LmrTxvNJpQRCtr2pTC84nWxCmbhoDxDoWR2Ung5nn222zZvDeedBbi6sXWvO33knXHtt2YHQsSqE3Jx5JhwOiiE8J4toDjhvISIiIiIiIlIeqhCSeq9EhdADD5gwyGaDE0+EwYPh4os9n1RKIMTatQSQTyPSyZi1GCg2Xeyll0wYBPDFF2a7aZOpRgoPhxEjzLF16+DwYdfzKhEIxcdDfCfTWDqG/QqEREREREREpEIUCEm9Vljoyl7CwoC9e+H5582B996DRYvgl18gKcnziaUFQla/IaDRil8At4bS+/ebQMjy3Xdw5IgJfwA6djTpUVycCYhWrXJdu2eP52uWl2PZs2gOOHMogM2b4f33zXsXERERERERKY0CIanXsrNd+2FhwKxZZspYjx5m+lZZSguENm927vZMN4GQs0Loo49M9+ru3aFZM1OWNHOma7pYhw6mIqlHD/PYfdrYhg1m265dxd5cdOkVQrfeCtddB19/XbHbiYiIiIiISMOhQEjqNWu6mK+vY4Gun34yB9xX+SrNMQKh/iwikBxXhZBVCXTeeXDhhWb/yy89K4SgZCBkt1c5ECreQ+iff8x24cKK3U5EREREREQaDgVCUq+59w+yYXcFQsOHH/2Jx5gyFkQu/VnkqhDascNsmzWDiy4y+9995wp+OnQw2+KBUGqqGaSPD7RuXYF3hseUMSsQys93DWXx4ordTkRERERERBoOBUJSr3k0lF650vTrCQmBk08++hOPUiG0ii4ADOGX0gOhfv2gaVMzhWzFCnO8eIXQypWmyc/69eZxixYQEFCxN+c2ZczqIbRjh2lRBLBsGeTlVeyWIiIiIiIi0jAoEJJ67dAhsw0Lw1UdNHgwBAYe/YmxsWa7f79ZOSwz0xkOfc6/AejEGteUse3bzbZZM1PtM3Kk6142G7Rta/bbt4fgYNPcaNOmyk8Xg1IrhLZscZ3OzYW//674bUVERERERKT+UyAk9ZpVIRQeDvz+u3kwdOixnxgfD127mnKbiROdSYu9cWM22Mz0r0RSTIXQoUOupeObNTNba9oYQMuWjgZGmGZG3bqZ/eXLqxYIuVUIHTpkhuoeCIGmjYmIiIiIiEjpFAhJveYxZSw11Txo3vzYT7TZ4I47zP6rrzqndtlatyYvxpQFJbCHuDhc08WiohzJE9C/PzRpYvat6WIW9z5C1RAIRXMAu93kUlYgZLOZrQIhERERERERKY0CIanXPAKhffvMg7i48j35sstML6Ht2+G558yx1q3xbWIaByWRgq+P3bN/kMXHB/5tppbRq5fnfasrEHKbMgZmZpsVCA0caLYKhERERERERKQ0CoSkXvPoIbR3r3lg9Qc6lqAguPlms79kidm2aUNAC1MhFMJhkziVFggBPPkkfPAB3Hef53ErEPrrL9i40exXoUIoinR8KCQjwxUIWSvfr1ljVh4TERERERERcadASOo1q0IoMjgP51Jc5a0QArj/fhg2zPW4dWtiW4aSiWNqWEqKZ0Npd8HBcPXVEBHhebxbNzOnKzUVjhwxfYVatiz/mCyOQMgHO1Gkk5HhXAiNE080RUp2u+mLLSIiIiIiIuJOgZDUa1YglODnWD7ex8cZpJRLaChMnw433ght2sBpp9GxI+zBsd78nj1lVwiVJSzMs7F1u3bg71/+MVn8/JxhUzQH2L3bVQTVtq1zRpnzmIiIiIiIiIjFz9sDEKlJViAUi6N/UOPGJhSqiIAAmDDB+fDqqyHz1URYu8FUCFU0EAL48UeYMwdmz4azzqrYeNxFR0NmJjHsZ8UKM+0sMhIaNTIz4/btc7VOEhEREREREbEoEJJ6zeoh1LjIUSZTkeliZQgOhuDuCbAWzwqh8qxeZvH3h+HDzU9VxMTA1q1Ec4Dly82hVq3MNi4OVq9WhZCIiIiIiIiUpCljUq9ZFULRhY4ymfI2lD6WRNNY+qg9hGqDY/pbHHudfa9btzZb662qQkhERERERESKUyAk9ZoVCEXlVV+FEAAJjh5Cq1ZBTo7Zb9q0eu5dER07AtCD5c7gp0sXs61MIJSfD4cPV+P4RERERERE5LikQEjqNSsQish1pCLVFQhZFUK//mq2zZpBYGD13Lsi+vUzG/5wHurWzWytt1qRKWMXX2yyrm3bqmuAIiIiIiIicjxSICT1mtVDKPSwIxWpriljVoWQlTi5rxpWmxyBUC/+IoBcALp2NafcK4Ty8lwz28qycSNMnWp+Z9Om1dB4RURERERE5LigQEjqjV9+gU8/9Txm5TUhh6p5yphVIWQ57bTquW9FtWlDTlgMgeSRzAoCAswq9uBZIXTVVdCiBfz1V9m3+vhj1/6cOTU3ZBEREREREfE+BUJSb1xyCVx+OWzaZAKNU06BLVvMuaBDNdRU2jJsWPXct6JsNtLbmyqhE/mdTp3Az7F2oHuFkDWzbcGC0m9jt3sGQr/+CkVFNTRmERERERER8ToFQlIv5OVBaqrZX7MGXnkF5s93nfdPr+YKoZgYV/LSvTvEx1fPfSvhcPcTAdNHyJouBiYQGsg8/m/jKFqlLATM72btWhgyxFRUWRYuNOFZWBiEh8PBgyYUmjgR0tNr8c2IiIiIiIhIrfDz9gBEqsOBA679jRth/Xqz37OnWYbdb1Y1N5X28TEh0K5d3psu5lDYpx9MMhVCO7u5jrec8ADzeBaKwIcCfuNk1q6F996DuXNNH+whQ8y1s2aZ7bnnmjDohx/gzDPNAmqrV8P48bX9rkRERERERKQmqUJI6oX9+137a9eaaWNgmiR/9WkutsxMc6C6poyBq3vzv/5VffesBL/+fQFow2aSWzu6aO/eTchrzzqvaYWZO7d2Lfz5pzmWkeG6x549ZtuunSskyskx2x9+qLGhi4iIiIiIiJcoEJJ6wT0QmjUL8vMhOBiaNsU00QEzxSsqqvpe9P33zby0U06pvntWQnjzRmQRCkC3WEeyYzVPcojHzKfbswcWLzbHrIwMXL+i2FgYPtzsh4aaQqi1a2HHjhobvoiIiIiIiHiBAiGpF9LSXPubN5ttu3Ym0GCv25LzNlv1vWhSEgwYUH33q6SYGMgMNU2uk2wp5uC2bQBsDugIQCIp+JEPuCp/3AOhvW4tlrp1g5kzTSXRCSeY49aUMhEREREREakfFAhJveBeIWRp396xY82Hqq7+QccZmw2SeplAyLbHEQht3w7AxkZ9yMMfH+y0Ctrj8byyKoQATj8dOnVytUdSICQiIiIiIlK/KBCSeuGogdD775tt9+61Np5al5BgtimeFUKHoluymyQALuy/y+MpZVUIuTv9dLP9+WctQy8iIiIiIlKfKBCSeqG0QKhDB2DVKvj6a3PgvvtqdUy1KtFUCDmroRwVQjkJLdhFEwBOab3T4ylWIJSf71pa3tlzOysLRo/m5H9FMyroW9LSzGpjIiIiIiIiUj8oEJJ6wb2HkKV9e+DJJ82DCy90rQpWH1mBULEKoaImzdlJUwBaB3pWCOXkQF6e63fn4wPR0Y4TJ54Ib7yB7eBB7vd5DnBlTSIiIiIiIlL3KRCSesGqEGrUyHWsQ+P98NVX5sEjj9T+oGqTeyBktzsDoaT+rgqhZj47iYiA1q1dT8vMdE0Xa9zY0YR78WL45x8IDweg5+HfSGIXBw7U1psRERERERGRmqZASOoFKxDq189sGzeGRr//aBrfdOtWv/sHgWcglJ5upnwBp17djCvHmAqh4P27WLECFi6EkBBzeWamq6G0s3+QI0yib1846SQALmCKAiEREREREZF6RIGQ1AvWtKezzjLbk04CvvvOPDjnHK+MqVZZTaX37HEFOrGxEBJC42RTIcTOnbRsaS6NjDSH3CuEnP2DrOe3aGGm2gEX8pUCIRERERERkXpEgZDUC1aF0ODBsHEjfPZhHsyYYQ42hEDIqhBKSzO/AIDmzc22qakQYperh1BEhNm6Vwg5AyFHQ2patICRIwEYyHxyt6fW0OBFRERERESktikQkjqvqAgOHjT7MTHQpg2ELJ1v0o64ODjhBO8OsDbExICfn9n/80+zbdHCbJs4KoR27TL9hfAMhEosOW9VCDVvDs2bszehGz7Yidnwe82+BxEREREREak1CoSkzktPN6EQmFyEvDwYP94cOOssR6fkes7HxzVt7HdHcGNVCCUlmW1urrOUqtwVQkB6y54ANN61oqZGLyIiIiIiIrWsAXxTlvri779d4YU7q39QeDgE+BbCBRfAjz9CQADceGPtDtKbrGljf/xhtlaFUEAAxMeb/c8/h927Sw2E4uLwWKHMCpSOdOgBQJO05TU6fBEREREREak9CoSkTti6FZKTS28HZPUPiokB5syB6dMhKMg0lbaWHWsIrAqh3Fyz7dzZda5ZM7O99Vbo14+osAIAMjKKNZVOS4MjRzyeU9QtGYBWmaoQEhERERERqS8UCEmdsHmzKV5ZtgwKCz3PeQRCGzaYB8OHw+mn1+oYvc6qEALo2hWGDXM9fvxx8zg0FHbupHfmL0ApFULWdLGEBAgMBMCvtwmEmudvNk8QERERERGROs/rgdAbb7xBy5YtCQoKol+/fixevPio17/88st06NCB4OBgmjVrxp133klOTk4tjVa8xcoh8vJg927X8cOHXYFG48Z4Lpne0AQHu/bHjvXsnXTmmTBrFlx+OQAnbPuS+3iWqycN4kiq+eXGxlLq7y+ydQw7MCuV2VesrNG3ICIiIiIiIrXDq4HQ5MmTueuuu3j00Uf566+/SE5OZvjw4ey15rAU87///Y8HHniARx99lDVr1jBx4kQmT57Mgw8+WMsjl9p26JBrf8sWs92+3VS1XHuteRwTQ4mGyA2K+xQxx3LxJVx8MQDJ/3zKszxAu93zOCFzFlCsQsjt9xcTA8vpAUDe4uXVPGgRERERERHxBq8GQi+++CI33HAD11xzDZ07d2bChAmEhITw/vvvl3r9b7/9xsknn8yll15Ky5YtOf3007nkkkuOWVUkdZ/7TKXNm832p58gO9t1PCKCEg2RG5SrroJx48y0ubJWVjvlFIiNJSD/sPNQPKn4+kJUFKX+/kJCYJWPmTaWv0R9hEREREREROoDrwVCeXl5LF26lGFufU58fHwYNmwYixYtKvU5J510EkuXLnUGQJs3b+aHH37gzDPPLPN1cnNzyczM9PiRuse9QsgKhJYs8bymVSsadoVQYCA88AC0bVv2NX5+ZhU2N4mkkJjoyJBKmTJms8GmiB4A+KxcXr1jFhEREREREa/w89YLp6WlUVhYSLy1HLZDfHw8a9euLfU5l156KWlpaQwYMAC73U5BQQE33XTTUaeMjRs3jscff7xaxy61zz3Hs6aMWYHQCy+Y7bWX58EDjgZDDbFCqLwefJDdqw+ybH4WZ/EDiaS4ZptZgVqx39/B6LaQDr4pO2p1qCIiIiIiIlIzvN5UuiLmzp3L008/zZtvvslff/3FlClTmD59Ok8++WSZzxkzZgwZGRnOnx079IW2LipeIZSbCysd/Y1HjoS774ZG2TvNUmSBgY6GOFKq5s3Z9NRkpmAqhZLY7QqErP8+rGXqHYpiYgHwz0iDoqLaGqmIiIiIiIjUEK9VCDVu3BhfX19SU1M9jqemppKQkFDqcx555BGuuOIKrr/+egC6detGdnY2//nPf3jooYfwKaVvSmBgIIGO5bOl7ireQ2jlSsjPNw2PnbOb3KtbbLZaH2NdEhEBKZhl6p0VQnl5YDV0b9LE43qfuMZmW1QI6ekQHV2LoxUREREREZHq5rUKoYCAAHr37s3s2bOdx4qKipg9ezb9+/cv9TmHDx8uEfr4+voCYLfba26w4nXugdCePTBvntnv08ct+2nIS85XUKmBUEqKOenvD40be14fG0gGEebBvn21OFIRERERERGpCV6rEAK46667uOqqq+jTpw8nnHACL7/8MtnZ2VxzzTUAXHnllTRp0oRx48YBcM455/Diiy/Ss2dP+vXrx8aNG3nkkUc455xznMGQ1E/uU8YA9r3zDQ/xD769H8D5MS6j/42UFBEBu0kCII69BLQrgI27zMmkpBKrlEVHwz5iiSTTBEIdOtT2kEVERERERKQaeTUQGjVqFPv27WPs2LHs2bOHHj16MGPGDGej6e3bt3tUBD388MPYbDYefvhhdu3aRWxsLOeccw5PPfWUt96C1BL3CqFAcnh4/RWEkc2qlBDgLjhyRBVCFRARYQKeAnzxo5Do/FTY5QiEik0XA1cg1JZNkJZWy6MVERERERGR6ubVQAhg9OjRjB49utRzc+fO9Xjs5+fHo48+yqOPPloLI5PjiVUhFBEBJ2fOIYxsALpMfgT2zoEff3Q1O1YgdEz+/hAU7EPqkXiasNtMFztKIBQTYwIhQFPGRERERERE6oE6tcqYNFxWhdCQIfAvpjmP2w4fhunTPVe+0pSxcnHvI3SsQMiqEAIUCImIiIiIiNQDCoSkTrAqhJ54rIiro78zD156CRIT4eSTYfx4c8zPDzp29M4g6xj3PkLs3u0KhJo2LXFtdDSk4Wg0rUBIRERERESkzvP6lDGRYykqcgVCTff+RdCB3RAWBjffDLff7lpm7F//goMHTUgkx9S+PaRsKF+FUGSkKoRERERERETqEwVCctzLznbth8//wewMHw6BgZ4Xtm1be4OqB/73PzhyfyJM4JiBUFiYAiEREREREZH6RIGQHPes/kF+fuC327G0fI8eXhtPfRERARE9S5kypkBIRERERESk3lMPITnuWYFQeDjY0g+aB9HR3htQfWJNr1u9GnJyzH5SUonL3AMhuwIhERERERGROk+BkBz33Jec58AB86BRI6+Np16xAqHNm802JgaCgkpcFhparELIbq+lAYqIiIiIiEhNUCAkxz33CiEOqkKoWnXpAnFxrselTBcDCAiADH8TCNlycyErqzZGJyIiIiIiIjVEgVADkJsLaWneHkXleVQIWYGQKoSqR3AwPPdcuS71CQ/lMMHmQV3+QImIiIiIiIgCoYZg4EDTFsbqGVzXeFQIWVPGVCFUfa64wrXfoUOZl4WFQRqNzQP1ERIREREREanTFAjVc5mZ8OefkJ8PixZ5ezSVY1UINQrLd01VUoVQ9fHxgS1b4MYb4YknyrxMK42JiIiIiIjUH1p2vp77/XfXfliY98ZRFVaFUELgQdfBqCivjKXeatkSJkw46iUlGkuLiIiIiIhInaUKoXpuwQLX/pEj3htHVViBUKyfIxCKjARfX+8NqIFShZCIiIiIiEj9oUConlu40LVfVwMha8pYrK+WnPcmjx5C+/d7dzAiIiIiIiJSJQqE6rH8fPjjD9fjuhoIWRVC0TYtOe9NYWGQQaR5YP2hiIiIiIiISJ2kQKgeW7ECsrNdj+tqIORsKm1XhZA3hYVBJhHmQUaGdwcjIiIiIiIiVaJAqB777TfPx3U1ELKKUSKKVCHkTR4VQgqERERERERE6jQFQvXYunWej+tqIGRVCIUXOAIhVQh5hQIhERERERGR+kOBUD22Y4fZBgWZrW1/GuTmem9AlWRVCIXmOqaMqULIK9RDSEREREREpP5QIFSPWYFQ+/bQhJ3c/0YzOOcc7w6qEqwFrULzVCHkTeohJCIiIiIiUn/4eXsAUnPcA6GwlbMIKMyBZcu8O6gKKiiAA47CoJAjairtTaGhmjImIiIiIiJSXygQqqcOH3ZV1rRvDy1YZB5kZIDdDjab9wZXAfv3u4YbkK2m0t5UYspYHfociYiIiIiIiCdNGaundu4029BQSEyE/lYglJ8POTneG1gF7d1rto0bg+2gKoS8ySMQKiqC7GzvDkhEREREREQqTYFQPWVNF2vWDCLsGXThH9fJ9HSvjKkyrEAoNhY4qAohbwoLgyMEU4CvOeCFaWN798KUKfDii7Bnjzm2ZAls3FjrQxEREREREanTFAjVU1aFULNm0HT3Ynywu07Wof4vViAUF2t3NRNShZBXhIUB2Mi0efYRWrcOXnvNldfVlEOHzPTHkSPh7rvhqqvgt9/ghBOgSxf48MOafX0REREREZH6RIFQPeVeIZSw9XfPk3UoENq3z2ybxhyBvDzzQBVCXmECIde0MXtGJg88AF27wm23wSmnQGpqzb3+hg3moxsUBP7+8NNPcNllppVRXh5cfTV88UXNvb6IiIiIiEh9okConnIPhGI3LvI8WQenjLWIcJSf+Pq6kgmpVdavPd1uAqGV8zN49lmzElxwMKxaBaeeah7XBGuKWKdOcP31Zn/rVhMQnXuueTxzZs28toiIiIiISH2jQKiesgKh5kkFNFrzGwCZPnVvyXArEGoa4jZdTCtbeYUVCGUSAcCOVeZzdO65sHKlCYX++cdMIasJViCUkAAPP2yCIIDbb4fhw81+TU9bExERERERqS8UCNVTViDUOecv/LIzSCeSJYEnm4N1MBBq5rPL7MTHe28wDVxwsMnirClj+zebz1FyMrRtC02amOusVk/VzT0QSkqCd96BK6+EBx90tZWqqdcWERERERGpbxQI1VNWINRqyxwA5jKY/UWO3jt1aMqYs4dQtqPspEMH7w2mgfPxgZAQVyCUsSMTMA2dwdXaqXgok5trQpvfi7WyqigrEEpMNNsr/p3Ph28dJiLCFQipQkhERERERKR8FAjVM7fdBjExriKgmBUmEJrNqRwoijIH62CFUGz6erPTvr33BiOEhbkCocMp5nPUtas5FxNjtsUDoYcegnHjTNPpqkhJMduEBMeBq66C8HB44AGiQ3IABUIiIiIiIiLlpUCoHsnKgrffNl/I27OOXpGb8Pt9AQBzGMr+grrbQyhyjyqEjgdhYa4eQsH5Gfj7Q7t25pxVIZS/cRscPux8znvvOY7nV+213aeMkZICn38ORUXw7LMkX92TE1mkQEhERERERKScFAjVIz//bJbfHhi/nlW+ySzJbAdHjlAUF89qOnPQsTpUXZkylpsLmWZWEsHbVSF0PHCvEIogk/btzRLwYCqEBjCf655pC9ddB5gVx6orf/QIhL780qw336YNJCQQsHktCzmZ/2SNr3LwJCIiIiIi0hAoEKpHfvjBbB9s/gn+hbnY7HYAigYPBWykE2UuqCMVQlb/oHDfw/jscjRFUiDkVe6BUCQZzv5BYCqE7udZfIsK4I8/AFi82HU+JKRqr+0RCE2ebB7ceiv88w9Fl1+JD3bGcw95jzxZtRcSERERERFpABQI1RN2uxUI2Rm4+3Nz8JZb4Npr8fk/8wXZ+iJfVwIha7rYCY02mJ3oaGjc2HsDEo8pY8UDoTaF6zmb6eZBSgrY7cyc6Tp/+DAUFlbudbOyzA9AUuEO+O03s+TZRRdBdDQ+H3/IEwH/B0Dos2Phn38q90IiIiIiIiINhAKhemLFCti1C04K+ovQXRvMGuHPPAMTJ+LTrg2BgW6BUB2ZMmYFQj1DNV3seHG0CqETfn/V9SAnB9LTPQIhcE0BrCirOig0FEJ//tY8GDjQrD/v8H7iQ/xOP/Ng7drKvZCIiIiIiEgDoUConvj6a7O9J+kzs3P22ebbu0NwMHVuypgVCHXxczSUViDkdQEBnj2E+vRxnCgspNXCjz2uLdi+myVLPJ9f2Y+ex3SxVavMg4EDPa5p1Ah20Mw82L27ci8kIiIiIiLSQCgQqgfy8uDddwHsnJ75lTn47397XBMcXPemjFk9hNoUOSqEtMKY18XGuj5HzSIyaNHCcWLtWvwPZ5JFKBv8OgKQviaFwkLw8THPg8oXp3kEQhscUwit5c0cGjWC3TgqhhQIiYiIiIiIHJUCobrM0TT6yy8hNRWGxa4kNG2bSX9GjPC4tESFUFFRLQ+24navz+IJHqHnnhnmgCqEvO6GG2DouY5l53PdgkVH9+gl9GF7kanSyVpvQpn4eNeS9JXJInfvNi2JQIGQiIiIiIhIdVEgVBe98w60bQuPPALAa6+Zww91m2Z2TjutxJJOHhVCdrurQ+9x7NSvbuYR/o+wI/vMXKUTTvD2kBq8rl3hlUmOz1FurvkBZyC0mBPYUWRCmZzNJpRJSICoKHOZVSG0YwecdBJ88snRX+/HH6FJE7jtNvO4WeMj5smgQEhERERERKQKFAjVRUVFsGkTLFnC1q1mhW8/Pzg5zdFs99xzSzwlOBhyCKLIz98cOM6njR35cxUjDnwKwIHHX4ONG6F5cy+PSgAID3ftW12irQohnxNIIRGAwh0mlElMhMhisxV//BEWLYJX3fpQl+bHHz0fdwrYZHaiokqsOKdASEREREREpPyOi0DojTfeoGXLlgQFBdGvXz8WO75clmbw4MHYbLYSP2eddVYtjtjLevUy26VL2bTRTBs7ucVO/FcuNUtxl/K7CA4GsJEXEmUOHOcrjWXf9Qg+2Pk+6EIaPTIamjXz9pDE4uvrCoUyMuDIEVi5EoD1kSc4QxlbqpnnVVqFkNUTaPXqo89ePHzY83HLPEc/qXbtzGfdjQIhERERERGR8vN6IDR58mTuuusuHn30Uf766y+Sk5MZPnw4e60lpoqZMmUKKSkpzp9Vq1bh6+vLRRddVMsj96Lu3U1JUFoaacvM9JkLAqebc/37m6YtxZhACPKC60Bj6S1baLxgKoX4MGvgE8W/98vxINFUAbFtGyxfDgUFEB9PTmwzZygTuN+zQugcpnHui4NgyxZSU83Ts7NdM8BKY/UOsnTwKb1/EBQLhNLTS6ZJIiIiIiIi4uT1QOjFF1/khhtu4JprrqFz585MmDCBkJAQ3n///VKvj46OJiEhwfkza9YsQkJCGlYgFBQEXboAULRkKQC9Cv8054YOLfUpViCUG1gHAiFH0+A1dKL58E5eHoyUqpPjz2XNGud0MU44gegYm3PKWFimKxCKioJbeIMW2+bBF1/gnveuXl32y1iB0EcfwZw50CLv6IFQJhEc8QnxfLKIiIiIiIiU4NVAKC8vj6VLlzJs2DDnMR8fH4YNG8aiRYvKdY+JEyfy73//m9DQ0FLP5+bmkpmZ6fFTL/TuDUDoGhMItc4yU3ZITi71cisQOhIYZXaqMGXsvvvgxhtrbqEy+46dAOygGf3718xrSBWVEQjFxLiqdKKOpAB2EhJMhVBrNpvrNm50VggB/PNP2S9jzfzq2hWGDKHMFcbAWsnMxl4/TRsTERERERE5Fq8GQmlpaRQWFhJfbIpTfHw8e6wmI0exePFiVq1axfXXX1/mNePGjSMyMtL506y+9KJxBELxu5biQyFxaY5v1d27l3q5FQgdDqhYhdC550K3bo78qKiIfYs28vzzZqGzNWuqMP5iMjLgxRdh715IW7ELgN22ps52SXKcKatCKBpnhVCgPZdGHCQxERpFFNKCbea6DRs8AqGyKoQKCnBWEiU5Mp6jBUKNGpltis1xsSqEREREREREyuT1KWNVMXHiRLp168YJR1mOfMyYMWRkZDh/dhytYUld4giE2qQvpQ2b8Ms9bFKfNm1KvdwZCPmVPxDauxemTYNVq+ClFwrhvPOIPakd5/ENAEuXVv1tWN54A+6+G1q2hI1zTYVQUJsmBAVV32tINbICoSVLzApwAH37EhMDeQSSHRwDQBK7SUyEhMJdBJBvrisWCJVVIZSaCna76WEdGwtkZblCnqMEQjsKVSEkIiIiIiJyLF4NhBo3boyvry+p7t8OgdTUVBISEo763OzsbD7//HOuu+66o14XGBhIRESEx0+90L07dl9fGhft40x+MMe6djXfnkthBUJZflFm5+DBY77EihWu/ejnHoDvvgPgYr4AqjcQsl7ryBFI/8cEQt3ObFp9LyDVq2NHsz10yGzbt4dGjRzTtmC/v6kSSiSFhARIOLLF9dzdu8nPyHY+XL3aBD/FWdlPfDz4+ABz55oDTZu60h831qHtBQqEREREREREjsWrgVBAQAC9e/dm9uzZzmNFRUXMnj2b/sdoHvPll1+Sm5vL5ZdfXtPDPD4FB5PfoSsAt/KaOVbGdDHH5QAc9I8zO/v2HfMlli832xP4g9vzX3AeH8bP2ChiyZIKj7pMjRu79pOKTCDUdbgCoeNWeDi4T790VOlZgZBVpdM2eDfBwdA4c7PH09uwCT8/s1heVhbs3FnyJaxAyDld7AsTRHL++aUOyVra3pqyVjwQsttLD55EREREREQaIq9PGbvrrrt49913+fDDD1mzZg0333wz2dnZXHPNNQBceeWVjBkzpsTzJk6cyHnnnUdMTExtD/m4sWvIFQC0sZr1dutW5rVWIHTAzxEIFavKKo0VCF3S2vSIme13Oods4cSSRg+Ws3w5FBZWZuQlWYUmAE0x6YBPcwVCx7VObivAOQIh6z/H9dkmxWkfav4sIw9u8XhqWzYSH28Ki6D0aWNWnpOYCOTmwrffmgNlrCjo6wsREW5Lz7sFQvv2mWBp1CiFQlIFn35q5i9+8423RyIiIiIiUmVeD4RGjRrFCy+8wNixY+nRowfLly9nxowZzkbT27dvJ6VYc9h169axYMGCY04Xq+8WJ99AOpGuA+WoEErzcQRC7ut+l8EKhM7tbBr5Li1IZo59CABn+/9E3uF81q6pnm/XWVlm27frEWI4YB40VSB0XCslELIObac5AG38Tc+usL2egVA7NhAf77p+3bqSt/eoEJo1CzIzTTp08sllDik62i0Q2rXLeXz+fNizB7780lVoJFIRG9YWUjTmIUhLg6uvhs2bj/kcEREREZHjmdcDIYDRo0ezbds2cnNz+eOPP+jXr5/z3Ny5c5k0aZLH9R06dMBut3PaaafV8kiPL5v2RTCBm1wHylEhtM/HsaLbMSqEjhyBtWvNfmK2CYQ20I6fOB2Au+zjOUwIPnfdXrnBF2MFQg9e7fgSHxpq1iqX41fnzmbr7w/JyYDJJIcOdQVCzdkOQOBu8+V5FV0AVyDUtq25hX3+Avj9d4/be1QIffWVeXDhhY6GQqVr3Rp20cQ82LXLWQ60fr3rmrvvdn3eRMpj9my4v/N3+OxwrJSXmQmXXgpFRd4dmIiIiIhIFRwXgZBUzrZt8Aq3kxUaByee6NmIpxgrEEq1u1UIHWXuzKpV5rtOXBwEbjeB0GafdszChHBRBWn4U0CHWa97dp+uJOsLeuMjjmYyTZqAzVbl+0oNGjDAzNMaOhT35eDGjHEFQo0Pm0DId7upELICRWvKWNu2cBbfc9vXp8Cpp0K2q9m0VSGUmAgsWmQenH32UYfUvbtbIJSdbb644xkI7doFb71VqXcsDVBuLtxyC4y2vwrAtoGXQ0gI/PEH/P23l0cnIiIiIlJ5CoTqqNRU831kD4lMeW4TzJt31OutQCil0BEI5eU5vyyXZtkys+3dPR/b1q0AtD2jHRtox/qB15HScTC/MBgf7PDQQ1V9O85AKCrLEQhputjxr3Nn0/zns888Dp96qisQij2yDY4cweZId6xAqB0biIuDbn5r+B+Xms/R4cOueYq4AqEmcfmu6TlWVVIZunWDI4SQ6We6W5/Tcyfz5rkCoRNPNNu4d5+CK64w3/ZFjuKll8C+bh1D+YUCfLluz1PYk3uYk2vWeHVsIiIiIiJVoUCoDnrzTVNZsWKFKaLpemKYmbZzFFYglJEXbFaIgqNOG7OKfga33Go6R4eE8PynSXzzjY22c99jyXO/8B/eocDmB9Onm3SqCqxAKCJTgVCd0qFDiSXgbTaYscqsQBaYl+UMeTJtESzG9Bpqwm6aRmXRbfJDRODWUXzpUueuNWWsReFmKCgw0wibNDnqcKw2Wtvt5vOTt2Unr78OG0yRG9deC63YzBUbHoFPPoGpUyvxpqWh2LABHn8cBvErAAv9BjF7Q3O2BjuaX1nzakVERERE6iAFQnVQSIgJUPr2hblzoVevYz/HCoSOHMHMA4OjNpbeYXoB0y3I8U26bVsiIm2cd55p4RIWBhtpx7zQM835xYsr81acrEAo9KACofqgVZcQ1xTGuXMB2B3YmoNEk4r5/LXNX0Pwmr8A+JlTATgwaylHjpgM0sorkw45Ok63b3/MaYSdO5vP57ZC8/lpyk5+/tn1Ub/wQrjF7x1TkQTw4YfV8G6lPioshGuugZwcOD/JBN65PU2J2cq8juYiBUIiIiIiUocpEKqDrrjCrMD9++9wyinle44VCB0+DDhWcDtaIGRN12ma4wiE2rXzOB8W5riORLNz4ED5BlIGKxAK3q9AqN5obqaNWUt074gwDaVX0RWA1qmLsG0zTXo/4koAUr5fQrdurn69wcEQleoIhDp0OOZLhoSY6rmduAKhgwfNucREaBSSy3W2ia4nzJxplh8TKebDD2HhQvN33anhJvAu7G0WPFihQEhERERE6gEFQnWQry/8619HXWypBCtf2bYN8hs5KoSOMmXM+o4cl3H0QGhvoenVUpVAqLDQUbkEBOxzrDJ2jKlBUgdYgdCffwKwJbE/4AqEmi4y67/vD0x0NivvyFpSNmXzxRfm8/3qq+CzofyBEJhpY+6BkKV9e2DKFKLy09hJEzbFnGBSp08/rdLblPrJ6mN+938OEbD+HwCCB5kpj4sOOAKhdes8VhorKDAL4m3fXqtDFRERERGpFAVCDUSLFuYLcUEB7Mg/eoVQUZErEIpMPXoglJrnCISsMoxKcFtYCt/0/WbnKCumSR1hBUIOvW4+kdhYVyAUumwhAGnxXdhDIrtJxJci/tt/Oa1bw/ffw/XXY750Q7kDoW7dXIFQu6BigdB33wEwiav5wHaNOVGsKbYIuPLy3iwxKzI2b07TPgkAzN/REntAgJlP5kh/0tPhrLPgootMryoRERERkeOdAqEGZMQIs129z1QIrfm19AqhAwdMcASuJedp29bjmuqsELKmi/n5AYccK59FRlb6fnKccA+EgoLoc2131q2D5Eu7elyW09pMJVtCHwCeu3gJmzbBGWc4Lljn1kOoHNwrhLrHuAKhdu1wNj+fxyDeSzvPnPjrL0hLK//7kgbBystbpzn6o51wAi1amMq17Fw/Clo5QvI1a7Db4fTT4aefHJfOH489IQGWLKn9gYuIiIiIlJMCoQbE+oI9b72pEPrnl738/XfJ66z+QS1isrBtNz1eildnhIaa7UEcK0xVIRA65FhkKizUji1TgVC94R4I9ekD/v40agSj3+ricZlvsgmIltIbANuMH01FBpjKs337zH45A6FTToH8OBMIRWW5AqGuCWnO5evtvfuQSgJpCV3Ma/3yS4XfntRvVoVQwjbHCor9+uHv7/pYH0wwK42t+3Ytf/xhZkaGhsKp/vP4v7x7saWmwuuve2HkIiIiIiLlo0CoARk0CIKCYEu2qRCKYy9r1pS8zpouNjjiLzN/rGlTVyNqh4AAs9L9AaqvQigmLBfy882DiIhK30+OE82aufZPPNG1HxHhERZFnWwCoilcQJGvn2n0PNHR+NmqDkpKgvDwcr1sdDTM3WgCIZ+MdM4alEV4OPTzMb2MaN+ec682QeZPhcPMsdmzK/jmpL6zKoQi1zuqfE4w/YNatzYPf95h+ggt+mAtb79tjl16Zjqf2C53rWL37beQl1dbQxYRERERqRAFQg1IcDAMHgx7Hct+x5NqFUwA8OCDMHKkqyFqfz/HF+i+fUu9X1hY9QZC8cGZnjeXus29Qsg9EALo6po2lnhqZ046CSJO6gb/95Q5eNtt8NFHrmXhy9k/yCk83BkqfvnyLrZuheiNrqk///63maL4v32OQOjnnyt2f6nXsrLMioyhZOGXssMcdHxm27QxD6dvNoHQkLwZzJ+0EYAxOY+SkLeDjbThUGi8aSyk6jMREREROU4pEGpg7rgDApqaap849rJpkzmekQHPPANTpsDUqeZYcl4FAqGDB13TfCrICoTighyBUHh4xZZQk+NTQoKZ+ufrCyed5HnOCoSaNcM3OpKFC80S3z733QPDh5tl5666CiZMMNedf37FX9+xtF7w/p1ERwOLXYFQ48ZmCuU8TqHQ5gubNsHWrZV6m1L/WNVB3YJM0EPjxqb0DFeF0AxGsJtEWrCdP+nLEyHjaPnDGwDcxATmNrrAXPj117U5dBERERGRctO37gZm+HCYtcJUCDUine0bcgH47TdXnmP9g3a7DEcg1KdPqffyCIQKClzJTgVZT4sNdARCmi5WP/j4wIwZ5icx0fOcFTIW/2z5+JhEctw405AlNhY+/xxGj6746zsCIXbuNB9ut0AI4NJL4RARrAzuZ47PmVPx15B6ybnCWPh6s+PWv8oKhA4Qw8CgJfwV1J9GpPPI4QexFRZyYPD5zGYYkw6NNBd+8w0UFtbi6EVEREREykeBUEPUqJHp1QJkbDQNexcscJ3Ozc4nmv3EpDvmkx0lEMohmEL/QHOgktPGnD2E/BUI1TsnngjDhpU8fv75MHly6U13g4LggQdMM6vt22HUKLDZKv7aViD0xBOm03Ramml81aMHAJ1MT2D+tDvCKatfkTR4ViDULbDkCndWIATQcWgSeTPn8nHXZykKCYXISELeHI+vL3ybMQh7YKD53O3ciYiIiIjI8UaBUENks2FPbAJA9O5V5OXB/PnmVAu2sp8YFmOqKGjbFho1KvU21kpjeWFV6yOkQKgB8vWFiy82zaLLEhZmwqHKsqapbd7sSjxHjIBAE2DGmUI5NuaY/xbYtavyryX1ijVlrL29ZIWQ1UMITMXliacEcMXf9+GzayesX09Qp1Z07AiF+HEkyvH51mdLRERERI5DCoQaKJ+zzwTgQvsXrF/vmk0zghlEcIg2OKqDyugfBK6+zznB1RMIRfkoEJJqdN11sHq16eHy6admXfApU5ynY2PNdqfdEQjt3u2FQcrxyKoQapHnCITcmpo3amT6pfv6mj5UTlFRzpTRapF1MFiBkIiIiIgcv/y8PQDxDtsl/4YJb3EBU3jlf2+RmxtIbCz0P7gECqAAX/wohKFDy7yHFQgdDoqmESgQkuNPp06uuWHF+PlBTAzs3q8v7eLJVAjZSTxUskII4McfzV937dqV/nyrZVZaUBOagMJGERERETkuqUKooRowgLSgJkSRwY53ZwAwcCD0D1gCwFV8SPaP8+Caa8q8hRUIZQW6rTRWCVYgFIECIald8fGwC7cpY5VcKU/ql9RUaEwawTnppn+V+zwxoHNnGDCg7Odb0xH3+ChsFBEREZHjlwKhhsrHh1VdRgEwLO0zAM4aeoQ2Of8AsDjwFEKGDzTzIspgBUKH/Ktnyli4XYGQ1K64ONiN40t7djYcOuTdAclxYe9eaI+jOqh5cwgOrtDzrUBoZ5H6U4mIiIjI8UuBUAOWNuRiAM5iOif0yOPKHivxLSoglTiKkpoec2EnKxDK9HE0nXYEQikpFVtp2foOHlaYYXYUCEktiY+Hw4SSGxRpDuiLu2AqhDpQcoWx8rICoc25CoRERERE5PilQKgBCx/alz3EE04Wn944D7/lZrrYcp/eDBl67GW+rVXG0n08K4RuuQUuuMD02SgPq0IopEAVQlK7rC/u6WH64l7r9uyBLl3gsce8PZIS9qbaGYGZSluZQCg+3mw3ZDuqz9RDSERERESOQwqEGrChw3zY2OEsANqumw5LTCB06v19ePfdYz/fqhDaj2cgtHSpebhuXfnGYQVCwfkKhKR2WYFQWkADWWksKwtOOQWGDIGCgmNfv3kznHMOzJ9f/WOZMsWsAvfGG8dV76b8fLjg4HtczJfYfXzgsssqfA/rc7UqXf2pREREROT4pUCoAfP3hwHjzjYPpk2DhQsB8DuxzzGni4FbIFTkCoQOH4bt283DlJTyjcMKhILyFAhJ7bIqORpM899bbzXhzty5zv/ei/v6a1Pht38/MHYsfP89vPZa9Y9lwQKzTUuDHTuq//6VtH/Zdl7jVgDsTz0N/ftX+B6xsWa7Nc/xuTp8GDIyqmuIIiIiIiLVQoFQQzdsmEmGNm+GDRtM89STTirXU61AaG+Ba5Wx9etd58tbbGEFQgE5CoSkdlmVHNsLG8CUsc8+g0mTXI+//bbUy5591vQA++il/fDVV+bgnj3VPx4rEAJndaK3FRbC0jFfEUQuS/xPxOe+eyt1n9BQ83OEEAojoszB+l59JiIiIiJ1jgKhhi48HAYPNvuBgeabYOPG5XqqFQilFrgqhNyniVW0Qsj/iAIhqV1WhdCm3Ho+ZayoiPTbHgHgSK+TzbGpU0udxmRlYkUffAi5ueZBNQVCkydDo0Yw+4PtnlVBx0kgdPnlEDHnGwD2DbsUfCr/P5FW2Jgb0wDCRhERERGpkxQICTzwAJx8Mnz3HQwfXu6nWU2l9+S4Vhlbu9Z1vqIVQr6HFQhJ7bK+tG/Iqt9TxqbfNpOotE2kE8n4k6dAUBBs2QJ//+1xXWGhlf3YOWv3O64T1RQI/e9/kJ4OMx5e4HnCajzmRfPmwZzPUzkZM5XujAnnVul+Vth4KFKBkIiIiIgcnxQICQwdaqZvnHZahZ5mVQjtznFUCB0+zObVOc7zrXf8Cr/8ctRmqkVFkJ1t9n2zFQhJ7bK+tG/Oq79f2n/8EexvvAHAB1zDt4vi4PTTzcli08ZSU81/ky3ZSkfWUeTja04cOmT64FSRlT+13O3oXzRwoNkuWeLVpst2O4wZA+fwHT7YoXdvaN68Svd0rmAXXL/DRhERERGpuxQISaVZgVBKdoRzakXK6oMAhJPJ1COnm7DpjDPK/DJkfcf0Jw9bjiNMUiAktSQ0FEJCYBeOQGjPHlMmcxRPPWV6LdcFu3fDI5dt5kx+AOBN/svSpZA99BxzwZw5Ja4HEwgBbPdvY/qKgUmLquDQIVOUBDAAUyFkv+lm08PswAHYtq1K96+KGTPgt99gpI+ZLsZ551X5nlYgtNe/nk9HFBEREZE6S4GQVJoVCB3K9sHeyEwbO7DRLD3fjB0EkmcumDkTbryx1HtY08UiOOQ6GB5eI+MVKU18POwlziwxXlh41OAjLQ0efhg+fHIb+Z26wYQJtTjSirvmGuh7cCY+2Ck6ZTD+ndpht8MfeT3NBf/843G9lVn0iDLhzPrcFtidS7FVbdqY9VLRjex0wDQb2xzbD7p3Nye82Efom2/ARhGDfeebA2efXeV7WoFQis1RIbRzZ5XvKSIiIiJSnRQISaVZgVBREdgbmWljQTkH8fODXvHF/jV8xoxSv1BmOmaJJYY6doKDTcWASC2Ji4NC/Dgc28Ic2LixzGutUOMivsR/7Sq4887j9ot+Zib89BP0YDkAPiedyKmnmnPT1nc0O/v2mR8HKxDqHbMVgK20pDA2wRysYiBkTRcb2DOLIEyz6nXp8dCtmznh3pG+li1bBm3ZSHD+IdNfqWvXKt/TCoRW09ns/PGHV6fFiYiIiIgUp0BIKi0kxLVfGGkCoWgO0LYtdIo03yz3dDsN+vUzlReffVbiHnv3mm3zKPUPEu+wCmD2N+5gdo4STFiBUBccOzk5x+38sQ0bzLaP/wqzk5zMsGFm98d5odCqlXngViVkzexs42cqhLbRgrxGjkCoilPGVq402xNamQDqMMGs2xkKsbHmxP79Vbp/ZeXnm7CqF3+ZA8nJ4OdX5ftan6uFRf2xh4SY39+qVVW+r4iIiIhIdVEgJJXm6+tqL5IfZqaMRXOANm2gZZBZc35/UBJceaW56MMPTaXQ+vXOe1hFBy0aKRAS77B6B/+d5wiE3JfKK6ZEIAQwaRL8/nvNDK4KNm4EHwrpXOgozUlOZvBgsNnMf4K5bbuY46tXO59jVQgl5rkCoSMR1TNlzKoQSk4ygdA+Ytm0CYiJMScOHKjS/Str7VrIzYUT/R2BUK9e1XJfq0Lo5/mB/HTkFMeDn6vl3iIiIiIi1UGBkFSJNW0sL9RVIRQbC818zDfLVFsijBplpoGtWGEaTJ90Ehw5Ys47ig6ahCsQEu+44Qaz/X6DYxrVMSqEbBTRGUeI0revmQZ0zjkeQefxYMMGaMMmgosOm+S2fXsiI6F1a3M+JdoRCLlVCFmBUHSWKxDKCqv6lDG73RUIdYh2BUIbNwLRjlUKvRQILVtmtgNCayYQAvjJ7ijNmjWrWu4tIiIiIlIdqhQI5eXlsW7dOgoKCqprPFLHWIFQTogrEIqJgbhCUyG0oygJYmLYMcRUCdl9fMzUkO++A1yBkLOHkAIhqWXdu8PIkbCW8k0Za852wsgmD3+YOZPCnr0hLY285D7kXHAJPPssTJ/u9X4xGza4+gfRtasp6cOtZY+vo7dNsUDIh0LCDu4ATCCUEVz1QCglxeQ9Pj7QPLhYhZAVCHlpypgJhOx0OuIIhHr3rpb7ugdCszgNAPuvv5pyJBERERGR40ClAqHDhw9z3XXXERISQpcuXdi+fTsAt956K88880y1DlCOb1YgdDjIfKlrxEGioyE6x5QabDmSSG4u9F32DlEcZMWI+80T/vc/wPUdMy5IgZB4z9ixsBZTIWTfvLnUL+1795pVxqzpYuvowMffN6Ltuh9YSTcCcg4R9M3n8MADZpWqadNq9T0Ut3EjJOPqH2SxFvX6/VDJCqFduyCRFHwK8imw+bGbJA4EVD0Q2rTJbFu2hIB00zhsH7Fs3QqFUd6dMrZsGbRgG6G5B00lY5cu1XLfmBhXKLTOryupxGE7fBgWLaqW+4uIiIiIVFWlAqExY8awYsUK5s6dS1BQkPP4sGHDmDx5crUNTo5/ViCUFeCqEIqOhrBDJhBadyiJKVMgdZ8PGUTxbehl5gk//AAHDzorhBoHOAKhyMjaHL4IYEKS0NYJZBKOrajIlWC4sXKTgY3Mzj904YYbYOvhOC5ut5zLW//G44xlZUg/c+Hnn9fW8Eu1YUPpgZBVITRrZyfTUCgtDfbuJTfXFOm0wEwX2x/clEL8SPN19BCqQlPp7GyzjYzEuarZAd9YCgogJdd7U8bsdli+HHqz1Bzo1g0CAqrl3r6+8NtvsGYN9O3n46wS4scfq+X+IiIiIiJVValAaOrUqbz++usMGDAAm83mPN6lSxc2lfJFSuqvUgOhRnYCD5gpYyvTknj5Zdf1327sYr595+fDV185iw4a55vrnQ1mRWpZy1Y2Z5VQaY2lrUCob6jpH/QPXcjNhYQE+PsfH55f0J/HeJz/HH7FXPj992YVMi/IyDC5izMQ6tHDec4KhP5aG4LdWmls9WpSHP8JtnWsMJYe0QKAPbhVCFVyGtzhw2YbEoIzELLHmNXFNh10C4RqeZrdxo3md3Wiz5/mQDX1D7K0aQMdO5qio+85G4D8b75z9moSEREREfGmSgVC+/btI869QYJDdna2R0Ak9Z81w+uA3bXKWJz/QXzyzJSbTYcTWLzYFCKAWXo6/4JR5sHMmc6ig+h0R5DYpk1tDV3EQ6tWZhoYUGofISsQ6lDgqhACuPZaM9MoMdF8fBfTl5yYJpCV5bVVpTZuhGj204yd5oA1Twxo2xaCgkxf9+y2PczBX391hhRdw00glBltAqGUIkeFUE4OZGZWajyOHvIegZBvovnfkHVpjhC4oAAOHarU/cslNxeuv970eHJwzFzl7LC5ZmfAgBp56S5dYAYjKLD54b9hDed121Sjb1VEREREpDwqFQj16dOH6dOnOx9bIdB7771H//79q2dkUickOIoHduW4KoSshtL5EdF06G6mFF5+OcTHQ2EhrI82U2rsK1Y4A6GwVEcg1LZt7Q1exE2rVhy1QmjpUrPCWPzBNYAJhGw2kzFYBgwAOz781eoCc+Drr2t62KXymC7WqpVHby5fX+js6Ce9tt05ZmfKFHbtMrvtAkwgdCTWBEJph0MgPNycrGQfIatCKDgYZyAU0sJUCK3bHmwSKqjZaWPPPAMTJ8KYMbB7N0VF8MEHEE4mHbKWmGuGDKmRl+7cGTKIYp59IABDD3zJ8vv+V+rURBERERGR2lKpQOjpp5/mwQcf5Oabb6agoIBXXnmF008/nQ8++ICnnnqquscoxzErENqR5TZlzNFQ2r9ZIkuXwoIF8M47cMIJ5tqFmY5qhU2b8M3JwkYR/ttVISTe1bJl2RVCR46Y5sNd+Af/3GwKgsPYRBv+9S+Tt1gGmu/7fJ430ux8841XeuOU1VDaYk0bmxN6jkmIVq5k8f82AtDeth6A3MSWgJlS5fwPvZKBUGkVQlHtHFPG3Fcaq6nf1Zo18PTTZt9uhy++4JdfYNs2GBEyH5+iQvN3T/PmNfLyVp/q7zAB3DOMYeCEy8x8stGjvTa1UEREREQatkoFQgMGDGD58uUUFBTQrVs3fvrpJ+Li4li0aBG9q2nJXqkbrO+Ja1LNF7pIMojMckxTSUrCzw9OPtkUAPTtaw7/ujoWEhOx2e1042/ahaZgy8kxX0xbtPDCuxAppULIrZ/N0qVmRtOZEQsA8B3Qn3kL/fjkE897WDOO3ls7gI1BXSAjA/v9D9TG8D2sX3/0QMiaQfbulBgO9xsMgG3qN3RiNZ33/AJAVvLJAKSnY+bDAc5GQxVUWoVQbGcTCG3dSs0vPX/PPZCXB1FR5vFnn/HBB2b3hnbm/dZUdRCYvycbNXIFQgCZhJsP1RtvwLff1thri4iIiIiUpVKBEECbNm149913Wbx4MatXr+aTTz6hm/XPztJgWN8T/1hvegj5YCdos5lSQ1KSx7VWhdDixTi/pHZnJb0bOaqDmjc3zVhEvKBlS9hIW4qwmbKYvXud56yVws9yBEK2AQM46SRXU3VL+/YQGwtH8ny5OmeCufa9d2Hhwtp4C05Ll5beUNry73+b/3Y3boQHl5pqpn/zGe80fcJccP752DqaaqmMDKBJE3PcmldWQVYgFOl/2PkgtKUJhNLTcTWTr4kKobQ07DNnAmCf9h34+MDixWyZZSqiTjziCISGDq3+13aw2UyV0Cba8sWAV3mvyViasYO1vS81F2jqmIiIiIh4QaUCoR9++IGZjv+D7W7mzJn8WMEldd944w1atmxJUFAQ/fr1Y/HixUe9Pj09nVtuuYXExEQCAwNp3749P/zwQ4VeU6qPs4fQXn/zL96A7Z9V5qCVFjlYxWMbN0JeR1OikMwKugVruph4X0IC2IKC2IJjDphbHyErEErOMoFQWc2HbTYYNcoUu21rOoD3uM6cuPPOWltBKzMTNq7OozNmNbTSKoSSkmDJEujfHybnnkce/vRiGQN2TjYXPPigWSKeYoHQzp2VGpM1Zayx3VQHERBAeJL5+yI9nRqdMpY/5TtshYUsowc/5wyAU08FYOjez2jEAcI2LDMXDh5c7a/t7qGH4LzzYMDkW8m863EyiWRZpqNn2vbtNfraIiIiIiKlqVQg9MADD1BYWFjiuN1u54EHyj89YvLkydx11108+uij/PXXXyQnJzN8+HD2uv3LvLu8vDxOO+00tm7dyldffcW6det49913aWJ9WZFaZwVCAAcxVULO5ZiKVQg1buzWcyjafElNZgXtfdVQWrzPZjMzFt37CB04ALt3w2+/QTO2E5m+3aQ9/fqVeZ9XXzWhzLhx8BBPcdgWAn/+CdOm1cr7WLoUOrCWQPJMM+mWLUu9LikJ5s+HrxYkMv3aKRxp3MycGDEC+vTxDISaNjUPqlgh5AyEYmOJamQWI8jMBHujsqeM7d8P554LU6dW6qXZ+app7D2FC5g3D9PhHriGD7gl4hNsdruZQ1cswK5uI0aYllJJSdC6tTm2ucDRs0iBkIiIiIh4QaUCoQ0bNtDZWqbGTceOHdm4cWO57/Piiy9yww03cM0119C5c2cmTJhASEgI77//fqnXv//++xw4cICpU6dy8skn07JlSwYNGkRyKf8CbsnNzSUzM9PjR6pPXJxrSfkDOL7UWV9urG89bqzmqivspkKoOytpWbDBHFSFkHiZ+9LzO39eS9u2JgtJTYVBvo5pX716QWhomfew2Uzz5LPPhoP+8bxkv8OceOQRKCqq4XdgsifndLHu3V3/gZbC19f0+Dp/4tkEb1sLU6bA558DrnY76elUecqYVSHUqMAVCFmBk90OeeFlTxn78UeTpb34YsVfN2t3Jkn/zALga0by++/AhReSFxxJa7bwQPbD5sIbbqj4zavAmiG3LsfRM23btlp9fRERERERqGQgFBkZyebNm0sc37hxI6FH+aLkLi8vj6VLlzJs2DDXYHx8GDZsGIus+RnFTJs2jf79+3PLLbcQHx9P165defrpp0utVrKMGzeOyMhI50+zZs3KNT4pH39/U/kDboEQmD4dpUyr6drVbBcd6EC+TwDhZNF5l/nCpkBIvM29sfTfX63j4EHXTK8LYxy9ZsqYLlZcVJSZnfQC95ATFAl//w0//VQDo/a0ePHR+weVKSQEzj8fK6mxApvDh6EgvmpTxqwKoah8VyAUFORabf5wUNlTxqxDjl7UFfLbw9MJJI/1Ph1YQycWL4aioBCWdDJVQqGFh8z7vuKKit+8Cqy/M1dnuVUIVXZK4Q8/mM+WiIiIiEgFVSoQOvfcc7njjjvY5NYIc+PGjdx9993861//Ktc90tLSKCwsJD4+3uN4fHw8e8pY2njz5s189dVXFBYW8sMPP/DII48wfvx4/u///q/M1xkzZgwZGRnOnx07dpRrfFJ+1jQwj0CoTx/XN0o3VoXQyjX+bI7oAUDwkYPmoAIh8TL3pefb2ddx4YXw11/w6j3bOSf9I3PRGWeU+37nnAPpNGJ243+bAzNmVPOIS1q8GDqwzjyw/oOrhIgI1/6hSMeUsd27K1XlZFUIReQ6pgPHmobS1l8RWQFlTxlLTzfbygRC4T9NAWDvgJGEhNjIzDStoSaHu1UEXXJJqX9X1SSrQuifQ45/oMjKcr3RivjqKzjrLBg2TEvXi4iIiEiFVSoQeu655wgNDaVjx460atWKVq1a0alTJ2JiYnjhhReqe4xORUVFxMXF8c4779C7d29GjRrFQw89xIQJE8p8TmBgIBERER4/Ur1KDYQcjVuLsyqEVq2CRyNe9jxZyhQzkdrUpo2rQqi1bQuTP8qlZ0+4NeVBfPJyzdLkblWNx9Knj9l+lXGa2anBCqGHHjIr+e3YAc1xTNsso39Qefj7m+IZgIOBCWbqWUFBpZIZq0IoPMdVIQSuaWmZAWVPGbNykgMH4CjFoCXYDx8heZdZcCD0ygucfxZ//AE/7k5mFsMo9A+EW2+twDupHlYP7RyCKYoxv4vyThtbvtz8OT97/wHst9xiDu7dC19/Xf0DFREREZF6rdJTxn777TemT5/Of//7X+6++25mz57NnDlziLL+H/4xNG7cGF9fX1JTUz2Op6amkuDeqdhNYmIi7du3x9fX13msU6dO7Nmzh7y8vMq8FakGpQZCZSzhbLWe2r0bJm/vz+n+v2APDTVLkBVfw1uklp1xBpx1TTx5wRH42Ivw2bzRfAP/9FNzwfPPH7UnT3Hdupk+PVMPDcXu4wNr1pjEpgY8/7zpHwTQyscRLjRvXqV7OvsIZfuDVc1ZiWljVoVQ6OHSA6F0W9lTxqxAyG6v2CJkuz+YSQiH2UYLOl/WixNPNMfnzTOrvJ/Lt+z7Y0upq7DVND8/13vPTXT0ESpnY+lnnzV/zo2eewDb3r3mcwVwlH8YEREREREpTaUCIQCbzcbpp5/Ovffey+jRoznllFMq9PyAgAB69+7N7NmznceKioqYPXs2/fv3L/U5J598Mhs3bqTIbcrC+vXrSUxMJCAgoHJvRKrMWpzHucpYQACcdFKp10ZGgnsbp9iLBmPbsQMWLKjhUYocW0gITHzfRkA3UyXEmjXwyitmf9QoE1xWQHAwdOxopo2lt+1rDs6aVY0jNvLyID/f7N9yeQYRRRnmQRUDoepaacyqEArJKj0Q2m8/9pQxgLS0CrzmJ2a62B9NLiAwyOZcGO7rr82sN/+IEOJ71OzKYkdj9RHKjnb1ETp48Ogz8vLzTZPtAHK5DBNSLrz5U5M6LligXkIiIiIiUiHlDoReffVVchw9Cl599dWj/pTXXXfdxbvvvsuHH37ImjVruPnmm8nOzuaaa64B4Morr2TMmDHO62+++WYOHDjA7bffzvr165k+fTpPP/00t1hl8+IVVoVQCo4vVyef7JprUgr3tibXXQc0auTqLityPLCaMb/8snPVLW6/vVK36tXLbP9OPN3s1EAglJXl2n/5bkcFUnR0lavuPAKhKqw0ZlUIBR1yBEJxcR7331fkNmWsWHNlKxDypYC8mb/ABx+YeV9lGDcOWjfLJ/7P78zYT70AgIEDzV9Lhw6Z6zp2rFCxV7Wz+gilR5pAaNv8bURHw9NPl/2cBQvMn8WZEQsJ5TApJPBJ/ig47zxzwUcf1eygRURERKRe8SvvhS+99BKXXXYZQUFBvPTSS2VeZ7PZuO2228p1z1GjRrFv3z7Gjh3Lnj176NGjBzNmzHA2mt6+fTs+Pq7MqlmzZsycOZM777yT7t2706RJE26//Xbuv//+8r4NqQFWIPQN57Pxyidoe+/5R72+a1fTW7dVKxg8uObHJ1Jh991nvlwvdCw136MHzjlHFdSzJ3z8McwoPI1TeBJ++aX6xulgBUJBQeC3q3qmi0EZgVAlpoxZFUIBmaVXCO0tcFQIFRaaxMat15sVCL3IXSTf+Zp5EBwMKSmlNoMePx6S9q8hgnTSiaTFv/s7X3L6dBg50uROVj8zb7EqhPaFtKAtsP8vM2Xs++/h4YdLf853JuPi+uYzYRX8xOnMm2+DR0aa0qca+GyJiIiISP1V7kBoy5Ytpe5X1ejRoxk9enSp5+bOnVviWP/+/fn999+r7fWl6qwpY9mEkX7rI3CML1qXXmoWx3nqKbM6vchxp00bePBBGDvWPL7llkqXk/TsabYztnbkaYDUVNOc2a/cf/0ekxUIhYXh6kVTjYFQejpVmjJmVQj5p5ceCKVlB5vyncOHYc8ej0DooGMRwp4s87zhn3+W2uD7yBHoyFoA1tKJ/gNcPecGDzZPe+cduOGGEk+tVVaF0B5/8+cUkGr+3FavNkVSxT9udjtMm2b2Tz5kVqubwQjWrIH9XQcRA7BsmfnDKmcvPxERERFp2Cr8dTw/P582bdqwZs2amhiP1EHuPcCjo8u+ztKzJ2zZYlZ7Fjlu3XefqQrq0qVKH1Zr9tnfO6NcByuzxPhReARC1mpVLVpU+b5WrlDVKWOHD5u+N77ZjvlaxZtKp+NaZXDTJo/nWr+qeBwLECQlmW0p/zCQlWVeywqEGp/SifBwz2tat4ZnnjGZnzdZFUI7bCYQiskyf26HDhX7FR88CC+8QMYN97Br0xFa+O8mattKsNnY0cGsXjdvYxK0b28aEM2fX5tvQ0RERETqsAoHQv7+/s5eQiLgqhAC1796i9R5gYHw22+wahWEhlb6NlFRZnpkAf4UhDjSiYosl1UONV0hVJUpY3a7qdqJxVEd5LbElkcFUrt25sGGDc7nFhU5Xhu3QOjcc822lEDIWsCtm78JhNqe1bFCY61N1t+VGwpaAZBoTyGGNO7lORqd2J7sCR+T9/wrpgv/vfcSNXE8t/IaNyRNN0/s3ZtuQ0yqNG8eMGSIOV5KZa2IiIiISGkqNWHnlltu4dlnn6WgoKC6xyN1UGSkmf715JOltvQQqbuqqeuwNdsqN9RRQmfNg6omNR0I7d+Pa3nAbdvMsmbllJdngh1nINS4sfP36lGBVEogdOiQCZQCySGSTHPQPRAq1oDaCoS6+JpAiI7HfyC0PTuGA82TATifb3iEJwndtYHQm68k4L47IDsbu6Mq6i5e5Ka0J80TR47EWtxz3jxcDdkcgVBmpumH7t5wXERERETEXaUCoT///JMpU6bQvHlzhg8fzgUXXODxIw3Pgw+W3QhVpKGzpi3lBDsCoZqsEKrGKWPdupntjz9CXvO2EB9vyn3mzSv3PayG0s5AyDFdDIpNGbMCoY0bneeLTxfLswWY4CMw0KRUxaaX7dgBNoponXf8B0LWlLG0NFiaeA4A/8fDhJNFJuHk40cWodzEWzzzny2kBrcggVRisndAy5Zw++3OQGj5ckhPHmQeLFsGBw/y9NNmpuMbb9T6WxMRERGROqJSgVBUVBQjR45k+PDhJCUlERkZ6fEjIiIu1urvhwMbmZ1qDoSspdQjQ/Jh927zoBoqhM44w0wJ3bsXpk7zgbPOMie+/77c97AaSsfbyhkIuVUIFQ+EDvjFmTCoVy9zoti0sR07oBk7CCo6Av7+rr5ExyGrQmj/fpiSbwKhePYC8A7/oQ2bOKvTFt7mJib9L4BXA+9zPfnVVyE4mCZNoEMHU4E1d10idOpkqqZ++MHZSshv3hyYOLE235qIiIiI1BEVCoSKiop49tlnWb9+PatWrSIuLo4333yTDz74wONHRERcrEAoK7BmK4Sa+uw26UBAgKnmqSJ/f7j+erM/fjy8s/ts8+C770pM1yqLVSHUJKBkIOTRQ6htW/Ng61bIz3cdxxUI7cXxnk480WwXLfJ4rR07XA2ladeuWldyq27OZef3wWcb+pCCqzv/ZEaxg+a8+HEsfn6wfj2MT7+WL7mQnNH3wDnnOK899VSznTMHuOgiAIr+9xnLlpmpdjf9dL75Q1yypLbemoiIiIjUERUKhJ566ikefPBBwsLCaNKkCa+++iq33HJLTY1NRKResKaMHfKr2R5CzYoc08WaNQOfShWAlnD99eZWixfDXTNOI5cA2LwZ1q0r1/OtCqFEX1P9UlqFUEYG2BOTzNLzhYVmGUJcgVC7cBMI7S50BEIDBpjtjBkewZRHIHQcTxcDV4XQgQOQcciHmb6m+mozrVhCH7p3h969XW81lyDubfElQa8973GfoUPNds4c4N//BsD200yCj+znVGYTWuDovbRwYU2/JRERERGpYyr0jeGjjz7izTffZObMmUydOpXvvvuOTz/9lKKiopoan4hInWdVCKX71GyFUEKhY71yq4t1NWjeHK66Cnx9IZswfsGxmtV335Xr+WEfv8Xr3EKizx5zoJRAqKAADh+xuaqEHH2ErECoYyNHIFQUT3Y2MHw4BAebYGrZMuf96mIgZFl76i3QrBkT4x8CbFx4oTl+5pmua5KTS95n8GDTo/uffyA1uhMkJ2MrKGAkX3M+37guLFZNJSIiIiJSoUBo+/btnOn2/06HDRuGzWZjt9WzQkRESrAqhNJtNdNDyAqEYgocS7NXw3Qxd++9Z6Z+NWsGP3KGOVie5c23b6fl+Fu5hTcZcXiKOeYWCIWEuGZ1ldZHyCqkahZo3lcq8aSlAaGhrqTkyy8BUyi0Ywf0wTE1qlOnir/RWhQQ4AoKAU64sSds306bp69jyBD4z3/M8TPOcF1TWiAUEwM9epj9OXMwnaSBm5jAv5jmunDRIubMMbPtto//0swBLOMfc/LyTMBUzlmBIiIiIlJHVSgQKigoICgoyOOYv78/+Y5+DyIiUpL1xX8/NVsh1Ci/ZgIhHx8TYLRoAQs52RxctKjMQMHp9dexFRUCEFGUYY65BUI2W7E+QsUCIatCKLbIFQjtc7Qisvrl2L/6ikGn2OnZE1pk/0NflmD39XU11zmOuS8JbwU/115rgh3rj7BLF1d/cKuXdnGnn262jz0GGedcTrZPGL1YRhz7SCcSu48PbN/Os7fvZusfe2hy36Vwzz0weXKp97v/fujatUK9w0VERESkDqpQx0273c7VV19NYGCg81hOTg433XQToaGhzmNTpkypvhGKiNRxViCUVlizPYQic2omELI0bw5fkEy+fzD+Bw+absdlTc3KyoJ33il53C0QAjNtbP9+00fIGQitXw+4AqHovGIVQmBWPAsKwrZxIxkbV7CCHrzEuwDYzjnHLI9WR4SHmxlwpbHZ4KOPTEjk1kvaw913w//+Z35t59zUBB/bj3zHGYSTxbecyyXtlxOwdiVhqxZxPWvwLSowT3zgATjvvBIvbs3CW7267NcUERERkbqvQhVCV111FXFxcR5LzF9++eUllp4XEREXa8rY3oJqrBAqKoJJk+Dvv52BUHh2zQZCLVpAAf5sje1rDvz2W9kXf/IJZGSQG9rI83hcnMdDq4/QFVfA2C+7mQdLl0JRkTMQsoKuVOLZ42hFRFiY6SUEjGAGgeRwBR+bczfcUPE35wU332y2n39+9OsGDYLHHzd9nEoTGwvffANBQTB/PvxaOICLG/3MlJDL+D8eJr1TfwAGsID/4Ajp/Pxg+3Z45ZUS99vlaEXV67vHTWikuWMiIiIi9VKFKoS0pLyISMVZFUJ7cquxh9Drr8Ptt0NyMlnBywEIzar5QAhgefBJtGOeCYSuvbbEdatWQatvZxEKrBh2D22/fYFoHFVRpVQIgekPPW5zDx4NCsH3wAFYu5b09M4AhGa7AqHZs+Hqqx1PPvVU+PZbhvALW2lJDAfYE9CMBEdQdLx76SUYM8b0Zqqq3r3h119NpdD+/XDhhf148sl+bFwK25L6E8fbjOZ1/ClgH40JHPcUEffeaBpEPfCA8z52uwmEktjFaQsfg4WYgK1Nm6oPUkRERESOK9WzLrGIiJTJCoRSct2mjFWl6iI1FR55xOyvXOmYbwVBmbUTCM0rOMnslLJy1bp1ptfN/hl/ArC60cmuvkM+PhAd7XG9ezZWgD97mvczDxYuJD0d/Mkj6LAJk1KJ5/vvTdNjwCyxhal8uSvmIwAKL7mi7FKa40xgYPWEQZYTToCXX4aPP4Zzz3V9DN7ddSYbaYM/ZqrYe1zPX+1GmflomzZBSorzHunpcOQInIRb9dfKldU3SBERERE5bigQEhGpYdaUsV1HHGFIfj5m/fRKuu8+yMw0+3Y7bQ78CdgJSN9rjtVwIDR9/4lmZ/XqEv2QZs2C6Pw9NGcHRdi446NeLGCAORkTY0IhN1ZDZMvSIEd45AiE4jDvye7ri39cNOnpbgucdenCoaDGhHKYE/b/CECTey6p+hutJ6zZeVPmx9Ke9Tx46h+81usDHuMxlm+JhO7dzQULFzqfY00XUyAkIiIiUv8pEBIRqWFWhVDqoRCzXBdUftrYBx+YLsNgloICuhz6nSjS8cl3lM7UYFNpgC1ZsRS2bmseLF7scc3ChdAXR3UQnckoCmcWp5mT7duXuOcjj8Avv8DUqebx9HRHILRgAenpEI+perLFxfGv88z/ZD35JAwZAlOm+rCy0SDXzTp3dv5OxPUx2L8f7PgQOuQE0s6+mjwC+ftvYIAjqFuwwPkcKxA6GVdIpEBIREREpH5SICQiUsOsCqEjOTbsjSrZR2jDBnj2WbjpJgBWXfS4s3ly95w/nMEJkZGmu3ANCA01RT4Ame37mB1rSSoH90DoT0zz6WX04s1L5sEXX5R6z8GDoZ9jptjk7f2xO6Yy+R9Idb2v+HjOP9/sLlhgqoSefx7m+Qxx3WzUqOp4m/VGsf7ddO8O3Rx9u0sNhFau5MSr2vMc99ITtz9XBUIiIiIi9ZICIRGRGmZVCAEURVVi6fm//4bkZNP8Ny+PbziP7l8+zK6mJkU5oeh3j+CkJlnTxlLie5qdv/5yntu+HXbsgH6YqqHFnOA8t7f9QEhKKvO+CQnQtClkEEl2a5Na9Dz0K6cxy1zQti1Dh0KnTq5V0lesgGmHFAiVpXgglJzsKqD65x8oOskRCC1bZkqDRo4kMnUD9/IC/hSQYXOsGrppE86l7ERERESk3lAgJCJSwwIDXX2OCyIqsfT8yy/DkSPkd+rGwxGvcgmfYceHf/x7YA8IIJY0+uNo8FxLgdCGMEcg5FYhZFrR2OnnayqElvn2dZ6zQpyj6fv/7d1ndFRVG4bhe9IhpAABEiD03jsCUhSkiIgigoqgKBYEFbCBBWwUC9gAsXyKCgoiolhAECnSu1TpVUgCBJIQIPV8P/aUhBogyaQ811pZM3PmzJk94ZBknnn3u+27by3TCYAXGcXDHvbVLR9+GB8f2LjRZGn+/qb58crY6rzJS5x96U2oWvU6Xlnek/ZUCA42DawrVTIFZGfOwK6zpUktWw5SUzlR+QbYvZtUm+vPgvnWLVihoaYB+pYt2T5+EREREclaCoRERLKYzeaaNpZU6CoDoRMnzFriwHtVJjEy9kkSMFPCDkb6klSrAQB38LPZP5sCofWWPRDavdvZ4HrZMqjKDoJTosHHh/iKdZyPK1jwysduYi8o+sT/Gc56B1CPfwhMjTEphr37tI+PCdjq1nU8ysYY/zfxe+OlTHh1eUvaCqE6dcx56OVllqgHWLkSVvq3A6Do2cMk4MM9ReezA9PraTGtSKlh/zfUtDERERGRPEeBkIhINnBMG0soeJU9hL78Es6dI7VuPUYtbAZA5crmrkOH4HRd04T5BlaajVkcCFWoYC43Hw3BKl0agJmvbARg+XJ4mg/MDjffTLkqPs7HZaRC6Cb77K9v5hbjXdvzrjsGDLhgdbIGDVzXS5c2YYekd34g5NC8ublctgwej3mLvnzBY4WmUp3tzDh+M+2ZxyDe4zMe4Uxle/KmQEhEREQkz1EgJCKSDRyB0JkCV9FDKCWF+HcnArC26UBiYm2ULg29e5u7Dx6EYzd0Sf+YLA6EHAuF7dgB0eVMKnN4/E8kDBlGhX9m8RBfmB2GDUu3qFhGKoSaNoUuXSA5GcYkDmavR0WssDB48MEL9k0bCIWHX+OLyeOKFXNdd1VUuQKhmTNh839F+NanL+Vfuo99mLTvIGX5gEEk4Mep8LRdqEVEREQkL1EgJCKSDRxTxs742gOhEyeu+JiIyXPxj9xHNIXp9PW9APTq5Zq2degQRFRswQmKuB6UTYHQ7t2wN9BMG3s69T183xvDD6nd8CURq1UraNXKWckEGasQAnj3XTOt6Qz+jOrxD7adO00DnPPUr++6rkDo4ry8TLNugHr1XNsdgZCjSK1FCxPEpVXEfkodC6luruzYkWXjFBERERH3UCAkIpINHBVCsT4h5srx41d8zNGXxgPwBQ8Rfc6U2PTu7QpADh2C0+e8+I3OrgdlcSBUpozp4ZOYCHMjXanMca8SpNh/pdhGjAC46gohx2NeecWEGX36+6dfoi2NGjVMPyEwU8bk4iZNgtGjXX2DwEwlq1TJdfuWW8z30xEeeXu7piUeCbA36o6MvLqV8UREREQkx1MgJCKSDRwVQqd87I1doqIuu/+qqbupHzmXVGx4DuiPzWYqO2rWTB8IxcXBbG53PTCLAyFPT1dYMHHTjURTmC3UpHbyBmqwjY97L4ebbwZIVyHk43ORg13C8OFw7hy0anXpfXx8XEuoq0Lo0rp2haFDL+yx5KgSAhMI2WzOfzbCwlwVQscTAlyJ27//Zv2ARURERCTbKBASEckGjkKXk172xi7Hjl165+hogp/uA5gl2AePr8ju3TBnjrnb8f787FnTR+gPOpDg4Wfe1WdDOuKo/IlIKkppDlOPjUQQxk6qUrxrM+d+pUq5HnPmzNU9h6fnlfd5+WXo3NmEHnJ1Wphe5BQp4pp+Z1/IjQoVoLC99/nJk0C1auaGAiERERGRPEWBkIhINnAEQsdtVwiETp8mtWUrqp5YwUmCsY0cCZg36YGBZhc/P9cKUtu2wWkCGNtqtlmePm0Kk0WqVnVdP0tBUvBy3k7b7NnDA4YMMc2i27bN/HHceSf8+mv61bQkY7p3hzZt4NVXXeFbr17w1lswbpyrbZMCIREREZG8y+vKu4iIyPVyTBmLwp5enDplGvGcP5fqs8/w2LaVo4TSq/if/HlfzYseLzzczDrbvt3c3lfpFrgna8Z+vrS9gcqVM9O7IiJMVUm5cun3HTs2e8YkV6dIEVi4MP02Ly94/nlz3VEhdOoUCoRERERE8ihVCImIZANHhdCx5MKukozzG0snJcF77wHwCm9Q4+6aeFzip7RjZpgjELpE7+UskbZCqFYtaNzYXG/Q4MJeNZI7acqYiIiISN6nQEhEJBs4Vxk77QFFi5ob508b+/57OHSIKFsJpnA/d9556eM5AqG4uPTHzw5pA6GaNeHWW831jh2zbwyStS46ZWzPHlPVJiIiIiJ5ggIhEZFs4Jgydvo0rqY356809tFHAHxgPUnBwn6XXWWrTJn0t7OhdZBTkSKuTKtmTXjsMdiwAQYPzr4xSNZKVyFUsqRJHFNSTCgkIiIiInmCAiERkWzgqOCJiwOKpW8s/cMPUK5wDKmrVgPwJX0ZPhy8vS99vLSLibVqBb17Z8GgL+P2202T6zZtzDSxevUytjKY5A7pegjZbK4qIcccRRERERHJ9RQIiYhkg4tWCB07xqFD8PDDUOPUMjyw2EUl6nQoyVNPXf54LVpAUJCZpvX77+Dvn6XDv8D//mfyrGxY5V7cIN2UMYDatc3l+vXuGI6IiIiIZAEFQiIi2cBRIXT6NM4KISsyiocfhthY6Bm6BICdJVoxeTKXbCbtULq0CWTmzMn+MAhM0cj5C6RJ3pFuyhhAkybmcvVqt4xHRERERDKfAiERkWxwsSlj+1YfY/58KFAAeoSZQKjzW60IDc3YMS83pUzkejgCoYQEOHMGaNrUbFizBlJT3TYuEREREck8CoRERLKBY8pYXBzOKWN7VpoeQi8OOkOBLWvNDi1bumF0IukFBrpaXS1cCNSqBX5+pqnQrl3uHJqIiIiIZBIFQiIi2cDx5vrkSUgIMjcKxkdRvDgMabEKkpLMUmHly7txlCKGzQa9epnrX36JKUdr2NBs0LQxERERkTxBgZCISDYoWtQ0gQY4kmgCoWIc4+WXoeCqheaOli3NO3GRHKBvX3M5ezYcP46rj9CqVW4bk4iIiIhkHgVCIiLZwGaDSpXM9R0nzZSxYhyja1fMMmEAt9zinsGJXESdOqYoKCkJpk7F1UdIFUIiIiIieYICIRGRbFK5srn8bbWpECrMKcKtg7Bunbnj1lvdNDKRi3NUCX3xBViN7RVCGzeabtMiIiIikqspEBIRySaOCqHv5xcmGU8AbFO+MRsbNSLDy4uJZJN77wUfH9i0CTacLAfBwaZk6N9/3T00EREREblOOSIQmjBhAuXKlcPPz4+mTZuy+jLl6JMnT8Zms6X78vPzy8bRiohcG0eFUNRxD44TYm5MnmwuO3d2y5hELqdIEbjzTnP9y8k2qFvX3Ni0yX2DEhEREZFM4fZAaPr06QwZMoQRI0awfv166tatS4cOHYiKirrkYwIDAzl69Kjz68CBA9k4YhGRa+MIhAAOU9pc2b3bXCoQkhzKMW1s6lRIrlHH3PjnH/cNSEREREQyhdsDoXHjxvHII4/Qt29fatSowaRJkyhYsCBffPHFJR9js9kIDQ11fpUoUSIbRywicm0cU8YAHuMTTlax92QpX961pLdIDtOuHZQuDSdPwmabPRBShZCIiIhIrufWQCgxMZF169bRrl075zYPDw/atWvHihUrLvm406dPU7ZsWcLDw+natStbt2695L4JCQnExsam+xIRcYeQENfS8+tpyMnfV8Ly5bBwIXi4PZ8XuShPT9cCeOuTFQiJiIiI5BVufQdy/PhxUlJSLqjwKVGiBBERERd9TNWqVfniiy/4+eefmTJlCqmpqTRv3pzDhw9fdP/Ro0cTFBTk/AoPD8/01yEikhE2m2vaWIECULacDZo1g7Jl3TswkStwVLetjKtpTuTISPMlIiIiIrlWrvtIulmzZvTp04d69erRunVrfvzxR4oVK8Ynn3xy0f2HDRtGTEyM8+vQoUPZPGIRERfHG+tq1UzlhUhu4Dhvtx3wd93YvNl9AxIRERGR6+bWQCgkJARPT08iz/uUMTIyktAMLr/s7e1N/fr12e1ozHoeX19fAgMD032JiLhL1armsmZN945D5Go4Ktt27wbqqLG0iIiISF7g1kDIx8eHhg0bsmDBAue21NRUFixYQLNmzTJ0jJSUFDZv3kxYWFhWDVNEJNM88gj07w9Dh7p7JCIZV7GiuYyKgnNV1UdIREREJC/wcvcAhgwZwgMPPECjRo1o0qQJ77//PvHx8fS1r3Pbp08fSpUqxejRowF4/fXXueGGG6hUqRKnTp3inXfe4cCBA/Tr18+dL0NEJENKlYKJE909CpGrExgIxYubQOhogYqUB7hE7z4RERERyR3cHgj17NmTY8eOMXz4cCIiIqhXrx5z5851Npo+ePAgHmlW3zl58iSPPPIIERERFC5cmIYNG7J8+XJq1KjhrpcgIiKS51WqZAKhg2eLmUDo+HF3D0lEREREroPNsizL3YPITrGxsQQFBRETE6N+QiIiIhn0wAPw9dfw+RPreXhiQwgLgyNH3D0sEREREUnjajKPXLfKmIiIiGQ/R2PpbceKmSvHj0P++kxJREREJE9RICQiIiJX5Fht/p//QsyVpCSIjXXfgERERETkuigQEhERkStyBEJb9xYAf39zQ32ERERERHItBUIiIiJyRRUqmMuICLCK2quEjh1z34BERERE5LooEBIREZErCg4Gm81cTy6cpo+QiIiIiORKCoRERETkijw8ICDAXE8MVIWQiIiISG6nQEhEREQyxLFy6blAVQiJiIiI5HYKhERERCRDHBVCZwqoQkhEREQkt1MgJCIiIhniqBCKK6AKIREREZHcToGQiIiIZIgjEIr1VoWQiIiISG6nQEhEREQyxBEInfRShZCIiIhIbqdASERERDLEEQidsNkrhBQIiYiIiORaCoREREQkQxyBUJRlrxDSlDERERGRXEuBkIiIiGSIIxCKTLFXCMXEQFKS+wYkIiIiItdMgZCIiIhkiDMQSiwMHvY/ITRtTERERCRXUiAkIiIiGRIQYC5j4jygaFFzQ4GQiIiISK6kQEhEREQyxLnsfCxQTH2ERERERHIzBUIiIiKSIRcNhI4eddt40jlyBOrVgw8/dPdIRERERHIFBUIiIiKSIekCoRo1zI1Nm9w2nnR++AH++Qdefx2Sk909GhEREZEcT4GQiIiIZEi6QKhBA3Nj/Xq3jScdxzhOnIC//3bvWERERERyAQVCIiIikiGXDIQsy21jAjh7Fqx1aYKpWbPcNxgRERGRXEKBkIiIiGSIIxA6cwaSq9YEb2+IjoaDB90ynn//ha5dISz4LClbtrnumDXryiHVTz/Bvfea3kMiIiIi+ZACIREREckQx7LzAKeTfKFWLXPDTdPGRo2C2bOhSuJmvEghObgo+PvD4cOwdu2lH5iYCI8/DtOmwV13QUJC9g1aREREJIdQICQiIiIZ4uMDvr7mek7oI/Tff+ayAeb5T1VuDLfdZjZ+8smlHzhzJkRGmusrV8KQIVk4ShEREZGcSYGQiIiIZNjVNJY+ejRr2ws5Mp2G9kDoSGgDePpps/Hrr12Jkd2YMWYRMmvCBLPhllvM5cSJOac5toiIiGSPqVNh0iQ4fdrdI3EbBUIiIiKSYRcNhNatu2C/mTOhZEmY+Px+mDEDTp3K9LE4AqEWBU2YszugATRrBq1aQVISvPeec9+oKBg2DGaO+AfbsmVYXl7w1VfQq5fZ4cUXM318IiIikkO99Rbcfz/07w/h4TBuHKSkuHtU2U6BkIiIiGRYukCodm1zIzLSLPeexp9/msvWn/eGHj1MOnTbbSZ4iYi47nEkJ5un9CCFyuc2A7DZs56584UXzOWkSRAXB8CuXWZTN340t6vfDmFh8Npr4OUFf/wBixdf97hEREQkh/viCxg61FwvVcp8aPXMM3DTTfY/cPIPBUIiIiKSYY5AaPx4aNnRn5SSpc0GR+Jit2ULeJJM5Zg1ZsPZs/DbbzB6tJm3dZ2OHTPT0cJt/+Gdmkgi3mw5Xc7c2akTVKkC8fHOJeh37zZ3tWERAN/HdDQbKlaEfv3M9Y8+uu5xiYiISA4WEQGDBpnrL71kVkr95BMoVAj+/hs+/titw8tuCoREREQkwxyB0G+/wdKlEBFU1WzYscO5j2XB1q1QkT34WglYBQua5s2O/j4rVlz3OBzTxeoH7wPgAGU5fNTTbLTZTBk4wJQpAOzZA36cpbltpdl8uA1nztgP5th38eKsbXokIiIi7jV0qKkebtzYfEDl4QGPPuqaZv711/nqbwEFQiIiIpJhjkDI4WhAFXNl507XtqNw8iTUYgsACRVrQtOmrtW8Nm82FUPXwREI1fI3gdA+ynPkSJodHL2BFiyAo0fZvRuasQJvK5EjHqXYkVrJ1Ue6cWPw84Pjx2H79usal4iIiORQa9ea/oFgSp090sQhd99t/hbYti1fLTShQEhEREQy7PxAaL+3PRBKUyG0dau5dARCx0NrmQ3h4VC8uGnauHHjdY3DEQhV9d4LwF4qcOQIpKbad6hQAZo3Nxu+/Zbdu13TxXaVagPYWLnSvq+Pj2lGDeojJCIikle9+6657N0bmjRJf19QEHTtaq5//XX2jsuNFAiJiIhIhp0fCG1PtU8ZS1Mh5AiEamOaPe/ztwdCNpupxgFYs+biT/DTT64DXIYjECqHqRDaT3mSk83UsLlz7dXeffqYnV55hfDt82iH6XR9pnEbAFatSnPA1q3N5ZIlV3xuERERyWUOH4YffgBgWbNnefttiIk5bx/H3w3ffmtWK80HFAiJiIhIhnl6pr/9z1l7hdCuXc7ynC2mMMhZIbQ8thbVqkHPnjgDocRl6QOh06fh1Iz5cOedprJn27bLjsMRCJVKMBVC0UHlAfPwTp3gm2+Avn3h1lvh7Flmnu5Ac0zvoiJ33QTgqhACVyCkPkIiIiJ5z8SJkJLCqoJtuPGJOrzwgpnN/vff5k8YywLat4cSJaByZTP/PR9QICQiIiIZlq5PD7Ahuix4e8O5c3DoEGAKfHw5R2XMymPjF9Vixw6YORNSGphA6MCMVRz78Fsznx9Tpb3s3vHmoLGx0KULLF9u1pe/CEcgVCzOVAjFh1ZwPjfA7NmYqWA//sjJll3MYW2B0K8fNbtUwMPDfFj433/2AzZtavY/etS1JJmIiIjkakeOwMcfJZP88acAjDnzFIGBJvfZsQNatTILkz73HODlZXoJLl8OZcq4d+DZRIGQiIiIZNiAAVCwoGul9sMRXliVKpkbO3c6Vxirxr94ksoJinA4NQwwrYOOlmoEQGVrF8We7gUtWhD37S/s/usAHVN+NccpWRL27oUWLSAkBO6664KVyaKizKphheLMJ3ipZcqnu/+vv8zz4evL3Md+ojiR3NbiFHz2GYUCbNSubfa76Sb44w+gQAFXP4FlyzLzWyYiIiJu0q8ffPHUBrxOneAkwawJvZ1//zWtDLt0gaJFzX6zZtkfULiwu4bqFgqEREREJMMaNTJz7j/6yNxOTISkcq7G0vv2mdVc63qY6WJbqAXYnI/f8F9x9mLCm2QPb0hMxL9PN2ZzO56ksrd8WzNtq2dP80dZTAz8+CN06JCucicyEsqx39wICCCgbJF04zx50rVIyJ59HhyjOJUqu8bxzjvmj8Bdu0zedO4ccMMN5s50zYVEREQkN4qMhHnzXItKLLW14vuZnoSFQWioqSbeu9csNrZ374VV0PmBAiERERG5Kl5eZmVWx4dosaGuQMjRk/mOkKUAbPGsl+6xixdDX77kRUbSs8V/cN99eKQkU5dNAMwMfQIqVYJp0+DYMdPop3lzkzLdcw8kJADmj7zy9obSVKhAyVKusKd4cXM5f755vp9+MrcrVnSN45ZbYP9+EwrFx8PmzZhpY6BASEREJA+YMcNUC3cNWgRA4+fa0Lx5+n0CA6FOHXM9PxYIKxASERGRaxJmZoIRUdz1l5RZtd3ipnO/A7C9bEfAFR4tXgxLaM1oXmTJ9mJY30zhyTqLeZk3eIZ3mXzyDtcTeHqakGbaNChSBNatgw8+IDXVZEUVMA2lKV+eUqXM1UKFYOhQc/2NN6BNG/MwuHCF2UKFoGFDc33dOlyB0KZNcObMdX53RERExJ2++w48Sabxub8BCL2nzUX3u/FGc7l0aTYNLAdRICQiIiLXxBEIbQ9vb5aU37CB7X/+Rw22ERx7CPz8eHByGz74wPQeAtc0LoDjx+HgIRuf72zFSF5mHM+wc7cHK1ZAx44wdqzpL014uJnjBTB+PCcik0lJSVMhVL48bdqYNkCPPQadO5vN586Zftf9+pk/8m655cLXkC4QKl3a1JCnpKQfqIiIiOQq+/eb3tAN2IBvQhwEB7tKgc6TNhA6d85MJ88vFAiJiIjINXEEQvviizvLb2of/p3bbKY6iJtuolHLAjz1FJS393y2r0zv9NVX5o+vwoUhIMAsKnbvvabR87PPQtWqcOAAcN99UKwYHDrE2Wk/400id3vMNAepVYvKlc3S9e+8Y1aLffBBs+L8xo3w2WemP/XFpAuEbDZNGxMREckDFi0ylw+Ws19p1cpUHl+E42+EjRvN3x23337JRU7zHAVCIiIick0cgdDRozjLcm7jV3oUsgdCnTo59z1/9VbH32SffGIumzeHGjXM9QMHzGXZshARAX37QqqPnyn/AQL+9x6P8BllUg+Yip577gFMU0ibzXx9+SX89pvrmJfiCIS2bLG3J1IgJCIikuvt2GEuW1mLzZU2bS65b+nSUK6c+dDq4EHTtnDPniwfYo6QIwKhCRMmUK5cOfz8/GjatCmrV6/O0OOmTZuGzWbjjjvuyNoBioiIyAXSBUK33QZAB/6g3mkzVz9tIFS2bPrHtmplLh0retx5J9Ss6bq/dWv480+zxP3ChfDhh8Djj4OXF4W3LmMCA82OL71kdrpGZcua6qSkJBMKKRASERHJ/f791/QPqhxp/5ukdevL7j9kiFnTYvRoM2WsatVsGGQO4PZAaPr06QwZMoQRI0awfv166tatS4cOHYiKirrs4/bv38+zzz5Ly5Yts2mkIiIiklbaQGjGrnr8R0n8SMDTSoGuXc1fVnbh4ekf27Wr6/pDD5mvtIFQnz7m4e++a26PGQMpoaWwPv8fpzxMh+r4ouHwyCPX9RpstvOmjTluHDwIp05d17FFREQkk5w9e1W7//sv1GMjvudiISgI6ta97P5PPmmCoKFDTU/C/MLtgdC4ceN45JFH6Nu3LzVq1GDSpEkULFiQL7744pKPSUlJoVevXrz22mtUqFAhG0crIiIiDo5AaMkS6N3HxmDeY1OFrjBzJvz4Y7p9/fygRAlz3WaD3r2hUSPo1QsmTTLbHIGQnx90726uP/yw+TsuMhJWrIDN9ftQPnUPA70+hrlzwdf3ul+HIwNaswbzZI4ly7Ztu+5ji4iIyHU4dMj8UVCwoFlxYuXKKz4kKclM+WrDIrPhMv2D8ju3BkKJiYmsW7eOdu3aObd5eHjQrl07VqxYccnHvf766xQvXpyHH374is+RkJBAbGxsui8RERG5fo5ACEz/ncSuPai58yfo1s009DmPY9pYyZJmFfk1a2DKFLMSGMDNN5sVwcaPh8BAs83HB7p0MddnzTJZ0ykKc+jWx/FvdIUGQRnkWF3kzz/BsnAlU1u3ZsrxRURE5Bps3w61aplf/mBWnGjRAhYsuOzD9u0zoVBbj0Vmw2X6B+V3bg2Ejh8/TkpKCiUcHxnalShRgoiIiIs+ZunSpfzvf//js88+y9BzjB49mqCgIOdX+Pk16yIiInJNypUzq4cVLw4TJ8KMGZf/AM7RWNqx4tj5vL3NimDnf97TrZu5/OEHEyAB3HXXdQ09nTZtTPC0f799qVkFQiIiIu6VnGyWDI2NhQYNTFVwly6m83OvXrB6NXz+OVSvbpaUv+sumD8fMA2lPUjhRuz9gxQIXZKXuwdwNeLi4ujduzefffYZISEhGXrMsGHDGDJkiPN2bGysQiEREZFM4ONjPrzz8spYJbajQqhcuat7ng4dzHz+gwfN7WLFzJKwmaVQIWjZ0nzgOHcuVHEEQpoyJiIicu327zflwHfdddHK4csaO9aEPkFB8PPPZimwli3N4g9btrgWgXD48Ufz1bs3/4WPpDvLCUjNWP+g/MytFUIhISF4enoSGRmZbntkZCShoaEX7L9nzx72799Ply5d8PLywsvLi6+//prZs2fj5eXFnousDefr60tgYGC6LxEREckcvr4Zn5Z///2m0rtfv6t7DkfbADDtfRYtMh8GZibH8efMQRVCIiIi1ysmxlTm9OgB77wD69ebFUkd078uZ/FirJdfBsAa954Jg8D8QTBjhpl7HhhoppO9+y4sXw5PPGEaEn7zDf1GV2A695jHdOum/kGXYbMsy3LnAJo2bUqTJk346KOPAEhNTaVMmTIMHDiQoUOHptv33Llz7N69O922l19+mbi4OD744AOqVKmCj4/PZZ8vNjaWoKAgYmJiFA6JiIjkEnv2wP/+Z1aed0w9y0xbtkDt2qahdfT+WAqEBpk7Tp7M/PRJREQkr7v/fpg61Vz38YGAADhxwoQ248ebAOc8x4+Df/Qh/G5siO3YMaZyHy+VmcKwF2089lgGnnPlSrNM2OLFAOzs8CRVfnrb/HLPR64m83D7lLEhQ4bwwAMP0KhRI5o0acL7779PfHw8ffv2BaBPnz6UKlWK0aNH4+fnR61atdI9Ptj+R9r520VERCTvqFgRRo3KuuPXrGk+gDx8GJZvCaSt48bWraasSURERDLm669NGOTpSWrtunhsXA8nTmAVLYrtxAkYMMCEQ76+8MEHMHw4EV0fo1IlmOY1jNtijrGe+jzCZ5w9aOPxx82q84MGXeF5b7iBpPmLaF90HWfjkpg05gbIX1nQVXN7INSzZ0+OHTvG8OHDiYiIoF69esydO9fZaPrgwYN4XO18QxEREZGrYLNB/fomA9q5E9rWrKlASERE5GqtW0fqo4/hAey9fzhRXR6mRPcbiSOAoN/nU/a3ifD66zB8uOsxAwaw40hlisZXpCPTAOjH5zw+uCAFC8LIkTB4sJk23qmTqRbevdu0JRowAO67z/weB/jiC1gU15DixU2/abk8twdCAAMHDmTgwIEXvW/RokWXfezkyZMzf0AiIiKS7zhWP9u3D1My9McfaiwtIiKSAUuWwI/TEhn76114JpzjF25j3L6Xuf2gBy+wgyS8+XKbjQdfew2KFIFBg7B8fbE1agTLltHonR58RgO8SGE+7djq04DfnoPQUIiPh/ffh2HDYONG10w0gBUrTAg0cSKEh5usCeDll00BklyeSm9EREREOC8QciyFduSIu4YjIiKSa7zwAmz8eDmehw5wjBDuZworV3uwYgUk4QPYWLLE7Luz09M09tpAmxL/Ys2bD40b43/2BO0xy8a/zfM88ACEhZnKnzffhJAQ00/QMX189GgT/vj5wV9/QZ06ULWq+bVdtiw8+qh7vg+5jQIhEREREVwZ0P79QNGi5sbx424ajYiISO6QnGwqd9ozD4C5dCSWIM6dMyvGOzgCoY8/hrXJ9VhysBw7DhaARYt4J+A1TuPPiYbt6TulHe+953qcvz8895zrdtOmJoB65RUzs7tjR0hMNDO9Ad5+W9VBGaVASERERITzKoQcgdCJE24bj4iISG6wcyecO+cKhObTntq1zX2JiebSZjMVPrt3Q9quL3//DVGnC/J83HCCiMXnz9+5r5cNf//0z/HEE1C8uLn+1luunkEVKsDvv8OaNbB0qakQ6tEj615rXpMjegiJiIiIuJsjEDpxAuILhODvuCEiIiKXtHEjhHCMBqwHoPukdtQ9Dc8+a+4vVcr0Alq3zjSEPnXK9di//zZTvAAqVvYgIPjiz1GokAl8IiPhxhvT32ezQaNGmfmK8g8FQiIiIiJAYKDpcxkdDYfOFKUamEDIslwfRYqIiAgA48dDUpKpymnLAjywoE4dbn8sjFWrXPs1bAgVK5pAaMECs+2mm2DhQhMI1aplttWvf/nnq1zZfEnmUSAkIiIiYleunAmE9pyyB0LnzsGZM1xQuy4iIpKPxcTAk0+a66GhMNI+XYz27QET7hQoAGfPmkDonntMIBQXZyqGJk40v3P374dffjEPrVcvu1+FqIeQiIiIiJ1j2tjuiELg42NuaNqYiIhIOpGRrusREZazf5AjEPLxMc2eAdq1gypVYPFiWL/eBEDh4a6KoKVLzWWDBtk0eHFSICQiIiJi5wiE9h+wqbG0iIjIJURFua5XZzul+Q/Lzy9dg58vv4RNm6B584sfo1Ur1/W+feGWW7JosHJJmjImIiIiYudYet650tjRo1p6XkRE5DxpAyFHdZCtVSszT8wuKAjnamMXM2gQxMfDXXc5C4skmykQEhEREbFLt/R8SIi5oQohERGRdByBUEgIdIqeB6lcdapTpgx88knmj00yTlPGREREROwcFUIHDqApYyIiIpfgCITuvj2B9r6LzA2V+eQ6CoRERERE7BwZUEwMWEXsNzRlTEREJB1HINQoYRm2s2fNUmOO9eMl11AgJCIiImJXqJDrelKgKoREREQuxhEIVYtdba60bg02m/sGJNdEgZCIiIiIXcGCrr9nzxVSDyEREZGLcQRCYTH/mis1a7pvMHLNFAiJiIiI2NlsriqhswU1ZUxERORiHIFQ0ajt5kr16u4bjFwzBUIiIiIiaTgCoXg/TRkTERG5GBMIWfgfUiCUmykQEhEREUnDEQjF+mjKmIiIyPmSk82vxpIcwTM+Djw9oXJldw9LroECIREREZE0HIFQjJemjImIiJzP8Wuxps1eHVSxIvj4uG9Acs0UCImIiIikERBgLk962gOh06chMdF9AxIREclBnEvO+2u6WG6nQEhEREQkDUeF0MnUYPCw/6mkaWMiIiKAKxCq46NAKLdTICQiIiKShiMQOn3GA4oUMTc0bUxERARwBULVUhUI5XYKhERERETScARCcXFAiL2xtAIhERERwBUIlTunQCi3UyAkIiIikoazQug0EBpqbhw96rbxiIiI5CRRUdCKxQSfizQrjFWr5u4hyTVSICQiIiKShqOp9OnTQOnS5sZ//7ltPCIiko+kppqFDI4fhwEDoEED+PTTHLW4wYmIJCYwwNx49FHXL07JdRQIiYiIiKSRrkKoVClz4/Bht41HznP4MPz1F5w75+6RiIhkrthYM/3K19dUqE6cCBs2wGOPQePGOWaBgzpLJ1CLrZwrVBTefNPdw5HroEBIREREJI10gZAqhHKW1auhbl1o2xaKFoUXXjCfpouI5AVvvgk7d5rrKSmcqVoPXn8dihWDTZvg1lvtv5zcJzY6mdt3jQXg1LMjXYsvSK6kQEhEREQkjXRNpR2BkCqE3G/1ahMERUebT8/PnIG334aBA8Gy3D06EZHrkrJ9J9b77wPwdefplGMfDVLXkTzsFVi0yAQvq1fDnXdCQkK2ji01FW67DVq2hHVv/E44h4n2DCF06IPZOg7JfAqERERERNLQlLEcKCkJHnrI/KPcfDMcOwZffAE2G3z8Mbz6qrtHKCJyTc6ehZdegt8avIItKYmIBrcyeEUPDlCOHbs8+PproEYNmDMH/P3hzz+hVy9IScm2Mf79N/z2GyxdCknjJwHwT/2+JpyXXE2BkIiIiEgaF20qHREBycluG1N+FzNyPGzdSkJAUU5+OsP8I/XtaxqtArzxBvzxh3sHKSJyDaZOhfGjYmh/7mcAumwaSXS0ybsBXnvNXhDUpAn89BN4e8PMmfD999k2xq++Mpfl2Ee75LkAFBz0aLY9v2QdBUIiIiIiaaSrECpe3Cypm5ICkZFuHVeusmkTbN6cKYc6tGgPttdGAPBE3FuUa1CEkSPt+Vy/fvD442bKWK9epnJIRCSHO3YMfvnF/OhavhzuZBZ+JLDLpwZrk+sC8OKLEBYGBw/CkCH2B7ZrZ+4AGD8+W8YaHw8zZpjr3QL+xAOLZV6taNizUrY8v2QtBUIiIiIiaaQLhDw9oWRJs0GNpa/s7FkYPNg0fm7QAH7//fqOFxeH511dCSSO1d4tWFqpL7Gx8PLLcNddpo0Q770HtWub1XfGjcuUlyEikpVeeAFuv91U3qxcCffyHQBe998L2PD2NivOT5pkKoUmToQJE+wPfuwx8PIySdL69Vk+1h9/NL8PK1aEwZ1Nw+vUuvXx8sryp5ZsoEBIREREJI10TaVBjaWvxjPPgL0pKsnJ0L07rFhxbceyLBLuf5iS0Vs5QhhJU79n+w4PvvzStK2YPRs6d4YkTz/Xssfjx5um0yIiOdjWrebyf/+DE9ujaMsCAMoPu4e5c02boLAwExqNHm32ffppmD8fc8fdd5uNzpQo6/z2m7ns1QtKnzGBUMuHqmT580r2UCAkIiIikoajh1B8vH1FczWWzpjkZJg2zVz/8kvo1MlUDHXvDsePZ/w48fHEHkvgp17f4zt7Bkl48WLVH2nevSQeHvDgg+bNUkCAWXhn8GCgSxdTlXT6tKkYEhHJTCdOwAcfmB9AffqYRDop6ZoPd/SouVy6FLozAy9SoHFjqFSJDh2gVSvXvs8/b54yJcXkQDt2YFZXBJgyBfbsufiTJCWZKqKJE8003mt06JC5rFMH2GkCIaooEMorFAiJiIiIpOGoEAL7lCRHhZCmjF3Ul19C//7w03PL4ORJKFoUevc2TSeqVYMjR8wKYVdYGj4iAlbMOAzly+MbXoybvjMNS0fyEre+foOzwSrAjTeaRqxgPiD/foYNXnnFbHj3Xdi2LSteqojkUZZl/xGVkHDhz6qff4aaNWHQIDPH65tvoGtX088nMfGanisiwnXbMV2Me++96P42m+mf37w5xMTAiBFAs2bQvr15/uefNwf9+Wfzw7FnT/jhB7MyWYsWZu5Zx47XHGAdOWIuw4olu8InBUJ5hgIhERERkTQKFHCt7pJupbH8VCF0+DDceis0anTZICwmxvR1njQJ9rw/G4BDdTub3kv+/vDdd+DjY7qnXmFqQ/fusKvHi3DsGL4JcQQRy/6gOpSd9KJzdkRaXbrA0KHm+gcfAN26maqkc+fgvvvsy/KIiFzZQw9BnaADpJQpD7VqwbZt/PADfNH1Z7jjDrOoQLVqZsmvwYNNieKSJa4Gz1fhxAlXNhPOQW5kGZbNZoKcS/D1hXfeMdfnz4eUVJvpmebpaZr8lCxpxrlsmVl97O67YfduKFLE/Cw+ehTmzr3qsVqWq5qpDAfNwP38XL8XJddTICQiIiKShs12XmPp/DZlbPVqqFcP5syBdevMm4wzZy6666pVZlpd0SIW3X1MIDT4ry788IN9h3r1XO9inn0W/vnnosc5cADOLVtLH74BoL/HJ7zBy3j9+jN9H/NJVx2U1lNPgYeHmRWxc5cNvvgCQkLM83z22bW9fhHJV06fNhWHQ+NexDPqKGzbhtWkCft6vUS32Q8AYD30MGzYAMOHmyDm66/Ng8eOdTXZySBHwAJwD2aabVz91q4FDC6hSRMIDDRt0tavB2rWJOWx/ubOiAgsf394/nkON76DVGysrXY/yTv2mCbUYH4+XqWTJ13ZevEY+3SxSpXMD17JE/QvKSIiInKedI2l81OFkGXBk0+aj7Dr1zfTv9auhSeeuOjuy5eby4da7KBs4m6SPHz4gw4MGJCmt/OTT8Jtt5l3FT16pO8nlJAAP/9Mwh09WYJpmvEN9zMp9VEmV3iD0jeWu+xww8KgQwdz/euvgdBQ83xgxi0icjmpqaz9aiudkn6mF9+Sig2aNMEWH89ziaMIJoblNOO+mI955yM/V5hzxx0mkQZ4+GHzc23LFtNEf/fuyz6l4xiVK0MfHxMIFXjo4tPF0vLygptvNtfnzTOXIwq+y/18Q0uWUCkgip0Pv8Wt52ZRkDM0/vcb7ngwmJQ+fc3Ov/4KUVFX8c1xTRcrUgR89ql/UF6kQEhERETkPI7G0qdPY/5qB9i3L33jhzzk8cehalXY/NEiUyHk52emF8ycaT4J/uorWLjwgsc5FhC71X8xAJ6tW1KmRgBRUaYdxuefQ/wZm2k0VLq0aUjaqRPJ0bGc3HnM9OW44w6qbPyegpxlI3V5gbcAaNs2Y2N/8EFz+fXX9kKmGjXMhu3br/n7ISJ5XFSUme5VtixtBtbiZ+4AYHZgb1i2jPcaf8tC2rDNrz49mc60md48/7z5dTB2rL3N0FtvmZ83kZEmJKld2zT6qVzZTJe9BEcgVLfkMWolbsCy2fC+564MDbt9e3M5f76ZzTt2vC9TuZ/NQS3ZG1GQO++EzZvB5ueHn58pXloSXcs0rE5OdlU2ZZBjrCVLoobSeZQCIREREZHzpJsyVqKE6aVjWVc9NSA3OHPGLH28cyccfXoMAFG3PwzFi0Pr1qZjNJjGpGmakqammiljALWSNgDg0bQxn31mpt3NmwePPAK33AKpRULMO5iQEFi7lgPVOrCy2gOwZw8pRYsxjiE0ZC3P3ryBo5hpE45Pwq/k9tuhcGGzEk7NmrA6rrq5499/r9jIWkTygeRkGDXKTF+NiYHXX4fy5c167ocPc8ZWkCiKsZlaDIgdxf7DXgzbdC83s5DEFev55LdwXn7ZTNmKjzezX2fNwgTn33xDss0LTp7E8vU1VYpgGqtdgiNkaeRjVv6yVaxoqjEzwBEILVtmiiHPnTN9pJcvN9m9o5/+ffeZlmoAGzcCj5om/Xz0kfl+ZJCjQihdIOT4kETyBAVCIiIiIudJFwiBSR3ANEfOY9auNe8PqrOd9swjGU+afv8MX31l3+GNN6BYMVNxc/PN5uNnzM2YGNOvtOhBEwhRrx7Nm5viov79zfdxxQp764pq1eCPP7CKFKHisZV0suaQgA8jW8/nGcbi1aQhA590NQvKaCDk52cW1AkPh/374bF3KplGq7Gx6Zt1iEj+45iq+tJL8PzzJBcOMct0nTnDsXKNmNvvB4pYJyjpGUXHkps5QimeecY8rHp1qFvX9Nd/4w3zs2zwYHPYYcPMz80NtgbcZv3CYMYx5K6DpsLSZjMNpw8evOiQHD+WaqSYn6XUrp3hl1OxIlSoYJ571iyzbfRoU6jUt69rv8cfN2MHe+u2++83P8cPHsTV5O3KnCuMhQG7dpkbqhDKUxQIiYiIiJzngkCoSxdzOW8enD3rljFlFUcfoCF1FwCwqejN7Kc8r75q/yC5cGHToLlgQVi6FFq2hGPHnI+7oVEyts3mk27q1wfMp9gTJ5oFeQBeeAH27gUaNOCv4Ys5QhgALzKKET/Wde7TsaN58/Xkk6ZAKaNuvhl+/91c33/U17xjAlMlJCL5VsqDD5vkxMeH4wXD8bKSiaIY9/ItxfevptPnd5GAH02bukLoH380lw89RLqG9h4e8OqrptBx504zJfaTT+APOvI+g3n/2+Is2RcOrUw/tEtNG3MEQhXirz4QAvOcd9xhCldfeMFUCIEpfAoPNy3bGjc+LxDy84OBA80G55y3K3OMNbx4gun+D6oQymNyRCA0YcIEypUrh5+fH02bNmX16tWX3PfHH3+kUaNGBAcH4+/vT7169fjmm2+ycbQiIiKS16VrKg3mL+vwcBMGLVjgtnFlBUew09K2FIDaA1oREmKqbRxvjOja1ZQEValiyoJ+/RXHn2u3Vdlp5i0UKmRWn0njySfNe53oaHM5YQJM2ViLuvxDv0qLGMcQwIRH3brh7Hnx4YdX/zrCTMbEqVOQUtU+bUx9hETyrVOzFuI5bSopePB9n1+pkLiDrvzEvXW380fhe7npJhtNmkBwsJkRe8MNrsfWr+/qGZ1WYCC88oq5PmQION6GNmliLl9+GejVy9yYOvWi43KELGHH7EH6VQZC7dqZjGvNGhgzxrW9ZEmT2TgKWR2B0LZt9tm+/fubH7Jr18Jzz2UoFHJUCFX1O2D2L1jw6tJ6yfHcHghNnz6dIUOGMGLECNavX0/dunXp0KEDUZfogF6kSBFeeuklVqxYwaZNm+jbty99+/bljz/+yOaRi4iISF6Vrqk0mI+JHVVCc+e6ZUxZwbIcgZBF+f9MIOTdpoXzg+R33knznqFMGdMpGuCXX5wL6TTxtk8Xq1v3gqWIvb3Nm5M2bUyvooEDzXuk4xTj/s9a8913Nn791dWm6HoULmxW4QE4E17NXFGFkEi+kZgIH39scunSoclE9HwagEk8Ts/PbyEuuQBRN3RlwcaiREfDX3+ZPmgnT5qeO45KGx8f03vZx+fiz9O/v6lkPHvW/FyrWhXnFNu1ayHlzu7mwZs3w6ZNFzz+6FHwIIWg/7aaDVcZCF1O2oqmcuVMgJWYaP9RWKwYvP++uXPsWFNG9PDDMGVKmmUh03MEQmWt/eZK+fLpn0RyPbcHQuPGjeORRx6hb9++1KhRg0mTJlGwYEG++OKLi+7fpk0b7rzzTqpXr07FihV5+umnqVOnDkuXLs3mkYuIiEhedf6UsU2b4ES1FubG+vXuGVQW2LXLrDBf2ecgPsf+M4lKkyY88YTrg+QPP4S//4ann4bjzVxT547uOwdA2Wh7IGSfLna+smVNUdWrr5rbSUlmCeMbb4R77oHOnTPntXh4mPc7ACeKq0JIJL958kl44gnYswfaRE6jWtJmom1FmFnndec+jrD7YurWhcmTTeZfq9al9/P2NhU6d9kXBhsyxMyiKlDAhES7TxR2/WA7r0rIskwgVIG9eCacNT9oz6uszCw2G9SpY67/849942OPmbluHh6wbp1p8Na7t3kBFwnQnauMJe4zV8qXz5Kxivu4NRBKTExk3bp1tGvXzrnNw8ODdu3ascKxjullWJbFggUL2LFjB60cczXPk5CQQGxsbLovERERkctJGwgtWWLeKPT9oJ7Z+M8/kJLitrFlJsefW/eXs3+w1qAB+PtTrJhrKsKQIWaxsQ8/hIkrG5h5CfHxVDy0CICih1wNpS/FwwOGD4ehQ83te+5xVfNkphIlzOXRIHuFkAIhkXxj5UpzOWwYvHejme9qPTGAT2cWJTjYtBbr3v3yx3jgAbjppis/l48PzJgB+/aZ1RQ9PV0h0j//4Jo29t13ZklGu7g4U1VUB3vlUM2a5sFZJF0fISAqCpZWfZhNM3eRPGWaaUJUoYKpELr7bjM4O8tyVQiFxCoQyqvcGggdP36clJQUSjh+e9uVKFGCiIiISz4uJiaGQoUK4ePjQ+fOnfnoo4+45ZZbLrrv6NGjCQoKcn6Fh4dn6msQERGRvMfRj2bWLPMGAeC3PVWxChQwfzA7VlvJ5RzFTu387IGQY84Epn/Ggw+a9zKOaWNbt9lMx1Lg1tRf8PZMxXfb5SuEHGw2sxrO9u0wblxmvgoXR2uLfb72QOjIEbPaWA7yww8mGEuNPmXmcojIdbMsE84A9OlxjmIb5gFQtG9XKlWC3bthwwbw9c2857TZzLQsxwwqR/iyaROmQigoCA4dMiWWdq4l56+tofTVcoxpxgzTCq5UKbMuQN07K1B/TE+ihowxa9iXKAFbtpiAyC462vUjqtBxBUJ5ldunjF2LgIAANm7cyJo1axg5ciRDhgxh0aJFF9132LBhxMTEOL8OHTqUvYMVERGRXKdPH1NBf/iwaa4MkIonSdXt9fcbNrhtbJnJsWhMtWP2NyxpAiGbDSZNgrffNv1HwTQn5fbbAbiLmXQPWYTt5EnzxudycyzSqFYtc9+UpeUIhP6LD4bSpc2Ndeuy5smuwZkzJmT7+a3tpIaXcfWlEpHrEh3tWgSg/IFFEB9vqhkbNACgaFHTTycrXbCql2NO2UcfOauEjh4FP87SDXvH/iwOhJo2NZcHDsDs2WblyHLlTBXsli1wyy0Q7RNqppEBfPtturGC+d55HrAHQuXKZel4Jfu5NRAKCQnB09OTyMjIdNsjIyMJDQ295OM8PDyoVKkS9erV45lnnqF79+6MHj36ovv6+voSGBiY7ktERETkcgICTKW/t3f67TEV7FUweSgQKkEERY7am5umCYTABDfPPWdW4AHYsQOSb7qFcwEhhBLJ6Dj7Hd26XboDazZyFJ1HRuJ6LWk+nXe3338371Nf5VW8zsTBvHlwmap4EckYR3VQaCj4zrMvs9W5c7Y2QE5XIQTQr5+5nDnTzFVbsYJdv+3kfzxM1cTNZv36e+7J0jHVqQO//mqC/VGjTFi1b5/JyUNDzVjffBPo0MGsIBYdbU/+XdPFwsJwfYNVIZTnuDUQ8vHxoWHDhixIs3xramoqCxYsoFmzZhk+TmpqKgkJCVkxRBEREcmnGjaEadPM1KkqVcy2yLAcGgidPQt9+5rym6vom3PgANzMX+ZGvXqursznCQ8Hf3/TEHrPIR8217oPgLJn7E1IHauPuZmjQigqCnD0l8xBgdD06VCLzfTke9fGefPcNyCRPMKZV5SzTAIC2V6B5yj2OXjQrFxGs2amqbSPj5l/3Lw5j7xblfv4jlQPT/j+e1PFlMU6dzbB/rBhribTVaq4ioImT4azyd7QvLnZsGQJ4KogrRx2Go4fNzcUCOU5bp8yNmTIED777DO++uortm/fTv/+/YmPj6dv374A9OnTh2HDhjn3Hz16NPPnz2fv3r1s376dsWPH8s0333D//fe76yWIiIhIHtWtG3zwgem5CbAv2B4IbdyYZj12Nzt1Ctq2NX/V79gBDz2UoabXcXHmTUs7/jQbLtGPEUxT6Or2hbu2bYN5YQ+47ixWLGNdWLPBRQOh5ctNknUxy5fDnXeaMC2LG4WfPg2//QYjeA2AZE97RdXcuVn6vCL5gWNqb4ui/5pExtfX/FzMRsHBZlVFMJU3J0/C4DX3Mf3xhSR07MrpQiU4jT//etcidfI3bv+52bGjGe/Jk6bHkPNnpj0Q2rvX3GxU1J62FS5spgdLnpIF6ztcnZ49e3Ls2DGGDx9OREQE9erVY+7cuc5G0wcPHsTDw5VbxcfH88QTT3D48GEKFChAtWrVmDJlCj179nTXSxAREZE8zjGT/V/v2nTx9DSflv73n6tPTUZYllnyd8MG6NQJ+vd3da++BqdPmw+f28wZTdUVK8wf68nJZqmdjz++/PrKmPdMYNHe9idYQJpVXy+mRg2zDP22bbA0vj6bqUVttpiVabJiybBr4JgyFhWFSbCKFDFTINavdzXTSEoyKd/UqSbYc3joIdNtNQtERsIbb0DA2Uju4CcAvmn0AX1X9TcVQikpWbrSkEhe56gQapVkn3ly441mClQ2q1vXVNaMG2d+TSxfDtAc7P/vAT6dANV6Z/vQLuDpCY8+Ci+9ZPrF9RnT2tyxZAlYFnv3mul2Nf33m+2qDsqT3F4hBDBw4EAOHDhAQkICq1atoqnjFzawaNEiJk+e7Lz95ptvsmvXLs6ePUt0dDTLly9XGCQiIiJZyhE0/HfCz1Vz/9dfV3eQH3+Ezz4zqcobb5g3LDEx1zSe77+HMmVg0ONnCfnZ1P3HfvAFvPWW2WH48EtXxdgdPAiV2UVp65CZ0nBe/6Dzpa0QOnDQxiDe53iTW+H556/pNWQFR4VQZCSmrMkR8Ng/8SY52Uxve+452LiRVE8vrPAy5r5Zs7JkTDt2mJl8EyZAT6bjRQqraMJ0/4dNl9sTJ3JU42uR3MgRCNWOsgdC2Vwd5PDEE6b33OzZJgwKDnb1FvLygptvdq1cmRM89JAZ14oVsL94E/O74OhR2L3bWSFUwab+QXlZjgiERERERHIyR4VQRARm7V4wjUIzaNOqsxzv+6y50aOHSXP27jUfz17D1LNRo0yZ/4CQ7ylKNAcow+AFXczxQkLMnStXXv4gs2fzKY+a6y1aXPHT9Bo1zOW2bSZM+ou2nJzym2uORA6QtkLIsuBkHTMF4tCor9k5YT7rwrvCzJmkePnwcvB4iqVE8Gfn98yDfvop06cBJiebN3+nTpnv34gKUwCYwv0cjvR2VWXNmZOpzyuSLyQmmkY4U6eybx94kEKp3YvMfW4KhDp0gAULzEzaQoXMNNENG0z2n5Bg7ssB/fedQkNdP9u37vFzVVIuXuwMhMLOaYWxvEyBkIiIiMgVpAuEunUzN/74w8zbuoKoKPjtlvcJidtPdMFS8MUXpsTHy8tcTpt2VWNJTHQuAsPIUhMAmMTjzP7Nk1SbJ7Rvb+68XG+av/+m06SutGGxud29+xWf1/GmYeNGs1IWmGbTOYmjJ3ZysglhXvvnDuIpSPipLVQZ2J6GEb+TiDddk2cy8tQAoinK7ykdzBLR+/alWR4oc4wdC6tWmbYbf07YQdG9a7A8PZlOT7Okc+fOZsfZszP1ecXOskyJ1owZ8O+/7h6NZKaVK03Z4iOPwP33027PJBqwHu/Tp8x/uIYN3Ta0li3NtLGDB02fZpvNFAN65NB33o5FE3buxJQwAYm/zCU62mwP/m+LuVKpUvYPTrJcDj0tRURERHKOdIFQrVrmD+OEBLOO+GUkJsJj98TwWNw7ADyTMJqoeH/zKezLL5udRo26qsqUbdvMbLAmAdvx/WcNlrc30/z7cfy4aZVDp05mx0tUnSQlQdKHEwH4nU5MeWq16Wd0BeXLmzc1DiVKmBwlJ/H1dfU8/e03+GB2BRqynr8LdgBgScidPH/TWn7jNuen9Jv3+puP9SHTp419+aW5fOcdCFs6A4Ckth05RnGio+Fcu9vMu8X16+HQoUx97vwqNdW00GrUCCJa3m3m6/XoYTY4pg5K7paQYKZ+7t0LAQEAvJ88kLE8Y+5v08btPbkKFDBt3XKDqlXN5Y4dOENqzwXz8CaR8GLn8Fqx1OzgaDoteYoCIREREZErcARCkZGYN/B33WU2/PjjJR+ze7eZiVVr4YcU4SS7farzdcp9fPaZfYennzZzCrZsuaqVphx9kB8O+RkAW9u21LvFlMb88QeuCqENG+wJlktqKvS8KQrrBzPd7SVG4tOisXlNV+DpCVOmuKpwHJ8q5zSOaWO97U1bm/apyg2n5rJ1zRlaRv3Iewvq8M8/JjAC+5ugO+4wN376KVPHcviwuWzTBli2DADvLp3w9TXbI1KLu5Z6VpVQxsTEmCXyLiIlBW6/3fRx8Vi3mtBlM7E8PEwpW3y8CUvVrylX27ABhhf7GPbvJ9IzjO3zDhHZ6QG8SKEVf5ud3DRdLLdKVyHUsCGUKIFnfBw3spSuxZbDuXNmAQRHIznJUxQIiYiIiFyBI2Q4dcr8bewMEObONSlLWklJsGQJ3bsmsWdtNM/YxgFwtN9wUvFk4kT7MYKDzapjYEpIMsgRCLU/awIhunZ1Frj88Qems3KjRmk2uEyZApWWTcaHJFbTmI3Uv6oWQF26mDcN770H48dn/HHZydFYGkxbpDffNE1eazYqgM1msq86daB+fbPP4cMQf3MXk3j984+rO+11iotzTa0LC7Vg9WoAbDc0dS4ud/Qorp5UP/+cKc+bZyUnmwbmRYqYUrWSJeHbb9NV161YYYI+Pz94zXc0ABtq9jYnbdu2cOaMWU5JcpWYGJOXHjkCj/Y4xdNxbwDwUsrrvPFhEDM7fMZD/I8fSj1tgvaHHnLziHOXdBVCHh7OKtPO/EY7W5om3Rn44EByHwVCIiIiIlcQHOxqBBoZiQlcChY071S2b3ftaFnQsye0bs2obV35ht4EW6egZk2avHM3JUuaNzUvvmjff9Ag00to4UJYujRDY9m4EUoQQdnIVWbD7bc7A6Hly+0Ll3XsaDbMm+d83OnT8OILKTyOeUP8CSaMKlPm6r8Xgwa5FlvLadK2dfrgg0v3OSpa1HwB7DxR1DUdIpOmjR05Yi4DAqBQ5B6IjjZz2urUuXggtHDhNa86l6elpMAvv5DasrUJTh0B7NGj0KuX+f5t3Qq4+qg/2mIrnRJ+IhUbff99gb1H/EyvGXDuK7nHCy+Yf+bwcGi8+1uKEk1c6WpM5kFmzICRb3vzJQ9x6Jn34f33wd/f3UPOVRwVQkeO2Ivvbr0VgNv4lYYn55s7HQ3wJc9RICQiIiJyBTbbeX2EvLxcq7EsX+7cL3rMp85A4Vbm0JnfTbnCN9/gW9CTTz81+733nllthtKlXZ9mv/LKFcdhWSYQ6sIv2CwLGjeGkiUpX978UZ+SAosW4WwMysKFzgqKF16AxhGzqcA+Yr2LMI178PFxVT/lFY4Zc3XqwMMPX37fdJ+M33mnuZFJgdDRo+ayZElMZ2kwZUk+PukDoSpVzECSk+HPPzPluXMzy4LRo80XO3aYeZe3347HyuXE2/w5+O73EBsLr79u/h/+8ov5xx43zhkIPRL9FgBLi3VjU1J1+vcHq2Ytc+eWLZm+mpxkrcX23vepqXAHPwEQ8NRDNG/pRXKyCTLCwuDxx903xtyscGHXVOBdu4D27UmyeVOVnZQ+ssbcoWl4eZYCIREREZEMSBcIgbP3S/KS5SxZAv3b7cLvxcEAbG3Qm3PYG8VMmuScn9S5s2uW2MCB9mKHl1825UeLFsFff112DAcOQEyMxf22qWbD7bc772vd2lwuXw40a2aqUY4ehV27mDYNJk6EIZjpa2d7P0aKrz9Nm+bclW+u1QsvwOTJ5vtwpRkO6QIhxzTAZcvM0nDXyVEhFBaGKxCyh4iOQMixj7MR+FX0ksqrduwwFXTTX9xISt36sGoVsbZA3uFZalhbqfPG3cxbEWAC1E2bTJCXmgrPPEPxhdMpxz5q/vMtAGUnDcPX1xTKTV9f2cwdjItTA+9cJD7e/v8T+OHzU7TzXGhudO3KU0+59nvpJdPIWa6No0poxw4gKIiXQj7lDAVcd5Yu7baxSdbKY38CiIiIiGQNRyC0fr39j2Z7ILR36nJat7a4fcFTFOQsf9KWp4Mm04D1TOm7AB54IN1x3n7btED591/7QmDh4fDoo+bOoUMv7EmUxrp10JWfaW0tNiHS/fc773P0Jl62DFOVdMMNAETPXEi/ftCQtbRkKXh7U+KNgezYYYor8poiRcy3PCOzRtIFQuHhZiqgZV22WXhGXbRC6LxAyLGPc4rf3Ln5vnrF0Vu7Px/jmXCWTYWaUcPayhfV3iG8RVliYsyicA8+CO/+Vp21L/5o5jAC70X34Qe6Y0tNgfbtKdutoXMxvxEjfVz/4Fu2ZPvrkmvzzz/mv0TJknBXwTl4pCSb5sZVqnDHHaZhe4sW0K+fu0eauzn+a+zcadqojT3xILXZzJlej8CHH7p3cJKlFAiJiIiIZIBjatXrr5uV5/cUbwZAFWsngwr9j07MJdHmQ38+ZsFCD7ZTg+BuN19wnMBAV/4zdqx940svmRXH1qyBqVMv+vypqfD+mHOMY4jZ8NxzUK6c8/4WLczl2rVmVWZuugmAfyctIj4e3itiGrHSsyeULEnZsq4l2vOrdIEQwD33mMuvv774A6KiTGOiiRNNNddlwhtH2FO6WIKrE3iTJsBFAqFWrUyId/gwbNt2Ta/lWlkW7NmTJofcudP0YRk4MNvHAiak9CCFOzFT94acfp3TQaWZOxfmzzcriAF89ZX5L9C6Newf+C6HmvfAl0Qast7sMGwY4Mpj9+49b9qY5AobNpjL+vVxrQJor+ZL237NsXKfXJu0PwvHjTM/Dyq1r0jBKZ/ibFIneZICIREREZEMKF/edT05GVbtLMz+gjUAeO+0aVi7osVz7Kayc7+6dS9+rCefNItaLVwIS5Zgyo9eesncOXRous7IJ0/Cr7+a9+it1o6lAvtICSvlfMPrUKmS6QORkGBfWbtNGwAqHFxIC5bRMnq2eVJHyYSk+1TcsoBevbA8PWHFCobcttMVkkREwKuvmm/yoEEwYIDpqdGhgykZO3PmgmM7poPVTd0AiYmmg3WFCoArEFq9GkaNgtikAs5/r+yeNvbdd+ZltW0LsX+uhtq1YfBgmDDBNJI9cCDbxnL8uJnq15K/Kc4xTlCExbTmvfegbFkzJWjCBJPFDRhgCkXOnIEBT3nyUfNp9GA6/xWpZSrn7HMoHUFucjKcqWAPhNRYOtdYb8/3GtZLcf3fcDRil0zjWFF+9mz43//M9eeec994JPsoEBIRERHJgCeegI8+cn1Yuns3rPBo7tqhfXt8X3vRebNIkUu3XShTBu6911zv1MkEPgwaZFKnI0fgrbec+/bvb5Z7f++ZQ7zIKAA8x75zwZwom81VJbR8OdC0KQkefoQSyW8F7jJ39O3rSkGEihXNYnGnT9tzstBQYm8w/8C3/PY0cdUaQZkyWOHh8Nprpv9M/fqmQsHPz5SsNGxo/i3CwkyTqJMnAVf1T/XoZeZK8+bOpkZ16piKhuhokwOOGIFz2ljizNm8/3761dKy0nffmcuVi85yrPMDJrxq2NCcJ0ePmhM0m1Y/+/13U5nweNEfAPiJO2h/qzcPPph+v5tugvHjzcw+b2/zuHfH2phBD+aP3QzffOP8Xvv4QEiIedyxEqoQym0cgdCNobtNM/GCBc3UTslUHTuaUDg+Hs6eNT/m1Ec6f1AgJCIiIpIBQUFmFo2jefO2bTAs/hU+ox/RX/wEc+fSqFVBgoPN/XXrXr6p8YQJcMstpsKhWzfYvMsP3n3X3Pnuu3DgAJblWnjqXdvz+HOG1OY3uqY2nSdtH6Fz+DGDu83Yz0aaBGLEiOv7JuQxPj6m8gpMpc748TAvzMwx6sRcgnatg0OHsCUns4zm9GQaD9ddy9lvZ8GmTZxs3pnkwMLmABERJpkYPx5wVQiVPmgPhBxpHVCqlFnNZ/hwc3vKFEjs1BXL0xOfFUv4afAis8pWFjt3ztXH/C2/EVRM/Jcoz1COTJ5nTrzSpWH7dtMJPRt6G/36K3iRxK0JpofTzRO6M2vWpf8fVavmKnizLPN/7rbbLtyvZElzeSDAHght22aW5JMcLSHBVcxVj43mSu3aptJRMpUjWH30UTOtecyYKzfllzzCymdiYmIswIqJiXH3UERERCQX+v57ywLLKl7cXBYoYFmpqa77e/Qw24cMufKxEhMt67bbzP5NmlhWclKqZbVubTbcfbe1a5f9Pu/1lgVWqs1mWevXX/J4y5aZ/UNCLGvxYsuykWJ1KLLaSn1zpGXNnXv9Lz6PGjXKfN+CgiyrXrWz1r9UsWIIsF60jbQasdoqyz6rWjXL8vAw+1WrZln9+pnrXl6WNXV8tGV98IHZEBpqWefOWQEBlgWpVlJR+4ny998XPG9SkmWFhZm7f/zRsnZ3fMKywNpAXevPwDstq21by9q/P8te97x55rlrhh63UvwKWBZYtzHbqlPHspKTLctascKyPD3NTp98kmXjsCzzfIULW9bjTDTPV6yYZSUkXPFxqamWtXSpZR0+fOl9OnQwh/zi8xTzHxYsa+fOTBy9ZIV168w/VZEilpU6dJi58dhj7h5WnpeS4u4RyPW6msxDFUIiIiIiV6FSJXPpWJm8QoX0n6SOHm0KKp555srH8vaGTz4x1UerV8OEiTZTsuLhATNmcGrEewCMCRgJgO3ee51L2F9Mo0bmWMePm4bVFh74t2mM7aUX1Rj0Ml54wVSbxMTAxn/9qMdGbqp1nFHWi6ylMV2fKsf27WaGWGioWSHu88/NY5OTodfAwnzt39+UokREcO6r6cTFQUX24HUiypQiXWSai5cX9Oljrr/7LtyzdThxFKIe/9A2dhYsWGBWJps61TSczgxJSc6rc+aYyxGl/4fHubMk1qjH8sK3sWkTTJ+OWalupDn3eOwx098qzeMz07p1kHQyjtdsr9oHNcJ8367AMVWyVKlL7+OoEDoa6eHs45SdvZHk2qRtKG37Z6O5canGbJJpPJQQ5Cv65xYRERG5ChUrpr/teH+Z9vakSa43oVdSsqQpzwfTOii5Vj3nm/BG3w7hQ57kpuiZZocXX7z4Qex8fEwbG3At3928+aX3F8PDI32P7jpNCphlyjF9NBwz+W6+2Uxh6dPHBIM//2ymEQJMnurtvOExZiShHKWtr326WKNGpufQRTj64yxfDmsPlWBM0BiS8eR77uZE6ToQGWmaJJcrB5MnX9Prs5KSzTlVt645SapUgRdfZNFv8XiSzK37JwDg8+xTPPOsSTffeMM+q+q550wXdDAnaLduZq5ZRqSmmmTpmWfMXJQ1ay6+X1ISJ96YwB90oLgVBZUru5biywSOJt5HjnCRJd4kp3L0D2rQALP+PCgQEsls2VCxlKNoypiIiIhcL8d0MbCsp5++/uMlJJgZMmBZs2dbZh7Mk0+6ngQsq1u3DB1rxoz0D1ux4vrHlx8kJlpWuXLme/bGG2bbjh1m++Vs2mQeExBgWSlRx50nx0FKW//4NjZ3PvfcZY9x//2WVaKEZT3yiJnJ9PqrKRZY1k2NYq3ZVZ+1osvWNcfx87OsLVsy/JqOHbOs3refsuZ7dUh/Uti/lnCj9b7tadc8w7NnrZgYM3ULLGvkyDTTR77/3jw/WFa7dpZ16lT6Jzt1ypxsR45YqamW9coz8dbK0t0ufN5+/ZyPTUy0rA7tkq35Re523p/i4WlZv/yS4deYER99lOa/UO/e5sZbb2Xqc0jmu+EG8081c1KUuWKzWVZsrLuHJZLjacqYiIiISBZyTBuDCyuEroWPDzxgehnz6aeAzcaZUe9zv8e3zKUDiZWqw5tvZuhYHTua/tFgilIaNLj+8eUH3t7w7bemMMVREFOlitl+OdWrm4WP4uJgx/GisGwZsSWrEs5h6iTYK2JuvPGyx/jmG9OT+tNPTXHMrbeZP9EXrg3g9h3vUCVuPantO8C5c6T2vIen+ifx/vum6S5798LMmbBoUbrKnZ07oVf1dbwyuxHtkv/gDAUYV2USO+fuhalTOecXREuW8rT1gXnACy+Anx+BgWZmGJgV0MqWhYAAuGva3aT+NsesqPbnn9CkCXHf/cr8p2aT3LQFBAdDs2ZQqhQnytTn6bHhND38I4k2HxIfHeBaVu/zz01j4JEjWXHvhzz95220i55BIt4MZTT/zdt28c7Q18E5ZewoqhDKJVJSXEVBjX3sVypWNCejiGQaL3cPQERERCS3qVTJvrQ7mRMIAfTrZ6Ym/f67aSO0e7cHU1PvZUHovRzZCWRwxZdChaB9e/jlF2jcOENtWMSuWTPzdTW8vMwq7X//DStWwEdrK/FXykpu5Qs6VdrFLXcFwa23XtUx69c3q77v2GGOfzzag1X9v6LZhtp4bN1C/NavGczDbH57Dp8fvwNbUiIAVuXKxLz2AYE713Ju7Fx+i1uFFynEBIVz67lZLN/ZkOc7w/jx5ZlXohoTDnQmoKgPhT7/ALp2dT7/s8+aMPHFF12ti378ESbe1IaBixfDHXfAzp0E3NeFW9IOvEQJiIwk5PBGACIpTnfrB7x2tmT2bAh44gmTfO7dCy+/TCv7w1Lw4D6+ZUOF7ozJgqWuNWUs99mxwyx/XqgQlDphD4Tq1XPrmETyIgVCIiIiIlcpsyuEwAQArVvD4sUweLBr+803X/3yv08+CfPmufrTSNZq0sQEQm++Cfv2AQSzy2MIDUYA91/98Tw8YMkSOH0aXnsNvv4aZiwpQbOhQ+GZZ3iJkcRTiIlH+2Aj0ZQyRUdj27WL4PtM+FTHfqwzt3YnaMqnTDlVmMGDTd+jJ54Ay2rAn74H+G+PFwR5XPD8Tz0FPXrApk2m9c/LL5siosZ/NST0x3Vsu+15SketozAn+Z4e/FD2WR5/vSQfPXeQclGrKHFDBfq8VZN/bvMjbpEJKb/66kaOTfyHrU9Owv/AVvwTTxIZUosb376dAxOb8OLj1/5vcDlpMyArNMxkq0eOZM2TSaZwNJSuWxc8Nqx33RCRTGWzLMty9yCyU2xsLEFBQcTExBAYGOju4YiIiEgu9N13cN995np8vJkylBn27DENqffsMTNz2raFu+821yXnmj4d7rnHdfupp0yQExx8/ceeNcv0cS5fHvZsPsOJoPKEpEQ57/+ROyk4ezqVS8SyuWk/ujCbP2nHDO6mzRvtuP/lcs59Lcuct9Ommds9ethXE7uC1FRzLi5aZG57epopPYGBps/14MHpF+2qXRv++gtCQkyY1KEDnDx54XELFzbNz68wo+66JSS4enrH/LKEwC6tTaq7a1fWPrFcs2eegXHjTLj94S/lYf9+k3LfcssVHyuS311N5qEKIREREZGrVL26uSxTJvPCIDAtMt55J/OOJ9mjSRPXdV9f03snM8IgMJU1fn6m8uiPvwvyZ8pzvMtzACxs9Bw9146k3BBvatQoymxmcdONSYRX8CYoCO47b1E6m830Kdq4Ef79N+MVZB4eJkQaPBhmzIDkZPO+/N13oU4dU9n26qswcaKZ1TN3rgmDwExbXLoUnn/eTIcEeOQR6N/f/D9y9LvKSr6+UKQIREdDhC2MQNCUsRzOscJYi7KHTRjk4QE33ODWMYnkRaoQEhEREblKlgWffAI1akCrVlfeX/I2y4JixeDECRN2fPpp5h6/a1dTSVOhAhzYm8ybpSYxdFo9TtW6kerVTUNqh+XLr9wH6eRJ2Lr12ipzjh41DbSrVLn4cQMCTN+ji/nvPxNKOZo8Z6fatWHLFljw82lu7mpvTBwbqybFOVBqqgnwYmJg35jplBt6j2nUtXatu4cmkitcTeahVcZERERErpLNBo8/rjBIDJsNHnsMypVzrdCVmZ591lzu3QspePHfnQPhxhsJDoaVK12zaDp1ylhT7MKFr32aVljYxcMgx3EvFQYBlCrlnjAIXH2EDp8q5AqBVCWUI+3cacKgAgUg/MDfZmNWzysUyacUCImIiIiIXKeRI820rsxqMp5Wy5bQq1f62w5ly8Iff8C6dfDDD5n/3HmFVhrLPVauNJeNGoHn8qXmhgIhkSyhQEhEREREJId7+23TxNnP78LKNJsNGjTI3H5WeY0jA4qIQIFQDucIhNrUjzHL3AG0aOG+AYnkYWoqLSIiIiKSw5UsaaqA4uMhNNTdo8l9HE2uT5xAgVAO5wiEOvr/bRp0Vazo+jcTkUylQEhEREREJBeoVMndI8i9HIHQ8eNA9bTzxyQniY+HzZvN9Xq77XMgO3Vy34BE8jhNGRMRERERkTwtXSCkCqEca+1as8pYhVIJFJz3k9nYo4dbxySSlykQEhERERGRPK1oUXOpKWM5m2O6WL+y881SY2Fh6h8kkoU0ZUxERERERPK0i1YIHTjgtvFklaNHYeJE04u5YonTvBsyBo9SYXDPPa5ULAebOdNcdk383ly5+27wUA2DSFZRICQiIiIiInmaIwuJi4PEWg3w8faGPXtg+3aoXt29g8tEzzwD330HHqTwE/fiwa/mjueeg/nzc3S1zZo15quc939U3/aj2ajpYiJZSnGriIiIiIjkacHBrkKTQ6cLszKwvbkxfbrbxpTZ4uLgp5/M9Rmlh9CFXzln88OqXh3OnoXhw906viuZONFcTgsbjO1MPNxwAzRv7t5BieRxCoRERERERCRP8/BwVQn99BNMONETgORvp5ulzfOAWbNM7vNkyZl0O/whAPdb37DmtTnmG/DXX64lvHKYyEiYNg1u52eaHpxhxjtpEths7h6aSJ6mQEhERERERPI8RyC0bh38TFfO4YvXrn9Nw53cKjkZ9u+HjRtZOmkLt/ELb0X3A2BOvaHMpDtjvisLd95p9v/oI/eN9RKSkuCenhZ9zn3CTFt3s/Gpp6BuXfcOTCQfsFlWHonEMyg2NpagoCBiYmIIDAx093BERERERCQbtGwJS5dC1aqwYwfMpBvdmAU1a5rymsqVs3U8M2aYqWy33GLfcOwYvPsuJCURWacdxTyi8TgdCxUqQGoqVkwsD05px58rCzGx6Ve0Pv4DgRuX4JGUeOHBmzRh4/il1G/iTYECcHL23/je0gr8/GDrVnPMHCAywuKjPmtoPf8lbuFPs/G+++B//zNjFZGrdjWZhwIhERERERHJ8+6800wXs9nMLLGabGE+txBGBBQsCE88AS+84FqSLAv9+6/pZW2zwdix4D33F+7/6yGCk49f9nHxFCSWQDNmuwR8OEFRfEkgtlBJyndrAKNGYZUsRenScOQI/DHXov3om2DxYtNYevFi8PS8+oGfO3f9QU1CAil/LuSfN2dTYtVsSln/AZDi5YPnG6+ZfwNNFRO5ZleTeWiVMRERERERyfMcU8YcH4cfKFSLBqfXMzfoHurGLDHVObNmwcqVnCkYwrlzUKRI1ozl999dY1kw5Fdm0Q1vktlkq8N6qz7NWc5RwjhFMBXYSxLeFLAlUN3ahj9niPQNZ7L/QH7x6MqK45XxD/DgqafMYmIEmWPbgI4d4YsvYM5cG+0nT4Y6dWDZMnjySXj3XTbvKUh0tKme8jhxzDTymTcP/vvPDM6ySEm2iImx8I+LwDfmGFSqBLffDi+9lKFvkGWBdeYsZ7+ewZ73Z1Nx9x/4p56mgf3+sx4FiW1zOyU+fRMqVsyC77aIXIoqhEREREREJM8bOhTeest1+5NPzDLtp09bjG07h8G7n8B24ACJN7Sk0ZFfiI06x5/3fUGlwChTTRMebqqHUlMhOhp8fKB3byhU6KrH0r69WQW+Z7lVTN7fGj8S+COkF12Of0ESPrz0Evz8s5na1rmzY/Uwi1v9lzBtwgkC7ukMvr4AJCaaghpv7wuf54cf4O67zTS58ePB+vobbvmmDwDJZcrTMfJrVifUYVSRsTwcM5YCKfEZfxFhYTBmDNx228WDoZQU1n67kxlPL2XAyTcpw0HnXf9Rkj98uhD+xO20G3UztgKaHiaSWTRl7DIUCImIiIiI5D/vvAPPP++6ffiw6SfdpQukpMDyz7dxw5Bm2GJjOYsfFjYKcvbyB23RAubMgYCAC+9LTYVff4UlS0x4U6kSdOpEfEAoRYpAUmIq8bWaUmDLWqzbunDu2x8Z/Y4XFSvCAw+YMZ05Yw794oswejS89x4MGpTx13zqlMmwUlJc2+5gFp/5PUXIucMk40ksgRThJADraMB0erKFWqTigYUNCxshITb+PV6UI5Tk0weW02XVS9j+/RcAy2bDVro0VKxIctmK7D/iw+md/1H56BL8E085n/cQpfm1WF/KDLgdGjSgyQ0eFCuW8dciIhmjQOgyFAiJiIiIiOQ/X34JDz1krnt7m3Y4Hh5mQauPPjJVO3eXWEKLbx6jOibsWElTFnIT3iRRlgMEcwoLG3Fehbmz4B94xMbAjTfCwoWMetuLV181x7y13DY+j+lOkYjtF4zjYMdHqTb3PfqF/MyHx+8zFUa7d0OJEpcd/7FjJty52vY6jmbaHh5w8832fCoxlkkeT3Bf6lQAYkpW45+7R3Ko0Z3s3GXDz8+8rMBA0zKoShVTTfXee+aYdSqf5Y5db9OdH6jNlks+92n8+a9wLYr07crhbk9Tu2lBvNS0RCRL5bpAaMKECbzzzjtERERQt25dPvroI5o0aXLRfT/77DO+/vprtmwxP3gaNmzIqFGjLrn/+RQIiYiIiIjkP7/8YlrfAJQvD3v3muv79pnindRUx54Wc0ZuoHWLZN5b2phjx20UKQKFC5uZUaNGmYW6Pn1sHY98dzPExnLqnc8Ie6Uf585BKQ6zgmaEc5hTBDGvyL0UCPCiKasofmANAIcpRVGfOAokxsIbb8DLL2fZ6/7rLxPkPPsstG5txv/SS+Z1PlR0Np+8fxave7pzpaTGsuDDD00wlJJiAiY/Pyh45hiV2UVF9lCBvRQNTqVM/aIcq3gDHo0a8MDDXgqBRLJRrgqEpk+fTp8+fZg0aRJNmzbl/fffZ8aMGezYsYPixYtfsH+vXr1o0aIFzZs3x8/Pj7feeotZs2axdetWSpUqdcXnUyAkIiIiIpL/LF9uZngBtGkDCxe67rvnHpg+3VwfMMD027mU6dPN/oULw5Hn3sPvxSHE+IdRMn4XLRon8WtMS3x2bmGfbzUaJ/zNCVyrlr3dYQG9/+hFKJFmQ5kysG0b+Ptn7ou9jKQkaNTITJd766300+gyYtkys0hZr15QurQZ/p9/mrZKt90GTZpokTARd8pVgVDTpk1p3Lgx4+0/dVNTUwkPD+fJJ59k6NChV3x8SkoKhQsXZvz48fTp0+eK+ysQEhERERHJf3buNM2VAfr0ga++ct23aZMJMho1ggULnP2aLyolBWrWNA2fP3wngf4fVcfr4D6m04NbakdSZPNi03B5xQoifMuydq0Jn8aNM48P4hTPt1zJ0BG+eNSr41r+LBsdPQpz55qe2KreEclbcs2y84mJiaxbt45hw4Y5t3l4eNCuXTtWrFiRoWOcOXOGpKQkilxiycOEhAQSEhKct2NjY69v0CIiIiIikuukzV3Klk1/X506ZqX1wMCLr9aVlqenaezcvz9M+NyXwLrv8MDB7vTke9iM6QL9++9QtiyhmKqZzp3NlKv33oOGNwfzzO8d8bhM6JTVwsKgb1/3Pb+I5Awe7nzy48ePk5KSQonzGqiVKFGCiIiIDB3jhRdeoGTJkrRr1+6i948ePZqgoCDnV3h4+HWPW0REREREcpfgYNP3Bi4MhMAERlcKgxx69TK9oHfsgId+u4s2LORYw45QsiTMmgX16qXb32aDsWPN9Kp58y5fgSQikl3cGghdrzFjxjBt2jRmzZqFn5/fRfcZNmwYMTExzq9Dhw5l8yhFRERERMTdPD1NU2gwrXuuR0AA3HefuZ6aCmcatyFkzRxTZtS27UUfY7NB9epmHCIiOYFbA6GQkBA8PT2JjIxMtz0yMpLQ0NDLPvbdd99lzJgxzJs3jzp16lxyP19fXwIDA9N9iYiIiIhI/tOjB1SubPoFXa/HH3ddHz5cjZRFJPdxayDk4+NDw4YNWbBggXNbamoqCxYsoFmzZpd83Ntvv80bb7zB3LlzadSoUXYMVUREREREcrkJE8w0r6Cg6z9W/frw+uswdKjpESQiktu4vaf8kCFDeOCBB2jUqBFNmjTh/fffJz4+nr72Lmd9+vShVKlSjB49GoC33nqL4cOH8+2331KuXDlnr6FChQpRqFAht70OERERERHJ+TKzkueVVzLvWCIi2c3tgVDPnj05duwYw4cPJyIignr16jF37lxno+mDBw/i4eEqZPr4449JTEyke/fu6Y4zYsQIXn311ewcuoiIiIiIiIhIrmSzLMty9yCyU2xsLEFBQcTExKifkIiIiIiIiIjkGVeTeeTqVcZEREREREREROTqKRASEREREREREclnFAiJiIiIiIiIiOQzCoRERERERERERPIZBUIiIiIiIiIiIvmMAiERERERERERkXxGgZCIiIiIiIiISD6jQEhEREREREREJJ9RICQiIiIiIiIiks8oEBIRERERERERyWcUCImIiIiIiIiI5DMKhERERERERERE8hkFQiIiIiIiIiIi+YwCIRERERERERGRfEaBkIiIiIiIiIhIPqNASEREREREREQkn/Fy9wCym2VZAMTGxrp5JCIiIiIiIiIimceRdTiyj8vJd4FQXFwcAOHh4W4eiYiIiIiIiIhI5ouLiyMoKOiy+9isjMRGeUhqaipHjhwhICAAm83m7uFcs9jYWMLDwzl06BCBgYHuHo6IzknJkXReSk6jc1JyIp2XktPonJScKLecl5ZlERcXR8mSJfHwuHyXoHxXIeTh4UHp0qXdPYxMExgYmKNPRsl/dE5KTqTzUnIanZOSE+m8lJxG56TkRLnhvLxSZZCDmkqLiIiIiIiIiOQzCoRERERERERERPIZBUK5lK+vLyNGjMDX19fdQxEBdE5KzqTzUnIanZOSE+m8lJxG56TkRHnxvMx3TaVFRERERERERPI7VQiJiIiIiIiIiOQzCoRERERERERERPIZBUIiIiIiIiIiIvmMAiERERERERERkXxGgVAuNGHCBMqVK4efnx9NmzZl9erV7h6S5FFLliyhS5culCxZEpvNxk8//ZTufsuyGD58OGFhYRQoUIB27dqxa9eudPtER0fTq1cvAgMDCQ4O5uGHH+b06dPZ+CokLxk9ejSNGzcmICCA4sWLc8cdd7Bjx450+5w7d44BAwZQtGhRChUqxF133UVkZGS6fQ4ePEjnzp0pWLAgxYsX57nnniM5OTk7X4rkIR9//DF16tQhMDCQwMBAmjVrxpw5c5z365wUdxszZgw2m41BgwY5t+m8lOz26quvYrPZ0n1Vq1bNeb/OSXGH//77j/vvv5+iRYtSoEABateuzdq1a5335/X3OwqEcpnp06czZMgQRowYwfr166lbty4dOnQgKirK3UOTPCg+Pp66desyYcKEi97/9ttv8+GHHzJp0iRWrVqFv78/HTp04Ny5c859evXqxdatW5k/fz6//vorS5Ys4dFHH82ulyB5zOLFixkwYAArV65k/vz5JCUl0b59e+Lj4537DB48mF9++YUZM2awePFijhw5Qrdu3Zz3p6Sk0LlzZxITE1m+fDlfffUVkydPZvjw4e54SZIHlC5dmjFjxrBu3TrWrl3LzTffTNeuXdm6dSugc1Lca82aNXzyySfUqVMn3Xadl+IONWvW5OjRo86vpUuXOu/TOSnZ7eTJk7Ro0QJvb2/mzJnDtm3bGDt2LIULF3buk+ff71iSqzRp0sQaMGCA83ZKSopVsmRJa/To0W4cleQHgDVr1izn7dTUVCs0NNR65513nNtOnTpl+fr6Wt99951lWZa1bds2C7DWrFnj3GfOnDmWzWaz/vvvv2wbu+RdUVFRFmAtXrzYsixzDnp7e1szZsxw7rN9+3YLsFasWGFZlmX9/vvvloeHhxUREeHc5+OPP7YCAwOthISE7H0BkmcVLlzY+vzzz3VOilvFxcVZlStXtubPn2+1bt3aevrppy3L0s9KcY8RI0ZYdevWveh9OifFHV544QXrxhtvvOT9+eH9jiqEcpHExETWrVtHu3btnNs8PDxo164dK1ascOPIJD/at28fERER6c7HoKAgmjZt6jwfV6xYQXBwMI0aNXLu065dOzw8PFi1alW2j1nynpiYGACKFCkCwLp160hKSkp3XlarVo0yZcqkOy9r165NiRIlnPt06NCB2NhYZ0WHyLVKSUlh2rRpxMfH06xZM52T4lYDBgygc+fO6c4/0M9KcZ9du3ZRsmRJKlSoQK9evTh48CCgc1LcY/bs2TRq1Ii7776b4sWLU79+fT777DPn/fnh/Y4CoVzk+PHjpKSkpPshCFCiRAkiIiLcNCrJrxzn3OXOx4iICIoXL57ufi8vL4oUKaJzVq5bamoqgwYNokWLFtSqVQsw55yPjw/BwcHp9j3/vLzYeeu4T+RabN68mUKFCuHr68vjjz/OrFmzqFGjhs5JcZtp06axfv16Ro8efcF9Oi/FHZo2bcrkyZOZO3cuH3/8Mfv27aNly5bExcXpnBS32Lt3Lx9//DGVK1fmjz/+oH///jz11FN89dVXQP54v+Pl7gGIiIhciwEDBrBly5Z0/QdE3KVq1aps3LiRmJgYfvjhBx544AEWL17s7mFJPnXo0CGefvpp5s+fj5+fn7uHIwJAp06dnNfr1KlD06ZNKVu2LN9//z0FChRw48gkv0pNTaVRo0aMGjUKgPr167NlyxYmTZrEAw884ObRZQ9VCOUiISEheHp6XtBtPzIyktDQUDeNSvIrxzl3ufMxNDT0gobnycnJREdH65yV6zJw4EB+/fVXFi5cSOnSpZ3bQ0NDSUxM5NSpU+n2P/+8vNh567hP5Fr4+PhQqVIlGjZsyOjRo6lbty4ffPCBzklxi3Xr1hEVFUWDBg3w8vLCy8uLxYsX8+GHH+Ll5UWJEiV0XorbBQcHU6VKFXbv3q2fleIWYWFh1KhRI9226tWrO6cy5of3OwqEchEfHx8aNmzIggULnNtSU1NZsGABzZo1c+PIJD8qX748oaGh6c7H2NhYVq1a5TwfmzVrxqlTp1i3bp1zn7/++ovU1FSaNm2a7WOW3M+yLAYOHMisWbP466+/KF++fLr7GzZsiLe3d7rzcseOHRw8eDDdebl58+Z0v7znz59PYGDgBX8UiFyr1NRUEhISdE6KW7Rt25bNmzezceNG51ejRo3o1auX87rOS3G306dPs2fPHsLCwvSzUtyiRYsW7NixI922nTt3UrZsWSCfvN9xd1druTrTpk2zfH19rcmTJ1vbtm2zHn30USs4ODhdt32RzBIXF2dt2LDB2rBhgwVY48aNszZs2GAdOHDAsizLGjNmjBUcHGz9/PPP1qZNm6yuXbta5cuXt86ePes8RseOHa369etbq1atspYuXWpVrlzZuvfee931kiSX69+/vxUUFGQtWrTIOnr0qPPrzJkzzn0ef/xxq0yZMtZff/1lrV271mrWrJnVrFkz5/3JyclWrVq1rPbt21sbN2605s6daxUrVswaNmyYO16S5AFDhw61Fi9ebO3bt8/atGmTNXToUMtms1nz5s2zLEvnpOQMaVcZsyydl5L9nnnmGWvRokXWvn37rGXLllnt2rWzQkJCrKioKMuydE5K9lu9erXl5eVljRw50tq1a5c1depUq2DBgtaUKVOc++T19zsKhHKhjz76yCpTpozl4+NjNWnSxFq5cqW7hyR51MKFCy3ggq8HHnjAsiyzFOMrr7xilShRwvL19bXatm1r7dixI90xTpw4Yd17771WoUKFrMDAQKtv375WXFycG16N5AUXOx8B68svv3Tuc/bsWeuJJ56wChcubBUsWNC68847raNHj6Y7zv79+61OnTpZBQoUsEJCQqxnnnnGSkpKyuZXI3nFQw89ZJUtW9by8fGxihUrZrVt29YZBlmWzknJGc4PhHReSnbr2bOnFRYWZvn4+FilSpWyevbsae3evdt5v85JcYdffvnFqlWrluXr62tVq1bN+vTTT9Pdn9ff79gsy7LcU5skIiIiIiIiIiLuoB5CIiIiIiIiIiL5jAIhEREREREREZF8RoGQiIiIiIiIiEg+o0BIRERERERERCSfUSAkIiIiIiIiIpLPKBASEREREREREclnFAiJiIiIiIiIiOQzCoRERERERERERPIZBUIiIiKS5z344IPccccd2f68kydPxmazYbPZGDRo0GX3LVeuHO+//362jMvxfI6xnTp1KtueV0RERHIGL3cPQEREROR62Gy2y94/YsQIPvjgAyzLyqYRpRcYGMiOHTvw9/d3y/Nfypo1a/j777+566673D0UERERcQMFQiIiIpKrHT161Hl9+vTpDB8+nB07dji3FSpUiEKFCrljaIAJrEJDQ932/JdSrFgxihQp4u5hiIiIiJtoypiIiIjkaqGhoc6voKAgZwDj+CpUqNAFU8batGnDk08+yaBBgyhcuDAlSpTgs88+Iz4+nr59+xIQEEClSpWYM2dOuufasmULnTp1olChQpQoUYLevXtz/Pjxqx5zVFQUXbp0oUCBApQvX56pU6desM+4ceOoXbs2/v7+hIeH88QTT3D69GkA4uPjCQwM5Icffkj3mJ9++gl/f3/i4uJITExk4MCBhIWF4efnR9myZRk9evRVj1VERETyJgVCIiIiki999dVXhISEsHr1ap588kn69+/P3XffTfPmzVm/fj3t27end+/enDlzBoBTp05x8803U79+fdauXcvcuXOJjIykR48eV/3cDz74IIcOHWLhwoX88MMPTJw4kaioqHT7eHh48OGHH7J161a++uor/vrrL55//nkA/P39ueeee/jyyy/TPebLL7+ke/fuBAQE8OGHHzJ79my+//57duzYwdSpUylXrty1fbNEREQkz9GUMREREcmX6taty8svvwzAsGHDGDNmDCEhITzyyCMADB8+nI8//phNmzZxww03MH78eOrXr8+oUaOcx/jiiy8IDw9n586dVKlSJUPPu3PnTubMmcPq1atp3LgxAP/73/+oXr16uv3SNqEuV64cb775Jo8//jgTJ04EoF+/fjRv3pyjR48SFhZGVFQUv//+O3/++ScABw8epHLlytx4443YbDbKli17bd8oERERyZNUISQiIiL5Up06dZzXPT09KVq0KLVr13ZuK1GiBICzcueff/5h4cKFzp5EhQoVuB7pigAAA4dJREFUolq1agDs2bMnw8+7fft2vLy8aNiwoXNbtWrVCA4OTrffn3/+Sdu2bSlVqhQBAQH07t2bEydOOCuWmjRpQs2aNfnqq68AmDJlCmXLlqVVq1aAqULauHEjVatW5amnnmLevHkZHqOIiIjkfQqEREREJF/y9vZOd9tms6Xb5li9LDU1FYDTp0/TpUsXNm7cmO5r165dzhAms+zfv5/bbruNOnXqMHPmTNatW8eECRMASExMdO7Xr18/Jk+eDJjpYn379nWOu0GDBuzbt4833niDs2fP0qNHD7p3756p4xQREZHcS1PGRERERDKgQYMGzJw5k3LlyuHlde1/QlWrVo3k5GTWrVvnnDK2Y8cOTp065dxn3bp1pKamMnbsWDw8zOd333///QXHuv/++3n++ef58MMP2bZtGw888EC6+wMDA+nZsyc9e/ake/fudOzYkejoaK0uJiIiIqoQEhEREcmIAQMGEB0dzb333suaNWvYs2cPf/zxB3379iUlJSXDx6latSodO3bkscceY9WqVaxbt45+/fpRoEAB5z6VKlUiKSmJjz76iL179/LNN98wadKkC45VuHBhunXrxnPPPUf79u0pXbq0875x48bx3Xff8e+//7Jz505mzJhBaGjoBVPTREREJH9SICQiIiKSASVLlmTZsmWkpKTQvn17ateuzaBBgwgODnZW8WTUl19+ScmSJWndujXdunXj0UcfpXjx4s7769aty7hx43jrrbeoVasWU6dOveSS8Q8//DCJiYk89NBD6bYHBATw9ttv06hRIxo3bsz+/fv5/fffr3qsIiIikjfZLMuy3D0IERERkbxo8uTJDBo0KN10sMz2zTffMHjwYI4cOYKPj89VPXbRokXcdNNNnDx5UpVDIiIi+Yw+IhIRERHJQjExMRQqVIgXXnghU4975swZ9uzZw5gxY3jssceuOgyqWbMmnTp1ytQxiYiISO6hCiERERGRLBIXF0dkZCQAwcHBhISEZNqxX331VUaOHEmrVq34+eefKVSo0FU9/sCBAyQlJQFQoUIFTSUTERHJZxQIiYiIiIiIiIjkM/ooSEREREREREQkn1EgJCIiIiIiIiKSzygQEhERERERERHJZxQIiYiIiIiIiIjkMwqERERERERERETyGQVCIiIiIiIiIiL5jAIhEREREREREZF8RoGQiIiIiIiIiEg+83/vGoidyJleiQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(14, 6))\n",
        "plt.plot(y_test[:, 0], label='Actual Prices', color='blue')\n",
        "plt.plot(test_outputs[:, 0], label='Predicted Prices', color='red')\n",
        "plt.xlabel('Time [days]')\n",
        "plt.ylabel('Price')\n",
        "plt.title('BTC Price Prediction')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3tj9W9Xc7PtT"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}