Clase_9__Churn_Analysis__Tensorflow_Keras.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4OEtECGjKtp"
      },
      "source": [
        "# Curso Deep Learning\n",
        "\n",
        "<img src=\"https://yaelmanuel.com/wp-content/uploads/2021/12/platzi-banner-logo-matematicas.png\" width=\"500px\">\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_ny9c9fjbLP"
      },
      "source": [
        "## Creando nuestro propio Churn Analysis 🤓📊\n",
        "\n",
        "En este lab aprenderás:\n",
        "\n",
        "* [Tensorflow](https://www.tensorflow.org/)\n",
        "* [Keras](https://keras.io/)\n",
        "* Descargar un dataset, prepararlo, entrenarlo, realizar finetuning y guardarlo.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fxgR3AqoTxQP"
      },
      "source": [
        "### 1) Descarga del dataset 🤓\n",
        "\n",
        "Utilizaremos un conjunto de datos de un proveedor de Telecomunicaciones para su Programa de Retención.\n",
        "<br>Para más detalle acá se puede ver el dataset de Kaggle: [Telco Customer Churn](https://www.kaggle.com/datasets/blastchar/telco-customer-churn/data).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5WVoiMyphoSc"
      },
      "outputs": [],
      "source": [
        "!pip install --upgrade --force-reinstall --no-deps kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lv-i750ghtiv"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kgDVQkKGibgT"
      },
      "outputs": [],
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 /root/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b9JJy0CIhxNn"
      },
      "outputs": [],
      "source": [
        "!kaggle datasets list -s telco-customer-churn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z10nbuiQh4av"
      },
      "outputs": [],
      "source": [
        "!kaggle datasets download -d blastchar/telco-customer-churn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T-6HAPlAiza9"
      },
      "outputs": [],
      "source": [
        "!unzip '/content/telco-customer-churn.zip'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HX8Q_QXsTts3"
      },
      "source": [
        "### 2) Preparación de la data 👌"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BYa0Tjnv74mE"
      },
      "source": [
        "#### 2.1) Instalamos las dependencias 🙌"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DRk5qVls96PG"
      },
      "outputs": [],
      "source": [
        "!pip install ydata-profiling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xu_qILeYSLv_"
      },
      "outputs": [],
      "source": [
        "import joblib\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ull0vjyr9Ihl"
      },
      "source": [
        "#### 2.2) Explorar Dataset 🔍"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DSj6JT8QWbtG"
      },
      "outputs": [],
      "source": [
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r__2J3X5sSPX"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"WA_Fn-UseC_-Telco-Customer-Churn.csv\", sep=\",\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0akcxXCsUJl"
      },
      "source": [
        "**Tip:** Para visualizar todas las columnas del dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2V22BAwcsaxX"
      },
      "outputs": [],
      "source": [
        "df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1I8KfBKFsSlQ"
      },
      "outputs": [],
      "source": [
        "pd.set_option('display.max_columns', None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8BgfGwLWsa2g"
      },
      "outputs": [],
      "source": [
        "df.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Xjwnyq7sijv"
      },
      "source": [
        "#### 2.3) EDA (Análisis Exploratorio de Datos)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X24Wz6o87MD7"
      },
      "source": [
        "Visualizamos qué tenemos en el dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "64SawhYT61gS"
      },
      "outputs": [],
      "source": [
        "# Crear un resumen utilizando funciones nativas de pandas\n",
        "def summarize_dataframe_with_pandas(df):\n",
        "    summary = df.describe(include='all').T  # Descripción general\n",
        "    summary['Type'] = df.dtypes  # Tipos de datos\n",
        "    summary['Unique Values'] = df.nunique()  # Cantidad de valores únicos\n",
        "    summary['Examples'] = df.apply(lambda col: col.dropna().unique()[:3])  # Ejemplos de valores\n",
        "\n",
        "    # Reorganizar columnas para mejor visualización\n",
        "    summary = summary[['Type', 'Unique Values', 'Examples']]\n",
        "    return summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6cEslJiv76pP"
      },
      "outputs": [],
      "source": [
        "summarize_dataframe_with_pandas(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNwp7HdKdZ1g"
      },
      "source": [
        "Una visualización más detallada e interactiva"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Zn1NtF3yddWT"
      },
      "outputs": [],
      "source": [
        "from ydata_profiling import ProfileReport\n",
        "\n",
        "ProfileReport(df, minimal=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X_nMMYAZdjEp"
      },
      "source": [
        "#### 2.4) Valores únicos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FzYhKk6LgPpC"
      },
      "source": [
        "Eliminar columna con valores únicos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jBpBlS8JdpvW"
      },
      "outputs": [],
      "source": [
        "df = df.drop('customerID', axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BbX5yn_hdrcE"
      },
      "source": [
        "Eliminar columna con que puede generar Bias o Sesgo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dkkBy6Ladt_y"
      },
      "outputs": [],
      "source": [
        "df = df.drop('gender', axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6uO2wy_utdOb"
      },
      "source": [
        "#### 2.5) Valores faltantes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2Cx-F3t6tgpS"
      },
      "outputs": [],
      "source": [
        "# Evaluar cantidad de valores faltantes\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UpB8G4J8t6sb"
      },
      "source": [
        "#### 2.6) Columnas Categóricas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_JusynMTbFK"
      },
      "source": [
        "Reeplazo de valores binarios en columnas categóricas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zaW6B2TVSOHf"
      },
      "outputs": [],
      "source": [
        "# Evitar warning por uso de Replace\n",
        "pd.set_option('future.no_silent_downcasting', True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aT8zAREQEu-v"
      },
      "outputs": [],
      "source": [
        "categorical_columns = list(df.select_dtypes(include='O').keys())\n",
        "\n",
        "for i in categorical_columns:\n",
        "    df[i] = df[i].replace('Yes', 1)\n",
        "    df[i] = df[i].replace('No', 0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sBC3NCDLuLMX"
      },
      "source": [
        "Label Encoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3HdfTTXrpLIi"
      },
      "outputs": [],
      "source": [
        "# Inicializar y aplicar LabelEncoder único\n",
        "label_encoders = {}\n",
        "for col in categorical_columns:\n",
        "    le = LabelEncoder()\n",
        "    df[col] = le.fit_transform(df[col].astype(str))  # Codificar las columnas categóricas\n",
        "    label_encoders[col] = le  # Guardar el codificador único para cada columna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L3NOyy6OZgqu"
      },
      "outputs": [],
      "source": [
        "# Guardar los label encoders\n",
        "joblib.dump(label_encoders, 'label_encoders.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oNOGq3DnTgQN"
      },
      "source": [
        "#### 2.7) Columnas Numéricas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bjnijN7ovJrL"
      },
      "source": [
        "Escalar la data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ywAu-R-TvNBx"
      },
      "outputs": [],
      "source": [
        "scale_cols = ['tenure','MonthlyCharges','TotalCharges']\n",
        "\n",
        "scale = MinMaxScaler()\n",
        "df[scale_cols] = scale.fit_transform(df[scale_cols])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kSO9nFvyZwyW"
      },
      "outputs": [],
      "source": [
        "# Guardar el escalado de datos\n",
        "joblib.dump(scale, 'scaler.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5CReMaiYSDt"
      },
      "source": [
        "### 3) Entrenamiento 💪"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "wvL5xN1kvayu"
      },
      "outputs": [],
      "source": [
        "x = df.drop('Churn', axis=1)\n",
        "y = df['Churn']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TdTSu9R6vdZU"
      },
      "outputs": [],
      "source": [
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2)\n",
        "\n",
        "print(xtrain.shape, ytrain.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wpy8DqtWeb7j"
      },
      "outputs": [],
      "source": [
        "print(xtest.shape, ytest.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NdRZS4ZactJU"
      },
      "source": [
        "### 4) Red Neuronal 😨"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "4ibs8FIvCePP"
      },
      "outputs": [],
      "source": [
        "# Obtener el número de columnas de entrenamiento\n",
        "num_columns = 18"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "vEyU0ZtdvptB"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Input(shape=(num_columns,)),  # Capa de entrada con 18 características\n",
        "    keras.layers.Dense(16, activation='relu'), # Primera capa oculta\n",
        "    keras.layers.Dense(1, activation='sigmoid') # Capa de salida\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmLmslKX-4L7"
      },
      "source": [
        "**Explicación de porqué 16 neuronas:**\n",
        "\n",
        "- **Entrada:** El modelo utiliza 18 características.\n",
        "- **16 neuronas:** Elegido por ser cercano al número de características, siguiendo buenas prácticas iniciales.\n",
        "- **Balance:** Simple, funcional y eficiente para entrenamiento rápido.\n",
        "- **Base ajustable:** Diseñado para optimización futura con herramientas como KerasTuner."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "Q1eO2NYB9avD",
        "outputId": "a75bbf72-4fc5-4c52-c516-54e7fbdbc2b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "keras.utils.plot_model(model, show_shapes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f6EgOaxIGHbX"
      },
      "outputs": [],
      "source": [
        "model.compile(\n",
        "    optimizer = 'adamW',\n",
        "    loss = 'binary_crossentropy',\n",
        "    metrics = ['accuracy']\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UvlFGWFzYwJk"
      },
      "outputs": [],
      "source": [
        "# Agregamos el Early Stopping\n",
        "early_stop = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "82F_GwtrIem-"
      },
      "source": [
        "**Probamos con 50 epochs**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rOiVYTaGGUeU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03c162b2-f4c2-4d9b-d39a-4858bee10219"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m177/177\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.8034 - loss: 0.4162 - val_accuracy: 0.8169 - val_loss: 0.4034\n",
            "Epoch 2/50\n",
            "\u001b[1m177/177\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7968 - loss: 0.4242 - val_accuracy: 0.8169 - val_loss: 0.4040\n",
            "Epoch 3/50\n",
            "\u001b[1m177/177\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7991 - loss: 0.4175 - val_accuracy: 0.8169 - val_loss: 0.4043\n",
            "Epoch 4/50\n",
            "\u001b[1m177/177\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7948 - loss: 0.4184 - val_accuracy: 0.8126 - val_loss: 0.4029\n",
            "Epoch 5/50\n",
            "\u001b[1m177/177\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7953 - loss: 0.4185 - val_accuracy: 0.8105 - val_loss: 0.4034\n",
            "Epoch 6/50\n",
            "\u001b[1m177/177\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7979 - loss: 0.4162 - val_accuracy: 0.8105 - val_loss: 0.4032\n",
            "Epoch 7/50\n",
            "\u001b[1m177/177\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7990 - loss: 0.4238 - val_accuracy: 0.8119 - val_loss: 0.4038\n",
            "Epoch 8/50\n",
            "\u001b[1m177/177\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7967 - loss: 0.4206 - val_accuracy: 0.8126 - val_loss: 0.4042\n",
            "Epoch 9/50\n",
            "\u001b[1m177/177\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8002 - loss: 0.4169 - val_accuracy: 0.8176 - val_loss: 0.4042\n"
          ]
        }
      ],
      "source": [
        "# Implementamos un callback\n",
        "history = model.fit(xtrain, ytrain, epochs=50, validation_data=(xtest, ytest), callbacks=[early_stop])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BkY3Xe9fDhN3"
      },
      "source": [
        "### 5) Métricas 📊"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5VCtXik4HUv5"
      },
      "source": [
        "**Matriz de Confusión**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YkaiSeFJHWSK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "15c48e48-1e0a-4649-bdb6-0106ed6edc55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "y_pred = (model.predict(xtest) > 0.5)  # Convertir las probabilidades a 0 o 1\n",
        "cm = confusion_matrix(ytest, y_pred)\n",
        "\n",
        "plt.figure(figsize=(6, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Greens', xticklabels=['No', 'Yes'], yticklabels=['No', 'Yes'])\n",
        "plt.title(\"Matriz de Confusión\")\n",
        "plt.xlabel(\"Predicción\")\n",
        "plt.ylabel(\"Real\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3o1bxyhoMHDX"
      },
      "source": [
        "**Reporte de Métricas**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PfAXHDpUMIpC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38022bab-3c8a-41d0-9ac7-c04e95354c9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "          No       0.85      0.90      0.88      1043\n",
            "         Yes       0.67      0.56      0.61       366\n",
            "\n",
            "    accuracy                           0.81      1409\n",
            "   macro avg       0.76      0.73      0.74      1409\n",
            "weighted avg       0.80      0.81      0.81      1409\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report = classification_report(ytest, y_pred, target_names=['No', 'Yes'])\n",
        "print(report)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zr7H5aBH5Fz"
      },
      "source": [
        "**Gráfica de Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m9lhFjPmH5pU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "116c6cd2-1ded-4681-908a-69a63c0f55d7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'], label='Accuracy Entrenamiento')\n",
        "plt.plot(history.history['val_accuracy'], label='Accuracy Validación')\n",
        "plt.title('Accuracy vs Epochs')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-IdFBT7gIAXu"
      },
      "source": [
        "**Gráfica de Loss**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wz3_0iMjICL6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "859aa95c-68cb-4852-b500-4028bdda9d3e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'], label='Loss Entrenamiento')\n",
        "plt.plot(history.history['val_loss'], label='Loss Validación')\n",
        "plt.title('Loss vs Epochs')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IOsroFjDkjrZ"
      },
      "source": [
        "**Curva ROC**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A2EZoDDgldfW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "2a95e741-1e54-45a4-c2d7-a56214dfe134"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m177/177\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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06HzbT5w4gZs3b6JevXpo3bp1gccLDg4GgHw1cLds2QIAGDVqFKpUqfLEmHbt2oX09HQ899xzqFmzZqG3891332H//v2YNm3aY4+nUCgwfvx4vP/++1i6dCk6duyoHzt58iROnjwJX19f9O3bt0RjGD58+BPva0kQj6mj6e7ujsGDBxtt9/b2Rt++fbFmzRpERUWhU6dOAIBNmzYBAMLCwgo8XpUqVdCmTRts3rwZR44cQe/evfON9+zZE25ubo+N98KFC9iyZQsuXLiA1NRUaLVaAMCdO3cAALGxsWjatOljj1FcAwcOLLD+dJMmTQAAN27c0G+7fv064uLiAMjn7KPs7OwQGhqK+fPnl2iMRTVo0KACtzdp0gRbtmzJd19yabVaREVFYf/+/bh16xbS09Mh5MQLAPlvXpjQ0NDHxpOZmYlt27bhyJEjiI+PR2ZmJoQQSE1NLfDYue8HERERsLa2fuyxi6osnrePateuHTZv3owXXngB7733Hrp16wYHBwfT7sBjaLVa/PnnnwCAf/3rX099vLS0NKSlpQEAqlWr9sT9C3rd1KtXD1FRUahVq9ZTx/O496+SkHsfc99fqPxgMkxlpnPnzqhfvz4AICEhAXv27EFqaipeeOEFNGjQAO3atdPvm5sgXLx48YmNLRISEvR/X7lyBQDQuHHjIsWUezs7duwo1u08zsSJE/HBBx9g5cqVmDdvHhwdHQEAS5YsAQCMGzcu3wfz08bg7e0NJyenIsWWl6enJwDg/v37yMnJgY3N498OcnJycP/+fQCAl5eX0Xhuc5WC1KlTB4BM/nLl3u+xY8di7Nixj73tgu734xoTaLVaTJs2DQsWLHjsB2BKSspjb9cUtWvXLnC7q6srACAjI0O/Lfffw9PTs9Avb6Y0YPDy8sK1a9cQHx9f7OvmVZz7AgDnz5/H0KFDERMTU+gxH/dv/rj7evDgQYSFhT22ecKjxy7u+0FRlObztjCvvfYa9u7di+3bt6Nv376wtbVF8+bN0bVrV4SHh6Nt27bFPmZB7t27p09eGzVq9NTHS05O1v/t4uLyxP3Hjx8PQDaKuXjxIg4dOoSLFy9i1KhR2L59O+zs7PLtn/seVtTkM+/rIfc9LO97WXx8/FPd79zXRWJiosnHIPNgMkxlZvLkyZgwYYL+cnJyMoYOHYpdu3Zh5MiROH36tD6p0+l0AIDq1aujT58+jz1u7huiKXJvp379+ujcufNj9y3qB2pAQAC6d++OnTt3Yu3atRg1ahSys7OxYsUKADCaHX/aGHKT7eLKnXHPysrCiRMnnviBevLkSWRnZ+e7bnHlTUxz73ffvn3h4+Pz2Ov5+/sbbXvc/Z4/fz5++OEHVK9eHV9++SU6deoEHx8f/WzaqFGjoFKpSmWmyMqq+I09H/clyJQuh61bt8a1a9dw5MiRYl83r+Lel9DQUMTExGDgwIF4/fXX0bRpU7i6usLW1hZZWVmwt7d/7PULe0wfPnyIkJAQ3LlzBxMnTsQLL7yA+vXrw9XVFdbW1jh37hwaNWpUJh3ASvN5WxgnJyf8+eefOHLkCLZs2YL9+/dj//79OHr0KL788kv83//9H7799ttiH7e0ubu76/9OTU3VJ4uFWbp0ab7L+/btQ79+/bBnzx68/fbb+PTTT/ONt27dGr/++iuOHz9epC/0hw8fBiBncHO/lAQEBKBq1aq4f/8+jhw5gi5duhTtzhUgN/n38PAw+RhkHkyGyWzc3NywcuVKNG7cGFeuXMGXX36Jt99+GwDg5+cHQL5pPfoG+Ti5M1lnz54t0v65t9OoUaNi3c6TTJw4ETt37sSSJUswatQo/P7777h79y46depkNPNQWjE8SfPmzREQEIDLly9j2bJlT0yGly1bBkB+eDRr1sxo/HEtU3PH8v7U6efnh7NnzyIiIuKJP48XV2RkJABgwYIFBS7dOH/+fInenqlyl8UkJCQgLS0Nzs7ORvsUpRXto4YMGYJ169Zh69atuHPnzhOTtpJw9uxZnDp1Ct7e3li7dq1RYvI0/+Z//fUX7ty5g1atWmHx4sVG44Udu3bt2jhz5gzOnj2Lnj17mnz7eZXm8/ZJ2rZtq3+d5uTkYN26dRg3bhy+++47hIaGonv37k91/GrVqsHJyQkPHz5EbGwsgoKCnup4Tk5OcHZ2RlpaGu7du/fEZPhRnTt3xldffYXJkydj/vz5mDp1KurWrasfHzRoEGbMmIHk5GSsX7/+scvFhBD45ZdfAORfymRlZYVBgwbh559/xrJlyzB9+nQT7ql07949ACiT1xuVrOJPYRCVIC8vL30C/PnnnyMpKQmAfNP39PTE6dOnH/uT66Ny1+KqVCr9z32P89xzz8HOzg5RUVFP/ZNyXsOHD4ebmxt27tyJa9eu6ZdIPDorXJoxPIlCocAbb7wBQCaNR48eLXTfEydO4IcffgAAzJo1q8DZyqSkJPz+++9G2xMSEvRrN3PXeQNAv379ABgS15KUu5yjoJm5mJgYnDx5ssDr5f4Mm5OTU+IxFcTPz08/Q6VSqYzGs7KysHr16mIfd/To0QgICEBWVhZeeOEF/WxmYY4dO4b09PRi305euf/mNWrUKHCG7tdff33qYxe2bKOwY+e+HyxevFi/XvxJnvQcKM3nbXHY2NggNDRU/8tZ3ue0qc9ja2tr9OrVCwDw008/lUicrVq1AgCcPn3apOtPmjQJLVq0QFZWFt577718Y/Xq1cPIkSMByKUkuZ8fBfnuu+9w6tQp2NjY4LXXXss3NnPmTNja2uLvv//GvHnznhjTnj17Ctyu0WgAmP7LGZkPk2Eyu//7v/9D7dq1kZycjC+++AIAYGtri3fffRdCCAwdOhR79+41up5Wq8XOnTtx8OBB/bbBgwejZcuWuHnzJkaMGKH/pp4rIyMDf/zxh/6yj48PXnrpJaSlpWHQoEGIjo42up3MzExs2LChyLPNgPwpNDw8HDqdDp988gm2bNkCJyenAk+6Ka0YiuJf//oXBg8ejOzsbPTt2xcbN2402mfLli3o06cPsrOzMXjwYEyZMqXQ482YMSPfuuDMzEy8+OKLSEtLQ7t27fItA/nXv/4Ff39/rFq1CjNnztSfBJXX7du3TfpQzj1Z7dtvv82XCN66dQvjxo0rNEnInbkuzhewp/Xyyy8DAN59912cO3dOv12n02HWrFm4du1asY9pa2uLyMhIODg4YO3atQgJCcGlS5eM9rt//z5mz56Nzp07IzMz0/Q7AaBhw4awtrZGdHQ0oqKi8o39/vvv+Oqrr0w+du7juWPHDqOk6scff8TKlSsLvN7kyZNRq1YtnDhxAlOmTDH6gpySkoLt27fn2/ak50BpPm8L89133xV44uHt27f1X2LzfvHLvQ/nz5/XL20qqrfeegs2Njb45ptv8N133xktPbly5QqOHTtW5OPlzlYfOHCgWHHkUigU+OijjwAAy5cvz/caAeRrPCAgAJcuXUKPHj2MHrecnBx8+eWXeOWVVwAAn3zyCQIDA/Pt06RJE3z55ZcAgOnTp+PNN98s8HE9d+4clEql/jX7qNz72KNHDxPuKZmVeYpYUGXypKYbQsiuUwCEi4uLuHfvnn77a6+9pi97ExgYKIYMGSLCw8NFcHCwcHd3FwDE999/n+9Yly9fFo0aNRKAbLrRu3dvoVQqRdeuXQttupHb0tPKykq0bNlSDB8+XISFhYnOnTvrSwM92vQCKLw2phD5S/bgnzqnhTElhsd1qyqOjIyMfDVy69evL4YPHy5CQ0P1DVEAiLFjxxZYGzVv04327dsLJycnMXDgQDFy5Eh9eTxvb+8CyxZpNBoREBAgAFk4v2vXrmLUqFEiJCRENG3aVCgUCuHj45PvOkUpf3bw4EF9TeT69euLkSNHir59+wpHR0cRGBiob0rx6HPy9u3b+n/rzp07iwkTJoiIiIgCO3w9rgNdQQrrJJeTkyP69esnANlwpW/fviI8PFzUq1dPODo66svqTZkypdD7W5jDhw/rX38KhUK0atVKhIaGipEjR4r27dvra3zXrVu3WO2YC3sMcusiW1lZiW7dugmlUilatWqlL59Y2GvmSa8lIYQYMmSIAAyNScLDw0Xjxo2FQqEQb731VqGvhePHj+tLArq7u4sBAwaIsLAw0alTp3xNN3Jt3LhRfzsDBw4UkyZNEhEREfnKApbW87aw13RuHeU6deqIQYMGidGjR4vevXvrm7n06NHDqFZ4br31Ro0aidGjR4uIiAgxc+bMIsXz888/69sT+/v7i9DQUDFs2DDRokWLYjfdyO1AV1hntifVGc7VtWtXAcimJY+6fv26/v4qFArRtm1bER4eLgYPHqxvhGFnZ1dg18+8Fi9erH/9Ozg4iK5duwqlUimGDh0qmjRpoo+zoFJuT7qfZNmYDFOpK0oynJOTo+8a98Ybb+Qb27dvnxg9erTw9/cX9vb2wsXFRTRs2FCEhISIhQsX5qvlmSs1NVV88sknom3btsLFxUXY29sLf39/MXjwYKFWqwuMYfPmzWLYsGGiZs2awtbWVri7u+s7b61YscKoxWpR3sADAwP1+xWWWJgaQ0klw7n27dsnJk6cKOrVqyecnJyEo6OjqFu3rpgwYYLYu3dvodfLW9P0wYMH4rXXXhN16tQRdnZ2wsfHR0yYMEFcvXq10OunpKSITz/9VHTs2FG4u7sLW1tb4evrK9q2bStee+01o3rNRUkqhBDi1KlTYvDgwcLX11c4ODiIBg0aiNdff12kpKQ8NnH966+/RM+ePYWHh4e+1Wtx2zEX5HFtlbOyssSnn34qmjZtKuzt7YWnp6cYOnSoiI6OFu+//74AIGbNmvXY+1uYzMxMsXDhQjFo0CBRs2ZNYW9vr29HHBoaKlQqlcjKysp3HVOTYZ1OJxYtWiRat24tqlSpItzc3MSzzz6rf809TTKclZUlPvvsM9GsWTPh5OQkqlatKnr37i22bdv2xNdCQkKCePvtt0WzZs2Es7Oz/rkdFhYmtmzZYrT/Tz/9JFq1aqWvj13Q41oaz9vC7sfGjRvFCy+8IFq2bCm8vLyEnZ2dqFWrlggODhY///yz0eMnhBBXrlwRo0aNEr6+vsLGxsbouE+KJyYmRkRERIg6deoIe3t74ebmJpo2bSqmTZtWaIv6wnTq1EkAEKdPnzYaK2oyvH//fv0XrYKOo9VqhUqlEkOGDBE1atQQdnZ2wtXVVTRr1kzMmDFD39b7SRISEsR///tf0aVLF+Hl5SVsbGxElSpVRFBQkPjXv/5l1OAkV267+p9//rlIt0OWRSFEGZx+S0QVVlRUFLp3745u3boZ/TxOT69Hjx7YtWsXVq9ejWHDhpk7HKJi++233zBixAhMnz5dvxSuIsnIyICfnx9sbW1x6dKlJ1ZNIcvDNcNERGZ28uRJZGVl5duWlZWFOXPmYNeuXfD29kb//v3NFB3R0wkNDUXnzp2xYMGCCtmQ4uuvv8bdu3cxd+5cJsLlFEurERGZ2b///W+cPHkSzZs3h6+vLxITExEdHY1bt27BwcEBP//8c6l0HCMqK19//TXatGmDDz74AN988425wykxycnJ+Pjjj9GuXTuMGzfO3OGQiZgMExGZ2ZQpU7B8+XKcOnUKhw8fhhACNWrUwKRJkzBjxowSbxlNVNZatmxZ5PJ25Ymbm5tR1SIqf7hmmIiIiIgqLa4ZJiIiIqJKi8kwEREREVVaXDMM2enp5s2bcHFxKbDNLBERERGZlxACqampqFGjBqysSm4+l8kwgJs3b8LPz8/cYRARERHRE1y7dk3fdrwkMBkG4OLiAkD2XHd3dzdvMFTqdDodEhIS4OXlVaLfLMky8fGuXPh4Vy58vCuXpKQk+Pv76/O2ksJkGNAvjXB1dYWrq6uZo6HSptPpkJGRAVdXV755VgJ8vCsXPt6VCx/vykWn0wFAiS9p5TOHiIiIiCotJsNEREREVGkxGSYiIiKiSovJMBERERFVWkyGiYiIiKjSYjJMRERERJUWk2EiIiIiqrSYDBMRERFRpcVkmIiIiIgqLSbDRERERFRpMRkmIiIiokqLyTARERERVVpMhomIiIio0mIyTERERESVFpNhIiIiIqq0LC4Z/uuvvzBo0CDUqFEDCoUC69ate+J1oqKi0KpVK9jb26N+/fpYunRpqcdJREREROWfxSXDaWlpaN68Ob799tsi7X/p0iUMGDAA3bt3x8mTJ/Hvf/8bkydPxtatW0s5UiIiIiIq72zMHcCj+vXrh379+hV5/x9++AF16tTBF198AQBo0qQJ9u7di6+++gp9+vQprTCJiIiIqIzsP30OP877vFSObXHJcHEdOHAAPXv2zLetT58++Pe//13odTIzM5GZmam/nJKSAgDQ6XTQ6XSlEidZDp1OByEEH+tKgo935cLHu3Lh412xXUm8gQ/WRGL/iV8wb9vf+Pgy8HMp3E65T4Zv374NHx+ffNt8fHyQkpKC9PR0ODo6Gl1n7ty5eO+994y2JyQkICsrq9RiJcug0+mQnJwMIQSsrCxupRCVMD7elQsf78qFj3fFcy/9Hn4+shkrTm3ADZt9GBwrsGcD4PUQSCml2yz3ybApZs2ahenTp+svp6SkwM/PD15eXnB3dzdfYFQmdDodFAoFvLy8+OZZCfDxrlz4eFcufLwrhpTMFKyKXodvolYiOm07hFUOnATw/UZg6jHDfvedqwBpD0r89st9Mly9enXcuXMn37Y7d+7A1dW1wFlhALC3t4e9vb3RdisrK76YKgmFQsHHuxLh41258PGuXPh4l0/p2enYdH4TftirRtTNTdAqMuSAFdDqJrB8NdD4nmF/MSQE7l98DtSvX+KxlPtkuGPHjti8eXO+bX/++Sc6duxopoiIiIiI6FHZ2mz8Gfcnlh1XY33sOmSIVDmgkP+z0gH/2emG/+5Lha34Zx24kxMwbx4UkycDycmlEpfFJcMPHjzAhQsX9JcvXbqEkydPomrVqqhduzZmzZqFGzduYNmyZQCAqVOn4ptvvsHrr7+OSZMmYefOnYiMjMSmTZvMdReIiIiICIBO6PDXlb+g0qix8tRvSM6+Z7xTmhesz47EIM8QvHvpBdiKf5Le1q2BFSuAhg1LNUaLS4aPHj2K7t276y/nru0dP348li5dilu3buHq1av68Tp16mDTpk149dVXMX/+fNSqVQsLFy5kWTUiIiIiMxBC4OjNo1BpVFCdWonbD28a75ThBpwZhgaZSrwyuDvGvGUDNzcAR1YAzz4LzJgBzJkD2NmVerwWlwwHBwdDCFHoeEHd5YKDg3HixIlSjIqIiIiIHicmPgYqjQpqjRoXEy8a75DtCMQOhtOlcEzo1A9TX89CM/8UoGaedLRtWyAuDqhZs8zitrhkmIiIiIjKh7jEOKg1aqg0KmjiNcY7aG2BC30BTTi6+gzG8xOrYOhQwPHkAWDMGKB6dWD3bsAmT0pahokwwGSYiIiIiIrhZupNRMZEQqVR4fCNw8Y76KyAy90BTTiqJw7DJGVVTPoAqFcPQE4O8OGHwAcfAFqtnAX+5BPgrbfK/H7kYjJMRERERI917+E9rD6zGiqNCrsv74ZAAUtar3UENOGwOjMSg7pXx+T3gL5980z6xsXJ2eADBwzX6dQJGDWqTO5DYZgMExEREZGR1MxUrI9dD5VGhW0XtyFHl2O80+3mgCYc0ISjgVcAIiKA8evk6gc9IYBffgGmTQNS/ymnZm0NvPsuMGtW/iUSZsBkmIiIiIgAABk5Gdh8fjNUGhU2ntuIjJwM453u1Qc0SkATDofUphgxApj8IdClC6BQPLJvYiIwdSoQGWnYVrcusHw50KFDqd6XomIyTERERFSJZWuzsePSDqg0Kqw9sxapWanGOyXX0s8A41YrtGqlwOT3AKUScHcv5MApKUCLFkCekriYMAH43/8AF5eSvyMmYjJMREREVMnohA57r+6FWqPGqtOrcPfhXeOd0ryAmBEyAb7WGW6uVhgzBoiIAFq2LMKNuLoCQ4cC8+cDHh7AggXAiBElfl+eFpNhIiIiokpACIFjt45BFa3CypiVuJF6w3inDFfg7FAgWglceg7Q2SA4GJj8ETBsGODoWMwb/fhjICNDVovw8yuJu1HimAwTERERVWCnE05DFa2COkaNC/cvGO+Q7QCcGyQT4Av9gBwH+PoCE2YCkyYB9esX4UaEAH76SZ4YFxFh2O7gAPzwQ4ndl9LAZJiIiIiogrmUeAlqjRrqGDVO3TllvIPWBrjYRybAsYOBLBdYWwMDBgCTJwP9+hWjyENCAjBlCrB+vZw67tQJaNKkRO9PaWIyTERERFQB3Eq9hciYSKhj1Dh4/aDxDkIBXA6WCfCZ4UB6VQCyGcbkycD48YCvbzFvdNs2ecXbt+Xl9HRg40Ymw0RERERU+u6n38fq06uhjlEj6nIUdEJntI/VzfbQ/a0EYkYCD2S26+AAhP5zMly3bgWURHuSjAxZI3jePMM2T09g8WJg0CDT75AZMBkmIiIiKkceZD3A+rProY5RY+uFrcjWZRvtY5fUDFlHZS1gXVId/faWLeUs8KhRjymJ9iTR0cDo0fL/ufr2BZYseaTbRvnAZJiIiIjIwmXkZOCP839AHaPG77G/Iz0n3Wgf58x6eHhYCXEqHFkJgfrtbm4yd42IAFq1eooghAC+/hp4/XUgM1Nus7cHPvtMdpcr9vSyZWAyTERERGSBcnQ52HlpJ1QaFdacWYOUzBSjfVxETQhNGB4cUCLtZmsAhoS0Wzc5CzxsGODkVAIBPXgAfPGFIRF+5hnZSS4oqAQObj5MhomIiIgshE7osP/afqiiVVh1ehUSHiYY7eNiXQ2u10fgxtZwpF7pAggr/Vj16rLJ26RJQIMGJRyciwvw669A9+7Ayy8DH30kFx+Xc0yGiYiIiMxICIETt0/om2FcS7lmtI+zjQtqpw3FtS3hSP27J1J1tvoxKytZEi0iAujfH7C1Nbq6adLS5H/e3oZtXboA584BdeuW0I2YH5NhIiIiIjM4e/esvhnGuXvnjMYdrB3Q1HYgUvaH48Lm/jiTk7/9W716MgEePx6oUaOEgzt2TC40rlkT+PNPmXHnqkCJMMBkmIiIiKjMXEm6ArVGDZVGhb/v/G00bmNlg7ZVe8P2bDiOLh+C44mu+cbt7YHQUENJtLw5aonQaoHPPwfefhvIyQFiY4GvvgJmzCjhG7IcTIaJiIiIStGdB3cQGRMJlUaFA9cPGI0roECnGt1Q/V44TqmH48Dfnkb7NG8uT4YbPRrw8CilQK9dA8aNA6KiDNtaty53dYOLi8kwERERUQlLTE/EmjNroNKosOvyrgKbYbSt0Q4tbcJxY9tIbPuwJrIfKRfs6irrAU+eLEuilWrlsshI4PnngaQkeVmhAN54A5gzB7CzK8UbNj8mw0REREQlIC0rDRtiN0ClUWHLhS0FNsMI8g5Cn5rhyDwWjvUf1MOPxufKoWtXuQwiNLSESqI9TkqKrAzx88+GbX5+wC+/yHUYlQCTYSIiIiITZeZkYsuFLVDHqLEhdgMeZj802qeuR12ENg6H120l/lwehC//lP0r8vLxkSfCTZoENGpURsEnJ8sp57g4w7awMOD770txLYblYTJMREREVAw5uhzsurQLao0aa86uQVJGktE+vlV8ERYYhvbOShxa2xaL3lfg3r38+1hZyVJoERGyNFqJlUQrKjc3oEcPmQy7uADffguMGVNuO8mZiskwERER0RPohA4Hrh2AWqNG5OlIxKfFG+1T1bEqQpuEYkg9Ja7v64Il71lj3kHjY9WpIxPgCRNk5TKz+uorID0deP/9ClcyraiYDBMREREVQAiBk7dPQq1RQx2jxtXkq0b7VLGrgpDGIQgPVMI1oRd+XmKLkWrZqyIve3vZFnnyZCA4uBRKoj2JEHIdsK0toFQatlepIrvKVWJMhomIiIjyiL0bC5VGBbVGjdh7sUbj9tb2GNBwAJRBSrTzGIDfVI54/W3g9GnjYz3zjKEkWtWqZRB8QRITgalTZcWIKlWAdu1kxw4CwGSYiIiICFeTr8oZYI0aJ26fMBq3VlijV71eUAYpMbhhCA7vccXCOcCodTAqiebiYiiJ1rq1mZfgRkUBY8cC16/Lyw8eAL/9BsycacagLAuTYSIiIqqU7jy4g1WnV0GtUWPftX1G4woo0MW/C5RBSoQ2DcXDu55YsgRovhi4arxiAs8+KxPg0FDA2bkM7sDjZGUB77wDfPqpoXSFuzvw44/AiBFmDc3SMBkmIiKiSiMpIwlrz6yFSqPCjks7CmyG0aZGGyiDlBgZOBLeDrWwYQMw5m1g2zbjkmje3oaSaI0bl9GdeJLYWDk1ffy4YVtwMLBsmawhTPkwGSYiIqIKLS0rDRvPbYRKo8IfF/5AljbLaJ+mXk2hDFIiLDAMDao1wOnTwFfvyfzx7t38+1pZAX37ylnggQPNUBKtMELImd9XX5UVIgAZ3IcfAjNmmOGsvfKByTARERFVOFnaLGy9sBUqjQobYjcgLTvNaJ8A9wAog5QIDwpHM+9mSEtTIDISGL8QOHDA+JgBAYaSaLVqlfpdKL7kZNk+OTcRbtQIWLFCNtagQjEZJiIiogpBq9Mi6nIUVBoVVp9ZXWAzjOpVqiMsMAzhQeFoX7M9AAUOHwb+9TagVsvzy/KyszOUROve3cInV93dgaVL5bT11KnAF1+UQT/n8o/JMBEREZVbQggcuH4AK2NWIjImEnfS7hjt4+HggdCmoQgPCkc3/26wtrLG3bvA/PnAwoVATIzxcYOCgClTZEm0atXK4I6YIiMDePgwf822Pn0AjQYIDDRfXOUMk2EiIiIqV4QQOHXnFFZEr8CKUytw/cF1o32cbZ1lM4ygcPSu1xt21nbQ6YAdO2QCvG6dLLiQV5Uq8ryziAigbVsL70ocHS2D9fcHfv89f7BMhIuFyTARERGVC+fvnYdKo4JKo8LZu2eNxu2s7TCgwQCEB4VjYMOBcLKVSwSuXQOWLAEWLwauXDE+bufOMgEeMUImxBZNpwO+/lrWCc7MlLPAP/wAvPCCuSMrt5gMExERkcW6lnwNK2NWQqVR4fit40bj1gprPFf3OSiDlBjaeCjcHNwAyFnf1avlLPDWrcYl0by8DCXRmjQpi3tSAm7dAiZOlHco1zPPAF26mC+mCoDJMBEREVmUhLQEfTOMPVf3FLjPs7WfRXhgOLp5dUNT/6aw+ufMtjNngEWLZEm0hIT811Eo5LllERHAoEHy5LhyY/16eRZf3jpvr74KfPQR4OBgvrgqACbDREREZHbJGclYe3Yt1Bo1tsdth1ZojfZp7dsa4UHhCAsMg5+bH3Q6HeLj45GWJjsML1oE7DNuJAd/f0NJtHLXcyItTdYIXrDAsM3XF/j5Z6BXL/PFVYEwGSYiIiKzeJj9EBvPbYRao8bm85uRqc002qexZ2N9LeCG1RrqtwsBHD4MfPONKzZsUCA1Nf/17OyAkBA5mfrccxZeEq0wiYlAx46yo1yukBDgp58AT0+zhVXRMBkmIiKiMpOlzcK2i9ug1qixPnY9HmQ9MNrH380f4UHhUAYp8YzPM1DkqZRw7x7w669yFjg62gpA/jq6gYEyAR4zpgLkix4eQOvWMhl2cpK14CIiLLzMRfnDZJiIiIhKlVanxe4ru6HWqLH6zGrcT79vtI+Psw9GBo6EMkiJDrU65EuAdTpg506ZAK9ZU1BJNIHwcAUmTwbatatgueK338qOch9/DDRs+OT9qdiYDBMREVGJE0Lg0I1DUGvUiIyJxK0Ht4z2cXdwx/Amw6EMUiI4IBjWVtb5xq9flw3VFi0CLl82vo1OnQRCQ1MQEeECV9cKkAFHRgL29sCQIYZt7u7yGwCVGibDREREVCKEEIiOj4Zao4Zao8alpEtG+zjZOmFIoyFQBinRp34f2FnnL+mQnS17SCxaBGzZImeF8/L0BMaNk6sFGjcWiI9PR5UqLqV5t0pfSgrw8svypDgPD+DUKaBWLXNHVWkwGSYiIqKncuH+BaiiVVDHqHE64bTRuJ21HfrV7wdlkBIDGw6Es52z0T6xsTIB/vlnID4+/5hCAfTuLdcCDx5sKIn2aKJcLh04IHs+X/rni0NiolwU/cYb5o2rEmEyTERERMV2PeU6ImMiodKocPTmUaNxK4UVnqvzTzOMJkPh7uButE9aGrBqlUyC9+41vo3atWVTjIkT5d8VSk4O8N//yv+0/5SRc3GRa4THjDFvbJUMk2EiIiIqkoS0BKw+sxoqjQp7ruyBgDDap7NfZyiDlAhtGgqfKj5G40IAx47JznArVsCoJJqtbf6SaNbWRoco/+LiZMJ74IBhW6dOcka4Th3zxVVJMRkmIiKiQqVkpmDd2XVQaVT48+KfBTbDaFm9JZRBSowMHAl/d/8Cj3P/PrB8uUyCT50yHm/a1FASzcurpO+FhRBCtsabNg148E9JOWtr4J13gDffBGyYlpkD/9WJiIgon/TsdGw6vwkqjQqbzm0qsBlGo2qN9M0wGnk2KvA4Oh0QFSUT4DVrgMxHDuPsDISHy5PhOnSoYCXRCpKYKLvJ5SbCdevKbwgdOpg3rkqOyTAREREhW5uNP+P+hEqjwrqz6wpshlHbrTbCA8MRHhSOFtVb5KsFnNeNG4aSaJeMC0qgQwc5CzxypFwmW2lUrSq/GQwdKntD/+9/lewfwDIxGSYiIqqktDot9lzdA1W0Cr+d+a3AZhjezt4Y2XQkwoPC0dGvI6wUBfc1zs4GNm2Sud4ffxhXeqhWzVASLTCwNO6NBcrKktPheRPekBDg6FHZWY4sApNhIiKiSkQIgSM3j0AVrULk6UjcTL1ptI+bvRuGNxmO8KBwdK/THTZWhacL584ZSqLduZN/TKEAevUylESzty/pe2PBYmOBUaOA+vUBtTr/GhAmwhaFyTAREVEloInX6GsBxyXGGY072jhicKPBUAYp0bd+X9jbFJ65PnwI/PabnAXes8d43M/PUBLNv+Dz6SouIYAffwRefVW2UT5+HBgwQE6Lk0ViMkxERFRBXbx/UXaDi1FDE68xGre1skXf+n2hDFJiUKNBqGJXpdBjCSHzutySaCkpjxzLVnYRjoiQs8EVsiTakyQkyGnwDRsM2xo1AoKCzBcTPRGTYSIiogrkZupNrNSshDpGjcM3DhuNWyms0D2gO5RBSgxrMgwejh6PPV5ioqEk2t9/G483aSLzv7FjK3BJtKLYulWeFHf7tmHb1KnAF18ATk5mC4uejMkwERFROXfv4T38dvo3qGPU2H15d4HNMDr5dUJ4YDhGBI5A9SrVH3s8nQ7YvVsmwKtXG5dEc3IylETr2LESlER7nIwMYNYsYN48wzZPT2DxYmDQILOFRUXHZJiIiKgcSs1Mxbqz66COUWPbxW3I0eUY7dOieguEB4YjLCgMAe4BTzzmzZuGkmhxxsuK0b69nAUOC2NFMACyk0hwMBAdbdjWty+wZAlQ/fFfOMhyMBkmIiIqJ9Kz07H5/GaoY9TYeG4jMnIyjPZpULWBvhlGE68mTzxmdjawebNMgDdtMi6JVrWqoSQal74+wsNDNs6IjpalMj77THaXq9RT5eUPk2EiIiILlq3Nxva47VDHqLH2zFqkZqUa7VPLtRbCA8OhbKZEy+otC22Gkdf584aSaHmXuebq1UsmwCEhlawkWnEoFHItSXq6XBvMbwvlEpNhIiIiC6MTOuy5sgdqjRq/nfkNdx/eNdrHy8kLI5qOgLKZEp38OhXaDCOvhw/lGuCFC4G//jIer1VLlkObOBGoU6ck7kkFs2GD/GbQp49hm6enPHmOyi0mw0RERBZACIFjt45BFa3CypiVuJF6w2gfV3tXDGsyDMogJXrU6fHYZhh55S2Jlpycf8zGxlASrXfvSloS7UnS0oAZM4AFCwBvb7kswtvb3FFRCWEyTEREZEYx8TH6WsAX7l8wGne0ccSgRoP0zTAcbByKdNzERJn8LloEnDhhPN64sUyAx41jXvdYx47JTnLnzsnL8fGyUsQbb5g3LioxTIaJiIjKWFxiHFZqVkKlUSE6Ptpo3NbKFn3q94EySInBjQY/thlGXkLIkmiLFskOcRmPnF/n5ASMHCkrQnTqxPO8HkurBT7/HHj7bSDnn0odTk6yhNrkyWYNjUoWk2EiIqIycCv1FiJjIqHSqHDoxiGjcQUU6F7H0AyjqmPVoh/7liyJtngxcMF4chlt28r8LTwccHV9ijtRWVy7JruI7N5t2Na6tZxqb9jQfHFRqWAyTEREVEruPbyHNWfWQKVRIepyVIHNMDrU6gBlkBIjmo6Ar4tvkY+dk5O/JJpWm3+8alVgzBi5FOKZZ572nlQikZHA888DSUnyskIhl0TMmQPY2ZkzMiolTIaJiIhKUGpmKjbEboBKo8LWi1sLbIbxjM8zUAYpERYYhjoexSvbcOGCoSTarVvG4889J2eBQ0IAh6ItL6Zcd+8CU6YAKSnysp8f8MsvQLdu5o2LShWTYSIioqeUkZOBP87/AZVGhY3nNiI9J91on/pV6+ubYTT1alqs46enA2vWyIoQUVHG4zVrGkqi1a1r4p0gWSbt+++B0aNlm73vv5eNNahCYzJMRERkghxdDnbE7YBKo8Las2uRkplitE9Nl5oIDwpHeFA4Wvu2LlIzjLxOnJCzwMuXG361z2VjAwwaJGeB+/RhSTST5OQAWVnyxLhco0bJgstduvAMw0qCyTAREVER6YQO+67ug0qjwqrTqwpshlHNsZq+GcaztZ8tUjOMvJKSAJVKzgIfP2483rChTIDHjQN8fEy8IwTExclF1Y0byzMP8+ra1TwxkVkwGSYiInoMIQSO3zoOlUY2w7iect1oHxc7FwxtMhTKICWeq/McbK1ti3kbsiPcokXAqlXGJdEcHQ0l0Tp35oTlUxFCrgN+8UXgwQPgwAGgXz9gxAhzR0ZmwmSYiIioAGcSzkClUUGtUeP8/fNG4w42DhjYcCCUQUr0b9C/yM0w8rp9W54It2gRcN74JtCmjaEkmpubKfeC8klMBKZOlRUjctWtK0+Uo0qLyTAREdE/Liddlt3gNGr8fedvo3EbKxv0qdcH4UHhGNJoCFzsXYp9Gzk5wJYtchnExo3GJdHc3WWJ24gIoHlzE+8IGYuKkv+w1/PM7E+YAPzvf4BL8R9HqjiYDBMRUaV2+8FtRMZEQq1R48D1A0bjCigQHBCM8KBwDG8yHNWcqpl0OxcvyqWpS5cCN28aj/foIRPgoUPlsggqIVlZwDvvAJ9+KpdIALJCxIIFXBpBAJgMExFRJZSYnojVZ1ZDrVFj1+Vd0Amd0T7ta7ZHeFA4RgaORA2XGibdTkaGoSTarl3G4zVqGEqi1atn0k3Q49y7B/Tunf9MxO7dgWXLZMUIIjAZJiKiSuJB1gNsiN0AtUaNLRe2IFuXbbRPM+9m+lJodT1ML9j7998yAf71V+OSaNbWsiRaRATQt68skUalxMND1g4GAFtb4MMPgRkzAKviVfigio0vQSIiqrAyczKx5cIWqDQq/H7udzzMfmi0T12PulAGKaEMUiLQO9Dk20pONpREO3bMeLxBA0NJtOrVTb4ZKg4rK7kuZeRIYP58oFUrc0dEFojJMBERVSg5uhz8GfcnVsasxJoza5CcmWy0Tw2XGggLDIMySIk2NdoUuxlGLiGAvXtlArxqlewUl5ejo1yWGhHBHg5lYts22YM6b51gX19gzx7zxUQWzyKT4W+//RafffYZbt++jebNm+Prr79Gu3btCt1/3rx5+P7773H16lV4enoiNDQUc+fOhQObshMRVQo6ocOBawewInoFImMicTe94GYYoU1DoQySzTCsrUxv2XbnjqEk2rlzxuOtW8sEWKmU1SGolGVkAG+9BcybJ9cCnzrFNspUZBaXDK9cuRLTp0/HDz/8gPbt22PevHno06cPYmNj4e3tbbT/ihUr8MYbb2Dx4sXo1KkTzp07hwkTJkChUODLL780wz0gIqKyIITAydsn9c0wriZfNdqnil0VDG0sm2H0rNuz2M0w8srJAbZuNZREy8nJP+7uDoweLZPgli1NvhkqJpszZ6B45RUgOlpuuH4d+PFHYOZM8wZG5YbFJcNffvklpkyZgokTJwIAfvjhB2zatAmLFy/GG2+8YbT//v370blzZ4waNQoAEBAQAKVSiUOHDpVp3EREVDbO3j2rrwUcey/WaNze2h4DGgzAqGaj0L9BfzjaPl2dsrg4Q0m0GzeMx7t3lwnwsGEsiVamdDrgf/9DtTfegCIzU26ztwc++wyYNs28sVG5YlHJcFZWFo4dO4ZZs2bpt1lZWaFnz544cMC49iMAdOrUCb/++isOHz6Mdu3aIS4uDps3b8bYsWMLvZ3MzExk5r5wAKSkpAAAdDoddDrj8jpUseh0Oggh+FhXEny8K4YrSVcQeVrWAj5556TRuLXCGr3q9UJY0zB0qtoJdWvWhdU/FQNMeewzMoC1a4ElSxTYscN4oa+vr8D48cDEiQL16xu282lWRm7dgmLSJFht26bfJJo1g/j1VyAoSC7mzq0pTBVGab2PW1QyfPfuXWi1Wvj4+OTb7uPjg7NnzxZ4nVGjRuHu3bt49tlnIYRATk4Opk6dijfffLPQ25k7dy7ee+89o+0JCQnIysp6ujtBFk+n0yE5ORlCCP2HJVVcfLzLr4SHCfg97nesu7AOR+4cMRpXQIEOvh0QUj8EA+oMQDXHavrHOz4+3qTH+/RpG6xY4YjVqx2RlJT/+tbWAj17ZmLUqHT06JGpL4kWH2/S3SMT2W/ZArcZM6C4f1+/7cGUKXjw5pvy5Dk+IBVWcrLxybAlwaKSYVNERUXho48+wnfffYf27dvjwoULeOWVV/DBBx9g9uzZBV5n1qxZmD59uv5ySkoK/Pz84OXlBXee6VDh6XQ6KBQKeHl5MTmqBPh4ly+J6YlYG7v2sc0w2tZoi/DAcIxoOgI1XWvmGzPl8U5JkSXRlixR4MgR41ng+vUFJk0SGDcO8PW1A2Bn0n2jEpCQAMW0aVCkpQEAhK8v7n/xBdxGjIATX98Vnp1d6bz2LCoZ9vT0hLW1Ne7cuZNv+507d1C9kKKMs2fPxtixYzF58mQAQLNmzZCWloZ//etfeOuttwp8M7S3t4e9vb3RdisrK35YVhIKhYKPdyXCx9uypWWl4fdzv0OlUWHLhS3I0hr/QtfUqymUQUqEB4WjftX6BRzFoCiPtxDAvn2yGkRkJPDwkfLDDg5AaKisC9y1q8Lk0mtUwnx8ZMWIKVOAIUMgfvwR2TodX9+VRGk9xhaVDNvZ2aF169bYsWMHQkJCAMhv+Tt27MC0QhbDP3z40Ogfx9palssRXC9ERGSRMnMysfXiVqg0KmyI3VBgM4w67nUQHhQOZZASzXyalcjt3rkjO/EuWgTEGp97h5YtZQI8ahRLolkErVaW7cg7gRURIcun9ekjv9VwWQQ9JYtKhgFg+vTpGD9+PNq0aYN27dph3rx5SEtL01eXGDduHGrWrIm5c+cCAAYNGoQvv/wSLVu21C+TmD17NgYNGqRPiomIyPy0Oi12Xd4FVbQKa86uQVJGktE+vlV8MTJwJJRBSrSr2a5EZmS1WtmLYeFCYMMG45Jobm6GkmhsUGZBrl2T7fqCgoCvvzZsVyhkH2uAJ8lRibC4ZDgsLAwJCQl45513cPv2bbRo0QJbtmzRn1R39erVfDPBb7/9NhQKBd5++23cuHEDXl5eGDRoED788ENz3QUiIvqHEAIHrh+AKlqFVadX4U7aHaN9PBw89M0wuvp3fapmGHldugQsWSL/u37deLxbNzkLPHw4S6JZnMhI4PnngaQkICoK6NcP6N/f3FFRBaUQXEuAlJQUuLm5ITExkSfQVQI6nQ7x8fHw9vbmGrNKgI932RNC4O87f0MVLZthXEm+YrSPs60zQhqHQBmkRK96vWBnXTInxqSn67BsWQpWrXIrsCRa9erAhAnApElAgwYlcpNUklJSgJdflu39cvn5AcuXy37Wj+Dru3JJSkqCh4cHkpOT4erqWmLHtbiZYSIiKp/O3TsHVbQK6hg1zt41Lodpb22PAQ0HIDwwHAMaDoCTrVOJ3XZ0tFwH/MsvCty/755vzNpaTipOniwnGG1Nb0JHpenAAWDMGNnlJFdYGPD992ytTKWKyTAREZnsWvI12Q0uRo3jt44bjec2wwgPDEdI4xC4ObiV2G2npAArV8q1wIcP5241zAbXqyfXAY8fD9SoUWI3SyUtJwf48EPggw/kAm8AcHEBvv1WJses5EGljMkwEREVS3xaPH47/RtUGhX2Xt1b4D5d/bsiPDAcoU1D4eXsVWK3LYScQFy4UCbCj5ZEs7cXGDAgAy++aI/gYCvwl3MLd+8eMGiQfFBzdeoE/PorUKeO+eKiSoXJMBERPVFSRhLWnV0HlUaFHXE7oBVao31a+7aGMkiJsKAw1HKtVaK3Hx8P/PKLTIILakjavLksPRseLpCdnfzPGtISDYFKg7s79K38rK2Bd94B3nzTsI2oDPDZRkREBXqY/RAbz22ESqPC5vObC2yG0cSzib4ZRoNqJXtGmlYL/PmnoSRadnb+cVfX/CXRFApAp2PZ2XLF2lp+yxk2TC6L6NDB3BFRJcRkmIiI9LK0Wdh2cRtUGhXWn12PtOw0o30C3AMQHhgOZTMlmnk3K/HubJcvG0qiXbtmPN61q6EkmlPJnYNHZWH3blnHrl07wzZ/f+DoUa4NJrNhMkxEVMlpdVrsvrIbqmgVVp9ZjcSMRKN9qlepjpFNR0LZTIn2NduXeAKcmQmsXy9ngbdvN+6l4ONjKInWsGGJ3jSVhaws4N13gU8+kWuBT56UJ8nlYiJMZsRkmIioEhJC4NCNQ1BFqxB5OhK3H9w22sfDwQPDmwyHspkS3fy7lVgzjLw0mtySaPJcqrysrGRJtIgIYMAAlkQrt2JjZX/r4/9UG4mLk+XSXn/dvHER/YPJMBFRJSGEQHR8tL4W8OWky0b7ONs6Y0jjIVAGKdG7Xu8Sa4aRV2qqoSTaoUPG43XryhngCROAmjVL/OaprAgB/PQT8O9/A+npcputrSyjNmOGWUMjyovJMBFRBXf+3nmoNWqoNCqcuXvGaNzO2g79G/SHMkiJAQ0GwNnOucRjEAI4eNBQEi3tkaXI9vZyDXBEBBAcDFaCKO8SEmR5j/XrDdsaNQJWrJBnOxJZECbDREQV0PWU61ipWQmVRoVjt44ZjVsrrPFc3eegDFIipHEI3B3cSyWOhARDSbQzxnk4nnlGngw3ejRQtWqphEBlbetWOa1/O8/Sm6lTgS++4BmPZJGYDBMRVRAJaQn6Zhh7ru4pcJ9naz+L8MBwjAgcAW9n71KJQ6uVJ8EtXCgnBgsqiTZqlJwFbt2a505VKHfuACEhQEaGvOzpCSxeLBtrEFkoJsNEROVYckayvhnG9rjtBTbDaOXbCuGB4QgLCkNtt9qlFsuVK4aSaFevGo936SIT4NBQwLnkV2KQJfDxAT7+WK4T7tMHWLoUqF7d3FERPRaTYSKiciY9Oz1fM4xMbabRPo2qNdI3w2jk2ajUYsnMlA0xFi0Ctm0zLonm7Q2MHy9PiGvcuNTCIHPR6eRPAXlLfbz0ElCrFjB0KBd/U7nAZJiIqBzI0mbhz4t/ymYYsevxIOuB0T613Wrrm2E092le4rWA84qJMZREu3s3/5iVFdC3r1wLPHAgS6JVWLduybXBLVrI+sG5rKzk2ZBE5QSTYSIiC6XVafHXlb+g0shmGPfT7xvt4+3srW+G0aFWB1gpSm8m7sEDWQli0SLgwAHj8Tp1DCXRatUqtTDIEqxfL9e83Lsne2b36QP06GHuqIhMwmSYiMiCCCFw+MZhqDQqRMZE4taDW0b7uNm76ZthBAcEw8aq9N7KhZC1gHNLoj14ZELazg4YNkzOAnfvzl/FK7y0NFkjeMECwzYfH/PFQ1QCmAwTEVmA6DvRUGlUUGvUuJR0yWjcydYJgxsNhjJIiT71+sDexr5U47l7F/j1V5kEx8QYjzdrZiiJVq1aqYZCluLYMVkG5Nw5w7YhQ+STxNPTfHERPSUmw0REZnLx/kV9M4yYBOOM09bKFv0a9IMySIlBDQeVSjOMvHQ6WRJt0SJg3TogKyv/uIsLoFTKJLhNG5ZEqzS0WuDzz4G33wZycuQ2Jydg3jz5ZOATgco5JsNERGXoRsoNRMZEQqVR4cjNI0bjVgor9KjTA8ogJYY2HgoPR49Sj+naNVkObfFiWR7tUZ07y5xnxAiWRKt07t6VD3xUlGFb69ayk1zDhmYLi6gkMRkmIipldx/exerTq6HSqPDXlb8gIIz26eTXCcogJUY0HQGfKqW/BjMry1ASbetW45JoXl6yJFpEBEuiVWpuboaF4goF8MYbwJw5crE4UQXBZJiIqBSkZKZg/dn1UGlU+DPuT+Tocoz2aVG9BZRBSoQFhsHf3b9M4jpzRibAy5bJVsl5WVnJogC5JdGY7xBsbYHly2VXue+/B7p1M3dERCWOyTARUQlJz07H5vObodKosOn8JmTkZBjt07BaQ30zjMaeZTPl+uABsGqVPM9p/37jcX9/OQM8YQLg51cmIZGlOnBArgdu3tywrWFDQKNhqRCqsJgMExE9hWxtNrbHbYdKo8K6s+uQmpVqtI+fqx/Cg8KhDFKiRfUWpdoMI5cQwJEjMgFWqQouiTZ0qEyCn3uOeU6ll5MDfPgh8MEHMvk9elQmxbn4BKEKjMkwEVEx6YQOe67sgUqjwm+nf8O99HtG+3g5eWFk4Egog5To6NexVJth5HXvnqEkmkZjPB4UJJdBjBnDkmj0j7g4+YTI7aRy5gzw3XfAf/5j3riIygiTYSKiIhBC4OjNo1BpVFgZsxI3U28a7eNm74ZhTYZBGaRE9zrdS7UZRl46HbBzp0yA1641LolWpYosiRYRAbRrx0pY9A8hZD/tadOA1H9+0bC2Bt59F/j3v80aGlFZYjJMRPQYMfEx+mYYFxMvGo072jhicKPBCA8KR7/6/Uq9GUZe168bSqJdvmw83qmToSRalSplFhaVB4mJwNSpQGSkYVu9evJnhQ4dzBcXkRkwGSYiekRcYhzUGjXUGjWi46ONxm2tbNG3fl+EB4VjcKPBqGJXdplmVhawcaOcBd66Vc4K5+XpCYwbJ2eBmzYts7CoPImKAsaOld+mck2cCMyfLzurEFUyTIaJiADcTL2pb4Zx+MZho3ErhRW6B3RHeFA4hjUZhqqOVcs0vrNnDSXR4uPzjykUsiRaRAQweDBLotFj3Lolnyy5a2k8PIAFC+TPB0SV1FMlwydOnIBKpcLZs2fx8OFDbN++HQBw5coVHDp0CD179kTVqmX7gUFEVFT3Ht7D6jOyGcbuy7sLbIbRsVZHhAeFY2TgSFSvUr1M40tLM5RE27fPeLx2bWDSJDmpV7t2mYZG5ZWvr1wT/NZbQPfu8ttVrVrmjorIrExOhl9//XV88cUXEP+0LcpbKkgIgVGjRuGLL77AK6+88vRREhGVkNTMVKyPlc0wtl3cVmAzjOY+zREeFI7woHAEuAeUaXxCyKpWuSXRUh+p1GZrm78kmrV1mYZH5Y0Qci1N3ifKzJmyoPTo0SyZRgQTk+ElS5bg888/x6BBg/Dhhx9CpVLh448/1o8HBASgXbt22LBhA5NhIjK7jJwMrDmzBitPr8TGcxsLbIZRv2p9fTOMpl5lv9j2/n157tKiRcCpU8bjTZvKk+HGjpXrgomeKCEBmDIFaNlSzgbnsraWTyQiAmBiMvzdd9+hSZMmWL16NWxsbGBXwAK1xo0b65dNEBGVtWxtNnZc2oEV0Suw9sxaPMh+YLRPLddaCAsMgzJIiVa+rcqkGUZeOh2wa5dMgNesATIz8487OwPh4TIJbt+eJdGoGLZulS0Fb9+WZ1z27g107GjuqIgskknJ8OnTpzFlyhTY2BR+dR8fH8Q/epYHEVEp0gkd9l7dC7VGjVWnV+Huw7tG+3g6eWJE0xFQBinRuXbnMmuGkdf168DSpbIk2qVLxuMdOsgEeORIntxPxZSRAcyaBcybZ9jm4WG83oaI9ExKhm1sbJD1aFX3R9y8eRNVWNiSiEqZEALHbh2DWqPGypiVuJ5y3WgfFzsXDG08FKOajcJzdZ8rs2YYeWVnywm6RYuAP/4wLolWrZqhJFpgYJmHRxVBdLRcBxydpxxgnz7ym1f1sj35k6g8MekToVmzZti5cye0Wi2sCzh7I7eyROvWrZ86QCKigpxOOA21Rg2VRoUL9y8YjTvYOGBQw0EICwxDa9fWqF2jNqzMcLJQbKxMgH/+ueCSaL16yVngwYMB+7Lr10EViU4HfP21PDEud62NvT3w6aeyuxxPkiN6LJOS4UmTJmHy5MmYOnUqvvnmm3xjKSkpmDx5Mm7fvo358+eXSJBERABwKfESVsashEqjwqk7xmeZ2VjZoE+9PlAGKTG40WC42LtAp9OV+ZKthw9lSbRFi4A9e4zH/fwMJdH8/cs0NKpo7t2Ts8Fbtxq2NWsGrFgBBAWZLy6icsTkZHj79u1YtGgRVq5cCXd3dwBAu3btcObMGaSlpWHChAkIDQ0tyViJqBK6lXoLq06vgkqjwsHrB43GFVAgOCAYyiAlhjcdXubNMHIJARw7JhPgFSuAlJT847a2wJAhcha4Z0+WRKMS4uwM3LhhuPzqq8BHHwEODuaLiaicMXnh3IoVK9C9e3d888030Gg0EELg6NGjaNKkCV5++WU8//zzJRknEVUi99PvY82ZNVBpVIi6HAWd0Bnt075meyiDlBgZOBK+Lr5miFJKTASWL5d1gf/+23i8SRNDSTQvr7KPjyo4Bwf57WvIEOCHH2TVCCIqlqc6i2TKlCmYMmUK0tPTkZiYCFdXV540R0QmeZD1ABtiN0ClUWHrha3I1mUb7dPMu5m+FnAdjzpmiFLS6YCoKDkLvHp1wSXRwsJkEtyhA0uiUQk6dkw+wRo3Nmxr1gw4dw54TIUnIiqcSa+cv/76CwEBAaj9T/9PR0dHODo65tvn2rVruHTpErp27fr0URJRhZSZk4k/LvwBlUaF32N/R3pOutE+9Tzq6RPgQG/zllm4cUOeCLdoERAXZzzevr1MgMPCWBKNSphWC3z+OfD223It8MGD+c+4ZCJMZDKTXj3du3fHu+++i3feeafQfZYtW4Z33nkHWq3W5OCIqOLJ0eVg56WdUGlUWHtmLZIzk432qelSUzbDaKZEa9/WZd4MI6/sbGDzZrkMYvNm45JoVasaSqLxfCUqFdeuyXU2u3fLyydPAt99J9cHE9FTMykZFkI8cR+dTmfWDzAishw6ocP+a/uhilZh1elVSHiYYLRPNcdqGNF0BMKDwtHFv4tZmmHkdf68nAFeuhS4c8d4PLck2pAhLIlGpSgyEnj+eSApSV5WKIA33gBefNGsYRFVJKX2u8r58+fh5uZWWocnIgsnhMCJ2yegilZhZcxKXEu5ZrSPi50LhjYZivDAcPSs2xO21rZmiNTg4UO5BnjhQuCvv4zHa9UylEQLCCjz8KgySUkBXn5ZrsvJ5ecH/PIL0K2b+eIiqoCKnAxPmjQp3+V169bh8uXLRvtptVpcu3YNf/31F/r16/fUARJR+XL27lmoolVQx6hx7t45o3EHGwcMbDgQ4YHh6N+gPxxtHQs4Stk6flwmwMuXG5dEs7ExlETr1Ysl0agMHDgAjBmTf2F6WBjw/feytTIRlagiJ8NLly7V/61QKHDy5EmcPHmywH0VCgXatm2Lr7766mnjI6Jy4ErSFag1aqhj1Dh5+6TRuI2VDXrX643wwHAMaTwErvauZR/kIxITZUWqhQvlEsxHNW5sKInm7V3m4VFldeMGEBwMZGXJyy4uwLffyuSYSw+JSkWRk+FLly4BkD991q1bF//+97/xyiuvGO1nbW0NDw8PODs7l1yURGRx7jy4g8iYSKg0Khy4fsBoXAEFugV0Q3hgOIY3HQ5PJ08zRJmfEPIcpIUL5XKIjIz8405OcgIuIgLo1Im5B5lBzZrAf/4jG2d06gT8+itQx3xlBIkqgyInw/55eoYuWbIELVu2zLeNiCq+xPREfTOMXZd3FdgMo13NdggPDMfIwJGo6VrTDFEau33bCosXA4sXAxcvGo+3aycT4PBwwNX8k9ZUmeSekJ73m9ecOUDt2vJJyZJpRKXOpFfZ+PHjSzoOIrJQaVlp+mYYWy5sKbAZRqBXoL4WcL2q9cwQpbGcHFkK7aefFPjjDy9otfmneatWlUsgIiJkzwKiMpeYCEydCrRtK2eDc9naygoSRFQmnuorZ0ZGBo4cOYKbN28i89EWTP8YN27c09wEEZlBZk4mtlzYAnWMGhtiN+Bh9kOjfep61EV4YDiUzZQI8racArvnz8sZ4KVLgdu3ASB/Etyzp0yAQ0JkJ1sis4iKkt/Grl8H1q4FnnsOaNnS3FERVUomJ8PffvstZs+ejeRk44L5gFxbrFAomAwTlRM5uhzsurQLao0aa86uQVJGktE+vlV89c0w2tZoazG1xNPTDSXRcvsS5OXrq8WkSVaIiFBw+SWZV1YW8M47wKefGpZIVKmS+82NiMzApGR4zZo1eOmll9CsWTPMnj0bM2bMQEhICNq3b4+//voLf/zxB4YPH46BAweWdLxEVIJ0QocD1w5ArVEj8nQk4tPijfap6lgVoU1CoWymRJfaXWBtZTm1xU6cMJREe/R7uY0NMGgQMGmSDi1bJsDX1xtWVpaRvFMlFRsLjBola/nl6t4dWLZMFrEmIrMwKRmeN28evL29ceDAATg5OWHGjBlo0aIFZs6ciZkzZ2LFihUYP348XmSHHCKLI4TAydsn9aXQriZfNdqnil0VhDQOgTJIiV51e5m9GUZeSUmyJNqiRflzilyNGsllEOPGAT4+sn1yvHGOT1R2hAB+/FG2T05Pl9tsbYEPPwRmzACszNttkaiyMykZPnXqFEaOHAknJyf9Nq1Wq/971KhR+Pnnn/H+++8jODj4qYMkoqcXezcWao0aKo0Ksfdijcbtre0xoOEAKIOUGNBggEU0w8glhOwIt3Ah8NtvxiXRHB2BkSNlXeDOnVkSjSzI/fuyZeGGDYZtjRrJb3StWpkvLiLSMykZzs7OhpeXl/6yo6MjknL7pv+jefPm+PHHH58qOCJ6OleTr2KlZiVUGhVO3D5hNG6tsEaver2gDFIipHGIRTTDyOvWLdmNdvFieWLco9q2NZREY/d3skj29sDZs4bLL7wAfP65LGpNRBbBpGS4Ro0auHXrlv6yv78/TpzI/0F75coV2LA+IlGZu/PgDn47/RtUGhX2XdtnNK6AAl38u0AZpERo01CLaIaRV04O8McfchZ40yYgz49OAGQ32jFjZBLcvLl5YiQqMmdnuah9yBDghx/kQnYisigmZatt27bF8TyL9fr27Yv58+dj7ty5GDx4MPbu3Ys1a9agZ8+eJRYoERUuKSMJa8+shUqjwo5LOwpshtGmRhsog5QYGTgStVwt72SdixflDPCSJXJG+FE9eshlEEOHsiQaWbDoaJkA161r2NamDRAXJ2eJicjiKITIre1SdGvXrsWbb76JP/74AwEBAUhISECbNm1w/fp1APIEHTc3N/z1119oVg6q2aekpMDNzQ2JiYlwd3c3dzhUynQ6HeLj4+Ht7Q2rcnziysPsh/g99neoNCr8ceEPZGmzjPZp6tUUyiAlwgLD0KBaAzNE+Xjp6cCaNfJkuF27jMdr1JDLLSdNyp9bFEdFebypaMz2eOt0wNdfAzNnynrBe/awe1wZ4Ou7cklKSoKHhweSk5PhWoLtQk16pQ4dOhRDhw7VX/by8sLJkyexcOFCxMXFwd/fH2PHjkXNmpbRipWoosjSZmHrha1QaVTYELsBadlpRvsEuAfou8E1825mMbWA8zp5UibAv/4qq0PkZWMDDBwoZ4H79GE+QeXArVvAhAnAtm3y8sGDwPffAy+9ZNawiKhoSuxjxsPDA6+99lpJHY6I/qHVaRF1OQoqjQqrz6wusBlG9SrVERYYhvCgcLSv2d4iE+DkZENJtGPHjMcbNJAJ8LhxQPXqZR8fkUnWr5cL2O/dM2x79VVgyhTzxURExVJqcy579uzB7NmzERUVVVo3QVRhCSFw8PpBqDQqRMZE4k7aHaN9PBw8ENo0FOFB4ejm382immHkEkL+WrxoEbBqlaHEai5HR2DECJkEP/ssS6JROZKWJmsEL1hg2ObrK/uA9+5ttrCIqPhMSobv378PGxubAtdrHDx4ELNnz8bOnTufOjiiykQIgVN3TkGlUUGtUeNK8hWjfZxtnRHSOAThQeHoXa837KztzBDpk92+LZtqLVoEnDtnPN66tUyAlUqWRKNy6Ngx2Uku75M7JAT46SfA07KqsxDRkxUrGV65ciVmzZqFK1fkh3RQUBC+//57dOrUCbdu3cKLL76I9evXQwiB1q1bY86cOaURM1GFcv7eeX0CfObuGaNxO2s7DGgwAOFB4RjYcCCcbC2zPmlODrB1qyyJ9vvvxiXR3N0NJdFatDBHhEQl4No1oFMnIOufE1adnID58+UTmz9tEJVLRU6Gd+zYAaVSCQBwdnYGAERHR6N///7Ytm0bhg4dilu3bqFVq1Z49913MYi1FIkKdS35GlbGrIRao8axW8YLaK0V1uhZtyfCg8IxtPFQuDlY7vRpXJyhJNrNm8bj3bsbSqI5Wk5TOyLT+PkB//d/wLx58ieOFSuAhg3NHRURPYUiJ8Pz5s2DlZUVVqxYgZEjRwIAVCoVxowZg169eiE7OxsLFy7EpEmTSi1YovIsIS0Bq06vglqjxp6rewrc59naz+qbYXg7e5dxhEWXkQGsXStngQtaEeXrayiJVq9e2cdHVKKEyD/rO3cuULs28OKLgJ1lLlUioqIrcjJ85MgRDBgwQJ8IA4BSqYRKpcKmTZuwZMkSjBs3rlSCJCqvkjOSsfbsWqg1amyP2w6t0Brt08q3lb4WsJ+bnxmiLLpTp2QC/OuvQGJi/jFra0NJtL59WRKNKoCUFODll4F27eRscC4HB1kxgogqhCJ/XN27dw+BgYFG2wMDA7Fp0yaEhISUZFxE5dbD7IfYeG4j1Bo1Np/fjExtptE+jT0b62sBN6xm2T+xpqQAKpVMgo8eNR6vX18ulxw/Xs4IE1UIBw4Ao0cDly4BK1fK9T5Nmpg7KiIqBUVOhrVaLRwK6IGau60kO4EQlTdZ2ixsu7gNao0a62PX40HWA6N9/N38ER4UDmWQEs/4PGORtYBzCQHs2ycT4FWrgIcP8487OMiSaBERQNeuPG+IKpCcHOC//5X/5Z4Famsr+4UzGSaqkPhDJpGJtDotdl/ZDbVGjdVnVuN++n2jfXycfTAycCSUQUp0qNXBohNgALhzx1ASLTbWeLxVK5kAjxolq0MQVShxcbLkyYEDhm2dOsl1QXXqmC8uIipVxUqGv/nmG6jV6nzb7t69CwBo2rSp0f4KhQIxMTFPER6RZRFC4NCNQ1Br1IiMicStB7eM9nF3cMfwJsOhDFIiOCDYIpth5KXV5i+JlpOTf9zNzVASrWVL88RIVKqEkN8Cp00DHvzzq461NfDOO8Cbb3IBPFEFV6xX+N27d/XJ76POnj1bIgERWRohBKLjo6HWqKHWqHEp6ZLRPk62ThjSaAiUQUr0qd/HYpth5HXpkqEk2o0bxuPdusmT4YYPZ0k0qsCSkoDnnwciIw3b6tYFli8HOnQwW1hEVHaKnAzrdLrSjIPI4ly4fwFqjRoqjQqnE04bjdtZ26Ff/X5QBikxsOFAONs5myHK4snIANatk8sgtm83Hq9eHZgwQZZEa9CgrKMjMgOFAjh0yHB5wgTgf/8DXFzMFhIRlS3+9kOUx42UG1gZsxIqjQpHbxqXTrBSWOG5Os9BGaTE0CZD4e7gXvZBmiA62lAS7f4jS5utrYH+/eUscP/+/EWYKhk3N+CXX4Bhw4DvvpNnhhJRpcKPPar07j68i99O/waVRoU9V/ZAQBjt09mvs74Zhk8VHzNEWXwpKYBaLWeBDx82Hq9Xz1ASrUaNso+PyCxiYwFnZ6BWLcO2Ll2Ay5fldiKqdJgMU6WUmpWKLX9vwcrTK/HnxT8LbIbRsnpLKIOUGBk4Ev7u/maIsviEAPbvl7PAkZEFl0QbPlzOAnftClhZmSdOojInBPDjj7JZRocOcp1Q3hcAE2GiSovJMFUa6dnp2HR+E1ZEryi0GUajao30zTAaeTYyQ5SmiY83lEQr6FzWFi1kAjxqFODhUebhEZlXQoJ8AWzYIC/v2iUT46lTzRsXEVkEJsNUoWVrs/Fn3J9QaVRYd3Zdgc0warvVRnhgOMKDwtGieguLrwWcS6sFtm2TCfD69QWXRBs1SuYArVqZJ0Yis9u6VZ4Ud/u2YdvUqcC4cWYLiYgsi0Umw99++y0+++wz3L59G82bN8fXX3+Ndu3aFbp/UlIS3nrrLaxZswb379+Hv78/5s2bh/79+5dh1GQptDot9lzdA1W0Cr+d+a3AZhiejp4ICwqDMkiJjn4dYaUoP+sFLl+W5dAWLwauXzce79rVUBLNyanMwyOyDBkZwFtvAfPmGbZ5esoXzqBBZguLiCyPxSXDK1euxPTp0/HDDz+gffv2mDdvHvr06YPY2Fh4e3sb7Z+VlYVevXrB29sbv/32G2rWrIkrV67Ane2xKhUhBI7cPAJVtAqRpyNxM/Wm0T5u9m4Y3mQ4RgaORKBTIGpUrwGrcrJoNjMzf0k08cg5fj4+hpJoDRuaI0Iiy2Fz5gwUr7wiy6jk6tMHWLpU1g8kIsrD4pLhL7/8ElOmTMHEiRMBAD/88AM2bdqExYsX44033jDaf/Hixbh//z72798PW1tbAEBAQEBZhkxmpInX6JthXEy8aDTuaOOIwY0GQxmkRN/6fWFvYw+dTof4+HgzRFt8Go1MgH/5Bbh3L/+YlVX+kmj/PP2JKrcrV1CtXz8oMv85J8DeHvj0U9ldrpx8+SWisvVUyfDatWuhUqlw9uxZPHz4EBcuXAAgu9Ft2LABo0ePRs2aNYt8vKysLBw7dgyzZs3Sb7OyskLPnj1xIG+v+Dw2bNiAjh074sUXX8T69evh5eWFUaNGYebMmbC2LrgNbmZmJjIzDSdPpaSkAJCNRdhcxPLFJcZhZcxKqDVqaBI0RuO2VrboU68PwoPCMajhIFSxq6Ify32MhRAW+1inpgIrVwKLFytw6JDx+uW6dQUmTRIYNw7I+/Ky0Ltjdpb+eFPJ0vn5ISs0FE7Ll0M0awbx669AUNA/g3wOVDR8fVcupfU4m5QM63Q6KJVK/PbbbwAAR0dHpKen68c9PDzw1ltvQavV5ktsn+Tu3bvQarXw8clfx9XHx6fQds9xcXHYuXMnRo8ejc2bN+PChQv4v//7P2RnZ+Pdd98t8Dpz587Fe++9Z7Q9ISEBWVlZRY6Xys7ttNtYf3E91l9cjxPxJ4zGrRRW6FyjM0Lqh6B/nf5wt3cHADxMeoiHyF9fTKfTITk5GUIIi1kmIQRw9KgtVCpHrF/vgIcP88dlby/Qv38GRo9OR8eOWfoJrnIywW1Wlvh4U+nR6XRIefVV1KhZEw9feEHWE+QLpcLi67tySU5OLpXjmpQMf/XVV1i1ahWmTp2Kjz/+GF9++SU++OAD/biPjw+6dOmCTZs2FSsZNoVOp4O3tzd+/PFHWFtbo3Xr1rhx4wY+++yzQpPhWbNmYfr06frLKSkp8PPzg5eXF9caW5B7D+/htzO/ITImEruv7C6wGUbHWh0RHhiO0KahqF6laGsBdTodFAoFvLy8zP7mmZAgu8ItWqTAmTPGs8DNmwtERAgolUDVqvYA7Ms+yHLOkh5vKmFpaVD85z8Q7dvLRfMwPN5OH36IKny8Kzy+visXOzu7UjmuScnw0qVL0bZtW3z33XcAUGApqvr162PTpk3FOq6npyesra1x586dfNvv3LmD6oWc9ODr6wtbW9t8SyKaNGmC27dvIysrq8B/OHt7e9jbGycVVlZWfDGZWWpmKtadXQd1jBrbLm5Dji7HaJ/mPs2hDFIiLCgMAe4BJt2OQqEw2+Ot1QJ//mkoiZadnX/c1TVvSTRFuSn1ZsnM+XhTKTl2DBg9GoiNhWLFCqBbN9lWEXy8Kxs+3pVHaT3GJiXDFy5cwIsvvvjYfapVq4Z7j57x8wR2dnZo3bo1duzYgZCQEADyW9+OHTswbdq0Aq/TuXNnrFixAjqdTv+PdO7cOfj6+pbaNwgqWenZ6dh8fjPUMWpsPLcRGTkZRvs0qNpA3wyjiVcTM0T59K5cMZREu3bNeLxLF5kAh4ayJBpRobRa4PPPgbffNhTX1unk2ab/JMNERMVhUjLs6Oj4xHUbppY3mz59OsaPH482bdqgXbt2mDdvHtLS0vTVJcaNG4eaNWti7ty5AIAXXngB33zzDV555RW89NJLOH/+PD766CO8/PLLxb5tKjvZ2mxsj9sOdYwaa8+sRWpWqtE+tVxrITwwHMpmSrSs3rJczpBmZsqmVwsXytngR0uieXsbSqI1Kj8N74jM49o1YOxYYPduw7bWrYEVK1hTkIhMZlIy3LJlS2zduhUZGRlwcHAwGr9//z62bNmCrl27FvvYYWFhSEhIwDvvvIPbt2+jRYsW2LJli/6kuqtXr+abJvfz88PWrVvx6quv4plnnkHNmjXxyiuvYObMmabcNSpFOqHDnit7oNao8duZ33D34V2jfbycvDCi6QgomynRya9TuWqGkVdMjFwGsWxZwSXR+vUDIiKAgQNZEo2oSCIjgeefB5KS5GWFAnjjDWDOHIC/AhLRUzApGX755ZcxdOhQDB8+HAsWLMg3dvHiRUyaNAnJyckmz85Omzat0GURUVFRRts6duyIgwcPmnRbVLqEEDh26xhU0SqsjFmJG6k3jPZxtXfFsCbDoAxSokedHrCxsrjy10Xy4IEsibZwIVDQ07FOHZkAjx8P1KpV9vERlUupqcBLLwE//2zY5ucni29362a+uIiowjAp6xgyZAhmzpyJTz75BP7+/nB2dgYAeHt74969exBCYPbs2ejRo0eJBkvlx+mE01BFq6COUePC/QtG4442jhjUaJC+GYaDjfEvDOWBEMChQzIBVquBtLT843Z2si1yRATQvTtr/hMVW2YmsG2b4XJYGPD994CHh/liIqIKxeQpuLlz56JHjx745ptvcOjQIWRkZECn06Fv3754+eWX0adPn5KMk8qBS4mXoNaoodKoEB0fbTRua2WLPvX7QBmkxOBGg/M1wyhv7t6VE1MLFwKnTxuPN2smT4YbPRqoVq3s4yOqMDw95axwaCjwzTfAmDFyiQQRUQl5qt+je/XqhV69epVULFQO3Uq9hciYSKg0Khy6cchoXAEFutfpDmWQEsOaDENVx6pmiLJk6HTA9u0yAV63zrgkmosLoFTKJLhNG35eE5kkLg5wdgbyNl/q1UuWY2EdeCIqBSaXVqtfv35Jx0LlxP30+1h9ejVUGhWiLkcV2AyjQ60OUAYpMaLpCPi6+JohypJz9aosibZkifw8ftSzz8plECNGyM9wIjKBEPKM02nTgK5dgY0b83+jZCJMRKXEpGS4YcOG6NixI8aOHYuRI0eiatXyO9tHRfMg6wHWn10PlUaFrRe3FtgM4xmfZ2QzjMAw1PGoY4YoS05WlqEk2rZtxiXRvLzkiXAREUDjxuaJkajCSEwEpk6VFSMAYPNm+e1z0iTzxkVElYJJyfDAgQOxdetWHDx4EP/+97/Rr18/jB07FgMHDmSjiwokIycDf5z/AyqNChvPbUR6TrrRPvWr1tc3w2jq1dQMUZas06cNJdHuPlL5zcoK6NNHLoMYOJDVnIhKRFSUrB18/bph24QJ8qcWIqIyYFIyvGHDBty7dw8qlQq//PIL1q9fjw0bNsDNzQ0jRozAmDFj0KVLl5KOlcpAji4HO+J2QB2jxpoza5CSmWK0T02XmggPCkd4UDha+7Yul80w8nrwQE5ILVwIHDhgPO7vL2eAJ0yQFZ2IqARkZQHvvAN8+qnhpxcPD2DBAibCRFSmFEI8+gNw8V24cAHLli3D8uXLcenSJSgUCvj7+2P06NH44IMPSiLOUpWSkgI3NzckJiaa1DWvvNMJHfZd3Qe1Ro1Vp1ch4WGC0T7VHKvpm2E8W/vZctsMA5Atvu/cicfly95YvNgKarVMiPOyswOGDpWzwD16sCRaeabT6RAfHw9vb+9S62tPxXT2rCy1cvy4YVv37vInmacsws3Hu3Lh4125JCUlwcPDA8nJyXB1dS2x45ZIMpzX/v37sWzZMixZsgQ5OTnQarUlefhSURmTYSEEjt86DrVGjZUxK3Et5ZrRPi52LhjaZCiUQUo8V+c52FqX/1ZpsiSaDj/+qMXZs8b3JyhIJsBjxrAkWkXBD0sLExcnX2jp/yy7srUFPvwQmDGjRL518vGuXPh4Vy6llQyXaKuv8+fPY+vWrdi+fTuys7PL/c/nFdmMbTPw1cGvjLY72DhgYMOBUAYp0b9B/3LbDCMvnQ7YsUOuBV67FsjKsgJgeNOsUsVQEq1tW5ZEIypVdesCw4YBy5cDjRoBK1YArVqZOyoiqsSeOhm+e/cuVCoVfv31Vxw9ehRCCHh4eOD555/H2LFjSyJGKmGZOZn44egP+ss2VjboXa83lEFKDGk0BC72LmaMruRcu2YoiXb5svF4p04CkycrMGKETIiJqIx8+61cjP/WW4CTk7mjIaJKzqRkOCMjA+vWrcOvv/6KP//8E9nZ2bCzs8OQIUMwduxYDBgwgFUlLNiRm0f0lSH61e+HX4b+gmpOFWNNQFYW8PvvchZ4yxbjkmiensC4cQJDhtzFs89Wg5UVp4GJSk1GBjBrFtCpU/6T4tzc5NIIIiILYFIy7OPjgwcPHkAIoa83HBYWBg/2ii8Xoi5H6f8ObRpaIRLhs2dlAvzzz0DCI+f/KRSGkmiDBgE2NgLx8Za/lp2oXIuOlifJRUcDS5cCHTqwHAsRWSSTkmEvLy9Mnz4dY8aMQb169Uo6Jiplu6/s1v8dHBBsvkCeUloasGqVLIm2b5/xuL+/rNk/YQJQu7Zhu05XZiESVT46HfD118DMmUBmptyWng4cPcpkmIgsksntmKl8ytJmYd9VmTnWcq2FOu7lq1OcEMCRI3IWWKUCUlPzj9vaGkqiPfccS6IRlalbt4CJE4GtWw3bmjWTJ8kFBZkvLiKixyjRahJk+Y7ePKpfLxwcEFxuKn7cuydPPl+4UP7q+qjAQENJNE/Pso+PqNJbv16+CPO2bnz1VeCjjwCH8l+VhogqriIlw++//z4UCgVefPFFVK1aFe+//36RDq5QKDB79uynCpBKVt71wt38u5kvkCLQ6YBdu2QCvGaNPDkuL2dnWRItIgJo354l0YjMIi1N1ghesMCwzddXrhPu3dtsYRERFVWRkuE5c+ZAoVAgLCwMVatWxZw5c4p0cCbDlidvMmyp64WvX5efo4sXA5cuGY937CgT4JEjAZeKUQWOqPxKSQFWrzZcDgkBfvqJP9EQUblRpGR4165dAIDa/5yFlHuZypdsbTb2XZPrhWu61EQ9D8s5+TE7G9i4Uc4Cb9lifJKbLIkmk+CmTc0TIxEVwNdXvnBHjQLmz5cvUv5MQ0TlSJGS4W7duj32MpUPR28excPshwCAbgHdLGK9cGysoSRafHz+MYVC/soaEQEMHgzY25snRiLK49o1uUapalXDtiFD5M843t7mi4uIyEQmnWu/bNkynDp16rH7aDQaLFu2zKSgqHTkK6nmH2y2ONLSZPLbpQvQuDHw2Wf5E+HatYE5c+Rn65YtslY/E2EiCxAZCTzzDPD888YdbZgIE1E5ZVIyPGHCBKxbt+6x+6xfvx4TJ0405fBUSvKdPBdQtrP7Qsgyo1Onyl9VJ0wA9u41jNvayqR3yxYgLg54911ZJ5iILEBKinzRhoUBSUnAb7/JcmlERBVAqZVW02q1sGKRV4uRrc3G3qsy+/St4osGVRuUye3evy9Loi1aBPz9t/F406ZyGcTYsYCXV5mERETFceCA7CSX92zWsDCgf3/zxUREVIJKLRk+ceIEquZdU0ZmdfzWcaRlpwEo/frCOh0QFWUoiZbbhCqXszMQHi6T4A4deK4NkUXKyQE+/BD44ANA+0/7chcX4NtvZUFvvnCJqIIocjLco0ePfJeXLl2KqKgoo/20Wi2uX7+Oy5cvY+TIkU8dIJWMsqgvfOOGoSRaXJzxePv2siZ/WBhLohFZtLg4mfAeOGDY1qkT8OuvQJ3y1bWSiOhJipwM5018FQoFLl++jMuXLxvtZ2VlhapVq2LEiBGYN29eCYRIJSHfyXMlWF84OxvYtEnOAv/xh3FJtGrV5BKIiAh2YyUqFy5cAFq1MvQ6t7YG3nkHePNNwIZNS4mo4inyO5suT5ZjZWWFOXPm4J133imVoKhk5ehysOfqHgCAj7MPGlZr+NTHPHfOUBLtzp38YwoF0LOnnAUeMoSVIIjKlXr1gOeeA9atA+rWlYv+O3Qwd1RERKXGpK/5u3btQkBAQAmHQqXl+K3jeJD1AMDTrRd++FCeRL5oEfDXX8bjfn7AxInyPz49iMophUJ2kPP3l+uFuaaJiCo4k5JhNt0oX3ZfNn2JhBDA8eMyAV6+XFZYysvWVjbEmDwZ6NVL/qJKROVEVpZcAtGlCzBggGG7pyfAZW5EVEkUKRnObZ4xdOhQuLi4FKuZxrhx40yLjEpM1JUo/d9FPXkuMdFQEu3kSePxxo1lAjx2LGvtE5VLsbGyhfLx48CSJcCpU4CPj7mjIiIqc0VKhidMmACFQoEOHTrAxcVFf/lxhBBQKBRMhs0sR5ejry/s7eyNxp6NC91XpwN275YJ8G+/GZdEc3KSlSAmTwY6dmRlJaJySQjgxx+BV18F0tPltsREYN8+YNgw88ZGRGQGRUqGFy9eDIVCAV9fXwDAkiVLSjUoKjknb59ESqZc29DNv1uBX2Ju3pQnwi1aBFy8aHyMdu0MJdFcXUs7YiIqNQkJ8sW8YYNhW6NGsptcq1bmi4uIyIyKPDOc1/jx40sjFioFeesL510vnJ0NbN4sE+DNmw019XNVrWooidasWdnESkSlaOtW2VL59m3DthdeAD7/XP7sQ0RUSbFoZAX3aH3h8+dlU4ylS/N/JubKWxLNwaHs4iSiUpKRAcyalf+EOE9P+UYwaJDZwiIishQmJcPXrl3D+fPn0aFDBzj9M6Og0+nw2WefYcOGDXB0dMSrr76KAXnPTqYyp9Vp8dcVWQPNxcoLL4xogr92G+9XsyYwaZIsicbmUkQVTHy8PEEuV9++8nL16uaLiYjIgpiUDM+ePRu///47bueZWvzwww/x7rvv6i/v3r0b+/fvR9u2bZ8+SjLJztN/69cLp0Z3w1+7DeuFbWxkSbSICKBPH5ZEI6qwatcGvv9eftv97DNg2jSe/UpElIeVKVfat28fevbsCVtbWwCycsQ333yDxo0b4+rVqzh8+DCcnZ3x2WeflWiwVDyvzo8yXLgiS6o1aiQ/D69fB1avBvr3ZyJMVKHcumVcEFypBM6fB156iYkwEdEjTEqG4+Pj4e/vr7988uRJJCQk4KWXXkKtWrXQpk0bhISE4MiRIyUWKBVPVhZwJj1Kf3lI82Ds2QOcOQP85z8sJ0pUIa1fDzzzDPDyy8Zjfn5lHw8RUTlgUjKs0+mg0+n0l6OioqBQKNCjRw/9tpo1a+ZbRkFl69ARLXS19gAA7LWeWLOgKZ59lpNCRBVSWhowdSoQEgLcvStrJa5ebe6oiIjKBZPWDNeuXRuHDx/WX163bh18fX3RqFEj/bbbt2/D3d39qQMk00RGnQIckwAAgc5dYaUw6XsPEVm6Y8dkJ7lz5wzbQkKAbkXrNklEVNmZlCENHz4c+/btQ2hoKMaMGYO9e/di+PDh+fY5ffo06tatWyJBUvH9ed5QNqJ/YLD5AiGi0qHVAp98AnToYEiEnZyAn34C1qyR5dOIiOiJTJoZ/s9//oNt27ZhzZo1AIBnnnkGc+bM0Y9fuXIFhw8fxhtvvFEiQVLxZGcDF3Ki9JdD23CGiKhCuXZNdsXZnadWYuvWspNcw4bmi4uIqBwyKRl2dXXFwYMHodFoAABNmjSB9SMlCdasWYM2bdo8fYRUbEeP6aCtJesL22mroplPkJkjIqISc+4c0L49kJQkLysUwBtvAHPmAHZ25oyMiKhceqoOdEFBBSdZ/v7++apNUNlaGRUNOCYCAJo6deN6YaKKpH59mQxv3SorRPzyC9cHExE9hadux7xv3z6cPHkSKSkpcHV1RYsWLdC5c+eSiI1MtC02CgiQf/dvyg9JogrFykp2kJszB/j4Y8DDw9wRERGVayYnw/v378fEiRNx4cIFALLxhuKful0NGjTAkiVL0LFjx5KJkoosJwc4n21YRziibbD5giGip5OTA3z4IdClC5CndCV8fYEFC8wXFxFRBWJSMhwTE4PevXvj4cOH6NWrF7p37w5fX1/cvn0bu3btwrZt29CnTx8cPHgQTZs2LemY6TGOn9Ahp6ZMhu20HnimejMzR0REJomLA8aMAQ4cAGrWBE6dAqpWNXdUREQVjknJ8Pvvv4+srCxs3rwZffv2zTc2c+ZMbNmyBYMHD8b7778PtVpdIoFS0ah3aQCn+wCAxo6sL0xU7ggh1wFPmwakpsptt28Du3YBj5SwJCKip2dSphQVFYXQ0FCjRDhX3759ERoail27dj1VcFR822INSyT6NQk2XyBEVHyJiUB4ODB+vCERrlsX2LuXiTARUSkxKRlOTk5GnTp1HrtPnTp1kJycbFJQZBqtFojNjNJfHtmOJ88RlRtRUcAzzwCRkYZtEyYAJ0/KxhpERFQqTEqGa9SogYMHDz52n0OHDqFGjRomBUWmOfm3Djk1ZH1hW607mld/xswREdETZWUBs2bJE+SuX5fb3N1lUrxkCeDiYtbwiIgqOpOS4cGDByMqKgqzZ89GRkZGvrGMjAy8++672LVrF4YMGVIiQVLRrNx5GnC+CwBo5NAF1lbWT7gGEZnd9evA11/LtcIAEBwsT5YbMcKsYRERVRYmnUA3e/ZsbNy4ER999BEWLFiAdu3awcfHB3fu3MGRI0eQkJCAunXrYvbs2SUdLz3GlrNRgJ/8u2/jYHOGQkRFVbcuMH8+8MILsozajBmyljAREZUJk5LhatWq4eDBg3j99dehVquxefNm/ZiDgwMmTpyITz75BFVZBqjM6HTA2QzDyXNh7YPNFwwRFe7uXcDJSf6Xa9Ik2UWufn3zxUVEVEmZ3HTD09MTixcvxoIFC3D27Fl9B7rGjRvD1ta2JGOkIoiOFsiuIZNhG60bWvo2N3NERGRk61Z5UtywYcC33xq2KxRMhImIzKRYyXBiYiK+/vprHDlyBAqFAu3bt8eLL76IZs3Y2MHcVu48AzgnAAAa2nO9MJFFyciQJ8nNmycvf/cd0L8/MGCAWcMiIqJiJMP37t1D27ZtceXKFYh/TvTYtGkTli5dikOHDnFJhJltPhMF1JR/92nEkmpEFiM6Ghg9Wv4/V9++QOvW5ouJiIj0inyWxkcffYTLly8jODgYarUaarUawcHBuHjxIj7++OPSjJGeQAjgzMMo/WWuFyayADqdPDGubVtDImxvD/zvf8DmzUD16uaNj4iIABRjZnjz5s2oX78+tm3bBmtr+RP88OHD0bRpU2zcuBGffvppqQVJjxcTI5Dlm7te2BWta7Ywb0BEld2tW8DEiXKNcK5mzYAVK4CgIPPFRURERoo8M3zlyhX06dNHnwgDgLW1Nfr06YPLly+XRmxURCt3nAWqxAMA6ts9Cxsrk8+LJKKnFRsrO8nlTYRffRU4fJiJMBGRBSpyMpyRkQEvLy+j7dWqVUNmZmaJBkXFs/m0oaRa74bB5guEiGRViKZN5d++vjIp/vJLwMHBvHEREVGBWNm9nBMCiEmL0l8O78CT54jMytoa+OUXYOxY2Umud29zR0RERI9RrN/T9+7da7Q2eO/evQCAzz77TF9lIq/XX3/9KcKjJzlzRiDTNwoAYK2tgra1Wpk3IKLKRKsFPv8c6NIF6NTJsL12bWDZMvPFRURERVasZHj79u3Yvn17gWMzZ8402qZQKJgMl7LIneeAKncAAPVtu3C9MFFZuXZNzv7u3g3UqQOcPAm4upo7KiIiKqYiZ05LliwpzTjIRJtiooB/KjT1rM8lEkRlIjISeP55IClJXr58Gdi2DQgNNWdURERkgiInw+PHjy/NOMgEQgCa1N36ZFjZKdis8RBVeCkpwMsvAz//bNjm5yfXCHfjl1EiovKIv6mXY+fOCWRUjwIAWGud0Y7rhYlKz4EDwJgxQFycYVtYGPD994CHh/niIiKip8JqEuXYqp3nAZdbAIC6Ns/C1trWzBERVUA5OcB778mT5HITYRcXeYKcSsVEmIionOPMcDm2MXo34CP/fq5+sFljIaqwLl4E5s6VlSMAWTXi11/lSXNERFTucWa4nBICOJUapb88qiPXKxKVikaNgE8/lfWD33vPUD2CiIgqBM4Ml1MXLwqke8vOc1ZaJ3So3cbMERFVEImJgJMTYG9v2PbSS0CPHmynTERUAXFmuJxateMi4HoDAFDHpjPXCxOVhKgo4JlngLffzr9doWAiTERUQTEZLqd+j47S/92jbrC5wiCqGLKygFmz5Ozv9euyq9yOHeaOioiIysBTLZPIysrC9u3bcfbsWaSlpWH27NkAgIyMDKSkpMDT0xNWVsy3S8Op5N2Al/x7VMdgs8ZCVK7FxgKjRgHHjxu2de8u1woTEVGFZ3KmumHDBtSuXRuDBg3Cf/7zH8yZM0c/durUKfj6+kKtVpdEjPSIS5cE0ryiAABWWkd0CuB6YaJiEwJYsABo2dKQCNvaypPltm8HatUyb3xERFQmTEqG9+3bh9DQUNjb22P+/PkYNWpUvvF27dqhfv36WL16dYkESfn9tuMS4HYdABBg3Rl21nZmjoionElIAEJCgKlTgfR0ua1RI+DgQeC11wD+okVEVGmYtEzigw8+gLu7O44dOwZPT0/cu3fPaJ82bdrg0KFDTx0gGVv/dxTgKf8ODmBJNaJiiY0FgoOB27cN2154Qa4TdnIyW1hERGQeJk1/HDp0CEOGDIGnp2eh+/j5+eF23g+bYvr2228REBAABwcHtG/fHocPHy7S9dRqNRQKBUJCQky+bUv3d3KU/u9RnYPNFgdRuVS3LuDnJ//29AQ2bAC++46JMBFRJWVSMpyZmQlXV9fH7pOUlGTyyXMrV67E9OnT8e677+L48eNo3rw5+vTpg/j4+Mde7/Lly/jPf/6DLl26mHS75cHVqwIPquXWF3bEswFtzRwRUTljawssXw4MGwZERwODBpk7IiIiMiOTstW6deviyJEjj93nwIEDaNy4sUlBffnll5gyZQomTpyIpk2b4ocffoCTkxMWL15c6HW0Wi1Gjx6N9957D3Xr1jXpdsuD33ZcBtyvAgBqW3WEvY39469AVJnpdMDXX8MmOjr/9gYNgNWrgerVzRMXERFZDJPWDA8fPhz//e9/sWTJEkycONFo/PPPP4dGo8Gnn35a7GNnZWXh2LFjmDVrln6blZUVevbsiQMHDhR6vffffx/e3t6IiIjAnj17HnsbmZmZyMzM1F9OSUkBAOh0Ouh0umLHXJbWnYgCqsm/u/l3s/h4LZFOp4MQgv92Fd2tW1BMmgSrbdvgXr8+dMeOAVWqmDsqKmV8fVcufLwrl9J6nE1Khl977TWsXr0akydPxooVK/SJ5euvv44DBw5g//79aNGiBaZNm1bsY9+9exdarRY+Pj75tvv4+ODs2bMFXmfv3r1YtGgRTp48WaTbmDt3Lt577z2j7QkJCcjKyip2zGXpROIufTLct3GzJy4dIWM6nQ7JyckQQrAOdgVlv2UL3GbMgOL+fQCAzYULuL9yJbK4JKLC4+u7cuHjXbkkJyeXynFNSoarVKmCPXv2YNq0aYiMjIRWqwUgZ4QVCgVGjhyJ7777Dvb2pf8TfmpqKsaOHYuffvrpsSf05TVr1ixMnz5dfzklJQV+fn7w8vKCu7t7KUX69G7eBB54/gUAsNI6YHDrPnCwcTBzVOWPTqeDQqGAl5cX3zwrmrQ0KP7zHyh+/FG/Sfj64v4XX8BtxAg+3pUAX9+VCx/vysXOrnRKyZrcgc7DwwPLly/H//73Pxw5cgT379+Hq6sr2rZtazSrWxyenp6wtrbGnTt38m2/c+cOqhewvu/ixYu4fPkyBuWZ8cmdRrexsUFsbCzq1auX7zr29vYFJupWVlYW/WJas/My4H4FAOCn6AgnO579biqFQmHxjzcV07FjspPcuXOGbSEhEAsWIFun4+NdifD1Xbnw8a48Susxfqp2zABQrVo19O3btyRiASCz/tatW2PHjh368mg6nQ47duwocNlF48aNEf3IyTFvv/02UlNTMX/+fPjlllCqANae2A24y7+7+rO+MBEAQKsFPvsMmD0byMmR25ycgHnzgMmTZac5LiciIqJCPHUyXBqmT5+O8ePHo02bNmjXrh3mzZuHtLQ0/cl648aNQ82aNTF37lw4ODggKCgo3/Vzlzo8ur28O37fkAyP6hRszlCILMfZs/kT4datgRUrgIYN5WUhzBcbERFZPJOS4R49ehRpP4VCgR07dhT7+GFhYUhISMA777yD27dvo0WLFtiyZYt++cXVq1cr3c8ht28DKR5RAACF1h7B9dubNyAiSxEYCHzwAfDmm8AbbwBz5gCltK6MiIgqHoUQxZ82eVIiqlAoIISAQqHQn1xnyVJSUuDm5obExESLPYHuu+VX8eIFfwCAn64brr4XZd6AyjGdTof4+Hh4e3tXui9VFUJqKuDoCNjk+S6v1QInTgBt2hjtzse7cuHjXbnw8a5ckpKS4OHhgeTk5Cc2fysOk545ufV4H/0vKSkJO3fuRPv27REaGmrxZcrKkzXHd+v/7uoXbL5AiMzpwAGgRQvgv//Nv93ausBEmIiI6ElK9GuUq6srgoODsXXrVhw+fBgffvhhSR6+Ujt2N0r/t7IjT56jSiYnB3jvPaBLFyAuTi6L2L/f3FEREVEFUCq/Kbi4uKBfv35YsmRJaRy+0klIAJLc5cywQmuHHg07mDkiojIUFwd07SrXAucuu+rQAfD1NWtYRERUMZTaAhsrKyvcunWrtA5fqazZfg2oehEAUBPt4WjraOaIiMqAEMCyZXJZRG4rdmtrOUO8ezdQp45ZwyMiooqhVEqrxcXFYdWqVQgICCiNw1c6q4/tBlzk38/WCjZrLERlIjEReOEFYOVKw7a6dYHly+WsMBERUQkxKRmeNGlSgdtzcnJw48YN7N27F9nZ2Xj//fefKjiSjt41JMNK1hemii42FujVC7h2zbBtwgTgf/8DXFzMFhYREVVMJiXDS5cufex4o0aNMGPGDEyePNmUw1Me9+4Bia5RAACFzhY9G3FWjCo4f3/A3V0mwx4ewIIFwIgR5o6KiIgqKJOS4UuXLhW43crKCu7u7nDh7E2JWbfzBlDtAgCghq49nGydzBwRUSlzcJAd5GbOlIlwrVrmjoiIiCowk5JhhUIBOzs7VK9evaTjoUesOrwbqCL/7lSTJdWoghEC+Okn4NlngaZNDduDgoBNm8wXFxERVRomVZOoU6cO3nzzzZKOhQpwNF994WCzxUFU4hISgJAQ4PnngVGjgMxMc0dERESVkEnJsIeHB6pVq1bSsdAjEhOBe1X+qS+ss0XvJh3NHBFRCdm6FXjmGWDDBnn577+BjRvNGxMREVVKJiXDXbp0waFDh0o6FnrEhl23AM9zAIDqurZwtnM2c0RETykjA/j3v4G+fYHbt+U2T0+ZFA8fbtbQiIiocjIpGZ47dy5OnTqF999/Hzk5OSUdE/0j8tBu/d+dagSbLxCikhAdDbRtC8yfb9jWp4/cPmiQ+eIiIqJKzaQT6D799FM0a9YM7733HhYsWIDmzZvDx8cHCoUi334KhQKLFi0qkUAro8PxUUCA/Du8A0+eo3JKpwO+/lpWh8hdF2xvD3z6KTBtGmBVao0wiYiInqjIybC1tTXmzJmD2bNn56szfOvWrULbLjMZNl1yMnC3SpS8oLNB38BOZo2HyGTR0cD06TIpBoBmzWTptKAg88ZFRESEYiTDQggIIQAUXmeYSs7vUbcBz1gAQHVtW1Sxq2LmiIhM1Lw58OabwH//C7z6KvDRR7KWMBERkQUwaZmEv79/ScdBj4g8uBv4J1/o4MslElSOPHwok928yx/eeQfo3Rvo0sV8cRERERWAi/Us1KE7hpPnwtsHmy8QouI4dgxo2RL44ov8221tmQgTEZFFKlYy/OgJclQ6UlOBeKcoeUFnjf7NuF6YLJxWC3zyCdChA3DuHPDWW8Dx4+aOioiI6ImKlQzPmTMH1tbWRf7PxsakVRiV3qaoO4DXGQCAd04buNi7mDkiose4dg147jngjTeA3FKLzzwDVOE6dyIisnzFylZdXV3h7u5eSqFQrpUH/wLs5N8dqgebNRaix4qMlO2Uk5LkZYVCJsVz5gB2duaMjIiIqEiKlQy/+uqreOedd0orFvrHgdtRQG3598j2PHmOLFBKCvDyy8DPPxu2+fkBv/wCdONzloiIyg+uY7AwaWnAHYd/Tp7TWWNQ887mDYjoUbGxQP/+QFycYVtYGPDDDwB/OSIionKG1SQszObd8YB3DADAK6cVXO1dzRwR0SNq1QJyzwdwcQGWLQNUKibCRERULjEZtjDqA3/p/27nE2y+QIgK4+wsO8gFBwN//w2MHSvXChMREZVDTIYtzIFbhvrCI9sFmy8QIgAQQs78XryYf3vr1sDOnUCdOuaJi4iIqIQUec2wTqcrzTgIsnHXbfsoeUFnhZCWz5o1HqrkEhOBqVNlxYj27YE9e2TzjFycDSYiogqAM8MWZOueuxDeGgCAJ9cLkzlFRclawZGR8vKhQ8DGjWYNiYiIqDQwGbYgqv2G9cJtvVieiswgK0vWCe7RA7h+XW7z8ABWrQKGDjVvbERERKWApdUsyP6bUUAt+feItsHmDIUqo9hYYNSo/G2Uu3eXa4Zr1TJfXERERKWIM8MWIiMDuGmbW1/YCkNbc70wlREhgAULgJYtDYmwrS3w6afA9u1MhImIqELjzLCF2Lb3HoTPKQBAtewWcHdwN29AVHmcOCFPlMvVqJEsndaqlfliIiIiKiOcGbYQqn179H+38Qo2XyBU+bRqBUyfLv9+4QU5O8xEmIiIKgnODFuIvdej9OuFh7fmyXNUijIzATu7/KXRPvoI6NsX6NXLfHERERGZAWeGLUBmJnDDNkpeEAqEtu1i1nioAouOBtq0Ab7/Pv92e3smwkREVCkxGbYAO/bfh/CW64WrZrWAh6OHmSOiCkenA+bPB9q2BTQaYMYM4PRpc0dFRERkdlwmYQFW7N0DKAQAoHU1LpGgEnbrFjBxIrB1q2Fbgwbmi4eIiMiCcGbYAuy5tlv/97DWweYLhCqe9etlJ7m8ifCrrwKHDwNNm5ovLiIiIgvBmWEzy84GrtlEyQtCgRHtuF6YSkBamlwKsWCBYZuvL7B0KdC7t9nCIiIisjRMhs1s54FECO+TAAD3zGdQzamqeQOi8u/cOWDQIPn/XCEhwE8/AZ6eZguLiIjIEnGZhJmt2LM3z3rhYPMGQxWDjw+QlSX/dnKSSfCaNUyEiYiICsBk2Mz+uhql/3toy/9v787DY7reAI5/J9skiAgRokKIpUUqP9SuolX7VrS2qlBaRdXWqlaFoihRqrW2jS2htS9Vu2gtRS2tvUVQJWLJRvbM+f0xnZExE5JIMpG8n+eZx8y559773rkxeXPmvefKxXMiG7i4wPLlUK+e/u5y/fubzikshBBCCCNJhq0oJQWu2jy4eK5b/RetGI14aq1aBf/8Y9rWqBEcPAhVqlgnJiGEEOIpIcmwFe09FIXO/TgALgk+uBUuYeWIxFMlJgb8/eH11+HNNyE11XS5jAYLIYQQjyXJsBUt/3Uf2OgA+J+rn1VjEU+Zgwfhf/+DJUv0r0NDYfNmq4YkhBBCPI0kGbaiXy4/KJF49X9+VotDPEVSUmDCBGjSBC5d0rc5O8PSpdChg3VjE0IIIZ5CMrWalaSmwmVNqPF1j4ZSLywe49IleOMN/aiwQcOG+ovlKlSwXlxCCCHEU0xGhq1k35EYdO7HACiaUIOShWXaK5EOpfQjv76+DxJhW1v9CPHevZIICyGEEE9ARoatZPkvD+qFfYvJlGriEX7/Hfr0efC6YkUIDob69a0XkxBCCJFPyMiwley5HGp83rGmn9XiEE+BF16Ad97RP/f3hxMnJBEWQgghsomMDFtBaipcVg8unuvZSOqFRRrJyWBnZzo1WmAgtGkjF8kJIYQQ2UxGhq3gt2OxpLofBcA5oRqlnd2tHJHIM86f14/6GqZMMyhcWBJhIYQQIgdIMmwFy/buBxv9DRJquvhZNxiRNygFCxbo5w4+dgzeew8uXLB2VEIIIUS+J2USVrA7LBT+Gwxu/7xcPFfg3boF/fvDxo0P2p55BuLjrReTEEIIUUDIyHAu0+ngki7U+Lp3Y0mGC7Rt2+D5500T4YED9aPDPj7Wi0sIIYQoICQZzmVH/rhHqvvvABSJfw6PoqWsHJGwioQEGD4cWrWC8HB9m5ubPimeNw8KFbJufEIIIUQBIWUSuWzpngf1ws8XlVHhAunCBejcGU6efNDWqhUEBUHp0taLSwghhCiAZGQ4l+269GBKtfY+ftYLRFiPqyvcuaN/rtXCV1/Bli2SCAshhBBWIMlwLlIKLqaEGl/3flFGhgukEiVg8WKoWVN/d7n33jOdU1gIIYQQuUaS4Vx09OR9UtyPAFA4virPuMhIYIGwadODumCDV16Bo0ehRg3rxCSEEEIIQJLhXLV0zwGwTQHAx9nPusGInHf/vn5miA4doF8//VcDadnaWicuIYQQQhhJMpyLdv4danzetoaUSORrR49CrVr6G2kA/PwzbN5s3ZiEEEIIYUaS4VyiFFxIeXDx3JtSL5w/pabCtGn6Wyr/9Ze+rVAhWLQI2rWzbmxCCCGEMCNTq+WSE2fuk+x+GIBC8ZUp51rGyhGJbPfPP9C7N+x98EcPtWtDSAhUqWK9uIQQQgiRLhkZziVLdx8E22QAahTxs24wIvv98IP+TnKGRFijgTFj4MABSYSFEEKIPExGhnPJjr/2gpv+eZtqflaNRWSz336D7t0fvPb0hGXLoKmUwgghhBB5nYwM5wKl4O/kUOPrPn6SJOUr9evryyMAunWDP/6QRFgIIYR4SsjIcC44dT6OpJKGeuFKeBV/xsoRiSei04HNQ39Hfv01tG0Lr78uN9AQQgghniIyMpwLluz6DeySAHiukIwYPtUuXYLGjeHHH03bixbVjwpLIiyEEEI8VSQZzgXb/wo1Ppd64aeUUrB0Kfj6wsGD8M47+tkjhBBCCPFUk2Q4hykFfyU+mGpL6oWfQpGR+gvk+vSB2Fh9W/HicOeOdeMSQgghxBOTZDiHnbsQT2LJ3wBwiq+It5unlSMSmRIaqp8yLW1ZhL8/nDihHyUWQgghxFNNkuEctmTXoQf1wk5+1g1GZFxSEnz0Ebz0Ely7pm8rVkyfFAcFgbOzVcMTQgghRPaQ2SRy2NZzoeCqf97yOSmReCpcugSvvQbHjj1o8/PT1wx7ysi+EEIIkZ/IyHAOO5cYanzeR+aefTo4OcHVq/rn9vbwxRewa5ckwkIIIUQ+JCPDOej8xQQS3fT1wo7xFahaqryVIxIZ4uEB330Ho0dDcDDUqmXtiITIdsnJyaSmplo7jGyn0+lITk4mISEBm4fnAxf5jpzv/MPe3h5bW1ur7FuS4Ry0ZOchsEsE4FlHGRXOs3buhP/9D0qUeNDWoQO0bq0fGRYiH4mJieH27dskJiZaO5QcoZRCp9MRGxuLRub9zvfkfOcfGo0GFxcXSpcunevnMs8mw9988w3Tp08nPDycmjVrMmfOHOrWrWux76JFi1i6dCmnTp0CoHbt2nz++efp9s8tW8/thWL65y2r+lkzFGFJQgJ88gnMmgVdusCqVaY3zZBEWOQzMTEx/PvvvxQpUgQ3Nzfs7e3zXQKhlCIlJQU7O7t8d2zCnJzv/EEpxf3797l16xZOTk4UK1YsV/efJ5PhH374gREjRjB//nzq1avHrFmzaNmyJefPn8fd3d2sf2hoKD169KBhw4Y4Ojoybdo0WrRowenTp3nmGevd+vhsQqjxuX8zGRnOS+zOnkXz/vtw8qS+Yc0a2LpVPxosRD51+/ZtihQpQtmyZfNt4iDJUcEi5zv/cHJyIjExkYiICFxcXHL1fObJApuZM2cyYMAA+vbtS7Vq1Zg/fz6FChXi+++/t9g/ODiYQYMG4evry7PPPsu3336LTqdj165duRz5AxevJJJQ4iAA2vjyPFvay2qxiDR0OvjqK0q0bo3GkAhrtfDVV9CqlXVjEyIHJScnk5iYmOu/ZIQQIqOKFi1Kampqrl/PkOdGhpOSkjh69ChjxowxttnY2NC8eXMOHjyYoW3ExcWRnJxM8eLFLS5PTEw0qZeLiYkB9IX4Op3uCaJ/IGjbb2CfAEBVrV+2bVc8gRs30PTrh8327cYm5eODWr4catTQ3y5QKSsGKHKCTqcz1hUWZMnJySilsLOzQ+Xzn3PD8eX34xR6cr7zD1tbW5RSJCUlWbwgMqc+x/NcMnz79m1SU1MpVaqUSXupUqU4d+5chrYxevRoypQpQ/PmzS0unzJlChMmTDBrv3XrFklJSZkP2oJNp3bBf9djNSpbm4iIiGzZrsga7bZtuIwYgebuXWPbvQEDuPfxx+DoCHJ+8i2dTkd0dDRKqQJ9tXlycjI6nY7U1FRSUlKsHU6OUUoZR5VkBDz/k/Odv6SmpqLT6bhz5w72Fq7biY6OzpH95rlk+ElNnTqVlStXEhoaiqOjo8U+Y8aMYcSIEcbXMTExeHp6UrJkyWwr2r6QfMD4fFCbNhZrnUUu2b8fG39/40tVujR3Z87E5bXXKFSAk6OCQqfTodFoKFmyZIFOhhMSEoiNjcXOzg47u3z30W/G0i9SkX/J+c4f7OzssLGxoUSJEhZzOAcHh5zZb45s9Qm4ublha2vLzZs3Tdpv3rxJ6dKlH7nujBkzmDp1Kjt37uT5559Pt59Wq0Wr1Zq129jYZMsvy8vXEokroU+GHeI9qV6movzFak2NG8Orr8K6ddCxI2rhQpJ1umw73yLv02g0Bf5829jYoNFojI/8SillPL78fJxCT853/mL4fErv8zqnPsPz3G8GBwcHateubXLxm+FiuAYNGqS73hdffMHEiRPZunUrderUyY1Q07V4+xGwjwegioOf/AfNbQ/XjWk0sGgRBAXpE2I3N+vEJYR4qvn7++Pl5WXtMKzi3r17uLu7ExwcbO1QxH/OnDmDnZ2dcVpZkXV5LhkGGDFiBIsWLWLJkiWcPXuWd999l/v379O3b18A3nzzTZML7KZNm8ann37K999/j5eXF+Hh4YSHh3Pv3j2rxL/lzF7j8xZV/KwSQ4H1zz/w0kuwebNpe4kS4O9vOo+wECJfSDvibWNjg4ODg9lIuEajITQ01NqhmggNDTWJz9bWFnd3d7p27crZs2fTXW/z5s20atXK+FVylSpVGDVqFHfu3Hnkvjp37kzp0qVxcHDA3d2d9u3bs3bt2gzFOnv2bJydnenevbvF5R9++CEajYZu3bo98lhXr15tcfmQIUMsDhylpqYSFBSEn58fxYsXR6vV4uXlRd++ffn9998zFHt22rhxI7Vq1cLR0ZFy5coREBCQoRr88ePHm/08pn3s37/f2Pfw4cMMGjSI2rVrP3Iu8GrVqtG2bVvGjRuXbcdXUOW5MgmAbt26cevWLcaNG0d4eDi+vr5s3brVeFHd1atXTYbK582bR1JSEl27djXZTkBAAOPHj8/N0AE4cz8UnPXP+/jJ/MK55scf4Z13ICoKTp+GP/+Ex5TWCCGefsuWLTM+V0qxdOlSdu7cadIO8Nxzzz3RfhYtWpQjV7MPHTqUF154geTkZP7880/mz59PaGgop06dMisPHDVqFIGBgdSsWZPRo0dTvHhxjh07xtdff83KlSvZtWsXVatWNVknICCAzz77jMqVK/POO+9Qvnx57ty5w5YtW+jSpQvBwcH07Nkz3fiSk5OZPXs2w4cPt3i7XKUUK1aswMvLi02bNhEbG4uzs/MTvy/x8fF07tyZrVu38uKLL/Lxxx9TvHhxLl++zI8//siSJUu4cuXKY0sos8vPP/9Mp06d8PPzY86cOZw8eZJJkyYRERHBvHnzHrlu586dqVSpkln7xx9/zL1793jhhReMbVu2bOHbb7/l+eefp2LFivz111/pbnfgwIG0adOGixcv4u3tnfWDK+iUUNHR0QpQkZGRT7ytf64nKj4upBiPchhdVul0uicPUDxadLRSffoYJkbTPzw9lTp61GL31NRUdePGDZWampq7cQqrkPOtFx8fr86cOaPi4+OtHUqO0ul06t1331UZ+fV2//79XIgofXv27FGAWrVqlUn7vHnzFKCmTZtm0h4SEqIA1a1bN5WSkmKy7NChQ6pQoULKx8dHJScnG9tXrVqlANW1a1eVlJRkFsPWrVvVpk2bHhnn2rVrFaAuXLhgcfnu3bsVoHbv3q3s7e3V4sWLM3ysBoMHDzY7Z4a2L7/80qx/SkqKmj59urp69apKSkrKld+11apVUzVr1jR5fz/55BOl0WjU2bNnM729q1evKo1GowYMGGDSHh4eruLi4pRSlt+XtJKSkpSrq6v69NNPM73/vOhxn1ORkZEKUNHR0dm63zxZJvE0W7z9d3CIA6CSfVOpF85pBw+Cry8sWfKgrVs3+OMPqFXLamEJIfIWPz8/atSowdGjR3nxxRcpVKgQH3/8MQAbNmygbdu2lClTBq1Wi7e3NxMnTjSb+P/hmuHLly+j0WiYMWMGCxcuxNvbG61WywsvvMCRI0eyHGuTJk0AuHjxokn7hAkTcHV1ZeHChWYjtHXr1mX06NGcPHnSpBTh008/pXjx4nz//fcWZ1xo2bIl7dq1e2Q869evx8vLK92Rx+DgYKpVq0azZs1o3rx5ttQVX7t2jQULFvDKK68wbNgws+W2traMGjWKsmXLPvG+MuLMmTOcOXOGt99+22Q2lkGDBqGUSrf841FWrFiBUopevXqZtJcqVQonJ6cMbcPe3h4/Pz82bNiQ6f2LByQZzmabT4canzev5Ge1OPK9lBQYPx6aNIGwMH2bszMsXQorVoCrq1XDE0LkPXfu3KF169b4+voya9YsmjVrBsDixYspUqQII0aMYPbs2dSuXZtx48bx0UcfZWi7ISEhTJ8+nXfeeYdJkyZx+fJlOnfuTHJycpbivHz5MgCuaT7H/v77b86fP0/Hjh0pWrSoxfXefPNNQF9TbFjn3LlzdOrU6YnKFg4cOECtdAYXEhMTWbNmDT169ACgR48e7N69m/Dw8CzvD/QlCSkpKfTu3fuJtnP79u0MPdLeiMuS48ePA5hdoF+mTBnKli1rXJ4ZwcHBeHp68uKLL2Z63bRq167NqVOnjDcQE5mXJ2uGn2anY/dCYf1z/2Z+1gwl/7p8GXr21I8KGzRsCMuXQ4UKVgtLiKdVnTrwhLlLtildGnLquqjw8HDmz5/PO++8Y9IeEhJiMhI3cOBABg4cyNy5c5k0aZLFqTjTunr1Kn///bcxea1atSodO3Zk27Ztjx11BYiNjeX27dvGmuFhw4ah0Wjo0qWLsc+ZM2cAqFmzZrrb8fLyomjRosaL7wz/+vj4PDaG9KSkpHDx4kU6duxocfnmzZuJiooyXljXqVMn3n77bVauXGlxRDejsiN2gJIlS2aoX1BQEP5p5qN/2I0bNwDw8PAwW+bh4cH169czFdfp06f5888/jRcePomKFSui0+k4d+4cdevWfaJtFVSSDGejGzeTuVdcf0WofUIZfD2lmD1H2NjAf78YsLWFcePg44+hANxIQIicEB4O//5r7ShynlarNc5KlFbaRDg2NpbExESaNGnCggULOHfu3CMTUNBf9J12FNdQ5nDp0qUMxdWvXz+T1yVLlmTZsmUmF1XFxsYCPHaE19nZ2ThCaPj3SUaF7969i1LK5PjSCg4Opk6dOsaLw5ydnWnbti3BwcFPlAxnR+wAO3bsyFC/6tWrP3J5fLx+ulRLfxg5OjpmelTWUErycIlEVhjOze3bt594WwWVZA/ZaMmOo+BwHwBvW5lfOMeUKwfz58Mnn0BwMNSvb+2IhHiq5aVJV3IylmeeecbiHaxOnz7N2LFj2b17t1lSk5Hbv5YrV87ktSE5iYyMzFBc48aNo0mTJty7d49169axcuVKs5sLGJJCQ1KcntjYWOMdTw3lFI9bJyPUw/O3A1FRUWzZsoUhQ4Zw4cIFY3ujRo1Ys2YNf/31F1WqVMnS/rIr9ubNmz/R+gaGP5gslVMkJCRkuMYX9O9lSEgINWrUeOQNwjKzPZCbjjwJSYaz0aaToVBI//xlb5lSLdv8+ivUrAlp6+S6d4dOnSCdW24LITLOCtO1WoWlhCUqKoqmTZtStGhRPvvsM7y9vXF0dOTYsWOMHj06Q1OpWZpuDCwnkJb4+PgYk7ZOnToRFxfHgAEDaNy4MZ6ensCDaeH+/PPPdLdz5coVYmJiqFatGgDPPvssACdPnsxQHJYUL14cjUZjMbFftWoViYmJBAYGEhgYaLY8ODiYCRMmABhvrWsYYX1YXFycye1308bu6+ub5fgzWrvs4uLyyITWUB5x48YN4zkxuHHjRqbKE/bv38+VK1eYMmVKhtd5FMO5cZMbSmWZXECXjU7Ghhqf+/v5WSuM/CMpCT76CJo2hffeM18uibAQ4gmFhoZy584dFi9ezPvvv0+7du1o3rx5umUBuWHq1KkkJCQwefJkY1uVKlWoUqUK69evT3e0dOnSpQDGOuUqVapQtWpVNmzYkOWbUNnZ2eHt7U2Y4ULlNIKDg6lRowarVq0yezRv3pyQkBBj3/LlywNw/vx5i/s5f/68sQ9A69atsbW1Zfny5VmK28DDwyNDjx9++OGR2zEk5A/f6OP69etcu3YtUwl7cHAwGo3mkXM7Z0ZYWBg2NjZZHoUXkgxnm5u3koktpq8XtkvwoLZXZStH9JQ7fx4aNIBp0/QzBy9dCtu3WzsqIUQ+YxjVTTuKm5SUxNy5c60VEt7e3nTp0oXFixebjGyOGzeOyMhIBg4caDbt29GjR5k2bRo1atQwufBuwoQJ3Llzh/79+1u8U9r27duNs0+kp0GDBmZJ4D///MMvv/zC66+/TteuXc0effv25cKFCxw6dAjQJ6W+vr4sX76cqKgos9h/++03WrdubWzz9PRkwIABbN++nTlz5pjFpNPpCAwM5Nq1a4+MfceOHRl6tGzZ8pHbqV69Os8++ywLFy40ee/nzZuHRqMxuelXdHQ0586ds1hik5yczKpVq2jcuLFZeU1WHT16lOrVq+Pi4pIt2yuIpEwimyzdcQy0+r+8K9rI/MJZphQsXAjDh4Ph6zR7e5g8GbKp9ksIIQwaNmyIq6srffr0YejQoWg0GpYtW5bhEoec8sEHH/Djjz8ya9Yspk6dCugvtjpy5AizZ8/mzJkz9OrVC1dXV44dO8b3339PiRIlWL16tcl8wt26dePkyZNMnjyZ48eP06NHD+Md6LZu3cquXbtMRnAt6dixI8uWLTOpAQ4JCUEpRYcOHSyu06ZNG+zs7AgODqZevXoAzJw5k5YtW+Lr64u/vz9lypTh7NmzLFy4EA8PD8aMGWOyjcDAQC5evMjQoUNZu3Yt7dq1w9XVlatXr7Jq1SrOnTuX7u2fDbKrZhhg+vTpdOjQgRYtWtC9e3dOnTrF119/Tf/+/U3ubrhu3Tr69u1rcYaKbdu2cefOnUdeOHflyhXj3RMNf4RMmjQJ0I+wp51uLjk5mb179zJo0KDsOsyCKVtv4fGUyo470DUePU0xHsV41Lvfzs++4AqSiAilOnQwvZNc1arp3kkuq+SOZAWLnG+9gn4HuqZNm6rq1atbXGf//v2qfv36ysnJSZUpU0Z9+OGHatu2bQpQe/bsMfbr06ePKl++vPF1WFiYAtT06dPNtgmogICAR8b6uLuy+fn5qaJFi6qoqCiT9vXr16tXXnlFubq6Kq1WqypVqqRGjhypbt26le6+du3apTp27Kjc3d2VnZ2dKlmypGrfvr3asGHDI2NUSqnExETl5uamJk6caGzz8fFR5cqVe+R6fn5+yt3d3eSObb/99ptq166dcnV1VXZ2duqZZ55R/fv3V9euXbO4jZSUFPXtt9+qJk2aKBcXF2Vvb6/Kly+v+vbtq44fP650Ol2u3YFOKaXWrVunfH19lVarVWXLllVjx441u7NfUFCQAlRQUJDZ+t27d1f29vbqzp076e7D8HNh6dG0aVOTvj///LMC1N9//50dh2d11roDnUYpK//5mwfExMTg4uJCZGQkxYoVy9I2XAa1IabUzwAc7HWW+pWezcYIC4Bt28Df33Sy03ffhRkzoFChbN2VTqcjIiICd3d3syu2Rf4j51svISGBsLAwKlSoYHKhUn6jlCIlJQU7Ozv5hi4bTZw4kaCgIP7+++90Lxi0hoJ+vjt16oRGo2HdunXWDiVbPO5zKioqCldXV6Kjo9O9+UxWFNzfDNno9t0UYor9CoBdQinqeVe1ckRPmV9/hVatHiTCbm6wcSPMnZvtibAQQojMGz58OPfu3WPlypXWDkX85+zZs2zevJmJEydaO5SnntQMZ4OlO44b64UraGR+4Uxr3FifDG/dqv83KChvTXwqhBAFXJEiRYiIiLB2GCKN5557zuJFkSLzJBnOBhv+CIX/bkrjV0HmF840jUafAK9bBwMH6l8LIYQQQuQCKZPIBn9E7TU+92/qZ71Angbh4dC2LezaZdpeurS+RlgSYSGEEELkIhkZfkJ3o1KIdtHXC9smuNOgslw4l66NG+Gtt+D2bfjjD/2jRAlrRyWEEEKIAkxGhp/Qsh0nwFF/L3svZH5hi+7f15c/dOyoT4QBdDq4fNmqYQkhhBBCSDL8hDb88aBEws/Lz3qB5FVHj0Lt2rBgwYO2Tp3gzz/17UIIIYQQViTJ8BM6HhlqfN77Rbl4zig1VX8r5fr19bdWBv00aYsWwdq1+unThBBCCCGsTGqGn0B0TCpRRQ31wm68+Gw1K0eUR1y7Br17Q2jog7batSEkBP67lacQQgghRF4gI8NPYPnOP8AxGoDyUi/8QHw8HDmif67RwJgxcOCAJMJCCCGEyHMkGX4C646HGp+/WM7PanHkOZUrw1dfgacn7NkDn38ODg7WjkoIIYQQwowkw0/g2N0HF8+9+aKf9QKxtsOHIS7OtK1vXzhzBppKHbUQ4ulz+fJlNBoNixcvNraNHz8+w98AajQaxo8fn60x+fn54efnl63bzG/u3buHu7s7wcHB1g5F/OfMmTPY2dlx6tQpa4eSLkmGsyj2XiqRzr8AYJNQgqbVCmC9cEoKTJgADRvCqFGmyzQaKFLEOnEJIQqUDh06UKhQIWJjY9Pt06tXLxwcHLhz504uRpZ5Z86cYfz48VzOQ1NPhoaGotFojA9bW1vc3d3p2rUrZ8+eTXe9zZs306pVK0qUKIGjoyNVqlRh1KhRjzwHoaGhdO7cmdKlS+Pg4IC7uzvt27dn7dq1GYp19uzZODs70717d4vLP/zwQzQaDd26dXvksa5evdri8iFDhlj8gyg1NZWgoCD8/PwoXrw4Wq0WLy8v+vbty++//56h2LPTxo0bqVWrFo6OjpQrV46AgIAM3brZ8Adfeo/9+/cb+y5atIimTZtSqlQptFotFSpUoG/fvmY/u9WqVaNt27aMGzcuuw8z28gFdFkUsuskOEUBUE41xUZTwP6uuHQJ3ngDDh7Uv543D157DZo1s25cQogCp1evXmzatIl169bRu3dvs+VxcXFs2LDBmJhl1dixY/noo4+eJNTHOnPmDBMmTMDPzw8vLy+TZdu3b8/RfT/O0KFDeeGFF0hOTubPP/9k/vz5hIaGcurUKUqXLm3Sd9SoUQQGBlKzZk1Gjx5N8eLFOXbsGF9//TUrV65k165dVK1a1WSdgIAAPvvsMypXrsw777xD+fLluXPnDlu2bKFLly4EBwfTs2fPdONLTk5m9uzZDB8+HFtbW7PlSilWrFiBl5cXmzZtIjY2Fmdn5yd+X+Lj4+ncuTNbt27lxRdf5OOPP6Z48eJcvnyZH3/8kSVLlnD16lXKli37xPvKiJ9//plOnTrh5+fHnDlzOHnyJJMmTSIiIoJ58+Y9ct3OnTtTqVIls/aPP/6Ye/fu8cILLxjbjh8/ToUKFejQoQOurq6EhYWxaNEiNm/ezB9//EGZMmWMfQcOHEibNm24ePEi3t7e2Xew2UUJFR0drQAVGRmZ4XVajPtSMR7FeFTvr2fnXHB5jU6n1JIlSjk7KwX6h62tUhMmKJWcbO3oMiQ1NVXduHFDpaamWjsUkQvkfOvFx8erM2fOqPj4eGuHku3i4uKUs7OzatmypdLpdCopKUnpdDrj8pCQEAWolStXZnibYWFhClBBQUFZiglQAQEBmV5v1apVClB79uzJ0n5zwp49exSgVq1aZdI+b948Bahp06aZtBve727duqmUlBSTZYcOHVKFChVSPj4+KjnN7wzDcXft2lUlJSWZxbB161a1adMms/a053vt2rUKUBcuXLB4HLt371aA2r17t7K3t1eLFy/O8LEaDB48WD2cOhnavvzyS7P+KSkpavr06eqff/6xuL2cUK1aNVWzZk2T9/eTTz5RGo1GnT17NtPbu3r1qtJoNGrAgAGP7fv7778rQE2ZMsWkPSkpSbm6uqpPP/30kes/7nMqMjJSASo6OjrjB5ABBWw4M/scvRNqfN67iZ/V4shVkZHQowf06QOGryMrVoR9+2DcOLCTLxqEELnPycmJzp07s2vXLiIiIsyWh4SE4OzsTIcOHbh79y6jRo3Cx8eHIkWKULRoUVq3bs0ff/zx2P1YqhlOTExk+PDhlCxZ0riPa9euma175coVBg0aRNWqVXFycqJEiRK89tprJl8pL168mNdeew2AZs2aGb+aDv1vmkpLNcMRERG89dZblCpVCkdHR2rWrMmSJUtM+hjqn2fMmMHChQvx9vZGq9XywgsvcMQw808WNGnSBICLFy+atE+YMAFXV1cWLlxoNkJbt25dRo8ezcmTJ01KET799FOKFy/O999/j729vdm+WrZsSbt27R4Zz/r16/Hy8kp35DE4OJhq1arRrFkzmjdvni11xdeuXWPBggW88sorDBs2zGy5ra0to0aNyrVR4TNnznDmzBnefvtt7NL8Th40aBBKqXTLPx5lxYoVKKXo1avXY/savs2Iiooyabe3t8fPz48NGzZkev+5QZLhLLgfp+NOkf/qhROL87JPDStHlAv27oWaNeGHHx60+fvDiRP6G2sIIYQV9erVi5SUFH788UeT9rt377Jt2zZeffVVnJycuHTpEuvXr6ddu3bMnDmTDz74gJMnT9K0aVOuX7+e6f3279+fWbNm0aJFC6ZOnYq9vT1t27Y163fkyBEOHDhA9+7d+eqrrxg4cCC7du3Cz8+PuP8uQH7xxRcZOnQooP9aetmyZSxbtoznnnvO4r7j4+Px8/Nj2bJl9OrVi+nTp+Pi4oK/vz+zZ8826x8SEsL06dN55513mDRpEpcvX6Zz584kJydn+rgBYyLv6upqbPv77785f/48HTt2pGjRohbXe/PNNwF9TbFhnXPnztGpU6cnKls4cOAAtWrVsrgsMTGRNWvW0KNHDwB69OjB7t27CQ8Pz/L+QF+SkJKSYrE8JzNu376doUdiYuIjt3P8+HEA6tSpY9JepkwZypYta1yeGcHBwXh6evLiiy9aXH7nzh0iIiL4/fff6du3LwAvv/yyWb/atWtz6tQpYmJiMh1DjsvWceanVGbLJBZtOmEskSj3YaecDS4vCA1VSqN5UBbh6qrUjz9aO6osk6/NCxY533r5uUxCKf3X0R4eHqpBgwYmZRLz589XgNq2bZtSSqmEhASzn4WwsDCl1WrVZ599ZtLGQ2USAQEBJl+RnzhxQgFq0KBBJtvr2bOnWZlEXFycWcwHDx5UgFq6dKmx7VFlEk2bNlVNmzY1vp41a5YC1PLly41tSUlJqkGDBqpIkSIqJibG5FhKlCih7t69a+y7YcMGBVgsP0jLUDrw/fffq1u3bqnr16+rrVu3qkqVKimNRqMOHz5s7Lt+/fp0SwbSKlq0qKpVq5ZJHI9bxxJDmURSUpLSaDRq5MiRFvutXr1aAervv/9WSikVExOjHB0dzfaZ2TKJ4cOHK0AdP34807GnBWTo8biynenTpytAXb161WzZCy+8oOrXr5+puE6dOqUA9eGHH6bbR6vVGuMrUaKE+uqrryz2M5TPHDp0KN1tWatMQr7XzoI1Rx9MqdakrJ/1AsktjRvDiy/qR4ebNYOlSyGXvvIRQuS8OgvrEH7vyUbIskvpIqX5/e3MX31va2tL9+7d+fLLL7l8+bLxIqCQkBBKlSplHKnSarXGdVJTU4mKiqJIkSJUrVqVY8eOZWqfW7ZsATCO5hoMGzaMkJAQkzYnJyfj8+TkZGJiYqhUqRLFihXj2LFjWRpZ3LJlC6VLlzaOdoL+6+ihQ4fSo0cP9u7da1Ja0K1bN5NRXEOZw6VLlzK0v379+pm8LlmyJMuWLTO5qMowo8fjRnidnZ2NI4SGf59kVPju3bsopUyOL63g4GDq1Klj/Llwdnambdu2BAcHWyxvyKjsiB1gx44dGepXvXr1Ry6Pj48HTH/ODRwdHTM9KmsoJXlUicTPP/9MQkICZ8+eZfny5dy/f99iP8O5uX37dqZiyA2SDGfB77dCoaT+ec9GBWAeXVtbWLYMVq2CYcPARqprhMhPwu+F82/sv9YO44n16tWLL7/8kpUrVzJ27FiuXbvGr7/+ytChQ421qzqdjtmzZzN37lzCwsJITU01rp/ZmSauXLmCjY2NWY3qw7MkgD5JmTJlCkFBQfz7778opYzLoqOjM7XftPuvXLkyNg99JhvKKq5cuWLSXq5cOZPXhuQkMjIyQ/sbN24cTZo04d69e6xbt46VK1ea7duQFD5qmjvDcnd3dwBjOcXj1smItO+rQVRUFFu2bGHIkCFcuHDB2N6oUSPWrFnDX3/9RZUs3iE1u2Jv3rz5E61vYPijy1I5RUJCgskfZY+jlCIkJIQaNWrw/PPPp9uv2X+zSLVu3ZqOHTtSo0YNihQpwpAhQ8y2B+TJu/VKMpxJ8Qk6bhfRjwzbJLrS6n/p/4A8lW7dgoEDYcQIaNToQbunp75NCJHvlC5S+vGdcsmTxFK7dm2effZZfvjhB8aOHWvxwp/PP/+cTz/9lH79+jFx4kSKFy+OjY0Nw4YNQ6fTZcchWPTee+8RFBTEsGHDaNCgAS4uLmg0Grp3756j+03L0nRjYDmBtMTHx8eYtHXq1Im4uDgGDBhA48aN8fT0BB4k4n/++We627ly5QoxMTFU+29+/meffRaAkydPZuxALChevDgajcZiYr9q1SoSExMJDAwkMDDQbHlwcDATJkwA9KOn8GCE9WFxcXHGPg/H7uvrm+X4M1q77OLi8siE1sPDA4AbN24Yz4nBjRs3qFu3boZj2r9/P1euXGHKlCkZXsfb25v//e9/BAcHmyXDhnPj5uaW4e3lFkmGM+mHPafB6S4Az6S8mL/mF962TX9RXHg4HDsGf/wB6VwAIYTIP7JSlpBX9ezZk3HjxvHnn38SEhJC5cqVTb7GX716Nc2aNeO7774zWS8qKirTv6TLly+PTqfj4sWLJqPB58+fN+u7evVq+vTpY5KMJSQkmF11n5lRs/Lly/Pnn3+i0+lMRmjPnTtnXJ6Tpk6dyrp165g8eTLz588HoEqVKlSpUoX169cbb4DxsKVLlwIYSziqVKlC1apV2bBhA7Nnz6ZIFm7YZGdnh7e3N2FhYWbLgoODqVGjBgEBAWbLFixYQEhIiDEZNrxnls6hoT3t+9q6dWtsbW1Zvnz5E11EZ0hiHycoKAh/f/90lxsS8t9//90k8b1+/TrXrl3j7bffznBMwcHBaDSaR87tbEl8fLzFkemwsDBsbGyyPAqfk/JRJpc7Vh8JNT5v9Ew+KZFISNCXP7RqpU+EAe7dg7/+smpYQgiRWYZR4ICAAE6cOGFW62hra2s2Erpq1Sr+/TfzZSKtW7cG4KuvvjJpnzVrlllfS/udM2eOSZkGQOHChQHzqaksadOmDeHh4fyQZpaflJQU5syZQ5EiRWjaNGd/R3l7e9OlSxcWL15sMrI5btw4IiMjGThwoNnxHT16lGnTplGjRg26dOlibJ8wYQJ37tyhf//+Fu+Utn37duPsE+lp0KCB2d3e/vnnH3755Rdef/11unbtavbo27cvFy5c4NChQ4A+KfX19WX58uVm5+Do0aP89ttvxvMO4OnpyYABA9i+fTtz5swxi0mn0xEYGGhxur20duzYkaFHy5YtH7md6tWr8+yzz7Jw4UKT937evHloNBq6du1qbIuOjubcuXMWy3SSk5NZtWoVjRs3NiuvAf3PmaVR+MOHD3Py5Emz2SxA//5Vr14dFxeXRx6DNcjIcCYdvrUX/hs86NnIz6qxZIuTJ6FnT0h7z/BWrSAoCErnna9OhRAiIypUqECDBg2M85k+nAy3a9eOzz77jL59+9KwYUNOnjxJcHAwFStWzPS+fH196dGjB3PnziU6OpqGDRuya9cuk7rUtPtdtmwZLi4uVKtWjYMHD7Jz506zOmVfX19sbW2ZNm0a0dHRaLVaXnrpJWN9bVpvv/02CxYswN/fn6NHj+Ll5cXq1avZv38/s2bNypa7qz3OBx98wI8//sisWbOYOnUqoH/Pjxw5wuzZszlz5gy9evXC1dWVY8eO8f3331OiRAlWr15tMp9wt27dOHnyJJMnT+b48eP06NHDeAe6rVu3smvXLrOLEh/WsWNHli1bZlIDHBISglKKDh06WFynTZs22NnZERwcTL169QCYOXMmLVu2xNfXF39/f8qUKcPZs2dZuHAhHh4ejBkzxmQbgYGBXLx4kaFDh7J27VratWuHq6srV69eZdWqVZw7dy7d20MbZFfNMMD06dPp0KEDLVq0oHv37pw6dYqvv/6a/v37m0zTt27dOvr27WtxtHnbtm3cuXMn3Qvn7t27h6enJ926daN69eoULlyYkydPEhQUhIuLC59++qlJ/+TkZPbu3cugQYOy7TizVbbOTfGUyujUavEJqYoP3RTjUZoxLir5oTvrPFVSU5WaNUsprfbBlGlarVJffaW/y1w+JlNtFSxyvvXy+9RqBjqdTn311VcKUHXr1jVbnpCQoEaOHKk8PDyUk5OTatSokTp48KDZtGUZmVpNKf37OnToUFWiRAlVuHBh1b59e/XPP/+YTa0WGRmp+vbtq9zc3FSRIkVUy5Yt1blz51T58uVVnz59TLa5aNEiVbFiRWVra2syzdrDMSql1M2bN43bdXBwUD4+PmbTbxmOZfr06Wbvx8NxWvK46cb8/PxU0aJFVVRUlEn7+vXr1SuvvKJcXV2VVqtVlSpVUiNHjlS3bt1Kd1+7du1SHTt2VO7u7srOzk6VLFlStW/fXm3YsMFi/7R3oEtMTFRubm5q4sSJxuU+Pj6qXLlyjzw+Pz8/5e7ubnLHtt9++021a9dOubq6Kjs7O/XMM8+o/v37q2vXrlncRkpKivr2229VkyZNlIuLi7K3t1fly5dXffv2feJp17Ji3bp1ytfXV2m1WlW2bFk1duxYszv7BQUFpTtdW/fu3ZW9vb26c+eOxe0nJiaq999/Xz3//POqaNGixuN96623VFhYmFn/n3/+2WRqu/RYa2o1jVIZrJzPx2JiYnBxcSEyMpJixYql22/59lP0PugDwDP323Pti425FGE2u3ED+vbV1wgb+PhASAjUyP83ENHpdERERODu7m52JbTIf+R86yUkJBAWFkaFChVMLgDKb5RSpKSkYGdnlyevWhfZ6+HzPXHiRIKCgvj777/TvWBQ5L5OnTqh0WhYt27dI/s97nMqKioKV1dXoqOj072pS1YU3N8MWfDj4QfzCzf08LNeIE/q7l347/aeAAwfDocPF4hEWAghRP41fPhw7t27x8qVK60divjP2bNn2bx5MxMnTrR2KOmSmuFMOHwz1Fgv3KPhU3zxXPXqMH06fP45LFkCLVpYOyIhhBDiiRUpUoSIiAhrhyHSeO655yxeFJmXyMhwBiUlKW466UeGNUlFaV/H17oBZcYff8DD05wMGQJnzkgiLIQQQogCTZLhDFr761kofAsAj6Qm2D0NtUipqTBtGtSpA598YrpMo4F0blsphBBCCFFQSDKcQSt/CzU+r/801Av/8w+8/DJ89BGkpEBgIOzbZ+2ohBBCCCHyFEmGM+jQzQcXz/Wo72e9QDLixx/h+edh738xazQwZgxk4jaMQgghhBAFgVxAlwHJyYqbjqEAaJKc6VjP16rxpCsmBoYO1V8UZ+DpCcuWQQ7fiUgIIYQQ4mkkyXAGbNh/HlVYf3Vq6cQm2Nvmwbft4EF44w24dOlBW7duMG+e1AYLIYQQQqQjD2Z1ec+Kg6HG5/VK58ER1tBQaN5cf8EcgLMzfPONPjmWSeeFEEIIIdIlNcMZ8Ft4qPF5t7xYL9yoEdSurX/esKF+KrXevSURFkIIIYR4DBkZfoyUFMUNrWF+4SJ0aVDLyhFZYG8PwcHwww8wejTYyWkVQgghhMgIGRl+jE0H/0IVDgfAPbGx9euFIyOhVy84etS0vVIl/VzCkggLIUSe4e/vj5eXl7XDEEI8giTDj7HiwIMp1eq6+1kvENDXBj//PISE6BPiuDjrxiOEEHnE4sWL0Wg02NjY4ODggL29Pc888wz+/v78+++/1g5PCJGHyTDiYxy4HgrF9c9fr2eli+eSkmDcOPjiC1BK3xYRAadPwwsvWCcmIYTIgyZMmEC5cuVITk7m0KFDLF68mH379nHq1CkcHR2tHZ4QIg+SZPgRUlMV1x1C9S+SCvNaw9q5H8T589CzJxw79qCtWTNYuhTKls39eIQQIg9r3bo1vr6+2NnZMWDAANzc3Jg2bRobN27k9ddft3Z4Qog8SMokHmHLoQuoIjcAcE9ojNbePvd2rhQsWAD/+9+DRNjeXj86vHOnJMJCCJEBTZo0AeDixYvGtqSkJMaNG0ft2rVxcXGhcOHCNGnShD179pise/nyZTQaDTNmzGDhwoV4e3uj1Wp54YUXOHLkiNm+1q9fT40aNXB0dKRGjRqsW7fOYkz3799n5MiReHp6otVqqVq1KjNmzEAZvvn7j0ajYciQIaxatYpq1arh5OREgwYNOHnyJAALFiygUqVKODo64ufnx+XLlzP0noSGhlKnTh0cHR3x9vZmwYIFjB8/Hk2aGYgMx7548WKz9TUaDePHjzdp+/fff+nXrx+lSpVCq9VSvXp1vv/+e7N158yZQ/Xq1SlUqBCurq7UqVOHkJAQ4/LY2FiGDRuGl5cXWq0Wd3d3XnnlFY6lHRASIpvJyPAjBO8PNT5/oWQulkjcugX9+8PGjQ/aqlbV1wrXyoOzWQghRB5lSBBd09x8KCYmhm+//ZYePXowYMAAYmNj+e6772jZsiWHDx/G19fXZBshISHExsbyzjvvoNFo+OKLL+jcuTOXLl3C/r9Bku3bt9OlSxeqVavGlClTuHPnDn379qXsQwMXSik6dOjAnj17eOutt/D19WXbtm188MEH/Pvvv3z55Zcm/X/99Vc2btzI4MGDAZgyZQrt2rXjww8/ZO7cuQwaNIjIyEi++OIL+vXrx+7dux/5fhw/fpxWrVrh4eHBhAkTSE1N5bPPPqNkyZJZeXsBuHnzJvXr1zcm7yVLluTnn3/mrbfeIiYmhmHDhgGwaNEihg4dSteuXXn//fdJSEjgzz//5NChQ/Ts2ROAgQMHsnr1aoYMGUK1atW4c+cO+/bt4+zZs9SS338ipyihoqOjFaAiIyNN2j3f76UYj2I8avGuA7kX0NGjStnZKaUfH1bq3XeVun8/9/afz6WmpqobN26o1NRUa4cicoGcb734+Hh15swZFR8fb+1QckRQUJAC1I4dO9T169fV1atX1erVq1XJkiWVVqtV//zzj7FvSkqKSkxMNFk/MjJSlSpVSvXr18/YFhYWpgBVokQJdffuXWP7hg0bFKA2bdpkbPP19VUeHh4qKirK2LZ9+3YFqPLlyxvb1q9frwA1adIkk/137dpVaTQadeHCBWMboLRarQoLCzO2LViwQAGqdOnSKiYmxtg+ZswYBZj0taR9+/aqUKFC6t9//zW2/f3338rOzk6lTQkMxx4UFGS2DUAFBAQYX7/11lvKw8ND3b5926Rf9+7dlYuLi4qLi1NKKdWxY0dVvXr1R8bn4uKiBg8e/Mg+ael0OpWUlKR0Ol2G1xF51+M+pyIjIxWgoqOjs3W/UiaRjtRUxb/2ofoXyYXo1rhO7u28Vi2YNAnc3PSjw3PnQqFCubd/IUTBM3OmvvzqcY8OHczX7dAhY+vOnGm6Xmzso5dnwSuvvEKZMmUoV64cXbt2pXDhwmzcuNFkhNbW1hYHBwcAdDodd+/eJSUlhTp16lj8Or5bt24mI8uG0otLly4BcOPGDU6cOEGfPn1wcXExiaVatWom29qyZQu2trYMHTrUpH3kyJEopfj5559N2l9++WWTqdnq1asHQJcuXXB2djZrN8RkSWpqKjt37qRTp06UKVPG2F6pUiVat26d7nqPopRizZo1tG/fHqUUt2/fNj5atmxJdHS08T0tVqwY165ds1hiYlCsWDEOHTrE9evXsxSPEFkhZRLp2HbkIroi+ul43OIb4eiQg/XC586Bt7e+Jthg1Cjo2xfc3XNuv0IIYRATAxmZgszT07zt1q2MrRsTY/paKdP1Hl6eBV9//TXe3t7cu3ePoKAgfvnlF7RarVm/JUuWEBgYyLlz50hOTja2V6hQwaxvuXLlTF4bEuPIyEgArly5AkDlypXN1q1atapJgn3lyhXKlCljksgCPPfccybbSm/fhmTb86HzYGg3xGRJREQE8fHxVKpUyWyZpbaMuHXrFlFRUSxcuJCFCxemu1+A0aNHs3PnTurWrUulSpVo0aIFPXv2pFGjRsa+X3zxBX369MHT05PatWvTpk0b3nzzTSpWrJil+ITICEmG0xG8/8H8wi+4+eXMTnQ6mDNHf9e40aNhwoQHy2xtJREWQuSeokXhmWce389SbWnJkhlbt2hR09cajel6Dy/Pgrp16xpnk3j11Vdp3LgxPXv25Pz58xQpUgSA5cuX4+/vT6dOnfjggw9wd3fH1taWKVOmmFxoZ2Bra2txX+qhC95yQnr7zumY0l5Ml1ZqaqrJa51OB8Abb7xBnz59LK7z/PPPA/qE//z582zevJmtW7eyZs0a5s6dy7hx45jw3++/119/nSZNmrBu3Tq2b9/O9OnTmTZtGmvXrs3y6LUQjyPJcDr2XQuFYvrnXerkwMVzN27oR363bdO/njQJ2raFunWzf19CCPE4I0boH1mR9mLfzHB2hmvXsrZuBhgS3GbNmvH111/z0UcfAbB69WoqVqzI2rVrTZK+gICALO2nfPnyAPz9999my86fP2/Wd+fOncTGxpqMDp87d85kWznB3d0dR0dHLly4YLbs4TbD6HdUVJRJ+8Mj1yVLlsTZ2ZnU1FSaN2/+2BgKFy5Mt27d6NatG0lJSXTu3JnJkyczZswY4zzQHh4eDBo0iEGDBhEREUGtWrWYPHmyJMMix0jNsAU6neKa7X8jw8lO9GyazTe22LBBfyc5QyIMMHSovk0IIUS28fPzo27dusyaNYuEhATgwahq2lHUQ4cOcfDgwSztw8PDA19fX5YsWUJ0dLSxfceOHZw5c8akb5s2bUhNTeXrr782af/yyy/RaDQ5mvDZ2trSvHlz1q9fb1KTe+HCBbNa5aJFi+Lm5sYvv/xi0j537lyzbXbp0oU1a9Zw6tQps33eunXL+PzOnTsmyxwcHKhWrRpKKZKTk0lNTTV5/0CfwJcpU4bExMTMHawQmSAjwxbsPBqGzvkfAErENcTpvwstntj9+zBypH7+YIPSpWHJEmjRInv2IYQQwsQHH3zAa6+9xuLFixk4cCDt2rVj7dq1vPrqq7Rt25awsDDmz59PtWrVuHfvXpb2MWXKFNq2bUvjxo3p168fd+/eNc6pm3ab7du3p1mzZnzyySdcvnyZmjVrsn37djZs2MCwYcPw9vbOrsO2aPz48Wzfvp1GjRrx7rvvGhPzGjVqcOLECZO+/fv3Z+rUqfTv3586derwyy+/8Ndff5ltc+rUqezZs4d69eoxYMAAqlWrxt27dzl27Bg7d+7k7t27ALRo0YLSpUvTqFEjSpUqxdmzZ/n6669p27Ytzs7OREVFUbZsWbp27UrNmjUpUqQIO3fu5MiRIwQGBubo+yIKNhkZtmD5vlDj89ol/LJno0eP6meJSJsId+wIJ09KIiyEEDmoc+fOeHt7M2PGDFJTU/H39+fzzz/njz/+YOjQoWzbto3ly5dTp07WZw1q1aoVq1atIjU1lTFjxrB27VqCgoLMtmljY8PGjRsZNmwYmzdvZtiwYZw5c4bp06czMxtm03ic2rVr8/PPP+Pq6sqnn37Kd999x2effcbLL79sdrvqcePG8dZbb7F69Wo+/PBDUlNTzUaQAUqVKsXhw4fp27cva9euZciQIcyePZu7d+8ybdo0Y7933nmHe/fuMXPmTAYPHsz69esZOnQoy5cvB6BQoUIMGjSIEydOEBAQwPDhwzl//jxz585lRFZLeITIAI3KjSsA8riYmBhcXFyIjIykWLFiVBjRh8suSwFYUO9X3m7V+Ml2sHs3tGwJKSn614UKwaxZ+htrpHORgsg5Op2OiIgI3N3dsbGRvwfzOznfegkJCYSFhVGhQgWzpCc/UUqRkpKCnZ1duheBCXOdOnXi9OnTFuue8zI53/nL4z6noqKicHV1JTo6mqLZcMGtQcH9zZAOpeAfm1D9i2RHevllQ71wo0ZgmGuydm04fhwGDJBEWAghRK6Lj483ef3333+zZcsW/Pz8rBOQEFYmNcMP2XP8MqnOVwEoHteQwo7m81Nmmlarv5XyihUwbhxkVw2yEEIIkUkVK1bE39+fihUrcuXKFebNm4eDgwMffvihtUMTwiokGX7Isl9Djc9rFc/ClGoxMfqL5IYNg+rVH7RXr66fPk0IIYSwolatWrFixQrCw8PRarU0aNCAzz//3OJNQ4QoCCQZfsgvV0PhvzKUV//nl7mVDx6EN96AS5fg8GH9w8Kdj4QQQghrCQoKsnYIQuQpUjOchlJwRWOYX9iR3i9l8AYYKSn6u8c1aaJPhAHCwuDPP3MmUCGEEEIIkS1kZDiNA6evkup8GQDX+/VxdsrAFdeXLulHg9NO1t6wISxfDhbucS+EEEIIIfIOGRlOY8WB/cbn/yvu9+jOSsHSpeDr+yARtrXVjxDv3SuJsBAiT5LZNIUQeZW1Pp9kZDiNA1f3QUn9806+j7h4LjIS3n0XfvjhQVvFihAcDPXr52yQQgiRBfb29mg0Gu7fv4+Tk5O1wxFCCDNxcXGA/vMqN0kynMZVzX8jwyla3nzpEUnt2bOwatWD1/7+8NVX4Oyco/EJIURW2dra4uLiwq1bt0hMTKRo0aL58kYFchOGgkXOd/6glCIuLo6IiAiKFSuGra1tru5fkuE0UotcAaDYvfq4FH5EvXDDhvDJJzBnDixcCK+9lksRCiFE1pUuXRonJyciIiKIiYmxdjg5QimFTqfDxsZGkqMCQM53/lKsWDFKly6d6/uVZNiCmsUeKpEIC4Ny5fQ1wQaffgrvvAPPPJO7wQkhRBZpNBqKFSuGi4sLqamppBhuEZ+P6HQ67ty5Q4kSJQr07bcLCjnf+Ye9vX2ujwgbSDJsQaeafvonSulHfocPh4AAGD36QSd7e0mEhRBPJY1Gg52dHXZ2+e9XgE6nw97eHkdHR0mOCgA53yI7yE/Ow1Ic6PNyfbh1Czp1goEDIT4exo6F48etHZ0QQgghhMhG+W9Y4AkVvVcP1wO/6C+KCw9/sKB/f6ha1WpxCSGEEEKI7JdnR4a/+eYbvLy8cHR0pF69ehw+fPiR/VetWsWzzz6Lo6MjPj4+bNmyJdP7dEiBbw8mQqtWDxJhNzfYuBHmzYNChbJyKEIIIYQQIo/Kk8nwDz/8wIgRIwgICODYsWPUrFmTli1bEhERYbH/gQMH6NGjB2+99RbHjx+nU6dOdOrUiVOnTmVqv6GL4bVDaZLuVq3g5Elo3/4JjkYIIYQQQuRVGpUHb0dUr149XnjhBb7++mtAXyDv6enJe++9x0cffWTWv1u3bty/f5/Nmzcb2+rXr4+vry/z589/7P5iYmJwcXEhGigKoNXC9OkwZAjIVC35jk6nIyIiAnd3d7ngogCQ812wyPkuWOR8FyxRUVG4uroSHR1N0aJFs227ea5mOCkpiaNHjzJmzBhjm42NDc2bN+eg4bbHDzl48CAjRowwaWvZsiXr16+32D8xMZHExETj6+joaABiAFWtGmrRIqhWDf5rF/mLTqcjJiYGBwcH+fAsAOR8FyxyvgsWOd8FS1RUFJD9t23Oc8nw7du3SU1NpVSpUibtpUqV4ty5cxbXCQ8Pt9g/PO0FcGlMmTKFCRMmmLV7Apw5A40aZSl2IYQQQgiRs+7cuYOLi0u2bS/PJcO5YcyYMSYjyVFRUZQvX56rV69m65sr8qaYmBg8PT35559/svVrFpE3yfkuWOR8FyxyvguW6OhoypUrR/HixbN1u3kuGXZzc8PW1pabN2+atN+8eTPdW/SVLl06U/21Wi1ardas3cXFRf4zFSBFixaV812AyPkuWOR8FyxyvguW7C6JyXMFNg4ODtSuXZtdu3YZ23Q6Hbt27aJBgwYW12nQoIFJf4AdO3ak218IIYQQQgjIgyPDACNGjKBPnz7UqVOHunXrMmvWLO7fv0/fvn0BePPNN3nmmWeYMmUKAO+//z5NmzYlMDCQtm3bsnLlSn7//XcWLlxozcMQQgghhBB5XJ5Mhrt168atW7cYN24c4eHh+Pr6snXrVuNFclevXjUZIm/YsCEhISGMHTuWjz/+mMqVK7N+/Xpq1KiRof1ptVoCAgIslk6I/EfOd8Ei57tgkfNdsMj5Llhy6nznyXmGhRBCCCGEyA15rmZYCCGEEEKI3CLJsBBCCCGEKLAkGRZCCCGEEAWWJMNCCCGEEKLAKjDJ8DfffIOXlxeOjo7Uq1ePw4cPP7L/qlWrePbZZ3F0dMTHx4ctW7bkUqQiO2TmfC9atIgmTZrg6uqKq6srzZs3f+zPh8hbMvv/22DlypVoNBo6deqUswGKbJXZ8x0VFcXgwYPx8PBAq9VSpUoV+Ux/imT2fM+aNYuqVavi5OSEp6cnw4cPJyEhIZeiFU/il19+oX379pQpUwaNRsP69esfu05oaCi1atVCq9VSqVIlFi9enPkdqwJg5cqVysHBQX3//ffq9OnTasCAAapYsWLq5s2bFvvv379f2draqi+++EKdOXNGjR07Vtnb26uTJ0/mcuQiKzJ7vnv27Km++eYbdfz4cXX27Fnl7++vXFxc1LVr13I5cpEVmT3fBmFhYeqZZ55RTZo0UR07dsydYMUTy+z5TkxMVHXq1FFt2rRR+/btU2FhYSo0NFSdOHEilyMXWZHZ8x0cHKy0Wq0KDg5WYWFhatu2bcrDw0MNHz48lyMXWbFlyxb1ySefqLVr1ypArVu37pH9L126pAoVKqRGjBihzpw5o+bMmaNsbW3V1q1bM7XfApEM161bVw0ePNj4OjU1VZUpU0ZNmTLFYv/XX39dtW3b1qStXr166p133snROEX2yOz5flhKSopydnZWS5YsyakQRTbKyvlOSUlRDRs2VN9++63q06ePJMNPkcye73nz5qmKFSuqpKSk3ApRZKPMnu/Bgwerl156yaRtxIgRqlGjRjkap8h+GUmGP/zwQ1W9enWTtm7duqmWLVtmal/5vkwiKSmJo0eP0rx5c2ObjY0NzZs35+DBgxbXOXjwoEl/gJYtW6bbX+QdWTnfD4uLiyM5OZnixYvnVJgim2T1fH/22We4u7vz1ltv5UaYIptk5Xxv3LiRBg0aMHjwYEqVKkWNGjX4/PPPSU1Nza2wRRZl5Xw3bNiQo0ePGkspLl26xJYtW2jTpk2uxCxyV3bla3nyDnTZ6fbt26SmphrvXmdQqlQpzp07Z3Gd8PBwi/3Dw8NzLE6RPbJyvh82evRoypQpY/YfTOQ9WTnf+/bt47vvvuPEiRO5EKHITlk535cuXWL37t306tWLLVu2cOHCBQYNGkRycjIBAQG5EbbIoqyc7549e3L79m0aN26MUoqUlBQGDhzIxx9/nBshi1yWXr4WExNDfHw8Tk5OGdpOvh8ZFiIzpk6dysqVK1m3bh2Ojo7WDkdks9jYWHr37s2iRYtwc3OzdjgiF+h0Otzd3Vm4cCG1a9emW7dufPLJJ8yfP9/aoYkcEBoayueff87cuXM5duwYa9eu5aeffmLixInWDk3kYfl+ZNjNzQ1bW1tu3rxp0n7z5k1Kly5tcZ3SpUtnqr/IO7Jyvg1mzJjB1KlT2blzJ88//3xOhimySWbP98WLF7l8+TLt27c3tul0OgDs7Ow4f/483t7eORu0yLKs/P/28PDA3t4eW1tbY9tzzz1HeHg4SUlJODg45GjMIuuycr4//fRTevfuTf/+/QHw8fHh/v37vP3223zyySfY2MgYYH6SXr5WtGjRDI8KQwEYGXZwcKB27drs2rXL2KbT6di1axcNGjSwuE6DBg1M+gPs2LEj3f4i78jK+Qb44osvmDhxIlu3bqVOnTq5EarIBpk9388++ywnT57kxIkTxkeHDh1o1qwZJ06cwNPTMzfDF5mUlf/fjRo14sKFC8Y/egD++usvPDw8JBHO47JyvuPi4swSXsMfQvprskR+km35Wuau7Xs6rVy5Umm1WrV48WJ15swZ9fbbb6tixYqp8PBwpZRSvXv3Vh999JGx//79+5WdnZ2aMWOGOnv2rAoICJCp1Z4imT3fU6dOVQ4ODmr16tXqxo0bxkdsbKy1DkFkQmbP98NkNomnS2bP99WrV5Wzs7MaMmSIOn/+vNq8ebNyd3dXkyZNstYhiEzI7PkOCAhQzs7OasWKFerSpUtq+/btytvbW73++uvWOgSRCbGxser48ePq+PHjClAzZ85Ux48fV1euXFFKKfXRRx+p3r17G/sbplb74IMP1NmzZ9U333wjU6s9ypw5c1S5cuWUg4ODqlu3rvrtt9+My5o2bar69Olj0v/HH39UVapUUQ4ODqp69erqp59+yuWIxZPIzPkuX768AsweAQEBuR+4yJLM/v9OS5Lhp09mz/eBAwdUvXr1lFarVRUrVlSTJ09WKSkpuRy1yKrMnO/k5GQ1fvx45e3trRwdHZWnp6caNGiQioyMzP3ARabt2bPH4u9jwznu06ePatq0qdk6vr6+ysHBQVWsWFEFBQVler8apeR7AyGEEEIIUTDl+5phIYQQQggh0iPJsBBCCCGEKLAkGRZCCCGEEAWWJMNCCCGEEKLAkmRYCCGEEEIUWJIMCyGEEEKIAkuSYSGEEEIIUWBJMiyEEEIIIQosSYaFEPmKn58fGo3G2mHkCV5eXnh5eWVqHXn/hBAFjSTDQgiruHz5MhqN5pGPqKgoa4eZZV5eXibHYmtri5ubGy1atGDDhg1Wi2v8+PFoNBpCQ0OtFkNGGWJN+yhUqBA1atTgk08+ISYm5on34e/vj0aj4fLly08esBDiqWRn7QCEEAWbt7c3b7zxhsVljo6OuRxN9rK1tWXs2LEAJCUlce7cOTZu3MiOHTuYMWMGI0eOzNH979q1K9PrLF26lLi4uByIJuu6dOlCjRo1ALh58yZbtmzh888/Z/PmzRw+fBitVmvlCIUQTzNJhoUQVlWpUiXGjx9v7TByhJ2dndmxbd++nVatWjFu3DjeffddChUqlGP79/b2zvQ65cqVy4FInkzXrl3p3r278XVCQgL169fnjz/+ICQkhL59+1oxOiHE007KJIQQedrRo0cZMmQINWrUwMXFBScnJ3x8fJg6dSrJyckZ2oZOp+Pbb7+lbt26FC9eHCcnJ8qWLUv79u0tlgv88ssvtG/fHjc3N7RaLZUrV2bs2LHZMmLaokULqlatSlxcHKdPnza2b9q0iWbNmhmPsWbNmsycOZOUlBSzbezZs4fWrVtTpkwZtFotpUqVokmTJixcuNCk38M1w35+fkyYMAGAZs2aGUsPHu6TtmZ42bJlaDQaPvvsM4vHc+zYMTQaDb169TJpj4iIYPjw4VSqVAmtVoubmxtdunTh1KlTGX6v0uPo6Gjc39GjR02WXb9+nYCAAOrXr4+7uztarRYvLy8GDRpERESESV8vLy+WLFkCQIUKFYzvh5+fn0m/sLAw+vfvT7ly5dBqtXh4eODv78+VK1ee+FiEENYnI8NCiDxt0aJFbNq0iRdffJE2bdoQFxdHaGgoY8aM4ciRI6xZs+ax2xgzZgxffPEF3t7e9OzZE2dnZ/7991/27dvHzp07TZKfefPmMXjwYIoVK0b79u1xd3fn999/Z/LkyezZs4c9e/bg4OCQLcdmSDpnzpzJyJEjKV68OD179qRw4cJs3LiRkSNH8uuvv7J27Vpj359++on27dtTrFgxOnbsiIeHB7du3eKPP/5g2bJlvP322+nuz9/fH4C9e/fSp08fYxJcrFixdNfp3Lkz7777LsHBwYwbN85s+bJlywDo3bu3se3ixYv4+flx7do1WrRoQadOnYiIiGDNmjVs27aNXbt2Ua9evcy8VemyszP9NfbLL78QGBjIyy+/TL169bC3t+f48ePMmzePbdu2cezYMVxcXAAYNmwYixcv5o8//uD99983vg9p/zg4dOgQLVu25P79+7Rr147KlStz+fJlgoOD+fnnnzl48CAVK1bMlmMRQliJEkIIKwgLC1OA8vb2VgEBAWaPgwcPKqWUunLlikpJSTFZV6fTqX79+ilA7du3z2RZ06ZN1cMfbcWLF1dlypRR9+/fN4vjzp07xuenT59WdnZ2qmbNmur27dsm/aZMmaIANWPGjAwdX/ny5ZVWqzVr37lzp9JoNKpw4cIqLi5OXbhwQdnZ2Sl3d3d19epVY7+EhATVuHFjBailS5ca2zt37qwAdeLECbNtPxxz+fLlVfny5U3aAgICFKD27NljMW5L798bb7yhAHXo0CGT9pSUFFWqVClVunRpk3PUsGFDZWtrq7Zu3WrS//z588rZ2Vn5+PhY3PfDDLGuWLHCpD0+Pl7VrFlTAWrVqlUmy27evKliY2PNtrVkyRIFqEmTJpm09+nTRwEqLCzMbJ2kpCTl5eWlnJ2d1bFjx0yW/frrr8rW1la1a9cuQ8cihMi7ZGRYCGFVFy9eNH51n1axYsWoX7++xRpWjUbD4MGD+f7779m5cyeNGjV67H4cHBywtbU1ay9evLjx+YIFC0hJSWHOnDmUKFHCpN+HH37IzJkzWbFiRYYvfEtJSTHWDCcnJ3P+/Hk2bNiAUoqJEyfi5ORESEgIKSkpjBw5Ek9PT+O6Wq2WadOm0ahRIxYvXmwy8grg5ORktr+HY84uvXv3Zvny5Sxfvpy6desa27dv387NmzcZNmyY8b09fvw4Bw4coF+/frRs2dJkO1WqVGHAgAHMnDmTU6dOGS+Ke5zVq1dz7tw5QF9+8dNPP3H16lVeffVVOnfubNLX3d093WN477332LlzJ5988kmG9rt582YuX77MZ599xv/+9z+TZY0bN6Zjx46sX7+emJgYihYtmqFtCiHyHkmGhRBW1bJlS7Zu3Zru8qSkJL7++mtWrlzJuXPnuHfvHkop4/Lr168/dh/du3dn7ty51KhRg+7du9OsWTMaNGhgllD+9ttvAMav8h9mb29vTMoyIjU11Zjo29jY4OrqyksvvcTgwYPp0KEDoE8eAbM6VYAGDRrg6OjIiRMnTI5l7dq11K9fn549e/Lyyy/TpEkT3NzcMhxXZr388st4eHiwcuVKZs6caSxNWL58OWBaImF4D2/evGnxwkjD+3fu3LkMJ8Nr1qwxK4d57bXX+OGHHyzOibx27VoWLFjAsWPHiIyMJDU11bgsIz8vBoZjOX/+vMVjCQ8PR6fT8ddff1GnTp0Mb1cIkbdIMiyEyNO6du3Kpk2bqFKlCt26dcPd3R17e3uioqKYPXs2iYmJj93G7NmzqVChAkFBQUyaNIlJkybh6OjI66+/TmBgoDGRvHv3LgCTJ0/Olti1Wi0JCQmP7GOYK7dUqVJmyzQaDaVKleLff/81tr322musX7+emTNnMn/+fL755hs0Gg3NmjUjMDAQX1/fbIk9LVtbW3r27ElgYCDbtm2jbdu23Lt3j/Xr11OtWjVq1apl7Gt4D3/66Sd++umndLd5//79DO9/xYoVdO/enZSUFM6fP8+oUaNYtWoVVatWZeLEiSZ9AwMDGTVqFCVLlqRFixaULVvW+EfPrFmzMvTz8vCxBAcHP7JfZo5FCJH3SDIshMizjhw5wqZNm2jZsiU//fSTSZnDb7/9xuzZszO0HTs7O0aNGsWoUaO4fv06e/fuJSgoiKVLlxIeHs62bdsAjF91x8TE4OzsnP0HZIFhnzdv3qR8+fImy5RS3Lx50+wr+I4dO9KxY0diY2PZv38/a9eu5bvvvqNVq1acO3fukRfEZVXv3r0JDAxk+fLltG3bljVr1hAXF2dWvmGIdc6cOQwZMiRbY7Czs6N69eqsW7cOHx8fJk+ezKuvvmpMxlNSUpg4cSIeHh6cOHHCpGRCKcUXX3yRqf0ZjmXTpk20a9cu+w5ECJGnyNRqQog86+LFiwC0bdvWrN73119/zdI2y5QpQ48ePdi6dSuVKlVi586dxMfHAxhnODB8PZ4bDLWolqZ4O3ToEAkJCemO9jo7O9OqVSsWLlyIv78/N2/e5NChQ4/cn+F9TFs6kBE1a9bEx8eHDRs2EBsby/Llyy1OqWZ4Dw8ePJip7WeGo6MjM2bMQCnFRx99ZGy/ffs20dHRNGjQwKx2+Pfffzee57Qe9X7kxrEIIaxPkmEhRJ5lGCndt2+fSfvp06eZMmVKhraRmJjIgQMHzNrv37/PvXv3sLe3x8ZG/1E4aNAg7OzseO+997h69arZOlFRUcYa3+zSs2dP7OzsmDlzpkk9a1JSEqNHjwYeTIkG+qnDLCVuhjl0H3fXPsMFg//880+mY+3duzfx8fF89dVX7N69m6ZNm5pc9AdQt25d6tWrx4oVK/jhhx/MtqHT6di7d2+m9/2wjh07UqtWLXbs2GH8w8jd3R0nJyeOHTtmMid0ZGQk7733nsXtPOr96NixI+XKlWPmzJn88ssvZsuTk5PNfjaFEE8fKZMQQuRZdevWpW7duvz444/cuHGD+vXrc/XqVTZu3Ejbtm1ZvXr1Y7cRHx9Po0aNqFKlCrVr16ZcuXLcu3ePzZs3Ex4ezqhRo4y3861RowZz587l3XffpWrVqrRp0wZvb29iY2O5dOkSe/fuxd/fn/nz52fbMXp7ezNt2jRGjhzJ888/z+uvv07hwoXZtGkT58+fp2PHjia3qx46dCjXr1+ncePGeHl5odFo2LdvH4cPH6Z+/fo0btz4kfsz3Gzj448/5vTp07i4uFCsWLEMlTT07NmTjz76iAkTJqDT6cxKJAxWrFhBs2bN6N69O7NmzaJWrVo4OTlx9epVDh48yK1btx5bS50R48ePp0OHDowbN449e/ZgY2PDoEGDCAwMpGbNmrRv356YmBh+/vlnypcvT5kyZcy28dJLLzFjxgzefvttunTpQuHChSlfvjy9e/dGq9WyevVqWrduTdOmTXnppZfw8fFBo9Fw5coVfv31V0qUKJGpiyqFEHmQVSd2E0IUWIZ5hlu2bPnIfhEREapfv36qTJkyytHRUfn4+KhvvvlGXbp0SQGqT58+Jv0fnic3KSlJTZs2TbVo0UKVLVtWOTg4qFKlSqkXX3xRhYSEKJ1OZ7bPw4cPq+7du6syZcooe3t75ebmpmrVqqU++ugjdfbs2QwdX3rzDKdnw4YNqmnTpsrZ2VlptVrl4+OjAgMDVXJyskm/lStXqtdff115e3urQoUKKRcXF1WzZk01bdo0s/l1Lc0zrJRSixcvVj4+Pkqr1SrApI+leYbTat68uQKUo6Ojio6OTrff3bt31dixY1WNGjWUk5OTKlKkiKpcubLq2bOnWrt2bYbek/TmGU6rTp06ClC7du1SSunP9+TJk1XlypWVVqtV5cqVUyNHjlSxsbHpvh9ffPGFqly5srK3t1eAatq0qcnya9euqffff9+4zaJFi6rnnntO9e/f37hfIcTTS6NUmjmKhBBCCCGEKECkZlgIIYQQQhRYkgwLIYQQQogCS5JhIYQQQghRYEkyLIQQQgghCixJhoUQQgghRIElybAQQgghhCiwJBkWQgghhBAFliTDQgghhBCiwJJkWAghhBBCFFiSDAshhBBCiAJLkmEhhBBCCFFgSTIshBBCCCEKrP8DduSPHHIU9ZwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Obtener las predicciones de probabilidades\n",
        "y_pred_train = (model.predict(xtrain) > 0.5)  # Convertir las probabilidades a 0 o 1\n",
        "\n",
        "# Calcular las métricas de la curva ROC para entrenamiento\n",
        "fpr_train, tpr_train, _ = roc_curve(ytrain, y_pred_train)\n",
        "roc_auc_train = auc(fpr_train, tpr_train)\n",
        "\n",
        "# Calcular las métricas de la curva ROC para validación\n",
        "fpr_val, tpr_val, _ = roc_curve(ytest, y_pred)\n",
        "roc_auc_val = auc(fpr_val, tpr_val)\n",
        "\n",
        "# Graficar ambas curvas ROC\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr_train, tpr_train, color='blue', lw=2, label=f'Train ROC (AUC = {roc_auc_train:.2f})')\n",
        "plt.plot(fpr_val, tpr_val, color='green', lw=2, label=f'Validation ROC (AUC = {roc_auc_val:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='red', linestyle='--', lw=2, label='Random guess')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate', fontsize=14)\n",
        "plt.ylabel('True Positive Rate', fontsize=14)\n",
        "plt.title('Receiver Operating Characteristic (ROC)', fontsize=16)\n",
        "plt.legend(loc=\"lower right\", fontsize=12)\n",
        "plt.grid(alpha=0.3)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F2MHfLfbxrtc"
      },
      "source": [
        "### 6) Guardar el modelo 💾"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "icAp1SniSDuN"
      },
      "source": [
        "### 7) Hacer Predicciones en Producción 🤙"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wl7v6oyFkREF"
      },
      "source": [
        "### 8) Conclusiones\n",
        "\n",
        "- Aprender sobre los distintos objetos y métodos que nos ofrece Tensorflow + Keras.\n",
        "\n",
        "- Realizar el proceso completo de entrenamiento de un modelo con Tensorflow + Keras.\n",
        "\n",
        "- Aprender tips sobre implementación con el uso de la GPU.\n",
        "\n",
        "<br>\n",
        "<br>\n",
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0sQ95LlzkUZN"
      },
      "source": [
        "---\n",
        "\n",
        "<br>\n",
        "<br>\n",
        "\n",
        "\n",
        "<img src=\"https://static.platzi.com/media/avatars/platziteam_8cfe6fc7-1246-4c9a-9f5d-d10d467443ee.png\" width=\"100px\">\n",
        "\n",
        "[Platzi](https://platzi.com/) 🚀\n",
        "\n"
      ]
    }
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