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Laina  female  26.0      0   \n","3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n","4                           Allen, Mr. William Henry    male  35.0      0   \n","\n","   Parch            Ticket     Fare Cabin Embarked  \n","0      0         A/5 21171   7.2500   NaN        S  \n","1      0          PC 17599  71.2833   C85        C  \n","2      0  STON/O2. 3101282   7.9250   NaN        S  \n","3      0            113803  53.1000  C123        S  \n","4      0            373450   8.0500   NaN        S  "],"text/html":["\n","  <div id=\"df-5ac6c73f-6ed3-4b3b-b75e-62d6c8ad129e\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Braund, Mr. Owen Harris</td>\n","      <td>male</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/5 21171</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>PC 17599</td>\n","      <td>71.2833</td>\n","      <td>C85</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>Heikkinen, Miss. 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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":4}],"source":["import pandas as pd\n","\n","datos = pd.read_csv(\"U4_04_train.csv\")\n","\n","datos.head()"]},{"cell_type":"code","source":["import seaborn as sb\n","# mostrar cuantas personas sobrevivieron y cuales no\n","conteo =sb.countplot(x=\"Survived\" , data=datos)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":466},"id":"QfmSC-BuFpTN","executionInfo":{"status":"ok","timestamp":1701379389630,"user_tz":180,"elapsed":373,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"37d3fdd7-91bd-4801-9739-a0098d1b8fe4"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: xlabel='Survived', ylabel='count'>"]},"metadata":{},"execution_count":9},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["conteo =sb.countplot(x=\"Survived\" , data=datos ,  hue=\"Sex\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"FRmfHByKGbtk","executionInfo":{"status":"ok","timestamp":1701379447775,"user_tz":180,"elapsed":813,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"a2e50df5-f287-4ff4-c418-87b4e8a70e4b"},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# datos vacios\n","\n","datos.isna().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cQlx_nnjHhYa","executionInfo":{"status":"ok","timestamp":1701379505377,"user_tz":180,"elapsed":4,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"3404d0eb-c5dc-4dd2-fd98-01c93733bfad"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["PassengerId      0\n","Survived         0\n","Pclass           0\n","Name             0\n","Sex              0\n","Age            177\n","SibSp            0\n","Parch            0\n","Ticket           0\n","Fare             0\n","Cabin          687\n","Embarked         2\n","dtype: int64"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["sb.displot(x=\"Age\" , data=datos)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":523},"id":"tKiBLrwxHrpi","executionInfo":{"status":"ok","timestamp":1701379608803,"user_tz":180,"elapsed":524,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"59277725-01c8-470f-8753-40eed5678d7f"},"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<seaborn.axisgrid.FacetGrid at 0x7fcba1961030>"]},"metadata":{},"execution_count":15},{"output_type":"display_data","data":{"text/plain":["<Figure size 500x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# proceso para llenar los 177 datos vacios de las edades\n","\n","prom_edad = datos[\"Age\"].mean()"],"metadata":{"id":"eevE9tJAIDmt","executionInfo":{"status":"ok","timestamp":1701379791247,"user_tz":180,"elapsed":239,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["datos[\"Age\"] = datos[\"Age\"].fillna(prom_edad)"],"metadata":{"id":"M3K6-luRIaKi","executionInfo":{"status":"ok","timestamp":1701379853293,"user_tz":180,"elapsed":253,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["datos.isna().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"81mSO03SIm07","executionInfo":{"status":"ok","timestamp":1701380931542,"user_tz":180,"elapsed":253,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"ec4e246f-6190-4a08-929b-29eb1bb22cb8"},"execution_count":43,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Survived    0\n","Pclass      0\n","Age         0\n","SibSp       0\n","Parch       0\n","Fare        0\n","male        0\n","Q           0\n","S           0\n","dtype: int64"]},"metadata":{},"execution_count":43}]},{"cell_type":"code","source":["# ELIMINACION DE CELDA Cabin\n","\n","datos = datos.drop(\"Cabin\" , axis=1)\n"],"metadata":{"id":"WeuLQtrrJK2B","executionInfo":{"status":"ok","timestamp":1701380120230,"user_tz":180,"elapsed":241,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}}},"execution_count":26,"outputs":[]},{"cell_type":"code","source":["datos[\"Embarked\"].value_counts()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Pk1p1IcpKIf5","executionInfo":{"status":"ok","timestamp":1701380156888,"user_tz":180,"elapsed":255,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"d42c2b5a-d89d-4439-94fc-ea6a02e2f1da"},"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["S    644\n","C    168\n","Q     77\n","Name: Embarked, dtype: int64"]},"metadata":{},"execution_count":27}]},{"cell_type":"code","source":["# eliminando los valores Non A Number\n","\n","datos = datos.dropna()"],"metadata":{"id":"MzQVTbffKOpI","executionInfo":{"status":"ok","timestamp":1701380198803,"user_tz":180,"elapsed":276,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}}},"execution_count":28,"outputs":[]},{"cell_type":"code","source":["# eliminando columnas innecesarias\n","\n","datos = datos.drop([\"PassengerId\" , \"Ticket\" , \"Name\"], axis=1)"],"metadata":{"id":"281zaxxWKY3R","executionInfo":{"status":"ok","timestamp":1701380310489,"user_tz":180,"elapsed":2,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}}},"execution_count":30,"outputs":[]},{"cell_type":"code","source":["# transformando los datos de sexo en datos numericos\n","\n","dummies_sex = pd.get_dummies(datos[\"Sex\"], drop_first=True)\n","\n","\n","# la linea anterior contenia una columna irrelevante para el analisis , se reemplazo por una sola en la cual indica 0 = hombre , 1 = mujer\n","# multicollinearity -> sucede cuando el modelo encuentra relaciones donde no deberia haber"],"metadata":{"id":"O0Txmx-jK224","executionInfo":{"status":"ok","timestamp":1701380689215,"user_tz":180,"elapsed":337,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}}},"execution_count":37,"outputs":[]},{"cell_type":"code","source":["# juntando set con los datos de sexo en numeros\n","\n","datos = datos.join(dummies_sex)\n","datos = datos.drop([\"Sex\"] , axis=1)\n","datos"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"b-TH7sySLXSG","executionInfo":{"status":"ok","timestamp":1701380692194,"user_tz":180,"elapsed":262,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"894c343a-f73f-409f-916c-38ef69253f2a"},"execution_count":38,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Survived  Pclass        Age  SibSp  Parch     Fare Embarked  male\n","0           0       3  22.000000      1      0   7.2500        S     1\n","1           1       1  38.000000      1      0  71.2833        C     0\n","2           1       3  26.000000      0      0   7.9250        S     0\n","3           1       1  35.000000      1      0  53.1000        S     0\n","4           0       3  35.000000      0      0   8.0500        S     1\n","..        ...     ...        ...    ...    ...      ...      ...   ...\n","886         0       2  27.000000      0      0  13.0000        S     1\n","887         1       1  19.000000      0      0  30.0000        S     0\n","888         0       3  29.699118      1      2  23.4500        S     0\n","889         1       1  26.000000      0      0  30.0000        C     1\n","890         0       3  32.000000      0      0   7.7500        Q     1\n","\n","[889 rows x 8 columns]"],"text/html":["\n","  <div id=\"df-d0efee1c-5639-4624-b9fc-caff943f424b\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","      <th>Embarked</th>\n","      <th>male</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>22.000000</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>7.2500</td>\n","      <td>S</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>38.000000</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>71.2833</td>\n","      <td>C</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>26.000000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.9250</td>\n","      <td>S</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>35.000000</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53.1000</td>\n","      <td>S</td>\n","     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<td>0</td>\n","      <td>3</td>\n","      <td>29.699118</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>23.4500</td>\n","      <td>S</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>889</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>26.000000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>30.0000</td>\n","      <td>C</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>890</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>32.000000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.7500</td>\n","      <td>Q</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>889 rows × 8 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0efee1c-5639-4624-b9fc-caff943f424b')\"\n","            title=\"Convert this dataframe to an interactive 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3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# verificando la relacion de sobrevivir con la embarked\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Survived\"\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Embarked\"\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdatos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mcountplot\u001b[0;34m(data, x, y, hue, order, hue_order, orient, color, palette, saturation, width, dodge, ax, **kwargs)\u001b[0m\n\u001b[1;32m   2941\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot pass values for both `x` and `y`\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2942\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2943\u001b[0;31m     plotter = _CountPlotter(\n\u001b[0m\u001b[1;32m   2944\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2945\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrorbar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, estimator, errorbar, n_boot, units, seed, orient, color, palette, saturation, width, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[1;32m   1528\u001b[0m                  errcolor, errwidth, capsize, dodge):\n\u001b[1;32m   1529\u001b[0m         \u001b[0;34m\"\"\"Initialize the plotter.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1530\u001b[0;31m         self.establish_variables(x, y, hue, data, orient,\n\u001b[0m\u001b[1;32m   1531\u001b[0m                                  order, hue_order, units)\n\u001b[1;32m   1532\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[0;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[1;32m    539\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\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    540\u001b[0m                     \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Could not interpret input '{var}'\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\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    542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m             \u001b[0;31m# Figure out the plotting orientation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Could not interpret input 'Embarked'"]}]},{"cell_type":"code","source":["# dummies de embarked\n","dummies_emb = pd.get_dummies(datos[\"Embarked\"], drop_first=True)\n","# agregando al set de datos\n","datos = datos.join(dummies_emb)\n","datos = datos.drop([\"Embarked\"] , axis=1)\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":512},"id":"AY-81Pn0MjUj","executionInfo":{"status":"error","timestamp":1701381027591,"user_tz":180,"elapsed":253,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"3bcd4e6b-37e7-45e5-ccc3-224c5fb95763"},"execution_count":44,"outputs":[{"output_type":"error","ename":"KeyError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\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   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 'Embarked'","\nThe above exception was the direct cause of the following exception:\n","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m<ipython-input-44-4309e26b5677>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# dummies de embarked\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdummies_emb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dummies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Embarked\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_first\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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      3\u001b[0m \u001b[0;31m# agregando al set de datos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdatos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdummies_emb\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[0mdatos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Embarked\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\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   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 'Embarked'"]}]},{"cell_type":"code","source":["# heatmap\n","sb.heatmap(datos.corr(), annot=True , cmap=\"YlGnBu\" )"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":499},"id":"Miqy2eKRNuJe","executionInfo":{"status":"ok","timestamp":1701381158873,"user_tz":180,"elapsed":1031,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"209c3f34-130e-42f9-e820-1a836804b1ff"},"execution_count":47,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":47},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAj4AAAHRCAYAAABqyLNmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3xTVRvA8V+60nSvdO89aAtl7z1EQJQNioAgoCzBVwUFxIUCooIMBwgqyAbZe8ves6W0lBYohc50Jh15/wikhKaFMgTq+frJR3Jzzr1Pbu49fXLOuTcStVqtRhAEQRAE4T/A4FkHIAiCIAiC8G8RiY8gCIIgCP8ZIvERBEEQBOE/QyQ+giAIgiD8Z4jERxAEQRCE/wyR+AiCIAiC8J8hEh9BEARBEP4zROIjCIIgCMJ/hkh8BEEQBEH4zxCJjyAIgiAI/xki8REEQRAE4bHt3buXjh074urqikQiYc2aNQ+ss3v3bqKiopBKpfj7+7NgwYKnHqdIfARBEARBeGy5ublERkYya9ashyp/5coVXn75ZZo3b86pU6cYNWoUAwcOZMuWLU81Ton4kVJBEARBEJ4kiUTC6tWr6dy5c7llPvzwQzZs2MC5c+e0y3r27ElmZiabN29+arGJHh9BEARBEPRSKpUoFAqdh1KpfCLrPnjwIK1atdJZ1rZtWw4ePPhE1l8eo6e69v8ImWevZx1CpVy99GLFu/O6ybMOoVIyVS/W94norBerGTh+68U6HvZ2kj/rECpl6pnEZx1CpTR0Uj3rECqtodPLT3X9T/Jv0ocDgpg0aZLOsokTJ/Lpp58+9rpv3ryJk5OTzjInJycUCgX5+fnIZLLH3oY+L1aLJwiCIAhChSSSJ/fla+zYsYwePVpnmVQqfWLrfxZE4iMIgiAIgl5SqfSpJTrOzs6kpKToLEtJScHKyuqp9faASHwEQRAEoUqRvCDTd+vXr8/GjRt1lm3bto369es/1e2+GHtHEARBEISHIpEYPLFHZeTk5HDq1ClOnToFaC5XP3XqFImJmnljY8eOpW/fvtryQ4YMIT4+ng8++IDo6Ghmz57NsmXLeO+9957YvtBH9PgIgiAIQhXyJOf4VMaxY8do3ry59vnduUFvvvkmCxYsIDk5WZsEAfj4+LBhwwbee+89fvjhB9zd3fn1119p27btU41TJD6CIAiCIDy2Zs2aUdGtAfXdlblZs2acPHnyKUZVlkh8BEEQBKEKkUgkzzqE55pIfARBEAShShHTdysi9o4gCIIgCP8ZosdHEARBEKqQZzW5+UUhEh9BEARBqEJE4lMxsXcEQRAEQfjPED0+giAIglCFvCh3bn5WXpi9s3v3biQSCZmZmU91O/369aNz585PdRuCIAiC8LQ8qzs3vygq3eNz+/ZtJkyYwIYNG0hJScHW1pbIyEgmTJhAw4YNn0aMADRo0IDk5GSsra2f2jaeBw3rBPPekA5Ehfvi4mRL94Hfsm7rsWcSi1qtZt7sraxbdZic7HzCq3sz5uPX8PCSl1tn9bIDrFl2kJs3MgDw8XOi3+DW1GsUrHf9/3t3Hof/ieHL796kSYtqlYpt15+bOL75IAW5+XiG+tDh3W7YuzlWWO/wun0cWLmTnAwFTj5utB/aBfcgLwAyUtL4vv9neut1H9uPsMY1AJjYfmSZ17t++CbhTaMeGPPBvzZydtsBlLn5uAb70HJID2xdK4751Ma9HF+9g9xMBXJvN5oP6opzoLf29e2zl5B4OoacjCxMTKW4BPvQuG8n7Nydy6wrX5HLn+99TU5aJkP//AZTCzO927y6fTdXNm1DmaXA0sOd0Nd7YOPnrbcsQPKR48SuWkd+ahpmTo4EdX8Vx8jSz7OooICYZWtIOXGawpxcZHJ7vFs3x7NFE22Zc78tIvV8NMrMLAxNpdj6+xLU/VUsXMu+j4fxqrcLvfzdsJOaEKfI5fuzcVzMzNFbtqOnE209HPG1NAcgJiuHny8mlCnvZSFjSKg31e2tMZRISMjO45Nj0dzKVz5SjPdSq9XMnLGY5cu3olDkEhUVwsRPh+Lt7VpunZkzFzPrxyU6y3x83Ni0eY7e9b89aBL79p3gx1njaNWqXrnrvbB5D2fX7iA/U4Gdlxv1B3RDHuBdbvkrB09wfMkGcm6nYeUsp/brnfGICtPZ9omlG4jZcQBVbj5Owb40GNQDa5eyx35xYSFrx04j/ep1Ok/5CHsfdwCyb6Wx7N2JZco7zxmBX1j5sd0bw5r5m9m77hB5Ofn4h/vQd3RXnDzKb89iTsWxeckuEmKukZWmYNiX/YlqHK5TZt5Xf/HP5qM6y6rVCWL0tMEPjEl4Niqd+HTp0gWVSsXChQvx9fUlJSWFHTt2kJaW9kgBqNVqiouLMTKqOBQTExOcnR+tAXyRmJtJOXshkd+X7mbpL2OeaSyLf9vNyr/2M+7zHri42TFv1hbGDP2VP1a/j1RqrLeOo6MNQ0a2x93TAbUaNq87xtiRC5i/dBQ+/rqf37I/9z3yjbb2r9jB4bV7eXV0H2yc7dj5x0b+GD+Xd+eOxdhEf2zn9pxgyy+r6TisO27B3hxas5s/xs9h+M8fY2FjibWDLe//+blOneObD/DPyp341wrVWd75vd741wzRPje1ePAvCR9bvZ1T6/fQduTrWDnZc2DxBlZNms2bMz/GqJyYY/YfZ+/81bQc2gPnQC9OrN3Nqkmz6TdrPGY2lgA4+nkQ3LQWlg62FOTkcWjJRlZ9OpsBP32KgaHuN7ZtPy7CwcuVnLTMcuNMPnyMi3+tpNqbvbD28+Hqlp0cnTaDJt98itTKqkz5jNg4Ts+ZT2C3V3CsHs6Ng0c58cNcGn42Fkt3NwCiF68k7WIMkYP7I3OwJ/XcBS78vgSpjTVOUZEAWHl74lq/Dqb2dhTm5nJ59XqOTp1Bs2+/QGJQuW+eLVwdGBbmw7dnLnMhI5tuvm58W68avXceJ1NVWKZ8dQdrtl+/zbn0eFQlJfTxd+fb+tXou+sEqQUqAFzNTJnVKIINiSnMj04kt6gYH0szVMUllYqtPL/+soo//ljP11+PxN3diR9+WMTAtyayYeMspFKTcusFBHgy/7fS49bI0FBvuYUL1z7U+Rb/z3EOL1xNw7d7IPf35vyGXWz+chZdf5iAzNqyTPmUmHh2fb+AWr074VmzGnH7j7F9ys+8MuVD7Dw1SduZv7dzYdMemgx7A0tHe44vWc+WL2bx2neflDn2j/zxN2Z21qRfva43vpcmDMfG3UX73Mtf/7lzv02Ld7J95T4Gju2Ng6sdq3/dxLfv/8SXv3+IcTntmbJAhYefK43a12HWJwvKXXe1usG89VFP7XMjk2c7i6Sq9tQ8KZXaO5mZmezbt49vvvmG5s2b4+XlRZ06dRg7diydOnUiISEBiUSi/YGyu3UkEgm7d+8GSoesNm3aRM2aNZFKpcyfPx+JREJ0dLTO9r777jv8/Px06mVmZqJQKJDJZGzatEmn/OrVq7G0tCQvLw+ApKQkunfvjo2NDXZ2drzyyiskJCRoyxcXFzN69GhsbGywt7fngw8+qPB22/+GrbtPM2naMtZueTa9PHep1WqWLdpH30Etady8Gv6Brnz8RU/SbivYt/N8ufUaNgulfuMQPLzkeHrLeXv4S8jMTDh/JlGnXGz0dZb+vpePJnV7pNgOrdlDk55tCK4fjrOPG6+NeZ3stCyiD54tt96B1bup2a4BNdrUw9HTmQ7DumMsNeHk1kMAGBgaYGlnpfO4eOAMYY2rI5VJddZlai7TKVdesnVvzCfW7aZO97b41Y1A7u1Gu5FvkJueRdzhM+XWO/H3Lqq1qU9Yy3rYe7jQamgPjKQmnNtxUFsmom1D3MP8sXayx8nPgwZ9OpCdmoHilu6XkdOb9qHMzadm5xYVxnpl8w48mjbEvUkDLN1cCOvXC0MTE67tPai3fMLWXTiEh+Lbvg0Wri4EdumElbcHV7fv0ZbJuByHW6N62IcEYia3x7N5Yyw93MiKT9CW8WzeGLvgAMzk9lh7exLQpRMF6Rnk3a78l6oefm6sS7zJxqRbJOTkM+3MZQqKi3nZ00lv+c9PXGJNwk0uK3JJzMnnm1OxGAA1HWy0Zd4O8eJQSgZzLiQQq8jlRl4B/6Sk602kKkutVvP772sZMrQ7LVvVIyjYh2+mvMetW+ls336owrqGhobI5bbah61d2eT04sV4fpu/hi+/GvHAWM6t30lQywYENq+PrYcLDd/uiZGJCZd26v/8z2/YjXv1ECJeaYWNuzM1e3bA3teDi5v3aN/b+Q27qN6lLV61I7DzcqPpsL7kZWRx9ehpnXUlnTzP9TMXqfPGq+XGJ7Uwx8zWSvswMtKf6N1LrVazbfleOr7RmhqNq+Hh58rAj3uTmabgxP5z5daLqBfCa4PaU7NJRIXrNzY2wtreSvswt9Tfk/pvEUNdFavUu7KwsMDCwoI1a9agVD5e1+5HH33E119/zcWLF+natSu1atVi0aJFOmUWLVpE7969y9S1srKiQ4cOLF68uEz5zp07Y2ZmRmFhIW3btsXS0pJ9+/bxzz//YGFhQbt27VCpNN/gvv32WxYsWMD8+fPZv38/6enprF69+rHeV1WRfD2d9NRsatUN0C6zsJQREu7J+TNXH2odxcUlbN90ioJ8FWGRXtrlBfkqJo1dzHvjOmPvULaRfpCMm2nkZCjwrR6oXWZqLsMtyIuki1f01ikqLCL5cpJOHQMDA3yrB5IUnaC3zo3YJG7GXyeqTf0yr22Ys4Jveo7j51HfcmLroQcmzFkpaeRlKPCMCNIuk5rLcA705kaM/piLC4tIiUvSqSMxMMAzMojkGP0xFxYoOb/jEFZO9lg62GqXpyUlc2jZZtqOeqPCxqykqAhFQiIOYaVDkxIDAxzCgsm8HK+3TubleOzDdIcy5dVCdcrb+vtx6+QZCtIzUavVpF2MITflFg7VQu9fHQBFSiXX9x1EJrdHZm+rt0x5jCQSAq0tOH47U7tMDRxLzSTMtmyPhT5SQ0OMDCRkF2qSGglQ38mWpNx8vq0Xxtq2dfipcSSNne0qFVt5rl1L4fbtDBo0iNQus7Q0JyIykFMnYyqse/XqDRo36kerloN4f8y33LhxW+f1/Hwl74/5lgkTBiOXV7wvVapCUuOTcL3vmHONCOLWJf3H6a1LV3CN0P383SNDuHUpAdAMUeVnKnANLy1jYi5D7u/NrXuO4/xMBfvn/kXT4X0xqqCHa9s3P7HorY9Y/8l0rh4t/0vDvW4np5OVnk1ordLz38xChm+IJ3HnEsqv+JCiT11mZKcJjO0zmd+/XUFOVu5jr/NxSJ7gf1VRpfrjjIyMWLBgAYMGDWLu3LlERUXRtGlTevbsSURExRnx/T777DNat26tfd6nTx9+/PFHPv9c02V76dIljh8/zp9//qm3fp8+fXjjjTfIy8vDzMwMhULBhg0btInL0qVLKSkp4ddff9V27/7222/Y2Niwe/du2rRpw/fff8/YsWN57bXXAJg7dy5btmypMG6lUlkm6VOri5FIHvyt40WSlpoNgK297h8KO3sL0u+8Vp642GSGvvEjKlURMjMTvvzuTXz8Sr9pz5y6lmqR3jRu/vBzeu6Vk6HZvsV9f8QsbCy1r90vT5FLSUmJ3jqpSbf01jmx9SByDyc8Q310ljd/vT2+kQEYm5pw+UQ0G2YtR5WvpN4rTcuNOS9TAaAdnrrLzNqSvAyF3jr52bmoS0ows7EqUyfjWorOstMb97Lv978pLFBh6+ZIl0/fxdBYc3oXFRay8dsFNHmzM1ZyO7Jult+DosrOQV1Sgom17jZNrK3ISU7RW0eZpSgzBGZibYUyq/R9hbzRnfO/LWLXe2ORGBqAxIDw/n2wCw7QqXd1xx5ilq6mWKnE3MWJ2v8bicEDhsHvZ21ijJGBhHSlbk9MhrIQr3LmNN1vaKg3qQUqjt1JnmylxpgZGdHH351fo68y50ICdR1t+aJ2CCMPnOVUmv7P8GHdvq2ZE2dvb6Oz3MHehtTUjHLrRUYEMXnySHx83Lh1O4NZs5bwep+PWLtuJhZ33uvkyb9So0YwLSuY03NXRoYCdUlJmSEtmbUVWdf1f/75mYoy5U1tLLXHfP6d/8vuO/ZlNpba19RqNXtn/Ulwm0bI/bzIvlX2GDU2lVKn76s4BfshkUhIOHSK7VN/IcSmPzUaVdyWKO58Plb3nf9WdpZkpVfcnj1ItbrBRDUJR+5ix60baaz8eSPf/e9nPp4zssxQs/B8eKQ5Pi+//DL79u3j0KFDbNq0iSlTpvDrr7/SrFmzh15PrVq1dJ737NmT999/n0OHDlGvXj0WLVpEVFQUwcFlJ8UCtG/fHmNjY9auXUvPnj1ZuXIlVlZWtGrVCoDTp09z+fJlLC11D/SCggLi4uLIysoiOTmZunXral8zMjKiVq1aFX57nzx5MpMmTdJZZmgVhrF1eDk1XgxbN5xg2ucrtc+/+XHAI6/L01vO/GXvkZtTwK5tZ/hy/FJmzhuKj58T+3ef58TROOYtHfXQ6zuz6xjrZi7VPu8z6elPGixUqji7+wRNerUp81qz3m21/3bxc6ewQMU/K3fqJD4X9xxlx5zSSaedPxnyVOMNblobz+rB5GYoOL5mBxum/kaPr9/DyMSYf/5Yh527MyHNaj/VGCpyddtuMuOuEDVqKDJ7OzJiLnP+jyVIba1xCCudK+Vavw4OYSEoM7O4smkbp2b9Qr1P/ofhA4YSn6Q+/u60dHNgxIGzqEo0bcHdb777b6axLP4GAJcVuVSzs+QVL5dKJz7r1u5m4sTZ2udzf5rwSLE2aVpT+++gYB8iIwNp0Xwgmzftp2u3NuzccZjDh86wavX3j7T+f8uFTXsozC8gsnPZ8+0uUysLwju21D6X+3uRl5HF5iW7yiQ+B7ce5/dvl2ufj/pm4JMP+o66LWto/+3u54q7nysf9fyS6FOXCa0ZWEHNp6eqDlE9KY80A8vU1JTWrVvTunVrxo8fz8CBA5k4cSL79u0D0EkcCgv1j3+bm5vrPHd2dqZFixYsXryYevXqsXjxYoYOHVpuDCYmJnTt2pXFixfTs2dPFi9eTI8ePbSTpHNycqhZs2aZ4TMAubz8WfwPMnbsWEaPHq2zzDHs6Z1U/5ZGzUIJDffUPi9UFQGQkZaNg7z023x6Wg4BQeVfZQKa8W53TwcAgkLdiT6fxIpF+/jfhK6cOHKZ60lptG+k29CPH/M7EVE+zJxX9jMPqlsNt6DSobLiQk1sORnZWNqVXuWXk5mNs6+b3pjMrMwxMDAo0yOUk5mNhV3Z4Y8L+09TqFRRvWWdCt8rgHuQF3v+2kJRYRFGd3pZ/OqE43LPlVdFd2LOy8zG4p6Y87Kykfvoj1lmaY7EwED7zfneOma2uj0sUnMZUnMZtq6OuAR6M/v1D7l86DTBTWqRdOYSqYk3+P61U3dKa87PuX3HUqdbG+zav6Jdj4mlBRIDA1RZuttUZSmQWusflpRaW6FUlF++WKXi0oq/iRoxGMfqmi8IVp7uKBKTuLJpu07iY2wmw9hMhrmzIzb+PmwfOoaU46dwrf/wSVuWqpCiEjV2901YtZUak3ZnonJ5evq50SfAnfcOnCNOkXffOktIyM7XKX81O58I+8oP1zZvUYeIyNI/iqo751taWiaOjqXDZ6lpmYQE+z70eq2sLPD2duVqYjIAhw6dITHxJnVq99IpN2L419SsFcoff3yls9zW1gqJgQH5WbrnSX6WApmN/vcps7EqU74gM1vbU3m3Xn5mNma2pcd+fmY2dt6aK7ZunLvErUtXWNB7lM56/v5oCn6Na9F0WF+925YHeHFh9cUyy6s3CsM3tLQ9KyosBkCRkY3NPcPrivRsPP31n3+PytHVHgtrc25dSxWJz3PqiUw9Dw0NZc2aNdqEIjk5mRo1NFnwvROdH6RPnz588MEH9OrVi/j4eHr27PnA8q1bt+b8+fPs3LmTL774QvtaVFQUS5cuxdHRESs9V6IAuLi4cPjwYZo00VxSW1RUxPHjx4mKKv+yZKlUilSqO9G1KgxzmZmbYmZuqn2uVquxc7Dk+OHLBARrGobcnAIunk2kc7eyc14qoi5Ro7rzh7/PgOZ0eLWuzutvdv2W4e93okFT/fM9pGamSM10Y7OwtSL+9CVc/DQNZ0FeAddjrlL75UZ612FkbISLvwfxpy8R0kAzLFtSUsKVU5eo07FxmfInth4iqG41zK0tHvj+kuOvI7Mw0yY9ACYyU0xkujGb2VqRdCYGR19NzMq8fG5eSiCynf6YDY2NcPLzIOnMJfzraeZ+qEtKSDpzicj2ZWPWbgs1qNXaBLHDh29RdM8E3JTLiWyduYjuX43CxtmBe6edGxgZYeXtSdqFGJxqVtduM/VCDF6tmundno2/L2kXYvBpW/ptPPV8NDb+mj/YJcXFqIuLy1xRJDEwQF1Sfu+qWq1GjZqSoqJyy+hTpFZzKSuHmg427LuZrtkWmonKq64kl1uvt78bbwR4MObQeWKydC9jL1KruZiZg+d9V+95WMi4mVdQqfgALCzMtENRoHmvcrktBw+eJiREs99ycvI4c/oSvXq99NDrzc3NJynpJp3kzQEY9HZXunbT7UXp1HE4H419ixbNyyaTJibGOPh6kHw2Bu86pcfcjbOXCG3XpEx5AMdAH26cjaHay821y66ficbxTuJv6WiPzMaKG+ditJemq/LyuX05geC2mmO/fv+u1OzZQVs/LyOLLV/Movl7/XGs4DL6tITrWOtJPGVmpsjuazOs7Sy5cDwWzwBNe5afW0D8xUSad36yt2FJv5VJriJPb1zC86FSiU9aWhrdunVjwIABREREYGlpybFjx5gyZQqvvPIKMpmMevXq8fXXX+Pj48OtW7f45JNPHnr9r732GkOHDmXo0KE0b94cV9eKexaaNGmCs7Mzffr0wcfHR2fYqk+fPkydOpVXXnmFzz77DHd3d65evcqqVav44IMPcHd3Z+TIkXz99dcEBAQQHBzM9OnTn/oNEh/E3EyKn3fpZd/eHnIiQr3IyMwh6caj3TLgUUgkErr3aczCX3bg7uWAi5sdv87agr3cisYtSu/PMXLQTzRpUY0uvTSNx9wfNlKvUTBOzjbk5SnZtvEkJ4/F8+0cTa+YvYOV3gnNji42uLo/3ERRiURCvc5N2btkK/aucmyd7Nn5x0Ys7a0Jrl865Lhg7I+ENIigbkdNg93g1Wasnr4ItwBP3AI9Ofj3HlRKFTVa6yZiaTduc/VcnN4htZjD58jJyMY92AsjE2PiTsawb+k2GnRpXqbs/TFHdWzG4eVbsHF1xNrRngOL12NuZ41f3dL5cSvGz8S/XgTVX9YMm0W90pwtP/yJo78nzgFenFy3m8ICJWEtNfM1Mm+mcmn/CbyqByOztiAnLZOjK7dhJDXGp6bmc7Jx0e3hzFdoJl7auTtp7uOTpRurT7uWnPllIVY+ntj4epOwZSfFSiXujTUJ7+mfFmBqa0NQ984AeLdpzuHJ07myaTvyyGokHz5G1pWrVOuvuTDBWCbDLjiA6KWrMDAxQeZgR3p0LNf/OUxwry4A5N26TfLh4zhUC8HEypKC9Azi12/B0NgEeWQYlbU07jrjagQSnZXDxYxsuvm6IjM0ZGOSZp7KxzUCSS1Q8tNFzUT93v5uvBXkxWcnYriZV6DtLcovKib/zuXqf12+zqRaQZxOy+JEWhZ15bY0cLJjxIHyryR8WBKJhL59OzF3zjK8vVxxc3dixg+LcHS007nfTr83P6FV63q8/romSfjmm/k0b14HV1c5t26l8+PMxRgYGNChg+aYv3ul1/1cXeW4e+i/PUi1Di3YO+sPHPw8kft7c27DLoqUSgKba+LYM/N3zOysqd1H01MY9nIzNkz8nrPrduARFUb8P8dJjUuk4eBe2vcW9nJzTq3cjJWzXHM5+9INmNla41Vbk1xZyHXPfWNTzZdLKyc55ncmt8fuPoSBkZE2eUo4fJrYnQfp92GPh9q/rbs1Yf3v23Byd0DuYsfqeZuxsbci6p5hsqmj5hDVuBotu2i+WBTkKbl1PVX7empyOomx1zG3MsPeyZaCPCVrF2yhZtMIrO2suHUjleVz1uPo5kC1OvqnafwbRI9PxSqV+FhYWFC3bl2+++474uLiKCwsxMPDg0GDBjFu3DgA5s+fz1tvvUXNmjUJCgpiypQptGlT/rjtvSwtLenYsSPLli1j/vz5DywvkUjo1asXU6ZMYcIE3aETMzMz9u7dy4cffshrr71GdnY2bm5utGzZUtsDNGbMGJKTk3nzzTcxMDBgwIABvPrqq2RlZenb3L8iKsKXrctK38uUiZou3j+W7+HtMXP/1Vh6929Gfr6KqZ+tICe7gPAa3kybPVDnHj43rqWRlVl6BUNmeg5ffrKEtNsKzC1M8Qt04ds5A6ld/8l2+Tbq2pLCAhXrZi6lICcfzzBfXv9siM5l5RnJaeTdc3VFtaZR5Cpy2PnHRnIyFDj7uvPGZ0OwuG/Y6OTWQ1g5WOMXFcT9DAwNObJ+H5t/WQ1qNXauctoO6kzNdg/uBav1aisKC1Rsn/2X5gaGIb68NuEdnfuYZN1M1SYmAEGNapKflcPBvzaQl6EZFnt14juY3xk+MDIx5vqFOE6u201Bbh5m1pa4h/nT4+vRZSZSPyyXurVQKXKIXbUeZZYCK093ar8/XDt0VZCejsSgtPfGNsCPyCEDiF25lpgVf2PuJCdq5BDtPXwAqg99i5jlf3N67nwKc/OQOdgR2LWT9gaGBsbGZFy6TMLWnRTm5iG1tsI2yJ9649/Xe++gB9l5IxUbE2PeCvLETmrCZUUu7x86R8adCc9OMqnOkHxnbxdMDA34onaIznrmxyTyW4ymT2zfzTSmnY7j9QB3Rob7kpiTz/hjFzmb/ngTm+8aOOg18vMLmDBhFgpFLjVrhvLLr5/q3MMnMekmGfdMhk+5mcaY0dPIzFRgZ2dNzZqhLF02Fbt7hlMry7dhTQoUORxfuoH8zGzsvd1o+/G72iGrnNR0nd47pyBfmo/sx/G/1nNs8TqsXOS0+uBt7T18ACJeaUVRgZJ/fvoLVV4+TsF+tP34nXLvX1WeUys2a7ZvYICNmxPN3xtA4/YPlxi/1LsFygIVC6ctJy8nn4BwH0ZPe1vnHj63bqSSfU+bkRCTxJSRpXOxlvz4NwAN29XmrXG9MDCUkBSXzD+bj5GXk4+NgxVhtYN49a2XMH6m9/IRiU9FJOpnfeOaKkDm2evBhZ4jVy+9WPHuvF7+pa3Po0zVi9XoRGe9WD/Zd/zWi3U87O306HMKn4Wp991z63nX0KnieVvPo4ZOLz/V9TuHjn1i67p5YfITW9fz4sVq8QRBEARBqJAY6qqYSHwEQRAEoQoRiU/FxN4RBEEQBOE/Q/T4CIIgCEIVIhF9GhUSiY8gCIIgVCFiqKtiIvERBEEQhCrk/puFCrpEWigIgiAIwn+G6PERBEEQhCpEDHVVTCQ+giAIglCFiMnNFRN7RxAEQRCE/wzR4yMIgiAIVYgY6qqYSHwEQRAEoQoRiU/FxN4RBEEQBOE/Q/T4CIIgCEIVIiY3V0wkPoIgCIJQlYihrgqJxOcJuHqp17MOoVK8Av961iFUSkLMi7V/J5+2eNYhVIqjafGzDqFS9nVyfNYhVMqAfTeedQiVcjXH7FmHUCk38w2fdQiV1tDpWUfw3yYSH0EQBEGoQsTk5oqJxEcQBEEQqhDxW10VE2mhIAiCIFQhEgye2KOyZs2ahbe3N6amptStW5cjR45UWP77778nKCgImUyGh4cH7733HgUFBY/61h+KSHwEQRAEQXhsS5cuZfTo0UycOJETJ04QGRlJ27ZtuXXrlt7yixcv5qOPPmLixIlcvHiRefPmsXTpUsaNG/dU4xSJjyAIgiBUIRKJwRN7KJVKFAqFzkOpVOrd7vTp0xk0aBD9+/cnNDSUuXPnYmZmxvz58/WWP3DgAA0bNqR37954e3vTpk0bevXq9cBeosclEh9BEARBqEokkif2mDx5MtbW1jqPyZMnl9mkSqXi+PHjtGrVSrvMwMCAVq1acfDgQb1hNmjQgOPHj2sTnfj4eDZu3Ej79u2fzn65Q0xuFgRBEARBr7FjxzJ69GidZVKptEy51NRUiouLcXLSvVbfycmJ6Ohovevu3bs3qampNGrUCLVaTVFREUOGDBFDXYIgCIIgVILBk3tIpVKsrKx0HvoSn0exe/duvvrqK2bPns2JEydYtWoVGzZs4PPPP38i6y+P6PERBEEQhKrkGVzO7uDggKGhISkpKTrLU1JScHZ21ltn/PjxvPHGGwwcOBCA8PBwcnNzefvtt/n4448xMHg6fTOix0cQBEEQhMdiYmJCzZo12bFjh3ZZSUkJO3bsoH79+nrr5OXllUluDA01d+JWq9VPLVbR4yMIgiAIVckzuoHh6NGjefPNN6lVqxZ16tTh+++/Jzc3l/79+wPQt29f3NzctJOjO3bsyPTp06lRowZ169bl8uXLjB8/no4dO2oToKdBJD6CIAiCUJU8o7GcHj16cPv2bSZMmMDNmzepXr06mzdv1k54TkxM1Onh+eSTT5BIJHzyySdcv34duVxOx44d+fLLL59qnCLxEQRBEAThiRg2bBjDhg3T+9ru3bt1nhsZGTFx4kQmTpz4L0RW6oWc49OsWTNGjRr1rMMQBEEQhOeOWiJ5Yo+q6Jn1+PTr14+FCxcCYGxsjKenJ3379mXcuHEYGVXNjii1Ws282VtZt+owOdn5hFf3ZszHr+HhJS+3zuplB1iz7CA3b2QA4OPnRL/BranXKFjv+v/37jwO/xPDl9+9SZMW1Z7ae7mrYZ1g3hvSgahwX1ycbOk+8FvWbT321Lerj1qtZv4c3f07elzF+3fNsgOsWa67f998W3f/Tv18BccPx5J6W4HMTEq1SC+GjHwZLx/Hh47t6vbdXNm0DWWWAksPd0Jf74GNn3e55ZOPHCd21TryU9Mwc3IkqPurOEaWfp5FBQXELFtDyonTFObkIpPb4926OZ4tmgCgysnl8ur1pJ67QH5aBiaWFjjVjCTgtU4Ym8keGG/0lj2cW7eD/EwFdl5u1OnfDbl/+fEmHDzByWUbyLmdhpWznJp9OuNeI6z0/R8+Rcz2/aTHJ6LMyaPjNx9h5+2ufT3nVhorh+v/1td01AC860c9MOb7qdVqZsxYxPLlW1EocomKCuHTT9/B29u13DozZy7mxx//0lnm4+PG5s1ztc/feGMsR46c0ynTo0c7Pvvs3UrHeK/knbu4vmUbqqwszD3c8e3VE0tfH71l867fIPHvteRcTUSZloZPj264tm6lu75de7i5ew/KtDQAzFxd8OjYAdvwJ9MuvOLpTHcfN+ykJsRl5zLzQjwxWTl6y7Z3d6KNmxxvS3MALmXlMO/SVW15Q4mEAYGe1JHb4iIzJbeomBNpmfwac5U0peqR4kvYvpv4jZpzzsrDnbA3HnzOxazUnHPmTo4E99A95zb0Haq3XnCPV/F7uY32ecqps1xesxFF0nUMjI2wDw6k1qghj/QeHlnVzFeemGeaYbRr147ffvsNpVLJxo0beffddzE2Nmbs2LHPMqynZvFvu1n5137Gfd4DFzc75s3awpihv/LH6veRSo311nF0tGHIyPa4ezqgVsPmdccYO3IB85eOwsdf9xLBZX/u+9d/ldfcTMrZC4n8vnQ3S38Z869u+36LF+xm5eL9jP28B65udvw6ewvvv/Mrv68qf//KnWwYPEKzfwE2rz3GuFELmLekdP8GhbjTun0UTs42KBR5/DZ3G2OG/sLSDWMxNHxwp2ny4WNc/Gsl1d7shbWfD1e37OTotBk0+eZTpFZWZcpnxMZxes58Aru9gmP1cG4cPMqJH+bS8LOxWLq7ARC9eCVpF2OIHNwfmYM9qecucOH3JUhtrHGKikSZmUlBZiZBPbtg4epCQVoa5xb8RUFGFlHD364w3isHjnP099XUG9gDeYA3FzbuYvtXs+j83QRk1pZlyt+KiWfvjAVE9eqEe1Q1rvxzjF1Tf6bD1x9i66lJMoqUKpyC/PCuF8XBnxeXWYeZgy3df/pKZ9ml7f9wbt123O5JoCrjl19W8scf6/n661G4uzvxww+LeOutCWzcOBup1KTcegEBnvz22xfa5/o+4+7d2zJiRB/tc5ns8e5rcvvIUa4sW4Hf672x9PXhxvYdnP9+BlFfTMJEzzFSrFIhlTtgX6smV5Yu07tOqa0NXl1eRebkCGq4deAgF3+cTfUJn2DmVn7y9zCaOTswJMSH78/FEZ2VzWternxTO4x+e0+QqSosUz7S3pqdyamcv3AFVUkJPX3dmFI7jLf2nSRVqcLU0IAAKwv+vJxEXHYelsaGvBviy+c1Q3jnwOlKx3fj0DEuLl5JtX69sPHz4cqWnRyeOoNmU/Sfc+mxcZycPZ+ge865Y9/PpfHnpedcyxlf69S5feY8Z+b9iUvtGtplyUdPcHb+IoK6vYJ9SBDqkhKyr92odPyPzUBkPhV5pkNdUqkUZ2dnvLy8GDp0KK1atWLt2rUA/PPPPzRr1gwzMzNsbW1p27YtGRkZetfzxx9/UKtWLSwtLXF2dqZ37946P4qWkZFBnz59kMvlyGQyAgIC+O233wDNbbaHDRuGi4sLpqameHl56b0d9+NSq9UsW7SPvoNa0rh5NfwDXfn4i56k3Vawb+f5cus1bBZK/cYheHjJ8fSW8/bwl5CZmXD+TKJOudjo6yz9fS8fTer2xGOvyNbdp5k0bRlrtzybXp671Go1yxft4407+9cv0JWPP9fs3/27Kti/TUv3r4eXnEF39+/Z0v3bqWs9qtf0xcXNjqAQdwa925ZbNzO5eSP9oWK7snkHHk0b4t6kAZZuLoT164WhiQnX9uq/jXvC1l04hIfi274NFq4uBHbphJW3B1e379GWybgch1ujetiHBGImt8ezeWMsPdzIik8AwNLdjajhg3GqEYG5kxz70GACu3bi1qmzlBQXVxjvhQ07CWjZgIDm9bFxd6H+wJ4YmphweZf+eC9u2o1b9RCqdWqFjbszNXp0wM7Hg+gtpfH6NalDZNeXcA0P0rsOAwMDZDZWOo/Eo6fxrh+FsWnlkwq1Ws3vv69l6NDutGpVj+BgH6ZMeY9bt9LZvv1QhXUNDQ2Ry221Dzs76zJlTE2lOmUsLMwqHeO9bmzbjlPjRjg1aoiZqyt+r/fB0MSEW/sP6C1v6eONT7euyOvUxsBIf1JvVz0Su4hwZE5OyJyd8HqtM4ZSKdnx8Y8VK0BXH1c2JqWw5fotrubk8/35OJTFxbRz198LOvn0JdYm3iQuO5ek3Hy+PXsZiQRq2Gv2bW5RMR8cPc+em2lcy83nYmYOMy/EE2RtgaNp+Ulqea5s3oFHs4Z43Dnnwvv1wlBqQtKecs65LbuQh4fi93IbLN1cCOraCWtvDxK2lR7DpjbWOo+UE2c055+jpke5pLiYC38uJ7jna3i1aIKFixOWbi641q1Z6fiFp+u5muMjk8lQqVScOnWKli1bEhoaysGDB9m/fz8dO3akuJwGu7CwkM8//5zTp0+zZs0aEhIS6Nevn/b18ePHc+HCBTZt2sTFixeZM2cODg6ab/gzZsxg7dq1LFu2jJiYGBYtWoS3t/cTf2/J19NJT82mVt0A7TILSxkh4Z6cP3P1odZRXFzC9k2nKMhXERbppV1ekK9i0tjFvDeuM/YOZb/N/BdUtH/PnX74/btjs2b/Vovw0lsmP1/Fxr+P4eJmh6OzzQPXWVJUhCIhEYew0qEziYEBDmHBZF7W/wco83I89mG6Q5nyaqE65W39/bh18gwF6Zmo1WrSLsaQm3ILh2qh5cZSlJePkcwUgwouEy0uKiItPkknQZEYGOAaHsTt2Ct669y+dAWXarrxukWGcPtSQrnbeZC0+ETSE64R0Fz//T8e5Nq1FG7fzqBBg+raZZaW5kRGBnLypP7b59919eoNGjV6k5YtBzJmzDRu3Cj7y9Lr1u2mbt3edOjwLt9+u5D8/IJHihM0x0jO1URsQkO0yyQGBliHBD+RJAVAXVLC7SNHKVapsPTzfax1GUkkBFpZcCI1s3T9wInULEJtyvYI6iM1NMRIIiG7sKjcMuZGhpSo1eQUVZyo36+kqIgsfedcaPnnXMbleJ3yAPLwUDLKKa/MUnDr9Fk8mjTQLlMkJFGQkYlEImHfJ1+yffiHHJk2k+xr1ysV/xPxBH+rqyp6LibTqNVqduzYwZYtWxg+fDhTpkyhVq1azJ49W1smLKz87u4BAwZo/+3r68uMGTOoXbs2OTk5WFhYkJiYSI0aNahVqxaATmKTmJhIQEAAjRo1QiKR4OWl/w/eXUqlsswv0yrVheUOpdyVlpoNgK29bsNgZ29B+p3XyhMXm8zQN35EpSpCZmbCl9+9iY9f6e+hzJy6lmqR3jRu/vTn9Dyvyt2/dhakpz14/77T987+lZnwxfQ38fbT/b2Z1UsPMPf7DeTnq/D0ljN97iCMjR98+qiyc1CXlGBirZuQmlhbkZOcoreOMktRpjvexNoKZZZC+zzkje6c/20Ru94bi8TQACQGhPfvg11wwP2r08Zxee0mPJs1qjBepUITr+l9Q1qm1lZk3dAfb36mAlOb+8tbkn9PvJUVu/Mg1m7OOAY92h/p27c1vcP29jY6y+3tbUhN1d9zDBAREcjkyaPw8XHj9u0MZs36iz59PmLduh+1vTodOjTF1dURR0c7YmISmDZtAVeuXOfHHx/t94UKc3KgpARjK919aGJlRdbNm4+0zrtyr13nzORvKCksxFAqJfidIZi5Pt4wl7WJMYYGEjLuG9LKUKnwsCjbO6bPoCAv0pQqjqdl6n3d2EDCoCBvdianklfJxOfuOXf/OSS1tiK3gnOuzDlqpXvO3eva/kMYmZriXKt0mCvv9m0AYldvIKR3F8wc7InftJ2DX31HsymTMLEwr9T7eCxVM195Yp5pj8/69euxsLDA1NSUl156iR49evDpp59qe3we1vHjx+nYsSOenp5YWlrStGlTQJPUAAwdOpQlS5ZQvXp1PvjgAw4cKO0+7tevH6dOnSIoKIgRI0awdevWCrel75dqZ0xdUabc1g0naFPvY+2jqJIn7708veXMX/YeP/05nFe61efL8Uu5Eqc5gffvPs+Jo3GM+KDTI6//RbR1wwna1v9Y+yh+zP07b+l7zP1jOK90r89XE5aSEKfbQLZuX4Nfl4xixryhuHvJmfjBnyiVZecy/FuubttNZtwVokYNpcGnYwnp2YXzfywh9fzFMmUL8/M5Nn0WFq7O+Hfu8AyirZwilYr4f45Vqrdn7drd1KjRTfsoKiq/J6EiTZvW4qWXGhEc7EPjxlH8/PNEFIpcNm3ary3To0c7GjeOIijIm06dmvHNN++xbdtBEhOTH2mbT5PM2YnqEz4hctxHODdrSuz8BeTdeAZzTu7R09eN5i4OTDwRTWFJ2bvzGkokTKgejAT44Xzcvx/gQ0jaewDX+nUwNCn9wqu+8178O7XDpXYU1j5eRAzqi0QiIfnIiWcVqqDHM+3xad68OXPmzMHExARXV1ft1Vwy2YOvOrkrNzeXtm3b0rZtWxYtWoRcLicxMZG2bduiUmmuBnjppZe4evUqGzduZNu2bbRs2ZJ3332XadOmERUVxZUrV9i0aRPbt2+ne/futGrVihUryiYzoP+XarPU28qUa9QslNBwT+3zQpWmIc5Iy8ZBXvrNIj0th4Cgir+BGRsbaSffBoW6E30+iRWL9vG/CV05ceQy15PSaN9ogk6d8WN+JyLKh5nz9F+J8KJ76P2bnoN/YOX37/LF+/jf+K7aMhaWMiwsZXh4yQmL8OTlxhPYt/McrV6qUd5qATCxtEBiYIDqvm+OqiwFUmv9w5JSayuUivLLF6tUXFrxN1EjBuNYPRwAK093FIlJXNm0HYew0iGTovwCjk37ESNTKVEjhmBgVPHdUKVWmngLsnR7yQqyFMhs9Mcrs7GiIPP+8tnIynl/D3L10CmKlSr8mtZ56DotWtQhMjJQ+1x1pzciLS0TR0c77fK0tEyCgx++F8nKygJvb9cKk5rISM2w4NWryXh6ujz0uu8ytrAAAwMKFbr7UKVQYGL9cD0o5TEwMtJMbgYsvL3ISUjgxvad+Pd9/ZHXmaUqpLhEja2Jbi+3rYkJ6Q+4Aqubjyu9fN3535HzxGfnlXldk/QE4SST8v6Rc5Xu7YHSc+7+c0j5gHOuzDmq0F8+PSaW3OQUot4dqLsOG81nZeFaegwYGhtjJncgP+3h5gM+MWJyc4WeaY+Pubk5/v7+eHp66lzCHhERofN7HxWJjo4mLS2Nr7/+msaNGxMcHKwzsfkuuVzOm2++yZ9//sn333/Pzz//rH3NysqKHj168Msvv7B06VJWrlxJerr+A1X/L9WWHeYyMzfF3dNB+/D2c8LOwZLjhy9ry+TmFHDxbCJh5cwnKY+6RI3qzth4nwHNWbB8NPOXvqd9AAx/vxNjJ/Wo1HpfJOXu3yNl92+1yMrt35IStTaR0ket1sxpqKjMXQZGRlh5e5J2Iaa0fkkJqRdisPHX/wfYxt9XpzxA6vlobfmS4mLUxcVlruCTGBhov3WCpqfn6NQZGBgZUnPUOzrfTstjaGSEva8HyWd1400+dwl5gP5Lq+WBPiSf0433xtlo5IHeD9yePrG7DuBRKxxTq4ebLwJgYWGGl5er9uHv74lcbsvBg6VXBOXk5HH69CVq1Ch7K4jy5Obmk5R0E7ncttwyFy9q5oFUVKYiBkZGWHh5knWxtLdOXVJCVnQ0lr6PNx+nDLUa9SP2ht1VpFZzSZGjnZgMmpGVGg7WXMgsf1i5h48br/t58NGx81xSlL3s/W7S42Zuyv+OnkNRwfyfihgYGWHt7Unqed1jOK2Cc87W35fU+8652+eisdVTPmnPAay9PbHydNdZbu3jiYGxETk3S3uLS4qKyUtNw8zB7v7VPF1ijk+Fnos5PvcbO3Ys4eHhvPPOOwwZMgQTExN27dpFt27dtJOS7/L09MTExISZM2cyZMgQzp07V+Yn7SdMmEDNmjUJCwtDqVSyfv16QkI034qnT5+Oi4sLNWrUwMDAgOXLl+Ps7IyNjc0TfU8SiYTufRqz8JcduHs54OJmx6+ztmAvt6Jxi9L5SyMH/USTFtXo0qshAHN/2Ei9RsE4OduQl6dk28aTnDwWz7dzNN827B2s9E5odnSxwdX96Z9s5mZS/LxLL6v39pATEepFRmYOSTfSnvr275JIJHTr05jff9mBu6eD9nYB9nIrGjUv3b+j3v6Jxi2q0aWnZv/+NGMjdRuW7t/tm05y6lg802Zr9u+Na2ns3HKa2vUDsbE151ZKFot+24VUaky9xiF6Y7mfT7uWnPllIVY+ntj4epOwZSfFSiXujTVDOad/WoCprQ1B3TsD4N2mOYcnT+fKpu3II6uRfPgYWVeuUq1/bwCMZTLsggOIXroKAxMTZA52pEfHcv2fwwT36gKUJj0lykIiBvenKD+fovx8AEysLJFU8KvHoS+3YP/sP7D388TBz5uLG3dRpFTi36weAPt+/B0zO2tq9n4FgJCXmrF50vecX7cD96gwrhw4TlpcIvUH9dKuU5mTS25qBnkZWQDa+UJ3r+C6S3HzNikX42j10eP1VEokEvr27cScOUvx8nK9czn7nzg62tGqVT1tuTff/JjWrevz+uuaIcBvvplH8+Z1cHV15NatdGbOXIyBgQEdOtwdPk9m3bo9NG1aCxsbS2JiEpg8+Vdq1w4jOFh/YvgwXFu3Inb+Aiy8vLHw8ebG9h0UK1U4NtRMnr007zdMbGzw7vIqoJnAm3cjWftvZWYmOYlJGEql2h6ehJWrsQ0PQ2pnR3GBktuHj5AVc4mwUSMeOc67Vly5wYcRAVxS5BCdmUMXb1dMDQ3Zck3zpfPDiABSC1TMu6S5sKCnrxtvBnjy1alL3MxTanuL8ouLKSguwVAiYWKNIAKsLPj4+AUMkGjLZBcWUVTJH6z0adeS078sxMbHE2tfbxK27qRIqcSjieacO3XnnAu+e861bc6hr6YTv2k7jpHVuHFIc85FDOits97C/HySj5wgpHeXMts0lsnwbN6Y2FXrkdnZInOwI36jZjTApU7l70MlPD3PZeITGBjI1q1bGTduHHXq1EEmk1G3bl169epVpqxcLmfBggWMGzeOGTNmEBUVxbRp0+jUqXTOi4mJCWPHjiUhIQGZTEbjxo1ZsmQJAJaWlkyZMoXY2FgMDQ2pXbs2GzduLPOLsU9C7/7NyM9XMfWzFeRkFxBew5tpswfq9BjduJZGVmau9nlmeg5ffrKEtNsKzC1M8Qt04ds5A6ldP1DPFv59URG+bF1WOsw2ZWJfAP5Yvoe3x8wtr9pT0btfMwryVUz7vIL9m5RGVkbp/s1Iz+GrT5aQllq6f6fNLt2/JiZGnD5xheWL9pGtyMfW3oLIKF9mL3wXWzuLh4rLpW4tVIocYlet19xMzdOd2u8P13ajF6SnI7mna9o2wI/IIQOIXbmWmBV/Y+4kJ2rkEO39RACqD32LmOV/c3rufApz85A52BHYtZP2BoaKhCSy4hIA2PuB7jBo02lfYCa3LzdenwY1KVDkcGrZBvIzs7HzdqPV2He1CUpumm68jkG+NBnej5NL13NiyTqsnOU0/9/b2nv4ACQdO8s/c/7UPt/7g+Z2EpFdX6J6t5e1yy/vOoi5nQ2uEQ/fK1OeQYO6kJ9fwIQJP6JQ5FKzZii//jpJ5x4+SUk3ycgoHeK4eTON0aOnkZmpwM7Ompo1Q1m2bJr2knZjYyMOHjzF77+vJS+vABcXB9q0acA77zxe76q8Tm2KcnJI/HstKoUCcw93wkaN0E64Vaal6/TwqTIzOf1Z6b2GbmzZxo0t27AKDCT8A839tAqzs4mdtwBVVhZGMhlm7m6EjRqBTVj5V/49rN03U7E2MaJfgCe2UhPiFLl8dPS8dsKzo6lU59e1O3o4Y2JgwKdRup/rwthEfr+chIOpCQ2dNMfkL410h49HHz7L6fTKTZR3rVcLVXYOl+455+r8r/Scy79vf9oF+FFj6ABiVqwlZvnfmDnJqTVK95wDSD50DDVqXOvV1rvdkJ5dkBgacuqnBZSoCrHx86beR6MwNv8XJzaDmNz8ABL10/zt9/+IWwVrn3UIleIV+NeDCz1HEmLKJrzPs8mnHy4hel44mj76xPBnYVz1yg1dPmsD9j3bycSVdTXnwUOiz5NIO+WDCz1nptdt8VTXH9Bu/hNbV+zmAQ8u9IJ5ru7jIwiCIAiC8DQ9l0NdgiAIgiA8IjHUVSGR+AiCIAhCFVJVf1X9SRGJjyAIgiBUJeI+PhUSc3wEQRAEQfjPED0+giAIglCViA6fConERxAEQRCqEjHHp0JiqEsQBEEQhP8M0eMjCIIgCFWJmNxcIZH4CIIgCEJVIvKeComhLkEQBEEQ/jNEj48gCIIgVCVicnOFROIjCIIgCFWJSHwqJIa6BEEQBEH4zxA9PoIgCIJQlYgujQqJxOcJ2Hnd5FmHUCkJMb2edQiV4h3017MOoVKOn+nzrEOolNzCF6tbfMTB6886hEqxeMFa2U6euc86hEoRV27rIYa6KvSCnZKCIAiCIFRI5D0VEh1igiAIgiD8Z4geH0EQBEGoQtRi/K9CIvERBEEQhKpEzPGpkBjqEgRBEAThP0P0+AiCIAhCVSI6fCokEh9BEARBqErEHJ8KiaEuQRAEQRD+M0TiIwiCIAhViUTy5B6VNGvWLLy9vTE1NaVu3bocOXKkwvKZmZm8++67uLi4IJVKCQwMZOPGjY/6zh+KGOoSBEEQhKrkGY10LV26lNGjRzN37lzq1q3L999/T9u2bYmJicHR0bFMeZVKRevWrXF0dGTFihW4ublx9epVbGxsnmqcIvERBEEQBOGxTZ8+nUGDBtG/f38A5s6dy4YNG5g/fz4fffRRmfLz588nPT2dAwcOYGxsDIC3t/dTj1MMdQmCIAhCVWIgeWIPpVKJQqHQeSiVyjKbVKlUHD9+nFatWpWGYWBAq1atOHjwoN4w165dS/369Xn33XdxcnKiWrVqfPXVVxQXFz+1XQMi8REEQRCEquUJJj6TJ0/G2tpa5zF58uQym0xNTaW4uBgnJyed5U5OTty8eVNvmPHx8axYsYLi4mI2btzI+PHj+fbbb/niiy+eym65Swx1CYIgCEIVon6Cc3zGjh3L6NGjdZZJpdInsu6SkhIcHR35+eefMTQ0pGbNmly/fp2pU6cyceLEJ7INfV7oxOfgwYM0atSIdu3asWHDhmcdjg61Ws2uPzdxfPNBCnLz8Qz1ocO73bB3KzvB616H1+3jwMqd5GQocPJxo/3QLrgHeQGQkZLG9/0/01uv+9h+hDWuAcDE9iPLvN71wzcJbxpV6fcwf85W1q06TE52PuHVvRk97jU8vOTl1lmz7ABrlh/k5o0MAHz8nHjz7dbUaxSsLTP18xUcPxxL6m0FMjMp1SK9GDLyZbx8Kt43T0LDOsG8N6QDUeG+uDjZ0n3gt6zbeuypb1cftVrNXz9vYfvfh8jNySc4wofBH3TB1bP8/btywQ4O7T7Ltau3MJEaExzuRd9hHXDzKt13cyYv5/TRWDJSszCVSQkK96bvsJdx93Yqd70PG+/KeZvZte4Qedn5BIb70P/9rjh7lB9v9Kk4NizexZWYa2SmKRj1VX9qNQnXKVOQp2Tp3PUc23eOnKxc5K72tO3amJadGzx0bInbd3Nl01ZUWQosPd0Jfr0HNr4+5Za/eeQ4l1etJT81DTNnRwK7vYo8sjSuooICLi1fza0TpynMyUUmt8erVQs8WjTRlsm7dZuYJSvIiI2jpLAIh/BQQl7vidTaqsrFC5rP/+iSjVzYdgBlXj4uwT40ebsHNq4Vn7dnN+3l1Jod5GUqsPd2o/HArjgFeGtfP7/1H2L3HeN2/DUK8wt4649vkJqb6axj41c/kZpwnfysbKTmZrhHBlH/jVcwt7OuMN4jf23k/PYDKHM18TYb/OB4z2zcy8k78Tp4u9FkYFecAjXxFmTncnjJRpJORZOdmoHMygLfuhHU7fUyUnMZAKlXrnF81TaSL8aTn52LldyOam0bEdmxWYXbfV5JpdKHSnQcHBwwNDQkJSVFZ3lKSgrOzs5667i4uGBsbIyhoaF2WUhICDdv3kSlUmFiYvJ4wZfjhR7qmjdvHsOHD2fv3r3cuHHjWYejY/+KHRxeu5eOw7oz6Lv3MDY14Y/xcylUFZZb59yeE2z5ZTXNerdl8Mz/4ezryh/j55CTmQ2AtYMt7//5uc6j+esvYSKT4l8rVGddnd/rrVMuuH64vk1WaPGC3axcvJ8xH7/GT38Mx1Rmwvvv/IpSWf57kDvZMHhEe35ZPJJfFo8kqrY/40Yt4Mrl0q7OoBB3PprUgz9W/Y9psweiVsOYob9QXFxS6Rgry9xMytkLiYz6ZP5T39aDrP5jFxuW7WPwh135Zt5IpKYmfDbyZ1QV7N/zJ+N4qWsDvpk3gk9nDKa4qIRJI36mIL90zN0v2J3h43swc8mHTPjhbUDNpBE/P/b+Xb9oJ1tX7GPA+92Y9PMopDITvhn9U4XxKvNVePq78ubo18ots2jm35w+HM3Q8X2Ysugj2nVrwsLvVnF8/7mHiiv58DGil6zAv3MH6k8ah6WHO8enzUSpUOgtnxEbx5m583Br0pD6n32MY43qnJwxl+xr17VlYv5aQerZC0S83Z9GX03Eq01LLv65hFsnTwNQpFRybOoPIJFQ+4P3qPvx/1AXFXPi+1moSyrezy9avHedXL2dMxv20HRID7p8PQYjqZT1n8+mqII2LXb/cf75bTW1ur9Et2kf4ODtxvrPZpN3p03TxKbCs0YINbu0Lnc9buEBtBnTn94zx9P2g7fIupnK5qnzKoz3xOrtnN6wh2aDe9DtmzEYS6Ws/ezB8e7/bTW1e7xEj28/wN7bjbX3xJubnkVuehYN+3Wm9/djaTW8D1dPXGDnrMXaddyKS0JmbUnrUX3p/cM4anVty8E/13Jm454K432inuBQ18MyMTGhZs2a7NixQ7uspKSEHTt2UL9+fb11GjZsyOXLlym55xi8dOkSLi4uTy3pgRc48cnJyWHp0qUMHTqUl19+mQULFui8vnbtWgICAjA1NaV58+YsXLgQiURCZmamtsz+/ftp3LgxMpkMDw8PRowYQW5u7mPHplarObRmD016tiG4fjjOPm68NuZ1stOyiD54ttx6B1bvpma7BtRoUw9HT2c6DOuOsdSEk1sPAWBgaIClnZXO4+KBM4Q1ro5UppuRm5rLdMoZmxhX+j0sX7SPNwa1pHHzavgFuvLx5z1Ju61g/67z5dZr2DSU+o1D8PCS4+ElZ9Dwl5CZmXD+bKK2TKeu9ahe0xcXNzuCQtwZ9G5bbt3M5OaN9ErF+Ci27j7NpGnLWLvl2fTy3KVWq1m/ZC/d+reibtNqeAe4MvLTXqSnKji8p/w/+BN+eJsWHerg6euMT6Arwyf05PbNDOKir2nLtHm1PmE1/HB0tcMv2J3eg18iNSWTW8mPvn/VajWbl+/llb6tqdm4Gp7+rgz5pDeZaQqO7ys/3sj6IXR7uz21m0aUWyb2XAKNX6pNaJQ/chc7WrxSH08/V+IvJJZb515Xt2zHvWlD3Bo3wMLNldA3e2NoYsz1vQf0lk/cthOH8DB82rfBwtWFgC6dsPLyJHH7bm2ZzMvxuDWsh11IEDK5Ax7NGmPp4U5WfILm9dg48lPTCB/4JpYeblh6uFFtUD8UCYmkX4ypUvGC5vM/s343Nbu2xadOBA7ebrQc8Qa56VlcOXKm3Hqn1+0itHV9QlrWw87DhaaDe2AkNSF6Z+lk18iOzYl6rQ1OgeX3eEV2bIFzkA+Wjna4BPsS9WprUi4lUFykfxKsWq3m9Prd1OrWFt+6mnhbjdTEG3+4/HhPrd1FWOv6hN6Jt/kQTbwXd2jitfdypf2HA/GpHY61ixz3iCDq9+nIlaPnKLkzITe0VX2aDOyKW7UArJ0dCGpWm5AW9Yg7eLrCffxEPaP7+IwePZpffvmFhQsXcvHiRYYOHUpubq72Kq++ffsyduxYbfmhQ4eSnp7OyJEjuXTpEhs2bOCrr77i3XfffaK7434vbOKzbNkygoODCQoK4vXXX2f+/Pmo1WoArly5QteuXencuTOnT59m8ODBfPzxxzr14+LiaNeuHV26dOHMmTMsXbqU/fv3M2zYsMeOLeNmGjkZCnyrB2qXmZrLcAvyIuniFb11igqLSL6cpFPHwMAA3+qBJEUn6K1zIzaJm/HXiWpTNpveMGcF3/Qcx8+jvuXE1kPaffOwkq+nk56aTa26AdplFpYyQsI9OXf66kOto7i4hB2bT1GQr6JahJfeMvn5Kjb+fQwXNzscnW0qFeOLLOVGOhlp2UTWKf28zS1kBIR5EnP24fYvQF5OAQAWVmZ6Xy/IV7Jz/VGcXO1wcLJ55Hhv30gnKy2barVL4zWzkOEX6knsuYRHXi9AQDVvTuw/T/rtTNRqNRdOxHIz6TbhdYIeWLekqAhFQiL2oSHaZRIDA+zDQsiMi9dbJ/NyPHahwTrLHMJDdcrb+Pty69QZCjIyUKvVpF2MITclBftqmp7VksIiJBIJBkalswUMjY2QSCRkXLpcZeK9S5GSRl6mAo/I0s9Eai7DKcCbmzH627TiwiJuxyXhHlFaR2JggHtEEDdjEh64zfIUZOdyae9RnIN8MDQy1FtGkZJGXkbl470Vl6RT52HiVeblY2JmioGh/ljuljG1NH/AO3vx9ejRg2nTpjFhwgSqV6/OqVOn2Lx5s3bCc2JiIsnJydryHh4ebNmyhaNHjxIREcGIESMYOXKk3kvfn6QXdo7PvHnzeP311wFo164dWVlZ7Nmzh2bNmvHTTz8RFBTE1KlTAQgKCuLcuXN8+eWX2vqTJ0+mT58+jBo1CoCAgABmzJhB06ZNmTNnDqampnq3q1Qqy1zKV6hUYSwt7ZbLydB0i1rYWuqUs7Cx1L52vzxFLiUlJXrrpCbd0lvnxNaDyD2c8AzV/abU/PX2+EYGYGxqwuUT0WyYtRxVvpJ6rzTVux590lI1cdra68ZjZ2dBepr+93BXXGwy7/T9EZWqCJnMhC+mv4m3n+78ktVLDzD3+w3k56vw9JYzfe4gjI1f2MOx0jLTNMMa1na6+9fGzpLMdP1DHvcrKSlh3ndrCI7wxsvPRee1TSv+4fcf11OQr8LNS87EmYMfa//ejcnqvuPTytaSrPSKj4cH6fvea8ybsowRr36GoaEBEgMJb33QneDqfg+sq8rOQV1SUmaeiomVJbnJ+q8kUWYp9JZXZZXu95DXe3B+wSL2vDcWiaEBSAwI6/86dkGaLwI2fj4YSk2IWbaawK6dUaMmdtlq1CUlKLPK//xetHjvysvUlJFZ637+MhtL8jL01y/IzkVdUoKZjVWZOhnXU/TWqcjB3//m7Ka9FClVOAV68/LHQx4Yr9l98ZrZWGpfu1/+nXhl9+1rMxtLMsuJN1+Rw7HlmwlrXf58tOToeC7/c4IOn5Qf7xP3DH+ra9iwYeV2IOzevbvMsvr163Po0KGnHJWuF/IvTUxMDEeOHGH16tUAGBkZ0aNHD+bNm0ezZs2IiYmhdu3aOnXq1Kmj8/z06dOcOXOGRYsWaZep1WpKSkq4cuUKISEh6DN58mQmTZqks6xFl3bcii0daugzafBjvb+HUahUcXb3CZr0alPmtWa922r/7eLnTmGBin9W7qww8dm64QTffrFS+/ybmQMeOTZPbznzlr5Hbk4Bu7ef4asJS5n561Cd5Kd1+xrUqhdAWmo2S37fw8QP/mTWgneRSis3JPei2LP5OHO/XqF9/vH0gY+9zp+nriIx/iZf/VS2kWnSLorIOoFkpCn4e9Fupo37g8m/DMPkIffvP1uPM3/qcu3z96c8frzl2bpiH5fPX2X012/h4GxL9Ok4Fk5fha2DtU4P07/p6vZdZMZdocbId5A52JEeE8vFP/7C1MYa+7AQTKwsiXz3bS4sXEzi9l1IJBKc69bGyssTySPc5v95i/fSnqPs/mmJ9nlFSca/pXrnloS0qk/2rXSOLtvE9h9+18QlkRCz5yi755bG2+FfiFeVl8/6L+Zi6+5MnZ7t9ZZJu3qDDZN/oXaPl/Csrv9vylPxwo7l/DteyMRn3rx5FBUV4erqql2mVquRSqX8+OOPD7WOnJwcBg8ezIgRI8q85unpWW49fZf2LYvdjDKvtBeouLBIs42MbCzvueogJzMbZ183ves1szLHwMCgTI9QTmY2Fvf1CgBc2H+aQqWK6i3rlHntfu5BXuz5awtFhUUYlfOtv1GzUELDS993oUrzHjLSsnGQl34DSk/PwT/QtUz9exkbG+Hu6QBAUKg70eeTWL54H/8b31VbxsJShoWlDA8vOWERnrzceAL7dp6j1Us1Hvh+XkR1GocRGFY63Fd45xjJSs/GzqF0/2amZ+MToP8YudfPU1dxbP8FvvzpXb1DWOYWMswtZLh6ygms5sUbrcZzePdZGrd9uCv7ohqF4RdaejwUqTTzFxQZ2djeE68iIxtP/wfHWx6VUsWynzcy6qv+1GigGZbx9HflauwNNvy164GJj4mlBRIDgzK9FipFNiblXK0ktbaqsHyxSkXsir+pMXwI8uqaiwIsPdzJTrzGlU3bsA/T/AFzqBZKk6lfoMrOQWJggLG5GbtGfICz3OGFj9e7Tjg97lzJBKVtWn5Wts6VVPmZ2dj76P/8TS3NkRgYlOlhyc/MLtML9DBkVhbIrCywcXXE1t2J39+eQMqlBFyDffCpE6698ureePPuizcvMxuHcuKV3Yk3/759nacnXlV+AWs/m4OxTEr7jwbpHXJLT0pmzcQfCWvdgNrd2lX6/QpPzwuXFxYVFfH777/z7bffcurUKe3j9OnTuLq68tdffxEUFMSxY7qTV48eParzPCoqigsXLuDv71/mUdFscqlUipWVlc7DwsYKe1e59iH3dMbC1or405e09QryCrgecxWPEP0T+IyMjXDx99CpU1JSwpVTl/AI9i5T/sTWQwTVrYa5tcUD91ly/HVkFmblJj0AZuamuHs6aB/efk7YOVhy/Ejp+H9uTgEXzyZSLVL/fJ3ylJSotYmUPmo1qKHCMi86mbkpLh4O2oeHjxO29pacORqrLZOXU0Ds+USCwsvfv2q1mp+nruLwnrN8NmsoTq72D964WlPvbrL1UPGameLsLtc+3HycsLa35Pyxe+LNLSDuQiIB1bwfer33KyoqobioGIP7eh0MDCQPNS/NwMgIK29P0i9Ea5epS0pIuxCNjZ+v3jo2/r465QHSzl/UllcXF6MuLi4zXCAxMNAcrPcxsbTA2NyMtAvRqLKzcaxR/kTuFyVeE5kp1i5y7cPWwxkzGyuunSmdCK3KyyclNgHnIP1tmqGxEXI/D66fKW3T1CUlXDtzCecgb711HtbdY+NugmMiM8XGRa592Hk4Y2Zb+Xgd/TxIuj/es7rxqvLy+fvTWRgYGfLyuMEY6blwJC0xmdXjZxLcvA71X+/4WO/1kTzDHyl9EbxwPT7r168nIyODt956C2tr3Xs4dOnShXnz5rFs2TKmT5/Ohx9+yFtvvcWpU6e0V33d7db98MMPqVevHsOGDWPgwIGYm5tz4cIFtm3b9tC9RuWRSCTU69yUvUu2Yu8qx9bJnp1/bMTS3lrnsvIFY38kpEEEdTtq7rXR4NVmrJ6+CLcAT9wCPTn49x5UShU1WtfVWX/ajdtcPRend0gt5vA5cjKycQ/2wsjEmLiTMexbuo0GXZpX+j1069OY33/ZgbunAy5udsybtQV7uRWNmodpy416+ycat6hGl54NAfhpxkbqNgzGydmGvDwl2zed5NSxeKbN1gyV3LiWxs4tp6ldPxAbW3NupWSx6LddSKXG1Gv89LuCzc2k+HmX3lPC20NORKgXGZk5JN1Ie+rbv0sikdChZxOW/7YdFw8HnFztWfzTJuwcrKjbtJq23IR351CvWTjtuzUCND09e7ecYOzUAcjMpWTcmStkZi5DamrMzetp/LPtFNXrBmJla0HarUxW/b4TE6kxUQ0eff9KJBLadWvCmoXbcPJwwNHFjhW/bsbG3oqajUvj/WrkHGo1qUabLo0BzT16Uq6nal+/nZzO1djrmFua4eBsi5m5KcHV/fhr9jqMpcaaoa5TcezffIw+w195qNi82rbi3C8LsPLxwtrXm6tbd1KsVOHWWDPv4uzPvyG1tSGw26sAeLZuwdGvvyVh0zYcIsO5efgoWVeuEtqvDwBGMhm2QQFcWroKQ2NjTB3syYi+xI1/DhHUq7TX8vq+A5i7OGNiZUnm5XiiFy3Dq01LzF3037PkRY0XNJ9/RIdmHF+xBWsXR6yc7Dny13rM7azxqVOaOP09cSa+dSMIb68ZVo/s2JydM/9E7u+JY4AXZ9btpkipJLhFPW2dvAwFeZkKspJvA5rhIROZKRYOtphampNyKYFbl6/iEuKH1NyMrJTbHFm8AStnh3ITKIlEQmSHZhxbvgUbF0csnew5vFgTr2/d0njXTJiJb70IIu7EW71Tc7bP+BNHP0+cArw4vX43RQVKQlpq4lXl5fP3pNkUKVW0GdUXVV4BqjzNBQYyKwsMDA1Iu3qDNRNm4lkjhOqdWpB7Zw6UgYGkzBypp+YZzvF5Ebxwic+8efNo1apVmaQHNInPlClTyM7OZsWKFYwZM4YffviB+vXr8/HHHzN06FDtjZgiIiLYs2cPH3/8MY0bN0atVuPn50ePHj2eSJyNuraksEDFuplLKcjJxzPMl9c/G6JzWXlGchp5WaWXz1drGkWuIoedf2wkJ0OBs687b3w2BAtb3W7Wk1sPYeVgjV9U2ateDAwNObJ+H5t/WQ1qNXauctoO6kzNdvrvo1CR3v2aUZCvYtrnK8jJLiC8hjfTZg/UmYdzIymNrIzS95CRnsNXnywhLVWBuYUpfoEuTJs9kNr1NUMWJiZGnD5xheWL9pGtyMfW3oLIKF9mL3wXW7sH9149rqgIX7Yum6B9PmViXwD+WL6Ht8fMferbv9erbzSnIF/FnMkryM3JJyTSh/E/vK0zD+fm9TQUmaX7d/NKzSXP44fO1lnX8PE9aNGhDiYmRlw4Fc+6JXvJzc7H2s6CsBq+fP3rcGz0DJlWRoc+LVAWqJg/ZTl5OZobGH7wrW68t66nkn1PvPHRSXw1ojTWRTP/BqDxS7UZ/HEvAIZNeoOlP21gzmd/kqPIw8HZjm5vt3/oGxi61K2FKjuby6vXocxSYOXpTs0xw7UTgvPT0nW+udoG+BEx+C1iV63l0sq/MXdypMaIIVi6lw6BRA4dyKUVazjz03wKc/OQ2dsR0OUVPJqX3hAwNzmFS8vXUJibi8zBHt+OL+HVtmWVi/euGq+2okipYvfcv1Dl5uMS4kuH8e/o9HgobqaSryj9/AMa1aRAkcORvzZoh5k6jH9HZ+jo3Jb9HFu2Sft8zSc/ANBiWB+CW9TDSGpC/KHTHFmykSKlCjNbKzxrhNKma1sMjcufsxb1aiuKClTsmvOX5gaGIb50vC/eLD3x5ityOLJkA7kZ2ch93Og4oTTeW/HXSLmUAMAf7+jeTLbvT59i5WjP5YOnyFfkELPnKDF7SkcaLOV2vPmz7vxQ4dmQqCt7nfML6ssvv2Tu3LkkJSU98XUvidv8xNf5NDV3VT3rECrFO+ivZx1CpRw/0+dZh1ApuYUv1rfDPy7rv3RfeDL8rMq/wd/z6EXs3BgeWvailCfJZ+TfT2xdV354uJ7XF8kL1+PzsGbPnk3t2rWxt7fnn3/+YerUqU/kHj2CIAiC8Fx74Wbv/ruqbOITGxvLF198QXp6Op6enowZM0bnjpGCIAiCUCW9iN1g/6Iqm/h89913fPfdd886DEEQBEEQniNVNvERBEEQhP+kKnoZ+pMiEh9BEARBqErEUFeFxBQoQRAEQRD+M0SPjyAIgiBUJaLDp0Ii8REEQRCEKkQthroqJIa6BEEQBEH4zxA9PoIgCIJQlYgenwqJxEcQBEEQqhJxOXuFxFCXIAiCIAj/GaLHRxAEQRCqEtGlUSGR+AiCIAhCVSKGuiokEh9BEARBqErE5OYKicTnCchUvVj9ipNPWzzrECrl+Jk+zzqESqkZsehZh1Ap8veHPOsQKqVe+IvVqA8IyH3WIVRKvMLwWYdQKfHZ4s+YUDniiBEEQRCEqkT0+FRIJD6CIAiCUIWoxRyfCr1YYzSCIAiCIAiPQfT4CIIgCEJVIro0KiQSH0EQBEGoSsRQV4VEXigIgiAIwn+G6PERBEEQhKpEXNVVIZH4CIIgCEJVIhKfComhLkEQBEEQ/jNEj48gCIIgVCWiw6dCIvERBEEQhCpELYa6KiQSH0EQBEGoSsTl7BUSc3wEQRAEQfjPeGESH4lEwpo1awBISEhAIpFw6tSpZxqTIAiCIDx3DCRP7lEFPTdDXbdv32bChAls2LCBlJQUbG1tiYyMZMKECTRs2JDk5GRsbW0rtc7Vq1fzzTffcPHiRUpKSvD09KR169Z8//33T+dN3EOtVnPwr42c3XYAZW4+rsE+tBzSA1tXxwrrndq4l+Ord5CbqUDu7UbzQV1xDvTWvr599hIST8eQk5GFiakUl2AfGvfthJ27c5l15Sty+fO9r8lJy2Ton99gamFW7navbt/NlU3bUGYpsPRwJ/T1Htj4eZdbPvnIcWJXrSM/NQ0zJ0eCur+KY2Q17etFBQXELFtDyonTFObkIpPb4926OZ4tmgCgysnl8ur1pJ67QH5aBiaWFjjVjCTgtU4Ym8kq3EflUavV/PXzFrb/fYjcnHyCI3wY/EEXXD3l5dZZuWAHh3af5drVW5hIjQkO96LvsA64eZV+TnMmL+f00VgyUrMwlUkJCvem77CXcfd2eqQ4K6NhnWDeG9KBqHBfXJxs6T7wW9ZtPfbUt6tP3whXBkd5IDcz4WJqDhP2XOZ0SvYD63UMkDPrpVC2xKUyaMN57fJ2fg68Hu5CuNwSW5kx7RYf40Jq7hOLN3X3Lm5t3UKRIguZuwduPXph5uOjt2zBjevcXLeWvKtXKUxPw7VbD+QtW5UpV5iRwY3VK8k+f44SlQqp3BGPN/th5uX92PGq1Wo2LdjEwY2HyM/Jx6eaD91GdsPRvfzj9/KZOHYu3UlSbBKKNAVvTRpARKMI7evFRcVsmL+BC0cukpachqm5KUFRgXQc2BFrB+tKx3for42c217apjUf/OA27fTGvRxfs4O8TAUO3m40G1japhVk53JoyUaunoomOzUDmZUFfnUjqN/rZaTmmnbgws5DbJu5SO+6B/32FRjq/7twZftu4jZq2jQrD3eqvdED2wratBtHjhO9UtOmmTs5EtLjVZzuadMAsq8nc3HZatKiY1EXl2Dh5kKt4W9j5mAHQG7KbS4sWUn6pThKCouQR4QS/kYPpNZWFe6jJ65q5itPzHPT49OlSxdOnjzJwoULuXTpEmvXrqVZs2akpaUB4OzsjFQqfej17dixgx49etClSxeOHDnC8ePH+fLLLyksLHxab0HHsdXbObV+D62G9KDXlDEYm0pZNWk2Rarytx+z/zh756+mXs+X6DP9Axy83Vg1aTZ5maV/XBz9PGgzog9vzvyYVye+A2o1qz6dTUlxSZn1bftxEQ5erg+MNfnwMS7+tRL/V16mwaRxWHm4c3TaDJQKhd7yGbFxnJ4zH/cmDWj42TicoiI58cNcsq9d15aJXryS1LMXiBzcn8aTJ+LdpgUX/lhKyonTACgzMynIzCSoZxcafTmeiEF9uX3mAmfn/fHAeMuz+o9dbFi2j8EfduWbeSORmprw2cifUSnL3+fnT8bxUtcGfDNvBJ/OGExxUQmTRvxMQb5SW8Yv2J3h43swc8mHTPjhbUDNpBE/U6xnnz9p5mZSzl5IZNQn85/6tirSMUDO+MZ+fH84gZeXHOdiag5/vhKOvcy4wnrullI+aezH4euZZV4zMzbg6A0Fkw/EP/F4M44d5caKZTh36EjguPGYursTP/N7Css5pktUKkwcHHB59TWMrPQnBEW5ucRO/QaJoSG+w0YSNHESrl27YWhW/heKytixZAd7V++l+6huvPfje5iYmjD3o7kUVtBmqPKVuPm50nVEV/2vF6hIir1G29fb8P7cMbz16QBuJd3il/G/Vjq+46u3c2rDHloM7kGPb8ZgLJWy5rOK27RL+4+z77fV1O3xEr2+/QC5txtrPitt03LSs8hJz6Jxv868/v1Y2gzvw9UTF9g+a7F2HYENoxg4/0udh1eNENzC/DGzsdS73euHjnFh8UoCO79Mk8/GYeXpzuGp5bdp6bFxnJg9H88mDWjy2TicoyI5+v1cFPe0abkpt/nni2+xcHGmwdjRNP3yEwJfeQlDE03/QZFSyaGpMwAJ9T8aRcPx71NSVMyR72ajLnn6bcXzYtasWXh7e2NqakrdunU5cuTIQ9VbsmQJEomEzp07P90AeU4Sn8zMTPbt28c333xD8+bN8fLyok6dOowdO5ZOnToBukNdd0VHR9OgQQNMTU2pVq0ae/bs0b62bt06GjZsyP/+9z+CgoIIDAykc+fOzJo1S1vm008/pXr16vz00094eHhgZmZG9+7dycrKeqz3o1arObFuN3W6t8WvbgRybzfajXyD3PQs4g6fKbfeib93Ua1NfcJa1sPew4VWQ3tgJDXh3I6D2jIRbRviHuaPtZM9Tn4eNOjTgezUDBS30nTWdXrTPpS5+dTs3OKB8V7ZvAOPpg1xb9IASzcXwvr1wtDEhGt7D+otn7B1Fw7hofi2b4OFqwuBXTph5e3B1e2l+z/jchxujephHxKImdwez+aNsfRwIys+AQBLdzeihg/GqUYE5k5y7EODCezaiVunzlJSXPzAmO+nVqtZv2Qv3fq3om7TangHuDLy016kpyo4vOdcufUm/PA2LTrUwdPXGZ9AV4ZP6MntmxnERV/Tlmnzan3Cavjh6GqHX7A7vQe/RGpKJreS0ysdZ2Vt3X2aSdOWsXbLs+nluWtgDXf+OpfM8ospxKbnMXZnLPlFJfQILdvTeJeBBGa0DWH6oQQSswrKvL4q+hY/HLnK/sSMJx5v6vZt2DVsjF2Dhpi6uuLe+3UkxiakH/hHb3kzbx9cu3TDtnYdJEb6O8Jvbd2MiZ0tnm/2x8zHB6mDHMvQMKTyins8HoZarWbPqr20eb0N4Q3DcfNz5fUP+5CVmsXZ/WfLrRdaN5SXB7xM5D29PPeSWch4d+o71GhWAycPJ7xDvekyvCtJl5JIT3n4/a5Wqzm5fjd1upW2aW0epk1bu4uw1qVtWoshmjbt/J02zcHLlQ4fDsS3djg2LnI8IoJo0KcjV46e07YDRlITzG2ttA+JgYSks5cIa1W/3O3Gb96BZ7OGeN5p0yL69cJQakLiHv1t2pUtu5CHh+L/chss3VwI7toJa28PEraVtmnRK/7GMTKM0J6vYe3tgbmTHOeoSKRWmt6c9Etx5N1Oo/rbfbHycMPKw40ab79J5pVEUi/EPPS+fhIMDJ7cozKWLl3K6NGjmThxIidOnCAyMpK2bdty69atCuslJCTw/vvv07hx48d41w/vuUh8LCwssLCwYM2aNSiVygdXuON///sfY8aM4eTJk9SvX5+OHTvq9BCdP3+ec+fK/6MHcPnyZZYtW8a6devYvHkzJ0+e5J133nms95OVkkZehgLPiCDtMqm5DOdAb27EXNFbp7iwiJS4JJ06EgMDPCODSI5J0FunsEDJ+R2HsHKyx9KhtLs3LSmZQ8s203bUG0gkFX/EJUVFKBIScQgL1tmuQ1gwmZf1fxPPvByP/T3lAeTVQnXK2/r7cevkGQrSM1Gr1aRdjCE35RYO1ULLjaUoLx8jmSkGhoYVxqxPyo10MtKyiawTqF1mbiEjIMyTmLNXH3o9eTmaP9AWVvq/xRfkK9m5/ihOrnY4ONlUOs4XkbGBhHBHS/Ynlf6hVAP7kzKIcim/C39UHS9S8wtZeuHmvxBlqZKiIvISr2IZEqJdJjEwwDIkhLz4uEder+L0aWSe3iT8PJfz/xtNzJefkbZv75MImbTkNBTpCgKjSo9fmYUMrxAvrlxIeCLbuKsgNx+JRIKZxcMPKSvutmmR97VpAd7crKBNuxWXpFNHYmCAZ0QQN8tp0wCUefmYmJXfDkTvPoKRiQkB9avrfb2kqIgsfW1aaDAZ5bRp6Zfjkd/XpjmGh2rLq0tKSDl9DgtnJw5NmcGWd//Hvk+/Ifn4KZ3tSiQSDO5JnA2MjZBIJKRfevTj7lFIJE/uURnTp09n0KBB9O/fn9DQUObOnYuZmRnz55ffY11cXEyfPn2YNGkSvr6+j/nOH85zkfgYGRmxYMECFi5ciI2NDQ0bNmTcuHGcOVP+NwmAYcOG0aVLF0JCQpgzZw7W1tbMmzcPgOHDh1O7dm3Cw8Px9vamZ8+ezJ8/v0xiVVBQwO+//0716tVp0qQJM2fOZMmSJdy8qb+xViqVKBQKnUehSqVTJi9T0516fzesmbUleRn6u1rzs3NRl5RgZmP1wDqnN+7lx55j+LHn+yScuECXT9/F0PhOd2thIRu/XUCTNztjJberaPcBoMrOQV1Sgsl9Y9Am1lYos/THqsxSaL/llFc+5I3uWLg6s+u9sWx5axhHp/1I2Bs9sQsOKDeOy2s34dms0QNj1iczTbNtazvdfW5jZ0lmuv73cb+SkhLmfbeG4AhvvPxcdF7btOIfejUbS69m4zhx8CITZw7G2Pi5mSL3VNnJjDEykJCapzukkZpXiNzMRG+d2i5W9Ahz4cMd/+43XYDinBwoKcHovmPUyNKKonKGOh6GKvU2aXt3I3V0xGf4KByaNOP6siWkHzzwuCGTnaEZ+rG01T1+LW0tyS6nzXgUhapC1v6yjqgWUZiamz50vdy7bZr1fW2ajaX2tftp27T72pYK6yhyOLJ8M9VaNyg3lvPbDxHUpCZGUv3H3t027f42SvqgNu2+OKVWVhTcKa9UZFNcoOTy+i3II8Ko98EInGtW59iMn0mNvgSArZ8PhlITLi5dTZFSRZFSyYW/VqEuKaHgMUcRniV9f/P0dVCoVCqOHz9Oq1alc+MMDAxo1aoVBw/q72kD+Oyzz3B0dOStt956KvHr89y03F26dOHll19m3759HDp0iE2bNjFlyhR+/fVX+vXrp7dO/fqlXZ1GRkbUqlWLixcvAmBubs6GDRuIi4tj165dHDp0iDFjxvDDDz9w8OBBzO6My3t6euLm5qazzpKSEmJiYnB2LtuNP3nyZCZNmqSzrPGr7UiPKx0a6fzJkEfeDw8juGltPKsHk5uh4PiaHWyY+hs9vn4PIxNj/vljHXbuzoQ0q/1UY3iQq9t2kxl3hahRQ5HZ25ERc5nzfyxBamuNQ1iITtnC/HyOTZ+Fhasz/p07PNT692w+ztyvV2iffzx94GPH/PPUVSTG3+Srn4aVea1Juygi6wSSkabg70W7mTbuDyb/MgwTacVzXP6LzI0N+a5NMB/uuERGQdGzDufJUauReXnj0vk1AMw8PSm4cZ20vXuwq1/+H2p9jm0/xtLvlmmfD/7q7Scaqj7FRcUs+GwBqKH7yG4Vlo3ec5Sdc5don3f6+Om2aaDp6fn7i7nYuTtTt2d7vWWSo6+Qfu0mbUa98dTj0aFWA+AcFYFfu5YAWHt5kHE5jqs79+EQHIjUypKawwZxduFfXNm2G4lEgmu9Wlh7eyD5l++r8yQ3p+9v3sSJE/n00091lqWmplJcXIyTk+5FH05OTkRHR+td9/79+5k3b96/foX2c5P4AJiamtK6dWtat27N+PHjGThwIBMnTiw38XkYfn5++Pn5MXDgQD7++GMCAwNZunQp/fv3f6T1jR07ltGjR+ss++XCVgrzSjPgokJNY5+XmY2FXelEybysbOQ+bugjszRHYmCg7S26t46Z7X3fRMxlSM1l2Lo64hLozezXP+TyodMEN6lF0plLpCbe4PvXTt0prTlh5/YdS51ubWjQ62WddZlYWiAxMEB13zchlZ5vQNrtW1uVmSR4b/lilYpLK/4masRgHKuHA2Dl6Y4iMYkrm7brJD5F+QUcm/YjRqZSokYMwcDo4Ya56jQOIzDMS/u88M4+z0rPxs6hNO7M9Gx8AvTv83v9PHUVx/Zf4Muf3tU7hGVuIcPcQoarp5zAal680Wo8h3efpXHbqIeK90WWnl9IUYkaBzPdJM/BzJjbeaoy5b2sTfG0ljG/Y+kVMXevio0f1oTmfxzhqp45P0+KoYUFGBiU6d0pylaU6QWqDCNra0xddHsCpc4uZJ44Uel1VWtQDa+Q0uP3bpuRnZGNtX1pm5GdkY2b34OP3wcpLirmt88WkJ6SwbBp7z6wt8e3TrjO1aTFd9u0rGzM723TMh+iTbuvbcnLzMb8vp5tVX4Bf382BxOZlA4fDcKwnHbg3PYDyH3ccfLzLDf2u23a/W2Uvl6du/T1BikVCkzvlDextEBiaICFm+7nb+HqQvqly9rnjuGhtJz2OcrsHAwMDDA2N2Pr8A8xkzuUG+/T8CQTLX1/8ypzoVF5srOzeeONN/jll19wcPh3989zlfjcLzQ0tMyE5nsdOnSIJk00l0cXFRVx/Phxhg0r+239Lm9vb8zMzMjNLb1kNjExkRs3buDq6qpdp4GBAUFBQXrXIZVKy3zo5tZWcM+FIGq1GjNbK5LOxODo6w5ovs3cvJRAZDv9QzmGxkY4+XmQdOYS/vUiNespKSHpzCUi25c/4UuNGtRqbcPU4cO3dK6ySLmcyNaZi+j+1ShsnMseXAZGRlh5e5J2IQanmtW12029EINXq2Z6t2nj70vahRh82rbULks9H42Nv2Z8tqS4GHVxcZmTT2JggLpErX1emJ/PsakzMTA2ouaodzA0efjeE5m5KbJ7Gm+1Wo2tvSVnjsbiE6hpiPNyCog9n0i718r/Nq5Wq/ll2moO7znL57PfwcnV/sEbV2vq3U22qrrCEjVnb2XT0MOWrfGaOXQSoKGHLQtPXy9TPi4jj1Z/HtVZ9r/6PliYGDJxz2VuZD/8PL5HYWBkhJmnF9nRF7GuXgPQHNM50Rexb/bgyf7lMffzR5miOwSuTEnBxP4hjpn7mJqZYmqme/xa2Vlx6UQs7v6aNqMgt4CrF6/SqGPDR44ZSpOe29dvM/zbYZhbmz+wjonMFBOZbnx32zS5zz1tWmwC4RW0aY532jS/uve0aWcvEfFSaZumzMtnzaTZGBob0XHcYIzKaQdU+Upi/zlJwzc6Vhi7gZER1t6epJ6PweW+Ns27nDbNzt+X1Asx+LYrbdNun4vG9k6bZmBkhI2PNznJKTr1cm+mYGZfdkqB1NICgNQL0SgV2ThH6Z98/rQ8yR4ffX/z9HFwcMDQ0JCUFN19lJKSonf0JC4ujoSEBDp2LP08S+5c/WZkZERMTAx+fn6PGb1+z0Xik5aWRrdu3RgwYAARERFYWlpy7NgxpkyZwiuvvFJuvVmzZhEQEEBISAjfffcdGRkZDBgwANBcsZWXl0f79u3x8vIiMzOTGTNmUFhYSOvWrbXrMDU15c0332TatGkoFApGjBhB9+7d9X5QD0sikRDVsRmHl2/BxtURa0d7Dixej7mdNX51S0+AFeNn4l8vguovNwUg6pXmbPnhTxz9PXEO8OLkut0UFigJa1kPgMybqVzafwKv6sHIrC3IScvk6MptGEmN8akZBoCNi+49P/IVmiTPzt2p3Pv4+LRryZlfFmLl44mNrzcJW3ZSrFTi3lgzlHj6pwWY2toQ1L0zAN5tmnN48nSubNqOPLIayYePkXXlKtX69wbAWCbDLjiA6KWrMDAxQeZgR3p0LNf/OUxwry6AJuk5OnUGJcpCIgb3pyg/n6L8fABMrCyRVPJyAolEQoeeTVj+23ZcPBxwcrVn8U+bsHOwom7T0p6HCe/OoV6zcNp30zTWP09dxd4tJxg7dQAycykZd+YKmZnLkJoac/N6Gv9sO0X1uoFY2VqQdiuTVb/vxERqTFSDEL2xPEnmZlL8vEuPRW8PORGhXmRk5pB0I62Cmk/Wryev8W3rYM6mZHMqJZu3qrthZmTAsjsTl79rHcTNXBXfHLiCsljNpfQ8nfoKpSZJvHe5tdQIN0spTuaaRtXPVnN83s5TcTvv8W474dCqNUkL5mPm5Y2Ztw+3d26nRKXCroEmiUj8bR7GNra4vKoZtiopKkKZfAMAdXERhZkZ5CclYiA1ReqouWpL3rIVsVO+IWXTBmxq1iYv4Qrp+/fi3ufxh10kEglNX2vC1kVbkbvLsXe2Y+NvG7F2sCa8Ubi23I/vzyKiUQRNOmsSB2W+ktvXb2tfT7uZzrXL1zCzNMfOyZbiomLmT/qNa7HXePvLQZSUlKC4M+fNzNIMo4ecpyaRSKjRoRlHlm/BxsURKyd7Dupp01ZO0LRpke3vtGmdmrN1xp84+t1p09Zr2rTQO23a3aSnUKmi7ai+qPIKUOVpegNlVhYYGJa2A5f+OUFJSQnBTR88jO/briWnflmIzZ02LX6rpk3zbKJp007eadNC7rRpPm2bc+Cr6cRt2o5jZDVuHDpG5pWrRAzorV2nX/vWHJ/1K/ZBATiEBnLrzAVSTp6l/tj3tGUS9x7A0tUZE0tLMi7Hc+7P5fi2bYGFy6P/PXlRmJiYULNmTXbs2KG9JL2kpIQdO3bo7ZAIDg7m7FndKxY/+eQTsrOz+eGHH/Dw8HhqsT4XiY+FhQV169blu+++Iy4ujsLCQjw8PBg0aBDjxo0rt97XX3/N119/zalTp/D392ft2rXaLrOmTZsya9Ys+vbtq70hYo0aNdi6datOb46/vz+vvfYa7du3Jz09nQ4dOjB79uzHfk+1Xm1FYYGK7bP/0tzsK8SX1ya8o/NtJutmqjYxAQhqVJP8rBwO/rWBvAxNF/KrE9/RdgsbmRhz/UIcJ9ftpiA3DzNrS9zD/Onx9ehy72fxMFzq1kKlyCF21XrNzb483an9/nBtt3BBejqSe+7gaRvgR+SQAcSuXEvMir8xd5ITNXIIlu6lXd7Vh75FzPK/OT13PoW5ecgc7Ajs2kl7A0NFQhJZcQkA7P1ggk48Tad9gZm88t+iX32jOQX5KuZMXkFuTj4hkT6M/+FtnXk4N6+nocgs3eebV2ompo4fqvuZDx/fgxYd6mBiYsSFU/GsW7KX3Ox8rO0sCKvhy9e/DsfG7tH3+cOKivBl67LS/TNlYl8A/li+h7fHzH3q279rXext7GTGjK7njdzchAu3c3jj77Ok5msSFFdLU+7pzHsorX3tmd669EqaWS9prvj77nAC3x1++Cvx9LGtVZvi7GxurvubIoUCmbsHPsNHYnxnqEuVnq7ztbgoM5NLX36ufX5721Zub9uKeUAg/mP+B2guefcZMpTkNatJ2bAeEwcHXLv1wLZuvceK9a6WPVuiKlCxdPpS8nPy8Q33ZcjkwRjf02ak3UglNytH+zwxJpEfx5TeomPNnDUA1GlTmz4f9iEzNZNzBzRXtk55e6rO9oZ9+y4B1fVfbKBPzTtt2o45pW1a5/EVt2mBjWqSr8jh0BJNm+bg40bnCaVt2u34a9y8lADAwnc+09le/58+xcqxtB24sP0g/vUikZo/+L5JbvVqocrOIeaeNq3u/0rbtPw03c/fLsCPqKEDiF6xlujlmjat9qghWN3TprnUqk5Ev95cXr+Zc38uw8LFiVrD38Y+yF9bJjc5hejlf6PKycXMwZ6ATu10epH+Lc/qp7pGjx7Nm2++Sa1atahTpw7ff/89ubm52qklffv2xc3NjcmTJ2tvQ3MvGxsbgDLLnzSJWq2uZHNVdXz66aesWbPmsSdWzb249ckE9C+Jznou8t2H9nZw3oMLPUdqRui/y+zzSv7+05+4+iTVC3+xbks7IODJ3Y363xCvqPztJJ6l+OwXqz0DmFb30YdcH0bgL0/mNgsAlwY1qVT5H3/8kalTp3Lz5k2qV6/OjBkzqFu3LgDNmjXD29ubBQsW6K3br18/MjMzK5zi8iS8eEeMIAiCIAjPpWHDhpU713b37t0V1i0vIXrSROIjCIIgCFXIsxrqelE8FzcwfFY+/fRT8QvvgiAIQpUifpy9Yv/pxEcQBEEQhP8WMdQlCIIgCFWIGOqqmEh8BEEQBKEKEYlPxcRQlyAIgiAI/xmix0cQBEEQqpB/+0dRXzQi8REEQRCEKkQixnIqJBIfQRAEQahCRIdPxUReKAiCIAjCf4bo8REEQRCEKkT0+FRMJD6CIAiCUIWIxKdiYqhLEARBEIT/DNHjIwiCIAhVSFX9ja0nRSQ+T0B01ou1Gx1Ni591CJWSW/hincXy94c86xAq5fa0uc86hErJ+HnYsw6hUnwtX6zzTaF6sc63q7nqZx3Cc0cMdVVMDHUJgiAIgvCf8WJ1VQiCIAiCUCHR41MxkfgIgiAIQhUiEZN8KiSGugRBEARB+M8QPT6CIAiCUIWIoa6KicRHEARBEKoQkfhUTCQ+giAIglCFiMSnYmKOjyAIgiAI/xmix0cQBEEQqhBxUVfFROIjCIIgCFWIGOqqmBjqEgRBEAThP0P0+AiCIAhCFSIRXRoVEomPIAiCIFQhYqirYv/ZvHD37t1IJBIyMzOfdSiCIAiCIPxLnpsen379+rFw4UIAjI2N8fT0pG/fvowbNw4jo+cmzId2dfturmzahjJLgaWHO6Gv98DGz7vc8slHjhO7ah35qWmYOTkS1P1VHCOraV8vKiggZtkaUk6cpjAnF5ncHu/WzfFs0URb5txvi0g9H40yMwtDUym2/r4EdX8VC1fnB8YbvWUP59btID9TgZ2XG3X6d0PuX368CQdPcHLZBnJup2HlLKdmn8641wgrff+HTxGzfT/p8Ykoc/Lo+M1H2Hm7a1/PuZXGyuET9a676agBeNePemDM91Or1ayct5ld6w6Rl51PYLgP/d/virOHvPz3fSqODYt3cSXmGplpCkZ91Z9aTcJ1yhTkKVk6dz3H9p0jJysXuas9bbs2pmXnBpWO8V59I1wZHOWB3MyEi6k5TNhzmdMp2Q+s1zFAzqyXQtkSl8qgDee1y9v5OfB6uAvhcktsZca0W3yMC6m5jxVjZTWsE8x7QzoQFe6Li5Mt3Qd+y7qtx/7VGO7q5OlMNx837ExMiMvOZdbFeGKycvSWfcndidaucrwtzQGIzcphfuxVbXlDiYT+AZ7UkdviLDMlr6iYE2mZzLt0lTSl6onEq1arWfTzFrauOURuTj4hET6882EXXD3LP36XL9jBgV1nuX71FiZSY4LDveg3vAPuXo7aMmOHzObciTideu1erc+7Y7tWOr6df2zi2OaDFOTm4xnqQ6dh3bB3c6yw3uF1+9i/Yic5GQqcfd14eWgX3IO8tK9npyvYMu9v4k7GoMxT4uDuSNOerQlrVB2AjJQ0di/eQvzpWHIysrG0syKyRS2a9myDkXH5fxvit+0hdoOmDbb2dCeib3dsK2iDrx8+wcUV68hLTcPCyZHQnp1xrl5Np0z29WTOL1lDanQs6pISLF2dqTPybcwc7FDl5HJx5Xpun71IXloGUisLXGpGEtK1I8Zmsgfv4CdIIrp8KvRc9fi0a9eO5ORkYmNjGTNmDJ9++ilTp06t9HqKi4spKSl5ChE+nOTDx7j410r8X3mZBpPGYeXhztFpM1AqFHrLZ8TGcXrOfNybNKDhZ+NwiorkxA9zyb52XVsmevFKUs9eIHJwfxpPnoh3mxZc+GMpKSdOa8tYeXsSMbAvjSdPpPb7w0Gt5ujUGagfsC+uHDjO0d9XE9nlJTp+/SG2Xm5s/2oW+Vn6/wjfioln74wFBDSvT8evP8KzdiS7pv5MRuINbZkipQqnID+ienfWuw4zB1u6//SVzqN6t5cxMpXidk8CVRnrF+1k64p9DHi/G5N+HoVUZsI3o39CpSwst44yX4Wnvytvjn6t3DKLZv7N6cPRDB3fhymLPqJdtyYs/G4Vx/efe6Q4QZO8jG/sx/eHE3h5yXEupubw5yvh2MuMK6znbinlk8Z+HL6eWeY1M2MDjt5QMPlA/CPH9bjMzaScvZDIqE/mP7MYAJo6OzA42Ic/Lycx9MAp4rNzmVwrDBsT/fs30s6aXcmp/O/IOUYeOsPtAiVf1wrDXmoCgNTQAH8rC/6MS+Kdg6eZdPIi7uYyPosKeWIxr/x9F+uX7uOdj7oybf5ITGUmTBjxc4XH77kTcbzcrQFT543g85mDKS4uYcLwnynIV+qUa9u5Hr9vnKh99B/eodLx7Vu+g0Nr99JpeHcGf/8eJqYmLPxkLoWq8uM7u+cEm35eTfM+bRk68384+7iy8JM55GSWti0rp/1J6rVb9Jk4iGFzPiS0YQRLJy/gxuVrAKQm3UKtVvPK8B4Mn/sRLw1+laMb/2H7gvXlbvfaoWOcW7SS4FdfptkXY7HydOPANzNRltOmpV2K49is+Xg1bUDzL8biXDOSw9/9hCKptE3LTbnN3s+nY+HqRKOP36PFVx8T1Lk9hsaaY6ogI4uCzCzCer9Gy68/IertvqScucDJX/6s1H5+EiSSJ/eoip6rxEcqleLs7IyXlxdDhw6lVatWrF27lunTpxMeHo65uTkeHh6888475OSUfnNbsGABNjY2rF27ltDQUKRSKYmJiSiVSj788EM8PDyQSqX4+/szb948nW0eP36cWrVqYWZmRoMGDYiJiXns93Fl8w48mjbEvUkDLN1cCOvXC0MTE67tPai3fMLWXTiEh+Lbvg0Wri4EdumElbcHV7fv0ZbJuByHW6N62IcEYia3x7N5Yyw93MiKT9CW8WzeGLvgAMzk9lh7exLQpRMF6Rnk3U6rMN4LG3YS0LIBAc3rY+PuQv2BPTE0MeHyLv3xXty0G7fqIVTr1Aobd2dq9OiAnY8H0VtK4/VrUofIri/hGh6kdx0GBgbIbKx0HolHT+NdPwpjU2mF8eqjVqvZvHwvr/RtTc3G1fD0d2XIJ73JTFNwfF/5CUpk/RC6vd2e2k0jyi0Tey6Bxi/VJjTKH7mLHS1eqY+nnyvxFxIrHeddA2u489e5ZJZfTCE2PY+xO2PJLyqhR2j5vXMGEpjRNoTphxJIzCoo8/qq6Fv8cOQq+xMzHjmux7V192kmTVvG2i3Pppfnri7ermxKSmHL9Vsk5ubzw/k4lMXFtC2nd+LrM5dYl3STuOxcknLzmX7uMhIJ1LC3BiCvqJiPjp1n7800ruXmczErhx8vxBNobYHc1OSx41Wr1axdspfuA1pRr2k1fAJcee/TXqSnKji0p/zjd9KMt2nVoQ5efs74BLoyakJPbt/M4PLFazrlpKbG2DpYaR9mFqaVju/gmj007dmGkPrhOPu40eX918lOy+LigbPl1juweje1XmpAVJt6OHo503F4d4ylJpzYekhbJuniFep1aoJ7kBd2Lg4069UWU3MZNy4nARBQK4TXRvfBv2Ywdi4OhNQLp1GXFlw4cKbc7cZt2olX84Z4Na2PlZsL1fv3wlBqwtU9B/SWj9+yC8eIUAI6tMbSzYXQbh2x8fYgfttubZkLy9fiFBlGtV6vYePtgbmTHJeaEUitLQGw8nCl7si3cYmKwNxJjjwsiNBunbh58iwlxcWV2d3CU/ZcJT73k8lkqFQqDAwMmDFjBufPn2fhwoXs3LmTDz74QKdsXl4e33zzDb/++ivnz5/H0dGRvn378tdffzFjxgwuXrzITz/9hIWFhU69jz/+mG+//ZZjx45hZGTEgAEDHivmkqIiFAmJOIQFa5dJDAxwCAsm87L+b+KZl+Oxv6c8gLxaqE55W38/bp08Q0F6Jmq1mrSLMeSm3MKhWqjedRYplVzfdxCZ3B6ZvW258RYXFZEWn6SToEgMDHAND+J27BW9dW5fuoJLNd143SJDuH0podztPEhafCLpCdcIaF7/kerfvpFOVlo21WoHapeZWcjwC/Uk9tyjxwUQUM2bE/vPk35bs+8vnIjlZtJtwuvoT+oexNhAQrijJfuTShMUNbA/KYMoF6ty642q40VqfiFLL9x8pO3+VxhJJARaWXAiLVO7TA2cSMsi1MbyodYhNTTESCLh/+zdd3QUVRvA4d9ueu+9F9IgoVcpIlXFTlFBBEFEikpRikqxIe2zAIKNIkhHkd679N5CEgIppPdN3U2y+/2xsGGTTUggGAj3OWfOYWbuzLy5zM6+c++d2dzikkrLmBnooVSpyC9+8C+1lMRMsjJyadKq7Pw1MzchoKEn1y7FVns/+XnqhNjCylRr+YEdZ3mz2+eMfH02yxZspaioZt1zWckZ5GXJ8GtaFp+xmQnugV7EX9N9nSgpLiExKh7fJmXbSKVS/JoEEB8eo1nmEezDpUNnKcjNR6lUcvHAWUoUJfiE+VcaT1F+ESYWpjrXKUtKyL4Zh0ND7WuaQ8MgMq/rjjXz+k0cyl3THMNCNOVVSiUp5y9j7uzI0Znz2DbiEw5OnUXi6fOVxghQXFCIvokxUj29KsvVNtHiU7VHcvCMSqVi79697Ny5k9GjR/PRRx9p1nl7e/PVV18xfPhwfvrpJ83y4uJifvrpJxo3bgxAZGQka9euZffu3XTt2hUAX1/fCsf6+uuv6dSpEwATJ07k+eefp6ioCGNj3XdEcrkcuVy7GblEoUDfUH3Xp8jNQ6VUYmil/QVmaGVJXlKK7n3myDCyrFhenlPWNRb8Vl+uLPmT/WMmIdGTgkRK6OD+2AY10Noudu9BItb8TalcjpmLEy0//hBpFWOk5DJ1vMZW2l8IxlaW5CTqjrcwW4axdfnyFhTm6O7Kq46ofcewcnPGMbDi/1F1ZGeqj21pox2XpY0FOZn3HjdTlYFjXuX3WWv54JUv0NOTIpFKGPJJX4Ka+N3X/mxNDNCXSkgv0O4iSC8oxs9G98W8pYsl/Rq60HNl3bakPA6sDA3Qk0rIKtcFkyVX4GFmVa19DA30IkOu0Eqe7mYglTA0wJv9SekU1MLdfFaG+vy1ttU+f61tLTTr7kWpVPLr/zYS3NgbLz8XzfJOPZri6GyDrYMVMdcTWTp/KwmxaUyeNaja8eVlqT9D5uU+X2Y2Fpp15RXI1IlM+W3MbSxIv5Wqme83eRBrZyxjRt/JSPWkGBgZ8ubnQ7Bz1T22KSMxjeObDtFz6Es618tz71zTtK+pRlYWlV6Di7JlGFtqx2lkaYE8W133clkuJUVyorbsIrj3C4S8/jKpF65y8odfaT/5Q+yDAyrsU56bR8TG7Xh3fkrnMR+m+pqw1JZHKvHZsmUL5ubmFBcXo1QqefPNN5k2bRp79uxhxowZXLt2DZlMRklJCUVFRRQUFGBqqv6iMDQ0JCysrLvi/Pnz6OnpaZKayty9jYuL+mKRmpqKp6enzvIzZsxg+vTpWstaDxlIm6Fv39ffXF2xuw+QHX2TZh+9j4mdLVkR17myfDVGNlbYNywbZ+DathX2DYORZ+dwc/tuzi/4lTaffYxeJWMbHgUlCgU3/j1N41d7Vnubf3edYfHsdZr58bOGPozQANi1/jDXr8Qy9tsh2DvbcO1CNMv+9xc29lZaLUwPi5mBHt91D2LC3kiyiipvgRBqRz8fN552tmf8ycsUK1UV1utJJHzeJAgJ8OOV6Io7qIYDO86wYMZ6zfyU7x78/F006y/ibiQz85dRWst7vlLWiurt74KNnSWfjVxE0q10XNztde7rwr7TbJq3RjM/YPp7DxxfZfb+sY2i/EIGfTMCUytzwo9dZM2MpQyZ/QHOPq5aZWXp2fzx2SIadWhCi2cf7OGCmlCp1OeBS7Mw/J/tAoC1lweZUTe4ufdIhcSnuKCQ43N+wsLNmaBXaz6e6kGJn6yo2iOV+HTu3JmFCxdiaGiIq6sr+vr6xMTE0KtXL95//32+/vprbG1tOXLkCEOGDEGhUGgSHxMTE62R7CYm1RtFb2BQlhDc2b6qgdGTJk1i7NixWssmny/rNza0MEcilaIo1/qhyJFhZKW7G8PIyrLCwOe7y5cqFESu/4dmH7yHYxP1E0eWnu7I4uK5uX2PVuJjYGqCgakJZs6OWPv7sOf9caScOY9r25a6j22pjreo3KC/ohwZJta64zWxtqQou3z5XEwq+fvuJfb4eUrlCvw6tar2Ns3aN8QvpCw5LVGo77plWbnY2JfFIcvKxdPf7b7iAlDIFaz9ZRsffTOYpu3U3Yqe/q7ERiWyddX++0p8MguLKVGqsDfVTkbtTQ1IK6jYBeFlZYynlQmLXyh7wuTOhe3GqI50Xn6SWB1jfp5UOYpiSpUqbMol+zZGhmTd4wms3t6uvO7rzoRTV7iZV1BhvZ5EwmdNAnE0NuLjU5fvu7WnVYeGBDQse7KpWKFOaLMzc7G96/zNzszFN+De5++i2X9x6shVZvw8Ensn6yrLBjZSf26S4itPfILaNMI9qCy+kttdfuqnqspazfKzcnH20x2fqaUZUqm0QotQXlauphUoMzGdE5sPM2rRRJy81DeeLr5uxF6+wckth3lxdD/NdrKMHBZPnI9HiA8vftCPyhhZ3LmmaV9T5Tm5lV6Dja0tKZJpxymX5WJ0+xpoZGGORE+KhZuLVhkLN2cyIrST3+LCIo7Nno++sRGtP3oPqf5/280l3NsjNcbHzMwMf39/PD09NY+wnzlzBqVSydy5c2nTpg0BAQEkJibeY08QGhqKUqnk4MGD9yxbE0ZGRlhaWmpNd7q5AKT6+lh6e5JxtWyQtEqpJP1qBNb+urtxrP19tcoDpF+5pimvLC1FVVpa4RFFiVSKSscdqea4KhUqVChLKm8l0NPXx87Xg6RL2vEmXY7EoYGPzm0cAnxIuqwdb+KlazgEeFd6nKpE7T+KR4vQCk3NVTExNcbZ3UEzufk4YWVnwZXTUZoyBflFRF+No0Gj+4sLoKRESWlJKdJydS+VSjR3gTVVrFRxKTWXpzzKxl5JgKc8bDibVLFbIzqrgK4rTtFz5WnNtPtGBsduZdNz5WkSc+UVtnmSlahURMryNAOTQV2/Te2suJpdebdnXx83Bvh5MPn0FSJlFR97v5P0uJkaM+HU5SrH/9yLqZkxrh72msnT1wkbOwsunLrr/M0rIvJKHEGhXpXuR6VSsWj2Xxw7cImvf3ofZze7ex77RqT6+nn3DUJ5RqbG2Lk6aCZHT2fMbSy5cT5SU6Yov4hbEbF4BOm+Tugb6OPawENrG6VSyY3zkXgEewPqGwuo+Pi1tNy1TZaezeIJ83D19+DVMW8ilVb+1SXV18fax5O0K9rXtLQrEdj6647V1t+HtCvXtJalXQ7XlJfq62Pj60Vuua6yvKRUTO1tNfPFBYUcnTkPiZ4+rce+X2ct7VJJ7U310SOV+Oji7+9PcXEx8+bN48aNGyxfvpxFixbdcztvb2/efvtt3nnnHTZu3MjNmzc5cOAAa9eufegx+/TsQvzBI9w6coy8xCSuLFtFqVyOewd1k/OFn5cSsXZjWazdO5N+6Qo3t+8hLzGZqL+3kHMzFq+u6m46AxMTbIMacG3NX2SER1KQls6tw8dI+PcETs3VY5oKUtOI3ryDnJuxFGZkkhUVzfn5v6JnYIhD46ofDw95/hki9x3l+sHjZN9K5vhvayiRy/F/ug0Ah+f/wZmV/2jKBz/7NAkXrnJl815yEpI5v24rGdFxBPUo61aU5+WTGXOL7AT1QNycxBQyY25RmK39xS5LTiMlPJoGzzxYs7VEIqFnn45sXLabM0cuEx+dyM9frcTazpLmHcpaSr75cCG7NhzWzBcVyImNSiA2Sv3qgLSkTGKjEkhPVg88NjUzJqiJH6t+2szVs9dJTczg0LaTHNlxusL7fmrit3O3eKOhC72DnPC3MeWbzg0w1Zey9vbA5e+6BTKhnfqiKy9VEZlZoDXJ5CXkKUqJzCzQdMdYGekTYm9GA1v1u2j8bEwJsTfDwfS/u/iamRoRFuJFWIj6y9rbw4GwEC88XO/9hVybNsQk8py7M91cHfA0M+GDhn4Y6+mxM0E9tuST0Aa8E1CWUPTzcePtBp7MuXyd5EI5NoYG2BgaYKynvkTqSSRMaRJIgKU5316MRCqRaMro18KAColEwouvd2TN4j2cOHSZmOtJ/G/aSmztLWnTqez8/XTEQrasPaKZXzjrLw5sP8P4LwdgYmpEVrqMrHQZ8iL1+KakW+ms/n0318PjSUnM5MShy3w3bRUNm/ri08C1QhxVxdf25U4cWL2L8OOXSL6ZyIa5K7CwsyK4XdnnYMnE+RzfdEgz3+6Vpzmz4xjndp8kNS6ZzfPXoZAraNatNQAOHk7Yutqzad5abkXEkpmYzr8b9hF9LoLgtur9ytKz+X3CPKwcbOg59CXyc/LIzZSRm1n52Ce/Z58h9sC/xB06Tm5CEheWrKZULsezk/oafGbRUq6s2agp79ujM6kXrxK1bQ+5icmEb9hC1o04fLs9rSnj/1w3Eo6fIWb/EfKSU7mx6wDJ5y7h01X9LrU7SU+pXE6zdwdQUlhIUbb6Efd7vVKktkklqlqbamrBggV4e3tjbGxM69atOXnyZKVlf/31Vzp06ICNjQ02NjZ07dq1yvK15ZHq6tKlcePG/O9//2PmzJlMmjSJjh07MmPGDAYOHHjPbRcuXMjkyZMZMWIEGRkZeHp6Mnny5Ices0vrFihkeUT9tQV5jgxLT3dajh+taWYtysxEclcqbdPAj8bD3yFqwyYi1v+DmZMDzT4cjoV7WRNyk/eHELHuHy4sWkxxfgEm9rYE9H5R8wJDqYEBWZHXidm1j+L8AoysLLEJ9KfN5+MrDJwuz6ddc4pkeZxfu5XC7Fxsvd3oOmmkpqsrP0M7XsdAXzqOHsS5NVs4u3ozls4OdP54GDaeZRfS+NOX+Hdh2fsrDv2wBIDGvZ+lSZ/nNcuv7z+Gma01rmHaT1Tcj179n0FepGDxrHUU5KlfYPjJ3GEYGpV98acmpJObXfZSvxvX4vnmg7JB8n/OUyd4HZ5tyXufvgHAqOlvsebnrSz8YgV5sgLsnW3pM+y5B3qB4eaoNGxNDBjbxhsHM0OupuXx1j+XSC9Uf2G5WhhTRWOeTt187fhft7J6XPCsumvuuxMxfHei+k8GPYhmYb7sWjtFMz9rqvpzunzdQYaNu/cNS205mJyOtaE+bzfwxMbIkGhZPpNPXyH79oBnRxMjVJRVcC9PZwylUqY21T4P/7gex/Lr8dgbG9LOSZ28/fxUU60y405e4mIVX8LV9drAzhQVKZj/zXry8woJaezD9B+0z9/khAxkd52/2zeou9knD/9Ja18fTulH116t0DfQ4/zJSDatOkRRkQJ7J2vadQ6l3zvdahxfhz5dKC5SsOnHNRTlFeLZ0JeBXw7H4K5WjcykDApkZfGFdmpGfk4ee1dsIy9ThoufOwO/HI65jfraoqevx8Av3mPXks2smPYLikIFtq72vDquPwGt1Dds189FkJmYTmZiOrPf0n7p6Zfbf9AZq3sb9TU4fIP6Gmzl5U7bT0ZpBjwXpGdp/aCVXYAfLUa8Q/i6TYSv3YSZswOtx7yHpUfZNc21ZROavPMGkZt2cvGPdZi7ONHqw3exC1Q/fZYdE09WdAwAu8dpx9ntuy8xc/hvk/+6sGbNGsaOHcuiRYto3bo133//PT169CAiIgJHx4qvkjhw4ABvvPEG7dq1w9jYmJkzZ9K9e3euXLmCm9v9D1G4F4nqftvrBY2Pju+r6xBqxNH48XqnRDe32nkz7n/ltVXm9y70CEmb898lJLWhfbnBu4+6BW3r7r1K9+N8xiN/P6zlTMaj++BGZWa27PJQ9//sriP3LlRN27u3r3bZ1q1b07JlS+bPnw+ouzY9PDwYPXo0EydOvOf2paWl2NjYMH/+/Go1btyvx+sMFwRBEAShSrU5hkXXK1yMjIwwMtJ+0axCoeDMmTNMmjSpLA6plK5du3LsmO6X4ZZXUFBAcXExtra29y78AB75MT6CIAiCINSNGTNmYGVlpTXNmDGjQrn09HRKS0txcnLSWu7k5ERycvVeujphwgRcXV017957WESLjyAIgiDUI/czKLkyul7hUr61pzZ8++23rF69mgMHDlT6AuHaIhIfQRAEQahHavMxdF3dWrrY29ujp6dHSor2I/8pKSk4O1f+G4QAc+bM4dtvv2XPnj1aLxV+WERXlyAIgiAID8TQ0JDmzZuzd+9ezTKlUsnevXtp27by32CcNWsWX375JTt27KBFixb/RaiixUcQBEEQ6pO6atEYO3Ysb7/9Ni1atKBVq1Z8//335OfnM3jwYAAGDhyIm5ubZozQzJkzmTJlCitXrsTb21szFsjc3LzCD4rXJpH4CIIgCEI9UldvXO7Xrx9paWlMmTKF5ORkmjRpwo4dOzQDnuPi4rTeur1w4UIUCgW9e/fW2s/UqVOZNm3aQ4tTJD6CIAiCUI9IanFwc02NGjWKUaN0v2vrwIEDWvMxMTEPPyAdxBgfQRAEQRCeGKLFRxAEQRDqkfr646K1RSQ+giAIglCPiK6cqon6EQRBEAThiSFafARBEAShHqnNNzfXRyLxEQRBEIR6RIzxqZro6hIEQRAE4YkhWnxqwZlUw7oOoUYOv+hY1yHUyAfHEuo6hBppE/p43W5l/aL7nRuPqiPD5td1CDUiOzugrkOoEUvDx6ub5FWvoroO4ZEjWjSqJhIfQRAEQahHRFdX1URiKAiCIAjCE0O0+AiCIAhCPSKe6qqaSHwEQRAEoR4RXV1VE4mPIAiCINQjYgxL1UT9CIIgCILwxBAtPoIgCIJQj4gxPlUTiY8gCIIg1CNijE/VRFeXIAiCIAhPDNHiIwiCIAj1iGjxqZpIfARBEAShHhFdOVUT9SMIgiAIwhNDtPgIgiAIQj0inuqqmkh8BEEQBKEeEWN8qvZYJT6DBg1i2bJlFZZHRUXh7+9fBxFV3yveLrzh74atkSHRsny+vxRNeHaezrIveDrRw8MRXwszACJy8vglPKZCeS9zE4aHeNPEzgo9iYSY3AI+O32N1EJ5rcSsUqn48cc/WbduFzJZPs2aBTNt2gi8vV0r3WbevJXMn79Ka5mPjxs7dizSzL/11iROnrysVaZfv5588cXIascWt+cAN7fvQpEjw8LTnaAB/bD29am0fPLJM1z/axOF6RmYOjsS0OcVHBqHataXFBURue5vUs9eoDgvHxMHO7y6PoPHMx01ZQpS04hYvZ6sqGiUxSXYh4YQPOB1jKwsqx33HekH9pO6ayclshxM3D1w6/cGpj664y9KTCB58yYKYmMpzszAtU8/HLp0rVCuOCuLxL83kHvlMkqFAiMHRzzeHoSpl3eN49PlRU9n+vi4YWtoSHRuPgvCbxCRo/scftbdiW6uDnjfPoejcvJYHBWrKa8nkTC4gSetHGxwNjGmoKSUsxnZ/B4ZS4ZcUSvxVsdTrYIYM7wXzUJ9cXGyoe/QuWzedfo/O/7dVCoVG37bwf7Nx8nPLSQgzId3xvfG2cOh0m3Cz0ezdeV+bl67RXaGjDEzBtOiY6hWmaICOasXbuH04cvk5eTj4GpHj94d6PpKuweOd/vS7RzbdpzCvEJ8GvnQ58M+OLpXHu/1i9HsW7OP+Kh4ZBkyhkx/h7D2YZr1pSWlbF28lasnw8lIysDYzJjAZgG8MPQFrOytHjjev37fwYHNxynIK6RBqA+DxlVdv9fOR7Nt1X5iItT1++HXg2lern4BEmJSWLtoC9fOR1NaqsTN24nRXw3C3snmgWIWHo7HboxPz549SUpK0pp8KvnCqExpaSlKpfIhRVjRM672jGrow9KIOIYePMf1nHzmtmmEtaGBzvJN7K3Yk5DGB0cvMfzIBVIL5cxt2wh7Y0NNGVdTYxa0DyMur5AP/r3EoAPnWBYZj6K09v6uX3/dwPLlW5g2bQRr187BxMSYIUOmIL/HF1ODBp4cOfKHZlq5cmaFMn379tAq88kng6sdV9KJ01xbvR7/l3vRdvpkLDzcOTNnHnKZTGf5rKhoLi76HbeOT9H2i09xbNqEcz8uIvdWgqZMxKr1pF+6StiwwbT/Zipe3bsQvmI1qecuAFAil3N69g8gkdDykzG0/vRjVCWlnP1+AaoanktZp0+RuH4tzr1eIGDy5xi7u3Nj3vcUVxK/UqHA0N4el1deRd9S98W/JD+fqNkzkejp4TvqQwKnTse1dx/0TE1rFFtlOjnb816QDyuux/P+0fPcyM1nRouGlZ7DjW2t2J+UzscnL/Ph8YukFcn5tkVD7IzU57CRnhR/S3NWRMcz4tgFpp8Lx93MhC+aBddKvNVlZmrEpatxfPTZ4v/0uLps+XMfO9cfZvDHffji148wMjbk27E/o5AXV7qNvFCBp78rg8a9WmmZFfP+4eKJa4yY0p/ZKyfybN+OLPvuL84cvlzpNtWxd/VeDv19iL4f9WHM/DEYGhuyaOIiihWVx6solOPm50rvD3rrXl+kID7qFj0GdGf8onEMmfYOqfGp/Pr5bw8UK8DWlfvYveEwg8b3YerPH2FkYsjscfeo3yJ1/Q4cW3n9piSk89XIebh4OjLpxxF8vXQ8L73dDUPDumtXkNbiVB89dn+XkZERzs7OWtMPP/xAaGgoZmZmeHh4MGLECPLyyu5Ely5dirW1NZs2bSIkJAQjIyPi4uKQy+WMHz8eNzc3zMzMaN26NQcOHKj1mPv5ubE5Lplt8anE5BUy5+J1ikpLed7TSWf5L89GsjEmmeuyfOLyCpl5Pgop0NzeWlNmWLAXx1OyWHg1hihZPokFRfybkkl2FRedmlCpVPzxxybef78vXbu2ISjIh1mzxpCamsmePcer3FZPTw8HBxvNZGtb8cva2NhIq4y5efW/oGN37sG901O4dWiHuZsrIW+/iZ6hAQmHjuosH7d7H/ahDfF5rjvmri40eO1FLL08idtzQFMm+/oN3J5qg21wICYO9ng83QELD3dybsSo10dFU5ieQejQt7HwcMPCw41G7w5CFhNHZnhEtWMHSN+zG9unOmDb7imMXV1xf3MAEgNDMo/+q7O8qbcPrq/1waZlKyT6ui+mqbt2YGhrg+fbgzH18cHI3gGLkIYYOTjWKLbKvObtyvb4FHYmpBKXX8gPV6KRl5bSw033/r+9GMnm+GSic/OJzy/kf5evI5FAUzv1uVBQUsrE01c4lJzBrfxCwnPymH/1BgFW5jjcleA/bLsOXGD6nLVs2lk3rTx3qFQqdqw9xMtvd6NFh0Z4+rvy/udvkp0uqzJBadI2mL7DnqNlp7BKy0RdiqHDsy0JaeaPg4stz7zUFk9/V6LD4x4o3oN/HaL7gO6EPhWKm58rAyb0Jyc9h0tHLlW6XUjrEJ5/53kat9cdr4m5CSNnj6Dp001x8nDCO8Sb10b3Jj4ynsyUrAeKd+faQ7w4sBvNb9fve5++SXaGjLNV1G/jNsH0fvc5WnSsvH7X/7KNxm2CeX3EC3gHuOPkZk+z9o2wtLG473gflFRSe1N99NglPrpIpVJ+/PFHrly5wrJly9i3bx+ffPKJVpmCggJmzpzJb7/9xpUrV3B0dGTUqFEcO3aM1atXc/HiRfr06UPPnj2Jioqqtdj0JRICrMw5k5atWaYCTqdn07CaHwwjPT30pRJyi9VJjQRo62RDfH4hc9s0ZFOPVvzcoTEdnG1rLe5bt1JIS8uiXbsmmmUWFmY0bhzAuXPXqtw2NjaR9u3fpkuXoYwbN4fExNQKZTZvPkDr1m/Sq9dI5s5dRmFhUbXiUpaUIIuJwy6krGVAIpVi1zCY7OgbOrfJvn4D25AgrWX2oSFa5a39fUk9f5GirCxUKhUZ4RHkp6Rg1yhEfdziEiQSCdK7Eg89A30kEglZkderFfud+AviYrEI1o7fIjiYghvR1d5PebILFzDx9Cbml0Vc+XgsEV9/QcbhQ/e9v7vpSyQEWJpzNiNbs0wFnM3IIcS6BuewREJucUmlZcwM9FCqVOQXlz5gxI+ftMRMsjNyadgiQLPM1NwEvxBPoi7HPNC+G4R6c/bIFTLTslGpVFw5E0VyXBqhrQLve58ZSRnIMmUENCuL18TcBK9gL25efbB4yyvKL0QikWBqbnLf+0hLyiQns2L9+gZ7cv1KzH3vV6lUcuFYOM4eDswa+zMjX5jCtGHfc+ZQ5cnff0EiUdXaVB89VmN8ALZs2YK5ublm/tlnn2XdunWaeW9vb7766iuGDx/OTz/9pFleXFzMTz/9ROPGjQGIi4tjyZIlxMXF4eqqHrMyfvx4duzYwZIlS/jmm29qJV4rQwP0pRIyyzWnZsmL8apmK8f7Id6kFyk4fTt5sjEywFRfn/7+7vx2LZaFV2No7WjDVy2D+fDoJc5n6O4yqYm0NPXdlZ2dtdZyOztr0tMrv/MKCwtgxoyP8PFxIy0tiwULVtG//0Q2b56vadXp1asTrq6OODraEhERw5w5S7l5M4H58yffMy5Fbh4qpbLCuBpDSwvyk5J1biPPkeksr8gpq6fgAf24svRPDo6ZhERPChIpDQcPwDawAQDWfj7oGRkSsfZvAnq/jAoVUWv/RqVUIs+pfn2X5uWBUom+pXY8+haWyJN1x18divQ0Mg4dwKFrNxx7PkdhbAwJa1cj0dfHtu2DjeWwMjRATyohS1H+HFbgYVa9cRdDA73IkCu0kqe7GUglDA3wZn9SOgWlT17ik52pPoesbLUTSStbC7Izch9o32+PeZXfZ65l9MtfoKcnRSKVMHRCX4Kb+N33PnOz1DFZlLt5s7CxIDfrwa8/dxQritn062aaPdMMYzPj+95Pzu1ropWNjvrNvP/6lWXlUVQoZ8uf++g99Fn6vd+Liyeu8eNnS5n0w/sENX20x54+qR67xKdz584sXLhQM29mZsaePXuYMWMG165dQyaTUVJSQlFREQUFBZjeHuNgaGhIWFhZc+WlS5coLS0lICBAa/9yuRw7O7tKjy+Xy5HLtQcPK4sVSA0eTvN8f393urjZ88HRSyiU6uxbgrr98UhyBmtvJAJwXZZPI1sLXvJyua/EZ9OmA0ydukAz//PPU+4r3k6dWmj+HRTkQ+PGAXTuPITt24/Qp093QD2Q+Y7AQG8cHGwYNOgz4uKS8PR0ua/jPqjYPfvJjr5J0w9HYGJvS2ZEFOHLV2FsbYVdw2AMLS1oPHIYV5etJG7PfiQSCc6tW2Lp5YlE8gi0B6tUmHh54/KyeiyCqacnRYkJZBw6+MCJz4Pq5+PG0872jD95mWJlxTtIPYmEz5sEIQF+vHL/rV6Pk393nuH32WU3bB/PHvrQjrVr/WGuX4ll3Mwh2DvbcO18NEvn/oWNvRWNWgbcewfA6T2nWfPdWs38e98Me1jhapSWlLL0i6Wggr4f9qnRtkd3nWHJnLL6HTfz4dSvSqU+n5u1b0jPfp0A8GrgxvXLMez751idJT71tYuqtjx2iY+ZmZnWE1wxMTH06tWL999/n6+//hpbW1uOHDnCkCFDUCgUmsTHxMRE6wsqLy8PPT09zpw5g56entYx7m5RKm/GjBlMnz5da5nH64PxeuMdneVzFMWUKFXYGmkPArUxMiCjqOpBwq/7udG/gTtjjl4mWlZQbp9KYnILtcrH5hYSZlfzJ4wAnnmmFY0bl10EFbfv7jMysnF0LOtCy8jIJijIt9r7tbQ0x9vblbi4pErLNG6sbnKPjb134mNoYY5EKq3QyqKQ5WJYydNVRlaWVZYvVSiIWv8PTUcPx6GJ+okNCw93cuNucXP7buwaqrul7BuF0HH2Vyhy85BIpRiYmbL/g09wdrCvMua76Zmbg1RKSbmBzCW5sgqtQDWhb2WFsYt23Rk5u5B99ux97/OOHEUxpUoVNoblz2FDsu4x0L23tyuv+7oz4dQVbuYVVFivJ5HwWZNAHI2N+PjU5SemtadZ+4b4NfTUzJco1H93TmYuNvZl50FOZi5eDdzu+zgKuYI1P29jzIzBNG2n7rb19HclNiqRrav2VzvxadSuEV7BXmXx3u6yzM3KxcqurNUvNysXN7/7j/eO0pJSlnyxlMyULEbNGVnj1p6m7RviF1JWv8W3u09zsnKxrsX6tbAyQ09Pipu3s9ZyVy9HIi/evO/9Pqh6MYblIXrs6+fMmTMolUrmzp1LmzZtCAgIIDEx8Z7bNW3alNLSUlJTU/H399eanJ2dK91u0qRJ5OTkaE0evQdUWr5EpSIyJ09rYLIE9UDlK1mVN7G+6e/G2wEejD9+pcIjwyUqFeHZeXiW6/P2MDchuaB6Y2XKMzc3xcvLVTP5+3vi4GDDsWMXNGXy8gq4cCGSpk2DqtiTtvz8QuLjk3FwqPyxzvBw9VibqsrcIdXXx9Lbk8yrZeOMVEolGVevYe2nOyGz9vfVKg+QcSVcU15VWoqqtLTCbZJEKgVVxRYKQwtzDMxMybh6DUVuLo5NKx/4qCt+U08vcq+Fa8Wfdy0cU9/773ow8/NHnqLdVSZPScGwitbL6ipRqYiU5WkGJoP6HG5qZ8XV7MrP4b4+bgzw82Dy6StEyio+9n4n6XEzNWbCqctVjv+pb0zMjHF2d9BMbj5OWNtZcOVM2fjCgvwioq/G0aCR930fp6RESWlJaYVWSameBKWO1rfKGJsa4+DmoJmcvZyxtLUk8mxZvEX5RcSGx+ITcv/xQlnSk5aQxsjZIzCzMqvxPkxMjXFyd9BMbt5OWNlacPWu+i3ML+JGeBz+De8/Xn0DfXyCPUmK0x7HmByfhp2zeJT9UfXYtfiU5+/vT3FxMfPmzeOFF17g33//ZdGiRffcLiAggP79+zNw4EDmzp1L06ZNSUtLY+/evYSFhfH888/r3M7IyAgjIyOtZffq5loTncDkpgFcy8kjPCuXPr6umOjpsS0+BYBPmwaQXiTn5/BYQJ30DAn04ouzESQXFGlaiwpLSim8/bj6qusJTG8RyIWMHM5m5NDawYZ2TrZ8cLR2BtVJJBIGDnyRhQvX4OXliru7Ez/8sAJHR1u6dm2jKff225/SrVtbBgzoBcDMmb/TuXMrXF0dSU3NZN68lUilUnr1UjcDx8UlsXnzQTp1aoG1tQURETHMmPEbLVs2JCioeq8l8OrRlcu/LsXSxwsrX29id+2jVK7ArYO6S+fSL0swsrEmoM8rAHh2e4ZT384lZvtu7BuHknziFDk3YwkZ1B8AfRMTbAIbELnmL/QMDDC2tyPrWiSJ/x4n8I2yx24TDh/FzMUZQ0sLsq/f4Nqfa/Hq3gUzl8oTZV3su3YjfuliTL28MfX2IW3fHpQKBbbtnlLX0ZLfMbC2weUVdbeVsqQEeZI6mVeVllCcnUVhfBxSI2OMHNVPVTl06UrUrJmkbN+KdfOWFMTcJPPIIdz7v1Wj2CqzISaRT0IbEJmTR0ROHq94u2Ksp8fOBPUF/5PQBqTLFSyOVJ/D/XzcGNjAkxkXIkkulGtaiwpLSykqVaInkTClSSD+luZ8fvYqUolEUya3uIQSHQnnw2BmaoTfXXfr3h4OhIV4kZWdR3xixn8SA6g/bz37dmTjst04u9vj4GrL+l93YG1vSfMOjTTlvvlgIS06NqJ77w6A+h09ybfSNevTEjOJiUzA3NIUe2cbTM2MCW7qx6oFmzE0MsDe2Ybwc9Ec3n6aAR+89EDxdnq1I7v+3IWDuwN2zrZsW7INK3srQtuXvedm/vgFhLUPo+PL6njlhXLSEtI06zOSM7l1/RamFmbYOtlQWlLK4ulLuBV1i2Ffv4tSqUR2e/yTqYUp+gb395UlkUjo0bcj/yzbjZO7PQ4utmz4bQfWdpY0u6t+v/1wIc07NqLba2X1m5JwV/0mZRIblYCZpanmHT3PvfE0C6YuJ7CxLyHN/Ll44hrnjl5l0o8j7ivW2iDe3Fy1xz7xady4Mf/73/+YOXMmkyZNomPHjsyYMYOBAwfec9slS5bw1VdfMW7cOBISErC3t6dNmzb06tWrVmPcl5iOtaEBQwI9sTUy5Losn/HHL5N1e8Czk4mRpq8Y4GVvFwz1pHzVUvudJosj4lgSoX4E9XByBnMuRDOggTsfhvoSl1fI56fDuZRZewML3333NQoLi5gyZT4yWT7Nm4fw22/TMTIqS/Ti45PJumswY3JyBmPHziE7W4atrRXNm4ewdu0czSPtBgb6HDt2nj/+2ERBQREuLvZ0796OESP6VTsul9YtUOTmcv3vzchzZFh6utN83GjNAObCjEy46w7XpoEfYe8NIeqvTURu+AczJ0eafjAcC/eyJu7G7w8lcv1GLv68mOL8AkzsbGnw2kt4dC57gWF+UgqR6zZSnJ+Pib0dvi88i1ePLjWuV5sWLSnNzSV58z+UyGSYuHvgM/pDDG53dSkyteMvyc4m8usvNfNpu3eRtnsXZg0C8B/3MaB+5N1n+PskbfyblK1bMLS3x7VPP2xat6E2HExOx9pQn7cbeGJz+yWck09f0bw+wdHECBVl53AvT2cMpVKmlmsd/ON6HMuvx2NvbEg7J3Vr1M9PNdUqM+7kJS7W4nlclWZhvuxaWzaebdZU9XVj+bqDDBt37xuo2tSr/zPICxX8PmsdBXnqFxhOmDsMw7u6yVMS0snNydfM37gWz9ejyx7iWDHvHwA6PNuS4Z+9AcCo6W+xZtFWfpq+gjxZAfbOtvR97zm6vPxgY7+6vN4FRZGCNf9bQ2FeIb6hvgyf8R4Gd3WJZiSmk39Xi3VcRBzzx5WNJdy4cCMArbq3pP+E/mSnZ3P5qPrx8lnDZmsdb9TckTRo0uC+433+TXX9Lpm9TvMCw/FztOs3NVG7fm9GxDPjg7L6XTlfXb/te7Zk2Kfq+m3RMYxB43uzZcVeVvzwNy6ejoz+chCBYdUfElDbxBifqklUqv/o1qoe67DpSF2HUCOHX6ydd7v8Vz44lnDvQo+Q5CK9exd6hGTJH694jwybX9ch1Mjhs5V3hT+K0oserxEQNob/3ctoa0trR909CrVl6tk9tbav6c0qviX+cffYt/gIgiAIglBGtPhUTSQ+giAIglCPPF5tuP+9x6tNUxAEQRAE4QGIFh9BEARBqEfEU11VEy0+giAIglCP1OWPlC5YsABvb2+MjY1p3bo1J0+erLL8unXrCAoKwtjYmNDQULZt23aff3X1icRHEARBEOqRukp81qxZw9ixY5k6dSpnz56lcePG9OjRg9TUij9UDXD06FHeeOMNhgwZwrlz53j55Zd5+eWXuXz5ci3UQuVE4iMIgiAIgk5yuRyZTKY1lf+9yjv+97//8e677zJ48GBCQkJYtGgRpqamLF68WGf5H374gZ49e/Lxxx8THBzMl19+SbNmzZg//+G+skIkPoIgCIJQj+hJam+aMWMGVlZWWtOMGTMqHFOhUHDmzBm6di17749UKqVr164cO3ZMZ5zHjh3TKg/Qo0ePSsvXFjG4WRAEQRDqkdp8j8+kSZMYO3as1rLyP9sEkJ6eTmlpKU5OTlrLnZycuHbtWoXyAMnJyTrLJycn6yxfW0TiIwiCIAiCTrp+n/JxJxIfQRAEQahH6uJxdnt7e/T09EhJSdFanpKSgrOz7h9ydnZ2rlH52iLG+AiCIAhCPVIXT3UZGhrSvHlz9u7dq1mmVCrZu3cvbdu21blN27ZttcoD7N69u9LytUW0+AiCIAiC8MDGjh3L22+/TYsWLWjVqhXff/89+fn5DB48GICBAwfi5uamGRz94Ycf0qlTJ+bOncvzzz/P6tWrOX36NL/88stDjVMkPoIgCIJQj9TVb3X169ePtLQ0pkyZQnJyMk2aNGHHjh2aAcxxcXFIpWUdTe3atWPlypV89tlnTJ48mQYNGrBx40YaNWr0UOMUiY8gCIIg1CN1+evso0aNYtSoUTrXHThwoMKyPn360KdPn4cclTaR+NSCQy861HUINfLO4cS6DqFGzB+zs/SdBvl1HUKN+FqU1nUINSI7O6CuQ6iRDs1W1HUINbJk/9t1HUKNrI8xq+sQaqy1Y11H8GR7zL5SBEEQBEGoiviR0qqJxEcQBEEQ6hG9OuzqehyIxEcQBEEQ6pG6HOPzOBDv8REEQRAE4YkhWnwEQRAEoR4RLT5VE4mPIAiCINQjIvGpmujqEgRBEAThiSFafARBEAShHtETj7NXSSQ+giAIglCPiK6cqon6EQRBEAThiSFafARBEAShHhGDm6smEh9BEARBqEdE4lM10dUlCIIgCMIT44lq8Rk0aBDZ2dls3LixrkMRBEEQhIdCPNVVtScq8alrKpWKeT+uZN26Xchk+TRrFszUae/j7e1a6Tbz5q1kwfzVWst8fNzYvmOhzv0Pe3c6hw+fZf6CyXTt2ua+Y03at5+EnbtR5ORg5uGO7xuvY+Hro7NsQUIicf9sIi82DnlGBj79+uDarav2/vYfJPnAQeQZGQCYurrg8UIvbEIb3Vd8cXsOcHP7LhQ5Miw83Qka0A/rSuIDSD55hut/baIwPQNTZ0cC+ryCQ+NQzfqSoiIi1/1N6tkLFOflY+Jgh1fXZ/B4pmPZ35maRsTq9WRFRaMsLsE+NITgAa9jZGV5X3+DSqVi+9LtHNt2nMK8Qnwa+dDnwz44ujtUus31i9HsW7OP+Kh4ZBkyhkx/h7D2YZr1pSWlbF28lasnw8lIysDYzJjAZgG8MPQFrOyt7ivOu+P985ed7Np4nPy8QoLDfBgx4TVcPSuPd93SvRzdf4mE2FQMjQwICvVi0OheuHs5aspMGv4Tl89Ga23X85W2jJzU+4Hj3fDbDvZvPk5+biEBYT68M743zh6Vxxt+PpqtK/dz89otsjNkjJkxmBYdQ7XKFBXIWb1wC6cPXyYvJx8HVzt69O5A11faPVC81fFUqyDGDO9Fs1BfXJxs6Dt0Lpt3nX7oxwV1fe5fsZ0zO45RlF+IZ4gPvUb2wc7NscrtTmw+zNEN+8jLkuHk48Zz77+Ge6AXAFkpGXw/+Aud2/WdNIiGHZoCMPW5Dyus7z3hbUI7Nat2/In79nNrh/qaZu7hjt+blV/T8hMSid1Ydk3zfb0PbuWuafFbt5N+9hyFSclIDQ2x9PPFu8+rmDo7Vzumh0V0dVVNJD7/od9+/Yvly7fw7bcf4u7uxA8//MnQIVPZum0BRkaGlW7XoIEni5d8qZnX19PTWW7Zsk1IJA9+xqedPMXNtevxG/AmFr4+JO7Zy5Xvf6TZV9MxtKz4JV+qUGDkYI9di+bcXLNW5z6NbKzxeu0VTJwcQQWpR48RPv8nmkz5DFO3yhM/XZJOnOba6vU0fPtNrHy9id21jzNz5tH+22kY6YgvKyqai4t+p0Hvl3FoEkrSsVOc+3ERbadPxsLdDYCIVevJCI8gbNhgTOztSL8STvgfqzCyscKxaWNK5HJOz/4BC093Wn4yBoDrf23i7PcLaPP5BCTSmvca7129l0N/H6L/hP7YOtuxbek2Fk1cxKTFEzEwNNC5jaJQjpufK62fbc3iqYsrri9SEB91ix4DuuPq50phbiF/LfiLXz//jfELx9U4xrtt+GM/W9Yc5qOpb+DkasufP+9gyge/8NOaTzA00h3v5bPRPN+nHQ2CPVGWKvlj4TamjP6Fn9Z8jLGJkaZcj5fb0H9YD828kXHln4fq2vLnPnauP8x7n72Jo4st637dzrdjf2bWigmVxisvVODp70qn51vx/eSlOsusmPcPV89EMWJKfxxcbLl0MoIlczdgY29J8w73l8hXl5mpEZeuxvHHmgOs+fXB/j9r6sj6vZzYdIhXxvbH2tmWfcu3sfzzRYxcNKnS8/XywbPs/PVvXhjVF7cgb45vPMDyzxcy+pdPMbe2wMrehvErvtTa5syOo/y7YR/+LUK0lr885k38mwdr5o3NTaode9rJU9xYsx7/t25f03bv5fJ3P9L8a93XNKVCgbGDPfYtmnOjkmtaTmQkrp2fxtzHG5WylJgNG7k89weafzUNPSMjndv8V0TiU7VHdozP008/zejRo/noo4+wsbHBycmJX3/9lfz8fAYPHoyFhQX+/v5s374dgNLSUoYMGYKPjw8mJiYEBgbyww8/VHkMpVLJjBkzNNs0btyY9evXP5S/R6VS8ccfmxj+fl+6dG1DYJAPM2eNITU1kz17jle5rZ6eHg4ONprJxrbiBzU8/AZLFm/k628+eOBYE3fvwalDe5zaP4Wpqyt+A/qjZ2hI6pGjOstb+Hjj06c3Dq1aItXXfQG0bdIY27BQTJycMHF2wuvVl9EzMiL3xo0axxe7cw/unZ7CrUM7zN1cCXn7TfQMDUg4pDu+uN37sA9tiM9z3TF3daHBay9i6eVJ3J4DmjLZ12/g9lQbbIMDMXGwx+PpDlh4uJNzI0a9PiqawvQMQoe+jYWHGxYebjR6dxCymDgywyNq/DeoVCoO/nWI7gO6E/pUKG5+rgyY0J+c9BwuHblU6XYhrUN4/p3naXxXK8/dTMxNGDl7BE2fboqThxPeId68Nro38ZHxZKZk1TjOu+PdtPoQfd/pSptOjfBp4MqYaW+QmS7j+MHLlW43/cdhdO3VCi8/Z3wCXPloyuukJWdxPfyWVjkjYwNs7C01k6m58X3HeifeHWsP8fLb3WjRoRGe/q68//mbZKfLOHO48nibtA2m77DnaNlJd/0CRF2KocOzLQlp5o+Diy3PvNQWT39XosPjHijm6th14ALT56xl087/ppXnDpVKxfGNB+n4eneC2obi7OPGq+MGkJuRw7VjlZ+vR/8+QPOe7WjavQ2Ons70GtUXAyNDzu1SX/OkelIsbC21pvCjF2nYoQlGJtrJg7GZiVa5ypItXRJ27cG5Y3uc2z+Fmasr/m/1R2poSEoV1zTfvr1xbF35Na3RmA9xat8OMzdXzD08CBgyCHlmJnkxsdWOS6gbj2ziA7Bs2TLs7e05efIko0eP5v3336dPnz60a9eOs2fP0r17d9566y0KCgpQKpW4u7uzbt06rl69ypQpU5g8eTJr1+rO1gFmzJjBH3/8waJFi7hy5QpjxoxhwIABHDx4sNb/llu3UkhLy6Jdu8aaZRYWZoQ1DuD8uaq/OGNjE+nQfhBdu7zL+HFzSUxM01pfWChn/Li5TJnyHg4ONg8Up7KkhLzYOKxDyu6sJFIpVsFB95Wk6KJSKkk7eYpShQILP98axyeLicOuXHx2DYPJjtYdX/b1G9iGBGktsw8N0Spv7e9L6vmLFGVloVKpyAiPID8lBbtG6rtOZXEJEokEqX5ZI6megT4SiYSsyOs1+hsAMpIykGXKCGgWoFlmYm6CV7AXN6/G1Hh/VSnKL0QikWBagzvk8lISM8nKyKVJq7J4zcxNCGjoybVL1b/Q5+cVAWBhZaq1/MCOs7zZ7XNGvj6bZQu2UlSkuO9YAdISM8nOyKVhi7J4Tc1N8AvxJOpyzAPtu0GoN2ePXCEzLRuVSsWVM1Ekx6UR2irwgfb7KMtKziAvS4Zvk7L6NDYzwS3Qi/jwmzq3KSkuIel6vNY2UqkU3yYBxF+L0blNYlQ8yTcSaNa9bYV1WxeuZ+brk/nlo7mc3XUclap641iUJSXkxsZhHax9zbAOCUJWyTXjfpQWFAKgb2ZWa/u8X1JJ7U310SPd1dW4cWM+++wzACZNmsS3336Lvb097777LgBTpkxh4cKFXLx4kTZt2jB9+nTNtj4+Phw7doy1a9fSt2/fCvuWy+V888037Nmzh7Zt1R8yX19fjhw5ws8//0ynTp1q9W9JS1PfbdvZWWstt7ezJj298jvxxmGBzJjxIT4+bqSmZbFgwWoG9J/Ips3zMDdXf3nMmPEbTZsG0eUBxvTcUZyXB0olBpYWWssNLS3JSU5+oH3n30rg4oyZKIuL0TMyImjEcExda9bNpcjNQ6VUVhhXY2hpQX6S7vjkOTKd5RU5Ms188IB+XFn6JwfHTEKiJwWJlIaDB2Ab2AAAaz8f9IwMiVj7NwG9X0aFiqi1f6NSKpHftZ/qys3KBcDCRrueLWwsyM2q+f4qU6woZtOvm2n2TDOMze6/FSUrQx2Tta12vNa2Fpp196JUKvn1fxsJbuyNl5+LZnmnHk1xdLbB1sGKmOuJLJ2/lYTYNCbPGnTf8WZnqmOyKhevla0F2Rm5971fgLfHvMrvM9cy+uUv0NOTIpFKGDqhL8FN/B5ov4+yvNvnq3m589Xc2kKzrrwCWT5KpVLnNunxqTq3ObvrGA4eTniGaI+96TzgOXwbN8DA2JDrZ6+xdcE6FIVy2rx07+t0ca76mmao45pWWMk1o6ZUSiU3Vq/F0t8Ps9vd53VJr54mLLXlkU58wsLKmpv19PSws7MjNLRsoKGTkxMAqanqD9GCBQtYvHgxcXFxFBYWolAoaNKkic59X79+nYKCArp166a1XKFQ0LRp00pjksvlyOVyrWWGRooKY3Q2bzrA1Kk/aeYX/Tylir+0ch07Ndf8OzDIh8aNA3im81B2bD9C7z7d2bf3BCeOX+Svv7+/r/3/l0ycnWgy5TNKCwtJP3OWqMVLCf1kXI2Tn4chds9+sqNv0vTDEZjY25IZEUX48lUYW1th1zAYQ0sLGo8cxtVlK4nbsx+JRIJz65ZYenlWa1zV6T2nWfNdWevje98Me5h/DqAe6Lz0i6Wggr4f9qnRtgd2nGHBjLJu3ynfDX3geBbN+ou4G8nM/GWU1vKer5Td3Xv7u2BjZ8lnIxeRdCsdF3f7au37351n+H32Os38x7MfPN7K7Fp/mOtXYhk3cwj2zjZcOx/N0rl/YWNvRaOWAffewWPg4v7TbJ63RjPff/p7D/2YxXIFlw6cpeMb3Suse/rNsvFfLn7uFBcp+HfDvmolPv+F63+uIj8hkcYTP67rUIRqeKQTHwMD7b5ViUSitezOF45SqWT16tWMHz+euXPn0rZtWywsLJg9ezYnTpzQue+8vDwAtm7dipubdoZuVMXAtBkzZmi1LAFMmTqSadNGay3r/EwrwhqXXQQVihIAMjKycXS01SxPz8gmOKj63T2WluZ4e7sSG5cEwPHjF4mLS6ZVyze0yn0w+luatwhh+fJvqr1vAANzc5BKKZZp38UpZDIMrR7sqSCpvr56cDNg7u1FXkwMiXv24T9wQLX3YWhhjkQqrdDKopDlYljJ01VGVpZVli9VKIha/w9NRw/HoYk6sbbwcCc37hY3t+/GrqG6idy+UQgdZ3+FIjcPiVSKgZkp+z/4BGeHe385N2rXCK9gL818SbH6fMjNysXKrqxec7NycfN78DvG0pJSlnyxlMyULEbNGVnj1p5WHRoS0LAs3uLb5292Zi629mX1nJ2Zi2/AveNdNPsvTh25yoyfR2LvZF1l2cBGngAkxVc/8WnWviF+DT018yWKUgByMnOxuSvenMxcvBrcf/0q5ArW/LyNMTMG07SduhvU09+V2KhEtq7aX28Sn8DWjXALLPv/L719vuZl5WJhW3a+5mXn4uyruz5NLc2QSqUVWoTysnMxL9cSB3D1yAWK5QqadGl1z/jcA704uGqn5nNUFQML9TVNoeOaZvCA1zRQJz2ZFy7ReMJ4jGwfbKhBbZGKx9mr9EgnPjXx77//0q5dO0aMGKFZFh0dXWn5kJAQjIyMiIuLq1G31qRJkxg7dqzWMkOjimMczM1NNV1RoB4c6OBgw7FjFwgOVic6eXkFXLwQyRtvPFvt4+fnFxIfn8yLDp0BeHdYb3r30b5DevGF0UycNIRnOres9n7vkOrrY+7lSU54OHZNm6hjVyrJuXYNl86da7y/KqlUqErufeEqH5+ltyeZV6/h1Lwsvoyr1/Ds8rTObaz9fcm8eg3vHl00yzKuhGN9e3yRqrQUVWlphQ5tiVQKOsYRGFqYq/dx9RqK3Fwcm1Y+EPYOY1NjjE3Lkg+VSoWlrSWRZ6Nw93cHoCi/iNjwWNq/8NQ991eVO0lPWkIao+eOwsyq5mMOTM2MMTXTjtfGzoILp6I0iU5BXhGRV+J47rXKH+NWqVT8POdvjh24xIyFI3B2s7vnsW9EJgJoJSz3YmJmjEm5eK3tLLhyJgrvO/HmFxF9NY6ur9x//ZaUKCktKa3QyifVk6BU1p8vGyNTY4zKna/mNpbcuBCJi9/t87WgiISIWFo+317nPvQN9HHx9+DGhUiC26k/I0qlkpvnI2n1QocK5c/uOk5g60aYWZnfM76kGwmYmJuib3DvrzCpvj4WXp5kh4dj36yJ+u9RKskOv4brM/d/TVOpVESvXE3G2fOEfTIW42rcAP1XHunBu4+AepP4NGjQgD/++IOdO3fi4+PD8uXLOXXqFD4+ut/TYGFhwfjx4xkzZgxKpZL27duTk5PDv//+i6WlJW+//bbO7YyMjCq0CKm496O3EomEgQNfZNHCtXh7ueLm7sSPP/yJo6Ot1vt2Br39GV27tWHAgF4AzJy5mM6dW+Hq6kBqaibz561EKpXSq5f6/TJ3nvQqz9XVAXeP+3ufhGu3rkQtXoq5lzfmPt4k7tlLqVyB41PqL7jI35dgaG2N92uvAOrBgwWJSZp/y7OzyYuLR8/ISNPCE7Phb2xCG2Jka0tpkZy0EyfJiYik4Uc1fwrNq0dXLv+6FEsfL83j7KVyBW4d1PFd+mUJRjbWBPRRx+fZ7RlOfTuXmO27sW8cSvKJU+TcjCVkUH8A9E1MsAlsQOSav9AzMMDY3o6sa5Ek/nucwDfK3iWTcPgoZi7OGFpakH39Btf+XItX9y6YudS8niUSCZ1e7ciuP3fh4O6AnbMt25Zsw8reitD2Zd2588cvIKx9GB1fVn9RyAvlpCWUDW7PSM7k1vVbmFqYYetkQ2lJKYunL+FW1C2Gff0uSqUS2e3xLqYW1fuiqCzeF1/vyJrFe3D1sMfJ1Y4Vi7Zja29Jm05lj3B/OmIhbZ8OpVdf9Zfhwll/cWjnWT6d8w4mpkZkpd+OxdwEI2MDkm6lc3DnOVq0C8LCyoyY64n89t0mGjb1xafB/XeBSiQSevbtyMZlu3F2t8fB1Zb1v+7Autwj5998sJAWHRvRvbe6fosK5CTfStesT0vMJCYyAXNLU+ydbTA1Mya4qR+rFmzG0MgAe2cbws9Fc3j7aQZ88NJ9x1tdZqZG+HmXnW/eHg6EhXiRlZ1HfGLGQzuuRCKhzcudOLR6F3auDtg42bFv+TYs7KwIalt2vi6dNJ/gdmG0fkF9fWr3ytP8/b8/cWvgiVuAJ8f+OYhCrqBpt9Za+89ITCP2crTOLrWIE5fJy8rFPcgLfUMDos9FcHjNbtq9Vv2kxa17VyJ+X4qFtzcWPt4k7NmLUq7A6fY1LeK3JRjaWOOj45qmKilBnlXxmha9YhWpJ04SMnoEesbGKHJyANAzMUHP8MFfxyA8PPUm8Xnvvfc4d+4c/fr1QyKR8MYbbzBixAjN4+66fPnllzg4ODBjxgxu3LiBtbU1zZo1Y/LkyQ8lxqHvvkphYRFTpixAJsunefMQfv1tmtb4oLj4ZLLuGtyakpzBuLFzyM6WYWtrRfPmIaxZOxtb2wdvoq2MQ6uWlOTlEffPJhQyGWYe7jT86ANN15A8I1PrjleRnc2FL77SzCfu3E3izt1YBgQQ+on6XSPFublE/b4URU4O+iYmmLq70fCjD7BuqP2ujupwad0CRW4u1//ejDxHhqWnO83HjdYMYC7MyIS74rNp4EfYe0OI+msTkRv+wczJkaYfDNe8wweg8ftDiVy/kYs/L6Y4vwATO1savPYSHp3LXmCYn5RC5LqNFOfnY2Jvh+8Lz+J1VytSTXV5vQuKIgVr/reGwrxCfEN9GT7jPa3HdDMS08nPydPMx0XEMX/cAs38xoUbAWjVvSX9J/QnOz2by0fVj2vPGjZb63ij5o6kQZMG9x3vawM7U1SkYP4368nPKySksQ/Tfxim9U6c5IQMZNn5mvntG9SPC08e/pPWvj6c0o+uvVqhb6DH+ZORbFp1iKIiBfZO1rTrHEq/d7TH3t2PXv2fQV6o4PdZ6yjIU7/AcMJc7XhTEtLJzSmL98a1eL4eXRbrinn/ANDh2ZYM/0zdnTxq+lusWbSVn6avIE9WgL2zLX3fe44uLz/8Fxg2C/Nl19qy8YKzpg4EYPm6gwwbt+ihHrt97y4UFynYPG8NRXmFeDb0ZcAXw7XO16ykDAruqs9GnZqRL8tj3/Jt5GXJcPZ1560vhmNuo92ad27XcSztrfBrVvHJOKmeHie3HGbHr3+DSoWtqwM93n2Z5j0rPvlVGYdWLSnOzSN2o/qaZu7hTsMxd13TMrWvGYrsbM5NL7umJezcTcLO3VgFBhB2+5qWdED99O+lWXO1jhUw+G2c2j/8c6Eq9fVprNoiUVX3mUChUipq/h6XujTkcFJdh1Aj5vqP1yn6nEdRXYdQI74WpXUdQo3Iih+vq3qHZivqOoQaWbJfd2v3o2pP0oO986ku/Nb+6Ye6/4NJ22ptX51cnqu1fT0qRFegIAiCIAhPjHrT1SUIgiAIgniq615E4iMIgiAI9YgY41M1kfgIgiAIQj0iEp+qiTE+giAIgiA8MUSLjyAIgiDUI6JFo2oi8REEQRCEeqQaPx/4RBOJoSAIgiAITwzR4iMIgiAI9Yho8KmaSHwEQRAEoR4RXV1VE11dgiAIgiD8pzIzM+nfvz+WlpZYW1szZMgQ8vLyqiw/evRoAgMDMTExwdPTkw8++ICc2z8OWxMi8REEQRCEekRai9PD0r9/f65cucLu3bvZsmULhw4dYtiwYZWWT0xMJDExkTlz5nD58mWWLl3Kjh07GDJkSI2PLbq6BEEQBKEekTziP1kRHh7Ojh07OHXqFC1atABg3rx5PPfcc8yZMwdXV9cK2zRq1IgNGzZo5v38/Pj6668ZMGAAJSUl6OtXP50RLT6CIAiCIOgkl8uRyWRak1wuf6B9Hjt2DGtra03SA9C1a1ekUiknTpyo9n5ycnKwtLSsUdIDIvERBEEQhHpFUovTjBkzsLKy0ppmzJjxQPElJyfj6OiotUxfXx9bW1uSk5OrtY/09HS+/PLLKrvHKiO6umrB7ItxdR1CjcTmmdZ1CDXyomd+XYdQIzdkenUdQo3IFI/XIyCWho92M355S/a/Xdch1MjgzsvqOoQakcVMqOsQHjm1+VTXpEmTGDt2rNYyIyMjnWUnTpzIzJkzq9xfeHj4A8ckk8l4/vnnCQkJYdq0aTXeXiQ+giAIglCP1OatjJGRUaWJTnnjxo1j0KBBVZbx9fXF2dmZ1NRUreUlJSVkZmbi7Oxc5fa5ubn07NkTCwsL/v77bwwMDKoV291E4iMIgiAIwgNzcHDAwcHhnuXatm1LdnY2Z86coXnz5gDs27cPpVJJ69atK91OJpPRo0cPjIyM2LRpE8bGxvcVpxjjIwiCIAj1iFRSe9PDEBwcTM+ePXn33Xc5efIk//77L6NGjeL111/XPNGVkJBAUFAQJ0+eBNRJT/fu3cnPz+f3339HJpORnJxMcnIypaWlNTq+aPERBEEQhHrkcRi19+effzJq1Ci6dOmCVCrltdde48cff9SsLy4uJiIigoKCAgDOnj2reeLL399fa183b97E29u72scWiY8gCIIgCP8pW1tbVq5cWel6b29vVKqyBxmefvpprfkHIRIfQRAEQahHxG91VU0kPoIgCIJQj4i8p2picLMgCIIgCE8M0eIjCIIgCPWIaPGpmkh8BEEQBKEeeViPodcXoqtLEARBEIQnhmjxEQRBEIR6RDT4VE0kPoIgCIJQj0gkj9cP+f7XROIjCIIgCPWIaPGpmkh8asnVHQe5tGkvhdkybL3caPtOHxwaeFda/uaxs5xZvZW8tAwsnR1oOeBlPJo11KxXqVScXbOViL1HUeQX4hTkS7t3+2Hl4lhhX6XFxWyaNIfM2ARenjUROx93AHJTM1g7cmqF8g4ffYqRt1+Vf89Lns709XHD1siQ6Nx85l29QUROns6yz7k70d3NAW8LMwAic/L4PTJWU15PIuGdAE9aOdjgYmJMfkkpZzOy+S0ilgy5oso4KqNSqTi1ehtXdx9FXlCIS5APHYf1w9q1Yv3c7dL2Q5zfuJeCbBl23m50GNobp7v+n67s+peow6dJu3GL4sIihiyfiZGZqdY+tn3zM+kxCRTm5GJkZop740DavvUSZrZW94z5+KptXN5zFHl+Ia5BPnR+rx8294j5wrZDnLkds723G08P7Y1zgDrmotx8jq/eRuz5a+SmZ2FiaY5f6zDavvE8RmYmAFzdd5zd8/7Uue93l3wDzmaVxrtv+XZO7zhGUX4hniE+vDiqD3ZuVcd7YvNhjqzfR16WDGdfN55//zXcA70063MzZez8/R+iz0UgL5Bj7+5Ip9e70bB9EwCyUjI4sHInNy5EkZeVi4WtJY2faUGn17ujb1D9S5ZKpWL70u0c23acwrxCfBr50OfDPji6V/4jitcvRrNvzT7io+KRZcgYMv0dwtqHadaXlpSydfFWrp4MJyMpA2MzYwKbBfDC0Bewsq/6/19XfPtXbOfMXfXba2T16vfoBnX9Ovm48dxd9ZuVksH3g7/QuV3fSYNo2KEpAFOf+7DC+t4T3ia0U7Ma/Q3V8VSrIMYM70WzUF9cnGzoO3Qum3edrvXj3A+VSsWCeetYv24vubn5NG0ayOdTh+Ll7VLpNgvmr2PhgvVay3x8XNm87buHHa5QS57oxCc+Pp6pU6eyY8cO0tPTcXFx4eWXX2bKlCnY2dlVez83/j3DiWV/89Swfjj4e3Nl6352fL2A3j9MwcTKokL5lIgb7P9+KS3efBHP5o2IPnKaPbN+4aVZE7D1VP9A28V/9nB1+0E6jnoLC0c7zqzews6vFvDqd5+hb2igtb+Ty//B1NaKzNgEnfE9O2U01u5lH+RDufZV/j1PO9szPNiH7y9Hcy0nl1e9XJnZsiGDDp0lW1FcoXxjOyv2JaVz5epNFEolr/u6MatlQ4YcPke6XIGxnpQGluasuB5PdG4BFgZ6jAz25cvmwYw4euGe9avLub/3cHHrQbp8MAALRztOrtrKli9/4vUfPq1QP3dEHTnDv0v+ptN7/XAK8OLilgNs+eIn3pj3OabW6v+nErkCz6bBeDYN5viKzTr34xbagGavdcfMxoq8zGyOLtvIjtm/89qMsVXGfObvPZzfepDuHwzA0smO4yu3svGLn3jrx8pjjjxyhsNL/qbz8H44B3hxfvMBNn7xEwPnq2POy8whLzOHDoNextbdmdy0TPYtWkN+Zg7PfzIEgICnmuHVNERrv7vnraBEUXz771bqPPbhdXs5vukQr47rj42zLXv/2MayzxYx+udJGFQS76WDZ9n+y9+8OLov7oHeHNt4gGWfLeTDXz/F/HYdb5izgqL8QvpPfRdTSzMuHjjDmhlLGf7DeFz93UmPT0WlUvHS6H7YutqTEpvEPz+sprhIQc93X66yju+2d/VeDv19iP4T+mPrbMe2pdtYNHERkxZPrDR+RaEcNz9XWj/bmsVTF1dcX6QgPuoWPQZ0x9XPlcLcQv5a8Be/fv4b4xeOq3ZsAEfW7+XEpkO8MrY/1s627Fu+jeWfL2Lkosrr9/LBs+z89W9eGNUXtyBvjm88wPLPFzL6F3X9WtnbMH7Fl1rbnNlxlH837MO/hfY58PKYN/FvHqyZNzY3qVH81WVmasSlq3H8seYAa36tWR09bIt/28SfK7bz9YwRuLk7Mv/Htbz37jf8s2UuRkaGlW7n7+/Ob4s/18zr6T9azwmJNzdX7dH63/oP3bhxgxYtWhAVFcWqVau4fv06ixYtYu/evbRt25bMzMxq7+vyln0EdmlHQOe22Hi48NSw19E3NCRy3zGd5a9sPYB7k2DCXuqKtbszzV/vhZ2vB+E7DgLqu5ArW/fT5LUeeLUMw9bLjU6jBlKQlUPsKe1EIf7cFRIuhtPqrVcqjc/I3AxTG0vNJNGrOt/t7ePKtvgUdiakEptXyPdXopGXltLTXfed6IwLkWyKSyY6N5/4/ELmXrqORAJN7dR3wPklpXxy6goHkzO4lV9IeHYe867eINDKHEfjyi8ulVGpVFzccoDmvXvg0yoMe283unzwFvmZOdw8ebHS7S5s3k9It7YEd2mDrYcLnd7rh76RIdfu+n9q/EJnmr3aHacAn0r30/iFZ3AO9MHC0RaXIF+avdKNlMgYSksq/4VglUrFuS0HaNWnB36tw3DwdqP7h+qYo09UHvPZTftp2K0tDbu0wc7DhWeGq2O+slcds72XK70mDMW3ZSjWLg54hAXSrv8L3Dx1GeXtXyzWNzLEzMZSM0mkEuIvRdKwa9sq4z228SCdXu9OcNtQnH3ceG38AHIzcgg/eqnS7Y7+fYAWz7ajWfc2OHo588LovhgYGXJ213FNmfjwm7R5sSPugV7Yutjz9Bs9MDYzIfF6PAANWgTz6tj++DcPwtbFnuA2obR/7RmuHq28nnTFf/CvQ3Qf0J3Qp0Jx83NlwIT+5KTncOlI5fGHtA7h+Xeep/FdrTx3MzE3YeTsETR9uilOHk54h3jz2ujexEfGk5mSVaP4jm88SMfXuxN0u35fHaeu32vHqq7f5j3b0bR7Gxw9nek1Sl2/527Xr1RPioWtpdYUfvQiDTs0wcjESGtfxmYmWuUqS7Ye1K4DF5g+Zy2bdj4arTx3qFQqlv+xjWHDX+WZLi0JDPTim29Hkpqaxd49p6rcVk9fD3sHa81kY2P5H0VdPdJanOqj+vp33dPIkSMxNDRk165ddOrUCU9PT5599ln27NlDQkICn376abX2o1AoSL8Rj2tYoGaZRCrFNSyQ1MibOrdJjbyJa1iQ1jL3xsGkRsYA6i6qwmwZrqFlZQzNTHDw9yY1IkazrDBbxpFFq+g0eiD6Vdyd7J75M38OmciWz/5H7Kmqvzz0JRICLM05m56tWaYCzqbnEGJdsfVKFyM9PfQlEnKLSyotY6avh1KlIq+KZKEyspQMCrJleDQuq3MjMxOcGniTHKG7zkuLS0iLjse93P+Te1ggyXfVaU0V5eYTeegUzoE+6OnrVR1zlgzPcjE73yPm1Oh4rW0kUime94hZXlCIoakxUj3d8Vw7cBJ9Q0MatG1S6T6ykjPIy5Lh1zRAs8zYzAT3QC/ir+mOt6S4hMSoeHyblG0jlUrxaxJAfHhZvB7BPlw6dJaC3HyUSiUXD5ylRFGCT5i/jr2qFeUXYWJhWun68jKSMpBlyghoVhaLibkJXsFe3LwaU/mG96EovxCJRIJpDVpM7tTv3XVlbGaCW6AX8eGV12/S9Yr169skgPhrMTq3SYyKJ/lGAs26V0xyty5cz8zXJ/PLR3M5u+t4rf0A5OPi1q1U0tOzads2VLPMwsKUsDB/LlyIqnLbuNhkOnccTs9uo5nw8Y8kJaY/7HCFWvREdnVlZmayc+dOvv76a0xMtC9Wzs7O9O/fnzVr1vDTTz8huUebYVZWFiqlskKXlomVJTkJKTq3KcyWVShvbG1BQbZMsx7ApFyiYWJtoVmnUqk4tGAFQd3b4+DnRW5qRoXjGBgb0WrgKzgF+SGRSIg5fp49s3/FbogRJo2a6ozNytAAPamErHJdWlkKBR7m1RvD8G6gFxlyBWcysnWuN5BKeDfQm31J6RTcR+Jzp54q1Lm1BQVZMp3bFOXmo1IqMbW2rLBNViX/T1U59sc/XNp+iBK5AqcAb57/dHiV5fNvx2xaLmZTawvNuvIK78RsZVlhm8zKzi1ZHifX7aBRt3aVxnJlz3ECOzavMlnOy8oFwNxGO14zGwvNuvIKZOpEpvw25jYWpN9K1cz3mzyItTOWMaPvZKR6UgyMDHnz8yHYueoee5ORmMbxTYfoOfSlSuMtL/d2jBblYrGwsSC3knPkfhQritn062aaPdMMYzPjam9XWf2aW99H/VpbkB6fqnObs7uO4eDhhGeIdgtm5wHP4du4AQbGhlw/e42tC9ahKJTT5qVO1f4bHnfpt2/u7Oy0r2t29lakp2VXul1YmD9fffM+3j6upKdl8dOCDQwcMJWNm+dgZvZwugtrSnR1Ve2JTHyioqJQqVQEBwfrXB8cHExWVhZpaWk4Omp378jlcuRyuWY+L0/3gN+H7er2gxQXFtH45e6VljG2NCf0hS6aeQd/Lwqycojbt6PSxOdBve7rRmcXe8advEyxsuIdpJ5EwpQmQUiAH65EV2ufkQdPceDn1Zr5eyUZ/4UmL3chuGtbclMzObV2O3t++IPnPx2uSZSvHTzFvkVlMb/4H8QsLyjkn68WYevuTOvXn9NZJunaTTJvJdP9o7e0ll/Yd5pN89Zo5gdMf++hxbn3j20U5Rcy6JsRmFqZE37sImtmLGXI7A9w9nHVKitLz+aPzxbRqEMTWjxbeTJ3es9p1ny3VjP/3jfDHlr8d5SWlLL0i6Wggr4f9qmy7MX9p9l8V/32f4j1e0exXMGlA2fp+EbFa8TTb/bQ/NvFz53iIgX/bthXrxOfLZsPM33ar5r5nxZOvK/9dOhYdu0MDPQiNKwB3buMZMf2Y7zW+5kHjrM2iLynak9k4nPHvZp2DQ0r3hHPmDGD6dOnay0LCAygMEf7Lq0wR4aJte5+XxNrywrli7JzNa0Rd7YrzM7F1KbsbqQwOxdbb/UTW4mXI0mNvMnSNz/S2s8/E2fh16EFnUYN1HlshwZe3DgfoXMdQI6imFKlCpty/f02hoZk3uMJrD4+rrzh687HJ69wI7egwnp10hOIk4kR409ernZrj3erUPrdfooJ1F1AAIU5uVpPUhVm52Ln46ZzH8YWZkikUk1r0d3blG8Fqg4TS3NMLM2xdnXExt2JP4ZNISUyBudA9Z21b6tQzZNXd8dcUC7mguxcHCqJ2eROzDnaMRdk52JWLmZFYRH/fLEQQxMjek18t9Jut8t7juLg446Tn6fW8qA2jXAPKnvyquR2vOqnqsrizc/KxdlPd7ymlmZIpdIKLRZ5WbmaVorMxHRObD7MqEUTcfJSD7h38XUj9vINTm45zIuj+2m2k2XksHjifDxCfHjxg35UpVG7RngFV4w/NysXq7vu6HOzcnGrJP6aKC0pZckXS8lMyWLUnJH3bO0JbN0It7uebCutpH7zsnNx9q1h/WbnYm5bsRv66pELFMsVNOnS6p5/j3ugFwdX7dTUW33U+ZkWhIU10MwrbrdqZ2Tk4OBoo1mekZ5DYLB3tfdraWmGl7cLcXHJtRar8HA9kWN8/P39kUgkhIeH61wfHh6Og4MD1tbWFdZNmjSJnJwcrcne15OkS2XJhEqpJPFSJI6VDJB1DPAh8ZJ28pFw8RqOt78oLRztMLG2JPFyWRlFQSFp12NwDFSXaTu4Ny/PmcTLsyfy8uyJdJ/8PgCdxwymxRsvVPq3Z8QkoGdZeZdViUpFpCxPMzAZ1HcPTe2tuJqtuwkeoJ+PGwP8PJh4+gqRsoqtYHeSHjczYz4+dRlZDS6whibGWLk4aCYbD2dMrS25dVG7flKiyhKPCsc30MfBz4OEi5GaZSqlklsXI3G+Xaf3604CXXrX32RoYoy1i4NmsvVwxtTGkvi7YpYXFJJ8j5gd/TyILxdz/CXtmOUFhfw9bQFSfT1emPxepU+IKQrlRP17joZd21RYZ2RqjJ2rg2Zy9HTG3MaSG+fLjl2UX8StiFg8gnTHq2+gj2sDD61tlEolN85H4nH7i0RxO3ku34UslUpR3dVCKEvPZvGEebj6e/DqmDeRSqu+VBmbGuPg5qCZnL2csbS1JPJs2ViNovwiYsNj8QnxrnJf93In6UlLSGPk7BGYWel+HcDdytevw536vXBX/RYUkRARi0dw5fXr4u+htY1SqeTm+Ug8gir+TWd3HSewdSPMrMzvGV/SjQRMzE1r9LqAx42ZmQmeXs6ayc/fHXt7a44fLxtMnpdXwMWL12ncuEEVe9JWkF9EfHwKDg429y78H5FIam+qj+rvWV4FOzs7unXrxk8//cSYMWO0xvkkJyfz559/MnLkSJ3bGhkZYWSk/XRE6AtdOLRgOfZ+njj4e3N5635K5HICOqu/YA7O+wNTWyta9lePUWj4/NNsnfo9lzbvxaNZQ278e4b06Dieeu8NQP2l0PD5zpzfsANLZwf14+xrtmJqY4VXy8YAmDvYasVgYKyOydLJATM79Qcw6sBxpPr6mvf6xJy4QNS+Y1i/PqjK+ll/M5EJYQ2IlOVxLTuP17xdMdbTY+ftcRoTwhqQXqTg98hYQN299XYDT745H0lygVzTWlRYWkpRqRI9iYSpTQNpYGnOp2euIkWiKZNbXEJJDQdVSiQSwno9zZn1O7FyccTSyY6Tq7ZgZmuFT6uyp3H+mToP39ZhhD6nbr5v/EJn9s1bgYO/J44NvLi4+QAlcjlBz5QlAgVZMgqyZeQkpQGQEZuIoYkx5vY2GFuYkRIZQ+r1WFyC/TAyMyUnJY2TK7di6WxfZQIlkUho2utpTq7bifXtmI+tVMfs17os5g1T5uHfJozGt2Nu9mJndv24Akc/T5wbeHFuywGKi+SEdFHHLC8oZOP0nyiWK+jx0UAUBUUoCooAdauUVK8sYYj89yxKpZKgTi2rVcdtX+7EgdW7sHVzwMbJjr3Lt2FhZ0Vwu7LBoEsmzie4XRhtXuwIQLtXnuavuX/i1sATt0BPjm08iEKuoFm31gA4eDhh62rPpnlr6Tn0JUwtzAg/dpHocxEMmPYuoE56fp8wD2tHW3oOfYn8u94fZWFbvdY5iURCp1c7suvPXTi4O2DnbMu2JduwsrcitH1Z/PPHLyCsfRgdX+6grs9COWkJaZr1GcmZ3Lp+C1MLM2ydbCgtKWXx9CXcirrFsK/fRalUIsu8PX7LovqJg0Qioc3LnTi0ehd2rur63Xe7foPuGmy7dJK6flu/UFa/f//vdv0GeHLsH3X9Nr1dv5q4E9OIvRyts0st4sRl8rJycQ/yQt/QgOhzERxes5t2r3WuVuw1ZWZqhJ+3s2be28OBsBAvsrLziE+sODbxvyKRSHhr4HP8suhvvLxcbj/OvgZHRxu6dC37jAwZ/CVdurbkzf49AZg9azlPP90cVzd7UlOzWDBvHXpSKc89/1Rd/SkV1NN8pdY8kYkPwPz582nXrh09evTgq6++wsfHhytXrvDxxx8TEBDAlClTqr0v36eaUyTL48yareruFm83enw6UtNllZeeqXWH6xToS+cPB3Fm1RZOr9yMpYsDXT8ZpnmHD0DYS10pKZLz78+rUBQU4hTkR49PR1R6N1+Z8+t3qI8vlWLt5kTnMe8Q6Vn1B/RAcjpWhvoMauCJjZEh0bJ8Jp66ohnw7GhspNVN+IKHM4ZSKdOaaT+ptiwqjj+ux2NvbMhTTur3Iv3aXnts0dgTl7iQWfPBpk1f6UqJXMGBRatQ5BfiEuxLr8+160eWnE6hLF8z36C9+v/p5KqtFGTnYu/jRq/PR2h1dV3eeYTTa7dr5jd+9gMAz4zqT9AzbdA3MuTG8QucXL2NErkCUxtLPJuG0L13D/QMqv6/af5KV4qLFOxduEr9AsNgX14uF3NOuZgD2jenUJbH8dVbKchSx/zylBGarq60G7dIvv004LIR2i+uG/zzNCwdy95HdXXPMfzbNK7wQsbKdOjTheIiBZt+XENRXiGeDX0Z+OVwrceeM5MyKLgr3tBOzcjPyWPvim3kZcpw8XNn4JfDMb/9uK+evh4Dv3iPXUs2s2LaLygKFdi62vPquP4EtFK/wPP6uQgyE9PJTExn9lvaL+D8cvsP1YodoMvrXVAUKVjzvzUU5hXiG+rL8BnvacWfkZiulVjFRcQxf9wCzfzGhRsBaNW9Jf0n9Cc7PZvLRy8DMGvYbK3jjZo7kgZNqt9S0L63un43zyur3wFfaNdvVlIGBTll9duoUzPyZXnsW77t9gsi3Xnri7L6vePcruNY2lvh1yyQ8qR6epzccpgdv/4NKhW2rg70ePdlmves/PUGD6JZmC+71pZdT2dNVXfDL193kGHjFj2UY1bXO0NfpLBQzrSpv5ArK6BZs0AW/TJJ6x0+8XEpZN3VvZiSnMEn438kOzsXW1tLmjYL5M/VX2FbzaRcqHsS1ZP2DONdYmJimDZtGjt27CA1Vf3StFdffZXly5djalr9R2dnXdz9EKOsfTsTqv+3PQpe9My/d6FHiMFjdrtlb6z7BYaPKkvDx+uSlS1/vE6IwZ2X1XUINSKLmVDXIdSYgbTJQ91/YoHul6/eD1fTyodOPK6eyDE+d3h7e7N06VKSk5NRKpVMmTKFXbt2cfFi9V+UJgiCIAiPEkktTvXRE9vVpcv06dPx9vbm+PHjtGrV6p4DKgVBEAThUSN+nb1qIvEpZ/DgwXUdgiAIgiAID4lIfARBEAShHqmvXVS1RSQ+giAIglCP1Nf379QWMYhFEARBEIQnhmjxEQRBEIR6RDT4VE0kPoIgCIJQj4iunKqJ+hEEQRAE4YkhWnwEQRAEoR4Rg5urJhIfQRAEQahXROZTFdHVJQiCIAjCE0O0+AiCIAhCPSIRLT5VEomPIAiCINQjEonozKmKSHwEQRAEoV4RLT5VEYlPLXjKSVHXIdRIcqFeXYdQI9LH7DN8I/fx+ljF5j9ev+T8qldRXYdQI+tjzOo6hBqRxUyo6xBqxNJ7Zl2HUGOFcavqOoQn2uN1hRYEQRAEoUpijE/VREegIAiCINQrklqcHo7MzEz69++PpaUl1tbWDBkyhLy8vGptq1KpePbZZ5FIJGzcuLHGxxaJjyAIgiAI/6n+/ftz5coVdu/ezZYtWzh06BDDhg2r1rbff/89kgd4S6Po6hIEQRCEeuRRf6orPDycHTt2cOrUKVq0aAHAvHnzeO6555gzZw6urq6Vbnv+/Hnmzp3L6dOncXFxua/jP9q1IwiCIAhCDdVeV5dcLkcmk2lNcrn8gaI7duwY1tbWmqQHoGvXrkilUk6cOFHpdgUFBbz55pssWLAAZ2fn+z6+SHwEQRAEQdBpxowZWFlZaU0zZsx4oH0mJyfj6OiotUxfXx9bW1uSk5Mr3W7MmDG0a9eOl1566YGOL7q6BEEQBKEeqc2nuiZNmsTYsWO1lhkZGeksO3HiRGbOrPr1AuHh4fcVx6ZNm9i3bx/nzp27r+3vJhIfQRAEQahHajPxMTIyqjTRKW/cuHEMGjSoyjK+vr44OzuTmpqqtbykpITMzMxKu7D27dtHdHQ01tbWWstfe+01OnTowIEDB6oVI4jERxAEQRCEWuDg4ICDg8M9y7Vt25bs7GzOnDlD8+bNAXVio1Qqad26tc5tJk6cyNChQ7WWhYaG8t133/HCCy/UKE6R+AiCIAhCvfJoD98NDg6mZ8+evPvuuyxatIji4mJGjRrF66+/rnmiKyEhgS5duvDHH3/QqlUrnJ2ddbYGeXp64uPjU6PjP9q1IwiCIAhCjUgkklqbHpY///yToKAgunTpwnPPPUf79u355ZdfNOuLi4uJiIigoKCg1o8tWnwEQRAEoV559H+ywtbWlpUrV1a63tvbG5Wq6t8RvNf6yogWH0EQBEEQnhiixUcQBEEQ6hHxI6VVE4mPIAiCINQrojOnKk984pOWlsaUKVPYunUrKSkp2NjY0LhxY6ZMmcJTTz1Vq8dSqVRsXLyDQ5uPU5BXiH+oDwPH9sbJo/LH/yLOR7Nj9X5iIm6RkyFj1NeDadYhVKvM79+s4t8dp7SWNWoVyNg571U7tpg9B7ixbTfyHBmWHu40fKsf1n7elZZPOnmGiA2bKUzPwMzJkaB+r+DYuJFm/daB7+vcLqjfK/g9310zn3L+Etc3bkMWn4DUQB+7oABafDT8nvGqVCpOrtrGlT1HkecX4hLkw9Pv9cPa1bHK7S5uO8S5jXspyJZh7+1Gx6G9cQpQ/51FufmcWL2N+PPXyE3PwsTSHN/WYbR+43mMzEwASL95izN/7SYp/AaFuflYOtjSqEd7Gr/wdJXHvbnnANF31W+jt/phU0X9Jp48w7W76je43ys43VW/ALkJSYSv/ZuMa1GoSpWYu7nQYvQwTO1tAchPSePq6g1kRkajLC7BISyE0Lf6YWRlWXXlAjd2HyRqqzpeK093wgb2rTLehBNnCV+/mYL0DMydHAl5/WWcm1SM98rqjaRfi0KlVGLh6kyrD9XxKvLyCd+whbRL4RRkZGFkaY5L88YE934BA1OTe8ari0ql4q/fd3Dg9uetQagPg8b1xrmKz9u189FsW6X+vGVnyPjw68E07xhaoVxCTAprF23h2vloSkuVuHk7MfqrQdg72dxXrACJ+/Zza8duFDk5mHu44/fm61j46n5aJT8hkdiNm8iLjUOekYHv631w69ZVq0z81u2knz1HYVIyUkNDLP188e7zKqYP8Kr/e1GpVCyYt4716/aSm5tP06aBfD51KF7elf+m0oL561i4YL3WMh8fVzZv++6hxVmVp1oFMWZ4L5qF+uLiZEPfoXPZvOt0ncQiPBxPfOLz2muvoVAoWLZsGb6+vqSkpLB3714yMjJq/VjbV+5jz4bDDJ30Jvautvz923bmjv+Zr/+YgIGRgc5t5EUKPPxcaf9cKxZ8trTSfTdqHcSQia9r5vUNq/9fm3j8NOErN9Bo0BtY+/lwc+c+Tsz+kadnTcPIsuKXZGZUNOd+Wkxgn5dwbBJK4rFTnP5+ER2+nISFuxsAXX78VmubtItXuPj7ClxaNtUsSzp1lkuL/ySwz0vYBQeiUirJvZVYrZjP/r2HC1sP0vWDAVg62XFi5VY2ffETb/74KfqGuusy6sgZjiz5m6eH98M5wIvzmw+w6Yuf6D//c0ytLcjPzCE/M4enBr2MrbszuWmZ7F+0hvzMHJ79ZAgAqdHxmFhZ0O2jgZjb25B87Sb7F65Coich7LlOOo+bcPw0V1duIHTQG9j4+XDjdv12rqJ+z/60mKA+L+HUJJSEY6c49f0iOn45Ccvb9Zufksa/X83Fs1M7Al95AX0TY3ITEtG7/f9eIpdzfPaPWHq403biRwBc27CZk9/9RPspnyCRVn5HeOv4aS7/uYHGg9/Axt+b6B37ODpzHl1nT8PIyqJC+YzIaE4vWExI35dwbtqI+KOnOfHdz3T+ahKWHq6aeA99+T+8OrUl6LVeGJgYI7uVhJ6B+v+qKCuHouwcGr75KpZuLhSkZ3J+ySqKsnJo9eG7lcZala0r97F7w2HenfwmDi62bPh9O7PH/cyM5RMwrOLz5unvSsfnW/Hjp0t1lklJSOerkfPo9HxrXnmnByZmxiTcTMawBp+58tJOnuLGmvX4v/UmFr4+JO7ey+XvfqT519Mx1HGOKBUKjB3ssW/RnBtr1urcZ05kJK6dn8bcxxuVspSYDRu5PPcHmn81Db1qvpCuphb/tok/V2zn6xkjcHN3ZP6Pa3nv3W/4Z8tcjIwMK93O39+d3xZ/rpnX06+7FgszUyMuXY3jjzUHWPPruDqL40GIrq6qPdGJT3Z2NocPH+bAgQN06qT+0vLy8qJVq1a1fiyVSsXudYd44a1uNO2gvhMe+umbfPTyVM4euUzrLk11bhfWJpiwNsH33L+BgT5Wdve+k9fl5o69eDz9FB4d2wEQOugNUi9cIv7gMfxf6FGhfMzO/TiEhmhabgJ7v0j6lXBidh8kdPCbABhbW2ltk3L2InbBAZg6qu+2laWlXF2xjqDXX8WzU1nLmoXbvX9tV6VScWHLAVr06YFv6zAAun74FosHT+bGiYsEdGiuc7vzm/bTsFtbQrq0AaDz8H7EnrlC+N5jNH+tO3Zerjw3oewFWVYuDrTt/wK7vv8DZWkpUj09Qrq21dqnlbM9yRE3iT52odLE58aOvXg+/RSet+s37Hb9xh08RgMd9Xvzdv36367foN4vkna7fsNu1++19f/g2LghIa+/qtnOzKmsJSMzMpqCtAw6fjkZAxN1i0nTYW+z4/1xpF+NwKFR5edU9PZ9eHV+Cq9O6r+1yeA3SDl/mdiDRwl4sWK8N3buxzEshAa9ugEQ0ucF0i6Hc2P3AZq8o4736rpNODVuSKM3dMdr6eFK6w+Haa0L6fMiZxYu1dR9TahUKnauPcSLA7vR/Pbn7b1P32T0S1M5e/gybbrq/rw1bhNM43t83tb/so3GbYJ5fUTZS9Oc3OxrFF95Cbv24NyxPc7t1Z8F/7f6k3nxMilHjuLxXM8K5S18vLHw8QYgZsPfOvfZaMyHWvMBQwZx4qPx5MXEYhUY8EDx6qJSqVj+xzaGDX+VZ7q0BOCbb0fSqf0w9u45xXPPV96Crqevh72Dda3HdD92HbjArgMX6jqMB/IwH0OvD57ojkBzc3PMzc3ZuHHjA//a7L2kJWWSk5lLSIuyC46puQm+wZ5EX4554P1fO3+dD1+cwqT+M/hj7nrycvKrtZ2ypIScmDjsGwZplkmkUuxDgsi+fkPnNlnXb2iVB3AIDSGrkvLyHBmpFy5pEisAWUw8RVnZSCQSDn/2NXtGT+DknHnk3kq4Z8yylAwKsmR4NA7ULDMyM8GpgTfJETd1blNaXEJqdLzWNhKpFPewQJIjYio9lrygEENT4yq/eOUFhRhbmOlcV1X9VlZfmddv4FCufh3vql+VUknKhcuYOztxfNaP7Bz5MYenzSTpzHmt40okEqT6Zfc2UgN9JBIJmZHRlf4typISsm/G4dBQu54cGgaReV133WZev4lDo3LxhoVoyquUSlLOX8bc2ZGjM+exbcQnHJw6i8TT53XsrUxxQSH6JlXXfWXufN4a6vi8Xb8SU+P93aFUKrlwLBxnDwdmjf2ZkS9MYdqw7zlz6NL977OkhNzYOKyDyxIuiVSKdUgQsmjd58j9KC0oBEDfTPe5+qBu3UolPT2btm3LugYtLEwJC/PnwoWoKreNi02mc8fh9Ow2mgkf/0hSYvpDiVEQ4AlPfPT19Vm6dCnLli3D2tqap556ismTJ3Px4sVKt5HL5chkMq1JIS++57FkGTIALG20uwosbS3Iycx9oL+jUesghk5+k4+/G06f4b2IOB/Ndx//grJUec9tFbl5qJTKCl0uRlaWyHNkOreR58gwLDdOxNCy8vK3jhxH39gY5xZld9kFaWkARP29Ff+XnqXl2BEYmJpy7JvvUORVnbQVZKuPY1qu28XU2kKzrrzC3HxUSiUm5eKuchtZHqfX7aBht3Y61wMkXbvB9X/P0rC77jL3W7/lx+EYWVpSdLu8XJZLaZGc61t24hDWkDaffIBz8yac/vEX0q9FAmDj54OekSHha/6mRK6gRC7n6qq/UCmVFOXkVPr3yG/Ha1z++FYWlcZblC3D2FL7/8LI0gJ5dlm8JUVyorbswjEshHYTRuPSvDEnf/iV9PDISuOI2Lgd7873N84u5/bnzarc583K1oLsB/i8ybLyKCqUs+XPfYS1DuKT/71H846h/PjZUq6du35f+yzOzQOlEsNydWhoaUlxFf9XNaFSKrmxei2W/n6Y3e4urW3p6dkA2Nlpt/ba2VuRnpZd6XZhYf589c37LPp1Ep9PHcKtW2kMHDCV/PzChxLnk0FSi1P980R3dYF6jM/zzz/P4cOHOX78ONu3b2fWrFn89ttvOn9sbcaMGUyfPl1r2eBxbzDk4/5ay47tOsMfc9dp5j+aqf0bI7Xp7m4ydz9X3P1cmfj611w7f52Q5rXfpF1T8YeO4tq2FXp3jb1RKdUvnvJ/sScuLZsBEPbuQPZ9NJmkk2fxeqaDpmzEwVMcWLRaM9/r03sPfn5QioJCtny1CBt3Z1q9/pzOMhmxiWyd8Sst+z2LZ5N7d0fWmtsv7XJuFoZfzy4AWHl5kHU9mth9h7EPCsDI0oLmo97l0rJV3Nx9AIlEgmubFlh5e/znzeB3XjLm0iwM/2fV8Vp7eZAZdYObe49gH6x9jhYXFHJ8zk9YuDkT9Gqvah3j6K4zLJlT9nkb95A+b3f+lmbtG9Kz3+3u8QZuXL8cw75/jhHU1P+hHPdBXf9zFfkJiTSe+HGt7XPL5sNMn/arZv6nhRPvaz8dOpZdvwIDvQgNa0D3LiPZsf0Yr/V+5oHjfBJJnuw2jXt64hMfAGNjY7p160a3bt34/PPPGTp0KFOnTtWZ+EyaNImxY8dqLTuTva9CuSbtG+Ib4qmZLykuBUCWlYu1fdndtCwzF0//2r0Dc3S1w9zKjNRb6fdMfAwtzJFIpchl2nfzulod7jCyskRR7u5fIdNdPjMiivykFJqN1P4iMro9BsjctWxMj56BAaYO9hRmZGqV9WkVqnnyCtTdVgAFObmY2ZbdXRZk52Lvo7suTSzMkEilFJaLuyA7F1Nr7bgVhUVs+mIhBiZGPDfxXfT0K3a1ZMYnsXHqfBp2a0fLPhXHYNxxv/VbvnVFLpNpWmEMLcyR6EkxLzceytzVhczIslYHx9AQusz5EnluHlKpFAMzU3aNnoCpQ+XjUYxux1tU/vg5uZXGa2xtSZFMuxVFLsvF6Ha9Gt2Ot/z4LQs3ZzIitLvdiguLODZ7PvrGRrT+6D2kOupel6btG+J31+et+PbnLafc5y0nMxevBvf/ebOwMkNPT4qbt/aTUa5ejkRe1N0VeC8GFuYglaIoV4cKmQwDK6tKtqq+63+uIvPCJRpPGI+R7f0/dVZe52daEBbWQDOvUKhbvjMycnBwLDtORnoOgcHe1d6vpaUZXt4uxMUl11qsgnA3kRbqEBISQn6+7u4WIyMjLC0ttSZdT4iYmBrj5O6gmVy9nbCyteDqmbK+7sL8Im6Ex+HXyLtW489MzSZfVlCtwc5SfX2svD1JvxKhWaZSKsm4GoG1v6/ObWz8fUm/GqG1LO3yNWx0lI8/eBQrb08sPd21llv5eCI10CcvOUWzTFlSSkF6huZx7DsMTYyxdnHQTLYezpjaWHLrYlkMioJCUqJicA7U/fivnoE+jn4exF8s61pRKZXcuhSJc6C31n7+mbYAqb4ez09+T+cTYhlxSfz9+TyCOrei7YCqfxW4svpNvxqhs74AbO9Rv1J9fax9vMlLStEqk5+cgqmddt2BOvEwMDMl/eo15LJcnJuFVRmvtY8naeXiTbsSga2/7rq19fch7cq1cvGGa8pL9fWx8fUit1y8eUmpWv/XxQWFHJ05D4mePq3Hvq/VQngv5T9vblV83vwbeld7v+XpG+jjE+xJUlyq1vLk+DTsnO8vqZDq62Ph5Ul2eLhmmUqpJDv8GpZ+us+R6lCpVFz/cxUZZ88T9vEYjKtIeO+HmZkJnl7OmsnP3x17e2uOHy8b75SXV8DFi9dp3LhBFXvSVpBfRHx8Cg4OtZekPXlEV1dVnujEJyMjg2eeeYYVK1Zw8eJFbt68ybp165g1axYvvfRSrR5LIpHQrU9Htvyxm3NHLnMrOpHfvl6JtZ0lzdqXve9k9kcL2bvhsGa+qEBOXFQCcVHqQb/pSZnERSWQkZKlWb/2p01EX4khPSmTq2cimTd5MY5u9jRqpT3gtDI+PbsQf/AItw4fIzchicvLVlEil+PRUf1Uz/mfl3Jt7UZNee8enUm7dIUb2/eQl5hM5F9byLkZi3c37aeaigsLSTp5Fo+nK47TMDAxwbNzB6L+2kLapavkJSVzeZn6d1tcWjW7Z1027vU0p9ft5ObJS6THJrL7h+WY2VppnvIC2DhlHhe3HdTMN3mxM1d3HyV83wky45M58PNaSorkBN9+yktRUMg/03+iRK6gy8g3URQUkZ8lIz9LphkvlRGbyMbPf8SzSRBNXnxGs74wp/JxI749uxB38Ajxt+v34rJVlMrleN6u33M/LyX8rvr16dGZ1EtXiN6+h9zEZCL+2kJ2ufr1e64biSfOELv/CPkpqdzcfYCUc5fw6lJWJu7QUbKu3yA/JY1b/57g9Lzf8O3xDOYuVb/Hxe/ZZ4g98C9xh46Tm5DEhSWr1fHefsrrzKKlXFlTFq9vj86kXrxK1DZ1vOEbtpB1Iw7fbk9ryvg/142E42eI2X+EvORUbuw6QPK5S/h07QiUJT2lcjnN3h1ASWEhRdnqR9xVynuPVStPIpHQo29H/lm2m7NHLhMfncjPX93+vHUo+7x9++FCdpf7vMVGJRB7+/OWlpRJbFQC6bc/bwDPvfE0J/adZ/+mY6TcSmP3hsOcO3qVLq/c/3u/3Lp3JfnQEVL+PUZBYhLXV6xEKVfg9JR67FjEb0u4edfTW8qSEvLi4smLi0dVUoI8K5u8uHgKU8oSsugVq0g9doLAYUPQMzZGkZODIieHUoXivuOsikQi4a2Bz/HLor/Zv+80kZFxTJ64AEdHG7p0bakpN2Twl6z8c4dmfvas5Zw6eZWEhFTOnYvgg9Fz0JNKq3wK7GEyMzUiLMSLsBAvALw9HAgL8cLD1a5O4rkfj8OPlNalJ7qry9zcnNatW/Pdd98RHR1NcXExHh4evPvuu0yePLnWj/fsm88gL1KwbM46zQvVxs4ZpvUOn9TEdHLveiIrJiKeWR/+pJlfPf8fAJ7q2ZIhk99AqichPjqJf3ecpiCvEGt7Sxq2DOSVIc9iUM33iri2aYEiN4/Iv7aoX7Dn6U6rj0drujYKMzK1PgC2Dfxo+v47RKzfRMS6fzB1cqDFR8M17/C5I+n4aVSocG3TEl2CX38NiZ4e539eilJRjLWfN20mfoRBNZ46afZKV0qKFOxfuEr9AsNgX174fIRWC01OcjqFsrK6OJb1JAAAHXJJREFUbNC+OYWyPE6u3kp+Vi4OPm68MGWEpqsr9cYtUiJjAFg+4gut4w38eRqWjnZcP3aeQlkeEQdPEXGw7KWRFg62vP2L9tivO9xu12/EXfXbulz9Uq5+m73/DtfWb+Laun8wc3Kg5UfDNe/wAXBp0YSwQW9yfcsOLq9Yi7mLEy1GD8MusGyMSX5SCtfW/YMiLx9TezsavNgT39tjgqri3qYFClke4RvU8Vp5udP2k1GarraC9CyQlN0z2QX40WLEO4Sv20T42k2YOTvQesx7mnf4ALi2bEKTd94gctNOLv6xDnMXJ1p9+K4m3uyYeLKi1XW/e9xUrXi6ffclZg41/9J5/s1nkBcqWDK77PM2fs4wrRba8p+3mxHxzPig7PO28vbnrX3Plgz79A0AWnQMY9D43mxZsZcVP/yNi6cjo78cRGDY/bfOOLRqSXFuHrEbN6GQyTD3cKfhmA80DxHIM7XPEUV2Nuemf6WZT9i5m4Sdu7EKDCDsE/W7Z5IOqJP+S7Pmah0rYPDbOLWvfMD+g3hn6IsUFsqZNvUXcmUFNGsWyKJfJmm9wyc+LoWsrLIbhZTkDD4Z/yPZ2bnY2lrStFkgf67+Clvb+3s9x4NqFubLrrVTNPOzpg4EYPm6gwwbt6hOYqq5+pmw1BaJ6n5/3lTQ+Ddla12HUCMbYu7vTbh1xceipK5DqJHY3MfrfkJP+nhdAl71KqrrEGrk18iH8/j4w7KwnXVdh1Ajlt4z6zqEGiuMW/VQ969Qnqm1fRlKdb8X7XH2eF2hBUEQBEGokniqq2oi8REEQRCEekV0dVVFpIWCIAiCIDwxRIuPIAiCINQj4kdKqyYSH0EQBEGoR+rrY+i1RXR1CYIgCILwxBAtPoIgCIJQr4g2jaqIxEcQBEEQ6hExxqdqIi0UBEEQBOGJIVp8BEEQBKFeES0+VRGJjyAIgiDUI+KprqqJxEcQBEEQ6hUxiqUqonYEQRAEQXhiiBYfQRAEQahHxFNd96ASHklFRUWqqVOnqoqKiuo6lGoR8T5cIt6HS8T7cIl4hUeJRKVSqeo6+RIqkslkWFlZkZOTg6WlZV2Hc08i3odLxPtwiXgfLhGv8CgRY3wEQRAEQXhiiMRHEARBEIQnhkh8BEEQBEF4YojE5xFlZGTE1KlTMTIyqutQqkXE+3CJeB8uEe/DJeIVHiVicLMgCIIgCE8M0eIjCIIgCMITQyQ+giAIgiA8MUTiIwiCIAjCE0MkPoIgCIIgPDFE4iMIgiAIwhNDJD6CIAjCQ5Weno5MJqvrMAQBEImP8IRRKBRERERQUlJS16FUW2pqKocPH+bw4cOkpqbWdTiCUC3Z2dmMHDkSe3t7nJycsLGxwdnZmUmTJlFQUFDX4WkcO3aMLVu2aC37448/8PHxwdHRkWHDhiGXy+soOuFhEO/xqWOvvvpqtcv+9ddfDzGS2pGdnY21tXVdh1FBQUEBo0ePZtmyZQBERkbi6+vL6NGjcXNzY+LEiXUcYUW5ubmMGDGC1atXU1paCoCenh79+vVjwYIFWFlZ1XGEuikUClJTU1EqlVrLPT096yiiqkVHR7NkyRKio6P54YcfcHR0ZPv27Xh6etKwYcO6Dq+Cw4cP8/PPPxMdHc369etxc3Nj+fLl+Pj40L59+7oOD4DMzEzatm1LQkIC/fv3Jzg4GICrV6+ycuVKgoKCOHLkCBcvXuT48eN88MEHdRbrs88+y9NPP82ECRMAuHTpEs2aNWPQoEEEBwcze/Zs3nvvPaZNm1ZnMQq1S7T41DErKyvNZGlpyd69ezl9+rRm/ZkzZ9i7d+8j+SU3c+ZM1qxZo5nv27cvdnZ2uLm5ceHChTqMrKJJkyZx4cIFDhw4gLGxsWZ5165dtf6GR8nQoUM5ceIEW7ZsITs7m+zsbLZs2cLp06d577336jq8CqKioujQoQMmJiZ4eXnh4+ODj48P3t7e+Pj41HV4Oh08eJDQ0FBOnDjBX3/9RV5eHgAXLlxg6tSpdRxdRRs2bKBHjx6YmJhw7tw5TUtETk4O33zzTR1HV+aLL77A0NCQ6Ohofv75Zz766CM++ugjfvnlF65fv45CoeCtt96iW7dudX5tO3/+PF26dNHMr169mtatW/Prr78yduxYfvzxR9auXVuHEQq1TiU8Mj755BPV0KFDVSUlJZplJSUlqmHDhqnGjx9fh5Hp5u3trfr3339VKpVKtWvXLpW1tbVq586dqiFDhqi6detWx9Fp8/T0VB07dkylUqlU5ubmqujoaJVKpVJFRUWpLCws6jK0SpmamqoOHz5cYfmhQ4dUpqamdRBR1dq1a6fq2LGjatu2bapz586pzp8/rzU9itq0aaOaO3euSqXSPi9OnDihcnNzq8vQdGrSpIlq2bJlKpVKO96zZ8+qnJyc6jI0LV5eXqodO3ZUun779u0qiUSimjZt2n8YlW5GRkaquLg4zfxTTz2l+uqrrzTzN2/eVJmbm9dFaMJDol/XiZdQZvHixRw5cgQ9PT3NMj09PcaOHUu7du2YPXt2HUZXUXJyMh4eHgBs2bKFvn370r17d7y9vWndunUdR6ctLS0NR0fHCsvz8/ORSCR1ENG92dnZ6bwbtrKywsbGpg4iqtr58+c5c+YMQUFBdR1KtV26dImVK1dWWO7o6Eh6enodRFS1iIgIOnbsWGG5lZUV2dnZ/31AlUhKSqqym7BRo0ZIpdJHolXNycmJmzdv4uHhgUKh4OzZs0yfPl2zPjc3FwMDgzqMUKhtoqvrEVJSUsK1a9cqLL927VqF8RKPAhsbG+Lj4wHYsWMHXbt2BUClUmnGpDwqWrRowdatWzXzd5Kd3377jbZt29ZVWFX67LPPGDt2LMnJyZplycnJfPzxx3z++ed1GJluISEhj2SyUBVra2uSkpIqLD937hxubm51EFHVnJ2duX79eoXlR44cwdfXtw4i0s3e3p6YmJhK19+8eVPnjUhdeO6555g4cSKHDx9m0qRJmJqa0qFDB836ixcv4ufnV4cRCrWurpuchDJjxoxR2dnZqebOnas6fPiw6vDhw6o5c+ao7O3tVWPGjKnr8CoYOXKkysvLS9W1a1eVnZ2dKjc3V6VSqVSrVq1SNW3atI6j03b48GGVubm5avjw4SpjY2PVhx9+qOrWrZvKzMxMdfr06boOT6cmTZqozM3NVQYGBio/Pz+Vn5+fysDAQGVubq5q2rSp1lRXcnJyNNPevXtVbdu2Ve3fv1+Vnp6utS4nJ6fOYqzKuHHjVO3bt1clJSWpLCwsVFFRUaojR46ofH19H4lumPK++eYbVUhIiOr48eMqCwsL1eHDh1UrVqxQOTg4qH788ce6Dk9j8ODBqo4dO6rk8v+3d+9BUVZvHMC/u8DC4kKECXhDLRjAQMPEYVICVzAxbbyRGpoohSUpZUV4mdCowEsqeK2JFXS8gMYQyzREam5SoCbmGqCiboJFaUEoKvfz+4Ofm8suSBc9Z/H5zDDD+777x1cX2Od9zznPaTC6Vl9fzwIDA9ncuXM5JDN29epVFhAQwCQSCbOzs2NZWVkG15VKJVu6dCmndOReoFVdAmltbcXatWuRnJysvwvt3bs3YmJi8OabbxoMgYmgqakJycnJqKysREREBHx9fQEA69evh52dHV566SXOCQ1duHABSUlJOHXqFOrq6jBs2DC888478PHx4R3NpDsft98NryEDqVRqMFTIGDMaOrx9TrSngEDbCrTo6GikpaWhpaUFlpaWaGlpwQsvvIC0tDThfucYY/jwww+RmJioXxJubW2Nt956CwkJCZzT/eXy5csYPnw4rK2tER0dDU9PTzDGUFZWhi1btqChoQHHjx8XaqVfbW0tFAqF0XteXV0NhUIBmUzGKRn5r1HhI6jbzb7s7e05JyGkYxqNpsuvDQwMvIdJ/j7GGCorK9GrVy/8/vvvOH36NOrq6uDr6wt3d3fe8TrV2NiI8+fPo66uDoMHD4ZCoeAdyYhOp8OCBQuQn5+P2x8zEokEISEh2LRpE9zc3DgnJA8qKnwE09zcjMOHD+PChQt44YUXYGdnh19++QX29vbC/XFLT0/HI488gmeffRYAEBsbi08++QSDBw/Gnj17MGDAAM4J/9JR11iJRAJra2vh7+bq6+uRkZGBGzduICQkRPgPZnPQ2toKGxsblJSU0P/nPVRTU4Py8nIAgJubGxwdHTknIg86KnwEcunSJYwbNw4VFRVoaGjQN9mLiYlBQ0MDtm3bxjuiAQ8PD2zduhVKpRKFhYUIDg7G+vXrkZubC0tLS6EaLrYfkmmvX79+iIiIQHx8PKRSvnP+Fy9ejKamJmzcuBFA2939iBEjUFpaCltbWzQ3NyM/Px9PPfUU15ztbd++HQqFAmFhYQbn9+3bh5s3b2LOnDmcknXs8ccfR2pqKvz9/XlH6VB3a3JKCG+0qksgMTExGD58OGpqaiCXy/XnJ0+ejIMHD3JMZlplZaX+cXV2djamTp2KqKgoJCYm4siRI5zTGUpLS0OfPn2wdOlSZGdnIzs7G0uXLkXfvn2xdetWREVFISUlBUlJSbyjIj8/HyEhIfrjXbt2oaKiAuXl5aipqUFYWBg++OADjglNS0xMxCOPPGJ03snJSajmendKSkrC22+/jR9//JF3lA7d2eT0bl+EkC7gMaOamObo6MjOnDnDGDNsTqbT6ZhcLucZzaRevXqx4uJixljbCqQdO3Ywxhg7f/4869GjB89oRpRKJcvIyDA6n5GRwZRKJWOMsR07djAPD4/7Hc3I7dVFt82YMYO9/PLL+uOTJ0+y3r1784jWKWtra6bT6YzO63Q6ZmNjc/8DdYGDgwOTyWRMKpUyGxsb9vDDDxt8EUK6H2pgKJDW1laTK18uX74MOzs7Dok6FxISgpdeegm+vr44d+4cxo8fDwAoKSnBwIED+YZr57vvvjM5VOjr64vCwkIAwKhRo1BRUXG/oxmRSqX6yaAAUFRUZNC3x8HBATU1NTyidcrJyQlardbovT916hR69uzJJ9RdbNiwgXcEQsh9RoWPQMaOHYsNGzbgk08+AdA28baurg7x8fH6okIkmzdvxvLly1FZWYnPPvtM/+F24sQJzJw5k3M6Q/3790dqaqrRUFZqaqq++/Qff/whREdkLy8vqNVqLF68GCUlJaioqMDo0aP11y9dugRnZ2eOCU2bOXMmFi1aBDs7O313YY1Gg5iYGMyYMYNzOtNEnHd0N/v370dmZiYqKirQ2NhocK24uJhTKkLMCO9HTuQvlZWVbPDgwczLy4tZWloyf39/1rNnT+bh4cF+++033vHM2ueff85kMhkbMmQIi4yMZJGRkWzo0KFMJpMxtVrNGGNsy5YtQjSKzMrKYjKZjCmVSubs7MwmTJhgcD02NpaFhYVxStexhoYG9vzzzzOJRMKsrKyYlZUVs7CwYHPnzjXZyE40t27dEr7pYnJyMlMoFOy1115jMpmMzZ8/nwUHB7OHHnqImuwR0kW0qkswzc3N2Lt3L7Rarb7JXnh4uMFkZ9HcvHnT5N3nkCFDOCUy7aeffsK2bdtw7tw5AG2r0ubPn4+6ujp4e3tzTmfo4MGDyM3NhYuLCxYuXAhbW1v9tZUrVyIwMBBBQUH8ArbD7uiJc/nyZfzwww+Qy+Xw8fERqq1Bezdu3MA777yDzMxM/PHHH0bXRWu66Onpifj4eMycORN2dnY4deoUHn30Ubz77ruorq7Gpk2beEckRHhU+Aikvr4eNjY2vGN02dWrVxEREYG8vDyT10X70LjTtWvXsGfPHqhUKnz//fdCZzUH5toTJzo6Gl9//TUSEhIwe/ZsbN68GT///DM+/vhjJCUlITw8nHdEA7a2tigrK8OAAQPg5OSEr776CkOHDkV5eTn8/f1NFm+EEEO0nF0gTk5OmDNnDr766ishNyVt7/XXX0dtbS2OHj0KuVyOvLw8pKenw93dHTk5ObzjmfTNN99gzpw56NOnDz766COMHj0aRUVFvGN1qKamBmvXrkVkZCQiIyOxdu1aVFdX845lRCqVwt3d3ew+eNVqNbZs2YKpU6fC0tISAQEBWL58OT788EPs2rWLdzwjLi4u+vff1dVV/7Or0+lA97CEdBHPcTZiKCsri02bNo3J5XLm4uLCYmJi2PHjx3nH6pCLiws7evQoY6xtCfbZs2cZY23zaUaOHMkzmoGqqiqWmJjI3NzcmJOTE3vttdeYpaUlKykp4R2tUxqNhtnb27P+/fuzyZMns8mTJzNXV1dmb2/PNBoN73hGcnJy2KhRo9jp06d5R+myHj16sEuXLjHGGOvbt6/+5/nixYvCtWRgjLHIyEj95qmbNm1icrmcBQcHMwcHBzZv3jzO6QgxD1T4COjatWtMpVKxkJAQZmFhwdzd3dnKlSt5xzJiZ2en79vi6urKCgoKGGNtHxqi9B2aMGECs7e3ZzNnzmS5ubmsubmZMcbMovDx9vZmL7/8sj4zY4w1NzezqKgo5u3tzTGZaebYE8fHx4cdPnyYMcbYmDFj2JtvvskYa5tE3LdvX57RTGppaWFNTU36471797KFCxeylJQU1tjYyDEZIeaD5vgIrrS0FOHh4dBqtcLNQ/Hz88P777+PZ555Bs899xwcHByQmJiIlJQU7N+/HxcuXOAdEZaWlli0aBFeffVVg7knVlZWOHXqFAYPHswxXefkcjl++OEHeHh4GJw/e/YsnnjiCdy6dYtTMtPS09M7vS7S0vGLFy9i4MCBSE5OhoWFBRYtWoQDBw5g4sSJYIyhqakJ69atQ0xMDO+oRurr66HVanHlyhWDIXGJRIKJEydyTEaIeaA+PgKqr69HTk4Odu/ejby8PDg7O+Ptt9/mHctITEwMqqqqAADx8fEYN24cdu3aBZlMhrS0NL7h/q+goACpqal48skn4eXlhdmzZwvbU6a9YcOGoayszKjwKSsrw9ChQzml6phIhc3duLu7o6qqCm+88QYAYPr06UhJScGZM2dw4sQJuLm5CbcqEQDy8vIwe/Zsk3OpJBKJcDdHhIiInvgI5Msvv8Tu3buRnZ0NS0tLTJs2DeHh4fpmcKK7efMmzpw5A1dXV5N7NvF048YNZGRkQKVS4dixY2hpacG6deswb948obpia7Va/fdlZWWIjY3FwoUL9ZtoFhUVYfPmzUhKSsL06dN5xbyr+vp6o/YG9vb2nNIYk0ql+PXXX+Hk5AQABkvDRebu7o6xY8fi3XffFbKJJSHmgAofgdja2mLChAkIDw/H+PHjYWVlxTtSt3T27FmkpqZi586d+PPPPxESEiLMKrTbu8jf7ddSxLt7c+qJY66Fj729PU6ePInHHnuMdxRCzBYNdQnkt99+E+rpgymLFy/u8mvXrVt3D5P8cx4eHli9ejUSExOhVquhUql4R9LT6XS8I/xjsbGx+Prrr7F161aTPXFEIpFIIJFIjM6Jbtq0aTh8+DAVPoT8C/TEh7Nr167phwCuXbvW6WtFGCq4c8+ozkgkEhw6dOgepyEicXV1xY4dOxAUFAR7e3sUFxfDzc0NO3fuxJ49e/DFF1/wjqgnlUoRGhoKa2trAG39fJRKJXr06GHwuqysLB7xOnTz5k2EhYWhV69e8PHxMXoqvGjRIk7JCDEfVPhwZmFhgaqqKjg5OemHOdpjjAk5tEH+ezk5OQgNDYWVldVdh9+ee+65+5SqaxQKBUpLS+Hq6op+/fohKysLI0aMgE6ng4+PD+rq6nhH1Js7d26XXrd9+/Z7nOTvSU1NxSuvvAIbGxv07NnT4O+FRCLBxYsXOaYjxDzQUBdnhw4dgqOjo/57c3jcflttbS1aWlr0+W+rrq6GpaWlEE+ozM2kSZP0c08mTZrU4etELIQfffRR6HQ6uLq6wtPTE5mZmRgxYgTUajUcHBx4xzMgWkHTVcuWLcPKlSsRFxcHqZQa7xPyT9ATH/KPhYaGYuLEiViwYIHB+W3btiEnJ0eooQ1y761fv97seuKYG0dHRxw/fpzm+BDyL9Atg0Dc3d2xYsUKlJeX847SJUePHjU55ycoKAhHjx7lkKh7KCwsRG5ursG5HTt2YNCgQXByckJUVBQaGho4pTPW2tqKVatWYf/+/di5cyfi4uIwcuRInDlzBrt378bJkyep6PmPzJkzBxkZGbxjEGLWaKhLIAsWLMDu3buRkJCAYcOGYdasWZg+fTpcXFx4RzOpoaEBzc3NRuebmpqE6ypsTt577z0EBQVhwoQJAIDTp08jMjISERER8PLywpo1a9CnTx+sWLGCb9D/++CDD7BixQoEBwdDLpcjOTkZV65cgUqlwoABA3jH61ZaWlqwevVqfPnllxgyZIjR5GZRV1ISIhIa6hLQuXPnsGvXLuzZswc6nQ6jR4/GrFmz8OKLL/KOZmD06NHw9vbGxo0bDc5HR0dDq9XiyJEjnJKZt969e0OtVmP48OEA2uZ1aDQaFBQUAAD27duH+Ph4lJaW8oyp5+7ujrfeegvz588HABw4cADPPvssbt26RfNQ/mOdraqklZSEdA0VPoIrKirCq6++KuReXd9++y2Cg4Ph5+eHMWPGAAAOHjyI48ePIz8/HwEBAZwTmicbGxuUl5ejf//+AIBRo0YhNDQUy5YtAwD89NNP8PHxwfXr13nG1LO2tsb58+f1eYG2f8P58+fRr18/jskIIcQY3Y4J6tixY3j99dcxefJknDt3DmFhYbwjGRk5ciSKiorQv39/ZGZmQq1Ww83NDVqtloqef8HZ2VnfyLCxsRHFxcX6LSsA4Pr160J19W5uboaNjY3BOSsrKzQ1NXFKRAghHaM5PgJpP8SlVCqxatUqTJkyBQqFgnc8vdbWVqxZswY5OTlobGyEUqnEp59+CrlczjtatzB+/HjExcVh1apVyM7Ohq2trUEhqdVqhVrVwxhDRESEvhkg0LZX1yuvvGLQEFC0ZoCEkAcTFT4C8fT0hJ+fH6KjozFjxgxhNyFsP5k1JSUFV69eFWrrB3OWkJCAKVOmIDAwEAqFAunp6ZDJZPrrKpUKY8eO5ZjQkKld2WfNmsUhCSGE3B3N8RFES0sLVCoVpk2bhocffph3nE7RZNb7o7a2FgqFAhYWFgbnq6uroVAoDIohQgghXUOFj0BsbGxQVlaGQYMG8Y7SKZrMSgghxFzR7blAvL29zWKvHZrMSgghxFzREx+B5OXlYcmSJUhISMCTTz5ptFO0KHtftd/ZGjC9uzVNZiWEECIaKnwEcuf8mDs3KxVtd3Zz3dmaEEIIocJHIBqNptPrgYGB9ykJIYQQ0j1R4UMIIYSQBwb18RHIN9980+n1p59++j4lIYQQQroneuIjEFM9cO6c6yPKHB9CCCHEXNFydoHU1NQYfF25cgV5eXnw8/NDfn4+73iEEEKI2aMnPmZAo9Fg8eLFOHHiBO8ohBBCiFmjJz5mwNnZGWfPnuUdgxBCCDF7NLlZIFqt1uCYMYaqqiokJSXhiSee4BOKEEII6UZoqEsgUqkUEokE7d8Sf39/qFQqeHp6ckpGCCGEdA9U+Ajk0qVLBsdSqRS9evUy2heLEEIIIf8MzfERQGFhIXJzczFgwAD9l0ajwdNPPw1XV1dERUWhoaGBd0xCCCHE7FHhI4D33nsPJSUl+uPTp08jMjISwcHBiIuLg1qtRmJiIseEhBBCSPdAQ10C6N27N9RqNYYPHw4AWLZsGTQaDQoKCgAA+/btQ3x8PEpLS3nGJIQQQswePfERQE1NDZydnfXHGo0GoaGh+mM/Pz9UVlbyiEYIIYR0K1T4CMDZ2Rk6nQ4A0NjYiOLiYvj7++uvX79+HVZWVrziEUIIId0GFT4CGD9+POLi4nDkyBEsWbIEtra2CAgI0F/XarV47LHHOCYkhBBCugdqYCiAhIQETJkyBYGBgVAoFEhPT4dMJtNfV6lUGDt2LMeEhBBCSPdAk5sFUltbC4VCAQsLC4Pz1dXVUCgUBsUQIYQQQv4+KnwIIYQQ8sCgOT6EEEIIeWBQ4UMIIYSQBwYVPoQQQgh5YFDhQwghhJAHBhU+hBBCCHlgUOFDCCGEkAcGFT6EEEIIeWD8D4Ffi7MinuD5AAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"code","source":["sb.countplot(x=\"Survived\" , data = datos , hue=\"Pclass\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":466},"id":"4WcWsLU0PfQ3","executionInfo":{"status":"ok","timestamp":1701381568781,"user_tz":180,"elapsed":688,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"1279fdc8-17fc-47fb-e7de-68cf9c698cc6"},"execution_count":48,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: xlabel='Survived', ylabel='count'>"]},"metadata":{},"execution_count":48},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# ENTRENAMIENTO\n","\n","X= datos.drop([\"Survived\"] , axis=1)\n","y= datos[\"Survived\"]"],"metadata":{"id":"-islcMCqPnPf","executionInfo":{"status":"ok","timestamp":1701381849520,"user_tz":180,"elapsed":265,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}}},"execution_count":49,"outputs":[]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","\n","X_ent , X_pru , y_ent , y_pru = train_test_split(X , y , test_size=0.2)\n","\n"],"metadata":{"id":"xZMKK8xAQr2_","executionInfo":{"status":"ok","timestamp":1701381947516,"user_tz":180,"elapsed":245,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}}},"execution_count":50,"outputs":[]},{"cell_type":"code","source":["from sklearn.linear_model import LogisticRegression\n","\n","modelo = LogisticRegression(max_iter=1000)\n","\n","modelo.fit(X_ent , y_ent)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"id":"sIjUe8e3REvM","executionInfo":{"status":"ok","timestamp":1701382045428,"user_tz":180,"elapsed":6,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"bb70c7df-ca6d-40fd-9103-39f55dec369d"},"execution_count":52,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LogisticRegression(max_iter=1000)"],"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":52}]},{"cell_type":"code","source":["# Predicciones\n","\n","predicciones = modelo.predict(X_pru)"],"metadata":{"id":"0gJBX0wORc-z","executionInfo":{"status":"ok","timestamp":1701382097301,"user_tz":180,"elapsed":275,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}}},"execution_count":53,"outputs":[]},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score\n","\n","accuracy_score(y_pru , predicciones)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YuJ93OvRRpvO","executionInfo":{"status":"ok","timestamp":1701382162230,"user_tz":180,"elapsed":4,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"4caa900a-2f5f-4da3-bebd-d56055554c27"},"execution_count":57,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.848314606741573"]},"metadata":{},"execution_count":57}]},{"cell_type":"code","source":["from sklearn.metrics import classification_report\n","print(classification_report(y_pru , predicciones))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Rn9W3n3wUs5Z","executionInfo":{"status":"ok","timestamp":1701382952823,"user_tz":180,"elapsed":3,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"857c2548-99df-4f38-d1e6-61cd30a60348"},"execution_count":59,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.87      0.89      0.88       109\n","           1       0.82      0.78      0.80        69\n","\n","    accuracy                           0.85       178\n","   macro avg       0.84      0.84      0.84       178\n","weighted avg       0.85      0.85      0.85       178\n","\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","confusion_matrix(y_pru , predicciones)\n","#"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3sWjo_S2UylV","executionInfo":{"status":"ok","timestamp":1701384120884,"user_tz":180,"elapsed":251,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"39a89a24-981f-4d9f-fdf6-0110af277283"},"execution_count":68,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[97, 12],\n","       [15, 54]])"]},"metadata":{},"execution_count":68}]},{"cell_type":"code","source":["pd.DataFrame(confusion_matrix(y_pru , predicciones), columns=[\"Pred: No\" , \"Pred : Si\"], index=[\"Real: No\" , \"Real: Si\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"ZhFe02yyVLDD","executionInfo":{"status":"error","timestamp":1701384131822,"user_tz":180,"elapsed":240,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"3f360653-ce1d-4f4e-a5d5-fc2bdfd356af"},"execution_count":69,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-69-e47f2eb8d555>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pru\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mpredicciones\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Pred: No\"\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m\"Pred : Si\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Real: No\"\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m\"Real: Si\"\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[0m","\u001b[0;31mTypeError\u001b[0m: 'numpy.ndarray' object is not callable"]}]},{"cell_type":"code","source":["X.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"kSwGwoVsZZDq","executionInfo":{"status":"ok","timestamp":1701384229754,"user_tz":180,"elapsed":8,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"afc19948-7e82-48d3-c26e-e629ae6b307d"},"execution_count":70,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Pclass   Age  SibSp  Parch     Fare  male  Q  S\n","0       3  22.0      1      0   7.2500     1  0  1\n","1       1  38.0      1      0  71.2833     0  0  0\n","2       3  26.0      0      0   7.9250     0  0  1\n","3       1  35.0      1      0  53.1000     0  0  1\n","4       3  35.0      0      0   8.0500     1  0  1"],"text/html":["\n","  <div id=\"df-120d6208-1794-4a90-b773-90aa2960868a\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: 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