Unidad_2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## **Librerías**\n"
      ],
      "metadata": {
        "id": "gB-TNGrXrWHc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "id": "4MpjFoHLkdk-"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from scipy.stats import spearmanr\n",
        "from scipy.stats import kendalltau\n",
        "\n",
        "# Librerías de clasificación\n",
        "import seaborn as sns\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.feature_selection import SelectKBest, f_classif\n",
        "from sklearn.feature_selection import chi2\n",
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# Librerias para SVM\n",
        "from sklearn import svm\n",
        "\n",
        "# Genera datos de ejemplo Regresión logística y Correlación\n",
        "np.random.seed(0)\n",
        "X = np.random.rand(100, 2)\n",
        "\n",
        "# Genera datos de ejemplo para Selección de Características (100 muestras y 10 características)\n",
        "X_feat, y_feat = make_classification(n_samples=100, n_features=10, random_state=0, flip_y=False)\n",
        "X_feat = np.abs(X_feat) # Fuerza que no existan carácterísticas negativas"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Clasificación Simple**"
      ],
      "metadata": {
        "id": "u6lTm3D2rte9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Ny3PMKpymOjP",
        "outputId": "4ca2ea82-a7c3-44cc-9472-7e6edcaf99c8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Clasificación simple\n",
        "y = (X[:, 0] + X[:, 1] > 1).astype(int)\n",
        "\n",
        "# Visualiza los datos\n",
        "plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], color='red', marker='o', label='Clase 0')\n",
        "plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], color='blue', marker='x', label='Clase 1')\n",
        "plt.xlabel('Característica 1')\n",
        "plt.ylabel('Característica 2')\n",
        "plt.legend()\n",
        "plt.title('Conjunto de datos sintético')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Regresión Logística**"
      ],
      "metadata": {
        "id": "ZmAawuRmr8Zq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xIWELIq3mg9V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48afc17a-cb3e-4a68-ae73-2b218fdfee17"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo: 95.00%\n"
          ]
        }
      ],
      "source": [
        "# Implementa la regresión logística\n",
        "class LogisticRegression:\n",
        "    def __init__(self, learning_rate=0.01, num_iterations=1000):\n",
        "        self.learning_rate = learning_rate\n",
        "        self.num_iterations = num_iterations\n",
        "\n",
        "    def sigmoid(self, z):\n",
        "        return 1 / (1 + np.exp(-z))\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        m, n = X.shape\n",
        "        self.theta = np.zeros(n)\n",
        "        for _ in range(self.num_iterations):\n",
        "            z = np.dot(X, self.theta)\n",
        "            h = self.sigmoid(z)\n",
        "            gradient = np.dot(X.T, (h - y)) / m\n",
        "            self.theta -= self.learning_rate * gradient\n",
        "\n",
        "    def predict(self, X):\n",
        "        z = np.dot(X, self.theta)\n",
        "        return np.round(self.sigmoid(z))\n",
        "\n",
        "# Añade una columna de unos a X para el término de sesgo\n",
        "X = np.hstack((np.ones((X.shape[0], 1)), X))\n",
        "# print(X)\n",
        "\n",
        "# Divide los datos en entrenamiento y prueba\n",
        "X_train, X_test = X[:80], X[80:]\n",
        "# print(X_train)\n",
        "# print(X_test)\n",
        "y_train, y_test = y[:80], y[80:]\n",
        "# print(y_train)\n",
        "# print(y_test)\n",
        "\n",
        "# Crea y entrena el modelo de regresión logística\n",
        "model = LogisticRegression(learning_rate=0.1, num_iterations=1000)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Calcula la precisión del modelo\n",
        "accuracy = np.mean(y_pred == y_test)\n",
        "print(\"Precisión del modelo: {:.2f}%\".format(accuracy * 100))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Correlación**\n",
        "### Correlación de Pearson:\n",
        "* También conocida como \"correlación lineal\" o \"coeficiente de correlación de Pearson\".\n",
        "* Mide la relación lineal entre dos variables continuas.\n",
        "* Supone que las variables siguen una distribución normal y que la relación entre ellas es lineal.\n",
        "* Se utiliza comúnmente para medir la relación entre dos variables cuantitativas, como edad y ingresos.\n",
        "* Los valores van de -1 a 1, donde 1 indica una correlación positiva perfecta, -1 indica una correlación negativa perfecta, y 0 indica ausencia de correlación.\n",
        "\n",
        "###Correlación de Spearman:\n",
        "\n",
        "* También conocida como \"correlación de rango de Spearman\".\n",
        "* Mide la relación monótona entre dos variables, es decir, si el aumento en una variable se asocia con el aumento o disminución en la otra.\n",
        "* No requiere que las variables sigan una distribución normal ni que la relación sea lineal.\n",
        "* Se utiliza cuando las variables son ordinales o no siguen una distribución normal.\n",
        "* Se calcula mediante la clasificación de los valores de las variables y la comparación de los rangos de los valores.\n",
        "\n",
        "###Correlación de Kendall:\n",
        "\n",
        "* También conocida como \"tau de Kendall\".\n",
        "* Mide la relación de concordancia o discordancia entre dos variables, es decir, si los pares de observaciones mantienen el mismo orden o no.\n",
        "* No asume una distribución normal ni una relación lineal y es robusta frente a valores atípicos.\n",
        "* A menudo se utiliza cuando se tienen datos ordinales o cuando la relación no es necesariamente lineal.\n",
        "* Se calcula contando los pares de datos que son concordantes y discordantes."
      ],
      "metadata": {
        "id": "XZKoYfTesLWg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calcularemos la **Correlación de Pearson** entre dos características presentes en la matriz **X. X[:, 0]** que representa la primera característica, y **X[:, 1]** que representa la segunda característica. Usaremos la función [np.corrcoef](https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html) que calcula la matriz de correlación entre estas dos características. Se utiliza comúnmente cuando se asume una relación lineal, como en el análisis de regresión, para evaluar la relación entre dos variables cuantitativas."
      ],
      "metadata": {
        "id": "6UnI-OKeshM2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "3JyfTKNRmRH2",
        "outputId": "e80fa29e-4d7d-4026-a3fa-530af9d70df9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calcula la correlación de Pearson entre las dos características\n",
        "correlation = np.corrcoef(X[:, 0], X[:, 1])[0, 1]\n",
        "\n",
        "# print(\"Correlación de Pearson entre X[:, 0] y X[:, 1]:\", correlation)\n",
        "# Anota el valor de correlación y el valor p en el gráfico\n",
        "# plt.annotate(f'Correlación_X = {correlation:.2f}', xy=(0.5, 0.9))\n",
        "\n",
        "# Grafica un scatter plot de las dos características\n",
        "plt.figure(figsize=(6, 6))\n",
        "plt.scatter(X[:, 0], X[:, 1], label=f'Correlación = {correlation:.2f}')\n",
        "plt.xlabel('Característica X[:, 0]')\n",
        "plt.ylabel('Característica X[:, 1]')\n",
        "plt.title('Correlación de Pearson')\n",
        "\n",
        "# Anota el valor de correlación y el valor p en el gráfico\n",
        "plt.annotate(f'Correlación = {correlation:.2f}', xy=(0.5, 0.9))\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calcularemos la **Correlación de Spearman** entre dos características presentes en la matriz **X. X[:, 0]** que representa la primera característica, y **X[:, 1]** que representa la segunda característica. La función spearmanr devolverá dos valores: correlation_spearmanr, que es la correlación de Spearman, y p_value, que es el valor p asociado a la prueba de correlación de Spearman. Se utiliza cuando no se asume una relación lineal, como en la comparación de calificaciones de dos pruebas, para evaluar la relación entre variables ordinales o cuando se desea ser menos sensible a valores atípicos."
      ],
      "metadata": {
        "id": "S69Vw6vQt0zb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "-lD8gFhRnr_b",
        "outputId": "7d3a9d56-9cfb-4fb3-8413-6542945c4017"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlación de Searmanr entre X[:, 0] y X[:, 1]: -0.08118811881188119\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "correlation_spearmanr, p_value = spearmanr(X[:, 0], X[:, 1])\n",
        "\n",
        "print(\"Correlación de Searmanr entre X[:, 0] y X[:, 1]:\", correlation_spearmanr)\n",
        "\n",
        "# Grafica un scatter plot de las dos características\n",
        "plt.figure(figsize=(6, 6))\n",
        "plt.scatter(X[:, 0], X[:, 1])\n",
        "plt.xlabel('Característica X[:, 0]')\n",
        "plt.ylabel('Característica X[:, 1]')\n",
        "plt.title('Correlación de Spearman')\n",
        "\n",
        "# Anota el valor de correlación y el valor p en el gráfico\n",
        "# plt.annotate(f'Correlación = {correlation_spearmanr:.2f}', xy=(0.5, 0.9))\n",
        "# plt.annotate(f'Valor p = {p_value:.4f}', xy=(0.5, 0.85))\n",
        "\n",
        "plt.grid()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calcularemos la **Correlación de Kendalltau** entre dos características presentes en la matriz **X. X[:, 0]** que representa la primera característica, y **X[:, 1]** que representa la segunda característica. La función kendalltau devuelve dos valores: correlation_kendalltau, que es la correlación de Kendall Tau, y p_value, que es el valor p asociado a la prueba de correlación de Kendall. Se utiliza en situaciones similares a la de Spearman, pero es especialmente útil cuando se trabaja con datos ordinales o categóricos y cuando se desea una medida robusta ante valores atípicos."
      ],
      "metadata": {
        "id": "b-T44Kqbwytq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "MX3UkDfCnt-X",
        "outputId": "df3c8cd0-c512-4cfd-c7b9-0324ecdf7373"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlación Tau de Kendall = -0.05\n",
            "Valor p = 0.4602\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "correlation_kendalltau, p_value = kendalltau(X[:, 0], X[:, 1])\n",
        "# print(\"Correlación de Kendalltau entre X[:, 0] y X[:, 1]:\", correlation_kendalltau)\n",
        "\n",
        "# Grafica un scatter plot de las dos características\n",
        "# plt.figure(figsize=(6, 6))\n",
        "# plt.scatter(X[:, 0], X[:, 1])\n",
        "# plt.xlabel('Característica X[:, 0]')\n",
        "# plt.ylabel('Característica X[:, 1]')\n",
        "# plt.title('Correlación de Kendall Tau')\n",
        "\n",
        "# Crea un mapa de calor\n",
        "labels = ['X[:, 0]', 'X[:, 1]']\n",
        "correlation_matrix = np.array([[1, correlation_kendalltau],[correlation_kendalltau, 1]])\n",
        "\n",
        "# Configura el mapa de calor usando seaborn\n",
        "sns.set(font_scale=1.2)\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', xticklabels=labels, yticklabels=labels)\n",
        "\n",
        "# Mostrar el valor de correlación y el valor p\n",
        "print(f'Correlación Tau de Kendall = {correlation_kendalltau:.2f}')\n",
        "print(f'Valor p = {p_value:.4f}')\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aplicación de carácterísticas, mediante **SelectKBest** que se utiliza para seleccionar las mejores características de acuerdo con una métrica específica. En este caso, se está utilizando la métrica [chi-cuadrado](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html). **Chi-cuadrado** se utiliza para determinar si existe una relación estadística entre dos variables categóricas en un conjunto de datos. Esta métrica es útil para medir la dependencia entre dos variables y se utiliza comúnmente en el análisis de tablas de contingencia\n"
      ],
      "metadata": {
        "id": "aly4CnTM0Oei"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJPcWvQoqgsi",
        "outputId": "3739b9c7-fbe6-4abc-a42e-2cef223c025c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[4.76898369e-01 5.21303748e-01 1.25198379e-01 1.48449581e-01\n",
            "  6.11927193e-01]\n",
            " [4.51303037e-01 7.07505698e-01 7.31712082e-02 2.65687975e-01\n",
            "  3.97667346e-02]\n",
            " [5.21064876e-01 1.07961859e+00 1.16940705e+00 5.75787970e-01\n",
            "  8.13364259e-01]\n",
            " [1.51999486e+00 6.84501107e-01 5.54893707e-02 1.71958931e+00\n",
            "  3.70825001e-01]\n",
            " [9.44446256e-01 3.17218215e-01 8.49123046e-01 4.10049693e-01\n",
            "  7.86327962e-01]\n",
            " [6.58552967e-01 1.29802197e+00 3.01549448e-02 5.14233966e-01\n",
            "  2.69622405e+00]\n",
            " [3.09114445e-01 8.07648488e-01 5.77822712e-01 2.33466662e-01\n",
            "  1.44653470e+00]\n",
            " [5.64008535e-01 1.05188010e+00 3.21693913e-01 1.28455230e+00\n",
            "  2.49720039e+00]\n",
            " [1.74235620e+00 1.66069981e+00 4.69537098e-01 1.30324275e+00\n",
            "  1.15147873e-01]\n",
            " [4.26258731e-01 7.81198102e-01 1.94473774e+00 6.76908035e-01\n",
            "  1.49448454e+00]\n",
            " [1.33701560e-01 1.34671751e+00 1.43043530e-01 1.07774381e+00\n",
            "  6.93773153e-01]\n",
            " [2.22605681e-01 1.04797216e+00 1.09140294e-01 9.13079218e-01\n",
            "  2.84279671e-01]\n",
            " [2.65917224e+00 1.18894496e+00 7.65997056e-01 6.06319524e-01\n",
            "  7.73252977e-01]\n",
            " [2.24258934e-01 1.50699840e+00 3.00495195e-01 3.02249730e-01\n",
            "  6.76460732e-01]\n",
            " [8.59307670e-02 7.82601752e-02 3.65380582e-01 1.94292938e-01\n",
            "  3.95228983e-01]\n",
            " [1.56506538e-01 4.81481474e-01 5.11600970e-01 2.32181036e-01\n",
            "  1.54079701e+00]\n",
            " [1.09634685e+00 7.78547559e-02 8.50891944e-01 2.34215801e-01\n",
            "  3.82732430e-01]\n",
            " [3.17160626e-01 1.55224318e+00 3.30687508e-01 5.20040615e-01\n",
            "  2.86904488e-01]\n",
            " [6.43314465e-01 2.49458580e-01 1.32411247e-02 1.57062341e+00\n",
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            " [5.61330204e-02 1.22619619e+00 1.06126205e+00 1.38504274e-03\n",
            "  1.83339199e-01]\n",
            " [8.62789892e-03 1.96557401e-01 6.15748704e-01 5.27004208e-01\n",
            "  3.54757693e-01]\n",
            " [2.36417382e+00 2.67594746e-01 2.65030274e-01 2.03341817e-02\n",
            "  6.78025782e-01]\n",
            " [2.91837363e-01 2.22675101e-01 2.93988382e-01 7.61492212e-01\n",
            "  3.53431749e-01]\n",
            " [1.95180410e+00 1.11831192e+00 8.48214735e-01 6.59891730e-01\n",
            "  1.31054012e-01]\n",
            " [6.66383082e-01 1.65813068e+00 1.76399739e+00 4.60719787e-01\n",
            "  1.18164045e-01]\n",
            " [1.60183605e+00 5.10292740e-01 2.78561851e+00 8.87180942e-01\n",
            "  1.83925494e-01]\n",
            " [7.55395696e-01 3.96086585e-01 2.68913472e+00 8.67407411e-01\n",
            "  5.91402668e-01]\n",
            " [4.48165363e-01 1.26306835e+00 1.85583449e+00 6.49337928e-01\n",
            "  4.52489093e-01]\n",
            " [1.12859406e+00 9.94394391e-01 7.61804328e-01 4.96000946e-01\n",
            "  1.31913688e+00]\n",
            " [6.96326654e-01 2.11497013e-01 2.16052537e-01 2.90397101e-01\n",
            "  7.04921353e-01]\n",
            " [4.21714513e-02 7.30677753e-01 1.84696289e+00 2.86887192e-01\n",
            "  3.84879809e-01]\n",
            " [1.30106954e-01 5.82224591e-01 4.75683472e-01 9.39532294e-02\n",
            "  2.09460307e+00]\n",
            " [8.62596011e-01 1.02943883e+00 6.33420331e-01 2.65561909e+00\n",
            "  9.08763246e-01]\n",
            " [9.36742464e-01 8.88971358e-01 9.17515596e-01 1.41232771e+00\n",
            "  2.42117961e-01]\n",
            " [2.25556423e+00 8.80178912e-01 1.28252298e-01 1.02250684e+00\n",
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            " [7.25597378e-01 3.19328417e-01 1.55399781e+00 1.38336396e+00\n",
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            " [1.75498615e+00 9.88001942e-01 2.65199620e-01 1.32988422e-01\n",
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            " [1.56821116e-02 3.77088909e-03 2.67892177e-01 1.60928168e-01\n",
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            " [2.19541028e-01 3.04614305e+00 1.05512600e+00 1.08403662e+00\n",
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            " [2.74516358e-01 1.07305276e-01 1.91692582e+00 8.90915083e-01\n",
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            " [1.06850940e+00 4.50934462e-01 4.33105451e+00 4.53385804e-01\n",
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            " [9.38981573e-01 9.12783494e-01 1.19466313e+00 1.01702099e+00\n",
            "  2.19509556e-01]\n",
            " [5.45711974e-01 1.07919473e+00 3.06165066e-01 1.88458584e+00\n",
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            " [1.63928572e-01 2.55918467e+00 9.80739928e-01 9.63213559e-02\n",
            "  1.18137860e+00]\n",
            " [1.17915793e+00 5.81268477e-01 8.53410903e-02 1.30142807e+00\n",
            "  1.63263453e+00]\n",
            " [9.60504382e-01 1.81338429e-01 3.97369747e-01 7.93117363e-01\n",
            "  5.02816701e-01]\n",
            " [1.36453185e+00 3.49654457e-01 2.95958826e+00 6.89449185e-01\n",
            "  7.64143924e-01]\n",
            " [1.68823003e+00 4.05204080e-01 4.67783070e+00 1.12465983e-01\n",
            "  4.98052405e-01]\n",
            " [5.32702792e-01 9.49246474e-01 1.34986807e+00 1.09074973e+00\n",
            "  1.50239657e+00]\n",
            " [2.30391670e+00 1.30184623e+00 1.29370156e+00 1.06001582e+00\n",
            "  6.28087560e-01]\n",
            " [2.75670535e-01 2.51484415e-01 8.35101353e-01 7.09727966e-01\n",
            "  2.01640663e+00]\n",
            " [4.79655814e-01 5.21189312e-01 2.23460699e+00 6.20358298e-01\n",
            "  1.84306955e+00]\n",
            " [3.70704003e-01 4.49712100e-01 1.05004467e+00 9.45615796e-01\n",
            "  6.72756089e-02]\n",
            " [6.52408582e-01 2.77259276e+00 3.59865169e+00 3.90953375e-01\n",
            "  1.95591231e+00]\n",
            " [3.84645423e-01 1.20857365e+00 9.64902950e-01 4.43836093e-01\n",
            "  2.42019830e-01]\n",
            " [1.02993528e+00 5.53132064e-01 2.57374908e-01 3.49943365e-01\n",
            "  4.57039607e-02]\n",
            " [9.55945000e-01 3.79151736e-01 3.04373307e+00 3.45981776e-01\n",
            "  2.25930895e+00]\n",
            " [1.02893549e+00 9.96212640e-01 1.14462475e+00 7.22870076e-02\n",
            "  1.21793851e+00]\n",
            " [9.36445726e-01 1.09463837e+00 1.14032083e-01 3.50951769e-02\n",
            "  2.34821526e-01]\n",
            " [2.80441705e-01 3.94849514e-01 8.82342712e-01 9.93123611e-01\n",
            "  2.67733537e-01]\n",
            " [1.10540657e-01 1.16103939e-01 2.93668633e-01 1.02017271e+00\n",
            "  2.03068447e+00]\n",
            " [3.10886172e-01 1.88778597e-01 5.08589018e-01 9.74001663e-02\n",
            "  5.23891024e-01]\n",
            " [1.64813493e+00 1.65671510e+00 3.09590732e-01 1.64227755e-01\n",
            "  9.85510738e-01]\n",
            " [2.16949570e-01 7.84957521e-01 6.20716743e-01 4.45393251e-01\n",
            "  5.54309627e-01]\n",
            " [7.04700276e-01 3.12292251e-01 1.43683401e+00 9.43260725e-01\n",
            "  1.57667016e-01]\n",
            " [1.01697275e-01 2.46121252e-02 1.89634239e+00 1.92793845e-02\n",
            "  7.19983730e-01]\n",
            " [1.63242330e+00 1.79132755e+00 9.32664558e-01 6.71341546e-02\n",
            "  5.85865511e-02]\n",
            " [3.64693544e-01 1.21407740e+00 1.37977014e+00 1.56703855e-01\n",
            "  4.40922632e-01]\n",
            " [1.82425666e+00 5.55786964e-01 9.05415370e-01 3.03603904e-01\n",
            "  1.03493146e-02]\n",
            " [5.17519043e-01 2.20144129e+00 1.45899162e+00 9.78829859e-01\n",
            "  1.99300197e-01]\n",
            " [7.45864065e-02 1.31247037e+00 2.13703895e+00 1.07709907e+00\n",
            "  8.58972388e-01]\n",
            " [3.57680719e-02 2.83455451e+00 1.19266118e-01 2.38074535e+00\n",
            "  2.11679102e+00]\n",
            " [7.82629156e-01 1.15107468e-01 1.53297707e-01 1.10389299e-01\n",
            "  4.57415606e-01]\n",
            " [3.09012969e-01 3.97271814e-01 2.78637404e+00 1.67600381e+00\n",
            "  1.32880578e-01]]\n",
            "Características originales: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
            "Características seleccionadas: [0 2 3 4 5]\n"
          ]
        }
      ],
      "source": [
        "# Aplica SelectKBest con la métrica chi-cuadrado para seleccionar las mejores 5 características\n",
        "k_best = SelectKBest(score_func=chi2, k=5)\n",
        "X_new = k_best.fit_transform(X_feat, y_feat)\n",
        "# print(X_new)\n",
        "\n",
        "# Obtén las características seleccionadas\n",
        "selected_features = np.where(k_best.get_support())[0]\n",
        "# print(selected_features)\n",
        "\n",
        "print(\"Características originales:\", list(range(X_feat.shape[1])))\n",
        "print(\"Características seleccionadas:\", selected_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 738
        },
        "id": "PPAcGfvR8Znt",
        "outputId": "a307cf27-d94e-4889-f676-f16d6da43745"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Puntuaciones de características:\n",
            "Característica 1: 0.59\n",
            "Característica 2: 0.00\n",
            "Característica 3: 0.68\n",
            "Característica 4: 0.74\n",
            "Característica 5: 1.14\n",
            "Característica 6: 0.85\n",
            "Característica 7: 0.00\n",
            "Característica 8: 0.09\n",
            "Característica 9: 0.02\n",
            "Característica 10: 0.09\n",
            "\n",
            "Características seleccionadas (índices): [0 2 3 4 5]\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Aplica el método de filtro utilizando ANOVA Ref: https://towardsdatascience.com/anova-for-feature-selection-in-machine-learning-d9305e228476\n",
        "num_features_to_select = 5\n",
        "feature_selector = SelectKBest(score_func=f_classif, k=num_features_to_select)\n",
        "X_filter = feature_selector.fit_transform(X_feat, y_feat)\n",
        "\n",
        "# Obtener las puntuaciones de características\n",
        "feature_scores = feature_selector.scores_\n",
        "\n",
        "# Obtener las características seleccionadas\n",
        "selected_feature_indices = feature_selector.get_support(True)\n",
        "\n",
        "print(\"Puntuaciones de características:\")\n",
        "for feature, score in enumerate(feature_scores):\n",
        "    print(f\"Característica {feature + 1}: {score:.2f}\")\n",
        "\n",
        "print(\"\\nCaracterísticas seleccionadas (índices):\", selected_feature_indices)\n",
        "\n",
        "# Graficar las puntuaciones de características\n",
        "plt.figure(figsize=(18, 6))\n",
        "plt.bar(range(len(feature_scores)), feature_scores, tick_label=[f'Feature {i+1}' for i in range(len(feature_scores))])\n",
        "plt.xlabel('Características')\n",
        "plt.ylabel('Puntuaciones de características')\n",
        "plt.title('Puntuaciones de características utilizando ANOVA')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J94O6Do490DZ",
        "outputId": "09ab7868-f656-4ba9-b605-321b9b3a2953"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Características seleccionadas:\n",
            "Feature 1\n",
            "Feature 3\n",
            "Feature 4\n",
            "Feature 5\n",
            "Feature 6\n"
          ]
        }
      ],
      "source": [
        "# Inicializa el clasificador (puedes usar otros clasificadores según tus necesidades)\n",
        "classifier = LogisticRegression(solver='liblinear')\n",
        "\n",
        "# Inicializa RFE para seleccionar 5 características\n",
        "num_features_to_select = 5\n",
        "rfe = RFE(estimator=classifier, n_features_to_select=num_features_to_select)\n",
        "\n",
        "# Ajusta RFE al conjunto de datos\n",
        "rfe.fit(X_feat, y_feat)\n",
        "\n",
        "# Obtén las características seleccionadas\n",
        "selected_feature_indices = rfe.support_\n",
        "selected_features = [f'Feature {i + 1}' for i, selected in enumerate(selected_feature_indices) if selected]\n",
        "\n",
        "print(\"Características seleccionadas:\")\n",
        "for feature in selected_features:\n",
        "    print(feature)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Hzm3rmXBSXZ",
        "outputId": "2253e060-eb4e-4e42-a302-1a39017efca0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Características seleccionadas con Forward Selection:\n",
            "Feature 3\n",
            "Feature 9\n",
            "Feature 5\n",
            "Feature 8\n",
            "Feature 7\n",
            "Feature 2\n",
            "Feature 1\n",
            "Feature 4\n",
            "Feature 10\n",
            "Feature 6\n",
            "\n",
            "Características seleccionadas con Backward Selection:\n",
            "Feature 4\n",
            "Feature 6\n",
            "Feature 8\n",
            "Feature 10\n",
            "\n",
            "Características seleccionadas con Recursive Feature Elimination (RFE):\n",
            "Feature 1\n",
            "Feature 4\n",
            "Feature 5\n",
            "Feature 6\n",
            "Feature 10\n"
          ]
        }
      ],
      "source": [
        "# Divide los datos en conjuntos de entrenamiento y prueba\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_feat, y_feat, test_size=0.2, random_state=42)\n",
        "\n",
        "# Inicializa un clasificador (puedes usar otros clasificadores según tus necesidades)\n",
        "classifier = LogisticRegression(solver='liblinear')\n",
        "\n",
        "# Inicializa una lista para almacenar características seleccionadas\n",
        "selected_features_forward = []\n",
        "selected_features_backward = list(range(X_train.shape[1]))  # Comenzar con todas las características\n",
        "selected_features_rfe = None\n",
        "\n",
        "# Realiza la selección de características con Forward Selection\n",
        "while len(selected_features_forward) < X_train.shape[1]:\n",
        "    best_feature = None\n",
        "    best_accuracy = 0\n",
        "    for feature in range(X_train.shape[1]):\n",
        "        if feature in selected_features_forward:\n",
        "            continue\n",
        "        features_to_use = selected_features_forward + [feature]\n",
        "        X_train_subset = X_train[:, features_to_use]\n",
        "        X_test_subset = X_test[:, features_to_use]\n",
        "        classifier.fit(X_train_subset, y_train)\n",
        "        y_pred = classifier.predict(X_test_subset)\n",
        "        accuracy = accuracy_score(y_test, y_pred)\n",
        "        if accuracy > best_accuracy:\n",
        "            best_accuracy = accuracy\n",
        "            best_feature = feature\n",
        "    if best_feature is not None:\n",
        "        selected_features_forward.append(best_feature)\n",
        "    else:\n",
        "        break\n",
        "\n",
        "# Realiza la selección de características con Backward Selection\n",
        "while len(selected_features_backward) > 0:\n",
        "    worst_feature = None\n",
        "    worst_accuracy = best_accuracy\n",
        "    for feature in selected_features_backward:\n",
        "        features_to_use = selected_features_backward.copy()\n",
        "        features_to_use.remove(feature)\n",
        "        X_train_subset = X_train[:, features_to_use]\n",
        "        X_test_subset = X_test[:, features_to_use]\n",
        "        classifier.fit(X_train_subset, y_train)\n",
        "        y_pred = classifier.predict(X_test_subset)\n",
        "        accuracy = accuracy_score(y_test, y_pred)\n",
        "        if accuracy > worst_accuracy:\n",
        "            worst_accuracy = accuracy\n",
        "            worst_feature = feature\n",
        "    if worst_feature is not None:\n",
        "        selected_features_backward.remove(worst_feature)\n",
        "    else:\n",
        "        break\n",
        "\n",
        "# Realiza la selección de características con Recursive Feature Elimination (RFE)\n",
        "num_features_to_select = 5\n",
        "rfe = RFE(estimator=classifier, n_features_to_select=num_features_to_select)\n",
        "rfe.fit(X_train, y_train)\n",
        "selected_features_rfe = rfe.support_\n",
        "\n",
        "# Obtén las características seleccionadas\n",
        "selected_feature_names_forward = [f'Feature {i + 1}' for i in selected_features_forward]\n",
        "selected_feature_names_backward = [f'Feature {i + 1}' for i in selected_features_backward]\n",
        "selected_feature_names_rfe = [f'Feature {i + 1}' for i, selected in enumerate(selected_features_rfe) if selected]\n",
        "\n",
        "print(\"Características seleccionadas con Forward Selection:\")\n",
        "for feature in selected_feature_names_forward:\n",
        "    print(feature)\n",
        "\n",
        "print(\"\\nCaracterísticas seleccionadas con Backward Selection:\")\n",
        "for feature in selected_feature_names_backward:\n",
        "    print(feature)\n",
        "\n",
        "print(\"\\nCaracterísticas seleccionadas con Recursive Feature Elimination (RFE):\")\n",
        "for feature in selected_feature_names_rfe:\n",
        "    print(feature)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Codigo de KNN con Rechazo**"
      ],
      "metadata": {
        "id": "wUt6GlSpMnFw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inicializa el clasificador KNN con un valor de k\n",
        "k = 10\n",
        "knn_classifier = KNeighborsClassifier(n_neighbors=k)\n",
        "\n",
        "# Ajusta el modelo a los datos de entrenamiento\n",
        "knn_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = knn_classifier.predict(X_test)\n",
        "\n",
        "# Calcula la precisión del modelo sin rechazo\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "# Umbral para el rechazo\n",
        "umbral_rechazo = 0.5\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba con rechazo\n",
        "y_pred_with_rechazo = []\n",
        "for i in range(len(X_test)):\n",
        "    distancias, indices = knn_classifier.kneighbors([X_test[i]], n_neighbors=k)\n",
        "    promedio_distancias = np.mean(distancias)\n",
        "    if promedio_distancias <= umbral_rechazo:\n",
        "        y_pred_with_rechazo.append(y_pred[i])\n",
        "        y_pred_with_rechazo.append(-1)\n",
        "    else:\n",
        "        y_pred_with_rechazo.append(knn_classifier.predict([X_test[i]]))\n",
        "\n",
        "# Calcula la precisión del modelo con rechazo\n",
        "y_pred_with_rechazo = np.array(y_pred_with_rechazo)\n",
        "y_pred_with_rechazo = y_pred_with_rechazo[y_pred_with_rechazo != -1]\n",
        "accuracy_with_rechazo = accuracy_score(y_test, y_pred_with_rechazo)\n",
        "\n",
        "print(f\"Precisión del modelo KNN sin rechazo: {accuracy:.2f}\")\n",
        "print(f\"Precisión del modelo KNN con rechazo (umbral {umbral_rechazo}): {accuracy_with_rechazo:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wg2P7o2QLt6W",
        "outputId": "298515e2-8908-4c79-db82-b78a8219b989"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo KNN sin rechazo: 0.35\n",
            "Precisión del modelo KNN con rechazo (umbral 0.5): 0.35\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Codigo de KNN con Distancia**"
      ],
      "metadata": {
        "id": "zzFQUizuM17-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inicializa el clasificador KNN con un valor de k\n",
        "k = 10\n",
        "knn_classifier = KNeighborsClassifier(n_neighbors=k)\n",
        "\n",
        "# Ajusta el modelo a los datos de entrenamiento\n",
        "knn_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = knn_classifier.predict(X_test)\n",
        "\n",
        "# Calcula la precisión del modelo sin distancia media\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "# Umbral para la distancia media\n",
        "umbral_distancia_media = 0.5\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba con distancia media\n",
        "y_pred_con_distancia_media = []\n",
        "for i in range(len(X_test)):\n",
        "    distancias, indices = knn_classifier.kneighbors([X_test[i]], n_neighbors=k)\n",
        "    promedio_distancias = np.mean(distancias)\n",
        "    if promedio_distancias <= umbral_distancia_media:\n",
        "        y_pred_con_distancia_media.append(y_pred[i])\n",
        "    else:\n",
        "        y_pred_con_distancia_media.append(1 - y_pred[i])  # Invierte la etiqueta original\n",
        "\n",
        "# Calcula la precisión del modelo con distancia media\n",
        "accuracy_con_distancia_media = accuracy_score(y_test, y_pred_con_distancia_media)\n",
        "\n",
        "print(f\"Precisión del modelo KNN sin distancia media: {accuracy:.2f}\")\n",
        "print(f\"Precisión del modelo KNN con distancia media (umbral {umbral_distancia_media}): {accuracy_con_distancia_media:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gupqiE4uM4n0",
        "outputId": "3b285aaa-4509-494e-c3d4-7196bb90dade"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo KNN sin distancia media: 0.35\n",
            "Precisión del modelo KNN con distancia media (umbral 0.5): 0.65\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Codigo de KNN con onderación en ejemplos y características**"
      ],
      "metadata": {
        "id": "gM_RJgJdP_Qb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inicializa el clasificador KNN con un valor de k y peso uniforme\n",
        "k = 10\n",
        "knn_classifier = KNeighborsClassifier(n_neighbors=k, weights='uniform')\n",
        "\n",
        "# Ajusta el modelo a los datos de entrenamiento\n",
        "knn_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = knn_classifier.predict(X_test)\n",
        "\n",
        "# Calcula la precisión del modelo con peso uniforme\n",
        "accuracy_uniform = accuracy_score(y_test, y_pred)\n",
        "\n",
        "# Cambia el peso a distancia inversa (mayor peso a ejemplos cercanos)\n",
        "knn_classifier = KNeighborsClassifier(n_neighbors=k, weights='distance')\n",
        "\n",
        "# Ajusta el modelo a los datos de entrenamiento con peso de distancia inversa\n",
        "knn_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba con peso de distancia inversa\n",
        "y_pred_distance_weighted = knn_classifier.predict(X_test)\n",
        "\n",
        "# Calcula la precisión del modelo con peso de distancia inversa\n",
        "accuracy_distance_weighted = accuracy_score(y_test, y_pred_distance_weighted)\n",
        "\n",
        "print(f\"Precisión del modelo KNN con peso uniforme: {accuracy_uniform:.2f}\")\n",
        "print(f\"Precisión del modelo KNN con peso de distancia inversa: {accuracy_distance_weighted:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7n7Xu0bTP_xh",
        "outputId": "1f857e63-b8d7-41f8-d3d9-56ef9482f138"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo KNN con peso uniforme: 0.35\n",
            "Precisión del modelo KNN con peso de distancia inversa: 0.45\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Support Vector Machine: Linealmente separables**\n"
      ],
      "metadata": {
        "id": "CLtYsUzfQqV0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inicializa el clasificador SVM lineal\n",
        "clf = svm.SVC(kernel='linear')\n",
        "\n",
        "# Ajusta el modelo a los datos de entrenamiento\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Calcula la precisión del modelo\n",
        "accuracy = np.mean(y_pred == y_test)\n",
        "\n",
        "# Visualiza los datos y la superficie de decisión\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Dibuja los puntos de datos\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, marker='o', s=100)\n",
        "\n",
        "# Dibuja la superficie de decisión\n",
        "ax = plt.gca()\n",
        "xlim = ax.get_xlim()\n",
        "ylim = ax.get_ylim()\n",
        "\n",
        "plt.title(f\"Precisión del modelo SVM lineal: {accuracy:.2f}\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "3rGl5cQmQyw5",
        "outputId": "8a8649b4-82df-4f69-a1d9-05c170731081"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Support Vector Machine: Regularizacion**"
      ],
      "metadata": {
        "id": "a_kHfDdERd2u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inicializa el clasificador SVM con regularización (parámetro C)\n",
        "C = 1.0  # Valor de ajuste\n",
        "clf = svm.SVC(C=C, kernel='linear')\n",
        "\n",
        "# Ajusta el modelo a los datos de entrenamiento\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Calcula la precisión del modelo\n",
        "accuracy = np.mean(y_pred == y_test)\n",
        "\n",
        "# Visualiza los datos y la superficie de decisión\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Dibuja los puntos de datos\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, marker='o', s=100)\n",
        "\n",
        "# Dibuja la superficie de decisión\n",
        "ax = plt.gca()\n",
        "xlim = ax.get_xlim()\n",
        "ylim = ax.get_ylim()\n",
        "\n",
        "plt.title(f\"Precisión del modelo SVM con regularización (C={C}): {accuracy:.2f}\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "WDrZZcF7Rf5q",
        "outputId": "2cc8825c-fbb6-44cc-b5dd-a14b60b80fd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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0NGw2m9+rDQFg586dqK6uxu233+5zrvPPPx9Dhw7F119/3a7N6ZPxp06disLCwi7Ps3btWrjdbtxxxx0+z+Guu+5qd+zHH3+MqVOnIiYmxud1mjNnDkRRxA8//OD382vruuuu8xkymzRpEhhj7SatT5o0CWVlZfB6vQCAb775BgCwdOlSn+N+//vfA0DraxSO5yiKIlavXo1FixZh0KBBrbenpKTgmmuuwcaNG2GxWDp9DbRaLbZt24Z77rkHQHOP1U033YSUlBTccccdPsOYMTExWLBgAb788svWoS7GGD744ANMmDCh01JcI0aMwOjRo1sXHr333nu46KKLWnsLu1NTU4MffvgBN954IzIzM33ua3ldA3kdbr31Vp/fy9SpUyGKIkpKSvyKK9D387p16+D1enH77bf7tL/jjjv8Oq+/BEFoXSgoSRLq6+vh9XoxYcIE7N69u93xl156KRISEmSd4/HHH8dXX32FFStWYPjw4a23a7Xa1v9uampCbW0tpk6dCrvdjsOHDwNo7ikrKirCXXfd5TOaAnT9uebv+6/F8OHDMXXq1NafExISMGTIkG4/o+rq6gA0/9231fJ35G+vJ9D8Ge7Pv/nz5/v9mN1xOBwd9qK2fK6fvoir5Xme3kvLGOu21zOQ83XmzTffxMqVK/HKK69g2LBhcDgc7aZrfPnll7j33ntx0UUX4cYbb8T333+P+fPn4/nnn8eJEyf8Og/5BQ2791Evv/wy8vPzoVAokJSUhCFDhoDnfa8lFAoF0tPTfW47duwYzGazz3BVW9XV1QB+GcYfOXKkrLhuv/12fPTRRzjvvPOQlpaGefPm4YorrsC5557baZuWL94hQ4a0u2/o0KHtVi1qNJp2X1gxMTEdzlnt6DyDBw/2uT0hIaHdh/2xY8ewb9++Tr8YW14nuU5PZKKiogCg3dBQVFQUJEmC2WxGXFwcSkpKwPM88vLyfI5LTk5GdHR063MLx3OsqamB3W7v8Pc1bNgwSJKEsrKyLocXo6Ki8Mwzz+CZZ55BSUkJ1q1bh2effRYvvfQSoqKifEolLV68GJ999hm++OILXHPNNdi8eTOKi4u7He665ppr8Nxzz+Huu+/G5s2b8cADD3R5fFstSUJXf/+BvA6n//5bfifd/e0CPXs/t/xdnP73Exsb2+7voqfeeustPPfcczh8+LDPXN2cnJx2x3Z0W1dWrlyJRx99FPfffz8uvfRSn/sOHjyIBx98EN999127pN9sNgMI/HPN3/dfi9N/z4B/n1Et2GnlxVqGlZuamvyOec6cOX4fGyxarbbdHFgArVM72l4gAL88z0BL6sk9X2cmT57c+t9XXXUVhg0bBgB49tlnO23DcRzuvvturFq1Chs2bMC1114rJ/QBj5LPPmrixImYMGFCl8eo1ep2CakkSUhMTMS7777bYRu5vRCnS0xMxN69e7Fq1Sp8++23+Pbbb/Hmm2/iuuuuw1tvvdWjx24hCEJQHqcrkiRh7ty5uPfeezu8P9Di953F3tntp38JBbPuaaieo1xZWVm48cYbcfHFF2PQoEF49913fZLPCy64AFFRUXjvvfdwzTXX4L333oMgCLjqqqu6fNyrr74a999/P2655RbExcVh3rx5oX4q3fL399yRcL2fO/sb82dB3zvvvIMbbrgBixYtwj333IPExEQIgoDly5e3Jn5t+ZscAM2LKRcvXoy5c+e2q+Pa2NiI6dOnw2Qy4bHHHkNubi40Gg12796NP/7xjwEtEuyIv++/QH/PLXP2T09Shw4dCqB5rrO/Kisr/TouKipK1u+hKykpKe0WiQJARUUFACA1NdXn9pbnGR8fH5bz+SMmJgazZs3Cu+++22XyCfzSaXD6ginSPUo+B5jc3FysXbsWU6ZM6fIDJzc3FwBw4MCBdlf73VGpVFi4cCEWLlwISZJw++234x//+AceeuihDh+rZQHKkSNHWlfttzhy5Ejr/T3V8jjHjh3zGSatqalp92Gfm5sLq9Uakd6DjmRlZUGSJBw7dqz1qhwAqqqq0NjY2PrcwvEcExISoNPpOqzvevjwYfA879ck/9PFxMQgNze3XYFntVqNyy67DP/5z39QVVWFjz/+GLNmzUJycnKXj5eZmYkpU6Zgw4YN+M1vfuNTC7Q7La9dR8WmW4TqdZDD3/dzy9/F8ePHfXob6+rq2v1dtPSENjY2+gxN+zM14JNPPsGgQYPw6aef+iRqy5Yt8+v5dMbhcOCSSy5BdHQ03n///XZJ+IYNG1BXV4dPP/3Up35wS/WPFm0/1+T83fv7/uupzMxMaLXadnHn5+djyJAh+OKLL/Diiy/6tUlCSkqKX+d88803A9757HRjx47F+vXrYbFYfBYBbdu2rfX+tlqeZ9vXNJTn85fD4WjtLe9KywhJTzttBiKa8znAXHHFFRBFEY8//ni7+7xeb2t5iXnz5sFoNGL58uXtVsN2dfXeMmepBc/zGD16NAB0ODwCABMmTEBiYiJee+01n2O+/fZbHDp0COeff75fz607c+bMgVKpxN///nef59DRqu4rrrgCW7ZswapVq9rd19jY2DoXM1wWLFgAoH2szz//PAC0vkbheI6CIGDevHn44osvfMruVFVV4b333sM555zTbvVpWz/99FOHK3FLSkrw888/dziMvXjxYng8Htx2222oqalpLTPUnSeeeALLli2TPbcxISEB06ZNwxtvvNGu5EvL69rT1yEY/H0/z549GwqFAq+++qrPMS+99FK7di0JWts5vzabza+Ri5Yev7Z/e9u2bcOWLVu6fzJd+PWvf42jR4/is88+63CaQEfndbvd7bZvPOOMM5CTk4MXXnihXSmdrj7X/H3/9ZRSqcSECROwc+fOdvc9+uijqKurw80339zhe3P16tX46quvWn8O9ZzP2tpaHD582Gdjk8suuwyiKOKf//xn620ulwtvvvkmJk2a1O5ibNeuXeA4zmfYG/C/1JKc85WWlrbO/W3R0dSi4uJirFu3zmdksb6+vl3Pv8fjwZ///GeoVCrMnDmz21iJL+r5HGCmT5+O2267DcuXL8fevXsxb948KJVKHDt2DB9//DFefPFFXHbZZTCZTPjrX/+Km2++GWeeeSauueYaxMTE4KeffoLdbu/0i+jmm29GfX09Zs2ahfT0dJSUlODvf/87xo4d2+nVrVKpxNNPP40lS5Zg+vTpuPrqq1tLLWVnZ+Puu+8OynNvqQe6fPlyXHDBBViwYAH27NmDb7/9tt2wzz333IMvv/wSF1xwQWuJFJvNhv379+OTTz5BcXFxwENFgRgzZgyuv/56/POf/2wdYty+fTveeustLFq0qPXDL1zP8YknnsCaNWtwzjnn4Pbbb4dCocA//vEPuFwuPPPMM10+lzVr1mDZsmW48MILcdZZZ8FgMKCwsBBvvPEGXC5Xh9v5TZ8+Henp6fjiiy+g1WpxySWX+PW6TZ8+HdOnT/fr2NP97W9/wznnnIMzzjgDt956K3JyclBcXIyvv/4ae/fuBdCz1yEY/H0/JyUl4c4778Rzzz2HCy+8EOeeey5++umn1r+Ltr2U8+bNQ2ZmJm666Sbcc889EAQBb7zxBhISErpNCC644AJ8+umnuPjii3H++eejqKgIr732GoYPHw6r1RrQc/z666/xn//8B5deein27duHffv2td5nMBiwaNEinH322YiJicH111+P3/3ud+A4Dm+//Xa7hJLnebz66qtYuHAhxo4diyVLliAlJQWHDx/GwYMHO7wQA/x//wXDRRddhD/96U/tevOuvPJK7N+/H08++ST27NmDq6++GllZWairq8PKlSuxbt06n9q2gY7avP322ygpKWlNKn/44YfWaQ6/+tWvWnt5X3rpJTz66KNYv349ZsyYAaB5oeTll1+O+++/H9XV1cjLy8Nbb72F4uJivP766+3OtWbNGkyZMsWnRCDgf6klOee77rrr8P333/v8TYwaNQqzZ8/G2LFjERMTg2PHjuH1119vTSxbfPnll3jiiSdw2WWXIScnB/X19Xjvvfdw4MABPPXUU92OwpAOhH19PemRznY4Ol135WT++c9/svHjxzOtVsuMRiMbNWoUu/fee9nJkyd9jvvyyy/Z2WefzbRaLTOZTGzixIns/fff9zlP25JAn3zyCZs3bx5LTExkKpWKZWZmsttuu41VVFS0HnN6qaUWH374IRs3bhxTq9UsNjaWLV68uLV0VHfPq7PyMKcTRZE9+uijLCUlhWm1WjZjxgx24MCBDnf/aWpqYvfffz/Ly8tjKpWKxcfHs7PPPps9++yzPuWSIKPU0scff+xze2e/z5bnU1NT03qbx+Nhjz76KMvJyWFKpZJlZGSw+++/v7WMTLif4+7du9n8+fOZwWBgOp2OzZw5k23evLnL14ExxgoLC9nDDz/MzjrrLJaYmMgUCgVLSEhg559/vk/JmtPdc889DAC74oorOry/bamlrvhbaokxxg4cOMAuvvhiFh0dzTQaDRsyZAh76KGHfI7x53Xo7Pfc2XtBbsz+vJ+9Xi976KGHWHJyMtNqtWzWrFns0KFDLC4ujv3617/2ebxdu3axSZMmtb6Hn3/+eb9KLUmSxJ566imWlZXF1Go1GzduHPvqq6/afU509bs6vdRSy3k7+tf2MTdt2sTOOussptVqWWpqKrv33nvZqlWrOnx9N27cyObOncuMRiPT6/Vs9OjRPuWNOvo88ff919lOOP7uBlVVVcUUCgV7++23O7x/3bp17KKLLvJ57yxcuJB98cUX3T62P6ZPn97p6932dWx5jU5/bR0OB/vDH/7AkpOTmVqtZmeeeSZbuXJlu/M0NjYylUrF/v3vf7e7D36WWpJzvpbn1dayZcvYhAkTWExMDFMoFCw1NZVdddVVbN++fT7H7dy5ky1cuJClpaUxlUrFDAYDO+ecc9hHH33kV4ykPY4xP2a6E0II6XcaGxsRExODJ554ot3OTSRybrrpJhw9ehQ//vhjpEMJmRdeeAHPPPMMCgoKgrbgifQdNOeTEEIGgI5qHrbMYWwZNiW9w7Jly7Bjxw5s2rQp0qGEhMfjwfPPP48HH3yQEs8Bino+CSFkAFixYgVWrFiBBQsWwGAwYOPGjXj//fcxb968Tuc6EkJIKNCCI0IIGQBGjx4NhUKBZ555BhaLpXUR0uk1MwkhJNSo55MQQgghhIQNzfkkhBBCCCFhQ8knIYQQQggJmz4x51OSJJw8eRJGozGoe1sTQgghhJDgYIyhqakJqamp7bbBbatPJJ8nT54M+R7JhBBCCCGk58rKypCent7p/X0i+TQajQCan0yo90omhBBCCCHyWSwWZGRktOZtnekTyWfLULvJZKLkkxBCCCGkF+tuiiQtOCKEEEIIIWFDySchhBBCCAkbSj4JIYQQQkjYUPJJCCGEEELChpJPQgghhBASNpR8EkIIIYSQsKHkkxBCCCGEhA0ln4QQQgghJGwo+SSEEEIIIWFDySchhBBCCAmbPrG9JiGEENIX2arKYCk9AtHjgkpvQszgcVDqDJEOi5CIouSTEEIICSLGGKp2r0fhqndQe2CLz328SoPMaYuQM/9aGNNyIxQhIZHFMcZYpIPojsViQVRUFMxmM0wmU6TDIYQQQjokeT3Y+6+HcOLHL8DxApgktjuG4wWA43DG7U8jbfKCCERJSGj4m6/RnE9CCCEkCBhj+On1R3Bi45fNP3eQeLbczkQRu176Ayp3rQ9niIT0CrKTzx9++AELFy5EamoqOI7D559/3m2bDRs24IwzzoBarUZeXh5WrFgRQKiEEEJI71WzfzPKvv8U8GtAkQEM2PPa/RA97pDHRkhvIjv5tNlsGDNmDF5++WW/ji8qKsL555+PmTNnYu/evbjrrrtw8803Y9WqVbKDJYQQQnqrotXvNg+p+43BYzOjYht9H5KBRfaCo/POOw/nnXee38e/9tpryMnJwXPPPQcAGDZsGDZu3Ii//vWvmD9/vtzTE0IIIb2Os6EaVXs2+Nnr2QbHo2jNu0g/Z2FI4iKkNwr5nM8tW7Zgzpw5PrfNnz8fW7Zs6aQF4HK5YLFYfP4RQgghvVXTyUL5iScAMAlNJwqCHxAhvVjIk8/KykokJSX53JaUlASLxQKHw9Fhm+XLlyMqKqr1X0ZGRqjDJIQQQgIm9WDepuSlOZ9kYOmVq93vv/9+mM3m1n9lZWWRDokQQgjplFIfeBlApc4YxEgI6f1CXmQ+OTkZVVVVPrdVVVXBZDJBq9V22EatVkOtVoc6NEIIISQoonNGQGWMhbupXlY7jheQcubcEEVFSO8U8p7PyZMnY926dT63rVmzBpMnTw71qQkhhJCw4BVKZM+9CuDlfa0ySUT2nKtDFBUhvZPs5NNqtWLv3r3Yu3cvgOZSSnv37kVpaSmA5iHz6667rvX4X//61ygsLMS9996Lw4cP45VXXsFHH32Eu+++OzjPgBBCCOkFsmdfCUGlATj/vlo5XkDC6HNgyswPcWSE9C6yk8+dO3di3LhxGDduHABg6dKlGDduHB5++GEAQEVFRWsiCgA5OTn4+uuvsWbNGowZMwbPPfcc/v3vf1OZJUIIIf2KJiYRk/7wKjhB6LYHlOMF6FOyMf63z4YpOkJ6D9rbnRBCCAmi+mN7seulP8BRU95uf/eWn5POmIkzfvPnHi1UIqS38TdfC/mCI0IIIWQgiR08FnP+uho1B7agaM27MBcdguRxQakzIumMmciecxUMKdmRDpOQiKHkkxBCCAkyjueROHoKEkdP8btNY9FBFK95H/VH90B0OaDQGZAwagqyZ19JySrpVyj5JIQQQiLIWlmC3S/fg8aC/b7D9HWAtbwQhd+sQOKYaRj3mz9DbYqJbLCEBEGvLDJPCCGEDARNJ47jx4eugLnoZwDwmR/a9uea/Zvw48NXwmmuDXuMhAQbJZ+EEEJIBHiddmxZfjO8Dlu7pPN0TBLhqD2J7c/+H/rAOmFCukTD7mRAcXslFNXbUNxgh9MjgeMAg0qB3Hg90qO0EHgu0iESQgaI8s1fwdlQ1f2BpzBJRGPBPtQd3oH4YRNDGBkhoUXJJxkQGGPYX2HBoeomSKd1GtjcIqqsLqgFHuMzopEVo4tMkISQAYMxhsJV7wAcB8joyeR4AUWr36Pkk/RplHySfo8xhq0l9ShucHR5nEuUsLm4Hm6vhMEJhjBFRwgZiBx1FWgqOya7HZNEVO5cB8YYOI5GakjfRMknCZjbK6Hc4vhl+FqtQKpJAz6IH4huUUKdzQ2PKEHgOcRoVdCpBFmPcaiqqdvEs62dJxph0iiQZNTIDZcQQvzisTYG3JaJXoguOxQaffACIiSMKPkkslmcHvxc1YSSBjskBnAAWgaN1AKPwQl6DEkwQqUIfD1bo8ODozVWFNXb2g2Tp0VpkJ9gQLIfyaEoMfxc3STr3ByaE1ZKPgkhocIrVD1srwxSJISEHyWfRJYKixM/FtZCYr8knG1zQ5co4WBlE4rq7ZiVlwCDWv6fWGGdDdtKG3yS2rZOmp0oNzsxJMGAcWlRXQ49lTU64BHlrQxlACqaXLC6vAHFTwgh3dHGpYBXqCB53bLbamKTe5y8EhJJVGqJ+K3W5sIPhbUQWcdJYQsGwO4W8d3xGri8kqxzFNfbsa20ofVxOnt8ADhSY8VPJ81dPt4Js//D7acr70FbQgjpikKrR9qUC8Dx8qYRgeORM++a0ARFSJhQ8kn8tqus0e9FmS0J6KEq/4e83aKE7acST38dqrai3t55z4Hc5LcFh+ZeXEIICZWcudd0W9/zdBzPI3PGpSGKiJDwoOST+KXe7ka9w9Nlj+fpGIDjdVaIp0/a7ERxvR2izOLJHICjNdZO7xd68Bcu0EpSQkgIRQ8agUHnXSerzcjr7ofaFBuiiAgJD0o+iV8K6mwIJBXziMzv4euuksjOMAAlDXZ4OumlNKmVAcXNAJg0NKGfEBJaIxbfi+w5VwNAp0PwLbcPv+Ye5MylIXfS99FqCuKXJqdXVq9nCw6A1eXt9jjGGJr8OK4jEms+R4yu/QT8vHg9jgSQ1KoFHmlRA2+1u7n4ZzQc3wev0w6F1oC4oRNgTBsU6bBIBDDGUHtwK4pWv4u6QzvgdTkgqDSIzT8Dg+YvRsKoKeB46r/oKY4XMGrJQ0gcMxWFK/+D2oNbT7ufR/KE2Rh07nWIGzo+QlESElyUfBK/SD3YS9ifUXc/R+Y71dlwvUmjRKJBhRqr2+/kmQOQl6APar3S3owxhvJNX6Hg27dgLjoIoPkLj0nNvclxw85E7oIlSB4/M5JhkjCQvG7UHNiKxqKDKF7zHlyNteB4oXVeotfrQc2+jaje+z10iRmYuPQlmDLzIxx138dxHJLHz0Ty+JmwVpbAXHgAXtepC8Ah46GJSYx0iIQEFSWfxC8apdBp6aOuMABqZfe9IwLPgecCT0JVXUzunJARg9VHqiFKrNv4OQAmjQLDEo2BBdLHSKIXe//5IE78+AXA/fIatiSeAFB3ZBfqDu1A3sKbMOyq39OuKv2Q01yLolXvoHjtB/BYfStInL4gpuVnR+1J/LjsKpyz7B1EZQ8PW6z9nSE5C4bkrEiHQUhI0ZgJ8UtmtDbgYfe0KK1fx6ZFaQOan6lXCTB2UY8zSqPErLwEKAWu28eP0igxMy8Byp6sVOpD9r/5OE78+GXzD6yT1f2nEtHj/3sdRz97NUyRkXAxlx7Bhj8uwrEv/9Uu8ewKk0RIHje2/PlWeJ32EEZICOlvBsY37AAjSgwlDXZsKKjFN4cq8c2hSnxfUIuyRnvAw+dpUVqoZSZkHID0aC10Sv/q2A2O1weU4OYnGLrtjYvTq3DesGQMSzJCJbQ/1qASMD49GnPzE6D1M96+rrHwAEq++why+rOP/PclOOoqQhcUCSt7TTk2P3F981aPkvzSYkwS4bbUoXzLN8EPjhDSb9Gwez9TUGfD3nIz3Ket/rY4vThpcUKt4DEuLQo5sfL2BBZ4DiNTTNh1olFWu+FJ/g9fJxrUiNUq0eBnSScOgFLgMcjP56JTChiTGoWRySZUW11wekRwHAeDWkCcTjXghpOLVr/rM5/PLxyHknUfYegVd4YuMBI2B995Gl67VXatSR8ch8KVbyNzxqUD7j1ECAkM9Xz2IwcqLdhe2tAu8QR+6dtyeSVsLWmQVfy9xeB4PfLj/Uv0OABnZ8citoMV6J224ThMzY2H9tT80u4en+c4zMiNl72HvMBzSDFpkBOnR3asDvF69YD70vQ6bTix+Wv5SYckoXjdh2A9WIBGegdnQzUqdq7rWeIJAIyhqewo3Jb64ARGCOn3KPnsJ0ob7NhfYfH7+L0nzbK3nuQ4DmekR2NcWhSUHQxdt9xiVCswIzcemTE6WY8PNPdOzhuSiHiDyucxTz+HXi1gbn4C4vS0v3EgnA3VYF5PQG3dTQ0QXbT1aF9X+v1n7d9gPeCx+//5QwgZ2GjYvR9gjMlKPFscqLAg3c/FQC04jsPQRCMGxxtQ1uhASYMdDo8I/tTwdW6cHomGnvUkapUC5gxORIPdjWO1NlRYnPBIEhQ8hzidCoMTDEjq4TkGOkkMrKZqa/sAE1fSe1hPFoIDF9A8644IKnmfJYSQgYuSz36g1uaGJYAC7Q0OD+rsbsTJGBpvIfAcsmN1yI6V37vprxidChMzqWczFFTGmIDbcrwApc4QxGhIJEheT9CmTyj1Jqij4oLyWISQ/o+G3fuBskZHQKNnHIATjTR8OhBpouIRkzfGp7anPzheQMrEuZ1uA0j6DpUxOjg7FPE8sudcDV5B29ESQvxDyWc/4PLKL5ESjLakb8s591ed1/bsBJNE2lu6n0iZMAesh9MvAAAMyJp1ec8fhxAyYFDy2Q/wPZj62JO2pG9LOXMu9CnZfvdicryA2CFnIHbohBBHRsIhfsRZ0CWm9/hxRl33AHQJaUGIiBAyUFDy2Q8Y1YqAFg0wAIYudgYi/ZugVGHyff+GOiqu2wSU43nok7MwcelLtNCrn+B4HvmX/F+AbQUAHEZc+0fkzF8c3MAIIf0eJZ/9QE6cPrA5nxyQE8IFQ6T30yWkYdoTHyNh1NkAuPZJKMcDHI+USfMx9bEPerRQifQ+mdMWIW/hzbLa8EoVMqdfgunLP0XughtCExghpF+jbq9+QKsUkB6txYlGh989oByArGgt1ApaODLQaWIScdYf/wlbVSmK132IhmN74bFbodKbED/iLGTNuhyamMRIhxkQxhic9VVwNFSBeb3QJaZBE50YnIU2/cSwq5ZCE5uEwx+9CK/D2nxV2rIKnuMBJkHQ6pE14zKknDkXpsx8KHX+71xGCCGn41gf2KrEYrEgKioKZrMZJpMp0uH0SlaXF6uOVMMjSt0moBwAlYLH/CGJ0Kvo+oP0P16HDaU/fIbjX/4LzoZqn/sElQaZMy/F4Atv7bNJdSiIbifKt3yLsh8+h7O+CuA46BJSkTn9EqRMnAteQWXPCCFd8zdfo+SzH2mwu7G+oBZub+cJKAdAreAxKy8BUVoqjUL6n8aig9jy1E3w2MxdH8hxGLVkGXLmXBmewAghpJ/zN1+jbq9+JEanwrlDk3C0ugnH62zwiL4pqErgkBdvQH6CAVolDbeT/sdSehQbH70WktvZ/cGMYf8bjwCilxbNEEJIGFHy2c/olALGpkVjVEoUKixO2D0iOABalYAUowYC1VYi/RSTJOz46x3+JZ5t7H/rCcQOHY+orKEhiowQQkhbNOu+nxJ4DunRWuQnGDA4wYD0KC0lnqRfqzm4Bbaq0oDaFq9+L8jREEII6Qwln4SQfqF49ftAQEXHgNIfP4fHZgluQIQQQjpEySchpF+oP7obCGi7BYB5PTCXHA5uQIQQQjpEySchpF8QZc71PJ3XYQ1SJIQQQrpCySchpF9QaHq2W5dCow9SJIQQQrpCq91J0DDG4PJKEBmDkuehUtC1DQmf+BGTUb75awQ09C4oYKLV7qSfk7xuVOxYi+K1H6CpvABM9EJpiEbqpPnInn0ldAlpkQ6RDBCUfJIec3pEFNTZcKzWCodHar09WqtEfoIBWTFaKGg7QxJiOfOuQfnmrwJoySHjnAuhMkQFPSZCTud1OVD38za4zPXgFAroEzMQM3gsOC601Ugqd32Hvf/4E9zWxtZtUwHAY7Pg+Fev4/j//o30KQsx5uZHIag0IY2FEEo+SY8U19uxrbQeUgedTY0OD7aXNuCnk2ZMz41HnI625yOhEzN4LExZw2ApOSSzJUPOvGtCEhMhLew15Shc9Q5K138Mr8Pmc58+OQuD5v8KmTMvDUnid2LTV9j9yr2/DAowyfcASWo9zl59ApMfeAOCSh30OAhpQd1RJGBFdTZsKek48WzL7ZWw7mgN6u3u8ARGBiSO4zBx6d+h1MvbgnfE4j8iOmdEiKIiBKg7vBMb/ngRila+3S7xBABbZSn2v/UkNj56LVyW+qCe21xyGHtevQ9gDN1OSWES6o/txf63nghqDIScjpJPEhCry4ttpQ1+HcsASIzhh8JaSCywUjiE+EOXkIZpT3wCbXyqH0dzGHn9n5B7/g2hDosMYObiQ9jy51vgdTnAJLGTo5oTQ0vJYWz98y3wOu1BO3/BN2/Ja8AklH3/GZyNNUGLgZDTUfJJAnKsVl5ZGgbA4ZFQbu5ZORxCuqNPysCcF1Zjwp0vwJCe1+5+XqXBoAXXY87f1mLQ/GsjECEZSH7698OQvO72Q90dYJIIc8lhFK16Jyjndjc1oHzzV10kvZ3EwRhKN/w3KDEQ0hGa80lkEyWGgjqb7DXFHICjNU3IiNaGIixCWnG8gNRJ85E6aT4k0Qt7bTkktxsqQxRUpljwAn30kdBrLDqIxsID8hoxCUWr30PewpvA8UKPzl97cBuY6JXfkEmo2LkO+Yt+3aPzk9Dwuhw4ueVblHz3EWxVZWCSCHVUHNLPWYjMmZdBExUf6RC7RZ/ARDaL0wOPKH/4nAGotdG8TxJevKCAISkr0mGQAajku4/B8YLsnkdnQxVq9m9G4pipPTq/uwdbxnqaGnt0bhIapd9/hgP/ebJ57jDHnZrLC3hsZhz++O848slLyJ63GCMW39OrL7Jp2J3I5u1uhVEXJAaa90kIGRCaThyXnXgCADgeTScLe3x+QRl4hRFBTeWWepuCb1Zg7z8e+GXR2unfpUwCk0QUrXwbO/92d2B/e2FCySdpJTGGcrMDu040YmtJPXaWNaCwzgav5DtXScEHXo+O5wA+xPXsCCGkN5A8roDacRwHydPzUSJj+uDAzs8LMGUO6fH5SfDU7N+Mg+887efRDJU71uLIp6+ENKae6L19siRsGGM4UmPF4eomODwS2qaGDDbsOtGIvHg9RiaboBR4mDRKKHkOHpk9oByAeD3V+iSR1Txf6hsUrf0AtsoSMNELlSEaaWcvQNbsq6BPTI90iKSfUJlifYZG/cUkESpjdI/PH5UzHKbMobCUHfVrwVPb82fPuarH5yfBc+x//5Y9haPw27eQt/BmKNS9b50F9XwOcBJj2Fxcjz3l5tbdiVibf0DzMPuRaivWHquByytC4Dnkxusht/+SAchPMAYveEJkKtv4JVb/Zir2/vNBmIsOwmtvguhywFFXgYKvV2DdXfOw6+V7IbqpKgPpuZQJs2UnngAAjkfS2Ok9Pj/HcRh07q9kJZ7geRjSBiF2yPgen58Eh7WiGLUHtsgeRvc6bDi55dsQRdUzlHwOcLtPNKK00dHtcQyA2eHB9wV1kBhDXrxB1nk4AFolj7QomkdEIqNozfvY88of4XV2PF+q+YOdoXzz19jy55shBmHYkwxsaWdfAEGtk9WG4wWkTJgNTUxiUGJIn3ph88Ilf7Y45njwggJn/ObpkG/3SfxX/dOPzT3ocnEcqvZsCHo8wUDJ5wBmdXlxrLb9bhudYQDq7G6Um50wqhWYmBnjVzsOzXM9pw2Kp/meJCLqjuzG/hWP+3cwk1B/ZDcOvv3n0AZF+j2FRofc85fIbMWQt/CmoMXACwpMuOtFJI4+tXK+kySU43ko1Fqcdd+/ED1oZNDOT3rObTUHVnaLMbiagrtjVrBQ8jmAHau1yh46b6nVCQCD4vQ4KysG3a0/Ugk8Zg9ORCzt7U4ipODrN8BxMj7uGEPJ+o/hbvJvFy9COjPkkt8gddK5fhzJAeAw5tYnEZM3JqgxKNRaTPrDyxj/u+cRmze23f1KfRTyFt6CmX/5CvHDJgb13KTnBJU6sOkbAARV75vvCdCCI794RAluUQLPcVAr+H7Te1dUZ5ddKJ4BqLa6YXeL0KkE5MTqkWzUoLDOhqM1Vji9v8wtitIoMCTRiKwYLRT+DPkQEgKOukpU7lovb94bmofhS7//DHkX3BiiyMhAwPECxt/xLHSJ6Sj49q3mou9tEomWRSTqqDiMvumR5nmiIYoj7azzkHbWebCeLIK1ohiS6IHKEI2YwWN7VJaJhJYxPS+gskkcL8CUkR+CiHqOks9OSIyhwuLE0RorKpt+KZeh4DnkxumRF6+HSaOMYIQ9IzEGlyjvy7gth6c5+QQArVLAiGQThicZ4fJK8EoMSoGHSuBo3hCJuJoDW2QnngAAxlC1ZwMln6THOF7A8Kt/j7yFN6Hsh89RvvkbuCx14AQFDMlZyJp1BZLOmBG2ouCG1BwYUnPCci7Sc4ljpkIdFQ+XuVZWOyaJyJp1eYii6hlKPjtg94j4/ngNGp3edsPSXonhaI0VR2qsGJ5kxOgUEyVYp3AcB42yZ9vBERJsHpsZ4PiAElA37fJCgkhliEbughuQu+CGSIdC+hBeUCBn/mIc/vjvfn+OcbyAuGFnwpCSHdrgAkRjoadxekSsOVoNs7N5P9yOhqVbbvu5qgm7y81hiy2YeI6DWgj816+lJJP0EYJKE1jPJwChF9bHI4QMPIPOux5R2cP8WnjE8QIUGj1G37gsDJEFhpLP02wrbYDDLfo9F/JojRUn/ChV1BvlxOkCWnCUaFC1DrkT0tsZA5zzxPECorKHBTkaQgiRT6HWYnLbSgSdLaDkeCgN0Tj7wRW9ttcToOTTh9XlxUmLU9YiHA7AkVOrv/uavHhDQAuO8hPk1fgkJJJi88edmt8m71KLSSKyZ18ZmqAIIUQmlTEGZz/0H4y99YkOFxKpoxMw9LLfYuYzX/b6C2ea89nG8VobOHQ81N6ZltXfFqenzy1AMqoVyIvX47iftT45ALE6FdKiaCiS9B3Nu7xch31vPOp/G15AVM6IXv8BTggZWASlCpkzLkXmjEthLj0Ce/WJ5moJpjjEDB4TtkVrPdU3ogyTaqtLdk9gixqbu88lnwAwPj0aLq+Esm6mDnAAojRKTM+N6zelpsjAkTnzMlTsWIuag1sAqZv5nzwPQaXBuNueDE9whBASgKjMIYjKHBLpMAJCw+5teLr7UuqqbQ/KFkUSz3GYkh2LcWlR0Cia/xy4Nv+A5vJS+QkGzMlPgFpBcz1J38MLCpx599+QOPqcUzd0MV9KZ8LZD66AMT0vfAESQsgAQj2fbSh7UAhd2YOV45HGcRyGJhqRn2DASYsTVU0ueEQJCp5DjE6FrGgtFH34+RECNG91OOkPr6Bix1oUrX4XdYd2+NyvjklEztxrkDXrcqhNsRGKkhBC+j9KPttINKhQb3cHNPQer+/7u0PwHIf0KC3SaU4n6ac4XkDqpPlInTQftqpS2KrKIHndUJtiET1oZGD7JxNCCJGFks828uINOFRtldWGQ3PiGdUH53sSMpDpkzKhT8qMdBiEEDLg0FhqGwa1AqkmjayCLAzAkERjqEIihBBCCOlXKPk8zaTMGGiVgt8JaH6CAelRmpDGRAghhBDSX1DyeRqNUsDc/ESYNM0zEjpKQltuG5ZoxBlpUbS3OyGEEEKIn2jOZwd0KgHnDk3CSYsTR2usqGpytd6n4DkMitNjcLy+T9b1JIQQQkjfZ6sqQ8l3H6Lsxy/hbmoAOA66uBRkzrocmdMvhdoUE+kQO8UxxgKtqx42FosFUVFRMJvNMJlMYT+/R5TgFiXwHAe1gqci64QQQgiJCMnrxr43HkPphk/B8TyYJPoewHHgeAFDL/8d8hbeHNbRWX/zNer59INS4Pt0HU9CCCGE9H2S6MX2536L6n0bAbD2iScAMAYmenHog+fhsZkx/Oo/hD3O7gSUUb388svIzs6GRqPBpEmTsH379i6Pf+GFFzBkyBBotVpkZGTg7rvvhtPpDChgQgghhJCB6Oh/X25OPP0ctD7+v9dxcuvKEEcln+zk88MPP8TSpUuxbNky7N69G2PGjMH8+fNRXV3d4fHvvfce7rvvPixbtgyHDh3C66+/jg8//BAPPPBAj4MnhBBCCBkIvC4HCle97XfiCQDgeBz7379DF1SAZCefzz//PG655RYsWbIEw4cPx2uvvQadToc33nijw+M3b96MKVOm4JprrkF2djbmzZuHq6++utveUkIIIYQQ0uzklm/hddjkNWISzEUH0Vh4MDRBBUhW8ul2u7Fr1y7MmTPnlwfgecyZMwdbtmzpsM3ZZ5+NXbt2tSabhYWF+Oabb7BgwYJOz+NyuWCxWHz+EUIIIYQMVDX7NwG8/NmSHM83t+1FZC04qq2thSiKSEpK8rk9KSkJhw8f7rDNNddcg9raWpxzzjlgjMHr9eLXv/51l8Puy5cvx6OPPionNEIICSrGGDw2C0SXHQqNHgqdkWr6EkIixm0zA5IkvyHHw2PvXZ14IV/tvmHDBjz11FN45ZVXMGnSJBw/fhx33nknHn/8cTz00EMdtrn//vuxdOnS1p8tFgsyMjJCHSohhMBtNaPsx89RtOod2KtPtN5uSBuEQfN/hfQpC6HQ6iMYISFkIFKodWje5kZ+hUxBrQ16PD0hK/mMj4+HIAioqqryub2qqgrJyckdtnnooYfwq1/9CjfffDMAYNSoUbDZbLj11lvxpz/9CXwHXchqtRpqtVpOaIQQ0mNVezZg59+WQnQ7232+W8uLsO+NR/HzB89j4u9fQvywiZEJkhAyIJmyhqJi1zpAkpd8MtELU+bQEEUVGFmTB1QqFcaPH49169a13iZJEtatW4fJkyd32MZut7dLMAVBANA8rEVIf+Pyiiiqs+FQVROOVDfhpNkBif7We73K3eux7dn/O5V4MrTvXWj+2euwYctTN6Hu0I6wx0gIGbiyZl7W4Zbf3VFHJyBp3PSgx9MTsofdly5diuuvvx4TJkzAxIkT8cILL8Bms2HJkiUAgOuuuw5paWlYvnw5AGDhwoV4/vnnMW7cuNZh94ceeggLFy5sTUIJ6Q8aHR4cqrKgpNEBxnwHRzQKHvkJBuQnGGjDgl7IZWnAzhfvBsC6L2PCJDAJ2P78bzH37+uh0OjCEiMhZGDTxCQi+cy5qNyxtuPi8h3heOTMuwa80Lv2FJIdzZVXXomamho8/PDDqKysxNixY7Fy5crWRUilpaU+PZ0PPvggOI7Dgw8+iPLyciQkJGDhwoV48skng/csCImwE40ObCquA2O/JJxtUxinV8K+CgtKGuyYmZcArZIuvHqT0u//C8nr9r9+HpPgsVlQvuVrZM28PLTBEULIKaOu/xMaju6Fy1zbbQLK8QKic0cjd8GSMEXnP9rbnZAeqmpyYv3xWr+mgHMAjBoF5uUnUg9oL8EkCWvunA1nXaW8hhwHY/pgzHz6i9AERgghHbBVn8CW5TfBXlUKcFz7i2ZeACQRccMnYuLSl6DUGcMWG+3tTkgYMMawo6zR77WHDIDF6cXRGitGJNOFVG/gbKiWn3gCAGNoKjsKr8sBRS9bSUoI6b/0iemYsfwznNj0FQpX/gfW8gKf++OGnIGc+dciefysXjfc3qJ3RkVIH1FtdaHJ5ZXd7litFcOSjOCpbmTEeZ0ydww5vb3DSsknISSsFBodsmdfgaxZl8NaXgCXuQ4cz0MbnwpdQlqkw+sWJZ+E9EBBnS2gqmsOj4SqJhdSTJpQhEVkUGh6VrOzp+0JISRQHMfBmJ4HY3pepEORhSadEdIDTU5vAOV+m1kD6DElwaeJSYQmJqn7A0936kOfVrsTQog8lHwS0gMya/36EHv/Wr8BgeObS5GAk/lxyBhy5v8qNEERQkg/RsknIT2gUQb+FtIoqNxSb5E541LwCiXgbwlnjodCa0D6lPNDGhchhPRHlHwS0gOZ0YENufIckErzPXsNdVQcxt/x3Kncs5sElOPBcRwmLn2J5nsSQkgAKPkkpAeyYrRQ8PJWrHMAsmJ0UCno7debpEyYjTPv/jt4paq5dt7pOA7gOCjUWpx1378QP2JS+IMkhJB+gFa7E9IDCoHHsCQj9ldY/G7DccCwxPAV/SX+S5kwG/NeWo/S7z9D0ap34KiraL1Pn5iBnHN/hYypF4W1aDMhhPQ3tMMRIT3EGMPW0gYU19u7PZbjgKk5cUiLorqQvR2TJLibGuB12qDQGqAyxoCjuqyEENIp2uGIkDDhOA5nZcbApFbg56omeE9bAt9SB9SkVmBiZgwSDOqIxEnk4Xge6qg4qKPiIh0KIYT0K5R8EhIEHMdhRLIJQxINKG1woLTRDqdHAs9xMKoVyIvXI16vop4zQgghAx4ln4QEkYLnMShOj0FxtAqaEEII6QgttyWEEEIIIWFDySchhBBCCAkbSj4JIYQQQkjYUPJJCCGEEELChpJPQgghhBASNpR8EkIIIYSQsKHkkxBCCCGEhA0ln8RvfWAnVkIIIYT0clRknnTKLUoorrfjWK0VVpcXEgNUAo/MGC3y4vWI0aoiHSIhpBvOhmpU7FwLl7kOvKCALjEDyRNmQ6HWRjo0QsgARckn6VBBnQ27yhogntbZ6RYlFNTacLzWhlSTBmdnx0IpUAc6Ib1NY9FBHPvin6jcuRaMMXC8AABgohcKjR6ZMy/D4Atvob3rCSFhR1kDaedIdRO2l7ZPPFu03FxhcWLtsRp4RClssRFCulexYw1+fPgqVO5cByZJAGNgohdM9AIAvE4bila9g+//dBmsFUURjpYQMtBQ8kl81Fhd2F1u9utYBsDs8GBnWUNogyKE+K325+3Y+eLdYKIIJomdHsckEa7GGmx+8ka4zHVhjJAQMtBR8kl8HK5uAifjeAagpMEBu6fzLzlCSHgwxrB/xeOnFgd2v0CwJQE9/tUboQ+OEEJOoTmfpJXdLeKE2Sm7HQNQUGvDqBRT8IMihPit4dheNJ04LqsNk0SUfPcRhl5+BwSVJkSRkf7C2VCNkvUfo7HwILwOG5QGExJGnIX0cy6CUmeIdHikj6Dkk7SqsspPPFtUWJyUfBISYaUb/guOF7ocbu+I12FF5a7vkDZ5QYgiI32d01yL/SueQMX2Nc03sFNz/TkOlTvW4eB7zyJ79hUYdtXvISipEgrpGiWfpJWnsxVGfnDToiNCIs5WXSY78QQAjhdgrykPQUSkP7DXnsSmRxfD2VDzS9LZ4lT9Z8ntROHKt9FYeABn3fdvKuVFukRzPkkrBS9ntmfw2hJCgqNlNbtsHAJKWkn/J7pd2LL8Zjgbarr/G2EM9Uf3Ys+r94UnONJnUfJJWkVrlQG14wDE6AJrSwgJHk1MIjhe/sc6EyWoTbEhiIj0dSe3fgtbRZH/FydMQsX21bCUHg1tYKRPo2H3CBMlhtJGOwpqbWg6tYuQWsEjO0aHQfF66JRC2GKJ1akQo1WiweGR1Y4BGBxPE80JibTUs87Dya0rZbfjBAHJE+aEICLS1xWuegfg+PbD7V3geAHFa9/H6BuXhTCy7rmbGlD6/Wco/f5TOOurAACa2CRkTr8EmdMvhsoYE9H4BjJKPiOopMGOHWUN7eZaukUJByotOFBpQW6cHmekR0MI07D2kAQDtpb6X7ezpdczVkcTzAmJtOTxs6AyxcFt8b9uJ8cLSD3rPOr5JO3Ya8phLjooux2TRJzY9L+IJZ+MMRz99BUc/fy15h5b9st3rLXcip/ffxaHPnoB+YtuQ/7Ft4PjaNpYuNGwe4Qcr7Vic3F9p4t8Wqr0Ha+z4cfCWkgs8MVAcmTH6pAR7d9EcQ7Ncz0nZ9GXFiG9AS8oMPTy3/nfgOPACQIGL7w5dEGRPstlrg24rddhg+SVN4oWDIwx7H/zcRz570vNc6A7+u5kDMzrwZFPXsL+FU+cqotLwomSzwiosbqwo6zR7+Mrmlz46aR/uw71FMc1J5OZMc0JaFfXgyoFj9mDE2DS0HxPQnqL7NlXYPCFt3Z/IMeDExQ4866/wZSZH/rASJ/D8T0ZHOXA8eGbNtaidP3HKF77vt/HF695D6UbPglhRKQjNOweAYdO7SIk51rrWI0VI5NNUAqhv14QeA5nZ8ViUKwLR2usOGnxrf+pVwnITzBgUKweKgVdvxDS2wy76m7oktJx+OO/w9VY41P7s+W/o7KHYdT1f0Js/rgIR0t6K21cMsBxHfcedkMdEx/Q4reeYJKEY1/8E5D1Dcvh2Bf/QuaMy8Iy/O6xWVCzfzNcTQ0QlCrok7MRO+SMATf0T8lnmNncXpQHsIuQyICiejvyE8KzsIfjOKSYNEgxaeDwiLC6vJAYg1ohIEqjGHBvFEL6mqyZlyNj2sWo2vM9Tm5dCZe5BpyghD4pA5nTL0X0oBGRDpH0cuqoOCSNnY7qn36UV4qL45E9+8rQBdaJmoNbAqhXy2CvLkPtwa1IGDk5JHEBQFN5AQq+eQsnNn4ByeP2uU+fnIWc+dcia+blEFTqkMXQm1DyGWaVFlfAbcvNjrAln21plQK0YVx1TwgJDl5QIGXCbKRMmB3pUEgflTNvMar2bJDVhuOaL37CrXrvD+AEhex6t5ygQNWe70OWfFbu+g47X7wbTBI7TOJtVaU48NZTKN/0FSbd+xpUhuiQxNGb0JhpmLlFqct5lF1xeWkXIUIIIeGTMOpsJI2fCcgYQs+/5HZoYhJDGFXHPDZLQFMEwFhz2xCoPbQdO/76O0heT+e9x6x5iXFj4QFse+bXEN2Bd1L1FZR8hpnAc7Lmep7elhBCCAkXjucx/rfPIWHEZDTPpezke4hrTicGnXc98i++PWzxtcUr1c3drnJxHAS1JujxMEnC3n88eGo1ffff/EwS0XB8H0rX9/8FUDTsHmYmTWAvOYfAdyAihBDSXIan/ugelG/+Cq7GWoDjoY1PQca0RYjKHBLp8HothVqLSfe8iqLV76Jw1Ttw1JQ3r2TnODAmAZKE6JzhyL3gRqSddV7E4jRl5ge0TSwTRRjTBwc9ntqDW2GvLpPdrnDVO8ied02/XltByWeYJRnU0KsE2Nzy3iAMQF6cPjRBkX6DMYZ6uwcOT/Pfl04lIEar7NcfYoT4o3L3ehz64Hk0nTh+asW/BHAAx/Eo/GYFYgaPxYjF99Lq/07wCiVyF9yAQedeh5qDW2Au+hlepw1KnQnxIyYhOifyC9jSpyzEwXeegeSRN2zNK1VIn7Iw6PEUf/eRT6UJ/zDYKotRf3QP4oacEfSYegtKPsOM4zjkJxiwp9z/up0cTm19SbsIkU54RAmFdTYcrbHCetqFjUGtwJAEA3JidWEp1UVIb1O89gPse+Ox1hHj1mSAAYw1/3fD8X3Y9Ph1OPOuF5E8flaEIu39OJ5H4qgpSBw1JdKhtKPUGZExbRFK13/id8LH8QIypi2CUhf8xbzW8oKAemIBwFZR3K+TT/omioDB8QbE61V+LTzi0DzXc1Im7UFLOmZze7HqSDV2l5vbJZ4AYHV5setEI1YdqYbNLW8VKCF9XeXu9c2JZ3Om2fmBTAITRex48S40FsrfUpL0DsOuuBPa+FS/CtxzvABtfCqGXXFnSGIRTyupJIfkDbxtX0DJZwQIPIfpufFIMHTdk8kBUAocZuUlIIrme5IOOD0i1h6rgdXVfVJpdXmx7lgNXN7ArsQJ6WsYY/j5/ee73qrNtwUgSTjy2cuhDIuEkMoYgykPvQV9chY6XyDVfLs+JRtTHnoLKmNoOnfUpriA24Yqpt6Cks8IUQk8ZuYl4KzMGMR2kFiqFTxGJBuxYFgy4vQ03E469tNJMxxu0a8KCgyA3S1iX0VoSooQ0ts0HNsLa/lxWeV3mCSiavcG2GtPhjAyEkrauBRMe+JjjFryEPQp2e3uN6TmYNSShzDt8Y+gjUsJWRypk+YFtPqeV6qRMOrsEETUe9CczwjiOQ45cXrkxOnR6PC07iKkUQiIN6jA0yIR0gW3V0Jxg11W6S4GoKjOhrGpUTT/k/R7JzZ9BU4QwES5vf0cTm5dibwLbgxJXCT0FBodcuZejew5V8FScgiO+ipwHAdNTCJMWcPCsggzY9rFOPTBX2UNof8yB9UYwsgij5LPXiJaq6RSSkSW4gY7pACKxooMKK63Y3AEdssifYvHbkVjwT54HFYo1FqYsoZCE50Q6bD85mqsaV7VLhPH83A21oQgIhJuHMchKns4orKHh/3cKkMUcuYvRsHXK+DfXvMcOF7AoHOvC3FkkUfJJyF9VKPDAw7+faS1xQEwOz0hiIj0F5YTx1C06l2U/fC5T9kajueRPGEOBs2/FnHDzoxghH7iAu/d53rQlpAWw65aCmtFMap2b0CXn9YcD47nMeGuF2BMGxSu8CKG3l2E9FFiIN2eQWhL+rfS7z/D9/c1l6s5vV4ikyRU7lyHTY9fh5/ff+7Uzi29ly4hNaAkkkkitPGhmwtIBg5eUODMu/+G3PNvAK9QtZsD2rIqX5eYhskPvI7kM2ZGIsywo55PQvoolSLwa8eetO1tJMZgcXrhESUIPAejWkHzWQN0YvPX2PuPB7o8pqVu4fH//RscL2DYlXeFIbLAZExdhIKv35TdjuMFpE0+PwQRkYGIFxQYsfheDL7oNpT9+Dkqtq2Gy1wHXqmCMS0XWXOuRPzwSQNqMxBKPgnpo1JNGhytscpux0617escHhEFdTYcq7HC6f1lXh/PATmxegyO19PGDDJ47E3Y+48/ATImcxz74h9InTQfUdnDQhpboEyZ+YgZPBYNx/cBzL+5n82J5wKoTf271A0JP5UhCrnnXY/c867v8H6v04bqnzbCZW7e+lWXkIaEUWeDF/pfqtb/nhEhA0SyMbCtWg1qBRIN6hBFFR4VFid+LKrrcPqAxIDCOhsK6mwYlWLCiCTjgOpRCNSJH788tSrX/6F0jhdQtOZ9jL3lsdAF1kMjFt+LTY9fByYydPvcOB6CWov8S24PS2yEAIC9phwF36xA6Yb/QnQ5mofmT01pUUfFIXvu1ciZdy1UhqgIRxo8NDY1wDk8Ig5WWrCxqA4bjtdgU1Edjtda4RXlrxAl4cVxHIYnyS/H0deTsWqrC98X1HY5b7Xlnv0VFhysagpPYH1c4ep3ZbdhkogTG7+A12ELQUTBEZs/Dmfe9SI4haLLXW84XoBCo8NZ9/0LhuSsMEZIBrK6I7ux4b5FKF7zfnPiCfjUpXWZ63Dkv6/ghwcvh72mPEJRBh8lnwOU0yNic3EdvjhQgX0VFpQ1OlDR5EJpowM7yhrx2YEK7C1vpIUpvVxuXPPwsr/y4/XIidWFMKLQEiWGjUV1slb476+woN7ev7eq6ykmSbBVFMsqxt5C8rh7/Zdi8vhZmPrI+0gcN625V4njwQkKcIIC4DhwggJpZ5+PaU9+gtjBYyMdLhkgLKVHsXX5zfA67V3vAc8kOGpPYvMTN8Dd1BC+AEOIht0HILtbxNpj1bB3sTOOV2I4VG1Fnd2NGbkJEPi+21PWn3Ech/Hp0dAqBRyotHRa95PngFHJJgzr472eJ8wOuLzyeuU5AMdqrJiUFRuaoPoBucPtpxPdzuAFEyLRg0Zg0u9fgaOuAuVbvoWzsQYcz0MXl4rUyedBbaK/DxJe+996ovm958d8ZCaJcNRV4OgX/8TIa/8YhuhCi5LPAUaUGNYX1HSZeLZVbXVjW2k9zs4OfI9aElocx2FEsgmD4w0orLehsM4Gh0cEwEGn5DEoTo+cWH2/WOEe6AKr4gY7xqVF94vXIBR4pRqcoAATvQG1V+pNQY4odLRxKbRzEYm4pvIC1B3aIasNk0SUrv8Yw664E4Kqby8apeRzgClttMPilPcFU9LgwIhkD6I0tANTb6ZS8BiaaMTQxP67LVuDPbDi+BIDLC4P4hV9e6FVqHAch6QzZqBq1/quh//at4QuMQ36pMyQxUZIf1S64b/geEHm+w3wOmyo2L4G6ecsDFFk4UHdAANMID1HHIDjtb13QQEZOMQeFDX30vzlLuXMvUb2FyE4IGf+teB4+iohRA5rRbH89xsATlDAWlkSgojCiz4xBhCb24v6AHqOGICieko+SeQpejD3mArPdy1+xFkwZQ7tckW4D56HUmdCxtRFIY2LkP6oeZ51YJi372+PTJ/GA0jzPMDAeEQGqZdvpUf6vxSTBoGknyqBQzRNG+kSx3GYdM+rUEfFdZuAcrwAXqHCWX/8R7+qPUhIuKhNsf5f6LXBJAkqY9/fAIGSzwEkkqucRYmhqN6GtUer8en+k/hkXzn+93MF9p00w+YObJEDGXgGx+tlr8nmAOTFG6higx+0ccmY9sTHiM4bDQDtvhxbftYmpGLqI+8hJm9M2GMkpD9IOXNuQMPuYAzJE2YHP6AwowVHXWCModHpgcPTXAZBpxQQpVH02VI1OqX8q6wWWgUPPsDnXdpgx/ayBnhE37TBI4r4uaoJB6uakBOrw5kZMZQgkC4lGtSI1ihgdnr9TkI5DsiTUQt1oNPEJGLqI++hsfAgite+j+p9G+G1WyGotYjJG4PseVcjYcTkoMzzZIz12c9TQnoi6YyZUEfFN2+l6SeOFxA/cjL0SRkhjCw8KPnsgFeSUFxvx5Eaa7uV4VEaBYYkGJEVq+vR/LNI0CoFpBjVqGxyyeo94gDkBvjlXVBnw/bSzovitsRRVG+Hze2lmqKkSxzHYVpuPFYdqYbbK/n1dzwlOw56FX3UyRU9aATG3vpEUB9T9LhRsW0Vila/C3PxIUheNwS1FgmjzkbOvMWIH3EWJaNkQOAFBfIv+Q32v/m4320YY8hfdFsIowofjrHeP5HPYrEgKioKZrMZJlNo68nZ3V6sP14Li6vroeAojQIz8hJ61JsYCSfNDnxfWCe73UUjUqBTyXuudTY3Vh+tltVmcLweEzL6/nwWElpWlxc/FNbC7PSCQ8fl0ZU8h7Nz4pBq6tv18PqLmoNbsevFu+G2NgIc71NYu6XkjDE9DxP/8Cr0iemRC5SQILBWFKHku49hKTsKye2CyhiDpHEzkHb2gtYanYwxHHh7OYpWvt3NozVfkI299XFkzrg0tIH3kL/5GiWfbbi8IlYd6XrnnxYcAINagXn5iX2qcDVjDN8X1Mrq/RyRZMToVPmLCtYdq0a1Vd6KPp4DFo1MgVrRt5J6En6MMVRZXThWY0WFxQmRNb8vo7VK5CcYkBnT90Yn+qvqnzZi219+DcZYl7u5cLwApd6EqY9/RAko6ZOsFcXY98ajqD241beO56kLLoVWj9wFS5B/8a+b72cMhd++hSP/fRleh9XnwqylvSY2CaNueAgpfWCuJyWfAdheWo/COrv/c8kADE4wYHx6dMhiCgWPKOGHwlq/EsPB8XqMT4+WPRRW1eTEd8f9n8vS1tjUKAxL6r+F0kloSIyBQ2QX1pH2nA3VWHf3fIgel197x3O8AENqDmY8/SX9LkmfYi7+GZueuAFid3u1o3nB0YQ7/9q6iE90O3Fy60qc2PQVnA1V4HgBuqRMZM24FIljzgloZXwk+Juv0USoU9xeCUX1/ieeQPNQX0GdDaNTTH2qhqBS4DEjNwFHappwtMYKh0fyKV/D0DytYFiSEdkxuoC+AHaWNQYcX7nZQckn8ZvZ4cEJswNuUYLAcTBqFMiIpl7P3qJk/ccQPW6/Ek+geQvBphPHUXtgCxJGnR3i6AgJDpe5DluW3+xX4gkAFTvW4uB7z7bu0y6oNMiYtggZ0xaFONLegZLPU4ob7AhkAxRRYihpsCMv3hD8oEJI4DkMTzJhaKIRFRYn6u0eeCUJKoFHklGNOJ0q4F6HJqen2zmzXXGJnQ/LEdLipMWJg5UW1NrczRdPpyZ/MgC7yhqRG6/HiCRTn5oW099IohfFa97vcqi9IxwvoHD1u5R8kj6jeN0HcFvNMv7WGYpWvYPBC2+GOipO9vmc5lqUrv8vyrd8DbelHpyggD45G9mzr0DKmXPAK1SyHzOcKPk8pdHh6XThQlc4rrltX8VzHNKitEiL0gbtMQvq7T1qr6ChNtKNw9VN2FNubv2Ztf5PM4/EcKTainKzE7Py4qGj1e4RYS0vhMssf4Ejk0TU7N8cgogICT7J60HRavkXWUySUPr9pxh84S2yznXw3WdQvOb9dnOonY01qPt5G5SGaIy5+VGkTpwnK55woi6BU8RA931mPdtvuj9qcvasaHy0lnaiIZ0rqrP5JJ6dYWheFb/+eC081JseER67JeC2ktsZWBFuQsKs/tgeuC3yL7LAJJz48Uu/D5dEL3a8cCeKVr/b/N44PdmVmn/2WM3Y+cJdKFn/ifyYwoSSz1N6MjSn6kPzPcOhp9tw5iX0rSkMJHxEiWF3eaPfxzMAFpcXBXW2kMVEOieoAx9R4QRFn1lkQQY2t7k+4LZyiswf/vhvqNq9wY/50wwAw0//Xob6o3sCji2UKGs6JcWolj3kDjT/ilOojqAPtYIPaP9tANAqecTpevdcFRI5ZY0OuEX579SjNVb0gcIe/Y4hORu8Ui2/IcfDlDU0+AEREgKcEPhFEif4NyXI67ChcOXbkDM5kOM4HP/6jQAjCy1KPk9JNmkCKhhvUAlIMgTw4dqPpUdrA0rkAWBCHytbRcKroM4a0IWNzS2i1iav5uxAxxjrccKu0OqRMfUi+T2YTMKgeYt7dG5CwkWXEGBNWo6Hzs+tMss2fgHJ7ZL18EwSUbnzOzjqqwKJLqQCSj5ffvllZGdnQ6PRYNKkSdi+fXuXxzc2NuL//u//kJKSArVajfz8fHzzzTcBBRwqPMdheADlfYYnmagW3WlSTRpolfL/tGK0SqRH60IQEekvbH5sANFV2/7II0ooqLVh94lG7CxrwL4KM+oCTLQdHhEHKy348mAFPtxbjg/3luOz/Sext7wR1gArWGTPvVre3E2Oh1JvQupZ5wV0PkLCzZQ1FMaM/OYVyHIwCdmzrvDr0Mpd3wUQWfM5qn/6IbC2ISR7CeiHH36IpUuX4rXXXsOkSZPwwgsvYP78+Thy5AgSExPbHe92uzF37lwkJibik08+QVpaGkpKShAdHR2M+IMqL16PersbhX6u1s6L12NQHCVLp+M5DiOSTbJrfY5Lk7+LEhlYetIR19+G3V1eEfsrLCist0OUWPP33qmneLCyCTFaJUYkm5AR3f28S8YYDlY14UCFpV1y7/RKOFxtxaFqK3LjdJiQEQNexpdsVNZQDL3iThz+6MXuD+Y4cBww/o7nIahoRIn0DRzHYdC51+Knfz0sq51Ca/D7IsttaYD8ejzNZcvc1u4XaIab7O6p559/HrfccguWLFmC4cOH47XXXoNOp8Mbb3Q8r+CNN95AfX09Pv/8c0yZMgXZ2dmYPn06xowZ0+Pgg43jOEzMjMGIJGOXQ3scB4xMNmFCADv/DBR5cXrkJ+j9Pv7MjGgkGWnuLOmaNoCpMS00PWjb29jcXqw6Uo3jtbbWSh2MtSwzaNbg8GBjUR0OVHa94pwxhj3lZuzvIPFsPebU/xfU2fFjYZ3sRYWDL7oNQ6+4EwA6H4LnefAKJc5c+hISR0+R9fiERFr6ORchOneUrCkmo67/k98XWXyAF2OMSa17yfcmspJPt9uNXbt2Yc6cOb88AM9jzpw52LJlS4dtvvzyS0yePBn/93//h6SkJIwcORJPPfUURLHzYRiXywWLxeLzL1w4jsPo1CgsGpmCMSkmGNUKKHgOCp6DUa3AmNQoXDwyBaNSaLi9KxzH4Yy0aIxLi+pwp5mWWzQKHlOyY/tckX4SGdmxgY00qE9tntAfeEQJ64/Xwu7nFIT9FRYcq7V2en9JgwNHajq//3Qtxf3l4DgO+Yt+jelPfYrM6ZeAV/ouKlQaopF/0W2Y/fwqJJ8xU9ZjE9IbCEoVzrr3H80L5bjOU6vm5JTDyF/dL2s3I1NGfmDVHxiDMXWQ/HYhJmvYvba2FqIoIikpyef2pKQkHD58uMM2hYWF+O6777B48WJ88803OH78OG6//XZ4PB4sW7aswzbLly/Ho48+Kie0oNMoBQxPNmF4cuj2ku/vOI7D0EQj8uINKG2wo7jeDodHBMcBepUCuXF6pEZpZA3hkYEtJ1aHveWNkLPgnQOQl6DvN39nx2ttaJI5/3JvuRk5sTooeN8vRcYYfq6Sf3F/pMaK4UkmCDK3MI3KHoYxtzyG4YvvgbWiGKLLAaXOAGN6Xq/fkYX0PebiQyha8z6q9n4Pr8MKQalB9KARyJ57NZLGTgt6KS+VMQZTHn4bhd+sQNHq9+Ay1zafg+NO1eVkiB8+CXkX3oyEkZNlPXb27CtQsu5D2TFp41MRP+Is2e1CLeTbfkiShMTERPzzn/+EIAgYP348ysvL8Ze//KXT5PP+++/H0qVLW3+2WCzIyPBvRRjpfRQ8h0FxegyK838YnpCOKAUew5NN2F/hX8LEobmG7+B+0rPOGMPRLnoxO+OVGEobHO3eg3V2N8wBbArhERlKG+zICfA9rdQZEZM7KqC2hHTHZa7Dzr8tRd2h7eB4oXXBm+i0o2b/ZlT/9CO0cckY/7u/Inbw2KCeW6HWIv/i3yDvwltQvfdHNJUfh+h2QaU3IXHcdBiSswJ63Kjs4YjOHQVz0UEwyc9NMzgOOfOvBcf3vsJGspLP+Ph4CIKAqirfZftVVVVITk7usE1KSgqUSiWENnWwhg0bhsrKSrjdbqhU7a921Wo11Or+MURG+g+PKKG43o5yiwMurwQFzyFaq0JenB5RtCtT2IxIMsLu9qKgruuFgRwAhcBhZm58j+aK9ia1NjfsAa7aL6iztUs+Ky2uwLYVBlDZ5Ao4+SQkVJzmWvz40JVwniovdHqlhZafHQ3V2PT4dZh8378RP3xi0OPgBQWSx89E8vjgTSMZc8vj2Pjw1RA9rm638uR4AdGDRiJn7jVBO38wyUqHVSoVxo8fj3Xr1rXeJkkS1q1bh8mTO+5CnjJlCo4fPw6pTaZ+9OhRpKSkdJh4EtLbSIxhb3kjPttfgZ0nGlFhcaHe7kG11Y1jNVZ8c7gKa45Ww+L0RDrUAYHjOJyZEYMz0qKgObUzWdvB35b/TjZpMH9IEmL60aYFPSkX1VFbtyjJrg4DNCer7tO2LPWKEmxuLxwesce7nBESCMYYdjx/B5z1Vd2X95IkMFHE9mdvh1PGLkORFJU5BJMfeB0KrR7orDfz1Bs6Jm8MJt37j15bNUL2sPvSpUtx/fXXY8KECZg4cSJeeOEF2Gw2LFmyBABw3XXXIS0tDcuXLwcA/OY3v8FLL72EO++8E3fccQeOHTuGp556Cr/73e+C+0wICQGJMfxQUIuKpo6L+7Z8xdbZ3Fh1pBqzBycgth8lO70Vx3EYkmjE4AQDys1OlJube6MFnoNJrcCgOD0M6pDPKupj2ieECp4LuG6qgucgMYYKixNHa6yobPMeaZlqMzheD5OGRgVIeDQc/wkNx/b634BJ8LocKF3/CfIX/TpkcQVTbP44zHzmfyhe8z6K174Pj813CpIxfTAGnfsrpJ9zIQRl7/0ukv3pfOWVV6KmpgYPP/wwKisrMXbsWKxcubJ1EVJpaSn4Nhl5RkYGVq1ahbvvvhujR49GWloa7rzzTvzxj38M3rMgJER2lTV2mni2xdC87/iG47VYMCypX5X16c14jkNGtNavWpbdYYyhssmFBrsbXolBKfBINqp7Vc9pIJs3/NK2/d9kjE4ZcO1UvVqBVYer0Oj0titN55UYjtVYcbTGimFJRoyh6iD9BpMkVO/biLIfPoe9ugyMSdDGpSB9ykIkj58FXhG5i43iNe/7zPH0C5NQtOpd5C28GbyfW11GmjY2CcOuvAv5l9yO+sM74bLUg1cooU/KgClrWJ94r3GsD1RetlgsiIqKgtlshslEq89JeNjdIr44WCG73egUE0ZQlYQ+Q2IMR2usOFJjhd0tNidSpyZCMgCxOiWGJRqRGRP5DSUkxvDFgQo4vX4uOGhjQno0Bif4LrySGMPnByrgkvl4HACNkofTI/nVczo4Xo/xVBe5z6vYsRYH/vMUHHUVvkkezwOSBJUxFsOvXorMGZdGJL5vbp4Ir70poLbTn/ovorKHBzmigcfffK33LYEipJcIdB/xozVWmvPWR3hFCRsKarGn3Ny6kIfhl4LtANBg92BTcT32lDdGfJcknuOQnyB/5b7AcR3WSOU5TnYlAA6AWuF/4gkAx2ptOGF2yjoP6V2K132EHX/9HRx1lQBOW8hzak2Hu6kee//5II58+nLY42OMweuQXwmihccWWNJKAkPJJyGdKGlwBDQfzumVAt5bm4QPYwybiutR3c20ipa/gcPVVvxcFfkvqLx4PbRKXtaF0chkI5RCxx/3w5KMiNMp/Xo8Ds0bQzi9/ieeLe2OVEf+tSOBqTmwBfveeAS+e2h17sgnL+HEpq9CHZYPjuN6VCs20B2ESGAo+SSkE05v4CuLAxkWJeFVYXHipMUpK4naX2GBwxP430UwqBUCZuYlQKXwLwEdHK/HsCRjp/creA4zchOQYGj+4u7qMQ1qBdKitLJHBBiAGpsbZqoI0Scd/fQVdP2XcToOhz/5W9hHCozpeV3uLtQZTlAEXH+TBIaST0I6Ia9v6bS2NLWt1ztaI39aBQNQWGcLRTiyRGmUmD8kEenRvySCp/+/TingzIxov+ZaqhQ8ZuYlYEp2bGsS6ns+BSZmxuDcoYlocHgCXiFfa6URgb6mqbwAdYd3dltX0heDvaoMtT9vC1lcHcmZe43MOJvrYaZOOhcqY0yIoiId6RtLuwiJAINaAZc9sC9Lg4reWr2ZwyP6VcWgI8drbb1iQZlepcA5OXFweEQU1dlgdnkhSgxqBY80kwYpJo2sBT48xyEzRofMGB1sbm9zXVDWvLDIqFa0PpbH391VOtCTtiQyKravlr+CHAAnCKjYtgoJYdzaMXXyeTjw9nJ4HTb4u3UCk0TkzOudhdj7M/qGJKQTefF61JXKTz5jdUra8aiXC3SXIACwe0QwxnrNym2tUsDwICfDepUC+k4uoJQ92KqvJ21JZLgsDQhkJwImSXBZ6kMQUecUai3OuP1pbH/+twDzb++uQeddh9j8caEPjvigTwJCOpEZo4UigPHzQFYjk/DqaTWCgVzLINGgDnhCSnwHQ/qkd+MVAfZRcVxEan4mj5+F8Xc8B04QwPEd11tuuX3Qub/CiMVUczwSqOeTkE4oeB4TMqKxtaTBr+M5NH+5ZvWCepCkaz3ZBEApcOB7Sa9nJOTF63FI5sp1DkC8XoUo2u2oz9EnZcoecgcADhz0iRkhiKh7aWedh6isoShe8z5K1v8XosveJjAeSeNnImfe4rBOCSC+KPkkpAs5sXp4RIZdJxrR3SBOnF6FaTnxAzox6SsMKgHRWiUaHfJWX3MAsgf4xYVBrUCqSYMKGZUCGJoXNR2ubkJWjK7D3ZZI75Q2eQEOvL0ckkfeFCQmSciIULF5ADCk5GDkdQ9g6BV3obFgPzx2CwS1DqaMwdDEJEYsLtKMkk9CupGfYEC0Romfq5tQYWkulN02EdWpBOTHG5CfYIBAy9z7BO5Usfbtpf71ardggOyi7P3RpMwYrDpaDYdb9DsBLTc7UW52Ym+5GenRWoxKNtHc6D5AqTchfcqFKPvhM797QDleQPyos6FPTA9xdN1TaHSIHzEp0mGQ01DySYgfEo1qJBrVsLq8qGhywu2VIPAcojRKJBvVvWbxCfFfVowOP1dZYHP5n0BlRmspYULztIW5+YnYcLwG5lN7u8vpBT3R6MBJixPTBsUh2agJYaQkGIZc9ltU7VkPd1Nj9wkox0NQaTBy8b3hCY70SbTgiBAZDGoFBscbMCLZhKGJRtnlbEjvoeA5zMxNgMbP3YIS9CpMyooNeVx9hU4p4NyhSZg2KA5JRnm7wzAAosTwQ0EdGgIsZ0bCRxubhLMffAsqU2yni3iA5h5PhUaHs+77V3PBd0I6wbFIb1bsB383qieEELkcHhE7yxpwwuzssAdP4DjkxesxJjWKplV0wSNK+L6wDjVW/+uncgCSTRrMyI0PXWAkaJzmWhR+8xaK130Ir73pl92EmARBpUHmjEuRe/4S6BLSIhsoiRh/8zVKPgkhBM21PwvqrKi3e+CVJKgEHslGDbJjdZ3ui05+YXV58b+fKwNqu3B4MgxqmgXWV4huF6r2fg9nXSUYk6COTkDyuBlQaPWRDo1EmL/5Gr3bCSEEzQvHRqVERTqMPqu4wS5r7mcL7lTbkb1g1yjiH0GlRurEeZEOg/RhlHwSQgjpsR7tGtWDtiR4mCSias8PKP3hM9irTwBMgjYuGelTFiJl4lzwCtokgAQHJZ+EEEJ6rCczuPrA7K9+7+T21Tjw1lNwNlQBPA9IEgDAUnYMVXu+h9IQjWFX3o3s2VdEOFLSH1DySQghpMd6Ujieis5HVtGa97H/zcd+ueFU4gkAYM3/7bE2Yt/ry+Csr8TQy38X5ghJf0Oz6AkhhPRYVowuoD3v2am2JDJq9m/G/jcf9/v4o5+9irIfvwhhRGQgoOSTEEJIj0VplYjXy58TGK9XUeH+CDry2SuAzFrFRz55Caxt7yghMlHySQghJCjGpET5VbC/BQdgdAqtco+UphPHUX94V+vQur/sNSdQe3BriKIiAwEln4QQQoIi0ajGWTJ2gTorKxZJtL1mxFTsWNPljkWd4QQBJ7evDkFEZKCgBUeEEEKCJjtWB42Sx95yMxocHp/any3/HaNVYmxaFO3rHmEuS4PsIXcAYJIEd1NDCCIiAwUln4QQQoIq2ajBuUM1qLO7UVxvh8PTXMdTqxSQHatDnI7qRfYGvCLAFIDjwCtoni4JHCWfhBBCQiJOp6JEsxfTJ2eBifIL/HPgoE/KDEFEZKCgOZ+EEELIAJQ2eQF4pfweTCZJyJxxaQgiIgMFJZ+EEEJImDHG4DTXoqm8EPaackhed9hjUOqMyJi6SNaiI44XkDjmHOgS0kIYGenvaNidEEIICROvy4HyzV+jaNU7sJQeab1doTUga9blyJp9JQzJWWGLZ8il/4fK3evhttSDSd0MwfM8BLUGI669LzzBkX6LY31gU12LxYKoqCiYzWaYTFQTjhBCSN9jLj2CrX++Ga7G2uZV5qd9/XK8ACZJGH7175F7wY3gAliJHoim8kJseepGuMw1nRaP53gBCq0eZ/3xn4jJGxOWuEjf42++RsPuhBBCSIg1nTiOTY8shttyqkRRB/0+zT2PDD+//yyOffHPsMVmTBuE6cs/Rd7CW6DURzXfyHGtZZgEtQ45867B9OWfUeJJgoJ6PgkhhJAQYoxh/T3nw1ZZ2v3QdhtTH/sg7Mme6HGj+qcf4ayrBGMSNNEJSBw7DQqNLqxxkL7J33yN5nwSQgghIVT383ZYTxbJa8Tx+On1RxE//EyA46GLS0Ha2edDHRUXmiBPEZQqpEyYHdJzEELJJyGEEBJCRWveOzWfU0ZNTSbBUnIITSeOnfpRxMF3n0HqpPkYctlvYUjJCVG0hIQezfkkhBBCQqj+yC55iWcbTPSCiV6AMTBJxMltq/DDg5ej/uieIEdJSPhQ8kkIIYSEkNflCNpjMUmE1+nA1j/fAmuFzKF8QnoJSj4JIYSQEFJo9MF9QCZBdDtx5L8vB/dxCQkTSj4JIYSQEIofPknWLkL+aBmCd5nrgvq4hIQDJZ+EEEJICOXMuybgOZ9dYZKI8i3fBP1xCQk1Sj4JIYSQEIoZPBamrKFB7/3keAGOuoqgPiYh4UDJJyGEEBJCHMfhzLv/DqXOGILh9463wySkN6PkkxBCCAkxfWI6znnsA2gT0gAgKEkok5p3ICIDk8fehKo93+PExv/h5PbVsFaWRDokv1GReUIIISQMDMlZmPXs16javR6Fq95F3c/beviIDKmTzwtKbKTvsJw4hqJV76Lsh88heVw+98UNn4RB869F8oTZ4DguQhF2j5JPQgghJEx4QYGUM+ci5cy5ED1ueOwWCEoNNj56DZpOHAcY8+txOF5A4pip0MWnhjhi0puc2Pw19rx6X+umA6erP7wTdT9vQ/o5F2LsrU+AVygjEGX3aNidEEIIiQBBqYImKh5KnQHDr/o94F/eCYADeB5DLv1tKMMjvUzFznXY/fI9zbtedVI9oeX2E5v+h5/+9TCYnxcz4UbJJyGEEBJhSeOmY/RNjwDggK6GSzkenCDgzDtfQPSgEeEKj0SY6HZh7z8e8P8ChTGU/fg5avZvDmlcgaLkkxBCCOkFsmdfgUn3vApjeh4AgBOE5kSU41sXKMXkjcGUh/6D5PGzIhkqCbOT21bCY7NARvc4OF5A0ep3QxdUD9CcT0IIIaSXSBo3HYljp6Hh+E8o3/w1nA014HgO2rhUZEy7CKaM/EiHSCKgeO37AMcDzP/SWkwSUbVnAxz1VdDGJoUwOvko+SSEEEL8xBhDrc2NY7VWnLQ44RUZBJ5DjFaJ/AQD0qK0EPierTLmOA6xg8cidvDY4ARN+rymsuOyEs9WjMFWUUzJJyGEENIX2d0ifiyqRb3dAw6/DIB6JYYamxs1tnqoFTzOyY5DolEdyVBJPyOJnoDbiqeVY+oNaM4nIYQQ0g27W8Tqo1VosDcnAZ3NvHN5JXx3vAYVFmf4giP9nlJnDLityhAVxEiCg5JPQgghpAuMMfxYWAunR/JruQcD8GNhHeyejsvhECJXyplzA9oVS2WKRVT28BBE1DOUfBJCCCFdqLW5Ue/wyFhnDEiMoaDWGrKYyMCSPefqTmt7dorjkTP3ml5ZaJ6ST0IIIaQLR2uskLuEiAE4VmuD1EuLfJO+xZSZj4Qx5/jf+8nxUKi1yJp1eWgDCxAln4QQ2Xrrrhmk5+xuLw5UWrCluB6biuqws6wBlU3OAf07r7A4ZfV6tnB5JTQ6Al8oQkhbE377HPQpOeD4blI3jgcvKDDxnlegiUkMT3Ay0Wp3Qki3GGOotrpwtMaKyiYXvBIDzwExWhXyEwzIiO55eRkSWVaXF3vKG3HC7Gzt5WMAODT34BlUAkammJATq49glOHHGINHCjzxdosBlMchpANKvQlTH30Pe169H5W71oHjBZ+h+JafdQlpGP/bZxGTNzqC0XaNks8IsDg9OF5rQ7nFCY8oQcFziNWpMDhej0SDGlxXW6sREmZWlxc/FNbC7PT6lJeRGFBvd2NLST12neBxTk4skoyaSIZKAtRgd+O74zXwiM2/3bapVst/W90itpY0oMnpxejU3rd6NlQ4joPAAWKA+aeCLspIECl1Rkz8/UuwVpagZN2HqNy9Hh6bBYJKg6jsYciZew3iR5zVfe9ohHGsD4ylWCwWREVFwWw2w2QyRTqcgLm8EraW1OOkxenzJQ6g9WejWoGzs2MRq1NFJkhC2rC6vFh9tBpub/erfDkA03PjkWKiBLQvcXhEfHu4yq/fcYsJ6dEYnGAIaVyBECWGskYHam3NvfMKnkOiQd3jwu+rj1Sjzu6W3Y7jgItHpkKt6N2JACHB4m++Rj2fYeLyilhztAZWlxdA+xpxrb0LLi/WHq3BrMHxiNdTkWISOYwx/FBY63dS0lJeZuGIZGiV8kuCkMg4Um2VlXgCwE8VZgyK0wdlqoXN7UW52QmXVwTPcTCqFbKTRVFiOFhpwdFaKzwiA9fm6v5YrQ1qgUd+ogHDk4zgAxhZGpygR12JvOSTA5AVraPEk5AOUPIZJhuL6mB1ebv9gGcARMawoaAWC4cnQ62gL3ESGVVWF8xOr6w2ImMoqLVhZErfHaEYSESJ4XidVfZiGo/IUNpo79H8z1qbCwcrm3DyVDH2tvNMVQKH3HgDhicaoeomefOIEjYU1KLW9ktyePp4nkuUsL/CgmqrC9MHxctOmjOjddh9ohFuGWPvDEB+L+wdJqQ3oEuyMKizuVFtdcv6gPeIDAV1tpDFREh3jgVQXgYAjtZaqbxMH1HR5Gyd5ylXcb094PMW1duw9qjvLkAMv4wAuUWGw1VNWH20GjZ35xdAjDFsKq5Hnc2/XsmqJhe2ltTLjlfgOZydHSerzfAkI+L0NH2KkI5Q8hkGx2oD/BKvoS9xEjkVFlfA5WXMTiov0xc4erADj90dWNtyswNbSxp8ks2OMDRPQ1p/vLbTFePVVpfsMkiljQ7UBzB/M8WkwdRBceA5dPp53nL78CQjRlPvPyGdouQzDE40OgL6End4JJipRhyJAIkxiD248Am0N42EV49mbAbQmDGGnWWN/h8PoMnlxbGajncKOlZrkx0Gh+YL+0CkR2lxwfBkDE0yQin4npkDkBmjxdz8BIxJjaKqJYR0geZ8hhjViCN9Ec9x4Lj2c+f8ReVl+gZdgAvDOAB6lfyvjwqLM6D9zo/WWDHstMVCHlEK6MKeAShpsGNiZkxAi4/0KgXGpkZhVLIJDQ43PGLzqnqTRkFz9AnxEyWfIcZxHHiuuSZiIKhwN5HD5RVRY3XDLUoQeA4xWiVMmsD29Y3RKlFvl9/zznPNJcNIcIgSQ7nZgUaHByJjUAk8Uk0axAShHFuySQO1gofLK+8ilwEYFKuTfb6COlu7MnP+cHolVDW5fMp4uWSu0G9LYoDbK0HTg6oMAs9RRRJCAkTfEGFg0igD2mKNA2AIoHeBDDwNdjeO1FhR0mBvd6ETr1dhyKldiOQMBebHG7C1tEFWHByAnFg9lALN6Okpjyjh56omHK+1wt1SPggAGLCvwoJYnRIjkkxIj9YGfA6e4zA4Xo+DlU2yEjm1gg/ovFaXGHDC2NXCI0JI30LfEGEwOF5+ORIOQHq0tkdX5mRgKKyzYdWRahTXt088AaDW5sam4npsLqmHKKMLPjNG125eW3cYAvt7J76cHhFrj1bjUFVTa3kfxk79O3VMvd2DH4vqsL/C0qNz5ScYoFUKsuZOjkuLCmjIOvC+yvajR2oFH/CcVZ5DtyWcCCGhQ+++MMiO0cmeA0c14og/Shvs2Fba/crh5mMd2F5aD383NRN4DlOy42R9wY9KMQVlOHggE6XmOr9mZ/d1gQHgQKUl4AU0AKBWCJiZF+93MjcuLSrg+p7aHsyJ1JyWLCqF5t7XQBYcZcXoAkqeCSHBQclnGCgEHpMyY2S1yY3TIYFqxJEueCUJ22QOixc3OFDZ5PL7eDnlZUalmDAiySgrHtJecYMdDQ6PrD7CvSfN8PRgcaJJo8S5Q5MwKE6Plutkrs0/AIjVKTFtUByGJgb+O84KYJ4o0Hwh1NG2rYPj9QEtOKILe0IiiyYUhklmjA5eiWH7qWShow/Mlon4g2J1mJARQ6U6SJdKGhzwylzJ1lJmRs7+62lRWiwcnozjdTYcr7HB1SbJ4bnmnv3BCQbEUo9njzHGcKS6SXY7UWIorrf3aL91rVLAxMwYjE2NQkmDHU0uLyTGoFLwSI/SBuX3mxWjxe4TjbL+bjk0fyZ2NI840aBGikmDShm1PjOjg/NcCCGBo+QzjAbF6RGjU+JotRXFHSwMSTKqkZ9gQKpJQ4kn6VZntQ+7wgCcPFXuRk6ZHZ1KgdEpURiRZILZ6YFHlKDgeRg1CqhocVHQmJ1e2Vuatiios/Uo+WyhUvBBeZyOKHgeI5KN+Omk//NUBZ7DkE56WzmOw5Ts2Hbba3Ym2ajGWVmxfp+bEBIalHyGWYxWhUlZsRiXFo0am6u1Rly0VgkDlachMjS5Al/9a3V6A6rxKPAc9RqFUE9WdNsC3HEo3IYlGmFziTjezfbBHJpX408fFN9l6S6lwGNWXgIOVlpwrG1lgFMX9wyAWuCRn2jA8NNqhRJCIoOynQhRKXikRQVeIoWQnmy92pPdi0jo9OTXwnqwkjycOI7DhIxoGDUKHKxsgluUfGp/tvx3rE6FMzOi/VrAJvAcRqdGYUSyCWWNDtTaXPBKzRf2iQY10qO1lHSGiNNci9L1/8XJbSvhttSDVyhhSM1F9pwrkTRuOjieKraQ9ij5JKSPUgryi4O3UFOZmV5J24PSapo+tLsOx3EYmmjE4HgDys0OlDU64PRKEDjAqFEiN06PaK38zREEnkN2rA7ZAS5sIv6TvG4cePvPKFn3UXMFDfbLZ5GjrhLVP/0ATUwixt76JBLHnBPBSElvFNA30Msvv4zs7GxoNBpMmjQJ27dv96vdBx98AI7jsGjRokBOSwhpIyOAMjMAoFXwAX2xk9CL1SmhUwWWROb0wYRL4DlkxugwJScOswcnYEZeAsanR9PfZy8neT3Y9uztKF77AZgk+iSeAJpvA+BsrMHWZ27DyW2rIhEm6cVkJ58ffvghli5dimXLlmH37t0YM2YM5s+fj+rq6i7bFRcX4w9/+AOmTp0acLCEkF/kBVBmBgAGJxhoCLKX4jgO+fHyF/twAHLjqLg/CY+D7/0FNfs3dz9P5NTOCLte+gMspUfDExzpE2Qnn88//zxuueUWLFmyBMOHD8drr70GnU6HN954o9M2oihi8eLFePTRRzFo0KAeBUwIaRajVSHFpJHV+6kUOEpSerncOD10MnccGpJooN3QSFi4mxpQvPYDGROUmxPQgm9XhDIs0sfISj7dbjd27dqFOXPm/PIAPI85c+Zgy5YtnbZ77LHHkJiYiJtuusmv87hcLlgsFp9/hJD2zs6OhUmj6DZR4QAIHIcZufGUpPRyKgWPmXnxUPm541BmjBZjUqNCHhchAFD6w+dgoryqDEwScWLTV3BbG0MTVD/WVF6Ak9tX48Smr1C19wd4nV1XiegrZC04qq2thSiKSEpK8rk9KSkJhw8f7rDNxo0b8frrr2Pv3r1+n2f58uV49NFH5YRGyICkEnjMzU/EtpIGlJkdPquGgV9WDhvVCkzOjqUySX2ESaPE/CGJ2H2iESfMzg5Xg6sEHsMSDRiWZKS6wCRsKravCagsA/N6ULN/M9ImLwhBVP0LkySc3LYShSvfQcOxPT73CWotMmdcipx5i2FIyY5MgEEQ0tXuTU1N+NWvfoV//etfiI+P97vd/fffj6VLl7b+bLFYkJGREYoQCenzlAKPcwbFwery4nidDSfNDrhFCQLPI1arxOAEAxL0KkpQ+hi9SoGpg+Jhd3tRUGeH2emBKDGoBB6pURqkR2kh8PQ7JeHlttQH3pZ6Prslul3Y9dLvUblzHcC3H5wWXQ4Ur3kfJes+woQ7/4rk8bMiEGXPyUo+4+PjIQgCqqqqfG6vqqpCcnJyu+MLCgpQXFyMhQsXtt4mSc2r4hQKBY4cOYLc3Nx27dRqNdRqtZzQCBnwDGoFxqZGYSwNwfYrOpUCo1JMkQ6DEAAArwr8u1lQ+r+t70DEJAm7Xv4DKnetb75B6riUHpNEMEnCjr/+Dmfd/28kjDgrjFEGh6w5nyqVCuPHj8e6detab5MkCevWrcPkyZPbHT906FDs378fe/fubf134YUXYubMmdi7dy/1ZhJCCCF9iCljcMCF4w2pOUGOpn85uX0VKnesbVe6qmMMjDHseeWPraWt+hLZw+5Lly7F9ddfjwkTJmDixIl44YUXYLPZsGTJEgDAddddh7S0NCxfvhwajQYjR470aR8dHQ0A7W4nhBBCSO+WPftKlG/+WmYrDobUHMQMHhuKkPqNolXvNA+1d9Lj2Q6T4GyoRtWe7/vc8Lvs5PPKK69ETU0NHn74YVRWVmLs2LFYuXJl6yKk0tJS8B3MUyAkmBhjcHkleE5toadW8FS7khBCQix26AQYUnNgrSjxs4cOABhy5l9L8867YD1ZhPoju2W343gBRWve73PJJ8dY79/k2WKxICoqCmazGSYTzX0ayDyihOJ6O47WWGFx/VLuQ6PgMTjBgNw4fY+2KCSEENK1+mN7senx65pLLnWTQnC8gNj8cZj8wOvgFb2v2obocaNixxrU/bwdXqcNglqL2MFjkTp5ARRqbdjiqNixFjv+ekdAbTWxSZj30obgBhQgf/M12tud9Bk1Vhd+KKyFW2z/Yef0SthfYcGBCgvOyoodsHs7M8bAAOoFJoSETOzgsZh0z6vY8dxvIXk9Hc855DiAMcQOOQMTf/9yr0s8Ja8HRz//B4pWvwOP1QxOUIBJEjieQ+n6T7D/P08he/aVGHLZHWFJQiWPK/C27sDbRgoln6RPqLW58N3xGkjd7eYGYEtJPRhjyBkgO/m4vCIK6uw4XmuFzd38JaDgOWRGazE4wUC1PQkhQZc4agpmPP0lila/g5L1n0B02n3uN2XkI2f+tciYemGvSzxFtwvbn70dNQe3tPbcthTOZ6fyaNFpR8E3b6H25+04+4E3oNSHdtRVaYjuQdu+V+GEht3b8IgSTlqccHpEcBwHvUpAiklDvUgRJkoMXxysgMvr7/yi5kLcFwxPhkHdv6+vjlQ3Yc9Jc4cjXy3FyFOMakzJiYNSoLnYhJDg87ocqPt5G9xNjeAVSuhTshGVPbzXzvHc+felOLl1lV9zVjmeR+yQCTj7T2+CC+F6Fq/LgVW/OaddEt8tnkfeBTdh+FVLuz82DGjYXYYmlxeHq5tQVGeHeNq3uFrBY3C8HvkJRqgV9OUdCSfMDlmJZ4vjdbZ+XfPyYKUF+yo633q25S+5ssmFtcdqMGdwAiWghJCgU6i1SBo3I9Jh+MVc/DNObvnW7+OZJKHu0HbU7N+MxDHnhCwuhVqLrJmXoWjVu/JKJ0kM2bOvDFlcoTLgv4mqrS6sPFyFglpbu8QTAFxeCQcrm7DqSBWsLnn72ZLgOFpj9WuP67YYgOO1VojdjdP3URUWZ5eJZ1sMgNnhwc6yxpDGRAghvV3Rmg9k1ynleAFFq98NUUS/yJm3GJygaJ4v6w+OR+rk86BLSAttYCEwoJPPBrsbG47XwisxdJWiMAB2t4h1x2vg8va9Yq59GWMMdTZ3l7+fznhE1m8vGA5XN8lKyBmAkgY7HB76+yWEDExMknBi4xeyi7IzSUTV3u/htppDFFkzfVImzrzrxebkmOsmPeN4ROcMx9hbHg9pTKEyoJPPXScaIfk55ZUBcLhFHKxsCm1QxIfEEFDi2cLjb7HePqTJ5UVlk0v268IAFNTZQhESIYT0eh57EySPO7DGjMFlrgtuQB1IGjcdkx94HZrYRABo10vL8TzAcUibfB7OfvAtKDR9s7LLgJ3zaXZ4UGOT90fY8uU9OtUEBRXSDwue+2XhTCCU/fD3VG0NvKxGpcWJkclUK5cQMvD0dAFUuBZQxQ+biLkvrkX1TxtRvPYDWEqPQPS4oDJEI+XMuciadXmfHGpva8AmnwV1toCSGq/EUNboQE7swCjjE2kcxyFOr0KtzAsFAFAJXL9c7e4RpYATcrfY/3qCCSHEHwqtAYJKC9HtkN+Y46GOjg9+UJ2eTkDSuOlIGjc9bOcMp/7XLeSnJpc3oC9vDui38wh7qyEJBtltOACD4w0Q+N5Z6qMnBJ4LuCeYeuwJIQMVx/PImL4ooAVHyRNmQ6kzhiiygWfAfhP5O9ez47ZBDIR0Kz1aC42Cl73iPTe+f/ZOx2oDK9jMAYjT965iz4QQEk7Zc64KaMFRzrxrQhTRwDRgk0+NQpCdzADNQ51U7zO8eI7D9Nz41vmf/picHQu9qv8NuQNArE6JaK1SdjsGYHA/TcgJIcQfpox8ZEy/xP9yRjyPxDFTET98UmgDG2AGbBaVEa0NeOgyPSr0+7wSX7E6FWYPToSqmyLpPAdMyY5FVkzfXAHoD47jZE9F4AAkGlQwaeQnrYQQ0p+MuWkZks+Y2f2BHI/YweMw4c6/9trdmvqq/tk15IfUKA00Ch5OmVs2JhvV/XIRS18Qp1fhwpEpKG2w40iNFY0OT+t9OqWA/AQDBsXpoFbIm8/TF+XE6nDS4kRZY/cT5zkASoHHpMzY0AdGCCG9HK9Q4cy7/4aCb95CwbdvwdVQ3Vzc/RQmeqE0RCNn3mIMvuhWCEqarhRsA3pv92O1Vtm7vswZnIAEgzpoMZDAeUQJblGCkuehFLgBd2UqSgzbSutR0uDocvW7TilgZl489XoSQshpmCSias/3qDu8E167FYJGh5i80Ug5cw54BSWdcvmbrw3o5JMxhj3lZhypsfp1/KTMGAyKozlzpPdgjKHa6sKxWhtONDp8ElCTRoEhCQZkxehoT3dCCCEh52++NqDHjzmOw7i0KOhVAvZXWuARffPwlt4knUrAhPRopNFcT9LLcByHJKMGSUYNXF4RNrcIiTGoFQIMKmHA9QYTQgjp/QZ08gmcWryRaERevAGljXaU1Nvh8EjgOMCgViA3To9ko5q+xEmvp1YIA2K+KyGEkL5twCefLQSeQ06snnYuIoQQQggJIZoIRgghhBBCwoaST0IIIYQQEjaUfBJCCCGEkLChOZ+EEEJIBDHG4PBK8IoSFAIPrYKnRa6kX6PkkxBCCIkAl1dEQZ0dx2qssHvE1tt1SgGDEwzIHSA7tpGBh5JPQgghJMzKzQ5sKqqD2ME2L3aPiJ9OmnGgwowpOXFUY5r0OzTnkxBCCAmjcrMDPxR2nHi2JTLgh8I6lJsd4QmMkDCh5JMQQggJE7dXwqaielltNhXVw+WVQhQRIeFHw+5dqLe7UVBng9nhgcQAjYJHerQWmTE6KHiaDE4IIUSewnobRNZNl+dpRMZQVG/D0ERjiKIiJLwo+exAvd2NHaUNqHd4Wvd3b1FucWLXiUYMTzJieJJxQK1INDs8aHR6IEoMKoFHgkENtYI6zwkhA5vEGDig2+8DxhiO1lgDOsfRGiuGJBgG1HcO6b8o+TxNdZMLGwpqIJ3KODu6PvVKDPsqLDA7PZicFduvPwwYYyhtdOBItRV1drfPfTwHZMXoMCTRgBitKkIREkJI+NXb3ThaY0VZowPeU18YepWAvPjOV6m7RQk2t9judn/Y3CLcokSr30m/QMlnG1aXF98X1nY7CbxFSYMDRrUFo1KiQhtYhIgSw5aSepQ1OtBRei0xoLjejuJ6OyZlxSAnVh/2GAkhJJzcooRNRXWobHK1GxmzuZtXqe+rMOOMtGjkJxh82nr8/XLphEdkUNO3NukHaMy0jSM1VoiSvA+HQ1VWeMT+NxGcMYatpxJPoOMe4JbbGYCtJQ040UgrMgkh/ZdHlLDuWA2qmlwAuvhcZMCuE404WGnxuV0p9OwrVyn031E2MrDQNdQpXlFCQZ2t0w+TzoiMobjejsGnXeH2dSctTpTKTCa3ltbjYlMqBFqMRQjpBbwSQ2mDHQV1Nlhd3uaFo0oe2TE6DIrTQ6uUN4S9s6wBZofH7++JfRUWxOlUSDZpAAAqgYNRrUCTyyvzmQBGtQKqHiavhPQW9Jd8SmWTS3avZ4viBnuQo4m8ozXWDofau+IRGUob+99rQQjpe4rqbfh8/0lsK21Arc0Np1eCW5RgcXqxr8KCLw5UYFdZAyQ/V547PCJKGhyyOig4AIerm375mePaDcX7K58WG5F+hJLPU5zewCaBA4DTE3jb3sjm9qKyySW7FxgAjtXYgh4PIYTIcaS6CVtLGuDpokOBAThaa8PGojq/EtBARsYYgIomF6xtejqzY+WX6lPwHLJjdTLPTkjvRcnnKXwPrih70rY3sjjlDwm1tnV5ghgJIYTIU9XkxO5ys9/Hl5udOFBh6fa4Cosz8Jisrtb/Vgk8/r+9Ow+uqzzvB/495+77LsmSZcnWYoNZnNhgbKAscfDvl0xa/uiESTpAM2nTNpDpxDNtIKSYNE0gGdphJtAwpUvyR1LSdAKTX8JAwEApxAnB2CzGi2R5kRdJ90pXd1/PeX9/XEm2rO2eI+mu38+MZuyrc6RXOrr3PPd93+d5/mBDoOyVJQnAzRsCXHKnhsK/5ilOs77trxJKe3EaSbnLUPOe23i5V0RURz4aTWjeMnQsnERxicRRvYml0jzntrqsuK03CNMSM6AmWcKtvUG0uay6vjdRrWqsqGkZQk4zHGaD5hpsAkBPsLFKDC2ncLzZ2FizwERUPxK50pYhrYqqwOloZtHXcqOs73VRAPMus7e6rPijq9bgdDSNY+HkrBUnt8WIjS1OdPnsy86QJ6pFDD6nTG8EP6hhuQYAbCYD1rgb612p326GxShr7iUsAej0cl8SEVXHcpbGz8UWDz4DDjMm0nlde+H99vmbcJgMMnqDTvQGnSgoKoqqgFGWGHBSw+Nf+CV6Aw54rSZNSzbXdXobbs+nLEnoCzo0L10JAH0NNgtMRPUjr6iaX7em5ZZYVu8NOnQFnj6bacHg81ImgwybycDAk5oC/8ovYTTIuLU3CLfVuOgLmDT1cUOXDx0eW4VGV1m9QScMsqRpU3yHxwq31bSawyIiWpBhGRMBBklCJJXDYCSJY2MJnBxPIXNJJROP1YSQ06w5uNVbWomokXHZ/TI2kwGf7G/BsXASA+EkssWL76Sn3/V2eKy4stWNgKNx+5nbTAbc0hPEa4NhQCzcyQMoBZ4emwk7uvyVGh4R0Rweq1HX7CQARDN5vHw8POsxCcBarw2bWpwIOiy4YZ0fLx0bQ0FRy/o+nV4b1rNEEtEckhDLSG2ukHg8Do/Hg1gsBrfbXbHvqwqBC/Es4tkiVCFgMcpo99hg19gVo56Np/PYf2oCiVxxTh/j6f+v89pw/Tofl4uI6piiCpyPZ5DMKVCFgNVkQIfbCmsdvd6pQuAXhy8gU1i5shvTr3PXdXrRG3Qini3gtcEI0gvUd54+vstnw/Z1fnZ8o6ZSbrzG4JOWJIRAOJnH8UgS0UwBiipgNkjo8NjQG3TAobNMFRFVX66o4MhYEoORJAqKmLXSIwFY57PhylY3vLb62FJzeCSO98uo26nHji4/uv12FBR13iz16ZnSvqADLU4LOxJR0yk3XmPUQEuSJAktLgtaXJZqD4WIVlAyV8Srg2Gk88rMqsalsxECwJloBsOTGdy0PlAXe9w3trhwNpZBNF1+D/ZyvTMcxVqvbSZLvSfgQDKvIFdUIEsSHGYDLMb6mSkmqhaukxIRNaFcUcG+ywLP+QgAqgD+9+Q4wkntNTQrzShLuLUnVFaGuVYFVeBMND3zf0mS4LIYEXRYpkrUMfAkKgeDTyKiJnRkNIHMEoHnpYQozfzVwU4tWIwyPtEXwvZ1vnm3CyynVeVgJLWcoRERuOxOTWwyU8BAJInxVB5FVcBskLHGbUVP0NFUSWXUfBRVYHA8pXlZejJbxES6UNOVPoQQCKfySOcVSBLwsQ4PzAYZ6fzFRKqCouKNoXFdXz+RK6zwiImaD4NPajqJXBG/PT2BSCo/J4N/Ip3H4ZE41vlsuK6TGfzUmM7FMigo2mcwJQAnxlM1GXwWFBWDkRSOR5JIX9Ym2WKU0Rd0oi/ogNVkwNlYRvf3UWt/4peo5jH4pKYymSnglYExFKduvJffR6b/fyaaQSxTwCf6W5a1REdUi5LzlE4rh5g6t9ak8kW8NhhBYoGx5YoqDo/EMRhJ4rbeECzLeE6bjXw9IFouPouoaRQUFa8NhlFUxJI3XQEgli3irZP6luaIatlyqmAqNbbnM1dU8epAeMmgWEwdu28gDItRhlVHECkB6PKxaDzRcnHmk5rGyYk0ssXyb7sCwEgih4l0flUyZ6k+5IoKTk2kEc8WoYjS3uC1Hlup1WKd1nG0GmVdZYgklLqf1ZIPR+JIlZk4JVB6E/re+Rj6Qk58oLEeqADQG3DoGSYRXYLBJzUFIQSOhZOaz5MADESS2L6OrUObTaag4L3zMZyOpqEKzOrpfSychMtixFVtbnTXYfvEDo8N7wxP6lp2X+etnVqfRUXFkMbEKQHgbCyLzW1uHBtLoFDGSsi0Lp8NTgtvm0TLxWV3agqJXFHXXjUBYDiqPzmB6lMiW8BLx0ZxaiI9k2AiLvkASn9T+09P4ND5WF2UH7qUzWRAp9cGrfO2FqOMtTUUfJ6ZzKCoIwNIAnA+nsWtPSEYZKms30PIYcb1fBNKtCIYfFJTyGlYbr9cQRVQ6yy4IP3yRRWvDkaQLahlzYgdGU3guI5Z9Wq7stUFrbsGVCHw0rExfDgSR2aB3uaVFM8WNP8MQOkNRDxbKhn1yf4W+KZqgc73pSSptNR+W28IRvZpJ1oRXD+gpiAvY2+ehPlvStSYBiJJpDUGVu9fiKMn4ICxjioj+Oxm3NgdwJsnx8tedi4oApOZAiYzBXx4IY71fju2dfpgqFJQpqNa1MVzp2ZMvTYTdm9qxUQ6j4FIEpPpAopTe3s7PDb0BOzsXES0whh8UlNwmPXfPBwWQ90mlpA2qhAYiGifxSyqAqejGfQE6ysZZa3Xhtv7QnhnOIpYVlv5JQFgaCKNeK6I23tDVQlALQZZe70olN5MXh5Q+u1m7u0mqpD6eZtOtAxWkwFrPVZdM5h9QeeKj4dqUziZQ6agb4vG0ER9tl1scVrwfze14pP9IawP2BGwz21HuZhIKo/fD0dXaXSL6/BYdWXti6lziag6GHxS0+gLOTXfqGQJ2OCvr9ks0k9v4AlgTledeiJJEoIOC7av8yPosGh+k3ZyIl2Vn99nN8OvMVgGAJup1EqXiKqDwSc1jVanBV0+bZm6H1/rZUcTahpFRcUJHT3fS203q5N0tbnNrf2cVvey9oET0fJwzyc1DUmSsH2dH6qYwPDkwuWTpve9bWn3cMm9ydhM+t9o2Jexr7hWjCVzukoXCZTKHl29xrPyg1rCWo8N165x470yC8b3Bh3orbO9uZVUUFScjqYxEEkhmStCnUq+6vLZ0Rt0wG3VPtNMdDkGn9RUDLKEG7v9OBVN4/hYEhOZwpxj2j1WbAq50OKyVGGEVE0hpwU2k6xr+b0Rtmfkl5E+nl9GObPlurLNDYvRgIPnJlFYIHg2SBI2t7mmSkxx1nM+J8dTeOfs5Jw3INmiiuPhJI6Fk+j02nBDlw9GmStCpB+DT2o6kiRhvd+B9X4HJtJ5RDMFFBUVZoOMFpcFDjOfFs1KliT0B51lz6JNM8qS5i0dtWg5CevVXsbuCTrQ5bdjOJrGifEUUlN7UG0mA9b77ej222Gqo1JYlXY8nMSBs5MLfn46HD07mcGreQW397HuKenHuyw1Nb/dzL7tNEtv0ImB8RQyZfYLB4Br1rjrqsbnQvQuqUoAPNbq306MsoT1AQfWs/+6JuFkbtHA81ICwHg6jwNnoyxNRbrV/6slEdEKMhtl3N4bgtUkl5X1fWWrC/2hxtgb7LWZdGWPC5SqSVB9OjqW0F7hYDyNbA10uaL6xOCTiGqGKkRN9El3WYzYvbEV3X77zFK0hNndrtwWI3Z0+XFtu6eh9hDqCaRZuqh+pfNFnI1lNVc4EACGxuuzti1VX/XXSYioqSVyRQyEkzg1kUZOKSWtWI0yegIO9AQdVduDazMZcEOXHx/r8OLURArxXBGKKmAxltouhhzmhgo6p3X57Dg9kcZIIldWQCIB2L7OX/U9n6TPSCKn+9xz8Syu1FHqiojBJxFVhSoEfj8cxdB4ek5bx2xRxUejCRweTWBTixNbqji7aDHK2Njiqsr3rgZZknDT+lLP9wuLBCYSAEkCdnYHOOtZxwqK/ioF+WWcS82NwScRVZwqBN4cGse5eBbA/O25px87OpZErqhi+zpfQ8401iKjQcYf9ARxaiKN4+EkopeVJDNIwPqAA/0hJzys+1jXDMvIWDfy+Ug6Mfgkooo7MpqYCTzLcXIijaDDjF4W/a8YWZKwIeDAhoAD0XQesezFguOtLgvLFjUIr01/hQMfK4WQTgw+iaiiFFXg6Jj2VoxHRhPoCTg4+1kFPruZgUaDCtjN8FiNiGWLms4TAPrYKYp04ltXIqqos7GMrr1iybyC0aT+5AgimkuSJGwMadvTLAEI2E18Q0K6MfgkoooaSWQ11xQESje80WVk5hLR/NYH7GgvM2lMQmmf6PYuFpgn/Rh8ElFFFRShuabgxXOZXUu00mRJwo3rA+jwlALQxd4cmg0yPtEXYqIZLYuu4POpp55Cd3c3rFYrtm/fjrfffnvBY5955hncfPPN8Pl88Pl82LVr16LHE1FjM8qSrplPYHmZuUS0MKMs4eb1Ady8IYAWl2XO520mA65t9+DTV7ayJTEtm+aEo5/+9KfYs2cPnn76aWzfvh1PPPEEdu/ejWPHjqGlpWXO8a+//jo+97nPYefOnbBarfjud7+LO+64A4cPH0ZHR8eK/BBEVD/8djNOTqQ1nyemziWi1SFJEtZ6bFjrsSGVLyKZK0IVpVq3XpuJjQRoxUhCYy+77du347rrrsOTTz4JAFBVFZ2dnfjKV76CBx54YMnzFUWBz+fDk08+iXvuuaes7xmPx+HxeBCLxeB2s5sCUT3LF1U89+F5qBrX3s0GCXde1c7ZTyKiGlVuvKZp5jOfz+PAgQN48MEHZx6TZRm7du3C/v37y/oa6XQahUIBfv/Cm5VzuRxyuYuJBfF4XMswierSeCqPofEUErkiFCFgMxnQ6S3NQjRSwGU2yljvt2NoPF323k8JQF/Q2VC/ByKiZqUp+IxEIlAUBa2trbMeb21txdGjR8v6Gl/72tfQ3t6OXbt2LXjMo48+im9+85tahkZUtyKpHH4/PInJTGFOm8nhyQzMBhlXtbnQH3I2TI3LLe1ejCXzSOaKSwagpWLWJvaQJiJqEBXNdn/sscfw7LPP4rnnnoPVunBZhwcffBCxWGzmY3h4uIKjJKqc8/Es9g2EMTnVvnC+QCyvqHj3XAzvnp2Exl0yNctsnMqYnequMl9IPf1Y0GHGbT0hGDnrSUTUEDTNfAaDQRgMBoyOjs56fHR0FG1tbYue+/jjj+Oxxx7DK6+8gmuuuWbRYy0WCyyWudl2RI0kli3gzaFI2Xsfj0dScFiM2NSirSB0rbKZDLijvwXDkxkcDycxns7P+nyL04L+kBPtHisTHYiIGoim4NNsNmPr1q3Yt28f7rzzTgClhKN9+/bh/vvvX/C8733ve/j2t7+Nl156Cdu2bVvWgIkaxZHRhOakm8Mj8Yba+2iQJXT77ej225HKF5EpKAAAu8kIu9lQ5dEREdFq0Fxqac+ePbj33nuxbds2XH/99XjiiSeQSqXwhS98AQBwzz33oKOjA48++igA4Lvf/S4efvhh/OQnP0F3dzdGRkYAAE6nE06ncwV/FKL6kSuqOB0tP+FmWl4RGJ7MoNtvX5VxVZPDbITDrPkliaiujafyGIgkEUnlUVRVmGQZbW4r+oIOuFnInRqU5lf6u+66C+FwGA8//DBGRkawZcsWvPjiizNJSGfOnIEsX9xK+oMf/AD5fB5//Md/POvr7N27F4888sjyRk9Up87HMppnPaedjqYbMvgkaibxbAG/OTWB6GWJhhmoSISTOB5Oos1lwY4uP6wmrgJQY9Fc57MaWOeTGs2R0QTeOx/T1WbSbzNh96bWpQ8kopo0mSnglYExFJdoNSsBsJsN+GR/C2wMQKkOlBuvsbc7URUsJ3+GyTdE9augqHhtMLxk4AmUZkPTeQVvDEUaptIFEaBj2Z2o1sWyBVyIZ5EvqjDIErw2E9a4aytj2mk26pr1lAA4LZwBIapXp6NpZItq2ccLABPpAsaSObS6Fi5RSFRPGHxSwzgfy+Cj0QTCqVLJHmlqI5UAYDXK6A85sbHFVRP1Ite4rTAbZOSV8m9CQOln6QkwUY+oHgkhcCyc1HyeBGAgkmLwSQ2Dy+7UED4aieN/hsYRSV2sFSnExU382aKK9y/EsW9gDHkNsw6rxSBL6As65i2uvhiXxYiQ07wqYyKi1ZXKK4hni5rPEwDOTma49E4Ng8En1b3BSBLvXYgDmL9D0KWi6QL+ZygCRW+q+Qra1OKCw2woOwCVAGzr9DZMi02iZqN1peNSAkCxBl63iFYCg0+qa4Wp1pPlEgAiqTzOTKZXb1BlMhtl3NYbgn2JAFRCaQvBjev9aOOyG1HdMizzjWOjNJcgYvBJde1UNK1rFvP4mPZ9V6vBaTFi98ZWXNHqgtlQurFIuNjXXAKw1mvDHf0t6PSytidRPbObDdAbP9pNhppKmiRaDiYcUV07EUnpOm8iU0AsU4DHVv0OIhajjGvbPbiqzY1zsQyS+SJUUUqS6vDYWN+PqEGYDDK6fHacmtDe3awvxERDahwMPqmuJfPaN+9PS+WLNRF8TjPIEtb5OLtJ1Mj6Q06cnNC27UeWgJ4AXxuocXDZneracpI/uXWfiCrNbzdjo8ZZzK1rvbAYuQJCjYPBJ9U1q1H/n7BlGecSEen1sQ4P+oIOAFgw2VC65NjeIJfcqbFw2Z3qWrffjsMjCc2zmHaTAQE762USNSJVCFyIZ3E6mkG2oAAoJfdtCNgRsJurXq5MkiRsXetFu9uKY+EkRhK52Z8H0Om1YWOLE0GHpTqDJFpFDD6prvUEnPhwJKH5vP6Qs+o3ICJaeScnUnjvfByZgoKpJmcAgLFkDifGU/BYjdi61lv1bkGSJKHdY0O7x4ZkroiJdB5FVcBkkBB0WJhoSA2NwSfVNbvZgL6gAwNlZr1LAKwmGRsCjtUdGBFV3OGRON6fajgBzN7XPf3vWLaI1wYj2Nntr5kEP6fFCKeFt2NqHtz0RnXv42u96HAvPYshoVTq5LaeEPd7EjWYUxPpWYHnYgSA35yawPgl7XiJqHJ4B6a6J0sSbtoQwOY2F4yLVHAOOszYvbGlpsorEdHyCSHw/oXyO51N+3CkvGCViFYWg09qCLIkYWPIiZBz4SSicCqP356ZQGoZtUGJqPaMJHJI5RVN5wgA5+NZvh4QVQGDT2oI2YKCXx8fw0g8t+hxkWQeLx0bQyLHGw5RozgzmV6wZNFShiczKzoWIloag0+qe0IIvDE0jlROWbLkkgCQL6p4bTCsqyf8SlOFQDqvIJEtIFdUIJZTNZ+oSWULSz/35yNJQLagrvh4iGhxTK+jujeWzGE8XX7igACQyis4M5nGen91st5T+SJORFIYiKSQVy7e/DxWIzaGXOjy2aCK0qxMulCEEIDNZECn1wYrS7AQzSLpnfcUpdaVRFRZDD6p7h0PJ2fV8yv7vLFkVYLPgUgSB4YnAcwdcyxbxNvDUbxzNgoAUMVUpxOp1Er0wNlJrPPZsLnVzcQpoilOi1HXa4AA4GCJI6KK47I71TUhBM7FsrqW3CYyBWQK2pIUlut4OIl3hichsPiNUhWlD0wdJy7595loBi8dG8NIPLu6gyWqE+sDdl2vAQYJWOe1rfh4iGhxDD6pIoQQq7KfsaAIXTedabli5fZ7TWYKOHB2ctlfRwBQhMAbQxFMaNhuQNSofDaz5na5EoD1AQdMBt4GiSqN6w20auLZAgYjKZyKppErqpAAOMwG9ASd6AnYYTEuf++ivMz7RiX3ew1E9G0PWIgqgEPnYri9L7RCX5Gofm1d68UrA2MoJ49QAmA2ytjc6lr1cRHRXHzLRytOUQX2nxrHr46M4ng4OTO7KAAk8wreOx/Dcx9ewJHRxLJnQw2SBKvObkWyBNgrlLxTUFScHE+vWOAJlH6fo8kc4tnCCn5VovoUcJhx84YgZAmLph9NB56394ZgN3P+hagaGHzSilJUgddPhHEqWqqdt1CwJQRw6Hys7HZ4C5EkCb1Bp+ZcVwlAt88OY4WW3GLZApRV2HYgATg5kV7xr0tUj9rdVuze2Ip1PtvMa4KEi8GoQZbQF3Li/2xshZcJe0RVw7d9tKLevxDDWLL8fYgfjSYQsJuxdhmb/nuCDs1t8gSAvpBT9/fUqqCsXv1OdmghushrM2FndwAf71BwNpZBpqBCkkpbftZ6bNzjSU0hmskjnMyjoKgwyhKCDgv8dhMkqTZqizH4pBVTUFQMRFKazpFQCkCXE3zaTQZsaffg0Pnyezv3h5zwa0xQWA6TYXWe8JdmwhPRRVaTAb3Byr3BJKo2IQSGJzM4OpacqX19aZ6B12rExhYX1vvtVQ9CGXzSijkVTWvuGiQAjKfzmMwUlrUMtqnFiaIq8OFIfMmknp6AHR/r8Oj+Xnp4rSYYZGnFuypJAIvOE1FNUVSBsWQOmYICCYDdbEDIaYFcI7NujUgIgQNnJzEQSc3ahnbpHWcyW8TvzkQxksjihi5/Va8Hg09aMcupO3khnl1W8ClJEq5e40bIYcbRcBIX5hlLyGHGxhYn1npsFX/XZzTI2OC3YzCSWvGkI9YpbB6KWipZZpClqs9cEF0uU1AwEE7O6dwGlFao+kIO9AadMBtkTGYKGIgkMTyZQUFRIUsSPFYT+kIOrPPaYWDrKU3evxCfWXlc6h5zOpqBUY7i+nX+1R/YAhh80oq5/MWmXJJUWrJfCW1uK9rcViRzRYRTORQUAaMsIWA3V70jUF/IqXlbwlI8ViOCjsptH6DKS+WLGIykcCKSQm7qeWKQgHU+e8W3jxAtJJrJ47XBCPJFdd7gJ11Q8N75OAYjSdhNRoRT+VmrVKoQGE/nMX46jwNnJ7Gjy48OD99YlyOZK+Kj0YSmc06Mp9ETdGquj7tSGHzSijHqLbopAOMKv8t1Woxw1ljbPI/VhOs7fXh7OLpiX/Oadg9nwBqUEAIfXIjj8GhizlYSRQCnJtI4OZFGu9uKnd1+JtJQ1SRzRbw6EEFBmT/wvFQqryKVL+1HXOjYgiLwxtA4dnb70eWzr+hYG5GeGtISgIFwEoGu6sx+8tWqRhUUFfFsAbFMAdkKt4DUy2c3aS55BJSeML4mmb3pCTqwfZ1vRYrbb13rxVrODDSk6f1bh6dmM+a7qUw/diGexWuDERRXeD8xUbnePTdZVuCp1f5TE4hlWMd4MUIInBjXvp1LADgdTaO4QquOWtXW1FCTE1PLDgPhJE5PZmZlMYccZvSHnFjrtdXspu2egAOHR7RN/QOlvUBtLssqjKg2bQg40O624sR4CgORJDKFi0/+gN2MFqcF5+MZxLLFWe9mp//tNBuwpcODTi9nBBrV6Wim7C0a00l7h85NYlunb3UHRnSZVL6IczH9+/2XcjySxHX8u15QQRW6S/mpAsgUVbiqsGrC4LNGKKrA74ejODmRnnf6PJLKI5yagMdqxK09IdjNtZfh7DAb0eG24nw8q+ldWH/I2XRLx1aTAZvb3Liy1YWCIlBUBcwGaabo/bXtboyn8zg5nkaqUAQEYDMZ0OW3o9VpabrfV7M5Oqb9TdyJ8RSuaffAzOV3qqCh8fnvWStBADg5nsKWdg+3lSxguV0Cl3u+Xgw+a4AQAvtPT2B4cuGuQNOPxbNFvDIwhjv6W2qyxM62Th9eOjaK3AKbzi8lAQg5SzO6zUqSJJiNEi7fdCBJpaLAQUfzzAhTyXg6j6iOpUZVlG7UG1vYr5wqJ5Fb3WVxRQDhZA7t3GI0L5NBXlbwb9HZnnq5+FaiBgxNpGcCz6UIAOm8gt+vYNLKSrKbDfhkf8uiM7PTc3ZtLgv+YEOQJTWILjGWyOnaOw0AY8ncio6FaCmKujqznpfKr2KHuHonSxLWem26Wky3OM2wGKszicWZzyoTQmheYhMAzsaySOeLsJtr7xI6LUZ8alMrTkXTOBZOIp6d3f6xxWVBf8iJdre1ZvevElVLQVGhdypDb7kzIr0sxuXNvJVjpauhNJq+oKPsCaxpAqjqqmPtRS5NJpLKzwnOyiGhtMfr6jWV7dRTLqNBRm/QiZ6AA4lcEdlL+ivXYsBMVCuMsqT7Tm7SW+6MSKcOTyl5cjVVu0ZzrWtxWhBymBFJ5ct66ZAAuK3GqtZR5StVlU33X9VKoBS41jpJkuC2mtDisiDktDDwJFqC32HWPYvkZ8MBqrA1bitsq5R/IAFodVrgqrGazbVGkiTcvCEIl9W45PK7BMBmNuDWnhDbazazoip0L1noLa9AVA9yRQWnJtJI5RWoQsBqMmCd1wa3tbFnQVqdFjjMBqTy2ur7SiiVOyOqJFmScFWbC78fnlzxr13tpeF6YjHKuKO/Be8MR3E6mpkTU0zHGR0eK67r9FU9YZnBZ5WZZEn3LIfZwH0w1HiSuSI+uBCbqXU7swotgA8uxNHiNOOqNjdaXdZqD3VVSJKETS0uHDg7Wf45ADq9tlWbgSJaTE/AgVimgOMr3D6422dHh6cxn+erwWSQsaM7gC0dCobGUxhN5FBQVJgMMoIOM3qCDjhqZPWxNkbRxFqc+kvptDRRYXZqDhPpUn/oS7ulXN64J5zM49XBCLav82FDg8709QYduBDP4nx86eLdEkpVJrZ1eld9XETzkSQJH1/rhc1sxIcjcSiXPWmnZ93MBgk9AQcGIikoqph34mX62PV+O65f52NNYx1sU3WkN7dVeyQLY/BZZT67GX67CRNpbbXSJIlLbNRYUvninMBzPtOf+92ZKCxGuaqb5leLLEm4aX1gpv7vfFtzph9zTzWeqFbJFCKgFIBe2epCX9CB09E0Tk6kkSkokFBqQLIh4ECn1waDLGFjiwuDkVKHt1xxdoWGdrcVfSEn2lxsptHIGHzWgCtaXHjr1ETZx0soLUfwZkON5MhoQnN/6HfPxdDutjbkTcogS7ix24+xZA4DkRTOTs7exxWYbrnrsbFWboXlFRWnpuoz54oKDLIEj9WE3qADAbu5If8ey2WaqnTSG1x4r6bNZMDVa9zY3ObCRDqPfFGFQZbgsppg59aRpsDgswZ0em3oCzrK6uUsAfBYjdi61rvq4yKqlIKiYmgirXn/czJXxFgy19D7P1tdVrS6rMgVVWQKCoQQsJgMvElXgSoE3j8fw7Fwcs52kGi6gJMTaXitRlzf5UfAzsoDS5GnOrlR82GppRogSRK2rvXiytZSW7z53jNPPxZymvGJvhb2uaWGcjaWmbNPrBwSgJMT6ZUfUA1RhUA4mUM4mUMqX4RBlhh4VoEqBP53aBxHxuYGnsDFbRGxbBGvHB/DaGLp/bpEzYoznzVCkiRc2+5BT8CBwUgKg+PJmVJKEoBOnw39QSeCjuZe0qHGlM4rukqOCZT2ijaibEHB4HgKA+EkspftiwvYS0vu63w2dgmrkIPnJstKABMAhADeGBrH/93UCidrVBLNwWdFjXFajNjS4cG17W4UVQEhAJNBYsBJtADRgOVux9N5vD4YWbBd5kQ6j/2nJ3Bi3II/2BDgSsgqyxYUDIS1lRFSVIGBSBIf6/CuzqCI6hhfsWqUJEkwGWSYjTIDT2p4NpNBV73bUpmhxnoPHcsW8OpAuNTjfQHTv6twMof/ORHRtWWByjc0kdI1Kz8YSaHIa0M0B4NPIqq6tR4b9CRsCwDdvsYqtfS709EFayBeTgAIp/IYjCRXe1hN7Uw0o+u8oiowxr2fRHMw+CSiqjMbZXT77Ev2Jb6c3WTAGnfjZLpHM3mMp/OaZ9mOh5MQjbj/oEZcvudWi8vrWBIRg08iqhFXtro016vc0uFpqG0pJyIpzQE4ACTzCsaSuRUfD5Usp4yqzBqsRHM01mYpIqpbLqsJt/QE8fqJCNQylp23tHvQ5bNXZGyVEs0UdO19BUolfqaqtdEKc1uMSOcVXdfGqWNPcipfxEAkheFoGjlFhSxJcFuN6AuyqQA1BgafRFQzWpwW3NHfgkPnJnEhkZuZBRSY3U7ymjUedHoba68nAN2JQ9IyzqWl9QaduJDQPrPssRrht5vKPr6oCvz+zARORS9vqSoQTuYRTk7AYpBxfZcPaxuwrSw1DwafRFRTvDYTbu0NIZkrYmgihVROgSoErCYD1nltDV3r1mLUtxNKADCz3NKqafdYYTXKmvd+9oecZf+tFlWB1wbDiKTyABaueZtTVPzv0Dh2dPnR7W+smX9qHgw+iagmOS2lGc5m0uGxYUTHDBsAtLnZpnC1yJKE69f58MbQeNnnWIwyvNbyZz0PDEdnAs9y/Pb0BDxWI3xs40l1iG+ViYhqRLffrjm5RQLQ4bbC0WD1TmtNh8eGHV1+SJi/BfLl8kUVLw+E8dKxUcSyhUWPzRQUXW1ij4VZYovqE4NPIqIaYTbI6As6NZ0jAFzBTKOK6PbbccfGFnSWUVt2etk8mi7g18fGMJFeeFbzxLi+Ivano2nkiorGM4mqj8EnEVEN2dLhQaur/CX0bWu9CDm55F4pfrsZN3YH0FFmfVmBUjLYa4ORBQPFc5P6itirAhjVuU2DqJoYfBIR1RBZknDLhiA2BErJJAst8RplCTu6/OgLaZsppeVLZAs4Fy+/c5EAkFdUnIjM3x8+t0gr1aXkl3EuUbVwkxDVNVUIpPMKCooKoyzBbjayBh7VPYMsYfs6P65qc+PEeAqnJ9LIFVVIkgSXxYjeoAPrfHYYq/S3nswVMTiewoVYFvmp557fbkZfyIGAvXGrEUwbmGoGoLkTVSSFTa0uyJf9fpbzmsXXO6pHDD6pLmULCobGUzgeSSJTuPjO32yQ0Bt0ojfoYAIG1T2HuZTxXytZ/3lFxe9OR3E2dnkdSiCRK+JUNA2vzYSdXX54bOVnetebM5NpXQXnMwUF0UwBgcsy1L02ExLZoq6v6dGQUU9UK7jsTnXnfCyDXxwewXsX4rMCTwDIKwJHRhP4f4dHMDQ+/xIXEWmXL6p4+fgYzsVK+xMvD5Sm/x/LFPDr44sn2NS7/HJ6vRfm7vvsCzp1BZ5emwl+llqiOsTgk+rKhXgWbwyNQxELv1SLqY/fnYkyACVaIW+eHC9rdm46web1wciygrRatpxtBfP1eg85zHBbjGWVcLrURu73pTrF4JPqRlFR8dbJcU0zBG+fiSKVL67amIiawUQ6j9FkruznnkApiWZoojHf/Lks+rf0zNfrXZIk3NDtR7kxrQRgjdvKDkdUtxh8Ut04FU2joKN/9UIZpkRUnuPhpOZZuenzxCKrFPWqN+jQfI4EoNVpgXOBwDVgN+O2nhCMsrTk73qN24qb1vvnJC4R1QsGn1Q3juvo5iFQykxVdAStRFRydjKja09iKq8gnm28lYduHZUGBEq93hfT4rLg01e04opWF8yGuV8/6DDjxm4/bt4QgFGur9t3tqDgXCyDUxNpnItlkJln7ys1D6YDU11QhUBM500sr6hIF5RlLZURNSshhK4Vh2nLqWFZq4wGGds6vfjt6WjZ53R4rOjwLF2Y3m424tp2D65qcyOSyiGvqJCnSmy56zCzPZLK4Xg4iTPR2W9gJJR+JxtbXGhhk4Smw7sx1YWisryZy2ID3gCJKkGSJEgSoHf1vFHLUK73O1BQBA6cnVyy5ucatxU7u/2aEpUMsoRWV3ldlGrVkdEEDp2Pzfv7EQDOxbI4G8tic5sLV7e5G74+LF3E4JPqgnGeJSgtTIb6WqIiqiUui1HX8rmE+RNsGkV/yAmfzYSjYwmcjc3teOSxGtEfcmJDwNF0+zOPh5M4dD4GYOHAfPrxwyMJGCQJm9vcFRkbVV/jvipQQ5ElCUGHGeOpvOa9ZzaTAXazYVXGRdQM+oJOHDg7qekcCcBarw1WU2M/90JOC0JOC9J5BSOJLAqKCoMswWszNUW3p/mkCwre1fj38v6FONZ5bXDV4dYC0o7TQVQ3+kP6CjH3h5pv1oFoJa3322HQ+BwSAPp0ZIXXK7vZgA0BBza2uNAbdCLosDRl4AkAQ5GU5tdqCaXkUGoOuoLPp556Ct3d3bBardi+fTvefvvtRY//2c9+hk2bNsFqteLqq6/GCy+8oGuw1NzWemywGmVNJV8MkoQN/ua5ARKtBpNBxnWdXk3nbPDbmUjShIQQGIjoq0xyYpyVSZqF5uDzpz/9Kfbs2YO9e/fi3XffxbXXXovdu3djbGxs3uN/85vf4HOf+xy++MUv4uDBg7jzzjtx55134sMPP1z24Km5GGQJt/QEIZdRBw8ovZO+aUOg4Zf9iCphfcCBbWu9ALDk86/bZ8N163xNO/PXzAqKQFZnZ6uiKpAtsgRTM5CExgrA27dvx3XXXYcnn3wSAKCqKjo7O/GVr3wFDzzwwJzj77rrLqRSKfzyl7+ceeyGG27Ali1b8PTTT5f1PePxODweD2KxGNxubkhudhPpPP7nRGTRFziTLOGm9QG0ues7W5So1oyn8jgaTmA4Orf2Z8hhRn/IiU6vjYFnk8oUFDz/4QXd53/6ita6LClFJeXGa5oSjvL5PA4cOIAHH3xw5jFZlrFr1y7s379/3nP279+PPXv2zHps9+7deP755xf8PrlcDrlcbub/8XhcyzCpwfntZvzh5jU4G8vgeDiJSCo/8zmP1YiNIRe6fDYYmeFOtOICDjNudASQ7VAwlsyhoAgYZAk+uwkeBg1Nz7zM193lnk/1QVPwGYlEoCgKWltbZz3e2tqKo0ePznvOyMjIvMePjIws+H0effRRfPOb39QyNGoyBllCl8+OLp8dqhAoKgJGg8TEIqIKsZoMWOdjb3GazSBLaHVaMJbMaU468ttM3CbVJGryLcaDDz6IWCw28zE8PFztIVENkyUJZqPMwJOIqAbor0yyePtRahyaZj6DwSAMBgNGR0dnPT46Ooq2trZ5z2lra9N0PABYLBZYLMySJCIiqjftHitcFiOSuWJZQaiEUj1mzqQ3D00zn2azGVu3bsW+fftmHlNVFfv27cOOHTvmPWfHjh2zjgeAl19+ecHjiYiIqH7JkoRbe4Iwl1EaT0Kpg92tvUEYGrUXK82hedl9z549eOaZZ/CjH/0IR44cwV/91V8hlUrhC1/4AgDgnnvumZWQ9Nd//dd48cUX8Y//+I84evQoHnnkEbzzzju4//77V+6nICIioprhtBixe2ML/HYzgLnluab/77GZsHtjK5PVmozm9pp33XUXwuEwHn74YYyMjGDLli148cUXZ5KKzpw5A1m+GNPu3LkTP/nJT/CNb3wDX//619HX14fnn38eV1111cr9FERERFRTHGYj7tjYgol0HsfDyZnqCEZZQovTjL6Qs2lbkDY7zXU+q4F1PomIiIhqW7nxWk1muxMRERFRY2LwSUREREQVw+CTiIiIiCqGwScRERERVQyDTyIiIiKqGAafRERERFQxDD6JiIiIqGIYfBIRERFRxTD4JCIiIqKKYfBJRERERBXD4JOIiIiIKsZY7QGUY7r9fDwer/JIiIiIiGg+03HadNy2kLoIPhOJBACgs7OzyiMhIiIiosUkEgl4PJ4FPy+JpcLTGqCqKs6fPw+XywVJklbka8bjcXR2dmJ4eBhut3tFviZVD69n4+C1bBy8lo2F17NxrNa1FEIgkUigvb0dsrzwzs66mPmUZRlr165dla/tdrv5JGogvJ6Ng9eycfBaNhZez8axGtdysRnPaUw4IiIiIqKKYfBJRERERBXTtMGnxWLB3r17YbFYqj0UWgG8no2D17Jx8Fo2Fl7PxlHta1kXCUdERERE1BiaduaTiIiIiCqPwScRERERVQyDTyIiIiKqGAafRERERFQxDD6JiIiIqGIaOvh86qmn0N3dDavViu3bt+Ptt99e9Pif/exn2LRpE6xWK66++mq88MILFRoplUPL9XzmmWdw8803w+fzwefzYdeuXUtef6ocrc/Nac8++ywkScKdd965ugOksmm9lpOTk7jvvvuwZs0aWCwW9Pf387W2hmi9nk888QQ2btwIm82Gzs5OfPWrX0U2m63QaGkhb7zxBj7zmc+gvb0dkiTh+eefX/Kc119/HR//+MdhsVjQ29uLH/7wh6s3QNGgnn32WWE2m8W///u/i8OHD4s///M/F16vV4yOjs57/FtvvSUMBoP43ve+Jz766CPxjW98Q5hMJvHBBx9UeOQ0H63X8/Of/7x46qmnxMGDB8WRI0fEn/7pnwqPxyPOnj1b4ZHT5bRey2knT54UHR0d4uabbxZ/9Ed/VJnB0qK0XstcLie2bdsmPvWpT4k333xTnDx5Urz++uvi0KFDFR45zUfr9fzxj38sLBaL+PGPfyxOnjwpXnrpJbFmzRrx1a9+tcIjp8u98MIL4qGHHhI///nPBQDx3HPPLXr80NCQsNvtYs+ePeKjjz4S3//+94XBYBAvvvjiqoyvYYPP66+/Xtx3330z/1cURbS3t4tHH3103uM/+9nPik9/+tOzHtu+fbv4i7/4i1UdJ5VH6/W8XLFYFC6XS/zoRz9arSFSmfRcy2KxKHbu3Cn+9V//Vdx7770MPmuE1mv5gx/8QGzYsEHk8/lKDZE00Ho977vvPnH77bfPemzPnj3ixhtvXNVxkjblBJ9/+7d/KzZv3jzrsbvuukvs3r17VcbUkMvu+XweBw4cwK5du2Yek2UZu3btwv79++c9Z//+/bOOB4Ddu3cveDxVjp7rebl0Oo1CoQC/379aw6Qy6L2Wf//3f4+WlhZ88YtfrMQwqQx6ruUvfvEL7NixA/fddx9aW1tx1VVX4Tvf+Q4URanUsGkBeq7nzp07ceDAgZml+aGhIbzwwgv41Kc+VZEx08qpdAxkXJWvWmWRSASKoqC1tXXW462trTh69Oi854yMjMx7/MjIyKqNk8qj53pe7mtf+xra29vnPLmosvRcyzfffBP/9m//hkOHDlVghFQuPddyaGgIr776Kv7kT/4EL7zwAgYHB/HlL38ZhUIBe/furcSwaQF6rufnP/95RCIR3HTTTRBCoFgs4i//8i/x9a9/vRJDphW0UAwUj8eRyWRgs9lW9Ps15Mwn0aUee+wxPPvss3juuedgtVqrPRzSIJFI4O6778YzzzyDYDBY7eHQMqmqipaWFvzLv/wLtm7dirvuugsPPfQQnn766WoPjXR4/fXX8Z3vfAf//M//jHfffRc///nP8atf/Qrf+ta3qj00qnENOfMZDAZhMBgwOjo66/HR0VG0tbXNe05bW5um46ly9FzPaY8//jgee+wxvPLKK7jmmmtWc5hUBq3X8sSJEzh16hQ+85nPzDymqioAwGg04tixY+jp6VndQdO89Dwv16xZA5PJBIPBMPPYFVdcgZGREeTzeZjN5lUdMy1Mz/X8u7/7O9x99934sz/7MwDA1VdfjVQqhS996Ut46KGHIMuc36oXC8VAbrd7xWc9gQad+TSbzdi6dSv27ds385iqqti3bx927Ngx7zk7duyYdTwAvPzyywseT5Wj53oCwPe+9z1861vfwosvvoht27ZVYqi0BK3XctOmTfjggw9w6NChmY8//MM/xG233YZDhw6hs7OzksOnS+h5Xt54440YHByceQMBAMePH8eaNWsYeFaZnuuZTqfnBJjTbyxKeS5ULyoeA61KGlMNePbZZ4XFYhE//OEPxUcffSS+9KUvCa/XK0ZGRoQQQtx9993igQcemDn+rbfeEkajUTz++OPiyJEjYu/evSy1VEO0Xs/HHntMmM1m8d///d/iwoULMx+JRKJaPwJN0XotL8ds99qh9VqeOXNGuFwucf/994tjx46JX/7yl6KlpUX8wz/8Q7V+BLqE1uu5d+9e4XK5xH/+53+KoaEh8etf/1r09PSIz372s9X6EWhKIpEQBw8eFAcPHhQAxD/90z+JgwcPitOnTwshhHjggQfE3XffPXP8dKmlv/mbvxFHjhwRTz31FEst6fX9739frFu3TpjNZnH99deL3/72tzOfu+WWW8S999476/j/+q//Ev39/cJsNovNmzeLX/3qVxUeMS1Gy/Xs6uoSAOZ87N27t/IDpzm0PjcvxeCztmi9lr/5zW/E9u3bhcViERs2bBDf/va3RbFYrPCoaSFarmehUBCPPPKI6OnpEVarVXR2doovf/nLIhqNVn7gNMtrr7027z1w+vrde++94pZbbplzzpYtW4TZbBYbNmwQ//Ef/7Fq45OE4Nw4EREREVVGQ+75JCIiIqLaxOCTiIiIiCqGwScRERERVQyDTyIiIiKqGAafRERERFQxDD6JiIiIqGIYfBIRERFRxTD4JCIiIqKKYfBJRERERBXD4JOIiIiIKobBJxERERFVzP8HF/tsO+L64jAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "u6lTm3D2rte9",
        "ZmAawuRmr8Zq",
        "wUt6GlSpMnFw",
        "zzFQUizuM17-",
        "gM_RJgJdP_Qb"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}