VK_Energie__Conv_Bayesian_NN_(Uncertainty_Plots)_.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/victor-armegioiu/Time-Series-Prediction/blob/main/VK_Energie__Conv_Bayesian_NN_(Uncertainty_Plots)_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uph2zf8vc0fQ"
      },
      "source": [
        "# Praktikum Project (VK Energie - Forecasting Heat Demand)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4XNcEHNdAUu"
      },
      "source": [
        "# Boilerplate \n",
        "\n",
        "This section does necesary imports, loads/parses the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WxS_3Li63y8S"
      },
      "source": [
        "# Non-neural based math imports.\n",
        "import time\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_probability as tfp\n",
        "\n",
        "import scipy \n",
        "from scipy import stats\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SE2Wellp38cv"
      },
      "source": [
        "# Data loading\n",
        "import pandas as pd\n",
        "import csv"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHTXJGQjO2pA"
      },
      "source": [
        "# Drawing stuff.\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "#matplotlib.style.use('ggplot')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZFD7pYxdjmZ"
      },
      "source": [
        "# Neural networks stuff.\n",
        "import torch\n",
        "from torch.autograd import Variable\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data as Data"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-HdkIun_PXQb"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "reader = csv.reader(\n",
        "    open(\"Oberbayern.csv\"), delimiter=\";\")\n",
        "\n",
        "rows = [row for row in reader]\n",
        "df = df.append(rows[1:])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ir9lyF5pQmNG"
      },
      "source": [
        "df.columns = ['TimestampUtc', 'Heat Demand', 'Ambient Temperature',  \n",
        "              'Solar Irradiance']"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfRsrPw3VHDJ"
      },
      "source": [
        "for column in df.columns[1:]:\n",
        "  df[column] = df[column].astype(float)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bkCOz0_jdLis"
      },
      "source": [
        "# Data Analysis\n",
        "\n",
        "This section covers the following analyses on the dataset:\n",
        "\n",
        "\n",
        "*   Peaking at samples from the data.\n",
        "*   Missing values count\n",
        "*   Overall statistics on the regressors/regressands (mean, max, std, etc). Plots for the histograms.\n",
        "*   Correlation analysis - Making sure the correlations between the features and the targets make sense, as well as ensuring we don't have too strong a linear correlation between the features. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
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          "height": 419
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        "outputId": "953a2a92-6491-43db-8520-21fa78a3298c"
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      "source": [
        "df"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TimestampUtc</th>\n",
              "      <th>Heat Demand</th>\n",
              "      <th>Ambient Temperature</th>\n",
              "      <th>Solar Irradiance</th>\n",
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              "      <td>2020-02-26 17:00:00+01:00</td>\n",
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              "      <th>5941</th>\n",
              "      <td>2020-11-02 12:00:00+01:00</td>\n",
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              "      <td>5.718750</td>\n",
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              "      <th>5942</th>\n",
              "      <td>2020-11-02 13:00:00+01:00</td>\n",
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              "      <td>1.140625</td>\n",
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              "                   TimestampUtc  ...  Solar Irradiance\n",
              "0     2020-02-26 17:00:00+01:00  ...          0.050000\n",
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        "outputId": "91b52d92-def7-446f-d49d-00bbf73e9525"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TimestampUtc           0\n",
              "Heat Demand            0\n",
              "Ambient Temperature    0\n",
              "Solar Irradiance       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48QbG6f1X4ma",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77083c6a-50ab-475c-910e-18432bc0dc2a"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['TimestampUtc', 'Heat Demand', 'Ambient Temperature',\n",
              "       'Solar Irradiance'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 36
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_2CO-n7qQj_G"
      },
      "source": [
        "# Compute and store pairwise correlations between features and\n",
        "# targets.\n",
        "correlations = {}\n",
        "for col1 in df.columns[1:]:\n",
        "  for col2 in df.columns[1:]:\n",
        "    if col1 != col2:\n",
        "      r2_correlation_coeff = stats.pearsonr(df[col1], df[col2])[0]\n",
        "      correlations[col1, col2] = r2_correlation_coeff"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4tMqblL4Yk3Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "outputId": "2cc2107d-22c6-461a-9441-92f2926fdee6"
      },
      "source": [
        "# Plot all pairwise features/targets/\n",
        "relevant_columns = df.columns[1:] \n",
        "\n",
        "for i in range(len(relevant_columns)):\n",
        "  for j in range(i + 1, len(relevant_columns)):\n",
        "    col_i, col_j = relevant_columns[i], relevant_columns[j]\n",
        "    plt.scatter(df[col_i], df[col_j])\n",
        "    plt.xlabel(col_i)\n",
        "    plt.ylabel(col_j)\n",
        "\n",
        "    r2_correlation_coeff = round(correlations[col_i, col_j], 3)\n",
        "    plt.title('R2 correlation coefficient: {}'.format(r2_correlation_coeff))\n",
        "    plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnUC6mU6Vl9y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d7394a3b-025e-4043-ee67-24aacea52ef6"
      },
      "source": [
        "for column in df.columns[1:]:\n",
        "  print(df[column].describe())\n",
        "  plt.hist(df[column], bins=500)\n",
        "  plt.xlabel(column)\n",
        "  plt.ylabel('Counts')\n",
        "  plt.show()\n",
        "  print()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "count    5946.000000\n",
            "mean     2753.058015\n",
            "std      1530.114730\n",
            "min        43.000000\n",
            "25%      1514.583328\n",
            "50%      2222.491250\n",
            "75%      3754.158808\n",
            "max      9681.533203\n",
            "Name: Heat Demand, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "count    5946.000000\n",
            "mean       13.007976\n",
            "std         6.339653\n",
            "min        -5.020000\n",
            "25%         8.600000\n",
            "50%        13.198389\n",
            "75%        17.370000\n",
            "max        31.371857\n",
            "Name: Ambient Temperature, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "count    5946.000000\n",
            "mean      105.020707\n",
            "std       184.026273\n",
            "min        -0.260000\n",
            "25%         0.000000\n",
            "50%         1.812500\n",
            "75%       131.907500\n",
            "max       787.470000\n",
            "Name: Solar Irradiance, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "duPE-KIhd9Cj"
      },
      "source": [
        "# Building datasets for training\n",
        "\n",
        "This section covers the setup needed for creating datasets than can be fed to Pytorch NNs. We also do some preprocessing shift/scale our inputs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SCBebg5ZoDvN"
      },
      "source": [
        "def DfTrainTestSplitter(df, test_proportion=0.2, as_numpy=True):\n",
        "  \"\"\"Split dataframe into train and test data.\n",
        "  \n",
        "  In order to have the test distribution as close as possible to th\n",
        "  train distribution, we split the initial datafram into 4 chunks,\n",
        "  one corresponding from each season.\n",
        "\n",
        "  We build the test set by concatenating random samples from each season.\n",
        "  \"\"\"\n",
        "  cnt_test = int(test_proportion * len(df))\n",
        "  season_cnt = 4\n",
        "  df_train, df_test = None, None\n",
        "\n",
        "  for i in range(season_cnt):\n",
        "    left_idx = i * int(len(df) / season_cnt)\n",
        "    right_idx = min(int(left_idx + cnt_test // 4.0), len(df) - 2)\n",
        "    \n",
        "    if df_test is None:\n",
        "      df_test = df[left_idx: right_idx + 1]\n",
        "    else:\n",
        "      df_test = pd.concat([df_test, df[left_idx: right_idx + 1]])\n",
        "\n",
        "  df_train = pd.concat([df, df_test]).drop_duplicates(keep=False)\n",
        "  if as_numpy:\n",
        "   return df_train.to_numpy()[:, 1:],  df_test.to_numpy()[:, 1:]\n",
        "  return df_train, df_test"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eT0h10RvgG2k"
      },
      "source": [
        "train_data, test_data = DfTrainTestSplitter(df, test_proportion=0.1)\n",
        "X_train, y_train = (train_data[:, [1, 2]].astype(float),\n",
        "                    train_data[:, 0].astype(float))\n",
        "\n",
        "X_test, y_test = (test_data[:, [1, 2]].astype(float),\n",
        "                  test_data[:, 0].astype(float))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qAhcM4vPgZBE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "917f7ccf-4e99-4989-bcb0-0b816cdfdcfe"
      },
      "source": [
        "X_train.shape, X_test.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((5350, 2), (596, 2))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0WoIeHxbpzcX"
      },
      "source": [
        "scaler = MinMaxScaler().fit(X_train)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VQZtL6nxglLY"
      },
      "source": [
        "# Use the same scaler in order to ensure that we don't inject knowledge\n",
        "# about the test distribution in our training biases.\n",
        "X_train = scaler.transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIUpN7Vkwj8A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "05668423-3a33-422b-bc58-2babd99ecfb0"
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
        "fig.suptitle('Inputs Train/Test distributions')\n",
        "ax1.hist(X_train, bins=4)\n",
        "ax2.hist(X_test, bins=4)\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ldHY_xK2Y9O"
      },
      "source": [
        "# Scale both dataset by `y_train_max`. Same reasoning\n",
        "# as in using the same scaler on inputs. This is done to avoid\n",
        "# exploding gradients.\n",
        "y_train_max = y_train.max()\n",
        "y_train /= y_train_max\n",
        "y_test /= y_train_max"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VEdTaSi9w5iC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "1a5577ba-f658-4f59-862f-7d90db4ce208"
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
        "fig.suptitle('Labels Train/Test distributions')\n",
        "ax1.hist(y_train, bins=100)\n",
        "ax2.hist(y_test, bins=100)\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yOtg4Bb3lVjN"
      },
      "source": [
        "# Batching Logic \n",
        "\n",
        "This section defines the necessary utilities for producing batches of variable length sequences."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWXCuJmTXgYp"
      },
      "source": [
        "def GetInitialBatchIndex(X, input_len, prediction_window_len=48):\n",
        "  \"\"\" \n",
        "      Returns a random index from which to sample `input_len` inputs.\n",
        "      This was written separately so that we can test that it produces an\n",
        "      even distribution among potential starting indices.\n",
        "  \"\"\"\n",
        "  left_idx = np.random.randint(\n",
        "      low=0,\n",
        "      high=max(len(X) - prediction_window_len * 2 - input_len - 1, 1))\n",
        "  right_idx = left_idx + input_len - 1\n",
        "  return left_idx"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BaOJOfx0bhHs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "561af409-7a5c-4e53-c741-6a79430b100a"
      },
      "source": [
        "# Confirm that starting indices for bathches are evenly distributed.\n",
        "no_trials = 100000\n",
        "start_indices_samples = []\n",
        "\n",
        "for _ in range(no_trials):\n",
        "  start_indices_samples.append(GetInitialBatchIndex(X_train, 15))\n",
        "\n",
        "plt.hist(start_indices_samples)\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jCUO69OREbVh"
      },
      "source": [
        "def GetSample(X, y, input_len, prediction_window_len=48, use_noise_reg=False):\n",
        "  \"\"\"Return a single input sequence and its respective targets.\n",
        "  \n",
        "  Args:\n",
        "    X: np.array from which to extract input sequences.\n",
        "    y: np.array from which to extract the regression targets corresponding\n",
        "       to the selected inputs.\n",
        "    prediction_window_len: length of the prediction window, which describes\n",
        "      how many predictions we have to do for a given input sequence.\n",
        "    use_noise_reg: bool indication whether to inject the labels with noise.\n",
        "\n",
        "  Return:\n",
        "    X_tensor: tensorized inputs.\n",
        "    y_tensor: tensorized targets.\n",
        "\n",
        "  Note, since we can use forecasts, we also include future data in our\n",
        "  sequences.\n",
        "  \"\"\"\n",
        "  left_idx = GetInitialBatchIndex(X, input_len, prediction_window_len)\n",
        "  right_idx = left_idx + input_len - 1\n",
        "\n",
        "  # Add `prediction_window_len` to our right index, since we can use future\n",
        "  # values as well, given that we always expect to have forecasts availabel.\n",
        "  X_sample = X[left_idx : right_idx + 1 + prediction_window_len]\n",
        "  y_sample = y[right_idx + 1 : right_idx + prediction_window_len + 1]\n",
        "\n",
        "  return X_sample, y_sample"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2swVcYlWkhRW"
      },
      "source": [
        "def GetBatch(X, y, batch_size, input_len, prediction_window_len=48,\n",
        "             use_noise_reg=False):\n",
        "  Xs, ys = [], []\n",
        "  for _ in range(batch_size):\n",
        "    X_sample, y_sample = GetSample(X, y, \n",
        "                                   input_len,\n",
        "                                   prediction_window_len,\n",
        "                                   use_noise_reg)\n",
        "    Xs.append(X_sample)\n",
        "    ys.append(y_sample)\n",
        "  return np.stack(Xs), np.stack(ys)vk en"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HvXIlWjne_ha",
        "outputId": "0e00332f-20a0-4771-e123-5a9fadf44226"
      },
      "source": [
        "xb, yb = GetBatch(X_train, y_train, 10, 1)\n",
        "xb.shape, yb.shape"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10, 49, 2), (10, 48))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ic5KL4tghLas"
      },
      "source": [
        "# Neural Models Definitions\n",
        "\n",
        "We set up a small Bayesian Neural Net, train it via variational inference using a flipout estimator ([Wen et. al, 2018], it costs `2x` more floating point computations than typical reparameterization, but has less variance).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPMu7uIA6R-k",
        "outputId": "7834695c-4fc2-4a7e-f610-5deba8169ef8"
      },
      "source": [
        "tf.keras.backend.clear_session()\n",
        "\n",
        "# Training params.\n",
        "epochs = 20\n",
        "batch_size = 32\n",
        "val_batch_size = 10\n",
        "seq_len = 1\n",
        "prediction_window_len = 48\n",
        "input_shape = (49, 2)\n",
        "\n",
        "iter_per_epochs = len(X_train) // batch_size\n",
        "\n",
        "# Loss, optimizer and divergence function to use for variational layers.\n",
        "loss_fn = tf.keras.losses.MeanSquaredError()\n",
        "optimizer=tf.keras.optimizers.Adam(0.001)\n",
        "\n",
        "kl_divergence_function = (lambda q, p, _:\n",
        "                          tfp.distributions.kl_divergence(q, p) /\n",
        "                          tf.cast(len(X_train), dtype=tf.float32))\n",
        "train_acc_metric = tf.keras.metrics.MeanAbsoluteError()\n",
        "val_acc_metric = tf.keras.metrics.MeanAbsoluteError()\n",
        "\n",
        "# Bayesian Convolutional NN.\n",
        "model_tfp = tf.keras.Sequential([\n",
        "    tf.keras.Input(shape=input_shape,name=\"input\"),\n",
        "    tfp.layers.Convolution1DFlipout(\n",
        "        64, kernel_size=5, padding='SAME', activation=tf.nn.relu,\n",
        "        kernel_divergence_fn=kl_divergence_function),\n",
        "    tfp.layers.Convolution1DFlipout(\n",
        "        32, kernel_size=3, padding='SAME', activation=tf.nn.relu,\n",
        "        kernel_divergence_fn=kl_divergence_function),    \n",
        "    tf.keras.layers.Flatten(),\n",
        "    tfp.layers.DenseFlipout(prediction_window_len,\n",
        "                            kernel_divergence_fn=kl_divergence_function,\n",
        "                            activation=tf.nn.relu, name=\"out_tfp_pred\"),\n",
        "])\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    start_time = time.time()\n",
        "    print(\"\\nStart of epoch %d\" % (epoch,))\n",
        "\n",
        "    for iter in range(iter_per_epochs):\n",
        "        with tf.GradientTape() as tape:\n",
        "            x_batch_train, y_batch_train = GetBatch(X_train, y_train,\n",
        "                                                    batch_size, seq_len)\n",
        "            logits = model_tfp(x_batch_train, training=True)\n",
        "            loss_value = loss_fn(y_batch_train, logits)\n",
        "\n",
        "        grads = tape.gradient(loss_value, model_tfp.trainable_weights)\n",
        "\n",
        "        optimizer.apply_gradients(zip(grads, model_tfp.trainable_weights))\n",
        "        train_acc_metric.update_state(y_batch_train, logits)\n",
        "\n",
        "        if iter % 20 == 0:\n",
        "            print(\n",
        "                \"Training loss (for one batch) at step %d: %.4f\"\n",
        "                % (iter, float(loss_value))\n",
        "            )\n",
        "            print(\"Seen so far: %s samples\" % ((iter + 1) * batch_size))\n",
        "\n",
        "    train_acc = train_acc_metric.result()\n",
        "    print(\"Training acc over epoch: %.4f\" % (float(train_acc),))\n",
        "\n",
        "    train_acc_metric.reset_states()\n",
        "\n",
        "    # Get 5 batches for validation.\n",
        "    val_batches = [GetBatch(X_test, y_test, \n",
        "                            val_batch_size, seq_len) for _ in range(5)]\n",
        "    for x_batch_val, y_batch_val in val_batches:\n",
        "        val_logits = model_tfp(x_batch_val, training=False)\n",
        "        val_acc_metric.update_state(y_batch_val, val_logits)\n",
        "    val_acc = val_acc_metric.result()\n",
        "    val_acc_metric.reset_states()\n",
        "    print(\"Validation acc: %.4f\" % (float(val_acc),))\n",
        "    print(\"Time taken: %.2fs\" % (time.time() - start_time))\n",
        "      "
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:2281: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n",
            "  warnings.warn('`layer.add_variable` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Start of epoch 0\n",
            "Training loss (for one batch) at step 0: 0.1590\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0529\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0363\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0303\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0177\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0159\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0246\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0143\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0150\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.1335\n",
            "Validation acc: 0.0850\n",
            "Time taken: 12.06s\n",
            "\n",
            "Start of epoch 1\n",
            "Training loss (for one batch) at step 0: 0.0155\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0163\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0107\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0115\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0143\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0113\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0108\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0126\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0109\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0809\n",
            "Validation acc: 0.0735\n",
            "Time taken: 5.71s\n",
            "\n",
            "Start of epoch 2\n",
            "Training loss (for one batch) at step 0: 0.0077\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0111\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0090\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0133\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0109\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0064\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0091\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0075\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0069\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0690\n",
            "Validation acc: 0.0646\n",
            "Time taken: 5.70s\n",
            "\n",
            "Start of epoch 3\n",
            "Training loss (for one batch) at step 0: 0.0070\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0076\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0056\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0059\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0085\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0063\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0099\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0079\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0072\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0639\n",
            "Validation acc: 0.0675\n",
            "Time taken: 5.49s\n",
            "\n",
            "Start of epoch 4\n",
            "Training loss (for one batch) at step 0: 0.0072\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0068\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0065\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0081\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0053\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0068\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0070\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0081\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0058\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0610\n",
            "Validation acc: 0.0633\n",
            "Time taken: 5.49s\n",
            "\n",
            "Start of epoch 5\n",
            "Training loss (for one batch) at step 0: 0.0056\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0090\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0068\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0070\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0066\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0059\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0074\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0083\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0060\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0598\n",
            "Validation acc: 0.0439\n",
            "Time taken: 5.45s\n",
            "\n",
            "Start of epoch 6\n",
            "Training loss (for one batch) at step 0: 0.0075\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0062\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0052\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0073\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0061\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0055\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0060\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0062\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0055\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0565\n",
            "Validation acc: 0.0427\n",
            "Time taken: 5.41s\n",
            "\n",
            "Start of epoch 7\n",
            "Training loss (for one batch) at step 0: 0.0050\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0056\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0065\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0050\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0069\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0071\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0065\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0048\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0059\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0557\n",
            "Validation acc: 0.0523\n",
            "Time taken: 5.44s\n",
            "\n",
            "Start of epoch 8\n",
            "Training loss (for one batch) at step 0: 0.0047\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0047\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0047\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0055\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0040\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0062\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0049\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0051\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0059\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0553\n",
            "Validation acc: 0.0508\n",
            "Time taken: 5.44s\n",
            "\n",
            "Start of epoch 9\n",
            "Training loss (for one batch) at step 0: 0.0052\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0052\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0064\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0052\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0058\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0043\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0045\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0062\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0063\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0537\n",
            "Validation acc: 0.0614\n",
            "Time taken: 5.42s\n",
            "\n",
            "Start of epoch 10\n",
            "Training loss (for one batch) at step 0: 0.0070\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0041\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0069\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0057\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0053\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0060\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0048\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0045\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0053\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0533\n",
            "Validation acc: 0.0505\n",
            "Time taken: 5.37s\n",
            "\n",
            "Start of epoch 11\n",
            "Training loss (for one batch) at step 0: 0.0040\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0040\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0050\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0053\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0040\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0065\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0050\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0053\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0070\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0525\n",
            "Validation acc: 0.0495\n",
            "Time taken: 5.40s\n",
            "\n",
            "Start of epoch 12\n",
            "Training loss (for one batch) at step 0: 0.0055\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0061\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0053\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0051\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0044\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0057\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0049\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0049\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0043\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0519\n",
            "Validation acc: 0.0451\n",
            "Time taken: 5.44s\n",
            "\n",
            "Start of epoch 13\n",
            "Training loss (for one batch) at step 0: 0.0064\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0039\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0055\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0060\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0033\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0065\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0053\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0051\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0036\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0513\n",
            "Validation acc: 0.0454\n",
            "Time taken: 5.44s\n",
            "\n",
            "Start of epoch 14\n",
            "Training loss (for one batch) at step 0: 0.0043\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0040\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0047\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0054\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0054\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0056\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0066\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0060\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0036\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0509\n",
            "Validation acc: 0.0495\n",
            "Time taken: 5.37s\n",
            "\n",
            "Start of epoch 15\n",
            "Training loss (for one batch) at step 0: 0.0076\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0062\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0053\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0052\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0069\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0035\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0041\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0042\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0062\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0500\n",
            "Validation acc: 0.0466\n",
            "Time taken: 5.28s\n",
            "\n",
            "Start of epoch 16\n",
            "Training loss (for one batch) at step 0: 0.0061\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0045\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0050\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0040\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0039\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0060\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0042\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0034\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0042\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0491\n",
            "Validation acc: 0.0496\n",
            "Time taken: 5.36s\n",
            "\n",
            "Start of epoch 17\n",
            "Training loss (for one batch) at step 0: 0.0064\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0051\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0049\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0053\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0056\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0056\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0050\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0049\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0051\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0483\n",
            "Validation acc: 0.0462\n",
            "Time taken: 5.36s\n",
            "\n",
            "Start of epoch 18\n",
            "Training loss (for one batch) at step 0: 0.0047\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0031\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0064\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0043\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0037\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0046\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0049\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0044\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0067\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0484\n",
            "Validation acc: 0.0430\n",
            "Time taken: 5.42s\n",
            "\n",
            "Start of epoch 19\n",
            "Training loss (for one batch) at step 0: 0.0047\n",
            "Seen so far: 32 samples\n",
            "Training loss (for one batch) at step 20: 0.0052\n",
            "Seen so far: 672 samples\n",
            "Training loss (for one batch) at step 40: 0.0039\n",
            "Seen so far: 1312 samples\n",
            "Training loss (for one batch) at step 60: 0.0053\n",
            "Seen so far: 1952 samples\n",
            "Training loss (for one batch) at step 80: 0.0046\n",
            "Seen so far: 2592 samples\n",
            "Training loss (for one batch) at step 100: 0.0038\n",
            "Seen so far: 3232 samples\n",
            "Training loss (for one batch) at step 120: 0.0045\n",
            "Seen so far: 3872 samples\n",
            "Training loss (for one batch) at step 140: 0.0046\n",
            "Seen so far: 4512 samples\n",
            "Training loss (for one batch) at step 160: 0.0046\n",
            "Seen so far: 5152 samples\n",
            "Training acc over epoch: 0.0481\n",
            "Validation acc: 0.0545\n",
            "Time taken: 5.41s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nuNYMWJWAcxw"
      },
      "source": [
        "X_sample, y_sample = GetBatch(X_test, y_test, 1, seq_len)"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "pQt9KF_opWGf",
        "outputId": "24504ad2-274b-4186-b880-d83f576f4058"
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 7))\n",
        "\n",
        "ax1.set_ylabel('Heat Demand')\n",
        "ax1.set_xlabel('Time')\n",
        "ax2.set_title('Sample predictions')\n",
        "\n",
        "# Number of times to sample new parameters and compute predictions.\n",
        "no_trials = 30\n",
        "preds = []\n",
        "for i in range(no_trials):\n",
        "  preds.append(model_tfp.predict(X_sample).flatten())\n",
        "  ax2.plot(preds[-1].flatten() * y_train_max)\n",
        "\n",
        "# Get average prediction.\n",
        "avg_preds = np.mean(np.array(preds), axis=0)\n",
        "\n",
        "ax1.plot(avg_preds.flatten() * y_train_max)\n",
        "ax1.plot(y_sample.flatten() * y_train_max)\n",
        "ax1.legend(['Prediction', 'Ground Truth'], loc='upper left')\n",
        "\n",
        "mae = tf.math.reduce_mean(tf.abs(avg_preds - y_sample) * y_train_max).numpy()\n",
        "print('Mean Absolute Error:', mae)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 355.0710785437739\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JQqSjDgmShDgZpZRLKZXdcx+4AtgJPA/0VKi7BfiLef954Gazyt0qoMtMy3sNuEIplW9WwrvCbBsfPJshx8wOjEqQJIQQIOl2QqQn3+Gj95Px0euHEGNbCfCsUgqM98Pfaq1fVUptAv6glPoyUAd81tz/ZYzKdgeBEHAbgNbap5T6F2CTud8PtNa+kbuMM5BKQuNWWHA9bP2NpNsJIYRJRpJGUEtLC5///OeZMWMG5557LhdccAHPPvvsiPahtraWhQsXHtO2Y8cOli5dytKlSykoKKCyspKlS5dy2WVDW2OntraW3/72t72PH3vsMb7+9a8Pa7/FKZJ0OyFOSmt9WGu9xPxZoLX+V7Pdq7W+VGs9S2t9WU/AY1a1u1NrPVNrvUhrvbnPuX6ttT7H/Hl0tK7plLXthVgAKi4CS4YUbhBCCJMESSNEa83111/P6tWrOXz4MFVVVfzud7/D4/GcsG8ikRjRvi1atIjq6mqqq6u59tpr+dGPfkR1dTVvvvnmkPp0fJAkxoC+I0mJ6Oj1QwgxtvUUbShdAc4cmZMkhBAmCZJGyNtvv43dbueOO+7obZs+fTp33XUXYIy+XHvttaxZs4ZLL70Un8/H9ddfz+LFi1m1ahXbt28H4N577+X+++/vPcfChQupra2ltraWefPm8dWvfpUFCxZwxRVXEA4bK6dXVVWxZMkSlixZwoMPPjjkPl9yySX8/d//PStWrOAnP/kJt956K88880zvdrfbDcA999zDu+++y9KlS3nggQcAaGxs5Morr2TWrFl861vfOs1XTZyWVAo6ao/OMZB0OyHEQBo2gzMPCmeCI0fS7YQQwjQx5yS9cg807xjec05eBFfdN+DmXbt2sXz58kFPsWXLFrZv305BQQF33XUXy5Yt47nnnuPtt9/m5ptvprq6etDjDxw4wNNPP80vfvELPvvZz/KnP/2JL37xi9x222387Gc/Y/Xq1dx9992ndFmxWIyeFeFvvfXWfve57777uP/++3nxxRcBI+Crrq5m69atOBwO5syZw1133UV5eXm/x4th5m+EZBSK50B3g6TbCSEG5qmCshWgFDhzJd1OCCFMMpI0Su68806WLFnCypUre9suv/xyCgoKANiwYQNf+tKXAFizZg1er5fu7sHfvHrmEgGce+651NbW0tnZSWdnJ6tXrwboPedQfe5znzul/Xtceuml5Obm4nQ6mT9/PnV1dad1HnEaelLtiuYYtxIkCSH6E/VD624j1Q4k3U4IIfqYmCNJg4z4nC0LFizgT3/6U+/jBx98kPb2dlasWNHb5nK5Tnoem81GKpXqfRyJRHrvOxyO3vtWq7U33e5M9O1T3+dOpVLEYgN/+D6+LyM9z2pC6ynaUCxBkhBiEI1bAW2MJIGRbhc4NKpdEkKIsUJGkkbImjVriEQiPPTQQ71toVBowP0vuuginnrqKQDWrVtHUVEROTk5VFRUsGXLFsBIz6upqRnwHAB5eXnk5eWxYcMGgN5zno6KigqqqqoAeP7554nHjbku2dnZ+P3+0z6vGGa+w0aVqoIZxmMJkoQQ/ekt2nCucSvpdkII0UuCpBGilOK5557jnXfeobKykvPOO49bbrmF//iP/+h3/3vvvZeqqioWL17MPffcw+OPPw7AZz7zGXw+HwsWLOBnP/sZs2fPPulzP/roo9x5550sXboUrfVpX8NXv/pV3nnnHZYsWcIHH3zQO8q0ePFirFYrS5Ys6S3cIEZRRw3kT4eMTOOxBElCiP40VBlfpmQZad44c6VwgxBCmNSZfGgeq1asWKF7ig302LNnD/PmzRulHomByO/lLHj4QnBPhjX/BI9cAjf9DuZcNdq9EhOUUqpKa73i5HtOPP29V40YreG/5sKMi+HTjxhtf/13eOc++K4PLNbR6ZcQQoywgd6nZCRJiHSiNfhqjW+HrXajTdZJEkIcr7sBAs1HizaAUbgBJOVOCCGQIEmI9BJsh5jfDJLM4hmyTpIQ4nieTcZt2blH25y5xq2k3AkhxMQKktIxtXA8k9/HWdBhFvIoqARrhnFf5iQJIY7n2Wx8kVKy6GibQ0aShBCix4QJkpxOJ16vVz6YjxFaa7xeL06nc7S7kl561kjqm24nQZIQ4ngNVTBlMdjsR9t60u1krSQhhJg46ySVlZXh8Xhoa2sb7a4Ik9PppKysbLS7kV58hwEFedMgFjTaJEgSQvSVjENjNay47dj2npEkSbcTQoiJEyRlZGRQWVk52t0Q4uzy1UBuOdgcR4MjCZKEEH217oZE+Oj6SD165iRJup0QQkycdDshJgTfYWM+Eki6nRCif71FG46reCuFG4QQopcESUKkk46ao0GSpadwg1S3E0L04amCrCLIm35su0PmJAkhRA8JkoRIF5EuCHmNog0AFosRKMk6SUKIvho2G6NISh3bbrODzQlRCZKEEEKCJCHShc8s/53fZ+6d1S7pdkKIo8Kd0L7/xFS7Hs5cSbcTQggkSBIiffQt/93DmiHpdkKIoxq3GLelAwRJjhwp3CCEEEiQJET66AmS8iuOtslIkhCiL08VoKB0ef/bnTkyJ0kIIZAgSYj00VED7hJwuI+29S0FLoQQnk1QNPtoJbvjSbqdEEIAEiQJkT58Ncem2oGZbidBkhAC0Nos2rBy4H0k3U4IIQAJkoRIH76aY4s2gKTbCSGO6qg1KmCWnTvwPs4cGUkSQggkSBIiPcTD4G8cYCRJCjcIIYDtfzBuy1cNvI9D5iQJIQRIkCREeuioNW4Ljh9Jcsg6SUII8B6Cd/8LFtwAJfMH3s+ZB4mwfLkihJjwJEgSIh30lv+WdDshxHG0hpe/afw9WPvvg+/rzDFuJeVOCDHBSZAkRDroLf99fJAk6XZCTHi7noVDbxO/+Du8t+MwqVRq4H0dZpAUlZQ7IcTEJkGSEOnAV2OkyWQVHNsuI0lCTGyRLnj1OzBlKbtdF/DGG2/g8XgG3r93JEmCJCHExCZBkhDpwHf4xKINIOskCTHRvf2vEGiBqx9gV10rALVNbQPv37N+kqTbCSEmOAmShEgHHTUnzkcCWSdJiImsYQts+gWc91UoXc5BTwsARwYLknrT7SRIEkJMbBIkCTHeJePQWd//SJKk2wkxMaWS8OL/BVcxrPlnALo7OwHoMG/7Jel2QggBgG20OyCEOEOdR0AnTyzaAFK4QYiJatOvoKka/ubX4MwlEk+iYyFQEOweZJRI0u2EEAKQkSQhxj9fjXHb70iSrJMkxITT3QRv/QBmroEFnwZgS50PF8aocjTkH/hYSbcTQghAgiQhxr/eNZIGSreTkSQhJpTX/tFIs/3k/aAUAB/t82BRmpi2kooG0Vr3f6zFCna3jCQJISY8CZKEGO86aiDDBe5JJ26Twg1CTCwNVbDrz3DRP0DhzN7mXbVNAATt+SidIhgMDnwOR47MSRLDa6CgXIgxTIIkIcY732Gjsp35jfExpHCDEBNL1eOQkQWr/q63KZFM0dDiBSDlKgagc9DiDbmymKwYPrXvwX3TjPmzQowjEiQJMd75aiC/ov9tNodR1CGVHNEuCSFGQTQAO/8EC244WqUO2NPkJyMZBsCRZ4w4d3UNEgQ5cyTdTgyfI+8bc9x2Pz/aPRHilEiQJMR4lkpBR23/85HASLcDGU0SYiLY/RzEArD85mOaN9X6cKsYLreb/IICADo6OgY+jyNHCjeI4dN+wLjd+9Lo9kOIU3TWgySllFUptVUp9aL5+DGlVI1Sqtr8WWq2K6XU/yilDiqltiullvc5xy1KqQPmzy1nu89CjBv+RkhG+19IFox0O5AgSYiJYMsTUDgLys8/pnlTrY8ie5yC/HyKct1EtZV230nWSpI5SWK4tO83bus3QmCQhYyFGGNGYiTpG8Ce49ru1lovNX+qzbargFnmz+3AQwBKqQLge8D5wHnA95RS+SPQbyHGvsEq20GfIEkq3AmR1tr2Qf2HxihSn/mJWms21frItcbIz8+nyO0gqO20+wYZSXLmSrqdGB5aGyNJ0y8EnYL9r452j4QYsrMaJCmlyoBPAb8cwu7XAU9ow0YgTyk1BVgLvKG19mmtO4A3gCvPWqeFGE+GGiTJWklCpLctT4DFBktuOqa5pj2INxDFkoiQl5dHodtOQDvoOq5wQzylORiKGA8k3U4Ml+5GIwV0wfWQNw32vjjaPRJiyM72SNKPgW8BqePa/9VMqXtAKeUw20qB+j77eMy2gdqFEP5m4zZ7Sv/bJd1OiPSXiMG238Gcq8BdfMymTbU+XCoGWpPtdpM4VE1A2wkFunvXSkppzR27a/nER/sIJpJGul0yBvHIaFyNSCc9qXbFc2Du1XDor0aBESHGgbMWJCmlrgZatdZVx236DjAXWAkUAN8epue7XSm1WSm1ua1Ncl7FBBHygSP3aIGG4/UWbpB0OyEGMu7nzu5/BULtsPzEp91U28GUTON7yj2bq/ifF7ag43GSiTjhsFHx7r9rW3iprYu41rTGEsZIEsi8JHHmeoo2FM2GuZ8y5tAeemt0+yTEEJ3NkaSPA9cqpWqB3wFrlFK/0Vo3mSl1UeBRjHlGAA1AeZ/jy8y2gdqPobV+RGu9Qmu9ori4+PjNQqSncAdkDTJFT0aShBiK8T13dsuTkFMKM9ecsGlTrY/5hVYA3tjRxPbcRfgSTsAoA/5SWyf31zYzK8tI6vDGE+DMMw6WlDtxptr3G0G3uwTKV0FmgVS5E+PGWQuStNbf0VqXaa0rgP8FvK21/qI5zwillAKuB3aahzwP3Gx+U7cK6NJaNwGvAVcopfLNN50rzDYhRNgHmYN8FrOZ2axJmZMkRH/G/dzZLg8cfBOWfgEs1mM2tXZHqPOGmObSgKbTOc1otxaiNWxp83HXniMsy87iv+YY30W2xxJH11iS4g3iTLXvh6JZRjERq81ICd3/qmQ3iHFhNNZJekoptQPYARQBPzTbXwYOAweBXwD/G0Br7QP+Bdhk/vzAbBNChHzGN3MDkXQ7IU5mROfODntqePVvAQ3LvnDCpo9qjbdKZ3cjKh6jOWsyOSj8lkxqVCH/6IuSbbXw6KJKpjqNUWdvvE+6XVTS7cQZat9vpNr1mPspI42z7r3R65MQQzQiQZLWep3W+mrz/hqt9SKt9UKt9Re11gGzXWut79RazzS3b+5z/K+11ueYP4+ORJ+FGBfCPsgaLEiSdDshBjLSc2dhmFPDUynY+iTMuATyK07YvKnGR44tSVdzPQkctCUVn8dOdirOB85z8KYUjy6sZLIjg8IMG9CTbpdrnEDmJIkzEekGf9OxQdLMNZCRBXukyp0Y+0ZjJEkIMVxCHScZSZIgSYhBjOjc2WFX8w50HoFlX+p380e1HayN7ySuLIQCxtv9UqwUFmQQ60zxydZGlue6AMi0WnBZLZJuJ4aPt0/Rhh4ZmUagtPclYw0lIcYwCZKEGK+SCSMdZigjSQkJkoQ43rifO7vlCWNO4tyrT9jUHYnTfKSOkroP0TY77fEsMoC9U53sXTIZhabjsP+YYwozbMel20mQJM5Aez9BEhj/Xv2N0Lh15PskxCmwjXYHhBCnKWIuBikjSUIMt6eUUsWAAqqBO8z2l4FPYsydDQG3gTF3VinVM3cWRmLubMhnLMy54suQ4Txh8+ZaH5e2rsNisYJSNJXOpSKawf0LnKz0xvGlvGzz5xOKJciyGx8Fiuw2YyTJ7gaUjCSJM9O+31jguKDy2PbZa0FZjdGk0uX9HyvEGCAjSUKMVyHzM9hg1e16gyQp3CDEYMbd3Nntvze+/Fjef6rdtldeZkqsmanhBEmtqAlZyMyykbAofrArxrJIDTFt5ZlNdb3H9I4kWSxGyp3MSRJnon0/5FeeuI5fVgFM/5iUAhdjngRJQoxXYTNIGnSdpJ7qdjKSJETa0NpItSs9F0oWnLA53NiAbcMfcUdiTP/SLfh0FvGkJpKbQUkkRnFUMzfeSYEK8vj7dWhzbkhvkATGItWSbifORPuBE1Ptesy9Gtr2gPfQyPZJiFMgQZIQ41XvSNIg6XayTpIQ6ae70fjpp2BDKhTizTu/RsxmIbnyWsJ5+bRpNwBNk53khhoByFNu5llbOeQN82GN8bekJ91Oa22OJJ08SNK1RNwAACAASURBVAr7uwl1dQ7jxYm0kEwYAVDxQEHSJ43bvVLlToxdEiQJMV71jiQNZU6SpNsJkTZyS+Ef9sGSm07Y1Pjsnzlo07RbJnHODX9DZ2cnHZZ8JrnstOVloGIHSOk4hSqXSqsXt13x+Pu1gDGSFNcafzJlFG8YwkjSaw//hBd/8p/DfYVivOuohVR84JGkvGkweTGp7c/T+fLh3tFMIcYSCZKEGK+GMpIk6XZCpKcMZ78FG7a/9hIpBS9OuYyVFQV0dnbSksxiSp45qhzfR9QWpNBSiI0UH5+seH13C42dYYrMAg7emLlWUuTkI0Qthw8S8LUP66WJNNC+37gdKEgCmHcNqqWK0PqdJFpDI9MvIU6BBElCjFfhDqNykCN74H2kup0QE0a0vY3DgQ4yrG4ml5WS77Lj8QboTlhJZaawpjSh5G4686Lk2QqxJOIszwmiteapD+t6F5RtjyeGlG4XDQUJ+LxEgsGRuDwxnvQESYXnDLzP3E+h0GRaPyR6SIqEiLFHgiQhxquwz6hsp9TA+8g6SUJMGLuffIyo1Yo17uTCQkgkEhzuNtKYfC4LMwJJnE4rjfkBMu15WKJRLCEfl84r4emP6sm2GH9LvLHEkNLtvJ56AKLBoKRLTRR1H0BsCKM+7QfAXQKZeQPvM2k+CTUFp2Uj0cMyr02MPRIkCTFehXyDp9qBEUBZMmQkSYgJYOdH75NtcdC2sJI5216mq6uLtpSbDAs0FGQxOxBhavZU6h0tKGXBnoSOjg5uuaACXzDGtn1eoGckKdcYSRok+PE2HOHdlZfx2seuIhGXvzFpr3ErPHolfPDgyfdt3z94qh2gEynC8fNxWqqJHvKgUxJoi7FFgiQhxqtwx+BFG3pY7RIkCZHm2nfvojUVJ15SBkBTKopv925aU27KC+1E7DbmxJMUZRZx2GqMAGXpDELhMOdNz2FmsYvnNnmAnjlJOaCTEBs4lc7rqae2/BzqSmcSlZS79LfxYeN27wuD76c1tO+DolmD7hZvCxNKrkGpBFmx14m3yLwkMbZIkCTEeDWUkSQwijdIdTsh0tqWpx4DFN68fKyxGF15eTQ8/Tu8OouMvCQA5yQjFH1YRG2sAYCcVCYA3d3d3HTeNHY2dJEVSxlrJTlyjBMPknLn8xwh4MohlOkiGpIgKa35W2Dnn4z3nKZt0Fk/8L7BNmMh4qI5vU3JfkYk9+1uI65ncDA1C5f1VaKHOs5Gz4U4bRIkCTFehX2DLyTbw+aQdZKESGPJRJz9B/ficuWTUjB9dzW2WIwj/m4mB7x0uiNkJTR5KoGKKYpaiwnZFUXaKPrS2dnJ+ZWFAGT7E0cLN8CgxRtaGz0EM91EHZkEAoGzfp1iFG3+FaQScMPPjcf7Xhl4397KdsZI0q89bSx6byd14WPfh3bvbCWF5lfJT5BhqSe5a/3Z6LkQp02CJCHGq3DHEEeS7DKSJEQa2/vaK0TR6OkzmNTSzMq9NVy0bh31ZWV8fv+btGdlML8rSTARBmBqaCoNWQEKrcWAESTNnZKNM8OCtSt+tAQ4GCMC/YhFwrQEQmiL8TGiPSipUmkrHoFNv4LZa2H2FcYI0WCLwPYp//22t5t/PtCAL57k+dajxRl8wRiRliBdGRaeS64ioVzYm/4o85LEmCJBkhDjUSwEicgQ5yRJ4QYh0kmbP8qtj37EG7tbAKh+8VkcCU0riqIGIw1qkq+DVRs/pMLiZVJ3gtLc12jsrMKlHdhQ7EwdoCBjMugULZ56MqwWFpfmEfdFzHQ7M0gaIN3O56nH78rpfdwSCp/dixajZ+efINQOq/7OeDz3k1D3nvFFXX/aD0BGFvtshXxtVy3zXE7mZdp5oe1okPRMVT1l2oJ7qpswTuqy15Kp3yVeO0ganxAjTIIkIcajcM9CskNIt5PCDUKklfysDLYe6eS1Xc34Gj00+9pwFU5Ba4092EbIDu9dkcXUpiaywiG+9NJzLOiAWCyXPJ3FsvgMvDEvXbYIlniClsOHAVg2PY9gR5S2cLxPul3/I0nehnoCfYKktoik9KYlrWHjQzBpPlRebLTNvdpIvTvwRv/HtO8nUXAON++oxWm1cK8b8nZtYbs/TF04Siql+d2H9VQoK/nlOeRlZfDXwmtQKk7qg6dG7tqEOAkJkoQYj0I9QdIQ0+1knSQh0obNauHi2cWs29dK1Z//iNKa0LQKinNzyQ5HqS+G99cuYdPsOUxpaeVjO6q4aEMlwUQSpzNEZmEtmdpOle0w9qSms9MYEVhWnk8qpfF5Q+ieRaoHGEnyeo4QdB9dA8cblZTetFT3HrTsgPPvOLom39Tl4J4Me1/q9xDdto/3bVNojsV5bGEllg4vlW2NALzY1sUHh70EvSEcGlK5Ia6sfJdNlnJilnnYap8etOy8ECNJgiQhxqOekSQpAS7EhLRm7iTaAzHWVe0mP5qiLZGgzGohP5DgSLFih283v5pxOdsWL8IRj2P78FdEiOPOsxGY/xRzbKU0WTvIJotQ0vhQunyaEfSkOmN029zGEw00kuQ5QmLy1KOPE4mze8FidGx8yPgybvFnj7ZZLDDnKjj4pjFfqQ8dC6K66tlom8oDc6dxbq6L7u5uciIhSqMhXmzt5KkP65jvMBY696be5vLS3xIMtxCfciO2eB269r2RvEIhBiRBkhDj0amOJEmQJERauXh2MRbggL2UjLJKALLq6siMa+qLFZFUkIZcG9WzZ7J77lyC2qg+V1hWhMsex3teDS7tIOVwkrRm0NHcRPDQTvKzHVi6Yni1HSy2AavbeRvqiRRMYpLdBoAvKd/+px1fjTFatOI2yMg8dtvcqyEWgJpjK9I9s2sTALOnL+HTJUY6eHe38W+ovLGWrf4Qrx5q5+pSY5vfuhkAa/IwatnfkNIuUht+eTavSoghkyBpmEQTSXY29P+NmxDDrmfC7JALN0gqjBDpJN9lp0x3UOecRvuUUioqKkjUGHOLGicZaVFu9z4+nLWMHUsW01xhrFmTvb0OgIh9GzMTFXgtQebGJvHkt+7iz/fdy6LcKJbOGN5E0lgrqZ90u3g0QldrCyF3LqUOO5mxCJ2pEbpwMXI++gVYrLDyKyduq7wI7Nmw72jK3evtXby1vwqAa+ev7G33+/3Y7XYqW431uWLFDlZkZ6GcVjpjRlDlstRhnzWZUPITWGpeOvpFoBCjSIKkYfLzdw5zzc82UO+TMqhiBJxK4QZZJ0mItNNeX8fUjv20ZJbQGIfFixdDh/F3IVqWRypWgMVdx57yWVi14uBsc2HPPz5HaK8Ld9ID1klkJm105DQz+/I25t10kAXxGlQkyUFvwCgD3s9Ikq+xAbSmy5HJN/b9lAf2/yddqJG8fHG2Rf2w9UmYfz3kTD1xu80Bsy4z1ktKpWiNxvm73XV8PNGIRmEpPKd31+7ubmbPnk1xKk5OIIhreg5Z/jiWQtAYWQ6l7iMEMyxEcq5DpWJQ/duRulIhBiRB0jDQWvOX6ga0hvUH2ka7O2IiCHVAhst4ozoZWSdJiLSz/c1XmRGqBaBJ5zNv3jzs4TBdWeCPZZMIzSDurAdSzLFMI2CLozWkEprut8vJ9QfxNP6GKUVvMfO8p8ko9eDIiVPk326c39NlVLjrZ06Sz3MEAK+ysaJpHWs6N9KtrCN16WI4rP8RPHcntO7pf3v1b41RxJ6y3/2Z8ykItEBDFdv8IYLJFFepVlT+dMhwApBKpfD7/eTn55NdXEplq4euTAvRthDJnKMB+IzcOlr9EayzlhHV89BVj0kBBzHqJEgaBnua/BxqCwKwfr8ESWIEhH1DS7UDWSdJiDQTj0bYve5N5rcewaVidGSWkgwGcEVjeIoV8UghyVAlWMJM0UdYHClHa/BrBx9MWcisvQ3UVU3jnGs+oHTREbzt06jbdSkA2n8Ih46wv6F7wHQ7b0M9CYeTZDxIYaCenGSQzFRgpF8GcSaqnoDq38D/twqevgnqNx3dlkrBhw9D2UooWzHwOWZdbsxb2/siHrO6YW7nYSia3btLIBBAa01OTg4HI27mtdfjSmjwx4lm1tPzMbTQ6aOlO4hjRh7B+JUo7wGjsp4Qo0iCpGHwwvZGrBbFFfNLeP+gl0RSkrPFWRbyDS3VDqRwgxBpJh6NMj2ngGybk1JLJ4dCGdRXbcYdiVMzCWK6CGeoDIDijgO4cZLSGXRrJ9HpGcS+FmDhmgNYnJmUb/o2B3dfRLPfqGxXWjSJCt1IffPA6XZezxEs02cyN1iDwvi2vyzRNHIvgDgzWkOwFZZ9ES6+B458AL+6DB79lFGx7sDr4DtslP0eTGYeVFwI+17GE4nhJIXNd/CYIMnv9wMQtzh4u1GRFw5wfsiohOi3bweMz0tWi8bbdQjHjFzCqY+jrdlQ9djZuHohhkyCpDOkteaFbY18/Jwirl9Wij+aoLq+8+QHCnEmTmkkSdZJEiKdZGbnMG/PYboXLGaGM0Q4rtn0bjVWrambpAgkssnDSdJaRNy3EwCtUkzK7GLx//qAyEwLvtfKWN+yhqyOeWRbHMTCdlIpK0WFuSwIN9DeHiJpH2AkyVMP5ZXMDxzqbauINaAlPWp8iPohEYGiOfCJ78Df74S1/2YERr/5DPzhZsieCvOvO/m55l4N7fuJt+5jmfahEhEomtW7uaey3esHayno7iDX7mB5rbFmUoPF+LfpbS4CIOzfiDXbjnVSAeHMtbD7LxD0DvPFCzF0EiSdoa31nXg6wly7ZCofn1mERUnKnRgBId/Qyn+DjCQJkWYSra3EdIo6t4vLFk3DbrPgqW0F4EixIhB1YXdB3DmPoP0AR4hgU0lmFNZhsSZofPUbzHnBy9s7F3GICDn2PNCaSMRFPMvLFclJkEjgSzpPmJOUiMfpbG4iWVLKguAhknY33Rn5zA3V0iELyo4PQfMzirvEuHW44YI74Rvb4LoHoWS+ETxZM05+rjlXAVB55E3OjRnV6yia07u5J0ha+6Pv8vBb91OyfTsVvhAJBYezcgD4TeNnALAlNhrdmZGLv3uN8b61TQo4iNEjQdIZemFbI3arhSsWlJCblcHS8jzeOdA+2t0S6S7ccWojSVK4QYi0kVFSQvy//5uE1qxctphVMwpJdYXQQEMR6EQ2qSIHKcdcwirAnx1G2e+8LB/xUB7+2DzaJ61kxZH9PGz3kY0LrFaiERfRnHbmupeyKNpKQ9hmjDqkjqaQdzR60DpFJL+IeYFD6EkL8GZNY27wME0B/yi9IuKUBFqMW3fxse02u5GCd/s6WH7z0M6VWwZTlrK8cR0LIvVGW590u+7ubpTWOCNRXrppCcv+7g5K4naassBvcZNM2qjYdRhfOA+nNkrYO2bmEo9NIzVpBWx96owuVYgzIUHSGUimNC9tb+KSOcXkOI1vXFbPLma7p5POkHxzL86SVAoinacwJ0kKNwiRbnbs2EFubi7l5eVcMquQkkAHXVlWYhmKfLuVjqI8ZuQtBmCXGSRl5TaDSuLMr+HAzKu5rvVdPoi5aQllgrIQibjRWR0opbhNuagJ2AANsaPBj9esbBfIymFB8DC2KYsIZlcyO1RLS1f/C8+KMSZgjDrimjQsp0vM+RSLunax0FtlZDi4Cnu3+f1+HJEwe6Ypnp5xkEkrFpKPi2BWFy6CBDpdTA952Ns6iwyLn1QqjqMyF4BY1segbQ+EZQqDGB0SJJ2Bj2p8tPqjXLPk6BoCF80qRmvYcFBGk8RZEukEnRp6up2skyREWgkEAhw6dIhFixZhsVhYlpekwt+MpyATgElWL+3uPC4oOYfcRAE2WztWaxyHu52E30XRoj+TyMgnaV3K7FgtTycySWmIRN1o/Ox3HGK5fSqtXnPtoz7FG7wN9ShlwRrvIDsZhJIFJPJmkpmKEW47MBovhzhVvel2wxMktcy4HAuaWfVvHzOKBNDZ1oY7EKS60kosFeXVmlfI0VnYnHsp13VE243S8cHWbGyWJM1t72N127GVZBEJTDdO0rRtWPopxKmSIOkMPL+tkcwMK5fOO/qHZklZLjlOm8xLEmdPuMO4PZV0O52CVPLs9UkIMWKcTief+9znWL58OQD2lsNMDbRzoMiNTjqwO4MkrVaW57qwBGeQbQnjyjK+jY8cWQARL1kFu6ibtpY7Qs/iQbErWUIklAVA/cIIiVSMZSFzHbY+85K8niPkTZ6M27fXaChZSEaxOQelde/IvADizARaQVkgq/Dk+w5BjXsmtU7zy+I+RRsAutu9ZIbC7CifwuKixfx1xxtYtCIzr4pc1U2s3cjCKW8IkkxZeGf7swA4Z+YRbjHP2VQ9LP0U4lRJkHSa4skUr+xs4vL5JWTZbb3tNquFC2cVsX5/u1T6EWdHyGfcDrlwgzn5VlLuhEgTYbKy3sNmMybK+zZuxILmYIkTu8om5ioFoEwrmoPTKYzmke0yRoO6Oi5hyishJq34IwmbE2v7Si6wBdiWKCUcdgGQN7uAPV0fUKnMvzF9Ktx5PfUUlE6jqMMMiCbNJ2fKAlIoMjsPjtD1izMSbIWsIrAMzwLAnmicV4suNB70GUnSWuOPRVHJCL6CIm6ccyN4jfmx0Wxj/lLY5wCVQV57lJZQMeHgNrrCcRwzcknGs9GuUhlJEqNGgqTTtOFgO52h+DGpdj1WzyqmuTvCwVZZXE+cBWEzSDqVkSSQIEmINKGUlYOH/oO2ttcA8G/bAUDdJE2mNZvurCm4Ugk2fNRAPDSDnHgublcHYCU3bxa2xsnYLK3kOLfQWHAJt6oNALSFjVLMuU4/6+3NBJJGup0OGyNJyUSczuZGCsrKKe86gC97Gjjc5OQVUeecQl73IcQ4EGgdtlQ7AE8kzvPFlxgPpi7tbQ/7/SSVIpXw8fOfbuOCDT5mJqYBkMg0piR4/FOJuUoIxjUOa5Ty7Hp+vX4rjhm5oCDhnAuNMpIkRocESafphW2NZDttrJ5ddMK2i2YbFWPekZQ7cTb0pNudymKyIGslCZEmrNYsst0L6OyqAkC1NJMCWgvDJBNZeLOLOEcl+EN1I8tjxWSkbGTltOBwFFNUlk3AMQXXRguTLvwjGvA2TucCHachPolk0gpdB9hXMYv9ncY6NrEDHgA6m5tIJZPYSqczN3CI7oK5ADiy3Bx0lFMSrB2FV0OcskAruIpPvt8QeSIxPEVL4BvboXJ1b3tblfHvM5XsxhFP4bvvR1xTU0Yko4sEFmJBG4ctlbQVFBOyJInFM7AoTdX+N/AmU2RMdhGLzwDfoRNK0QsxEiRIOg2ReJLXd7Vw5YLJOGwnDleX5mUys9jFeikFLs6G3nS7UwySZCRJiLSRm7eC7u5t+H0tuEJh4lYLOAL4/Tl0ZLqZ5Fc0hmJclbSirDGyXB24XHMoKnPT7ZhM3tspVIGPosgHdNiWMq/LQQg7kYibaOtODk2fy2F/DQCR7XXoRKq3sl2yIJ8ZYQ/RSQsAsGdlcjhjKpPDjRCPjNprIoYoOLwjSQ3RGGVOO+RPP6a9J0hqyonwb5+14vzG7bhDLizeZlIxiPgc1GeWc2BKCUmSdHuySWkLs/J28+BfD2Kflk3E31O8Yfuw9VeIoZIg6TSs29dGIJroN9Wux+rZxXx42EskLpPlxTAL+4xJt868oe0vQZIQaScvdwWpVJT9W18jOxIjYrOBJUY8losKJAi1JclXioJkjKysLiwWTTJzDoWlbgKuqVi7FbU+C4WLX8KWjJCf9KOTDqIRN8loI22Fkwn0zHsMdRH8sAmvpx6UwpbyYkFjnbwQAIvFisdagpUUtO8fxVdFnJTWEGgb5nQ7M0g6jnffPgCa8+NUz1SsX12AbfIMUt2N2DKihL1Oup3FBHONUS3VkMSiUqyYvJ+nPqwlkGEhGq40TibFG8QokCDpNLywrZFCl52PzRy4Mszq2cVEEyk21fpGsGdiQgj5jADJMsT/vr2FG2RBWSHSRcJvfLBsqXkLZyJJh9v4f55KZOOqa2Pb4U6u0hm06DBut/E+VBN3UVjmJuiaAkDbQSss8VHW+BYpawmTE4pI1IXdFSVLaYKzlxJPWYhlReh68wjddc3kTipBtRtFG1xTF/X2p8lqfuhu3T1SL4E4HbEAJMLDtkZSSmsaInHKHMcGSYmODro7OkBrAk5jMeJn9j2Djis6Vx9A2cDWAJ898BY3hI0v/Gr0TACcVh9TXC28We8jRS7aXSrzksSokCDpFAWiCd7a28InF03BZh345VtVWYjdapFS4GL4hX1DL9oAxjpJIGslCZEmulqb+eP3fkgykk1S7QGgc4pRTTXlyCPRCImU5rKkjaAlgtvtI5pSfNRei91pw142laTNjmO3IhYrILfoQ+zRDiqjTiIRNzZHgrxQG91zFxFLWWmgBZIpprZOo3BqORmtuwhYMymadE5vnwKWAqIqQ4Kksa5nIdlhGklqjyWIaU2pM+OY9uB77xPOzERZFd1ZoDTU+mvZkXWAUEEjAN3eIj69+20ufflxklYbjVnldOMG4JblzbxRZwT3qYKFMpIkRoUESaforT0tROKpQVPtADLtVlZW5rN+v8xLEsMs5Bt6+W+QdDsh0kxOcQmrv/S3dB2xYZ/cidftwFthjhRnGW/rKwtcEFckrVFcbh9+ncnm1iq01hSV5xLOnkpxaCVdhy4kdUkr0zx/JSueRzRifEgtSTWzxZZFNJVBKtBA7rUzyFeTmGlfQp53LwfdM8mwHp2T61aKA1nTSbbsGvHXQ5yCniBpmAo3eCLG+0r5cel2wQ0bCGfnEHMqvNmKC7onk21x80ree0SIoZPwaP5neG3+x1CHt5KtneR1d/BE8itYVBaLivcTyjCqK6ay54P34DGLGgsxEiRIOkUvbGtkco6TFdNPPml+9axi9rX4ae6SiaxiGIU7Tm0kSdLthEgrSimWrb2awpLrsWUm2ba8iHpdDhoyMjaglOZGl4umhMZiD+B2+3BkVtAebqe2u5bCMjddzlKiudfh7VhBdBo4bE0obSMUzgZgSvQIbSlFCCfZiU5acsPU+neS35rPVN8+PLnHLhqah2avqxLdumc0XhIxVMHhHUmqjxpBUt85SVprgu+9R7SwgIAlTJdbMSejlCvsF7MhewvhlJ9Il5MGewlbFizHVzib/FCYsuYjfGC7iJZ4LkH/JlYtMt7n4nZzseJmKd4gRpYESaegKxTnnf1tXL14ChaLOun+q81S4O8ekJQ7MYzCHUOvbAcykiREmqraVwLA5LxOJnkUl28qISe5h/+42cq5vgSdCY09rxGrNcn0SZ8AYFPzJorK3DRNPp+4PZ/AzDZ2eueh5rSgUESCxvyQUksnUauVsMWFS0V59qU3qPK+gS27k8ykn+7s2cf0Jd9qYa+rElt3g5RrHst60+1KhuV0nojx5VvfICm6fz+JtjZCdjt+3QxApbucKwPnk7AkUa4Q8a58UIrSSC7VC+/C0d1FRixCcaiJ52zXk0pFqJxSSxJNd8KYqzTUeUkdzUFC3fJ+J86cBEmn4K29LcSTmqtPkmrXY+7kbIqzHVIKXAyvU063M+ckyTpJQqSNSDDO9toAyg8l+V1sWJLDZJ+DazdMoeiInwafMQfR4TY+pM6cvJbizGI2t2wmMzuD7pxKJrVuwRbezTuej6Gmt4NOYY06iScyyLMFCdkzySuagt2apH7ffuI6Rv5Fxt+R+Q1T0Vr39iffZmGva4bxQEaTxq5Aq1EdNWvgwlOnoiESI9tqIafPcijBDRtIWK1EkklC2viSuCJvJmWt+SxMFOLKTBINVuJKwRR/MdZkDJ1RAkqxfM97fMDHSWorxY7tdKDp6s6EnNIhzUvSWvOXH1ez8TlZ2FicOQmSTsFf97VR5LazuDR3SPsrpbhoVhEbDrSRTOmTHyDEySSiEA9C1qmMJPWk20mQJES62FbVjDtUj+OQIj5Dc7A8ysbLEsRsmq2/fpJd3pdx0I4zqxOtFW73rP+fvfeOkiw/67s/N9S9lWPnNN3TPXFndmezNmi1CkZC6BVCwGsJg+EcGSzgxQS/IDDBhnMA+YABWzbGAl7LBh2BERIIxWXR5tm8s2FC90xP6txVXTncfH/vH7e6J+7uzE7YmdH9nDOnum5V3bpVU911v7/n+3wf7ui/gxdWXuC1xxZAkpg89mWic8tM13bjDSjoVo2Iq2KaSWJ+CV9RqDU7RBRBurVKorcfVZwAoHdlgNbepY3j6dUjTCe6cc1heMO1S7sYCCT53BmPb4XzxX+3nnwSb8cOAEypjiQEY71bEQ2Zd7nrSXY57rBURhef4p5nfh0zGyQl5o9VeAd7mWE7rrGXMgK7bsHgnguqJLVrNu2aRaN8kW0Odhs+/4NQnL64x4Xc0IQi6QLxfMETR0o8sLX3gqx267xray/VjsP+xdB+EHIZ2BgkGwY3hIR8J/MXTx1isr6ANivj9woKfhk7H+Gr9y2zY9u76FiHqXT+Joj/9gvIssYdA3dQMko8dmgviayO6nVILJTJx2NU1HcTtdZQXA3bTJCQiuD5rNZNoopL1q4RKQxgLe9nTh9gbaKf+tePYy+2AChoGgt6P3YkAauhSLpmaZUuW/w3nCuS/E4H44UX8W+7DQBD6VBowPFiCwQMm8HsyOfVVfbYKjnnIBG3gxXdBEIwvFTi4/wF0/4OFO84Va0KLQeG9gThDVbzDY+nNBeEO3TqF5nmuvQyHHkIjj92cY8LuaEJRdIF8vJ8jVrH4cFtF/fH5f6pHiQp7EsKuUwYXZF0UcEN6yIpDG4ICbkR6LQM9tYbjLWKaEeDRbuxaBMZmWQiQ0/0neiZT5DrGSGZqFA+Bl//zO9zt76bnNvLP23732R3KrQTg+QXW/SkdIrGBLq0huwnMa0khVgZuWLRUOLokoOEYNVWkIsHOJCcovI9m1ASESpfmMa3XAqJOLLvs5qeDO121zLtIiQvT7IdwIJlM3yaSOo8/zzCcXC3BsEehu6QNWJ8LCVJnQAAIABJREFU9aW9HJNX8bQqlqswG5/GU9pY0U08ffdvIrsuURfyVo2UkmHCCxaW3b6DREwvqCQhYPmNwxuKc012xP6RdPuli3shpW4FqbV6cY8LuaEJRdIF8thMEVmCB7b0XNTjCkmdXUOZMAo85PJgVIPLi6kkqesiKZyTFBJyI3ByucGdnsaIJSMvR8GVmYi2SHaivMPdw/yKQSKaIj9pEdEs8oU9HHn+ab74y7/EB5/ciaua/KX+n6knBhgsuuTjCotmCi26hhBZTDNBTLVIlcuIvvHgSVUZs1YhWj3KocRm+rMx8h/bjls2qH/9ONFEgpjZ4WRiAooHQIQW82uSVvGyhTY0XI+G6zOin5qR1HriSaRoFKunB4GgFRXE/aBF4WX1BHKmjmIOImSf2d4XOd7zHsxYD6prEpEzuBEQc33cnZim5SWg5wBxVyD6bwme4E36kprHj/Ng+k/YpXwR1/Yu/MWsHe7uIBRJIacIRdIF8shMiVvHcmTj2pvf+SzunSqwb76K7fpX4MhCvqPYsNuF6XYhIZcLSZIUSZL2SZL01e71CUmSnpUkaVaSpL+WJEnrbte712e7t4+fto9f6W6fkSTp/VfyeLeNFvh1JUfBltFzW4k0NzOu+7xz4WY++eoPUrR9RvrjOIllAO5474/zo3/033FS40Q6J/hg9D5mzAP87T1lkqZgQJQ42kwQyZRQfH1jVlJfZwlpNEixM+IZxqxZJOFzMDnJgB5B35whuqOAdayOHosTM9sciY8Hiznhivy1hxCBSLpMM5IWzXPjv9tPPkn8zjtpdjrYuoujSkSUHqKySlVuES2YRKpbGWhM0F9P4akxACRMRGyUth5B/3Yb21mk7GwhnTuIhI9HBlJDb9qXlF/9ErLkU4jMXVzCXVhJCjkPoUi6AEpNi9cW6zy49a39Ydk5mMbxBMfWWpf5yEK+4wjtdiEhV4KfBU73iP1H4A+FEFNAFfhEd/sngGp3+x9274ckSTuBjwE3AR8A/liSpMvTGX8eJE3hc0PHcJtLOPV5FpomIxGffYnjfNPugCwxMRmHaAMhIJXawZcOPESlN9BujZLNj2z7lzyz5RiP7pYYqB7jYC2G2lNC9nTMdZEklagmgjTXSFRmVA0cEbOpKXLdNDM1q+M1bfREkrjZ4WBsU3CQYXjDtYfdAte4fDOSzhokay8sYp84QfKd99NoNGjF2gDERI7dUi85zUSJ+BRXh9m89B56O3sYWHkWAF/2sNVBfFlGf2UZqePj6dvRI02s1BzVYifoS3qDSlK72mGL/C0AUkoJY6184S+m1K0khSIp5DRCkXQBPH446Cd69/a39odlx2AagJmVN244DAl5U95ScEOYbhcS8npIkjQCfA/wZ93rEvAe4Ivdu/wv4CPdn7+3e53u7e/t3v97gb8SQlhCiOPALHDXlTrmBduhfmwfshBIZp3XrDiyBHeXbgYVPvpLtxPVThDRDFwnS90xeenR45wYyGKrEWolk5+54/9hrLmZP/2AjNPaT93RUAZrSELBMgKRVIhVmKkEPU+DKeiNtrGIYGY2EbxskFMRhOkR0aLEjDYvR8eDgwzDG6491mckXabghkUrWHhb70lqP/lksPv7A5FU1YIQhaSTZNgcYEQJrldbaQbrNyH5HUZW/x7FNfBlFZQgltxQZOJPy6TSY8F+e15lZakZ9CWtHXnd8IbW818jpZSo9n8YAGfxwIW9ELMOzW5SYyiSQk4jFEkXwKOHS/QkdXZ2xc7FMtGTIKJIHFoORVLIJWJUQI2CFn/Tux5ebfLN/SunzUkKe5JCQs7DHwG/BKz7oQtATQjhdq8vAMPdn4eBeYDu7fXu/Te2n+cxl51B0+feYycAcBUd+cRHEL6EO/IshR9p0j+epukeIBptgjTGZ57/b2xeuZXWkKCS7SXVVDhYPsjH5H9DuiPxlcEXkZQ2ak8ExbORzCiuozOYqDDbPa++9babyEdNlu0M/dHoxrHMdYeJaiJK3GxzPJILTsLD8IZrj3Y3QOoyBTcsmDaaJNGnqcHun3oSdWgQbWKCZrNJTamh+BJZJ0bByxHVlxEC2laMXl9heO1hjuWaRL0qnpJEkgMb+VJPH4nHVfpjLebdCdo9+6kV20ElCQErr533eLSDf0nHy6C85xeDDasX+BlcOxJc9u8K3iP/InqZQm5orrhIut683mfjej6PHy7xrouM/j6diCIz1ZdieqVxmY8u5DuOTvWCqkiu5/NTn3+Jn/vrfXhS8AUW2u1CQs5EkqQPAUUhxItX8Tl/QpKkFyRJeqFUemupp1JSJVIv0UwM8cKdn2KgOkXFSOENP89gPqgSVHmNaLSDK49w4Nl5dC9OJelRyfaQaxg8vvgsk6Oj/OtvDdOKuESHvwDkiDplVFfFMpMMRcsslIPvvUxUok9vU+1oZN0glEEIwWdfnANA9VRiRpuWJOP37QzCG0KuLdarJJcpuGHBtBmKRpAlCeE4tJ9+huR99+P7Pq1Wi4ZcI2UlyJsOEhJkSljtPFprEzXZZmrmUY73CyKihhvJokR9JDQWUznUEqSff5J98q0YmVnatXI34Y7z9yU1lsjWHue4/AGSUzfhCB2leoEzj9b7kSYeAOFDOwzaCgm4GpWk68rrfTavLNSoGw4Pbru0lZcdAymmw0pSyKViVC+oH+mvnp9nttjCdHwWaybIkdBuFxJyLvcBH5Yk6QTwVwQ2u/8MZCVpfXWBEWCx+/MiMArQvT0DlE/ffp7HnIEQ4rNCiDuEEHf09r617xXb6ED2Dl687RdxI1G+PPptpoXFuObT211EqUdOAPBircjNq+8iPqgxr6WIxCUyrTr/OH+CwU15CsY4n3gI1MRR/q5joMtlZC+KacTp08vU/aBqnbFXSCgWFTNGZPooAC+crDLTCoZ2SqYgYQc/mz3bgqGcfhhWdE1xme12C6bNiB5Y7Yz9+/FbLRL33Uer1UIIgSE1SfgZhuVNWH4bOdfCrY+jeFEEr6J4DmLrZgyxjB3J4kRqoPZiST4io6F+Y5ZZsQtkH5+XINUPqcHz9yW99BdI+FSGvh9ZUaiJTeitIxf2QkozgeNi5M7gemi5C+lyRUXS9ej1PptHZ0rd6O9LE0nbBlKsNExqnfBENeQSMCpvmmzXNB3+6OHD9KYCm91sqRmEN4QiKSTkDIQQvyKEGBFCjBMsxn1bCPEvgEeAH+je7UeBv+/+/JXudbq3f1sIIbrbP9Z1REwAW4DnrtRxO1aEuYEPkW4e52brDyllT7DsJ9BlSIoaZruEo3YAOLjskG8Nkb3JxJMU7hpMAbBWUdH7Be3EIO992UUp3czDVhElVgKRwrATJGMVWnJgrUusBSemJTOBMh1Uib700iJlgqqSXbfJiEAU1fLbgoCA6vEr9RaEvBXaJUCCeOGy7G7RdDb6kYx9wecjfsftNBoNfHws1Qz6kcQgT5W+SCTVwqwO04qZjDUDy9zkXe+jIS+BJNNR6shyHh0T6QN7iLzmkKhmwImhxLvCaHDPuZUk38N/8X8xZ91CanIHAC11grh57MJeSGkGClOQ7jpk18VkyHc8V7qSdNW83pfDwnA+Hp0pcdtYjkw88uZ3fgO2d/uZpsPwhpBLofPmIulPHjvKWsvmP/1gMFfiyGormJUUiqSQkAvlU8AvSJI0S/A99Ofd7X8OFLrbfwH4ZQAhxAHg/wAHgW8CPy2EuGKNDYmoz237/pBbX/4MekKmEGmz6gVhC8I4TH31JSTJx7LiTNUeRNVlTC0IUtj9jqCpPWXkOWC8TCMTLAAOLQXVBStdRBJxLDOJrNrkMw4ddOSVYIjnjDJKbPko9Y7N115dwlSD04hmxSAjBS+5mA0GiYZ9SdcYrWIgkBT1ze/7Jti+z6rtMBINzo0aL73A7IcKfOXP/wvf+L3fQiy8gJAgY6c5XtpLK3EcSQKrPsLLBZ1MrYGr62TGt9DQu+dsvoMiJZFwaT/4bgA+8PSjONWdKLnXEELA0B7E2mGOHPwNFhe/EDxu9mHk5iIHOu+ndzRYBDBik0RFBdoXkHC3NgO9206l/rVWLvn9CbkxuGIi6Wp7vS+HheFsik0ziP6+RKsdBHY7gOnlsC8p5BIwKm9ot1uqGfzZE8f5yJ4hHtjaS09SZ7bYCitJISFvghDiUSHEh7o/HxNC3CWEmBJC/KAQwupuN7vXp7q3Hzvt8b8thJgUQmwTQnzjSh6rpMhMfuLDSAjsZB+q1KAjdGqeSr3+ErXqS6iqQ7OVY6h4E9vuGmC2XeV9qxbDX2sTVVNkW/DIyb1UhxIAfGwt6Muo5MpnxIBvjdVpiDiS71BVcswMbGfQWOJPH5mhYbr8wF2jVPExqia5bt/uYnoqONBQJF1btEuXrR9pyXIQBDOShBAcMA8R/+AypvoSaiJJfHASgKFmAnnXK6RG7gagvzPGUeGTbRlU0mlEW6KcCESSbkXQu5+hot2HebNgz1NPUW3uRopWObbwTU46zyMhqM98jqPH/gAhfHjhf+JECpyw7qRnLDjXctLBfC9Kb/IZdAyonuyKpO57E9rtQrpcyUrSVfd6X24ePxw07z247dL9u70pnVw8wsxqWEkKeYsIEfQkvUFww+9/awYB/L/v3wbAlr4kRzZEUhjcEBJyIyDHYkijgTvh1cQQDaVBrp1kjSz1+guUm88RiZi02z3gSuza0cAwJ/itV238sslY4Sby1TW+vbrEv4r/OmrM473Fg6R8nxOFMop/SiTtkCo0RNCXtJSY4MTmrajC48mnX6Q3pfORW4epIHAaFjklOKUookFuPAxvuNZorV7WZDuAEV3DWVykngsMQrHdOtt2fZBIKgPAlsMLuPoo0ewavqsz7vdSb7exVTi4excvfu1Fksn7wG+gOTokAvvm6pEyzu1RYo0WzfIEAC+++mssikD0TOjvxHEqtFeegCPfYk77IKm+FHosOL30CoHtzn+zKPq1I4AIRJIWBz0NzVAkhQRcMZF0vXq9T+fRmSK9KZ2bht5a9PfpSJLE9oF0GAMe8taxGuC7r1tJem2hzpf2LfKJ+ycYyQUnNVN9SY4WWwglEkaAh4TcQHSOByd/X5H6MRSLnZVxDGUY216j5e1HksBujDI4mSGzuJcfOTrOaz0KH783SjKWo7e6ykpsB6+kJ2glBplrjjHpOpzUq8iejmUFImlMlDDkoO9kIT3F/MgEQpKIFo/x4ZsHGcxEqSAQLYd8JLBelW0X+naGlaRrjVbxsoY2QFBJMvbtQ+QDq2VSfo3+aZtp/QSapzFUadBcuQUtdwLX6OGb8Zf55/rLPHXfvRT7+lhW47y0+W6amRlkP46VtgCFxvGTqHcG1aB4yUBrDZPQctz57qchOUC2HZy+us/9NxA+r1TfQ2+3igSg9Qxj+gm8xf1v/EJKM8FlT7CwSLI/rCSFbPB2zEm6pr3e67iezxNH1njX1t6NoXmXyvbBFDMrTXxfXJb9hXyHYVSDy/NUkoQQ/PbXD1JIaPzUg5Mb27f0J2laLq4UptuFhNxIWCeP4kcFx0QWgFGzn2QlONFTpKANuLM2wu67+ik+O4Uly3xpW5UjqQhHhm4iVy/jaDv5u/HbKUZ3k6zVGTNh0ffQ3Da+HcG1NOLRNXwtsOTNpqeQJQkrO8youcCesSzOioHpCxTDJZGIE7VMyk5XJK0dCRdnriXapVN9N5fIQnc+1lA0grFvH2rGRvggyx7Nwj6O64uknCSaGqNeL6BnF6i04jiqTKMq8eAjj/DTH/q/ODK4mVfGtrGWByNu0tQ8FCmFV16hd+qDGGNxBpZnSaztRpPnkeUIDO1BKR4mHh0nPv043vi7Wa5kzxBJ8UyUijv25rOS1mZAUqDQ/d5M9ofBDSEbXBWRdD15vdd5eT6I/n73ZbDarbNjII3heMxVOpdtnyHfQXQqweV5ghsePlTkmWMVfu59W0hFT4WMTPUGq8GWr4Z2u5CQGwThC1Tjnchb7sAiWDP8u9wjfKb1VexuTJLjaKjuAKkXFvG8KD97e4yqE/wNmUnHSckpUobCUWeRZmIA1XOINnIYQkKT66iugt2OEUmsIdRAiL3EEAnLYC3WT59V4tirS3z1v7xC1pWJWj5aLEHMbFNxXOjbAcI7Nagz5O3FaoHTgcTls9v1ayq6LNN5+WWiaQu5tgnFzDI78AQNrUW+FeXo4MeREkUiEYudtTvoH3uQwrFl+qs1Ku4A1WgPAKafoJFZxEYl5oCwKgwN/Ev6v+uHGJ6bIbG2G1lyqVafDRLu1g4z1upFMwzKg98PcJZI0qi4o8iVmcCq/nqUpiE/AWp36HqyLwxuCNng7agkXRc8OlNCkSXu39Jz2fa5bT28IUy4C3krGF2RdJbdzvF8fvfrh5jsTfDxu8bOuG2qPxBJHV8OK0khITcIXsNGlvtITf5rPucN8kDjdk7oi/xA+buIycHfh1Yrz02xFO6axSv9DzOTVljSgxPkg2mZvugY+VqJaXmQpXxgp6MUREOLaAXZj2KbCdRkGUQeQ2i83CwQt00WpCQyAv+xfXiuj++DKiCmJ4kZLdYsB/pvCvZZfJOekJCrQ7tbHbmMg2SHoxp+u03zyBFiKZ+kOcTh4h4iuROYisme+ffi9XgMbv8nABKtcVZVmKwvoU1O8cxXT1JOBVVK35aQ/QggE7NM8BvMHyqRfeCdSEaFWG0LnqdRrjwGQ3tA+Ay8+hyWJjHbCb731pPtYF0kbUJx6tB8A9FTOgy9209dTw2ElaSQDUKR9Do8erjIbWNZMrFLi/4+na39KSQJplfChLuQt0Dn/Ha7Lzw3x7G1Nv/ugztQlTN/pXuTOumoSttVQpEUEnKDUFGqNB7+DZ6o/SmS5PEri5/gj4//Gj9sfQThBANd2608uY5KbuRhnhoqkHTbLEQH0CSJ6YxCITrGWHWRVmwX+8eCk8hEMagYtZNFJJHAsNNE4mvsMz7Mr2qfpFqRSVgmqpYFKYJrn2T8jl4cL1ipj6pJ4kabNdsO5s7IkVAkXSusn/hfpuCGRcsO+pFee41mVEFJmChGgUeWJqkLj4/Kvdz+zq8z8f7fpDB0iHp5O7HaJCu+y5b6IkZuE0stC0MLvrOWCzFS9a0gCaojweL07PMzxPfswcBG9jXqla2Uy48HlSRAaZVZ6o+xfKJCuidKNHHqfC2R1im73UXD1/sMeg5UjkLP1lPbkn1gt4LKW8h3PKFIOg/Fpsn+xcZlSbU7nZimMFFIMB2GN4S8Fc5TSWqYDn/08BHu2VzgPdvP/bxKksSW/hRNRwpFUkjIDUK8Y1AqCMo9RT6ReZTfGfkzPMkj0oD+gz8GQLNZYOiBCRLVzzKd3MywGTSjf6gvi6FImH2TbO80caI7ON5zHCNa4F4lQQafpdwqsh+l7aRQFBe7oeCO7sKr+aRNk7vaIyiREZreSUoa2F03U1SJEzM7lG0PlEiwQr/8ytv0LoWcwbpIugzBDb4QLJoOI3oQ2tDuVZBkQV2p8l1bn2I4Inhg+CR4Hs7+MZ57+p9z8PBH+UdVYvXkPEm7w4l6Fn93IMoVz2a+N49m51GdJNWBAdxkhvlDx5A0jfmdO/BcE2NtF4Zxko5qdStiEvWp3VSXpDOsdgBKRKatBal4rxsgUjkWhCGdXklKDnTfrzC8ISQUSeflsZkgs/9yzEc6m20DqTAGPOStsd6TFM1ubPrCs3NU2ja/+j07XjdgZKo3ScMORVJIyI1CvHeIW778pxRv/XmiMYt9+cP89MTv4HygiNbpp/nij1BdG6dvYgHhGkxH+khGAkvdjwwFlrrZfIpNTQchJyhlNVqJIXLFJUaQWEiUurOSghNPLVYmHlGQPMHUdJyIG2H3u99Bxq1xYGkJsxtGpPo6caNNzfODwZ+jd8H88+Bf8aylkDdjw2536SKpZLvYQjASjdDZtw9rKqj8iPGnGMyWWDl6P9MP/SqF3/dpLu3C8VT2duC3nDbOTJAm99/zBb6gGABkG0usFgaQXIOo0Y/i2JiD49Qri3QaNtU77wKjRnIlCCYpV56AHR+GWz6GXng/VjNNYSR6znEq6T4sOff6s5JKwWwwes+qJEFouQsBQpF0Xh49XKIvpbNz8NKjv89m+0CaE+U2Hdu97PsOucExqhDNnDEt/cnZNbb1p9g1nHndh23pT9L2ZFwnFEkhITcCsqxTKNzPiXoOTW+hKRpxLU6n72nm7voDDpd7SCezyLPfYiU6SE1N4aYGGdIj3J1JkJJlDqYVCg0NfJ9GcguNZB/e/EnG5STHI5UzBspqiTWOzR8GoGppWD3Hufm99wKwPH8Qq1tJ8myFmNnGBRquB2P3gN2E1XBe0ttOqwRIEL/0PuvFbvz3sBbBeOVVGAgW7oSQKB/8Leov/ShP938bfXSNhhwsNrsiyhf3jLOlvoSPxKZN24j2RFFsn1a1hhOJYis1om4SvDZClmn3mMwdLCPuvhdh1hlqZLDdHirlx+F7fh++708Qxp0AxPIL5xxnPK1RZ/z1K0ml4DN9pt1ufaBsGN4QEoqkc3A9nycOly5r9PfpbB9MIQQcXg39riEXiVE5I9nOcj2eP1HhnsnCGz5ssi+JjYpjGVf6CENCQq4iq3UTSW0hSzJ98T5qtecR9i4c1aTQ3wMz32Q6FUQbr0lRdqdiyJLEzek4B7IyeamPHX4DJ7qL6VEVyfcY62Soqy0UT8Gyuk31mTXuOXk7WU9iIV+kqSyQ7O1HTaRIG/PYAjwhcA2JuBGkt5YdD8beERzo3DNvy/sTchqtVYgXzlhke6vMW4FI6i8X8et14ikZBJjtMWoHNWZ7nscY2Ie5x8cgsIf3ijg9IxkmaotUE318+ifvI9OfYHdKR2oGkYyVhI3sRnG1JD0njuLGVZ559ml6JjbRwqIgySxVNlOpPo3vB9HyRnkYACn+/DnHGc9olJ0xKE6D75/7QkrTkBmDbsQ9EAQ3QFhJCgFCkXQOB5cbNEyXd5+nv+NysH094W45DG8IuUg6lTNCG/bN1TAdn/um3nhlcEtXJLlOOK8kJORGYqVh4kl1fOEzHk9jmgt0SlvwVYus7kNzkUN6cBK5ZDnsTgZDpvek48ymFDKJMd7j+zj6FI/fVMFO9DB03AFJoAoT31exTR03V0HzImjJCEtulHi8Sr3RYOLmPQyaCwghaCLwDIiZbYAgBjw7CukRmHv6bXuPQrpcgRlJhYP7qUc19JSDAIzlrRC1eHz8iyQ7McxbfDqeii9gk4hzwrKZrC9ij04SzygcabW57cTfIzWCBelyCoSvIUkaPctLqM06Jyv7yTYNFlNRopE40/PD+L5BrfYCAGsLBnqySct8/JzjTKR1VjvD4LShPnfuC1mbgd5tZ26L5YO5SW+UiBfyHUMoks7i5pEsT/3ye65IPxLAaC5OXFPCGPCQi8eonBHasPdoGVmCuybOHS57OkOZGEKKINzQbhcScqNgNsrUDQtbNLE9myk9WCkvHRvDwyFrzgMwnb+ZnKoggJtTMQD2pOK4ksRiLsueWhMkleN9UZb67mLwhSUAfK2C5EvYnRhOvspj2xZZ2Byj48RxEgaVSoWJm28l7nWwRYW2D8IQxLsiqex0LeVjdweVpDeaVRNy5WkVL+uMpLQqo7z0InOjI5AtI0nQWd1KvTCDHTGJrWTwk2DqddpCY1tE55knj9Jn1BjdEeXYn38fFjJ7nAUGLBPNKrOSjwISihdFUVX04hwCn/1ffpSZvjySomEf0pGkCOVKIIpKc02yQ4JW6xC2XT7jOOMZjTV7NLhytuXO787wOlskyXJ3VlJYSQoJRdJ5Gc7GiGuXXpI+H7IssW0gFcaAh1w8Z1WS9s6usXs486Yx9bIsEY/FwuCGkJAbhU6FyB/fzq/pf4LAp+N0GJSbKEqaei2wDuVKgcVtuud2CpHg++zm1KlKEsDBtMLAsoksXFrJzRwZ2UlyTSKr+NSTq8iejm3ESWkl9m6PomYDoVOWo1SrVcZ23QKA5Z3EFALZ9ImZXbvdet/t2D3QXIL6/NV5b0LOT7t4WSpJrttk0bQZ1jWa+15GFKZwY2sIAZ3SFEVmAcjMJsCRcFSDhogylU/Qc+ybAAxV/w+H4uMA7PjAr7JTFNGsRZYKQW+t4sZw9TiybVGI5VhtzFKOB31P26sd4vouyuXHsQyXetFgYCLoI6pWz6xYJjI6Vfd1RFJtDlzzXJEEQV9SmG4XQiiSrg4n98Jjv7dxdftAiumVZpD+ExJyoRjVjUpS23J5eb7GvW9itVsnkUgg+86VPLqQkJCrhRJhdfMPcJcWWI4ebLdI+6to0s20k3PEozG2NPfi6RkOiziyBL2aSn938W9Ej9ATUdmfAbUsMaF1sKO7ODKwihjdw6AiWEmudQfKJulhDU8bIRYz6InbzJt9VKurZPr6Id2Db8/hCIiYHjm6QmqjkhT2JV0TtEqXPEjWdZs8+dS9HG+VGFYkTtZK9MRH8dUOdq0Xvd7hzhdfBWCs6GAf70XyBR0RZbL0Kb7P/AIA+g/9DtP3/jsUCbb0DLIzWgWnxEJfL74Eih+jnQ5aE3aO9yBkj/zqSQBuatdw25totw+zfDwQZCNTkyhKkkp17xnHG89o2CKBGx86VyStrYc2vJ5ICu121wWuBY/87hWbaxWKpKvBc5+FR34bunan7QNpah2HYjPsEQm5QDwHrMZGJem5ExVcX3Dvm4Q2rJNKxFGFQ9MMhVJIyHWPnuL5rT/PJ8WPA/DDnSaqV2b16ACu1uBdQw4aLidu+ySmL2h6PruTsY0wIkmS2NMNb0haKe5Px/C0EU72HmF16E6GXJ8FrYLi6Rh2GhWXnNRBFVVu3xTnSG0z7XYw/yg+NI7iruEKiDmChK6h+d4pkdS3E/R02Jf0dmK1gr6cS7TbmeYSntdhyRIMNGqc7MlQyCdBgLW0hYRV5LbpEnuOSmyu1inP3YSMRExyGNBeZLqxjWrEXX/8AAAgAElEQVQyh/rAj3OoY7E5phNVZLb36gi7ihOJUI87qF6MWj6L4oPXqLBpZDP1yAIOLoO+z+KBIM5+ZekRAPo2Zcnl3kG1cm4lCcBMTJ0rks6K/267Ht+/b5YDLQNS/aHd7nrh4f8Aj336iv19CUXS1WD5FUAE5V1OhTccCsMbQi4UoxZcdtPtnj5aRlNk7tj0xv1I62SScSK4HC21r9QRhoSEXEVWGybFblLZ3/anEULi8EmNiKdzx/LnAIlDu34suK/lcEvXarfOnlScuXgEP5LkfQQnk0f74xyRtzNsSVRiFWRfp+UEozB6WcWxlrlvy2ZqVpayFXyfpXv7Uf0WjusjA6l4nqRjnRJJshLMSworSW8fl2lGkmWt0iFOmyjRk9OYiQxS3zzI0KjeQmfHICt5lU9+3aPHbLFa2QVALr6K5cdwKh5rg8GA14Mtk+2JGIeeeISFl46jtIM+7VrCQXZitJNporZDZXGe973/3fiyy7SyhJmdpPXMMro+QMt4kmROJ57WyOfuxTDnMIxTts54JhBT7cjmIKTBO230SulwUDHqfqcebJs8VWvxD8VasL1dCud7Xesc/hY888ccMCZZ8IauyFOEIulKYzaCqc4A1RNAUEkCwvCGkAvH6A6S7drt9h5d49axLDFNuaCH51IJFElwZKV2pY4wJCTkKrLasND0YNEjklIoFrdjoLDJAsUowcBupu3gK94HdndDG9bZk44jJInptMLUSRPJa9JK7KCitRh2MzT1MrKnY1nBot4vHlvAc4rsHAnE1oKt4HkevUODSPg4nglAMpYnYZunepIARt8BxYOBZTjk6tMqBZeJSxVJRdboWrybL9Onj2Daj4MPnbVtVEWNz363Tqbtk+g41PXgs5PLz6F6Jr31NYxNU7RcjznTZmcyyuFnn6LV6DBQDBaNqwkfxYsiVJ2MYbE4fZDZf/oGSt8Ar6onWRu8g+ziKj2Ffwax5+kZ97rPEcztOt1yF9EVVE2mLo0HPbnr52IQVJJO60eaMwJnz0uNdiCShA/ttUt6v0KuII0l+LufxEpP8o8nB1AuQ7T9+QhF0pVm5bVTP1ePA5CJRxjMRMMY8JALp9MVSbEctY7NgaUG905e+FDATCoYCnliNTxJCQm5EVhpmCTiHTRFY0qDuZN7UO0UUzuC4Zrc8zMcahv0dEMbdp+nkgTwSsqFEwa6dRAnuotSaokeaQRPsZA9H9MMgiBG/CJZs40aLZHSHE52BimXjzGET7pjYXrBol9cTxMzO6cqSXCqL2n+uSv4joS8LhuVpEuz21n2KmsE+8hlTzKlFLDjy/i1BL6doOmVODBq89BdSSRA81bwBQwPnGDVTCAjkCY2MdMOBPX2RJTF6YMATFY75OpFShkZ2YuCkOi3BLsfeC8vfeMrxI8ewJBsivEaUuZ2VOMeJNkjM/4EAIn4FJrWR6Xy1MbxSpJEPKNTdrrhDaWu5U6IoCfptH6kue6A3JebHfz1ilsY3nBt4nvwpZ8Ax2B+58/jCRk9kbwiTxWKpCvNcuDbRlI2KklwKrwhJOSCOK2S9MyxMkLAfVMX1o8EoKiBnebkalhJCgm5EVitm2haC12OQGkrpqmSaE7Qk64Hdxi+jemWSVSWyEcURvQzUzB7NJURPcLLKRep6NPnHMBXsnzzrm202EpBFThyByEUMNM4sTW2tSIstBbYPaBxpDrJ8oG/R/30p9m9UMJwg+fV5BSxTouKc5pVafh2kNXQcvd2sd5fc4nBDZa1SlXZBMBo4STx6cewNws65ghC8rAyLYQkeP5WGV+S6THnMESEocQyq07ggkhmDQ51RdJgq4bRDBaLh1s2+foCy7kYUjcGvFzo4aZb7uX7f+U3UZs1cm6UE/oJFofu59m/OUp7dTue/g2E8JAkiXzuXqrVpxHi1ODYREZjzRgCpFN9Sc3loMe391yR1HB9liJdG3sokq5NnvgDOPEEfPD3aIgg9TCaDEXS9cnyK5AcgJ6tUDm+sXn7YJqjpRa2e54p0CEhZ7NRScqz92iZuKZw80j2wh+vBCdI8+VQJIWE3AisNEwktUnSl5mf30V/IYvmZOlRjoKkYKRHOW5Y2EKwOxnfCG04nT3pOEdyUVRH5S7XJl/6DLVEgX+/6UNkVJ1JPagiWW0NN7bGlnaS+cY890yNsdbO4/7+FxGNJrrjYrrB3yiFKNF248xKkhaHwT2hSHq7aBUBCeIX7j44H5a1SpURFN9l9CEHKzMPGrRKNyF0QWZzEOE97Ji0kiMYiSi9tTK5po/XENS1OP3tvRxqGcQVGY4G4QkDk1vItD2SzQWW8xk8KYgBXxkcYHHmAON7budDv/NH9Pu9GLKNIX2NSrGf2uyDuP4y5fJjAOTy9+A4FVrtwxvHHE/rNBoy5CcCyydAaSa4PMNuZ5NTA/v6KyLdfd9CkXTNMfcMPPq7sOsHYM+/wGwFxYZoWEm6Tll5FQZvCX5Bz6okOZ7g2NqViS0MucE4rZL01Owad03k0dSL+PVVggbWYrWJ6YTNqCEh1zNCCIoNC09qMFkdx3U1NmXuRI3IpK0DkBvniOXhE0Rxn92PtM6eVJxSIk4tInFLZyuK8Rz55U/joGLZH+UOaQRFKNgNFSNe4hZjkPnmPA/sGOaHZh4mfrSOlnHRXA/HrWAJgeJH0Zt1Op6P4Z22CDj2Dlh8MYjsDbm6tItBP+sl9m0YrQpbv7iTj3/7cVIPeRTvCMREff5Osn1ZloxgEPGWtk0tOUYtnWV4bQXxRIzIokNzUGOw8hSHalW2J6IsTR8gnsmy813vRbZ9YsYyrqpSSckoIk49k6F4tBspnsuxOLSDrJ/AzCewmp+nuCCjaX0sLP4lAPlc0JdUPc1yl8hodOoW9O44VUnaEEnbN+43Z9q8K58iocg863atqaFIurYwqvC3/wqyo/ChPwRJwmw30eMJZOXC+rMvllAkXUnsTtAcOHgL5MYDkdSdjbQe3jATWu5CLgSjCnKEVVPlaKl9wdHfG3TtdhFcjoUJdyEh1zW1joPt+UiORW9lM9n+ZZxKnPxQArkyC4UppruWJk+cG9qwzvpQ2ZeTNlvqIwDI7hF+6dmj/PKJ2+hINpKnYTbTiOga21sx5ppzTMxP8/GZh5kdHyI32UYGVHuNlhDIfoSoGfyNObMv6R7wLFh6+cq9MSHnp1W85NAGgGbVpJN4mY888rd4kQj6TVl8X0Z0eugfSrJirRATMcZaHid6RpEUiYhnoT2soC4pZLeUmbtVcLDVYWcixuL0AYa372R4204AUs3AFljJyMSVLK6m0VpZYKG5wE/+009Q1i1ucyfw9ShSIoffeJze3u+jXH4cw5gjGh0iHp84I7whntGwTQ+vsB3KRwORvjYD0exGJLrjCxZNm/GYzp5UnGc7fhBb3wxF0jWDEPCVnwmskt///0E0OIc2W60rZrWDUCRdWYoHg4SUwVsgNxHMKWgHKTObexNEFIlDy6FICrkAOhWI5dh7rAxwUaENwIbdLoLLkWL4mQsJuZ7JJTSe+7X72VLZjIREz1aHtYUWhaFEcCLYs4VDLQO167A7O/57nVtScSQELyQ6DJXzIAAJNguVyXaK/519CMnTqdW68bqFEtGDC6x+6lOUU1n+x+7vxR8aAEC1K3SEQPUixI1AJFVOF0mjdweX4bykq0+7dMmhDUJ41KsSk8dfoFCpkLjjo7j5OVwjsH0PTaRZ89fISHmybThZCBbyOmMpJFcge4LHtPexEh+gJunkGy/QKBUZ2X4TPaObiOg6uWoN2feppiUSahDNXUXio1/5KPvX9iMlI4z7fdiuisikkX2PpcNJJElmcTEYVJvL3Uet9hy+H/QYrc9KspJTIDxYOxJUknq3Q9eCumTZ+MBYVOPWdJyDLRM/2R9Wkq4lXvhzOPQP8N7fgJHbNzZb7dYVC22AUCRdWZa7K2aDNweVJNiw3EUUmcneJNMrYcJdyAVgVCCeZ+9smUwsws7B9MU9vmu30yWXo8XQ4hkScr2zXDrBeGsctfcIyfRNmC2HQsEF14DCJNNtk7SikFZlNkW18+4jpSpsUmUOZmTips6Hcx9g1Bpgwuyl3vsij+eeRfF0qvYAvq9h5Gf45NcTuLUqR+8eZr8yycJIcDKs200sARFXJbZeSTo9BjzZC4WpsC/p7aC1esmhDbZdwagl2fXafop9O/EnR0BxsapjAPRvTlFX6qTlOFoTKolAnGjDA3xu53fjSTKzgx8gnv75YIfTfw1A7+QgsqIwuGU72bbKUGmVtRRgBtbAdnaY23pv5csf/jL5nh5kJJb9UVqZDMQSTD/0LD2F97G0/Dd4nkU+dy+e16HRCGx68XTw2W9pk8HzFg91RdLWjdc2ZwSCaiwWiCRHCNqxnlAkXSsYVfjWr8Lke+GenznzplaTaDJ1xZ46FElXkuVXgkFlmdGgJwnOCG/YMZhmOqwkhVwInSoilmPv0TL3bC4gy+c2Yb8hXZE0lolwJBRJISHXPXsf24sruaSH9hM1bgWgkOjOdena7QSw63VCG9a5NZPgWD6DAH65/+f4t0s/jCPb1Ma/QTJmIfkRrIhKpdZPJzdDVh+jcIfD1v4gBOYlPziZjbourm+jewpxowOcZbeDoC9p/lnww8Ciq0qrdFF2u2Z5Dde2z9hm2au4xRiK79MevBMjH/T1tFduwpRdOpEalmKRUUBqKFiaF8R/9+X40tSDfPy7/z2JTZuY7b0PgLETAjnic3T5JymWvsXQth2k2yqblhdYyaiYTQ+ERDuZ5j+mfozB5CCJbBSAmDJIxDCR0j0YxTKKeQeOU6VY/Dq53N2AtBEFHu9WkhpiOEhYPPEEdNbO6UeCoJJ0W9eCWtQKoUi6VijNgGvC3Z8E+UzZEtjtQpF0fbLcDW2QJMiOAdI54Q0rDZNax37dXYSEAGBUMNQMizXjoqK/N+iKpImcymwokkJCrmtqtRrFuSIz2RkMxUauBZanghIswtUykyxbDg3Pe91+pHVuz2doxRMsx1yaD82xzdzMV/v+BkkoDCjgyC5IEvq0i51aQBrtoXdsmb57fxgFj8dbgY0uCG8wiAmZhGkAZ9ntIOhLMipQPnKZ35GQ18VuB1b/C7TbCd/nLz71b/j25/7HmbuxikiV4JQxneilnZ/GMRQ6a1O0VZ9Dq0EoQla1sI0eZMXER2VPZAEJn6aWYDgX45Dl0++3cI+WGd66g3himP37f5b+yU1ISOSrq6ymdRwJJC+GkGXmvvxlAHK54LM8EYsRadm4ehwpGeXgt2aJxzezsPh5IpEsyeR26o19QBDcANBpCShMIaa/Grygs2YkKRIM6RqDusaAFuGEkj0VnR7y9rI+BDi/+ZybzFaTWNiTdB3i2kFP0uAtwXVVh/TwxkBZgG0DgfoN5yWFvCmdCst2sMJ1z8X2I8EZlaTja20cL1zJDQm5Xslms4x81wCz6VlWHQmvpBDPaMTahyGSYFoK7LieeP1+pHVu7d7+XLqN33E5mT7Cl1J7ke0c/apHWw2qQtkDTrDP7TbV2TilBeiR2rzW2YEvS+iOh+MGCzAZV0ZGUD47SXPsnuAy7Eu6eqyf6F9gJalZXsNoNjj0xKOYrVMLaqa1QqQeLOj2ZLIY2SN4lobdHMRLNHhlPpgJ2ac3WZQnScgm8ahOpnWM7XrQSzuSjTHdMtmqKZTNKGP9GTZv/gWEcEgOBKFW6WYRX5YppxRkKZhXNPfqfj7zdy+w0rZwJeiPKNgighGPY/dHmdv/KtnYB2g09tFsHiCV2kWzeQAhBNFEBFmWaNdtnOwkUic4ljPjvy2GdQ2169C4LR0PfofsFljhouLbTuU4SHK32HAKIQRmqxn2JF2XlKbBs0+JJDiVcNdlR7evZHo57EsKeQOEAKPKsbZGf1pnsjdx8fvYEEkKri84We5c5oMMCQm5mhS9IyC7rLg6jRWbnuEklGehMMmhzqmY7d3JN64k3ZSMoQif5/UWSlZnaedBqqqM58cZ1A2akeD7yahlwYvQzFX5SvIDPHTsGANykwoJDD2K5nobA2VjSoKM8M/sSYJgJTjeE/YlXU26YVEX2pNUXpwHwLUtDjz2TxvbbatIvNnGVRTk3gaoFp5VAKGg5U5yuHIYWciMyVVmUhOkJYuhoSGoz3FXJlgI7s9EOdwxGZECi+aIe4hU8qZg//5RKMRJtAIRU8woDKWDUJCF0WGOff5v+E9fOcSaLuG2bCpuBCQJpWSg6jonn3aR5SgLi58nldqJ41SwrBUkWSKe0WjXTI4cC0ZpOL6CEztVWZszbcZO69u7NR1nWgpmPoWWu2uAyrGgbUU9s7fSMQ2E74d2u+uS5WBVhYGzRNJpPUl9KZ1cPMLMalhJCnkDnA54FodqCvdO9rxhf8Hr0k23G04FX06zYcJdSMh1zUrzKClF0JGGqCy3KWyIpCkOtQw0CeKyzOa4/ob7iSoy48JhOqWS+altjI1OAdCQfQaiNnW9CsCXHvwQDWMMI3+YRv9Othw5wojSRiDRjCaI+j6GW+3uM0HKd8/tSZKkoC8prCRdPdZP8i/QblfpiqT88Civ/OPXEN3+MctaJd5qYcRTG/1IdmsUgHjPfpasE/SpvfSbBifz/aiSz9YtE1Bf5H0jHtGIjJ7RsHxBbnURRZYYWPs2M02fv1I+SaNxgPj4EMlmHcXzqORVxgcmiEQiLA8Pc3+2yv/8wVsoaxI9skJNBBXQaEdm0+49TD/xNIXc+1lZ+Qqx2DgAzeaB4PjSGsuH93LkZNBHV7ZiHHz8kY3XfNKwGYudKZKKWuHM9y/k7aNy7HWsdkGVL4wAvx5ZeRW05Jn/sflxaK0E85MASZLYPpDmxZNVRHd+UkjIOXSC1a9FO37x85HW6c5JGkgEv/JHVkMLQUjI9UyxvUJclhnwt+K7gsKgDrU56NnCTNtEk2V2p2IoF7CosisaYaV3mLX5ObaMBI31ZalDjyroKGXwBX3VJV5lN1ZyAauvyAOpNJtrc0j4VPUUUc/DdIPgiLiWJuna5/YkQWC5q56A5srlfDuuexzb4qVvfAXfu8zDvi/SbldemCOWSnP39/3fVJeXmNsfpMSZVpFku4kTy2AUZjCrGnZzGF/ySGdP0ohU2ZToJ1r1KSeDgIX+hATC452TWfb/h/ezKgWCK3pkP/2bRlGFyX89dIh/8P8Zs40VCpObUQQMllao5FRaZZtt27ahShKvDQ9RfvKrNGIyQ6rCqtq1lGoRbMPAc13qs4P4vkFz7QCyE98QSarWpnT862hjtwHQ0fp54atfwvc92p7HmuMypqo88vlpGmsGt6TiFLV89/0LRdLbzuuIJKMVLPa+LZUkSZJue6N/V+yIbhSWX4GBm89M4sh1E+5qJzc2feTWIQ6vtvjWgfAXMeR1MAKRVBNJ7nmrIqlrt4vKHsPZGLOlUCSFhFyveJ5J1WoiSTKD1jgAhWQNhI/IT3Gw1cH0/TcNbVjnzp4clh5j//IK+d6b6PE85r065mqM2w7UkR2TqN3hqejNIAn6IlWy//ZnyTdr9Ml1SloGzXEx7OB7LKZnSdrmuZUkOK0vKbTcnc7xfS/wyOc+y+L0gcu743W7XeLCelnLiwvkh0fZevd9xFJpXn7oawAstzvEO21EIksne5hOKYpdH8HU28Q0jYbkMJHJkFn0MPVg0TcvdweXZ8dQFZnplokCSAf2MXLzXVg9O3jYDgTVAVNheEsQyz1Y+v/Ze/MwOa7y3v9zqnrfe3r2XdtotyRbtmUbvIF39j0Jly0JIeFygbAlN+QSbm64uUmAH1z4sQcCIYaAWYyNAQNe8SLJsvZtpNHsS890T+/d1V1V5/5xerRYI81ImrEl05/nqWfsmu6q0zWlrvM97/t+33HGA4J0vEhzczOmlKw5dJj9w8OkNRO/YTPhjKCbJvmgztC+PXSsu4w9DzxLMHAZw0f+g/a9Hyab249tW8SP3IOUguve87fQuAbvpteQGh/jyNYnGao62zWOV9j/2Ci92ycIOnSCkWpvsJp5wwtLIQml1BlNGwA8L1BN0qer2xeBp4GvAl+r/vcXF21ELwZsC8b3nFqPBCdE0kl1Sa+/vJ2lDX4+/atDWHYtmlRjFqqRJFeonvbo2Yuwz0g13Q6rzPLGQC2SVKPGJYxtG+TwkrMkkXwTmiaICrX4NhpeRtaSmBLWB+b3fbGlRUUZdqQyoGn0WC5SB0v03tuFQKJZbqTTxVCpFWwnLb48k41uYmGBH5Mpdwhn2cK20xSkxOMM4jOKp9ckgeob6PDWRNJzmB4bBSAVX+AIWy4O3roTz4CzIKUkOTJErL0Dh8vFuptv5ej2p8kmpuifdOIxDPQuB1IvUc44KaXbMDSTRFE1fm3QcxTiMdy6AQjCZlVghFXB/YF8kQ4dtLJB2+q1PL7u3RQ0JZL65FLqGxyYmkVseoKEVyOZKdEQU/fmshUruGbbdrIYBMsSszGOXs5jejxYQoDTSzGbQR/dRNk7ijQNKn0lnrn/p+SSx3D6bsYfa4K/eJKm1/wNkeYWtv70Hgaq9XveYZXhMzmoJt7LYi1UhI6sRTxfWGZKVGYRSUZezWP6dmwjM7U4YvaMIklKeZOU8iZgDLhcSrlZSnkFsAkYWZTRvFhIHFF1JC2Xnbp/pqHsSXVJDl3jQ7espDee48fP1i7rojD4FHzlBmWFegliZdXq7JLOrvM/SDWSNCOSjk7maqK8Ro1LFKH7yVQM0pbEk44Qafahp48AsNPZcvx1l80zkrQq4MNpmewrS9LxcVY800Jjr4/o8jSTV7lAepFOF958Ea3cgyc0ykBqgKx3OT5hMuUOo0mJ07Ip2BZuLYC3mGPatDCf+z2jO6F9c60u6TkM7lXN50cOLHAk6RwayRYzaUq5LLE2VWu04eW3I5Hs+vXPyY6pelbZrUSFWY5glSII28GRafV8CRWe5mBpHSFRwu0JomWG1YHD7QAcyJVoLaRBCFp7VnNPaCPCLuIuD9DHMlzmGFlfiWAmgRSCqaCOz6EMFIqbNtF59CjVTD5E48+ZCBbQNB99kSb6d+2gvrMb+6kOtIqPdOfjhHa/nMe/920al2xEc66mmFEOjZqms/kVr2Oir5dj+6rphMfUhHtGJG0MB5h01pFPj533pa+xAJzV/lv9zbb/7Edk4s+zSDqJlVLKPTP/I6XcC6xelNG8WJgxbXhuJMlXB+7QKZEkgDvWNbOuLcRnHzyMYS5wPnINOPYYjO2EqUuzN0d88BC2FKxcte78DzIjkswyKxoDGKbNyHRxYQZYo0aN55WCWWBlnbIwFklP1bShF/yNbC9KNMCtCVb4PPM6nkMTdJVy9Opuvv3R96HnHDyycZKu6xN0+J2YaEiHk/pUgpK2nkpghJHvTxLPrcYnKiQ9qj7EbVqU7DIuzYsnryab0+ZsKXdbVN2uUTOQmWF6VC2SpiYWeFKen5y3aUNieBBQpg0A4cZmlmy8gj2/+QXEVSTKapvGKoZwe7oB8Bk+hosFApaHDb23ccS1mpAwaKqvV6UFgSZwesibFgOlMtHxIRo6u3H6/PwyY+Iq7kIYhzkmllHMHyYbg2BOZU/EwzqVvIbX6yWpa7jXrMYzfBRDlDFdcaa9eWxngNC1y9DtCqa/mQa7C3dlCZWmUXyFbrpCq9n8qncihKCQOeH6uPaGl+ELR0j/+n7CFkwP5XD7HGSmSpTyFS4P+Zh01ZFPjS7UX6LG+ZDsA8SJIMNJzNQkAURb2xbl9PMRSbuFEF8XQtxY3b4G7F6U0bxYGNsFDs8pzcoA5ewT7TqlVxKApgk+etsqRlJF7n568Hkc6O8JmWqELnVpXtvU8EHGibKlp/X8D/KcSBLAkcnaBKVGjUuRkCvEBy//IC7Tg5kRxNr8kDgKseVsTxfw6xprA97jfV/mwyrdZjzaRLR7Gde/9WqOtRYwKz66QpKcQy3etU/H6bNXgZCEzRGu9f4IryiTcqvvFJdpUbZLeIQXd05Zh89el7QFpA29D8LkIRjaBr2/hj0/hG3fgCf+r2rG/nuClJJ8SjkDZpOJhT14Lj5/04YRFfmZEUkAG2+9i0I6Q3iwgBSSSuMo+XE/QlPHdEkHWUee9yRfR3D0jawOqEhSS1sjpIaO97Y5mC8B4Dmyj7ZVa9mWypKXLtbKIXxGHyW8HEyPU+wIEMkk0CyLyZBOZqpIU1MTExMTRN/4Rlz9B+l3j4KQZF15pO6kvcdPJtyOZ9TCoTnJpzSK9gip8iSbmm8g2qzqsfLVPk8ADpeLy+94FY7efVzTP4a0JWtfqp6xU8M5Vvm9TLpj2DXjhheWZB+EWsF5+oJPKZdFaBourw9fOLIop5+PSHonsA94f3XbX91X40yM7YKmtaA7Tv9ddMlpkSSAl66oZ8vSOr7w0BHyxiwPlRrnT6a6EnSJiiQxfYyEq51Y4OxWvmdlFpFUq0uqUePSZaIwQV1BTepm7L+N2Ap2ZvIYtpyzP9JzefOGdZhOF853f5h1K29El5K86aDBV2TcPw62RVM2w/3TTQjLgaNrK1f67iUgTKbdyl3KVbEom3k8uHFm1KR/1rqk9qtUc8gfvhO+eBV84+Xw3dfDPX8M9/8l/Orj8JWXwjduhd3/CaZx+jFeRCRHhvCJELe1vQvyC5xNkp+EwPxEUnJkCKfHSzB2wuShe+Pl+OtCNEymqTSCdBqk+qFSioFLPUNiMshNU9ciRIbLnG50IWloqFfP3LASXAeqIqlufJj21Wv5ev9+kCZ/vu5lLLGVMNybt2FJAw7LIjqdIFHnIBVXIikejxO86y7C2UmOeNSzPO9UKfRTyUle9oY3ssq7jJSdZ+RoHLQyA3U/x1n04xxUgr2QPvU+2nDLnZhONz27HkVzCNbdoNICJwezODWB5W/EXZg8n6teY6GYPjZrqh2odDtN04i2tJ1fa5R5MKdIklKWpJSflVK+trp9VkpZWpTRvE15ITgAACAASURBVBiQUq2APTfVboZoN0wPQLX3wAxCCD56+yqmcmW++btjs7+3xvlxCUeSpvNl6ssjJ0w/zpfjxg0VIj4X9QE3R+I1kVSjxqXKZHGS2IxIitmQn2RPdCMVoCwllwXPzeTl5S0NrPR7+MrwFK6GVXRVTFK2RDinmQyOoJfKeIBjQT96dgmO0CBapIsmr0aqKpK8QqdsZnAJB76UmpgmK7NM+j0heMt/wF2fhtd/A/7oh/DHD8J7t8KHDsGHDsNtn4L8FPzoT+Ezq+HXf6eenS9CBvfupsnbScTVQJ1oXrgDl/NQzoF/nul2I0PE2tpPmXBqmk7nlm7C+RLFLrVQV5p2Y2RjVHzTZJx53pG4i4qjgsvxdyQ0ZYBQF41Cevh4JOlArohH2oSzKVpXruGhaYNA5Qi3d72U1U1L0KVBH0sIWyl8hkFDcoJ4SCM1nqc+2kilbJIqV+hat4IjnmE0dHIO9QxLJVNcc8U1NHo62Z87jJFVDddjN2cwokMYT46icWokCcDt97N3zZW443uJtUqCdR4CUffxuiRPqJmwMU1ltpTRGs8PyT6om33+Y+RzSCmJtlxAls0czCmShBDXCSEeFEIcFkL0zWyLNqJLnel+MNLK/ns26paAZUD29LzjyzujvHx1E195tI9UoTzLm2ucF5ewSHpifz/1IkOsY+XcLz4bQqhokqVW0nqaAuwaTtX6c9WocYkSL8RpNrpw+xwELPXdts237Pjvrwz7z+l4Qgj+rKOB/fkSjxkueiwYlwYVkSSm12OXi0iXg7LDiW0uQfNPUGpbT09zHVmXF0toeE9qKBsuqmfYrOl2ACvvgCv/BNa/AVbcAh1XQcNKCDZDsAmueS/81+3wX36sbMN/9zn43Ab49zfAw/8Iu38AIzuglD6Pq3dx0b/zGYJO1Zcn6mqilF+gBawZ++p5GjckR4ZOSbWbwVrbhLdUpNyusmNKaRdGuomcu0BdIM3a4jJGA9vYJz38VihxHEwVwK5A5EQkqSWfJtrUzLNWjrwIc33EiT1Y4JXHtuA2+uljGaHJXbhsg/rpcVJenb6+FFu/laZh4qV8/+O7+O3US+k89Fre9fT/YcPwrQCYiQLFPQmEECTTT/O44wYAvP4QE8u+i50usyLoPC2SNG1a7Fh5NSCwSs8A0NAZZGpIiaRItA0dm96p4XO46DUWjFJGRULPEEkqZNLYlkW0ZXHqkWB+6XbfAD4DvAS48qStxmyMV/OozxZJgtPqkmb4yG0ryRkmX3rk6MKP7feRcgGK6qF9KYqkA/uVCUjTkrUXfjDdBZZy97lzfQuHJ3LsGExd+HFr1KjxvBMvxGkodhBrCyCS6nmxXUTxaRoNLgcrfOeenvv6pigNLgdfGp5khTPCGAZSGKxo7CCrT4EQXHV0kqKjB4TkWDjKa155Fy5pk3X78NgWpYoquo+YOnCGdLv5ommw7GZ4y3fhA3vg+g8rg4qH/xF+9CfwtZvgHzvhn5fDN26Dn3/0+HfcpYK0bUYO7SdQFUl17hYmjs3/+f/4dJZ/7DuD2cNMj6R5pNsZhTy5ZGJWkTSo6XgKBcwmSbHgQXNFkbaTrA430Mx+bx9W7gBPWRs4JPLoUsN+LIWUQKQLKSUHckUiY4O0rVrLF3t3APCXTVeQ+PZ+lh2qJ5Ifpl8sxePzYWkl6qeVwGt7XTdXv7qbXOAYDesEm27vYE/Lo5jOAt3T6zBlES3lprAjjqPDT0kr4Z2axpR+JFCsO4jWLVimQyl1qkgaLJaJFf1orlVM9D1JMZuhoTPI9ESBcsmkJaYm30cnXpwRzIue6TPbfwMUUtPYDhf92QJTU1OLMoT5iKS0lPIBKWVcSpmY2RZlNC8GxnaB5oDGNbP/fpZeSSezsjnIaza28a3f9TORqWU1XjAzETtPRImkSyhyYtuSiYGDAOixC0y3A5VyZ6nV3ddsaiPgdvDdp2pf/jVqXIrE83GC2XpirX5IHEEKja0FsJFcGwmcV46+W9N4V1s9DyWzeCObyFjqGJev72DSMwpSsnIsTly0ImwHCZnFHYsRK+XJuP24KhVKlpqsuEItBDVx5kjSuRJuh5s/Du/fBX8zDn/xFLz53+Hln1RRKbsCW78Cxx5dmPM9TySGBzEKeYIO1Wso4mpk4sj8nVj/bSTB5wYmqMzW0mEmkjSPdLtk1bQhNotIGkuCp1TEbqhQSerIoEpv2lRuwSedfKnx+2jFGE9bKwhhENA9VMahZG+GSCfxssm0aVE3MUTzypU8ndNpMuM03DeNXVD3R1suQRk3hfqVFD0nRJK1OszmO5bi7cwjG+LErrd5suunOHx5/OUIRb1ApBzBjBcIbG5h/U23EPQUmCo1YZoqymhuHsAJhOOFUz7XYKlMV7yCy38lVqXMzl/eT0NHECQkhnM01KkapZFELZL0gnAW+29Q7na228PhwWHy+cVp8TIfkfSQEOKfhRDXCCEun9kWZTQvBsZ2QcPqWZ04APVFL/RTeiU9lw++vAfLlnz+N5emZfVFRbr65dZ5DZSzJ6JKlwB7RtLEjOr4L7QmCaqRJCWSAm4Hr93Uxn17xkjma6mdNWpcauSSBrrpJNauTBsGG65g0rQp2ZLrIuffgf7tbfV4NcHjkdtIV0VSk8dF1mugGUWcdpLRSYknvZQyAwghaLALpDxBnGWToqkmK85QK45CjoHUIqTDOT3QuBpWvxJe8gF41f+Fd9yvmtQe/uXCn28RGdy3B4Eg4Iww7rKVO9ux+ZsF7M0VkMCYMcv3eH4m3W7uSFJiZAiAWPvpIil0QCKkDXUGRspFLNJGWLe41qzj14EdpESZCWMVfTQTFiWiYT8OX4m0+Q5ksI1dWSVO6pMT7PHnKDk7+d8DfirDOUK3qP5/bXn1mrjtJR2waJqaQDNNDiXUtZhxuNuf2A9ACyEEGnmniVfzIHXwra9n/Lpb+I/X/imDspFicRC3q4msdweZoIsWw8TKnbhOg0WD7rhJc88yll5+Jc/+4mdEmpXJ0eRQFhFUaYrT0zUb8BeEGZF0hvlPuVhEOlS9dTAYXJQhzEckXQ1sBj4FfLq6/cuijOZSR0oY3XnmVDtQq/mRjjNGkgA6Yz7+4KpOvr9tiIHEpdkA9aJhxtmuc4v6eQml3D18aJIubQLbV68KnS8U3Q3miQfEW7d0UTZtfrB96MKPXaNGjecN0zbRppV73UyPpO1NLzn+++ui5y+S6pwO3tRcxwNaF3kzBkC9DnmPiV7MU3HlqQz5sdIdSMcIppmlSS+TdIVwGhXKdhFbSkKpDKFSgYPTmQv7sPPF6YWlN8DhX1xSGQND+3YTCTSjCZ0HWtUEXU7NL/qWMy2OVWu/Ro1Z0gxzVbE1r0jSELrDQbjxdOOIpqECdhiEy8JIubCtOjYGBGkk32m4nw4jxKC1lIzmx6sZNDXXE+rYiSm7Se3N8b/7xohUSiwt5fhWdoRXjlS47IhN8MZ2AtepqNRKGcWFQVy0k/ILYrkidakpdvXtgoEnaGpqIplMsm9qH248LDfVM9FGp6JZpNotfpUv8LExVU/U7+qmXJ7C7+8hm9tHfkkETUoyvz0xBxhOFWlKWXSujHLlq15PMZvh2Z9/D2/IpcwbZmq5suNkaz0sn3+Sfepv4D79+8ysVLAtE+FRBjUvmEiSUt40y3bzoozmUic7BoUpaDmDacMM0e4z1iTN8L6bl+PUNb5cq026MGZMGy5FkXQ4zlpPAu0MoeZz5qR0O1CpnVd11/EfWwexZ0vVqFGjxkVJvpJnldwIQF2LDxJH2R5agy6gyeVgqfcC2gUAf9bRSAVB3nMbALqRwhEOoBdzoNmEpiW5XAyETSr9DI0+nSm3EklS2hRlBZ/tpE2axHXn82cQs+JW1cB06vDzc74LRNo2wwf2UldNX3u80YlhG3iK82sCvC93oiH4SGmWSFJuArx1J9xNz0JieJBoSxuarp+yv2IWaJhMYzarv2HZuYLGYjMR4eYbIkXCHaetYvP9kIlflBFCUt/aild/Eqd7mM/tH+ZAvsSdW39F05J2mG7hr/aXcC+PELqlG83jQPM5WCrbCVsjjOhrSQUgWijRlBhjn7sN+5t30rTvqwDsmdhDh3MJEaGmr5rlAQEPuKZ5975+1gd8vMHlY9DRDYBd9pLPH8XZ6GSwLMk/PY6ZVKUMub4sAmhbGaV99To23fFKdjxwL07HPiYHc+D0UnGFaKgkj0fDajyPJM9s/21UzU10XwCPx4PTOfc9fj7MJ5KEEOIuIcRHhRD/Y2ZblNFc6ozNYdowwxl6JZ1MY8jD5u4o+0afp1W4FyuZEfWQaKi6w10iIimZL7NzKEW3Fj+j/eU5c1K63Qx/tKWTgUSBx44sTtFjjRoXM0IIjxBiqxBilxBinxDik9X93xJCHBNC7KxuG6v7hRDi80KII0KI3Senngsh3i6E6K1ub1/McYfdYV4WuJNQvQdXZRIqBbY529AQvCQavOCeIUt9bm6r8zMavpG07aaUHyPW2IpeUBMT05mhlF4Ots701FM0hbwkPUF020a3BUWrhEf302ZXqOgOBrLPU0ZEjxJ1l0rK3eRgP6VclqBDNcIc8GvE5TRBovN6/56TRNJpkSTLhJFnINgyr2MlR4ZnNW3onxolmJ46LpIuu/3D9Gheko5pnnD3IYXk/spG4rrEu6TaG7KhCZEeZHzZMb7epnOHpdG2ZysjgQr/cKCNikej7i0rEbq6T/WohxazgXKxl0F9JYWgg2i+RPfwEbKuAM/e+gWaCoeRSI6mDrJMb0DPjiDsEu6Kiijdm7Hp8Xu4e8NSbm2MMoqqJ+rfuRuw0dyTHCqpaFDmQVWLKwbzWA5BU7c6xo1v+xOWXn4l8b77mBzYi1mx0IKNNBkJdmRqIul5J9l3lh5JKmIoPN5FiyLB/CzAvwy8GXgfIIA3Al2LNqJLmbFdgICmdWd/XbQbCgllb3gWumI+BhK1f5gXRGYUQm3KuMEdumRE0mO9kzhlhaAxccYviXNGd57m/HT7umbqAy6+82TNwKHG7yUGcLOUcgOwEbhdCFENO/MRKeXG6razuu8OYEV1ezfwJQAhRB3wCVR6+lXAJ4QQ85vpnieJkdzxJrJ5zcs+6aMiL6we6WTe09VCyRHgt/JG0vlB2uo6MWUJYVlMh3OUUx1400tJTj5JU33weK8kj+bFsIp4HH7aqy0Hnp14nhZhwu3QtP6SEUlD+/YA4LH9lKiQcgoGHAVCznqkac/xbtibLRJzOog4dIafG0l6+FMwthOu+29zHscsl0nHJ2YVSSNHEohCklKbhlV20/wsOAU8HX2WslvV+Obpwn15hBvGlIj+23iedC7JX7VdTVAKPvB4CpfmYWViE00lSfmNK9ADruPncETcREpB7NJhTJyUw26CxTJLBg4jbItfNN1M5AOPU3LnMITNusFdFJ/6Iv5CnEBJNb4NZLN8b8MyIk4HmxqDTBZjmNKJbavURVMepSRBrqqjsDOOMZolNmpgt3nRHdWolKZz1/s/Srixg3L2ZxzZuh892EKHleLZmkh6fikXVHbWGeqRUvEJAKTT+cKKJOBaKeXbgGkp5SeBa4CeRRvRpczYLqhfMWv+5CnUnd3hboauOj/pYqXWM+lCyIxAqFX1CYp0XjIi6eFDk6zzpRDIhTFtAHC4j/dJmsHt0HnT5g5+e3CCkVTxDG+sUePFiVTMNKVxVrez5Ya9Gvh29X1PAREhRAtwG/CglDIppZwGHgRuX6xxm2WL1ERBmTZM9fJsaBUStSp/IfVIJ3N12M+K0ggPaa8gY4zQGewk76ngMnKYzgzZggPv9Epy5f00N/uZrj73vGgYVhaPHqCzor5T9iXn32rgkcOTvPHLT7B35DwNH3puhcEnLwmTnqH9ewg1NuGxfUxqJSjbHPVW0IVO9ujEnO/flyuyPuCl3eM6NZJ0+Ffw2Kfh8rfBhrfMeZzpsRGktGc1baj0l9ALacotAiPbgHY4RX/Z5ruZJRRcKaTUiKyt4+2TT9GQy4OEPSbctf4zPKvF+Js6P3VFuKX7nWzMt/KltS7WrTq1RkqPevDmHDhLqgwhE3SjAf6STcv4ML8YHUVzerAa1T2+2XE1spjAnxunLt+CjUWgXIKyihS1hL1o0xXGRAutazswSzojx34GQL4rhPA4mLi3j4a0hX/pqfW+Lo+Xu97/30G4+c2//hMVV5jWyjQ7MvlaX8GFIDs+v95mM/PjM2TSJKtGI5bQXnCRNDNzKgghWoEKML/47e8bY7vO3ET2ZObolTRDZ0wVpNWiSRdAZlSJJLhkRJJtSx45PMkdrdW/+4JFklyz9hD5w6s7kcD3tl7816ZGjYVGCKELIXYCcZTQebr6q3+optR9VggxU+TTBpzsdDJc3Xem/bOd791CiO1CiO2Tk/N3MTsZ25Jc+YoldK2NQeIo2yKbAGh1O+n0uOZ49/wQQvDnZi8JrZXtdjsdoQ7yXgtXMYWvUiDrN9CSywGbaHPiRCQJG8NM4dH9xEp5nKZJb37uBZh0scJHfrCLt//rVrb1T/NY73lGn3puB2nB0d+e3/ufJ2zbYvjAHlp7VhNwRBhxmWhTJQ4GlRBI7u4/6/vLts3BfIm1QS+tbueJmqTUEPz43Sqidsc/zWssx53tZokkBcbAUcxgN9rY+UYEMF7JMWDUIWJJNEcD9x74Kx50/AREkohp8aGY5Ii/mw7NxPfruxksHiQgQ9zfoiE2t6A9Jx3UEXUjLKgrl/BRouiOUHQJwhUXK4/uodd00JsrUggU0KRGYFcSNCe+YhyPGaakFQiIEk8eVd1pdE1Qb8AI7VT0KTzOZdhO1U6jULQI3dSB1p+h3iFo7Ymc9pmbl7YRaHg95WKeI/v7iJYmmSibs5tj1Dg3/u1V8LP3z/26Oey/p8dGkIBRMQkEFmZhaDbmI5LuE0JEgH8GdgD9wN2LNqJLlXwCMsNz1yPBSSKp/6wv646pjukDyZpIOi8qRZXWGK7OVWZE0kW+GrR7JE0yX2ZLtJqOuWA1Sc7TapIA2qM+bl7ZyN1bhyjPI8WjRo0XE1JKS0q5EWgHrhJCrAP+GliFapxeB3xsAc/3VSnlZinl5oaGuV3HZsPldXDlXUtoXhqGxBG2xjajAy+Jnl9/pDPxxqiDoDXJL7VbaA+0U/CYkFffS4nmHJWpHrB1nIFjTHvURMVtVSiZaXShIwsGsUqRgfLZncF+e3CCWz/7CPfsGObPb1xG0O1gPH2eke22K8AXu+hT7iYH+jHyeepbO/A7wgz5BFrOZMDjwrAKlAbOvtp+OF+iIiXrA17aPC5GjIpyL/3BO1Q90pv+TTn+zYPE8BBCaERbTtX1dtmiOSFxW1lE0IS8yiDN5x+BjQFc+jjXVXI4fC6mXNPocoqIQ+OxrIHLLuPo7+PYM1spXu3gq61b+dRaH3c2np6FqkeVUcVS2UGTmCKvN5MKQJth09N/AIBf9D7LpDZJz7if4lNPIVbdhs9Q0cKio0KEAr87qbZ2maYzShumMUJL90vwN+WQ0mBo/1EC17RSdAjWeHV6loVPG48QguZly6nrfD2TySJOs4i/kuex6ey8ruepF7H2TD1OfgqmDqkFDHsOt8DjImn2+U8mPoHUHdhSsssUjM5mXLIAzMfd7u+llCkp5T2oWqRVUsq/XZTRXMqMbFc/5yOSPGFlJjCHSOqsU5GkwZoN+PkxY/8dOkkkXQK9kh4+FEcIWOGIqzoqX2xhDjyLccMMb93SxVTO4Ff7xxfmXDVqXGJIKVPAQ8DtUsqxakqdAXwTVWcEMAKcvNzeXt13pv2Ljp04wjbfcizgusjCpp04G5ZzRf4xDok1TNsh8h4LWShjI0h4MqTKTjyZbrK5HUihYSNwVioULfXMsnMmrdiMCcesx08XKnzoP3fxrm9tJ+J18ZP3XsfHbl9FS8TD+Pk2U9d0WH4L9D4490TsBWRonzJ6CrlVTc2xoJNQ7H4ywk3SGEebI0Nxb9W0YV01kpQ2Lcq/+riai7z6CxBbNu+xJEeGCDc24XCdGoUsD2aRpTSysXods+r+MipxXvHUPWhmnLWZcR4RZTbXX07e62VPVw9PVrz8/aHP8+rHfko81swDywN8e4kDl0vjmoj/tPM76pRIWutYidMcJaN3MO2XNBbyhPJpYslJfjExxVB5iFt3SHA4CKy9joBbiUALjYBm8FjviRTFnrCPsXIrAhu3qx4hbBwug8G9h3nix3ezp2IR1QWNvbMLn4bOIIVcKx1X3wrAm7f/lF9MnUMKqJGD/3w7/EMTfPUm+PlHYc8P1bzvIl+oXTSGq/PkUlrVy52NZJ+aJ3tnL+3MJhPHeyT9KGvQXzRmfd2FMh/jBl0I8SohxH8D3gv8sRDiLxdlNJciE/vgx38O3/sjJX7mI5JARZPO0lAWwOvSaQy66a+l250fx0XSSel2cNGn3D10aJIN7RE82QG1irJQK8O665Q+SSdzfU8DHXXemoFDjd8rhBAN1UwJhBBe4BbgYLXOCKHCMq8B9lbfci/wtqrL3RYgLaUcA34J3CqEiFYNG26t7ltczDK9hiSnqWzAhapHOk5sOdck9+GVeb49lsPyCYSUWAj0Uob+0Ai+5Boy2d006xlybh+OSoWSVXXBy1l0ux2knW5y5VNTlR46FOeWzz7CT3aO8L6bl3Pv+67jsnaV+tQU8jCePk+RBKouqZg8MSm7CBnat5toSyukVaShN+LFso6R13Mky+N4TC+ycmaRtzdXxKtpLPG6afe4uGvyEVxbvwJXvwfWvuacxpIcGaJulnqkzJFp8sXx4852YloJnN6uy+geO8amw2GWlQ0e1cu8OnQLGa+fezpXcb01Rsf+IzhzGfpvfxPfyy+h4r+aW+sjuLTTp516RN2/S+mkmNuHqdeRCgicxTRFPUTz4CDPuDsxDAebD2Qxrrgcb0eMSFBdH7fhxiEEU6kCQ9XMm/aoj9HpaqRWqMl0uNmNy1fPUz++l7G0xYhDMn73dr738Y/x669/kd2/+QWVsppsN3QGsU1JdOUNAFx2+EkO7d9HwZpHZGi6H75xKxy4F9a/CVx+ePY7cM8fw+c2wL/0wN1/qETT7xMj26Fq3U7fI2d/7Vmc7QCKmTS41H1TcrgITY4t1ChPYT7pdj8D3gHEgOBJ2+8vUqo/8L+/Hr50Lez/CWx+F/zZo+A9Pb91VurmtgEHlXI3WBNJ58dMj6SQsgK9FERSImewezjFTSsblYheKNMGOGskSdcEf3hVF08fS9I7cR4pBTVqXJq0AA8JIXYD21A1SfcB3xVC7AH2APXA/6q+/udAH3AE+BrwFwBSyiTw99VjbAP+Z3Xf4pIaYHtwNaDqkdoXqB7pONEu2vMubuUXPJCySbRdC4BX2kTzGfqXjOJPrAEs1tf3kXb5cRoVStVIkjBgZdAPQrBr4kT9VaFs8p7vPEPE5+Sn772OD926ErfjRH+elvAFRJIAlr0MhA69F2fKnapH2kf7mvWYCRUROlznR7NSuN3DTJqTaGiUx86cRbI3W2RtwIMuBEuKw3z20P8h3bQJbvn7cxuLZTE9NjJrPVKuL8WoHcdskkhLw5sJYEkb/7px9saaWN8XxjURZiDUTGuujkd7NoIm+FjfD9k5GWPDy+/gs7e8FKw0lvBwe/3pqW3A8V5JbVYT+dxObD1Myg+imMUd7mDtkR0AtKc34alYjG7YSN3rOmhr+SnOcoZoVom3oCjxxFGVctdR5yM+GcVGMFZIousBnN40/kgbd77vXwD4WbeO3xGmVS7h4O8e5cGvfoFDTzwGQEOHmuYmUurYfmeFlsFeHknO0Zbl2GMqcpQZhj/6Ibzmi/CO++CvhuDPHoO7PgPLXwYTe5Vo2vq1c/p7nRHbhl9/EiYv4h5hI89A01poXAPH5hJJZ+6RJG2bUj6H5vFhSYErbxANvEDNZIF2KeXrpJSfkFJ+cmZblNFc7EgJe++Br94A336VMmq4+ePwwX1w5z+dqDWaD9FuSA+p3OGz0BnzMZCspdudF8dFUtVn5BIQSY/1TiEl3LgiqpoiLpRpA5xVJAG8aXM7Ll3ju09fvNenRo2FREq5W0q5SUp5mZRynZTyf1b33yylXF/d99YZB7xqCt57pZTLqr/fftKx/lVKuby6ffN5+QCJI2wNrUUguTG6CJME3UlHuYHX8X3WOSfY0/Em+jp7iCHRpSQbc6OlloLloqf5GNPuIK5SmaKtVuO1ssa6mFo43D11QjM+MzCNYdr8zV1rWNd2+sS5OeQhnjWozGfVfja8Eei85qKtS4of66NcLNCx9jJIWxSsPM3mKPcO7KZTbmXAoYRTZTg36/ttKdmbK7Iu6INKkTU/fw+m0PnNyz4PjnMTyqmJcSzTPM3+W1ZsHCN50uUEZrPEyIbxakFKto3mSfBMt4/xuhK7h5fyssht/HIswVBdEx8OOjjw9DA+t8ZL/uBtTGQPEor/E68IF7glFjrDKFRdUp0RQrOS+J0+Un6BVjFYv3Y1nVMDeIwUhu9y8o319LtdiGgbnmgJX3GScKEOgHp3id8dUeYNHVEvVhKmaGAi00swsBrhGqOQKTNxLE/ZKUhvacW9PMJy9wbe84Vvo+k602Nq3hBu8OL06MQnVSpgU2OQJaPHeOBsKXfbvg7feQ346+FPH1Ji6PgHdEDLZXDlH8NrvwzvewZW3gk//zA8++/n9DeblWQfPP4Z2PODCz/WYmDbSiS1XQFLboDBp6ByhoUQ01Dz4zPMf3LTSaRtY/v8TEk/4/sEe6fOPpc+X+Yjkh4QQty6KGe/1Njxb/DDdyn/9ld+Hj6wF67/CPjqzv1Y0SVgm2q14Sx01fmYyBgU5yh8rTELmVHVH8lVzYG+BHolPXwoTszvYn0gq+6PhTJtgFn7JJ1MLODmzvXN3PPMMHljcb5watSosYBM9fJE5HIkYuFT7ap0i1YcWLzZ/g+apGc9SgAAIABJREFUiv3ce8tbKISVsHFPuxjVMriSK2iN9TPlCeEplSg7/VjSwmU7uLylCYCD6RMT/qf6EuhCcNksAgmgOexFSpjMnqgzyJkWjyazfHtkivJ8iuF7blOr9emzP2NfCGbqkTrWrEcv6iRlmpsTj7OiXOKy3H4mPBoFu0BpYPbCpMFSmZxlsy7ghSe+gCu+l/ev+u/0Os7dCCR5Bme78lAWzZLIwhTlFiilvLgdIXJCwyqB7p2kd/0YwlnBee9hfuOL4DVKbI4fYyLn5MYt7Xj8AbaNb8NhjvEva9bg0c885XRE3fjzLgTQ5tZJVW/nnu42BNA+uJsDS9aR2tDOVCKBKQUi3EJAJnGZzQC0+DI8cTSBlJKOOh+iZBGnnUqxj2BwLbbeT8Ww6N+TYKDeQaffQ/j2buyCSf7xMUL1jaQnVF2u0AQNHUFGRzXQHDQ3hmiZGOK34wlM+zk1RWYZ7vsg3P8hFcX8k1/PXROmO+EN34RlN8NP/+uFp95NVDOCUxdpynzyqKpFatsMS28AswTDW2d/7fQAIM/qbAdQDIRJC1WX1ll/ZgF+IcxHJD0F/FgIURRCZIQQWSHEHPHGFyFGDh76FHRsgfduhSveDk7P+R9vJuo0R11SV72a4A/WHO7OnfSIai44w0XeK8mqWn/f0NOAlqreFwsZSZqlT9JzeeuWLrKGyb27RhfuvDVq1FgUkokhhrwqUn7dYkSSAG+ojrKlYeYP84He/4G3lOPLa69lJFyPmC7zbGAfoeRafL442Zgbt2GAK0DJKuDGQ8TjJlgucax0YoHm0cNTSCRff7xv1nM2hVStwd3H4nzs0BAv23aQnsf28KZdR/no4WF+NDEP852e29TPizCaNLR/D9HWdgLROtyWl0ktz6b8IQCusgwKLjdT5QmMwdmjFnuyVdOGgFf1hGpaz77W6xkxzt3ha8b+u66t/ZT9xjF1bn9hEjsmKSUtvI4wSQ2MghunZ5QuSgSv0cmg01vfylVDR9n2n9+h259k5cZ1AGwd30pPtIeI5+ylCHrUg0hbOHBQp5WZ9qv0y7DpRGoOrt29g4rTycEVEWzbZmpqCsLtRLxJbEcdFUqERIapnMHhiRwxvwuvUydrdxCwhvD5V6O7VSpePmXQ3+ig0+PG1R7Eu6GB3OMj1Dd0kZo4YV7U0BFkariA9DcSDeoIy8Q3fIynThL85BPwndfC9n+F6z4Af3C3qk+fD04PvPm70HUd/OjdsP/e+b1vNib2qZ/TF6lImqkPbN+sPq/Qoe/h2V87ffb5z4xIKnj8pHUlklojvoUc7XHmI5I+g2og65NShqSUQSnlnJJNCOERQmwVQuwSQuwTQnyyuv9bQohjQoid1W1jdb8QQnxeCHGk2pvi8pOO9XYhRG91e/t5ftYL48kvQm4Cbv1fMEvh4Tkz74ayM72Sail358xMI9mTuYhF0u7hFNOFCjesbDghnhe8JunsfR6u6IqyvDHAfbtrIqlGjYudZwoqw6DV7aTZ7VyUc+h1EQzTjWmlWFua5JaHv4Ep4LerLscqGTwR3o4/uRYA59ICDsvCqeuUrBwe3Y9RLNBkVxiRyoCmUDbZO5LGlnDf7rHTGnTmTIuPDKjvn08fHOWeiWnqnU4+2N3E9zYspc3t5P7JebiM1feoxcjeXy3o9bhQbMti5OA+Oteuxy6auIWHUVeZ9XklGJeXK5TckCmOYE9XsI3Ts0j25YroAlb5PSqC0LyeNreLkdK59/FJjgwRqIvh9p3qOmccSzMQFIScSdDBmNbxaD4m3ZKJfISSM8WySoV1N7yT6TvfgqXrdB3cim2ZvKz5KCLaRdkqszO+k6uarzrD2U/giHrAlKz29qBVhpgOKdEvpxKEmptZMTCMv5jjmeb1AExMTEC4nYaYulc0s4zHUovJvzsyhRCCzjof6UILTiqMyhgO74n7pr/RSadXpSaGb+tG2pJl+jrS8ZNEUmcAs2JjeRrw62WEprF0tO+Ey93EPvjajTC8DV73Nbjlk8pd8Vxw+eAPv6fS0H74LtUI+HyYiSTNo9b9BWFkO7iC6t+lJ6Q+75nMG+bRIwnAEDqUbV4+tB1ffv4Nq8+F+cz2h4C98txbDRvAzVLKDcBG4PaqGxDAR6SUG6vbjA/gHcCK6vZu4EsAQog64BPA1Sgb1k9U3YOeP3Jx+N3nYM2roePKhTlmsEVNWudoKNtVayh7/pzcSHaGi7hX0sOHJtEEXL+iQX1JODzqPlkoztAn6WSEENzQ08D2/mlKZ3FWqlGjxgvPVjsIUnJD3SJ2nG9sRjfCuHUT3deIszzGG576CUWXmwdXX8lL0teiZ9uhHCDcpSaPbmlSMnN4dT/lQoFOh8akw41lWfzntiEk0BRwM5Ao0Bs/te5meybPqFDpdO9rinHopev5/sZlfGRJCzfWhbirIcIjySxZc47vJyFUY9m+h1WK/EXCxLEjlItFOtZeRmFYRcRGvTbdeTXx6y7bVPw5ksYYAkFl9PS6pD3ZIj0+D55iQi3eNq+j1eNk9DwiScmRodPrkSwbYyDD01EdT0At0JZSHtyag6lAhX7DixSSJstm86rX86grSF0uTcPwUba8dBMRVwkiHeye3I1hGVzZPPe8SY+q6OE6xypy2Z0kQ2qaZ05MESrnyXlcBOLTPFa/GYRFfPAwhNtp9Ko+Sr4COMpqznTCvMHL+JRqoXE4O43TV70PPDoTEf1442VHnYfAlhbCmTqCZphSXl3z+k7176pEHVpxiuZlK1gTH+CByTTywH3Kwc4sw7segMvedM7X/jjuIPzRD6BpDXz/rWeOsJyNGZGUG1c9Ii82Rp6Btk0nROTSG2B0h0rBey7Jvmr7k9lLWZKjI9gIHGaFlqk4H3rme5SPzh6VvlDmI5L6gIeFEH8thPjLmW2uN1ULXGf+dTur29lmpq8Gvl1931NApGrDehvKcSgppZwGHgRun8e4F46H/1GlKb3sEwt3TE2HSNecqj/icxH2OmvmDedKpQSFqRPOdjNcxL2SHj4UZ0NHhKjfpe6LaPfCRC1nmMO4YYZrl8UwTJsdgxffNapRo0aVUoaHA2tACG5YpFQ7AL2lk2CxmbAu6Y11YHihqe8QN/UdYiIY5YGl11KRAl9iNc3t40gkbsugZOXxOPyU8jmW+TyUHU76ktN8+ZE+dCl524jB7ZkkD+6fOOV829J5NKeGy6EhSxb6c1og3NUQpiwlv07MI+t/xa2q9qH/sYW8JBfE0L49ALSvXkd+QDn+Gf4sTmmRCeiEyhUs12Gmy+q6lGcxb9ibK7A24IUJdSya1tHmdjFqVE6LzJ0NKSWJkeHT65GGc1Cx2es1oF7Vp1qF5QghyITyTFYNNWKhDo4Wy/R7Alx1cDd/8IG/5qp11YltpJNt49sQCK5oumLOsTiqDWVXaEsYS++jEIpgA+XxYaIH+rA1DW28QE64yEW8TPQ+C5FOIkJlhoTyLhy2g2u6Izzdl8S0bNqjPg4Oq9S3sWwvkZiqjyu3e3Ho2inRV+uaFv5Uy9PS/CaS9/ciLUm0yYfDqZGrhCE7Qee6DfhHB3jjwa8pMVPfA+9+WEVFLhRvBP7LT1Qt091/oIwN5ksprRZ/G5TT5UWXLVMpwfjeU6/TkhtA2tD/u9Nfn+w7a/uT5Ogwea8fb7lE47Qy6nAvW8DShJOYzwzsGPAbwMU5WoBXeyztBOIoofN09Vf/UE2p+6wQwl3d14aKWs0wXN13pv3PPde7hRDbhRDbJycnn/vr82eqF575lrL4PofmbPNiHr2SQK2M1CJJ50j2OT2SZrhIHe4SOYPdI2ll/Q1z9gg4L3S3+lKaw1HxqiV16JrgiapLUI0aNS4+KomjHPCr74jFMm0A0JvbiKZXUO+Q7PPZOCMBRLHC9QNjXH1sPzsag3ytx0MwuRavL4vZBA5TNZR1az6MXI61UZWhf/fOPibSBa4wiiC9rKzY/Grvqf1NnkkXWBXwKhvwWXolXRn20+hycN/kPNJrul8CTv9FVZc0tG83sfZO/JEoo8cGsKWNx6XmLPEWFT1pEqOUrDwpYVAeObUlw2S5wkTZZH3QC+NVkdS8nlaPE8OWTFXmb7qTTUxRKRVPiyTN1CPFzSmMVihnHbj1lQBYvgwexyialGxc8Wr+rW8EIW1u27uDti3XIjLDKq3KE2HbxDZW1a0i7J67RmcmktRuNlOxKwS9ITJ+QeGpX9JQTa9sGx7AYUoGWnuYyJTB4cGpGbisLO6yH4FgZX2FrGGyeyRNR52PyYKHkohgFI4Sii0n0HyQkRU+2j3OUwT4o0NJDto2j5RHsXfkmPzKLuyUQaw9QKoQhMIUnT3LubP5AH81+E0OLLkT3vnzE+65C4GvDt72U/DVKzvv+RJX0TRW3al+Xmx1SeO7wa4o04YZOq4Ch3d2K/CzzH8ss0JmMk4q2oAuJU2ZKXJOD49NnacT5hzMKZJOsvz+53O1AJdSWlLKjaju41cJIdYBfw2sAq4E6oCPnf/wTznXV6WUm6WUmxsazt3h5Yz8+u/A6YPrP7pwx5xhplfSHCs/nXU1kXTOPLeR7AwXqUg6MJZFStjcHVX3w0L3SAKVbgdzRpOCHieXtYePpyzUqFHj4mN/fBhTc9LigAbX4tQjAQivg/DoFmwJLscI/jqVvtQSN6jLZ1iWGOVHHS5c02sAKK7R0MsGJSuHEIJSIsvGpnoAHjyqoiNrqtrHKaJMHj14XAzZUrIjm2dz2H/GhrKaENxRH+a3iezcjT0dblh2kxJJF0GKtWWajBzcT/saVVeTHktSMDN0yXEqmo65/HoAOjT1uYe17Gk24HtPNm0Y3wuhNvDV0e5WqWPnUpd03NnuOY1ky8fSZKIuoskxjBYopdwEnerZWXbkiXj6aTdNostv5yeJHB3JOBv9PoQQ6tka6cSwy+yK75pXPRKA5la9kurLyuChzh0g5ZfIbJZ8ACy/g7biGJG4wZ5wFxkCFA4/CkDIkUdWqzAiTrW498SRKTqiqqi/4ugmZg8z6b6c9us/ze4WSZfHfcr5H3ziEP/6q0+RH9tKckWKSrzAxOeeZWnAwVTKD9Km44kPsDo8yX2um/mvq/4WnN55X+t5E2iEVXfB6LNz1hAfZybVbtVd6ufF5nB3smnDDA43dG45vS7Jqqh76AwiKR2fQNo2Uw1tSAnNmSnGAzFWN/lnff2FMqdIEkJcI4TYDxys/v8GIcT/fy4nkVKmgIeA26WUY9WUOgP4JqrOCGAEOPlfant135n2Lz6DT8HB++Al74fAAgqvGaLdYGTmTP3qivkYSRXPv2fE7yPHRdJzgo4XqUg6VjXmWFofgOw4mMWFtf8GlW4H80q5u25ZPbuG02TPoxC4Ro0ai89TKRVheEl0caxvZxBC4JZuyskGLvMUoapbWpbVMR6qY/nYEHmnYJenCa1QT26dD5dRJm+rF5bv/ikromF0y6JSLuGWGg2iDrffgdACXJY9xoMHlHg6XCiRMW2uCPnP2lD2lY0RirbNQ3M19gTlcpcZhvj+BbkecyGl5HP9E/TmTx97YniQilGifdUapJT4ik6ylSQ95SEyoXr06HLKTo11tsBwOhkhhTlVxC6eiA7tzSmRpNLt9kKTcpFr9SihPN+6pG+NTPGbA8pR7+R0O1mxMI5lOFjnYGl8ABpsjJQXn0OJkJI0sF0JllRsHnV1kUCwof8Q9WuVSCY1BJEOdsV3UbbL86pHmkGPeggUVNpdUHeQ8qtIzwObJN6OJjrNSbRpg5TTyWQwwkSfiqRFomUqDpVKl5gaoacpwLODKTqrNd2ITloZYa+lnqmDpcpx0wZQzrKl7dtoKSRpz6YYt/pp+sDlONsCNA5laUR9dpEZ4TFup3eqlf0Fg4GigVEocPjpWVLGLoT2zWoOMN97dnyvanHSsknVMl9s5g0j21XpQ7D51P1Lb4DJA5A9KeU2PVRtf3J204ZEfQsGOp3ZCWxNw7/zDHbiF8h80u3+P1RdUAJASrkLuH6uNwkhGoQQkep/e4FbgIPVOiOEEAJ4DVCVwNwLvK3qcrcFSEspx4BfArcKIaJVw4Zbq/sWFynhV3+rCue3vHdxzjETKZjTvMGPZUtGUxdhMd7FykxvjOdGki7SXkn9U3k8Tk1Z3x63v1ysSNLcwufaZTEsW7KtPznna2vUqPH885uyWjm9tWHxfYy0iJ+W6Tvx62A5lPuX0ePHEDrt03F02+apegf+xFrspXk8RpG8Xi3QnswylTMIFXJkLC9rKjpIwVWvUJOgFeX/x957h8mV32W+n5OqTuVcnXOrlcNImqSZ8cj2eMaJtU24gEkXbHaNYQnPhUv0Aja7xrCAL2AW1qxtjI1hMbbvjDGTg2WPZqTRKLakVuhcHSvndM7ZP35VCqPQLY3aI+x6n6eeflRVXSd06Zzv+/u+3/ct8vSRSUBI7QB2+5y0ewVJutqMzT0+N0FNWZ3L3bpGzOOZx1/fSVgl4rU6H5+Y50vzV147k3PivhTq6eNkfJSQGSRlptlYGMeIDKM7usi7FLaZUNZlUlVxrquxi92k4/kSPboNP3VYHsOMbqZSqtN1g52k/z6xwAunx1Ccbhzei3K40skkVtXg60GJgeI4ks2inOtElwVhSVdqpG1Vhh1R/nExjb1W5d5jh3Fs2yY+oNFJOrBwAFmS2dm282qbvyrUgB0pUyeoB9GsDAkv1FSJZ7dJdI5sRC+nqczFkUyLbGCEQvFBUB0E3BlMOYhsGCSXEmxo93J6IUdPQJCkYq0TN3lOFuuUJQ9pQ6ZHv0iSjsykGFoS991gMU9mcQHVrxP52a0o93TglDZRYC/1H3oMZfN7MOemsVdK/NtyhhPPPcVjf/px4tOTqz7OFdHsuMweXN37F0cFWZblVc26f8cROwRdV/keDDwofk588+JzKznbzQmSVLM7Mcsm/mqBmqzi2rPnVu7xBaxqKtyyrJnXPLUa26sO4DlJko4BBxEzSV8HvihJ0nHgOBAG/qDx/m8gTCLOAZ8GPtzYdhL4WOMzDgIfbTy3tjj1qAi6evNvC4vGtcBqs5IaNuCTLcnd6pGdE1kF9tdo9W/TrKTJeIH+kEvIFVa4SNw01Ia8YIWsJICdfQFsqnwhvbyFFlq4vXBEEeqGPWto2tCEEgoRGd9LoqoR6BIzRIu5ZTzFEoppMlgp8GJEw53cjGyvYg/kKKmCJCkV+J9/8peEsglKRZXdkp1Ql4sN94pVZUUOkTpxgGy5xivZAkFNYdBhp92nU62bpIpXFv2qLPH2sI+n4hkqKwXLetqhY8fNWyvfIGZzGb594MeIjn31iteaq+D+9g5eOPMsdslOWV0kWM/i7LkbXe8k71LoLGYp2grk8+I+VYvlqDeUJKO5ElvdDlg+DZbB+bl2vvR7L+GXJXRZuiwrqW6YV1WgLFdrxGt1wuk4M94QR3MXF2CLry6i+Gw87QOfJmalqrn1yDKUJUiWchgSdITv4N/iGYaXZuman0PfsgVKaahkwNfDwYWDbApuwmNb/fdTCejUUxUGvAPky4t8+T6Z//pTYQoOiS1bRRHckV8gmq5zNhLFW38HhtJGUBdOgI5ShcpylhGfg1i6hGFZBF02YjnRZZrNnKHgECbLl3aSnh9bZlOjDosW0qQXxXdckiWi7x7km0UPieqvkXlZp3fzNrAs9iRiPB7PEJ+ZFJ99anTVx7ki/H1iLmn20MrvNU3RcWoTNvwE+m8vuV0hLkjbpVK7Jjq2izpt4vmLzzXr4Wt2kuYwdAc208CREQqc+a63MDe5NvXxqizAJUnaA1iSJGmSJP0qcGqlX7Is65hlWXdYlrXNsqwtlmV9tPH8WyzL2tp47sebDngNCd7PW5Y11Hj9lUs+6zOWZQ03Hp+9yWNdPYyamEWKbIQd71+77TRJ0kpZSaFGoGwrK2n1yM5dKbVr4nYkSQlBkgBBkmQVfL23diM3ILfTNYXdfQFePN8iSS20cLthqVwlqzhpM4uEbOqab0/x2JCQSFY3MuTOU9YMEstz9NcqJN1eRgppJtwKyYIo1KwhlYoq9kuTdNwnn8NTqeHLmPiKFiN3t2PTVfxtTjRXPxsyJ3l+bJlXMgV2esViUbtXyK7mM1dXULwr4idnmHwzmbvq65dh5O1i0TO/dGtOyHVQntjHUGmWLVP/dsVrqbkYpegQyCqnzgipmOoQa9Cu/rdh1zvJu1RUo47uTGHPLbJkMzlxaJ6dH3uKxXyF8VLlotQOOD8XpZCpEp8pXJGV9DtfO8FPf/bKbsTJvJAC9mQTlMJt/OTxcWbLVYxclfLZFNbWEFVFRvKJc18ubgcZkhrYqkLKPhl9O1ULNixM0aVpqIGAkEoBJU87x+LHuLPjxiJTRFaSyUb7CAuFBRI+idFoGkX2869xF5KiMGTFiRQMptwqM06VfOlBAtJZAOxlC6VgEN0n9vHMQo6egINzSTET56lPc0wT3Yse28U8o33HZxhOxzAkmc5CnOzyEkZdSBwVVUbtcDPv1CidSBBQ2lFtdnbFZziQKTC5IL5Ts6dOcMsgSYJUxF5Z+b3pKajmLyFJfcK44TaYwQNEFwkuN21oQlag/wExl9Tc3+SE8AFwt13141LzMbLeEOFsEk9O/N+v6z04PLarvv/1YjUk6UPAzyMc5WKIzKM10p/dJjj0OVGovu2jNx4MdiOwNb4IK8jtoh47uia3zBtuBNnYyiTpNrmIGKbFTLJEf7hJkibA1wPKLS5+bkBuB3DfcJhT81kS+ZU7Ty200MJ3Dr5aBt2o8LC2CoJwC+C+r5PAD66jf9NP4JQsig6DXCLOkCaz7PYTnhMr108GXKiZXqzBKoamUTMrWLqPtNbJcHSAzRUFS4L1d4kuUqTXg83eQVt1mSdfOsrZYoXdXqGcaPcJkrR4jbmkBwJuvKrM11cjudv0H4Sz56nHbsHZuD5c558GYGviMMZrnETPzSX5W9fDfPAL+7BnRG2h28U8htS2Fd0uSBJA2FXEVqtySqmjLJXIlus8PrGMBQ1nuxNYmpOJGQeWmWd2LEmXrl3WSXppPMHh6dQVksVT+RKOUgGzkOP7tmykZJj8xLFxUq8uggnHuoUhgRk2MMoKci2CKkPCLuEyp5Esi+cZoK1aYt1CjMDGpvW0IElHKFM366s2bWii6XA3Ig+SqWaQkJGwMO1d/JFUYz7YhreyiH9SyA+fbJPIVR/BWxwFy0QyNeqyyqAszuGphRzdQSdjyzqS7KSTGE+UhwEImuI7u5QtY5w6iWqZTAxuQzdqKDXIxi8S6kivh9FUBSVgJ/eNKTpHNuGZOoMJHFDFuZo9PXpD9usrons3xM+sHFey2OhgNWbT8Petatb9O4bZV0CSoXPH1V8f3CvIdVNB03S2u5b993yMOX+EQCGDL5OhrGgo9iCekL4mu78ad7u4ZVk/ZllWm2VZ0Ub357t3ebmcFblI/Q/Auret/fYCA5CcvO5bZFkkR7fkdjeAbOzKeaQmbrOspLl0iaph0t8cMl0L+2+4oU4SwL1DwsXqpfHWXFILLdxOKOoBfqS7g/dsvAX5LKuA1u7Ctbudu7r3crSkUHPUqaazrPN7STvdOFJxuusWL/tNPMnNqB1LyPYapXoeyx3mjvsfwirJbKopWO0KLr8ohiO9HqolGUtxMzUvugG7fWKxqEmS5q/icAdgk2UeDvl4Ip6hZq5QnEY3QWgdnPzaLToj14Bl0TXzPAXZgc/IszR16JKXLGYSorh/4XSBjsR9GFYdr5Jg3tkBdg+aFqDkdmFJEiNOQaLG83k6kfEhsX9G2J4LZ7vj1HzrqeT3U8n+PTOnEnTabcw1OkmFSp2pZJFC1WD5NQtdJwslBhNi3mnDwCB/u2WAM8Uy5/fPona5ebxUIpBOoITq1DNOJGR0GTJ2E8u2jE/q4nChylBsnOhsDMc24dbXVGgcLM2hSAp3RO+4odPXzErqNcW922sXUr132o7ypeqX6VE6aM8vcEfBYkPG4O8HdFKaj2JxL/Z6norqxFRVops8uJEYW8jSG3QSS5dxu4YYlOeYqWroVhG5cASA588ss7FRg9UfFu5wWk0is7hwYb8ivR4qlTy2B7qpLxbZGL6bfGyGwVqJ011DRPoGKKSSF2R6twTNzkvs1eu/b/EEIEG0QVSbCqXbRXIXe0X8/7Ndw31ucK/42bQCb2YkXQXVUpFCKknCH8ZTrRDOpoi5I7jcOjZ9bTrq1yVJkiS9WZKkr0iSNNp4fFmSpL1rsie3C7IxcIZEF+kaTPaWIjh4kUFfB71BF9OtQNnVoV6BwvL1O0lw20juJuLi79ofdl20/77Vpg0gcpJAJISvAtu6fLjtKt9uWYG30MJthYCm8ocberk/Ev6ObjeoB5mVh8BZR8pV2NDVTtoh5j7vTC1xMuJCSW9BUgx83jSJyhxaYIj7vCbZiSJ+U2ahSxCGD/7dK4ya4lrkHdgDbpCAOzxisSjitiNLXNUGvIl3RXyk6wYvpq8MXL0MkgSb3wuT34L8LcxRfC0S5wjlZ/mbnv8LgOL5i/bGxUyaTF0QH19ojN5KlHwtTSdJFgIjjd2UsLm6qLi99EuiPItJolO2y27j9EKWoKbQYVNh8TgZZRizdh6sEnNnZ+hSFRarNaqmydhi7oJYYjJ++QLrqXyZ4fPCOc1VrfFg0MNfBiN0pes822PjaCrPjlOHsPsr1Aui6+eRJPK2Gil7iar7rSjA8Pw0bYuL6Fsbpg2ZGdCcHEyMsjm8GZd2Y7bMzU5StCbMSJyq+C5sjd4D9q9y7451qEaVHZbFT50tU1RlfmGXjQRvw23kMYRXGPOZswwic2ouS0/ASc2wkLQ+uhvGyG1Sklz2MAAvjC2zNTVFzNtG+x7R+dKrJulLSJIePsK69/4SC/JZ7IM+/PN+bLLOm+bOMtk9zMjbBLmK3cq5pK6dgHRRrnYtLJ4QGZ7N2flAn/h5O5g3WFYy0nvcAAAgAElEQVTDtOE6izmhYfB0CsmdaQhl1TXiT1LzQkaZ8oexmQbRXJIFT2TNukhwHZIkSdK7gM8AjwHvB34MYa7wGUmS3rlme/RGI7oRPvzS1Z041gKhIRF8Wrn+Rb4/5GQ6Wby17dzvVuQaqzm+fx8kaaoxazYQdonuViWzRp2k1eUkNaEqMncPBNnfmktqoYUWGhhoezOms4ZWlQh3dVBpSNIHv/kENU3lkLUODBV/IM5ieRrV5qY0voBjrkxNhtGwwWyqxNOnFtm3IDojfUO7WGrrJFQq4WoYPqiKTMRjvy5J2hv04lRk/nU1wbKb3iskd6fXUHJ3VphDjA7/AGPOfmxT+y68lJqPUVSENMsI/SO9MhTrc3QaSXKRbRfep9s7STlc+AvimBbaxPHf63SwEC+yxe1AysagnCGWCWGZ4n318hLRpRoWsFCpcXr+ohRz8pJ55rppcaZQpn1+GsUwyX38E1jVKm+erWJK8FFHhTHNYtP8UTSnQaUwRFGy8CJRlMtMahrLvj1ss6q4axUiyST6pqbcboqir5sT8RM3LLWDi1lJroIdm2xDkcR34YH1v4BpVrBvngRgvaRBsgKSxBmvxq9v66PND5oh5lgWZk8whMzYQo7uRneqZPbgNJfRrRKdthqZzGFqhsm+M4tsSEyS6N9ApK+DnObAWalf6ApZlkk89ykk2WRx6Wn8/2EIahbbwnvpOj+KoWrMjWzD4fHeWvMG3QeR9Ss73C2OXpxHAiG3gzc0ULZimpQMExLnoZy5umlDE5IkrMAnvilciY3qivbfiqoh1w2CpSzL3gjeN4IkAb8GvNeyrM9alnXUsqwjlmV9BmHbfUsCYG9byKsy/bs1CAl97ErdpL6Qk3LNZCnXmg9ZEZlGjNb15HZw25CkiXgRh6YQ9dgvOrvc6iBZuGG5HcCe4TAT8ULLfr6FFloA4O72u0nZhcHtROEwwWwWE+jMJdENi8cDGnp6CE9klsWGhXVmrsJIRWEqIjGjKhybFd2R00s5vBEHRtXDfLSH4PQ45iXSuXaf45pZSQAOReatQS/fWM5grLSA2LZZ3G9Hr3Sdu1WwzjzBGWc/3e3DHAjsJLpw8MIMaHIuRlFxIksmmlwjaulgnQbgQLLzwgKoIffwaPwOOqUEqlxHL8eZtcNGSaGUqbLJKaR2AONzF53rLDOOY0Zcp2OVGqfms7jtKqosMRm/SJLGSxUqloUvk8Cr2ameOs3Sp/6K4uElnBuC3NvpB8kkLIu/XSnbR0YzkJEoSilKjq0UFTfrF6YJVyq4BweR9UaRmp7hsC9M3arfUD7SpVACOka6Sp9PFPt93j56Q7uIRB4mWfoyEX8vuqRy0Kojl+sMJBbZF1X5hzt60QzRScrHFxhCIV81sDWs6JMVUQ90EqNPd1AqT3Pg/CT+5Tnc1SLS1m20+xzMuqN4yxXmZkQdsbj4dQqFMUxDpWK8LKSn93Qw6NqGdz6Lo1Li2UKNrg2bmT19C80bQEjuZl+59vx0JS9qhuY8EoDuBUfwDeskFeoG7z50lp0vjvLcsWfFk1czbbgUAw9CKXlxZvAaJCk+F8NCIlLO422YNqRdETzhNQj1beB6bKC9kYl0GSzLOgZc3XaihRtHkyQlzl33bb0N57NLL3YtXAPXCpJt4jbLSppMFOgLOdfW/hsuIUmrD4i9b1jMJbVc7lpooQWAXW27mNNE0XZ87GusK2TJ6U6sO3ezx1Q53ObAldyMHohR9YTJ11KYSg86EqURGwmbzuGpBCE9iVE5i7evxMJsnKpmY3j2FC8fO3thW+3eyztJ2fgSn/2VD10mhXpXxEe8VudAZoV7oySJbtJaSe4qOZh6kaeC99DjsDHefjf2eunCTElqPkZJcyGrRe7S70OTZGyyWBT78hk3/98zZ6kZJp/Yt50DlQ0ABNwFIolFTrohUjSQDIsOU4LFE1hIzKVKyDYXpmbHZk9THReF41y5yumFLBs7PPQEnZd1kk42wmhlo0J4aBjf+95H5mvfwsxVce6M8jtdbfSMnkH3iQXZcrqPQsO/qmwuU3a9CY9s4j13ksj0DI6tWy+eg/Q0B2wKqqyyI3KNQf0VoAbsGKkyA94BJCS++M4vAtDf9yEMI0dnRATfnsQgMFvGtLt558Q8j/V6ObjJhVyvUa1WGbLEuESmVEOSIJYXZet/jOT50U4hU33yxBk2Nzou7Xvuxm1Xmfe1ESrmSMzPYZo1xic+iar6KKf60Dzj5NJxfG/rw1IttrjuY9fceZ5KZPFu3EZmcYFc8hbK07t3C/JwLXOv5dOAdXknCYTk7g2YSTIsiw+dnOJkocSwU2fi3IsUFCf/Stv1VVCDjbykQ58TP69R/0zMTJN1++iYn8adEd/1vCuC36FgrTSXeJO4Hkm63hWnVanfKjS/DInz131bc6h/Ktkyb1gR2RU6SbdZVtJkoiCkdnDxYtjUFd9KqE2StPpu5EjUQ8hl48VzrbmkFlpoAdw2N8E2UajOzp1h10gXSZeXOPDWgIecS2OpJGYQ/N0uEuVpPM42KizTPuymomqY+c/z8fs/yu/v+QTuwZ+l7y0/y+esH+UH7/kXErM/xoFDP0wuP0aHz3EZSYqNnSI5N8v82dMXnnso5MUuS6uT3G1eQ8nd+AtIZo1nQvfQq9vIdt2DiQSTIigzNR+j4vBhKmmGq6LL4tDmSKhetmzdxCefPst7P/VtDs462TQk1qejvir+bJLjIRuuskk3Mmq+LkwbnD1UavPI/giG3YFpLJOdyWOvmsyWq5yez7Gh3Ut/yMnEJTNJpwplvPkshiwRHRii7bd+E9u6vVj1EvZ+J5l8lTtePYDur2DVJWrFEI1RKkosUHHuZI9aQzZNojPTF00bKnkoJTloFdga3opTu7l8yWZWUr+3n1ghhqPhHuf1biMYuB+fZqNqVqjqBo5klVmni8Hpw7wzVuHJLU5OdPRScjgYLAoifG45T6fPwfmkD0lSud8ZZ3d0C5Kksu9cgd35GTI2FxvvEkQjE+nCVy1iLC8wP/8vlEpTgIyslpBki8kzzyI7NdS7/bQ5+vjR2Twlw+RXvb0sB9sum0tKzM4wtn/fFce4alwIlb2GFXijo3glSep/Q+R2v39ujqcSWf5gXTeP7hzmB+rnGfNt4AMnZ3jv4XO8mr0GdfB2CmOVxFkxO32NBe7luRgpf4jQ4izObJ6apKC7A6gnnqRw+haaZlyC65GkIUmSHr3K4zFgDZa5v0dhc4K3e8VOUqffgSJLTLcc7lZGNgZ2H9ivE2J3m5CkumEykyxeYv89Li4Q2hq0j29CbifLEvcOhXjxfKI1D9dCCy0AsGtIBHuWcgrDA4uknR4yySRvGxCd5wP4kGoOPNEJzHIBm6LjL7/ABnOJX+dj3Nf2L8TK9/BXR36G6cLPs3Ts+zlUfgeJhTaKywa5zCvsP/c3tPt0cpU6+Yqw0k4vCJVAdvmiPbNbVdgb9PCN5QzmipK7LRAcgtE1cLk7+wQ1m4cD3q306DbaAm2ccg1hjjdI0lyMoupEUnM4kkIW5rPHGXWv4w9+YBs/3VbjI3/7y/z8hiI7+g5j2t10eQycpTxPdWgYErwLjVSyBIsnSNQ7wKpQ0m2YdgflfALTrLMpaXImXiBXqbOhw0N/2MVUonDh+n0qX2Lrkrj3dWzaimRzoLZtpzZzgOVP/hmLmTJdlTncvjJG2guWfKFSLOgZkGz05XKokkQonrho2pA8T16SOFlJ3LTUDkANiqykdbZBTMtkOnvxPt3X/yG8VjvJyjx7Q2Vy2Qp1CTI+lZ8ZPcHd8Tr7NtzJif512Arn6NRUTi/k6A44mE5WcTj6KBbOoSg6VXk340md4aVxxtsGiXiEZLDSKRYA3KUc4xN/gcs1Qr2ewuZZxKjpxOPfAsBxZ5R0dYntxQG+tmUQQ1b44vv+E1+ZFsX69Ilj/MPv/D/865//8YXMpRtGZKPIDLoWSVocBZvn4hxSE/4+Ud+Yxs1tdxU4+NhX+NJHfu3Cvz8Xi/M/Z5f52e4wP90VRqpX8MVPsWPDm/ijkW7OFyu889BZfm50ksXKVdQszW5SoP+qIy+WZVFfmscItaEZdTzZHHPuMAOBBWZ3/xFF5y2cB7sE1yNJ7wH+5CqP/46YS2rhViE0tCJJ0hSZLr/jsrZ5C9dAdu7aXaQmbpOspLl0mZphMRC6JCNpLeaR4KbkdgB7hsIsZMuMt6SeLbTQArCn737KNoNq0Ym29DlKLg+WaeKWa/hyNfaFVZzJDbijY0wqYga0o88kkPrPrLdO87nRH8HV9l+ZKd3NkexekqffQTb+kxS1X2Lm8U4KNYlCcepCoGyzm5RuuFtdmmEDIlh2rlLjSHaFRcQLLnf7oHALu+OWBWefYqrzPuqySrduY8hhZ5//Dpg9gFkpkl5cIC/ZkZU89rSNmlUkpOY45R4matf4cCCDr1rkp9sMkCRqwU46tBJavUbcDt8Kq7xLsjEdW4DkOLPpRlFvd2DqDrBMZCnFhmWD8SVhBLWxw8tA2EWxalyYZz5ZKDE0N8Gr3u28aLZROpEAAxybfaS++EWyLzyD0yqg+ytUc20UqeOVxX0yZRMLbLX5edoBVdexDw+Jc/DiX/Cqy4OBdVOmDU0oDaOFfqsbgMns5IXXfM47cVa6SVbmGJESVAwTKVOlFI3wihrnj46UaMsXeXTXmzhan2NIVhhbyNITdDKTKuJyDVEoCtXOqex9+Cp5opklCusu6cT0CMIR2ZSiWl3A5xU25g5HmGq2nxrCSCE9H+PVxNPoOBk+muKJ3evpLuf58/YNfOJrj/KVj/8XjHoNyzTJJ29Srq6o0Lnz2qGyTdOG1zoxB/rArF00sVoDnNm/j7kzp8jGl3kukeW3z87yUMjL7w03ukALx8GsIffs5ie7wrx0z0Z+ua+Nb8QzfORc7MoPHGiQpGtI7XLpFEqljN/nw9I0/NkM0542uoKTYEn4O27+O3c9XJMkWZb1wvUea7I336sIN9qMKxTsfQ2HuxZWwPUykpq4TbKSJhqkt++yjKS1Ikk35m7XRGsuqYUWWrgUOyI7KOgGRl6hWpki6BeFbTweZzBvcLwjhJXegeaOU/ZZzA3+I7YHn6JeUnj0mbexL7aH7T0BNnd6GV3MkXbKdKcNOu/0U7IZVHIa1JavCJRNNRzHcvHLZ4oeDnlRJVYZLPveWx8su3gCcvMcbruPgKrgURUGnHa+7b8DuV6mMPokhlGnYGhIao5QWaVmnkSVTGb865ElifqEkFpLKdF1KPuCBOpxJCx82SRf7dYIWhJDM8K6O5Y20X2dWKqGaW9I0kIFOheqzC+L+8r6Ng99l8wzZ+sGs+UaocVZjvq28VffnqZwaAElpBP9tQ9iGxzE/dX/haSYyD6DUr6PpGLQppqUFFhq2EzX5mNEFxdxbNqEpChi7ur4P3Nw4C40WWN7ZPtNn0q1YQPeVhX3nYnMxXmc+nwByZLJaVMoS0dQZAn3bIkpXy9ppUSxVOA3Ds+hGgZfuGMHW9MJzi8X6PDpLGYr2PVBSqVpTLPGkYVe7soJowXXzot5To6+Hqp2Gde9WSRzhGJpErd7A17fdqj7UPQl0qlxErPTLJdnmCmcJvvcNOGqxR8bCd719D+hfOnTZNp7efBDvwxc+X29IXTvgvljUHuNgYllXels10QzK2mNJHe1cpnFCUE2Xzl2lP84Osl6p85fb+pDaRK2JrFrmDa4VYXfGOzgnWEfh642PzjwAEiKaBpcBa+Oi+9Bey6PJMn481lmPFF8wXHsxQ6k1NqYS30HbdxauCZCw8ImsXj9IrQv5GSqJbdbGdm5a9t/N3GbONxdZv9dyUNhaW1MG+CSnKQbc0jsDTrp8jtac0kttNACALqqU3PJqEXQtCBtzhkAFuNxdtnsmLLMcUPIsHrf/Alyw4/jnXmAnrZfZDbThWLV6XHLbO70Mb6cZzYgoy9V+ML4XzPeaUBCR6unLnSS5q/oJF1edPo1lQcCHr6+nF5ZFty+VUjubmWw7JknAHg2eDc9uujYDzrsvOTfjinJ1MeepizrWEjYbDUGJMAUc1XJkLDPrpwXahJjOYHNFqbo1lHMMj6tTN/sefaHFVIq3F8RxeJSoYKrXdzHnMEQyDI2PYMzWaO+XKYv5MRlVy+oFCYTBU41TBuUVJK86mYmWWJsPIXrjiiKw0HnJ/6QjGrh9pRAhnJ+gHnVJKRA0iaRsgWRLBNntUxodBR92zZRrD/1X8AZ4oAmsT2yHV29eUtmpUG4lZxFm7PtMpJUnRbD+vXIEsnJaXb3+JDjZUYdXrZvfIq50EmGKjrRXIqpji52TezHMC1sqih1y1YPllUnm5/g4IzCnsIR6rJM/56LOT6RgJvFh21Ibovl2E4ymUOEgg+i2zuRtCwA02efIj47je72cCz5Tahb5F+aJzs7xaZzx2FwhM+84yf4zbwgDK/tfN4QunaLrlBz/qiJzKyIC7kaSbpgAz5589u9DubPjWGZwlnxn146gEOR+fttg7gb9v2AkAh6OsHbcdnvbvc4iVVqLFdfo2hxBOAnvgp7fvGq2zzeWEToO3YEV6WGjMWsJwz+cWwzAarTa1PLtUjS7YBVOtz1BV1kSjXSxRvrBHxPoV6F/NK1ne2auE1I0kS8gNOmEPHYL5o2rHkn6cbkdpIksWcoxP7xxGX2vC200ML3LnS/D1dZQfHcTZd1kLKqcW5hkbv7QuilEi/qOkoxhCTXcT62k45TH0CpLDIZ2UCkOs/7vvgIRW0/pgWTLolqosK5xXG8Wx6hmtWwUyTqEUXXYrZMuZCnlMsiKyrZ5aUryNAjYR/T5SpT5RXuj03J3cQ+KNyi7vjZp6BjByfw0uMQJMmvqWgOH7OBTaixlykqogvjUmTClh1VmqCGQj00jGVZVM+K+399cQnd3knWJY4vYi/QuTiDIUu8ElQZkSap4iRXt6FEA3i9Xto7OpGdbupVUYx3xeusaxMzuZ1+HU2RmIgXOVUoo5eLpCT7hV3fTx3nHVEA9M2bmff7CPkEGalm25nULAKSQlozyWgRXPUyDk3Dv7QsTBvOPAGT+8je/yucTp99XfNIALJdQXapwuHON8B45mI8SnUmhxKw07X1TuoViW36S1SqBsWKRCrgYE5OgaHjL+ZZCEUITr+IYhpU66KgT1REwX588ijFqslIfJ5Mp86WweiFbbS5q7C3gDyqkkvPY1l1QqG96HonNu85aoUQydS3ScxOE+7tx3CaZOxJks+dZ+xb+5BkmV3r1/OlXRuZ08XfIL60ePMnpLtxPl8ruVts2I1fav/dhK8HkNbM4W721ChIEtm2bvyxCT6/dZCuxuLABcReEV2w12C7V/w/OJq7Sudn8EHwXN08e3Z2BkNW8Jw7i7skFnqtLglLK2Gf0tGi0av+3uvFiiRJkqQfWs1zLbwONNuLK5GkpsNdq5t0beTmAWt1cjt4w0nSZLxAX8jVsP9ukqS16iTduHFDE3uGQ6SLNU7OZ2/xTrXQQgv/HtHW3oe9pnA6U6HfOETa6WFhOU5Xm4feiRlebvfQfeg3mH/yY6SnxEJgcTxHoapS6D/HnJ7hzKzowFRcohS5Q76XD+z6fqoZGVkCozyL36kxnymRXhBSu871G6hVypTzucv2Z9gpCv/ZlUgSNCR3xq1xuSsmYfYA1rq3MVuu0n1JsTjk0HkluAtX/jyS2wdAh+FCQsKlxJiwdxPRnRjJJEZGSAXri4voehdprYCFRFgv0Ds3QTAd52mpjk2eJF/vRJJt5CSIRqMEg0Fqmp3sUgxJV+gvQkdY1AuqIgsb8HiBk/kSA6lFsqoXADuw326hhoRcb2lqAjDQogaWBdV8lJgi4bdUikqZshbGUy7TbbcjWxaOzZtEFyk4xMGOEUzLfF3zSE00He52RncymhjliUnxPalO57D1eFi/S5SgkYz4+ynLZYrTQyxnQsyXVQLFPDVVI+20c8/CSRZTDWv0XBRJUkgvfYo3tR/At1DAGq7j1cUCYn15GZ/xzygOA/+jEk79LKrqwee7A7veiazWMApbqEuvkohNE+7po2fzNg5PPolmarzlzT9D5/qNxMZOsSfg5nN3jFBwuHh1eubmT4a3Qyz6vjZU9gJJ2nTl76g28HWvWScpNnYSf5uLLb4lRrITbNJe84ZCQmz7KvlIW90OJODoSvODl8C0LIqL89TdPuqqjLdQxARCXcLRUjtnoUYiN30818NqOkm/ucrnWrhZ+HpB1lZBkkTbvGUDfh2slJHUxG2SlTSZKDIQvmQeCb4Dxg03QZKGRK7E/tZcUgsttACs7xNyulMLSYIkKbtcFFNJ2rw60dlFUi4H57U2Og0b855O6oUFluLrMa0qpn8/AInKPKgS+ZooYt+tvRvXb/4Kg2NChjz2ymO0e3UWMmVSDWe73i1i3uW1krtOu7i+xcqr6JS3bxWLUbfC5e78s2CZpAfeSsm0LsjtAAaddp7ybEfGoD8k9qut5AEsvLZljjsHabdrVM6J+Q7Z7aa+tIiud1KsL0JwkKijhC+f4ZHxf+PlpTSKNIlkrsMTHiaRTBJ2uXCmk9RtGsVcHLWjTD9VgoE6tVoa06wzEHI15HZlNqUWyKqiw/EQGseqVTJFsW+TR0Wmky1gYGa9YNeoSeAzFGpyGksJ4yoWaEunUYJB1IWnIT4GD/0e++b349E8bI/e/DxSE2pAx0iV+cDWD7A9sp2PfPsjnJk+iZGpYOvxEu7ux+bQ6dL2EtU1bAtlDns2E6nNEatCR1F0Gib6+nn31IvsP3aWTdoyUymLLZv/nGK1zk9t+wLx361i3penUJgk/ulPc/odD1BL/wNT5/vRZyQi6jLBwP3Isoaui4VXXduIpJbQvBnCPb2su/Melioz1F0m7ZUeejZsYWniPNVSkZ0+F5YvyPT8AhXTvN4hXx9du650uFscFbNH13Lx9fetyUySaRjMnTnNDn2aH1Ke4QNDB7H+Zi+8+JcX66/YIfGz+0qS5FYVhp12juZWX8eOFcq4U3EchkUx2o4nX2DJFWQgMItS8aLES8gu1y04uitxTZIkSdI7JEn6C6BLkqQ/v+TxOeAm/QxbuCoUVUisVgqUDTY6SS2XsWvjQkbSCiTpNshKumD/HbrE/tsZFonZa4GbNG4AaPPqDEVcfPt8ay6phRZagIEuEXg6tRhHApxeFaVcwquBaz6LZJo8G7Hos8uU9RFKy2eYyPagBV5CJkug7CEuZTA8GolUkpq9gPXkOSoTEwRnRdE+fuxpQZKy5Qv2372bmyTp8jmPDru4vs1VVnF9awbLTnzz9Uvuzj4JzhATATEb0nspSXLYecK1EcOSGNAFqRuxbMgkcSgVjvo20m7XqI4LkrTkGKI6t4hd78A0S1jREaKOMhYWWmmBHhZRpQqWNMJw104Mw8C2+CWM9t/l7vc+w/YPjjF054e5672/xLbS+/jmvl3se+Yefpxn+P7FKg8dSPLumBO7cz024N2KDcOCF86KfRs/+iopmxfNW6Wc76LqkVEAX12mJMcx1ADuSonA2BiOzZuQnv849NyDteHd7Jvdx72d96LJr20r3DiUgJ16qoIma/zZ3j/Drbn5zJN/DYCt14Mky7QPjZCOZbi/LwjpCi+5t7PBimFgEi0IkjS7bT3bF8+iFKvcpUxSOPEMS4Vt/MY3f53iq4+gxCXqXfDyi49wbvyPyL7XwKLKk2NvAcCZqaPqdwOg2wVJ8gajWJaErxdC3b1suO9BfuFz/0TkkfXU5gt0hTZiWSZzZ8TMWU9HB3o2xf9eSN78Cem+U0jnLg1BXhy9utSuiUD/msjtlibHqVfK+GSDVL2TF5YGqJWL8ORvw59ugs++E17+HyDJ0HH1QOHtHucNkaT9qRz+TILwwgLzkS682SxT7ja6A1M40sOoNxfJtSpcr5M0B7wClIFDlzweBR5Zu136HkVoeMVAWYdNoc1rb3WSroeVgmQvxRtMkmLpEnXTupiRlJpYu3kkEIWBYrspkgRw33CYAxNJasbrWBFroYUWvisQbhMWzfVcBUX1E3KLxbtYfBlZUtk0cY7nQnWcsoeNDjvxYpFRqthDz1O1b6GzMkhZqyN5KhhFJ9HUHGkCRD78YZQ0UIe6tYyzmmUhUyY9P4cnFCHQKRbAcsuXkySHIhPS1NV1kqARLGvA6a/f/EkwDTj3NAw/xExVZNK8tpNUVJxM14IMch6kOiPoSLJwqDvq20C7TSNz9DR1RSfj7sUq5LAjOve1YBdeJYemWNRTCbYqougt1NvpkMV8jeGcxci5mDy3lbmXI0xNvomlIz9EOvXTRE+/H60Ywez9NCP3fJQHSq+w0eih4uyiE5nd2zsIumw8d3qJSilLxfw2u993CkI1iskRUppBWBJr4kktiyWpeOsVnKMn0X05YTT08Mc4kz7LUmmJB7ofuPlzeQnUgMhKMvM1Is4In3zzJ2lP+zEkE7ldyCoDHd2kFuZ4eGs7lgWpgh2HV8JQSngrFrZ6jamN7QDcP36IuD6EUk7z8c89ioVM6FCZ2qfbwARlqU7hIYvSHhPPmTCTdkE+1EWJiXnxPbfZQkiSDXc0TjnZh7s7T6hbyPZtugPnHRFkl4pz1o4ky2JuBxjo6MBfyPCpqUXqNzvT2+zINOeSaiWxqH4104YmAn1i/KB2a13fpk4JmZ9XKpOpd3DKvIvHjffBLxyCvb8JhWXRXW3bAnb3VT9jh9fJYrXOwtXykq6Co6dPo5oGbakMc3YXnlyO+TY/HmccPdmDGgndsuN7La5nAX7Usqy/A4Yty/q7Sx5fsSzrjfVN/m5EaEiQpBVasn1BVytQ9nrIzgkZ3Wq6MW9wVtJEoyPYf2lG0lrNIzWh2G7YuKGJPUMhilWDozOrSLZvoYUWvqvhDorCxFlSqaoROnWxyHckNofs9XL36BGmA37OOM8xbJcpevs5EtyHpNNzVVgAACAASURBVBYxvN9HWRUFrFseB0ujmF+g6GyjZkhIloQSB0cIKtOnieerJBfm8bd34PB4UW32K+R2AF12jdhqOkkA7duEtPn1uNzNHRautOseZqYktnvZTFJjTmpU6afPmMKjLNNtepAk0WU46R4iLMss7T9OydNB9A4xn5wfE4YVZV8QCQjrRew5g7sC+zEsiTPZIq6kjMOyYQYL5BJbmYltJ3Gqm7FjXSyPv41thx4kUnoPdz/yKGrovzGnVEjt+FMOb/plcsoh+vrchH9ohIfX21CLn+PFl/bS82AMGRvmvw6TOP12Zqwq/XZxn5q3C4fBXkVCNk0cxW/DpvdAz13si+0D4P6u+2/+XF6CZlZSPSW2uS2yjYe1Bzlvn+GTx/4cgEBHJ5VCgXsGnKiAvFzmeCiCvZpEMXT8xRzTLj+Fjs08PH2A7d9+nheqG5gniF+p4TwzimIaaNMSdm8399zzLNHxvXgfH8AT8JF1OmFK5ex5MSsmSTK63o7BDOXECM5wnEtN/CRNwXV3B9UzafoGtjPbIBO+SBSlVmUxleax5Zu8d3bsEPbYTcnd0ilhY389ktR0uEu/jnmoq+CVY0fJO/04lRwly4dq62b+7GnM4ADs/XX4+QPwcy/Cj3zxmp+x3dM0b1i5lrUsi+LBb2FKMj3RDqRCHsU0qTRG+e1zvjWbR4LVzST1S5L0ZUmSTkqSNN58rNkefa8itA6MCmSu/4XuDTlbgbLXw2oykpp4g7OSJpskKewUttyZ2bWbR2pC0W66k3TPYAhJauUltdBCC6BqGqZDxV1WmatKdMvfwkRifGEJWzjIPccPA/CFaJ2qVWKwYwQ8B4kUh4kobhKufgB6MqKrUn74TQCc3T+B495fQs04sQdMrMVJAFLzMQLtnUiShDccuaqtcqeuMbfK1ekLLnfjLwjzhZvBmSeErGjoLcyUqxcykprodwiSdNCxEQWTu9Tj+CsBNGmCjOUhp7qZfWwaWypGYOdGRh4WRe/Uc+IaW/AIQ4WIliNY0umpHydZcXKsUkRCYpsthOrMU0n34lRtaF4/cnqRu9wammERfP9GFF1jsOf7+Mi3f4vHYu/CGSnzc2/5DA8PfJ7TY7/DQ+EP8fa+xyhnA5z7xgDTj/8e5VM7wJQ4W6/Q1QiQXdDFcQ0XxX1LD5Thrb8LwL7ZfWwMbiTsCN/ceXwNmllJRoMkWaaFL+HA7NT4/MnP89j5x/C3i/t8MbHEepsddbnEQX8vvnQMpebGX8wTkyLYeu8lUsqg5tL88EtfYbbuYkvuPPZClmgpgz98FyVPAjXtJXDu/8a98cP4EwXmvUEcCy76zxsUjyxRT5TQ7Z2Uy3OUlqJIskki/uJl++2+pwNkifX+O1k4N0a9WsUTFgX85nqRv5haXNmi/mqwOQUhapo3LIou1YpyO7ilkjvTNEmfPY3p78Ehp9FCnVTKEaqlEvHpxnYkSexr0xzrKtjsdiCzOpI0mSvQd/owlurEuHcvkYIwj7L35LEMFW28/oaTpM8C/wMxh/Rm4PPAF9Zsj75XsUob8P6Qk6VchVKjtd/Ca5C5QZIEb5jkbjJRxGVTiLjtjQFLa23ldiCykm4wJ6kJv9PGuqibw9OtRnILLbQAqs+Fs6zwcnIepxSn7HSSTCRwd7QzPDtJMF9gLNBBwvoGdlniVxd+jHcsfh8D1iTT0e1gQaA8iU2Gg4U8fkWip/2tqG2bsVU60ew5gpqBzahQyefwdzTmQiJRsstXdpI67TbmVuNu18Sm1ym5O/skdN8FziAz5eplUjsApyITMmq8HNhOBY03S2NoVR+6NMuC0s6OWI3x589hr2YJ7tqEo1ccX/JkjFqul4JWxlAcROwF5KrF9rLEcsXJmBbgnFKm0yau5ZVUDw5Domx38xbnZrqROFI0yBqiIO/0OzAljdmzgxz+p408MfkW2mz7WFj4CpHoe/jd/b/F6Df6KeY247AS5Ny9qKRZqKqEGyNGcZswCBg6dwrNXUe9/2cgNESmkuHo8tFbJrWDi1lJ9aQ4vvpSEatqcNeuB9jdtpvf3//7xHVB1tILc9zV5oOyyUv2EULJKayaE18pT1LxwUiUtO4hJNc5F+ihXrd4U0J0eV555P1E7/oRTLNEZvooAKUDf0PAn2TaFUVN1NhWDZH8xzEW/vgVNCtKuTxHajKPUbMzN/Ps5fvttePcFsGXDSAZMgvnz+ANC2vqH7RbnCyUeTpxkw6x3btF59I0BUnSnNdfVA3c+qykx8fOohfz9HnbUCQDvzuCaYmOcGzs5Ko/x6nIrHfpHM2uLAV89rFHcVRKDMgaR3c9RKiUByDctoya7cdKzqOukf03rI4kOSzLegaQLMuasizr94B3rdkefa/iAkm6/lxSb0OaNd2aS7o6snMrmzY08YaTpAL94Yb9d2qN7b+beB1yO4AdPX6OzKwisLGFFlr4rocrGMJVUjjXkJrJHif1TJpIZ5iC5uCuiVligQij7iVOVqrcXV7PO8tD7Mrlqege9KqNkqeKjwJavMZ9bgXTsqhX09gqXchanTvv24y/LiRP/nYxh+O5VifJrpEzTHL1VS4idmwXK+4nvnLjB59bhPkjsO5tAIIkOWxXvC1aypGI9HDEGuJNTCJRwiknmVW7eeTlPN2NTCLb0BBqVGTEOOoZUmfeQ7kyT80/RNhewKEa+K0Sy2U3Z7xu/s3MgUfYopczPWhl8EuDbPTu4tUoxGoWM6dFh0yRJRSXSltqgbwV4stn38OJ5Y8zPfWfOTd2JwHDg5RexLRCVFWDtH8E2VGgZin4G0YMWcWHatTp3P9NHBEL3vT/ArB/fj+GZfBA160jSZdmJYHIRwJw9Pr5k71/QlAP8pHR/4YkSaQW5njzetFJWMx7qSllajU7/qIopifCWY5vvJ+h+bN8q2MrD00fYMv4GFVNo/6ud+Pz7QQgnTyEpCsYyVHctnHOu7qRalX+evYf0H94BACtGqZSWaRWmqK4tIFMbv8V++6+rxOpDoOebcyeGr2kk1Siy67xF9M3GSzbtRsqWYifEfbf0U0gX6eEd7eBqr8uknTsmSf4+1//Jeo1UTM8dkDI/SKW6HBKMQ1N9mJ3+pgbO3VDn72tYd5wvVqicv48s88/Rd7p5l1/+AkOJQ38xQJpj4seXwxPeh1mbgE1feSWywqbWA1JqkiSJANnJUn6BUmS3gdcfRqrhZuHOwo2zyoCZZtZSS3J3RUwapBf/PdDkuKFy+eR4LaW2wHs6AmQKtaYSd7aYdAWWmjh3x8CkXZcZRW3awjLAo/HwFXMY3PZSNnd7Dg/Rlmz88/tIZ50HmG5buBFYvuiHwBb3UNOyfFej5ffssLUZDiwvEzdZ2AzxdDBXQ+sx1cTJCnQkFh5w1FK2Qy16uVd8eY80KrnkiQJtr8fxp+DheM3dvDnnhI/Rx7BsqwrMpKa8KeXSXgCfKu+mR5rAVU9jCRBKtGJLMHOreJ99uFhFLcL2eWiI1QnOb6FzHIeqW0zEb3IhuEgAMu1buLuKk9ZFiXPFErRj1Sx4Su52KtsI16eZXo4T8ItM3FKkKSSYVLVFQK5BeqSKN9mzkwQXy4xeybB7pi4/9jM9ZhmmK65fdR7hGGD23RSVKCk+HFXi8ipEo67HgCXmEnbN7sPn93H1vDWGzt/K0BkJTVI0nQOyaGihh0E9SCffPMnSVZT1Nwqqfk51g8HCSCjLJeZGmwH006gQZLGfRWSO9+CbFk8NPMKb+r341LtLIfDSPFz6HoXNluEdPU4/3uTi9L27Thd00y7RYekM7/ImcafVauEABPNmcPIb8GU5igWL5ez2bo92Pq9bAjezezJE2KGzm6nkFjiw71RDmQKvJTO3/gJaYbKzh5sONtdZx4JGg6+fTdNkmKnT/LM//orlibPE5+a4FiuSOn8GDhdWI0OkGEG6A7p2Fw9l3WSPvXcOT7x+Onrfv52j4N4rX5NeWx1NsaZD34QZy5ObvNunJ2dHJ5J48sXiI+40GQDZ3od1fwCSycfox5fmymg1ZCkXwKcwC8Cu4AfB35qTfbmexmS1DBvaAXK3jRWGyTbxBuYlVQzTGZSJTGPBML+2+YB163RdF8Tr8PdDmB7jwhEPDzTkty10ML3Otrb+7DVZTZ5NxOvS7hsU2imQaZWIGX3sHX0AABnw14mHcscKQpjouHYCI5qBbsVYHf9Dn4y4+IsBsf1FAlHmGREx2aIBSNFSxGqN0Ij7UKG5Y0IeU3uiqwk0fVYtcMdwN3/Cew+eP4Pb+zgzz4Jng5o20K8Vr8iIwnE0LlrfoaipfGyuRkJC5f9q2Lfc/3MvzWKvDCNpOso7R28Op1CbWsjqJeQZJg9PILasxNdqTPoEnNKcdajVvwk0Sm4p9Dz/YRtde6wbaQsWby49P/jyywy0aaxeDaNYZicKZaR7OArJSgY4hwGck78S7tQp4bxlZapy07O627ajI+x7tw/Y9t2DwAu00FKq1FVfXgrgqw6v//DAJiWybdi3+K+zvtQZIVbCZGVJEhwdSaLrccjVBfAptAm3jf8Ppb1PKmFOYKdLkYqMnK6yuGhPmTLIFqqIFkWU24Z3FFejazjkemDTG65B9vyAmdCfUyfO0U+n8fn3cEh3eATEYt/ePtb+T/svXeUHNd95f95VV2d4/R0T84zGORAggBIQIyiRAVLlCwd2bJlK/3E9ZEta7WO8m9tr2T9tLa8ltdry2sdS5ZXYSXZohUokiIpJhBEIkHkwQCDybmnc+6uqvf7o3qQAQJEIEXOPWfOdFdX91S/qe56993v996AL8GE1zrHGgopDs6nEXYVrRACwO6tEgxsBSAe337esXu3tuASXhivIE0TfzhCZiHGrzaFCWs2/ufY3JUPSLjXOk+PPQTFxKX7kRYR6nhFPUm5ZIKffPmLuP3W9X52eIj/PRGjbXaMps5+XFifR5MgrSEHerWB7EKMbHyBUtXgH58+yTd3jqFfwgl3/SXMG/SFBcY/9lGOuzQUKWm7416S+Qqj8xn8uRzZPutcc6S7SNpNfpxdyfh1mpO8LEmSUu6VUuaAhJTyI1LKX5ZS7rouR/NGR7j3ZUlS0G0n4NIYSywpSefhcoNkF/EqZiVNJYsYpjytJCVHoK7TOqbrCdvVkaT+Bh8uTWX/ksPdEpbwhseiDbijIEnjIaBaZg3JQpqk00ddbIZ6cpRcKzhaXkbeMJjIxEHa+I3dT/CW5CY+FfsQuYjg0xSYzs6T9cX4g3obo2qt12HiMI3kKSpODj35MwD8YauEKXOODXhzjaRcVlbSIlxB2PJb1uRz5uDlPSc5apk29L8NhGCi1gfVfg5JKqRT+GMziIqJIvsxpQOvMUDJsPFicw/uVSHKJ0/i6O7m2ZNx3vuV58n5QshkjNbVKRJDN1H29wMQze4jp2uUdS8RWcCuVJCeOZzZDja4PbgVJ18kQ0pTEbNTjDZomBWT+dEsA7kiYTOBgiRu8+MVBv5cO5pm464P9aNp00y4W9nrMqgvJZF1GhOFKnalgtdwMKcmyDvchFMz2HwajrWWJfVAfIBEKXFN+5EWoYYc6KkSZkmnOlfA3nZ2aOrmps2kXBUSM5PYNIV1HhdIeCHQibuaxGXYCJRKjDtd7IxleLRzMw2FJB0PfxeA0Z6NSCnZvXs3XrGafQ6LlD8S7cLvyBJ3+TE0O/5SmcGRGdSAHTVjKaC+qJ22vnVU83XMTj9z3rG7VoUx3ZJu11rmR4dP9dC5VYVPtEZ4KpHl0BXkBAFWaV3rzRY5h5dXksAqJU2OXZGDr6FX+cmX/zvlYoH3fvZzuHx+jo6P8/joOMF0nEC4m4BqERJDhggKMAyrTHT6+ABPD8bIlXVyZZ2BmexF/84KrwubgAPZs6tSjEyG8Y//P1TnY0x1tDDV0Mbmvh72T6Zoyc9jMwxor5DKR7AZGsONDTy95a2U+zdc9nu8ErwsSRJC3CqEOAocq91fJ4T4ynU5mjc6wr3WhP1lGus7wu4lJelCWMxIClwmSYJXjSSNxBed7c4ot7vepXZw1UqSTVVY0xJYIklLWMIS8NVswI+M7ONgtYF5YYVNJ3MJUg4farVMObcLw7GcotmIKE0zUFaYEgmEWSVQcbLdt4/BVSepKIKxTIpj7WWORep5LuxBydtJzRwiZGTIO4IceOxhKqXiKSXpXBvwRruGAkxfiZIEFkm6EjXpkT+yLJnf9HsAp0jSopIkpaSUrzJ6cJTGtItbTpb4gO6hbK4AYL7k4dGbG2h02CifPIm9p4ehOasEa0zxoM/Ns/xNClKq7DtgA8CtlomVfBjVNMvlLC3eGYQiyVV8OITK4WKJHaiYvjCl6QlGo9bzJo8lGMiVaCpZYzXvDBNWdRTTQf+WZsLNJUq5DFVbO9OqwJdUUJoCHJqaJ+JewKvbmFUSFBxOGmem8Wy66ZSi8+zUswgEW5u3Xtl4XwasrCRJ6VgCJOeRpI0NG8l6dPRiiWI2w/q2EDYFFkpOtEoSm2EnWMwxarZxoFBmZ9Nqsk4fN734BIZQ8G7czIoVK3jhhRfQMl0cYD0uYbKgeIlnmpBCIR9pxFuuMD4+gRp0oCYsZcXf5KKlL0R+bhXZ3G5MUz/r2IQicG9uJOpqZ3bvAL76CNm4Nf4fbgnjVZVX1pvUstGy/obLI0nBDquP6QocfJ/55teZHjzKWx/4FJH2Thp6+vgP4aZlxponKbYWQloaKTVsrY3Y0mUUNYJqszM1eJSfHJzG67DOvd0jF3fCdakKyz0uDmROz2XNQoGJB/6TtXDwJ39ENRHj2PKbWO9zs388RU92ConE15ginepByAyHe/rYu/5NFNzXJyvpcsrt/hYrPDYOVn4ScPt1OZo3Our7AHm6P+UiaK9bIkkXRPoKgmQXEWh7VUjS6JkZSaZhrUxeb2c7uGrjBoD17UGOTGeo6EuhsktYwhsZvpqiU6/7eD4xz7cykqqoYsz+iFzDUTTTwJ49gKlomEEHqpigpPkYUmsTdiXNXzX9CyPxY/RGPBxy+DgZtV5zxG2i5UPI8giuUpK0K0wpn+Pwk4/hrQsjFIXsOeYNNkXQcCVZSTVIZwBu/SQM/hSm919652MPw/FH4M4/OrUgd2ZGkmlKvvPnu/naf9nO099JElbu5r7BKmsVlZhhTWxjFT85j4dGo4I+M4Ojp+dUdcjhsgM9FiPcEMHftocju6rkhTU5X6g209gTIKTk6PJZ163MujZ+ruxjp6EgEdgCITKz06iaSTXqYGIgwdF8kd7iAiXFQcwVImzqCATBiIuxg5b6F1A6kSgoMRVbSzPj8SIN7hjBCkzWbMA7J2fxvO2XTw3Fc5PPsaZ+DSFn6IrG+3KwmJVUOGgR73NJUtAZxN9gKRjJmWmibV6apQ0lViZj01F1N75ChnmlgbCi0xLxs6tvMwqSk4FmVnU3ctttt1Eqldh9XGdcdPEBzwBumeMldRMA6UgrnlKVwsIc0qthpgR6ScUdVvAEHcjCeqTI84Pdj2OeExRbd0c3uqzC0TL++iiFdIpqpUxAs/Hhlnoemk8xXLhCp9nFUNlAm6WAvhwWHe4us+Tu6LNP8tKjP+Hmd9zP8q13AODrWcbu1mVsS81g0+yUcgECziwGQTybGqFqEvU7cfrbmBw4ys8H5njPhhba69zsGbm0tf46n+ss84ap3/t9igcO0PKlLzGcimHYbDg3bMapKuyfSLGhGseIgNtZRE/1ohdnGW20PoMdKf1Sf+oV43JIElLKc20jlvynrwfCtXSs+IlL7tYZ9jCVKlK9RL3nGxKZabB7rT6jy4W/2cpKKl9cFr4eGIsX8Dps1HvtlgJmVq+/sx1ctXEDWA53Fd1kYOYVWpkuYQlLeF3AW1cHQvBrre/jqfd8l99rLFJwVPAYrWQ9Vt/Ar4o6hJTYw4JUtIKUJpPKAk5hRwKBapCR5AmWuQxGvQ2Mhy0HuzG3QCs1ojozqJUCc8JPU/9KDu/+BqZp4K0Ln1duB1Zf0pUoSTu+/y2+8yefwdz0CXC+jJpUKcAjfwiRFZb6VMOZGUn5VJnUXIFlmxpoWz6PXvg+u/tVHIpgbpmlFi3YmkAImqethT1Hbw9j8QI2RTBl84JhoBXchFc8gl6BhGmRgbS6nBV33AN2jV7vKKWqg0jPOh6se46Yas0H7C4n0jRZlk+x0Oli5mSa8bk8jalZFuz1ZOweAjW3Mn/EzdjBfdicDSxriuKQOZwZA0d7L4mCSr0zhV+XzNst5ahzNo5nqxUYmyglOLRwiG2t1yZA9lwsZiWVBhPYwk5Uj3bePn3da63xnB4n3OJlVV4gqibDIT9U3QQLWQxV5V31syyP+vhZp9VndbSui3VtAVpbW2lvb+ehinUt68/9K7eygz2NmwmaZRbqW3BXdcKlBKMii5mpUMlqaF5r/KraTUgp2H7op/zbi2dPkxWnjbQvSagcwRe0eo2zCxbhe6Atgl0R/MP4FfYmtdRI0uWoSHA6Kyn58iRpbuQkj3/172lbuYbbf+0jp7bvau2nYnfSPHGCxt5lxKdLuJUkplKHs98yE+mMupCykdjYCEa5zAZPmq3+FHtHE5d0r1vnc5PUDSZKFfRYjNyTTxJ+4BO4776LgR3PcrxrJTdH6zEMEwbS9BZi5Put7xVbqpf5xAli0Va8pQJzU6OXNyZXiMshSRNCiNsAKYTQhBC/B1yZ198SLg91iyTp0n1J7WE3himZTi05jJ2FzJTVj3QlfT0+64JM9hU0Ul4FRhbydNa7rbKFG+VsB1eVk7SI9W3WCtaByaWSuyUs4Y0M1abhCQTJxhfwe3ppc2i43Fl8uo+SeT8Av1Vcjz1ZRA3bKLkVbM4ZqkqVDRVrlbupUM9UcRJHcpJ82EfFZsNfqDLtVrFX21C9ZVAkSdVPy11xWu44yPED364Fyp6fldTitF9+oCwwceQQsydPMPjifrj1dyyVaPqlC++8/a8hPQ7v+B/WglMN42dkJOUSliPbss2NmNUTBKMqt2VN8kiKq8M8sb6Tifo2AEITlhpk7+lhPFHg7uVR8n5r4qmmwBGYpqEvy0zGIklK0+3MzVvkod07zkS2lYMjXgbz25hXTTQMFNWalHam5znW6QQJLUNZ7PPTpFxWGVnItK6Rbr9gcuAoUrait5ZZqVoqWim6HEMq1AknOUpkNEvV6fR7sYUs1ej56eeRSG5vuT6FRYtKEoY8T0VaxMZlb8IUksHh/YRbvPRUVUAy6K9D150Ea5k6Tc3TdHoUjtqC/OmWj/HQmrfQXW+5/N225TZOBEMEzTxt8iR32V6kbLOjyQrjNfOGsHmCjxc/xohjCjPvRGg5/umZk3z7RIFSooMtjSf4y0cHSRfOPu+0Th+qULGXrHNj0bY+Ytd4X0MdD86lyBtXoDl4wrD+12HN+y9v/+DlZSUVc1l+8jf/H06fj3d++g9RVIuIVE3Jg6aD7rFBirPTRDr6KRd07DKJdNRjCzhQgw7qbIJqNQrSZLmaJD46gC8zQrJQZWj+4k5+6/yL5g1Fioes7Crvtm0M7d1JpZDnUP9NbA56OXRgnk1Zlbp4nNwylXzVRTDfzGxmkli4kXA+Q7T9+sSSXA5J+k/AJ4EWYApYX7u/hGsNp9/ytr9MG/DRpZK7s5GZvrJSOwCfdfGxnPFuHEbjeTrONG2AG1Rup111uV1TwEnE52D/+BJJWsIS3ujwhevJxmMoig2vp49Wfx5vuUgqYE1sJ77zfYyETsbvRxgGWc8cSEFTpYmg6iRSbCBp1ykU5jAbXDglrB0pkXFoGOVmhGK5iW1Z/QK6fBqAZOIl/PXRC5KkZofGdLly2VluqTnru3/nD76LecvHwRW6sJq0cAJ2/B2s/RXoPLsHZ/KMjKRszbbaF3KSnJ4i1NjKm4oKzyk6Lpcb4c/h7bQW5VzjowhNQ2luYSpZpDfqpW+ldR2ozCfRtDDNPfsZKL+fnen7CLRu4uTRMcCk3r/AaLaVw//8IH/y86eYFToRs0SmUkXVNKKJeU44TFxtHtYOzYCuU3ZYC1x+U0VzKMQnBjENHUXrZDQ4ynqOAPBE2ro2+Y0gc0qarNPKxenacLo5fvvkduqcdawIr7iscb5SKHYVpaYeXYwk3dKyibzLYGriBL6wk4BNErJXmUJDGA4CNRvwUZ9EKU8iJextXEFbTwuKYhHF9mALU6EGevOTCGCL0OnQKxS8To6rVlVKkBkkklHHNLLgI1+J8cVHjtGzKkx+biVRzzBh7RhffuL4WcfnX2HNR8x5q3rjTDfG9zaEKJomjy9cYUXG/f8Aa953efs6/db5fIlyO9M0ePjvvkQuEeddn/ks7sDpMr6HYilmqwZ3HNkFUuLyW6RLNZNWbA3g6PTjyFZQ1CYkcJsvSyaTRi/lAcnuS5TcLfc4sQvBgWyB4qGDoKo4V6zg8NNPQKCO8ZZubvG7GT64AFLiSS9gdBqcTHVRh8IsJZK+IE3GFJr3+lRWXY673YKU8teklA1SyqiU8tellBfvxlrC1SHc+7KBsovN/uNLWUlnY1FJuhKcUpJmr/3xXARVw2QyWaTrVEbSsNUrdKXH/kpwlcYNAEKIU6GyS3j9YjZd4omjc+TK16fWewmvD9S3dzJzYpBqqYTHu4x6r/W9kPLUnObSJZR4GYSgIiCv5dAqQeJVQbQaIFCtI+M2CShFjKiTLl0hWvtqmTet78Toujj39D2H6Xo3lZydUvkk/kiUXGIB0zx7Jb7FYadkSuLVl1+hr5SK5JMJGnuXkZyZ4tgLL8Gtvw3HH4WpF0/vKCX89L+A5oa3fP6s15BSMnFGRlIuYSn17oCN1Nwsda5O/FLh504TQ7cm7f66CcI2gXFyGHtnJzM5Hd2UjD8xRVvIcvU7fugkDqUBpfISHk83+4oP4Cwa5EppPK48mmYwkW1l5egBNs4PYlZTNFTLmFISaGzGuzDDfEUnv8JPKGVd30wsFSqouq1STg8QzgAAIABJREFUu0P7Qag09S1n2DjBOmMUgO9NWZ95txliTkmRczixVzLUb70NAMM02DG9g20t21DEZXVtvCKotZI7e/uFS+h9dh9m0EFuPoYQgnAU+owymZKOajjxVEo4dJ1JhxszbeXoqEKwof10D9WLEylKmkb9TAxhQMN8gff5naTDAQ7ht0pCS1Y5fkxLQDmMphT447e28eWP3Ex14V6oNvAHt/w9Q6PfO6sMPbKii5KRR8YqCKGcFYC8OeihwW7jx/PX+Tq66HB3ERx68jFGD+zjrg8/QFNf/1mPPTiXpMWh0VO15pomDXgUA4U0ImDNneydASjoeJ1uyrYwDaUZcrkcpmnQ5lUu2ZfkUBRWeJ0cyBYoHTyEo6+PfLHA2KH9TK3ayAqvm4BmY+F4GlnNYrMVsNWXGEp14bXpDLd2YCoqXbYTOF1NVz9WF8BFz24hxP8SQvzdxX6uy9Es4bKykqI+B16HjZ3DS1z1FIyqRXSuxNkOwGddkMjdOJI0uWj/faazXbADrnHOxAVxDUgSWCV3wwv588oLlvD6wY6hBT7+f15gPlN6tQ9lCa9hrLrzzVSKBY7tfBavtx+nw4piqLgt0pD2hVDSFTzoTESiIMBX8TCdnKJ+IYsqVew4KZlFsKt4M2UaDWuVf1JYjlX1K1PsnV3PrPwkZjGEIWbwhSOYhkEucfYkrNlpqQ+XYwOemrVUpJvfcT+Rji52PfhdzI0XUJOOPAgjz8A9//XUCvoiFqo6pTMyknKJEg63jWI2jmnouHJh8pjsdgvy6X0YVYFDy7PeNmo52/WeNm3wFk2SuwoYQmH4yDBiyoPuSXLHA2vo9mswk8Ow52moZSaNZ5ppy1nqRE9qgkjFWk331EdR56aQwLONAtOYQ7E5kFWJQOIwnQQjLob3vYiitrLsllYWSiM05UpUVBtj0o1DLWMz6phT0qRcGr5CAte6dQAcWjhEupy+LtbfZ8IWcoIq0Jo8F90n0NiELV0lX8kTbvbQn3FTFVAQAk2q1BdLzCoNBEvTODWFt69t5Lfu7Dn1/KeSWRQp6UpnmH3+ViIDL/GB3mZwqMRMG2YoRChvnUvzWgKjbM0Zfu0WB4qi0NDRw+Szf4LLsYGPrP42j+z47Cm3O4fbQ4Ykakrgqas7S0lSheBd0SA/T2TI6texzf8SgbKmYbDje98CoJg9W9HK6AbPJLK8MxqEWklgYrpCW30VIUxEvUVKHJ0WgXW4BJqthczU2CkV9+YmO3tGXr4v6UCmQOHQIVxr1nD02SdBSp7sXsPmoJdsokQ1UWZCn6LSbb3OWKqbkjHBXJPVx73ccxC7PfIKB+jSuNQSwAvAi7Wfd51xe/FnCdcD4V7Ix6B48dUFIQQf3dbFw4dm2bVElCzk5riiINlFOPxgc91QJWnR2a5rMUg2OXJjSu3gqnOSFrGh1pe0f6kv6XWLsXgeRUBryP1qH8oSXsNo6V9JuLWdQ0/8DK+nH6fTWnX3KlXyNiceDP548MdsODHIS61Ww3nbiQFSrmbcB54AIFwKczzSgGIYzB2foD9s9YuMuGxgqpSzdr5x5DeYyVSxyRYUZxpfvaUGnB8oW1OwLsO8ITVrEbpQUwu3vv+DJGemGdj7Itz2O1YezeQLUMrAo5+FpnWw8aPnvcais91iRlI2WcYbcpCcmUKgEMw42amU8HqToE8TOxDGlAobjeepTk7i6OllaNqaoG7Z3Mz6O9rJOvykx6ehGEb3JCg35Di2+mnUTAnpKBFQZ5CGYDbXQFPOMgPoT41Tb1ifVc0fxEyncJYKPF8pUbUtoKgRilLikRXMkorDUyY1O4middBzUwSHMoq2ICjWzDii7hhCd5EUOTJOJ2GjhNAsArp9ajuqULm16daXP0GuAt6tzQTf1YOwXXyq2tG+HLuu8MLw84Q7IzRUPWiaSdousZt26vIFpmml0V6lzl8mka/gd57uJ3vWrLCmKLgjmOW47GVW99K+sJe2QhIQ5BrraExak/M5W5xWTy8ApZJ17rSvqCOfcHDg2x8leeIu1tU9wkPffz8P/s12HvvaEQp2J07dTSjcdJ7RyLujIcqm5NGF9DUeuTMQ6oT0hOWiew4Gn3+WYsb62zu+9032P/bwqcceX0hTkZJ3hLzkktZCxPxYmiav9fk26twkEjuwRd3gVNFME6fSTOUMwtcfUpnNlJhMXrx/fp3PTcYwmXS4cKxZzZFnniDYt4JZb4jNAQ/jR6w5rq0yS6XbxDQF+UwnidIYC5FmhDRZmfZD5fpkTF70zJNS/uviD5A8835t2xKuB8J91u/EpUvufuuOHlqCLv7sR0cumWr8hsGVBskuQghLTbqBJGmkRpI6wh6rjCMxemNMG+CaKUlrWgMIwVJf0usYo/ECLSEX9ktMUH7RIYRwCiH2CCEOCCGOCCH+W217lxBitxBiSAjxPSGEvbbdUbs/VHu884zX+uPa9kEhxFtfnXd04yGEYO09b2VmaJBSwo2qGri9NgJmgTl3iL7p42wZ2sPm+ByxYB16VTKwwlKI8t4V2MtlooUwx3o30JZPMp9x4Aku4KmUma3LoxXrqcSd+F0uptNFJqsbQIDqtohF5hwb8BaHNQG+HBvw5EyNJDU20btxC9HOHktNuvlj4KqDp79oKUq5OXjHly+o9o+fk5GUS5bw1ln9SA2uDhyG4HHTZKXf8ruSuV5OKGvoLT0HUuLo7eHA0QVsErbe3c6bPtCHzVuHv5RmR7oHwyzwnaP/ytfl9xl1jVI1Szgdc2SKUcK5DPaaarEsOUGYEKoQmE6LLNUn5jBME6U8j6SepMNPA1UwoVKwemGjnSuRnioRLYUtDqWIpZRE3TGoFpECinYvzY7TxGL75HbWRdYRcARedoyvBo7OAN7Nly6jWtVrOb7tG9xBfVcEgeB3yruINzhRDSf+YoY4YVRPkao6wuDsaSfbhXKVI07J7aqdTdpx3KLCd7mf7OFH2Fq1yMNwxEVzAqJ6PXO2BUKiE4BS2VIhl21u5IN/vpl3/vbN9K/4bxw69iFc9Yfx9v8Js+MDzKQDCCGIeNvIxM8m9Df73bQ6NX44d/Z19MDjDzOw/amrGbrTCHVY1/xz+q6labLzB1aw7sZfei/dN2/i51//RwZ2WOG4D8XSNDk0WhOzGNUqoFHKmQQUa1xmSk+x/8DHMcwc8YBGq6IgbM2Y2ulA5UaXNTe9VF/SOp/V7zbY0U066Cc5M01xg+VCuDnoYexwnIpD0FpZoNwL09kWAoaT2fwk8UCYaDlD10ufpDR2+VlQV4LLvfpdH9uIJZyPsLVK8XJ9Sa7Yfj7/lkYG57J8c9fleeC/rpGrudOdUwpxWfA13VglKZ7H57AR9tghv2BZkN8I+2+4JjlJAD6nRm/Eu+Rw9zrGWDxv5Xi9vlEG7pZSrsMyJbpPCLEF+Evgy1LKXiAJfKy2/8ewFg17gS/X9kMIsRL4FWAVcB/wFSHEDaiffW1gxe13o2oaA8/uQdNC+L0VwtU8n9v8YX77zv/MS3//b7z5Y78JQE6xY7p17L4qw11vIRyL4zaXkXcF2JCyGvRPlB+k2TbKuNOOvdCAy2PgVnSeGYzx+SPrOJrop1iyrpHnrs7X223Yhbgsh7vU3AyeYAi7y3IavfX9HyQ1O8PRPS/A1k/B0BOw+x/h5t+E1psv+BqLQbKLPUnZRMkybZiZojOwhpKosgeTjfZ96EWVFVt/lV3yFtzKLNUGib27m+MTGcKqQlXJkv35OH57kEgpzfaYZSz0/MROAPZ4LGMFJVxgINNBX9laZR8LNNKXmsRA4lU0yooV5hmJzxJKxxF6Bc3RQMLuo76W55OYHADhZsW2tZxMnaRJM1EXBMNaPVFXnOXeUUwjTVmxYaouuhss8hQrxBhIDFz3UrvLRWOrtcA4PHqYumbr+2qLUaGilhBVD55iCikUZrxVMgyykKsQy1p9Y0+NxZFCcFedj1xiBlPYyAkP3zoieHPA+n8e9Bex67B2zsGCPYWrEkEI7ZSSJIQg1OihY1WYNbe3cPs7f5e/fvGTSGeGtju/QKnOIsdeM0B2YQFpnl7UFkLwrkiIZ5IZklWL7Eop2fH9b7Pj+9+6NgN0yuHu7Hni0Au7SM5YFvSrbr+bd376D2ldsYpH/+FvOPrCbp5MZHhHJMD04FEA3CFrburULeUypU8iZYWFhafYVanQKhRc9hCK67TJhmYUCbg09lwiVLbf48JuGgx2L+P4yUE0h5P9natod9qJKjYmjyWZ9wraC3NUO0yGU100GGXiiiTu8dNZschRUuy7NuN1Dl6/S4S/qAh1glAsJ52LIT0FX3srdx38A27vq+dvHjt+6kP/hkWh9iF011/5c30NN9TdbjReoLPeY9l/30hnO7hmShJwyrzhcl2klvCLhdF44XVPkqSFRY9arfYjgbuBf69t/1fg/trtd9fuU3v8HiGEqG3/rpSyLKUcAYaATTfgLbwm4PL66N+yjYHtT+PU+nA6EwQqReY8dZwMtrC2M4w+NoK7XGQi3EBvKEjccRwTF7i6ONHci2IavG/Q6n+bERUiRpwJJYRWjKD6y8xmqyRrPZDj6RYymaM4ff7zlCRFCJocGtOly1OSgo2nlYqemzfR0N3Lrge/i3HTR8AdtvqT7vmzi77GmRlJ1bJBOa/jrXOQnJqm2dXDiHeMKpJ+13HSsz6it97OC7VTo7RBkjDrmK9W6XYn+epXv8rRp17C0dFMtJLlhAiR0VVO5qwJ7iH3CTStiOIzGMm0cKvdqkr4WdtG3HqZamkapw6pQhGn10drap6GmDWZj7Y1klc0gtIiUAsTJ1BsbfTeHOXIwnE6qhKlJDhoePnTLd/kzsBBKmYKsxYUu7ypFYDnpp4D4E0trw2S5I80gBBk5ucoK0X8zjQJtY0ViVGk4cRftJSjGY+NgGapj4tq0pPzaQIVk/UNTn6UXUvJVAj7XcRMP4Mpa9xOBCwVpG4oRVGtUKxUcDgaKddI0rm4qT3Emu67+PPnP4Niq6dx29+TUWdwlt2Yhk4+dbbicX9DEF3CwzFLockuxChm0qTn5071zF0VFrOSznC4k1Ky+z++j6pphNs6qG/vRLM7uP/3/5T69k4e/dv/Tv3UCGtVO088tQt/tAFf/UoAbCVLDUubkwCMT/+Uh5PWeHY3uzHtQagRwXg8zi2ddZc0b9AUQV9slrHOLgZ3PUff5tvYWdTZFPAwfTJFtWwwpJqEnRNgh4FUD43FaYqBMBmXh/ZigaqoMuxwXP1YXQCXMm7ICiEyQogMsHbx9uL263I0S7B6RoIdlzZv2PUVMKuI0e381apRSrrBXz167MYd42sRp0hS3ZU/19d0Wom6ARhdyJ9t2gA3ttxOL1tlfleJ9e1BEvkKE4mlvK7XG1KFCulilY7w678fSQihCiH2A/PA48BJICWlXLT1m8SKwKD2ewKg9ngaCJ+5/QLPOffvfUII8YIQ4oVY7HwL619UrHnzfVSKBdIjYTT7BIqh46IKwmRlk5/BwWP0ZOY51NNPn6FTssXR7QkqLGe4vpmVo0P0zB2nzqYznm3FO91MxuHCKDYgHDo2tYRdVaj32plMtFAonsBfHzmvJwks84apy1GSZqcJNp7uYxVCcOv7Pkh6bpaju/fAb/wYPvzTC15XjjzzcyaOHGTizIykmv23N+REXQANO9N1h+nSxnC5ShxjNfNSJSHCqHE/5Y02Hn30GVKqTnPJuoYdcU3hvXUZ9nIRPV/kmYVWEBDONzPsnsDnttR7j2cFt9hyZDUXexqsCaw9O4pWgFQqRX1bBw2JeZoWZlA1DXfIEjbrTDs6FSqFJP5IC96Qk0Pzg7SlrWtCKhDAbZtEZD0UZJqy07pWLfNaZGn71HairijLQssu46y4/lBtNpzhEL68jX1z+6gPFogX67m3u+4sG/CYI8g9NQvrF8cSmFKyvVxiS8Jk38BzTNFIR72buXSZe9hBPJdBsaWZqbemu41xa+I/V5nD6Wg6pSRdCH9433JyegPfHf5dhGIQ6/0JrrI1jueS+jVeF10uOz+at8jT7MnTNuKjBy+S13UlCLQB4izzhrED+5gbHsKoVlmx9Y5T2x1uN7/82c9R8od43yPf4tmf7kafPsmQiCLUBuwyi6InkIoDQwWns5V0ajsnlQJSETR57RiKC1GtgGkSj8fZ3FXHaLxwUQMgWanQNzhAZOQQRrVK433vJl7V2RL0MnY4jmpTGM4nUBstEnki1YW7PEW8vhmEoCMrmdHiGHrD1Y/VBXCpniSflNJf+7GdcdsnpbywH+MSrg3CvRcnScUkvPgNWP3LEF1F486/4IHbmvm3FyfZN359ajJ/IZCPg+YBzXXlz/U1QiUH5ezL73uVqOgmk8kCnYuTz8QwIKy64RsB1Q7ICzZxXikWQ2VfmngDn3evUyxmsL3elSQAKaUhpVwPtGKpP8uv89/7qpRyo5RyYyRyfRyZXg209K+krqWNqX05HA6rJCdgLyOcGXS9yMjICLfaSmS8PlLpJMuWLeOmzlFi7gAZt5fuuRHye7/KGk4wkWmnvmxVR5yo2YDf5jhCxTDpjniZzjehM3XRrKQWh52pl1GSKqUi+VSSUOPZZj/dN91CQ3cfux/8Hkb9coienwOUTSzws3/8n/z7F/6U4VTmdEZSLUj2yHPjRMwWKkJn0jPDnd7tADwWfRsvpKzrjLZfUGkuMZJ/AR2VjlrPj17VWah1OLSbefbnfTgUlU9s/DgVpULFb5Gpt67fRiQxw4QvyrS3npzmJJQZQeQFlUqFYHMrgfgs3YkZIu2dzBSs1fyg7iBnWP+fxh5rcW48dQxv3FKYNt0ikaJCOtaJgUG2dlztLicVo8LO6Z28qfVNViXEawTR5nYCeTt7ZvcQjghS1Sjtd29ENZxopkGoXGFWNLOmEiQqsnzlqSEen0ywoEhuqlZ5as9BlssTvKOpHkVRyPj6ebvvGE7XMFm3IOd2EklbJGnelsShNlIqX5wkRXwOPn3vMsxH9lA8toFq2y4UVxqH4jrvfBVCcH80xHPJHLFKlZmh4yiqDW+4nrFrQZJstXiRM8rtdv3H97G7rTnI8q1nhwFLj5f/+/bfRHG6aH7+X3CZJY6aYWbiKl6yqCKJ4bAoQHfXp1Aoc0/fKI5WL85SGWnTUPQKqlEll8txS6dFrveMXlhNKh0/wbKTR1hxfD/db3kHAy5rXrE54GH8cJz6bj+bJvdT6ZGU8y6S5RDSliThtY6hL+lh2h5jVL9wltbVYqnc7rWIxaykC6327/1na0K/7TPwtr+E9Difcj5Cg9/Bn/3oCIb5Bi19KsStNOpXAm/NBvwG9CVNJAuY8ozJZ3IEAq1guz5S8XlYTIm/BiV3/Q0+XJq6lJf0OsRYLYOts/71ryQtQkqZAp4CbgWCQghb7aFWrCB1ar/bAGqPB4D4mdsv8Jw3BIQQrHvzfcTHF1DKFlnwBwxkQ5a9h/diGAbv6rJKtnbaXHzwgx9k65YgM81YBgakwSzxG8//gPlsGFmyFOrDwiKSa6S1wt4UcDJXiYJawBd1k4nNn1fy2+zQmK1UMS6hmC+WMgXPIUlCCG57/wdJz89ZdsQXwNFnnkRKk2BTM1MVHX/GWijKJS1iNz0wSou7j3HXJOmSi+WhE1TLToY8/fxw7zPYdB3vDmshIlprbpeG9X7twsbuE9Yi6b1RhQWZZZknwFtW3wWAGpgjWQzxltV9VEdHmQ80IIXCuD9Cc3ocWbYImyMURqmUCU+PEu3s4XjW+s53VVTKtajLtpVWn0m1cpLZUet/s2blBFRdxIoWOU06FYQ0aXBo/PvxfydXzXFf130XHddXA+HmNgIFO3tn9lDf4gEUKtkUqmF9hBvyJWZoploc572pCBgmf7rT6mnT44ewq3DH1A7if/IlWkfHeCkVZvnIM7hdI0jTxkhjG4GidS7NawnsZoRyeRYpL77Y+Bu3dvDmxDHEU41gaiz0/oA6x/kOdwDvigYxscwS5k6eINrZRde6mxg/fADTuAb24KHOU+V2kwOHmTp2BM3hpKmvn0C08axdn4xniXkCZN/xAGaNInz0/rtw6Da8SgWFJBWnjYqMMpzfRrbi4c6OI9g7A6ipCqZq1lRkE9M06amz47arp0runj0e48j0aTe/wsH9OHJzZN0+Kne+nV3pHHWaSiRvkpwt4On0ct/Ybsq9gtlcKy6jgO6ykXD50YwqK5MBJp1ZbmoMcT2wRJJeiwj3QDV//qS9WoRd/xt674XG1dD1Jlj1Huw7/5a/uCvAoak03907/uoc86uNQtyqH38l8N04knR68nlGud1izfCNwCIZuwYkyaYqrGkJLJGk1yFGFwqIN4D9txAiIoQI1m67gHuBASyytBhr/5vAj2q3f1y7T+3xJ6U1Q/8x8Cs197suoA/Yc2PexWsHiwYOmRMObDboqDPQO6MMHBvA7XazYfUt9MTG2d1gKedKyyqONklakjnqyg4G3tJC83SKDx95mAcXLBOeRM12ulWxVuCDLo2ytJMsB3FHqlTLJUr53FnH0ey0Y0iYr1y85O6Us13T+bERXRs20ti7jF0Pfg9DP/s1pJQcfupx2lau4Z7/9wvoNo2Fp3/G2KH9p5SkiK2MXXWS9R/GHIdAcxaHZz0IwRF/F21z02izIDNBou0DgCQvrWtDiQp50yI6N3mzCEcMn1GHVtXwlANEPXFEpRVRLKDHYlSbLW6eCIdoz86R161ri+K1VtalYRDt6mFct2GTBgoCTY8jUena0EOylCQg0mQmg2SdXgz5PEqskbTixiMdLHg03KJIWS/yTwf/iU2Nm9jcuPlyTocbhmBjM6ouGZ09gdZqVZOc3BfDZVNQpEIkX2SGFghMUVo2yD15O+OqJJIpkE1P85ZIityLThwrVrChu4uKamPHyAZUbRil0Mh4YyveisSGyrwtgVaNIKVBuXw+4VmETREsS0/im4+RHLyXbONeIi1OsvHzlc8VXhf9Hic/mokzOzxEQ88yOtbeRKVYYGbo+AVe/QoROp2VtPuH/4bD4yWfTLB8653n7fqTWIqwZmPXsMHATb/O3R95gC1dPSgInDY7ikhStFf5v8d/nU988yVeml9LQNmDEcUqUlGraMIJSYuIx+bnuLkjdIok/dEPDvI3j51+T0ee305JU3h+69s5VIXdqTybA14mjlr7K6UJOn3jyIDBVKqVtuIUutdPylVHczGJS2pMuKp0epxXP04XwBJJei3ilMPdOSV3+78NhQXY9unT2+79PCB488Tfs7mrji/9bJBk/to05v9CobDwC0GSRhYWy5hehYwkuKZKEsC6tgBHpjNU9Ivb0FcNk0cOzbxxVc5fQIzF8zQHXDi1171BWxPwlBDiILAXeFxK+RDwh8BnhBBDWD1HX6vt/zUgXNv+GeCPAKSUR4DvA0eBR4FPykstM79O4fL6WLZlG6mhEB5PlfZqGVPrZH58gWXLlqFqdm6dH+RAex/FapXRQB8nI2HWDI8iBBzvqCN+m533nnyWX5kcxFEuMe1UUct+6jwWAZE1d87pXCOKy1Jwzl2dX7QBv1RW0mJGUrCh8bzHhBDc9r4PkonNceSZn5/12OTAYVJzM6y+614WFIvMtGgKP/yrzzM3PIPdZaPF7qRilFBad9GYmUdzG/SIAC69REWzs2ZyEIEgN7qSutAx6mxZKkJHQZARRdZ2WsYOcuEwAAsL3czPz6MV24nay7TZllEZsfpZb3lTP02eGYorl6FK81SvTFU9bcUc7exmFifNZhmBwKEnqNjr8IXcDKWG0ErteNNl1OYAhpFHizkoSZMGM0jc6SCkVvnm0W+SKCX43Zt+9zVVagenia4vb2PQlcAmiux/QVDWbWimnUAxR1G40T1V+laH6cYGITvNyUmamjpofOZ59KLA93ufYd1nP0u0vp6TN3VT9MaRhXYCriYa3vRnbMr1EdOS2IpWj1qpPM1YPM/nHzp63vVNn5/Hnk5iL8ywMPgWRMWDfe3+CypJAO+OBhkaG6NaKtLUu4z2NetAiGtTchfqhOwMcyeOMLr/RaKd3Qih0H/rtrN2KxgmT8QzbHY5GZrP8c47bmLDfb9EfMoi8FFvEIUkGUrsmmwnXzaI6duQZoFDCw9RpAJCopjdiIr1eR08eoRNnXUcm80Sy5aYzZQYrS0WF7MZ9s9PElHtODds5vF4mrFShc0By/o7EHHh2P4I6ftNTMPFwdm19DAHikrS46GlWFNuXQK/7fpcq5ZI0msR9bWspDNJkqHDjr+Dlo3QsfX09mAbbPvPiKM/5Esb02RLOn/92OCNPd7XAgrxV+ZsB2eQpOvvcDe6kMfntFHnsVs9UPnYjTNtgFpPEtfQ4S5ERTc5NntxL5f/9eQQv/XtfTx08OI13Et4bWE0nn9DmDZIKQ9KKTdIKddKKVdLKT9X2z4spdwkpeyVUr5fSlmubS/V7vfWHh8+47W+IKXskVL2SykfebXe06uNtW++D6MiENU0zlyOpnQcqib9/f0AbK3EqNjt7B6d5KcZa3HlPfv2ggSxsI7QnQlwKnx0eCdN2QKzLs1yuAtZhCeVspTryWQTurS8Ms7LSqoZKVzKvCE5O33K/vtC6Fx/M409fbzw0A/PKuc7/NTj2F1u+jbfdioj6QMf+giBaAPjh4dQlSrNzjAzxiiVfJJIvbWiHtn7EJFa3+uK2CwSSM/fhiIM7muyyFBLsJGSqNJua6JitzN94iCqdHBgaivvH07hogFFQK7sPUWS1MD/4c9v/Styq9YA4MyN4LFpZPJ5AtEGhKKgBetICxedWOOtGnEqTut6OZQaYjx2G82FOM5miYobLV1FotNgBshrHupUg28c+QZ3t93N2sjaS/z3Xx0sOhTWl9zszZ7kdv/X2bpuhBW3NaPqTjwF65xJet2EZYD9XQmkELQmYrw0qZDal0Hb1MK3/uFLnNj9PBs3beJGDDY/AAAgAElEQVS4B6Qi0Ut93O66CcVdx/0jG5ljAlvOKu0qlab5+nMjfO25EYZjZ6uZpcPW//SRllWIkkQdvAc9cpKKHLjge3h3NEjDvOUY19jTh8vro7G799qQpJoN+JH/+Dp2l5v0/Bxtq9fiCZ5dovZ0IkPBMDGmCzg1heXODF/96leZGorjcNsI2lzYlBxjlSYqhkUf2hu3YlP9zGUeJu6wxkAxw9z6bit4+cgzP2djqxUO/fjReUwJE4kihil57lv/go5ky7pbWOd3M1ILZr7Z42JqMEnHMg/e+GNUV5jkM9vIVP1E7WUqqo2Mx0lbrSQ37rt+16olkvRahK8ZbK6zSdLRH1o1pds+bQWgnomtn4JAO+27P8dvbmnhO3vGeXHs4paLr0sUEq9cSXL4QXPfEIe70XierkX770VnuxuVkQTXniS1W02WFyu5Ozqd4StPWefxw4dunM36Eq4OY/GCFXa8hCVcIVr6V+KLuKku5Mln0iyfH0ZXBD09PQDcFnKhGjrPTE7x09k4fePDrC57qSoGUneSm7wJf7eBOXeYzrwk7fKgFOswQhXcssxsPEXQqTKZbKZiWiQpe87q/GLw6aXMG851tjsXQgjWvvltJKcnma2VPJULeY7v2sHyrbej6gaTOasyoK8+zPv/6xdQbEHc+RnsisaM/ySxQ3W4WsqkikFc+SK+iiUutieylJx1NBl58tUga8KHAOjttqpIkpMLqI2tKJkUiq0XXXeQOJSmqbZafjiRpDwyAoog6xlGFSaqYzfzrgDhXAqtopNIJGhetoLG3mVMT0yQlQ7aVQ0pK2BmQbXUkMOzoxyfW0OkkKTomyOY66ZU44QRM0hZ81MpT1HQC/zOht+5zLPgxiIQaUBRVXpkM3vmXmBF43HWR54n1OQB3Y2zaCmOL9lvZmJskKl2B3a9yvpCmLc+8R3Kdo3KljWYhsH08QHWrl1Lym3NoT5SWIXH5qecmWCluJmsTFH4xrcReSgVp3n8qDVvmEye7fJaPHwYVJXnN74NT2GG/Mlt2Ip1+LqPXTA2o8ftZFVqFt3uINRs9YN1rL2JmaFByoX81Q1QraR/W/Zf+Hj3Lt7tfYS3eZ6A7/wKPPgA/PxzYFR5KJYmZFN58cUZ3r6qkeeeforp6WmGDk0QaXKjVa3F0IO5fhbjU/saQwixnkBgFD1q9YAphgOH1o0AyqUysz/5Og4FdgxZpYYVw2Tg8AAHn36cjoU0zbdtY12N6LgUhfB0Gb1q0pTeT/4dBbL5APNzy5FVHcXtwgxYpaQdxQIVUUULv4J8zMvEEkl6LUJRrL6kRZIkJez4Wwj3Qf87zt9fc8FbvwDzR/j98E5aQy4+/PW9bxy3u2rJMrN4JfbfYJFO743JSho9M6DzRmckwRkk6eoDZQGaA04iPgf7x88nSVXD5Pf//QBBt5371zfz9GCMfFm/wKss4bWETKlKPF85XRK6hCVcAYQQLNu2HiNpfdZ75meZDEWoKtZ0I9S/jpUjQ/ykItiXL3P7S3uwBYJkvRNU7WkGEh8jdOdKQNI5N0vW5aZQDKJ7c/Tmp5hNl+hr8DGTb8RgFpvDfp5jWMCm4lYVpsuXIkkzZ2UkXQjLtmzDZndw5JknABh8fjt6pczqO+/l+G1bqfviX1CnqXhtKu5AEEX10+Rwo5sV5j3HyE548DQXmIot51jbn+FMW9+T0fFx8p5mVio/5OD8SsJ1x7Eh6akZKWRkHrXzZvw5nZR3HZqtirJQprVugaKpsFsfI3t0J3rYpL3rY6TMW+j3P8FotIG2zALVXJpkMsm9n/htfvmPP8fA4El0VBrtLqRhKVtuvQ4pJXsHdUKFPKqUVOvK5CeipPECKmhepLAxnTrAL3X/Er21QNHXGhRVJRBtoKHiZyg1RLxhOcwexF/vQhhOnJUcmmGyj41ksoNMeML0JlJsPXGC5clx/mHVexjIWL1MsdFhnE4nlUiVVZkVfEB6+Iks8UPGUFUHd2U3Uzi2m+jn7Rx+fIjptFVWNpk6mySVDh/B0dtLR3uUglIhWfVTf/I9OOqzTE/+6Lz3ANC5MM10fTMTtetkx9r1SNNk/MjBqxug1o0MuN/MkVwzSUc3Wd2F2x+E9CSMPAPb/wfl8T08tpBmpWIjV9K5vaFKPBFnRbUVWbRTyc6hCuv8PZzv457lFjHJlaqcHA6gaRXKQcuQoSUUYPpECrfHg+4LMLx3J+8u7eHgZM2wQUqe/+ZXcWp2+maTOFevPkWSbva7mTyawKYpFOa/id4m2Zn6NQr5Ko3VGNKmUbGEKbqzJjNajK5g+9WNzyWwRJJeqziTJJ18EmYPWYqRcpF/2Ypfgq47cG3/Iv/2oWXUee186J93s/citouvKyxmJHleYbkdWFlJ17knqVQ1mEoWz7H/5tUpt9OvTfiwEOJUqOy5+OqzwxyZzvAX96/iVze1U9ZNnhq8eKPrEl4bGK/Zfy8pSUt4pVh919tRdWvSqJiSkfoWHqr1AGmrt3HzwEEm7db5dftLe3GHRxD+GRAm6RJMuu9ED2o0nXwJKQTjZhSEZEN5kKxho9EjmNMbkOjUdbjPK7cTQtDi0JguV5HVKvN//deMP/DAqRX8SrFwQfvvc+Fwu+nbdCvHnn8WvVLh0FOPUd/WQZ3mQJbL+MZGaXVY36mlXBVDlzQ7wsT0eYxEAVekhNNRYT7RzpMD7QSKORTj/2fvvMPkqM50/6vqnMP0TE/3ZM0ojUYZCQkBEsGAbGzCGhvj64ADe53W63Xa67V3be/13vV1xGF3r3PChPXCGhswQSCShBLKM6PJOfR0zt1V1ef+UaMEIhgkBPK8zzPPM12h+1R19TnnPd/7vZ+Gd2ocpaoe99KFbJ9ajsGoEPbFCNTrtV6S5hJJaz3+LGycTLCm+lkA2q19jFT8HHQMku07AHVOWls/g6vqJlzmLMkmBzWFJOV0imw2i5BkLHY7naO65bdDtiAb9L7aJgUY6UkwMtHCWlmfhFeCFqYjRsa0EGaDh5HZocqgxvjoio++8gfiNYC3Nowto3+/u7zVEO3F49YwaBYkIJgvkZAC9NeGyVrtXDUpYX72vzEtauFgwzx29+mLyjPDg+SKCqPKJJ+cvpE4gq2xbrbOX4iaHOKt8YspXv0J8JjZdl8ZGYFRlhhL5I+1RQhB8dAhrB1LWBRyccDqIC0MOCbOR6R8DA59l0rl5AVDVVFgcozJmnrujejfUXjBIkwWK8MH9r2qe5NJJHhgr0JyxSe5d7CRQ7U3I3/wT/CRp+Dm+wHoHT1MVquQGczQ6LMR6d2P02Cjo9yGhAFKaQzS7D0SXr509RLMBplDAxOMDLuQJCsxrR+DkGlrkBnvH8Tl8CDMVlou2EhwfA8Nw48BsDDbQ2akj6U2D/b6eow+H9WyjK8rRVteMHwwSkswSXzZANmoj6xhI4VSiRpDBioV4kYVRyFPY9bJhHmGQOFVzP1eAnMk6fWKqjbdjURT9CiSKwTL3vnCx0uSbgleylC751vcect6gh4r7/3pTrb3x16zZp8VHCsk+wrldqDnJZ1hkrRzME5FwPLZ+kLEB/U8KutrWHbsNEeSQK+XNBDNkcoff8+e6Qy3PtLLW5aGuKojxHnNfgJOCw8cPPPmGHN4dRj6C7T/nsPphb96Cb662X5ZghGvmz9G9GiP5K5h7ZCer9GSTTFPLUD9fdjd+n7VHuGZ7oXEa0yEx3SZ26Ck9+3LzDPkMWPMRSkIK+myC3edTPoURXnrLGZy4xMMv+e9xH7yU3KPP4E2m8+UnNb7oVM52z0XSzZeTimXY9+Df2Sqr4eOS64g+/jjAHjSKcKzxUqziRIegy4XKnhsaEMy1Uv0KEPG5SOvpfAWsoSjEYyain91OyPnfYHu+Hw01UQgOI7ZYkGyWjlsKVBja8abN3BdaDVXND/GSncXQfsUGFpBKMjT4F96FbJsZkXb5YxnQ1hb9ZwnS0ohaXPw7v39fPTwICMZvW82aCaEFkVIBhTZzR9/302x5GODaQ8Atuo2LNIkM4Yq7LKHIYcuD9zcsIaw86Xv1dmEtzZEcSaBw+hgp6wCApfai6zprmfhfBlJVPhj8HIALnjqD8hqkdKFF/Gvpp/gzEUQBhPFXJY3f/MXvHXmAhrUar5JkbbUDGvHijwrBnCbw+QkE9q/nse2hctpjw0SzMcYjRzPy1XGJ9ASCWwdHSyqdbPTE6ACpMtZHEeupKSMMjV190ntjw4PIjQVe9M8fj9LkgxGEw1LljJ84NlXdW+GD+5DVCpUNTTOutodLyCLpxFkExMTXbgNMkcOz3Btm5mJiXEWKkFSBv265odCIOmL7hZznqYqOyGvlc7hKdxuP4HApcSzozgkM/mmL9Nw8fcROd1N19LWjn/5OlbH9rI2u4sLE9upBBqo7RvGtnQp0WyJd/94B4WRLA8+Msh0rIDX9Ws0P9w+8HZqXVZyiorDKkGpzJi1lubJMbylAGOWNPLQ6ZvPPBdzJOn1iqo2qKjQ+XsYfALWfeSla+nULIa1t8CeX1Cb7+GOW9ZR77Nx8y928mTvuVPZ/XnI66tkp4UkvUhdjVeLLV3TWE0yG9pmVz1ea2c7OO3udnC8qOz+Mb1j1yqCz/7uAA6Lga9cs0T/WFniqo4gj3ZHKJT/4ky/3lAYno0kNfrnSNIcXhlk2UTjKgeSUsJts4DSxTMZQWW2f12Wn6YmFePKvTux1NqIVqtUW/LkDXnypiEyWRODVRcQiul9+7CsJ5gHvWVKwkh+egiAiUwt9kD5lLbKa7v286l/+BSlnh4811wDgDqt548ctf9+sZyko2joWIqrqpq9D/4R2WBk8UWbyO/cBYA/nSQ4oBO5TLxIrVGmUs7jv+uLXNA5gdsXZypXjb95Ie5GQTCZ59379TIdjZevZKRkRwiZWKwOu6+PWw71M2myoVrKWCQbJn8bl6w4n1prnHe0PIBkEqz0mgmkJIyqYCy8QL8vbhvPRi8ntGgQTZKpYOeu8y7lqWyRuyMpJk36Qlw5L1BLUWR7NV0WgTZQoEbOUl+aQsiCaNaHT6QQkowHB8M2ffL58Y53/Dlf/1mBrzaMUiqy1r2cXTk9V80c3Y/DqsvogrkyAomybMWdn6Lq0EP0NW9gZyzMSnsSa6VEt00fj1dWpnhXdDOJcIntlDlSvxR3yclv57sQ5Rz2LMTLzQxJQTZf3EFNeoaRweNy/aOmDQZ/FQsPbyNqlJDQSCgFauKbMFaaGBi8FU0rHjtnsl9/jtZ1dHAoW6Avr+9rWraS5NQkqcgrX2Ac7z6M1eFksucIJquNeavWHN9pMFLxtyBi/TQqEjLgSfdjtVkJzn+U/GWfxeIZxWO1oJr0hY8aaZpsIkbAJhPJqpx//vkEg5spFo2E6vsomoaweocpJXSCNTg4yAVvryHm9nH+zG5sWoHReZvQJidJLFrGO/5jO/0zWb74lsWkiwq77FGybfuxTdXwTHIp1dYKiiKQTCYMRpURVyNNqQRmYWLMVkbpSlLMnRmiNEeSXq84agP+4BfA4oHVN7+88zb9Pdh88MDnqXFauOOWdTRXOfjgL3fzWPc5KnWarST+it3tQCdJSk53nDsDEEKwpTvChW2B47bK8aHXVmoHp7VO0lEsq/cgScfNG3761AD7R5N8+W1LCDiPE/s3d4QoKBqP95yjz+Gfg8wUjOw42604JYaiOYJuC3az8aUPnsMcXgDhBQvwFXpRD+7hw3c9wMYH/4sHH36IYjaLPVjFHV/5OO+44xdY6GEmYKUpt4iYNUbZoGJzyOSkNgx+sBULJGQPVIyYZrt4WdPlwsMz9RjsSfKpJEpZ3yY0jZnvfY+rvvpFZjxewr/7T7w36iqMoyTpmP33S+QkAciygcUXbiQ9E6F52Qrsbg+lLt2hzCAEddu3IVRVJ0kmmWRyDIOmYVfLmNxlUqN+Grw+SlIKh9HL/H26zNqzfDFDk2kapDKxWAOSKU/vzE6aawJYKjkUFIyh5RhTKWy2Olyzduf51JOsTOrRkf9TMdOb0yfTBsdmDthXMOSppTYZoS0yzmd7d2FBMGatoqqUp5RT0dQovmAd260KAtgs8hijElqVxP7BamwVfbIZ1OxM2DRkUabFeebkTKcLR6WTywzzGcqOMe1t4NnRJ5g2RJCEhK+QA0lCEhoXPrsTXH62bKgml6hjSNOdF+vXXoyExPuUJRQMJdresw67pJA0a9SqWQKVCuPp7YRs89hxUM+DufKKDupsMmMZBVHR3QOLhw+B0Ujk299G++qXqKrkEdYKSdWIRbZiiFxKqTTF2PhvjrV/ur8Xu8fL2xbohge/n9bH06ZlKwFeluSuK1sgqz5/EXK8+zChBYvo27mNtjXrMFlOrikUczXRmB8h2pPgsiYLo8ODhBv7ydQ9DbJGePVtZCZjlA1JMsJGtVXiwK6dyIUkOSysWrWKKv9GVNVMVcMzmPJ6AWhvte7Wl80OMD7+HR6pvZxpZ4iRUAPdRTMjzho+NFHNTLbEbz54Ph+6aB6X2J1saLoNYRE4PX8NgF1JY9TKIARSQ5K01UV9SZfzjtgl3DmNqaEUZwJzJOn1iqMkKTsNaz748iVZNi9c9k8wsg0O/RdVTgu3f3gdC4JObvn1bh46fA7KnU6L3G52sDxDDnc901nGEgUuXaRrzlFLkBo9C5Gk0+tuB+CymmirdrJvNMnATJZvPdTD5YuDvG35yau0a1v8VDnM3DcnuYN7/wZ+eTUUz0zH/mow52w3h9MBl2shi6/cxzUf+yjJJjONE4N0/vT7/NuHb+JRycagw0NZArXDjmKSmB9tIWaJYZCt2GsUKoZGpHYfdTPTlGQZueBH+MpY1BKSsxqLpDGWDCFM+qJLJjqDGo0y8sEPEf23fyd+5WY+9rl/Jh6qwxTU+13laCRpagKHz495NsrwUnD4dVMgu9dHpVhEmZwkP3tu/fAAuR07KEzl8Rol0lE9v2fqgxLCCuue7mPp7XeTiERYsqoNV3kaKRDE4HQwOJyiXSqRSNShCSPvKd7HmiofuWyOTlMXxtrllKem8XpasJtSIMBub2NtWncKjASrec/BAQ5m8uyrdfID42foq21g8dQQb+raRfnIYc7LJ0ljpVkrH3O2S493UqLMfrNKTTKMLV5BC7gwVlSSRT2CXFe2EbFKuKTC664u0qngDemOcA2aPkH/W7+d9xW7yNoSGCtm3Hm9r30Xv+KvH7mbkbduJLwugIRMVzyEbDDwxZvfzLK6S6hWqnl08X7iMwrOikRWFjSN3E04JfhN3S4k2UDjXo2wY4JaZ5KWJW0kTXaiT24DdGc7g8uFMjwMwObiCNNO66whBkjDYXzedYyP336s/ZN9PdS2zidstbDKbefxhL5g6w/X46wKMPQSkruZUplf7fwM79/5J4YLx3OOc8kEickJ7B4vxVyWRRsuft65B811zMuPkxxJscw0hcebJVT9OLbYYqL73ocl0I+haj9ZJU7aYIO0icd27EZJTVMQJmSjGVm2UV0zhMFUIHTgI5ikKsILdJJU459CCAsxLcBu9xqkkJHhrMxnL/4YqmzkzlvWc16zn3JRZXVmivZFnch7bEw1XgFAJTOD0SBBIU+hXo9GBxWdDE44zdze8XlGPJ0v+1n5czBHkl6vsPvB5geDRZfa/TlY+T8gtAIe+hKUsvgcZm770Drawx4+etuzdE68cE2bNyRyUUDSCeIpkCoo/H7f+CltN4/BOUtezpDD3ZZufXC+dNYRhuQIIF5b+284I3I74Jh5w+f/6wAWo8zXrut43sBqNMhcsaSWR7umKSp/wZK7SDf0Pqh/B0def+V0dAfGOandHF4dHM6FyCZBzUInhqsW8Mt3/RVP3fQJ1l5zA2Wjme66AE8saqQrvBxJGKiLVSgb9dXhnDqEbPBgCVYRSkaJ2ayoBQ+qJ8eqRIRH09U4KDJZCIEhhcGskdy9m8Hrrqewbx+hr30N7R+/TMlsYbyosHdoCAEoU8cjSd7gS0eRjmJo7x5ko5GJnm7d2lkIxlt1qVtNNkP6gQcwTOiTWiW+j7JZwj5fT8x/pHwBzvsf5JJHH2Oe30GjJ4N9kV4LcXgmS71cQtNMDCqLaM3toPy9WwE4YD2C7Kgm99Q+jEkZMcvnlnZ8j9aCg6wVbvYXmCiUedPuHvqFgmtgEktjCqdSwJtTUCSZls5noaAhrNZjznaB+gYW+c1EzEew13RjjAmyVhcLxAgRtQpZFVSpRuIWI37jCxcKfz3BHahGNhiwpit4LV66RZGbk2muXnIxkmrDNmsDrmFidFmZJ5apXLlwNRPuXqZiClUNTZDRmG9exUipF2O7h8d+041fspDHxKgNfHYfzzQpZJLdrLNUU2uNUCpOMG+tXqOq+657EUJQ2LcfLZHA/4EPYPB4WBPtoU9VyAgzSqWMMSXj8Z5HoTBCpVKmlM8TnxijdvaZWuqy05UtIIRAkiSal61k5NB+KpUXHjc7EyNczp9oLj7C5j09PJPUc+XGug4DkI3HsLrcNC1dedJ5SkXwmFSDRZRZZZkhPdnFoiWPI1QrzoFbiPWuR47NZ2bBXQhjEmwGwlqZvGxAOI0IYDJVIJEYoK6ui3zeja0wD1d5JWZnJ1AhP91OLPsuNE3w5a0/4wO/2cpFQ3uxUuF3H7mA9rAeBBg/kqBuwZ3IFcHWgxdy+26dZCUjY0hmM+mSRG5WElmrSZSkMlmHA8VYYL53/ul5kJ6DOZL0esayd8LFnwXnn+kBLxtg8/+FzAQ89W0APDYTP3vfeWhC8FDnObaSn4/ppFI+dcXlnzw5wCfv2MeOwRdx+jsaScqcmUjSlq4IHXVuaj2zYe6jNZJea7ndGYgkgV4vKZ4rs2sowZeubifotp7yuDcvrSVX1nii5xzOkXspbP+BXgfNGYTD/322W3MS8mWVSKY0F0maw6uG06FLmLK5I7R4WiC3k+3uIA3XvIN3fuhvuOjIKE5FYf+jeYYfXIRS7sMjbKioxNJDAFSKfgL2caZ8PkpZO4o9yttTaUyygYywENH0sdHpLFD8+jdBkmi+8068f3X9sVpJQ5ksWx5/nKLVSmFMn3QlJidelmkDQCYWZWj/Xpo6VhAfH2Xsia0AjG8KkZlvJOB1k3n4EWzJEhk1iy2XQmusoNp9xMrV/FvbtWT+5//Ek0rBpz9Dqa8Py2zNqJFsCYuxhFVYCDyZoVIlcL5viqqqEbJlva2F/VPk79gCgMuyGKdzIf50iUm/RGxsO1+8/Q/89X1H+NU/f473/fF+MkF9HLTOpJGcLuzZDHJBxWzzUJklSW/+xKe5+zNXUbP2l1S33YshKzGp1tAuJonJfgS61DZtthE0vzGmiboNeC2p6Ul+9KYf8V8r/56/SySpMseRNSvlShZfWWWSMJPrAuwq7WWZsDLt245WjuHyN5C4uxchC/ZNP4yl208qUmDJ4iAlYWSgdR4NLiPesokHXTsxW72sGvQTSU7QWK3X7Rk81EvmoYcQhQLG2lpq/vaT2C9YT9PAIQYUPbqTKEexlR3Y7S1AhUJhhOmBPhCC2lZ9ot/usJLRKozO1vlqWrqCUi7HdH/fKa8doD+tL/C+1R3FZzRyw75+7pyMM959GIPJxPiRThau24DBeFxGPVQo8cnuEQ5Z9CjctZ4+2uY/g0mO4z38AYStHpAJdL0HzVgg3xqjZNOoa2nlkbZlHDh/FQIYTxQYGPwWkiQoldxogST2ySVoWhqnK0YuVUtnZy3N6SmqCykyBjuf3nsntx74NQ2m4y5/Q907cMzbj2Orkcqmd/J4zwxGNFKz8tio5CFpWYEvnaIaJ5PmKCaTA5fJRa2j9rQ8R8/FG+Pp/0vF5n+FjZ99Zec2ng/LboRt34dYPwBVTgsdYQ/b+s4xt7t87AWldkII7pstYnrHzpEXfg/X7A/sDESSYtkSz44kuOyo1A7OTo0kOMEC/PRHkgA2Lqjm7avrX/C4dfOq8NpNPHDoHCPqLxeZaThwJ6x8N3S8Hfq3vK4kd0dNG5rnSNIcXiUsllqMRjex2FbmuRuxFHS50MOxNOaGRlzFMhfMc1C3YYr0pMydQ4L5I3aSlgRTHhcVFNJZL7ZQHNVoJBW3UjEVqLWkubk2gIqBvLCRKdpZ0h2HZJL6738P60J9NT5s1UnStiO9lEolCjYbpYkJyoU8+VTyZZk2ABx+fAtCVLjwxvdgNJnp3r8HJImmxY+Q+VSezFVRVK2EW9EYSx/EVSyjtmiocpq+TBMmc4HRhnq2X38dpuoAolzG3NZKLpJnulJBkMcmLCw/YKOpshLVm6F9yeNsvLiX6ZYfI1bOJ7xBz80INlwHgDYySs7rYtfYw6DNoyZbzZE1F7NImqDt8RwFgxl3YgxNMpATZkDCiRFNxJBkI55gLdFiBL8pi1PokrCy3EZVJUbS6kExOyhLUDA5aLC+hGHU6wi+UJjk5ASLqxYzr+0qANxqH7JmpSSp1BUUptVWWr1ZJouTjE/tYYm0A0QBR7aeUn+KA+EeClqG1OM5Fl0QorXZSwkTaZsT29hhXGULv1qwF7WY5G2ag1sfr1Dv0yPv03Y/U1/5KgDBL3wByWzGuWED5lQce34SJEiUk3iMASwGfZzM5weZmjVtCM6SpCVOPWzYNZtv1rh0BUgSwwf2vuC1T+R0IiEXe7lv9XzO9zj430/tYMvevWiKnme27PLNgE6O/rZrhA07urh/JkmNU1e0BB1PUV09xMNJB86pVewdymKxG3FWWvAOX0E6JBiyltkZqmIk2EABEA4jk7G9pNMPMTbWTqBqMWnvDiz9espIdfUIFVMJadLFqsgRAP7lTe/l5+2b8UwMMXDtdeR37SKXKpGX/gMpJxHMb+RT79qAySBjlEGeLaabNAcYF2GaJ0ZxVfxMmCIIoTLfN/+MSULPGEmSJMkqSdJOSZL2S5J0WJKkr8xub5EkaYckSX2SJN0pSZJ5doN85HIAACAASURBVLtl9nXf7P7mE97rf81uPyJJ0pVnqs3nHN70FX1S/OA/HNt0QVsVe0cT5MvnUFHPFyFJPdNZBmZyBJwW7j80dZJN9UmwuMBkPyM24FuPzCAEXL74BJIUHwCTAxzVp/3zXhRnKJLUHnLztes6+NY7lr9oZ2UyyFzRHuSRzmlKp0gwPeex80e6/fq6j8KSa193kruhqD4YNc3J7ebwKiFJEg0NHyAW24pl6ntUiylCJoWHoimMwSCSzUZpjYGapRne841v0+gp4BlK45kp4CoXiTvzTOWbcNv0sSo3G3w+uG6G0fRTvHmhvjBjuseJd1IhdvF6bMuXH/t8h8GAxyDTPROjvr6egt2GGomQmNIXwnwvw7RBVCoc2vowDe1LqWlppXXNOobzKVRzBYucR03YSS+eYeZLJfLV+0kkupGFoLRIIIRCZ7wJm0VhYmIC9+LFNN95J7X//FU8b30rRw5OIxCURZ5AJk/Dt79L20U/Z7zXwP5DG9G0FpKt2xne+E2GWv4IgNu9BC2bQ41EMJptjDkNlE36Al/ZNo9Dy5Zy4ILzGfCGcc9kseTz5NCj+sGSoEwcV1UIWTbQm+ylzVJh1rAMzTCPWMlD2WRGM7uIWPV+fJ7D9SqfhNcO3towielJXVpv94O3CXd+LwZNJ3p1GYUJKYTZlqLJXGH71E5WJTRkDDQVgxiDdu4L6nlFFjnDhr9qI+jWz80LE0pPD1bFgWIusUudwO9rIb5HoTeSwWyQqbYb0eJxkGVcl2wCwLFhAwArIkfAbSKhKhhkI0q/LmPM5weZ7u/FUxPE7vYAsMihf2edWV1+and7CLa0MvQiJClW0HPzyuUZMv3PctNDv+X9v/shjvgMI8vW8e5bf0whWMenunVy9N+RBB+sq2bHunbGOmHGacU4b5yM1EK2/xKezKhokmDDh9tJSIL8wBVYihpqnY1fW/3YCvpYIbwmjNkfIOFmbHQJ9fUbSXmfxKi4MJSdBAIJhEvjSaONVTM9THhqudi3j/9ccAlP3PwpJLOJ4fe9n+2f+jjWQBeuB2WqrnsXfoeZloCDoiYjFL0PsBoq9KsS8yZG8ap+xixJ8sVJ5vvOjNQOzmwkqQRcKoRYDqwArpIkaR3wdeA7Qog2IAF8cPb4DwKJ2e3fmT0OSZLagRuBJcBVwL9JknRqXdUcToarFjZ+DnoegN6HAdjQGkDRBDtfTHr2RsOLkKT7D04iSfDNG5ZRVivcs3fs1O8hSfr9yp5+krSle5oal4Ul4RPMN+KDej7Sa50Qe4ZIkiRJvPv8ppPc7F4Im5eGyJRUnu6LntY2vO5RzsGun8Diq/Vi0XXngbvudSW5GzpWSHaOJM3h1WNeyydY2vFDtNIEn60tskB0sy2ZJYNE022/Idscx+dbj792HtdeVEWyYx6WbA4JyNjHiatN1Bf1aUpU023AQ0YLW4O/Yv29/8T6iUPMezJJapXMUDFL7Be/OOYwBuAqF8larFx77bUUbDZELHaCs91LR5LGug6Rmp6i41I9gXxRx0oUWSZSr6/0Z0aWUv09O5LwML7qu/gbegFIByVA4nB0Hi6LIBaLEQ6Hke12fDfcgGy18sOxGYJoKJJKTX0dlvnzweJiV3ghkxkX8dEraX3keqqP3IikyBiNLlyuJZSHhgBwjkdoTHYgIWG2GahzL+ait3+Ih/Ey6auhLh1BAHJG7+trCgLUGIY6PYLRn+ynzlRBjur3V7E72FbUrdJls4cpq759setVGCK9xvDVhlFLJXKJ2flNeCWO2DaMswlddTmVjNFMVgTY5DHzTLofg1KDs+5ifrPAwTuXmnjA9VbuvuomHrl4Ht+LRHkWFS1oZdxZRVE2ICo6Kf33DhuiovHxYpov3nOIlSRY3b0dAEM4hGTSI5mmUAhzayvr431kbBIpofetmSf6MZmqyOcHmezvOZaPBOAwGmi2menMHrcIb1q6gsnebsqF40Vrj0KpCAql4+Pp/f/v80z2dLHowk10zl/OXRdczVXdk1ywo4t7po+To6/Or2Mmmmd0ZoKudidGRebBzkW0D74NmwzlS4O87Tc76S2WSVeyLOjPYbAILjLt4k1P/QFbReP8ukM4pUNolasRwkpj45WUXZMoljju9FoslmnuLVYxKpVYFh2gvHINjr4UPmuS/dVhpK/8iKnQOpyhraCBqz+I86KLAF0JtFAbw2AAtQJOW448Ei0zcUzCxLitjFqeYIFvwfPuyenCGSNJQkd29qVp9k8AlwK/m93+S+Da2f+vmX3N7P7LJH1J+hrgDiFESQgxCPQBa89Uu885nP8R3SnvT38Papk1zX7MBplt51KB2RchSQ8cmmRts59NC2tYWufhjl2jL2zg4Aqd9khSWa3wRE+UyxbXIMsnEKLEIPibT+tnvSwcM244c8XXXgobWgO4rEbuO/AXJrnbexsUk7D+E/prWYb2a15XkrvhWI6A04xrVqo0hzm8WtTUXMX5a/9AqmJlo3I7qoBHo3HUBoliaZSaal0WNVK7lttXb+S+1b1QqWCo6KGjQKQGsygz7NJJTb0Ai2rl9+cl+cyzv2UkUE3yJgWT5ifxu2fJPq1HAqanpzEk42jeKgKBAKrHi5zLkRzVZde+l0GSDj32MGabnflr1wNQlStiLatMNulyVLOnBfqhfvs/Yei8GHchQsUkGLND0VBNMu/DY9SnWeHw8c87kM7xUKXEAknPU6k7T0+m1yoau8px7HKWxNQoVrcPb9ca2ga/y0UX7sJodFE4qLvnGaoW0BJfhmLN0XFxHZO9SZaFq5hQ6om6QpgqGm/tuRfX0DQmTcNQLGFS0vT6dDvvgXgXNhkqUxYUk5Fl3i1MGpsAsBrcTM9GktqcpzZEej3iqKV7YpYIE16BnBzE49ajYf5Z2VbB8TbCVgeP2NfzqYu+yJfeehk/aTWTzWURokTSW8N+f4DvDE3z42QSZUUVT6xZy6+vuIZkXp9r+G15RtQJmj3zUUfH+fATv6Roml0kzBdOmmc4L9zAokg/40qePB5KWp7yaAa7vYVMppdMdOZYPtJRtDtsdOUKx143LVtFRdMY7Tz4vOvuTiRpH9uDmF0fWHLFCj78w5/hrgqw+sgefraoHgPSSeQoaDEhhOD/PNDJ3yz7JYoZFnRDx8Hr8TklNtU72DWRQtEEt4oCsnWA6liZSWUeb+cOQvmdNCbGud59J4lyHenUUtxuN2azH3/VxZSCQwwMr0GSKjR7+3lHdABTRcW4dA31fQlqrFEMfRX+9Mt+old8mOIldmxdFqpv+ADSbN7UdKrA+tw+NKOFjLAiuXT1SWtKJ48jdjCo0TcmSQKQJMkgSdI+IAI8DPQDSSHEUa3XGFA3+38dMAowuz8FVJ24/RTnnPhZt0iStFuSpN0zp6i+/RcLoxmu+jrE+mDHv2MzG1jZ6D13VvGFeEGS1BfJ0DOd5c1L9Y7zxrUNdE9l2D/2AhNSZ/C05yTtHIyTLakn5yNVNEgMvfamDXBCnaTSix93BmE2yrypPcjDnVOU1TeGc9KrRkXTDRvq1+r5gkfRflRy96ez17YTMBTLzZk2zOG0w2ZrYIdxE7lyHJdIcXvv/YyO/hyQqa6+HE0IPmm5CAx+8lYFrZTCKlWQKCNFqrFpUxxsDiLnzAhnhr/eupxb7iugmDR+tv7tmOwaa1svxdpxA4n7tgKwZcsW3EqJ9OzEVa7WyUGmrw+Hz4/JempzmaOoaBr9z+5k/toLjtWVKR04QDido6w7grOwaSmRtlVYJAfRzreiHWwm31Ch2VLhSF5BaHbcs4tjoZA+DikVwSf39WPJqjRI+mQvOE8nUN2JbjJKhlBVNQnFiMGcR53cS7kvBaqMqFSI/+znCODAyrXUpxYyVdVL89IAlYogOZBmQaCG+Gwh6Bx2pu1+5hUzCE2PrjxtcTNeKFFKbUOSQJ42UXGaWObdBnIKqQJOycmoXe+b66zmV/ntv3Y4asZxtFgwYZ18+h2AAHdBd/b9hrqZv5N+SMT3LswVO5/qLnLDMw+xafejVE98i69N7OcTv/kGwxd3sG31AsxPTzNvYoYdC5dzuP1CKpKV1tQ0P2oPIJud3Nq/nbqZEX60Rq/HpcXjFA8cONYux4YNGDUFdbwTSXYSL01hlLzYDGFyOb1uVm3byRP9xU4rA/kSeU3/HsILF2O0WBjaf7LkbrL3CI/+02cIF8bJFVxU8OJvtmC22hjr7iTY0sbmUIAd64+To6N4uHMa89S9tAR6sPY0EUxGGa7dzcYaJ/YGF/sGozSRZQrBYSmKBNxbvg6LJGhZN8Om/N0E5Snu7trM1Og4Tqducd7efiv3uC7g8/FaiqqVq2qeIZzqpiIZSeZDNMdmWBevZXHUzvzVNVz0ngSanGX+u76P/+b3A1AoaxiKKRzpCMJiJ4OVtEf/DdeVdeoyYrcga1HavG2v+Jl5KZxRkiSE0IQQK4B69OjPojP4WT8SQpwnhDivuvo1zvN4vWP+5bBgMzz+fyEzxYa2AJ2TaRK50yu5OisopqCiguP5xe4eODiFJMFVHXp4/G3Lw9hMBu7c9QIGDq6Qnlj/YlbhfyYe6ZrGYpTZ0HZC+9IT+sT4tbb/hjMmt/tz8ZalIdJFlW395whZfyl0/QGSw3DBJ07eXr8GXGHofH1I7vQaSXNSuzmcfjR7Wrl9pswmn5ndaitjU/fg9a7BbA7wH6MzPKPa+N8DP8anuima00gGA6qzl5mom0I5wkxVA8a4EcU2w4UxlXACvnud4EhYr49yULsbjQrFeJHB7m56enpYGq4lpVXIaRqmWn0cyI8Mv6wo0mTvEUq5HPNWnXdsW2HfPpoqBkxOfZ3X5apDDq9AExpRzYMjFSG9XGCUYFcmD5KGQ9Vwu93HJpDfG5ygS6vQOqNQbSpjQMZXp48PuyZ3AbCw/TI0jBQj21En94MmKPUmiP3kpyijoyhuN0rBg0EY2et9AmsdWJ0mhg5EWdXkI+FRiFtcFHIWpp1+GpXCMWe7qK+Grx7ZS6OsO7kaohIuRwZzZRrEJCmbC69mYNRWwSwK2AxvHH8vV6Aag9FIcnp2sTOk56h5TFEMFQuWYpqgpuEw2XinfC+fnvoKv91Z4fpSBskTx2VQuWr8apoWLkEtl8hOT9HisuEsC+ZNZbmg+wCK30ky+I+k5QzP1tnJl2ME6tawZ9lGHq5aTEk2ItlsJH7722Ptsq9ZQ8VoonGmC0kyES9Nk7dVk+4rUxEpDGZBsOXkif4Sp40KcGTWvMFoMtGwuIPhg3pRWU1V2faft3H7P36WsqKQaKhmUgnQmwgST3ailEtM9fVQt3jJKe9VSdX42v1dXOXcDQpoTzchSxXaL0ggYiUmLBIL5CE2Wbp4LxKxkv4cNNeuZl7Lx2moybMu/DQHWc62wgqikWmmOg/w6y9/gZu+vYX/dzjCVcKKS11CtX+UtokjTPoaifSX2HX+l6lOB3nKqrD+fyxkOnoPZnMNVVUbj+U0T6WLLCgPI6tlZKOJeZRRfVWEUtNIVVUUpTIzdicNVgdW6eXVO3sleE2efiFEEngMWA94JUk66kFYD4zP/j8ONADM7vcAsRO3n+KcObxcXPk1fWL8yJfZ0FaFELB94ByQ3L1IIdn7Dk5yXpPvmB21y2ri6mUh7t03Qa50CuMKVy0oOShlTkvThBBs6Z5mQ1sAm/mENLqz5WwHIM/+9M6i3A7gwvkBnBYjD/wlFJYVQneZ9LXAorecvO+o5K5vCxTPbv2yoqIxmSrOOdvN4Yygxd2CUlG40F9NDicR7/tpbvprOrMFvj4wyeYqF++MPky95ifhT4OmkbLEKGSqsSlRCrYqynGJsnUac+MGrPM3M+24AM3/NE+lrGQcfXRXiuBaykP33ovL5eL8Vn0haqKoYKvTBSjK1NTLykca3LcbSZZ1ZzGgUixS7O5GszlRAhbUkoltDz5Enb2NLEXMSgKTUiTfJtAE9BRlDLZRTMXCManddEnhO0PTvGkgQklRMUlFvEYnsqxPxXZM7aDF00JTWLcIz5Zm0GJ9YITMkz3M3HorFbebqMNByLEAg7HChGuYx0e309RRxfChGKsavETsFQZ9dZjjCpNOP8GSitBiSAaJ5WEr98crhC1mRAVsmSJOR5GdxjYmAw6iThcLZ1QmbQK3XDz1zXmdQpZ1G/Dk0UiSzQf+ebgrg8ialZSU4z/6x3li7SI+6U5zxcAmhNCwXVxLbaoaqWLErBl45kg/QpKJDA0gSRJBt5UCFtQJBevuCBVDgPsWXMPiRJq7mp0Y/K08dv47AJjxh/Bcdx3p+x9AjenzE9lmQ162guUzPRhcRuJKku9KZX4+qJPjmoW+50U224863GVPlNytJDExxvCBfdzxj59l++9uZ/GGjez/wOdwWMukS26GUyGyuV7Gj3RS0VTqX4Ak/fzpIYZjeQLeCKYxCcmqf85m0QzAo4NTzDdEkSS4UlIIW/VruT7YTFPjh8BUi1GqcBvvRfXakKx2/HWN/Djbxu64zGXJJ/lC2ItrZilWa56gbZrucB0CgWJyMt54hO1Wlf7JHmKxrYRqr0OWj9uTT6YKtGQHqBgMaDIsN8ww4Q4xb2oU2R1g0jyDwWBnKefx4089wUjnmZnPnkl3u2pJkryz/9uANwFd6GTp7bOHvQ/4/ez/986+Znb/o0IXdd4L3DjrftcCzAd2nql2n7OoaoX1H4f9t7PMOIrDbDg3JHcvQJIGZrJ0T2XY3HGyg9GNaxvIlTX+eGDi+e91zAb89Ezc+yJZRuMFLlv8nDpXZ6tGEuhGEQbzWY8kWYwGLl9cw4OdUyjaOS65G90B47th/cdOXctryXW6/LHn7EruRuJzpg1zOHNo8ej9XZAxzJJEr/v9OH0X8fHOYdxGA99Y1IRUvYiGioke1xRyIYNq0JCERF1eRZWNjMoLUG1x5EAT5cwo1z5UQM3N4560TDxQpEuJYzeFSeZUNm3aROPsRHOipOBqbgZATqWO5a68GAb27qZuYTtWhx4BKh46BKrK9sZ5KAELSkYm1TOGw+QlWirhyulrtyIoGC7LKELCYB9AKmQJh8MITeOJX/0WVZZ5R8TCBFAUOfwOPeenpJYZ7o2wNrgWn083qch460BoGOxZSr1p5HAdIp+nUBWgHDPTGDRS8L6Tvxu20bjISSmv4i9niFpTTIdK+LJZhOsIXkVGVCJYPEU2ZP8VRbLzrLwBLWrFqFXYV9fOO877Fs5ykcZcEp9SIWKR3zCFZE+EtzZ0PCcJILwST+EgsmYhJxUxJlQkSSIwvYiqzCq6UzspeSyUylmkkg9XaiHTM1GKDW1MD+pSuBqXhaxmYEj1snq0i7bh7yNQeKamlq31+venlvUISHzhcvzvvgmhKCR/91/HmuHfdDEt6SmEIUNCVTiCxuGCbqIRaHm+g2Cj1YzdINOZO5kkAfzua18kOT3F1X/792z++Kc5qIJbpEiXXSxqWIFJLvPQE/frl7+w/XnvHckU+f6WXq5qtUEwh3lIIhbVbcg9Ud0lbyjVhYSuqum2p3FWaSSEk6//vpeKMLG449/5UdRCjDokj5GsZuCOaIBJYxX/4vxvVqefpatvK+5B/fNLiytEmlpJu49gVPL0OfT8otGx3yKERij09pPaOJnI4stOgVu/vxUpz7hsoXFiHKspwIR5BoGgKbeYiirwh5wv8WS8MpzJSFIIeEySpAPALuBhIcQfgc8DfydJUh96ztFPZ4//KVA1u/3vgL8HEEIcBu4COoE/AR8TQvwFegefBqy9BQDT2DOsm1d1TpOko3V4jkrtjmJVo4/5NU5u3znK83CUJM063D3RM3NqMvUy8UiX3tlcuui5JGkAZBN4Xrie0BmFwXLa6yS9EmxeGiKZV9gxcA45LZ4K274PNj+sePep9x+V3J1ll7uj9t9zkaQ5nAkcJUmTmUE2+Jw8FE3xzcEpOnNFvr2ogYDZCMEOmsspEqY0JWMRSTZQNsfxxPRV9l7bapAhb42grroRj0NQHL8JszBzl6nCmOsQD5j2YlYFI0eOML7tSVYNH+HQnl0UJQnFaMSiaC9ZSDYbjzEzNEDLypOldgC/W7cJj72EkjfTGjwPIQQTORVHegzNJrDboK9oJiAbMNoHsaEQdLsZ/fAtHDxwGABPFpyiQo4CVX597Hronj28Zf/HWBxbh8fjQZZlki2bAFD6tyKZHGSvvRmDquJZfjlKUaMldxdlxwbyRi+fHPwcmqRyx5bbUKofZ89Cvf5Ru7gbk9EEagyju53C/nrCygR/4i1khnSy+IP2d7F++CASkKsZxWqAhMVCyPzGMxL2hcIkpyaPuxyGVuAuHcKgWSmhYs9aEEJQ3BukKDL0iSf5xT2/gopMxrMdvzmGr7AQ1eFmT/8QmqYRdFsZSmmkhZULgiqhWISaiX+huSw45DVwV4MJU0bPdY43tGFpbcW+fh2JO+5AqLpqxXPxhQAYI4fISk4mEUwXqhEVcAaff59lSWKxw3qSw11VfSO1rfNpWbGa933jByxcfyHRskq0VMRGmsXDI1zp0+cVicgeXKEGbM7nE7Bv/OkIZa3Cld7DYBGYhiSqJguULC6Y6SNpKuI2zVDSajEJAwddRWorcQQ+9o6n+N6jfYQ9HaQqVbhzvVTMMveXFxOR/Pz8hmZu+vz3+Kv5o4ynu7AUaxAxM/kOiZKlgbJ9hrLFwmDcAwik0qN4PKtwOE5OPxjdsw1ZU3FW6QmAExYbKtA0Oo5TBBg3xxCVIpZpLxV3kRn5lc/VXgxn0t3ugBBipRBimRCiQwjx1dntA0KItUKINiHEDUKI0uz24uzrttn9Aye819eEEK1CiIVCiNdPUZE3Gly1OpmYOsgFbQGGYnnGk4WXPu/1jBckSZOsbPQS9p6sVZUkiRvXNrJvNEn31HPkTa7Z1cXMFI/3zPCBX+zi03ftJ118ZdK0LV3TLAm7CXmeo5dNDIKv6dRRhdcCBtNZjySBXnjWbjYcK/Z7TiLaB933wZoPgfkFIjTHJHePnFXJ3Vwh2TmcSXgsHqqsVQymBrki4GGwUOYHIxFuCvm5IqDXhyG4hObiEAB91oOgqWj2AVpm9MhKlEYAekOP4DNVk16yClmzE45eSh7BrtBTFKQyFSr09vXR/ewe1g51MfP0E2zZsoWCxYJVUV8yJ2lw/x4AWlasPrYtv28fWihMT9M8/IYk9QvWsSB8HpKcJi2sODOjFNZUkCTozHjJZRZhsA1jkUoon/s8+V27mH7z1dQgkdIqLJDKCAmqQzVk4kWGt+qLFNlnbAgBXq+XpK0BgxWU7ocRWpnSgD5hLsViGKUCsdX1lI36JNguOSGcZ3VxIxeXr+R6RV+Bnz8hUKwmKiJPzcIabvNuZ6N0mDGpiYdVvbjopdo+bnlQF/UYNZmSUaZkNNFoe+NFlb21dXo+0Qk24G7DNLJmAQlkzURu9zTllJeu1BOMexTkjBFrsZqPG/fQ5rwNY6qGgALpCtxzzz0E3WZKmh5VWXPhaqrTFcrSND+OwML8BP+33Uq9I42hohGp0mWdvptuQp2cJLt1KwCWhQvJOz24Rg8SM+syO1WYyOVsmBy5U15Lu9NGZ/a4U54kSbz7X77D9f/rKzhnyXVXtoAbnaAtHB1H3L8HkAk5J+mmGvU5So0DY0n+c88YN69vpDz2GAC5XAOBNEjO+UiZIZ4R/VSAerURn3ASyOZZkB/GLny8rdbLDx7tZecP3k9HYgI1PozxYApFGPjB2xdz4erlYLYT3PhuLql+lJySwnYYlDYNT16X0ynmFJW8j6WBLmxyhHDohudde2nvowhZxu/Qf/vTnmYA2iIpDJgYs5YQagx1wswR614SxcSf85i8bLxxMvLm8OohSVC7FKYOsqFN/4G94aNJudn2n0CSRmJ5Do2neXPHqSUV162sw2yQueO50SSn7kA3MTrAR36zhxqXhZJa4U+H/nz5XTxX5tmRBJedWED22M7BsyO1O4rXgdwOwGoycOmiGh46PPW8jvycwTM/1O/32g+/+HFLrj3rkruhWA6v3YTHPmf/PYczgxZPCwOpAa6o0mvGNVjNfLXtBLPa2g5aVH3Cl60yY8wkyZsLNEZrMQiVlMVHNtaOtfUJMs4hrrKZqGcSeSTI1WYbrdlWjGYbN5TP5/qnt/EP//AP3HPZ9WSuvAYhBAWbFaui4g2+uNxucO9unP4qAo3NgJ5fWti3n8GmFuxKFoOUxe9fTCVSRvYpqEYbzuwIpaUVyhWJwUw1scRKkFVUxrGUyzTd9huGqmpoGhllwlKkcdb+O9gc4unf9VERGiPLd5KNljjyzBR+v594IomxpgY1nWOaKUJyGCEZGC0vpKnVxIMr3ou5IpCFYG3RxcaOGrS4kXdoLi42jjHqrKZ12IdR08U3yZkteO1ebgqO4yaDLxFHAH8z83OS6It5zalqJmz6pLzN6X7evXm94ygBPloPi9ByzHIB22y+S0YqkvxDPxXjJL2Jw2TtISSgPdyEqFvEUroILLIiRZyYI2McOnQI0/heQFAl5bDY7TSo+tR5LDnApfv1hWbh8uApZZm26oTfdcklGEMh4rfdBugEJ9OxipbpLmLG4/c1mfGgZPtPeS3tThtJVWOy9MILtZ25Ap5ZkiSnJUq79mM2hnH48xxU/fz71uPvLYTgK3/oJOA0c3lYxWGeQM7Cjird7KxcqCGlJhgzTHFEq2G52YWv4kBSsvhyVaiyn0+rSRqZ5m8n34TsvpnskUUgS6yqnuL8+Scod9Z8mEZ3HhOHsR+oIJkEIe8oslahbEngrhjYWP8MimaipubNJ11TIZPGkIygOL3IZTuygESgDUkImvL6XGHUDnUpgSjKTLj7qLKdmXpecyTpLw21SyHSxcJqGwGnmW1vdJKUj4HRCubjq98PHNIjE8+V2h2F32Hmyo5a7tk7TlE5QblpcVEx2Xl090H8DjP//bENRYArogAAIABJREFUtAQc3PPsn+8TsvVIhIqAy54rtRNitpDs2SZJZ9e44SiuWVFHLFfm3v1nJlR+VpGLwr7fwvIbwVnz4sfWrz3rkjvd2W4uijSHM4d5nnkMpgYJW0x8fUE9v1jagtN4QkQ92EFrWeEK0cEn4u+nUswhZImKKYdHS5NwK0w9/SEM0v9n7z3D5DivM+27Qnd1ztPTPTkBg5nBIJIAQRDMFCUxiFSmMmXZlGR92rW9lvfy5ZVsyXntlW1ZK0u26VWiAhVIiiLFHAEBJHIgwmRM7jCdu6u7K3w/agAIIkBSJEGQ4tx/+kJPVeHtmber3vOe5zzHx8yqr6EU+1gfERmXG3FMX0hbqY2jQhpRcqLnRSp79xJ3Ophy+mhtbaXsduPQjBe0/9Y1jYn9e+lce8FJp6369DR6KsVj7T1ceeRpAMSU9V3R4h7kehmnmqHWbjKi2vCWJZw1K1tQdC3Q+eMfoQwOMlSs0DZ8gKlKgvCi/XdNVRjZnWBX84P0b2ki2uFj58/HCQaCZDIZpPblTLt9HCkdxiY5qXdfQdkI0LFlkJ8nc1yiinSoJs95ltNZ+QkASlLD0O3sifYxkDAQFgPPgfkRbl15K1r5IP/ov4+3Hn0a2aWjlSXybjduQWJdvpf9YSuj3ed7vmvs653n9Upy+CC8DL9kBaVFVKgZ/JvjHgwBIkIb9mqIrkt6CLVtJmAYmP2H0Y0wSnqO1St6Kc8Mc5E8waBtnuHhYRqia+jOd7MnMYdWsYKkgt2H09CYWaxNEmQZrruOZ/MFdm7fzvDwMMKaNfhqZRzVLA5Dxw1kShFUY/aM/Rv73dY8fa50dgONQ8UKbZL199IrCtWhYYySH2eoysC6tfzzI0McmrH+/vfsm2HXRIbPXbuC5/bvxRkpIB23UxGtTdPyvMzTQh8GAge0OE2mwLzHS1Wok6+/l0LVjS2l80/du0kIYX50cC2CLUt1YwPFoA+3+1eeH95GGHwPzqmfYx8SMHQRT/BeWrJ5VOc8LXKRgfBz7EsOIEmnP3ee+PZ/IpgGWiRERVfwYjDlC+EqqjgcVjB03G1noGhtQs/6Rog4z81cXQqS3mw0DoJeRUgPs6k7wtaR9Nmbq74RKC9YWSThVKPW+w7MsrrFT2vo7FKB91/YSq5S54FDp7JEiWKVac1PAwt86+MbiPoc3LSmme1jaWZ+Q1niI4cTNHgVBpv9vzbeNNQK5zeTJNvPa5+kX+WqFVEGmnx8+eFjv309k/Z9HzTVMmx4MUQR+m88r5K78XSJjiXThiXOIZ3+TvK1PAvqAh9tjpx08DqJO4LN08jf2DpYWemi6PGCrqE6knjKVRZcCprqxaX9CXjmSfb+gJtKQTwOqBWi1IU6jrqGjoHcvIbcXXfT7LAxo1ZZvXoVFbcbpV7H1M9e1jxz7DC1Svk0qV1lj1WPtKN7BVsmLN8oc8KBHHZQi0TwlKbR/SamB45VTZbPZ7iyZRd6Ncp0i4gcDjOl1igLAtORKKOSgEeq4hYdbP/pcUS/xr6mR7my7Qo23tBJYUGllpWoVqscrarsb2kguXAAzVARui5HFAUyHS5mqnXe7vOyLKtzwL8G3+i3CTUqzKWiHDFbeLrn7cwGujD1NLoAZdXgnf5llMtjDASaiefmkbwSlaybvNeHz+OltRbjYMi6B/W433iZJG8kgiTLp3olATStJWJYm6cFocxu92EKpWHq/jAmMu56kDnb9/C1WD3sMoUH6FxjdaxplGUG1lzACjlJh5hmaGiIybyHNek1FO1pRHkESddJ2iUcislU5lRAc9Dl5ODgSu79xS/4zne+w7Y5q4qkMXsEv1gmhEai3o5pMygce/Z5n6XvDA53v87hosoy1fqsjqKCFOmlcLyO4q/zZ+9eRdBt549+uI9cuc7f3n+EwWY/V3Q4mZwcgrCKPhfm+h3ziIEAU9MaB+glaThwCwqOukneZck5E7RS1FdiEGTlu/6BL920kmv6I7jbv4bXUJnzhk66NJ7kok9Tm5tBczaSy0dxtswTEK21x4bWJ7BLNR6Z3EK+OHXylHpV5fC2pzAkmUBMoiho+MwKo3YFdypLLdiIKlSZd/rpWYhgeKporgou+dw8u5aCpDcbsUHrde4gm7vDJAtVhhPF8zumV0I5Ba7QyX9OZcrsm8rxtsEXllNs6grTFnKdlNwV1Dofu/1Z5owAmxt1uhosp5Sb1jZhmnDX3peeTappBk8eS3JlbxRRFE7/4Qlnu/PRI+kErxO5HYAoCvzxtb1MLlTO3r/qjcroYxDphYbel3b8SZe7B87tuM5AVdOZyVaWMklLnFNOmDeM5cbOflDjALb800iOMoqvCVt+gbojhS8LSZsfV6jKgQdC6HWFXOtjhN1TbJSnEG0ldkd240Rkr+swSvdG8vffT5MsEXnmcXb+x1coKwqiyUlrZoC/H5vlI/tHqS4W+o/t2YkoybStXHPymMrevdQdDhINjazEalZvjthwrolSsQXwFCap9lrnD1UlLoi1sSp8AMrtTNTH0QyNAwctI5/tq9azZ1kTOmW8oofMbImhvq20B1txVI/Q0ucn3u1n9pC1MN4v1JANg9UXXcJkaQiPI0hLb4BfFIrYBIGrB0W6HPPMyE7SZpQO72Fmq73sV1ZwRc3DXMvVmHoKRdP4WoeH+pFvAyBoDYhFA5vHoJxvpODz4nFYQdGQpwymcVrj0TcKoigR61nO0W1PUa8tbgY2rSVgTiDqNp5172V73y+5Igl6OIqseXBFj1HI/AKifQBo8wfYdPNKENwMP/sc737Hddx22200NTURjUb5wHtuxZdcjyvXwfXNP8Snlph3QaNSJ1WsnlSo5ASB4MICn1i7jltvvZXrPvABZoJRVsyPEaSEIhaYqHUAkHz42yc/w/j4vzE7+2N8skSLw8ZzZwmSNMPkaEklnrSc6cRUAWQH+ePW0t5mTvC37xzkyFyBG7/6NLM5lc/f0M/u3bsIuJIggppqw6FmEGSZvb4gMhqyXuCikPUsaKK2+FmmkQXLXa8+U+SWDW38+0c20hlsJFzMkPAFMIzTNzoNfzeVlJ1stIfMQjPOYJWpwiiuQo2mpt1k1ADHMj0cmzly8pw999+LXq+heYO0KCWKgopDLDOHSUMyhe5vYMaexJAb8CT9VKIpws7wyazvq81SkPRmI7LMWiTP7T/Z4PQNXZdUToPrVJr1RP3Q284itTuBKAq878JWfjmaZmi+wCe/s4tj8wXa2rvw1JInj2sPu1nfHuSnu6dfcsbt2fEFClXt+dbfcH57JJ1Asr1u5HZgGThs6AjxL48OU66doX/VGxGtChPboOvyl37OCcndeWgsO5WpYJgsZZKWOKecCJJGc6NnP6hxACF1BKf+GHF7P3I+gymYNOQMVMFJ4zVPUq/WmHziDxHUJmYH/pOAkqUslUh7pqiicliYwxSCGBWd0MQYq/Y+TSmzQBnr/lKZtja9fjqf4f+Mz/NgOs//GrLeG9uzk5a+fhSXi7GxMcbHxynt3cvh9m4uHTuG1GIVkruibfiuaKVcNvEWJ6n2mZTqCsVigJzWQ3K+m25FxxBUHtyzg317TxnUZNpCJOwalBWa+/08JtzDhnAH+w98kumZO1h5VSO1xYSybndwwdgsF131VvLJQ9hFG1FbmnsTWS4Lejg+9Fl8gW8BsMP3edqL38dAJqdsQkGg5mnB0NNE8xVap0QembGK9RmfRauIOF0lMjkXdbsdX0lhTJkmJck4KSGdo4XnuWbz+z5MIZ1k988XO8w0rcEnzSHqDjrVCn+dPMq00IJmd+EsNlPumMZnJCgLBVSHj+Zyjqx7Hk+ohfTUGGqpTjwep7GxkXKhyo7vz6CLGqNt30UKFghWysy5ZZpMBeCkIVamUsGXz+OYmaa9vZ1Vq1Yx3L2OFQsTtJSSuIUyR/NWPVD2yJMYqkoq/Tgjo/+bqenvAVZT2UPFM8vthisqNdMkmhnHqEmINQE9PUx7/XIAisUjXNXXyHsvaGEiXeaG1U2sbfGxd+9eehqsfpDVZBPIIrM4OR5vZIOxlw5hlmbJquHqcz6AXapRlA5gEzwYpol6vEB66jhDO7YxEBmgIZ+ialc4ljt9nOWduzB1kwPRFWQy1sb1qut7cZb2EgjMMz3VCQgcm7GcGLVajWfuuRMBqHmDNIl2ylSZ97oxgfaZaWzuRmbsSTqKLvS6Qjo4ec7qkWApSHrzIdms3ZL5g7SGXLSGnGwdeQM3lS2nTzNtuO/ALANNvpe0I/6e9S1IosB7v/5Ltg6n+ft3r6KxqcPqk/QrAdHNa5sZShQ5NPPSZFCPHE5gl0UuWXYGjezCKCBAoP0lXeuc8DrKJIFV0PrHb+0lWajyzW0T53s4rw5Tz0K9DF2XvfRzTkjuhh561Roav1Qm0pa70lImaYlzScwdwyk7XySTNAh6DYewjYFaiAog6BreWhaAcXMfsQtvR13oYnrP72HaKgwu20WmHMUrGyTsI6g6JIU8yrKLcD1yH76Sde+uLcqMR3bs4Eipwh8emeSWEY3PPVPlWzNp/uvYOKnJCTrWWNbf9913Hw/+7GdUjxxhX+cyrtn2NHowgqi5iN6yDkEWKS6oeArHUXtNRvJxNs9tQVM1KslWbowfBuAbT/yCUUcVV8VykDQlkV0t7Qh1J+LmJBoaa7yWemF4+P9w7+P/hV56FEyTmOLFX6mh7t+PkJOp6ipHEwmmq3UusQ9RKByinWEADtlaiejjOKQiYcOygjbNKphF7A6ZjzxicP+8gE0KIR/cDgjYnBqpmrUUjGRd7PAcoGg68Yuvn2fEb0pr/yA9F25ix113UspmILYKn5RA0h3k8FOZOUrW34pkCDR4WgmtWI9uwtDxb0K0n556nZ3zO+la24ehLbD7F1ZQ7/P5EGdaKKZVDvTfT8aZoOiWiVdLpJ0KgZrlKDiVqVCv18nl8/iB6tip+Z5YvgbZNBiYH8MrVkmrATDt1L1lMg/8lCOH/xQAVbVULv1uJyMVFfUM5kaHF4MnP1nMyqJ0tV6iuRBHxEGxZGVo/tf1/Xz2qmV8/vp+Dh8+TKVSwe9JIs2DqCpEPvlJ9q1Zj6xptGXG6BTnUEyDugDV9scJuQ0WBA0WRTv5wwtsu/MO7vvKP9Af7Ceet5zlHktkTxtfaetWTNnG/eEeyuUAhuomvNyk/60DVnl2qgeAnXv28vT3v8Uzd99JtVQCmx3T50QsxkGAoWAbgmmyeWYcl2z1SOpKWgH8pOcoYcdSkLTEq0njIMzuB9Nkc3eE7aPpN667WOlUkDSbq7D7eJa3v4jU7gRRn4MrV0TJlOv8z7et4J3rWiyb9Hr5tEXq9avi2CSBn+55ccldXTd44NAcF3eHcdnl5x+wMAa+ZrCdvXD4nPM66ZP0q1zYEeKK3gb+7YkRcpXXT5brZTP6BAgidFzym53Xf9N5kdyNp07Yfy9lkpY4d4iCSIevg7H8C8vtAJTBXjoNidl4B1Ihg0uzVAJHtDjNfTUCjU5mshXGRtfhjoxxaUMBv2Qy7R0HQ2efbRzHqivRRw5RcHkRnS4008okTR04yCcOjuORRdYcr6OMlbhadPD5qQzzkThday+gXq+TSqXQjhxF0HWmelawQRxAUwo4nHEkv5U1yCeLKJ45zICJlLHUA6E1TQiGhFKI4NIjHLUfZdgt4KmUUUyDzrkih5o6Cfe1srX0GEElSIuYQxA86HoRT/YZqIwh6CJFyVqZlrfvIBVew4IksL+1E9EwaJ37O3zeVQTFCiFRZbjZjiwWaHcdQU5IaFQwdWsTtPPDt2ICt9wFh2br+I5ZBhSmCQWPtTkSMNzs8O5Hl0L0vwFNG36VSz/4MfR6ja0//A4oHnxRN6KuUBQcHNW60D1+7AUfvRua2NB6NUdUkcT8Pdjjq1lW19g19yytA8sBg32P7KOcr1EYVVCqYdZe14SnVSIra9QVkdZ6Hl2SME07DsOS/S8sWBbkIY+X2uip+T7f1osq2ejN5PAJKiYipq0No8PJyOw/UaunaGh4C7VaCl1X6fM4rQCu/Pxs0qFCGVnTsAVFxJLtZG12eXYP9nwzxfxRALwOG394zXIavAq7du0iEPBTc8wiTSrYaxlm1rWwEFFYfvQY5YyLTmGOFl1Ak4qYskbUJZAUwoRvaEEUBEiUmRs5hlav0aHFaCwWEAyDndnTrcxLW7eSblzOIcmOYtioZTtYWHgawfckpbkBUuUwilglJwd45q4f8csffQ8EgZo3iNeToVay5uDhUAdSoc5yTUNCZtpRIZ4W8YhJpo1z52wHS0HSm5PYoFXLU5zn4p4IBVXj4EvMkryu0OtQzYHb+iK9VKndr/LFdwzw1Q+s47ZLF2uEfqVX0gkCLjtX9Ea5e+/MiwaT//n0GNPZCh/aeJZMUeY8O9vB66ZP0q/zP67tJVep8+9PvoAU543C6OPQvB4c/hc99DRaN1pz8NBPz8mwzsZEuoRXkQm57a/p/7vEm48Ofwdj2RcJkq75EsK1X8SzIogebsSWTeOtFhEMg3vLLajh9xJdYafimmFh5HJqqV7iK+6jS5IpO1RsmSQTQoq5uklFMtnfdwH4AhiCgCEIVGdnmMnl+dfWJooJSxr1KdWBp65yz9s+hBCNk0wmMU0T/2L90o3ObuxN69H9aRzeU32WtPFRKv3W4rSQjbGjaQcln5OiKDI1OcDaShzJNc74jIps6ARFmevGEtRsdp5qFXl0/FHilTiTx7eTSgaY2beKwlGTFZdehFsJkXV50SSF9J6jFHztKCvjPBZTWD47S+LxOr29f4Hfv552YZKREIiCSiN16hWdolLGWAySurZchRKL0JQG+a4qubKVudLkGFmfDxGBulRjb0MvmtzAxeGX/hx9PRKMN7Pm2us5+OhDJCfG8LR3Iet2DEFkm7QBDANnpZPlGxpZFlzGoVoAUc+RCTpwGgaTk9toaLee1Vo1wYP/cZDJZ1VUxzzRPhtt4VaSsoBhQqduBUTpQI0eTWQqUyG9OG/CjY3UxsZOyvWLpsj+SDex7Cy+RRv4sh6n3iFQWJ6i2fNeog1vBUBVp+j3LDrcnUFyd2Bqlra5aWpBG/a8gOhvpOJQKE1vQ8m1UsgdPq1MIJVKMT4+zrp1negOFX2uAXlliad23QOiTvPocdSsQJc4Q3fJxPANARBzgIqDejxAzWVDNE3qC9b3RklpSKaBUlA5XDk1xvp8gurQEEfcVrbIVTPIpVvRtDymkCA3tplxo4OgrUCy5uPWf/5Xlm24GEyTui9I0D9HteqlLkoMeeOYqSohwXo+TTghsuAkrjxHViucM2c7WAqS3pzEVlqvcwe5uPsN3C+pvNgsbtG44b4Ds6yIeU+aLrwU4n4n162Knyr68y72NSqc3uD0neuaSRWrPP0Cv6fpbIV/fniIq/saubr/DP2RwJLbBTte8vjOCa8nud3he+Ebl0NmnIEmP9evinP71jGShdeH+97LQs3D9C7o/A2kdicQRejYYmV6X0PG02U6Iu5zVvy6xBIn6PR3MlOaoaKdxbFLEGDzZ6m6QzwR28twuIZUKSJqOl61ii43ktDtjC3sRzAlHLVmntj7AUTdzlUumZLNoEgVMHmmehBBEPAZ/aDGQBBY8HpxlSt82igQm7Xug3aHROJgmhsf/D5Fl5ffP3yc2bl5ANz5Aqmei9g4bFCf2Y3myuNQrM000zAR5kaY2RxEVV2MNlRRggrTswWOEqJWc9MrNiFIFYT0BHXFCcki7cUFmjMp7tEFKmadbjpxOPN4zH7SO3W8LVUaNzxHPOJGk2tMtl1FQrEW7PmLY0y7RK7Nl1k4EmD82VmCwU00awc5pkFdkPCJg4iCiSTKlrMdEp+8e5T5TW1MXmFwyV6YHHWCZDImeNnV58dnOPl/PRUKoQ/xtoifT7a+SNuCNwAXvev9KC4Xj3/7PxGaVuPBmnMJuQF7IUesrZlgzI0oiARDWzBMyCrWMZFigpyrhmxX8EdKTB/LEog5KPiHyOfzSGqauiBQNKA3abnLJYI6vRqnBUkNHe0YpRJawqp1ns2q7Ir24iwkaCyUcKAzm/ejyUWkaQHvAzYcDqt3WEWdotOp4BQFnis9//tyuFyjOzGLaVex5QxUKUDa24SrlMeWtqELBYqTpzYkdu/ejSiKtMatYKay0IU2qJNOtzGhNTHvDsOCiM/UaKwaiK4pmps/SMxmPY8TqTSeyy0Z52BwCwCZaeuzy3mVSUNHXwzKStu2AfBcxDIu8moVUplGQEQSfRRn1hBzN+KWDRLlCGVtknpVxe71YTjcNPvylMwas/4wuiih5Go4Fmu+8gRxVG34o3kMzCW53RKvMo0ngqT9RDwKK2LeN2iQdKqRbCKvsnMi85KldmflRCapOH/a21esiOJ32rjrBSR3f3HPIUxM/vzG/jMfUC1CKfk6ySS9DiRtcwfhJ78LM3vgx58Avc4fXrOcqmbw1ceGz/foXj4TW8HUfzPThl8l0Ab5adBfOxOLiXSJ9iWp3RKvAV1+K2s/kT9z/WHdqPPlXV/m6juv5vMTf0XJPkve40euZPCpBQSxlfGxcWaSE7hKbYy076ZYjFA4cgt2Z47lisioN4atVGDOqSHYu7lwXEHWrR3tscYoSrWGd2KE6WMZqk4RaXWAmeEs0elp/sCp89hCgW+kS8iSRHO2QrzvFkS5TmXff1E3siiKlWUpZCscby3iimY4lu6h6pyl0dXIQlLjiNqEx5OmVvQgmAKSY4Ss3Uk0L1KWKmweG6aieNA8l/Lfb76FSsrOyC8SBBrjXHXb+8kVduCVJzGlOhOtlzHXuAGPmeMpakimyXXzYdr7B3n0v/4NPddOO+PUEBlquhRPIEVYEvBV/Zh6GskXJVcsI/v3It+kMROFUEql6jX4zKYsboJMRtr5XnsPYX2cfxtoR/51Z9Y3IE6Pl03vvoXjB/YylvMQWGy6CmDPSizfcGozc2PTFuY1gfm65bLaU6uzO7mHhrYObEqGZRc28tbbBkEwyOVy6CnrGZWuyjSNziLpOimfSGvdxkyqSDqdxuPx4O2x5l1tbIy6bjCaKjLSZklKW+ZSBIU6I0krsIjNbqBw989RBMvMQa1MIQkCvW7n8xzukpksCaeLwYATu1REytSoOsJkgv2IhoG5dQcAiccexzRMNE1j79699Pb2Uk5shzqUCj2k7ZaKaGc9it3bjDtbpq43IyDQHOxkRe8XaTSsAC+ZTNKwKU6qOk2HZyURdwuZGcu+WyjmqQswXLYCqtLWrWhOP8ejTQTVPKGGADkdIpmVtDT/HqZho02xUTA8pCphnvz5HYzv30PdH0aQdIL1CEVBJREIIhoGVwad1Bw+KoKKq2yt0+zLLaVGWFRe+WQ5C0tB0psRZwD8bTB/EIDNPRF2TmROb6z6RqC8aDjhCnPHM8cxTat+6BXhXZQY/FomSZElrlsV54FD85Sqz1+8PnJ4ngefm+ezVy2jJXiWxWbOKsQ8r6YNALJy/vsklRfg+x+w5Ghv+3vL6OCxv6arwcN71rdwx47jTGXK53eML5fRx0F2QuuGl3d+oNUKsgqvTYPdum4wlanQsWTasMRrwAvZgJumyRd/+UVuP3g7G2Ib+MY13+Ar2keZbOpCXkjirxQxbFHyB/L4fX6cpWYW1DSRvknumV+JWHdysWKnNdeKUmoCScIW7eCRgQUM0Y6JQN6l4KuUmZ2dZezoJA/Leb54dIo73DVyyko+s3qA98VC3C17KIViRAc+hCTZEITdELcDJoojhq7r/PSuH6M3l7DZquSH/aSqaaKuKNKCl7qh0NlxnEoJeip9iKFp6oqNcNmgKJRZMzGBU5tBC97M0ccfYujudmyKg5v/5At09nwUt3s5Vae10K3ZIe/roNlf5K7ZWQbqw4SqNq56y224/AEe+r8/IDJvFc0far2KwNubiNtUTAMMPU338m7ub/8e3uYcch7SHSHKdtjfINM2LzDjb+N7A8vxapNcLf0S5df73byBWf2WtxOMN/HEL54ihLWxKhVz2IwGll1wKki6KH4RUzWRQvkopr+FlbrIzvmdNLR3kpme4JqP9xOOe/F4PGSzWZwpS4o2V/FApkxDpUbeaUM2QZvJkU6nCYfD2Dut+V4bG2U0WaKum9i7Okm7g8TmZ/EIFYZz1rrFu+VSjEIB9eFnEUU7lRPmDR4Hh4qV06Rzu56waspWD1qZHTFTpeQIkQ1dCIBt1AqqipUhSs/OceTIEcrlMuvXryeZeQbblICmOKhrVjDsNO3Q0oHN0KlUrDWK3G9ZfrvVGVxinUQigWCTOFbaTd2osqbhKvKJeRBAL1klCnvyJUzDoPj0VpK+XlSpRrScpWFwEF0w8O26ke74e7A7JBoEmUTFgazpzGw9jjsUIe8OYHep2IstlESVmUAYTzbHzU12DL/lbNeca0DyipS72gAIL7yAfPcV8tvzTVjiNyM2CHMHANjcE6amGeyayJznQf2GLAZJqi3IN7eNc3Vf428ktTsjihds7tNqkk7wzrXNVOr6ydqnE1RqOl+45xA9UQ+fuOQF+h9lF4Mkf+srG+Mr5XzL7XQNfnSrFYi+7zuw8TZY9xF4+ssw+jj/7eplIMA/Pzx0/sb4Shh9Ato3WcHoyyFg3fhPzpdzzEy2gmaYS5mkJV4T2n3tiIJ4Rhvwr+//OncN38WnVn+Kf7z8H9nUtIn+ZVHm4y3YSkUCZZWarFBTFa55yzXIioRXDbPxLZ2kdRlPcg0rXDU6jR4k9SBSuYTqrtKsZzDU3YhSA4Jkw1EqIYoic+pxhgXoy0+RE3S+Fb+QD3/um7zr6W20p+cZSASRwj1skw5Tnz6KsOhY6lBifPf2HzF2fIS+0m7rcx3IkjTzRMwgBU3Ehk57pwuvV6Ur043otOTh4YpOQaggqznsC3dRFILcsW0Od6zCB//mywRicURRZvmyP8PmtXbwfTZr007BSni7AAAgAElEQVTr8zJRE9nk3IPotaEPlXnnn3wBp8dL8W4TWa+xz9OPNHgNvaGfnnS2i5ljcPDHFGNRlIyPjfU4B28qU1xjcvVzq3lg5QY6q3VC6X8i5gq8FtPgNUOSbWz54K0szEyj6gu4FsI45o/T0NaGO3DqHh33xKnKjchGgVpsGf26wM45K0hSS0UKaetv4ff7mU3P0lO01ksJ1UH9ohgtZZ28w0XZXqSxZGc+tUA4HEZubERwuaiOjXFkzsrarG4N8nhsFcGZCQaTRziWWax9apKwd3eT/eGdOBzNqBVLudLvcbJQ10n+SouMfc9ZvZGWdVrlBmJeIG8PUXc0IDa1IlYEbHUvWnyO3P3jHNixl0AgQEdHG5o9gXTcjtyqkyg0UjMl3o2TRzqWA6Cq7YCBrX+xoXIpQdRpnKzTS1UnSaiThGikls3jdDsR9VkEzWBPvszuBx7gWDjEQrCPbK1CTKgS7rLWRhmzAePeL+EN2XBrJpgCV6SfwKyapAONIEoo/jRyoZuUvc6sN4I9W2Wj30RyRZmxJ2lNu2hZFiTtsez4I9MHX/V5c4KlIOnNSmwQ0sNQK7OhM4wsCm88yV3JGu89w1Uy5Tq3XfYqNWj1xs4YJK1vD9Iacj7P5e6rjw0zlanwpXesxC6/wFcqt9gsNXC+g6TzLLd7+AtWtuX6L0OLZbXLW/8OIsvhJ79HXC7xkYva+fHuKYYTr60V9iumMAfJwy9fagdWlhcg+9o01x1PLzrbRX4tkzT/HDz+d1BMnuGsJZZ4eSiSQrOn+XmZpHtG7uGre7/Kjd038qnVnzr5vrs/jMfnQACCeWuROR2A3uUrMJ0a7QsDTH3LwXUFO975DYj2MuXWf8YopyiLCYqiysD4OEbtCKISo2pUkOp1mjwNjCsVaqLE74QKfDw5zOUVkcPORj4y4mX53btoyytU5/czLM2xkExAmxcAWW5gdOoIXi1GIDRHIeOljxI1QcedtJFDwGnXcLnaaG07iqdmJ6xZO/4erQICzNuS3PTIBP58hn2XXs7Kd4l4g6fqgEKhzQQnrUVxOJBg+bEf8GRTDcHUeX/PFbhWNaAeXSAUbeFDf/NP9K1RiKQTPDpVZOLgAaKXr8BlWlKo0NzTaOs/RIUcHmU5lUNDfLixnT55C3/70c/gq5T4sihTqiXPqVPY+aLngoto6V/J+EIKey6HWKvSf+n65x3XEr4YgLmwn8ZKnnRpDjNq3ReTE9Z89fv9pBZSDBg1FMFkoS5RC6+kqyyRc3qY9xforkskizqerEziq3uxt7dTGx3jyFwBWRTY1BUk13Ezuy/4A2559n6kEoCHcmWc4Hvfg7pvP3Y9eDKT1OdeNG9YrEuqHT/OYVMkpNUx1cXsWB4ydsvYoNa1GgQBW8JOvWEWs64TH1FYt24dpfIxBNlEm48htyUYS/dQw8ZN2HlI8FGVbNT1ViQxjaDYLPvDYoIGr41EIkE+lUCtFjELMwiCgN/wsuHhJ/jM9l/ykZ/+EMdPf8Jj27ezd+0azJWDJLDTEgsQCFjBd1FQUQ9O4cs8hZSc5cLiHpaVRqgu91C3W0FrU+Qo9kIrx/weTEGkV/FhW1jAZYuQFkt4VYGOFRHSVSt7Gp7YdlrblleTpSDpzUpsJZgGJA7jUWRWtwbeeP2SFo0bvrYjw7q2ABe0B1+d63rjZwySBEHg5jXNbB1JMbfYNG0kWeTrT45w89pmNnW/yMMlOwmiDTzn2TXofGaS9v0AfvmvsOE2WPuhU+/bXfDu26GShbs+xacu68Rpk/jHB4+dn3G+XEafsF5fjmnDCfzWYuqkPPMcc6pH0q9lkqZ3weN/DfXSGc5aYomXT6e/87QgacfsDr6w9QtsjG/kzzf9+WkGIpJfYbmokfUFaUhYlsZ5sZf//KOnMbIS3lqIpCTwUNDAkRpA1200xSZBhMc7DuI1HZTdDsCgJeLDNDVmwl0EsnYmcWPX63TIRYT6CBdWFb73gYv5QCs8E+vnVkXkX4M+NEEipVYxYtYiLptSQDCh7KS2zCRdDKF2WgFOZSKPio2gR8ahtBAMHSCvKDTUrFpVX70IQL1sECxKrNSfY9TdwoRjy2m/I71QIPS9CrJcJWtL0jK/lQdlN4PyNCviV+Nc3QC6SeVQGtlu57LOCj2eIeYCDdz5V3/GwwehTb4XAFfkfeQ33QJAoG0LZq3G7sG/5Pcu+h281TrX799KtM+q7ziXTmHnC0EQuPzDn6BW19DVZxBED32X9DzvuAvb3wnAc3IOydBorWuM2qz65MS4lfn0+/3USjV8doGQbJLXRWYND10lAV2SyPireEwBox7AfqRCfaoIUoTa2BhH5wr0RD04xyt06DaKnmaUWo0/2vV9iqUA5fIY/ne8A8FuxzgwS6V0Qm5n9UA60VQ2d9fdjDa3MeB3k8pbWUaj7KQUXIXDLZP2LgPTRDxapFwdp9wv0mPE6K42cvj4zwAoL3SzR7NhGhIh0cG0V6RQ1JkPxjHkJmRhcTO4mgdNJRr0UqvVGD1kKZCk0a0AhOxN6MUSW0YW+Nijd/O2n9xBURDQZRnVn6Eu2Wjv7cTvt+ZX2Vmn0v45fFEfhXyZLeltzDhiTMZjhMM+HA47Da4MgurkeCCCXa/xvr5W6rMZREGiqlsBY/PyAOlKGocg48rPQuK5V3HGnGIpSHqzEhu0XucXJXfdYQ5MZcmWXyeuZy+FcpqazcdYpsbvXdr96jlzeRufV5N0gpvXtWCacM++aUzT5H/ddRCHTeJP39734tfNTYK/2XIwO5+crz5JM3vgZ5+F9kvg2r96/s9jK633hx4kfPB2bt3cyf0H5052L39DMPYEOIMQW/Xyr2FzgKfxtcskpcq47BINnl+TBxYXNwrOd1C/xG8dnb5OxnPj6IbOUGaIP3jsD+jwd/Dly7+MTbI97/hVNoGJpi6C06P4izpJf5TWDR6STSPMBkX+ZYubcrdI2pRRFzqJxCrozU5am7uJKB7yNh3d4WK5Ye26TzT1YR5XmTYCtNeTJEsZPKEqsl0kfTjHdWsCfEqZ4RMNzzHXPswv6r2MB2MYIZAkFzPHLQMAry+NqYDgFMi3WsGFXLFTMW0sb4wzMVEmI4TY07YCXbY20WKL/Z5KcolyKMl4+Vt4zDy3Fwep/crmVXVoGDkt4HOKLDirHP2bzUyJzdzc3IUgCNhbvUhBhcr+JBg6ntF9LHeMUVZcdL/jfex74nH2pXUkQULQrqZQsBaRkTXvwJQkPluScKoVPrH7ANEaFFxWRvlcOoWdTxq7elixdjVg4HF5UZzP72O4MnohC7pM2rD+RmtMO7uz+wnE4iczSTaXDdEUybgdBCWTEgbz+RLxqrWZpNs1dAyc1RDOzgTeq9owtSD1mRmOzOZZ4TQ4eP8IolzBNG08u24zFyaOYkzUKZfHkAIBon/0h3A0jWbkmPqLP8WTTNCk2DhcrGAaBum772G8pY2BkJ/ZI1awUqQdEDHtIuN1a6NNHtcwzTrHI8eYEhcwH0lQPPQUYgEK+grunrsQH1UccpXvNMsIuoktGkFwxpHNMTCMk0qCaMSa3+NDxxBFEVdyHIMqISXO5PXX8733/Clv/51/4u8//mkARE1mvmAFlh0re3A6nSiKghoRUMdqeFddQ6X4CLoo81zjIMlyBDWfJhQSUMpNlASVmUCE5blx3jYQQ0tYmwuGGkJVdAKNLlJqirAzgrDiemvT/xywFCS9WQm0g+I7WZd07coYhgnf3fHaLMxeDcxyipTuoSvi5pqzWW6/HLxxy93uDOnbzoibNa0BfrJ7mnv2zbBtJM3nru2lwfsS6k+yk+e/HgnOT5+kYhK+/yFwN8B7v2mN4Uxc+AlYcT089AU+2GZlCu/Z+9oYGLxiTNOSEXZe9soDYX/raxYkWc52Z7D/Lsxbxhrns/HxEr+VdPo7qRk19iX38elHPo1DdvC1q7+G1+494/FrmiIcb+5C0nRW+yuMNYL38gpH3Dt5csCNXxT54KpOEhi4ZtYhOwzoqrOxcT3HXEexmzKBWA+uw4eRbS4KDolCsUrBVGhwqRybnaJr7Tra+sOM7U8xOzvHCiXI5hU/4hOD3+FtvffxLx3Xsa8aRFFizM1a2QVvaAzTEGgLuNgpW9I2s1zkLXi5dqFEchuU8uuZDrdSVNw4a1VEQ8dhyvx80wwbD5b4/uYv8hbu46DRzdvu/RSTeSt7UD1mZdEbG3pQqwEe8gQRMLm5xbJUFgQB56oG8kMpjj79INNVFz2GdV91XPgW3nfd57mp47Nc1/FJzLJOZnIXDqUJZ7CVycuvYtTh5iP33YNsVAg6/aQqVgD525hJOsGlt34aWRTo6wud8eeiIKLbm7EbaWqCyGZbkF3zu2ho7yQ5bgVJxzXrvpx1KYRkiYJUIZVP01izFvEVu8KMXSVQ81EfeBDvla3YuzopygpzWZXWoylEuUq2xwogUq19bGsapGkoTa2WQNOKhD76UZo+9WcALGy9h5Fr30rX7BSHsgXKz+5kXDeoSTLOYwdRJ/cg1ESyHssxr1CsU9IciK0d2Kate3q2cIh9rXM4Vzdgl+axTQik/XFKgohDqLHbeYAnhb0AtISLCJINsT5FfWz/SaffhrhlSz47M4NfsiPb3eBSCCtxnDaNwRttGEWDkFpEk720RvooigZeQaUlYn2v/X4/JZcGBkxvvxdTnyPf/y6m7K2ESnZKdTtNzh2Q7WJK0Vjw+FmZHcfrsKHnLOmprdBAOVJCEATSlTRhdxTe/91TG/+vMktB0psVQbCswBeDpIEmP5f3NvAfT41Srr121sOvhFxqjjnNxSe2dCG9mnal3hjUy1aa+QzcvLaZI3MF/uyug6xq8fOBszWOfd6Ap04V5Z9PTsjtzpGG93loVbjzo5bRxvu/e7L57xkRBLjxK+CJEn/o02xqsXP33rPbrr+uSA9b1t1dr0Bqd4JA62smtxtPl+g4k2lDcW4pi7TEOaErYNWPfubRz5Cv5vnqVV8l5j77XOvu6iTbaMnZVuQWMCUfjyaGmfLBULOd9zk83LC6lRQ60bm1aDWRSCDLBZEOnnEfoFdvYtZpp3z8OPGuLgxtBnUxGGiyFSi7vHStvYDO1RFK2Srz41miikjdmcLjWcFlrVv55Prb+Zs9m3lw/FLSqTSCYcMf3kc100JRDvB0/QgASk7ls6aLi+YlLi5czDXbP8Cjj1eQ7SHc1Qozcg7BIRCy++icM1Hmh3gL96MIBsdtG/jdh36XZDlJdWgI0eUi0txGtepkp3gFF/gUYsqpDSZphZ975Z1879Ed/Ae3MLzdkpx//clH+f7wIzwYOMKwKwsCaMdreL3WQvLxS69C0jUum1XJCWUi0QZSi7Utv401SSfwNjbzya98jc3/7YtnPaYxeCFh2eCZxnb6dJguTuOMN5Cdn6VWKXOgYK2ZSh4bMXeUiqRSpUZQryMbBnmnm3l3Hq9uJ5OaJb3wCOrV04x7Y1xUlXGUQ1zxoZUcDFlBtYsAd6y9ETVj1T4Vk4cA8Cz2s4x+5fP4b7qJtp3PMFRSefDrX+donxUQpe+9E0dDALEgkfFZ6xB73aSKSb17DfK8AIZAtTpKrCnG/FUl6sEs9uMKywNxbm4bQhRAlVTE+lackkDUZTnfGoU5yvffAaWENc5wCx6Ph1yhgDeRwrn2MtRKFbfNjzlXYlN0BbZqlXguRa27nes2tYBp0i2laQ5acsFAIEC+XCAbyXLk2CNI9n4a29bRZOZoNsosj40QDB9AXehnZ1gC4IJF17yqChWhiq3qxNVojTGtps955nMpSHozE1sJ84eslCrw/125jEy5zh1vkGxSLj1HUQrwznXNr+6FTywMz1CXBHDD6iZkUaBY1fjLm1a+tABNq1kSvtdFJsmyscV4DSzfiwn45g1W76AbvwLx1S9+jisE7/x3yIzzJds3OTJX4OjcG8DAYfRx67Xr8ld+LX+rFVQb50ZCcALdMJlcqNB+Jvvvwvyp5spLLPEq0umz3LzK9TL/cNk/0Bd+YbmyFOqkR5+h6AsSP2ItIndkK0zHN+GoGby1KOGyyzjMFD4zyFjWQVNoji6Xm6SSJWT3YALpcJhGfwDTyGA4QvSXszRW8+jBCC0Dg7QPhhEEKM9J2JzW5syK3r9CH93C8tAwf77p7/nZ0Xam00ns2FCiM5QSAzw6Nk8g0Iq/ZOIsW8XzT9mOYL6viW+uGuKbLQYZux2fWqEmaEzZprksfgkCUEjswUuZD8XDlJwbOez+OJc9/TBfiLRy+wd/hydcIfbHu5gwG7kx2nDyd6LrOndvvY+MWOJyrYdrayu5Wl1GpFKhGmmgc2AZYkhhh3GUpLdI48GP4Tl4MYZh8EBjKxc8dwB3eBk1QaOxq4l0xapJ/m2V251AibQg2s6u/FgRewsAu0MOYkVLzZDyWYvy8cP72Z7dDpjUXSZNHut5LiomVUOgWa2Tc3qoOa3nVXV2M/sPfJJJ8Q6ON6xkkyoTa3LRe2EnSWUfuqAh1N3IisB3Gm4CYObb/4hpmjgXG8rWXCXiX/oimz58Cwiwr7WFmbZmRF1ny+BKBJ8DKQ8lV5y8aG18pjwCKe8yBENASkjYlQSxWIzt4z8AAcxknF67zHuSVl2WKqnY5AMM+jKIi5sV9XSC2q6HMQuLPSM9UYJ+H3XJjr9Yxn/zlZRdVlmCkgXX/52nR8ohmSZPBeP8w0SSY544rY4cXsUKeAKBANl0im3HfoRL9tPkvRKHmmKjfJxJ3Y+3fQTVAa5inL0hBZtWZ9PinkANhbRoZes626zvWLqSPueZz6Ug6c1MbBBqRchYaeT17UEu7g7z9SdHX/c9kw7P5rHXMjQ0NuGwSa/uxb0vHCSF3HZuu6yL//GWXla1vES71Pw0YJ4qyj+fyNYN5pz3SprdD9+4wnp9z/+DVe956ed2bIaLP0vP7M/oEBNvjGzS6ONWpjD4KjQLDrRZ2b5fa2r8ajObq1DTjbNnkryvsO/YEkucgYAjwA1dN/ClzV9iS8uWFz9B8bCyPMZISw/VfTux6VlG9CgV9xo2jdTQkta9zG/MI4t2ntXs2Ox1itntDEQGmPUlwIR0YyP+vFV7Y+hzXJKrEkwn0ZxuanUNp8dOuNWFrxqm7h5BwI7X20/gaJSDB64kqGT4y83/G7d3DJd7HiSTUqKPaOUygvYooQJIPkvK1bF+BS2rOngg5GZcSJFQTJyLC85JZYYret+O6PdTqozgcrXz3zubeFcsSHegmzw+nmju4DtrLuIbqsDWZauQgesarOJ30zT5+c9/zvDwMFf3baZX99DXmOWS33873a6jJIMy73rvu/jYxz6G1+vll77nKDTsxr6/ncd/dowZU+Cq/bsoRi1lQ0NzI6lKCr/iP2NN2JuJcGANAHO2EnJuiojs4Yh3HqfXx9M//wF5PYfLXgBZozVgWWZHnT7y1RqtZYOCy0WQPCnJoD67hVDwEgbX3oHQeDllwWRtsUr66DSz6jQFJU29ZsMplHjcPmgZyc3tJfvDO7HZwoiiE1W1nn2D7c2ESnlLbaHVadJUrr/ts9iEBciDYfNQbXViAEXTZLzaBIAwI+J2Z4nH48wmrd5KmYVl7PLvRihbdVllm4Ig6HjdD6A5V2JqJaoLNUR1Dm38EAgSOEM4BAFDcdAQjVPa+gQzz96FYRp4ZD+uC6J0ikWebV/BmOLmO+FmHr3gIu7cfA2btx3kfx6b4qAnRDkxQ7GQ4eL2m2nyiUzNbSdvKjxZ72KmYj2HotUARwNu2nOzeCNWsChKfoqGQdlusKw1gGZoZNTMOc98LgVJb2YarXTuiaayAJ+5sodkocqdO18bqc/L5d+fGCFEga62cyBfO7EwPEuQBPDH167g9694vjvOWTnZSPb1kkni3NYlPXcP3H4tYMLHfwEDN//m17jwEwD8fsNe7t47c1ojvdcdhg7jT1n1SK+GgcgJWeY5ltwdnLYK0Huiv9ZfzDStTJJnKZO0xLnhr7f8NTd03/CSj+81Uow3d6PXq3SPj1PT49zw4He55PDDjO95iMNbnyCftRaU6XIYXZPJZJ9lY2wjT9qeIWh6WOjoQRhLgwlyYR9+pRkWCoDA8PAwAJ5miJgeKoFhvI5+RNGOd2qKSiWAIIDTrrB6zf20d22HOmhFP33Va0hUkkiSH3kxSLpgywVUR0aYkltoHj5ETXGQUUYZ84yx4F1gY3wjjt5eVDmJ272MBruNr/S18+hF6/lmg8pPP/dJ/uzRP+eXq1q4ZcdDfM1Rpclh3bu3bt3K7t272bJlC5svjRJXPkrgci/2Ji99LoFp3UdJ01AUhWuuuYZEqsCe2C8wxCr3ZPLYDXhbzwZysvUMiEQiLKgLv/VZpJeC3R7GkAL45SpHbRJv9XayM7WbVVe/lYVDQ6yoOP5/9t47TJKzPPf+vVXV1Tl3T85pgzYH5QwSEkhIgEQOxrLBBB/4MNjgc3w4tgHbYMAGjI0FmGxMkARICIFA0q7Srlab48zs5Dw9HaZzd1W93x81u6uwqzi7WtDc17VX71W5aiq8z3Pfz/0Q99h1sm0RWxmhVUyqmLTkBGm3l7CVp1+rMDvlZ0X31zl8fxin4uagOo077iH7kwG8hpuMKwGWA79Soip1yoUwRk+A6c98hnJfH253E8Wi/Q3ocruoy9qW127T4LyaKEJRcKgZzJJd89O2LIwa0XGVLUYMDbW1A21CweksUNBnqcmnUadh3tGJf81B9hq2KVLW0Y5ZqmfCsQ9DW4ZQi5QyLtSwYOrILqSvBhQFa24aFJXAa66jMjxMXhekZYawoxblggC9PXU83rYcgM/tSNK1c5zNg4dxFXL8aCrJF0wXt77uvTz+uj+m/pI17NT3Ylkmj0T3UUVjphDDLHlJ6z5SHg/LU0M44k2YxRIuLUK14mY47qAlFCVdTiORS3K7JZxG1KywMwQLdUkAF3RE2dga5j8eGKBinF6pzwvFRLrIvXsHcYoqzmDNs6/wfHFMYpQ7dZD0vHG2NJKFE6YJp6NXkpTwwGfhR++A2nPgT++DhnUvbFuhZmi5kFeZWxhPF9g5chY3O57cDaXM4kjt4MR9cprNG7b0JY63AHgSSmmbafQv1SQt4ezACkeZo23LEe2XUWvMUXJ58VWLlBOPMzt0N7/80ufILfRNiVQjzORrKBSG2VS7nj3uw9RaQWbdTmamQTVVtPIACJWyazUej4feBaME6Z8npBuUA0MEo3YfN3VoiJBuu5e59Q+RnY/hj06hDTlwRSeZOZonIecxnVHiaTvw0B74OMN3fAlZlpTKdvDmCZbZGd/JhtYNOFUn+spuqoESXveTE27nF+1E3QP6Ub524J+JmRUcGfv9t3//fu69915WrVrFFeeugm1fs1dquwiAtaE6JAqPz9o1UqtXryYarTI0tJGy5uDeRp2LEgbuTDsZUUATKoFAgEQx8Qdt2vB8EA6upUm3uN/j5kItzEh2hMaLz0UiuWSygaDXlia2RDahojCnpAgpfpoLYCgOSrqLhHMeieChn/ax974x9uoVAskjRN60DCUnuXn8BrymjmY6CGAzodlcHfkGHcXrZfaL/4LL1XScSUqNDtGQOOG621nMUq6WUZwFyhU7UDh3Qx0dyyLUGQr7HCaV7g24h+0a88fGf0aDR6IPKeT8zcTqu5nAvqdKZoRqehNjToNyMYAadGDkJFZApShzZE1bnlgYOApAef16KkPDFDwekiJDyFnPhw4Nc6Cpk+ioXcPUF/Yxn1ZYn6/wiu2/Y8+mbv7VWWJF725+G2/jr0Z/QUrk6C50YfoKgGSmECObamFHxFYHrUsfxl3fQnHXoG3/XXExERcEAzUn5KFLTNISThscboh1w9QJJkkIwQev7GI8XeSOXWenxOmbDw4SZsFUwXMaHhCnH3TfMzJJzxvHGIGzQW53upikSgF+8m6479Ow5s3wrjtffE3L6psI5AZYo41xx66z2OXuWD3Si+mP9ESETn+QJKVkS+8sF3RGcahP+RQc16EvMUlLODvQHIzgtwr0nnMpQ122AUH2Ta/mwjf/M67Q+3n7P3yJa//yowDEqiEeybYhqNLmKFF2GPjcLqrCYjLaQhEPRYdFNLEb6dtE3lFLf38/pmmSyk/jiw4jFYNQZANmLoeZShH124PN8QmVffteSfmRVtjSRqgxjWVJorkWwvPQmphHkEUcuZ2DQyO84tH7yXrtmr/V07sQUvJKj80Uy+VhUMFZeHKPv3JfHwDXvuK93DV4F4bTIJlMMjw8zO23305LPMCN1TtQvrgSdn8PVt10/NuyeUEh8njCHtAKIeju2Ue16uD2jgwJDV7fVYPicZARBcLuAIqi2E5hS0wSAJHgOmo1yVafhxWGrWD4+cw9jNQWYEji0tNUqy48rlpq9DizeoqO81fQXLATyxm3F82Rw9BKHH5kCl/cxW/dJq3JMRRnkXsad5G1qjgRKFIlhF3eMJevQTpT4NTJP/oobpfNJJXyOX7+hc8Qy88z7bfvlUg2zWTS/iaWjHryismK1hCt3WGcCCZ1ixlfF/qYrWwYGdmBy1NBHXZgREEXGynrGSQSWYwSKy4jYngRFQW9xd6HI10iaM0zXYCDj20nn7EDk9lkEiuTsYMkK8Xfbghxj3Sycegw1sEcDckEe4IK81WLWGsPhmHQd/gQgbH9XL3lZ3iLWR5u2EyX0sFmGrnK/0kUIZgrNyMmN7M9ouKqllmZP4qztoncgyOUqTBVleSj8+CJnTE3xqUg6eWOutVPYpIALu+Js6oxwFfv78cwzy42KVOs8t/bR3ht90Lh5ekIksDOoJ+iV9ILQnrUNoTQnoNV+OmGunAMxiLWJFVL8K1Xw4E74Kq/g9f9x+JYR6+8ERSND8Z3cde+Sapn2f14HAMP2PJVX/zZl30ucPrtfkunUW43mMgzlipyac9JjvnYvb/EJC3hLIEz1sPa7BG21ProFLwAACAASURBVCgMqzUIq8isFSZU4wHhwumto2ZFNxKLmBHmwbkmzKqT9Ny9rImvYdZrD6qGamIktTCGphKYewAhnEyMBCiXy4yMjDA9NY0etQMMt2M11THbhSwUtgfL2/oOIE0H8Z9KMvl1FFUPljBoTq/AmVDRdT8qc/C/dnI4tIFlfdvJBW1jiss7LubnqSqvObIFALPFzphrE09+r5V6e1EjEd550Z/zzpXvZMwao3+4jx9+978IyTRvnv0ntNGH4bw/gw88Bjd94/i6nYEGvBTYn7UTidXqPA7HAXp63PxKt3AZFhfNGMTfs5q0KBAN2vLARDHxB+1s93zg95+DEJD1qlQz43gdXr578LscbMtSrUhyCY18Pki1WqU+1Ei6yyDYHT8RJHk8xJUco8EqQkiiVzZgCGifnyB76BCD6WlUFCzNlrv5DCdOLBLVOKqjQrkwhSwWcVYimGaOe/7zn8jMzuIzKkyEYmTcXkQywfSUfW8WaMEIOxFCUNseACBoCO4tx2FeQEGw0mUvayRqcYVmKc61YTgKlJUyxnyEDeYw11u2vbxjxYJpREojriSpVBw89JWvIKWFS1WYHrGTd9lIlC+t6+TeOgevHDjMZXOTFHHSc/gw2+IallultaWZWCzGIzse4Z7tP0ZDclnfPtJePwc2bsClCC4sK1gSppULGJs7n+0RlYZ0ggjzDOSyGHNODpMgr5Vwe2ZB1ZgrnRmjkaUg6eWOutUwPwaF5PFJQgg+eEU3Q3MF7tq3iIHCIuD724bJV0xuXrFQaO45TVkEX92JbPpiIDNydtQjwemR2x36hd0s9vX/CRd9aHHqcgC8Ueh8BZeUHiCVL/Fgf2JxtruYqBZh5NHFk9odQ7D5hEzzNGBLr90k8LLukwRJxwwj/sAtwIUQzUKI+4QQB4UQB4QQH1qY/v+EEONCiN0L/179hHU+IYToF0IcEUK86gnTr1mY1i+E+PhLcT5/yHDHlrM2d4Sk7sBlmDhK++ktOwnEbXvh9EwBoQpwGsSqIYpGlcTkamZm7mFTzXoeUh/DJR3kPSWmXDZDWtDmmdOKtJb9WAj27tuPzFWpBI9i5WNM9irHgyRvGExTRRQ1NNODZuUZXlbLwX6DqitJS3oF86IBxR1CqFUs3BQSowgUCvFGhDQ4f9O7adtwC2LgPkj0UfbPgwkcSj/pXMt9/Th7ehBC8Beb/oI6vxerLLGMIm9rHMFz07/DRw7ZzbfjPU9aVwhBl56nt+zGsqpks7ZS5MJLNjMQa6Ank8TaNklxMktOFInGYxSqBQpGYUlutwC/37bYbtIttmSPsr5mPWWzTF13D3Fvldk+H/lciPn5eep99UwVplC8DuqKEk2aZL0uapQcv6xzc9MlWxnGZiFb56fYdfddaIbGsqZmTLUEgNcMEhAVxkxbZjnWE2XW70YbWGAv+x9m3Y1vAilJ+EJkAmGS01NkZ+x7s2g1Em2x65LCtR4cLpU2VLYrgnQ4BpMaAWceqpDLLidUp5GeLGFqZUpahVLOzXp5iCu9thJiS2U3JY+TPY4ePrD8//CVje/jgeXr2LbuYoa7V7PF0nh82Tn88NwrOVJXz8f3zdM9cphIv51cyDtHUQCzw09T0E1ddx2zE7OE572owTAXuDWujwX4ntfgiEvQNVVCulQOLPNz+8YwCbcdJOmyyo9u+w4IlZGSxnhknmbLHqsuye2WcGZwEvMGgKtX1tJT6+Mrv+vHss6OgvmyYfJfDw1xSXeMFpftUITn5E3hXjROB5N0NtQjwemR2+38NoTbbNnHYmP1zbiLk1zmGjg7G8uOPGrX7yyW1O4YQi2nlUna2pegLeqh5WTOdsekpn/4FuAG8BdSypXA+cAHhBArF+Z9UUq5buHfLwEW5r0ZOAe4BviqEEIVQqjAvwHXAiuBtzxhO0tYBKiRDtZkjwBwfmYUvbSflKmQCthsTGbGzsrrQZ14NYpwzLNrbiWmmaXbZbDP20etFQJtHnChmSZZj4uDqkLYUpiuBtl74BBxy08x1E813c3g3gSVUXsgOmb1Uql48EgHjrLKaNt65twZGrxh/J4S/nIUzEaEKwQOldH7fkswM43iWk8+WsUrU7T4m2DjH9nv4O23UigO4Mg4qRzuO36e0rIo9/fj7O4GQJGS1xT6MUQVuVEjcsuPYdXrn1GVsMrnYUQ2kZrfezxI6tVXUHLoNEz2M1SZZuzeQ0gBNU21J7LyS0wSAE69FocjynKnxn2ixAURe5x0RePFrIqOUEo5yU74yGQyNHgbmM5Pg1tBBWqNDDmvC5cwENUqydxODk9laQy58ToUcgeP0Bfso2NNN05FIpG4pI+QKLE/bQeps+0KR+qi5LYcAqBtYxfBTjsYTviC6NE4mUyG0qzN6BilICvPsdcViqCmNUC35mRAs5iKtOMetRkux6hg3tNObWsjyckCljApKhIQrFMHafOcR0mp8J3+75OORPjUVe/nV7GLuT+4nPs2X8qW86/h9tp2ftK9jo9++P8w5Q/zJ7++nXPGepEC2kbt4+kVRTaUBGaDh6OTvXx56stIJA49Rs/kLK/q7+dTPc04FYVPr3Timyvj6A6AKjgUtccnjalZvKqTq2c2kyrPYJXCDNY4aJa2vXqimMCtufFoJ/l+LSKWgqSXO451KX6K5E5RBB+4oou+mRz3HFjE2pwXgW0DSWazZd51QZvdmBROs9xuanEarlqWbQF+1jBJx4KkRWKSkgO2s9v6d4ByGl4py64Fh4c/izzOPQemzr5mx4MPgKJB64WLtkkppR0kpUdOS9PfimHxyMAcl5yMRQKbSXJ4bdnfHzCklJNSyp0L/88Ch4Bnarx2A/BDKWVZSjkI9APnLvzrl1IOSCkrwA8Xll3CYiHQwKWpbdyc/AVp+W1cRdtoYWexiO5SyczYiTMtGqSmGgJPnnuSq7BMF4FKPwW1hAs/bqVMx/wIoUKZvK4xoy1s3teCWS4Q0MuYzgwuz1qG9yeYm99OqdlB1hynmA9hCgN30WLfilrcjjJXl9cz32x/I9coboQzgKWoPPLLO6g6XOj6ZtJeQUykEUKAr8Z2+9z9A3K5I7gqMcqHDh8/zer4OLJQwNm9YOZw4HbOz26lsm6QO3P3UTJKz3qp1kdbKAsX+2d2MZ/dj8vVyC+SVQKqwnrVYru7n5mkzcrHm+qOZ+WXmCQbQggC/nPocGtsd7u4yBmnNdDKdf5uGuoTqE6D0pAkk8nQ7G/GkAZ7CnYwWl8uMu+xB+61xTSPyhBHJjMsq/WS8fkIFnIcDB2kfdkyYtKHpZbQpAefUmAqF0QaKo5wkaxLJ/ngLgCWX7aOqakpdF2nJR6jp8UeS5hlO2lolAJsWHsioVXbHkDPmyjAjshq9IXycn1IkPU309S5htRUHsUSlCyFcz0jbGiPY6YkeV+Zw+YRtl11DQdbevj0kX/h3+76G/7vlz7Bx3/wBd7+yN3cvON3/N87vscbd/yWK5JTjBoTOKTKhr//O/wusKoh6hec+G4dHyUQCFATDVMNRgmNjFP83X3UCMlfd9RzMO7g6x06uQYPgYEs0bKFw6gSKmZxi05qjRqGzAhTgX529LTRotjjlrnSHBFXxH6mTiOWgqSXO3w1dnH21P6nzbpuTQPtMS9f/l3/WWG//EDvLLqmcFFXzA6SFA1cwdOzM38dGEUoz7/4beWmbdbmbGGSFrtP0q7vgVBg3dsWZ3tPhdMHy17NxtwDVCtl7j00c3r280IxcD80nWsf54uAlJL7j8xw7b9u5cZ/e4iytxGqhSdJYRcLO4aTFCrmyeuRwE4Q/OGzSE+CEKINWA9sW5j0QSHEXiHEN4UQxyrrG4En0ntjC9NONf1k+3mPEGKHEGLH7OzsIp7BHzgUlYJD4y2j32G80E9E5HEaebYcniZY4yEzazNJasRLzAghnDnmTReTE2soZx5FlYLBsm2g0ESGUL5EWREkhR10XNFlZ+pFyP4z1jStoVqyOBQbZ+jtK2iRPqwB+0/qsCRVp0JPWy9aTjAaGiflmqbNDCIUlXRmjvG5GSYbL0KoLpK6i1rxhOf43PdgVrMUS6N4XB0YMzMYSXv+MdMGV0+PnWDb+nmILePmzR8iW8lyz9A9z3qp1gbt5OGu5DjZ7D6cvnXcPZvh1fEQ119zDfNmgccctuV5NBp92TSSfT7w+8/BI/JYAgan93Dn6+6kLpegHFCJrkgjU0VmRke4pv0a6r31/MPOfwSHoKFQIeUIU5YqPbkRekd8HJ3N4ywlSXk8ePNpavw1RINxwk4/plrCMh34RRGJQjXlxBmsIBWBYVooipeqMc3k5CT19fX85tzlvH/dAkktZpEVDWmYeP06ZraCtCS1bQGkJVlhwP3RLpSRhdq3IY2iJ4o/XMtEeR6npVMQTv7D/DTKsmsxZgqUNBNLKHxn7WbaJka5/tDv0HfMknc72XDFldSbBtH8POFcilAxj9Z/lBlnhQYrQqXqpCnsxaqG2DfRj28sz8FIJ5+54lZqdQfS4STfHqCiZyns3Mm7mmK0pE2+3qnjr1isHyxiCogVslQ0B4nc9VSlZKt3N4lz9lJ1qLSrdlZjrjh3RpjPpSBpCSc1bwBQFcH7Lu/k4OQ89x156QemW3pnOa89gltXoZCwWaTTlUV4Dr2SnjMytlzjeO+blxqLKbczDdj1fei+GgKnsfHo6ptxVNJc7zvMz8+mxrLFFEzsho4XJ7XbP57h7d/Yxh/912PMF6vsG89w674Fxiyz+A53W/sSaIrggs5TfGRy03/w9UhPhBDCB/wU+LCUch74d6ATWAdMAp9frH1JKf9TSrlJSrkpHl8ko4+XCVLuEMGCnaGuDdQSqA6z3axS79NILzBJatCJUzoJUEJVTbZNbsA053mFs5XHTTemFLiiEUKFEgiIWHM4PRqeioYUCmZwGGG5KPJDhFphbMsHmXjkT1GdOZSjdmKp6FcJBKapMRsRDoVJc4bRwACYdiKsPzmMKlRc2lpEnYN5EaGGJ9S4Nm2i0LoKkATidq+d8mGbTSr32kGS3tUFR34JMwfh0o+yqf5c2gJt/Lj3x896nXq8LlQsDhUtisUR9qsXkTMtbqwN0dHRwYoVK8iLMj7Ng9PpPGNOYb9P8PnPASy6VZP7ZnfaE2cOkPfq1K9VEMDYzu24NTd/tfmv6Ev1kdNKNOQlZeEihU6DTBHtH8FhFClM9OFf1oMvXWSNzzZIiMeimGqRakUlsBCsl1MOnEH721x0OvDozRSKY0xNTVFfb39jXS4XkUgES0lglAO4vSZmvsrUZx8jv23yuHlDl2Ew5dTJmR3E/knDGK3FGzGYncpxd2QCVaoQChERWaz2qzDTZabmk7gCN5B0hnnP7f9N9qgbUdCQQEPPCt707ncD0NdgN6uVoSAlp0azFSXXP0NrxI9qxpiaL1M3lgAE/z1VpTo5hjANjl7eRuYtFrn7H0ARggvmBaYiaC1YXOt0kdYVVs2nUEwvXmo5xAS72n5IrX8NAO0LifFEMUHMdfrv16UgaQl2XdLsYTCePmh+3fpGGkNuvvy7/pfgwE5gIl2kbybHpcfkQYXk6ZPawQlXr8WoSzo2yD0b7L9hceV2/b+x+0lteOeL39YzofNKcIf54+AO7j8ySyp/GhvhPh+MbAMktF3yglYfTxf5yP/s5vqvPMjBiXk+ef1K7vvo5Xzy+nO4e3TBYOM02IBv6Z1lQ2sYn1M7+QIvIyZJCOHADpC+L6W8DUBKOS2lNKWUFnArtpwOYBx4IiXctDDtVNOXsIjI+WI0GXbyoDPYiWIcZNqtoOZLFOZKmIaFGrRrdWpKAmetxW/T3RTNMDWeGg6qElP6yETC+Er2dlaqc0QbfSTGsrikiiM4RrlYw3x+C4WOHYRTh9nUtAstZVEsBEFCUdFwTF9EY+9NuFZEmCnNMecdwCns5ylhzFN0RWlKQam1hCU0YnKKavWEQUN+5aUABF22NKt02K63Kvf24mhoQPV6YcvnINwO57weIQQ39dzEntk99KZ6n/E6ORWFDpdkWNqJuQfKHUQcKheHbPns1VdfjaZpxJvsZ3yuNIdAEHaFT7nNlxsCC+YNl+kaW0oTmJYJ0wfIBT1E67vRaurJ9B+iWilzZcuVXNRwERNMUZ+zh9UqeUynG6moxCtzNHqh/XL7O7GubF/3lq52TK2IZSoEpG0Dnsx70QMVLMUi43biToaZS8xhGAZ1dScSV/V19UhHjmo5TG2Tl/JABlm1KPWm8AadaD5wWQ4cEo42noM+rJDzthGu8/J39x6h4rRZ7JCuQ6gVw7S3PaOXSYevw1E6zIb+XWQGPcz57GeqrrObWoeOXi5jqaotBf/TPwUgXnVjTBRoDLuxqmF0q5b1sRqun6jyg4lZpvsPE8hPkkg2U2zXyW65H8OSPFin4q5I9oc0ftNlm7A0jI9TW9ZRheCrHd/kbb7LmNY0dFkm7rPv0WQpucQkLeEMoW41WFVIHHnaLIeq8O6L2tg1kmYsVXgJDs7GcSeuZceCpLnTGyQdy6IvhsPd2dRIFp7gbrcIgcbO79hyze6rX/y2ngmaDitvZGVmKw6ryC/3nyWui8kB+ze+/Hmtli1V+ce7D3PFP9/Pnfsmee+lndz/sSt490Xt6JrCuy5s4+JN6wHYtXfvoh7ybLbMgYl5LjuV1A5sJsl/GpnBswTCFrR/AzgkpfzCE6Y/8eRfBxzTI/8ceLMQwimEaAe6ge3AY0C3EKJdCKFjmzv8/Eycw8sJ5UA9QcuiXQ/TEeqgknscgN1eWOFSyM6VUEP2gC5mBMk2eCjh4F8qH+VL/vdRcRvE9DBJt8DSVNwVg/ryJLEmH4nxPFFLx+tLYVar4LqcX7tvY/3erxLusPDeqzIfDKGabhxagZp8A5ohkD1eMjKHy3UEj2oHXgUzRzK+BtWC8Yj9DYmQoFg8kfDIh0MIC/z9v0OrraV02C7SL/f14ezpgf7f2k2qL/kILEiMbui8AV3R+fGRZ2eTVgdCjIg2Sji5P6tzfTyEptjKi3A4zFve8haufpX93k4UE4RdYTTlFEmTlyFcrmY0zc8yn4c0Fntm9yCnD5B3mnh93USWr0FWK9x15zf51oFv8ZFNHyGtZmlM2aUJMz67XsZ0e4lVZrn2Na9gqtaWvfXML9QsdTfhVGxTBa+p4zXL5EoCRYVKbZGM24k+4iAxZ7NMx5gkgKAexqGXMUsBlm1opHzUDsArw/NYpsWMNo3P1OkxVX7hssceaedyHs4WeWAyTY9qlxI0V+eg80qyI7bcc9f65eSkA2/6h4w32fLUwRoX+J34whEqw0NEE3M4NY2a2loGZ2YIGwZGMYmaUWgMuTFMhVLZTVtzmD8arBBKTGFWq8TrBrEsjZlMI4XyAN872Me4LnnVjhx1Jvy2zkE4b+IupqgXCgc94wy581y+6p1MOVzErTmEL45hGaRKqaUgaQlnCMfNG55elwTYNUDYxgkvFbb0zVIXcNFds1D3kU+cZiZpIYu+KEzSqF075Qq8+G0tBharT1J2CnrvgXVvPRF4nU6svgnFKPL28AF+dra43KUGwRl4Xi6LVdPilm/t4GtbjnLd6nru++jlfPza5QTdT76GH7vhPIrCw54D+3n46OJZnz/YbyccLj2VaUM5B5Xcy6WR7EXAO4Arn2L3/VkhxD4hxF7gCuD/A5BSHgB+BBwEfgV8YIFxMoAPAvdgmz/8aGHZJSwirAXzmz8qCxr0MGp1nKAq2NXhpcOpkt6fQA3aTHmsGsLylXDWwH7Pckyh0dI1S5fuwBKQDkUI54q40uNEGr2YFYsGfwkhJNOZOJ/dcSkXJOZxx8tsHZ/C+7BCLhxANTy4XSqNDhV0hSk9TVUrsFpOE/cUkdJCy6eoerqwFDik23Lr2FOCpFxxALcIohy5B2dnC+XDR5CVCuXBQdu0YctnIdBkN+ZeQMgV4qq2q7hz4E4K1WdOWq72+0kS5WHH6ylakhtrn8wSdXZ2Hh90L/VIejqEEPh95+B3qzik5P7e2ygZs1jCwtRqmQwWMJ0utv/ydr6w4wt86tFP4curdOR0FMsk3VCHlFD0hIlZSe5w+DnkSWMBdXN2YKQ3+AkoduDkM4IEtUnknD0szzdlybp15IF5svN+NE0lFjshLxPzDnR3CaMUoHFtC+WBNKgCq2Cw4+AjDBqSkKVwZW2YnZF2ttev59H6dfxo3uDi6UMEhF1K0FXuh84rGN7dT0KHXwWCXOA3cVSO0huzg/6Sx0tmoU9YZWiIYCaDYVR4Z/X7jIyM0BGNks+O4Kq6afLox4+xOe6jPeTh6mE7Uax25wiFHExOdZNZpvO5qQyrFQerRqt82GPL6JpnyiAkAYL8MngvRf+r+WRSMOPwUWvOgq+GdDmNRJ6RGrqlIGkJEO0CzX3SuiSAZbV+Qh4H2wbnzvCB2TBMi619CS7riZ9wMjndTJLTD7pvcWqS0qMQPEvqkWDx+iTt/gFI03a1OxNouRD8DbzVvZ3tg0nG08Uzs99nQmrItj5/HrVxn7rzINuHknzxjev4wpvW0Rhyn3Q5TVPRY630OJO873s7GZjNLcohb+1NEPHqnNNwiqD9WI+kl0EjWSnlg1JKIaVc80S7bynlO6SUqxemv1ZKOfmEdT4tpeyUUi6TUt79hOm/lFL2LMz79EtzRn/YqNavY9Ch8fqh3TT+7MMIJMvkNLtCgrwpEQ+No7g1JJK4EUZYGYorIoh8lYg1i1nnwTlt1/5ka5qI5EtQLuLy2AGHJ2gnX2ZnVjEwP8n7qimaL07if3w/likoaRLFdFK2NOpdCu7lEQ6OHgYhaTULhIIuimaeYNEiWPJSrncxVLaf2yizT2aS8n34wutBKLh885QHBij19oFh4AxJGN0GF3/4hNHOAm7uuZlcNfesBg6rfPZ75WfcRJ3u4Lyg95TLzpXmlkwbTgK//xyKJDmvWOS+kd/xUNhmgP7y0X/h4dQjVMO1RLM6f9Pyv+DhIURqHo/iwWFOYwVVUtJNxRuksTTNDyaT3J+dIxlS0Ubtd6xwKHhd9rfDafjQor9DztlN2NVoBSkEyaM5crkIsVgAVVWPH1tlBjRXCfIOdM2BMVPEu8FObP36oQfol3bQsaEhQMhS+Nq61/ILv0mXR/DevgcpKDY7VbtvitQdnyM/luY/e3TKUrJe7EEguHuNQfLNV6OVBEOeOapW1Q6ScvOYKOxKubEsi56Nm6hkhhBC0Fw+0Ri5MezG2Rli5YF95N0+7gu8gQsueAW5bIzbz30Dc6qDj8SiCMC6fYzXPZLjBp8dlujALsdO3rlrhnuoMqA00VgdB2/sjNbQLQVJSwBFhZoVMH3yIElRBJvbImwbfGmYpD1jabIl44QTl2XaBfPe0/yA+OvsepsXi8zo2WP/DYtj3CClLbVrvRiinYtzXM8GRYHVb6At/Qghsvxiz1nAJiUH7SDpOeLHO0b59iPD/MnF7dy4/pmcpm2ooRY2hfKoiuCWb+8gXTj13yxTrGI+S08zy5Js6UtwcVcMRTlFYHeMPX15MElL+D2CL9rNaxvrefQ1n6bhHLsnW9PwbYxVTdL+/8Jd3ENlNIv0qsSqYTTFScXlRjuUITCXoF9dRqq0A7/lIllbb5s3AIXMCCCx/JNQjlIqqmwOzVGnGiDgnIPjJM/1gQBNCgpGHl1KHA0+do/Z9UExU6IqTkpmjqp3BfGMRG3TKeLDKSxCDg+FhSDJNEsUiyN4Q2tgxXW4ynvBMMj+yo65nXO/sZ+/9W9/2jXYULOBjmDHsxo4rFwIkhIG3FATQnmGRM5ccW7JtOEk8PtXYckqV1plhowsd8Xtmq5rl/0xH7v4Y1SDEXS3h+SPtrK2N0BBB01oiGqCSd3FjOVDczlwORTqFMl95RVkG8KUBweO78MVcWAqZapS4mIEs6RSNV24F8wb0sUiuVyESOTJUsiRBddTkZPHpXbec+sw3JIjEw1MahKQJGfuZ1VFZcLpRwH+/ZaL2bPqzzE1B6aQRB6b49d76sk5/fy8Qedt9VGGEw+xLLwMo6mGOzryAEwFCvTOHaby6C+IVuyE+TaxEV0x6bzwAijagZ9vZh6h5hHaPI0hO0gqloYp1br5tfpKmleuRtUks4UGLtu1ndZZ+xk0DYu/fdsaLu2079s9vl6u2lHiT/r62ZisIoVCQ3kavPEz1kgWloKkJRzDMYe7U1h9n9ceYXiuwFTm2Xs0LDYeODKLIuDiBdkfxRQgTy+TBHZNxotlkqQ8uxrJwuIESUMP2lKz023Y8FSsvhlhGbwnvp/bdo69tNb0lgnpYYi0P6fF94ym+d937OfCzigfv/Y51jCFWtBzY3ztHRsZTxV53/d2ki8bHJyY57adY3zml4d4xze2ce6n72Xt3/6av/nZySWzx3Boap5Ernxq6294QiPZP3wmaQm/X9hQu4FPnPfXbNjwHuKv+SIOxUGgwb5PB2MVapx/hbHjN+hhN6i1lPUOXGaBtUqKmaMBLKGy/4IO4mWNRMCNr1RBKAoTvTvQvQlkeICIZzNCCFZbNgM0vT+IUpHMnGt/f3yaiqVUKVDGCqocTNmD1UBoJVa+QtHMM1//GgQgGrKYWpQ6XcHjaTnOJBUKRwGJ19cN574Xp9ce5GbuvAsUBT37KFz45+B4OssshODmnpvZl9jH4eThp80/hqiuUe+0VQM31IZOuZyU0rZTXmKSnga/37ba3uRy8K7MPNfLKk5nA+/f8Bd01HWAotK4bhP5VJKuCy4hF7DLAWJGipQaY68VIGc4KDV18tbD26hSw8MXX0NlcAhp2YyLrymAkFkyokos48GOxNuIu0yElOQcoOUtQiFbHv+z3eNs7ZtlcsFSXimYlAcyCJeKUu/mcVc/uyoxmvQZnMEZ8mNVNuTLtFYV3ukO4quCdM6jWxplTdD2hij9nT18vyuEwzL40NRt7Jndw6a6TVzXeR2TP5SPJgAAIABJREFUfXat+lywzK4tf09lbJJ4zJaX56SbDmsQLT9FpLOFbDVJdXgOb/uX8bT9GyGvxAhWKUhJa2ycslT5r5kM882NtM1Ocv3j/dz/w6MAdG+qpaY1wMx+W576UOARXrVTYkVv4v9N76BT9nJBdj/ovhPNj5fkdks4Y6hbbQcfmdGTzj6/w74ZXwrJ3QN9CdY1hwh6FmRip7uR7DH460/Yd79QlNJQyZ5dTJK2CEHSzu+AMwgrX7s4x/RcUbcGYj3c5HiE3ukcW/oWr1bneSM7aV/D58AkJXJl/ux7jxP3OfnKWzegqc/x1RtqhlKGzXUq//iG1TwyMMc5n7yHV39pKx/50R6+9fAQyXyFS7rjXL2ylh9sG2Fr36l772zpta/Xpd3PkDU+JrdbYpKWcJbBoTh464q3oqs6ilBo8DVQ1XKENJU7Gj5CxarF2fcZHEEnt7d3IaRJSXFxbedOXtfVSNiYZXvDudTqGUqapOD14lI0BvZsxRceQtHzRGvOp6WlBZF2YlYF6QE/xjKThLAHhn6X/Tun5Jgx01SVDACty69BlMB0qGA5KGtQ8B/B0qK0eby4XSeCpFzetvn2eruh9UL0jmUIDYypKfSIA8UXgU1/fMrrcH3n9ThVJz/p/ckzXq8NAQ8dbifr/Z5TLpOv5imZpSUm6STweNpRFDfVaJSPJtN4vU58vm4AQiE78IyvP48bPvY3dF31GkrClrC3Gxmk4kY238dvSx3os+PkJ6d442O/5oHm9WQRGNP2e9bfHsOUUzirAZqScQqKG8W1lmbdouKukvE4CaXT+Hy2xPzD/7ObW76+nbJmB0nVfIV0/wzO9iB3D9/N/1ST5BDctOZHeGIjlArdNM4P8sa8k43NYVKTBUqeGVymC6nC495O0uEm7q3XuSU1zOzDn6JsltlUs4FbVt1CY85P3mcRcrrYM7GDat6JZ8MriETs56CbQdj1PWo3n0+yPEl5Mo/QsiiODD/q+wFH7/2hvZwV5oaaEF8fS3BXZAWqlEzUraBeTxCscVPKV5GWJDE8hYJg1egEAT0M3k7Cvm/zd3yCK7LTIMSS3G4JLwFaLrB/Bx446ewV9QH8To1Hz7B5QzJfYe9Y+smZ7zMVJEU67CCp+iLYs7PN2Q5ePJNUTMHBn8GaN54003laIQSsvpl48nHW+HN87YGjZ3b/T0Ry0P4NPzOTVDUt3v/9nSTzFb72jo1EvPozLv8kHLtv0qO8fkMT//rmdbz/8k6+/Jb13PuRSzn4t6/irv91CZ9/41q+9Jb1dMS8fPyn+8iXjZNubmvfLMvr/NQEXKfeZ3bKNvdwL9kBL+HsRoO3gcn8BOeFvBzxOzhUfDcOc5D7GWNHzEd96i4QCtI9yedvXkfP8BEOsBqxfBiARCxGXEtSmAPVcRAAf2wTbQ11lKwQYwdjCMMkc62LfDaOYuoIYQ/M5kSWsfQUAcc4mpQ4opegCxfuOnvwOFGjMzK/B6HV0Oxy4Xa3UC5PYVll8vk+hHDgcbeBEIgL3nu8N47TnYYLPgD6qWuIgs4gr2p71bMaOHxuWTO3re86Uct7EhzPyi8ZNzwNQqj4/SvIehUsoOCo4vXaTYddLhe6rpMrFOjadB7JZOp4kNQq7Xe8DGcww0fwJ0epZxxL5njFge38+PobKfTbbVVizfX0xYfwVANEsipzeoSZcg8OBXJteXIunUAqjaZN8D/bR2hU0ry7WcGv2mOximExlp5B7wjw1V1fpc+ooyM4RL2WYfn666hKJ+GoLdWLNHhJTeXJ+ewgSVcsHsnWs721i3DZ4s82XsmOZVcCsP6RrxNUdJrzIWYDRYxqjiEZRJoWensbx3q8dbU0wM7vErv0UubKE/ilj/P4E4JyA9/Y9w32H/wVCElT9nV8qCGOLBlcvwMc5TAV/zDL9/w7waiL+USJ8tE0hWqJqlLm1Q+BGu7GVVcgFZggUFBwOu19zhXncGtuPI5TB/+LhaUgaQk2as+xmZP+35x0tqoINrWFzziT9GB/Ail5sl1xfoE9ON1BUrQTkHZx/gvFMWbubGKSXmyQtO8nYJbPvNTuGFa9AYHkr1sP8fDROfaOpZ99ndOBY/fFszBJn77rENsHk/zTG9awqjH4/PYRarV/F+6jG9Y18pfXLOf6tQ101fifxEi5HCqfvWkNE5kin7vn6Xb+hYrBjqHUM0vtYKGRbO3pa9S8hCUsEhp8DYznxrkg6GNSsXjIOp+U3MhnYh46siaNid8CcLBaTzE7z7rtj2MKjQfr7DqhRG0tbcoEvnonwncIs+KmMB3C/a1voFWrZIfcuDdvpthVolL2oBoe5hNVfMJD0pnn4PAYPm2aqCUYP2oPWuvXtFP0q2S7fRxK9lNVfDS6HLjdLYCkWBwjn+/D42lDURbUEavfiCu6UMAfVeHcP33Wc7+552by1Tx3D959ymUiDo065zM7jx7Lyi8FSSeH338OWTVL0a1iCQuf12aShBAEg0EyGZtJnJubQ/Ha17rdXDDFUesRzbehewpUpybY3bGdSns3HsXk1q0PMjk5ybaRBxkLz9iuiJUiWXeUvl5bWidac0ghcM3nmO71s//R+3mlo49yYhu1YTvISrpC/IdI8PPSOD0FN6lSmIv9R5nqeyN1Hvue8rY3oyiC+s4gqakCU8EELtOFUN2MhGo4Eo9zy0CFUMTJ44EInXqYyJF7yH3ttZQz82xWkyRVlYS0g0C9tZX169dz/vnnEzzvrTA/xiMzP2PAYX+nPr/+jXz9uk9SMcsMZiv4/BYeo5H2mTIf21WlJWHQ1ZXBAEacOmFjivm5IomHB8koeTQxh2/WQAl147oywrxfIzaTBV+Nfa1Lc0Rcz91R9sVgKUhagg0hoOsVcPR+ME+ehT6vI8rAbJ7Z7Iu0jn4e2NI7S8jjYE3TEzTVZ4xJWjAkSL4ItuI4k3QWudsd64XxQtztpITHvw31a6F+zeIe13NFtBMaN7I5fz9+l8bXHhh49nVOB1KDINRnZAl/+vgY33p4iFueo1HD03AsuH6ODWU3tUV41wVtfOvhIbY/xWjl0YE5KqZ1auvvY3gZNZJdwu83Gn2NJEtJNvjtwen+mIPPt36ccZefjx8q4zG9xOQMQ3TQ+9hv6RkapC49zaNiE421o8zF48iEC6X9Ijy1OfIzfo5++bvEBvew/uAu1KJB5c2vRQiTqqmhGh7qxU5iSoBZMc/h0RksR46oHmZ6vy2hC7TW8c0bIjjWeJmt2nUnDU4dt8f+BhSLI+RzfbbU7hh0D661mwFwnvtKu2XEs2BtfC1doa5nNXB4NpxJ6dLvI/y+VZhUmG2wB+VP/Ls9NUjyxmxjhy4ZR5UG3c5zQML2KBTmJW2hVv7iLW/kvu51pCyLW2+9lbu3/ppwOoG0MghpUXGHODxaxqwqRKO2ikVaZfYeaaTFmqS+ew05ZTVuFUxDIxmI06lN8Kstj7Gv93pqvGVcczGiRhDHoW0oZgUj3swtn7+Exp4wyck8E85pdNNJqSwYaawhXDG4sT9BafAIu2d2s6HtlfDGbzM6Zn93XtHWw7WtryIREjy4UqC3tbF8+XKuueYaWPYaRv1x/vrIt5nRprCkiZzIszzWwavcTTiSTnz1zaAKCv1p1LEia69o4twro7hc8/Qu7yYwvpuGqgWH8mREno6Mnfh0b9xErmYUhCA2Vzlu1pUoJs7Y/boUJC3hBLqugnIGxh476ezz2u2XxFMHX6cLUkq29M5ycVcM9YlOXGcqSIp22L9zLyJIyoyC5jr9TnzPB0LYcqoX0idpcrftgvhSsUjH0HweaqKXt5/Xwt37JxlK5M/8MaSG7CBGPXkDxkOT83zi9n2c3xHhE8/VqOGp8Mbt++c5BkkAf3nNMpojbv7qp3spVc3j07f0JnA5FDa1PYuM7hiTtIQlnOVo8DUAECaJX1U4sNLLt1vj3DA9wIaUiUeGaJd9DDva2HP/fxOyJJdu38Z+1uJt6SXj81BJ+illCrgiZfKTFqPTk6Qur6f9yBBDHe3syBylUnFjoeA0NS72f5O46SZnFmkwhphRVRqC7WSGbcOTXRgkqyZxEpiq/d5vdDlwu+wgKZfvpVgaPS7bOgbfu/4a38o43rf97+d07scMHA7MHeDg3MEXfA2PO4UtGTecFMfMG6Z6ugCB19t1fN5Tg6RQLAKaoEZGCDOLcIcJz3eR81dQTYX+wf386NDXubZ/P9879ypKcZWOZDcrCsswFvoWmU4vw9UQzqxBs9tEqip9q9dQVQr0Wx28+003cijnxSM0RFanXgbQFYUOLcGl2jBva4eKrBDOuylseYCgMUti1kR3axhVk1yiSEYkURCYmsbeaDdvSFTQMlOMbvsduWqOjfHVDLpHeDwYB0WivPYd/O3Ff09dCv7j1SoDWur4NShh8ZG6OoRZ5fxID+nKLMXDk1iWwebCBA5TYU99Cb3FT+pIEtOwiNR7CYXPpaHhCHOROKW8YI1H5WHfHiSCyFwexekkfPP5JBL34ZQufHnT/h7CGTUaWQqSlnACHZfbmfFTSO5WNQbx6OoZk9wdnsoykz2JE1dhzu5h5HiGuorFgDsM7siLY5IyoxBsOvukS6r+wpiknd+xe2qtumnxj+n5INgM1Tx/vCGEpih8/cGXgE1KDj5jPdL/PDaKIuDfno9Rw1MhhH2upzBUORk8usY/vn4Ng4k8X/xN7/HpW/pmOa89isuhPsPaLDBJ9c+8zBKWcBag0Wezs1P5Cc4L+TgSEGgGvP+gXc8TNsO0FA8xp8dJi3lqHS5WHd2GJVT2uFYQikxScnmo89lOdqXpGLPuCcS+JAjJntWrSST6KBRsZscp3EQdYyw3dgKwThxmWtOoLzegW7aM+XNzSZpdOnFjP5ZmJxabXDq6HkNVPcwl7gPkcdnWMTi6VtN82xbU2ueuOriu8zpcqutFsUmJYgJVqIScp3bAeznD6+1CCJ18vg+3qxlVPVEHEwwGKRQKZLNZCoUC0VgU1esgaPoJWpNMa142TF/IVMiWti/LRvnq7q/yq8afUTPzc+703k6SHVTdfjJ1s1TCcSxNYYoooVSVkCYxVrZRcbuJTdaxTm+iUjYpmBLNNY+aV2k2ajCkyZpNt6FpJfwVO2COVnQKj+8kVqMyO5LDNCzS00VcShXLsnsMznZ2IZDcOCLBzJDY+SibPQa+yX9iYPBfKCVq8NdC3/CnUK0CtzzqwVWFD9/3YTLlDFJKPvXopzhsZPiH2QRdUUGyPIkxU2Fq/E6MeXvcs5V97G0cIj1hJzPDdV4C/jXU1Y2hAX3xMPcGtvHT5ocAcCQq6C1t6K0e5pJbifo2IQC8ttwuWUqeMXnoUpC0hBNwh6D5XOg7eZDkUBU2tobZdobMGx7otV26LjtZkOQ5M3pUop0vjkk62+y/j0F1PP+aJMuEfT+1He3cL/EHNdgEQNya4Q0bG/nxjjESuTMnAwVONJI9BbYuBCVRn/PF7SfU/LyYJICLumK85dxmbt06wO7RNGOpAgOz+WevR6qWbEfGJbndEn4PcIxJGs+Nc0HItl++en+BaMWDRBKrhqiZsxMo03WNeHIFupUh4nKaR+TFNDQeIhUKEvJNgxSEjUswrTxDpTByo0rW7cWhFyguBEnVcoh5fQ2t6h1Y0mSdepCiIvCOV/A4Aliq4NFyib9sr6M/dQifx1Yj1DsdCCFwu5pJZx4HeBIj8UIR0ANc034Ndw3cRaqUevYVToJkKUnEFUFVniV58jKFouj4fDbr5/U9mf0LBu37YmDAvsei0SiK14Gn4sRpTDMtoywrr6XiMZFCog6s5NYrbyWmBiiZP8OSBjfcc5Rl0RxaqUq5rpVWdQ4PZWZT9r3jCybwDB2mnJrFOyX57fZx4qaC5prHUdSISVvip1saHk+GdNqWqgVnx8Co0rCuBdOwmBvPkZrKU/UkcJlOTKGwI1TPVXOPUZexMJvLGJft5W3RCh5XPWtWfZfspEXH2isxjCyHj/wNXZOSax+zmCpM8fGtH+fHvf8/e+8dHsd9nmvfv5ntHdhd9F5IghUEm0SJFEVJlGRZEh251zi2JbcvJz45iSOXOPlkOz5OnM9xbCfusSM7sSzZapbsyKqkJIq9g53odQEstpcp3x8DgARJECAJgKC893XxArk7Ozs7XOzO+3uf93l+xRMnn+CBpQ+wvnAVwcgWBtPdCF2ma89/kWi1Y7M78AYL+W7qJ0QUQ36aV+hAwkz1yU9Rr5Ry3NTFL5y/5V02w9HRFk7iWreGcHgHqhojUPluY2G2diOKpjCUGsoVSTmuEnW3Qs9+iPZe8O7ravwc7Y0yGL8C++gp8soxw4mr8FwnrsQAOGZJvpZfC4NX0KWYa0Gyo8iWSy+SBk8bcszq9TNzTJeC74zr20fX1ZBRNX76WsvsPX9qGJKDE2YkdYaTnOyPs+5iVttTxVt+ZrbtEnjwLQ0UuG389aP7eL65D4Cb5k1yPGP237mMpBxzn4A9gEWy0BXr4n3F+Xx9Xhkb02YyAlRZI6D4CITbEbpGT14Zoj+EozxDILWXQ2Ip1vxhItU2LO52LNEi5r/tPcjWRlr9HlqXLuRAthhvwEQ87kNoMntkweP2P8MkwrxHeoak2ViY0U8PkOcroc8qWOCy8yeFeTQPNOOwVxK0mLBKxqWWYd6gIYQZu71yWs7Bny76U1JKiv88/J+X9fhQMpQzbZgEt3sRMH4eCSYukkRSx6YNkhIOgo1mmiIBwq4M1nSU55/v4MHSAI3+BUQCn8AseXGvux57Zwv2vjg2FDZbD7I1fhOKKiN8R5AyKdzZA2iK4KkXTlGkCkzWYSxpB8GRBWNJUnE4IiQSJvz+PKTYcTBbKd9gzA73no4w1B2nx30am2rjZLCEsGTlvSHD5KdvyTaEReWIsoqVKx8j1e9CUxWql66jtuYz9Pf/HqqGAZ13z383Wzu38tC2h1hbspZPLPsENH0IX/oUA2kjjFyPWEh2OSlduJi/WPEXHI+f5Ki5D6tFwmqVGHi4GXtrA6e9xwDBffsbSPYaM1iOeALPW24nNPAikmQlP7gB3v4jKFjAUGrIWACx5WaSclwN6m8zfp58/oJ3r56luaR4+iJOXImBmZ9HGsVfC5FOyExsszoh2STE++eWacMopssokvoOGT8LFk7/8Vwqo+d0uJ3aoItNCwv52eutE1pfTzuTONttHckqWjeZScJU8FVAInTJ70GPzcxX/2Qxx3pjfO3ZIxR7bdQGXRd/UC5INsc1xGhWUmesE5/ZxAdLA9Q1FdCT1tB1nWA2Dz2ZpJhOejxVAAyVFdMV3oYmJHbq12Ff2oua34nbuhzbqd2Y7Ddik+DoEcFBpZD57m6S0XxkxYHQBX/bW8AL6hoa9BO0m6zYUzLp7hCyLZ9ui+DBmmLi2SgdsQ4kcyElZ7nLGUXSaP7OxV3npkqtr5ZNVZv4xZFfMJwevuTHDyQHckXSJLjdiwHOk0iOZiWdOnUKIQR5eXlITjNaPItHNrom2eo0jf0NDHiylCjdeF0/JGzayepuyDjW0LNpMYXFdaBEsMY9KLpMv+bCKtJ0p6x48w0paF6mj4HgMIdSKco0HdkWx5R0464N4HQNYLPF8XjNKIqNoqIMSucBTIEG3AEndreZvpYIg91xmn2nsKpWDpVUU5MJUTI8YiikLafg783UDy1HCImO5kMgBKXzF1JR8RE81iVE3qkQLgGvxcu75r+LGm8NX1v3NaML2XA3ZqcXjRCKnsbeuQSTVEhZ1WJuDW5kRV4TPSTwyDr9Pz5EsnmQ/yh7nKeKniVPThD2u0n19iN0HZcew9ywjFDoefLyrkeWz0SNzLZlfa5IyjGewiWG7nMCyd3SMi9WkzTjc0mjTlznSe0A4rNYJOWPmDcMnb70x44G0b5ZOkm9hwEBwcs0IZhOHPnGbNTIOX7gplqGk1l+uePSOy6XxSQZSVuOhyhwW5lXOElRMhV8ZwrCS2XjgkLetryUZFZlfX3wonkpAMRGiqSccUOOa4QSVwldsa6xf9c0BunPasiaREDJox8rtZzgpFyFDnTX/gnm7Al8QmenfjsFpafRzRm8riVkf/ItBCZq8vPRonE2ZQ8hMu0kU15k1cEGp4t/qynmp9r7yOgyB+w1lIQMpUNWs6K6TGzyezg6aKzOJ4WbMtuZXLTRIuncjsSVcv/S+4ln4/y8+eeX/NhQKpQzbZgEf/56PO6l+PLWjLvd7XYjhCAajeLz+TCZTMhOM1osS5HN+Ozvd8XxxeqwuJKYslBtaeGJY++l/pfGQnPnsjJcUQnQkeQANtXKdrWK36tLORotosCmIEk6CU3mx1kLg0LHazZme+RUHkpVF8XFxxACaqrfAoBV6UAd6kMuWES2K05ekcqhl77F6ZaddNuH0eVF9Hr9NByWSPatIq7q7H55PUdq3ktJmyG77DhyiGB5JTaXCyFkqtLvBQErV0vsC+3lC9d9gcfvfZw824gRkNlGZNntyMEoQ2oHvqHreUvZxyjaHaTny2/w5dc+ykflOq6zyGRah/lBzW/4nX8bnwimaCjJkHTY6Zc6sCeTOIskkqlWksk2Av6N4875mNFIrkjKcVWQJENyd/KFC1qBW00yTRUzP5f0yrF+7Gb5wk5cs91JgsubSxqdI5mTM0mXUyQdNM6HZeYD3CZFiHGzOk0VeayuzudHW0+TVbWZf/6LdJJUTWfriRDrplKUTIWzAmUvh79960JuqPPzjpVlk288KrPNdZJyXCOUuEroip8pkvKKnGT9diQkgtk8Tsp+qjlJ2JLHYEERce9CBLDMmmC/XEtCGKvU8rZWCPXgMIWIWxdyyl1PXfc2+odkMqoZk+KgpN5HU1rw9erl7N/wY9JLb6e03w5uD56sxLwiD0IImgea0YFBRabUen6RdG5H4kqZlzePWytu5eHDDxPNRKf8OF3XGUgO5Oy/J8FuL2PVqt9gs47/XJRlGbfbmAny+41rEslpRs+ozHMWI+kqR+Nhugo6uKnGKGxi+wt4qmUNrpGv3zavlUyPcT0l5AB2xUWDI0JEd9ASrkAW4AzESVkkvrjoF3xBdSLbIgCY1ALCztfw5XWTTLoRwpBwJhOGvNpUuJj06WHCXc+STXYQ6zvAumP3MOxZi0lVuaNzNwFHAlN1iGh9G91F17HlxFJe+/Uxuo42U9qweOy1Sq0JPI/KBF1J7LFtaLo27vutp+dJdllfwe5N80bXs5w8/Uu29T+N554qfHfX4NhYzpGUylbrUb5R/j1+b3+Vf0j8FZWZUlyOkzjicVKyhiMex1HtJTTwAgCBwM3jzvmYZX1ObpfjqlF/qzG83bX7gnevqcmnuSfCcPIy3NGmyMvH+rm+1o/VdM4waTYJ2Tg4Z6uTdAVZSXMxSHYU2Xzp7nZ9h+eG1G6Uc1zfPn5TDZ3hJE/v77rIg6aJodNGoW7znHfXoa5hwoks6yeb/5kqY52kSzNvGCXPaeHnH72OlVVTMDuJ9RgOl7M185cjxxUympWUyJ6Ro5Y3FZDSdJyanbDJRVWyBYDWpiYiceO7Y4GlHQXBq7GNqEkr8Z89iXfzvQTNJxlUankhby0mk8yRHUZBIysOKhflo4SSWAIOVt68mYRZoyxkp7V4Pg4VyguM7sGRwSMEHJUkNH2c3M7lasBk8pKXd/20n4cHlj1ANBvlF82/mPJjIpkIWS2b6yRdAaNzSWcXSQB1lgp8ej/taYV5q5/C6zMc5bpEgI/Iz3BSeMhPDdKlW+htOYImgZB8OFQ3mzy9fGvBcUqjRoHtKY4Ss1moC++iqtiJaaRIstjL6R5+Bps1Tn9/JT09PQihEVVUzPU1WCrKOP7Gawx2HAJMyPEBBE6OlARZFulnQ8kPsSgunHV+nit/mKLsDygIH2LXMztQMmlUpQh1JOsr09KC+1Aeim0ht7miHO15GQBdVzlx4v9y6PBn8HiWsUB4iKtxjudFSAZTeFYX4HK8SHrvX3M0pfFfec+xxbaXh07fR/1QGR5bI5miNmqHjSLSEU/gXFROKPQCLtcCbLaScec7J7fLcfWpuRmENKHkbk21H12HnS0z001qG0jQMpBg/YWG3mcrI2kUm8fw5r+sTlK7ccHpLpl829nmUnOSMnFDYla4aOaO6VLxjTc02DCvgHmFLr738il0XZ/Z576Is92W48ZK1w1101RouIuMAOBLdLi7LKK9Rqq5lPtqyHFtUOI0Pl+7491jt9U0BhlWjc8AWXZT02387pwuC9LWD0Jzo6eaKbWaeSNxM4d330Ta5SLwoXspNJ0mm/Wjyg42NJlJZoyFEDkjUVzuRk+rmAJG9ync1oElKxEqHCmkPMZFbfNgM6X5ywEoPUtuZ7UGuWn9bny+ldN+HhbkL2BD+QZ+dvhnxLNTy40blS7lOkmXz7lFkjxSJJVJxbi0Hvp1P8OazNIDEhmbSsdggs9Yn+C5po2IpEYfhfSePoiSZ8Ek6ZgVJwMxle6Mg4KUie6swFSVBSFInzYz2J/EajFmXuU6N1q2DyEgFKqgq6sLk6ySSHuQbp+HKLHwxv7HySutgPyF6NoAjzZtR5Vlbk10ETEb3+eZigL6En14llawcN/3WdhkLDgc2ynxy6/uQM1qZFpbsVRVUTf//yWjQ9uJL5HJDLJv/8dobfs+paXvZfnynxFcejsAw0ODlLhS8M2l8MSnGMoYEu5PVN/Nj/oi3FqxnaL/s5LCpRtR9Rg1C4KYslny4mGk6lKGh3cS8I/vIoHxnrWb7DjMs6NoyX0T5jgfRz6UrpwwL2l5hQ+LLPHGDJk3vDwy9H7T/ILz75ztIgku3+FuuB08JROGjV5VLjUnqe8IoM+tIslbZhgaZI0VOkkS3L++liM90TH7+BnjIhlJW473s7DYQ+BKrb9HkWTwlF623O6SiPXk5pFyXFOcbQM+SqDcRXQkDywfO+Y+jaDWy775bpY5vspHg2kqE8/vO4hfAAAgAElEQVSyRuzmRKCMkFLI9rfeRXhoJwFTCyBY5XNRZ9qBe74CmoYaeRZ9eETmNFIkKaf60YFlVQ2AUSQlsglOD5/G7zZmN0tt02PQMBU+vvTjRDIR/uvIf01p+9FV+VyRdPmc10lyGf/fhSJIKt1LB6W8KK1BVUvw2rM4h3T2L9rAP+T9OwI3vRTR09qJHMzDahpGUm3EMxlO9cep1AfpSltwBlOATrLLwUBUwWYyZkdTRS3oQFiVsZhrGRoawqVKJBIeskvN7G1/nqQSpcfmIZkvoSNIem6hIDLIgmyIjNaALmfZIxl5epXXG8ZdQ6f24Ssq5uYPrGCoO07n8SEyLS1Yq6qoyW/kmVg+pmwnr71+M4ODrzJ//kMsmP8QkmQh7/p3jp2bkoHnoXAhfOA3DC34c2SzxB33vJ9lN/4lUstzcPwP+HyrALCucnDX07+laXgfg840uq4SCIyfR4IRN8ZZ7HzmiqQcF6b+NujaA7HzLzZtZpnGch9vnJoZ84aXj/ZTnm+nyn+BlYKxImkWP9QvNytpuGNuziOBIbdTklPffi45240y5nDXMXbTPctKKPLY+NHWyzDamCpq1njOC3SS4mmFXa1DrJsuqd0ovorLMm64ZKK9uXmkHNcUo4GyZxdJQgjsDYa8NF+zMng8QFWqhZP2KorMpymyqDj0YVbrW1BkE7qSoiur8MLOQ1jNRtep0SaR0iKoJjuyYkFX+3jie/9ARkuPFUmO9hR9+Vbe6TcMhmSPle0921F1lbyRTJ2zZ5JmmkWBRawrXcfPDv1snPxwIkbnO3LudpfPaHEUCBif+aNyO3vGgpw+QVbYUdy3IuVXMl8axhM38bjNhTezlu90DJNI29ESGociFVhN3aiq8fhkMkVQD5FWnVhkFbcryVC6jKQGdtGOyAjCYgvo0CtVUFRUTCaTIRBJkEq5aAmd5ODu58n4gpSXzaep9FZaqheQdBSyuPMUVi2EKV4PhVl29e/Ca/VS23QzWCx0d3VQ1rCYeWuKkM0SLXt6UXp6MFdWIoTA6r2eAxkfsmxj+fKHKSt979j5cBWWYTYbC8PFH/kRfOA3ULuRcG8CX4GDVzpf5mDdOsMU6/cPYjMHsVqLyXraGSpaQXBehAG5F7M5H49n2XnneyA1u26MuSIpx4Wpu9X4efKFC969ujqfg10RYtNsuRxLK7x+MjSxE1f8anSSaowV9nTs0h4Xbh8LPZ1z5FdD/zEjIHYq9B4Gs2PC7slVYSwr6YwMzWKSeMfKMl49EaIvkpqZ5x1uB129YEbSG6cHyKo666fD+vtsfBWzI7fLdZJyXGME7AGssnWcwx1A2XVGsR9QvLRHCln0fBvdUjH5u53sFB/hq90W7l7+NUqsZvbdfR/Bik6ODBaxJVhGGp3SVJS0VSIRz0PWfFQ3fYD+nlZe6XkE1arS3NONf0gmVVWIP21I+ySPhVc7X8VusiNbijALQdAyu0qCB5Y9wFB6iEeOPjLptmND8LlO0mWzdOlS7r///rGOkuQwihw1nmW+NYac7eSwNg9bRR2V9hACwd6T+4nJd1HZI+MfMhaiMw4zbtFFVpNAN659nBaBLWm8f/L9UcIWKzaTilN0IiXMpDIdCAEO343k5xuLAs7ubkDi6MsZMJm5oehO7vKvJd6ZYMey1diTcWr7u0DvwhatwF5VwK7eXTQVNCFbrGQa5pNRFUoXLMJskSmbn0fLfqNjaq2qAqCxoJEf9aZZuOIp8kY6QaMIIcgvr8Lh9eFddCZTcagngafQymdf+Szf2POvcPs/QOgYbP8BPt9KotHdlHzkDmyBLCHlBH7/TQhxfsDxQHLgzdFJEkKUCyFeFEIcFkIcEkL8r5Hb/04I0SmE2Dvy5y1nPeZBIcQJIcRRIcTtZ91+x8htJ4QQfzNTx5zjLIobjW7NBJK7NTX5qJo+7XNJP9/WSjyj8o6VE3RgrobcbtTh7lIkd6pi5CvNRdMGgPI1kIlC/5Gpbd97EAoa5tasymiX7pwOy72NpWg6PLW/+wIPmgYu4my35XgIq0liReUFXBmvBG+5kWGkzGCIs6pAPATu4pl7jhw5phkhBMXO4nGdJIDi+jxSZAkoeeyfX0sEC7qQcLkVbrQbnaCW8Gk29unsTJr5t9pP0Vbhozm8Fsl3CtNwjITdSSrtxKQ4WLxhPRtWvp/BTA+/+frf86PfP4mEYHHjItThNMImI1lkXut6jVVFq+jJaBRbzUjT4XB5CSwLLmNtyVp+cugnJCdRCwwkBzBJJjyW8w1ockwNk8lEScmZuWPJbgIJtHiWCk859uhznM6YOVK2lAKbMSsWHLKhpvMxaRYqQ0aR9MBbVCrMxwGBrBpS7fLiAooiMeIqWCstqEQxeV8FZwpJMQqIIUUwv/h2XC7DNGR/jRFbkoj7uOG9d7CobjHplmG6MmZaC2tZcnwfsq6hKzpCN6GUeWmLtrGicAUAw6XG4kLpPEMuWrnYT3RYJWEvwFxpuOc1BhsBwf7+Qxc8Jyvu2swN73z/2EK3klWJhJLEnYMklAQHQwfJ1m6E2lvgpa/hszeQzvRSOW+IiMeEosUvKLUDGEwNvmk6SQrwl7quLwSuAz4lhBjV6vx/uq43jvx5BmDkvncDi4A7gO8KIWRhlJLfAe4EFgLvOWs/OWYKSYK6W+DE8xfsNqyozMMkiWmdS0plVX6w5TTr6gM0lvsuvFFiwDCVsE9w/0xwOQ530W6j2zBX5XZlI6s/7W9Mvq2uzz1nOzAu5oU8Tm4HUFfgYnGphyf2dk7wwCvkIhlJW46HWFPjx2Y+fwXsivBVADpEOibd9LKJ9xnP4c51knJcW5S6Ss/rJEmSIGuFQNbHYH4eL3qWArDXNY/1oT2gw77H+qj+XR8ffTmGNzTMM9UbeKTpNg6VZOiLWWlxFQECWXFQVOOh1FzHTcveQ/eJoxQ++zRpk8rChqVokQyyx0p7pJ22aBtrS9bSmcrM6jzS2Xx82ccZTA3y6LFHL7rd6HzHtEQV5ABASALJYQTKrixcSYPcidck80OtDLcpjSSZWJVahMA453UDUXSLhMfSTIFsfL6bFRcZYaGkuoHKSIjTGQm5JA3ouAZeQHPr6KjowN6EzJLAEiwWC5Kq4h4yrslMAYnggiSWKi/qUJp9tUYRtaDlIACyanQP91qbAVhZaJiJDJglrFkF27BhJV+52ChIBvyLsVRWAYas0ySZ2Nu/94LnoOHGDSy99Y6xfw/3JdF1OIHxXCk1RfPQEbjjHyAbx3d4GwDh6B5C+RaEMOHPX3fefhVNYSg1NKudzxkrknRd79Z1fffI36NAM1B6kYfcC/y3rutpXddPAyeA1SN/Tui6fkrX9Qzw3yPb5php6m6D5CB0nf+L4LCYWFLmnda5pEd2thOKpfnUzXUTb5QIgT3PGGafLUYDZS9lLmku23+D8ZocAWjfMfm2sT6jOJ1Lpg1gGGJ4Si5oaLC5sZT9HcOc6r9EieRUGGox3AHP6bh0Dyc50Re7sCvjlXIBaeG0Ex0Nks3NJOW4tjg3UHYUs99BQMkjX8uQSFmwKhlecK/Fe+RpbmnZTPKglRV3VvLgZ1bxvWNP8MNDXyQqbDy15AZ+ut7DnoyREyOrdpw+C8pAktrFq1n+wF8A0BlMUeguQo1kkD0WXu16FYAbS2+kM52Z1Xmks1lesJw1RWv48cEfk1Imlh0PpHIZSTOB5DSjxrK8c/47eWrzr3hfsZ/fxk102YI4TFbmpc8ssFVFUmQCfiKxfbhHDBncipcifx4E6qnOZBgKF2P1DiFbFUxdWTS3jiZlEMCAqQqXxUUymWTN69so7m5FZDM4Sr309bzKG9u6yQh4tcqKJbkX3W4UZ55EJcIZ43/6nydoD9Lgb0DXdXqHQuTHkqT2HzC2C9hxyzEGi5Yju5wAWGUrC/0L2dt34SLpXIZ6jPm47aktNBU0AbCnbw8E58Pq+3Hu/A0myUE4fYJQwIbPuwqTyX3+flJD6OhvDrnd2QghqoDlwOiy9aeFEPuFED8WQozqUkqBs692OkZum+j2c5/jfiHETiHEzv7+GXa2+mOhdiMgJpbcVfvZ3zFMMjPFuZaLkFU1vvfyKVZU5rGm+iJ5LrMZJDuK1WVcOF6K3G70wn3UXGCuIQSUr4aO7ZNv22usPM25IgnOy0oa5a1LSxACHt87A5lJQ6chr/I86eGo9fe66Z5HgisOlJ0So0VSrpOU4xqjxFXCUHroPLMCb1kegWweVSJKhTmCOZLksHchIhXmrrBgoP44a+6pwem1Ull8lGWhQ0hbQ9x4pJM+r5N/Lvwke8vqcNt9kFDQMxqmgJ2BBY38952NvLFokEJn4bgiqdRVSqmrnO50dpz992zzwLIHCCVD/Pr4ryfcZiA5u0PwfyzITqOTNMqHywLowE9K78Nn1fErfkKmIQBKUzoxfxGqniLrjSGERkWmhAKHB4Lz8aer8LbcAoDPHyeRMaE5QTdDRJUozLsOgGRrK7bhQTrNAq/LQyzuJZbYT0domBcLZYatEvbYH9D9QdB1vMP1mApltnZuZWPFRiQhMdzXS3w4TEATJA/sHzv+YOIUQ84qMskzM+grC1eyv38/g6nJ1UThXkNm2CKO8fZ5b6fcXW4USQA3fRbhyMcbE/TTTtwhTSi1m+2MJJiFIkkI4QIeA/5C1/UI8G9ALdAIdAPfmI7n0XX9+7qur9R1fWUwOAMXKX+MOP1Q2jRxXlJNPoqms7tt6Iqf6jd7OukMJ/n0zXUXb/0nBq9O0OWlOtyNBn/OVeMGMIqkgRNnzDAmou+w8bNgLhZJZRcsHIq8Nq6v8fPk3s7pz0yaICNpy/EQBW4r8wpd0/t8YFiAI2bW4S6W6yTluDYZdbg7t5tkzXfg1hyocoI/q0yTjMr0W10MKCUsyH+SN6qeNL5vdB1L9z62ywHMAm49IvGnL++heHiAg6W1+IvcKCFjvsfkt3MgliTmSSHsFlyyCzWaAbeJ7d3buaHkBvqzCqoOpdarI7cDWFW0ihWFK/je/u9NeCE7kMx1kmYCyWlGS5wpksptFu4Menm4+G4Cfp18axE93kFiUgKPcNHnCYAOfQEHmiVBWpOI9g+T6MvjePLPsXatRtMFvkCMeL4ZJNBtsDshsaygETWRQPz0xxwqC2KxeEkN5pFMyQhJ4c7PWHiyUicvlcKcOojDV4ikqlgVHx2BBEklyS0VRhHWecSYMSquqB7rJAHkde5EFxLtzWfeR2+teSuKrvDbU7+d9HwM9STQXRl0k8r6svUsL1jOnr49xnez3Qcbv4ivt4+sZJyzQOD8fCS4OkYjM1okCSHMGAXSz3Vd/zWAruu9uq6ruq5rwA8w5HQAncDZ2qSykdsmuj3HbFB3G3TuMoqTc1hZmYckuGLJnarp/NtLJ1lU4mHD/EkK3HjIyHGabfJrLm0mKdxuFHOW2Qk8uyzKRn71OiaR3PUeNi6cnXNwxdFXbhhkXGBubnNjKS0DCfZ1DE/f8+k6DLacN4+kaTpbj/dzY31gZvT9JsuE0sJpI9oLCCNMNkeOa4jRrKSu+PgiyeQ1BuAVobBErcAZiaPIgr1lH6RcO4116BQZNQNDp7Gmo+xRF7OiMo8PNv2K4sFyansHidnsUOZACRmyNVPAzoFoEh8RCp2F6AkFNJ1u0UdCSbC2dC2dKeNir+QqdpIAPr/m80QzUR56/aHzFos0XTPslGdRuvTHgnROJwngY2VBwiYXB2uXYZOdFLgLiJiiOE1ejvqK8GRsDOTbiFhDxDUdLZZh4OenOJisIC0l6ctYcBQlyXhHvl/MsC8u42/T+Nn9H2Rfnp2U1YrZcxf1iyrRgUzGzZHwAd7wOakaPoVAxyNkrCO7eNlyGI/Fw8oiYx6po/kgNqeLwsYm0idOoMXjqNEo7vY9mGWVloNnrvXq8+pZ5F/E4ycen3QhcqgnwYCti5WFK/FavSwvWM5gapDWSKuxQdMH8cmG6sah2nE4LuyiOxp+/KaQ2wnjSuFHQLOu6/981u1nC/nfBoxoeXgSeLcQwiqEqAbqge3ADqBeCFEthLBgmDs8OVPHneMc6m8D9AtagbttZhaXenn9CoukZw50czoU51OTdZHg6sjtwOgkxfshFZna9sPtc3ceaZSS5SCZJjdv6D1oBMLNRbzlhkFG9Hwnu9sXF2GRJR7fM41rKolBwxXwnE7Soa4IQ4ns9Ft/n423fGZnkmI9xu+WfPVWv3PkuBxGO0kd0fHGJrLXKFJ0XaCcNLEwZNgpN1+3GVUycV8kQlukDTp2ArAzvZIbagM4+l6hMhiioce4XOnwm8gOJEEWCK+FA9EEVj1MocOQ2gEcyhzFJEysKVpDR8q47Wp2ksC4kP308k/zh7Y/8NvT41f8w+kwqq7m5HYzgNFJUtDVM8XDGq+TpWo/vyq5Dh3o7mnH63TjMvloD1ZzJApJh0bU1kdcA5Nuot9qIqzqSCV/4HhWRy7NYnYZkrfBVi9Nz1ey4/s/IRWL4BcO0hW3svkv72LFBsOZDn0Zj4ZUJF3DktiFJnmRE2GcspmsluHZnt+xoXwDZsl4n3YeOURpwyIcy5aCppE6fJhMSyuSrlFSLGg9OICunXlNm+s2c2zoGM2DzROeC13XGeyJ0WVq4eYKo0M0bi4JQJJx3/R1TIpGkKoJ9/Vmk9vdAHwA2HiO3ffXhRAHhBD7gZuBzwDoun4IeAQ4DPwO+NRIx0kBPg38HsP84ZGRbXPMBiXLwZ4/oeTutoZCdrQM8dLRvsvava7rfOfFE9QGndyxaBKZj64bRZLzKsgDLtXhLtw+d53tRrE4oGjJxTtJqgL9R+ees90ovolndbx2MxsXFPD0/i4UVZue5xsacbY7JyNpywljDvKGuhl8b/rKz8g4Z4JckGyOaxS/zX/BrCR5pJMk6yYKBfxZfAA0nV+1pYjV3szdsTitg0egfTtxYeKYXs7aQgXi/fhLnQT7ssiqzkkHKKEkpnwb7ZksUSVNOt1NgaMANZIGYHtsF8sKluGyuOhKG12EsqvcSQL40MIP0Rhs5KtvfJXeeO/Y7WOr8rkiadqRXUbRcbbkTgjBR009nHAGeC0fWjuaSWSHcZq9pK12/lMqAwG6tw0daMhU0zyQxmXNUG79HadSgAXcZcZ8T89rAZy6ieVtfdxV0UDr/IVYZBcFVZ6xYNuMtoDnMovZOLiNYfspNHMxmUQMNy4G0114u7LcWmFkYsbDQwx1d1G6YBG2JUsASO7fT6alBYCqxQGSkQz97dGx13Rn9Z1YJAuPn3h8wnMRD6dRMzphex83lxtFUrW3Gp/Vd6ZIAuTqDawp/ntqVvzLhPsKJUPYTXYc5tlT6Myku91WXdeFrutLz7b71nX9A7quLxm5/R5d17vPesxXdF2v1XV9vq7rz551+zO6rs8bue8rM3XMOS6AJBsGDieeg+T5s0cfW19DXYGLB399gEgqe4EdXJznm/s40hPlkxvqkKRJukipsNE1uFqdJJjaXJKuG7bUvjlq2nA25WsMOaU6QSjw4ClQ01C4eHaPa6pMkJU0yublJYRiGV47OU0ujBNkJG05FqKh2EPQbZ2e57kQvgqIdE38f3Wl5IJkc1yjCCEMh7tz5Hayx/h9dOhWOnWVJnMx+XGF46k0fdb78Gg60uEnUdq3sU8UIZtgqWKswQbmVSBrUDKocFgoRpEUsLM/msQx/BSJzBBvrXnrWCdpV2IvN5TcAEBnKoNblnCbZtGFdQJkSeYrN34FRVP40mtfGpNGjc132HIzSdPNaKDsuZK7e30W/Oksv6iUScY6GRjuQhYm/GmduqiHtAbuPOO7LJJ2ElZ1VqyKUpOOcjpjXK7nVRlqlpP1ITbta6cqWIzn859DFxAozEeSBE6nE5vNxm61jrDI497o/xBWe3FYS0kndVyKj36tk8p+F9eXXA9AR7Pxvi9rWIQpPx9zWRnJ/QfItLaCEFSvqwMBrWdJ7rxWLxsrNvLM6WcM2eoFGOo2zFQ8hdYxWawQgsaCxnFFEoBtyQeRRhekL8BsB8nCLLnb5bjGWfURSEfhp3cbM0FnYTPL/NM7ltEbSfHlpw9f0m51XefbL56gLM/OPY0lkz/gxPPGT8/FnORniNEZlKk43CUGQEnObdOGUcpWQTZxxsHuXPpGmrZzVm43co4nKJI2zC/AbTPx+BVkJoUTGd73w2387PUWtIGR/39f5dj9iYzCztbBmbH+PhtvOWjKBaWF00Kuk5TjGqbEVXJeoKwwS2SsKgHFxwedB3msuIU10RZ0t5lv7cwno5Wycs/ryL2H2aMuoKHEjKV7B5id+BcakqWyAYXDqTTxoRSmgJ1Xe4/giDzF7VV3ckPpDaiRDDo6Q6YIa0vXAtB1lZ3tzqXCU8FnVnyGV7te5dHjRnbSqHQpZ9ww/UhOo0hSzymSLJ4y7mvP8kaBg0GPg96Q8X1SmtRoVDwgFVGZZxjonExrWB2ChrWlVGUVwqpENmvB5FZBhzufTmCSTJR959t0dhndndIqQ+4thCAQCPCyKYhPH6TK3YqqRimyFqDpMg7dxmHnaYoHbMgja24dzQcxWa0UVBlFin3pUpIHjE6SubgYp99FYZWHlgPjFxzfVvc2htPDvNj+4gXPRUe7oTJqnDf+GqKpoImWSMtYR3MqDKRm340xVyTlmJzKtfCe/4bQCfiPu85YBY/QWO7jgZtqeWRnBy9eguzutZMD7G0P8/GbajHLk7wVY33wzF9BSRMseOvlvIorw+IwirOpdJJG50bmutwOjE4SQPsEVuC9h4zA1sD82TumS8HiNDqLExga2Mwydy4u4vcHey7bqv755j5ePTHA3z5xiJe2bUdxFI4z5Hjj9CBZVZ8Z6++z8V28a3ZFaJoRJpsrknJco5Q6zwTKdsW6ePzE43x+6+fplHoIZvNwVPyIcGUN16VfQLfJPGPV2eNcgVtrQ+gqu7KNrK8vMmIRSpvwBF2YbTL1KUFW1zniANlv46Wj30SSrPzN6r8GQItkSFjTeO0+GvIbAKOTdLUykibiXfPfxZriNfzjjn+kPdqek9vNIGNyu3OKpGy6iPvaNcy6xu7F1xHPhgGoSWRptZWwOBukyDMEwviu8i8SyIXz8WkaXslKf3qkoklDWb8g+I2vYSkvp+Wo8b6vWnBmsVkOFnLM7mVT+nm2WIzrq3JhHJdDt/CK/whC1Wk9YGQddR45RMm8BmSTMbdnW7oEpaubxO5dWKqMRcHKxX76WiMkIme6RmuK11DoKJxQcnfk1GnScoKb540Ph11esBxgyllLkOsk5ZjL1N0C73/UuBj9yZ2GnOws/uLWeuoLXPzNY/sZTk5NdvftF05Q4Lby9hWTdFx0HZ7+DGTisPnfjBDRq8FUHe7mepDs2XjLjFDUifKSeg+Dvw7Mttk9rkvBW3bRwmFzYynxjMofmnsn3OZivHK8n4DLwj++fSmeVAd7Yz7+9fnjZBRjzmnLsRBWk8TKqrxJ9nSFjHavZsLhLjFgdKly9t85rlFKXCWE02HufOxObn/sdr746hfZ0rGFtMPoJAlJRXe3smhECqW6zXzLdx3ZkcugvVodm+qLoecAlK1CSIKiGi/LnXYA9vtknlFeIBE/xILKj4x1YJRImj5pgOtLrkcSxr460hlKbXPLAEUSEg+tfQhZyHxh6xfoT/Rjla24zDMQWfBHzmgn6dwiKTNgxZ/RuTvbxcH5TQxKhmPigniCNmc5+SeaEUIn5ewjZhmCQBicfnSHnyLSnNCNkQRzt+DZ230UrDfmibpbDIVPYemZAmKvrwBdCN7a+xyPR1UUczlVqiHV06UY7cEIFrudk7u2k4rH6G9roWzBmZgP+9KlAChd3ViqqgCoWmJYlbcdOtP9kSWZe2rv4bWu18bNvI3S3z1M3DlEg79h3O0L/QuxSBZ29+2e8nm9Gpb1uSIpx9SpuhE+8BtDcveTO2Hw9NhdVpMhuwvFMjw0BdndrtYhXj81wP3ra7CZJ9Ft738EjjwNGz8PBQuu9FVcPlPNShoLkr0GiqTRUNmJHO76Ds1dqd0o3vKLFg5ravwUeqw8cRnBspqms+V4iHX1Qd6xspzlrmFUXyXfeO4Y93x7K/vaw2w53s/q6vzJ38dXyqi0cCYc7mK5INkc1zYri1ZS4iyhPq+ez676LI/d8xgvveslGqqWEFB86JqJPmUfi4qqAFhQ52VHeyVPON20alWkhJtF+kljsaDciEe442OLue+DiygXMrvydL7Z9l2y1nncUXPv2PMmBiP0yYNj80gDGYXBrEr5HJLbjVLsKuazqz/L7r7d/Pr4r/Hb/DMTWfBHjuQwFnLV2DlFUkcMSRrmgcSrZC1W9s1vJKlpVEUTtDkrcPb3kNWtsOw/eXbBDzgSbUbTshyud+C1KRxNGsVXWIfIWqMgyqZVhgbDCIxZJABV13lJWCkd6qM3nGVIUUl47iEY3QdAm62bNcUrqGpcyek9O4x8JF2nrOFMkWRraADZ+E6zVBoLdIFyFw6v5TzJ3ea6zWi6xlOnnhp3eyKbgLAVV4HlvPeZRbawOLD4vLmkiVA0hXA6nJPb5ZjjVKyBDz1pzCj95C0QOj5217JyHx+/qYZHd3XwwpGJV+1bQnG+8tvD+Bxm3rN6EnODSDc8+1eGLOz6T0/Xq7g88mshOXhBA4txDLeDxQX2Ge4sTBdlq40L73NklKSjhlHBXAyRPRtfhdHZnCCrQZYE9ywr4eVjfYQTFx4unYiDXcMMxjOsnxeAbAo51s2appX84IMrCSeyvO27r3K8Lzaz1t+jmO3gDM6Mw100FySb49pmWXAZv3/77/nWxm/x/oXvZ17ePCQh4fR78KguzKlCmod34KtcTWmql0Behowi+CKbeZvyICt0M5w0LiIpNXJjLHYTFpuJ5VmJnXkaCSVJNP/PWOZ2jj2vEkkTMoXHBuCfGzBy2dbnu2f3BEyRe2vvZXt8RuQAACAASURBVEP5BqLZaG4eaYYQsoSwm8a52wFk2qNYnL0sG9zFvHiYnU0bGHTLFCdU2kwBkK0oGSe1RccZcnZyMnWYffs+Ro83yYJYmua0QEg2dgUllkUMR9XOY0MoIo3D7kSSjEv6Vwaj9KrQ0N3CS6IYYQpQ6QxgSxgy8YOOdm6pvoPaplXEw0Ps+u0TSLKJovozsnrJbsc6fx7AWCdJCEHlYj/thwdQz3KMrfBU0FTQdF5m0taW13FmvFRXXnjmvKmwieaBZpJKctJzOpQaQkfPye1yXAOULIcPPQ1qxiiUes90jv78lnrmF7r5m8cOMHzOB8TJ/hj/+5d72fiNlzjUFeHzb2nAab2IdE7X4ak/ByVjyOykq+wUNOpwN5l5w6j997WyQjfRXFLfEeNn4RwvkrzlkI1ftHi9t7GUrKrz2wOXZnrwyjHji2hdfXCkg6NDXhW3LSzkf/73et6zugK31cStC2epA+OrPOOwN51Ec52kHG9OzD5DLrfCXUMo2c8xp4dF8VO0KVnW1XtJDm1gUHOzAhPxwzHDpMc1ftHDO9xOyuyktvxP0c2lNLiMfeqKhjVtQnKbxwqO34WGKbGaWTqyzVxDCMGXrv8SPqtvzG0sx/QjnxMoq8azKAMpLL4EhNv4l+uXkbY7+acmO76MlaRkYpf/k7j6h3HLsMbu5aaSHQwNbWXBsShN/Qk0BKed9/FcxMLS3pMQ66ft8CC6nMGX7x17roe7B8gzydSEukhoXoY997FCfw09VoakC47YT7Gh/GaqGlcghET7of0U1dZjtox3Z7UvMSR3o50kMKzAMymVnhPjQ9o3122mNdLK3v4zM0avHzLyxxbV1V/wHC0vWI6iKxwMTWAcdRZjbow5uV2Oa4KixfDhZ43C5eH7IGusBIzK7gbiGf7+acMZ7XhvlP/133u47Z9f5pmD3Xzkxmq2fPZm3rFyEjnanofh+P/ArX93pkC5moxaUw5MUiQNt10b80ijFC8F2XL+XNJcd7YbZQoytEUlHmqDTp7Yc2mSu1eOhVhc6iHgsp6XkeSxmfnK25aw/+82UR1wXmQv00h+DQy2TP9+Y7lOUo43J6NZSbVm4+JqS+92FskpTuLkLatkrMH/wVb8CNaSk8R6y9FLV417fEpJsS31MAAR563UO204RoyGooPG4H2g0Pi9SagaLw9GuSPgndMytoA9wCNvfYQH1zx4tQ/lTYvkNKOdJbfLjOQLWYpMEO1mudfB39WVsMUh8+tSH7Km85uum7G2GgtV7/D3ELBkqTyqUarXU5k1TBteH+xCFw5qs1k4+QLthweR7Fk8Hg8A/Zksvw8N886Ag6wcJk/JJ+28jpVsR0sXYsNMwBHBb/fj8HgpnmeMMJQ2nL8Y6r3nbtybNmEuOzM3XtaQhyQLWg6Ol9zdXnU7dpN9zMBB0RROtBjfyYHiC3dVlwWXIRDs7p18LulqBMlCrkjKcSUE58Gf/ACiXbDrP8ZuXlLm5ZMbavn17k7e/8M32PTNV3jucC8fW1/D1s9u5PN3LaTAPYkRQLgdfvcgVN4Iq++f2dcxVfKqAHFx84Z0zCiizsnRmdOYrEZ38NxOUu8hQzboneN5T1NwfRNCsLmxlO0tg3SGJ2/tA0RSWXa1DZ2R0k2QkTSrF0P51RDpACU9vfuN9oLNO7cNOnLkuAxMI0WSKSZYkL+AF9tfZEg6iaf3b/nqnvux5G3D5D7Md73/yoeq/p1/xzxuAP3f9/47Q+oBbLrGqaTKkrM6RAdbDHlebamxUv7yYISkpnNnwMtcp9hVTL4t/2ofxpsWyWkeZwGeaY+CAEvFiAx/uIMPlwbYlBzi2/Ns5Gd0MqV29nV9jt6kDUU3851+K7vjVjrXf4aKbBYBHAwdZHFwGZLDT+TgNsK9CRTSRpGkqfyyexBFh0WRlwlZ4rjUAursMkV0k0qbQejc4q4aO67aFcb8XdkFiiTHihWUfetfEKYzih+LzURJvY/WA+PjYBxmB5sqN/G7078jkU2wt28vlqgHJB1P8MJdVa/VS62vdkpzSWNujDm5XY5riup1ULUOtvwzZBJjN/8/G+tZWOxhb3uYT26oZetnN/LgnQ3Givxk6Do8+WnQNbj32yDNkbep2WZIuy5m3rDnYUP6teSds3dc00HZKujaO/7iu/cwFCycO+d/IkaLuHMcF8/l3kYjX+uJKWYmvXZiAFXTuWneSJE0eBrMTmMu6GqRX2P8Xky3eUOsJ9dFyvGmRPaOGChEFRLZBPv79/Pb8Mvowsymkndxe/nbKLUt4Gvlb6cwm89341vY9NgmPv38p/nlkV/y08P/wabwahbLVhKaxlL3mQu+k+1HAagrN1bjnw0N4zXJXOfLOcb9sXOu3C7THsVc6EQKjizqhVsRQvBPvc8TSOtEzALvhmLctiSDL3yeR48vpSNl4Z98bu7c8XdoQuBRVTJahnvq7oHajbQ1D6MJBUXN4jen0b9ewy9aTrPG6+S3px9GNQ1jS8FbAkGWL/w5qaxCSk6xsfBMt3TJxk1cd997qFzSOOXXVrUkwFBPgkho/ILj5rrNJJQEf2j7Ay+2v0h+sghv0I58kYiXpoIm9vbvRdUuHtExKrfLdZJyXHvc/DkjY2Xnj8duspgkHv3E9Wz73C381e0LyHdegtPPzh/DqZdg00Nj0qY5g/8iNuCaCtu+a8z4lK+68DZzlfI1oKahe7/xb12/NpztABz5YHZMao1d4XewqiqPX+5oR9MubPJwNq8c78dlNdFUObLyN9RidJGupozmUkKNL4Vob24eKcebEmGWyVgUPCkHwxljjuL/LP8MSuCvsYr5PHTTX/PIfd/mrkyKf+laxY87vsyHF36YQwOH+PIbX8Ylu/ho359QMuJWV2c/023t6TUWXKx5ThRN57lQhNv8HszS3JXa5ZgdJKcZLZFF13R0TSfTFsVS4TaMhmBsoSs/uouv7E+SkeDJcJS779PQMybqj95NMBVEtsj8/Q0PoefXslh2Ueur5e7au6H2Ftqiddi8RiFW2/ErXrNWcUqzsdYxzO50PwU2NxI6N5o0HLH5JEUGVQ5TXLBk7Djtbg83vPN9yKapW9ZXLjYKlXNd7lYUrqDcXc7jJx7nxfYXKcpWkF908QWD5YXLiWfjnAifuOh2A6kB7CY7DrPjottNN7kiKceVU7kWajbAq980soxGcFhMuC5mzHAhlDQ89yVjfyv/bBoPcprIv4gNePNTEG69+i58l8OI5e3YXFK02zBCmOvOdmAULd6yKbm+vf+6SloHErxyvP+i2+m6zstH+7m+1n8m6Hjo9NUv2vNrjJ9n2e9PC7lOUo43MfZ8N7fm3cwf3v4H3GY3p2KtLFL6OZSRsJvseK1eaN+Os7Cd4mg+91vfx3Nvf47v3PIdvln8VTyqC4vNMA5SMRZY2iJtmOKgSTqSw8QbwzGGFJU7rgGpXY6ZR3KaQQM9paAMJNFTCpZyt5FLKJmMIklVkMO7WTascEtYpy2V4any67jX831cqWIKk4VE9Riri1djL15GdSpJa6QVTddQq26mI7MUv6+XIAPktz7DL6rfh0eJcrT5/5KvagwW3gRAIBkjdLyTFBl8dJ9ZbLtMfIUOvAV2Wg+Ol9wJIbi39l529Oygc7gTW9xDXtHF53WbCpoAJs1LuhpBspArknJMFxs+B/F+2PHDK9tP70HIRGHFh+emO5y/FlJhSAyef9/r3zY+fBbcNfvHdaW4i4wVrtG8pFHHwrnubDfKJFlJo9y5uJiAy8LD21ovut2pUJzOcJL1o1I7XT/TSbqaOANgcU9vJ0nXc52kHG9qzD47rpQNm8nGdSXXsbVzK4usOofMRWjJsLE417Mf6/wAksdCYlcfJsnE+rL11MZKERaZ4ZHi6HDMCADd2rmV/KwX4TYjhOB3oWGskuDmOWr9nWN2kVwjocXxLJm2EdOGCjfIJvCUGkVSuBWhpZCdKu/v07FKgi+0DJC4dTPVpRGK4oZhwraubRCYR1VskKSSpC/RR2+/jazuwEUzt/Aq/fZinvat5qbhN9gROcJ7I3Fe9Boy0FAoROexFnQBC5TQtBhL1S4voP3wID2nxrvc3Vt3LwKBO+0HTZBXfPHOT7GzmEJHIXt6Lz6XNJAamHWpHeSKpBzTRcUaqN0Ir/6LYV5wuXSOrCaUNk3PcU03Yw5353ST2t6Ajh1w3SevvlX55VK22jBvGJXawbUhtwPjQ3+SmSQwZKDvXlXB80f6aB9MTLjdqPX3TaOmDdEeUFJXv0gSAvKrzjjtTQepsCG1dBdP3z5z5JhDyF4LSjiNnlVZV7qOvkQffreFmMlJ2+ld0L0P1AyifBXO5QWkjg2iRo1MtWwoiSlg42gijUuW2Bkx1BKvdb1GCYVYfQ50XefZ0DDr89w4Tdfo53+OaUV2GkWSFs+SaY8irDKm4EjB4KswiqTQMQBMPgtFcZWspuOUZe533ErTR2/DlwriUJ283v26USRlDWldS6SFtsMDCKETSG1jASf5zqqvkNUhKx3Eq6oUyfOJyhasLhehUAj6jcdWWi2GWdMVsuKOSpx5Vp7/aTNK5sw8UZGziA3lG1htuxGAvMKLd5KEEDQVNLGrb9e4jKVzGUgOXJVcr1yRlGP62PA5SAzA9u9f/j669oAjYHQG5iJjWUnnFEmv/yvYfLD8fbN/TNNF+RpDZjfcYTjbuUuunUBcbxkkQuPMQybiPWsqEMAvtk8sz3v5WD/VAScV/pEvtVFnu6stt4MRG/Bp7CRFR5y8XLlOUo43J9ZqL3pSoecbu7h+eCnoEBOGGuBQ13FjgQugbBWOFYWgQWKvsVCiDCSJB220pTLUOqzsHI7zaudrbOncQqlWiOyxcCiWpCOVvSZc7XLMDtJokRQziiRLuRsxOqvmqzSKpH7D+MNU4MMTU9CAL9YWcyyR4puRIfyBfILxAl7v2Ibmr6NqxAb8VPgU7YcHKSoWVNNCi7mEn0k1bPRJ7Iru4v3RBC8GbsYjS5QEC+jq6ETTjOd2eabnO91iN7HxAw2EexO88eT476Nv3PQN3lP0p8ZLLZp8hqixoJG+RB/d8YlzDHNyuxzXPuWroO42eO1bkIpc3j46dxtdpLkotQPjw01I4ztJg6eg+WljhsoyS3k5M8Go2UTHdkNud61I7WDKDncApT47tzYU8ssd7aSV8x11UlmVbacGWF9/1qrVaOfmCrXc00JeNQy1GkYh00F05IvJnZtJyvHmxNFYQOBjS5BsJpRHu/jXrs+ROL0fWVc5OPz/t3ff8XFedb7HP78pGvWRxipWc5Etx9hO4haXdFJsp0BICGwSCCHkJuGSANkLLGF3uWFZshDYC+zeDXWTS8ICSSCFkDUpmOD0Yhun2I5b4iZ3W1a16pz7x/PIHllyLNkjjcr3/XrpNTNnnhn/5ljS0e95zvmdOu8KenQM5IwmXJRJuDybpuW7cB1xOmqaWTfK+4N3XjSbmvYObnvxe0zIm0BuWxbB3DT+uLeWAHBhQW5qP6gMGp1JUnttC207Gr31SJ3yxni/d3e+BdnFBAujpB3sIKPdURpJ48byAu7fvg83eQxFzYXUtdeyJtBOUUecHAvz7de+zXcLP8/9Y+/km0VpXDfpMzTH47Tv+SXZ4Wz+pjXE06Pmc2E0THo0wr79+6g37wRidqwoaZ+x4gMxpp5VysolW9mx8fC0u3AwTN3uFjKjaUQyjr0ufWbx+69Laou3caDlgKbbyTDwwa95C/5f+2nfX9tSD3vegbJZyY8rWUJp3i+4xCtJr/zYW4g5WPZzOl7F07wqcZtfgr1rh85UO0jYK6l3pbGvnT+W/Y2tLH6r+5mrZZtqaG6Lc85JCaW+azZ5yfFguMIZq4R4W68Swl5p6LySNPKSJDOrMLNnzWy1ma0ysy/67TEze8bM1vu3+X67mdm/m9kGM3vTzGYmvNd1/vHrzey6VH0m6Vn6hDyKvjCDvMsnUtE8ms8tv5Q7VzeyqTUKm1/sUpE0a1YxbTsbObhqH8Rhbbb3p9L5+V5lu+a0Sv7t9B9AS5xgbhpP7q1lTjSLwrTeVwiT4S2Y5SUHLetqIO66J0kA7z4LBZMIxbzpb6UH42xpbuUr40soSAvxYGY+hc3eOPSH5c9h+WO5O62STxTdQOW+Uyhu3cOqSAHrYmeR1vgyq3Y8yTWTPsaaUBn7w1Fm7HyKp6ufxAH7Cry1dNlFyR3DTv/oRHLy01ly32raEqbd1exoJL8XV5EAqvKqyApnsXL3ym7P1bXW8f1l38fhKM4c+NkOSpIkucpmwaRF8NJ/QHPtsY9PtOMNwEHpIF2P1Cmxwl3Tfm9vpJM/BrlDfE1HMOz1/dsPQ0fr0Khs16kzeell4nDGhALGF2Txy5e7F3B4bv0e0oIB5lUmnLXa/x7klntJcqodqnCXpCl39Tu925FZuKEd+JJzbgowD7jFzKYAtwNLnHNVwBL/McBFQJX/dRPwY/CSKuAOYC4wB7ijM7GSwcMCRvbcEg58JpvHY8/ywWr44tsf4MCBy6hruoiGl7bTuHwXFglCAGoXe1eQV4cdZZEwj751JxZvZPqYqxntvD9et2cFWdXQrKp20oWFg1hagGb/CkuPSVLTPj9J8vbeqmh2bD7YQm4oyD9UlrA2HmBLQRUFrSU8v+lF4rFJzKjZwexdC7i1poCfVm+mJO0GsAi/nTqX337ot3w2NosnC84gLd7Gfdt/yQeaxgKwnwOk0UJaQWVSP2daeojzPjWZ2t0HefX33pjknOPArqZjrkfqFAwEmV44vcuVpPZ4Ow++8yCXPHIJv1rzKz5a9VEurrw4qbH3hpIkSb5zv+YtBn/lJ3173WAv2tBp1ATvD1TnvD2d2ppg/i2pjio5KuZ4VwJhaE23yykBC/aqwh1AIGB8ct5YVmw5wNvVXZP5pWv3MHtcPplpCdMEajZ5BRMGg851Uckq3tCwy9skNzLyqnI553Y451b49+uBNUAZcBlwn3/YfcBH/PuXAfc7zytAnpmVAAuBZ5xz+51zNcAzwKIB/CjSB6eMmc6vy5/iX87cxsr8EA0dl1K3ejQHHt9IzW/XUfPQOohDxwFvc+2amoPkut38acszTErvYGt7Nh21XmGHPwW9BfGLCpUkSVeBrDC0xwnmRwjmJJxg60ySAApPIpjvXUn6QKuxpdn7vvr46Bgnp4d5pXIqU0bNojpjA1saCnB717Nj9TZOy3yAvWPOZUnJTObteYM5u5czOTaZUPUKHi84i1DrGrLa27i0fi4Ae2rryKapX4oPlU+OMe2cMt7481a2rz/Awfo2Wprae7UeqdOMohlsqNlAbUstL1W/xMf+8DG+9eq3qMqv4qEPPcQ3Tv8GGaGMY79RkilJkuQrnQ4nXQIv3w0HD/T+ddtXePPCswa+gkmfxCZASx3UbfeKVFR+EEZPS3VUydG5X1IgBAWTUhtLXwRDkFsKtb1LkgCunFlOejjQpRz4ztpm1u6q55xJhV0Prnkv9ZXtOuWUQjCS3CtJI/MqUhdmNg6YAbwKFDvnOudi7gQ6O6gMSPwm2+a3Ha29p3/nJjNbZmbL9ux5//26pH+EA2Hml85nZdvz3DYrk00l/0LZHadR8o9zGf2V2RR9YQa5C70z8A74388fIGfjci4afxEfLp3E2sZmDtR505eeaW3mA1npjMs48YphMrx0rkvqchUJDu+VBFAwiUBWGEsLMDEhSQqY8c3K0RwMR9iZdw4dgXb+uKsd62hhFj8hPb6P/5j6VdoDQa7d9SJsXALAozveYWd6CaM61vHz2gvIas0izd8oNpvGfhvH5l8+gdxR6fz5/jXs3uytSe/tdDvw1iU5HNc/dT03/+lmWjpa+OEHf8g9C+5hcmxyv8TcG0qSpH+cezu01MIrP+r9a6pXQNmM/ospWTor3C29yzsLf/oQ3Dz2aMr9JGlU1eCYWtYXvdwr6dDhmWEuO7WMx1ZWU3vQOxvcucns2YlJUkuDtwfYYCjaABAIeANdsjaUbdg1ItcjJTKzbOBh4DbnXJeqM86rS3v02rR95Jz7mXNutnNudmFh4bFfIP3izLIzaah/E4BVMz6LZaQTzE4jNCqDtNJscs4uJ5Ad5pVxLdSFjR+8M5t/iP0tc6JZOGBFXSMHwsZrTQc11U56FDyUJB1R0KNzrySAgkmYGcH8dMoPxtl8sPXQYfOKYkzZtZWXKYa0MbyR5SXmUzKXsGfi5dx/MJMJe6qZNiof3nue16tf5GvhInBx7jvjs4RbP0SabaQw6iXw2YFWyOyf4gdp6V61u9o9B1n6a69q37E2kk00rWAa6cF0djTs4EuzvsRjlz3G+WPOx1JcxEtJkvSPklOgagH89VfetLRjadwHBzYP/vVIcHhNyIr7oWgKTDg/tfEkU9YoKDnV2/dqqOnlXkmJrp0/lua2OA8v9163dN0einIiTB6dcOZvyyvebUFVsiI9cbHK5CVJI/xKkpmF8RKkXznnHvGbd/nT6PBvd/vt1UDiyudyv+1o7TJInVF6BoF4HTmBVt4unN3teQsGiFxZwb+WvcINczIJFGVQ/+sNTNpQTwBY3tzC86Vh4sBFmmonPTh0JWlMD1OZ88Z4G4PnlgIQiqVT0NjBvrZ2Gv2qq2bGgprtRFyceNHNbBntla2PE+Ce6V+mycGszWvJmTCHF0IdfG7JrbRlzGRmoI7xuzNprwuRnfMcBS3ebInsSKhfKweXnZTPyeeW01DTQigSJDuv91dXM0IZ/OaS3/DfV/w3n572adKCg+MkrZIk6T+TL4W6bbB79bGP3e7vtjzY1yOBVwY8EAKctxZpsJYrP16fXgwXfTfVUfRdtBzqqqGjvdcvmVYWZcaYPP7rlc20d8R5Yf1ezqoqPHz2yjl47nte0YaqBf0U+HGIVXpTAHtzAuJYRvCVJPP+o+8B1jjnvp/w1ONAZ4W664DfJ7R/yq9yNw+o9aflPQUsMLN8v2DDAr9NBqnirGIm5U8ivX07L9U0sKulrcvzq/at4rZ3b2e3RXGZxpjPziIyMZ+2RzYyyQX5q2tj6egwZZEwJ2cP/FoJGfyCeREsHCCtNLv7k9OugFnXHfr7IZSfTnZ9Ozh3aModQGl2Juft28a+wBg2RIrYFChnY8m1/Of+OKcHOog11bMsBz5fXMhol09TZDwfKsij4flqgrlpZJ55KgUNawDIzur99LfjNf/yCeQWZjCqNOvwvlC9NDF/IrH0WD9FdnyUJEn/qbrQu13Xi78VqpcDBiXT+zWkpAiGvEQpu9irajfcRLKTsiP3gItWgOs4vO9PL107byzv7m3kJ0s3UnuwrWvp7/eeg62vwJm3Da4+iY33CoZ0lu8+Xi0N0NowkvdIOgO4FjjPzFb6XxcD3wEuNLP1wAX+Y4DFwLvABuDnwOcAnHP7gX8GXve/vum3ySB2ZtmZdOx9iJq2dhYuW8eKuka21G3hK0u/wlVPXMWmuk2MypvLzGguwfQQBZ+aQuaMIqZtPcgboTiv5AZYVBBN+ZQgGZxyzi6n6JbpWLiHP7VnfwYW3nnoYTCWTrAtTrSta5KUm5vL5K0bGZ8ODfnXsOKqb/PIBbfT0BHn0tZatuZu5R+X/zNTSefCNm+Wy6L0Ulo2HCD7jFLstOsoCDYCkB3N698PDIQjQa748kwW3TQ81mkrSZL+k1sKo0+G9c8c+9jtK7xCAelDZDO+Rd+GK342uP5wHukO7ZXU+3VJABefXEIsK40f/mk9ZnDWxITCIUvv8golzPxUEgNNgs4KdydavKEzyRqhSZJz7gXnnDnnTnHOTfe/Fjvn9jnnznfOVTnnLuhMePyqdrc45yY45052zi1LeK97nXMT/a//l7pPJb11ZtmZBJvf4uslNQQtzoeWv8OFT9/F0m1LuemUm3j0I0+woz2NU3K8M/AWCpD/sUmcNjpKY8hoDmiqnRxdID1EuJfrckL+HlxlBx1bEtYlRaNRGuvr+M7EMcRDhfxoRwv/Wb2XSwqjrN33FK+Neo3TRp/Gz8Z/jGdHncHk1p1EV9RgaQGyThsNGfmUn3wmMWooKy3tl895pKxohGz/8wx1SpKkf1UthK2vHi4r3RPn/KINQ2CqXadJC6Hy3FRHIYn6uFdSp/RwkI/PrqA97jilLEp+lj8XetML3iaTg+0qEhwuInGiSdLOt7zbvLEn9j4iQ9D0oulkhbP4/eofwuYvEmxeR+2oGzl35q/5n6feytbWMB0OTs45PJ3OAsY5Z48DIBoMMDfaw1QqkT4KjfKSisoWx5bmlkPt0aiXhJ8SMsbwHm/Hq6jviFPZ9jKLWxZTGa/k7vPvpnnCIl6NnsLl7bU0vbGHrNmjCWR6a6Jyzv4cX8hfSvHUswf+gw1xSpKkf01a6E2B2rDk6MfUVUPj7qFRtEEGr2i5d3tgS59f+om5YwgGjPMmJxQw+Mt3vCmVg+0qEniLfi144sUbVj8GWYVQflpy4hIZQsKBMGeWncnG2o2cVzqLv5x+Ov+jvID7dzZwzZsbeaGmHqDbmqOx6WmURcJcXJRHuI/rLkR6EvSvvExuDXSpcNeZJNXW1nJDsYN4M+NsKw+8+W0mtE7g6qyriQQjLA1VELcgixqrIO7IPiPhqlFsPHxx5fDZqmQAhY59iMgJKJsFGTFY/zScfGXPxwyVTWRlcEvL8sqb9nG6HUBFLJOnbjuL8nx/Yevml2DT87Dw2xAehIuyg2EvUTqRK0mtjd56wVOv9tbZiYxAX5/3dW6dfivjouMA+FYUpmRncPvabTxX00BeKEhFetdKW2bGk7MnkRnUeWZJjkAkSCArRGWL44kj1iQB1NXVcfH40/i3FZ+goaOej068guCzQfLH5AOwZH89pRYge3UT6VNHERo1CMetIUg/4dK/AkGYeAFs+BPEO3o+ZvsKr1pcsc5yyAnq415JiSYW5ZAeDnoPlt4FWUUw69PJiy3ZYuO9CnfH6GTdWAAAEhtJREFUa91TXvGHqZcnLyaRISYaiR5KkDpdUzKKR2ZMpCgtxOl52T0WZihMC5MVDA5QlDISBPPTKW3yCjc4v3Jp4pWkipwKLqmYy00nX89XZ3yVeHucnJwc4s7x5/113Lo/gGvuIPus8lR+jGFFpw+l/01aCG895F0xquhhWk/1ciieCuHhsdBPUihaDnvXn9h7bHkV3v0LLPgWpPV/ydTjFquEN3/rrek7nupaqx7xphOOPT35sYkMcbOjWbw+fwodSdtGWOT9hWLp5G+upakjxN62dgrTwqSlpZGRkUFtbS0A3zvnewDs2uUV3cnNzWVlfRO1Le2cvbaVtDE5RMYOkQJYQ4CuJEn/m3AeWADW91AKPB6H7Su9aXkiJypvjDfdrqf9g+p3wi8uhcc+B03vU5156V3etL3Zn+m/OJMhfzy01L5/UZSjaan3qk5Oucy72isi3UQCAU2pkwETyk8ns76dgHNsPWJdUmeS1Km+3lsvl5OTw5J9dXxoexvpta3knKOrSMmkn37pf5kxqJjb835J+zdCS52KNkhyRCu8KWRHJkF718M9F8K2ZfDmg3D3XFjzh+6v37YMNi6B0z/vrXEazGLenhjHVbxh7ZPQ3gxTr0huTCIiclyCsXQs7ihs7r5XUl1dXZdjOx/n5uby/K46bt3YRtrYXNKnjBrQmIc7JUkyMKoWwM43oe6IjT5VtEGSqbPCXW1Chbtty+CeBdDaBNcvhhufhZxiePCT8NvroXHv4WOXftcrNHLajQMb9/E4lCQdR/GGVY96+z9VzE1uTCIiclw690oqPRjvVuHuaFeSWiIZTF11gLzmONGLxmlj4yRTkiQDY9JC73bDERvLbl8B4UwoOGngY5LhJ++IvZLWPulNsUuPwg1Pe8l4ySleovTBf/CuJt09F95+xEvY1z8F82+ByBDY+yTf39uor8Ubmuu8n8OpH4GAhgARkcEgFPOSpJNardteSc3NzbS0HG6rq6sjMzOTl3fVcd17rbROihIZp42Nk00jpAyMoimQW9Z9yl31Cig5VSWIJTmiY7zbA1thxf3wwDVQeJKXII2acPi4YBjO+Tu4eamXWP3uevivKyA9D+bclJrY+yqc4f1M9fVK0trF0NGqqnYiIoNIMC8CBlPbA7x8oJF4DxXuOtXV1ZGbm0t8aTUZ7VB2yYQe31NOjJIkGRhm3pS7d/8C7f7ZkI42bwqe1iNJsmTGvCuTr/4EHv88VJ4Ln/5vyC7q+fjiqXDDn+CCb3jT8c76X5A+hCoDxSr7niStehRyy6Fsdv/EJCIifWahAMHcCKfFQ7x7sIWn9x5edwR0WZdUX19PLJLHzLUNvF2VTaR4kK+hHaKUJMnAmbQQWhtgy8ve492rvcXjWo8kyWLmrUs6sBlOuQquefDYU+eCITjzb+H2zXD6FwYmzmTJH9e3wg0Ha2DDEk21ExEZhIKxCCVNcSrS0/jR1t3A0a8kle8vpsPAPliWklhHAo2SMnDGnw3BCKx72nusog3SH+bcBOd9HS7/iTetrrfCGce331AqxSqhcbdX0rs33lkM8TZVtRMRGYRC+enEa5q5uaKQ12obWVbbSE5ODmZ2KElqb28nsyHImD0hfj0+jdMrYimOevhSkiQDJy0Lxp91eL+k7SsgI9/b70UkWebcCGd/eeglPMcj5v/s1Gzq3fGrHvX2ktKJCRGRQScUS6ejvpWrCvLICwX58dbdBINBsrOzD023q6ur47T2idSFYfUpeeSFtaa7vyhJkoFVtRD2bYB9G6H6r1A6Y2T8MSvSH/pSBrxpP7z7rFewQT9zIiKDTjA/HRxE6tu4rqyAxXtqea+ppUsZ8PpVuymLx/jphAhnjM5LccTDm5IkGVhVF3q3q3/vrUlS0QaR49d5FbY3SdI7T0C8XVXtREQGqc4y4B01LXymrICwGT/dtudQkuTiDl7cz95QKw9XhDlv1BAqNDQEKUmSgRUbDwWT4OW7wXVo2o/IiUjPhcyC3hVvePsRL6kqmd7/cYmISJ91Jknt+5spjoS5cnQ+D+7YRyCaR11dHU1/3UXwQAe/LG9nVCTMlKz0FEc8vClJkoFXtQCa9nr3dSVJ5MT0pgx441547zlNtRMRGcQCOWkQMtr3HqRtdxOfawjxiXXN5K3J5MONs6h5eD1NOR38rjKf8wtyMf0+71da7SUDb9JCePk/IKcEcktSHY3I0BYbD5tfev9j1vzBu3I7TVXtREQGKwsYobx0Gl6opuGFatKBm4CdGUZdoJnY9HL+2LGFlnAeF2iqXb9TkiQDb8x8iEShbFaqIxEZ+mKV8OZD0NYM4aNMvVj1CIyaCMXTBjY2ERHpk+iicbRurSdUnEm4OIvl4TiXr3qXs9dt4J+mhXjxnSAB5zgrPyfVoQ57SpJk4AXD8MnfQVZhqiMRGfpilYDzNtAtPKn787tWw6YX4KwvaaqdiMgglzGtgIxpBYcez3OOk7MivFE+kZoDtayJZDOxvZmcUDCFUY4MWpMkqVEx5/AeLyJy/A5VuOuheEN7CzxyI2SOgrmfHdi4RETkhJkZt4wtpjYzm0caWtmTkc3MQHuqwxoRlCSJiAxl77dX0rN3wq634bK7Iaug+/MiIjLoXVqYT7S1md8EswE4IzMtxRGNDEqSRESGsswYRHKh5ogrSZtegBf/HWZd7xVLERGRISkUMM5p2EeHBchubmJKNDvVIY0ISpJERIYyM2/qauKVpOZaePSzXvuCb6UuNhERSYrzgu1ktDYzfu92cnNV2W4g9FuSZGYVZvasma02s1Vm9kW/PWZmz5jZev823283M/t3M9tgZm+a2cyE97rOP369mV3XXzGLiAxJR+6V9MevQt12uOLnENEZRxGRoa4oN4e/eX0J895drSRpgPTnlaR24EvOuSnAPOAWM5sC3A4scc5VAUv8xwAXAVX+103Aj8FLqoA7gLnAHOCOzsRKRETwijcc2AId7bDqMXjjN3D2l6F8dqojExGRJIhGo6S3txF0cbKzdfJrIPRbkuSc2+GcW+HfrwfWAGXAZcB9/mH3AR/x718G3O88rwB5ZlYCLASecc7td87VAM8Ai/orbhGRISdWCfF22PY6PHEblM6Es7+S6qhERCRJotEoANnZ2QSDKv89EAZkTZKZjQNmAK8Cxc65Hf5TO4Fi/34ZsDXhZdv8tqO1H/lv3GRmy8xs2Z49e5Iav4jIoNZZTv9313ubyl7xc28/MhERGRY6kyRNtRs4/Z4kmVk28DBwm3OuLvE555wDXDL+Hefcz5xzs51zswsLtUmpiIwgnWXA63fAwm9BwcTUxiMiIknVmRzl5OSkOJKRo1+TJDML4yVIv3LOPeI37/Kn0eHf7vbbq4GKhJeX+21HaxcREYDs0ZCRD1ULYPYNqY5GRESSLBKJkJubS0GB9rwbKKH+emMzM+AeYI1z7vsJTz0OXAd8x7/9fUL7rWb2AF6Rhlrn3A4zewr4l4RiDQuAr/VX3CIiQ04gADc/B1lFXklwEREZdm688UYikUiqwxgx+i1JAs4ArgXeMrOVftvf4yVHD5nZDcBm4OP+c4uBi4ENQBNwPYBzbr+Z/TPwun/cN51z+/sxbhGRoSdvTKojEBGRfqSpdgOr35Ik59wLwNFOaZ7fw/EOuOUo73UvcG/yohMREREREenZgFS3ExERERERGSqUJImIiIiIiCRQkiQiIiIiIpJASZKIiIiIiEgCJUkiIiIiIiIJlCSJiIiIiIgkUJIkIiIiIiKSQEmSiIiIiIhIAiVJIiIiIiIiCZQkiYjIiGRm95rZbjN7O6HtG2ZWbWYr/a+LE577mpltMLO1ZrYwoX2R37bBzG4f6M8hIiLJpyRJRERGql8Ai3po/4Fzbrr/tRjAzKYAVwFT/df8yMyCZhYE7gYuAqYAV/vHiojIEBZKdQAiIiKp4Jx7zszG9fLwy4AHnHMtwHtmtgGY4z+3wTn3LoCZPeAfuzrJ4YqIyADSlSQREZGubjWzN/3pePl+WxmwNeGYbX7b0dp7ZGY3mdkyM1u2Z8+eZMctIiJJoiRJRETksB8DE4DpwA7g/yTzzZ1zP3POzXbOzS4sLEzmW4uISBINy+l2y5cv32tmm0/wbQqAvcmIZ5hQf3SnPulOfdKV+gPGpjqAvnDO7eq8b2Y/B57wH1YDFQmHlvttvE/7+0rCWKXvr67UH92pT7pTn3Sl/jjKODUskyTn3AmfnjOzZc652cmIZzhQf3SnPulOfdKV+mPoMbMS59wO/+HlQGflu8eBX5vZ94FSoAp4DTCgyszG4yVHVwHX9ObfOtGxSt9fXak/ulOfdKc+6Ur9cXTDMkkSERE5FjP7DXAuUGBm24A7gHPNbDrggE3AzQDOuVVm9hBeQYZ24BbnXIf/PrcCTwFB4F7n3KoB/igiIpJkSpJERGREcs5d3UPzPe9z/J3AnT20LwYWJzE0ERFJMRVuOLqfpTqAQUb90Z36pDv1SVfqD+lP+v7qSv3RnfqkO/VJV+qPozDnXKpjEBERERERGTR0JUlERERERCSBkiQREREREZEESpKOYGaLzGytmW0ws9tTHU8q+LvM7zaztxPaYmb2jJmt92/z3+89hhMzqzCzZ81stZmtMrMv+u0juU/Szew1M3vD75N/8tvHm9mr/s/Pg2aWlupYB5qZBc3sr2b2hP94xPeJJJfGKY1TPdFY1ZXGqaPTONU7SpISmFkQuBu4CJgCXG1mU1IbVUr8Alh0RNvtwBLnXBWwxH88UrQDX3LOTQHmAbf43xcjuU9agPOcc6cC04FFZjYPuAv4gXNuIlAD3JDCGFPli8CahMfqE0kajVOH/AKNU0fSWNWVxqmj0zjVC0qSupoDbHDOveucawUeAC5LcUwDzjn3HLD/iObLgPv8+/cBHxnQoFLIObfDObfCv1+P94uljJHdJ8451+A/DPtfDjgP+J3fPqL6BMDMyoFLgP/0HxsjvE8k6TROoXGqJxqrutI41TONU72nJKmrMmBrwuNtfptAccIu9DuB4lQGkypmNg6YAbzKCO8T/3L9SmA38AywETjgnGv3DxmJPz8/BP4OiPuPR6E+keTSOHV0I/p3ciKNVR6NUz3SONVLSpKkz5xXN37E1Y43s2zgYeA251xd4nMjsU+ccx3OuelAOd7Z7ckpDimlzOxSYLdzbnmqYxEZ6Ubi7+ROGqsO0zjVlcapvgmlOoBBphqoSHhc7rcJ7DKzEufcDjMrwTsrM2KYWRhv0PmVc+4Rv3lE90kn59wBM3sWmA/kmVnIPyM10n5+zgA+bGYXA+lALvBvjOw+keTTOHV0I/53ssaqnmmcOkTjVB/oSlJXrwNVfpWPNOAq4PEUxzRYPA5c59+/Dvh9CmMZUP583XuANc657yc8NZL7pNDM8vz7GcCFePPfnwWu9A8bUX3inPuac67cOTcO73fHn51zn2AE94n0C41TRzdifyeDxqojaZzqTuNU35h35VU6+dn1D4EgcK9z7s4UhzTgzOw3wLlAAbALuAN4DHgIGANsBj7unDty0eywZGZnAs8Db3F4Du/f4831Hql9cgre4s4g3smWh5xz3zSzSryF5DHgr8AnnXMtqYs0NczsXODLzrlL1SeSbBqnNE71RGNVVxqn3p/GqWNTkiQiIiIiIpJA0+1EREREREQSKEkSERERERFJoCRJREREREQkgZIkERERERGRBEqSREREREREEihJEhkgZjbKzFb6XzvNrNq/32BmP0p1fCIiIiLiUQlwkRQws28ADc65f011LCIiIiLSla4kiaSYmZ1rZk/4979hZveZ2fNmttnMrjCz75rZW2b2pJmF/eNmmdlSM1tuZk+ZWUlqP4WIiIjI8KEkSWTwmQCcB3wY+C/gWefcycBB4BI/Ufq/wJXOuVnAvcCdqQpWREREZLgJpToAEenmj865NjN7CwgCT/rtbwHjgJOAacAzZoZ/zI4UxCkiIiIyLClJEhl8WgCcc3Eza3OHFw7G8X5mDVjlnJufqgBFREREhjNNtxMZetYChWY2H8DMwmY2NcUxiYiIiAwbSpJEhhjnXCtwJXCXmb0BrAROT21UIiIiIsOHSoCLiIiIiIgk0JUkERERERGRBEqSREREREREEihJEhERERERSaAkSUREREREJIGSJBERERERkQRKkkRERERERBIoSRIREREREUnw/wFgCBwJ2M+7PQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "M8xTc2nEon3u",
        "outputId": "ac713a30-3833-4aa7-9361-82413eed2b5f"
      },
      "source": [
        "# Plot mean and std.\n",
        "plt.figure(figsize=(8, 8))\n",
        "y_std = np.std(np.array(preds), axis=0)\n",
        "plt.plot(avg_preds, label='Predictive Mean')\n",
        "plt.scatter(range(0, 48), y_sample, label='Targets', marker='+', color='black')\n",
        "plt.fill_between(range(0, 48),\n",
        "                 avg_preds - 2 * y_std, avg_preds + 2 * y_std,\n",
        "                 color='red', alpha=0.2, label='Epistemic Uncertainty')\n",
        "plt.title('Prediction')\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}