GluonTS_1_2_deep_factor.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gomlfx/ApiaryFund/blob/master/GluonTS_1_2_deep_factor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v28DJzHXeDW9"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ma1yzIATZTMD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3ee0673-ff26-4453-ef95-ccaa320d4b0c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mxnet~=1.7 in /usr/local/lib/python3.7/dist-packages (1.9.0)\n",
            "Requirement already satisfied: gluonts in /usr/local/lib/python3.7/dist-packages (0.8.1)\n",
            "Requirement already satisfied: graphviz<0.9.0,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from mxnet~=1.7) (0.8.4)\n",
            "Requirement already satisfied: numpy<2.0.0,>1.16.0 in /usr/local/lib/python3.7/dist-packages (from mxnet~=1.7) (1.19.5)\n",
            "Requirement already satisfied: requests<3,>=2.20.0 in /usr/local/lib/python3.7/dist-packages (from mxnet~=1.7) (2.23.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet~=1.7) (2021.10.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet~=1.7) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet~=1.7) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet~=1.7) (1.24.3)\n",
            "Requirement already satisfied: holidays>=0.9 in /usr/local/lib/python3.7/dist-packages (from gluonts) (0.10.5.2)\n",
            "Requirement already satisfied: matplotlib~=3.0 in /usr/local/lib/python3.7/dist-packages (from gluonts) (3.2.2)\n",
            "Requirement already satisfied: typing-extensions~=3.10.0.0 in /usr/local/lib/python3.7/dist-packages (from gluonts) (3.10.0.2)\n",
            "Requirement already satisfied: pandas~=1.0 in /usr/local/lib/python3.7/dist-packages (from gluonts) (1.3.5)\n",
            "Requirement already satisfied: toolz~=0.10 in /usr/local/lib/python3.7/dist-packages (from gluonts) (0.11.2)\n",
            "Requirement already satisfied: tqdm~=4.23 in /usr/local/lib/python3.7/dist-packages (from gluonts) (4.62.3)\n",
            "Requirement already satisfied: pydantic~=1.1 in /usr/local/lib/python3.7/dist-packages (from gluonts) (1.9.0)\n",
            "Requirement already satisfied: convertdate>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from holidays>=0.9->gluonts) (2.4.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from holidays>=0.9->gluonts) (2.8.2)\n",
            "Requirement already satisfied: korean-lunar-calendar in /usr/local/lib/python3.7/dist-packages (from holidays>=0.9->gluonts) (0.2.1)\n",
            "Requirement already satisfied: hijri-converter in /usr/local/lib/python3.7/dist-packages (from holidays>=0.9->gluonts) (2.2.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from holidays>=0.9->gluonts) (1.15.0)\n",
            "Requirement already satisfied: pymeeus<=1,>=0.3.13 in /usr/local/lib/python3.7/dist-packages (from convertdate>=2.3.0->holidays>=0.9->gluonts) (0.5.11)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib~=3.0->gluonts) (1.3.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib~=3.0->gluonts) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib~=3.0->gluonts) (3.0.7)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas~=1.0->gluonts) (2018.9)\n"
          ]
        }
      ],
      "source": [
        "#install GluonTS\n",
        "!pip install --upgrade mxnet~=1.7 gluonts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1vH7mX0edOyn"
      },
      "outputs": [],
      "source": [
        "#metatrader 5 history file any timeframe is ok\n",
        "df = pd.read_csv('/content/EURUSD_H1_200001030000_202202031200.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q0wCDd8sftJx"
      },
      "outputs": [],
      "source": [
        "#split mt5 into columns\n",
        "df.columns=['header']\n",
        "df = df.header.str.split('\\t', expand=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 424
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        "id": "gsjnTnmNkW5i",
        "outputId": "20c3a4e7-5c51-4d33-dd42-65090cc51703"
      },
      "outputs": [
        {
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              "                 0         1        2        3        4        5     6  7   8\n",
              "0       2000.01.03  00:00:00  1.00730  1.01320  1.00730  1.01280   194  0  50\n",
              "1       2000.01.03  01:00:00  1.01290  1.01410  1.01200  1.01370   113  0  50\n",
              "2       2000.01.03  02:00:00  1.01400  1.01730  1.01340  1.01710   149  0  50\n",
              "3       2000.01.03  03:00:00  1.01700  1.01900  1.01700  1.01750   214  0  50\n",
              "4       2000.01.03  04:00:00  1.01730  1.01770  1.01640  1.01670   162  0  50\n",
              "...            ...       ...      ...      ...      ...      ...   ... ..  ..\n",
              "137126  2022.02.03  08:00:00  1.13007  1.13013  1.12905  1.12914  1540  0   0\n",
              "137127  2022.02.03  09:00:00  1.12913  1.12976  1.12881  1.12973  2556  0   0\n",
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              "\n",
              "[137131 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "#check the splits\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IKMZcqOfkZMd",
        "outputId": "a16470ac-0916-4b5c-a77c-b3043defc8b8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 0         1        5\n",
            "0       2000.01.03  00:00:00  1.01280\n",
            "1       2000.01.03  01:00:00  1.01370\n",
            "2       2000.01.03  02:00:00  1.01710\n",
            "3       2000.01.03  03:00:00  1.01750\n",
            "4       2000.01.03  04:00:00  1.01670\n",
            "...            ...       ...      ...\n",
            "137126  2022.02.03  08:00:00  1.12914\n",
            "137127  2022.02.03  09:00:00  1.12973\n",
            "137128  2022.02.03  10:00:00  1.12952\n",
            "137129  2022.02.03  11:00:00  1.12842\n",
            "137130  2022.02.03  12:00:00  1.12793\n",
            "\n",
            "[137131 rows x 3 columns]\n"
          ]
        }
      ],
      "source": [
        "#merge date and time to convert object to datetime to use as GluonTS index\n",
        "selected_columns = df[[0,1,5]]\n",
        "new_df = selected_columns.copy()\n",
        "print(new_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tRRqEkwdlXmT"
      },
      "outputs": [],
      "source": [
        "new_df['date_time_index'] = df[0] +' '+ df[1]\n",
        "new_df = new_df.drop(columns = [0,1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "td9O8CwRqiBs"
      },
      "outputs": [],
      "source": [
        "new_df = new_df.rename(columns={5:'price'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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        },
        "id": "RO96StzjrLVJ",
        "outputId": "9caffd56-b267-4dd7-f3d8-518966bfbffb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                       price\n",
            "date_time_index             \n",
            "2000-01-03 00:00:00  1.01280\n",
            "2000-01-03 01:00:00  1.01370\n",
            "2000-01-03 02:00:00  1.01710\n",
            "2000-01-03 03:00:00  1.01750\n",
            "2000-01-03 04:00:00  1.01670\n",
            "...                      ...\n",
            "2022-02-03 08:00:00  1.12914\n",
            "2022-02-03 09:00:00  1.12973\n",
            "2022-02-03 10:00:00  1.12952\n",
            "2022-02-03 11:00:00  1.12842\n",
            "2022-02-03 12:00:00  1.12793\n",
            "\n",
            "[137131 rows x 1 columns]\n"
          ]
        }
      ],
      "source": [
        "#convert mt5 object formats to datetime and float\n",
        "df2 = new_df[['date_time_index','price']]\n",
        "df2['date_time_index'] = pd.to_datetime(df2['date_time_index'])\n",
        "df2['price'] = df2['price'].astype(float, errors = 'raise')\n",
        "df2 = df2.set_index('date_time_index')\n",
        "print(df2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9rlTE6bgtEKd",
        "outputId": "75877dba-61e2-47d8-e265-260efa3c95a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 137131 entries, 2000-01-03 00:00:00 to 2022-02-03 12:00:00\n",
            "Data columns (total 1 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   price   137131 non-null  float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 2.1 MB\n"
          ]
        }
      ],
      "source": [
        "#check it is done\n",
        "df2.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "yYzS14iSZjRi",
        "outputId": "50a2d4ea-e14e-4f42-a175-cde82a4bb2bd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#print the data range from above\n",
        "import matplotlib.pyplot as plt\n",
        "df2[:100].plot(linewidth=2)\n",
        "plt.grid(which='both')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y2jkSwYoZjz7",
        "outputId": "81ebb8e3-5cd1-40b7-830f-16944e25ac9e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gluonts/json.py:46: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.\n",
            "  \"Using `json`-module for json-handling. \"\n"
          ]
        }
      ],
      "source": [
        "#import Gluon\n",
        "from gluonts.dataset.common import ListDataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "_BAzJE6l1LS0"
      },
      "outputs": [],
      "source": [
        "#choose data range to train\n",
        "training_data = ListDataset(\n",
        "    [{\"start\": df2.index[0], \"target\": df2.price[\"2021-01-01 00:00:00\":\"2022-02-03 00:00:00\"]}], \n",
        "    freq = \"1H\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QjFfjoxq2zKd"
      },
      "outputs": [],
      "source": [
        "#import model \n",
        "from gluonts.model.deepar import DeepAREstimator\n",
        "\n",
        "from gluonts.model.deep_factor._network import DeepFactorPredictionNetwork, DeepFactorTrainingNetwork\n",
        "from gluonts.model.deep_factor.RNNModel import RNNModel\n",
        "\n",
        "from gluonts.model.deep_factor import DeepFactorEstimator\n",
        "from gluonts.model.deepstate import DeepStateEstimator\n",
        "\n",
        "from gluonts.mx.trainer import Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sl6QDq3u26Ma",
        "outputId": "18664679-33c6-466c-8179-8ccb965a4acb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gluonts/transform/split.py:36: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version\n",
            "  return _shift_timestamp_helper(ts, ts.freq, offset)\n",
            "/usr/local/lib/python3.7/dist-packages/gluonts/transform/feature.py:352: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version\n",
            "  self._min_time_point, self._max_time_point, freq=start.freq\n",
            " 10%|█         | 1/10 [00:18<02:48, 18.72s/it, epoch=1/10, avg_epoch_loss=0.632]/usr/local/lib/python3.7/dist-packages/gluonts/transform/split.py:36: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version\n",
            "  return _shift_timestamp_helper(ts, ts.freq, offset)\n",
            "100%|██████████| 10/10 [01:38<00:00,  9.88s/it, epoch=1/10, avg_epoch_loss=0.607]\n",
            "100%|██████████| 10/10 [01:29<00:00,  8.90s/it, epoch=2/10, avg_epoch_loss=0.527]\n",
            "100%|██████████| 10/10 [01:29<00:00,  8.93s/it, epoch=3/10, avg_epoch_loss=0.463]\n",
            "100%|██████████| 10/10 [01:28<00:00,  8.88s/it, epoch=4/10, avg_epoch_loss=0.395]\n",
            " 20%|██        | 2/10 [00:18<01:14,  9.35s/it, epoch=5/10, avg_epoch_loss=0.347]"
          ]
        }
      ],
      "source": [
        "#train model\n",
        "estimator = DeepFactorEstimator(freq=\"1H\", prediction_length=24, batch_size=24, num_hidden_global=1000,num_layers_global=10, trainer=Trainer(epochs=10, num_batches_per_epoch=10))\n",
        "#estimator = DeepFactorEstimator(freq=\"1H\", prediction_length=24, trainer=Trainer())\n",
        "\n",
        "predictor = estimator.train(training_data=training_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "k2FSTP4e3q65"
      },
      "outputs": [],
      "source": [
        "#choose data range to predict\n",
        "test_data = ListDataset(\n",
        "    [{\"start\": df2.index[0], \"target\": df2.price[:\"2022-02-03 00:00:00\"]}],\n",
        "    freq = \"1H\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "F3s2XjNb9BWr",
        "outputId": "ec207b21-9f76-4222-ad15-b0224d178856"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gluonts/transform/split.py:36: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version\n",
            "  return _shift_timestamp_helper(ts, ts.freq, offset)\n",
            "/usr/local/lib/python3.7/dist-packages/gluonts/transform/feature.py:352: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version\n",
            "  self._min_time_point, self._max_time_point, freq=start.freq\n",
            "/usr/local/lib/python3.7/dist-packages/gluonts/dataset/util.py:128: FutureWarning: Timestamp.freqstr is deprecated and will be removed in a future version.\n",
            "  freq = start.freqstr\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#plot prediction graph\n",
        "from gluonts.dataset.util import to_pandas\n",
        "\n",
        "for test_entry, forecast in zip(test_data, predictor.predict(test_data)):\n",
        "    to_pandas(test_entry)[-24:].plot(linewidth=1)\n",
        "    forecast.plot(color='g', prediction_intervals=[50.0, 90.0])\n",
        "plt.grid(which='both')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "epAi4P4TdMB8"
      },
      "outputs": [],
      "source": [
        "from gluonts.evaluation import make_evaluation_predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MIuHz2lpdUhk"
      },
      "outputs": [],
      "source": [
        "\n",
        "def plot_prob_forecasts(ts_entry, forecast_entry):\n",
        "    plot_length = 120\n",
        "    prediction_intervals = (50.0, 90.0)\n",
        "    legend = [\"observations\", \"median prediction\"] + [f\"{k}% prediction interval\" for k in prediction_intervals][::-1]\n",
        "\n",
        "    fig, ax = plt.subplots(1, 1, figsize=(10, 7))\n",
        "    ts_entry[-plot_length:].plot(ax=ax)  # plot the time series\n",
        "    forecast_entry.plot(prediction_intervals=prediction_intervals, color='g')\n",
        "    plt.grid(which=\"both\")\n",
        "    plt.legend(legend, loc=\"upper left\")\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OgvLlzBHcpdH",
        "outputId": "de05ba8e-610a-4a96-ad5f-fbe5969414e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gluonts/transform/split.py:36: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version\n",
            "  return _shift_timestamp_helper(ts, ts.freq, offset)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Nm9frexIEQRBuP00tZr4/W8P0SH97sn90sJqTZXr66znBnOocrLKVk1UnabW24uvqi0qhorCukErD1eWZ/SRGzHob2eorTk4/1spSKBT21wqFwl5QXJZltmzZQnh4+BWfPy0tjd27d3PgwAFcXV3tgR2ASqWy/+AqlcoOBczbu/RJlouv3dzc7NtkWWbt2rXMnDmzQ9sbmet20cXPsKf3JAiCINx+9ufX0Gy2khLpb98WHTSAT46WUlnfTIDa+Yb2p85UR5WhigsNF+xlmNROaupMdZQ3ll/1AwBixKyfzZw5k7Vr19qj/WPHjgEwadIkPvzwQwBOnjxJdnZ2p2P1ej1eXl64urpy+vRpDh48eMXX3759OyaTidraWtLS0khISOiyj2+++SatrbZyqGfOnMFgMDBp0iRSU1OxWCyUl5fz7bffdjo2KSmJ9PR0CgoKANBqbWVXPTw8aGho6NRerVbj5eVlzx/btGmTffRMEARB+OnanVuJh7MDCUN/nHm5+ABAf6xndqb2DADndLZpTFeVKz6uPrg7ulPfXE+RvuiqRvJEYNbPVqxYQWtrKxqNhpEjR7JixQoAnnzySRobG4mMjGTlypWMHj2607GzZs3CbDYTGRnJ8uXLSUpKuuLrazQa7r77bpKSklixYgVBQUGd2ixdupSoqCji4+OJjo7miSeewGw2M3fuXEJDQ4mKimLRokWMGzeu07F+fn6sX7+eefPmERMTwwMPPADAPffcw7Zt2+zJ/+1t3LiRZ599Fo1GQ1ZWFitXrrzi9yUIgiDcPixWmT25VUwJH4hK+WPoEhU0AEmCkxdufGCWX5uPxWrhbO1ZDK0G1E5qQr1DGeg2EIACXQG1xitfI/+yamXe7MLDw+W8vLwO23Jzc4mMvPYyDbezVatW4e7uzhNPPCFKMvWRG/VzKMrB3NrE/bv1iXvYt44W6bj/zR/4+4Ox3Bsb3GHf9L99R4iPK28v7jzjc7mu9P5daLjAZ3mfUVhXSOrJVAr1hUT4RvCr2F9xqPQQW09vZXbobNZMX8PIgSM7HS9JUre1MsWImSAIgiAIN7XduZU4KCSmhA3stC86aMANXzLDPo3Z9jSmg8KBIeohBHsEM8RzCCqFioK6gqt6AOAnkfwvdG3VqlUAXeZ6CYIgCMLNYk9uJYlDvVG7qjrtiw5W82nWBaobmvHzcOri6OvLbDVzXnceY6uRwrpC6pvr8XT2ZIT3CMJ8wtAatbiqXCmtL6WiseKKzy9GzARBEARBuGnlVTRwprKRGVH+Xe4fGfRjBYAboaiuiBZLC2dqz1DfXI9FtuDp7Mkwr2GE+oQS6h2Ku6M7+mY9VYYqjK1XVjJKBGaCIAiCINy0th4rRamQmB3T+eE0gJHBAwA4deHGTGfm1eYhyzKna0+jb9YjIRHmE8Yd6jvwdPbEz82PALcAAEr0JVc8nSkCM0EQBEH4CbgVH/azWGW2H7vAlDA/fN27nqYc4KwixMeVE6V9P2JmbDVSWl9KWUMZDc0N6Jv1uDu6E+4TTphPGAA+rj6EeIYA2PLMrnA9s9syx2zdkXWUl5cTaAi8odddNkYU4hYEQRBuTmUNZTgqHe3LOdwKDpyrpaLexIrZUT22Gxms5nhJXZ/3J1+bj1W2kludS7O5GZPZxED1QIZ5DWO413AAfFx8GKwejEqh4pzuHMX6YsYOGnvZ1xAjZn3o73//O9HR0YwcOdJe6xFsi6ympKQQGhpKSkqKvUj3pTZu3EhoaCihoaFs3LgRgObmZmbNmkV0dDRvvPGGve3jjz9OZmZm376hNkuWLOGTTz4BbGuc5eTkdNs2LS2NH374wf563bp1vP/++9fchwsXLjB//vxe2/35z3++5mtdjvZF2wVBEG5G1YZqDpUe6u9uXJGtmaV4ODuQHNlzMBkdpKZUZ6SuqaVP+3Om9gyGFgPF9cXUNdsCwUjfSEI8Q3BztFXM8XDyINA9EDeVGyX6EmqNtdSZLj9oFIFZHzl58iQbNmwgIyOD48eP88UXX3D27FkA1qxZQ3JyMvn5+SQnJ7NmzZpOx2u1WlavXs2hQ4fIyMhg9erV6HQ6vv76ayZMmEB2djabNm0C4Pjx41gslm4LnV+Oqy1v9PbbbxMV1f03mUsDs2XLlrFo0aKrulZ7QUFB9uCwJ1cTmFkslqvpkiAIwk2tuqmasoYySvQl/d2Vy2JoNrPzVAWzNYE4q5Q9to2+AXlmOqOOmqYa8mrzsFqt1DTV4KpyReOvIdQntEPbYV7DcHN0Q2fSYWw1ck577rKvIwKzPpKbm8vYsWNxdXXFwcGByZMns3XrVsBWBmnx4sWArUj3p59+2un4r7/+mpSUFLy9vfHy8iIlJYWdO3eiUqloamqitbXVni+wYsUK/vjHP3bbl1WrVrFw4ULGjRtHaGgoGzZsAGxB08SJE3nggQeIiorCYrHw7LPPkpCQgEaj4a233gJseQlPPfUU4eHhTJ8+naqqKvu5248U7dy5k/j4eGJiYkhOTqawsJB169bx6quv2lf4X7VqFa+88goAWVlZJCUlodFomDt3rn3kcMqUKTz//PMkJiYSFhbWqTIAQGFhIdHR0QC89957zJs3j1mzZhEaGspzzz0HwPLlyzEajcTGxrJgwQIA/v3vf5OYmEhsbCxPPPGEPQhzd3fnv/7rv4iJieEvf/kLP//5z+3XSktLsxeEf/LJJxkzZgwjR47kD3/4Q7efuSAIws2mpqkGgENlt8ao2denKmhqsTAvflCvbaPbnsw80YelmQrrCrHKVvJq8mhsacRkNhHoHshQz6EM9Rzaoa2fmx8B7m0PANSXcE4nArN+Fx0dzb59+6itraWpqYkdO3ZQUmL7llJZWUlgoC3/LSAggMrKzomBZWVlDB482P560KBBlJWVkZKSQmFhIUlJSTz99NN89tlnxMfHd1lKqb3s7Gz27t3LgQMHePHFF7lw4QIAmZmZvPzyy5w5c4Z33nkHtVrN4cOHOXz4MBs2bKCgoIBt27aRl5dHTk4O77//focRsIuqq6t57LHH2LJlC8ePH+fjjz8mJCSEZcuW8bvf/Y6srCwmTpzY4ZhFixbx8ssvk52dzahRo1i9erV9n9lsJiMjg9dee63D9u5kZWWRmprKiRMnSE1NpaSkhDVr1uDi4kJWVhYffPABubm5pKam8v3335OVlYVSqeSDDz4AwGAwMHbsWI4fP87y5cs5dOgQBoMBgNTUVB588EEAXnrpJY4cOUJ2djbfffddlzVMBUEQbjbN5mbqm22jSTVNNeTX5vdzj3q3NbOMwd4ujBni1WtbLzdHgj1drlvNzNrGZgzNHWeStEYtJfoSDK0GqpqqUEpK4gLiGOY1DCeHjg8m+Lj6MMxrGADF+mK0Ri1ao/ayrn1bJv/fDCIjI3n++eeZMWMGbm5uxMbGolR2HoqVJAlJki77vA4ODvbi5q2trcycOZPt27fz+9//nuLiYhYtWsScOXM6HXfvvffi4uKCi4sLU6dOJSMjA09PTxITEwkJCQFg165dZGdn26cI9Xo9+fn5pKen89BDD6FUKgkKCmLatGmdzn/w4EEmTZrE0KG2bw3e3t6d2rSn1+upq6uzFyhfvHhxh1GqefPmATB69GgKCwt7/VySk5NRq23fmKKioigqKuoQ2ALs2bOHo0eP2gu1G41GBg605S0olUruv/9+wPYZz5o1i88//5z58+fz5Zdf8te//hWAzZs3s379esxmM+Xl5eTk5KDRaHrtnyAIQn+6OFp2UUZZBsO9h6OQOo/PWKwWlIqepw77WoXexPfnavj1tNDL/hsZHTyAzCIdFquMUnH5f1cvJcsy89cdoLnVwj8XxBN3hy0w1Jl05Nbk0mJpoc5Uh7+bPxG+EZ2mMQG8XbwZNGAQjkpHzunOMZ3pnNOewzu457+NIEbM+tSjjz7K0aNHSU9Px8vLi7Aw26O0/v7+lJeXA1BeXm4PDtoLDg62j7ABlJaWEhzcsT7YG2+8waJFizh48CBqtZrU1FT+93//t8u+XPqDffG1m5ubfZssy6xdu5asrCyysrIoKChgxowZV/HOr52Tk+3bh1KpvKz8t4vtezpGlmUWL15sf395eXn26gfOzs4dAucHH3yQzZs3s3fvXsaMGYOHhwcFBQW88sor7Nmzh+zsbO6++25MJtM1vlNBEIS+d2lg1tDSwKmqUx22aY1aPs/7nE9Pf0qLpW+T6HvzaVYZsgxz44J7b9zmnpggLuhN7Mm98jJI7V3QmyioMVDd2Mwv3jrApgOFWK1WiuqKKGsoo7qpGoAQj1ic5FCGqId0OoePiw++rr64OrhSrC8GuOzpTBGY9aGLuVjFxcVs3bqVhx9+GIA5c+bYn7LcuHEj9957b6djZ86cya5du9DpdOh0Onbt2sXMmTPt+3U6HV988QWLFi2iqakJhUKBJEkYjV2vMLx9+3ZMJhO1tbWkpaXZR40uveabb75Ja2srAGfOnMFgMDBp0iRSU1OxWCyUl5fz7bffdjo2KSmJ9PR0CgoKANvDCwAeHh5dlnxSq9V4eXnZ88c2bdpkHz27nlQqlf39JCcn88knn9jvi1arpaioqMvjJk+eTGZmJhs2bLBPY9bX1+Pm5oZaraayspKvvvrquvdXEAShL1wMJtrLLM+k1dJKs7mZ/cX7+fjUx/bA46v8rzBbr+6hsGslyzJbM0uJv8OTob5uvR/QZtbIAII9XXhnf8E1Xf9okS3f+V9LEpkwwpcV20/x6/8cIa/mPGazkmpDHa5yNBUFT/HObh/yKgydzuHk4ESwRzDuTu5ojVqMrUbqTHXUNtX2en0RmPWh+++/n6ioKO655x7++c9/4unpCdiS0r/55htCQ0PZvXs3y5cvB+DIkSMsXboUsE0FrlixgoSEBBISEli5cmWH6cEXX3yRF154AYVCwcyZM9m3bx+jRo1i4cKFXfZFo9EwdepUkpKSWLFiRZc5aUuXLiUqKor4+Hiio6N54oknMJvNzJ07l9DQUKKioli0aBHjxo3rdKyfnx/r169n3rx5xMTE8MADDwBwzz33sG3bNnvyf3sbN27k2WefRaPRkJWVxcqVK6/iU+7Z448/jkajYcGCBURFRfGnP/2JGTNmoNFoSElJsY9cXkqpVDJ79my++uore+J/TEwMcXFxRERE8PDDDzN+/Pjr3l9BEIS+UG3oHJgZzUZ2ndvFRyc/4mTVSWRsD5SZrWbKG8vZdW4XVtna+bhWI3k1eX3W11MX6jlT2XhZSf/tOSgVLLkzhEMF2mvKNTtaqMVFpSRpmDfvLE7g9ylh7Miu5rsjkygtHYdFNqG2/ow7I/UMcFby4henuly819fVl0A3Wz75lYyaSbfiSsCXCg8Pl/PyfvwhsS8wGygWmAXbU5nu7u4888wzXe5vaGjAw8PjBvfqpyE3N5fIyMg+v05aWhpTpkzp8+sIfUPcv1vfzXwPWy2tvHPsHaoMVZTUlxAXENdlblmLpYX9xftxUjox/g7bF8/hXsOZPmw6kiTRbG7meOVxTlSeoNXayr3h9xLocf3/zv7xixw2HSgi44VkPF0dr+jYelMr4/68hxkjA3j1gdjLPq79/Zu9dh8eTio+ejzJvv/dgwf469c5FLEaq0LLo3GLGTc4CZomsmL7Kd5cEM/PRnX8LA6WHuTVA6/ycc7HTAmZwi9G/oIBTgN4eNTDSJJ0VJblMV315bZM/l82ZtkN+4MoCIIgCDezi/ll5Q3lZF7IpFhfzOQhk/F2+XEWprKxkm8LvuWs7iyNLY1ED4xG7azmnO4cqiIVA5wGcLziOM2WZvsxP5T8wP1R91/3/h4u1DImxOuKgzKwlWf6RcJgNh0o4vlZEQSona/oeEOzmdzyBv6/KcM7bL/Dz4DK7zVM1SWEDAgh0s+W9D8m+g4+OFTMSztymRoxsMN6a94u3gQPCEbtrOZg6UHmRc6jvrm+y9HL9sRU5k/AqlWruh0tEwRBEG5vFwOzj05+xOma01yov8D209s5Vn4Mi9VCVkUWX5z5gtO1pylrKEPfrOezvM/sx5+uOU1GWUaHoAxseWvXe0rTYpU5U9lARMCAqz7Hr+4cilWWef9A4RUfe7y0DotVJv6SJToK9YWUN1/3ooMAACAASURBVJajkBRMGDwBlVJFmE8YDkoFK2dHUaozdspt83bxxtfVF18XX4xmI9mVtuWVepvOFIGZIAiCINzGqpuq0Zv06Jv1NJmbyK3JRWfScbT8KB+e+JDDZYcp1BdSaajEz9UPZwdnjlUco97U+yr6GWUZ1/UhgWJtE6ZWKxEBV59ec4ePKzOiAvjgUDFNLVfWt6OFtsT/+ME/BmayLHO6+jRaoxYfFx/iA+PxdfW1jzjeOcKXmSP9+ee3Z6ms//FJfS9nL7xdvPF28UalUPFd0XcAvVYBEIGZIAiCINzGappq7KM0Q9RDUClV5GvzqWiswGQ2UagvpKaphgC3AAYPGIy/mz9Gs5Gvzvb+5Lmh1cCx8mPXra95FbZgMOwaAjOApROHoje2siWz7IqOO1qsI8zfHbWryr6tvrme3JpcZGRi/GNwUbkQ7hPe4bgX7orCbJF5eedp+zalQomXsxe+rr74uPpwpvYMWqOWhpbOKxW0JwIzQRAEQbhNma1mdEadfcpxmNcwInwi8HL2oqyhjBNVJ9AatQS5BxE8IBhJkuwjPJnlmfZqAT05XnmcxpbG69LfvIpGJAnC/N2v6Tyjh3gRM0jNu/sLsFov7yFHq1Ums0jH6EumMXUmHaX1pQCMHzwehaRghPeIDm3u8HHl0YlD2ZpZxrFinX17++lMgAMlB3rtx22Z/P/gJw9SX1/PgFNXP0d9Nf4z/z839HqCIAiC0JPaplpkZAr1haid1MyNmEt6UToKSYGbwY2yhjIGDRiEv5u//RilQslAt4GUNZSx5/we5kbO7fEaZquZQ6WHSB6WfM39zaus5w5vV1wdry08kSSJRycO4+mPjrH3dBXTo/x7PeZcdSP1JjPxd3QMzLRGLVVNVbg6uBLgYRtVdFG5dDr+/0wdwSdHS1n276P8/cE4kob54OPqg7+7P04OTng4erCveB8/C/1Zj/0QI2Z9KCQkhFGjRhEbG8uYMT8+FavVaklJSSE0NJSUlBR78e5Lbdy4kdDQUEJDQ+0L0jY3NzNr1iyio6N544037G0ff/xxMjMz+/YNtVmyZIm9bNPSpUvJycnptm1aWlqH2prr1q3j/fffv+Y+XLhwgfnz5/fa7s9//vM1X+tytC/mLgiCcLOobqpGlmUqGysJ9AhEpVQxbeg0xgSNIcAjgLiAOHtQFuQRxF2hd5EQlICfqx8KScGR8iPoTb2vCZavzaewrvCa+3u6ooFw/+uzfNPPogMY6OFE6pGS3hsDR9oWlr10xEzbpEVv0jPQ3ValJ8wnrMvj3Z0c2PirRNwcHXh4w0Fe230GtZMXQ9RDcFG54Ovqi75ZT15tzw9MiMCsj3377bdkZWV1+KO9Zs0akpOTyc/PJzk5mTVr1nQ6TqvVsnr1ag4dOkRGRgarV69Gp9Px9ddfM2HCBLKzs9m0aRMAx48fx2KxEB8ff9X9vJyyR115++23iYqK6nb/pYHZsmXLWLRo0VVdq72goCB7cNiTqwnMLBbL1XRJEAThplPTVIPOpKPZ0mwvqi1JEnGBcUwfNh0nBydCPEOYEz6Hu0LvIsgjiCi/KDycPPBz9UNr1JJelH5Z19p5difbcrdRVNd1RZXemFotFNYYCL/G/LKLVEoFd40KJP1MdaeC5F05WqTD282xU7WBM7VnMJqNDBowCCel7fPqTlTQAD779QTuiw3mtd35LN9cRlOzigifCDydPVFKyl4/TxGY9YPt27ezePFiwFa8+9NPP+3U5uuvvyYlJQVvb2+8vLxISUlh586dqFQqmpqaaG1tta80vGLFCv74xz92e71Vq1axcOFCxo0bR2hoKBs2bABsQdPEiRN54IEHiIqKwmKx8Oyzz5KQkIBGo+Gtt94CbE+kPPXUU4SHhzN9+nR7SSPoOFK0c+dO4uPjiYmJITk5mcLCQtatW8err75qX/l/1apVvPLKKwBkZWWRlJSERqNh7ty59pHDKVOm8Pzzz5OYmEhYWFinigEAhYWFREdHA/Dee+8xb948Zs2aRWhoKM899xxgq7BgNBqJjY1lwYIFAPz73/8mMTGR2NhYnnjiCXsQ5u7uzn/9138RExPDX/7ylw4F1dPS0uyr/z/55JOMGTOGkSNH8oc//KGHuywIgtD/qg3VnNWeBSDKt+OX6CHqISwYtYDpw6Yz0O3Hms0OCgfiAuIY6DYQCYkj5Uf4d/a/+TjnYz7L+4yvz33N98Xfd5lXVmmo5KuzX7H51Gbya/O7XBG/O2erGrHKXLfADGBWdADNZivf5lX12jazSEf8HV4dakvLsszR8qOAbbHdYV7Dei3w7u7kwN8eiOWVn8dwsqyRf+0OpKpiEuamGLycAu3LZnRHBGZ9SJIkZsyYwejRo1m/fr19e2Vlpb0qQUBAAJWVnQuulpWVMXjwYPvrQYMGUVZWRkpKCoWFhSQlJfH000/z2WefER8f32WJpfays7PZu3cvBw4c4MUXX+TChQsAZGZm8vLLL3PmzBneeecd1Go1hw8f5vDhw2zYsIGCggK2bdtGXl4eOTk5vP/++x1GwC6qrq7mscceY8uWLRw/fpyPP/6YkJAQli1bxu9+9zuysrKYOHFih2MWLVrEyy+/THZ2NqNGjWL16tX2fWazmYyMDF577bUO27uTlZVFamoqJ06cIDU1lZKSEtasWYOLiwtZWVl88MEH5Obmkpqayvfff09WVhZKpZIPPvgAAIPBwNixYzl+/DjLly/n0KFDGAy2+mepqan2epkvvfQSR44cITs7m++++47s7J5/wQRBEPqLxWpBZ9KRV5uHhMRg9eBObboLMsJ9w+1LQtQ01dDY0ojepLdVD9CXkFuTy5bcLZyoPNFl2SatUcuegj3syN+ByWzq4gqd5VXYnla8lqUyLpUQ4o2vuyNfnajosV1Di8z5GkOnacz65nrO684DMNRzaLfTmF2ZP3oQn/96AoO84VSRJ/WV81HVPd/l59XebZn8f7PYv38/wcHBVFVVkZKSQkREBJMmTerQRpKkDtF5bxwcHPjwww8BaG1tZebMmWzfvp3f//73FBcXs2jRIubMmdPpuHvvvRcXFxdcXFyYOnUqGRkZeHp6kpiYSEhICAC7du0iOzvbPkWo1+vJz88nPT2dhx56CKVSSVBQENOmTet0/oMHDzJp0iSGDh0K0KGuZ1f0ej11dXX2wuWLFy/uMEo1b948AEaPHk1hYWGvn0tycjJqtRqAqKgoioqKOgS2AHv27OHo0aP2Au5Go5GBA23fEpVKJfffb1vB2sHBgVmzZvH5558zf/58vvzyS/76178CsHnzZtavX4/ZbKa8vJycnBw0Gk2v/RMEQbjRtEYtVtlKcV0xns6ehHiGEOEbwffF33daLPZSCklBfGA8lY2V1BprOac7h6vKFZVCZfunVOGqcuVQ2SHO6s4y4Y4J+Ln6dTpPSX0JH5/6mBnDZ+Dv3nMC/pnKBhyVCkJ8Lr9weW+UCokZIwP49FgZxhYLLo5dB6Jn62yzJ53yy4xayhrKUEpKhnsPv+ISVCMGuvM/96k5UZnD6fIG9p4pQNsUTDPdL+MhArM+FBwcDMDAgQOZO3cuGRkZTJo0CX9/f3stz/LycntwcOmxaWlp9telpaWd6rC98cYbLFq0iIMHD6JWq0lNTWXatGldBmaXBn8XX7u5/fgLIMsya9euZebMmR3a7tix44re9/Xg5OQE2AKmy8l/u9i+p2NkWWbx4sX85S9/6bTP2dkZpfLHX9gHH3yQ119/HW9vb8aMGYOHhwcFBQW88sorHD58GC8vL5YsWYLJdHnfBAVBEG60i4n/VYYqQn1C8XHxIcwnjEEDBrGvaB8FdQU9Hj/cazjBA4LRmrRojVqaWps6jfa4OLjg4eTBOe05xgSNIWlQEs4OHcsgGVoNbM/bTtKgJDT+3X+RPV3RwPCB7jgor+9k3l3RgXx4qJjvzlQzKzqgyzb5OisqpYRmkLrDdp1JR01TDV4uXvYcvSvl4+KDUgEjgz3IbzhPY7WS4h7aX9a7lyTpXUmSqiRJOtnN/ghJkg5IktQsSdIz7bY7S5KUIUnScUmSTkmStLrdvqGSJB2SJOmsJEmpkiQ5tm13ant9tm1/yGW985uMwWCgoaHB/v9du3bZc6LmzJljf8py48aN3HvvvZ2OnzlzJrt27UKn06HT6di1a1eHgEmn0/HFF1+waNEimpqaUCgUSJKE0Wjssj/bt2/HZDJRW1tLWlqafdTo0mu++eabtLa2AnDmzBkMBgOTJk0iNTUVi8VCeXk53377badjk5KSSE9Pp6DA9ouu1WoB8PDwsH8O7anVary8vOz5Y5s2bbKPnl1PKpXK/n6Sk5P55JNP7DlyWq2WoqKuk1QnT55MZmYmGzZssE9j1tfX4+bmhlqtprKykq++6n3xRUEQhP5S01RDjbGGFmsLw72G4+tqW0vLVeXKzBEzSRmW0imIak+SJEYHjSbYI5hRA0cRFxBHrH8s0X7RhHmHEeQehIPCgWpDNed05/g452PWHVnHqapTnQI4q2zlh5IfSCtM6/Z6eRUN13Ua86Kxw7zxdFWx82R5t23O1lkYGaTuUOsSoNZQS31zPYHugR1qi16JEd4jcFLaBg9G+o3s9TyXG5a+B8zqYb8WeBp45ZLtzcA0WZZjgFhgliRJF8u1vwy8KsvyCEAHPNq2/VFA17b91bZ2t5zKykomTJhATEwMiYmJ3H333cyaZfsIly9fzjfffENoaCi7d+9m+fLlABw5coSlS5cCtqnAFStWkJCQQEJCAitXruwwPfjiiy/ywgsvoFAomDlzJvv27WPUqFEsXLiwy/5oNBqmTp1KUlISK1as6DInbenSpURFRREfH090dDRPPPEEZrOZuXPnEhoaSlRUFIsWLWLcuHGdjvXz82P9+vXMmzePmJgYHnjgAQDuuecetm3bZk/+b2/jxo08++yzaDQasrKyWLly5VV80j17/PHH0Wg0LFiwgKioKP70pz8xY8YMNBoNKSkplJd3/YuqVCqZPXs2X331lT3xPyYmhri4OCIiInj44YcZP378de+vIAjC9VJlqCK/Nh+ASN9Ie2B20XDv4TwU/RCjA0ejUqi6OgVD1EM6PBigVChta3I5eRDoEUiYTxixAbGEeYfh4uBCXm0eW3K3sDVnK2X1nafrTtecttfubE/f1EpFvem6Jv5fpFIqmBHlz57cKprNnZ+6bzFbKdBbO01jAmRWZGKRLQxRD8HLufP+y+Hk4ERcYBxgW+DX3bHnxXOly31iom3k6gtZlqN7aLMKaJRl+dIADUmSXIH9wJNABlANBMiybJYkaRywSpblmZIkfd32/wOSJDkAFYCf3ENHw8PD5by8H9cFsS8wO0AsMAu2pzLd3d27LWTe0NCAh8f1/2UQIDc3l8jIyD6/TlpaWqepbuHWIe7fre9mu4f5tfnsKdjDe1nvkVGWwet3vc6yMctQSF2PxzSbmzleeZwTlSdotbZ22FfeUM4PpT+gQGHPi7ZYLWiN2g7trLKVIn0RWqPWltOmDmHc4HGdpi+HqId0WmQ1o0DLL946wL9+lcDU8M7pPdfq29NV/Oq9w7y7ZAzTIjrmuh0r1jH3jR94Y0E8d436MYdMlmUe/ORBNuds5ndjf8dfpv8FJwenS099WcxWMx+d+AhDq4GjF47y9r1vH5VleUxXbfs8x0ySJCVwFBgB/FOW5UOSJPkCdbIsX0wEKgWC2/4fDJQAtAVtesAHqLnkvI8Dj4NttKZ9PtYy32Woh6sZMaJjyYS+1tWU3c2gubkZlUrVbf8sFstN2/dbnclk6vCz2VcaGxtvyHWEviHu363vZrqHVtmKzqTDR/ahXl9PiEsIgbWBpH/X+3pkwxiGocWAyWxCxjYe4oMP0T6dx2RaLa0YWgyY5R9zemVvmb21e9lauZUaaggaEISbxa3DlGljVSO7S3bjoPgxBNlTbAsGtedPklZ+/ReMMFtlXBzg3W+yUFR0DK6+ONcCQHNZLmntFn+1yBasOtuUbJw5jgP7ey+n1BM/sx/OLc5MV07nbd7utl2fB2ayLFuAWEmSPIFtkiRFYxsFu9bzrgfWg23E7NJvKrm5uWIUqE1Xye7tiRGzvuPs7ExcXFyfX+dm+7YuXBlx/259N9M93Hl2J9V11VhlK/lH84n0jcQz3JMpQ6dc9jkqGitIK0yjzlTXYzurbOV0zWkyyzN/XBZDYXtwoKCugD+e/yMRvhFMDZlKhG+E/Th3tTvTQ6fbX3+z7QQezheYN2vqFa1UcCVmVWex93QV4ydOQtX2gMHOk+Vs/+YYYV4K5s7quOJAga6AQ1mHcFO5oQ5VMyV8yjVdX5ZlNp/ajM7UdbWfi27YOmayLNcB32LLVasFPNumKgEGgf3Z0TJgMEDbfnVbe0EQBEEQepBf+2NppCpDFWarmRHeIzrll/UmwD2An0f9nLiAuG6nP8G2rEaUX5StbWAcA5xsKURqZzVhPmGYrWbya/P5rug7e74bQJG+qEOu2ZlKW+J/XwVlYCvRpDe2cvC8LaTYnlXG//nwGKOC1fwmvvNDEDqTjlpjLb6uvng6e17z9SVJIjE4sdd2fTpiJkmSH9Aqy3KdJEkuQArwsizLsiRJ3wLzgf8Ai4HtbYd91vb6QNv+vT3ll/VEluU+vcmC0JOr/LEVBEG4Kk2tTewv3m9/fab2DAARvhH4uPpc8fmUCiVjB41luPdwyhvKUSqUKCUlSoUSi9VCRlkGhlbbQtxODk6MDhzN6MDRVDdVU6Ar4LzuPGarmbPas5zVnkUhKVAqlPZlJw6XHeZnoT9DlmVOVzRwb2zPC6Vfq0lhfrg6KtlxooILdUaWbz3B2KHevLM4gcMH9ndqf6H+Ak2tTQwaMOiqE/8vNdRraIeC8V25rMBMkqSPgCmAryRJpcAfABWALMvrJEkKAI4AAwCrJEm/BaKAQGBjW56ZAtgsy/IXbad9HviPJEl/Ao4B77RtfwfYJEnSWWxPez54eW+3I2dnZ2pra/Hx8RHBmXDDybJMbW0tzs7dP4ouCIJwPaUXpXdYODa/Nh+FpCDYI/iKR8za83X17fL4IZ5D2Fuwl2J9x1W5/Fz98HP1IyEogV3ndmG2mimoswVqSpS4qdzwd/e3j5q1tLjRYDJft+Ll3XFWKZkWMZCtmaV8lFHMpDA/3vrl6G4XnT1QasspG+Y17LqMmF00dtDYHvdfVmAmy/JDveyvwDYdealsoMsEG1mWzwOdxvRkWTYBP+98xJUZNGgQpaWlVFdXX+upbnsmk0kEEH3A2dmZQYO6+rUQBEG4vs5pz9mnMC8qqS/Bx8UHT2dPHJWO1/2azg7O3BV6F1kVWWSUZXRau0ySJCbcMYFKQyWtllZKG0op1BfyzflvmBsxFzdHNw6XHcbZOhqA8IC+X0lhtiaQL7LLmRHlz9qH43By6Dook2WZrIosAEZ4jcDL5fqMmAEEefQ8MnjbrvyvUqns5YGEnqWlpd2QBHVBEAShb1y6NpjFaqGmqYZRA0dd02jZ5YgNiCXQPZBd53bZpzYvcnN0IzE4kRZLC63WVioNlTgqHfnm/DfMDptNkb4Ig862KENfj5gBzBwZQOrjScQP8bI/ANCVU9WnKNYXo5SUBHoE9rr22PUkipgLgiAIwm3mUNkhLLKFEd4jriq/7Er5u/szN3Iuaid1p30RvhEEewQT7BGMl7MXZQ1l5Nfmk15kW77jYGExgWpn1K5dL3J7PUmSxNhhPt0GZXqTns/yPmN/8X4qDZV4uXhd9Yr/V0sEZoIgCIJwGzleedwe9ET6dV7xv6+4O7pzX8R9+Lh0DgQnDpmIo4MjIZ4huKncKKgr4ETlCY5XHKeotpXBPv0/gXes/BibT23mQsMFZFlGZ9IR4BZwXfPLLocIzARBEAThNiDLModKD7GvaB+VhkocFA4EuAd0GSj1FReVC3PC53R68tDd0Z3EoEQUkoLhXsNRKVWc053j+5ID1NQ74OxU2ylH7UYxW83Umerso4wAtcZazFYzd6jvuK75ZZdDBGaCIAiCcIuzWC18V/gdewv2klOdQ6ullUl3TMLFwQUPpxu7gLiTgxOzw2YT7BHcYXuEbwRBHkGolCpGeI3AIlvIrz2P2dqMh1sDJ6tO3tB+XrS/eH+HMlQ6o84+4jjCe4QYMRMEQRAE4cp8ffZrdp7bSXF9Me6O7oz0G0lMQMwNyS/rikqp4q7Qu7hDfYd9myRJTAmZgr+7Py4qF4Z5DcNkaaTG8a8YOcfRC0d/rB5wg5zTnuN0zWnMVjOHSg+x+dRmtuRu4VTVKcC27tj1WsPsconATBAEQRBuYQdKDvC3Q3+jvrmeQR6DCPUOxd3JnQD3gBuWX9YVpULJrBGzCPEMsW9zVblyd+jdjPQbibtiCF7mhRiVh8ms+Ryj2cjhssM3rH+NLY2kF6VT31xPnamOE1UnqG+uB6DJ3ISbyg1XlStq584PNPQlEZgJgiAIwi0s0CMQL2cvInwj8Hf3Z7j3cOZHzmeA04B+DczAVrJpxvAZ9tX+L25LDB4HdY/iYZmBAgUXGsrIq8kjpzoHrVHb5/2SZZnd53fTbGnmeOVx+7b65noK6wrRm/T4ufrh4ejRY0mqviACM0EQBEG4hYV4hvBY3GP4ufqRMjyFaUOn4aJyAbihif/dUUgKUoalMMJ7hH3bt9me6BoGMCO2Dh9XH3QmHZnlmbRaWzlQcqDP+5RZnklFYwWNLY3kVOXwScUnnKg6Qb42H51Jh7eLN8nDkm944j/cxgvMCoIgCMJPRbhvOGoXdYcV/pWSsl8Ci65IkkTy0GQUkoLPs0s4ctaD0SMaGD1MSbk5kS/zv6TSUElOdQ4OCgfO6853GGXrisVqQanoeuX+nlQ2VnLkwhEAjlcc55zuHCdaT+Du6I6Piw9qZzUKScFQzxufXwZixEwQBEEQbnlDPId0Krvk5eJ1w6fheiJJEsMGJPLVER8CvZqZpqkDYOIdE1FKSrRGLdmV2bRYWthXtK/HBwH0Jj17CvZcVT9OVp1ERsbYaiTjQgYNLQ3M85/HCO8R9s9MISnwdPa84U9kggjMBEEQBOG21N/5ZZeyWGWe/igLldKBeeP0XFx8X+2sJsA9gDpTHU2tTZyoOoHRbOSHkh+6PI9VtrL7/G4KdAU0NDdccT/KG8sB22hZib4ER6UjE7wm2Pf7uvoyfvB4lIr+GXEUgZkgCIIg3IZuhvyy9g4V1JJdqmfl7JHMjkzqsC82IBaLbKG+uZ5TVacwmU2cqT1DUV1R5/OUHqK6qRoZmdya3CvqQ0NzA40tjZjMJr4v/R6j2UiQRxBOCiei/KKYGzGX+yLuI9w3HECMmAmCIAiCcOUcFA44Kh1xVDripHTCSemEn5tff3ergx0nynFRKblbE0ikX2SHHLLxg8fbpzNbLC2cqT0DQHpROi2WFnu7En2J/SlKgLyaPGRZvuw+XBwty67IplhfjIuDC0HuQXi5eHHn4Ds7rPvmqnLtND18I4jkf0EQBEG4xY0OGs3ooNH93Y1uWawyO09WMC1iIK6OttBj8pDJVBmqaGxpxMvFiwD3AMoby7FYLeRW5zJq4CgMrQYOlBxgcshkjK1G9hbstZ9Tb9IDUKQv6rBWWk/KG8ppsbSQVpRGi6WFEV4j0ARokCSpU9v+SPwHMWImCIIgCEIfO1RQS01jC3drAu3bnBycSB6ajIQtKIoNiMUqW9E362loaaBYXwxAbk0upfWl7C3Yi9FsBKC+ud6e/J9TnXPZ/ShvLOdk5UlK6ktwd3TH392fKN+oLtv2xzQmiMBMEARBEIQ+9mW2bRpzavjADtsDPQIZOXAkAEmDklApVOiMOgBOVZ+yt9t5dicl9SX218fKj6E1aqkyVFGiL6GxpbHXPhhbjdSZ6viu6DvMVjPBHsHEBsSiUqq6bN9fS42IwEwQBEEQhD5jtlj5+lQF0yIH4uLYed2xGP8YFJICX1df/N380TfrsVgtXGi4YA/SzFazvX2dqY6zurMAnKk9g4zM6ZrTvfajvLEcnVHHWd1ZPJ088XfzJ9I3stv2YsRMEARBEITbTkaBlprGFmaPCuxyv4eTh/1BAI2/BhmZOpNtjbOupikzyzNpaG7gVPUpcqtzabW0crrmdK8PAZQ3lLO/eD9W2UqgRyAxATE9LlArcswEQRAEQbjtfNH2NOaUS6Yx24sNiAUgISgBR6UjtcZaZFkmX5tPs7nZ3k5r1HJed55ifTEms4myhjIK9YU0tjTac9K6U95YTk6NrbKAn6sf4T7h3bZ1VDri5uh2he/0+hCBmSAIgiAIfcJssfL1yQqSu5nGvMjX1Zdgj2D83f0Z5DGIhpYGdCYdZquZfG2+vd3RC0epaarBaDaiUqioaarhdLVtGrOnNc1aLC3UGGq40HABD0cP4gPjux0tG+Y1jHmR867yHV87EZgJgiAIgtAnDhVoqTW0cHc305jtxQTEIEkSSYOScFW5UlJfgtlqJqc6B1mWqW6q5rzuPGUNZbip3BjqORSLbCGnJof65nqK6oq6rQRQ0VjBmdoztFhaGOg2kFCf0E5tBg8YzPyo+cwYPqPf8stABGaCIAiCIPSRL0+U4+rY8zTmRXeo78DbxZthXsMIUYdgsVoo0ZdQ31xPSX0JRy8cpcJQgdlqZtCAQbg7uuPs4ExlYyVnamwPARwoPdDlucsbyjlafhSAxODEDjVEJSQ8nT25O+zum6KMlQjMBEEQBEG47swWKztPVpAc6d/jNGZ7Mf4xBHoE4uvqS4B7AFqTFr1Jz8HSg5zXnaeysRIvZy/cHd35f+zdd3hc1bXw4d+ekWbUe5fVbMtyk7FxN8U2NrExNZCbAAmBhFwgISGEQArhS3LTLjeFEEghJCEkoYOBQOg2CGNw70W2XGSr9zqqM5r9/TEjWbIkq400Z6T1Po8exmefc2aNDpKXd1lbKUVcG07KKwAAIABJREFUUBwtjhZ2le1Ca83J2pMUNRT1um+prZS8mjwsZkuvuWXpEen4m/oumeENkpgJIYQQwuO25ddQ09TO5dkJg74mMzqTUEsoy1KXkRCSQIBfAKfrT1PbUktxQzEAk8ImAWBWZqICozApEwV1BZQ0lgB0rbzs1OHsoLihmMqmSiICIogKjOrxnvMS5430o3qUJGZCCCGE8Lj/7B/8MGYnkzIxO242aeFpZEZlkh6ejt1p50TtCWpaa4gPicdithAZGMnqyasxm8zEBMZQ21rbtYdmXWsd+8rO7KdZ3lTO7tLddOgO0sLTemy/lBSaRFzw4OMbC5KYCSGEEMLjNh+v5KLMGAL8BzeM2Wlm7EysZitLU5Z2FZ1tbG/Ez+RHQnACVrOV1RmrSQlPIT44ntjgWDSa3aW7aXW0ArCrdBdN7U2Aa35ZZ9I2M7bn9kudZTqMRBIzIYQQQnhUZWMbhTUtLEiLGvjks1j9rFyScQkBfgEsTVlKUmgSEdYI0sLT8DP7sTJjJeEB4QDMSZhDgF8AYdYwKpoq2HhyI07txOF08EnhJ4Brfll+bT6BfoFkRGR0vU90YDSp4ame+cAe5OftAIQQQggxvuwpcG2ldH7a8MpOpEWkdfVmTYmc0rWKcn7i/K45Zn4mP1LDUokMjCQ2KJYTtSc4XHmY6KBolkxawonaE0yvn05eVR61rbXEh7h61zoZsbcMpMdMCCGEEB62u6AOf7NiVlL4sO+xKHkRiSGJLEtZRoBfABkRGV3J1KzYWVw57UqUUsyJn0O4NRyL2UJZUxkHyg90FaXdcHIDW4q2oNGkhqV2JXihllCmRE0Z+QcdBZKYCSGEEMKjdhfUMjMpfMjzy7ozKROrJ68mKjCKlekruTjtYhSKpZOWclHaRcSHxJMWnsaUyCmEWcNICkmi2d5Mia2EzQWbqWyqpK2jjUOVh4Ce88vmxM/pUcvMSIwZlRBCCCF8kr3Dyf6iOs5PHXn1/GBLMKsmr2JS2CQC/QP51JRPcV7CeV3tC5MXYlImsuOziQ6KJjowmjJbGdXN1Ww4uYFmezOFDYWE+IeQEp4CQIBfADNiZ4w4ttEic8yEEEKICUZrzaGSBracqObGxakEWz2XDhwta6TV7uT81EiP3G9S2CSWpiwlISShV2mLmKAYMiIycDgd7CndQ2p4Ks32Zk7VnSLQL5BXcl/B1m4jKeRMWYxZsbPwMxk3/TFuZEIIIYTwqIqGVl7dW8z6XcUcLXftK+nUmtuXe26+1e6uif+eSczANfTYnwVJC8ivy2dW3Cx2luxkSuQUcqtyOVF7goQQV3Hb1PBU/Ex+mJSJWXGzPBbXaBhwKFMp9YRSqkIpdbCf9ulKqS1KqTal1L3djqcopT5QSh1WSh1SSn2zW9vzSqm97q9TSqm97uPpSqmWbm2PeeJDCiGEEBOZ1prvrd/Pkv/dyC/ePEKQ1cxPr5nN3JQInt9RiNbaY++1+3QtcaFWksIDPHbPc4kOimZy5GRmxMzAYrZg9bOSEZFBi6OF0/WnMSkT06KnATA5cjJB/kFjEtdwDabH7Eng98A/+2mvAe4CrjnruAP4ttZ6t1IqFNillHpPa31Ya/25zpOUUr8B6rtdd0Jrbcw1rEIIIYQPysmr5LkdhXxuQQq3LZ/MlNgQAAL9zdz74j6259eweHK0R95rd0Ed56dG9qiwP9oWJC0gvzafxcmL+ajgI8IDwkkISaDMVkaYNayrxMbsuNljFtNwDdhjprXehCv56q+9Qmu9A7CfdbxUa73b/boRyAWSu5+jXE/ts8CzQw9dCCGEEANxOjW/fPsoqVFB/PSa2V1JGcC67ARCrX48v6PQI+9VZWujoKZ52PXLhisqMIopUVPIisnqKhrbOa8sPjie+JD4ro3RjW5M5pgppdKBecC2s5ouAsq11se6HctQSu0BGoAHtNYf9XPP24DbAGJjY8nJyfFw1BOHzWaT75+Pk2fo2+T5+T4jP8OtJQ5yS9u4fY6VTzZv6tW+MA5e31fMqqhagv1793IdqHSggewY84C9YHsqHACo6lPk5Hgm2RsspRUxLTFcH3I9tX61ro3Mo12FaCOqIwhpDCGnIqfPa430/EY9MVNKhQDrgbu11g1nNd9Az96yUiBVa12tlJoPvKqUmtXHdWitHwceB8jKytIrVqwYlfgngpycHOT759vkGfo2eX6+z6jPsN3h5IcPfcj0hFC+e/1FmEy9E6uYzHref3Qz1SEZXL40vUfb4ZIGHnlvM/YOzdyUCO79VBYXTI3uN0Hb9vYR/M0nuemKFSOqYTZce0r3sK14G/m1+WzM3wjArLhZrExaybVzrsVs6jsmIz2/Ua1jppTyx5WUPa21fvmsNj/gWuD5zmNa6zatdbX79S7gBDBtNGMUQggxvrXaOyitb/F2GF7x/M5CCmqa+e7a6X0mZQCzk8OZlRTGs9t7LgJodzi554W9hAda+J+rZlHR0MoX/raN6x/fyo5Tfc9w2n26lpmJYV5JygDmJc4jKzqLjMgMpkZNBSAxJJEZMTP6TcqMZtQSM/f8sb8BuVrrh/o4ZTVwRGtd1O2aWKWU2f16MpAJnBytGIUQQox/v92Qx9qHP8Le4fR2KKOi3eHkvcPlNLc7ehxvbnfwyMZjLEqPYkVWbD9Xu1y/KJXc0gYOFJ9Zi/fo+8c4UtbIg9dmc/OydD64bwX/c9UsTlY18V+PbeGZbQU97uHocLK/qJ55HqpfNlzL05eTGJLI0klLCbYEkxiSaPgSGd0NplzGs8AWIEspVaSUulUpdYdS6g53e4JSqgi4B3jAfU4YcAFwE3BJt/IX67rd+np6T/q/GNjvLp/xEnCH1rrfhQdCCCHEQDblVVHfYudwSa9ZMT7P3uHk68/s5r//uZOLf/kBT2zOp9XeAcCTn5yisrGN76zNGnBu2NVzkwjwN/GcexHAvsI6/phzguvOn8TqmfEAWP3M3LwsnU33reSizBh+8p9DHK9o7LrHkbJGWuwdHq1fNhwmZWLt1LXEBcexZsoapsdMJ8QSMvCFBjHgHDOt9Q0DtJcBk/po2gz0+3+C1vqWPo6txzX0KYQQQoxYfbOdI2WuhGzHqRrOSxnb1YIj9diHJzhc0sADV8wgLrRnXbAOp+Zbz+/l3cPl3LF8CvsK6/jJfw7z+KaT3L58Mo/lnGDV9DgWpEcN+D5hAf5cnp3Ea3tLuO9TWXz7xX3Ehlj54ZUze50baDHzm8+ex9qHP+KuZ/fyyp3LsPqZzxSW9cBWTCNl9bOyLnMdL+e+7BMlMrqTvTKFEEKMW9tP1aA1mE2KXadrvR3OkLy4s5AH3zrCa/tKWPvwR7x7qKyrzenU3PfSPv6zv5TvXzad7102nWdvW8IzX1lMYkQA//P6YRrbHNy7JmvQ73f9ohRsbQ6uf3wrxyts/N9n5hAe6N/nuXGhAfzyujkcLm3g1+8cBWBPQR1xoVaSIwJH9sE9JDwgnCuzriQ5LHngkw1EtmQSQggxbm07WY3Vz8SqGXHsOFWL1npMC58O19aT1dz/ygEuyozh/nUz+PYL+7jtX7u4fmEKD1wxk5+/kcvLu4v51uppPbZTWjY1hpenRJOTV4mt1cGMxLBBv+eCtEimxAZztLyRGxensnzaueelrZ4ZzxeWpPKXj/JZPi2O3QW1zEuNMNT3NyYoxtshDJkkZkIIIcatbfk1zEuNYOmUGN48UEZhTQup0cbekie/qok7ntpFalQQv7/xfMID/Xn1zgv47YY8HvvwBG8cKKWx1cHXVkzhrlVTe12vlGJlVlwfdz43pRR3rpzKM9sKuH/djEFd84N1M9l6soa7n99Dla2dGxelDvl9RU8ylCmEEGJcami1c6iknsUZ0SxwT0jvr8yDUdQ1t3PrkztQwBO3LOwaSrT4mfju2uk8999LSAgL4I7lU7hvzcCT+ofq2vMn8dJXlxFiHVy/TaDFzO+un0t9i2vzH29P/B8PJDETQggxLu06VYtTw+LJUUyLDyU0wI+dBp5nZu9w8tWndlNU28LjX1xAWnRwr3MWT47mvXuW873LphtmyHBWUjj/74qZpEYFkZ0c7u1wfJ4MZQohhBiXtuZXYzGbOD81ErNJcX5qJLtOG7fH7K2DZWw5Wc2vPjOHhYNYSWkkX1yazhfP2jVADI/0mAkhhBiXtp2s4byU8K4q9AvSIskrt1HfbPdyZH07XNKAv1lxzTzfWkUoPEsSMyGEEONOU5uDA8Wu+WWd5qe75j/tKjBmr9mx8kYmx4Tgb5a/micyefpCCCEMR2tNbunwK/XvOl1Lh1OzePKZIcG5KRGYTYqdp4w5z+xoeSOZ8b5ToV6MDknMhBBCGM7m41Vc9ruPhr2Kclt+NX4mxfxuqwSDLH7MTgrrcwGA06l5+2BZ1+rCsdbU5qCotoWs+FCvvL8wDknMhBBCGE5RbQsA7xwsG+DMvm07WUP2pHCCLD3XuM1Pi2JfYR3tjp4bmv9r62nueGoXn/7DxxyvsA0v6BHofM9MScwmPEnMhBBCGE61rQ2A93LL0VoP6dqW9g72FdX1mF/WaUF6JG0OJwdL6ruOldW38qt3jjJnUjj1LXY+/YePef9I+cg+wBDllbs2A58mQ5kTniRmQgghDKfK1g7A6epmTlQOrQdrT0Et9o6e88s6dRaa3dVtntmPXjuIvcPJozfM47VvXEhqdBC3/mMnf8w5PuSkcLjyyhux+Jn6rF0mJhZJzIQQQhhOTVM7YQGuYcj3DlcM6dqt+TWY1JkkrLu4sABSo4LY6a5n9u6hMt45VM43V2eSFh1MckQgL92xjCvmJPHLt49y74v7xyQ5yyu3MTU2BLPJGEVjhfdIYiaEEMJwqpvayIwPJTs5nA25QxtW3HaymtnJ4YQG+PfZviAtkl2na2lstfOj1w4xPSGU/75ocld7oMXMI9fP5ealaazfXURlY9uIPstgHCtvlGFMAUhiJoQQwoCqbe1EBVtYNSOO3QW1VNkGlxy12jvYU1jH4oz+K+fPT4+kytbO3c/tpayhlV9cm92rdphSijWzEgBXb9Zoami1U1LfKhP/BSCJmRBCCAOqsrUTE2Jh9Yx4tIb3j5x7OPN0dRMPvZfHpb/9kHaHkwumxvR77oI0V9K28UgFNy1J4/zUvjfenuruweqcmD9ajrkTPymVIUD2yhRCCGEwTqempqmN6GArs5LCSAwPYGNuOZ9dkNLr3LcOlPK3zfnsPF2LUnDh1Bi+s2Y6y6fF9nv/zLgQwgL8CLSYuW9NVr/nxYZYiQjy59gol8841rUiUxIzIYmZEEIIg6lrsePUEB1iQSnF6hnxvLSriFZ7R9e+lwCfHK/ia8/sJiMmmO+unc6n5yWTEB4w4P1NJsUjN8wjJsTa7zw0cA1nTosL7UqcRkteuY1AfzOTIgNH9X2Eb5ChTCGEEIbSWcMsOsQKwOqZ8bTYO9hyorrrnNqmdu55YR8ZMcG88Y2L+OqKKYNKyjqtyIpjdnL4gOdlxoeQV944qisz89xbMZlkRaZAEjMhhBAGU93kqmEWE2wBYMnkKIItZt5zr87UWnP/KweobmrjkevnEWgx93uvkcqMC6Gh1UHFKK7MzCtvJDNOhjGFiyRmQgghDKXaXVy2s8fM6mdmeVYsG3PLcTo1L+ws5K2DZdz7qaxB9XqNROe8r9FaAFDX3E5FY5uUyhBdJDETQghhKNVNnUOZlq5jq6bHU97Qxuv7S/jxa4dZNiW6R+2x0dJZwuLYKJXM6CzFIRP/RSdJzIQQQhhKla0dpSAy6ExitnJ6HCYF97ywD4ufiYc+O3dM5mTFhFiIDPLnWMXo9Jh17ZGZIImZcJHETAghhKFU29qIDLL02J4oKtjCgrQoOpya/7sue0gT/UdCKUVmXOioFZk9Vt5IiNWPpDH6PML4pFyGEEIIQ6m2tRMdbOl1/DtrszhS1sja2YljGk9mfAiv7StBa41Snu2lyyu3MTUuxOP3Fb5LesyEEEIYSnVTW4/5ZZ0WpEfxhSVpYx7PtPhQGkdpZWZeeaNU/Bc9SGImhBDCUKqb2rtWZBpB5ihtzVRta6O6qb3r/kKAJGZCCCEMptrW3lXDzAjOlMzw7DwzWZEp+iKJmRBCCMNodzipb7EbqscsOti9MtPDPWZ5skem6IMkZkIIIQyjtrmzuKxxesyUUmTGh3p8KDOvvJGwAD/iw4yThArvk8RMCCGEYVR17pNpoKFMgGnxIRyrsHl0z8xj5TamxYfKikzRgyRmQgghDOPs7ZiMonNlZnmDZ1Zmaq3Jq2js2llAiE6SmAkhhDCMru2YDNZjNjXOsyszX9xZRF2zncUZUR65nxg/BkzMlFJPKKUqlFIH+2mfrpTaopRqU0rd2+14ilLqA6XUYaXUIaXUN7u1/VgpVayU2uv+Wtet7ftKqeNKqaNKqTUj/YBCCCF8h5F7zMAziVlFQys/e+MwizKiuOq8pBHfT4wvg6n8/yTwe+Cf/bTXAHcB15x13AF8W2u9WykVCuxSSr2ntT7sbv+t1vrX3S9QSs0ErgdmAUnABqXUNK11x6A+jRBCCJ9W3dSOv1kRFmCsjWliQqxEBVs4XjHykhk/eu0QrQ4nD16bPSb7fQrfMmCPmdZ6E67kq7/2Cq31DsB+1vFSrfVu9+tGIBdIHuDtrgae01q3aa3zgePAooFiFEIIMT5U29qIDrYackJ8ZlzIiHvM3j5YxlsHy/jmqkwmx0phWdHbmMwxU0qlA/OAbd0Of10ptd89VBrpPpYMFHY7p4iBkzkhhBDjRLWt3VClMrrLjA/hWPnwV2bWt9j54b8PMjMxjNsunuzh6MR4Mep9xUqpEGA9cLfWusF9+E/ATwHt/u9vgC8P8b63AbcBxMbGkpOT46mQJxybzSbfPx8nz9C3yfM7I7+0hSB/ZczvR72dxjYHr7zzAVEBPfs1BvMMnzjYRmWjg6/NVnz80aZRDFQMlZF+Bkc1MVNK+eNKyp7WWr/ceVxrXd7tnL8A/3H/sRhI6XaLSe5jvWitHwceB8jKytIrVqzwaOwTSU5ODvL9823yDH2bPL8zHtj2PuelRLFixVxvh9KL9UQ1T+VuJXpyNsunxfZoG+gZfnK8ik1vb+P2iydzy7oZoxypGCoj/QyO2lCmck0Q+BuQq7V+6Ky2xG5//DTQueLzNeB6pZRVKZUBZALbRytGIYQQxlLT1G64Uhmdprk3Gx/q1kx1ze3c99J+0qKDuHv1tNEITYwjA/aYKaWeBVYAMUqpIuBHgD+A1voxpVQCsBMIA5xKqbuBmcAc4CbggFJqr/t292ut3wR+qZSai2so8xRwu/t+h5RSLwCHca3qvFNWZAohxMTQ3O6gub3DcKUyOkWHWIkOtnBsCJuZa62598V9VDS28tIdywi0mEcxQjEeDJiYaa1vGKC9DNeQ49k2A30uq9Fa33SO+/0c+PlAcQkhhBhfztQwM2aPGcDs5HC25lejtR7UytG/fpTPhtwKfnTlTM5LiRiDCIWvk8r/QgghDKG6yZWYxRg4MVszK4HT1c0cLm0Y8Nxdp2v5v7ePsHZWArcsSx/94MS4IImZEEIIQ6ju2sDcmEOZAGtmxWM2Kd46UHbO82qb2vnGM7tJjAjg/z4zx5B12YQxSWImhBDCEHxhKDM6xMqSyVG8eaC033pmTqfm2y/uo8rWzh9uPJ/wQP8xjlL4MknMhBBCGEJVk/F7zAAum53IyaomjvazOvOFnYW8f6SCH1w+gzmTZF6ZGBpJzIQQQhhCja2dIIvZ8CsX18xKwKTgzf2lvdrsHU5+/8Fx5qZE8MWlaV6ITvg6ScyEEMIHOZ2aNsf4qiZU3WTc7Zi6iw21sigjijf6GM58bW8JRbUtfOOSqTKvTAyLJGZCCOGDHt6Qx6rffIijw+ntUM5pb2Edlz/yEbmDWMVY5d7A3Bdcnp3IicomjlWcqWnW4dT8Iec4MxLDuGR6nBejE75MEjMhhPBBbx0so6i2hY9PVHs7lHN6cWchh0oauPmJ7RTWNJ/z3Gpbu6FLZXS3ZnYCSsEb3YYz3zpYysnKJr6+UnrLxPBJYiaEED6mtL6lq6fm9X0lXo6mf1prNh2rZHZyGK32Dm5+Yjs17lplfalu8p0es7jQABamR/HWQVdiprXm9+8fZ0psMGtnJ3g5OuHLJDETQggfs/lYFQBzJoXzzsEyWu3GnGt2urqZwpoWPrsghb/dspDiuha+/OQOmtsdvc7VWlNt8405Zp3WzU4gr9zG8YpG9lZ2cKSskTtXTsVskt4yMXzjIjGrbNE4nX3XkxFCiPFm8/EqYkKsfPtTWTS2Ocg5WuntkPq06ZgrroszY1mYHsUjN8xjf1Eddz69G/tZc+MaWhw4nNqw+2T25bLsRPdwZhmvn7CTEhXIVecleTss4ePGRWLWZNd8dLzK22EIIcSoczo1m49VceHUaC6YEk1MiMWww5mb8ipJjQoiPSYYcJWZ+Nk12XxwtJIfv3aox7nVXTXMfKfHLD4sgAVpkfx180lO1jv56vKp+JnHxV+rwovGxf9BJgVPbz3t7TCEEGLU5ZY1UN3UzkWZsfiZTazLTmRDbjm2tt7Dg97U7nCy5UQ1F2XG9Dh+4+JUvnxBBs9sLyCvW4HWzn0yfWkoE1zFZhtbHURaFdfNT/Z2OGIcGBeJWai/YuORCsrqW70dihBCjKrO+WUXuhOeK89Los3h5L3D5967caztLqilqb2Di6fF9mr7xiVTCbb48buNx7qO+cI+mX1Zl51IgL+JK6f4Y/UzdmFc4RvGR2JmUXQ4Nc/vKPR2KEIIMao2H69iWnwI8WEBAMxPjSQpPIDX9hprOHNTXiVmk2LZlOhebZHBFm5Zls6bB0o5WubqNaty75PpK+UyOiWEB7DjB6tZmeLn7VDEODEuEjM/E1yUGcNzOwoMX2xRCCGGq9Xewbb8Gi6ceqYXymRSXHleEh8dq6L2HKUoxtpHx6o4PzWC0IC+N/C+9cIMgi1+POLuNevcwDzSh+aYdQoN8Je6ZcJjxkViBvD5xamU1rcadnWSEEKM1I5TNbQ7nFw0ree8rSvPS8Lh1Lx10BjDmdW2Ng6W1HNxZu9hzE6dvWZvuHvNqpvaiAjyx18mz4sJbtz8BKyaEU9cqJVnthd4OxQhhBgVm49VYTGbWJwR1eP4rKQwJscG89q+Yi9F1tPm41VoTZ/zy7r7ykUZhFj9+N3GPFcNMx/sLRPC08ZNYuZvNvG5hSl8cLSCotpzb/shhBC+aNOxKs5PiyDI0nM+k1KKq85LYlt+jSEWQX2YV0lEkD+zk8PPeV5EkIUvXZDOmwfK2F1Q61M1zIQYLeMmMQO4flEqCmQRgBBi3KlsbCO3tIGL+hkevOq8JLT2/u8/rTUfHaviwqkxg6qAf+uFrl6z0vpW6TETgnGWmCVHBLIiK47ndxT2qiothBC+7JMTrjIZZ9cF6zQ5NoSVWbH8dkMeD7x6wGvbNB0pa6SysW3AYcxOnb1m4Hs1zIQYDeMqMQPXIoCKxjY25pZ7OxQhhPCYTXlVRAT5Myup/+HBP9+0gNsunsxTWwu45g8fc9y90flY2pR3ZhumwfrKhZOJCbEyLT50tMISwmeMu8RsRVYcSeEB/L9/H+Khd4+SX9Xk7ZCEEGJEtNZsPl7JBQMMD1r8TNy/bgZ//9JCKhrbuOr3m3lpV9EYRuoqkzEtPoSE8IBBXxMe5M8n37uELy5NH73AhPAR464intmkePTGeTy84RiPfnCcR94/zvy0SK49P5nPLkiRpdhCCJ9zotJGeUMbF03texjzbCuz4njzrou4+/k93PviPo6WNXD/uhker7Vl73ByqKSB3FLX1+GSBnYX1PLlCzKGfC+Ln/xuFgLGYWIGMD8tin/dupiy+lZe3VvM+l1F/OCVg5TUtXDfmuneDk8IIYZkd0EdAAvPKpNxLgnhATz9lSX85PVD/OWjfOqa7fzvtdke3WT7q0/tZoN72kiI1Y/pCaF8YUkaX75w6ImZEMJlXCZmnRLCA7hj+RRuv3gydzy1i6e3FfCNSzIJ8Jf9zIQQvuNQcT3BFjMZ0cFDus5sUvz4qllEBlt4eMMx6lrsPHrDPI/8DjxeYWNDbjk3L3UlYimRQZgGsQpTCHFuE6LvWCnFly7IoK7Zzr/3GqMAoxBCDNbBkgZmJYUPK/FRSnH36mn8+MqZvHe4nFv+vp3GVvuIY3p2ewF+JsXXL8kkLTpYkjIhPGRCJGYAizOimJ4Qyt8/PoXW2tvhCCHEoHQ4NYdLGpiVHDai+9xyQQYPf24uO0/V8rk/b+VYeeOw79Vq7+ClXUWsmZ1AbKgUhRXCkyZMYubqNUvnSFkj2/JrvB2OEEIMSn6VjRZ7B7PPUSZjsK6Zl8xfbl5AaX0Llz+ymT/mHMcxjJqPbx4opb7FzucXpY44JiFETxMmMQO4em4yEUH+PPnxKW+HIoQQg3KwuAFgxD1mnVZmxfHePctZNSOOX759lOv+9Al5Q+w9e3pbARkxwSydEu2RmIQQZ0yoxCzA38wNi1J593CZ7KcphPAJh0rqsfqZmBob4rF7xoRY+ePnz+f3N86jsLaFKx7ZzIs7B7eV09GyRnadruXGRakeL78hhJhgiRnATUvSUErxr62nvR2KEEIM6GBxA9MTwzxa5gJc0zuumJPEu9+6mLmpEfz4tUNUNrYNeN0z205jMZu4bv4kj8YjhHCZcIlZUkQga2cl8Nz2QlravbOXnBBCDIbWmoMl9cxO8swwZl9iQqz833VzaHM4eXhD3jnPbW538PLuYtZlJxAlG44LMSomXGIGcMsF6dS32HlVSmcIIQyssKaFxlYHs5NHPvH/XDJigvnCkjSe21HI8Yr+55v9Z18pjW0OblycNqrxCDGRTcjEbEFaJLOSwnhSSmcIIQzsYEk9gEdWZA7krlWZBPmbefCtI/2e8/S202TGhbAwPXI2ykBxAAAgAElEQVTU4xFiohowMVNKPaGUqlBKHeynfbpSaotSqk0pdW+34ylKqQ+UUoeVUoeUUt/s1vYrpdQRpdR+pdQrSqkI9/F0pVSLUmqv++sxT3zIPmLmlmXpHC1v5IOjFaPxFkIIMWIHi+vxMymmJXhu4n9/ooItfG3lVDbkVrDlRHWv9t0FtewrqufGxTLpX4jRNJgesyeBtedorwHuAn591nEH8G2t9UxgCXCnUmqmu+09YLbWeg6QB3y/23UntNZz3V93DCK+YblmXjKpUUE89F6e9JoJIQzpYEkDmfGhWP3GZhu5L12QTlJ4AL94Mxen0/V7UWvNc9sL+MJftxEdbOHaeTLpX4jRNGBiprXehCv56q+9Qmu9A7CfdbxUa73b/boRyAWS3X9+V2vtcJ+6FRjzn3R/s4m7VmVysLiBdw+Xj/XbCyHEOWmtOVQ8uhP/zxbgb+a+tVkcKK7n9f0lVNva+O9/7uJ7Lx9gXmoE/7nrQsKD/McsHiEmojHZxFwplQ7MA7b10fxl4Pluf85QSu0BGoAHtNYf9XPP24DbAGJjY8nJyRlyXJFOTUKQ4qev7Ma/IhDTBO2et9lsw/r+CeOQZ+jb+np+ta1OqpvasTZXjOmzDdeatDATP3plH04NzQ7NDdMtXJrWwtE92zg6ZpH4FvkZ9G1Gen6jnpgppUKA9cDdWuuGs9p+gGvI82n3oVIgVWtdrZSaD7yqlJp19nUAWuvHgccBsrKy9IoVK4YVny2qmG8+t5fm6CyumJM0rHv4upycHIb7/RPGIM/Qt/X1/DYcLgd28ukV85mfFjWm8QSkVnHjX7YxPSGUh6+fy/SEseu181XyM+jbjPT8RnVVplLKH1dS9rTW+uWz2m4BrgA+r92TvLTWbVrravfrXcAJYNpoxnjFnCQy40J4eMMxOpwy10wIYQwHS+pRCmYkjn1StGxKDO/cfTH//voFkpQJMcZGLTFTrmU7fwNytdYPndW2FvgOcJXWurnb8VillNn9ejKQCZwcrRgBzCbF3auncbzCxuv7Snq1OyVZE0J4wcHiBqbEhhBkGZMZJ71kJYzdogMhxBkD/sQrpZ4FVgAxSqki4EeAP4DW+jGlVAKwEwgDnEqpu4GZwBzgJuCAUmqv+3b3a63fBH4PWIH33Muut7pXYF4M/EQpZQecwB1a634XHnjKZbMTmJ4Qyu82HuOKOYmYTYpdp2t5elsBbx4oZdmUaB767FwipdK1EGKMHCqpZ1HG2A5hCiG8b8DETGt9wwDtZfS9qnIz0Odseq311H6Or8c19DmmTCbFPZdO47Z/7eK76w9wqKSeI2WNhFj9+NSsBN45WMYVj27msS/MJ3vS6Bd6FEJMbNW2NkrrW8eksKwQwlgmZOX/vlw6M545k8JZv7sIP7PiwWuz2Xb/Kh69YR4v3rEUgOv+9AnPbi+QumdCiFF1qMS13mlWsszvEmKi8c7kBQNSSvG3mxdSZWvrNdn2vJQIXv/GhXzzuT18/+UD7Dpdy3fXTic21OqlaIUQ41nnVkyzpMdMiAlHesy6iQ219rsCKirYwpNfWsRdqzJZv7uICx58n/te3MeRsl6VPIQQYkQOFTeQGhVEeKAUcxViopEesyEwu+eiXTM3ib9/fIqXdhXx4q4iLpgazT2XZjE/TTb2FUKM3IHiembLMKYQE5L0mA3D5NgQfnrNbLZ8/xK+szaLo2U2vvrULm+HJYQYBwprmimoaeb8VPmHnhATkSRmIxARZOFrK6bylYsyqGhso7HVPvBFQghxDh8crQDgkulxXo5ECOENkph5QGpUEAAFNc0DnCmEEOe2MbeCjJhgJseGeDsUIYQXSGLmAZ2JWaEkZkKIEWhqc7DlRLX0lgkxgUli5gGp0a7E7HS1JGZCiOH7+HgV7R1OVs2QxEyIiUoSMw8IC/AnIshfhjKFECOyMbeCUKsfC9NlKyYhJipJzDwkLSpIEjMhxLA5nZr3j1ZwcVYs/mb51SzERCU//R6SIomZEGIEDpbUU9nYxiqZXybEhCaJmYekRgVRXNuCo8Pp7VCEED5oY24FJgUrsiQxE2Iik8TMQ9Kig3A4NaX1rd4ORQjhg94/UsH5qZFEBVu8HYoQwoskMfOQFKllJoQYptpWJweK67lEVmMKMeFJYuYhUmRWCDFc+ys7AFg1Pd7LkQghvE0SMw9JDA/E36wkMRNCDNneyg6SIwKZFi/V/oWY6CQx8xCzSTEpUlZmCiGGptXewaHqDlbNiEMp5e1whBBe5uftAMaTlKggCqT6vxCiH6eqmvjOS/tJighgRmIYM5PCqLa1094Bq2bIMKYQQhIzj0qNCmRfYZ23wxBCGNTHJ6rYfqqG2FArr+4t6TpuNcPiDKn2L4SQxMyj0qKCqW+xU99sJzzI39vhCCEMpqi2BX+zYtv3V1HfYie3tIHDpQ3Ul5wkwN/s7fCEEAYgc8w8SEpmCCHOpai2heSIQEwmRWSwhWVTY/jKRZOZHy//RhZCuEhi5kFSMkMIcS5Ftc1MigzydhhCCAOTxMyDUqMlMRNC9K+otoVJkYHeDkMIYWCSmHlQiNWP6GALBTVN3g5FCGEwrfYOKhvbJDETQpyTJGYelhIltcyEEL0V17UAyFCmEOKcJDHzsLRoScyEEL0V1XYmZtJjJoTonyRmHpYaFURJXSv2Dqe3QxFCGEih+x9s0mMmhDgXScw8LCUqiA6npsQ9bCGEEHCmhllcqNXboQghDEwSMw+TkhlCiL4U1TZ31TATQoj+SGLmYWnukhmnZc9MIUQ3rlIZMowphDg3Scw8LD40AIvZ1DWfRAghwJWYpUTJxH8hxLlJYuZhJpNiUlSgDGUKIbq02juosrVJj5kQYkCSmI2CtKggGcoUQnSRUhlCiMEaVGKmlHpCKVWhlDrYT/t0pdQWpVSbUurebsdTlFIfKKUOK6UOKaW+2a0tSin1nlLqmPu/ke7jSin1iFLquFJqv1Lq/JF+yLGWGhVEYU0zWuuuY/Utdv7+cT62NocXIxNCeENRbWepDEnMhBDnNtgesyeBtedorwHuAn591nEH8G2t9UxgCXCnUmqmu+17wEatdSaw0f1ngMuATPfXbcCfBhmjYaREBdHY5qCu2Q5ASV0L//XYJ/zP64d56N08L0cnhBhrZ3rMZChTCHFug0rMtNabcCVf/bVXaK13APazjpdqrXe7XzcCuUCyu/lq4B/u1/8Arul2/J/aZSsQoZRKHOTnMYS06GAATtc0c6SsgWv/+Amlda1cODWGf245xfEKm3cDFEKMqaLaFixmE7EhUsNMCHFufmP1RkqpdGAesM19KF5rXep+XQbEu18nA4XdLi1yHyvtdgyl1G24etSIjY0lJydnNMIelvJGV9X/R17bxpZSB1az4jsLAgi3NrPrlOaef27mngUBXo7yDJvNZqjvnxg6eYbGtvtoK5FWzaZNH/bZLs/P98kz9G1Gen5jkpgppUKA9cDdWuuGs9u11loppXtf2T+t9ePA4wBZWVl6xYoVngjVI5rbHTzw8Tu8X+ggMy6EJ7+8iOQI19ySisCT/OyNXHTCTFZOj/NypC45OTkY6fsnhk6eobH99tDHTEv2Y8WKxX22y/PzffIMfZuRnt+or8pUSvnjSsqe1lq/3K2pvHOI0v3fCvfxYiCl23mT3Md8RpDFj5mJYSyZHMVLdyzrSsoAvrg0nckxwfz0jcOyn6YQE0RxbbNM/BdCDMqoJmZKKQX8DcjVWj90VvNrwM3u1zcD/+52/Ivu1ZlLgPpuQ54+47WvX8Bzty0lPMi/x3GLn4kHrpjBycom/rnltJeiE0KMlZb2Dqps7TLxXwgxKIMaylRKPQusAGKUUkXAjwB/AK31Y0qpBGAnEAY4lVJ3AzOBOcBNwAGl1F737e7XWr8JPAi8oJS6FTgNfNbd/iawDjgONANfGumH9AY/c/8578qsOC6eFsvDG/K4Zm4S0TIhWIhxq7hOSmUIIQZvUImZ1vqGAdrLcA05nm0z0OeOvVrramBVH8c1cOdg4vJVSil+eMUM1jz8Eb95L49ffDrb2yEJIUZJoRSXFUIMgVT+95KpcaHcuCiVF3YUUt9sH/gCIYRP6qxhliJDmUKIQZDEzIs+fX4yDqcmJ69i4JOFED6pqLYZi5+JGJmyIIQYBEnMvGjupAhiQixsyJXETIjxqqi2hUkRgZhMfc7qEEKIHiQx8yKTSXHJ9DhyjlbQ7pDSGUKMR0W1LSTL/DIhxCBJYuZlq2fE09jqYMepfne8EkL4MFcNM5lfJoQYHEnMvOzCzBisfiY25JZ7OxQhxAjsL6rj4+NVPY41tzvcNcykx0wIMTiSmHlZkMWPC6fGsCG3HFelECGEL/r1u3l86ckdnKi0dR0rllIZQoghksTMAFbPjKewpoW8ctvAJwshDKmmqY12h5PvvLSfDqfrH1lFXYmZDGUKIQZHEjMDWOXezFyGM4XwXXXNdmJCrOw6XcuTn5wCXKUyAFKkx0wIMUiSmBlAXFgA500K573DkpgJ4avqmu1ceV4iq6bH8at3jnCqqomi2hapYSaEGBJJzAxi9Yx49hbWUdHY6u1QhBBD1O5wYmtzEBlk4eefzsbfbOI76/dTUNMsNcyEEEMiiZlBrJ4ZD8D7UmxWCJ9T3+LaVi0yyJ+E8AD+3xUz2Z5fw3uHy6WGmRBiSCQxM4jpCaEkRwTKLgBC+KC65nYAwoMsAPzX/ElcPC0Wh1OTEiUT/4UQgyeJmUEopbh0Zjybj1fS0t7h7XCEEENQ23ymxwxcP8//e202UcEWspPDvRmaEMLHSGJmIKtnxNNqd/YqUimEMLZad49ZpLvHDCA5IpBt96/ihkWp3gpLCOGDJDEzkEUZUYRa/Xj/qAxnCuFL6t09ZhHuHrNO/mb5FSuEGBr5rWEgFj8TU+JCKKxp9nYoQogh6Owxi+jWYyaEEMMhiZnBxIZaqWxs83YYQoghqG22429WBFvM3g5FCOHjJDEzGEnMhPA9dc3tRARZUErqlQkhRkYSM4OJDbFS09yOvcPp7VCEEINU12zvWpEphBAjIYmZwcSGWtEaqm3t3g5FCDFItc3tRATK/DIhxMhJYmYwsaGuPfVkOFMI31HXbO+1IlMIIYZDEjODietMzGyyZ6YQvqK2ub1HDTMhhBguScwMRnrMhPAtWmvqWqTHTAjhGZKYGUxMiCRmQviSFnsH7Q6n1DATQniEJGYGE+BvJizATxIzIXzE2ftkCiHESEhiZkCxoVYqbZKYCeEL6qTqvxDCgyQxM6DYUCsVDZKYCeEL6vrZJ1MIIYZDEjMDigsNkB4zIXxE5z6ZsipTCOEJkpgZkGzLJITvkDlmQghPksTMgGJDrTS3d9DU5vB2KEKIAdS7e8zCJTETQniAJGYGFCslM4TwGbXNdoIsZqx+Zm+HIoQYByQxM6CuIrMyz0wIw5Oq/0IIT5LEzICk+r8QvkP2yRRCeNKAiZlS6gmlVIVS6mA/7dOVUluUUm1KqXsHc61S6nml1F731yml1F738XSlVEu3tsdG8uF8VWdiVtEg+2UKYXR10mMmhPAgv0Gc8yTwe+Cf/bTXAHcB1wz2Wq315zpfK6V+A9R3az6htZ47iLjGraggC2aTkqFMIXxAXbOdxIhAb4chhBgnBuwx01pvwpV89ddeobXeAdiHeq1SSgGfBZ4dVLQThMmkiAmxyFCmEKNMa91VuX+4XHPMZChTCOEZ3p5jdhFQrrU+1u1YhlJqj1LqQ6XURd4KzNuklpkQo+/Bt49wwYPvU9M0vOTM6dTUt9hlKFMI4TGDGcocTTfQs7esFEjVWlcrpeYDryqlZmmtG86+UCl1G3AbQGxsLDk5OWMR75gxtbdystQ2Jp/LZhub9xGjR57h0J2s7+DxLa1o4OH1H3JJ6tB7vZrsGqeGqpICcnJKhx2LPD/fJ8/Qtxnp+XktMVNK+QHXAvM7j2mt24A29+tdSqkTwDRg59nXa60fBx4HyMrK0itWrBiDqMfOm1X7+DCvkrH4XDk5OWPyPmL0yDMcGnuHkwcf3UxcmCbY4sfhJgs/WbFsyPc5VdUEG3NYkD2DFfMnDTseeX6+T56hbzPS8/PmUOZq4IjWuqjzgFIqVilldr+eDGQCJ70Un1fFhlqpsrXjdGpvhyLEuPPnD09wpKyRn149m+vmT2Ln6VoKa5qHfJ+ufTKDZY6ZEMIzBlMu41lgC5CllCpSSt2qlLpDKXWHuz1BKVUE3AM84D4nrL9ru936enpP+r8Y2O8un/EScIfWut/FA+NZXGgAHU5NzQgnJgshejpeYeORjce5PDuRT81K4Oq5SQD8e2/xkO9V1+Ja8xQhc8yEEB4y4FCm1vqGAdrLgD778M91rdb6lj6OrQfWDxTTRNC9yGyMe4smIcTIOJ2a77+8n0CLmR9fNQuASZFBLEqP4tW9Jdy5ciquxeKD07miUyb/CyE8xdurMkU/pPq/EJ739LbT7DhVywOXz+j6GQO4el4SxytsHCrptc7onGqb3D1mgTKUKYTwDG+vyhT9kI3MhRg+rTXfeWk/m49X9TheZWvjoswYPnPWRP3LsxP58WuH+PfeYmYnhw/6feqa21EKwiQxE0J4iCRmBiUbmQsxfC/uKuLFXUWsnhFHVPCZYcYgix9fXTGl13BlRJCF5dPi+PfeEr532QzMpsENZ9a12AkP9B/0+UIIMRBJzAwq2OpHkMUsPWZCDFG1rY1fvJnLgrRIHr9pAaZBJk3XzEtiQ245W09Wc8HUmEFdU9ssxWWFEJ4lc8wMTKr/CzF0P3sjl6Y2B/97bfagkzKA1TPiCbH68eqewa/OrGtuJ1yGMYUQHiSJmYHFhVqpaGz1dhhiDFQ0tHL1Hz4eVi0tccZHxyp5ZU8xdyyfQmZ86JCuDfA3s3Z2Am8fLKPV3jGoa2SfTCGEp0liZmDSYzZxHCptYF9hHe8cKvN2KD6r1d7BA68eJCMmmDtXTh3WPa6Zm0xjm4ONuRWDOr9OhjKFEB4miZmBxYZIYjZRNLgLlW49OSHrKXvEIxuPcbq6mZ9/ejYB/uZh3WPplGjiQq2DLjZb12wnXHrMhBAeJImZgcWGWmlodQx6WEX4rnp3YrY9v5oO2YZryPYU1PL4ppN8Zv4klk0Z3MT9vphNinXZieTkVWJrc5zz3HaHE1ubQ3rMhBAeJYmZgXWWzKiSkhnjXmePWUOrgyNlQytyOpHlVzVx93N7uPZPnxAVbOEH62aM+J7rshNpdzjZmFt+zvPqWjqr/kuPmRDCcyQxMzCp/j9x1LfY6VxAKMOZAyuqbea7L+1n9UMf8vahMm6/eArv3H0xkcEj771akBZJXKiVtw6ce75ffbPskymE8DypY2ZgcaEBgCRmE0F9i53YUCtWPzPbTlZz64UZ3g7JsD46Vsmt/9gJGm5aksbXVk7p+lnxBJNJsXZ2As/vKKSpzUGwte9fk7VdiZn0mAkhPEd6zAyss8esQhKzca+hxUF4oD9LJkexLb8GZz/zzMobWmkaYO7TeHa8opGvPb2bjOhgcu5bwY+vmuXRpKzTuuxE2hxOPjja/+rMWtnAXAgxCiQxM7CoYAtKSY/ZRFDv3tpnyeRo6lvsHClr7HVOm6ODKx7dzKf/+PGETM6qbW186ckdWP3M/O2WBSRFBI7aey1MjyImxMqbB0r7PadeesyEEKNAEjMD8zebiAqyyH6ZE0B9i52wAH8WT44GYFt+da9z3jxQSmVjG3nlNu57aR9aT5zVm22ODu54ahcVDW385YvzmRQZNKrvZzYp1s6O54MjlTS3950ES4+ZEGI0SGJmcFJkdmJoaHX1mCVHBJISFcjWk70Ts39tOU1GTDDfu2w6bx4o48+bTnoh0rGnteb76w+w41Qtv/nsecxLjRyT912XnUiLvYOco5V9ttc22/E3K4Isw6uZJoQQfZHEzOAkMZsY6lvshLn3XFycEc32s+aZHSyuZ3dBHV9YksbtF0/mijmJ/PLtI2zK6ztpGC9K61v4+Ru5vLynmHsuncYVc5LG7L0XpUcRHWzhjX6GM+ua24kIsqDU4PfjFEKIgciqTIOLDbVysrLJ22GIUdTh1DS2OroSsyWTo3lpVxF5FY1MTwgD4KmtpwnwN/GZ8yehlOKXn5nD8Qob33h2D69//UJvhu9RTqdmT2EdHxypYOORCnJLXTXdPjN/Et+4ZHjbLA2Xn9nEmtkJvLqnmJb2DgLP6hlzbcck88uEEJ4lPWYGFxtqpdLWNuT5RFpr/vHJKdkE3QfYWl1zmMK7esyiANjmrmdW32zn1b3FXDM3uWv7nyCLH3++aT5aa277107aOnx/vtmBonqu/sPHXPenT/jThycIDfDje5dN571vXcyvPjPHKz1T62Yn0tzewYd5vVdn1rp7zIQQwpMkMTO42BAr7Q4nDS1DW4V3uLSBH712iKe2nB6lyISndG7H1JmYpUQFkRxxZp7Zi7sKabU7uWlpWo/r0qKDefj6uRwpa2RLie+u0mxqc/CT1w9z9R82U9bQyi+vm8PuBy7lhduXcsfyKWTGh3ptuHDJ5Cgig/x5s49is3XNdiICpcdMCOFZMpRpcF3V/22tQ9oseUe+q7dlx6naUYlLeE5nYhYWcObHccnkaD44WkGHU/P0tgLOT41gVlJ4r2tXZsWRHBHIgar2MYvXkzYcLueH/z5ISX0rn1+cynfWTu9KUI3Az2xizawEXt9XQqu9o8fm6LXN7cxNifBidEKI8Uh6zAyuq8hsw9AWAGw/5UrM9hTW0u5wejwu4TkNrT17zAAWT46ipqmdv3+cT35VE19cmt7ntUoplmfFcqiqw6ee8+nqJr7yjx185Z87CQnwY/1Xl/LzT2cbKinrtC47kab2Dt46eGYRgNaauhY7EcHGi1cI4dskMTO4tOhgzCbFt17Yy18/OtlvTaXutNZsz68lMsifVruTgyX1YxCpGK6uocxuPaJL3fXMfvXOUaKDLVyWndDv9SumxdLaAbtOG793tKW9g9+8e5RLf7uJT05U873LpvOfb1zE/LQob4fWr6VTopmdHMYPXjnIwWLXz1KL3ZUISw0zIYSnSWJmcMkRgTx162Imx4TwszdyueDB93l047Guv8z7cqq6mSpbG1+6wLXfYuewpjCmM0OZZxKzSZGBJEcE0uZw8rmFKVj9+q+VtWxqDGYFOX1MUDeSjbnlrPpNDo++f5zLZifw/rdXcMfyKVj8jP1ryN9s4m83LyQi0J8vP7mD4rqWM/tkGrCHTwjh24z9G1EArn+xP3vbEtZ/dRnzUiP5zXt5rPntpn57z7a7q8avy04kIyZY5pkZXENL76FMpRSLJ0dhUvD5JWn9XQpAiNWPaZEmPuynEKoRtNo7+OrTuwm2+vH8bUv43fXzSAj3/B6XoyU+LIC/f2kRLe0dfOnv2ymobgaQVZlCCI+TxMyHzE+L5IlbFvLYF+ZT1tDKB0f6/ot4e34t0cEWpsQGszA9kp2n+98UW3hffYsdP1PvCvLfWj2Nv928kORB7AmZHWvmSFkjpfUtoxXmiBwqqafd4eS+NVld2075mqyEUP5803zyq5r41vN7AaSOmRDC4yQx80GXzownJsTS7wbLO07VsCA9EqUUC9KjqGu2c7zSNsZRisHqrPp/dkmIlKggVk6PG9Q95sS4VnQatddsT0EdgM+vYlw2NYYHr51DWYOrPmBksPSYCSE8SxIzH+TaYDmBjUfKew1nltW3UlDTzKIMV6/EonTXpOrtMs/MsBpaHSNejZgcokgMD+h3X0dv21tYR1J4AHFhvjN82Z/r5k/ivjVZhFr9fGo4VgjhGyQx81GXZyfRanf2Gs7sLJPRmZClRQcRG2plxylJzIyqvsXeo4bZcCilWJEVy8fHq7B3GK9sxr6iOuam+nZvWXd3rpzKnh9e2mPBhhBCeIIkZj5qUUYUMSFW3jhQ0uP49vxqgi1mZiSGAq6/sBelR7FTFgB06XBqQ9X86r6B+UgsnxZHY5vDcGUzqm1tFNa0cN6k8ZOYgav4rBBCeJr8ZvFRZpNylRw4UtFjOHNHfi3np0X2+EtjYXokxXUtFNcZc2L4WDpW3sjKX+dw+792ejuULo0tdo8UVr1gajR+JmW44cx9ReNjfpkQQowFScx82OVzEmm1O3n/iKt+VV1zO0fLG7s2we60wD2sOZ7rmbXaO3h1TzFff2Y363cV9bkKdfOxKq790ycU1jbzwdFKCmuavRBpb57qMQsN8GdBeiQ5R41Vz2xvQR0mBdmTem8pJYQQoidJzHzYwnTXcGbn6szOemUL03smZjMSwwi1+nXNPxtPTlU18Ys3c1n6vxu5+/m9fHi0km+/uI8rHt3Mx8erus57ettpbv77dpIjAnnx9qUoBet3F3kxchetNQ2tnukxA1iRFceRskbK6ls9cj9P2FNYx7T4UIIssjWvEEIMRH5T+jCzSbEuO4EXdhbS3O5gx6kaLGYT5501ZGQ2Kc5Pi2TnOEvM7n/lAM9sK8BsUnxqZjyfX5zG0inR/Gd/Cb98+yif/+s2VmbFkhwZyFNbC1iRFcujN8wjNMCfZVOieXl3Md9cldmrTMVYarF3YO/QHkvMlk+L5cG3jvBhXgWfW5gKuObUHSiuJzE8gPgxXhWptWZfYR2Xz0kc0/cVQghfNWCPmVLqCaVUhVLqYD/t05VSW5RSbUqpewdzrVLqx0qpYqXUXvfXum5t31dKHVdKHVVKrRnuB5soLs8+M5y5Lb+GOZPCCfDvvX3Poowo8spt1Da1eyFKz/vkeBXPbCvghkUpfPK9S/jTF+ZzYWYMZpPi6rnJbPz2cr5/2XR2nq7lqa0F3LIsnb9+cQGh7lV0186bREFNs9d3RehrO6aRmJ4QSkJYAG8fLOM/+0u45/m9LPz5Bq75w8d87endHnmPs23Pr+HiX37QZ3Hb/KomGlod427ivxBCjJueq8QAABiLSURBVJbBDGU+Caw9R3sNcBfw6yFe+1ut9Vz315sASqmZwPXALPd1f1RK9b9JoGBBehSxoVZe3FnEoeJ6FmX0vRl05/DmToOt2BsOp1Pz8zdzSY4I5EdXzuqzFyjA38zty6fw4X0ref62Jfz4qlk9FkSsnZ1AkMXM+l3eHc5saHEt3PBUj5lSiuXTYvngaCVff2YP7x+tYPm0WK6em8Su07VdWwl50lNbT1NQ08yz2wt7tXVN/B9HpTKEEGI0DZiYaa034Uq++muv0FrvAHrtqj3QtX24GnhOa92mtc4HjgOLhnD9hGM2KdbNTuDDvEocTs3CfhKzOZPCsZhN46Ke2b/3FXOopIH71mT12TvYXVSwpc8tgIKtflw2O5E3DpTS0t4xWqEOqL6PfTJH6msrp3Dvp6ax/qtL2fXApfz2c3O5b00WAK/vL+n3ujZHx5D//2hud/De4XIAnt9RgOOsGmp7C+oIspjJjAsd4qcQQoiJyZuT/7+ulNrvHu6MdB9LBrr/s7vIfUycw7ps1/wdpVz7afYlwN/MnEnhPr8DQKu9g1+9fZTs5HCuOi9pRPe6bn4ytjYH7x4u81B0Q9c1lBnouemeadHBfP2STOanRWE2uebPTYoMYmF6JK/uKUbrvvdN/dXbR/mvx7bw7PaCQb/XhtwKWuwd3HphBuUNbWw80nNF6N7COrKTw7viEEIIcW7emvz/J+CngHb/9zfAl4dyA6XUbcBtALGxseTk5Hg4RN/h1JoIqyLMoti99eN+z4s3tfN2gZ23N3xAgN+ZvyhtNpvPfP/eONn+/9u78/Aqq2uP498VEgKEQAjzEGZIRJFREKSKM9pB64Ba69A6ttZqbS+1WjvcPvZeh+pTa28tdlDrUFHEuQoWqBMoECBhniGJESRAAiSBDOv+cQ6QQM5JQHKG5Pd5Hh7Oed993rOSlQMre+93bz4rruCawfD++//5UteqdqdjK+PJWbm037X2OEV4dD4tCBRmq3Ky2bHu2H9PakgOT2hTwTOb9vPsm3PISK39XqUVzrPzSmlhcN+MXPZ+tpaBafXPIvh7djlpyca4Nlt5Jdl4/F9LSP4iMLRcUe0sKyjl/L5JcfPzFS3x9BmUuimH8S2W8heVwszdtx54bGZPAm8GnxYAGTWa9goeq+saU4GpAJmZmT5x4sRGiTVeTO27g5aJCWEX8WzbdwdvPTGP3WkDmTT60Ld57ty5xMP3r2jPPm6fM5dzTujC9y495bhc81sVq/njnHVkjTg1KvsebvhwI+Su4LyJE0hrc+wbYjckhyfv3c/zq96jILEH10zMqnVu6vvrKa9axbM3jOVnM3J4ckU1b9x+Kl1SQ39PiksrWDZrFteO68s5Zw3h2qo1PDZ7LQNOHkNGehsWb9lJ1cyP+fr4oUwcqrsyw4mXz6CEphzGt1jKX1SGMs2s5r/S3wQO3LX5OnClmSWbWT9gEPBppOOLR2P6pde7svqoPh3o3zmFaQuPnKQdD/4wex2lFVXcfUFW/Y0b6JKRvah2mLG4zvq/0R0YykyNwJ6L6Skt+cqgTryx9LNaC/BWVFXz9482cWr/dCYM6sTUa0ZTUlbJbc9lh9266p3lhVRU+cEh5SvHZGBwcCh0aZ4m/ouIHK2GLJfxAjAPyDSzfDO7wcxuNbNbg+e7mVk+cBfw82CbdqFeG7zsg2aWa2Y5wJnAjwDcfTkwDVgBvAPc5u7Rm5ndxJgZk0dnsGDTTtZ/sSfa4TTY/spqPlq3nWfnb+aKUzIYeBwnkvfrlMKoPh2Ynp0fcu5VYyopryA1OTFic7AuGt6Tgl1lLNpy6O7ct3IKKSwu5+bT+wOBBYkfuOxkFmzayf1vrQh5rTeWFtKnYxtODq7o3719a87K6sK0hfnsr6xmSd4uuqQm0y3Ca6eJiMSzeocy3f2qes5/TmDIscGvdfdrwlzvfuD++uKSY3PJyJ489O5qpi3M42cXnBDtcOrk7vx75TY+2VhE9pZd5BYUs7+ymvatk7jznEHH/f0uHdmLe2bkkpNffMTivI3teG3H1FDnDulKq6QEXltSwCl903F3pr6/gQGdU5g4uMvBdt8Y1oPc/F08+cFGTuzZnsmjM2pdZ9vucj5ev53bzhxYa4Heq8f24b2VC5i1YitL84sZnpEW1QV8RUTijbZkama6pLbirKwuTF9UQEVV6GGqaHF3fvn6cm58ZiFPz9sMwHXj+vB/V49k9o/PCDvn6Vh99eTuJBj8e+XW+hsfZyURLsxSkhM5d0g33soppKKqmnnri1hRWMJNX+lPwmG9dj+dlMVpAztyzyu5zF5V+3vzdk4h1Q5fP+zO2NMHd6ZnWmue+M96Nm7fq2FMEZGjpMKsGbpidAbb9+xjzqrY2uz6QFH2zLzN3Hx6f5b96nymf2889351CBcO7U7HtsmN8r7tWyeR2a0di4NzoiKppKyS9sdxqYyGuGhYD3aWVvDh2u08+cEGOrVtycUjjlyVJrFFAk98exRZ3VP53rPZfLKh6OC515d+Rla3VAZ3rT2s3CLBuGpMBrkFxQAM14r/IiJHRYVZMzQxszNdUpNj6iYAd+cXrwWKslvO6M/PLsiiZWLkfjxH9k5jSd6uWpPiI6G47PhtYN5Qpw/uTPvWSfz+32uZs/oLrh3XN+RCvamtknj6O2Po1aE1Nz69kGUFxeTtKCV7y64jessOmDw6g8QEwwyGBuefiYhIw6gwa4YSWyRw6ahezFn9BdtKyhv0mr37Ko/pvYr27Ku32HF37nttGf+YHyjK7p6UFfF5SSN6d2B3eWXEb4ooLqs4bvtkNlTLxAQuHNqdJXm7aJWUwLdP7RO2fce2yTx741jatU7i2r99yuOz1wGEXOC3S7tWfH1YD4ZnpEXkblMRkaYkWgvMSpRNHp3Bn+au5+XsfIbU0/bVxQVMeTmHp75zCuMHdmrQ9Yv27OOXry/nzZxC0lNaMq5/R8YN6Mj4AR3p1aENeTtL2fjFXjZu38snG4t4b+U2bj1jAD+dlBmVyeIjgnOhsrfsZFDXyG0fVFIe+R4zgIuG9+CFT7dw2ahepKfUv35a9/atefbGsVz+xDxeXJjHiN5pZKS3Cdn+wctOpjoKd7mKiMQ7FWbNVL9OKYzpl85LC/P5xajQ/4Hur6zm4Zmr2V9VzZTpObx75+mkJIf/sXk7t5D7Xl1GSXkFN07ox47S/Xy8roi3cguBwNZRNf/P7tAmcLflHWcPitodfP07pdC+dRKLt+ziilN6R+Q9K6qqKd1fFZXCbGy/dP7nkqGcf2K3Br+mX6cUnvnuGG58egHXj+8btm1SC3XGi4gcCxVmzdjk0Rn85KWlrNnZijNDtHlpUR75O8u4/ayBPD5nHQ++s4pfX3RSnW2L9uzjF68t563cQob2bM/zl59KZrdA75O7s6molI/WbWdbSTl9O6XQL/jny6x4f7yYGSN6p7F4S+RuADi0T2bkCzMz46oxR1+ADunRjo/uPktLYIiINBIVZs3YhUO78avXlzN7SwW31HG+vKKKx2evY2TvNO46dzC7yyt56uNNXDi0O2P7d6zVdvGWndz0zCJKyir4r/MzueX0/iTW6DUxs4OFWKwakdGB/6xZQ0l5ZOZ9lQQLs2j0mH0ZKspERBqPxhuasTYtE7l2XB8++byKl+q4Q/Ofn26hsLicH58XmPc1ZVImvdPbMGV6DmX7D23I8GbOZ1w5dT5tWrbg9dtP47YzB9YqyuLFyD5puENOXnFE3q84TgszERFpPPH3v6ccV3edO5gTOyZw74xlLNp8aJue8ooq/jh3PWP7pTN+QKB3rE3LRB649GQ2F5Xy8MzVuDuPz17LD55fzNCe7Xn1ttPI6tYuWl/KlzYsIw2zQO9fJBwaylTHtYiIBKgwa+YSWyTwvWGt6J7Wilv+sYjC4jIAnp2/mS927+OucwfXGroaN6Aj15zah799tJHvPLWAh2eu4eLhPXjuprENursvlrVrlcSgLm3JjlBhVlIeWIJEPWYiInKACjOhbUvjL9eOpryiipufWUTRnn38ae56JgzsdMRcMoC7L8iiR/vWzF39BT86ZzCPXjGc5MS6FyiNNyMyOrA4b1dENjSP5uR/ERGJTSrMBIBBXVP5/ZXDWfZZMV997EOK9u7nrvMG19k2JTmRf9wwhhduOpU7zoneEheNYUTvNHaVVrCpqLTR3+vA5P9ILzArIiKxS4WZHHT2CV2Zcn4Wn5eUc2ZmZ0b27hCybf/ObRk34MjetHg3sk/ga87e3PjDmSVlFSQnJoTcDklERJofzTqWWm49oz9d2yUzfkDDVvhvagZ2bktqciKL83Zy6ahejfpe0dgnU0REYpsKM6nFzLhkZOMWJLEsIcEYlpFG9ubGX2i2pLxC88tERKQWDWWKHGZk7zRWfV5C6f6j37h9z75Kpi3IY19lVb1t1WMmIiKHU2EmcpgRvTtQ7ZCTf/QLzf727ZVMmZ7DIzPX1Nu2uKyCdq3UaS0iIoeoMBM5zPCMNIBa65ntr6zm+U+28PKi/JCvW5q3ixc+3UKntslM/WADn2woCvs+JWWV6jETEZFa9Ou6yGE6pLSkf6cUFm/ZRWVVNa8sLuD3762lYFdg8d22yYlMOqlbrddUVTv3vbaMTm2TefP2CUz+8zzumraUd+78CqkhlsPQUKaIiBxOPWYidRjeO435G4o479H3mfJyDh3btuSv141mWEYaP562hHXbdtdq/+KCPHLyi/n5V0+ga7tWPDJ5OIXFZfz6jRV1Xr+62jX5X0REjqDCTKQOY/uls7u8kpaJCUy9ZhSv3XYaZ5/QlSe+PZLWLVtw8zOLKCkPLBC7Y+9+Hnx3FWP7pfONYT0AGNWnA7edOZCXF+XzzrLPj7j+nv2VuGs7JhERqU2FmUgdLh3Zi1e+P563f/gVzjux28HdDbq3b80fvzWSLTtKuevFpVRXOw+9u4rd5ZX85uKTau2C8MOzBzG0Z3vumZHLtt3lta5fXKrtmERE5EgqzETqkNgigZG9O5CQcOR2U2P7d+S+rw3hvZVbufPFJfxzQR7fPa0vg7um1mqX1CKBR68Yxt59lfx8xrJa54q1HZOIiNRBhZnIMbh2XB8uGdmT15d+RpfUZO44p+59RQd2SeUHZw5k5oqtrNl6aF7agWFQDWWKiEhNKsxEjoGZ8dtvDmXy6F787vLhtE0OfYPz1af2ITkxgac+3nTw2IENzFWYiYhITSrMRI5Rq6QWPHjZMCYMCr+vaHpKSy4e3pNXsvMPzi07OJTZWivWiIjIISrMRCLguvF9Ka+o5sWFW4DA4rKgHjMREalNhZlIBAzp0Y6x/dJ5+uPNVFU7xWUVJBhhh0BFRKT5UWEmEiHfOa0vBbvKmLVia2CfzNZJtZbXEBERUWEmEiHnnNCVnmmteerjjZSUazsmERE5kgozkQhJbJHAteP6MH/DDhZt3qnCTEREjqDCTCSCrjglg1ZJCeTvLNPisiIicgQVZiIRlNamJd8c0QvQHZkiInIkFWYiEXb9+L6A9skUEZEj1VuYmdnfzGybmS0LcT7LzOaZ2T4z+0lDXmtmD5nZKjPLMbMZZpYWPN7XzMrMbEnwzxNf5osTiUWZ3VK572tDuOKUjGiHIiIiMaYhPWZPAZPCnN8B/BB4+CheOws4yd1PBtYAP6txbr27Dw/+ubUB8YnEnRsm9GN4Rlq0wxARkRhTb2Hm7u8TKL5Cnd/m7guAioa+1t1nuntl8Ol8oFeDIxYRERFpomJh2fHvAi/WeN7PzBYDJcDP3f2Dul5kZjcDNwN07tyZuXPnNnacTdaePXv0/YtzymF8U/7in3IY32Ipf1EtzMzsXqASeC54qBDo7e5FZjYKeNXMTnT3ksNf6+5TgakAmZmZPnHixAhF3fTMnTsXff/im3IY35S/+KccxrdYyl/U7so0s+uBrwFXu7sDuPs+dy8KPl4ErAcGRytGERERkUiKSmFmZpOAKcA33L20xvHOZtYi+Lg/MAjYEI0YRURERCKt3qFMM3sBmAh0MrN84JdAEoC7P2Fm3YCFQDug2szuBIa4e0ldr3X3vwKPA8nArOAmzvODd2CeDvy3mVUA1cCt7h7yxgMRERGRpqTewszdr6rn/OeEuKsy1GvdfWCI49OB6fXFJCIiItIUaeV/ERERkRihwkxEREQkRqgwExEREYkRKsxEREREYoQKMxEREZEYocJMREREJEaoMBMRERGJESrMRERERGKEBbepjGtmthtYHe044lh7oDjaQciXohzGN+Uv/imH8S3S+ct099S6TtS78n+cWO3uo6MdRLwys6nufnO045BjpxzGN+Uv/imH8S3S+TOzhaHOaShTAN6IdgDypSmH8U35i3/KYXyLmfw1laHMheoxExERkXgQrm5pKj1mU6MdgIiIiEgDhaxbmkSPmYiIiEhT0FR6zKQBzCzDzOaY2QozW25mdwSPXx58Xm1mGhKOYWFy+JCZrTKzHDObYWZp0Y5V6hYmh78J5m+Jmc00sx7RjlWOFCp/Nc7/2MzczDpFK0YJL8xn8FdmVhD8DC4xswujEp96zJoPM+sOdHf3bDNLBRYBFwMOVAN/Bn7i7iHvFpHoCpPDXsBsd680swcA3P2nUQxVQgiTw3x3Lwm2+SEwxN1vjWKoUodQ+XP3FWaWAfwFyAJGufv2aMYqdQvzGZwM7HH3h6MZn3rMmhF3L3T37ODj3cBKoKe7r3R3rQMXB8LkcKa7VwabzSdQqEkMCpPDkhrNUgj8wiQxJlT+gqcfBaag3MW0enIYdSrMmikz6wuMAD6JbiRyrMLk8LvAvyIdjxy9w3NoZvebWR5wNfCL6EUmDVEzf2Z2EVDg7kujGpQclTr+Hf1BcErB38ysQzRiUmHWDJlZW2A6cOdhv6VLnAiVQzO7F6gEnotWbNIwdeXQ3e919wwC+ftBNOOT8Grmj8Bn7h5UTMeVOj6DfwIGAMOBQuB30YhLhVkzY2ZJBH4Qn3P3V6Idjxy9UDk0s+uBrwFXuyaPxrQGfA6fAy6NbFTSUHXkbwDQD1hqZpsITCXINrNu0YtSwqnrM+juW929yt2rgSeBMdGIralsySQNYGYG/BVY6e6PRDseOXqhcmhmkwjMbTnD3UujFZ/UL0wOB7n72uDTi4BV0YhPwqsrf+6eC3Sp0WYTMFqT/2NTmM9gd3cvDD79JrAsKvHpF+vmw8wmAB8AuQTuwoRA93sy8AegM7ALWOLu50clSAkrTA4fI5DHouCx+bqjLzaFyeENQGbw2GbgVncviEqQElKo/Ln72zXabEKFWcwK8xm8isAwpgObgFtqFGqRi0+FmYiIiEhs0BwzERERkRihwkxEREQkRqgwExEREYkRKsyaATO7OLh3W1a0YxEREZHQVJg1D1cBHwb/FhERkRilwqyJC65sPIHArfhXBo9NNLM3a7R5PLg4KWZ2oZmtMrNFZvZYzXYiIiLSuFSYNX0XAe+4+xqgyMxGhWpoZq2APwMXuPsoAuuaiYiISISoMGv6rgL+GXz8T8IPZ2YBG9x9Y/D5C40ZmIiIiNSmLZmaMDNLB84ChpqZAy0IrGj8GrWL8lZRCE9EREQOox6zpu0y4B/u3sfd+7p7BrCRQN6HmFmymaUBZwfbrwb6m1nf4PMrIh2wiIhIc6Yes6btKuCBw45NJ3ATwDQCG7RuBBYDuHuZmX0feMfM9gILIhiriIhIs6e9MqUWM2vr7nvMzIA/Amvd/dFoxyUiItIcaChTDneTmS0BlgPtCdylKSIiIhGgHjMRERGRGKEesybIzDLMbI6ZrTCz5WZ2R/B4upnNMrO1wb87BI9nmdk8M9tnZj857FqbzCzXzJaY2cJofD0iIiLNhXrMmiAz6w50d/dsM0sFFgEXA9cDO9z9f83sbqCDu//UzLoAfYJtdrr7wzWutQkY7e7bI/11iIiINDfqMWuC3L3Q3bODj3cDK4GeBHYBeDrY7GkChRjuvs3dFwAVUQhXREREglSYNXHBNclGAJ8AXd29MHjqc6BrAy7hwMzg3pk3N0qQIiIiAmgdsyYtuIH5dOBOdy8JrIAR4O4e3A2gPhPcvSA43DnLzFa5+/uNFLKIiEizph6zJsrMkggUZc+5+yvBw1uD888OzEPbVt913L0g+Pc2YAYwpnEiFhERERVmTVBwcdi/Aivd/ZEap14Hrgs+vo7AnpnhrpMSvHkAM0sBziOwW4CIiIg0At2V2QSZ2QTgAyAXqA4evofAPLNpQG9gMzDZ3XeYWTdgIdAu2H4PMAToRKCXDALD3s+7+/2R+jpERESaGxVmIiIiIjFCQ5kiIiIiMUKFmYiIiEiMUGEmIiIiEiNUmImIiIjECBVmIiIiIjFChZmIiIhIjFBhJiIiIhIjVJiJiIiIxIj/B4myw1Nb7bZeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "forecast_it, ts_it = make_evaluation_predictions(\n",
        "    dataset=test_data,  # test dataset\n",
        "    predictor=predictor,  # predictor\n",
        "    num_samples=24,  # number of sample paths we want for evaluation\n",
        ")\n",
        "\n",
        "forecasts = list(forecast_it)\n",
        "tss = list(ts_it)\n",
        "\n",
        "plot_prob_forecasts(tss[0], forecasts[0])"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "GluonTS_1.2_deep_factor.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMPPDLk+t64dVdQY3n7/ijj",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}