{ "cells": [ { "source": [ "\n", "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES\n", "# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,\n", "# THEN FEEL FREE TO DELETE THIS CELL.\n", "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n", "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n", "# NOTEBOOK.\n", "\n", "import os\n", "import sys\n", "from tempfile import NamedTemporaryFile\n", "from urllib.request import urlopen\n", "from urllib.parse import unquote, urlparse\n", "from urllib.error import HTTPError\n", "from zipfile import ZipFile\n", "import tarfile\n", "import shutil\n", "\n", "CHUNK_SIZE = 40960\n", "DATA_SOURCE_MAPPING = 'breast-cancer-wisconsin-data:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F180%2F408%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240706%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240706T233729Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%3D2a42b19591dbfb7e3dadedf38ba5c4a2f41943260a2d6207aadbbdd6dc68ac198d85c58f17405095296f8c79de5c9517c6b9fdead7a5db588fea525cfb3a0474d6648706bd7ed55b1eec6b7718d64035647349365aa3b684519ef9f3ee4b750db4f314a520cd629a09d7a6ab3553ca46600d66b8613a67f2335fcfb93a051a47237d3adde9a5dbeccff7f24f0de64e5dc4346b7d5fcf85ce9ef16e62007599a879c970761ea4b4dfdc90568736428bca9722b7c679b20b5843c031092316569902ec1e5e413c2fb039207260c95e5cea134c8a4bc1f27e559256bb1c78141d4a53f01b9253fa597423bf463719f5f3d47f21afdf5c9030c3fd43009a347010b5'\n", "\n", "KAGGLE_INPUT_PATH='/kaggle/input'\n", "KAGGLE_WORKING_PATH='/kaggle/working'\n", "KAGGLE_SYMLINK='kaggle'\n", "\n", "!umount /kaggle/input/ 2> /dev/null\n", "shutil.rmtree('/kaggle/input', ignore_errors=True)\n", "os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)\n", "os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)\n", "\n", "try:\n", " os.symlink(KAGGLE_INPUT_PATH, os.path.join(\"..\", 'input'), target_is_directory=True)\n", "except FileExistsError:\n", " pass\n", "try:\n", " os.symlink(KAGGLE_WORKING_PATH, os.path.join(\"..\", 'working'), target_is_directory=True)\n", "except FileExistsError:\n", " pass\n", "\n", "for data_source_mapping in DATA_SOURCE_MAPPING.split(','):\n", " directory, download_url_encoded = data_source_mapping.split(':')\n", " download_url = unquote(download_url_encoded)\n", " filename = urlparse(download_url).path\n", " destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n", " try:\n", " with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:\n", " total_length = fileres.headers['content-length']\n", " print(f'Downloading {directory}, {total_length} bytes compressed')\n", " dl = 0\n", " data = fileres.read(CHUNK_SIZE)\n", " while len(data) > 0:\n", " dl += len(data)\n", " tfile.write(data)\n", " done = int(50 * dl / int(total_length))\n", " sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n", " sys.stdout.flush()\n", " data = fileres.read(CHUNK_SIZE)\n", " if filename.endswith('.zip'):\n", " with ZipFile(tfile) as zfile:\n", " zfile.extractall(destination_path)\n", " else:\n", " with tarfile.open(tfile.name) as tarfile:\n", " tarfile.extractall(destination_path)\n", " print(f'\\nDownloaded and uncompressed: {directory}')\n", " except HTTPError as e:\n", " print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n", " continue\n", " except OSError as e:\n", " print(f'Failed to load {download_url} to path {destination_path}')\n", " continue\n", "\n", "print('Data source import complete.')\n" ], "metadata": { "id": "4vms5FNmFWX3", "outputId": "c87e5a05-33f2-4628-b12c-9eebd3064044", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Downloading breast-cancer-wisconsin-data, 49796 bytes compressed\n", "[==================================================] 49796 bytes downloaded\n", "Downloaded and uncompressed: breast-cancer-wisconsin-data\n", "Data source import complete.\n" ] } ], "execution_count": 17 }, { "cell_type": "code", "source": [], "metadata": { "id": "ZHaohxVEFYaV" }, "execution_count": null, "outputs": [] }, { "metadata": { "id": "LEnE79HkFWX3" }, "cell_type": "markdown", "source": [ "# 🦀 Breast Cancer Prediction Using Machine Learning." ] }, { "metadata": { "id": "Xw5V8dhhFWX3" }, "cell_type": "markdown", "source": [ "

Table Of Contains

\n", "\n", "---\n", "\n", "> ### Steps are:\n", "\n", "\n", "1. [Gathering Data](#1)\n", "- [Exploratory Data Analysis](#2)\n", "- [Data Visualizations](#3)\n", "- [Model Implementation.](#4)\n", "- [ML Model Selecting and Model PredPrediction](#5)\n", "- [HyperTunning the ML Model](#6)\n", "- [Deploy Model](#7)\n", "\n", "\n", "\n", "\n", "**Hope** you guys ****Love It**** and get a better **learning experience**. 🙏" ] }, { "metadata": { "id": "A0ZqpbjrFWX3" }, "cell_type": "markdown", "source": [ "
\"Breast
" ] }, { "metadata": { "id": "h5DQYlxmFWX3" }, "cell_type": "markdown", "source": [ "### Attribute Information:\n", "\n", "1. ID number\n", "- Diagnosis (M = malignant, B = benign)\n", "\n", "Ten real-valued features are computed for each cell nucleus:\n", "\n", "1. radius (mean of distances from center to points on the perimeter)\n", "- texture (standard deviation of gray-scale values)\n", "- perimeter\n", "- area\n", "- smoothness (local variation in radius lengths)\n", "- compactness (perimeter^2 / area - 1.0)\n", "- concavity (severity of concave portions of the contour)\n", "- concave points (number of concave portions of the contour)\n", "- symmetry\n", "- fractal dimension (\"coastline approximation\" - 1)" ] }, { "metadata": { "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "trusted": true, "scrolled": true, "id": "_OtxTDnNFWX3" }, "cell_type": "code", "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "pd.options.display.max_columns = 100" ], "execution_count": 18, "outputs": [] }, { "metadata": { "id": "dY0xSlXeFWX4" }, "cell_type": "markdown", "source": [ "After installing numpy and pandas package, we are ready to fetch data using pandas package, Befor we use it, We need to know where's our dataset located. Means what is the path of our dataset" ] }, { "metadata": { "trusted": true, "scrolled": true, "id": "Dk2lLFMbFWX4", "outputId": "23279e80-db06-4162-8ec4-ff5af031e897", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "#\n", "\n", "import os\n", "for dirname, _, filenames in os.walk('/kaggle/input'):\n", " for filename in filenames:\n", " print(os.path.join(dirname, filename))" ], "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/kaggle/input/breast-cancer-wisconsin-data/data.csv\n" ] } ] }, { "metadata": { "id": "9xaNdDHmFWX4" }, "cell_type": "markdown", "source": [ "
\n", "\n", "# 1. Data Collection." ] }, { "metadata": { "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a", "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", "trusted": true, "scrolled": true, "id": "tITKxSISFWX4" }, "cell_type": "code", "source": [ "data = pd.read_csv(\"/kaggle/input/breast-cancer-wisconsin-data/data.csv\")" ], "execution_count": 20, "outputs": [] }, { "metadata": { "id": "82qG80ohFWX4" }, "cell_type": "markdown", "source": [ "After collecting data, we need to know what are the shape of this dataset, Here we have attribute(`property`) called `data.shape`\n", "\n", "For that we have 2 type of methods to show the shape of the datasets.\n", "\n", "1. `len(data.index), len(data.columns)`\n", "- `data.shape`\n", "\n", "Both methods are giving us the same output, As you can see in the below cells`" ] }, { "metadata": { "trusted": true, "scrolled": true, "id": "1f6Pu6ztFWX4", "outputId": "2870e0f5-6343-4b7f-d688-9b1f5c4a1fd0", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "# Cell 1\n", "len(data.index), len(data.columns)" ], "execution_count": 21, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(569, 33)" ] }, "metadata": {}, "execution_count": 21 } ] }, { "metadata": { "trusted": true, "scrolled": true, "id": "kAHv18SEFWX4" }, "cell_type": "code", "source": [ "# Cell 2\n", "data.shape" ], "execution_count": null, "outputs": [] }, { "metadata": { "trusted": true, "scrolled": true, "id": "cU62-4GbFWX4", "outputId": "bbe56c2c-3fb9-4d31-cc54-9e6e70b6c2e0", "colab": { "base_uri": "https://localhost:8080/", "height": 244 } }, "cell_type": "code", "source": [ "data.head()" ], "execution_count": 22, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " id diagnosis radius_mean texture_mean perimeter_mean area_mean \\\n", "0 842302 M 17.99 10.38 122.80 1001.0 \n", "1 842517 M 20.57 17.77 132.90 1326.0 \n", "2 84300903 M 19.69 21.25 130.00 1203.0 \n", "3 84348301 M 11.42 20.38 77.58 386.1 \n", "4 84358402 M 20.29 14.34 135.10 1297.0 \n", "\n", " smoothness_mean compactness_mean concavity_mean concave points_mean \\\n", "0 0.11840 0.27760 0.3001 0.14710 \n", "1 0.08474 0.07864 0.0869 0.07017 \n", "2 0.10960 0.15990 0.1974 0.12790 \n", "3 0.14250 0.28390 0.2414 0.10520 \n", "4 0.10030 0.13280 0.1980 0.10430 \n", "\n", " symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se \\\n", "0 0.2419 0.07871 1.0950 0.9053 8.589 \n", "1 0.1812 0.05667 0.5435 0.7339 3.398 \n", "2 0.2069 0.05999 0.7456 0.7869 4.585 \n", "3 0.2597 0.09744 0.4956 1.1560 3.445 \n", "4 0.1809 0.05883 0.7572 0.7813 5.438 \n", "\n", " area_se smoothness_se compactness_se concavity_se concave points_se \\\n", "0 153.40 0.006399 0.04904 0.05373 0.01587 \n", "1 74.08 0.005225 0.01308 0.01860 0.01340 \n", "2 94.03 0.006150 0.04006 0.03832 0.02058 \n", "3 27.23 0.009110 0.07458 0.05661 0.01867 \n", "4 94.44 0.011490 0.02461 0.05688 0.01885 \n", "\n", " symmetry_se fractal_dimension_se radius_worst texture_worst \\\n", "0 0.03003 0.006193 25.38 17.33 \n", "1 0.01389 0.003532 24.99 23.41 \n", "2 0.02250 0.004571 23.57 25.53 \n", "3 0.05963 0.009208 14.91 26.50 \n", "4 0.01756 0.005115 22.54 16.67 \n", "\n", " perimeter_worst area_worst smoothness_worst compactness_worst \\\n", "0 184.60 2019.0 0.1622 0.6656 \n", "1 158.80 1956.0 0.1238 0.1866 \n", "2 152.50 1709.0 0.1444 0.4245 \n", "3 98.87 567.7 0.2098 0.8663 \n", "4 152.20 1575.0 0.1374 0.2050 \n", "\n", " concavity_worst concave points_worst symmetry_worst \\\n", "0 0.7119 0.2654 0.4601 \n", "1 0.2416 0.1860 0.2750 \n", "2 0.4504 0.2430 0.3613 \n", "3 0.6869 0.2575 0.6638 \n", "4 0.4000 0.1625 0.2364 \n", "\n", " fractal_dimension_worst Unnamed: 32 \n", "0 0.11890 NaN \n", "1 0.08902 NaN \n", "2 0.08758 NaN \n", "3 0.17300 NaN \n", "4 0.07678 NaN " ], "text/html": [ "\n", "
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iddiagnosisradius_meantexture_meanperimeter_meanarea_meansmoothness_meancompactness_meanconcavity_meanconcave points_meansymmetry_meanfractal_dimension_meanradius_setexture_seperimeter_searea_sesmoothness_secompactness_seconcavity_seconcave points_sesymmetry_sefractal_dimension_seradius_worsttexture_worstperimeter_worstarea_worstsmoothness_worstcompactness_worstconcavity_worstconcave points_worstsymmetry_worstfractal_dimension_worstUnnamed: 32
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0842302M17.9910.38122.81001.00.118400.277600.30010.147100.24190.078711.09500.90538.589153.400.0063990.049040.053730.015870.030030.00619325.3817.33184.62019.00.16220.66560.71190.26540.46010.11890
1842517M20.5717.77132.91326.00.084740.078640.08690.070170.18120.056670.54350.73393.39874.080.0052250.013080.018600.013400.013890.00353224.9923.41158.81956.00.12380.18660.24160.18600.27500.08902
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "data" } }, "metadata": {}, "execution_count": 31 } ] }, { "metadata": { "trusted": true, "id": "jGHnpSfGFWX5" }, "cell_type": "code", "source": [ "diagnosis_unique = data.diagnosis.unique()" ], "execution_count": 32, "outputs": [] }, { "metadata": { "trusted": true, "id": "L3Rjyc9pFWX5", "outputId": "e0d54a81-0f01-4c84-eda5-26dc8071735d", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "diagnosis_unique" ], "execution_count": 33, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array(['M', 'B'], dtype=object)" ] }, "metadata": {}, "execution_count": 33 } ] }, { "metadata": { "id": "JFeBzXs7FWX5" }, "cell_type": "markdown", "source": [ "
\n", "\n", "# 3. Data Visualization." ] }, { "metadata": { "trusted": true, "scrolled": true, "id": "cy8xlYZ2FWX5" }, "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "\n", "%matplotlib inline\n", "sns.set_style('darkgrid')" ], "execution_count": 37, "outputs": [] }, { "metadata": { "trusted": true, "scrolled": true, "id": "v1sliSJdFWX5", "outputId": "11b4aefc-02a3-4186-80c5-0f1c79cb0d58", "colab": { "base_uri": "https://localhost:8080/", "height": 504 } }, "cell_type": "code", "source": [ "plt.figure(figsize=(15, 5))\n", "\n", "plt.subplot(1, 2, 1)\n", "plt.hist( data.diagnosis)\n", "# plt.legend()\n", "plt.title(\"Counts of Diagnosis\")\n", "plt.xlabel(\"Diagnosis\")\n", "\n", "\n", "plt.subplot(1, 2, 2)\n", "\n", "#sns.countplot('diagnosis', data=data); # \";\" to remove output like this > \n", "\n", "# plt.show()" ], "execution_count": 39, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 39 }, { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} } ] }, { "metadata": { "trusted": true, "id": "qmATqv37FWX5", "outputId": "2ac56225-8ba5-4a31-daa9-217dae11b287", "colab": { "base_uri": "https://localhost:8080/", "height": 542 } }, "cell_type": "code", "source": [ "# plt.figure(figsize=(7,12))\n", "px.histogram(data, x='diagnosis')\n", "# plt.show()" ], "execution_count": 40, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
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\n", "\n", "" ] }, "metadata": {} } ] }, { "metadata": { "trusted": true, "scrolled": true, "id": "H_RkQNEIFWX5", "outputId": "c033665e-0d46-4c06-b39b-5d0ed2d26b80", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "cell_type": "code", "source": [ "cols = [\"diagnosis\", \"radius_mean\", \"texture_mean\", \"perimeter_mean\", \"area_mean\"]\n", "\n", "sns.pairplot(data[cols], hue=\"diagnosis\")\n", "plt.show()" ], "execution_count": 41, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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mvbuiiF8d24crJmRJEEMIsU80TbGlJsAVLy7h5MFpXDulN0/Nadnj8zen9G/3654Qh5Nfadz6xkpWFe7OcfHp2h2cOTyde08diMMwufmEvszeWIr3Rz0x+qZEM7x7rCT7Fp1Chx9C8swzzzBt2jRGjx7NpEmTuOmmm9i6dWuzZS6//HIGDhzY7L8//elP7dTiI4/WWIrpit+vz5jR3bDVbDs0DRJCtKCZFpeM7c4/Lx5F3xQPNk0xKD2G568Yy0kDkrHacQiJsmk8N29bs+DFLk/P3Uq1jFMXQuyjRhNue2slgbDJp2tLcNo0/nreMPqlRmPTFAPSonl6xhjOHJLWrtc9IQ4nXdeYvamsWfBil0/X7iC30oumKRLtGh/cdDRnDEvHadOIc9u54Zg+vHjlOKKkt5LoJDp8D4wlS5YwY8YMhg8fjmEYPPbYY1x77bV8+umnREVFNS03ffp0brvttqZ/u93u9mjuEUlrLMNyJuzXZ4zodDRfOSrYgOWIPkQtE0LsyW5aHNMznrFXjsO0QFfg1sBo5zeQXsPinRWFbc7/dO0ObpjUg1BIEo0KIfau9keJOx//Ooe+KdFMH5dJRpyLQRmxJNtVu1/3hDicvIbFrEX5bc5/dVEeD50zFCtskGhT/PmMQfzxrCGEwwZRmsI0TAlfiE6jwwcwZs6c2ezfDz/8MJMmTWL9+vWMHz++abrL5SIlJeVwN69D0Lw7MKPS9uszpicDAL12O+GUYYeiWUKIVhiGiXPXPywwjoSYgAWhvTxMeIPSA0MIsW+MVrq455Y38NDnmwB491eTSNDsh7tZQrQri70n8/aHTaydQzUtCzTTJDXRQ0VFPaYE+0Qn0+EDGD9WX18PQFxcXLPpH3/8MR999BEpKSmccMIJ3HTTTfvdC6MjZdTf1dZ9abPeWIaROIj92T0relcAYxtGascPYOzP8erq2uNYtcf30hn/Jva2T7pNoyFkYSmwK4VTWfs8VtapRRKHfbOprNX5pw1LxzCMQ3IsO9v3dKTtT3uc50fKvh9Msm9tf9ZQGn7TQgFRuiI+yk6000ZDoGXg023XSfI4UOrwPJC15/e2L9vszH9X+6uzH4tYh86D5w+nzh+ioMrLm0sLyKv0Ns0/f3R3bAqMHx2Hzno89pccj86lUwUwTNPkwQcfZMyYMQwYMKBp+llnnUW3bt1ITU0lOzubv//972zbto0nn3xyv9aflBRzsJt8yP1km00DfJW4ElJxxUXtfdlmosARTWy4BJI73nFpS0f8jtvL4TxW7fm9dMa/iR/vU3l9gPdWF/HM91upaAgwMiue358xmMEZsXic+/Yzcffpg1iQW4nvR8NEJvdNpm9qDEkxzjY+eXB0tu/pSNmf9mjHkbLvh4Ls226GabG9opG/f7WJrzeW4tA1LhybyfXH9uHB84dx25urWnzmnjMGkRHvxmnf96TjB8ORfh505r+r/dUZj0VlQ4CvNpXxr29zKKz2MTAthltP7MfGknpm/rCNvinRTOqbREJcyxeznfF4HAg5Hp2DsqzOkxr+vvvuY968ebz++uukp6e3udzChQu56qqrmD17Nj169Njn9VdW1tNRjpZSkZP0p9qsvBUkvTCKxgm/Jdxtwn5twzPnHsLdJtBw4qMH2Nr2t6/HSxyaY5X8E0Gw9vheOuPfRGv7FFSKv3yWzefrd7RY/sWrxjEqLRpjHxLlKU1RFTT57/db+X5zOTEuG1cd3YuTB6Xisg5dor3O9j0d7v05ks69zvZd7kn2raUaA879z3waf1QtISvRzaxrJlBY7eOJr3PIrWikV1IUd5zUn/7JUYe1otGh/N4OxrnXmf+u9ldnPRaGUjyzII8X5m9vMe+PZw4mzm1nYu9EorDY85Gusx6Pn2vP4yFBjI6v0/TAeOCBB5gzZw6zZs3aa/ACYOTIkQDk5eXtVwDDsuhwF4GfarPmLQfAdMXvd3IfMyoFrWZ7hzsme9MRv+P2cjiPVXt+L53xb2LPfar2h1sNXgD8+eMNzLpqPK59WadhEa8r7jmlP3ec2A+lwKMpjMOUOKyzfU9Hyv60RzuOlH0/FGTfdtI1npuzpUXwAqCgysfS7dWc3C+Jf00fQcgEmwKnAtO02iUR4ZF+HnTmv6v91dmORYNh8dKC7a3O+9e3W/jo5qNxmSZtvSbobMfjQMmx6Bw6fBlVy7J44IEHmD17Ni+//DJZWVk/+ZmNGzcCSFJPdgcwLGf8fn/W9KSj1+Ud5BYJIQ4FpWk0WIrVBTUUecP4UNjsGqsKWpZk2yWv0osvvJ+9JwwTNxYuy9qnnhtCiK7HZ1h8m13e5vxP1pZgADbTwo2F3dr3nDxCdGRK1/CiKPKGKfGFya/20taffq0vRJ1PkmSLrqfD98C4//77+eSTT3jqqafweDyUl0d+EGNiYnC5XOTn5/Pxxx9z3HHHER8fT3Z2Ng899BDjx49n0KBB7dz69tfUA8MZ9xNLtmR6UtEad4ARAl0yggtxpAprih+2VfHnj9Y3vfFM8jh46rLRxLrb/hlQCmyaZLwSQhxcGhDjtFFeH2h1fpzbjq5o862yEJ2RqRQri+r43XtrqfWFcNo0/jF95F4/Y9flN1p0PR0+gPHGG28AcPnllzeb/tBDD3HBBRdgt9tZuHAhr7zyCl6vl4yMDKZOncpNN93UHs094mjeCixbFNj2P8GeGZWCsky0hmLMuJ6HoHVCiAOllKKgNsBd76xpNr2yMciMmUv44o5jsOuq1TKoJwxIIcqmaPP1jxBC/AxRuuLKo3ty30cbWp0/dWga9WFzn4avCdFZlPvD3Pj6iqZhDoGwiWVBrNvWak+LwRkxxDh0GRchupwOH8DIzs7e6/yMjAxmzZp1mFrT8Wi+ckzX/ve+ADCjUgHQ6wslgCHEESqs4N/f5rQ+z7RYmFvBPy8ZzS2vr2gWp8iIc/GHMwdjs9pnzLkQovMyDJPjB6YyofcOlmyrajbv4vFZ5JQ2EOeyMyjBJc9momuwaTz/zZYWf+/PzdvK384bzp1vrya4x7DM+Cg7j100Epe8YxBdUIcPYIgDo/kqsRw/M4DhTsZCodflE2LyQW6ZEOJgCJqwpbyxzfmfrSvl8WnD+fL2Y/hyfSkFNT6m9E1iRPc4ojVk3LkQ4pAIBMOcNSKDyyb0YH5uBU5d45gBKawuqOGJr3P4y7lDGZIUJbl0RJcQDFtkl9a3mL6msJbXF+fz4tXj2VBcR05pPeN6JTChVyIx8hstuigJYHRxmrcc0xn78z6s27FciWj1hQe3UUKIg8ahQb8UDyW1/lbnD8mIwaEU8bpixpjuaBqEwyamaWHKc4MQ4hBx2DQe+XwTTrvOqKx4QobJO2+uxB+KXHh6J3sw5SIkugiHTTEwLYaNJS2DGAu3VpIW6+S+0wehkY5hRJJky+khuqoOX4VEHBjlLcf6uQEMwIxKRq8vOogtEkIcTDYLbjuxf+vzNMXF47IwwpHEnuGwQTBoyBsdIcQhF6NrXDO5F1WNQb7dVMa8nIqm4EW3OBc9EtwyfER0HWGT66b0RrWSk1MpuG5Kb8LBMMGgIb2SRJcnPTC6OM1XiZH086uxmO4k6YEhxBHMsiwyY508euEI7v94Aw2BSCKwJI+Dx6ePJMGuHdgAWl2jIWwSCJu47ToxupKbKyG6MKVr1O+8JrjsOjE2hdlKOWbTMLlkXBZV3hBvLi3A2HkdGpgWw38uG41HxvaLLiYtys4HNx1NaV0Aw7IorPbxwcoibjmhLykumyTrFGInCWB0ZZaF5qv8WSVUdzGjUrDvWH4QGyWEONhslsUJvROZcMtkavxhsCwS3HY8OpitVB/ZVwFN49m523hrWQGBsEmsy8aNx/XlnBHpOOTJQ4guJ6hpzJy/ndeX5OMPmcQ4bdxwbB+mjeqGvZX+7g7T5PZj+3DN0b2o8YVw23VinTpuJWP7Rdei6YpSX5g/f7SBlQU1QCSY99fzh9E7zonVShBQiK5KhpB0YSpYjzJDWI4DGUKSgtawA0zjILZMCHGwWaaJR1mMzIqne5QNN1abwQulawRRWLqGaq0/KxBSigc+3cAri/II7LyxqvOHeeTLbN5cXoTS2/55UbqGD4UXhalrrXaZFUJ0LEFN48Evsnlh/vamoSD1gTD/mL2ZV5bkE9Q1/Ch0W/NrgzJNYjXo4bGT4tBwWpYEL0SXoJTCtGk0KkVNGKY/u6gpeAGQXVrPZc8totIv99hC7EkCGF2Y5qsAOLAcGO5klBVG85YdrGYJIdqLpqg2LB6fs5VrZq3grg/WkVPjJ6S1jDDUh0y+2tD6ef/cvK00tPK2SNMUXqX4x3e5/P3rzWworWdDWSPlIYuwBDKE6JD8KD5ZU8yO+gCfri1pdZkX529nfUk9FzyziHfW7CCoye2n6Np0h061abG+tJHy+gDvrihsGuK5p5Bh8czcrbCXlwJCdDVyNnRhyhepvX5AQ0jcyQBoDcUHpU1CiINLKUVYUzRYUG9CnS/UaqBA0xQF9UHOfnI+ry3JJ7u0nu83V3Dp84t5b3UJxh4fUgqKa3xtbtMfMqlr5Uas0YIZM5fQPy2G1FgXv35rFVe/tJTT/zmP299eTb1Jmz0+hBBHnoBS/PXzTcQ4bTjtOk/NGMNvpw6kW5yr+XJhk5BhUd4Q4MHPN/F/X20m3EpgVIiuwNAUszdX8If311FU7cO0YGzPBE4dmobeynmxLK8av+SWEqKJBDC6sIPSAyMqEsCQSiRCHHmUpigPGvzm3bWc8NhcTnx8Lre+sZLKkIX2o5skvwV3v7+2aTjInh6bvZnGPYabWBbERdn3um23XW/2b13XWLStit7JHmp8QZ6ft62pmznAku3VXP3yMtoOiwghjiRKKUrqA1wzpQ9vLivgnCfnc9NrK/hy/Q7uO2coxw1Iaba8Y4+hIx+uLqYuKA9kouvRNMWmCi9byho5a0Q3npmby8XPLuK2N1aSGuPi35eOxvmjYVapMU5sEvATookEMLowzVeJhcJyxPz8ldijsGxRUolEiCNQrWEx7emFLNwa6W1lWfD95nIufGYhdT8aUtsQMtlc2tDqekwL1hfXNQt6JEc5Wrxl3eXovklE/yiAYSnFZ+t2cMGYTF5ZmNfq57ZXeims8ctQEiE6ALtdw7DgjjdX8vnaHU1VRNYW1XLzayu48uhexLkjgc6xPRNYX1zb7PObdtS1CKQK0dn5LfhkTTHpcU7++OE6CqsjYfvGoMGri/J4bXEet53UvPT5jcf1wdEejRXiCCUBjC5M81ViOWNAHdifgelOQpchJEIcUZSuMWtRPt5gy+Rfdb4wH6wuQtvjLc9PPUb8OKjg0WDmleNIiXY2m943JZoHzxuG3Wr+dlVTkBjlwGnTqPGG2tzO5rJ6NBkfL8QRT9MUuWUNFNf6W8wLmxYvL9jOhWMz6Z3s4bdTB/DSgu3Nlolx2bGkLKToYgwLJvVN5uk5W1udP39LJQPSYnDszHnxy2P7MDgtWhLbCrEHKaPahSlfJZbj5+e/2MV0J6E1tJ64SwjRPvyGxbwtFW3O/35zBZeM6d70I+CxawxKj2HTjvoWy2oKhmTENruBMk2LNJfOO788ivxqHwVVXvqnRpMe68RNy3L1RthgxsQelNT5cdt1fKHWs6r3TPTIjZoQHYBpwfy9XGOWbq/izqkDOGdkN+763+pmgcsoh06fZI8EMESXoxTEumyUNwTaXKai3s/jF49kQGo00TYNm/wmCtGMvObqwjRfxYENH9nJdCeiNUgODCGOJDZNkehpu9NpkseBbY9uFS4FD18wvMXYW4C7TxuER2/ZR8MwLKKwGJzo4rQBSfSOdeCyrFYfSiwLMqIdBEIG08Z0b7NNvZOi5KFGiI7AssiIb30YGUCix8HGkjp21PrYXLZ7eJpdV/x3xhii5Q5UdEEeTZEW69rrUMm0OBeTMmOJ1xU2+T0UogXpgdGFab4qTOfBCGAkYy9bfRBaJIQ4WOxY3HBMb5Zsq2p1/jWTe6FMi123RqZpkemx8+mtU3htST5Lt1eREefmhim9yYx1ou/lJsqyIsGMn2yTZTGpZwJjeiZS0RDgi/WlTfMy4lzMvGIcMbrap3UJIdpXMGgwbXQmM3/Y3ur86eOyGN8zAaeu8Z/LRrNkWxW9kjwcOyCZGF1hyVtl0QUZhklylJ0TB6byzaaWpcijnTb6JHnkd1CIvZAARhemfJWYMa2/Cd0fljsRzVcJRgB0509/QAhxyJmmxZC0aGZM7MFri/ObzbvhmN70TnC36OlgmRZxGtwypReBST2xaQrNNA9qjwi7ZWHH4v6zBnPnKQMoqvER57aTFOUgWo/c3AkhOoZEl84D5wzhvo83NBs2dsLAVM4b1Y1oZWEYJkd1j2Vyj3gsyyIcNiPjT4ToohymyR/PGszWika2VTQ2TXfZNZ6/YizRmgT4hNgbCWB0YZq/EiN58AGvx3RHSqlqjaWYsT0OeH1CiIPDYVncdGxvLpvQg3k55eiaxvEDU4j+iW6pZtjEDmDs7qFxsGlhk3hdkZActfPBx8JoPS2GEOIIpZsWUwemcMyA45m7uZx6f4jJfZNJjrbjtqymyiSmaWGacoKLzk3TFH4LGkMmmorklrJbtHxZYEG0gleuGkdelY+VBTVkJrgZlRVPjAaWKYF8IfZGAhhdlWVFhpA4Yg94VaY7EQC9oVgCGEIcIXRdURWy+PNH61iyvZqB6TF0j3cxvHsscfEuDllkYj/J8F4hOjabgsr6AAtzKyip9fPUnFyGd4/lgXOGkezQJCmv6BIsTbG1LsAfPlhPdmkkGfb4ngk8cO5QUp16i/PAsixcwKBEN0NTIrmfDMPCktiFED9JUih1USrUgDJDWM6DEMBwJQFIJRIhjiD1Blzy3CJ+yK0kaJisLarli/WlXPb8YkoaQ6g2MogpBQ6Hjt2uH+YWCyE6Ak1TTdcIpaDMZ3DJs4v4Yn0pqwtr8QYNFm+rZvqzi6iXhzHRRVQFDP797RYy4lzER9kBWJpXzcXP7f082DWsSnJeCLHvJIDRRSlfJcBBCWBgd2PaPRLAEOIIYbNpzM+tpKIh2GKeacHfv8om3EoAI6AUW2oDPDZnK/9dkMcOv0Fob6nShRBdhlIKn1Ks3NHA/32byyvLCqkKW8zPrSDcSi+LWl+IrzaUYmulspEQnUlQ0yhvDNIr2UN6nIv7zxnKn88ZitOmUecL89m6HehyHghx0MgQki5K2xnAOBhDSAAsdzJ6Q/FBWZcQ4gBpiu9zytucvaqglqBp4d5jWkDT+N37a5m/pbJp2jNzt3LdlN5cc1QP7DLWQ4gurRG4+qVlbN0j6eDj3+TwhzOHcNaIDD5Z0/Ilxg9bKrhgRPphbKUQh1dIKV5cmMfzP2xrmvbm0gKO6pPI/104gtvfXMX83AouHJmBvA4Q4uCQcGAXpfkipRUPSg8MwHQloDXuOCjrEkK0TbfrKJu217eaGpCZENXm/JQYJ7oGpqaotyLBi283lzcLXuzy/A/bKKoP7LVmvRCic9F1DWXTI9cbBegaT36X2yx4AZEcNn/9dAMXjs1s9RrRLd6NDEYTnZVSUFQfaBa82GXR1ip21Pq5c+oAjumXjE1+RIU4aCSA0UXt6oFhOWIOyvpMd6IMIRHiEDKUoixo8th3ufz2/fX8b80OGlFoWsubomDQ4MIx3dsMOtw5dQD+sMXdH23gpMfn8snaHby8YHuzZfqnRvOLiT24eHwWH68pQbfJY4gQnZ2mKXwovtlaxV0frOcvX2azvT5ISCminTbOGdmNKEfza4FlwdqiWgalt7yfmDGhR6RsqhCdiKYp0DU0u40v1rf98u7tZQVkxrvxhUyqQwZKl8cuIQ4GGULSRSl/JaY9GrSD8ydguRLRytcdlHUJIZqzNMW8bdXc9c6apmlzNpfz5JwtvHX9Ua1mOE9w6Pz9whH87t21zcanXzQ2k6HdYrnw6YVNOTKcdo16fxiAOLedB88fhsdpw65rBEIGKbFOvJaF4zDsqxCi/TRYMOOFJcS57Vx1dC+Soh0oTaOwxkcgFCmD+vjFo5i3uZxZi/ObPhc2LNx7JP7VNcX9Zw8hxW2TUkOiUwkoxdriet5cWoBlWZwxPIMXrxpPYzCMphS6pqioDzDzh23U+cPU+sP885scnpmby+vXTaRXjEMSdgpxgCSA0UVpvqqDNnwEdvbA8FWAGT5oQREhuopdFUF+XCt+lwbD4p731raYXucL8/sP1vHkxaOw/6guqm5ZTOmZwFd3HMvaohq8QYPRWfF0T4zivRWFzRJ8ri2qZVLfJD5cVcxj00dg13VmLcpj9sZSLCvSTfbUIWnce8Zg3FKfXojOSdd4Zk4uA9JiOGdkNx76fCOldQEAUqKd3H36IL7aUMovX13Ob6cObJb3YkLvRC4Y3Y2FuZU47TojM+PwaApNgheiE/ErxZ3vrmXJtqqmad0T3AztFsujX26m1hcCICvRzR/PHMK64lrWFNZEPhsy+c3bq3n16vG42qPxQnQi7f6kaZomeXl5VFZWtrh5Hz9+fDu1qvPTfJUHbfgIgOlOQlkmWmMZZky3g7ZeITozS1M0GhZbyhpQCvome4jSFdoePSY0TbGxqK7VLP8AK/JraAwZxOstx4tolkWMgik94lEKTNPCoWt8l908weena0p47opx1DQGMUx4ddE25uyxjGXBF+tLMS34y1mD0QwJYgjR2fhMi4/XlPD0L8ZyxQtLMPa45pQ3BLjrndU8f+V45m4u5/GvN/PcFeP4ZE0J43om0BgIk+DQmNo/CcuKXGuk54XoTGw2jRXba5oFL1JinEzul8wtr69stmxBlY/b3lzJBzdN5ryn5jdN317ppT5g4HLIUBIhDkS7BjBWrVrFnXfeSXFxcYvghVKKjRs3tlPLOj/lq8Q8mD0wXEkAaI0lEsAQYh+EleKLTeX87dONTcEJm6b445mDOXVQCvoeDw+B0N4DBqZlwV7ym+8aXqIU2HSNjNjm738CYZP/+3ITj1wwgsrGYLPgxZ6+2lDKXacOJF7uvYTolK6a1IsPVxU1C17sYlrw7vJCzhyRwTvLC8mvauTOqQPonxrD6sIaJmbGStd40Wk1WjBrcV6zaReOyeSVhXmtLu8PmXy4qphRWfEs2ro76NHauSWE2D/tGsC47777GDZsGM8++ywpKSlN3ajFoaf5KjCjUg/a+ix3YmS9kshTiJ+kFBTWB7j/4w3NpodNi/s+3sDQbpPoFWNvepM5rHvbwcbeyR48dn2f33bqmuK6Y3qTGO2kqjHAZ2t3UOsLsa6ojmV51aTH7b1za40vhN3jwKMReaL5GWw2DROFpiC8c1y9EKJ9hDVFQ8gEBeeO7sav31rd5rI5ZfWcMTwDAKdNZ21FLY/N3syXdxxLSM5l0cnYbBoGYCqYl12JXdO4aFwmabEuCqq89EiM4s2l+W1+ftOO+p0VwSIBjCSPgziXDZAghhAHol3fo+Xl5fGb3/yGvn37EhsbS0xMTLP/xKFzsHNgWHYPlu5El1KqQvwkU9N4Zm7Lsmu7PP/DVkxt9+U5xqZx1dE9WyynKfjLuUOJaqUSSas0RW5ZAy8t2M6c7DLK6gM8eP5wrp7cC4BAyCDRs/dUnd5gmDP+/QNLi+ow9zPorHSNegtmrSjizg/W8dT87VSFLSxNunQI0R6CmsaspQWU1AWYtTifD1YW0zOp7RLMPRKjKNuZFyM91sWHq4p46PxhdIt3H64mC3HoaYpaE2YuKeC376+noDbAjjoft57Uj3p/mO82lQEwrHssPRL3cr4kRVFW52/69wPnDiW6leGeQoj90649MEaMGEFeXh49e7a8MReHluarxHTGHbwVKiWlVIXYR0HToqjG2+b8gmofIXN31Q+bZXHD5F6M65nIf7/PpawuwMisOG4/qT+pbhvGPuSk0HVFbm2AGc8vIbhz+fXFdXyzsYx7zxjE7DuOwWPTCKEY1zOBZXnVLdYxsXciNd4QT1w8ipBhURk0iXfqOCyzqQOIpin8FgQMC11TeHSFZZjouiKvPshlzy/Gt/NN7Zzscl6Yv52nZ4xhdHo0lnStFeKw0W0aH6/ewbheSdz02goaAmE8Dp0nLhndlJzzx6aPy+K3/1vN1Uf3wqErvrz9WGJskRKr/vrDvANCHAK6rsiu8nPli0sJGiaJHgceh40TBqVSVO0nLdbF/JwK1hfXsaqghntOH8SvZq1osR6bpjh1aBq/frOEY/olcftJA8iMcezT77UQYu/aNYBx+eWX88gjj1BRUcGAAQOw2Zo3Z9CgQe3Usk4u5EUZfizHweuBAWC6EtGkB4YQP8mlK0ZnxbOxpPU7/lFZ8Tg1hbXHeHK7aXF0ViyjLxtN2LJwaho2y9znh36vCXe/t7YpeLGn//sim1MGpWG3LFw6PHrRCH791mpWFdQ0LTO6Rzz3nT2ED1cV89KC7QTCkfWM7ZHA3y8cQawOIQs2V/n4y6cb2bSjHpdd46KxmdxwTB80y+I3/1vdFLzYxTAtfv32aj65ZTKefdoTIcTBUB+28IcNnpmbS0MgUka5MWjw+boSHjh3KA9/vglvMHK+uuwavzttEGmxLp75xRh6JUbhBAzDREmyTtGJNBhw+1urCBomIzPj+H+nDeKBTzawILcSTcEJg1L5z4wx3PfRerZVNJJb3sjdpw3isdmbm35fY902/nnxaHonunn7hok4NA27ZbVZaUwIsX/aNYBx6623AnDvvfc2TVNKYVmWJPE8hDRfJcBBHUICYLkSpAeGEPvACptcOakX/1teSOhHSe8cusaVk3pitRJoMIxIrwwHgGnu1yjahqBBbnljq/PCpkVOeT1j0mIwjEjlkv9eOopqf5jiWj9Om0ZlQ5DZG0p5Zu7WZp9dnl/NVS8v5Y1rJ7CtysuMmUua5vlDJq8uymdFfg3/umQ02ypa335DIMyOOj99Y537sUetU7pGfdiiIRDGbdeJsTev6iKE2EnB8QNSeeLrnGaT31tRxEVjs/jXJaPxhw2wwG7TeGNxPg99tom3fnkUbk1JzgvRKdX4Q5TVB3DoGndOHch1Ly9rCrybFnyzsYyV+TX846KRXP3SUh79Mpsvbj+GCb0TCYYN7LpGsscR6X0YNiMPWvv5ey2E2Lt2DWB888037bn5LkvzVQAc/B4Y7iTsO1p2oxNCtJTo0Jl17UR+9+4atldGhpP0TvbwyAXDSbBrrSbIjJRdhdJ6Pw5dIyXaQYxDwwiZTZVG9kdClJ2+qdFM6J1IWqwLU4s87CulsAEJLhsfr67iv99v5Z+XjGLmD63n7cir9FIbMPjLp60HndcX11Fc6yMhyk61N9TqMsHwvgyD0WjYGfBx6QrNbP6ZkKbx4sI8XlmYRyBsohScMjiNP54xGDemVHUUgsiLIt2hEfQZ2HeOx0+PdXHJhCy6xbuJc9mw62pnRaJSvssux79HJaSn5+Tyt3OGtFfzhThkdF3DZbfx1IwxJHkcfLOprEWvQYCqxiBLt1cxoXciS7ZVUVzjI9HjZFCSm1DIBKxmPSiFEAdXuwYwunfv3p6b77I0XyQbsuk6iDkw2DmExFsaqYYgFWWE2DvTpH+8i1lXj6c+aKCAaIdOlEarpQjDmuKDtTt4fHZOUzfV+Cg7D50/nORoB+kxTtxYbT6kRzt0+iR72FrRyIC0aH5/5hCCYYPiGj/pcS62VTTyysI8rjy6F5qy+MdXOdT7w5w6LJ3nrhiHZVnU+cNt7k4gbLBpR9uD4BdvrWJMjwS+2Zn8bE82TdEjMQpTV3jDJoZlEWXTcBKpwqIU+JTGW0sLeWNJPt6gwQmDUrjjxP4kOXcmANU1XlqQx3PzdgdZLCtS+rWiIcC/Lx6JQyIYoosLKIXfsNheWM8jX27ithP68c4vJ+J22imp8ZEa6+KFH7Yxe0MpdpvGmcMzePoXY7nrf2sob4gk71y8vQpvyEDSdorOJKRpfL+1iie/20JJrZ8hGbFcf2wfLh6fxVtLC1osvyyvmiEZsWyraKTaG+I3/1vNBzceLUMhhTgM2jWAscuWLVsoLi4mFGr+Zu6kk05qpxZ1bqqpB8bBrfRiuZNQRhDlr24qqyqEaJthmDgBp33nQ7hlYbTSK1vTFOt3NPDIF9nNptd4Q9z25kqeu2Icd7y9iiemj8ZF6z0ZojR4+ILhfLauhPNHZ3Lty0upaAg2ze+XGs2fzhrC5TMX88i0kfzmlP58uaGM//tiE0keJy9ePR63XW/1bRRESirubX5qjJMJvXq2GsD445mDMSyLP36yia83lmJakezu9589lB4xDnwW/HLWcjbskTPks7U7+G5TOR/efDRJQH3I5OWFea1ue0V+DdW+MGlOvdX5QnQFQV2joNpHjMtGRUOAa6f0ZmleFacNzeDGF5bwj+mj+MXMxdT5dgYqgwavLc7nhy0V3H/uUG56LdLDMiHKESl7LwFB0UkYmuLZ+dt4acHu35CVBTXc9NoK/nbeMLJ31DfLCQWRHozeYJi7Tx/ES/O3U+MNsXh7Faf0TZJEnUIcYu0awCgoKODmm29m8+bNTbkvINK9EZAcGIeI5qvAdMSAdnC/fnNn0EJvKCYsAQwhWqV0De/OHhZROyt0/BS/BU98m9PqvJBhMS+nAqfdxpcbS5k2LI1wK8MxDMOiT4KLC8dmcf0ry5oFLwC2lDXw9Pe5XDahJ/d9tI7fTh1I9o46/nHRSO54axUrtldx2cQerQ4jSYl2Eue0MX1cZqtBBF1TTO6bhEdXvPPLo3jimxxKavxcPqkn43oloqnIzWJ+lbdp5My6ojoufnYRn902me2VvmbBi118IYN/fbuF/7twBI1BoymxaGsKq32kZ8RIEjXRJdntOquL62kMhAAX7ywvZFleNY9fPIrb31zFhN5JfLKmZHfwYg95lV4Kq30MSo9h0456rpvcm2hdEQ7LuSQ6h4aQ1WYA/IlvcvjdaQNbBDAuHJuFy6Yxa3Eea4tqAViyrYrTBqRIAEOIQ0xrz43/7W9/IzMzkwULFuByufj000+ZNWsWw4YN49VXX23PpnVqmrcS62CWUN3JdEWCFlKJRIiWNE3RgOIf3+Vy5pPzOeup+fxz7jYaUWja3odcGRbkV7ZddjW/yktarItZi/KackTs2mZY0/ArhWXTaQxbFFR7Kan1t7qeBbmVjOkZT2ldgGiXjdkbylhVUMMJg1J5+MtsLh6fxenD05t9JjPBzSvXTCBKs7huSm9GdG+eW0fXFP+6eBTRNoVuWfSKcfLEhSN5/qpx9EnxUFDt5f2Vxcz8YRvXH9OHc0d1A8Bp04iPsjMnu4JP1hS3ue/fbiqj1hfCbdfZ22FMjXFK8EJ0WbVhk/k5ZYzIjGNreSPL8qqJcujoCsobAozpGc8POeVtfn5eTjljeiRw8uBUjh+Q3GqQVIiOSCnFloqGNjsUldcHiHI0f+H3q+P60C3exfWvLuPL9aVN0wekxWCacm4Icai1aw+MlStX8vLLL5OYmIimaSilGDduHL/5zW/461//ygcffNCezeu0Ij0wDm4CTwDLFY+lNKlEIkQrGiyY/syipnHkAK8uymP2hlLeumEiUXv5rE1FhnisyK9pdX6/lGiW5VXhDRpNN2Fhpdhc4eVf326hoMrL8O6xXDOlN9WNrSfR3CW4862qIhINeHtZIX89bxg/5FSwqaSOHglRvHjVeDxOnfgoB8GwwdqiWtYri0l9knjq0tEU1vpZmFtJcoyTo/smEa3vUQlEQXFDgD9/tIEV+dUoBVP6JXP3aYN44JON/P6MQRzbP4Uop05FfZDhmbHkV7UdvPE4bGhKEWPXOG1YOp+tbRlAzUp0kxLtkC7voku77KhebK/0Ydc1XrxqPPO3VFC1M6luIGQS5Wz7ltDjsDF9fCa+oIFJJM2VnE6io1MKAprCru/9fW5mgpsbju2DTVOcPiwdb8jgq/WlzZLb2nXFyYNSJbgnxGHQrgEM0zTxeCLpbhISEigrK6NPnz50796dbdtaz3b/Y8888wxfffUVW7duxeVyMXr0aH7729/Sp0+fpmUCgQAPP/wwn332GcFgkClTpnDfffeRnJx8SPbrSKd5y7GcBzf/BQBKw3IlSQBDiB/RbBrvLytqFrzYZUedn682lDJteDpGGzc+TgW/PWUAl+1RonQXt11nYp9E/vt9Lr+Y2AOPTRE24ZP1Zfzts93D8JZur+aUoemkx7nabKfbrqNp0DMpivL6SFsbAmEcNo1ThqQxb0sFn6wuYUzPBOasLee95YXUB8KMzIzjl8f15fUlBVw6LpN+cU4Gjs/EsiJ5PvasqFIdtrjo6YVNwz0sC+blVLC2qJb/XDYGXVM88MmGpsosUQ6df186mlmL81tt82UTs0j2OKiubuSe0wZR1RBk0baqpvk9EqOYecU4olSrhV2E6PRCusbSbdX88YN1TTlqNAW/OKon43omAPDF+h2cO6ob//hqc6vrOHtkBje/toKiGj8jusfyn0tH45LCkKKD86N4f1Uxxw1IIcqh4w22zOE0IjOOH7ZUsHRbFfFRdk4YmIKmFO8s353YM9Zl4z+XjSHWJj80QhwO7TqEpH///mRnR5LSjRw5kueff57ly5fzn//8h6ysrH1ax5IlS5gxYwZvv/02L774IuFwmGuvvRavd/cbuwcffJDvvvuOJ554gldffZWysjJuueWWQ7JPHYHmKz8kQ0ggkgdDb5QAhhB78octPl/X9tCqT9aUENjLTY9pWvRJcPPohSOIde+OO2clunniklH865scYt02rpncCzNs4jXg0S+bJ/wMhE36pUSzprCG4wemtLqd647pzWdrSrj79EG8OD8SRE6PdeENhpk+LouPVxdz9+mDeHpOLi8v2E59IDJefnVhLTe/voLBGbGU1AcARThsthwHrGs8P29rq7kqarwhLMvi9++vawpeAHiDBsu2V3PV0b1afGZYt1guHJOJvvPtmcs0+ce04Xx26xSeu3ws7984ideuGU+iXf2sMrNCdHS6rqjwhvh/76xulmDXtOCVhXlsLq3ngjHdWVNYS8/EKMbuDGjs6dxR3civ8lJUExl6tqaojg076n9y6JsQRzKloNIX4oSBqbhsGg+cOxTbj/6m46Ps/PmcoXy3sYyVBTWcMTyDJI+D7rEuXrl6AjOvGMsb103k45snMzjJLcELIQ6Tdu2BceONN+Lz+QC47bbb+OUvf8mMGTOIj4/n8ccf36d1zJw5s9m/H374YSZNmsT69esZP3489fX1vPvuu/z9739n0qRJQCSgccYZZ7Bq1SpGjRp1UPepI9C8FYRSRhySdZuuBLT6okOybiE6Kk0Dz14qYEQ5I8Mg2MsbTd2yOLFPAhNumkyVL4Q/ZFJY7eU/3+YwKD2WRy8cQZweeVAvqfM3lVoFiHXb+OfFo9lQXIcvZDB9XBZpsS4+WFlEIGwS67bxy2P7MrZnPON7JfKf77awtaIRgFtO7EefZA93vbMWh66R5HGw8kfJzCDSk+KpObncdeoAwnFu6oNhNKXw2BQ2CyzLwm9YLNha2er+KQWGCbnlDS3m/ff7XG48ri8f3HQ0760oos4f4uwR3RiQ6iHqR1VXHJZFikMjNc0T6eJuWXJPKbouXeej1fltngPPz9vGQxcMR1OKP364jv936iB+MbEHc3PKcdl1pvRLIbe8oUVA9N0VRUw4Zwim2XrVISGOdLrTRqA+xJfrCgmZFgVVPp6/chwLcisprvExJCOWAekxPD1nC6cOS+fsUd2Y2DsRl65QIQO7BiNTd/7OYGHJyBEhDpt2DWAcc8wxTf/fs2dPvvjiC2pqaoiLi2uqRLK/6usjmerj4iI9DNatW0coFOLoo49uWqZv375069atawYwTAPlrzqEPTCSsFVK9Rgh9uRUcNXRvViRv6rV+Vcf3QubZbVRAHU307CIAqLcNvRoje6xTib2TMCtR57+d/UysOvNr59/OHMID32+kc2lDdw5dQB1/hBJHgf/umQ0TrtGWqyLaIfO60vyeXbeNgzTItZt49cnD6BHYhQXPr0Q04LRWfGsKqxps30bSupIi3VxzavL2VBSB8Dkvkn86awhpDh1dNMiyeOkoMrX4rMum059oO38HP/9PpczhqVx5wl9iPTwMDBNq82Qj4zPFwKCwPadwcjWFNX42FrewFG9E7nh2D4EQgYOm86JA1P422eb+N27a2gItKxM4rK1awdeIQ5IUNMoqw9y+czFnDkig3p/mO+yy/h+cxnjeyWSFO3gkzUlbPgym1i3jd9OHUSs24Zlmqg9ejLJ74wQ7aNdAxi75OXlkZ+fz/jx44mPj//ZmeJN0+TBBx9kzJgxDBgwAICKigrsdjuxsc2TViYlJVFe3nbG7db8zJhKu9jV1h+3WQWqUZaJ5YzjUOyO5U5CbyhBYXWoA9bW8RIttcexao/v5WDup2lajMmK46RBqXyzqazZvNOHpTM0PRrLsvZrW6ZpEq0r/CZUBwwM0yLaqROtK9JiXcQ4bdQHwiR6HNg0xebSSM+Gf3y1mR6JUVwwpjvlDQHq/SHS41xs2FFHcrSTp38xhrBhkeBx0D3eRUmNnyiHjYZAmL4pHib1SSJsWMzeUEpRTfNARJLHgS9kcMfJ/QmbFsGwyVvLCrj4uUV8cOPRxOpwwzG9uen1lS32xxcy6JnYdipTu66IdtoI7XHzqNT+f0+aptB1Dcuyjshka0fatag9zvMjZd8PpsO9b0pFHtLq/WEGZ8Ty9cayVpcb2i2W5BgnM+dtw27TCIVMju6XiBEM0z3B3WrwAuDSCT0wDfNnnYMdSXvu275sszMf+/21r8cirDSqfSFeWridQNhke0UjU/onM3tDKaYFi/fIoQQwNCMWfziMHoB4m8LqIMda/jaak+PRubRrAKO6upo77riDxYsXo5Tiq6++Iisri3vvvZe4uDjuvvvu/Vrf/fffT05ODq+//vohaW9S0iFIfHmItWhzaSQRnicpDeL2Vvfg524wA8I+kqMNcLccS3uk64jfcXs5nMeqPb+Xg7nth6eNYGt5A/9bXoCuNC4al0mvZA/J0c79XpdpWmwuq+e3/1vNuqI6dE1x5ykDOGlwKuX1AV66ZjyXz1xM3xRPU436XfKrvDzxdU7Tv4/pn8IvX13RYhvDusfyp7OG8MJV41Aovt1Uxj+/ycHjtHHLif0IhE3+8skGDNMiOdrBs5eP459fb2bulgosKzJ++JfH9mFszwS+yS7nmsm9mOiwc8n4LN5cWtBsW3ec3J+kaAfHD0hhzuaWweVpYzLJKW9kSEYsma0EOvble9pR62fTjjrmZJeTEefilCFppMe5WpTIOxIcKdei9mjHkbLvh8Lh2rf8Ki//mp3DwIwYxvRIaApo/titJ/bjltdXMigjhkHpMQRCBt0SIsnVp4/P4tO1JU3Bz13OG9WNAekxJP3ouiXfW/ttszMf+/31U8eisKoRj8vGwtxIoGJFfg23nti/zXPk2il9SIhykBbrwmFreyjokUr+NpqT49E5tOtd20MPPYTNZmPOnDmcfvrpTdPPOOMMHn744f0KYDzwwAPMmTOHWbNmkZ6e3jQ9OTmZUChEXV1ds14YlZWVpKS0nsiuLZWV9R2mu5hSkZP0x222F+cRB9SFXVi1bZcm/Ll0M5pooDovByN58EFf/6HS1vESLR2KY5WcvPcflPb4Xg7FfiqgX5yTP506EIBQKIzlD1LhD+73uhosxW/fXs2Mo3qSHO2gW7ybYNjk/ZVFrC2qpVucmzevn8SOOn9TPovWRDl0imtaDukAWFdUhz9kYNM0bn9rZVNlEoCFuZWcOjSd304dwCNfZPPAucO483+r2bbHtmq8IR75Ipv7zh5CXkUjlTVeMExuO74PV07qyfwtldh0xeS+ScTYNWxhg7+eO5S/z97Mx2tKMEwLp01j2phMxvZM4FezlpPkcfL2L48iRu0s97qP35MXjatfXkpu+e72PfzFJp6YPopJPeLQjpAT/3Bfi46kc68zX4cP174ppai3FL//YA03HtePGm+IR7/M5olLRvH3r7LZWBIZZpsW6+TXpwzA47Dx4AXDsWuKWYvyuPGYPlRURJZxKJh5+VhWFNTy/soi3A6dGRN70DvR3ey6Jd/bz3Mwzr3OfOz3108dC01T+JRGQ9BgR62fRI+jqRdha+dIaoyTP5w5mNRYJ7plUVdz8O+ZDyX522huz+MhQYyOr10DGPPnz2fmzJnNAg4AvXr1ori4eJ/WYVkWf/nLX5g9ezavvvpqi+olw4YNw263s3DhQk499VQAtm7dSnFx8X7nv7Csjjfe7cdtVo2lQCTZ5qFguJMA0OqLCSd1nADGLh3xO24vh/NYtef3crC3bVkWgTa6Ze8rm01ncU45v5k6kD9+sA6PU+fhaSO4cuaSZm+Q/re8kL+cO5STBqXy6JfZGK1k8rt4fBbvrWw78W51Y4g5m8ubBS92+XL9Di6dkMXr103EsKxmwYs9PTt3Kw9dMBxdQUBpBAwTp03j3BHp2CwIhw3Ymc/CaZncO3UAvzquL1srGtGU4ot1O7jrndWYFpQ3BHjyuy3cfXL/SNbPnfb6Peka//w6p1nwYtdnfv32Kmb/+lhij7BupUfKtag92nGk7PuhcKj3za8U2ysb+OWx/bj1jZU8dMEI8qu8/PGDdVxxdC9+fbIHw7Jo8IfZvKOe4d3i2FBcy9BucVw5qRd202zKK2NZ4MLimJ7xTO4VjwIsw8JqI/eMfG/tt83OfOz3V2vHQtMUpf4wKI0rXlhCjNPGVZN7NfVO3FBS1+wcSY11Eeeysamkjn9+k8ODZw/B6qDZoOVvozk5Fp1Du2Zh8nq9uFyuFtNrampwOBz7tI7777+fjz76iH/84x94PB7Ky8spLy/H74+U+4qJiWHatGk8/PDDLFq0iHXr1nHvvfcyevTorpfAE9AayzHtHtD37fjuL8uVgKU0NCmlKsR+UwpCmqIqbJHfGKLGsDC0lpdpE8hM9PDrt1ZRVOPjjpMGcN+H61vt/vrAJxtoCIR4fPrIFiXiRmfFc+WkXsxtZcjGLj2SovhiLyVgv1i3A3/IYG1hbZvLlNT6SYt1UREweHLeVr7JLmPjjgZWF9dRFTRQP9pHh6Z4e1kBN722gl/NWs4Hq4qaVVH4ZE0JPmPf70IaDYuPVrceFDctWLy1qqkUqxAdlW7T2FrpJRC2ePr7XKq9IZ7+PpdHpo2gyhvk4c83ccOry7lx1gqe/n4r08ZmMvOHbSzeVsVtb67ks7Ul2FpJzmkYJlbYxAybPztHmRDtqcGEhVur+efXOZTXB9ha0YhpwTkjuzUtU1zr5+HPN7GxpI5l26sIhE1+/8E6bjquL7p55OVLEqIra9ceGOPGjeODDz7gjjvuaJpmmibPP/88EydO3Kd1vPHGGwBcfvnlzaY/9NBDXHDBBQDce++9aJrGbbfdRjAYZMqUKdx3330HZyc6GM1bhuWMP4Qb0LFcCWgNEsAQYn8oBY0ofvPWGpbnVzdNP31YGr8/fTDOPW6gbLqiuMZHrS9StSMx2tEiz8UuIcNiQ0k9yR4Hz1w+lrI6PwXVPk4YmEJ+lY9vNpVy3qhuvL6koMVndU3hsuu0XesjEgCYvbGUo/oktbmMx6HjsmlUNwY5dWg6f/8ym9WFtcS6bFw4NpMrj+7Fj7NamHt5UNrfZyjDtAjtJeBR5Q1KYi/RYSmlsNt1GgyLhCg7MaaNBbmRcsWrCmqY+cM2/n3paAqrfZTVBRjfK4G0OBe6Us16X+0clNUu+yDEoWCzaegOG4tzK+mb4uFvn+2ukvfAx+u56YR+PH/lOFYV1BDjtDGsexwp0U7Of2o+k/om8eezh9A9xtFhe18I0Vm1awDjrrvu4qqrrmoqdfroo4+yZcsWamtrmwITPyU7O/snl3E6ndx3331dNmixJ81bhumKP6TbMN1J6A37NgRICBERQHHrG6tY86NAxOfrSnHadO45pT/azpso0zAprN6dt8IbNNgbBczbUsGsRfm8e+MkpvRLJlqDeLeDG2ct57FLRrG6sJb1xXVNn9E1xQPnDGV+TjlTh6S32YPh2P7J3PvBWq6Z3BuPQ6exlbb84qielNb5KW8IctubK5sCEHX+MC/M3866oloev2gkjp1BmnDY4Ozh3XhpQV6r2zxjePrO0rH7dlPp1BUD02LILq1vdf6kPkkYhrxhEx1PQClK6gL4wgaZCW6ChonzR4kGV+RXc/0ry8lKdJMQ5SAp2kHPpChW5tc0W+7EQamR4VxCdHC6rqgLw/cby/AGDXxBgyHdYpsNozQtePLbLTh0jf5p0Zw3qjuLt0byMh3TP5lop87RPeOx9qO3nxDi8GjXPrMDBgzgyy+/ZOzYsZx00kn4fD5OOeUU3n//fXr06NGeTeu0tMZSLGfcId2G6UpEq297TL0QoqUaf7hF8GKXj9eU0BDe48bLtBjabXdS4sJqL72SWq8qpBSMyopnXnY5107pTe/kaKIwCYdNkpwa/718LO8uK+C6Y/rwn8tGc8dJ/Xn28rG8f+PR5FU28uhXm5k+LpMkT8thZycOSqW41k8obFFc6+Oxi0cR624eFz9hYAqXTujB2qI6/v1tTqu9J5Zsr6akPtDUC8KyID3GwZnD01ssmxBl59YT+zXLf/FT3AruO2tIq70sJvZOJD3aIeNiRYcT0BTP/bANt9PGuyuKwFKYpkWtN8iw7rEtli+o8rGmsJYxPRLwBgwe36MS0UVjM0lyHXnVeITYX5qmKPEZnPPUfO77aAO6phjcLZZg2OSoPoktlg8aJuuL6+iXGs2UfsnM/GEbvzyuL26lJHghxBGq3X+tYmJiuPHGG9u7GV2G1lhKOHnIId2GGZWMrXzdId2GEJ2JUlDWSpLMXQzToiEQbgoOmKbFwNRoUqKdlDcEeHlBHnedOpDb3lzVIlHndVN6Ewib3HhCP1YW1PDS/O2cMTyNBIdOld/gsucXc/6Y7kQ7bSR5HKTHuZm1KI/y+gDjeiXw1Iwx/HdOLq9cO4EPVhYzL6ccj9PGuaO6YdMUf/pwPb84qidvLS0gr9LLg+cNJ2CY1HpDDOseRzBk8OKCbUzsndSiHOOeFm2tpO/oboTDkcCEw7K499SBnDk8gxfmb6chEOaUwWlMG9OdWB2M/bixNE2Lvgku3rr+KP722cam4StXTOrFJeMym3p+CNFR2Gwapd4wY3snMTenjJuP78ey/Go+XVNCZryLP501hF88v4TgjwJ9F43NJM5tY+n2aqKdNib2TuS80d0Z3zMBu0TxRCfgs+A3b6+mzh/mT2cN5oRBaazIq+a77DJuO7E/qwqW4g81Py/OGJZOtFPn2bm5vHzNBNKjnSjpjSTEEavdAxiBQIDs7GwqKysxf3QTedJJJ7VTqzovzVuK5ZpySLdhupPRG0oir1FlYLkQP8myIDW2ZULjXXRNEeOy4UXRGDRw2TXcdo1Xr53A7W+uIru0nvdWFjHzynG8vjifNYW1pMe5uG5Kb/qnRXPTayvJLd8dPPjXtzn89dyhBMIm/3fhCGYtyqfeHybJ4+CpOblNy/2wpYIkj4PHLx7FLa+t4IlLRtMzyU1BlY+ZP2yjqNrHReMyGdotlpff3Y5lwS1vrCTWZSPKYcNp1/jDmYPZXuFlQq8k7LpqMxdFYlTLXhAOy2Ji91jGTB+JYVm4NYVhmBg/475Ssyz6xjl56pJRhHdemqJ3rk+IjqbOgNvfXEW/VA93nDyAX85a3qwK0LZKL7Oum8grC7ezbHs1SdEOfnlcH3oleqj1hXHqGtdM6UVOWQNPfruFF68cB5r8XouOrz5gkF1az2vXTsDjsnPDK8vIKYv8/hXX+Hn28nG8t6KQRVurSPDYuW5KH8b3SsC04M9nDcWjQygkwQshjmTtGsCYO3cuv/vd76iurm4xTynFxo0bW/mU+LlUsB4t5MV0t+xCdzBZ7mSUEUD5q7DcbSf2E0LsFu/UGd49lrVFdS3mnTUig7wqL7e/tYo6X5hYl43p47M4fVgGj0wbTtAwafCHMUyL4wakcFSfJLzBMGHT5IUftjcLXuzyhw/X88mtU/jzR+tZkV/N81eO55qXlrZYrrIxyAvzt3H8oFSufHEJb143kfE9Ezl9aBpxUQ6+yy7nd++uaRZ8qPOHqfOHSfQ4CIUtJvRKYE52GacPy2g1l4amYGKfxFaDCaZpoWOhs3+9LlpjmhYOwAFgHfj6hGgPyqbx7HdbKan18ehFI5i9vhSPU2+Wg2ZBbiWrCpbwv19NwjSh1heke3wUtf4g93+8nksm9ODp73PJq/QytkcC0XZN6guKTiFsWjx56WjK6wNsKKnHYdOagueRfDDLOGtEBs9fOY78qkZ6JnnwBg1SHBqmaRGSmLYQR7x2DWD89a9/5bTTTuPmm28mOTm5PZvSJWgNkVKIpuvQBjDMqMh3qdcXEZYAhhD7xEnkpus3b++uQqIUnDY0jeuO6c20pxaiaXDf2UNIjXGxvriWFXnVHNUnkTiHnbVFtfzlk+ZB339fOpoPVrWej0ZTkeSfy/KqGZQew9rCmjbbNndzOZeMzyIrwU2c04bNNMFmR9chNcZBWwnaj+mfTJ0/xOR+ybw4fxl/nz6SdUW1bN3jTbFS8Pj0UURrSh6ghNgHjWGLd1YU8tupA3DZNBx2jWP7p3DT8f3YUevnkS82EQibeIMGL83fTmMgzOnDM8ivqiCv0ktJrZ/EKAd5lV6yEt08euFwHLCXWkNCdBxJ0Q5susIfMsmv9nHioFR+fcoAlm+v5um5uQTCJu+uKCIQNjl3VHccNo14h4YpkQshOox2DWBUVFRw9dVXS/DiMNEaSwGwDnEPDHNn0EJrKILUEYd0W0J0FpYFHmXx7+kjqA+ZkZwXLhsuu84FTy8gbJr897KxPDt3K8vzdvdas2mKx6aPZFyPRK6c1BO3Q2dreSPfbipDU4pAuOVN2bDusRw3IAVNQe9kDzZdEdxLbwTTgr4p0YzJiEHtMdTPMCxGdI+jf2p0UxfdXaKdNi6b2INab4g/fLCO568ax/99kc0tJ/YjZFisLqime0IUUwenEWtXTRVWhBB7ZwEnDEol0ePk8a830zclmmpviHveW8uAtGieuGQUN722AsuKJCi85/RBlDUEeHlBKaV1fl69dgKr82t47doJ9EhwE6UivZOE6AxChsXS7dXkVXmxLJiTXc6/v93CBWO68/szBvPXTyOB/mDYZHBGTKSagQwZEaJDadcAxqmnnsrixYul4shhojWWAGC6Eg7pdixHLJbuQJdKJELsF8sCOxaJNkWizQ5AZSBMeX2QkwansiC3slnwAiLdZe94axWf3DqFKm+QTIebS8ZncftJ/alqDDKmRzwrdpZLTPQ4+Nv5w9hW3sg3m8pYnlfNlUf3ItZpIy6q7Z+D0VnxeGxas+DFLlFYvHDlON5YWsBbSwvwhwxOGpzKr47ry/qiWv7w4XoCYZP3lhfyjwtHUO8Po4ATBiTjBILBMG124RBCNBPSFKGwxa9P7k9pXYC0WDcLcytJjXXxt/OHsbqglmXbqzmufwpzNpdz7shukSCmBTef0JcYu44Ti5P7JWGaJpZlyeknOo06b5BA2KQxYLAyvwabpvjFUT2Iddv5w/vrGNczkcwEN4XVPs4d1Q3DsHArC+l7IUTH0q4BjD/96U/cfvvtLF++nAEDBmCzNW/OFVdc0U4t65z0hmJMZxzoLcshHlRKYbpT0OpbjnUXQuwf587xu2cOz+BPH65vdRnTgtkbSrn66N68sTSf3723FqdNY/q4TB69aCQX/ncB1d4Qj0wbwcOfbyS3fPcQjkVbqxjfK4HfTh3IGcPT+Wztjmbrduga/++0gbh1RSudObAscFkml0/sQZ8UD3ZdY1NJPZc+t4haX7hpOV/IJEpT2Gw7EwUGwwQP/PAI0WWENEVOhRewcNpt/PLV5fia3hzXMntDKbec2I+qxgBnj+pGQyCMw6bz3soiLp+Qhc0wwTR3PqxJ1EJ0Lj5No6ouwC1vrGhW8WrxtirG9Ejgr+cN4/Ul+Zw9ohsLcisYkRmPB1NyIQnRAbVrAOOTTz5h/vz5OBwOlixZ0myeUkoCGAeZVl/cNLzjUDPdSegN0gNDdHx2h45pgRk2D1s3a1PTaAib1PnDJHns/OmsIQxIi6EhEG7zM/WBMM/MzeXL9aVN0x6bncNna3bw9i8n8cbifFbmVzcLXuyydHs1ueUNnDIknfG9EvnfskIqGwNM7J3EpROy+HRtCb2P6Y2+lzY7sMgpbeCZuVtbnT99bCahUNvtF0LsXRio84UYnhnHza+v3CN4sdtT321h5pXjSfDYuXPqQHyhMCnRDpyAdJIXnZGuK7woTNNibk55q+W6V+RXc+6obkQ5dKb0T+bSCVlEWWaLsuNCiI6hXQMYTzzxBLfeeis33HADmqa1Z1O6BL2hCMt1+AIYWl3BYdmWEIdCAMWSbZW8vjifYNjkgjGZDEz14DrEiSYDmsajX23m4zXFRLts/PPi0awrqsMbNBjaLZb1xS2rlACMyoznw1Utez1tKq1nQ0kdM47qwfWvLG9zu1+uL+WWE/qREevk7tMHsr3Cy+bSehoDBlF2HZumYZmtPwJpNo2GsMUFY7ozpkcCby0r4LvssqacnBeO6U63WCeWJOkUYp8pFbkOhSwLXdMIhE1SYl2UNwTbvA6YFuRXeemZlMT6oirK6vxcNDZTygWLTsnSNbbU+gmbFrXeEP9bVtjmsl+s38FFYzLpkxwFpoUpPS+E6LDaNYARCoU444wzJHhxmGj1RRjxfQ/LtsyoFOzlaw7LtoQ42IJK8cjsnGYlPz9bt4OxPeL558WjcLaSC2JPNh08ZjW6rwLMEGZUKl4tnqC590uuuTN48eHO7f7hzCE8/PkmskvryUxwc/dpg7jljZUtPtc3xYMFlNcHWl3vh6uK8dj1vXYaNy2LaJfO/R9ns2hrVdP0T9eW8NYNk8BoPXgR1DQ+WbOD/36fS403RIzTxhVH92TGxB68s7yAX0zsSa8ENw4JXgixz5RS1FvQ0NBAuq2OuHAVJe6+3PPeev7faQP3+lmXXeOp73I5bmAqx/ZPxmWZMmBEdDo2m8a6Ci9Rdo2b31jFLyb1xNzL74xlWQzKiCFoWET9xG+4EOLI1q6Rg/POO4/PPvusPZvQdVgWWkNRU4nTQ82MSkbzVULYd1i2J8TBommK3Epvs+DFLsvza/g2uxybre1Lp10ziK1eiePFE9BnnoD+4lTszxxFTPYbRGnevW67IWzy8ZrIdpM8DmyaIru0HoDCah+zN5by+MWj6JsSHdmWrrhgTHf+dclonvk+t831Om0aKDh9WHqby1wwJpM1BbVcO6UPqTHOpunV3hDV3mDrFU51jVeX5PPQ55uo8YaAyFCW/3yXy+wNpdx/5hAG7kfwQtcVPhRFvjCbqv3UmmBoap8+K0Rn4gfqqsrot/VlUl6ajGP2PczbUMi2ikYKqnwMSo9p9XNqZ2Whd1cWcesbK3l1UR4hJeeQ6HzqLXCaPj5fU0h5Q4AFWyo4ZUham8tfNDaLWLedGCXhPCE6unbtgWGaJs8//zw//PADAwcObJHE85577mmnlnU+KlCDFvJiRqUelu1Z7hQA9PpijITD0+tDiIPB0jReWZTX5vxXF+VxYv9IBY3WRBvl6K9PA2OPFJVhP9pX9+BK7o8/aVKzXBqaprDZNCwL6moDTRUB+qVGs7qwttm6P1xVzNrCWmYc1YP0WDeD0mOIdWj8sLWKYweksK6NbuVTh6YTNi3G9UokK7GYgqrmgcURmXEkRzuIdtr466cbuXRCD/75TU7T/HpfCBXrQClFwIKwBXYNQqbFCz9sb3Wbby8r4PopvYndxzC5pimKvGFueHU5JbV+IPIwduGYTO44sR8OeWMmupCGoMFg/0oc8x4EoKbXqby3KRIAfWXhdv541hBue2NlizLJ107pzXsripoCjs/9sI2Lx2Xt83koREeg6xoKyPRYfLq+Aogk67zumD70Sopie2XzlwXDu8cyrlcCUZYlSTuF6ATaNYCRnZ3N4MGDAdi8eXOzeUreGBxUel0+AKbn8AQwzKhIAEOrL5QAhuhQTMtqNTneLr6Q0WbJNbtdR1vxv+bBiz3ocx7EPe0NGvGglMILbCqp57vscjLiXJy6Rw+JhkCYxCh7i3VsrWjkL59sJMqh8/mtUyBkML5nAkO6xfL95vIWY+PPGZFB/9RoGgJh7n53DX86ayirC2v4ZmMpNk3jzOEZdIt3MXtDKeeM7MbyvGqundK72Tp6JkURQpFd7uVf3+aQV+mlf1o0t53Yn2lju/PGkpb5bkwLKhqDxMbsW9WjBhN+MXMJtb5Q0zTLgv8tLyQzwc3lY7p3ynH8Sil0XcOyrE65f2L/aZoi2qjBMe+hpmm6GcKmR6IQhdU+XvhhG89dMY4PVxWxurCWtFgnV0zqxfriWp6ft63pc5YFm3bUc1Rm7GFLQizEoWbZdWKCdQQr1mPTXU3T7353DQ9PG87aolq+3lCGpsEl43tw3IBk3KaJXGKF6BzaNYDx6quv7tNyO3bsIDU1VXJlHIBdCTUPVw8M052IpTT0+gJCP724EEcMG3DeqO7M31LZ6vwzhmXg0VWrCcA0wmile8n9UrUV3QoCHhqBK19c2uxNkTdkcMLAFL7LjgQifjt1IJqC1p47LpuQhUdXWIaF0zTRHTr/umQUG0rq+Wh1MQ5d45QhaZTW+bngvwv4/RmDOXZACte/sowJvROZOiQdw7L4aHUxG0rqePbyseSWNaD/aMjGBaO7E+3U+XTtDv762aam6ZVbq1i0dTH3nT2Eo/okNsubsUuUQ6fOhGDYxOPQidZVqw/pmqbYVFLfLHixp5k/bOO8kd2IavvIdjhKgR/F9mofS7dVkRHvZkKvBGJkyEyXZ5oWSW4FVbsr+sTkfMAVY89myc7YxOJtVazMX8apQ9M4e2Q3RmfFU1Dl5V/fbGmxPqdda30ImBAdTFgpakMm6zaXc3KmSdKm17ls+K955PtIr8LKxiDXv7KcsT0TOGVIGqcMTiXJ48QWDkseGCE6kXYNYOyrM844gw8//JCsrKz2bkqHpdflY9mjsOyew7NBzYblSkKrl1KqomMxDJOJvRLok+xha0XzkqNJHgeXTsjCbOM1jokNs9tYtOzPW51vJQ/AUE7QNJ74anOLbq4vL8jjyctGEwibLMit5PUl+fz1vGH88cP1zcq9jc6K46pJvbD2aIfNMInXFWOy4lm6rYoaX4j7P15P9c7cFH/5ZAOf3nYM324qY8m2KpZs2x1wOH90dxI8Dv7x1WZOGpRKQZWXP58zhAGpMfRJisIXNnn0q+a95HZ54usc/nresBYBjN5JHnwhg9cW51Nc4yN7Rz13nz6Io3rEY/vR05SmKba2Ut51lzp/mKBhEqV3nod7L4prX1lOTtnukn92XfHs5WOZHOdux5aJ9hTSFDUBg7qAQd/kAVC2MTKjcgsTXEUc3TuOBdsiQ8uChsnHa0oYneWjZ2IUGfFubJoivMe1wmnT6JsSLRWARIcXUopZywqJddk5d1Q3SqsriPVWcd7IIB9s8pBduvs3ZHleNalROheNy8RuShJbITqbDhHAkB/eA6fXbsfwpEde+x0mRlQKen3bJa2EOFJFAS9dNZ53VhTy1rICQobFmcPTuWZyb+L0NgtyEAoZmIPPR5v3dwj7W8w3j/89Pjw0GhafrC1pMd8XMrj1jZW8cOU47jtrCJWNQdJinHx5x7HM31JBSY2PYd3jCBom60vqGJ4eg/1H18cNJXWt5vAImxa3vLaCl64ez4LcSr5cv4Nop41zR3UnM8HNs99vpcYX5KnTxlBQ1UiNP0xelZcNJbUM6RbXYqz9LrW+UCRJ6B5OH5bGrSf25+1lhRTX+OiTEs01U3rz3LyteBw647rFNuuJYRgWgzNiWz+oQHK0A4euQSe5DbU0jX98tblZ8AIgZFj8atYKvv7NcUgIo+sJahp/n70Zj9PGxeOzaJh8D9HvX9E0P/Xza3nitGdYM7o3s9b7wYKTh6Thsmvc+/5abjmhHz0So5oCr0rBoxeOwKNBm+PehOgAlIKCugAnD0pF1zVyyxv4cHUVvx1/JxkfXcpLp7/AkvruvLMpgNOmuHK4m4E9MtAMU54hhOiEOkQAQxw4vWYrpqft7MyHguVOQq9rOTZeiCOdZVlEKYvbTurPRaO7Y2ERpWtYhtlm8GKXBj2VmMs/Qn/vGqjdGcBzxmCe8iD+hCGYpoVhRh5WW+MNGizPq+bisd1RCsrqA3ybXcac7HI8ThuvL8mnzh8G4M9nD+GswalNPTE0TbGlrAGXXWN49ziUUqwtrG3K6ZFT3kBRtY8FWyr5f1MHEuO2s6GkjufmbmNYZhx3Th1AtTfIG0sLWJhbSbTTxgVjMtF+IvCZ6HHwj4tGsq2ykRHdY3E7bJz7n/lN+zg3p4LXFufx2PRRvL28kCHpg3ESuSk1lYbftOib4mF0VjwrC2parP/WE/oRY1MY4c5xI9pomHzaSgALIBA2WV1Yw9GZsZJsrgux2TRmb65gVI94eid7mJtTgdl9BP1OfBDPD3+DYCOEfKT+8CdGnvUSG7LiKK4N8OzcreRXRXpypcW5uO6Y3ny1oZReSVFcPC6LRKeOktwXoqPTdQprvPRJjuaqF5dw+0n9CYZNXsqN4YZj7ifj0ys5NzaDqT2OR7O7aYw+jYBmR8k1VIhOSQIYXYReu51Q90mHdZtmVAr2wh8O6zaFOJhsuoZbWVgWzYZr7E3IVNTGDMNz+RfYAlVghjHdSTRqiYSMSCDApSsGZ8SwsaS+1XUc1SeJJ+ds5aWFebxw1XhemL+92RCSXR79MpvjB6Swa2CYaVpM7pdM39RoFm+rwrQsrp3Sm00ldby8cDtXT+5NepyLc0d1I2Ra5FU2Ul7vZ0zPeDIT3BiWxY2vraC0LgBEhm48+d0WxvRMIM5tbzVHRWaCm82l9fz9q82kx7oY2zOB37y1ukWAJmRYPPDJBu4+bRAh08KhK8r9Bv+Zs5lVBbWkxbq4+YS+lNUFuPeDdZHjZNe48bi+nDIoFaONHiAdUciwmnXz/7GKhsDORNZy891VNBgWG4truXpKHz5cVcwna0p4a6nJzceexhnXnI7VWIHd4WRdjZ273itle2XzMs+6phidGUeCXefUASloCsIho/UEOkJ0MIZlMbZHAo/N3sw/LxnNF+t2MHVIGje/vpIN/fpzx1mfkazqsTSdrV4XeeWxHBOnESOp84TolCSA0RUEG9Ebd+CPzjismzWjUtG85WAEQG+r6KQQnY9hmNQRD474yAQL2KPnhkvBfWcN4bLnF7d4vpjcNwkUvLQwD4euUecLtRq8AGgMGtT6QnjckUt5wFK8sSSfN5bu7vn04vztnDUig9euPYr7P17PP77azM0n9KO6McjrS/Kbra9/ajR/O384N7yyrFm7/v1NDn+/aAQ3zlrRYnz9Py4ayf99mU1VY5CqxiCmaVHeEGi1veX1AWLcNpw2jS2VPi5/cUnTvhXV+LjuleXcckJfvv3NsdT7w8S6bMTYtH0OHnUUbptGZoKbwmpfq/PH9EiQiiRdTNiCyyb24rY3V7KuaHclod99sIl/xDh59MIRvLu4kBuP702tr+XQzL+cMxSPpgiHIxca+esRnYWlFPl1fuLcDs4fnckNry7DHzLJSnRz1ogMPllTwvdbqpuWH5QWw+/OyMCmKyR7rRCdU4cIYEhJ1QNjq41kMjdjMg/rdk1PKgoLrb4YM773T39AiC7CNC16x7n43y8n8dDnm1ieX028287VR/fi/DHdue6V5QCETROXXd/ruhw7SytqmiKnorFZ8GKXT9aUcEz/ZAprfEQ5dIZ1j+XGWStaLJdT1sAPORUcNyCV77LLmqavLKghv9LLBzdP5u1lBeRXeemXGs3pw9LJ3lHP2TvLrwJt5srYJcquY1pw7wdrWw3M/GdOLueN6k63nUGZzha8APDoij+eOZhftvIdTOiVSLd4N/hbL8UrOh9LM1idX4+mtGbBi13K6gN8s6kMb8igvMHPP6aPIresgfm5FXSPd3Pp+CyS3TY06W0hOiGvZfHoF9k8cO4wnp27FX8o8pvw6JfZ/L9TB3HKkDS+Wl+KP2xw6tB0usW5aQyEiNaUDMMTopPqEJ2rJAHPgdGrcgAworsd1u2aUSmR7ddLHgwhfkyzLHrFOPjX9BHM+c1xfHjj0cwY2x3LsCjYOabdtMAbDJMc7Wh1Hf1So4l1RgIcQU3x/LxtbW7vu+xy7jt7CM9cPpaEKAfXH9OHOLe9xXIfrS5m6tCW+XJK6wJ8sqaYvEovKTFOcnbU47TpPPjZJraU1vPPS0YxIjMOXSmiHK0HXTwOne7xbuoD4RYVWHaxLNhUUofWicuJGobJiPQYZl4xlj7JkQFAUQ6d66b05vGLRpAcLT3Wuopoe4D6kEV6bBSxbjsnD05tNdf2NxvLOH5gCg1+g5teW87UoWlcOCaTG47pTZpTR5fgheiE3E5FrT/M/zttEJqm6JkUhcseeXQxLXj4i038+aP16JriwjGZDE6PwakrRmbESC82ITqxIyqA0dDQwNdff01ubm6z6Z999hnduh3eh+/ORK/OwXQngT3qsG7XdCdhKQ29Lv+nFxaiCzJNC7tpEYWFGwszbOLQYERmXNMyz87dyl/OG9Z007ZLnNvOPy8ehUuB0jW2Vnip9bfMUQEwsXciF4/L4slvt3D5zCVc+twiNpbU8a9LRtE3pXlpZV/QaOrVsaczRqTz4vztfL+5nI0ldcw4qidfb9jBqUPTmLU4n//7IpuTBqXidur87rRBrbbjD2cNJlpX6G30qtM1xfRxWfROjabYG6bOBKuVtnQGNstidHoMr1w1jm9/cyyf3zqFX03uidOSm+6uIqRp/G9dDQ98tpmiWh+BkMl1x/ThvRuPpmdS899rm64Y2i2ObzaV8si0EawpquGe99bKCx7RaZmaxpbqIKV1AcobAuRVNjKlfzIvXDWeJM/uoH61N8RHq4vZXtlIepyLvvFOCegJ0cm16xCS22+/nfHjx/OLX/wCv9/PtGnTKCoqwrIsHnvsMU499VQAMjIOb+6GzsZWsREjtsfh37Bmw3QnoddJKVUh9pXNgrumDuTCZxZGeiPsqOe5uVt5asZYNhTXUlLrZ3RWPON6JhCjga5MXGY1/a1SXjozmoUl0Tz8QxUFVZH8Ci67xk3H9+X6V5YT3PlGyrLghy0VrCuu5fHpo7j6paVN2z9+YArL8qqatenWE/uREmXn01smYwIrCmr480frqWgI8szlY1lbVMvm0gYe/zrS2+vqo3vx0tXjeeq7LWyr8NInxcPtJ/Wjd4IbDBOPXWNot1jWF+/uLq9risemj2ReTgXn7axgoik4Y3gGvzt1IC6z8z3YG4aJE3AqAAsrbB3OSteiHSld472VRWyv8nHq0HQe+mwjFQ2RYUP9UqN56ILhPPTZJtYW1QJw0djMSNnjkZGyx9OeXsDNJ/QjRtcw5U2z6GQ0TVHmD7Esv4Ynv9tCjTcSnB/aLZa7Tx/EQxcM44ZXmw/BO2FgKikxLioqWk+OLYToPNo1gLFs2TJuvPFGAGbPno1lWSxdupT333+f//73v00BDHFgbJUbCGWMb5dtW1GpaDKERIh9ZlkWmdEOXrhyHH/8cD2F1T5W5NfwzPdb+L9pI0h06oRCJoZh4sKHe+vnaLP/gCsQCQacmdSPMec8xXVf6awvbuDsEd343/LCpuDFnmq8IZbnVzO+VwJLt1fjcejcfEI/1hTWUD0sRJzbzqXjs0iNsmM3LVx2DUMphqbHMiA9hh2by7n9rZX88cwhaJpiVX4NPZOiOH5ACnF2jX9eNIKQaWHXFA4iPU4gksT04QuGc8mzi2gMRpIOXjI+iznZ5by/sqipfaYVyd/R4A/z0LlDsMlbNdFJNBgmby4t5PdnDW6Rj2ZLWQPXv7yMp2aM4coXlzIgLZoJvRPZUFLHlrJ65uda3HhcX84ali7BC9EpKS1MTlkjf/10Y7Pp64vruPWNlcy6diLpsS521PkBuPmEvsQ5OmdvPSFES+0awKivrycuLtJVet68eUydOhW3283xxx/Po48+2p5N6zSUvwa9oRh/bM922b7pTpYhJELsA11TeKwqbP4qMIMck5zCh9ePodyvsGmKKLuGwwK/PwxE3lC5KlejfXJb8xVVbiHj/Wk8c/FsLvlfmIvGZXLHm6va3O6KvGpGZMWRFuvikvFZfLOplF6JHs4f1Z2VBdXEOm3YLYugpsgpb2TmD9up8gaZ0i+Zayf35q+fbuSud9bQPzWax6ePpHu0k3AoDIaJjZ0/MqbVrCqCaVpkuG18fMtkPlxdwpJtVZwzqhuXPbe41TbO2VxOfcgkQZfuCaLjc+gm0eFK3rqsF7/9YnuryzQGDVbk1/DUjNGkxbqpbPDjDYaZNiYTZUGMrmSMv+iUPI4gDUGLp7/PbXV+jTfE8rxqrpnSi9UFNVwxqRdZ8S7snbCXnhCide0awMjIyGDlypXExcUxb948HnvsMQDq6upwOFpPWif2j618HQBGO1UBMTypOCvWt8u2hTjS2W0Ku+kFmwNXzWb0d66A+pLITJuTxOPuJnbodOqseEzTYs/+B1HUo3/7QOsrDtTjIMSfzx5KTlkDSdFOimv9rS6aGuNk2uhMdE0RNEyyd9Qz84dtxLrsXDg2E7tNI4TF0/O28eqi3cHI9cV1vLE0nzevPwrTtIhx2ojWiQQv9oFlWsQouHJcdy4b040djW2Xi4VICdaEeNc+rVuII5Vb7ew19dU96Kc8xcYdbeemWltUy2UTsvCFDFKiHcTZFMGdPZakuoLojNxOA2d9AbV1frJL2x4KsraolntOH8QZw9L5w/vrePi8oYexlUKI9tau/a2uuOIK7rrrLo477jhSU1OZOHEiAEuXLmXAgAHt2bROw1a+FsvmxoxunzwiZlQKmq8CQr522b4QRyJdUySocmJX/wfP+5cRVbMBfdY5u4MXAOEA6pv7sW3/lvhQPnbNaJqlaQpFCMo3tbr+mkl389SqENe9spyZ87Zx4di2SyifOiyd615eRrU3xEVPL+SztTuo84UprPbxxNc53PP+Wqr8RrPgxS51vjB//2ozyVF2orAwf8ZDlRE20Uyrzcolu8RHSVBbdHzuus2RXlPBRhyNxXSPd7e5bGaCm40ldZEeWJbZFLwQorPRNEWCVokzUIX+6jnodfl7PTf6p0azZFslj3yRzclD0nB24qpVQoiW2jWAMWPGDN58800efPBBXn/9dTQt0pysrCzuuOOO9mxap2ErXYUR1wtU+3zVZlQqAHq9JPIUAkApiAsXYpt5ItqcB8EyUVvntBnkUwufRN/6NTHBIpSKfL7WsKgNAAm9Wn5As1HW8yxeWrIDgK0VjVjAOSNbVnK66fi+LM+r5vThGTz13Rb8oZZdcBsDBt9tKmtzf77LLsN/ELqyR9t1jumX1Oq8gWkxxDn3HuAQ4kimlMJbW46a+0jTtMQ1z3HHUXGtLq9rihMGpnLLG6sImxZSbER0ZrFWJSrUQChvIfhrSV39FLceldjqsg5d48RBqXy9sYy1RbUc2z+ZcFiGjwjRlbR7xpvhw4dzyimn4PHsLuV3/PHHM3bs2HZsVedh27GccGL/dtv+7gCGJPIUAsCt/Gizfw++6siE2O5QuaXtD1RthZgMtLmP4NKChJRi8bYq/u+HKirG39ly+ZRBLCgMNpt0/8fryUxwM/PKcdx+Un/uO3sIL141noqGIM/P28boHvH8sKWizSa0lgB0T6al0G0H9nNit0z+dt4wRveIbza9f2o0/50xGhk8IjqyBgv0QC2qImf3xKqtjPPN545j0tH3eIPsceg8dMFw3liST0MgTHl9oB1aLMThYbNp6FWbaAiYqB1rIxOLV3KyaxPXjE9pVpkp1m3j+SvH8dKCbWTGu3nl6vG0PQhLCNFZtWsOjHvuuWev8x966KHD1JJOqrYQvXEH/oT2G45juROxlI4mPTBEJ2HXFXarAUvZCFiupsoaP0Uphc2m4Qw3oHK/2T2jthAG7KXiUlJ/qC1CbZ+L3WjEazpIjnHx7ooixqUO4uyjfkv0kifA3Jl7whWLcsUCO5pWYVnw1JxcHLpG31QP954xmCteWNL0VjdsWDhtOr5Qyy7qG0rq+N3pg/j3t60HWY7tn8KHq4uJdto4Y2gajp+ZSM2yIEpZPDl9JDX+MDvq/KTEOEl023ERqc4iREdk6RrPfJfLA1PskDyg2VCxhO/u5vqR13D+lVewPRCDV/NgWfDygu0s3hYpZ6yQ7vGi87IpA82dgMMMYqSPbJqe9MWv+PXYm7niqulsr7OIcjlITE7H4XDyqyl9iLIpzLCJ/DII0fW0awCjrq6u2b/D4TA5OTnU1dVx1FFHtVOrOpH8RQCEkwa2XxuUhhmVIpVIRIenaYo4qwJt7Tto2Z+AIxr3xJsIpY6i3oxp83NKQYyqxVa9BbX5M9SwC2jWH7x4BRx/Nzg8EGxsuYKJv4IF/wR3ApbSMUyL0M4eEfd8UcT6MVO554ZLqSvdjmVzUW9PZlx0KrC5xaqChkl8lIP5WyqaNeHrjaWcNTKD/y1rGWgMhE08Dp2Lxnbnf8uLms2Lddm4enIvbntzJXW+MPO3lPO3c4fi+JnlTi0LHFikOnXSUiMPcliW3KCKDi0E3HFKf7RAERx9C2z7vtl8z+oX8Kx7Fe3SOZz26hYaA7sDidFOG8nRDpCzQHRC0boXR+UmrEX/xt1Qhnn6o+BOiPRQtCxilj1JzLIn6eWKI3DKw8ytPpERqQ40LMywnBNCdFXtGsD4z3/+02KaaZr8+c9/Jisrqx1a1MnkL8SI7oblbH2M7eFiRqWg1Rf99IJCHMHizFJsL58GDbvzQejb56GGTSP6hL/RYEa3/jmtBts7l6OKV0YmuGKg59GQt2D3QnMehguehc/vhtqdw63sUTD5NqjNh8pczDMew6fH41Hgsmm47Br+kMmsFRUENTfxUd35fE0J954Vx8IlBVw9uRcvzt/erC2xLhu/P2Mwd769umlaZoKbxkCYO08ZwNJtVWyv9Db7zHVTerO5tIFLJvTgqD7JvLuikBpviEl9krh4fBZ//HAtdb5I7485mysobwjRPerAf1oOZ4cLpRRhpQiaFk5doZum5BwQB0wpRSOKNQU1TExoQHv7bBh4Opz2EHz3EAR2vsSJTiN4ztPcP7e2WfBCKXjoguFE6wpLqo6ITsat+XCsfA593qNN07SPb9v5W/i7yPBJAJsT79gbCfU8gSFaDJqUDxaiy2vXAEZrNE3jqquu4oorruD6669v7+Z0bNt/wEga1N6twHQnSw8M0aG5bGG07x9rFrzYRVv3LrbxvyIQPYQopTD2uLly2Cz05S/tDl4ALH8ZznsKildBaGewoHgFzL4P86wnwBWLVl8SSby76nXI/gxrwOmE+50WSVSma3icNmZM7MnMH7YB8PayAn59ygAeu2QUf/lkA2sKa7n1xH7865JRfLi6mKqGIEf3S+L0YRmU1Pp4/OKRlNYFiHLoFNf62LSjnjmby3lqxhhyShv4bF0JsS47l07owcKtFWiaYtp/F5Ie6+L04el4nDZM02RrRQN3nTqIqsYgy/OqeWNJPj/kVjBjdLcOk1TN0BQFdUGe/C6H3PJG+qdGc+sJ/egW7UCXKIY4AI3AFS8uYVIPD6fYX4aGUihYTFmfC6iZsRhNKRI0L0k2P/ga+N3xyaTHR7OupIG+KR6mjc3k/RWFDM2IJUZGkYhOxm3WUm+5qJ7+DUEDYo0q0pY/jvbZXVgn/glfbB8a/X4cMcmY7iRqQnbiNJBaPEKIIy6AAVBQUEA4HG7vZnRoylcF5ZsIjzm5vZuCGZWCvXxNezdDiJ/NadSjrXunzfnG6rf4d/AKzhvVnX4JLtg5hMJt1KItn9l84cZymPMQXPIa1voPUXk/gCeZshE3Mr80mXWlAa4Y1ou4ojnEZIyGKXcRjEqnwYhBKQhbFkU1PmZM7EGCx86zc7dS5wvz3zlbmNw3iTWFtQD8+9stpEQ7ePaKcazMr2FOdhlPzcnFsqBHopunZozl6peWNksQ6LbrPHv5WH532gDiXHbeXFpArNvB03NyAdhR5+fF+du56fi+xLoc3P3eWmq8IQCm9EvmycvGUFrrx7IiQ25sNh3Lsgi1klvjiKBpzNtWxV3v7L4+FVb7+C67nH9dMorJPeJ+VmlYIdA1/vl1DnmVXp44PRXXh+/hG3AeK4f+jt99uoOCqkhQc3BGDI+e1Yvebg3qfaTFOIhyJXL8wBRufX0llY1BkjxOrp/UA7ODBAWF+CkOp42C+hju2Xo0C78uBSDJ4+D3Jz7CSfUfEvfuNdRftYinNzsIhX1cf4wTl41mLwiEEF1XuwYwfpyk07IsysvLmTNnDueff347tapzsBfvzH+RPKSdWxKpRKL5KiNvm+2SL1p0QpZFaX2A299axVc3DCEmXAX+WrS4bnDKAzD7T7urjgAUrYDXp2Od+giBY+4maNn4bouPpxbm4g0YeHFz0/HXoeyKYMAAE8JKUdIQ5Knvt7K9MtJT4LpjenPK4DTq/GHq/SECYROnTSOw80FnYp8k3lxawFtLm1cBmj6uB/e+v7ZFdQNfyOD2t1bxwa8mEQgajOuVhMOmkV1a37TMsO6xpMa6+PNH65t99octFeRXeXnlmvE0GBZrimv5Lruc7vFuzhyeQbxdQzuEPRp0XaMhbKEUeHS1Tz1AGk2rxX7s8ocP1/HxTZMlw734WXyGQV5lIxN6J9IzyQOOaLaPvZvb/lfIWSO6kR7norjGx0eri5n+8gZeu24iF7y0oGno0qiseOr8keDgwq2VXDEhC3s77o8QB0tI1/CGTP7fJ1uZ0DuJ4waksaGkji/W7eA3Hxfw3LTzOSXlY6oaA7w4PzL8eHhmPKf0TZLeF0IIoJ0DGBs2bGj2b03TSExM5O6772batGnt1KrOwV60EGIysKKS27spmDvboNcXYbRjSVchfq6AFoN9yPmoNW+0Or+q73lsy27krekZxL97CfQ/GbqPhWWfYykdddlbkPsdfP/w7uQORogyPZXXl9YyY2wmp/SL4ujeiVhYuHUNDBMjrOF2KhoNxbwtVfy/d3f3FNhS1sCX63fwyLQRzFqUx+rCWmZMzOK8Ud15a1kkYHHa0HR++87qFu0dmB7N37+qbX1fGoOUNgR48LONrCyo5bHpI4l12ajzR3rFXTq+B0/t7JHxY/lVXrZXevn7l9msK96dpPmjNcX88YzBDEuNPihBDE1TBFCELQtdKYqqvby3oogPVhWjlOKS8VmcOjgVN9Zec1lUNgZpDLZ+S1znC1PtDRF1EPJ5iK4l2u4nFFQ8cXoansbtBGwx1J7wV5ZUOPjLecN4Y3E+X28spV9KNI9MG8HXG0v5cn0pR/dNYv6WShKi7IQNk9DO3j8pMU7smgLpDSQ6uLCmETQtGgJhzhrRjXeXF1LtDTG+VwLPXzmOf3yVzUPfVzDq5PtZWLI7CP38vG1M7p2Isx3bLoQ4crTrndmrr77anpvv1OxFCyFtaHs3A4j0wADQ6gslgCE6JL9hI+qYu9BzZ0NjRbN5gQHnML8yht9OUfT++noYPh0qsuHNywAiBRDnPwEjL4Gpf4Mv7wXATBlKjurFk9/lcu6o7sRpEGcz0TAI4SRaq6a20ce6egcDEhQj3VV8OCOLNzb4eXdNBSHj/7N33/FRlPkDxz/PzPYkm55ACL33IkpRmorYRUCx99698zxP72c5u3d6p556d4oNxYYVFRuCiIggvfde0/u2mef3x0MIIQkCQnYTnvfr5UvdmZ15ZnY3u/Od7/P9SmwJT321kntO78S/vlvFJce1xO9zMrBtKi6nQWaCh3tP78zrP21gXW5Vh5Pwb1wIbSsK0CTRyzCfm++W7+S8vs331NtIjnOxtbCizufOXp+Py2FwbKtkTmiXRp+WyazYUcKircWkJXhI8zpw/Y4ghiUE64uCPD55BQu2FPL6Fcdy3ydL2FJQNaZHvljOxF+38PKlffDsp3uDKfZfWOA3FmtaNUIIAkLyzbJiUv0+4lx+0pN6ky4iBFqPwF5RwI1vzduz/paCCqatyuGhs7uyMa+M1mnx/LqhgEdGduelH6raFl97QmuErbvxaA2b2wiRZFSQLxP454/r+XTBtj3LNuWX8+XiHbx4cR/++MFCSjL68p8vqoLvhRVhDrG5laZpjVCjuLU0Z84cxo0bx5IlS8jJyeGFF17g5JOraj/cc889fPzxx9Wec8IJJzBu3Lh9N9UoiEABZt5K6HRatIcCgPQkI4WBWbKFcLQHo2mHqMjMxH/lFIzF76o2qu4Edna9hnl2e+6btIXvL0mF8jyIT4fvHqi5gYXvQouBkNaRspYnsrH95Xw4r5Rxo7LJFjk4Q2WImc8iKvKQXc7FyujKxuI4Ohb9QPKXj5FSuhMcbjp2uYCLLr6Rse9spCJskVMapF16PO9cfRx+I8SinABPfb1yT5ChVaqPP5/WiTdmbuCX9flkJ3txOwwS3A5KgjVrDQkBXTPcjEh4G+H0UNj1UiLedEAybsYGQhG7zucCZCV5ufvUTmzMKwMEV742h8juX57PTlnNSZ0y+NtZXXDZBz+X2TQNlu4q48o35iAlDGibys/r8qoFLyqt2FnCLxsKOLFNSp3zppN9TpJ8zj11PPaWHu8m2etEt6/UDoQQEBA2P64ppDgkeeKDRXs+I/1aJ/H4yG784+uVtT736W9W8t9Lj6GwPMxJnTJ46Ye1LNlajBBw50ntaZ7oQeqCsloD5jIs4nf8hGx+LDsKrWrBi0oVYYv//biOywe0ZFV+hF17TXEc1C4Nr6mzkDRNU+o9gHHuuefy+uuvk5iYyMiRIxH7ucW1b9ChLuXl5XTs2JHRo0dzyy231LrOoEGDqtXccLlcBzfwBsS5fQ4CqTIwYqHekWGqTiQlW6I9Ek07ZJYlKSAVV++bCXW5lAm/bmfitGI2528mwe3ALM+FzmfB4rqLfbLoPTafMYEXfi3iVp+bfxyzGUfBOpg8Gbwp0OdS+Po+xKRbcXQdRa9WgzC/ubPq+ZEgnkVv0K1wNRMveZYZ64tZVySRUtKEfFYXCi5/fTHWXreqNuSVc8e7C3j32uNIDmzFl7sAV/EOfry2D8/8XMibc/OqDXFs70wyF75AeWJbVicPZty0YgoqCjipUzrf/mEw2wrLubh/C/7zw7oah+d1mvTITmTUiz/x8mXHcvUbc2rcNZuyYhd9WyYztldTrIMsSlhmSx6ctHTPtJABbVL5bGHNH8KVPpi7hUGtUzDqWB5nCp4e05Pr3ppb7Zw5DMHT5/cgzgRbT7rWDkCCM0C8EJjC5smvqgcqfllfyOrcirqnKwUiuEyDY5snUhCIcG6vLEb1bkbflsnEmQKHDl5oDZzPLkBm9Sa/qJQvlxTWud7Pa/P4w/AO/PPbVXse8zgNbhjSBnQBT03Tdqv3AMZJJ520J3iwd5bE7zFkyBCGDBmy33VcLhfp6emHZX+xzrntF2xvKkZ8JhTXnepdn6Q3DUMHMLRGIBQGy0wkPTXCjiLVVrUsFCESlwmueKgorP2JCU2gxQDyQgYPD0nA+cuzsOY7cLih22jI7Aaf3gznvAATxkKHUzC/+WutmzI2zaTLkC103fIYoaTWYN5CaOtGXlrSrNqFeKVgxOaLBVv4c/nfca6cpB4Ugr8OfYB2pwzj/m+2khrn4vpBLRiVsAIrz8OL+cfyyldV7Y/nbCjg5R838PxFvenbMoVhHUuZurKqrazf6+CVy/ry8KRl9G2ZQiRYyheXNsdtlxM0fLy3PMhbc3OI2JJxP63nzO5N8Bzkua8I22zIK9/z/1JKHEbdQXDTEPudBiItm+6ZcXxxy/FMmLOJFTtK6dLUz0XHNSfZaegOJNoBifdIXKESQuEw/eKLmHJZExbnm/zjp/w92UG/FYNwGAIZtkgyBSPapwIqaPqbT9S0GCeEICKceLBxuH0keUrrXNcQEO92MHt9PgAntE3l3tM7k+I0kHoOiaZpu9V7AGPvDIm6siWOhNmzZzNgwAD8fj/9+/fnjjvuIDk5+aC20VDmQzu3zyaS0hGXECBAxMDffNuXhlmyNWbPYeW4YnV8sSQa5yoar8v+jtOB5OT2qQy4fTCrc0qwbUhMB7muAtFyAGybt9fKbhg9DjyJyNVf08uXB6+fD+X5Vev88BRk9YZh98HSj+Gi99WO96m3UW182xdCeR6uLXNg6QcUX/4Ti7fl1Ln+vK1lVLTphJPdAQwpcU19kIuu6M/gu4YQCodpt/DvGD++zMrzpvLK6xsBaJnq49T2qoXrt2vKGP/zRsb0yeacXllcNqAlxRVhkrwGfo+TQMQm3uPg3kHJtFr8DM7F74AdAcPBX7pewBkX3sxF724gryyErOPc7o9jd0Ci8ppu6socTu/elBU7Smpd/9L+LXEgsfezHyElaS6DO4e0IWSDy0Blhki5u4BJ/Yq1v0XR+JzHyrEfCCEEntAuxKZZuL9/mJYl26HFANq2O5lhF5/BrV8VMn1NPqGIRbLPSUEt05XS4l0k+5wqcxKwd1+oNZTz0BBftwMVzWM7kH02hHMvBLhdLkTeKpLXfM/J7S7nyTrWHd45k3i3yfd/GIwtJR5D4EQFq3/rGBvCuahP+nxUp89H49IoamD8lkGDBjF8+HCys7PZvHkzzzzzDNdeey3vvfcepmke8HZSUxOO4CgPk3AAcpZA36sASPTHSBPApKawYTppabF9DhvEaxwj6vNcRfN12bPvUAUEi8BwQFxVd5/WGfFVKx9/KxRuggUTVNvUDqfC4Lth+adQsBHR8niQFsRlVA9gAGybr+Yr7FgCTXtBWnsw9jOHIbkVBHZ3EpES9+pJZCcNZf1exTr31jLJgbus5nQLc9a/md3ifpwyTIfNP0NWb75dF8DjNPjPyGy6RJaTsfJfIG2uHngBa709qTAEM9bkcnbPLFxWGUNm/RHLtsg54SEePTWb9B/vw7nso6qd2BHci9+iR6SCmwbcxI+bgyT4XCT7VDZeRShCTmmIwvIQXqdJSpyL1Pia9ebdAVUj4LvlKvNjweZCrhvchq5Zfpbu1fUE4Pi2qRzTMpkU/8HmecSGWPlbFI1xxMqx/6ZQGVQUIPO2wmc3Q3JrFXzcPBvWfEfi1rm8OvR6JrRuyms/beDJUd24ccKCWqYr9SQ7JQ5jP9lEDUGDed0OQax/DmL53EeKdiKCRVC0FXYtIcM3hZsH9eWFH6u3906Nc3Hbye0xBDRLi69ja78tls9FNOjzUZ0+H41DvQcwjj322P3Wvdjb7NmzD8s+zzjjjD3/3bFjRzp27MjJJ5+8JyvjQOXllcR8Nqdj+68k2WFK4lqTABQVl8dEDTqnmYi3dCd5O/PBjL1u9kKoP2oN4TWOtiNxrn4rsBWN16XyOIvyi4gP78CY+S/E+mngTcbufwvhlkMpsRP2eU4ivsQOeK74EhZPRKS2hVeHVwUhln0C3mQY9T/47DYo2V59p2u+hRYDIFwOSz+BzmerjIx9eRIhsQUkNleFQ4GEBa9w8zmX8eOavJrrA1f3jsP98Sc1j7N4K/1bxPHT1ghlbc8kbsO3hG3437nZDJx3N44tM/esm75+OilNelE08k2unLuFLxdv57Ur+kJCU8zF79Fk2avIAbcgltdev8i94iPOHns7x3dpjQyEKA2UYtplrM2r4KGpuczZUABA1yw/z43tRbJT7LkbXfma3HtaJ5ZtK2ZbUQCAP09cxCPndiMcsflo/lZMQ3BJvxZ0bZKAEQqTm9uwSgfX99+iWPrsNaS/w6Zp4M+fg0hti/HTP8EZB6f/Az6+rlrmlGPll5zX9yaC7cfQ1VfEV9d25tX5JazZVUqb9HjO7plFq2Qv+fl1p9XHuob0uh2sI3lsh+OzF+vn3uky8csg/PQszH8LgMRVX3PN4L8x7OLhjFtQTl55mIFtUzmpUwbxLpM4Abm5tWfV7U+sn4v6ps9HdXufDx3EaPjqPYBx77337vnvwsJCXnrpJU444QR69eoFwIIFC5gxYwY33XTTERtD8+bNSU5OZuPGjQcVwJANYDqqY8c8pOnCTmihHpAxEb/A9qYhpI0o3YHtbx7t4dSpIbzGsaI+z1U0X5f44GbMV0+G8O56MsXbMD69EVens4kb/hSldtWdIimh1HZTZjYnudclmP89vmYGRUUBTH0MjrsWpvyt+jLTBS0HwsxnodclkH0s5K2BHYur1nEnqDoZPzypin5+sUA9XpZD54Qg/3dqG574dv2eVqluh8GjZ7Sm1cqX1R3jfVgtT+DpH7bz6eJd9LnyNDouHs/pgxJIyZtXLXixZ4g7FuBZ9x3dmnVgydZinv9+DZ07H4cfC9I7I3JX1/1iSUkzd4DkRCeJ4S0YP/4dse57unmS+G+Pa1gxYChXfrCJpduKuey1ObxzzXF4ZbWnk2gK3r22H4u2FvHjmjyyk7z0aJaI3ykY3DoFIcCQEruBt52Mlb9F0RhHrBz7/vjsIkxsZCSgPp89zodfX6112pf31xe5/JqxGD//k3U9/0BZIELXrES2F1Uwb2M+nVObYdXe1KdBaQiv26GK9c9BrJ77BLsASnchdgcvKiVPv5++3n/SfdiDhOKbsdJoSnqCC4eUhMO/r1hnrJ6LaNHnozp9LhqHqHQhqXTrrbdy2223cckll+x57LLLLuOtt95i5syZXHHFFUdkDDt27KCwsLBRFvV07FyAldhGpbnHENunUu7N0q0xHcDQtGoqCjG+ua8qeLEXseIzXMffCZ72NZYZhsDIW6cyKWqzbT4Mvqvm453PUcU+vckgDFj0vpoO5kmCnBVqWVw6/PiMqrPR++Kq57r9JIpSLuzsY3iTTNaXOjANQUtvkPT4AJ4p42vuz+kj3Otyfn5Z1bu4+pMdjB81gWbuCuJXvFVz/d18i99gVKdnWLK1mBlr8iga2g9/Rgt4+zy48N06nwfg9CaQGNyEOe4kiKgsCsrzSZl2L/1aDWXOTX9nZ34hhZaH4oogPq+7WgtJ25Z4gQHZiQxqlYyUkJTkIze3ZE+9H12rXjtShIBEUYjDrkDOexsx6A+Q1BxaD4YPr67zec41XzG7y91c+85aiitUtCI72cu9p3U66G48mtYQOJ0mQcODZ9mnta9QUYD7m7txXfQ+XZLclAPydwYvNE07OkT1KnfGjBncdVfNH/GDBg3i6aefPuDtlJWVsWlTVbX8LVu2sHz5chITE0lMTOTf//43I0aMIC0tjc2bN/P3v/+dli1bMmjQoMNyHLHEuXMe4Yye0R5GDbZXBTCMkrpbHmpazAkUq2kjdRCrv8LRuxORSFWWhccMEyeLEMHiOp8HgL3PD7VuoyGjM4wfCac+DvPGQ9uh8PmdKusiMVt1OKmcdhKXBnvv46T7Yfrf8bYZRovP76CFJwmkrdZpORDGvArTnoQdi9T6Wb1h2L3YW+Yy4eLjOGPcSrYUVHDqGxt555KO9LH3c0vYjmDungpoCCjzNsda9x0mwK7l0OwY2Dq35vOy+2K7/Di+vL0qeOFLheSWUJ6PuWEa/vwl+L/4A0hJ8IS7kV1GU2LF1XL6JKGQpQtyafXKLwpxbP8VEZcG7YaR787CM/Rv+CJF6vNWFytMQcAiK9FLerxkZK8szu7ZlHhBjVbDmtbQCVNgigDbK5y09qWDL6Vm3SdQGYoJTQhKQ38QNE07YFENYCQlJTFlyhSuuuqqao9PmTKFpKSkA97OkiVLuOyyy/b8/+OPPw6obI8HH3yQVatW8cknn1BSUkJGRgbHH388t99++552ro2FCBRglmwh0GFUtIdSk8OD7UrAKNUBDK0BEUJlM9V1Me/0UjlJSwhIEgWY0x5FLP0ILpgAKa1V9kbJjurPS2gCCZnQ7iQ1d77H+eoivmiLqo/h8MLgP4LLr1qzBktUYGAv8rjrERt+gk5nqkyMZZ/C6m8RXUaqFQKFVStvnAkOD5zxDyjd3fo0ZyVMugNf8VbadhnJvOv/SG65xQ9bJJY7ibwO55O6aVath13RZSxfLFcBiBM7ZTBzfQEtivPwAcx8TnVd+eavsGtZ1ZMyuxIZ+QpIG7F2isokGf43lWmyazn4m6qaHjmroGlPWDcN93f3YjucODtctGdKjKZFi2kaOMKliBb9IGcVMqMrntWTcFbkQPO+MOYN+PTGWqdqibYnMnz1O/S65DYs4cJrCKyIra/ZtEZJCBt3qIC0zb+CFYBTn1Dfo9/eX32aVcfTwemjMBRH/L5BfU3TtDpENYBx66238te//pXZs2fTo0cPABYtWsSPP/7Iww8/fMDb6devHytXrqxz+bhx4373WBsCR84SAKykNlEeSe1sr2qlqmkNhjcF2fkcxNIPa10s248gsjv9O0EUYb5/ISJnhZoe4vTCcTeoorX+pjDrP7D+B/XEoX+Bn1+EIfeouhcON+xaAV/9GUp3qnWSWsLZ/4aL3oOJV1YFHoSAHhcgmh2jUte9qfDhVSrIAWAFoVlf6HQ6pLZTj62bpoICH10PBevVPjuergInJTsQyycR1/MC4t6/jIs7nMHk1EcwEvuTnNYZI7d64ITk1oQ7ncPsyUtJjXNx2YBW3PfxYk47dQi+Oc+rGh8fXQtD74GEplC6C5nUAiulI4UyBb/MA28KnPsfmHw35K2t2rYrDs4fD/nr9jxkTH8cX7sRFJHye15JTfvdfG4bw0yAcBBCJYi3R+PbO7iZfZwK3r13SfWgZ5dzwJOAmd6eNDufApFZrROJpjUmbrcgIX8R4u0xuPaeRpnaDs79L3xwhfq+8iQij7+dsOHFb9ZMStQ0TatLVAMYo0aNom3btrz55pt8++23ALRp04YJEybQs2fsTYOIdY6cxUiHFzu+CbGYVS29KRg6gKE1JO44rKH34dj0U40sCnvQ3QScqWCrmIKjaANi5xL1A23ZpzDtiaqVHR4485+Q2Q2aHwurv0WmtIHi7Qi3Dxw++PBKVeui/02qhSoAEha+pzIVnHFghSG9A6z+Dt4erapRnf1vcPurAhjrf4ThD8HX98H2BaoVa4fTkNnHIawQtD1R7WPpx7DuB0hqobI+rDAktcDcsYBAswKumrKdN8e8Soud35G8YgJIm6KO57E1+wwKiuO5ZVg7BndI4/1ft3BatyY4MuKws/pgbJunOqN8+ScVvHEnYl/wLkWkIKUkaCbiGPE4xqyXqgcvQN25nnglnPU8zH9TPVaejxGpgAPveK1ph51hCNzBXMhfr7KnPriiZmbWltkqWDjicfjlJfAmQc8LofVQWD8DvrwTrp95lDSw145GQkBCeCfinQtq1oDKWwO//A8G3KK+T7ucA24/xSFPtTpHmqZpvyXqX6M9e/Y8qHoXWt0cuUux/C1VSnYMsr2pmEUboz0MTTsoxWYm/su/xlj7LcaqL5G+NOy+1xKMb0m57QNUarnYPAtanQB5q2Hll9U3EgnAZ7fC9dORu5ZjD7yToDMZb9FKWDkZCtaplqk9L4DZL8Oi9+DC92DTTGg5AAo3w8J3Vf0L0wmXfgqpbVQAw+mF4++EJe/DcderaRhlOdD7ElX/In8drPgcsX2B6l5SkQ/vXFB18bVtnmrvetZzKnNjyUR6pkFRRZhzxq+nT4tjGNXpeAzgszVBchfm8N+Ls7iyRQ4JC18i+7ibeGxGCcP+s4kJY1+kzbrxxC9+E4IlyKw+2MMfpTSu3Z52qAHLJL5pL/jk+tpPeKAIwmUqmBMoBNOFNN2H/4XVtIOQYhYiCraAAaz8ou5pZfPHw9i3ofel2L4UjIzO6jM39W+Q0ATbmRAbrcE07QjwuMKwZUn1+kx7W/MtDL4La/W34Emm3JmGbFidrjVNiwFRD2BUCgaDhMPV/4rFx8fXsbZWG0fOYqzkDtEeRp1sbyrOrbXPqde0WGXbkgKZiqPDRbg6jkEKJ4GwrFavT0qJ9DeDHmNrtkbds6EIcvW3WL0uo8zyEBfJRWycqe5C/fAk9DgH3r1YFQI861ko3AiLJ6q7VmntYdAfVMZCVm+Y+xosehciQXWhf+a/oO81agpK5fzilDZwysMw41+wZY6qr+H0qUBKbRdfX/1Z1e1oOYD0OIMxfbKYOG8b8zYVMm8TnNsthQcHJdDKkY+7cCZChgmkteeZmYV8vjgHgHPfWs/wjmdz2akXkOJzkJWeRkTEY+1V5BTAssI49nfHrSIf3PEQKMTucQEBMwmsulfXtCMp3mdAyAJvopriVUuNiz1CZZR5mrAp/VSyUv34IoU4371ATa0a9QrlZiroriNaI+RwGsSFCxDleXWvJG0qcLGrwxVkJrgIRtzovlGaph2sqAYwKioq+Pvf/87kyZMpLCyssXz58uU1n6TVLlyBWbSBUKvh0R5JnWxvKkaoWP34c9XsKqBpscpjBPGG8zE2zwJp420+gIAjmXLbC4BlSWR2P8hZrLIf6iDy12MG8vCHylVx0PJcWPoh9L4UvrtfBS86n6WqtU+6veqJ2xfCpzfD8Edg+yKY90bVMtOhpolMvA78WdD/RtW1ZOdS+OgGOP91eHuMytYIlUHR5toHF65Q00h+epakgg3cc9abHJvdgRd/2saF3f1c4vuZ+IkPgRVS67vicZ79AmJTVWTBsiVfLc/jq+XqB+xHNzVlzc4cBrVOrRawsJ0JqvtIXT90UztA11HIlDbY7U4jEI6ZWLt2lElwBXGHShFf3KXuHvtSYcRjdT8hozNrShwkpmVRUr6TxA/GgMuHPXYCRqvjiZTpizWtcUo0y6GiXNVvqktcGmFXEqleH/lhF05d+ELTtEMQ1bkGTz31FLNmzeLBBx/E5XLxyCOPcOutt5KRkcGTTz4ZzaE1OI78lQhpYyXu54sjyipbqZpl26M8Ek07cD5RTtziV3G8dBzGpFsxPr8d86Xj8M79N/GGuhMrBAjThfRnQ5MedW+sWW/Eh1cjvvwjomS7mu61eKIq8lmwQa3T8wKVkVGbaY9B1j71gXpcAHNegZMfgmH3qcDF8s8hPhPGvgm7VkKr3S2jjd/4kx8oVNNUpE3aZ5cwdvF1fHCWh6valhD//X1VwQuAUCnmh1dy2zFuXGbt212zsxRDmCzZWYppVlXmKTdSsU+8v9bnyPanIH2psG0eYs7LGHP+QzI5mMb+K/s4TAO/KCSJHNXqso4xadqB8jnDuO1yxPS/q+AFqKCbHYaMLrU+J3Tyo0xYWkFF2CLZ5yJ82ZcEL5pEUZOhqiaGpjVCPpdEWBWIr+5RAfc2Q2tdT554P8KTSFjq4IWmaYcuqr/wpk6dygMPPMCIESMwTZO+ffty0003ceeddzJp0qRoDq3BceQtRyKwEppHeyh1kt5UAIxSHcDQGg538VqMaY+qDIa9GDOfxZW3BMMQxIsSjA8vQ8x6AU76v9o35G+mpntsX6imdLw9WrUSzeqtMh8q2ZaqmVGbcHmNcZDSGloMgPy1Kktj/XTVvvTXV+H9y6Bpd8joCkIQ9LdUWRq1cXjUeuf+F85/E9qdDNsXkLHpS5wz/l77c6RN2vLxnNQptdbFKfEu4twmz3+/hoq9fquGLUlF61Oxz30ZErPVg6545MDb4Pg7EW+epQIxOSswfvonjnHDSIxsRdQVwyjLxb/6XdxvDMf5Qm/cb55C4rqJewJMmnYofFYBIlSqatLs7eu/wskPqi4+5u527GntkRe9z5NL4pm8ZDsrdpTw3ooQJWYmxbYfy9IXa1rj5HeU4gvvVMG9jTPghyeg71Vw7DVV2baJzWHkf4i0HAy2RSSkPw+aph26qOblFhUV0by5uuCOj4+nqKgIgGOOOYaHHnoomkNrcMy8FdjxTVU7xhhle1QbRKN0W5RHomkHKFyO8csLdS42Zj6L5+yeOMt3IrbOha1zVVHNc16EaY+r6RpCQJthqvL6Z7dW38CP/1ABg40/QbM+sHWemg6yP/tmUZTuUgGM8SNrrhssgR/+Dt1GERo7kXeWRzjjxGdI/+xiFSjZiz38YYypj6oCpO4EOOEPkNkV4jMQhRvqHI63cDXNk2pGFjo3TaCjr4wkK4fEE5LAEGBVBV/K7ThCLc7Ad+kADDuANFyYRDA3TFfnpCxXdXvIWws/PIXx/UN4RjxHhVX9b5zHDMPMf2P89M+qB4u3YXx+G+4h9xDueQNBS09B0Q5Okg+wfBAsrR5gBJWp9P6l0GUkXDlZtQpOaMIrq7yMm72O209qz6fzt3LjkDbY+i6z1oglmkU4dy1FWiGMeJVlSySoukl1PF0VhzadUJ6HzOjEFjuJVJcbIrqokaZphy6qv+qys7PZsmULWVlZtGnThsmTJ9OjRw+mTp1KQkJCNIfW4Djylsd09gUAphPbnYSpMzC0hiISVFM96iDKdmJiqeyHSvPfgh2L1R3axOYqqLhkIky8Sl347C1UpjIq5rwC5/wbPrxWdeGIz1TFAvflbwahfVrTbVugsifqsmE6nP4UO0sFT3yzhjntErlv7LekLfwPrpxFRBJbkdPrZkRKK5p8c596TrAEpjykupYUbkZmdEUUbal183ZWH5IMH0JUJYf0bp7I8yP8ZE88DUp2MDC5NZEL3qPI0WxPNxKASMSmmCQQ4HSYJJatgFkvQO7qqh1k94Ux4xATr8Ztl1LBPgGMSCH8/HytYzN++ifebmMJkl73+dG0ffjc4AjmITb/ogKSTl/NlpCRoMrM6D4GmdCUDbIJT3w7n8sGtCQ72csXi7eTlajbQ2qNl8M0cKz6HNHxNBXAD5Sw54vAtmD5JPXPbvKmWSR5nASDOnihadrvE9UpJKNHj2bFihUAXHfddbz99tt0796dxx9/nKuvvjqaQ2twHPkrsf0xHsAAbG+KnkKiNRyuBOzWQ+tcbLccRAgvJDStvmD7QhWs+Ph6lYUx8/mawYtKQqhgxbcPwOiXIViu7lrtG5RweuGMf0BiM4hLq3q8dMdvH0fBRsoDQQJhmy+WF3DSWzk8yLWM7/QiT8b9iVM/DDB3hwWD/lj9ebNegvh0xAm3U+v8DYcbo+2JXNRsF5NvHcj4q47lmxt6MG5kE7JDG2DE4zD2LUhuiePd84mXBXUO0WcXIj65oXrwAmDLrzDrP9D3ylqfJypy625pGQliVOTv58RoWnWJznJ8kVyVdfTJ9bDkQ+hzWe0rZ3RFJrdho0wl5Ijjs1uOxxDw9ZIdjLvsGLz1OnJNq19euxDDGYcMB+Cja2H9D9DxjFrXle1HIN1+ZGT/tYw0TdMORNQyMMLhMFOnTt0zVWTgwIFMnjyZpUuX0qJFCzp16hStoTU4oiIPoyIPqwEEMKQnVU8h0RoO04Hd82KM2f+FUGn1ZQ4P8rjrCVoGnoTmmP4sKN7rve1vBgXrVTZDXRkV8RmqBkSzY1TnkeWTYMg9gAHXfAfLPoXcVdCkJ7QeBN/8VbUYPe3vam7xzqXQYiC4vCpjojZthsKmmXg7XoXD2ErEllSELSbMq94tpWmiGzJOhvx1sPQjlTafuxLZYgBi9Xdw5rPw/cNVXVaSWiJH/oewvxVhw8+/v1jFA4PiSN80WU2fqazj4YqD4X+DTbNwlO+os5ChWZEDOStrLkjvCJ1OR2b1xoyUkeBwUG4kYVVORzF/Y9pcDE+r02KL3xnAaZchrAgYThjzuprS5UpQn4f546sK2bYeAqc+TkX+Nm79WvDvC1MpC4a5tF9L4h0CU8oa5Wo0rTExHQaywymITbPgvNdV1kWPsdBmiKoTYwVBGMgu52Cd9DdKjGSskM6+0DTt94taAMPpdLJyZfUfq82aNaNZs2ZRGlHD5chX5zHmp5CgMjDMoo3RHoamHbASMwP/FV9hTL4LsXmWejCrD9bpz1BsNgEbSkQKSZd+hvneRSrg0P8mSGii5v7+/AKc+oTKxti7i4fpUoGITbNVzYy8teBJgGlPQPFWVROj/Skqfd3fVF1A9b9J/VsIcMVDZheYOw7iMlXRtF9frT54tx9OehDeHkVax1Gc0z2NDxfWbPOanewly8iHLbNVwOCi92Hy3QAsynewxh7M4CRIP+0pMJ3IxOaEXGmUGcmqOKEl+etpHUgrXgDfPVB946Ey+PIuuOAdKMtB+DrXnlZfW0vVdiftbjH7EKJgPQLwpLbDfdZzWP7mVBBPxJOCmdQSCmv5u5LalrA7BXQZAu03GIbAJSsQkRDMfE5ND7Ej6nPa80LoNkq9H+2I+ky64gh99ygz2v2Zkb0TSDDB59pdv8aW6NiF1pg5DHCUbUcYDlg/TU2dtEKqPXj38+H6aeo7zXQSTu1E0PASCejghaZph0dUa2CcffbZTJw4kbvuuiuaw2jwzPxVSMOBHd8k2kP5TbY3Bee2WdEehqYdMMuGAldrfCPfxGUVg4SwI4Fy4gHwG4U4SrYg8tciz3ga4tIRO5bA7P9Bl3NUm9S5r8EFE2Dt95C3BlLbqYuib/4Km36GG3+G/DXw7V6tRddOUcU0k1vBj0+rNqvpHWHwnyC5tbqQ+vl5WDlZrX/iX1Xxy8XvQ1ketD1RZW2U7QIp8ZVt4U+9UsgrS2bamqqpHC1Tfbw6MpMmO3+AcAWsm6amvIx5lby8XO6avIPVu0ppkeLjnZGtafbuyVg3zCKHJCqCFi6HxOty4rcDMOPZ2k+ilLBkImLIvXXXBNh3Go7DowI2E8ZWnyKStwbx1rk4LniH+MUfYg25By58B147vfo0HW8y1pg3KSMR9OWk9hv8riAiFIHvHoRVk6sWWCGY94a6m+zwqs9y25MoHXAXv3a8h5+2CK4f0gRLFyXUjiJ+chGGE355CRa8XbXAjsDCCRAsUt9zG34kMnoCthEH6M+IpmmHR1QDGJZl8c477zBz5ky6deuG11t9xuhf/vKXKI2sYXHkr8aOz1KR7xhne1IxgkUQqVA/BjWtAZBSUiZ9lAkfCMBWSRDJcifmO2PUtItKSS1UDYsNM+D0f8Cu5aq16YYZ0HowJLZQLUq/vV/NGQZ1kfTdg9V32u8GCBTDt3vVftg6F965AM54RqXprtzrQuv7R9SUlM5nqyknPS+AF/urDiVD/wLrf6BJyQ7+2a4/uYOGsK0kQorPQUbZajI/vwXO/KfKlpjxLwgWI5d+yuo2N7F61xoANuWX8/LieO7tdTlB4SF350aySpeSseEzwk4/DLgZUbCOOhVswDLcdf6GDTmT8bQZhlg3VT3Q5WxY+E7t9S0iQVj2CSJUgvnhFXDhu4Sv+QFj26+InYuRmT2ws46hRKRVKxqqaXVxhIqgcEP14MXeFn+g6rnMex059B5Wl6fTKjuR21pFCOlOI9pRxOkQGMU7VIB94Tu1r7TiC7h4InarwXi8PspKdfBC07TDJ6pXvKtWraJLly4ArF+/vtoyUVvBOK1WZv5KrISGMfVGeitbqe7ATmod5dFo2qGLEyWYH11TPXjhz1IF/6wQjB4Hm35R7Ui9ySqzIS4d0juoFPV109RzUtrAtnkg97oIGnyXCkS8fGLtO//+YRWY2FfpLtXRBKD5cWqbG3+CtPbQ9iSISyP5gytILruP9q541VnBtmDIn1U9DcMBwWIAxKovKc6oXjjzg4V5XHvtbZgVQXpMvQojVxVhdgI4TGRGF0TemlqHLJv2psLw1xnAKLXjcJz5PI4pDyCWfQwpbWHpx7WvDKrTS+shiOWfQdFmitwdMLJPxWhxOpZlq8CFLkKg/QYhINUTgE2LwKqjGCyoz4ltw9nPI8rz6BXvILx1BhsyR5AaJ3SgTDtqxDnDiECxqnMk9xO8c7gRic3JLY1qvwBN0xqhqAYwxo8fH83dNxqO/FUEW50U7WEcENubCoBZuk0HMLQGzRUuUIEH0wmdzoTuYyCpJSz/HN6/TP24y+oNQ+6GSAgKN6m7u5VdNoSANsOQg/6A2LvzRrfRat59zsq6u2tUFKh2q/uzd7HMua/Dqq+g/81wyUeweRas/gZ8adB1pLo4qyiASbdVPcfhIWRV/3EasmzKnYm0mP/MnuDFHks+QowZBys+r/mj1nRi972GYLgqMG2YBmW7C3F6DIGwbQqtJLwnPYVnyL0IITG2zEXk7LOfSonZUJ6r/nv7Ysy2nYhEbCxL3+nTDlyqmYfYshzWTYf2w/e/cnJL+OxpOOURSt2ZhJqlI/BgR3QGhnb0cAgLQiXg3H97aulJIuxKgor6GZemaUcPHRZt4ERFPkYgH7uBZGDYnt0ZGGUH0PpR02JZqFxlXFz4riqo+fGNMO4UyFutajKktYdt82HiVaoFap/LIauPytDoNhrOfwua9kR8eC34s6u222MszPinCmL8lsxutT/eon/N7hyeRNVBZONPan8n/h8Mugt2rYCpj8EnN6oCobuVHHcH6Qkevri4KZ9d0pzL+2ZwatdMnJEA7kW1BJ9DpbDgHeToV9RUlkqJ2VgXf0SJQ9XoEUJQIQxemrmRM1/8iVOe+5GHv15JoQXCEFRYbgpEJgWiKfbxd9Z97D3GwrLP1H/Hp+Om/LfPl6btJdVVDOt+UKnwcamqzkxGl9pXbnmCyqwq20UkLp2dVgKrSj0kOfXPKO3okewJq9bgHr8qvNy8X+0rNukOnkSKKmJ/arOmaQ2P/uZt4BwF6s5tQ+hAAoDDje2K1wEMrcGT3mQ4/WmYdIdqrxgsVlMylnyoghanPqlaMEaCsPwzNY0ks6vqMNLrIhWkmPFPKNkOG35URT0dHnWRFK4AaYEnqfadp7ZV3U6G/00FSvaW2Q0G36225UtRwZNz/wPHXKEyLxZMUJkjJTtAGOq/dyyqtgl70F14krPoN+1iun44jB4fn8i99v94angqKe5IVYvUfS37BLlrJaGrvse6djqR634keOlXFCUdQ9hWHRrKgctfn8MrM9ZTXBEhELaZtGg75740k8JIVRq+lJKAvx32iCeq1/cxXXDyg2oKTrBYXXzaFt5J15FkFKBnH2oHIsEsR4TKEIFCePMsyD4OfnoWTnlEFR/cW5PucNL9kL8BeeF7bArE4XM66JjiAz11RDtKCAFmpBSx6H2YeI0K3A/5s/pe21tae+TIlwi495+hoWmadqh0aLSBM/NXI4WBHd/0t1eOEdKTilm6PdrD0LTfJeRKw8z7HFG0uebCigLY8itc8K7qXmCF1bSKgo2wfZG6c7X116r1f35etVrN7guuuN2PvQCn/x0+valaZgSueBj+sOpgEipTBTpTWquWdfEZKiCydoqaFnLGM6pjydf3qsyLStsXqO4mF74LA29TU2BWfK4CJ91GI7zJON8eU7W+Fca97H1kziIcF04k1OYUXCs/rfW8FGQOwDbTcPhSiERs1QBk91QU0zSYt6GA9bllNZ5XGozwyoz1/OnEdvjsYpyyHIFBuPMYzHanYOathEARwpukqt4vn6TaxI58EX58GrFtPo5JNxN31suUyrgDeg21o5NhCNxlmxDueBXkGzteXZ2d+gRMuh0G/RF8qVC6Q9Wo8aaAFUJm9WSLTMfhMHFalu6poB1VEl1BCJSrAtJZvdXf39yVcMyVqm14yXaIbwIuH/hSKS3TnxBN044MHcBo4MyCVdhxTRtEB5JKticZo2wHO4oDzNtSxM6SIA5D0DzJS49mflJ8B5A6f4BKAhGW7ShhS1EFwYhNss9J96Z+spN0BxTtwAkBXiOA01aTeSvMRGwrjFj1de1PyOoNWb1g0q0q0wFUtkPvS+GMf8La76qvLyVM/rPKpjjvDfXDcOtcVeH9ggmqi0llenvH0+HzO6uKh35+h2qfOuslKNyoghkXvKO6JvhSVS2NvYMXlQo2wJKPoMOp0LQXZHZXNSU8iYiPrqn9POSswJGzhPDwh2D9dyqAUikuDXnW88TLIM5Zj0Jic+w2J1FmphKyHXtO5GeL6g5eBgMVJJUuxfjqHsTWX8FwYHQ+G4bdR1GTIXjsItylmxFNe0K74arOx49Pq6k6gNgwXdUmMXUAQ6tbslmACJaBwwXT/6E+C6BaD5/7H5UllbcW2gyFinz12bXCWKun8V7xCVzUJ3t/m9e0RskRKUEsek8Voa6sz9T7Uuh3PWyerb7HklsjvYkU2YmArg2jadqR0XCuerVaOfJXN5gOJJXWmq34+7pefL9sNhKId5lEbEkgYiOA7ll+zuiSwSmdMoh3H/xbdGdJkG9X5jBlVQ7LdpRgSzAEOA2D4O47wb2a+bl1cBt6ZPkP78FpjY7DkCQEN2N8/yBizXfg9OLodQly4B1IVzy1zlgYcreaRhIqrXpM2jDvDRVgSO9c+85yV8Ps/8E5/4aJV8La72HdVDX/PrG5asP64VXq4qpSXLoqaJnVCwbeoqaM2JZqsVq0WdW9qMui96DjabD+R2jSBSryVAbIruV1PkWsm4rpTqL8mhm4f3wSc83X4EvHHvUyxkdX4d5rbIZhknDeeIozjidsmwgBCZ7aP9N+r4MHBzoxXxte9ePYjiCWfgRbZpN46afk0QSn4cRc9J4qYlq6q+aGAoUQpy8wtdrFmQGEFQR/Jrx5dvX30Nrv1T+D71ItjCsKVPDC6YMZz7C9+610i/PjMUDXitWOJslxNqz+BWY8U33B/PGw+H24+EP4/HZk835IZwLhch280DTtyNEBjAbOLFhFuNnAaA/jgH2xweZvG0aTSCmX9M2mVzM/cW4HUkoKK8Is31nKvC1FPDFlDf+cto6TO6ZzRpdMemcnYhp1T27PLQsxfU0uX6/IYf6WIhyGoEuTBC4+Jpt26XGkx7kwDEF5yGLZzhK+WZHDte8u4IbjW3HFcc11216tTgmhrZjjTqyq+xAqw5j9X9i+ADn4T7B6nyyMtPYqwLB38GJvs16CSz+Gs55TGQSGCVvnwdzXoDwfDKdqIXr5F7D0I5VpkdEFepwPC9/dPW3iJVX7wTBVYGPZJFV/Y/o/VNDi6m9VYCKzG8x/U+1XGGraCEDB+qoWo2W7VODitdNV4GDUy2qaSl3jj0vHnPkvSoY8xdPGtQw66SYyEjx0WPMFRlZvdbG3Y7Fa17YwJl5O/A2zKSAdK2JxyXEt+HTBthqbvbxPKv6fn6i980rRFlg3jbgO5yKd8VWdXGpTV90QTQO8jggiGFEtUcdO2J1dEVL1L1ZNVivNeglG/kcFwzK6wrjhlPX/I8sr/HRvmYhl6Ysz7ejhNi3MUCnCmwI3zFSBvWAhLPsUFr2v6jytmwbNjgVPIoVBJ2ruoKZp2pGhAxgNmAiVYJbtJJDQMO42Tlhl89Q8mxMSi7im7H8EWr4JpnoLCiFI9rkY2DqFga1TKCgP8/OGfH7eUMDnS3fi9zg4JjuRDhnxpMW5MA1BUSDCpoJylmwrYXVuGYaAjhnxXHpsNr2bJeJ1mTXG4HOZ9G2eRJ9miXy+bCcvzthAaTDCbUPa1Pfp0BqCUBnG9CdrL1q5+RekLwV6XohY+E7V4wlNVcvUupTuVDUtpv9dBRsAWh4Po15RtTC6jlIdRCo2qountichU1qDFUF0PB1aDVJTUyrvHCc0heEPwY4FVdszHCqLomADdDxDBTk6jICdy9QFW0Yn9ePTk6SCFd8/XDW+ZZ9Azwtgzis1x244oGlP+P4RUoY/ziXHd6KstJCOngLMNVvVD9muo2DYvfD1fSr4YoUR237FyD4d25Zk+d1cMaAlr/+8sdqmT2sfh/iolqkuu4kN03F3OhPpcCI7jKh1+o5sMZCQM1lnLms1CAHJrnJwJahaL5FS2L4Q1k9T8/dPuEN1B/roGjU1yp8FG35C2hLriq8oM1Lpbvhw2PrNpR1d4kUJomwXrPhCTanqMEJNk2w9FFoMUFMaC9YjT7yfsDcNq1QHLzRNO7J0AKMBMwvWADSIFqpfbFDBi1Oaw9iUItzLwgSDhUhfWq3rJ/ucnN4lk9M6Z7A+v5zF20pYk1vGr5uLKAmqO7Ruh0F6vIsWSV6Ob5NCt6YJBzzlxDAEZ3drQpzL5M05W8hK9HDDyR0P2/FqjUSgELHu+zoXGz89S+iUp3D2uVwFMaywuoAPFKqpIPsyndD/FjWd5JRH1P+vmaKyJD65AS79FATw9qhqQRBhuuDcl1XGxrsXqudXKtmuWqBe+J66C9a8n1qvRX81npYDIGcFvH1e1XOEgOPvhO7nVQ9eAKz6StXh2LEYNv9Sfexn/lMdlycJabjIEqW4XDsR66bDhhlqP8snqWkt5/4XPrxa3a0rz9ud5SRxScm1x7diZO9mfDRvK+XhCOf0zKJValgVVKwr8yOhCWLmcxhzX4Mx45CRIGLdtD2LZYuBWOf8hzJdwFOrRYqjGBEJI8JlgK0+Q9l9wOmB7x+B2S/DaX+Hfjeq2jOBImSXsyhwZUEogrAkDqkvzLSji09UqKmTSc1VJyuBmpb43UNw4n2Qswran6K+d5xxFOnghaZp9UAHMBowM381EoEVH9sBjGX5kodm2wxsIhjTFihPAMCoyMOqI4BRSQhBm9Q42qRWXZTYtkTCfqeUHKiTOqSTUxri6e/XckLnJjRx687C2l5sW03VCBTVulh6U6kQiYQT2uFrM1RdpG+ZDc2Ogbg0KMutWtl0qiyL1V/DuN11HgwTuoyE0a+qi/3tC2DjzzUzOKwQrPteTRORtdwBti1VtLPfzdBqoMq+aNIDPrtFdSaZ9eI+A5dqLnPL49Vc/3239dF1cNL/wfF3wK5l6iIvtR388j9YOwV71DgcZdsR3z+E2LFIZYEccwUg4au/qB+4M59TBd5mPgfZ/aql3bukpJnXwR+HtQEEkYhFUAi8/W5EfP2X2l+LNsMQH1yusjwmXo0YcDMMuEXVIfGlEDASKZNx6GtMbV9Oh0AEChCuONVZ5IenVPvg+Aw45io1f//Dq+CrP8M134E3GZnano1BH/F2WL+ntKOWT5QiWhwHc8apduBI1bXq5Adg40zI6gO9LkZmdiNoxoHuzaNpWj3QV2sNmKNgFXZcBjjc0R5KncrDkj/PtMiKg0s6qoCEdKvCmUZF3iFt0zDEYQleVBrdsylN/G7u+mAhET23WdvNMITKHuhxQZ3r2L0uJRSKEJBeSO8Ev/wXpj8FSz+Ei96HzK5VK/e/SU3PWDBhryKVFiz5UN3x7XeD6jZSkV/7zvxNYeeSugecsxJ6nKeCHE6fqqUx7D61v7r8Ok7V1thXuFwFItZ+rwIGc8bBhLGwdgqy/00IdzzGa8MRG35UwZ2cFfDVPSpw0u96tY31P0Dz45CthxLy1d7m2bCD+EI7SIxsxysLsTudhWx7Us0VT34QVn2txlI5vul/h7fHIDfNhtT2Onih1SnRLEW4k9SUptfPUIVxA0WqlsrXf1HBvHNeUMHBbQuQfS4jtyzM+gJ9MaYdvRwuE+yw+tv/6zgVmC7Lhbmvw1ujodUJajpW0x7g8lGi26ZqmlZPdAZGA2bmr8aOz4r2MPbrXwttdlXA/X0Fzt1BB+nwgOlEBOq4UKtnTtPg0mOb88R3q3ln3lYu6ds82kPSYoBHlsGXd6nAQ/axsGVOteXyhDsJJ2RDGNwihJj6iJpDf/bzam790k9g+MOqlWnpLkjIhP8NqX1nqyarKSDbFqiLq9qU7IDkNnV3CElprWpoVAYs2p4Ipz6pam7UpXSHqmlhulSWx95MJ/S6GNvtRzY7VgVWMruBKx7zzTOpNVowfzxc9IGaZiIl0ptM5KwXKLXiq60mBCSJAozpT2Eseg+sEI6UNtgjnsA+458YRZv2dHyRHUYgFr6nipxmdIYeY9U5zV0FCyZgrP0Wjru67mM8QKZp4LMLcETKkKaDoJlIwHbroEgDl+qtgGCZChbuXKqyLipbG1daMlEF3jK7QqgMy+HhrDeX8s41/fTrrx2VhBAkuCUs+lK1E87uq4IVyyepttwl21Ub7u7nIQ0XQdOPLtypaVp90QGMBsyRv5JwZu9oD6NO83Mk76+RXNRekOGrnjFhuxIPOQPjSGiR7OXkzpm88vMmTu+SSYrPFe0haVHmkEF1wfPJjapexXHXqjoPTi+0GYoo2YVj77e1lGoe8FujoXB3gcqZz6mimWf+Uz2vrqshKdWPw85nwbzXa19n6ccqOLDyi9qXdz9PTf2otPZ7KN6mgi85K2p/TssToLwALvkQPr+jqj1ralsYNQ4pBDJQjExsTiS9O2UygcTQxqpiobUdR/5aiG8C7gSkL50iO7HGagmiCPP9ixA7F1c9mL8O453zsS98n+Img/Ec2wkhIyAtXJtnw2lPqu3PfV39eG7aC85+Hpm/QdUIIVj7mA6Axwjiy5mP+fXd6i69MHB0OBXP8EcpMjKwbf3DvCFKdRYi1s9G/PQv9ZnM6AynPgFrp6qWxnvbPBuyj0O2OoGr3lnFpf1b4jMF6NdeOwpFBJiBfETzY2HHQvjuQVXwucdYlS34yQ2w8kvoNgpMF2W11LnWNE07UvQUkoYqXI5RshU7RjuQRGzJo79atPHDkFpKdEi3HxFDAQyAMcdkY0vJ67/UcXGmHVVs4VCFy8Ll8MUf1JSK4q0qA+L9yyFvFSKsCk6GcGEPukvNra8MXlSSNnzxR/DUvJCvxt9MpSac8Ieay+IzYMyraltnPA3OOHVHrN1JkNZBXdwvn1S9AKa/GZRsha7ngsNTc5uuOOh9CVgBmPxnOO56GPsWnD8eznkJuX464vXTMV8ZiuM//fG8fx5JkS2qE8n+OH1qiszQe7CdNad1CAGOovXVgxd7Mb65F3c4n+Kwh6JIPKUkYY94HLYvUtNUclao9P/1P8B7F6tgi+PQA46GIfAVr8R8Z4wKXgBIG7HySxzjzyaB2MgU0w5OgiuEmD8e8eFVsGORes9smgUfXAFNuqvuCXtzJ0ByKyqcSYw5Jpuze2Rh6OCFdpRK9DkgWAwTzlfB84oCFbj+8R/w079gxGMqQ84VT4Xp10FeTdPqlQ5gNFCOwrUIJFaMdiD5cK1kbRFc1EFgiJr1KqQrAbMit5ZnRk+Cx8nJHdKZuHAbu0oO/W6u1jgEzEQ44c6qB8rzVMeQ9dNVIKHNUGRETbswhY1wJ9SdHWFHoKIQmvevfXmzY9Q0E6cXklvCmNdUYAJUC9QL34Uv7oLXTlXrXfUVdDgNklrCiX+FlLawdkr1bXY9V00nmf53OO81lbGw9/4umAAOL0y8UhXqnP1fVYtj7RTY+iviu/tVO8lKOxZjjj8L4XCrwm21cXjUmM5+Dtvho8yu2RHENA3Epp9rfz5A3hpMu+p2nmVJ8Phhwds115US8dWfa04JOAg+SjG//WvtC4s2Y+5YqOqhaA2KO1yA+PHp2hdOexz6XlX1/4YDmvfDbncKUzbZfLZgGw6hL8i0o5NlGngCuxDfP1L9O6DS1rlgupADbgFnAmVl4fofpKZpRzUdwGigzPyVAFgxmIFREpK8tNhmQBNBy4Taf/jb7kREjAUwAE5sn4bTMHh77pZoD0WLAodp4BeFJLELXyQPWgxU6bJirz+V7gRV52L1FMLOJBLNYhLmPYvIXVl7h5BK2+aqTImmPas/ntkNhv1VZVnMfR2++atKzR35ouqIcO5/VGZHwXrVrs6fpWppfP8w/PoqvH+ZqtUx8j8qq6KSx6+Krm36Gb78E3Q5R2VYjH0LOoyAn19UUzG8KXD+m3DstSp7osvIml1LKpXugtyVWGf/G7zJ1ZcJA855ASlMrLgmlKf3JWTXzNaQEtW1pC5Or+rOspthCOSO2rM1ACjYUGeXmAPhkEHVkaIOYv1UHA79VdmQpDqLVDZNZbHcfZXnVSt+Lc/8F5Yjjj9/X8StE1cyuk82ehKhdjQSAhJECSJUAmu+q3vFDT9CiwEUR/QnRdO0+qdrYDRQjvzV2N50dcERY8avtCmLwMg2da8j3X6MQJEqrLbXxUq0eV0mQ9ql8tHC7VzdvwV+jzPaQ9LqSbxRinvtVxjTn1QX9nHpMOiPyL7XINqdrO7yOz2q4OW8N7FOfhhbOHBPfxhjwVtw2lNqOkNlHYl9pbaDdy6Eyz9Tldzz16lWq1YYEjLg2wdhzbdq3ZyVqi1qj/Ph2Otg9Tfq8WOvgXcvqhkoKdiwu6PIBerfALuWI7P7IXJWQtEWmPJQ9ecMuEWlBZ/9PHx2q5oeA6pFXlHdATyxaRalx/2JuKunYqyZjNgwQwVV2g2HpZ8g255ERbMhhIQPl0tgWbJaC1XLsrFbDMQwnerY92H3upQKI3lPNz4pUVNm9scwD7l+nI1Rs+Xt3hJb6PToBiTFUYiY/nf1md0ftx/63wg9L8KOS+eKDzbx4+p8RvdpRo9mCfo1145KTqeJDwuCJSqYvG9x50ruRHB4CJXqziOaptU/fVupgTLzV8bk9JH8gGT8CsmJ2ZDsrjvt2nYnqnnmgYJ6HN2BGdY+lYgt+WTRoaelaw2L27RwL34T44s7VPACVPbCV/cgf30FK6U9dnk+cs332OWFRE59miJnC9yRAoyFu6c2/DoOBt9d+w7aDIO8NVCyDQo3qYBByXbVxjFcUT14sbdF76v0dmlDcmvIW133XeVVX6lq8ZVKdyH6XV97kNPth54Xqukpv7xUFbwACJWoC/o6yIzORCwbKW3Ess9U1kf+OhVYWTgB46OriStZSdLGz0iYcT+JmyaRJPIwzaqvm1IjFfv8CSoYtPe2mx2L1f82glbVulJKZHrnGuvuWZ59nMoiOUQVRjJ2/1trXygM7I6nE4no9soNgWkKjLxViHlvqFvJu1t215DWAcIBGHALVv5GdoW89G+dwue3HM8fT2yHSwcvtKOU0zSgaKuqp9RtdJ3ryR7nUWHr7AtN06JDBzAaKEfeypicPvL6CvVD/9QW+58zLnf/sDRicBqJ3+PkuBZJvDd/KxH9Q/ao4LUKMGbUPl/emPMy0rYo7nYtxSc+Q2GbMRTIVCxbIspzqzqL5KxUKbfnvV5VI8KXCkPvhUF/gKmPVW20dIeaArL4fdX+tLbgRaVwqSrI6fRCoLju9WwLklqojiLXTlM1Mr65j8hlnyOb96tar+XxasrIjoVgGDXThBe+C33raEvqTsBo2gO/WYqx7CM1PWXR+6o2yF6BFTH1MYzc5RhzXsb85Hqc44aSGNq4p5ZE2DYpTjuWyA2/YI38H/bJD2Nd+Q3BUW9QaCfV2G2ZkYx1zn/URenevMnYZz4LvkMPYIQtSaTrecgOp1VfYDqxx7xBuVl3MEeLLUlmCcx+Wf3PzOfh3JdqFp11xcPwh2DHQmS4gpmRjmA4uah3FpluE6fum6odxbyiDDH7fxAoUdl4ae1rrtT/RkjIoqy8jmC6pmnaEaankDRE4XKMki3Y7c6M9kiqyQ9I3lstOTkb4p37D2DYbtWRwajIIxYTEIe1T2PmhgKmr83jxPb6AqaxMwL5EKmjcKttIcp2Eo7LqLls36kNiz9QF/V9Lofjb4f0jjD/LSjPR3YdiVg8UWU8DLlHBSRCZVCeqy7Ma7twMhyQvwmGPwyf3QxNe9R9EKntYPMv8NWfIa0jDLsXNs4knwQSz30Vd+4ysIKwbT58dI2aMnHRezX3u2GGasna62JV1LNyukp8Jpz5T8TH1+Fo1rfuVqoAOcur372rKMD86Gp8Yz+ilHgAwraDAtIxmp+BYQgsy66zhEjIdlDWbChx1/+MWPg2RsE67FZDsdudQomRRnLtTztgRZaf+BH/wjXkXsTWOUhPMrJpL8qM5FrreGixJ9FRhljzNSK4O8i3+RdYMRmum6aK2RZsgIwu0KI/rJsGx17NtsIgcUmZuKQkYunAhXZ083lMkBaEimHqw3D6M3DSgypbcN00cMdDtzHI1PaUiiRABzA0TYsO/cusAXIUrFYdSPzNoz2UasavtBHA8OYHULHfdCMdHnUHOwY1T/bSNtXHB/O36gDGUUDuVdCvVnXUmgm7UzDT2qsuIFm91HSQZZ/B1EdV14+uI+Hnf4PTh33jLOQJf0YEizDiUhE//gPmj4f+N0HbE1UWQyVXPAz5M6S2RXoSEZ4kuO4HVV+jzTBYN7XmYAb/CWY8owISOSsgbw3WiffjMyzc67+DSbfXfE7BBnWHLXd19cc/v0NlYVw/HXYuA5dPZXd8dK3KNIlLg5T9FLlJbqWyTPa2axnOUAE44qs9bNsSKcEnKnDaxQgpCTviKad6HYKg7SJoZuPqfy9CWljSIBKxEYcpS6rUjgNXa8x2bZFSqn3rmSMNgmGAM1KKaN4f0jtD2S7YOg/mvQEbZ8CpT0K3Merz6U2C7L5YpQU4ElrS3HDpeheaBvisXEQkqL57CjdDsAhMtwqOZ3RR3wM5qyHdSSCggxeapkWPnkLSAJl5sdeBpDiksi+GNIO438i+qGS7EzHLc47wyA7d4Hap/Lq5iE0FFdEeinaERVwpkN6p9oWJzYm4U2tdFDCTkWMnqIv7ZZ+qu74Db4VzXoSTHoCfXwDDxB71CiUykQKRSaGvI4TLVPAC1N3h/jeDJ0n9v9MHY8apCzCHG7H8M5Ux8dktyHAFnPEMDLuvatpEVm81bWXNdyq4UGnF55jNjyPeZYDhgkF/VIGFvc0Zp6a47Ds1Q0qVYbFyMky6TU0rmfdm1fbXTYP2I2qm51fqe7V6zj6EVTPLxTQEyZFNeCddifPFY3C81BfvxLEkla/AadTMzwqFLIJhjlhdCsuy9QVtAyIEJBtFEClX2U/rp8H0f8CW2XDuf1W3kXfGwoTzwHRCWR6ySQ+C/uaYwgP6tdY0HA6hsu02z4ENM2HXUjUNa+scVXvp7dHwxlnIjI6UO2v/PtQ0TasvOgOjAXLkrcCKawIOT7SHssd7qyVhG4ZnH1jwAgB3IqIidgMYfbITeX/+Nj5ZtJ3bhuznbrPW4JXhxzn6dczxZ1bvRuFJwjr/bcqMZNgnxVwISAhvRbw6HAKFVQs2zUJ2GwM9L8Q+4Y/ItsMpM1MJ26rbTqJRiFj/Y9X6gUL4+l4VtFg7VbVTXfAuHHMZTLxKdQqp3Ofm2cjOZyOG3qvmJxdvVVNCvvm/mlM6mnRXg/zpWdg4U2VNHH8HhMvhm/tUkCJ3FWydi7xiMkx5CLFljtp/70tVhsWnN0NyS+h7pWrXWsm2YM7LcM6/YfKfq9qYmk4YeLvK7CjcVH087gRsT3KNbiF+mYP5+mnVz+H2hZivn0bCddPJN2InUKvFHr8riAiWIRa8pYJ4Do+avpTcSgXfzn4e5r+tOvjEZQAWLPmIcK+bkTIWJzBqWv1LdJRB4U7Y/LOaRuj2Q8+xKhswZ4UKmjfvD750ystrdo/SNE2rT40igDFnzhzGjRvHkiVLyMnJ4YUXXuDkk6taqEkpee655/jggw8oLi6mT58+PPjgg7Rq1Sp6g/4dHPkrsBNiZ/pIICJ5e5XNwCaQuJ/OI/uy3YkYZbHb6cNpGvRrmcznS3dy0wmtcJg6Yamxsm1Jkas5CVd+j7FzEWLHImRGZ8zmfSm2U7BqmR/vMUIYUx6ufuG9m1gyEbv/zRR3ukxlCuxOFnA6wFj2CXiqT6MgZwW8PQZaD4XOZ6upInPfqBa82LPt5Z/BMVfA0k8go7OarrKvhKbQfSy8cRZEAuqxgvWwZY7qPjLwdvjpX5DYHNl1FJTtQva7UbWCdfqQuatVYOSiiQhPAuKdsapex95WfQ2mG+uaaVC6S7XbS2yG2DgTY9ItNc/xsPspN1LYu+iN0yEwFn9U6znECmH89C88Q58gYDWKryrtMEtwlOMMlSDeOFO9ByvtXArZx8LQv8COxXDygyrotuZbmPwn7Mu/IBzWwQtNAzBNEOW5iLfGQHCvQtHbF0D7U6D1EOhwChKDUiMZXftC07RoaxRXZOXl5XTs2JEHHnig1uUvv/wy48eP58EHH+T999/H6/Vy9dVXEwzWUbQvxpl5y2Oq/sWn6yXFQRjxG51H9iU9iRjlObUXL4wRx7dJoaAizI/r8qM9FO0IsyxJoUyhsMkwivrcSVGzUyCpBVYdKeZuuxSx8ou6N7j8M8Q+UzM8VjHGvFdVlsO+GVRSQnme6ubRejD8xrYp2QoJmarWxr4G/wm+vb8qeLG3he9AywHqbvQ5LyLeuxjxzgUYH1yG+O8gxEv9kd5kyo65jfykY7HcKRAsrXUYdrtTKDYzyY/rSr6/N/kyg1Crk1VnEP/uNs+p7bDOe5NA+3MIWdXPh0MGMdZ9X+dhik0/47TL6lyuHb18ZhD3zrmqY8LewYtKW+aof+9coj5TW+fC9CeRnc4m7G8Ty187mlav/I5yFQgP1tLlavU3kJgNFUXgite1LzRNiwmN4rbWkCFDGDJkSK3LpJS8+eab3HjjjXuyMp566ikGDhzId999xxlnnFGfQ/3dREUeZnkOgcSW0R4KAJYtGb/Cpk+GIN17cAEM252EiARUm0hXwhEa4e/TLNFD6xQfnyzezjBdzPOoYNsS27ZqlIWo3X5WEmaNh2Tlc2a/rLIdPr9DzTt2xcPZz6nCaeEKEIb6p85tG2pjk+6AM5+B0p2w/HO1h85nQbNj4Is/1P38HYvhwneR3z2AKNlefZkVxnx7FJ7rZhKQqZQYafgv+QTz/YtVgAVUXY9+NxFqdXKNWhQldjyONmPwtToZAwtLOCgXSVhWzZoVtnAgE7LqPotxaQjDICZbFWlR5bMLESmt4fNaitNWWv0NtBqsAnDSQp75HMGMXpTYsfl9o2nRYIZLESsn173Chh+R/W+mWCagKxtrmhYLGkUAY3+2bNlCTk4OAwcO3PNYQkICPXv2ZP78+QcVwDiwC5ojy5m/AgDb33J/l05V11UCxBG80zRtq2RLGVzRab+XcrWSHtX80CzfhRXlAEbla1tbN8sBrZN5d95WcsuCpMf/RreKo8De56q+91mffus4LYcXR+ezEEs/rrmw/XBEj/OIL18HDg8BZzIBy0XIkYin18WIKQ+BKw4uel91FGl7Enz/CGybp+bvtz1J1bhY8mHtO28zFD65CeLSoWSHqnfRbvjuN6+NlHK/n0fpSQJfMmLDj7WvECrDyF+NkZqKJQXFid2Iu2oqZvkuCJcj/dkEzCQCtqvW82PZkhISd+8MkHat64VtA/vY6zEXv1/7OPpchmvWs6QccyUlziwids2NROP9eCTF2vFE43O+v32aBiS5KiCAan1cVyFZUMtaDkB6Ugm1zKLM9mLbMib/njRk+tiO7L4PZJ1DHZ/XY0DZ7s+RFap9JdMFTi/hUO1/x2NFY34fHgp9PqrT56NxafQBjJwcVSQyNbV61eTU1FRycw+uhWdqagzctVm9FkwXCVmtwah5h3dfif7a2z8eDlJK3lxdRpdUg65Zh1BQ1JUJQDzFkHjkxnkw/LWcr5O6uZi4YDvTNhRx49C2URhVbKrPz0M0P3u17tsKw+rpqtjl+ulVmQkAwx+GSADx6ik4AkVgmMR3Hkn8KQ9DYjPocT4snADLJ8HKL6H7+aoA57Z56vlLP1IBjP43wYYfa6bH99hdWK3tidBtlGq/mtIaPr5BbcObjLjiSzVvef0PtR6TSGkLeWv2e9xmsHifY08AqjK/4nf/87v52sGIx6oKi1bqfQmEShGz/4M5/w2Srp0GGXV0iiFG/j4fRrFyPNEYR537lFJlKa2apgrXZveFLufArJdqX7/7+eDyIxxO3PHpxEL4OVZe1yNBH1v09nnI48tfr6aOdDkHFn9Q+zrdz0M44knzN4zXtzG/Dw+FPh/V6fPRODT6AMbhlJdXEvV5s/Eb5uHwt6Cs5DfqdwgVvCgqLq9R9f9wmZ8jWbjL5tbugrLSWuba/yaTONNNIGcTwaTeh318B0MIFbwoLi6v9TXule3nnV82MqZreo26BkcbIdQXwOH8PKSl7f8LJRqfvf0dZyJ5OD++QXX2OPc/sOEn2DBdFeG0I9ULa9oWLP0QWbiR4KjxlJFEykUfwJrvEIvfV4U4l35Stb6U8NnNcOx1cMknsGqyCnS4/dB9NASKVVeFfjfAOxfAmFfh3QtVUAVgyJ/hq3vg+Ntgx6KahUCH/kW1e01qDvGZavpJLaz0LhTklvzOs3ggTDwdxuJtNwJj4wyEOw5S2qqxf/FHtUq4Avn9w5Sf8hwVtqvas4/E+zGa6vt4Yumz91vHnugsx1m0WdW36Hiq6jpy3HVqqkje2uordz4bmdEZy3BTGPBAoD7ey3VrbO/TveljOzSH47P3e8aXRB6OUBF8eqvqghWXDqu+gvx1VSv1vQbpS6U4KAiXR/cz9Fsa8/vwUOjzUd3e50MHMRq+Rh/ASE9PByAvL4+MjIw9j+fl5dGpU91382ojZfTrTTpyl2L5W/xmTGLPtBF5xOIXvLnCoqkPuqUe+j4sbzJGWS0F2OpZ5eta1+s7oFUyz/6wnkXbSuiR5a+/gcWw+vw8RPOzV9u+jfJd6q5VsBjePk8V3Ww1CNoOg/curXU7Imc5zlAh0uknT6aR3OlsjIQmiPhMWLdPpoSUMPu/6p/uY2HIPbDmG5j6GBRvgxPuhGmPQ/cxMPf1quCF6VIF19b/oAITo15WLfG2zFY/TruOgrSO8M75EJ+BHP43xMfXV993kx7IAbdgOJzEO0OUhKoHDI6ECulGuNLxZXaBRe+rAE1aO7jwXfj5BVg3FbHqK5wnl1Euax9PLPx9Ppxi5XiiMY5aP3OGwBEpU61/i7eCMw5OfUJ11znpAXUnec234PDCMZerTjx2hCIZj4yFE7lbrLyuR4I+tujt82DH53AYmDmrwTBg2D3q+8QOqwLQ3iRY9IHK8EvvhBROQuGG88I25vfhodDnozp9LhqHRh/AyM7OJj09nZ9//pnOnTsDUFpaysKFC7nwwgujPLqDZAUx81cTajYg2iNhU4lk6la4pKPA+B0ZCdKdjFG6/bdXjLKOGfGk+pxMWrJDBzA0aoTs1k9X/2QfV7MlqNsPJ90PcekYG38gJak1Vlonyuw04oWBcMap9N21U2rfVVJz+OmfsHFm1WMZXdSFfdeRqiNJJW+yCnBAVWvWVoNUjYxAMUy6DS7+AAo3QeEmxIgnYORLMPN5KNgAI1+E0l2In/8NkQrcnc7A1ftyCsjArqMby+HgMMCbMw8xYYzKYAFVG2Tu63Duf6E8Fwo2HrH9a7EvReQi3j6v+tSn5Z+pKVVpHWDZJ3DctRAOqild3z8CF7x9RN+3mtZQmQaIpObw3QMq66LS6m8hqzec8ihMeRDOeYmg3egvFTRNa2AaxV+lsrIyNm3atOf/t2zZwvLly0lMTCQrK4vLLruMl156iZYtW5Kdnc2zzz5LRkbGnq4kDYUjbyVCRrCS2kR7KLy10ibBCf0zf992bE8KjoJVh2dQR5AhBP1aJfPNihz+OKwtHudv1x/RGi87LhPT6YNwedWDya0huaUqhlZ5Ee70wuhX1JSS7QsRqGK3htuPcfFHyMTW8OkNcPo/oEV/2DSr+o7SOkCzPvDjP6o/HixW01cCxeBLg7Ld9XwCRWpayN42/Kj+ATW20O62pJldYeuvqnDbiMfAlwKT74FNVYESMeNfiAUTSLziGwo4cl144mQ+xkdXV523SlYYvr4XTrofe8cyKoxE3ZHkKORxAbNfrb1uy6L34ML3YOZzULBJFbVtMwTKcojERNULTYs98Y4AcusqxN7Bi0rb5qusvaZ9kA43QenjyOXyapqmHbz99OlrOJYsWcLIkSMZOXIkAI8//jgjR47kueeeA+Daa6/lkksu4f7772fMmDGUl5fzyiuv4HY3rB83jtwlSGFg+aPbQrUwKPl0vWRoM4HL/H31IKQ3BaMiryoFPoYNaJVMedhiyqqDK/6qNT7lRhL2afsEFYY/BAvfVW1MK/W4ABZMgO0Lq68bLMacMBrh8iCTWkFFPvS/GU57EloeD82Pg5MfUlNAPrul5gAWT0Qee63qUtL7kqrHIwEIlUByq9oH3mUkLPtMBS+GP6wCIy2Ph+8fVjUn9gpe7FG6C3POf3E5jlz7PLMir3oh1L2V7ABvKvax1xGyGsVXlnaQ4sI5iAUT6l5h+WfQZpjq3NNmCHx+J3LArZSJ5PobpKY1EF4zhAgUIea9UfdKi96D3hdjO+IJR3TwQtO02NIoMjD69evHypUr61wuhOD222/n9tv30y++AXDsWoSdkA2O6AZe3l8tsSUMbfb7t2V7U0DaGGU7sf3Zv3+DR1B6vJuOGfF8tmQHZ3T9naknWoMWskzKWp5C3FXfIn78ByISgIpCmPMKnPc6FG1RhQbbnQQfXF77RgJFiF3LkMMfRm5bgJHRHqygaqMaDqh5yIap5vqTX/25m2chzvk3bPwJ3Anqwm3F52rZtCfgrOdg8p+qFTaU7U6GwXcj7Aiy+3mIDy5TmRumU9XFWPVNnccrlkzEc+xNhDhCF4Ry/2kV0pdKoUxF3wU8uhiGwC/zEHaw7haPAOEKGHI3/PoqzHkF2W0U4a7nE7b0+0XT9uU1AoiK0v1/piJBpCuewqAT/XdX07RY0ygCGEcLx65FWImtozqGQETyzmqb45tAguv3d+OwvSot3SjdFvMBDICBrZN57ZfNbC6ooHmyN9rD0aIoYHsI+jqTcuqTiO1zYeMslQHx4dUw7F444Q7wpqguJHUQeasRs15EnvMC0o6AMBEOFzTpoTosTH0EOXoczPw3YtVkkDaktoXBd8P8CXD87RAsgd6Xqbarm38BYagg59i3VVZDyQ5keicCzjTKZBxpGQlYu1bjqOxOUp6ntrlzad0Ha5ioyS9Hhu1Nqzklp5LbT8SbHlOFGLUjTwhItHfg+OkZ1VGn/Sn7afM4Bkp3QJPuyBtmUOFMp8yKq98Ba1oDYUTK1d/a9qeo2k216ToK2+XHDui/u5qmxR6dj9tQWEEcecuIRLn+xecbJIVBGN788FzMSFc8mJ4GUcgToHezRHwuk0+X7Ij2ULQYICUUk4KV1gWSWqgHQ2Xw9X0w8SrVCcSXWvcGUttBRlfEii8QL/ZHTLwSPr0FXh2ugiHnj4dwEPuMZ5A3zIDrp8OZz8KCt2DG02r7M5+HqY/A5Lth2acq9ffb+2HnEnjzbJh0O3LOq4SNqoBbmSMN+7zxqmvJtCegzVDVlrIOds+LCZhJh+ek1aLcTMYe8Xjt+z71ScqMlCO2by02eUUAc/s86HO5KszZ5zJVpHZf2X0hsTlEQjDrJSJmPGW2Dl5oWm321FyXElLaQHrHmislNEX2vJCSsLNex6ZpmnagdAZGA+HIXYawI1gp7aM2BsuWvL7Cpk86ZPgO391Yy5eKWbLlsG3vSHI5DI5rkcSkJTu4YWBLHKaOAR6tDEPgpQxTBgm5UnB3PANj6qMq8ADg9MHKyTDoj6oQ5b6yj4Xi7dB2CLx7cfVlUsJP/4Im3RG+NIxpjyEWvQuRoCrqecIf1PYn3QFnPI2UErHgbfXczmeqC71Jd6jsj1ApxtpvcfX/AyGRCEDYdlCccTzxN/yCsXMhSBCZ3RAdToNVk6uPJaUNdu/LCe1TX/NwClkmFa1Px3NZO4zpjyPy1iDTOmAP/gsV/vaErSOX/aHFHiEEPlGGKM+H2S8DtqoJM+Y1WPoRrJkCLh90G6PuIi/9GLxJ2G1OpMJM0YVeNa0OLpcJIammDhoOOOlBVbBz2aeqFlnHU+GYK7A9yYQr9O8bTdNikw5gNBCOnQuQhjOqBTy/2yLZUgqXdzy8FxO2Lx2zeNNvrxgjBrVJYdqaPKatyePkjunRHo4WBT6jAk/BUsxpj0Duakhti3XK49iXfIzxzb0w4GbVUaN4GzKjC+Lq7+CzmyFnpfrh2PVc6DoKlk+CzbPq3pE7HibfhchdXfXY1nmqrsbYt9R/f3w98pqp0G00YtcyVYdj7/UBvMlIw1FtKrOXMkTeaow134DhwOpxMcaIx+GYyxFzXoZwBXb3sVith1FUD/Unym0vgcReeM56HVMGsISbCulD6jaYR50UIx+Ruwb8TdUFVXyG+jx5k1Tgr+UJ4HBBXKb6DGQfg1z0PtbwxwlG9EWXptUlzghAyU4VvIiEVJvuuEwY/CdVTym1PdIdT7nOvtA0LYbpAEYD4dwxV9W/MKPzpSKl5NVlNp2ToZX/8AYwpC8TM3eZmt8vYv/HZ7MkL+3S4pi4cJsOYByFXKaNd90XGJ/fUfXg1nmYr43AvuAd5NnPI94araZ3sLtyRFp7uGAC5K4BOwyrvoL3L4O+V0HB+tp3lJitimzuG4wAlVkx898qrX7dNESwGOHxw+z/1bopq/8tVIgE9RkDEkQxrsm3I9Z8u2cdc96byKzeRM6bQPD0VxDSImT4iERs6quIm21LyvEAnt271MGLo07hVpXF9P3DsH1B1eOeJDj3P6rAbfMBgKU+B6ltwIpgn/QghRF/lAatabHPMARGqBDx3f0q8AcqkNFlJAz6AxguCJeBw01AZ71pmhbDYv9qUQPAueNXIikdorb/GdslKwvh9JaH/0vNissAK4RR1nDqSgxpl8LczUWszS2L9lC0euazCzC++Wuty4yKfMS7F+0JXuyRuxr5+Z1QlqMCFwsmqArwuauhac/ad5TZFTb+XPdANs2EzG7IEY8iPrwScldB36trrCY7n02k+QlYVlUbVMeuBdWCF5XEtvmYq74kLNyU257dwQtNqx9CAMFixLTHqwcvAAKF8MmNEJ8Ou5aqOi/z3oCJVyEDRZSRGIURa1rDkewoUTWPKoMXoDKblkyE106DXUuRRVuwnQnRG6SmadoB0AGMBsAo3Y5ZsgUrSgEMKSUvL7Vpmwgdkw7/9u24JgCYhRsO/8aPkN7NEknyOnh//rZoD0WrZ0ZFLoRKa1lggicRCmufDiU2zIAm3WHQXSolXhjqblevi8FVS9HBcAX4m9Q9EG8ydkYXpHBAeb76YerywYXvwoBboN/1cP545LC/Umzt9YM0VIa5bhqc+S84/00Y+RK0GVZ1GPNexWMVHdjJ0LTDyOk0IZAPyz+tfYWKAtU9wemDAbdCfFPklZMpa34yQdtVv4PVtAZGhIoQSz+qfWGgCILF4M+iMOSp34FpmqYdJB3AaACc22cDEEntFJX9z9opWZQHZ7YUCHH4MzCk04d0J2IWrjvs2z5SHKbBoDapfLFsJ4Xl4WgPR6tPRh0z75w+9SNwf4o2q5oXw+6D0eOg9WD47iEY/YpqnVoptR0cfyd0OqvOTdnHXk+Jq1n1GhEzn1cZHlt/hR1L4JMbkcF9soSCJapt6vSn1Lpf3wtZvWHkiyqoYoXQUze0+uY2QsRFdql5+ftpPUx5vpq370vF7nga5WYqFRE9G1bTflMkpAp11qUsFxmfga3rDmmaFuP0t34D4Nz2C1Z8FtKTVO/7llLy4mKbNn7oegQ7GVrxTTEL1hy5HRwBQ9ql8vWKXXywYBvXDoxecVWtfkXcqZj+LCjeJ/smWAIJTet+osODjMtQmRgbZlRbJOMzkeePR+StRlghqCiEKQ9BRmfkqU8ivr5HdSap1Pw4RM+xhMJgx6VjOr0qYwNUAGLT7sKgnkRsb+qeeITLlLB4InxzX9W2KgpgxjPQfQz0uwHbk0TQ9MMR7DqiaXtzmIL4nbMwgkWQ2U110akoqHVd2aI/0ptCKBim3EiuNjVK07TaxTvKwRIQl6ZqK9VCthhAsZ2EDmBrmhbrdAZGA+Dc8hORtC5R2feP2yWL8+Ds1kcm+6KS7c/GUbBm/3feYky828GA1im8N38r5aGGM27t9ykTyVijXgPHPmm2pgs7LgNZR9aE3f9mIoltsAfdpbI1AEwndu/LiAz+C/l2KqGUzliGG7nhJ+wWA5F9r0a4E+Ci92HI3dD/RtVKsvt5MP3veEx1EWef8ljt+zz1KcqM5D3/77XyYNrjtR/Ykg+h/SnY3S8grIMXWj2Ks/Mxvvs/MF0qS+n4O2pdTzbvj4zLJC/go0Qm6uCFph0gT6QQ8dO/Ycifa10umx2D9GcRjujghaZpsU9nYMQ4o3Q7jsK1BNvVnUp+pNhS8vxCmw5J0CX5N1f/XayEFmAFMYs2YCW3PbI7O4xO6ZjOjLV5fLhwG5ce2zzaw9HqgWVLShI6E3/9TxjLPsbYNhe7SU/srmModWTgO+VJHP4sjHlvqG4KnkTsAbcR6n4xJWEfrj634etxCSJcjnR6CZjJBCz1p7hYJmKk9sdx4vE4HALv9IcQv7yoqhs26QlOLyx4BwKFCNOF+4R7KJLJVLQ5E89l7TF+eAKRvwaZ1hF7yF8oT2hHeK9q8kagqPb6HQBSIq0IRRxay1QhBE6ngZQQDuuAnnZgHA4D045AZndVuHPOK3DZJBjxGPz4NJTnqe5b3c6DIXeTFznCX0aa1sgYhoCiHbDma+h5PlzwLnz1JyjcXNXWe8hfyLeOYJqtpmnaYaQDGDHOuXk6EkEko3u973vyRsnqIvhznyObfQFgxWeB6cTMWdKgAhipcS4GtE7hjdmbGdWzKXEu/ZE6GoRtgwIycfa8GaOnhY2pLtotKMKPe8B9eI+9EWEFsB1eKsxUQruzGkIRgxBpUNkReZ9rfduWhEIRpDTxVgYSpKzZlWGvlsPltpdAYm88Z7+OKQNYwkOF9FavjwHIfbNG9mF7kg5p/rPfKMJRsBqxYhJ4EpFdRhP2puO0KxCRChACy5lIiYzH0rENDXCZFgnWLsSyyZC3FjqeDlm9YNU3MO5kOOZKGPu26kNshWHpJ0RsnTSqaYckqbkq2rzsM3C4YfSrquh0RSGs/BKCJUinbguvaVrDoK+2Ypxr41Ss5LZIV/22tQpEJM8ttOmdDu0S66EfuGFi+Vvg3LWIUIdzjvz+DqPTu2Twy4YC3v51C9cNbBXt4Wj1qCrToPpVeTBiECQNTFQywyFMyYhELGS3MfDLS7Uut7ufT9Dw79m1bUvK8QCe3QkUNQMRYVcSjmZ9qrfRqxSfgeXLPOjkiySzEMeHVyK2zql6cMYzGEPuQbjjYepjkJiNcew1JLUaTLEzi7ClL0SPZk7Twp87G/HuhaqNI6iWqPGZcNmn8N7FMPc19c9udt+rqTCTdG0WTTtIySIXvrwL1kypevDnf8MxV0HTHrDyC6w+V4GekaVpWgOhf0XGMiuMa/MPhDN71/uu31whyQvA6Db1ELzYLZLcAUfeUtUmrwFJ8bkY2j6N8XO2sLMkGO3haI2ElBDyZWP3uqTmwoQm2Mf/gYBlHtQ2y2U8jHq5ZrFRdwLWBe9RKg4uhdjpAHPxu9WDF7uJH56AlNYgbchdBZPvxpjzPxJk/kHtQ2t8/HYe4oPLq4IXlUp3whd/gDGvV3tYNumBNfBOghH9k0XTDkaSqwKx6ivE3sGLSnNfhaSWyDGvU2Kk1f/gNE3TDpHOwIhhzm2zMEIlRJr0rdf9bi+TjFtuc2I2ZPrqMYCR2hH3usk4d8wl3HxQve33cDi9cwa/bCzgX9PW8vhZ0Sm4qjU+pXYcDLoPV5dzMWa/hAgWY3c6B7vjmRSJtOqdSQ6AlBLS2hG+/GuMnGWI7fORaR2ws46h1EjDsg5ue16rEGPOy3WvsPpbaD1I/Rtg7msYvS7BGZ+p62QcpYQAkbe67kD1xpmAjTzjX1C6HdlyEOHE1pTYifU5TE1r8IQAR/lOxIK36lxH/jqOwBkvEQno9AtN0xoOHcCIYe61X2L5MrASW9Xrfp+aZ+E14cxW9Re8AJDuRCx/c1ybpze4AIbXZTK6Z1Ne+2Uzp63NY3Db1GgPSWskSu0EjNT+uE/vg5ARQoaPSEQedPBib0WkIDIGYTQZjG1LVffiEOIJAqnax9YlWFLVcQVASkTuSkx/R8IHvzutETAMAcHi/a4jywsIdLmAQMjGsmykvrbStIPmcJiQv22/f6NFsHh39zd9OaBpWsOh8zFjlRXGvWYS4az+KoxeT77fYjN1K5zfXuB11G8AAyCS0RvHzoWI0p31vu/f67gWSXRvmsCj36wivzwU7eFojYhtSyosJ+W2VwUvDtM2IxH7kIp2VgoZcci2J9a9QqsTYNuCag9JTyKWLsZ41LIsCRn7yVJLaArCIBhW78/fEafTtKOalECwFFrVfUNIdjqLEPsv7qxpmhZr9K/IGOXa/ANGsJBw9gn1ts+ioOSxX216pkHfKBWjDqd3Qzp9eNZOis4AfgchBJf0zSZsSe77fAXW77gw1LSGIGC7sIfeB7V1N0nvCK54KNxY9ZgrDtI66OkjRznLnYzsdl7tC4fcjYxrot8jmvY7RSIWdnpn6DYK3P6aK/ibQbuTCYV0ZVxN0xoWHcCIUZ5l7xBJbI2V1Kpe9iel5LG5NuURuLjDkW+bWifTSTirH64NUxBlDS8LI9Hr5JoBLZi3pZCnp65VNQe0o5bHDJNELklyJ/FGqUqfb0SkhGJnM6yrpyDbn6Jau7rikf1vhhGPw+S7q1Y2TOS5L1Nq6lZ9RyPDEMQbpSTJnUg7Aif9FXni/1UVlG3aUxWY9SQScCRFdaya1liE3GnIws1w3uvQ6UwwHOD0Qs8LkZd9RpGRGe0hapqmHTQ96S0GGaXbcW34jorul9fbPj/fIPl6k+SaLoJkd3QvskJZ/XFu/xXv4jcp7/+nqI7lUHTMiOfCPs14e+5W/B4H1w9sGb2AkBYVQgiSyMWc+ihi6UdgR3A26YHr1KcoS+hI0HZGe4iHTcQ2KHC2xHvaizjtckAQdCThs/Iwht2H2PAjMq0DstsYypxNCehOEkcdtxEmrngF5td3w47FYDqRx14HA25GtjsZAkUQLEYmtcZIbEZ5uf5pomm/V6JZjGPe6wirAlLaQOezkMdeA94UpDeVQpmMFdEFZjRNa3j0r4QY5F38OphuQs0H18v+1hZJHv3VZmATQb/MGLjQNp0E25yCZ8VEHFt/IdKsX7RHdNAGtU2lPGwxbtYmSoMR7hzaFrOR3X3X6uYX+TjeGgkF66se3LEI843TibvqO0Lejo0qO0dKKLc8UDmXOgwB0nC0vwiz48XYEjUlQGcqH3UMQxBXvg7zzTPYU43TCiNmvQCrJhO65HMCcR2QqPdImi8ByvdTGFbTtN8UZ5Tj/O4+xLKP1QOzX4bWgxGuOOz2p1Hc4jQsSwcvNE1rmPStsBgjgkV4lrxJsNWJ1av3HyHFIcmdP1qkeuCiDkd8dwcsktYVK7UTvgX/g0BhtIdzSEZ0yuDCPs14f8E2bvtwMbllurDn0cAwBOaOhdWDF5WkjTHlfnyijhaSjUwkYhMMWbqewVHMSxnGd/dTayuR/HWYOxcQjtiEQvo9ommHiztcUBW8AIgEYPU3sPRjjK/uxmcXRG9wmqZpv5MOYMQY74KXEVaQYLuzjvi+wrbkzzMt8gJwYzeB24ytDIFAuzMBG9+8F39Xy8hoGtIuldsHt2bFrlLGvv4rny/dgd1Aj0U7MA6HibFqcp3LxaafcchAPY5I06LHIQOITTPrXG6s/gqHQ/8U0bTDRQgBeavqXiFQiBEsqr8BaZqmHWb6V0MMMcp24lvwX4KtRyA9yUd0X1JKHpljM3snXN9NkOmLreAFgHTGEWh/Ds6d83Gt/TLawzlknTIT+L9TOtAxI56HvlrFFW/PZ+b6/EY1hUCrIqVE+pvVvYI3BUnsfd407UiQGOBLrXt5QrOGGp/WtJgkpQRvyv5Xqq1zlKZpWgOhAxgxJO6nR5CGk2CHc4/ofqSUPDXf5tP1kis6CTonx+7FlJXcjlCz/niXjsfMXxPt4RyyBI+Dq/u34I/D2hCK2Nz+0RIuGT+PL5buJKiLaDUq4bCF7DqqzuV2v5uoMI5sgFLTYkWFmYx93I21LxQC2WWknmKkaYeZ5W8OvtqDGLLVIEK604+maQ2YDmDECNeGKXhWf0yg6yVIV/wR248tJU/Ms3lnleTiDoL+TWI3eFEp1Go4VlxTfLOfhmBxtIfzu7RPj+euE9tyx5DWOEzBg1+t5PT/zuIf369h2Y4SnZXRSJQ707HP+jfs031Gth5KpNtYwpZ+nbWjQzgisbpfiGw9tPoCIbDPeoFyR1o0hqVpjVqpkYJ1wXuw7+/JpJZYZzxLufRGZ2CapmmHge5CEgOMsh0kfP8Hwpl9CLUYcsT2E4hI7v/F5tvNkks7CgZnxX7wAgBhEOh0Hr6F/yPul39QdvxfwXRFe1SHTAhBp8wEOmUmsLMkyIx1+Xy1fBfvzd9GdpKHUzqmM7R9Gp0y4nX71QYqYLuRrU7Hd2N/jA0/QqAA2Wow4bhsSqwjF6DUtFhUaPlJOPMlnGVbERumgycZu9Ugys0UgnbD/VuuabHKsqA4rjPx183A2DYfkb8WmdUHK7UDJSQjbR1E1zSt4dIBjGiLVOCffC1ISXmfG2vcsT1ctpZK/vSTxZoiuKGboE96w7owlm4/gU5j8S4dj2/2M5Qf94cGHcSolJngZnTPpozs3oQVu0qZu6mQ9+Zv49VfNpMe52Jg6xSOa5lEn+ZJpMU1/OM9mgRtF0HRBLPdWIQAy7JrbcSgaUeDEjsB4euE2b0LUqrPA/rzoGlHTMSGQtIwmp2C0VxgWXJ34EIHLzRNa9h0ACOarCD+r27AkbuM0hMeQLoTD/supJR8vkFNG/GYcHcfQcuEhhW8qGT5m1PRaSzeFe8RP+MhyvrejozLiPawDgvTEHRtkkDXJglYtmR1ThmLtxcze1MBny7ZAUCW30O3pgl0zIindaqP5slemvo9uHUF/5hmWfoqTdNANZOK6Jo/mlavbFti64wLTdMaER3AiBIRLML/1fU4t/1CWb8/YSW3O+z7WJZn8eB0i9m7oF+m4ML2EOdsmMGLSlZyWyq6XY575UT8U+4k2OpkQi2HYftbHrHslfpmGoJOmfF0ylRTDYoqwqzOKWNdXjnr8sqYvjaPwF4XAQluB4leBwluBz6Xicdh4nIYuEyByzTwOE3iXCZ+j4Nkn5P0eDfNEj008XtwGI3jnGmapmmapmma1vjpAEYUOLf9QsKUOxAVBZQNuJdIetfDtm1bSubukkxYZTN1a4QmPrith6B7auO5ULUSmlHe+0ZcW2fi2vQD7rVfYntTiWT0JJzZm0hGT3A2ngJViV4nfVsk0bdFEqBe48KKMDmlIfLLQxRVRCgNRqgIWwQjNqXBCJEKSdi2iViSkGUTCNuUhSJUhKsCHw5D0CLZS/v0ODpmxNM5M4FOmfHEu/WfBU3TNE3TNE3TYo++UqkvUuLYMRffgv/hXvclkZSOlA29Gzsu83dvuigoWZgn+WWHZMoWyY5yaOqD63q66JMUxmyMd9lNF6EWQwk1H4xZuB5HwRocOYtxbfweTCfh9J6Em/UjnNkH3P5oj/awMoQgxecixXfwNTHClk1hRZjcshA7i4NsKw6wJqeMaavzCO6e6pCd5KFLZgIdMuJplxZH61QfmQnuxvk+0jRN0zRN0zStwdABjINwIDMUzJwluNd9BZEKjGAxRul2zILVmCVb96wTankioeZDMAIFGIGCGtsIWoJPtiWQE3IQsSEiBSFbUG4ZlEYMCsMmeUGT7QEHAbt6/YNsb5jRWeW0TwjhkQ427oj87uOOffHg6QVNemGESjCLNmJs2wDbPgU+BcD2pSG9qdguPzjcYDiJpHRQvdIBAcRVWJSVBY6K8lZO0yA72Ut2shdaqlopO4qDbCyoYFNBBd+szOGblTk1nud2GKTFuUjze4hzCOJcDrxONU3FZRo4DIHDEBiGwBAQ53JwVrfM353VEY3ZQZX7bCQzkwB9TA1BrB1PfY4j1o79cNLH1jBF89gOZJ+N+dwfLH0uqtPnozp9PhoXIaU8Gq7X6s/TnaBk++/axPuRIdwduf4wDUjTouvmYW3504hO0R6GpmmapmmapmkNnA5gaJqmaZqmaZqmaZoW83T/RU3TNE3TNE3TNE3TYp4OYGiapmmapmmapmmaFvN0AEPTNE3TNE3TNE3TtJinAxiapmmapmmapmmapsU8HcDQNE3TNE3TNE3TNC3mOaI9gIYkN7eEhtSzJSUljvz8smgPo8HQ5+vAHe5zlZ6esN/l0frsNcb3hD6m2FefxxNrn73G9lruTR9bw3Skju1wffYa87k/WPpcVKfPR3WV5+O3Pnta7Gt0GRj/+9//6NixI48++uiex4LBIA899BD9+vWjd+/e3HrrreTm5kZxlEeeEGCaBkJEeyQNgz5fB+5oOVeN8Tj1McW+xnY8B6MxH7s+toYp1o8t1sdXn/S5qE6fj+r0+WhcGlUAY9GiRbz77rt07Nix2uOPPfYYU6dO5V//+hfjx49n165d3HLLLVEapaZpmqZpmqZpmqZpB6vRBDDKysr405/+xCOPPEJiYuKex0tKSvjwww+55557GDBgAN26deOxxx5j/vz5LFiwIHoD1jRN0zRN0zRN0zTtgDWaAMbf/vY3hgwZwsCBA6s9vmTJEsLhcLXH27ZtS1ZWlg5gaJqmaZqmaZqmaVoD0SiKeH7xxRcsW7aMiRMn1liWm5uL0+nE7/dXezw1NZWcnJyD2k9DmjdVOdaGNOZo0ufrwEXjXEXjdWmM7wl9TLEv1o4nGp/zWDn2w0kfW8MUzWM7kH025nN/sPS5qE6fj+r0+WhcGnwAY/v27Tz66KO8+uqruN3uI7qv1NSGV7W2IY75t4QiFrtKglSELLwuk4wENy6HeVi23RjP15FSn+cqmq9LY3xP6GOKfbFyPNEYR6wc+5FwMMd2JL/rjgT9ukVvn4353O+roCxEQXkIKSHR5yQtvvpv/6PpXBwIfT6q0+ejcWjwAYylS5eSl5fHqFGj9jxmWRZz5szh7bffZty4cYTDYYqLi6tlYeTl5ZGenn5Q+8rLazhtVIVQH9KGNOYDETQM3py1iTd/3khF2MLnMrliYCsuPrY5bmkf8nYb6/k6Eo7EuUpL2/8XSjRel8b4ntDHFPvq+3hi6bPX2F7LvR3ssQWFwVuzN/P6zA1UhC28TpPLBrTksn4tftd33ZGgX7dDczg+e4353O9LGIIdFRb3fryYhVuKAGifEc+jI7vROtGNgTxqzsWBOJreGwdi7/OhgxgNX4MPYPTv359JkyZVe+wvf/kLbdq04dprr6Vp06Y4nU5+/vlnRowYAcC6devYtm0bvXr1Oqh9SUmD+yPQEMdcF9sweHbKGt6fu2XPY+UhixenraWoPMztQ9og7N/3w64xna8jrT7PVTRfl8b4ntDHFPti5XiiMY5YOfYj4UCOTRoGL/ywlgmzN+95rCJs8d/p6ygoC/HHk9phWLEVxAD9ukVzn4353FcqjkjO/+/PlIWsPY+t3lXKxeN+YdItx5PmVGX9joZzcTD0+ahOn4vGocEX8YyPj6dDhw7V/vH5fCQlJdGhQwcSEhIYPXo0TzzxBLNmzWLJkiXce++99O7d+6ADGFp0lUZsJs7bUuuyd3/dTGkM/qDTNE3TtINRErF579fav+smzttCaVh/12lHF9Nh8OmCbdWCF5XCluQ/P6wDs8Ff0miadoAafAbGgbj33nsxDIPbbruNUCjECSecwAMPPBDtYWkHqaA8jF1H5NSyJQXlYRLinPU7KE2rb9JWtxCM2J0Lr2naoSuqCGPV8WVnSyisCOPX33XaUSRkw8/r8+tcPndTARURfWtd044WjTKAMX78+Gr/73a7eeCBB3TQooHzufZ/wfZbyzWtQbMj+H59Fu+iVxGRIIHO51M68P/A6Y32yDRNO4y8v/Fd5nXq7zrt6OI0ICvRU+fyzAQPDp2AoWlHDf1x1xoMv9ukY2bthXe6Zvnx6wCG1lhJm4Tv7sT367OEsgcRaH82nuXv4Z9yh57QqWmNjN9t0qWpv9ZlHTMT8Lv1d512dJGWzRUDWtW5/MYhbXDr9piadtTQAQytwfAKeOGi3mQnV7/j3CLFx/MX9OLINtHVtOjxLnoNz+qPKe97O4HulxHsNIbyY27FvfYL3Ks+jvbwNE07jNwSnr+gF81Tqn/XZSd7eeGi3nj1hZp2lJESmsQ5efCsLphG1QdACLhhcBs6ZyRg1zXHWNO0RqdRTiHRGifbliQ7BO9cfRxbCgNszC+ndaqPLL8Hn4H+8tIaJaN0G3E/P06w9amEmw3Y83g46zjCTY8lbtaTBNudBaaeE69pjYGUkiSHYMJVx7GtKMD6vHJapvjITvLgE/q7Tjs6mVJyWqd0BrdPY/HWIiK2pEezJBIcAlPaoAN7mnbU0AEMrUGxbYkHaJ/kpkOyByklUkp+Z/dUTYtZvl+eRpouKrpcUGNZRafz8E+9G9eGbwi1PSMKo9M07Uio/K5rm+imXdJe33U6dqEdxQxbkiDghBZJwO5gnp5GqWlHHT2FRGuQpFRfXPp7S2vMjJKteFZOJNhhJDh9NZbbia2IpHTEu2R8zSdrmtbg6e86TavJtqXORNK0o5gOYGiapsUo76JXkQ4vwVYn17lOqMUwnFtnIspz63FkmqZpmqZpmlb/dABD0zQtFllBPMvfJdRyGDjqbh8XbtoXAPf6r+trZJqmaZqmaZoWFTqAoWmaFoPc677GCBYRannifteTbj+R1M641ukAhqZpmqZpmta46QCGpmlaDHKv+ohIcgfshGa/uW4koyeubT+DFaqHkWmapmmapmladOgAhqZpWowRwSJcm34glD3wgNaPpHdHRCpw7px/hEemaZqmaZqmadGjAxiapmkxxrXhO4QdJpzV74DWt5JaY7vicW6ZcYRHpmmapmmapmnRowMYmqZpMca1/lsiye2Q3tQDe4IwsJI74tw+58gOTNM0TdM0TdOiSAcwNE3TYokVwrVpGuHMPgf1tEhqBxw754NtHaGBaZqmaZqmaVp06QCGpmlaDHHu+BUjXEqkycEFMKzkDhjhMsyCVUdoZJqmaZqmaZoWXTqAoWmaFkOcm2dgu/xYia0O6nmR5LZIYeDcMe/IDEzTNE3TNE3TokwHMDRN02KIa/N0IundQBzkn2eHBzuhOY6cJUdmYJqmaZqmaZoWZTqAoWmaFiNEsBhHziIVwDgEVmJLHDmLDvOoNE3TNE3TNC026ACGpmlajHBun4OQNpG0QwtgRJLa4MhbDlb4MI9M0zRN0zRN06JPBzA0TdNihHP7bGxPMnZc5iE930pshbBCmIVrDvPINE3TNE3TNC36dABD0zQtRji3zSKS2gmEOKTn2/6WADhylx/OYWmapmmapmlaTNABDE3TtFhgBXHsWkQkpdMhb0K64rC96TjyVxzGgWmapmmapmlabNABDE3TtBjgyFmCsMNYKR1+13YsfzZm3srDNCpN0zRN0zRNix06gKFpmhYDnDvnI00XVmLL37Udy99cFfLUNE3TNE3TtEZGBzA0TdNigGPHPKzENmA4ftd2LH8LzNJtiGDxYRqZpmmapmmapsWGBh/AmDBhAmeddRZ9+vShT58+jB07lh9++GHP8ksvvZSOHTtW++f++++P4og1TdNqcu6cTyS5ze/ejp2QDYBZoDuRaJqmaZqmaY3L77vVFwOaNGnCXXfdRcuWLZFS/j975x0mRZH/4beqJ4fNGZacMygiYMScxYCKOYdTT+/0PL3g6d1PPU89PeN5BlQUMaJiwoAJkJxz3gV22ZxmZid01++PgV2GnVny7gL9Pg/Pw3Z1V1f3dHVVf+obmDRpEr/5zW/4+OOP6d69OwBjxozhzjvvbDjG6XS2VnNNTExMmiDqK9FqC6nvMXqf69I9eQBoVWuJ5AzZ5/pMTExMTExMTExM2goHvYAxatSomL/vvvtuJkyYwIIFCxoEDIfDQWZmZms0z8TExGSXWEoXA6DvBwsMLA50VyaWytUE9702ExMTExMTExMTkzbDQS9g7Iiu63z11Vf4/X4GDx7csP2zzz7j008/JTMzkxNPPJHbbrttr6wwhNifrT2wbG/rwdTm1sS8X7tPa9yr1vhdWvI6raWLUBYXyp3D/jid4clDq1jdpO2H4nN+qF1TW7ue1ujnbeXa9yfmtR2ctOa17c45D+V7v6eY9yIW837EYt6PQwuhlFKt3Yh9ZeXKlVx66aUEg0FcLhdPPvkkxx9/PAATJ04kLy+PrKwsVq5cyRNPPMGAAQN47rnnWrnVJiYmJtt472ooXwOnP7Z/6pv1MmxdAnfO3z/1mZiYmJiYmJiYmLQBDgkBIxQKUVRURG1tLV9//TXvv/8+48ePp1u3bk32nTFjBtdccw3ffPMNHTp02KPzlJfXcrDcLSEgPd17ULW5NTHv1+5zIO5VRoa32fLW+F1a8plIfXMkkYy+1A+4Zr/UZ1s/Bcei1ym/ZQ1o1obth+JzfqhdU0tfT1vqe4fab7kj5rUdnBzIa9sffe9Qvvd7inkvYjHvRyw73o/09Ob7nknb55BwIbHZbHTs2BGAfv36sXjxYt58800efvjhJvsOHDgQgI0bN+6xgKEUB91L4GBsc2ti3q/dpyXvVWv+Lgf63CJUh1azkfpuZ7G/TqN7chFKR9YUoqc0jatxKD7nh9o1tZXraY12tJVrPxCY13Zw0tb7waF87/cU817EYt6PWMx7cWhw0KdRjYdhGIRCobhly5cvBzCDepqYmLQJtPLoO0lP7rTf6tTdudG6q9bvtzpNTExMTExMTExMWpuD3gLjySef5LjjjiM3Nxefz8fkyZOZNWsWr776KgUFBXz22Wccf/zxpKSksHLlSh599FGGDh1Kr169WrvprYpLBrBHqhDBapQ9iZAlBb9ymcqkiUkLYylbipIWDG/7/VancqahNDta1TrgpP1Wr4nJwYZL1mPXKxH11WDzErKm4FNuc6wzMdkNo2iCcwABAABJREFUbJqBy6hE+stAWog40vGJVHTD7EAmJiatx0EvYJSXl3PfffdRUlKC1+ulZ8+evPrqq4wcOZKioiJmzJjBm2++id/vJzc3l1NPPZXbbruttZvdqqRoVVi+uhex6suGbVqXE7Gd9R+qSG3RiZ3FogGKSMRouZOamLQhLOUr0D3tQO7H17GQ6J7cbQKGicnhSYpWjeXbPyGWf9JgN+zoMALreS9RRVqriRhCRMc+pcyxz6Tt4pIBnGsnI7/5M4R8AGjeXCwXvk6ttw9hY/8ZcVssEiEEkYhuiosmJia75KAXMB555JGEZbm5uYwfP74FW9P2cWkBLFP+GCNeAIh1U9E+uw33Oa9Qp9wHvB0hISj3h/l+ZRGaFIzqlUWqXcNqjlwmhxmWsmUYSfn7vV7DnYNWtXa/12ticjDg1EJYpj6EWDYpZrsomI720bW4L3ibOuVp8XYFhWBzdZCfVpeS7LRyQo9MvBaJZo59Jm0IKQWOyqXIz++OLagtQht/Hp6bp1NJ9j6fJyIEtRGDqcuKqa0Pc0KPTHK9dmxmfzAxMWmGg17AMNkz7JEqxMrP45aJDT9jC1eCdmAFjJCUPPHtKj6ev6Vh25PfrOK6kZ24YXhHU8QwOXxQCq1iJcHu5+33qg13Dtai2fu9XhOTgwFHpBKx5IO4ZWLzXGyhcrC0rICxtaaeu95fxOwNlQ3b/u+LFfzjvL6c3D3DFDFM2gxOfGg//CN+YSSIXPoB1kF3Eg7re32OiBR8sayEv3++vGHbc1PXMrJrOv+8oD92w7ROMjExic8hGcTTJDEiWNN8CN76qgN6fikFizbXxIgX23lt2gbWVwYQQhzQNpiYtBVk7SZk2IeetGcZkXYHw52DrNsCenC/121i0tYRoVpQzXwA1W5tucYA0iKZOLsgRrzYzp8/WUrVPnwImpjsbzQVhLI1Cctl0QIkkX06R0W9HiNebGfa2nK+WFKMZjE/UUxMTOJjvh0OM5QtqfkdHCkH9PwhBf/7JXFmhNenb0BJU8AwOTywVKwEOCAChu7JQaDQagr3e90mJm0dZfOCaGaK48lqucYAvohi3PSNCcs/X1SE1aq1YItMTBKjCxukd01YbuQMxNgHI26bTeOj+ZsSlr8+fQO+iGmRZGJiEh9TwDjMCFpSUD1Oj1umOowgZE09oOfXFVQH4qe4BajwhYmYZrQmhwlaxSqUxYVypu/3ug13TvQc1Rv2e90mJm2deksqqu/ouGUqbzBhe0aLtkcB1YFwwvKSuhCm8aFJWyGAB/34B+IXajaMvhftk/sIwNaaxNaBVf4w5kzQxMQkEaaAcZjhN5xETn8C1e3kmO2q0zHo572E7wAH8HRoguO6ZyYsP6lXJnZzFmdymGCpWIXubc+B+HJRjpRoKlVTwDA5DAnoNiKjHkb1PCtmu2p/FPoF41o8gKdNwFGdEi8QnNQry8xIYtJmMAxFfXp/jNP/BVZnY4EnC/3yj6iz7JsFk64bnNYncRDQ4V3SsJlfKCYmJgkwg3gehlTpKbjPeAFbpBJRXwN2LyFrKj7lPuDpq5RucMXRHXh/7ibqgrH+k6kuK2f1z0XXzUmcyeGBVrEC3dvuwFQuJIY7G606scuWicmhTJWejOvUp7GPehBRX92iY93OWIEHzuzD+S9MQzdiT94lw02vbA+GYa45m7Qd/IaLSPcxuLqdgvSXgrSiO9Kpk6no+xiyRdcVA9sn0yHNRUGFP6bMqgl+f2pPLEqZVhgmJiZxMfXNwxSf4aJStqPC1ZsKrT11RstN6FIsgg9uHs4JPTMRAqSA0/tm8/7Nw0lqCRdgTeJH4EdgSGGa7Zq0DsrAUrkWw9v+gJ3CcGUhqwsOWP0mJm0dv+GkUua1yli3I0opumd7eP+moxmcnwKA3SK5bGg+464ZiusAnFPTJEEh8CEIIMygiCZ7TMjQqFIZVDh7U2HvRrXad/FiOy7greuGcvER7bFp0WfzyI6pfHDzcDIdWov1U02T1LNDP9HMfmJi0tYxLTBMWhxDV2TYBI+d24d6QwECpwRNKXT9wI1YUgpqdPjPlFVMXlxExFCc0COT+07rRabDHLBMWhZZuxkRCaAnHTgBQ3dnYylbdsDqNzEx2X0cVo1OSTaev2QgQUMhhcCtCZRu7PeV5rAQzNlcw+Nfr2R9mY8Mj41bju/KGX2ysZnpKU3aAEopXMAfTurG7Sd0xVAKhyawQYtZI4WFYNqGSp78dhWFFQGyk+z85oRunNwzA6tpEWVi0mYxv9pMWgWlQDMUbsCNQhrqgKvtdQZc+sqvfLxgC2E9er6pK0u54KXpVIXNgcqkZdEqoynqDM8BciGBqAtJ7SYwzBSNJiZtAcNQWJXCI8CFQh0Al0lpkfywtpxb357H+jIfAGV1If7x+XKemboG3cz0ZdKW0A1cRPuExVAt50olBZOXbuWu9xZSWBEAooFF//rpUl6dvhFDmp9IJiZtFbN3mhwWaJpk6qrSuFGv/SGdV6etp34fI2qbmOwJlso1KM2O4Tpw2RAMdzbCCCN9xQfsHCYmJm2Luojisa9WxC17b84mak3B3sQEnw7//nZ13LJxMzbiM+OxmZi0WUwBw+SwIAJMWbY1YfmPq8qoaSbFnYnJ/karXIPuyQNx4F7Dhisa5V2r2XjAzmFiYtK2qKmPUBOIJCzfUO4zYz+ZHPZU14cJJFi40g1FcTNpXk1MTFoXU8AwOSzQBKS4bAnLk51WLJo5ozNpObTK1RjevAN6DsOViUKgVZsChonJ4YLN0vxY5nVYWiWQqYlJW8K2i2CdLltLRJU3MTHZG0wBw+SwQOkGVw/vmLD8+pGdSHPbW7BFJoc7lso16Acw/gUAmhXlzEDWFB7Y85iYmLQZvFaNQdsynexMstNKbpKjZRtkYtIG8do1emR74pZleu2kOa0t3CITE5PdxRQwTA4LlIL8ZAc3HtO5SdmpfbI5unNaK7TK5HBF1Fch6yswPAfWAgOiVhimC4mJyeGDDfjXhQPI9MaK8naL5H9XHoHbnPmZmOAU8Mwlg0h1xQoVLpvGy1cMMfuJiUkbxkyjanLYYDUMrhvegdGD2/H10mKCusGpvbPJ9tiwKzNYk0nLoVWtBWgRAUN3Z5kuJCYmhxFKKdKsgg9uOpolRTXMK6ika6aH4Z3T8GoCZaaHNDHBMBTZDguTbh3Bwk3VLNxcRY9sL0d1SsMrWy6Vq4mJyZ5jChgmhxUWQ5Fpk1x7VD4AkYiOUgp2chkWAixWDQUYEcMcyEz2K1rVOgB0T84BP5fhysJauviAn8fExKTtYBgKF3B0uyRGdkjBMBS6bjQRL8yxzuRwRtcNXMAxHZM5rnPqDv1k18dqmkBIiRAQCetmXBkTkxbEFDBMDkvCzaRMDQnB5pogE2YXEgjrXDC4Hb2zPThR5gBlsl+wVK7FcGaA5cD7ohvubGSgHEI+sLsP+PlMTEzaDoahMIz4411ICAqr63l3diHBiMGFQ9rRK8uD3RzoTA4zdF2h64nnhTsihCAALCioZvLiIpIdVi47Kp9stw2r2XdMTFoEU8AwMdmBIIKnp67l/bmbGrZ9taSYAe2SeGHsENPVxGS/oFWtRffktsi5DFdW9Jy1BRj23i1yThMTk7ZNSAge/2Y1nyzc0rDtyyXFDOmQwjNjBuEwxzoTk7j4EVz7xmzWlvoatr03dxM3HduZa4Z1MEUME5MWwAxRY7LXOLQwSaISr6g+ZFKQFlbXx4gX21m0uYbPFxehWcwuY7LvaFVrMVpKwHBnR89pZiIxMdkrhACnFiRJVOIRtWi7SL/Y1hFCsLbcHyNebGdeQRXfrSw56K/RpHWQUuCRPpJEFS4ZQIhDY264HaFJ3vx1Q4x4sZ2Xf15PWSDcCq0yMTn8MEcokz3GIhVpxmY839+L/bUTcLxzDsmrJ5Cs1ezyWKFJ6hQU1+tU6wp9m/9gWyCiG7w9syBh+duzCvFFTGXdZB9RBlr1Rgz3gY9/AaDsySjNjlaT+Nk2MTGJj1XqpIY24P76TuyvHY9z4mhSNn5Ckqzdq/oMKakxoLg+Qq0CJVt+GiY1yfhmxrq3fi3Ar5tjncme4ZE+Ukp+xvnhpdhfOw7XZ9eRWrcYhww22VcIQVgIKiOK4nodPwJ5EIhmfl0xcXbTRa7tfLKwCKtVa8EWmZgcnrSqC8mGDRuYOXMm5eXlGEasueLtt9/eSq0yaQ4hBEmhjWivngSR+uhGfznyy3uwrvoS75nPUWt44x4bkpK3Zhbw+rQNBCPR3/uEHhk8dG5f3KL140sYCvzNxMYIhHUU5qTOZN+QdcUIPYjeAhlIABACw5WFNAUME5M9QkqBt3YF2ptnwvY4Ev4K5Ce3Yh14Ge7j/obP2P24Mltr6nnsm9V8unALuqGwSMH5g9tx16hu2I2Wc9nQlSLQzFgXjOgY5lhnsgc4ZAj7kreQU//RsE1s+Blt3Om4LhxHpP0pRLYtAEkZFS7u/WAR8wurAPDaLdx9SndO75XV5l0wts9f41EXjLSZRTkTk0OZVpM733vvPc4880z+85//8PXXX/Ptt982/Pvuu+9aq1kmu8ApA8hv/tIoXuyAWPsd1rrCuC9vYZFMnLuJl35cF/Py/2FVGbe9M5/gzmlAWgGbRXL+wMQflaf1ycZ1EKwQmLRttOr1ABgtkIFkO4YrE63aFDBMTPYEFzVoX/6uUbzYAblwAvZQ2W7XFRGCv326lI/nb0bflukjYig+mLuJx6eswpAtNwZq0OxYd3rfbFymu6TJHuA0qpA/Pha3TPvqHtx6RcPfPgVXvDqrQbwAqA1GeHjycn7dWIWlDT97dgkn9spMWH5WvxzCYTN+jInJgabVLDBefPFF7rrrLm666abWaoLJXmAzfIj1UxOWixWTsQzr3yTLR13Y4JVf1sc9ZumWGkr9Ydo5Wz+m7KD2yfTM9rBya13M9mSnletGdkbp5sBksm9oVetRQjYE12wJDFcWWtXaFjuficmhgDVSB1uXJiwXBTPQundG341xoS5s8OWS4rhlny3awp2jupHcQt9tum4wtFMqXTPdTXz509w2Lj+qI6qZVWYTk50RVRvBiMQv9JUhg5VgS0VKwariOoprmi6CATz5zSqG3nAUBz4/196hKcXvT+7Bz6vL8Idi57lHdEylS7oL1cYtSExMDgVaTeasrq7mjDPO2Od63nnnHc455xyGDBnCkCFDuOSSS/jxxx8byoPBIA899BDDhg1j8ODB3HHHHZSV7f6qyeGMzSpwamGs1sbHRCFAWps5yB3XFcQf1pu87HdkQ5kP2YIrUIlwCcUrVx7J3Sd3JyfJQarLymVD8/n41uEkm26NJvsBrXo9hisbZMsJdoY7C612E63up2Vi0sbQNInDEsFuVU0CDiqh0Zw9uLK6YDddLaoCCT7uiHbL6hYO/ucCXr96KHeM6kZ2kp00t40rhnXgw5uHk9z6awkmbRwhwG5VOCyRqMWExd78AdvGOyklizZXJ9xtc1WAUBuOv6IUpNkkn9w2gvMH5ZHstNIuxckDZ/TimTEDzRTEJiYtRKsNU6effjq//PILl1122T7Vk5OTwz333EPHjh1RSjFp0iR+85vf8PHHH9O9e3ceeeQRfvzxR55++mm8Xi9///vfuf3223n33Xf305UcethlCHd4K2LW68jyVRjthmIMuJRamUVQJGPpPwa5YHzcY1Wvc4hEmgoVDquGRQoiRvyXe26yo02o1kqBQxlccUQ7Rg/MAxQuTaJ0AyNB201M9gStej2GJ7tFz2m4shCRACJQBiS16LlNTNoiQgiSRQWyYDra4vdQNhfGkTcSSulO3bY4TiEtCUvXkxBrvo1TgUS1H4ae4GPLatUQAsJhA6UUHnvzCrjH3rLTMaUUThTXHtmeiwe3AxQui0RFDPTEaw0mJrilD3vteuTslxGBSoze50GX4yCjB5StanpAejcithQwQCmDzhmJ48akuKxYpWB3hcHWQBmKFE3wp1N78LuTuiMAt0Wgm1ZLJiYtRqsJGB07duSZZ55h4cKF9OjRA4sltilXXXXVbtUzatSomL/vvvtuJkyYwIIFC8jJyeHDDz/kiSeeYPjw4QA88sgjnHnmmSxYsIBBgwbtl2s5lLBqBp7i6cj3r4JteeDluh+Qvz5H0hWfUu3tg3HMvcgNP0FVrE+9cdx91NsyIM473GORnD8ojw/mbW5SlpPkaDMCxnaMiIFz2/9NtxGT/YlWvQE9uXOLnlNvSKVaAHRp0XObmLRFUkQFlndGQ/kaAASgLf8M+4CxcPxfqTM8BJQDx6mPohUtAF+s5aZx5pP4tdQm411YCMrrI0yevQlfMMIZ/XLomOrEa9MY2D6ZhZuarj4P7ZiKxypbxUJK13cY68wPMJNd4JJ+HHOeR874T8M2uW4qJLdHjX0f8cpJEPY3HmBzo5//P3wkAwpdVwzOT8Ft0/DFscq98ZjOuC0C42DI+LZD39EPhvaamBxCtJqAMXHiRFwuF7NmzWLWrFkxZUKI3RYwdkTXdb766iv8fj+DBw9myZIlhMNhRowY0bBP165dycvLMwWMBLiNSuSkmxvEiwbCAbRPbsZ1+WSqRQZJV0xGFk5HWz4J5c7EGHIt9a72+A1X3HpDCi46sj1FNfVMW1PesL19qpNXrjoSt4hmATExOaRRBlp1AeG84S162u3xNsxAniYmYLOAnDuuQbzYEbnoHWxDrkJ4+kVdOyx5JF37HXLtt8i132AktUcNuRq/PY+gYYs5NiwEb80u5MUf1zVsGz+zgKM6pfH0mAE8N3YIN701h+VFjSlY++Ul8cTFA7CbOa5MDgIcwZIY8aKB6k0w43mMW2bA3NeRZcsx2g/D6H0+tTIrxoLVaxGMu3Yot4yfR7kv1LD9nAG5nDUg15wMmpiY7JJWEzC+//77/VbXypUrufTSSwkGg7hcLp5//nm6devG8uXLsVqtJCXFmkynp6dTWlq6x+c5mFIjbW9rc23WNImUAn0H9whZuxlCsUG9EBK6nwJ9zsOm/BjSRg1pyE7nYul8FkpohCMGSsU/n6ZJfl5fyf0fLea2E7tx3cjOlNQGSXFZqQ6EqQmEybTZW/X+7s79MonSGveqNX6XA3Gd0rcVoddjeHJaNu+O1YlhT0KrLQQOref8UOu7be16WqOf7+9z7jzWOfQq5IK34jRAQreTkRE/KbKSEE4CykkV6Vh6XobWawwKjVDYgJ3GOyGguC4UI15sZ9aGCj5fUswNx3bhlcuHUBGIUFJbT1aSgzSHBce2NOJt5TffU9raM7s/ac1r251ztkT7hABN07BYJGLRJ7GFmhV6nQM9T0dIK4a0UHfUH5Do6FiiLsU7Pdt1EcU/v1rBn8/ug1UT1AUjZHsdTFtbxm/emcf/Lh+CbS+u51B+DvcG837EYt6PQ4tDIlRT586dmTRpErW1tXz99dfcd999jB8fP0bDvpCe7t3vdR5o4rY5EobazbD+JyhfCx2GQ1YvkHYwgrH72r0w+r+w7gf46gFkqBZX5xNwnfIQZPQES2Jfxu1U+kK8Pm0DwYjBv79ZhRTgcVgIhHTCuuLk3lk8O3YwTmvrP45paR621tRT7gsR0RXpHhtZXjs2ixnBc2dasj+0Zt/br+euWwiAO6cTJMe3VjpgeHNxBbcAB+e7bFccatfUVq6nNdqx386pR6BmM2ycDqUroN0R0G4ICA30nYJmbh/r1v+I+PB6LPXVWDofj+vUv28b62zxz7ENw1D8c+qihOVvzNjIeYPa0S7DQ7v9cW0HiLBuUFITpKwuiEWKbWOgY7eCbLeVZ/ZA0Nb7wQFrX10JVKyDVVPAkQzdT47OHxe8A55sOO95WDYJJt8Negitxxkkj/oLpHcFGX/etHFjBXM2VjFn4wLsFondIqkNRho8qHwRRV723l/PvtyLCl+Q8roQvpBOitNKhseOx9H6c9N94VDul3uDeT8ODVq1VxYXF/Pdd99RVFREOBw7mbj//vt3ux6bzUbHjh0B6NevH4sXL+bNN9/kjDPOIBwOU1NTE2OFUV5eTmZm4jzOiSgvrz1ogvgLEe2kO7fZIg28VYvQ3rkQItvEimlPQ3J7OPdZQEQV9e2Tu9MehR8eg+IdJmZrv4MNP6Ff/z1V9i67jF1RT1Rh346hoGaHiOzVgTA1tUF8RmAfr3rvEQKSkt0sKKzkjgkLGlJ8uWwaD5zRi5N6ZGA9WH78A0yiZ2tfyMhofkBpjb53IK7TXrAcD4IaPQmq/bs+YD/itKcjStZi5eB6l+2KA/E7tSYtfT1tqe/tz2vXJCTVrUAbf16sVaEnC3XZRET/i2HWy43bT3sEfvwnFC1s3LZuKvzvRPTrvqPK0bXZsc5i1ajyJ84kUheMYCjVpp/TiBBMW1/Jg58taxizMz12nr5kID3TXWDEj5FxqPXBHTmQ17Y/+t6BbF+yrMbyyY2IjdNjC0b9BYZcBd1Ogc9/D1UbG8uWfwprv0e/8QcqRU7c9tbtMP8LRgyCO8VeCYQilJXV7nzoLtmXeyGEoNaAu99byPzCKgCkgPMHteP3J3fHvrNb9UHAodwv94Yd74cpYhz8tJqAMWPGDG699Vby8/NZt24d3bt3Z/PmzSil6NOnzz7VbRgGoVCIfv36YbVamTFjBqeddhoA69atY8uWLXsV/0Kpgy8L4c5tdhuVaBMvaxQvtlO9CX5+CgZdDiN/Cz89EVXXpYwVL5LbAwJqNiG/fRDHWS/jV81n7HZogpN6ZfLKL7645Wf0y8EKtHbmrC3VAa54dVbMYOoP6fz5k6V0uG4ofdNdZiaSHWjJ/tCafW9/nltWb0Q5M1BaM6mIDxCGKwtr0Wzg4HyX7YpD7ZrayvW0Rjv2xzkbxrqdXSLrShCT74Zzn4PF70OgEjxZ0TSPO4oX9iRwZ0BdCfLbv+I4+5VmxzpDV5zVP5cpy7bGLT+hRyZJDiv+2kib+F13RghBQXWQ338Qa0VSWhfkmnGz+eKOY0nZhSFiW3lmDwRtvR/s7/ZZLaAtfKepeAHw/d/hyo9g6/JG8cLuBXdmNNhtsAYx4zlsxzxEUI99aJSKxj6TCeKeJTktJDus+xTUfW/uRT1w0/i5rNpa17DNUPDR/M24bBq/Pb4LHKQB3Q/lfrk3mPfi0KDVBIwnn3yS6667jjvvvJPBgwfz7LPPkpaWxj333MOxxx67R/Ucd9xx5Obm4vP5mDx5MrNmzeLVV1/F6/Vy4YUX8thjj5GcnIzH4+Ef//gHgwcPPiwDeAoBsnIdBBMo2xt+hqNvifbu0S+hti5DbPw1WjbgEuh/UTTomTIgowdi+WdYVQBoXsBQusHYozrw3txNMZYXANlJdk7qmYXewgODENEJ23ZBwmLR+Gze5iYrAdt56pvVvHDJQFr+s9PkUEKr3ojuzmqVcxvuLKSvqKnpvInJIYjmKwZfglhXRQugrhgueg2WfQLheijYNtaldIATH4i6mdRshpSOiJAPG/X4mxnrdN1gcH4yndJdbCiPta5yWjVuO6ErLrsF/54vLB8wpBQoFY2/ERGCZ75bHXe/sK54b24ht43oFDdNusmhh1OvQs5+OfEO636MTqS8OTDqz2B1RTPTJbcHI4Kc+TI2o5YgKU0O9VgE14zoxGvTNjQpu//0Xng0MFr4MSvzh2PEix15b84mrhvZCa8ZO8HEpM3QagLG2rVreeqpp6KNsFior6/H7Xbz29/+lttuu42xY8fuVj3l5eXcd999lJSU4PV66dmzJ6+++iojR44E4IEHHkBKyZ133kkoFOKYY47hwQcfPGDX1ZYRQkB9VfM76RH4+UlIaoe64iNY8gFi+O1gdcI7lzRmJxECht+OlAp2Y6BJ1gQf3TKCZ75bzVdLi9Gk4PxB7bjluC54ZELL1P2OkAKfAQUVAWoCYbpne/BaJBZgwTazwXisK60jojAFDJN9Qqve0JARpKUx3NkIZUB1IbDnLnQmJgcLQpBYqN9OyAcfXg+9zoK+50PREkhqB2f/Gz77bdQqcTvpXdE6HI0QotmVYRfw5rVDeX36Rt6bU0h9xOCkXln8/pQepNvaThyliBTUhAzWlNSS7LKSn+JEKsWa0vgfcADLimoIK9WywYdNWg0hVNQ6KRF1JdGYMue/GHUjqdgheG1yPpz/Akpa484PpaG4YUQnemR5ee6HNWyqDNAz28u9p/agb7YHo4XMcaUU+BVUBMKsL49vIQwQ0g0CIR2vve30YROTw51WEzBcLldD3IvMzEwKCgro3r07AJWVzbw0d+KRRx5pttxut/Pggw8etqLFjhiGgsxeiXdwZ0AkGvuBms2o2hJUn4vQypfD+9fE7qsUTH8W2fUURHL6Ls39DEORLOEvp/Xk3lN7AOCUAnSjxcQLpGBVZT03vzWX2h1icowelMefzuhF/3bJfLu8JO6hnTPcWASYee5M9gWtZiORjH1zkdtbDFd29D+VGyDZFDBMDl2UApXSIapkxBub7Nv8n41I1AJj5Rcw9j1IyoUv74sVLwDK1yIn3YJ79NvUqcSBq5VSOIHfHNOJa4d3BAF2IdCUgWqxga55QlLyzykr+XRhUcO2JIeFiTcdTddMD1trgnGP652ThEWI3VmvMDkECAsH1k7HItZNjb9Dh6Mhbwh8dmeseAFRkfyLe5CXTUpYv9UwOK1HOiO7pKEESMAlQW9B8aIqorjuzTkUV9fz9KWDErdVEzjbkABpYmISfWe0CgMHDmTu3LkAHH/88fzzn//kxRdf5IEHHmDgwIGt1axDnqA1HdVndPzCY34Hc8dF/29PwkjuQNiRhlr8fsL6xPRncMj63T6/MAycSuFUqsX9Cet0uHbc7BjxAuDjBVv4cP4WzhvUDrslfpe4++Tu2M2lJ5N9QASrkcFqDHd2q5zfcKajhIwKGCYmhzhBLQVjyLXxC0fcAQvebvxbD2MIKyqnX9RNMh6b5mAL797iitINnETHOWkYbcbnWlokHy/YEiNeANTUR7j+jTncMapb3OMsUjDmyPbopvvIYUPAcGGc9LdobJidSe8WdRkJ+WDL/PgVlK7EUl/R7DkiEQPHtn5iV6rFxAuAgIJb3p7HxnI/wYhBeV2IrpmeuPteNKQ93gRzQxMTk9ah1Xrk/fffz4ABAwC44447OProo/niiy9o164d//d//9dazTroiebrlgnzHPsMF+GTH8E48S/gTI1uTO8K5/wn6u9bMAMsdoxL3iFgSUfXFaKmKH5lgKjdgoVw1D2lDaNpkmlry+LGuHBYJZuq/Fg1yRvXDiXTa48p+9s5feiR4TYDeJrsE1pNAUCrCRhILeq+YgoYJocIzY13fsNBeOS9GKc+Eg0uCJDSEXXus1EH+9VTGvZVvc4hktIVpe/iAz3sTzi2HgyElCCiFHee1I0RXdNjyrZU12MYiscu6I9rh9XmdLeNV68+klSr+QF3qCOEaOhPSilqHZ3Qr52Cyj86uoPFAQMvgzP+CV/9EbWreErhxC5JrU11UGd1SWP7nv52FQ+e04d+7RozFgoB5wzI5TcndD1oA3iamByqtJoLSX5+fsP/XS4XDz/8cGs15ZBASoFXVaJVr0eUr0aldwdrN6RMaqJqV+terANuxtV3DFLpSAmqYj2yZDlc8D/I6Y8MVOKtXYGR1AGjwwjklnlxz6vaH4Vt83RS7cno6T2pJXW/fegLAVarBgjCYX2folJLKdgQx8dxVK8srhrekY/mbeaa12cxoms6468/ivqwQUQ3SHfbcGu0fooUk4MeWR2N1t5aMTC2n1szBQyTg5yG8a5mA6JsNSqtC3pKF2pFWsz4U6N7sfa+GmePc5EqjC5sKIsTm38zUgFpnSGnP0IPY/EXI9zNuFZpViQGqb5lhD351Br7Nw2flAKLRQMUodD+t3QICsHM9RX8sLKU+rDO8T0yufHYLvx50hI2V0VTmP+ypoybRnRkeOdjKPOF0KQg1WnFLUGZAv4hiyYFXlWOrFiFqC5EZfYh4s2nViVR5eqJ8/w3cKg60IOIosUQCiCu/gyEjFpoGJGmlQqBtNhIVVvxa2kEjb2PIHYg+kZNIFZ8qa2PUB0I86czexPRFYGwTqbXTobHhlMpTPnCxKRt0WoCBkBNTQ1ff/01BQUFXH/99aSkpLB06VIyMjLIzm6lVcqDECkFKfoWtLdHx/ruJrcn+fJJVGm5TUSFcASq2WaBYYA9Ix2PJxNZsgwmXgHlaxA2N9rAy1BDb4S5r0I4EHtiix1x5LWIyg3w5e/RlCLliklUisx9NpkNCcHmmiAfzd+MYShGD25HfooD+w4VKymp0w2q/GGcNo0km4ZDEFdA0XWDIzqm8covGxq29crxct6gPK5/Yw76tmNWl9TxxoyNPHxuH07vmYkwdi9IqYnJrtBqClEWF8rWevnHDXdWU39lE5ODCCEEyXoxlncuaEzhCGjeXFKumESVpX3MGBCOKMLbMyEoIAxuRzbO3mcjZv4XJt8FkXpkVh/UOU+j+l2EWPJB0xMPuTq6Qr1mCrJwJtq5L1GlJ++Xa6oXgrWlfj5duAW33cIFg/PIdtuw7jyQatExr9IXxmaRJNstuLVdxw0ICsF9k5YwbU15w7YVxbV8smAL/7ywP9dtGwOP6JCKETFwoejg3v7BqRpid5scemhSkFy/Bm38+TFBO2VWX1IumUCVSsOn3PiFh1StBJnWCWa9DMs/RQwcCwPHwvw3m1bc/2JE3VYs75+O+/yXIfe4PRYxhIAAghXFdXy+uIg0t43Rg9qR7rQ09A1NiwZmr66PRINtan4cUu7SYiLDY4v5+y9n9+GdmQXMWFcesz07yc7EG48mceQbExOT1qDVBIwVK1Zw7bXX4vV62bx5M2PGjCElJYUpU6ZQVFTE448/3lpNO6iwaIIkVYpWuQ5OfwxCdTDzv1G/xOpNaB9chfuSD6ml+Y8mJ7XIovnw6R2NG0M+mP0KQg+jrv4c8fndULQwWpbdF064H6b8GcpWw3nPw2e/RX5+F95z/4vU60FBWLrwK/ceWU+EhOD/vlrJF0uKG7a9N3cTp/TO4sGz+mBXBiEpeeGndbw7u7BBfOiV4+W5ywaTahFNRAzDUPTN9ZKT5KC4Jhqz46rhnXj8q5UNx+/IPz5fwbHdM4nvEWlisudotYXRFKqtaINuuLKgaOa2wIYHsS28yWGJVVN4jVK0yjVw+qNQWwwznouKcrVFaO9djnvsp9SS1Gw9Dr0KMelW2LqkcWPJMsS4s1A3/4RyZyHmvhYNam3zwJCrIKsPvHoy9L0A0ec8tAVvYht0BwYSl1GBVCEMYSWgpRGO7P54F5SS305cwLyCqoZtb/26kWtGdOSmEZ0aPtTCQvDRgi08+/2aBlfI9qlOnr9sMO091oSZG6QUrCn1x4gX2ymuqWfKsq2c3DuLxZuq6ZHlMV0lDzO8ogatfCWc+2x0XNCDMOt/iMKZaF//Aefpz+HXHbiED1mxFr64p9ENcf5b0eMcyTD3teic0eqMihodh0djqp3zDNqH1+K99Vec0kZQeqk37Lu1yBUQkhvenMPKHdKbvjZtA3ef3J0xg/KwCiioC3P7hPlsqowusNktkt+e1I3z++c2FQB3wGOVnNonmynLtpLltWO3yCbiBcDWmiBvzdzIzcd0xmIodNOVxMSkTdBqAsZjjz3G6NGj+cMf/sDgwYMbth9//PHcc889rdWsgwq7DOMum4v26W3RlFYAnmw45SFYPhlWTIayVVjC1aAlFjCEAK2+Ipo+NR7z3oAjriVy8XgsZcshEowOYF//qXEF7OsHYOSdiC/vw1a5GjF+NBgRLB1GYD/tUWodnQgbu47iLKVgaXFdjHixnW+Wl3DuwDyO6ZTKmzMLeHtmQUz5iuJarn59NhNuOApnnLrdAt6+/igemLSEmesrSHPbGkxndyakGywrqqFHlocUqwRzUmeyj2jVGzFcrZv9w3DnQLAWUV+FcqS2altMTPYEl/TjXD8F+c2foL46ujGtC5z2f/DTv2DzPChfgzVUibAmJfxAsts1xObVseLFdvQw4uNbMS55B464ClG6Mpo6fMmHMPPF6Afekg8h/yjk2u/wDByLWP8j8sdHobYI3BlYR/6OcK/R1Oi7trTSLJIvFhXHiBfbGTd9I2f3z6WT14aUkmkbKnliyqqYfTZVBrj81VlMvn1kwhViqUnem1OYsA1Tlm7lwXP78PtTemLXhOkueRhhlTqWypXw7UPRGGgArnQ46a+Q1QexYDz2SBV+kYNNBRCV62NjKCkDPrsLrvsK2g2JupQALP8UPropWp43GNodgVg+CeuKz7Ek5+MY9TdqLLnNWg5JTfLWjIIY8WI7//52NSf3ysJjs3D5KzPx7eBWEowYPP71KvJTXYzskJJQcLAYir+e1ZsUl5VQxGDqyvgZ6AA+WbCFQfmp+IIRRnZJx0nbCcxrYnK40mpRmRYvXsyll17aZHt2djalpaWt0KKDD3eoCG3CmEbxAqBuK0y6DY64Bs78F4x5E61iNWnBNXhlTYJgmwJhhJsP7lcwHRGshXfGwHtXwjd/iTHfpWwVpHSI1haoaPCJFAXT0V47BU+4CCl3veIbEYLXpydux+vTN1KrG7w2Lf4+m6sCbKoMxF3kNgxFsiZ4+qL+fHPXseSlOJptS33E4KKXZrCqIoCmmQHMTPYNWVPQqvEvoDGAqFazoVXbYWKyJ0gpcFQuQ352e6N4AVHLiw9viFoDDhgDY99DVheQWjWXFFGGVTb1zZdSg43TE5+saAGgopYd718NH1wbXQzY8Ytl/njocSrSX4qcfGdUvADwlSGnPID112ewaCECgGhm7PBFFG/+ujFh+YTZhWgWDb+heOrb1XH3qQtGmLm+IvEYJWh27BUCkp1Wxrw8g8+XbcXYjXHa5NDAq29FvHNxo3gB4C+PulX1vxgueRspos+I0CxQ8GvTSjodA4vfj/aT96+O/lvyIQ1+R/PHR/umvxxsbsSKyVjGnYZXlTZrjOgzFO/OLkhY/tniItaU+WLEix156tvV+Hex8GQ3DO4d1Y37Tu+J1kxjhBBU+cP88aPFXP/mHAKm9aKJSavTal9lNpuNurqmyuqGDRtIS0trhRYdXNg1Hfnrc8R1TlUGLP4Aaorg3bGI965Ee+UEHO9eQIoqaSJiKKWiZrLx0mVtx50NYX/zjdLD0cwmxk6RqfUQ6sfHqQr4iOzCfN5QCn8wTkCobfiCEcIRlXDQAlhf5kfK+I+2UgqLoUjRBKl2C/lp8Ww1omaISQ4L1fUR7vlgET5zVcpkX1AGWu2mNiNgbA8oamJyMOCiDm1qgkDfIR8ULYL8YdHx7t3L0Mafh/W/w/Fu+QG7DMXsHonokNwu8clsbhQgarYk3sdXikrtjFj3fdxiOftlaiqKuPC/M3li6lpqDBBxhAEF+JsZy6oDYRRRo4iCisTj7+LN1Wha/LFVDxuMOaJ9wmNP75fDuOkbqPKH+b8vVphj3WGCzQJi3hvRedvOKAXTnobVUxCTbiZFVKBLB7gymu7rSAJfM4uOvtKoi0m7I6ILXQD+ckKLPqbGUKhmBLPm5nnVgTCFVYn7xPoy3+4F3tQNrBGDMUfmJ9zl9L45/LAqulC4qqSOr5eXYDHTqpqYtCqt1gNHjRrF888/Tzjc+PLcsmULTzzxBKeeemprNeugwWIEEFsXJ96hZCns/BFfugLLh1fjFrVNdg9aUlB9zk9wMjtG3hEoZ1pikUNqYLHDyLtg3ltNiq0FP7F20xa+WVUGzQxYdik4s19OwvIz+uXgtErctsTuKJ0yXBjGrocutyb414UDsMaZ+N1zWk/enR01uy2qrqe6GVHFxGRXSF8xwghHg2i2JlYX2JPRTAHD5CBCUyEoXZl4h6KFUauKHbMhhANoH12LOxTrjhgO66hupzSau++EGnwVAZmM0fWUxOdrPzQaB2rOa/HLlYHNX0KlP8S7swsZ/eJ0qsJNhQGnJhjVK7Fb2XkD88AwkIKEYjtAv7zkhOb4Sim6pLs4qVfTd0/7VCejemUxdUX040w3FMuLanfLWtLk4EZTwWjcs0SUroSUDojNc9G+uBvdUKiBTa2mKVkG7Y9MXE/7oVEr4UgIfGUNm90bvuHjmavYUB2M+7zZBRzXPY5gso0z+ubgtSVedOuU7trtDxylFJ3TnAn7yEm9G/sIwMTZhabQZ2LSyrSagPHHP/4Rv9/PiBEjCAaDXHnllZx66qm43W7uvvvu1mrWQYMuHdFUqYlI7RQNcLYzxYuwhSqAqFmg3apwamECyo068c+ozN6x+2tWjDFvUytSCWgpGEfeEP98gy5HOTOig9XQG+DSd+CkB8G7TYxwplEdVDz65Qp8zWgLesTg9H455CQ1de/I8Ng4Z2AegbDBU2MGxuSq305esoP8VOdu+SfqukHXFCef33EMVwzrQP92yZzRL4dx1w5lY5mP75Y3Dlimv6PJviBrotmBWtsCAwBvToyAUR0I88asQv7z4zqWFTcVN01MWhsdS3RMS0R6t/jjnVKIua9is4DVInBqYWwWqNMyUBe+GhXed9w97wj0Yb8hEASjx5ngimMNqllRx96LoTlhyNVw2QQY8yZc9Bp0HdWwm2FxEdlmwl5TH+GFH9cSCO8khOsGtx7XFY+96YdY10wPffKSqNPBa5H87uQecS/dbdMY1iWt2eCCdqV46OzevHj5EIZ3SWdQfgr3nNqDxy8awF8/WRIT4kk3B7vDAl3YMDJ6Jd4hrXNDnxJrv8MWriTsykGd+URsIOqy1dF5Y9JOVk02D4y4A057NGod5UyFPuc1FEcc6VQG4S+fLKU+ziOnKcUfTuuJPY6lw8B2SXRId9E9x8urVx/Jb0/qTpo7NqvI3Sf3wLUHQtz2PvLSzn3kwgH8ZVJsHzEUUfMpExOTVqPVgnh6vV5ef/115syZw8qVK/H7/fTt25cRI0a0VpMOKoKGBdfwO9CWfhR/h4GXwse3JDi4BpctgMNXgJzzP0RdMUa30zB6noW67EO0ytWIghmopHYYHY+hTqQSNjTCaNiOvgurJws5/T9QXwWOFNTw21H9xyAKpsOUP0V9HQFyB8GlE6B2CzUhwbgffATCOuW+EG534nRaHgHv3jiMV6etZ9L8LRhKcWb/XM4blMe142ZTWOlnVM8sxt8wjGtfn0V1IDop7JHt4YWxQ3CLPYi5aRikaoLbTuhKcU09a0t9zFxfwfE9s/A6rLz441oyvXaSnZa9UjGEgIiQhAyFIGr1YUaxPvzQaqO+vK0dxBPYJmBsAKJZCG6auJAyXwi3zcI78zbz19N6cGYfM421SdshIJOwHX8/2ntXNC3UbND5OJj277jHyvI1eFQlYvFE5MZfUCkdMY64gfq8kdhunY3c8EPUJaTjsYSTOlFrRDOY1MgMkq/+CvnNA4i130Xf/7mDUGf8izpXZ9zhYlg3FX78Z/REjmQ4/j7odhL8+iKr/C6gMavBlKVbuee0nuw88iVbBB/fOoJnp65hyrJiHFaN8wa14/gemVz00gxsFsn/nd+PYZ1S+d0p3Xl+6tqGLCTtUpy8MHYwXhnfm3RH7EpxVJ6XARcNoNQXZPb6CqYs3cqfzurDsi01/Of71Qigb27SXmciEUIQFoKwOd61eUIRgRp6A8wbF//hGXI1fHlvw5+yZhPatKcxhlwFt/4K66ai6mtRXUYR9uZjufIztO/+ilj5BdiT4JK3UNOeRbx8bLTvWF0w7Fa46UcoXU61pyeT3y6jyh/GHzGwaTu7NkOmXeOT20bw7+9W88PKUrwOC5cP68ipfbK59OVfKa4JRpvaIZWnLxnEHz9cRLkvxB0ndmNoh+Q9fvbsSjE0z8vAiwdQ4Q8zbW0Z3y7fyl/P6cP8gipe/HEtSsEFg/NwaWKX6Yt3hRCgS0lwWz0es7+YmOw2Qu1JfsvDnLKy2ja1Eu+QQdybvkN+fnfUDxii/rtnPoXY8DMsGB/3OP22WYiVXyC/+1tsgSsd/ZqvqNLysFo1fLpBhT+CFOC1abhkNN+81QIuvQKpBzE0O/WWdNylM9HevqDpyVxpcP6LhLeu5CvtRO74tJBJt42gnXPX2pnQJH5DUVQT5J1ZBXy5uJjQDi/3/u2SePbSwZT7QjitGkl2DadgjydfISGZvLSY7CQHFimYW1DJh3M3cWb/XPJSnPTNTWJwjnuPByslBSWBCP+asopf15WT7LRy9fCOnDcwD/tuuLi0VYSAjAzvfu0PmZnNR+xvjb63P6/TNftpnAv/R80Z/9s/jdtLBJC8/mOM1d+y9ep5XPP2PMp9Ye4+oQspTitvzdnE3MIqxo0dTI+sgyOJ8IF4HluTlr6ettT3mrt2j/RhXzIe+dNjjX77rjSMi8Yh1v2A+OWpuHWqY+9BbF0Kq76M2W6c+wJ1Hc8gIuzRDwndQEqJz1DUBnUEkOTQSJUBrJEahDIIax78ePGqCmxvnh4b/HA7571Abc4wznt7E+vKfA2bMzw2Jt9xDJZwJO79VNvOPWdjJV8vLeb7FSUNQrwQMOnWEbTzWqkNQ4U/hM0iSbZb8Gjs9tgUkoInv13Dtyu2cvER+QzukEJEV5T7oh+CvvoIlwzKQ+7hDy4EuJKcLN9Sw+Nfr2R+QRWZXjs3HtuZU3plYTPHu7jsj763L+2zyzCerdORn9wKwZroRqszKsTVFsHM/0a3SQtc8hZMuCz6t2bDuPYrKpP6UFYXQlfRvpImQ1gjVWgCxAdXI4oXNT3pMXdDVSHB7IFM0U7gjk8L+e7u40hKFIN2h498KWB9hZ+rXp/d5Fpzkhy8df1ROKwaDow9zqajWSR1EYUhYH5BFf/8aiXFNfUN5WOOzKdDmov35hQy/rqhOPfxYRBSUBbS+fe3UXHGbbcw9qgOXHpkexxq/2Q5OdTGxn1lx/uRkbHrLFEmbZtWs8AAWLRoETNnzqSioqJJzIL777+/lVp18FBv2NHbn4b7punIumIQ0TSJEYsHx7qpceMkq55nITRrU/ECwF+O/PYv2M94nvnFBvd/vKQht3bXTDePXziAjl4b4YiimrSoA5ICj16LFq8+AH8FlK7CungCJ3au4Lph55LqtLI79ndKNzAUXDdudtxgTos311AdCNNhuzWHUru0vBBCsKNmZ7FolNdHWFNax9PfriakGxzbPYNnLxvMU9+s4pyBeXRPc+yxeCGlYJMvzIUvzWgwIy6pDfKvKav4aXUZT13YH5s5ohw2yJrCtmF9AeDNRfpLeW/OBlaV+Ljv5G6kbzO/vfyIdhRWBnj8uzX879KBCbIWmZi0PHWGm3C/63D1uQBRVwSaHd2ViV+m4e3lRZv276ZWclYXossJ8EvTFOHy89/iumU4lWzrl1KwotzPfR8vprAiOu51Snfxr4sG0CkpO2rWZ0RDS2llS+OLFwA/Pob94ncarCS2c+mR+WR47FRVxo+nZJUwad4WnvmuabYRpeCZ79fw6Dl98AgDT4MFo0JPHOcwZrzbnqJ8Q7mP/1w6mHHTNjBu+gbsFsk5A/O4fmRnUuwSEdlzsUHTJAsKqrj81ZkNP8HmqgB/+2wZszdU8KfTe6IBQUNhEQL7Xiw0mOx/goYVI/tY3Df+jObbigiUIVDROGarvmrcsf/FsOrrxr/1EHLiWNaf8iEXvRN1R8xPc/LP0f3pnpZFmn8FMp54ATD7VTjnaewfXMeJR/+OB065CJclsdmsUiB1AydQrwT3f7wk7sd4cU09G8t9nNwnJ/rB3sx17zwPDEnJx/O28Nr0DVT6Qwxqn8KD5/bh80VFTF4UzTD03pxCXr36SN65figOtW8eJEJAWUjnvOenN7wngpEQz01dw9SVJbw0djAO00fFxKRZWi0GxksvvcSYMWP46KOPWLJkCcuWLWv4t3z58tZq1kFH2NCoUulUuPtS4epLlUqnLmwnfMJf4YhrQds20ZEaasClRE57HNZOTVifWP01WriGa8bNaRAvANaW+hj7ykyqIk1fqhph2LokcSO3LobUznjmvsjvh6fgSRx/swn+sL6LjCO+XQYck1LgR7CuNsScoloqIorwtmNqdMVNb83l/TmbCIR1dEPxw8pSbhk/j9+f2pOJswrQhWRrUKcyoggLsVsfdSHg758vbxAvdmTm+go21wabTSFmcmih1RS0KQEjoGy8PmsTx3RJo1Oaq6HIqklGD8hh4ZYapm+obMVGmpg0JWhYqSSTCs8AKpw9qVZphHXwOTtiXDoRvLmNO2d0R101Cb57KL77nx5GlK1oeJ+XBXWuen12g3gBsKHcz+WvzKIy3PhRr2kSsXlu4kZWFWDzbeHWo1IaNnXL8nDJ0HwszaRUjSiYuzFxn1uyuZrgblgyCBG1tCgJ6swpqqXAFyYoBIYmeevXjdx1cg9uGT+XH1aVohsKf0hn4uxCbh4/l/qdqpeaxI9ga1CnSldEpIw7/vl0xZ8nxf+w/HxxMWWBCH/7YiUXvTyTW95dwNyi2oYx2KR1CRuSKpVOuasPkawBqCUfweop0ULNCoOvjMZ2mb9TcPbaYrItjZkECysCXPX6bKojBpTFT/kLRC09RHQS6JnzPFcNcGLdzcWcsFIx89KdWVBY1ezxESmp0hVzi2tZXR0kIARhKXlg0hKe+GYVFb4QSsH8wipue3sep/TJpkuGu+H4ibMLcchY8WNv0KXkqW9WNxE5AZZuqWFV6a7ntSYmhzutZoHx5ptv8sgjj3DBBXHcDkz2mRojiYzTHiFy9B0QqgObh3otlbCwk9RcOlSlKKsNoMf58A5GDN6bs4lbRnRA3+HFaygJKR2gckP8OpM7wPofIRLEEanGr+1+IEOHVcMiRVwhACAn2dHsYCI1QUFtmBvenEO5rzGd3ul9c3jwnN7MXVcRNz1dIKzz3pxC+uR6eePXjbz04zoABuen8M8L+5Nhk81aZQS3mQIn4vsVJfQa2Ymgmd3ksECrLSScM6S1mxHFm8u7+onU6Aan9W7aF/vmeOmc5mL87E2M7GymtDZp+wQNG3rmSFxXT0ELVYOQRGwpWHQ/lk2zEx8YqUeIqPvGK1PXxh1nQrrB2zMLuPO4zhgRI2ot2lwAbWcq+Ms5tlMnTuqdxfkD8xjYPhnXLlZUNSnokuHmlzVlccvbpzqxCLHLWEwBIfn9+wuZtYMA2T7VyctXHsHxPTJ5e2YB9eGmH07ry3wsLKxiZIdoRpOIEPyyroJHvlhOpT/qsnNEh1T+eWF/Ui0ixoIiEDFi3GV2ZurKUlaX1lFSG6SkNshN4+dx6/FduOqofCymJUaboUpPwTnqXziOewDCPjSLFTHjWZh0CxhNF5LETtsihuL16Rt4eGDitL1o1sbguZEg1mAVyr574r5FCFJcVqr8cVK/Al0yErs9hqTkme/X8N7cTQ3bspPsPD92CD+ubtrnlIJnv1/D5Ud34O+To4uqVYEwQQW2JnvvGUFd8eOqxKlnJy8sYuCZPcGcH5qYJKTVLDCklAwZ0kYm9IcqNhdVIotKWxcqySKgW4lEDOh8QsJDVPuhzCpObPEwt6CS0E4TjoCWinHMPfEP0KzQYRhsm0QqS+JUcPHwWCTnDsqLW5aT5CAvydHsfK5Wh6temxUjXgB8tbSYGesq+GJxUcJjp68tZ1CHVGauq2jYNr+wirGvzKS2GbNdAIGIGz17OxYpCbRAGi5pkfiUoDKi8CPQmlkBNDlAGBFkXXHbyEACKHsSb+inMTTZR4a76VRMCMHx3dKZU1jFxjjinolJWySiK2pUKpXWTlRaOlBrJBGRTkjtHP8AIVDZ/TEMRVBXLNxUnbDuBYVVDYH2dF1htD8KbO74Ox9xDaz4nLwUF4+f24eRHZJxKrXLVVs9rHPZUfkJLfNuP7Eb9l0syipN8s+vVsSIFwCbKgPc+OZcRnZLZ8a6+AIJwOTFxQhNommSxVvruPeDRQ3iBUTH/7GvzKRuJ/1DE6JZi0KPXaM+HDtovvTTOnxxLDr3N1KT+ICKiIEfgWxmXDaBgG6jUmRTaetCRHPB0klxxQvsSZSqpCabZ66vJJzUsTED3c70HQ0rG+PR7Mmc0G0R3HBM/P7ssmkM7pAct0yzSL5ctjVGvABId9ubFRLWlNSRk9TYvqO7pDFxTiH6frCOcMbJorcdt11jS4L0sgcT2+OX1BpQpSuCwpyDmuw/Wu1Juvrqq3n77bdb6/SHNUF7JsbAy5oWWOwYp/+L7zfEV7cBOqa7sO70Uo1EDEKdT8YYenNsei17Epz/Isx4Ifp33mDC1pQ9aqvQDe4+qTsju6XHbG+X4uSNa4fibuYJllKwrKiG2gQq9s+rS0l2NpMNxW7Ba7cwfyezxLK6EPMKq9C0xIOL2yo4L4HwAjCkYyoVCVYR9hchKXl5RgFnPvcLpz7zM+e8MI13F24hJM0BpCWRvq0IFWkzLiRzSww2qBxOcG9IuM8R+cm4bRqfLomTmtLE5CDBL1PRz3oaRNN3njHibuq1FABsmiA/NfGHVH6aKyZLQq1IR13xYdM0q31HQ2ZPjJwB+EQKRsTYo/hJaTaNZy8djMPa2F5NCn5/Snd6Z3l2GTeiNmzwRYI+u7kqanrvtSce89LcVoQCv6H451cr4u5TUhtkSVFNzMeV2yo4sUf895tFCjqlu5uY/isFy3aqZ38TkpJxsws569lpnPbML5zz/DTenrvZHAN3E79MwTjtsbhllSc8wtMzm6bdzk914rNkoI/9qGlq1c7HR1OpLn4/+nfeEMLW1N1ujxExGD0wjwsGx9ab6rLy1nVHkZTg49inK/67zYp2R+rDOu44KYy3o8lGYS7Ta2dg+xSenbqWun0U3twWwdijOiQsP65HJm/9ugHLQSy2SSmo1uG+T5Zy0tM/ccrTP3P1G3NZXu7HOMiFGZO2Qau5kFx//fXcdNNNnHzyyXTr1g2LJbYpzz33XCu17NDHZ7iRx/0Va9eT0Wb8B3xlqE7HYoy8mzpbLmOPCvH54viToGuHd8KI47dXq3twHn0vzqE3IctXIPRg1M9xxnNR64vkfCLn/w+f8rKn4Y8chsG/zu9PdShCYYWfdI+dTLcNt2w+EJjFIllflngF+ZtlJbx69ZF8OC9+MLYrju7As9/H9+Wctb6CUV3T0BNFUIsorj+mM7PWV7ChPLYNd5/Sgy8XF3FjgpWE/UFESv71zSomLdjSsK0mEOHxr1dRUx/h+mEdwEzX1SJotYVAG0mhCnywMkym5qOPvpJE3sRWTXJEfjJfryjl9mM7m8E8TQ5KdN2gLnUgnht+RP78OGLLPEjKRR/5e0JZQwgYDgCkobjluK5MXRl/NfaGYzrHvC8jhsDn7YX7yk8Q5auhvgaS28GGX1CLPiBy1n8I74X1t1SKo9on8dWdx1JYGSAUMeiS4cKlCeRuxL+oD+vNBrIuKPdz+bAOPP71yrjllxyZTzisE0GwtjSxS8icDZWM7JCCsW1l3qIUD57bl6X/ncHWbaktIbqe8Zez+zBhVkHceuwWecCyI+hS8p+pa5g4p3HVvaY+wlPfrqYyEObWkR0RLWAFeTAT0jUCnc/AcfUXyJ8eQ5SvQWX0IHzsH3lxvsaPa5r2l1uO74qKGFTZOuK56mss/qKoiG9xQNEC+PAG0EOQnI8++n/4lIc9mRPaDIN7T+7GLcd3YUO5n2SnlRyvHY8GKkEfiSgorQs22b6uzEfPbC+aFHHdpk/unc30tWWc1T+XS4bm86dJi1EKqvxhkj2JhcBdYUQMLhmaz9SVJSwvihWBrhvZiZnrK7bFyzl4x906Ay59ZSaltY33fW1pHVe+NosPbxlBvtu6z7FETA5vWk3A+Mc//sHMmTMZNmwYKSkp5gS5hak1vGjtz8B50UiE0glLF/W6FSLQJdXJ/af34l9TVjb4BNstkn+c349MpyWhD27AcBAQDiw5OXj0CmT5CugzGk74E3pqN2pJRRkKuS0I0p68u6zKIMMqyczxbnvpKXY1n/NHDPLTEq+quWwaNovkluO68NJPser80V3S6JTu5pEv4q9Cdd3FaphSCoem8ftTe1IdCDNrfQUpLivH9cjku+VbWVFci9sqd+nPvLfUhY0Y8WJHXp+2gUuOaI/H7HItgqyNTqANZ+sLGGFD8eX6CMd6y7H4ErtPARyRn8JPaytYXFTLgLympsImJgcDIcNCpb0zjlOfwaL7MaSVALHvb6UUHZLt/OXs3jz6xYqGcc+mSR46tw85rqaT7UDEgm7Lx53tRRQvgNI10Hs0oSG3UKc3puiTexj0TxgKN9A7zbGtbSTM0LAzLpuGwyrjxrjY3paumW6O6JjaJGDo7Sd2JctlBaWQRNO+ltWF4tbTJcO90/2Djhlu3rvxaOYXVvHDqlLyU52cPTCPj+dt5tvlJU3qsGmSHlneA/YR44sYvL+Ty8B23pqxkSuHdcBrjoG7xG84CXgH4jznVTQ9iC7t+HDTuV0ZNq28IbW9VRP88fRedEi2o4zo/K6GFHCmYPH2xaOXRUW4E/8CWX3RU7tSSyqo6HO5J1lpLIYiVROkZbu3TaEUhk5CN6aIoeia6Y4ryr0zq4DHLxzAPR8sjJmO5ac5uevk7mwo8zF1ZSk3vzWXwDY3KLd9DyLRJ8Ar4a9n96GgIsD0tWV47BaO75HJrA0VvPLzej68ZTjhvVFB2wCaJpm5tjxGvNiOoeCJKSt54oJ+WEz9wmQfaDUB4+OPP+bZZ5/lhBNOaK0mHPboukEd24Ie7WBIYFGK8/tnc2qfbNaV+dCkoFO6C5eMTq52RSSiqCIVkT4CmTkyOjApqAe2+sKsK60jL8VJ+xQnnl1YUezMnkx2wkZ04OqQ5oobqPPWE7qyrqSOK4d14JwBuUxeUkxdfYQz++XQIdXJD3ECO0F0oD6xZ2Y0nkgzOKUiFNb5709r6ZObzPoyH+/OKsRmkbx74zAciTOH7TMltfUJy4IRg5r6CB5nq2ZRPmzQajZh2JPBYm/tpjB7q6I6CEPbBZGFlRCpB4sj7r7dM9wkOyx8t6rUFDBMDmqUivr2gw0MiLfia1GKc3pnMapHJuvL/QigU7obt5Z43AsZFkJkIPNOQQiBrhtgROPIBIDi2iAbyny0T3XRLiV+P2uuzXuK1yK5bmRnXvhhbZOywfkpbCj38+SUlfz5rD7cfFwXfl5dhtdu4az+OaQ6LA3ZIDwWwS3HdeEfcQR8u0Uyslt69Fp3wi0Ux3VK5cSu6Sil0HWDcwbm8vbMgiaunI+O7odLY9vvsf+p8IcSjq8RQ1EdCON17f0q+uGEUgq/7gSc0ecbg5O6pXP0b49lQ7kPBXROd+HWRNy+EokYVJGGSB+JzDymYc5XZ8Ca0jqq/GH65CaRbNf2KL387uwqpaCwzMcNx3bh/o8WNylfUFjFfaf15PVrhrJkczVF1fUc2z0Dqya56tVZTSw3+rVLIsmm7fPikx4xaJfs5KlvVpHmtlPtD3DHhPkEIwaXHplPrsd20FooaJpoNrbIvIIqwkYrfoCaHBK02vOTkpJCfn5+a52+zSIE2CwgjTBhYd/lR/KBQhoKj4CBWdEUi0qpPZ5oRCcwCiHAh+DGt+ayamtj2q0sr503rx1Kpl3bbRFDSkFQQW1YJxRRJDkseDSBEWcyJQW8/WsB/zi/Hy/+sJYZ68oB8NotXH9sZzqlu+mY5sKuDLIdGved3ovqaj/hsIEyDE7onsFlQ/OZMLuwoU6P3cKLlw8mSdu1+iAMxcjOqfS6/AjemVnAxmCEO0/qxln9ckmygHEAzVe9jua7tsO67ysIJruHrC1sM+4j325SZLsE7VPdUAjSV4yR3CnuvlIK+ucl8dOacu46votpJWdyQLBZBZoRIiJsRFrZpF9sG/cGZO7ZuBcdv6JtFwJqDbjhrTkxK755yQ7eumEYqZrYo7gYUpPURhS1wQgOq8Rj1bAqI+73k9INLh8anVe9Nm099WEDKeCk3tlcOjSfOyfMpz5s8OdJS3j8wv784aRu6LpBOKzHfJDpEYMz+uawcmtdjBVDksPCS1ccgbeZ8U/XjRhxI8dp4dPfjOSLJUX8ur6CDqkuxg7rQLpdQx7ADCSuXYxxh+MYaLFILIRQQiMUEfv0Db69r/Rv6Cvsck60fU6IFKwo93PTW/MarBoATumTzd/O6o1tN9ylNE1Qp0NtMIKUgiSbhjNOyAjDiM4Tp68r509n9eaFqWsaAtMO6ZDCXSf3YEt1PSuLarjyyPawTXirjkCq2xojYHTL8vDsJYOxs6eO0PFxoXjyogHM3ljFpwu3cFKvLK48uiMdUhy7nVq2LaIU5Ke6EpZneu2tF4DR5JCh1QSM22+/nWeffZZHH30Up3PPMlMcqngtPuzhStiyCgGo5HwMZyZVKnWPrBT2J/v6DtU0iUtVECnfysunOiioT+Gf06pZuqWWktog1785lwnXH8XuPAFWq6QirNhQ7qM+bBAI63w8bxPnD2rHiE6pTV74Lim4anhHbp8wjyuGdeS6YzoRikSjwX+yYDNn9MvBq4EyGk0Pw2G94ZpthsGdJ3Th+pGdWFPqw+OwNFiNqN38PTRDkWWT3DOqKxFDYRHR1Yh4Qb33J6lOK/lpTgormkY5GJyfgvcAuq+YxKLVbsJwZrR2M1BK8dMWxZE5FnBFg+LKuqKEAgbAgLwkfllXwcaKAJ3SE09ITEz2FLsM41EViNpyRFUBypESzRgS2dckhfvO3rwaLRq4jQpksIqwL8K9I1P45y/R9KQAW6rrufGNObx5zZHsyhZLCAgLQV1EUVoTIBDS+WppMR/O3cSwzmk8cn5/krT41os2w+C6o/K5+Ij2lNQGqakPM21NObe/M7/hY9Fp1RjaMZX6+sSBpG2Gwe9GdeWmYzuzpqSOJKeV9ilO3FIkjDUQD0OPfuiOHZzHmEF5DWPggR5/kh0WumS446Z37ZO7f1bRDxasFkGSvhVRsRHhK0Ml56NsSdTb0vEZCbLp7CZ7cgstmsBtVCLqK+kQqef5c/N4fFoVK4qjcSC+WbaVfnlJXHFEu7ix1hrOadEoDoQpqPBj0SRzN1QyfW0ZD5/bl6Tkpsdle+1MWbqVdilO/nZuXyxSYtUEy4tq+GbZVk7uncU5/XII7WAl5JWC1648gjJ/mC1VAfKSnWS4rTjYM0vg5lBK4QRO7JzKcZ1T0aTAiBgHreXFdsJhnfMH5/Hfn9fFfT5uOrYzbosg0gJZiEwOXVpNwHjrrbcoKChgxIgRtG/fvkkQz48//riVWtY6JGk12JZ/hPju4WiAI0BYnWgnP0xKt1OoFBn7NNZqmsChfEgVISQ9BPW91z+tFoFTr0AaIQxpI6ClEY7zIrJJHU/VfLRPbiWnOrqK08mbS5+T/s3fFqUweVkVm6sCVATCtNuVO4MUrKyo5/fvL2yIZp7psXPv6T35cVUp6R4bA7PcMStbum4wND+Za4Z34oUf1jb4Nae7bTw/dggZdq3ZQRKivpbJmuDI3GjwUaUUai+MYiLbJo4t5dHoEvC/K47gytdmx6wg5Kc5efLiAdjYPysIJrtGqykknDWwtZvBqiooDcDgbA1lcaKsLrS6omafyV5ZHmya4Od15aaAYbLfsGoG3voCxLw3Ye7roIxouDpHClz0GvaMI6kP7/0YJQQ4ZAiLESAindQbtr0aPx2ajsOoAkMnojnxkxRXNHDIIK6iaWhf/A58ZeQCuWldGHTms9zxg5fZBdEPtHVlPirqI0jAbbPgtQjUTtaDQoAfyYOfLm0IKmrVBKMHt+dfFw3k3g8WctW4Wbxz3VEkckpRuoEHcKQ4GPdrCW/9urEhSGGmx86Llw/Ba5FNzr0zFkORogmG5iXROP7t3cix3Zq0pcZAp4CXLh/Cla/Pigks2i7FyX8uHbTfVtHbOhZNkVyzGDHxCvBHrVCFEIgBl+IceBkquRd+o+m7XdMkDlWLVBGCmpdQZNf90amFsevVgEFEuvHvEGvGLsO4y+ahfXY71BbREeiY3J5+Jz/DA7NT+HZVFQCvTdvA6IF5CRe1QlLyv5/X8fbMAsLb5nvHdMvg7lN6cPP4uUy8eTg7SzIuAa9dfSQ3j5/Hb99d0LD91D7Z/Pms3nhk1OpoR5RS2IF2TgvtXUlRUUGpA/LMbLdYShQT/mAk1abx5EUDuPfDxTEBUs8dmMeJPXbtgm1isitaTcA4+eSTW+vUbQ4hBLbqdYgpf44tCAfgy3uRV07CkZq0zYd3z0mStVg2/Yo281kIVGHrchLuYbdQI7PZk3eIRRMkqVJk9WZEsAZqtsDCiVj7XkCo5/nU6p6Y/T3hIrTxo8HYYcpSW0TGp5fzwCXf8N1qjUBYp6imnmy3F0szE6PqiOKq12YR3KHBpXVB7vtwES9feSTPTV3Dfy4eSMMd0iQ+XRGOGIw5oj2jB7WjuKYeh1WS5rKRZBX4I4q6bfW545iTSimoV6Ar0ACnlHH9ftsihqHIcmh8cPPRbKjws6HcR/dMD/mpTlxiz+KOmOwDyohaOXRq/ffdL0UKhwa90yVBPxjOdGRd/Aw827FZJN0zPfy6sZIrh5oufyb7B7fwITZOgzmvxhbUV8GES3HfMo16cveqbocM4vKtR/7yL0TpSlRmT5wj78Hv6Uy9sXtxKJyWCO5IKVQUIvQQbJyGdeMMbCc9iC+pD0HVaEMhhMBVtxbt/atiK6lYR87HF/H4xd8w6vXaBgFlfZmPe95fiCYF14/szNih+TEm80EEd06cz4LC6oZtYV3x3pxCNAnnDWrHx/M3s77cT590V8xqrRAQERK/HnUxcQrF9Ud34LKh7fGHDaSIBkv02DUkMaGvEiItEl9EAQKHJhAH0RiYbpO8f2N0DFxf7qNrpocOKc5dZjA7lEgyShHjL4jOJ7ejFCycgEjrjMPbDj+xAkaSrMFS8DPazBchWIOt++moI2+gWmbHzdZhs0q8kWJExXpEuB7qirFs+AXb8DupdXYhbGi4Q1vQJlxMzApQ9SayJl3K3y75lp/W1hDSDaoD4YQil7BIxk3fyLjpG2O2/7KmjKpAiJuO68qnC7Zw5RF5hLcFshUWSe02V6rXrj6C2voI1YEwWV47SXYL/rDO1noDp1XisUpUnEmxUgo0SWCbYOK2iF0ufh3uSENxTKdUvrnrWBZuqsYXjDCkQyopdg3rHlhwmZgkolVdSHaHyZMnM2rUKFyu+Kt///3vf5kyZQrr1q3D4XAwePBg7rnnHrp06dKwz5VXXsmsWbNijrvkkkt4+OGH9/4C9iMuSwimPZ2wXMx/C8fJf98rAcMt6rBO/QtyyQcN22TFOlg0gaTrvqHSkt9kZUrTJFJGA5JtH+QdMoh760zk53dB3baI4mld4JSHkTOew1qzBXHkXSjhAN3AaVPIn/4bK15sx9DJWPgSo/vfyDvzSrFbNOZtrmF4++S4AoFm0Xh/xsYY8WI7SsGEWQX0zk0mosAuICAkL0xdy0fzNxOMGLRPdfLAGb0YnJeEFUVICLb4IxSU+7FZJCuLa/l6WTF/PbsPHb020BURIVhe4uPJb1axpqSOjuku7jqpO4PyklrUN1HTBEpIhGCPTQt1XeEC+qa76J/pxjCixx8m87Y2gfSXIIxwm3AhmV5k0CsVLFIQBJQjDa02fqaaHemd7eGTJcXUh/XD0m/cZP+jBSth9ivxC/UQYuUXWPreuMerdFbNwF34PfLjGxu2icoNaKu+xn3+y+gdziCsN43lYrFIhBBEIjpJsgbr9KcR814HPRxVBbqdAsf/AW3i5bgufZdiR38smsCjSVz4kT8+Er9B4QDZGz/j6C4nMmNtBQBJDithXRHWFc//sBYpBFcPbd9gDVFZH4kRL3bko3mbeebSwXw8fzNLt9TQP8vdYIYtpaA8bPD3yYv5eU05HdNd/OG0nrRLdVIfjgoakxdt4Z1ZBWR47DxwRi+O7pCCJcGYIraNpe/8WsDEOZuoD+uc3DubO07sRqpV7LUlxp5iscroIoIQDZaMu4thRE30+6S76LfjGHiYfD9pmkSu/zlWvNiR2a8iO4zAmpoXjYMCeGUttq9+h1j9VcNucuYLsPAdkq/7hgqRi5QCTZMYhsKqAniKZiA+/13M3FCc8jDaN3/Gfdrj1DrbI396hrjmq3qY9KXjOKPPZXyyuIyumR7K64KkpDiaWEXUhg3enLGxaR3Aks013DnKyYRZBVw2JA+IWmu8MX0j42cW4A/pZHrt3H1Sd07olg5SsrS4FgX4Qzpeh4VwxKBPjgfbDs+2lIJqXfHSd2v4YkkxmhRcOKQdVw/viKcVF4OkFCD3bm7YUghD4RVwXMeUxiDHbbCdJgcnbT4I7F//+lcGDhyYUMCYNWsWl19+Of3790fXdZ566imuv/56Pv/885hjxowZw5133tnwd1uKu6Hp9YjKDYl3qCpA6Il9VZvDFiiOES8aCNUhv/0LzjNfwq9HV6WsUsdjlCNXT4XqQlSnY9DTe1FLKi7/RuR7l8e+fCrWRXN6X/o22rtjqew4hleXKu4dlY+rdj2iaEHidpUuolcvjRN7ZrKyuIZ3Zxfy9rVD4/oGh5ViQWFVwrpWba3lhB6ZSKKrV3dMmM+CTY0TQLtFY0t1PZ0z3bhsFv744SJmrKtoKB/SIZW7T+7BjW/OYcINR5Nu1/huZSl//mTpDueo47Z35nPPqT0YMzB3t7Kx7AtRM2LBwoJqPpq/BadVMnZYBzqnOvcoSjfsEDjLpMWRtVELh9YO4hmIKBaVw8VdGz/eDGc61so10T7dTIDOPjlePlhYxPzN1QzvlNYSzTU51BECquJ/iABQshzZX7KnkaPdRiXyi9/HLZNf3oP7xmFUkd6wzSUDOEKliGVfQCQI/S9CzHoNMfvlxgOVgtVTotYhx92DZcr9zOnxDH/5poiz++fy6CmZiK1Lm55w+zm2zuH6Iy5gxtoKTuubw6/bgklv55Vf1nPhkDzcRK05Nlcl+NgkmkHK2Pb+75juivl4qjVgzMu/UuUP0yPbwx/P6M1fP1nS4HIZ/fBqz/+d348HPl7C3e8t5OUrj+CodkkNH6874kdw/RtzWF3SGHj704Vb+H5FCZNuG0HygY7CJwXVYYOP5hayZHM1vXOTuGhIO1Ksco/Tdx2uY6CUAkpXJd6hbivIxs8AIcBauzFGvGigvgo591XSjroVihchNs9FZfdDpnVCvHdlwrkhPzxKxbAHSStemLAZztKF9My/AoDfnNiVx75awVMXDWTnWb8/pMddyNpOUXU9ffOS0YSgXgoe/mI5Xy/dysD2yVx8ZD5pbhtldUFKA2FCEcU/v1rJyq21Dcef2iebm47rQsckO2wTFGt0uPClGQ2BPyHq5jJl2Vbe2c34bfsTISCAYPHmGj6cvxmbJrlsaD5d0117PDdsKXYMcmxisr9o8wLGrlTFV1+NNUF97LHHGD58OEuXLmXo0KEN2x0OB5mZbSMTwM6EhANbzgBE+Zq45Sq7HxHNs1fpxsSqLxOXrf0Om16HHwdWqZNUPhs58bLoqhPA9KfR0rqQcsUnyJ8ei6+cRuphzbfQcSTt7QE04URWbkAu/B+kdIQt8+OeO5LSmRSvl5uOS+ODuZvYWO4nbCjscb6jLAK6ZnqYub6iaSHRaMddM914LIK1VfUx4sX1x3Sme7aHzxYUAfDTqtIY8QJgXkElz36/hutGduG1aeu59YSuPPpl0/RxAP/5bg1n9c/FE7d0/xFActNbc1mxw+D6xZJizh+Ux70nd2+zA5VJLFptNPZLawsY80sVYQN6pzZuM5wZiLAPgjXgSE54bG6SnRSnhTkFVaaAYbJ/0JyQ1QeKm6Y1BKDD8L1y15OB8ujzHI9gDTJQBo6ogOGWPhzz/4f85YnGffIGRmNyxKNwFhzzO9i6hDPP8eJKyWLinEJ+WFfNmakdwZcgbWBKJ45M8XHr8V3pm5fE3e8tiCkOhHXqgjpuu4ZSiixv4hCfUkTjYXjtFvrmNsbj0CySX1eXke62URMI87tTenL3xAVUBxo/unQj6oaSnWRneNd0Zqwt57EvV/Dc2MGk2rQYSwxNEywqrI4RL7ZTF4zw35/W8YeTujV85O1vNC0a8+rq12cT2naOn1aX8eov63n9mqH0Tne2mAXIwYyuG6h2R5JQnk7rgnKkEIlEBSyrVUMs/Sj+vp4sRPdT0V4/NSp8AAy7GRa/2+zc0CoFHVNs0flg6cq4VYdTu1IV1vi/0f1wWDTmbqwipBu4tNiWO60amhRx3VgAMjx2hnVKQylFdUjn66Vbefi8vjisGm9N38DiLTW0T3Xy+jVDufPdOWws98ccP2XZVpKcVn47qhsOohmAJswoiBEvtrOpMsBPq8s4q1fLxnMICMlt78xj8ebG99yXS4o5vW82fz6jF3ZzbmhymHDIZbKprY1+8CUnx07IP/vsM4YNG8bZZ5/Nk08+SSCQeJUjEUIcmH8hZUWNvCuqhPe9AC5+Y9u/cdG/j7qROt2xx/UCoDWT51xIEAIpwasqkF/dC97c2NXYinXIovmIrUsS11OyHPKPotyazcWDMkia9xws/xQGjEl4SHjE3czaEuT16RsQQpDmtqEJEfc6lG5w5bAOCReJrz+2M92z3CgpWLdD2rphndMYlJ9MpsfOyX2yGNwhlZ9Wl8WtY+7GSnrneplbUEWlP4wvFN9UNaQblNYGD9izIER0Mjpp4ZYY8WI7kxZsoaCqHinj36uW+gf7v77mOFivU6vdhGF1I6wuBLTav5lbFal2yNke3UyA2paJRPM170YihKB7pofZBVWt+sy15PPY2v9a8npao+/5Lamo4/8IQ66CS8ZHx7sL/gddR4EjBaPzCRiGsefXsssLirpHOi0RHHUbkL8+G818sq0voIyGINpxqdsKnlxWlvj522dLOb57JmURF/px9yc4nQY9Tyd56gOc2c3GbycuaAg8uB1NClxWreEaMt22hAFzT++bQ3ldkGcuHcT0deWEpUSXgrKgTnUgwoVD2vPODcOoqw/HiBc78vbMAi4c0g6IBhVdX+Zn2oZKNItsaIPUJJ8uLEp4G75dvpWAHvv7wP57Pup0+O3EBQ3ixXYihuKuiQvwGYdGf9wV+9o+pRR67hBwZ0J6Vzj9URjzZrTPjfoz6sQ/EbanNxwffSATzBmH3QIzngers3Gf1M5QsizxBYT81A67C7H2Oxh8ZeLrHH4bvdpnMnNtOatKanFaNayabHI9HqvknAHxY+N0SHPRJcPF4i1VzN5cTcSAN68bSm6yg82VAS4f3pHnxw4hx2unpDbYRLzYzqcLtuAP69H3lK6Ysqw4Ybs/W1REWLXcc2ixSKYs2xojXmznq6VbWVvuR9Ni54bQsn2lrf/bfj9MDn7avAXGnmAYBo888ghDhgyhR48eDdvPPvts8vLyyMrKYuXKlTzxxBOsX7+e5557bo/qT0/37u8mNxLuAbf8DLNfhUm3RH0WbW4YegPCmUZ60t6t+cteZ8HUf8Qv7H0uVqebdN8miISiA5QehsxesOqrBh9lUTQ/qp7XxPnQsTgoGfk3fi138cZnm7l2kJdBJUshVAcrPofTH4PvHoawv2H/+pMf5ZkFgvEzC/jH+f14+ad13HRsZ9qlu9FkgjdLbT3/uXQQf/xwcYO4YLdI/nB6L5IcFv7w4RIMpRg9pB1PjhnIXyct4bYTu1FaG+T3780jpBs8P3ZIs/cqENbJS7JjtzSv6zltGhkZB+5ZKKmpZ+KcwoTl78wu4N+XDMIiW1d/PKD9oRXPtV/PHS4BbzbJya2bwWNeWR19MiQeT3R11+12gCMPhMQTKYXkI5o9flDHNF79ZR1Wt4NkZzOiaCvSms/IgaCtXM8Ba4fqHxW6378mGivJmQpH3wZn/AstrSMZezPLrM6OihH+8qZlrnQ0bwbp1YthyzxIagcXvQZla6LHOFOiWVCEjO+rv60OX88LeGNRgCp/mKBu0C3Fw1K6MOD4P8IvTzZaMNqT4PRHYO44RMkyQvX+uCvHZ/TLISvZgcfR2K9ev/YobnhjNmt3EORHdkvniqM7MnVFCflpbr5aupXVJXUMap/MnRMXNuw3pEMqR3dNbClV4QvhskWnfslOK/Vhnb9/vpwRvz2WvIyowqkbCq8j8fTQbbfgsFvJSIoNirq/npWlW6opqQ3GLSv3hagJRujYPmW/nGt3aY3+uCfnTLyvF66bEhUavnsIyra5lOQNQZzzNPakDOw7ziX6Xwy/Ph9bRc8zoddZ4EyLWjhl9ICiheArg5QOTeeGNjfFZ73BdH8+61bB72p+jvaxUx6GqY9ErTMArC44+W8UBN387r2F3HZCV2aur+DyozuQl+rEZmkac+ne03pS4Q/z06pGi6cuGW5euHwIE2cXsmJrLf3ykslNcTF/YyX//q7RsjnFZeX5sUMorqlPeB9DukEoYpCRkxLTV+LhtltI8tpxWFvmU6qsNsg7sxLPDd/6dSPPjx2CfadYVW1lLGkrmPfj0OCQEjAeeughVq9ezTvvvBOz/ZJLLmn4f8+ePcnMzOSaa66hoKCADh067Hb95eW1Byz+jFMGcU37D2LhhMaNIR9MewYjWId/xJ+pN3b/w0GIaCf1W9JxDL8DOeNZcKVFB6fkfAhUYQy+CjbPR5Yshe//DsYOVgdDb4CTHowOeIsmos75D+Lti5qcp+S0l7j7+3qmrY0OYAOyrZyW0hVryXJY8DZ0OwkueDk6YCmF4c3jw8IUXpq5kb55SbhsGkd2TOXc/rlUVjQ1VwVASj5cXMyA9sk8OWYQYd1ANxTdszx8saSIMf/9tWHXn1aXMbB9Mo9dOIAMj43rx81uSJ/qsMptqxLxT+OyWbjp+K4k2TTy05wUVjS10snw2Eh2WCgra2od0RxSE9TpUBUIoxSkOK14LQIjjgluvaJZP89ASKe2ph490jo5t7Y/W/uzP+xKEDqQfS8R++M6k0o3IGyp+Kvjr/a0BHVhxdIygyt6Cny+etxuBz5fPShwOlKJlGwgkN18+zp4rRgKvl24meO7pTe7b0tzIJ7H1qSlr6c1+p5H1GH/5BZEwfTGjYFKmPp/YHNTY7mK0F7k27TIJJLO/y/y3UuiH1sDxkStCqsLMQZcBos/Qk79O1zzOXz3d9j4S+PBdi9c9DoM/w1Mf7Zp5endMCJh1nYey9fT1nPNiE6U1Qa54Zs5DO+azmtHdsU55i2orwbNFhVCZv0XNk6H3IF0yUmnXUpJTIyLozqlct9pPamvq6e+rvGjKlkKxl19JJtrgtTWh9GkZPHmaq57Yzb1YYNxMzbwxMUDefXndQzvGhsguKg6QPuUxJ75mV47tfXRm3vJ0Hw+WbCZ6kCYstp6bJHodiHgsqH5fDgvfpaiK4Z1wGroDePgjs8sQAhBbdigLhghyWHFaxVoe+DysT2DRCJCEWOPx+C95UD2x/3R93anfalhP9r7V8cGVd8yD8adReTGn6ii0cXR7cjFPuQa5LxxkN0Pjr416u71479g2aTGOnqcDoPGQkY3KPg15nxbz3iV236xM69wLUd2TKW2e1+Sf/kH9DwjKhqGfNH+YbHDmm+YWz+UdilOjuyUxuqttVx7dEdqqpqOSZomWLyphiM6pHDNiE4YhoHdqmG3SK55fXaDMDFjbTkTZhXw5nVHMWH2pobtVf4wf/xwEf++ZFDC+2m3SNy26BxP0yTXjOjIHz6M7+p29fCO+GrqqduPD0a8uaJnW7rlgBKEmsm1GowY1NTVN2QKOtTGxn1lx/thihgHP4eMgPHwww/zww8/MH78eHJycprdd+DAgQBs3LhxjwSMbWmgDwj2SCVi0btxy+S8cTiG3UaA7D2u169ciCNvx97/YkTdVsTMl2DFF6j0boiuoxDOFPj2b00PnP0KnPtsVF0H9PSeyFP+jvz+4cYVpvQeLHEMYdraRveSDxdVcNOY35C7anJ0w5rvov8sdhCS0os/Y/y8Cv54Ri9O6JGJRcLwjilYDCNhiB9fRPH0t6t5csxAbnt7HjZN0inDxeXDOvL81LVN9l+4qZqi6gC6oRrEC4Bf11VwUq8svl1e0uSYkd3S0QR0TXNiF/DspYMZ+8pM/Du4ktgtkucvGxJNwbYH2oGSgsVbfdz7wSLK6qLmySkuK4+O7s+QPC9yp4mdS5Oc3jeHt37dGLe+C4e0x9iWJq81OZD9oTXPtT/PLWsL0ZM7t2r4qnmlCgPokUJjHC0V/a9ypiNqm0+lCpDhtpHmsjK3sIrjurYtAWM7rfmMHAjayvUciHZYg1tjxYsd+fGfOLufRVDt+XMW1qE2/Ui8t85CVK5BzHgBln+GyuyF0EOIspXQ+1xY+G6seAEQrIX3r4brpqA2z0fsWJ7RnciF4/hpi+D2T6Lv5eN6ZHLduNkAzFxXztbje9Fp4qioK6ihx7iiGMfdj7Am8eGtw9lcGaCkpp4OaS5SHBZsccY+XVcEdEVxdT2Pf7WCopr6mN8grCv+MXk5fzyzF+GdRPCi6nqSnTYyPXZK65paMVwzohPvzynk5N7Z9MtL4n8/rwPAImXDOZSCPK+da4Z3ZNxOWR8G5Sdzdv9cIglEBp+S3P/xYqatjVrBCAFn9cvhj6f3wr6b6T9SXFY8dgt1waYqltOqke6ytXjWhdboj3tyzkT7OrQw8pen4meEC9Yil7yHZeDthLdls6lTbhj5R+zDbkGs/wHx64tRi9rOx8PY92DKn6Juw6u+igobKR3huHujmfT0MKR3ZU6wHfMKo5YCcwsqKR91OsmWf8HKL6P/LA5AQSRIxdmvUhVI4bVruqEBD53dB2uC+WBtBP722XKKa+rJ9Ni56+Tu5CQ7uPb12TFzPYjGavnrp0u4cnhH/vV1Y+yNwsoATptG9yxP3BgvVxzdkWSrwIgoIhGDEV3SGdktnWlrYq26Rg/Oo9tOgXT3GSlYWurnd+8vonSbBVKy08o/zuvL0PbJuDTBmf1y+e9P6+IefuHgdmgKdo5X21bGkraCeS8ODdq8gNGuXTsslsTNVErx97//nW+++Ya33nqL/Pz8Xda5fPlygDYV1FMEKhL3KkNHBKrAuecCBkBQuLCXrkJ8fEPj+aoLYd1UOPvfcMbjUb/G+mpY/H7UNBBg/lvQ7yLCOYOp8QXwdjsdS88zUFWbMKSVEnsnJnwX6x9YUx/hleUat5/2HKlT74sq7QDSijr73yRntOPVK5NxWQXVQYOIoYgoQJP4wwZWKfBaRExQpMpAmEBYx6pFzRxDusHIbhl8tSSxb+KnC7dwx6huMdvenLGBZy8bjBSCb5ZvbbjdJ/bM5G/n9kUC1cEIbptGxyQbn99+DN+vLGF+YRX98pI4pXd2dGDbw2jmFUGdG96cG2M+XOUPc9s78/j0tpHkOrWYn17pBteN7MTkRVuaBI/ql5dEv1zvYZPD/mBH1m4hnHtUq7ZhbokixQ5ZcRZldWcGlsrVu6wjGgfDzbwE6R1NTHYXKQWUNfPM1VdHg8ta9lIoEwI2/oKY/NvGTVUF0UwiZz0JGT1hzTdRgT5QBYsmwvYYTyEfFMyA855HBatRNcXUOzKpsaRzxYT1rCmJjmc9sj0s3tzYFwwFD/1Uw+PnTSDzq5vAvy1QtGZDP+EBgtlDiEQMcjLc2A2DnCQ7gbBB0ACLVUPT9ZgxQAjBmpJanLZoBq14lNYF8dgtyDiuNo9/vYJnLhvEQ58ua8iyYLdIbji2M0d0TGVk1wymryvj9+8vJNvroGO6iyS7FjMHsSrFjSM7cf6gdrw/bxO+YIRzBubRLdONExU3pngQwe/eX8icjZUN25SCyYuLsWiS+0/tgdyNwJ9eTfDgOX2494NFTcr+dGYv3Bp7FdT8cMRq+BGbZycslxt+xjLgBsI75IATGIgpf0as/XbbThr4y6B6E4x+GaY9E7XGmDcOTvk7ypODumU6VBVQ5ejAO183BkpXCh78sZonz3+XrC9uiLp3RepBs6JOuB9H12M5NeImYiicFokUUGOAJsGjyYb0wgC+sN5gTeELRcjw2NhSVd9EvNjOks013DGqe5PtK4trefSC/jz25YqGZ1WTgkuOzOeq4R2pCOo4LBK3FNh1g3+e348NlQE+nr8Zqya5aEg7cr32/R5MvTJscO24OTHXUx0Ic8e7C/j41hHkuy1cNjSfj+ZtbiJO9sjycESHlL0KfmxicjDSqgJGTU0NX3/9NQUFBVx//fWkpKSwdOlSMjIyyM6OfqxPnjy52ToeeughJk+ezAsvvIDb7aa0NOoX5/V6cTgcFBQU8Nlnn3H88ceTkpLCypUrefTRRxk6dCi9evU64Ne429jczRYr69770LuNCuSXcVLLdTsZkttHrS0KfgV3RjTQ0tAbYfJvobYYo/e5WFdMJv2DbQGYktujn/0sdal9+WBO/Kjrr84uY0P3Ltx7wTd0s1ViqS8HBGLOq2iLP0ac/iLj55Tyv5/XUx0Ik51k57qRnTGU4u2ZBdx7ak+GdkjGaiikJtC2RaJeuqWaIzumMmdjJVKIJitPOxKOKHKTY7/YghGDOybM58rhHXnj2qNw2y0kOyxommBBYRVba4J0znBRXF1Pn1wvHb12LuiXzUUDcjEMRSSio/ZQvBCaZNxP6+P6PisFL/20lgfP6NVg8redZA0+unUEb87YyJdLirFbJWOH5nNW/1wcKrG1iknbQQSrkeE6DGfGrnc+gMwrVXRLFog4HzrKmYHcPAP0IGiJsx8AdM90887czdQFI3jsbV77NmmjKKXA04wYL+Qun8XmcBmVyK//GL+wYCbkDoSiRTB3XLQdQ66Kjr+f/z4a+yJYi/jhUVg8EZGcj+XY+6H9KDZXRj+cumd5uH5oBrVGbH+auqaaG+u9PHDap7S31pBlC4MrjQ/WSpb9XMoNIzvjC0bYWBfi/z5fzuwNlUgBJ/TM4v4zepFulQ0fHxEJVotsEvBzZ6QQrCttuoq8sdzPvIJK/nJ2bwJhnVBEYdUE3y3fyi+rSrFaJJsqA7x85ZFsrPCR5rYRNqL77GgRqAFlviCBsE59xOBPHy8h2WnliYsHkOeyNBHzq4N6jHixI58u3MIdJ3YjaTdCNxm6wbGdUnn3xmE8891q1pT46Jzh4s6TutM1zbnHaVQPZwxhBW8OVBXELVfJ+RjC1vC3EAJr9fpG8cKbGxX7Vn4ZjaemDOh9PoydCJ/dBRk9ECEf4oVh4EpHnv0KEBsb5ed11Vxf7+VPp0yii9NHuqxDE1DlzOef3xXx8YIighGDntle7jypG18v3cqUZcVccmQ+NxzTGYdhEJKS6kCjVVPUOlagJ4pXs/3644gMaW4b/pDOvaf1JMVpxR+KZgGqDxtMX1OOLxShR7aXdLeNTIeG3VD0SnXy19N6AhAOR/a7BZBmkbwzY0NCMea5qWt45JzeeAW8f/PRjJ9ZwOeLi7BogkuPzOfcgXk4zbmhyWFEq81CV6xYwbXXXovX62Xz5s2MGTOGlJQUpkyZQlFREY8//vhu1TNhQjRmxJVXxkY4fvTRR7nggguwWq3MmDGDN998E7/fT25uLqeeeiq33Xbbfr+mfSFsS0PL6h01zduZ3EGEbal7veIgAxVR64odSekIQ66GCZc2+kPUV0fdSXqdBcffhypZiVz5Bfy8Q5q56k1o71yI98YfuPCIbnQucPPNsq1Nzvnd6io6ZqXgtacxunMKnb64HCo3UD/sd/z7u3W8N68xwvnWmiCPfrmC35zYjT65Sfx24gIeHd2P07pnUBNRLN5UTddMN+Omb+A/lw7mL58s4dd15ZzVPzfhZGn04DyyPDY6Z7hZX9YYCC0YMXjl5/Uc2z2Drik2VpbXc+Obc6ipbzSvHNE1nUyvnWSnjWSp2JelnpChWF6UIK0fsKK4jpCu2Hm6rusKD/CbYzpx7fAOCARui0CPmAPUwYLc5pphuFpPwKiPKJZVwEXd4pcb7kxAIeuKMJI7NVtX90wPhoKFm2sY2cVMp2qydygFenInNHdGNAjgzvQ+l3prKuxFDAwA6SuJBsHemYzu0PN0ePXUxiCd9dUw5c/Q70IYeRf88lTUbXL6f6INrSrA9tmtuC58h3dvGoGvfAvd/fPJXPZ31pzwPDuHx16wqZYxE2o5unMqL/VbR8q3oznq0p/44+cF/LqunGcvG8wl//21IcaRoeD7FSUsLKzig1uG4yFqobKouI6wrtCkwG3T4mbFSnJY6JLp5qHPmmYIc9s0zuybg00KimuDTFm2FbtV46rhnXDbNNaX+whGDK5/Y3aDFqBJwZ/P7M3pvTKxogDB5roQN7w5N6bu4pp6LvvfTD77zUg8O2mi210k42EoqA1GSHLu3rRTU4puyQ6eurB/VFyRAhuY1od7SAA39pG/Q3v30rjlxtCbCUYaf0irVSKXfti4w9lPweS7oXqH4JFzXoGVn8Pol6IuU5//LtqnfKWkfPs7xo78sInLxeIttVw6sZb7TuvB9YFPKMs5lis/K2JVSWOsi5Vba7l9wnyevWww8woqeWPGRhZvrub5ywbx8s/rcVi0GHeOJ6as5OlLByWMbdY5w01JTaylwml9s8lNdiBReC0amlLYLVZmbqjkr58siYk/dvaAXO45tQduosJraG8C8+wmIUOxdEtiC8dVJbWEDIUE3MAtIzpy1bD86NxQE9GUuQesdSYmbY9WS2Pw2GOPMXr0aKZMmYLN1qj+Hn/88cyZM2e361m5cmXcfxdccAEAubm5jB8/npkzZ7J48WKmTJnCH/7wBzyevcvqcaDwkYR+0ZsNMScaSOuCfsFr+NQ+BJwRcX7modfDT4/HD+aw4nPIGRAN3rRzNGqIDlQ//osULcywbMXIzslNdmmf6uTMfjn8Z+p6Ln6vmM2nvgxAWZfRvD8/fnq216etZ/S29G7/mrKKegS/rC3jxR/X8qez+qAU/P79hdwxqhs3HtuFo7um0zWzqeXK9nNbIgbjrjmSswfkokmB3BaY7PM7jqFTqouqsOLacbNjxAuA6WvL+Xl1GRW+EFU6LKsIsDkQISBE1Px5D7Brgu5ZiZ+1rplubFriOpVu4AQcKPQWzDVusu9o2wWMVrTAWFYBEQXdm3ZRoLFtWs2mXdaVtS2A7fzNphuJyb5RJ9LQx37YmL50GypvMJz2f9TrtgRH7gbxxjuIBqae+kj8DCNLPoQOR0N236j4EYgVxpN+/jvtRBlHL/oTmVN+A5tmk7VqArcendWkKrdN45YTuuHPHAzKIGXLj/TNS6Jfu2T+++O6uAGay30hvl62FatVo17Bk9+s5n8/rcNj17jrlB5N9gd48Jy+pNkkL449gsH5KY2X2TGV924eTpImsCtF5yQbN43sxJgh7fDaNJKtEpsmeeGHtTGGDLqheGjyMjbVhlhRWc+WQISttUGyk5paw9QFI3y9rBjLTlki0t2Jfzch2GPLLaUUFkPhBCyGMsWLvcAwFKHMwRgj7orNHyk1jDOfIuBs3+QYZdn2O+YPg83zYsWL7dQWRS2aQn6o2CEmgx7hyFwrQ/Kbzlm7ZLg5uksaW3pdx2o6xIgXDedW8OIPa7nsqOhceF5BFTVBnXdnFTJhdgE3H9eV3OSohcfqkjq+WbqVG4/t0qQeTQr+enYfPlkQHYedVo0bj+3M387uQ5bLil3TCCmw2DTK/SEe+Hhxk745eVERXy/bimbTEFLgR7CuNsTKqnrqFBh7OB9sDqsU9MhOPM/vkuHGtkO2mJi5oek2YnIY0moWGIsXL+bhhx9usj07O7vBDeRwwjAUVVoe3iu/QKspgIr1kN6ViDefOlJQ+zBw6440NE8W1O0QvDK1ExTHj6wMYJStRoo1jTEsdsKydSGOcAWe90bz72MfYVb/dry+0E8oYjC6l5PT8w18kRKkEKQ4rSys9dIu/2g216mEoT78oUY/4GSnFSGjbiObKgM89uVy/nXRAAoq/MzZUEnvnCRcORqvXzOUZVtqeOb71fw/e+cdH0Wd/vH3zGzfzab3AiShBULvXZFmQYogitjr2Xu705/d09OzYkEQUYoIigooqPTee09vpLfNbrJl5vfHhCRLNoieR7y7fb9evu6Yvpudeb7z+T7P57E7PUzsEcvEnjGYBfU7NQF/G9uRR0d1QBEEFu7I5rrZ2wmz6LllSDsvk86mfL0njzFdohj11oaGZbFBRj65vjeRBgnPeZaSyG6Zmwa3Y+nePJ+f+y/DkxDPYWDq5z8X0ZaHImpQDEGtdg17SxSMEsS1oKEpGgOKLgCx+tcFDEEQSAozszfXL2D4+dfwyAqVhmQsN69FqkhX2zCGd8JtikIfGAv/QocJ2RSBZAiC2grvFQHRUHrK1y4AKCUnES79Byy6tvlKRzlWWzpi+tqGRdYd/+T2EREMvXooH+2xU1LjpHebYMZ1jeLvPx5jaoqZa6N7oHMUY9Zr6Bxt5dNNGS2ef8OJYqb0iEEURWqcbo4U2HlvTRqPj+3I3Jv6MmtDOhklNSRHWLhvZHvaBhmodctEB+iYOa07NS4ZQRAwagR0CiiygiAKFNd5ePPn42w8WUKAQcPLE7vy2ZbMFq/jk00ZWI0avtqVS1K4mdev6s5jSw40az25I7OcKd2jvZYFGSS6xQZywIfIOaZLJAEawV/+0QrYZDOe3ndj6HEdQv4e1X8iqjt2MYg62Vt0crk8KF2nwvYPVVEv7ZeWD3z0O29RBKDfbUR+fz0zhzzJ1upYPj9Qg1tWmJJi4pKoWpzuInaUGjlZ6CNLqp7D+VX8ZYSaNmg1aqhzywSZtBRW1fH40gM8fVlnKuwuNWNBgOn9ErisazS7ssrYml6KXiNy94hkIk1a/jmlO3VuGZ0kYtUKVLg8vPlzOj8cOo1GEnjgkvZUOtw+y3wB5m3JYmxKFCeLbDz45X6q641ltZLAg5d0YEK3KLR/wG9adsvM6J/Awh3ZPm+Rey5KRlIU/1jRj596Wk3A0Ol02GzNazczMzMJCfnfTE+WZYVKghACghCs3VEUpX6y6F97ZNWIwUgTZiEtmNzEiVo4Z797p8aK1pZH8y7c9ddqjUeoqwZLJBGUcXmoxNhxgdSWF2Le+wnC5i3k3bCDT67vw8G8SvJqJU6OW4ipzg20/LIUZNLy6Y19OV5YzY+HC5nUK57IQCPvrjnF7Z/vJinczJguUSRFWJi9OYOThTaSws28NrkbwUYtRlENBE0DgOhRB3Y3zd1JekkN/duFcE2/BKKsem4Y1JZv9uQ2y8JwuDzNHNDzKhxcN3sHX985kN/iSBKml5h5bU8eX3qw4TxmncSLE7oSZdZecDd1PxcGqToX2Rje8ozwBWB/iUxiID6N/s4gm8KRbL/eiQRUH4yl+wuodXkwaFt6Ovjx8+t4ZIVKQhCCQhCCBWRZQVBoVk73W7EJwQRO+Ajxy2u845vwK79XUwgsf6BZ9gUAnS5Hm7MFrngbjMFqHD28jOANf2WQKZTOl/yTkuhhnCi0sTOzjHsubk+prQ5neCpVMYMJLtXSNcZKsFnXoilnmEVHicPF5lOlPHhJB9qGmflqVw4TZm5hUFIoY7tEopFEksPMhJp0zN6axdb0MiKtem4Z3I74QD0aWQFZfckRBCis9fCXBXu4skcMY7pEUWyro7C6jvzK5t1JzlBYVUubUHUMllZcw9++PcR9I9vz1DfeEx5J4WYkQfAqsNSj8M60Hjy4eD97cyoall/UMZynx3ZGPM8uJH7+eByyEYdgRIy/DKgvxfHx51AUcJpi1VaqHiecy39NawTXWVkUwW1BbyFKKWZihIfLLw2krjQH84F5lJhnkK9tgwcPodaWM1PNOgmrUcPfJ3fDoBXZnl7GfRe3J8Si4701p7hnwV7igo10iLAwKCmMwwWVfLEtm1qXzJU9YrioQzhmQUF2ezABJo0AKFS6YNKHW6mwu+jfLoRLU6OJDzFSVFWH1aBpNg4E9X7wKAp3fLHHS+RweRReW3WcjlEB9Igw/yHZQSF6iY+u680jSw5Q6VAN3E06if+7IoU4q/5fmsj04+e/jVYTMC6++GLef/993nrrrYZl+fn5/OMf/2D06NGtdVl/CtSWR3/Mg0oQQEsdSkgiyh0bYe/nCKcPILudCO1HI5z40cdOImK7wTiddowbfXuRFPe6jzCdSfXLWPsiVJ9Gow/A0mM69LmJsg6TmX2wjjlbGt2vXwIW3TaAiAA9RdXNB1Dd4gLRCALXNanLBRidEskrk1J5bMkBQsxaesQHceOnOxrMjvblVPD13jzendaTAXHWZseVJJFdGeXkVzp479qeHC2o5u8/HqPS4aJ/u1DeuaYnH65PY1t6o3N2dKCBirM6gICa6ptRaqdrmOm8/0aiotAvxsr3dw+mzO5CURRCzDosIv6A9F+MWJ2PbGy9lqOKorCvBIbHnHs72RiGWOnb4O1sksNUt/jDp6vp3SRt3Y+f38sfGe8AtIILObwzwu0bYM9nCMVHkaN6QHgKQrthCBkbmu8kaiC2j5r56ANnt+loT++B9a9BVZ5q+tn9Gpg8h6rM3Swvj+eZLzd6Zdld2SOGi/vdjs0QR7uwQt76+QRT+sRx+LsjPs9xebcYrvpga8MMr1kn8ckNfZjWL54b5+xkS1op8cFG3p/ei8ve2+SVPbjqcCGPjenIpNQopPqLcAsiW9KKeGR0R+ZsymDW6Qxig43cMqQdj4zuwF3z9/i8jq4xVi9j0KxSO8FmLTpJxFmfqi4KcFWvOFwu7wxGRQGLoPDe1d2pdnqornMTaNQSoBXR+MWLPwXn86LtFvToBj+AYjsNEV0QMjf53jB1Cuyc5b0sKAES+qveMvZStMZgtH1upnbsG9yxtJA9ufsQBPj0xr6ILSTkXNU7nkirnv/77ghpTX6LVoOGN6Z2562fT9AuzMKNg9ry/tpTrD3emLW9J7ucT0KMzL+5H01t3AVJZP6WTBxOD+9d25Njp6v5cH0alQ4XAxPVceBH69PZmu7t3ZEaG0haka3FDI1315zig2k90PwBuRGCrNAzOoDv/jKIMrsLWVEIM+swSQL4y0T8+PGi1aYGn3jiCex2O4MGDaKuro4ZM2YwevRozGYzDz74YGtd1n8dgXIR5lX3onm3O8JHQxFcDuTRL1GbMBLPJS+p7tRnIV/6Bk5tCHZjLNWXfVDfs7seUaJyyF/J0XdAPPED/PQMVNe3M62rVtMOj3xLdexw5mxpXjf59LJDzJzeC6vBWzuLshp4dVIqf1mwp1lAW32kkFKbk1uGtOX/xnflr8sONXNqVhR44uuD2HzNKAgC3+3P58lxnflsSybvrz1FQWUtdqeHtceLuH3ebm4bmuhVv3v7sEQW7fT9UpdZVoOk+W21j2o5i0KcSUO8WYsZxS9e/JcjVeeitKKAkVUNVU5ICjz3b1U2hyPVFIKnuWB3NrGBBkxaif15LRvT+vHTGgiCQLBQivnnh9C83xPhk4sRZA/yJc9TO+BhyohAHvsPtdvWWchXvIddCsEzfSkYApselLqLn0fI34uw4mFVvAC1tHLnJ7B7Lu4e1/O3VbnNSgS/3ZfPhvIQ3l2fyYfr09meUY5GFLk0tXnMvWt4EnuzKxrEC4Aap4fb5u3G6ZKZf1t/EkKMzLq+D89+e9hn6ePrq49ja1La6BEE9FqJexfuZW9OBQ6Xh1NFNp78+iD7ciqY3q95y3mDVmR0lyivF0KAkmonAfUx26iVmHltL4J0voePigI6RSFUK9LWoiNYEtTMED9/egQBgqRyLBv+ijSzH8Li6xCC4lE6jG22rZJ0Mc62F3kbxHe/BuXAl6rQZ68XAhzlsPENhF2f0DNGlRQUBRbuyOb5K7sineUj0TM+iAk9Y3hl5TEv8QKgqtbNY0sO8I8pPYgI0JNb7mj2WwXIKXOwaFcuoqbxN+rwKKw6UsiTl3Zm3tYs3lvTOA785Zg6Drx1aDvCLN4lNY+N7cgPh0+3+J3llNkpq3XhPEeW429B8cgNY8UEsxYTil+88OPHB62WgREQEMCnn37K7t27OXbsGHa7nS5dujBo0KDWuqT/Pipz0XwxXu3dDWo67e5PEffMRX/DD1QFdsNyw2qkrPWIJ1chW+NQeszAoY/ChRFBUKhLuozya3qiq8xEkV3UBCSyIU9heriIsKyFTjHHlqPv+7hPZ+i0YhsrD+Sz8LYB7MoqJ6fcTnKEhWirkZUHC1p0MV+0M5uZ03uRU+bwmb0BqrFYia0O81kBSAAirHpCzDp2ZjZPD3Z61M4kV/eN58udOTxwSXvKapwcaKHWP9ikI7/GTZRB8puK+WkRsToXV/zQVjv/vhIFAWjXPCnJC9kYDooH0XYaObD5S01TRFEgMczk98Hw86fDKpSjmT+xiaGgB3bPQdw9B/11y3AE96VSG4v1pl8QM9Ygpv2CHNQGpft07LooXBjwhPZCf9tmaotO4bBVUGtNRKPVEbfkUt8nTV8LQ2xoJcFny9OPN6RzRfcYvkc1rn6mvhxjYs9YDuRWYtZrGJIcxuJdOczbmtVsf1udm1PFNaw4kM8Xt/TH6fZ4lWY0RVHUbMSL2gWrxo0embd+Pulz21kb01lx31C2Z5Zzqkh9SUyJtvLgqPa89fPJZrPN3eICeW1SKjqtRHyQEbOE38vivxCLUIVm6U0IefVG+jUlMH8KwugXUHrfBAe/AhTkHjPwhHakSgqm+IrvCXSeRnSUERLfGd0nvmOefu9spk2dwewd6r9XHS5EVuCT6/uQVVpDQWUtqbGBlNmdlNc4WXfCtxdeud3FycJqbLVuVh9pWVhYuieXab3jGrIwRAEiAwyEmnXsyChrtr3TI/PxhnSm9oln5ro0YgIN3HdJew7lVZIQ3HIZTYeoAJbtK6DC7uSBi5LQ+UuC/fi5ILSKgOFyuejevTvLli2jd+/e9O7duzUu478aURQgd3ejeNEURUH8+Rn0Ez+nQglBajcZTdJEFEHC4C7HWHUcU8Y6MIcjt7sIKTiKUksstjoPJp3EuDARTW2amnHRAprKTCZ3j+G6LnpChWoUUSLNbuTlDeXsy60ixFLEop05hFv0fLcvnwGJoQ3pqb4otTnJKK5BOkfHDvA9ppI9Hm4c2JaFO304adezNb2Uv17emeQIC0fzKzHofN8abUJNON0yT35zkA+n9UB7zqvx8z+Lx4loL1bFgVbiQIlCrPlM/W/LyGa1k4JUnfOrAgZAUqiZn44X45GVZrNnfvy0BqIoIJUc8e6G0ATpp6cxTV2CzRNAOaFokq9Gaj8FRZBQ3E4schnm7BVQkY0SPxBjREcqw4MptdXRVZsP9uYvPGcQSo4Tbgknv7IWs07ispQQbuxmJECuQtDocRskFgYZyatwICvw1s8n0WtEOkdbeWhUB3LK7D7FizOU1dSRXWbnZJGNhGBji9sByIrS4Kloq3NTVuN7QkBW1JnjGwe1IcxiINikpbrOzf99d5jccm9zxR7xgYQZNcQa1Y5fiuLbO8HPfz7amvxG8eIMHif88DhCZBdc1y7DI2jQ2gvRHFxEiCIT1H401dqOZDsMhLpOtZzJFxhPXIDEhusjEGQnFYKVj/bUcNPcnTw/PoViWx1PfaP6hM2c3qtFs3dQS3n1WpGqlq1c6seCjQcxSQJ3DU9k/UkfrZvr2Z5RxrNXpNA1NpCqWhdfbMvicH4VC28bgHFDOg5X88yn6we24a/fHKK0xsn0/gnEnmebYD9+/PxrtMqdptVqiY6ORvbXRP7b0GhESPu5xfVCRRZarYhWlnC5PHg8ECSVofn6BjWAiRLE9UNMW0NgjxloowdSrdFhEF0EeEoQxXNXHwVGJ/KSvA/98iehTk03T7DGMnDCJ2TpO7IpXe0ucmawdKKwmgk9Y/nxkG9FvVdCEPtyK0iJthJq1lHqY2Bm1EpEBOg52/RUUepbQJ4jsOg1ImlFNh5avB+A/xvfhVuGtGP+9ixqXervtE+bYO4b2Z6HF++n2FZHjVsm6FcEFT//m4g1pxFQkE2t10L1QKnyq9kXAIrGiKKznFcnEoDkcDPfHjrNqeIaOkb+udpR+/nfRJJEhMyNLW9QdAStJKDTSLjdsvofoBWdBJTvRfrhUQhNhNoq2PQGUkg7oq/5GqM1HMltwGc6YT06axgjO0dycacI2pocxKV9ifbrN8Bdb9YZ1oGvJn3EXzdbuCQlklCLHllRqHPJOD0ydqdHNfBsIfswMdxCQWUt3+zN4+lxnegSY+Vwvu8Srp7xQQ0dsjS/Ii4KAry44ii1LhlRgBX3DWVIchhL9+Q2ZJMM7xDGC1d2Ra/I53yh9POfjySJCAX7m68QRIjvB4ZAJI8DadtMxB0f1i/vj5i3g8CILqT0ugVNbQtTOpFdYOQzGJdeR0K9yBivNfLPYU/y4LCJlLhN/GP1iQYTTbdHxmrUUOVobqoJEB9iYvbGDO4d2b7FMeOV3aMxSSJK/cSY2y2TGhvInpyWswf1GpG04hruXbjXa/lrPx7ji1v78fDi/WSWqqaloWYdD43qwJqjRQ3j0eUHCrhnSNtm3jB+/Pj542k1D4w777yTN998k4qKita6hP9qZBkIatN8hc4C49+FUS+gWf8y1p2vEeLKJEBjR9rxgSpedJ0M1yyCuD5gDEK0F2L0lFFhq6L29HHse5cgpP0CCQN9nzyyK4qtGP3yuxvECwCq8jAsmIjBXoBLlrlvZHLDqmOnq0kMM9cLEN6IAjw0qgNLducyb2sWj4/r1KxzF8Bz41Mw+/hFu0SRTWmlXNw5ssXv6/Ju0fx8tLHN7P99d5jCqlq+umMgM6f3Ytb1fRjZOYJjp6v4+1WpzLq+D4IooPgFDD8+kKrVWvnWMvG0uRTSKiHxV/wvziCbIpCqW85QakrbECMaUWCfj1aJfvy0BoqiQEvZQx0vhelLkQ4uwLr+cYLyVxMkliGKAmahCqmuEgbcBcYQ9UXt2i8hMhV+epp1R7MoVgJQkkf5PrYhCCmsPQatyDtrThKYsxbt+pcaxQuAkhPELJvMm2PDmLkujTs+381dX+zhpRVHqa51kVFi467hST4P36dNMDlldurcMiadhEkUeOnKrug1zQPdXcMTCahfLooCJp1Ex8gAn8cN0GtwupUGcV5WIKOkBo+s8P61vXj/2l4svK0/T4xT21XmO9zYEdDqJDW7089/HYqioAR4t8Wl8xVw7WKIHwDGUITcHYhtBkCfW9T7JK4vGEMQA+OQ6spxuVwQ3qn5wS/+K3x9u3eGlMuB9pdnCC7cisMtMyqlcXy2ZHcutwxJ9HmdQ5LDOFpQRW6FA48s07dtcLNtIq16ruvfpkG8OIPbJfv0oDnDpanR/Hy0sNnyPdkVhBh1zL2pLx/N6M3M6b342+Up7Mwq50BuJRpRYFBSKKNSIsmqrqOwzoMDAUlqvQ5kfvz8t9NquU7z588nKyuLoUOHEhMTg8nkXWP2zTfftNKV/Xfgdnsg5UpY+1JjKzlBgIkfwqY3IW8PAqo/BJv/iTjiSQRJA53Hq0FpwdTGGaf9C5GC2xF51VdM/aGWYNMQHhgQxIAxI5C+uh4qmqS/6q3UXDEL46oWjFjdtQSnf8v6rJFc3i2aztEBHC1QS1GeX36E967tyYfr0ll7oghFgQ6RFp4f34UIi4724RZ2ZpXTOSqAuTf2ZeHOHE4W2mgTauKuEUnEW3TNakhkUeDzHdl8sD6d6f0TuOeiZN5be8prm7ahJsZ3j+G2ebvVj6AR6RgVQF6FgxKbk7/M30O4Rc8bU7vz5k8n2Fdfg6wRBa7tn8CdQ9uh9Zss+WmC2CBgtE4GxuFStZVi0nlkYIDaSvV8O5FoJZG2IUb25lVyda/Y33+Rfvz8QbjdMnLiSERRArnJ7GfSxdBxHCyYglC/XNozD8kaQ9CM5YiKUzWibhrDtrwD415Hm7aWK5IkDtslGPIi0WVpUJrWuJ3OjHLVHHaW6Zm18RCzJsYSsrkFXyh7Ga6snYhCRMOiYlsdDy3ez8cz+nAkv5K/T07lzZ9OUFhVh14jckX3GEanRHL/on2YdRLXD0jA7fYQZ9Gy/J4hfLolgx2Z5UQE6LljWCLtQ00NHUhsCjz45X4eH9eJBxbt9WoPqREFnruyC3M2ZzTEugCDFpdH5qvduXy1O5dRKZGM7RrFvQv3NmSGhJh1PDe+C2FmHdGBBiyS0OwF0c9/LrKsIEekIOks4LRB+9GQNLJ+LKj+nYX9C1ShcNp8mHt54wTV/oWIwe2ou/pr3FfOxrRoEtjqhYDwTup9U+tb8A7Z9ipVvTtwebdY1h4vpqzGyYaTJfRpG8JTl3Zi1oYMim11GLQi1/RNYELPWKZ+tBVQM4j+cVV3xnaN4tt9+dS6PIzvHsMV3aKxCPUTefUoosjiPXkUVddx78XJvLvGexzYJtTEhB6N40BJFOgUFYBWEvHIMloRdILA8v35BJt1XNQxgsQwM+1CTQzrEM7h/Cpum7er4V6LCTTw9rQetPO3P/Xj599CqwkYl1xySWud+n8Hayzy5DmIX9+q9q1vNwJydkBe8/ZpwrpX1KDUbijMn9I8XbY8g4AtrzK0zZ18urOI6ZnlPDkyllvGv4+mKge5IhdXaEdOiInYSz30Lzne8mWV7qdN8Fg+3JDGm1N68O6ak2glkXGpUWSU1JAUbuaxcUPRCAJGScAgqO1G/3l1d6pq3dS5ZTyyzP0XJ1NU7USvEWgToEPwESRqPApfbM9GIwrM357NDYPaMuv6Pqw5Vkilw8XYLlG0C7dw06c7cMkyd1+UTI/4IPZml2PSSQSbtNwypB0dIi088+2hhvRBALesMG9rFma9hlsGJKC0kDYoSSK1soJHAaN/0Pc/gWTLQ9YHgqZ5RtGF4EApmDQQ2bL3mBeyKQJtwU613lnS/er2SWFmduVUoCgKwh/kvu7Hz79CjRRKwNT5iF9dr/6OAfrfAV/O8BY1AKryEVc9pgr2FWf5TygK/Pg4TFuIs66WyR9tJ9Ss45+XfkZ7zWmMRfvRh7VFHxhOdVkRr25RjTIjTEJjlxIfGEsPEhN0GdlljTFEUVSD6mirga3ppcy/tT9ZpXYkUUAUodYls+C2/siKwv7cKkqCjLQLNRGiFXhoRBK1soJGAC2N7TElSWTdsSL25FTw+qpjvD2tJwfyKjlWUEVcsImxXSPZcKKYAYmh3Dk8qSHWhZp13DKkHV9sy+LafgncOm+Xl5lnWY2T+xbt5ZPr+zD5w618ML0XiYHeL2d1CMiovjuy2x/n/tOwCaFYr/0KacFV0PdW+HJ64wTYGSpz1ImxHtfA9o8al5dnYNrwIjMDH+CyCd/Rtu44npJT2GIGEXhsccvp3mXpJARqeXNzBjOn9+KTjek43TIBBg3J4WaeuqwTnaOsGCQRi0bAocBlqVFM6hVHsFmHw+khwKDhtcndMOkkYoNNlJfZOLtC3eaRmb05nTqXwjX9E5h1fR/WHiuiwuFkQGIoQ9uHsWBbNk6PzPT+CVzSOZI92eW4PDKjU6KQBAFBlnl4dEc+25LJTXN3AhARoCcx3MJflx3yOl9+ZS3Xzd7ByvuGYP2VEClqROxu1b/GLAm4/feOHz+/SqsJGPfcc09rnfq/CqPkRO+uQPDUIWvN2KVQXO76AYXORHX0CMx37UQoOY4Y2g5h3viWD3bqF2g7rHnAqkd7dBkTJ93Pp+pzm1fX5DEmIY62m/5J7cBH2K0fyIw5O3lmZBT9QxIhb7fP49iCU8jNc5NT5kCWZWKDjcQEGimxOfnnTyd4clwnrDoJgwAet4ysgFMS+f5AAbM2ZlBW4yTSquemQe2ICTbQKy4IQZYbUltlQcDhURBFgdo6N89ekYJFr6W61sWH69NYtCObQUlhmHQSAQYt87Zm8sKErtS5ZNYfL+b9Jhka/1h9gr8MTyI+2OQlXjTlsy2ZXNUrjmCNiOcscaJOENmdVcHszRlUOVyM6BjOjP5tCNIKyD5c6/9IRFFAqk8pdrtkNc3azwVBrM5rtewLgIOlMu2sIJ6nuCCbI0CREavzkYPa/ur2yWFmVh0rJq+ylrigcxsL+vHzR6HTKJjcZQhuO4rGQK0UTK1HHcY4ZQ1V4QOw3LkDofQkYmAUlJxA8Pj2lhDSfoE+N/s+keyBkpMct6plmKU1Tq7/KpuYIAOpsRdReqqOt4fJSOjJKVfjQmktalvyat81+Y7gThQebe46ePx0NRd1DEcB1h8rYlxqNKuPFDJzXRpF1XWEWXRcN6AN4RY9N322kyCjlnk39yPOrMUgCtg9Ck7AoJGwuTzkl9qJDjTy3jU9+WRTBjfN3Um3uEASQkwczKtg7bFCXp3cjfnbsr1iHcCMAeqL3ff785t1IgFVcFm6J5eLO0Vw82e7+P6ewQRKInYFluzK4dMtmTicHsZ0ieTqPvFYJRo8OS4UWq0EAsge+YKf+z8RnUbB5ClDcNXfU0Gd0d25A032RoSWDDlP/gRT53kLGIB07FuGT3qASz/LYNXtPThV14k568p5p2MKLRZTBrUhv9rN0dPVnK6sZUKPWLLK7HyxLZuIAD23Dm1LqFGD4PageBQ0osDApDAeW3KA/MpagkxarumbQHKkBbfbw2XdYhBFoWEOrlZRx4Qul4fnxndFJ4kIgsDsjekYdRpMOok5mzLoFhvI4PZhDO0Qzta00gaBAuDD9elc0S2ap8Z2JL/MzqdbMhvWXdU7jnlbM/FFnVvm+/0F3Ng3zqcvhigK2BT4Zlce3x/IRyuJTO+XwIgO4Rhpfd8ZQVDvJ4X6sbg/k8TPnwi/Xe5/KIIAQUI50s/PIBz7Th1wGYPRjHiauuQrqFFUx3CXoqFKjCAwsAah6PA5O4cotVUIcgsBC0B2IzWxH+8YYcZqNsDQh9DXVhFZb0Axe3cll495hIi8a5ofQ9JSmjSJLRtyCA/QE2mSWbwrlzCLjrem9WDJnQNZtDOHHw6dJjXWqg6C9BIfrs/g822NM2WFVXW8+uMx7hyeiAj0jA9mX24F1bVu2oWZkUSBz7ZksvxgQUMQiLTqeWliKv9YdZy1x4uQRIEJPWP4alcuxwqquPuiZKKDDMyc3guALadK+GZvHisOFdAmrOWpbLvTQ1mNk3S7k5TIAHT1ApBTEHh51TFWHmwc0M7dksWS3Xl8fedAQrTCvy1A1QoCp4pqWLo3D40kcnXvOBKCDP4WXxcIqTqv1fwvFEXhQAkMiTn/fTwmtVuKVJV1XgJGYpgJAdibW+kXMPxcEAKkanT7v0Dc+o4axyQtUrdrMAx9nApPIAAuWUOVGE6gtRphxUOq/0VLKIqamdjSatnD5wdVcSLIpOW1MVF0MVegK9uLOyAOa0gi7rRjdI7qwvaMMt7fZaNXnwcIWvtE84MZAikO7kFGSfNuIwkhJpLDLaw6XEh2mZ1As44AgxZbnXptJTYnb/18kmv6xXN1n3gW7czh5rm7mHNjH5wemfnbs+gcHUhJdR2fbMpoEB4CjVqeG9+FJbtz2XSqhAO5lRi0IkOSw6iwOwkN0DXEup2ZZSzdncvn27KZ1jeeY6dbHiecKrJxaWo0tjo3u7PLMWolJFHg8a8PNpz7g/XpfLkrl6V3DuRC2fy6BIEiu4svN2ZQbndxWdcouscFYgK/eN8CVqka7d65iNveV8tGJB1Sj+tQhjwEtRUt76jIzbOaAGQ3CVaRn6cFEZy/nrKwkWxOO0Xp0IGE6szgrGm2S1m/h3l/ZzXxwSY6hgjM2l5EaqyVv09OZWt6KR9vyCDSqueGgW2JtepZcbiQF1ccbdi/wu7ig/VpjO8eQ3iAnpQSO5kl1dS5FTpHWymx1bEtvYxZG9Nx1/8+A/Qa/m98F1YcLGDFwQKM9aLX3fP38Na0nny8MZ2xXaMY1zUKrSSSX+FgwfZssitq+WRThtf1xwYZWVjfitgX+3Mr8fSJ87nOJsPVs7ZzuqrRM+dv3x2me1wg703rgYHW+906BYHTNidf7kzHVufmyu4xpEQHYERpdWHFjx9oRQGjU6dO50w9Pnr0aIvr/ECAUIX01XSE0wcaFzrKEX94BMNlEnXtpzYstihlSAsmqZ4Y7YbBiR99HlPpMhECYmjxrxLZhWP13eRSoi18PiGckM0vQvYWpJoSIodUMLrjUFYfL2XJ6Uimj3iRwE0vNhqamUIpGvshz22sRlbgngGhRGR8y5DkVDpFBVLtcDN91vYGG4vtGWXM25rN3Jv6sivTdxu7uVsyee+aXmzNKOORr1QH7REdw0mJtvL9gQKvbQur6nho8T7evronFQ4XFr0GSYAFt/bHLSuIAuzOKmfmujQkUWBkpwg+uK43L688Qsw5XtLOGKq9u/YU7ULNPDaqPXoUCmucXuLFGWx1bl7/6TgvXpaC2CTbRRDUOk2nrKAXf3+pSZ0o8vCSA169zpftzePyblE8Mbojen/0+bcjVufiCenYKufOq4EKJyT+Wt5qUyQ9siEIqSqHc0iYDZh1GmKDDOzLq+SKri2bovnx80egl2R0e+cgbny9caHHhbh3HkJ1PpZx72OTVdHeopQhzZ+kvoANfajlgwa18TbbPAtPmyGsXlWIRa/h2+uTiT/4LmL2ZiiuL480h6FcPZ+7IxLZnlHG7qwKNnYfzEV97say56NGcSQwDtdV83j0m1Kf57luQBvuXbiPYpuanbHxZAk94oN4eWIqD3y5r2G7xbty+XhGbxbtzKHYVkdmqZ0nvz7I3y7rjCDASyu8x0yVDhePLtnPrOv7kBJjVYULh5PEcDOKrGZ+zN6UgQAM6xDOe9f24vnlR9h8qpSkcDMHWzDpbRtq5nSl+r0VV9fx5c4cOkYFcP/I9rz504mG7cpqnMzamM6DI5LgV2KZpBGp9agp9HpBaJbJ+Gu4BIGFu/N4t0lGyY+HTtMuzMxnN/bBL7E2xyB50O76AHHLO40LPU7E3XNQbIUIwx5ueefQJKgpar48IgVr1UmC9n4OteXE9bDQITKUx34u48OJS4haeTNU14/LJC2Ofvfxi7s7h/PzmDc1gRDbSS7p3IHkcDNXfbTVqwvJd/sLeHxsx2Ztfs/w/YF85tzQl8+3ZVFhd7L6SCEJISYeGd2BD9aneW1bXefmsaUHWHLnQGYMaEOlw4ksw4fX9eZIfiUfXteLdceLefLrg9idHpIjLNxzcTKVDhdF1d5ZVKeramkTaqbcXuHzujpHByAJSrPOw6JG5MvtOV7ixRn251ZyML+K/rFWr6wHQQBZFHHJCnpJQPk3lZq4BIGPN2fyWZP2zisPnaZzVAAfz+iNoYUsbT9+LiStJmC89957Xv92u90cPXqUb775hnvvvbeVruo/A0EAjS3PW7xoun7dywQkXgxYEASQKjLBVgTHf4TJn0D6umYDNyW8M0JoMgIKJI5QtznrpEVDXuTtH8q5tW8Yjw4woT+8AEwhMPpFcDsJ/Pk5nr9iICZ9BG9sLOJo5z78ZfIvRImVGAx6DlboeWl9OQfzK7ihTziXW9MwHf+RBGtPruodx6SZW8724MTpkXn4q/08cEkHHl/a/PPWuuQGh/YzXNkjlme/O9RsW4Aqh5uqWhefbEjncIFqQGXSiXx+ywCmf7KtwZXdIyusPlLI3pwKXpmUSpBRS0yggfzK5sFmcq84PIpMZmkNB3IruXVIO+IDdPx0pOW2lL8cLeKv4zpzJq/DIwgU2l18tDGdjJIaUqKt3DKkHWF6yae3R0toNCI/nyjxEi/OsPzAaab0iqdruMmfCvjvRFGQbPm44ga3yukPlKh/2/NpodoUxRSBWHV+Rp6g+mDsyfV3IvHz78foKUPc9p7PdcKpnzG4SrBJZjXeVWaq4kXn8Wpnkc5XwtFvm+84+gWwRPlukZp0MU7RQN+EQN4aE0JI3g8Iiht6XAdh7WHNC1B4GOGrGwmesIoXJ3Tlnz+d4N7vcrihzwRuvnY60WI5Wo8DoboA96Fl3NN/Ko+scFDjVGeu9RqRR8Z0ZE92eYN4cYZ9ORUU2+roGBnA8UI1G8IjKzicHkRB9aoutzsxaiXK7S6W7vEda1wehX05FRi1EtfP2YEkCnx6Y1/unr+H6voMDwVYd7yYvdkVLLp9AN/szuWWIe34dn++z5nWq3rH8XB9u/EzLV4zS+1M7hWHQSs2xFBQ20reOTSxRQFBFAVqFFh18DTf7s9HJ0lcPzCBPvFBv0loL6/zeIkXZ8goqeHTLVncPaSt33vqLAyeMsTtH/pcJxxfAUMfhPZj4OSqs1YKcNFfYf3fmy1XxryEaC+DwDiI7UV4SAhzJsbwxE8lXPdDHX8b/iXtzQ7MkhvFHMHrm8tZvCGfpy6OplvZamStiQ6de/G3ZYd8tlB9bdVx5tzQlwU7spv9NhUFiqrrqK51EWhS27lO6hXLrI0ZzY4D6v303f580opsbDhZAqim8R9e15vHlhxgV1Z5w7animw8tHg/X9zSjyHJYRzKa+yu9/WePB4a1aHB3L0pWkngks6RuH38lO1ume/25/u8NlAFy37xXRqM6d2iQIHNyccb0skqs9MtNpCbBrUlVC81M6//VxAEOG1zeokXZzh6upqle/KY0SfW73Hjp9X5U5l4jh07luTkZFauXMmUKVNa4ar+MxBFEaHQ9ws6ADXFiC510KPRSKri3WYQDHkQjiyDq7+A7R9C+lrQmlF6zUDoczMsvRWKjsDEjyCmJ+z9HOylKHH9qRj6LDYxlGWTqgjUyUhpq+DAYtVpevdcdfsr3yVqySRe7HUHd9/7IFVOcIkK2qIjVNQGo5OieWxwEHGWIEJOLiZg5XvYBjzMFe3bUulwNQyozqagvs6xJcx6iaMFjQFFrxFb7B8OcCS/Ck2T9qeXpsYwd3OG18DrDMXVdRw/XYUkBDLrhj7ct3AfacVquqAgwNguUYzsHEFGsb3hnJtOFXNdn3g052ih5eVNIIpsySzjoa8aBZqjBdUs25fPJ9f3pnuE+bxree0ehXnbmgeeM8zdmsnrE7r8oQHPjzdCXQWC29FqHhgHSxWiTGDR/jZzTY85Ak3JkfPevn24mfWnSimpcRJm/nXjTz9+fi+CswpcvmdeASg9ibVNAjVOCWrL1Tbgh76GBVfDpa9DVFfYNVuNV9E91baOhkDYNQemfq52HsndCaYw6H8XpIzH6Krls7E6REemas55aKmaAm8IhEkfww9PQHkGoY4slu/X8tfLU4gPNhDgqcBctB3dztcb2kYagZFtD7HqxpfIJwK3RyE6yMjiXbl80sIL1soDBVzcKaJBwADQa8WGR3dMkJEyu+oH1dKsNMCJwuqGeDOyUwSrDp/2GWsrHS6+3pNL9/ggQs1a3p3Wkye+PthQymLWSTwypiM/HS2kus5N37bBZJWqLV4BdmWV0ynK6vUiJ/1Ky9UaBa6dvcPr+vdklzMoKZTXJnY9LxFDq5VYsatl89Qvd+Vw08A2/iyMsxDrKhvNbn1hL4XB90N0N9jzGdQUQ2wfGPIQ6APU7j72koYxIqP+D0FRQGcCSaveU5veJG7CTN7vIVIUNRS7rMXtKsShEcktdXJ5exN3dtMQduAjjEcWUXvdd5QqClvTfWfcKgocLagiMczSMA5rikWvISXayppjanZIQrCpwaPGF1mldsIDGo2288odnCyyeYkXTXn2u8N8fH0fvtiW3XBf5FU4OFVk48FL2jNzXVrD/RBs0vLsFV2YuyWDRy5p7+P3J5zz/tBIAoI6pQiiwLpTpTz5TeO4/2iBKiZ8dlNfOoeafnPWUktotRoW7/b9TAJYsCObST1i/PeTn1bnT+eB0aNHD5555pnWvow/NbKsIARGt7yBpAOPC354goDwFISYVBj8ACyargas/Yug+zXQ41oQtQhR3WH+ZChV3dT56kZoNxxGPguBcShBCVhzdxP847XgqH+wx/SCCTNh5aPqIC1/L2RuhLZDsex4h6TuE9laquWlbVV8MCyc6CUTiBY16rW56gOKxoCSOpVog5FTRc1rI5vS0mO+c3QAgUYtC3fkNCzTiALhAXqKq5sbpgEkRVj4ek/jgCcl2srHG9SBZq+EIG4Y1Ba9RkKpr/Xbn1NBh0gLGkXhrau7U1rjpLrWTYBBQ3F1HaIg8MoPjem7Oo2E2+1hTEpks1ZdZ7gsNRqjBHigRlZ4etnhZtt4ZIXHlh5k6R0DzjtYyAo4nL67oQA4XB48Ci07gvv5l5HOtFA1tY6Asb9UoW3Ab99PNkUiOjaptdC6X69cbx+mpuzvza1kVMfw335CP37OF+252+kIhkB0m19D0340YkRn+GIiVNZnJXx9GyQMhIv/psYtVw3orPDxMDUTMW0N9LpeFS7qqkFvgROrENa8gOCuVZXq9qPhqk9Vkb+2En58EgbdAyseJoIy7urXgUKPzPoTJVzdpoawn+5p5hGgy1pLpN6F0aDHhcjatFK2pJW0+JlcTYypAeKCjRRXqy+cg5NDOVZQjUdWiAky0j7SwoEWsqE6RgbwS/0LXUqMlZUHC5ptE2rWccfwJDpFBaAoCiU1Lvq3CWbFPYMpqXFS55axOz3M3ZLJrqwypvaJZ2TnCO5ftLfx82lE3Ge1fpjcKw6z5NuwWpJEvtyR41N82ZJWyoniGrpHmM8rW9DWwuQHQK3L04pOAn9eRN2vjCoU4LPL1cmvi54GvVUd6+mtMH8SxA9Qx4g6C4R2QFhyozqGFCU1++mqObDkFvj2XgKnziMg9xsq209Gu/d7LJtfIUrSqdueESaD2lCsjaPAR5br2Zfla24ozKLDI8t0iQ3kjZ9OYDVq6BAVQIfIAJ8ZqaDeG7uzG8WKtmFmr/uoS4yVm4e0I0CvwS2rBqJaCb66YwDPLT/CtnqhZWdmGU+O60SvNsFUOlxoJZFal4c5mzIoszt9jl/NksDU3nG8vvqEj7UwvV9CQx/YGhn+7/vmkwvu+jHiolv6YWjh+/qtKCjYav33k58/P38qAaO2tpZ58+YRERHx6xv/j6LRiKohVWh7dSbIV2/tLpMQdnwE+xepL6ozlqFs/7DRjd1RDttmNm5/zSJwnmXalbFe/U8QEG9dA9/c7r0+fw98cydc9iYsnqEu279ITcvN241Yls7gdc/Q7rJ5fJtlYsqY9whe/3TD9bq7XUPegOfIs0uEih5CLTqirHpOVzUXHYJNWhJCTAxrH0aAQUt+hYO9ORWEW/S8Nrkbu7PKuTQ1CpdH4eejhQSatNw0qC2vrWreyjU60IBBI3ql7Nrq3ASZtHSLC2R0l0ie/e4wFXbVCcCsk3ji0s5oBBDdMrEmLYEGDVnlDqocbraml7LiQEGD6g4wNDkMt1smxKjhhoFtmqXihVv03D8yGaF+UFdUXYejhRasxdV1VDrcGA2Sz/VnY9IIjO0SxYf1gszZXNk9Bp0ALYcnP/8qYrX64tQaGRh1HoUT5TA1+be3NpXN6nNXqsrGE5byq9sHGrVEBujZ5xcw/Pwb0WhEPGIwUtLFCGlrmm8QGA9VeQhb30XK3ozSdXKjeHGG7K3qf/3ugPCOaonJmTLK6gLvdHhLhCp2nFmvKHBiFdSUwIjHYdXT6otcgDqJoNEZGLb5bnJGfcBLK7JJK7Dw3Pj5hP98n1q6CWAKxTHubfKFWPSIlDtcRFkNPD62E09+c5AsHx2uruodR3G1k+EdwjmcX8nzV3bllZVHmdI7jpGdI7l/0V6Sws1kldZw8+B2Xn4ZDR9FryE1Loh//qxOTqixzjtbKtyi542p3fn7j8d4OV/NZBQEVWR/YkxHEsxaNBqRao/CHcMTmWaP58fDp7l7/p4GU0SAXgnBfLCu0WsgLtjIjAFtkFuYGbZ5FL7Z23LmxKKdOXS/IsW3WWQT3G6ZMV0iW8w8HNExAoMIzUwI/seRtSaktkMgc1PzlcHt1PtCkdX1TbfpNhVSJsCBL9UxIiB0uxqiUlUBQ/bA4W9UMXDgPbDxH1CRjRiSSPDSq7FPmI29rhLT3lmN4kVMT7Iueo8CTyCZpTbemNIdi0GDrKgv0/O2ZjV4sgxrH86qQ6cZ2zUKj6ywLa0UBfhgei8seg3rTpTQJcbKoKQwPlyXxg0D2/oUMIxaiQGJIV7+GDX1Y0GAgUmhTO+XwHPfH2kYLxq0Io+O6cilKZH8c3I37PXjvlq3zI2f7qTYVocoqCLLmeShR0d3wCw171Dn8ciM7xbDN/vyOXWWCeiIDuG0D28U73IrHF5jzKbkVTioqnNj0J/fGPHX8LhlxveIYbkPoRPULC6TJIC/w4+fVqbVBIy+fft6mXgqikJNTQ0Gg4HXX3/9HHv+bxIgVqO1F0D2FjCGoCQOR5g8B76+tTErAiBhAKSMV7MozlBbhVAfaHxy/AeI7u67/ZuitOxGXVOsptaGJKoDuroqdaas/52wbwFU5RHz3dX0GrOMe7cn8uClK4jR1iAGxbMirZa3Pt5HVb3S27dtMB9f34fb5+32MjUSBHhjSnccLg/92oVwrKCaS1IieXliKoFGDS5ZodzuZGtaKQatxO1DE9FJIhpJ4C8jkvh0c2aDONA9LpAXJnTlzs+927uuOFDA1X3jaRdm5tZ5u0gOt3BRxwhq6txsOFnM35YdovOdA2lr0SLLMpIgEGbR8+CX+yixeadgPjyqPQE6EWQFraxwx+C2jE6J5LOtmZTbXYxOiWR0SiQWgX+LD4XslpnWN56vdudSWuN9bW1CTQxOCvX3GP83I1XnoYhaFH3gBT/3sXJwK5D4O04tG8NAlM5bwABIDjOxJ7fit5/Mj59fIUC0obXnqzEvsA2MeRmW3gKFTbLVAqLgirdgxSPqv2P7IBxb2fJBT/2kljuePtjyNrYidXLgbPJ2w9CH1fR4j0s16Uy8CAoOIBTsIW7NfTw69FX+urqA3KoAHrt4KW0MDsItOvKcZpymCNKK7by0cn/DLHNSuJlnLk9h5ro0dtenrUcE6Hl5Yiq2Oje7MstJDDfz6JiOmLQiD47qwOrDhdy7cA8uj8KYLlEs3JFDt/ggnh/fhTd+OkGlw1V/bAv/mNLNq6Tjh4OnuefiZK8XugdHtee574+QVVrD8A7hhJh1ZJXWsPxAAVaDlgdHJOJ2y5gEiAk08NCX+5t5djx4SXvCLTqGtQ/D7vRwRbdohrUPwywo//ZqRUVRaBNspG/bYHZmeqf+G7Qij43ugKQo/lnjegRBNYEXZBll1AsIy+6C4mONG1hj4Mr31AkqX6Svg2GPqQLGGSpzVAGjKad+hn71E1/OGjWzr2AvpiXTOThyHtqkazF7qgkKDKRKDGTVqTqGttcSH2zi5ZVHOVn/Uh9p1fPI6I5sPFlCsEmLw+Xm3pHJrDhQgE4jMuv6PkQFGtiVVcbCHTkoisJlqdFc3CmCabO2YTZoeP7KLvzzpxOU109MtQsz89SlnXirXtg7Q2apncQwM3qNwJ3DErl7wV76J4ZgNWg5UVjN4fwqXlh+lKRwC6lhJur92zHoJfq2DWblodNev/eOkRbGd49psbzDiMKn1/dmW2Y5S/fkopNErhvQhpRIS6uZrcuyQudIC11irBzOr/JaZ9FruPui5F815fXj50LQagLGk08+6SVgCIJASEgI3bt3JzDwwg/8/6wIAgTpapE2v4Owew646wcOCQNRxr6KcNmb6kxRTQnE91PreJfe6l3b6KpR0/x8ZWsAGIOgPNP3OkkH5rNmV9sNU0tMUMBVp6r1ZenQbZo6G+aohLRf1G1rSohz53LktIbJCyq4tGs0Q9q7eX75Ma9D7sws564v9jD7xj68tOIo2WV22kdYuG1oO0Bg2sfbGhTo5QcL+HB9Gsv+Mpjr5+wgr6IxBXV7RhkXdQxneMcI0oorePPq7igKaCWRY6erKKtxYj8r2+F4YTVJ4WY2nSpl5vReZJXUsDOrnCCTlnem9WRzWikz16XxyvgUnArcOX8Ptjo3f5/cjYN5lezKLCfMomPGgDbEBOjQNIlgWkWhY7CRly5PwaMo6ERB7afdJDZFBOgxaiWfWRgRAXoCDRr1uz5PrBIsuXMgszdlsOJgARpR5KresVzTN15tKXfeR/LzexBt+cimcPXmvcAcKFXQihBr/h07CyIeUwRSZcseKmfTPtzCloxyKh0uAo0t+9T48fNbCBSr0Gx5E6H0hPpyZSuC29bCwHvVGvuqfAhKUGd5Vz4K5fU12+5atQSkJXQWtbY/8hwCXWgSVJ01+xjcFrpMUtPne90AB75SY93wx2H5gwCIudsZOkS95w/kVXPdYjWr8aMZvfF4ZIxuJ/ct2uv17E8rruHuBXtYdPsAHv1qPxpR5NXJ3bh7wR6v0opPN2fywpVd6BEfxKH8Slz1s5/xwSZ+qitk3fEixnXpxksTuyKJApIgkFfh4N6Fe5kxoA2Xd4tm+YEC8ioc1NS5mdAjlmX78tBrREItOlKiA/jb5Z355WgRBZUOhrQP496L2/PG6mNUu2UC6n1OTVqJd67pwY6MMnZmlhNs0jIuNRqjVqJNqJnXJ3TBLYNeVDMjziVeWCSBiT1jeb9J1kZTru4T35BC/2voFYV/TunOykOn+WxrFtW1LoZ1COe+i5IJ0Ykofs+nBgLFCjSrn0AQtXDRkzDwbnUMl79XFQRNIXD0e1WU8IXO0rx7T1R3dfIqrq9abiVKkLERBEkV+RIvhgX1vnbFRwlWKhg6rxhFgfeuaUvvhEAmd4Nyl8zdC/Zgb1IGW1hVx2NLD7DwtgFEB+p5aPGBBrEPYGBSGK/8cJT9TUo/9mRX8O2+PF6ZlMpdX+yhb9tg/m98F3QaEUlQvSf2Zlew14fx5objxXx2U3/yKx28Na0HPx8ppMRWx6Wp0TxwSQdeXHGEt34+ycyru3Mm4ukUhcfHdmJM16iGjNwRHcMJs+g4XlhFt8gAn2bsiqJgAEYmhjCsXYhqQqwozSa3YgMN6DWizyyMuGAjAfrfNkb8NYwofHhtL77Zl8eCHTnYXW5GdY7kruFJBGt9l4T58XOhaTUBY8CAAURHR/tspZqfn09MTEwrXNWfC4toQ2/PQ9j9LYIowZS5kLVVNUfK3oog6VQzpe0fqtkTIe1g9V+bH+jod5A6BXZ+4vtEHcZB3l7f63pMh9oq0BrVc13xDuRsV1MHJT20GQLDH4P+d6gZGQe/UjMyrlkE61+D/D1oqrIINnWmrMbJdQMTeKTewfxs8ioc5JU7uGN4IrZatypqmfXMmL2j2YN7YGIoc7dkUGF3cv/I9gxIDEUjCZi0kmrMZNGxeGc2d32xx2u/wUmhfHFLfx5avI8TharCb9GrqYrjUqO4e/4erxrMr3blcu/FyXSItOCUFT7dmtWgSt/y2S56JQTRKcqqpuYaNV7ixRkURQGPgoTqa3E2ZlHg5YldefCs70USBV6bnIpFAs+5s2i98HgULMADwxO5fUg79RwaAdkt+8WLC4BUnYtsDG2Vcx8sUf0vNL9intcSiilC7eJwniSHm1GAfXlVDE9unc/s57+MOhsaez6CKRiEDtB9mrr82Eo1fX3Da6DRQ+mp5jHtxI8w6nm15MMX3abCvvkw4km1VORMiUdThj3W2GpcEOCyf6oZGTtnqanxXSbCrashezsU7IfeN6hiyqa30Hi8vZzahppwuWWsRi2fb8vy+UJf65JZeeA0z13ZFatBw6yNGT59If727WF+eXg470zrgUdW0EgiFr1EcrgZu8vDL8cKWbA9u6HLyRleX3WcxXcM5KcjhdS5ZV5aeZTnx3fhLyMSsbs8uN0Ko7pEcvPcnQ3X9/PRIqwGDW9N64nLI4NGRNKILNmXz99XHadnfBCdo9W49/iSA1TXuVl5/1AitAKSAueT5OfxyFzdJ45v9+c3+7yDkkLpeJ7+F2fQyzJXdYtiXEokCqCXBESP7BcvmmDSedBU5CC0GQhlmWAvh4guqqi38xNwlKmiw+X/VMeVvug2VTWDP4PGAJ0vVwUMjRayNkFsX9U3I28nhHWAyiwY96o6Pq3KR3KUYtBIyIpC52grWo+MKImsPFjgJV6cQVHgw3WnuGFwOy/xIjnCQllNHWEBej6a0RtRgBCzjpo6Nwu35yAJAo+O6cDCHTncv2hfw36SKLDiviEUVDr4dl9+QynUsPZhDO8UQVWtk+paFw81GZP9fLSIMIuON6f24MlvDuBSaBAwFFHkjdUn2JpeyogO4WgkgS+2ZZNWbEMjCqx+YCgB5wjJHo/c4Evm69axSALPjU/hia+9zfs1osDfJ6VikYTzNno/HxQFDMhc1zuWCd3VdzGjJIBH9osXfv40tJqAMXLkSDZt2kRoqPegt7y8nJEjR3L06NEW9vzfwCpWo/v5CYSm7d+2vgd9blYHXwe+VFP0Sk7A2FfV9TqzOkNU5532xYlVMG0hStZWhKKzzCKHPAjHvldVeEMgHF+h1j1q9ND9Wojvq6b+KTJc8bZabtLpMupi+lMc0JlK2UR7ctAtvUWdDTuDMRimL4G6KgQCKVpbQnKEhVCzzmcr0jPszCxjd1Y5e7IrAHX26ux0VVD71s/akMYnN/TBLStkltpIDLewK6ucHw+dxqAVuXNEEmU1Ll5cfqQhQF3ZM5Z5WzP5YHpvCiodVNWbcbYJMfLaqhM+DaTeXXOKRbcPwOFW6JkQTLes8gajpz3ZFQ3XmhIdwNXdo397iYYsMyghiG//MohZmzJIL65RzaMGqy2yfm9gUjxyg/mn7KuPl59/C2J1bqt1IDlQqtDtX9ARZHMEuuwN6v0u/LrVa5hZR6hJy57cCr+A4edfRie64MRahK9vVX+DADuB8E6qieaCq2Hk3yCis5o1eLaAYSuCmhKU1CkIB7/yXpc4Akyhaqet3Z/CtYth+UOqnxOAIQiGPKDGwbIMSBoJI55QY2x5Jgy6D8VRiRzcDumTUd5x1hgM078iQjLz6lgzz68pxO1ReP7KrjjdMjlldq9OWWezP7eCWrdaJrnigO/a80irHlutm4O5FYRY9GQU2xiUHMaGUyVszygjMkDPm1f3YNPJEj5v4gfhlhVyy+18dlNfRFGgutaN2yOz7kQxb6w+wbvX9OSxJQeaiStVtW7eWH2cmdf2BEXB5lYa/Jz25jSfvV6yK4f7h7XDeQ4T6bMxC7Dgln78dLSIZfvy0WtErh/Yhl5xgb8rhd7jlhvNDP0vWl5IkoDJloGwcFpjNu7OWdD1KjWTqLqgMXO35IRqbLtnnvdBEgZBZCqsqx9zhibBJc+p5VR7PofOl1PZ4SrMFcfRfDqm0b9kx0fqttOXQv5eZGs8DlcWf5/cjUCNgOJR8AC7zioBasrh/GpKzyrZHdY+DEkQ6NMmhML6bnXfncxnXGo0o7pEMndLJhpR4K7hSUiSwLPfHqbOLTOyUwQ/HjqNVhJZeNsARFGdXNJrJe6dv4ePru/DnWdNfgGU2Jx8sjGdmwe1w+6WqQMCdBJ1HpmVBwtwywpf7fb233HLCntzKhjRNvj3lw3LCiMSQ/nmroF8tCGD7DI7qbGB3Dy4LSG65v4afxQed+MY0n8/+fmz0WoChtJCcLLb7ej1ep/r/leQJBFNziZv8eIMu+ao7s6mUDXY7P1c/Q8geaTa9mrNC977KDJK5iaUqZ+jFB1BOPkj6CwIicMhbS1kb4O2Q6HtYOhxjRqMBFFNI1z5GNz6Cwx/AupsENGJqlPbWGGewHMLj/LxhFi6rJ/hLV6A6sux7C7odxuBkom51w0nNDgESQCrUdNim9PoQCMltkJAnfxyn+OhefOQdoiCwCOL9/HypFSeXHqQ9JLGGbC1x4sZ1TmCpy7txHf7C7h1aDvWHisiLsTExxvTMGg1DE4KRRQEnB6FHw/58ACpZ39OBWuOF3Gy0MazV6SwZHcuG096O8iX1jh9ZhSdD6KiEGvS8OzYjjhlBb0koLhlf7vT/0Ck6jzcIR0u+HmLHAqn7TCh3e8vXfGYI8FTh1hzGtlyfllwyeFmdvtIxfXj57di8pSpvk7KWQPy4mOw7X1VUF9yk7pswgcQkaIKEk355XmUOzah9LsT4cAi8DgREkeoLVRXPKyWgvS7Db6+Xc1MHPaIGvNkt/rC1m4EJF2klo3MvazhpU6O7E7NxLkELLyi+SRBfbzT9r2Nq9K/Zfht72HThSMKYK/z0DHSwnc+Mg3OEBtspNTmRBQEL2PMpjw8uiNHC6rYml7GrqwyXpnUjRmzd3h14Pj+QAEPj+7A+O4xfLc/v/HyXB5KapxEBxpoF2oiu9zByyuPYTVqqHS4fLYQBzicX0WtW1ZN+4CqWpfP7UA1o/6t4U+WFYzApK6RjOscgSAI6AX+bS9k/8uYlQqEr65vXkp8aAlYwmHQvbDxDXXZ+ldhyMNw9ReQvl4120wcpmbdGoPhph/UrI3qAtg9F/reCgn9yVIiwe4icOmNzc1XS9Ng3SsQmkxIZC9+uG8IwXoJpf5vrdOItA0zs+GscdUZYoONlFR7TzAFmXQEm3UEKfDlzhy2pqtlwC8sP+Ll37DhZAkDEkP4++RUskrtxAWbeGzpAcw6iXFdo3hh+RGuHdCG7nGBzL+1P2uPF7c49Np4qoRHxnTk5rk7KaisZWj7MF64sisaqeV7t7zGVT82/P3jOUlRiDdreeGyTv4xoh8/tIKA8corrwCq58Xbb7+N0djYysnj8XDgwAE6dep0oS/rT4VBqUba9l7LGxz+BsI6+jBN+gWie6jpf1vfUwNGQBTKkIeh7RDEokNqhkb7MaoJWWACtB+lKvB1lfDT39TlTRn3d9j7hdpy1VYIq5/m2OhlPLkgE4Ako1018vRFyQmwxqL56Rl6Xt0Pxa3O3iy5Lpk7vskho8Q73dask4gLNpJdpjqy94gLom2oiZnTewGw9ngR3+3Lp84ts+lUCQ+MbM8z3x2mc7SVzadKvMSLM/x0tIjrBrblmn4a1h0r4uo+8URY9Tz9zSFGdYnC6ZEpLqlBrxFbDD4ANU43LrdMWY2TR77az6zr+7D5VIlX7BjeIfw3Z19IGpFqt0KtW0YvCVhEtZ5S8WdM/GfirkV0lKAYL3xXjkOl6m+mnfX3H0M2RQEgVmadt4DRIdzC/N25Da2F/fj5PUiSiJi2sbl4cYaDX8G0RY3mgT8+CZM+huMr1XXOGpQ2g5FHvUitJhRjWTqCMRg6XQ6ioAoSt6xWhXjZrWZrrH3J+xx9b1Xja8qVMPsSr2vJG/EPpIpiAs7ucHKGkpMQGI8mtB1R7jxkqQZZa6HWEky1ArcObcd2H90QhrYP45Yh7cgutRMeoOfO4YnM2pjhVW5o1kkEm7SU1bhYcbCAh0Z14O2fT/hsH/rWzyeZdX2fBgFDEgV6xAeRX1HLvpwKIgIMfL1H/QxaUWxRvDiDyyODJKEXBQYmhja0ZD2by1J/R/ZhPYqimg87XB50koBFI/qNAv9gNLWlUJHte+W2D+D29ar/xbYPVf+Lg4vVrInB96smnChqJtLGf6ith88w4inQBVBU5eDpnTbeTDnVfBx5huMr4dovMZYfp1OwE9ljxKkJxCZYKHK4mdonns+3ZdF0frNTVADT+7eha6wVj6xw/cA2LN2dS43Tg9WgYf2JYhLDLGxNL6VHfBCnimzNzCcBtqWXqV1xFIXZmzK4fWg7JveO40BOBY+P68Te7Ao2nixhQo9Yyh3OZvufQVHUVqJnsnU3nizhpRVHuffiZF5f5bslap+2wb9JlBNFgTqFhi4nJo2I/ozxu0dGB/4xop//eS74aPPIEXW2RFEUTpw4gVbbaPym0+no1KkTN99884W+rD8VouIGR0XLGzjKwRIJpw+h9LkZYdecxnUb34CobshTv0DUB6DIbjjyLcLHwxqDSsJAddbpqxth2hdqzWPBfrjlZyjPUl2mIzqr/b8d5SBoVPPQY8upSr2Bt7erCr4ogODxPaPUgCDByGcQv5ymCipAh9Bklo1/m3vXB7IhTT1WiFnHq5NSeW/tKQBuH5ZIdKCBmz/bSWFVHVpJYFzXaGZO78UDi/bxy9FC7q13U3/2ihQ+Wu+7bSjAVztzuGt4IsOSw9DWB5FXJ6Xy0Ff72ZlZzhXdojHpJHolBLOnSU/wpgxKCuOTjapRnMujsPFkCQOTQtl8qhSArjFW2gQbW8ws8oVLEPn+cBHvrjlJud2FQStybb8Ebh7UFv15mpf5+XMh2dSXBtl04UtIDpQoBOshxPD7MzAUnRlFF4BUmYU7duB57dMhwoyswL68SoYm+ctI/Pw+BAGw+559BdQYZA5TTQSdNrU71qJroeOlKFM+QwlOolYw48BMiCsHIWMdBMbBvCsaZ51DEmH8e2rmxm2/QGm6msFx+pDqZSFp1WzC6gK1lORMh6/QZHaX6ugXWHHuD2GwguxG+GIikseFpNEj9bqJ9HY3ExUYyb0XJfPB+rQGsfy58SnU1HmY/sl2KuwujFqJa/vF89bV3Xlo8f4Gs84Qiw6dJPLdfnWyoENkAG/+5PtlySMrZJbUEBtkJK/CweuTUymrcXLX/N3UumQeHt2B7DI1bpfWOIkLNiII4Ct0RVkNCIKAKAoIirrvxpMlOM96GWsbaqJHQhCy67c36HYJAuvTy3ht1XGKquvQSSITesZw70XJGBXZ53X5+R24m5fielGeCSd/gRnLoDpfnYDa/pGaSdtuOIx7TZ0AG/ow9LlV9UTTGdXSq5M/cTpgGKU1ZWhqm4t0Dchu9RjL7kKwFSEBhvj+cOk7/PUnG38ZkcSLE7ry4vKjOFweruodR/92Iby75hTZZXZEAS7uFMEH1/Xm4cX7Meo0pERbWbhDNRwd0TGclS20/wSYvz2bmwa35aKOEcRZ9Thlhc1ppSzd0zgJ1ykqgJTolhsJdIoKaDb59vOxQh64pL1PAWNwUijhJq3vG8wHgiiS73DxzLeHG8q0esYH8fyVXYg2alH8Y0M/foBWEDA+/1wtd3jyySd5+umnsVjO4Rh+Hnz00UesXr2a9PR0DAYDPXv25JFHHiExMbFhm7q6Ol599VVWrlyJ0+lkyJAhPPvss4SFtU6d+q/hFC3okkYi7p7je4O2Q1BkBU/KZNyiHm3bEUib/wltB6N0vBQQUDxqGYiwa45q+tmU7K2qmdK0BSgeF4IkqlkUX92gdh3pdYM6wJs3Xh0kdhinlpeIGmoD2pJZqirPsgI1WrX1os9e7VqjOoCcNcJbkS89ReCXE/nkto38eLotVqOOuGATszekcSC3ku5xgcQEGfm/7xr9Olwehe/255NWbOPFiWpd8Zl6QkkUfJpjNuwrK2SWOZCBGIsWFFiwLbuh5VpWmZ0SWx13jUji7vl7mg3OhncMx+mWvcylMktriLIaMOskrukXz40D22JQmptkCgJIkgQoXrNToiSybH8+rzUJeLUumTmbM8kps/PcZZ3R+kdu/3GI1epAqDUEjP0lCon/QvbFGWTzbzPyDDPrCDFp2ZVT4Rcw/PxuPB4Zpc2QljeISEHWW1FuXYew7T1ERzn0uAZFZwGNHg8STsGAVVuLcHglxPZSy0RAjVFR3VWBInOT+jK38Q2oyFRNDIc9DPn767MQ69Qykkkfw4Z/qKbV1hiOlrjoEhYKokZ9ETsbrVHN2Ni/qHGZuw5xx4ekOB2saXMfwzqEMbpLJJmldmKDDKw9Xsy7a041bO5weZi9OZPLqqJ5bnwXnl9+hGCTjpsGtSXMonpgAPx6U1CFwUmhTO4dS2SAgcve3dSQaZFdaqdDpIW0YtXE+pejhUzv34YvmvhmgBq7HhrdgSN5lcQkh+J2K4TrJZbeNZBXfjjGlrRS9BqRq3rFccewdkQHGqmoqEFRBDwez3m9r0mSyLr0Mh5dcqBhmdMjs3hXLicLq3n36h7o/dbTfwiKKbSxBfDZaPRgjYXx76Bs/AfC7rne62uK1bKpYyvg2HL1t546RRUEtzwGQx+mwK4js8SOPbIPIS1dREgiFOzzMs8VcrZj/OJynp/2A4tOFDMkKYyFt/fndGUtOo3IzXN3NWwrK6qZ5olCG89ckUJuuZ1Iq4Hq+tKmc5VggSruGbUa8iochFl07M2u8BIvAGqcHk5X1jCuaxQ/nFVWLIkCT1/Wmb//6N1FT1HU3+2CW/rz0g9HOZxfhdWg4YZBbZnWOw7dr4gOoiggSSKyrFDukpn60TavrnR7cyqY+tE2lt8zGOuvW1P58fM/QavdCo8++miL4sXx48fP+zg7duxg+vTpLF68mE8//RS3280tt9yC3W5v2Obll19m7dq1vPXWW3z++ecUFRVxzz33/Muf4d9FnUdC6f8XdabpbCyRKF2nYutxJ+VKKNUeC5UxI3FP+RxFlhHmXYEwdxzSl9dCTZHvziNaI/S+EaoLEDy18NnlsPktVYEvOQGrn4bVT8GYl1Vh4thymD0aul+LsTKN9uGNZT/zDzmw9WqhX/jAeyFzo++A6XHi2foh206e5s4vdnP5uxsZ0zWaXglBTOuXwIcttFY7nF9FsEnH7E0ZOD0yEQF6tqaVMrJzRIvf50WdwnlpxREmztzC/tM2ajwKn29rTKU8kFtJUkQAC7ZnMev63oxKiSTQqKVtqIlHRnfksTEd+es3B72O2T0ukEdHd+SHe4dw52A1Y0IQBK8aYKcgkGVzMXdXDksOnqbco+Cp7w5R7VF4f63vz/jT0SKqfoMRmp8/D1J1LgrCBTfxdMkKR8oh0fqvt271mKJ+UytVQRBo7/fB8PMvoijgDkiA+P7NVwoC8thXKVfCKBOiqB36V5QuE+Hr2xE+uxxh9ii0c0ZirctGU3ZKFc7PdFHodYPaFSv5YuhxHWgN8MVEtVtCZS6cXAVzxqhxMbKLeiHpa+HLGaq5oc4ClbmkRmj44pADW2/f8U4e8jDsnO1znfbAFwyOUrjx051c8d5mnl52kBKbk1kbfWcOrjhYQOdoKy9NSOXO4UmkxgZi0EkMSlafK/kVDtpHtDz507dtCAmhJtoGGUkrrvHqTLLiYAFT+sRzplHRF9uzSQgx8cKVXekcHYDVqGFgYigfXtebzadKCLXoG80HZYVog4Y3JnVl3cPD+en+oTx4USJ6AdKKbXx14DRzd+WQZXPhPA9DDJtH4bVVvsd7e3MqKappOZXfz2/DIQUjD7zf5zplyIMoeXugrhph34LGFYKolohM/gQWTlN9aCqyVE+aNS+o/+5zEyy5kdhgEw6Xhx3lJpwJw3xfxPAnfN8jNcWIuTv4dm8+kz/cyts/nyLCauAfLZRkZJfZEQRICrcQG2RkYL1wvjW9lEvOMRa8JCWSF5Yf4cUVR6l1yXxcn1XblKW7c3F7ZIa2D+epSzuTFG4h0KhlRIdw5tzYB4+scCjPu0RFFMCq19AxxMDH1/Zk3cPDWXHPYG7qF39O8UIUBRyCwIGiGj7Zns1Pp0qpqHXRPb55BojD5WH+jmx1wtGPHz+tJ2BcccUVrFu3rtny2bNnM2XKlPM+zuzZs5k0aRLt27enU6dOvPrqq+Tn53P4sDp7X11dzdKlS3niiScYOHAgXbt25eWXX2bv3r3s27fvD/o0fzxVUhSem39CaT9anQoRNSipU3Df8ANlQiS1nsbSGxEQj32PsH1mYyZEwkDQGNWWckMfUdV1UI818SM4vEx1ht63QPW2OJuC/Wqabkh9JktdFeybT4BcwwMDGh+uc3cVsyZkGuUXvwYB0erCoARV/Ijvp/pntICxcDeJVjXzoNYl85cFe3juyi5EWvWcrmq5U8mZNlofr0/jr5d15qcjhVyaGk24pbn5a8/4IAQE8itrccsKf5m/B5dMs9rhN1YfZ/qANvxj9QkirQYeH9uRGwe3IyHEyMIdOWQ3MV/Ta0QuS41GcrkxolAnK5yu9bAmo5xDJQ7sCDglkae+PawG419O8dLKY4x+ayPLjxThEQRsde5m7e6akl3u+N2GoH5aD7E6F8UQos7SXkBOVkCdBxJbznw9b2RLFKKjRL3/z5OOERZOFNWc0+TPj59fo1qxwpS5yIPuV1PUAaJ74Ll+BTZrF2RZQRBAb89HWHpTo5mmpIWJHyCWnkAoO6VmEBYfh363qwaF86eo2RSWcFjzYvMTKwqseV419zyDu1btVpI6BcrS6RlUy3dHKvglaCrlF7/uFe/qLn8fscMY7/aSTZE9VJYVNTzzqxxu3LJyTv+JtGIbz3x7iGe/O4zDJXPXF7u5qnccYRYd87Zm8dDoDmil5jHiugFt8MgKn2/NwqNASY136YDd6WHRjmxen9KdSKsaM19eeZSVBwt4eWIqr03uRmpcIE99fZBt6aUkh3u3M1UUBY2sYEbBiILLo7DiaBEj31jPSyuP8fYvp5j84Vae+PYwdeK5h5gOt4fi6pZLG47kVyH5X9j+EGo9Wpw9b0W+/G2w1vsbBcYhj38Ponsg7J6tPvO7TlbLRS55DibNUu/Dre+ppp1nk7sLEMAcQWTZTlKiA3jyxwIO9nsNW/8H1TIsgMiuyNOXqsJg8bHmxwECT2+jbZgZUP3OBODIOTr3bDlVyrytWaw/UczNg9th0klsTSulX7tQ4oKNzbbvGBlAtNXA8cJqCiprOZRfSZkPgWxLWilmvYbsshpWHsxnSp84Hh/bicHJYZj1Gh5rki10hiu6xWDRqBkUEmrG8O7sCrZmV1CtCMg+2poLAlTLcN2cndz02S7eW5vG418f5NpZ25nevw292wQ322dbehl1ftNOP36AVuxCcuONN3LvvfcyadIknnzySSorK3nsscc4ceIEb7zxxu8+bnW12g0jMFB9cB46dAiXy8WgQYMatklKSiImJoZ9+/bRo0eP8z72hXyf9ChQoY3HMO4DdLINBIE6IYBaWa2la3otJrkMcXOT72zoI2rJxw+PQfFR1bzs4r+q2RX5eyFvt1pGMuZl2Daz5Ys4sQraDIay+lmik6thylyST67hvQnDeXpVAZUOF/d9l8MlnXrx+rU/ECg6EPP3wo6P1RTewFgoPOTz8O6AOPJqGh/GFr0Gl1shNsiIThKblXKcIcyip9LhQhTNhFt0fDijN3O3ZPLG1O6sPV7EmmNFGDQS43vEkBBi4vGljQGn1iVT7nDSNdbqpaIfzq/i/747zM2D29GrTRAAQUYt6UU1fLevMcUwNsjIW1d3J0gjoMgKtYLII0sPsC29se5zSPtQhrcPZ70PN+0XVxxlYLsQjNpzD8pCTFpAuaC/ufPlzDVdyGtrje/h93xOqToP2RTGhb7cAyUKGgHaWjj3uYXG/xVaGAcp5kgANJWZuMO7ntf5O0RYUIC9eZWMSL6w2Set8Xv8d/Jn+zwX/D63xmDr9wiG3rciKDJuQY9DCGgQLwySG3HrO4015aHJqnH14WWqSaDWqLaAnPo5yC619Sqoy2srG1tFno2tCLRm72UZG2DUC7D7U2J2/p1FN33EXxYf4zNjCncP/ZIIs4jZqCes8jD6lsys63FKJqCi4d/aX3kxbxNi5oFL2jNncyYmncSJQhuPLN7PZzf3Y/amDBbvyOGTG/rw1a5c9uVUEGk1MLVPPCW2On4+VsSVPWPQCAop0c3rylYfKSSvwsGT4zrTNtSEJAlUO9z8bdkhDtUbIHaODuCdq3tiEeFcGfBldR6eX9687f3GkyUsP3SaKd2jUdy+xXqdJKIRW077j7QaUJTWjYOteT+ezzl/y/XZFAuapCkY241EVNzIghZJFNC810Mtm9JZ1Ptj4xuq+NDnFnUy7Owy5Kac+gkSBhD+y4N8fONOHvrmBFPmp3Nxh7HcMmYycYFawnROjGXHIWN9i4exW5Moy1fvzYs7RaDXiAQatVQ6fIviYQE6DuZVMnNdGr0SAlly50De+vkkT39zkJcnpbI7s5yVBwsQRYFLU6PoEhPoJT5sqPcxW3JW21NQx2nvXduTK3vEklOmmutGmHUogkLn6ACK6kU3QYDLU6N5dHQHJEXGKQrM3ZbDrE3pDY8njSjw4oQujEgKRdOkrsojCjz37ZEG0/oz1LllHl96gDemdm+YrDtDeIAOnSggnKeI8WeLJa2N//v476LVBIzbbruNwYMH89hjjzF+/HgqKyvp1q0b3333HeHhv8/BX5ZlXn75ZXr16kWHDmobw5KSErRaLVardxAPDQ2luLj4Nx0/NDTgd13Xv0YAoL4QaAGfSaNlJaqR0phXIaornD4An09sXG8rgpwdajrswLtVUyZQ63VFra8jqkha71pfQyDk78HiKmVsQjW9bkuh1KEguWyE1KQTZHcg7J4DHcfBsEfwBLVFrKtCOLHK5+GLu9/F0qXqi3+U1cBrV3Xj8aUHSI0L5LJu0Xyzt/mA0KiViLDqKbU5ufuiZHLLHaSX2BiUFEp1rZshyWFc1SuOn48V8eOh0xzMq2x2jLwyO0+M7cSMOTu86nRzyx3M355NoFHL3uxybh+WyNCO4fxw/1DKapxIkkiIWUeUVe00X+vy8PbyI17iBcDITpHM3tQ8NfEMX+3J49ExHRmSHMqmU81nNcID9MSGmAgLbD6L8GfiQt4PrXPv/Y5z1xaANZLAQNO/74J8cKTKTmIQBAUazmt7s/kc25liQdJiduZDYL/zOl5goIlIq55DRXauGtA6f6vW/I38O/izfJ7WuA5rkBVQY7YW8HoS1pSqsWv8e2rcC0uGuZeCvclz+Of/UztyXfJ/jcsUWY1p5+LsjAFDICT0h6nzEGpK6LjuLhYNuoqy4O7UyQJBOg8R2hrEyGTqRB16X21dASVhIOtzvV86DuVV0qdNMLvOekkBiAk0kF5Sw/cHCvh4Rm8yi228flU3TDoNuzLLuX5AG3LKHdjrPAxODqNHXBClNU7e+eUkeRUObhjUhjuGJWF3ywQYNAxJDmPTKW9B/XB+FWU1TuZtzWJPdjmvT+7Gu9f2pLrWjUEjEWrREeojq/FsZn5/uMV1n2/N4pLOkSRH+P4NmZxuruwR08yHANTOK51jrIQGX9hnaUu0xn3wW875266viVBXeEQtnRr5V/hkJLjqX6hthfDj45B0sTp2XP6A70NJWjXz11FOnDOdjy8Po5QkbHUugpQqQqqOYYztBNZYlEH3I/z4uM9jlMWNZN9P2cwY0IbYYCNvrD7O1D7xPsusJFGgZ3ww7/xyiiCTlthgMz8dLSQ53ML4HjFUOVyEB+j4++RubMsoZd3xYt755ZTXMVYcKGDZ3YP5fr/a2a4pIWYdZp2G2+bt4uWJqdzw6Q4+v7k/3eODeOeanpTanFTXurEatYRZdAQY1OfKL0cL+fis63XLCk98fYiV9w0hJaYxRTKrtIZ1J3y/g9idHqocbkLMOq8skbuGJxMeYva5z7n4s8SSPwv+7+O/g1bteZeQkED79u1ZvXo1AJdeeunvFi8AnnvuOU6ePMmCBQt+fePfQWlp9Z/SETtQ1KId9QL8/IwaaH7wESBA9bm49ZfGDicnVkGXCbDpn76373QZ/Pxc4797XAtBbWHFw2g2vUkMEAOgM8MV74Jogagu4HZAZArS0eVqSuKIJ2HD641iiKihYthzLMwwUuVQM2YeGNWev317iKxSO1mldmZe14v0Yhv7cxsFCJNO4h9TurP2aCGzb+zDqz8cZXNaKYOSQrltaCJv/3ySvTkV/H1yNxZuz26xDCXAqGXJ7lzm3NCXV344yolCGzpJZFxqFBN7xvLAl/uosLu4uFMkgRrQe2SiDZK6s9NFSYk6I2BTBBb7UO8teg0V9pZT6QuranHXOXnxyq7cMm8XacWNjtZBJi2f3tAHvcdDSUl1i8doTQRBDQB/5P0QFnbugNIa997v+ZzBZVm4onpTV2n/9Y3/QHbku0kNhRpby6VXgJrtazZQU1N7zpb0BlMEntMnccSe/+dIDjWx4XgRJSUJ573PH8G/4/fYmlzoz/NnuvfO57NbJA/6DmMRfnkOEgaoBoT2suYbFuxTu4kEtVHr9t11qmCvM9e3hTyL4HZQfVY5ZfdpUHAQ9s1X09+jexAeaCG8ZpfqMRU/EEqzwRqH5+gKGP0irHykMWsRICIF52Xv8OaH3qL2Z1szeWdaz4a4d4ZQs46XJqbyf98fJqvUzl+XHeKR0R25f9FeyuwuksLNvDo5ldkb09mX21ygB7ikcySPLtnP5lOlXNsvgbsvSqJtmJmv9+Rid3qICzZy5/AkskrtDZ23Hl16gM9v7kdKqNpNS6l1UlJ7bg8KrU6i8BzlnuV2JwUVDkJ1Ap4WWqw+MLI9JwurOdAkI9Ksk5h7U1+MitzqcfDfeT/+Effev3p9QRoLmvHvw8/PNooXTUlbA/3vVMXC2orm6zuMhZWPqtm6J1YTtPEfBIFaYjXyb2AKU8ea4Z0R2g2D1Klqi9Yz6CwUXzabFzZWEmLW0T8xhHsW7FWrnXvGMTi5sdsbqFk7L03syufbshjeMZxbBrfjngV7CbXouH1YInM2ZbAlrZRLU6NIjQ3ijdW+fTRCzDpyyuzMnN6LD9elsTOrHEGA4R3CuXlwO/7vu8Pkljv4cH0a1/RN4IN1p3jx8s7gkbEKYDVKgEydrZY6Wy0uQfAy5D2bz7Zk8vgl7ZHrs5EcznN32KmwOzHpJMrqH1X3XpxMmyD9b7of/tti479K0+/DL2L859NqAsbu3bt59NFHCQoK4rvvvmPPnj288MILrF+/nueee66hBOR8ef7551m3bh1ffPEFUVFRDcvDwsJwuVxUVVV5ZWGUlpb+ZrFEUc67E9IFRXJWweIZqkAg6Rpbv52Nu05d13YYpK9RDTYH3KW2TC06KwW0/Wi1ndwZf4z2o9UU3J0fw7T5sPV9yN4GAVEwZR6selJ1az+DzgITZqqph4EJMH0JpVIEpWIoNbKESxYoP3AaSRTQSgIBem3DIM7pkbl/4V4eG9uJO4YncbKwmg6RASSHmyl3OJnaN4E92WXsyipHUWDzqVJ2Z5Xz8Yw+3DZvF9/vz+Ovl3XmnoV7m30Fl3SOYH9OJcv25XPb0ETuGJaEUSchKwprjxVxx+e7G9T4BTuy6DGxK4rie+DldMvNlHuAQ/lV9GkbzLrjvtX1MSmReNwKFgE+u6EPeZW1HD1dTUKIiaQwMxYRPJ4/4Q/tLC7k/dCa9955n1t2I9oKkE3hF9Q3v8ihcNoOE9oJv3rehrIR5Zz6BbI5Eqmy5SwiX3SMsLA5o5wSm5NQs+437ftH8Gd9Pv9e/iyfpzWuo6VziqKAtuIkwjf13UXaDIJ1r7Z8oGPLVZGjot6UducstXRy+QPeJ9AYVL+oNc83LksYCOGd4Zs74OovIGWC6qHxzZ2N3huglkqOegFp76dwbBmMeAp7eDdKxHDKXBoMWi0ByFzatZZFO3Madquwu3h48X4+uK4XDqeH3dnlxAQZMWklXlxxpCEe7s+txGLQ8NqU7mSW1PDumlPcu2Afb0ztzozZ2zk7m7x7XCB55Y6Gl75gs46XVx4lJsjIpzf2xeHykFvuYOGObA7ne3sMzN6cwWtXdjnvFHWPW2ZUShQrDp72ub5fuxDSi210CjO1+MAxIvPBNT05bXNyKL+SKKuBjpEBWEQBuYUy0tbgz3Qf/KvbNkVU6qDsJKT90vJGaWug06WqZ1pTUiZAVYEqIo5+EU4fVEu66qpV/4xv726890D1Yps2H3rfSJEQSilW6mQJWdRitWQxvnswi+vvEUWBhxbv58FR7blxUFuO5FcRaTXQp20w5XYnD4/qQFaZnQe+3EtZjYu0YtibXc5HM/qQU25nWt8EtqSVMColgp+OFHE2916czDtrTlJT5+a1q7pTYlPLQnZklHHPgj1U1Xf82ZJWyk2D27LhZDF1boWWIptL4ZxiXm6FA7esNMRgo1YiPEDfogfMoKRQgowaZAX6tgnGrBXRyL/ef8gXf5ZY8mfB/138d9BqAsYNN9zAjTfeyP33349WqyUpKYn+/fvz6KOPcsUVV7Bhw4bzOo6iKLzwwgv89NNPfP7558THx3ut79q1K1qtlq1btzJmzBgA0tPTyc/P/03+F38mBAGMogOduwpR0iBsfLsxu0H4NcMrAUY8Dpnr1bS/b++Gy95QhYoTq9VA1OcmCGqnquQD/qL6aZSmwfIH1fPk7oKe10G/O9RgtfU9b/EC1MyLZXephqErHyWz+0PcvaKMw/mZgOrafGX3WF6/qhvv/nKyoQ3WGWqcHp797jAmnURskJG/XR7MqLc2NqwflBTKu9f04p4Fe6hzqyagOzNKWXjbAPQakfxKB5/e2Ie//3icY6erCTHruKZfAikxVkqqall8x0AMWpH3154ivcTHbBxQbnfhcsstOt0aNKLPAPTNnlzentaTzadKcJ0lRMQFG+mVEISnfmCmB5IC9bQPNiLLCoqinLPe2M+fF7GmEEHxIJt+fxbZ7+FAifob+yMMPM8gm6PQFu5XOwj9Wtp9PR3ruyLszqlgdKeWneD9+Pm9mLAh/vxM4wKPS41ZLaExgb5J+WjGBjCFwrVfwbGVUHoSYnqq2YUep/pCZi9VZ5MdZWoM8zjV2ekJH6kdu+rOMhbM2wO75qhmn3u/oNRt4KPdHubsPNDg7ZAUbub9a3oSG2zko/Xp2OrcRFkN/OWiJDacLKZLTCArDhRQVuOk1IexYFapnUeX7KdjVADvTOvBW7+coLzGyYr7hrLpZAnvrzuF26MwfUACozpHcsOcHQ371tS5uDQ1mu/3F7A1vZSVBws4UejboLe8xolbVji/O14V2nvFBxEXbCS3idE1gFYSuGFgW9KLbWgE8NF4FlBfJnQotLFoadcpoj4GKih+s8J/OxadC2HbZxDTQ33Ou1swVBVE6D5dNcc99bM6mdXnVnU8WJkLY/8OX92gTlxd+b6a6bTiIW/xAqAqD2XNyxwb+jZ3Lj5OVqnaiU0niVw3IIFRKZE88XWj2OD0yPz9x+PoNSLxISaSwy2cKKxmzuZMAEanRPLc+K488OU+PLJqKLv+RBFzb+zHofxKCqtquX5AW3rEBzF7UyZlNU46RQVwx/AkDudVYNFrSAg2kV5s4/Gl3p3mmuJ0K3SLDUSvEVDcvn+XekmgZ3wQBZW+xbyB7ULRCHDGDSZAEnhqXCceXLy/2baXdI4g1KBhVLLaXcXjUWimVPrx8z9OqwkYc+bMoV8/7/rqhIQEFi5cyAcffHDex3nuuedYvnw5M2fOxGw2N/haBAQEYDAYCAgIYPLkybz66qsEBgZisVh48cUX6dmz53+kgCGKAkGeAsSVjyFkrIWLn4HcxsEKNSVqF5CK7OY7G4KQ3bWU6BIwX7cS0y9PI+TtUgdp/e+Ekc/CgUWw/CGY8AFYIlQhIm2dOnPVcI5i2Pw2dLxU3afDWDVDI3srHP0OqvLV7Zw1YCukcNR7XL+sxMusSFbgm315hFrUOsWmPa+bYnd6yC6zU+P0Hv5sSSsl0KhlSp94vtqVw+tXdSOr1M6Di/dR5XAxtH0491yUzIfTe5FeUkN4gJ51x4tJCjNTUOFg+YF8eiYE8cqkVB78ch/5lc2V84s6hqOlMeCcTYBG4JFRHXj8a+/AV1Xr5oeDBSy+YyAvrTjKrqxytJLA+O4x3HtRMmbBOxYpCg2Chp//XKRqdeboQgsY+0oUwgwQrP/jnKlkSzQoHqTqHDxBiee1T6BRS4zVwI5sv4Dh59+DllqEgn2NC078CF0mwpZ3fe/QaRxY41TR4fDXquBRXQCKB8yhqudF0RHI2AhVp6HXtWqXkpWPeGcy2stUkX/QvRDeCXQmtUxk91woPKzGvclz8JTn8E1VBz7enu91GWnFNVw3Zwdf3NyHhBATWkkkKdzCE0sPsDengk9u6ENasa3FdxSrUYvLo3A4v4oKu5NpfRN4d80pCqtr6REfxNyb+iEICvnltTy0eB81Tg8hZh3PXpGCWSexI7OcsV2j6J8YgtWg8Wm6CTCiQzh6SUBu4SXNF2ZB4bOb+vL2L6f44VABLo9Cr4Rg7hqRyILtWTx1aWfcLZSPNMUfBy8cgiAQRAlS2kaEnK1QU6iKdwe+9Lm90n4UwvzJENpenbxKGAiHlkDyJbD1XSjPVDc0hardf2L7qMbxVfmwZ64q8tWT1+shps095GXO6fTIzNmcSWK4hdEpEczamOl1/jq3zKkiG33aBHsJZauPFBIRoGd0SiQ/HSnklUmplNqc3P75LkprnAxMDCXMomNaz1guT43G6VHYnl6GJEBKTCBuGWKDjXSLC2JMlyhWHW4uPoSYdbg8MjcPbodyjt+xJMvcfVEyq44U4jnrRg7Qa7gsNdqrjMrjkRmQEMRH1/Xi1R+Pk1FSg9Wg4YaBbZjWJx6tLONpuVGdHz//87SagHFGvMjKyiI7O5u+fftiMBgQBIG77777vI+zcOFCAGbMmOG1/JVXXmHSpEkAPPXUU4iiyH333YfT6WTIkCE8++yzf9AnuTCIooCJavRKDeJn41QRAdSBmTW2seZ220w1RXbprWobuIYDSCgTZpJt6cbRzALahkfQ4bK3kPCo6rqjAta+DKdUPxI2/gPi+oIhGFKvUmuAz9RHSjo1syJ7K8y6SBU5AqJh6MMw6ROozIFVT6kzWY5ycoyDyW5aE9yEL7Zn0SMhqEEZP3a6eX3ftH7x/OAjRXX1kUI+mN6LpHAzC3fksDW9sU7yu/35rD5ymg+v682Di/fzwfRe9GoTzISZmxta183bmkWU1cDrV3XjvkV7KW/iWxFs0nJ5avQ5B1Rut8zQxBBentCVf/x0grIaJ6IAY7pEcc9FSVhEeHdqN2o9CqIgYJIE8Mj+DIv/UsRq1Q9FNl5YAWNvsfKHZl8AeEyRgIBUkXHeAgZAxwgzO3yYEvrx81sxaNwYPJWgyLhFE3VSAJK9Um0BeUagz1gP/e9QZ4XPLoNMnaLGJa0ZecDdCEMfQXDaVDNPe6lqbn1wMbgcqohx1adQWw0Jg+DwN97HShyhzjof/wHWvqSmQSaPgitngu00fP8AKDJF3e7i/eW+SwdLbE6yS2t4eeUxCqvq+PL2AezNqQBg08kSxnaNYqWPODcgMYRD9WbUNw9ux6ZTpXzVxHtp3fFiNpwo5q2re1Ba4yTErKeouo43p3bnheVHvDyWNKLAu9f0ZHRKJKuPePt9BBq1TOgRi3weYkNTZFnBKgncOSKRK7rH4PLIHD9dzcLtOTwyugNWqbG7u58/B4FCBZoFk6DjZarAd+RbmLYQsrao47cmKIPuR9Ca4NafVZWppgRWPKCKEjE9Qaz3B4vsqprmbn4Hvr9PXdZmMIx9Fepq1GWSln0VBiodPjxrgPfWnOKTG/rwyabMZmn+WklgXNcobvlsl9fypXvyeP2qbnSNDeSHQ6dZc6wxg+OHQ6f55WgRi+8YQJXDxbHT1XSOsfLyyqMcLWgca4oCvDQxFVBYddj7vvjLiCQSQ02E6MRzZkEoCoQbJL64uR9PLzvUkNnbPS6QlyemYtU0vw80ikLfGCuf39gHp6wgCQIBkuAX8vz4OQ9aTcAoLy/ngQceYPv27QiCwOrVq4mPj+epp54iKCiIxx9vwYjyLI4fP/6r2+j1ep599tn/ONHiDHrRhbn6ONLOWRCd2ihegDoDdfEzqp8FqK1St76v1hmmrVF7bod1gqSLcFrbEJT2A+N2v6uWjexbCXs/V4UJY7BaEpJ0UaOfRe+b1JmqVU/BlLnqLFfmRlWo2L8ITjbpLlJdoM5aXfw3tdxk4kfw5XUQ1Y3M096ppU2pdckICLz50wnevroH769LY0eGGtw0osCEnrFc0S2GN39ubsR0RuUONeu8xIumx56zOYPp/RMIMmmZNmtbg3hxhtNVtby79iSPj+3IE18fQhRUA7THxnTEKgm/6kWhVRTGdghjcFIodqcHvUbErBEQZQWPR73BLAKAovbG/RMgakRsbgWHy4NOI2LRiAj+gPkvI1XlIusDz53S/gfjcCscK4er2//BfcEkLbIp7Df7YHSKtLD2VCm5FQ7igv7cXXT8/HkJEsoQ17+GeHAxeJxoYvugH/8ewqY3YeC98MOj6oaKAsv+Ape+rmZJnElv7zpZjUluJxz5AtESoQofa15UX9BEDXS6HKbOg69vV2eKNQY1szAgEq54W/3/ZelgiYI+N6vlIx5X43lPrlbbkk/8ECZ8CDVF1OkiKfdlKFrPiUIbUVYDBq2EWa9BEgU8ssL87Vm8Pa0nAD8eOt3wnjSiQzj3jmzPjNnbkUSBwclh3Dx3Z7Pjygq8+dMJ7r4omat6x3Eor5LFu3K8xAtQOyLct2gvK+4bSq3Lw8ZTJQjARR0jeHxsR1Vs+B2hQFAUUqIDsWolbE4PyeFmpvaMwSCA/CeIe4IAbkHE7pZxemRMWgnL/+iLolmqQ1N0QB2nZW+Fi56GI8vUcuJLX1d/8xkb1A48vW+EwDjVGP7Uz6r4F5oEwx5Xx38/PArXfw+b3oTu16plJE1LrLI2qx3xpi2EK96BnbM4VNRSMZE6Hqt0uHhpQir/WH28oQNHlNXAy5O6surI6WYtdx0uT0NG0+urmr8POD0yz31/hBsGtaWsxsnKgwVe4gWo98/T3xzkqzsHsjWtlKpaN/EhRh4Z3ZFe8UGYhfPLDhJkhY4hRubd2Aeb04MkCpg1Ivpz3Acej4we0NePE/8T/M/OF1EUqFWgpn7cbdaK6jPBXw7j5w+g1QSMV155BY1Gw7p16xg3blzD8ksvvZRXX331vAWM/3ZEUcBsO4n02Th1UJa323uD6tMQ1kEVFTb9Uw0w2VthwTS4ao7a29sUAnU2dPZC9IGBMPQR2DFLNTg7g6Mc1r8Kwx5TZ6LWvqwO6A4tUc+57C7oezuMfEZdvu4V3xe89T01CB5YBAPvAVsRbYNiWvx8Bq3qMFFhd3Hvor3cOKgt91yUjF4jYtKAy2Fj7aFMpvaJZ8tZLUf1GpFgk45v9zVvwXaGjSdLeHxsJ/IqHFQ5fAfOHRnlPDamE2sfGY6gKBhFEUmRz3tw4/HImACTrt4t40/8cHYJIquOFPH2LyepsLvQiAJXdI/hoUvaY/CnhvxLiNU5Fzz74kiZqosl/8EZGKD6YEgVv03A6BBuQRRgR1a5X8Dw8/uozEMzf4JXJw8hbxdC6Qk4+i0MeRB6TFezAkGNXd/eo4rsFz0FkgGq89QMwqJjah1+ZFfVjPMMslt9aSs+rppNfzkDNDo49r0qUkSkqGWVQW0guA2sf61RvGhKTbEaHx0VEJqMXlNJsEnrlc3XlPaRFtJKHdw5PBmLTmJSzxhKqx1M6qiH6kIGJQYzuVccCqowv/rwaSrszobOIScLW+5AkFlqx2rUklfu4MoeMcyYvcPndi6PwqaTJfRPDOGZK1KQAKMkolHkf/nFwoCCvj6moyh/ilAoCAI2BV5YfoQ1x4tQFIi06nlqXGf6xQei+R9y9NNIAvrywwjpa9UFpw+ok1djXoWfnq6fdEqF6J6QOBwC4xE+u1z1uThDaRosuwOu+wbKs6EyG2L7qpNbZ/vDgFpKfPhrMIVDfH+6aC1Ac1NNUP8uWaV2luzO5W+Xp5AQYsTj8WCSFDIKy+geF8yC7d4ZIoFGLXqtyDYfk1hn2JtTwUuRFnQakQe/3OdzG1mB7Rll/PPqHtS6ZCrsTvolBKPxeFosI/ZFgyDxJ7sPLjSCKJBb4+KpZYcazII7Rwfw8oRU4i1av8eNn3+ZVhMwNm/ezOzZs706hgC0bduW/Pz8Fvb638OEDXH1k+qMj6MCgts2rgxNhvHvwun9an3utV9CWYaa0hcYDweXwNCH1EyMzW8h2IpU8WHGN97iRVO2vgszvoVJH6vu0rJbFU4G3K22n6vIarnLCajrRC0c+Rblrq1gLyFeNBMfYiSnrHkmxrS+Cfx4qACAKoeb7ell3JBqQkj7hZB9H4C9lK7xQ/CEP8qQxEA2pTe2jbu6bzy1bjdBppYtx3SSiKDgVW/pi0qHi+QIC257HYosX9AuEhcKUSPy4+Einl9+pGGZW1b4Zm8e2aU1vDO1O7r/ocHcH41UmY1sCrug59xbrGDUQOxvbw3/q8iWaHTZ69UpWfHXzIFVjDqJdiEmtmeVM6l7y8KlHz++EEVBzWrwVXLodqpGmi67GgenfwUlJ0EfoIr02z5Qa/NFSRXYFVmNkRM/gq9v833C4qMgGVBu+wUBSY1dHpd6jLg+qgGo7IasTS1fdM4OsMagtBlMuNvFXUM1vLyq+fWHmnV0ijAxICoCtyiAIvPyxSG4d83FuGMxKDLVnSZTGX0d2e5Qrp+9g+o6N+/HBqkf36Ng0Ern/P5CTDru/GI3i24b0GymuinldidbTpVyeddoTCjwXxrzABzA9Z/u9PLgKqyq4/4v9/HxjN70iQ74n8nEMMnlSJvfUDvRgWraqdFB8RE1S6IyR61zCGmn+sJ4nN7ixRkURS2lmvgxyHWqOLHy4ZZPnLMDUsajxPenlykSqzHb54TSjYPasmS3mjmUHOCmjW0X1m2vQ1kGncM64Rr2JGn9wvl4R2MW8q1D2lHrdBNqaTnzURIFSmuchFv0PjvHnaHU5mTm0TR2Z5XTNtTE8OTQ1ntJ+g+n0q1w9VlZz0cLqpk2axvL7xlC0LkfZX78/Cqtdm/a7XYMBkOz5RUVFeh0F74F358VjVKLcCbrImM99Lsddn6i/nvMS5C/D/Z9oRqJ7f1CTZOVPY3tT6NSVfXbVq94S1ooS2v5hC6HGrT0FghNVGsas7dDxjp1UCjp4fI3z33RkgY8LoSiI/DL80SOfIbPrx/CXV8ebkjdEwW4qmcMdw6Oxe6RuG5AG6pq3URqarCsfRr98W8bDqc79g2cXMHrU77nymI95XYn1/RL4M6hiVh1IlaDlvfW+v5Ml3WLZnNaCb3bBLd4ucEmLVGBBoJMOkrsLbhw/xdgcyu8/ctJn+t2Z1dQYncRY/SH69+LVJ2DK6rXBT3nnmKZJCuIwh9cQgJ4LNHgqUO05SNb4857v06RFtanleKRFSTx/9k76zg5ivQPP9U9tjuz7vHdTbJxJ25ESSAECCRAkOBwOD/guAPO4JBD7nCXECSChACBECRG3N1to5t1nZ2Z7vr9Ues7GyNOP59PCNtdXd09md6q/tb7ft8Tf10W5y42mw4b6hDXvbkqYgLUy5OmK48Lf4nytADYv1KlR5aXvs7aCvl7gwsi5eyYjUhqr1aib/kVijOhIAO8eSp0vvlQCI2tHENrEhoD3nwo2IcIieOy9okczC9l/KJ9FSJCcqybt0an0fjzYQRGf0aulESIHOwfj8BeboAIhC1+mbBNX1I0/HMKSgMIoSINATIKvLRMCsOui1rVrQC6NI7CpgukVGHzjWNCK0qx1qRVvXCklITYNajDQPtcQNMEWzOKqokXVXn6+41MGNeFU5f0d3rR/YWwfbZKw9Lt0Ol62L8aln+k/ngS1HOVv0+ZsxfV8Z0HJSLuXay8LTpco56Duih7RoS/iHrf38yk61/h1smbKha17Lrglj5NGNQynsYxbhJCJcl7v8Tzy6OV1150CH3CXG4f+hrrUpJZlp7P2G6NuaBNIvEuGylxAf5NcHPagS3imbvlEFee14iWSWG1UkjKad8ggs/L/GVu65uCWxcEjsHQ1kKh6RqfLNxZK2UblCHrRwt3cm/fFOQfRDi0ODkc3bLaSaBLly5MnTq12jbTNHn33Xfp1q3b6bmoMxCJpkpTgVoZWvuFMuls2FWJF5445XtRTv6+SvECYP8qlcNYjr8EnEeIN7c54KORsHMeTLtHRV78/C91fl+hyh8OqUMQSDlfqe0JbZQzddZWmHwdTXZMYsIVDZlxZ2e+uqUjv9zagsfjZhNNIU5NlSP929Q1RAUyqokXFRg+En/7G5OuTWPybT24p38KdsPA6/Xjcdq5qXeTWoc0iAphZPt6rN+XT6jDxoh2SUEv+d6BzRASvOfwRA5URZfDRaJszihUK6AWx44ZQCvcjxl66qpvGKZkVSY0jTg5/2amW0XH6bmHefkLQsvEMApLDTYeJtzdwiIYUkoITwy+M7KRyqkvFytMQ60OF1cJHffmqSiKqpgBsIfWfdLoZFj1KUy4BL7+kwqRT58PS96DPUvUmNvxmrqPbzkCMjYicnaivz+IGP8B/q/+Jn65qyNTb+nID3d0YtIFkuZfDIYG5+FzROPTNbzrvq2s3lCVvD0kpn9H9+Qo+jWPo2m8h6//1JOZ9/dFAH8d3pKaemVkqJ27BjTlUGEp7RpEkFXk4+4BTYNe7nlNovD5TVrXj+CzJXswz+Hf+bqusfQwpsI7MovwnUOeA0dC6g5l2r7wdbj4VWg9qvr8sfBgZRU5b54SNIJhc0K7MTD1dhXFseFraDe67hO3H6Oe3YIDiO0/0/LrC/l8ZCjf39mVr25ux883NOKuyIU4NMH/TV5FqC8Lz5x/Be0qevajvHJREv8b05GAaeLSBT5fgDCnzoND0mq1Twh3ck33xrSpF8HjU9dyZ/+mtZ4fgDb1wyksDZBX4md4m0R6NY09quo5FrUpNSWLdtTtBbR4Rw6lf6DnzuLkcNoEjIceeojJkydz88034/f7ee6557joootYunQpDz744Om6rDOOEj0Ss8vNlRtWT1IeF8P+oyZXpgHRh6kSkNROuUaXYwYqK5cEo1EP2DJTVTBxx6r83zWTq7eZ918Y+aoK3a1KdIoqM7dsvPLkWPp+5b6VHxNbsou0KQPoOKElTb65nJDWw1me62L53jz+9c06Xr66ExF759Z5KyJ9ER6KSQh1IH1KbJASPHaN7skxvHFNJy7rVJ+BLeN57MKW/HV4Sx78fDXX92zCr5sy6N8innsHNiOqLOWkSUwoL1/ZgeYJYUxels6hglLQNIqkYE+xn30lAUoQaPq5McFz2rTDrognhDnVC4TFMaMV7kdI45QKGFvyoCgAzSJPTv/S5sIMiUbPOzYBIzk6lBC7xoKdVjUSi2PD7zfUi1FNhFC59NEp1QX5mjjDlPF0VdZ/rTwzgqE71Evalpnq587jlBdUSn/YNF1ty9igBJDWl9W+pr4Pqdz/XncpwSPghfkv42rai0ZzHqTD5G60+KAF8Z9fgoxLI9DvL2zNl8xes42wzV/UeRsRW6dyTfswHr+wFR4hqRdiI1xAmMvG6j15vHtdF67u2oiBLeO5b1AzXhzdgflbM4lxO/jbRa149vuNFfn8aQlqnA5z2rilTzLPXNaOPK+f+yau5JVft1JogNBP21TwpGKakobRdYtX4S7bHypKrNQWgWw+TKUVr/xELU7FtQzeOH0RJPdTgkdNmg6Gjd9RUSqkKFOlL3f/U+22na6Doiz197IP1DYhSJj3OC396+j4cRsa/vZXbM2HsCOrmHev70KUkaWEkWB4cynKOchni3dzQ48mlDstOUyTIa3jeee6zlzRuQEDW8bzyAUteGJkG579YSO6JvhtWxY/rDvAK1d1pHW9cABCHTo39GrCc5e3J98b4L3ru5Aa72HR9ix0XSOgaeQakt1FfnIMiV+IoAKIRSV2TZAUUbcHVmKEE/s5Mq+2OH2ctnhxj8fD9OnT+eyzz3C73RQXFzN48GDGjh1LIFC3S/EfDV9AYHS5BbFrHmJvWfmoDd8AIMPqITQbdLsDvr2v9sH2EGjYXaWegIqc6DxOhd2Oeg9KCyB9ISx6S+3reqtyZl/3BSR1gANrVOjswfXV+z2wBuY8r3KLfcUqJSU6RU0cF70Jl78Hyyeo1aX4lsrluuAAcvcCFT0S3xK/LYKNhS5unbCIF0d3YNbmTBw2nVdSDpPMr+lEhTrIkdVVcS1gkJYYxn0TVxIf4SIyxM6K3Tn8vCGDQS3iyS7ysWBbFnO2ZNIzNYZHL2xJZKiDnCIfb87ZzqH8Uu4c0JSD+WH9zHkAAQAASURBVF42HyjgyekbKC4TSOI8Tl4a04Hm0a6z3nTIY9e4sG0S01bV9piJCrXTODrUEjCOE71AGYudSgFjxSGJTUBy2JHbHi+mJwk959gEDF0TpMV7WLgzh5t7ND5JV2ZxzhLeAHP4C2hVc+qlRLoiEU0HqvSQsCRVZaQmnW+svqKs21UVkc7XQ9NBIFARgoveguhUuPB55aPR4iLY/L3yvMjfpxYGqo4z3z0A/R+B7jNVhQaApPaqrwZdYfuvcHAttBqJaDcaNv+AmdgO0f1PSGc4UtgIOKP5Jd1E4mV/vk+lY9aFzUnfZgkEBNWqEkTYdZJjQrnj4+X0aR5LZIidOZsPsWZPHvcMbMbdny3n8Ytasz/Py5Sle1iyI5uruzWmQZR6mZi/NZNpK/fy08aMCi+A1XtyCXPaaJfoQZzlY1xNDMOkW5NoHLqGL0i4+vU9GhNmExh/kDQBr+nENfjf2A5tUN9jM6Cq7oRE1fY2s4eo6N+bf1HeZzanMqw1DRVpu3ep8lQLeFX7WU8r49tbflV9B7yqzOqhDRDXArb+qp6pKz9V/eXtRZYWIe9aRqkezocr82nfyMU7s7fx2vmHiZgCEiLdPDsyFZs0KzQU05R4bBpr9+SR7/UTEWLn+7X7sesa57eIY/uhQgC+Xb2flem5XN21EXee35SAYTJ97QEO5HqZsjSd3dnF+A1Jz9QYejWN5bGv1/LLpkrPjR4p0Tx7WVtChaxV6tVCIUzJrX2S+WnDwaD7b+ubimbKc9Z3x+LUcNoEjIEDBzJv3jzuuOOOattzcnLo168fGzYEz2X7I5JnRhB22UfY8nchtv8C7jjMlIFo/kJE5kYVQtv7AVj4WqVqHZYIw5+HrTPB7lYCxah3lfjxyeWVk7OU8+GmH1UO7/z/wZopyvui2+0qF3LlJ8pHY9/y6he1bwVMvFpFXCS0Vyq97oD6ndVqVko/5XA96J/K/b0khwq91ROPcfXXpOdEEhfmZG+uyoP8acNBDnTrRV2vPDLtIrxaOMEsoUOlyctXdmB3TgmaJliyM5v8kgDdU6JpHOMmNd7NnC2ZzN+Wxfxt1d2qW9cLJ69Ylev669S1gDL/9Lhs5BT7GPfhEqbf05uI07xIpZeVPy3yGzh0Dbddw2aaRz2ICsPkoSHN2Z1dxMr0SjPUyFA74284D492fCX0LEDLLxcwTp2J57IMk5QITupKhumph2P3nGMy8gRolRDGxBV7KSwN4HFavioWx4ArjKLUSwi5oy9i20yENxeZOgg8Cei+QlUKdeRr8MOflfgAavxpe7kad5aNV9s0G1z2jhrz3uxdOealDoBbZ6kUzO8fVhGJyf3g6imAUOOelGp12ldWhlSaqjLX3BdUucjuf1KRGXHNwRaixsnWl0GjbvDxZQhpVox3ZsOelF7yDjmmh3fmrmRY2ySmbSjgmr7XE7/rt6AfQWarcZTiwWFUH+xsUnJ5h3r0bRbHp0t20yTGzbU9GrNgWxZfrtjDny9oSdN4NzFuO1lFfnZmFfPU9OpzqX9c3LpaKmGIQ+evU9fywbjziHecuZEYQtcoCJh4/SYuu0aYTTuqHHqPDu+P68ItHy2jpEqa6PlpcVzZpSHGHyhNQErIFXGEXz0NPXsTmtAQqyYp0/bpD1WmNLkilUnurGdg289qm9CUoXv7q5RhZ1JHuPITVW64XNRb9CYseQcGPwGNe0PBPpAoQ/kOYyGlrzLULXuuxIJXERENsV07jU8X76ZvWhy/bs5kfZdGdAkmqgBENEQ6o9CDGM86pOSG3slsP1TIrqxiLutYn4U7sjFNSZfUaP5vSHP+99MW9uSU8J8qJVfdDp0LWidWKzsc6tD5Yd2BCvHC7dCx6RoLtmfz0Bdr+O+ottjP4FdwXdcoNiVbMwrBlITaNBxwShappJQ0jHDy56FpPPfjpooqLELAA4OakRx19i8IWpx+TtvMsq6HqLi4GKfzj2KpdHRICfkyHBHWFlvnDggBLn8Wmi2A9MQj1kxRK0dXfKgEDN2u8hd/fQr6PqhU847XqpDYtTXCVrf/Ct/cq/J4N/2gtuXsgK0/qxzJNpeplarVE2uH9Ol2FWo7cWylCl/OwL+pQWz6g7UHocIMQqbeRMeRk7izf1Mi3Sqlw5Tw0Vov9/R+nIh5T1Q/xpOAcf7f8JrBDV6lBLuQZBR4uX/yqoqX+nfmbqdlUhgvX9mRjxfuDmp+duV5jZi6Yg+t6oWTGO7inoHNiHY7yCosJT7cyY7MYqav2c+1netX5ETa7BqGBB1OSZ5kQBPM2pbNsz9sJLPQhxAwIC2exy9siUcc/aAUIk1eG9OBQ8V+Nh8sIDHcRePo0DLxwhpQjhc9fzemK1qJeKcAKSXLDkGPOuwCThSGp95xGXm2SgzDlLBkdy7nNzu1lVkszn5KpROvloSt5TiEEAQCBpoQRCW0RkQ2gGl3Q697IbKhGpfsIbD1JyjOVpEQoELWN35Xe8zb9ovyuki7UAnxoHwv1n4J10+D66YBGnS9DebVMKwOlKr+t8yAHx9T23reDamDoMNV8OloairKevp8jMXvUdzxHvJK/CTHhDKsTRLFCY0JJA/AtuOXau3Nxn3Y4OpIWFEp9YOYKtulpH6ojYcGNuOXrZlc+vr8ilOOn7+LtvXDeevaLox5eyFGjd/pIXad+pEu9uSU0KdZLNf3aKJeKoY0p9hnYIbY0MpEAV3XkEKgCTACxmldbfZpGu/P38nHC3dTGlACxnXdG3N998Y4jqS6m5IW0SF8f09vtmYUkl3so3W9CKKcNux/QMVeSkkekWgx3XG7BM6kdojpD6nnyZOgTNyjkuGHv8Du+VUONNUClzQhua8yk1//FVz+PuTsUlEV6gRQvxN8MkoZ71YglJmnr4hq5KVj++Zu3rz8dUr8BjFuB0/PzeXNoW8QN+0aFSVSjs2Feek7FGnRUId4FRnqIDHMydcr9zF+wa6K7a/P2sbQ1on8Y0RrHv96bbVjLuvUgOlrqkd0XdO9MQ99vpquydHc0KsJXr+J12+QGO7ix/UHyPebxNiOvHhgs+uYEjTkKfPUMIVgXUYRj09bW2GU2rlRFP++pDVxTv2UzPVspuTStokMbZ3Amr15SAlt60fgtgl0a65pcQI45QLG008/Daja3C+99BIhIZV5UoZhsHr1alq0aHGqL+usQErA8OHJXYU+9TYVQtvpOpWi8f5QVW2kKjFNQejQ6hIV7vrugOAd71miPCuEqD75+uERuOIDNZBd9i788q/KFa/oFBj2LCx4vbZ4ASofeOznkLs7+Dkz1iMLDzFrs8ZtfVMY2b4eX6/ax3tLMvH06scVV/QkftPHOEoyMJoNx0wZQL6IRZoSXRcIITCM6tEHhQHJA1XEi3I27C/gg9928NrVnbjjk+XVJnVXdGlAwDD5vyFpfLxoF/+5vB2Pf722mnt7x4aRPDKsBSYCNEGu3+SLpbvZuL+AtvXDGdmhPpF2gTxJpkQ2m8bCXbk89Pnqim1Sws8bM9ieWcRH47pQu55PcKQEB5L6ITYapkQjpZrQ/AHncScUPW8XhrsOw7OTwM4CyCmF5pEnN4/U8CjjWz132zEJGLEeNYlcsDPbEjAsjpvyCb8QgnAzA377Hwx4HN4brKIn1E71iy2mqaqccMFTyjy6QTf4YGjwjtMX1x7zvLkw90VwhauKDJe8qdoselO9dAlNiR4dx8IXVXyplk+AqyYqUaOOt/ywVe+xvdEVPHlJGzILfSzakc3PGwO8MPxZmnfbhX3leDRM8lqNZY+zGbd8vI2pdzQElJAgRG2hPLc0wJ+/WFPrXGv25jNj3QFu7pPMW7Mr07/cDp3/XN6eN2ZtY0S7JLqlxHDXZ8srKgXomuCeAU0Z3bE+flOyaHs209ccIDLUxtiujagX5sR+GlQMQ9N48ectfLF8b8U2r9/k7bk7KPAGuL9/KuIIA5g0JW6gQ4IHIcrEevnHHvQ8Ih/7nFcQaRco0eLb+9UOIeCqSdXFi6qsnwpjPlYChuFTURrn3QQz/wYRDVXk79qpNcQL1Pz0ig9h0Ru1uhS75lHPXsQhZzS39EnhmR828uiKSB678hditkzGnbORwtgOODpdRbEeh2GYCCHQdYFpylov5DuyS6qJF+XMWHeA3k1jaBITys6yOV7P1Bh6pMZw92crKtqNbF+PehEhtGsQwbA2iTw4eRVFZWnFmoCx3Rof0QdDaho5PoMpi3ayNaOQjo0iuahdEhFHGTl0vAgh2Ffk44bxS6r9Olq2O4cr313E1Dt6chKqrgdFl5IwAb0bRQJlz50lXlicIE65gLF+vfJTkFKyefNm7PZKx3CHw0GLFi248cYbT/VlnTWE+fejf3JZpSq9/CNlnnTtVOSspxE756rVqNaXQetLVfpGcZYacMzDVNkozlT5jqVVKgf4CsHvLXOP3q88MiIbKxM1m0uZe5aHF9Ykf29tM7UamH4v01blMn3Nft4fdx5ZRaXM25rFS79l8M4SnXsH3E/f1EiiPW60gIEEiiQs257DwXwvXRpHUT/ChQuJrmvMXZ9R5+/GL1fs5fIuDfnh3j6s35dPvtdPs4Qw8kv8pMa6cZf5Q/x92rpapedWpOfy9pztPD2yNZuyirnhw6UV5fFmbT7E23N38NENXWka6TwpynZhQPJslXDHquzILGJndgkto0OOOTTQirg4cej5u5Cn0P9iWYZEE5AafpJPpDsxQ2Kx5WzF36jfMR3aKjGM37ZnI6VEWK5nFr+DMJGL7dMrlDCRuwuu/QpmPasMNG2uyvHuy1vVeBeVrP4cbswrylTmnFVXhDdNh0veUOPq1NtVZOKNM1QFLm8ebPwWPr9BVfMqx5uL1HQoLaTOb7k3jziPnZeW7GXS0j0Vm0d8UEDj6FDevPolVu7OIdEZxq0fLaNZfBgep40cv8H8zRn4Aia9UmOIctmwS4nNpjFzbRAPkDI+W5zORzd2pVOjKNKzi0mOdZMc62bx9mySIlxc26MxY95eWO0FxzAl//1pC23qR/D6rG0sq1K946sV+7i1TzLjujU65SJGUcDkyxV7g+6bvGwPN/dOJvwoM1+ktHwLABw2E/vy99EWvQ57l8C1Xyvz2g3TVBUfX/Cys4B6powq1cwOroUmr8DoCVCSrbwyNk8PclzgsKJRTn4Bt/+wjFeu7sjWQw34Yvkeft6SR5+mF9My7gouaNWYJLsT3TQpEbDtUBHr9+eTHOumVVI4YboSqkr8AT6cv6PO83y5fC8vju7A5oMFtE4KJzLEzo6sQoa1TsRu07iySwMaRLgoDphc270xN41fWm3Ry5QwYeEuujSJok+jSIwgYoTQBCsPFHDbx8sq5qSzNh/ijdnb+PTmbjQJc1TztjmRBITghR83B/2e5xb7mbslk4taxp3SCivWXNPiZHDKBYwJEyYA8Je//IVHH30Uj8dzqi/hrMKpGzjMQmUCZnMjtiyCgX+HQAmsn6YGj51zwf4EYthz4C9Sk6tVn8LEqyoHmpAotYJU1wASEl07tA+okJmzt6uIDIBe90HbK5SYURcJrdVA1+A8lYbiDIMDa2HVRKXM25wUaOFALgFT8vjXa/nfmA5c16OUAm+AaLeDtXvz0GxObKaJqQnWHyri1gnLq5lxtakXzpvXdCJUExwqrMO1GrViowE2TdC7UQR2u45hSgQh+P0GOpKECBc7MoN8BsAvmzLILW3BPRNXVogX5ZQGTO6bvJJJN3cjJOjRvw+fKdmTU1Ln/pXpubSJC7XqlZ9G9PzdlDYeeMrOtyRD0iQMXEcRwvp7McPqo+dsOebjWieF8cuWTHZkF5MSc6rWfCzOZoQASgvwkIehOfESSogoxu7LhaYDlTixY7YS0bvdARe+oPybDm2Ar++sLKmas0N1VjOqsCqh0eCv8aKm6dXHyA3fqCjC4S+oMqvBiGiIyNyCTBuuSr2GRiuhZcXHUKTy580G5xGwhTBp6eZah+/KLuaf0zdzS58Ubp+wnMhQB69e3ZGvVu7jhZnV24/uXJ8HBjdHwmHHu8LSAFlFPu75bAX/N7g5XeqF4fcbnJccRUGpn6+W763zY3nt1630SI2tJmAAvD13ByPb1yPeqdd53pNBTrG/zms1TEluiZ9wtz14A4taaJrAY2SirfpULUqlXaiek6aDlbeLaRze70iI2qmS+Xth8rXq/5PaQ6fr4ZcaacAOj5qDdrlJmXv6i1WFoF2/gSuSbDOU7ZmZLNiWRctEDx+MO4+sQh9upw3DlMS4HeimSW5Acu37i9mXVxn5Wz/SxYc3nIdT1wgU+8kpqrtcfG6Jn4gQG0OaRisRQZq0jnXz5IUtQEDAbyClxBViZ8qyPbXSsMp57detdL62M8ES3gtNuH/yqloLal6/yf9NWc2EcV2CHnci8JmS1Xvz6tw/b1smI1qdusUWC4uTxWnzwChPJbEIjk2ThBsHEPNeQ9v+C9TrjOz/Z8S+5bB9lhIE2o0B950qhSN/H8x/Sf2d1E4NICU5sOl71aHugBYjVL3umiS0VpO0muJGSJTqWwhl9tl0kBqAig7B/tXKsNMdW71Mazk971HRGy1HKMd3wwd9HlQ5xoUZSHsopb7K8gm7soqxaRoBw+RAfgkep87F7ZII05ULe6EUtcQLALfTxr48LxMX76ZfWt2/lFvXC2fJzhwGt4zHNCWlpdUr3UgpyS+pu/qNlGpSmFMcfGDcn+clzxsgxHXiJ3c2TeB26BUhjDVpEHXs0RcWJxBfEVpJFqb71EwKpJQsy5B0OUVzECOsPs4dPyoxVD/6F4XmcW4cuuC37dmWgGFxRFxaKaHFO2HGf3AdXI/s/idCE1rB3BcR2duVYebFr6goic7jVFTg9w8rsSC2uQpd3/IjrFCLJBxcXzbmTat9ssS2yrCw5u/NViNVKkh4PWVWGJYEeemqGldEQ/X/NelxJ0Qnq2tc8REUHIDEdjDiZSg+BKExiLAkXKaXQWnR/LQpu1YXC7dn8+CQNF69qiNpCR4OFfpqiRcJ4U4ubFePt+bs4LdtmdzWN7XOz7JToyjsusBnmPRtHouvbOzw6IJOjaKYvTnImF3GnpwSYj3BvXy+W3OAW3s0UuVuTxGhjsOPqe4j7LeoxKMV4Ti0Et0dDcP/A0s/gGl3QXSy8nzZMlMZv1/6thIi9q+q3UmzobBzXuXPDc5T4sSwZ1Vq1oZpylS+JoOfUObyJVlwcB2k9leVfVyRGHn7eGeRWjz6euU+XhzdnpXpuRzM89KuQQTJMaG4pKQUePDz1dXEi56pMdzaN4Wnpm9iyc5s+jWPpW/zOJbWEODK6ZasqtJUjZxQaSjVv9M6kl3ZdS8c7c0poa4gioP5XgpLg88nd2QWkV9qEHeSDHM1AfHhLvK9hUH3N4oOtcrAWpwTWPbwZyBCQLgvHf2DwSoqwuGGTtci3h+qwljLmblWCQu97oEJl1Zuz0tXhmZjP4eO14HpV32c/xfluL7918q2ie3gktfh41E1LkKDIU/Cqkkqv3fHXJULbAag5UiIS4PCQ3DlZ8oNfm9ZlZLQGFV5JL41/PxPNRi641TJ1QWvKfdqKREhUbTv/yj/HNyZv89UobB2DbrVD6dHw4iKvEbDUCsGK3bm1hIvot0ObuuXyrXvLaY0YNKlSTRt6oezdm9+rc/zjv6pPPP9Rjo3isQdZLVGSmgQWXf8hEPXcB9h5ck4SSKCRxdc070xb82pXc7SZdfo0DDypIUjWhwZvUD5vJjuk+yoWcbuAsj0nnz/i3LMsPpgBtDzdmBENz/q4+y6RlqCh3nbs7n2vIYn8QotznZsOoTu/w19ynVqQ/MLEEYpfDSyslFeepm59CtQmAGTr6uyb496eRo9XqWS+IpUlEZy37Ixb1Zl26T2MPw5+Oyq6hcRXg/ajlYVFQb9Q/k4ZW9X4kjTwTD6I2XcWV45xBUJ/f8MjXrB8g9g6fuVfR1cC7oNNs+ETd8hpEm8M5wXu93PjNQBPDS9ekqEEBDjdpAc5gBN8PyCnbU+oycvactfv1pbUbXLlJK0hDA2HSyo1k4TcFu/FJDw4hXtiXLoFXnnmilJjQqhc+Mo5m0NLmK0SgqvlUZZTrH/1Je4j3DqtEgMY+OBglr72tQPJ8yh1x1lY1FBiObFufwttN3zlFjx5c2Vn1teuvreD3gcmg1Rc8aLX1Xi4IJXlRE8qGo9ncepNCpQC2mDn1D+F3m7IXUgjP0C0Cqjn+yhKmq3QRf1PF/yuorEnXi1ivZweNB63M19PS5lxoYsdE0QZhMMTIlGlJURllKV3CwISFak51bcU1yYkxt6JXPLR0srDNq/W3OAyzs3JMbtIKvIV+0z8DhtDG+bxIrdOfRqFHnY1AZpSrolRzFj3YGg+1slhePQgCBBzb4jzMfqiuo4EYRqgj/1S+GBKatr7RMCRnWqXyFoWliczVgCxhlIiPCizXysMqWj3Rg1OfIGCQvb/it0vKZ6HW97KIx6R7mvr56sQvWiU9Tg1Pl66PEn1ZczTJly/vY/VZZu8ww4uAaim0KXG2Def5VJ6IxH1UBWzqI3VCTHddPg0ythzAQVlVGSA0YprPoM3DFKvAAY/C9VjSS7ygt4SQ7a9w8y6sI3+KJBI0p8BmEOPaghk6YJMgpqG4WO6dKQd+Zsr6hn/9T0jfx3TAd+3ZjB1JV7KfYZtK0fwe39Uvluzb7DpmEAxIY56JEaw4IaZVYBruneCLdDx2XXKkzPqhLmtBEVYoeTUFbLNEyu69aITQcKmLW5sh65x2njves6E3aYzCCLk4+ep8zCzFPkgbG4zP+iWcQpOR2GOxE0G3r2lmMSMADaJIYzeeU+CrwBwlzWcGMRHLeZjf7dfZUbOl6jyi0GY+bf4KIa1UEiGsCIl9Q4ufkHMA1k/S6I8/+iSj92/5MSMsrHvH2r4IJn1RhZmqciBaNSYN1XENVEeWmUU3QIxl8EF/1XrUxnb4WSPJABWPkpxLWqLl4ADHhMlXutuoJdmk/4nH8ytJ/Od826MmtLZSRGv+ZxhJRVgjKBQwXVX7w6NYpi7d68CvEC4Inv1vO/MR2Zse4AX6/ci9dv0qFhJLf3S2XK0nQeGppGy7hQtBrjqd8XYGT7JN6es71aWVFQLzg39k6uZmhYlWGtEwkETu3Lj0vAa1d1ZNz4JRUVFQCaxITy8piOODkZo+65h8vIQZv/X7WY9P3DtUUfTVcpUJlblCm8Nw/C60O/h+GCZ9QkQ7OrhaykDtCoJzQdoBawyr/nKz9RlX/GfQe3z1f+bDKgnpef/gG971fzyvRFlef1FSJmP03zvpILW59Pnxb1cJSZtNektMZ376quDXlz9rZa1eX++c06/jumA5OW7OaHdQcxpaRfszjG9WrCM99v5La+yUf8vAIBk/PT4vnfT1vI99YW7h4cmoaDoPoFDSJd2DRRK90YlFAZ4bJxsr61hmHSvUk013RvxMcLK030HbrGc5e3I8puCX4W5wbWjPIMxCGLEDuqREk06a3MOOtix2ylbm8pEwwG/R3m/U9VFykne7tSzUe+DnOeh8xN1Y2YctNh1Luw8HXI36/Caxv1UCkpVcWLcvL3qRzfnveoga7ccdrhVitWu8sGqNAYNSvKrh09AOCZ92/u6/0pUfGNcIngBsWGYdKpcVSt7W0bRPDG7G0VPxeWBrj942UMapnA05e1JTHcxe7sYp6avoG9uSWEOW1Eux3UNXBEu508d1lbnvlhI9PXHsCU4LRpXNO9ETf2aIIDyV8uaMHfv1lf69i/jWiFWz+ib+lx4zBNnrq4FTneABsPFhAd6iA51o1HE0irhMhpRc/bibSFIJ2nRlFYclCSfIr8LwAQGoYnCT372H0w2iaF8dlyyYKd2QxpYeXdWgRH9+aoEqigXqTMQHWjzKqU5ICoMXUZ/oIqrZq/r2KT2LsUPrsSrvwUPr9J+UZVHfMGPK4iKFZNhIY9lHjf+lKYNDb4eZd9CA27qYUF06+qlSS0gUM1xgN7CHgSg4ffA+GL/8sdQ6dVCBgRIXb+OqwFtrJVZocmOD8tjgXbK4X0zo2jmLvlULV+8ksC3PrRUoa0TuDlqzoS43Ywb2sW//xmHQlhTsJsWi3xouIabBqf3dKNBz9fzdYMFWqeGO7iyUta0yAqBH+Ql8deTWOoH+E65emKpimJtAk+ubErB/JL2Z1dTOPoUBLDnYQKyyDwaNA0gdizSr242kIqvWKq0uNu5flSNeUqfy98c68S5HYvVFFObS5XIkjeXhg/vPbLsK9QPUtRyeqZc4SqlK15L0LXm6uLF1XQF77CPaMu5pAWQgCCGuKGu+yEOnSKyyIIWiSG8/LPteenO7OKuf3jZTx1aVuu6NKQYp/B0p3Z3DNxBYXeAG3qRRzV9yZch89u6c6Dn69iw34VARTncfKPEa1oHFG3aXuoLnhgcHP+E8R8/V8Xt8aji5MaNWs3Te7sncz13Ruz/kABzrJoSLcuENbzYnGOYAkYZyRClT+VZaqvaYBmqz75qopmp6IOpsOt8narihdVmfMfFR47ZVzlNnuoipLI36/CB32FEJ2qJmff3V/3ZQa8EBIBn45Wq1vlfQ34G0TUVz9HNYaMjXX3kbeHbg1D8YU4CfgM5Q+la0hJhQJfguBgfilt60ewpoo5kWFKnDatIgKjfNuMdQeYse4At/ZNYdmuHPbmliAEPH1ZWzxHEBlc0uTxYS24f1BzSvwGboeNMJuoKHs1JC2OlLjz+N9PW9mVXURqnIf7BzWjSaTrpJVRLccuJfFOncQmURXlT6U1GJ129LydKkrhFCSWSilZnCHpeWqyVSowwhpgyw5eCedwRLsdNIx0MWdbliVgWNSJFFXywaVU49/hqPqsNemtfCeqiBcVGH6V599+DCx+p3J7dIoSK7K3Q9owNW4mdVCGoMGEk/hWqlz5p2MqfTCEBh3GQsuLq7cNS4LsbbX7KKckh4Zug86No+jfPI6R7ZOIsGsYhsQwTAIBkyGtEnhj9rYKzyW/YeKy1/5MAqZk+poD7Mv10jM1htdnbSPErvP2NZ1x1rEgACANk4ZuOx9e15kCn4FpSsKdNtxl2SbT7uzFm3O2MXtTJmEuG+N6NmFAWhzO0ySWm6YkBEgJd5Aa4SyrJiKtiozHQrnxZrBxSgho3EtF4wbjt5fVAte2nyG5j5pzLnm7tnghNLj0TRWFsX5q5f6YVBjxv+DPaDm+Iuq5Aoz5cDnT7+lDiE2rVilD0zUO5XkZ17MJr89Sz5cpJXZd1IrAACj2GWzJKGTulkOs3lM5b3x4aBpu/TDmvlUwDUmiS+fdsZ0o9Jv4DZNwp61MgKj7WdBMyaXtEmmRGMbLv2xlT04xaQlh3D+oGQ3CnIc99kRhk5Iom2Bkh/pkZRVaJUwtzjksAeMMxKt5sLW8GLHuS7VhywxodYlKzQhG6vmVIa9RyXDoMC8aOTshthk0GwwFB1WURadr4JenYHOZ4WdEQ+To8Yid80GvwyvZ5oLmQ+Hjy6oPBP5imPEIXP+tSmspzobwpLqvx+HhQKFBdAiEafnYC9JVKVh3LGbjPpTYolm+t5RHvljNS1epcNlpK/dRGjBZmZ7DyA71mFylLF1VBqTFs2BbFhe3r8ctfZJJ8jiwC4EpzMOWkNIMk3ANwp06IKsJEzYpaRUTyitj2uE3JQ5NqDDCUzgwWCtOZxZ67g5Md8IpOde2PMgthbRT5H9RjhneCG3vAkRJJjIk9piObZsUztzt2QQME5t+cozLLM5uDGcUtvD6ZeW3TRWB4YoInjYZXg9KqxjUdf+Tigasi90L4KpJKiLCCEC7K6BhdzV25exUbRqcp1JQvMGN/zj/r2qMLY80BHWdKyYosT91YGVJ8ZIc8Bzm94FuJyYijHeuTMEjC9FzliPSFyDDGyIbdqdQiyZc2Jh8a3ee/mETv27K4KcNB7lnYDPmB0lvBBjVqQG/bcvkxl5NGNutERE2DYHE4bAhpQxqummaEifgtJc/k8pzCiBCgz8PbMY9/ZuiAW6bOKVlF+uiXLi3ODZMUyIT2qiFsOJslXKVV2Xe5IpUJVDrojRfzefGfqHSrCIb1a5EAkrM271QtalK1jb46na4elLd5xAaJabO11cmkrBxPMJfgkw5H19IIoWmh0JDcsuEZdzeN5VHLmjBe7/tYPbmQwxtnci3q4OXFL6oXRJFXj+GKUkMd3Frn2QaRbqwSYndoQMCf1nVkcN9dg4g2iaUWQ/yqKInbKakfbyb18e0xy8lDk3DXia8nWqsZ8biXMQSMM5AvIaDkP6PYts1T5mVrZ+mjDR3zq0+6ABml5sgNAatvBxcaQGEN6i7c90OvhJo0FVNEpsNgXcHQaBKSba8dERRplLY+/xfddPPctKGw5opdavYv72kfDUmXq3c2x3uoGVai9rfgFcPI8zIRv/6JsTuhRX7NKHhvvRddh9Ko8hncMfHyxjWJokXrmiPEILc4lK6p8SycHs2u7Orm47d0S+FBlEu3ryqAy6bRr7PZNraA6zek0eb+hEMSIsj3KZVRFYcC6YpsQN2gLKcZYs/LnreTvxJ552Scy06KLFr0PQU+V+UY4QrE05b1ib8DY5NwGhfP5zpGzJYuTefLo0iT8LVWZztFGnR2C59G/3jS1XFqsVvwdB/w7R7qhv86HbMS95CaBpC01V0oidBpSrWRUiUEvUbdlfiR2gMvHN+9TbOcCVG1O+syqAWV3mZi2ykKp5UFS+qsuAVJZBs/1Vda0mOEvhDY4KG6vtaXUFpYTZREQImjkUc2lC50+Yk7MpJFER1JMqm8/TFrThU0pzd2cVEhto5Py2eXzdlVOuvR2oMKXFuDuR72Z1ViK4Jik3JyvQ8ftmUQb3IEC5ul0SkQ0c/lhcZw6woC26V6D77KdYi8Qx/Hm3RGzDk38pjxijzWvGXqAohh0PoMP0htWA2/xVVsWd1DUGi9SUw9U/Bjy/YrxbN4lrAodpRuUbaRYSV7CF+8pjKjb/8E1fLkWiDn2FfYQi5xX6e+WEjnRtH8eCQNMJdNpJj3axMz63lcXbPwKZ8tXwPt/VN4RbDxK4JNFNSKmF3oZ8vV+ykNGAyskM9mkSF4DwJL/nV54qm5dViYXECsQSMM5Q8LZGIcTPRtnyPtuk75P7VcO1U2DwDsWUGOMORnccholOUqHHVRKV6CwENOoHNWV2UKKflxcrTYu9SpbovfL12O92hFPfy0qpVV5fKqd8FNkyt+wayt6kB7+afYdc8uPxDNWBWmQT6Uoawu9m11HP50JaNV+KFOxY6Xquqo/gK0VZ9zMW9n+QpAX5DMm3VPqatUmGIV3RuwLp9+Tx8QRqHCkpZtCObiBA7A1rEs3B7Ft+t3s+VHeuxPbeUse8tqsibnLpyH8//uImPb+pGcpjDimiwOH6MUrTCfaesAsmigyap4eDQT20EhrS7MUNisWVuwN+g1zEd2ygqhKgQO7O2ZloChkVQDMOkILwNYbf9hr5yAuxbjmmaiNvmwtL3EJmbIa4lssuNiN0LEFtnwtVTYOO3aqxKG1ZZPrUm3W6HdV9AxgYVSfHT32u3iWyoRI70Rcrcc+odSuAH9WJXHqkRjOIs9SJ462xlmp27S42pV0+Bz0ZXKzMeaNgTX68HCd/yNTIrAtH3/5QIs3kGbPwGAqXoE6/EfdtCcolFNyV7sov50yfLsWmCvwxvwZXnNWT62v2YpqR/i3iKSgPc+OESSgMmoQ6dm3qncueny6sZfr4+axv/GdWWfsnRaNZq7B+SUtOBlnwRIYkd0QJFiJt/VgLE/pXI2OaIsKQ6RTcadVflT20OJeht/Un5WrS9HNZ8XqWhUFG4dZGzU6WifHK5Svsqw6jXGfP8xwl5t19ZNwJSBkDrSxGOUOx523GHtq9ov2xXDsvKyqQ+NCSNpy5ty9aMQhbvzCYqVM0B52zOZMLCXbSuF0GfxhEYhkmpELw6ezufLakshzxl2R56pETz3Kh2py1FysLC4tixBIwzFNOU5BCDLe067C3GEOrdi/bJ5XDZO2UmYgHEgteg223KqKzrLSpaYu9yyElX0Q9T/1SpsIPK421/JdI0Ea0uVaVQv76z9skNn3JrB/j+z2olrO0o2DhdGUg0vwBS+iuRIn1x8BuIa6FSVRa9BW0ug60/I6/9ClmSS3FeJsURzcgnlOjiXUREJaMtfVeltXS5SZVbnf+yWjlrfzVxDh9dGkexZGf18N7miWF8MG8Hny5OJyXWTZv6EWQWlnLvxBV4/SY9UmMY1iaRuz5bUSFelOP1m9z56XKm3NK9YpXJwuJY0fN2I5AYnpMvYARMydIMGNLw9BRxN8IbomfVNrA9EkII2tUPZ9bWLP7v/FSEVYTeIgh+UydHSyJ24D8ozM/FJYvRv7gBEZagyoW3GIHI2gIz/gLnla0ed7xOjUkbp0P/R2DWM9U7bTYYmg5S3kz+YqjXKXgefv4+lX659D31Unf1JFXNJHu7Si+Jawm8FvzCwxJVFMi0e2Hgo6qPRW+ClJjXT6c4Zz+lufshphl6aCQhK8YjO16BmP2sEi50h/LjuGoSfP0nKDiAlrEaLXEgpilpnuCpqGjwxLcbeGhoGvFhTvblenlm+kYOFVYuQFzasT5vzN5aTbwo55Ev1zDzvr6EW1lcf1hKTBcipB6haz+CxDYQ3xLqdUSE11PpJSNfUwtNpVVK1kY0hL4PK8+z3PTKZ+XXfysj3FHDlJDoK1LeZ86w6sdXJbKR8lC/8jMozkYWZ+GPSmVTSQQtF7+lnlF7KFz2tkpF+env4M1Fa9yblEH/5NK2sXy1pnr533pRIdw8fikNo0NpXS+cg/ml3PXpigpvtF83ZdA/JRrTNNiZU1JNvChnwfZsZm/JZHha7BmRKmVhYXFkrKHsDCdgmAgzgPjxr9DjLti3Qg080oSUfhASCUOehJYjVY36qbfDzMfA4VHlTQc/oSqFXPaOyhX+9SmEN0e1W/mJKhkXjEOboH4nNUmc/hD8/C+VBhISBelLYNl4FS6oBdHAhIAed6p85vhWcHA9NOmNWDMF7YeHcSc2I8Y4RNOCpcTt/E65zjvDoNP1ygF+51y1KlWUCfNfRsz4C/8aWNsAUAAxHuXRsT2ziGmr9vHzhoyKMqeJYU7yvYGgkzmAg/ml5AYpj2VhcbTouaq6jumpd9LPtTYbigPQonZBnlOCEdEEPT+90rD3GOhYP5yDBaVsOFh45MYWf2x0HZ90IvYvR+xdolI6mg5WXlAHVsPVkyG5r0pf/PJmVcLUDKiIwbFToP9foNd9cOUn0OFaNVZNu0uVjsxYp8awmmz7RUVxaDa1uvzpaCVehNVTFbWik8FThwltz3uVAXb/h9ULWMYGZP3OyMveQqyeiHvzVCLjGxLuzyBywhCczc9HfDBcVXwwfMo0e8UE+PZ+GP48AKIwo0Loc2uCxy5sWXG6jIJSNuwv4Ls1+6uJFwC9m8Xy04bqKSblmBIW78xGt3xo/tC4/FmI+DQVbRTfSgkTBftVpTtvnjLhHPYf6Hm3EjQG/R0WvqHa+ApV6nDn69Uc9Od/wvQH1XPjjoOVE6F7kEUxUKKHKxxydyqTT088YvFb2Be/TuvEUGz5O1W7IU8qM9EFr6poENOAHbPR3hvEM71tJIRX92Ur8PqJ8TjYdqiQaav28cvGjGrG7vUiQ5BSInSNjxfuqvNzGb9gJ0Un2YjdwsLixGGNZGcBDrMIsXOeCuPbMUf5Ssx9XtXVfv8CVVIu4IUtP6oDDm1Uk7D8/Wrb7vlq1Wrz96oc1o+PqXZrPof2VwU/6bz/wiVvqZxgUOF+qyepdJWWF6kVpt9eUkp61dzJ0Bi46CVY9oEq67ruK0hqp6534euQuQWx9nP0JW8iN01H9v4/CmUo9HlQndOsbTYmdi+gmT2Tt67pRPeUaDo1iuSJka0Z3iaB2/qm1Pm5XdujcdBycFU50n4Li8Oh525D2kJPSQnVhQckbhs0CTvppwqKEdkEAFvmumM+tlmcB49T5+fNmUdubPGHx64Z6Ks+UaK7Ox7e7gszH4fZz8JHI9XLV+ZmlQ656lOo1xFCY+GLW9Rq8J4lKhox4IWFr1Z2vHoydB5X+4RmQBmBXvmpEjhMA7b+rF7WUvqr/Vd+CgmtK4+xudRLnjRgyVtKXHHHQ4uLEEUZiNx0xPyXEcs+QB9/IfY1KrJDbpgWXATM3aXMDhPbIut1rqhUoEnJ0BZxfHF7D4a1SWRnZiHXdG9U52dnHCYlMt/rPxXFkizOUHRdIA6ugZBo5dXyzvnKF236Q+qZObQJcnap52T3Apj1tJpDDnkSfn1KdVKcBTvnqcUxe4jqZ01ZOleTXmrhq8uNqt9y6neCC1+Az28oq3jSQ81l9y5DrP0Ckb8X2Xy4evZc4bBnae2LNwM4fn6U6be0YUyXBrRrEMHlnerTOzWam3onB71fIeDi9kn4/QamlBSU1l2CrqjUOJrCJBYWFmcIVgrJ2YAQKox1z1I1SNRk60zI2VF925J31aSu8w3KzT0mVdWxn3xdpZmmNxe2z4KhT8PP/6j0wrCHqFUs0w+XvAlFGUqZ1x2wdxl8cZNaadr6E7S+DIY8pXIjQZlBLXlHpZZ0HgeaBsWZsH915bVt+RHajUb88iQlvf7Cf5ZK/t6tG/bvHqjzI9C2/USP8x6gfb12SClxaQLTlHRqEMGYLg2YVKMSyZ+HppHkdlBqgsuuVURlVMVp04h222ttrwtd1ygxJQJwaYcvo2Xxx0DP3Y7hSTolJVQXHDBJiwJdOz1vINIRjhkSi/3QWgL1exzTsbomaF8vgp83H+KuPk2sNBKLIyCQzghE454w6ZoguzVY+4X6fynh23uh2VC48HlV4tEeoqIQx19U3QR0+68qb7/t5er48jcWTzy0v1Klilz0P9U/KE+mvUvh41FKsBjypGpbklN5DfNfgeR+kL1DlaKc/aw6NqmDitzI3KJ+Xj8V+vwfonyhIRg7ZmO2uQK/OwmqvGvZTElyuIN/X9RSrS4LwW19U3hrzvZqh7tsGi2TwtiwP3gIf/fkmKMOkdc0gR+VuuLSxXEZXlucWUgJMjROVRxZ9oHaaAup9L2Y9yI07ApdxoEjTH3H3XEw828w/Dn49gE1b1zyrnrerp2q5o2GHwwvzH8V0hdCuyvhyokqMiNQDAfXqdSU8oiK3Yug6FDFdYlN3+Nrfy2O5FkqdaQOxO4FhIsSHh7YlFJDKi8oQzKsdQKLtmcxs0r0kSbg2VHtiLTrICU6cFG7ROZtDS6iD24VT4guqlWdqxNdo7SsCp1mnp7KIhYWf3TOCQFjyZIlvPfee6xdu5ZDhw7x2muvMWjQoIr9jzzyCF99Vb2sU+/evXnvvfdO9aUeF6VaOLautyHm/Cd4A2lCMH/jfSvUH5sTLn9fDTo1WfqeSve47hslgghNRVGE11emYs0vUCteyKDRETjc8M09wcvdNewGm6aDN1+liTTpoyaEUOHNkVtq8tmSPdzcLpkmdRmPAjIkCsOQ6IaJ0AR5AcnmjAIO5pdyY69kru/ZhIXbs3DoGt2SYwizC3RTYrdpPDQkjSe+21Crz3sHNsOjaXAE4yZNExRK+HblPr5ZvR+7rjG2WyN6pcbgskyf/tDoudsxPYcpE3yCKPRL1mTBVc1O74u/EZmMLWPlcR3buWEEv+3IZmNGIS0TTlMYicVZgc/QkD3uQix4OXgDKStFhvKfN/+g/oAqD97+quriRXm7r+9SkRO3/AoH16oKJIZfRRL6i9VL3IHVgKh9/K7fVGrk5hnVtyf3UT4czYeonyMaQkIrdR15e5SwDyo60uGp876lI4z9TS7FNMJwlY3pQhMUmrBhTz6ZRT7a1Y8gNtTOuK4NuaxjfRZuVy+f3VNiiHLq/GNEa656ZyE1AzEGtIgnNtRed+WwKviFxtr9+bw9dwe5JX76NYstK88qMK0w+7MW05QQkwLf3Ve5saaYnL64urfZFePVIlnxIbjoRbWQJU1VinXPMvjhz4Cs/r1a9ZmqoBeTAovfqdzuSVALaNJUz12rS1R5Y4eb9bkaCT3+TuKWz6hzlLOHINEwAyZOTVAYkGzLLGJ/XgkPDknj7gHNWLorB7dTp0vjKDy6EhhAGQX3TImhYXQI6dnV04rDQ2xc173JEUU6qQmySg3e/nkrq/fm0yg6hNv7pdIowonNMoO3sDilnBMCRnFxMWlpaYwaNYq77roraJs+ffrw9NNPV/zscASpYX2G4jXthCa1RxTXEX69Z7EKtV38dvD9rS+rVX61GtJU4oS/SOUpajZl1GQPhU3fK4OxNVNqH+eJV5EdQcqjktiu0scisjFc+paK9sjfp1JhYptBbDMMU2KYkreWFfCv1mOwr/oo+CU2HUwgoMSL7fml3PjhUgpLK/0rujSO5NWrOuEwTRUZUTaYSMNkWMt4GkSF8OLMLWzPLKRJjJsHBjejfb2II4oXAIUSrn53cTUvjUe+XEPnRpH8b3R7XJb6/ofFlrsNX6Pzj9zwd7LkoMSU0Cr6pJ/qsASimmLfvwRRuB95jMJNWryHMKeNmRsPWQKGxWGRUmKExqNVLWdala0z1bi2f2Xw/e2vrC0+VHRuqlXe1peqly5nuIrY+O0VsNmVZ9QXN1FrUUAIVT58Yo20y/D6yktg0/cqBP/St1TqytovlcB/WVlE4vyXYdcCZLfbEd/eF/TSctveyNjPtjF+XJlPhybYmF3CzR8trRZF2D0lmudHtSPaJhjRUnlzGIYJhkmTCCdf3tGT/8zYxNKdOUS7HdzYuwkXtk7EcRTjnV8I/vvrVqYsq5wzbM0oZNLSPXxxew9i7MIKtT+LCUgdvUplHLx5EJakPC5q4gxXf0tTpWR9fqMy0O08Tm2LSan7Oes4VvnOVKXvg0rYaD5UiRyaDj3vgkY9KcnwMvSTXcy76RLC5zwXtEuz/TV4tUg0KdhXHODaDxaTW+yv2N+mXjjvjeuCwzBVpFENUcEt4OMbuzJ+wS4+X74HX8DkgtaJ3HV+UyL0w08HdV1jfWYx13+4pCJNa9uhQn7ddIh/jGjFsBZxFWKJhYXFyeec8MDo168f999/P4MHD66zjcPhIC4uruJPRMTJz1k/UUgJJXo0snGf4A18RUoVbzu69r6wRGg3GmKbBzfctIdAh7HKxEzYwBmhyqsueh1m/FV5V7S5TK0kVcWToCZmi99WJVwb91bbnWFw3s2qXN2Mv8KwZ1VURcE+lQbTbJAycPriZhj6NB6nRtv64QxOCUU77waIaVrrEs3hL1Kiqze3QhNu+GBJNfECYOmuXF6YuZlgGY52KTmvXhjvX9uJn+/vywfXdaZb/QjsRzGZ03WNKcv2BjUCXbY7l/X7C9BOU0i/xelFeHPRSrIwwhqc9HPNPyCJD4H4kNMcgRGRDJqO/eDKYz5W1wQdG4QzY2MGpvUGZHEECs1wZNM6xvT9q1SKZFKH2vsadFHpHmGJwQ07QYkUPzyqqpIEvKrEasFeWDVRif3d7qi+Mm1zwYhXILweNB2iXrxsTjW2XvyKGusGPQEtLirL449UCwCrJ6sUGMOnoj4adEGmDEA2qT2WBzrfxPcHI+jYMJLQMqPNQgNuGr+0Vgrkwu3ZfDB/J0LXMAyzWjqjZkoahNp54bI2/HR/H6bc0o3L2x6deAGQ5Q1UEy/KKSwN8PQPGwlY6V9nNV4tHJk6oHLDojdValTN+aEQMPhfKiW4nIQ20O1W5Qmze75KDR74t+p+F5oOA/+uxA+hlx3XWpmDOtzqudn1m0q7anGR8t744kY6JzkY3iIc4StSfdYktjlmj7spNTSKTLhx/NJq4gXA2n35/OWLNfjq+KqbpiRUSu7s3YRv7+zFj/f04dEhzYnQyqJTDkORKfnzl2uCesz8e/oGiuu217CwsDgJnBMRGEfD4sWL6dGjB+Hh4XTv3p377ruPqKjTZOd/HJQYdlx9HkLf9K0Kd62KJx4coapKSfMLYPVnKm2j2WAVCfHNfRDbVNXf/vlfyl0dIKk9XPiiCsWNqA9zX1BGYnEtVFpJfCtl1Pn5TdDvz2oCVlqgDKCimsDXdyhfjk3fK0OnYc+o8Nq1X8Kc55WDdUJr5dtR7mIdGq1KpbYZBbP/Q9So93jliiTil/wH/ZfP4YJnVBjv7gUQEo1MG44/IhWv4UTTBJv3F1DkCz5STF25lz/1S8ETZH5lGBJHjZ+PhiLDZOrKvXXun7h0D50vaVVL6bc499FztgJghNU/6edasF+e9ugLAHQ7RkQT7PuX4ksddsyHd20UxZxt2azcm0enBpEn/voszhkChonRbDi2eS+oSL6q6A5kSBRiwGPK+HLDNDWOtR2toii+e0BFRlz2Dsx6Sq0eA6QOUKUfdQf0uV+VET+wCjyJqrJCx2tVSmTH6+C2ecokVLMpYX3GX1WFrysmQJ8HoPCgMvpcPgGu+FCNy1/eonyiNJt6ORvzsTrHojdV9ZSE1ohf/o1odbE637Zf1Qtd6gCk38++3SZ3nt8cDBNdFyzfnlOtokJVPl2czrXdG+MOsk9KiU1WTvCMwNGNTzabxq+rgqzElzF78yG8pgx6TouzA19AYHS4DtvS99R87sAaFQ1x9WS1uHRgNUSnQtebldH7znlKuLjov0rcmzhWGehGJas24Q3U97zggDpB/c6qikjubrjmCyjOVgLhmi+VMJG9Tc01D6wuKyF8CQx+Avuy9/l7n6sJebeneg6vnqSeD28uNO6F2bgXuVINgvvyvbUq8JTz6+ZDFARMIg6zPGsGTELKfzjKuWBBad0V7fyGZFd2MW1iQ6zoJAuLU8QfQsDo06cPgwcPpkGDBqSnp/Piiy9yyy23MGnSJHRdP+p+TufCg6YJcIYjr/sG8eOjapIkNGh+AXLAYzDrGUTDbmXq9z9VvuKy8fDLk6qD3F1qQOl+h5qsOULVwHNwHXjilO/FBU9BcY6qFjLtbhj0D0gdCNt+VvW4dTukXQR9H1Lb/F7Vd9EhVZY1NFrlHTfsCi0uhIS2sGaScrIupzgb5jwHHa6GpHaYvhLq6wHsK9+vnABGNlLCR/4+xOK3sd02H6GHoWmC/fneOj8jvyHxGRJhP/Z/qPJ/W7tdR0pZxehMoB3mH17TQENg/oEWpco/jlP5PJyOZ+9I92nL2YwUGtKTVHfO7glgd4FkTxFckiJ+/3lE5d/iOCdaRnQaju0zEP4ipP3YXmVSYkOJdTv4YUMGnRtGHt8F1OB0fB9PJmfa/ZyO51wIcGtFCNNAXjcNfv6XMr+Upoqw6P0AwpuvXq6iGkPfh8HpUZW5ds5VnWRuVuLBeTdB7weUGaGvUJUvbXmxEsr7Paxeyha8BrOeUdt73a8iD4WmIjF++jt0uAoyN0FJLnw0Qo1PbS5XPhdZ21SU4YcXqpVpUH+vn6rG6WH/gcnXIrf/ikhsi1j9maqcEhqtohLNAHx5K3ajlHtuX0yBTSBNiaYJ9uXVPd6V+A0MU1Ysch8vuq6hlZlSC8FhxztR9l9R45fHmfadPZGczns7mnMez/X57ZHo13+HmPmYqgay9SclSDTpA3aXEiO+uVcJCdd9A55YtSim25UIUZoPC9+EGY9Ct9uUELJ5hrqIS99RwgeoynVtRqnFsk7XKkP3z2+oTDsxfCpKad9KRN+HcRXvVSHHyz9SokqjHipqY/az0P0ubC2vwzAk2UW+Ou9NSij1GwjX73wwUPNuXddUdEZdYR3lbcWZ9/0/l5/L48H6PM4t/hACxoUXXljx/2lpaaSlpTFo0KCKqIyjJSbmNOZtZ25Wynd8S2WkVJwNpYWwZwli1SQY/E9Y97WaDGVugoz1kLOzRh9bVLksUI7ShzYr8WLK9ao/UMZMQ56EpR+osqYjXlJiBSiBoSRLVRwJlKpIiu/ur+y/OFtNBIWAa74Eb7ZS4oOxaqJS2M0ANsNbPaokd7f6U4ZedIDYlDQA2h9m1TY+zEl4qJ3YiJA629TFoQIvi7ZnMWfzIaI9Ds5Piycx3EWUXWd0lwY8/+PmoMdd060x0VGhx3y+c4FT+TyczmevznN7d0NYIhHRkSf1/MvSfdiEQacGLkJsJ2bkdbtdx39wg7awbTrheWsh9dj9P3o3i+OnDQd5+ooOuOy/f5JZzmn9/XwSOFPu53RcR4wtH6beodJAWl6sXmQ6jFWRDbpdpU0u+0AZYv72P5XGuHepqq5QlcKDqvyjpsP138KuBarc6viLlJgByo9p+HPw8z9VJMfVk9V5pAmYSmwvyYFuf4If/6qOObhO/QHofqcy/iwXL6qSlw75eyC2OcKbp85ZvkRbnF3LDNThzycmvjKN8rwmdYddNYoOJSzUTmzY8T3LAcNkf56XRduz2ZZRSMdGkbSpH8Hg1gk8PzP4eDe4VSKx4U48zuDVu86U7+zJ4LQ8B8dwzqNu6y+B7fPUAlSPO5V/mhlQacabpqs5XDkz/go3/giZW1VUU3nlkLAklV6yZgoseU+VGF75qdrnrGJS6y+GFRPUn4tfUT4wwTwzMjdDwEs1id7wK3GlDG3nHCLPuwlsDlL9davvYU4bkW4Hsb9zXrYvt4TVe3JZlZ5HswQPnRtH0Sg6lN3ZxbXaOm0ayXEeYqLPzLngufxcHg/W53Fu8IcQMGrSsGFDoqKi2LVr1zEJGFlZBaclPCycHBwTLlWTtg5PwruDK6p4VLD9F4zRn6BpOmLlx2oi1nkc7FsevNOG3ZVIMGls9e15e+DLW+Gqz+DT0VBzzbftaLXKVZylhI6hT6l0j5xdKiTQ5oQhTyKFDVGcXem+XhNpQmkBS4uhVYyd8MPcv+GMICdTlYWLc9tp3yCCVXtqVz15eGgaLtMkMzN4Cbm6KBEad3+2gpVV+nzyuw08e1lb+qVEc3G7JL5YvpcdmdXNSns3jaFZbOgxn+9sRwg1AJzI5yE29vADyul49o50n+F71yFCkyjOqz2hOZH8tD1As0gwvaUEscs9NoQSL4qKvEELFx0dLlzhDZFbfqEottsxH90xycPUlXv5fOFOLigzIPw9nIzv4+nkVN/PmfTsCQExoQby2/uUaXX7MbXHKFBmmkntVTTGjllKUF8/FdqNUSUfa5JyvjLSjGyoVparkrlFRf5d+iZ8dpV6mQqvp0qt7l8Fy8cro8MLnoUe90DONjUW7/oNNDuy3WjEh8PrvqndCyGhDUarUZT6AhzuFcfQQyrGOoAGEU6ax3vYnFFYq+1fh7XAHjCOa/zRNMG2vFKue38JJf7KlMz4MCeTbu3OuJ6N+XD+rmrHRIbaeWhIc0oLvXgLqkeGnGvPYFVO5r2diGfvWK8v2tiDlr5YLXCt+1Jt1GzKfDahtYo8KhcZmg4BoxSmXFe9ykjBfiUwXj1JmbOXFqg+QqOV98uVnykftZ2/qb40XaUm71lS53XJPUsRTQfWud+MTqGgKIDfX0qYXdC/eRyzNh+q1e7ugU0JFfK452WaJsj0mVz97iIyCyvn2W3qhfPsqLZc//4SfDWqlTwxsjUueexzz5PNufxcHg9VPw9LxDj7+UMKGAcOHCA3N5e4uLhjOk7WqBR1KhAC9PxdSljoebfykqgqXmi6ql2PQGRuoTi+K6ExTRGHNqnVqWaDYcvM6p22KzP7LK8DXhPDp3wtUgeCXuUrUq+jir7Y+K1aESvJViq5w6NWqZq8DroTKU1K3fVx5m4+bMi7DKvHf7/P5Y6u0fRr3Buxa17tRtEp+J2xFZ+7E8mrV3Xkfz9vYdqqffgNSVyYk4eHptGzSdRR17gvR9M1Pl64u5p4Aerf+c9frmHGvX2ItmmMH9eFuVuy+GLFXpw2jWu6N6J9vXCcUh7/e+BZzql8Hk7Hs3ekc+vZm/AndTmp//7egGRJBoxoIk7IeSoiv+Xv0C+AQFxbnNtnqFBi5+Hkx9okhDlpGutm2poDDG3x+wWMck7nd+RkcKbczym/jqJMlS5y0X9h1rPB26QvVOPhzH+oleCwJJXL3/1PSqzY/mv19q0uUeL63BeC9+fNUxGJ8a2UEXXjPkgzgPjyFrU/NFpFUiS1VWHw0akqlN4RVvbiFhO8GhdAaAxSs5PrSWXV/mIGxLdSEZI1qd8Jnz2q2gK1S0jeva4zz/24melrD2CYkqQIF38d1oL2SWHHPN6VU2DArROWVRMvADIKSnlg8ipeubI9A1sk8MH8HWQX+Tk/LY6R7esRdgSzwzPlO3syOB33diznPJq2DhuIJeNV5Ny8F9VGIZTHS1iiiqa44BlVGlVKSBumjNqDdWwGYO0XKl3Y5oSwejBmgno20xerOeLgJ6AkD8ISkEYA4YpUnhbBrj+iAQFnFEFje4RAth+Lr8z/zC4lT45szVtztzN56R5KAyZRoXbuHdiMi9vXx/CWHve/VYkJ909eVU28AGUQOn7+Tr69uzefLNrFyvRcGseEclOvZBLcdjDP3LngufxcHg/WZ3FucE4IGEVFRezeXZlysGfPHjZs2EBERAQRERG8+uqrDB06lNjYWNLT03nuuedo3LgxffrUUdXjDELTBOTsUD/U66T8Kco572ZVjmrPUpAmwuFCx4tM6ohwuFXI38C/q9Db7bNV6G3rS5ViLg1V8q0uDm1Ste29eeq8bUZBSj/4YBgktlVeGpOuVQ7uoPIVneFw1UTEsvHYzn9cHZvYVk0saxJej1xbLP/tl0ns5rcQA/4K3/2fMnsqJ6IhxpiJFInIit84UoJLmjwyqBl39U/FZ0hCbBoeXVRzYj9aigzJJ4t2B90nJfy04SBXdahHSMBkeItYBjSPRQNsyCO6Vlucu4jSfPTCfXjDG53U8yzNkJQa0CbmpJ7mmAnEtsa5YwaO9Dn4ml50zMf3TI5iwpI97MvzUi/id6SzWJx7lOarv0NjlHdTVdyx6qVIaGpMa3cF2EORodGIhDYqumLwP6HTdWUREjY1jpXkqfTIzMONeRvVi1xSewBESa4qE56brlanZ/xVRWuUM/9lGPKUMuDsdgdixl+C99tmFKK0kKilL9G3660qLfPLWyvHdYCYpgQueY8i6aGqtCglhCB57II07h/UDH/ZeBdmE8ctXgAcLCglp0YFh3JWpOdS7DNpGR3Csxe3JiAlTk1gBMyjqTpucQajSx+i8CBklX2P214O7a5U5Yh3L1QRTP4iuPpzSF+kzNqrzslqcmgjNBsCdjdcPRE+uUIZuZcz5zm45E2IaoLY+ZOaiy58rXY/QiCbD0PP3QmdrldRTxUX7cAY+QZF9gSo8v1zmib39E3hxp5N8AVMXHadcJsgyuMg0xvc4PNoKPAZrNuXH3TfD+sOclufFO7pm4zPlNg1AYaJtOaCFhannHNCwFi7di3XXXddxc9PP61MIy+99FL+8Y9/sHnzZqZOnUpBQQHx8fH06tWLe++9F4fDUVeXZwymKSGmmfohUKKiHbx5qp62r0gNGGWIOc/h7HITvp4PYr9qCtpnV8CPj4ErAup3QTYdBCW5iCa9VeREVBOVoxuMqCbQbCjsmA0p/ZUZkzQgsjH0f0RNFAM1DMZK8+Gbu2HMp+izn0LsnAOjJ6iwwZIcWP+1WnlyRWKM/gQ9fSH1frhLHbvpazj/MXDHIX2FmJFNCHgaUCiigwsFhqmqjdgEII+6qkhNJNQqyVqVzEIfoszxJxAwKx4Yax73x0bPVi9CxkkWMObul8S6oN4Zllor7aEEolvg2PkTvtQLj9kVq3PDSKas3MfXaw9wR68mJ+ciLc5OXBHq+yQ09XdF+F2YEhK+f1gZZ5bjcCOv+Qqu+AgxYST88BdVyrTZEGXImbNDrQjX6wiRTaoLB1WJTob6XWHB67BmsvLG6Hor5O1V1Rmqihfl/PhXxG1z1ep1q0tUNa+wJCW8rJ6ixsp9K2Dm42hSou2eB/0fhT7/p667KBNvbBtKPQ0IiGhkHUuDmmESVmW8CxxlVZFgCAEF3uDiRTmlARNpE2BIbBC0dKTF2YchnJjRTdEcYdDxGjW3/PSK6kvSzQZDmyvAFa7M3mNSVFpVMKKSoflwyNsFP71RXbwA1e/Xd8K1X8L3D8HYz9Vz5i+BQxtg3VcQKEWOeBn9txdVREe322DsFMjajoxogBHbiiItCp8Z5HWlfB5o1/g988Cq1FX1p5xCXwAzYMcGlnBhYXEaOScEjG7durFpU90rK++9994pvJoTi5QQCGuIHp0C66ep9I81n6u0kS9uqtVeLH0Pe+ogCpN6E/6nhRj71yEKDyAT2yFDY7B9OkpN4FpeqiZR5Y7t1TrRoMuNMO0eVWKuHHuIMu50RdYua1dO1jakvxixf6UybVr2oRJBnGGqz3odwRaCiSD8pwcrjyvOVhEj9hBodgGlA5+jyHSd9Fgvu4BOjSJZvjs36P5+zeOOK7LD4tzGlrUJKTRMz8kroSqlZM5eSdsYKkS0Mwl/UhdC1oxHz1iNkdD+mI512jS6Nori6zX7uaV7I2z6YWreWfyxcMchW1yMSF+k0kG2/aK2d7pOpVBWFS8AfEVon16B/+a5yGu/R8/bicjaAg3PQ/z0N0T6YmjcA3bPV+VPp91d+5z2EFX29Kd/wJYf1bbMLSoio1EPeG9I3de78VsVfdhhLCx4VVU5iWupfKT8Xlj4auU4lrlFrXD/+iSUFiBCosnv9wy5oW2I1k7Ny5CU0Ci67upB4SE2wk5ABQeLMw9fQCLbXA65O5Q57qejazfaMhOZMgCaDkZ8MEyJhpt+qN1OCGUCOvMxJfRt/Sn4Sc2AisK98EW1YLZ6IhQeUhV4rpuGdEUgds1X4gXAordU2kpYPWjcg9Lzu+EzTt2rSkSIHbdDp8hn1NqnCagXeewm8RYWFicea9Z4mgjR/UTJg0R7NxNt7MWjFdW5iFlIJIGrPleiQeoAOO8WWPt5nX1rC17CZpZCZCNy4/uQmzqabFdzAtKm/C1MA9Z9rmrYD/k32KqEcLsi4IrxqsJJ3wfh8g+gYZlRn6ZD/Y6V9b7rQBg+GPpvmDJOhQLm7FQD2PQH4dd/g+FD27MEPEHy3/0liPVf4TBqm3SeDJwC/jq8JVqQz75FQhgpMaFWqohFLWzZGzA99VRa1kliWz7sL4a2MWeeeAFgRDTB8NTDteXr4zq+b2o0WUV+Zm/LOsFXZnEm4tBNIskk2ruZKP9OwkSeSpGsiSucwOB/IwszocddylAToHHPyopYNfHmoeVuJ59IciI6kNt0DMWu+ohAqcq53/S9SlFEQK97VWpJOe44VXkkfx90vUX5aGhlL/ChMZC9s3a0YVXc8So68dMrlFifuxu2zIAPh0PBPmh2QfX2e5cpQ0NfEeSlk7DufeJdtV+WTiYeu8YVnRsE3ffg4OaEWYLiOUF5OeIoY6967uRBfI4YjOjUSmEw2HGL3lTPSEk2bPpOGdjaq4QBOsPg0rch4FOlVh2e4NVFyolKUZFI39yrUp5zd6k57IcXIooy1byzKlJC/l7E2i9wBE6tMaZHF9w3qFnQfWO7NsJtPRsWFmcE50QExtlGhJ6Pbd5zaCs/UmIC4GrYHfvIN8kVcbXCSE1TkisScF/2MY5AHnpMM/h6Vt0nKMpEmMqASMpKr4ZSPRxHy5FoC15R7Ra8otzcL39PDUSeBGXS+dO/YGdZ+SpXJFzwlJrINRsK66aq1SzNVrtsnBAQ31pNOGc8qlJdarLtF+h1LyK8PtTvokIOzSCTN2nWKoASDE0ThFCEJn0ERAhe6TymoA3TlDQMczD51u488d0GVu3Jw2XXGN2lIbf0TiZEmmesMZPF6cN2aN1JTx+ZvVfi1KFF5Ek9ze/C36A3ro2T0TM3YMS2PKZj60eG0CzOzeQV+xjY/NgMlS3OLtxaMa7NX6H98q8Ks0tbdAr2UR+QF5KKUWMIyDMjCen/BE6zAP3aqbB/NcLuPmxEnijOQkQJTFNiGCY+3YnZ7kq0HbMrG027SxlOX/kJINT4VrBfpZ0cXKvGsFaXwiVvwPxXVRnWg2uUiJ++qPZJIxsrj423+9feJ6WqiHLFh9W3h8bAvpWVP5t+HJjUncgITpuJwyhAouHVI36X/wWAzTS5f0BTmsZ5eHvudrKKfDSKDuWhIc3p3CAC04o6POsRAiLJQv/6DsTu+WqjpqO1vxpz4BPIgv11T7GKlagsG3RFLPsQmg6Cy94Gh1vNzXzFsOQdVebUHavEjLajVepVtYvQIDpFpSRX9bUox/DBjL/AiJeVR03enur7peRoE3YdNhOnkQ+FxdhsbvyHKbV6OKRhcmGrBGI9Tl6YuZk9OSXEeZzc3i+FYa0S0C0jGAuLMwJLwDjFOHUD26JX0ZZXrwAi0hdim3wVntFfUBCkqKiKzpBIuxtZtB/RepTK6w2CTO6L3VZ7aPIFBGbnm9BWT6ys571puvrTpJ9amapZDs6bq0Jur50KnkRY8REUHlAhg1UNRTtdDy0vUiGCO+aqcN+QSFj+Ue0LXPc1WufrlLP71ZNhzvOqFGs5CW3w28KOOG65tWKcWWvQ5z4LubuRCW0I6fcXikOb4DWdhz+4CpopSYlw8sENXckrKkUXglBdIA1LvLAIgjTRs9ZT2uySk3qa2XtNWkWDXT8zIzAAArEtMDxJhKz7mMK+Tx6zF0a/1BjeXbibLYcKaRbnOUlXaXE60XUN5/5FaD/8ufqO7O3oH40g/JY55BBb6zivDEETBlL40Oqfh16SpcaM4uyg5xGxqUQWb8F0RVKsR+EzwGjUGy22efUc/pWfqujDcd/ChyPUeFaOlKq0pDtWmQ/Of1lVNBnxP2VaXS7atxkF7a9U0Rm75sPo8arPRW9Wv6jiLPBWMQQUmhJD5j5fsclsP5ZS6SLYgKfrEGFkIOa9irblB3B4cHa9nUDqUPLMiFrtjwW7aXJFu0SGtU7AkBKbELiP0wzb4szDI/LRp1yLOLC6cqNpoK2YAAltVKRR60vVQtPqSep7XE7Drph6CNrQf8OHF6r0kLx0Ne/7/uHqi05FmTDxKrj5Z1g7pVJk7HY7NB0I2TuUD9rYKeo8a2pED+9fBb5C6PswmH744RHl0QbI+l0I6IefC+q6INw8hLbwbbSN00B3EN75JowWI8mTkceVhWyXkv7JUXS5sSsBKdGFwGNTRrYWFhZnBpaAcYoJMXLQlr0ffGfGBuwl+5V5UhVCtRJcmSvRf/mHMsEMb6DEhhEvwbf3VV+VsociWo7ANn44XPoWNk8L/EZlyFvAGYN+w/eIlZ+A7lSO66ExamL4zb3Br8s0VF5vVLLKCV41EQb9E4Y9C4vfUYOg4VP5lOXXIgT0uBv6PFhtsqau0QkrPoUl78KKj2HUu2qlK3s76HaM4S9STBiHK/To0ny41n+K9vM/KraJggPo237GfcXHBBL7EzgGQyfTlMS6HZglpUgpkSfADMri3ETL24XmL8aITD5p58jyStZkwfUtzlzxQiHwJQ8hZM147Omz8Tfqf0xHd2wQQVSInUnL9/HY0OYn5xItTiuhMg991pPBd5bmo+2cjZ5yRbUXZ5fwEpK3Af2nv6kKCZ4EZPc7EVdNgg8uqB21lzoAsfUn9F+eRLeHEtbvL/hbjwYpkFdNhDWTEasmKUPqNqNU2kjW1uriRVVWfqJMOV3hamxa/K6K2lj4hjI+DEuET8dUD5vvPE5VSJn5eI3OysYSTVeiyPIJlVW14lsRaNirTtEgwr8P/f1BlZVZAG36A9ib9CVsxJsUmMdWwrgmhmESAmXRjifGBNHizMBeklFdvCin/ZVonngVdbR/hUqB6ny9WoT6+k5AIvs+jARE3h64bTbsWwVxaTD19uARs4FS2DITrv8WfnkC0i5UXmtVTOYRGvT/i6qet+Td6seX5MA396hIjyH/ViKJzYk57DmKcXO4uWC4mYHtg8HVfNm0mY8i1k4hfNTH5BF5LB9b5S0FTFxQ+Wz8DuNcCwuLE4+VzHWKEf6iw+fT5u6ulhds1yFk90/on10BB9epiU9euvKT2L8Ghj5VuerZqIcKV/3132rw+GgEYQEVaaFpgiiRhfOXvyJ2L1KpI3sWw6SrYfxFqmxqXe7soPaHJSkTUSHgp7/DsvFKxW/YHX57qbqQIqVavYpvqQSSqqQMgJUfq/8PeJVxWp8HkG0ux7hlDgWe5kf0nQgxc9F+DTIplhJt+v24zeCrdBYWvxdb5jpAeUCcLGbvlQig3RlWPjUYRkQTAnFtCVnzoZqIHgO6JuibGsP0DQfJKfadnAu0OK1oMlB3FQNA7F2KXiPKKDRjEfpHI1TevJRQcAAx83Hkojfhmi9VuDmo3PvzblEvX7P/o7b5i9F+ehzH9h+xf3UTYuEbiJjmMPojNXZ9egVMuR4yNtZ90b4iKM5UlUw0m/LemHqHSntsc5l6SauZ87/sQ+UNENWkcltEA3BGwKB/wFUT1Wq3OwZiUjEHP0lgzGTyZWTQS3DpPrQ5z1QTLyo+s51zsOduOyPNfS1OP0KI4BXmElpDkz7w+Q2wb7l6tgoPqmdn/VS44Bm4/EPEvBfQszaqlODCQ0rQy96moinq4uA62DwDWoyAyIawtMZCnTTV3DSlv0pFKadxz8qUqq0/QXh9ZNdb1VwwJOWwc0GHzURb/GZQU3mxfyX6wZXBfXYsLCzOeiwB4xQj7aGHN/4Lr1/tF3aomY3241+Dt132HjTuDWO/UOVKm/RWOb57lqr9gVLE2knYbRrhMgvbxxcjtv0MiW3ho4uVH4WUKnpi1zyITav7uuLS4MBqNWG87D1lupSxXv1Z9Hrdx62eqFayyml/FexdCv7iym1ZW5GJHSgc8BzZekP8wcplVUEIELm7antwlFOYgVaae9g+LCyOF/uh1ZiuGKTz94VwH45f9pg0jYQwx9kx+SpNuQAQhK5445grB/VJjUYAn6/af1KuzeL0YqKp6L06kAntqr+k5O+vnW5Shlj7BTI0Djn2c7h6Clw3VUVRfHGjGseqtp33ghLcmw4EGYAvb1YvVf4S9dJWLoIEwxWhBPvFb8Mlr1emruTsgKWHqWq2YgK0uVz9v+5Qq8nLPoBVn6nV6F+exOxxN96rviG/1Q3kmnWHuDvNIsSm7+o8lbZ2Cna7NYWzqI2UstL8tirn3Vwp9NVk0/eQ2AZ+/gdsmYn49UnAhE8uV9XqcnereWBdxLdSlXxsTlWNpy7WfQVpw9T/uyKh9wOw/MPKa1/1GYHeD5Ot1cdvHr4ajtPIR9tQt4m0vvoT7LqV9mFhcS5ijX6nGK8ehdn+6uA7o1MIuKsPOlppXt2rmlKqtIvpD8Hka2HOc1CYUf34fcuwawbangXK+bnfwzD/FTWJq8rqSXBe7bKsgJqINR8Kv/1PnSc0Gi76nxJNOl4HBQfrvuGCg0r1b3GhMoGKbQ5zggyggRL8OOrup8Ztox+hrWZ9tS1ODraDKwlEpZy0/ov8ksUHof0ZWn0kGNIeirfZSOwHV+DYMu2YjvU4bXRvEs2UFfsotXKMzzmKtSjMvo8E32kPxUwdVN2U0ptX28yvKvtXIb66FRa9ASs+UemNwcLac3ZCdKoS9P1eFUVYTkmOEsAjGwc/R5cb4eB6VaFr0Vsw7D8w5mPofX+tMbYahRlqjOtykyqjuuwD5alRfm4zANKkQIbjP5qQ9MOMc7JqVQgLixoEQpMgtkY1jdAYNQ+si6o+GIc2q2eoXBhcPkH5WgTDGabEjwkjVYTHkZ6RpI4qDXrUu8rotkoEhTAD6KVHW4VOHP4Z0Z0clRu8hYXFWYf1lneK8Ro2jN5/RlaNSgCIb4lx1RQKRVT17Ucq02h3BVfayzDjWyM0Hb1cpY5KgZ2zazcszlYK/IiXIaTKNUQ0gMveKUsRMdXgc2CNEku+fwgCJdCgS93XV78LaDbk0Gfgx7/Bz/+svUIb0QBRsB+nLA7eR7D7Cm9QPQyxKjGpBOyRR92XhcVRI01sGasxIlNP2inm7pP4TOh0lhXmMKKa4mvQi5D1n6IfWntMxw5qHktuiZ/v1h9GDLU4KzEMk9KG/TD7/rn6eOZJwLhmKgV6DQPPI4nTjlBVyjt/H0QfxocmvKzMcXI/2L2w9v5fnlAiQ71O1c993s0Q2UhVR2h7uSp7+sXNqrKW7qzeviYNuyLrdVDVtT4dDdtnVd+fNgyyd6IfRSnGEj2i7sUOQLYdg893asuvWpw9FBKBMWaSWkAqR9MPb7TsDKtc3IpqAoeqpFnl7FA/D35StSsnOkXNEX99Sh2bvkjN++qiUQ8VrbF3uYruyFhffX/zobDuCxyOw0dfAHi1SMyO19e53+x8E76AJWBYWJyLWCaep4FcI5zQgc/j7P9oWQ3scPyOKIqIqJXv57dHYktqr5yaa+KKUCs6Ha9RIX410WzQ/mpsvmxki4sQzQap8D5XRHCFfOUnENUYbpyhBitXBNhDoCQX2l6hKpdkbFATuvZjILGdygu++BUVwVEzqsMeCq1GwCdXIO9cguh8Pcx+tnobocGAx2HdV4j4rkf9GRZpUYRf8hbalOuq5yLbQzBGvkWxFgmWIZnFCUbP3YHmL8SIanrSzvFTukmTMIgNOfsmXr7GA9AL9+Ne/F8Kzn8WGVq7ukQw4sOcdGgQwcdL0hnZJhHdyls+pyg03QQ63E5I2ysRRQfB5sJwxVAoomsbR4ZGI5P7I3bMqt2RzaXEhcKMymgHh0dVMahJn/9T5oPZW1RFrJr4ilTefquLYcgT6sXOEQZGqQpzr2q+mXaRilDM2qLKpi77oHaZcN2O7Hor0haK8BXVjgoJiVIlXA+sQ8T1OOJn5gtomN3uRNvyYy1/KrPLTZSG1jvaCpMWf0BMU5IjEvCM/hy7L1tVwwlPQms6CLFlZu0DdAdENFTCIEC/P6sSw574yvni/Ffg4pfh8g9VlKszDDS7qlaX1F6VHF72oWqz8ZuKaiIVuCKgUTfAVM9STep1ApsTkbOLo4mc8BsSo80YxLovERnrqu2TrUfhj2yKPIKfmoWFxdmJFYFxmig2XeSIJLI9bcm2NabADA9qVlRMGMbIN1XaRlV0B1z0X5j7olKw+/9FiRPlhETB6I/Qlr6D4/3zYcsM5XGxZoryoaiLxr2VIFCcDZOvg7f6wUcjVd5kvz8rA6bIhio0d/L1KoVl7otwzVeQ1KGyn3od4fL3lCofXo8ADswG3VQaSZM+St1vcaFaAdswDTOhPT4trI6Lqo3f0ChM6IVx22+YXW+HlPMx+z5C4NZ55HvSLDd1i5OC7eAKAIzIk5NCUhKQzNsPHePO0hd4oeFNG4XUNNyLnlcvg0fJ0BZxpOd6mbW1tiGbxdmP17CTQxzZ7jZkO5uSJ6Mwgr1chERiDntBeVBURdORo95HlORWbpv9LFz2lqoKUqWdiqJoAnHNlTFgSr/a5/EXgy1EjUVL34fxI+CtPvDeYMjaBpe/ryqO3PCDMt6ceJUKv1/yrgp9b1hFcI9vBaPex5SCUumE7reriiSJ7dQK9Xk3w6j3YPpDyMZ9qqfMHIZcGYP/mmmYF78BqQORrS/FuP57Sns8RJFZRwSihUUZUkoKzDCybY3J9rQlR8ZiDHkGwutXb6jpcOELqqqc7oDzH1Nzuw3fwLDnoPMNlW0PblBRUAtehXcHwdv94ONRqgTqJW8qsXDPcjUnjG9VeVzDripSY+bfYfrDyGu/hu53QEyqajfgMeh5N0x/GLPNKAKBo4suypOR+MdMwrjsA1XFpOUIjGu+pnTAkxSYRz+ntLCwOLsQUh5PleQ/JpmZBcdVU/r3ousaYeYhtPQFaOnzkbEtESn9EDMegR1zVKOWI5QwYfjVZC7gVSGy5YaeoMJpL/9ALSwtfbd2eGvv+yF1EEhD5TLWvFndDldNAtNUzuhL3oHdC9S+y99XecXueLWSdWiTmhTm78UY9SH5DYYQZmZi+/w6tYLljlMD5JrPwRlGYNwMcswaIs1R4rALNOnHwH50ecVBEAJiY8NO27/x2cTJ+Kzi4g4/0Tgd/y7B7tPz659x7JlLwYDnD3/wcfLjbpOH55v8u7sg/iREYAjA7XFRVOg9TGG6349euJ+QNR/gq9+Lkk5/OnzYchX+N2s7ppR8fG2no66wcK49u6f6fs6kZ6/83rOzCwkzs9D2LUXbORsZlYJMG462ZQYiJBLmvwSZZSu4cWnQ8x714qTZVeTf6knKUDOiIQx/Djb/oFJKfn2q+glv+kl5aaz9ovbFJPeDHneBNweWfwQ756mIj173qlTIzuOUQCFNZXC4eQa+EW9QQBSRRevRv39QpYzYQlRY/ZYZ0GwY3gteoFQLxzBkneVTa6LrGnbhRwoNX0Cccd/zc+0ZrMrJvLcT8ewd6/VpmiCSTLSdsxHpC5VQ2PJiFZFUeADcCcqDZuptlQcNexY2fKsifYc9q56X9MW1O+94DXQYWxbdtF5FS5WnrRxYoyKXCjMwe94DnW9Em/mYSkE2fLDpB9i3HFn/PHyXfUi+eWwm2bqu4dD8eDyhZOX5jljJ7o/AufxcHg9VP4/YWEvcOtuxUkjOAgzDJJcYQpIvwpkyCClNDClwdRiLyFivDJA2fKMmUQP+DgGfipqo1ZFfeVe0GQXNBqsJ2M55Kiy3zeVqImYG4LfXglcSMPxqIhjREGY9raIpygWM6Q/CpW+rAWrzDyp8NiwR85K38NbrRSBgkqfFEDFqPNrSd9DmvwxSUtruGko730oesYRq8rgGHZ9for7K1m9oi5OLff9iAlHNjtzwOJmxW6WPnAzx4lRieJLwpl6Ea/NXGFGp+FIuOKrjLmgZx/9m72D+zhx6JR+foGlx9mOaknwRg7thX/TGfTHQMQxwb/pOeR+N/gh++IsS4Q9tgu//DF1vBXcszKhStSsvXYn4mZvVmHT1ZJV77ytQ5b8d7rJ0kSDsmK1WhKfdowT6nfNUP/tWQL9HYO7zKtxeaMjmwzAueoV8GaVWvd3NCRvxKtqspxC750NINOYFz1HabDhfbS5l1Z6ttKsfQc+UGMJ0jhjmbhgmBkf2BLCwOBKmKckmhuhmQ9ECpYj1X8G8F0Ho0PoylVL17T3VD/r1abjyE9jcSflbfB+8ShCrJ6mSxltmQP5+Fa277RdVjc5fAs5wzP6P4m97DcV4CD//b4h5z6FtmQkON6X9/4ZsO4ZCIjnW+ZxhmHhNHY/NgZRHH/lnYWFxdmIJGGcBmiaIkBlov72Otv4r0GyY7a9Bdh6HGP6CmrQFSlWZ0/kvqbradbF/FfR5UFUTcUWovEUpMTtehyzMQAuLR1Q1bgp2vKaDvxjpK0KEJak8yeJs+OUJzMvHYw58AgI+DLubYi2GQNkqk2lKcrVYsts9wJ6Y0ZhS8vXmUr5/ZRNu5zYm3tKNeKduKecWZyTCm4stZwulyYNPSv+FfsncfTCiydktXpQTiG+Hv3A/Ias/xAhvhBHb6ojHpMV7SIkJ5d0Fu+jZJOqoozAszi3cWhHOg8vR5jwDObuwxbdEnv8YZpdb0EIj4aNLof2V0O02sLtVqtLSD2De9NqdbflRGQPOeQ5WfYq8+DXMxr3R/EVQkoOQh4mCKMlRwn7BfhV2n78X1nyOecUEjOt/QPiKkbqDUlsEXtNJeUCr39TJcSTjGvoqdlkMQmenP4zL/jufojLjzUlL0nE7dD69uRsNPXYr7dHilKBrggjzAGLWKwibHS54Vgl9EQ3UM/R57ZLEeHPLoirWqpSPujD8StRbNQkufF6VLm43BnnD95iaA1N3U2yLxhcQCCHYJpP42vEnzht4N8V+k4/XlrBk5mrev74LLWNCLf8KCwuLOrEEjLOACHkI2wdDlIlmGdq852H9l8hL31amaBPHqh2xzap7YdREiMpwbm8e7JiDbDuGIlscvoj6eGxeHJGNEdnbgx8f1biibKoM+DDGzUAUHgB7KH5ntDIiRVYGRNQIkS0yYcy7y8gprm7ulFfi575Jq3j/2k5HWUzVwuLUYt+vQmaNmBYnpf9f96jqI10TTkr3p4XS5MFoxRm4Fz1PQb+nkZ7D35wQgmGt4nlt7k6W7M6la+Oow7a3OAfxFeFc8zHar09UbBK7FyDGX4i8dipsnqFC3X/7H/wG9LhTRUVULQFZFZuj0kzQNDAT2pIdiETTo4h2HeEFyRUOhg8pwbxyMhilGCGxFIso/KYAW1mUUJB0fSmhRDopwUmJFNzwwaIK8aKcIp/Bnz5dwcSbu+I6qg/HwuL3EWEeQH9voBIlIhoq/4nvHoArPlIRtHVh+lVUUrfb6m4jNJVqXJqPTGyPOW4GZmgcRVoUfqPMci+g/vIBf/5iDSvSc2t1c8cnK/j2rl5YLi8WFhZ1YZl4nuE4bBJtxYfVxIsKsrdDzm5kcU7ltqytqmxWXSuXyf2rl5QLjcHs+zBew45pSgr8Luj7YPBjhVDpJ5vUKpdMbE+OGU12aCuy7U3qNCKtyqFCXy3xwq4LokLtbDtUSL5VFs7iDMW+dwFmSCxm6MlRGL7bZdI8EqJd51DUgdDwtrgcqTtwL3hKeeccgTaJYTSJDuGt+buwLJr+gBQdQpv9dNBdYudc2Le8+sbNP0DNsuRVaX0ZbP4eALPLzXgdqj6xaUq8jlhk0zoiqhLbVfhsmPU6k21vTLarOXkyWokXx0CeN8C+PG/FzxEhdkLsKiVkb24JuSWBY+rPwuJ4cOoGYv7LSrwAFU1xYK1S2wyfMn8PRkRDKMoC3YGMaqK8YIKRNhx2zEbW74JXjyQ7pIUyojVqv2oU+s1q4oXTphEZakcTUFgaYE9uydFaJ1lYWPwBsQSMMxynUYC2YVqd+8XaSWpAKUdKVWmk/19rN3bHIgf/S03KEtti9nuEwI0/k6cnVjlcUhzWHDn8+eqRHA4PDH8BVk+GgBeZOhCfK/6Y76fEXylQxHmcvDGyPrPGRvH9BUXMujaGRFuBNWhZnJHY9/xGILbVURtSHgsZJZLFB6Bbwrn35Ze2ELytxqL58vH89q8jihhCCC5sncDqffks3pV7ai7S4swhN115MQUjbw8yslH1bVnbVFWEJn1qNZeNe0FkI6QnEeOqzynt8SDFZmjF/sKAC2PYi8hGNdIuE9vCoL/DvBeRyf3xhSTye/AZJkLAn7rH8+sNDflheAm/XG7jq7GNOK9RGP6jNPO0sPg9OI18tM3fVW4ozqqs4LPoDbjgGdBqBGbbXDDsWcyiLIxb5pBnq4c55jOIqxGJmNwXOo6F5R9hDv43xXgOey2+su98apybz8Y05NcrQ/lhWDGzxtXjsYGJFJUGOJpSqhYWFn9MrBSSMxyJpgaQuvZLMMMboVWt1b16MnS5Ca78FLnxO0ThQcymQzCbDqVAj8Mx7DWEGcCne/AHgBq5t8VmCIHUy3HfPgitIB1hmgijFBa8BvtWYPZ+AKPTzRQahx+gglEv0oUmICrUweQxiST/eBNU8dyQSR0wLh9PLjGWa7LFGYMoycKWtYGSRneclP6/2ynRNegSd1K6P+2YIVGUtLmWkLUTCJv1CMXn3YcRXWMVT0q0/D3YMtdyXn46TZ2teGPGAnpc2ggzvs3puXCLU8/hUiA3TYexXyqRvirf/R9c8Ayy+x2w8lOENDE6jsOMb43f1DFH9KBUuoJGCObKKNwj38fpz0bkpSM0XZl1fns/ZofrMLrcelxjXVWiQ+28enFD+h+agHviOxUm2UnOcN648D0CYRpYGobFSUaKGvPJA2tgwOOqdOqepSoC4+pJKqopezsyoR2yw1h8tjBKE3rjC2gQgGyRSNiVX+IozYCc3Qh7COxfAfP+h3H1lxSFNTtiNG6Y00a3JlG8dL6NxG8uh8KDFfuua3YhRqfnKLQ8MCwsLOrAEjDOcLx6OM4ut6BNvz/ofvO8WynQYgi/+kv0Ty+rFDGWvgeHNmGOep9iw0UAm6o9HzAJlGfbBlnksuvgNPORUidfi8fwxKLrGqFmDvrw/yKFjRI9qsKEKUQrxmEUgRB4tXC8hr2iLyHAbtMQGARMHcMw8eiCG3sl0zrSR/LPt1cTLwDE/pXo39xJ6Ij3KZKhNS/PwuK04Eifg0DiT2h/wvuWUjJth0mHWEGo/dxdcTJD4ylufzOujVPwzH4Uf0JHArGtQdPRC/Zgy1iNVnwINBtGaByjnbk8lX8BSyY+xvlNYyno/zQy9BxVeCwqCUtSBtPevNr7PPEEPEnow19E++HhykgNw49ZmEFp6gj8F6hIDL+hYZaL8wbUrGogBLg0H7rpJSBCKLI1QotrQqiZix6Zgmx2UcVYV45dF4Sa2QgZwBAOSvQoNa6W77draNLAFDb8VaINI5w6A/QVhCx/u/r9lOYT+/XVBG5bSI44h8xvLM5IvHokjs43ov3yr8qNC16FS96Ab+6FLTNV1ZCU85HNLiDQ8lJyS1215ooOLYBpQrGrAWa9FFxGLkSlEmhzHT5bBC5/NiHkYQobJXo0/oBECIHDBiDxG4IwTfDGxQlETxiozHKr9r/lO8zYFHxdH1KiiYWFhUUNLAHjDCcQMAmkDsHeqAeivGRpGbLVpfhjWhEISPKcyXjG/Yyev0u5QMc2R49sQG6pC9OEYMs7uq7hNrPRfXngDEPDgFWT0NZPBXsozvNuxWjUhzwjggLKanJLIAA2TRJeuhNt5qOIHbNBs2FrNZLQ/o+RK+JxUYSrKB1t+fuI4kyMlpdgNuxFPjHc2KMx4UXb4Kc1Qe9Z7JyHw59DkW4JGBZnBo7dswlENEG6Tryp5Kos2JEPlySf8K7POKQzgpJ2N2LPWI0tYyUhGz8HDAxXNEZEE0qTh2BENAHNRjLQeqXJMyV3cP7eh4iaPIy8EZ9gxKSd5ruwOKmEJWGOeh/tszHVU0kcboxL3iFfRmFPvYTQO85HZKwDaSDj21CiR+INVI3eqC1YhGhenEYeQgglls37LyJ7GzKhLaE976PI1YACGQ6EV4x15YTrBdjXf442/39QnIU9sjGO8x/D26Avfpx4AhmIxZ+gHdqA2aArZqvLKNDjCBiCUH82rgX/DX6/hh9t07fY2txSTQyxsDjR+AMSo/VoxPqpiAOr1cYdcyBQihw7BVlaiCzJg4Q2lNqjKSpVi11qrpiDjg/d8ML8NxDpC5CeJMye9+GLaU2hjMZDEWE7vkGb9STk7gZ3LPaeDyBbXgQ5u9GWf4AIlGC0H4uR2BE9f3Mt8aIcbdn7hHa+GR8xp+rjsbCwOIuwBIyzgDwzgvCR72LLXI+28mPQHZidrscfkUqBGQaAYUryiEKERyEiOiKlJNYThvQWBO3TqflxZ61EP7AK4luATIDPxlRGcAD617ejJfcn/KLXyTfDqx0fHtiP/v4gCJQZk5kBxNov0HfNJ3LcD4iVn6DN/U9lX5tnoEc2JuKaaeTIGELMwsPes/AXYZW9tzgjMA0cu37B17B2jv2J4MttJnEh0OKPUnBDaPgTOuBP6HDEpqOaCp5YEsL45s9x48EniPx6NLmXfYURmXLyr9Pi9KDbKIjpgue2BYi1k9Ay1mM26IbZYgQFWjymKSnFQSlxiITzEUIZch4uBUMIiBTZ6PuWItwJcGgDTK80qxaZW9DXf4Vn9CcYCf0I1EirDNW92Oc+g7ZifOXG3F1oX92Ca9T7uJzhaBPHgKmiLrQtP6LNe5Hw66aR526JJk3I21339WVuQtM0rDwSi5NNnowk/PJP0TNWo6/5DGn3YHYah8/TmCK3ByFEtefJrhmEFW1G9xeqKiOfjIJAKVD23Oycg7PXA9DlDpwbvkD7sYr/WlEmmjCRP/0dsf6ris36pulobS6H+p3rvlBfkUpdtgIwLCwsgmD9ajhLyDcjyI3tScEFr1Mw6CVywjtWiBdVkZIj5h4KAe7SvejZWyBjLayepFJOqogXFW13zMKWvQlNqwyjdekBtN9erBQvqlKchVZ0sJp4UUHuLrTfXsSpG8iQw6jqQkM6Iw57DxYWpwrbgWVo3mz8Seed8L7zfZIZuyW9EgWa5V5bi4YeQc8kwasbQ9jX5TGkLYSIb65BeIOv2lmcG/hNnWwtifzO/0f+sLfJa3MrOcRTM0BBSnnE8Q7AIwrQszcjln4A3hyY+XjtRlKifXsPbjO71i5nIAdt5UdB+9Z0O9qXN1WIF5U3UYz+1a2EmnkYOCChbh8X2agnhmXkaXEKkFKJGLkJ/cgf+ib5A18gx9OaQtMTdP4YJjPRiw4qv4wZf6kQL6qi/fYiLn8W2qwa1YPcceCOqyZelCO2/ayqoNSFOxZTDzmue7SwsDj3sQSMswjTlJT6JKV+83eVF3RqfrRVnyglY83nkNK/ojRqMLRVH2O3VX5V7GYhYses4I0bdEFs/KbuvtZMxGXm4bNFIZsPD9pGtrsKr+0YlqN1jUIJh3wGhRKEbn2tLU4cjh0zMJ2RGFFNT3jfX2+XGCb0qXfCuz5nuDQZ/Aa8sslNUfc/I7w5hM+8G6T1wneu4/cblPohEPh95bXtgQLEjjmwez4YfvCXBG9YlInurS5gCAEidzdBXaU1G0gjuF8HQPZ2bL4civFgDPhH8DYhURiNe/8uAUPXNUoQZPpNcg1JQNMQliBqcRhMU1Lql/h8Zp2G6U6biZa/D6bdCZGNlIhRF+lLwFPDo6jpQNhQx3ywJAfhK4Lo4NF0Zt9HKNajj+JOFMKmUWAKNh8ooMAUCJs1D7SwOJexUkj+gNjNEkREA1j5mdogTRUaWBearVo2sURXbtUFB4K0tSMDpXUXvzJ8CKDIDMF+wXPYXOGItVPU6pVux+xwLYFeD1JiOI7qXnyaxttzdzBxSTqlAROXXePa7o0Z170xDtN6wbH4nUiJc8s3KvricM/IcWCYkolbTDrHC8Id1stGXUQ4BRcnw+StkouT4+nY+W7cC54mZMUblHS683RfnsUZjqYJhK8YVkxQG470HNd48ZcScNaOdlSd62D4Dt+dGcA0Jd7oNrgvewd+eESVrwRIbIdxyVsUiLjgAslRYAjB6gOF/G3aOvbmKmGmU6NInr60LXFO3YrssDhubEaJinYrzj5y+XDdVltU1uzBI3XLmfci5tVTEN/drwRGAIcHs8+DlDYdgT9wdM+ET9N4b95OPlm0m9KAidOmMbZbI27q2cSaB1pYnKNYAsYfEENzID3xiKJDasPWn6DlSJVGEgSzw7X4/ZWDQIkWgbP7XWjTgrw87F8FfR+ExW/X3gfI1IH4tFAwINeIIKT/07h6PwT+IqTDg1ePxmsc3dfS0ATP/riZaav2VWzz+k3embuDotIA9/VPRViTN4vfQ/pi9MJ9FHe49YR3/eteyd4iGNfihHd9zjGgASw6CH9bZDBpaHtszS7GvfA5/A36YCS0O92XZ3EGY5oSYXdVmgUKAQ4P+IL4MIUlEXBG1fT/xHAnortjoSiz+o5AKXgSQLeryI6ahEZjuFR/JTIEd5tR+Op1QyvNA92O3x5JMWFHlQYTDCEEewp93DxhaTX9Y/nuXK58ZyFf3dET93H1bGEBps2F9OarBamMDVC/E+xdXruhEMh6XRCuGqm/6QvhvJvVHDNY/417U2CLw3bRezgDuRAoQTojKNajq1X/ORyGpvHfX7bw+bK9FdtKAybv/7aTvBI/Dw9shmaJGBYW5xznRIzVkiVLuP322+nduzdpaWn89FP1X5ZSSl566SV69+5Nu3btGDduHDt37jw9F3sGUGKGIGNbQMOuasPmHyBtmAoRrIFscTH+iNRqKSuGYeJvcj6yxYha7c1ut2NEJCNTBtQ+sT0Ec+C/KDEr65CXGHZyRAI5jhRyiT9q8QKg0C/5ZvW+oPsmLd1DoeXobvF7WT0JMyQGI+bEqgxSSj7YYJIWCU3CreiLI6EJwfUtBOkF8OpqE2/LMRgRjQj76R4I1JEOYGFRhqmHVhoGLnkXhjxRe0VZs2GMfJMirXbYeqGIxhjzGThqyAGeBMzwRpj9Hw1+3gueo7hqf5pOPtHkOJLJ0RtQaHqOW7wA8At4/sdNQYM3cor9zNuaic0Kpbc4TrwBHZLKBOKl70O/P9d+BgBz4D8ptsVgXPIOeOIrd2RuAU8CMr5V7c5DYzDPuw2fX6PYDCFHS1LzQBlz1OIFQFHA5Mvle4Pu+2rFXmseaGFxjnJORGAUFxeTlpbGqFGjuOuuu2rtf+edd5gwYQLPPPMMDRo04KWXXuKmm25i+vTpOJ3OID2eu2iaQNMEJa5EQrvfidj4rVpFmnYXXPgiHFyn1HJ7KEbX2/DHtQtqFppvhOMZ/ByOXvcjtswAewiy2VC8tliKjRAiLnoV25bv0Ra/DiW5yNRBmL3/j3xb0glLXc8p8dUZdWuYkrwSP2Gh9hNzMos/HoESWDMFX+OBJzx9ZMEBybpsuLedJV4cLQ08gktT4KNNkq4JGn073UnYrEdwL3wOLnnudF+exRlI+XhXIGKJGPwEYvyFqmykJx6umqg8oHJ2IBPbY553K4W2RAyj9qBimJDvbknYrb/x/+zdd3wUZf7A8c8zsy3ZTTYd0oEAofcuRRT07CL2fmf359n1UE+Rwzs5xX7YPc/esXcFbEjvvZcQSG/bd2fm98eSQExCzabxvF8vXpqpzzw7szP7nef5PiJvPqJoPUb6QPSU3lSKRKJ6XIw1pSfqzw9D2TaMlJ7ox9+HJ6YzwaNL33FAQR1W5jWQfwP4bXMJp/dIaXC+JB2IYUDIloIp9zTE+i9hzr/h/Ddg47ewawlGTCrGsJvwOjri1Sz4TenEXvkDSsEKRP4SjORu6O0GIC58H2XluyhLXgPNj95jAsaQ66lUkqGe6+1wlHuDNBQD1I3w/Fi7fA6UpLamTQQwxowZw5gxY+qdZxgGr7/+OjfccAPjxo0D4JFHHmHEiBH88MMPnHbaaU1Z1GZjUgxijBJE3iJExQ6MtIFoiV1Qr/wavvkbIm8hvHcJxpDrMc56Dp8pHq9mOeDbIZfuAFtXzAO6YxhGeAz7vYtXaLGYul5EVM4pCDQCigOfZm7UUeKiLQc+fQ82X5IOxLrpS/BXEsw6vlG3axgGz6/S6RgLPQ89R5kEjMuE9eVwz+86b5+UQefuF2Bb9hL0OxscsiuJFGZWdBxGKSJvIaJiJ0b6ILT4HNRLP0F887dw4GL9txgn/B39+L/jVhLwh8QB708hHcpIQs06A6XDmWiaHr4/GgZuovEmjSDqnHdQ9QAhxYbXiMY4itYVh0IB2jttbC5y1zu/Y5K9TncYSToUDsWFxVeAWP8LjL4LI6kLYuHL8Na50Otc9BMnE4rvSmUouuallK4blJOA0n4savqJ6LoeDghqYO5zA7aeFyEw8Cux+DXlqIMXANEW9ajmS5LUOrX5X3h5eXkUFRUxYsSImmkxMTH07duXpUuXHlYAozUl9a4uqxDh4EVs5WqUt86BoGffQgmdCF36KaGJb2IKujCEQlCNwWvYMEIAxiEdc3WG+D8uq2k6LmLDf+iNX39xVpWu7RxsKKjbl7lfZhwxFgVxiInR9q8v6cCao66a43OJWvFfSBuAHpOKaMQfAXN2Gawogdv6NsPQqWLffxvzmJqKKgRXd4d/LjH4v580Xh93Ghl7FmP65HrEBd+B2dHcRTxqLe27qDmu86PZp0kxiClfifr2xNoJBJO6ErrkE0IXfoxJc2MoJvyqMxxY1w59n7quU92lfv91DMPAgx1EddCg9v0zEp+rXRXcOCaHOz5cUWeeIuDsfmmEQlrEP8OWds42puY8tkPZZyTKF6NUYZ71d5RVH+3duAK5p2BcOhPDFkdIicajJoRfWtWz7/ALrdrPhSHNwMXeHBmN+DwYa1XpmRbL6vzKOvN6psXitKqH/BzY1rTl6/JIyPpoW4RxNONxtkC5ubnMmDGjprXFkiVLuOiii/jll19ISdnXlPKWW25BCMGTTz7ZTCVtQuU74fnj6h/qrevJcM7LYItt+nI1gq3Fbi7/73x2lu7rB5+TbOe/Vw4mO1GmL5OO0I558N+T4YQH9uWKaQQBzeDkD9w4zHDvcNvBV5DqVeDWmfybjzSHwttjqoj/7mbofR6c9UxzF01qbuU74NlhEKinVUL3s+DsZ8Ha+gNd1Upcfp74YSNvztteM81qUnjygn4cn5tMlGyJKB2uZe/CJ9fVP++6nyG1b9OW5yC2l7i54r8L2Fay7wVdh8RoXvvLEPkcKEltlLyzHYaSkqojHemsyQkBiYkxlJZW4SzZgNrQOPUbvyNUWUi5q3WGJJ2K4O2/DGFPlZ+dpV6yE6Np57AQhUFxcdUhb6e6vlrTZ9xcIlFXSUkNDFO4V1N/LrE//hs1NhM1YxAVlZ5Ga4b9yhqd7ZU6kwcJ3K4DDC8XKQLsdhtut69VNy13ALf3EUxfpnPOD3Ze73UjmUsfo7L9KAI5pzR38Y5KU38XtaRr72iPXQhBXNFq1PqCFwDrPidUOZnyZmh+FMnP9aZRHbhiWBZrdlcSbTHROcWOQxG4K700UBONqi3fPyN5bI1x7TV2+exUETX3yQbnGwteour4aQSCLeeDdgjBG38eTEGVn7wKHxlOG+1irNjF4T0HtjVt+bo8EvvXR2Liga89qeVr8wGM5ORkAEpKSmq1wCgpKaFbt8MbWcAwjnio9mYk6g79tj/DgJAPo5XmONI0AxvQwWGhU6y1pk/ykXY9bp2fcfNoyrpqyn2Z9izBsn0WnoF/JVooYDTOb/2tleHcF+MyIM0hmiV+UPO7rZGOqTmlOQR3D4CnVxicuXQg0+PPY+ysOylL6o0em9HcxTtqLeW7qDnKcaT7VBSgenjwejesgxbAaMZu8ZGoT9UwiFMFI7Pi9m7fAN1o8mu8pZyzkdDSr4PGKp8iQgd8ZhRVuxG6hmG0nNFtDGPvc2CMhUEdEykursIwDLQIJtBtTdrydXkkZF20DS3nGyhCMjIySE5O5vfff6+Z5nK5WL58Of3792/GkjUNXTegviGsqtmT0C1tIxJ5NMPRSRIAhoF97j8JxWYTzDiu0Tbr1wzu/V0jwQZndmydrZ1aovbRgvsGCrJjFa7aM4H7fJdifH1z7dwH0jFD0wxof4BkrjHt0Uxtt0m5rhu0sV7BUhMLKHaMDqManK93OYVQ23/3KUlSC9cmAhhut5u1a9eydu1aIJy4c+3ateTn5yOE4PLLL+e5557jxx9/ZP369dx9992kpKTU5Mlo6wLWZIwuf6p3nj52cu1x6iXpGGbd+AmW3fPx9bq00YZONQyDfy7U2VQB1/QQWFUZwGhMDrPgrqFWLukq+CQ0nBPzrub991/B4w81d9GkZhCwtcPoeHy98/QT/4FHSWzS8khSa+LTLOij7gLVUnemIwU9Z1xN8k5JkqTm0ibCqKtWreLyyy+v+fvhhx8GYMKECUybNo1rrrkGr9fLAw88QGVlJQMHDuTll1/GarU2V5GblEu3YzrlcdTkbiiLXoaAC5yZ6CdMxpsxOqLj1EtSayE8RTh+eYBA2nBCKX1pjDCDYRg8tVzns20GV3UXZMXI4EUkKEIwNl3QP0nli7UBHt/dgxee+5kz+nXg7N7t6ZAY3dxFlJqIS3dgOmMG6oJnUZb8L5zMMy4bbdwUfKkjCGnyx5ckHUiVJZ2YK79F+fZuRN5CEApG7qloJzxIhUiSbfAlSWp2bW4UkkgK96tr7lIcGiHCCaL2L7PFZBCllSH0ILpiw6PGy0j6XvXVl1S/SNRVcvKBuzFF/HPRNZxfXIapcDlVJzyGYY1FAE5nNBUVniPqS+7XDP61SOfTrQYXdBaMy2z+4IUA7A4bbpev1efAqFbfMVVtW8QP2/38JIbg1lS6Jts5oWsSIzsl0jXZjmjB46g19XdRs197+2nMY7eYdKK0coQeRFNseJv5fteW7zHy2I5MY1x7kSqfogiicWHS3CAU/GoMPt3aoj/ftnweHglZH7XtXx8HS6ArtXxtogWGdGgCIUGAhPATvwHI4IUkAWCf+0/MO3/BPeI+DOvRDSlsGAa/7TaYvlRnlxv+3F0won3L/cHcFsV0GMQF6s9cuO3fLEg7j7nKQP43fyfP/7adhGgzgzLjGJDpZEBGHB0Solp0QEM6MoGQIu93knSEdN3AhR3E3pwxsqWuJEktiAxgSJJ07DIMouc/SvTyF/H0+QuhlAMkAGyAbhgUeWFzhcHSIoMfdhpsrYIuTrhvoCDdIX8cN4dA5mjMZgfHbX6fYXELqRh7AxsDiawtqGJjkZsfNhShGxAXZWJARhyDs+IYmh1PZnxUcxddkiRJkiRJaoAMYEiSdEwS/koccyZh2/QZ3p6XEuhUf6Lbcr/BxgrYVmmQ7zbY44ESn0GpD8r84X/a3uaZDjP0TBBMyIFuccg3+80s2H4Aur0d1g2fkPDT3fRPH0HPDiei9eyGTxdsKfGwocjNxkIXczYVoxuQFmtjZKcERnRKYFBmHFZTm8h1LUmSJEmS1CbIAIYkSccWzY9t/Uyi509HCVThHnwrwfQRALiDBmtKDVaWwKoSnTXlVexxh6MTCpBgg3grxFog3QG58eC0COKt0D4akqPCCSWllkOLSccz4AbMexZjzp+HJe9XUK04YjOJj05moCkKEs144xXWeWJZ4Ylj1uoK3l+Wj1XRGdJOMLxDPIO7diA7MaZRglKBkE5BlZ8it59KbwhfSEc3DKwmBYdVJSdoYNVCOCwmGQSTJEmSpGPQZZddRrdu3bjvvvs44YQTuPzyy7nyyiubu1iHLC8vjxNPPJFPPvmE7t27N+q2ZQBDkqS2TQugVu7EVLQS867fsGz+hkpfkJXJY9nc6Uy2VcSwaYfG2lKD7VXh7vI2FTrEwOD2KqlWjTQHtIsCkyJ/TLZKQiGYOphg6mBUVz5qxVYUdxGquxA0Pxg6DgwGAYPRMWwh8lU7SwMZLN+Tw+O7NUK/lxOvuOlnLyM3TtAh2UlqcjKJSe2xO1OwWS2oAjTdwBvUqfAFKfMEKajyk1/hI6/Cy44yLzvLvBS5AoeURNVuUcmMjyI7Poqs+Cgy4qJIi7WREmMl0W6RrUMkSZIk6Rjw4YcfEhXVurq4pqam8uuvvxIfH9/o25YBjMPQml6EVZe1NZW5Ocn6OnTNUVcH25cIVBH78fmYi1byjTaI64O317NUAnDG3n9A3t5/1E7ul6K6SFMrUbwG+bsEu9pY+m4hBG1t8KnDP6asvf8Owmxg00Lk6nls0DMo0+3MrrIzuwrYCeAFtu79d3isJgWnzYTDasKiKggBId3AG9CoDGhUeoO4AxrrClysK3Ad9vajzSr/u6QfnZLsh73u/prjOm+L38Py2Fqn5jy2Q9lnW677wyXrojZZH7W19vpISEho7iIcNlVVSU5Ojsi2ZQDjMCQmtr5hd1pjmZuTrK9D15R1ddB9+QFrODKtHOIAoYmqlwyLi0STH9FmBhWVGp8KQHt215paFVLI80dTpMcQrOdWasNPIpW0F6U4M3sioo/+4cMT0NhV7mVPhY+AduBRNSxmhYQE+1EPF9cc34lt+XtYHlvr1NKvg7Zc94dL1kVtsj5qa6n14fF4ePDBB/n++++x2+385S9/qTX/j11IXn31VWbOnMnOnTtxOp2MHTuWu+66C7t930uL999/nxkzZlBeXs7IkSMZNGgQM2bMYNGiRQA888wz/PDDD/z5z3/m6aefpqKigtGjRzN16lQcDgcAgUCARx55hC+//BKXy0WvXr2455576NMnnPS+oqKCf/zjH/z22294PB7at2/Pddddx8SJE+t0ITnQsodLBjAkSWr9rDFw9fcAnARsa9bCSJIkSZIkSdKheeSRR1i4cCHPPvssCQkJPPHEE6xevZpu3brVu7wQgvvuu4+MjAx27tzJlClTePTRR3nwwQcBWLx4MZMnT+bOO+/khBNOYO7cuTz99NN1trNjxw5+/PFHnn/+eSorK7n11lt56aWXuO2222rK9e233zJt2jTS09N5+eWXufrqq/nuu++Ii4vjqaeeYvPmzbz00kvEx8ezY8cOfD5fvWU+nGUPRgYwJEmSJEmSJEmSJKmJud1uPvzwQx599FGGDx8OwLRp0xgzZkyD6+yfzDMjI4Nbb72VyZMn1wQw3nzzTUaPHs1VV10FQMeOHVm6dClz5syptR3DMHj44YdrWlyceeaZ/P7779x22214PB7effddHn744ZqyTJ06ld9++40PP/yQq6++mvz8fLp3707v3r1rytKQw1n2YGQAQ5IkSZIkSZIkSZKa2M6dOwkGg/Tt27dmWlxcHB07dmxwnblz5/LCCy+wZcsWXC4Xmqbh9/vxer1ERUWxdetWxo0bV2udPn361AlgpKen1wQvAFJSUigpKQHCrTOCwSADBgyomW82m+nTpw+bN28G4KKLLuLmm29mzZo1HHfccYwbN67W8vs7nGUPRqYwlyRJkiRJkiRJkqQWLi8vj+uuu47c3FyeeeYZZs6cyQMPPABAMBg8rG2ZTHXbMhxOUvQxY8Ywe/ZsrrzySgoLC7nyyiv597//fdTLHowMYEiSJEmSJEmSJElSE8vMzMRsNrN8+fKaaRUVFWzbtq3e5VevXo1hGEyaNIl+/frRsWNHCgsLay3TsWNHVq1aVWvaypUrD6tcWVlZmM1mlixZUjMtGAyycuVKOnfuXDMtISGBCRMmMH36dO69917ee++9Brd5OMseiOxCIkmSJEmSJEmSJElNzG63M3HiRB599FHi4uJITEzkiSeeQDQw5mt2djbBYJA33niDE044gcWLF/Puu+/WWubSSy/l0ksv5dVXX2Xs2LHMmzePn3/+ucFt1ic6OpqLLrqIRx55BKfTSVpaGi+//DI+n49zzz0XCCfm7NmzJ126dCEQCDBnzhxycnLq3d7hLHswMoBxGIqLqziMVjXNLiHBTmmpu7mL0WrI+jp0jV1XyckHHtaqua69tnhOyGNq+ZryeFratdfWPsv9yWNrnSJ1bI117bXluj9csi5qk/VRW3V9HOzaaw533303Ho+HG264Abvdzp///GdcLle9y3br1o177rmHl156iccff5xBgwZx++2387e//a1mmYEDBzJlyhT+85//8OSTTzJy5EiuvPJK3nrrrcMq15133olhGNx999243W569erFyy+/jNPpBMI5MR5//HF27dqFzWZj4MCBPP744/Vu63CWPRhhHE5Hl2NcawpgCAFJSTGtqszNSdbXoYtEXbW0H1HQNs8JeUwtX1MfT0u69traZ7k/eWytUySPrTGuvbZc94dL1kVtsj5q278+kpJaXgCjKfz9739ny5YtvP32281dlKMmc2BIkiRJkiRJkiRJUhvxyiuvsG7dOrZv384bb7zBJ598woQJE5q7WI1CdiGRJEmSJEmSJEmSpDZixYoVvPzyy7jdbjIzM7nvvvs477zzmrtYjUIGMCRJkiRJkiRJkiSpjXjqqaeauwgRIwMYknQMUhSByaSg6wahkN7cxZEagaIIVDXcKzAY1Jq5NJIkSZIkHcuEAJNJBSAU0mQuDqnRyACGJB1DFEXgNmBFXiXztpSQnRjNCbkpxJoE6PLO0hoJAV4Em4s8/LiugES7lT/1akecRUWVn6kkSZIkSU0sIASF7gBfrtwJwKm92tPOYcEioxhSI5ABDEk6RggB5SGDS15ZwJ5KX830f3+znpcuG0ivFLsMYrRCXqFw7RuLWbunqmbakz9u5B9n9uCkrsmo8mFBkiRJkqQmEhCCx37cxMyluxACVCF4+detnNk3jb+N7yKDGNJRkwEMSWqjhAA/gkq/Rpk3SILdwsq8Cip9wVrLhXSDG95ewjc3j8LeTGWVjoxQFV6bu61W8KLaA5+tYegto0gwiWYoWT0UBbduUOTyI4AkhxWHAoYMmkmSJElSm6AogrUFLjYUVvHkBf2wmVWCmk6UReWzZfms2lPFoNQYdHnvl46CDGBIUhskhMBlwE3vLmXVrsqa6cM7JfL0Rf3569tL8e6XJ8EX1NlU5KJfigNDRsZbDY9u8M6CnQ3O/3b1Hi4bmEEo1Lw5MTRF8Nu2Uu7/dDWeQLgsdovKw+f0ZmiGE0Wec5IkSZLU6oWE4PctJVw/OocHPltNqTsAgFkVXDWyI2WeIEEEKvK+Lx05pbkLIElS4/NTN3gB8PuWEt6at4PLhmfXWafSG0S0kJf10qExDHAHGg5OlHma/zMVAna7AtzxwYqa4AWEy33Le8so9IWasXSSBGrJeqKWvYji2t3cRZEkSWrVNN3gxO7tuP395TXBC4CgZvD8T1sAEPLXp3SU5CkkSW1QhV+rE7yoNmdDIUM6JtSZ3iM1Vjbpa2UsimBox/gG55/QLaXZR5nRFYUXft5S7zzDgFd+3QomeSuSmodSlU/czLNx/PYP4j46E+Etbe4iSZIktVoOq8qCLaUEtPqfPV79bRsNzJKkQyafGiWpDSrzBBqcZxgQ+MOP2jP7phJrVSNdLKmRmQ2De0/pjkmp28yid3osHRKimr1LUFA32FLsbnD+piIXQU0GzqTmEb30WUBQecJjCH8l9oWPN3eRJEmSWi1DN9hQWDcvV7UdpR5C8mVZmzFp0iRyc3N54IEH6sybMmUKubm5TJo0qdH3KwMYktQGJTosDc5TBDis4fQ3yQ4r953ajb+dlItZ3lBaHcMwSLObmXnDcIZ3SkAIiLGauH50J567eABRLaCPqUURdG8f2+D8nqlOrKrsuyQ1Az2EdeOnBLKOR4/NxN/5DGxr3kZ4S5q7ZJIkSa2SrhsMyGq4ZWjnFAfylh8ZFZ4AmwtdLN1RxuYiFxUHeJnZmFJTU/nqq6/w+faNcOj3+/niiy9IS0uLyD5lEk9JaoNizSrDOiYwb2vd5tBn9k2jc1I0P90xBgWwmwRaM3czkI6CbpAWZebxib0J6CAIf6Z6SD+i8IUQoKoqhmGgNUY7T03n2tGd+HxFfp1RelVF8OcR2ejy/JOagWnPEhRfGcG0oQAEOo7Htv4jbOs+xNv/umYunSRJUvMymRRAoGkah9qYMxTSOTE3hce/31Ar71W1O0/qik1Q53lAOjr55V7+9tEKftlYXDNtdJckpk3sQ1pcVET33aNHD3bu3Ml3333HmWeeCcB3331HamoqGRkZEdmnbIEhSW2RgH+d05vjc5NrJikCzuqbxp3ju2LWdKIxsGHI4EUbYBgGJt0gGoMojCMKCAgBfiHYUO7jvwt28sW6Iip10OvpnnK4kmwqL142kKT9WgYlx1h55fJBJMquS1IzseTPxzBFo8XnAGBYYgimDsK27v1mLpkkSVLzCQlBmWbw4co9vLZoJztcQQKHkRE8RoW3rx5KVkL0vmlWEw9P6EXXpGiZb62RVXgCdYIXAD9vLGbSRyuapCXGxIkTmTlzZs3fH330Eeecc07E9idbYEhSGxMQgunfb+TbNQVcPjybS4dlEwjptIu1khpjxSyzJ0n18AqF699awur8fclfFQFPX9ifwRmxKEfxwKHoBv3bO5h53XDKfSEE4LSZcKigyfwXUjMx7V5IKKFrrZT4wfTjsC+Yjlq6ES2hSzOWTpIkqemFhODLtYU89OXammlP/riJMV2S+NfZvbDoB3+GNHSDLIeZt/4ymEq/RkjTiYsy41AFunwGbXTFrkCd4EW1nzcWU+wK4IxuuGt5YzjzzDN57LHH2LVrFwBLlizh8ccfZ8GCBRHZnwxgSFIboqqCpTsr+HhZPkB4yKqf9o0A8d61w8iJtRxyU0Dp2CBUhRd/3lIreAHhJp63vLeM724ZRexRttfTNYMoIMpW3eLCQGt4BFhJijhT8UqCGaNqTQu264dhsmHZ+i1eGcCQJOkYU+oL1QpeVPtpYzHfri3g7B7tDql7qaYZ2ACbRSHc4N9Aly8sIqLSFzzg/KqDzG8MCQkJHH/88Xz88ccYhsHxxx9PQkLdEQ8bS4vvQrJw4UKuv/56Ro4cSW5uLj/88EOt+W63m3/84x+MHj2aPn36cOqpp/LOO+/UWsbv9zNlyhSGDh1K//79+etf/0pxcf2RKklqzXw6vPjL1gbn/2/uNlBlk32pNrdm8MHivHrnabrB3C0lqGqLv11I0iETnmJUTxGas0PtGaqFYHJfrFu/bZZySZIkNReLRW3wWQDgv79twy2DEC1OrM18wPkxB5nfWKq7kXz88cdMnDgxovtq8U+kHo+H3NxcJk+eXO/8adOm8csvv/Doo4/y1VdfccUVVzB16lR+/PHHmmX+9a9/MXv2bJ588kneeOMNCgsLuemmm5rqECSpyWiGQbmn4UhriTuAJvseSn+gGQa+YMNvVIqq/IjD6P8qSS2dqWQdAJozq868UPv+mAqWIXxlTV0sSZKkZmMABVX+BueXe4LIDiAtT5LDwuguSfXOG90lqVb+sUgaNWoUwWCQUCjEyJEjI7qvFh/AGDNmDLfddhvjx4+vd/7SpUs5++yzGTp0KBkZGVxwwQV069aNFStWAFBVVcVHH33EpEmTGD58OL169eJf//oXS5cuZdmyZU14JJIUeVEmhVGd6/8SAxjXLQVzi7/qpaZmVQS57WIanD+8UyKa7O8htSFq+SYMxYQe3a7OvGBKPwQGlp0/N0PJJEmSmoeuGZzco+53YrVhnRKwNUJib6lxOaMtTJvYp04QY3SXJP49sU/E819UU1WVr7/+mq+++go1wq29W30OjP79+zNr1izOPfdcUlJSmD9/Plu3buWee+4BYNWqVQSDQUaMGFGzTk5ODmlpaSxbtox+/fod8r5a0wvI6rK2pjI3pzZTX5rOn4/rwMfLdtUZvirRbuHkHu3QNf2ojrM56qo5Ppc2c07sp6FjilLggdO7c8krdZMt9U6PJTPOBhgtsi7a2ufU0o6nOa7zptinqWwTuiMVodTzkBWVgBaTgSXvVwJdz2qU/bW0z7UxyWOL7L4PZZm2WPeHS9ZFbUdSH7quMyArjoz4KPLKvLXmmVXBbeO6YkLHaIV13NbPj7S4KJ65qD/FrgBVviAxNjNJDkuTBS+qORyOJtmPMIzWk84vNzeXGTNmMG7cuJppgUCA+++/n08++QSTyYQQgoceeoizzz4bgM8//5x77rmHVatW1drWueeey9ChQ7nrrrua8hAkKeJCms7mIjcPfbmGXzYWowg4uWd77v5TLh2TmuaLRWp93P4QK/LKmfL5GtbtqcJmVrhwcCbXjckh1RnZMcQlqcm9fjaEfDD2vvrnz38e9qyEW1c0abEkSZKa285SD098v4HPV+QT1AwGZsfz4Bk9yW3vwGKSedSk5tfqW2C88cYbLFu2jOeee460tDQWLVrElClTSElJqdXqojGUlFS1mtEbhIDExJhWVebm1NbqK8ksePTsnng1AyEE0SqohkFxcdVRbzsSdZWU1HD3BWiea6+tnRNw8GPKjbfxymUD8GsGqhA4TAIjGGqU8yZS2trn1NTH05KuvaY89vjiTYRS+uOr8NQ73xTTFfu6Lyjdth7dkXbU+2tr5+n+5LEdmca49tpy3R8uWRe1HU19RAH3jO/CLSd0RjcMbKrAKqCyvP7vy9Zg//pITDzwtSe1fK06gOHz+XjiiSf4z3/+w/HHHw9At27dWLt2La+88gojRowgKSmJYDBIZWUlsbGxNeuWlJSQnJx8WPszDFrdl2JrLHNzaiv1ZRgGKuAQAAZoNHripaasq+b8XNrKObG/ho5J0wwsgGXveaOHWs+Bt7XPqaUcT3OUI+L71EMoVbvROp5MQ7sJJXUHwLRrAf6uZzfarlvK5xoJ8tiab59tue4Pl6yL2o64PjSdaAAB6AZtJQuWPDfahladzi8UChEMButkx1dVleqeMb169cJsNvP777/XzN+yZQv5+fmHlf9CklqKttp/Tzo88jyQpCOjuHYjjBB6dEqDyxhWJ5o9FdOexU1YMkmSJEmSDqbFt8Bwu93s2LGj5u+8vDzWrl2L0+kkLS2NIUOG8Oijj2Kz2UhLS2PhwoV88sknTJo0CYCYmBgmTpzItGnTcDqdOBwOHnroIfr37y8DGFKrElIErqDOjlIPsVFmUhxWHArocljUY4YQAh9Q4glSVOUn1WkjzmbCiiHfKkjSIVIrw88Uur3hAAaAltAF856FTVEkSZKkRqMrArdmsHp9IRZVIdVpw6GAIZ8XpTaixQcwVq1axeWXX17z98MPPwzAhAkTmDZtGo8//jiPP/44d955JxUVFaSlpXHbbbdx0UUX1axz7733oigKN998M4FAgJEjRzJ58uQmPxZJOlJBVeGR7zbwybL8mmlJDgsvXTaIrBgzuiZvSm2dEIIqA659YzEbC1010/tlOnn6gn5ECxnEkKRDoVTtAkCPanjIaYBQfBeiVr4WTvZpsjVF0SRJko5KUFF4e9FOnvtpC9regIXdovL0hf3p084OMoghtQEtPoAxdOhQ1q9f3+D85OTkmqBGQ6xWK5MnT5ZBC6lVUlWFtxbvqhW8ACh2Bbjsvwv44qbjsDdT2aSm4wNufHtJreAFwLKdFdz3ySoemdAbk9HYmU4kqe1RXfno1jhQDzy8nBafgzBCmIrXEGo/oGkKJ0mSdIRUVbAwr4L/zN5ca7o7oHHtm4v5+uaRxKuy/6nU+rXqHBiS1NYJAS7d4L+/ba13vssfYuWuShRF3pDaugq/xtrd9Y8G8sumElzBlpFiSwgwm1VMJnl7kVomxbULPSrxoMtpsdkYiglTkRxKVZKklqW++6xXh6dnbap3eU03+HRZPmazHAZVav1afAsMSTpWBYRgjyuAbkClL9TgcluLXYzMdspcGG1chTd4wPkuv0ZcdPN+pfuFIK/Cx88bi4mPNnN812RiTAqq7NsitSBqVf4hBTBQzWgxmZiKVkW+UJIkSYcgIASF7iCz1+djURVO6JZCnFXFbBhoBuwq8za47sZCV4MjL0lSayIDGJLUAvkVhQc+X8OsdYU8fE5v2sVaKaj017ts73QZvDgWJDkabu6uKoJYWzMHLxSFm99bxpId5TXT/vnVOh6e0IuxOYkyiCG1GIprF1p850NaVnNmywCGJEktQkARPPTVer5evadm2vTvN3DDmE5cNiQTM4Ju7WNYuL2s3vWHdEiQ44hKbYJs4ytJLYzJpPDThmJmrSsE4P2FO7lqZKd6l01z2shJtssAxjEgxqxwfNfkeudNHJBOTDN22VBMCu8s3FkreFHtno9XURmUuTmklkNxF6LbEg5pWc3ZAVPZBtAbbgUnSZIUaaqqMH9bea3gRbXnftrCrgo/FgzuPKlrves7rCZO7JZCKCTvx1LrJwMYktTCuEMG/527L+fF0p3l+IIat5zYBYd131v2QdnxvPGXIdhl+otjgkk3mHpWT87ok0p1yhOzKrhkSBa3jO2M0JvvocStGbwxf3uD879ZvUf2u5VahqAXJVB5yAEMPTYLoQVQKxo+vyVJkiLNqxu80kA+NIDX520HRZAdZ+M/F/Wv1WqzS4qDt68eQqxsdy81skmTJpGbm1vzb+jQoVx11VWsW7cuovuVp7IktTA6UO6pne/g8e83MDY3hWkTe2O3mkh32nCYFCwgW18cQ2y6zv1/yuXWE7vgCYSwW03hlhda875RMQyoOkCelsIqP0IG2qQWQHGH314aUYfYAiM2CwC1ZC1afE7EyiVJknQgulH32XB/Je4AmgGqbjAi08nM64fjDuoIw8BhUYkSoGvyebFN85aBuwh8lWBzgj0JouIjvttRo0bVjAhaXFzMk08+yfXXX8+cOXMitk/ZAkOSWhibKjiuc1Kd6bPXF3LT20sprPCRZAknbDJkX8ZjjtB0YhVobzMRI2j24AWARYEhHRq+Scpmq1JLoXoKANBth/ZQZ1hj0a1OTGUbI1ksSZKkA7KpglFd6j4bVjupR7uat9KaphONQffUWJIsClbDkC+72rqKXfDBX+A/g+HlE+E/g+DDq8LTI8xisZCcnExycjLdu3fnmmuuYffu3ZSWlkZsnzKAIUktjNB0/u/4HKz15DRIddoY3ikBrQX8aJWkahZg0p+6o9YznG+XFAedZZ4WqYVQ3IcXwADQHOmoZfUPTShJktQkNJ2rR3Ykqp7umMkxVsZ2TZbPhscqbxl8ehNsmVV7+uYf4bO/huc3EbfbzWeffUZ2djZxcXER248MYEhSM1JVBS+CCh08CJS9QYtEi8LM64czvFO4mbNZFUzol8Y7Vw+VOS+kI6IogqAQ7ChxU6WDpiqN1q1D1w3SHWbev3YY/TPjALCaFC4dmsUrlw8iSg7cJrUQiqcIQ7WCKeqQ19Fj0lBLZQsMSZIiTwhBSBFUGVBlgKYIxN6bdZxJMPOG4YzqHB4G2qQIzuiTynvXDMUhf9Edu9xFdYMX1Tb/GJ4fQXPmzKF///7079+fAQMGMGvWLJ544gkUJXInpcyBIUnNJCgEszeX8Pj3Gymo8nFyj3b8ZWRH4qMtWIE0h4XHJ/bGrxsIBNGqAE2nGXM1Sq2UUAT5nhAPfr6GxTvKEALG5qZw7yndSDALNM1AqAqukEFI17GoCjEmcVjdPoRu0CHGwn8u6EtAN1CEwK4KDE2X4QupxVDcBeHWF4cRvdMdaVh2/gqGDkL+SpAkKTIUVVDk03joy7X8urkEgJE5idx3Wnfa281UBnTMquChs3thUgWhkEGUIhC6Lls5Hst8lUc3/ygNHTqUBx98EICKigreeecdrrnmGj744APS09Mjsk8ZwJCkZqCoCl+vLmDql2tRBDwysQ8bC11c/foiKr0hkhwWbhrbmfG5ydgBMEAmX5KOUGlQ59wXfse/NyBhGDBrXSHLdpbz8fXDURWFl37ZynuLduIL6mTER/G3k3MZmOHEfBh5VnTdwAJYBICBIc9ZqYVRPEUY1rjDWkdzpCI0H4p7D7ojLTIFkyTpmFcRgnNfmIfLvy8p9u9bS9lY5OLLlVW8Nnc7Vf4QyTFWbj2xC2O7JDbrCGRSC2GLPbr5RykqKors7Oyav3v27MmgQYN4//33ue222yKyT/kqQZKaQVXI4Mkfwk2SLx/egV83FfPiz1uo9IZvWsWuAA9+vob3luxCqPIylY6CqvDCz1tqghf7K3UH2FPl5/YPVvDa79vxBcPL5JV5+eu7y1iwsxxVlX2WpLYj3ALDeVjr6PZUANTyhocwlCRJOhqKSeH9RTtrBS8Arh7Zka9W7uE/szdTtXdeUZWf+z5Zxacr9shnRAnsyZBzYv3zck4Mz29CQoS7Pfn9/ojtQ571ktQMXIFQzY1odNdkPlueX+9yL/y8haqQfIstHTmvZvD73qaof2QzK3gCGgu21Z8petrX63FpkSydJDUtxVN42C0wdHsKhlBQK2QAQ5KkyPBpBj9vLK41TQgY1imRb1btqXed/8zehEu2dJSi4uHMZ+oGMXJODE+P8FCqgUCAoqIiioqK2Lx5M1OnTsXj8TB27NiI7VN2IZGkZlA9wohFVajwBmmolb4/pFPpC+KIkpeqdGRUAQkOC/kVvjrz0uOi2VBQ1eC6eyp9eEMaUfWMiCNJrZHiLSbYrt9hrmRCj05GrdwRkTJJkiSZFUGi3VJrWozVREFV3Xt3NXdAo8oXxG6Tz4jHPGc6nPtKOGGnrzLcbcSeHPHgBcAvv/zCyJEjAbDb7XTq1ImnnnqKoUOHRmyf8oyXpGZgNysM7ZjAgm2l2C11h8Tan0X+eJSOQpQiuHZUJ25+d1mdeS5/kIz4hkdjMCkCi2yeKrUVuobwlWFYD68LCYAenYJSIQMYkiRFhmoYXD2qI79s2tcKwxfUibGaD7ie1XTgZ0jpGBIV3yQBi/1NmzaNadOmNek+QXYhkaRmYTEMHp7Qi/S4KEo9AdKctnqX65UeS+xBAhySdCCapjMwM45z+tfNBH3p0Cx6pMbWO648wKm9U7HLAIbURghfKcLQ0Y8wgKFWbmv8QkmSJBFOgt0l0c5Vx3WomRbQdHwhjeQYa73rDMqOx2GW92jp2CNbYEhSMzAMcKqCt68awu4KP09d2J9r3lhEuSdYs0z7WBtPnt8PK8hhKKWjYtF17jyxM1cd14HftpRiUmBk5yRizApm4L9XDOLK/y2slegzt52DO8d3lRnOpTZD8RQBHFkLDHsK5j0LG7tIkiRJNcyGztXDszlvYEZNS4x+6bG8fPlALvvvgppE7wAZ8VE8MrE3FuQzonTsafEBjIULF/LKK6+watUqioqKmDFjBuPGjau1zObNm3n00UdZuHAhmqaRk5PDM888Q1paeLgzv9/PtGnT+OqrrwgEAowcOZLJkyeTlJTUHIckSUA42h4F5DitmM0Kn944gvV7qthc5KZb+xhyku3YBXJsb6lRmA2DdjaVa0d3oqzMTSikg25gAF3ibXxz80hW7Kpkd4WXvhlxZMbZsKHLByOpzVC84R8Eh5vEE0CPTkbxVyICVRiWmEYumSRJUpjJMEg0K5zbuz0AoZBOnN3M5zcex5o9VWwrdtMjLZaOidHyGVE6ZrX4AIbH4yE3N5eJEydy00031Zm/Y8cOLr74YiZOnMjNN9+Mw+Fg48aNWK37mlv961//4qeffuLJJ58kJiaGqVOnctNNN/Huu+825aFIxzDFpBDUDSyKQPvDcJaGYRAIaEQDA1NjGJwei6YZGIaBvC9JkaBpfzgHdQOHgJFZThQlDk3TMQwjYsELRRHoQqAD5nrKI0mRoHjCAQzdGnvY6+pR4WHolKo8tMTujVouSZKOXSaTQkAHRYBiGDUBidB+z4q6ZhANDEmLYViGfEaUpBYfwBgzZgxjxoxpcP4TTzzB6NGjufvuu2umZWVl1fx/VVUVH330EdOnT2f48OFAOKBx6qmnsmzZMvr16xexsktSSAgKvUH+N3c7eWUeBndIYGL/dJwmBaOepvm6bshoutRsmuL88wvBugIXb8zbQSCkc3a/NIZ3SiCacDBPkiJF8ZZgmGxgqj/n0IHo0eEAhlq1SwYwJEk6aooicBvw07oivlixG7vFxOXDs+mSFI2lgXuhfEaUpLAWH8A4EF3XmTNnDldffTVXXXUVa9asISMjg+uuu66mm8mqVasIBoOMGDGiZr2cnBzS0tIOO4AhRGMfQeRUl7U1lbk5RaK+dCGYvbGEez9ZVTNt4bYyXv1tG+9dO5QMu7lV3oia49xqjvO4LV5DzX1MfgRTvlrH92sKaqb9vqWEnGQ7/7tiEFFHUK7mPqbG1tKOpzmu80jtU/GVoFudHNHmbXEYihm1aucRla+lfa6NSR5bZPd9KMu0xbo/XK2tLlwGXPLKAvLKvDXTZq0vZEK/NO4c1wXrUbaBbG31EWmyPtqWVh3AKCkpwePx8NJLL3Hrrbdy55138ssvv3DTTTfx+uuvM2TIEIqLizGbzcTG1m4ympiYSFFR0WHtLzGx9fV7bY1lbk6NWV87Sj088NnqOtO9QY1JM1fxxlVDSHLUn1m6NWjKc6s5z+O2eA011zH9uqm4VvCi2uYiNx8v383/HZ+DeoSjnrS1z6mlHE9zlCNi+9QrICoOpzP6yNa3J+HQinEkHXn5WsrnGgny2Jpvn2257g9Xa6iLQEjjuW/W1wpeVPt4WT6XDu/AwOzGGQ6zNdRHU5L10Ta06gCGvrcJ/oknnsiVV14JQPfu3VmyZAnvvvsuQ4YMadT9lZRU0VpaOAsRvkhbU5mbU2PXlxCCNflVhBpoYbFmdyXFVT4MX+Dod9bEInFuJR3kB0FznMdt8RpqzmNSTSpvzdve4Pz3Fu5kQp9UosThFaytfU5NfTwt6dqL9LHHlO1GMTnwVHiOaH27NZ5Q4TZcxVWHvW5bO0/3J4/tyDTGtdeW6/5wtaa68BiCj5bkNTj//YU76BJvIxAINbjMwbSm+mgK+9eHDGK0fq06gBEfH4/JZCInJ6fW9JycHBYvXgxAUlISwWCQysrKWq0wSkpKSE5OPqz9GQat7kugNZa5OTVWfQkBWj3BiyizCoRbYbT2z6Ypy9+cddXaP6f6ROKYhDjwNnXDIHiAZJ0hXcfAOOJytbXPqaUcT3OUI1L7VDzF6Lb4I26YrdsSUFy7j6psLeVzjQR5bM23z7Zc94ertdSFpjVcSIPGO47WUh9NRdZF29CqAxgWi4XevXuzdevWWtO3bdtGeno6AL169cJsNvP7779z8sknA7Blyxby8/NlAk8pYnTdoFv7GBQBugHH5yZzydAsqnwhDCDZYSXWZgI5+oLUiimKwGtAqTdIsStA+1gbTquKtb5AhG5w3sAMflhbWO+2zuybhl1V0OU1IUWI4i1Gc2Yf8fp6VBLmPYsasUSSJB2LolTBqb3b896i2q0wRnVJ4rJh2VjNCsv3VJEZH020CkorzJcmSZHU4gMYbrebHTt21Pydl5fH2rVrcTqdpKWlcdVVV3HbbbcxePBghg4dyi+//MLs2bN5/fXXAYiJiWHixIlMmzYNp9OJw+HgoYceon///jKAIUWU3SS4+YQu7Krw0r19LH99Zym+YPjHmUVVeOD07pzYJQmTDAdLrZCiCMo1g2vfWMzmInfN9KEd45l+bl9sRu1AhK4b9Ggfw4CsOJbsKK81LyXGyqVDsmTwQooo4SvFsDqPeH09KhHFvQcMHcSR5WqRJElC07ludA7frSmgzBME4Ox+6QzIjuOWd5fhDWoAmFXBpFO6cWq3FPmsKEn7afEBjFWrVnH55ZfX/P3www8DMGHCBKZNm8b48eN58MEHefHFF3nooYfo2LEjTz/9NIMGDapZ595770VRFG6++WYCgQAjR45k8uTJTX4s0rFF1Q0uHZzJzkofE56dW2teQNP5+6eryb1uGB1jLLJJm9TqeAzqBC8A5m8tY+qXa/nHad1Q//DWyGYYPH1BP37aUMzC7SWc0K0d7WJtJDks2E0KRkhrykOQjiUhL0rQg26JPfiyDdCjEhB6COEtwYg+vC6okiRJ+0uwCD6+cQQFlX68wRB2q5nzX/i91vNgUDOY+sVaerSPpUucTQ41Lkl7tfgAxtChQ1m/fv0Blzn33HM599xzG5xvtVqZPHmyDFpIEaGqClWagS+kYVEV7CYFoemEhGB3hZdXft3W4LrP/7SZh8/oiTDkm2ep8QkBQSHwhAw03SDa3Hhvjcu8wTrBi2rfry3gbyfnElvP7qy6zrjcJGwWlYe+XEthlR+zKji7Xzo3n9CZKEOXAT2p0SneUgAM65EHMAxbAgCqew8hGcCQJOkPqrtVeoI6iiKINgksBnUCD7oQrCz0MPmz1WwpdnN6n1QsJqXBe9+MOZuYPqEXqrw3ShLQCgIYktSSBYXgpy2l/Pvb9RRV+TEpgtP7pPK3k3PZWebFF9KZODCdM/ulsXxnOe8s2FHTXBBgW4mXgG7QegdTlVoqRRGUBg1mzN5It7RYshOjsZoUqoI6yTYVQvUHzaoDct6ghtWk4DApdXK1CAHFroZH0DEMcAdC4Twv9Wz/5y2l3PHhipppQc3gg8V5bCysYsYF/bAccZpFSaqf4i0Bji6AodvCwxoqrj2Q3LtRyiVJUttgKIItlX7+/slq1heERyoa0iGef5zZk2Srir63RaKiCLZXBbj81QUM65TITSd0pn2sjaCuoyqCz5bl4//D/Xl7qYegbqA2+VFJUsskAxiSdIRUVeHnraXcud8PsZBu8MmyfM7un06VL8Rj361nW0l4yL5hnRJ48oJ+TP5sdc203umxWFUBB8hGLUlHwqXDI9+u47xBmTz5w0bW7K4EoE+Gk3+c2ZNMh6VOzomgEHy9rognf9xIqTuAWRWc1S+dW07ojE3ft6xhQPtYW4P7NqsCh7X+24tLM3jku/pb1S3bWUGBK0Cm3Xy4hytJByT2BjCOpguJYYvDEEo4D4YkSdJ+SvwaF700n9B+XScXbCvj/Jfm8dmNx2HfOy0APPLNOm4f3xVFCB76cm3N/faUXqn85+IB3PLuUjyBfV0qe6bFYlHrvkyQpGNVxLJQFRcXc9dddzFy5Eh69OhB9+7da/2TpNbOpRk88m3dH2KJdgtRZpWb311aE6gAmLellDs+WM59p4XPf1URXDuqk7whSY3OZFL4fUsJlw3vwF/fWVoTvABYkVfBxS/Pp/wPb3gUk8J364t44LPVlLrDrSuCmsGHi/O4/f3lBISotbzTqjK0Y3y9+79wcCYxpvpvL76QTkGlv8Gyr8qvRFVlgkSpcTVGCwyEgmGLlwEMSZJqMRSFZ+dsrhW8qFbpDfHVqj2oe++JQR1se7tzPvrt+lr328+W5/PcnM3cPr5rzfqKgP87vjOqLp8VJalaxFpgTJo0id27d3PjjTeSkpISqd1IUrNp6IfYuQMzePrHjfX2ZSx2BdhY4GJ0lySuGtmRJJsaHmdVkhqTIqjwh1iwbFedpqgAnoDG+4vyuG54Ftre+a6QwZM/bqx3cwu3l1HiCZIate+WYcVg+rl9eejLtXy3tgDDCLe8uGhwFteN6thgYM6sCsyqINhAq6N2sVaZqExqdIqvFMNkA9VyVNvRbQko7oJGKpUkSW2BTzdYtL2swfm/bS7m3L6pCEAAFwzO4r5PVtW77JIdZVw3phNWk0JctJmHJ/QmJcqEIZ8VJalGxAIYixcv5u2335atLaQ2y6wKLKpC4A8/1DqnOHhz3vYG11uxq4KHzu6NSYDQ5KgLUuNTgA6Jdj74wxjz+5u3pYQrh2bW3AQ8QY3y/fKz/NH6QhfpHeNr+vEaBtgMnSmndePuk3NxB0I4rKZwy4sDtCpymBTO6pvGh0t21Zlnt6jkpsTU7EOSGoviLUG3HPkQqtUMqxPFXdgIJZIkqa0wCUiOsbK7wlfv/DRnFCYh0ACHSZAcYzvg/bbUHeCz/zuOKJOCQwVNdjOWpFoi1k43NTVVvkWT2jSHSWFC//Q608u9QVIOkB8gIy6KT5btIiS7jkgREghodE1xkBzTcHrY1L0PVNWsqoKqiAaXbxdTf8sIVTeIVSDVZiJGcPAuUZrOLSd2oW9G7R+T0RaVV68cjENtuAySdKSEt+Touo/spdviUTyyBYYkSftYBdwwOqfB+ZcOy0LbO0x4KKSTYLcc8H7rtJlJiVKJwpDBC0mqR8QCGPfeey+PPfYYeXkNvwGUpFZN07lpbA4DsuJqTf521R6uGdWx3lWEgLHdUlCFgd0kf6hJkeNQBVcdV/95CHDVyA6I/frUOswKp/RqX++ycdFmshOiG2140yhD59kL+/HR9cOZcmYPXrh0AF/9dSSd4mwYsp+vFAGKtxTD4jjq7ei2eFTZAkOSpP3oukHf9BiuGJ6N1aRQ/W5AEfDgGT1oF1U7MbXTLBq83zqjzHRt5yAUlPdCSWpIxLqQ3HbbbXi9XsaPH4/NZsNsrn3xLliwIFK7lqSI0hWBWzNw+zVsFnjwzJ5YVIVyTxBfMESHRDsWRXDugPRazeTNquC+03qwIq+cs/umozcwjKUkNQpNp3s7BzeN7cyMOZtqgg+KgLtPziXDacOtGRS5AoR0nfaxNu4/tRun9U4FAYoQ7Crz8uHiPP41oRcOBRortmAYYMEg22GmY7cUDMNA1w0M+aZJihDFW4xxFCOQVDNscQhfCeghUORAbpIkhYdG1YDzB2dydv90oi3hAU9tZgUboPyxW2RI5+6Tc9le4mblrn1Jtp1RZl68bCCxZjniiCQdSMTuvvfee2+kNi1JzSaoKLw8dxuxUWaEELz88xaq/CEgPN73tHN6E6OAruvcfmJnrh7ViWU7y7GaFDq3c2BTFewmBbN8yyw1AYuuc+nAdCb0S2PpznIUIRiQHY9NwNpCF7e8t4xKb/j8ndAvjRN7tGPqF2tqktN2SXHw6Ll9yIq1oEXgbZBhgCYf0qQmILwlaDF1u/wdLt0WjzB0hLcUwy4TlEvSsU4ogjxPkDs/WMHGQhcQTkZ918m59MuIw2ES1HeXizZ0nr14AAVVflbnV9I+1kqXdjHEqEIGLyTpICIWwJgwYUKkNi1JzUJVFb5aW8ji7WWc0TeNh75cW2v+gm1lXPbfhbxz9RCiALNukGgSjM9JQFEEhrG3L6PMDSM1IdUI56gY2zEeISAhwc7GPZVc/fpitL1vheKizZzSO5Xr3lxc6/TcWOji4pfn88VNx4XzW0hSK6X4ShslB4ZhjQtvz1OEJgMYknTMq9Lhopfm4wnsS8peUOnnrg9X8NJlg7Cm2KkvK1p1Iuxsu5mc7sn7nhFlS0RJOqiI5cDYn9/vx+Vy1fonSa2NSzOYMWczFwzO5JVft9a7zK5yL5uL3Cj7JWfSdYNQSJeJmKRmpetGzb8Pl+yqCV4AnNM/nTfmba83tuYJaHy5cjcmU5PcLiSp8WkBlEBVo3Qh0W1xAKgykackHfNMJoXPV+yuFbyoZhjw2u/bKPUGaz0T1kc+I0rS4YlYCwyPx8P06dP5+uuvKS8vrzN/7dq1dVeSpBZMN2Bgdjx9M+O4//QemFXBxgIX//1tK8WuQM1yK3dV0K+9Qw4FKbVIQU1nfUFVrWnZiXY+X7675u9B2fFcOiwbq1lBEeHWQ4aQTTCk1knxlQKgN0oLjPDoOcJTdNTbkiSp9VFVgUuDSn8Iw6/RMy2WXumxrNovl0W1tbsr8QZ1FEXIZ0JJakQRC2A8+uijzJ8/nwcffJC7776bBx54gIKCAt577z3uuOOOSO1Wkg6bqiq4NIPV+RUYukGs1VTvuNuqKshOjGbic3Px7c0H0DvdyePn9+OemSvZVe4FoEOSXd6opBbLYlLonxnHnPX7foAVu/ykx0dR5PJz7sAM+mfGMeXz1ZTtHac+Iz6KJ87vR8dYC8bec1tRFao0nQpvCJOqEGtViRbIc19qcYQ3HMBojBYYKCZ0S6wciUSS2pDqoESFL4QBOG0mYlRRJ0eToQhWF3u488MVNbmikmOsTDqlG9+t3sO3q2u3zEqPi8JhVWXrCklqZBELYMyePZt///vfDB06lHvuuYdBgwaRnZ1NWloan3/+OWeeeWakdi1Jh0wXgqW7q7hn5kpK3OFWFO1irTx+fl86J0RTFdSp8oVw2EwUVXh4be72muAFhFtb3P3hCu49tRs3v7sMu0WlT7pT/oiTWiwhBKf3SeXZOZvx7x0J5+Olu/jrCV3YUephXPd2XP/m4lrr5JV5ufSV+Xz115HEKhASglkbinn463W49iax7Zhk56kL+pIeY6UyoFPuDWI1KTitKrb9AhtCgC4U/LqBWRWYdAND5oWRIkjxlgA0Sg6M8HacCK9sgSFJbYIiWFPi5Y4PltcEJRLtFh49tw892jnQgaBuUO4JYiAoqPKTaLfWLFtU5eeuD5bzyhWD+XlDMd7gvu4klw7LJt5mlsODS1Iji1gAo6KigszMTAAcDgcVFRUADBw4kClTpkRqt5J0yISAAm+wTuLCMncQl09jytfr+HrVnpp5Y7om89LlA5m3tRSH1cTmIhdfLN/NnkofvpBOlxQHj0zsg0MBQ96rpBYsziR46+qh3PreMvLKvOSVeSnzBHj4nN68/vs2AOKjzZzZL51Up43dFT4+XbaLj5ft4uphWazIr+K+T1bV2ubWYjcLtpXhCWjMmL2pJjjStZ2DZy7sT7JVIWhAkTfEcz9tYHV+JVkJ0dx4fA7ZcTZMMugnRUh1AENvjBYYgG51oniKG2VbkiQ1r7KAzpWvLiS03z1obLcUgrrBJyv3EBdlZto36yjf2yKxXayV+07tzoeL8/h5477vgbW7K5h+Xh82FbmZva6QoR0TGNwhHhtGvaOQSJJ05CIWwMjIyCAvL4+0tDQ6derE119/TZ8+fZg9ezYxMTGR2q0kHTJdKDz305Y6iQsvG57NzKV5dZoCDuoQT7ErwLzNJRRU+eiTHsfzlw7glV+3sqfCx+t/HoxF12XrC+mwqCYFdyh8ztjrabIaCYZu0CnWyjtXDaHCF0I3DJw2M0LAxgIXFw7OZExuMu/M38GPawvISXYw7Zw+bCt249Xh8e831Nlm/8w4QrpRZ96GgvBIJjNvGM7mIjdXv76I6ktkW4mHnzcWM+WMHpzSLRkhrx0pAhRvCYZiBlN9YwEcPsPmRPHILiSS1NopJoU3526pFbwY1imBPhlO7vpwOdPP7cu1b9RukVhQ6eeOD5bz8uWDmLu5hNQ4Gw+d1YufNxbz/qL1WE0qlwzLYmzXZKIMQz4TSlIERCyAMXHiRNatW8eQIUO49tpruf7663nzzTcJhUJMmjQpUruVpEPm1w3W7amqM31ETiJXv157lJFLhmZR7Arw2Hf7fpztLPXy3Zo9PHVhfxwWVQYvpMMihMADfLokn5lLdyGAiQPSOaNPKtFQp1uFEAI/UBnQ8AQ04qLMOMwKyhEGPDRNxwbYrOreKQYhITitd3uyEu3c8OaSmmXzyrz8tKGIaef0RlXCyWv/6LxBmTz948Z691XiDrA6v5IXft5CfZfIP79ax+guyThknlApAoS3JDz8aSMlotWtTkwl6xtlW5IkNZ+ATp3km5cOzebej1dyVr903lmws971gprBN6v3ML57Oy4amsUdHyynqMpfM/+BT1czMCuOR8/rS5k7SKzNRIxZRZVdSSSpUUQsgHHllVfW/P+IESP4+uuvWb16NVlZWXTr1u2Qt7Nw4UJeeeUVVq1aRVFRETNmzGDcuHH1LvvAAw/w3nvvcc8999Taf3l5OVOnTmX27NkoisJJJ53Efffdh91uP9LDk9oAqyrokBjN1mJ3zbTkGCuJDgsvXDoQzTAocwd5c942xnVvx19eW1hnG0Et/Mb55csHocsbk3QYPMBlry5ke4mnZtqj323gwyW7eO3KQUTtt6yiCMpDBje/t4zV+eGHLUXAeQMzuXlsDpZGOvcswAWDszj3+d/rnf/PL9cytGMCmQnRbC6qHcSIizazp9LX4LZX5VdiaWAo1oCms6vCS25cw2/IhRAEhcAV1AhpBg6riqOJWqxIrZviLWmUEUiqGdY4FJkDQ5JaPYsCnZIdLNlRjtWkcM6AdDom2Zk2sQ/tY228t2hHg+tuKnRxzoB0vl61u1bwotriHeXM31rKf2ZtIr/Cy596tuO+U7pjlc+KknTU6n+abGR+v5/09HROOumkwwpeQHg41tzcXCZPnnzA5b7//nuWL19OSkpKnXl33nknmzZt4tVXX+X5559n0aJFPPDAA4dVDqntUXSDG4/Pqfm7OofFg5+u5to3FnPDm0t4ds4mrhndCX9Iq9PVpNqmQhe7K7yEDjLOtyRVU1WFH9YV1gpeVNta7Gb2hiJUdd/Xs9uAP7+2qCZ4AeFhfd9btJPX5m1HqI3zVa7rBt6AVpOY84+q/CEqvEFuH9+FTkl2khyWmnkuf6jW33/UpV0MhfU85FVTD/B2XFUFJUGd/3tvGeOf/IVTnvmVC16az6L8KkJyeFfpIBRfCYal8bquGlYnir8StMDBF5YkqcXSQzp/HpFNtEXlPxcPoMoX4qwZv3HjW0u4/L8LSLLbePDMnvWu2yHRTny0lR/WNtydbPa6QoZ0TMAw4OtVBUz/fgO6fFaUpKMWsQCGpmnMmDGDUaNG0b9/f3buDDfDevLJJ/nggw8OeTtjxozhtttuY/z48Q0uU1BQwNSpU5k+fTpms7nWvM2bN/PLL7/w0EMP0bdvXwYNGsTf//53vvzySwoKChrYonQsMAyDzFgr087phc2scM+p3bn1vWUsy6uoWSavzMvt7y/HGWUmwd7wj7MKbwhXUEbVpUPj0w0+Xprf4PyZS3bh22/Ujj2VfnaU1g12ALw+bztVocY79w4lHpDmjOKSYVnceVIuz186kN7pTr5bvYfrxuTUu3yszUS/DCdaA12soswqqc6GW19UanDhS/NYsqO8Zlp+hY9r3ljMjoqGgyKSBKB4ShptBBIIdyGBfclBJUlqvZJsJt65Zigv/ryZL1bsrsmH4fKHmDFnE/nlXk7u2a7WOoqAS4Zlkeq0YjpAQEJVRK373mfL83GFZFdjSTpaEQtgPPfcc3z88cfcddddtYIKXbt25cMPP2y0/ei6zl133cVVV11Fly5d6sxfunQpsbGx9O7du2baiBEjUBSFFStWNFo5pNZJ1Q1OzElk1u1j2FrsosIbrLOMYcCM2Zs4d2BGvdvok+FkU6GLqgbeWkvSHwkBZrXhhx6TqtQEEoQQbCtxN7isL6jXjPhxKCq8AcpCBnmeIBUGuIUgoCr4hUJIESREm4mPNte7bqLdQpHLz4Tn5jL1i7VMmrmSuz5Yzv2nd+fB03twWs92/OW4Dqj7PdClOW28ddVQ4swKj53XF8sfWosIAdPO6U10A3cjVVX4bXMJZZ661ybAo9+up9wj34RLDRPe4kYbgQTCLTAAFK8ciUSSWjtFNzApCgu3ldU7/8152zm7X3rN37FRJp65qD+6Hm4xeVrv1Aa3Pb5HO+Zu3vc9oRvgDshnRUk6WhHLgfHpp58ydepUhg8fXqv7R25uLlu2bGm0/bz00kuYTCYuv/zyeucXFxeTkJBQa5rJZMLpdFJUdHh9WFtTS+V9P36atxytgmEQrcK8LaUNLrJiVwW3nNiFF3+ufe7aLSq3j+vKpJkrOblHyjFR381xbjVHvUbyOG2K4OIhWbVaFOzvsqFZ2BSBZhiAQXZidMPbMivYzCpCHPytjk8o3PfhSnqnO0mwW3hr/nbyyrx0T43hyhEd2VRYxfFdknn8/L5c/friWm+OVEUw9eyePDNrU61tVvlDXPW/RXx+03E4hMH1Izpw6dAsCqv8RFtU4qPM2BXQQho5cVa+unkk7yzcwbKdFXRKsnPF8GySo0wohgH11LXJJJi7ueE33avyK/EGNSxt5Nprad/dzXGdN/Y+FV8JWGPrO72OyL4ARtEhl7Wlfa6NSR5bZPd9KMu0xbo/XEdaF0IIth/gJYEnoNHeaeOda4ZiUhQqfUGe/2kzC7eVMaJTIg+e1ZNZ6wrZUlx7Gyd0S6HCG6TYtS/ArgiwW0xN8nnJc6M2WR9tS8QCGAUFBWRlZdWZbhgGoVDjRB9XrVrF66+/zsyZMxFNcEYmJra+4V9bY5mbS1ZCwz8S28faKPMEefGygXy+PJ+CKj/9MuI4s18a98xcSa/0WNo5o4iNqv/NdVvUlOdWc57Hkdr3qK4qA7PjWby99lufwR3iGd45ifjYfV0qgiYTmQlR7Cz11tnOFcM7kBYf3WCCzGqeQIhHP19DrM1EmSfA9O/2jaIwb0sp87aU8q8JvXjg89XcdXIu39wyijfmbWft7kq6t4/l/MGZPPXDBlbs18WqWpU/xJYSNyd0CzezTQE6pdRfb0nApFO64w1o2MzqQcsN0Cm54YTL7Z02zIrS5r7rWsrxNEc5GnWfoQD4K7HFJWNzNvwdf1gc4TeuTsUFSYdX1pbyuUaCPLbm22dbrvvDdSR10c5Vfws/CAcdil1+bnxrCc9c1J8Eu6XmPjh3SwkzZm3k1T8PZuHWUj5asgurSeGsfmlU+kJM/WJNrW2d0SeN9nE27Name1aU50Ztsj7ahogFMDp37syiRYtIT0+vNf2bb76he/fujbKPRYsWUVJSwtixY2umaZrGv//9b15//XVmzZpFUlISpaW136yHQiEqKipITk4+rP2VlFQ1mMixpREifJG2pjI3J92kckK3FP7729Z66+v6MTnM3VzMB4vyGNejHVeMyCbFYeWc537juJwkHjy9BwG3j2J3w6MwtBWROLeSDvIjoDnO40hfQyrw1Hl9WJFfybsLdyKE4MLBmfROjUENBCku3vdAZVEE/7tyMH99Zxlrdu8/CkkGlw/NorK84bdH1Sp1mLkkjxcvG8TVry+qd5knvt/IpFO78dd3lvLx9cO5bUxHKkIGj367nh2lHr4/QLKy3eU+ysrchzUqyKF2/DijTyozZm+qdwjWG8bkkBRjrfmcFEXgNyCo733b1cpGKmnq7+6WdO1F4tgV1x4SALceRaii/jwyRyLGbMdblIe3uO5Q3PVpy/dkeWxHpjGuvbZc94fraOoixW6mXayVgsq6OZXGdkvht00lBDWDJ3/YyK3junJKr1Q+WbYLgM9W7ObiodmMzElkdKcEFAE+A255d3lNPg0h4OQe7bjrpK54q3x4qyL/rCjPjdr2rw8ZxGj9IhbAuPHGG5k0aRIFBQUYhsF3333H1q1b+eSTT3jhhRcaZR9nnXUWI0aMqDXtqquu4qyzzuKcc84BoH///lRWVrJq1Sp69eoFwLx589B1nT59+hzW/gyDVvcl0BrL3NQURVAZ0HlnwQ6mntWLqV+sqZVT4ILBmSQ5rPRKj+N/c7fz6bJ8XP4Qf/tTLl/eNBKHWcWk6xxr1dyU51ZznseNue/qH9deLbzBaBMMy3AyOMOJECB0A1036pxLmmbgVAQvXtIfn6YDArtFRcVAhA7t3PMGNWwmlcIqf4PJNItcfqLMKsWuACWeIDFxNl78eSNfrtzD2G4pJDusFLn2PeDlJDuIsZnYUuyiW/sYQo2YTHR/cWaFZy8ewC3vLat1bV46NIuRncJdBA0DQkKwrczHI9+uZ83uStLjorhpbGeGZsdhbuCYW6qW8t3dHOVo1H16wt2PdGtso35HG9Y4hLv4sMvZUj7XSJDH1nz7bMt1f7iOpC7squDVKwdz5asLa42W1TvdyaVDs7nxrSUArNtThRDhlpLVAQzDgEBIp9KvkWgW6JqBTcAz5/ehKqjj8oeItZmIMauozfCsKM+N2mRdtA0RC2CMGzeO559/nhkzZhAVFcXTTz9Njx49eP755znuuOMOeTtut5sdO/aNw5yXl8fatWtxOp2kpaURHx9fa3mz2UxSUhKdOnUCICcnh1GjRnH//fczZcoUgsEgU6dO5bTTTqNdu9pZhaVjj6EIKnQodfv5ZtUeXL4Q/7l4AEVVfvwhjQ6Jdn7aUMSSHWWkxFhr1uuc7MCqKkQbBsgxvaVDIBSF3d4gD3y2uib3xdCOCTx4Rg9SbCp6qG7gojYDA8HmIjcfL8vHblG5aHAmqTFWLIdwR442q6iKwGFVD7hcdQLOIpcfIQRjclPolOzgo8V5XDO6E//6ai3Hd03m8hEdWL+nkjJPkOtGdyLZYUWgH/HDgRAQEgqekI5mGESbFGwiPLSr0A0GpMXw7S0j2VTkxu3X6JEag8OsYDbC15+iKizeUcH/vbO0Zpubi9zc9v5yrhrZkWuGZ6G2siCGdPSqE21WjxzSWHRrrEziKUlthEs3+NdX65hyZk9sZpXNRS4y4qPZXeHl5neW4g1qQPg+pSqCwH6t+hQBfk3nzg+W89wl/bFgYBhgxiDBJEgw7e0uIp8VJanRRCyAATBo0CBeffXVo9rGqlWraiXofPjhhwGYMGEC06ZNO6RtTJ8+nalTp3LFFVegKAonnXQSf//734+qXFLrpZgU3CEDVRHsqvSRX+6lX2YcJ/Vsx1cr9zBnQxGJdgtmVaGwyoduwMc3juCBT1cB4aSJw3MSCWlGBMfxkdqaipDOeS/Mq3kQApi/tZTzXpjHZ/83gpiDpPHxCoXr31rC6vzKmmkfLdnFpUOzuGFUx3qDGJoi9r4B0khyWLhhTCc6JjuwW1TcAa3O8l1SHOSVechJdpDqtLG12MMLP28mxmrmkmHZrN9TxTMX9ccX1Lj29UU1zWMB+qTH8uzFA7AaB39IUxSBzwBPSEcQTobr0wwe/Gw1P28swjAgt10M/zirJ50TonAHdTDCyU/7t3MA4cAG+r7En66QwYOfr6l3f//9bSsXD84kVl6vx5zqoU6NRhyFBMCwxqJ4Di8RuCRJLYuqKniBKm+IO0/uiioE0RaV+z9ZSZErUGeEr1GdkwiGdL5fU1Az7ZReqfyysZgVuyrYXOSmZ1J0+P4kSVLERDSAUc3tdmP84eHa4XAc0rpDhw5l/fr1B19wr1mzZtWZFhcXx2OPPXbI25DaLr+i8MGiXbwxbzuPnteHokof8XYLO0q8XDg4iyXby9lT6aPEva93/h0ndUURsHJXJRnxUfz9tB7M31pC98GZ1NspX5L+QKgKr8/dWit4Uc3lD/HB4jyuGZaF1kAXDNWk8PHiXbWCF9XenL+Ds/ul0THWUvOCRwjwonD/p6uYsyH8ltikwBd/HcX079Yz+cye3DNzZa2uJHaLytSze/HAJ6uYdEo3bnl3GeN7tOP/ju/MDW8toX9WHEt3lDMiJ5HLXllQK3gBsGJXJS/+uoWbR3XCOFDOCUWwwxXg75+urjmewdnx/O2Ubrj8wZoWHLvKvVT6grwyr4i3F+zA5QtxXOck7j45lxRb3VYkVf5Qre4t+zMM2FzsZkA7R517kdS2Kd5iDFMUmKwHX/gwGFYnauXORt2mJElNJygEC3aW0yPNiS+kMe3rdXRMctAlxc4lw7L59ze1f3skOSzcc2p39lR4mbu5BEXAn3q157Q+qdzybrjl35er9tB3XGcC9bwgkCSp8UQsgLFz506mTp3KggUL8Pv3PVQahoEQgrVr10Zq15JUixACP+A34M73lrFkRzkdEqPJK/XQOz2Op2dtJCM+mllrC5hyVk+2FrtZtK2U+GgL43u0Y1V+BUIIZt44Ak0zmDFnE1PO6IEigxfSIfLpxgGHAv1tcwmXDc6kobzk7pDBOwsb/rH01oKd3Dm+C1bFwNAN/Aju+HA5C/Yb115VFDYVuvhhbSGKELx8+SBmry9kV7mXbu1jGdwhHrtZ5d7TuvP49xvYXORm809b+NufchnSMYEPFuXx1tVDmL+1tE7wotr7i/K4fFgHKn1B4qPMOEwK/CGYURrQueDF+bWa4C7cXsafX13IBzcM54FPVrFgWxn3n96DJ3/YWCto89OGIn7fXMLMG4bT3qbW6q5iUg/chCXaosIxl6lGUjxF6Na4Rt+ubnVi8i49+IKSJDU/VcEV0inzBrGaFOKjzLy9MI9e6bEsz6vg2Tmb2FDg4i8jO3LzO0s5Z0AGL18xiB/WFFDs8tMvM57c9jHklbqJsZl54bKBtI+18fHSXdzy7lKCe/NaxdpMMseCJDWBiAUw7rrrLgD+9a9/kZiY2CTDnErSHwkhqNLhgc9Xc+7AjJrcAw6biQ6Jdl78eQsOqwlFGHRKdvDE9xsA6JEWS6UvyM3vLkURgqyEaNLjook2K0w9owdRyJ9C0qEzK4LkGCsbC131zk92WDErArT6zyodo97WG9U8AY2nftzEtaM74VSgMqDVCl4A2Mwqlb7wyCbfrSngx3WFHJeTSHKMlXlbSpgxexOvXDGIq16rPULJK79u5e4/deOemSsprPKzraThkRx8QZ31BS7+7+0lWE0Kfz2hM+f0TcNc3TREVXhh1sZawYtqVf4QM5fs4p5TunP7B8uJjzYTH20hOzGa7fvtM6DpPPLteh45uxfqft1VYswqPdNi622lEm1RyYiLkq0vjkGKtxjD2rjdRyDcAkPxloKhg5B9kySppQoKwYdLdjFjzuaaLiFv/GUIb83fzvRz+2CYFTYU7Ls3982IY0eph3s+WkHfzDhio8x8s3o3079bzy0nduGbVXvYXeHl3tO687+522rt68y+aQQPcK+WJKlxRCyAsX79ej766KOaZJqS1By8wFWvL6Kd08bC/X7QbS/20C7WhiIElw3LJq/MS0iD4TmJdGsfw7NzNrN4e3j50V2S2F7sYVB2Ag4MDONgyRYlqTaTYXDtqI4NtsK4emRHRD2jj1SLVhXGd2/HB4vz6p1/Sq/2+EIav2ws5vQeKRSXeessU+kLkuqMqvlb0w1+3rgvCWGM1VRvkKTYFcBhNTEoO54lO8rok9FwMsTOKQ52V3hRBCTHWHlz3g7ax9o4MSeBUEjHpxks3F7a4PrLd5bTO93JUxf2Y/H2MrISojmxewppcVH8++t1bCkODxf766ZiAoZB1H7r2hSYfm4fLnhpHpXeUM10VRE8c2E/7Aogc6gdc8ItMCITwBCGhvCVY0QlNPr2JUk6eiaTwqxNJTz+w8Z90xRBuTdIWlwUm4vcRFlUjuucyNjcFNrF2BiQFYc3qHPxkCyKXX6mfb2uJvCR2z6Gl37ZwondU1j8h5cEN43NIclmksNcSFITiFgAo1evXuzZs0cGMKRmVVDlZ0uxm7hoc63RFxLsFqItKjeM7cRfXl1Uq++81aTwxAX98Ic01u6u4srjOuIPhoizKAT9ofp2I0kHpOsGXZMcXDOyIy/9urXWvL+O7UyHeBvGgbokaTrXj+7Et6v3UOmrfQ52ax+D1aww/bv1HJ+bTJWWTIK9bn//03unkuiwcFrvVL5cubvO/KtHdeSjxbvqTHdGmfEFNa4e1ZF7Zq7kjpNy6ZEay5rddVs63Hh8DrvKvLxyxWA2FbkwqwrJsVbcGlgJ5+FIdljZWVo3wALQOz2W2CgTF744D89+fYgT7BaeOL8vf/toJXsqfdgtdW9dum6QYlX57Mbj+GVjMb9vLaFLSgxn9G5PrFmR+WqOUcJThO5IbfTtVo9qoniL0WQAQ5JapKqQwZP7BS8ANMMgyqziDWgMyI4nENI5f1AmGwqqOOe5uTXLvTFvO8M6JfDouX24+d1lDMiKo6DShyoEfz2hC9uLXfiCGnHRFs4bkE6y3YxJ3mckqUlELIDxz3/+k8mTJ1NQUECXLl0wmWrvqlu3bpHatSQB4ZEOtpeGm57nl/sY170dXyzfzSMT+5Aca+WnDUV8sDivTuI/f0jn3o9X8ui5fVEFJNjNWFXrgX9gStJBmA2dvwzL4tyBGczfWooiwsOoxpiUQxreM84k+PjG4/jP7E3MWldIlFnl7P5pDMyO5473l1PpCzF7XRFdUhyM6pJcqzvFab1T6ZcVx8Tn5vLIuX1JjbPx/sKdVPpCtI+1ccu4zlR4Q8xeX1hnv5cOyyLVGcUrv26h2BXgkW/WMeOSAXy8ZBefLc/HH9LJSXZw49gc7BYT+RVeHtvbFQvCb7seOrsXx3dKwALcMCaHa95YXO8xnt0/gytfXVAreAFQ6g4w7Zt1/Pm4Djz89TouHpqFXRHof+hyo+sGduD07smc2TMFwyDcnFdeu8csxVOElti90bdr7B/AoGujb1+SpKOnGQa7K3xAOLl1v4w4eqc76drOwXOXDuDRb9czd3MJj5/fjxmzN9dZf96WUoZ3SuSuk7rSLTWW71cX8OENw9F1nQHtYxiyt0ViICDvM5LUlCIWwCgtLWXHjh3cc889NdOEEDKJp9RkdN0gO8HOvyf2waQIkh0W/vfnwXgCId6Yt51RXZJYkVdR77rlniAxNhOxVpUf1hZycq/2aCHZr1E6OiYjPC786d2SAdA0/ZCbm+q6gVOB60Z1ZHTXJDLjo3nihw288PMWDAP6Zji56YQuPPbdelblVzD1rF488NkqVudXcu7ADP7y2kIMA25/fxljuiYz5axeWE0KURaVDglRbC/1kh4Xxa7ycOsIRcB5AzM5q186f/nfQgZlJ/DONcPQjfAY9xcPzeKqUR0xCYEnoDH9u/Wc3LM97yyonWw0pBtMmrmSz286DjCwmBSuGJ7Na79vr1lGCLj5xM6UugMUuwLUZ+3uKm4fb6d7+xguHZqFfoCRTkINjOYiHWMMHcVbim6La/RN17TA8BQfZElJkpqLKgTtYq0MyIrj0mEdMAyDuCgTmmGwdEd5+PmuZzt+WFvQ4DY+WrKLly4fiElR6JgUzW+bihnXLQVdN+RoI5LUTCIWwLj33nvp0aMHjz/+uEziKTWbdrFWpnyxmhO7t6NnegwCgVlV+WBRHsM6JR5w3QpvEMMw6JBkJ96qYsgfRVIj0Q40zOgBGAbEWsKjbzz/02Z+2xTOqSEE3D6+Kx8tzuPmE7sQazPhC2nceVIuDquJpTvKa+IkhgFz1hcxZ30REO4y9eLlg3jws9XcdEJnkhwWPAGNWJuZ2esL8QU1LhuWTak7wOX/nV+TbT3ZYeUfZ/fkw0U7mXRKd87pn8HLv23BpAgGd0wg1mZi/Z6qmqSfb87bQYUvyDer9vDMRf04o28a87eWoiqC3HYxfLN6NxXe+oMX1eKiLLx46QCiDP2I8tCoqsCjg18zUBVwqMqBh3yVWjXhK0MYIYwIjEKCKQpDMSO8MoAhSS1VrFnhtT8PZmepl90VXpZuL+PioVmUe4K8vjeIHm0xUekNNriNCm+Qck8QX1AjLT6Kjgl27EI2uJCk5hSxAEZ+fj7PPfcc2dnZkdqFJB2QalJ4f2EeVb4QJ+YmYzGpTPtqHWf3S8cb1NB0g9goU62Ef9WEgDSnjbwyLwMz4xAyeCG1EGbDYHROAj/ufWPULzOOa0Z3xKQqtHNGccf7y/EGNYSAMV2TueXELuyurD/nBIS7TLl9IbaVeLhn5srwPlRRE6jomRZLcoyVh79eV2u9Ipef295bxnOXDGTyZ6uZelZPBmTFc/v4XH7ZWESJK8CVx3Uk2WFlyuer2V3hJT0+nHbzr+8s4y/HdWBcj3Ys31HO92sKOK1XKmnxUSgNPBg6rCZSHBZsR5hEVxOCNUUepn65lk2FLmxmhfMHZXLNyI7hgIh8GG1zFHf4GolECwyEwLDGyRYYktRCKYqg0Bcir9zHs3M2c2rv9lw0NIufNhbXGpVr5a4KLhqSyXdr6m+FMaxTIjtKPaQ5bfRJj8OucMAWgJIkRV7EAhjDhg1j3bp1MoAhNZsqDb5auZsZFw9AN3Tu+mA5i7eX0yMtlgFZcby3cCc3junMtG/W1Vl3Qv90nNFm0u1mNBm8kJqY2awiRLjbSL3dIfZmSD+hezvyy734Ajpz84t56ZctNYtUt7TwBjRuOD6H//66rd599UqPZWuJu/bm98stkR4Xxf2frq53XV9QZ3leOS5/CG9Qo1Oyg7/8b2HN/M+W55MRH8X08/qyeHtpzSgsMVYT87eW8vOGYvpmOrn75FzUoIYm4IrhHXj1D0PTAdw+vgsOtW7ei0OhqgqrC91cuV/ZfEGd13/fzoqd5fznwn5Y5dhCbY7iCbcy0m3xEdm+bnWieIsism1Jko6O14C7P1rBpcOyOaV3ewZ1SGDt7ioe+WY9Z/RJZXinRD5YnMemQhepzigyE6LqJJi2mhQuGZpFqtNGik0lENAwZK8RSWp2ERu8fOzYsTz88MM888wzfPvtt/z444+1/klSpGmGweXDO7AqrxyEwuLt5QC8s2AH14/J4fctJZR7A/xrQi86JtmBcLP4e0/txi0ndsFuGDJ4ITUpQxFU6vDG4jzu+WItn6wpxGUIFLV2FzzDMMiKj+bz5fk89t0GUmKtvDZ3e73bnL+1lESHlX6ZcXXmKQLuPaU76+oZUaRaSoyN7X8IcOxv235DEv/j87qBjrwyL28v2MGJ3dqxJr+SqWf14uGJvRnTNZkLh2RyZt80dpX7UBSBohtcfVwHppzRg5SY8EgqWQnRPH1hP/7ULeWI33p5dIOpX9afd2lZXgX5lX5kL8e2R/GEk9JWJ9xsbIY1tiZIIklSy1LhC5Ff7sPl1xjVOQm3P8RTP4ZHJPlm9R7O6JuGwxp+jzv5s9U8dFYvJvRPx2oK/zQ6rnMiL10+iFibiSSrKvNdSFILErEWGJMnTwZgxowZdebJJJ5SUzAMg74ZTlx+jc2FrprpBZV+3l24k+cuGcALP2/hx7WF/HlEB7qnxZLssOAwKxDUZP9GqUkJVWFdsZu/vLaopgXEVyv38LjVxNvXDCUtyoS+30lZ4gnU5LGo8oVwHWCI340FVUw/rw8fLd7Fm/O2U+UPMSArjrv/1A2bSXDuwAx+WFuIN1j7Ae2sfmkUVfnonOJgQ4Gr3m13TnGQX+5hwdayBq+Z79cUcMGgTJ66qD/P/LiR5fslz7WoCk9e2I92djMWwKLrnNE9hRNyk9EMMAmIVsQR5w0B8GkGm4vqLz/Agm2ldOmfJpN/tjGKuwDd7ADVEpHt61anDGBIUgvlCWoUu/x0TLTjDer4Qjp5ZeEWFkHN4N/frOPZSwbwv7nbmLO+kP97eyl3ndSVv57QGU03UBTBuvxKOidFEwrK4IUktSQRC2CsW1e3Wb4kNTYhQBcKvr2/nOyqQDcMXJpBIKhjNat8tCSP0V1Taq03a10hK3dVcOHgTLq3j6VDUjRCgF0RGDLKLjUDt2bwf+8srdV9A6DKH+K295fzv8sHYt07zWRS+HXjvr73ihCYFEGogQhCbJSZic/O5ZTe7fny5pEYuoFZEUSZFcr9Gg6bhZk3juDdBTuYvb6IuGgzV47ogN1q4oHPVnP1qE41+TH2F2M10T01hr6ZThZsKW3w2DTdIMqs8tHSvFrBC4CApnPru8v45pZRWPa2gtA0HVv1AgZoR9BtZH8mRWA1KfgbCFAkx1hlDow2SHEXYEQlRGz7hs2JUrrh4AtKkhRRwqTg0Qx2V3gxqQqEdBLs4cClP6SxaH0pHZLsOKPMVOxN2Lk6v5L/e2sJEwdm8PRF/cmIi+KrlXvYXeFlQ4GLPulO+qTHYtGPLGm0JEmRE7EuJIfqjDPOYPfu3c1dDKkVEoqgNGTw4NfrGP/UL5z+7G88//t2ykMG93+2hlJPAJcvyJ96pZIeZyM9LqrW+kVVfp6ZtYlPl+fz/qI8HFYzQpdvYKXmUVDlrzehLMCmQheV/n2BNUVViLbuiz/PWlfIqb1T6103OcZKlS9EhS/EuwvzCGoGdmGgA+8s3sX5L87jT0/+zD+/WMOE/um8e81QHjuvL0kOCyvyKqj0hdhZ6uH28V2Jtqg1281OjGbGJQOIjzbTMSGKvllxDR5bj9RY2jmtfLYsv975AU1nyY4yVDUy/TgcqmDigPR655kUweDs+KNq4SG1TKqnIDIJPPcyLE4Ub/EhD4UsSVLjUhSBG8HjszdzytO/csL0n/j395twIbCogmcuGkBOip1e6bG0j7FyxYgOtdav8of439xt3PXBCopcAT5dvgsQvPjTln1Jo+XlLUktTsRaYByqvLw8QqGGmz5LUkPKgjpnPzsXz94WE/6Qzku/bGXWuiJmXNyPUk+QR79bz6JtZfRKj+W5SwZw49tLapoQAgzMjue8QRlsLKgiyWHBfYDRGqRjk2oKD1tq6HqtLhyNzXeQJqqariOEglcovP77dgZ12Pdm+ZOlu/jPxf0pqvLz+5aSmuntY21Mm9ibKZ+vqZlmUgV+YNLHK/l1075lF2wrI6/cy6+bivnf3G2UugMMzI7nn2f34qMl4cDHY+f3pV2MFYtJxWFVsSkCs2FgGAZdUxyM7prEzxtqj8qgCPi/sZ0BGmwBAVBQ6ds73Hbj17Gh6dwwJofleRWszt+X78OkCJ69ZAAxqhwTry1SXHswrJFJ4Amg25wIzY8IujAsMRHbjyRJ9XPpcMFL8yio9NdM+3BJHrPWF/LWVUOYs66AjknRzFy8i9kbCnn03L6c3ieVL1bse3EaF21m2jl9eHb2Ju46uRv/mbWRFy4bSLS8LUhSi9XsAQxJOiKqwowfN9QEL/YX0DQKqwJc+eqCmpvPirxKbnx7Cc9c2B9fSGdbsZvkGCsFVT58QY3Te6cSZTHRcKpC6VjjR/DrpmLemredgKZzbv8MeqXFEEVk3shkxEehKgKtniemuGgzsTYzXuCmt5ewfFcl5w70c8f4rjz2/QYCms7N7y7lryd04doxnfD4QyAEHn+IyZ+tZnuJB4DjchKJtZnZWuyqFbwAuPPkXN6ev6NmpBCAhdvKWLx9MU9f1J+Xf9mKRVFId1hQdCP81lkLt+QAUAyDu07KZUROEq/+trUmAHLVyE68v2gnDmsWHRKj2ba3LH80MMKtIKIMnRcu7s/Och/zt5bSLtbK0I4JOGTwos1S3AUEnVkR275hjQvvx1OEJgMYktSkVJPCdyv31ApeVCt1B/h0WT5Xje7EOfu96Lr9/eVcO6oTn910HFuL3ZiUcLfjb1bmc9tJXYkyKTw8oTcxChF9YSFJ0tGRAQypVfJqBrPX15887bKh2Xy8NI8ROUms31NFkSt8c8sr8zLhubl8dMNwkhwWUuNsJMdYsajgUOSNStrHLwQPfb2eb1bvqZn249pC+mY4+c9F/bEZjf9D264Krh/diRlzNteZd/9p3YkxKeyo8tE7M54e6U4KK/10a+9g5g0jeH/RTso8ASwmBauqUKkb/O2j5fiC+8qZaLdw32ndeWfBdlz+2oG/aItKZkJUreBFNd2AGbM38cKlA3EIo958FCEhqAxofLFiNymxVl66fBCabvDDmgLumbmCYleA7IQo7j+tO1e9vrjO+j1SY8hw2jAi2FbXMMCKQZc4K90GpYfjL5ougxdtlaGjeArQbZHLgVHdPUXxFKHFdYrYfiRJqsuvw9er9jQ432JS+H5NAX0z41ixsxx3QEPTDZ77aTMv/LyZV64YRGpcFJpuoPZU6JZsR9XCLS1lb2JJatlkAENqlQTgsJpqkjEBqIrg7pNzGdsthS7tYli3p5Kz+6dhGDD1yzU1+QXmbi7h+NxkSl0B0uOiiDUd3QgHUtuiKIJNRe5awYtqy/Mq+GFdIWd1T2n0c0bRDS4ZlEG39jE8PWsTO8s85LaL4a6Tc+mcEEVlSOfXTSWM7pJEUNNZtK2MKp+GosC5A9PZUODif3O38c8CF2f2TePZSwby04YiCip9jMxJZETnJLYUu+me6sTlC3Hfad3RdIPZ6wrxBDRW/iG55v7W7q4iENLR6smaFBKCD5bl8/gPG2tNH5GTyPmDMil2BQDomeakV/sYXrpsIA99tZbtJR4sqsKE/uncNDYHm9E0idIMAznayDFAeIoRegg9KjFi+6hugSHkSCSS1ORUQc0wqLWmK/DwOX3ok+5kV7kXu0XlsmHZbCt289j3G9B0A92At+bv4J5TuuHxh+iX6UQENeSdQZJaBxnAkFolu0lw8dAsHv12PQAdEqOZfl5fPl++mytfXYjNrHB6nzSizCZe/HkzT17Qn+veCA9PGR9tYWuhmyGdElADIQzNQEQmd6DUChmK4I15Oxqc/9b8HYzPTa4ZEaQxmQ2D47LiGHjFIPy6gSLCicjKvEEueXk+j57Xl+nfrWft7qqadZ76cSMPn9ObSl+oZqjTz5bn89XK3YzISWRETiKpcVHc8f5yCqv8PHxOL1bvruCLFbsxqwqn9GrPkI4JbCpseJhRkyIwNZCfosATrBO8gHCgcFinRPpmOCmq8jMwMw5F0xmYGsObVw7Gp+mYFYHdpIAms7xLjUt1h/u4G5EMYJjtGIpZDqUqSc1ANQwuG57Nz/uNyJXqtPHiZQP5bHk+T/+4EZMiOKVXKpkJ0WwoqOIfZ/bkvk9WAeCMMrOj1MPiHWVcMzSLQHMdiCRJh63FBzAWLlzIK6+8wqpVqygqKmLGjBmMGzcOgGAwyJNPPsnPP//Mzp07cTgcjBgxgjvuuIN27drVbKO8vJypU6cye/ZsFEXhpJNO4r777sNutzfXYUlHSQvp/Klne2atK2RFXjn/OKsX176xmFL3vlvQUz9upE+Gk8uGZfP1yt38qWd7vly5m0HZ8Tw7ZzMDs+NQD7AP6dhkGAdONukPaRHNSu7TDX7fXs7j329gV7mXSad047vVexjaKZEf1hbUCl5Uu/fjlXx8wwiU/dI5hHSDTYUuLhqaxTWvLwLgv1cO5q4PV9TqM/zsnM38sLaApy/sX2v9/Z3WO5Vok4A/dB8xW1Te+Xlrg8cyc0ked/8pl+7tYrEroOv7hki1VY84Ils/SRGguMIj3kSyBQZCoNviZABDkpqBrht0SLRzaq9Uvl69G0XAvyf24bo3FpNf4atZ7rmfNpOT7OD28V0pcvnpmGRna7GbU3un8td3ljHj4v6yVZ4ktTIRCWAEg0GuvvpqpkyZQocOHQ647D/+8Q8SExt+wPB4POTm5jJx4kRuuummWvN8Ph9r1qzhhhtuoFu3blRWVvLPf/6TG264gZkzZ9Ysd+edd1JUVMSrr75KMBjk3nvv5YEHHuCxxx47quOUmk9QUQgGNO4Y35Vkh4XXft9eK3hRbUVeBUHNYPH2Mm4b35WB2fEoAs4bmEEwpGORLS+kP1CBc/qn8+um4nrnn773x7weavwohlAVPluxm2nfrK+Z1jHJzpId5Tx1YT8mfbSy3vUMI9zi4b7TeuC0mbDbTOyp8OGMMvHs7M3oBhyfm7y3S0ndhGcbClys3FXBjIv7c+PbS2sFaDITorh1XBcUXce/N9dFhTdIfLQFpwlcvmCd7VWr8AbpmRpLtCH7FEtNS3HtxlDMGJbYiO7HsMaheAoiug9JkuoXbVa4fXwXrhiRjUkJJ97eP3hRbXORi7wyD+v2VHFq71R8QQ2H1USa00anxGiZsFOSWpmIBDDMZjPr168/+ILAGWecccD5Y8aMYcyYMfXOi4mJ4dVXX6017f777+e8884jPz+ftLQ0Nm/ezC+//MKHH35I7969Afj73//Otddey913312rpYbUOvgUhXs/WcUvG4txRpl55YpB9eYrqPbThkL6ZMSRk2wnLc7Gmt1VrMmv4MqhWcgBvqU/0jSdQdlxdE1xsOEP3SqSY6xcOCgTPUJva1whg6d+3FRrmr73HDWrCt4DDLVa6glw/qAMXtk7lLDFpHD+oAxuPrEzd3ywnGEdE3lnYcNdYz5ZtovTeqfy3ysG89vmYio8QfpnxRFrMzN/awnDOyVxy7tLWbZfroyRnZO477TuzFpXVG/ZRnZOIkoIeZ1JTU6t2oUenUSk+wcaVieKW7bAkKQmpwi2FHu45d2lnD84k1N7p/LZ8t0NLv7jukJG5CQyvkc71u+pIqQbvHLFIKKaKP+SJEmNJ2JdSM4880w+/PBD7rzzzkjtol4ulwshBLGx4bcuS5cuJTY2tiZ4ATBixAgURWHFihWMHz/+kLfdmvIkVJe1NZX5UIQUhSmfr+GXjcVEW1SeurAf24o9mNV6sgvuZVYV0uJsGAZM+XwNFw/N4k8922Pb26Qd2m59RUJz1FVTfy52EX6w+XzFbt5ZuINgyOD0vqlcOiSLGJWItSYo9wbrBALK3AEePLMnGfFR9M+MY+nO8nrXHdUlievfWMyW4n3DlD7xw0Zy28XwjzN7savce8DrxKqqlHuCbCp0cemwbDz+EG/M28H7i3fyyMQ+3PzuUpb/IdHnr5uKmf7tev5vbA7Tv9tQe3smhZvGdkYx9HDW3UNkMim4QuEka1ZVYDL0A8Y/2tq129KOpzmu88bYp1qVhx6VdDin3hHRbXGort0HLXNL+1wbkzy2yO77UJZpi3V/MOUhg7+8tpD7TuvB8p3lzFpXiMXUcEVYVIWOSXZW51fwt49W8sNto4naO6JYW6y/Y/ncqI+sj7YlYgEMTdN45513mDt3Lr169SIqKqrW/HvuuafR9+n3+5k+fTqnnXYaDocDgOLiYhISag+jZjKZcDqdFBUd3luTxMTWN857ayzzgWwtcvHjukIALhicyeu/byeo6ZzWJ5WXf6m/L/647u2It5u57s3FFLv8DMiMJzMhiihL3dO/rdVXJDVlXTXH55IIXDcmh3MHZWAYEB9txmKKbNaU0j2181tkxEfRIdHOo9+u5/k5m5lyVk9ueHNxnTwVPdPCAdv9gxfV1hdUUeEN4g2EmDggnX9/U3/ruIuHZuGMMvHewp2c9MTPRFtUzhuUyRPn9yMl1loneFFt1vpCJp3SjR0lHj5Zlk9A0xnVJYl7T+1Op2QHZlPDQZM/Kqry8dWaAl74eQulrgBDOiVwx/hccpLtWM0Hrvu2du22lONplmuvMfbpzQdne5zO6KPf1oE4U6BoBUlJh1bmlvK5RoI8tubbZ1uu+/poms5/v99AVkI4l93HS3fRO93Jab3TWLu7/nvcab3bk+q0cdFL8zm+azIpsTZibOamLHazONbOjYOR9dE2RCyAsWHDBnr06AHA1q21f1iKCIS/gsEgt9xyC4ZhMGXKlEbfPkBJSVWraQktRPgibU1lPhAhIIhCmSdYczzDOyXy2txtGMAVIzowZ31RnZEUTunVntz2Dp78YRMFlT5eunwQiVYFd6UX9x+235bqK5IiUVcHe/hvjs+l+jjVQPicqww0nOuhsdjNgq7tHDWjiUw6pRu3vreMwqpw3ooPF+fx3KUDeeGnzSzZUY7donLBkEwm9s/gr+8saXC7c9YXcemwLAwjHOxYnV9Za/7oLknYzAqbi9x8uGQXAJW+EK/8upXTeqdy0ZDMBrdtGOD2h7hrfBeuHdMJly+E3aoSa1apKHc3uN4fBYTgH1+t45vV+/IJ/Li2kJ/WF/HuNUPp5LTW20+5rV27TX08Lenaa8xjTyjbQSC2C/6KukG9xmTBjs1TTElhOSgNB9na2nm6P3lsR6Yxrr22XPf1UVSFqpCBalJYX1DFmf3SeH/hTgBW7qrgujGd6JPhZMUfAu6jOifSLyue299fRvtYGw+c1oOA20exq26+jLbiWDs3Dmb/+pBBjNYvYgGMN954I1KbriMYDHLrrbeSn5/Pa6+9VtP6AiApKYnS0tJay4dCISoqKkhOTj6s/RhG6+vK3RrL/EeqKigJ6Pzz69XcdVJuzUgJQc2oeRN95wfLeeisXhRU+fhpfRE2i8olQ7Jw2Ex4AzrH5STyt5O7EqMqB8xf0Bbqq6k0ZV015+cS6X2rqoKihIO6QtN5+oJ+XPzKAqLMKpXeYE3wAuD7NQWsyCvnoiFZ3HB8Dl2SHRRW+fh9S8kfBwipJdqikJUQzXnP/87kM3tS6Q0ya10hJlVwcs/2+IIaHy/N57JhWXXW/XLlbv5vbE6D21YEOGwmlu6qwBvU2V3h46MleWTGR/Pg6d2Jxjik+iv2hmoFL6qFdIPJn6/hxYv7YznAdtratdtSjqc5ynHU+wx6ULwlaNHJEe/brtniEYaO8BSj2w+eU6ulfK6RII+t+fbZluu+WlBR+HzFHjYUVNE300nvdCfOKDNFrn33yL99uIJ/nN0Lb0DjhzUFqIrggsGZdElx8OGSPG4+oQu90mKJ5thJLH0snBuHQ9ZF2xDxYVS3b9/Ojh07GDx4MDabDcMwGrUFRnXwYvv27bz++uvEx8fXmt+/f38qKytZtWoVvXr1AmDevHnouk6fPn0arRxS5FRqcMFL8xnfox15ZR5O653G5yvy0XSd2CgTld4Q5Z4gN72zlC4pDgZ1iCeoGditKst2lDGqSwpn9kghGNQil7xAkvYjBEQpPiyhSoShETI5cBNbqwWBoghcOszbXMKPawtpF2vj/EEZtLebef/aYWwqcrNoW2mdbRdU+nnyh40AzL59NIkOKz+sKeD0PmnMmL2pzvIAZ/ZLB6BzioPb3ltGxyQ7QzsmoBkGj323noJKP5PP6EGFN9zKJLddDON6tMOiChZtL0PTDIZ3SuD3LXXLc1qfNMo8QZ78YRObCl1kJUZzxfAOlHsDvPLbVv5yXEcEYFEEJsPAqOfpwWRSmLelsMH6XJ1fiVfTsSiy86p0cGrldoBDCigcLSMq3EVV8RQ2yf4k6ZikKvzv9+28+MtW7jwpl27tnagKfLosn36ZcXy/Jhz8dgc07nh/OdmJ0QzvlMgJ3VLYWFhFe6eNK4dnY9J1QkEd+SQoSa1bxAIYZWVl3HrrrcyfPx8hBN999x2ZmZnce++9OJ1OJk2adEjbcbvd7NixL3N+Xl4ea9euxel0kpyczM0338yaNWt44YUX0DStJq+F0+nEYrGQk5PDqFGjuP/++5kyZQrBYJCpU6dy2mmnyRFIWgFVVfhhVQFxUWauG92JdbsrufnEzugY5MQEuW1MFlO+2VKz/MZCFxsLXQzrlICmwwnd2mEShIMXktQEFEUQp+WjfD0JsWUWGAamlO6YT3kcV2x3AroJIaBCM7j45QXsqdzXhPWNedt54PQe+IMaczeXcEL3lAb3k2C3oApBlBJO4Nm5XUy93UNO6dWebcVupn29lmkT+3Dhi/PYWuxma/G+7h3tY21kxkezp9LHY+f3pdQd4LNl+fhDGqO7JhNtVXlkYh/u/2w1c9aHv2MVAaf1TuW60Z0459m5BLTwI+GmQhf3f7qKW07sQp+MeCY+/zsV3iDDOiZw18ndSI028cckHoYBdmvDze8VAWobzrylKALDOLSWKtLBqRXhZwbd3j7i+9Jt4ZcmimsPJPc+yNKSJB0JV8jg1bnbyIyP4pz+aYDguzV7mNA7CatZZfa6QkL73Ve2l3io8AaZODCDgdkJxNpMiECICIx+LklSM4hYAOPhhx/GZDIxZ84cTjnllJrpp556KtOmTTvkAMaqVau4/PLLa20XYMKECdx0003MmjULgLPOOqvWeq+//jpDhw4FYPr06UydOpUrrrgCRVE46aST+Pvf/35Uxyc1DQ3IK/Py8hWDePz7DXyxYjc3je3M/7N33uFVVOkf/8zMLUnuTe899N6rFJEmKiKCNEWKiiiWtetaf+vadXVl17piAwQEUVREUVSU3nsLLUAIpPfcOjO/Pw5JCLnBRkgI83keHmDmzJwz5965M/Od9/2+t3ULo+XG/yMupifWK3rx2sosckrcWE0yYzrHMKxjIhF2K3aLAm5vXR+GwUVEsJ6D8vFVcHppxaw9KLOuxn7Lz+RbG6NJMq98v6eKeFHOM9/s5qPJ3Xj5+31M6p2Cv1nxWaL01r6NsCsSqqoxskMsRwqcPDC4OQ6vyqItGVhNMqO6JBBhtzJjxSEO55RR5PDw1g2deWXpPg7llCJJ0K9ZJDf3aUSJ00ug1czHa9JYd7gy0iI1s4SvtmawYGpPXrymNcUejVKXF7ufGatZZth/V1aIF6fz9vKDvHVjZ3JK3ACsOJDLmkOrWXBbT5LslirRKKqq0aNRODVVXB3cOhqbSQYf/VzIeCSJApfK4ZxSQm0W4oP9sP9+z1ODGlCKjqArVnRrcK33pVuD0SUZubR6+pOBgcG5weHx8tqYjiSF+uP0akz+YAMvjmpHvHaU4F/+xac3PMHjy3LYe8oIu3fjEP4xrA26rHA0t5TgUD+8hnphYNBgqDUBY9WqVbz//vvExFR9A5KSkkJGRsbv3k+PHj3Yt8+3ozBw1nXlhISE8Oqrr/7uPg3qD2ZFZmz3BPZnlfDD7kyu6xxP2/ggWgYWouxZROieRYxL6kP/q+6izBKJFTcR+z/EE3AvxeYAQ7wwOK+YTDLygZ+qihflaCryL8/jN+RNslUr3+/2nTKh67A9vZCWMYG8+dMB/j22I3//fDsFZZUmosM7xjGkTQweXUcGTJpO02A/VJPML/tzCPYz4VZ17vt0K8VOL3f2b8qUvo24bdYmfnnoMp4f2Y5ipwdNgyN5pTg9Ki1jA9l6rKCKeFFOVrGLj9ce4Y7eKYQoEiEBZtySRGaxmwKH73OsaZSdSLuVt8Z3JqvYxfwNx9h9oohnv9nLG2M7YDrDncCuSPzzmjY8+eWuKsujg6w8MqQFsqbVup/B+cQlyzz51S5+3lf5XQmzWfhwUldCQxrSkZ5/lILDqPbY81MvT5LR/UKRS0/Wfl8GBhchsizh1nQ2H8knOtDKyp0nGd8ziWS7RvhPr6Ic+IYuubuY3fVeikPbIEsQcnw55iP7SI0bQavoQLxn8T4zMDC48Kg1AaOsrAw/P79qywsKCrBYLLXVrUEDo8ij8e4vhxjcKoov7+rNjBWHeOzznSwd5qbcglU+upLYoyurbFfcdgJecxQNv0CWQX1CkTTkAz/UuF46tg6zXoamW1F9VNQoR0PHrMhsOVbAukM5vD+pKxkFToqdXmKD/Vh1MIfB//6Ft27oTLe4IFRVA3SOFzh5eOGOavub/uN+3ri+E8H+ZnafKMbtVUkJt6PI0DgyAF0HiyKx72QRZkXiijYxtEsvlbP6AAEAAElEQVQIwelR+XbnCfacKObLrRlM7pmMP+Lh+6mvd3Nn/6Y+x//wkBb4mRXumruZY3kOEsP8ubl3I8rcKq8s3YdT1bCf8Wyp6DqDmkXQ+a7eLNiUzokiFwNbRtKzURg2CZ8VSC5UJEXm/ZVpVcQLgLxSNxM+WM+39/TFWkdjawgo+QfOS/pIOZpfGHLpifPWn4HBxUSJBrfP2szrYzsQZrPQJNLOaz/so5E1mPj0taJRfhoRy+4l4vQNmw0mtvl1yA0scs/AwKAWBYyuXbuyaNEi7r333oplmqYxY8aMitQOA4PfIqvYxchO8cQE+3GyyMWAltFc2TYWf2vaWbez+NnwlJcrMTA4T+goaMGJ1JgFYItEw4S/SfJZ6q2cy5pHciirlO3phVzSJIIb3luHJIHVpFDkrCwl/NgXO/ni9kvwB3RZ5n8rDvvcH4gSrHf2b8qWo/l8t/Mk/xzeljd/PlARcZEQ6s+jV7bks9svYfbao8xck4bdamJEp3im9LHy6vf7UAGHJHE0r4yNaXmYFYmksACO5lWWqrymQxyFDg8vL62MjjuW5+Dpr3dze7/GXNsxDlmSAB1Vlin1ahQ4PARYFIIsCtF+Ju67rDGaDrqmo6pagzuNS1SNuRuO+lxX5PSy92QxnaJtqGcrLWNQI0rBQTwJfc5bf5pfKEqJEYFhYHCukSSJA9mldG8URkiAmS3HClh7KJeHr2hJc7sLbFFQmuNzW489HrNsOiXwGxgYNCRqTcB46KGHmDx5Mjt37sTj8fDKK69w4MABCgsLmTt3bm11a3CBISsypaqOBNhO5fOXY7Eo6HgIsJp44+eDfL09o+LB7fMbU+gcFAdF1dORtOQ+5BOEtaE99RjUe9weFb3jBFj/rs/1Wq97ccjBWHWdp4e1Zsz/1uI54yF1SOtoIvzNPDK4Gbf2aUROmRvXqfBXp6fqjVhuqZtCpxd/PwWPppNeUIYvZAkyCh30aBTGxA/W89J17bl//tYKfwoQXjN3ztnCOzd25pfU7IryrS98u5e+zSJ4bkRbXvh2D8v2ZNG/ZRTvTuhCRoGDp69pw11zNlPqFj4dwzrEcdeczT7H8eGqNGZP6YFNkXDpEq/9uJ/PtxyvOK/bxAXx33EdCfZqDSri4kzcXr3aZ3k6R/PK6BxjhwaVNHN+kNzFKGVZOAPjzlufun84Sv7+89afgUFDRDHJlHp1dHQCTDK6V0OWJY7llxEb7Iciy7i9GjariWmzN5ESbmPuJXcSsfRO3zvsejOa4XthYNAgqTW7sObNm7N06VK6dOnCwIEDcTgcDB48mC+++IKkpKTa6tbgAkGWJUqReHfNUcbNWMcNH67ni12ZOGSZbLfG4WI3WU6V6CArX2/P4KttGVXM/R74LpusYbMhILzqjsMaUzzkdYqxYzauWwZ1QJk1Fu3q/4JctaqG1v56PCn9RUSBphNvt/DlHb25vHU0Qf4mUsIDeHZ4G54a2gqLrmPWdaL9FGyWmqtzQGWav1WR6JxUWUY6IdSf565ty3sTu/Kf6zvx/Ih2SBJ8OLkb2SWuKuLF6cxYcZjRXROrLFuxPwerSWH94Tw0HX7ck8XUmZtIDrcho/PfGzpz36BmXNMhDqtJrhBczsTl1SjPHHl/dRoLNx+vcl7vyiji5o83UtbAz12rSSbCXnMqZZu4YDSj5POfQslLBUALTPyNlucOzT9cVCExMDD4w0gSOCWJT7eeYPyHG3jtxwMcLnSRVurhaImbXo0jaBMXRLHLQ36Zh5lrjuBRdfZnlbBeak9Z2/Fn7FBGveo1PAHxPst2GxgYXPjUWgRGRkYGsbGxTJs2zee6uLjz93bEoP5RosGY/62teMsbFWilWXQgt83exJ4TxRXtrmobw7AOcXyy9miVElmHc0q54WtYcOMytKw9BDvTcYe15IAajeaNIM5mQm/Ab3AN6i9OzYreaCgB03ojpa9HcpeiJ12CyxJOqWqraCdpOlFWmWeuboVT1VEkEYV0ptlYpN2K3WqixFXdLDM22I8gqwnQQde5qVcKMUF+JIT6Exviz4MLtnEkV0RlyBKM6pLAlW1jWXswt8bx7zheyA09qovMP+7N5JErWxFht+D26ng1jTUHc2gbH8yJQiepWcWoGr95w+hnkin2anyyzncKRVpuGZnFLpLP8oB/oRNokrh3YDOeOMOwFKBJpI2U8AB0t8fHlga/hSkvFV2SUQPjz1ufmn8YsqcEyV2Mbgk8b/0aGDQEHEhMnb2ZPSeLGd8jiaZRdiZ9sJ6yU1F9Qf4mXr6uAyH+Zj5enVZl27u+PMbD/W7mqnFTiC7YjG7yh8QeuMyhuL0N9xpiYHCxU2sRGAMHDiQvr7qbfX5+PgMHDqytbg0uABRFZsHm9ArxAuC+Qc35v692VYgXTSJtPDO8Ldd0jCfQz8R7E7vSPNpeZT8HskpZeBA2mjuzJmwkuRFdiIhMINFmRjLEC4M6xKVZyCeKgqRhFDS7njxTEqWarVo7XQdZ1QhAx6rrPp3S7Qq8MqpdtYIKZkXi1VEdsCsSsixR4NVZl5bH4u0nsJhkbpu1qUK8AGEHM39jOhuP5NGj8RmRS6cRHeRXpeIJCKFkaPs4vthynFtnbuLOOZt56stdeDWdYH8z7/5ykA4JoYzsFE9SWAAJof4+950Q6k+w1YTDo/qM0lBkies6x+NnMZHpUinSQFcaXl1R1asxoFkETw5tRZCfeI8gSXBZi0jen9iVqKDqBtgGvw8ld68w8FTO38OL5ifOJ7n491dYMzAwAEWR2JpeyJ6TxUQHWrmqXSxPf727Qrzo3iiMf17TFh2RdndznxSiAistjjUdXlyeycBZJ/nEOwBvm9GUmWNwq4Z4YWDQkKm1CAxd15F8lDArKyvDajX81S9mSlWdxdsrHdujAq0khQdwOKcUgF5NwrmxZzIvfbe34gEsIdSfR65oycJN6SxPrXTubxRhIzLQyokCBwE6aLre4Az/DC5chI/DX/tC6qpOl7ggvrmrD7PWHmF/VgntE4K5vlsSIWYZVdVwyTIfrT7MrLVHiQq0kl/mIa+0eoqIWZFYeyiXl69rjyKDL2+zm3un0CTKzps3dGZ5ahZfbc3g0Stbctcnm8kodFa0K3R4eH7JXiLsVjomhbD3RBGXt4oi2CTx5vWdGP/++ipRI3aribdu6IRNAa9ZqZZqosgSr47uwJpDuYx8azVuVUORJYa1j+XBwc2xNrCUCrOuM6JNNINaRlHi8mI1K9hNMmYa1nGeb0w5u1CDks9rn1qAqImllBxHDW9xXvs2MLiQ0SWZhVuOI0kw/fqOvL38UMW6Sb1SSAkP4J+Ld1dcz9onBPPyqPb88+vdHDp1zwjgUXUiAq34W004S13V+jEwMGhYnHMB44UXXgCEc/Drr7+Ov3/lmzhVVdm+fTstW7Y8190aXEBIElhMMuE2C3+/siWBfiZOnnowMisSt/drwpSPN+I+7ekqPd/BfZ9uZdYt3VlzKBeXV6NxhI3k8AAe/mwbr43u2KBN/wwuTiRJRGlImk6kRebBAU3xaBoWWcKl6RR4VDIKnTQK9yfJ3829fWLYXwg5xa5q+5nWrwmdk0PZebyQVQdzWXRnH+asO8Lc9ccq2l3XOYFwu5UJ76+vKKf60U3dyCp2VREvTue1H1KZM6UH/hKgani9Ogl2M4vv6s26w3nszCikbVww3RuFEaSAquoEmmXG90jig1VpFfsZ0zWBFfuzWbj5eMUyVdNZtDWDQoeH565pjbmBneOqqhEABFhP+ZxoGnp13d/g96LrmHJ242py1fnt1i8UXZKNCAwDgz+MzqVNI7ipVwq6Dmm5QpRICgugQ0Iw98/fVqX19vRC7p+/jRdHtmPqrE0Vy0MCzLSODUaRG17EnoGBQXXOuYCxe/duQERgpKamYjabK9ZZLBZatmzJzTfffK67NbiACJAlJl+STEKYjX98tYvjBQ7endAFgEGtolm8/UQV8aIcr6azYFM60y5rwrI9mbw2piOvL0vlbwOaYZPBeHFpcCEjSWAyKSiyip87F7n4OHid6CEpOOQQnJoFzauiIHKGn/tuH9vTC/l4ZCwxO7/h1rSvwGLD1fMeftViq+z7qatbs/VYAW8tP1hl+cNDWjD7lu4czCmla1IoX2/P4J55WwDxRuvr7SeQJInEMN8pISDERa+qwWkP3pqqYwMGNw3nihYRqOqpcqgiKhjdq3FLrxSKnV4Wbk5H02FAy2hun73JZx8/78umxK0RajKe7g1qRi46iuwuQg1pdJ47Vk4ZeR7/7bYGBgYVuDVwqRqTP9rA41c0p3mUnSO5ZYzrlsiMGsqC55W6OVHooGmUnQNZJaSEB/D62I4Em43rg4HBxcI5FzBmzZoFwKOPPsrjjz+O3W7/jS0MLjZUVaNf8yge/WJHRQhgrF2mebSdlHAbKw/4rukNsCO9kLv6N6Vn4zA2puXywKAW2BUw8kYMLmQ8kkRmqZtdxzK4Jvgg5sVTwXXKzFZWsPV9GFP7yZRoNiRF5sPVR1i2O4ulk5No9OVwKMms2Jf1yGpaj/+FqEArWcUuGkXYUGSJL7dWfzv88tJ9vDuhCzI6T325k01HC6q12Xm8kF5NavbMCPY3o0gSvlJlVFVDVX1vZ9E0HhjQlNsvbUyBw4MkiYiLmsgucREaYnhDGNSMOUu8rVVDmpz3vjX/CJSiY7/d0MDAABCi/ckSNy99t49r2oQyImQ/3bs14oc9WbSLD+aV7/fVuO2u40W8cX0nSlxeQgLMBJlkJF85kQYGBg2SWou1euGFF7Db7Rw5coQVK1bgdIrwY6OkkQGAw+NlxSmholtyCDFpX/D+0BASQ/1IOsvb3vhQf44XlJFVWMblLaOxS7ohXhhcsEiShGRW+GZ3JpM/2sjVjSSCTCoMfwuufAkimoOmIv/yAtbMjciyRIlXY/a6I9zZN4GkPe9VES/Kif9mIrMnd6RlTCDDOsSyYGN6jWP4ZvsJWsYG+RQvAA7llNIk0o6/2Xc511v6pGD/k5ERiqYRJEOSzYzdenY9PSTAMGUzODumrG2oAVHo1qDz3rfuH4FSbAgYBga/F1lRmLX2CCZZ4qEeAYR/eSNN9rzNu9c1IjbITFxwzfeCSeEB/JqaTWGZxxAvDAwuQmpNwCgoKGDSpEkMGTKEqVOnkp0tjBcfe+wxXnzxxdrq1uACQdVg0iUpPDykBU8MTiR4/1ck+jkYF7yb2zrXHLUzqksCR3NKGdDIhu6uXlbSwOBCQJIgSC4ktHAzoZtfZ5RlLZtuTyJ4+RMwf4L4s2EGXPoQtL4WAHnFywToxbi8GpGBVm7sYEfZ9ZnvDgqOkLLtNe7q35SBraLJLanZ1CynxIVXFdVEamJXRiH/vaFTlTZmReLJq1sxvEM8XkQllL9CoFmmdw2RHs2j7QRbfQso5wqTWUYyyZhMRg71hYr55EbU0KZ10rdmi0I2IjAMDH43Xl1HkuDZa9tg9pbibjUSW1J7Lk+Wabzj39zVM8zndmZF4pIm4QxrH02nGJshXhgYXITU2p3a888/j8lkYvny5fj5VYb9XnXVVaxYsaK2ujWo52iKTJEOhU4vg1tHY7OamLU5j10DP6DMrSJ/ej2NDnzEi1cmYDmtfKJZkbh/cHM2H8mndYwNt2a8jTWoO2RZqlbW9I8QLOVjmT8OZdYwlF9fIjTIjumTEUj7lwrXToCc/fDFVGg3CuxRkH8ERfJgNSu8eUNnPD7KkFbBWchX2zKYveYInZNDa2zWNSWUA5kl3H5ZY5/rJQmSw21MX5bKs9e25fWxHfnv9R356q4+HMouZfJHG5g2dytrjxfh+gsGamZd5/kRbWkXX/XteeMIG2+P78zZkke8qkapLpHh8HLCqeJAQlZ+3wekyxIFqs57a4/x4Be7+GD9MQo14C8KMgbnGdWFKXsH3rDmddK9FhCJUpYFXt9mtwYGBpXIsoRLh/E9kgm3+7FPacYXyU+Qlngt+rKnkVe9zuCAVCZ2iaxyrQ20mnhlVAd0j4tQSlFVIwLXwOBipNbKqK5atYr333+fmJiYKstTUlLIyDCcui9G3LLMG8sPMn9jekWue+ekEO4d1JybZm7lpasSuSy2M4Gb3uLaFhn0vvEuDpdZ0XWdxNgY3l1zkn2ZxdzSKwXVUNwN6oAA2YGfNw9O7gZrEFpYU/LkUJyqgr8sIesaZ8uSUxQZJA/KileRMneIheFNoeg4FPn4XdR1WPMmdJqIfnQtBwvh1gXrSc93cEefBO5vMwrT5g999pXTdBTrF+Xh9Ki8O6EL3+/KrGaOGxJg5vLWMXy64RitYgOZM6UHLy/dx9ZjBYB4hn9ldAd+2pvFnhPF3D13C4mh/jxzbVtGvrUah0cYXBymlM1z8hnVJYH7BjTFommgyDhUHZMEVonfrBKk62CTdN65vhN5Di8ZhQ6iA/2IsJnxP8v2qiSxbE8mf/98BwVlHgAi7Vb+Nbo9rSICkM7Sr6xI7Ml1cNNHG/CcuhFenprN/1Yc5uObutEsxM+obnSBYMragaS6UcPrpsqZFhAFgFJ8HDX0/HtwGBhcKJjNMiccKn+bu4X9WSUAWE0yEy9Jocyj07frkzQ9uoqIb6fyYLd7uGnydRwq1LD5WbGFRPL2r2k8fmULio0gXAODi5ZaEzDKysqqRF6UU1BQgMVivD2/mJBkCZck88Gqw1VKNgJsPlrA01/v5t5BzXlkyX6+vOoRYr8ci9++RSTuW0SiXzAgUdx+Mk0iJ3BX/6ZYdc2HXaCBQe0SpBRj/vV55K2zKpYp5gBMV83ghW2hSCYLDwxuQahJqvbQa5G9BOgFeAuzUfwDkbfPrVwZ1QqO+66+AYh13W6hJLE/E+ekkn0qHWTGmgxGT55Ko9Ql1Xww1BZXs9MZSUHZEXo2DiXcZuX9yV15ZamoXCJJ0KdJOFP7NeGeeVs5mC1uIv3NCh9M7sbujAIC/Sx0Tg5l94kiksMDeHdCF7anF6JqGm8vP1ghXpzOZ5vSmdwrBass8c7P+1l3OI8Iu5Xb+jamfXyQEDbOgq6DBZ0YP4VY/0DhmaTrNdrcSBKcLPUw7ZPNVYSj7BIXt3y8kW/u7kPYWfw5SlS4Z97WCvGiHJdX455Pt7Lg1p7UnIVtUJ8wn1iHbvJDDUquk/5Vm3hZoxSmGQKGgUEN6LJEvkfnxhnryC11Vyx3eTXeW3GIR65owfxUlbs73ELgutcIWv86QRum08g/FLwujoz9kUeuaIlVB924EzQwuGipNQGja9euLFq0iHvvvbdimaZpzJgxgx49etRWtwb1DVm84Sx1e/lk/VGfTQ5mlxASYKHI6aHAEkeVApDOQgACIpMZ0yIOj0s1LlkG5x2TImM+sKSKeAGAp4zwryfyyNifGfDRMVYdyOXLO3pjP+2Z2S6XYt09F/mXFzF7nTD6Y/Ce5knhKICYdjV3botEC23M9HVesksKKxa7VY0Jn5/koxGLSM5YgnmfKKOa1WYK9qaXEO+wseiOeLy6znVvrybMZuGGHklMu6wJ0YF+bD6az91zt1RELQA4PCp3ztnM2+M74/CoDH9jJaXuSqFiQMso7hvUjDd+rlqO9XR+Tc3mp71ZbDySD4gyq9PmbObGHknc0bcR5t9p5Px7DJ81WebN5Qd9Rr14NZ1P1h/hnksbo9WQbpPv8FS5iT6dzCIXBU4P/n61dpk0OIeYM9bhDW0Ocu16pdSE7h+GLpuRi47USf8GBvUdVZbYfLyIEpe3xt/dj1ance+g5hQFDCBw3Wtioa5DWR74hRAbYqNQM6QLA4OLnVrzwHjooYeYP38+U6ZMwePx8Morr3D11VezceNGHnzwwdrq1qCeUazq3PLxRjQdnJ6a375mFDgIDbAgST4uS4oZPaUfHlcN9RgNDGoZfy0fedW/fa/UvIQd+55OiaEUOb18sv4I0ikjSEWRsWSsQf7xH5W58cUZorpIOUdXQ+PLqMlUQ+91N4dNzZixoXp54fR8B4M+OMzmpEm8l/Iar0c+yyJnRwqkYJ5YtJNVB3N4bvEeXF6NE4VOXv0+lWmzN5Nb6uLZb/ZUES/KySt1Expg4bZZm6qIFwA/7c3C7dXO6v/hVjWf0Rmz1x2lyH1uz2GXqpOaWVzj+p3Hi/GcJQXkbGVbf896g3qCpmI+sQFvRKu6G4MkowVEoRQaAoaBgS9ynSofrEqrSBvxRWaR65RZtI+y3N1vo0Sp2c/JwMDg4qHWBIzmzZuzdOlSunTpwsCBA3E4HAwePJgvvviCpKSk372fDRs2cPvtt9OnTx9atGjBsmXLqqzXdZ3p06fTp08f2rdvz+TJk0lLS6vSpqCggAceeIDOnTvTtWtXHnvsMUpLS8/FYRqcBUWRWXUwF7cqHnhON+U8k5ggP2xWhVC9qOoKkxVtzCeUyL6rExgYnA9kSRM+FTUQUHSIMJuo0PHT3mwcXnHzFaAVoCx/tmrjjR9C3wcq/6+psG0eXPESyFXf9uutrkFrfhUlrupCw+kUuzQW7XMQFRnBgOZRHMguZXTXRC5tFsmWU34Wp3NmysTpNI+2s+5wLq4aohZ+3JtFv+aRNW7fPiGEvSd9iwprD+cJH5BzhFWRaBxZc9Wi5tF2zGcxFg0PsNRYHtZuNRFqlG69IFDy9iG7i/GG16GAAWi2aEPAMDDwgclsYs76o2QWO0kIDaixXYTdgr9ZJiTtuyrLPY364+owEV01zJUNDAxqMYUEIDAwkGnTpv2lfZSVldGiRQuuu+467rrrrmrr33vvPWbNmsWLL75IQkIC06dP55ZbbmHJkiVYrVYAHnzwQbKzs/nwww/xeDw89thjPPXUU7z66qt/aWwGZ0eWJdJyywD4YXcmwzvFsWBjerV2SWEBlHlUnhsUTbRnM4z9BAqPoluD0RJ6UKJE4FHrJizYwABAxQIxHeDEVp/rC2Iu4fBhIYoG+plQJEAHBRVyD1RtnJMKh5bDyP/Bitcgey+kfoeW3Btp2hq0jK1IziKkxK5QcBRl3jhih89hUs8EjuS5WHUwp4oA0STSRoi/mSvaxrByfzaXt4yiwGnio9WHuX9wCySJaikWmq4TaDVR7KrughZut5Je4KhxLmavO8KcKT3ZcrSAQkdVYeX2fo35NTW7xsgF8zkULwBkTeOOfk34aW9W9XUSTOyZjOatOerDpsBjV7bkya92VVv35NBW2GUJ3YjCqPeYM9aiy+Y6K6FajmaLQcnbW6djMDCojzh0nWN5ZRzLcxAT7Eewv7na9QNg4iXJtAjyYms5CFd8J0oKsnFEd+a4GkqKHIZ8lt9zAwODi4daFTBcLhf79u0jNzcX7QzztoEDB/6uffTr149+/fr5XKfrOjNnzmTatGkMGjQIgJdffplevXqxbNkyhg4dysGDB1mxYgWfffYZ7dqJPPMnnniCqVOn8vDDDxMdHf0XjtDgbKhAmzhREjHS7ObGSxrhcrn5emdWxQNVi+hAXhzZllA1m7gVj8PhH9G7T8Xb+wHm7SrlMikaP6NMlkEdU4Ydy6B/osy6pvpKexQngztxMFu8eb3jsiaEKmVIqgtkGeI7w4lt0PwK8UdWIG0VrJqO64rXOK6FEBdqx6WE4PbKKIlxBGvZyB8MoqT731BGzsR+aBlPO5fjiE4mp/co3tzsYt62PIL8TTw+tDV//3wH/ZpF8NiVrdB0nf/+uJ/UzBI2Hcmnd5MIVh6omn7y2cZ0pvZrzKvfp1Y7nHbxQXRNDuXdGuYi3GZl38kipo/ryJqDuWw8kk+YzcL4Hkk0jbTx94U7qrS3mmR6Ng7H36LQu0n4Oa0gpOuQEGTh1dEdePLLnZSdSlEJ8jfxynXtCfdTqNEBFEDTGdQ8gpSbuzP9x/0czimlSaSN+wY1JyXED/03TEcN6gfmjPWoIY1BqduIGdUegyXte9C81aKpDAwuVmRZ4lheKR0TQwixqLTzz2fOhFbctmA/6flCLFdkiRt7JDKidTBxC64gv/OdPHa4Iweyo8guKeTTqW0M8cLAwKCCWrvC/vrrrzzyyCPk5+dXWydJEnv27PnLfaSnp5OdnU2vXr0qlgUGBtKhQwe2bNnC0KFD2bJlC0FBQRXiBUCvXr2QZZnt27czePDg393f2fK+6xvlYz3fY5ZlCbuWj1J2khI1kq6x/tzZK4Zbg9cR8t5VPDvwJe695FLyylTsAf4EBQYSuf5FzJveB9mEu/sdOLrdycF8M5c0icImw/l4hqir+boQqYu5qovP5fTj1HUdR0hrAkZ9jLz0ESg+CYCW0JP0S1/mri+zAXh3VGMGWfegLH0fGl2KHtEc/Zo3kCQZNsyAH54C1QPNh8CQ5zlJJLmmcEJkKxISdrkQk+5F3rOIslaj0ZsMwH/mECjLBcAfSNz4Fk9e/Q7XdOpDbHgIJhnen9CFIIuMzZvDcYc/m48V0LtpOGk5pdx2aSN2ZhRW8btYnprN+J5J/Gt0e6b/uJ9jeQ7CbRZu69eYoW1jcHhU4kP8Oe4jEuO2fo1555dD7M8q4ZIm4dzQPZHLmkdi0lS8Xp2nrm7N6P+tpdDhYXKvFHo3DeeXfdmUur2kZpXQKtpOgKSftdzsH8GMzrAOsXRNCiG7xI0si9QQu4KInviN744ZnTYR/vx3THs8GlhksJSXba3j7119oC7O8z/Up65jPrEeb3zPuvi4qg7FHoukeVFK0tGCU6qsq2+f67nEOLba7fv3tKmvcy/LEoFmFwmmQsa3tWEK3034rKHEhTVm4dWvkWttRZmqEB4SROThr7DP/AelvR9msbsz3+46QZ+m4bw2tiORfnKNZszl1Pe5ON8Y81EVYz4aFrUmYDz77LNcccUV3HnnnURERNRKH9nZ4qEhPLyqP0J4eDg5OeKNY05ODmFhYVXWm0wmgoODK7b/vYSHB/6F0dYN533MOakwexT4BRF46cMEf/d37r1hIfqPK0kbs4yfj2kc2VJIz3gT8WQSvfNz3L3ux9txAnluM6+tLWRwpkL3FDthduv5HTsX5mdcV5zPuarLz6Wy70AIGw5J3cFZiFcyUYCdL7eXMLi1mQldIkk8MBd529fQ5z74+QWkk9thzExY9g/IO1S5050L4cCPxE/5ifjQUEyOHEj9Hta9Be5SaHwZ1kvuga/uqhAvKtB1bEvuotu0DWSZFIL9TditZsjaA3PGII1Zxns3duLHvdkoMtgtMvNu7clX2zLYfDSfCLuV67snYbcqZBa6eGBwc9olhFBQ5mHLkTwi1Uy0Te8zd9T1PL68iBUH8rGaZCLtVm7p24jUk8UVJmxrDubSOjaIQ9mlXNoikhbRgTQLM7P47j7sO1nM5qP53DqzskTsF1syaB5t5+ObuhMbUrVAqdOjUuz0YDEpp0zczo7bq1Li8mJRFCwmheRIO8k1W3NccNSX36K6GMcf6rPgKJRloSR0wBpcc279eUFpDECYlgkRvisL1ZfPtTYwjq3u+qy3c5+5Gwqzifv2Ubj8GaTv76a0/WROtr6Z7w+5yXLk0T/ZTKJ6FCm5F9njl1GghBGV6+R/E+JoGRNIUrjtD3VZb+eijjDmoyrGfDQMak3AyMnJ4aabbqo18aIuyM0tPmdvDWsbSRIn6fkcc5BUgOWT0VBwBIa/iXxkNVw3A1PxMVw976DAofDd/mLWHSniow0QG+zHvFG3kugt4aFfnSzcJsqsTu3fCs3pJsfpu8xWbVAX83WhUhtzFRFx9gtKXXwuNR9nIMiV453YNRgAm+ck8vLnYNwc+HQ8eBwQ1Vo8YJ0uXpST3AulLAcyd6KXZiKFpkBKX1j3Dmz6CKX5FXB0le/BqR70E9t5a38xgf5m/tbVRsCsEWQPeYtXfjrKNzsrPSHe+TWN8d3iuLV3Elc2t9E4UONQdhbzt5Sw9piDt2/szMMLtnOyyMnSmxphmXkFFJ8kafts3hn0PKaruyCXnESSZNJ1Jy8fKKwylF5Nwrl99ibeXH6Q+wY1Y2ynOOyyRJNIG/cv2Fpt6KmZJXyw6jDTeiWjqRqSLJHr1nh/ZRrrDucSYbdy+6WNaR1jx+LjQ5dkiXyPzsdrjrDucC5RgVamXdaUxqF+v7tEa33mfP8W1adz788cu2X/CoKAIr8k9MKyWh3fb6LbCJLNlB7dgTO0arn4hnyNMY7tz3Euzr36PPfBUj7mr+6GTjciWYPAPxRt9EwUVUOWIM+p8eH6LD5cDy2ibXwwIRKL7OCNn9P4cmsGV7eLpfNVLcjJqbnS1OnU57moC4z5qMrp82GIGBc+tSZgDBkyhHXr1v2hiiN/lMhI8botNzeXqKioiuW5ubm0bNkSgIiICPLy8qps5/V6KSwsrNj+96Lr1c3w6jvnc8xKWSbkp0H7sZDUU1Rt+HgYkubFD+hoDeKNK97mKf9Qvt1bwIlCJ0/8UsR/rklmcnuNhdvgirYxBJmVOss9vxA/47rifM5VXX4uv9W3+5Tvgr3wCDQdCHu+FuIFQGJ3OPhz9Y06XA8x7ZA+ukrky5fTeRIMeQ6WPi5STc6C2ZXLM36rKbbGYvEOhuRerPM05pud1Q0pP9mQwVVtY+iV+hrStk9oh0SbtqNQJz7A4iMF7M8qZvYt3fDb93FFegwtrsCmlsD7/cEjHgxTTH481+8ZUoI78tbabB4a0oKluzIrTEUPncgmrL2M6eAv+Bdl8e2Inmwvi+GBJScq/CkA5q0/xoTuSdhkifQSD2P+t7ai6klabhlTZm3ipl4p3NoruYooIcsS6aUexrxb2T41s4SVB3K5o19jJnZLRGkgJ3B9+S2qi3H8kT5NJzej2mLQrMG1O6jfgySj2eNQ8g/XOP768rnWBsax1V2f9W3uJQnkgiMQmgyFR+HS++GTUchlufgBjSSZh7vdRofhN3DXl8fYl1nKOyuOMqZzDJMuSeab7Se4s38TULU/fFz1bS7qGmM+qmLMRcOg1sqoPvXUU/zwww/8/e9/54MPPmDmzJlV/pwLEhISiIyMZM2aNRXLSkpK2LZtG506dQKgU6dOFBUVsXPnzoo2a9euRdM02rdvf07GYSB8TfTSTCFc9JgGx7fAz89XfThzFRH59UQe7WVHkUUS2ooD+WSrAYRZdW7qlcyTV7bEZBjnGdRTzpo7qVggokXVSiVeJ1gCwGSFgDBh4GmyQtuRot1Vr8CVL0GjU0bFmz8Gkx+EN4GSTAhrXPNYguKRN75H8K//h+mDgRT0fIQZq2su9frB6qM4dBN4XeB1Im+djfmjK+gaWsYbN3TmSMYJTPu+Eo0tNmg9HL59uEK8KD+e0B8fYlobL7Nu7k5hmYc4Pw+P9IvmnksT+L/mx7C83Q15yf347fuC+JKdDAnLZOVtTWgeXVnutMyjggQuHZ74cpfPkq0frk6j2FN1uUuHJxb5bv/WL4co/o0caYOGhzlzK2pok7oeRgWqPQYlf39dD8PAoE6RJAmpKF2Y2bYaDp9OqJoOqWuY17/NEGUD7ROE+Lhg83HSi1WC/RXmTOlBhFUxHjYNDAx8UmsRGIsXL2bVqlVYLBbWr19fZZ0kSUycOPF37ae0tJSjR49W/D89PZ09e/YQHBxMXFwcEydO5O233yY5ObmijGpUVFRFVZImTZrQt29fnnzySZ5++mk8Hg/PPPMMQ4cONSqQnGM8gYlIA59G8Tpg7Zu+G2kqCWmfc12nkczfJEqqniz20jEskrsvDUb1GC7TBvWPANmBVS1AyjsC1kBUeywlchjqaV9XLTAexV0C9tN+VzJ3w7D/QLvR4CiA8KZg9gddAyT46Vnx/7bXQY/bYNE02PSxiGLa+D4M+gcsmFT9lUGHGyBtJWinBuBx4D26niJHYo3HUOBU8VqCqi4sySQmfQlbg0bTLjEC9p4Kq2x5NWxfUOO+7Oun0/qKN2hddpDwLW+C14V65Sso708Tx3bZo2CLhHVvI+ceJCy8KQv7PcrCvBT+sewEfZtFYJEkSjwqO44X1tjPhrQ8rmoRWVG5pNSrnbX9pqMFXN40DNWoXHRxoHkx5ezC2XJ0XY+kAs0eh/n46roehoFBnaJpOkWxvZDi+xB0eAmS1+mznXnVa/xzyGdcO7MQp0dD08Dh0WkaYjV+xw0MDGqk1gSM119/nbvvvpupU6ciy38+0GPnzp1VxI4XXngBgBEjRvDiiy9y66234nA4eOqppygqKqJLly7MmDEDq7XSAPJf//oXzzzzDJMmTUKWZS6//HKeeOKJP39wBhXIskSpBoVOD3Y5hlhLMUr2DuGDUdM2OXt4sF8wCzanEx3oh8OjIpkshnhhUC8JlIsxr3gBeUtl5JgSEE7wuHkU2Vri1URYRqkcSlCTgcieMkj9DkKSYND/wYKJwgejnOTe0Ode2P2liNAAWDUdotvClS/D90+Afxj0mIZuDUa66TtY/gIc3wyBMdBlMkS3hhPbq4wz6MgPDGjxEIdySn0exxVN/LAfq/5gZd73FbQfwviZJ/l80BSiD/8i+klbWeOcSPmH2J6WRVTUJQTavsBScBBl/3dCvGh9rUh/+eb+yg1yUgn86iZG9n2KHe37cEu/Fph0Dek37MBFpFblTexvmYfLhrv4RYWSfwDJ60ANblTXQ6lAtcfjV5qJ5C5Bt9h/ewMDgwaGKks4dZi7uZS7+iYKIb8mCtNpFylMmzsmBHMwu4TG4X6GeGFgYHBWak3A8Hg8XHXVVX9JvADo0aMH+/btq3G9JEncc8893HPPPTW2CQkJ4dVXX/1L4zCojixL5Ho0bvpoI6DzyZQeFLhdxBQchciWcKSGt1BRrbGnL+eyZi0Y1SURm0nCo1k5/UHFwKBeoKmYdy+oIl4AUJaLMvtaAqeuIh9hVOxRZUoiumPX8pAuewwpOB6+vhcKj1Xd9sgqCAiHTjeK0qrlZO4U6RrNr4TkXrBtHlJsR1h4M7S4EtqOAkcebP0EMnfB4H9CfBc4Lqp8WPYvZsJt/2b+pgyKnN4qXUbarVyZrCOv9nFOWoPIKVU5XuBgvTuZQS2vQ7GEIsd0xHR8U/X2gDuqHT8dKmPe5+uYM+UlUswFROx4TwgM7cfAZzf53C5ozcs8d9sISmQFXdOxmSTGdI1nQ1oBh3NKaRMXxC0dbUQEKGzL8tCjUXiVG9kAk0ynxBC2HCuotm9Jgs6JocaN70WEKUekhnpD6o+AoQXGAaAUHMIbZaSpGlxceGSZORuO0SjKToDFxJFCL23iOsHmj3xvENEcio7Ts1EI9w1qzktLUxnRIdYwKjAwMDgrteaBce2117JkyZLa2r1BPaBMg1tmbuTaTvE8P7I9GQVOXl+ZRWlZMfS53/dG5gBI7k3Apnd57vI4kkOsNIoMNB46DOonJZnIq6f7XucuRTq2FkWp/Bl1aWZyicbZcQp6WOPq4kU5+76BJgOqLz/wI1xyJ+Qfgf1LIXsv5B6A1f8VJVV/eEqIFyCqlXSaULmtbCLeXMYXt3XlytbhKLKEWZEY2T6ShTe3IeG7m30ORe02ldbxInXk3sXH+TLmbtKb38jJlpNA8VHOVJJxdv8bC3fk4lF1Zq45QqYUgaPRYLFe8wqfDV94nZgdueiaTqBcQmTZPp6PXcmCS46w8+4mzO1fzMhtt3LpD0O5M/tZkl37sMqVZqZWCZ4Z3oYAi1Jt1/cNbIbdbIRgXEyYsnej2qLFdaWeoNrLBYyDdTwSA4Pzi6LIrEnLo9jlJcJu4cq2sdw/fzt6Yg/wD/W90SV3omz+kNevTmDPyRIevLwFNiOUzsDA4DeotQgMTdOYMWMGK1eupEWLFphMVbt69NFHa6trg/NETpmb6zonkF3sYtIH6/ny9q7c2MaC1T4IJB1u+QG+e0SEvoMwJLz8GREOb7UTa3XhbwnF6zZSRwzqKaqnqvHYGUg5qciNpCpeGACqhqjIUxOaWtXgthxzAJRkgeYRHhKZO6u3KacoQxiDnkLv93dU2UKj/DW8EraaJ2+4AnSNkINzCTjZCSJbVB9T6+EohUfpoB5lXIeOzNuWx+ur87jDGsovu0t44dp5RC27BwqFXw2BMWQPeI0MPZoy93Gig6xc1yWRG99fz/ShsfSNbI0sVxcXqqBYCFEKMS2+GyksBZpdToTXBSVpEBQC8V1h+zzkwz/DB79gG/8F3rBuqKqGpunE+Jv4+q7efLohnTWHcokOsjL10sYkBllRNEMIvZgw5e5CDaq9Smd/CnMAml8oSr4hYBhcXJSpOm8tP8T/DWvNhrRcrmnmx/+uCkR3FcOkxbD3a/jlZZFqaA0SqZT5aeiOfKJL9jCgZR/8JanC88jAwMCgJmpNwNi3bx+tWrUCIDU1tcq638p7Nqj/SBIUlnnomBjC5A838MSAGFofX4h5+TOVVQtCG8GIt8HtBG+peOD6/knIOwRXvw6K6dST3m888BgY1BUmK4Qk1+jposd38Xmz5efNRwptJKIpPA7Y+42oKlKO2R+fjg4db4Clj0L/x0Q509CzhMYHxoCzEKJaQffbkEpOYl1wA/qQ57FvfBP7xtOMdLeb4Ib5okxr6lJxAjcZIKI7vn8cP13n1nG/Mm9bHhaTjNur8eP+AiYUBfJInzk0DnAhoXPE6cfLK4u44zKx28m9Unhl6T4KyjzcvTiDT8d9QHM5AyU4oVL0OJ3gRDS/MJSts5A6jIVDy+HT8ZVmpAldYdh/4dBPQsjRNZRvHyDg+q8oRhiQ6ppOkAS39Upico9EzIpEZKiNnJxiIwntIsOUuxd3Uv+6HkY1VHucEYFhcNGhARZFRP6NbaqRsOymyhdYsgIdxsPtK8V1R9dg8yw4+CPSmFloISlYZQmMSlIGBga/g1oTMGbNmlVbuzaoB+g6NImy88r3qbSLD2ZSXDrmz86Iqsk/DJ+Mhuveh3njK5e3uAo9oRsOJRiv2xAvDOoxgTFoA55E/nyK73VR7dFOe+uvyBJ2qQjF64WjayF9g3jTNOj/oDQXlj0lTp4uN8Guz6vur8MNIJuh5VAIjIVr3gSLP/gFC6HiDPQ+D0BES6T240TVnxxRulE6uR09uh1S5o7KxppXCItf3gmJ3SEoXggk4U3hqn/B1k9IKt3J7HEdWJEBXZNFZMe+zGJuXlhcpd/k8AByS9wANI2ysyujCIAih5fhM9O4tVc894x4H8uckeA+zVDUYkMd9RG6rCAXpYMjB7bNrXpQ6RuF6emVL8GCUz4auQcweYtBqVpBRfNqmAHJSD+7KJHKspEdufUvAgPQ7LFGBIbBRYdZhlfHdEAvPknC9xOEUFGOpsKWmWD2g9Js2PWFWN5mJHpgDE5rBLLHEC8MDAx+H7UmYBg0fPwtCpqus2BcHOb5D/hu5CpGzUtD7/8USmGaKCcZlIDbZKfUbTmv4zUw+DM44/vid8XLyD8/Cy7xsE58Z9Th71IkhVWYjSkKBDsOoLgK4fNbq0ZcHFgmvvuXPQalOcLoMmObSFExB0DnCSJHOH0jHF0jhI9Ww8AWAzcsgM+nVFYykU3Q/TaIbI7kKYGNH1RGiJj9ofgk+rhP0L95ALksF1Q3ZO9DswYjOwshqg2EplSKHkFx0G0K5vBk+vz0IN3b38CmskCGtY/l6+0nqsyFJMG9g5rz9nJxY3qmz5pb1XhzxTFWHw7k5eu+JzlvFeaTW9FjO6E1GUSxHIldzYXml8MXt/me8Jz9YPIX81IezSUblyqDqpjyRGSnGlRz6eC6QrPHYUlfJd4yS7VmNWZgUK/wV2QOOxz09MurKl6czpbZaDcsQPcLR2l9NXpwIl6vilPzwzByNzAw+L2c07vCu+66ixdffBG73c5dd9111rZvvPHGueza4DyjyRIOh4tHB8RhLTsMOak1tpVObMU9+CVMagmaJKOf2EFJxCUYFyuDC4Ey3Yan+fUENB2C5CoExYrHHEKpbkc/LfoiUMtF2TILnPlVxYtydiyAm5fC/h/QD/+K2noUeotrMDkykRQLzJ8EqlOUSQ1OFOKGPRIyNsOAJ0UkhiSL6IytnyDNHC7SSIb9B+ZdD/3+DvGdRcju3sUw8P/Q8g8jFWVAo75oshW5261ogbHIi++tHFdRBvz4T+h2K0S1xvL1nbTt/RgjOk2kbXwwc9cfJbPIRYfEYB64vAX7M4tpGmVn2qWNaBrpT0iAmYIyT5VD3ZJezOUfFfPrQzcS1Hoimqbh9WqggVf2w6xYRGpNTeQdFv4ehWXojfrhUoJEfLKBwSmU/P3osgnNFlPXQ6mGao9D8jqQS05WVCUxMGjoOHWdLhEa0p5tNTfylOG1hqD1exSlNAPNVYpkMYzcDQwM/hjnVMAIDAz0+W+DhoOiyJTp8MXW4+xNz+G/V0UgZWwRBp01KO5yVCv8MzfB/u8huh3upP5Vwu4NDOo9ugboSM5CQMccbMUsWXHr4idUliXkE1sh+RJYNK3m/ez+CnrcjrT1E2TNiZSbJgSGgjRI7gmxHWH1f8S5dNN3wkA08NQD2vZPYfeiU94y74pSq+VmaBMWwdq3YdXr0OxyZGsg7PxM+Fy4S+Gdv2Ma/E/cPe7EMqOf77FtfF/4ZGx4j8B1rxEdfxWvbivgxp7JhASYOZBVQrHDQ8cohdFBWZiX34O6LZgnB7zAA4urV1uZ2DOZYqcXyaJgPS1Uoww7fiFJSCZrzdVKAmPAkQ+2SLQrXsGh+5/14zG4+DDlpaLZ40VufT1Ds8cCohKJIWAYXAxYLApFbjcWVzqSPbLmhiY/LIoMSx+CoDiktqMplsMMgdrAwOAPcU4FjBdeeAEAXde5++67CQsLw8/P71x2YVBHSJJEGcK4M6/UyfGsXKZfEY6suiC2A/R9EBbdXn1DcwA0uxyOrETvegtOOYhS1XgYMbhw8JNdBBxdhvzNvZVRAyYrgZe/gKPJMMo0f2RZQso9AOGNT4kdNaCrwqDSHoWseUUesD0SslMhqYfwqJBNMG4O7JgvTM68TjD5QcfxcO07YLHB+hmw6zMRpZHQHTqMhdBk6Pgu7PkKio5D8ysgpj0oFtg8k9K8E5Tl5xDpw09DjE0TKRvjF4K7hBZBKs8MjMRPdiCrBbRqbCMxqBh7yWFkzQmXPoiy/n8MLlrIzDFjeHFFHntPFhEX4s+Ensk0irBxotDJi6vTeP7atlg1MS+apuO0RuHXaQLShhnVxxHaCCw2tMtfQE3pR5EUUSXSxcAAQMlLRT0lFNQ3tIBIdElGKTyMJ7FvXQ/HwKDWkGWJMh2cqkaoXoiUsRli2tVsft15IlgCIaErevMrKSIcj2akWRkYGPwxaiWxWNd1Lr/8chYvXkxKSkptdGFwnilDorQ4j1iLi2/3l/D3ZscxyWHw3ZNw8GcR4t77HvEWWBUGfwTGwKgPROh7q+GojmJDvDC44PAvO4a86Ay/Bq8Lecn9+E1ug8PeBk3T0WM7QOoSIRzsXex7Z0mXwFd3Cy+NgDARSZGXBi2vFiWHAbpNgR0LhYBR0Z9TREh4HcLDYvtp5pctroTiE2C2wac3Vi4/8CPYo2DMLGh1DTmNR6J5Fc7ybkwIJfPGgerBBHSO6wwDnoATa0CxwoJXKqMmrIFw+XMEHfyRS1eMp02nOyhtdAVrMyVmrklj5/EiOiaGMLxjHJ9vOc74LvFopxzmSzx+mC65H5OzEGnnZ5VmGlGtUUfPoswaixoloes6+u8sqWdRVPy9+UjeMnRTAA5TGC6vcWPcUDHlH8SdVEM0UV1zKrVFKThc1yMxMKhVSnUI0ouQvV5sB76Gxv3g5+dg2Ouw9DHI2iMaShK0vha6TwV3EbQdjdfjwqMav9EGBgZ/nFoRMGRZJjk5mYKCgtrYvcF5RjbJ7D1eRF/bcYrNKYxs7iakoAB+fl6IFwA/PQPtRsGYmaLigX+YKINojwVdRUtbjyO2txEmaHBh4XEir3uzxtXy6n/jd8VbOFQLWngLlMOPwlUvQ9pKcBZUbdz8CvFGqtwItCwPvn1EiH+28ErfjCYDYO5Y3x1umwfXn1G5wxokoqBmj6zeviQLVv4bWl2DU5PZnivRKKYdnNxRvW1AmIj+SLkUTm4TZqMZm2Hl69Drbpgzump7VzEsvhdu+BT2jSf8p4fw73KALzNHsvO4OMatxwqYdlkTnv71ENd2iON0+bJADSLgshew9n0YqSwPrHY8ljDcUgABrpNIx9YhleWgJ/bEY0+gWDtLWmJxJvZ105E3vi/EHnMASrdb8et6O4Wqkc7Y0JBchciObNR6nJ6h2aINAcOgQSObZMyuYuxaLlp2KlJCV/juUUhbARlboPe9wjDa6xT3hCarqMale9EkE2VKdF0fgoGBwQVKrUmfDzzwAC+//DKpqTWbOxpcGLg0aBUTgDkwBtuJtcQqRZDUU4Sqn86Oz2DuOJg/QRgZhjVGd5egqxrO2B44NWvdHICBwZ/F60DKq7kcopR3CEVzIkkizUq/ZjpsmS0ij7pNgYjmkNAVrnxZREr8+M+qO8g7JG7q/IIrl3kc1ct7lKNr1X0jHLlwYmvNx7D/e7BHYsPBm+sLSB/wXwgIr9rG5Idn1GxR6i4kEYY8D8Omi3SVuI7oq6bXPJ7dX4k0MaAstBWZRVXHdyS3DJPi+1JTpvmRL8WSZ2tDnikZF/7Yi/ZiOrEZxRqAbAtD+flprF/dQojiO/XFX3bBsn8gr31T3CgDeMqQV0/HtPIl/BV3zXNjcEFSXqJUs9dnASMWpfBQXQ/DwKDWcOs6isUPxT8Iy/Y5ImU4bYVYWXwSvvu7uCf87CaYeY1Y/+PT6PYoypQw3Fr9868xMDC4MKi12nSPPPIIDoeD4cOHYzabq3lhrF+/vra6NjiHmEwyDq9GhJ6LyVuEKSwe/dByiGxRc66/roOmnSqFaCVPCzNMOw0uTMw2tNguyMc3+1ytx3bEqwRg14pQPr0eqeSECJH1uqDzZGg6CAIiYNFtkFuDEKJ5IfU7aHwZHFouBI2zoZxWfjg0BeK7nV3A0DXwOIg69B3DO9zKxK8y+c/VXxFdsJXA7M2UhTSnJKEvQSVHCPGThQCz6SNR0WTEu1BwBKkgreb956eJ3wOLnZzwrhzIOkpSWAB3dA+hebiZ6BCJ3OIoLLIEv/E7ECgVIO/7Snh/uIrAFiFMT01+KJvew9L1Adxeqco2Vm9+1ZSa05C3zsKv599wSFFn7dfgwqJcGFDrsYCh2mOwpP0gRMF6aDRqYPBXCZBdBLmzkVxF0Pc+kcroi3JB3lmAntgDpzkCh9t8/gZqYGDQ4Kg1AeOxxx6rrV0bnAdkWaJEg8NZpXQNLiFg32dQcBRkE9KmD2HsbFDMwkTQF0Gx6H6BFHgDDfHC4MLFZEHvNgW2fFT9uy4raL3uwek1EeDMRMo8lZbx8/Pi7+veh6/vgetmiHPHF7JJCBJr3hJRG7kHIO8gRLeFzJ3V28d2hIJTFT+CE+Cqf8E398LQf9d8DAldwePE0nooEwKDQNcZPfcoscEJJIU3p6kcwM22MEIWDIUuE4VwcWy9KMd6bB0ExqNHtkYqTPe9/6iW4Cjk5MjPeOSHfO7qFcmkpBwi1zwBWbsgKJ6Hej+AKoVRhL3GYdpNTqSfnq/q/VGaAz89C73vQS4+ib+aj5uwKttJjryaI1Y0FcmZB/6GgNGQUAoOo/mHC8+Weopmi0HSPMglJ9CCEup6OAYG5wxZljDJLoL2LUT67hFRvWrxvTD0VeF1UcPvsR4Qjt59CiWGeGFgYPAXqTUBY8SIEbW1a4PzQLEG//5hD88OisJWehy2zILRH4s3s50nigec9uPE8jOJ74IeEEWhasdr1PY2uMApMcdgv3ERypfTKoWIoDjUYW9QYolHRoJ8H6HiAeHgLhGpVp0nga+KG92nQlA8NB8Ce7+B8Z+B1w2N+8O88ZB/Wg59WGMY9h+QEAa5ve8Vxp/5R+D4Rmh1TfW0LsUCV7wkwnpX/IuIoa9xZ+HHjB57E8W6HwEmnfDD87EvPwS974bSbJEOkrFFCDbb5sHQ13D2fhD/gz9UvzE1WdG63EyJZOO5H3Lws2jcEpVK6M4voMM40f7AD5iW3IfcdScBlzxKmeb7odPiKUA6Xbw4ndTvYPibyIoJWZOqiqIWm+9tfu96gwsOpeAQqi2mrodxVjSbyO9XCtMMAcOgQVGmQ1TJUaRvHxLG1IXHIao1uEqg/+Ow/EURWXg6zYeAfxi5rppFbAMDA4PfS60JGABHjx5l4cKFHDt2jMcff5zw8HB++eUX4uLiaNasWW12bfAXkBSZY+nHeTppO4Hu9uAuhVEfioe34hOALt4Et71OvEHeMqvyYtVkIPrQf+GQ7XhqCM4wMLiQ8GgKhcGdsN34LYo7H3Qd1RpKmRx6SqDThQhxOlf/W5wbtkjYOgeuegX6Pwbr3xMigS0Cet0DfkEwfyJM+gZKs+CL20U6SLcpInKjLOeUGW6UEBT2fQtNLoNhb0BAaGVayi8vwdDXRCnWLZ9AWS6k9IFL7oSdn0NyL+g0Hr5/AsuJrSTsXlD9QDuOg7CmQuy4Yb4wGM1PwxHWitfWFHD70PcJ//lhERUBEJJE9uVv8OBXeVispdw7qDnxSiEhxU4oaCYEG0kWwkrPachf3oW1+zTKpBoePIt8RHhYbGLuPE745SVMSIR0vglvVHuKNOEb4rGEoUS1hqzd1beP7YjbHGqYBzcwlPyDaPXYwBNOK6VadAQPfep6OAYG5wTZJJOdW0DC7g9hyHOQ1EukTGbugM0zRUTexC9h9X+F8CxJ0HIYDP4nXl367Q4MDAwMfge1JmCsX7+eW2+9lc6dO7Nhwwbuu+8+wsPD2bdvHwsXLuQ///lPbXVt8BfxINHGvZ0QuUw8hKHDsv8TlRXK2f+DCE0f+H/QbLAQMMKagMeBLvtR5rXUuH8DgwsNVdUoIhjMp8w2deC06CLVnoiS0F2kTDS7XPhC7PgMetwmUiCWPATJvWHQP8BiP1WpJxhmjxI7cBfDgkmiSol/KDTpDxs+gGaD4Nd/CSHDXSrarnxVpHBJpxljaqpIV4lqDW1HihKnyX3gg8vFdsfWCYHjh/+r+SAztorUlQ3vwc7PYNQHqOve48OtJby3LotNJ6J5/prFNLW7ySzxcMThR5lD4e+XuNAkme82HeC+nkFiHIXHKve76nXY1wyu/jdS7n6kyFh0XUdRZFyajgb4K5KopnImw6bDunchfUPFImX/98iNLiX86v9QIoVSqgfhN3YWzBop5q+csMZ4R35AqR546gMzaBDoOqbCwzhjOtf1SM6ObELzj0QpOvLbbQ0MLhAcXp2WwR6klN6QsU281FowqTLF8vAvQrweMxt6TBPXuoim6JpKsWrH+C02MDA4F9SagPHqq69y7733ctNNN9GpU6eK5T179mT27Nm11a3BOSBYLyBg/asw8j3xttVVXFW8KCd9I5zYJsLMVRdcPw/dHEChHoxek8GngUEDQpIkAqUClNIT6F1vQbIEQFgjUYkn77AIm+1zP6x/F46sEn8Se0C/R+DLO8RO/ELETWD5w3f7sUJ06H4LzBwuzr/TUd2w5EG48fPq+cZZu+Gn3cKf5rYVlaJH+gYhqsiKEDt8ERAOq08Jy4582PEZRX3/j3c+EOPaebwYl39r9iLz3vpdPN6xkKhVj0H2XpBkWvR+AGm3pap4UU7OflFxJbkfuq7jkmTWH85n5tojuL0aw9rHMrl9FP5B8VB0XGwT3RaKTlQRLyrm/fCvSAd/wF6Sg9pxAoQ1xT1hCUrxMTHvYY3xBiZQQgi64cHToJDKspG8DrR6nkICoNkiUQoNAcOg4WA3e/F3OYTQntAVvrm/uj+Uporr21WvQO5B9MiWuEyBqG7jt9jAwODcUGsCRmpqKv/617+qLQ8LCyM/P/+c9aOqKv/973/56quvyMnJISoqihEjRnDHHXcgSSJcTdd1/vOf/7BgwQKKioro3Lkz//jHP0hJSTln42hIWHWHSA8pyhCh67++XHPjXZ9DmxHQ6FJ0awh5Lj80ryFeGDR8JEkihCxMc8eIB/RyQhsJM7NF0+CHp6Dl1XDTd0JAUEzCX2LhLUIkAGh0adXtmwyAwyuEf8WZ4kU5BUdFKkr7sUJAPJNOE6q/6Dr8C7QbLfZdnFFV+JAkCG8CGadVWznwA0ea3UmZS2VA8zAe7h2CTcvlkCOYx7tJRH06qrISka5hMpth58KaJyz1O2g9Epcm8dDn21lzKK9i1e4TRSxPDeWTcfMwzbpGzE3zIbDri5r3t2sRcmwHpEVTYMxsivRgsAcjBbZD1/VTQzNumBsapkLhC6PaY+t4JL+NFhCNXJhW18MwMDhnBKoFSBabSE3scpMol+oLRz74h6Kn9EU32yguq9WMdQMDg4sM+beb/DkCAwPJzs6utnzPnj1ER0efs37ee+895s6dy1NPPcWSJUt48MEHmTFjBrNmzarSZtasWfzjH/9g/vz5+Pv7c8stt+Byuc7ZOBoCJpOMZFaQAFoNE29kPWXijW9NeN3QpD96YCzFms2oOGJw0WCTSjAtulWID7ZIaDcK2o8BdFGK9PJnRSTE3sXw7cPgKYWFU8VDfnn6h38o9JyGHtaocscBYbBxRs2REuWU5ojSq5fcJdJSAPyCofc9ENcZsk/zhGg5FJpfAc2GQO+/CY+LblMq1/d/XKS8nC5qmPxoEenPinF+TE/6lZY/TMKcvQvNkU/E2uerl1HWvFVLvJ6JYsUt2UjNLq0iXpSz7nA+Hx+w4ZnyC+rYuWjJvc/+26O6QFaQjq6t8M8QFZz1GouSGFz4yIVp6EgVJpn1Gc0WhVLkIyLJwOACRZYVcS0LTgKv8+yNTX4QFEeByxAvDAwMzi219qsydOhQ/vWvfzF9+nQkSULTNDZt2sRLL73Etddee8762bJlCwMHDuSyyy4DICEhgW+++Ybt27cDIvpi5syZTJs2jUGDBgHw8ssv06tXL5YtW8bQoUPP2VguVCRJogxYuS+Hy5MlpIwVItQ8OFFUG2k+xGcYNwDtRqEHRKCa7LgcXt9tDAwaIBZPvkihuuJFIUTs+1Y8xF/+DPiFiWiGMbPg6BrY+AGY/GH8ArGNLQJCU9DtMejI4C1FCowFZ4FIgfA4wBIAJqswSDsTWwR4HbDoDmhxFdz8nUjRUN2wfb7Yxlkg2na8QaRjzLu+MtRXkqDbVLh+LiBB6lLY/mnVPtpeh79Win+wPwR0gaBQouJSsEgm5OObqo8pdamIxjq53ed8aV2n4FJszNuwq8Y5fW9lGoNbdccWdSkWs4yt7RhkX+VkQQgye5eIf2dsQ2nSHK8R/dXgUQrT0P0jzi6W1RO0gGhkdxGSq1CIiwYGFyiSBIpZgZJsca3odoso6e0XDM7C6huYA8AeDZIJ1bg1NDAwOMfUWgTGfffdR+PGjbnssssoKytj6NChjB8/nk6dOjFt2rRz1k+nTp1Yu3Ythw+LsNK9e/eyadMmLr30UgDS09PJzs6mV69eFdsEBgbSoUMHtmzZcs7GcaGiKBJlksTEDzfQMthFuF6AFN0W7DHCwFNTIbKlCC8/k5AkaHElaF6KjLreBhcb7lIY8gIc+hm+uA12LxLRFp9OgB//ASGJcGCZMPWcvERELHz7kCh9+vkU4SGTuRN58d3IP/5TiB0hyZUPZls+gb4PVu9XkmDAU6Kiia6JPg/+LAw6F04RJe1aDoVmV4iby1bXwNLHquYp67rw5dA0OLIWNn1YtY/kXtDhesg9BFtmwsGfICQZef07hKx8Bka8LcZxOie2CVEmvmu1IeuNB+CJ6YKm6dU2O/PQQERROF0qWutrxZycSXgT8btUnvISGG1Ef10kKIVpqBdA9AWICAzAiMIwuKBRFJkyScbiyEDaOkekGJedEvD7P+Z7o8seRVcsOCX/8ztYAwODi4Jai8CwWCw8++yz3HnnnaSmplJWVkbr1q1JTvZxM/oXmDp1KiUlJVx55ZUoioKqqtx3331cc801ABVpLOHh4VW2Cw8PJycn5w/1dbYb7/pG+VjPNmYHEkdznfyyP5txnSPpFOOHlHsMfngSMneJMPeJX8Jnt8A10+HoWtjztXhoajkU2o5GtwbjVmU09ewPJvWd3zNfBoK6mKu6+Fx+6zh1W4SIkEhdWn1lfprwwtg+Hz4eJs6ZkGS49EERzRQYC8c3In33SOU2xzcJc8+oliKiY+9iCE2G696HTR8Jk8/IFtDrXtj8UdWoKIsNWl8DEc1FieM1bwozzQmLRPRHTax8Ffo/AfGdhFFvWCNRySQwFj4dX9WbY82bMPifyJk70TN3IzXuL4SNcuK7iLSzQU9BXhrs/RokBa3LTXgi2lGsByFrGuO6JbJ0V6bP4VzXOR67SUYC/GQ3HiUcJnyNvH0O8vZ5p8qyDoOkS+CruyqPPTiBYD2HYiUC9QIXMurbb1FdnOdn69NUcBjNFkU9mZ6zogeUCxhH0KLbAuLY8krdrD9aQGpWCbmlbryajr9ZIdJuITksgKYRNhqFB6DIF8JR1r/v7LmkLo/t9/RZm+OTZYkSDbYeLaDE4eb65GJRGWvR7cL7YsS7oiLdmJniOpOTCmGNhTdGYAya4k+Zx3re5q4hfw//DMZ8VMWYj4ZFrSamLViwgI8//pi0tDQAUlJSmDRpEqNHjz5nfXz77bd8/fXXvPrqqzRt2pQ9e/bwwgsvVJh5nkvCwwPP6f7OBzWNObPIyf2fbGZohzicLg+39QpD8pSKsPTmV0JJpsix3zADLvs7fHqjKMvY5SZx9pflotvCkcwBWIOCsZ7n46otLsTPuK44n3NVl59Llb7dpSApYPaDMq+IuvDFkOfhs5tEWkc5BUfgq7tFdR97tAi7vW4GeJyw5ysRrfH136DPg3Dt2zB/ohAN7FHQbowwA03sBl/9DTJ3ihBdEOdsRHNI/VYIJroGNy4U5Vs1tbKqhy+KToi/wxoJv44tn4htdn5eVbwoZ9n/wQ0LkL64Da59RwgYsgLtr4eet0PeIaTiE5D6AwTHg64iZ+3GmnQJVn8xj23NJi5rHsny1KoeSYlh/ozvkUwYOWK/uz4Xx9h5InSZDB3GCQPSnQtg1XSxkTkAhr8Fi+/FlHuQ0EmLIaZtzcd7AVFffovqYhw19qnrUHwEkrtiCQ44v4P6M+j+YPYnSMuG8EC2pxfw3x8P8OPeTDQdogKthNosmGQJp0clt9RNQZmIlPI3K7RPCKZjUgjt4oNpFRtEclgAJqXWAmf/MvXlO1sb1Kvz4C+2/b2k55Ux5n9ruL1fI27sGAIlZcL3rMstsOF/whg6KFZUImk/VqQbl1eOuuQulKAown+zl3NPQ/4e/hmM+aiKMR8Ng1oTMKZPn85HH33EjTfeSMeOHQHYunUrzz//PBkZGdxzzz3npJ+XX36ZqVOnVnhZtGjRgoyMDN59911GjBhBZGQkALm5uURFRVVsl5ubS8uWLf9QX7m5xReMOZwkiZPU15gVRWZtWgEbjuTz+rUpxDUxiTfB+06Fuaf0gbFz4JeXYPeXIq/+5qWQvkk8dDUdgG6PoYhQPKU6lNZQKeEC4mzzZVCV2piriIizX1Dq4nMpP868vBIC9TyUI78i7/wM3RKA1vVWtPAWmH35UwTGCg+K08WL01n5Glz3AeQdhI3vCwPO9mNg3ByRirJmOrT+GW5bKaIYsnZDcAI06gtf3yOiMgY8ccrjQhJvvI6uFW/CylHdInXk2DqI71oZKREQDuhQdspEM76zKOO69LHKUsntRsHPz/oeu66LfQYnooU2Rr91BbKkwY4FSB8MEQJPeBPofR+c2AobPhbDaX4V+aXiwUsCnr2mNdsyipi55ggur8bwjnEMbBFJlJYFs6+tOnf7lkDr4WiD/oknZSDm8KbISb3AFg5BcULMSN8o2s67HveNSygi5Gwfbb3mfP8W1adz77eOXXLkEu4qplQJx1tYdn4G9Rex+0dRmH6Q5+dt5rOtJ4gNsjK2UzydEoII8queelnmVjlW4OBIXhlpeQ4WbDjGu7+I80GRJaLsFkIDzASYFRRZwqPqODwqZW7xx6VqKJJEsL+JlLAAuieHcnnLSEL8ay/NsyFfP2vz2M7FuVdr45Nl3vnlEJqmM7ElSMueQtpxyl8pph1c/bp40XX4VyG45x0W94dtR6JbQyjRA3HlnN97w4b8PfwzGPNRldPnwxAxLnxqTcCYO3cuzzzzDFdffXXFsoEDB9KiRQueeeaZcyZgOJ3OinKp5SiKgn7qbE1ISCAyMpI1a9bQqlUrAEpKSti2bRvXX3/9H+pL17ngfgR8jdmlwdwNR7mufQRxcj7SwikQ11GEZh9YJh5kZg2HW34QqST7vhWmgondIaIlurMIT1Bj3K4LbDJ+BxfiZ1xXnM+5qsvPJUjPxfTJtRUP1RKg7P0Gqc+D6O1GI5U/+JcT3lQ8vNdE1h4ozQLZDFFtRNt178KR1TD0NSGA6G5Y/S5oHohsJdJCvG5xo3hgGSyYVGnuafITgkbnibB5plimemDVv0UUxfjPoNNEaD0MCtNFGkZgDGydC10mwcltleIFiPW+hJlyXMUQ3gS35IfuZ8Pv56eQDiwV4gVA7kGR3nH1v8W4T2xFOrIauelYVFWYbFrRuSQhiC6j2qEDVklCkVTkDTN8Cz+7v0RqPxY9pofw9dj3DTgKRJrM6RQcRXHloVtCah7/BUJ9+S2qi3HU1KdSkAaAaou+YArkHrc04tadnTmsnmTSJSn0iA9EPktqSIBFoUWUnRZR9oplRU4vxwsdZBW7yStzU+zy4vZqeFQdRZYIC7AQFyzjZ5IxKzKarlPs8nK8wMmrPx9k+i+HmNA1gZt7JmGuxQiO+vKdrQ3q03nwV9v+HhyqzqKtGXx/UwrSvOuRik9C6+GiMnXqtzB7BIxfKITyuddDj9tFOp/XDRYVp1c5d4P5gzTk7+GfwZiPqhhz0TCoNQHD6/XStm31UN42bdqgqr9RHvAP0L9/f9555x3i4uIqUkg+/PBDrrvuOkBU2Jg4cSJvv/02ycnJJCQkMH36dKKioiqqklx86Gg6PHRpNBKlMGx6ZWWEvqdy9Jc8AGvfhqtegfkTRBi7o1BcoMIaUeiq/w7wBgZ/GdWDvPkjnw/V8sp/od+xTkQc5B6sXOEsECa4vojrJEqWZu4Bqx1aXiVM0H55SXhgmAOEeDB7RHURYfCzEN0Kvvt71eVeJ3z/BIydDbu+ECagpdkiPzl7X4XgwNxxlaVZFTNc/hwgVa8+kpMqIjOOb/Z9DIk9UM2BSLKCtfQYUlx7aD5ICB+/vFSZerLiVbj0IRE14iPpVFV1ym9xVXRsciHStjm++wSkPYuxFp1ACoyGkztqbIenDIyfpwaJUijMujVbDedXPeNYsc6tJ8ejahqPDG5G66QwCv9E5EiQn4kgv0Ba/Qnv0iKnlx9Ts/lw/TFWp+Xz7xFtCAswThCD3yY6yEqMKw1pwBPCw+nENvH01+tuyD8My1+EgU+dEjY0iO+Mbo+miGA4d7f5BgYGBtWoNQFj+PDhzJ07l0cffbTK8vnz5zNs2LBz1s8TTzzB9OnTefrppyvSRMaOHcudd95Z0ebWW2/F4XDw1FNPUVRURJcuXZgxYwZWa0NxbvhjWCSJN0Y2JdJUCl89KvIYy/ELgVHvw7A34NeXofutYnnHG8Hshx4QRYEUjXF1MrgoKM1B3ja75vUbPkAb/wXSlo+Rts0VZVQTugth4pcXxf/LCW8K/R6GBZPBXVK53GKDkf+DH/8pTHKtdt8REPmH4Miqmseyfb54G5bUE1b/By75G/z6ivDjWPZ/VduqHvj2YWHyKZ0RWr7pI7jyJRHloZ1xnid2Rw9NBv8ILAtvRDq+sXKdLRKGvwnfPy5EjMJ08XsC6Em9KqIvakLSdc5ab091IR1ZCe1G11xeVlbQbZFn7cfgwkUpSEPzCxNRR/WcbIfO1J9VJFnmH8rHKMG9fnujWiDIz8SI9rF0Sgjm7ZVp3DpvGzPGdSDUEDEMzoK/InH7pY2RAvPgwA9VrweSLF529X9UGE63Gi4qUJks6GYbbmet2usZGBgY1K6J52effcaqVavo0KEDANu3bycjI4Nrr72WF154oaLdmSLHH8Fut/P444/z+OOP19hGkiTuueeec5a2ckEjSxR5vcRZVVj1ZlXxAsTb4/mT4KYlwthPtsAVL0JIEnpxJqXmCLwuQ7wwuEhQXVXLj55JWRYlSiT+XW/D3HQQmG0ixcHrEmVRP5tc+aDd62/w7SNVxQsQqRdLHhY3g5m7IXuv777sUXB0Tc1jKTgqoi6KjkPLYcLYs+kgUW61JjZ+AN1vgSMrKpcVHRepKGNno69+E+noKvALQe9+K3qHG3FJdvyWPVhVvAAR9fHVXXD5s/D5VGHuKStofR7EYQqFs+sXuOQgzK2uQdoy03eDJgPgp2fEzXO3KcLg9Ay0rlNwKqGGvtpAESVU63/0hdOrc8+vKk4VHm98grCDuRS5iwFbnY0pJSyAB/o34V8/H+T+Rbt4d0wHLKb6awhqULd4NJ2ejUOh8JAwhD4dXRMvuMZ/JiIAo1oCEqge8t3+cMEkeBkYGFyo1JqAkZqaSuvWrQE4evQoACEhIYSEhJCamlrR7kz/CoPaQ1JkdmaV0jWkGItHgy2zfDd0l4hQwX6PgDUYgpPQj2/Ck9gHx1lS4w0MGhKyLEFJtnBW3zbPZxu9w3g8Xg3ZZMOUdxjJHglf3iGqhDTqB6M/EpEImgbRrYXI4Iui4+JNVttR8NGVvtvkHYaY9sJDwxexHWHbHOGnEdZIeGm0GQGL7635IAvTITgJQlNE6ddy9n8PpdlI181A0yU8WHAoIXi8EKpnIdVUfaUkSwgM1kBo3B89JBlnVDccmn+NQ1AUGUkClwq23vcg7VtcaTJaTmIPISQVn4SdC2HIcyJ0ed27wkguIBytz/14Wo7EoRpvlhsqSqEooVrfeXGTxv5CeKSzRJgkvCzk0kyIrFvxJSrQyrTeKbz680H+8+shHhzQtE7HY1A/8UgS76w8zOP9wpDWvVtzw7Vvw2WPgcWOXngMZ3AzNLchXhgYGNQ+tSZgzJpVw8OxQZ3h0HQSzIXYZBW8XmHMWROFxyHxEpBl9KiWeKwRFDqMtzUGFw/+erGImBjwuHig9/FQrUe0QFN1kd5gDYIdC4V4ASK66fAvooKIX6gwyj0bJj/x4B/erHIfp5P6PUz+GnZ+Vj21QzELk86514sKJFl74PMpMOojiO0gvDB8Ed9ZVEK56hU4+LNIYUEXQkqnGyH/KJItHEWxYZadeCU/8buhnyWcojRb9Nn7HrTSfMrMjXw2s8hebFoe8uFVUHwCPbkP3pAmmG7+AWnDe0ip34E5AL3zJKSQJFh4c+XGSx8XqTIDnkAPTkAKb0axHorba9w8N2SUwsN4w1vV9TDOytKjGosO60xqKZEcKKF5QwCQy7LPvuF5olF4ACPax/LplgwubRJO9+TQuh6SQT0jq8zDwexSLLodik/U3LD4hEjbM/mhhzalxGmIxwYGBucHI1HtIkGWJcK1HPwt+Uj5ueKBZ8Q7wjQw7xBs+liYMpUT30V4XsgKqhJgiBcGFx2K7oGcfcIrYuQM2LsYDvwIZn9RajShG6ImCWiSBd0/BGnXwuo7KkwXfyx2sa0v4dBkBUsg6KpIwZg7ToTmlpN0iTDdPPSrECVO7oC4DsIzQpKE18Sq6UK8OJ2fn4fRHwhzzzNTYUxW6DIZXA6QVCFatBstfhNkkyiX+uPTSCWZmCQJpekgrENeRrfYwRoEriLfExfXSUSTzBkNY+b4dPy2yF4Cs9YgL5hQxSdEju2Id9RsXL2ewL/bbSBJeC2hmPd8juxxQHIvaD8O/EMg9wCseBVt6OsoIYl4/mLJPpMiE6DloriLQLHgNoVQpgdUVLQyqFskZz6yqwitJoPcekCOQ+fZDRpdoyR6nxqmbvIDk1+9ETAA+jcLZ9vxQl74YT/zJnfFaqSSGJzCbFZYtOYo712XiCTrkNwXGl0KcZ1FSuXur0QVEk0V94mBMeAuIV87VaLbwMDA4DxgXLUuAvwUD8FSLv6yF8lVBKU5cHwLLPsHzJ8IOz6Dy/4u8soBIltAeBN0HVSLnXyP/az7NzBoiKiyRfhI5B4QD+MlWcLUtt1o2LsEDv+KrglRwOWVICDs7H4ZWXtEWpYvet4Je78GZ5GoAjR5MXS/TaSFdJ4Ig/5PpJas/JcQB07ugE8nCGO1r++BjM2Q0LX6foc8C6v+IwSYsMaVyyOaw4QvQfEHd6EwFp0xAN7rL3w70jeIVJiSTNFe15H2/4DpkxFg8kPrc7/v40jsDid3whe3izm0x/lsZtPyqokXANKJrShrXkfTNPL0SPK0CIqcCt4ml6NfPxdS+sDPz4nfrV1foA9+Bi2sWc1z/jvxl5wEZfyA9ePLMf2vN6a3u+H/9U2EaCfOWvLS4PyhFNT/CiQvblKRgBuaVU2PVf1CkUvrj4AhSxLXd4nnRJGTOZvS63o4BvUISYKuCTb8dSf8+iK0GQ4ZW8V1YfH9wotp3Fxh2tzzDtC8eCUzmmaIFwYGBucPQ8Bo4ATKxdjWPI9J9yJl7RHlHg/+BD8/K/LJQYSrf3EbRLaES+6GcXOgNBckKCwxLkoGFycO7KLEKYi3TXsXi3KlPz0jjDZT+uI9Va9TkkA32yA4seYdmiwQ3RaGvyVEQkmGiGYw9FVRiSTpEvhiqnhA/+AK4Ysx8CkhLC55SJiBDnoaljwo3oCVp3E48oUYabELr4hyUvoK088d88U+e94hjEXHzoauN0PWbhFA8sXUyt8CgM6TYMUrvo8hPw05czvuNtejDXpaGJaCiNhoMwL6PCAqnpgDUMfOo1QKq7YLRZGRj6ysJl6UI2+djb+aX2WZFzPs+Bx+eblSVDm5A2n+BOSMjX+psLssS/gX7ET5bFKVcGkpbSWmWVcTpOf+6X0bnDvKS6iqttg6HolvVmRoLEuHMc0kAi1niF6WYOSyrLoZWA3EBvlxadNwPlp3jPwy929vYHBR4K8VclUKSLmHhBn0h1eKVEhdF/5oGz8Qv/GTFoNfKLqrBAdBdT1sAwODiwxDwGjIeF2YN76DfPgX8LrFw1VoI9j1ue/2v74CnSeAYkW32inSgw1V3eCiwiaXEaplEFa2h2D3MYhqhX7FS0IcKCc4EUa+h2oNokwOxU92EVq2F2n9u6K0nC/iuwiRYM4YiGkLQ18TQmH3qZC1VwgPGz+oNNLUvEIw+eQ6QBIRF37BIr2jpiola94Q5285Kb0hdan4d06qED7mT4BPb4Tv/g7b50HJCeHtERAuqqRc974IF849WOMcScfW4pSDKG5zC94pv6BP/RV9yo/oHW9E9w9Fu34+3qmrKApshdeHVYYkUSlC+MLrrCZuWD25SDsX+GyufP/o2fO0f4MAilGWPel7ZfFJ5BObUBQjCqOuUQoOixKq5poNYesKt6rz0iaNVqHQ3YfHqOofglR6lu98HTG0dTQaOjM3GFEYBuCneDFv/QhJcwOaMEk+My0RRDRh7gF0wOMXistj3CcaGBicXwwPjIZMSSbyhv+JnHpZAaxQcOSs7SnLRY+MIl8NRTUM8QwuIkKUAkxL7kU6sKxyYXIf9BHviooihUeRAB0JLbQRJXIksu7BdvQH5C9FygQ9boOrX4eV/xbnmskPOt4AzQbDwikiasJVLHw12o0WokSHcSJF5UyTUBBvvTxl4t/BicKToyZKMoU3xenbmqw1t1es4CyGlldDp/Gw9h1h6Dn8LeGp4SzwvV1oCpqmY9adyGkrkJbcW8VUVO80Efo+QqA3E8ldgm4NwqGE4lTNAKiqhp7cu+ZxRTRHVQIqyq7KsgTHd9TcvihD+HFIgTW3OQsm3S0EohqQ01agJF2Bqhq1WesSUYGkfqaPzN2vk1EGT7WRfFZW060hKI5N1DePALvVxICmESzYmsGNXRMItxkmjBcz/mo+kqaC2wVBcZD2a82N934DiT0pdJzlGmNgYGBQSxgCRkPGWQBdb4EmA8XD1Kr/CNO+s6Bbgyj1WlHPrHJgYNCACZCdmJY+XFW8ADiyEunzKTiHf4QaEYmiufAqATi9JtAgRMpF/vaByvbr3oWYdtD7b2CPFqKDowC+uhvcpaKN1wmZu0CxQN8HAMm3eFGOo0B4YZRmidKoNWH2B3uMSOUIawyN+wuBJGOL7/ZtRojUlbbXwbzxlSkpW2eLCiRr3qi+jcmKntwHTdMJcJ9EXnx3tSbylplIcR2RNswQaSqyCVvH8Vj7PEKhGoSugzcoBTm2I9KJrdW2Vy9/gTIpmHIFQ9epTFWpCdnyp58NNWTxWdUQFaKHNTEi0eoBSv5B1Hpo4Fno0nlvl8alsRBv8x2po/mFCn8cRz7gd34H+BsMbBHBT/tzmLv5OHf19V0xyODiQPEUi6pZFj9xnbIG1nht0m3haKYAwLhXNDAwOP8YKSQNFFmWwGwH2SzCsVf/Fw7+CCGJ4qHFF0m90P2CcLiMC5LBxYVVzUfat8TnOunoGiyePByqlRI9SIgXp5BLsyqFiXJO7oBvHhCpGu5S4X1xxQunoqAQXhbxXYV4sWgaHF4uKnfUhCTBgCeEkGGLrBplcTrtrxcPSL3+JoSTj64UgkdSz+ptU/qKSkRmP/jlxaplUQ8sE+VVmw2uuo3Fhj56JtL+pYS6DyFv/rDmIW+YISq1AGhe5M0fY1rxIn6KCEcu1oPwjpqN1vVWEaUCENkCdfxCysI7oqqV49F1HS2iRY2pA3qjfsJA9U/iUELRet/ne6VsQmt2BV5fuTAG5w9dRyk8hFaDKWxd8v4eDa8GwxrVnGakW0PEP073mqkn2CwmLm0SzmdbMyhx+falMWj4SBLgFwhNBsDRtaIyXZebat6g7Wjyio17RQMDg7rBEDAaILIsESIXitDzTuPFK8zEHhCaAt89Cte+Ix5eTscejX71vynQ/vyDgIHBhYrk+o0SnI6CGjaUhFgw4EkYMxNGfSAiGsrPL2eBMEHbMgv95qXoHW+EI6thyHPw5V0inWTjh0LMKBc4TqfbrcKfAh1u/k54YIyZKSqRnE5KX2g6AD4dLyqJHFsHg/4JzkK47FEYPROaD4EWV8I1b0DbkcITozQbss9IS9F1Yeqb0A1u+g6GvCC2uek7pJWvIi/9O8r6t5EKj9U8XyWZIg3lNORtc/BXCyq6KNBCKOn1BJ7b1+G5YxPu8YtRw1pi0R1YTVUFg1I5DG3UTGEWejqBsWhXvirKqv5JPF4dT4vhaO3HVV1hDkAd9yklpog/vW+Dc4NcehLZU1ZjVZu6IsuhMy9VZ1ACBJ1p3HkaWvm5UFz/fDAABjSPwOnVWLSj/gksBucHWZZB08HkL35nYztAq2t8iuv6oH/gDYiug1EaGBgYCIwUkgZIsFyErLnF254Dy0SJRa8LLn0Q8tJg04fCQPD4Jig8Dond0VP6UiBFoRpvGg0uQnRr4KlSIjWkCpwpGJRvZ4+CIc/DilfhxFYRTdD2Ohj7CSx9XAgIAKnfoXW+mbLLnsemFyJl70Eq95goPAZbZovqIGveEpVDQpJF6dO8AzDvelHCFdATe8Cw6TD1V8jdD8UnkQJChQixcEql+eW2uWJ/BcdEVIYlEPo/Kc795c8L3whzANiifR+36oGfn4chdtjzzamKKE/CsfViffZeaH4F7P/e93zFd6luNqqdKuNsrXQ5dKkKbimCECUPZeM7yFtng+bB3GoEtkvuolCOQlV1PKpMcUR3bLevQ97/HVL+IbTkvmhxnSmWwvmrsmuRGkhAv3/i1+teyNoF1iC08OaUSiF4VB/CksF5RSk4BIBmr18VSN7frWGWYXDSb5i8KhZ0iw2p5CTUQz0sxN9Mt6QQ5m5KZ1zneExG6eCLDqsZcAPH10P6Rsg/DDs/g6v/DYXp4rfeYhcRGuFNKfDUEMlrYGBgcB4wBIwGhKLI2JUSFICSfMjeA55S8eY1uo2oMtJkgMhr/GQ0xHUGWzh6QjfyiTTEC4OLFpcSitLiaqS9X1dbpyf3wW0OrTCVLEeWJeTik7BgUmUKhtcJWz+BjK1CxNj7jRA0jqxGi2iBVzdB2mokR3bVnaV+B8c3QqcJwrcmriMc3wyf31qlmXRsHcy8BsZ/Bn6hsG0O7P5SCA5nsn0B9JgKpTmQukT4Y3S7RURflGaLt2y5+9GbDEI68EP17WVFlHk9ugp63wM7TqsCcnwz9HtERIeU5VbfrtuUamNHktBPr+ZyimApD9Oca6tUPpE3vQ97FhF084/kn3ric2sKbikaU+ubkGUJr1dD03Qk6dz4U5RpAZQpAcjxiei6SF2pZ56LFy1KwUF0SUGz1Z+3vpllOp8f1BmaLBFg+u0Hfs0vDKUeppCUM7BZBGvT8vnlQA4Dm0fW9XAMziOyLBGgFSAVnxBRg5pH3DOm9IH1/4PE7uK3Pn0DBMXhju5iWF8YGBjUKYaA0QCQJAiWCjAdXYF0dA10nQKzR54yDDuFOQBG/k+IGJf9XTz0ZGxGv/QhvOYgo+KIwUVNmeaH5fIXMOlqVS+Mxv1Rr/4PpVr1t00BlCB9/1hV/wgQhpsDn4SdC0QucWAM+phZuGU7fmoh8ro3xTl4JqU5onpJ3/tP+dZM9z3YkiyxX02F7FTf4oU5ALrfKsxD8w5VLl/+gkghC4oDVxF6fFf0+G5IJ7dXN7Ec/Axs+QS9+21IYY1h1Rnj+f4JGPk/9FXTkQ6fcquPbCn8Oja+Xxl9cgq92RBcSkgVIUhRZJTDv/ou21qWi7z5QyzdHsTtrXxArG0/CsOws/6h5O0X0RdnphDVIR/u0bAqMDDh97XXrcH10gOjnMRQf5pF2pi76bghYFxEBMolWPJTITRBlNc+vSLT6v/AwKfgxE5I6QWHf0VvdQ3FbguGumtgYFCX1J+7AYM/TYhcgDJ3FFJJpnjr+8WtVcULEH4Y39wPg54GZOh6C3rHG/Da4ynwVH8ramBwsVGghhBw+X+wDngayVUI1iCUwCgKHSafmSUm3Yl0fGPVhUFx0P9RmD8J3CUVi6UdC/C/ejrexoNFyc+cVOFbkbai6rZdbxE+FTn7z1rakxNbISRJGHSe3F59ffuxsGFGVfEChNjy5R0wbg76mjdxD/8AXZKxTloMR1YhHVqOHpIE7cagywpa0yuRXQVI7w+o3kfOfvSNH+K55l1kTxloKl5zICbJi6nguardJvXCO+QVyrSqFRgskgt55/waD1Pe8xXWzlNx8xtVSAwaNKb8A6j1KH0kxyGiL65MlvD7HdEXICIwyN1ZyyP7a/RvFsH/Vh8hNauE5lHGfUFDxy6XYFn+D2RFEZWefF1zfvwn3DAfrEHo175NsRyBbkTrGhgY1DGGiecFjtkkoeyYi+QuheveF6F/Z5rylVOSJdJHAsLQE7rhtcUZ4oWBwWmUaX7ky7Hk+bckX4mDgNAabTF0ZHE+nU7XW+DnF6qIF+XISx5A0ZzoLYfB8hehx23QeZKorhHbEYb9B/YuhtwD4MgTgkZNBCfAgR+FD8UZZpkANB0EPtJhABHdkZOKdvnzKHiwfjEJ6c1uSOv/B7KCVJSBhkSBOYl8IvH6R6FHtam+H9mE1vchitQg8qVo8pU4irVACrQwnGMXot76K+oNn6Pethrn8A8o1Kv7iOjI6DVUFwHA7Cfm2eCiRsnfjxYYX9fDqGDWPg1FhgF/YEi6X5goSam6am9gf5EOcUGE+puZvzWjrodiUMtIEliK05B3firMojfPrLnxkVXoAWHoliBcbkO8MDAwqHuMO8MLGEmSsGu5SFtmn6pqcCc4i86+kdeFbglEa3QZBd7As7c1MDCoEYcSgtb5jDJzkS1FdIQvNC9k7kLvdgtY7bBgshA6rn0bhr8h/p+xBSQZdi6suYSdYoZG/UQ+8vdPwKj3hXdG3wdh9Mdw4+cQ1likmNSArnnRguJQfvoH0tHVYmHWbtHvzs9QPhmJXcsDoEgPxjtmHlrPu4SJG6Cn9EG9+QeK/VKEV8Tp+9Z1irUg8iyNyQ/rTp4piRLN7lMIcmsmUUq1pnF2uRmzSceiGAnXFyuSqwil9CRqYGJdDwWAQpfO/AM6l8VBgPn3m11qfkLAU8qyamtofxlFlujbJIyle7IodholVRsyZrOCvHUWdBwvInR9iO4VOIvAbCPfa7zwMjAwqB8YAsYFiln2Euo6gHJ8PQTFgmyGYa9DSCJYanCHlhWIbInqF0Ke27gQGRj8FXRdR+p6M3p5mTlrkDgXz4bqxi0HwviF0OEGOLRc+NHsWFh5A7nzc4huC4Ex0G5U1e0tNiFSFJ+ES+6GRn3BbIMuk4X48dlkmDtOuMZHNK9xGFJMO0xF6Ui7v/DdoCQTpehIRYGSAi2E4u4P475tDZ67tuMY9iH5fs3xaGev0FFT9Eo5mqbjCW+N1nZU9ZUJ3ZACwjC92YXAPbOxyaVn35lBg0TJ3w+AFvg7zSZqmU/363g1GJT4xyp16P6iVo5cUn99MAB6NwrDo2os3l0/S74anBv8JJcwnW45VFQcSe5dc+OWQ0XEnRF8YWBgUE8wBIwLEEmCINcxlA8GIWXugqGvidJ/88YLk75ef/O9YY/b0f2CKTQiLwwM/hImGQILtiPN6I/UeSJcNwPGzoLs/RDetPoGZn/oOB4ptiO6rqMhC5f3AU+KNJL09ZVtd38hIjmy9kJwIoxfAFe+DKM+hJuXwrp3YeEt0OZaYeDpKYOPr4aDPwrFIDBGRHhc9nfxY3EmjS+DnANIhcfOGqVB8Qmk07Z3qzKFWigFehilmn+1yIs/S7EWiKv/M6iTlqC3HwutrxURKZ0mCBNSjwP5h8exnlyHohiXrIsNU94+dElGtZ8lpeo84fDqzEnV6BMLQZY/KGBY7KBYkUvrt4AR7G+mY0Iwn23NOGfnuEH9wqKomPcsQGoyAPzDQXVDr7t9m+TGdoDwpriMgBwDA4N6RIO4G8zMzOTBBx+kR48etG/fnmHDhrFjR6UZka7rTJ8+nT59+tC+fXsmT55MWlpa3Q34LyBJEqFyPtKKV8QDTUAIuMvAFikMOj0OEao+9DUIbyI2Ck6Aq6ejd7uVPDXUcNk3MPiL2PR8lM8mCbPcxfcJT4uyPFjxCvR/vOqNYOP+MHY2uteFPGcU/l/cCMUn0BtdCt/cBxmbq/pd6DosvheK0oU3hq5DfFeRMjJjEBz+RdxUHlsvbjy3zBbnfTn9HoHPp8K+JTDqI1EuGcA/FPrcJ8SQZU8J8eJMDw8QokdEc+SYNlhlHxVOaoESLZCikI7oHW+EpgPB6xIpLeZK009l+XME6AXnZTwG9Qcldy+aLRZM1roeCl8e1ilyw+A/GH1RgT0CuaT++0v0axLO0XwHG44W1PVQDGqBADUfefnzEN1O/N7v/VZcD6Ysg0vuOmWQYRf/Hv0xuteFQzJefBkYGNQfLvgqJIWFhVx//fX06NGD9957j9DQUI4cOUJwcKVr/XvvvcesWbN48cUXSUhIYPr06dxyyy0sWbIEq7Xub4r+CMFSPkrmNmg7Eg7+DD1vFyHnOxaIBu1GiweWVf+GblNEKLpfEHraajxYDfHCwOAcoDiyoSy3ckFwAqStFAacWz+B6+cKU7SyPGHWOXccUnm50/zDyEfHoPe+F33iYqRja8R5W34Ol7P7S/HnmjdEBaHC9Mp1HcbBuv9Br7uEeFKO2V+kmRQegx3HRLnVzpOg9z3gKRWpKok9hECw/VORhrL8+crt240WFUyydiPt/x5bch+s9jgKvEHnfhLPwE9yInkd4vcs94BIgbl6Ohz4Qcxlzn5k3fDCuNgw5exBDar79BGvpjNzr0bXKIlI/z8pYPiHIxfXfwGjWaSNuCArC7dl0D25uvmuwYWNyZkjov/SN0JKb0juBT8+LV5+tb0Opq0WlbAyd6Ij4bUE4fUY+SMGBgb1hwtewHjvvfeIiYnhhRdeqFiWmFhp9qXrOjNnzmTatGkMGjQIgJdffplevXqxbNkyhg4det7H/GfxM+mYjq0FkwW8buh6M8wZW7VU4vIXhIHf5c/CvBsgsQf6yPdQWwyjUDUUdAODc4Lqrvp/d4koQwdwYJkQDtpeBz1uh28eEKkeZyCtng7NBkPTweiHf0Ua+BT8/FxlWockQc+7oDSrqngBIuKqKB3cpaLf0myx3Bokqg2VU5gu9nk6nSeKv4+uEWkmmgrr3hLjDUmCOaMrzCskwNRmJEEDn6OoFn8/zIqG9fB3SF/ecdrYj4m0mCtfgkaXQlEGGmf33DBoYOg6ptw9uBoNruuRsOyYTkYp3NLqL+zEFoF8fPM5G1NtIUkSlzYJZ/7WDLKKXUQFXlgvegxqRpIAxSKqV8W0hc9ugsxdlQ1+fQW2zYXhb8KuRdB+HAUe497RwMCgfnHBCxg//fQTffr04W9/+xsbNmwgOjqaG264gTFjxgCQnp5OdnY2vXr1qtgmMDCQDh06sGXLlj8kYPhKJz+f2ORSpD1fCbOl+G6w95uq4kU5eYcgJxUSukJCV3RzAAVOvzoff32mfG6MOfpt6mKu6uJzOdtx6rZocRNYLmQc3yxSN1aeauAugc0fi3Km2Xt9d6Drwnhz95dIve6GXV/AuLmQnybKIUc0B/8w+Oiq6tvmHhThv7sWQYfr4adnxHJHnogGqQlzgDggWYEhz8N3j8LQf0NCF9HX+4Oqz8OuzzE3HoCp8UhUtXbewtm0fORvH/K98ufnYdh0NFXDoYQgnTGGhnbu1rfjqYvzvPxvufQksqsALTiFupwOXdf5cI9G61BIDvyTI5EAWxSyswDJU4pursFsu57QIyWURTtO8sX2E9zeJ+Wsbevbd/ZcUpfH9nv6/KPjM5kU8CgiffDQL1XFi3IK00VEYfux6BY7Uv2t/FuFhvw9/DMY81EVYz4aFhe8gHHs2DHmzp3LTTfdxO23386OHTt49tlnMZvNjBgxguxs8WYyPDy8ynbh4eHk5OT8ob7Cw+tQhXY7oKBAhH8n9hTh4Knf1dx+95fi4antKGSLnQi7/3kb6oVMnX7GFxjnc67q8nPx2bdbgcsegx//If6va7B9Pgz+J/zwVGW737pSmv1FRZGoVmANBnexiKDSNVj9JiRdAmM/gc9vFX4bIMqs6joMfApmXStSVBr1E94YqkcIIPFd4Pim6v11vRmcJcIMdMVrIgpDc8OWT8A/pMZhymumE9piCIRGnf14/izpqSKaxBfOQvAPQ45uS6it5oe+hnbu1pfjqYtxVPSZJ0r82hJagj3gvI+jnBXpXvYVlPFYTys2+1+IAlLF+ROk50Bw5DkaXe0QDPRtHskXO0/y8NVtsJh+2zKtvnxna4M6PQ/OcVtysoTv0po3am6z63MYOwfZP4SICywCpyF/D/8MxnxUxZiPhsEFL2Douk7btm25//77AWjdujX79+9n3rx5jBgx4pz2lZtb/JtlAWsLGyX4OwqFkdmvLwt/C+UsH59sgtbXovmHkVfkBYrP21gvRCRJ/KjV5Wd8oVAbcxURcfYLSl18Lr91nLY212ONaI78ywsi6in3IHS8UYgDqUvBWYAekoQU31lEaJyJrEBMexj1gdj2yzsrfTWsQXDF86JE6s/PoY+bi+TMF54a9ijY9y0UHINr3xbRFz3vgK43wZHVwtT32neEuLJviRA7TH5oPe9A6zIFU3AMas5BlNRvRV9leRDZHDJ31jwZpTm4nU6KnLXzOxKqS2dNDtGsQeQ5LOCo3n9DO3fP9/HUp3PvzGP3P7gef4udYq8dCsvOzyB88N8NXpIDoZGfm9KSPx+BYbNFABKOEwdwW5LP6Rhrg15JwfywO5N5qw9xVevoGts1tHPwdGrz2M7FufdHx2e1mrAHxomIprPeQ/4/e3cdHsd1NXD4d2eWtWK0yMzsOKY4cZiZOWnSONBAIW1DTUMNtEm/NNAGmzTMzAxOHFPMzCTbYlyeme+PtWVvJBlkSbuSz/s8fRrPHc3emZ3Z3Tlz7rl2LLubiuoQEGp5vQTSlc/D1pDjEWvH4yFBjM6v0wcwsrOz6d27d8yyXr168emnnza2A1RUVJCTs/3pYUVFBQMGDNij17Is4vchYLdhaTaUOz2aMu7NhkEnRWciaIY1+hJUch5VAZdMhbYH4voedzIdeazi+b609Nr1RhL+3El4zxiBLVSJtupreP+aaPrtwBMxJt2I5fBim3RjdJxxqD52AwffCNOfjBbkfPW82ClNg7Xw7tVw1gvRrKvk3GgmgjMZArXRIpcz/wuXfQOH3xENhthd0WFji96NFgQ95XEYcR6WbsdK60W9nkXI0MnSdHx6JkknP4H2zuXw/f0w6Ybo8JKlHzd/DIrHE9I8tFcNTcOdie5O355lsiNvDhFn5i7f/6527SbK/sSjH9te01Y6DyO1F1Ycc34XVVpML4XJgxUoRWsPhbIA3Y7pzkSrWdeWXWw33VJcDMz18vKsjRw9ICdmWuXmJMo52x7ieR205bqmaUHEBw4PDDwRVnzZ/PZGXUTAlo7VCadP7crnYWvI8Yglx6Jr6PTTqI4aNYrVq1fHLFuzZg0FBQUAFBYWkp2dzdSpUxvb6+vrmTt3LiNHjuzQvraWpimcVkP0x4PNCZqC2k2Q0QcK92+yvlU0FrPHQZCUJcELIdqJpilclg/LMgi6cgkOPJ3ImS8RmTyF8JH3YmkOVKAaKzkXLnoPDro+OiRk8KnRwET9Fgj7YNE7scGLHS37HIafjXrtInj5LHjtQvjoeuh7ZDTr4rULwJOOtWYKvHU5fPQnrORuWKc8hrniSwx3FoGMoVSQS9DYnuMQtBz4iw/FuuonrIEnYWk6Vu9DwdvMU1bNhjnpJgJm+6URN6gMjFOejAZifvHaxslP0KDJTAj7IlvpXIy0nnHtwzOLTXLcsF8bjfgwk7LRaztHAAPg0H5ZLN5Sz7yS2nh3RbQBt1ULmhadiSqzT3T44S9YeUOh/7HUB6RYgBAiMXX6DIyLLrqIc845h8cee4xjjjmGefPm8dprr3HHHXcA0WraF154If/5z3/o3r174zSqOTk5jbOSJLp0rQpt1v9g+uPRceKaDYaeGZ0a8ZBboHJltKAnYI04F7NgDFWRVLLi3G8huiqP5sdVvRjt23tQ5UshozfGwTcTSB+MQ4WwfX0Hav5r0Wnp7B6sMZNh8KmoYAP4K+DtK6IZGWOvgE1zW36hQSfAC6dGh3ls46+Cz26Bk/8Na3/E0NwEJ/4F19gro5kblauwQgGsEedTZ3qJRJoGMZ1aCNfGKajPbwF3Ouh26H0Y1oXvwZe3oZZ9Gq3DkTcM45j7qXMWYbXjLHoRE+ozR+Od/APq52fQShdi5g7DGnUR9bY8IjJ76j5Hq9+E3rCFQHqfuPVhba3FF+stzu+v0NooC8T05GLfPDP6GLATVJMbnJdMtxQnL8zcwPCC1F3/gUhodjOAWvZZtP7FwTdGi08PPQMWvwtGBIacCj0OpMLM3PXGhBAiTjp9AGPYsGE88sgj/POf/+TRRx+lsLCQm266iRNPPLFxncsuuwy/38+tt95KbW0t++23H0899RROZ2IXJnJpIZI0H2rmU/DDg9sbzAjMfQnqNsF+F0fTxruPx7IUPm8vfL5QZ/hdJESnZNdN3Ks/RXv/6u0LfZXoL5yM+8L3UVMfQS3/dHtb2If64UGsSCAafJj32va22o2Q1TdaTPOXsgdEa2PsGLzY0Y+PYJz1EvVaFt6G1WjPnQCB6u3tnkxSLvyAantxNG14B0lGBZrNDgf+Pjolq68Cvr0PNf1xzIs/JnL43SjTIGzz4iO5yd+3h5Bpo1IvxDH+L+hWEEM5CUWAdgyciMRlK50DQCSOAYz/LjZJdcD4vLbbpunthgrVo/wVWJ7Ef8ygKcWhfbN4adZG1lb66J4Rv2KqYu943Ros+Hj71Nqf3AA9JsL+l8Gkm8CRBApMGXIghEhwnT6AAXDIIYdwyCGHtNiulOK6667juuuu68Be7Z0kzYdr0ctohaNg2mPNr7Tqaxh3Bfz0GIy9HDMUwK93jmJLQnRWSWYV2mc3Nm1QCs0IwI7Bix2bZz0Dpz0N817dvnDZp3D2SzDnxabDSNJ77LywZtliDEvHZVSjv3ZebPACwFeB/sZFJJ39DnWkbF9evR71zpWwceb2ZRm94MRH4N3foD67meBxT+HHuTV40LG/ZEMRgMQOLov2Z988C9OdheWOz5PgTQ0WH6yxOLWXwq613RMB09sNAL16JZFOEMAAGNcjnQ8WbuH5GRu45ah+8e6OaAWlFK7gZtR3f49tWDMl+j+AM/4HzhTCaXtWH04IITpap6+B0RVpmsJZtQjty7+C7oBIoOWVazdB/kisjbMIJxdL1FyIdqb5KyDYzGwcDi/Ul7b8h5FgtFDmjowQzPofnPkCeHa4UXOmRIeJZexk/H9qESYatmAlVLcwpr58Gbbw9sKYHi0AH/0BtWPwAqKzqHz8JzjoelTVGmzWTj5zhOgA9k0ziGTE72b5mcUmbhsclN+227UcyViOFGxVK9p2w+3Irmsc1i+LDxdtYXOtfDZ0RkmqDhWsazmjD6BiBVZyN3xaSsvrCCFEAugSGRhdjQsf2vf3R/9hd0fHybYUmfBkYmX1I6QnU2fKtEBCtDvVwoSfYR+403b+t64UOPFhmPMSNJRFi/AOPQMWvo118YfR7IhIALCis4yMvzpauDcSbLIpa+L1+PV07OENO+9uJND4Se+IVMHyz5pfsWIFJHfDyhtORPOA1J0Q8RL2Yyudh3/IBXF5+S0+i7dXWRzfQ+Gytf14TCOlAL1iaZtvtz0d1DuTz5aW8ez09dxweN94d0fsIUeoKjptqu6IBs6bk1qI5UzBCMuTMCFEYpMMjARkI4zaNs1abQn0Pqz5FdOKsTJ6EXTlUGtKxFyIjhBxZUJyt6YNpgH+muhwjGZYvQ6FddPg279D0RgYcW70h+TLZ8OyjwlrSQQzh2B4cjA3zsbsMQkrrUc0rde9wywcSoNRF2EVjsY0Layk7Kazd2xjc2K6Mrb/aci388HN/krMA/9AwLTvxpEQon3Yt/yMMsNEsgbF5fWfXmTi1OGQgvbZvplcjK16BRjh9nmBduCy6xzRL5t3529mY40/3t0Re0gL1UJDZXQWrOa407EK9qOWtA7tlxBCtIYEMBKQphTkDo7+47O/wJF3QbcRsSulFMCZz4M7nfqQ3GwI0VEaVDrGqf+NZkbsSLdjpPfCOPsVSC2KabLyhmEc9yBG7yOimRo//Au+vAMWvBmdKvSsl6lX6dSZyVR5B1O7/5+oG34l5sY58N3f4bgH4PT/RutUnPsa6Ha0d67AY9Xh19Mwx1zZbF/NA36HX98e/LCcKdEZR1pg5QymwVkgQ9FEXNnXT8F0pGAmF3b4a5c0WLy10uLIIoW7HbIvAIzU7mCE0auWt8v228shfbNIcug8/sPaeHdF7ClncrR454RroPsBsW1JWVjnvIpl9xIOS9VkIUTikyEkicgyYMzkaIG/iuUw5UE48SGo2xydMjW5G5YrHTyZVBmpWHK3IUSHMUyLutTBeCf/iLbwdbSSnzHzhmEOPYt6PQcDG94LPsZWvzE6y0h6T8KeXOqtFDS7IvmSr9BKZqFKZkH2QMziA6jTszC2DtmwLAiHDRwOHW3dd7DxZ3jjkmjARHdGp0oFUAqdMPWGF/uYq3GkFUWHnjWUQXI3zIP+TLD30QSN7R/zAVs63v0uiU7J/AtWr4MJuvMJGo42PV6apjpkFhPRddjXf0ske2g026iDPb7AxGNvv+wLAMObh2V3YyudhxGnLJPWcNg0jhucy0uzNnL2qAIG5cmw1c5A0xRWQw0qtQgWfwBH3BGthVG5Ary50YzC1AIqQt54d1UIIXaLBDASiEsL4bFq0RpKowUBr54JM5+BqQ/Duh/hgN9D78OwlIbPkYcvCB09Q4AQAsKGRpXKxT7iWrQREUxshCNm46wdtaRBUhpa8pDozfvWh1qGYVFNJlrBUejFx2CaFoZhNltvwjQtrMx+ND4DjgRja2GkFGBuTaKrM7zY+5+Pu89xaGYIU3Pg0zKIGLFP0wKGDe9Bf8BUOtqsp6PbUxrWoJOJHHYHdUZSq4+JQweHWYeldIKaF7dZg8O/BVW5AislHyO5mFrSJZghdq6+DFvpfPwjm88qak8rayzeX21xRp/2qX2xncJI6419y2yCg85ux9dpewf0zOCbFRXc/9VKnjpneDRjVCQ0XZkod1r0wZhlRqdLtbmjNdZsbrBMIiGZwU4I0XlIACNBJGk+3FumoZQGs5+PZlpk9o0W8Rt5fjQTo3YjvHYhxpkvbw1eCCHiKRwxiY7Eaz7ttqWbddO0MH85beovRCImZr9j0L66vdmx8uYBv8OvpcPWIEU4YhEmDRTRuKbRQiqwN5eGcX/GNfoyVKge7B4Cehp+o3VTl2qaItUqR5v9ItqS98DhwT3mcnAkod64BIwQCtBS8sk47w2q7N0xDAliiBas+ByAcO6IDn/pf801yHTDpHbMvtgmktEP19K3UL5yrE4ynSqArinOGpnP/32zivcXbOakoc3UAxIJw66bpNQtRgVrYd1UWPZJdBjh0DNhwHEw7TGsYD2Bg+6Uws1CiE5DAhgJQCmFy78BVbcpOpXhNuXLYdnHcNp/YfH7sPJLjLNepk7Laul+SQjRhdRrWSSf/Sr66xdCqL5xuTnyIsJ9jm2SYbG7goZOgGxwZG/dYOv7mGaVoj9zZHToylbq7cuh18HR+j3bPtNqS1AvnUnahR9QQee5YRMdbNF7GBn9sFxpHfqy0zabfFcCkwcp7Fr7ZxVEMvqBZsNe8hOhPse3++u1pf45XsZ1T+fBb1cxoWcGOcmtC36K9uc1ytGCNfDRH6F6h9olm+bCgjewTvo3EXsyAUNqqQkhOg8p4pkAXFoAFaiGr+5s2mhZ8OHvsQ65mfCvv6c2ZSgRU1I2hdgXhE2d2ozRhCf/gHH+OxhnPE/kyun4J95CrRH/8edO3UD9+K+Y4EWjVd9Ep5X1ZG5fVrMBVbsBrQNuEEXno4I1sPJLwvljO/R1I6bFP2ab9EmF0Tkd9KK6k0hGXxzrvt75zEAJ6owR3dCV4s5Pl2F2wv7vC3RdQy9bDGu+jw1ebFMyG0oXU0d60zYhhEhgEsBIAHYrhIoEIFjX/Ar+KghUU21lSPBCiH1MxNSotjKpShtNVe6hVGn5+Ex3vLsFgNOsRVv4VssrrPgCuk+IXVa1Bke71hcQnZVjxYdghAkXTNj1ym3o1eUWK2vgrD4K1YE1HcK5o9Br1nW62UgAkpw2LhxTxNQ1Vbw4c0O8uyOaoWkKdB2WftTiOmrOC7j1SAf2Sggh9p4EMBJARPdg/XJKxibkB78Q+zLLIjFnHNrZTBFKixaN21F6d0z5PBPNcC16CfJHYbkzOuw1t/gsHp1vclA+9Ejp2PPSSO+N6c7EseKDDn3dtjI4L5mjBmTz8Her+WFFeby7I37BMExIyt7FZ7RCfl8KITobCWDEiaYpvFo9aaoKW6QBUouhpTG/SVkY7szm24QQCcths0hVVaSqKjxagK5WsD+gp2IOO6vlFfoeCWt/3P7v4vFYnmzCYakWJ2LZSudh3zIH+h3VYa9pWRZ3zzSwa3Bqr3hcnIpwwXgcG39Cq9sYh9ffeycOyWNArpcrX5jFyvKGeHdH7MA0LUxvAQw8qcV1rP0uxhfRO7BXQgix9ySAEQduLUBa5XTcr5+B/ZGhOJ87HCscwDrhQZrc4Wg61kn/pk5J0TshOgulIE2vInnG/TiePADHo8PxfHQ56aE12PWuU4E3FNEwx1wFKU2nbbAGnAh1m6ND4DQdBp2MdfjtBG0ZnXHIv2hn7tn/wfDkQFHH1b/4aK3FtyVwbj+Fxx6f6GI4ZwSWMwXn4lfj8vp7S9cUl43rTkaSg9+8Pp/1Vf54d0lslaLXo9Z+B8PPgax+TdqtHhMxcofL1NZCiE5HZiHpYDZd4d7wHfpbl2xfWFuC9sSBmBd9BJd9C9MfR5UtwcodAmMmU+coJCLTDgrRaaSoamyvnoPasqBxmVr5Jfqa70m+9CuqHD26zE18jcoi9cKP0ZZ/hLboLbAnYYy9EpUzEPxVqPPfxLK5sFwZBO0Z1JueeHdZJBi9chnOlR8SGHYJbq1jngaXNFjcM8tkbK5iVHYcU6M0nVDxJJzL3yNUuRwjo2/8+tJKbofOn48ewO3vL2Tyq3N55PSh9M5Kine39mkezY/9h7+jzfovFI6Bs16IZsPNewU0O9Z+v8IoGE2V0XHDtYQQoq1IAKODJZmV6J/d0Gyb9r9jMa6cSfDQe9EMPxHNjT+kZG5uIToRTVPo5YtjgheNjBDa17fjPuY/+AxXx3euHZimRRUZ2PpfiKPfaVhKJ2g5MSMWuisDm6cXoBGKmE3KYQgBkPTj3zDdWYS6H0pHlKcNmxZ//tHApcO5CRAvCOeOwLZpOu75z1J/0F1NMzE7gTSPg99N6sXD36/m0pfncO8JAxnXQ26O48VpVEeDFwAbpsOjY2DgiTDqQrAU4cIJ1ES88e2kEEK0kgwh6WBaqBbqS1teoXQB/rBGXcQTDV4IIToVm01DW/J+i+1q5dfYra6XZh0xTHymG7/haExJNgyLYBiCYbPLZJyItuVY8yXOtV8SGHweaB3zTOWfc0wWVcJlg+I3dCSWItTzKPTKZdjXfxvvzrRaqtvOHw7pTc9MD9e+uYAnf1xLRIYndDilFKp8WdOGxe/BO1fBu1ei+ZqZ+loIIToJCWB0NN2x83ZXSmLONCCE2C2WBZZnJ08enclyMy8EoEJ1eL+9gXDOcML54zrkNd9aafLyMouz+ip6pSZC8CLKSO1BJHsI7vnPQaiFKdU7Abdd56oDenDsoBye/Gktl748hxVlUtyzY1ngTN75Krv6LSqEEAmsywUwnnjiCfr378/f/va3xmXBYJDbb7+dsWPHMnLkSK655hrKy+Mz5VfIlopV1MIPNUcSZlovubkRohMLhw2sIWe02G6O/jUBLb0DeyREYvJ+9xe0QA2+EZM7ZNjEdyUmd800OTgfDs5v95fbY8GeR4MZxj3/f/Huyl7RNMUJQ/K4/pDeVPpCnPf8LP7+5QoqGkLx7to+wbLATO3eYhDDKhhN2C7fQUKIzqtLBTDmzZvHK6+8Qv/+/WOW33333Xz99dc8+OCDPP/885SWlnL11VfHpY8+Kwnz+IfAmxPboNsxzniOBk3GjArR2fkdOZiH3d5kudVtBMbIiwlJXRuxj3MueR3X0jfwDbsEy5Pd7q83bbPJ9VNMhmfC2X0VKgHrTFiOJEI9j8ax7ltsG6fFuzt7rXdWErcc0ZeTh+bx4cItnPTUdP7x5QrWVvri3bUur15lYJzxPOj22IakLMwTH6XBkiKrQojOq8sU8WxoaOCPf/wjd911F//5z38al9fV1fHmm29y//33M378eCAa0Dj22GOZM2cOI0aM6NB+WpZFta2AlIu/QNs4HW3tFKzMvph9j6ZezyJsdqmYkhD7JL/phkHn4u5zBGrR2+CvxOp/PJGMftSZqfHunhBxZStbQPI3NxAsPoRw8UHt/nrfbTS5/geTfmnRuhe6lnjBi23CucPRK5fimf0f6lK7Y3nz4t2lvWLTNY4ckMMBvTL4alk5Hy8u5bU5JQzPT+HIATkc1DuDvJSuUdA4kURMRV36SLxXTEMt/xStfClm9wMwC8ZSp2Viycx2QohOrMsEMO644w4mTZrEhAkTYgIYCxYsIBwOM2HChMZlvXv3Jj8/f48DGG31wMayLGrIQC8+Bq3HcViWRSRigtV2r7FtOwn4kCkhyfHaffE4VvF4X/Z2PwOWm4C9GPvo3wMQiZiN9W3idZ51xfO8q+1Tou1PW/dDq99EyocXYyQXEhh+KTGbV9v/X7XB/ZVlWby03OL+n01GZEeDF/Z4BS/2YN+CfU/EPfdpkqbeTcNBd2A509q7d3tlx3O2pSGwSQ4bJwzJ4+iBOfy8oYbpa6v459cr+MdXUJTuZmRBCoPykumf46VnpgevMzF+nsbzetyd19xZ/yKWTrXKwTboIpRSmKaFYZhgWgnz+dKWEu2zM97keMSS49G1JMY3xF768MMPWbRoEW+88UaTtvLycux2OykpKTHLMzMzKSvbsyrMmZm7KIqUgDpjn+NJjtfu68hjFc/3pSueE7JPiS9R9qdN++GrhNcuAkw44lZSPWnNrpaa4tnrl6oJWtz8nZ8PVxkc28vGeYPsaAnwyzUpaXeyDVww+mKY8QQpU/4Kh/0FUovbu2t7LWU337cjM7wcOayAhmCE+RtrWFhSw7xNdXywcAvbJi3JSHLQPdNDUbqH/DQ3eSlO8lJdZCe7yPI6yEhy4HXaOmwoUDyuxz15zUT5vEgEcixiyfGIJceja+j0AYxNmzbxt7/9jf/+9784nc52fa2KirpOU2BTqehF2pn6HE9yvHZfexyrrKydf6HE433piueE7FPi6+j96ahrT/nKSH33XLS6jTQceBtm2A01v6iFoKLBi5paH7TyNU3L4qO1Fg/MNgkYcPlgxf45Jv6G4N7vxN5Q0eBFQ0NgN/ctCW3IxTgXvYz23rUE+p1CqM9xWLa9D+60NaWiwYvaWt8enysDM90MzHQDEDZMNtUG2VwboLQ+RHl9iCUlNUxdWU6VL9xkSlabpvA6dbwOG0lOnSSHjSSHjsehk+S0key0kea2keFxkJPsoFuKi7wUF7Y9yMJpz+uxLa69rvb5tzfkWMSS4xFrx+MhQYzOr9MHMBYuXEhFRQWnnnpq4zLDMJgxYwYvvvgiTz/9NOFwmNra2pgsjIqKCrKz96xwmGW1nB6ZqDpjn+NJjtfu68hjFc/3pSueE7JPiS9R9qct+mHbMoeUTy5HRfzUT7wVM7mw2fUah1ZYex6/CBkWX6y3eGaJyfJq2C8bzuqrSHeq1sZC2lRr9s1wpeMbfhmOdd/iWvoWzuXvE+p+CKEeh2OmFLVXV/fYtvNjb88Tu65RnO6mON3dzGtYNIQMavxh6oIR6oIGDcEIvrCBP2wQCJsEIiY1gTBb6oMEwya+sEFdMEIgbDZuR9cURWku+mQl0S/HS/8cLwNzvaR7dj6taDyuxz15zUT5vEgEcixiyfGIJceia+j0AYxx48bx/vvvxyy78cYb6dWrF5dddhndunXDbrczdepUjjrqKABWrVpFSUlJhxfwFEIIIfYVWn0J7p//g3vBcxhpPamfcDOWJ6vNtl8dtPi5zGJKicWXGyxqQjA4Hf48UtEnLf7DRdqEbifU83DCBWOxl0zHse5bnCs/wsjoS6j4YELdxoKr6xcGVkrhddpaVRsjFDGp9ocpbwhRWh9kc22QdVV+flxdhS8cnRIqN9nJwFwvA3K99M320jvLQ7cUF3oCDDsSQggRq9MHMLxeL/369YtZ5vF4SEtLa1x+2mmnce+995KamorX6+Wuu+5i5MiRexzA6EzfY1KsZs/I8dp9UsSz85J9SnyJtj+77IdlYt/wA1rDFlSoHs1Xhl69ElvJdHRfKQCh4oMJ9jkezQhBXUmLm1pWp1Oyzk29L4xhKUImBA2FL6KoDWtUhRSlQZ2NPo31vtifLyPTQxzQI0Q3twEmlFbu7Z63PZfPTiAQbv0GUkaBdzh69Wr0ysVos9+C2W9h6U7M1B6YSbmYrjQsuwd0B2ZyIYYnZ9fb3UtKQYOlqK8PJPTTzQyPnQyPnQE53sZlZfUh1lX5WVvlZ87GWr5ZURHzN7qCwnQPmVv/Ns1tJ9llw+PQcdl0HDaFQ9ewaQpNU+hKUZzupt8Or9Fae1vEc18jxyKWHI9Ycjy6FmVZifx10zoXXHABAwYM4OabbwYgGAxy77338uGHHxIKhZg4cSJ//etf93gIiRBCCCF2sOhdeO3Cvd5MjeVhePCpNuiQEPE385bDyfK2b102IYTYV3XJAIYQQgghhBBCCCG6Fi3eHRBCCCGEEEIIIYTYFQlgCCGEEEIIIYQQIuFJAEMIIYQQQgghhBAJTwIYQgghhBBCCCGESHgSwBBCCCGEEEIIIUTCkwCGEEIIIYQQQgghEp4t3h3oTMrL6+hMk85mZCRRWdkQ7250GnK8dl9bH6vs7OSdtsfr2uuK54TsU+LryP1JtGuvq72XO5J965zaa9/a6trrysd+T8mxiCXHI9a247Gra08kPsnA6KKUAl3XUCrePekc5Hjtvn3lWHXF/ZR9SnxdbX/2RFfed9m3zinR9y3R+9eR5FjEkuMRS45H1yIBDCGEEEIIIYQQQiQ8CWAIIYQQQgghhBAi4UkAQwghhBBCCCGEEAkvrgGMxx9/nNNOO42RI0cyfvx4rrrqKlatWhWzzgUXXED//v1j/nfrrbfGrFNSUsLkyZMZPnw448eP57777iMSicSsM23aNE455RSGDBnCEUccwVtvvdXu+ydEvGk2DR+KWhP8KGy26Pg/U1P4UYSVQtdlQGCXoGv4LQhufZ8TjVKKiKaot6DeAkOTsahCiPal2zT8KGotaEARselYeuJ9PgohhNh9cZ2FZPr06Zx33nkMHToUwzD45z//yaWXXsqHH36Ix+NpXO/MM8/k2muvbfy32+1u/G/DMLj88svJysrilVdeobS0lD//+c/Y7XZ+//vfA7B+/Xouv/xyzj77bO6//36mTp3KLbfcQnZ2NgceeGDH7bAQHSikabw7p4Qnvl9NtS9MTrKT6w7ry7heGdz78RLmbKgmL8XFlZN6M6IgBbtpxrvLohWUpqgKWzzxzQq+XVZGitvOryb04OC+WTgS5D1VmqIsaPC3DxczZWUFSsGBfbO5+ZgBZDk0TLMTTe8khOgUwprGO/M3859vV1HZECLb6+SiCd3pl5tMj0wPGXYNSz57hBCi04lrAOPpp5+O+fe9997L+PHjWbhwIfvvv3/jcpfLRXZ2drPbmDJlCitWrOCZZ54hKyuLgQMHct1113H//fdz9dVX43A4eOWVVygsLOSGG24AoHfv3syaNYtnn31WAhiiSzI0jUe/XclL09c3LiutC3LzOwu4YlIvbLqirC5IWV2Qq176mUsn9uTX44uxyY+5TkUpKA+anPKfH/GHDWD7+3xwvyz+duJgHAkw93OtYXHG4z9RH4xmxlkWfLesjDnrqnj3qgPwSiaGEKINmZrGkz+u4Zkf1jQuK6sPcv9ny7jkgB58unAzv5rQgzyXDSsBPiOFEELsvoTKo6urqwMgNTU1Zvn777/P2LFjOf7443nggQfw+/2NbXPmzKFfv35kZWU1Lps4cSL19fWsWLGicZ3x48fHbHPixInMmTOnnfZEiPhqiJi8PGN9s23P/riGk0YUxCx7espq6sPyI66ziWgaf/9saWPwYkffLCunpDYY92Eamq7x0vT1jcGLHdUGIrw5eyO6pHQLIdpQg2Hy3NS1zba98NM6jh6Sx98+Wkw43h+QQggh9lhcMzB2ZJomd999N6NGjaJfv36Ny48//njy8/PJyclh6dKl3H///axevZpHHnkEgPLy8pjgBdD477Kysp2uU19fTyAQwOVy7VYfO9P33La+dqY+x1NXO16bawO09FApEDYxm2lcWFLDQT3SdpnOH49jFY/3pTOcE0HT4pulpS22f7poC9cc2IPw1gBHPPYpYFp8t7ysxfZvl5Vx7n4FOFrZp87wPu2JRNufeFznibLvbUn2rWNV1IcwWvguCxkmoYjF1FWVBAyT5J3ET+O5b7vzmol47ONFjkUsOR6x5Hh0LQkTwLj99ttZvnw5L730Uszys846q/G/+/fvT3Z2NhdffDHr1q2juLi4Q/uYmZncoa/XFjpjn+OpqxyvTcGd1z6wN1O40+2wkZHh3e3X6MhjFc/3JZHPiXC1H7uuEYw0/367HTqpqZ4myztyn+oCYTKSHC22Z3odpCW78Dj37usokd+n1kiU/YlHPxJl39uD7FvHKA3V7rTdYVPYNIXDrpOV3vQz8pcS/Tro0P41lMNzJ0LeMDj5Pwl3R5hI52EikOMRS45H15AQAYw77riDb775hhdeeIG8vLydrjt8+HAA1q5dS3FxMVlZWcybNy9mnfLycoDGuhlZWVmNy3Zcx+v17nb2BUBFRV2LT7UTjVLRi7Qz9TmeutrxSnXqdEt1sakm0KRtcH4KK0sbYpbZdUW/nCTKy+t2ue32OFZZWTv/QonH+9IZzgm7rnHyiHxenbmh2fajBuXGvKfx2CdNU0w+sBc/rapstv3XB/TEXx/AV9e6DnWG92lPdPT+JNK119Xeyx3JvnWsZIdGUYab9ZX+Jm19crysr/RzzJA87Ja10++99ty3trj24nHsPdMewbNlIWxZSE3PkwkXHtAxL7wLiXgexpMcj1g7Hg8JYnR+cQ1gWJbFnXfeyeeff87zzz9PUVHRLv9m8eLFwPbgxIgRI3jssceoqKggMzMTgB9//BGv10ufPn0a1/nuu+9itvPjjz8yYsSIPewvne5DoDP2OZ66yvHy6vDEBftx3tPTqPVvrz2Qk+zkhmMG8PvX5sasf/cpQ0nS1R5VZO/IYxXP9yWhz4mIyZWTevP98nJKfhGsuvygXmS6bc2+px25T4ZhMSDbywXjinn+p3UxbZdO7EmvDHebzEKS0O9TKyTK/sSjH4my7+1B9q1jeDR4/Lzod2CVL9y4PMvr4C/HDeQfny7lobNHoAyT3elyol8HHdk/+5ovCRUegF61CsfStwkVJEYAY5tEOg8TgRyPWHIsuoa4BjBuv/12PvjgA/7973+TlJTUWLMiOTkZl8vFunXreP/995k0aRJpaWksXbqUe+65h/33358BAwYA0WKcffr04U9/+hN//OMfKSsr48EHH+S8887D4YimLZ999tm8+OKL/P3vf+e0007jp59+4uOPP+bxxx+P274L0Z4MwyLfbeO9qw5gYUktK8rqGZyfQr8cL5pSXHpAD6auqqQo3c05Y4rJcuoomYGkU0rW4JXLxvLjqgo+XrCFNI+dC8YWk5/iTJhZZeyWyZUTe3LumGK+W1aGUoqD+maR6tCxya8JIUQbM02LXJfOO1dOYNHmOpZsrqN7podsr5PKhiCPnjMSr4ZM4bynIgFs5QvwD7sU05mGY9230TvCBBtGIoTo2uIawHj55ZcBuOCCC2KW33PPPZx66qnY7XamTp3Kc889h8/no1u3bhx55JFcddVVjevqus5jjz3GbbfdxllnnYXb7eaUU07h2muvbVynqKiIxx9/nHvuuYfnnnuOvLw87rrrLplCVXRppmmRBIwrTGFCcSqmaUV/rFkWZw3vxunDu6ErRSRsSEi6EzNNCzdwZJ8sDuuThU1TGBFjj7JpOoLdssiya5w5vBsAkYgp550Qot2YpoUHGJOfzPiiFCylETFMbNkeIhETc+elokQzbFXLUZaJkdoD052Ba+WHaLXrMFO7x7trQoh9SFwDGEuXLt1pe7du3XjhhRd2uZ2CggKefPLJna4zduxY3nnnnT3pnhBdQmPgYgeRrUUfm05sKTorw9j6njadUTWhRFooOCqEEO1h+3egfO/tLb1yGQBGciHKjA7NsW+ZTVACGEKIDrSTyaOEEEIIIYQQAvSatZjOdLC7sZwpGEm52Mrmx7tbQoh9jAQwhBBCCCGEEDul167FTMpp/LeR2gNb6dyd/IUQQrQ9CWAIIYQQQgghdkqr3YDpyWr8t5HSHVvFYqlnJIToUBLAEEIIIYQQQuyU3rAJ05XZ+G8zpRgtWIPm2xLHXgkh9jUSwBBCCCGEEEK0zLLQGjZjujMaFxkpRQDolcvj1SshxD5IAhhCCCGEEEKIFqlgNcoIYbm2BzBMTw6WZkevkgCGEKLjSABDCCGEEEII0SKtoRQA05W2w0Idw5uPrWpFfDolhNgnSQBDCCGEEEII0SLNXw6A5UyLWW5689CrV8ahR0KIfZUEMIQQQgghhBAt0nxbMzCcqTHLTW8+epUEMIQQHUcCGEIIIYQQQogWab5yLN0JNlfMcjOpG3rDZoj449QzIcS+RgIYQgghhBBCiBapQGU0+0KpmOWGNw8AvWZdPLolhNgHSQBDiC5I0xQ+FCX+CJsCEfwoNF3t+g/Fvk1X1Fuw3hemLGQSVApNk/NGCJHYNE0RVIotQYP1vjANKJQuP3HbkuavxHIkN1luJuUCoNes7uguCSH2UbZ4d0AI0bYMpfhpfQ23vLuQGn8YgOxkJ/88YxgDMj1gWnHuoUhEYU3xysyNPP7dKoIRE4A+OV4eOXsEOS4dw5DzRgiReDRdUeKLcPXLs1lb4QPAZdf43eH9OGFIHnbTjHMPuwYtUNFsAMNypmHpTvTa9XHolRBiXyThaSG6EKVgU0OYa16Z0xi8ACirC/KrZ2dSFZYfcqIpm03jq2XlPPTVisbgBcCK0nrOfXo6dUYcOyeEEDtRZ8C5T05rDF4ABMIm93y8hJ83VKNLJkabUC1kYKAUZlIueu3aju+UEGKfJJ/qQnQhpqbxyNfNz8ceMS1emr4e3SaXvYhVF7F46Kvmz5vKhhCLNtfJUBIhRMLRdY2fVldSF4w02/7A58vxSdZhm9D8FZjOZgIYgOnORpMaGEKIDiJ3MkJ0IUHDYllpXYvtC0tqCEkShviFiGlRWhdssX1RSS2aJl8XQojEouuKeRuqW2xfXd6AjH5rG1qwpvkMDMD0ZKPXyRASIUTHkF+kQnQhDk3RKyupxfZ+ucnY5aoXv2DTFFleR4vt/fOSMWUcuRAiwRiGxaBuKS22F2W4kfrVbcCyUMFqLLu32eZoAGMDWBItEkK0P7mVEaIL0S2Lqw/p03ybprhgbHfMiNyIilhem+LKg3s325bqtjM0PwVT0rCFEAnGMEwO6JOFx6E32/67w/qRJBGMvabCDSgzguVoOYChIn5UoKqDeyaE2BdJAEOILsSyLApTnPzjtGExP+hS3XYeP38UGU655EVTRsTk6IG5/HpiD/Qdal0Uprt58dIxeOW0EUIkqBQNXrx0LHkprsZldl1x3WF9GdcjjYgE7feaClQD7CSAkQWAXl/SUV0SQuzDZBpVIboYm2lxcO8MPr5mIuUNITSlyPDYSdIVliE/5ETzHKbJ5PE9OHdMMRX1IVx2nTS3DY9Csi+EEAnLNC2KvXZeu2ws1YEwwbBJltdJkk2h5DuvTWjBaoCWh5C4owEMrW4jZA/pqG4JIfZREsAQoisyTDxAcZJ96wILSyqZiV1QpkmyguTkrfUwLAuJXQghEp1pWrgBt8sGLgALqd7ZdrZnYDRfY8typmJp9mgdDCGEaGeSGCyEEEIIIYRoltpFBgZKYbqz0Bo2dVynhBD7LAlgCCGEEEIIIZqlBWsAsOyeFtex3Blo9RLAEEK0PwlgCCGEEEIIIZqlgjWY9iRQLd82mK4M9LqNHdgrIcS+SgIYQgghhBBCiGZpwZqWh49sZboz0Bo2d1CPhBD7MglgCCGEEEIIIZqlgrUtFvDcxnRloDVsAUuKpwoh2pcEMIQQQgghhBDNUqFaLPvOAxiWOwNlhlGByg7qlRBiXyUBDCGEEEIIIUSztGANls2903VMV0Z03YYtHdElIcQ+TAIYQgghhBBCiGapYM0uMzBMVzogAQwhRPuTAIYQQgghhBCiWbsTwLBcaQDoEsAQQrQzCWAIIYQQQgghmqUF67Dsnl2sZMN0pqL5SjumU0KIfZYEMIToZDRNoZSKdzdEHCkVPQ+EEGJfse1zT77+Ot7uFPGE6DASzScZGEKI9mWLdweEELsnrCmq/BEWba4jM8lBr6wkvJrCMs14d010EE1TNFhQUhNgfZWfHpke8pKduAFLpq4TQnRBmqbwmVBSG2BdhY/umUl0S3HiUWCa8rnX7owgygjuVgDDcqai+co6oFNCiH1ZXAMYjz/+OJ999hmrVq3C5XIxcuRIrr/+enr16tW4TjAY5N577+Wjjz4iFAoxceJE/vrXv5KVldW4TklJCbfddhvTpk3D4/Fw8skn84c//AGbbfvuTZs2jXvvvZfly5fTrVs3rrzySk499dQO3V8hWiukadz07gK+XVbeuMzrtPHUhfvRJ82FJT/iujxNU1SGLS56ZjolNYHG5b2zk3jqwtEkK5AYhhCiK9E0RXXE4uL/zWB9pb9xefdMD89cNJoUTUnwtp2pYB3AroeQsDWA0SBDSIQQ7SuuQ0imT5/Oeeedx2uvvcYzzzxDJBLh0ksvxefzNa5z99138/XXX/Pggw/y/PPPU1paytVXX93YbhgGl19+OeFwmFdeeYV7772Xt99+m4ceeqhxnfXr13P55ZczduxY3n33XS666CJuueUWvv/++w7dXyFaQ+ka//tpbUzwAqA+GOHiZ2dQZ8iPt32Bz4LLX5gVE7wAWFnWwB/fmEcIyasWQnQtAQt+8/LsmOAFwNoKH9e+OodgnPq1L9FCtcDuBTBMV5rUwBBCtLu4BjCefvppTj31VPr27cuAAQO49957KSkpYeHChQDU1dXx5ptvcsMNNzB+/HiGDBnC3XffzezZs5kzZw4AU6ZMYcWKFfzjH/9g4MCBTJo0ieuuu44XX3yRUCgEwCuvvEJhYSE33HADvXv35vzzz+eoo47i2WefjdOeC7H7GgyLF6eta7YtEDaZs6EGXZeb166uJhBhVXlDs20z11ZRF5ahREKIrqUmaLBkc12zbQs21lIbMjq4R/seFawBdjcDI02GkAgh2l1C1cCoq4t+SaWmpgKwYMECwuEwEyZMaFynd+/e5OfnM2fOHEaMGMGcOXPo169fzJCSiRMnctttt7FixQoGDRrEnDlzGD9+fMxrTZw4kbvvvnuP+teZCkdt62tn6nM8JfLxihgWDTv5kbahyo/WK73DxgLH41jF431JtHOiNhDZabs/ZKDsO/9IT7R9agtdbZ8SbX/icZ0nyr63Jdm31vHtIkDhCxkoR/s9i4vn+7Y7r9kR/dNCWwNI9qRd5vlZzjRUxI8KN4Bj1zUz2lJXvsZaQ45HLDkeXUvCBDBM0+Tuu+9m1KhR9OvXD4Dy8nLsdjspKSkx62ZmZlJWVta4zo7BC6Dx37tap76+nkAggMvl2q0+ZmYm7/mOxVln7HM8JeLxMmoDFKa72VDlb7Z9v+7ppKV17A8F6NhjFc/3JVHOiVqr5W9du67I8DrIyty98yBR9qktdbV9SpT9iUc/EmXf24Ps256ppwHVQn0fXVNkJTt3+3NvbyT6ddCu/dsSzWZOycwExy6yMHw5AGS5fJCR13592omufI21hhyPWHI8uoaECWDcfvvtLF++nJdeeineXWlRRUVdpymSp1T0Iu1MfY6nRD5eDl3jz0f155pX5jRp65mVRFGai/Ly5lNs20N7HKusrJ1/ocTjfUm0c8KjKY4b2o0P529q0nb+2O54NHZ5HiTaPrWFrrZPHb0/iXTtdbX3ckeyb63j1jROHVnAmz9vbNJ25n6FuNWuP/f2RnvuW1tcex1xXjnLt+BFUesD/L6drqtFXCQD1RvXEjGz26dDLejK11hryPGItePxkCBG55cQAYw77riDb775hhdeeIG8vO0R26ysLMLhMLW1tTFZGBUVFWRnZzeuM2/evJjtlZdHix3uuM62ZTuu4/V6dzv7AqJPADrbh0Bn7HM8JeLxikRMRhelcv/pw7j3kyWU14fQFBw6IIdbjh24dQrNju9XRx6reL4viXJO6JbFTUf3J8vr4JUZ6wlGTDwOnUsO6ME5o4uwIrtfAyNR9qktdbV9SpT9iUc/EmXf24Ps257RDJPfH9aXdI+DF6atJRA2cdt1LhzfnQvHFqMMk444nIl+HbRn/1SwDuxuLLXroTqmMy36N76yff47O1HI8Yglx6JriGsAw7Is7rzzTj7//HOef/55ioqKYtqHDBmC3W5n6tSpHHXUUQCsWrWKkpISRowYAcCIESN47LHHqKioIDMzE4Aff/wRr9dLnz59Gtf57rvvYrb9448/Nm5DiERnMy0O7Z3BmMvH0xAycNg0vDYN3TLlw3gf4jBNrjmwJxeP704gYuK2R8+DPQleCCFEZ+IwTa6Y0J3zxhQRiJi4bBrJNg3LkM+9jqCCtZj23RumYzm8WEpD81e0c6+EEPuyuM5Ccvvtt/Pee+/xwAMPkJSURFlZGWVlZQQC0WkCk5OTOe2007j33nv56aefWLBgATfddBMjR45sDD5MnDiRPn368Kc//YklS5bw/fff8+CDD3LeeefhcDgAOPvss1m/fj1///vfWblyJS+++CIff/wxF198cZz2XIg9Z0RM3FhkOTRSNNBMCV7siyzDxKsgy66RBBK8EEJ0eTt+7nkVErzoQFqoFms3AxgoDcuRIgEMIUS7imsGxssvvwzABRdcELP8nnvu4dRTTwXgpptuQtM0rr32WkKhEBMnTuSvf/1r47q6rvPYY49x2223cdZZZ+F2uznllFO49tprG9cpKiri8ccf55577uG5554jLy+Pu+66iwMPPLAD9lIIIYQQQojORwVrsWy7nkJ1G8uZivKV73pFIYRopbgGMJYuXbrLdZxOJ3/9619jgha/VFBQwJNPPrnT7YwdO5Z33nlnT7soRNwoBYbSCFoWWODRlTx1EglNKUVYQcgEBSTpClPOWSFEK+i6RoNpYRFNF/ZoCkM+TzqcCtWBzb3b65vOFDS/BDCEEO0nIYp4CiFiKU1RETL5x2eL+HppGQ5d47RRBVx2YM/osAEZOyISjKUpNvsj3PfxEn5YVUGy08a5Y4o5b2wxbqnVIoTYA2FN46d11fzzi+WsLm+gOMPDbw/ry7geadhN+TDpSCpYg+XY/alqLUcymmRgCCHaUVxrYAghmlcVtjj53z/yxeJSDNPCHzZ4Ydo6zv/vdHY+iZkQHU8pxWZfhFP+/SNTVlZgWVAbiPDYd6u44sWf8aPi3UUhRCehdI1352/imlfmsLq8AYB1lT5+//pcXvu5BEuTz5OOpAVr9mwIiSMFLSA1MIQQ7UcCGEIkEKVrhDWNH1eWYzbzyHp9pZ8Za6vRdbl0RdtSCtA1wkqh2fbs/IoouO+TJUSaeTK6sKSW1RU+lJKbDiFEU7quEdEUhq6haYq6iMW/vlze7Lr//mYlDUYHd3Afp0J1WPbdD2BEh5BUtmOPhBD7OhlCIkQCMJSizB/h6R9WsKq8gb45Xh6/YD/+9+Navl5aGrPuh/M3cXCvjDj1VHRFEaVYVxvkqSmr2VIbYHyvTM4cXUiqrrB2I107aMKPq1p+4vbF4lKGH9KLUEjuPIQQUUopfMDXi0t5d24JDpvGBWO70z8vucUhZyHDpKIhhNdr79C+7stUcA9mIQEsZwoqUAWWCUoetggh2p4EMISIM6Uppq+v4bpX5zT+aJu3oYZ355Rw/xnDKa0LsLCktnH9dI8dTYGUMhNtwdAUb87ZxP2fL2tcNm9DDc//tJZXJ48j323D3EUQQylIctioD0aabU/32KUGhhAihg84/78zWFe5fWDkT6sqmdgni78cP4hb3lnQ7N859zBDTOwFy0SFG/YsgOFIQVlGtHaGK70dOyeE2FfJt4AQcVZvwg1vzW9ygxcxLe75eDEXT+gRs/zcMcUYEXmSLdpGQ8TigS+WNVnuCxnc+u5CgrsReEjSFOeOKW6x/ZgheYTDcs4KIaJ0XeONnzfGBC+2mbKiHLddJzfF2aQtL8VFqkuevXUUFapDYe3xEBIALVDVXt0SQuzjJIAhRJyV1QfxtZBav6U2SLJre6rs5Qf1opvXIU+zRZvQNMXP66pbPJ9mr6/GF9n1yWYaJheMK2ZQt+QmbX85biCpDn1vuyqE6ELqDYs3f97YYvunCzdz2ICcmGUeh85/zhtJkvxy7TAqGM3+3JMAhuWIBjCUXwp5CiHah4SxhYizXQUjvE6dKw7qxfHDupHptmGTKeREG1EKLHZ+PkXbd12A022ZPHHeKFZW+Ph80RYykhwcOySPVIeOTSJuQohf2Nl04KZlcc0hfRhWmMq8DTUMzk9lQq9MknV2OaRNtJ3GAIbDu9t/YzmjgWwp5CmEaC8SwBAiznKSnbjtOv5mUuyzvU66Z3gYMqF7NAVffriJNmQYFiOL0lpsH1qQSpJNjxZj2wXLAicWQ7I8jDi0N5ZF9JyV4IUQ4heSbIqTRuTzn29XNdt+9ugiHKbJkX2yOKZ/NoZhYRjm7nwUiTakBauBPc3ASMZCyVSqQoh2I4l4QsRZkk3j9pMGN1muKbj31KF4lCX1A0Sb0HWN8vogQRS2rYXwku0avzm4d5N1nTaNO08ajFPtWQDCNC1CIUPOWSFEi8yIydn7F5Gf6mrSNqZHBv1zvZhmNGgRChkYhkQu4kGFtg0h2f0inigNy+GNzkQihBDtQDIwhOggSoHdHq0FEA4bmEpRHjB47IsVFGW4eebi0bw0fT1ryhsY1C2FyQf1IsulYxryBFvsHU1T1Fvw3s8lvDe3BLuuce6YIib1zcZtmZy3fxFje2bw+HerKK0LMqZnBheP70G6Xe1WurbNpqFpGoZhyo2GEGK3PhO8Cl6+bBwfzt/Ee3NLcNo0ThlZwLhemdhQsIvhbaL9NQ4hse1+BgZE62DIEBIhRHuRAIYQHSCkFJvrQ7w3dy2mBScM64bXZePUx34kEI7+uEtx2zhhWD4Xje9Ov6wkVESGjIi20WDBuU9NZ2O1v3HZLe8uZHhhKo+cPQKXaTI4y8MDpw0lYlq4NIVlmFi7Ov80Ra1h8dX8zSzbUsfoHhmM7ZFBsibj1IXYF6mtnwlfL9jCks21jCxOZ3yvTFJa+EyobAgxdWU5Rw3OI2yYvDx9Pbe9v4grDurFRWOKpH5OnGnBGiybC7Q9K8RsObwyC4kQot1IAEOIdhZSivu/XM7bs0salz3/01oO7Z/DrccP5qa35wNQ64/w4rR1vDZzPZ9ddyDeXddNFGKXNF3jzZkbYoIX28zdUMO8jbWML0rBMCx0LHTA2o2sH6UpllUFuPiZGYS2PmF9fdZG0jx2Xv71WHKcugQxhNiHKE2xojrIhf+dTjCy/TMhxW3j5UvHkue2xXwmBCy45d0FLCyp5fsVsfUSHvtuFaeNKiBNly/CeFLBGiz77hfw3MZyeFGSgSGEaCdSA0OIdqRpiqVlDTHBi22+WlpK2DDpmRU7tjRsWKyu8KHkd5toAz7D4p25Tc+/bV6btQFjN2YZ+aUGE6588efG4MU21b4wv3ttLgGJXQixT2kwop8J24IX29T6I1z32lz8v/hM8EVMFpbUtri9Weuq0SWAEVdasBpzT+pfbGU6ktH85e3QIyGEkACGEO3KQPHsj2tabH93zkaOHpzXZLmuyY820VYstJ1Ew3bWtjNb6oLU+MPNti3ZXEddSIp4CrEvKW8IUdkQarZtRWk9tcHYzwS1i8CpTVMyiVGcqUAN7MEUqttYjmQp4imEaDcSwBCiHRmWRV0g0mJ7XSCCyx57GTptGsUZbvnhJtpEkk3jzP0KW2w/b0wR2h6ebEqBL9TyeQ0QkuKzQuxTmpsKfEfhX2Rreexai9M4KwUjitJkGFqcqWAN5h5MobqN5UhGC0oAQwjRPlpdA2PNmjVMmzaNiooKTDP2S+nqq6/e644J0dlFZx3R+MsJg1hd3sBnCzfzxeJSjB1+kE3sm8Xs9dUxf3fXyUNI0pQU8BRtwoiYnDQ8n145Xvwhg2+XlfHR/E0EIyYT+2QyYOt0hY3raxr1EZPKhhBJThupTh23ii3AZ1lQlO6hpdM01W0n1WWjuVkEdF3hM6EmGMEwLVJddry6wpTZS4TolDRdUVrrJy/VzcPnjOTndVW8OWsDtTsE75OdNjxOG9WGhdeuYbcsnFjcdfIQznx8Kg2/yNj681H98eoKieTHlxasxnSm7vHfWY5kVLAOzAhoUm5PCNG2WvWp8tprr3HbbbeRnp5OVlYWaocUZKWUBDDEPk9pivKQyf0fLuSbpWW47TonjsjnqQtH8/vX5lDlC5PusXP+uO68/NNaav1humcmcfGE7uR67CgJXog2oOuK6ojFw58t5aMFm1EKjhnSjecvHYsvGKFPlgfnDjcIQU3jH58t4715JY33DX1yvDx23ijSbbFTqibZFL+a0IOnf1jT5HVvPnbA1qBE7HmsNMWa2hC/fW0u9cEIp+9XyMBuKfTM9FCY4sTaxRNcIURiUbqiMmzx7DcreG9uCb6QwcS+WTx8zkj+9eVyfl5XDcBVh/Tmzg8W8d3ycsb3yuCeU4biBXJdOu9ffQBvzd7IjysryUt1csmEnhQkO9AleBF3KliD5S3Y47+zHMkorOjfuzPboWdCiH1ZqwIY//nPf/jtb3/L5MmT27o/QnQJFSGTk//9Q+MUqXXB6AwjP62q5PaThvDl4i1ce2gf0nW4amIPgqaFXVNYEVMyL0SbqTXgtMemUuXbXqvi7dkbmbKinDcuHxcTvEDXeGrKat79RcHPFaX1XPzsDF6+dAyuHZbrpsWl43vQPy+Zh79ewcYqP/3zkvnjkf0ZmJ3UbEZFrWFx7tPTmNQvm9P3K+KZH1bzwk9ryU52ctnEXhzaLwu7KZkYQnQWdQZc8cLPrCitb1z2zdIyflpVwRMXjOafny3jnLHFrCyr57vl0aKOU1dVcvkLP/P0BaNwmhbJCi4ZU8T5owuxaQoME0uCFwlBC1RjtaIGhulI3vr3VRgSwBBCtLFWBTBqamo45phj2rovQnQJpq7x0GdLG4MXO1pZVg+WxTn7F6OhMAwLsLABlgQuRBvSbRqvTVsfE7zYpqwuyCcLt3DGsDyMrTMG1EdMXp6+vtltbajyU1IToHeqMyaj226ZHNEnkwk9MzABHXD9YrjJNjabxvuzNpKf6ub4Yflc/vzMxljd2goft7y7gJOG5/PnI/pil5sXIRKe02ljyoqKmODFNoGwycvT13HzcQOZX1LNE9+timlfuqWO0oYQRR47EB3qpiPfgwnF2ppB0coinoAU8hRCtItWFfE8+uijmTJlSlv3RYguIWhYfLesrMX2jxdsxmZT+COSLi/ajy9i8fmiLS22f7JgM8EdbhYCYbPJ9Ic7WlfljxkuuE0kYuK0LNyWhcOyWiy6Z6KYva6a88d158EvljebaPTu3BKqg3JdCNEZaLrii8WlLbZ/t6yMNRUNdM9IwqE3/bm5tsKHJjNuJSwVrkdZRisDGNG/0fyVbd0tIYRoXQZG9+7d+de//sXcuXPp168fNlvsZi688MI26ZwQnZECkpw26oLNz9LgddlYvqWeg/pmgQQxRDvRNUh2tfwRn+yyoSvFtkKbbruG2663OJNAj0zPXs0IoCnom+ulW5ormonUgp/XVXFs/2wMKeopRGKzINXT8meM12UjEDZZWFJLUYanyXVfkOaWoSIJTAWqAbDsexHAkAwMIUQ7aFUA49VXX8Xj8TB9+nSmT58e06aUkgCG2Kcl6YoLxhXzj8+WNdt+2IBclm+pw2vXCEkAQ7QTp4JfTejBz+vmNNt+8YQe6JbFtjCB16Zx8YQe/OfblU3W7ZmVRF6yc69mBIiEDc4YVcjqSh9qJ5MLJDltclMjRCfg94c5c78iXprW/NCzU0YW8NGCTRw2IIeQEftdV5DmJi/FKdd6AtsWfGhNBgaaDdOeJENIhBDtolUBjK+++qqt+yFEl2EY0WkrP1+0hTkbamLafnVAD2avq+LUUQWE/E1rEwjRVgzDYmRhKkcOyuWzXwwlOWVkPv2zk2KnRjVMzh9TRH0wzMvT1xPZ2jaiKI3/O2M4bpqbFHXPpDk0uqW4OKB3FlNWlDdpt2mK4YWpe5XpIYToOLleB1cf0odHvl4Rs3x4YSojitJ58vvV/O7wvqyv9De29cj08MQF+5Eks4UntG3Bh20FOfeU5UhGC8gQEiFE25PJmYVoB07T5MGzRzBnQw0/LC/H7dA5sG8W01ZXkpHkIMWmyfz2ot05LYu/HjuAyyb25L15m9AVnDyqgBy3vdlCmQ7T5JoDe/GrCT2o8YfxOGwk2zUc0CZPSpVpUZBk468nDOLcJ6dRVh/c3qbgvlOHkqTtJD1DCJFQbIbJBfsXcNTgXN6YtQFfyGBc70xCEZM/vD6Hv586lMIUF59ddyCbawOkeeyku+x4tOaL/YrEsS34YO1FAEMyMIQQ7aHVAYzNmzfz5ZdfsmnTJsLh2CfJN9544153TIjOLlnB6KI0uqW4mLaqgmkrKzh5ZAGZLhs2uUETHcRhWfRMcXD9Ib0ASE31UF5e12I2hTJNkhUkb50dAMva68yLHZmGRYZN8cbl4/hpTSXfLC2jMN3NaSMLSHPoaHJtCNGp6KbF0MI0cpKdLN1Sx4fzNpHitvPm5eNJd+hopkm6TZGe4d76FxYyW3LiU4EqLM0OurNVf2/Zk6QGhhCiXbQqgDF16lSuvPJKioqKWLVqFX379mXjxo1YlsWgQYPauo9CdEqmaeHEom+ak/77F6KAUMiQp8uiw1lW9NxrZhKRuDBNCw9wRO9MjuqXBRaEw3JtCNGZ6eEIQ7KSGHZEX7mmuwDNX4nlSKG1XxyWwysZGEKIdtGqaVQfeOABLrnkEt5//30cDgcPP/ww33zzDfvvvz9HH310W/dRiE7NMCzCISMavBBCNDIMk3DIiN7oCCE6Pbmmuw4tUIXpbN3wEZAaGEKI9tOqAMbKlSs5+eSTAbDZbAQCAZKSkrjuuut46qmn2rJ/QiQeXaPegtKQQb0Fqpn57YVoD6auUWtCWcigwVJoNjn3hBBtS9c1/CjKQgbVhkVYU6hESd8SHUb5K1pd/wKixT+1rVOxCiFEW2rVEBKPx9NY9yI7O5t169bRt29fAKqqJF1MdF0hTePf367i9ZkbCBkmTpvGeWOLuXRCDxwyqFe0E6XAh8bdHy3ms4VbMC1Idtr4zSG9OXFoN+xy7gkh2oChFFPXVXPnB4sbi+yO6ZHO3acMJd2mpPDmPkTzl+9VACNaxLM6OoxIAmBCiDbUqgDG8OHDmTVrFr1792bSpEncd999LFu2jM8//5zhw4e3dR+FSAgRTePuT5by4fxNjcuCEZP//rAGX8jg9wf3RsmNpGgHARS/efln5m+sbVxWF4xw7ydL0ZTi9KF5GIace0KI1tM0xcIyH9e+Midm+fQ1VZzz1DTemDwOT3y6JuJA81dgpPdu9d9bjmSUFUGF6rCcKW3YMyHEvq5V+cc33ngjw4YNA+Caa65h3LhxfPTRRxQUFPC3v/2tTTsoRKJoCBsxwYsdvT5rA7URkwAKm6T1izZW1hCKCV7s6JFvVlBntPxUVNuaDu5DYWpybgohmhew4N5PljTbVlYXZO7GGnS95Sfp1tbPGj8yvK0r0AKVmI7WBx4shxdACnkKIdpcqzIwioqKGv/b4/Fwxx13tOrFZ8yYwdNPP82CBQsoKyvj0Ucf5fDDD29sv+GGG3j77bdj/mbixIk8/fTTjf+urq7mzjvv5Ouvv0bTNI488khuvvlmkpKSGtdZsmQJd9xxB/PnzycjI4Pzzz+fyy67rFV9FvuuSl+4xTbDtFheWs/t7y9i8kE9OWpgrgwpEW1C0xQryhpabK/1R/CHDTz22BsGpcCvNF74aR2vzFhPIGxw6IBs/nDkANJsCrYGPaKZvQpLZgsQYp8WsWDJ5roW26evrmRSz3QMw9haE8OKjg7QFKvL6/m/z5fz6cLNOG0ap+9XyMXju+MB+WzpjEwjOo2qK63Vm7C2Bj+0QBVmavc26pgQQrQygAFQW1vLp59+yrp167j00ktJS0tj4cKFZGVlkZubu1vb8Pl89O/fn9NOO42rr7662XUOPPBA7rnnnsZ/OxyOmPbrr7+esrIynnnmGcLhMDfddBO33norDzzwAAD19fVceumljB8/nttvv51ly5Zx0003kZKSwllnndXKvRf7Iq8zerloClx2HV/IoDDdzYC8ZOqDERw2jc21Ae74YDHTV1dy6zEDsMsPN7GXLMsiL8XVYrtdVzibedrpR3HJ/2ayvLS+cdnHC7bw7bJy3r1qAql2DZ8Jayt8BCMmvbOSSLIpbBbY7RrWtmkQhRD7BE1BdrKTsrpg4zKHrqFUdLhkz6wkwhY0oKhqCKFrGikuG8qEEx/+gbpgBNg+tPKLxaW8cMn+uOO1Q6LVVKASZZmYztRWb8OUDAwhRDtpVQBjyZIl/OpXvyI5OZmNGzdy5plnkpaWxmeffcamTZv4+9//vlvbmTRpEpMmTdrpOg6Hg+zs7GbbVq5cyffff88bb7zB0KFDAbjllluYPHkyf/rTn8jNzeW9994jHA5z991343A46Nu3L4sXL+aZZ56RAIbYIxluGw+dMwKnTachGKFvjpe6YIS3Z2+kV5aXTK+Dk0cU8M6cjXyycAtXH9KHXKce726LTs6yoDjdTWaSg4qGUJP2E4fn47VpsEMNDE1TzN9YGxO82MYXMvh2WTnd0lz84fW5BMLRv3PbNZ66aH+wLL5aUsrEvtlkeh1UNoTI8TpJdek4regTVyFE15OkK66c1Is7PljMpH7ZnDu2mGDYwAI8Dp0BucmsrPSzorSeWeuqSPc4mNgnC7ddZ/+eGXy1pDRme+sqfUxfU8WhvTKkRk8no/nKALD2IoCxrQCoJgEMIUQba1UA49577+WUU07hT3/6EyNHjmxcPmnSJK6//vo26xzA9OnTGT9+PCkpKYwbN47f/va3pKenAzB79mxSUlIagxcAEyZMQNM05s2bxxFHHMGcOXMYPXp0TObGxIkTefLJJ6mpqSE1tfUfzmLfoWmKypDB/Z8uY12lr3H5YQNzOG1UIde+PJsnvl/FzccNJGKafDBvE7PWVXHCgBz54Sb2mleD//1qfy5+dgbl9duDGGN6ZvC7w/rGBC8AdJvGe/Oar9di1xV9cr386tkZMcGI+04bxr++XM6SzbU8fPZI7v9sGfM31jS2jyhM5V9njyBJSRBDiK7IiJgcPTAXh00jbFj89pU5+LdmYTltGredOJiNVT4e+Xpl4988PWU1Nx07kN8c0odvlpbyy0lK3p+3iYN7Z3Tkbog2oPnLAbCcaa3fiM2JpTvRApVt0ykhhNiqVQGM+fPnN1v3Ijc3l7Kysr3u1DYHHnggRxxxBIWFhaxfv55//vOfXHbZZbz66qvouk55eTkZGbFfjDabjdTU1MZ+lJeXU1hYGLNOVlZWY9ueBDA60yxQ2/ramfocT7s6Xg0WXPjf6WypDcYs/3JxKZlJTo4fls87czZy90eL+e9F+/Ph/E0kO22A1eXeg3icW/E4hol0DVmWRb7HxpuXj6ekJkBZfZBeWUlkuG04LBN+0UcFpLrtzW7rwL7ZfLZwS0wQYnhhKmsrfUxfXcn1R/bnoa9WxAQvAOZsqOGPb8zjwTOG4SBxIhiJ9D61hUTbn3hc54my722ps+ybE5OhBamc9OiPMcuDEZMb35rPf84fRYrbRq0/0th2z8eLefOKCYzrmcmPqypi/i7ZZUNTCjPB97sl8Xzfduc126t/ui+aTWO50n759bJHLEcyWrCqQ45fZ7nGOoocj1hyPLqWVgUwHA4H9fVNU5PXrFnTJKCwN4477rjG/+7fvz/9+/fn8MMPb8zK6GiZma2fDzteOmOf46ml4zVtVUWT4MU2787ZyP+dNYJ35mzEsmD+xhoGdkthdI8MMjKSmv2brqAjz614nseJdA1lAN2zvbu17nljinl1xvomy7O8Tkpq/DHLjhnSjddnRdcdlJ/C/Z8tbXab09dU4TMgPydxjsk2ifQ+tYVE2Z949CNR9r09JPq+BcIGT382r8X2N2Zt4Pih+bw0fV3jMsuCn1ZXMLQwtUkA46LxPchI6/yTryb6ddD2/asBh5fUjLS924w7FY9Vjydr3/i9kIjkeMSS49E1tCqAceihh/Loo4/y4IMPNi4rKSnh/vvv58gjj2yrvjVRVFREeno6a9euZfz48WRlZVFZGZuaFolEqKmpaaybkZWVRXl5ecw62/69LRNjd1VU1HWa1GmlohdpZ+pzPO3seGmaYl1Fy7NABCOx6fv1wQh/Pqo/LmVRXt5yRffOqj3Oraxd/LiJx3nc2a+hLI+NyQf25InvV8cs1zUY0yODb5Zuz5Zz2jXqAtEnqoFdFO6s9YUS6rzu7O/TL3X0/iTStdfV3ssddZZ9CynF6vKWv+/WV/oYWtA0c7UhGKGiITbIf/7YYgpSHAn1ebGn2vN9a4trr736l1S2AYczjfoa365X3gmP7sGs3kJdB5wDneUa6yhyPGLteDwkiNH5tSqAccMNN3DttdcyYcIEgsEgF1xwAeXl5YwYMYLf/e53bd3HRps3b6a6uroxODFy5Ehqa2tZsGABQ4YMAeCnn37CNE2GDRsGwIgRI3jwwQcJh8PY7dGU6h9//JGePXvucf0Ly6LTfQh0xj7Hk2WBUoqgBQ0RE8O08Np1eu3kqbfXacPYYeDvkYNyKU52oAwrgRLt215HnlvxPI876zVktywuHlvM8UO78dacEuqDYY4fms/A/BR8gQiPfr2ChlA0WDF3fQ0H9Mni7dkbcdlbLjyrFKS47Al5PLa9T5pNoy5i4g+ZuOxakwKnnUWinHfx6Eei7Ht7SKR907TY77okh06SrjG0IKXJELJt+ucms76y6U3tIf1zCBsmGgqXXeeUkfnkJjmwm13jezDRr4O27p/ylWI60/b6vbMcyShfRYceu0S6xhKBHI9Yciy6hlYFMJKTk3nmmWeYOXMmS5cuxefzMXjwYCZMmLBH22loaGDduu1piBs2bGDx4sWkpqaSmprKI488wlFHHUVWVhbr16/nH//4B927d+fAAw8EoHfv3hx44IH85S9/4fbbbyccDnPnnXdy3HHHNU7lesIJJ/Doo49y8803c9lll7F8+XKee+45brzxxtbsuujiNE2xJWBw09sLmL2+GoDumR4eP38/emd7WVnWdOjUuWOLeW9uCQAH9Mmkm9eB+mUlMyHiwG5ZdHPb+N2knoDCMAwyUlxUROp55bJxXP/GPJZuqePjBZv478X78/miLUxfXcGhA3KazCgAcPTgXNx2DV2RkMVpw5ri7dklPPbtKuqCEey64pSRBVx7SB+cZuL1V4h40XTFZr/BjW/PZ96GaLCiV1YSfztlCBeN78FrMzcQ+cX3mK4pLj2wFy9NW4tD1wht/QwY3yuDHplJqFCYm4/sB1iEQobcKXRiWv0mLFfaXm/HcqSg16ze9YpCCLEHlGXF7xtm2rRpXHjhhU2Wn3LKKdx222385je/YdGiRdTV1ZGTk8MBBxzAddddFzP0o7q6mjvvvJOvvvoKTdM48sgjueWWW0hK2l57YMmSJdxxxx3Mnz+f9PR0zj//fCZPnrzH/S0v7zxpWEpF0xM7U5/jadvxWlNWz4n//iGmQBlAltfBK5PHccs7C/hpVXTYktOmce7YYs4cXcRVL87itFGFnDI8H0cXv1Fqj3MrO3vn6XzxOI+74jW04z4ppQhYUBcyMCxIc9vwhQw+WbiFIwfn8q8vlvPJws2YFmgKjh/WjbP3L+ZvHy3ijP2KOHxADi7LjPuxadynqgZemLWRB79Y3mSdiX2y+PvJg7HHu7O7oaPPu0S69rriNbdNou1bvaU44dEfqA/GftfpmuLDaw6grC7En96cx6aaAAC5KU7+fPQAPl+4hSSnzpGD87j9/YWcvX8xp47MpyAjKWH2rS215/vWFtdee/Uv47nxhPNGEhh8/l5tx7nkdRzrvqHyV7PbqGctS7RrLN7keMTa8XjsaviWSHytysAAmDdvHtOmTaOyshLzFzdsu5vdMHbsWJYubb5YHMDTTz+9y22kpaXxwAMP7HSdAQMG8NJLL+1Wn8S+7fPFW2KCF90zPVwwrjuD81MIRkwuPaAnkw/shT9sYtMVH8/fzO9fmcOzF48hWUvMp9IicSldo8GwqPaHAUhz2/HqCrMdzyPTtPBZikDYxKFrZLltWBETDBOXXXHYwBxO+fePnDm6iMcvGE0oYuKwacxcW0nQMFlYUsfCkkW8+fMG/n3OSFwJkiBeHzZ5/NtVzbZNWVFOTcggy651cK+ESDy6TeODnzfGBC+GFaZyzphiUt12avwRijLdvPTrsZTVh6hoCFIfiPD0lNUsLKkFYG2lj5cvG4dXo10/r0QcWBaabwuma++L8luOZLRA1bbxuW3QOSGEaGUA47HHHuPBBx+kZ8+eTQphKvmAEp1UMGzww8rtFdQPHZDDqaMK+NcXy1leGh06Mr53Jlcc1It/fLqENRXbxwGXVPvpm+bs8D6LzstUijkltfz5rflU+7YGMDx2/n7aMIbnedHaYRhSSGm8NH0d//f5MioaQjhtGmeOLuTyA3vhNE0MNB79ehm+kMGzP67h2R/XxPy9Q9fYr3s6P6+rYm2Fj3klNYwvTMUw4h/EqA8Z+HdSgHRdpY/svGTimHQoREKIWDB11fYC6L86oAc9s5J46MvlbKoJYNMURw/O4zeH9uFPb8yN+a7bZsaaKqoaQrjdNrkv7WJUsBplhLDaJICRgjIjqFAdljOlDXonhBCtDGA899xz3H333Zx66qlt3R8h4samaxSnR6d7S3HbOG9sMZOfnxVToHPqygqWba7jvtOH8ev/zWxcPnt9NQMyuxGJyJMosXvKAhGuePHnmNTOal+YK16YxftXH0COo+Vimq2h6xrvLtzCXR8ublwWjJg8/9M61lb4uPekIURMkxlrqlrcxtz11Vw6sQdX2/uwuTaAy26jwVQk6cQ9iOGy6yjV8rD7zCSHBC+EAGyaojDdDcDg/BT6ZHu5+Z0Fje0R0+KD+ZtYWVbP5ZN6c+Nb85vdzpLNdRT1ycCUmk9dilYXrellujP3elvbghbKXyEBDCFEm2lVPq2maYwaNaqt+yJEXOma4uz9iwA4dWQh788tYfKBvbjr5CFcfWgf8lNdAFQ0hFi8qTZmKrm8FJf8iBO7T9d4asrqZm+2TQuemrImOt9pG6o1LB76sml9CIDvlpdTHYygK0V2csuZRDkpTlaXN/CrZ2dw41vzufR/MznzyZ/Y7DfQtO2PYS1Nw48iqBQ2W8cM20i2axzaP6fZtrwUFzleyZASAsAIG1wwrjsA54wp5j/frmx2vcWb6yhKd3P7iYO5/cTBnDg8H7u+/TrPTnbSxUs+7ZP0hk1A2wQwTEc0aKEFKnexphBC7L5W/bK86KKLePHFF9u6L0LEXaZL59FzR3DaqAIOH5TLzLWV/PPzZfywvJw/HNmfKyb1AqJPovvlRqdWddo0RhalSQBD7LaQYbFkc12L7cu21BFq4XzSdI1AK4IDDcEItYFIi+2ryuvx6IrLD+rV4jonjyjkka9ib3Y21QS44L/TqTdBaYrKiMUdny7llMemctH/ZvLR0nJCWvsHMWyWyV+PH8igbrFP+bKTnfz3otF42zahRYhOLdtt44kLRjEgL5kNVf4m7clOG49fsB8z11bx5PereOSrFThsGk9ftD99crwkO230ykqSrKYuSKsvwVJa28xCsjXrQvNLAEMI0XZaNYTk0ksvZfLkyRx++OH06dMHmy12M4888kibdE6IjmYpRUG6h8WbavnTm9vTZisbQsxeX83vDu/LUYPzSPfYKa8P4rJrPH3h1psjeRIldpNDV/TN8bYYxOib48Whq+hg9a10XVFrwMs/rePjBZtx2TXOH9udg/pl48TCodhpEG1nQyx6ZyfRM8uLZVmMLkrjnP2LeHnG+sZ2TcFNxw7ki8Wbm60zUdEQYmNNgGSXjdMfm0pw61CqioYQN7+zgCMG5XLbsQNw7MbNjqYplNrzISmWBR4snjhvJGUNIVaUNZCf6qIozY03AYa4CJEIlFJENEXAhA1VATRNw2XXCIRN+uR4yUxysK7Sx/VH9eehL5c3Fu0EeGPWBj5ftIVHzx2J26bj1cCS770uR68rwXRngdr7wLPliM72oPnL93pbQgixTasCGHfddRfTpk1j7NixpKWlSeFO0SXUBUIsKWsgN8UVUydgR//+ZiWPnDuSVLeDZVvquOGYAaTpCkuyL8QeUKbJZQf24oP5m5oEFJSCSyf2hF/UU6kx4PTHplLREGpcdut7CxlZlMavD+pFfoqLQq89OgalGUk2xaH9c/hySWnjssMG5HD5pF4s21LHB/NKGFGUxoDcZH53aG8uGt+dmWurcNp1RhanEY6YHPNQ89fFtn7f9eGixuDFjj5ftIWrJvWiKMmOZYFm0wgZFnZdA8PAssDUFD7DYu66avwhg5HFaaQ4dOx7cG1ZFjixKPTYKe6ZjmVZWJaF0XJtTyH2GUpTlAYNttQGeeCzpSwoqeWEYd24+uA+DMxPYXNNgPpghOIMDxHTiglebFPjD/PR/E38/pA+WBG5sLoirX5jmwwfiW5Mx3Qko/wVu15XCCF2U6sCGG+//TYPP/wwBx98cBt3R4j40HVFZX0Im6aoD7Scah+MmDhtOjNWV7Bf93RSJXghWkHXdfK9Og+fPZKb3p7feL6luGzcc+pQcty2mECE0jWe/nZVTPBim9nrq6kPRLj249k8cf5+ZDu0ZrMsbJbFrccPZF2lD49D4y8nDGZTdYDvl5fTPdNDv7wUbn1vIaYFL/16LNlOG8f2zwYsDMPC59C3TrEYbnafMpIcTFvdcgHQb5eVcdGYIsp8Ef43ZRXzN9bQMzOJSyf2IMvj4IdVldzw9vyYorlnji7kukN642jFNSZDuoSIVWtazNtQQ5LTxoKSWnpmJTEgL5kjB+VRUuMnYlpU+UIML0rlmR/WtLidTxdu4YoDe+HuuK6LDqTVbcRy7/0MJNtYzlQ0CWAIIdpQqwIYaWlpFBUVtXVfhIgLpSCIwgQe+HwZV0zqvdP1PQ6d44Z225o+KzdJYveFlaLcH+GrJSUopThuSB7v/eYAqnxhFJDusePVmt58+wyLjxZsbnG73ywtZWRROo9+s5Jbj+qPaiav27LAq8FLvx7DhuoAF/13ekygrjDdzT9OH851r8zm+tfn8q+zR+LUFJoFurLw6oqrDu7NPR8vabLt3tlJOHQNm6aItHBNuO0662tCnPrYj43rVDaEyJ7r5Mz9C3n95w0xwQuA12ZuYEyPDA7tlYFhSK66EK2l6Rq19SGqGkJ8s6yUf545nNpAhKWbatlY4+fql2ZTH4x+Hmyo8uO0tVw0xmnTAPnu66r0+hLCeaPbbHuWI0WGkAgh2lSrBrhdffXVPPzww/j9TQs/CdHZBJUGSvH0lFXMXldNZUOoccaRX0r32CmtC1LpC7U8X6MQzQgpjX9+tYITH/2BB79cwf99sZzDH/yep39YTU6SnQKPDQ9Wi5kDNq3loXp2XcO0LKatqiTQzN/bbBp2u46lYHNdiMnPzWqSZbShys/DXy3ngvHdmbuhhhWl9bw1p4Spa6spD5tYpsUJg/P47eF9cdu339xM6J3J0xeOJklXHDMkr8U+Tuqfw9UvzyZiWtg0xR0nDeaqg/uwcFMtf3x9HqO7p/P0RaMpSIt9rvvYd6vwSaBQiL0SsOCZH9dw5OBczhvbnWd+WMNt7y1kQp8sfvvqnMbgBcD3y8s5fGDzM/oAnLV/Ed4Oml1IdDDTQKvfjOnJartNOlPQfGVttj0hhGhVBsbzzz/PunXrmDBhAoWFhU2KeL799ttt0jkh2puma0RMi4aAwU8rK0l22vho/iYeOXcUXy7ewk+rK5m1NpoWb9cVt504mMe/Xcl+3dO59sCehJspaCjEL2maYtHmOt74eWOTtud/WscRA3MZnOVpMXjh1RVn7FfIo980P93hYQNzuO29heQku9AUjQ9Hw0pRHTT4ct5mghGDk0YUsK6igbL6YLPbmbGmislbZyEJRkz+8elSbjxmAC9NW8vtJw4m12Pnwv2LOHlYPrXBCG67hteuY7dMLMPi94f3Y8aaKjbXBmK2e/0R/dAVrKvyAfDHo/rz/fJyPl+0pXGduRtqyE1x8o/Th3Pp/2bgtOmkeezUByKNo2k0TRFRCsO0cOkKo5l6G0KIpkKWxUXju2NZsKXGz8KSGpIcOoZpUe2LHRbWMysJr8vG308bymPfrmJVeUNjW//cZCb1z0ZTCvn263o0XynKikSLeLYRy5mKXrO2zbYnhBCtCmAcfvjhbd0PITqU0hQ+E7As6oIGvpDB2WOKGNMzg1VlDdz3yRICYYMjBuXyx6P6M31VJYcMyOaBz5Yxd0MNI4vT4r0LohMJo3hyyuoW25+espp/nDIE1UJatmGYnD26iI8WbGb1DjcTAEcOymVzTYAqX5hbjhuER1MYhkVYKV6YuYFXZ6wn0+ugNhCmX14y9YGd33aEIhbJThvW1r48PWU1vz+yH/d/voyD+2UzfXUl544ppjjNFZ1VxDQbe52iw6uTxzJ1VQWfLSoly+vg3DHF5HrsVG6r8+G2kZ/m5vNmhqJsqQ0yZXkZT104mhp/mM21QXpnJ6GUIqhgYUkdz05dS0MownFDu3H04FyS5UGwEDsV0jS+WrSFoYVpLN5cR0PI5OmL9mfW2iqqd6hpk+V18LdThrJkcx0PfbkCu664+tA+ZCY5eHXGesb1ziTFZefrxaX8aowMI+6KtPoSgDYNYJjOVOx+ycAQQrSdVgUwrr766t1a74MPPuDQQw/F4/G05mWEaBdKU6ysCfLGzPWcP74HN741j0Wb6rjtxMHc/+lSvlu+fazm3A01FKS5ee6SMXw4r4RvlkW/hE8Ylk9EKrCL3WRYFtW+pgU4t6nyh4mYFvadbMONxf8uHs1Pa6p4Z/ZG3A6dY4bkUeMPc/dHSzh5RD7jeqRjGCZKQUUgzKBuKdxwzAA2Vvvpne3FoWskp7b8Kl5n9CvhsoN68cbMDQCU1gVJcdn5blkZJw3P5/15m/hxZQV3nDSYPtleAhGDZJedVLuGGTbwAEf2yeKIvtloCiJhA6xoUCQvxcWQghS+Xdb8j9n8VBcT+2Zzw5vzKKnZnsVx32lD+WpJKZ8u3J6xMW9DDc/8sIZXLhtL25WbE6JrMWwa66r82HSN85+aRmiHWjJnjC7kmKHRYV9KwX2nDePODxaxpsLXuM6MNVWM7ZnBlQf35rb3FrKmwsc7V02Q7MMuSq+LZgm25RASy5WGClSBaYDWcm0VIYTYXa0KYOyuW2+9leHDh0sAQySUOgMu/O90Xpk8jgc+X8qiTXVke50ku2wxwYttNlb7eWXGOoozoufxMUPyyPM6sKQGhthNLk1xcL8clm2pb7b9sAE5OHWFGWn5nLIsCzdweK8MDuqVSdiymLa6EtO0eO+qCaS77djN6M2Jbrfhqw1x2/sLKa/fHjh57pIx/LSqgqMG5/HpwqZFQa86uDd2PTpEY1uwLsVlIxQx8ThsBCMmBWlu7jl1KP/8fBlz1lcDkOTQueLg3pw0PB+PZTUW3NxxgIdXh3tPHcrLM9aR4rLx+yP60S83GYC566t5ZcY6fnNIH259d0FM8CLdY8dl12OCF9tsrg3w7NQ13HTswBaPmxD7Kl3XWF7hxwJue39hk7JNr8/cwOju6Zw3ppg1lQ1MW10ZE7zYZtrqSk4aUUDYsDh2aDfSPHY0JTP9dEVa3UYsuwfsbfe73XSmoSwTFajE8mS32XaFEPuudk2+lRs8kWjsdp3SOj8fXjuRJKeN75ZFAxZje2Xw1eLSFv/undklZCe7+Pe5I7nl6AHR1HkhdpNpmJyzfxEprqYx4zSPnZOG52PuZj0HwzDRDQOXaXJorwxOH5pHjlNvDF4A1EdM/vD63JjgBcAbszbgsEWLbV46sSfJWzMucpKd3HnSYCb0zuRfXy7nX18ub/ybc8cU8+6cEk4akc8nCzdzwzED+NMb8xqDFwANIYMHPlvGF4tLCevNFxs1DItB2R7+fHR/jh3ajS8Wb+GKF2ZxxQuzmLO+mv87awTd0lxNbqDG9Mzgm6Utpx+/PbuEymaml22Ormv4UJSHTepMMHUZfyK6rhCK/DQXnyzY1GLN6f9OWcPpowv51YSefLKTmY4+X7SFe08byrheGZz35HTqpfxMl6TVl7Tp8BGIZmAAaA0t/8YSQog90a4ZGEIkEk1XVIQMfGGLr2eu55ih+Y1tO9Q9bJaFxcBc79YpLuWXm9hzKTq8ccV47vtkKV8tLUVBtMbKkf1J1qA1p5VpWdQbUFoXImSYFKW7qWwIEzQMNlTFzhLVN8fLpP7Z9MpKosoXwqEr/n76MLwuG5lJTizL5OJnZsYU+DxuWDcO6JvFN0vLOG9cMbe8PZ/DB+Y2KdK5zZPfr2J093QKPLZmn84q0yJiWJz31DSCOwRspq6qYFV5PfecOrSZraqdTvhj7eZ0jhGl+H51Jfd8spSyuiCaih7/m44ZQBIScBddS1jTeGduCYPyk9lU0/z1CrClLsC6Sh+Fv5j955csLH5YUc5j364C4KfVlRzRJ1OmN+5i9PqNmK62HZRnOtOi2/ZtwWBwm25bCLFvkgCG2GfUW4onv1/FKzPW88g5IwlGDFx2jUDYZNrqSv50dH8+nL+p2b89aXg+STa120/Jhfgl07TIsCn+dsIgAsdFhzy4NdCtlqdO3RlLU8wvbeC6V+ZQF4zw56P789bsjXw0fxN/OyU2EDA4P4XfHt6Pm96eT1ldkNwUJyeNKEDXFGluO2c9PpW8VBe3nTgIl12nxh8m3eMg1WMnHDG5bFIvfv/aHPpkJ7OitPlhMACbagLUByPYUpyNNWJi9k3XeOKb5THBi2221AZJ9ziaLJ+xppK/HD+Id+Y0ncEF4MRh+WQkOamtjjTbDtFpZKetq+H3r89rXGZa8OnCLaworefZi0bT/MTJQnQ+Std47eeNrCn30Sc7iXG9MvmihQzDoQWpJDttfLxgM0cPzuOJ71c1u94RA3P5z7fbZ0H6bPEWjuiXhUxF0rVodSWYSbltuk3JwBBCtDXJnxX7BJtdY02Fj4/mb+L1y8dSWhfkualrOXdMMRAtVBgIm4zvndnkb/NSXFw4rjuWBC/EXrIs0E2TJCySsNBMa6fZBTtTHTa57PlZ1AUjZCY56Jbq5q2fNxIIm2T8IhDw28P78rtX51BWF82u2FIb5InvVjH5+VlMWVHOYQNyuObQPlT5wny8YDMrSuuw6QpfKEKK284d7y+i1h+hvD5IYXrLT2qHFaaSk+xkRZWfWZvr2RQw8CkNU0WHlQSMaN2OliwsqeXIQbE/nisbQlT7Qhzcr+nY6exkJ5dO7InDtvOvsvqIxb2fNJ31BGBlWQNrq/wo1fzQFyE6m7qIyZeLtjD5oJ4UZnjol5tMqrtp8V6lYPJBvfjD63N5+ofVHDM0r9nre1RxOnabFpPVle11RqdsFl2K3rAJ0930d9Be0WyYzlQ0X9M6RkII0RqSgSH2CQYab/+8gf9dMoZN1X7+8+1KyuqC3HnSECYf1IsXf1rLnR8s4m+nDOHEYd14fdYGAmGT44blcfzQbnhV61L8hWgPNrvOG9PXYGzNbjh0QA4f7ZA9NHVVBUcPyeOTBZvpkelhfaWf+mDzGQpPfr+a168YzyXPzmDtDvUn/v3NKm46dgCDu6Xwwq/H8uas9URMi/27p3PLcQN5f24JczfUNK7fPzeZm48dyHlPT2NL7fZhKCcM68ZlB/Uix2XDpiArycn6ytjhLdvMWlvJX48fSLbXyeuzNhAyTDwOnU01AX57RD8OHpDDe3NK8IcjHDOkGycN60bybhS1D5pWkyE1O5q9rprBWR4iOymiKkRnETYsrj+qP1e/NJtrDuvLv79ewYNnjeChL5cze2vtmqIMN78/oh92XaPKFybJoaNrihcuHctnCzfz7twS7LrGccO6keV1ctNb82Ne49wxRYRDkn7RpRhBNH8FVhvXwAAwXRloDRLAEEK0jXYNYBQUFGCzSYxExJ/SFAf3z2H5lnqGFKQ0Pon+y7sLOGJQLvedNgxNU2hKYTks7jttKPVBg56pLsKhCFJsXXQ0TVMtDi0xLVi2w1AOp12nvsbPeWOLOWRADuGIybFDu3HaqALenr2R9VVNZxbYprIhxJaaQEzwYpt7Pl7CZ789iEynzgXjuvPU96u59LmZeJ02ThlZwKUTe/LHN+YRjJjcduIgLnt+JrX+2EDJ+/M2kZfq5uzRhaTZFJcf1IsrXvy52b6cP7Y7TtPkD4f14aSR+ZTVhbCweH/uJk759w/0zEziiEG5uB06Z4wqwBYxdiuwaNcUHoeOr4UbrsJ0t8yoILoMm03jmpdnc86YYnzBCMtL6/nTG/M4f1wxVxzcG8O0qGoI8dg3q7j2sL6MKk7n6kP74NA1vDqcPiyP4wblYumKp75fzd8+XByz/RuO7k+2206r08dEQtoWYDDdbT8xteVKQ6svafPtCiH2Te0aXfjggw/ac/NC7BalFDZdMbQglT++MY8Tw/kUZ3hYVxm9Yft80RY+X7T9ycA/Th/G+3M3cdZ+hYRDLY+rF6KtaZrCZ0WLcm6uDVCY7iYryY7Tih1qomExqiitcXaOxZtquOnYQbw0bS2XPz+rMTNjeGEq958xnOWldTzzw5pmXzM/tenMH9tYFkxbXcG4npmc/O8fG7M4thDk/s+WMa5XBm9eOQFNWayt8DcJXmzz6ox1HNw/C2+2l/QkB6fvV8Abs7bXtFAK/nhkf/K3Tk+sWRYfztvE/6aujdnOqvIGHv9uFXZdceqI/N3+AvPaFOeOKeapKaubtLnsGiMK0ySAIboEy6Yzc0UFfz56ACtL60nzRIeOlNUH+b8vlses63Xa6JHp4cC+WVTUB3HoilxXEpGIgQPAsLjsgB6cPLKAb5eV4XHoTOqbTbJdwybBiy5Hr49m8bVHAMN0Z6LVN19jTAgh9lSrAhiGYfDss8/y8ccfs2nTJsLhcEz79OnT26RzQuwtpRR+TVHvD3PViz+zdEsd/nCEXx/Yk1vfXdhk/cwkB6OK0zmgR3rMtJRCtDdNU1SGLX71vxkxwx2GFqTw6Dkj8ajtQYxIxOT4Yd34bNEWLpnYk8I0D58s2MSbP8cWupy7oYbLn5/FkxeNJjvZ2Zh5tKNrD+vLS9PXtdivdI+Df36xrNkhKD+tqmRTjZ+Rhal8vbS8xW3UBiJEDIuXp6/noa+Xc9sJgzlr/2JmranEAg4fmEuyTaFvDSJEIgbHD+3WJICxzdGD83AptdtPgM2IycXju7Nkcy1TVlQ0Lk9y6Dx10WiSdbDkchednM1pY0WFn7BpUFIT5L8/rqFvXjK9s72sLGtafPdXB/RgXZWPkcVpvDlrA5dN7NlYfHcbh2VR4LZx4X4FQPSzRzIvuqZtAYa2noVk2zbtW2a3+XaFEPumVhXxfOSRR3jmmWc49thjqaur4+KLL+aII45AKcXVV1/d1n0UotXCNp2K+hBVvjBLt9QBsHhTHeX1QX53RD+8zu0xvEHdUnj+0jGkOzTs8gNNdDCfBZc9P7NJrYb5G2v5y3sLCf+iyGSaQ+f2kwZzz0dLKKsP8vxPzd/srypvYGOln3+eMZwhBSmNy5McOr8/oh89Mj07vR/pnpnEF4tarh7/6cItzFhbxaBuKS2uk+114rLrjO6exofXHEh5XZB/fLKETTUB+ucmU1Ltx7HD/lkW5Kc4OWJgTpNtpbhtXHdYX9QeBhidpsl9Jw/h/asP4L5Th/LUhfvx4TUT6ZvmwpLsC9GJWZqiMmKxuLSerxZvYURhOi9s/Ty4/9Ol/PWEQYztuf2m1GnTuOzAnpw4PJ+HvljOxc/MQNcUmZ6mhT63iUTMaPBCdFlaw2YsmxvsnjbftuXOQvNXQKTlKX2FEGJ3tSoD4/333+euu+7i4IMP5uGHH+b444+nuLiY/v37M3fu3LbuoxCt4lca4bDBD8vLKM7yxrQ99OUKDumfw32nDQOiY+DT3HYsy8IIS2Ey0fEq/eEWh3J8u6yc+rBJmr79Jr82bHLxf2dQtzUzIhBu+eZiyZY6PpxXwrFDunHPKUMxTIs0tx2nXWNFaT3XHtaXK17YPvQEwKYpRnVPJxQxcNg0Qkbz23foGu/MLuHGYweQn+qipKbpD9RLJvYgL8XJ+qoAZz0xtXGoyfQ1Vfxv6lp+dUAPemUmxUxl6rAs/nr8II4b2o2nf1hDfTDCYQOyOW9sd1I0WjXkw2FZ5Dl18ntnYFlgWZZkXohOTdMV80t9XPrcTN77zQEcNigXC6jxRzNjKxpCXP3yz1w0vge/PrAXoYhJt1QnNl3jp1UVOO06j5wzgpGFaTgk63CfpjVsaZfsCwDTEy0MqtVvwkzr2S6vIYTYd7QqgFFeXk6/fv0ASEpKoq4u+mT7kEMO4V//+lfb9U6IVrLbddbVBEh22unXLYVUt6PJOl8vLeXrpaXYdcX710zEsiy8OlgSvxAdTCmoagjtdB1fyCDNHf3I1jTF/PU1jcELpaJPVYMtPCHtlurCHzFYXd7AmaMLIRhGKVhZ6efCZ2ZwSP8cHjt/FB/MLWFc7yxykp0A5KW6mLe+mhOGdePlGeub3fak/tn89pU5XBOM8Mi5o7jzg0WNMx247TqXTuzJkYPy+HThFr5bXt5snYxnfljDWaOLcNlis0ycpslBPdLZvzgN0wK3rrAMc69nBJJ6F6KrCKL4aMFmfntYH5x2nWp/mCSnjRSXjdpA9Fqr9Ud4+KsVjX/z99OGsX+PdPrlJnPc0DxGFKXhNE3kqti3aQ2bMV3p7bJtc+vMJnrdRglgCCH2WqsCGLm5uZSVlZGfn09RURE//PADgwcPZv78+TgcTW8UhehoDRa8OWsDr8xYj2nB304e0uI44JNHFOB12CjM8FBeXheH3op9nWVFgwUtcegaya7tH9eaplhZ1tD4708XbubUUQW8PL1pkKEow02PTA8XjuvBzDWVPPH9ak4ank+qy8bfPlqMZcFPqyoYUZjKxQf05JqXZzcOY9EUnDKygMkH9eKHlRWNhW+3OWN0IQs21mCYFmHDYkOVj0sn9qQg3Y1hWqR67IDFl4s2U5SVxA+ftFwn48cV5ZwyJLdJmrphmGz7VrEMucUSAqL1nXzA/A01TOyTyYqyBo7513eEDYtD+udw7thiHvt2VZO/y/Y6GdgtmYUlNbw/bzOfLtzMF4tLefD0oVKYcx+nNWzBcqW1y7ZNd2b0Neo37mJNIYTYtVYFMI444gimTp3K8OHDueCCC/jjH//IG2+8QUlJCRdffHEbd1GI3acUWE4b78zYyEs73Mw9+MVyHjhzOPd9soSFJbWNy48enMfkg3rhalU1GCHaTopd56C+WXy3vOlN/gXji0m2aWCY6LqGZcGQgtTG9g/mbeL/zhxOrT/CRws2Nda06J+bzINnj+DWdxYwY21V4/pPfLeKW44bSL+8ZPrmJnPU4FySHDYueXYGVb7tRZlNC978eSN5KS6e/dX+fLWklCnLy0ly2jh6SB6ryxv4vy+Wcc7+RSzeVMudHyzGpqnGzJAkh857Vx/AlJUVnJOVtNNaGxHJihBitzUAFz0zgxSXjV8f1IuHvtw+w8g3y0o5Zmgep+9XyFs/b2icBrxnVhKPnDuSj+dv4uD+OQS3FuyctrqSupBJ+i8yoMS+RWvYQiRnWPtsXLdjujLRa5vP5BNCiD3RqgDG9ddf3/jfxx57LN26dWPOnDl0796dQw89tM06J8SeMu06vqDByrI6ThtVwMKSWpZsrqOsPsjvXp3D5ZN6c+dJQ6jxh/E6bcxZX0VdIEyG/HATcWa3LO4+eQgPfLGc9+eWEDEtXHaNi8b34KKxxYQs2OILs3hTHb2yvQzKT+Hs0UW8P6+EhpDB71+by0UTevDUhaOx6xpuh06Ky8ZrM9fHBC+2uevDxbx55Xjmrq/hL+8s5NrD+sQEL3b0wrR1DMpPYWRxGhUNQUqqA9zyzgJq/GGOHJTLZQf1YunmOu45dSghwyTL6+Sj+Zt4Y9YG3pi1gb65ySzYWMuYnhlMX13Z7GtM7JMlRQKF2B26xmNfr2RNhY93fzOB9+aUcOqoAuZvqGF5aT2WBTe8OY9zxxTzxhUTqPGHcdo1AiGDzTUBBheksrq8IWZWIn/YIN3Wqp+EoovQfaWE22kICUTrYOh1EsAQQuy9Nvm2GjlyJCNHjmyLTQnRaoausbrch8uuo5Si2h/m5JEF9MxK4rb3FrKpJsDdHy3musP68O3SMjZU+7nzpCHkJzmwJHVWJACnaXLj4X255uDe+MMGHodOsk3Db8HVL89mc22A208cwvyNNTw1ZRWZHgcPnTOSaasreeK7VbwyfR1JTh27rvHAZ8t4+JyRvPBTy1Okakrx90+X0D83ucnwkB3V+MOkexxkJzk4b3QRlf4wp40sIC/VRYrTxs8bqvnj6/NoCEWf6Oqa4uIJPbj+yP48+MUy3rpqAr/+30zuO20Y8zfU4P9FodxzxxSR5tJBsjCE2KUGw+Kt2Ru59fiBhCIWlb4wtYEwZ+5fREGam7++t5CyuiAvTFtHpS+ErhQlNQHuPXUoG6p8PP7tSm4+blBjNqLTppHikuDFvkyF6lERP2Y7DSEBMD3Z6DUtfx8JIcTuavU31jvvvMMrr7zChg0bePXVVykoKODZZ5+lsLCQww8/vC37KMQuKaVoiJg4bBqbawMcNTiPukCEp6espsoX4r7ThjH5+ZkEwiZD8lM5YlAubruNTJdGOGiAJGCIBKFMk2QNkp06ACbwwOfLWLK5jicvHM1Nb8+PmWr1tVkbuPbQPrxxxXicdp2NlT4e/WYlfXK85Ke6mhT2nNgni3PHFqMrRcS0uOfUYbz98wZ6/mKmnh1leR0Uprlwbw30dXPZ6Lb1hqcqGOGal+fEzGBimBZPT1nN3acMpSDdjS8U4dbjB/HEdyt5/IL9+GDeJmatrSTd42DyQb0YkufFLsELIXZL2LS4YHx3clNc1AUjHDEoF4dNY015A49/u5L7Tx/GZc/NImSYjChK59ABOdT6w1z8zHQCYZO/nz6MaSsrGrd3yQE98W4doib2TZovOlW25WzHDIykXOzrv2+37Qsh9h2tCmC89NJLPPTQQ1x00UU89thjmFtLwqekpPC///1PAhiiw4U0xZRl5dz78ZLGp7vZyU7+esIgXpy2jldnrOfE4QV8s7SUntlJXPjUdF64bGw0eCFEAmuImHwwbxMnjcjnjVkbYoIX2zz01QoO7p/DlpoAEdPi9hMHU+0PU+MPM7p7OjO3DiE5d0wxfXK8/OmNedRvrVORmeTg5uMGkpvsIifZSekOaeXb/PrAXqQ4tCbXi92u8eaMdTHBix29OG0tVx3ch6+XlGHTFfedOozSugAXji/mykm9cOkKh2XJrCBC7CbNrhGqD3PW6CL+/c1K3p2zsTFxaWC3ZG48ZiDfLI3WwPh80RYOHZBNtS/Iuko/txw/iEDYJBgxeHnGepKdNiYf1ItTR+RL8GIfp/nKANo5AyMH3VcKET/Y3O32OkKIrq9VpQtfeOEF7rrrLq688ko0bfsmhgwZwrJly9qsc0LsDl1XrCj38df3FsakppfVBfn9q3O54qBefLZoM0cNzuX/zhpBRX2QktqAJF2ITiEYMYmY0ZkFPlmwucX1Plu0mXfnbGRLXYDNtQHsuiI31cVfTxiETVNkJDmY2DeLOz5Y1Bi8AKhoCPGnN+ZhYnH/6cMY2C25sc1p07h0Yk/yUlz4w01vcEwUK5qZ2WebjdV+huSn8NrM9eSluEjWobv3/9k7zzCrqbUN3yvZfe/pvdB771UQRQGVInaw937Uz3osRz32Yzn2Yxe7CPau2FCqgPReZxja9Lp7ku/HghmGmQFBYADXfV1eMslayUr2zk7Wk/d9XgdpDh0fFjbDVOKFQvEnEZqgOGjgcuh8MGcjn8zfVCvravmWCu74ZDGdM+MY2SWD1y/sw9RVBbwxI4fbP17Mte/NZ/LcjXTMiOW183vxxbVHcXbPTBx/tS6x4rBHbBcwLGf8AduH4U0DUEaeCoXiL7NPERh5eXl06NChznKHw0EgUPftoEJxIPELjad+qF84Cxsmv60ppHfzRBK9shjj/32wgKaJHmwCVOF7xaGO266T5HWgaYLwbt6ShqIm5/Rrxl2fLWFNvhQV+jRP4P6TO/PhlQNYmFfKe7Przz+OmhZfL95COGoytnsWNw3zEeOyEeOysXJbJXPWFzO4eXwdsUFY0KdZAj8uz693u+3TY5i+thALGKxMOhWKfUbTBBUWVIQNLMvi3Qau5bySALouyE5wE4gYmKaF06bTrUk85/VrSo/sOJyWhdeuARZmVN0EFTKFxNLsWHbPAduH6U0HQC/LwUhse8D2o1Aojnz2KQIjOzub5cuX11n+22+/0apVq788KIXizxISgkDYYG1BVYNt1hdWcVSrJHxOG/NzS9lQ5OeW4W3xaioGQ7F7TNMiiCAIaLaDV2s3qmmUGha5VREQcNOwtizKK2VAq6QG+wzvmMYV78yrFi8A5mwo4ayXZ+GPGHTNjmd94e6vk6aJXhbmlaJpgtemreekZ6axelsFFw9qjlmP+BCNGozomI7PqTOgVRJn9MpmcJtkdlxaFwxszow1RUy8tD8xqlSxQrHPRHSN9UV+vl28BZsm6hjh7kxBRYg4t50bJy3gmHap3HRca545vQtHNZXihUKxK5q/UKaPiAP3XGS54rF0J3p5zgHbh0Kh+HuwTxEYF110Effddx/hcBiARYsW8eWXX/Lyyy/zwAMP/OntzJkzh9dee40lS5ZQUFDA888/X8s/w7IsnnnmGSZPnkx5eTk9e/bk3nvvpXnz5tVtSktLuf/++/n555/RNI3hw4dz55134vV6q9usWLGC++67j8WLF5OYmMi5557LZZddti+HrjiE0HUNC5iztohWKV7+yC2tt12LZC8ndM5g9bZynv1pNfeN6USv7DgMlfOraAAhICAEr01fz/uzc4mYJqO6ZDCuTxN8gj2mPQgBbi2IwyhHmAYRWwx+Yv5UukRQ07jn86X8tLKgetlL5/akQ0YMwzulM/7lWfUYcyZRVBWmLFC3DGpZIML83FIcukarFC+bSuuPkmufHkO/lonklfi55t0/qidIL/+6jtN7ZtNQuFKMXfDRVQP5+I9NrNpWSetUH1cc3QohLDLi3Dw0piPOP3HOFApFXXSbht0oJVBaSpfEJDYlesgrCeK26w2KGB0yYskvD/HaBX1IceqEt1cHUo5PiobQAgUHNH0EAKFhetPQyzYc2P0oFIojnn0SMM444wycTidPPfUUgUCAm266idTUVO644w5Gjhz5p7fj9/tp164dp512Gtdee22d9a+88gpvv/02jzzyCNnZ2Tz99NNccsklfP311zidTgBuvvlmCgoKmDBhApFIhDvuuIO7776bJ554AoDKykouueQSBgwYwL///W9WrVrFHXfcQWxsLGeddda+HL6iERFCvg2vCBtk2MI8+d16Zq4v45YR7eoVMBy6xhm9sol16GTHe/j0qoH4dIGpxAvFbggguPztP1i+taJ62QtT1/Hx/E1Muqw/uwuy1TRBnLEF/bvbEWt+AMvCltYJx4lPUBnTnrDZ8M+uoWk88PXyavHCoWs8enpXVm6r5KFvVtAs0cPbl/TlnVm5/La6gDi3nQsHtqBH03iuemdeg9uds76YW0a0o0NGLL+uLqyz3qYJzuzdhM8WbKZtegyBeXnV66KmxYaiKrqmeBC6RmXUImyYOHSNOIfOmuIA57w2u0ZUWQnvzMrh1Qv6kGAXRCPmQEjIsgAAmv1JREFUn66OKgREhaAyYhGKGnidNmLU9ar4m+K2RXAULsLx3S3EVGxl4ZhvueuzDZzYOZ0z+zThzRkb6vTJTnBTGYxyzquz6d0sgcdP74pPCFUqXLFbNH8BliP2gO/H9KQqAUOhUPxl9lrAiEajfPnllwwaNIgxY8YQCATw+/0kJTUc2twQQ4YMYciQIfWusyyLt956i6uuuqo6KuPRRx9l4MCB/PDDD4wcOZK1a9fy22+/8eGHH9KlSxcA7rrrLi6//HJuvfVW0tLS+Pzzz4lEIjz00EM4HA7atGnD8uXLmTBhghIwDjOEgCoL5uSU0Co2SlkgxOeL8omaFks3l3PriHY89/Ma/NvfNqXGOHnktC647ToiEiXdpQMWpqEe5BQNo+sa83JKaokXO9hWHmLSvDwu6dcEowE/h1irENtbJ0HlTr4Q25aivzUS36W/UOJoQUNzicqIyffLtlX/fcsJ7fhwXh7T1kjRIafIz9ycEk7pnsUHl/fHY9Nw2HW+XbqNBK+DzWXBerebHu9ic1mAP3JKuXtUR/47ZVWdKiQrtlZQGYoS57bTq1kC87ZXLgGw6xohofHBnDwmTN9ARShKgsfO1ce0IsZlrxMREjEsbpg4n8lXDiBBF38q+kIIQYUJd3++hN/WyBKPsW4bNx7fluHtU7Ero0HF3whNE7jK12B7byxYFoHuF/PKHzI97LulW3nsjG6c0iOr3iokt364CIC5OSVc9vY8Xj+/F65GOg7F4YHmL8B0JR7w/RjeNGyFSw/4fhQKxZHNXgsYNpuNe+65h6+//hoAt9uN273/yyHl5eVRUFDAwIEDq5fFxMTQrVs35s+fz8iRI5k/fz6xsbHV4gXAwIED0TSNRYsWMWzYMBYsWEDv3r1xOBzVbQYNGsQrr7xCWVkZcXFxf3pMBzA1cL+zY6yH05j3REQIPl2wmcpQlD4xEUKBCNHtT26vTVvPse1SeeLMbgDoQpAW62JjcRUxNg2jngoKO3Mknq8DRWOcq4O5L0PAh/M2Nbj+i0VbGNcrG3c9Y9J1DX3tL7XFix2YBtovD+E+4TkCprN6saVrBKIWuoDKcKRa3PA5bWTGuavFix34wwbv/p7L2sJKnjqtK3rUYGiHFGy64I5PltQ75mEd0ghGDF7+bR39Wybyn9O6omsCIeT2JkxfT4tkL+P6NOG6iQu4aXjbagHDZddonuTh6Z/XMGluTWRGiT/Cg1+v4OKjmjOmWyafL9xca58l/gjrCqton+rD8yc+vwBwwRtzyC32Vy8rD0S594tleBw6w1on1Zv2daRdu4fa8TTGdX6oHPv+ZG+PzSH86D/ezY4fhJA3i5yNMkXMtOCWyQs5u18zXjm/N6Goicehkxrj5ILX51BQWVMKeXV+JQVVYZp67fv1eHZGfW4Hdt9/ps1fHZ8WKMKIb3HAq7NZ3jT09d8hLAM0fb9u+0j+Hu4L6nzURp2PI4t9SiHp2rUry5cvJysra3+Pp5qCAhlCvWtkR1JSEoWF8oG+sLCQxMTairHNZiMuLq66f2FhIdnZ2bXaJCcnV6/bGwEjKSlmz40OMQ7HMTdETlEVQ9uncuGEOVySHkJ3xuFx6NURFz+vzOfnlTUTx3cu6cvAVskk+JwNbbIOR9L5OtAczHN1MPcVjBg4dmPYadc13C47yTH1fK+MCKz+rsG+YuMsvLYw3thkwlGD3GI/z/6wihlri0jyOnjs9K4IIecsHTJimJdT3OC2Zq0rJiIE6YnS72dwG8E5/ZrWqk6ga4I7T+qAQ9dI9jllVZA1RUzfHuGwMyd1ziBqWBRUhGifFsPL5/XCsCyaJHiImBaTd0or2Zl3Z+fy7PgedQQMAJdNozQYpRRI8DhIiXHistf/0DpjTWEt8WJnnpiyiqPbDiI9wVvvejjyrt1D5XgaYxyHyrEfCP7ssVUVlCDy5lT/7SleSveMISzerq2alkzVemeWNEQc0y2T8wc0q1UieQebS4P0bHbg366rz63x9vmXxxcsQo9LwRV34KqQAJDSFMwoyY5yiG96QHZxJH8P9wV1PmqjzseRwT4JGOPHj+eRRx5h69atdOrUqU4ERvv27ffL4A41iooqGgz9PtQQQl6kh9OYd4emCQwTKkNRyoMRTFc8qYte5vJ+N/DUb1vqtO+SFYsmBFrUoLCwbirArhxp5+tAciDOVXLy7m8oB/Nz0TTBuf2a8tOK+kuDnt23CXo0SmFhuM46h13HF5vVcHknbzLBCPiLK8mrinDGSzWGnAUVIT5dsJnhHdP4buk2IobV4GQfwL7dG2LH99sNXHtMS87r34y5OSWEoyYtkr1MnreR+75cxvAOqdw0rC0Xvzm3zrbiPXZapXoJRAwSvQ62lAWZMGMDwYjB5lI/j5/RvcHzH4qa1ZFQO+O0aXgdNp76YTWbSwOsL6zimmNacXqPTBw7bUzoGhEL/NvTV+ozIt1WHqIyGKYwUnfdkXbtHuzjOZSuvSPts9yZP3tsQgC6jkto4E2BCnl/c6z6ggvPupmJ8wWRXdIgbZrg1J5ZzF5fTGa8m7UFlbXWp8c6/9R9cF9Rn9u+sT+uvf0yvoif5EgAv+kmUla/iLy/0IgjBijbsJxIdsJ+3faR/D3cF9T5qM3O50OJGIc/+yRg3HjjjQD1VhwRQtRbYnVvSUlJAaCoqIjU1NTq5UVFRdUCSXJyMsXFtd9QRqNRysrKqvsnJydXR2zsYMffOyIx/iyWxWH3I3A4jnlXhCYoili8MyuHoe1TiRgWa4OxZG7+nXNbrcEc1IqXZ+cTjJgIAce1TeLGYW3RdBuR3ZSaq48j4XwdLA7muTqY+zIMi7YpXo7vkMoPy2uLGB0zYhneMY1oA/4XobCBt8cFMOeVetebA67HL+IIWXDvF8vqeEe8MyuHZ8f3wLLgpxX5XH9cG2Btvdsa3TUTr03DitZ8x21RE4cumDB9PaGoSV5JTcWR75fnc93xbbh3TEee/XENRVVSgOmUGcvNw9vx/dKtuB06lwxqQYzLRseMWHwuG72bJWDbQ8lhu15bsumcFcsDJ3dhzoZiEr0OejdLoG16DE9OWUVarIvhbZIwTIvSqMXb09czN6eEjDgXD53ShT9yS3ht2vpa2/M6dOyatlsjwsPx2hUCTKERNE00IfBoAnO718ehcjyNMY5D5dgPBLs7NqEJKiyYv76Y+RuKuaXnlcRMvUeuNCI0mXEX7417kJu+2VYdrZSd4ObWEe14Y/oGhnVMoyJYW+RrmughLcZ5UEw8/66f26Gwz78yPq1KRiybztgGak3tPwyPfDYX5RsP2Pk8kr+H+4I6H7VR5+LIYJ8EjB9//HF/j6MO2dnZpKSkMHPmTDp06ADIiiILFy5k/PjxAPTo0YPy8nKWLFlC586dAZg1axamadK1a1cAunfvzlNPPUUkEsFulzmgM2bMoEWLFnuVPqJoHGw2jfXlIca/IqscpMW66JwVyz0/F/POqLfJ/OwMrm1xPGeOu5gKy4XbYScmMZVPlpcwpnMGRPdOwFAoAJyWxb0jO3D+gOa8NXMDEcPi9J5ZdM+Ox22Zu33I8zvT8Y58Cu3rG8GqESjMzmcSaXEchmESMKm3ak4oavKP9+fz4nk9uXVEOyKmyS0j2vHYdytrtcuKd3NKzyxKglESHVq1Saaua8xZU8Tagqp6x3b3Z0u4Z1Qn7h7dEbuuYdMEa/Iref7n1fxjaBumrSmkTaqPc1/7vbqPEDDp8v5kJ7hrCSI76JwVS3qskz7NE1iTX8nAVslceFRzznl1FlXhmuvP57Tx1LjuvPzrWga1SiK/Msy4V2qqlyzdXM4Py/O54fg2dTw1zhvQbHv1oCPnycPSBAUhg2d/WsmcDSUk+5xceXRL+jTbv28lFYcHQkClBXd8soQZa2WK1/CzhtGn1Syca78BwLHhJ/pUXcR7Z75DTiSOipBJUWWI535eQ15JgCuGtCS/osb/ok2qjxfP6YlH8KcrASn+fmgB+X0znQe+Cgm6A9OVhF6eu+e2CoVC0QD7JGDs8L5Ys2YNmzdvJrJTWK8Q4k97Y1RVVZGbW/MjlpeXx/Lly4mLiyMzM5Pzzz+fF154gWbNmlWXUU1NTa2uStKqVSsGDx7Mv/71L/79738TiUS4//77GTlyJGlpaQCMHj2a559/njvvvJPLLruM1atX89Zbb3H77bfvy6ErDhKaLqg0wB+IcttHi6snOa9PX89/z+zO7R8v5oKvq3j0pC/ICK3Hu20uyU26Eo5rydaom5M6xSCUeKH4C7iwOLptCp1SvZimhY6FYexevAAImi6sFqPxXDUYkTcbEa7CajaQkCOZKkN6OAgBWgOTilDUZEOhHw3BTZMX8uz4Hrx6QW++W7qVUn+Evi0SyU5wc/PkhSR6Hbx6Tk92WBQLASX+MMk+B/1bJiGAWeuLKagIMaBlEhcPakFJIELbtBgW5JaytTxI1+w4BrdJpjQQwWHTsOsauiYwtg/OsuDGyQv53zk9uWjCnOrIDYDMOBcPje3CuoIqLh/ckqKqMG3TY/jHe/NriRcg07/u/XwpVx/TmohlcdvHi+tEoAA8+9MaXjm/d7WAMbxjGuf1bXpElVLVNMH6ijDjXp5VnX5TWBnmhkkLOaNXNneO7NDII1QcbITdxvLc0mrxAuCiD3P49/G3cXTvG0ksmgeuWKzsfrzzezkvzlhV3c5t13njoj40T3Ax5f+OpqgyRIzLTpxTxy34U1WAFH9fdggYlvPgvNQzPcnoFQ0bZSsUCsWe2CcBY+PGjVxzzTWsWrUKsVN9cbHd2vXPppAsWbKE888/v/rvhx9+GIBTTjmFRx55hMsuu4xAIMDdd99NeXk5vXr14tVXX8XprDHPe/zxx7n//vu54IIL0DSN4cOHc9ddd1Wvj4mJ4bXXXuO+++7j1FNPJSEhgauvvlqVUD2E0TTBxsoI5732O+9f3o/V+TX5vIWVYf750SJuPaE9hmkxsyhI+7R+tGw+lKBNw64L4sIGmqHEC8X+wYoaYMHefKNClpOQSENvfjIgZPWMnebfHpvG8R3SapVM3Zl+LRIZ9ew0AMqDUW6YuIDBbZLxOmxMnruRVdvkNbGtPERF2CDJLlM4TNNiaIc0UmJd/LIiH9OC205oh8euEzZMrn3vDxy6xiOndeXFX9eS6HHwzqwcSgORasHi3H4RjmufWmtsG4sDzFxbxBsX92Hp5nI2Fgdokewhxmlnfm4J93yxjKHtUxnbPRPLgk2ldSM1APJKAqTGOglGzOpj2BXDtKgKRXn9/F5kxruJc+rYjrASqkEL7v5sab3eIZPn5XHZ0S1J3L8G/YpDGE0T+I26RrkRw+KO7zbjtGkMaNWTK49uTZxl48QeyQzu2JSlm8uJddno2yKRWA2sqEm8BvGx25+RLEtFXij2SLWA4Tg4vgCmOwmton5TaIVCofgz7JOA8eCDD5Kdnc0bb7zBcccdx+TJkyktLeU///kPt91225/eTr9+/Vi5cmWD64UQXH/99Vx//fUNtomPj+eJJ57Y7X7at2/Pe++996fHpWhcKk24+M253DSibb25apvLgtzwwQISvQ7+PaYTny3cxGWDW1IetMjyONDUE5viEMEwLKgnZkM3TW4d0Y75uaW1Sh4C3HpCO35cLsUHp03DHzYIRIwGxY5Q1AS7RlQIyiMm//l2BT+tKKhe/9XiLQxqnczJ3TMJRU0Gt0nml1X55BT5ySmqa9j25eIt/PPE9nX2l1PsZ82MSn5ZVUCKz8n75UGaJnoY2EpWivppRT4/rcjn+bN77vacuG36Hkv1aQK6p8dsF36OvOs5EDVZtqW8wfW/ry9mZLvkBr1WFEcWQSzyGqjAA/IaX5NfxeLNZbRM9vLcT6t58qxubC610b9lEm7TwjoCrxPFwUEEizAdPtD2aUqw15ieZOxb/zgo+1IoFEcmDdcK3A3z58/nuuuuIzExEU3T0DSN3r17c+ONN9Zr7KlQ7A1byoOM7paJU9fwOHRO7p5JrKvujbUsEMHntBHvdshqDZY4osLMFUculgUJNsGHV/Tn32M6MqRtCmf0yubTqwYyqnM6L0yVxp2hqEm8205DHppuu06c247QBfM3l/P7+pJa4sUOpq0ppDIUpVWKj0SvE6fe8Ov9cNSs17TzqFZJ/La6kIKKEMu2lFNcFWZ83yZs2EUEcdg0nA2UoXXaNDLjXXjtGq1TffW20TVBpwz5JvBIrdcu9nBgTtvuDUsVRw5uLUhBVZjvlm7lkkHNyU5w19tuRKd05ueU4NA1Hj29G5qm8cPyfCoCEfVdUfwltEAxluPgecKZ7mS0qq21PKIUCoVib9gnAcM0TbxemcudkJBAfr506s/KymL9+vW766pQ7BYhBCVVYc7u24SuTeKZl1tK3+aJvHtpPyZd3p8OGTUhjlcf04rvl27h7H5N+eD3XOLrETkUikMV07TwYDGmQyqPj+3EHcPakOWxoSFNOnfw44p8TuuVXe82/jG0FT5dUGXAok1lfLV4c73tQHpQPHZGV7pkxdK/ZSKvXdCbYR3T6rQ7rn0qs9fVru7UOtVHMGqytTxYveyYtinYdY2x3TNrCR6fL9jERUc1r3cM1w1tjU8XuAU8cmoXHLtUL0mJcfLB5f3ZUh7im5WFLC8OEECg7aEKyuGG16YxuHVSves0AX2aJyrfgr8BmgZ+U+C02RnTPYsNhX5uOL4tb13clxM6p1e3y05wM6h1Mse0SyE7wY0uBNe+N58fl+fvttSyQvFn0AJFBy19BGQKiTCjCH/hnhsrFApFPezTjK9NmzasXLmSJk2a0K1bN1599VXsdjuTJk2iSZMm+3uMir8RlmXROTuOzxZs5okpq2qlkFw8qDmPnt6NyXNy6dMiCX8oyuiumSzZVMrQ9mn4dFDWF4rDjR1pAjs8Zz2a4B/Htua6DxYA8O7sHB4c24XMODfvzs6hsDJMZpyLG45vw6AWSViGSTBq0TfTRZw9ljX5lRRWhmvt48ZhbSn1RzjthRnV15RdF9x2QnviPXYmz5X5yLFuG1cd05p7v1gKyEiA03tlc8XRLSn1h/nXyPa0SPaREuOksCrMTZMW0CkzjpfP781bMzYwZ0Mx8zeWcna/prRPj+XJH1aRU+SneZKHG45vQ98m8WCYmECzGAdf/uMo3pyRw7zcEtqm+rhmaBsuf3turdSW1Bgnb1/cl+Sdqq0c7tgsk7tHdeSsV2ZTXFX7s7pnVEdSYpwEKur3EVEcGQgBVRb8kRfkl5V5fLqgxtRQ1wT/GtmB1ilehBD0a5FIVcigR9N4cor8xHpsLMor49QeWfhsGqjIQ8VfQASKMJ0HT8Cw3FK81Ss3E/WmHrT9KhSKI4d9EjCuuuoqAgH5cHXddddxxRVXcM455xAfH8+TTz65Xweo+PsgBIQ0jZwiP49/v6rO+tenbaBLVjwXHtUCy5JvKqeuymdw6xTibGK734BCcXhjGCa9m8Zz5dEtefm3dZgW3PHJYo5pm8KbF/fFbdOwCUGMTWCaFrGihIS8H2m37GOOccQwdtSFTC/P4tZvNmFa8u1tepyL/06pfU1FDIsHvlrOGxf1Ye76Yvq2TGJklww+mJPDpYNbcNuI9hT7Q3TMiMURNdA9DoTQuPvzpZT6I/RunsB/z+zOK7+t44YP5nNaz2xGds2gW3Y8yQ6Ndgluel/UB8MCXUhhxth5omVaJOiC/zumJSHDQmiCayYuqOPLkV8R4rK35/HuxX1wHYwP4CBgWZDk0PjkygH8tLKAqasKyIhzcXbfpqR4bHidNgIVjT1KxYEkIgQv/LKWNmmxtcQLkEa2936xjC/+MYhIxMASkBrjoqAiyNuzchjXpwmDWidx4/FtEEq8UPxFtEAhpufgCQnmdgFDq9p60PapUCiOLPZJwBg8eHD1v5s1a8a3335LaWkpcXFxe8ztVSgaIiA0fl9XxLdL6zcrBPhw3kay4118umAzp/bM4pQuGYRDUWVgpjjsEQLcIoDTKINIlBv6xXBO/2NYtbUSt10jM96NT2P7d90iGrVI0IqxvTMGSjYAMicwY+VXnNRpPKFhl/PUzGJuP6k9r/22ocH9fr14C3eM7MDUlQVEDZM4j4OXp64j1m1nVNcMwoYJQnDnZ0v4ZVVNyO8vKwuYvqaQ/53Tkzs/WcKE6Ru4dUQ7fLYaoaK6XpRFgwKjZZg4gOKwyfyNpfW2yS32UxqIku46csLlDcPCA4ztlMqYTmloAqIRA6H8DI547A4dV7iYy/omc+WHaxpsN3F2LucNaMZXi7ewcGMp09YUcufIDrRK8fLo2M7Yj7DqPIrGQQsUYyS0Pmj7sxwxWJoNrarhZz2FQqHYHfvNNCA+Pn5/bUrxN8Fm0zAR2DRBCBD+Uo5JKuON8lCDfQoqQlSGDM7uk02Sy044FD14A1YoDgCaJhAC4qKb0b6+EbHhNwBs8U3JOvEx4tN6EzSdgFXL88zjBG36C9Xixc54lr7P8ZffgS8xDcOw2FrWcDrCxpIAbVJ9tE+P5cyXZrKlrMbn4qcV+Vw0sDmjumbUEi92EDEsXvhlHf89oxupMU5i7Bq2fZyAB8K7z/+qCB1ZAsYOdqQQqano3wPdbuItWoj96xuo6nsn+RXeBtturQjitGm8MyuHJokeXj2/Nx1SvdhMi3pLdCkU+4AWLMZyxB68HQoN05WgIjAUCsU+o1wPFQcdTRNUmjBtZSFTlm8jNcbJlce0IqNoHrZF7zGwyf+xeFP9fbtlx5MW6yDT4yASUYYXisMXn1aFI1wI+cvR0jsh3j4ZKvNrGpTmor1/Fp4LvyUc06WW/4OpaZihIrSF79e77bJ+N/Ha3CJenrGZc/o1pVNWHJt3EiZ2pmfTeD6YuxF/2OChU7owcU4u3+0UBbUwr4yY3Rjk/pFbQlaCGx9/bVIV57bh0DUZ8VEPKT5nvcsVisMBISCv2E9i1Qbs74wB08BbtJCe2SP4aVVxvX26ZsezpqCKr/4xCAwTp+CI8YFRHCJEAohoANN5EAUMwHIloCsBQ6FQ7CP7VIVEodhXhIAyw+KMl2dx56dL+GVlAZPm5uEJFeH++R7sa75lfCcPHkfdN61Om8apPbOIddqVeKE4rInTy3F9dz36S0ehT3sMsfbH2uLFTmg/3E2MKKv+22bT2FIVZl1+BZiRejrYKGh5Ci/PkBVJPluwmfF9m6LXU8nD69Dp2zyRF6eu480ZOVzy5hyObZ9KvxaJ1W0ihom9gbKoIM1Ad4zrr+CzaVw4sFm9607ulonPrm5XisMXPxpTFq7D/tvjYMr7V8zCCdw0ML7eazPR66BzZiw3TJxPOGpitywlXij2O1qgCOCgViEBMJ3xKoVEoVDsM+qJUHFQMYXGY9+vJL+iJk0kyevAboWgaA2YBtk/X8+HZzejW3ZNXfJOmbFMuLAPzRJcuFTorOIwxmGzsM17BbHqW7kguR1sWdBge7FlPvby9cRolQBURC2K/WE+XOYn1GZU3Q4p7Zm+saayRWUoynuzc/jvmd1omuipXt4xI5anx/eoVe3HtOD+L5ZxwcDm1e2Wbi5jQMv6S34CjOiUzsQ5G/lyeQFh7S/cUgyTC/s346ZhbYndHvHhtutcNrgFtw5vi03l+ysOUxwOne+XbyPFEcG+dW7NimAprf54iPfGNadViq96cf+WiTx1Vjce+no5VWGDgsqG0yoVir+CFpTRP9ZBj8CIR6uqX7RXKBSKPaFSSBQHlSrD5PtldW9aUUsHVxwEy7BtnkPHr05hQq/rKDu2NyCI8bpxxZmETDCVgKE4zNB1DY9Vii1ahWYItDmv1KwMFENmj4Y7x2Qgti3FXr4NPfsEAqEowYjJpIWFXHbhdTRd9z0ESnbZob3Wnz8szyenyM9lg1uSHOOgWaKHX1cVcvenS+qkllSFDSKGicuuEYyYmBboQvDPE9vzyDcrarXNTnAzrk8TLn1rLsGIya8dUrl3ZAcc+3iN2k2Ts3tmMrpLBiHDwKlr+Gwalqq0oDiMKYuaTPx9I+O6xEBMOpRvrl7nWvU5/YqW8sHAmylL7U2FPYUfludzwwcL65TYVSj2N2J7BIZ5MD0wANOVgH3L3D03VCgUinpQERiKg4qFLBG3gwSPnXHdErC53IR6XlbTsGILib/cTovJw2jx8UnE+9yELZcKoVU0Og6bhU9U4NWq0PU9/4Q6RYi4otm43huD7YU+aNuWQriypkHODGhxNIgGttXzAlg0EX3a47jNMuy6hmVZ2HWNiz8vJPf0r4n0vRrim0FaJ8q6XUr/9k3qbGZ1fiX/+mwJb0zfQChq8si3Kxr0xYgYJrbt0RTHd0jFEpBXEuCV83szvm8TTuiczt2jOnLvmE5UbhdUAKYsz6fQX09ay15gRk28wiLRpuEVHDTxwmbTcDhsfzkVRqHYgdAEZRYs31pJ2DB5Z2E5Zb2vr9uwaC3J312FM1zCuFdm8dzPa6rFC69DJ8mr/F8UBwYtuD2F5GBHYDgTZPSH8dfuFwqF4u+JelJTHFRcmqBX0wQArh+UzvfjE7ix6imS3h+FvdVgrLYn1O5g9xAZN4mgKw3DUCV6FY2HrgkSyCdm9n9wvz8Kz4dnEZ/7BT6tnAAC6hEzNE3grVyD/t6pMkUKZLREXHZNI8uEeW/CqCfBtstEpdMp4EuDTX9A+WY0oth1jTX5lVw2uAVr8qsY+toGVnS+kU97vcGkjs+zOvNkPp6/lXP6Na0zHq9D5+YR7fA4bCT7HPUep00TpMW6aJni5YkzunH1Ma2585PFvDMrh2vf+4Nt5SHsmuDd2blc8+4fGKZFh4ya/Olpawqx2TSEENjt+p8SeRoToQkqLJi8aCt3frmcj5Zso9ICcYiPW3FoI4SgIGQw5rnpfDBnI2f1yub1k1PwuN3Q+xJIaAHx269RzUb4pKd4Y7moFgN3cO+YTsxYW4imhDXFAUALlGDZXKDXfz84UJgumSK8Q0BRKBSKvUGlkCgOKnabxv1jO1FYGUSLBvkxr4DWnW6jaWQ9qZ9dDb0vwhx0E1WbV2C5E3Gkt0PoOuGwQMZvKBSNQ5yxBX3CsFrpGtqnV6C3Gs7i1nfx6ya44uhWJDk1nJESNAyEEGiznq9dneOPt2DgP+Cb22qWrfgSIgEY9x4ESmWIeUo7yJ0FX1wHgJXZg6hwEzVNujeJZ21BFfeM7shr09bz9M8bGNgqmS2VQT7/YTXT1hRy47C2/PfMbnw6fxNFVWF6NE1geMc0/v3FUhy6xr9GdeT6iQvqHOflR7fEH4pyz6iO+MMGW8uDLN9SAUAoavLTinycNo0bh7WlRbKX3GI/Fx/Vgji3nZd+XUei10GVCeuK/MxcV0RGnJujWiURYxOI/RxBpWl/7XdB0wTrykOc9/rv1RPHLxdv4b9TVvHOJX1pHuPEUOkrin3A1DRe/GUVwYhBxDC4tLPAMWEk5V0vIrfL9SxIvQa7MOmW6SXJaWLftpAzeqRTGoJl+SFapHi5cGBzflyWz4x1hQxtk4x9z7tVKPYKESw+6OkjAJZzu4DhL8T0ph/0/SsUisMbJWAoDhqWXSdigk3T8IctdM3F+oCX+z/MIS02ljdPnkiTj8egzXkV17hJWFsW4yhbRySpg5pEKBoVlx5B++mRul4TgGPt9/TofjV3LI0wKEtwkns5+m//AbsbOp8B/a+CrB7w479lh81/QKtjYdh9MP0p8BeDEEQtDTOmGY5fHoOqrVCWB2ZU9hGC6DF3USrcOG0awahB56xYQhGTh07pgs9pI9ZtwzAtTnh6HQD/nbKKK49uybBOaWwrD7FiSzkXvTGnOoWrRXIBk64YwFM/rGLl1gqyEtxcc2xrWiR5cNg0nJogZEFukZ9Yt43ygByLrgmeHteDN2as5+Fvaso/uu06j57ele7ZcVzwxlzWFlSS7HNw3oDmrCuqItHrINnrIEYXGNG/dj2HhaA4EGVVfgUpPift7DYiCEpDUTQBsU4bHk3s8XejyoRr3ptf5623P2zwj/cXMPHSvrj+0kgVfzcsTeA3LVZsLmNI+xSuOqYlyS4LfryF4kH3Ms15NO4qJy6XA7sueGl2Pp3SPYzJzKLdewO4v/dVrOx7EbrTwzM/rWXKsm0M65iGXRNgKBFfsX/RAkVYjSBgmM54uX+/MvJUKBR7jxIwFAeFoK6RXxbk+2XbqnPwhYCj26Tw/Dk9uW7ifG7+qYKX+vwf8b/ejT3qxwoUEG3Wn3ISUdEXisbEaVYiVnzR4PqkdZ/xz+Ov4tjit9EXTpDpIFUFMrJi1bfQ9Uw450P46BIIlsFvT0DzwVhnvY/piMHUnWwKe3hzdjFXD/0PKT/eCCUb5Mbjm1J4zH/4vSABf0EJ78zKJbfYT5tUHxcObM66wko+mb+ZNfmVTLqiP3ZNMLRDGke1SqZrkzie+WE1P6/MZ9fgh4/+2ESXrDiOa5/KA2M74xJg1wR5FWHemZXD6b2bMGNtEaGIwdl9m/Hi1LUAHN8hjWmrC5i1rrjW9gIRg5snL+TtS/qyoaiKsT0yGd+3KY98vYInp6wCINnn4K6RHejXJB7bPhp9BjWNmz9axOzt+x/RKY0TO2fwn29XsGW7p0ezJA+PndaVFnFO6hz4TpQEIrUqIu3MptIAZcEoLmfdks4KRX2YmmD2xjLu/XwZI7tm0CEjhqmrixjZ0kZy4XIKj3qUKb9s4OslKzBMC5smGNU1k3ifl9yIoG2rYWxsfioXvLOU+0/uxK+rCgC4bFALhGmqu6BivyMFDN+eG+5ndkRgCH/hQd+3QqE4/FEChuKAY3PYiIQMflyRz0tT11UvtyyYuqqAwsoQNw5rx72fL6V4yBDiAUt34O94DgHLjaWMOxWNjQBEwxNZU9gY0tSB74fn4ZSXYNYLMtJiB7kzIb0bjH4aJl8ol+VMp0SLxfC2wGFZxNpMrh4cBwiCp72PHillS0klayrs+A0fTX0GP6zexubSAMVVYWavL2b2+mLuO7kTg1oncfuJ7Wme4OG9y/rz0R95PPPjajQNTuycwWsX9OHWDxfVKcdYUBkmNcZBnF0D02J2XhlvzczhmdGZ+MvWMDzZIjfoxhWfzvIt5UxdVcDILun88+PF9Z6HUNRkxdYK3r2kHzZdcMXb8yjaqZJCYWWYGz5YyBsX9qFLimfvTXl1jf/9vLZavPA6dM7q05RL35xTS6fIKfJz7mu/89U/BhG/G/0huoc32mHDBJSAodgzQkBx2GDS3DyeGtedt2Zu4ItFm0n0OIhxNmPI6Z/z+Dfr+H7Ztuo+UdPi0wWbCEUNhrZPZU3Lf3H7O6uxLHDadcKGyS3D25Id68RS1bcUBwAtUHTQDTwB0O2Ydi9aQAkYCoVi71EChuKAIQSEEGAZ5FcGeWP6hnrbLd1czrWxTpw2DX8UiEkn4k7Fb7oP6ngVioYIilhsXc5E++ONeteXtD4FrWg1pLSHqvza4sUOti6E4vWQ0R0CxUTOmkgsJlrJAijdiIjNxHLHY5VsxEpqxTYznrt+K+ChY2xkLP0vet4sOsdkct6oa/iqIIPX/ygjM97FpDkbeW58D9L0KgpDVVz97oJaUQVvzNjAzyvzeeS0LmzI20T/DDkhn77JoGPLRJoneTAjBn4EXy3I4bXjTLwfjiXiTWfbgLuxgLyicm44rjU3DW+LYVr4w0aD56qgIsTvG4rp0SS+lnixM49+t5JXzunB3trGVUZNPp6/qfrvUd0y+WDOxnqDLMKGyQdzc7n6qOYNpqwk+Rw4bRqhetZ7HDqJHgcq+kvxZ7A5bVhl27h5WCvOePl3ItvFsVJ/hDs+XcYHl/evJV7szLdLt3LegGZ8vbgMf9jgsdO74tQFU244Gp8u0JV4oThAiGAxRiN5UFjOOLSAMvFUKBR7jxIwFAcEIcCPIFWvoDJs4dEdu530bCoJkB7nIk4LYZz8ElUiATVxUBwqhAwdz8Ab0NZMgfJNtdb5O5/Dz1vdHJsSgVZDYdnnDW9o+ecET3wKB2HsX1wrhQ6hQZvh0PdyxG+PQ++LYMZTpKZ15eXjuuF693gwpBCgFa4mZf1ULjj6Fs48ZQjmqq8IJHbESza2gjV8uD693pSInCI/W0oquajqdbQP3wagQ7NBmN0eo1I0JQKUByP8c4AX7/vHEc4eyJyeD3PZxFz84ZpUkeM7pHLXyI40TfSQW+yv9xBbJnvJLfJTGYzyyilZpHo08v0mz82pZGFemTwNW8tp+NegYSKGVUtsaJboYfa6jQ22/yO3jPAAq8EYCq8m+L/j2/LItyvqrLtlRDu8GqDsdxR7wOWy8JUtxSGi5IVimXhWEz5dGWTSwsLq72thZf1iHshoxKhhkRnn5ItrjyLNY0e3LBmhpMQLxQFERmDE7LnhAcB0xqkIDIVCsU8oAUNxQIgKQZYzjBYK4KzciN/ZBpsmiDYQMp4a4+TULikkpSVQiXePod0KxcGmTKQQd/43aGu+RVv2CaYzjm0dL2J6RTr/+mYTk8c3JdPmlmVRd2BzQfezodVxskSqLw273Y32wgAwIrKNZUqfjC0LYOQTsGk+uOKwZ3TB/vWN1eLFzojfHsdz7iCY+xw+y4Kp8RSe+xNfLs1rcPyfLdrGyc1S2PGoquVMQ5swHO9lv1JKCqkejaSFb4ERZuvAe7norRzsuuCyfmmc1NqJEDBlXYjpawq54fg23DhpYZ19tErxkuyz86+jY0nY8hv6r49KwSc2i959b+bHrj25+etNZMS6EDtd4pquUWlYVIajuGw6Ppt867zr3M1p00j2Oaong4WVIbIS3Gwoql9MaZ7swa6JhlNVTJMxndNonuThyR9Ws6GoilYpPm4c1oaOab7d+mcoFLquEWsVofurENEg7mlP0LZoFfS6iJ69+3H3kNY8MrOKN2dvxmnffRnUlBgnZ3XPxIyaYJhKN1MceMwoWqisUUw8ASxHDJq/oFH2rVAoDm+UgKHY7zicNmKjW6GsBPHDPbD+V5J7XcnYbmfw4fy6IbTpsS4y4lz0btaCqr9YnUChOJBU2VMQHc5Ha3MaUc3G1nKTOWs3ckqPLPS4DIyEE9GdXsibAw4vnPYqLPwAJp8PpgEOH3rfK2DEw/D1zbU3XrEVCtfAxtnQtD8EiiF/Wf0DsSwoz4OUjpC/FIKlaDm/4rK3bnDsLruObuwSnRGqQFvwDlqfmzACldg2z4XUjszYbJGV4OaNk5PImPsojk++AsuiU6vh5He8g0JnNv8e04n//bKGbeUhdE0wqlMyjw5PwZ4/A23JXJj5XM1+yjeR8MP/MWLALfze7Xg6N0snxq7hNErRDT9lVRbvzCvnrbn5hA2Toe1TuXtkB7yCWrn/MTbBjce35Y5PlwDw+cLN3D2qI9PX1B+GfNGAZnJCuBvslkXfrBheP68nUQtsAlyCvffnUPytEEIQF8nDFDpYUXj3dETr42HEQzDtKcRP92N3eLmrxwWcfcmF/LSpim7ZcdVRSDvTp1kCsU59j99VhWJ/IoKyqpbZGB4YbE8hqdzcKPtWKBSHN7t/JaBQ7CVRXSc2sgVRsQUx4URY/ysAngWvc3PXMENaJ9Rqn53g5o2L+pAZ51Kh2opDEiEgTislvnAGMb/9C9/8Z3FHC9HNKE28du4Y1pa7R7Qj2+uiwtMKq8PJWKmdYMhtMP1pWPapFC8AwpUw7QkozYEOo+vubONsiMuSJVh15+4HFg1B+5HVfyYufo0L+mU12PzCri48q+umt+jrfmbB2jxemL6JSGwz8CSRW27y8pgUmn1yMo6Vn8vxWyb2Nd+SNXkUre3FfLZgEzcOa8fbF/flxys68njfSpyfX45mc8LvL9c7hpjfn+b/BiYwun0sMUVzcH9wKo7/9SLljQHcFHmZL85vToLHwY/L87n0rXkEdulvRE2GtE7ivjEdiffYKawMs3hTGbeMaIddF9XtXHaN/57RlRSPfffncMd2DQuHZeFB/l+JF4o94bMF0EIl2Ox2xLSnwJMMXc6AyRfUeOCEqxCz/0fLHy6nfUyQJ0/vRIeM2uH6nTJjefSMrrgsdQNUHFx2+E80WgSGSiFRKBT7iIrAUOw3wkKQYq/Cyl2MWPoJRIM1K40w6R+dwpOD7qFw2MlsKq7AFZNIms9GuidCuWnDrsJmFYcgcZSgTzwDUbCTT8IvD+Id/Rw0P5HgThkeEUunSKSQMP4DtPI8xJS769/o3Ndh7AuwfJfSrJ4kSGgpIzGaD5aGn1sW1O0vNIhvAt7kmmVbFzOkmYt+LRKYvb6kVvPh7RPpaq2Csrp+EZYvlekbKvh4URFXnHUFmd9czLB+sWRt/BT89UQ2hMqxz3+TNN8obvtoETZN8OulzbEHi2Hj7xAN15v2AoARJlWUQ1kR+jsn1+T3GxFcSyfSdts8fr30fTYXllEUtVMZCuN22mulkjgsi5EdUhnSJgV/2MBp00jwORjZOZ2cYj+6JsiOd2/3r1BChGL/IoQgTivFZgRlhOHoZ2DDb9D3Mpj2ZL2eFWLLAvr68oks+oS3hvRkQ2xfVhcESIlxYJoWcXYNYzceUQrFgaBawGikCAzTGYsWKJbXjBB77qBQKBTbUQKGYr+R6IxQUhkkzQzDhl/rNogGSfzldhK3/ErbpgMpyz6NGFGOWVqGw9cOQ002FIcakSDajCdrixfb0b78B+6rficoMqqX2TUDn1mCtmUJIlLV8HbDDaxrPxLimkKLY8CMwIgH4d0zILKLx8PRt8DiD2tFYKDZSPOv5tkTM1ie72PiijC6gHM6uWmTZCP5jXoiPgCr31U0zXVhWhZvrrRxZb+baR1r4Zn+XYPDd6z7nqPaj+WbZbIUpOZJgBWz5Ep997cVYbOjfXtr/eaEhavxbZ1F258eBCNEcPCd0G40FYanVjMzauIBPA4NISDB68QIhOmU5N6+WUtFdCkOCHGiBLswoWA1JHfAL9x4XHGQ1AY2z2+wn2P9jzi3LaWi6fH836TFlIcihCImn149UIkXikZBC0iD5saLwIhFmBFEuKLRRBSFQnF4ogQMxX4hwRWiKOqgqsoPCS3k2+U1P8DC9yFUUbuxKw6a9ifWbhHNXYaZ1QtDmXYqDkX8hWgL36t/nWUh1vyArcMFRKMmds0gpnge+gfjQehw6ivQ7CiIBGQUxc4h4kIDfaf0BiFg6L8gsSUs+RjKN0ObYRDXBMa9Cyu/hk1/QEwGdD1TppoESiBnes02el0ISz4mtfVQUrf8zFHHXogwIujBPCABepwPc1+rfQz9rkTLncH58c04+dyWVJoaK8KDaO1KwutMaDjH0BVP2XY7jd7NElhZKkjxZchqH5UFkNAcSjbU7ZfQApyxNSH2TftDj3PlMsuC5Z9LE9OU9rDuZ1zf3YRp07C3PpNIdM+/Eapgg+JAYrPp2DDlteeMwzzmVkIVVTjHPI+u65DVGzbNrb+zN4XCoY9zzad55JUGaJXi46GxnUh26ipSSNEoiGARlmbDsnv23PgAsEM40QKFGErAUCgUe4ESMBR/CSEElhahoqqSpMr1pH17CxSu3l4achic8SZ8fq2ckG3H6nImxGRSVVmOM7sHFaa6cSkOUUyjdirULohAMWJ76KvPKkafOE4KE8MfkBVI0juDMwaOvR0WTYLFk2XHdieBNxWOuR3cCdB8EJTmwhuj5HWT3BaK10FSa1j8EfgLoNlAOXH6/B+AgDPekPn2MenQ5zJw+uCb26DrGeBLxTZhuPTJAPClwviJWF3OQKyfCpoOLY+FkvWw8hu0mf8j4fTXSJh4JpltTuBn7Uk6db6UjLVT6j3uUN9r+eCbChy6xgMnd8AwLco9I0jgPpjxDJz4KHx8GQR3Mix0xWOc8RaGcOFwxUH/q8DuhR/vg8p8eb66nAHdx8P3/6rupv3yIJ4Wx1NGQj0jUSgODrquER/OQUw+HzqfBp1ORXx/FwnLPgMzKkW7Y++EDZ3gjzdrdxYC0ewotDVTePjk0yiNCDJinPg0lHivaDS0QBGWI67R0jdMZxwg76PEt2yUMSgUisMTJWAo9hlNExg6OMwwcaEtiHdPqXkFapmw6jvYtlRWXJh8gVw+5J+Q1JqIcGB6sig1lI+s4hDG6YPMnjURA7tgtRxKNGqg6wKRM1tOZM6YAL/9FzbNq9142P1SOChcBT3Ph/fOlNdGdj/I+RWWfgpj/wcL3pUToFNfgbU/Qa/z5fU0d4KsStJtPPS+GEryYNx70uxz3gTZL60ThCphzRQ5lh1U5sOEExFnvSeFkm7jZPv8ZRDfFE5/VYo18U3RC1dSVlbE1Egao7tdjHfh67UOI9LpDEJpPTi6VSGX9IyhvKKYce+sZly3eK4f8gDxU++CH/8to7BKNkDxOqzsPkSz+lEhUtCFiW34g2hV+TClRqggGoT5b0NZLrQdAbkz5fKqQrSoH3QlYCgajwRRgFj2CZz1rryO3x+HKFxV06BkgxTtTn0FcmdIIR/k5HDMszDzOWJTOyNcBgluH8I0MVTmiKIR0QJFjVaBBGq8N3Z4cSgUCsWfRQkYin1C0wTxrhBaVSEUr0ZMf6b++O2yPKxQBdaJj0GzoxA2BwEthqrwn6sOoFA0Kp4kjBGPoL9xQu0UEMBq0o9obDMsU0YiifJNMnpi/W91xQuQk/XLp8qJ+UeXYiW1gszuiGUfy7DznufD++Ok8DDqKTkhWjQRfn5A5tcPugE6nAw5v0GgFIpXQ/FayOgm00Myu0NyO1jzo4yuGPJPKSLuSBuJhqBgObQfBe+cVmO0mTtLRoaMeV6mvJTm0jXTx/CXl1F+zLmceNZ44nO/w62DaD0Ue/F67FP+j/s6n0nE1oYrPy0nEDGYMLcIW9++nDfuJ5LWfoxzyWeY3caxOWsU7tgUtIgBloVhCmg6EF47rv5zvm4q9L8G+l4hq7FsXYylO/bP56lQ7CV2uyDWFoJASF4zP9wNXc+SQmR9/PoY1qmvwsKJWDHpiNbHITb9AV1Ox7Z1CR4RodxUBi2KxkcLFDaq94TliMFCqEokCoVir1GvvxV7jaYJfM4wWtEaxMtHI6KhhvN+ATNnJovTTiXqTaPCnqLEC8VhRZWvLebF32M1HSgXuOKxBt+EecqrlG9PfzIME9G0L3QcK31fGmL559LA8+TnEae9inhzjFze7iT46X4pXnQYDYFi+OgS2LpYemhsXQQfXgyFK6DdaHh7LHx7myxX+tk1Mmph7c/y35hgc8oIC1csDPxHzf6TWsO3/6xbJcSy4OubYeB10PJYUorncXq3NB76eSvHv1vEtnbnoZVvQX/3dPj8Glg9Be2Ty7BPfYAbB8ZXb+aV3ws57u1tXLFlDNcELmeO6MKjvxXywdw8NFvN7cYMB2Q6TENU5kOwFDb8ipXRDU2Aru0+zNknKkmM5pBYNpfEaC4+rVIZ2yv+Ei47xEW3opXlIF45VlYZCZRI0a8hClexMeDgzdgrKO5yKdtIgZXfyOs5uxd+TUUSKQ4NhL8QyxGz54YHbAAaliOm2kxUoVAo/ixKwFDsFZomsNkMrGAl4tMrZdh3sAy8KQ32seKa0jLRRpXhIRTWD+JoFYq/jp0QrJuKaD5Q+k6c8DBi6yLEjKfxaZUAeEUlBMvBk1jb92FXAqXSuNLmkv+2opA3R6ZxFK2RbbqNg6mP1t//18chVFa7Kkl6V+kx02wgDLlVRn8s/lCa5bY4GloNA4cPNBtRXwaU5tS/7YgfKrZATDrxsXHc1lvw6IlZnNAxlZS1n6AterdONRRtzQ90t+WSGeeq2YxhMW1NId8t3UZxVZgrj27Fu7NzqdrZhNPmkj45DeH0gScZ3AmI6U+hv34cccbmegUJIYDSXJwfnY3+0lHo74xFf2kgrk/OJ14UKxFDsU9omsBnlSJ0h/Sd2VE5KFgmPWUawuHDEjaGtEvhnAnzMAtWYEWqMM7/Apr2xzBU9IXi0EALFFb7UDQWljMOEVQpJAqFYu9QKSSKvSJRLyUaqMAWKJAmgyDDz3ucB78+VreDEOidT6Y86sChEn4VhyGO0tVoP99fZ7kGOFoPQ0sZhCNYgPjsGjj/MylQNPSGNqsHfHSpFApS2sEpL8EnV4Lz1po2uzMOjfhre1sAdD5VzuLzl8GCnSqmFK6CJR/B+A+g86kEcOAPmiTt7mCrCuCbWyEaILnnBZzZfDCjm2Tj/uqdBrukrXiT49v+k7fm1B6zTRN0i60iY9mLfHzm6Ti1CJYpbzlBPR5v2xMRK7+qu0F3AniSYNsSSG0v00l+exzt5wdwDXuSgFE7ncRDFXx4CWIXnxKR9zv6F1fjGf06VVbjuOwrDk9sNo14iqRYGA3L7+IO8pdDehdp1mtE6vSN9rqYWfk6aRE/d57YnqQUE3/mawTxkOTwARV1+igUjYFMIWlcAcN0xKD5VQqJQqHYO1QEhuJPoWmCZEcFVmU+9l/uR4SqalbmzJBVE9oM26WTDeuUVyi1p+E0lXihOAwJV6HNfq7B1frMp3FrIShaLVMeJp0LQ26TJn+7ktIeNJsULwAKVsJPD8CAa2DzfDkpgvr77oy2y8+2MxZSO9QWL3YaP788DK2HU9DnFlaU2WTVknoPxiE9NFxxMqVk3htQsQV3yUopqjSEaWCr507yf0NbkKxVYvckkPXlOSTkz8Cuye0ELSfGsIewUjrUPZaTn4Pv7oD1U2H2SzDxbBhwHcKXgkuP1omocERKIO/3eocmNkzDGdlNqopCsRNCQIKthHhzG6I8D7HqOwhX1m046wUY/Yy8ZnbCaj6IZU3OpkVaAk6bhstuo9KKwW+6sVSNX8WhhBFGC5U3uoBhOWOViadCodhrVASGYo9omiBRL0GU5CIWTwZ3ksyxH/WkfFtrROCzq2VJyF4XyXz9uCZYGd0IepoQDam0EcVhihFGVBU0vN5fhDDDNSlUpRth9ouyfPD0p2V6iN0NnU6FTmPh48tr9988XwoeX98MJz0hPS186VJkqNhad39x2eDfJV/YXygjJxoiZzqc8DD+QIhHppcxYegTJH9+Xh1T0vBx9+PQ7TD0LilgfPtPmPs6HP9vOfZpT9a7ebPjWM6MT2RDqcmSzeVkJ7i5fkAi3Uq+xzPhbsjsASc9hj7jGbyjO1FKEpYFpSKJmHEfYivPha2L0OKzZUnaqY9K7w+Qb7mP+xcYQYiG0X97hMQuZ2B50zHRCOgJ4N/DG+1wBbh330ShAIh3hdEjIIww5M2VUUx2D5z3CfxwL2xZKBuu/UmKeme+JVNKitdhtRhCDhnYnEl8OyePj+bn8fnVR2GaSrhQHHrsEA0aO4UkYE9gXqHO4nl5BCMm8W4bLZO8dEyPwVGfMq5QKBQc4gLGs88+y3PP1X772aJFC7799lsAQqEQjzzyCF9//TXhcJhBgwZxzz33kJycXN1+8+bN3HvvvcyePRuPx8PYsWO56aabsNkO6UM/ZNB1QYKVj/juXlj+Wc2K+W9B2xPlpOuL66SI8eN9Mrd9xENY2f2ociQTUOKF4nDGEYvZahhafVVFALPFsURtPhzxTaQ/RmmuLAW69hdZRjR/GSS0gOlPyUiCekLOAajcBljQfBBMuRtOfEyWZNw5lcTuhlNfhXW/1O67YRq03iX6aWeEgJINeOP7smpbJQ8tTeSWcd+R/Mez2PMXY8Q3o6Dn9ZDUivSc72HxRClujP2fNB40wpjdz0VbNAnKN9XatJXRDdGkL601uGhAFq0TmuFeOpH4X16GsjzZaPN8uZ0z30YrXY+IlwKGZUG5FYeI6YIW1424qhXorw2tPfaTHocVX8LqKfJQADH3dWg/Cr3lseiFqxH9tlcriQTqPXbL1bgP6IrDA5dTR49WIfKXybLfO669RR+AM0Zee9/dXpM6uX6q9J9JaYvVYQxlzgxKKzX+88UyVm6r5K2L+uLTdx+8pFA0FjsqfzRWBEZF2OK1ZSaTV4+jyrTj+G09TptOVTiKaYHLpjG4VSIjO6XTv1nCHk2cFQrF34tDfhbfpk0bJkyYUP23rtdMiB966CGmTp3KU089RUxMDPfffz/XXnstEydOBMAwDK644gqSk5OZOHEi+fn53Hbbbdjtdm688caDfiyHGy67iY9yxPoZtcWLHaz6Rubf975YvpGKawJH3YCV3Jqg5iMQPuS/XgrF7tF1zK5nof3+okwR2RmHF/pdiXfLNLSfH5BvaxNbyqofWX2gaJ2sHDLuXVj6cf1lhoUAXxpcNROm3AM7PCEsE856Rxpy5i+X6SdN+8OKr6DjaGgxGOa8Aq546HKmTE1piJZDIW8OKb4mjO+RwhtztvH9ahtndbue9t0EGyssZkwP87/hpTJawe6Vk7Oti6DLWYTtMTw3u4JTT/6YpjkfoS39CHQHZvfz0FI7wNtjsVUVcNSwBxErFiMWvFt3DOEqWPUtdDptl8MXCCGruBCubRBKUitZLWW7eFGLFV9Cm+FoK7/E2rYETv4ffHhRnWZWh5MJ6QmgfBMVuyFGq8AZCSK2LpLRULt60IQqpHgx7H74/k6IyZBljeObYUZCrAomEOtyUeav5LqhbciMc+HVwDRU9IXi0ETzy6i9xojAWFRocct0g9IwHB9fyDEVX+A7+RmEpmGYFpvKAizdUsncjaVMWbmE9Bgnp3XL4OQu6SR4VElthUJxGHhg6LpOSkpK9X+JiYkAVFRU8NFHH/HPf/6TAQMG0LlzZx566CHmz5/PggULAJg2bRpr1qzhscceo0OHDgwZMoTrr7+ed999l3A4vJu9Kmw2gU/4ERVbpUlnQ8x5BZodBX0uhxEPY3lTCDsSqQw7D95gFYoDSIWWinHRd1itj2eHAYPVfDDGZb/KcqITx8nIhLYjpJltXFNp+heugAs+h4p8WV61PjqMkQJFuLJGvAAoXAk2txQuhAbrfpKlU2c+C6+PgLJN0GEsNB8Mb46UAknfy+tu3xUPx/wT8pfjWvsNV3c2OKNbCv5wlNd+38Yt327l19wQ/z3OR6pZIFNU8pfKvos/xOx3Ob8FW/LhomKOfXUtHzhOxRh6D+Zpr6Mt+wTeOUWmupgGWqQKsXF2wydy8x/gS5PRF5qgEsH8bZXMzCunzAQzLru2p0D7Ubv/7VnyEbQ7CZEzDeKbYXU5q6ayidCwOp9G9PgH8Zuuhreh+NuT4KjCaW2PvND0htOxitfJSIzeF0PfK2DjbIxv7yQvGktcXDz2iEnnZC9t4p14sLBU6ojiEEb4GycCY+omk0t/MohxwL/7Ck7PKiebbYioFLB1TdA0wcOJHVO5a3gbbjuuNc2TPLw8I4eRL83mji+XMX19MVFVzUeh+FtzyL8iz8nJYdCgQTidTrp3785NN91EZmYmS5YsIRKJMHDgwOq2rVq1IjMzkwULFtC9e3cWLFhA27Zta6WUDBo0iHvvvZc1a9bQsWPHvRrL4VSOb8dY92XMTpfAFy5AVJZCyXr59qkhQpVQtBrLkwy6HcvupSIgDqtzBX/tfP3daIxz1Rify459mpZFqS0L90kvYjfktRDRfThMP/rP98Ow++TEf/UU2LJAVtCI+KURpRmBo2+FflfKiI1Fk2RUge6AbmdD38tg0UTpY7EDTyKc85GM+PjkcoiGag/MsuRb4HM+kuKFZUnfjWNuh9NelZP+qkJodSw0HQjf3yV9Nn55mNTsftwTP4NrLhhNWdSGx2aRtG0GiSu2R3nYPbKvzQX9r8aIROhW+gOfDk2iOLYDT80u4Zjju5L+7fXS32NnAsXyzfSOcrC7YMVkEbHFYEUFM3PLuOXDRYS3P4QKAU+d2o7Rx/8b7bvbZQebS/6+NES4QqaOAGz+A+O4f8PRtyNCpViOGEK2BAKm47C7pg+136LGuM4P1j5jnVH0UAUCS0YRNh+0+w7BUpj5PJzxBhGrO9HuFxMjPBgAgmqjzgbL/Taw7nBHHduB3fefabO349MDBZh2H0K37/3A9pGZW01ummbSLRku7SiwawIrJCtE6eFyDIevVnshBC2SPLRI8nB6twxmbShhxoYSpqxcQozTxsAWCfRuGk+n9BiaJ3pw2rXt/Q7aIR3SHMnX5b6gzseRxSEtYHTt2pWHH36YFi1aUFBQwPPPP88555zDF198QWFhIXa7ndjY2Fp9kpKSKCiQb1AKCwtriRdA9d872uwNSUkx+3gkjcdejzlUgeUvlGHg0RAkNIOWx8pQ9vpodxK0GwnuBMQfbyH6XUGy9/A7Tzs4HD/jxuJgnqvG/Fxq9h0DSLNOO8Cm+XDs7bIKz5S7azosniyjkkY/DZ//Q5ZU7TAaelwg/x8NybSRonXw8hA56R94bU3/4+6Br26GAVfXFS92UFUA0UBtcfGXh8GbLCMXkttC+zHwyhApcMx6AU56DCIBfB2Oxyf8sHCi9Ojofg5kjAQrKk1GhQanvoz1+yvYv7mVHb+gqTYnj419k5C7HSJ3Zt0xLfkYjr8HNvxW75DFwGtxxSWTs7WcGyYtqJVRY1lw/Ucr6Xz1GFqd3wl+eUS+8W59vBxjfbQcCrkz5L81Hdsnl8LY5yGlByBvbt76ex4WHCq/RY0xjoOyz/LNEA5A8QZY/7Ms32t3N1geFYcPvKkw/n3Q7OhNm2NUFlGgx9M2/c9/0w6Vz/VAoI6t8fa51+OzysCTQFzcwSkxvabE4ObpVXRO0bihjxNbtadFAgA+PQC7GUsccFpqLKf2acqGIj9zNhSzKK+UKSsLMC3QBKTGukiLdZLoceBz2fE6dFx2HZ/TRqzbRkqMk6aJHtqmxRDjOnjCTWNzJF+X+4I6H0cGh7SAMWTIkOp/t2/fnm7dunHsscfyzTff4HId/LDgoqKKetPYD0WEkBfpnx2zpglibX70qB9RsALKNoLuhKzeELe1/qoI3mToehaW7kR8ezvGcfdSGnRhBQ6/Ovd7e77+zhyIc5WcvPsbSmN8Ljsfp0sEcUVLoWA52N2Yia3RNAe6MxaWf1G3c850GHAtjJ8ovR+mPQWthsrJ0ZS7YeTj8NlVsm3ZRohvJqMyzAgktYFti2rSIRoeYd1FVYWy/ClIIcOyoM1w6HI6vHNqTQUTuweG/RsG3QQlG2DjbJk6ktoB0jpDzgzErkJENETMJ+fhu3I6tBgCa3bxpqjcCjYX1uCbENP+W+P5ITTMYQ8Q8DYnUubnvdm5DX6Wb87N557+SWgdxyKsKDTpBwvfk8e1MzHp0KQv/Pa4PBZvCuRMgw/OI3jGB1Rah+8DysH+LTqUrr2Ddew+FzhLNiA8yaDbAAGth8prp+8VMLOe0snH3ilFjJePxjp9AsWeFmyLaCS6BIWFe77nHcn3GHVs+8b+uPb2dXwxRZvQ7LH4y/x7bvwX8UctLv/eINYBl7azCPlDVEvzUQdewF+cT8T958aSaBeMaJPEiDZJBCMGeWVBtpYHKaqKEDAsiitDbC4JEDZMwlGTYMSgKmIQjGyP+ANap3g5tk0yYzqnkR57ZKYZHsnX5b6w8/lQIsbhzyEtYOxKbGwszZs3Jzc3l4EDBxKJRCgvL68VhVFUVERKinxLmpyczKJFi2pto7BQPgjvaLM37HDOP5z4M2PWdUGs20KPGIhln8D632Ddz9JIMFQhoyxO/p98s7z8C7m8w2gYfCNW8Xqs5PZEjn+YCpFw2JeMOxw/48biYJ6rxvxcvKISx+ynpZHn9kHodjfWFb/Bsk/r79TjXBk58NP9NcsWT4Kk1nDqK7DmR5lWkt0bIkHYNFdW9PnuDgiUyDfAmk1OmML1pFFk95ECYo/zpZBQsWWXQSeDO1GKIX0vh/fHgRmtWR/xw9e3wHlt5FvnmAxpDLptKVZmD8TbY+s/LiOCWPEF5nH3oG2aK8e6g1FPw9qfEUKHsydLHw/didX8aPy2ZAKGA01ATnH9D6mxLhvXdQP9tWNq3oAntpTna+H7sPxLKep0GA1dz4QvrpdpJic/BzOele23LsIeLsGyHf4PJ4fKb1FjjONA7tPnBme4AOFKkJFMiyfLCMPyTdBsoExbOukxWdK4eD2ktJMpWAUrq4XFaEpHDMMk0efDZlrszVAPlc/1QKCOrfH2ubfjE1XbsFzxe/Xd3VeeXGCSVwl39hY4baL2Pm0u0HREqGyftu2y67RO9tI6WUZBxcV5KGtAlAlFTQoqQ+SWBFixrZK3ft/IqzNzGN0pnSsHNSfZe2QahB7J1+W+oM7FkcFhJWBUVVWxceNGUlJS6Ny5M3a7nZkzZzJixAgA1q1bx+bNm+nevTsA3bt358UXX6SoqIikpCQAZsyYgc/no3Xr1o11GIccCbYqqCxDVBVARnc5aTjqOpj+tDQH3JHH3+4kaVLoipNvRMs2YaZ2pMSIlz8I6ldBcQTi2Pgb2uwXai+MBBBLPq5bOQOkEWDHk+HdM+quK1ojUzf6XQk/3gtzXwenT1YSSWoFZ70Nru2C7O8vw/AH4Kv/q7m27G448VFpzjnlX4AGQ++EaFhWSYiG5CT/xMdgw3QY84wUHX1p0OcSKWgIAduWwrwJ8OtjMn2lYDl88Q8AxBlv7t73pjIfsegDosfeje3r/5PbO+4eKaL88aZs88cEaWZqGYjfnsBxwfcEcKABA1ok8svKuil85/VMImn2f2qH7xevg/fHQ6exWBd+LX97gmWI5V/Ic5jYAqY/IyNIdhAqP8zubIqDRYKtAj0qELpDlt01QtBpLLQZJq+bVd9Cp1OksHHCozLSx18E394Og66H6c9gDHuASkcKWkRDOyjTP4Vi/6P584kmdTjg+5m91WTSGouz2wgyvfWbD1h2L2LXKl8HAKdNIzveTXa8m4EtEglGDKatK+bbFfn8vKaQO4e3ZWib5D1vSKFQNDqH9GPef/7zH4499lgyMzPJz8/n2WefRdM0Ro0aRUxMDKeddhqPPPIIcXFx+Hw+HnjgAXr06FEtYAwaNIjWrVtz6623csstt1BQUMBTTz3FOeecg8NxZCqte4OmQaIjCOFKaWLmTZaTByMiw7L7XiFDzneEyCe1lm+jAjFYMRlEPWmUGXFKt1AcuVTmo017ov51q77B6nImImd67eWZPaQvRkNsmif9K7qeJU0D578tTTh3pJ3EN5XpERt+k6kR4z+ApZ/IVI9j74Cpj9b2mVjxhUzpOOUlWPY59LpApqSs+EoKEZYpU1amPgqb58s+2b1h9DMw+yW5j1n/q9leeZ68zgtW1j/+9K6IH+/DvHAK0atmowtAaIhAiTQC1e2yzOnC92siVopXIxL7EY0aDO+YzvO/rKU8KCNChACbJhjc1In4elrd/UWDsHAiIhLAyuiOSO8K63+R0R/F6+o0t9yJDZ97xd+WJHsZwogg7E4o3yrFwNhsKfqFK0Do0HWcTLVKag0nPS6/Y0s/hhEPwoZpGCf8h3xXS+xROyjxQnEYo1XlY2UN3HPDv0AwanH/HJO28TAkq+F2pt2Hto8RGH8Fl13n+HYp9G+ewLvzNnHb58u4bEBTLhvQDKGcHhWKQ5pDWsDYunUrN954I6WlpSQmJtKrVy8mTZpUXUr1jjvuQNM0rrvuOsLhMIMGDeKee+6p7q/rOi+++CL33nsvZ511Fm63m1NOOYXrrruusQ7pkCHGY8NpVUFpDmLK3XLy5IqDnhdCj3Ngy0IZiTHgamnsF6oES2Bl9Qabh3LiCUdUGSvFEU64SoaW18fm+Yjh90NCcyku7MDmkv3q4+ibIa6JNPcsXCUnSv2vhspt0rjSGSPFjKNvgZn/k5OnlV9D2xOg39WyfGp9Jpnrp0LP8+G4u6GyQIqQWb1h9gsw4Bp4c7QUAnaQNxc+vAjOmQxbFtfe1rw3YNCN8OlVdfeT1EqmtnQai61oGdoP90ihw5MEvS6C5NbwyRXSGHTkk/DlDbJf4Uq05P4YhkWsDd6/rD9P/7ia8R3stPUFsUerSMzwSCPF+lJmANyJiI2zIFIly8fOm1CnidVuJGFbAuzjT5MQYLPpWJZFNKp+344UEj0RBB6ECMoSxz/cK6OQ3AnQ+xLofBp8fSuMuF/eA+e+KiMyAiUw4DqIBgh2GseMIg8dfb7qaiMKxWFJuAotUonpSjigu5mw3GSrH+7pK9B2Jwg4Dk4ERkP4nDYuH9CU71YU8MrMXMqDUW46tpUSMRSKQ5hDWsB48sknd7ve6XRyzz331BItdiUrK4tXXnllfw/tsCbRa6EFtiC2LpR5+r0vgm7j4Yd7YMbTMh9/xEMQKJVh4ADtTsBqcwLY3RQGPezzDEGhOEyw2TQo3gjpXRqMqDCLchDnfQqzXkAsmQymAWldoP1JMgVkZ9qPlJP/L66vWVawAr78Pxh8M3QcK6MmgqUw+SLod4X8LxoCm1MKEHNfbXjAc1+XAsfUhyGhBRx1gxRHFr5fW7zYQbgKVn4jx7Qzhath4+8w+lnYMh9is2TbYJk8rlkvYrUfifb+uJo+/iJpqNlhtBRMfntC7r/tCFj1HSS3rfbHMQ2LLI+NF45zoE8aD2V5chstjsHqezliyr/qP75Op8r0kMptcjxjX5TlZ6fcDaFyrM6nEz3mbqoMd8PnaDfEauXYyjcg1v4I7gTM1sMJ2JIIms592p6i8bHbNOLMrZC7HFG8DlLby++qv0g2CJTI7+3m+XDcXTDxbBj3vozwMaNgd2Fh4td8lGhJdEkHDHXvUxze6P5tAFgHUMDYVGkxYbnFsCaQ7tm9EGA6YtBCJbttc6ARQnBCh1Q8dp33/tiE16Fz1aAWjTomhULRMIe0gKHY/yR5IoiiNYh3TpOTgR0kt4VTX4bJF8pojFA5rPleel4kNMdqcQyW7qIoeHBKbikUjY3LKIWf7oOB/6hfwHDFI5oPoNBIJn7Qrdj7XiZf4f/xlhQBmvSr7c3QbTx8fFn9O5v5HJz+uvStqNgioxB+2yV1ZchtNWVVHT4pDrjipQiZO1OKFIFimdufvww+uRyunLb7dJac6TDw+rrLN/8hK5dsXSQFEHcC9L4YSnKg2zjEd7fXv73lX8DZk6QPyLwJMPIJ2LIQI7FtrbfWXqMA/Z0xtU1A1/+CGPZvrBZDEOun1t7uyP/KMq+fXlnbnyO1I1z0DehOqkQ8AWPfSuPF62XYPr4Ikfd79TJtyl14Rz0DLUYqEeMwxOnUialcjXhnbE31HZARU2NfgI8uqVm+9kd5fQldCmrH34/lL8LM7EUZSRhYOE0lXCiODDR/PgCm68Cl2z2zyMRjg5Oa7TmKwbL7EGU5B2wse8PRrZMIRg1en72RrDg3Y7qkN/aQFApFPSgB42+CpkGirQwCAcR7Z9YWL0CGs89+UQoWs/4nJ1LxzUDoWOd+QsCZSlVAhc0qjmxsNg2PUYJmRdCxIH+59HMY8yz88jCUb5YNM3vCsPswNSe6AC1cLic+C96VVQ3sbjjlZWg6QJYBDZTIMqmRQP07jgZl2keX0+Vb4F1p0k+WQ0XISgnNB8sKKEVroNlRMPhGKN4gK53sILWDjFbwpcpIj/rwpUvxsvfFcvvREDi84I6Ht0+pLRbkzcHqcgYMvA5Rmd/wSSzfLE1DyzeD3Ytx7mdUiMRqPwxNE2hbF9YWL3bw3lmIcz/CKsuTIobDA21OkD4e751Z11w0fxnWj/cjxj5PsGrfbmcOG+h/vFFLvNiB9uV1eK7sS1DP3qdtKxqHWGcIR6QUMXF8bfECZLrX1P9An8vk/3ew8XdofRwUrcHqehYfLqviKDMJu0oXURxhaJWyapV5gPyCFhdZfJdrcUF7gcv2JwQMhw8tVCrvEYdA2sawdikUVIZ55IfVtEr20Ckjds+dFArFQUVr7AEoDjwOh06iKIJNfyDyl9c/cQBY8wM0P0r+25MIugPL6SOES4kXiiMen1ZF3PpPcb49Avvz3dEWTZRmmosmwZzX4Jg74My3Ydy7MpWicBUWFnGBteivDJGi4I6yqpEATDpPhqYfc4csQ7ynh0V3Amh2mTayM3FN4Kjr4Y2R0HoouBLgg3Nh8YfS++K3x6VPTZO+ULy2pl87me5Bt/EN77P/1bKPZpPbnHwBrPsFfn6o3kokYvFk6Q2wp+PodyU4vFjJbTDsPnSt5vdD0wSiIUGlcitMOAEzpQPRflcT7TyOSEwmlr9QptbUg1j1jUx320fcRgnanIbTDMWKz7Hb9X3evuLgEu/048hfiChY2bB/Te4syOxee5k3EYLlWElt+G5dmP5tM5R4oTgi0Sq3Ytq90q9pP2NZFk8tMMj2wsA/GbxgOWJk+mV4N9WvDiJCCM7skUl2vJvbv1xORTC6504KheKgogSMIxyf0ySWEsSGqQgzWvMGuT4sS95ENBs0HYCV2pFg1EaFGXPwBqxQNAIO3cC55B20z6+u8WT4/WXoe7lMh9iyAD6/VooSE8+BOa9iJbRAmAb6pHNqIpp2LgEKUmD48gaZOhINSjGiPmIzZVrIjKel+DH8ARnFANDvcmnwaYRk2dZf/1O3f6BEllXtcX7NMt0uJ3BlefI4dkYIadQZky6rovz+ssz5BymErPu54ZNVlofVfHD96xw+KXC4E+DoWxAznsXxyiBil04gRpPmnKZpYaV1bXj7dg9RU6MkGk+JSKfciINgecPtLbMmtWYfEFiwOwf8ym2HwktBxZ8gzlaJLVCI+PDi3X9nQJYe3oHNCendIHcmRkp7ZuVUgKHEC8WRiVa1BcuddEC2PWOrxbwCGNtyD8adO2E55DOmFizeQ8uDh13XuGRAU8oCUR75YXVjD0ehUOyCSiE5QhECCFbhjJYgjLB8Q2n3QWLzhju5E8AIw5hnsSzwe1vg30dDPIXicMJjFKP99ljthYESWZHjtNdlqHn+Mjk5b30c9L8WM7apjArwF8vUK1+a9GpY8K4UBXbFCMOoJ2WUw85VSuweGPWUjHhY9qkUQZr0h1NelNU9bE5oehT4C2SJ04beCq//tbZQkTsbWh8PPz8IfS6VFUe2LpHHkNYRIkEplsx9vfZ2LAsQNFQmUmyZjzX8fpnqsrMgqjtg9FMw81l5DCc9Dj/eB5aJ9sO/cITKcfa4lpChY6Z0RPelyRSXHbQ4GtoMx8rqRcQeD9vnl6ZpYaZ0osEYCE+irN4SaajB7okIF7amA+uWw92O1eZEVZHkEEcIiHMEsJkGonSjrPaT0KzhDnaPFCZBCvZjX4LNfxAZN4nTJ26hQ0Ysbl1gKRFDcQSiV24+IBVILMvi2UUmbeKg617oI6Zzu4ARKMGMa77fx7WvJHsdjOuZyeuzNzKkdRLD26c29pAUCsV2lIBxBBLjiOAkBKWFiIpt0tivLE9WJnAlQosh8s3wrgy+CSuzJ2h2ys1YwqYKm1b8PdCCJfVX6lj1LRSsxBr3LoSqwIpgoWP4MigniXjKZUrJvDflhN4VJ1M2+l8Fn11TExmQ3QeKVst0lEumwLqpMr0kraMskbp1saxOYkTkbKz72VIEyeotPTjWT5WT9L5XwMnPy7ZmPWGtO4sbuTOliWZCcymEBMtkRMb6X+Xb5xMeAjMil+9MzjRpELrym/pPVkZ3xNJP5TiK18uxx2ZCRjdpRrrxdykqbF0koyN2nOOZz+LuejYhUigXScSf9wX6xxfLMZ38vOw35zXE7BfxdDwVV6+LKRMpmKZF2JWCq8UxiPW/1BmOeey/0GLSodi/u4+4QfyWF+fx96NPGCYj0HY+nSntMZLbV1dQURx6xGiVOB0ahCsRldvAMqSoGCiTlWuWfly304BroaIAhj+A1eo40J2sKYly1Qdb2Fgc4PHTu2KpaiOKIxStYhOmJ2W/b/enPIsVJXBzd7FXJUgtu4zc0wJF+31Mf5U+TeNZsKmcR39cQ++m8SR6HI09JIVCgRIwjigcNosYUYkwo4g1U2RufPlm8CZLk75ACVRshWP+KSc1iyfJN7C+VKzBN0O7kZRp8UQjYKkHdsXfCMu2myoTJesxoxHKYzqhaQLDMDEMCyEsNE1I4WKHOae/SEY8tBgCx98rIxC6joOjrpN+FFsXwSdXwGmvwbYlstpHXFP46kaIbJ+A97xQmnO2OhY+vKi2Z80nV0DbE+W2v7+r9jgzukvTy06nSLGy41iswlWI31+CgpXSnLPrODj5OQhVwmdXS6NRZ0xtv4uFE+GMN6Wg4N/lgbLXhbKqyaa5UmhZ8hEktpJiy88P1rRL7STFjZ2JBtFCZeCUokSpLQvPmZNxWlWID85DFCyvbqrNfAZt8QfEXfgdJSRTafqwjX4efeZTaPPflmJTTDrmMXcRbD4Mj7bvYqtlWVR6WuK78Fu07+9A5M0Buxuz2zmYA66n3Irf520rDiyJegmaGUaUlcH3d0hvC80mSxa3OFoKgfFNpIdNuFIKa32vgJhMaD4Q053Ef6fm0j4znmvfn0+PJvFMvqI7CXZN3QMVRyx65RaiSR326zYN0+L5xSYdE6Bdwl7m3AkNy+FDBAr365j2B0IIxvXM4r7vVvLET2t5cNT+PW8KhWLfUALGEUK8vRKbTUdEDVkF4af7alZWFcLUR6HHuTJHvdlAaHkMtB8ly5N4U8GTRKGViBYx1IOb4m9H1J6ALaWdnOjvSmwWUWdSnTQCtwhKgaK+yiLrp8LRt8ClP8GG37CWfo4x8AZE3yvQqvIRm+fLVBMAIwrdxkFlPnQ/B7wpMP9dmDuhfsPdVd9At7NktMeO6AndLiMqbC7ofw0Ur4OiVYiPLq3pF66CuduFk86nyegIh1cKJjOfrWkXqoBvbpEiS97v0tzXnQQ9zpFVWXYIFYNulN4Zpbl1x9j3cvjq/+ostuw1ZZhN0yKgx+HY+CvaTuJFNZXb0Be8SUKvy6i0Yig14nAOuAt332sQZgRDd+HXkjBMk79a3Dls2ij1dsRzyjvYzACW0AhqcYQMFYV2KGKzacQ5goiQiYgGYcIJMkULZGTSss8gby6c+B9ofjR0OROq8qWHTMU2SO+Mqbs47Y2ljOiYQbfsOH78v6Nx2zScWCriRnHkEg2iBQr3uwfG9xst1pXDP3vum2GQ6Yw7JCMwAGJdNk7vlskbv2/kpI5pHNXywJWfVSgUfw5l4nmYo2mCRGcVNiuCWD9NlkPdNZd/Bwveqy4Tx+wXZFnE2EwMVxIFZhxEourBTfG3pErEYZw6Qb6h3RlnLMZZ71Kl1c0XdlOBWPtjwxvdNA+rYCVGs0H4O46nJBJDBCfiuztqp6us+laGumf3kdEcZRuh+UApVDTEhulS9IjLhk6nwSU/wMJJ8MpQeH0YxDeVlUTqY+Nsad7Z7yr45jZI6yDTXnauLiI0KXAUrMbqNh4G3QBf3VQ7yuKXh6Q/SHKbmmXeFBj9DKz8WgqnO2E16UfEFldrmYMQ+qL3a48vvqksGxuXjVj2GbYVnxG79kM8wk/I0CklmRItg3Irgeh+DPM3TYtK00spyZRZiUq8OERJ8BjEayVoK75ELPlQ+tPsEC92pnyTjAJa8RUs+gBMC5JaY6V3xnAlcMo76xncOoUxndNxmSaxGthNU90DFUc0eqX0LdqfKSSGafHiEpMuSdAqbt8EDMsZh+bfTXnuRqZfs3g6pPl45IfVBCLGnjsoFIoDiorAOIzRdUgQJYiqYlj1PVhRaD1MhsjOeLqu2Z9lSjPP+GZYIx4GTyJ+WyJVUTtC5fsq/saYpkWZszkxF/+CtmUeYstCrLRO6E37U24lYOxi5ufSwoiKLdK4ctcKGIktZW59+SZY/jkiqQ2uTqdhOZ0Iyy6riXhTpDlnNAR2t4y02JESotvlhEzoNOhMqdug/7WQ0kF6T7w1piYao8PJ8v/F6xo+4K2LZJrLj/+WaSy9L4bxE+V+E1qCpmEZEaxj7wLLQLzQr26FlU1/YBasJHr2Z2ihUjANTGc8uhVG3zkCDCChBcaYF6iyfOxsDmoJDUt3IgCS28LQf8k35UVroetZUqApy0P76v9wj8sglDq4zmeh+PuQZCtBFG9FrPkR7C4pyFfsprJW7gxoNgg6ngyWgWlEqXJnkuv38NSZPYixCeV1ofhboZVvBMD07D9Dyq9zLHIq4M5e+16uyXTGYStetd/GtL8RQnB2ryzu+24Vr87M4R9Ht2zsISkUf2uUgHGY4rMFcVGFmP6MDAvfwS+PyDD04+6FH+6p29HhxfIkgs1FleklENIRqAc4hcIwTEpJRMscjt7kBCzLJCHBh1FYtza92yxFzH9bTowWTapZ4UmCkx6DT65AbI9AELoDMPG2PwnKNmGd8AgiWA4n/0+WV213Ym2jwXVTITYLOoyW6WD10fp4+eZZt8OaKbWNOLuPh6qC7UJIAwKIO6HGsNIyYc6r8jjG/g+WfAhLPkREAtDuRKwB12GOfg7t0ytqbcLK6IbR8VTKjFiwxW5fCLqu4bvoJ/Ti1YjidVgpHTDim1NBQp30tLBpw+x9KXrhShjxIHx8ee20GU8SnD4B4rLRfroP95kfUYkq6/x3JMlWgvjqRunvtDND/wW9LoJ5E+p2cifIyKaCFVIIm/UCnrEvkelpTdS0VJURxd8OvXwjltAw3fsnDSJiWry0xKR7MjSP3XcBw3IlyBQS06ipEHSIkeJzcmKHVN6dt4kTO6TROsXb2ENSKP62qBSSwwxdFyQ5K3HpEcSm+bXFix0seBd8KRCTUXt5bCZWfFNwJVBixhMw7Adn0ArFYYRpWkQixm7f9IvKbVJ06HqWTHnYwQkPw29P1KRPaDY49RXYshDx8rGID85FvDkavrtdpnFc8Dm0GwnlW2q28cdbkNpBTspi0uvuvMsZ0stm6UeyisnG2TXrPInSqHf1d9BhTP2D1x3Q6jgZ3RDXpGb5SY9Kr5xp/5W+FlUFiD/eQnt9GGT1wrhyNuYxd2D2vQLj3E8Jn/4eZWZ8nc0bhkmZlUhJYn9K255DSVx3ysz4ekPzTdMiktwZa/iD8PWtdT0//EXw9U0w8B9QsAKdeiqvKI5ovJqfRGcAsfzzuuIFwE/3y6o59Rnxdj4dy5uKFdcEM6s3ofGfUOpug6qKq/i7opfnyPQRbf+8v/xivUVeFYxpse/iBSDLulomwn/oGXnuzLB2KaT4HDw0ZTVmQyXNFQrFAUcJGIcRMfYICcZmtG1LEeEq+OONhhsvmgQdd5rAOGOxznybaEwzCgNODJXCp1DsO3a3TP/47BoY/iAMfwCOv0emj+TMqGnX+VRpgrn6+9r9S3Pho0tkOVOAZkfVrIv4ZfWRotVw7sey4kiTfjI9bPwHcjIfrpIpJsFymY6yK4s/lL4WKe1rL9dscMYb8Mc7sqLJCQ/LaiLxzaQZ6dZFdbcVKEHMfI4qRzrl3f9B+cB7KYnvQ7kZVydLbWcsy8IwzN22AagwY7DisqFkff0NCldLoSW+Gaa6Zf1tEEJGXbhDW9DDpbKaTkOs+UFWHdmZof/CSmxF0J5AkZZNka055Was8rhQ/K2RAsb+SR8JGzL6ok+qoInvrwkYlktGhGhVW/bQsnGx6xpn98xi8ZZyPlu8tbGHo1D8bVEpJIcJSY4KRNlGROVWmaNetqluicOd8RdDu1Fg92KldoDsPlTZkgn4D96YFYojFcOdjB6bKcsUTzoP0jrL1JHyzfJNcI/zoNVQ8CTLdA4jJEUFa6dXvxVbpRARkyFTJWb9T5Z6BFkJ5IvrZVWRK6eBaUpzzy+ug0AxjHpKRmIs+VCmjO3wz/AXy+2ZUZmeMvwBKVpsWSCFjsyecnyznpMlJv1FcOE3sOE3WNOwIalY8TnOgTdRbtQ1M90fWJHQ7hsYEcyjb8WvJYDyLDjicdotfFYpFKxERPzSH2V397uqAhhyG2T0kOJi2xNg7U8YJlSGdHb2XVEo/s5opRswYzL3y7Ymr7EoCMA1Xf76tkxXHGg29IrNGGnd//oGDyBtU30MaJ7AM7+uY3CrJJK9jsYekkLxt0O9zjrE0XVBsr0UMfNZxFtjYNL5csIU9UPTAQ13bDkEcqZjdT8Ho8VxFBrJBPYwR1AoFHtGCIFmRWH00+Dc7v1gd8m3wKEKOOtdWRL1g3Ph9eHw0cVg98LJz9Wu9AFQskHm5/9wD5z2qjTk3EFSazh7Eky5R5ZFnjcBKrdJX4svb4DeF0nvC4evdrrIwomyhGugREaIfH8nbJ4PSz+RfX+4B+u4e6TZ58bf5YQvqxfYd1OM1ObGOoC3C8uT3HBIs+7Ais0k1PRYDCVeHPlYFr5oIWLOa4hJ58t7Xrhq9/e7NsNh2efye9ykL3x1I9aqbwlpf7XArkJxBGFZ2MrWY3oz9tx2D1RFLF5ZZjIgAzK8fy36QiIw3EnolXn7YVsHntO6ZSAQPP7TmsYeikLxt0RFYBzCJGpFaMFSxOLJ8u3sDkrWy4f9tifI8nA7G/iBnFR1OxvMKIYthpKgUocViv2FV1Rgm3weIOCUF6UIodlk1Y9oCGY8C+un1nSIhqT4YJmy9OmC92rWpbaH2a9IgeGrm6DPJVJ8sCyZSlK0BlZ+VXcQRgS+vlmmmKyeAq2OlZVEtiyUgkSrodBsIMx8DsryIKkNVv+rMGw+bGe+LYWWBe/KN9cbf4fMHtDuBFg8qe6+ALPXhQT1eIgemDfZAT0eb7+r0GY+W2edNeAagjEtqIy4Dsi+FYcGmiaIpRhKSxFLPoLpT9Ws3LIQBlwD636R0UU7E9dEihbZfaSIOPtFqNiKefEPBEwPKvpCoZBoVVsR0QCG768LGG+uMKmKwOjm+0O8kFieVPSynP22vQOJz2njzB4ZvDZrIz+vLuTYNsmNPSSF4m+FisA4BHE5NZKcVWhzX0VYJsx5pW6j+e9CxRY4/XU5WRFC/tdqKFz0NVbRGvAkUmYol2SFYn9iDxfD1sXSL2Li2dJId/2vkNVbGmPuLF7szML3of3Imr9T2kkfC7F9glW+CX68T0ZuTDpPRkzkzW14IBtnQ/4yaHkMZPeTVUdS2kN6Z1mN6KubIb0rdBiDFZuN355CuUiC8s2Il46GaU9Kb45vbpHpKUltoMuZdXZjpXXG6DKeyB7EC13XcNlMnHb5U7Tzcl3f/a0maDgI97kGc8Qj4N3+IOhLxTzhMUI9rlDixRGM06HhcVrEa6XYV34my+jOeKZ2o+lPg+6EM9+SURYgRcOOY2X536VfSCHux/uwUjtiXPozFa5mWMpkT6GoRi+R0QJmTNZf2k5BwOLtFRZDsyHJtf8EDNOXhlaeWzvV8hCmd5N4umXG8vCU1ZT6G6j4pVAoDggqAuMQwm2L4I3kw6x3EduWQkZX+Ta1+dGyVOLOLPsUnD5ZqaDrODjqOhmm7k7CdLiodDUjNi4Wq54SkAqFYt8RkV2MZIrWyv/aj4ZAacMdjXBN6dLmg2HQ/8GUf0G/q2QUxa6UbZSC5ML36q4DKTiU5WHZXIjJF8qJ3BfXyTSTHfz6GADmuZ8RtDz4KIWPL5XRHTvz4UUw4B9wzD+h53lYf7yFCFdidB2Pkd6T8nqqjVSfDyGIFcXoG2ehL5mE5YzF7HUJVlwThBFCFOWC0DDjmuHX4giZ9UeEVRg+7O3Pw91mJJoZwdTsBPTEPQonisMTjx7GE9oMv09AlGyQRrYdxkCwAkLltRuX5shUqGH3wzF3gKZJQcPuhhVfQ4ujoGgd1iVTCLiy8BuOw2UOpFAcNPSS1Via/S+beD63yETX4KSm+0+8ADB8WTiiQbSKzZix2ft12wcCIQRn98rivu9W8dCUVfxnTEeE2L/nRKFQ1I8SMA4R3HYTb+F8xPtnyfBwgLU/wsznZR58xVbYtrh2p/nvSAO/hBYgNCwhMHQPpeEYVNisQnFgsNyJ8u3vrqHs3/4Tzvmg4Y5CQGIrOO8T2DAdPrxYihpx2dB+FKz4snZ7uxuaD5IpYbtO6AAGXoflScaKzUZEg7DgHRhyqzT0jARkG82GNfRfBBM6YJoWerRUii31MfNZyOqBtfwrQiP+S9jUiBjaHqs2xItibO+dItNdAAHoSz7C6nkhouUxMONpiM1C6zYOn92LSOhC0Kw/oiIStYiQIDdiccBSVhSNi0uP4sn9HvHZVVSXqVnzI0x7Ci74UqY0bZ5fu9Pm+fDmKLj4O4jJhGCpvC9G/fDDvZhD7qDKnkEwqlImFYr6sBWtxPBlgqbv8zaWF1t8vt5iXBuBx76/BYwMEBq2ohWEDwMBAyDObefc3tm8NCOHTxdv5ZSufz09R6FQ7BklYDQymiawLAuvUYD4+LIa8WIHRhg+vUoaBr63S3h3QgssbyoIMLDhFwmEohpgoURgheLAENAT8Pa+BG3Xso7BEqxgOSKpdfVkfmes1sOwoiG03FmQ3UtGWCFg3htY/a6CvpfB3AkIMwpdz5QlWfPmwtkT4bNrpccGgMMLR98KvlSiKV2ImuBO7yKrnPiL4dRXZISFEcGKa0IkoS3+qDQzFOYe6idHQ5h9LqcybNsefr97AcFhs9DmvFbv8Yo/3oDWQ+W6zfNhxZdox9+LJzaboFAPeX9XNE3gNYoQX1xHnRq7oXL48noY+yL8r1/dzr5ULE+SvC9GQxCXjdX9XCI9L6fSUiVSFYrdYStajhnbZJ/7m5bFw/MMsrwwZP8UMqmN7sTwZaIXLIIWxx+AHRwYemTHMbhlIo//tIZO6TG0TfU19pAUiiMeJWA0AkKAT1Rg92+RFQgyekCwqOEycRVbwL1L+cKE5ljj3yfsTqbcv+9qukKh2DuChh1H///D7klCm/mcnHQ5fJj9riSa3AX9rInoE8+sERwAK6sXjHiEiObB1up4tKmPIPKXYsU3wzz6n4Ri2+K3PMSO6IQtVAwrv4PiDYgWRyOWfQb9r4bYDJmCIjRY8C7Wym+IjH0Lv+XFftqb2D4YB2t/kv85vJDWCePklyk3ah6mDGc8ujcZqgrrHphmw0rtSNCZifUnJ4IuowxtwVsNN1j9vYwiWfmN/Pvnh9CaDcQRm004vAcxRXFE4dX8OCNFsHUlwmaTIkR9bJ4vyw63PRFWfVOzPDYT6+xJRBwJVERcWO40qjU2A1TUoUKxGywTvXgFoban7PMmPltnsagIbu4u0LUD85bMSGiDffMsAkYY9MMnmuqM7pnklAS4+bOlvHVOT+I99sYekkJxRKMEjEYgXpSgf3Q+YkeI7KgnwZe+546X/gglG7BiMiAmnUpbGkG/emhTKA425YYPR7er8HQ6C2EEsWwu/Hoi4ahA033EnvMZtoqNiMIV4EtHlKyHV47B3vZEIkPvIzryJXQjiKk5CODd/ubYoizsQmiZ2LpcKiOzzCIcyz6VlYd2RgjMi74ngBfLsigVacSM+wRbYBuU50FcUyLuVCqtuFpGhn49EcdJT8DkC+ockzXkNgKebPwR558+DwILog1MRAGiQdB2epAzwoiSXERspz+9D8XhT6xegf3ne9AWT4LE1tJrZXcESmDEA3D0zVCSAzFpWL40ym2ZhEMmSqxQKPYOvXQ9WsSPEd9yn/oXBiz+u8BkQLqgXcKBC/GNpnTEkfsz9i1ziGQfdcD2s79x2DSuGNiMR35Yw02fLeX507vgsquXiwrFgUJVITnIuPUw+pQ7asQLgGC5rCBgb6BmvTMWHF6sFd9gNelHcUw3Cs1UgmH1EKdQNBbhqKCUJEr0LEqtJMJR+VBnmhbCCCLePQ1+ehAmnQ/f3QmhcrTFH2Bf+TlhPFQQT5XpqRP2blkQiRhEoyYVJBI951OszqdJ3w2A1I4Y531BlbdVdV/Lsii34ih2taUk7TiKna2pMGPrVGGIGkDr4zAu/A6r+WDwJEJmT4xxEwl2Pp+qvRAvAEKaD6v9qIYbtBoKeb/XPj7dgWGqHLe/Czabhn3l51K8AChZC2mdaDDPMaG5TKWc+T/46DKsvDkYzkTKtFTCEeXMqVDsC7b8BQAYcS32uq9lWTw410AAZ7Tav+PaFdOdjBHfEueqT8A8vK73JK+Dqwc1Z8W2Cv75xXIixuE1foXicEIJGAcZl1GKWPlV7YWrvpEixtE319vHGnYfpjuZQJ9/UBiJV3m+CsUhjM2mo634XBpp+ovqmH1qM57CbZb8qW2ZpkWJlUTl0MeIXDWXyNXzCJz1EaVxPRqs5rHH0pHOGEpjOuMfPYHQxVMJnPY+pSmDqTT3vuRy0LBjDroZXHF1V2b3ASMqjRZ3oNshtSPRqHqw+7vgMUrQZjxVs8CypFdLn8vrNhYaHHsHlicZY8D1RM79jKp+t1Jib0LEUI8rCsW+Yt/2B4YvC8ux9/4Mn6+3+HkTnN1WEOM48OJzuNlQ9LJcnKs/PeD72t+0SPJw5VHNmZ1Twk2fLiUYUamSCsWBQD0RHGwigbo1rnNngdMr89tPexWa9gdvCjQ7CuvMdzBbDqUo5KEqrMLRFIpDHU0TiKJ1DTeo2IIm9m4CHzQclJJMqUij0oz5yyKmZVn4TRflVgKVpvcvba9MT8e45CfMXhdDTDoktsQa/iAMuBa+u732fofdT5We9JfGrji8EJhQubX2wvfOkKa1o5+GjO7gS4U2w2Dce1CSS8iVRrGZSKmZQMCw1/H6VCgUe4d98+9EE9vsdb/15RaPzDMZmC7olXpwIueMmCzCTQbjWjYRe970g7LP/UnH9BiuHtScPzaWcdnEhWwtDzb2kBSKIw7lgXGQsRw+cMZAqKL2ik+vhjHPYVUVIDqcDN5krJgMovEtKTNiG2ewCoVirzEME7PlMegL362/QWZ3DA4fc7I9YZhQTBrOQffi6H8jCI2ILQZPcBNax1MQm+dhxTXFOuoGgrGtCRjK3OzvhCEcUqTYOW0y4of3z+L/27vvsCiu/X/g790FpCxSRYOFEJMFEaSoF0GIvUWxxBg1it5I7FGjqGgwEQFFY4xKNNFgIVhiBaKg1+hNNBJF9AZLbLFTDbAqCAvLlvP7wy/zcwVkRdjG5/U8PjozZ2bOZ/acOWePM2fx/lawXp+DV/4YKC2AUqEEv/NElFaYaS2/hBgaXsVjCMQ3UNmu1yvtVyZjCE1TwLoZMPbVxz5eS6VTT/ClxTC/EIsyvgnkjl01m4HX5NbKEqG922PzH/fxUcKfmN+7PQZ1cACPfiKQkAZBAxgaJhHYwjIgFPz/RrywQQyWvhGVw+OhUMgB8CDlW9Kj1oToGYVCCWVrXwiELYHSf6pv7xMFCYQwtIkIpXI+pLB+FpYMqBC0QbN3l8NYKYGC3wwVymZgCsOKmdRNAiFM+kZCkBCkukF8B9gzFoqQX1Ep54HxBJDyzGErFAIVT2s+GCHklRnn/AEeGGQt3NXeR65kWHRWgXwJsNiHB1MjTX/x5qHinaEwVcpgcWEtSv2/gMK+g4bz8Hra2Zjh837v4Kc/c7H06E0cvJSPqf5O6NrOmgYyCHlN9AqJhlXKeZC6jYGyXzRgav1spcAYzHMs5EM3o0RmijKlEGVKCxq8IERPlfDsIJ+QAubc4/+vtGoL5eifILFybTLz2EgVApQyS5QrTOqem4MYJKWSQWLlBuXo3YBVG249c+4BefBhFCuao4wJIVGaNZl6QYgmNbt/AormTmDm9mqlVzKGqPNK/JEPTHHj4Q0LLX3Z5vFRIXofCmFrWJz7Crwa/kNA11k0M8Infk6Y/a4znpTLMPPAFXwYfwFb0x/gZkEplNQuElIv9ASGFpQqhZB2+DfMXYLAl0nAjExRIbBGBT1aTYhBUCoZnvDfgNmQLWimKAEUMiiMhZAIbGlgkjQ5FcpmkLfqCfPg/0AgKwUExpAKrFDOTMFo0IKQxqOohMm9X1D5Zl/1kiufDV4cuscwqQMP7nZaflKAL0BFh9EwuxQHi/RVKO2xHDDWv1fM3FpZokNLIW4WlOGPe4+w/Vw2Nv3xAMJmAri1soRLCyHesjeHs50FnGzMIGxGX88IeRmqIVoiUwDFsAOM/m9CO5qomBCDwhggYWaQ8Mye3WkZABq8IE2UXK5ECawBI+tnK6gqENLoTO6fAL+yBJVtuteZtlTG8PlZBdLygX934KFbK914zYEZmaKiw1iYX9oK8wvrIfFdCPD17wFyHo8H15ZCuLYUQqZQ4q5YgtuFZXjwWIIj1wsgLqvk0rawMMFb9uZob28BFwchXByEeNPWHAK+bnwmhGgbDWAQQgghhBBiYMwub4PcRgRl87YvTXf10bPBiwIJ8KkHDx7afvLiBUpze5S7joTZtZ9gdnkbyj1DAD2eR8JYwOcGJqpUyBT456kUD59KkV8iRX5xBY7fKMTu/+UCAEyN+OjQyhIebzRHJ0dLeDg2h6254UwITsiroAEMQgghhBBCDIhxzh8wyUtH2b9Ca03zqIIh7qoSe28xtLUEPu/CQytz3RwYUNi8DWn7wWh2+zAAhnKPjwGB4XyNMTUWwMnWHE625irryysVyHpSjvuPJLgvluDQX/lIOJ8NAHBsbgoPR0u4tbKEW0tLvONgAQsTw7kmhNSGSjkhhBBCCCEGgicthvC3hZDbukD2hupPkDLGcO0xkHxXicP3GHg84P32PPRpAxjp+CsKslY+AHhodicFgkc3UPlmfzATS/AkBRBI/gEEPDQzbQlZq65QWjpqO7sNwsxEoPK0BmMMjyUy3BVLuNdQfv27CLL/m0+otZUp2ttb4C07c7i1tYGVEdDK0hQOQhMYCTT36g1jDFVTHPF5oF9eIQ2KBjAIIYQQQggxAALxdVie+Az8cjGevrsCj6Q8ZJcy3HrCcEXMcO4fhocSwNoE6NcW6N2GB6Gx/ny5lLXyhlLYEiYPfoXZ5W0AUwICUyjMbAEjI5g+PQXTv3ZCbu+GSucBkLX0AYxNtZ3tBsPj8WBrYQJbCxN0aWcN4Nnkq/klFch6XI7c4grkl1Tgan4J4jOyVfa1MjWCjbkxrEyN0dzUCOYmApgaCWBixIexgAc+j4eqMSzGACV79qs0SsYgUzDIFEpUKpSoVDBIZQpUyJWokClQLleiXKaAVKaEVP4sjfyFCZqN+TyYGgtgYSKAsNmzfNiYGcPWwgR25sawF5rA1twEduYmz/JoZoxmRvo31wnRDBrAeAX6NHhYlVd9yrM20fVSnzaulTY+F0MsExST7tO1eLRRz3Ul9oZEseknbcZW1znT7z/Gpweu1LI1/NlfqUBNs8TbCSToYCRGcQFDUsHr5FKbAgCj7uAxJRhPAFQCfBkPSr4SfNkT4KESeHgLwK3/vwtfAFkrHzATS21lWiOszZ4NDjA+Hw+flOORRIYn5TIUV8hRXCEHUK7xPMmUDDKpHE+lcuCptFHOMdmvHaZ2f7PGbYZ8n2qKeIzRjxATQgghhBCiL5IyczB37yW10jbjyeFs/BimPHkj50rHyKWAUqa6zl4EmFppJz+kVpVyJcSlUhSVVUJRz5/XHu7liHVjvBs4Z0QX0QAGIYQQQgghhBBCdB69XEQIIYQQQgghhBCdRwMYhBBCCCGEEEII0Xk0gEEIIYQQQgghhBCdRwMYhBBCCCGEEEII0Xk0gEEIIYQQQgghhBCdRwMYhBBCCCGEEEII0Xk0gEEIIYQQQgghhBCdRwMYhBBCCCGEEEII0Xk0gKHnzp8/j2nTpiEgIAAuLi44ceKEynbGGNavX4+AgAB06tQJ//73v3H//n3tZFbLNm/ejJEjR8Lb2xt+fn6YMWMG7t69q5JGKpVi2bJl8PX1hbe3N2bNmoWioiIt5Vh7du/ejaCgIPj4+MDHxwejR4/GqVOnuO2GdJ0MrQ4ZYjk39PL4ww8/wMXFBcuXL+fW6XtMtfn222/h4uKi8mfgwIHcdn2KuyHuHU+ePEFoaCh8fHzQpUsXfP755ygrK9NgFDWrK7ZFixZV+xxDQkJU0uhqbA11j8zLy8OUKVPg6ekJPz8/rFq1CnK5XGNx7Nq1C71794aHhwdGjRqFy5cva+zcjUVTderGjRv46KOP4OHhgR49eiAuLq6xQ6sXTZbVc+fOYcSIEXB3d0e/fv2QmJjY6PG9qoboCxjKtWjqaABDz0kkEri4uGDp0qU1bo+Li8OOHTsQERGBffv2wczMDCEhIZBKpRrOqfZlZGRg3Lhx2LdvH7Zv3w65XI6QkBBIJBIuzYoVK/Dbb79h3bp12LFjBwoKCvDpp59qMdfa0apVK8yfPx+JiYk4ePAgunXrhpkzZ+LWrVsADOs6GVodMsRybsjl8fLly9izZw9cXFxU1utzTHV55513kJaWxv3ZvXs3t02f4m6Ie8f8+fNx+/ZtbN++HZs2bcKFCxfw5ZdfaiqEWtUVGwAEBgaqfI7ffPONynZdja0h7pEKhQJTp06FTCbDnj17sHLlSiQlJSE2NlYjMRw5cgQxMTGYOXMmkpKS4OrqipCQEIjFYo2cv7Fook6VlpYiJCQEjo6OSExMxMKFC7Fhwwbs3bu30eN7VZoqq9nZ2Zg6dSp8fX3x888/Y+LEiViyZAlOnz6t0Xjr8rp9AUO6Fk0eIwZDJBKx48ePc8tKpZJ1796dbdmyhVtXUlLC3N3dWUpKijayqFPEYjETiUQsIyODMfbs2nTs2JEdPXqUS3P79m0mEolYZmamlnKpO7p27cr27dtn0NfJEOuQoZZzQyiPpaWlrH///uyPP/5g48ePZ9HR0Ywxw/mMahIbG8uGDh1a4zZ9jrs+946q2C5fvsylOXXqFHNxcWEPHz7UXObr8GJsjDEWFhbGpk+fXus++hIbY/W7R548eZK5urqywsJCLs3u3buZj48Pk0qljZ7nDz74gC1btoxbVigULCAggG3evLnRz60pjVWndu3axbp27aryOa1evZoNGDCgsUN6bY1VVr/66is2ePBglXN99tlnbNKkSY0c0et7lb6AoV+LpoSewDBgOTk5KCwshL+/P7fO0tISnp6eyMzM1GLOdMPTp08BAFZWVgCAv/76CzKZTOV6tW/fHo6Ojrh48aI2sqgTFAoFUlNTIZFI4O3t3aSukyHUIUMr54ZUHiMjI9GjRw+VvAP6/xnV5cGDBwgICECfPn0QGhqKvLw8AIYVtzr3jszMTDRv3hweHh5cGn9/f/D5fL14HSAjIwN+fn4YMGAAli5disePH3Pb9Cm2+twjL168CJFIBHt7ey5NQEAASktLcfv27UbNb2VlJa5evaqSPz6fD39/f71pl+qjoerUxYsX0aVLF5iYmHBpAgICcO/ePRQXF2somvpprLJ68eJF+Pn5qZwrICBAp++79ekLGOq1aIqMtJ0B0ngKCwsBAHZ2dirr7ezsdPadYk1RKpVYsWIFfHx8IBKJAABFRUUwNjZG8+bNVdLa2dlx17IpuXnzJsaMGQOpVApzc3Ns3LgRb7/9Nq5fv95krpO+1yFDKueGVh5TU1Nx7do1HDhwoNo2ff2M1NGpUyfExMTA2dkZhYWF2LhxI8aNG4fDhw8bVNzq3DuKiopga2urst3IyAhWVlY6H29gYCD69euHNm3aIDs7G9988w0mT56MvXv3QiAQ6E1s9b1HFhUVqXwJAsAtN3Z8jx8/hkKhqLFsvTg/giFpqDpVVFSENm3aqKSp+uyKioq4wQFd05hltbY0paWlqKiogKmpaaPEVB+v0xcwtGvRlNEABmmSli1bhlu3bqm8e01UOTs7Izk5GU+fPsWxY8cQFhaGnTt3ajtb5BUYUjk3pPKYn5+P5cuXY9u2bWjWrJm2s6NRPXr04P7t6uoKT09P9OrVC0ePHqWOoR4ZPHgw9++qSTz79u3LPZWhLwzpHkkMG5XVZwypL0Dqj14hMWAtWrQAgGqTOonF4mqji01JZGQkTp48iR9//BGtWrXi1tvb20Mmk6GkpEQlvVgs5q5lU2JiYgInJye4u7sjNDQUrq6uSEhIaFLXSZ/rkKGVc0Mqj1evXoVYLMb7778PNzc3uLm5ISMjAzt27ICbm5texlRfzZs3x5tvvomsrCyDilude4e9vT0ePXqksl0ul6O4uFjv4m3bti1sbGzw4MEDAPoR2+vcI+3t7as9hVe13Njx2djYQCAQ6GW79Doaqk697LPT1evX2GW1tjRCoVDnBpZfpy9gaNeiKaMBDAPWpk0btGjRAmfPnuXWlZaW4tKlS/D29tZizrSDMYbIyEgcP34cP/74I9q2bauy3d3dHcbGxirX6+7du8jLy4OXl5eGc6t7lEolKisrm9R10sc61FTKuT6Xx27duuHw4cNITk7m/ri7uyMoKIj7t77FVF9lZWXIzs5GixYtDCpude4d3t7eKCkpwV9//cWlSU9Ph1KpRKdOnTSe59fx8OFDPHnyhPsSoMuxNcQ90svLC3///bfKl+kzZ85AKBTi7bffbtT8m5iYoGPHjir5UyqVOHv2rM62Sw2hoeqUl5cXLly4AJlMxqU5c+YMnJ2dde71EU2VVS8vL6Snp6sc+8yZM3px332VvoChX4umhF4h0XNlZWXIysrilnNycnD9+nVYWVnB0dEREyZMwPfffw8nJye0adMG69evh4ODA/r27avFXGvHsmXLkJKSgu+++w4WFhbc+26WlpYwNTWFpaUlRo4ciZUrV8LKygpCoRDR0dHw9vZucjeuNWvW4N1338Ubb7yBsrIypKSkICMjA1u3bjW462RodcgQy7mhlUehUMi9w1zF3Nwc1tbW3Hp9i0ldq1atQq9eveDo6IiCggJ8++234PP5GDJkiN59lq9772jfvj0CAwPxxRdfYNmyZZDJZIiKisLgwYPRsmVLbYUF4OWxWVlZYcOGDRgwYADs7e2RnZ2N1atXw8nJCYGBgQB0O7aGuEcGBATg7bffxsKFC7FgwQIUFhZi3bp1GDdunMrkkI3l448/RlhYGNzd3dGpUyf8+OOPKC8vx/vvv9/o525MmqhTQUFB2LhxI8LDwzF58mTcunULCQkJWLx4sVZifhlNldUxY8Zg165d+OqrrzBy5Eikp6fj6NGj2Lx5s7ZCr9Hr9gUM6Vo0dTzGGNN2Jkj9nTt3DhMmTKi2fsSIEVi5ciUYY4iNjcW+fftQUlKCzp07Y+nSpXB2dtZCbrXLxcWlxvUxMTFcoy+VSrFy5UqkpqaisrISAQEBWLp0qc488qopn3/+OdLT01FQUABLS0u4uLhg8uTJ6N69OwDDuk6GVocMsZw3hfIYHBwMV1dXhIeHAzCMmGoyd+5cnD9/Hk+ePIGtrS06d+6MuXPnol27dgD0K+6GuHc8efIEUVFR+PXXX8Hn89G/f38sWbIEFhYWmgylmpfFFhERgZkzZ+LatWt4+vQpHBwc0L17d8yZM0flEXxdja2h7pG5ubmIiIhARkYGzMzMMGLECISGhsLISDP/N7hz505s3boVhYWF6NChA5YsWQJPT0+NnLuxaKpO3bhxA5GRkbhy5QpsbGwwfvx4TJkyRSMxvgpNltVz584hJiYGt2/fRqtWrTBjxgydGxBriL6AoVyLpo4GMAghhBBCCCGEEKLzaA4MQgghhBBCCCGE6DwawCCEEEIIIYQQQojOowEMQgghhBBCCCGE6DwawCCEEEIIIYQQQojOowEMQgghhBBCCCGE6DwawCCEEEIIIYQQQojOowEMQgghhBBCCCGE6DwawCCEEEIIIYQQQojOowEMonGJiYno0qULt/ztt99i2LBhWswRIYQQUj+LFi3CjBkztJ0NQgghpEkw0nYGCJk0aRLGjx+v7WwQojOCg4Ph6uqK8PBwnT4mIQQIDw8HY6zRz0N1mBBCCKEnMMhrqKysbJDjWFhYwMbGpkGORQhpXA1V7wnRdwqFAkqlEpaWlmjevLm2s6M2qsOkKZHJZNrOAiGkgdEABlFbcHAwIiMjsXz5cvj6+iIkJATbt29HUFAQvLy80KNHD0RERKCsrExlv8TERPTs2ROenp6YOXMmnjx5orL9xVdIgoODsXz5cpU0M2bMwKJFi7jlXbt2oX///vDw8IC/vz9mz56tdgxRUVFYvnw5unbtCn9/f+zbtw8SiQSLFy+Gt7c3+vXrh1OnTqns9/fff+OTTz6Bt7c3/P39sWDBAjx69Ijb/vvvv2Ps2LHo0qULfH19MXXqVGRlZXHbc3Jy4OLigl9++QXBwcHw9PTE0KFDkZmZqVa+SdOxaNEiZGRkICEhAS4uLnBxcUFOTs5Ly+C5c+fg7u6OCxcucMeJi4uDn58fioqKaj3mi69zAcCJEyfg4uLCLVfVz/3796N3797o1KkTAKCkpATh4eHo1q0bfHx8MGHCBNy4cUOtGKuOeeDAAfTs2RPe3t6IiIiAQqFAXFwcunfvDj8/P3z//fcq+9V1zqysLEyfPh3+/v7w9vbGyJEjcebMGZVj9O7dG5s2beLqe8+ePbF371618k30W1UbFhkZic6dO8PX1xfr1q3jnp6orKzEqlWrEBgYCC8vL4waNQrnzp3j9q+qL//973/x3nvvwcPDA3l5edVeIWmMdqa2OlzXfs/H/XzbXRcXFxfs2bMHU6dOhaenJwYNGoTMzEw8ePAAwcHB8PLywpgxY1TaOeDZ/WPEiBHw8PBAnz59sGHDBsjlcm57XX2Gqmt8+vRpDBo0CN7e3ggJCUFBQUGdeSZNw8v6W1V9rSNHjmD8+PHw8PDA4cOHAQD79+/HoEGD4OHhgYEDB2LXrl0qx129ejUGDBgAT09P9OnTB+vWrVN78IPaNEI0iwYwyCtJSkqCsbExfvrpJyxbtgw8Hg/h4eFISUnBypUrkZ6ejtWrV3PpL126hPDwcIwbNw7Jycnw9fWtdgN/VVeuXMHy5csxe/Zs/Oc//8GWLVuqfQmrKwYbGxvs378f48ePR0REBObMmQNvb28kJSWhe/fuWLhwIcrLywE8a2AmTpwINzc3HDhwAFu2bIFYLMZnn33GHbO8vBwff/wxDh48iPj4ePB4PMycORNKpVLl3GvXrkVISAiSk5Px5ptvIjQ0VKVzR0h4eDi8vb3x4YcfIi0tDWlpabCwsHhpGfT19cWECROwcOFCPH36FNeuXcP69esRHR0Ne3v7Go/5xhtvqJ2nrKwsHDt2DBs2bEBycjIAYM6cORCLxYiLi0NiYiI6duyIiRMnVhugfNkxf//9d2zZsgVr1qzBgQMHMGXKFPzzzz/YsWMH5s+fj3Xr1uHSpUvcPnWdUyKRoEePHoiPj0dSUhICAwMxbdo05OXlqZx7+/btcHd3R3JyMj766CNERETg7t27al8Por+SkpIgEAiwf/9+hIeHIz4+Hvv37wcAREZGIjMzE2vXrsWhQ4cwcOBAfPLJJ7h//z63f0VFBeLi4hAdHY2UlBTY2dnVep6GbGdqq8PqtE9V+Xm+7VbHd999h2HDhiE5ORlvvfUWQkND8eWXX2LKlCk4ePAgGGOIjIzk0l+4cAFhYWGYMGECjhw5gsjISCQmJmLTpk1cmrr6DFXXeNu2bfjqq6+wc+dO5OfnY9WqVWrlmRg+dfpbX3/9NVcOAwICcOjQIaxfvx5z587FkSNHMG/ePMTGxiIpKYnbx8LCAjExMUhNTUV4eDj279+P+Ph4tfNFbRohGsQIUdP48ePZ8OHDX5rm6NGj7F//+he3PG/ePDZ58mSVNJ999hnr3LkztxwbG8uGDh2qcp7o6GiVfaZPn87CwsIYY4wdO3aM+fj4sKdPn9YrhrFjx3LLcrmceXl5sQULFnDrCgoKmEgkYpmZmYwxxjZu3MgmTZqkcpz8/HwmEonY3bt3azyPWCxmIpGI3bx5kzHGWHZ2NhOJRGzfvn1cmlu3bjGRSMRu3779ynEQw/ZiHVCnDEqlUjZs2DA2Z84c9t5777ElS5a89JiMMXbw4EGVusgYY8ePH2cikYhbjo2NZR07dmRisZhbd/78eebj48OkUqnKvn379mV79uypM77Y2Fjm6empUocnTZrEevXqxRQKBbduwIABbPPmza91zsGDB7MdO3Zwy7169WLz58/nlpVKJfPz82O7d++uM99Ev40fP54NGjSIKZVKbt3q1avZoEGDWG5uLuvQoQN7+PChyj4TJ05ka9asYYw9qy8ikYhdv35dJU1YWBibPn26ynkao52pqQ6ru19dbfeLRCIRW7t2LbecmZnJRCIR279/P7cuJSWFeXh4cMsTJ05kmzZtUjlOcnIy6969e63nebHPUHWNHzx4wK3buXMn8/f3f6X8k6bj+f5WVV8rPj5eJU3fvn3Z4cOHVdZt3LiRjR49utbjbtmyhY0YMUKtPFCbRohm0SSe5JV07NhRZfnMmTPYvHkz7t69i9LSUigUCkilUpSXl8PMzAx37txB3759Vfbx8vLC6dOn650Hf39/ODo6om/fvggMDERgYCD69esHMzMztfZ//vF4gUAAa2triEQibp29vT0AQCwWAwBu3LiBc+fOwdvbu9qxsrKy4OzsjPv37yM2NhaXLl3C48ePuUeS8/PzVY79/LlbtGgBAHj06BHat2+vbvikCVKnDJqYmODrr7/G0KFD4ejoiMWLFzfY+R0dHWFra8st37x5ExKJBL6+virpKioqqj1SXpvWrVtDKBRyy/b29hAIBODz+SrrquqhOucsKyvDhg0bcPLkSRQWFkKhUKCioqLa/1Y9Xw95PJ7KeYhh8/T0BI/H45a9vLywfft2/P3331AoFBg4cKBK+srKSlhbW3PLxsbGKuWnNo3RztRE3f1ebLvV8XwMVU+aPB+DnZ0dpFIpSktLIRQKcePGDfz5558qT1y82Ceoq88AAGZmZmjXrh13DAcHB6qfhPOy/lZVX8rd3Z1LL5FIkJWVhfDwcHzxxRfcerlcDktLS275yJEjSEhIQHZ2NiQSCeRyuUobVRdq0wjRHBrAIK/k+UGCnJwcTJ06FWPHjsXcuXNhZWWF//3vfwgPD4dMJlN7QOFFPB6v2ozuz79mIRQKkZSUhIyMDKSlpSE2NhYbNmzAgQMH1JpIzchItdjzeDyVdVWd26o8SCQS9OrVC/Pnz692rKpBiGnTpqF169aIjo6Gg4MDlEolhgwZUu39SWNj42rnefE1E0JepE4ZBMDNqVJcXIzi4mKYm5u/9Lh8Pr9aXavpnd8X63JZWRlatGiBHTt2VEv7fIfwZeqqh1XrquqHOudctWoVzpw5g7CwMLRr1w6mpqaYPXt2tZhqOs+L14E0LRKJBAKBAAcPHoRAIFDZ9nw9MjU1VRkAqU1jtDO15Vud/erTHtfUXr2sDZNIJJg1axb69+9f7VjNmjVTu89A9ZO8jDr9refrrEQiAQBERUXB09NT5VhVgwuZmZmYP38+Zs2ahYCAAFhaWiI1NRXbt29XO1/UphGiOTSAQert6tWrYIxh0aJFXCNw9OhRlTTt27fH5cuXVdY9//5fTWxtbVFYWMgtKxQK3Lp1S2WU2sjICP7+/vD398enn36Krl27Ij09vcaO0+vq2LEjjh07htatW1drJADg8ePHuHfvHqKjo7m5OJ6fTJGQV2VsbKwysFVXGQSe/W/rihUrEBUVhSNHjiAsLAzx8fFc3XzxmABgY2ODsrIySCQSrsOnzkScHTt2RFFREQQCAdq0aVPfMF+JOufMzMzEiBEj0K9fPwDPOoi5ubkayR/RDzW1R05OTujQoQMUCgUePXr0SnMqNRR16nhNdVid/TTFzc0N9+7dg5OTU43b1ekzEPIy9elv2dvbw8HBAdnZ2Rg6dGiNaTIzM+Ho6Ijp06dz6158yqGhUZtGSP3RJJ6k3pycnCCTybBjxw5kZ2cjOTkZe/bsUUkTHByM06dPY+vWrbh//z527txZ5+sj3bp1w6lTp3Dy5EncuXMHERERKCkp4bb/9ttvSEhIwPXr15Gbm4vk5GQolcpaH7F9XR999BGKi4sxb948XL58GVlZWTh9+jQWL14MhUIBKysrWFtbY+/evXjw4AHOnj2LlStXNkpeSNPQunVrXLp0CTk5OXj06FGdZVChUGDBggUIDAzEyJEjERMTg5s3b2Lbtm21HlOpVMLT0xNmZmb45ptvkJWVhcOHDyMxMbHO/Pn7+8PLywszZ85EWloacnJy8Oeff2Lt2rW4cuVKo1wTdc7p5OSE48eP4/r167hx4wZCQ0PpCSeiIi8vDzExMbh79y5SUlKwc+dOTJgwAc7OzggKCsLChQvxyy+/IDs7G5cvX8bmzZtx8uTJRs9XXXUcqLkOq7OfpsycORM///wzNmzYgFu3buHOnTtITU3F2rVrAajXZyDkZerb35o9ezZ++OEHJCQk4N69e7h58yYOHjzIPWHh5OSE/Px8pKamIisrCwkJCThx4kSjxkJtGiH1RwMYpN5cXV2xePFixMXFYciQITh8+DDmzZunksbLywtRUVFISEjAsGHDkJaWpjLCXZORI0di+PDhCAsLQ3BwMNq2bavy9IWlpSWOHz+OiRMn4r333sOePXuwZs0avPPOO40SZ8uWLfHTTz9BqVQiJCQEQUFBWLFiBSwtLcHn88Hn87F27VpcvXoVQ4YMQUxMDBYuXNgoeSFNw6RJkyAQCDB48GD4+flBJpO9tAx+//33yM3N5X5dwMHBAVFRUVi3bh33RMWLx8zLy4O1tTVWr16N33//HUFBQUhNTcWsWbPqzB+Px8MPP/yArl27YvHixRg4cCDmzZuH3Nxc7t3+hqbOORctWoTmzZtjzJgxmDZtGgIDA+v17j8xXMOHD0dFRQVGjRqFyMhITJgwAaNHjwYAxMTEYPjw4Vi5ciUGDRqEGTNm4MqVK6/0iz31VVc7A9Rch9XZT1MCAwOxadMmpKWl4YMPPsCHH36I+Ph4tG7dGoB6fQZCXqa+/a1Ro0YhOjoaiYmJCAoKQnBwMJKSkrgnH/r06YOJEyciMjISw4YNQ2ZmZp191ddFbRoh9cdj9JIUIYQQQgxccHAwXF1dER4eru2sEEIIIaSe6AkMQgghhBBCCCGE6DyaxJMYjLy8PAwePLjW7ampqXB0dNRgjghpmgYPHlzrBGjLli2rdSI1QkjjO3ToEJYuXVrjNkdHR6Smpmo4R4ToNmrTCNEt9AoJMRhyufylszPrwizthDQFubm5Kj99/Dw7OzsIhUIN54gQUqW0tBRisbjGbUZGRtycFYSQZ6hNI0S30AAGIYQQQgghhBBCdB7NgUEIIYQQQgghhBCdRwMYhBBCCCGEEEII0Xk0gEEIIYQQQgghhBCdRwMYhBBCCCGEEEII0Xk0gEEIIYQQQgghhBCdRwMYhBBCCCGEEEII0Xk0gEEIIYQQQgghhBCdRwMYhBBCCCGEEEII0Xn/D+p2a+raHzJbAAAAAElFTkSuQmCC\n" }, "metadata": {} } ] }, { "metadata": { "trusted": true, "id": "J2uhUR00FWX5", "outputId": "5e40f325-e33d-457e-d28b-0687e9966c57", "colab": { "base_uri": "https://localhost:8080/", "height": 449 } }, "cell_type": "code", "source": [ "size = len(data['texture_mean'])\n", "\n", "area = np.pi * (15 * np.random.rand( size ))**2\n", "colors = np.random.rand( size )\n", "\n", "plt.xlabel(\"texture mean\")\n", "plt.ylabel(\"radius mean\")\n", "plt.scatter(data['texture_mean'], data['radius_mean'], s=area, c=colors, alpha=0.5);" ], "execution_count": 42, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} } ] }, { "metadata": { "id": "EdmdKh_uFWX5" }, "cell_type": "markdown", "source": [ "### Data Filtering\n" ] }, { "metadata": { "id": "mSmOrqisFWX6" }, "cell_type": "markdown", "source": [ "- Now, we have one categorical feature, so we need to convert it into numeric values using `LabelEncoder` from `sklearn.preprocessing` packages" ] }, { "metadata": { "trusted": true, "scrolled": true, "id": "rWgEptSNFWX6" }, "cell_type": "code", "source": [ "from sklearn.preprocessing import LabelEncoder" ], "execution_count": 43, "outputs": [] }, { "metadata": { "trusted": true, "id": "2WJ3WrWmFWX6", "outputId": "b474060f-03a2-4444-edaa-a35fde69597f", "colab": { "base_uri": "https://localhost:8080/", "height": 149 } }, "cell_type": "code", "source": [ "data.head(2)" ], "execution_count": 44, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " id diagnosis radius_mean texture_mean perimeter_mean area_mean \\\n", "0 842302 M 17.99 10.38 122.8 1001.0 \n", "1 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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "data" } }, "metadata": {}, "execution_count": 46 } ] }, { "metadata": { "trusted": true, "id": "CGb_TOwyFWX7", "outputId": "051e832e-4277-4729-8d9e-b8b314416b8c", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "print(data.diagnosis.value_counts())\n", "print(\"\\n\", data.diagnosis.value_counts().sum())" ], "execution_count": 47, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "diagnosis\n", "0 357\n", "1 212\n", "Name: count, dtype: int64\n", "\n", " 569\n" ] } ] }, { "metadata": { "id": "RhCkPl30FWX7" }, "cell_type": "markdown", "source": [ "Finnaly, We can see in this output categorical values converted into 0 and 1." ] }, { "metadata": { "id": "DU5ydaQYFWX7" }, "cell_type": "markdown", "source": [ "#### Find the correlation between other features, mean features only" ] }, { "metadata": { "trusted": true, "id": "jZWerv2JFWX7", "outputId": "8b20c761-48ec-4e77-b98e-3d5b740baec5", "colab": { "base_uri": "https://localhost:8080/", "height": 449 } }, "cell_type": "code", "source": [ "cols = ['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n", " 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n", " 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean']\n", "print(len(cols))\n", "data[cols].corr()\n" ], "execution_count": 48, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "11\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " diagnosis radius_mean texture_mean perimeter_mean \\\n", "diagnosis 1.000000 0.730029 0.415185 0.742636 \n", "radius_mean 0.730029 1.000000 0.323782 0.997855 \n", "texture_mean 0.415185 0.323782 1.000000 0.329533 \n", "perimeter_mean 0.742636 0.997855 0.329533 1.000000 \n", "area_mean 0.708984 0.987357 0.321086 0.986507 \n", "smoothness_mean 0.358560 0.170581 -0.023389 0.207278 \n", "compactness_mean 0.596534 0.506124 0.236702 0.556936 \n", "concavity_mean 0.696360 0.676764 0.302418 0.716136 \n", "concave points_mean 0.776614 0.822529 0.293464 0.850977 \n", "symmetry_mean 0.330499 0.147741 0.071401 0.183027 \n", "fractal_dimension_mean -0.012838 -0.311631 -0.076437 -0.261477 \n", "\n", " area_mean smoothness_mean compactness_mean \\\n", "diagnosis 0.708984 0.358560 0.596534 \n", "radius_mean 0.987357 0.170581 0.506124 \n", "texture_mean 0.321086 -0.023389 0.236702 \n", "perimeter_mean 0.986507 0.207278 0.556936 \n", "area_mean 1.000000 0.177028 0.498502 \n", "smoothness_mean 0.177028 1.000000 0.659123 \n", "compactness_mean 0.498502 0.659123 1.000000 \n", "concavity_mean 0.685983 0.521984 0.883121 \n", "concave points_mean 0.823269 0.553695 0.831135 \n", "symmetry_mean 0.151293 0.557775 0.602641 \n", "fractal_dimension_mean -0.283110 0.584792 0.565369 \n", "\n", " concavity_mean concave points_mean symmetry_mean \\\n", "diagnosis 0.696360 0.776614 0.330499 \n", "radius_mean 0.676764 0.822529 0.147741 \n", "texture_mean 0.302418 0.293464 0.071401 \n", "perimeter_mean 0.716136 0.850977 0.183027 \n", "area_mean 0.685983 0.823269 0.151293 \n", "smoothness_mean 0.521984 0.553695 0.557775 \n", "compactness_mean 0.883121 0.831135 0.602641 \n", "concavity_mean 1.000000 0.921391 0.500667 \n", "concave points_mean 0.921391 1.000000 0.462497 \n", "symmetry_mean 0.500667 0.462497 1.000000 \n", "fractal_dimension_mean 0.336783 0.166917 0.479921 \n", "\n", " fractal_dimension_mean \n", "diagnosis -0.012838 \n", "radius_mean -0.311631 \n", "texture_mean -0.076437 \n", "perimeter_mean -0.261477 \n", "area_mean -0.283110 \n", "smoothness_mean 0.584792 \n", "compactness_mean 0.565369 \n", "concavity_mean 0.336783 \n", "concave points_mean 0.166917 \n", "symmetry_mean 0.479921 \n", "fractal_dimension_mean 1.000000 " ], "text/html": [ "\n", "
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diagnosisradius_meantexture_meanperimeter_meanarea_meansmoothness_meancompactness_meanconcavity_meanconcave points_meansymmetry_meanfractal_dimension_mean
diagnosis1.0000000.7300290.4151850.7426360.7089840.3585600.5965340.6963600.7766140.330499-0.012838
radius_mean0.7300291.0000000.3237820.9978550.9873570.1705810.5061240.6767640.8225290.147741-0.311631
texture_mean0.4151850.3237821.0000000.3295330.321086-0.0233890.2367020.3024180.2934640.071401-0.076437
perimeter_mean0.7426360.9978550.3295331.0000000.9865070.2072780.5569360.7161360.8509770.183027-0.261477
area_mean0.7089840.9873570.3210860.9865071.0000000.1770280.4985020.6859830.8232690.151293-0.283110
smoothness_mean0.3585600.170581-0.0233890.2072780.1770281.0000000.6591230.5219840.5536950.5577750.584792
compactness_mean0.5965340.5061240.2367020.5569360.4985020.6591231.0000000.8831210.8311350.6026410.565369
concavity_mean0.6963600.6767640.3024180.7161360.6859830.5219840.8831211.0000000.9213910.5006670.336783
concave points_mean0.7766140.8225290.2934640.8509770.8232690.5536950.8311350.9213911.0000000.4624970.166917
symmetry_mean0.3304990.1477410.0714010.1830270.1512930.5577750.6026410.5006670.4624971.0000000.479921
fractal_dimension_mean-0.012838-0.311631-0.076437-0.261477-0.2831100.5847920.5653690.3367830.1669170.4799211.000000
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"data[cols]\",\n \"rows\": 11,\n \"fields\": [\n {\n \"column\": \"diagnosis\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.28004378082153986,\n \"min\": -0.012837602698431882,\n \"max\": 1.0,\n \"num_unique_values\": 11,\n \"samples\": [\n 0.35855996508593324,\n 1.0,\n 0.33049855426254676\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"radius_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.4264252322780478,\n \"min\": -0.3116308263092899,\n \"max\": 1.0,\n \"num_unique_values\": 11,\n \"samples\": [\n 0.17058118749299467,\n 0.7300285113754563,\n 0.14774124199260202\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"texture_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2840284213590684,\n \"min\": -0.07643718344813423,\n \"max\": 1.0,\n \"num_unique_values\": 11,\n \"samples\": [\n -0.023388515998423325,\n 0.41518529984520475,\n 0.07140098048331764\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"perimeter_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.41256121493342834,\n \"min\": -0.26147690806633256,\n \"max\": 1.0,\n \"num_unique_values\": 11,\n \"samples\": [\n 0.2072781636910072,\n 0.7426355297258334,\n 0.18302721211685316\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"area_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.4183251195109886,\n \"min\": -0.2831098116914261,\n \"max\": 1.0,\n \"num_unique_values\": 11,\n \"samples\": [\n 0.1770283772540016,\n 0.7089838365853902,\n 0.15129307903511224\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"smoothness_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2885968069214828,\n \"min\": -0.023388515998423325,\n \"max\": 1.0,\n \"num_unique_values\": 11,\n \"samples\": [\n 1.0,\n 0.35855996508593324,\n 0.5577747880728878\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"compactness_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.20999390796030692,\n \"min\": 0.236702222074372,\n \"max\": 1.0,\n \"num_unique_values\": 11,\n \"samples\": [\n 0.6591232152159234,\n 0.5965336775082527,\n 0.6026410484055158\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"concavity_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.227129108218085,\n \"min\": 0.30241782794389144,\n \"max\": 1.0,\n \"num_unique_values\": 11,\n \"samples\": [\n 0.52198376771426,\n 0.6963597071719052,\n 0.5006666171419609\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"concave points_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.27180825809830444,\n \"min\": 0.1669173832269923,\n \"max\": 1.0,\n \"num_unique_values\": 11,\n \"samples\": [\n 0.5536951727437609,\n 0.7766138400204371,\n 0.4624973883673585\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"symmetry_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.27038168156783055,\n \"min\": 0.07140098048331764,\n \"max\": 1.0,\n \"num_unique_values\": 11,\n \"samples\": [\n 0.5577747880728878,\n 0.33049855426254676,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fractal_dimension_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.43006351493092915,\n \"min\": -0.3116308263092899,\n \"max\": 1.0,\n \"num_unique_values\": 11,\n \"samples\": [\n 0.5847920019499775,\n -0.012837602698431882,\n 0.47992133005096926\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 48 } ] }, { "metadata": { "trusted": true, "id": "cZaEFFC5FWX7", "outputId": "4411cb03-236e-482a-fd82-c7e5d927eab0", "colab": { "base_uri": "https://localhost:8080/", "height": 932 } }, "cell_type": "code", "source": [ "plt.figure(figsize=(12, 9))\n", "\n", "plt.title(\"Correlation Graph\")\n", "\n", "cmap = sns.diverging_palette( 1000, 120, as_cmap=True)\n", "sns.heatmap(data[cols].corr(), annot=True, fmt='.1%', linewidths=.05, cmap=cmap);" ], "execution_count": 49, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "metadata": { "id": "f5WGZEvyFWX7" }, "cell_type": "markdown", "source": [ "Using, Plotly Pacage we can show it in interactive graphs like this," ] }, { "metadata": { "trusted": true, "id": "otD7z3xrFWX7", "outputId": "081a460c-bea2-4672-8cec-6217cd2cafed", "colab": { "base_uri": "https://localhost:8080/", "height": 559 } }, "cell_type": "code", "source": [ "plt.figure(figsize=(15, 10))\n", "\n", "\n", "fig = px.imshow(data[cols].corr());\n", "fig.show()" ], "execution_count": 50, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
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\n", "\n", "# Model Implementation\n", "\n", "---\n", "---\n", "\n", "\n", "#### Train Test Splitting\n" ] }, { "metadata": { "id": "SWBkiBWVFWX7" }, "cell_type": "markdown", "source": [ "##### Preprocessing and model selection\n" ] }, { "metadata": { "trusted": true, "id": "Ki1ttkFAFWX7" }, "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "from sklearn.preprocessing import StandardScaler\n" ], "execution_count": 51, "outputs": [] }, { "metadata": { "id": "F6s0axMsFWX8" }, "cell_type": "markdown", "source": [ "### Import Machine Learning Models\n" ] }, { "metadata": { "trusted": true, "id": "edjTxYkQFWX8" }, "cell_type": "code", "source": [ "from sklearn.linear_model import LogisticRegression\n", "\n", "from sklearn.tree import DecisionTreeClassifier\n", "\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "from sklearn.naive_bayes import GaussianNB\n", "\n", "from sklearn.neighbors import KNeighborsClassifier\n" ], "execution_count": 52, "outputs": [] }, { "metadata": { "id": "FbBPfOhZFWX8" }, "cell_type": "markdown", "source": [ "### Check the Model Accuracy, Errors and it's Validations" ] }, { "metadata": { "trusted": true, "id": "sPoH8kI6FWX8" }, "cell_type": "code", "source": [ "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score\n", "\n", "from sklearn.metrics import classification_report\n", "\n", "from sklearn.model_selection import KFold\n", "\n", "from sklearn.model_selection import cross_validate, cross_val_score\n", "\n", "from sklearn.svm import SVC\n", "\n", "from sklearn import metrics" ], "execution_count": 53, "outputs": [] }, { "metadata": { "id": "kWIk7D2JFWX8" }, "cell_type": "markdown", "source": [ "### Feature Selection" ] }, { "metadata": { "id": "X-XG6dSTFWX8" }, "cell_type": "markdown", "source": [ "Select feature for predictions" ] }, { "metadata": { "trusted": true, "id": "aH6yJ9_YFWX8", "outputId": "e22655d9-eb1c-4926-b075-5eaa7b810483", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "data.columns" ], "execution_count": 54, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n", " 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n", " 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n", " 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n", " 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n", " 'fractal_dimension_se', 'radius_worst', 'texture_worst',\n", " 'perimeter_worst', 'area_worst', 'smoothness_worst',\n", " 'compactness_worst', 'concavity_worst', 'concave points_worst',\n", " 'symmetry_worst', 'fractal_dimension_worst'],\n", " dtype='object')" ] }, "metadata": {}, "execution_count": 54 } ] }, { "metadata": { "id": "gWZQ3-MDFWX8" }, "cell_type": "markdown", "source": [ "- Take the dependent and independent feature for prediction" ] }, { "metadata": { "trusted": true, "id": "UG-OuP-bFWX8", "outputId": "b53e6ef1-728f-4b6a-c2b2-b2017c0a88c7", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "prediction_feature = [ \"radius_mean\", 'perimeter_mean', 'area_mean', 'symmetry_mean', 'compactness_mean', 'concave points_mean']\n", "\n", "targeted_feature = 'diagnosis'\n", "\n", "len(prediction_feature)" ], "execution_count": 55, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "6" ] }, "metadata": {}, "execution_count": 55 } ] }, { "metadata": { "trusted": true, "id": "5c4zu6ToFWX8", "outputId": "8b0e4f16-6770-4adc-ce14-e0e3fbbc48b2", "colab": { "base_uri": "https://localhost:8080/", "height": 424 } }, "cell_type": "code", "source": [ "X = data[prediction_feature]\n", "X\n", "\n", "# print(X.shape)\n", "# print(X.values)" ], "execution_count": 56, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " radius_mean perimeter_mean area_mean symmetry_mean compactness_mean \\\n", "0 17.99 122.80 1001.0 0.2419 0.27760 \n", "1 20.57 132.90 1326.0 0.1812 0.07864 \n", "2 19.69 130.00 1203.0 0.2069 0.15990 \n", "3 11.42 77.58 386.1 0.2597 0.28390 \n", "4 20.29 135.10 1297.0 0.1809 0.13280 \n", ".. ... ... ... ... ... \n", "564 21.56 142.00 1479.0 0.1726 0.11590 \n", "565 20.13 131.20 1261.0 0.1752 0.10340 \n", "566 16.60 108.30 858.1 0.1590 0.10230 \n", "567 20.60 140.10 1265.0 0.2397 0.27700 \n", "568 7.76 47.92 181.0 0.1587 0.04362 \n", "\n", " concave points_mean \n", "0 0.14710 \n", "1 0.07017 \n", "2 0.12790 \n", "3 0.10520 \n", "4 0.10430 \n", ".. ... \n", "564 0.13890 \n", "565 0.09791 \n", "566 0.05302 \n", "567 0.15200 \n", "568 0.00000 \n", "\n", "[569 rows x 6 columns]" ], "text/html": [ "\n", "
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "X", "summary": "{\n \"name\": \"X\",\n \"rows\": 569,\n \"fields\": [\n {\n \"column\": \"radius_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.5240488262120775,\n \"min\": 6.981,\n \"max\": 28.11,\n \"num_unique_values\": 456,\n \"samples\": [\n 11.87,\n 13.44,\n 12.96\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"perimeter_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 24.298981038754906,\n \"min\": 43.79,\n \"max\": 188.5,\n \"num_unique_values\": 522,\n \"samples\": [\n 92.25,\n 76.38,\n 119.6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"area_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 351.914129181653,\n \"min\": 143.5,\n \"max\": 2501.0,\n \"num_unique_values\": 539,\n \"samples\": [\n 556.7,\n 584.1,\n 458.4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"symmetry_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.027414281336035715,\n \"min\": 0.106,\n \"max\": 0.304,\n \"num_unique_values\": 432,\n \"samples\": [\n 0.1742,\n 0.1829,\n 0.1506\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"compactness_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.052812757932512194,\n \"min\": 0.01938,\n \"max\": 0.3454,\n \"num_unique_values\": 537,\n \"samples\": [\n 0.1661,\n 0.128,\n 0.06373\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"concave points_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.038802844859153605,\n \"min\": 0.0,\n \"max\": 0.2012,\n \"num_unique_values\": 542,\n \"samples\": [\n 0.1255,\n 0.05069,\n 0.07785\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 56 } ] }, { "metadata": { "trusted": true, "id": "tjtbJ1u2FWX8", "outputId": "ca93c5a8-4453-4627-add6-89815a51f065", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "y = data.diagnosis\n", "y\n", "\n", "# print(y.values)" ], "execution_count": 57, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0 1\n", "1 1\n", "2 1\n", "3 1\n", "4 1\n", " ..\n", "564 1\n", "565 1\n", "566 1\n", "567 1\n", "568 0\n", "Name: diagnosis, Length: 569, dtype: int64" ] }, "metadata": {}, "execution_count": 57 } ] }, { "metadata": { "id": "JLZZpXRXFWX8" }, "cell_type": "markdown", "source": [ "- Splite the dataset into TrainingSet and TestingSet by 33% and set the 15 fixed records" ] }, { "metadata": { "trusted": true, "id": "e-7H2LbVFWX8", "outputId": "e538243b-ce9a-41ea-f5d1-4932435a2a46", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=15)\n", "\n", "print(X_train)\n", "# print(X_test)" ], "execution_count": 58, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " radius_mean perimeter_mean area_mean symmetry_mean compactness_mean \\\n", "274 17.93 115.20 998.9 0.1538 0.07027 \n", "189 12.30 78.83 463.7 0.1667 0.07253 \n", "158 12.06 76.84 448.6 0.1590 0.05241 \n", "257 15.32 103.20 713.3 0.2398 0.22840 \n", "486 14.64 94.21 666.0 0.1409 0.06698 \n", ".. ... ... ... ... ... \n", "85 18.46 121.10 1075.0 0.2132 0.10530 \n", "199 14.45 94.49 642.7 0.1950 0.12060 \n", "156 17.68 117.40 963.7 0.1971 0.16650 \n", "384 13.28 85.79 541.8 0.1617 0.08575 \n", "456 11.63 74.87 415.1 0.1799 0.08574 \n", "\n", " concave points_mean \n", "274 0.04744 \n", "189 0.01654 \n", "158 0.01963 \n", "257 0.12420 \n", "486 0.02791 \n", ".. ... \n", "85 0.08795 \n", "199 0.05980 \n", "156 0.10540 \n", "384 0.02864 \n", "456 0.02017 \n", "\n", "[381 rows x 6 columns]\n" ] } ] }, { "metadata": { "id": "EyuVrcMyFWX8" }, "cell_type": "markdown", "source": [ "### Perform Feature Standerd Scalling" ] }, { "metadata": { "id": "0RXZhd90FWX8" }, "cell_type": "markdown", "source": [ "Standardize features by removing the mean and scaling to unit variance\n", "\n", "The standard score of a sample x is calculated as:\n", "\n", "- z = (x - u) / s" ] }, { "metadata": { "trusted": true, "id": "WDL9fOcZFWX9" }, "cell_type": "code", "source": [ "# Scale the data to keep all the values in the same magnitude of 0 -1\n", "\n", "sc = StandardScaler()\n", "\n", "X_train = sc.fit_transform(X_train)\n", "X_test = sc.fit_transform(X_test)\n" ], "execution_count": 59, "outputs": [] }, { "metadata": { "id": "PHiBPti1FWX9" }, "cell_type": "markdown", "source": [ "
\n", "# ML Model Selecting and Model PredPrediction\n", "\n", "\n", "\n", "---\n", "---\n", "\n", "#### Model Building" ] }, { "metadata": { "id": "2g2Ux7yGFWX9" }, "cell_type": "markdown", "source": [ "Now, we are ready to build our model for prediction, for the I made function for model building and preforming prediction and measure it's prediction and accuracy score." ] }, { "metadata": { "id": "Fjv9V3kMFWX9" }, "cell_type": "markdown", "source": [ "#### Arguments\n", "1. model => ML Model Object\n", "2. Feature Training Set data\n", "3. Feature Testing Set data\n", "4. Targetd Training Set data\n", "5. Targetd Testing Set data" ] }, { "metadata": { "trusted": true, "id": "_E9c1UVLFWX9" }, "cell_type": "code", "source": [ "def model_building(model, X_train, X_test, y_train, y_test):\n", " \"\"\"\n", "\n", " Model Fitting, Prediction And Other stuff\n", " return ('score', 'accuracy_score', 'predictions' )\n", " \"\"\"\n", "\n", " model.fit(X_train, y_train)\n", " score = model.score(X_train, y_train)\n", " predictions = model.predict(X_test)\n", " accuracy = accuracy_score(predictions, y_test)\n", "\n", " return (score, accuracy, predictions)" ], "execution_count": 60, "outputs": [] }, { "metadata": { "id": "IBgcav_oFWX9" }, "cell_type": "markdown", "source": [ "Let's make a dictionary for multiple models for bulk predictions" ] }, { "metadata": { "trusted": true, "id": "0No96bH9FWX9" }, "cell_type": "code", "source": [ "models_list = {\n", " \"LogisticRegression\" : LogisticRegression(),\n", " \"RandomForestClassifier\" : RandomForestClassifier(n_estimators=10, criterion='entropy', random_state=5),\n", " \"DecisionTreeClassifier\" : DecisionTreeClassifier(criterion='entropy', random_state=0),\n", " \"SVC\" : SVC(),\n", "}\n", "\n", "# print(models_list)" ], "execution_count": 61, "outputs": [] }, { "metadata": { "id": "i9CseWRyFWX9" }, "cell_type": "markdown", "source": [ "Before, sending it to the prediction check the key and values to store it's values in DataFrame below." ] }, { "metadata": { "trusted": true, "id": "Og6YiHC5FWX9", "outputId": "c3b6af4d-43dc-43f9-a3d0-0644ad01de42", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "print(list(models_list.keys()))\n", "print(list(models_list.values()))\n", "\n", "# print(zip(list(models_list.keys()), list(models_list.values())))" ], "execution_count": 62, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "['LogisticRegression', 'RandomForestClassifier', 'DecisionTreeClassifier', 'SVC']\n", "[LogisticRegression(), RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=5), DecisionTreeClassifier(criterion='entropy', random_state=0), SVC()]\n" ] } ] }, { "metadata": { "id": "l9jgF62RFWX9" }, "cell_type": "markdown", "source": [ "### Model Implementing\n" ] }, { "metadata": { "id": "Yd8V64pJFWX9" }, "cell_type": "markdown", "source": [ "Now, Train the model one by one and show the classification report of perticular models wise." ] }, { "metadata": { "trusted": true, "id": "fD-WnjbSFWX9" }, "cell_type": "code", "source": [ "# Let's Define the function for confision metric Graphs\n", "\n", "def cm_metrix_graph(cm):\n", "\n", " sns.heatmap(cm,annot=True,fmt=\"d\")\n", " plt.show()\n", "\n" ], "execution_count": 63, "outputs": [] }, { "metadata": { "trusted": true, "id": "NJO6q8L7FWX9", "outputId": "252b02c7-52fe-4c5c-89e2-f11d9b86aab2", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "df_prediction = []\n", "confusion_matrixs = []\n", "df_prediction_cols = [ 'model_name', 'score', 'accuracy_score' , \"accuracy_percentage\"]\n", "\n", "for name, model in zip(list(models_list.keys()), list(models_list.values())):\n", "\n", " (score, accuracy, predictions) = model_building(model, X_train, X_test, y_train, y_test )\n", "\n", " print(\"\\n\\nClassification Report of '\"+ str(name), \"'\\n\")\n", "\n", " print(classification_report(y_test, predictions))\n", "\n", " df_prediction.append([name, score, accuracy, \"{0:.2%}\".format(accuracy)])\n", "\n", " # For Showing Metrics\n", " confusion_matrixs.append(confusion_matrix(y_test, predictions))\n", "\n", "\n", "df_pred = pd.DataFrame(df_prediction, columns=df_prediction_cols)\n" ], "execution_count": 64, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "\n", "Classification Report of 'LogisticRegression '\n", "\n", " precision recall f1-score support\n", "\n", " 0 0.90 0.96 0.93 115\n", " 1 0.92 0.84 0.88 73\n", "\n", " accuracy 0.91 188\n", " macro avg 0.91 0.90 0.90 188\n", "weighted avg 0.91 0.91 0.91 188\n", "\n", "\n", "\n", "Classification Report of 'RandomForestClassifier '\n", "\n", " precision recall f1-score support\n", "\n", " 0 0.92 0.96 0.94 115\n", " 1 0.93 0.88 0.90 73\n", "\n", " accuracy 0.93 188\n", " macro avg 0.93 0.92 0.92 188\n", "weighted avg 0.93 0.93 0.93 188\n", "\n", "\n", "\n", "Classification Report of 'DecisionTreeClassifier '\n", "\n", " precision recall f1-score support\n", "\n", " 0 0.90 0.96 0.93 115\n", " 1 0.92 0.84 0.88 73\n", "\n", " accuracy 0.91 188\n", " macro avg 0.91 0.90 0.90 188\n", "weighted avg 0.91 0.91 0.91 188\n", "\n", "\n", "\n", "Classification Report of 'SVC '\n", "\n", " precision recall f1-score support\n", "\n", " 0 0.90 0.97 0.93 115\n", " 1 0.94 0.84 0.88 73\n", "\n", " accuracy 0.91 188\n", " macro avg 0.92 0.90 0.91 188\n", "weighted avg 0.92 0.91 0.91 188\n", "\n" ] } ] }, { "metadata": { "trusted": true, "id": "lIXGejK7FWX9", "outputId": "356aa316-4908-4042-b263-c9dd0202f843", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "print(len(confusion_matrixs))" ], "execution_count": 65, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "4\n" ] } ] }, { "metadata": { "trusted": true, "id": "x1RpTZyFFWX9", "outputId": "77e1c68f-4910-42fe-a58d-7b034073794e", "colab": { "base_uri": "https://localhost:8080/", "height": 228 } }, "cell_type": "code", "source": [ "plt.figure(figsize=(10, 2))\n", "# plt.title(\"Confusion Metric Graph\")\n", "\n", "\n", "for index, cm in enumerate(confusion_matrixs):\n", "\n", " up\n", "# plt.xlabel(\"Negative Positive\")\n", "# plt.ylabel(\"True Positive\")\n", "\n", "\n", "\n", " # Show The Metrics Graph\n", " cm_metrix_graph(cm) # Call the Confusion Metrics Graph\n", " plt.tight_layout(pad=True)" ], "execution_count": 68, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'up' is not defined", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfusion_matrixs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;31m# plt.xlabel(\"Negative Positive\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# plt.ylabel(\"True Positive\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'up' is not defined" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ] }, "metadata": {} } ] }, { "metadata": { "id": "9slq9CcUFWX9" }, "cell_type": "markdown", "source": [ "While Predicting we can store model's score and prediction values to new generated dataframe" ] }, { "metadata": { "trusted": true, "id": "0waHRNkAFWX9", "outputId": "c51acd8a-54d4-4458-cd5a-ddb5129d34ef", "colab": { "base_uri": "https://localhost:8080/", "height": 175 } }, "cell_type": "code", "source": [ "df_pred" ], "execution_count": 69, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " model_name score accuracy_score accuracy_percentage\n", "0 LogisticRegression 0.916010 0.909574 90.96%\n", "1 RandomForestClassifier 0.992126 0.925532 92.55%\n", "2 DecisionTreeClassifier 1.000000 0.909574 90.96%\n", "3 SVC 0.923885 0.914894 91.49%" ], "text/html": [ "\n", "
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model_namescoreaccuracy_scoreaccuracy_percentage
0LogisticRegression0.9160100.90957490.96%
1RandomForestClassifier0.9921260.92553292.55%
2DecisionTreeClassifier1.0000000.90957490.96%
3SVC0.9238850.91489491.49%
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df_pred", "summary": "{\n \"name\": \"df_pred\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"model_name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"RandomForestClassifier\",\n \"SVC\",\n \"LogisticRegression\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.04417981162888049,\n \"min\": 0.916010498687664,\n \"max\": 1.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.9921259842519685,\n 0.9238845144356955,\n 0.916010498687664\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"accuracy_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.007522412565814333,\n \"min\": 0.9095744680851063,\n \"max\": 0.925531914893617,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.9095744680851063,\n 0.925531914893617,\n 0.9148936170212766\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"accuracy_percentage\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"90.96%\",\n \"92.55%\",\n \"91.49%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 69 } ] }, { "metadata": { "id": "rFEi57tyFWX9" }, "cell_type": "markdown", "source": [ "- print the hightest accuracy score using sort values" ] }, { "metadata": { "trusted": true, "id": "XESa4dPyFWX-", "outputId": "5d8d95e5-6ff5-4b1e-dbbb-7e8a39aff709", "colab": { "base_uri": "https://localhost:8080/", "height": 175 } }, "cell_type": "code", "source": [ "df_pred.sort_values('score', ascending=False)\n", "# df_pred.sort_values('accuracy_score', ascending=False)" ], "execution_count": 70, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " model_name score accuracy_score accuracy_percentage\n", "2 DecisionTreeClassifier 1.000000 0.909574 90.96%\n", "1 RandomForestClassifier 0.992126 0.925532 92.55%\n", "3 SVC 0.923885 0.914894 91.49%\n", "0 LogisticRegression 0.916010 0.909574 90.96%" ], "text/html": [ "\n", "
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model_namescoreaccuracy_scoreaccuracy_percentage
2DecisionTreeClassifier1.0000000.90957490.96%
1RandomForestClassifier0.9921260.92553292.55%
3SVC0.9238850.91489491.49%
0LogisticRegression0.9160100.90957490.96%
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"# df_pred\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"model_name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"RandomForestClassifier\",\n \"LogisticRegression\",\n \"DecisionTreeClassifier\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.04417981162888049,\n \"min\": 0.916010498687664,\n \"max\": 1.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.9921259842519685,\n 0.916010498687664,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"accuracy_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.007522412565814333,\n \"min\": 0.9095744680851063,\n \"max\": 0.925531914893617,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.9095744680851063,\n 0.925531914893617,\n 0.9148936170212766\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"accuracy_percentage\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"90.96%\",\n \"92.55%\",\n \"91.49%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 70 } ] }, { "metadata": { "id": "wrwBynOWFWX-" }, "cell_type": "markdown", "source": [ "### K-Fold Applying ..." ] }, { "metadata": { "trusted": true, "id": "s3HUQA9jFWX-", "outputId": "69d6ae37-bcd5-4b59-d680-019966b1f12e", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "len(data)\n", "# print(len(X))\n" ], "execution_count": 71, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "569" ] }, "metadata": {}, "execution_count": 71 } ] }, { "metadata": { "trusted": true, "id": "FWnfoRaGFWX-", "outputId": "5aa41816-e5ff-4630-a667-f76d6786cfac", "colab": { "base_uri": "https://localhost:8080/", "height": 238 } }, "cell_type": "code", "source": [ "# Sample For testing only\n", "\n", "cv_score = cross_validate(LogisticRegression(), X, y, cv=3,\n", " scoring=('r2', 'neg_mean_squared_error'),\n", " return_train_score=True)\n", "\n", "pd.DataFrame(cv_score).describe().T\n" ], "execution_count": 72, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% \\\n", "fit_time 3.0 0.012574 0.008401 0.007037 0.007741 \n", "score_time 3.0 0.002489 0.000233 0.002238 0.002385 \n", "test_r2 3.0 0.534312 0.186125 0.325364 0.460291 \n", "train_r2 3.0 0.545196 0.051555 0.514363 0.515437 \n", "test_neg_mean_squared_error 3.0 -0.108902 0.043669 -0.157895 -0.126316 \n", "train_neg_mean_squared_error 3.0 -0.106321 0.012102 -0.113456 -0.113307 \n", "\n", " 50% 75% max \n", "fit_time 0.008444 0.015342 0.022240 \n", "score_time 0.002532 0.002615 0.002698 \n", "test_r2 0.595218 0.638786 0.682353 \n", "train_r2 0.516511 0.560613 0.604714 \n", "test_neg_mean_squared_error -0.094737 -0.084405 -0.074074 \n", "train_neg_mean_squared_error -0.113158 -0.102753 -0.092348 " ], "text/html": [ "\n", "
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fit_time3.00.0125740.0084010.0070370.0077410.0084440.0153420.022240
score_time3.00.0024890.0002330.0022380.0023850.0025320.0026150.002698
test_r23.00.5343120.1861250.3253640.4602910.5952180.6387860.682353
train_r23.00.5451960.0515550.5143630.5154370.5165110.5606130.604714
test_neg_mean_squared_error3.0-0.1089020.043669-0.157895-0.126316-0.094737-0.084405-0.074074
train_neg_mean_squared_error3.0-0.1063210.012102-0.113456-0.113307-0.113158-0.102753-0.092348
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"pd\",\n \"rows\": 6,\n \"fields\": [\n {\n \"column\": \"count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 3.0,\n \"max\": 3.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 3.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3089272795072835,\n \"min\": -0.10890188434048083,\n \"max\": 0.5451960826284363,\n \"num_unique_values\": 6,\n \"samples\": [\n 0.012573639551798502\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"std\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.069592191219818,\n \"min\": 0.00023279914944948236,\n \"max\": 0.18612495039971752,\n \"num_unique_values\": 6,\n \"samples\": [\n 0.008400526113484729\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"min\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.26558436695832255,\n \"min\": -0.15789473684210525,\n \"max\": 0.5143631920853446,\n \"num_unique_values\": 6,\n \"samples\": [\n 0.00703740119934082\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"25%\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.28760838955937307,\n \"min\": -0.12631578947368421,\n \"max\": 0.5154370108846654,\n \"num_unique_values\": 6,\n \"samples\": [\n 0.007740616798400879\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"50%\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.31731565133274625,\n \"min\": -0.11315789473684211,\n \"max\": 0.5952183690377559,\n \"num_unique_values\": 6,\n \"samples\": [\n 0.008443832397460938\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"75%\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3356706936094643,\n \"min\": -0.10275308984863213,\n \"max\": 0.6387856551071133,\n \"num_unique_values\": 6,\n \"samples\": [\n 0.015341758728027344\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"max\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.35413990679435675,\n \"min\": -0.09234828496042216,\n \"max\": 0.6823529411764706,\n \"num_unique_values\": 6,\n \"samples\": [\n 0.02223968505859375\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 72 } ] }, { "metadata": { "id": "10AttXOhFWX-" }, "cell_type": "markdown", "source": [ "Let's define a functino for cross validation scorring for multiple ML models\n" ] }, { "metadata": { "trusted": true, "id": "PVfCv1P_FWX-" }, "cell_type": "code", "source": [ "def cross_val_scorring(model):\n", "\n", "# (score, accuracy, predictions) = model_building(model, X_train, X_test, y_train, y_test )\n", "\n", " model.fit(data[prediction_feature], data[targeted_feature])\n", "\n", " # score = model.score(X_train, y_train)\n", "\n", " predictions = model.predict(data[prediction_feature])\n", " accuracy = accuracy_score(predictions, data[targeted_feature])\n", " print(\"\\nFull-Data Accuracy:\", round(accuracy, 2))\n", " print(\"Cross Validation Score of'\"+ str(name), \"'\\n\")\n", "\n", "\n", " # Initialize K folds.\n", " kFold = KFold(n_splits=5) # define 5 diffrent data folds\n", "\n", " err = []\n", "\n", " for train_index, test_index in kFold.split(data):\n", " # print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n", "\n", " # Data Spliting via fold indexes\n", " X_train = data[prediction_feature].iloc[train_index, :] # train_index = rows and all columns for Prediction_features\n", " y_train = data[targeted_feature].iloc[train_index] # all targeted features trains\n", "\n", " X_test = data[prediction_feature].iloc[test_index, :] # testing all rows and cols\n", " y_test = data[targeted_feature].iloc[test_index] # all targeted tests\n", "\n", " # Again Model Fitting\n", " model.fit(X_train, y_train)\n", "\n", " err.append(model.score(X_train, y_train))\n", "\n", " print(\"Score:\", round(np.mean(err), 2) )" ], "execution_count": 73, "outputs": [] }, { "metadata": { "id": "daT8OsWAFWX-" }, "cell_type": "markdown", "source": [ "Call the function to know the cross validation function by mean for our select model predictions." ] }, { "metadata": { "trusted": true, "id": "paXDgqffFWX-", "outputId": "cb0c6454-6ba9-4ae0-f15d-14222111e00c", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "for name, model in zip(list(models_list.keys()), list(models_list.values())):\n", " cross_val_scorring(model)" ], "execution_count": 74, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Full-Data Accuracy: 0.9\n", "Cross Validation Score of'LogisticRegression '\n", "\n", "Score: 0.91\n", "Score: 0.91\n", "Score: 0.9\n", "Score: 0.9\n", "Score: 0.9\n", "\n", "Full-Data Accuracy: 1.0\n", "Cross Validation Score of'RandomForestClassifier '\n", "\n", "Score: 0.99\n", "Score: 0.99\n", "Score: 0.99\n", "Score: 1.0\n", "Score: 1.0\n", "\n", "Full-Data Accuracy: 1.0\n", "Cross Validation Score of'DecisionTreeClassifier '\n", "\n", "Score: 1.0\n", "Score: 1.0\n", "Score: 1.0\n", "Score: 1.0\n", "Score: 1.0\n", "\n", "Full-Data Accuracy: 0.89\n", "Cross Validation Score of'SVC '\n", "\n", "Score: 0.9\n", "Score: 0.89\n", "Score: 0.88\n", "Score: 0.88\n", "Score: 0.88\n" ] } ] }, { "metadata": { "id": "IX9sSuzCFWX-" }, "cell_type": "markdown", "source": [ "- Some of the model are giving prefect scorring. it means sometimes overfitting occurs" ] }, { "metadata": { "id": "7A-KKCDiFWX-" }, "cell_type": "markdown", "source": [ "
\n", "# HyperTunning the ML Model\n", "\n", "\n", "---\n", "---\n", "\n", "\n", "\n", "### Tuning Parameters applying...\n", "\n", "" ] }, { "metadata": { "trusted": true, "id": "4fsa3xv2FWX-" }, "cell_type": "code", "source": [ "from sklearn.model_selection import GridSearchCV" ], "execution_count": 75, "outputs": [] }, { "metadata": { "id": "6oh5s8G2FWX-" }, "cell_type": "markdown", "source": [ "For HyperTunning we can use `GridSearchCV` to know the best performing parameters" ] }, { "metadata": { "id": "7_FkurdCFWX-" }, "cell_type": "markdown", "source": [ "- GridSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.\n", "\n", "- The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid.\n", "\n" ] }, { "metadata": { "trusted": true, "id": "YcOnd2dPFWX-", "outputId": "75a4398d-491f-4398-ce18-44b489f0b440", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "# Let's Implement Grid Search Algorithm\n", "\n", "# Pick the model\n", "model = DecisionTreeClassifier()\n", "\n", "# Tunning Params\n", "param_grid = {'max_features': ['auto', 'sqrt', 'log2'],\n", " 'min_samples_split': [2,3,4,5,6,7,8,9,10],\n", " 'min_samples_leaf':[2,3,4,5,6,7,8,9,10] }\n", "\n", "\n", "# Implement GridSearchCV\n", "gsc = GridSearchCV(model, param_grid, cv=10) # For 10 Cross-Validation\n", "\n", "gsc.fit(X_train, y_train) # Model Fitting\n", "\n", "print(\"\\n Best Score is \")\n", "print(gsc.best_score_)\n", "\n", "print(\"\\n Best Estinator is \")\n", "print(gsc.best_estimator_)\n", "\n", "print(\"\\n Best Parametes are\")\n", "print(gsc.best_params_)" ], "execution_count": 76, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", " Best Score is \n", "0.9238191632928476\n", "\n", " Best Estinator is \n", "DecisionTreeClassifier(max_features='auto', min_samples_leaf=9,\n", " min_samples_split=7)\n", "\n", " Best Parametes are\n", "{'max_features': 'auto', 'min_samples_leaf': 9, 'min_samples_split': 7}\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n", "\n" ] } ] }, { "metadata": { "id": "lnukcUd-FWX-" }, "cell_type": "markdown", "source": [ "### Observation\n", "\n", "Using this Algorithm, we can see that\n", "- The best score is increases\n", "- know the best estimator parametes for final model\n", "- get the best parametes for it." ] }, { "metadata": { "id": "BkuhsQkJFWX-" }, "cell_type": "markdown", "source": [ "- *Let's apply same criteria for* **K Neighbors Classification**\n", "\n", "[**To know the right params chckout its doc params**](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)" ] }, { "metadata": { "trusted": true, "id": "oJ7XhtfvFWX-", "outputId": "4dc859ff-72d0-4b1e-fd50-20b374c2517a", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "# Pick the model\n", "model = KNeighborsClassifier()\n", "\n", "\n", "# Tunning Params\n", "param_grid = {\n", " 'n_neighbors': list(range(1, 30)),\n", " 'leaf_size': list(range(1,30)),\n", " 'weights': [ 'distance', 'uniform' ]\n", "}\n", "\n", "\n", "# Implement GridSearchCV\n", "gsc = GridSearchCV(model, param_grid, cv=10)\n", "\n", "# Model Fitting\n", "gsc.fit(X_train, y_train)\n", "\n", "print(\"\\n Best Score is \")\n", "print(gsc.best_score_)\n", "\n", "print(\"\\n Best Estinator is \")\n", "print(gsc.best_estimator_)\n", "\n", "print(\"\\n Best Parametes are\")\n", "print(gsc.best_params_)" ], "execution_count": 77, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", " Best Score is \n", "0.9159244264507423\n", "\n", " Best Estinator is \n", "KNeighborsClassifier(leaf_size=1, n_neighbors=10)\n", "\n", " Best Parametes are\n", "{'leaf_size': 1, 'n_neighbors': 10, 'weights': 'uniform'}\n" ] } ] }, { "metadata": { "id": "WfcTFeAKFWX-" }, "cell_type": "markdown", "source": [ "### Observation\n", "\n", "Using this Algorithm, we can see that\n", "- A little score improved compared to previous model\n", "- Showing the Best Estimator Parametes for final model\n", "- We can see the Best Parametes for KNN Model." ] }, { "metadata": { "id": "FnfpwDZMFWX_" }, "cell_type": "markdown", "source": [ "- Finally, Implement same strategy for **SVM**" ] }, { "metadata": { "trusted": true, "id": "qttTBafbFWX_", "outputId": "cdf52f17-5daa-42a3-d1c8-8d7bfcc50550", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "# Pick the model\n", "model = SVC()\n", "\n", "\n", "# Tunning Params\n", "param_grid = [\n", " {'C': [1, 10, 100, 1000],\n", " 'kernel': ['linear']\n", " },\n", " {'C': [1, 10, 100, 1000],\n", " 'gamma': [0.001, 0.0001],\n", " 'kernel': ['rbf']\n", " }\n", "]\n", "\n", "\n", "# Implement GridSearchCV\n", "gsc = GridSearchCV(model, param_grid, cv=10) # 10 Cross Validation\n", "\n", "# Model Fitting\n", "gsc.fit(X_train, y_train)\n", "\n", "print(\"\\n Best Score is \")\n", "print(gsc.best_score_)\n", "\n", "print(\"\\n Best Estinator is \")\n", "print(gsc.best_estimator_)\n", "\n", "print(\"\\n Best Parametes are\")\n", "print(gsc.best_params_)" ], "execution_count": 78, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", " Best Score is \n", "0.9184885290148447\n", "\n", " Best Estinator is \n", "SVC(C=10, gamma=0.001)\n", "\n", " Best Parametes are\n", "{'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n" ] } ] }, { "metadata": { "id": "gx7sMuJcFWX_" }, "cell_type": "markdown", "source": [ "### Observation\n", "\n", "Using this Algorithm, we can see that\n", "- It's gives slight better score\n", "- Showing the Best Estimator Parametes for final model\n" ] }, { "metadata": { "id": "5Gk2RtyvFWX_" }, "cell_type": "markdown", "source": [ "Let's Implementing RandomForestClassifier for hyper Tunning\n", "\n", "> Remember while you run the below cell, it will take time for prediction and give the best params and estimators" ] }, { "metadata": { "trusted": true, "id": "G7T7rwGQFWX_", "outputId": "14c02e2c-d8fa-42a1-b3a3-22093e8a5f68", "colab": { "base_uri": "https://localhost:8080/" } }, "cell_type": "code", "source": [ "# Pick the model\n", "model = RandomForestClassifier()\n", "\n", "\n", "# Tunning Params\n", "random_grid = {'bootstrap': [True, False],\n", " 'max_depth': [40, 50, None], # 10, 20, 30, 60, 70, 100,\n", " 'max_features': ['auto', 'sqrt'],\n", " 'min_samples_leaf': [1, 2], # , 4\n", " 'min_samples_split': [2, 5], # , 10\n", " 'n_estimators': [200, 400]} # , 600, 800, 1000, 1200, 1400, 1600, 1800, 2000\n", "\n", "# Implement GridSearchCV\n", "gsc = GridSearchCV(model, random_grid, cv=10) # 10 Cross Validation\n", "\n", "# Model Fitting\n", "gsc.fit(X_train, y_train)\n", "\n", "print(\"\\n Best Score is \")\n", "print(gsc.best_score_)\n", "\n", "print(\"\\n Best Estinator is \")\n", "print(gsc.best_estimator_)\n", "\n", "print(\"\\n Best Parametes are\")\n", "print(gsc.best_params_)" ], "execution_count": 79, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n", "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning:\n", "\n", "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n", "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", " Best Score is \n", "0.9132253711201079\n", "\n", " Best Estinator is \n", "RandomForestClassifier(max_depth=40, max_features='auto', min_samples_split=5,\n", " n_estimators=400)\n", "\n", " Best Parametes are\n", "{'bootstrap': True, 'max_depth': 40, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 400}\n" ] } ] }, { "metadata": { "trusted": true, "id": "y6dqdDdzFWX_" }, "cell_type": "markdown", "source": [ "### Observation\n", "\n", "\n", "Using this Algorithm, we can see that\n", "- It's gives slight better score\n", "- Showing the Best Estimator Parametes for final model\n", "\n", "\n", "---" ] }, { "metadata": { "id": "7RYjlGpsFWX_" }, "cell_type": "markdown", "source": [ "
\n", "# 7. Deploy Model\n" ] }, { "metadata": { "id": "NroWtIOqFWX_" }, "cell_type": "markdown", "source": [ "\n", "- Finally, we are done so far. The last step is to deploy our model in production map. So we need to export our model and bind with web application API.\n", "\n", "Using pickle we can export our model and store in to `model.pkl` file, so we can ealy access this file and calculate customize prediction using Web App API.\n", "\n", "\n", "### A little bit information about pickle:\n", "\n", "`Pickle` is the standard way of serializing objects in Python. You can use the pickle operation to serialize your machine learning algorithms and save the serialized format to a file. Later you can load this file to deserialize your model and use it to make new predictions\n", "\n", "\n", ">> Here is example of the Pickle export model\n", "\n", "\n", "\n", "```\n", "model.fit(X_train, Y_train)\n", "# save the model to disk\n", "filename = 'finalized_model.sav'\n", "pickle.dump(model, open(filename, 'wb'))\n", "\n", "# some time later...\n", "\n", "# load the model from disk\n", "loaded_model = pickle.load(open(filename, 'rb'))\n", "result = loaded_model.score(X_test, Y_test)\n", "print(result)\n", "```" ] }, { "cell_type": "code", "source": [], "metadata": { "id": "4rQ2ZZcLKduo", "outputId": "5364191d-2030-40a1-8f5d-372c8bcc0cec", "colab": { "base_uri": "https://localhost:8080/", "height": 211 } }, "execution_count": 82, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'Y_train' is not defined", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;31m# save the model to disk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'finalized_model.sav'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'Y_train' is not defined" ] } ] }, { "metadata": { "trusted": true, "id": "UwCJE1S4FWX_" }, "cell_type": "code", "source": [ "import pickle as pkl" ], "execution_count": 81, "outputs": [] }, { "metadata": { "trusted": true, "id": "tUz4f9AvFWX_" }, "cell_type": "code", "source": [ "# Trainned Model # You can also use your own trainned model\n", "logistic_model = LogisticRegression()\n", "logistic_model.fit(X_train, y_train)\n", "\n", "filename = 'logistic_model.pkl'\n", "pkl.dump(logistic_model, open(filename, 'wb')) # wb means write as binary\n" ], "execution_count": 83, "outputs": [] }, { "cell_type": "code", "source": [ "!pip install datasets\n", "!pip install huggingface_hub\n", "\n" ], "metadata": { "id": "InvS2RvFFab0", "outputId": "468a1a73-2632-4140-91aa-2ca2511dcb94", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.20.0)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.15.4)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.25.2)\n", "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (16.1.0)\n", "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\n", "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n", "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.0.3)\n", "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\n", "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.4)\n", "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.4.1)\n", "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.16)\n", "Requirement already satisfied: fsspec[http]<=2024.5.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n", "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.5)\n", "Requirement already satisfied: huggingface-hub>=0.21.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.23.4)\n", "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.1)\n", "Requirement already satisfied: pyyaml>=5.1 in 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typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.12.2)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.3.2)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.7)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.7)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2024.6.2)\n" ] } ] }, { "cell_type": "code", "source": [ "from huggingface_hub import login\n", "from datasets import Dataset" ], "metadata": { "id": "m9AIMOfUFo7R" }, "execution_count": 5, "outputs": [] }, { "cell_type": "code", "source": [ "login()" ], "metadata": { "id": "9SOPOcurFw0u", "outputId": "f38e825f-e639-4fcb-9e85-6547c100935e", "colab": { "base_uri": "https://localhost:8080/", "height": 305, "referenced_widgets": [ "4fddc0e8881743c18fe70e3151b60fbc", "a19c22860d644022851b25fb27992292", "a4aeb7bc554d493d819fd7e6dc041c2e", "db5d00b42f96415eb369dd85439f9510", "9985ac9708c0444c9ec1d1512acd0fcc", "5b8fb054565045f99d9837199804cba5", "25d66f6ca77f4da39605f0e72691a8eb", "782e819069e34d6fbd142240a6472599", "c86bfc9371b449d29dd2ea1638b37c80", "e2cc46d1a1d249febb148af1a6442eaa", "c1e8fcfb1122421dbfd94f8784093602", "46e3f41f094348d692e4fa0f3295f80c", "ccc9aac24e0945b78bf9239cccacb2fb", "d3b3d4ac996e41b59caa7c568b7c6a9b", "cae6cb00b5c04123838b533446886efa", "0d73d883089d4c9e8b02c4da0227d89e", "6d5ff71c750841d0b5e852f2c5918de1", "6dca5f03fc21458599db227e5a8f753a", "fa15afdd41714a6dade8c6b1d04edef3", "2ab9074e11044559b5165fc5d1cf0b8f", "432cf5a2fa78426182de6f676e4c92fc", "04b6f19dca454aa6951b4346de339b42", "34b36f6b0d64478f800e29bf0abd2f59", "8ce7a01f570841219a4ffd114c70cf9d", "28fb782087354207a4a64ba1014a2e06", "85249e39e0184f6fb8abc142e37560c5", "307b712f89b543699d99e21b57ab67e7", "3badb4a2b18a4f00b5d47fbc0ef34860", "c1843c39d86149a2ac8542a7d12ea367", "387daefcb172483f9099c4230a27ea04", "f4a005314df64232b6cb8818a7b30827", "eda94bcc8d07432c86d54927ec28811e", "9c2f918527dc4f129b58392dcb0451da", "a96584af5eb444a8b447bc10413adaa6", "c186932ad02c4a0c8bf0a94bb8bb55b7", "867a7cc26f1c4fd68cdc546e6f4e3259", "27b849792e1a4121b652e6d53a0df994", "5218e2f4724945479fb7c0b25b721ae4", "807e1a68ce614ebf81a9fca8e7a52649", "f5ea70ff4945447f849930004202c977", "6ec67aee82ca427592b1deb4685efe57", "ceb14e74bc1e42caae63578f764dd393", "d6376b1859eb4549b531c8c7a9563cb0", "a65a2c349a2f44ed8867e4ba173aa5b9", "4d12e2ee009e45ecbd9676c73f0d34a0", "cedc3df33ea541488e6e1ee8f6ae9ec3", "e89f5617977b40758d15927e6172f9c3" ] } }, "execution_count": 6, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
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iddiagnosisradius_meantexture_meanperimeter_meanarea_meansmoothness_meancompactness_meanconcavity_meanconcave points_meansymmetry_meanfractal_dimension_meanradius_setexture_seperimeter_searea_sesmoothness_secompactness_seconcavity_seconcave points_sesymmetry_sefractal_dimension_seradius_worsttexture_worstperimeter_worstarea_worstsmoothness_worstcompactness_worstconcavity_worstconcave points_worstsymmetry_worstfractal_dimension_worstUnnamed: 32
0842302M17.9910.38122.801001.00.118400.277600.300100.147100.24190.078711.09500.90538.589153.400.0063990.049040.053730.015870.030030.00619325.38017.33184.602019.00.162200.665600.71190.26540.46010.11890NaN
1842517M20.5717.77132.901326.00.084740.078640.086900.070170.18120.056670.54350.73393.39874.080.0052250.013080.018600.013400.013890.00353224.99023.41158.801956.00.123800.186600.24160.18600.27500.08902NaN
284300903M19.6921.25130.001203.00.109600.159900.197400.127900.20690.059990.74560.78694.58594.030.0061500.040060.038320.020580.022500.00457123.57025.53152.501709.00.144400.424500.45040.24300.36130.08758NaN
384348301M11.4220.3877.58386.10.142500.283900.241400.105200.25970.097440.49561.15603.44527.230.0091100.074580.056610.018670.059630.00920814.91026.5098.87567.70.209800.866300.68690.25750.66380.17300NaN
484358402M20.2914.34135.101297.00.100300.132800.198000.104300.18090.058830.75720.78135.43894.440.0114900.024610.056880.018850.017560.00511522.54016.67152.201575.00.137400.205000.40000.16250.23640.07678NaN
......................................................................................................
564926424M21.5622.39142.001479.00.111000.115900.243900.138900.17260.056231.17601.25607.673158.700.0103000.028910.051980.024540.011140.00423925.45026.40166.102027.00.141000.211300.41070.22160.20600.07115NaN
565926682M20.1328.25131.201261.00.097800.103400.144000.097910.17520.055330.76552.46305.20399.040.0057690.024230.039500.016780.018980.00249823.69038.25155.001731.00.116600.192200.32150.16280.25720.06637NaN
566926954M16.6028.08108.30858.10.084550.102300.092510.053020.15900.056480.45641.07503.42548.550.0059030.037310.047300.015570.013180.00389218.98034.12126.701124.00.113900.309400.34030.14180.22180.07820NaN
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56892751B7.7624.5447.92181.00.052630.043620.000000.000000.15870.058840.38571.42802.54819.150.0071890.004660.000000.000000.026760.0027839.45630.3759.16268.60.089960.064440.00000.00000.28710.07039NaN
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569 rows Ă— 33 columns

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\n", " \n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "input" } }, "metadata": {}, "execution_count": 8 } ] }, { "cell_type": "code", "source": [ "dataset = Dataset.from_pandas(input)\n", "dataset = dataset.train_test_split(test_size=0.3)\n", "\n", "print(dataset)" ], "metadata": { "id": "ugocR-3UGMp4", "outputId": "d25c0275-d1aa-44c9-adcb-d2dc46e01fa1", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32'],\n", " num_rows: 398\n", " })\n", " test: Dataset({\n", " features: ['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32'],\n", " num_rows: 171\n", " })\n", "})\n" ] } ] }, { "cell_type": "code", "source": [ "dataset.push_to_hub('Tiburoncin/mom-cancer2')" ], "metadata": { "id": "aQfFDM40GUAI", "outputId": "36c886e9-1ef5-455a-9e20-38f55d554c87", "colab": { "base_uri": "https://localhost:8080/", "height": 180, "referenced_widgets": [ "79e2c345c7c14c5b87663f0d9b60a7a1", "73b8c261a08f427898353ebe36fc50f0", "f81b20ac25144b9882f3681b583493cd", "dc6d983e59f84f54b164667c38de208e", "356d55f0f0f34775939d52a4cba7bba0", "6db151909c964cfa805a57c878349d77", "97dd085a4ae6412baf1ff1db72254178", "a4a95a3891944e869aa45a1635fc218d", "0ec2137c43f3458c8345acf7efa85f89", "e205da5987734e08afb93ec0da31176b", "8acb0e6feeab4b108fae283a1c6b76e3", "bd2e0c3d265440cd92e3c3d077866044", "fc4094f52eef40c99f2a2c53649d8b38", "ff4defbee0ad4a059e4c743ffbb42e74", "6ffa7d0df1ff42f897585f8fc9c3e77a", "71b9eb354c4644e397b5c91ee0f370d1", "825a45c8f9074b788c8d72c2aa759966", "9fccd4aa9f7d4eaab02d27fbeeac7b3a", "2f7ecf4f17d84a3a9316e555ea7b6dc7", "00b50e9c6f05436999868dbbc55bae2c", "0577e012f26143029ea954fc4608a910", "fd74a26027244f2ba0c6f2e17c34763f", "da8635c5eb0b4f68ac0d3693fa813f18", "38da281666be4ebb872ba51798815bd0", "10083c9731494c4aba5cee8c42ba2f11", "7fb817fa8dc2472dbbd6e8bf7e2abe70", "71bbaf54d7844cecb70fa099b0504d7a", "564640640aef4c90ace3e52e1e099185", "37e4dda762ac4796b70496202034e949", "edd58ac1222b4b4caf4da863b1d16d71", "b026428b1f794a5d9d0133e04dc987af", "1732c65d401344539ae3f24c5498acb2", "93ca2a69e7224fd7b5cbdc8ccb8df49f", "4eaf6f8a0e464497b1e429cbf7c7909f", "82098e6bdfae4ffabc89b3f74a0c9b12", "3d1396e92b7b4cd1b71e49bfbb62e993", "7e900b23cba4433cb87b160ab800c0ec", "de0b7ade360a45a2b6949b743230239b", "8af02d923f6b45fca536def2b25ccfa6", "06ebf7f032e9419c9987f5b467630b1a", "3a829cb774e44b9997949afc96ab852a", "2ad9c5b21d9a4680ba41427ca0db7d71", "4d450c3abdc44d0c991d9c10a921a51f", "6ad6b4993847476388aed8240ed1b8b5" ] } }, "execution_count": 16, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Uploading the dataset shards: 0%| | 0/1 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mloaded_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpkl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# rb means read as binary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloaded_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'finalized_model.sav'" ] } ] }, { "metadata": { "id": "BnQCwjuoFWX_" }, "cell_type": "markdown", "source": [ "\n", "---\n", "\n", "\n", "---\n", "\n", "\n", "---\n", "\n" ] }, { "metadata": { "id": "XNOfLh8QFWX_" }, "cell_type": "markdown", "source": [ "### Conclusion\n", "\n", "- In this kernal, We had seen the data clearning and EDA using pandas methods and show some visual graphs to know the behaviour of this dataset and finnaly we train some model for it and calculate the prediction and it's acciracy scores and hyper tunning. I have wroted some basic codes in this notebook. So, After socessfully completed we can deploye our models to the live production mode using **exporting models and some python web applications.** For that we can use `Flask`, `Django` or `FastAPI` frameworks." ] }, { "metadata": { "id": "vTt8G93VFWX_" }, "cell_type": "markdown", "source": [ "### I hope you enjoy in this kernel and give Upvote it. đź‘Ť" ] }, { "metadata": { "id": "wfoto4rMFWX_" }, "cell_type": "markdown", "source": [ "---\n", "---\n", "\n", "
\n", "

That's it Guys,

\n", "

🙏

\n", " \n", " \n", " I Hope you guys you like and enjoy it, and learn something interesting things from this notebook,\n", " \n", " Even I learn a lots of things while I'm creating this notebook\n", " \n", " Keep Learning,\n", " Regards,\n", " Vikas Ukani.\n", " \n", "
\n", "\n", "---\n", "---\n", "\n", "\"Thank\n", "\n" ] } ], "metadata": { "kernelspec": { "language": "python", "display_name": "Python 3", "name": "python3" }, "language_info": { "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "version": "3.6.4", "file_extension": ".py", "codemirror_mode": { "name": "ipython", "version": 3 }, "name": "python", "mimetype": "text/x-python" }, "colab": { "name": "🦀 Breast Cancer Prediction Using Machine Learning", "provenance": [] }, "widgets": { "application/vnd.jupyter.widget-state+json": { "4fddc0e8881743c18fe70e3151b60fbc": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "VBoxView", "box_style": "", "children": [ "IPY_MODEL_432cf5a2fa78426182de6f676e4c92fc", "IPY_MODEL_04b6f19dca454aa6951b4346de339b42", "IPY_MODEL_34b36f6b0d64478f800e29bf0abd2f59", "IPY_MODEL_8ce7a01f570841219a4ffd114c70cf9d", "IPY_MODEL_28fb782087354207a4a64ba1014a2e06", "IPY_MODEL_85249e39e0184f6fb8abc142e37560c5", "IPY_MODEL_307b712f89b543699d99e21b57ab67e7", "IPY_MODEL_3badb4a2b18a4f00b5d47fbc0ef34860", "IPY_MODEL_c1843c39d86149a2ac8542a7d12ea367" ], "layout": "IPY_MODEL_25d66f6ca77f4da39605f0e72691a8eb" } }, "a19c22860d644022851b25fb27992292": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_782e819069e34d6fbd142240a6472599", "placeholder": "​", "style": "IPY_MODEL_c86bfc9371b449d29dd2ea1638b37c80", "value": "

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