{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "<img src=\"../Imagenes/logo-final-ap.png\"  width=\"80\" height=\"80\" align=\"left\"/> \n",
    "</figure>\n",
    "\n",
    "# <span style=\"color:#4361EE\"><left>Aprendizaje Profundo</left></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:red\"><center>Diplomado en Inteligencia Artificial y Aprendizaje Profundo</center></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:green\"><center> Regresion Basica con tf.keras: Predecir eficiencia de la gasolina</center></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "<center>\n",
    "<img src=\"../Imagenes/gasolina.jpg\" width=\"800\" height=\"400\" align=\"center\" /> \n",
    "</center>   \n",
    "</figure>\n",
    "<center>\n",
    "\n",
    "Fuente: <a href=\"https://commons.wikimedia.org/wiki/File:Dispensador_de_gasolina_viejo_en_San_Vicente.jpg\">Cesar Pérez</a>, <a href=\"https://creativecommons.org/licenses/by-sa/4.0\">CC BY-SA 4.0</a>, via Wikimedia Commons\n",
    "\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Profesores</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Alvaro  Montenegro, PhD, ammontenegrod@unal.edu.co\n",
    "1. Camilo José Torres Jiménez, Msc, cjtorresj@unal.edu.co\n",
    "1. Daniel  Montenegro, Msc, dextronomo@gmail.com "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Asesora Medios y Marketing digital</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Maria del Pilar Montenegro, pmontenegro88@gmail.com\n",
    "5. Jessica López Mejía, jelopezme@unal.edu.co\n",
    "6. Venus Puertas, vpuertasg@unal.edu.co"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Jefe Jurídica</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Paula Andrea Guzmán, guzmancruz.paula@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Coordinador Jurídico</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. David Fuentes, fuentesd065@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Desarrolladores Principales</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. Dairo Moreno, damoralesj@unal.edu.co\n",
    "10. Joan Castro, jocastroc@unal.edu.co\n",
    "11. Bryan Riveros, briveros@unal.edu.co\n",
    "12. Rosmer Vargas, rovargasc@unal.edu.co"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Expertos en Bases de Datos</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "13. Giovvani Barrera, udgiovanni@gmail.com\n",
    "14. Camilo Chitivo, cchitivo@unal.edu.co"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Contenido</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Introducción](#Introducción)\n",
    "* [Importa librerías requeridas](#Importa-librerías-requeridas)\n",
    "* [El conjunto de Datos de MPG](#El-conjunto-de-Datos-de-MPG)\n",
    "* [Lectura de los datos](#Lectura-de-los-datos)\n",
    "* [Preprocesamiento](#Preprocesamiento)\n",
    "* [Construye el modelo Sequential modo 1](#Construye-el-modelo-Sequential-modo-1)\n",
    "* [Entrenamiento del modelo](#Entrenamiento-del-modelo)\n",
    "* [Conclusión](#Conclusión)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Introducción</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Información general sobre Tensorflow](https://www.tensorflow.org/?hl=en).\n",
    "\n",
    "\n",
    "<a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/keras/regression\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />Vea el cuaderno original en: TensorFlow.org</a>\n",
    "\n",
    "En un problema de *regresión*, buscamos predecir la salida de un valor continuo como la probabilidad de un precio. En contraste en un problema de *Clasificación*, buscamos seleccionar una clase de una lista de clases (por ejemplo, en donde una imagen contenga una manzana o una naranja queremos reconocer cual es la fruta en la imagen).\n",
    "\n",
    "En este cuaderno se usa el set de datos clásico [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) y se construye un modelo para predecir la eficiencia de vehículos de 1970 y 1980. Para hacer esto proveeremos el modelo con una descripción de muchos automóviles de ese periodo. Esta descripción incluye atributos como: Cilindros, desplazamiento, potencia y peso.\n",
    "\n",
    "Este ejemplo usa el API *Sequential* de `tf.keras`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Importa librerías requeridas</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1rRo8oNqZ-Rj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4.1\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import, division, print_function\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "\n",
    "import pathlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">El conjunto de Datos de MPG</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "F_72b0LCNbjx"
   },
   "source": [
    "\n",
    "\n",
    "El set de datos está disponible en el siguiente repositorio [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gFh9ne3FZ-On"
   },
   "source": [
    "## <span style=\"color:#4361EE\">Lectura de los datos</span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primero descargue el set de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p9kxxgzvzlyz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\n",
      "32768/30286 [================================] - 0s 4us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/home/alvaro/.keras/datasets/auto-mpg.data'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_path = keras.utils.get_file(\"auto-mpg.data\", \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\")\n",
    "dataset_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nslsRLh7Zss4"
   },
   "source": [
    "Impórtelo usando pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CiX2FI4gZtTt"
   },
   "outputs": [
    {
     "data": {
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       "    }\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>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>Origin</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
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       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
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       "      <td>2</td>\n",
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       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  Origin  \n",
       "393          82       1  \n",
       "394          82       2  \n",
       "395          82       1  \n",
       "396          82       1  \n",
       "397          82       1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n",
    "                'Acceleration', 'Model Year', 'Origin']\n",
    "raw_dataset = pd.read_csv(dataset_path, names=column_names,\n",
    "                      na_values = \"?\", comment='\\t',\n",
    "                      sep=\" \", skipinitialspace=True)\n",
    "\n",
    "dataset = raw_dataset.copy()\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Preprocesamiento</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Detecta datos faltantes y elimina filas</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3MWuJTKEDM-f"
   },
   "source": [
    "\n",
    "El set de datos contiene algunos valores desconocidos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JEJHhN65a2VV"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MPG             0\n",
       "Cylinders       0\n",
       "Displacement    0\n",
       "Horsepower      6\n",
       "Weight          0\n",
       "Acceleration    0\n",
       "Model Year      0\n",
       "Origin          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9UPN0KBHa_WI"
   },
   "source": [
    "**Para** Mantener este tutorial inicial sencillo eliminemos las siguientes filas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4ZUDosChC1UN"
   },
   "outputs": [],
   "source": [
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Codificación one-hot</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8XKitwaH4v8h"
   },
   "source": [
    "La columna de `\"Origin\"` realmente es categórica, no numérica. Entonces conviértala a un \"one-hot\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gWNTD2QjBWFJ"
   },
   "outputs": [],
   "source": [
    "# retira 'Origin' de dataset y lo entrega. Se recibe en la variable origin\n",
    "Origin = dataset.pop('Origin') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ulXz4J7PAUzk"
   },
   "outputs": [
    {
     "data": {
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       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  USA  Europe  Japan  \n",
       "393          82  1.0     0.0    0.0  \n",
       "394          82  0.0     1.0    0.0  \n",
       "395          82  1.0     0.0    0.0  \n",
       "396          82  1.0     0.0    0.0  \n",
       "397          82  1.0     0.0    0.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['USA'] = (Origin == 1)*1.0\n",
    "dataset['Europe'] = (Origin == 2)*1.0\n",
    "dataset['Japan'] = (Origin == 3)*1.0\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Ejercicio</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Investigue cómo hacer esta codificación one-hot utilizando\n",
    "\n",
    "*tf.keras.utils.to_categorical*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Dividir los datos en entrenamiento y test</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Cuym4yvk76vU"
   },
   "source": [
    "\n",
    "Ahora divida el set de datos en un set de entrenamiento y otro de pruebas.\n",
    "\n",
    "Usaremos el set de pruebas en la evaluación final de nuestro modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qn-IGhUE7_1H"
   },
   "outputs": [],
   "source": [
    "train_dataset = dataset.sample(frac=0.8,random_state=0)\n",
    "test_dataset = dataset.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',\n",
       "       'Acceleration', 'Model Year', 'USA', 'Europe', 'Japan'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Descriptivo de los datos</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J4ubs136WLNp"
   },
   "source": [
    "\n",
    "Revise rápidamente la distribución conjunta de un par de columnas de el set de entrenamiento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oRKO_x8gWKv-"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f9cc5a02760>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ihuJMDe0gNUZYXcIx5eEcjAEury/hO95RzI0v/X9xt7uF+2i7vD5wHt6reLC64EjxE+m6OLcgnRLiBKKkOIacHi967C4YVLJEDyWofIIe//PJcbSYHMjRU80SObMsrVyw5m2oNcUjnUUhY1ehQSUYUyqpCAwMPs6xvqohOFOslIogEQHdNjfq2ntpJo1Elc/Hse1oK/Y3muDjwLQcrWB8zpmYBoVUhFn5eqSrpWjosiI/TQWVTBysP35jVyOaTY4z1gXTdTE5UVIcQ6d6HMjQyJPq4i0RizC3wID3q1vwowuKEj0ckgJy9TI8tKQcD7xzMDij8dCSckxIG9oveyOZRSFjl8/HcaLLinu+PRWPvX80GFN3XT4Fv3m7Gt02F/54/aywmeJVC0rx4Dv+r1MfVxJNDV1W1LT2BmvcKwr1ePiactz/9ulr3sPXlKPd4sCDGw8FH7vn21ORpZXjntf3h9zRWF/VgHsXTh+0Lpiui8mJkuIYauxOjnZsA1UUGbBp3ylKismQfNPtxDMf1wRn7TgHnvm4BoXGWchNO/PFm1ZXk/5Odlqx4u97YFDJsOLSEhQZ1TjWZsGL/z6JZpO/DRXnCCbEwOmaztsuLMazH9fSjBqJqlazM9iTOFevQOW0HDy9zV9DnKWVY2K6Cgcae/DER6Ex+dj7R7H8ouKQxwJdKWbkpQ36SxtdF5MT9SmOocZuOzLi1LZqOGbmpeFIixntlvj0mSWprdXshMvDg58zBrg8HK3mocVPYHW1Quq/3NDq6vEtMEPWbHLg8Q+Ower0YN3W2mBCDAB1HVYYVDL8/NISrKj0fxhUsuCiO+rjSqLJ1q/G/dq5+Vi3rQb1nXY8/sEx/OqNA9hR1wmzU7jO2MdDz2VQyeBw+/DliU7UtffCN/CAPnRdTE40UxxDjd22YM/NZCKTiDCnr4TiB/MKEz0ckuTy0hS4+fzC4ExK4FZ23hBr0mklP+kvSxs6Q6aSS8JnzCRMMOaCX6cZNRIlPh+HRCQKxiBj4Zt2+DggZhCc2e1/GcvV+6+VP3rxqzMuKqbrYnKimeIY+qbLjgxN8iXFgH/b5037TiV6GCQFiBjCtjt9amvNsPoUB1byzyvOQHGmhi7845hYBKxaUBqcIWvusWHVglIUGpX4+aUlWLmgBGV5esGY8/o4zaiRqDrZacX97xzAysrSkFnb/jbta8L0XB3uunxKyDG/XjgNpVma4GPXV+SHxe1gWzbTdTH50ExxDDX12DEzX5/oYQiaPTENf/r8ODp6nUlZ4kGSR3uv8Ha8Hb2uBI2IpLJmkwOv7KgP1qh7OZCtk2H5RZPx0Gb/IqaVC0oEY640S4N3V86nGTUSNa1mB+o77Xh1pz8mNXIxHlh0VjAWFVIR7l04HZVTsrDtWBuWX1QMH/dPFmiVEhg1Uvzrjvlo73XA5hIusaDFc6mDkuIYOtWTnDXFwOkSivcOtuCHVEJBBpGmlAreNtQrpQkcFUlV2ToFum0uPPtxbfCx574/F/e9dbo9lY8L36ouzY7P5h1k/AgseGs2OYIxWWhUYv3yebC7vcGyhpOdVvxi/d6wmFx+UTGWzFJhXnEG6tp7afFciqPyiRjx+jg6ep0wJmFNccC5Rel4e09ToodBkpzJ4Q67tbiyshQmhzvBIyOpSGiBkdPrC0kk3tjVGBZzVDJBYkEoHu9dOB0z8tJCyhoitVDzcX9Lt0jnorhNLTRTHCNtFgd0Cikk4uT9vWPWxDQ8/3kdmk125OqViR4OSVI6hTRkIwXOgfVVDXjsulmJHhpJQYEFRlPvmI+GLiskYhHkElHIDFuzyYH1VQ149dZz4eU8bBHSaLYNJ6Q/oQVvBQZVSHwVGFRQyQQWhPYttFPLJCHnCsS2SiZBti457xYTYZQUx0gyl04ESMUinFuUjo17T+E/Lp6c6OGQJCURcfz8khI8uLE6WGO3dnEZJGLhVkOEDMXRVgvu2rAXBpUMv1o4FasWlIZ0m/jZxSWwujy4qDQrJOGNxrbhhPTXf0vvgfFVaFTijspSPL2tBisrS8M2lFFJxXB5vSHnC8Q2xWfqoaQ4Rk71OJCepJ0n+jt/shEbqr6hpJhE5PExPPtJbchM8bOf1OKxpTRTTEYmsMWtQSXDf101Hb96fR8MKlkwxkQMcHm82N3Qg4J0dUgdMW2PS6LN4/GhutmEZpMDmRo5HtlyOBhfi2bmBXe2CyzGE4uAswsNOHTKjL9sP4Gnb5wTPBfFZ2qjpDhGmk12pKuSPymenqtDt82Nw81mTM/VJXo4JAm1W5yo77SHLIwCgPZe2vyFjEyr2QGDSoYfzitEbZsluJlH/xh7YtksmB0eHGu1AECwRIK2xyXR5PH48Pa+ppAtnVdWluLVnfVoNjlC+hb3j9EVlSX48+d1uPOyKXB5T8djpPis77RSmU8KSN6C1xTX2G1Pyo07BhIxhm9NzsDruxoTPRSSpDK18rC+nQqpCJlxLA/y+Tjq2nux43jHoLtEkdSQrVPg+gr/zmGBThP9KaQiKKViPPNxLX761924at3n2FLdAp+PB7sF9FdoVEIpFVN8kGGrbjYFE2Lg9FbN187NDx4jFJ9Ts7W47cJi/P2reqSrT18LheJTIRVhzzc9wRgG6JqWrCgpjpGmHntSd57o76LSDLy1pwlur+/MB5Nxx+by4M7LQpvW33nZFNhdnri8fqDG76p1n+OmP30ZkiCR1FRkVKMkUwOH2yfYaWLtknJ80+Xf6hkI3QRh4Ar/QM3nDc/vpPggw9ZsEp7ZDayR33m8Hb/77oyQ+Fy1oBS/f/cwXviiDg8sOgucI5jcFhhUYR0oVlaW4p9VjcEYpmta8qLyiRhpNjlwyZTMRA9jSHLTlMjRKfDxkTZcUZaT6OGQJKOWS1A2QY1XbjkXrRb/amyXxw25ND6XD6rRGzsCXSM6rU7k6hW4+4opcHh8+PpEJ564fhbcPg6xiOH5T4/jWFsv7rxsCl7afhIAcO3cfJzs7IXF4UaaUor1y8+H2+uFVCzCDc/vpPggI5KrVwa7SuTqFbh2bj7EImB+SQYun56FTqsbB5tMePKG2RAzBq+PI9+gxIUlRri8HE3ddmzc14QNVY3otrnwxLLZuGJ6Nl6+5Vx8XtsBtUwMr4/jurP9M89dVn/ZGV3TkhMlxTHSYnKE3FJJdvNLM/CPrxooKSZh0pRi7Gm34sGNe0K6T8wpiE9DeqohHRsCs2OPbDmMGyoKQlbxr15UhkfeP4L6TntwZq19Zz2e/OgY7v32VNjdPqyvaoBaJsa6bXtCVvVnamUUH2TEynJ1ePiacjy9rSYkLt/Z24QVl5bigXdO1xqvWlCKV3bUo9vmwtol5Xj245qQmH11Zz3u2rAX766cj0ytHJv2NeGGigI883Ft8BylWRpwgGI2SVH5RAy4PD6Y7f7ZjFQxr9iIqvpuNJvsiR4KSTLdVm+wHRvgv3g/uLEa3VbvGZ4ZHZFq9GiXqNQSmPFfNDMvmHgA/nhas7kai2bmBT8P1HT6W2KpsW5bjeDz7tqwFzKxiOKDjJhEIsI1s/Lwx+tnh8TXopl5wYQY8MfbU1tPx+WD7xyMGLNtFgeKjGo8tGRGWMze+8Z+itkkRklxDLSaHTCoZSm1ylQhFeP8YiPWf/VNoodCkkyrxSk4q9FqiU/3CdolamwIzPj3X80fEHh84OcKqQgOj2/Q59lcXooPMioSiQjuAbsqnilOB4vZTI0CJzqssDjcFLMphsonYqDZ5Ej6jTuEXDI1C+u21uCOBaUQp1BCT2IrWycX3MkpXjs1Ce04Ra2NUk//GX+heJqarUWuXoFmkwMKqQjTsrV48obZSFdJB31etk6B8yYZKT7IsPTfFTFLq4BaHrpjnVIqEow3zsP/HvhcxIAnls3Gic5ePLT5EO6+YprgOVQyMc4pTMe7FLNJh2aKY6DZZEe6OnVKJwImZaihVUjw2bH2RA+FJJFsnRhrF5eHdgdYXI5snThuYwjsODWvOAPFmRp680hBBQYVHr6mHDuPt2P1orKw1fmPf3AEP5xXiEKjEqsWlOJ37x7Gnev34kCTGfddOQ2b9jWFdakIzK5RfJDhGNj94TtPf469Dd3BLju5egX0SilWLQiNt7sun4I3dzcGO6Rs3t8U/NpDS8rx3dl5OCtXi4c2H8INFQV4/IMjYTG7srIUK1/bgw8Ot6LIqKaYTTKM87HZAqSiooJXVVUl5LX/79PjONBkwvfPK0zI64/GtiNtqGvvxQs/PifRQ0kWUbtSJTImR+OrE514dMth3HxBMewuD5QyCV7ZXodfLZyOcycZ4zKG/rM62bpxP6uSkjFZ196Ljw61YEKaCo99cAS/vGIaatss8PqAN3c3BmeIH186C7979zCaTQ4A/kRixaUlcHl9mD1RD6NKji6bCyqZBNk6OQrSx3UsJIuUism69l5cte7zsBncVQtK0ev0YnqOFnf37bJ47dz84C6L5RP0sLo8mGhQwcc5xIzB7HAhXS1HWa4eEokIO4534IvaTrzwRV1YR4uSLC3+X19sK6QivBuh2wRd76JiRN8wKp+IgVM99mB/zVRzwWQj/vFVA1pMDuToqeifAK1mJ6rqTaiq3xP2eDwEZnUCLYwCM4QLy3LojSKFtJodMDu9MLdaUN9px7FWC57ZFrpLosPtw5FWSzAhDjzm8PjwzLZavPjjCjSZHBQLZFQidbSxurx49uNarKgsEdxlcUVlCUqzNPjBC1+GxN+MvLRg/GXrFBCLIu+CF4jtSN0m6HqXWElVPsEYUzDGvmKM7WOMVTPG1vQ9ns4Y+5AxVtP3pyHRYx1Ms8mRErvZCVFIxTh/shHrv25I9FBIksjWyVFoVOLnl5ZgRaX/o9CojFtNcaQ+xSc7rXF5fRId2ToFxAwQM/+GG1OztVi5wB9PuX2/gAfqMvsL1G4qpCKkq2QUC2TUInW0Cdw4F7PQXexy9QqsXFCCiQYlxCImuKlMQJFRjXMK0wc9f+BzoW4Tka53B5p6aHOPOEiqpBiAE0Al53wWgNkAFjLG5gH4NYCtnPNSAFv7Pk9aqZwUA8ClU7Pw2tff0H9AAgCQijlWXFqKF76owzPbavHnz+uw4tJSyMTxiY/B+hST1FFkVGNGvh55aQr89OIS3PP6Pqzb6o+nH19QhPuunIqHrynHzDw9Co1KAAj2MP6yrh2PXjcTnbYInVDMFAtk6IQ62qxaUIrPj7Vh5YISzClIw0NLyoP1xTefX4jnP6vDvW8cwN3/3IcfzisM/iLncPvQZXUGt2w+2WnFeUXpeOS6mSHnf2hADXKkbhORrndbj7TRrndxEPXyCcZYPoAizvkXfZ/fBSBwf+DvnPPaSM/l/gLn3r5PpX0fHMASAJf0Pf4ygE8A3BvtsUdLqzm1k+JJGWqoZGL8+3gH5pemxq58JHY8XoZnPq7BbRcWB1sQPfNxDR5fOisurx+Y1RlY/0c9PVOLSMRQOTUbB5t6sGzADnR//6oe91wxDUdaLRAzYOWCUqQppdjdYMJzn9XiD9fOxK/f3I+rZ+VFXM1PyFAN7GiTqVFAKgGm5eqwp6EbFocX67+ux20XFmNajhb3vL4vJF7XbfNfD5/9uBaFRiWaehz4wQtfhZQ7fKc8FzPy9Gg1O+D2cqzbehSLZuZBLAIqCtNxQbFRsBwi0vXO6wPtehcHsZgpfgxAWr/P/wOAFf7kds2ZnswYEzPG9gJoA/Ah5/xLANmc82YA6PszK8JzlzPGqhhjVe3tiemg4PH60GV1IU2Vet0n+ruoNBP/+JJKKEYrGWJytMwON26oKAiZKb6hogAWpzsur099iqMrkTEpEjFYXd6QN/xcvQI3VBTg7tf34Zlttfi/z+rQ1lev/uzHtajvtGNHXSfqO+14Y1ej4Gp+t9cn+HokNSQiJvt3LJmUocaeBhN+9tddWLe1Fve8vg+V03Lw5u5GHG21ROxXrJCK8Nury3DvG/vDyh0aum0oztQgW6fA8lerUFVvwrMf12Ld1losf7UKDd02wXEJXe9WVpbizd2NdIcsDmKx0G4q53xzv89tnPM/AgBj7PMzPZlz7gUwmzGWBuAtxlj5UF+Yc/48gOcB/wrWYY06Stp7ndArpZCIkq0yZXguKMnAnev3osfmQlqKLhpMBskQk6OlkknCdmVat60GL8apQwn1KY6uRMVkYEW9iLGQmbBr5+aHxddTW2vwP9+bC8CfFMjEIvz80hJ/FwARsOLSEmRq5WjosmN9VQMWltP29Kks0dfJk53WsMQ2MBsMCPfHnjsxDY9fPws+zgWT5mOtFgBAp1W45CfSls6B613e8nnYeqQNXh/w6s76YMcKukMWW7HI3Ab+xBb0+/uQ+zdxznvgL5NYCKCVMZYLAH1/to1uiLHTkuL1xAEauQSzJuqxcd+pRA+FJJjV6RFeqe30xH0sY7SD5JjXvy/sL9bvDen/2n+lfoDD7UO3zQWFVIRHr5sJnUISvFPx3x/VQCxi6LK68MIXdbh34XQUGdXw+XiwrrOuvZdqL8mQRarjFYuAN3Y1hvUrXrWgFL95+yDu/uc+SMViwUV1tW29eHtvE5pNDvx64dRgDXLg64MltyIRw4y8NEzL0eGFL+qCCTHdIYu9WMwUWxhjUzjnxwCAc94FAIyxaThdLyyIMZYJwM0572GMKQFcBuARABsB/AjAH/r+fCcG446KsZIUA8CFJZlY//U3uPn8okQPhSRQWt+OYgNnStKU8SkRohZFqa//ivpmkwOv7KjHfy+bDavLgyytcA3lxHQV3l05HwzAlf16ygZmkl+99Vy8u3J+MEmgGCEjFamOt6IwHSqZBMUZavzlRxWwOr04cMqEV3bUB1urPfDOATxy3czgTLNCKvJvAiIR4f9tORJ8bNWCUryyox7dNteQklu6Q5YYsZgpXg1gM2PsR4yxGX0fP4Y/sV19hufmAviYMbYfwNfw1xRvhj8ZvpwxVgPg8r7Pk1KL2QFDitcTB8zM06PF5EBtmyXRQyEJ1Gl1B3d6AhC86Hfa4lNTTC3ZUt/AmbhmkwMHm814/INj6LI6BePL6nSjOFODlgizeL1OD1rNDpzstOJEB8UIGbkioxq//+6MkBh8YNFZePz9I/jDe0ew/NVdsDg80CgkWLe1NqSPdn2nHXlpCry7cj6e+8Fc3HZhMXycBxNi4PQvco9fPxP/umP+kH9Zo50a4y/qM8Wc8y2MsWsB/ArAyr6HDwK4lnN+8AzP3Q9gjsDjnQgtw0hazT2OMVODKxIxXDDZiDd2NeHeK6clejgkQQwqKba2mfCXH5+DDosTmVo53trdgDkFaXF5/cFastEq7NQgNBMnZkC3zYWadit2Hm/Ho0tngXMf0lQyHG22QCOX4uuTnVDJJCg0KuHy8ODuYjq5GE63D7saeiBmQNkEPcUIGTGRiGFuQRqWX1SMvDQlmnrseGZbbciuijl6BTxeHozj/jvVMTAUGFQQMeBQsxnpKhlun1+MN3Y1AkAwbrttbtjdXkzKoBKIZBWrHe1aAawDUNtXGzxunDLZUZCuSvQwouZbJRl48qNjuOfbU+m31HGqwCBHRVEGbn3p6+CtwLWLy1BgiM/mHdSSLfUFVtT3L2+Yka/Hw9eU47Wv6nHd3AL88YMjuKGiAPe+cSB4zMrKUqyvasDdV0xFs8mBJz48BoNKhpvPL8Qv+p3rwUVnodCoRH2nPfiaFCNkOArS1ZiWo8MjWw7jhooCdNtcAPxx9PA15WjosuHxD44GY/KGioLgAtHnP6vDY0tnQsREeP6zujOWUZRkalCUQb+sJaOol08wxm4HUA3gaQBHGGOLo/0aySzVN+4YqNCohlIqxtcnuxI9FJIgDV1OPLixOuRW4IMbq9HQFZ9tnqklW+oL1Ee+u3I+Xlt+Ht5dOR8lmRo8va0GN19QjDWbq7FoZp5gl5NFM/NQ09aLJz48Bofbh2vn5uOpraHHrd18CL9dXE4xQkYsEKMv/vhcnDPJgL/edh6e/+HZWL98HqbnaHHP6/tR32nHqzvrsXZJeVis1rT14pf/DC3hefKjY+i0ucLKKFrN8bl2kuGLxUzxLwCUcc7bGWPFAP4Gfz3xuNBqdiB9jJRPBJw/2Yg3dzfhvOIhNw8hY0irJcIuYpb4XNhpwcnYEKiPDJQz7DjegfpOO4719YFlTLgLxZQsDYwaGQxKKZxeH/LSVILHgXO8SzFCRmFgjAZsOdgcjLlmkwMtPY6QEgrGgDy9UjAuBzZBcbh9sLnO3Lkn0MKw1exAto7iOV5ikRS7OOftAMA5r2OMxeceaxLgnKPN4oRhDM0UA8AFkzNw/9sH8dA15ZBJUrv/Mhm+XL1csHwhRxe//9qR3qxI6gqUxQAI+XNgnB1r68ULb9bhrsunQCkRo7bNInhcQbqaYoSM2GBJaL5BiQcXTUemVoE0pQRyqRiFRmVICcWqBSWCcTkwj1VIRVDLJTjZ0Yv8NBUaum1hr0kddxInFhlOPmNsXeBD4PMxq8fmhkwsgkI6trYczdDIkW9Q4tNjqbkjGxkduViE1VeXhSQuq68ugyKOvyBRD9qxJ1AW43A68dCScmza1yS4W11gJ68nPvTfit5QFb6r3dol5cjTKShGyIj076N905++xFXrPseW6hb4fBw+H0d9pw2Pvn8Uv3/3MA6eMuOe1/fh3oXTQ0ooNlSF9zO+87IpyFDLUGhU4ueXlmDlghI8uWw2Ht1yGO8dbMF71c245aWvwl6TOu4kTixmiu8Z8PmuGLxGUmo2OZChGVuzxAHnTTLirT2NuPys7EQPhcRZfZcd//iyHo8unQW7ywOlTII/f3Ycyy+ejBkTDTF/fZo1GZsCZTEZGhn2NnRjyew8iETAo0tnwen24mSnLbiTF3D6VnSzyYFXd9bjtguLwRgwNVuLl7fXQSpi+K+3DlCMkGGLlIROWzkfAPDLf+4Lq2evbesNazP4yo56PLZ0Fo62WsA58NL2k8jUyHBH5RTc/3boAtLXvm7Aktl5WDQzD89+XBvymtRxJ3Fi0ZLt5WifM1W0msfWIrv+5hWn464N+2B1eqCWx6ppCUlGRo0Mx9p6sfIfe4KPKaQiGOMU64O9YdEbRGoTiRhazU6YnV48s602+PiKyhK88EVdxFvRzSYHnv24FgqpCLddWIzzijODCTFAMUKGZ7AklPPTte79695dXl9YuUS3zYWaNktILF9fkR9MiAPnDWwh7eNAQboSuXoFmk2O4GtSx53EiXp2wxgbdFEd53zMdqNoNo2dHsUDaRVSTMvR4sNDrbhmTl6ih0PiSCMX44lls+Dz+bd8ViskEDFALYtPmRDNmow9gVvEnVYnsnVyNHXboJCKYFDJcO3cfMjEIjxz0xzUtffC7PRCzIBMrRzefn1iAzNur+6sx/UV+RQjZMSydQoUGpVYNDMPjAFKqQhGlRRmuwdapSQkQQ3EqUIiwkNLytHYbcPHR9pwybQslGZpkaaSBtsDKqQiFGdoIm4hDQBNPXb8cF4hXt3p3+0usEh0YAtD6qYSH7GY8jsfwDcA/gHgSwDj5t5Vs8k+ZnazE3JesRFv7G6kpHic0asYDrd48eA7B0/3KV5SjjR1fP5r06zJ2BIoh3lky2F879xCiOHBhDQ17rtyGqwub/D2dKCn6xu7GtFtc+F318yAnbvxxPWz4PT4cKLTGkwkZuWnUYyQESswqHBHZSnuf/tgSOw9ta0aMgnD2sVleHBjNd7Y1RgWp4VGJf7zkhKs7mtbGeibrVVIUNPWi85ep2BsTs/Roc3swHOf1aHb5sLyi4oxLUcXXGxHHXcSIxYrZXIA3AegHMBT8G/L3ME5/5Rz/mkMXi9pNPXYka4eu802KgoN2NPQg85e6rE4nrT0nE6Igb4+xe8cREuPNy6vT32Kx5ZAOcyimXl48qNjOCvfiF+9cQBmhyes//BTW2tw7Vz/LPBv3j4AnUqOQy0WtJgdkIlF+PWV0/Do0ln4646TYYucKEbIUDV024IJMRAae/Wddjz7SS2euWkO1iwuQ2m2NiROF83MCybEgeeu3XwINW29WLe1Fn/+4kTYwtCHlpSj3eJPiANlE3MmpoXUwNMWz4kRi5piL4AtALb0tWO7CcAnjLG1nPOno/16yaS5x4HSrLF7q04hFWNOQRr+daAZN59flOjhkDiJRp/i0fTcFIkYrpiejfXL56HZ5ECuXoGyXD29SaSoQDlMoD6zoy++HB6fYJwxdvrvx1pP12sG6omf/bgWuXoFzi024vkfng2vjyNdJYPL58PJTivNsJEzilSiFYi9+k479jaaMC1Hi38f7ww5NlJ/7UDzk/4LQwvTlVDKJHj8gyMh25aLGTApY/hxSr2Moy8mK6b6kuHvwJ8QF8G/5fObsXitZOJfaDd2Z4oBYF6xEa/vaqSkeBzJ1gn3Kc4eYp/i0XaP8Pk4PjjcSt0nxoj+/YkLjcqwfsUD42xqthYrKkuwaV8Tzi0y4JFrZ0CtkMDmdOPJj/wJ8Q/nFeKprTXBLaD7l2BQrJAziVSixfnpv4sYkKNToLHLJnjswM/LcnV45NoZSFPL4PNxHG/vhVImwZ8+O45bL5gEmzu0VGhypgb5aSpIhtjqkrryxEYstnl+GcB2AHMBrOGcn8M5f4hz3hTt10o2rZax230iYGa+Hg2dNtRTv8RxI0srxtoBW+iuXVyOLO3QFtqNtufmiQ7h55/ooBhMRYFymJ3H2/HTi0vwxq56rF1cJtineNWCUvz+3cP48+d1+PklJVi39RjuffMA7v7nPnAw5KXJce3c/GC/WKEtoClWyJkIlWitWuDvkR34e2mWBuU5OuQZlLjr8inBYzftawrr4752STn+sOUw/ntrDerae3Hnhr14/INjuOf1fVhYngsf52Fx+qs39mN7XeeQ+2tTL+PYiMVM8Q8BWAFMAbCSseBvLAwA55zrYvCaCdfr9MDj5XFbkZ8oEpEI8/q2fb7z8imJHg6JgzaLF89+UhPsC8s58OwnNSjKmIWijDM/f7TdI+q7rILPb+iyYvIYLlcaqwKLiPLSFLjh+Z1wuH1YMD0bj143Cz12F1655Vw0mew40WHFKztO9yl+cGM1bruwGFX1JjjcPqzeWI2XbzkXJztOx0ekW9kUK2QwAxe2ZWoUYOAoydJAIRXDoJJiWrYOjT021LT1QiXzd+SxubxQSMTI1svw5LLZqGnrRVGGGn/84AjqO+34+aUlYcnvum01eGzpLME4rarvQr5BOaTrInXliY1Y1BSPy32Am3vsyNTK0e+XgDHrW5Mz8H+fHccvLisdF//e8a7V7PQvNvm4NuzxoRht9wi1TCL4fJWM+mWnKpGIwebywuH2IVevQIfVg1XrvwreBn5oSTnWbQ2Nt/41noHP2yxOTDAogy2wAOFb2RQr5EyEtpIvztIG/+7zcexu6MHzn9WFtQS87uz8YK37isqSYCxG+iXN7vKExGmuXoHrK/Ix0aBCe69zSLXB1JUnNuhKESXNJkfcNjNItMmZajAAuxu6cXZheqKHQ2IsWycP6eEJ+G8ZDrWmeLQ9N3P0cqy+ugxrNp1uebT66jLk6Md2/f5YF3hTv3ZuPh7afChkNq2x2yaYNOTplVhRWRJs08YB/OSVKqy+ugzPfVqLN3b5t9rtX6t51+VToJaLseN4By1GIiN2osOK+94K34Rj1YJSTDSosKKyBIC/x7FQX+P+i+oKM1R46oY5qG42QSUTQ8QYnvjw2LBqg6mXcWxQUhwlLaaxX08cwBjDhaUZ2PB1IyXFKWC0K5QlIo6fX1KCB/v14Vy7uAwS0dBq30bbc9Pt4Xju09qQ8o3nPq3FOYXnDPnfMFq0yjs6+n8fszRyPLZ0JmoGbJcLABuqGvHAorPw0OZDgovnVi0oRb5BiTazA7fPL8Zzn9bisetmwdu3MmrVglJYXV6oZWLk6hVY+tyOYS9Gop85CfD5OA43mwVnfXP1Stz9+r5gfD163Qz8/rszcN9bBwT7GiukIuQZlOixuuDjQL5BhcfePxJWGzz1jvmCJT/94/KsXC3+dcd8tPdSL+NooaQ4SppN9jG7m52QC0sy8es39+O3i8ugHON11KksGiuUPV4WTIiBvj7FG6vxyi3nDnkcQrcmh6qh2yZYvvFNtw0l2doIz4oeWuUdHULfx7sun4LpuTrB7XLNdjduu7AY03O0waQDON1DdtWCUjyy5WjwNrbL68WFpVnw+TiydQq0WRxQSsXBuuXAc4ey/TP9zEl/JzutqGmzCJYr1HX0hsTXr944gLsuK8UzN82Bw+ODXinFT16pGtDnvRrLLyrGM9tqQ8owAvXzkergKS5jb1zW/8ZCU8/4mSkGgHS1DKVZGrxf3ZLooZBBRGOFcpfNJThD0m1zR3WskQRqivuLZ50orfKODqHv4xMfHsPJDmtY14mVlaV4ZUc9nv24FkdaLYLxZ3V5g39ft60GGrl/N9H+mx4E6pYHPrfN4hj2WOlnPn61mh3YUNUYFqcPLDoL/6xqDDnW4fbB7PRixT/2gHPgyxNdg/YxDsTvtXPzg1+PdH2juIw9mimOkqYeGy6YPISl+GPIhSWZ+MdXDbTtcxKLxgpltVwiWFOsksfnDkG2Th5WJ7pqQemQa5pHq9XsCKkJBIA3djXSKu9hihSLVpcXnx1tw//98Gz0WN0Qixme//Q4AODnl5agwKDEqgUl2FDVGJxJ699DNnCeJpMdM31pITNmI12MFIuV/VSOkbqydQp021x4dWc9Vi0oxfRcHSwODzRyMW65oBAvbq8Pi02H2wepmOHcIsOgPZAB/7Fi0emvRbq+jSQuKe6Gh5LiKGkZRwvtAiqKDHh5x0k0dNpQYFQlejhEQDRWKKerpfjpxSVhC90MKmkshhwmP02FfIMSyy8qho8DIgbkG5TIT4tPzOXqFYI1rTk6WuU9HJFiUScXY2F5Lv7j1V3B7+99V06D3e3Dkx8dC/mev7KjHt02V/Dv/c9ztMWC6Tm6kOSgwKDCw9eUB7fwVUhFePiachQYBo+daK/sp9veqS2wqO2RLYchYgw//euukLj82cXF+N9P69BtcwVLIQKJ7wPvHMTKytJgL+3+sRygkIpwTmE67r5iCiZnaqCSi1CQHr5gbrhxSXE3fFQ+ESXNJgeMmvG1Gl4qFuGCyUb8s+qbRA+FRCDUlH64K5RtTm8wIQb8MxNrNlXD3nf7OtYaum14/IOj8Pa9D3h9wOMfHEVDty0ur+/1IazX6FNba4LjIUMTKRbnFRuDCQPQt/Wz1RVMiAOPPbW1Bvd/ZzpWXFoClVSMbpsreJ6VlaX4Z1VjWFlEQ7cNT2/z99heUVmC2y4sxtPbas4YO9H4f9Mf3fZObYHFwutunCMYlx1WF37/3XIsv6gYr+70/+K2+uoy/GHLYdR32oPbPK9cUIInl82GRi4Jid87L5uCX72xH49/cAx3btiL/DTh2dzhxiXF3fDRTHEUmB1ucD72N+4QcvGUTDzx4TH84vIpENNvnklntJ0fAKDF7BS8ZdcyxD7Fo9VqdggutItX+UKbRfiWZXuvgzaEGIZIsfjlic6w76+PC/d3PdxiwTPb/Fs733ZhMQrSlWjosgcTkYEzZiONnWj8vxk4DtpoIbX1763dX//64HOK0pGnV0KnlKK+yxrsV9xscgRjMNBScPlFxSjN0uJIiwUvbT8Zssgu0rVluHFJcTd8lBRHQXOPAxnjZOOOgQqNaqSppPjsWDsunZaV6OEQAaPp/AD4a3qFbtnFq6Y3W6fAFWdl4PvzJqHb6ka6Woq/7jwRtyb11CQ/eoRiUej7K2bCm3AE6jCbTQ688EUdHl86C2/u9vcsFpoxG+xn5/NxnOiwor7LCrVMgmydHPlpKjR020LqL6ORPFAMjQ2Rfo4iBmjkErRanFArJGjvdcDu8goeq5CIcPP5hShIV0ElE0OvCJ1M6x8XIS0MtQqIRf7Yz9YpcG6RkTb4iAEqn4iCUyY7MtTjq3Siv4tKM/H3LxsSPQwSIwUGOdYuLg+5Zbd2cTkKDPGJ+RyNDJdNn4D/eHUXfrF+L5a/uguXTZ+AHE18avijfSudhBL6/qarZLjr8ilhMfdlXXvw85WVpXjsgyO459tTsWXVfME6yUBNcf/zPHxNOfL1SmypbsF3nv4ct75UhR+9+BXeO9iC96qbcctLX+GmP32Jq9Z9ji3VLfD5htaPe7j/Roqh1CP0c1y1oBQlmRrc/fo+rPj7Htz9z32QisWYnKHCqgWh3Sp+e3UZ8gxKPLW1Bj//+x787G+74eXAzy4uRq5eERIXgXrgq9Z9jpv+9CW+8/TneO9gC+7asG/IsUlxN3yM89H/h09GFRUVvKqqKi6v9bcv67H1cBt+Mr84Lq+XbGwuD1a9thef3HMJMsZeXXXUpv/jGZP9jXb18Vd1nXj0/cO4+YJi2F0eKGUSvLK9Dr/69nScW2yM4cj7Xv9EJ27+y1dhsx2v3Houzp0U+9cHTn8Po3ErPQrGXEwWGFSobjZh65E2eH3Am7v9ba4Cu9g1dNuxeX8T/t+1M7GzrjN4TLPJAYVUhHcj9B2ua+/FLS99FeycwjmweX8T1t04J6R/MXA6wel1eoO3ugc790j/zUkSQ9GW8jE5VIE7DA1dVqhkEsjEDL/YsDdYKgH44+YvPz4HAIfT7YPby5GtlcPh8eJHL34dFnfLLyrG1GwtMjRynFOUDpGIoa69F1et+zzs2NsuLMazH9cOOTbHeNwNZkT/SCqfiIKmbvu46lE8kEomQUWRAW/ubsTyiyYnejikn2isPm61OFFVb0JV/Z6Qx9t641NT3GYRrmlut8Tn9YHRl6CQ0yLFpEElxbqtobW/67bWYkVlSTBJbeq2hx0zWI1kpJriZpNwrWWmRg7GnCGPRav+kmJobBCJGCZnaYI1v5v3nwpJiIG+xaK9Tvzq9f0hXU9aIsSdjwNHWy2YnqsLXpcj1QPLJaLg34cSmxR3w0NJcRQ0dtsxIU2Z6GEk1EWlmfjrznr8ZH7xuKytTlaRVh+faUev/rJ1csE+xVna+NwVyNIK1zRnxun1SXRFisn1y88P+Tnn6hXBmeIVlSXYtK8J2TqFYM9ipVQMn4+H/aKXrVOgolDvv8vh9EAll+Dl7XXI1SsFY6qh24aSLG3IY1R/SQYKudMR4fpkVMvw5A2z0W5xwKCSo77Tipn5ehQalSFJdKFRiWnZWojFDBaHB1+f7IRRLYeqb9OigectylAH/06xGX1UUxwFTT12ZMSpvjFZTcvRwuH2Yn+jKdFDIf0Mtvp4qOQS4OeXlOCFL+rwzLZa/PnzOvz8khLI4/QrNec+wR3POKeeaKkoUky6vd5g/WOgN/Tzn9Xh3jcP4M+f1+GnF5fg8feP4P8+q8PN5xcGazBXVpZi5Wt7BGss8/VKLDunEL96fR/uffMA7nl9H5adU4ipmRr8/rszwmLqn1WNaOhrV0X1l0TIwFrfP31eG7bmYvXVZXjkvSP4w3uHIRWLcc/r+/D4B8ew/NVd+OnFJSg0+ifRCo1K/PTiEtz9+j787K+7ccPzO/D1iW7c8tJXaDU7BK97p3psFJsxRDPFUXCqxz4Wa2mHhTGGC0szsKHqG8yamJbo4ZA+0Vh97PQAD24M7VP84MZqvHLruVEfrxDGRNh2pAWPLp0VMttXUWSIy+uT6IoUk+lqOeYWpGPayvk41WPH7a9UhfXGvu3CYuxvMuOprTV4fOks+AD8v3cPo9nkELwDcrjVjAffORgau+8cxNRsDablaHDXZaXITVPB5vSgw+qETMIwr9iI15afN97qL8kQDbzTUZqdhg1V9Xh06SxwH4dYxPD8Z8exv8mMn19aItjj/YnrZ6HX6UF+ugq3vvR1yNfX9fXVrj5lwtt7m3DbhcXBevhtR1rwyyumYd2Ns6FRSHCo2YRp2TpIJDS/GS2UFI+S18fR0esc1zXFAReWZODBd6qx+uoyyOg/aVIIrD4eWL85nBmG1gh9ituG0ad4NIv97C4PrptbgF+9vu/0jnqLymB3eYb8+qNFW6VGz2AxGah/rG2zCMZcQboSuXoFmk0OHGm1AEBIf9eBNZaRaodbzU5wcGiVMtzTL67WLi6DXMJQkZ8xpH8LxcX4M/BOB2MIrrlYUVmCZ7bVhnxNKP56nR6IRAwmuztinP9tZz1WLZiC+946AIfbF5xVvqUviVZIRbjr8ilo6rFjcoYG7b1OisEoSKqkmDE2EcArAHIA+AA8zzl/ijGWDmA9gCIAJwEs45x3J2qc/bWaHdAppZCKKQnM1CqQb1Di46Nt+HZZTqKHQxCdTQhyRtmneLSL/ZQyCdZs3h0627I5fjPVtFVqdAnFZIFBFZJc5uiEa37VMgl+elEx/rL9BEQMIbsKCt0BmRChdtigkuLz2g48/1ld2B2Qv91+HoAzJ7wUF+NTpDsdgc8Df8/VKzA1W4uVC0rg48Abu053S2ns8S8YXbWgRPBcVqcHP7vE/7UXf3wO2i0OiJgId/f9Agf44/WJD4/hyWWzseiZLygGoySpkmIAHgC/5JzvZoxpAexijH0I4McAtnLO/8AY+zWAXwO4N4HjDGrstlOxez/zio14c3cjJcVJZLSrj6fnqLF2cTke3Hiw34xaOabnDG22ebSL/Tp6nTCoZLh2bn5wod8buxrREafuFyc6hMc/9Y75tKPdCPWPyYHJZaFRibuvmIpVC0qD22v370t84zkFeHDRWbA6vfjjh0cBnO4/XGBQDXgd4L4rp6HD6oKP+zcFMaplsDjdEXfN67C4hpTwRmMRK0k9A+90bNrXhDWLy7B6YzXe2NWIVQtK8drXDbihoiDkLsTKylKsr2rAjecU4JUd9QCADVWnj180Mw9iEXBWrg4enw939ou9Bxadhc5ee4RdHs0Ug1GUVEkx57wZQHPf3y2MscMA8gAsAXBJ32EvA/gESZMU25A5zhfZ9XfeJCP+8VUDLA43tAppoodDouBwixXPflITUtv27Cc1mJShwjmTzvwL4Wi3Gs3UynHz+YUhCdKqBaVx6z5R32UVTMobuqyUFEfBwORy0cw83PP6fhhUMjy6dBZq2yzw+oBXd9aj2eTAU1tr8PwPz8YrO2pD+g8/va0GcwsMITHVYnbA7vYFZ4QVUhHuvGwKVDJJxF3z1HLxkBJeobg2qGRotzipnGIME4kYzsrVYvlFxfBxYEq2Fq9sr8NtFxZDLhGhOFODh5fMwE9eDa2JX7etBi/dcg7uXL8vWPLTbHLgvQPNWHFpKR5452DI9c2gkgXLfx7afAiPXz9LMF69oSFIMThKSZUU98cYKwIwB8CXALL7EmZwzpsZY4L7CTPGlgNYDgAFBQVxGec3XTYYqZ44SKOQ4KwJOnx4qBXXzs1P9HASLhExGW0tZqdgr9fWIdYUj3axHwMLJsSA/w3mqa01+Ott5w3xXzA6eoVUMCnXpegvfckWk0I1mg63D80mB461WkJqNAH/10x2T18dZ2i3m4G/aOkUUjz50bGQ2Hnyo2P4663nojRbGzYbfdflU6CSiSP+ItdqPn3+gXEd6Jjxoxe/olvZw5RsMXkmzSZHsF/2isqSsFhcUVkiGD8mmwfdNlfI45dMywomxIHjntpaE9ykI/AY5zwsXh++phxPb6sJnoticPSSshCWMaYB8AaAX3DOzUN9Huf8ec55Bee8IjMzM3YD7KehywYj9UsNcd4kI97a05ToYSSFRMRktOX09Sn++aUlWFHp/yg0KodcUzzarUZNdpfwG4zdFeEZ0SURCSflkhR9k0m2mAwkl/0F2rIFajJXVJYgV68Ifk0iYoIxOfAXLavTE3GhU1muCudOMuCFH1Xg6ZvmYN2NczA5Uw3OgW6bC898bw5m5ulCxuT28mDbt4FxfX1Fflic3LVhL072tXgjkSVbTJ5JIGYjxWjgLkR//uPlwa2fc/UK3LtwKkqztIIxGrgrlatXYOWCEjAwnJWrw8u3noMnls3Cr749FTqlFDeeUxD8v/Cbq6ZTDI5S0s0UM8ak8CfEf+Ocv9n3cCtjLLdvljgXQFviRhjqm247pmRrz3zgODK3wIAX/30CJpsbelVqzqaR08pytPj5JaVhNcVlOUOL+9Eu9jNqZBGb48dDe2+EHfXiVNM81gnVaD62dCYau+2CNZnfO7cQb+1pwM8vLQ22W4tUU1yQrhaMnTyDCgdPWdDYbQuZeVuzuAxrNx9Cfac92G8WX9bjWFsvVlaW4oF3DuDFH5+L4kxN2G30PL1yVGVCJHUUGdV45ntzUNPaKxijM/L1gh1WzsrVo63XibsuK4VWKcOaTdW4fX6xYIyK2OmZ34F3qbQKCQqMKqz4+x5MydJg+UWT8dDmQ7h9fjHF4CglVVLM/FuhvQDgMOf8iX5f2gjgRwD+0PfnOwkYnqDGLhsttBtAKRNjRr4e7x9qwbKKiYkeDhmlmg5rMCEGAqv0D2JqjgazJg4tMR3NYj+fF7jr8il44sNjIbe5fXHau0MuFQu+acml4vgMYIwT+qXJ4nDjnr4tcoHTNZmv3nouGAPmFafjhud3hnz9/rcPhtUUT8oQbv/m9HjR3GOD3e3F7fOLAfjrxFdvrMajS2fhWF+7t+c+rcXaJeX46kR3sKa5f4Ix8Db6aHuCk9QgEjFMMmqw4u97wmJ0/fJ5mJGXBgCCEwGVU7ORqZEH4/eNXY1YWVmKddtOJ76/++4MaOUSVFxnwPJXd4XdpVp+UTGydQrcdmExCgxKmOwurLi0BJkaueCOjxSDQ5dUSTGAbwH4IYADjLG9fY/dB38yvIExdhuABgDXJ2Z4odxe/2zReN/NTkhFYTr+tb+ZkuIxIFKv1xaTA7Pi8OPtsLkgF4uCM3IiBsjFInTa4lM+YVBJw2r5/Ath6C5ItAz8pWnLwWbBmOuyuvDt8lzsON4xpBmxSHcpPjnWCrVCiic+Cu1u8erOetS2+euYA4+Z7O5gbefABKN/XfEbuxpx52VTgjXMtOvY2NZmEb4u2t3e4F0woYkAkYjB5vIGn9tscuDVnfX+BDddiSKjGmcXGPDB4VZ0WYX7GEtEInRbXXjhizoYVDLcfH4hnvm4NuT69MqOenTbXPj9d2dAxCC4DToJl1RJMef8CwCRfmoL4jmWoWjuccCgkkFCPYrDzClIw1/+fQJmhztlFySNFaPdYCA3Qq/XHH18Zh/SlFI8/2ktbr6gOGRHu18tnB6X15+SqUV7rwPP//BsdFndSFdL4eU+TMmksqnhGmosninmRrJ4k/fbAVqnkOE//+a/9Xz7RZNhd3qgVkjw80uK0Wz2/7IVmPl75ZZzMTNPh2NtvWFJbuA2+v5GEyQiEUqzNFh34xx021xQyyRQy+m9YawYGLuBbcaHE4Mejw/VzSZ4fLyvX/bprjZiBjjdXmRp5WjotsGgkkIsYoKvMXuiHg+8czA4U3zKZA/pVvHU1hr83w/PhtfLcazNgh+9+BXuXTidFtwNQVIlxammocuGbB3dlhCikklwVq4OHx9pw5LZeYkezrgVjQ0GpmdrsXZJeUj95tol5ZierTvzk6PA4Rbe0c7pic+Ods0WO1pMruB2rYFa02aLHYVGqtMbquHE4plibqg7NUZ6Tb1CgilZGtx0bmFIXK1dXI5DTaeXrDjcPnxxvAM/OL8I500yYKIhPIl3eXhIy7f+s3SrFpSiMF2NogyKk1QWKY6e+d6cYAnFme4MeDw+vL2vCfe/fRAGlQz3f2c6LA5PWDeJ6mYz7v6nPyYrCvVhPeJXX10Gj9eHGyoKQkouAnc6AonxrvpurNt6+o7HI1sOY1qOlmqLz4B+jR2Fhi5b3HqlpqK5BQa8e6A50cMY1yL1Wx3OauRGkx3PfuxvEbSisqSvVVANGk32WA07hEIqwXOf1QZf//b5xXjus1rIJfH5nb7V5AwmxEDfjnqbqtFqooV2wzGcWDxTzAXKIt5dOR+vLT8P766cL5hcR3pNqUSEFZWlaDbbcft8/2sYVDI8uPEgvnv26ZqgQB/YB985iB6bO+z8Qhu7PLW1BvddNR23zy/Ga183DLl1IUlekeJoklFzxhgMqG424f63DwbbDRrV8rBOEfe/fRDHWk9vcX5ecWawR3zg/8Fzn9ZCKRPD4fGGxO66bTXBNqj9+xcH7ngsmpmH+k4rfD5/B5W69l7sON6BuvbeYEcVQjPFo3Kiw0pJ8SDOLjTg7181wOH2QkGLkhJitBtnBM4h1Kc4Xiuae2xuwVmRHrs75q8NAJ1W4ZZwndb41DSPFcOJxaHE3FAWb0Z6TavLA5PdHTLDG5hpc7j8dyBCHutLZPrX0Pt8HIebzYLnP9pqwZ8/r8PKylK4vd6hfYNI0ooUR+29DswrzhjSdXDg2gyzXbheuH9+yhjC/h/k6hX4psshGLuMhcZt//OKRcCeb3rg5RwuD6ftySOgmeJRONHRixwqn4hIp5Si0KjC9uMdiR7KuBWpB+xwViNH4xyjkaaSBhNi4PTMR1qcatUDLeH6U0hFMNIC22EZThxFK+YinUctk4RtmLBuWw2ur8hHoVGNlQv8s3KB29EKqQiZmtAJkJOdVjR22wTPz/npc2rl/jil2bnUFY14DNTJB6jkEsFzDsxLBx5zfUV+WDegQOzOnZiGx5fOwvqqhmD3icA5zpuUjnSVFA2dtkHv2Iz3OKWkeBROdlJN8ZnMLTBgy8HWRA9j3BrtxhnROsdoWBzCMyoWZ3xmip0eD1YvKgv5969eVAZXnGqax4rhxFG0Yi7Sedwen2BMFWdo0NJjRa5OiRe+qAsmxKuvLkOXzRWSIHRanVDLJcHNGALnv/OyKXhzd2PwnDa3N1iTetW6z3HTn77EVes+x5bqlnGXcKSqaMRjWa4OD19THjzHy9vrsHZxecg51ywuQ2mWJvjYpn1NWH116LWnOEMjGLsFBhVq2yzosjrxs4tLQp6zsrIU9711ACLGkK6WR7xjQ3FK5RMjxjlHY7dtyLt6jVdnFxqwdlM1vL4ZENOtmbgb7cYZ0TrHaOiVUsEV2Po4zRTLJRI891k1bruwGIz5uxg891ktHls6Ky6vP1YMJ46GG3ORulpEOs/JTqtgTOkUEvzm7SPI1Mjw6NJZcLg8yNYr8Mf3j+JYWy/eXTk/eKtcJhbhd+8ehkElC8aGiAE+zkN6xGbrFBFrUqf1Ox9JXtG4BkokIlwzKw+lWRq0mBzI0SswNVOLqdkaNJnsyFDLka2XI0+vQkG6CluPtMHrA9web7Ad5YwJOpzoEI7d9l4nZubrUd1kwuu7GvHUjXPg9vhgdXrQYXXC5eHosLrQaXVF7JoRKU6n3jEfk7PGR5xSUjxCLWYHlFIxVDL6Fg4mW6eATiXF3m+6cXZheqKHMy6NZuOMaJ5jpDw+L9YsLsPqjae7P6xZXAYPj0+tZrvFKVjf2m6hBVTDNZw4GuqxHo8P/zrYjHvf2C9YIyl0niKjGr//7gzc99aBkJX/HRYHum0uNJsc+NXr+7CyshRPflQTTHL71zQHes02mxwhsbFyQQmA0NnEL090Cs7OtZppp7FUEY1roEQiwqyJhpDa9FkFBsyCIeS4GXlpaOpx4K4Ne3H7/GI8s+30BjFCm308sOgseDw+PPLeESwsz4XJ4UZHrxMPbT4UUncsEgEvb68Pe/7vvztj0Dg93GLGpIz4TYQkEmV0I1TXbkVemjLRw0gJcyca8N7BFkqKyYhIRRL8zye1ITO1//NJLZ64fnZcXj9TK8cVZ2Xg+/MmobuvT/Ffd56gRbZJwOfj2F7XGUyIgaHNwopEDGcX+usvrS4PVDIJOnsd2Lz/FB5dOgsMQE2bJVhTDETeuGNgr9l5xemYkj0HOToFZk7QQyRiUMkkKDQqsWhmHlhfXrFpXxNUMuEFyIF+ts0mB3L1SpTl6iCRULXjeNF/Zrrb5uq7AwFMzdZCJmFgDHhs6SzYXB50WV0oy9Vh1fo9qO+0o73Xhf+6anqw1SBwuu748aWz0G1zBTcLCdzdKDQqUXWyC1anB6uvnt63GNU/6bDzeDvAgU+OtaHIqEaBQYWGbtuI+94nO0qKR6iuvTdumxekurMLDXju0+P4zVXTwdjY+c9D4sPscAvO1Jod8akpzk0T47LpufiPvu1W/f1sy5CbRh1VEu1kpxVV9V3D7rDi83FUn7Lg7pAexWdhWYW/b3Fgl7Duvl0ThWpICwwqPLZ0Jhq77SG9ZtVyCV7890l021x4+JpyXDMrDz7uw08vKsGazf16XS8qA+fhtZr9+9n2n8W+ZlYeJcbjiEjEUGRU40iLJdhpoqJQj/+8pCTkrtnaxeVw+Ty48ZwCPLXVf1ejts0i+H/C7fXhoSXleOCdg3j2Y38P4/uunIaqkz0hOzGuWlCKN3Y1QiZh+M9LSoL/TwqNStxRWRoSm2OtcwUlxSN0vL2XFtkN0aQMNRweH2raejElm3YBI8OjkkkEa+DiVbrU3OPFgxtD+xQ/uLEar9x6LiYazvBkElOtZgd8HMPeWUyodrKx53Sbq2aTA6/sqMfyi4pRmqVFTZsFZ+VqQ974G7ptaOy247WvG4KzbgDwty/rce3cfDz7cS3uf/sgSrM0EDEW7LUdOO65z2qx7sY5YWPr3882MLbAeWZRwI0rA+P0vOLMYEIMBK5FB/H40ll4Zcfp2d8p2dqw/xOFRiUyNHIcbjbjb7edB7vbg16HF2qFBD95pSqs1/ZjS2ehvtMa8nqLZuaFxeZYq42npHiEatp6cX5xRqKHkRIYY6goNOD96hZKismwmezusBq4lZWlMMVpprjN4hTuUUo1xQmXrVNg076msPh45LqZg3YGEOo76+troxbQbHJg3dZaPHLdDKzbWosLJhtDdqZrNTvAGAR7aIv6JnT9dcNOGNRSweNc3tAxBF5XKN4G9kkmY9/AOGUMgrFhdXlCattz9YqQmvlCoxI/vagEP3m1Khh/Dy0ph93lQUeLcB/2mjYLJhpUQ3r9ePWsjwdKikeotq0X159NV6ihqig04PXdjbijsjTRQyEpJl0txd5vOvF/PzwbPVY30tRS/G3nCZxTFJ9ZsyytXHAmkmqKE6/IqMa9C6fjkS2HcduFxRCLgIrCdFxQbBz0dm6gHrj/z1TcV1u5aGYe5BIRJmWo0dxjQ45egUKjMmzmOVunQFG6Gr8YMOO8blsNHu3rTKKQipCulkEqZth2pAWPLp0Fu9MDlVyCl7fX4VslRgCh3TOMaplgvGX09UmO1GmDjB2Bn7GIMcFYGPh5rl6BFZUl+OxoG66ckYtJRjUUMhFeuuUcNPc4YNTIsLyv/Avwx+kD7/hnmA1q4evbtBwtJGLRkF4/Xj3r44GS4hGwONww2d30pjgMU3N0aO5x4JsuGyamqxI9HJJCpmWrcdn0CQNqessxPSc+fZLtLo/gTLXDTX2KEy24IClHO6xWWYG+s/139ZpbkIa8NFVwY4TAz/nBdw7ijspSFBhCr1sFBhUONJkEZ85O9diDXVJkEgaPl+O6uQXBxU+BmmIf58HesIGxPPLdMqxeVBZWfywWIezYsVjTOd71/xkbVDKsWlAarFnfebwdaxeXBcu5AjH24hcnsONEF/7fd2egy+YK/qIW6LG9v1E4To+0Wvy9kAfE28rKUjyy5Qi+f14h/mvhNPy/LUfgcPuwaV8THr6mPKymOF496+OBkuIRON5uRb5BBREtGhsysYjhnEkG/OtAM3568eRED4ekkMMt1rAdnB7ceBCTMs7FOZNiP0OhlEmwvqohpPvF+qoGVBRRn+JkMJJWWUJ9Z7ttLvzHX3eHzfredmEx7n/7IOYWGEJeo6Hbhrr2XsGZs+m5Wtx2YTH+55Na/PH62eCcC9YUP750VljdqEohw8vb6/yzyi4PlDIJXtlehwcWlVG/43Gg/884UNv++NJZONZmQUmWFn/84EhYJ55FM/Pw8bEOnOi0BuviAX98rNlUjceWzhKMU84Bl4fD4nDhLz8+BzvrOuH1Idh15YkPj2HFpSXBuzALpmWhLFePuQWGhPSsjwdKikfgWKuF2rGNQEVhOjbuO0VJMRmWFrNwTW+rOT41vT12t2A9qMken5pmEhsDk+nN+08JxlmgjnJg3WSr2YENVcI9Yw83m4P1nR29TohFTDCGeuxueHw85HWbemyonJYTMqu8srIUbq9PsBZ6rNV0jncDf8bNJgeOtFrwzLZarKgsEezEE/hFa2BdPOD/vKnHJni3a8vBZvxwXiGe+KgmpB9y/+c6PL7g610w2QiJRJSwnvXxQEnxCBw6ZaakeATKJujxv58cR0OnDQVGKqEgQ5OjE655i9dukmlK6el60L6eti9vr0NF0fS4vD6Jj1yBOuPAbJpQ3WS2TiHY89XqcGNKlhZP3zQHL2+vQ75BCZvLCx/neGzpLDT12NDr9GJ9VQMeXzoLRk1ofPc6vdh5vD2s/nhheU5wTA63D7l6Ba6dmw+xCFBKJXA4PDjYYkaL2YFcnQIzJughi9AHmSQnn49DJRNj5YIS+Djwxq5GNJscEDOEbNs8MEbVMjHuvmIKCo1qrFpQgg1VjSH9tS0OL97c3Ric8Z2eo8PJTit+ctFk3NOvn7FQbJVmaZGr98d6tlaBkx29aDU7YXV5UJiuHnObelBSPALVp0y4bHp2ooeRcsQihvMmpeOdfU204I4M2YxcPdYuLg+p9Vy7uBwzcvVxeX2llGFZRWg96NrFZVBKx84bAQFmTNBj7ZJyPPhOaE3x+qoGwbrJAoMKv/vuDPzmrQPBnq+rFpRCIRXjN28fRLfNhUevm4n6Tht++c/QWd9N+5pwQ0UBfJyH1TfvPN6OZecUhsTbw9eUo8CggkjE8MSy2Xhky+GQmeftte1YVlEY+n9kSTmumTmBEuMUIVQvHoi/Gfl6/PH62Xj0/cNhM773f2d6Xxu1oyF9hl/ZUY9umwurry7Dc5/WotnkwAtf1OGP18+G2+dvu3b7/OJgQhzYKW99VUPYXY1VC0phVEtx8JQprC/3WKtpZ0LNw8eCiooKXlVVFfXzcs4xe+2H+MO1M5CmkkX9/GPd0RYLXt5xEtt+eXGqbOQRtUHGKibHA4fDgwPNJrSancjWyTEjVw+FIj6/0391ohM3/+WrsNmZV249F+dOMsZlDANQTMaIy+XF/lMmtFkcyNTIAQYY1XLBusm69l58dKgFXg6kq2TI1MpR22bBi9tP74K3ckFJSI0n4I+d2y4sxgtf1OHVW8/FOZOMwW4DbRYHlFIxbnh+Z9hz3u2rG/b5OA409YQcs+6mOSE7mAWe89fbzkNFUVx2EqWYHKW69l5cte7zsJ/h+uXzMCMvDQBwosOKZpMNMrEYVpcHEw0q2FweLBOIl8eXzkKWVo5svRweL9De668Btrk8WPrcDjjcPqyoLMGfPz8dn7l6BX5z1fTgZh39z/fE9bNwpNUiGM/vJmdN+4hikmaKh6nZ5ICIAXqlNNFDSUlTsjVwur3Y32jCrIlpiR4OSREKhQTnJCYBRWuCa5pJ/Mhk4iEnka1mB8xOb7AOc0VlSVhNZqQaz0CdcnuvP4b61zfvON4xaN2wSMRgc3lDjrE7PRFi1DG0fzhJuEj14na3N/gL2eQsDSZnhSafkeLlSKsFGVoZCo2a4HMBYMvB5rDZ4cCscLfNBbvbK3g+i9MTMZ7HUk07JcXDdKDJhMmZmlSZ5Uw6jDFcWJqB175uoKSYpIRsnRxXnJWB78+bhG6rG+lqKf6680TcappJcsrWKYK1noFEIdDnOPD2oJGJw+o09QoxZk00wKiSIkMjh8fjC9m+OVunQEWhHjdfUBxSU9y/pjlbpwh5rdw04Xpo2nU1dQj1zlZIRVBKxfD5eMTyhEjPm5mnh0QkwsdHWqGWS6CSiWF3e2FUy0Jqll/d6d+5sSRTDTBRMKYNKhmunZsPxvw9vCfoFVDLJSg0KlHfaQ8do2TwMaZSb21Kiodp/zc9KKRFYqNy8ZQs/PrN/bj/O2dBLacQJMltSo5wn+QpceqTTJLTBK0CkzM1wT6ynx1tw08vLsGaTaf7vT563Uz88fpZePT9I7ihoiBYr/mjF78KqRe+ZlZeMDGeoFVgWUXhgBr2ckzolxQXGFS4o7I02C+20KgMr7tfUo6ZE+JTd09GT6h39srKUqx8bQ/uXTg9Yt2u0PPuu3IaTnRYsfK1PcHH7rp8CuRiUbDncP+a5ZJMNZwejgfe2QeDSob7rpwGq8sbUjusumwK/v5VPf7zkhL8zye1qO+0B3toP/Svatx64WTBMaZab22qKR6m7/1pJ86fbERFYVzqtMas//7oGBbNmoAfzitM9FDOhGrlxjmqKSZCqk524Zf/3IsbzylAvkEFnVIS/MUpQCEV4fWfng+by4ub//JVsJZYqG501kRD8Lw/eOHLQeuDhepPC41KPL50FtosTmTrFJgZ3+4TFJNREKgX33qkDV4f8OZufxeJM9Xt+nwcJzqsqOvoxYEmE2RiEZ75uDYshpZfVIx1W2tDHnvplnMgEYlCYu7uK6YIPj8Qv48unYVjrRZwDmze34RFM/Pwwhd1gmOMVCsdhzrkEcWk6MyHkACfj2N/owmlWdpEDyXlXVGWgxc+r4PPNzZ/KSNjB9UUEyEtZgfqO+14ZMtR3PGPPWjpEa4Jbeq2B2MoUEs88JgWkyPkvGeqDxaqP63vtMPLOb4zcwIqitKp60QKCtSLr9tai2c/rg0u2AzU7Q72vMlZGmjkEqzbWguHxycYQwPfbgPHdPSGXuMiPT8Qv8f6+iY/+7F/xrh/L++BBuutnYzo3vUw1Lb3QqeQ0CK7KJieo4VULMIHh1qwsDw30cMhJKLsBPdJJslpYF9jlVwiGCc5egVcHt+gfWZz9QrUtff6ay61keLNXz7h72Ub/lqFRiWUUjF2HO9I+rpNElmkGuGBfbIHe27gOQPPMTAcAudVSoVjN1LP7v4FBv0fV0rF2NPQBY+Xo83iRI5OgZwI8TyUf08i0EzxMFSd7EZpNs0SRwNjDItnT8ATHx6j2WKS1KbkqLF2cXnImw3VFJNAX+NAXLy8vS4sTh6+phxluXqU5+iwdnE5Nu1rwsrK0rBj2nqduGrd57jpT1/ikS2HBeOtPEcXrM9c+drukPMUGpW4o7IUNzy/Ezf96Utcte5zbKluoWtrCgrUCPf/+Qv1yR7suUJxdtflU2BUyQTPW5arw8PXnI65TfuawmJwZWUpNu9vwkNLyrF5f1PY4w9fU45nPz6GI829+OFfvsLP/74H33/hS1Q19ODpm+aM6N+TCFRTPAz/+bddyDeocOnUrKied7zinGPN5kP4yfxiLD07P9HDiYRq5ca5r0504tEth/3dAFweKGUSvLK9Dr9aOJ1qise5QF/jwKr6smwtjnX0osXkQI5egbJcPSQSEerae/HcJ8dwzdwCWBxupCllaDM7kJumhFEtw5UDai4rCvW4Y8EUtJgcwXh7dOlsAAjWZ/bfdeyi0kzBOuQ49o+lmIyi/n2rs7TDm/UPPLfL6oRUJEKXzQWNXAJlX/cJmVgEm8sbdjfB4/GhutkUjF2DWop/7W9BplaOTK0cPVYXTpkcuGpGDjxeoKHLCrVMApfXC61cipXr9+CXV0wT7Jf9t9vPg0ElG9G/ZxSoT3Escc7xZV0Xvr0oJ9FDGTMYY/jBeQX4w3uHcfn0bOhVVJZCkk+r2YmqehOq6veEPU7GN6G+xrMmGjBrYuhxrWYHNuxqxoZdzSGPv7b8PMEa4qp6E74+2R3S97jN4gDv1ye22eTAsx/7v35Wrm7M948dT/r3rY7XcyUSUUjs7jjegUe2HA07bk5BGuYVZ4T0S95xvAP1nfaI/bJbTA6cXZieErFISfEQHWmxQCYRIYv6PkZVSZYWFUXpuP/tA1h30xzq/0ySDtUUk6GK1I91YF9hwH+LOlBXGal+s//ngWOFzpOrV6ZU3SaJvmj2AvbXrYtD+hk3mxwR69YDtcyR6upTqV82JcVD9MnRNtpsIkZuPGciVm+sxqs76nHzBUWJHg4hIab31RSH9IBdXI7pVFNM+hmsH+vAvsKBWuICgwoiEQvrM7t2STme/bgGAEKOBSB4nunZ2rBzJHPdJomuaPYCFjrXyspSbDvSghvPLQxuL97/NQK1zH/54jhWLyrDms2ne3WvWVwGl9cz6OYeyYRqiofou8/+G1eUZWN2Xy9JEl2tZgfWbj6EtUvKsGjmhEQPpz+qlRvnvj7Ribtf3xecnQv05nx86axEbT1NMZmEBuvHCmDQXq39a0iVUjEe2lyN84ozQ+LtxR+fO+h5iozqEdehRgHFZAJFsxdwpHP99bbzBq1bD8Rwu8UOH2dosziRppLipS9OYMeJrnjWtwdQTXGstJgcqG3vxV0TpiR6KGNWtk6BX317Kla/U412ixM/vqCISilIUmgxO1HfaQ/WbwZQTTHpb7B+rP1rgQd+rThTE1IHuuN4R18Nuynk+KGcZ6R1qCS1DRZ7w42HSOca2Mt44GsEYrjV7MBNf/oy7LypUt+eVC3ZGGN/YYy1McYO9nssnTH2IWOspu/PuE/Vbt5/CmcXGiARJ9W3a8wpNKrxwKKz8PL2k/iPV3eho5eSDpJ4OX01xf1RTTEJ8Pk46tp7IWJMME6ytIqQ/rEDvzbQYMcO5zxk/IhmXEQ6V65+aK+R6jGabFneSwAWDnjs1wC2cs5LAWzt+zxuOOdY//U3mF+SEc+XHbeydQqsWVwOhVSMy5/4FC9vPwGP13fmJxISI4Ees0J9Y8n4Fqi/vGrd5/jF+r1YtSC0N2ygrnc4vWcHO3Y0PWzJ2BXNuCgwqEJ6Fp+uW9cN6TVSPUaTrqaYMVYEYDPnvLzv86MALuGcNzPGcgF8wjmfeqbzRKsu6cu6Tty1YR8eXToTIrqdH1f1nVb87csGWF0ePLjoLFySmP7QVCs3ztW19+IP71Xj+/MmocfmRppKir/tPIFfX1mWqNuBFJNJYmD9Za5egesr8jFnYhoK+5LYQF3vcHrPDnbsaHrYxhDFZIJFKy7q2ntxy0tfha2hePHH5w65bj1JYnTM1hRnc86bAaAvMY6YGTHGlgNYDgAFBQVRefH//eQ4vl2WTQlxAhQa1fivK6dhV303fvPWQRRnqLF6cRlKspK/LikgFjFJ4qvV7MAHhzrwwaGOkMdvvTA1auQGopiMnoH1l80mB9ZtrcVry88Li43h9I8d7NjR9LBNVhSToxetuGg1OwTXUAynbj2VYzTZyidGhXP+POe8gnNekZmZOerz7arvQnWzGRdPoR3sEoUxhoqidPzh2hkoylDjuv/djoc3H4LF4U700IYk2jFJ4i/Va+QGopiMnrEWG4lCMZk8xntMp0JS3NpXNoG+P9vi8aIujw+/eesgllVMhEySCt+msU0iFuGqGbn4w7UzcLy9F5WPf4o3dzfC50uu8h8y9qR6jRyJHYoNMtaM95hOhfKJjQB+BOAPfX++E48X/d2/DkEjl+BbkxPSh5REkKaSYflFk1HTasH/fnIcL/77BO7/zlk4r5h+TiQ2RCKGhWU5mLZyfqJr5EiSodggY814j+mkSooZY/8AcAmADMZYI4DV8CfDGxhjtwFoAHB9LMfAOceTHx7DtiNteHBRGfXKTVKl2Vr8dnEZth/vxMrX9qA4Q4MVlSW4YLKRfmYk6lK5Ro7EFsUGGWvGc0wnVVLMOb8pwpcWxOP1a1oteGjzIbSYHbjvqunQKJLq20MGEDGGC0syMG9SOj6v6cB/vXkAYsZw3dl5WFieg8mZGkqQCSGEEDIk4y7rc3q8aLc4YXN50W114ZTJjiPNFnxR24H6Thu+MyMXt3xrEiQiRv1xU8j80gxcWJqBY60W7DjeiRe+OAEfB2bl6zE9V4cCowrZWgUMahm0CgmUUjFkEhEkIga1XAKFVJzofwIhhBBCEijp+hRHC2OsHUD9wMeNV66aqJl5ecR2EtznTb5vCOcMjCXfuEYqPv8exkRDS3S518MbHr9md4Qvd3DOB24oM8IBCcdkFGUA6DjjUYmV7GNMhfEdSYKYTPbvkxAac+yk0nVyJFLl5zBU4+HfM6KYHLNJ8VjCGKvinFckehzRMtb+PckiFb6vyT5GGl9qjWM4aMxkpMbaz4H+PZFRrzFCCCGEEDLuUVJMCCGEEELGPUqKU8PziR5AlI21f0+ySIXva7KPkcY3NMkyjuGgMZORGms/B/r3REA1xYQQQgghZNyjmWJCCCGEEDLuUVJMCCGEEELGvTGbFC9cuJADoA/6GO1H1FBM0keUPqKGYpI+ovQRNRST9BGljxEZs0lxR8dY6ktNxgKKSZJsKCZJsqGYJIk0ZpNiQgghhBBChoqSYkIIIYQQMu5RUkwIIYQQQsY9SaIHMFSMsTsB3A5/AfUBALdwzh2JGIvPx3Gy04pWswMT0hRoMzvRY3dDr5Si3eJEUYYKNqcXLWYnsnVyaORinOiwIVMrh9vrgVomRa/TC4vDg0ytDC6PFwqpBA63Fya7/zGVTIwemxtWpxc6lRQujxcT9CpMylBDJGJh41DJJHB5vTCq5Sgynj6GEELI8LlcXhw4ZUKz2YEMjQw5OgUK0kOvrRa7Ayc77Oh1edHZ60KOXg6F1H+9z9bJMSVHDZ1cjpOdVpjtTri8QLfNDYNKil6HG0aNAmW5OgBAdbMJzSYHcvVKFGYocKzFita+95BpOWrolIrguPafMqHb5oJeKYXJ5kaWToHSDBWqWyxoMTuRo5NjRq4eCkXKvMUTDP6eDgAnOqzotjng8zF02/1xZHN5oJFLwADY3D7YXB4Y1TJYnR4opRJYnG5oFFLYXR6oZBKY7W6o5GLIxWJ0Wl1IV0shETHYXF70Or3QKiTwci/UUiksTg8cbi/SVTJ0293I1MihVTB09HrRZvHHpohxGFQKFKar0NBtg8nugsfL0Wp2IksnR45ejgk6/9dazQ5k6xRJnaOkxP8YxlgegJUAzuKc2xljGwDcCOCleI/F5+PYUt2CuzbsxfmT0rFwRi7+55Na3FBRgHXbajAlS4ObzivEmk3VcLh9UEhFWLO4DFsONKOu04pfLJiCFrMFT22tCX794SXlcHttWLP5cPCxtYvL8OwntajvtEMhFWFlZSnuf/sg7l04HQvLcgAgOI7Ac1ZWlmJ9VUPwmGQNOkIISWYulxdv7z+FB985GLy+rr66DPVdVswvyYJIxGCxO7C9rhtdVnfI9X7t4jK8d6AZO0504dHrZkIkYti49xssmB76XhG8/l9TDrlEhHte3w+H24dlZ+eioigTD2482O+c5VhYngmFWIq395/Csx/XhJynolCPZRWFYc9ZPCOXEuMU0T+3GPie/sCis+B0c7yztyFiHK2+ugzPferPGQqNSvz04hKs2bT79NcXleG5z6qDOcWqBaVQScV4/rNaXF9RgNUbT8fwb68ug8tjw+/fOxJ87M7LpuD/Pq3FsooCPLgxNL/Zevg4riibgA+qT+HSqblYs/n01x9aUg6lzIy7/7kv+NgTy2YnbY6SSuUTEgBKxpgEgArAqUQM4mSnNRi0P75wElZvrMaimXnB4Lz9osnBCyQAONw+rN5YjR9fOAmLZubhRKc1mBAHvn7/OwfRbHaGPPZg33kDn6/bVoNFM/Nw14a9ONlpDRlHpGMIIYQM3/5TpmBCDPivr2s2VcNi9wavrYdbrHB7eNj1/sG+673D7UNtey/u/uc+fH9e+HtF4Pj73z6Imrbe4GPXzC0IJrenz3kQR1qswXENPM/NFxQLPudAsyl+3zQyKoO9p+9vNOGX/9w7aByt2XQ6Z1g0My8sLtdsDs0pntpag06bCzdfUBxMiANf++2manRYXSGPPfnRsb44C89vvj9vEu5/+yC+P29SMCEOfP2Bdw7iWKsl5LFkzlFSIinmnDcBeBxAA4BmACbO+QcDj2OMLWeMVTHGqtrb22MyllazI/jD7ba64XD7wBiCj9mdnuDfAxxuH3psbjAG+DgEv+4b0FUvcN6BnzvcPrRZHCHjiHQMSbx4xGQ8eLw+/HLDXiz93+1otzgTPRwyCmMlJmOpJcL11eryBK+trWYnrINc74HT13uh94r+x/e//ndYnILHtJqdwXENPE+k951Wc2r8X6WYxKDv6UONo0DOcKavBz738cixI5ST2F2R473/+IZyrmTNUVIiKWaMGQAsATAJwAQAasbYDwYexzl/nnNewTmvyMzMjMlYsnUKKKT+b1u6Whr8e+BPlVwS/HuAQipCmkoKABAzCH594F0EhVQEzsM/V0hFyNIqQsYR6RiSePGIyXh4a08Tqk+ZYdTI8Pt3DyV6OGQUxkpMxlJuhOurWiYJXluzdXKoFUO73gu9V/Q/vv/1P1MrFzwmWycPGVf/YyK972Tr5MP9pycExSQGfU8fahwNzBnO9HURixw7QjmJShY53geO70znStYcJSWSYgCXATjBOW/nnLsBvAnggkQMpMioxhPLZkMhFeHFL05gzeIybNrXhJWVpVBIRfjTZ8ex+uqykMBds7gML31xApv2NaHIqMaqBaUhX394STlydfKQx9YuLsPm/U3Bz1dWlmLz/iY8sWw2iozqkHFEOoaQaPnLv09gyewJWFYxER9Ut8Jkdyd6SITEzIwJeqxdUh5yfV19dRm0SnHw2jo9Rw2pmIVd79f2Xe8VUhEmZ2rw+PWz8Ned4e8VgeMfvqYcpVma4GNv7W7A2sXlA85Zjmk56uC4Bp7n5e11gs+ZkauP3zeNjMpg7+kz8vX44/WzB42j1Vefzhk27WsKi8vVi0JzilULSmFUyfDy9jqsWRx67G+vLkOGWhby2J2XTemLs/D85m87T+Dha8rx150nsHpR6NcfWlKOKdnakMeSOUdhnI94N7y4YYydB+AvAM4BYId/gV0V5/zpSM+pqKjgVVVVMRlPYIVom8WBXH1f9wmHG3qFFO29ThSmq2B3CXSf0Mjh8XmhlEpgdfm7T2RoZHD7vFBI/N0nzA7/ylG1/HT3Cb1SCpfXi9xBu0+I4fb6kE7dJ6Itat/IWMZkLJ3qsWPhf3+G//3+2RCJGP77o2NYenY+rq+YmOihjVfjPibjIdB9osXsgHEo3SesLmRr5VDKhtN9Qo6yvsS1utmEFpMDOXoFCjOUw+w+IUdphhrVLZbgc+LcfYJiMgoGe08HQrtP9NjdSFP5u0qoZRIw5u8+YXd5YFDJYHN5oJBK0Ot0QyOXwuH2QNnXfUIpE0MhEaPT5oJBKYVU7O8+YXV6oVFI4PP5oJJJ+rpP+GBQSdEzoPtEu8XfXULMONKEuk9YnMjSyJGTdrr7RJvFgSxt3LpPjOgFUiIpBgDG2BoANwDwANgD4HbOecSCqfH8H4tE1bi/2P/jqwa8e6AZ/3lJCQBg65FWtJmdWHfTnASPbNwa9zFJkg7FJEk2I4rJlOnVwjlfDWB1osdByHizvbYD03N0wc/PytFh876jCRwRIYQQEn2pUlNMCEmQfY0mlGRpgp/n6BVwerxo6rEncFSEEEJIdFFSTAiJqMfmQkevE3lpyuBjjDFMztTgYBP1QCWEEDJ2UFJMCImo+pQ5ZHFnwESDEtWUFBNCCBlDKCkmhER0tMWCiQZl2OOFRjX2U1JMCCFkDKGkmBAS0eFmMyakqcIeL0hX4VhrbwJGRAghhMQGJcWEkIiOtlgwMT18pjhLp0BHrxMOtzcBoyKEEEKij5JiQoggzjnqOqwhi+wCxCKGXL0Cx9tptpgQQsjYQEkxIURQl9UFEQO0Cqng1/PSlDjebo3zqAghhJDYoKSYECLoRIcVuQKzxAHZOgWOt1niOCJCCCEkdigpJoQIOtFhRY5OEfHr2To5TnTY4jgiQgghJHYoKSaECDrRYUWWVh7x69laBeo7qXyCEELI2EBJMSFE0MlOK7IGmynWK9DQRTPFhBBCxgZKigkhgr7psg86U5ymlMLu9sLicMdxVIQQQkhsUFJMCBHU2G1D5iBJMWMM2ToFvumyx3FUhBBCSGxQUkwICWNzeWB1eZGmFG7HFpCllaOxm0ooCCGEpD5KigkhYRq77cjWysEYG/Q4o0aOxm6aKSaEEJL6KCkmhIRp6rYPWjoRYFTLaLEdIYSQMYGSYkJImKYeO9LVsjMel6mV4xtKigkhhIwBlBQTQsI0dduRrj7zTHEGlU8QQggZIygpJoSEaey2IUNz5pniDI0cLWZHHEZECCGExFZKJMWMsamMsb39PsyMsV8kelyEjFVNPXYYNWeeKdYpJLC7vLC7vHEYFSGEEBI7kkQPYCg450cBzAYAxpgYQBOAtxI5JkLGsmaTA8Yh1BQzxpChkeGUyY7JmZo4jIwQQgiJjZSYKR5gAYDjnPP6RA+EkLHI5+Po6HUOaaEdAGRo5WjuoRIKQgghqS0Vk+IbAfxD6AuMseWMsSrGWFV7e3uch0VIuFSMyQ6rExq5BFLx0C4P6WoZTvXQYrtUkYoxScY2ikmSLFIqKWaMyQAsBvBPoa9zzp/nnFdwzisyMzPjOzhCBKRiTLaYHEOqJw4wqCgpTiWpGJNkbKOYJMkipZJiAFcC2M05b030QAgZq5pNjiGXTgB9SbGJyicIIYSktlRLim9ChNIJQkh0tJgcMCilQz7eqJah2UQzxYQQQlJbyiTFjDEVgMsBvJnosRAylp3qsUOvGvpMcbpGhmaaKSaEEJLiUqIlGwBwzm0AjIkeByFjXbPJgQlpyiEfn66WoY028CCEEJLiUmammBASHy1mBwyqoZdPaOUSODw+2sCDEEJISqOkmBASotU8vIV2jDEY1TLa7pkQQkhKo6SYEBKi3eKEYRg1xYC/hKKF6ooJIYSkMEqKCSFBvU4PfJxDJRMP63kGlQxtFkqKCSGEpC5KigkhQYHSCcbYsJ6XppLSTDEhhJCURkkxISSo1exA+jBLJwAgTUm9igkhhKQ2SooJIUHtFifSRpAUG9RS6lVMCCEkpVFSTAgJajU7oB/GbnYB6SpaaEcIISS1UVJMCAlqNTuRNowexQEGtQxtFmcMRkQIIYTEByXFhJCgZpN9ZOUTKhk6e13gnMdgVIQQQkjsUVJMCAlqMzuHtZtdgEwiglwqQo/NHYNREUIIIbFHSTEhJKhthAvtAP8GHq3Uq5gQQkiKoqSYEBLU0TuymWLAv9iu1Ux1xYQQQlITJcWEEACA1emB18ehlA5vN7uANJUUrWaaKSaEEJKaKCkmhADwl06MZDe7AL1SilZqy0YIISRFUVJMCAEAtJkdMIywnhgA0lQytNBMMSGEkBRFSTEhBEBgkd3I6okBf1s22sCDEEJIqkpoUswYMzDGZiZyDIQQvzaLc0S72QVQTTEhhJBUFvekmDH2CWNMxxhLB7APwIuMsSfiPQ5CSKhWswO6USTFBhXtakcIISR1JWKmWM85NwO4FsCLnPOzAVyWgHEQQvppNTlG3I4N8M8Ud1ld8PloVztCCCGpR5KI12SM5QJYBuA3Q30SYywNwJ8BlAPgAG7lnO+IyQhjwOHw4ECzCS1mJ3L1ciikYnRZXbC7vcjUyCAWMbSYnVBKxVDLJXC43RAxESQiESwONzQKKXqdbmjkUvg4R7fVjUytDJkaGU6ZnJiQpkCP1Y1mswO5OgXcPh/MDg8K09UoTFehoduGVrMD2ToFioxqAMDJTuuIHhOJztydwOfjI3oeSZw2ixPTcnUjfr5ULIJaLkGn1YVMrTyKIyPkNJfLi/2nTOixu6BT+H8Ry9Ur4PNxmJ0eONxeZGvl4AD0ShmMGgmOtljRanYiWyfHtBw1dEoFTHYHjrfZ0GZxQiUTQyOXoDhLhWMtVrRZnMjSymFzeaCSSZCjlyM/ja5hZHD93/eyNHLYPV60WRxIV8nh8Phgd3mgU0phdriglksgE4lhcXjg8nqhVUhhsrmRrpFBImJoNTuhVYqhlkrQY3fD5vYiSyMD5wwmhwtKqQQWhwdKmRh6pQQOty8Yt1q5GDaXDw6PF063D2kqKXrsLhjVCpTl6iCR0HKySBKRFK8B8D6ALzjnXzPGigHUDOF5TwHYwjlfyhiTAVDFcpDR5HB4sPFAMx7ceBAOtw+FRiX+85ISrN5YDYNKhlu+VYQnPjwGh9sHhVSEVQtKUZiuQrPZisfePxp8fGVlKdZXNeDGcwrwyo56dNtceGhJOQ41dWNKbhrWbPKf7+bzC/HU1prga91RWYr73z4YPM8Ty2ZDJmFY8fc9gz72zPfmwOXhuGvD3pDjFpblDPrm4PNxbKluGfbzSGK1W0a+cUdAulqGNouDkmISEy6XF2/vP4VnP67BDRUFWLetBgaVDD+7uBhWlzd43VNIRXhoSTnSlFZUnUTw2quQirB2cTmuKM/ER4faQ66LqxaUorHbji9qWrFhVzMUUhFWLyrDG7trsKyiAEWZdlQUZNA1jAgSet+778ppYIzhaEtvSGzeedkUGFQSdFrdeO3rhmAs94/FV3bUQyZh+OnFJSHv7ULHr766DM99Wov6TjsUUhH+37Uz0G5xhuQVd142BWu+OoQ7Kktxzaw8SowjiOt3hTEmBjCRcz6Tc/6fAMA5r+OcX3eG5+kAXATghb7nuDjnPbEeb7QcaDYFL8oAsGhmHlZvrIbD7cO1c/ODgQsADrcPT22tgY8jmBAHHl+3rQaLZubhqa01uHZuPhxuHx545yC+PSMPazadPl/gP1/gtQIX/sB57tqwF/sbTWd8bH+jKfgfvP9xJzutg/57T3ZaR/Q8klgdvaNbaAf464ppsR2Jlf2nTHjwnYNYNDMvmBRcOzcfHVZXyHUvcG3UK1Uh116H24cHNx7EsRZr2HXxqa01qG3vxTVzC4KPrdlcjZsvKMaDG6vh9TK6hpGIhN73OqwutPc6w2LzyY+OQSWT4qmtNSGxHPh64D1+0czw93ah49dsqsaimXnBz090WMPyiic/OhbMB6qbTfH+9qSMuCbFnHMvgMUjeGoxgHb4F+XtYYz9mTGmHngQY2w5Y6yKMVbV3t4+2uFGTYvZGQxOAGAMwc/7/z3A4fbB6vQIPh44PrC/gsPtQ7vFEfF8kc4/sOxT6DEfF35um2XwpKfV7BjR88aiZI3JgVweH3qdnlEttAMCHShosV0yS5WYFNLSd20ZeA2NdK1qtQhfi1oHXJMDj/s40NnrDHnM7vJfizt6nePyGhYPqRyTAULvez4eOTatfXEV6T2aMeFcYbDj+7/uYPkDtc6MLBHz59sZY88wxuYzxuYGPs7wHAmAuQD+l3M+B4AVwK8HHsQ5f55zXsE5r8jMzIzB0EcmRyeHQhr6re7/udDX1AqJ4OOcn/4z8FimVnHG8w38fOAdQKHHxEz4uVlahfA/tE+2TjGi541FyRqTA3VandCrpBCNcDe7ANrVLvmlSkwKye13bel/jYl0rYp0LcqOcE0WMcCokYc8ppT5r8UZGvm4vIbFQyrHZIBQrIlZ5NhUyySCsRz4vP97fP/Hz3R84HUHyx9y9BTHkSQiKb4AQBmAtQD+2Pfx+Bme0wigkXP+Zd/nr8OfJKeEGbl6rF1cHgzSTfuasGZxGRRSEd7Y1Yi7Lp8SEuyrFpRCBOCeb08NeXxlZSk272/CqgWleHN3Y7Bu7v0DTVh99enzrVpQGvJaD19THnKeJ5bNxsx8/Rkfm5GvxxPLZocdF1iAF0mRUT2i55HEaTM7R7WbXYBBJUUzJcUkRmZM0GPtknJs2teElZWlwWueUS0Lue4Fro0mmy3k2huoKZ6Sow67Lq5aUIqSTA3e3t0QfGz1ojK8sr0OaxeXQSzmdA0jEQm97xnVMmRo5GGxeedlU2BzubFqQWlILAe+HniP37Qv/L1d6PjVV5dh8/6m4OdFGeqwvOLOy6Zg835/PlCWq4/3tydlMM5To30SY+xzALdzzo8yxn4LQM05vyfS8RUVFbyqqipu4zuTQPeJVrMTOf26TzjcPmRopBCLGFrNruBvkA6PByLGTnefkEthdbmhlknBwdFtcyNDLUOmVoZmsxO5+tPdJ3J0Cnh8PlgcHhT06z7RZnEgSxvaVWIkjw2n+8Rwn5eEojboZIvJ/j481Ir/+/Q4fnnF1FGdp+pkF6rqu/HyredGaWREwLiIyUgEu0/oFPBxDktf2VmmVgYGQDeE7hPtFhcUMhG0kbpPSCXISaPuE2cwrmMyoP/7XoZaDofHi3aLAwaVHE6PDzaXBzqFFBanG0qZGHKxGBanB26vFxqZFCa7G+lqGSRihjazExqFGGqZv/uE3eVDpkYKjtPdJ3odHihkYugVEjg8PrRbnMgU6D6hV0lhtruQrpajLFc/XhbZjSgm4959gjGWDeD3ACZwzq9kjJ0F4HzO+QtneOodAP7W13miDsAtMR5qVCkUEpwzyRiTcxdmaP1/GoFZEY4pztSgOFMTtcfORCRiI3oeSYw2i2NUWzwHGNS00I7ElkwmRkVR+rCec+6k8NvFeqUCcwvDHxc6lpChoPe91JeIXxdegr8l24S+z48B+MWZnsQ539tXczSTc34N57w7dkMkZHxpMzugVUQhKaZd7QghhKSoRCTFGZzzDQB8AMA59wDwJmAchJA+rWZnVGaK9Ur/LUC313fmgwkhhJAkkoik2MoYM8K/Kx0YY/MAUNM8QhKo1eyAQTn6hXZiEUOaSoqOXpotJoQQkloSsaPdXQA2ApjMGPs3gEwASxMwDkJInzZLdGaKASBdJUOLyYFcvTIq5yOEEELiIe5JMed8N2PsYgBT4V8deJRz7o73OAghp7VHMylWy2gDD0IIISknbkkxY+zaCF+awhgD5/zNeI2FEHKaz8fRZXUhLQp9igEgTSmlDhSEEEJSTjxniq/u+zML/g08tvV9fimATwBQUkxIAnTbXFDJxJCKo7PEQK+Sodlkj8q5CCGEkHiJW1LMOb8FABhjmwGcxTlv7vs8F8Cz8RoHISRUm8UJgzo6s8SAv3ziVA8lxYQQQlJLIrpPFAUS4j6tAKYkYByEEPQlxVEqnQD8STHNFBNCCEk1ieg+8Qlj7H0A/4C/LduNAD5OwDgIIfBv3KFXRmeRHeDvPkEL7QghhKSaRHSfWNG36G5+30PPc87fivc4CCF+bRYn9MroXQrS1TK0WRzgnIOxEW0/n5Q8Xh9q23tRkqmBJEr114QQQpJHImaKA50maGEdIUmg1eyAPgobdwQoZWKIGIPZ7oE+Sm3eEs3icOP7f/4SzT126FUybPiP85EexTpsQgghiRf36Q7G2LWMsRrGmIkxZmaMWRhj5niPgxDi12J2RLWmGAAyNHK0jKG2bH947wjSVFI8deMclGZp8OA7BxM9JEIIIVGWiHuAjwJYzDnXc851nHMt51yXgHEQQgC0mZwwqKM7o2tUy3BqjCy2a+qxY9O+U/jheUVgjGFZxURsP96Joy2WRA+NEEJIFCUiKW7lnB9OwOsSQgS0WqI/U2xQy9DcMzZmil/efhLzp2RCo/BXmymkYlROzcRL208keGSEEEKiKRFJcRVjbD1j7Ka+UoprB9ntjhASQ5xzdPa6orbFc4BBJR0Tbdm8Po639zThkimZIY9fMjUL/9rfDJfHl6CREUIIibZELLTTAbABuKLfYxy08I6QuDPZ3ZBKGOQScVTPa1TL0dSd+klx1ckuqOUS5BtUIY8bNXLkGZT4d20HLp2WlaDREUIIiaZEtGS7Jd6vSQgR1mp2xqSLglEjw77GnqifN94+PNyKswvSBL82t8CADw61UFJMCCFjRNySYsbYrzjnjzLGnoZ/ZjgE53xlvMZCCPFrNTuQHuV6YsA/U9xsSv2a4o8OteH2+ZMEvzYrPw1PfnRszPVjJoSQ8SqeM8WBxXVVcXxNQsggWswOGGI0U9xiTu0NPJpNdnRZnZiUoRb8er5BCbfXhxMdVhRnauI8OkIIIdEWt6SYc76p78+X4/WahJDBRXuL5wCFVAyFVIROqwsZGnnUzx8P/67tRHmeHqIIST1jDGUT9NhZ10VJMSGEjAHxLJ/YBIGyiQDO+eIzPP8kAAsALwAP57wiqgNMUj4fx8lOK1rNDmTrFCgyqiESDT7zNpLnJGKcJPFO9US/HVtAllaBpm57CifF7ZiWox30mKnZWvy7tgPfO68gTqMaP/pfU1QyCVxeL4xq+RmvLXQtIsmsf3zm6hXw+oA2y8hilWI9+uJZPvF4FM5xKee8IwrnSQk+H8eW6hbctWEvHG4fFFIRnlg2GwvLciIG/kiek4hxkuTQYnZgVn5aTM5tVMtwqseOWRNjc/5Y+/pkN1ZWlg56zLRcLTbuOxWnEY0fQteUlZWlWF/VgHsXTo94baFrEUlm/ePToJLh5vML8dTWmhHFKsV6bMStTzHn/FPO+acAtAA+D3ze73EywMlOazDgAcDh9uGuDXtxstMa1eckYpwkObSaHUiP8m52AUaNDI1Rasvm9XG8tacRz392HN1WV1TOOZg2swNmuxt5BuWgx+XoFLC7vWOiJ3MyEbqmrNtWg0Uz8wa9ttC1iCSz/vF57dz8YEIMDD9WKdZjIxGbd9wIoIYx9ihjbPownscBfMAY28UYWy50AGNsOWOsijFW1d7eHpXBJlKr2REM+ACH24c2S+RV/SN5zmgl4jVTRbLHZJvZGbPyCaNajoYu26jPwznHqtf24LlP6rDjeCe+8/TnMY+tXfXdmJqjjVhPHMAYw9RsDXbX98R0PNGU7DEJRL6mMDb4tYWuRakpFWIyGvrHZyCW+xtOrFKsx0bck2LO+Q8AzAFwHMCLjLEdff8hBi/eA77FOZ8L4EoAP2eMXSRw7uc55xWc84rMzMzwM6SYbJ0CCmnoj0ghFSFLq4jqc0YrEa+ZKpI5Jt1eH7ptLqTFrKZYjvooJMXrv/4Gh5vNuO+q6Vh+0WScW2TEfW8eiMIII6uq7x7y4rlJmRrsbuiO6XiiKZljMiDSNYXzwa8tdC1KTakQk9EwMD5HE6sU67GRiJlicM7NAN4A8BqAXADfBbCbMXbHIM851fdnG4C3AJwbh6EmVJFRjSeWzQ4GfqBmqMgo3CJqpM9JxDhJ4rVbnNCrpBDHqP4sUytHU/fokmKby4PHPziK2y4shkzij6/vzsnDgSYTqk52RWOYgnbVd6NkiElxcYYae7/pidlYxiOha8rKylJs3t806LWFrkUkmfWPzzd2NWLVgtIRxyrFemzEfUc7xtjVAG4FMBnAqwDO5Zy3McZU8PcyflrgOWoAIs65pe/vVwBYG8dhJ4RIxLCwLAfTVs5Hm8WBLO2ZV5eO5DmJGCdJvBazAxnq2HWGyNTKcapndL2KX69qRHGmJqRXsEwiwndm5OJ/PzmOF36cHq3hBrm9PhxtsZxxkV1AcaYGh5tr4PXxmP2CMd70v6b4u0+I4fb6sLA8Z9BrC12LSDIbGJ85OgWuOCsH7b3Dj1WK9diIZ0u2EgA5AK4H8CTn/LO+x+czxrSc8+OMsVsjPD0bwFt9b6wSAH/nnG+Jx7gTTSRiKM7UDKsP6kieM1qJeE0yOi0mBwwxWmQHACqZBFIxG3GvYs45Xtx+Ej8QaHc2vzQTG6r2BFsRRdPRFgsytXIoZeIhHa+RS5CmkqK2rRdTz9DCjQzdSK8pdC0iyUwoPidnjSxWKdajL57lE/8NwMw5vzmQEPex930NnPOtQk/knNdxzmf1fZRxzn8X89ESMsY1m2LXozggR69AfefISij2fNMDt9eH6bm6sK8ppGLMK07Hm7sbRzvEMPsae1CcObxbkMUZahxsMkV9LIQQQuInnklxEed8/8AHOedVAIriOA5CCIBTPXakxWA3u/6ydQo0dI2sRdAbuxrxrckZEUsvzptkxKZ9zaMZnqA9DT2YNMy6vIJ0NQ409UR9LIQQQuInnknxYPc4B28GSgiJuqYeO4wx3m0uUyvHyY7hJ8Uerw/vHWjG+ZONEY+ZnqvDqR47volCh4v+9jf2DPt25KQMNfY10kwxIYSksngmxV8zxn4y8EHG2G0AdsVxHIQQAM09dhg1sS2fyNYqUNc+/KT4q5NdMKhlg9YLi0UMcwrS8NHh1tEMMYTD7UV9pw0F6aphPa/IqMbRFgt8vog72RNCCEly8ew+8Qv4F8t9H6eT4AoAMvhbshFC4qjZ5IAxht0nAH9N8ec1w2/G//7BFpxdaDjjcbMnGvDBoVbc8q1JIxlemMPNZuQblMH2b0OlUUigkUvQ0GVDUQa1RCKEkFQUz22eWznnFwBYA+Bk38cazvn5nPOWeI2DEOIvT+iyumLafQIAcvUKnOy0gfOhz6ByzvHBoVacXXjmdmvleTrs/aYHdpd3NMMMOtBkCmn/NhxFRjWqT5mjMg5CCCHxl4gd7T7mnD/d97Et3q9PCAFa+zbukIhiewnQKvybg7RbnEN+ztFWCzjnmGg481IDlUyC4gw1dp7oHM0wg/Z904OC9JElxRPTlag+RXXFhBCSqhKyox0hJLGae+zIjPEiu4B8gxK17b1DPn7r4VbMLjAMecOPsgk6fHZs+CUaQkYzU1yYTjPFhBCSyigpJmQcauqxj2hDjZHI1StwvG3oSfGHh9owOz9tyMeXTdDji5qOEYws1EgX2QUUGFU43ExJMSGEpCpKigkZhxq77UiPcT1xQK5eiSMtliEd22NzoabVIrhhRySTMzU4ZbKjs3foJRpCRrrILiBTK4fV6UG31TWqcRBCCEkMSooJGYe+6bLFvEdxQEG6CoeGOIP66bF2lE3QDysxFYsYpufosLOua6RDBAAcbDKhaJibdvQnYgxFGWocbqHZYkIISUWUFBMyDn3TbYtb+URBugo1rb1D6kDx4aFWzMzXD/s1puZosf346Eoo9n1jQqFxZKUTAfkGJQ43D21WnBBCSHKhpJiQcehUjyNuC+10SilkEhGaeuyDHufx+vDZsXbMnpg27Ncom6DH9uOj60CxbwQ72Q000aBCdRN1oCCEkFRESTEh4wznHKd67MjUxicpBoDiDDUOnGEb5F313cjQykdU1lGYrkK7xYmOEdYVO9xeNHSNfJFdwHBKRQghhCQXSooJGWfae51QSMVQSMVxe81JGWrs/aZn0GM+PNyKOSOYJQYAkYhhWo4WX46wrrj6lBkT01WQikd3SZyYrsKJDis8Xt+ozkMIIST+KCkmZJz5psuObF38ZokBoDhTjd0N3RG/zjnHloMtQ9rFLpKpOVrsGGFd8f7GHhRHYXtmhVSMDI0cdR3WUZ+LEEJIfFFSTMg409htQ5ZWEdfXLM3SovqUGS6P8AzqkRYL3F4fikax0G16rg476kZWV7y7vhtFUUiKAaCQ+hUTQkhKoqSYkHHG345NFtfXVMslyNUrcKCpR/Drm/adwnmTjEPexU5IkVGNFpNjRP2K9zWaMHmUi+wC8g1KHKKd7QghJOVQUkzIOHOiwxrXRXYB03N1gjvP+Xwc7+w9hXnFxlGdXyximJ6rw5cnhldX3G11oaPXifw05aheP6AgXY2Dp6gDBSGEpBpKigkZZ0522pCji2/5BADMKTDg/eqWsMd31nVCJhGNqnQiYGqOFv+uHV5d8Z5vulGapYFINPJZ6v4KjSocoV7FhBCScigpJmScaei0ITsBSfHUbC2aehxo6LSFPP7i9pO4eErmqEonAs7K1eHftcOrK95dP/r+xP0Z1TK4vD60W0a37TQhhJD4kiR6AMPBGBMDqALQxDlflOjxRJPPx3Gy04pWswM5OgUsDjdazE6kqaTosbmRpZXB6wNaLQ5kaOQw2d3I1clhd/vQZnEiWyeH18dhtnugV0nRbXMjWyuDxwe0mBzI1skhETOY7G6oZBK0WZzI0srB4YNMLEabxQmlVAyNXAKJiKHb5oJSJkGr2X9ug0qMNosbZrsHE/QK+DiHxemBw+31v7YXaLe6YFRL0Wl1QSOTQCkTo9XsH6/X5wNjImTr5MhPU6Gh24ZWswMqmQQurxdGtTy4xe6JDivqu6xQyyRQy8Wwu0O/Hvg+ZesUKDKqozbDNx7YXB6YHG6kq+NbUwz4yxsumpKBv35Zj/uumg4AqGm14OsTXfjeDbOj8hpFRjW6rM5gfAzFlyc6UTktKyqvDwCMMUzKUONQsxkXazOjdt5U5/Nx1Hdaccpkh0TMIWESdFqdUMokkIoAmUQMu9sDqViM9r5rmlQsQkOXHTk6OTQKMRo67VDJJFDIGMRMhFM9DmTp5EhTiVFk0EImi1+bQZLa+r/nZmnlsLu8sLo8aO91waiWIUMjgdXpf5/rdXiQqZXBy30QMxG8Po5umxtqmRhquQQWpxtKqQQWhxs6hRQ9djcUUjFkYgaJiEHEGCxOD+xuL/RKKZRSMdxe3nedksPLOXpsbmgVEuTqlJhoUKGxx4ZWsxMOtwdKmQRWpwcF6WpMyqD3vFhKqaQYwCoAhwHoEj2QaPL5OLZUt+CuDXthUMlw8/mFeGprDRxuHxRSEe68bApaTHb8/r0jwcfWXn0WOnpdWLOpOvjYqgWleGVHPbptLvzXwmmo77TiiQ+PBb/+/66dAbfHhwc37j59nsVlePaTWtR32oPnKDSq0OvwhB330eFmHGjqxc8uLobV5Q0Z431XToPLy7HqtaOC41l9dRne2NWABdNzkG9Q4vEPjgZfc2VlKdZXNeCBRWfB6eb45T/3hpxDJRXjL9tP4IFFZ8Hl4bhrw+mvP7FsNhaW5dBFYogaumzI1skhisKs7EhcNi0bD26sxi3fKkKmRo7fvHUAi2dPiFrPZJGI4awJevx/9s48Pqrq/P+fM/uSzGRPhoQkhCQsCWExKlpAJUqpZRMVtf1qFyzt96eFShe7qIjaxS620vJti9pW7SK0uIBVagUtWnEJCEjYEkISErIvM8nsy/n9MZlhJnMn6+x53q/XvDJz77nnPjM5y3PPeZb/1nVh7YK8EcvbHC6caDHga9dMD8n9PUxNVeHkRQOuKSWlGHCPcQfOtKO2fQAfN3VjWdkUPPDyYb9+npsih9nOsWXPpTFty8oy/O2DRpztGPB7v6mqBGqZGL/9Tz16TTY8sqoMHf1WXJGfQYoxMSJD59xvLiuBwwk8PDifFqQr8c1lM9DSa/ab5x5bXQ6ny4Ute0/6zX0yiRi//U8NbqvMx7YDl8pvvqEUealKNHab/Op5ZHUZtr/lP+965spNVSUoylSjsduEv3zQGFAnzXnhJW7MJxhjeQA+C+DpaMsSahq6jV5Fb+2CPG/nAQCL3YVfvnkWXUab3zGVXOpViD3Hntxfi7UL8mCxu9BtsnkVYs/5811GPLTH/5qH9tRgRUWuXx0uFwTLfX7hNKxdkIcuoy1Axi6jDT9/40xQebburcFdVxfhyf21qO0Y8LvntgO1WFGRi+PNeq9C7FtHt8nmPe/5nTznN+86ioZuigk7Wuo7jdBpQ+NQNh6yNAp8tkKH23//Pj7/9AewOTmWzc4J6T1m6zT4z9nOUZX9pEUPnVYBlSy06wMF6Sp80twX0jrjmYZuI4436/Hk/lp8fuE0PPDyiYB+nqKSexViz/Gte2tw95LpAe+f3F+LLqPNO748tKcGYibGcXJwJEbB0DlXIZF4FWIAWFGRi7qOgYB57oFXTqBFbwmY+x7e655HPcqr59wT/z4LlwsB9Tz0SuC862nLT+6vhd3B8cS/zwrWSXNeeIkbpRjArwB8B0DQVFGMsQ2MsWrGWHVn5+gmxVig3XCpkzEG73sPFrsLLu5/jdHqECznWQB08cB6hI75XuP5HKzuPpMdjI2vbovdBbPN4f0uQ+8ZrF7f8sHOd/RbEKvEWps832VEdhQiT/iysmIK1l0+FfPzU7H5hlKIQ7ziMTdPi3dqu+Aa2mkEOHSuCzN1od94KkxXoyZGYxVHo022Gyze/ttrtAv2454gx802h+B733HEYneha8BtNkPEH5Fuk0Pn3KFz3kjzkS+ecsHmbqNt+Ll66GffOThYnbE858U7caEUM8ZWAOjgnB8erhznfAfnvJJzXpmZGT/bltkaBRTSS/8K3/eez0P1BrVCIliOD3ZYMQusR+iY7zWez8HqTlFJx123QiqCUibxfpeh9+Q8eB2e8sHORzoRxViItTZ5tr0/qivFHhbkp+Ka0swJp1UWIkujgEomxslRKKXv1HahLAxK8ZQUJdoNFgxYHSGve6JEo01maxTe/pumlgr242DHlYOr+EPf+44jCqkIGUnyqDiQEhMn0m1y6JwrNOcNNx8FKydUXi0bfq4e+nnoHBxvc168ExdKMYBPAVjFGGsA8AKApYyxP0dXpNBRmK7GE+vmQSEVYffhZmyqKvHrEPddX4oMtczvmMlix5aVZX7HNlWV4MUjze4JRiXD5htK/c4XZqjxyCr/ax5ZVYZXj7f41SFiECz3l/fPY/fhZqSrZQEypqtl+NayGUHl2bKyDM+9V49NVSUoyUryu+fGpSV49XgL5uRp8Ytb5wXUka6Sec97fifP+SfWzfM64BEjU99phC4l8QfUeVNT8ObJ9mHLmGwOHG/RY6YuOeT3F4sYCtLUlMRjkMJ0NebkabGpqgR/fv88HltTHtDP+0xWbB0y7mxZWYanD54LeL+pqgQZapl3fHlkVRmc3ImKKdpofk0iThg651rsDjzsM5/uPdaC6VlJAfPcY6vLkatVBMx9D68sw95jLdi41L/85htKIRIhoJ5HVgfOu562vKmqBFIxw+YbSgXrpDkvvDDOR95ijCUYY9cC+NZI0ScqKyt5dXV1RGQKBR5P2I5+C7KT3dEn2g1WaFVuT9asJJ/oE2o59BY7cjTywa0UK7KT3R6sBrMDWuWlaxwcaDNYkJ3sjj5hMNuh9ESfSJKDMxekIjG6BmyQS0VIlksgFjH0DUaf8JRLVQ9Gn7A4oNMowL3RJ1zIHoyM0WW0IU0lRY/JDpVMDJVUjPYBt7zBo0+IYXe6kDYk+kRTjxGqwegTFrvT77znd8pKjkj0iZBVHu02yTnHnIffwC/WzYVGIY2aHJGg5qIeu4804/VNS4KW2X+qHb96s9YbCSPU/Om/53FFUTrWL5oW6qrjsk36R58AJEyMbqMNSpkYEhEgl4hhsTsgEYnRZXRHx5GKRGjqNSNbI0eyQowL3RYo5WIopIPRJ/QWZCXJoVWJMS2Nok9Ekbhrk75zbmbSpegTXQM2pAlEn8hIkoHDBdFg9Ik+kwMqmXsluN9mh1Li/quRD4k+IWYQgXnnS41SAqVEDLuLe+/tAofe6ECSQgydlqJPhIhx/UjxFn0iYRGJGIoyk0IaLzXUlGSHrq7hvuv0rCRMzwr+O8T67xSrtBuskIpZwivEADAzR4PWwZjI+UGSgvz7ZDsq8sK3sliQocaxC31hqz/eEIkYpmUmYdoY++7c/FTv+1k6/3MLQiEYMSmJ9Tm3MCMJhRmxKVsiEy/mE144528nWoxigogEtR39yEudeNa4eEAsYrh8Whr2HGsRPO9wuvBGTRsuL0wLmwxFGWocpwgUBEEQcUPcKcUEQYyPs+0DyJ0E9sQeFhVnYFd1M4RMxA7VdyNNHV7HrLxUFdoNVvRb7GG7B0EQBBE6SCkmiEnCqVYDpqREP/JEpCgZNMF571xg2ue/vN+ExaUZYb2/WOTObPdJM8XOJQiCiAdIKSaIScLJVgMKJpHXMmMMy8tysP2tOr/jTd0mHKrvwqLi8CrFAFCUqcbHF3rDfh+CIAhi4pBSTBCTAIfThfrOAUydJDbFHhaXZKCx2+QXnu3Hr5/C9bOyQ57FToiijCRUN5JSTBAEEQ+QUkwQk4CGbiPSVDIoJ1nIKolYhPWLpuHb/ziGf59sxy/+dQYnWvRYNTc3IvcvzU7C0aY+QbtmgiAIIragkGwEMQk40WJAYcbkMZ3wZZZOg68sLsIv3jiDFJUU3/3MLMgkkVkPSE+SQyISobHbNGl/f4IgiHiBlGKCmAQcvdA7qeyJhzI/PxXzfeLdRpLSbLcJBSnFBEEQsQ2ZTxDEJODoBT2KSCmLCiXZyfigPjACBkEQBBFbkFJMEAmO3enCmbZ+FGWSUhwNZuYk44PzPdEWgyAIghgBUooJIsE5edGAHK0iItEWiECmpqnQZ7Kh3WCJtigEQRDEMJBSTBAJzuHGXm8iCyLyiBjD7ClavHeuK9qiEARBEMNASjFBJDiH6rtRkp0cbTEmNbN0yTh4tjPaYhAEQRDDQEoxQSQwLhfHh+d7MFunibYok5o5uVq8W9tN8YoJgiBiGFKKCSKBOdlqQLJCgjS1LNqiTGp0WiWkYoZTrf3RFoUgCIIIAinFBJHA/OdsB+bkaqMtBgFg7tQU7D/dPnJBgiAIIiqQUkwQCcybJzswNy8l2mIQABbkp2LfibZoi0EQBEEEgZRigkhQOvutqO3oxyyyJ44JZuqScaHXhJY+c7RFIQiCIAQgpZggEpR/1bRhfn4qZBLq5rGARCTCFYVp2HvsYrRFIQiCIASg2ZIgEpTdR5pxRWFatMUgfFhYlI4XjzRTFAqCIIgYhJRigkhAGrqMON9pxLypKdEWhfBhlk4Dg9mBEy2GaItCEARBDCEu8r4yxhQADgKQwy3zPzjnW6IrVWhxuTgauo1oN1iQrVGgMF0NkYiNqsxoriUmF88easA1MzIhEdNzbywhYgzXzsjEs4ca8PNb50ZbnIjjcLhQ06pHq96CzCQ5RCJAq5TRmEXEPCPNsxM9T8QGcaEUA7ACWMo5H2CMSQG8yxh7nXP+frQFCwUuF8e+mjZs3nUUFrsLCqkIT6ybh+VlOd5OE6zMslnZeONU+7DXEpMLvcmO3Yeb8cOb5kRbFEKA62Zk4Zt/P4bvfmYmMpLk0RYnYjgcLrx8rAUPvHzCO1ZtWVGG3Uea8OVF02nMImKWkeboiZ4nYoe4WEbibgYGP0oHXwljlNfQbfR2FgCw2F3YvOsoGrqNI5apadWPeC0xudjxTj0qC1MnlcIVT2iUUnyqOB2//099tEWJKDWteq9CDLjHqq2v1uCuq4tozCJimpHm6ImeJ2KHuFCKAYAxJmaMHQXQAeDfnPMPBMpsYIxVM8aqOzs7Iy7jeGk3WLydxYPF7kJHv2XEMq36ka8lokek22RzrwnPH2rATfPzwn4vYvysrJiCnR814WIUwrNFa5wMNlaZbQ4asyY5sT53jzRHT/Q8ETvEjVLMOXdyzucByANwBWOsXKDMDs55Jee8MjMzM+IyjpdsjQIKqf+/QiEVIStZMWIZnVY54rVE9Ihkm3S5OL7zj+O4cY6OVoljnPQkOZbNzsHDe2oifu9ojZPBxiqlTEJj1iQn1ufukeboiZ4nYoe4UYo9cM77ALwNYHl0JQkdhelqPLFunrfTeOyNCtPVI5Yp02lGvJaYHPzyzbPoNdnw2QpdtEUhRsHKuVNwqtWAfxy+EG1RIkKZToPH1pT7jVVbVpThuffqacwiYpqR5uiJnidiBxYP8TIZY5kA7JzzPsaYEsAbAB7nnL8a7JrKykpeXV0dMRkniscztaPfgqzk4aNPDC0zmmuJcROyHzJcbZJzjqcO1uOP7zXgoRWzkaKShfweRHho6jHhx6+fwjNfqMRlBaOOKR3zbTIYvtEnMpLkkIgADUWfSATitk2OlpHm2YmeJ0LOuH7ceIk+oQPwLGNMDPfq9q7hFOJ4RCRiKMpMQlFm0pjLjOZaIjHRm+3YuqcGhxt78YMbZ5FCHGfkp6nwtSXTsf7Zavzqtnm4dkZWtEUKKxKJCHOnpmLu1GhLQhBjY6R5dqLnidggLpRizvlxAPOjLQdBxAodBgt2Vl/AH949j8sL0/DwqjIopOJoi0WMg7lTU7BpaQm+uesYlpfnYNP1JWRrSBAEEQXiQikmiMmKzeFCj9GGi3ozGruNONFiwIfne1DfNYCF09Lxvc/MwtQ0VbTFJCbITJ0GP147By993IKlP/8Prp2RiV/dNo+SrxAEQUSQuLApHg+MsU4AjdGWI0RkAOiKthAhJJ6+TxfnPCROncHapLq8KiXjs/dNH0td9p4WM3c6/Dsvd0nARI6JSRlmYl3GGJBPJFOIJdps+YVff/6oy6R3DjmdAeB0uNvkKIinPuyBZA4fYR8no0y8/B9Gy2T4PuNqkwmrFCcSjLFqznlltOUIFYn2fWKFePhdY11Gki++5BgLJDMxXhLt/0DfJzi0N0cQBEEQBEFMekgpJgiCIAiCICY9pBTHBzuiLUCISbTvEyvEw+8a6zKSfKMjVuQYCyQzMV4S7f9A3ycIZFNMEARBEARBTHpopZggCIIgCIKY9JBSTBAEQRAEQUx6SCkmCIIgCIIgJj0JqxQvX76cA6AXvSb6ChnUJukVolfIoDZJrxC9Qga1SXqF6DUuElYp7upKpGQtRCJAbZKINahNErEGtUkimiSsUkwQBEEQBEEQo4WUYoIgCIIgCGLSI4m2AImCy8XR0G1Eu8GCbI0ChelqiEQMNpsTxy/q0WawQKdRYM4ULWQycbTFJQhiDFgsDnzSqkebwYocjRxzdFooFDR8EgQROoLpEdGQodtohUwsgsnmHJMssfAdJgKN6iHA5eLYV9OGzbuOwmJ3QSEV4Yl187C0JBN7TrTioVdOeI8/srocayqmkGJMEHGCxeLAnk9a8dAen368qhyr5uhIMSYIIiQE0yOWl+VETKn0yPD4vlO4rTIf2w7UjkmWWPgOE4XMJ0JAQ7fR2wgAwGJ3YfOuo/ikVe9ViD3HH3rlBI5f1EdTXIIgxsAnrXqvQgwM9uM9J/BJK/VjgiBCQzA9oqHbGHEZVlTkehXiscgSC99hopBSHALaDRZvI/BgsbvQZrAKHm83WCIpHkEQEyB4P7ZGSaLE553aTnzvxeNo6jZFWxSCiAjB9IiO/sjpCx4ZGMO4ZImF7zBRSCkOAdkaBRRS/59SIRUhRyMXPJ6tUURSPIIgJkDwfiyPkkSJzfkuI+7968foNdlx1x8+gN3pGvkigohzgukRWcmR0xd8ZRiPLLHwHSYKKcUhoDBdjSfWzfNrTE+sm4c5Oi0eWV3ud/yR1eWomKKNprgEQYyBOTotHlk1pB+vKsccHfXjcLD9QB2Wzc7GF64qhEYpxStHL0ZbJIIIO8H0iMJ0dcRl2HusBRuXloxZllj4DhOFcT7uxB8xTWVlJa+uro7Y/Twelx39FmQmKSAWAa16C7KS5eg12dGqt5DXenwSMu+ASLdJInSYzDacaOtHu8GKbI0c5TnJUCll0RInYdukxe5E5WNv4me3VCBFJcPhxl4cON2OF//fp6ItGjE8CdsmI4mvHpGVHPnIDS4Xx/kuIxp7jNAqpZCKGIxBok8EizIR7e/gw7huStpZiBCJGIoyk1CYrg7wvtxUVYLnDjWi12SLO09MgpjsOBwuvHayHQ+8fCn6xGNryrFmbi4kEtpsCyX/retCYboKKSr3A0dFnha/+885dBgsyCKzMyLB8egRRZlJEb/3WCJHjFQ2Wt8hFNCIHmKEvC+f3F+LtQvy4tITkyAmOzWteq9CDLj79AMvn0ANRZ8IOe/UdqI895JZilQswrypWrx9pjOKUhFE4jOWyBGJEGUiGKQUh5hg3peMXXofT56YBDHZadUHiS6jp34cat6v78Esncbv2MwcDd6t64qSRAQxORhL5IhEiDIRDDKfmCC+djUqmQRiEYNCKvJrMAqpCB7TbYVUBKVUDJeLkwkFQcQBOq1SsE/naGk7P5T0W+xo7DahKMPfKadsihZ7Xj8FzjkYozGTIMKBJ3LE0HEuM+nSOOfRd0RMWM/JSlbEfUY7WimeAB67mhu3vYM7nvoAt+04hFMXDdh8Q6mf9+WmqhK8eKQZCqkIG5eWYOMLH2NfTRtcrsR0ciSIRKI0Qy0YfaI0Iz5t5mKVkxcNKEhXQSL2n5ayNXLYHC60UXx3gggbQpEjNlWV4Hz3AFwu7qfvfGPnUWyqCoxOkZ+q8tOJbtz2TtzpOrRSPAGE7Gp+vO80NlWVYMOSIsyfmoL8NDXMdgfMdiecLuD59xvRqrdg866jmLlxcdwaoxPEZKGmvR/b367F+kVFYAzgHNj+di2KMtWoLEyLtngJw4lBpXgojDGUZCfhaFMfdHOUUZCMIBIfkYhhti4ZG5YUwcXd45wnQMBrGxcDgFffadVb8NyhRq+eU5CuRmG6OqitcTzpOqQUT4BgdjVGmxO/OVCHFzZcielZSTh0rgvb9tcFlOvot8RNQyGIyUqbwYLGbjO2v+XfhykzZWg5fqEPBWnC8UwLM9Q40tSHz8zRRVgqgpg8tOotAboKAHT0W8C5f5Y7T9kXNlzp1WOGszWOF12HzCcmQLDsLZz7Z3FJhCwvBDFZ0QXpv5SZMrScbutHvsBKMQBMS1fjk5a+yApEEJOM4XSV0egxiaDrkFI8AYRscDYuLcGrx1v8srgkQpYXgpiszJlCmSnDjXPQOSc3Rdg8oiBdjdOt/UjUZFMEEQsMp6uMRo9JBF2HzCcmgEjEsLwsBzM3Lh6MPiGG3enC8vIcP49L33IxkOWFIIgxIJOJsaZiCooy1F6P6oopWshk4miLljA095qQopJCIRX+TVNVUgBAR7+VVugJIkyMpKuMpMckgq5DSvEEGW32lnjP8kIQkxmZTExOdWGktn0AeanCphOA29muMEONk60GUooJIowMp6uMRo+Jd12HzCcIgiCIqHKuc2BEZXdKigK17f0RkoggiMkIKcUEQRBEVDnXaUS2Rj5smSkpSpxuJaWYIIjwQUoxQRAEEVXOdw0gZ4SV4qmpKpyhlWKCIMJI1GyKGWNiANUAWjjnKxhjaQB2AigE0ABgHee8d7Ds9wCsB+AEsJFz/q+oCC2AJ6Vht9EKmVgEk80JnVaB7gEbWg0W6LQKpKtlaNW700C7uAsKqQj9Zie6jFbkpqhQptNAIqHnE4KIVQxmC063GdFusCJbI8fMHDU0SrJtDRWN3aYRleK8VCXOdxkp3TNBjMBYUy37lvfoKVKxCANWJ/otdmiVUmQly5E/GEdcqG7fOrKSFRCL3LGMszUK5Keq0NRriovUz9F0tNsE4BQAzeDn7wLYzzn/CWPsu4Of72eMzQZwO4AyAFMAvMkYK+WcO6MhtC+etIeP7zuF2yrzse1ALUqzknDHlQXYurcGFrtbAd6ysgx/+6ARZzsG8JO1c2B1uLBlz6Xzj60px5q5uaQYE0QMYjBbsO9EJx7ac8LbZx9ZVY7l5ZmkGIcAi92JPpMdGUnDm0+oZBIopGK0GSzQaSmzHUEI4dFLPJnlPGHRlpflCCqiQuUfXlkGm8OJH71+2nts8w2lmJGTBKPVFVD3slnZeONUu9/xTVUleO5QI2QShq8vLcEDL58YlTzRJipaGGMsD8BnATztc3g1gGcH3z8LYI3P8Rc451bO+XkAdQCuiJCow+JJabiiIhfbDtTCYnfh7iXTvQox4M7msnVvDe5eMh0Wuwv1XUavQuw5/8DLJ1DTqo/mVyEIIgin24xehRhw99mH9pzA6TZjlCVLDJp7TchMlo9qgsxNUeJcB/3uBBGMYKmWG7qF+41Q+Yf31qDLaPM79sS/z6Lf7BSsu6ZVH3D8yf21WLsgDysqcr0K8WjkiTbRWpr8FYDvAPDNB5jNOW8FgMG/WYPHcwFc8CnXPHgsAMbYBsZYNWOsurOzM+RCD8WT0pCxS+kPzVaHYJpDs80BAHANSZXoOd+mp5SxiUik2yQRetoNVsE+226wRkmiiRFrbfJCjxlZIzjZecjRKlDfNRBmiYhIE2ttMp4ZLtXyWMq7huTJsdhdMNqE9ZtWvXAdjMFPPxqNPNEm4koxY2wFgA7O+eHRXiJwTDCtEed8B+e8knNemZmZOW4ZR4tvSkPPX5VcIpjmUClzW6qIGQTP52hpGzYRiXSbJEJPtkYeJM3z6BS5WCPW2uSFXhMyRzCd8JCjUaC2nZTiRCPW2mQ8M9ZUy8HKD924UUhFUMuE9RudVrgOTwLKeEr9HI2V4k8BWMUYawDwAoCljLE/A2hnjOkAYPBvx2D5ZgBTfa7PA3AxcuIGx5PScO+xFmxcWgKFVISnDp7DlpVlfsrylpVlePrgOSikIkzLUGPrKv/zj60pR5mOUsYSRCwyM0eNR1YNSfO8qhwzc+IndWks09htQppaNqqyOVoFznWSUkwQwRhrqmWh8g+vLEOGWuZ3bPMNpUhWigXrLtNpA45vqirBi0easfdYCx5bUz5qeaINi2YuecbYtQC+NRh94mcAun0c7dI4599hjJUB+CvcdsRTAOwHUDKSo11lZSWvrq4O7xfAJa9Ng9kGhwvoGrCiIF0Jk9WFNoMFORoF0pPc0SekYhHMdjtSlHKYbYPRJ7RKlE3RkpNd7BIyT4BItUki9PSZLTjrE32iNEeNlOg52SVUm/zKcx9hVo4GV03PGLFsa58ZP//3Wbz33aURkIwYAwnVJuOFYFEmPMdHm2rZP/qEGJxzSMQiGK1OGCx2pCilyBwSfWJo3Z46eoxWSEUi9JhsUMklyE6WIy/FHX0iwqmfx3WDWErz/BMAuxhj6wE0AbgVADjnNYyxXQBOAnAAuCcWIk94EIkYCtPV2FfTH+CR+ZlynbexnGwNPL+8TBeT3pcEQVzC4XDhzZOdft7TFDEmdDT3mrG4eHRb5pkaObr6rbA6nJBLxGGWjCBil5GiTIwl1fJYywuV9ehCp9sCdZ38NHXcpH6O6ojOOX+bc75i8H0357yKc14y+LfHp9wPOefTOeczOOevR09iYUby9hyrNyhBELFDTas+wHuaIsaEjja9BRnJo7MplohEyEiW4UKPKcxSEURsE4t6RSzKNFZomSMEjOTtOVZvUIIgYodgntUUMWbiWOxODFgd0Cqlo75Gp1XifBcpxcTkJhb1iliUaayQUhwCRvL2HKs3KEEQsYNOq6SIMWGiVW9BRpIcojFkqMtKlqMxjlaeCCIcxKJeEYsyjRVSikPASN6eY/UGJQgidijTaQK8pyliTGi42GdG5ihNJzxkJctxvouUYmJyE4t6RSzKNFZiydEubhGJGJaX5WDmxsWC3pUjnScIInaRSERYMzcXJVlJaNNbkKNVoExHEWNCQUufedTh2DxkaRR4r64rTBIRRHwQi3pFLMo0VkgpDhEjeW+O1buTIIjYQSIRYe7UVMydOnJZYvS09pmRqhqbUpyjUaCRHO0IIib1iliUaSzQUgdBEAQRFVr6LGNWirOS5Wg3WGB3ukYuTBAEMQZopTgMOBwu1LTq0aq3QKdVokynoa1WgohjqE+Hh1a9GQunpY/pGolYhFSVDK19FuSnq8IkGUHEN8ESe8RqvbECKcUhxuFw4eVjLRTonyASBOrT4aNVb0Fa0thWigF3uufGHiMpxQQhwEiJPWKt3liCRvQQQ4H+CSKxoD4dPtoNljE72gFuE4omsismCEHClUQjEZJzjAStFE8Q/5zhErT0mQWDV1/oNaPf4oBYBCgkYlgdTrjgfm+0OTBFq8K0jLFtQyT6NgZBxALDJe8gx7vxY7I5YLW7kCwf+zSUniRHIyXwIAgvLhfH+S4jGnuMsDlcgmNWY7cRIuYe01QyCfotNqhkEljsTlidTmjkMljsTiikYgzY7FBKJbDYHVBIJTBaHVDJJCjNSsLi0ix4QovvPtyMdoMlbh3rhkJK8QQQ2kr47ecXQCEV+TVIhVSE3gEbvvX3Y9hUVQK1TIxkhRR/+aABVbNyoJKK8cDLJ3D/8lmj3oaYDNsYBBEL5KYqBPv0lNSxxdcl/GnTW5CeJAMbQ+IOD9nJCpxuM4RBKoKIP4bqA5uqigXHrI8v9OF0Wz+eO9SIXpMN911fCqVUhGf+ex63VeZj24Farz6xZUUZdh+pxdKZOf7HV5bhd/+pQ2O3GQqpCJuqSrwyJILuQeYTE0BoK+HhvTV4cMVsv+DVm6pKMD1bDYvdhSf316LLaMP5biPuuroIT+6vRbfJhhUVuWPahpgM2xgEEQvYHRybqkoC+rTdEWXB4px2g3VcphMAkKUh8wmC8DBUH9hV3RwwZm1cWoK/Vzfjyf21WLsgDxa7C7988yy6jG79w6P4Am59YuurNbjr6qLA43trsKIi1/v5yf21OHqhL2F0D1opngBCeb4bu83ot9ixflERGAM4B5471IjvpswE4G5ELu4ua7Y5vJ8Zu5QjfDTbEMPlGE+UbQyCiAVa+ix47lBjQJ/OS1VhQUG0pYtf2g1jD8fmITtZgQu9JnDOx7XSTBCJxFB9oFXvHrP+73MLcORCHzgHnn+/Ea16CwB4TR+G6h++WOwumK0OweO+Xc5id8FocyaM7kFK8QTw5PkeukVhsjmx/a06v2MpKinuXVqMvcda4NlhUMokUEhFEDHA6RpbjvBg946nHOMEEQ9ka+ToNdkC+nS2hswnJkKbwYIUlXRc16rlYgBAn8mO1HGuNhNEopCVHKgP9JpsMNmdePqd+gA9gfNL70UMcHEI6hMquUTwuOd63zoSRfeYsPkEY2zTaI4lIkJ5vr//mZkozlQHbLV+d/cnePqdenztmmLkpSgwLV2N596rx6aqEqSrZHj1eMuYcoQnQo5xgogHSnPUeGRVuV9fe2RVOUpzqK9NhNY+M1KU41NoGWOU2Y4gMOhg1z0QYC6x+YZSJCvEAXrCpqoSvHikGQqpCPddX4oMtQx7j7Vg41L/67esKMOz79UHHH90dTlePd7iV19FnjZhdA/GfVX+8VTA2BHO+YIhxz7mnM+fUMUTpLKykldXV4f9Pv7RJ8SwO13I1srR1mdF14ANp9oM+Ht1s3fbQiEV4c9fvgIOlwtODiglYpjsDugmEH0iXnOMxwkh+0Ej1SaJ0FLfOYCfvF6Dzy+chj6THSkqKf7y/nl89zNl0douTIg2+dXnqzEjOxlXTc8Y1/W/PlCLO67Ix8q5U0IsGTEOEqJNxiP1nQO4cds7SFXJsHZBHhgDRAxYNjsbs3VaAPDqCZlJCohFg9EnpGL0W+1QSSWwOJywOV1IlkthtTshk4phtNmhlEhgcVyKPpGfpkZBmgqNPSY09RihkkmQrZEjPy0mdY9xCTRu8wnG2B0APgdgGmNsj8+pZADd46033giW53tqahIOnevCtv11fsctdhccnGPh9Myw3ZsgiNDRbrDgjZNdeONkl9/xLy9KDBu6aNGut445m50vGUkycrYjJj0ee+JWvcXPxOvq6eleRXWonlCYMbFxa3pWEqZnJebYNxGb4vcAtALIAPALn+P9AI5PRKhEgex+CSL+oX4cHtr7LROyB85MVqChKzE83glivND4FFrGbVPMOW/knL/NOb+Kc/4fn9cRzjkFKwLZ/RJEIkD9OPRwztE9YBu3ox3gzmpHNsXEZIfGp9Ay4egTjLG1AB4HkAW3DQcDwDnnmonWHe+IRAzLy3Iwc+NisvsliDiF+nHo0ZvtkEoY5BLxuOvI1ihwgZRiYpJD41NoCUVItp8CWMk5PxWCuhIOsvsliPiH+nFoaTdYka6eWEi79CQZugdssDtdkIopDxUxeaHxKXSEQilun8wKsW/0iWyN+wnN5eKoadWjVW+BTqsE5xzNvWZkaeSwOR1IlslgcTjRa7JjSooSM7OScaHPjMZuIxQyEeRiMTKSZXA4gY7+S/XSkx9BRIc+swVn24xoN1iRrZGjNEeNFCXZ7I2Xjv7xxyj2IBGJkJYkQ0uvGYUZtFVMJD4uF0dTj3scMtocKEhTB0StcjhcXv0jM0kOkQhIVcngdF3SJ6YkK1DTZkCrwYJ0tQwpSgmMNicMFnedBWkqNPWa0G6wIFkhgcnmxIBV+H6JRiiU4mrG2E4ALwOweg5yzl8MQd0xzdB84wqpCL+/cwE6+2144OUT3mObqkq8ucYfXlkGh8uMx/55Cha7CwXpStxzXQkeeuVS+e9/ZibkUjG27KnxHnti3TwsL8tJ6MZIELFIn9mCN0504qE9l/roI6vKsaw8kxTjcdJusCJFOTGlGACyk93pnkkpJhIdl4vjwJl21LYP4Mn9tYK6gcPhwsvHWvz0jy0ryyBmHA/tOQmL3YXKAi3WVRb4jWdbV5Xh79VNqG7UoyBdia8vLcEDL59AqkqGu64qCHq/RCQUe04aACYAywCsHHytCEG9Mc/QfOMWuwv9Zqe3QXqO+eYaf3hvDTr6rd7zKypyvQqxp3yX0eZViD3HNu86mjC5xQkinjjbZvROIIC7Pz605wTOtlF/HC8d/RZoQ6AUZ5KzHTFJaOg24niz3qugAoG6QU2rPkD/2Lq3BiqZ1HvsrquLAsazLXtqcNfVRQDcOomnjrUL8oa9XyIy4ZVizvmXQiFIPDI03zgAGEfIFe7JNe5BKOe4iwvnIU+U3OIEEU+0G6yC/bHdYA1yBTES7XoLUlQTT8+cmSSnsGzEpKDdYBlRN2jVB+okFrsLRtulgGDmIDqKebCMr04ipJ8kui4SijTPpYyx/YyxE4OfKxhjD0xctNjHEx/QF7VCEnBMKNf40PO+iFngMYo7SBDRIVsjF+yP2ZqJOYpNZtoME7cpBoAsjSKhV60IwkO2RjGibqDTKgXPq2WX1j9VcmEdRelTxvf8ZNNFQmE+8RSA7wGwAwDn/DiA20NQb8zicnHUdw6g22jF4zdX+MUHTFaI8dia8qC5xh9eWYas5EuT7N5jLXhkdfmgbXExNlYVY06uFo/fPIfiDhJEDFCao8ZPb67Axqpi3Lu0GJuqivHTmytQmkP9cby0G6whWSnO1ijQ2E3mE0TiU5iuxpw8LTZVlUAhFUGnVWBjVTF+dstccO7WS8p0mgD9Y+uqMoBx77Fn36vHI6sCyzz3Xj0At07y6Gr3+d2Hm73385RNdF0kFI52Ks75h4z5LX8mbPKOoc51BelK7LizElIx80aJsFodyE9Tod1gxRStAiIGzNQlQy2TQG+2I0Upxa9vnw+5VASpmOFCj9lr2O5r+H7/p2fAaHPisoJUSMQMDd1GikJBEBFGDncs3R0H673986c3V3iPE2Ons9+K1FA42mnkaOk1g3OOIXMQQcQtQlGtRCKGa0uykK6WobLwCjR0mfCgj4P+j26ag+kZakxNk+O5L1/h7mMqGf703jlU5KXh8ZvnIDNJDoPVgaJ0NXZ+ZSGa+8xIU8uQJBdjy8pyWB1OmGxOmO1O/OaO+WjsNmFqmgpP31UJu9OFqakqSMQMH5zvTtioWKFQirsYY9MBcABgjN0Cd/pnQRhjCgAHAcgH7/8PzvkWxlgagJ0ACgE0AFjHOe8dvOZ7ANYDcALYyDn/VwjkHhdDnesau83Y8Hw1Xtu4GEWZSbDZnNhb04aHXjmB0qwk3HFlAXYfbsLNC/Lxu4M1uK0yH9sOXPLk3FRVguLMJHz9hY8DDN/vva4YIsbwpT99NGk8Pwki1qjt7sd3dh/365/f2X0cu756JSry0qIsXfzBOUfXQGhWilUyCWQSEToHrAm9pUtMHoSiWnnm/Ra9CafbBtCqN3sf0gH3mPT9lz7BhiVFUEjE2FndhO8un4XvvXQct1XmY2d1E26rzMf9uz/xq1MuFeFLf/oIpVlJ+NKiaWjTW/wiTXxr2Qxs2VODXpMN+zYtxsnWfkG5EkkfCYX5xD0Afg9gJmOsBcA3APzvMOWtAJZyzucCmAdgOWNsIYDvAtjPOS8BsH/wMxhjs+E2xygDsBzA/zHGorZEI+Rc5zE8B4DjF/XeaBJ3L5mOrXvdXp1bX63Biopcr0Lsue7J/bXoMdkE68xMkgeUT3TPT4KINVr7hB3t2vpsUZIovum3OiBiDEpZaIbxHI0CTWRCQSQIQlGtPPN+u96KrXtrgjrcuTiw7UAtVlTk4lSbwatzCOkem3cdxfFmvVdXOd9lDIg08fM3zngjZ7UbrEHlSiQmrBRzzus559cDyAQwk3O+iHPeMEx5zjkfGPwoHXxxAKsBPDt4/FkAawbfrwbwAufcyjk/D6AOwBUTlXu8CDnX+Rqet/kozR4vT8/fYJ6cKpmw4btKLhlWAScIIvwoZWLB/imXURa18dDZb0WaeuKrxB6yNXI0kFJMJAjDLbx1GS89oAdz6PfoGi4OiEUYVvfwRMIyWx1BFW026NxntAlHrUg0fSQU0SdSGGMbATwK4IeMsW2MsW0jXCNmjB0F0AHg35zzDwBkc85bAWDwb9Zg8VwAF3wubx48JlTvBsZYNWOsurOzc0LfKxiF6Wo8sW5eUMNznY/S7PHy9PX2FGrIrX0mPLhidoBzXkufadJ5fiYakWiTRHhJkksCnE02VZUgWR4K67PIE+022WGwhiTyhIcsjYLCssU50W6TscRwC2+5KSqvA9zGpf5j0sallxz6OXc7zM3NSxlW9/BYPajkkqCRLUQMeGLdPBSkqSeFPsI45yOXGq4Cxt4D8D6ATwB4HyM4588GvejStSkAXgLwdQDvcs5TfM71cs5TGWPbARzinP958PgzAF7jnO8eru7KykpeXV099i80CjxG8B39FmQl+xub22xOvHz8YhCb4jpBm2KVVIy9x1uwftF0nOscwLz8FORqleg2WtHSZ8H9g/aMiWrDE+OE7IcOZ5skwkef2YKDZ7pR1zkAFwdEDCjOTMKSGenRymgX123ylaMt2FV9AfdeVxKS+t6t60J95wB++z+XhaQ+YlzEdZuMJYazKXa5uDdjXapKhlsr8zA9MwndA1Y89c559Jps2Li0BDurm/C5Kwqg08rQb3Vhx8FzAbrHE+vmQSZhuPevHwe1Kf7xTXMwPz8F+WnuRb9gcsWoPjIuoUKhFB/hnC+YwPVbABgBfAXAtZzzVsaYDsDbnPMZg0524Jz/eLD8vwA8zDk/NFy90exYNpsTxy/q0W6woCBd5Q6ebbVDKZNgwGqHRiFDn8kGhVQMtUwMg8UOtVwKDo50tdxPyR5OASciAg32BPrMFpxtM6LdYEW2Ro7SHHU0UzzHdZt8+p16HGnqxZ0LC0NSX13HAP76YSNe37QkJPUR4yKu22SsMdy873C4UNOqR5veghytArOyNWjWm9FusEAqFmHAakfSoD6RmSQH50BHvxV2pxPJcilMdqc3cgQA7310WgX6zQ70mu2w2J2Ylq5GUWaSn74RZ/rIuAQLxf7f84yxrwB4FW4nOgAA57xHqDBjLBOAnXPexxhTArgewOMA9gD4AoCfDP59ZfCSPQD+yhh7AsAUACUAPgyB3GFDJhOjsjA0XukiEUNRZlLCZo8hiHggRanAFdMSa5swWnQYrNAqQmc+kaNRoKnHRGHZiIRhuHlfIhFh7tRUzJ166dhIOsK0Yc6NRb+YDPpIKJRiG4CfAfgBBsOyDf4tClJeB+DZwQgSIgC7OOevMsYOAdjFGFsPoAnArQDAOa9hjO0CcBLu+Mf3cM6dIZCbIAiCiDBtBgtyU5Qhqy9JIYFYxNA1YENmMmUZJAhi/IRCKd4MoJhz3jWawoMZ7+YLHO8GUBXkmh8C+OFEhCQIgiCiT7vBgrIpmpDWmatVoqHbSEoxQRATIhQxhWoAUDwcgiAIYkRClbjDlxytAuc7KQIFQRATIxQrxU4ARxljb8HfpnhjCOomCIIgEojO/tCGZAOAzGQF6joHRi5IEAQxDKFQil8efBEEQRBEUKwOJ0w2J5JCHON5ilaB4y36kNZJEMTkY8IjE+f82cEoEvmc8zMhkIkgCIJIQDr7rUhVySAKcZSIKSlKvHS0JaR1EgQx+QhFRruVAI4C2Df4eR5jbM9E653MuFwc9Z0DOHTOHZTe5ZpYLGmCICYG9cnQ0NlvRao6tKYTgNumuLXPArvTNXJhgkgwaHwKHaHYw3oYwBUA3gYAzvlRxti0ENQ7KRkum00MB8kOwBPku91g8QYKjyf5idgimu0pUfpkLNDRb0WKMvRKsVQsQkayDI3dRhRnJYe8foIIJxMZ32h8Ci2hUIodnHP9kKDp9JgyCoQ6QkO30du4dVoF1i7Iw+k2A3JTlJiTqxVs5LGmgFInJUJJtNtTQ7cRf3j3HH56y1yYrQ6o5BL84d1zmJmTnNBB7MNBR78V2hA72XnIS1GirmOAlGIirhjt+OY7z6tkEticTqSr3RnrPNcCgMXuwuZdRzFz42LB8SnW9IVYIxRK8QnG2OcAiBljJQA2AngvBPUmFL4NMStZAYkYONzYh++/9AksdhcqC7S474aZ6DPZ8Mt1c7Hrowu4fFq6N1f5joP1QTtKrCmgvoo9MHInJYjhaOg24u3TrfjDFy9HV78VmclyvHSkKWJKqd5sw80L8vGdfxzz9rEtK8qgN9vCfu9Eo8NggSaE2ex80aUocaatH8vLdWGpnyDCQVOPEafbDLh7sTvf2e7Dzdi86yhmb1oMF3fH9dZpFTjZ2o/H953CiopciEXArBwNHn/9FL54dZF3rgXgXUw7294PAH5KbyzqC7FGKJTir8Odzc4K4K8A/gXgsRDUmzAINcQHV8zGjoPnYLG7UJGrwc0L8rH+2Y+857euKsPfq5tGVCxjUQFtN1j8OqlHro5+CynFxJgx2x2oLMzAl/90qX88sqoMZrsjIvd3ODm2vlrj18e2vlqD5798RUTun0i06S0hj1HsITdFidNt/WGpmyDCgcvFcaSpDzsO1nvHto1LS7DvRCuONF1aNNtYVYxXjrbgtsp870KZR4+42GeCQiry7i7fubDAr4yv0huL+kKsMWFHO865iXP+A8755YOvBzjnllAIlygINcRHXz2JFRW5AIC7l0wPmHS37KnBXVf7Z8r2KJa+DKeARotsjQIKqX/TUkhFyEpWREkiIp4xWp14aI9//3hoTw2M1shke+/otwbpY9YgVxDBaA9DjGIPeakq1LZTrGIifmjoNnoVX8A9rmw7UIsNS6b7HXdxYEVFrlfZ9ZR99NWTsDk5HlwxGwqpCGsX5AWU2bzrKBq63YltYlFfiDUmvFLMGPs3gFs5532Dn1MBvMA5//RE604U2g0WpKpkWLsgDx7T692HmyEe1BstdqefDbGnjFzC8Os75uOpg+dwvMUAhVSEzCQF6jsHvPZAWckK71Oih2groIXpajyxbl7AFk1hujpqMhHxS7vBitKsJNy9ZLrXpvepg+fQboiMUpqjEe5j2Rp6yBsrnYNjYTjITVGiqdcEm8MFmSQUyVoJIrwEU1IZA9YvKgJjQFayDDOyk9HRb/PTE5IVYuSmqABwZCTJ8eTt8+ByQbC+Rh9b5FjTF2KNUJhPZHgUYgDgnPcyxrJCUG/CkJMsx11XFeDJ/Ze2NDZVlaAkOwkF6UroNAoUpCsDtkYeXV2O37xVi68tKYbsSBO+vGg6zncP4N6/fuwt85vPzY85BVQkYlheloOZGxejo99tQ03G/MR4KUxX4o4rC/xteleWoSBNGZH7z5mixSOry/HQKycumW+sLkfFFG1E7p9IdA6EJ/oEAMgkIuRoFDjXOYBZOk1Y7kEQoSQ7yAO3Wi7BM+/WozQrCV9aNA1HmvpgsTu9esLO6ibcVpmPbw/xc7A7nYL1fXyhD9v216EgXYnH1pTjgZdPxIy+EGuEQil2McbyOedNAMAYKwBFn/Cjx2z3KsSA+8ntyf21+NayUnxz2Uw8uf8MHlpRhnv+esSvzIOvnMD6RUXY+moN/rz+SmQkybD8yXf8ytz714+xb9NivBZjCqhIxFCUmUR2SsSEsdhd2Lp3iE3v3sjZ9MpkYqypmIKiDLV3h6ZiihYymTgi908UnC6OXpM9bNEnACA/TYlTrQZSiom4QGhX9fGbK7Blj1tpvXvJdNR19GPHwXqkqmR4cMVsbN51FOsXFQWYSWx9tQa/XDcPm6pKAhbgnjvUCABo7Dbj1wdqsXPDQpjtzpjRF2KJUCjFPwDwLmPsP4OflwDYEIJ6E4ZWvfAWSZ/ZgT5zP6ob9ahtHwi6jWKxu9A9YIXd6RIs02awYGFRBimgRELSZhC26W2LkPkE4FaMKwvTIna/RKTbaEWyXAKJKHymDXmpKtRcNGDtgrDdgiBChtCuarfRisZuMwDAbHXAxd3jXavegrqOAT+9wBeL3QWDxY7nDjXiZ7fMxZn2flw5LQ3f+cdxtOov2Qw3dpthtjuxsCgjot81XghFmud9jLEFABYCYADu45x3TViyBEIXZIuEc4Ax9/sBmyNoGYVUhBytAskKKdkDEZOObI08iE2vPIpSEWOlw2BFmjo89sQeCtPV2H+6Paz3IIhQIrSr6hnvVHIJxOzSZ5vT5XViFxoTlTIJek02nG7rxzPv1uOGWVnoNfmHjiSdYXhC9cjuBNABQA9gNmNsSYjqTQg8Nom+jXlTVQlePNKMvcda8PDKMuw91oKNS0v8ymxcWoJXj7fgsTXlKNNpvVstvmXIHohIdEpz1HhklX//eWRVOUpzqN3HEx394QvH5mFaphqnWvspzS0Rt/jO808dPIfCDDU2Vbl1g92Hm7GpqkRQX9iyogzPvVfv1RueWDcPZTot6QxjhHE+scGDMXY3gE0A8gAchXvF+BDnfOmEpZsAlZWVvLq6Opoi+GGzOXH8oh7tBovbm10mQkuvBWlqGcQiQMwY9GY7pBIRBiwOaJVS9FvtSFfLUabTQjLoTe1JAhJL9sMJTsh+3Fhrk/FEn9mCs21GtBusyNbIUZqjRopy0q52xGWb/NuHTfj3yXZ8ZXHRyIUnwKYXPsbOr16FaRk08UeQuGyTsYrvPK/TKmAwO9BrssFkcyI7WQ4OwOHiEDGGHqPNq0eIGIPd6UKaWu7VDSaxzjCuLxkKm+JNAC4H8D7n/DrG2EwAW0NQb0IhZJM4K8fdWLuNVihkIkglInQN2DBF6za36By0I65p1UNvtsNqdyFFJYXJ5oBCKsHkaNcEQSQCHQYrtGGKPOHL9KwkHLvQR0oxEXN4FNRuoxVKqRgWuwsWuxNGmwNTtEokySVoG3TmzU9VQcSA7gG3MmyyOTE1TQmZSIyGHiMUUjHS1FKUqJPQZrAgTT28wjvB9c9JQyiUYgvn3MIYA2NMzjk/zRibEYJ6ExpPlrvH953CPddMhxPM62HvCTn1tw8aobfY8bVriv3ObVxagp3VTbj98nyUZCdh6YzsyfLkR0xC+swWvHGiEw/t8QmJtqocy8ozJ/NqcdzRZjCHLRybL9MzklDd0IM183PDfi+CGC2+c/6Xr54GxgCjzekXKWLzDaX4438b0Guy4We3VGDAYkef2YEn99ciVSUTDO2qlonx2//Uo9dkC0jZTGmdx04olOJmxlgKgJcB/Jsx1gvgYgjqTVhcLo5PWvpwus2Aby2bCblEhBMX9X65z7furcHv77wMnzTrA8JRbTtQi5/dMhd1Hf2o7xhAUQaFPiMSl7NtRmx/u9YbzB4Atr9di8IMFa6YRkpxvNCmt2De1NSw36ckOwl//aAp7PchiLHgyWy7flERuged3zzpnQH33P7Ev8/i3uuKYba7YLI5kaKSY+urp2Cxu7B2QZ5XIfYk8DDbnZiapsJ91xdDIZOi32LH4cYezNFpcbHfgoZuI860GZCqknmjYFFa5+EJRfSJmwbfPswYewuAFsC+idabqAx9cvv+Z2YgRS0PyH3+/PuNaNe7t1GEQq+cae/H0+/U48EVs9FjtFIDJxKWXpM9ILHNxqUl6DXZoy0aMQbaDVakqcO/UjwtQ42mHhP0ZntEzDUIYjR4stcx5k7bDAiHVcvRKPDAYKKgjVXF3jKeMGw6rQJ3LizwGw8fWVWGX7xxGo3dZu9O2va3a72fPTqFRzHu6LeQzhCEcUefYIylDX0B+ATAuwDo1w6C52nR09B1KSpvpizg0krwrZV5UMgkaO41eT1HPXhCtXlyn0vFlNKUSFxSVdKAQPXbDtQiNYxJIIjQ09lvDVuKZ1+kYhFm5CTjw/M9Yb8XQYwWT/Y6ABAzeEOt+aKQitDUa/KOdS7uX0YhFWHtgryA8fChPTVYUZHr8/mE3+dtB2qxdkGetw4KyRaciWhThwFUD/4d+prcrqPDMDTX+fkuo+DTYlFGEp4+eA67qpsDwlFtXOoO5+Ypa7I5I/cFCCLCdBttgn2kx2gLcgURa7iz2dnCms3Ol5k5yXi3tjMi9yKI0eAJtbb3WAvSVDKkq2XeUGsAvL5Ef69u9l6z+3CzN/SaJxybWCS8wszYyJ8pJNvIjNt8gnM+LZSCTBaG5jr3BOMeGoS7a8CC4y2GwXMO/PSWubDanbjQa/Jug3jKZmvoqY9IXNLVMsE+Eu5EEETo6BqwIlkR3mx2vlTkpeD/3q7Dw6s4GCOHIiL6eLPX5SSjx2iFQiqG1e7C03dVwmRzIkcrh4gxv2QbrXoLdlY34ae3zMXZ9n6IGXBFYVrQRF/DfV5cnIG183MnU0i2cRGSEYoxtpYx9gRj7BeMsTWhqDNRGZqAY++xFvzwpjkBiT2eebfB+16rkuIXb5zGE/8+C6VU7O009NRHTAZmBEneMYOSd8QN7QZLRB9iCtNVsNidONdpjNg9CWIkPNnrKgvTUZ6bgssK07CoJBPLynJQkZeK2QLJNm6/PB8/fu0Unn6nHk4O/HTf6YAV5kdWlePV4y1BPz+xbh4uL0xDUWYSKcQjMGFHO8bY/wEoBvC3wUNfY4zdwDm/Z6J1JyJCuc7zU1WoyNXifJcRKpkYapkYpdlJkEnESFVKoVFJ8NOb58Jkc6AwXY1ls3PQOTDpAnETkxStUoFPl2eiMOMKb/KOGTlqaCkcW9zQboiMPbEHxhguL0zD3mMXcd8NpRG7L0FMhKH6QWaSAiLmNgdKkkuglIkxNy8FKpkYFXlamGxOTEtXI0+rRFGmGu2DMY7LczSoLEydjAk7JkwoQrJdA6CcD6bGY4w9C7fDHREEoVznJdnJKMlODnpNfpq/7+L0LPJlJCYPWqWCwq/FMW0GC1IjEHnCl6unZ2DHwXPYVFVCCgERNwjpB9NGESliaHKwIgWFah0PoVCKzwDIB9A4+HkqgOMhqJcgCIJIANr0ZqQoI2sDPj1TDalYhIO1nbh2RlbQcue7jPjTf8/jw/M9cHFgXn4Kvnh1IWbpNBGUliCIWCAUNsXpAE4xxt5mjL0N4CSATMbYHsbYnhDUTxAEQcQxLX1mpEbYMZIxhuXlOfjlm7XgAjlu7U4XfvavM1iz/b8YsDrwuSvzcedVBQCAzz31/mCoTIrsQxCTiVCsFD80lsKMsakAngOQA8AFYAfn/MnBOMc7ARQCaACwjnPeO3jN9wCsB+AEsJFz/q8QyB1TeHKie2yCCtPVcLk4Trbp0dpnQZJCApVUjI4BKzLUcohEQKpKBqcL6Oi3QKdVeN97rqctQyJRMJgtON1m9NoUz8xRQ0M2xXFDm96CmdmRX3n9VHEG/lXThr980Ij/WVjoPX6+y4iv/+0I5BIxfrx2jp+98/TMJFw/Kxt/eu88Vv/mv9hx12UoIGdmYox45vRuoxUysQgmmxM6rQIOJ0dTrwlJcglUMjHMdidUUjH6zHb0WxxIU8sgF4ugVUmRn0bzeKQJhVJcDcDMOXcxxkoBzATwOuc8WLopB4Bvcs6PMMaSARxmjP0bwBcB7Oec/4Qx9l0A3wVwP2NsNoDbAZQBmALgTcZYKec8YR7hhfKT/+Zz82EwO/D9lz7xy3P+3KFG9Jps2LKyDGLG8dCek4I50Sm/OZEoGMwW7DvRiYf2nPDJ4FSO5eWZpBjHCe5sdpEPoSdiDPdcW4xH/3kSerMdlQVpePtMJ/76YRNump+LZbOzBUO2JckluOfaYvz7VDvWbP8vfnJzBT5dljPi/awOJ6QiEY27kxzPnP74vlPebJxC8/TmG0qRqpKia8Dmd3zrqjIwcNR1DmDpjGxqTxEkFOYTBwEoGGO5APYD+BKAPwUrzDlv5ZwfGXzfD+AUgFwAqwE8O1jsWQBrBt+vBvAC59zKOT8PoA7AFSGQO2YYmuXOYnfheLPeqxB7jj25352VxmJ3YeveGqhk0oCc6J6ym3cdRUM3hSMi4p/TbUavQgxcyth0uo3ad7wQ6ZBsvuhSlHhoRRk+Ot+Lh/eeREO3EY+uLseny3KGjWHMGMOy2Tm47/pSbHmlBvf+9QgauvzbnN5sx8sft+Brzx/G/EfeQNlD/8LsLfvwpT9+iBMt+nB/NSJG8czpKypyvdnnhOZpd5hVScDxLXvc8/vxZj3N4xEmFCvFjHNuYoytB/BrzvlPGWNHR3UhY4UA5gP4AEA257wVcCvOjDGPZ0QugPd9LmsePCZU3wYAGwAgPz9/HF8lOgzNcge40zsOl7XGYnfBaHMAuJQTfWhZym8efeK1TcYS7QarYPtuN1ijJFF8E+k2OWB1wMU5VDJx2O8VjBytAl+9Zvq4ri3JTsaP187BP4+3YvX2/yIrWY6sZDk6B6y40GNGea4G8/NTsaJChzS1DGa7E++d68adz3yAby2bgc8vLAjxt0k8Em2c9MzpvnNzsHnaaHUIH7c54OKgeTzChEQpZoxdBeDzcNv9AsCIox9jLAnAbgDf4JwbhnliFzoR6DUBgHO+A8AOAKisrBQsE4sMzXIHXMqLHixrjUIqglom8Ts3tCzlN48+8domY4lsjVywfWdr5FGUKn6JdJts7TMjM0ke15nlFFIxbr4sD6vnT0Fjtwn9FjuSFVJMTVVBJvHfcFXJJLh+Vjbm5Grx49dOQS4V4ZbLpkZJ8vgg0cZJz5wO+M/NQuOYWiERPi6TQMRA83iECYX5xDcAfA/AS5zzGsZYEYC3hruAMSaFWyH+C+f8xcHD7Ywx3eB5HYCOwePNcId585AH4GII5I4Zhma5U0hFmJOnxY8EMt29eKTZmyPdZLP75UT3LUuZ7ohEYWaQjHYzKaNdXNCqj57pRKiRiESYnpmEeVNTMT0zKUAh9iVbo8C3Pz0Tj/3zFGoukinFZMIzp+891oKNS0uCztObbyiF2eYIOL51lXt+r8jT0jweYZhQqJqw3tC9XPAsgB7O+Td8jv8MQLePo10a5/w7jLEyAH+F2454Ctx2yyUjOdpVVlby6urqcH2NkOPxVPXNQOOJPtHWZ4VaIYZKKkbngA1pahkkIiBlMPpE54AFORpFwHuKRBESQvbDxVubDCVC0VXG0iYp+oQfcdUmd37UhH0n2rBhyfjMF+Kdd2o78cbJdry2cfGwSnScE1dtMhJ4xrweoxXSIdEnLgxGn1BOMPrERMfVBGdcP8S4zScYY7/inH+DMbYXAuYMnPNVQS79FIA7AXziY3v8fQA/AbBr0Da5CcCtg/XUMMZ2wR3/2AHgnkSKPOFBKIuNSMRQkZeKirzhr/XNbleQpsI/T7Ti/t3HKRIFERMIRVcZa5tMksuRkeSA08WRkSRHkpxMJ+KFi1GIURxLLCrOwAf13XjqnXrcc11xtMUhIoTQnO6heDB7ra9Sq9MqcVXR6JXaUIyrRCATsSl+fvDvz8dyEef8XQTX4KuCXPNDAD8cy30mIy4Xx3v13V6FGLgUiWLmxsVkrE9EBaHoKmNpkzT4xzfNvWakqSavUswYw/8sLMSWPSdwa2Ue2YgSACY+rk10XCWEGfdeDuf88ODf/8C9inuSc/4fzytUAhKjp6HbiOrGnqCRKAgiGghFVxlLmww2+FOoovjgYp8F6UmTVykG3NEvFpdmYtubtdEWhYgRJjquTXRcJYQZt1LM3DzMGOsCcBrAWcZYJ2NsTBnuiNDRbrDAxeE12PdQkK6EUirGoXNdqO8cgMvF4XJx1HcO+B0jiHDg64ntYSzRUdoNFpRmJWHbHfPx+No5+PUd81GalUSDf5xwUW9GuprMXVZVTMGeYxfR0meOtihEBPCdYxu6BnCuw3++nahSO9FxlRBmIuYT34DbPvjywaQaGIw88VvG2H2c81+GQD5iDGRrFF5vV0/A8IJ0Je69rgS37Xjfb4tGJmG4968f03Y0EXY8nthDtwlH61U9JUWBO64swHf+ccx7/ZaVZdBpafCPdTjnaNNbkJFESrFGKcXSmVn4zYFa/HhtRbTFIcKIr2lEsIyzs3XJEwqlOtFxlRBm3NEnGGMfA7iBc9415HgmgDc45/NDIN+4iXUP1nB4jXo64h/ePYe7ri6CxeZAQboaX/jjhwEdb8OSImzbX+d37DUBWyTybiWv6lAgFF1ltO3oWFMvbnvq/YA2vPMrCzE3PzVcIscycdMmuwesuPbnb2PHnZVhu0c8YTDb8a1/HMObm69BtiahHuripk1GgvrOAdy47R1Y7C7cc10xnnm3PmD82nvvInQNWNHUY4JKJsGz79Xjy4umj2lxarhxlebuCEefACAdqhADAOe8czAOMRGEcDkOiUQMy2Zlw+50eVfVNlYVC27RDLWWEMqARw5ORKgYzhN7JDoGhDPadQ5QRrtY52KfBVnJtErsQaOUYlFxBp5+5zx+8NlZ0RaHCBO+phHBMtmdbDX4RYl6dHU5rp+RNaa5Ndi4SnP3+JlI0ETbOM9Nes53hc9xqKnX5Bd9QsjGWCEVYWi/ENq2IQcnIhbQKqWCbVijpGfvWKelz4x0Mp3w4zPlOuz8qAkGiz3aohBhYqi9r9D4da5zwG9uffCVEzjVbhi23tH6AtHcPX4mohTPZYwZBF79AOaESsBEw+XiONVqCJvX6FDj/d2Hm70ZdYBL2e4q8rQjZsAj71YiFugz2wPa8MalJegzk1IR6zT3mpA+iWMUC5GZLMfcqSn4y/uN0RaFCBO+WWqFMtk9tqYcf69u9rvGYnehTR98bvWs/t647R3c8dQHuHHbO9hX0yaoGNPcPX7GbT7BOReHUpDJQkO3EbUd/cMa2PvaAqlkEticTqSr5QE2QUI2Q54nVE/drXoLdlY34c/rr4Td6fKWA4DXNi4e1sZzaF1D5SQmDzabE8cv6tFmsECnUWDOFC1kssgMAalKKXZWN2H9oiIwBnAO7Kxuws9vmRuR+xPjp6nHRE52AnymXIdfvXkW6xcVJXKWu0mLx5Rx51cWokVvRo5GgV1fXYjuARtUMglkYoZekw06rQJrF+SBMUDMgNxUZdA6xxKXmObu8TMRm2JiHLQbLNhV3ewXIUIhFeFHN83xpnYeagu0cWkJDpxuw8aqGZCKGbI1CuSnqvDGqfYAm6Fls7IDPFJvq8zHN/9+FPcvn4Urp6V7ld+RbDzJu5UA3Arxy8cv4qFXTnjbwSOry7GmYkpEFGOVXIx7ri3GQ3tqLt1/VRlUcnouj3WaekxYMDmdIYdlWoYaOq0Ce49dxM2XjZCylIgbfBeqHE6OB175BI3dZhSkK/G1a4qxda97DKss0OLxm+fgYp/FLypFabYGs3VaQbvf4VZ/h87jNHePH1KKI0y2RoFekw3Pv9/oXfkSMWBBfgpEIob6zoGAp8FtB2rxxLp52PB8tbeB77iz0ltOp1Xg81fmo99ixwfnu6HTyLHt9vk4cVEPpwv46Hw3vrlsJvotdnzS3AeVXIILvSaoZRJka+RB86uLRAzLy3Iwc+PiwVVrMWxOFxq6jZPRk3XScvyiHtXnO/GHL16Orn4rMpPleOlIE4oy1KgsTAv7/S02F7a/Xee3Urz97To8ceu8sN+bmBjNPWZ8uiwn2mLEJJ8p1+F3/zmHtQtywRiNpfGO0ILWgytmw+lyIUMtR32XET+7ZS5a+kwAgCS5BGa7E3cvLsLBMx1YXJqFM+0G5KUqMSc3UDEey+qv79w9nog/kxlSiiOM7xPc9rfqvE9w+WnuJ7hgT4On2wx+irInc51Oq8AXry7EL9886+2Im6pKkKWR4+/VzchMkuGOK9wxXoXiJW6qKkFJdhKWzsgOqhgXpqtxuq0fX/rTR+TJOgnhcKKyMANf9vn/P7KqDBzOiNy/22hDY7cZ29+qCzhOxC6cc7T0mSn6RBAq8rT420dNOFjbhWtKM6MtDjFBhMwbdhw8hw1LpmPz3y/FWL/v+lIopSLc45MnYMuKMvzuYB0au83YcbBecH4d6+rvRCL+TGZIKY4wIz3BBXsadA5+9Ngg5WqV2FRVDKlY5FWIAXdHfHJ/LTYsKcKtlXkozkr2hmdbuyDPqxAPLZuZJIfN6YJMLILJ5vSLa0g51ic3nIu8pguA+///0J4aPPflKyJy/ySFWLBPqMl8IqbpMdogETOoZDTNCMEYw43lOvzfW3WkFCcAQgtaKypy8eirJ/3Gzl++eRYblhT5Hdv6ag3WLyrC9rfqvPNr7oaFMNmcfn5Fy2Zlj+gLREwMGq2iwHBPcEJPgw+umI0dB89Bp1XgzoUFfrbIj60pDxqHeHqGGmabY8R4iS4OfFDfDYcLfpnwHl09B1Ixg4gxpKpkaPXxjA1my0QkHu0G4TjB7YbIxAnWaRTYsrLMa4/nyWg3hTLaxTSNPSbkJFaCipBz9fR0/ONIM45d6MPcqSnRFoeYAEILWmJR8Dl36DFfCxqL3YX9pzuwbX+d169oZ3UT7l8+C8vLcmjeDSOkFMcYQ+14pWIRzHY77v/0TLg48K3BVV8ASFXJoJD6r6LptArcWpmHkqxkZGvkUEpFUEhFSFXJMCNbOK2kiAE5KSrvirJOq8Btlfl+Nsybqkrw3KFGr2JMnqyThxyNHAXpSqyoyPUO3HuPtSBbE5lt8R6THb/7j79N8e/+U4fS7HmYFhEJiPHQ1G1KtKxtIUciFuHGOTn49YFaPP2Fy6MtDjEGfJ3qcpLl6DPb8fjNFajvHMBbpzvwmTk6TMtQY1NVMXZVN/vNnUJ5AnyTCyukIiilYtxzXTEYA6wOJ26/PJ92aCMAKcUxiK8dryd3+pc+VYgBq8NP+b1zYQF+9q/TuO/6UvzyzbOCNsOPrC7Hbz43H7XtA/j5G6cDol5sWVkGMTgauozeutcuyPOWAfzNLDxPruTJOnnITBKO/pCVFBnzhVa9RdCmeLiYnkT0aegyIjOJYhSPxHUzsnDfzqM409aPGTnJ0RaHGAW+TnVD592CdCXuva4ED/pE6/EsKvWabNh8QynSk2TeBSr3eFqO7W/XAnArxN9bPhMWh8ubHtqzY5yqktEObZghpThG8bXjXbsgD3/5oBHfWjYTG6uK3VnqJCKv4vqn9xpw/6dnYIZOg/fru3H34iLsPux+Mn3olRN46s5Kb4f1RL0Qi4DirGT84o3T+MGNs5GsutRJg5lZzJ+aghc2XEk51icZ7f1Ov+gPgDv6Q8HNc1EYAVNIXRA7e1qFjG3OdxmRQyYuIyKXiHHjHB1++e+z+N2dl0VbHGIUNHQb8fi+U1i/qAilWWqIRSL8v2uLYXO6kJ0k9yrEgHvufOGjJjx+8xzozQ409Rihlopx73XFsDhcEDHA5nDgm8tmoq6jH0tKMvFRQ0+A/8+jr57EhiVFtEMbZkgpjlF8jfaTFWLcVpmPb//jkgfro6v9bYnNdpdfdICNS0vw/Ptuc4cek80vmYdnxW3zDaVo7DbjxEU9MtQy74qzx+RiqBKSn6bG9CzKsT7Z6DPbcVtlvt8Ow8alJdBHKKPcnClaPLK6PCBOcsUUbUTuT4yPhm4j2cmOkutnZWPzrqM4edGA2VM00RaHGIFuoxW3VeZjZ3VTwNj4k7UVfnPnJXPEw36rvn/5oAm9Jhs2Li3B9rfrcWtlHgrT1ZiXl4I2vXAUqtLsZNqhDTOUSidGydYoUJCuxLeWlSI/TY38dBW+cX0JdFoFLHYXmntN3rSRQuYO2w7UYu2CvEHbX7lg7vXCDLU3ssWPXj8NF+dYv6gIJVnJAWkpN1WVQCzQWijHeuKTqpIKtq8UlXTUdbhcHPWdAzh0rgv1nQOCqUmDIZOJsaZiCv68/kps/9x8/Hn9lRFLHEKMn4ZuE3S0UjwqFFIxVs6dgp/uOx1tUYhRIBO7d2pXVOQGjI3nuwb85luh+fnRV0/ih2vK8bNb5mLfiVb0mmyoLEjFgvwUSCQizMpJFpyzZ+VoaLEpzJBSHKPkp6rw9aUl+M1bdfh/fzmCb/39GDgHvrakCDqtAruqm/Ho6nIopKKg5g5iEfDIqnJYHA5sXOqv5G5cWoK2PhM2Li3Bi0eaYbG7YLQ5sf2tOhxv0eO5Q24zi3uXFmP9oiI8d6gRbYZAG07KsZ74dBttgv/jnlHGCfbsJty47R3c8dQHuHHbO9hX0zZmxbiyMA2frZiCysI0UohjnF6jDQ6nC1rl6B+cJjvXz8rGqTYDDp3rjrYoxAiYbM6gpoa7qpuxZWWZd74NFoHiyIU+fPsfx/CZOTr8eO0ccHDkp7mz2p7vMQYsTD2xbh6mZdAqcbgh84kYpanXhAde9rdL8ji7rV2Qh2ferUdxphp/WX8lTHYnnhYwd/jU9AzoUuTo6rdhZ3WTn/f+zuomfHPZTPz4tVNo1Vv8vF/FDOg12fwcm4JFm6Ac64lPulom+D9OU4/OiYriXE8+zncbMSVFSZnaxoBULMK6yqnYurcG/9y4GGJaEYxZPPMegICxsddkQ5/JhnuvK0Zmkhy6FOE5kvNL8/qury5E+ZRLWW3v/evHSFXJ/LLeztYl0ypxBCClOEbwdVbLSlagqcco+HQpEYlQmqXGT2+ugEwqglzsTr381J2V3jzr7vjF7hjDBrM7YsVjq+cMOV+OX7xx2qsQe7xjFVIR5uRpR8yc4yvv0Hs/fnMF8lNVkf4JiTAhFzP87JYK1HYMwMXdD03FWUmQi0c3QLcbLEhVybB2QZ7XUW/34Wbyok5g6juNZDoxDq4qSsf+Ux3424eN+J+FhdEWhxDA5eJwujieWDcPAMf2z83HI6+e9M5/P72lAi29ZlgcLlzUm5GlleFHN83B91/6JMDnpyJXg7uXTEdTjxmcM5TpNN7dV1//H8Ad07owg8bLcENKcQww1FmtIF2Jx9bM8Uaa2H24GQBwa2UeijLUqO3o98ZB9A2/9qOb5kAtE6Om1YBfvHEGvSabXyiYR1eXQyZmEItFSFaI8eyXrkCbwYLMJAXEImB+fgp0WgV6jXZ0Ga147stXwGR1IC9VBYmY4YPz3cjWKJCfqsIbp9r9lOYf3TQHYhEgYiI8+149AOCz5TpIJGShE+9oVWLwXv9jfPD4aNBpFYLpxSmxQ+JS294PnVYZbTHiDsYY7rqqAI/vO41lZTm04xZjOBwu/PNEK+7ffdxvLPvWshlQysS42GtGZ7/VLzxbborSG71HLAJm5Wjw27frkJkkwx1XFOAXb5zGiopcnG3vR4/Rivw0lWDuAZPNifrOAYruFGZIKY4BfLeXPZ6qX3nuUuIMT670H71+2m/lduiW9Pdf+sQbS9jDk/trvekjH3zlBNYvKsIz79ZjU1UJ0tVyXFGY7u1geSkqvHysxWu2oZCKsHVVGfqtDnz7H5cGgR13Vgre21P3xqUleOLfZ5CqkmFRcQZ14Dinx+hES68ZOw7W+00EeSlKFGaMfL3TBcH04stm54RZciJanG7rx2X5qdEWIy4pSFfjmtJMPPDSCfz+zsvIBCVGcDhcePdcl1chBvzNGgFgZo7Gb25cUZHrje/uWfVVSEXYsKTIGxJ1aPSKH900B7/53HyvCcXQBQWK7hReaBkvBvB1VhPyVP3lm2fR5ePsZLG7cLrNMOb0kb6OAU/ur0VDl8nP4ammVR9gx7xlTw1qOwb8jlU39gje21O3xyu3urGHolAkAP1Wh6BS2291jOr6jn5hZ8zOAXLGTFTqOgaQm0orxePlpvl5ON3Wj1eOXoy2KATcu7nv1XfjSFNv0HnXxREwLwdzgi/NSobLxQWjV3z/pU+QlSTHaxsX41e3zQsYeym6U3ghpTgG8DXaD9aJhiq7Lg7BkC3DpY/0fW+xu2C0OrB511E09RhR3zmAC71mfOP6EnxrWSnuXVqMe5cWI1UlG/W9fesWi9wrhBSFIv6x2J2CbdJqcwW5wh/f9u2BnDETF7PNic4BKyVXmQAyiQhfu2Y6Ht5bg6ZuU7TFmfQ0dBtR3dgz7LzLefC5cehnjVIKuYQFjUzRqnf7W7g4p+hOEYaU4higMF2NJ9bN8/Nm9UVI2d17rMUbks1T5r7rS5E+mJkOAArSlfjlunmQS0TYVFWM7y2fiRePNHvLdw5YkaqS4UhTH27c9g5++M9T4Bz4zVt1+M2BOjz9Tj0231CKMp0Gv75jPipyNd57P35zRUCIN9+6Z+Zo8OrxFlJ8EoCsZDkK0pW457pi78NSQboSGcmjiz4h1L4pTXjicra9H3kpSoqeMEGmZaixet4UfPX5aljszmiLM6lpN1ggl4iw91gL7ru+NCCGf55W4U56JRFh++cWoCDdvUuy91gLtqwo8yu/ZUUZtu0/g+4BK+ZNTRGc7z2ZIGlBIfIwzkcfKzSeqKys5NXV1dEWY9R4ojn0GK1o6bP4GfLfd30pFBIRfrzvkk3xxqUlOHC6DV9ZXAyrw4kz7QNepXTtgjwkK8TISJL72QdvvqEUrx67iGtnZiE/TYV2gwVSEcMTb7q3Z+65rtiba92DQiry2gpvWVGG3Uea8NVripGmksFoc8DmcEEmEeNBn+gTD66YjZeOXMCdV03DlBQF0tXyeHYOCJnQ8dYmPdRc7MW5ThPqfKJPTM9KQnGmCrOnjM5u1NO+O/otAWnCiTET023yhQ+b8PqJNnztmukhrXcywjnH9rfrkJWswBPr5sayfXFMt8mJUt85gDdPtgGM4S8fNOLWy6YiK1mOLI0CHQYzek0O/PLNs0hVyXBrZR6KM5Mgk4hwvsuI1z9pxeLSLIhFwJXT0vD466dxvMWAX98xD1Wlmdh3qsMvMsVja8qxZm4uJBIRZYydGOP6gcjRLkYQiRiKMpNQlJmEBS6OwnQV9p/ugNMF/Om9BgDAz2+Zi7Md/XC64E3hfOLix3hi3Vw/ZXb7W3XYWFUcYB/8lw8a8fWlpXjg5Usd8NHV5UhVydCqtwQ13fAc3/pqDf68/grUdhjx9b997K3jp7dU4PbL82F3chRlqCGTivCNG2bgBy9dUpSpI8cvA1ZhR7scjXzUdfi2byKxOdlqQB7ZE4cExhjuXlSER189iR0H6/FVetCICvmpKuSnq9HZb8XqebmwOFxo7jNDq5IiWSHDQ3tOIlUlw50LC/yc5jYuLUHnwKWY/4+vnYPjLQYopCLM1mmhUsqwqmIKpmeq0aa3IEerQJlO643aJBIxLC/LwcyNi2lBIUJExXyCMfYHxlgHY+yEz7E0xti/GWO1g39Tfc59jzFWxxg7wxj7dDRkjiQiEcOc3BTMzNHgmXfr0aq3oNfkzh62bX8dtr9Vh1a926bIYnehqccUkP1mWoY6QMFdUZHrVYg91z74ygncWpnnLTOSrfCA1Ymte2v86vjOP45jwOrEE/8+i3v/9jE2/u1jfHi+B43dZm8Zcg6IX2wOLuhoZ3Uk5i4TMTFOtOhRQKYxIUMhFWPzDaV4+p16vPZJa7TFmZQ09ZrAOfDD107h79XNXvvhkxcN4HCPiUJO8tsO1GLtAvf86jFZ9MTy92Snk0hEmDs1FZ8u12Hu1NSAMKaeBYWFRRkoykwihTjMRGul+E8AfgPgOZ9j3wWwn3P+E8bYdwc/388Ymw3gdgBlAKYAeJMxVso5T2gjK5GIYdmsbOzcsBCtegt0WgWSZBLB+IUFaWqAATvuXACD2Qmj1QGFRBSQRSeYUX/5FC22f24+khUSLJxWiR+8/AlsDo5bK/MwNVWFjn4LKnI1uHZmFgYsDsE68lOVuHdpMQB3XGWhKBhDkzX4JgDJ1lx6Ag52nIgORqvw/9w4yugTxOTB6eI43daP/722ONqiJBTpSXJsXjYD33/pE2QkyXHFtLRoi5Rw2GxOHL+oR5vBgilaty1v14ANVocLmUlyyKUMm6pKoFFKsePgOdx62VRkJsuRqpJCIRUNu9OqkIqwZWUZ+s027LizElcXpdOcFqNERSnmnB9kjBUOObwawLWD758F8DaA+wePv8A5twI4zxirA3AFgEMRETZKuFw8IEHGbz433xufWCh+4dZVZfi/t+vQ2G1GZYEWj6wq88ZIdG/XaATTTZ64qMe2/XV+gcg5gO/4xCbesrIMv/tPHVbOzRWso0Vvxm8OXKpjaLKzoc4BwWylls3KDvjeZHoRXXK0csH/+VjMJ4jJwfmuAaQopUiSk2VeqClMV+P/XVuMrz5fjb9+ZSFm6TTRFilhsNmcePn4RTz0in+M/r9XN6Glz4r/vaYIUokYZrsTL3zUhM9dUYBfvnnWm6Bjy8oytOnNguPkVUVpWFKcAZvTCV1hGqZl0CJPLBNL0SeyOeetADD4N2vweC6ACz7lmgePBcAY28AYq2aMVXd2doZV2HDjm9ADcD9x3vvXjzFblxw0fuGWPTVYUeH+aa4sysT2t+vws1vm4t6lxVi/qAgdBkuAmcWmqhL8vbrZW8eT+2tR2zGAuiGxibfudde9+3AzNi4duY7Zudphow0Ifb/Nu46iplUveDxeTS8SoU26nMDmG/w9rjffUArX6CKyETFGONvksQt6FGWS6US4mJOrxZ0LC3DXMx8mVKi2aI+Txy/qvQoxcGk+vevqIqxdkIcuow1b99bAxd1miB6FGAAau8343X/qUDbFvRA1dN67qigDVxSlY1FJFqZnkflDrBMPj/NCLUjQmJFzvgPADsDtwRpOocKNb0IPDxa7C20GCxYWZQQ973FOZszdWc+09+Ppd9wOUvcuLcbuw81Yv6gIjAEzspPxo9dOee2TPXW4ODB10Bxi9+FmtOot3rpb9RY8/34j1i8qwsycJADMrw6dVoHPX5kPm9OJZ790BboGrMjWyFGeo/UbDILJ3220euUD4L1/Y7cxLs0oEqFNtugtePXYRfz0lrkw2xxQySR46uA56LRKLIi2cMSYCWeb/Kihh5wpw8xV0zMwYHPgc0+/jxf/92pkJUA86GiPk8HmI7lEhMvyU2C0OfH1pcUQixhMtktx23VaBdYuyANjgNXhgsXh9M5fi4szcHlhWtzNWZOdWFKK2xljOs55K2NMB6Bj8HgzgKk+5fIAJHyaH098wqFbMR4ThGDnfSPsKaQi78rutgO1AIBe0yVP2HuXFnsd+HyvETGgqcfsTdn8/PuN6DXZvHW36i145t16/OyWuajr6PfWodMq8MWrC/HXDxtxG/NPXfnI6nKsqZgCmUwcVP6CdCX0Jqc3kobHe3dndRM+vtAHs91FZhRRYGqqAp+Zo8N3/nHML/pEXkr8T8ZEaKlu6MUXP1UYbTESnhtm5WDA4sD/PPMB/v61q6FVSqMtUtzicLiQqpYKzkddA1Y8+upJ77j3rWUzMDcvCQqpSDDaxIMrZuPFI83oNdmwdn4uzVVxSCyZT+wB8IXB918A8IrP8dsZY3LG2DQAJQA+jIJ8EWWkhAdC57euKsOrx1sAuIOGP7KqDL0mG55/vxEblhRhRnYStqy8tL2z91gLHl5ZFmAKka6S4cUjzV7v2Vsr87BlxaW6PbEUn32vHiXZyV6TjLuuKoCLc3xr2UxYHU6kqtzJHSx2Fx565QQ+bu5DQ9cAHA4XOHeHmNtUVQzdoFPDo6vn4Hsv+eeV33agFvcvn4W/Vzf7Zd87dK4L9Z0D3hTVRPhwuIAXPmrC+kVFuHdpMe5eXIQXPmqCk356wge92Y6WPjMK0lXRFmVSsGZeLqZnJuHLf/qIkntMgJpWPZ544wy2DjF9eGjFbLQbLLh7cZE3u+vP3ziD811GbFlZhlsrA6NNPPrqSdxamUfJieKYqKwUM8b+BrdTXQZjrBnAFgA/AbCLMbYeQBOAWwGAc17DGNsF4CQAB4B7Ej3yBDByfELf82fb+/FJiwFWhxMrKnLBBlNOnrrYhz988XL0mmzIVMvRa7ahRKvA7/5nAY43G1CYoUaSTIRf3z4fNqcLSQoJTl404HcH6wEA91xXDMaAeXkpeP5Qg1/dyQoJHlxRhm6jFUWZSXjqrsvQprfiQR9HBc8qs8f8orHbiCNNvchLVeLnb5zxxjD+0U1zsCA/xVvOF4vdhbqOAW8EjiNNfX6BzskJL/x0G2348tXT0G2yeZN3fPnqaegx2ka+mJg0fFDfjZk5yZCIYmmtJXFhjOF/Fhbgt2+fwz1/OYLf33kZJGL67cdKq96C6kY9Vs1zek3EpqYp0dhtxitHW7CiIhdiEfDQitn47dt1SFHJ0NlvRUGaSnC+mj81BdeUZtGcFKdEK/rEHUFOVQUp/0MAPwyfRLHJ0IQHLhdHfeeAX6gyz7lv7DyKuxcXBWSk+1t1C9YvKsL2tz6GQirChiVFcHF47Yx/fcd8/PyN01hRkYv8VCUsdicyk2RYXq4LCELuUXAVUhF+uW4ebtvxvtf79idrK7wKMXBpldd9b3dUCoVMgif31GDDkiKsqMjF9rfqYLG78P2XPsFrGxcHNQmxOtyfb63M8yrEnnts3nUUMzcuJjvGYZhoiLvMJBnqOwcCkndkqEeX5pmYHPy3rgszdcnRFmNSIWIMX11ShJ//+yx+8NIJ/OTmObGc9S4mmZKihEIqQke/DT96zZ019tkvX47f/acOt1XmB5hHTElR4BdvnMW6yjzB+aogyPhKoUbjA3qsjBM8Icxu3PYO7njqA9y47R3sq2mDy8W9phR7j7UIRoZ48Uiz1+ThsoIUv3K9Jiu+do07vfP9L36C3x+sx/9eWxw0CLlnYPjJvlOw2F3QaRW4rTIfhxt7R4zR+PTBc15HPt9x2xPDWMgkxFf+0qxkwXt09FtACDNcuxktRptDMHmH0U5xiolLvH22E3NyU6ItxqRDIhZh09ISVDf24Fdv1kZbnLjBs8ikN9mwZWWZ37x46mI/VlTkCppHGEwO3HVVAXK1Cj9zRKEoS773mug4TESGWHK0I4YhWAgzzyrp8rIczMxJRo/Rip0bFqLHaMPptn4AwM2X5YFz4NcHavGHL1yBP37xCm85q8OFu/7woV+9p9oMgspnaVYS1i8qQr/F7s1W58nic/fiIsGn5pnZydhx52X4+b/OeNNbihjg9Kne40A41GQkM0kBsQiYn5+CrGQFOMewzodEICO1m9Hg623twWJ3wWyjmGyEm/NdRgxYHCgke+KooJSJ8a1lM/DIqyeRo1Xgjivyoy1STDM0Tv73PzMDq+flIjdVgZ/fMhcSMcPJVuF5UG+248n9tdhUVYIFBSnYuWEhTDbnsKu/oRiHichASnGcECxkjCdL3FBTi0PnuvD4vjMB9XQOWLzpIj3lhtbrCqJ8XtSbsf2tOmyqKvae92Tx2X24GfddX+qN3+hZ5eXgePCVE1774U1VJV6bYk+9vk/XQ78HABRmXDIf8SQv8bUpJoeG4IzUbkaDRiERbA9JCnFIZSXilzdq2jA/P4W27qNIikqGb396Bh775ylkJctRNSs72iLFLEOVVIPVid8cqENFrgZfu7YYTd1GzJ+aIjjudQ5YYbG7MCVFicumpgWkZRYiFOMwERlIKY4RRrI3Gi5Em9C1I4V081wjYiyg3N5jLXhwxWy/UDSPri7HFdNSMT8/BTkaBWbkaLB511Fvva16C/70XgPuva4YWcly5KYqkZuiRF6KCrN1WjT1GKGSSZCtkSMvRYWyKVpBB8LhGMn5kAgkW6NAZYEWd11dBLPVAZVcgmffqx/T6rpcIsa3Pz0DP/vXGW97+PanZ0AhIaWYcLPn2EWsnieYU4mIIDqtEvddX4rNu47hD1+sxGUFlA5aCCEltSBdic9WTPEqy4+tno0tK8uwde+lrLD3XV+KP73X4M0Q66sQDzeHjzQfE7EDKcUxQLCUx75RFTz2tkPL5KeqgqZLDraq6nu/VJUMm6pK/NJF3355PnZ+2IQNS4qQn6pCm8GC37xViz8WXIGFRRkAgPw0NWZuXIweoxUlWUm4f/dxtOot+M1bdd4sPh7Zp2clYXqW/9Pw0NXg0SK0kkwEJ0+rxLrLC/xiDD+yuhx5WuWo65BJREhTy7xOmiIGpKllkI1ihYRIfDzRYWZT2uGYoDgrCV+7ZjrufrYaf9uwEDNz6P8ylKFK6u7DzXhkVRm+/sLH3mPb367H/15ThE1VJchMkqOp14Q/vdeAXpMNm6pK0NhjxPTBXdqR5vBg8zftcsYejPPENPSurKzk1dXV0RZjVNR3DuDGbe8EPEW+NsTeyPMk6rtK2tBtDHqt5/zQVdWh99NpFbi1Mg/lU7RgcKcLPN6iB+fAi0eavdnqXthwpVcp9kVIrgRavQ3ZF4lGmzx2odcbJcSDQirCzg0LMXdq6qjq+FdNK7YfqMPdS6bDbHNAKZPg6YPncO/SEiwrywmX6ERwYqpNPrK3Bj1GG267nOxYY4n3znXhhQ8vYNfXrsK0jLArXzHVJkdCSIn9xa1zcc9fP/Yrp9Mq8NNbKuByudBvcaK+ywirw+VN0OGZo0czhyf4PBmLjOvHpZXiGGC09kZCq6QjXSu0qjr0mla9Bdv21+GPX6zEw3tr8K1lM70h2zwMt9VDq7exS7DYz216C+ZODXLREJIVEpztGMDGv12aMBRSEdRyMp+Y7BgsdvzjcDMeW1MebVGIIVw9PQNWhwu37ziEnRuuQmH4FeO4wdcUr7HbiI8v9AEI9KXpNdkgE4vgEjHc+7dARd0zz45mDqd5Mj6g/c8YwLOV48to7Y3Gc22wa/LT1Lh/+Sz8/I3TAaHdaKsnPtFplYL/6xzt6G3ZdBqlN2uh5/pNVSWYMgYTDCIx+eO75zFvagoyyTYyJrluRhZWzZ2Cdb8/hLPt/dEWJ6bwKKnXlGZhZo4GLb0mwXEuWyMfcZ6dyBxOxBa0UhwDTMTeaDzXBrtmWoYa0zLUfqHdRgo1Q8Q2ZToNHltTjgdevpRp8LE15SjTaUddR0G6GiXZSX42xSXZSSigh6RJTavejGfePY9HVtMqcSyzdGY2FFIxbvv9IWz//AJcPT3QBG4y41k1buoxouaiIWCcy09zj3PDzbNkUlC3bwAAentJREFUM5w4kE1xjDARe6PxXEv2TaMmrmzlhHA4XKhp1aNNb0GOVoEynXZUYYR8ofYSU0S9TbpcHF/444fI1ihw84K8UIlDhJETLXr839t1+No10/GVxUWh7r9Rb5OhYLhxbqQxkMbImGNcPz4pxQQxPAkx2BMJRVTbJOccj7x6Eh+e78F3PzMTEhFZ4cULnf0WbH/rHFJUUvzk5opQOuDROEnEGuNqkzSaEQRBEKNiwOrAfTuP4eDZTnyjqpQU4jgjM1mBB1fMRkl2EtZsfxffe/E4zncZoy0WQcQMZFNMEARBBIVzjoZuE149fhHPvteAuXkpeOCzs6GQUvSReEQsYvjsnClYXJyJ10+04abt/8W0TDU+XZaDhUXpmJmTTP9bYtJCSjFBEAThx5m2fqz49TuwO/3N6xYWpSEjSY59J9qiJBkRSuQSEapmZeF4sx4/ef30sGUrC1LxzBcuh1YljZB0BBF5EtammDHWCaAx2nKEiAwAXdEWIoTE0/fp4pwvD0VFEWiT8fC7xrqM8SDf6XC3SUlanizjs98oZGKpyN7XbnEZe+0cPnOF06GEWGIOhQwRg2QeFUwkYRJNhkysTpWJFMkSJleJmVgqsrXXD3S++Ei9y9zvFLgsnsbJ8RDr48JYmQzfZ1xtMmGV4kSCMVbNOa+MthyhItG+T6wQD79rrMtI8sWXHGOBZCbGS6L9H+j7BIe8JAiCIAiCIIhJDynFBEEQBEEQxKSHlOL4YEe0BQgxifZ9YoV4+F1jXUaSb3TEihxjgWQmxkui/R/o+wSBbIoJgiAIgiCISQ+tFBMEQRAEQRCTHlKKCYIgCIIgiElPwirFy5cv5wDoRa+JvkIGtUl6hegVMqhN0itEr5BBbZJeIXqNi4RViru6EikuNZEIUJskYg1qk0SsQW2SiCYJqxQTBEEQBEEQxGghpZggCIIgCIKY9EiiLUAi4nJxNHQb0W6wIFujQGG6GiIRi7ZYBEGME+rTRCSh9kYQ0YGU4hDjcnHsq2nD5l1HYbG7oJCK8MS6eVhelkODGkHEIdSniUhC7Y0gogeZT4SYhm6jdzADAIvdhc27jqKh2xhlyQiCGA/Up4lIQu2NIKIHKcUhpt1g8Q5mHix2Fzr6LVGSiCCIiUB9mogk1N7iB73Jjn0n2tBIDywJA5lPhJhsjQIKqchvUFNIRchKVkRRKoIgxgv1aSKSUHuLD5q6Tbj19+9Bp1XifJcR9y+fgc9dWRBtsYgJQivFIaYwXY0n1s2DQur+aT32YIXp6ihLRhDEeKA+TUQSam+xD+cc9+06iutnZeP+5TOxdVUZfvHGWbx9piPaohETJKwrxYyxBgD9AJwAHJzzSsZYGoCdAAoBNABYxznvHSz/PQDrB8tv5Jz/a/D4ZQD+BEAJ4DUAmzjn485YEkqEvISXl+Vg5sbF6Oi3ICuZPIcJIp4RiRiWzcrGzg0L0aq3QKdVoEynpT5NhAVqb7HPO7Vd6Oy3YvMNpQDcq/v/e+10fPvvx3HgW9cgWSGNsoTEeImE+cR1nHPfFDXfBbCfc/4Txth3Bz/fzxibDeB2AGUApgB4kzFWyjl3AvgtgA0A3odbKV4O4PUIyD4sw3kJF2UmoSgzKdoiEgQxQVwujjdOtVM0ACIiUHuLff7w7nl8pjwHInbp/1E2RYs5eRo8+WYtHlgxO4rSERMhGuYTqwE8O/j+WQBrfI6/wDm3cs7PA6gDcAVjTAdAwzk/NLg6/JzPNVHF10v4utIM/P7Oy2BzuHC4sQc2mzPa4hEEEQIauo14+eMm/P7Oy/Cr2+Zhx52X4eWPmygaABEyXC6O+s4BHDrXhU9a9Hh836mg0Sd8y9Z3DsDl4sMeJ0JLj9GGjxp6cOW09IBzt1w2FbuqL+BinzkKkhGhINwrxRzAG4wxDuD3nPMdALI5560AwDlvZYxlDZbNhXsl2EPz4DH74PuhxwNgjG2Ae0UZ+fn5ofwegni8hK8rzcDyOTp89fnD3if7R1aXY03FFMhk4rDLQcQukW6TROgZsNpRNcu/f29dVQaj1R5t0cYFtcnYQmjHcePSEjz/fiNa9e6IE57oE4XpasHdyWWzsuN6dTme2uSB0x2Yk5cCpcDcnqqS4doZWdj+Vh1+eNOcKEhHTJRwrxR/inO+AMBnANzDGFsyTFmhnsuHOR54kPMdnPNKznllZmbm2KUdIx4v4S8umoYte2r8nuwfeuUEjl/Uh10GIraJdJskQo/V7gro376f4w1qk7GFUFzibQdq8b0bZ+HepcW4d2kxCtKVyEpWBI1hXNOqj+vYxvHUJg+cakdFrjbo+Rvn6PDK0YsUQi9OCatSzDm/OPi3A8BLAK4A0D5oEoHBvx53zWYAU30uzwNwcfB4nsDxqOPxEu4z2QXjSrYbqFMQRLzT0W8NEjfWGiWJiEQiWFziuo5+/OZAHZ5+px5fX1qC/FRV0LKteoptHAlcLo7/nutGRV5wpVirlOJT09Pxx/82RE4wImSETSlmjKkZY8me9wCWATgBYA+ALwwW+wKAVwbf7wFwO2NMzhibBqAEwIeDphb9jLGFjDEG4C6fa6KKSMSwvCwHOq3CGz7HgzuupDxKkhEEESqykuXUv4mw4dlx9EUhFcE5qONa7C488PIJNPWagpbVaZVB2ijFNg4ldZ0DUMnESE8avu9/ujwHf/ugCSabI0KSEaEinCvF2QDeZYwdA/AhgH9yzvcB+AmAGxhjtQBuGPwMznkNgF0ATgLYB+CewcgTAPC/AJ6G2/nuHGIg8oQHkYhBKmbYsqLML67klhVlkIhj35aLIIjhkVD/JsKIUFzijUtL8OKRS640vjbFQjGMy3Qaim0cAT5q6MGMnOQRy+m0SpRmJ+Olj1siIBURSsLmaMc5rwcwV+B4N4CqINf8EMAPBY5XAygPtYyhQquUYfeRJvz0lrkw2xxQyiR47r16/PSWedEWjSCICUL9mwgnnh1HT2x7pVSMjS987HWyAy6t+g4t6xsHn+Ljh5/qhh4UjzLU6g2zs/GHd8/jc1fkgzH6P8QLlOY5BBSmq/HlRdMDPH/pKZ0g4h/q30S4EYmYN7a9y8Vx//JZQdubb9lgdRDh4XizHusXFY2qbNkUDexOF96v78FV0wPDtxGxCSnFIYCe0gkicaH+TUQSam+xicnmQEufGVNTlaMqzxjD0pnZePa9BlKK4whSikPE0Cf9oamfaUAjiPgnNpLLE4nOaFZ9aZ6JLKdaDZiaqoJEPHpXrMUlGdj0wlHvww0R+5BSHGKGS/1MAxZBxB/Up4lYg9pk5DnV2o/8NNWYrlHJJFhYlIZdH13AvUtLwiQZEUqikeY5oQkWXD1egqgTBOEP9WkiFIQyDTO1ychzqtWA3FGaTvhy7Yws/PWDJkq7HSfQSnGIGRpcXadVYO2CPJxt7wcAr7MEbXsRRHzQbrCgNCsJdy+ZDrPVAZVcgqcOnkO7wUJOTcSocLk4Dpxpx/FmPVwcEDNgTp4WS2dkC479I5lGBEvi0dFPbTJcnGo14DPlujFfV5ShhkIqxn/PdWFxSWxn6yNIKZ4wQwcvtVyCgnQlVlTkIlkhRrJCikdfPend4vrN5+bD5uC07UUQcUKKSoI7rizAd/5xzNtnt6wsQ4qKhs/Jzmjtept6jKhtH8COg/XeNvTgitk42arHbJ3W75qRTCNcLg6VTAKFVOSnGFOyjvDBOUddxwCmjtF8AnA73C0pzcTfPmgipTgOIPOJCeAZvG7c9g7ueOoD3LjtHbQbLPjaNcV45t169FucXoUYcD/JH2+O7xz1BDHZGLA4sXVvjV+f3bq3BgMW5whXEomM0Pi/r6ZNcJu83WDFk/tr/drQo6+exBsn2wOuGc40wnPPjS8cwcalJZSsI0J0G20AAI1ifA/CnyrOwMHaLvQO1kPELqQUTwChweuTFr13AmUMAVtcLh54jHLUE0Ts0tFvDdJnrVGSiIgFxmLXa7Q5BNuQiyPgmuFMIzz3bOw24/n3G7F+URE2VhVj54aFtNsYRs51DCA3VTXuJBxJcgnmTtViz7GLIZaMCDWkFE8AocFrqNI7NB+9mAUeo20vgohd0tQywT6bppZFSSIiFhhOeR1KQZpasA1xHnhNtkYRdI7wvWer3oLtb9Vh2/46mO1OUojDSF3nAKZoJzZHLy7OxM6PLoRIIiJckFI8AYQGL1+ld/fh5oAtrnS1DD+9uYK2vQgiTkiWS7BlZZlfn92ysgxJcrIpnswMp7wOZVqGGk+sm+fXhjYuLcGLR5oDrilMDyzrmSPGck8idNR1DCBbM7HfeE6uFu0Gi9fpnohNaFSfAJ7By9chYk6e1nusVW/Bzuom/HLdPNR2DMDhcmFKqhLXlmShPFdL2YoIIg4ozUpGi96Mn98yF0abA2qZBFIJw4ys5GiLRkQRofE/2AKHJ0vdjK8vxqk2A8629+P59xvRa7IFXDNcRrux3JMIHec6jbi8IHVCdYhEDJ8qzsDuw8343o2zQiQZEWpIKZ4AwQYvl4tj54aFaNVboNMqkaKSIFUt9Z73QNmxCCL2kcnEWFKUgU/aDGg3AFkaOebkaCCTiaMtGhFFhMb//FTVsNEoGAPS1TJUzczGkpIMpKnlgosiwTLaUQro6NDYbcTKirGHYxvKouIM/PyNM7h/+Uz6n8UopBRPkKGDl8vF8cap9mHD6VAmIoKIHxwOF/55sg0PvHzC22cfW1OONXNzIZGQBdpkxnf8H25sByB4bkF+2pjjFI8mBTQROhxOF1r7QpOmeWqaCmq5BB+c78FV09NDIB0RamhEDxGebEXVjT2CHsnVjT04dqEXHzUIn6eQbAQRm9Rc1HsVYsDdZx94+QRqLuqjLBkRSwwXjWIskSrGEuqNCD8X+yxIUUkhC9ED8NXT0/HikeaQ1EWEHlopDgG+KwRbV5YhVSXD2gV58ERv2X24GU3dJjzwygncvbiIMhERRBzRojcL9ukWvRlzMTE7QyJxCBaNot1g8UaZEDo3dNxv6Dbi8X2nsH5Rkbe9Pb7vFGbmJA87R4w2kQgxNhp7jMiZYOQJX64qSsf3X/oEj91UDrmETLBiDVKKQ4DvKkCKWoa7rirwBmpXSEXYVFWCHuOlWKeUiYgg4occjUKwT+dQnyV8CJZlTiUTw+Hkguek4sDVx26jFbdV5mPbgUvtbeNS9xwSTCkms7zw0dRjQlayPGT1pSfJMTVNhYNnu3DD7OyQ1UuEBjKfGAceU4lD57rQ0DWArgEr7l5chHuXFkPEEJC56Mn9tchNVeHepcU4eKYjIEzb4zdXID917OkjCYIIPxa7U7BPWxyU0Y64NB90Gy247/pSv7H9vutLYXe6oLfYAsb9jUtL0G8JzHAmE4u8CjHgbm/bDtQKKtAexmKeQYyNxm4T0pNCpxQDwJXT0vDyxy0hrZMIDbRSPEZ8n8hTVYGrwo+tKRfcJjvV1o+n36nHxqUl2HeiFRuWFCE/TQWZRIy2PhMO1XfjqqJ0ctwhiBjDaHUK9mmjlZTiyY7vfPCN60ugkoqxYUkRXBwQMUApFSEjSQ6ZRIyd1Se9JhGcAzurm/Dr2+cH1BmsvZlswdvbcIlEyCxvYjR2G1GaHdrwi1dOS8c3/34MJpsDKhmpYbEE/TfGiO8T+doFeQErSBd6TILbZB6bsm0HarFhSRHyUlXI0chx8qIBf3zPHa/y8ZsrsLJiCm13EUQMoVVJBfu0ViWNolRELOA7HzicHD9683RAO/lUcSY0Cgn+37XF2LKnxruAsnVVGTTKwDaklgc3wwCEbYezkhWC12QmkYnPRGnqMWFRcWZI69QopSjOSsKB0x1YUTElpHUTE4OWJceI7xM5Y4HOE7uqm/HYmjmCmYsAd/mSrGT8+VAD7n7uMJwc+NqSIqSqZLh/93Ha7iKIGKPPbBfc+tab7VGWjIg2erMNP71lLh5fOwezdRqkqvxTf1vsLnQOWNA9YIPV7sSGJW4zuw1LimC1O9E9EGg+YXM6Bdubzenyi0yxedcxvHy0Ba990gqLw4HNN/ibbmyqKsEwFhfEKGnpNYfUptjDFdPS8MrRiyGvl5gYtFI8RjxpNoM5zfWabChIV+Lnt8wFB1Db4c5c1Kq3eMvXdvRjebkOne834sn97pXjtQvysP2tOtruIogYI00lxc7qpoCt71/cMjfaohFRxOFwoa7DiAdfuRS/elNVCZ475D/eZyUr0Ko345n/nseKilwwBrg48Mx/z+NHN80JqFcmFgu2t08Vp3tXplNVMty5sMDPGe97y2fi3uuKYXG4wDnw3KFGzM9PQWEGzSfjpd9ih83pQrIi9KrS5YVp+Mv7jRiwOihlfAxB/4kx4ptmc/fhZmyqKvGzKf7VbfPwcVMfnvj3Wa/Nca/JvRrgeeL3pPdcv6gI29+qg4u7V50pCgVBxB5ixnD75fkB0SfEZOY0qalp1XsVYuCSA+amqhI8vu+MXwrmll6TYEQJLpDW1OZ0Cpa1O13encq1C/ICnPF+vO+0d04BaD4JBS19ZmQnK8BY6Pt6klyCWToN3jzZjjXzc0NePzE+SCkeI0PTbOZoFFg2OwedA+6MN10DVnxjp9vGrFVvwXOHGvHzW+bidHs/OIffqrFHEfbMrZTDniBij7Z+K5471Oi3cvfcoUZMy6C+Oplp1Qs7t+WmKPGHL16GdLUcZTotRCIGjUIqGFFi51cWBtSbrpYLrhQvL3dnxlNIRYKmexa7y2su4auQE+OnuceMjDCYTnhwm1C0kFIcQ5BSPA6E0mxOz3K/b+g2+g1WrXoLTre7I094juu0CtxamYfcFCV+eds8SEQMJVlJyE+jYOsEEWskyyWQSS71S8YAmYTRluckR6dVCjq31XUOwOkCnnm33hsr2GQPElHCHhhRojBdjQdXzMbxZj1cHJCIgAdXzPYquE+sm4czbQbBe1fNzMLV09ORlUzJO0JBc68JGWrZyAXHyWUFqXj2vQboTXZy3I0RyAw/xBSkqb3ODh72HmvBgytmQyEVQad1JwLYcbAe9+/+BPftPAqbg5NCTBAxilouxteuKcYz79bjNwfq8PQ79fjaNcVQyykb1WSmTKfBo6vLAxzi/l7d7F3J9cQKzkqSB8wL7ugQwquQNgfHjoPu9vb7g/WwOdxmFp6dypvm5+JHN/k7dD+xbh7m5KZgYVEGijKTaD4JARd6zUgLo1KskkkwJ0+LfTWtYbsHMTZIKQ4x0zLcNse+g9Xtl+dj54fu7bDv3zgrIIzbN/9+FOe7KOoEQcQiDifH1r01fn12694aOJyB9qDE5EEiEaGyINUbUWL9oiKvv4jHVNiTytnscGJTlX9EiU1VJYIJYEZKxCESMRRmJGHNvFy8tnExXthwJV7buJiy14WB5l5TWM0nAHfM4pcokUfMQPt/IcbX5riuYwBSMcP3XzqBVr0Fx1sMuHdpseA2WlOP0WuCQRBE7NDRbxXssx391ihJRMQKU1NVmJ6ZhPt3H/dzinv+/UYAl+ILX+wzC9qlT89MQvkQc9LRJuIQMuMjQsvFPguunp4R1nssyE/FM++eR4fBgiwNOUZGG1KKw4BnsBKLgPNdJm/0CQ9CtmBkn0gQsUlmslw4MUKYV5CI2Mbl4njjVDue+PcZrF9UhAy1FAXpamx9tQateos3QYeIuZ3nek02b2QIwN2G0gW25oeG/fSUpUgSkadVbxb8H4USmUSEysJU7Dl2EXcvLgrrvYiRIfOJMOJwAn/94Dx+6GP7tfdYC7auKvPbRnt0dTmkYhEOnetCfecAXC7aliWIWMHmdAT02a2rymB3UprnyYzHzKGx24ztb9UhPVmBFz5qwCOry/Gr2+Zhx52XYf+pVlgdHCabA/dd759c477rS2G2OwLq9YT9HGovTJEkIovV4USfyR6QkCUcXD09A/843Bz2+xAjQ8uTYaTHZMW8qenYtv8s1i8qglgEzMzR4OWPm/DEunlo6jGhME0Fm8uF259637v95vFYJvswgog+apkESpkDG5YUwcUBEQOUMjGUMnK0S2SE0in7jslDzRxkIuC6GTp89fnD3rF8y4oymO12qGRSKKUi/zYkFUEpDZyCh4b9pEgS0aFdb0V6kiwiv3uZToOnBqw43WbAzBxN2O9HBIeU4jAiE4m8sSl9A6qvX1SEx/edwoYl03GyzYAdB+sDnCpmblxMtmIEEQM4nMB3/nE8YDv7+S9fEUWpiHDiSafscXgTWqwYauagUcnwjV3H/B0yX63B81++AlIxw49ePx3QhnZtCIxTDJC9cCzQ0mdGRpDoIKFGJGJYVJyBnR9dwJaVZRG5JyEMmU+EkR6TTdBhgjFgRUUuHn31JFxcOAh7R78lkqISBBGE9iCOdu3kaJewjBQBAgg0c7jYZxZsJ50DVhhtwnGKjTYywYlVImFP7MuS0iy8/HELrAIRSYjIEfaVYsaYGEA1gBbO+QrGWBqAnQAKATQAWMc57x0s+z0A6wE4AWzknP9r8PhlAP4EQAngNQCbuFB+zBjBs+1mdbiwqaoYu6qbvVnsFFIROAfEokvKMDlVEETskp4kQ0G6EisqcuHJ9rr3WEtEJ0wisowmAsRQMwcRY4JjeYZajoxkuWAbyqZoAzFLq96ClAgm1MjRKpCfpsK+E21YPY8y3EWLSKwUbwJwyufzdwHs55yXANg/+BmMsdkAbgdQBmA5gP8bVKgB4LcANgAoGXwtj4Dc48Kz7XbjtnfwtT8fwe8P1uOuqwqg07q32r63fCaS5GLMytFgU1UxDp7pwMalJeRUQRAxSrpags03lHpT6IoZsPmGUqSryfosUfGYRvgitFghEjEUpquRlayA0erA9s8tQEG60lt+y8oyZGvlyE9V4etLS/wSwHx9aQnyU1UR+07E2GjuNSFNHdkIM1Uzs/GHd89H9J6EP2Ed1RljeQA+C+CHADYPHl4N4NrB988CeBvA/YPHX+CcWwGcZ4zVAbiCMdYAQMM5PzRY53MA1gB4PZyyjxehbbcn99fiF7fOhVQsgsFix4/3nfbaqW2qKsHrn7Riw5IilGYnY1aOBtMyyKmCIGIFCRPjYp/Fa/vv6bdzpmijLRoRJjymEUNtiocuVgjZHv/wpjlQy8RQycSQiIGpqWo0dBvxwMsn/OaFB14+gQX5qWQ3HKNc7LOgsiA1ovdcUJCKv3zYiI+bejE/P7L3JtyEe6X4VwC+A8B3Hyqbc94KAIN/swaP5wK44FOuefBY7uD7occDYIxtYIxVM8aqOzs7Q/IFxkqwbTepWIRsjTxgYHxyfy1+8NnZWDMvFzeW6zA9i9JzJhKx0CaJidE5YAvIQvnk/lp0DthGuDI2oTY5Mh7TiJEyxgktgvzgpU/wSYsBG54/jD6T2z50OHMMIjbbZKs+vCmehRCLGD5dloP/e/tcRO9LXCJsSjFjbAWADs754dFeInCMD3M88CDnOzjnlZzzyszMzFHeNrSoZBLBbbccjRzdRmHHu6HJPUaLy8VR3zlA8Y1jmFhok4mAw+HCsQu92HeiFccu9MHhcI18UYgwWOyC/dZgsUdMhlBCbfISQ8dQh8Pl/dzQbURhuhoLizJQlCm8WBFM2WXM3zkvW6NAQboS91xXjHuXul8F6UryHRkkFttkm94ScaUYAK6bkYXqhh6cbjNE/N5EeM0nPgVgFWPsRgAKABrG2J8BtDPGdJzzVsaYDkDHYPlmAFN9rs8DcHHweJ7A8ZjE5nRi49ISbyg2T5D2C71mnG3vF3TE+KTFgG/sPIof3TQHC/JTkJ82svnEaEIGEUQi4HC48PKxFu8ui0IqwmNryrFmbi4kkvC7RaSpZYL9Ni0CQf2J8CE0hj62phy/PlCLxm7zqMbUrCThbIeKwXbpWQ2uzE/D15eWBLRhsimOTcw2J8x2JzTKyDnaeVBIxVg5dwp+8tpp/InCPkacsM0onPPvcc7zOOeFcDvQHeCc/w+APQC+MFjsCwBeGXy/B8DtjDE5Y2wa3A51Hw6aWPQzxhYyxhiAu3yuiTnSVHLsrG7C+kVFuHdpsTvXPTi++fdj2FXdHOBU9+CK2UhWiHH34iI8uf8sXvy4Bftq2kZc9R1NyCCCSARqWvWC9pg1rfqI3F/MGDZV+ffbTVUlENPDZ1wjNIY+8PIJrKjI9X4ebkx1uTg6BqzDtg2Pc15Tr0mwDTf1msL9NYlx0GawID1JDhGLTh+/flY2zrT34z9nY8OUZDIRDffpnwDYxRhbD6AJwK0AwDmvYYztAnASgAPAPZxzT8C+/8WlkGyvI0ad7AB3qLX1n5qGLqMNLg5IRIAuRQmL3YVWvQXPv9+I9YuKIJeIcFlBCmpa9BiwOrH3WAtuq8xHbooCp9sMyE1RYk6uNugKxWhCBhFEItCqtyBVJcPaBXnecFa7DzejTW/B3KnDXxsKWvrMeP2TVvz0lrkwWx1QySV46uA5FKSrMA/kDBMLjJR9TojhTB98PwcbUxu6jTjc2Iu/fOBeBGEM4Bx47lAjbr4sz88574Pz3d576bQKb1vuHLBStroYJNIxiociFYtw11WF+P6Ln+CN+5ZALadIN5EiIr805/xtuKNMgHPeDaAqSLkfwh2pYujxagDl4ZMwdHQOWGG2u/w81besmI2CdCUau81o1Vvw4pFm3HVVAdY/W+0ts3FpCXZWN+Hby2Zi2/467DhYP+zW3dBsSgDFNyYSk7xUJe66qsDr7OZZjctNVUbk/jlaBT4zR4fv/OOY3/2zqa/FBOM1JQs2hvpGwB9uTG03WFCQrkavyebNWOq5ZsHUFGxYUgSZxD/7XapKhjsXFnjN655+Z/hxnogO0bIn9mXe1BR81NCNB14+gSfWzQWL0qr1ZIMy2oUYmViEX755dkiqz5PYuqrcu8V2a2VegDf7tgO1+OaymTDZnbh3aTFSVbJht+6GZlOi+MZEoqKQiAWjPygk4hGuDA12p0vw/nZX5Jz9iOCM15QsP1WFHXdWYmPVJce3x9aU49XjLQBGHlOzNQq09pkCTOI2Li3B2Y5+bNtfh3v/+rHXYe+JdfNwa2WeVyEei6xEZHHvTkXenngody4sxJHGXvzxvxS7OFLQmnyIMQVJ5ykTi7Bzw0K06i0QMyZYpm5wIPUMrM+/3xh0625oNqWs5NFtGRJEvNE5IJxmuXPAiuLs5LDfX28OEn3C7Aj7vYmRGY8pmcvF8capdr/V5cdvrsBnZudgQX7qqMbUwnQ1Zum0eOCVT7zmE2qZ+0EtVSnDvUuLsftws1eO5WU5ASvTwWQdjzkIETou9pmRGgOOtAqpGN9cVopHXj2JlEETMiK80EpxiMlKFs6EBMax/3QHTlw0wOp0CZZxDo6VFrsLO6ub8IMbZ8FkcwYNtyYSMRRlJg0bMogg4p1gYQ5VssisFE/RKoXDLGojm+2KEGa02ed8EVpdvn/3cTTrzSjKTMIVhekAgA/Odw87/l5ZmIZvVJXimXfrsftwM9QyMcx2Jy70mSFmwP9eU4ScwVTOnux3I8nqmxX1jqc+wI3b3hmV8zUROlr1lohnswtGZrIC9y+fiR/+8xR2ftQUbXESHlKKQ4xYhABv5M03lKJV786I9ZsDdfjZv05jy8qygC23F4+4c5TotArcVpmPb/3jGL78p2oaFIlJjSfM4dD+YnNGxnxBLEJAf92ysgwSegiNCcZjSjbc6vJolVKXi+PNMx341f6zWL+oCI+uLoPR5vSO878/WA+jzem1UXa5OEQM+NFNc4aVlSILRR+3TXH0zSc85KWq8P0bZ+EXb5zF0+/UR1uchGZU5hOMscc55/ePdIz4/+2deXxU5bn4v+9MJplM9o0kBBIIhH0HESmgggtaxH2pvWpberntrwhqvWr1qrVaq9WLLcXWi0utXr3ivlDEDRWwoIKyryGQEEgChGSSTDKZZOb9/TELM8mZZLLNxvv9fPLJzJmzPOfMc97zzPM+i/MX5ksbS32yke0OyX3vnC7HU1rdxDNfFvPST6ditrbQ3OLgiY/3UmF2dje6apJ23NmIxTNVZQnFGUesXu8pc+i+p1ZuLuMHQzOCcvyTDTae+bLY5/jPfFnMw5dHRO5v1NOdULKOEpX9GaVtx1/3eu5pdp0QmrHnk/LTKMhI8CQDppliWTirkGHZSYzMSWZwpq+sqrJQ6KmqCx9PsZv+qfHcP28Uf1i9hxa7g1+eNzTUIkUlgXqKL9RYdklvChKptO2IlJti9GQjL19bzNOfF9PU0j7OuLS6iVONNnYcNfP71Xu4fkq+x3ug16FagioULmx2O9dPyef5DU4P3PMbSrh+Sj4tQfIUN7XYsbWe9hIKAbZW2e4eVYSOroaS+fMu56eZOFxtCWj8raqzeqpJPL+hhBP12rHvjbZWDldbeHzNHhbMKOTqyQNwSHjio70IQTtZuxMOoug9WuwOzE0tpISgcUdnZCbGcd8PR/HSxlJe+bo01OJEJR16ioUQvwT+H1AohNju9VES8FVfChYJ+CsFtPzGiSx69XvPspE5yZpeCSQ4JNQ02lizs4Kl101gb2UdI3KSVLk1hcKFd0Mcb0/xxaNzgnL87KQ4zZJwWYmhT8RRdA+dTnDRyGxP8nNuipGR2cl8vKeKfZV1AY2/2clGn2oSJy3NmtvlpydQbWnm+in5Pp1OF88u4pSluZ33122wt32uqMpCweFEfTOpJkPYNudJT4jl7rkjeOiDXQzOSGD60MxQixRVdBY+8SrORhl/AO7xWl4vpTzVZ1JFCP6m2f5560xWL57J/qp6Dp20cLS2kYcvH0N5TSOvby6nptHGHRcO41C1hbe2OLvcCQF3vL6VYf0SGds/hUeuGMORU6fXV4Oi4kxFr4PbLhjGoZMWT0Oc2y4Yhj5IGRGNNrvmtPj4W6YERwBFr+NwSL44cJzt5WYcEvZU1FFhtvL4mj3YWiWLZxf5GLAPXz4G4drO7dkdlJHA0KxEj17876Yybr9gmKckp9uYLUg3UdNoaxcSt2ztAVYunNZONlVZKLRUmK1khFnoRFuyk438v/OGsvi17/lwySyyksJb3kiiQ6NYSmkGzMCPhBB6INu1TaIQIlFKeUanQvqL/TrRYGVaYSY6AYdOWnjy49OD5P3zRpGVGEd9cwvLPjtAhdnKmp0V/OK8ISyZU0RmYhy3vva9z/oWawsjc5LUoKg4I6mqt3KivtmnIc4dFw6jqt7KoMy+j7H0V2axscXuZwtFuFNabeFAVYOPTi2ZU8RPzhnESUsLOh08cc14Dp20UJiZwO9X7/E4J9yNNnQ6waDMBI93uMJs5cV/HWbhrELG5qUwJCuR/DQTH++pYm9lnZ/QCm0dcoeDqBji4FNVZyUtjJLs/DEmL4UZQ7P4zdvbefbmKaq5Ry8RkK9FCLEIqAI+Af7p+lvVh3JFBJ3FftkdtPMwPbxqN/GxelZtPeaJI75kbC61jS00tdi5/72d7dY3W+3sqazzWxpIoYhmWlolSz/xbYiz9JP9tLQG515IM8Vq3uep8Sp8IlI5Zm7S9P4Pykzg+Q0lPPbhPv7zzW0Y9IIjNY1UmK2aVSBG9Evibz+e5GkAEhsjyE83cf6wfhRmJVJW08gdr2/FIdHUoWxXuba2uSlqnA8dlWYrqWFQozgQrpqUx77Kej7aVRVqUaKGQCcgbwOGSylHSynHuv7G9aFcEUFnpYCO12t7kr89fIqzBmewZmcFC2YUMjw7id+t2k2MTruwu14Hu47VB1yaTQ2winCjJzrZ0NyqeV9YmoPjqa1vtmmWhGtobgnK8RVdIxBdq7dq69Qpi40FMwpZNHsoP59ZyGvflmH32t474c5dku2Xr3zHss+KeW59CbfNGcb8sf2JiXHqins20R0mp/WsUHWJw4tKs5XUMEyy08Kg13HL9EE89MEurGrmqlcItKPdEZxhFAovOov98lf2x+6AZWsPsGBGIc9vKGHSjRO595IRDOmXyOI5Q3FIeGtLORVmK0aDjhE5yTy8andApdn8Jf+5p/wUimDTU51MTzBo3kdppuA05EyINbB2byV/vGY8Tc2tmOJi+Me/SjhrUFpQjq8InEB1rX9qvKZOJcYZeH7D6ZCK2y8YhkT6rOOeCfQuyXbVpAEI4Vx2rK7JE9bjfgZUmK28vMlZqlOvgzkj+jE2LxWdTlByoiGgEnCK4HC0tomCDFOoxQiY0f1TGJhu4sWvDvOL84aEWpyIp0NPsRDiDiHEHUAJ8IUQ4jfuZa7lZzwdlQLS8iS7m3RYWxyMy0tmzZKZtLRK9HodC/6x2eNxuGlaAQUZ8Tx8+Rie+aLYU8O4s9JsqvC7ItzoqU7GxejbNcRZMqeIuJjgdLSz2Vu5elI+d725jbvf3sF/vrmNqyflY7Mrz0y4EaiujcpJbtdE45ErxvDoh7t9tn3q0/0MSk/wrOM9E9i2JJu7Ycd3ZbUeL6/3M6DCbOX5DSWMyEn2GMTu/agSnOFDpet7jSSumzyQ/1l3kDqrmr3qKZ25WpJc/8tcf7GuP0UAuD3JeQun8dne49gd8PKmUo8HuCg7CYeEslMWGmx2fj6zEHB6iZetPcAfrxlPZW0j24/WefbZWWk2VfhdEW70VCcr66x8uKPCx1P77LqDDAmSPsfFGHho1Xc+xtJDq3bxvwvODsrxFYETqK7FxOgYnGny0anYGEFpdVO7bWP0gtcWnq05E+hdks29/r3v7GDCwFSPk6SzShIdNRJRBJ+qOisZCZFl5uSlxTN+QCovrD/EbRcOC7U4EU1n1SceCpYg0YpOJxibl8qJhma2l5u5evIA+iXGMjwnmeLj9aQnxJKaEMfST3f61K98eVMpxcfrGeFV4ziQepVqgFWEGz3VybQEA5eMzeWuN7f5VApICVL4hK3VoWloBat5iCJwAtU1h0NSVt3E3W9v9+jU768cS0FGvI9hbDToSDIaOLuwfffEQRkJDM9O0tSN/VX1nnU6qySh6hKHD1JKTtQ3kxZhRjHA/An9+d0Hu/n5rEIS44IzNkYjgVaf+EAI8X6bv5eFEEuEEMraCgBbq2TFuhLe2lKOxWbnlr9/w8KXv2PdgZM80KbixLK1B7h2ygBG5iTzwoaDLL9xEk9eO44nrxlPQlz7r8w7sUQn6DD5T6EINp0lpHaGUa/XrBQQH6TwiaykOM3KAZmJqjZoONCd8a/kRIPHIAanTt33zg4enDfaZ9uH5jvfHz6pnbCXajJo6kbx8YaAE+bc3uTVi2fy2sKzWb14psoBCRHmphZi9AKjIThjS2+SmxLP6LwUXt54ONSiRDSB/pwoAbKA/3O9vx5nibZhwLPATb0vWvTgHed21aQBPg94h9Ru6Tw0K5F+SQYWzBjCw6t2UVrd5PGQFaQneBI5tBJLlt84kX/eOpMTDarwuyL09LQZQaWfKfHKumaCUQLH0tzKkjlF7TraWZpbg3B0RUd0d/w75KeV8+6KOp/OiX/9opjLJ+QRb9BTlJ3I7OHZnn0drraw66hZUzfc+ws0YU7VJQ4PKsxWMsO8cUdHXDYulyc/3sdPfzA4Ig37cCBQo3iilHKW1/sPhBDrpJSzhBC7+kKwaMI7zk2I9kaw1nRfrF6HxeZgT2UdC2YMpiAjgaq6ZkyxesyNp4PptRJLFr36PasXz2RaoWr/qAgPevLQNxr0mvdIWw9dX3GkpomXNpb6GEsvbSxlYJqJCfmqAkUo6e7450+nAJ7+vNhnXYd01ptfMqeIwszTOlxVZ6Wu2c5bW8rb6cbVkwd45KmqU/kckUJlnZX0CG7fXpCRQH56Au98f5QfTc0PtTgRSaBPlSwhhOcKu167Rxxbr0sVZZhiY3we4N6v39pSrplZf6jaQkNzK/8qPoFBp+c/Xt7CXW9u5843trHveAPfl52i5ESDylxWRD1JxhgevMx3WvvBy0aTZAxO3Fx2chw1jTae/ryY5WuLefrzYmoabfRTrVVDTnfHv8xEA49cMabduJtsjCE35XREoNGgQ7pm87IS4zhlafZ8lp1sRC/Q1A0pT29vilUeu0ihyhx5lSfa8sOxOfzti4M+9bUVgROoUfxrYIMQ4nMhxBfAeuA/hRAJwD/6SrhowSEdnli1tkZwTaMNk8FZcurJa8axZE4RJoOe174to7S6kZunF/LQql0+npAH3tvJkRorly5bT6tddthVT6GIdBwOeObLYk9ThQUzCnnmy2IcQcpzi9MLT2wpnI41jYtRIUmhprOuov4w6PQ0NreycNZpnXppYymPfriXa6cM8OzHXULTaNBRVtOIQX/6WIMyEhiZm9zOqXHHhcM82yyeXaQSMiOISrOVlAhp3OGPkbnO5PxPdleGWpSIJCBXi5RytRCiCBgBCGCvlNL9U/xPfSRb1KATgmfWOR/qcTE6RuUmsfxHE2m02dEJwalGGxPzU6m1tGB3SGoabdx50XCO1jYh0I45brI5OzL913s7ePzqcdz91uksapVYp4gmjtdbKa1uajetHazZkGpLC2t2VPA/N02mtrGFVJOBFzccIitxUFCOr/BPdys3nGho5niDjeVri9t9Niw7iT/fMIGDJxp4eVMpNY02T0Wgc7yqUOh0ggtGZPNt6SmevGY8EjhRb6W51cHVkwcgJazcXMbcMTm9fdqKPuJYFHiKhRD8cGx/lq8t5uLROQihfrx3hQ6NYiHEbCnlWiHEVW0+KhRCIKV8uw9lixoabXafh/q4vGR+ffFwio+acUhYt+84PxzXn6c+3Y+1xUFBRjy/uWQktlaJEGiWCYqPdX51pdVN5KUaWd3NJCaFIhAcDsnhagtVdVayk4OrY+7qD23jP7OCFL5QkJHAxkOn+Hz/SZ/j/9e80UE5/plKIDrX3STO7GQjibHaccVHTjXy0sZSrp0ygF9fNIxKVze6mkYb2cm+HuiYGB1nD87gcLWFU5ZmHFIqB0UEU2FuYtrg9uX3Io0pBWm8seUIGw9WM32oyi3qCp15is8F1gKXaXwmAWUUB4B37czcFCNzx+TyHy9v8Rk43Z6O3BQj10/J57aVpz0fD142mme+LPZUoPjd5WM41WAlN8VITaON9IS4HmUuh9LgUYQ/oW4drhNCM8NfFyQPyOBMbW/k4Exl7PQVHekc0G686ur4l59mYkxest/KERVmK8s+K8Zo0LHo/KHUNNr8GrjeSaSTHJKxeSnKQRGhVJqtEVmjuC06nWDeuFz+9NkBZRR3kc6adzzo+v/T4IgTnXhP8V01qX0HpL2VdZ73Wp8/9MEuXrjlLKrqrVSarVQ3NNPUYueBy0ZhitX1yBMRaoNHEf4cOqndOnf4rTMZ0q/vs+or66ya1R8KMkx9fmzoeUk5Rdfx1655+K0z2VdV3+PxqqymkWqLTVOvvLP2rS0OhvZLZPXimQF956q0WmRzvL6Z9CgwigF+MDSTd78/xjeHTjF1cHqoxYkYAoopFkJkA48C/aWUlwghRgHnSCmf71PpogTvh+r+qvp2McIOebosm1bJNmuLg2qLjT99up/rp+R7jGajQcfjVwdWqdWfN9jfwyeQ2pqKM4PSU9o1XctOWYJiFGcmnq7+4CbYzTOUsRNc/FWVKDtlYW9lHT+fWQg4q/d0Z7yqtjSTbDRo6tUgrxkAd/UI9SMo+rG22Gm0tQatqk1fE6PTccXE/jy+Zi9v/uIcFVscIIFWn3gR+Ajo73q/H7itD+SJCrw7LJWccHZCcj9Uh2UntcuW/mDbUR6+3Lc8kDdGgw6dgHnj8tp5ke9+azuHqy2dyrNmVyWXLlvPj5792qfTkirppuiMxLgYTZ0MVitRKaVm2UIpg1dySOueVvQd/qpKIAQr1pWwfG0xz60v4aZpBaSZYrs8XsXqdfz2g10snu2rV/fPG0VlbaPn/ZI5RVhbHH470ym9iB4qzVYyEmKDFpYVDGYMzeJ4vZW1e4+HWpSIIdCnWqaU8nUhxG8ApJStQgh7H8oVsfgLR7hoZLZryq65XbWIG87K5+WNh1kwo5C0eGf9zP96d6fn8wfmjSJGryPFqPdrwHbkJenIG9w23vmqSQPQ6yDeEOMx5hVnNqZYvWbsZXyQ6q/WNbVqTnMPDZLXVoUYBR+tqhKPXjmW376/02ccW7bW2VSjqyUo3cnPL2/y1SuLtYWB6Qksmj0UnYCcFCPL1x5g//GGdt5opRfRRWWdlfQI7manhV4n+NFZ+Tz0wW5mFGUSF6NqZndGoEaxRQiRgTO5DiHENMDcZ1JFMFoG6ONr9tBid3gM4YKMeFbcNAWDXqATgttWbvVsX9PUgl4H//jpWVTWNXPwRAN/WessCP/7K8ZqVqLo7IHQkTd46qAMll43gcfX7PEJzVixrkQN8ArAv1E6YWBqUI6fFK8n1qsmsBAQGyNIig+Op1qFGAUfd8jZ8Ftnsqeyjv1V9Z7SfN5YWxz0T40nP61r8eXZyUYKMuKZNy4Pt2Nw1fajPHjZaFrtkoFp8STExtDY3MKJBpum80HpRXRRabZGTTyxNxPz0/h833GeXlvMHRcND7U4YU9nJdluA74C7gLew1mK7SsgC7i2z6WLQLQM0Hnj8jwGMTjLqC18eTOrF88EnA/4trHCD18+huWfH/B5CNz37g6e+bfJ/OJ/fStXdJZo5+0NduM2pt0Pn7xUI9ev2KQGeEU7EuJiNGMvg9Wpy2SI4RfnDuWhD3b5VGQxGYJz/I5+VKp7o+/Q6QRCwJ1vbHO2b549VHMcO3iigbKaRr/fhVY+RX6aiUXnF3H/e6dn5H43fwyvfXOYj3f7lt5bMKOQ5zeUtHM+KL2ILirMVtJMkd24wx8/mT6Y+97ZwZyR2YwPkjMjUukspngA8GdgjWvdT4BXgelSym19LFtE0jYWLjfFyMicJH4+09k5yd1C1D14DspI4OHLx7aLFb7/vZ3MG5fns29ri4O4GB2rF8/ktYVns3rxzIA8ue6pSO/YOW9jWqcTNNrsKrZYoYnNbm8XexnMTl1ma6vHIIbTFVnqrK1BOb7bq/ir84eyaLbzryAjXnWNDALehudbW8o19fCNzeV+xyl/+RSlpxo9BjG4OoW+v5Oi7FSf7a0tDvQ6NJ0P3e2m11eo+Oaecay2KSrKsWmRnhDLT38wmF/87xb1TO+Ezkqy3QkghIgFpgDTgdnAfUKIWinlqL4XMbLwjoVLM8Vy8zkF3PnmNo83wt0ZqabR5vHUGvRC0yDVt/nJYjToSIjVdzkLPpCSUh15kxVnNhkJcazcXOYTPhHMTl1Nfn6wNdqCk9aQn2bi1tlFPnH+j1wxpstT9oqu4z0uVbiaaCycVUheSjxlNU0+Y6kWh6stPL5mj0d3AR5fs4cHLxsd8Jg7Z0Q/xualtnM+dLebXl+g4pt7ToW5iTF5KaEWo8+YOjidCnMTP372a17597PVs90PgQblxQPJQIrr7xiwo6+EimS8DdAT9c3c8vdv2iWGLJxVyIicZM/g6c8gHZmT7FnuTm4qPdVIcryB/PSulQjqrKRUOA3wivBiUEYCd88dGTLdyEiM1bw/MoLk1SmrafQYxOC8j//r3Z1Myk9T0+R9TNtxqabRRn66ib+sPeBpZtSRLlZbmtuFpi2eXUSSq6JKW52aUpDuM+YuvW6CpkEM4VW/WsU395wKs5Vzh2WFWow+Zf74/jik5PLlX7H8xolMLlD1i9vSWUzxCmA0UA98DfwLWCqlrOlsx0III7AOiHMd500p5YNCiHRgJTAIOAxc596fq7rFAsAOLJZSfuRaPhlnWbh4YDWwRAazHlMXcRug/mLOJg5M5dxh/TyDp5ZB+vjV46husLJwViEOeTq5qabR5jGqvb0Ara0OdlWYqTBbyU2JZ3RuMjExgVbc6/0BXnXJix50OsFFI7NZuXCaj34F6/s0xui4Z+4IHluz13N/3DN3RLup675CxY6GDq1xKT/NxJSCNKrqmrHYWilI9//jLFav85nlAOcsx6xhme3G3CVzipA4+OetMznRYCUr0YheB18fqu6wzXQ41K9WOtpzquqspJmiM3zCjRCCKycOYGCaiX9/aQuzR/TjzouGk5OivMZuOvMU5+M0ag8AR4FyoDbAfTcDs6WUDUIIA7BBCPEhcBXwmZTyMSHEPcA9wN2uhiA34DTC+wOfCiGGSSntwN+AhcAmnEbxXODDwE8zNPjzABe0GVz9Dfxr9x2nos6GEGCK1XHzOQVYbHaK+iXx+Jo9jMhJojArkdZWB+9uO9pueveK8XldNox7Y4BXU3nRhcMh+eLAcbaXm3FI2FNRx4kGK7OHZwfl+6y3ttAvKY4nrxmPpbmVBGMMOqChuaXPjw0qtChUtP1hPXVQBjqdwOGQ7K4IrKudze7gZ9MHU91owyFBL+Bn0wfT1GJnVG6SptNh9eKZTB2UEVFjmNLRnmFrdVDb2BL1RrGbKYPSGZmbzPvbjnHRU19y49n5LJ5ThCk2OhqX9ITOYornCmcblNE444l/DYwRQpwCNrrbQPvZVgINrrcG158ELgfOcy3/B/AFcLdr+WtSymbgkBCiGJgqhDgMJEspNwIIIV4CriACjOLuhCR4+7+bWuw8v6HEx5Px1pZyahptLJ5dRF2TjZITDVSYmzSnd4v6JTJ+YFpQvLbexzDFxvD4mj1qKi9KKDtl4UBVAyvW+eri0KxEBmUG9n32RAdj9Xoq6up54qN9nuP/58XDyUkOzgNfhRYFH4dD8uHOSn79xulr/t/XTuCSMTldChWIN+hptjt8dPeOC4cRb9BTYbay7LPidsc+Xm9FJ+iVznnBQuloz6iqs5KWEBuWP3j6ioS4GH40NZ+LR+fw2rdlXPyndfzPv01hVP/kUIsWUjr9WeAybncKIWpx1iY2A/OAqYBfoxhACKEHtgBDgaellF8LIbKllBWufVcIIfq5Vs/D6Ql2U+5a1uJ63Xa51vEW4vQok5+fr7VKUAk0JMHhkKwvPk59kx1LcyvmphZONjT7lHGztjj482cHWDCjkKc/L2bZ2gOM6j+ZG579mp/PLNScOjta28To3BQ+3lPVpx4PLc+wO6Gwwmz1yHMmTuWFm052h8q6Zk/jDjiti5Py0wIyins6c2C12z0Gsfv4T3y0jxd+MqVnJxYg4RQ72htEgk6WnGjwGMTg/M5//cZWhmfP4Hh9s+Z4V1XnHF+8f4ABLP1kv89+ln6ynwkDp2rWKv5g21Fyko18V1brY0i7x7NwHcMiXUdDrZMVZmtQ28aHE+kJsfy/84byVfFJbnxuE8/fchaTC9JCLVbI6HBuXQixWAjxmhDiCM744HnAPpwhEJ1GaEsp7VLKCThLu00VQozp6HBau+hgudbxVkgpp0gpp2RlhUfAvDskYVphJoVZiZqD1JEaC8dqm7nzzW3c/fYOblu5lQqzdoyYe/C2tjj4rqwGa4uDeINOszRQvEHPt6WnNL22nbWGdhNImR8tz82ytQe4atIAH3nOxKm8cNTJrlLX1KKpi+amwMIX/Hn2AtVBS7Of6hPNwWuqGch9HClEgk4errZofueHqy202B2a45211cHavZV8faiau97cyo+e/ZqTrsYbbfdjbmrxVBV5fsPpttG3zi5CSrj3nR3txrNrpwwI6zEsknU01DpZYW4KWuJuuPKDoZksnFnIv7+0mSOnGkMtTsjoLOB0EPAmMFVKWSilvElK+Vcp5TYpZcBFSqWUtTjDJOYCVUKIXADXf3dT7nJgoNdmA3BWuSh3vW67PGqoMje3q8NacqJBc+B3h1cYDTrcZWJ1QrBkjm/9ziVzirj3nZ389MVvuX5Kvqc+snv/gdQq9Ffjs61h7C/Jw13eSE3lRTYpJoOmLqbEB1bovqMkoICOH699/OQAj6+IPBKMMZrfeUJcDA+8v5P7541qV6/4t+/vZEupmZ+++C1XT8pnXF4yBr3Q1h2jgcPVFs2wsxMN2p7oYdlJagyLUo7VRm/jjq4wMT+NS8fmsOjV77CfoXWuO4spvqO7OxZCZAEtUspaIUQ8cAHwOPA+cAvwmOv/e65N3gdeFUIsxZloVwR8I6W0CyHqXa2lvwZuBv7SXbnCkZOW9oPw65vLuX/eKB5etdsnjvOljaU+He/A2fY2Rid44prxJBtjaG51sHztAU/owrK1B3jimvHsq6r3xCQH4vEINHbPX5LHnBH9mD4kI+Km8hS+WG2t3HfpSE40NHuSlTIT47C2BNY8o6dJQPXWFhbPLmpXVitYiXaK4GNrsWt+500trVw2Po8Mk4E/XjOepuZWTHExPLvuIKXVTQjhau6yahd/vGY8wuUwcIf/uMfRhDg9h/x4o2sbW/yWyFRjWHRytKYxaht3dJVLxuSytayWlzYe5qc/GBxqcYJOX6Ya5gL/cMUV64DXpZSrhBAbgdeFEAuAMlztoqWUu4QQrwO7gVbgV67KEwC/5HRJtg+JgCS7rpCXamo3CNc02qhravGUEjp7cDpNNjv3XTqSVJMBS3Mr98wdyfMbDpISH+vTAnfJnCKunjSAEw0lnjCMfVX1PLe+hCVziijKTgzI4xFomR9/SR7+6nsqIous5Dj2tUm0u+PCYWQlBxaD19MkoFi9XrN5yCOXj+3JaSnCmAGpJh54f1e77/w3c0eybt9x+qcY+Z2Xw2Dx7CLM1hbPTJq1xUGTrZVjtTZMBr2nyoROgMmgx9pix2jQaxq/pli9pr4OzlRe4mjlaG0TEweeuXG03uiE4OZzBvGHD/dw1cQBpJxhHvQ+M4qllNuBiRrLq4E5frb5PfB7jeWbgY7ikSOa0bnJPHLFGJ+SaotnO73CFWYrRoMOY4yO5Z8X88gVY9h9rJG6Zjt6Ab+aXcQv//e7dklQC2cVctWkATz9ebEn7ML92T9vnRmQsRqohy/SkzwUHWOx2jWTlSYOnBrwPmJjhI9hEhsTuG4kGfXccFZ+O29fUpy+y+eiiAwGZyVy18UjfapP3H7BMP6wZg/3zB3J7Ro5DEuvm8DDq3YDrpyK2BhqLDZe+NchTzKd3QEv/OsQL9wylfjWVk0vcprJwPQhmWo8O4M4VmvlgpHKU+xmYLqJifmp/M+6g9w1d0SoxQkqqihdGBATo2P+2P4MykjgeL2zpNmD7+/0GMS3XzCMF/912BPztnBWIcvXFnvqEWt5cx3SGVbh3t4hJYtmDwWgprEZ6DyDuisevnApYq/ofarq/GT71zcHtP3haguLXv2+3Y+r1QGWt0qI05GXFu9jVOelxZNgDE7zDkXw0ekEF4/KJnvB2aw7cAK7A17812EqzFb2VNZp6qOzPKXVE16Wn2akMNOEhHaGr14HJkMMCbG+XuSEWD2m2Bg1np1hVJibztjqE/6YPz6PB9/fxS/PG0KS8czxFiujOAxwOCTrDp7wNEdIjtPzyBVjqTRbOVzd6HkYwGmD1/36yKlGTW+uzhVykRCrxxij4w9e3cCK+iUyySE79XwoD7ACIDdFe8Yg0DrBPe22VWG2sWpbOT+eNphaSwupCQZe2XSI1PjBFGV37VwUkYHDIfl4TxV7K+va1RJ2SDT1cdyAFP7644nkpcYzOjeFmBgdGw+e5MMdFe3ijyfmpyIl/O3LEq6aNMDjRf7blyUUZScxWBnDZwyW5lasrQ6SjMoc8iY72ciYvGRWfnvEU6/7TEBpQRjgrzlCYVaip3mHG+8KFOBMyGsberFkThEF6SZMsXpG5ibz4Ps7fdqcLv1kH2PzUgIySJTHRJEQp+Oh+aN58P3TcesPzR9NYlxgntqeJtrZpYMJAzP4j5e3+IQXaZUHVEQH7iTfn88sbKc7H2w7yoOXjfbJo3j48jE8+dE+7v3hSMZ7xYbmphi5ZGwud725zWd8zEk2Ym5qpabRxtOfnza63THFijOHCnMTWYlxCKGcPW25aFQOz64v4Wc/GHzGOMOUURwGVPlpjvCn6ya0y8B2V6BwU9Noo76pxWkIZ5iIETriYnU88N5OSqubeHDeSK6fkt8ui/uUpdlj6HoXu89NMWJ3OLs69VX3O0Vw6WlHw2pLM3ExOp9p5rgYHdUWW0Db9zTRLiHW4NFfOB1D+tJPA49pVoQXnelkVZ2VNFMsCbH6dlV4FvxgMK12h48+6nRwzeQB7WYv7A40x9aLRuVgs2tXuGix+85qKKKbo7VWMhNVPLEWRf0SidXr+OrgSWYWhWdN895GGcVhgMXWqjm9LMGTdR9v0DEmL4XymkZqGp3GiDteeMX6QwBcO2UAI3OS+a60Blur04uWmWTkyY/3+niKV24u4wdDMwDfbmNpplhuPqfAJ/6ut7vfKYJLT7vJARj0MTz16S5PspJDwlOf7ueJa8YHtH1Pw3Asza2kmWI909zgbLtrsQVWEk4RXgSik/1TjTwwbxR7KusQAp6+cSJNNgdNLXb6pxi5990dlFY3efZpNOi444IizI0tbDx40mNoH6/XDt050eDUQ62qJnPH5AT1eihCy9GaJjJUPLEmQgjOHZ7FK5vKlFGsCB4F6Qma08tF/RL5+0+mcrzeSnyMnuuf3USaKZYFMwoZlGHyxBsD3DStwMfjcf+8UdRbW2iw2tp5iu+fN4oTDc18XVJNQpze83C6atIAj0Gcm2LkqkkD2FtZR15qPGPzUpRhHIEEWmu6I8xNLfxk+mAe94pLv3vuiIA72kHPwnAGZSS0+7G2ZE6RaqQQoXSmk62tDr4rreWVrw9z8/RCpHRQ29TKfa4uc0aDjgcvG016Qiz7Kuux2R2s23ecBKOB65/d5GNoj8pN8hu6Mygjgbvnjuz2DIYiOiivaTzju9l1xPQhmdy+civmxpYzojybSt8OAwZnOqeXvTs0Lb1ugseImFaYic3hYMGMQq6e7PSWJccbWLX9KFdNGsC9l45sN7388KrdNNrsZCXHa372fZmZW/7+DdvKzaSZnAOCu/B9boqRm6YV8PyGEpZ9Vsz1KzZqdrJThD897SYHkJ4Q6zGI3ds/vmYv6abgPEgcUnsKXKljZNKRTjockn+VVPPK14e5elI+d725jf1VFo9B7F73oQ92seuYmaWf7Oe59SX88ryhnhAL9zp3vL4VuwPNsdU9UzF3dA6rF8/ktYVns3rxTDUrdgZSdqpRVZ7ogMS4GMYNSGHVjqhqJOwX5SkOAzqbXnY4JMdqrZ6kO6NBxx+uGssvzx3Kbz/Yxc9nFvoty7a9vFbzM7cB/PCq3SycVejJ8DYadFw1aUA7Q/qO17eS8dOpZCXFqTjjCKKnSW4AJ/20vQ00prin+JsCP15vZUg/lQAaaXSkk4dOWthceoqbpxd6kuPcY5U3bavw+CvTdrze2mGNbJVIrCivaWJivmrc0RHnDMng7e+O8uOzC0ItSp+jPMVhgntwnlaYSWFWoo/Rebjawt1vbfcxUg+dtPBbV/Y14PGEuHFXqXCXL9L6zL2vYdlJFGTE89aWck8NT60HzPrik1y6bL3yGkcQ7iQ3LU9ZoMS7On95YzToiDMEZ/gwxcZoHl9VCYhMOtLJ0lMWHBKa2uRZdDSGgf9xzhSrZ9Gr37Pss2KWry1m2WfFLHr1ew5XW/ruBBURxbHaJrJUol2HTBiQyoGqeo7VNnW+coSjjOIwxuGQlJxoYH9VPT+fWUhuymnvnkOeNlzf2lLO4tlFPg+ZxbOLePu7cj7YdpRHrxyr+Zn7/f6qem6bM4y//GgC5w3P5KyCdL8PIbfXWD1UIoPemCJOiIthyRxf/Voyp4jE2OBMNLmrBLTVYZuqEhCRdKSTCbExbDp4gkEZCZ7vW2t8WzLn9BgGzjJtj1wxxmedR64Yg83u6HH4kCJ6sbU6qGm0kZ6gwic6IkavY8qgNFbvqAi1KH2OCp8IU7QytBfPLuLlTc7Wz3pxuoB9hdnKy5tKuevi4WQkxnHwRAMvbyqlptHGQ/NHs2H/cZ66bgIHTzRQmJXI42v2eDo/ufdZ02hjzZKZfFdWy58/29+uVFHbrnjeJd0U4U1Pp4hT47U7yqXGB+c3dVyMXrNKwAxXBRVF5OFPJ3NS4rh2Sj53vbXNMwZVmK2s3FzGX2+cRE2jDaNBjzFW5wmDMBp0/PjsAhwOB4vOH4q11YGU8Je1B/jLjyZphmpkJQYePqSIXo7VNpGREItehQN2ytRBGby39VjUN/JQRnGYopWhvWztARbMKOT5DSXkZ5i4/YJhPPXpfqwtzl+7+RkmjtU4pzeunjwAnYDmFjtFOcn8btVurp0ygFa7nceuGse3h2tobnV4jGxw1ku+15XQ8vKmUhbMKESvg5E5yRyvs3arK54i8qltdFDXptJEXVMLtU3B8dS2tDq44az8dtUnWuwqhCfaaLXjaRLjPQZNHZROyYkGHv3w9Bh0/7xR9EuKY8dRM3F6HU9/cZB54/J8mnG0tDpYMqdIs82zQnGkppF+AXbmPNMZnZfMX78s5lhtE/1T40MtTp+hjOIwxV+G9rDsRJ64Zjw1jc28+k2pj/fM1uLwPDTcGA06nrhmPBVmqzOebvZQspPj+esXxe3Wq7O2eJZVmK2eh8tffzzJYxC75Vj6yT4GZZhotNlVk48op8HWyrPrS3zqFD+7voTfXT4mKMc3W1t4aaOvrr+0sZQROUlBOb4ieGglVbpnJ57/6lC7Kjp/vXESyz4rdjb18KrFDs4xzZ/uTMxPZVCmmuk60zlyqklVngiQGJ2OKQVpfLijggVR7C1WRnGY4i9De39VA89vKOE/Lx7OnReN4D+92pf+97XjNQ3pxuZWclOMXDtlAHmp8Rj0gnsvGeHjdVkyp4jUeIPmMZONMT7LclOMXD8ln+tX+NYEVeWMohOHdGh2RZRBSrYsSE/wqRggXBUE8tNVPdlowz3upZlifWqvP7feN3wMXGObze4J6UqO09NgswOnk/cK0hM0Wzl3pfqKInopO2VRNYq7wOSCdFYpo1gRCrRa47ofCtYWB098tI/7fzjSxwNyvN6qadTWN7d4mh+kmWK5dsoA8tNNPHX9BEqrLdRb7by0sZSxecmaU42pJl9j2V/Jtq40hFBEDklxBp+YXnDG9P53gB3tekpBuolbZxfxX+/u9OjlI1eMoSDdFJTjK4JHfpqJFTdNocLcxLHaJtJMsVSYrT7hY24D12jQcaSmkeVrnZ7i3185losLUpk+JMNT1hLoUYtxRXRzuLqRoeqZFTBj81L42xcHOV5njdqwE2UUhynetYv3Vtax61h9Oy9JjE7H058Xk5ti5Mdn55NuiuX3V4716fz0+NXjyEmJ45YXvm3nffGuRFHTaCM3xYS11eGTUFWUnciI7GSfB4u/km1Vde7YZKsKqQgjHA7J4WpLt7+XOmuLpqe4vjnwjnY9oaym0WMQg1PX/uvdnUzKT1M/wiKMjnTR4ZB8vKfKb3Kxe+yB09Ul/vvj/YBTJ+57ZwerF89kWmGmzzF70mJcEd2UVlv4wRCVsBsoBr2OifmpfLS7ipumRWfNYmUURwBJRoOncYe7/bJeB7mpRsblJTN/Qh5PfrwPa4uDgox4/nT9BOxSMiI7mcGZCXx9qNrTxrmth3fZ2gMsnFXIiBznuoMzEyjMTGz3APF+sMQbYlixrqSdR7rFLrl02XoVUhFGaFUx6er3YjLEaOrN339yVl+K7qGjDmjKKI4cOtPFQyf9Jxc//bnTGzy0XxKLZg9FSoiN0XmcBO71tXRCNehQ+KO8pilqPZ59xeSCNP65/VjUGsUqBzdMcT9ALl22nkMnG3jwstEUZMTzi1nObGyHhC2lNdx2YRFPfryPNFMsvzp/KJeNz2NPRR3xMTqaWlo5dNJCU4udJXOGEhej0zQuJg5MZXh2El8fquZwtYVBGQntmoh4NxcZm5fSrvj+41eP4/73fFuxqnrGoUeriklXv5eG5lZNvbE0t/aqrP5wx5l6o+JCI4+OdNHhkJScbPDbfdPtNf7D6j0sX1vM8xtKiI/R+9RuVzqh6Aq1jTbsDklSnPINdoXxA1LZUW6mJkgdTYON0oYwxfsBUtfUyhtbDnHvpaMoOdHg8dK6yxIN65fI3DG5PtPbf7x6LMUHq1n6yX7Psqeun6AZc6zX6fjhX9p7eN1ytJ3qbOs5zko0Utto47LxeYCz2L57ulN580JLb3hZMxJjNfUmI0hdoLTi61VcaOThTxdPWZrZW1lPWbVFU8+mDkpj0sBJFB+v5+rJA9ALyEyM45HVzjKT7uoTSicUXaG0upHcFCNCqJnMrmA06Bk7IJVP9lRx3ZSBoRan11FGcZji/QCxtjoorW7C1urwJMHB6bJET14znjtdVSjcy4tPWHxCHKwtDh77cA+PXjnWU4u4Iw/v8Ftnsq+q3u9Up9tzPCgjwW+TkZpGW5c9Nz2Nf1X44q+KSVe+F+lAMwFTBqmhXNsfYSouNDLR0sWCjHhAsLeyjtH9UzT1bG9FHQPTE1j6qW8zIVurZGRuMq8tPFvphKLLHK62kK1CJ7rFlII0/rm9QhnFiuDgcEhMsXoWzxmKQ0JinB6jQUejn2lsh5SatT3bLiutbiLVFMPKhdM89YWrLc2UVvv2M7e2OCg7pT3V2bbChL8mI+445a54bnoj/lXhS294WSvqrJq1XvODWP1BxYVGPm11sSAjnltnF/Fvz3+NtcXB4jlDeWNzeTs9u+2CIuxS+lSieOrT/SycVUhWYhxTBqWH+tQUEcihExayk1SN4u4wMT+Vv391iDprC8lGQ6jF6VWUURxmaBmGt18wjHsvGeFqcarh9UuOa7fcuw2097pbSs08v6GER68cS26KkazE9tsaDToS4mJ8SnC5QyLaTrv7mxKdODCVc4f165Ix6y/mUJV66z694WXNSorTrPWapR4oii7QPmFX76l1Ds4f8lp6Vnaqiec3lLSrRDEkK5Fx/VNCdTqKCOfgiQby0qK3M1tfYoqNYVT/ZD7bU8WVEweEWpxeRSXahRlahuFTn+6nX5KRiQWpPHrlWE/SUUFGPCtumoLdLnnm3ya7piKdD5L8DBP/efFwn2Q4d/k1a4uDe9/ZwdvfH2XXsXqW3zjRZ72l103glMXG8xtKWL62mOfWl3DTtAIKMuLbTbv7S4Iq6MZUZkfxr4ru450k6Z08GSiNtlYWzy5qp0tNtuAk2imiB29dbLTZfe73t7aUa+qZe8xatvYAV00a4PlsZE4SsbH6kJyHIvI5dNJCbooyirvLlIJ03t92LNRi9DrKUxxm+DMMd1bU8dz6EtYsmcnqxTM5ZWnmaK2VhS9v9km6SzbGIBHE6sAe6+DJa8Zjd0h0OsGz6w761Dl2SPj1G1tZtWgGq708iToBc/+8vl1IxN9+PIn8NN8p895MguqN+FdF7xMbo2Pt3kr+eM14mmytmGJj+Me/SpgwUHnpFN2n7f1eYbaycnMZT14znhaHg+qGZlrskqsnOw3ht7aUeypRLJlTRLXFhsM1tikUXUFKSempRnJUTHG3mTIojX9sPBx1IRTKKA4z/BmGxhgdC2YUsreynmHZSaSZ4vj1G9t8QhxWrDvIfZeOYucxM2cVpHGoupEHPtjtkwB3osFGhdnZ+U664o4PVVuYMyLbE6Kw8eBJTcN817E60hNisdkdxOp1nrjkC4b3Y+XCaVSYreSmGBmdm9KtB5WqMhCexBv0/HjaIIqP1+OQztCcH08bRLzy0il6gNb9fv2UfH6/eg+xMYLFc4oorW4EnDr3y3MLyU6JZ8GMQl7aWEpTi3P8UaFViq5yoqEZASTHR48xF2xMsTGM6Z/Cx7uquGZy9IRQKKM4zNB6UNx+wTCMMTqWf77Xs2zZDRM1u4zZHZIV60ooumZ8u0oV7kL43vF5RoOOHUfNtNglo3KTqDBbMcXGUJAR75OAZzToaGpx8F1pDYlGA/e/t9MnWca7BW93k+NUlYHwxNbqoNJs9SkFuGROEf1TlJdF0TNiYwQrbprMN4drkBJPzPDdc4dzvK65nc4dq2n0NPKwO1AlHxXd4uBxCwPSVJv4nnJ2YTrvfF8eVUaxiikOM9yG4T9vncnyGye6Wi5L/rBmr4+Ba2t1aHYZS4jT8/OZhZ5l3lhbHAzLTmThrEJPybTFs4t4Y3M5d7y+lbe/P8qPnv2a61dsZNH5RT4xyotnF7Fq+1GG5SR5DGKAeePy+IvL2F40eyg/n1nI42v2dLtpR0/jXxW9T4tdtvuB9efPDtBilyGWTBHJHK62sOjV79HrBM+tL+Hpz4s94V0D00yaOpeZZPQZj1RolaI7FJ9ooH+q0p2eMik/jW1HajlR3xxqUXoN5SkOQ3Q6wZB+iQzOTGBUbjL7q+rbGbglJy2aRu/2cjPL1xazZM5QzTCMI6ec05G3zSmirKbJ45kBZ/a3ez/3v7eTv//kLDaWVGN3wMrNZSw6v4hdR82kmWK5atIAhICxeSkkG2N46tP9Ph7rU5bmTj04qiZxZNBo0y4F2Gizh0giRTTgzp9oaG7lkSvG+Mw2CaH9o14IWDCjkJWby7h77kgVWqXoFgeq6lWN4l7AaNAzqSCdD7Yd5WczCkMtTq+gjOIwwZ+B6DYs2xq4dodD0+htcr1/fXN5u0L43iWNFs8Z2q70kfRy/KWZYtEJwYSBqSTExXDJ6GwOnmzAAdx8ToFnv4vnDG3XJGTZ2gM8f8sUth2pxWa3k5EQ52PwOhySQyct7Kmo48Dxel7fXE5No63TTnodXSeFf3p6zVJN2h3tUlU8nqKLeOtikjGG380fRUq8Abtd+tQnjovRaepcTrKRzMRYrp6Up+59RbfZW1nP7OH9Qi1GVPCDIRm8saVcGcWK3qOzphVaccZjB6S0W+Y2esGZyf3SxlIfb6/bIDYadIzOTfE8dNzxei9tdG6bm2Lk5nMKuOXv33i1jR5HdrKRY7VWn2lNrSYh1hYHG0tO8dx6Z+yy26vjNnj9dcC74/WtjFoyk90V2p30tLZVzT06pjcaolhsLdx+wTCf2YDbLxhGY4sqyaYIHG9dTDPF8stzC7HY7Nz91nYemDea5zec/nF9zyXDNXVOIplWmBniM1FEOgeq6rnlnEGhFiMqGNM/hefWl7C3so4ROcmhFqfHKKM4DOisaYW/BDSAEYtnUlptwdJs54mP93pCIcBZCF+ng9yUeB76YJfn4fLgZaNJMDqrWRSkx+NwOMhOjefaKQNwSEiO03taqrrlueut7fz1xknkpca3M4K1PDruyhbL1h5g0flD2VtZh9GgIyMhjsfX7NFMAHz682Kq6pr9XgtANffoIr3REMUUa+CT3RXtSrJNHDiyL0VXRBneunjzOQWkJ8Rx0lLPnReN4G9fHGDx7CJPnkSTzc67W4/6eI9f/aaUC0dlh/o0FBHOSVepvzSTmunqDXQ6wcyiLF775gi/nT861OL0GGUUhwEdNa1wGy7+2twWZiVSVWfl3nd2ctO0Ap9qFI9eORaHQ/LMl8U+D5dnvizmwXmjefrzYs4flsklY/vzy//9zrPdI1eM8bRU9ZZna3ktDulrBL+1xX+Yhnu7/qnx3PvODs0wDvc67vqjFj/xq8frrR5Du6PrpPAlEN3qjOaWVq6enM9db27z+WFlbVWeYkXguHUxN8VIcryBO730afHsItbsrPCMU0aDnl+cO9Tnx/yjV45VMcSKHrO/sp6CDBNCqNnF3mLWsCx++/4u7rlkBEZDZJfqVEZxGNDTphXZyUZqGm28vKnU81DRCZiUn8qB4w2UVjf5xA8DWFxJUldPHuh5OIHTYPqvd3eycFYhyz7zjTm2O+Dt78p9PDo1jTYSYvUsOn8oWUlxmBtt2LwK7n+w7SjxBr3HyG7rGXbvWydg6XUTKEhP6PBaqOYeXaM3GqIYDTE88+Uun5rYz3xZzBPXjO9tcRVRjFsXr5o0gIdX7fZJ2G1utXPJ2FweX7MPcOroc7dM4anrJnDgeAOtDgeT8lNVmJSix+yuqGNguirH1ptkJxsZlJnAmp2VXDExL9Ti9Ig+K8kmhBgohPhcCLFHCLFLCLHEtTxdCPGJEOKA63+a1za/EUIUCyH2CSEu9lo+WQixw/XZMhFlP/HcMcNtWy0H6hVxb1/TaOPpz51tmUfkJJOfnkBhRoJmG+aquiaMBh0OpKYncUROEovnDGXR7KEsmTOU38wdwdvflVNhtnqM72f+bRL/vHUm/dPiWf55Ma9sKiXFFMvyz4t5a0s5OgG3XzCcyrombj6nwGf/et1pWR69cixXTcxj7ugcBmf6vxY9vU5nIr1xzeqbW/jVuUMYmZPEwNR4RuQk8atzh9DQ3NJXYiuiELcu6nUwrF8i988bRbxBx/DsJBJi9QzLTmJcXrKnO6eUDn75ynf89Ytiz3imUPSUHeXmdp1ZFT3n/OH9ePFfh0MtRo8RUvZNrVEhRC6QK6X8TgiRBGwBrgB+ApySUj4mhLgHSJNS3i2EGAX8HzAV6A98CgyTUtqFEN8AS4BNwGpgmZTyw46OP2XKFLl58+Y+Obe+wJ2V3d2mFVrbA5SdsvB9WS2/8QpfeOyqcaTE60lPiAME16/Y6GMYF2TEs+j8Ik89YqNBxx0XDuPvXx32hDwYDTpWu+JSHQ7JjqO1VNU1s/OYmRidjqJ+iTy2Zg+l1U2e6fZlnx3wJPqtXDiNpha75rl2dC16ep26Qa/tPFQ62dNrtq2shr1V9Tz4/ump7Ifmj2ZEdhLj89M634Git4lYnXQ4JHsqzWw9YubhVae7bd4/bxTvfHeEG6YWcKK+mde+LeMPV45FpxOqiU9kEDE6ecHSL/np9EEq5K6XsTskt7++lRduOYuxA1JCLQ50Uyf7LHxCSlkBVLhe1wsh9gB5wOXAea7V/gF8AdztWv6alLIZOCSEKAamCiEOA8lSyo0AQoiXcBrXHRrFkYa/mOHubu9wSNYXH6e+yY5OwIqbJmNpbmVYdjKDM50G8+FqCw3NLfzu8jE84GUA3zN3JLe3Sc5a+sl+T0hFW2+jTiew2R2caPDtQOUdO/zQB7tYdP5Qln9ezNLrJjA2z/9UaEfXoqfX6Uykp9es2e7wGMTg1IcH39/FSz+b2ptiKs4AdDpBq116DGJw6tPDq3az9LoJPL5mDzeclc8vzh1KeoKBEbmpoRVYEVU02ewcOdWoutn1AXqd4MKR2Ty7voRlP5oYanG6TVBiioUQg4CJwNdAtstgRkpZIYRwFwvMw+kJdlPuWtbiet12udZxFgILAfLz83vxDCKPIzUWjtU2t6s6EeOKgV+7r4rt5WaK+iXx9OfOGN/8tHiOmps4cLzBTze8JBbNHopOONuzwmkvpHTQ7kHnHTtsbXEwtF8iqxfPPKO8PtGgk9UNNk19qLbYQiSRoieEWieP1jZp6tPeyjrmjctjQJqJJz/ey3M3nRV02RShIVg6ubvCTH66idgY1cy3Lzh/RD/ueH0rx2qb6J8aH2pxukWfa4YQIhF4C7hNSlnX0aoay2QHy9svlHKFlHKKlHJKVlZW14WNIqrMpw1icD50HvpgF1XmZspOWThQ1cCKdSXsq6r3JOIdqW1i2WfF2OwOzTjkwyctLF9bzLLPiln06veUnbKwZlclly5bz6ZDp/x2oHJvnxAXc8a1bo4GnUyMi9HUh8Q4lacbiYRaJzMT4zT1ye7Ak2tQWt1E6anutYpXRB7B0smtR8yemVJF75MYF8O5w7JYsa4k1KJ0mz41ioUQBpwG8StSyrddi6tc8cbuuOPjruXlwECvzQcAx1zLB2gsV3TASUuzppF60tJMVV2zTwMO7weU0aDjrS3OChPeyVm3XzCMV74u89mXd01hf4a0dJVwWzKniKS4GByOvolhV/QdSUY9S+b46sOSOUUkxkZ26R1FcHE4JCUnGnBIB49cMdZHnxbPLmLV9qOMzEmmvKbR8yNaoehNtpSeojBLGcV9ySVjcnnru3JONjSHWpRu0ZfVJwTwPLBHSrnU66P3gVtcr28B3vNafoMQIk4IMRgoAr5xhVrUCyGmufZ5s9c2Cj/kpZo0jdS8lHgavWoBv7WlnNsvGOYxhpfMKfKUd1s4q5C//GgCKxdO49VvSn3qFretKaxlSN8/bxRJRj0LZxViMuhZsvJ71uyqDNgwdj9ENx486XyYKoM6JNjsdvLS4lk4q5BFs4eycFYheWnxtDjsoRZNESG4u9ldumw9N6z4ho93HeVv/zaZxXOGsmBGISs3l7Fw1hDqm2y89m0Zi2cXYWtV+qXoXTYfrmFYdlKoxYhq0hNimT4kg2e+PBhqUbpFX/4U/wFwE7BDCLHVtexe4DHgdSHEAqAMuBZASrlLCPE6sBtoBX4lpXSPir8EXgTicSbYRVWSXV8wOjeZJ64Zx4HjDTgk6AUM6ZdIaoIBhPDUrs1KjKUwM4GH5o/GFBuDubGZ526Zgl4ITLF6bHYHiXEG7p83ikWvfu/TKti7prC7VNvCWYVMHJjKwDQTDc2trN13HLsDnllXQoXZGnA3td5oT6zoHQw6PdlJMeQkZVJVbyU72YiUrRh0ylOsCAx3Nzvv2sRSOpicn0qd1c7Zg8eQEBtDRZ2VRy4fw8ubDnGR6l6n6EWO1jZha3WQk6zq2vc188fncc/b2/n3mYVkR9j17svqExvwXxJjjp9tfg/8XmP5ZmBM70kX/eh0Ap3Q+VSDuPOi4dz8wjfcd+koFs8uYuXmMq6fms+tr33vUzHiT5/s46ZzBnP3W9s9y/9w5Vj++uOJGA16cpPjKXBVnlh63QSP4VrTaCPeoMcuJYVZiXx9qNqnAQgE3k2tN9oTK3oHnR4OH2/mwfdPdz18aP5oRuSqNqmKwKiqs5Jmim3XdXPJnCK+KanmotG5PLTqdFLw7+aPYUBKZCbqKMKTr0uqGdk/WXWyCwLpCbGcNyyLpZ/s5/Grx4VanC6hUjCjlMPVFn79hq9R+eTH+5g3Lo9dx8ys3FzGry8aoVkx4ubphR6D2L38N+/sYEupmZ+9uJktZTU4HBKdTjAyJ8kzrb5gRiEvbSxl0avfc+ikxdPByptAu6l11J5YEVysNqlZks1qU+EsisDITjZy7ZQBHoMYnHr0588O8JMZgz0GsXv5A+/vZGdlR3nZCkXXWH/gBCNzVOhEsJg/Po+Pdlayv6o+1KJ0CWUURyn+jEoh4PXN5SyZM4zi4/Wa61i9YoXbbutuA727wkzJiQb2VNax7LNilq8t5unPiz2tnMtOWXrUTa0nBrWidzler520eaI+MhMpFMFnUEYCw/olaepRjaVFc3lVnfoBrOgdpJR8VVzN6P5h0VTijCDRGMP8Cf357fu76KsmcX2BMoqjFH9GpZRQ02hjUn4qc0Zka66TmxLvd1twPrAOVzdy6bL17K6o11zXFBuDTieYOzqH1Ytn8trCs1m9eGbAMcGqpXP40C9Zu4RWVlJciCRSRBo6nSA/Qzv5Nz3BoLk80mIRFeHL3sp69DpBborSqWBy4ahsymua+GhXZahFCRhlFEcpWkalu+zR0usmkJ+ewNi8lHbr3D9vFC9sKGlXSWLx7CLe/q7c8774hLPBh1bViSVzishOdhpM7m5q0wozu1SjuCcGtaJ3SYrT89D80T7f8UPzR5MYpxLtFIHT3GpvN1bcceEwKs1N7fTrd5ePYZzy6il6ic/2VDFhYKqKJw4yMTodP5k+iAfe20W9tSXU4gSEKgQZpbiNyhGLZ1JVZ8UUq6fF7mDumByfjnLudY7XW4k36Fn82veUVjext6qBBTMKSYmPYWi/RB76YBcVZitGg46HLx/D0k/2A3iqTiyYUUh+ejxHa5soyk4kP73nHl3V0jk8OGWxER+rY8VNk6lpbCHNZKC2yUZto+popwicjIQ4Vm4u44lrxnPopIWifok0trRy3zs7GdYvkSevGY9E0j8lnrH9U4hVdbAVvcQ/d1Rw1cQBna+o6HVG5iYzNi+F3/9zD49FQNKdMoqjmECMSu91HA7J3XNHcsfrW6kwW3l+QwlLr5vAjMJMlt0wkUqzlZwUI8lGAzVeBpF73X/8dCpnDUo/o9o4nwnkppi4792dzBuX5yqlBau2H+WFW6aGWjRFBDEoI4G7545kX2Ud/+OqipObYmTBjEL0OshOjmN8XqoyhhW9yuGTFirMVkbmJodalDOWG8/O5563d7Bu/wlmDQvvzq7KKFZ40OkEF43MZuXCaVSYreSmxDM6N5mYGB3jB6Yx3tVv0OGQPqXY3PG+Zw1KV8ZwFDI4M4H7541ie7kZh4QYHdw/b5Rql6roEu7Zq9H9k5iQn8b3ZTU4JHyw7Sh3zx3J5AI1fih6nze3lDN9SAZ6pVshwxQbw8KZhfz69W2suW0mGYnhm4+ijGKFB4dD8vGeqk4bZniHZhyvt9Ivyai8w1GOrVX61Lxeet2EUIukiFB2Hav3GWMev3ocF43MVuOHotdpsTt4ffMRfn3R8FCLcsYzJi+F6UMzWPLaVv7xs6lh+yNFJdopPPhrmHG42tJu3e4m0Ckij67ohULREVq6dPdb2ymraQyxZIpoZPWOCrKTjeSnm0ItigK4dvJAaptsPPHR3lCL4hdlFCs8qIYZCi2UXih6C6VLimBhd0j+/OkBLh2bE2pRFC70OsGt5xfx3tZjvPp1aajF0UQZxQoPqmGGQgulF4reQumSIli89k0Zxlg94wekhloUhRfJ8QbuungE//3xft79vjzU4rRDGcUKD6phhkILpReK3kLpkiIYHDnVyJMf7+OWcwap2sRhSE6KkbvnjuDhVXt46V+HQy2ODyrRTuFBJdAptFB6oegtlC4p+pqG5lYWvrSZH47NVbHEYczAdBP3zxvFf3+8j53HzDw0fwzxYVCOUXmKFT6oBDqFFkovFL2F0iVFX1FptnLDio3kp5u4dGxuqMVRdEJ2spHfzh9NZV0zFz71JWt2ViKlDKlMylOsUCgUCoUiYqmx2Hjt2zJWrCvh4tE5zB/fX4VNRAim2Bh+ee4QtpfX8sc1e3nio73cODWfi8fkMCAt+J5+ZRQrFAqFQqEIa1rtDt75/ihHa5uot7ZS3dBM6alGdh2tw2Z3kJcaz4+m5pOXGs+hk6pcZKSRGBfDz2YMZvexOv6xsZSH/7kHgJxkIwUZJnJTjKSaYkkyxmA06InV60iJNzB/Qn+Mht4LuxChdlX3FUKIE0B41vzoOpnAyVAL0YtE0vmclFLO7Y0dBUEnI+G6hruMkSDf3jDQyXC/TloomfuOPh8nY1JzY/P+49mxWtu0mo83y1abQ+uz3kBKR4wQuta+2n+wiYjzEQJ9UmaczhDXYZhv5f/du7e5bHs87e+Tbulk1BrF0YQQYrOUckqo5egtou18woVIuK7hLqOSL7Lk6ApKZkV3ibbvQZ2Pf1SinUKhUCgUCoXijEcZxQqFQqFQKBSKMx5lFEcGK0ItQC8TbecTLkTCdQ13GZV8gREucnQFJbOiu0Tb96DOxw8qplihUCgUCoVCccajPMUKhUKhUCgUijMeZRQrFAqFQqFQKM54lFEcZgghBgohPhdC7BFC7BJCLHEtTxdCfCKEOOD6nxZqWQNFCKEXQnwvhFjleh+x5xJq/OlHm3XOE0KYhRBbXX8PhEDOw0KIHa7jb9b4XAghlgkhioUQ24UQk4Io23Cva7NVCFEnhLitzTpBvYZCiBeEEMeFEDu9lgV0nwgh5goh9rmu5T19LGfQjtUVujNuCiF+4zqPfUKIi0Moe8DjY7jIHOn0pr4IISa7xrpi15gWslZ6vaFL4XI+QohUIcSbQoi9ru/pnKCcj5RS/YXRH5ALTHK9TgL2A6OAPwL3uJbfAzwealm7cE53AK8Cq1zvI/ZcQv3nTz/arHOe+1qHUM7DQGYHn18KfAgIYBrwdYjk1AOVQEEoryEwC5gE7PRa1ul94pL/IFAIxALb2upDL1+roByrG7J1adx0fbYNiAMGu85LHyLZAxofw0nmSP/rTX0BvgHOcY1lHwKXhPC8eqxL4XI+wD+An7texwKpwTgf5SkOM6SUFVLK71yv64E9QB5wOU4lwfX/ipAI2EWEEAOAHwLPeS2OyHMJBzrQj0jjcuAl6WQTkCqEyA2BHHOAg1LKkHa/lFKuA061WRzIfTIVKJZSlkgpbcBrru36gmAeq0t0Y9y8HHhNStkspTwEFOM8v6DSxfExLGSOBnpLX1xjVrKUcqN0WmAvEaLnWW/oUricjxAiGaej4HkAKaVNSllLEM5HGcVhjBBiEDAR+BrIllJWgPOGBvqFULSu8CfgLsC7BWeknktY0UY/2nKOEGKbEOJDIcTo4EoGgAQ+FkJsEUIs1Pg8Dzji9b6c0Bj3NwD/5+ezUF/DQO6TYF7HcPnOOiTAcTNczuVPBD4+hovMUUUP9SXP9brt8lDwJ3quS+FyPoXACeDvrnCQ54QQCQThfJRRHKYIIRKBt4DbpJR1oZanOwgh5gHHpZRbQi1LtNGJfnyHMxxgPPAX4N0giwfwAynlJOAS4FdCiFltPteK6wpqfUghRCwwH3hD4+NwuIaBEMzrGPLvrDO6MG6G/Fy6MT6GXOZooxf0JSy+k17UpbA4HyAGZzjZ36SUEwELznAJf/Ta+SijOAwRQhhw3qivSCnfdi2uck8vu/4fD5V8XeAHwHwhxGGcU62zhRD/S2SeS9jgRz88SCnrpJQNrterAYMQIjOYMkopj7n+Hwfeof00bzkw0Ov9AOBYcKTzcAnwnZSyqu0H4XANCew+CeZ1DIfvzC9dHDfD4Vy6Oj6Gg8xRQy/pS7nrddvlwaa3dClczqccKJdSumdB38RpJPf5+SijOMxwZUY+D+yRUi71+uh94BbX61uA94ItW1eRUv5GSjlASjkI5zT1WinlvxGB5xIudKAf3uvkuDNshRBTcd7n1UGUMUEIkeR+DVwE7Gyz2vvAzcLJNMDsnhYLIj/CT+hEqK+hi0Duk2+BIiHEYJfn+wbXdn1BMI/VJboxbr4P3CCEiBNCDAaKcCbkBI1ujI8hlzla6C19cY1Z9UKIaa593kwInme9pUthdD6VwBEhxHDXojnAboJxPoFkAaq/oGZczsDp3t8ObHX9XQpkAJ8BB1z/00MtaxfP6zxOZ8RG9LmEqX78AviFa51FwC6c2bibgOlBlrHQdextLjnucy33llEAT+PMEt4BTAmyjCacRm6K17KQXUOcxnkF0ILTu7HA330C9AdWe217Kc7s+YPua92HcgbtWF2Uq8vjJnCf6zz2EcKKAS5ZAhofw0nmSP7rTX0BpuD80X8QWI6rU3Ck6lK4nA8wAdjs+o7eBdKCcT6qzbNCoVAoFAqF4oxHhU8oFAqFQqFQKM54lFGsUCgUCoVCoTjjUUaxQqFQKBQKheKMRxnFCoVCoVAoFIozHmUUKxQKhUKhUCjOeJRRHGUIIaQQ4mWv9zFCiBNCiFWu9z9xvd8qhNgthPh3r3XnCiG+EULsdX2+UgiRH4rzUIQ3rjq+rwkhDrr0aLUQYpifdc/z0r/5QoiOOhNpbf+iEOKa3pBbEVkIIeyusWiXq+X2HUIIneuzKUKIZd3c7+EQNGPpMkKIQUKIG0Mth6L3EEI8JYS4zev9R0KI57ze/7cQ4g4/2/5OCHFBJ/v/rRDiTo3lqUKI/9cD0c8IlFEcfViAMUKIeNf7C4GjbdZZKaWcgLOe4aNCiGwhxBic7WxvkVKOcH3+CjAoGEIrIgdXEfR3gC+klEOklKOAe4HszraVUr4vpXysj+WL6cv9K4JKk5RygpRyNM6x7FLgQQAp5WYp5eKQStf3DAKUURxd/AuYDuD6gZcJjPb6fDrwldaGUsoHpJSfdvO4qYAyijtBGcXRyYfAD12v/Xbtks4WvAeBAuBu4FEp5R6vz9+XUq7rY1kVkcf5QIuU8hn3AinlVmChEOJy9zIhxCtCiPneG7pmKpa7Xr8ohFgmhPiXEKLE7Q12dblb7vJA/xPo57X9ZCHEl0KILS4Pi7vl5xdCiEeFEF8CS4QQ1wohdrq8i0qHowDXeLUQWOTSEe8ZiHNdHuWtQojvhRBJrs/XCSHecenSM24vszdCiHdd+rRLCLHQa/lcIcR3Lh36zLUsQQjxghDiW9dxLnct/4lrPx8IIQ4JIRa5vNrfCyE2CSHSXesNEUKscR1vvRBihGu55r0APAbMdJ3X7X15fRVB4ytcRjFOY3gnzq5raUKIOGAkgJ9xzjNrJoS4VDhndTe4dGeV1zFGucbEEiGE+4fjY8AQly49EYwTjUSURyU6eQ14wHWTjANeAGa2XUkIUYiz+1gxzpvzyWAKqYhYxgBbNJY/B9wOvCeESME58N+Cs3uUP3Jdn4/A2arzTeBKYDgwFqf3eTfwghDCgHM243Ip5QkhxPXA74GfufaVKqU8F0AIsQO4WEp5VAiR2oNzVYQRUsoSl2Hbr81HdwK/klJ+JYRIBKyu5VOBUUApsAa4CqeOefMzKeUp1+zat0KIt3A6jJ4FZkkpD7mNWpxds9ZKKX/m0qtvhBBuz90YYCJgxDmm3i2lnCiEeApne9k/AStwdk08IIQ4G/grMNu1vda9cA9wp5RyXneulyL8kFIeE0K0Cmdo4nRgI5AHnAOYgT3AU/gf5xBCGIH/4bR+tnV8jcDpvEgC9gkh/oZTl8a4ZoEVflBGcRQipdwuhBiE00u8WmOV64UQM4Bm4D9cDwTPh0IIdytFE7BCSqmMZUWnSCm/FEI8LYToh9P4eEtK2eqtWxq8K6V0ALuFEO7wi1nA/0kp7cAxIcRa1/LhOA2PT1z71ONsjexmpdfrr4AXhRCvA2/39NwUYYWWQn0FLBVCvAK8LaUsd+nIN1LKEgCX4TCD9kbxYiHEla7XA4EiIAtYJ6U8BCClPOX6/CJgvjgds2kE3HkXn0sp63F6/czAB67lO4BxLmN9OvCG1z0R5yWH1r2giE7c3uLpwFKcRvF0nEbxUZx65m+cA6fRW+LWT5yzwQu9Pv+nlLIZaBZCHCeA0DaFE2UURy/v4/T8noezX7g3K6WUi9os2wVMArZJKauBCa6BP7GvBVVEHLsAf4lvLwM/Bm7Ay7PRAc1er72NHa3+8wLYJaU8x8++LJ6NpfyFyxP3Q2CrEGKCS68VEYxrdssOHMc1zQwgpXxMOENtLgU2idPJSG31yOe9EOI84ALgHClloxDiC5yGrtDYFtfyq6WU+9rs52x8ddnh9d6B81mrA2o78NT5uxcU0Yc7rngszvCJI8CvgTpgLZDXwTgHneuHty7ZUbZewKiY4ujlBeB3UsodAa7/R+A+IcRIr2Wm3hdLEQWsBeKEb+WSs4QQ5wIvArcBSCl3dXP/64AbhBB6Vyzd+a7l+4AsIcQ5rmMahBCjtXYghBgipfxaSvkAcBKnB1ARwQghsoBngOVSyrbG7RAp5Q4p5ePAZpyeNICpQojBrpCL64ENbXabAtS4DOIRwDTX8o3AuUKIwa79u8MnPgJuFS4XnhBiYqDySynrgENCiGtd2wohxPhONqvHOQWuiC6+AuYBp6SUdtdMRCrOEIqVdD7O7QUKXTPC4NTtzlC6FADKKI5SpJTlUso/d2H9HcAS4CVX8P5XOD0xr/aVjIrIxGWQXAlcKJwl2XYBvwWOSSmrcMbE/b0Hh3gHOIBz2vlvwJeu49pweqgfF0JsA7ZyOmGlLU8IIXYIIXbiNLK39UAeReiIdyUG7QI+BT4GHtJY7zbhSqwEmnAmG4PTuH0MpzfuEE7d8mYNECOE2A48DGwCkFKewDkd/bZrn+7QnIcBA7DdpVsPd/F8fgwscO1zF3B5J+tvB1qFM9lPJdpFDztwVp3Y1GaZ2ZVQ2uE4J6VswllJYo0QYgNQhTP0wi+umbKvXPeJSrTzg2jzg1uhUCi6jRDChHNwnySl7HCQVij6EldohEpSU0QlQohEKWWDa9biaeCAlPKpUMsV6ShPsUKh6BVccZx7gb8og1ihUCj6lH8XQmzFOeOQgrMahaKHKE+xQqFQKBQKheKMR3mKFQqFQqFQKBRnPMooVigUCoVCoVCc8SijWKFQKBQKhUJxxqOMYoVCoVAoFArFGY8yihUKhUKhUCgUZzz/H0Elxdh2plj6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gavKO_6DWRMP"
   },
   "source": [
    "También revise las estadísticas generales:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yi2FzC3T21jR"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cylinders</th>\n",
       "      <td>314.0</td>\n",
       "      <td>5.477707</td>\n",
       "      <td>1.699788</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Displacement</th>\n",
       "      <td>314.0</td>\n",
       "      <td>195.318471</td>\n",
       "      <td>104.331589</td>\n",
       "      <td>68.0</td>\n",
       "      <td>105.50</td>\n",
       "      <td>151.0</td>\n",
       "      <td>265.75</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horsepower</th>\n",
       "      <td>314.0</td>\n",
       "      <td>104.869427</td>\n",
       "      <td>38.096214</td>\n",
       "      <td>46.0</td>\n",
       "      <td>76.25</td>\n",
       "      <td>94.5</td>\n",
       "      <td>128.00</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>314.0</td>\n",
       "      <td>2990.251592</td>\n",
       "      <td>843.898596</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>2256.50</td>\n",
       "      <td>2822.5</td>\n",
       "      <td>3608.00</td>\n",
       "      <td>5140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acceleration</th>\n",
       "      <td>314.0</td>\n",
       "      <td>15.559236</td>\n",
       "      <td>2.789230</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.80</td>\n",
       "      <td>15.5</td>\n",
       "      <td>17.20</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model Year</th>\n",
       "      <td>314.0</td>\n",
       "      <td>75.898089</td>\n",
       "      <td>3.675642</td>\n",
       "      <td>70.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>79.00</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.624204</td>\n",
       "      <td>0.485101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.178344</td>\n",
       "      <td>0.383413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.197452</td>\n",
       "      <td>0.398712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean         std     min      25%     50%  \\\n",
       "Cylinders     314.0     5.477707    1.699788     3.0     4.00     4.0   \n",
       "Displacement  314.0   195.318471  104.331589    68.0   105.50   151.0   \n",
       "Horsepower    314.0   104.869427   38.096214    46.0    76.25    94.5   \n",
       "Weight        314.0  2990.251592  843.898596  1649.0  2256.50  2822.5   \n",
       "Acceleration  314.0    15.559236    2.789230     8.0    13.80    15.5   \n",
       "Model Year    314.0    75.898089    3.675642    70.0    73.00    76.0   \n",
       "USA           314.0     0.624204    0.485101     0.0     0.00     1.0   \n",
       "Europe        314.0     0.178344    0.383413     0.0     0.00     0.0   \n",
       "Japan         314.0     0.197452    0.398712     0.0     0.00     0.0   \n",
       "\n",
       "                  75%     max  \n",
       "Cylinders        8.00     8.0  \n",
       "Displacement   265.75   455.0  \n",
       "Horsepower     128.00   225.0  \n",
       "Weight        3608.00  5140.0  \n",
       "Acceleration    17.20    24.8  \n",
       "Model Year      79.00    82.0  \n",
       "USA              1.00     1.0  \n",
       "Europe           0.00     1.0  \n",
       "Japan            0.00     1.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats = train_dataset.describe()\n",
    "train_stats.pop(\"MPG\")\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Db7Auq1yXUvh"
   },
   "source": [
    "### <span style=\"color:#4CC9F0\">Separe caracteristicas y etiquetas</span>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separe el valor objetivo, o la \"etiqueta\" \n",
    "de las características. Esta etiqueta es el valor que entrenará el modelo para predecir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t2sluJdCW7jN"
   },
   "outputs": [],
   "source": [
    "train_labels = train_dataset.pop('MPG')\n",
    "test_labels = test_dataset.pop('MPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mRklxK5s388r"
   },
   "source": [
    "### <span style=\"color:#4CC9F0\">Normalice los datos</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mRklxK5s388r"
   },
   "source": [
    "Revise otra vez el bloque de `train_stats` que se presentó antes y note la diferencia de rangos de cada característica."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-ywmerQ6dSox"
   },
   "source": [
    "Es una buena práctica normalizar variables que utilizan diferentes escalas y rangos. Aunque el modelo *podría* converger sin normalización de características, dificulta el entrenamiento y hace que el modelo resultante dependa de la elección de las unidades utilizadas en la entrada."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-ywmerQ6dSox"
   },
   "source": [
    "### <span style=\"color:#4CC9F0\">Nota</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-ywmerQ6dSox"
   },
   "source": [
    "Aunque generamos intencionalmente estas estadísticas solo del conjunto de datos de entrenamiento, estas estadísticas también se utilizarán para normalizar el conjunto de datos de prueba. Necesitamos hacer eso para proyectar el conjunto de datos de prueba en la misma distribución en la que el modelo ha sido entrenado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JlC5ooJrgjQF"
   },
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "  return (x - train_stats['mean']) / train_stats['std']\n",
    "normed_train_data = norm(train_dataset)\n",
    "normed_test_data = norm(test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BuiClDk45eS4"
   },
   "source": [
    "Estos datos normalizados es lo que usaremos para entrenar el modelo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BuiClDk45eS4"
   },
   "source": [
    "### <span style=\"color:#4CC9F0\">Precaución: </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BuiClDk45eS4"
   },
   "source": [
    "Las estadísticas utilizadas para normalizar las entradas aquí (media y desviación estándar) deben aplicarse a cualquier otro dato que se alimente al modelo, junto con la codificación de un punto que hicimos anteriormente. Eso incluye el conjunto de pruebas, así como los datos en vivo cuando el modelo se usa en producción."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6SWtkIjhrZwa"
   },
   "source": [
    "## <span style=\"color:#4361EE\">Construye el modelo Sequential modo 1</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6SWtkIjhrZwa"
   },
   "source": [
    "Construyamos nuestro modelo. Aquí, utilizaremos un modelo `Sequential` con dos capas ocultas densamente conectadas y una capa de salida que devuelve un único valor continuo. Los pasos de construcción del modelo se envuelven en una función, `build_model`, ya que crearemos un segundo modelo, más adelante."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c26juK7ZG8j-"
   },
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "layers.Dense(64, activation='relu', input_shape=(normed_train_data.shape[1],)),\n",
    "layers.Dense(64, activation='relu'),\n",
    "layers.Dense(1)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Compila</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c26juK7ZG8j-"
   },
   "outputs": [],
   "source": [
    "optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
    "model.compile(loss='mse',\n",
    "                optimizer=optimizer,\n",
    "                metrics=['mae', 'mse'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Summary</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c26juK7ZG8j-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 64)                640       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,865\n",
      "Trainable params: 4,865\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()\n",
    "plot_model(model, to_file='../Imagenes/gasolina_model.png', \n",
    "           show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0-qWCsh6DlyH"
   },
   "source": [
    "## <span style=\"color:#4361EE\">Entrena</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0-qWCsh6DlyH"
   },
   "source": [
    "Entrene el modelo durante 1000 epochs y registre la precisión de entrenamiento y validación en el objeto `history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sD7qHCmNIOY0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "...................................................................................................."
     ]
    }
   ],
   "source": [
    "# Display training progress by printing a single dot for each completed epoch\n",
    "class PrintDot(keras.callbacks.Callback):\n",
    "  def on_epoch_end(self, epoch, logs):\n",
    "    if epoch % 100 == 0: print('')\n",
    "    print('.', end='')\n",
    "\n",
    "epochs = 1000\n",
    "\n",
    "history = model.fit(\n",
    "  normed_train_data, train_labels,\n",
    "  epochs=epochs, validation_split = 0.2, verbose=0,\n",
    "  callbacks=[PrintDot()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tQm3pc0FYPQB"
   },
   "source": [
    "Visualice el progreso de entrenamiento del modelo usando las estadísticas almacenadas en el objeto `history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4Xj91b-dymEy"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_mae</th>\n",
       "      <th>val_mse</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2.237779</td>\n",
       "      <td>1.001212</td>\n",
       "      <td>2.237779</td>\n",
       "      <td>9.682216</td>\n",
       "      <td>2.348676</td>\n",
       "      <td>9.682216</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.730033</td>\n",
       "      <td>1.076314</td>\n",
       "      <td>2.730033</td>\n",
       "      <td>9.447591</td>\n",
       "      <td>2.344280</td>\n",
       "      <td>9.447591</td>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2.291314</td>\n",
       "      <td>0.975400</td>\n",
       "      <td>2.291314</td>\n",
       "      <td>9.515383</td>\n",
       "      <td>2.329066</td>\n",
       "      <td>9.515383</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2.148690</td>\n",
       "      <td>0.979652</td>\n",
       "      <td>2.148690</td>\n",
       "      <td>9.659124</td>\n",
       "      <td>2.401752</td>\n",
       "      <td>9.659124</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.307335</td>\n",
       "      <td>0.985725</td>\n",
       "      <td>2.307335</td>\n",
       "      <td>10.056418</td>\n",
       "      <td>2.424482</td>\n",
       "      <td>10.056418</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss       mae       mse   val_loss   val_mae    val_mse  epoch\n",
       "995  2.237779  1.001212  2.237779   9.682216  2.348676   9.682216    995\n",
       "996  2.730033  1.076314  2.730033   9.447591  2.344280   9.447591    996\n",
       "997  2.291314  0.975400  2.291314   9.515383  2.329066   9.515383    997\n",
       "998  2.148690  0.979652  2.148690   9.659124  2.401752   9.659124    998\n",
       "999  2.307335  0.985725  2.307335  10.056418  2.424482  10.056418    999"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B6XriGbVPh2t"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_history(history):\n",
    "  hist = pd.DataFrame(history.history)\n",
    "  hist['epoch'] = history.epoch\n",
    "\n",
    "  plt.figure()\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Mean Abs Error [MPG]')\n",
    "  plt.plot(hist['epoch'], hist['mae'],\n",
    "           label='Train Error')\n",
    "  plt.plot(hist['epoch'], hist['val_mae'],\n",
    "           label = 'Val Error')\n",
    "  plt.ylim([0,5])\n",
    "  plt.legend()\n",
    "\n",
    "  plt.figure()\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Mean Square Error [$MPG^2$]')\n",
    "  plt.plot(hist['epoch'], hist['mse'],\n",
    "           label='Train Error')\n",
    "  plt.plot(hist['epoch'], hist['val_mse'],\n",
    "           label = 'Val Error')\n",
    "  plt.ylim([0,20])\n",
    "  plt.legend()\n",
    "  plt.show()\n",
    "\n",
    "\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AqsuANc11FYv"
   },
   "source": [
    "Este gráfico muestra poca mejora, o incluso degradación en el error de validación después de aproximadamente 100 épocas. Actualicemos la llamada `model.fit` para detener automáticamente el entrenamiento cuando el puntaje de validación no mejore. Utilizaremos una * devolución de llamada de EarlyStopping * que pruebe una condición de entrenamiento para cada época. Si transcurre una cantidad determinada de épocas sin mostrar mejoría, entonces detiene automáticamente el entrenamiento.\n",
    "\n",
    "Puedes obtener más información sobre esta devolución de llamada [Aca](https://www.tensorflow.org/versions/master/api_docs/python/tf/keras/callbacks/EarlyStopping)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fdMZuhUgzMZ4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "......................"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The patience parameter is the amount of epochs to check for improvement\n",
    "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
    "\n",
    "history = model.fit(normed_train_data, train_labels, epochs=epochs,\n",
    "                    validation_split = 0.2, verbose=0, callbacks=[early_stop, PrintDot()])\n",
    "\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3St8-DmrX8P4"
   },
   "source": [
    "El gráfico muestra que en el conjunto de validación, el error promedio generalmente es de alrededor de +/- 2 MPG. ¿Es esto bueno? Le dejaremos esa decisión a usted.\n",
    "\n",
    "Veamos qué tan bien generaliza el modelo al usar el conjunto **test**, que no usamos al entrenar el modelo. Esto nos dice qué tan bien podemos esperar que el modelo prediga cuándo lo usamos en el mundo real."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jl_yNr5n1kms"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 - 0s - loss: 7.0616 - mae: 2.0794 - mse: 7.0616\n",
      "Testing set Mean Abs Error:  2.08 MPG\n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n",
    "\n",
    "print(\"Testing set Mean Abs Error: {:5.2f} MPG\".format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ft603OzXuEZC"
   },
   "source": [
    "## <span style=\"color:#4361EE\">Predicciones</span>\n",
    "Finalmente, prediga los valores de MPG utilizando datos en el conjunto de pruebas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xe7RXH3N3CWU"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions = model.predict(normed_test_data).flatten()\n",
    "\n",
    "plt.scatter(test_labels, test_predictions)\n",
    "plt.xlabel('True Values [MPG]')\n",
    "plt.ylabel('Predictions [MPG]')\n",
    "plt.axis('equal')\n",
    "plt.axis('square')\n",
    "plt.xlim([0,plt.xlim()[1]])\n",
    "plt.ylim([0,plt.ylim()[1]])\n",
    "_ = plt.plot([-100, 100], [-100, 100])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "19wyogbOSU5t"
   },
   "source": [
    "Parece que nuestro modelo predice razonablemente bien. Echemos un vistazo a la distribución de errores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f-OHX4DiXd8x"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = test_predictions - test_labels\n",
    "plt.hist(error, bins = 25)\n",
    "plt.xlabel(\"Prediction Error [MPG]\")\n",
    "_ = plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "m0CB5tBjSU5w"
   },
   "source": [
    "No es del todo gaussiano, pero podríamos esperar eso porque el número de muestras es muy pequeño."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vgGQuV-yqYZH"
   },
   "source": [
    "## <span style=\"color:#4361EE\">Conclusión</span>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vgGQuV-yqYZH"
   },
   "source": [
    "\n",
    "Este cuaderno introdujo algunas técnicas para manejar un problema de regresión.\n",
    "\n",
    "* El error cuadrático medio (MSE) es una función de pérdida común utilizada para problemas de regresión (se utilizan diferentes funciones de pérdida para problemas de clasificación).\n",
    "* Del mismo modo, las métricas de evaluación utilizadas para la regresión difieren de la clasificación. Una métrica de regresión común es el error absoluto medio (MAE).\n",
    "* Cuando las características de datos de entrada numéricos tienen valores con diferentes rangos, cada característica debe escalarse independientemente al mismo rango.\n",
    "* Si no hay muchos datos de entrenamiento, una técnica es preferir una red pequeña con pocas capas ocultas para evitar el sobreajuste.\n",
    "* La detención temprana es una técnica útil para evitar el sobreajuste."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "regression.ipynb",
   "private_outputs": true,
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}