{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Métodos de ensembles \n", "\n", "## Introducción.\n", "\n", "```{index} ensembles,bagging, boosting\n", "```\n", "En apartados anteriores se han explicado diversos métodos supervisados de machine learning, que permitían bien hacer una clasificación o una predicción mediante regresión. Cada uno de estos métodos tenían sus pros y sus contras.\n", "\n", "Con los métodos denominados de ensambles o también denominados de **combinación de algoritmos simples**, lo que se trata es obtener información de diversos métodos de predicción o clasificación y mejorar el ajuste buscado. De forma general, estos métodos se pueden agrupar en dos grandes bloques: bagging y boosting y los mismos son muy populares dentro del muundo de machine learning, siendo muy utilizados en competiciones online como Kaggle. \n", "\n", "Una clasificación un poco más exhaustiva de estos sistemas de aprendizaje se puede ver en la siguiente imagen\n", "\n", "![resumenEnsamble.PNG](figuras/resumenEnsamble.PNG)\n", "\n", "Entre los métodos de aprendizaje que combinan varios sistemas de predicción, se encuentran XGBoost, Random Forest o AdaBoost\n", "\n", "Estos sistemas de algoritmos ensamblados consisten básicamente en unir varios algoritmos más simples con la finalidad de obtener un algoritmo más potentes que mejores la acuracidad del modelo que se está construyendo, es decir, se basan en el principio popular de que \"la unión hace la fuerza\" como lo pueden confirmar estos algoritmos ensamblados.\n", "\n", "Como ya se ha dicho anteriormente hay muchas formas de ensamblar o unir algoritmos más débiles para formar otro más potente y de mayor fiabilidad, pero entre los más usados y populares son los denominados de tipo **bagging** y de tipo **boosting**.\n", "\n", "## Bagging.\n", "\n", "```{index} pasting\n", "```\n", "\n", "Los algorimos de tipo bagging (*bootstrap aggregation*), fue propuesto por Breiman en 1996, con el cual se reduce la varianza y se basa en utilizar técnicas de tipo bootstrap junto con un modelo de regresión o de clasificación. Si en vez de utilizar técnicas de remuestreo con reemplazamineto, lo hacemos con muestreo SIN reemplazamiento entonces el método se se domina *Pasting*.\n", "\n", "La idea que subyace en la creación de este tipo de modelos es la siguiente.Si disponemos de muchas muestras de entrenamiento (submuestreo con reemplazamiento-bagging- o sin reemplazamiento-pasting-), entonces se puede utilizar cada una de estas muestras para entrenar el modelo y hacer una predicción. Con este método, se tendrán tantas predicciones como modelos o muestras de entrenamiento. Cada una de estas muestras de entrenamiento se denomina en términos anglosajones *bootstrapped training data set*.\n", "\n", "Para un modelo que tenga intrínsecamente poca variabilidad, como puede ser una regresión lineal, aplicar bagging puede ser poco interesante, ya que hay poco margen para mejorar el rendimiento. Por contra, es un método muy importante para los árboles de decisión, porque un árbol con mucha profundidad (sin podar) tiene mucha variabilidad: si modificamos ligeramente los datos de entrenamiento es muy posible que se obtenga un nuevo árbol completamente distinto al anterior; y esto se ve como un inconveniente. Por esa razón, en este contexto encaja perfectamente la metodología bagging.\n", "\n", "Así, para árboles de regresión se hacen crecer muchos árboles (sin poda) y se calcula la media de las predicciones. En el caso de los árboles de clasificación lo más sencillo es sustituir la media por la moda y utilizar el criterio del voto mayoritario: cada modelo tiene el mismo peso y por tanto cada modelo aporta un voto. Además, la proporción de votos de cada categoría es una estimación de su probabilidad.\n", "\n", "(errorbagging)=\n", "### Estimación error de predicción con bagging.\n", "```{index} bootstrap (muestra),out-of-bag,OOB\n", "```\n", "Utilizando este tipo de métodos, se puede obtener un error de predicción de una forma directa, utilizando el siguiente procedimiento. Una muestra de tipo bootstrap va a contener muchas observaciones repetidas y en promedio, sólo utiliza aproximadamente los dos tercios de los datos originales para formar esa muestra. Entonces teniendo esto en cuenta y sabiendo que un dato que no se utiliza para construir el modelo se denomina *out-of-bag* (OOB), para cada observación se pueden utilizar los modelos para los que esa observación es out-of-bag (es decir en promedio un tercio de los modelos construidos) y así hacer una predicción de la misma. Repetiremos el proceso para todas las observaciones y se obtendría de esta manera una medida del error cometido.\n", "\n", "Para obtener la agregación de las salidas de cada modelo simple e independiente, bagging puede usar la votación para los modelos de clasificación y el método del promedio para los métodos de regresión.\n", "\n", "## Boosting.\n", "\n", "Esta metodología se encuadra dentro de lo que genéricamente se conoce como *aprendizaje lento*, en el cual se combinan muchos modelos obtenidos mediante un método quizá con poca capacidad predictiva, para que la misma sea mejorada mediante etapas sucesivas y así incrementar la calidad final del predictor. Así por ejemplo los árboles de decisión construidos con poca profundidad y por lo tanto quizá con escasa capacidad predictiva pueden ser perfectos para esta tarea, ya que así son fáciles de combinar y su generación puede ser rápida.\n", "\n", "Este tipo de métodos vieron su origen en el año 1984 gracias a los trabajos de Valiant y después fueron ampliados y mejorados por Kearns y Valiant en 1994. Sin embargo la implementación efectiva y práctica se logró mediante el algoritmo AdaBoost presentado en el año 1996 por Freund y Schapire.\n", "\n", "ver este enlace https://rubenfcasal.github.io/aprendizaje_estadistico/boosting.html\n", "\n", "\n", "## bagging vs boosting.\n", "\n", "Una vez indicado en los apartados anteriores, las líneas maestras que vertebran los métodos de combinación de algoritmos, a continuación veamos cuales son las principales ventajas de estos algoritmos.\n", "\n", "Dado que los métodos bagging se pueden entrenar de *forma independiente* para cada muestra utilizada, su principal ventaja radica en que todos los modelos que se construyan, se pueden hacer en paralelo, lo cual agiliza considerablemente el proceso a seguir, sobre todo cuando se está trabajando con una buena cantidad de datos\n", "\n", "En el caso de los algoritmos boosting, los modelos simples utilizado se usan secuencialmente, por lo que aquí no se utilizará la programación en paralelo que hemos comentado para los modelos bagging. El principal objetivo de estos métodos secuenciales es el de aprovecharse de la dependencia que se establece entre los modelos simples. En este tipo de modelos, se mejora el rendimiento de los mismos creando un modelo simple posterior que dé más importancia a los errores cometidos por un modelo simple desarrollado previamente.\n", "\n", "Por todo lo comentado anteriormente, la diferencia con los métodos bagging es que en el boosting los algoritmos no son independientes, el hijo depende del resultado del algoritmo padre, y en este sentido se van ponderando los errores que se cometen anteriormente.\n", "\n", "
\n", "[]: # Ver enlace https://machinelearningparatodos.com/cual-es-la-diferencia-entre-los-metodos-de-bagging-y-los-de-boosting/\n", "
\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## bagging (Bootstrap aggregating) en Scikip Learn.\n", "\n", "Recordemos previamente que esta técnica, tiene como rasgos principales los siguientes aspectos:\n", "\n", "* Se remuestrea repetidamente el conjunto de datos de entrenamiento.\n", "\n", "* Con cada conjunto de datos se entrena un modelo.\n", "\n", "* Las predicciones se obtienen promediando las predicciones de los modelos (la decisión mayoritaria en el caso de clasificación).\n", "\n", "* Se puede estimar la precisión de las predicciones con el error OOB (out-of-bag).\n", "\n", "La técnica de bagging se puede implementar en Scikip Learn, gracias a dos clases: [BaggingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html) y [BaggingRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingRegressor.html).\n", "\n", "Mirando la documentación de cada una de estas clases, se puede observar que las dos tienen como parámetro *oob_score*, el cual admite un valor booleano, para indicar si queremos que se utilice la técnica de OOB (out-of-bag) [ya explicada en un apartado anterior](errorbagging), para estimar el error o no.\n", "\n", "Veamos a continuación un ejemplo de uso de BaggingClassifier en Scikip Learn\n", "\n", "### Ejemplo de BaggingClassifier.\n", "\n", "En este ejemplo vamos realizar en primer lugar una clasificación usando sólo el algoritmo LogisticRegression. Sobre este modelo calcularemos algunos parámetros de la bondad del ajuste, y posteriormente haremos un ensamble de métodos utilizando para ello BaggingClasiifier con estimador base LogistidRegression. Al final podremos ver las diferencias entre ajustar un modelo u otro." ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from sklearn import datasets\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.ensemble import BaggingClassifier\n", "from sklearn.model_selection import GridSearchCV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comenzamos por cargar los datos de breast cancer, que ya vienen dentro de paquete de scikit learn." ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(569, 30)" ] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datos = datasets.load_breast_cancer()\n", "X = datos.data\n", "y = datos.target\n", "X.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Este fichero contiene un total de 30 variables numéricas, observadas sobre determinados enfermos de cáncer, 569 samples o muestras, y dos tipos diferentes de target o clases a clasificar. Veamos algunos ejemplos de esto." ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 212],\n", " [ 1, 357]], dtype=int64)" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Veamos los valores que hay en la variable target\n", "\n", "unique, counts = np.unique(y, return_counts=True)\n", "\n", "np.asarray((unique, counts)).T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generamos los datos de entrenamiento y de test" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1, stratify=y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estandarizamos los datos y luego realizamos la clasificación mediante una regresión logic. Para agilizar este proceso lo hacemos mediante la clase pipeline, que se utiliza para hacer una agrupación de operaciones y hacerlo de una forma secuencial, de forma que la salida de una operación es la entrada de la siguiente (es lo que se conoce como tubería de operaciones)." ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [], "source": [ "pipeline = make_pipeline(StandardScaler(),\n", " LogisticRegression(random_state=1))" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pipeline(steps=[('standardscaler', StandardScaler()),\n", " ('logisticregression', LogisticRegression(random_state=1))])" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Ajustamos el modelo\n", "pipeline.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Modelo test Score: 0.965, Modelo training Score: 0.991\n" ] } ], "source": [ "print('Modelo test Score: %.3f, ' %pipeline.score(X_test, y_test),\n", " 'Modelo training Score: %.3f' %pipeline.score(X_train, y_train))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notemos que se aprecia un pequeño overfitting en el modelo, ya que el score es de 0.991 para el entrenamiento y para el test, 0.965" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "matriz de confusión para train\n", "[[156 1]\n", " [ 3 266]]\n", "\n", " matriz de confusión para test\n" ] }, { "data": { "text/plain": [ "array([[50, 2],\n", " [ 3, 88]], dtype=int64)" ] }, "execution_count": 132, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Creamos la matrices de confusión\n", "from sklearn.metrics import confusion_matrix\n", "\n", "print(\"matriz de confusión para train\")\n", "y_pred_train = pipeline.predict(X_train)\n", "\n", "print(confusion_matrix(y_pred_train, y_train))\n", "\n", "print(\"\\n matriz de confusión para test\")\n", "y_pred_test = pipeline.predict(X_test)\n", "\n", "confusion_matrix(y_pred_test, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hagamos ahora esto mismo, pero utilizando para ello un clasificador Bagging. Utilizamos para hacer esto los siguientes hiperparámateros:\n", "\n", "* n_estimators = 100\n", "\n", "* max_features = 10\n", "\n", "* max_samples = 100" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Creamos el pipeline\n", "pipeline = make_pipeline(StandardScaler(),\n", " LogisticRegression(random_state=1))" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [], "source": [ "# Ahora definimos nuestro clasificador Bagging\n", "bgclasificador = BaggingClassifier(base_estimator=pipeline, n_estimators=100,\n", " max_features=10,\n", " max_samples=100,\n", " random_state=1, n_jobs=5)" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BaggingClassifier(base_estimator=Pipeline(steps=[('standardscaler',\n", " StandardScaler()),\n", " ('logisticregression',\n", " LogisticRegression(random_state=1))]),\n", " max_features=10, max_samples=100, n_estimators=100, n_jobs=5,\n", " random_state=1)" ] }, "execution_count": 134, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Ajustamos el modelo\n", "bgclasificador.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Veamos la salida del score, para poder comparar con el modelo anterior" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Modelo test Score: 0.958, Modelo training Score: 0.972\n" ] } ], "source": [ "print('Modelo test Score: %.3f, ' %bgclasificador.score(X_test, y_test),\n", " 'Modelo training Score: %.3f' %bgclasificador.score(X_train, y_train))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Podemos ver que a diferencia del anterior modelo, se ha corregido el pequeño overfiting que habíamos visto antes y por lo tanto, tendrá una mejor generalización del mismo que lo obtenido en el ejemplo anterior." ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "matriz de confusión para train\n", "[[148 1]\n", " [ 11 266]]\n", "\n", " matriz de confusión para test\n" ] }, { "data": { "text/plain": [ "array([[48, 1],\n", " [ 5, 89]], dtype=int64)" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"matriz de confusión para train\")\n", "y_pred_train = bgclasificador.predict(X_train)\n", "\n", "print(confusion_matrix(y_pred_train, y_train))\n", "\n", "print(\"\\n matriz de confusión para test\")\n", "y_pred_test = bgclasificador.predict(X_test)\n", "\n", "confusion_matrix(y_pred_test, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como ejercicio para que el lector práctique con este ejemplo, se aconseja utilizar las clases [RandomizedSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html) y [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) para localizar hiperparámetros que intenten mejorar los resultados.\n", "\n", "En lugar de utilizar un modelo de regresión logit, utilizar otro modelo de clasificación, y observar si se mejora o empeoran los resultados." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ejemplo de BaggingRegressor.\n", "\n", "En este ejemplo vamos a construir un modelo utilizando BaggingRegressor para comprobar la ganancia que se obtiene con este tipo de técnicas. \n", "\n", "En concreto se trata de crear un modelo de datos artificial, con la finalidad de ver qué errores se obtienen al aplicar diferentes modelos sobre los datos previamente generados y más en concreto, cómo queda la descomposición de esos errores en las tres componentes siguientes que conforman el error: sesgo, varianza y ruido aleatorio " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", " \n", "from sklearn.ensemble import BaggingRegressor\n", "from sklearn.tree import DecisionTreeRegressor" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# ajustes. Definimos los parámetros que configuran el modelo\n", "n_repeat = 50 # numero de iteraciones\n", "n_train = 50 # tamaño del set de entrenamiento\n", "n_test = 1000 # tamaño del set de test\n", "noise = 0.1 # desviacion estander de ruido\n", "np.random.seed(0)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Se propone al lector cambiar esto por explorar la descomposición sesgo-varianza de otros \n", "# estimadores. Esto debería funcionar bien para estimadores con alta varianza (por ejemplo, \n", "# árboles de decisión o KNN), pero mal para estimadores con baja varianza (por ejemplo, \n", "# modelos lineales).\n", "estimators = [(\"Tree\", DecisionTreeRegressor()), (\"Bagging(Tree)\", BaggingRegressor(DecisionTreeRegressor()))]\n", " \n", "n_estimators = len(estimators)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# función para la generacion de datos\n", "def f(x):\n", " x = x.ravel()\n", " return np.exp(-x ** 2) + 1.5 * np.exp(-(x - 2) ** 2)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# generación de muestras\n", "def generate(n_samples, noise, n_repeat=1):\n", " X = np.random.rand(n_samples) * 10 - 5\n", " X = np.sort(X)\n", " \n", " if n_repeat == 1:\n", " y = f(X) + np.random.normal(0.0, noise, n_samples)\n", " else:\n", " y = np.zeros((n_samples, n_repeat))\n", " \n", " for i in range(n_repeat):\n", " y[:, i] = f(X) + np.random.normal(0.0, noise, n_samples)\n", " \n", " X = X.reshape((n_samples, 1))\n", " \n", " return X, y" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#creamos los datos de entrenamiento\n", " \n", "X_train = []\n", "y_train = []\n", " \n", "for i in range(n_repeat):\n", " X, y = generate(n_samples=n_train, noise=noise)\n", " X_train.append(X)\n", " y_train.append(y)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Los datos de test\n", "X_test, y_test = generate(n_samples=n_test, noise=noise, n_repeat=n_repeat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Procedemos a continuación a generar las figuras correspondientes" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tree: 0.0255 (error) = 0.0003 (bias^2) + 0.0152 (var) + 0.0098 (noise)\n", "Bagging(Tree): 0.0196 (error) = 0.0004 (bias^2) + 0.0092 (var) + 0.0098 (noise)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Substituting symbol E from STIXNonUnicode\n", "Substituting symbol E from STIXNonUnicode\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 8))\n", "# Estimadores de bucle para comparar\n", "for n, (name, estimator) in enumerate(estimators):\n", " # computacion de predicciones\n", " y_predict = np.zeros((n_test, n_repeat))\n", " \n", " for i in range(n_repeat):\n", " estimator.fit(X_train[i], y_train[i])\n", " y_predict[:, i] = estimator.predict(X_test)\n", " #sesgo^2 + Variación + Descomposición del ruido del error promedio al cuadrado\n", " y_error = np.zeros(n_test)\n", " \n", " for i in range(n_repeat):\n", " for j in range(n_repeat):\n", " y_error += (y_test[:, j] - y_predict[:, i]) ** 2\n", " \n", " y_error /= (n_repeat * n_repeat)\n", " \n", " y_noise = np.var(y_test, axis=1)\n", " y_bias = (f(X_test) - np.mean(y_predict, axis=1)) ** 2\n", " y_var = np.var(y_predict, axis=1)\n", " \n", " print(\"{0}: {1:.4f} (error) = {2:.4f} (bias^2) \"\" + {3:.4f} (var) + {4:.4f} (noise)\".\n", " format(name, np.mean(y_error), np.mean(y_bias), np.mean(y_var), np.mean(y_noise)))\n", " \n", " # representamos figuras\n", " plt.subplot(2, n_estimators, n + 1)\n", " plt.plot(X_test, f(X_test), \"b\", label=\"$f(x)$\")\n", " plt.plot(X_train[0], y_train[0], \".b\", label=\"LS ~ $y = f(x)+noise$\")\n", " \n", " for i in range(n_repeat):\n", " if i == 0:\n", " plt.plot(X_test, y_predict[:, i], \"r\", label=\"$\\^y(x)$\")\n", " else:\n", " plt.plot(X_test, y_predict[:, i], \"r\", alpha=0.05)\n", " \n", " plt.plot(X_test, np.mean(y_predict, axis=1), \"c\", label=\"$\\mathbb{E}_{LS} \\^y(x)$\")\n", " \n", " plt.xlim([-5, 5])\n", " plt.title(name)\n", " \n", " if n == n_estimators - 1:\n", " plt.legend(loc=(1.1, .5))\n", " \n", " plt.subplot(2, n_estimators, n_estimators + n + 1)\n", " plt.plot(X_test, y_error, \"r\", label=\"$error(x)$\")\n", " plt.plot(X_test, y_bias, \"b\", label=\"$sesgo^2(x)$\"),\n", " plt.plot(X_test, y_var, \"g\", label=\"$varianza(x)$\"),\n", " plt.plot(X_test, y_noise, \"c\", label=\"$ruido(x)$\")\n", " \n", " plt.xlim([-5, 5])\n", " plt.ylim([0, 0.1])\n", " \n", " if n == n_estimators - 1:\n", " \n", " plt.legend(loc=(1.1, .5))\n", " \n", "plt.subplots_adjust(right=.75)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como resumen de todo el código y con el fin de ayudar a la interpretación del mismo, se puede decir lo siguiente.\n", "\n", "En este ejemplo se generan una serie de números en base a la función denominada n_samples() y posteriormente se hace un ajuste de regresión, y se mejora el ajuste mediante una técnica Bagging.\n", "\n", "En este tipo de análisis de regresión, el error cuadrático medio de un estimador, se considera que puede descomponerse en términos de sesgo ( desviación de los datos respecto de su media), varianza y ruido (es lo que sacamos en los dos gráficos inferiores). \n", "\n", "En la figura superior izquierda se muestran las predicciones ( en rojo oscuro) de un único árbol de decisión entrenado sobre un conjunto de datos aleatoria (son los puntos azules). Sin embargo el gráfico superior derecho muestra el ajuste utilizando un procedimiento Bagging.\n", "\n", "Los dos gráficos de la parte inferior, contienen la descomposición del error en términos de sesgo, varianza y ruido. De acuerdo con estos últimos gráficos podemos ver que:\n", "\n", "* El error cometido con el método ensamble es menor, ya que la gráfica en rojo de la derecha queda por debajo de esa misma gráfica de la zona izquierda.\n", "\n", "* Lo que más contribuye al error es la varianza (en color verde).\n", "\n", "* El sesgo es apenas perceptible en los dos casos.\n", "\n", "* El ruido del modelo está en unos niveles similares en los dos modelos, debido a la característica propia del modelo con el que se ha construido." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Método AdaBoost.\n", "\n", "```{index} AdaBoost\n", "```\n", "Este método se basa en un proceso iterativo, en el cual se aplica un clasificador en cada paso, pero al pasar de una etapa a la siguiente, se presta una mayor atención a los puntos mal clasificados en la etapa anterior. \n", "\n", "En Yutube, [se recomienda ver este vídeo](https://www.youtube.com/watch?v=LsK-xG1cLYA) que aclara la base de este algoritmo.\n", "\n", "```{index} Decicision Stump\n", "```\n", "\n", "Así pues para todo este se necesita un clasificador de base, que en el caso de scikit learn, se utiliza por defecto el denominado *Decision Stump*, que no es más que un árbol de decisión con el parámetro max_depth = 1, es decir un árbol compuesto de un solo nodo de decisión con dos hojas finales.\n", "\n", "![stump](figuras/stump.PNG)\n", "\n", "En este sentido el ciclo que seguiría este algoritmo sería el siguiente en el caso de construir un AdaBoost classifier. Se comienza el ciclo entrenando un clasificador base (por ejemplo un árbol de decisión) y con el mismo se obtienen el conjunto de predicciones de los datos de entrenamiento. Entonces en el segundo paso, el peso relativo de las observaciones mal clasificadas es incrementado (y por ende, como la suma de los pesos totales es uno, se disminuye el de observaciones bien clasificadas), y a continuación se entrena el mismo clasificador pero con los nuevos pesos y se hacen las correspondientes predicciones, los pesos son actualizados como ya se ha hecho en la etapa anterior, y así se continua el ciclo hasta terminar el proceso iterativo. \n", "\n", "\n", "
\n", "ver https://rubenfcasal.github.io/aprendizaje_estadistico/boosting.html\n", "
\n", "\n", "A continuación vamos a generar una serie de gráficos que visualizan el proceso de mejora del ajuste que se obtiene al utilizar la técnica de AdaBoost.\n", "\n", "Comenzamos por importar las librerías que vamos a necesitar." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import sklearn\n", "\n", "# importamos matplotlib\n", "%matplotlib inline\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "mpl.rc('axes', labelsize=14)\n", "mpl.rc('xtick', labelsize=12)\n", "mpl.rc('ytick', labelsize=12)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.datasets import make_moons\n", "\n", "X, y = make_moons(n_samples=500, noise=0.30, random_state=42)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generamos los datos y a continuación procedemos a hacer su representación gráfica" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(X[:,0],X[:,1], c=y)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lo primero que vamos a hacer es obtener un ajuste AdaBoost, utilizando como estimador de base un clasificador de tipo árbol de decisión básico, con un sólo nivel de profundidad. En este paso se le indica con el parámetro *n_estimators* un total de 200 pasos" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),\n", " learning_rate=0.5, n_estimators=200, random_state=42)" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import AdaBoostClassifier\n", "\n", "ada_clf = AdaBoostClassifier(\n", " DecisionTreeClassifier(max_depth=1), n_estimators=200,\n", " algorithm=\"SAMME.R\", learning_rate=0.5, random_state=42)\n", "ada_clf.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A continuación creamos una función de conveniencia que nos será útil para trabajos posteriores para poder dibujar las zonas y fronteras de decisión con el estimador que se le pase como parámetro.\n", "\n", "Una vez definida la función procedemos a hacer la representación gráfica de las fronteras de decisión que se obtienen en base al modelo generado anteriormente." ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from matplotlib.colors import ListedColormap\n", "import numpy as np\n", "\n", "def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.5, contour=True ,color=0):\n", " x1s = np.linspace(axes[0], axes[1], 100)\n", " x2s = np.linspace(axes[2], axes[3], 100)\n", " x1, x2 = np.meshgrid(x1s, x2s)\n", " X_new = np.c_[x1.ravel(), x2.ravel()]\n", " y_pred = clf.predict(X_new).reshape(x1.shape)\n", " custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n", " plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n", " \n", " if contour:\n", " custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n", " colores = ['Greens','Blues','Greys','Reds']\n", " plt.contour(x1, x2, y_pred, cmap=colores[color], alpha=0.8)\n", "\n", " plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", alpha=alpha)\n", " plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", alpha=alpha)\n", " plt.axis(axes)\n", " plt.xlabel(r\"$x_1$\", fontsize=18)\n", " plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)\n", " \n", " \n", "plot_decision_boundary(ada_clf, X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una vez hecha la primera presentación del método, a continuación nos disponemos a obtener el resultado que vamos buscando, es decir ver cómo va mejorando la clasificación con el método AdaBoost a medida que vamos incrementado el número de pasadas. Es to se va a ver claro con los gráficos que se generan con el siguiente código." ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "image/png": 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oFGNg0KK6svwDKfKh2+V/VRQ5I5/Tf42N1xONDhCNejHNGNGol2h0gMbG69Pel2n6EUJD05xomo6mORFCwzRVcf7yJFoUW/v4seO87qzXcfu621l7wVo+8t6PEAgEAPjOt77DNZdew+XnXc7nP/15pIy74H9874+59JxLWXvBWj72gY8B4O318sF3fZC1F6zlzWvfzGu7XptwLI/Hw+VXXo7D6SjcCSqmPfmuT610W5EqugZOuw0hBLFo/qYsy123ldGtGCGX8XvjyWXMnWF4kFJimkFMM4ZpBpFSYhierMepKCXyH2tysOUg6z65js07N1NZVcnP7/s5AJ/67KfY8OIGtuzaQjAYZP3j8XvgB9/5AZu2b2Lzzs18777vAXD3XXdz3oXnsXnnZr72j1/j9nW3533cCgXkV7NB6bYiTQrkPSln3VbhJYoR8l3rNFcxd273MoRwEY32YlkhNM2By9WIy9WUg1EqikX193PfWKHva32T/n1+83wuu+IyAG750C38x73/wZ1/dSebNmzih9/9IcFAEG+vl9Vnr+aGt9/A2eeezSdv/SQ3vvNGbrzpRgC2vrCVX/32VwBc/car8fZ46e/vp7p65jWKUBSWQtSnVrqtmAz7r1fnfJ/TWbeV0a0YoVxqnQ6Xh6qoWDqmPFQmU56K0qH/i/30er3Mrq8bao7jJRCOEZ1Xi6M+T0I4zjMjhCAUCvGlz32JDds2ML95Pt/+xrcJhUIA/Obx3/DCphd44rEnuOef7mHr7q0jU5jj96NQ5Jty0WxQuj1diXxwH35/mKjTyUD9fHz+MJUVUF+n5++gZazbKrxEMUIu4/fyiSoPNbOwEohjrmg70ca2LdsAePB/H+SyKy8bEer6hnp8Ph+PPvhofByWRVtrG1ddcxXf/M436e/rx+fzsfb1a/nNr38DwHPPPkddQx1VVVV5G7NCMUy5aDYo3Z5uSE0gHS40lwND1zDCYRyBgYIcu5x1W3m6FSMMi188E/4UdvssmpvfXZKiqMpDKXLBytUreeC/H+ALn/kCS5ct5eO3fxy3281HP/FR1p6/lgWLFnDhxRcCYJomn/7IpxnoH0BKyR1fuIOamhq+/PUvc8fH7mDtBWtxu93c94v7Eh7r3CXnMjgwSDQS5Q+P/IGH1j/EqrNWFfJ0FdOMctJsULo93YhqNoyqahyxGNZAEE9/N4PuBiC/CePlrNsikYt9unHRRavk1q0/LfYwFIqyJ5flyQ4erGDVqmUASOnG7/cnDC8Jz63B1VCTw7OIc/zYcd7/jvezZdeWnO87Uw7vP8zp5afHLHur/a3bpZQXF2lIRUFptkKRG3JdUvKMbrsIhsJgmrhiIcyeXgL+CFGhMTh7NnWz7Lk7iVGUu26r8BKFQpES+S5PplAoFIrcUQjN7h/0EbY50KurcDps2CwTh8+Xs/1PN5TRrVAoUiLf5ckKzcJFC0vKW6JQKBS5pBCaLaUkFImCYaDZdBACIa2c7X885a7byuhWKBQpEYl0oWmVY5bFy5N1FWlECoVCoUiG0uzSQxndCoUiJYbLk42mVMuTKRQKxUxHaXbpoYxuhUKREvkuTyaEQFP1rRUKhSInlFNJyZmCKhmoUChSIp/lySTgcDjweDzK8FYoFIocUG4lJWcCyuhWTFtyXSpJkb86uxKIWAK3x4OGdWbhNKlouuHpDdz1lbuIRqIYdoNvfuebXP3Gq4s9LIWipFCanXtUbfTMyYduK6O7xFCiM5FMrslwqSTDqEJKG/39O+jtfQ6PZzXNzbemdU3VZ1IYQjELXQiqjLHtg6VV/pZ3XUMd//vI/zJ33lz2vraXm2+4mX2t+4o9LEUOUPqQmHSvy2jNttvnEAyeYP/+r2O3z6KiYlla11V9JopckA/dVjHdJYSqgzyRTK/JcKkk04wSDB4CBJrmIRA4ntY1VZ9JYekciDASXTL0rxU183Ks48eO87qzXsft625n7QVr+ch7P0IgEADgO9/6Dtdceg2Xn3c5n//05xluIvbje3/MpedcytoL1vKxD3wMAG+vlw++64OsvWAtb177Zl7b9dqEY51/4fnMnTcXgNVnryYUChEOh/NyXorCofQhMZlcl9Hl7aLRXsLhDoTQicX8aV1X9ZlMb8pdt5XRXUJMtzrIuSDTazJcKikYPIEQNnTdhaY5EEKmdU3VZ1JYJJJCNsk92HKQdZ9cx+adm6msquTn9/0cgE999lNseHEDW3ZtIRgMsv7x+Of9g+/8gE3bN7F552a+d9/3ALj7rrs578Lz2LxzM1/7x69x+7rbJz3mow8+ynkXnofDkd9WyYr8o/QhMZlcl9Hl7c7otgeIpnVd1Wcy/Sln3VbhJSVEfCpszphl8Zqap4o0ouKT6TUZLpVkWWGEcA4tjaLrzrSuqfpMCsesSgcwLGjzhn6yo8/sn/Tv85vnc9kVlwFwy4du4T/u/Q/u/Ks72bRhEz/87g8JBoJ4e72sPns1N7z9Bs4+92w+eesnufGdN3LjTTcCsPWFrfzqt78C4Oo3Xo23x0t/fz/V1dUTjrdvzz6+/uWv8/v1v8/63BTFR+lDYjK5LsOareu1o3Q7rtmpbJ/NsRWZM39+06jfclOKcDrrtjK6MyQfMWOjRWeYmV5TM9VrMv7zcLsX4/VuQQgNy4ogRAzTjFBZuTita6o+k8LRNRimezDM8noHYqCHaLeXQChGoL6CiqaG/Bx0XKEUIQShUIgvfe5LbNi2gfnN8/n2N75NKBQC4DeP/4YXNr3AE489wT3/dA9bd28dmcIcv5/xtLe1c+vNt/LjX/6YxUsX5+V0FMlRml04UrkuyTQbQAg7lhUPGaisXJxw+2yOrcgdbW3tuJ0OqnWJ2d2L3x8m6PHgXpInzYay1m0VXpIB+YoZK/eaml7vDlpa7mb37i/S0nJ3TmLoUrkmiT4Pr3cLtbWX43QuwLJ8mKaJ270MTTPSuqbl/pkoJqftRBvbtmwD4MH/fZDLrrxsRKjrG+rx+Xw8+uCjAFiWRVtrG1ddcxXf/M436e/rx+fzsfb1a/nNr38DwHPPPkddQx1VVVVjjtPX18ctb7+Ff/infxjx0CgKh9LsxORDs2Hq6zKZZmuaG8NwAxZOZxM2W11a17XcPxPF1JSzbitPdwaMjhkDRt6oOzvXZ+U5KeeamuMzzy1rkNbWXwLk/Zok+zwCgaOcd973xnhUNM2d1jUt589EMTUrV6/kgf9+gC985gssXbaUj9/+cdxuNx/9xEdZe/5aFixawIUXXwiAaZp8+iOfZqB/ACkld3zhDmpqavjy17/MHR+7g7UXrMXtdnPfL+6bcJyf/uinHD10lHv+6R7u+ad7APj9+t8zq1F53wqB0uyJ5EuzR2+f7LpMptkrV35lZHyZXNdy/kwUqVHOui0SudinGxddtEpu3frTnO1v9+4vYrfPQdfPvLOYZoxI5BTnnvv9nB2nnGhpuRvLCoyIKEA06kXT3CMimi/U51GeHDxYwapVywAwpZv+YKyg4SXHjx3n/e94P1t2bcn5vjPl8P7DnF5+esyyt9rful1KeXGRhlQUlGbnH6XZikw4o9sugqEwfd6+goaXlLtuq/CSDBiOGRvNTI8ZG515Pkw8eaUr78dWn4dCoZgMpRETUZqtUBQeFV6SAY2N149Mw2laJZY1SDQ6QHPzu4s7sCKSz+SVqRKg1OehyISFixaWlLdEkT+URkxEabaiHCl33Vae7gyorV1Dc/M6NM1NJHJqKE543YyOGctX8koqCVDq81AoFJOhNGIiSrMVisKjPN0ZUlu7RgnEKPKVvJJqApT6PBQKxWQojRiL0myFovAoo1uREcmmD3MtoqrRwcxCE4IEpVIVCkWWKM1WKIqPCi9RpE2+at4mQiXczCxmVznONC0Y+kez68UbkEIxDVCarVCUBsronqFk0xRh9PShrtswjFoMo4rOzvU5H6dqdDBzcBk6dmFimdaY5Ym6hJUb27dt58o1V3Llmiu54sIreOz3jxV7SIoyQ2m2QlFY8qHbKrxkBpJtU4RCTh+qRgczAwHYMBnw+ah0u9CHF5a/vQ3A6nNW8+y2Z7HZbJw6eYorL7ySG95+AzabkmDF1CjNVigKTz50Wyn+DCTb7mz5LDWVCJVwM/0RQCQSIRAMUuFyFuSYx48d5z1vfQ8XXXIRu3buYtnyZfz4v36M2+3mO9/6DusfX08oGOKSyy/hBz/+AUIIfnzvj/nFf/wC3aazavUq/vOB/8Tb6+WzH/8sx44ew+1284Mf/4BzzjtnzLHcbvfI/0Oh0LTw3isKh9JshSJOueu2Ci+ZgWTbFEFNHyrygZSSQnfIPdhykHWfXMfmnZuprKrk5/f9HIBPffZTbHhxA1t2bSEYDLL+8fg0/A++8wM2bd/E5p2b+d593wPg7rvu5rwLz2Pzzs187R+/xu3rbk94rJdffJnLzr2MK86/gu/9+/eUl1uRMkqzFYozlLNuK9WfgWTr9Zip04dTNXxQZMf3v5/79s9f+NoXJv37/Ob5XHbFZQDc8qFb+I97/4M7/+pONm3YxA+/+0OCgSDeXi+rz17NDW+/gbPPPZtP3vpJbnznjdx4040AbH1hK7/67a8AuPqNV+Pt8dLf3091dfWYY1186cVs3b2Vln0tfOa2z/CWG96C01kYr76ivFGanTlKt/PLz372s5zvczrrtjK6ZyC56AY206YPs42pVEzNF7/4RXq9XmbX1+EIDRLt9hIIxwjPrcHVUJOfg46bLRRCEAqF+NLnvsSGbRuY3zyfb3/j24RCIQB+8/hveGHTCzzx2BPc80/3sHX31oTe+cmmIVeuXonb42bfa/u48OILc3o6iumJ0uzMULqdfz7xiU/gdjqo1iVmdy9+f5igx4N7SUP+DlrGuq3CS2YgqXYDyyZbvhTI5fgLmf2vKBxtJ9rYtmUbAA/+74NcduVlI0Jd31CPz+fj0QcfBcCyLNpa27jqmqv45ne+SX9fPz6fj7WvX8tvfv0bAJ579jnqGuqoqqoac5xjR48Ri8UAOHH8BIdaDrFg0YJCnaaizFGanRlKt6cn5azbytM9Q5nK61HuHoJcj181fJierFy9kgf++wG+8JkvsHTZUj5++8dxu9189BMfZe35a1mwaMGIV8M0TT79kU8z0D+AlJI7vnAHNTU1fPnrX+aOj93B2gvW4na7ue8X9004ztbnt/KDf/kBNsOGpml89/99l/qG+kKfrqKMUZqdPkq3pyflrNvK6FYkJNts+WKTzfgTxQAWOvtfURg0TeP7902MJf/qt77KV7/11QnL12+a6CGrravlgYcfmPQ47//w+3n/h9+f+UAViilQmj0xblvp9vSknHVbhZcoEpJttnyxyXT8yTq3ud2LVfa/QqEoWZRmT+y2qaq2KEoNZXQrElLurXwzHX+yGMBA4GhKMZWK3KNp+ZGphYsWsmXXlrzsW6EoNEqzJ8ZtpxoLr0gXDQugsBVegfLXbRVeokhILrLli0mm458sBjBZTKUqSZVf7KqetUIxJUqzzzA6bjuRbivNzgaBKW2YMlbsgZQl6mmmSEi513XNdPzpxgCWe/JSqSOmSx94hSLPKM1Wmp1vJCDR8ccC6BhoSp/TRhndiqSkU9c1E89Bqtuku+/x68+f/+GUzyNdb0u5Jy+VNkrQFYp0KFfNznQbUJpdSGKygagVoj/so8pWjR292EMqO1RMtyJrJktkyXabdPedyVhGMxwDGI0G8Ho309+/m8neTcs9eUmhUMw8SkmzM91mGKXZhSMm3VjSIhKLcSagWzlH0kEZ3YqsyaQBQarbpLvv3DVDiFFdfS61tWsxDHfSB0C5Jy8pCkvriVaaqpq491/vLfZQFDOYUtLsTLeZiNJsRX7IpW4ro1uRNZl4DlLdJhLpIhoN4/W+TE/PC3i9LxONhpPuOxdejHQeAKoklSIdvvKXX+HN17+52MNQzHBKSbMzHc9olGYr8kkudVsZ3YqsycRzkOo2Ugp8vn1IGUMIJ1LGhn5PPKWVCy9GOg8AVZKqfDl+7DivO+t13L7udtZesJaPvPcjBAIBAL7zre9wzaXXcPl5l/P5T38eKeNTqT++98dces6lrL1gLR/7wMcA8PZ6+eC7PsjaC9by5rVv5rVdryU83uMPP86iJYtYdfaqwpygQpGEUtLsTMczGqXZxUXTNJAy/pNnyl23ldGtyJpMPAfpbCMECCFG/eR2LONJ9wFQW7uGlSu/wrnnfp+VK7+ixLuMONhykHWfXMfmnZuprKrk5/f9HIBPffZTbHhxA1t2bSEYDLL+8bjH7Aff+QGbtm9i887NfO++7wFw9113c96F57F552a+9o9f4/Z1t084jt/v59/u+Tf+9h/+tnAnp1AkoZQ0O9PxjEZpdvFwOuxUGBpycJBwKIoFaFWuvB6znHVbVS9RZF2zNJNST6luI4TE41lFJNKBaQbRdScezyqEMJPu1+dr4eTJhzHNQXS9krlzb0rrfNLNhlc1X3NDVXU1VaN+dwz9ZEO/2Tfp3+c3z+eyKy4D4JYP3cJ/3Psf3PlXd7JpwyZ++N0fEgwE8fZ6WX32am54+w2cfe7ZfPLWT3LjO2/kxptuBGDrC1v51W9/BcDVb7wab4+X/v5+qqurR47z7bu+zR2fv4OKioosz0ihmF6afQbbUBIkuN1L0/I+K80uDkII6medebHJlbpNZ91WRvcMJ1c1S9MpVZXONnb7LILB9jHLLCuIy9WUcH2vdwde7xYqK5cTjYYIBI7S2vpfeL0v0dx8a0pjTOeBpGq+5o6B/n56vV5m19fhCA0S7e4jGI7BwlmIKnd+DjrOAyeEIBQK8aXPfYkN2zYwv3k+3/7GtwmFQgD85vHf8MKmF3jisSe455/uYevurSNTmOP3M5rt27bzyIOP8A9/9w/09/WjaRoOp4NPffZT+TkvxbRlOmr28PnU1q4lGDxBIHCIY8f+H52dy1IyiJVmFx6BwKELrNNtRHr6CIVjhO1OHCtnT9C/PBx87K9lpNvK6C5TcvWmXgo1Syc7F7d7MT09G9E0F0K4iMUCRCI91NdfOen5WFaUQOAQum4HKgiFTqQlrKk+kErh+k0nNCHQBWBZSCv/8YFtJ9rYtmUbl1x+CQ/+74NcduVlI0Jd31CPz+fj0Qcf5R03vwPLsmhrbeOqa67i8isv53cP/A6fz8fa16/lN7/+DX/z1b/huWefo66hjqqqqjHHeWLjEyP///Y3vk1FRYUyuGcYSrMn12zDqCUc7iQUagc0otHASOlAmFq3lWYXFo/hQAsMYg76CEdixDQNY+ms/BvclLduK6O7DMnlm/pULXTzjde7gyNH7sU0BzHNGLreis93kCVL7qS2dg2BwFHc7mVEo71YVhhdd+F0NhEIHE24v+Hz8ftfRdftaFo8kUfK0Eg2ey6FtdjXb7pRV12FLRLE7PcRjppg6AhPtkEmyVm5eiUP/PcDfOEzX2DpsqV8/PaP43a7+egnPsra89eyYNECLrz4QgBM0+TTH/k0A/0DSCm54wt3UFNTw5e//mXu+NgdrL1gLW63m/t+cV/exqsoT5RmT63ZAMFgK7puR0rbkGbn3iAu9vWbLmiIEeeIlBBy2qk0CtMsp5x1WxndZUgu39TTbaGba1pb7ycc7sJmc6HrbiBKONxFa+v91NauIRLpwuVaQEXFkpFtTDOWVCCHz8c0Q2jacDJHFE1z5EVYi339phN2mw17LITV108wGEFqAhbOAj1/Qq5pGt+/7/sTln/1W1/lq9/66oTl6zdNLEFWW1fLAw8/kPIxv/z1L6c3SEXZozR7as3W9dpRuh1B0+Iv27nW7WJfv+lL4ZrklLNuq+olZUguO2oVu2ZpIHAYTXOi6y503Yauu9A0J4HAYSD9rPTh85FSYFlhTDOIlDFcrgV5EdZiX7/phF3GkP19hHwhTClhXh3ClT8vt0JRKJRmT63Z0agXMDBN/4hmT7VtJhT7+ilmNsroLkNy2VGrFGqWjg8BG/17ugI5fD5u90Isyw9IXK5lxGI++vpexec7REvL3Sm3hZ+KUrh+0wHNMiHoI+YPETMtwi4noja/lT4WLlrIll1b8noMhQKUZqei2ZrmxmbzIKWJwzEPy7Lo7d1CX992wuFupdkKoPx1W4WXlCHplkdKxvhkmPnzP1xw4bHbGwgGjxCLeQHb0JSihcezAsi8tFVt7ZqR8wsEDhEOd+NyLRjxeA9fvzP7zjy5KZMqAIo4UsqhxBsJlkRaEguIuBzkt9Jr6ZEom14xPciVZkPxy93lU7NhOP79fny+3WiaB4/nbAzDOSYGPhclE5VmZ4ZEadVo0r0WyuguQzIRtfGUQtmkuOfCAuxADIhgWVEMYxbNzbeOrJeKQCYT4draNbS03I3TOW9CPGVr6/1ATJWOKhIOh0Vvbz91ddUFjAYsTaSU9Pf2E3VEiz0URR7IhWZD8XW7UJodj4G/eESzhxlu617sZ9dMJqpFCA+GkJXK8M5Et5XRXaZk+6ZeCmWTOjvX43I14XA0Egy2YpohpBQ4nU05fYFIlq0eCOymuvrcSa9Bsb1K05l58wJ0dHTS1dUNlg0CPmK+ANGYRSgwSPdgb7GHWFCijijeed5iD0ORJ3LhXS22bhdbsyORUyldA6Xb+aPX1cnx9lNoPhMRDmL5/ITDMcJ2A6fpK/bwCk66uq2M7hlKKZRNGh6DYdhwOBqBybPckzGVCCfLVgeSJDfFj19sr9J0x2aDBQsCWNKNzVuB+dD/cvQ/H+HA8V42f+ytvOVbNxd7iApFSVFs3S62Ztvts6a8Bkq384PEYDAa4bi/nZ/vfpoNN9+L67lN9Pz817z40lG2nncOr3/ii8UeZsmjjO4ZSi7LJqXrVRhe3+8/gt9/gsrKZSMCnskYphLhZPGUbvfSSa9Bsb1KMwGJQZdvKbPlWE9BVbWaulQoxpMr3S5XzW5ufjedneuVbhcYicAXa2TzqW38ftcWjrafQtNmelBgZiije4aSy2TMdLwKo9d3u1fg8+1jYGAPHo+JYTgzGsNUD6Jk8ZTApNeg2F6l6Y5E4AsvY8vxAO8ce5kzjvG+57az6GmfmIJZ3xTkr3+xN8O9KhSlQS50u5w1e3i50u3CIYGINYdXeg7w0J6NvHrwKPVuD3Y9N+bjTNNsZXTPUHKV2JOuV2H0+oYRL3IfCBwmEDhIbe2lGY0hlQfRZPGUya6BaqKQX8Kxhexslzyys4N3XFeXk2TKnnYXTSsCE5a3H3BntL+Z9kBQlDa50O1y1+yproHS7dxiyloODHTx2MGNvLBnP5qEf3/vh3LWC2emaXbRjW4hxOeAdcC5wANSynWTrPtF4G8BF/Ag8BkpZbgAw5yWjC/T1Nm5nra2X6WVeJKuV2H8+k5nI4ZRRyRyipUrv5LxeUDqD6JUp1ZzWeZLMZa4l7uGg909HGztA+rG/N1hN4oyrvHk+oGgUGRLtro9XTQ72bGVbueWsKzm2OAe9p1sw+cP8oWr38wlC5fARFksCUpds4tudAMdwD8C10Hy0rxCiOuAvwPeOLTN74FvDC1TZEE2iSfpehXy5YVItTJAOueaq9kARWIk8eJj48ucCqCuqqoII1IoyodMdXs6a/boZUq3c4eUEktaaEKwfFZjsYdT1hTd6JZSPgQghLgYmD/Jqh8Ffi6l3DO0/reA/0EZ3VmTTeJJul6FROsHAu0YRi3bt38M0/RjGB7c7mUpe9vTSQpK91xVE4V8Ep+fjLfDyQ3H9npoa5nYyTJmqsRMxfQiU93OpWbv3v1FpIzfx0LItGZJU9XtTM5T6Xb5MNM0u+hGdxqcDTwy6vdXgdlCiHopZc/4lYUQnwI+BbBgwezCjLBMySbxJF2vwvj1pRQIAZYVJhLpRgiNUCiEEK6UvDbpekFUkk3pYKEhZW67m8XCOlVzIxOW95605+wYivygNDs9MtWyXGm2YbiJRkP4/fuREioqVmNZgZzrttLs6c1M0+xyMrorgP5Rvw//vxKYYHRLKX8C/ATgootWTc9XphyR7fRhul6F0eu3tNyNYbjx+49gsznRNCemGSQa7aWiYumUXpt0vSAqyaY0kIihsJLc3pqG3cLfrydcrihtlGanRzZalgvNNoxa/P7t2GxupJSEw2243RcDU3vb09FtpdnTm5mm2eVkdPuA0YGew/8fLMJYphXFTDyJRLqQUicc7gQkQhhomhshzJS8Gel6QQpxrqXaDa1Ux5VLFp7ty2kSTX1TMOG29U3BjPanUOSKYun2sOaGw50jug06mmYfGUsudbtQ51mK+liKY8o1M02zy8no3gOcD/xm6PfzgdOJQksU6ZFo+hBstLX9is7O9TQ2Xj/q77m9+aUU+P37AW3od4lp9mGz1aTkzUjXC5LvJJtcdEPLh9CqLm2ZUQolphSKRIzWskCghWjUj6576OxcP7JOPjTbbp9FMHiCUKidYd0GE8sKEg53omlGTnW7EImR2eqj0uzSodQ1u+hGtxDCNjQOHdCFEE4gJqWMjVv1v4FfCiH+BzgJfBX4ZSHHOp0Znj4cfaMPexWOHr0XKcHtbsrLzS8l6Lob0xyetJBYVjglb0YmXpB8Jtlk2w0tX0KrurQpFNOP0c1inM55BdHsxsbraWm5C9DQdQ+mORzp6WZw8BBud1POdTvfiZHZ6ON01mwJWFISs0xi5rhwD6k6UmZC0Y1u4sbz10f9fivwDSHEfwJ7gbOklCeklOuFEP8CbOBMne6vT9ibIisS3ej9/fvQNJGXm18ISUXFasLhNqSMYVlRQEcIG83N66bc/5mHzv0EArsBsNsbaG29P+2a47kg26SfZELb2np/Vp6UmZKMVOpTiwpFrim0ZtfWrsHhaCAaDSBlBMOoQUqwLAuIpazbPl8LJ08+jGkOIoSB3d4wZna1kM6AbPRxMuN4+N9MdLsUNDsmGzjQf4xnj73CwRMduAw7q+bMBVmHaY01IF3uzIzwmabZRTe6pZR3AXcl+fOYOjJSyu8B38vzkGY0iW50KU1Mc+x6ubr549OMgZEEHIBo1IumudMU3RjV1ecSjYbx+fYhxGk8nlUpZ9PnimyTfhJd//g57cUwLs7YkzJTkpFKfWpRocg1hdZsALd7GZYVGDE0IT3d9np34PVuobJy+Yhmh8OnsdlqC67ZkJ0+JjOOA4GWrDzgxdZsS1Zw3Bfmj4c38syruwiHo3zn7TezpH4h3X3N1BHCM2r9So+WdF+TMdM0O7OrpJi2DN/ooxFCR9fHvp/l6uZvbLyeaHSAaNSLacaIRr1EowMjceSpMNrTEA63YbO5sNncRCIdQ62Lq8bEOeYar3cHLS13s3v3FwmHuwkG2zM+n0TXPxQ6iqZ5MIxadN2W0Tnl4jorFIrSo9CaDdnrSSlpdkvL3bjdizM+n0TXPx4u4x85x0x0u5iabUk7nWE3z7Q+x+M7X6bHO8CHLrqU91x4KYHgcp493E8oYk69I8UEiu7pLjemezZxolg7u70KKYc9GbnNHs9FksxoT4NlhYmnBQhMMzhyHvmakksUzxcXxwBC+NM+n0TX3zT9eDxnj1kv3XNSXdoUMxWl2bmv+JGtnpSaZnu9W6itvZxA4Gja55MsPl3XPWha5Zh10zmvYmp2yJrNK907WL9/O8c6Orl4wUK+et3bkOYi9pyM8dTeLm6cP9a7jwrxTglldKdBrhImSvkhkOhGX7z4zgnLcnnzZ5skM3oaTtMcSBkFBLruBPI7JTfaYxMKdRIMniAWG8Q0Ayxf/rdpn1ei6+/xrMYwnGPWy+ScVJc2xUxDaXb+DLZs9KQUNTsUOp0zzW5ufjedneuzDg8plmbHpMZAZIDBYBCbEHz2yjdit9mIRZ34IzGuXlKLUzfjjc2kBAENCxoKPs5yRBndaZCLbOJSKwOU7GGSrCV6KTLa0+BwzB+K6QaPZ9XIlFy+atcOe2xCoU78/gMIYUPTKjFNX8af6/jrP/ydgcLV4y1lI0OhSBWl2aV5z053zR6mkLqdT83WtTORyHVug3OrHXh7u3BKiQSEplHVUJV8B4oRlNGdBrnIJi6FMkDDlNrDJFNGexqE8FNRsQJgqMGOO69TcsMem2DwBELY0HUXlhVC1ytH4vdyUS0AMvNaZSLE0+V7oVAozS5NprtmQ+a6XdqarbGsWvLSlh3MqXAwF4lAIITIcW/h6YsyutMgF9nEuSoDlIu32lJ6mGRLsabhhj02sdjgkDcjhGlGqKxcnNO4xEzOL1MhLvT3os5jZ7xkC00FCCqyR2l26TLdNRvSP8dS12yn5uB42wG2b9/O1RedBwil1WmijO40yEU72lw8BHL1VlsKdUDLneHrffDgdzBNH7peSWXlYhyORqJRb0ZxibmaJpxKiJMdp5DfC03AXW9fSSw2iG4NT1UKZi2cXqUMFcVBabZiPPnQbCjMS1WxNVsC0WiMUDiMZVkIlT2ZNqpkYBrU1q6huXkdmuYmEjk1NA02dSOA0WRbBsjr3cHBg9/B7z+K33+EWKw34xJLyUodTbfazfmmtnYNy5f/LR7PYjyeJdhsdRmXdxp+OFtWYOjhHK9Z6/XuSHtckUhXkuz5rkmPU8jvxWXNVbjCXYSCISCeAK8bNmx25Q9QZI/SbEUicqnZkDvdLgfNVmSHerKlSbZTYtnG57a2/hLT9KNplUgZY3CwBQCbrS7tt9pceIEUcXJV3imX04STeegmO06hvhcum4Yn2s+Tzz1H45UX4wGEiMcHKhS5Qmm2IhG5LMmXK90udc1WZI8yuotApg+B4ZvOZouLt6a5AAgGW/F4jIxKyA3vV9Vuzp5cxCjmcppwMiFua/tV0uMU4nshpQs7UY4eOczOna/yzrVriPu5lcGtKD2UZk9PchVXnivdLgXNtqSDwWiAU75evAM+hBA4bUba+1EkRhndZcTwje1yLcDvP4BpBpHSltVb7WjRGY4Xi9/cpVMmbjqWr0t2Trls/TuZEE9VQzafSU4SG72RKtymRSwWIxKLDcVy5+VwJcc9t51FT7trwvL6puCMa4k83VGaPf01G3LXsr3Ymi3R6I81sKn9WR7Z9SLtnT28fulyLlqwMKv9lju51GxldOeZXIrP8I3tdDYCEAyewDQH0XVP2nGKicZZiqWoSnVc2TDZOeV6mjCZEBdrOlIi8Juzeen0K1xV/6a8HqtU6Wl30bQiMGF5+wF3EUajGI/S7Owo1XFlw1TnlEs9LZZmSyBozeWl07t4aM8L7DvaysLaOv7fzR8cU6d7JpJLzZ7ZVzLP5DIpDsYm9BhGHRUVS/F4lmTURWs8o+PFdN2WcaJPrinVcWXDZOeUi8SvVCjUccYTsWazs+cgjx54Ia/HUSgyQWl29pTquLJhqnMqhJ7m+xgxOYs9vSd4qGUj2/YdxGOz87P3r6PaldiwVJVLMmPGeroLMf2V69qZ+YznK9VSVLkYV6lNdU51ToWqX1voOrkSnf6oxr7eQ7S0tsPl8eUrli2mNtxP9PAxer1+pKZB5cSpPIUi3/ey0uzsmYmaDYXR03wdQwK+mJOWvkPsbTtBNBLjG297F8sbZydcv7KigrPm1mE99wJd7b3x8MC6ipyPazoyI43uQk1/5UMUp7rpMu1mFQx24PMdwjAqcbmacTgac1ZyKBsBzTZWrhSnOnMZtz2eUntYjUUAEkvGfwCcDju3v/UajKd+w8GnttPlC6PdejXa2c3FHaqi5CjEvaw0O/MxDaM0Oz1KRbMlYEkLaYFNaKyaPTfhekIIPnj9NdTv2kDHY5s4fmoA6/yFGO9dW9gBlykz0uguVPemfN6oiZhMrICEN/bwNnZ7A8FgG7FYgIGBfbhcPjRNzzpeLFMBHRaiQOAQ4XA3TmczLteCtOPYSrGDW75i80rxYTUVF86fhWfj72l77HlOdA5iXbIU4673q7KBigkU4l4uJc1O1gwFUJpdYJRmDyElC+qrqNm9gb5Hn+bAwU5ijVXYfvpZhNNe7NGVBTPS6C7UtFyhk9WSiVVr6/1ALOGNPXobm62CYPAEsdggkUh3wrjDdN/KMxHQ0ULkdq9ECNdQAlKQioplk07Pjh9fIHAIXa/H53sZywqjaQ4cjvkI4U/5uuaafE05l+LDKhlCCNxmCG3fZnofe4aDR7oI1FXh/vFnEdO8MU59UzBhAk59U7AIoykfCqHbpaLZw7HCiQ0yW1qaDenpttLsiSjNjqNbMepP7iP4hz+y75VjDAqB499vR5tTO/XGZUwuNXt6P92SUChvRq5u1FQFM9lDKRDYTXX1uQlv7NHbOJ2NOJ2NmGZsTO3P0eNI9608EulCSltaAjpeiCoqluBw1KJpblau/Mqk12n8+AKB00A7hlGFEE6kjOLz7aOiYkXS/Uzc5/0EAocBcLuX0tx8a9aCmI/YvFKN8UzEO1Zeiy3Yj3n0EN0nuvGZFp2fuZEls6qKPbS8o8oCZkYhdLtUNDsSOZXUIOvv301tbXwqfyrNHh5LOrpd7poN0Nr6ACdPPjxUKaaSuXNvorn5Aylvn4iZrtnISvRwCNqOMHjkJH3BKIcuP5/zLlle7JHlnVxq9ow0ugvpzcj2Rk1HMJM9lIAkrWVPpfwgG25lbJqBMTGEcMYrk+ghI6XA59uHzeZKWUAzFaJEDykhQMrIULhC/CfVyAWvdwdHj95LONyFpjmREny+Axw5ci9LltxZcp6IQk+NZ8oc11wWV1+FtHpAxmMJTQTu5ppiD01RwhRKt0tBs+32WUl1cHidVDS7s3M9Xu+LgI3KSheGYZvgTR+v2+Ws2RA3uFtbf4mmudC0aiwrOHL9szW8c025aDbY8fqWYDODGEikBCnBsai62AMrO2ZkycBilUvLhHTKL40uT2WaMaJRL9HoAG730hHje5jhGzvZNsOxgzC+lXHFSCvjcLgTTavE5zs0aZktIc60947/TH7Ow0KUaLyTEYl0TXi5EMIAnAhhw7KCCGHD41mFEHLyQTD8MBrAZnNjs7kxDDc2mwvTHCzJ8lepfJbFRkgbZ1VexdZjA4z+BFQEt2IqykW3c6HZjY3XJ9VBt3tpypptWQGkjOvusGYDk+p2LNZftpoNcPLkw2iaa6gTqIFhVKFpLk6efDil7QtJOWg2CEKh5Ww+4iMaM8f9RSl3usxITzcUvlxapqTjQUg2NQok9RClMp06VStj0/Sj6y58vsMj05GGUUdn53qEkHg8q4hEOjDNILruHBLQsTfvaNzuxbS3P4CUEk1z43A0pJQglMhroOs2hNCpqbloZFk06kXTpi5qH59mNQHnqKUGphkgEumacvvx5DtLPZ/lyXKBRCBkPbvaB3hy1yk+tbi+2ENSlBnloNu50Ozh5Yl1e92k2wz/7Uzct2tIt+0Eg60jVU6S6XYk0l22mg1gmoNo2lgPrBAuTLM/pe1HM3M0WyCRmKMqSw1jmYt4rV3yh9dOc93cxqG1FZkyY43ucmGq6adEopAshi7ZjT3Vg2yqVsZCCILBNnTdPjIdGQy2IWUQXa8hEDiKEBJdd+JyNaNpRlIB9Xp34PVuwelsJhLpwTR9BIMB5s//4JRClGj6WdcrsdmGRTu9KWm7fRZCnACigD60NIqu29Ke/itUlnqujJJcP2wk8cY4gbDFycEgnb0+QBndiulHrjR7KoMsFc0GRnTbsnSkDIx4U5PptmUFy1azAXS9EssKAsbIsvizqDL5RknOq5w0GzLX7YjVyIG+wzx/bA8nTnVR6XQwvyb+/TXNKrr8fjq6AyClMrizRBndJc5kcYzpiEI2N/ZUrYwPHvwOQgh0fbihiQ3LihIK9WC3yyEvihPTjDAwsAeHYxaLF9+Z8FitrfcTCnUgpYWmOaisPBtdNwgEjk45zkQPqSVL7pywLFVPQmPj9fj9B4diui2kBMsK4XDMSnv6r5yy1PPxsDFlPQf6O3FZlVipzRIrFGVJrjR7eFkm99xow39YtwOBw1iWHArLeXdC3Y7FfEAYy7KXpWYDzJ17E62tvyQajXu4pQxiWUGamt6X0vbDlJNmQ+a6bcpqDg328cjBDWzavQdpWnzvve+j3qOa3eQDZXSXOGemGe8nENgNxCtoQOFEYfRDxDDq0HVjZJqztnYNuu7BNINYVoi4dyGKlBYQQ9fd2Gx2otE+4v5OJzZbbdIse59vL5pWMZS4GMXvP4DLtSzlclHJHlKZd5O7c0z1koqKFRlVLymnLPVcf68s6eG4P8IzJ17gujmLANCUv0QxTSk1zda0SnTdwOmcNyYGPrFux/9frpoNZ5Il49VL+tH1Spqa3pd2EmU5aTZk9t2ypJOOoI0nj21k/c5XGPQF+NIbr+Xq5SsLNu6ZhjK6y4YY1dXnjnhO4omNPtzusTdHPkRhqmlOw6ghEukiEukFwGarwuVq5sEHL8TrnUss5sSybGhaDJsthNMJ3/3uxON0dq5H0zzouoam2QAbphkkFDpKdXVxPAu5mvpLNuUspaCl5e6idyMbTS4fNhKNvlgNm0/+mQ37dvOWRgtNkFY1AoWiPCldzYbEuv3UUzfj89Xh988uW82GuOGdbaWSctJsSF+3JTBozuLF08/xxJ7tnOzs5drVq/nMFW9IuL4ANE0gh7ZVZIYyusuAZG+wodDpgpUbSmZ8er07iEa9SCnR9RqEiIdgWFaAnp55zJ59EtM8E0un64N0dCxKeIxIpAu3ezGBwKGhJQZSSizLn5Ns7mK220005RwItCMEGIa7pLqR5baMlUHIjOANDeALhEaVAVMopi+lrNmQXLeDwUqqqwfweM6YBkqzS1+zIRPd1gmbMXpDAwz4ArgMgy+94Xo0LXFRu5svnIsNEyyJlBIE1C9QeTnpoozuMiDZG6yue4hGB0Z+z3f3tER0dq7H7W7C4WgkGDyBZYXR9fh0ZChUhxAdCBFBSjtCRBBCEo16Eu4rLhoBKitXEgy2DiVsCjye1VkLWrHb7SbyPMVLibmzmmrOx0Mpn/WQlbmtmAmUsmZDct2ORFxAVGk2+dNsKF3dttsSm4SNFXYurrcYGOilfsjXrWmCqob0klMVyuguGuncdMneYCsqltHYeH1Ryw0NP1wMwzaSsDPcHS0YbCIY7MMwvOi6H9N0EgzOJxhsSrivYdEwjCoqK88fJRq3Zj3OUkiKGe952r37i0mbFqVCvh5KpVPGSqEoHaaLZkNy3Y5GKwgGq5RmD5FrzYby020BrKwVbNu8nWWN1dRLiRAaQggVZpIByuguAunedJO9wRa7bu1kU1qDg2cD+wiFFmJZbjQtgK77hpYPTNhXPo29UkyKyTaMI58PpWJ/rxSKUmI6aTYk157BwQVUVXUozU5CLkLvSkW3pTQIm1EGI35CkWi89+eEZBsNQ3Nw+MQhXty2jaY3vR6GGiYpMkMZ3UUg3ZsuE2FLxyuTzVTXZA+XSGQ+fv+1OBzbMYxuYrFa/P61RCLzgb0J95evB1IuxDLXU4LZTgeW4kNJoZiOFEKzIXWNyVaLkmlPb+9aamoGlGYnIRchHKWg2xKNAbOeF04+xxOv7eBUj5e1i5fQXFM7Zj0zupRoxCIUCuH3+ePNj5TBnRXK6C4Cmdx06QhbOl6ZT3zCRiymE4ncgpRuhAhgt/ux2Wz87GexKY812cOlqSnC/v1nAWeN2aapKZLSeeSSbMUyH1OC2XqJcpvwqFAokpFvzYbUNSZbzR69v/HaM2tWI/v3z0dpdmJy4dkvtm5LIGTN4eXO3Ty053n2HmllfnUtP3rPrWOSKKU1j70n7cxyRxluUqk83NmjjO4ikO+bLh2vjN9/lOZmP1JqQBAAIfy0th4FmlM6XrKHyy9+cSjB2sUhW7HM15RgNl6ifCY8KhSKMxTCUEpVY3Kh2ZBYe5RmpzauYnvLsyEmZ7Gnt5Xft2xk294DOHUbP33/R6l1j0qWlTW0ds/iD3tPcvNZdYAq85orlNFdBPJ906XjlbHb+7As95gbyrLc2O19pCPg5UA2YpnqNb3ttmW0t9snbN/UFMn5A60UEh6nmr6V0sFAxEenv49gOO4tE8DCeg8MZcFLGV+o64lLVSkUxaYQhlKqGqM0OzVKUbOhuLotsdHWtY32Yz/iguBhGhcLVi95D6vnzB2zXiSygN0nfWxt6eKmFTVD3zVldecCZXQXgXRvunTj0tLxykQiNWhaK1KeycjWtACRyPQS72xJ9Zq2t9tZsSI0YfsDB5x5GVcxk7Kmmr61pIPTISfPnNjA+ldfIeCL4TBsOGw6f//WZcSip9GG5i2FplHbVDvJ0RSK4pFvzYbUNUZpdmqUqmZD8XTb632Zro77EWaQ7ogDjy3EIv0FBrwrqBo1HlNqhE1JNGqiOivkFmV0F4lUb7pM4tLS8cqEww1UVKwHJLFYBdFoHaAlzVafqRR7SrAUmWz6tqb2YvrNBjZ2bOLhV7fSerKL68+6AJdh55qlVfQdP0CkxoZjKFbQcNgQQnm6FaVLPjUbUtcYpdmpoTR7Il2d67HpNZi6DnjxmXZ0m4eezifHGN2K/KGM7hInk7i0VL0yXu8Oqqp0gsFmDKMXm20Qmy1AT89bJ81WT4epPD7peIQKPQ04mlxMCRazu1o+mGz6NmjNZXvnbh567Xn2HjlBc20t//y292ANPk/P8Z/Q2fkadnMF8zzuoeQd5UtRTA8yjSVORWOUZqeO0uyJRCKdCH0RMDiyTGhVRCPdk24Xi23ngguexM9GWip07At8sCe/Y52uKKO7xMm0vFAqXpnOzvVUV1/LsWPnjywTwo+UZk6y1afy+KTrESrGNOBospkStNvbitpdLR8kn76dQ284zK7uA+w70YYhdH7yvo8iQq2cbv9PBn0xQsEqRNRLa81LBOfoMOBBuB1FPBuFIjdkUxJuKo1Rmp0eSrPHYtibGAx2MRAKEo6aaAg0/Bj2hqTbxELPA7/E5ZJU6vOIBffjveQk8rgH4SnM5zidUEZ3iZPPrPlIpIs77vgTun7mazDcTfLcc7OPD5zK41OsjmPF8F5UVu4pene1XJNs+nZ+8/vxSQtLWliWhdOwsXLWYg7s/SGaVo2uSc5aoGOcOIzvZJDA60y0NdehLZmd9zHfc9tZ9LS7Jiyvbwry17/I3kuoUCjNzg+F1u3pptmWdEPV2+np/id6+3yYMYuLFjQiLR/1jTcn3S4S+B1QxbL5C3F1tRM43E/QsjDeCfKG/F+H6abZyugucfIZl5bvMlhTeXwy8Qhp2hEcju3YbF5isVrC4YsYX1N2MvLVgneYpqZIQi/O2Wcfzbp9cKmRbPq2pvYSfIEz61U7K/GHVtI9cJx6zyJmV4WxdRzFf/wkwYEYYoUT/X3vKciYe9pdNK0ITFjefsBdkOMrpj9Ksxn39+w0G/Kr2zNBsyU2uiNVbO6P8qfj81iu7efsWXaWNCygofG6SeO5pdVBhWMO7r5TRI614R0MYLkc6JfMwbTn34ScbpqtjO4klEosVz7LC+U70STZA+InP7mNlpazqK//S4QII2W8PmhDg5c77vh10geI3d6Gx/MUpllBNNqApgXweJ7Cbq9KeUz59tQki1NsaXkeywpMu0Y2iaZv5aj/CwR3Xfe3bDse5GRfDXM8XozeAULH2ugbDGE2GOjnXYAwspOi6eYNUaSP0uzsKUXNhvzq9nTXbInAH5vN5lPbeHDXC2w/ZHJ6ztV8/ubP4DSMKbd32+dh9x/HPH6Svu5BYuhoVy6CiuQhKakwUzVbGd0JyLc3NF3yVV4o3/VCkz0gjh69khUrQmha84ggW5abjo6KSR8glZV7MM0KpKxECJCyEtOML0+1Pm2xWvDm+2FZKgaHBMJWNUcHDrKr/ShVRjULa5bz1IEBjp56I5fOeoCwvxffoI+ox0Sc04jZfF3Wx51u3hBFeijNnr6aDcXR7UJUP8m3bksgYs1hR89BHtq7iVcPHqXe7eEn7/9ocoNb1nGyz2LPyUFOnh6gqeZdnOr6CrGIl5AZI7y6Dk+lJNb45qzGNlM1WxndCShm3Fq+SHZz57NeaLIHRDzLPoRlLcHvvxaHYzuG0Y2U9TQ3r0s6njlzjtLauoyxlS7qmTPnEKkKeLFa8ObqYZnocwRKxuAwZR0tfad55MCzPP/afpor5uGy25ES2nwXsqC6itOt30XWmVinNPyut1FVe0FBx6iYfkxHzYbJdTsflKJmQ3F0O5+anUlSaiaYspYDA1080rKBF17bhybh39/7IeZWVSdcX0oXPX3NPHWgmydfOs5gv4/66qsw+t/AsfBDMNsiZriJNX8AlG5nhDK6E1Asb2i+KKYXaKoHhGUtIRhcAkBPj5Pa2uTTSrff/jyW9dTIgxUgGvWiaW7gqinHcttty+jq+mcaGp4jEvEgpRshAsyZ08rXvpZYhHJJtg9Lr3cHR47ci2kOYpoxdL0Vn+8ghlFbEgaHJd20Biz+eHQTT7+6k2AwzGeuvxpd00fWqXVdQtWxt7DvV0/S0e3HfOvKgo1PMX2ZbpoNxdPtUtPs9nY7dvtY3W5s7ODjH//vvNfczpdmL1lyZ95fFC3ppi0g+cORZ3lq104CgRBfu/5tXLJwSZItdALB5Tx7uJ/HtrVyqsPL5WfbsOlQG1mG/5HFtB84zak7r2Hpey/IenwzFWV0J8Bun0UweIJotBfLCqNpDgyjDperqdhDy4jp4gXKdrovXr6qAU07fyix5xixWC379r2T2trSbyrR2no/4XAXNpsLXXcDUcLhLsLhTurrxz7ACm1wSGx4ozVsbP8Tj+96ic6eft57wcW889wL6Owv2DCmpL4pmHD6sr4pWITRKHLFsCc0Go0SDJ7AssIIoeF0Lij20DJmOuh2bjQ7BIzV7WPHVk7qYS8Vkml2a+v9CCHz9qIo0eN63PFn/vDqy3R293HT+Rew7pIrkm4Tiy5j+4kIj+08ycFDp2meJfjnT9SDOTZPp9BMN81WRncC3O7F9PRsRNNcCOHCNINEo4eor7+y2EPLiFL1Ao3Parfbr5l0/VxN94321ABEIk5y0VQi3wQCh9E0J7o+nHxiw7IkptlTlJCZsbjoj/ppHThFl7efOrebb914E4jSanoznRN0ZjKNjddz5Mi9hMNdaJoTITRMM0Qs5sXr3VHyxlkiSlG3i6XZkJ6HvVRIptmBwGFqay/No267GYgGaBs4Rae3j1qXm2+/7WZEUj0WhCIe2gd6aO8cRMfkb99fQ6NnNrEeCw1Axk3vyvrKJPvID9NNs5XRnYBA4Chu9zJiMS+mGcJmc2OzNREIHC320DKiWHHMyWhqinDsWDcNDa8SiVQj5VyECLBixSN4vdVTdm0rxwdorhivmUKAEC6i0binftibFAi0Yxi17N79xaIkVroMe0qZ8bliunlDFOlRW7sGw6glGh1ACAtdd1JRsRRNM8rKMzyaUtJtpdmZk0izpUw8C5Av3XYYNlzGxM6giQccH+P8umpi3jr8/n4qpBUfp65RN68u6/HAzNVsZXQnIBLpwuVagK6f8YYONyAoR5JN8dXWnk1Ly90Fr3jxi18coqXlbiwrMCHWr7PTPaMFejLc7qX4/QewLA0wgCiWFaKiYhXNzbeOeJOkFAgBhuEe+bzLvZPaVEw3b4gifYSQ1NZekrBxTDlSSrqtNDszkmm2x7NiwixAKel2bYWDJZXLeOWlnayaXYMHQAiEpuXsGDNVs5XRnYBS8jDkgkRTfLW1Z+P1bilaxYt8TZ2WSum8fNDcfCtHj95LJDKAlBGE0HE4ZtHcfOsYb1JLy90YhjutWNBsr5uUGjErRtQ0kVKOLVYwhMN2RrCLGSOomJ4o3c6vzinNTp/JNBvISrcnu25SCmIyRsQ0sZLo8Vg0TAtilsQyJf/0gQtpO9bGc889x8K3vQVBvOeCInuU0Z2AQtTnHKZQgjN+ii9+gxcvSScfD8ipsv2TdR5raopkfMzJxpLK55rO5x9ffueU66f7cMy2SoJEpy/m4cVTG9jYspeBwQBXn7tszDoCuONNSzGtzrhRDghNUFFfMeX+FYpUKJRuF9JILCXdVpqd3nrD55CKZkN6uj3ZdaupfR39sQq2nXyWjfv3MDAY4Iqzzp703M3oYl47FWTDvk46OrysmFtJx6Fj9PT2AiCEloLhrkgFZXQnIN8NCIYpZim/Yifp5OMBOVW2f7LOY7km1c81k88/lfjIdB+O2VRJkEDQnM2Lp3fy0O7nOXCsnaUNs7j7bTePWe+yhVU0xHqIRmPYiMcH2l12bDY9wV4VivQphG4XuwlPMXVbaXb+NBvS0+3Jrpuj+u1sPf0KD+5+npZj7Syub+Bf3nFL0uNKay77Tzl5ZFcbL+1swyZizKrW6VBWdl5QRncSUr1RsvF6FLMkVLGnYvPxgCz0AynZZ5/q55qvzz/dh2M21y1iNbKr9xgP7dvI9pbDVNod/Oz9H6XSccY75bZpzLeHeH7zS8y9YAk2QAiBpitRV+SWVHS7XDUbiqvbSrPz+/mno9vJrlso0sPunmM8tH8TL7ccomJYj50TZwsAkFW09zbyx32nePblE0RCIf7ug1XYDaXN+UIZ3RkwfOMGAocIh7txOptxuRak7fUYfeOEQp0EgyeIxYIIIfNe5qqQITTJyHVWez4fSMNNGs4cq42GBp3q6mu5444/jfnsU32Q5OuBk+7DMdPrZspqjgwO8MjBDWzctQcrZvG9W25hccOo7aQDp4jxyp7X2PbSNm48bzGglVwpQcX0ZjpoNhRft5Vm5+8lIR3dTnTdYmaIsKjnscMb2bjrNayYxb++970sndWY5IgCf2gJW4/38+dXT9LbPcBNV7q4+So3xOJr2Gw2PHYNAgGikejwZoosUEZ3moyeXopGA4BGKNSOYVTgcMS/3Km+9Y5u6OD3H0AIG5pmQ0qZ9ynLQoXQFJJ8lmA606Qhjsu1ASHCtLYuRtdtYzweqT5I8vnAGX44DhsbbW2/orNzfcLzz+RBbkknJ0M21h97mvWvvsKAL8AXrn4zb1p51qi1dILhRZjhCH6fH78/ADBJrdjCcs9tZ9HT7pqwvL4pOGMz66cj00WzR+97uui20uyxjP18u+jsXD9m+TDjr1vM9OMP+9ltLeOPr7zMwGCAO1//Rt6yarJYbp1ITMMXiREMhHE54CPXesbo8y1vvZbG1r30PfkcR9v6kLOq0C9eNsk+88t00GxldKfJ6OklKSNomhshYgSDrTgcjWm99Q7fOKFQB1LqaJrAsqJUVq4sSH3Z6VY/daoSTP/+72+iv1+nu7uKSGT+yHZNTZG0YwdtNi/RaMOYZcOf/fz5H07JiM231yrV+MN0H+TxxMk6nm17lkde3UrH6R7euHIVd171xjHrRSMr2NUWZkW1HKlWUiL2NgA97S6aVgQmLE9UO1ZRvkwnzYbppdtKs8eSqWZL2wKO29Zw/84TtJ/u4ZoVK/nCG96c9vFto0L+5tXXsKjPS/h3f2DfK8cI2Q30H30arbE6u5PMgumg2croTpPR00ua5kDKKGDHNOMF3dN56z1TKugbQ2LjprJyMQ5HY1nXly0mk5Vg6uxsorn5KLNmbSAYfO/INomy46ciFqtF08be/MOffapGbL69VunEH6b6IB+dOPng7udoGUqc/Ld3fwBtVA1XK7aA1zoEz+zvZMnF9QBoJWRwK2YOSrNLG6XZZ0hXs2tq1xC0mtjY8QoPvPQ4+4cSJ3/47g+O0eN00aRkCQOY6x+hZdNe+qMW4hu3oF+yPPOTUwDK6E6b0dNLLteCocL3UYRwEI16037rjQvOpROaDpRzfdlSIVH8nWW5MYzurPcdDl+Ex/MUQvgxzdgEj0eqRmw+vVb5iD+UsoYTfi/Ptb7C7iMn8BjGhMRJcNHjq2friU5eO9QNF9chBMxunIU9GsDs7SccigIC0qheMh2mFhWFR2l2+aA0Oz3Njlqz2N1zjN/v38TL+w/hMeyTJ06ORjoJRCx6fRECoSgC0DXQsGP3++GV5zn9/G5O9Qfpv+Ys6j96TdrnozR7IsroTpPR00uGUYfDMY9g8ASG4UHT3Bm99RY7OWa6kij+TtMCxGK1k2yVHE07gsOxHZvNSyxWSyBwDlKaJRtbmYv4w/HZ/g2zbiEiVhKMRbFMi7PmzBmbOAkIbMQsCMcsYrEYAnA67HzuPTdgPPc4R/60na7BMPLtaxBzU/8spsPUoqLwKM0uH3Kt2fHtz+i2lDo2W2haaLYpqzji8/HY4Y08u+s1rKjF9275AEtnNTLg3UFP55NEI90Y9gbqG6+jasx52uj3LWHTYS9Pbm+jt3uQa9YYzK03cEeXEg0OYAsGiYRixISg8mNvySgXR2n2RJTRnSbjp5dcriYWLvx4VjdusZJjpnMnMJj4YBTCj6778PvXJt0m2TVZsOAAAwOvEolUI+VchAhgt3fh8VzEued+P6fjztXnkq1hkCi+sL3tPmK1Hx5ZJ9UuZZesWEjdS09y6tFnOdbRj3lWE/Z7PlYySZWK6YvS7PIhl5rd1BTh2LFuGhrG6vacOa3Mn//hnNdvL6RmW9LByZCdJ48+MyFxcsC7g47W/8JmVGPY52Bag3S0/hfAkOEtiIRXsO14iIe3t3P0aDdLmzS+dVsdFXIxxw520uSUVA41MdPtNjRDmYq5Ql3JDMjH9FKhk2OK3eShEExM0nHg91+LZS1JuP5k1+Sv/3r9hOnkaNSLpv0B+ErOxpzLzyVbwyBRfKEl7QT6/gicl/I46gwLz+Ed9D+6npYDp4nUerD99LMIjyOt88k19U3BhB6X+qZgEUajyCdKs8uDXGr2L34RjxFPpNudne6cXbNCa7ZEpz9Wz8a2Z3l419YJiZM9nU9iM6qxG/GER12vHlleVbsGM7aQV9vh4Vfb2bWng0qXxffvqKfBMY/ethibNm3iXa+/mEoATZSUY2Q6aLYyugtMqXgqit3koVCMfjA6ncvYv98+YZ3hlsKTXZNCNXHI9eeSjWEw/pwlILR6QoF9dHibCEWiOA1j0n3MqjCo8HVi7nyZE6+1MRCz4B8/hH1B8WNfZ2pMoSJ9SkG3lWafIRXNrq1dUxDdLqRmSyBozYknsr/2fMLEyXhIydhz1rVKopFTSNnAkc4qHt3dxgs72sCM8E+319DcaGALz+bw4R289tprvOOKNQghUp7JLBTTQbOV0V1ASslTkYkYlcKDJxumKjE12TWRUtDf/xJSWmiag1//+i84fXoWUjro6TlTmzqTUlapjqHQjI8vlNKBN3SS474oL+47iEPX+Zs33TD5PmwamDGIxYiZkrAA+wWLCzF8hSInlIpuZ6oN5azb2Wi217uDYLADn+8gNlslv/71X3Dq1CKE8I/R7XLS7OHEyYf2b0yaOGnYGzCtwREPN4BpDWLY59Dpnc/6ltP86aUT+Af9fPadFVxxjgsQSAmmaRKNRuPe7RLycE8nlNFdQPLpqUhXWNNNsiuVB08+SXZNpBTEYl5MM4SmOTHNKLFYF0uWnGJg4F3U159pwJBJKatUxlCMqgij4wuFVsdAuJtu/wkeOlRHJBzlnne8h3PmNhVsPNNhalFRfuRLt/Ot2cPHmM66PZlmt7b+Eru9gWAwiGkGiUZ7WbSoH9Dw+68d0e1y0exEiZP/OpQ4OZr6xutGYrh1rRLTGiQWHcDT8Jf8+ZCXx19spfN0P9esMbjthoqcjnE8SrMnoozuApKvN+JMhDXdJLtSm9oc3+J3mGy8FsmuSSzmJxIZACAWG0DTDKJRJ7GYnjTWMFNKqSrCmfjCpxgMnaYjFOP/jsxmaxusu+Qy3n3BRQUdz3SYWlSUH/nQ7UJoNpSWbhdes/sQQqJpcTNHCAvD6KOv70M51e1CaHY8cdLgyWNnEic/9/pruDZBx8nhKiXx6iWnMOwN1M75C3Z0X8Ij249w9GjXSOKklufmCUqzJ6KM7gKSrzfiTIQ13SS7Ugp7gIktfofJxmuR6Jo4ndV0dT0OaICBrjuJe0qaqa1tz/hY6YyhmGWtamovRqt8O7taN/OTPX9g15GjXLJwEX9/3duSbhOLNdDmDbO/vZ+rF9cipR8pJUNR4Rh2JTuK8iEful0IzYbS0u1ia3Zf3zIqKtpz7ijJt2aPSZx8NZ44+YYVK/jiG96SdJuq2jUjxrfAw6neFbScPs3RE14chsm/3t6Ax5m4ec7q5cuoC/URO3iEnh4fUtPQajw5OReFMroLSr7eiDMV1nSS7Eop7CGfjL4mXu8OWlruIn6bxAXKNAPouhuPp5VYrD7vY0iHXMduSiBszeHlrr08+NpG9hw5wbzqGu675cMYeuKmNlLO4khnJY/ubsMZjnDd6tnEBvthqPyU3W3HcEyefKlQlBL50O1CaDbMDN0uZ82GyXV7dOLkQ6+9MJI4ee+7P5RGx0mBBCwkEoldh6ZZiU0/p8POx6+7EtuTv+HAUzvo8ofRbnsj+rkLMzo3xUSU0V1A0nkjTseAKoSwFjPsIdG1gLOm3C5bOjvXI6VE16sxzX5ABwSmGcTh8BEOX5v3MaRKPmI3Y7KBPX3tPNzyLFv3HsCh6fzkfR+h3pMkDlB6ON3bxBP7T3PgaDc/eO9KDux7lfo5HmyAEAKbPfPWxApFMciHbhfKGC6WbivNTo2pdPtMx8mNvLT/YHodJzPg/Pmz8Gx4kNbHn6e1axDr8hUYf//evBxrplISRrcQog74OXAt0A18WUr56wTrrRtab3QU/tuklM8WYJg5IZU34nQNqEIIazGbQSS6Fnb7G4CGnOw/2UMyEulC09xomkTTaonFBpHSBCw0rYE9eyY+RIZLWRWaXMduWrKC474wfzy8kWd27kohcdKGL7CMTUf7eOaVDv7lpuV0HGthy5YtXPqON4KgpOq9KhTpkGvdLpQxXAzdVpqdOpPpdlXNG0YSJzdMkjiZChKwLDky45gIVzSC2PMCvY89w6Ej3YQbq3H+++0IFQ6YU0rlav4IiACzgQuAPwghXpVS7kmw7hYp5ZWFHFyhSdeAKpSwFroZBCS/FpWVe4Crs9r3VA9Ju30WphkhFGpH1+1omgfT7AMkH/7wf2GzPYwQsiTKcOUydtOSdjrDbp468RSPvfISvX0DrLtk7aSJk4IK+oIaR3uC1Nhglltw+NQpLr/wHJwn9tG/8wC9gyGYXw/u4jbFUSjyQTq6XUhjuNC6rTQ7dZLr9ul4x8ljYztOJkqcnPIY0SYOdAbYfsRLf3+AhY0a4/MnXVYTwtePPH6YnrZefKZF9CvvwdVQlc3pKRJQdKNbCOEBbgbOkVL6gOeFEI8CHwb+rqiDKxKZGFDFMIgLQbJrMWfOUfbsuW7C+om8Fsmy5leu3MUddyR/SA57o5zOJoLBDiyrB9AxjLmEw6cJhU5TUbEaywoUvQxXrqarJRqD5iyeO7mZR3Zt4fjJzikTJ8duL0EAApY1z+OcyClC9/+Bfa8cJ2g3sP3bJ5TnRDEtSVe3lWbHSeZpTqTb9fWtNDZ+hDvv3AiUt2ZDYt02rUGwNbOxbfNI4uTojpPpIK357Ouw8fCuVnbsbsNps/j2J8dWLbFTQ7DbhT3Si2FJpATNblCxfG5OzlExllJ4+q0ATCnlgVHLXiX5K/GFQohuoBf4FfBtKWVs/EpCiE8BnwJYsGB2bkecQxJNlUkp8Hq3IYRE1524XM1ompGyAVXOzRDGk8yYvP3251m58qqU9pEsa35gQKBplWOWjX5IjvZGhUJtGEYjbvdSQqFWhHAjpSQcbsPtvnhkvVxc50xKa+Viunp04uRDr23ktcPHaUqSODng3TFUkqobw95AfePNwJmmN3ZdcJ7bxHzscVqe28dATKJ/673oFy1NeTylyj23nUVPu2vC8vqmoCqRlQXlotkwUWPd7sVDjVgOYRiVuFzNOByNKb/4Ks2eSCLdrqxs4dixsSEipaDZkBvdNq1BQtEwna7zxiROju44mTKyluNd9Ty+p4NNL59gec0O/uK6F3F5vXT4Z1Hd+BaqatYifPPYvn0bVyydizHkNNFsiRPly5VS0uxSMLorgP5xy/qBygTrbgLOAY4DZwP/B8SAb49fUUr5E+AnABddtCp5IFMRSTRVdvTovZimH8sKDzViiTAwsAeHYxaLF9+Z0T7Hv9GXk8DnM/YxEqmZ0js87I3avfuL2O1z0HUbfv9BNM0FSCwrNDK2XJXhSre01vDnGY36CIVOYxge3O5lU05Xj/8e1M+6heNIHm55li17D2DXdP5jKHFytJGNhGjMi8M1H8M+B9Ma5GTrT9Cr5gDL8Nh1ZkX74fn1HH96B6f6g/CB16Pfmt3UcqnQ0+6iaUVgwvJETSAUqVMOmg0TNTYYPEFPz0bs9kZMM0QsFmBgYB8ulw9N06fUqlRjwctFt/Op2bFYLUKMvfdKQbMhPd0e/VmCjWg0gBB+dHszocor+P3htoSJkxOdHdeNlAYcjZROegcWsPnAY1QP/Bdfuuwg82v7qPMsQLcvQFoD9LTej9tcxKHDFps2beKyhe8mnnwz/ZLdS0mzS8Ho9gHjA4eqgMHxK0opj4z6dbcQ4pvAX5PA6C4HEsW+9ffvQ9MEVVVnEwyewLLC6LoTm602JYGdKq6w3DqU5TP2cXDwbKLReNObqR4Oo703uu5Eyigg0bR4fHKxynCN/jwrKlaOnMNUD+Tx34OYGeTQsR+xLXQOz+zsGJM4OeDdQUfrf2EzqjHscxjo34Y0w9gdjeiGhq5XE8EgNPC/wFeZW+1A8/Vinu6krz/AgKZRc9f7VCIlpeVxUWTGeI2NxbxomgspY1RWriIYPEEsNkgk0s3y5X87pValEgteTrqdT80Ohy/Cbn+VaNQ7LTR7+LOMRgeY13QHXbYL+ePex9iwa/eExMnxOmxagyOdJ8ca3jrB4HK27F+PbeBfsSJ26urCeJw2oqEOdKMCm6MBgUbXqd/S2/shuro6AaE6vycg15pdCkb3AcAmhFgupTw4tOx8IFES5Xji3TbKlESxb1KamCY4nY04nfGbzTRjKb+RTxVXWEodylIlX7GPkch8mpvXpfRwGO29sdvn4ffvR0qoqFhNNOotWtfITD/P0dtJqWFSQWe4m0j30/T2NY9JnOzpfBKbUY3dqAZASInUnISDbTgc8YeWrlVhxRI3C5IATlWbG0rL46LIjPEaa5ohhHBhWeER3R7W7FR0K5VY8HLT7XxptmUtobu7Ck37w7TSbEtqnDj1RzaZA0kTJ8frsK5XjywfbXRHo8t46USEntP/zWC/HUurYlZVDE1zg4gRCbbGjW6tkmj4ZAHPvDzJtWYX3eiWUvqFEA8B3xRCfIJ49ZJ3AmvHryuEuAHYIaU8LYRYBXwN+G0hx5tLEsW+CaFPaM2azhv5VAl1pdShrBRI9eEw2nsjhB+PZwUAQsTQtNqidY3M9PMc3k4CUZyc9J/mSN8gTss3IXEyPpV55hhCc4AZxTLPVO40rQE0W7JyggrF9GG8xuq6k1gsgK6f8YblUrNB6fZoIpH5rFz5lSnXKxfNlghMMZuugVd5+IBtpOPk+MTJ8ToMoGuVREd9B6TVNJQ42cb5opWgWc81F9rRI8OefmNEt6U1iGFTyZKFpuhG9xB3AP8JdAI9wGeklHuEEAuAvcBZUsoTwJuAXwohKoDTwP3A3UUac9Ykin2z26uQkpSmz1Ld5+jtZ0KHsvE0NUUSxtWlW5+1FKsNZPp52u2z4lnyWhOdAS9HejuIBPqICPeExEnD3oBpDY54VpyuBQwO7EFodkzTwrQGiUUDGJ6/IBSz8BjxmMCRzu9ZoMIxFKXGeI212WqJRHpwOpswzVjONRuUbo9fniqlqtkxKwZa/LM0gW7/QQ73RyftODlehyFe6cSwD9U+l7Uc72oYSpw8TtPqGi5aEabGU0lUbybkP4C0ogjhIBbtQ0Z91M26k9ZWcDqc2KSJFY5gxsyszk9p9uSUhNEtpewFbkqw/ATxRMvh378EfKlwI8sviWLfhpMlM42HmyqerhBNGTLJ4s4nuTxmIZKZ0nnYZPp5NjZez4nW/8EbbudYXw++gU6qDJPzzrptQsfJ+sbrRmIHda0SoRs4HfXoRh3RyCkMeyOu+r/imePn8GrLcf79fauwBg8NNR0G3a6nn3k/RCmGY9Q3BRMev74pmGBtxXRjvMa6XE3U119JIHA0L5oN+dftUtNsyJ1uFyoBNRXdlkBtw/s5fPyHWJofRBVRs5duXysPH6ydtOPkeB2OOzv6aWy+aSRx8qkDPax/qZV+rw979ZuZXfl7YtE+NKMOwzGPSLAV3fAgNDe1ze+h0n4VsJVPvu/dePZto/vP2+joHoQls9GWZeYFV5o9OSVhdM9kkr2JZyMKk73dF6IpQ7rVN8qFQiUzpfOwyfTzrK65Cj3opO3ofYR87QSiNhYt+CAXLptYR3c4XjCeNX8Kw97A/CV3Di0XhEJn8aeWEI/vOMaX3rSIcN8pTGJxV7cAV0V5f+7jUd4aRa49qFPtL9+6rTQ7e1LRbVPWclpfwS65hmr/Nhy04bPc/OZAI3t63PzolluSdpxMpMONzTdRVfs6AoHlbDw0wGMvtdLR3sPFq2x8+J1XEh7w0N/5NGbkJIZrHg0LP4pnaD8aBvhhRfM8FnXuw//oelr2dBCucGP7yR2IadTErJQ0WxndM5BSnHIrNJl4dko1mSndz9OSdo527uRQ688ZHGjnlE/HVX01b7v4/Um3qapdk7A0lRlbwiutFr9/5SQ3Lq9mniPAjpd2sOT8RegCNKGhUuLPUEoeF0V5oXQ7fd0utmbHo+vi+ieli7aA5A9HNvLIzi78gbkg5hGJxugb8HHn698wacfJZOUChxMnH97RwYGDp5lXL7jn03XYdIGtds2IkZ0IG5IlkS4if3iMfS8eZBCB/t2Poi2fl9sLUcbkWrOV0V1AcjHNNXofUsZv5nTb2pZLvdd8kolnZzokM0k0OnoOcvTYv3DaO0hrn2RBlZOLmw4x4N2R0LBOhqCak95KNh05SbDPx9uua+LFFzbSeeok+spZmAN+ojETXBMfkjOVUvK4KKYmV1qZrW4rzY6Trm4XU7MtaSckm7CGjG5fJMCf2/7EYzu3cbj1FHUuN0IIBHDj2edM2nEyWblAadVz3Hc2D+9qY8fuNhy6yffuqKOmYurmNprlwO3rw9r2LK0v7KHbFyb80WuovGHmfa8mI9eaPaON7kIKWS6muUbvQ0p9TAmkVNvallO911Jg9HckGOzANCNUVCwZ+Xs6yUzFfnAOd5w82PYPnBgM0NYXxWN3cenSc9Gkf0LpqakxiFgSfyRGtdOGhoWh63z82isQT/6OI5v20BeOYfvIZYhxMd0q2UaRCeWm2eP3k4luK81On+Hvid9/BL//BJWVy3A44mEbhdBsicagOYu93v0Eh6qF9Ib6eOjVFzjcdpKz587lPz9wGy4jXkq1yumatI9BwnKB0s6J1id5vONiNr18nGg4zDdvq2Zl89RODoGOM7KQWKAD/H5CoSgRm47r429Kuo3S7NwwY43uQgtZLqa5Ru/D79+OzZZ+W9uJjR2iBALttLR8g9raS2esByUR478jphkhEIhPYbpcC9JKZiqFB2dMNrCnr52uvgOc6A2jazpXL1uJ02bDNMeWnsoEAVx97lIqn3uM1j88T2unD2vtcoy/m3h9Uk22KedwDPWQyi3lqNnj95OJbivNTo/R3xO3ewU+3z4GBvbg8ZgYhjPvmi2BkDWHl7r28MyxrQQj8UTK04N97Dx4lHq3h5+/fx1zqqqT7mM848sFSmkjYs6jc/AgTw4lTt76FjdvvSy1ZEWPuYzDB9pY5oxhlyCEwHAZaHpyD7nS7Nxo9ow1ujMR1Gy8LLmY5hq9D9MMZdTWdvQ+wuFOBgdbEMKGZYmUveVTkasSfcVm/Hdk2MMdiXSj6/a0kpmKEVso0Rm+xaV0ctwX5o+HN2L0RnFrUc6Zv5R6T1wcx5SeygAB1BkWjj1b6HnkKQ4e7cFcUIdx3x0II3OZKWfjtBSz+MuZctTs8fvJRLeVZqfH6O+JYYCmaQQChwkEDlJbe2leNFtiAPHZvJisZm9fOw+3bIx3ljQtAELhCLoU/Og9H2JudU1a5zS2XKCGaVbTNXCaQ51VtA8lTn7+5vGNvRPjks2cPDbA5s0vsOj1a7ATN7pz1TFYafbkzFijO11BzdbLkm2dVa93B8FgBz7fQWy2yngNZNJvazt6HD7fQUwzQLxSqA3TjGIYVVkbgsUqMZVrEn1HXK4F6Lqdc8/9ftb7ymdsoSWd+M25mMQF3x8J8Ezr0zz+yku4AzXceb6f5urRdbbjpafGkyx5ByAWq6TXH6WrP8iSegeunhNEX9rGsX0nGZQS108+i6irmLBPhSITylGzh0McgsFW3O6lGbUjV5qdHuO/J05nI4ZRRyRyKqWmOsn2A4m/b6asxm/WYg2lTXYGu3n80Eb+tPNV+gf8zKuqxqbr1BlOPrX2ai5dtIR0GV0uUNBM50AnPYOneWLXDWMSJ4fxe3cMVS3pQrfPorrxLXhq16BhR/qraWnZQl9vD47QAGZ3L/5gBIQG9hlrEhaMGXuF0xXUVN56J/OqZFNndfjhYbc3EAwGMc0gphnGNC2EMNJqazs8jnDYSyzWy/DbuaY58PsP4HItQwj/lGMqd1Lx7GTz0B3/XZBSFKy5hUSnL1bHzu5XCMbiU3p94QEe2rmZYyc7uWTBBVx63sX0dz8zrvTU2Id2suQdgMqaaznWVcljr7Wx5ZV2Vq+dhzBjEIsQMyU+m07F2Qtyfm6KmUs5anY8xGE5fv9+Bgb24HDMJRo9mVY7cqXZZ8inbmei2ZZ0cjJkY0/vS/GGN8Cx/g7+sPMlevsG+dilV/APN7wj4/MdZlibuzufo2/gBMe91fz2pRs50ruK//yb2jGJk37vDnpa70c3KtHtc5HWAD2t9w/t5wqkJTFsOp9+2zXozzzIoWdeoccfQXzwKsTc2oTHV+SOGWt0pyuoU731TuVVyabO6tjpsgqCwVakNAEdj2dJWm1th/9+8OB3iIu3jmFUo+tuTDNIKHSU6urpHx+Yimcn04duou9CNOolFvOmta9UpsbHrzOr8XqcVW/jxdM7efzQZnyhIALwBgLsOXKCedU13HfLh6nzVFBX/7pJzyNh8g7Q3fkngtzG+v1dPP1SK/5BP5essgMTp+UUilxRvpoNuq4zOHiIaLQz7XbkSrPPkC/dzkSzJTreaB0b2jaw6fguorEYc2hjjvUqH2nsg6Ya3nXJxAYzk80eTkZVzZvojdzG+sMdPPTsIQb7B/nmbVUTEif7O59GNyqxGTXxBXrNyPKq2isAuGRZM1XPP0r7Yy9w/PQg1uuWYHzzAzkLMVEkZ8Ya3ekK6lRvz62t9xMKdRAMtqFpDlyuBROm/TKtszr64eFwNOJwNGKaMSKRU5x77vfS3l9t7Rpcrnk4nc0EAocQQsOyTKSUWJafxsbr095nzpBOhMzN11KKKIhwxttn+tBN5GFzuyEaDaBp7pT2lcrU+PA6NqMWSzrxenfQ3bMZHP/N873NPLVrEDkUTxiJxXBoNn7yvo9M6DiZjPHJOwC6VkUwOMhLh/p4bFsrnaf6uPoCg9etckBLSrsF4okp3mNeGir34LT3E4pU0z14NvVNSvQViSlXzYa4bttsddNTs0uMTHQ7Vc2e3/xBamovQwJBs5IXT7/CQ7ueZ9fh46yq7OXy+cfoj+iE8PCWJfPobL8fm6aPGNWjZw8tqTPg3Y635zkqKlYzp/lD44xvAzHUkFtKjZ6B+Tx1oIcnX2qlzzuYNHEyHlIy1tgXWhVm5CRI8EgT274t9D7yFAeOdBKdU43x4zsQU4SWKM3ODTPW6Ib0BHWyt2evdwc+3140rQJNi8fs5XLaL9vYwuT7DFBZuZJgsBXTDCKlwONZXbRMeGG6Ed12CE+sw5rR/lwerHoNtMyzpjN56CbzsAnhTzmmMJWp8c7O9ei2BsKxGMFAC0LEY0QHQgdZEDnEIqMJT8MFOG02NE3j1osu55y5TSmfx9jknTimGSEsm9jZNkB7h5c5dfCPH6vDoBIph2Md5ZQNcf76e7/G1voAGBWgVYE1AFEfseYPABekPMZ8kU0W+/C2x/Z6OPTKmeQmw26x8GxfWWTxlypKs0tLs0uVdHV7Ks2WQMSaS38UToUtkHBs8AgP7dvIyy2HMaTgHQsHMEQ1NYab8+Y1U+F2EYn2jynFOjx7KK0I4cAhhG5Ho4Jg6MRI6F58XRvh8Fn4QvGwEdOCF454eeyl1pHEyb94d+LESd0+C2kNjHi4AaQ1gG6fhVPWY+s9ifXaq7QfPMWgKdH/ZR1a49SVVKazZg9vP16zIa7ba67tytk4Z7TRnQ6TvT23tNyNpnmACNGoD4gBAp9vH/X1a7M+ttu9mPb2B5BSomluHI4GNE1PKbYwGcMPJMOoorLy/FEPpFvHrJdq9n8mHR7HYBmIDgvrhceRwWC87lKWL9DCU4l25Zv56N8vp73dyHxsaZKLB24qSTyRSA9h0cygbzNSStAsotIkGgrTH3TzqfNsvO0Nn874PEYn7+haJaY1iBkLYVR8BlMCSOY1aMxyzWWw08SwrPg4gIopEij1zmfi4j1uClTvfAaz9oKMx5wrssliH952/PbtB9z881PbczZGxeTMVM2G1HQ7a83OI4Ue21Sabco6Wga6ONR/CMuKzx6+fPIAG3fvQZoW/+99H6Ep/FMM+xx0/UxPAl0bW4p1ePbQ738VodvRNTtCWkgZwmZUDxnoFxEJr+Dl42G6AxFAErUkj+44OabjpGFL/ICsbnzLSAy30KqQ1gBmdJDZzR8g3OfGGe1FmiamJcFlx3H+opSu0XTW7OHtr7hponHdfsCd04osyuhOg2Rvz5FIF4ZRRyh0iOGYO4gh5QBu9+Ksjun17sDr3YLT2Uwk0oNp+ggGA8yf/8GsvBupTMGlk/2fSYfHEaSG5q1A/um3DPxhA9FgNOPzGo29wkGVGaP9+OdYsXpgghGf0tgyIJsErGGmeghIDEx9PqcGj6CH+wljA0xkLEwoApWeepbXZ3d7j/bOjCRczl9HR/AqoA2AFXNqifTWsHfPa1xeK5BDNV9tjsmPLSK9YJ89dqFWhYiczmrMCsVoZppmDx8/Fd3OSrPzTKHHNplmW9LNiYDJE0efZXvbkRHHwqH2kwz6AvzlG97CNStWcbQlwczguFKsw7OHlhlEaMNe2ShCc4wY6GZsIa+2w+93neKkNz4rZlkWu/d04LBN3XFyuO17vHrJSXT7LBqa30u18W5eeXkXl85yYZMSwdQ6PRql2blBGd05wG6fhd9/AnADPuKl/AA8BAJHs9r32DCDeKmhaNSb9X5h6im4vNWWliBkNUIOCUfIhXzhj3Q//Ax7d7USFlO3sE0FJxbnyCeh/1aIVYE+/LlIEPmrP5tNAtYwww8BCzu6VoNp9WNGgzQ1r8OiAX/MQZu+Gu/gC7hjESwRBQSGJum3arls0RzsRvbl+qpq14yJMxRyDu1D0RF1FXY+9+bz2LH9Ffa9tpvL33BBPKokhWQcaa9DjJsCxRpA2uuyHrNCMRXDmq3rlZhmEBg28HS83pdobv5AxvsupmZPPH5hegKMIB1olifzzYWJFP05HFBq1NRehkUV3Z2PDmn2HJqa11FdexndYZ0/tT7Do69s4/jJrhHfjWlavHnlKj77+muAxDOD40uxDq8Tr4wSASmwZAy3ezWWFcWwL+RIVxWPvdbGhm3HCfgCCEBKicDiax9NreOkp3bNiPEt0PBEVrFzewtbtmzhdW+7Oq7RGgxVlE0Jpdm5QRndOaCx8Xq6uzcQn6IcviFMgJEOhplS6PrOhTi2Fq6D9n6Ixo1gebod3yN/YN+uVoJ2F3U3vZNvPvU+ugYnxqzNqhzgrnc+MuUxTJ8P78OPsm/HMWSkC7pD4ByaZtI18DiyOoepyDQBa5ia2jWErQbaT/0WM9yGbszG0/h+Bh2vYzBgcnyghd8c7uXk4QZuag6zsiZIDAdBGrhwViNYPuobb87hGQFo+EOzONDlZ/8xLxcsqMGQUdra27j83NXoh/fQvec4vnAU0Th5owaz8c3x+EAYEx9oNr89x2NWKCbS2Hg9PT2bkNIgrtXx8DMh3Ph8e/F6d2R8/xZTs4t6fMvOxz60lLbWiX+a3xjml3cfnHIXwrAhmgprxEls9EdnM+jw4Gh+HcNPhkFgwB9jZ9dOfv/qZg63nmJxfT3XrTwHgGqniw9dchmaFg8nSTgzOK4U6/D/T7b+D37/PoReiW6sIRRzYkX6cNZ8lqf3d/PMS60M9A3yhvPsLJ4bd0Itbza4/pL0m7R4rCUcPXiSDX/ewKqF83CcPIJ/zyH6BkPQWJ3ys1Bpdm5QRncOqK1dg6a5sSwf8WBkG4ZRg2VFiUazS8rJR0JOMY8tYpWw9yjW1k3IcBiQRNpOsv+FFnyWwHzfe+m67BLan57PvFUJ4rM65nP6kotTOpZlWvT/7kH8fX7M4z1sbDsbf9RBb9CNhQ3TglmzXofDYXHWWcGSiGUcJmLO5pgVYa/tOkwt/gKHNwDeZwHY23WC5/fsJxau45x3fIlVVX2jylDVpFyGKh2i0WW8fCLKQ9vbaTl4ijVXzkMXkjWrlnOOvxX/I0+wf08H4Uo3xndvm3xntRcQIx4PKCKnkfa6uHiXQGygYvpTW7uGioqzGBzcRTzuzMAwKhFCYJr2rLzCxdTsoh1fami9Htr2DbC8sm2oedsZDu2tJ/bHB6fcjeZ0ol12FRBP+P7zn6vx+3V6e3UsS2CaudVsicBvzublrl0cG2gdCR0ZzXNHX+PVQ8eY5anggY9+mtmVyR0K42cGJ1tnwLuLjvatdPefwE8F/XySnsFVPPbiCTpP9fGGNQbf/XQdmpZ5cpNLzqWzNcTGjZuwa5J3LGkg+sCv2LvtCAFdx3bPOoRjYr5TQpRm5wRldOcIu72eSERiszmJe02iSGmh65lPtUFu4oNL5tiWA3HcT/SPj9Dz3E5MS2KaFt4eH52+MMdXLudIKADPPovXuxxJz4Rd9Hnr2fDss5Me5okn3ovfVwMsx+17H22h5RzfE6HfrGKB6yTemAOPI0bY5mTu3Bj9/TorVoRKIpYRwJQ1HPL18eihZ9ndfpwmrYNz3Iep1IMMmi5eCyzlhXYbwUCIu254B5csjE9h59rIHo20mth/0uCRXW1sf7Udh26y7noPTkvjXCNA9PHH2PviIfyahv6v69CWTqxPO4HaC0oiAScR9U3BhAk4qVQeyWZbReFobr6V/fv/Hk3zDHWHjGKaEdzuZUQimVcrKKZmF+X4EjR/HfKFZ4h1Xkag9yRyXAJN2G/j5B82J93FX+77B06FZ6FpYNRU8kqvk5b9Dnq9OosXh+nr03G5LMJhwdy5Ufr7bVlrtgQiZiOv9hzhwf0baO3pBjnRwN177AQ2Kfj3W26d1OAezdS1uDUM1wc5ZL6L37W0M+CPIIBQuJ2jx7pYMk/wj7dlZ3DbqSLUU8HWLc9y9PAhvn7r2xGP/x8Hnn2NvoiJ9jfvQr/q7PR2Ok01Oxfbp0pKRrcQwgUcJB4BtFxKGR71t58BtwEfklL+b05HV0ZUVCwjGHQRi3kxzSC67sTlasblSr1EWyJyER9ciGNP2SlM6mhdDmJPP8jxP27hcFs/MV3HjMUwLYsTLicPWWGsl7YC0N9/C5HY6AQNAQiCfoNtL22bZNSSjvZbcHni2cYxt8TlbyBquohgoyfkJooO4RiGIwSmLb5v0w3YQOogzDF7TLWCyzCWrMES6U8DDtMRDPHEsY088cp2aiNHOX9xO4M+G6djOpW2fs43tnFENnPl+Vfx4dddnvFxUkZW0uGdxZP7T/Hcjlai4RB3ratiQZ2LiuPdmH/+I4c3vEpPIIz2+bejX3dh/seUZ7LJVs9lpvt0oRSfIbW1a/B4VhMIHMey4ppdWbkYTTPQtMzv32JqdjrHT6W7YzKEWY2w4tuKmMB65WV8jzyOr/98emwhpAZilOHti0R57Vhv0v0d6q+hyX4IEZNU+Q1m2RvwWzVEo4L+PjuxWFz/bbb4TPJUjNXsJuob30NN7diqNFJqHPJ18siBDTzzyi58vgCa0Cbsy6Zp/P21N3LxgkVTHhc0BnqPc7Ltt+h6DYaxHDPWz8nW34I1j+qhMURNF6+0mvx+Rwcvbj+OFYuOpMLUegQ/uKMej3PiWFJFw0APLODlndvZ/MIL/M26W/C89AzHnnyJk31BzLdegP0z12W8/1IkW90tlG6nZHRLKYNCiK8DPwPuAL4PIIT4NvBx4LMz2eCGM94Fj2fJGO9CLpoWZBsfXIhjTzrFJ0EbrEVu+iOdj27gaFsfcu48Di5exP4DLegVBqs+cwO3VJ6psXnvPzTQOC/+9dRxIGNOLAs6T7l5/8c/l/ggCOw2wfe/M5f6eWe+2laoi769bfi738LqxXvYd/xcKujHaRg4RQTn7G6qPE/TULmS/o4I1fOWjVQ6SaeCC4ApqzgZshGI9fF3n2mms2Ni0kvjvAj/fF+CwEfiCTMvntrBoztfpL2zl09cHGBh7UIs/UxSpGb6+MvZLi67+D157SB2xlsziC+ygPDgG/AN1rJmucHbL6/GCDUgvduJnmjHOxiiv9JNw19m3/JYMf0o1WdIc/OtI/d3Lr3CxdTsVI+faViGiFUijg1CoAOQyGCA8KMPs2/zQUzAcfYCbFWVaJYTKxbP1HN2VjHvC3+ddJ/OHyzE01hLsOUgA75BLqt5DvfqZfzfttW8/YpTPLZxFtUVQ86QEDgdA7hcv2PuXDstLY+NcYSM1mzDvoDBiA/v0X/DHfThrLxk5JgxK16R5MlXdzDoC/C+Cy7mlZ9/gr6ucXWadZ3nDut8+JKpr5cZXcrxEz/GsurRaRhK7XJixnSOtz5KRSxu6A6Eojy86yTbdraiyyi3v8NDXVU8dvu8pXaaZ6cY8jEOv3cH/Z1PY0X6sMkF9PUtwW7YaDBAnjyFt9dPrMaN/Z7bEFrmRr0ic9IJL/kl8EXgy0KInwKfAP4O+LqU8t/zMLayotjejVJGC9chX9pG/yN/pGXfKayqag7ecC1PvLiV8Kwq/urej9O8Yt6YbX71w2rmLrKjSQMRqqVjIEIkZhJz2uibl/ya1nvs1NTMoWmhfaiiRxxPfSUtj8CJSJhBWYPfqsAZ8zHH7MLfWc8zT7+J3sFavvDXz9LVX0ckGk9Eqq9vpbHxI9x550Zg8koAlnRyMmTwp7bn6A542XvkvTQs6Jswxr1HanjsyDNjllXEDtEQ2YFD9jM4KDH67KxoXM5lzWFczqYxtV9Ns55o5BQOW/6iw0Z3TjOMZggPcl7VT9kx6waqPefjiS2lo72LBZaFHHrhqWiuQ+ilJ+TZNk1Q5IxfUmLPEKXbaWI5Ecd8WE8+jNXdgwRkKMyhZ3bSHYhiNNVRuaYWW6yGnq4+AoF4Xo7fZqOltjbpbv02G30OJ6xYhu3V3fT1BbEdPQ6RJVhHDtPrbaTPGzdKHS4/1fW9PP/8+Zw8OY9o1IXd7qe7u4pIZP6IZn/uc88RsAxORULEwgHM4z/jmHtg5Jgxy+TxV7dzssvLW886m7vf/m5u+tF8Lr9oYqnCIwlmBMaHkNQ1fIA2/9l0Dh4lYDZxpiIOgBMnR9nr7QSgLxDl2ZdPEA6F+PIHq3jP1dmFoULc4O5pvR/dqMSwz8MMBqiufpT6+pVjgn2kroE7v4UEsmU6a3bKT20ppSmE+DvgMeBh4I3AvVLKb+ZpbGVHsb0bxWKyRga//Olp2HeU4KOPsG/bEUKajd6b3sGG13YTCAW49e/eOcHgBpjVFOLEATd6rILeQIzOgShmzMKo8vH4i8eTjqWqwoU3MIsl0Rqi9u4hgxAO7F6EtBycOnUxMcsG6FR5uhn016BrJhWOPtpOL2SW8xSzGp4iEP0Q6FEqK1s4duysMcdIVAlAotMfq+PZtmd5cNcLeAd9dPmuxdc9sYZp0Cd45JUzITKLnJ1cU7ePU1E7QWkgwz7etyTGpefdghGKTFn7NR8Md06zG9UgNdBnETKDXL5oE+eefQsdx/po2buX5nlxYRSaQCtBgxuyb5qgyA2l+gyZibqdUfMZqaN12ok9/Ts6H36WwcF4hFA0YtLW48f++tUsMGo4caAO/2CInp4AoVB8Hbf7FM8883TS8fT2noNpxePoXXU1NHZ289SJsxkwHTyyezVh00Abqm9X5epHx8RjjxCJuGhqCiKEn1mzNhAMvpfKyhaOHjuLiHTS4TvJ0Z7ThCMh6mxdPNJyRndN0+JI+2lWzZ7Nv970/pEqJKkwxilhn4NpBjl27Gds7bbAV4UhThKMnXnJcNm8hM1K/nj4BAD+QITe7gHedaWLm6/KjQ71dz6NblRiM2rQhI6w1WGap2lesD8n+y8k01mz03KVSSkfF0LsAN4E/C/w+dF/F/E+1P9v6O+NwEngR1LKH+RktIqSJHkjgwrE8RDRPzzCwQ2v0Rc2id54A0+1t3K68zRX33wpl1/3Ohqs5ejW2Dfvn/0UzKjOlsO9/HzjEfp2tWHYYggBHW2JxyElHIvZiAycRdegnTlVDaDFhTrss2O3g2F3EPMNbSBMfP5qqjy99PmqEFqMYJ+kwtUOPhOzoppIrBEhwpiycuQ4UbMH3b6QsFw4sixsSbaefpmHdj/PS3sPYhc6oVCUmH/idYmForS2n0nUesvy/fQMSnwxCUQQwsFFsxdihF5MqfZrPhjunAbxFwokhGQli+uP87qmGjY9uwFv5yloXA6mxJqY9F9WlIJnJdkYYMmiggygAKT6DLHbDWprr2Pu3Dpuv/1d/MVf3FKM4U5bUmo+IwVadBYiFjcTZCiK3PQYnY/8mT0tpzDdbhgyVB0rlnLhvT/iv6rttB05yiOPPMLTTz9NKBZGH+qceOBI8vH4g34QfQD0A6bTTiDkQidK1LQAiTXkr43E7NjCdgb6XGgiBpaBJWsxjNOYZj2RyFw0ESVm2aiyNXBWXQ3C7APh4qtLxoa42HSN8+fNx6nZsWIg8SDNifegRMOKnUk67Dp9H7o+H8MWrwgTjQk6Qx2Yff/DE7su4b0X/h4RieCLuqkwAti0QZ7YexUnu844YS5crvN3H6zOWYigGelCt8cT2IUUmBbEYk7cntMIy8SKROJdKKcJpazZkyVfpmV0CyFuAS4Y+nVQTqyvYwNOAdcCR4DzgCeFECellP+XzrEU6ZFusl/ekTpENGJP/57jf9zCSW8AcfHF/Mlu4/CeIyy+cAHvvuN66iKrOdXtJGKaE3bRH4zw+x3tvLa3A4/b5JMfqqCyIrk3YsBn8bP/8dMv+3h1Ty2dVTZsWnxK0u+HxjmSNWvtbHoyhssTZdGCg/gGdFYueg7DaSf48g1gRAh2GsiuY+hLVtEj1qDbttPtPwFaJViDYPXjmLWOfX3Hzhw77OehfRt5ueUQFYaDf7rxJr771FzmLZrYoKbDqOSfP7hu5Het6x7QGkCL344uu41Kw0E0ciql2q/5YLhzmq7NIhA2aB8I4B84jbtyLrFohFg0wgeufh38+SGObTuALxJDv+KsqXdcopSCZyXZGHY9a2QW4FmCpPoMWb58Ptu3/4Jduw7ztrd9iTlz6rnlljcVdKz5puQ0ezQStGA9tBxCBuNeCsvby8DDT9DScgpZU0PjX34eW018Bs65ZDG9g5KYt5vnn3+e559/nlA0xE2ffjPzl0+cyRzPv99VibfroqHfBJhO/LsN7KKLBfNbED1rqa6KO1Cqq49w2crHEVEnz4RvhqAfzRbAjLjQ/AP0mJeh2bbg8/VgUYtdBtBEBFH9OWyOpWOOK4TgaPeZ330h6Jt4C+ILwb6TZ34PD3aAthAtFv+9LxjlyCkbDtmG5jmPBcsqMCJ/QrN6sLR6oo6buOOcC0YdGM5eZMdu5C4nR7fPQloDCL0eYm56e7sZGDjJioWrsL28ibbnduMNRBCXLUfYctOArpiUsmZPNoaUjW4hxLXAr4DfE2+5+DEhxPellPuG15FS+oGvjdpspxDiD8AVgDK680S6yX6p7C+rh4EEom7o66bz0Q0caesjPLuRnWet4NWtW6ieXcHt3/gA/5+9946P4z7v/N8zsw27i94JgL1ThSLVuyiJapZt9e5uWZITdzty7i65OMXJOZd++fmcc0lix3aK2NTZKRaxAmxoBEn0DuwusH1n5vv7Y9Gx6GUXwLxfL4nk7JTvzO585pnn+5RsaRVXG0wcvNxGUB1udHd0B/m4pB6hR/j6l5O5bv3YnbjsSTJ/ETxGUpKdW25fSmZy1HPT9vuFZBZEqHVDBDsKZpo61pKdWo7JqeLzg2KJYHJG+OCd5+jqLkIplRDWTQS1Ilw+B/n5V3jghV20mK7H09oFrQf6r5nfy8HzF9FVnb969lkeWLOef7LZyU0Z/pLgs9m4bVm/+F8NL0HTA1jM/d70cMTTF0Iyntqv003Uw/4vRCIp1Hf7aeuoxya5saS8jCzBIzdfT9rxd6nfdZi6Vi/6Tcsxf++JWR2jwdxiIs+QzZvX/ndZltm4cRWPPHIbx45dmFdGd8Jp9hAkNQ1x9iLa7nfQ/VGPeLizi9KTVwjKZsIvvcgFsxl8PQbH+YsARMJh9u7bR4erk8c+fy/3P3fnuI73tzv7py+zIxsoqdL4nZfMqF0VNAeChDSNUE/3xFbXMpQUFa0rCLqGFGrlXPm1XKm4kVDIjG5bS0jPx+VNIz//Kvc/eYJm9Uu4XeuB9tgD6KHdZ0V3RYYt7/SZ2VXaPzu5zpSJLLWgEQ0hcfvCRPxthKUM/varmWSm5gC3juvcp4vUnAfpqPslJj0Fl9tFS8slCvOtLHOtonPXXqpqOlAXZ2L56y/N6rgMBjPekoG3AG8BR4CXgELgKeCHwKdH2c4E3An8r6kO1GBkprPt77Q8DLRkaGlEbe2mwt1Elyzz/uICas+cAZPEa3/yInmpS3G3ZLLrYiMHihsJhoYLXSgYxu/z8+ITSeMyuAGu32Dh2U8m8ZttfvYdr8Fqi27X5s6kW4k2KgqGrYRlla5ABh73NSxfV0aapRZVlTl14QbaOxeRoniwqQHk5DQ6Qpmc7MgmWHY9p9efISrcg8U7EI7Q1e3nd+/awgNroh7fvIJQzAScvILQoH//6Ed/gKaeQQ070IQdRfJjsvhQTJv4x/83/LpMlbFryEJK+mbCkSWUXvkNLncV7R47Nb6n+N9P3ofNZcJ29hSdO3Zz6XIb6qI0TD/+KpLFKPtvEJvJPkNUVeXIkXN85zsvzsYwZ42E0+wBSJodqaqd0Dvbqf7gDIFQ1J0bCqt4IoLIJx7ig7oaXBfODttWU1UaGxtZe/sKHv7MPRM+drq+lOoGKzvO19HRXYAleRmmYDnhcLgvhjwcsXPqwiY25Jdis3kJujUqL1yPyafjMLdjUlJp11M5VX8rgbNbOJOxsWfvV8c8vluz03ZxeLiAObmbXYf7t//PQ99idV4JoaCdYMSOxeTDZvMjW27gcy9Pv2ZDf2WSaBhJNqk5D/a1eodo63ernkf95f/E5TqPw5bOeh7E+lYxxefrCTqSMP3TV5HSh8++GsweYz4lJUlaB7wDVAKf7qmvelmSpJ8Cr0mSdIcQ4sgIm/8d0RCtf5muARsMZzrb/k75YaDZobMDrfoq/i4HnlSNd5Ytoi7cjdlk5auvf5Nbl96Pxy34sKKV3cdraW7uxGGPHWt22y0mPrE1VpzrcBaJQlJFGmseFHgbKzh8vgM10mPg6ioi3DMXqISJ+E0gBC41kx17XybNEqKzXtDRKeOTHEga+H0SJk0jNTNCQfZKPm7qxtcdY+6RaE3ah9et5xv3PtC37Cc/H7vtMUB97RpWrdVIsp7GZKpGVdMJhDZzqXwNcGFc+xgvwxKA9O6+mPGBhremLqOqax2/qizi4PGrpNhUfvX7WdglB7bGSkL791F1vp5uJGw/fgM5e3xNI+JBc7WNquLh43Omj12P2GDqTOUZ8s1v/h2pqU5efnl+1RROKM0ePAqkJlDf386VD05T3eJFzsiIllC1ATesZbcM586ew+IwI8Vo3pK/Jpcv/bdnByUmpulFONSxe1Y0uTTeKW3i0KlawqFsZAdoyQXgDhIIRh0YmmbmQuU6KstXI/tyOFj1CO1qLklSECkisKg6aZkB7tyQRvllE//2uRXsr+riL96vHNYpcyi59x4e8bNQd//f62qvAyXI2sKz5Dmu4PGlE9E242ldC5wf8zwnysDKJIolH6F30VH3S4A+w9tGNt7QUs6f91J8Jov/9dpLWHf8igsnqvAiofzZS8jriqZ9bDPBfNbsUY1uSZIWAx8SNZwfEUJ0Dfj4B8BniXqx74ix7f8m6uXeIoSY+1cqQYg1jTidbX8n8zDob7JggoAOLT68zU6SRT1N91zL5/7qNRy+9bT6TFjsTs7WBqjpDLDreB3NTS42rJd57eUUhvYlkCXISJfHlWiSJXIIe8NcCEQF7/lPCLbe5ehL8PvH2gidrdH4w+7lbtq63IQjKs6UJCw3nmXxsly+c3M2BU4bf/x1mdSO86S0NOO0WkjPXYRsW0ukIIf/95Wvxzy+hEReSsqIGfBjeZc1fTnewPIxz3OqDKpKAn1VUTpaP+gfj55LVYuTnefr+Lg4Wkf2z7+czuLMDCJuC1Z/N5EuP0FVI7gyF8fGZTM+7qmQtzRIwdbhjTnmQyZ8ojOVZ8j3vvcPHDlyjg8//Bsslrkb1p7Ymj2Ywiwdsf9dGnd9RG1LN5Z160n/7jeRzCYCgQDvH/uY8x99RFpeCl/5kxewJw93iKRmJmMy95sWTpFFoDOfy55gzDbrAznX5OWDE7V4XF4Kl0VwKhlY9EUE0v1oatRpkpbTxRe+XwlA+cf/wdn/PEjb5R9zTWEQ29VqrJIgIzUbc3shNl8ahZcO8pm8Ih74w3W0aqOHlwxE9Z4h7NmLUNuRTFlYUu/H5Ixq5OvnrCxetgZYgz8MNhukWiS6J9/IdFQGViYBQEnrW+5I34RJOFDdGRw79hH79u0jJysDm6Sh+/2EwhpadjLmT98yM4ObAeazZo9qdAshaoGYr0ZCiCYg5hWQJOlviGanbxFCjP9XbjAqI00jpqffhst1DJh629/xPgwGPkjefDObnOxHyYw8iPbWz7n6L+9Q6eukZnEWt/zLX5CrbeZYW4RjtS4iWjTE42pTN5evtJKbC9/5Sip2++TLzTmEE5PfxHvVe6nuGl6iD+CGb/d7MI5fqqS7upG7r1nHa594mj/9kUR1TRvvJ1tZtTgVdwTk1ZuRug8g+X2YalpIsdqwSNdQkJoHUijmMUZiLO+y2VKPM2k/JpOrz8ut6TNjgA+sStKLIicT6X1ACyeNnfm8W9bMvlO1NL57LUXJKfzvr5n531oSoUAQh3YbuW4HX5ZexZmdHOMoc4/eFsDVpQ7UUH+Skdmi8+bWzbOSET9SG2KIzMx89Sww2WdIfX0ru3ef5MMP/5asrLQZHOHMktCaPTDuW4AcyELsP0THT3dz6UobZGXT8KnHqLwSLTvidrs5fuIEklnitT99kcKV+WOPTdjBvYw9lZ1Utfn6SriOxLmqThobO1m/Tua//7icFazkXNNHHG4oIaT23wYXe/5sXuujdoWJYKVKXSBIUV4eorERT1MHqbqG6I4Q3PUOlkU5FDzxPElrUgn3hBmOhs91hg7Pb7CZk5GsixB6F5rnN6SkmHCkbyLF3sCijP2YTG5UNQ1faDMRfeacDwMrk/QiySlo4SZkzJgDSzlZXMLevXvZu/cV0rPWcfF0KjR+j67WLsLtJrI/XzDna1wPJJE1e9qql4wHSZL+jmj91fuEEDP03rcwGWka0e+/SlHR56alwUNvZ02I/TCIPkR+iddbiiw7sNuXoWt+6qt/DTUXCe08w+V6F+0OG9dt/+9ks56yOthe3MiZ0maE0HrGHMZq0fi9r47P4JaQMDPc2/Unn1+Luz6ZzoCLpq77CPfYJ0pKB/kv/FXMfbm6vRTl5fD6Jz+N1SLz9ddT+J8/9FBa3khNfQed7hy8UoCMNTdjuXAYyRvEXFMPqXnYu/MJJrt69iTQ6YIxHPGjeZcBsrLMyFIQNZKFLPtIcXxIl28rML5qIOOJ0e6lrypJzLrfZjzeFeyrcvHuiTraW7tINzu5+7ZKnJbzCNGC3+fA5N1M+UfZSJaZ64Y52/SK85tbN8ctI36kB8SjlivVM37wBEKSpL+z2SwUF/8z2dlp8R7OlEhYzdb9g+K+exMnfTt3UXa2hojFRsMnH2PPkSMEw1EnQzAYxB/y85nvPzGqwW0iWv5VQiYlsJ49l7vY9nEdTS1uQND43kY073Avu+IMknxHKTlZEr/3ahZFUj51HbVsKzvIsfIKNG24wR4IhYisMWE5ZKW7u4ErtiQWWS0QCBJpdNEdSeHCvoukp1SxzJpEWspn6VzkQJejzwmdCMR4ERjNswyQk2UDKUQkkoUk+0h17MbjexBYN/IXNYCx4rOHXZueyiS94wAQeheKJQe7upLSsivs2bOHsrIyZOV1bru7mSzTLpTr6uluNtF6dSlXGlaOa2xzhUTWbID9v4q9fFqNbkmSlgC/C4SAqwPCAj4SQjwyncdaiIw2jThdDR5G69DW67UJBhuRZSeKIuP3V5Hs2ISpy0dD+3Y6ysx4JInsX3yD/Oz1tDQls+t8PceK6wgHAzgcAglIccIXnk+mIH/sn6AJE8v1VchiuHHe1ZBC8qIm6luqCakeFFlBkWXC7YtQY1REASjMzuZbzz2H1RJNsszKUvidV5389F+7CQXDKPYuAm12GtoVQln3kl5/maZWWKWUwal2kq6/E3pKEepJhQSspaMa3qN5lztaP0ANP4cuJJBAF8mgQZL1NOMxuscbo93LyHW/nyAYWs3Raj/bT9VRXdPGmsUypqYW0p17CQYcuN1JOB1hknP3YHHeQ8Svg3nul54ySBx6nyGhUITVq5/rW37nndexa9eP4jewSZKwmp28BrM5hdbW98lIuRPpcjRxsuJwGV5dIvDEJ9hzqZK6+jqS0mzIsgRmePiVu7npwetjjkNCIke9FlmNdlfUdDhd42Xb6QYqKpuwWlWsZtC8Jixp7uHXyp3MsmwrP/ziWlYnOWnubmJn1X4OnLuI1x/AYUtiaKSh1Wzhhfsf5ER5OmWHV+DxqnRJOWQSwBzUyTS30KpYcTV5sL7zEUU5WWQ99jKBlIzoDpQIXXIZAnXQfkfzLHtad6OGn4/Gh0sghBNVA4f1NOMxuscTnz2U3sokveMQehdapJtFBV+h7nIHB/Yf4NSpU1gdZlZv0MlK3Q0+K2G3E9nipfDGMpqbGsYcm8HMM61GtxCihjH9fgaTZTrjAEdjpIdBa+v7/PSnnyESaUdVkwAFWQ6hawpmj8KLW/4ITyQT9fvPsOy2m4h0FPF+T5hCwBfglWfsPPbg+JIie5GQKNSWUNV5CU/YM8wp0RW8lZbORto8XiwmMzevX4/VYqah0s6ff+c74z7O2jUWfvQnmdF//OVlDh8N8tN/9uNMS+aTgVPc+F8/JVcyE9ixCVtHCyCBJCEvW4v12k2ELJUj7ns073Ik3E56Zoj6+oHfYRZWS8ewKiexGFeM9gBGqvvtSH6Kk9WC7cUNXChrIt0p+OvXM/hhaRnhkIOubhlJEthECqrbT1LeZUK1CqavxO9dejzNEX70+fVUFadQXzE4Y9/qiJBdOLEwIYOZp/cZsnnzWvHxx/8U7+FMmYTVbN2E1ZrFq6/+NJo4+d52rrx/mtbuMOKeu/jQ101tXS0b7lnDa3/0wri6NWaoq6huNNPmi9b11nSdXedaKLnQgMWi8ce/l0bRIhPfOmehcPXg+9YszDRWOvhvr0l49FJONkF5x1XeLymmy+vjyXvu4al77x3x2Pf/anDXxc7GTv7fa/8PERCU3f5prtn5DpeutJO07UMyM7Lwb7yfq61unMlOipatpNs0ePuRPcvZaOE2UjOD1NfnDNgiiyRLJ3kFY6evjRWfHYve5VHveBOKJZv8os9B4HYOH/6QQx8dIqyG+eofv8R//E0lhJLQ3SrhgIqqWxEZTrKSLwKZY45vJjE0ewbCSwxmjrGmEWeacLiN1tZFrFp1CUlqRwgbaDqK5Ka0/kb8LRJtD9zArb/zBLbutXxQ1ck7J+poa+3itlsVHn1g+JTiWOTrBdR01rCraj/tvu5hn7f41uHTouEe1yxfjnWaEq7uvN3G1RqN3fu6eb/oDlh/lltKT1K5/zxL2lyAhCyBY20pJkcy6qoCNCW2J2G0rpIdrR/w2hu/7jOaIVqjW5GTWLZmQ8z9DWSgFz0UbCcYqEVT/SAJulxnRjS8By3Xc6locrDzfP2gxMklWZlYzFfxemXUSJjsVCd0dRHsFlidPqTvfBLlrvg1xBlPY4KOhiRSs8MEfYM98p42K5oq8ebWzYOWVxWn0FZvZeMWFwYGUyXxNDs6u6coPioqCrBQgNj/XjRxsrUbZc1a9i/Ko/TEcXKXZfGF7z89LoM7Rc+ntTWFd8paqOuI3pOaLjh5tgE1EuYbrzopWhTb3FCQselJuAIe3io/gKpHC3JfamykoaWDG9as4om7757QeWcsyuCpP3iKf/u9X3Ow+AzSvXexevsuys7WcsOOt0lNzaQ7YKekpIS7pTvJW7EYv1Tbt/1InuXMok/had3Nq2/8W7/RDKgRN5JsZ9GatWOObaAXPRJsJxyoQ1P9SJLA5zozquHd+5lJOMBdyIGPD7N//37cbjef/OIWNt68gV2mUnS3TtgbQNUFIjUJ1Z6Kzech3ka3odmG0T2nGG0acTawWLKRJD+hUAEORxWaKoEeRtNAsQZprsxn8z//Dhmh9Ryr9rP9VD01NW0sXQJvvDK+drdWbFiIPhhsIolAt59dl/bz9ulTBEKRYdMobp8PyayxuqiQ9JTpTep74Rk7NfURqqo6+PcNL+Ksvwod7XQcuwyAIsGiqhaWp6RgdX6BUEEOuhRtJCHwAdHwlrG6Sk6lzXuvF12NRPD7KpElE8hmJKGPGmbSx5DEyYAvwLeeSebWNalI3kK83jJMpgYyk3ORfD7C3gCYwwSDaSivjv1SkAhs3DJyFvzQB0BdhYOQb+5WyjBILBJOszUAHV2XsJoC5NYvo2Pbh32Jk+fvvJWTR45gcZh57c9ewma3xtyvCQvmnhxYkzAT6SzivbIW3jlWTbfHjySBLgQBX4hPPmzjlk2D92PGjNSj5jbNRqu/jVZviD1nzvWt4wuEyM/K5KtPPjUuw38oK25cwX1fvJd9P9nH7spy6vOy2VLXSPnhcq7N3Mmtz7zM+YtuKssrSE+9CXtWAaoc9dIr6bcDgz3LmUWf6jN6RzLIx0OvFz0SUQn6KpEkM8hmEPqIYSYSMmbJSTSQQELxF3CyuIQ9e/ZQW1vLdXev5tGX7qXInUmk24ae7CaimRA2M1J2GibZSzA8N8oFwvzWbMPongMkSrvgaKkrH0KY8HWvxmapxGzpou1KPlfPrWXlsb/oS5zcVtzIhbImnE6d776WhnUcSXdOkcwibVGfp8Mf8fNu9V4+OFtCW6eH/Kws7LbB3vJOk4lF+XkU5eTE2uWUMJkkvvZaCj//VRcej8b7z7xBaPtPSO1pi5weDBBscGHbeYjCzGwsj7xEk6UQkHBYw5gtpQw0vCcS7jHeDpS9XvRQsBEhFIQsIekR7MmrkWTLiGEmUcy4vSvYd7mzL3HyoVssvPRAMrbICs6eq6C9PRmHLYcWj5lIVyqSbMXkiCBy1yDJsWuWT4TxTDdOFyX7Mvq8JyF/9M+6Cgc2h9Yn8jaHhqfNMiwJZ7RsdAODoSSiZnu9y0lKqsFi6aa7+xra6m/Bsv1digckTh44c4aQGuK1P36RnILYXlGzsJERuo5IJHoPhSOCA5c7ePd4HU0NLnKyddJSo0bysqUKz3168L1kIwm71h9D7Ql5uNrZhNdvwxZRKezR8qR8C5976FHs1tiG/3i4/fnb8fl81JbU0rDESbmsotS0cGX3aVZlZfDlrU+iWZOweNswk4ovqQhdkrCaFOQ0c0yvc6xQj4EG+Vj0etHDPZotyRKyHsGWvBpkU4wwExmntg4tGL2mQkBFRRV79uyhtKyUvGWZfPH3n6UgnI/t+MdY6i00F2YSjtjQLNmYm4KYLIBjCeCd9LXsxdDsqWEY3QnOdHcbmwrp6Ztob08hJ/Mwit5Id1MBzWc2EhaQf4MLpfX/Ut24jKP1W/i4OANEhG98OZnsrLGT7SxYyAxncbr9DEEtGrfV4XexreQ4DS0dbFq7im89+/wwj8ePDmfQ0VBAw5A+NNN1w6Uky3z9tbSef6XDD/6077Pao9X82UMZ+D/OxVnmwPTXMnqqifaQID/fym9/vRKzuWLsY0yhzXvvdlcr/hiEhCzZsSYvxWrNRtP0/lKAw5AIhlZHZyRO1vclTv7hK+nonS2cv/RXtLZe4L+9mctS2YwrdJKQKYTuV4iseQr5umsmNd6hjGe6cboI+hQcqdGXoF4vmz1Vw+fp/31u3NIZzQf48PS0H99gYZCImp2dvR+TyY/ffw2ujlvAq+K0n6H4hj0ECxXC8kPsKa3E5Xbx+Je2sOGW1TH3J6GQGlzP2boQXT1dhP1hrU9DliyGP/5eJjZrbCdLnlhEWpaXsxd1oq4VgScYorXLjpTcwvdefJG1S5ZM2/nLssxDX+5vrCTCKpEX/5Kvf/A6gR8uwfazdFCUqANZVshbpfC136tAUUysWL0EzXEJneFx2gNDPSZK73aNFX8GQiBLFizJqzFZs9A0HS3cNGh9p76M+isddHZ2AgJd6Bw5fJRTp05hc1j46p+9Qqq/luaKv6Tac55XPx2gKwK2rVYkOYxQnGj5j0HR9FQvMTR7ahhGd4Izvd3GokzFCxMOr8Bfl4JWUU57XRtkuyh8qI7m9hSEvppmTzO56t+y1PE4Nz56E9esHbt9u4xMgbqYE00nefvSUbyhaIiGx+ejsqaBRdlZvPFE7CnGeNYdXXz7UpoLl7O8/hhJAZmscCvmzLU0WrIpLhOUNpq4rqgQSa6f0XFEjfab0fTAoNjw/lKAw9HUZZTUDU+cNPvqqL38Y1pbA6jhNHKz3bQHqqBGQm1OQtqyAgtlqK4SSN84Y+dUXeoYFrsHM+NNMTCYThJPswsJBJ6J/kOXkSPVOJJ3QGsRoSaQlqYTcB5Frkhn/d038+BLd424r8zIGs7XarxV0kiHL2qMhkJq/6zm62kjGtwpIg3VG+Hmb/8Ue3MtQkSrdpdercPZ6eGzjzzM2iU3T+i6TBTJYsL0/71B++pCitRLZPiSonGCCExWK9WXriVNUtmx7yPC4Tu4dvMKvKZyYpUVnApRo/0mhO4fFBvem6zZS5LIp7UuxEcffUR9fTRnSNd1iovP9CVO5tpa8V36KXJDJ95LQUJZIZLWylHPdtJiJL0Lk+skqnOlodkJgGF0JzjT2S4YpuaFEVoKhSleKkt8lFRvJISJRTlXqDhwM7JJ4U/+KAXZ1s4dD/+UJ285wjUPjCzeP/z8etoaoqEiNpIIhgM0dF9Hl5RC+lN/AYCqaSRZraQc/it+sCNt2D4S4WZ2FqXTruWS09SMu9FFRtIVyjty6Oi08doLZtJsKZhNeYBOfkGEn/z8Sk+89/QyWrImgEQyEtEpQV23cWlAx0l5QOJk9dl/orNTw+3SuXnNYrzugxDSiKTKSLkrkPOLIOJGad2DNoMCroaUMb0p42lMMHCdkF/p85ZYHZFpiwOczelWg8QnkTQb+rtPlpYmEQoo5OfWA08gofH3kX/AWtHFPff8Kbff1cXml0ZOnPzjz91E3ZUkmrqCdHblo2tRX7Vs95G5pZpvfDmZnJ5ZzR/23BOmAb0VZKEQcVSjPXaSprbOvhyfUDjCHddfw4M33TSp6zNR5OwUWFeIVlFNhycAMhT7biWCnbDi4I3XzHRElvOrX8qsXpXEv/x6NSHFPawum0AjKDqYrEEeK1lTRPzkLv4CSVIuoBBsd/Lxsf3s2bOH9o62nhKFEA6Hefzz97Hx5g1oJX+J3OojeKWbUERHLkoCmwkiHnDKfdVRDM3uJ56abRjdCc50l5yatBdGKCjtFn76wN9wteFtnqn/e5KTm9n8RBl6uBBfWMLti9DSkA4WO+sWe0ZNnGxrsFG4OkCSSEINdVPZWY0v0oLatojNq9diMZuQJYm7N17PT/emxa34/XjIe2Atndu9SN1ezLXteMMR0h1mNq3VQYpgMimEhMLVKgse72pSHJUgTc7wHqkRzqix4SINV/dygmr04eAP67xX1tKXOPnNAYmTnR2V1NX5WLyoACXoQxUhIn4gRUFa3ZOIEwkj+Usxnf99hCUDLeeBGfWgjMR4xHFoGapeQj4zXS6FbpeC2SqGPRgmwmxOtxokPgmj2T38/OdVIGDr1o2stl3Bnv8rvC1WhGIhmJxMfWM6oYiJjTelj5g4aRcZNNXY0dN8+H0eJHuYvEwZiwW6Wpx87UvOQbOa7Q1JbFhpRtV6eyUImv1NlJ7VkBtaWJSdxZqixQCkJyfzyTvvGFei/XQhOZOQ7lmPXhttmB2sTMEZcSFpsEK9wMqVqxBmC5frNKx1Lajp+QT1wftQFBl7sgO/VDPqsUZqhDM0NtxsySMn9xtYxO1EOkHXdE6fOsOePXuoq69j2YZCCpZF493zl+bwwFN3UOTO5FJbKWqVi65ACJGZDFkRwAZ6sH8QhmYPIp6abRjdCY7dvoz6+n8DBIrixGLJRJaVSZecmpQXRoDcnYY49C4tO/dxpcFNRFLoXpKLCHegCS/eQBKhcBhJhhvWhjDZMsYci1mYkSIStV2N1Le3o6oa+RkZfPWpJyZ1bvFCkiXSP3EDgbeOI/uD6GoIRZExqRFaPH6sVgvOlDTcAY1j1V5uXbKC1ORSGNKQYSzGaoQTKzZcCCudXUs4Vt1FdzgMArpC6qDEyZcHJE42NISJRDqxJi1DRkMKK2CLgGSNNqYItoO3AmQrWHKjU5d1v46eySREvLrUMaweK0DQO71NdzoakrDaNeyp/Q2T7Kkqfo+JwjXeORMPaJD45OQ8zJUrf4+mlaNpKopiQlGSWb78dye1v+nwnEtqGnR5iTRV4zdbUZJUAtZM/EE/up7O2s152JJjJ6ObhQ1z10o6fRFcAS/BUJiiRTJrV0eN7HpJ4eYb+o11CQkbNjqC7fjCPqKde6G+sx1fIIllaan84ec/T7I9vi+l0qJ0pEXRFxipLRnUCGobuBs6SDObwGpFdGSi799G0s0PcMEr0eXtd5aYTWZWrVqOc1E2QWI33x6rEU7vfxIK9uBaykuv4nKVAKBGVPbs2cOlS5dYuqGA7/ztlzBbekw2AYuCediOfwzVXjyaHz0pCfneayFcBqoflJ7rO82aDVCyL32Y19nfrVCyLyNm5ZHJMF812zC6ExiX6wwu1zGSkhYTCrWjaT6CQT8FBS9MOjZwMl4YOZSBOHUKz453qShrJmCx0mYzU6BItHffhdP+FroeBJLISPWTJPsgZ/TySTISNj2JOl89Ne1t+INhCrKySFFTR90uUZEtJswPXk/o3TOoqoYUCGJytWM1J9PQ0EihJGNTUniruBGJAu5dtRqLtYyJTE1OtBEOyASCqzl8tZvtxU0EQlEj3+sLU13TxtqexEmnWMGVykb27dvHlatm7tsiSM0SqJ0qis8E6aAFLCiaDv6rIEmQvByUqU9dSkJCxLoGM+D0sjkGJ+AAhPxywma5G8xdJAl0Pfq71nWBaQpP2ql6ziXNjlTVDu0Ct8cNegH599aiBdxomkxqmk5KMlhy7h++bU/i5KGrXlq7kwgpQdJSJdasGHmaf5FWSCASxOVqxNeTnyOEoMPTjSQ5+Pbzz8fd4I5JZgp0h/AFVZS6NiwWE2GfCddbu0nr9rH2/qf4x30n8fqihrcsy3R0dHDvfXdiSvOjxpi9HG8jHIe2ksryWvbt3Y/H4waiISQlJSUkZ9p5449f6je4gQw1nfTzl/Bt30FXnUB8Uke6ZjGS1QR6KoRdYMuPtgWdZs3OLAhQVZyC1T6447PJrA+rrT1V5qNmG0Z3AjNwWtHpXA5AJOLC77866X1OtFmDpDmgspbQO7soP3GFoKRQt/UBIv9uQmgSl1oW4/Pfy4q80xRkd9LSnANFL476Bi0jY8VGm6+JKx3NdPl8pDodrFmyhKbLkz61uDA4Rs1OeMmt+MttZEU60apryFy1mkCqEz0SwmkWfOXGbNoiOp5AEjmma0D2jPtYkXA3ZksRiP6YS0W2EwnXIYnFw9YPq05O1UZ463QDJ87UoquRvs+yUyT+6vUMMiyFtNYEOHjoEEePHSUYTiNrw0OYTVWEqcDW7cBzUELc7YRwC+gqOFaDdUCCppyCFG6Z8LUDWLLBG3Oa7+Bv82KsPTLjidEbqfarEXdtMJ20tr5PUlIBKSn9FX4iEdekEymn1GBHN0c7Tr6/A3/n09h0HcQ6ii9mk5t7gaycIG0uJ7aiFzClbwIkUsUiTD29EkxqCmdrNd46XY/ftxhHtmDjBguSEvutOENk4vX6aPe5cEfaCUci9PZulySZgqxsluRN7N6eCYbGFvfGDiflgwjY8XgC4I/QHQlx5mwd64MHyc/M4ol7b+HohWj3Yb/fz0eHDpGWlsbNt9+ANWl48zbC3ZgtizFJ/ZotyQ7UcC0OKVqlRdatNNZ0ceDAAQ4c2I/X1x29ZkJgtpl57QcvkJeZR7Ka0uOLkMi60kb47e1UfFRGt88GL38aa1YHhFsQSflombeh+KujujzNmv3dn5fy5tbh4RnHd+VMKLp9oWq2YXQnMNOdkAMTb9YgRRyIhjoCNa10hTUiN97IyYAPITRUJYmm1m66uxfh1wqRUszUd9gh/cyoYyjQiwiEA7S6GunwdGMxW7hu5UrkEYQ8kYl1839rdZglLadxNylkJFVTuHg5yBqekJc7rK20ZeQTDrjxCytBJZuuoBZjz8MJaUsJ+rtRTP3iqantSNJSqtuGh/O4/GF2nmvi9NkGFCJ88TE7zqSo+N+2wcaSrCwCbQ6OHt3Hgf0H8Pm8vPjtT7L6ujtZUWtGP/4vlP9mL5FOM9zyDFx7P0rFXyLpQ7wMehfCMnY40Xiwy9WkW4tZt2o9Skcb7d0b8IcL+j4fycNhxFUbJArTrduTbrAjJGRPMmL/WzTuOkQw9Gmk7FQa7Uk0XbHS0HobN9y9gaYOJ6b04uix1GUE3Tn4e7z0Ln+Y7ecaOH2uAUkqZOM1FsyW2ImWduHAFrDzVvW7tHu3oCdHyMvIJNkRNazs1iRCbY5JXYPpZqhuv7l1c79++K9Dr+sATSCa0vEJG5WVrdh3fsiqz2ay6uZVAAgUPlqxlD379pKSksLixYXDD6QtIuLvQpj6a56rqhtJWoSvJfpiEwoFOXL4KIcOHcLr93LPkzeTlRed1Vi6tpB161eztNmO0tFTSlDXUQ98wNX3T9LSHUR7+hYsz38ObUhMfO9TZbY0+5q1i2lsWoKrWjE0exQMozsB6S0P5fNdweerJTl5JVZrNN5urGnF8ZSWSk/fNHGPiwQIwdXODq60NZOaGaHJvRKvL4TuWURHs5n958Fs0fnW1ui+swuCfH+IuGWLXDrdnfjsXmovORB6EkV5+bRXRyuZxLpBx5PxnEjk35FMw8HrkJo9JF0yYe/wI2QoSKmHve+Sds+n2Xu1DbPZzPprr+NkXZg27/BasENxspUC/pEIYVRSMNGFGRcNPIXX0zps/dbuEAdO1RIJBfnDz6bw+O39DzxFWPE2XqW89LvIcim33aaiOe7knsduZnFnKmLvf1B54X2an2jCvNKGvKkYzZWJlvMAprpfR3cip4DeBREvWtHjU75udrmafMceIpqDvCWdfO0P/hEiXtSiF+KS9DNe5trv02D6Gai7gUAjmhbum52E0XV7RjRbgBzIRBw9RMe23Vy60kam3U2lZQPttW58vjux+J3sfUvGbNF5besNmDCTkWnh4a+14AtHQ9Hau0McPF1LKBhk7bUqniYHnsFlpMkuCGLCTE44l331B9lZfBzVuhZnaA2pWi5SV3S9EIl7TwydsURKBxPk3BrA9urvEvjM31F25irXp+3CuiRqUEoWM3feeDtXFuWyf/8BVq9eNWy/JtN6kpLeQtM6AQfgQ1G8BAJPUlNzHIBAIMDevXtp72hj6/N38tTrD/dtLwuZxZ1pSAfeIVJ9FXdyPW05FXiTW+h+XEVvXIblzz7Tn4TqKkFp3YMU7uxLmJwtzV51bQMZmR7eePNvDM0eBcPoTjAGloey21fj9ZbR1XURh0PDbLaNOq04U00ZorezQBeCttY2klet5NYXIxw5t49MWwRb8YMsWx+dBirZl0ZdT2LcpeIUvrU1akxnFwT505/Vofhl3rt6EOmRgzg7u/jcI4/w4M2jl4qaa1NJ3/15KSIYJvLs/8JcUsPizHRkWYDQad+WTIZk4vqbtvKT7R8gdMGt6zbydx9uZ0Xye6TaWvEEczjX8SDV3UO/s8UsTX6W6zJ3k2qr6lnvWaq7FwPDa4F7ugK4O7t5YYt9kMEtIRNub6Lm6p/Q1tZNQ4NKVq6F1dddIbW5FvPJi1w9/Z80316P0G3I12xCEoFo8k3RC6hFL/QIe0tU2IsenxaBTbcWE9EcqMIJyNATBzkdpa6mS2SN8oAGQxmqu5oWxu+vAiApafGo4SAzptlqGuLsRXw7d1HW03Hy+d85xfbin5DT7cbt/zXrb4zGWx/fl0p1hRNZN1HsFRwpSUEXOhZnmPzHLuHq6Obh+6x84cXy2MdCokhbTnFrCf91/giVtQ2s+fL/5Y+/9GVslpntUTBdjH7vrkF8/wk8P/gPyg6Xk34xek7aim7UvF1svj6NslqVc+dW0NY+3NudnbWcxYsrsDvq8Puc1Nauoa29HdgHgM/n42r1VdbcuJwnXt3av6GAAn8O5sP76dr2Ho3WOjz3NSO3mfC1RdAzTCQ9LFADpWDbCK6SqHFtdg5OmJwtzZYgIhxgdhqaPQqG0Z1gDIzjNpujyRp+/2X8/kukp98y6rTiTDRlQLehaypaRIsmBkkSeVu/zMFyN4gIX/9yMj/7Wv/qAZ8ZR2rUSyJhYt1qM7KAK5eSyAwFeL9uL7tKTtDa4eHeGzbywE03Tm5cCY5ks2D6yVcJP/0XXG7tRghQAyGcTV1cZ9pDbmYWW268nvf27eETjku8sP63BNQUYDWBSAd26ac0N3ZQ3rZh0H6ryeMArww5WmPsMQA3rTPxzWeiZZd6S1cR9uHrukp7u4zVnMdtN64iLScVXXXTVfVzUrYnUb+8Bi1gQr77BiS7FYhWJ1Ba96Ct+c601XsdKKyOfAuBcB4gk57ljq4whdjDgUxUXEcS6upSB3d8ur9SQW8Wf1VxCm9u7V9/qoI++PhrhrvQDBKGobrb6+EOh9tRFMuo4SAzotnChlTbRWT3e1w6Uo5Xlwh86jH2XOrvOLntX+1A1HgJ+sykJpvwhTU0n05QaUeWIdjqwNrmYv1amVeeHTksZJFWSHVHDdvKPuJMxWWSrDa+/dwL2CxjN0abKyifv5/whWqa3y2hpbULaY2fpFs7sXVaSLMmsX5JDovyr0BwE3DdyDtywrLc4YutdjNrb1iBElGgJ/VG0SRCZ/6FC20/w31fB5GMCHqXCToUhNmGfOMGcIo+A1dp3RM1uIckbU63ZkO/bg/UbCCq24Zm9xBbtw2jO8EYGg9os+VgNmcQDjezZs3vT2hbmFosoaRlw+UqIh99xJXyRiJCIO56jJP1EgFfgJefSRqx46REtBlCOBwCBBHVxsnm02w7d5Qr9S0sK8jnc48+Oqu1WWeT3htQmO6BlKhHSbdGyKw/zx9e+DY37HyX2z7zCpeXFNLY8HMKCjPIcERj7GQpj9w0hcX5x+m23zHpMSgybFptxWyS+kpXWUy5hEP5BIPFOByQa19EEgLJ40HVIwRbyyk95iRyk4a0egNS2oCH7TSJ6UAGipxS8VY09nBAh7bxxB721nMdWnrQ6oiQXRia1LhO787CFCPHwNs5uGpDyGfGnqoiGNwYYqpxiQPjHc8dCE3uJAxmhVi6m5S0GEWxcO21fz3hbaeatyMJG7ibUVva8YU0tLw8Pgx4qa2rY8M9q3nwpbvY9q8DjoeJUESn26+iagJnEqxeZqKj3szrrzlZt9qM2TR64uSuyv0cPHcBVdP5xrNPkpc5PfHCs8lYHlHLn38O7YlKCEUwK/+OVhUh0BTAFm7DkeTElO5EMR9ktWkUo3sEJFlFqqgctMwVOkV16//F5/ER6pZRVllQchX0VQXIzjykNDtoep8mS+FOsAyx6GdAs6Fft2NqdsTQbBhZtw2jO8GYSnmo6WzKIDQ7cn0A9YNtVO0+Q2t3iMtrrmFf4VbarrZy2y0Kjz0wXKB6MWEmoqvUeBoQ6HQE0nmr9CPOXrpKqsPBt597Hot5erpLJSKDb0Cl78/6oxvxuiXKjl3i+qxdvPDMK7zX0EFLswWrNZr6oigmMjLSyE2uZ9O1I1/jieBp3Y3ZnIGk5dDWVo/QzaSlKYS6K7C1ZSMA1eQnWBvA5bdizipCKbAN3sk0Jt/EYrKxhx0NSaRmh4eVq/K0WVl7i3tSY1FDCin5w+PsO1vm72/WYHIkimbHQgiBx++ntLyM3GWZfOH7gztOWrRkNE2iKxghGI4gYWLTtRbsdpmwW2LTdbGb5UB/4uS26nd572wx3b4Az27ZwvUrV07L2GebsRL7JIsJ013rAVDO/xaRsRHdXYzbG8RUXYfJ7SBs96Gd/q9pGU/TusMEOwOEukBatxhSvaD5kU3dkNaTLzBAk4UlA0nv6vNwD/18JjA0e+IYRneCMZXyUFMqLTUQYUJxpaLv/yX1Oz+itrWbpvQMdn/ie9RUtLF0Mbz+SuqIXmoTJjQtQlBVqW5rRUMn1KVw5EIZCImvP/sMGSkpMbed70hZqUhPbKHjn/ZQtaeEtVmZrLhrLSWXy2lt7SnTZTKh627yFy3BQgphuqZ8XD3cgVlaS2tLGy0trVy3ai2h0DlCWpD2Og0cAmHT6N7rQLtrDcqW+1AafxvdeJqTb0YkfSMqTCr2cD6WljKYGySEZo+AEIJurxdLUQav/dlLgzpOmoSNcMhGMKwRkqKNsxwOGbs9dnWSgUQTJ/PYV3+AHSXHaWrrZPO6NTx+x+3TMu5EJ2rgBpDu2YD24Vlc7X5Mug+5UabivdGrd40XT1EroQ6ByMtA3rQcwh3QXQkRb7T+9hBNnsmEyRExNHvCGEZ3gjHp8lBT3LYPAbJ/EeLoLtq3f8il6g46zRY++sKfcOFyJ06nzndfT8Nm7Te4swuC1FdGPbIhv0K3EPhVjbDUheb3o8gyakRDCHh561bWLB5eU3ohoXz/KSLltdQfKsfxzhEWFVxLZGUrmp6CRDKCboTsJcf+CVK7C1CTliHkqYXhtCqr8LjqaGzsZO3SIpK7XGitEl6bQE+PoLtNqAcy0KRCrP/wFaR0J6oiz0jyzaikb5zW2MO5Tsm+dCBlbnaMWiDEXbOHIGlOCAVQAyE0TUeXJT7/P54mp6C/bF1egUpNWQat3jA+nxkUE0lJEilpesx9ysgsEcuxiKhjQNd1TraejCZO1jRQkJPFG59+YpAXfT7Ta+DKyU70m5cTKqtCtakED2TSro6vBOxYWNwKUr4J5a4NSLIEtixQvYhwZ2xNnoIBPCUMzR5Ef7fO2LptGN0JyKRK+k3DtgCSmo4o/hjvjncoP1ePRxfs2/okR+p0EBEyrt7BD18cHvuUXRDkj352hb/4zPVcuOLB5+5EhEOkhNeRkZpC2mY/X331K2Snp016bPMFyaRg/ofXiDz+J1yu6eC6/7jCks/eRHv6RcJcxqynkhW+h7QuFbn5EGL5Rmp8KkKM3nogGDxBMLgTIVqQpFxstk9is90MQMB3O+3tf0ludiZpupdA82X8wQjBXQVIX/sGcm4ayhNgKsxCcvR4wwwxHYYsD479C/plBAo2x/Q8aIcSFW99ZnZuMG3EU7MHoaXC1WYiez/g8snLeCMav3T8DP1/bOxbRQIUkQyOELkPn6LOU4RDtrO0KDoN3+tAyS4I9m1ToC+mo6sdbyTaAMYXCbCt7NCgxMkk68ihKPOOAQaunB+A7BuIKDej3LOOCfVkDJVhCh5FEm6ElIZqux2s6wAQoTLM6gcg+0Dr8VzLJrRVXx/ZkDY0exjx0Gx7qkpnU2zdNoxugz4kzY5U0Uzo7R2UH62gSxW8l5nOyYCVgBzglWfs7P2RncLVw0v2NFTaydcKefJ/7qDz2Ht0XK5h47KlfP+ll1GUXhlKm9XzmTZi1D6dqvdASndi+qevEnzqR5Sdr+f6txwszckH8kEIVLWKEFUIuw3zXZ3IRRs5dOb8iPuz2crIyHgXVXUghB1JqsVk+gs6Ox8lGFyHxxMkyXIPd19fR6jhAt6GAP5j6ci/9w2UuzeMuN+FitmiD2s/DJCcHuHPPzzd9++ByVcDhX2q9V57qwME/TIgT29vZYP5ibAitZhQ926n+p2Paez0Ub80G91xPUsGaLZNy6DNo3KyRMVc0Mq6Z5v5s++njRhWkiPy6HC382HNIbpC0ZhnTzDAwXMXEzdxcgY0exhDDNwJ+/hdJZjqdkOyE+QlSHoXlshu1JysnrEWoLqyZt9zPUdJJM0WKIyk24bRbRBFNyM16qjvb+fy7jO0+yI0rVtDqc+H1xvilvsUHn3Axt4fxd7cio3S1qO8deEwZVfryE5L4+tPPzPA4J6jjFT7FEYVv/HUGJXXFaH88GW83/w5549XYTX3XysBCFXDZpFZ6fKx5OUMZDXMviNHYx7vzjuPEAprhEMa9MSAW6xBIpG3OHz4DrIyMvjBKy9j3flbSv+znu5uG9L3nhjR4E602qajMRONDjZtbRvx/AcyU9eid79vbt3MuQNdnhk5iMH8QUhIXTmIw/9B6479XGlw0+awcf32/w4v9YemWbRkPD6Zq+1evD6NfKvG976aOqLB7RQpSD6J968eZPuZE/gC0YIMYVWl2xfgmS33JV7i5Axq9nQyaom/ASEj4/VcG5qdOJpdsNrPez+JrduG0W0AgBzMgJIPcR06Q12bF2nNWkrXr0WcOEV6ujRq4qRdtxNWQ7xVdoATZZWYTCa+9fzzJNsnX4InUQRkXMIYg/GOUfnUzehXmun8t0NRS3sA4UAYq9uH+lE512Tt4oWnX+ET99wWcz81rioUFmFS+m9pVVPRaOTJu7+NVQ2SdGA7V977mCZ3AO3xTVhee2jEcc2lFr0z8XtItIeUgcFoyKFFiJMH6NrxHuXlTXgkidxffgt7dn9YaW/iZE2nj6b2boSw8jtfcLIoL7YZYBEWMsOZvFe3m13FJ6luaMFpT0LqaZd2x8Zr+OQd/SVNF4pmTxfTXeLP0Oy5odmG0W0AgKRL6KEgajCMjsTlRXmcvXCBiJLEbY9cOyhxciBWYUFXBe1+N/vPXSAUVvnqk0+wJDdGB4AJkCgCMhu1T83f/CSm1x8GfbDVbQMif/BrWn97hKu7T7MyM4PMFSti7qPdLtClK5hJ7h8m3cjCSmbTYYTHTcuOA1Q3etDWL8Lyoy8gLZCkJwOD+YykJkNpGYGdOyg9cYUuVefMXdfy/M2r+9aRhQlCKdS5AtS3eomEVbKz7Nx4few4bBmZAm0xHzedYNvZY1xpaGFF4SJ+76WXMffMXlqHNL9ZSJo9HcSjxJ9B/DGMboNh6EJQUVlJwGKlreAxbGmxy/uZhIJFtVLdXUd7t4bF081jt9/GbddcM8sjnjlmSxglW+wmQ+Y/e5nIlSZqT13B/vZh0pdeirlech40X19PJGRGDinoVg3VGiHv7Aq8ze8T9nRTXtFMJMOJ6Z9+pz9ZchpJFE/XfCI6NbqQMtQMJoRuQ6r2EnlnG5cOXMAVVPk4J5P2AZVKJMCkptPQFaa61YvfHyI7UybLOnLoX4G+mMq2SrZdPMTZqmrSkqO9FZxJ09M3YCaZK8ZsXEr8xcDQ7emlP3Qmtm4bRrcBAAIZkBAIdF0nEAiQfMsTRPxFg9brLQ8oIWHDhjvoocEtCFsb2Lh8Kc/ff398TmCGiLcwSmYTph+/QeTxP6Gypp2k5pFrdotSK9zWDWkqNJngWDLVlS7ARSSi4TebUP7uS8iLx268Ybc0UJB0AKvJRUhNxxW6Ab++dNRtEsXTNZ/47s9L2f+rithvWgYLG6Egt1pQd/8n1e9+TJMrQNfqlZwxy/QXZZUozE/i/AVocKl0e2UsZifpFvOg6iQDyRF5dLrb2XnpAEculiNLEl9/5lnSkpNjrp9oxFuzx800l/ibjGaDodvTTe+LyqOW2LptGN0GIGzg9qM11uPp9COAjOXrKM+/CapasA5wwn6/5we1RF9OeXMZPzm5i7ZzpeSmpPD1p7889xMnhxKv2qcDkLNTUX7yBoE/+g2BwPCOW32Es+DgkGVLe/6UJEyv3De+SiWuEgqznChSkGAkC5PsI9+xhybfA8D6yZ2EgYHBtCKraXDpLIETJTS0dEHBIspvuYnQx8ew2KLl/9LVZfzu/wzw82O1uI5eIVkJ86dvplKQH/vR7xTJSD6J964eZHfJWQLBEJ9/9DFWFxXFXD8hSQDNHjfTVeLP0Ow5g2F0L3SEjOTJQj/07zTv+oja5i46nMlU3vUFrla0k5srcectg9uB5+mLaO5snNbEyYQmnrVPe0LpleuXobz1/Vk5pNK6h+TUR6ivW963zCz5UEU9mQXLZmUMBgYGYyAANYJQNTQkOlJS+Li4GNks8+hn78MpsnG3Z/L2hUaOnKlDU8N87UvOEQ3uaOJkVl/iZGunh/s2bWLL5mmqIT6bLLB61YZmzx0Mo3shI0AKLEYc/xD3jveoqGzGrSgc+cIfUXylG6tF43tvpOIYUE4qVaSh+iK8feXAgMTJJ6ecODmU2S7fZNCPFO7khTf2gDIg0VLTIdyCem3myBsaGBjEBaELmptb8Gcm88r3n2DFypUI9zI+KG/jw5O1dHf5eOoTNm7aOHri5PGBiZNFBXzu0UdGrFo1FEOz44eh2XMHw+hewEhaAdLFYvw7dlB6+ipdmuDMC6/zUXsSasTL737FSeGi/p+ITdhICaaxs+Z93ik+Taenm8duu43brpn+5ioLJYEjEZNY5koiUiKTiN+rwfxCCDMSIIRACEEwGODep+7ntq2bcfjXsbfKw64TdbQ0udh4vcLTnxh5JrJAL6KyrZK3Lh7iXFU1qU4H3372Ocym8ZsIC+V3nYj3tqHZ08NsfLeG0b2AkfwW9MpzdJbV4g5EKN94Cx/aN9LV1MmnH7Fx8w39XhEFhXytkEONh9lx9mNqm9tYv3wpzz8wvxInZ5tETGKZbCKS4enqJxG/V4N5hDAjdQm0K5forG0jrGpYlqXx8GsPkx5ez+nqENtON3DlSit5efD1L6Qgy7E91tHEyY6+xEmQ+MazcydxcrZJxHt7Ksmjhm73MxvfrWF0L2SEQOg6mibQgPP2xTQ1urj+WpnnPjX4R1aoL6G09SJvXThM6ZXa+dNx0mA4k0xEmoonIBG9RwYGCYkAybsI/egOOnbupaq6E7fVzC3//A2yWMmVBoXtJbWcvdCA1RoNERy54+SQxMlAiM8/9ujcSpw0mFLy6GT11dDsyWEY3QZ9uFw+8tbAN76UOsgrEjNx8rnnpiVxcqZv3IQSBldJjyh2RkUx5wFg8+yOYbzMciLSRD0Mn1t9B17X8NrmzvQwv6g8Mu3jMzBIFOTIEkTxUbw73qHsXD0eIUj5/95g0eINuFsmlzi5c1Di5OiatKA0G4bptt3yl0D67I9jLAzNnhMYRvdCRgjQ+/+pKDrffSNlUOJkSl/i5MHBHSfz8qZlCCPduEe2Z/Pm1uHiP1HhHbr/kn0ZBH0KVcUpvLm1X9hnXNBdJdHpP7MTLLlIehemul9jtzxMQgp4guN1WcjIH14+sbMpdpMhA4P5gKRlQWUV4Xd2UH60AndYxf+VR1j/0B1IrmXsrmhn96nxJ06eaDrJtrPHuDqBxMkFo9kQU7cLsw4j5GvHVQPboB9Ds6MYRvcCRVLz4GoZ4bMXaG7uQkViw70FFA1InLQKG6nBVHbWfMA7xacGJE7OfMdJNaTMSGxV0KfgSNWQkAbtf6bj8ZTWPVHhNqf1LIj+mZV8EY07Z/TYBgYG8wMpnIJoOIz/SiOdvjDNS3K55Q+ewy6yaPZqVLV5cbl8FBVKYyRORjtO/tfFg5NOnBzKfNNsiK3bathBlrUYf2DpjB/fYP5hGN0LEKElI9e6ibz3X1zaW0K7L8T+3NUsf+TGvnUUFBYNTJxsMhInp4IU7gTLkLKKcgr5eVc5fnHrsPUXYhILQMm+dEI+c9+/g36ZN7dunnNxgkZyksGMIaL/EwC5qTFXyEhRxkicbGd75X6OXCxHkozEyZGIpdvOzADuVgsNTYPv74V8bw/U7V7NhrkX3z0bum0Y3TOIy3WG1tb3CYfbsFiyycl5mPT0ODcaEGaUjiS0Pf/BpbLdtDzZhJ6rkGVWcarngJuAGImT6Ubi5FQYqaTT068d4Ik1N4643UIj5DNjT1X7/i2Ies9mszLAdMSUzqUHjcFgElK3e3B1HaHZ/FN8nyrDdG0YW1vnhLZ3ipS+xMm9Z88ZiZNjEEu3P/PGrxByEtqa5SNvuMAYqNu9mg2zV9FluvIAZkO3E8LoliQpA/gpsBVoB74vhPi3Edb9JvB7QBLwX8DrQojQbI11vLhcZ6ir+wVmcwoWSx663k1d3S8A4iLgQggAZF8B+vGd1J7dRvPt9YR9ZqpSb8WmdpMf+i24zOSmPUK7p5W3qw5zsvxSwnecHO2GSxSmUtJpIdDrYQj6ZQT9L3Y2hzbrY0nEkmAGs0Oi6fbQsdU3/BYTIUxuC5ItSPatNaiuM0ipS6MpOjo9nvDhRBMnM3mvbs+QjpOzn8w9FzQbDN0ejYFe4YG6bWj26CSE0Q38HyAM5AIbgXckSTorhLg4cCVJkh4C3gS2AI3ANuCPepYlFK2t72M2p2A2R5PkFCW9b/lkxXuqHhhJkpCCYfSmetqWN6B2y7RmrsEdUkAkY0pSoHU3jrRnqAnW0Nzlxh8I8ck775z2jpO9jDSdY7boMdaOzWg33ND9h/wKEhJWR2TY+jOaNT+Fkk4Lgd7r++bWzTG/y6E408MxE3Cc6cMTdQwMxst06/Z0es1bW/ZjipgxtXbgcYfQQwq6LQ219QC6/BonazsprmghFAyzYqlt0LaDO04enVTHyV4WjGaDodujMPD6jke3Dc2OEnejW5IkB/AUcI0QwgscliRpJ/AKw43pzwI/7TXGJUn6Y+BXMdaLO1GRHVzhQ5aTCYebJ7W/qXpghgqrlhpB1Ei4HQLdprFulQmHIxXCLT1r9LtLHEmDBXw6GUkYf/T59dMSWzV0/wNFeuD+MwsCM/+2PNMlnWKVJOw93mifzUEWUokpg9ljOnV7Wr3mIolwdzPmRi+emlZ8IRVvmoOcDeuQg36OXfWy42Qd9XWdrF4t8+SQJMrexMnejpNpyZNPnFxQmg0zq9uGZi844m50A6sBTQhROWDZWeCeGOtuAHYMWS9XkqRMIUTHwBUlSXoVeBVg8eKZ8dKOhsWSja5393lKAHS9G4sle1L7m24PjOIxQ3IQNaJRuFSmMF8B1QMJ0jZ2sl6KsRI6RttvrHJXc4YRShL2RkeP+NkcFvHZoLdcGUQ9bnM1QWguEG/NhunV7WnTbCEjuTOwtIXxttTR7Y8QsJrIeWQzNk3GHcpn2/l6KiqbyUgXfOcraZhN/U6W3sTJHZU9HSclia8/M/2Jk4ZmTxBDs2eERNfsRDC6nYBnyDIPEEsRhq7b+/dkYJDRLYT4CfATgM2b144Q5TZz5OQ83OfVkOVkdL2bSKSLoqInJ7W/6fDACGEGNQLhCJaTKSj3+Em3+Vm+QkFSPRDphqLHkXSJkBYhHFbH3mmCEe+EjngxUklCpXVP9N8jfDabzRTGQ6JV/egtVwYMKlk2339P8SDemg3Tq9vT5TWXI0vg3EFSDko0LQ0QTpZx3nkdFiEI+Lo41PhpTp1rQFFUvvt6Cqkp/X0WnCIZ2R9NnNxz9iyBYIjPP5pYiZOGZqf1LEjrXw5zRrMhsXQ70TU7EYxuL5AyZFkK0D2OdXv/HmvduNLryYjG8zVjsWRTVPTkpOP5puqBEQLwL0U/vZ3OfcdpPyXocKWx6I/zMUVaoh7uosfJTnuIZlcTe66c5Ozlq5jNJq5ZtmxSYzaYPUYqSSj1hguN9lkCkQieiIEPkN54UiBmTKnB/GI6dXvavOYRCeHpxFpmIXDaSeQ5G2nOELq6hBOdz/OfH2cRCnbx2mccLF/aX26zr+Nk7cDEyRvikjhpMJz5otkQf92eS5qdCEZ3JWCSJGmVEOJSz7LrgYsx1r3Y89m/D1ivZWhoSaKQnr5p2jLep+qBkcKrkc5+hHf7TspLavBogoYnv8KNN/c3ZnGKZBS/zPtXD7H77Fl8/hCfe+QRli1aNC3nMFMkUhb1lJlkHN9IJQlFT7jQaJ8ZjI7oy2+YWMKZwdxkunR7Or3mvb9AUWaj7eObuf7ZX7DrfCe/OXmJzo52HrrPyr139OfexEycLFzE5x59dMKJkzOBodmGZs8kiazZcTe6hRA+SZLeAn4gSdKXiFYv+RRwe4zV/wX4hSRJvwKagP8O/GKWhhpXpuKBEVoByuUyQru2UXG4HFdI5fwDd7LlK/0Gt5moV+SDun3sOnuC1g43d268lgduSvwa0hPNoh6JuE+RjRbjN4aIj1Xaai6XvZrxCgVDGJicVV/hxN4zVenzGDXqDcbPdM929mLWkwclTq5ZLfOZZx2D1hmaOJnqdPDt556fUsfJ6cTQbEOzp5O5pNmJcQfCG8DPgFaisdmvCyEuSpK0GCgF1gshaoUQ70uS9L+A/fTX6f7DeA16tpmMB0YIUNwS+sd7afjoPM1dQc4XFHLPzz7bt46ERKG6mBPNJ3nr3FEu1zWzrCCfLz32eEJ4RWaLeE+RjRbjN2Yc3xilreJd9moqIjyXarDGYrYfQAaJw5S95gJEJALeboL+aDuKrpDER6fHTpzceakncZL523Ey3vePodmxmeuaDTOn2wlhdAshOoFPx1heSzR5cuCyvwL+anZGNh8wQziI8PoIBsMEkUj+nU8M6ixZqC+mqv0Sb108xNlLV0lx2PnWc89jMZtH2W9iEnfPxxQYGOP3L3/7KVztaYBOkqWTyqZxZGCPVtpqpssVjsFcFWGrI4LfE70PQn65b7wT+T3N1XM3iD9SaAkUH6LrvX1cqmrDryhUXL911MTJ3o6Tu0vOzomOk4Zmb5z4Z7PAXNWt6dBsmLnzTwij22B6GdyQIYc80zODsk8dSVeh4iCEO7FYCmlNepAd1Q0cuVgGAr7+zDNkpgzNbZ0bxNvzMRUGxvi52tPIK2zFJHnRhI2C5MTKwF4obNzi6vt7Q6WdP//wdBxHYzBfidVEJyP5MaSyUoK7tnPefZ7Q57tQ1uVSEPkZy723cs/DN8dOnKybW4mThmYbTCeJrtmG0T3PGNqQQdN81Hr+PwociwEwrQuSm3oA9EVIlkIIBWhu/xuuVmXg85v57COPsHbJkviexBxiOqegBsX4oWOSvJgVH+2+26ZhpAYGBolI7CY6/4qsyDjfPc/FmmOEH+3Ep2RTqa9F1jr54t07KVyfQjQFarSOk4mROJlIGJptEE8WhNEdDDZx/vw3p9yGdy4wtCGDLOcimTpoy4gWg7He5ScicpHMGTg1B7VhNzXuANfbqzFd/zQPxkicNGJSR2Zap6AGxPglWTrRhI1232349aVTHqfB+JnL093zhYWs2YqSAREnzQ3/SuYxmc6bOgiEk2hZcQu+lm5SklPIW2SC1t19Mb4ZIotObztH6s9RXttAcNf/IJByA//jt4MTyQzNNjR7PjKXNHtBGN2gT70N7ySJNW04k8ce2JBBoBBRk5D1ZMJmL2bhQMnSsaZk4BTJtPhbudzeTHtXhKXpEjffH9srMpOxXYZBP4SeOL7Kps1905NTYULXN0HbDs+2oC7I313CsTA1G0CIZHQ9iCpVooZTkDJ1rHkb0JAQAnIyZUymFBhQs1lBwaerhDUVXReYwwUsWR8adqzpCnUwdHsA8dRsSEjdNjR7ZBaE0S1JCopimnLr9IkSe9rwF8DMPUB6GzLISjq6lkaXuxOH7MYccSIQCJeJJDTcATdXOhvp8HSTboOCwvWY45A4OZeSNWKJYVVxCm311kFxZInEuK/vFEpfjYepiPBcEtRYzCUvTKKwEDVbUdIRwk5XVwhJbcehRfNqhEtBXjzEgNa7ow3N4sRc0e15rdkwo7q9kDUbZk63F4TRPZDJtOGdLMOnDWf+AdLbkEHXU/B6PbS0VlKUY2VR53rapDK0Eymo93locHfQ7Ooi2RRhXUEm5oKHZ2Q884lYYlhX4SDkm/6Xldk21KZU+moczHURnopnb66fe7xZKJoNFoJBG83Nl8hI01nivxlVOoN62Ills48k4QIEZoIQ8cMcqdkcT+azZsPM6vZc162pzsbM1PkvOKN7Um14J8nQaUOY+QdIevomBCYaG47i811Ai1goFE+SXHaOK94g8qUUWsQ9tHrfJcsUIDlzMZZVL8Z9OipRGO1GnU1mW/DGbEm8wJkrnr35yELQbICWlmP4/VX4fBrX5b5C2pVqKtu80OmgtfFTuFPOkutsQFKyoejTfZotIWHX7VT4KqltbycUjqDIidcUZKZYqJoNhm6PRqJq9oIwuoXQ0DR1Sm14J8PAacNeZuMBkp5+GybTi7S1HGfrGhtJ7/6W8j1n6fBHyPnSU7Tnr2PblXYqaxr4/VdeoSB96YyOJ95M5I13ojeqzaHhabMM+3yuhQ6M1ZJ4ITLwd1NVnEJ9RbRlgNURSdip6fnCwtPsTaSkvEJXVyl3rhbkl+yl/p3D1Ld7iVyziV/5HubMyRsoLIA/eSINrP25N/l6IdWdNWwr+4gzFZdJslpIzkgj2l5lbmJo9vgwdHs4vb+dgZoNiaPbC8LoBnla2/COl/5pw6i3ZDYfIBJw87I8kvb9J7VvH6G+w4u4ex0F33uN8oaDE9rXXI9Jnck33o1bOqdcCzQRkpLGakm8EBn4u6mrcPS1Fu5tvGAwkyw8zUbAytxM0s/uo3PHbi5daUfLyWf7J77GuYvtOBw633s9DdsAgztdZOLr9rGrcj8Hz11A1XS+/swz7C7XDM0egfmi2WDodix6fzsDNRsSR7cXhNFts+Vz7bV/PevH7X1QRDPhZ/EBIsCuB1HOH6Z9114uV3cSys8g7R9eQzJN/CufSRGZ6wb9UCYjxmM9YKYi8OO+vmO0JB4PM/0gSpQHncHMs+A0G1A0nYyGC/h2vUPZ2ToCJivHP/N9DlV1IfQI33w1mZzs/rARu7BjDySxrfo93j9bQrcvwDNb7mPjqlVsnOH7YT7p9pzVbJiybhuaPfssCKM7nqSnb5r1GrOqnoXV04p6qZzm6g66dZ3I7z9HZtpi2rpaON9yhca2ThRJJsXhmNWxDWUu3XjjEcOZ8NBMZZ8Tur5TbDs80zF0iRqjN5P86PPrgTWr4j2OhUQ8NBtSED4fXK3AdakRT0jl0tZPs6PFit/r5qWnkrhmraVvbRMmssP57G84wPaS4zS2drB53Wo+eccdszLauaLb816zYUq6bWj2zDCabhtG9zxDE+nUu1JZTicIgUCgShIpSxZhDzrYXv0+758tpsvr48l77qEge+qxinPlbbZkX/qgrHV3m5mns+/DbNFZssELjF5OKpHOxWB2sTk0fJ6olzHkl/seGjPt2YveV6HhBZcN5g1CWPF685D9LpI0HSEEyDJnlALaWj3ccpPCJ7b266uERJG2hJLWs7x17giVNY0U5GTxxqefRJblcR3T0GyD+c5AzYZ+3Z6N2ZjRdNswumeI2W6wAKALG41dhRyrdbM8q3+5BGRqWRyu/5gdZz+moaWDjWtW8cTdd0/LcefK22zIZ8ae2p9c5Gk3k5EfxudR+sbfVm/D02adF0k2M0ICNmLoZSYNiY1bOvv+PtV4UIPEZfZ1WyYSWcq5c6WsyHCQJAAJhKTQ2trF4hslvvrZ1EFNy3oTJ98qO8TpnsTJbz/3AklW67iPamj2AsLQbCBxdNswumeAeDRYEEh0hVZw9KqXo2WtvHiHCRAgop9f6qjirdpzVNQ0kJ+Vye88+dS4vSJznd4pxqBfRtD/5ivJ+rB1pyPJZt4yww10psp0GxLzKW7VYGziodu6vpjLl5s4ePAQRY/eBwiEkNB1gc0m+N7rKcMSJwPdPnZd2s+h89HEyW888yx5mfOrWoWh2dPEAtNsSHzdNozuGSAeDRYgia6AiXpPkO/emQOlH+A5cZF2T4CQycR+XwWnKi5jNVv4zvMvYJ+AV2Su0/vG/ObWzYNu8OO7cuI1pEEkkkiM5nl4882/HLERw59/68VhJZogWqYpu3BuRkcYU9MLi3jotqo6aG2tYHF+Nvn+NkInimlocBOWFW75ZMHIiZMlJXR5Azx9371cv2rljIwtnhiaPX4MzR5Mouu2YXTPAPFosNDLYyucLG04hm/HLkrPVOPRBOdeu4UDV6tQVY3fffZp8rMyZ3wc84HJTH1NRoynNZt9iozmeRitEUNHQxKp2WGCvsFNOTxtVtbe4p6WsSXSg85g/hEv3Xbak3h602qkbb+k8tBFOkM6O9c8wsb1BX3rjJQ4+ak775zRsc1FJqrbhmYbmj2bGEb3DBCvBgtOk2CJt5Lw29uoOFSKKxih/MFr+CDTR1drNHFy05rVMzqG+cRkpr5m4i07Ud7cx2rEMDSGDqLXarrGnyjXYTaJPpwW0LRUHImXbl+blYTpnW1cee84Te4AZWtuZ9/yu9jY8/nwxMmGCSdOLiQmqtuGZg/G0OypM5puG0b3DBCXBgvCRKavBXHwPWr3nKHZE+DKsnzeuzeZhuoGNq5ZOW2Jk0OZK2+zQ8epaoLOJgtmiz5oeaKNO1FI9EYMNRed1FUML4Gpq1KMtecG3/15Kft/VXEp3uNYCMy+bktYgkEoOUTre4e52ujBl7+YDx/4Ilxt71treOKkdcKJk0MxNHthkOiaDVCyL2OYtz3kV/jR59fPWaN9NN02jO5JMlqW+2w3WBBIBCNLsPsr0dvb6PIE6EbioxdXUFlXH02cfGLmEifnyo0x1XH2ikPIr/Dm1s19yxOtzNaMMQ0NdGYSIQkkhhvYQhKzNoa5UoptITJWZZLZ1m1dL4KAD6m9FW9nN0Edjt/3HFUNXdissLTIHDNx8uvPPDPlxMm58lucjnEuaN1OcM3OLAhQVZyCzT44QTY1OxRTR2eC2dZsw+ieBOPJcp+tBgsCCKrLuNSmcf3ADyQ421iH1WmNJk7abDM+lvlOW50VWZbQVGmQR7XmonOUrRKboYLTm1hjdURi1r2dagOdmWTpel/cy6DNlVJsC43xViaZvcY42TQ1aaSrAWxCIADJZOJ8Y4SII8zXvuRkaVZyzMTJjauMXknjpWRfBq01SSgmMUi3bQ5tjC0Tl/mk2d/9eSlvbo2vZs62ZhtG9ySIT3WS2Kh6LuXNSZyq6eT6/P7lAoGq6rzxxBNG4uQ0IXQZU5IGQsKR2i/anU2WUbZKbIYKTlu9jaBPGVb3dqzp28lMV8+kh2FgU42gX+7zcC0I75bBMBJJs4Vw4HKlcOzYQe67ZgU2QJJkNCHh9wX4xKds3HWjg+yQkTg5VYI+BcWkY7KKQbo9sGnKXCOemg0zp9tDGyH16vZ802zD6J4E8axOMhCBldbuPA5Xt9Pe7IE8EANm0h++5WYjcXIKDBUlTQM1KKFYJheuMBdCD3oTayZa93Yy459JD8PAphqC/kYahsd5YZIomg2gqksoL7/AmdOneWBdEQQChMMaQgiWLZV5/kk7BaKIK+7LfFh1mku1jSzKzjQSJ8fJQN0O+RU0VQahz1vdnk3NhpnT7aGNkHp1e75ptmF0T4J4ZbnHGAkhFSK6xmdvL0TzXUEQDTkRksTWO26d5fHML4aK0tPZ95GRH570/hIt9OBHn19PVXHKsORDm0MjuzAYlzEZGMwEiaPZEqoKfr+f5x59gJTyk7TvPk59axduZxbP/d46FFnCrFvwqz66/UE0Tefhm2+dUuLkQmKgbr+5dTP1Fc5BxtxESTTdno8J4wsJw+ieBHGpTjIKj63JJNBajWpSkXtc3cLMoNbBBgZD6WhIwmbXsacOjm+cq1OvAz1cAzvZzeX4TYPpIdE0e0VBDoubSvHteI/yiw24ZBvW//s6ybnDjSkAkzI370mD6ScSlsnIHO78mathjiN1H52vum0Y3ZNgtrPcRyPNptDWUM+JkydYftNaZCBGAQeDacBk1fB7ht8yJuv8FIe5xlAPVyzv1EwzV0qxLTQSSbMVBEtD7YTf3UXZ8Uu4ItD+vZe49u4lsz6WhYDVEcHvMQ9aFvLLxj2ZIIzUfXS2mG3NNozuSTJ7We4jo4tUCPspK71IZ1sbpmABmttDOKIhJAnFYny908nmB9tHjO2bq8zHB1I8w2YSIcbTIDYJodm6DYvXgzh5iMZjpbR7Q1Teciu3fe22vnVkFHRNpzPgxuP3gYQRyz1J+nUsNGz5XL1X56vzJ15hM7P9OzCssjmKLuy0e7MQqg/FpPClR+5C/uA/qdp/nk5/mMYtK8lJTY73MOcVc1WkRyNWianp7Eg2GjPlYYi2Nw4NyoQH8LRZWHtLjJJaBgazgkI4vBhzsAUpGCAUUglKMnlf2TJorSJ9CaXtF9hZ+jEV1Y1kpaZyw2qjTOBkmI+aHe/SqDOl24nQZ2E2MIzuOYjARGdgBafrvWxMF9y9dhkpH+2g7u2PqG3r5urSFDb85FvxHqbBEKZNrFwlKK17+M8f30tT8zLauzfgDxcM2t9ceNjM5BjHeplI9IoEBvMPTVtKRUUtqxxg7bEjhGRCNvV7sfPEIppdTWwrP8Tx0kpMisK3nn+eZPv8quAwl5gW3Z4nmg0zp9tjvUzMF802jO4EYKxOaQMRgC+8kmPVAY5WtLHl7nRsF4/SvmM3l66205JsJv/Xb2K2GZnuica0CIOrJNrW1+yksXklhUXVLFXKafI9gF9fCozf4zEX4o+nW2h791dVnILV3j8da3NobNzSOe/KUxnMHBPSbbGImhoPhw4dYskDt2EFkGQYkOyeItJQvSrvXD7AvrPnCIYjvPapT7E0Ly/mPg1mhynr9jRqNiS+bk+3ZtdcdPLm1s3zRrMNozvOjLdTWj+pNHdZOVPXwYZMC7bOOvSzp6ipaMKjCdR/fJWUotxZPYeFQLzfsnuPvzizA5P0DSLCQfnZ1bQ2ZbHp5lOkW4vxB5ZOaJ9zwTswHeW6epsuBP1yX8vhkF9BCMhcFK0CMFcrthjEh4nptkIgkMqlSx/j93aR5HejNbXg9YYQSJjtFqzCRmowlbdrdvNOyWk63N08fOst3Hn9dbN+bvOF+ajZkPi6PV0lFnt129Nhpr7CScivoIaifTIyF4XnrGYbRnecmXinNBkhBEvSbTywxIze0YIeUdE1geawsPje+CYKTRfxFsyh9AqJXa4m3VqM1eQipKZzoezeWT3+kuRKgpEskHzUXC4i4Heg6g5s5vZZGcdcpLfpQm85KnuqhtetoIWNMj8Gk2Niui2j6wKzSeErj92Hac9bVO05Q2t3iPobb+XGW4pYpBVyuPEI284epaaplbXLFvPigw/O8llNDUOzYx/f0OzJ0avbnvbon163gskqUINzW7cNozvOTKZTmlmBrcvtXCm/QEZqT4VACZIcwwVvrpJoDQkA7HI1+Y49RDQHwUgWJtlHYdZhcHkhfeOsjCGkpmOSfajC2bfMJPsIqemjbJUYzNZDeej0a2/9V5tDI+ibm94Rg8RiMrq9eXkBKR/tpOHtj6hu6qIudwnX/fIzLGEpZW0X2VZ6mNKrdWSlpvLNZ56bc7W5Dc2OjaHZ42Mk3ZZkI5HSYBqZaKc0gUSmVeZsSSmnT55k85bNSBiNcCbLREQl3VpMRHNExVMCVThRww6U1u1osyTgrtAN5Dv2gAYgMCshzIqPdt9tY20ad6Zz2tHTZuXNrZsHLe/9zoZ+bwPrv368a7Y7EBrMRyai2wJI0jWUsqN07vyQysuttFuTyf/N77DYsZTmzma2lR/i44uVKLKRODkexqvbhmZPjdnSbBgeNtOr2/NNsw2jO85MpFOaACJaIX5/N9XV1axZUoCp8QpdlXV4gxGk3NTZHfw8YCKiYjW5eqYJ+5dFRBJSuHMmhzgIv76UJt8DpFuLyUhvprFpCafOPzUsE34+UrIvg6BPoas92nmtt6brZBJqFJNOOGjqq3cb8ss0VNrn7bUzmF4motu6thTF3YpeXkrz5Ra6VUHgD17g2pXLUL0q714+yF4jcXJCjFe3Dc2OL9Ot2WpIRtNk/B7TnNVsw+iOMxPplKbqi6jplHBqguWFi7jZ7CX0y3+n/Ew1AbMJ0w8/M9vDX1DEmiY0SwGEJWNWx+HXl+IPLMWxCuzCzh+8fRoYeVq7l0SLuRwPA6ccPW0WbHYdAItNxdHTvn4iCTU2h4bPo2Bz6kiyRuEab99xEvUaGCQe49VtXeTicumkRUJImoauC3SLiQ0Pric1mMbbNbt5u+QUnUbi5IxgaPbsM5OaDdEk+MI13oS+BqNhGN0JwHg6pekilSvtmZysc7O1UOHmTCv8+79ReagUT0RD+u/PoNy+dpZGvPDILAhwoexeCrMOo4YdREQSZilATl4dWs4Ds3L8qZaJSsSYy7GI1dr9+K4c7KnquPcx8NoN7Eg5V0XbIDEYS7cFDrzdmZw7d5Jb85OxEo1NtdpM5Or5fNx4Yk4nTiY6hmbHh+nQbOi/fkO7CM913TaM7jmAwEJr9xL2VXXQ0OThxTwr0kfvcuXDkzS5/OhP3YLlizMvIrPJRARrNrwBfftxeVFatyOFOxGWjKh4z0Js4KDz6Gm00DcG1+yMYbbo/T6rSx2oIYWgT0GPOjm4eCQdLQJet4Ji0sksiIy5v7ks0AZzE4FMKLyKsrKzfPTRYW586iGsSEiyjAJc7qhiR9OxOZ04OZSJGpkzrduGZs8e063ZMH912zC65wCCFDp8gsvtfgqtMiZPC6Gr1bR1+HDJEtl//tl5l0g5kRtuot6AgWJfVZwyLM5sVNI3zloCTkwGNFrAkoukd2Gq+zVqz9gSmfE+lHu/z/oKJyn5YVqrrZhsAr9bwerQ8PeWjgrJw/ZlYJAYpBAMhGhqaqaxqSm6SJL6Sk01+lqobmtFCMEXHntsXiROTtRImohuDzXQe3Xb0OyZxdDs6ccwuucIoud/khT9M/pvgWo2IdmN7pO99BbUh2jJod5s6YHek4Fi31Zv7Vvf02bpE5jJJGfMhsddad0TFW9zWs+CtL7lcX2wjINp81xIoAalOZ9QYzD/EX3/G9R8sudDCUmSSXM6WeiMpdtDDfRe3R6o2b3rTwRDs0fH0OzpxzC65xiCwTUr55d/e+r0FtQHECh9Qj2S13vjFlff3xsq7fz5h6cnfezZiL+Twp1gGdJxVE5BCrdM2zESFoloRzITONLVOZ9QY2BgEGWyum1odoJjaPYwDKN7DqDrVjQhUHUdkwTzq1R8/BnYJnxgHdFEFAZhyUDSu/q8JQDoXePKxp+OxJ54YrEJcpaE8HkUbn28bcoPXAODmULq6UKpaRqyLCEhEJqGrkXVWxM6uq7HeZRzF0OzDc2eqxhGd4Kjk0SrN5sTte2UXenkGy+sRXR2goiKt2I1vsKpMrBNeK/Xo2RfBlXFKby5dfDUY7xFXct5IBofCCCngN4FES9a0ePD1p2pqdO5WMbKwGC2EMiEw0VcuVxK8ZlTPP/YgzguleA6cpY2tx+9MIdD7mrqWtpxJtnISkuL95DnHIZmTwxDsxMHw2Ibgst1pqf2ahsWSzY5OQ+PWc5vptCR8QTX8HFNNztP1/P1+4pQve3outYTZiKRnJkcl7ElEgO9Ab2tYyGaGBmL6lIH9RX9cZSedjNet4Ku9wfrBH0KNrveJ+h2uZp0azHuVgtKxVuzlgE/jPSNqNCTCd8SzYQvejzmWGZq6nSmp2R7v09VE3Q2WVBVmXAXyDJ0NlkwW/QFGw9oMJxE0mwAVVtLTXUd+w/sZ0XhIu5wqPh/+S5l5+uJ2G0cfW0zhyorQcDvPvUUzqThxtBCYKK6PTDuu1ezNVWmZF8GG7d0Gpo9CoZmJw6G0T0Al+sMdXW/wGxOwWLJQ9e7+7qOzbaI60AgvJaShgDbixu4f2kqyxwhLpw/z9LlGYCEJEf/W+jEqgs6GpKQhsXGR6PjYwfu2OVq8h17iGgOAuE8JD0wKPu814tQVZwyyJi3OiKDYsanzdsQ72z8GSaenhfDIzS3SCTNBtDFElqavXz00Uf4PC6+ds896P/xSyqOVuJD5vKrd7LdU4fPH+Tlh7aybunSWR9jojAR3c4sCFBVnILVPtAglzBbNYK+4aUWx9JsiN7rQzUborqdXRjqW8fQ7LExNHv8GEb3AFpb38dsTsFsTgdAUdL7ls+2gGt6ITUuE3sqWwi6fTzxQD4ff3SAlvo6pCI7hCJouhHdPZTxxMAt2eAdJPC9hftH6pKVbi0mojl6uprJfVnovdnnvV6EtnrboAeAp8066O1+PN6GRBaQ8b5czGXmYjOKhUwiabYuUvF6nZw/d5jTp07zR6+9guXwLi4fPEu7L4LniRv5VaqHlloXt123gYdvuWVWx5fIjKXb3/15KW9u7b83BzZbiaXbY2k2RO/11OzwMKPd02Zl7S3uvnXGoweJqtuGZicehtE9gOj0ZN6gZbKcTDg8drvW6UQXSbjDZrwhFZcvRF6KBUlT0VWVV+6/Dfb9F9VHy+gKq5i2GG2DBzITAmc1uQhGsgaXiomRfT60XuxkkkYSWUB6x1ZX4cCe2u9x8nvMcRyVwUImUTRbYKIrtIxQyEeX14usyNglHdHlweePoDpt/OZ6C1X1tSxdlMerj39q3vVWmArTrduT1WyIau1M1hyfTQzNTjyMSuUDsFiy0fXuQct0vRuLJXvWxiAw0RFOo9J9FSF6gyAkJAm23ngNaWcP0bjrMLWtXvQbl6P8/tOzNrb5itUR6akdqtBQaaeh0k7Ir2B1RDtnhdR0TLJv8EbjzD43MDCYORJDsyV8oZVcaAqhatEmChKD63IH1TDnqmtJttv59vMvYDUbRs9U6NXsgbptaLbBXMDwdA8gJ+fhvnhAWU5G17uJRLooKnpyVo4vkPBpuXzccpLa9gCbM9chAbIkkWWTsZWcpHPHbi5dbkPNT8P04zeQLMZXOFFqLjr7ulD2IzBb9D7PdO+0XEOlHZflXgqzDqOGHaRntUPEPWL2eSIxU+WmbA5t0JTuTDc6SNSpW4P4E2/NBgiriylugIOX2lhyXSow2OAWCCKqBgK+/vTTZKakzNrY5hPDdTvqkurVbUOzR2a2NRsM3R4Jw2IbQG8MYDQTvhmLJZuioidnLTZQFxlc7Wpmf80Z1lpvJBBW8fo1PrUqHXtLFeGPDnL5Qh3dgO3HbyDnpM7KuOYbQhJIMdoKCak/Rn6oKPzjlzZCqIZgV4C/+8EbtHdvwB8uSKhs7NkSuaFTske2R72KHQ1JM1IzN1Gnbg3iT7w1W+CgqSuNI9VNuDq8aKoNn89Lakoy5nAQzdNFKKyhI3jyvnsXdOLkVBlLtyei2Yli9MVLs3sN7qGaPZ3HNnQ7NobRPYT09E1xLBFoIqiFWJGyik0Z9/LWuVaEL8DdqwoQNVVo3gBhVSdUmInjhmVxGeN8YOl634TFoLZ2DQWri+idsEzPhHT8feI1Hu/ETDc6iJfIqSFl3ojrXG9GsRCJp2aDmYgONpPCV+8p5PyZjzl/toTf/8zTyIff5vK+c3T4QjTesogH77srTmOcH0xUt0fTbBjfvT5fNTvex55O5ppmG0Z3gmFVkrg3/5O8X+qmpKKVv31mNY111WQKPTqZJknYM+bWTTGfqS4dGqYSpddbMFFPRiILyEhjM1sSt7PeRK9/onjADOYOkgRPrE+ntuwsB/bv40tPfoKssmM07jxIdZOHukV21v78TSNxMkGoLnUM8+7CYE2YL7o92rhinV+iMJHrP9c02zC6EwqZDGUN+y53sbu4gR88tpLWuiqqL1VyzbpcJIlobe4YU2wG8WEsL+9EvQmJLCAjjS3WAyxRmC/eHIPEJTtJofrSJfbt28d9t25irbce944PqbzUQnuSTNqvvoctJfbLucHsM56Zufmi26ONy9Dt+GAY3QmCACJ6Go1unWNXO9mQZSMnKcKHFy7g6WiDdXmDs3MMDOJALA9EVXEKbfW2mOW3DAzmM7rIJBzopry8jM72drasvR9p5y+pvliPG0H3n7/MilVF8R6mwQJmJK9xzUXnmI3kDKYfw+hOECJ6Nm2BbsKak1BEJ8uioGsakUgEXehRL3e8BzlPSNSpwLlALA9EfYUzZle46cL4vgwSEV2k0eRJQVG7CEcimC0WFHSEqqJqOj6zxLJPG3Hc04WhA5NjJK9xVfHMVtExvq/YGEZ3AqCLFKq9Pmo8zVjFOoBo/J/h2Z4RJjMVOBfimeMlclZHpK/75kwcN1Gnbg0WLrqw0tRVxOmGLjZn6P21ueM9sHnMRHXA0OzRMVm1GT22oduxMYzuOKMLK01BCx/U7scUyGFD5jpkCSPpJsGYC/HM8RK5jVtck+q+aWAwFxHIeAIrOXSlm7NXO9l0ayYYPpKEw9Ds0Vm63mdodhwwjO44IpDxqFkcbDjAtuJjPFT4IFKW1GNw99eMvnvTRqi/QldNK8GwBsm2uI15vjHVOqljeSpifV5d6kAS0ozVR43FQm1UYExxGkwnAvBHVnCiLsK2Uw08vjoNj6uD+ro6Nl+zBlNbPd7LDfiCKmqS0fB5JphpzY61TnWpAzWkYLboM9KLIBYLVbNhfuu2YXTHCQEE9HxOtpzjrQtHKKuu47lVdhRpsFDfdO16Nmmt+H/7LuUX6gk5bJj/4Pn4DHoeMtUs6bHEL9bnb27dPOyYJfvSqSpO4c2tg0XWaFQwNSZ67abyoFvID8mFgqov4mKTie1n61npVLh9sYndH+yGSIgn1hUS+e0vKTtehUfX8H7308iyYXhPNzOt2bHWiaXZEG0MNlb5wcmyUDUbJqbbU9Xd2dZtw+iOExE9m4udNbxVfoCTZZdwmCw8tuE66jr65yjtFhObnTrar3dRdrQSLxLKD19GXmdkw883Qj4zVrs2TGQTTWAn44GYiKjF23CdyoNuIT8kFwK6SONyWyZvlzbhavHwe0+s4MTRg1RVlPOHn3kC+d3fUrb3LO3+MJc+dQ0bv5DYLccNps5caAxmaPbMbj9RDKM7DugimWqvj51VBzlw/gJqROVvnnmJDIeTuo6oFzzDbiI33InYu4uqPSW0+8JIrz2E8qmb4z38OcvI5e6sbNziitOoEofxiOdkRHQiomYYrgaJSG/i5IeXOjh+oZm/e3Ytl8qKOXr0CN966Smcp/ZR895xGjq8VK3L5Nq//d14D3leYGj22Iyl24ZmJxaG0T3LRBMnrbxX+z7vlpzC5fHyxp338vC6G/B0p1LW6qG+0cP/fHEd8pUj+C5dpd3tx5XuIPvNJ+M9/DlNLHGoq3AQ8pnjNKLEIlHFs2RfBp42y6zGwBsY9DIwcXLniTqWJys4lDC1tbUkO+zkmzW0y5dpbvHQmaSw9N++j2IyHq3TgaHZY5OIum1o9sgYyjCLDEyc3FHyMfUt7dy9chXf3vIw4dAqjtcE2X6ygTfuWowU8qALHaHr6Eg41xUgmWauFrKBQaIS9CnY7HrCh94YzD+GJk5WXmrh5rsLUHpCtXuT3nWhIwB7VhqO7PQ4jtjAIP4Ymj0yhtE9S0QTJ/P6Eyev1rEkPYO/f+pF0JdztgG2lTRw12Ina1LDVF26TL4tWsFEliUUk5GQMxPYHBqeNsuM1ZiORawYu6BfJjU7PK7tJxNDN5+zwQ0MZoqBiZOnz9djVTS+sDUZ+m5VgegpNCVJEibFcIzMNImi2TD+mt+GZhv0Yhjds0Q0cbJ2UOLk/3v+c6TYllDVlMzO8/Wobj/P3reMwwf3ofu9sDoTo+XCzLJxS+es15iOJbK9ojyeB8lkphOna0pv4MOj5qKTSDj6Mmiyaixd7wPm9hTiVB50xkNyfqGLZGo6s3i/vImPztQRCYX44efTKci042oCXdfR9ajFLSEjGTo9KySKZkNUD8dzzxuaPXNMVXdnW7cNo3sW6O04ubPqIPsHJE6uyl5Oi6uA9ypa2Huyls/eVkjI343f282TN21AHNxJw7ka/BEVZcPSeJ+GwShMNYN7rgjewIdHXYWDjMyoy8/vMfUtj/XiMF5Ri7VuyK+Qmh2alvGPxVS+h7nyHRqMD02k0u5Tqe8M4PcGuHm1hU/emo4ezKSi4hQlZ0uwmhQkWTIa48xBpqPqxly45w3NntntJ4phdM8wvR0n36/dx7slp/B0eXn9jt7EyRXsu+Ti7eO1tLZ4WJa3GJMMj995Ixmn99Kw6yA1zV3oG5dg/vYn430qc56ZfKNNxGSWiTJT12ciojbeuuYGBrOF1NOoLMUhY1bXUlZWyd49e7l44SLX33Y9ZrMJNc5jnK8Ymj02M3GNDM2eOeJqdEuSlAH8FNgKtAPfF0L82wjrfq5n3YG/pE8IIQ7M8DAnzcDEye29iZMrBidObjvVQPXVdlYWyNx7fRJKh4yj/CPcOz/gUlUram4qpp98FclmiffpzHkSsVB+IpGondWMsA2DROCJGzdQd7WR/QcOcOLkCQKE+eTntyBr8R7Z/MXQ7LFJRN02NHtk4u3p/j9EU1JygY3AO5IknRVCXBxh/WNCiDtna3BTYVjHyat1LM3I5B+eegmZIi62KLxf2sCF8iZS7Dp//dVMkrBhbbhAcM9uKkpq6UbC+n++gpxnZMPPFonq/RgogFXFKdRXOAGwOiIJX692tGv6udV34HUNf6F0pof5ReURYG5M4RrMP4QwoemCsKazeUUmNyxycOjAPo4cPkKX38v9r93N2hVL4UI7elhF782oNJhVDM2eGUa6rke2Z/NCwT19seG9mKwamx9sn3Rt8IVC3IxuSZIcwFPANUIIL3BYkqSdwCvAm/Ea13QRq+PkPz33WVKSktBUB13BCC5vGDUc5ol7k1iWnYHuURCedsLtbnxhFe81S7HfvCrep2KQAAwUwLZ6G0FftEqCp83a93CZi14Er8tCRv7wqi2dTcbMjkH80IWTOlcyBy+3cKa0mUfWZxEJB+l0u/D6vSQXJPO5Fx8nr0VBPbyH2qNldAVVpFtXxnvoBgnCfNVsNaRgtWt9seG9+D2mmJ5xg8HE09O9GtCEEJUDlp0F7hllmxskSWoHOoF/BX4ohIgZTidJ0qvAqwCLF+dOz4jHycDEyYEdJ1flxB5HVnISim8xjQ0NLBU6QggkSSJ9afasjttg9ok1hVdd6kASEks2ePuWRbuw2di4pZONWzr7lvdm8ffuZ6abEQycNtRVqc84Nlm1Of0gMYg/8dTsgQjMtHqXsa/Kzdsnamlv7aJwSzrQ68mWWJKfTVFXNsrBd2jacZDqRg/aNYVYfvBy3MZtMHsM1e3qUgdqSMFs0ft029Bsg1jE0+h2Ap4hyzxA8gjrHwKuAWqADcBvARX4YayVhRA/AX4CsHnz2lmb9+tNnHyvJ3HS3Zc4eU3M9a0mmU9edw1lFyrxdrSxxBo1uCXZqMs9l5hsDFusKbz6CicCMWh5fYWzz1My3v3A9E+xGtOGBjNFvDR70BiQ6A6u5Fi1j20n66itaWdJPijpVQiiThBFknnhoU+SdPw47m3vUVHZTCTTGc29sVvjMWyDSTCVuOOheltf4SQlP4zPo/QtNzTbIBYzZnRLknSAkb3WR4DfBVKGLE8BumNtIIS4MuCf5yVJ+gHwXUYwuuNBrI6Td61Yxbe3PDRoPVUz4Y+E8QYjfOH+1Qi/j5MnT5HlTEJabDcM7jmIIWwTo2RfOp42K/5uBTXUb6goFkHmovE1CTIwmE4EEFKXcbpe560zjZSWN5FqhxWbi1GD6wmHQ4SDIdauWsHd+dmov/0FVSU1BEwm5L9/FbkoK96nYDABDM2eGCX70gn5zPi7FUJ+BZ8r2hjI0OyJMWNGtxDi3tE+74npNkmStEoIcaln8fXASEmUww5BAnWNiZU4uSQ9g3946kWUAUa00Aspb4Z3L7ZQWt7ES9dtIBIO4fP5SbdbjAYLs8RImdnN1baY6xvTb7EZT4Z7LI+Sp81KanaYkD8Jk63fqakGjd+/QXxQ9VzKW5LYeb6ej0vqkHWVW+6qIRLS+MSSuzh64DDHjh1jw9pVmLUwYa+fsCbQV+RgvnNdvIc/7zE0e3oYb1WSobrtabNitWuYzDqyLGGyRnXb0OyJEbfwEiGET5Kkt4AfSJL0JaLVSz4F3B5rfUmSHgHOCCFaJElaC/wP4D9ma7xjMVLHydSkAcaGSKemLZO3Lzby0alatHCITassCFckfgNfoIw0tQfMSKezkYSuutQxrnqmVkdkUAJOL/F8sPzo8+s5/nYuVvvgmmk2x+B/DxTygdch6FPQIuB3R6dgFTNoWjSJ0plueE4MZg9dDX/LkgAAKeZJREFUOKntzOHdshb2naol6Atyz50umn2V/O3936e05AJ79+6luaWZp19+GFkZYGgohtExGySKZo9Xc+eyZsNw3a4q7g1MkAgHJMKBntAZOarZJqtmvOiMg3iXDHwD+BnQCnQAr/eWC5QkaTFQCqwXQtQC9wO/kCTJCbQAvwT+LC6jHkJ/4uSBvsTJv376xUGJk0LY6OhazIeVHXxwsg6Py8srD9pZnGOmJvGrBxlMkZEeGP1CNjobt7hmvfXxWHQ0JGGz69hTBwu2zzN2HGN9hRN7qoYjNdC3za2PtyXcORrMf3TMtHqXs7eqk3d6EidvWBemTTnKD+74Jq1XG9m7Zy+XL19m+XVF3P7oJihtj/ewDWaYqcZczxfNHrqdY8C2Po9C0RpfQp1johNXo1sI0Ql8eoTPaokmW/b++zvAd2ZnZOOnN3Hyg9r9vFtyCpfHy+t33ssj668dvJ62hPKWMEcq22lq6OSG1Qpfe2qwwbWicBHCXUOgoxtV18E8+o1gMPcxW/RhIq5qAklIE/KQGM0IDAwmjo5MV3ADx6q72X6ynpqadhbngWnxYa5zbCSbZN4/e4SysjKcGUm8/oMXcWg2RGc7wY4uwqoGJkOnFxpD9VbVBJ1NlmF6bmi2wVDi7eme0/QmTh5qOMC24mPUN7dz18pVfGdI4iREGy2ouko4oiOEzqM3OTEpUl8VqltvuJYbLV4C73xARVkjEZsV5UsPzPIZzQ3i0YFspo65ZIN3WrwERlKQgcHE0IFAZC3F9QH+60wDF8uacCbprLyxmIOlFdxz5y0ITSccDqPpGqs2LKUwpYDM0iZC775DxekrBCQJ01cejvepJDzzSbNhevTW0OyFiWF0T5JBiZMXj1BWXceSjOGJkyMhSf1xgHabhTtzrGi/+Q1lRyvo1kH+kxdQrlkyg2cwd5mOUks1F53UVTiGLdfV2PGZidr1LNEZ+uDr7czm80gMlJ+QX6ah0m54eQxmDVVbRkWrxI7zzRwvqUNVA6y+rZTjl0vQIjr3r9sAXf16kJ2ZTWG9jvbuNqr2lNARiCC9/jDKYzfG8SzmBoZmzx1ivaxUFaegRQRDTcaQXzY0e4IYRvc4iTqkbUB0KjGiO/sSJ0+UjpA4OQ4kHYqED7FnF5c/PE1bdwjpi/djejpmPqnBNCEkEbNSjJCmrzzwXG8DPB6sjgh+j3nQsoFCPPTBV1fh6IknNHHL4619yxMt9tFgfqPqmdR0pvBeWRP7TtbS3eXFknOc0uYyvP4gX77zHlZk5dDcFe29Zk9K4tUHP4HY8za17xyjvt2Hdu86LN99Is5nsnAwNHt6mKhmQ1S3JaRBmg1R3TY89hPDMLrHiSbS8UScaCKaRNAR6hyUOPlXT71ArrmBqxU/IxJux2zJIjPnIVLSNwEWugNmalzdtHR0I0mQZJGQkDGFrMiXz9N1ppTGdh+u/HSy/9sz8T3ZBcDS9b4Z9YL0Zonb7NFapuGgRMhvRpIFlh6vLsyt+L2hHpCai04iYRmTVWPpel/f8tGmb20ODZ9H6fNsD9zGwGA20EmixVvEgcvtvHu8liyO8My971KU3USXmkS3+Ua+eN+DSKqTzo7LNDc3c8uNm8jxdRI+c46GJg/qojQsf/8VJMXoqTBbGJo9caZDsyGq2542S0JVYpmrGEb3ONBFEg0BwYXOE6h61PNR7W7uS5x84857uXORSmPdP2Myp2K25KHp3TTW/TMgYUl6maNXu9lxsp66mg7WLVG4f7Mdh7YCr9tLqhpBi2hoApLvuwbJYnwtc52OhiSsdq0vS9yeGv3d+D0mCtdMTxz3bDPUA9JWbyPoizZKGC+97ZANz7ZBPNCRcQfX8HFNF9tO1pEU2M9Tm/6TTauzsVpvQuhdaJHLCFcztW057N9/gNOnT/PJx7cio6NrGjogluUgpQ0PdTCYuxiaPTIbt3Qamj1NGNbdGAhMdIbT2F+3l31XSwir0RuxudMd7Ti5MtpxsvbSX2Ayp2IxpwKgKNE/21oO06q8yLaSJkrLm8hMgb96I4M0cxEt1X4CHa2kiOj0mKxImK3m2AMxMEgwgj4FR6qGhDRI2I2YSYNEpDdxsqQ+wPbiRi6UNfHFzftYtzQTuz0zupKShoKN5tq3OHXKw+HDh1H1CHd9YjNSm2/U/RsYJDqGZscfw+gegkACUhFEpw39mp1jLSf4r3NHOH+5BnoM5LCqsjQjsy9xMhpSkjdoX4qUg8vbxq66Fk4U16EQ4S9eTacoPZNAWxLHju0jN9nGWpNAkiWQjCYL4yEepZYSsbzTdGTnx6OqgIHBTCJwIrD0/D0Jev6uaTYqWiV2nm/m4+I6QiEfm1d4SEte3retLMyokWQ6Os6zf7+M2+Pmia88SN6SHGi7Go/TmRcYmh3F0GwDw+gegABCej4doUhf7HZtdyVvlX5E8aUrJMkmHlt/PZIkYTWZ+Owtt/clTpotWWh6d5+HG0BVg/jUbMrrPQR8AV593MGm1TasIo/6llZaW1p4YO1mOHqCtuo2VF1AblocznxuMR3CMlFBTkQxm47s/JnO8E/EB5/B/EUTqbSH7YS0EGbJjh7JIRiJOkoCEY33yprZd7KWri4v99/aRWH+YoTeBUoaEgqSloyrs5K6ugB19XXccN86HnruLtK7zOiXSumoaiYQVhE542tqZRDF0OwohmYbGEb3AFSRSZmnidKOClQ92pr9fGsNhy5cRNLhx8+/wu3LV8bcNjPnoZ4YblDkZDS9G1XVCFq/hq4LJBlW5PdcbiEhIXjq/jvIPn+Ipp0HudrkQb+mCPMX7p+Vc13ozIYg9yYNDmQ8JZbmkydjro3XYO6iCxtNQRPHW44hhMzGlIc4U+/CHQwB4A1qvHO8ltYWN4sKmvmrL24i4FHpqPslIGGRFuPqqMblqufgwWQKVubwhTefoTCUj/XjI7h2fMily61oealY/ttz8T3ZBUgiazbMH92eS2OdixhGdw+6cFLrC/PulYOcrqvqa1pzubEZvy/Imw88MqLBDfRUKYGO1g+IhJsxW7LIKHid6uYbgSuDVxawKD2ZtPNncO/8gMrKFtSsZEz/9FUku3WGztBgNhlJpMcjwIlaX3aoByTkV5CQsDoicRyVgQEIFFyRDPbX72dP1Vm+dsPXOVHjZ8fZZroDQQB8vjDVNW1Y7R389veuRZZlHD267Ws9RrerisbGEB98kE5LZybf//HL5JvySD1djm/7LkrP1BCwmFF+/DqyMSM575iKZkNi6rah2YnHAjG6TWgib9Q12kI6u2s/ZGfxCWobW/vqgUpIPLLhGr58+91jHiUlfVOf8Q0QCl0HTX76LPje0egSGc0VBN99h/LTV+lGwvIPryIXZE781AwSkvnoLRh6TgM9O0b5P4PpRRlTswcS1tNpC7QgRzJ4bvnztLgdbDtTw4kzNQRDQXo1WFIC/Ow7BaQ5+p0bmekPYQ0+xYkjH7Jjxw66vB5+5y+eZXFeIXmXfUTee5uKw+V06wLlj55F2bRiuk/WIAEwNNtgNlgQRndY17nq8466TknbRbadPUZ1Qyvrc/N4+vrNgITTauUT11w/qIPkeBAik3oXnKhxU1PXiVmBgmwTZuFE8mmIxhp8NS14ghE8W64n7/a1UzhDA4PxM7QBRG+XN5tD6yvpNx7m40PKIDGI6Iyp2b04lTy6vBbCaj7rU3PpCqj85kwzp8/VI/QQd97gwmqJ1l5+8o48rl2SMWh7s8jA1dlKU1MTbo+b2x7ZyPobV+EMO5GazxOqbsQTiKDftBLzS2M7XwwMphtDs+cPC8Lo9oS9vFu9f9R1Pr5azoXLNeSnpPAvL3+JLKdz0scTwk6ru5D3y9r48GQt3i4frz3u4JqlDhTfEtzudjJ1Pep7kSQyNy2b9LEMFi6TTXgZOA3aVm8l5IuWqRzY/MDwfBjEE2/EN6ZmAyxPXkWBeTEf17Tj6Ynddvk0Dp2qRQ2F+KPPZ/DYrYXjPq4kQWpm8qBlQpJAAikjecLOFwODgRiabbAwjO6Aj50lx0dd53JDM2ZJ4f8++5kpGdxgwhdYxYEqD7tO1tHS5OKu68186bEU7OpKLvz/7d15dFzlmefx71tVKq3e5N1GliEsAYKdQCBwoAOEhhxI2CZOAjFpIA5OAmQ/maF7QiBAQncnPZ2ZSbpnktDJpLtPutMdgpcsBsvIK94XbOQNS7Zka7Ns7Vupqt75oySrpNJSJdVyq+r3OUfnqEqvdJ/76uqpR7fuc9933qXQ9lLcf3NC43L1fyYSm3ictQhf2liLH4hTtPV0j5uzS6eVcNv1y/lTxTnWH6yns7sbt8tNR4ePluZ2PnNHAR+7UfcfFudQzpasKLp9vj6Onzwz5hi3y80L99zPkoXRnxUZeVuXsf1kN6/tOUNlZSOL5xu+/7liprhKqa1sYevWrVx/Zagh07i0hHAsMqU7fDy6ZZNkuwJPAU++f+WYYy6ecSm7q7tYu6uG48frMcZHXo4XA9xwpYevLYvutn42CIFAEL/fP7AMAwAu6wJ/kKDfT9Da0X+AjEl5W3lbBmVF0X3R9GL+ZtnyMccUFxbygYsWTWo7AX8ph84YVu2v5cChMxTmBvnRkzMpKnDhDhbR0XGK8+ebCQQDGKOCO1ZO7A7PBPs3FF9YGviZu6678HymvShK+ijyTmHx9KVjjjnV3Muq3SeprGxkTrGfby4rJt/rwe2Cay/PJccz/juIuRTT1uRn//79HDhwAOOCS5eUkhvMo7imhb6tmzj1djW+oMXcdHm8di+rZEPeTvY/FsrZ6Ssriu6i3Fz+/L1XJXQb1s7kZNM01hyqZcveGqzfx4tPTKd0npZ1l9gk+0UqnksDZ8tZLUmsTl+QtYebxhxT1dDBgYpapuRbXvnmPBbNjS3Xum0+tM9l545tlJWV0dR0lrsevoWl11/JosYigut/zak/vEVtczeBe5bi/extk9gjyWTK2RKtrCi6E22kxskvfLyQW5dGHsiSGdI1Uf3g8as4WVHIu/sG33rvanfT0Rxk1kU9k/75Tj+rla6/t2zT1tXH2m0nxxzT2toFfh8vrZwec8FtcJPXewl79h9i/fr1VJ2s4orrL+HBJz5KSfscPJv+SP2qN6k83ULgivl4/+7zuhwwA6Tj379ydvr9zsaionvSBhsn14Y1Tj7x8WEd8EEXvb299PR0M62wANvQTV+PD7CgXJ52nJ6oRnPuTD43P3B2yHPb18zGYIY06GSqdP29ZRuDn7bz58Yc4zaw8t4i/mxJ7Cc3CgPv4fjRGsrKyjh06BAzF0xn5XMPscA/l/ydu2hb9UeOHqnHN70Iz8+fxhTlTXRXxEHS8e9fOTv9fmdjUdE9SeGNkycqG1k8L9Q46XINXk+Yb0uoPXWWt97azqUlC7jatNOz/k2OH2sgkJ+L+yNLUrgHIiLOMq/Yzf/5r7PGHOPNMZTMif0lLN+WUHeqnfLycnbs2IHLa3jqpeUsLFjAzLer6Vq9msM7K+lyuXD9z8/hWjxnorshIjKEiu5JGLFx8qlQ4+SAXGbS2eBl69ZNNDXU8+1ld8Kr/8KRLUdDK5w992nc75tcA2e2UHf45Pzg8auGLKwwoP18DlOL/SmKSiRSjtvwnoXx74fJpZjORi9bt2xi48aNdPd2s+LZZVxy8WIWVPbQ94dVHC8/SHOvH/PN+/Dcfk3cY8g2ytsTp5ydeVR0T1A0jZMem49tm8OOHVvZu2cPzz62DO+br3F83R4aO3ph+YdxPXRLCvcivaTj9VsTkagXqXNn8skrCFIwLTDk+fbzHnq6XBHb1IuiZJLRGidvvP0DkY2Tdy/F+/Q9qQ45I2RD3lbOlmip6J4QF51di9hV08zWd+roaOtkxd2RjZMF9mIOV1Wza9du7r/zNopPH6Zl8x6qG9sJfmAxOd99WCucSYRkv0hNKfZTckUnMxd2D2lYOXcmn2fuui6mhhWd1RInGrFx8oMX8+DKu5jjm03Ood20btzFydpWApfNxfs/VqhxUqKWqpz916/vGdJoOJCzIfpGQ+Xs5FLRPSEu+oKGHn8Any9Anhc+dlNkM4+xbvr6+ujp6WH61CI4exp/j58AYD55C8ar6U9X6Zqocgv76God+rZ9b5frQsE92YYVJ57VCn9RCr8LQI43SOnVHYDzf28yOYWBS4c2Ts6fxsrnHsLlduHxubE93QR7+/AD5p4PYop056lMlI55e6ycDZNvNFTOTi5VfXHiivWEdczfIE7ixEQVjZG63c8cK+Bbv6gYssjC/g0z6O0MJfqeLlfMZ0+cJPxFafg9bbWEcubLtxdRd6ptSOPkk997hKJphSN/g3Jzxkq33AVj5+xw4TkbBvO2crazqOiWrJFp9/tMpN7OHAqmhRp1LO4LiS9db9Mk2SnUOJnL1i3r2bRpU6hx8jvLuOg981IdmkRBOTt64TkbBvO2crazqOieCDuF1u4gtS09tHf0YAzk5gw9O+K2ufR2BWg620Rvbw8XTcnHHqyl5XwHGAN5Wqky2TLtfp+xSse3VkUmanjj5Nmzjdz58C1cf/vgLVpd1kVepx8a6mhtaiMYtJCfm8KoJZxytnJ2plHRHSNrcznftogNx87x+q4aWps7eej2AuYWD06lwUVe33s4cOgo5eXlfO7Be5h5dCeNqzdyqq6V4JULyPnotSncC8lGOjMk2cLgJs83fMXJi3nwiTsHB1lY2DUHz7Zyzq0u48Sp8wQXzSTn07qjlDiDcnbmUdEdExc9PZezpaqD13bVcPr0eT5wuZuvf2rqkFGFwUuoOl7HhrINfGjJlSzxnaVjzTqOHqunr7gQz8+exhTqbIo4S/hZlZ4uFxY3AHmFgbG+TcRxCgOXcvxIDWVlG8JWnHwYt8d9YcysvllM2XeIjlW/5/DbNfQU5OL+v09iiotSGLlIbAbydnjOBuVtp1LRPQ7DdOg/kP19M9ld3cere05z9Fg982bAD79QjNcTvvrkAhqreyjfuJFcN9x3ySz8//YvVOyspMvtwfW/nsC1aHZqdkYcLdXXL4Zv45m7rhvxbd10pLdos0toxcmBxsntF1acLJo6eAwUBqYw51gTvjWrObLtKO0Y3C8vx321FiqT6KU6Z8Ng3lbOTg8quscQDJTS3DGTYDD0uK6tl1Vvn2bP22fwugP83ZdmUDx18D9LL1PpbirkrbfeZGP5Rl745tN4KndSX3GSll4/PHk37g9fnaK9Eacb6frF/RuKeXffVJ65a2hiT3RSz6Skp7dos8doK04uHNY4ObtnGpzYTts7VTR1+ODTN+N+4MYURS3pSjk7MTI5Z6voHoW1M6k6O41D9S309Vfdxxs62binmr6eHr7zF1O5avHgJSJum4ur8yL27N5JWVkZdfV1TCsuwlRagkGwLhfmmtJU7Y4QfVJKxdmL0Zf79TC1OBCR2BPdSJTJSU8y00grTt758C1c/5ElI39D0IK1WAMsWZzMUCVKytnRU85ODyq6R2BtAY0tF7HuyFm2vXueYH/RXVvfRvO5dj59ewH33Tz4h3ahcfLto5SVlXH8+HEuft9CiudOT9EeyEiiTUqp6JgfbbnftiYvkJnX5jnhrVnJDCOuODm8cVLSjnK2syhnT56K7ggeOrsvo/zdVtbsrKGq6iwQKrptMMi1l3v4RkTj5MWcOtFAeXk5+/fvZ8qsAp588RFcGLCAtcnfDRGHy/bbgclkGPLNXFwm9BLm8k/h2NGwxsn50yIaJ4dQShaJmXL25KnoHsbnu4ztJ7t5bc8ZTlQ2Mm96kAduDh1QuTmG+24pGNI4CQZ8RdTVHaG6upq+QB+Pf+VBFk5bgPfwSXr3HaC2rhXrceOaPyM1OyUSJ+l+piPd45eQwuBielvzCARCJ0RaW5qHNE4++b1HhjROhssPFlBQ20Dfvv3UnGrCut2YBcrNkrnSPe+le/zhVHSHCfhLOXTGsGp/LQcOnaEwN8g/fG0mpfOiWcjGYi24XIaFC+ZTcgYCf1zFu2X7OdfVh2vlnbivvSTh+yDpK7ewj67WocdaIBB63imGn+nYv6GYnk53ROOQU5OhztSkvzzm0FxnOVyxj16fD4DGxoYhjZOjrTjpsR5K6nMJrPtPqv60i4bWHoLLbsB7xyjXfYuMIR1yNgzNewM5GxiSt52asyGz8raK7n7WzuRk0zTWHKply94agn4fLz4xPcqCe1BBQT7X51+KXbeKmrVbOH22g+CH30vOM/8lQZFLpnj/R5ojntv62mxmX9TLtt/Noc/nuvC8JzfAM3ddl/JE2dPppnBaAIMZkhTTMRmK83lsEf6WGezYvpldu3bR5w8te93Y0DBu46SxhpKWWbg3rqF2zSZO1rcSWLoI7/cfxRgz4veIjCWdczYwJG8rZydHlhTduRC8dMwRja1F/OlII2/sqqajrZMnPlbIrUsj386IZLCW0PLBWJ750koKd2+lafV6Tpw6h790Fjk//iJmtGsLxXFSceul0bZ53Z1NfOsXFaPeg1WJUjKRIYepXBbxfF+Xlx179rJ+/XoqKiow/Zf6GeC9H7pk9MZJC/N75pC3czstr63j2LEG/HOmhhYqy/cmcE8kGZSzJV1kRdHt87uobBz7QN9d08zanTXU1zVzy5IcvnDvlKh+dkFwEaer69i3bx83XbuUm/J9dP7n7zl88DQ9hfl4fvYUZoZWOEsnqTgL4dS39RIpk+4rK/FlA4bm+sg7QJyuOUFZWRmHjxxmVsl07ll+Ky63C7fHzTU3XjFq4+QM/wxmHDpB56rVHN5bRXdODq7/vVJ9NhlCOTs5lLMnLyuK7tZuP6veaRxzzM7j5zhR2UjpPMPLK4pxucZ/uzGP2bTWwZYtW+hqb+Xh25YS/M2vqNh2nA4M7pcfwXVlSbx2QySjZOOLlkSnq7OHzZu2Rjx/8uRJdu/ZTV6Rl6/8zaPMWThz3J+VHyxgflUHvjWvcXRTBa19FvOdB3HfdEUiQhfJWMrZk5cdRXeXj99vPzXmmLr6Vgpzg/zoyWKKClxjjgXw2EL8LcVs376ZQwff5rlHl5Hz+m85UnaAc119mCf+HPf9N8RrF0QcYfiZjt4uNwbjuMah0ehMTXro6OzgjbI3Ip6vr6vHH+jjC88+ElXBPVLjZGDZDXgfvyMRYYs4UnjeG8jZ4LyGz9FkUt7OiqI74Pdztv7smGNyXfDdx6ezeP741/e5yCGnu5Sde/dTVlbGlx/5JNMOb6d+/Q5ON3UQvOUKcv7yE/EKXzJEJtz2aHic4fsUnhSdmgzTZZ6znT/YR9WpExHPG2O4b8UdXH1D5PXeEWOtoaRNjZMycZmQs2Fo3hu+TwN526k5GzIrb2dF0V0y28OPvlY85pgZU9yUzIlmOgwF/kupqKhi/foyjh49woKZ0zAHmulo68Gfm4P76w+ocVIiTOa2RxP5Tz8ZLxiZlAzFOWbMnspXf/hYxPN5+bksvGTu+D9goHFye1jj5Gw1TkpslLMl3rKi6M7zGpa8JzcuP6sgWErNiXOUl5ezZ89ucgu9FE7NB8LOnORmxbRKgsQr8WbSvU0lu+R4PVz6vtIJf/+IjZM/VuOkJIZytkRL1WEMwhsnN23ehM/vY+V/fwS3e/xrwEWipcQrMnFqnJRkU86WaKlajJLHFuJvLmb79p1s2LCBlpYW7n38dpbecBWmu5tAWwe9vX2AAV0vKCKSdKHGyTwC61YNNk4++EHcapwUEQdQ0R2FUOPkYvbtPUhZWRnVNdUs+fAV3P2ZWylpmQmb36DhjR3Une+EK+fjumJhqkMWEckqgytOrqN+oHFySQne7/2FGidFxBF0ecm4+hsnD1eyfn0ZFYcrmH/xbFb81SdZ6JtP3va3aF69jqPvNuKfOxXPT5/CeDWtEimTbnsk4igjrTg5eyqen38ZUxCffh7JPsrZEm+qDsdxoXHyzVDjZF6hl6e+/wgLPPOYse9Yf6POSXq8Obh+8kVc89SoIyNLdte4XjAk03isBxeRd4Yq8heGGidXq3FS4kc5W+JNRfcYwhsnN2/ejM/v46kXl1M6bxHz3m3Ht3YVRzcfps1vMc9/AncU944VGU+8Eq9uDSWZZJp/OvPap2Ksjfiau7E21Di5UY2TknzK2RItFd2jCF9xcsOGDTS3NHP/5+/g/TdcTUmdl8C6/6DyT7toaO8h8MkP4X309lSHLBnC6Yk3UxaMkPSRG8xjQXUvZv9arD9yFb2+2rrBxslPXK8VJyWpnJ73lLOdQ0X3CIavOFlTU8OSWwcbJ90bVlO7eiOn6tsIvL9UjTqSVXR7LEkmt3Wz6GwR9vVf07ltLwFfIGJMW0PLYOPky48pH4uEUc52DhXdEQYaJ6suNE7Ou3gWK/5ysHGyZfXrHH23kb45/Y2TeVrhTEanswwisTG4KAxOAWB2RxGeTX+ifvWbVB4/i98VedMtX58/lI9//mWtOClxobwtiaCie5hRGydzBhsnK/ZW0ePNwf0PapyU8eksg0hscvwuFp3xABaO7aZt1R84eriejvxcgteWRpzJtjkevF+5V42TEjfK25IIKrrDjLTiZHjjZO8aNU6KiCRcZyfBDasB8O05yOGdlXS5XLh/8gS5t1+T4uBERCZGRXe/4Y2TLS0tEY2TVev6Gyc/daMaJ0VEEiTQ0kHzf7wBwJnqJpp7/Zhv3odHBbeIpLGsKLoNHgrM/DHH2K5idu0La5z88OWDjZNvDmucfOmzatSRrKV7yUqi9fT52Hu4FgB/MEjwrmvwPn1PiqMSSU/K2c6RFUU3AQ+956aNOaSqqpL169f3N07OZMVffWqwcXKVGidFjTUDsmlfJTXsrCn0Le9/N3FGEd7HbseM0EApMhbl7JBs2leny4qiu6uri9279ow55u2Dh9i9e6Bx8rNqnJQIE22s0VkGkRhNL8T77KdSHYWkuck0QypvSyJkRdHd3t5BefnGMcdUVFRoxUlJiIGzDMPPupw7k88zd12XdWddRESc7lu/qBjxTPm5M/n84PGrlLNlQrKi6O7s6mTb9m1jjnG5Dfc+fvvQxkmtOClxpFtQiYikD+VsibesKLpnzJnGQ18duwlnyvQirv2zq4esOHlSK06KiIiISBxkRdFdNDWf2x64cexBFhb0zCNvx3aaV69T46SIiIiIxE1WFN3RKPbPYMbBY3S8torDe0+qcVIiqLFGRCR9KGeL02RV0V0UmEqxbzrDLxQx1lJQU4dv7SqODTROPqfGSRlKjTMiIulDOVucJmuK7rxgPiVnwNQdBGsjvh7YuW2wcXLZh/A+psZJiS+ddRERSR/K2RJvKS26jTFPA48B1wC/ttY+Ns74rwP/DcgHfgt8yVrbG8WWKGksgNd/i/9EJZElNzTsOhZqnFxaivf7apyU+NNZFxGR9KGcLfGW6jPdtcBLwEcJFdKjMsZ8FHgG+Ej/9/0O+G7/c2PyBjx4NpfRtHoD9afOjVh0N7V1hxonf6bGSRERERGJr5QW3dbaVwGMMR8ELhpn+KPAK9bad/q/50XgX4mi6HYFgvgrT1B3upmaHj/B/JzIQbOmkPOTL6hxUkRERETiLtVnumNxNbAq7PEBYK4xZqa19tzwwcaYlcDK/oe9BYeOHBp3C7d8NR5xxmoW0JSKDY/DqXGBc2Nzalzg3NicGhc4J7bSVAeQDMNz9j3ee8bP2anhlONiOKfGBc6NzalxgWKbCCfFNWLeTqeiuwhoDXs88PkUIKLottb+FPgpgDFmt7X2gwmPcAKcGptT4wLnxubUuMC5sTk1LnB2bJlIOXtynBoXODc2p8YFim0inBpXOFeifrAxptwYY0f52DKBH9kBTA17PPB5++SjFRERERFJnISd6bbW3hbnH/kOsBT4Tf/jpUDDSJeWiIiIiIg4ScLOdEfDGOMxxuQBbsBtjMkzxoz2j8CvgBXGmKuMMTOAbwO/jHJTP518tAnj1NicGhc4NzanxgXOjc2pcYGzY8t0Tp57p8bm1LjAubE5NS5QbBPh1LguMHaEhWKStnFjngeeG/b0d621zxtjFgEVwFXW2ur+8d9g6H26vxjdfbpFRERERFInpUW3iIiIiEg2SOnlJSIiIiIi2UBFt4iIiIhIgmVk0W2MedoYs9sY02uM+eU4Yx8zxgSMMR1hH7elOq7+8V83xtQbY1qNMf9kjMlNRFz92yo2xvzOGNNpjDlljPnMGGMTOmcxxuK4OUrmMRW2zViO+WTOWVRxJXvOjDG5xphX+n+P7caYfcaYu8cYn7Q5y0ZOzdmxxtY/PinHinJ2fGNz8nGlnJ05OTsji26gFngJ+Kcox79lrS0K+yhPdVzGmI8SWuL+DmAxcAnw3QTFBfATwAfMBZYD/2iMuXqM8Ymcs6hicfgcJeuYGhDVsZWCOYvlbzGZc+YBaoBbgWnAs8BvjDGLhw9MwZxlI6fmbHBu3lbOjmNs/Rx3XClnX5AROTsji25r7avW2tcYYaXKVIoxrkeBV6y171hrm4EXgccSEZcxphD4BPCstbbDWrsFWA18NhHbi2MsWTlHI4nh2EranMUYV1JZazuttc9ba09aa4PW2rVAFXDdCMOTOmfZyKnHCTgzbzspHzk1Z08gtqRSzo5NpuTsjCy6J+ADxpgmY8wxY8yzZvR7hSfT1cCBsMcHgLnGmJkJ2NblQMBae2zY9sY6a5KoOYslFifPkROPKUjunMUqZXNmjJlL6Hf8zghfdvKcZats//tSzo5/bODM48rJ+Uc5O0ZOOKBSbRPwPuAUoV/UvwN+4OVUBgUUAa1hjwc+n0L8/wMdvq2B7U0ZZXwi5yyWWJw6R049piC5cxaLlM2ZMSYH+Ffg/1lrj4wwxKlzlq3096WcHa1MyNtOzT/K2ROQdme6jTHlxhg7yseWWH+etbbSWlvV/3bFQeAFYFmq4wI6gKlhjwc+b09AbMO3NbC9EbcVrzkbRSyxxG2O4hlXgudnspI5Z1FL1ZwZY1zAPxO65vPpUYY5cs7ShVNzdiJiI07HinJ23GRC3nZk/lHOnpi0K7qttbdZa80oH7fEYxOAcUBc7wBLwx4vBRqstTH/lxZFbMcAjzHmsmHbG+ltmxE3wQTmbBSxxBK3OYpzXMPFc34mK5lzNhkJnzNjjAFeIdRg9Qlrbd8oQ9NlzhzJqTk7QbHF5VhRzo6bTMjb6ZJ/lLOjkHZFdzSMMR5jTB7gBtzGmLzRrjUyxtzdf20Qxpj3EuqIXZXquIBfASuMMVcZY2YA3wZ+mYi4rLWdwKvAC8aYQmPMzcD9hP6bjJDIOYsxFkfOUTKPqbBtRntsJW3OYokrFXMG/CNwJXCvtbZ7jHFJnbNs5NScHWtsJOlYUc6Of2wOPq6Uswelf8621mbcB/A8of+6wj+e7//aIkJvPSzqf/xDoAHoBCoJvUWSk+q4+p/7Rn9sbcAvgNwEzlkx8Fr/PFQDnwn7WlLnbLRYnDpHqTymxju2HDBnUcWV7DkDSvtj6emPY+BjearnLBs/YsmNKThWoo4tmcfKaPkoFXMWbW5M5vzEGptTjqtUz1m0caVgvjIiZ5v+4EREREREJEEy8vISEREREREnUdEtIiIiIpJgKrpFRERERBJMRbeIiIiISIKp6BYRERERSTAV3SIiIiIiCaaiW0REREQkwVR0i4iIiIgkmIpuEREREZEEU9EtMg5jTL4x5rQxptoYkzvsaz83xgSMMQ+lKj4RERlKeVucSEW3yDistd3Ac0AJ8OTA88aYl4EVwJettf+WovBERGQY5W1xImOtTXUMIo5njHEDB4A5wCXA54G/B56z1r6QythERCSS8rY4jYpukSgZYz4OrAHKgI8AP7bWfiW1UYmIyGiUt8VJdHmJSJSstWuBvcAdwL8DXx0+xhjzlDFmpzGmxxhTnuQQRUQkjPK2OIkn1QGIpAtjzKeA9/c/bLcjv01UB/w1cD1wU5JCExGREShvi5Oo6BaJgjHmLuCfgd8BfcDnjDF/b609HD7OWvtq//hFyY9SREQGKG+L0+jyEpFxGGM+BLwKbAWWA98GgsDLqYxLRERGprwtTqSiW2QMxpgrgd8Dx4AHrLW91toTwCvA/caYm1MaoIiIDKG8LU6loltkFP1vNb4OtAJ3W2vbwr78AtAN/G0qYhMRkUjK2+JkuqZbZBTW2mpCCyuM9LU6oCC5EYmIyFiUt8XJVHSLxJExxkPo78oDuIwxeUDQWutLbWQiIjIS5W1JFhXdIvH1bUJLDw/oBjYCt6UkGhERGY/ytiSFVqQUEREREUkwNVKKiIiIiCSYim4RERERkQRT0S0iIiIikmAqukVEREREEkxFt4iIiIhIgqnoFhERERFJMBXdIiIiIiIJ9v8BytTcmBvJ0foAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.svm import SVC\n", "from matplotlib.lines import Line2D\n", "\n", "custom_lines = [Line2D([0], [0], color='green', lw=1),\n", " Line2D([0], [0], color='blue', lw=1),\n", " Line2D([0], [0], color='grey', lw=1),\n", " Line2D([0], [0], color='red', lw=1),\n", " ]\n", "\n", "m = len(X_train)\n", "\n", "fix, axes = plt.subplots(ncols=2, figsize=(12,7), sharey=True)\n", "for subplot, learning_rate2 in ((0, 0.3), (1, 0.2)):\n", " sample_weights = np.ones(m)\n", " plt.sca(axes[subplot])\n", " for i in range(5,9):\n", " ada_clf2 = AdaBoostClassifier(\n", " SVC(kernel=\"rbf\", C=0.05, gamma=\"scale\",probability=True), \n", " n_estimators=i,\n", " learning_rate = learning_rate2, \n", " algorithm=\"SAMME.R\",\n", " random_state = 4)\n", " \n", " ada_clf2.fit(X_train, y_train)\n", " \n", " plot_decision_boundary(ada_clf2, X, y, alpha=0.2,color=i-5)\n", " plt.title(\"learning_rate = {}\".format(learning_rate2), fontsize=16)\n", " \n", " plt.legend(custom_lines, ['paso 1', 'paso 2', 'paso 3','paso 4'])\n", "\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como puede verse en los gráficos anteriores (uno para una tasa de aprendizaje de 0.3 y el otro de 0.2), a medida que se van incrementando los pasos, las curvas que delimitan las dos zonas de clasificación de los puntos, se van ajustando cada vez más y por lo tanto mejorando el ajuste que vamos buscando.\n", "\n", "Para apreciar aún con mayor detalle esta mejora en la clasificación a medida que incrementamos el número de pasos en el estimador, vamos a ver otro ejemplo similar al anterior pero en este caso, utilizando como estimador de base un árbol de decisión. " ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.ensemble import AdaBoostClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from sklearn.datasets import make_moons\n", "N = 1000\n", "X,Y = make_moons(N,noise=0.2)\n", "plt.scatter(X[:,0],X[:,1], c=Y)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "clf = DecisionTreeClassifier(max_depth=3)\n", "clf.fit(X,Y)\n", "xx,yy = np.meshgrid(np.linspace(-1.5,2.5,50),np.linspace(-1,1.5,50))\n", "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", "Z = Z.reshape(xx.shape)\n", "plt.scatter(X[:,0],X[:,1], c = Y)\n", "plt.contourf(xx,yy,Z,alpha=0.3)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Teniendo como base el anterior clasificador, procedemos a continuación a mejorar la clasificación viendo cómo queda esta clasificación después de realizar una serie de pasos." ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "data": { "image/png": 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zaRGZu48hDSNku+bT2fblLrr06xj23MKsIrYu20VNhYd+Y/owcEKfUIHFM4Tu1/n8hWXsXLUPVVWQwIybJzL5mnFn5Xo2NjbfDHat2oeuBU/IdE1n95oDXP3dOWHP89Z42bFiL8cP5tCuWxKjZg8nJiHqrN3n/g2HWPCfJWg+P4Zu0HNIN67/0VwiYyPP2jVtzg22A2bTIjSvhpQWO6TEV6uFPW/Xqn0seH4Jul/H0CX7N6az6fN23PPkzWdFTHHJKyvYvXofus+PHti28t31xLWJZej0gWf8ejY2Nt8MdL8efnuYZlIVxZW88IM38Hp8aF4Nh8vBunmbuf/Pt5PSPfmM32PO4Tzm/XMhmveko3hk91HeeeoT7v/zbWf8ejbnFnsJ8luM4TfYvXo/Hzz9GQtf+JLcI/mnPFaPIV0x9NAImNPtYPCk/pbn+Go1Fjy/FM3rx9BN702r9ZF7JJ/dq/ef8r2Ew6/pbPtqd5DxAtN5XP3RhjN+PRsbm7NLTkY+C/+7lA+fXsCeNQcw/KE2qKX0GtYdlEZeloCeQ7uFTcRf+tpKqitq0LzmJNPv8+Ot8TH/2UWnfB9Nsf7TVDQt2FHU/Qa5GXkUnyg5K9e0OXfYEbBvKbpf59VfvEfe0QK0Wg2hCHYu38OcB2czavbQVo8XGRvJ8JmD2bpkZ9B2v6aT1CnR8pzjB0+gqKGWTavV2L3mACNmDWn1fTSFz+NDhrHPlaXVZ/RaNjY2Z5fUJTtZ8spy/JqONCRpWw+zZckO7vn9zSiO1scOZt02hfTtmcEbJbTr1jbsOYe2ZiCN0NB/bkY+mtdv2Sz8dCjNLweL6ykOlYriqrC21uabiR0B+5aya9X+eucLQBoSzetn0UvL8Hp8rR9QQlrq4dDNhmT526stT3G6ndbLloD7LLREioyJIDLGupqpS9/wOWo2NjbnF7XVXha/vBzN6693gLRajZzDeexdd/CUxkxLPYxq4bilLt6Jt9preY7Dae1gKYqwnFyeLr2GdUW1uKbfp9O+x5lf8rQ5t9gO2LeUvesP1jtfDVFUhaz92a0er7qihpqKWst9x8KM17lfB1yRzpDtzggnYy4d3up7aA6hCOY8MCt4VqoInBFOZt817Yxfz8bG5uxwdN9xS2dJq9XYu/7AKY2Zvj0TXQvNA1MdCjmZ1ukZI2cPxeFyhBzfb2zvs5LDOm7uaCKiXEFFSs4IJ5OuHmMn4X8LsZcgv2EYhkHGzmOUF5TTsU8HOvZKsTwuIkyESUpOSfOmqYhVdLx1RZCiKNzxmxt4/TcfYPh1pARdN5h45Wh6De/e9AUl7N94iI0Lt+Kp9DBgfB8mXj2WyJiIJk8bPHkAUXFRrP5gAyV5ZXTq054Zt0whpYllBhsbm6+PouwSju07TlR8FH1G9cThDHVk3BFh7I04NfsFEN82luxDmAn3DTB0g9iEGMtzZtw8iZyMPLL2ZyMUs6o6sX0CVz12abPXK8ouYc3HGzmRnktylySm3jAxrL2uIyYhikeeuYfVH6zn0LYjRMVGMunqsQydahcQfRuxHbBvEBVFlbz883fwVHgwApIQ3QZ15bZfXRsyGxtz6QjSUjPqk0frcEe5mpSMCIfD5WD49IHsWrMff4Mkd6fbyZRrwwuxduiZwk9ef4yMHUfxVNXSY0hX4trGNnu9r95ew8aFW+ujeMW5pexavZ/HnrmnWQPcc2g3M7HWxsbm/EHCZ88tYdeafQiEuYznVLn3yVtCKgq7DeqMw+XEWxOcLuF0ORlz6YhTuvzEq8ZwaOuRIJuoOBRSuiXTtrN1bpXD5eCu391EXmYBeZkFJHZoQ9f+nZpVz889ks/LP38Xv1dDGpLC7BLSt2Vy6y+vbXbyGZcYwxWPXNLax7P5BmIvQX6D+OgfCykvrAiURPvRvH6O7cti3fwtIcf2GNqVqdePR3U6cEW6cEe6iE6I4s7/uxHRuBKohVz+0Gz6j+2D6nTgjnLhdDuYdM3YZpPpVYdK3zG9GDZjUIucr6qyGjZ8lhq0hKprOtVl1Wz7cvcp3buNjc25Zc/aA+xea07gNK+G1+PDU+HhnafmhUSlFFXhzt/eQGRcJO4o036pTgczbp5E1wGdTun6Xfp34opHL8Yd5cIV6cLhctC5b0du/dX1zZ7bvkc7hs8cbF67BeZzyasr0Ty+kwn8UqJ5NT5/Ydkp3bvNtxM7AvYNwVNVS3ZaTkhFjub1s+3LXUy7YULIOdNumsioS4ZxdO9xIqIj6Dm062kJoB7adoSiE8W4Ihwkd07i4rtnnLIxbIqc9FxUh4K/Ua2A5vWTvj2DiVePOePXtLGxObukLtlhmZdaXV5D/rHCkChYh54p/PT1x8nYfQxvjY/ug7uclgBqSW4ZaakZKA4Vd6SLsZeNMAWaz0IvyOy0E9b3kFd6Vqonbb6Z2H8F3xD0JvRv/BaJpXXEJEQzeLK1TldrMEvCV9SH77MOnOCN33zA/X+5jQ49m85raC0xbaIxLEqxhSKIbxt3Rq9lY2Pz9RDOTilCoIVpGaY4FPqM7HHa1y4vrOC/P3odX40ZlfJUeFj5/npqKj1cfNf00x6/MZGxkWjeypDtDqeK6rQXnmxMbAfsG0JMQhSJ7dtQeLwoaLvqVBk8qd9pj++t9rJ/Uzpej5dew3uQ3CAnwtANlr25OiSfTPNpLH9nLbf/uukQ/t51aXz5xkrKCiqIS4ph1m1Tmly27NirPQnt4inKLg6K+DmcKuPmjjrFJ7SxsTmXDJ02kPyjBSFCyYqqNJuc3hzSkBzZfYyCrCKSOibSe2R3FOWko7N+/hb8tVqQPdFqNTYt3MaU68Y3WdxTmlfG5y8uI2NnJqpDZejUQVx634wmc1EnXT2Wr95aE2QznW4Hoy4eHnRfNhc2tgP2TUDC7tX70XyBl1kRYEicEU5iEqKZcfOk0xo+c08Wbz85DwEBtftVjL54GHMeuAgEVJVWh/RQq7uvE+m5TY69f+Mh5j/zeb3RrSiq5PMXlmEYkjYp8WSn5RKXFMPAif1wRQQkKwTc9bsbefePn1CQVYSiKAhVcNWjl57xaJuNjc3ZpyCriPTtmWYUTAiQEtWpoiiC6390xWmlRnirvbz8i3cpzStD9+uoDpWYhGju/8vt9UuWR/cft1xFUB0qhceLw6ZS1FZ7eeGJN/FU1YIhMXQ/O1ftJe9YATf9+CoObE5HGpL+4/qQ2CGh/rwJV4ymvLiSLV9sN9Mp/AYDJ/bjknumn/Jz2nz7sB2wbwBr5m1i9YcbTuZPGBKhCqbdOJGJV44O0alpirrGsjmH82jXrS1Dpw3i3T9+gtZInHXbV7vpM7oXfUb2IDI2snGObD2GbrD83bWkbztCTEI0E64cQ69hJysQzchZaGugz/+7FNXpMPuruZ0sfmU59/3pNtp1NeUi4pJiefjvd1GaX0ZttZeUrsmnpH5tY2Nzbik+UcJLP34LX62vPtleUQVd+3fimu9dTkK7VqQVSMjYdZR9G9JwOFWGzxzC1qU7Kcourtf40jWdMp+fhf9dyi0/vwaApI6J5GUW0lgZ2lvjJXPPMVa8sxafV2PIlAGMvmR4fY7W9uW78Xu1IHV6XdPJyyzgmUdfwqxjk3z19hpm3jaFydeMNQ8ScOk9M5hx40SKc0tJaBdPVJyt42UTjO2AnedoXn+w8xVAGpJj+4+ffOFbQEVRJS/86A28NWZjWafbwcr31ltft1Zjx/I99BnZA6fbwciLhrJ1yc6QfpCeylrWfLQRGej1mLk7i9l3TWN8YKmwLL/Mcnzdb6AHsuw1jw9NwIdPf8bjz90XdFyblIQWP5+Njc35x6oPN5hLcQ18H0OXZB3MaZ2avISP/7GQA5vTTXuoCLZ9uQvDkCECq4ZukLblMIZhoCgKk68ZR9qWw/gtcs2Wv7O2/t7yjxawa9U+Hnj6dlSHSt6R0CVToMH1TtrDFe+uZcDY3kHtgtzRbjr2bt/yZ7S5oLBDCuc5ZQXlCGFhpCSkbzvCX+58lt1rWtbYevGrK6guP9lYVvP60Wq1kNyuOnT/SaM2575ZYeUr6pwvc0yNZW+sxheIqCW01IGSUJJXRkVRaOKqjY3NN5fjB0OrtwF0zc8/H3yRef/8vMlCojqO7Dl20vkCCLRXs1K3B5BS1jtWnfq0D6+/1eDWNK+fwhPF7NtwCDDlJ1pasWj4jfrzbGxagh0BO8+JTYwJXwEpzQjUZ88upk27eLr0P5nHUFvtZfMX20hLzSAuKYaJV40N21jWan1RKIJBk05WTyqq0mQlZuMBj+47Tt/RvZh9xzTm/XNh8CxSWF9TSklORp6p+3OKatcNKcwuYc1HG8jNyKddt2Sm3TAhpNTdxsbm7JLYsQ0luaWW+3RNN5cTHSpXfeeykzsk7N90iK1LduLzagybNojczHxLGYtwdOrdPii3zKq1kRWaR2PP6v0MnTqAkRcNZfVHG9E0vX4ZUqgCaRCynCmlpLyonNL8sjMSuffVamz5Yju71+7H6XYy7rKRDJ028KzIZticG2wH7DwnItrNkCn92bf+oGUoHEDz+Xnvj/NJ6dGO4dMH0XtED1740RtUl9eYIXdhRsta++Zm7jrK0KkDzB8EdOiZTG5GQbPnaV4/7/1pPrNum8Lka8fh1y5j4Ytf4q0yG95GJ0RTW+UNSezX/Trz/vUFuqYz5bpxzLh58ikbmxPpebz6y3fx+8xmvgXZxaRtOcwd/3cD3Qd3ObVBbWxsWs206ydwbG9WWPvl9/rZvnwPhdnFdBvUhfFzR7Pm4w1s/2pPvcOVm5FPRIwboQjrSaQFhdnF+H3++hzZPiN6kr49s0VO3KHtGbz8s3e44zfX89Bf7+TDv35GzuE8wFTj9/s0jEaBN2lIdq7az44Ve+nYK4Vbf3HdKed96X6dl3/2NkUnSuo7j+RlFpC5N4urHm++DZLNNwN7CfIbwJWPXcrwmUPCJ6FLqCqrJmNHJgv+u5SXfvoWVWU1J/MdpOkU6bqO6myZzy0Nya7V++tbHgFc/uDFON3OFjlFuqaz8r31ZO7JYu+6A0HLBNVl1Ri6jjNQ9Vi/tCnNiia/z8+6T7a0eGnVisWvLEdrWHZumErUX7xkK1Hb2HyddBvUmet+cAUxbaLDHiMNSdaBE2z4LJV/P/oSW5fuCnKUNK+Gp6q2VdWSUsKhrRn1Pw+dPoj4tnGoFr0nQ87VJSfSc1nwny8pzC4Okv/xeXwgzIia6lCCUjM0jw+/18+JQ7m8+8dPWnyvjdm3Po3inNKgtm9arcauVfsoDhNNtPnmYTtg3wAcTpUrHrmYpI7W/coaotVqlOaXW8pGOJwOXBEtD3rqfh1dO+mAdR3QiQeevq3FVZeaV2PNx5s4vPNokCFBmkuaAyf0Zcr147FKcdO8Gms/2dzie23MiUPW8hj5xwpDCglsbGzOLgMn9mX6TZOabYOmazpej8/yHfV7/SQkt7xiUhqSmgpP/c9Ot4OH/n5ni3o51t3L/o1pLH1tRUj0zvBLIuMimXXbVBLbJ4Se6zfIycijLL+8xffbkMM7rCN1QhFk7c8+pTFtzj9sB+wbQnlhBaV5ZS07OEyIXvNq+GqtlwGsSO6SFJKAqvuNVgkJVhRXolrMWnVNx1PlZcylI0IaiddRXV4DmLkQO1fsZfWHG8jYebRFSxARMdY5ZK4Ily2EaGNzDti6dGfLlg8llscpqqAkTFW15TBS0mNot6Bt7kgXRTmlljmolmMYkpIwdreqpJqJV48JOyFVVYWq8hqQcGxfNqs/2si2L3fjrfY2e924pFhLxXwhBDEJ4SOJNt8s7Bywbwh+v97ifCjFIRCIoKT5usRRS0FVK4Rg9CXDQza3PBHfnHEOGNeH9Z+mhuxTHQrtuycT3zYWZ4QrZIYpFEHPYd0oPF7Myz9/G79moHk1XG4n7bq25Z4nbwlyDssKKtjw6Ray03Np1yWJodMGsnXJzqBxnW4HYy8bYSex2ticA/wttT1QLzbdEEMSdnJpRbuubS2jU4a/+YrLOtp2TsRb46O8sCJkX3RCNIqq0GdUT4pOlIRKYRiS5C6JvPX7jzi673h9PtqSV5dz1+9vpnPfDvXH6n6d7V/tYdeqfTjdDvqP64OiqOgNZC4QAlekK3w1p803DjsU8A3AU+Fh+VtrgpfxmkBVVSZfOw6H24E72o3T7SCpQ5uTSvMtQUqWvbGawuySoM2d+rRv2RKeIohLjKXXiO7ouoXBE4Kxl41AKIIrHr7YdKYCjpHqUHBHuZl162Q+/NsCPJW1plCsIfF5fORlFrBu/snlycLjxTz/3VdIXbKD7LQcdqzYS+qSnfQe0RPVaX4GqsvB4MkDuOj2qS3/DGxsbE4fCZsWbrN0Yqxwuh1MvHIMcW1jcUW6AlXRribbBVlRcLyIFe+vC9ned3TP8Cc1mJypTpW5D822lrkQgmk3jgfMtkORMRFBuWVOt5NL7p7O7tUHyNybVZ+PqtVqeGt8vP/n+fVROMMweP3XH7DkleVk7c8mY8dRvnx9Fd0HdyEyLhJXpAun20nbTm2476lbTqtrgM35hR0BO8uU5ZdTnFtKcuck4trGtvp8aUhe/sW7lOSUhOyra0VUW+PF0E5GyG740ZX0HdOLydeOIycjn5j4KOKS4/jLHc+26tp+zc+Gz7Zw1WMnq25Uh0pUbCQVxc3odRmS8pJKlr620nLWqqgK0YE2IYMm9SOubSzr5m2iJK+cHoO7MOmasagOlYJjhSHLBX6fnx3L9zDj5kloXj8f/nUB3pqTSv7SkPi9fopySvjJ649SkldGQrt4ouOjWvX8NjYXOr5ajexDObgj3XTq3f6UoscbFqay/K21lj0gVYdCcue2FGQXoTpU/JrO0KkDufjuaVxy93RyMvLRNT+d+nbgo78uYP+m9BD5h3D4vX42fraVGTdPCko7aNspKawUTsNtQgjWztuMz0InUXUoJLRLACA6PorH/n0v6z/dQvq2TFP25+qx9BrWjWcffdly4uyprCX/WCEp3ZLZMD+V7EM5QY6eVquRuTeLR/5+F36/jtPlpG2nRDt6/y3DdsDOEn6fnw+f/ozDO4+iOlV0TWfA+L5c9/3LW9VSJ3NvFuWFFSFLf4oqGDypP1c+dglCCLIP5aL7dbr074QjMBNzR7npMaRr/Tlj54xgw2dbW2zApCEpyg51/KwMkhV+r5/8TGvZCsOv8+ov3qNNSgLj546iS7+O3PKLa4OOydyTFTZnxK/5yT9WxKu/fBdPg0TbhhRlF+NwOenUp4Plfhsbm/BsW7abRS99haIqSCmJiovkzv+7kbadmy8GqkMaktXvb7AUe46MjeDRf91LbGI0xTmllOaX065bW+ISY+qP6dTnpIr8jFsmk5Z6GN3f8mVI3a+jeTTc0SdzQj3V3hblgPl9fg7vyLQWkdUNlr25ms2fb2PI1IEMmz6Qi++azsV3TT95kISSMEn4ut+P7td587cfcmT3MctVBQEc25/N6EuHN3+zNt9IbAfsLLHk1ZVm9Z/PXy8HcXDzIVZ92IaZt05u8TjFJ0qRRujLaegSxaHUJ7CHaybbkKqymvAzPwtUp6N+XM2rsfL99RQcKzKjbtW1LRpH160P0v0Gxw+e4HhaDgc2pTHnwdmMmj006JjcI/lhx01s34b3/zQ/rPMF5gx7/r8X4df8DJk8gEGT+9kJ+DY2LSAnI59FLy0Lilr5an28/pv3+dHLjzRbzViH5vPj9VgnnXs9PmITzYTypI5tSOrYpsmxaqu9WJZMN0FEdASuSBcA+zaksWvFXnw+sw1bOF2yhggBWGmPGZLCrCIKs4rIOniC7V/t5t5Gy4M1lR7r9AvzdDL3ZHFs//HwKR1CcGj7EQ5sTqdTnw6MnTPCTsD/lmE7YGcDCTuW7w7pO6Z5/ayfv4XYNjEMmNCXmITml8TadW0LYZyGnSv20Kl3B0ZfMqzZcQzdMHW1WpHE6nI7mHDFaHLS83jxx2+2WAAxiOaibdJsJ7Lo5eUMnTowKLHeHWnmbukW/ds69m7PtqU7ww4rFIGhG+xdewCAjJ1H2blyL7f/+voWf3nY2FyopC7eEdoeSEJ1hYfPX1rGiJlDgpLIw+FyO4mIiaQmUNHcEL/XzztPzePGJ65qUbufFe+tDdt2yAqH28HFd01DIvn3Iy9TfCI0mt8cRpgJZEO0Wo3cIwUc3JzOwIn9Tl7fqaIoAsPCbkbFRrL9q91NOoFarcahrRkYfoPMPVls+nwbD/39TpI6NO2o2nxzsMMBZwFDGmg+a0OheTWWvLqcfzzwX/auS2t2rG4DO5PcOdFSPFDXDBY8v4TVH25odpxdq/a1yvnqM6onD//zLmLaRPPG7z48NefLijDOjyJCI14DJ/ZFhplBmmKIYRwpYQbnGt6zVqtxdH92oCOAjY1NU1SXVVsvvfn8bFu6k9d+9R7v/vGTIKFmSwTMvmOqKeBsQdrmwzz7+MvNjuOt9nJ07/EW339MYjQ3PnElI2YNYfnb607J+QqH1QROq/VxYMvhoG2uSBed+1mvTEjDaLKivK5JuRE4xu/zU1vtZfHLK071tm3OQ2wH7CygKAode6WE3a95/fi9fub9cyGeqtqmBxNwz5O3MKSuJZAFy99d16zg39YvdzV9nUYkpMTTJiUBT3Vtk8t8rSU2jBq2oUsio4O1uyKi3Igw0b9DWzOIjA2tilKdKn1G9cJpoc2jeXwc2JJ+CndtY3Nh0X9cn/pOFY0xdLOaL31rBtuW7m52rFEXD+Oqxy4Nq0Bfll/OqvfXNzlG2rYjYfUCrW8Seg4zNcC2f9X8PbYUoQrLanJFFZZFPvFJMSHbwEzB6DG4q6WGWFxSrHXkTUqO7D7a6nu2OX+xHbCzxNyHL8YZ4WqyZFjXdJa82vyMxh3pYvzc0TjChekNyY4Ve5oco7YF4n8NSV20g2P7jrcqatYcQhF06tMhxLALRdCmfTzJXdsGbdd1I2x+hLfGy00/vgpXpKv+c3FFuujQM4Xh0wehWETHFFVpdSm7jc2FyNBpA0nq2KbJpUHdb7DktRXNR8GAodMHEhUXPuViy+KdTZ5fU1mDbGHxEJit2epaAbXmvOZwuZ0oFo6g6lAZNTs0FSSc3RUCeo/sQbsuSfU5ak63A3eki5t/fk3YlnHhIok230xsB+w0KC+soCCryNIAde7bgceeuYfRlw4nNtF6FgSwZ82B0FwLC5I7JzW5DFh0osRSYVkakg+fXkDRieJmr9GYVR9uIDI2EneUq9XnWiENSXxSLFOuG4fD5cAd5cYZ6SKpYxvu+M0NIcc7nCrJXZIsx+rUpwNd+nfih/97mNl3TmPSNWO54Ykruf8vt9F/XB/LpU7VoTBy1lCL0WxsLjy81V7yjxZaOgkOl4MH/nIHl9wzk46924fNm9R8fg5tbdmyfreBncPu89Vai50CHNycztJXV7Qq/wvgyO4sAIZOCb960FpiEqK56rFLiUmMrtcoc0Y4ueo7l5FsUR3ab0xvS6fJr/npMaQrD/7tTq7/4VwmXjOW2XdN54cvP0znvh0YOm1AiBPmcDsYY1dEfquwk/BPgfLCCt7783wKjhUhVAWn08G135tD3zG9go5L7JDA3IdmM3TqQF7+2TuWCelCERTnlJLSrW3IvoY43Q4mXDmadfOs+yPu35TO/o3pDJjQh15Du1FTWUvX/p0oL6pg/6aDyBYkkzbGW2Ma5lt+cR2v//r9FstXNMW2ZbtJ6Z7Mj159lJzDeUTHR9GxZ0pYfZsrH7mE1//vgxAtnez0XDJ3Z9FjaFcmXDE6aJ/iVrjztzfy9u8/qo+gGbrBlY9e0qoSehubbyPSkCx5dQWpS3aiOhT8foORs4Zw+UMXBVUJO90Oxs4ZwZhLh/PHW/8VpLVXjyE5ujeL/mN7N3vdWbdNYe/6g5ZRdb+m88wj/yOhXTyTrxtHdVkNsYnR9B7Rkw+e/iyoJ22LCVzn0ntnsm9DGpUlVa0foxHFuWXM+9cXPPDn2/DVamheja4DOoeNFI6YNYTNX2yjOLe0Pp8LTFO67O01XPXIJfQf18ecNDbg8gcuoiy/nOxDOSiqgq4b9B7Rgxk3TzrtZ7A5f7AdsNYi4bVfvU9pfll9RErz+Pjg6c945J93W37Bdx3QidjEGCotxEsNXRIdH9miS19813TyjxaSviMzxIjVVQruXXOA/evTAInqdJhVOK3QzWnIqNnDAeg5tCs/eeMxlr25mt2r9rWqHVFj/D4/BceKOLLzqGVem69Wo/C4KXURnxxH14GdmXzNOFZ/tCHIifR7/Xzw9Kf85M3HLaUluvTryE/f+A6Z+46j+/x0G9wFd+SZieTZtBDpRfo2gX8/KIkI9zRQ2jd/ns1ZZd38zWxduisgkWNu27liD9HxUZYSOUIRTLp6LCveDVWVV10O4pJaJjCd1LENNz5xJR//fUFojpMh8fv8FGUX8+kzixCqwOlyIqHVka86EgOyFopD4YlXH2Xzou1sWJBKWd6pNcgGzKrtWh+LX1nOPX+4JXTiKE0Fft2vk9I9GafbwR3/dwP/euiloMMMv8HuVfsYOmVAkFZjHa5IF/c8dQsFWUUU55TSrmvbZmU6bM4C/jSkdyOgIVxjwTmUM6mGaztgrSTr4AmqLCqEdL+fLYt3MOeBWZbnXfXYpbz35/lBkgqqU6VTn/Z4q73ExEe36Pd6x29u4OCWdFKX7OTY/myzGrARJ6M+LRNMtSKpYxtGzBpc/3NMQjRTb5jAwS2HTzspX/NqfPSPhRzYdIirvzunPql1w6epLH9nLYpq9rHs0r8T1/3gcg7vOGIZwfNrOrkZBUFijQ1RHAq9hnWz3GdzlpEeZOUfwSgHfICC9K5DRD8IziHn+u4uaDZ8lhoijKp5/WxcuDWsRuHka8exYcFWahsVDSlCkNK9XX2fw+YYPLk/HXq0Y/1nqRzZdYyywvKgyFAdUpeWtq3FKIKbfnJV/Y9CEQyfMZjC48WkLt5x6uMCSMjcncUzj/6P6394Rb39yT9ayDtPzaO6vAYhBE63k+t/NJeyggpUpxqSz6p5NfauPWDpgNXRrmtbU4rI5mtHej4B7ypM+wVS2wvOgaYNO0NO2BnNARNCPC6E2CqE8AohXm/m2B8IIfKEEOVCiFeFEO6mjj9fqCypslQ/MHRJWWH4mVXf0T255O7pOAN5A3UVQTkZeTz//dd45tH/UXi86Tyt8sIKPnt+CWvnbSapQ5sWGbxTQXEoPPi3O+sLCCpLqnnhR2/wn+++Wr8sedoYkgObD/PxPxYCkJaawfJ31qJ5zV5pfp+fzD3H+Nu9/yH7UG74e1VtTa/zEVn7FRil1BkvMAANWf1G4L/PPy4E+wVmGxwrvNXesHmmDpeD+/50G4kdzMR8p9tpdvjQdd774yf86fZ/s2XR9iavq/t1NnyaymfPL6GqrJou/Tu2rK9sKxGKYM59M+nQM1CJLmHpayt5+q7n2Lly7xm7TvGJEl7/1XtUl9egef28+st3KcsvR6vV8Hl8VJdV88avP+Cz55dYdgJACITd1/H8xCgE70pO2i8AL2j7wH/ojF3mTH+D5wBPApcAYdfVhBCXAD8DZgbOmQ/8LrDtvKZz3w6WS3BOt4Pew3s0ee74uaMYNXsYxw5k8+6T84KEWotPlPDST9/ihy8+RGRs6Ee3ccFWFr+yvF59/vjBE6f3IE2gKArHD+bUN6196/cfkn+08MxpgQXQNT/p2zKpKq1m3SebQ41UM5eLiHLTvnu7M3pPdeRlFrDm400UHi+iU+/2TLl+gr0E0Bq0bYCVyKQGei6ozXduOAd86+0XQIeeKZxID53UtOvWtkmR4pRubfn+Cw9SnFPCx//83BxDgi+wjrnof8uJaRND/3G9Q9ICinNK+e8PXj+9qFYLkYYkfUcm4wO5oalLd7Jl0Y4QYewzgW4Y7Fq5j7ikGPxhtB/DVZI7nQ6GTx9sue908VZ72bhwKwc2HyIiOpIJV4wKyTOzaQJtf5gdPqS2B+HoF2Z/6zij7reU8hMp5adAcyV3dwGvSCn3SSlLgT8Ad5/JezlbxCfHMfKioUFSCqrTQWxiDMNnNv8yOd0OTqTn4NdCjYG3ysuf7niW57/76smoj4R187ew+OXlzTokZwq/z89Hf19AcU6pmYNwotTS+YqMi2TghL5hxVVbgsOpUFlaRWlBWYuOVxzCrD6KdnPrL68N+sKoa9L94V8XsPS1lRTnlp7SPWXuzuJ/P3mbvesPkn+0kB0r9vLfH7xOXpi+ljYWiHD+iwHnabDoQrBfAHMemGVW5tW9O0LgdDu4/MHZzZ8sTCmEnIy8EHtk6Abv/2k+f7rt36z7ZHP9/vyjhV+b81XH4e2ZbP5iGwDr51tM7gCEoOfQbpb6XS3F7/VTnFdKZWm19TUaowgcLgcOl4OJ14yhc7/gbgLHD55gwX+W8sm/vuBQasYpTXp9Hh///eEbrPl4E7kZBWTuPsZHf1/I8nfWtnqsCxYRgbV7pAJnTsroXOWADQI+a/DzLiBFCJEkpQwyfkKIB4EHAdq3O7dNlaUhObL7GLFJMQyfMZgTh3PxeTQGTezLpGvGWQr0WXHiUG54Z8qQ5B8t5LVfv893nr2PzV9sY+PCrWfuIRqgOhWzsa1FdaPX4+Oz5xYz4+bJZvNwi5XHpA5tuPnn1/DMI/87ZaVpQ5ckdUxscY/GDj1SGD93NAMm9A36vH0eHy/++C3KCswlAMWhsGXRdm76ydUh1anNsfCFL4OMqTTMfJTFr6zgnidvbtVYFyrCPQNZc4LgEL4ApQMo3/iclhbbLzi/bFhpfhnH03IYO2ckBVmFlOSW0q5rMtNumtikeHRDqitqmqyq9lZ7WfneehRVofvgrrzys7eb77soBEKhddXaiggbXZKGZOlrqxg0sT81YZZcFdXME9uz7gCfv/jVKVV5uyJddBvYhZqK0FZLVqiK4OK7p9N3VC8SOyQE7Vv5/nrWzduM5vODlOzbkEa/0b248cdXtSrlaPtXu6ksqQyK+Gm1Guvnb2H83FGn5XBeMDiHAe9a7FAQ7nFn7DLnygGLARomTNX9dyyNZp9SypeAlwAG9B30NcWAGt4A7F1/kC2LtnPicB5GQBzU6XbidDm4/y+3By1NFWWXsG3ZLqrLa+g7uhcDx/c1HZgAmtfPsQPNLx8afp21n2xi+1d7LJNUQ2jCGIUgBCnd2jJs2iAUp8ISq/YWhuTY/mzikmPDhu5PHM5l9YcbuPO3N/DiE29SU9665HxnhJNpN0xoseMKZrRx2IxBIds3fr6N0ryy+ns1/AaG32DeM1/w0zceb1IQtyF+n5/iHGtnMjvt7C37futwjQE9E7xrMWeNgBKHiHn4nN7WGaLF9gvOvQ0ryCpi3SebObLrGJWlVYF3QSAEXHrvDMbOGVl/rNfjY9fKfWQdPEFyl0RGzR4W0gD60NaMZq+peTVWf7SBtNSMFjW9RkpkS4sdFUFMfBS9RvRg0MR+zPvnQrzVodE1oQjSUjNo3z2Zo/uyQxwsw2/wwdOfcf0TV3BkTxb7N6S1apVBdarEt41l0MS+pC7e2SIbrPsNxl42IsQelRdWsPbjTSFOU9rWDI7sOUbPoS0vJkrbesTyM1cdCtmHcunXygnpBYmIQMQ8iqz6b4ONOkTeAUryGbvMuXLAqoC4Bj/X/XeoTsPXiYSsA9mU5JfRsVd72nVty4L/LmXXqn1otcHhZZ/Hh6/Wx0d/X8DDf78LgL3rDjL/mS/Q/aaTtm9DGhsXbOWep27BEUi637lyL77a5kPVuqazZ80By0bUlrQiVO2OcjL7jmn1kaGV76/HW2Uh4irh+e+8GnYcqUtWfrCektwyHvjL7ZQXVpKx6xjrP93crNMoFMGVj1zMsBnmsm3nvh0pK6xo9jkS2ydYbt+79oClo+j3aeQfKzyZkNsMqkPF4VQtDViEraLfCgQi8iZwX2w6YiIOHL04kyXc55Dz035hfpEf25dNZGwEvYZ35/jBHN787Ydomr/+3dKNk57O4ldW0G9Mb+KT46gqrea/P3yd2iovmlfD4XKw7uPN3PunW+vfH0+Fh9UfbmzRvXgqasncc+zMP6Qh6dArheu+fzlgtjra8GlqqPMkJUtfX4mu+cNGt47sOcaLT7zF1d+5lNl3TiNty2FSl+xsUUS/3+heXPPdOagOlZRuybjczmaXWVWHahntP7zjqGVxl1arcWBTeqscsPikGIQiQpYvDYkd/WoNjn6IhL+C/yBIAxz9AkuTZ/ASZ3S0lrMPGAZ8GPh5GJBvFb7/uqgur+G1X71HWYGpxiwNg879OnH84InwyZsScjPyqanw4HQ7+fTZxUFf3FqtRl5mAbtW7GXUJWabiv0bD7XYqWpc8h0WIVoVPvfW+IhpoM4/YvpgUpfuMg1VHYpAQLOJq4ZmsGP5Hnav2c+w6YOYftNENi5MbdYBc7qdCPVkS48ZN01k/4aDTdbHOdwOxlw2wnKfK4zGl1brR29FpZVQBKNmD2Prsl1B4q9Ot5OJV45p8Tg2AZQ25r9vF+ed/ULC0tdXsfmLbfXRFZfbiSvS1WRukq4b7FufxsSrx/DlG6uoLqupr0z0+/z4gfn/XsSj/7oHgGMHT7Q4mlx3X2eDiOiTX4TDpw8mddGOkEmT5vU3H32TUF5QzjtPziMmIZr7/nhriyslq8prcAf61/YY0pXYpBiKs8M7bqpDZdj0gZZzEKfbgRFm8llRZN0hIBzj5o5iz9oDQc8uFEFcUgyd+5zbJfBvHk5wnD3ZnDMtQ+EQQkRgrjmoQogIIYSVk/cmcJ8QYqAQog3wK+D1M3kvreWTZ76gKLvEjGx5fGheP1n7jyOb6XMmDYnm1cg+lGM9g/Fq7F57sqIiIvrMJCALxUxGd0Y4TyGoINi54qSRueiOqaR0b4sz0oXD5cAV6UJVlVYlgJoRu/1k7skiyqKKM+R4v05FUUX9NdwxEWEbb4P5vBfdNpUu/Tpa7u87OkxYXQiO7j3e/AM04OJ7ZjBgXF+zXVK0G4fLwcjZQ5h4le2AfZv5Jtuvg6mH2bJ4O36fv96GVZVVU9JcIYohyTtqFpekbTlsKQtRcKyoXn4mMjoCv+/U9QUBEOb77HQ7cEe5mqy8DMehbRn199q+Rztm3DwZ1eUwJTIinKgOpVUyPX6vn7LCCj54+jMGju/bonMrS6vQ/Xr9MzVp2wV06JXCnAcustzdb2zvk2M14kRGfrP30pAOPVO46jtzcEe5cEe6cLodpHRP5u7f3fQtCUB/ezjTEbBfAf/X4Ofbgd8JIV4F9gMDpZRZUsolQoingZWY5d7zGp33teKr1Tiy82iI8dH9BpZeVUMUwYbPUolvGxc2COVyn4zOxCWF7wvZEtzRbq77/uV0G9CZ42k5HNiczs6Ve4OWFZpFSvauO1AvGuuKdPHw3+4ic18W+UcLSerQhs/+s4SKwtatqGheP5sXbeea787h3ac+Qff7QxWvA+h+nS/fXM3K99Yz5rLh9BrWHYfLEVb12uFUmzTU8UmxKKoS+gUiJTmH81r1HA6nyg1PXEFVWTVl+RUkdUywlAax+dbxjbRfAKmLd4SkSbSUnIw8dq7Yi2rRZBpACIESiFYndWoT9p1uEYpg9MXDuOz+WZw4lEtpfjkL/rOkdfYLQJecOJxXPyGbfN04hkwdwKGtR1CdKmUF5az5qGVLpfUYktwjBVzzvcvZuXIflSWV4SNoAkrzyvn99X+n64DOXP3dyyg6Hj76JYTAW+PDFWEdqXdHuMJGC606qDTH0KkDGDSxL3mZhUREu20JnfOUM+qASSl/C/w2zO4gz0NK+Q/gH2fy+qdKuJmHSTPGxpBs+mIbqqriDzOO6lDQvH6cbgc+z6nPHlWnyqiLhlKQVUxSx0T6junF5kXbT6lVR8gygoAeg7vSY7Cpytx7eA92rtjTamPrq9XoNbw7j/zzbjZ9sY3inFKKc0qoKq0OXs6UmG09vBqpi3dQXlTZ5HNoXj/rP9vChCtHW+5P7pJkqTbtcDtanP/VmJiE6JAEZJtvL99U+wVm8rwVqkMBIZp8twqOF/PZ80sxdN0yd8jhUqkqq6JNSgJlBRW4Il2nJCkhFGEu1bWJIWNHJn1H98Lr8eFwqa22YRJQG9mw+OQ4xlw2HIDstFzWf7rlFIReDVRV4dF/3c325Xs4tPUIms9c4dAb6nxJ83+khGMHsnnpx2+RkBxL/jFroWppmELdOUfyratNBcQlxVJh4WzFJ8eFHt8CVIcatkuIzfmBLcMLRMZEkGCR3K2oCgPG9SGmTXSTn5TUzT5m4RLI9288xNN3PcvxtBw69kwxNXhaiVDM9jybF21nxbtreeGHr7Pi3XW065qM4mgmStdot8PlYEQzmmUzbp6EO8qN6jz54Ioq6hX8w1EnjNq2cyJzH5rNtd+7nMT2CfXOqVUUS/P6SdtymG6DOjc5vqeylrL8chb9bzmv/OJdlry6gvJCMz+iY6/2JLSLD9YkEwKH08Go2UObvGcbm286gyb2s1w2U5yqmcDdlKU3JLrmRxrSMu3AW+PjmUf+x5dvrCKpfcIpq9dLQ3L8YA4r31vHx//4nOe/9xrRCVHNV3lbmDd3lLvJiVXnvh3oPbx7iF5jS2xvYvsEXJEuxs8dxR2/uZ6eQ7shhDBti7CwYYE+ll0HdQ3blBtAUQQVRRWkLtnJa796n/f/PJ8ju08WKUy8ZkzIxNjpdnLRbVObvWebbyYXfC/I6vIaPv77QkrzyoK2O91OImLc9B7Rg8M7MkO7pwhalWDqrfHx9u8/4vsvPMhX765tmWhf3aXqXngp62eKhm6wfv5mbv7ZNWxZtB3D38R40hxDURUUVaFjr/ZMvWEi2Wm5rHx/HYXZxYE8ikmkdEum6EQJ7kgXjz97P+s/3UzmniwS2sUz+dpxdOrTnj/d+gzeGusZcJuU+JOXNSSv/PwdygrK6p3TcHllqkNh1m1T2bZ0F9uW7bI8JqVbMs9991V0zY/uN8hOO8G2L3dx/59vZ8viHZTllwU5wUkdErjtV9cTFRdZ/5kd2JRO+vYjxCbGMPKiIbRJSQj/udnYnO9IWP3RBtZ8vCkoAi8UgcOpctm9s1j+9hoURcFomM/aGtkaTMmGzV9sp+fQ7gyfOZitS3e2yv453Q40n15v93weHyW5pexeuY/O/TqSuScr/HiB7apTRXWYqQi3/fJaaqtqWTNvEwc2peOOcjJ+7mhGzBxCeVEFXo+PG35yFbtX7Wf7V7uQBoyYOZiRFw1lyWsr2LRwm+WlHK7gnNq18zaxbt7moIIcaZFrotVqqKrCVY9fxsL/LLWMSPr9BiveXUdxTmn955C+LZOp14+n66DOLH97bdC1VafCnAcvYuj0gfXbTGHoPWi1GgMn9qPXsO52Xtc3mAvbAZPwxm8+oCCrKGhWJxTBxKvHMPHqsTzz0IuheQCtdL7qMHSDnIx8ug/qwoGNLesnpbocTL1+POvmbQq5D7+mc+xANvc8dQv/+8lbTSbNK6pC31G9GH/lKMoKKnjxx29QkFVcb4TL8ss5uCkdoQocDhUpzeTWm39+DZfeOzNorElXj2XFu+tCruGMcNC2U2L9z0d2H6OqrLpFy5i63yC5SxJXfedSBk7ux/t//ARN08GQgYRdJ75aX9DSh64Z6JqvvoCi8edTVljB2nkbOZ6WQ2ybGCpLq6korsTn8SEUwbp5m7jysUsZMctuDm3zzWTL4h2s+WhT0IROKILOfTty9XfnsH3ZbmprvKFRplMQHdVqNbYu3cGEK8aw7ctdlo5IY1Sng/FzR5pi0o2O1zWd3WsO8MP/PcT/fvo2+UcLww8kzMrHy+6bSZt28ayZt4m0LYfNCueADZv/zCI+fW4xihCoTgeqU+Wa71zG/X++PWioGTdPYtPn2yxteFCulIS1Vi3SLHBGuujYO4Wh0wbSb0wvnv/ea0FpF84IJ72GdSNj17Gg8TSvxqqPNhLxuSskh08IhfRtGeYEWzdo0z6Bwzsy0TUdaUi2L99D1/6duPsPN59SIYPNueeCXoI8cTiP4tzSkJC6UMyESc3js375TiMH1a/pHE/LafHx7bokEdtEHpJA0LlvB2740RVNjqNrOoXZxWxZtIPPX/iSgqNFljNgqUs0rx+/z8+J9Fze/M0HIc87+brxRCdEBc+8BDhdLgZN6l+/qTSvLHjWHQan28H4uaNwB6Qk+ozowf1/uZ3Bk/qT0i2ZEbOG8PA/7qIgq8jy/NzMAsvfk67p7Fi5j6LsEjL3ZFGUXVzvwElDovsN5j+ziC2LdzR7jzY25yNrPtoY8rcvDUnB8SKSOyWSsSvTMr9KdZya6de8frIOZre4QloaOsNnhp/gCGEWAd3/p9uarjyU4PN40bx+Xv/N++xfn2Y+V6P7kLr5Xvs8PjwVHj7624IQuxEZG8mEuaND0h2cbgczb5588lkDFaXNoagKkTFuBgdsnzvKzaP/uocp148npUc7egztZtpnISwLJRRFUFsTmjvm9/nZvzGd3Ix88o8WcnBTOn6vv/6z1zWdzD1Z/Of7r+E/hTxgm3PPBe2AlRdVoFjMHAy/QUluKRHRbsL5D5FxrRdkM3SD7oM6t7htBUBhdjEJ7RMsJ6wSqCqtoqqshsFTBjD6suFNzoS8Hh+HUg+3uFpKGpLSgnKz71sDHE6Vh/56J10HdsYZIRg8voY5d0ge+dtEnOoRZOU/kWW/YMCw1SQmN/+sfr/O1i93sW3Z7vptHXqmcOOPr+SxZ+/lqscuZeMCC6HFAE1O/lrwRbH45a/qc8lsbL5JVIexJd4aL4ZuEJ8ULoFbtDpq4oxwMmTqAHJb0xNVCFKX7KBL/06h1wtINxw/eAJ3tJu7n7y56RxTRWH522tapqwfwK/pbP48dLnxkntnMH7uaJxuJ517a0y72sOdv+xOnxFxyOp3kWW/xOF9mhFTa2luxi2lpLqsmvn/Xlwvsh0R7WbGzZN47Jl7uOfJm1GdDtK3HbE83zBk+CKEFkQZC7KK2Ljg7LSrszm7XNAOWMde7fFroR6W0+2g+5CuuCJdDJ4cmtzqdDu55K4ZJHVMRHUG9jVhy+o0b+Y+cgmuCFfLWgsF0DWdI7uOMffh2ThcjuAkTUOybfke/nr3c3z4twVMunost/3qestEUNVpLg+2JCLVEEURVJZWh2xPSInn/icv4ZcvFnL9I2WMm5VNnPs1qPoX+NNAlhDpPsgDv84lpWvTs0ipS2qraln00lekpYa2OVk/f0uQblljmtIPaxFSsG7+ZmoqTrZSMgyDypLqVhl7G5uvm5Su1m1REtsnoDgUJl0zNsQeqA6FLv06Mvnacaiupotq6nBGuujSryNDpw4MyodqDsNvsGftAa793hyi46NwRjZIgpdQnFPK/376Ds8+/go1FR6++5/76dyvo6X8j8vtwFPdunZnZvVh6ORKURUuuXsqv3wlhvt/XcDMa4ro2m0NVP4GfOtAFoOexWW35DHr+qa11KQh0TWD/RsP8dFfF4TsLy+s4P0/fRLWydJ9/tPK45KGZPMX2yhsFOnzVHrwVLTu87L5ermgc8AS2sXRZ1QPDm4+HDTTcEW6GX2xqVx/5aOXoms6BzYfxuFQMAzJ9JsnMnL2UIbNGMSBjYc4npbD4Z1HQ14AMI1d3zG9mX3HNNp2NvOjUrq3I/9oy2aRkkB7pNwSxl8xii2LdgSHxQ2JBPauOcCh1Azu//Nt3P3kLbzxmw8wdAO/z48r0kV8chyDJ/Un+9Bx9FYoYfj9RthSZln9MoJqQisUTISQON2Si28s4a2/NV8OrXk1Vr2/vr5XmTQkGxds5cu3VjcZyToVGY6g8/06277czbYvdzP64mF06tuBxS8vr5/NDps+iMsfml3fTiocZfnlVJZW0a5bcv1yqo3N2WT2XdN487cfBi0Jqk6VOfebgp/dB3dh7sMXs+h/XyGlqXzffWBnbvzxlUTGRjJ4cn/2rU+jJK+MtK0ZaBZLbrFJMVzxyCX0Hd0TRVHoM7InGTszWzw50TWdZW+sod+Y3pTklXJ07/HgJUwpKcwq4qO/fsbEq8dyz5O38MrP3qEoxxTGVp0OFFVw7fcu590/ftKqz8fpdtB7RA/rnb7NCH0fJxvG1z1Pg++CCIMJl1SwYUk8nqqm339d85Ox6ygVRZXEtY0F4PjBE7z/l8+a/KyspD9aS0VxFS/86E0SO7ThikcuZsmrK8g9Ygq4JndN4oYfXEFy17ZNjuH1+Cg4VkhsmxgSGhRT2Zw9LmgHzFPpIWNHZkiYV0pZP2t0uh3c+JOrqC6voaq0msQOber3qQ6VwVMGMHjKADZ9vo1lb6yySNhXmPvQxcQmnszjmvvQbF7/zfstcxwMyYlDueh+HdXpCG4X1Aifx8fiV1Zwz5M38/0XH2Tbl7soPF5Mj6HdiIqLpPB4cZiQtsRqCuZ0OxgzZ6S1FpZRBXo24ZyvOoSATj2ttXGsKMkr5Z0/zOPoviwzNO8PzfM4G9Qly6Yu3UXq0p1Bv5tdq/eh+3WuDfSea4ynqpb3/vgJ2em5qA4Vw68z/eZJTLlu/Fm/b5sLm01fbLOMnqgNovYjZg1h6LSBFGWXEBUXFWSLOvRMoUPPFKrKajiwOT1kHKfbwcQrx9B/bO/6bcNnDmb9p1soaVR1bIkQ+H1+9q47YDoaUoZd0dO8ftbP38L4uaN48K93sH9TOgdT04lPiqP3yB7kZxbQoWcSuUfy0bWGD21tv1SnQnRCNCPDyNBI7zqgeduk+wXtu2hkHmg+Wqg6FL56ew2Hd2TirdXwe7VmnStFERiErxBvEQFNxfysQl79xTvmryUwXt6RAl7++Tv88OVHwk4M13y0kdUfbkBxmJpsXfp15OafX0Ok3f/2rHLBOmBVpdX85/uvWc5M/D4/BzcfZtCkfvXbouOjmmxkOnLWEDYu2EpFcVW9k+SMcDLm0uFBBk8akq1f7mzy3hRVQQjQdWlKTwTKy5tyvuo4fvAESNi2dBdrPtkMhsHuNftN0UKB2QhW+BsYQYGlBQ9UHk6YOyrMlSQ+r0BRBA5n04ajqrxlyxxg5q6kbT3c4kIHoQjadk6iLL88KBnZ6XagOFS81Y0MrALI8L0zrT5jv9fPnrUHmfPARZbtRj58+jOOp+Wga3r98syqDzaQ3DmJ/uP6tOxBbGxag4QF/11K2ubDIbt0TWfNRxvpNexkA2fVoZLS3Xq5EiAmIYqJV45m0+fb6nNEVadKdHw0owN9bOs4ujeLypLKsM6X6WiZ19T9en2RU0scDEVVOJ6WQ3R8FMveWmXaU7/O2nmbUB2mjI6uBz4A82qEW7+ThmTqDePDOh26buCvVXBHND2JVBwGJYUt+6r0ejT2rD3Quqi8ouBQCfouUhwKrghX2H7AikOxTmUxZOiUWJq5cHvXHKjvSdyQ/RsOsfrDjeb1A/eQdfAEH/9tIXf89oaWP4dNq7lgHbCP/r6AqrLQ3CYwl6TKW9kA1RXp4pF/3s36z1LZvyGNiGg3E64YXV8ZU8fedQfNhtxNvKDdB3eh17DuLHtrdavuAczkz9QlO1gzb1NQsr0eeFkDcqjND2RIPFW1fPKvL7jnqVuCduUfLeTTZxeRm9EJoUCfYTXc/HgBwiIVS0rYsLgFSs7CnAlKSYudL9XlIDLKzZ3/dwMbP99G6qLtoCiBLwYZ6nwBcYmxtOuaTMbOzFbNOFWHQlVpdYgDVlVazdF92SG/T61WY938LbYDZnNW2Ls+rcmm0WUF5a0ec/Yd0+jYqz0bF27FU1XLwPF9mHjVGNxRJ//mfR4f7//l0yaX1BwuJ1OvH8fBzemcSG9dGzBpGAgheOM3H4RUIOqaHnjPWpYwZeiSRf9bTv+xfYKi+JrXz6JXlrNrhcDQu9KmrcbtT+ST2C70maSEqlIH5UXNf1U6XA4M3Wi58yXA6XJw5WOXEpMQzfxnFuGp8mAYkqjYSGosnC9XpItOvVLIyypqVX6XVqtREuZvYt38UKkNXdM5sieLqrIaYhLCBx5sTo8L0gGrqfCQdeBE+Ko6VaFzX+umz00REe1m1q2TmXXrZOsDJGz4LLXJKkRnhJMhkwcwcvZQlr+zttWq06MuHsaajzedcl+4hkhDknXwBN5qL+6A41FdXsPLP38Lb7UPEGBA+q4oPDUKUTGh9yolHNjeRDsfYZaFd+7bgdK8Moqyw/dTq0cRuKNcdO7Tnj6jerHyvXUgoOew7niqvcS2ieagxXIKQEVRJRXFVaekg5TQLtSRrKn0oDoUy7y66rKWV7va2LSGzV9sC58Mrwi6Dezc+kEFDJrULyjy35g96w42++4ktk9g2o0TKThe3GoHzOF2kne04JTV9hsjgAMbDjFmzoj6bR//4zPStx3G7zOPKM53sXdLNFMuLw/J/ZcStq2JDT++InC4nSS0jaVz347sWXugRffujHQSlxjLiJmDOZGeR21NLd2HdqU0v4yk9m3CRtF8Hh+Ze4+38OlP4op00blPB8t91eXWdkp1KHiqam0H7CxyQTpgmlcLX4ItoHOfDnTt3+mMXrOipIq3fvsR+VlNiA1ihu2jE6JZ+f56y96GzVFZUhX2hTpVjh00FefLiypwRzrQtYDzFUD3Cw7uiGLE5BqECL7fwhMuM3m17vCGtluYlagP/+MuAD7824KWOWCGxFvlJWPnMTJ2HAvZ3WxSaxNfIIpDQVVVNJ+//jin28nMWyZb6hQldWxj+bekOBT6jAyT/Gtjc5pY6UbV4XQ5mHbTxDN7QQlLXl3Ops+3N2mTnG4nY+eMYN0nmy37GjaH3+enJKc0uG/saSClOUn68vVVHNlzjOj4aI7sygiZMKVtj2LiJZU4nMHPpvsF+1MDE0gLAW7VofKdZ+8joV0cmXuy2LPuQIvuS/NoFJ8o4au31oSMm30w57TywRJS4oNEYFWnSkK7uPripsb0HtmDbUt3hfxeFVUhyaJFn82Z44J0wOLbxhEdF2Wp/dS5X0fu+O2NllFub42XJa+tYs+a/Ri6Qd/RPZlz/0X1FS/hKMgq4u3ff0xZUUWTSasJ7eKJaRPNR/9YaFmN1BL2rjtIm/YJlhWZp0J0QhQf/Hl+/ZJDQrKO3xea07X60wQGj9UQwo/TpaP5BLoumP+yWXmjOlRiEqKpqapF8/hwuh2oTgfXfO9kYvuUa8dxoJnl2SDCfJSnaryEIrjnDzcTGRvJV2+tJuugqaA/7cYJDJ7c3/Ic1aEy54GL+Py/S+s/I9WpEhHlZsr1E07pPr5WpAfpmQe+VMAApTM4+4DaG+EazAWuVHPeMnhyP4pPlIQ4KqpD5eG/3UlShzaW5+1YsZeV766joriSpI6JXHLPDPqO7tnktWqrvSx9bSU7V+5t0vkSimDgxL4sfnk5hmG0Sm6nHinxeX043c5WtWtrYjg2fJaKz6uhazqKYlZm61rw33V2RgRpO6MYMMqLEBpSgl8TbFgST0m+E4fLQYeeKeRlFqD5/Ahh6iFOv3lSfWS8+6AuxLeNo/hECyaRQTfZ6MdTdb4EDJ02iKseu5Q1H21gx8q9SEMydOpApt80MaTPZB3TbpjAvnUH8Xp8pu0V4HQ5ufzBi1BOUbD360R6N0Dt5yDLQUkEtTc4uiNco0E0sfpyHnBBOmAIuPb7l/P27z821+z9Ok63g8i4SG775XXWcgMSXvvle+RnFdU7CAc2pZN1MIfvv/AgrojQJq/SkMx75gv2r09r0YzOW+OlurzmtAyPz+Nr0vlSnWqLHBzVqeJwOfBU1gble8QlalRXCDRv8ItZXalSXDICZ0Qbcrato+CEgx1rYqkqV1GdKt0Hd+G2X17H3nUHyT6US1LHNgyfOTioyia5cxIjLxpC6uKdrX/wM4DDqdKlXycUh8Ktv7yuxeeNmDmYpPYJrPt0C2UF5fQe3oOJV4+xrh49r5DIyr+DkUtddiBGJngzgS+RNU6IeQThGNjUIDbngPFzR7Nr5X7KiyrQajUUVaA6VG544sqwcgOpS3ay5JUV9fal8HgRH/xlPjf//Nqw0dpDqRl88PRnaJq/2YpHKSV71x08LVkYzetn37o08weLfpUttV9gFuK069qW3CMnlzQdLonfbxGxViWZByIZOO0+dn31Fj6Pxq4NMWQfNu2TUAQ3/+waygrL2bf+IIqqMmzawKDCBqEILr57Oh//YyGa5/Sdx9YihGDw5P443Q5m3T6VWbe3rIl3XFIsjz97H+s/TSVjV6bZ9/eacXQdcGZXgc4G0rsKPJ9QLyViFJr/tI1Iz3vgnIyIvo3ztWHmhemAAT2GdOXx5+5ly6KdlOSW0mNIF0ZcNDRsxUzmviyKckqDXn5pSLw1Xvas3m9ZXbJr9T4ObDzU4nC6J0zFS2uJS/IzdW4ZB3dEkX/cTZt2Kl0HDUHKSBI7tGHxK8ubFFNUVMHACX0Ze9lI3vr9R8H3WKngjpTomsQwzD9qh1PSubdGx94jwTWU40d6sGXFCqQhUZ0GPYd15cYfXYnD5WD4zMEMnzk45JqG3+DVX77XYn20s4GiKhzbn02PoV1bfW7XgZ251SLvprbay86Ve8k/WkiHnikMmzHo/NEI86eZxopwX2gaVD0Hsb8E9fw3xhcS7kDRz+5V+zi0/QjxSbGMmTOS5M6JlsdLQ/LV22tCJnea18+yN1dZOmDeGq/pfLV0QiixFFBtLUKRTL2iDK9H4cC2aBxOQa/hnXFFdSUyNoIju49xZNexJiNF0QnR3PW7G3n3j58ERe18tYKhE6s4sC365CRSSBwuyaQrklHcA+g68se8/6dPKc4twekWRMZGcMOPriQ2MZrYxGi69LPODzYd3OXnTLxZGpLdq/cHSYa0lJg20Vxyz3RgesiYh3dkcnDLYSKi3QyfOSTs39jXjwTPAk7quFmgrUN6ohCR135td9UaLlgHDKBNSkLgj655CrOKLcPvWq1GTmY+VmINW5fubF0yvMWMrzFxbWNJ6ZbMkd3HwvR4k9z/q1xi4v2MmVmXgyFAFCDingIlgoTkON7/86eBFhihxkJ1qEy9fgKRMREhz1yY4+KyO4o4nh5B+q4oHE7JiKk1zLguHpymYzXyoqEMmz6IkpxSIuOiLJM48zIL2PT5Vo7szsJTVUtUTAQVJVWnLarakMQOCZTkl7dKR6y1nQKaoiS3jBd//CZ+rx/Nq+GMcLLy/fU8/Pc7iU9uQWXo2UbPIbzzVYeBrF2KiL7367gjm1bgdDsYdckwy8lfY3xezbIqGKD4hLXSe9rWI61uV6SIZv6ihGDmrZNZ89HGsBPTWdeXMm5WBa4IyWW31S3n5SOih4FrGKNmD+OVX7xLWUG5pX1VHQpDJvenfY92RMVFUpbfsPpPUFXuYPpVpWxeHk9ttUK3frVcfFMVid3uBiCpQxse+/c9lBVUoGt+kjomhgRQPJUeti3bzd61BynJL8XhclJTXo2hnznNQsUZyEdtxXeIoZ85+ykNyXt/+sRsIF6roagKmxZuZe7DFzNiVvj+nl8b0kNLdNzwroDIucB5MvFtwAXtgNVh6Abr529h0xfb8dV46TGsG5fcMyMojyKpU6LlGrozwkn7Bu1AfB4faakZaF4Nb03rwtA9Bnch+1Bu2BdOdSrc8vNrSemezIp317Hty51I6aHf8CoiIg0y9kaS3FEjItJADVpFlSD9oG0F92T6jOrJT954jH0bD7H01RWBvnF1CecOug/pRkr3ZAzdIDo+NFdu+cftufe3fbjh8TRAB9cshHsKDXUoVIcadinky9dXsXHhtiDnL9yXwykjBNNunET2oRxSl+xokbSFlJxa9VgYFv53qRnVDDiAWq2G3+fni/99xa2/OA9mZGoKoHJSATwMevbXcTc2p0Hmniy+fGMVBceLSWgbx6zbJjNw4slqRpfbiSvSWleqTfsGqucSju47TlF2MeVFFS1VhDGvEelCb6ZoaMTMwUy/aSIdeqbw5RurKMkpplu/Gtp19lGc5yRzfwTjLqrA5W58ZS+y9nOEaxhRcZF859n7yNyTxZp5Gzm6N/ukLQlUJU66ZiwAvYf3IOdwflDhzbFDMbTt0pUfPWeAngdqX0TEJaAG2yurqmeA7LRcXv/N+/hqfQ3syhm2X0C7Lm25+rtzeOWnb7coquaMcDJ8RujqwqlyYFN6vfMF5vekoRssfOFLBk7oGyRPck4QEZhOVXNyHAKMClCa7gRwLrAdMGD+vxexf0Na/R/5wS2HObr3ON99/n5i2ph5PD2HdCU+OY7C7OKgiIrf56f7EHPJ6sjuY7z71CcIwJASv+YPL5jXGAGlBeV0H9SFjJ2ZljMpXZeUF1bQqU97pt80kQj3McZNS0MCiiKZfWMpuUddqE6r63mRRmH9RM4d5WbkrCH0G92L5e+s5cCmQzicDkZdPIwp140DYOELyyyb/Y6cNYyOAy5q/pksOJGex+YvtrVIVPZ0cLhUBk7sS9vOiexcsbfZZRShCG584krLSsdTQRqSI7uPhUTfpCHDNuX92nEMACUBjCKajFuoXb6uO7I5BTL3ZPH27z+qt1+Fx4uY988v8Hp89ZEKoQim3zSRr95aExJ56jPKrI6rrfby6i/fpSS3DGkYgDCrgVuIoRtMuW4caz/eHPb9LskvA6DfmF54a8pISXqL+CQfqirR/YKaKgVFhHH7jAbJ7QJ6DO1KjyFd2bp0J+s/S6Wm0kOPIV2Zfec04pPjOH7wBBs+Sw2peo6Oj+Kiu25DWIgqN4uED57+NESj7EwjFMHka8fToXs7ImMi0bzNV5T2GdWTfmNav/wYjj3rDlhHGFWFI7uzGDD+XGscKhBxKdR+QZPLkAhQzs/WShe8A1ZeWMG+xknyhtnWYdPn27joDjORUSiCy+6dyVu//yhkVvj+Xz7lob/dybtPfRLyYkrFFOhrNg9MmrpRo2YPM7+4rULJhmTJqysYOLEvH//tE665exOuRirOHbr7rKM9wo1f78TyV1eyfflutFo/iR3iueqxy7jy0Uu48tFLgg73VHrYtXKv5X1XlFRZPoLm9bNvw0GKc0pJ6ZbMgPF9EEKQlppB3pF82rRPIDczH+0MLjPWVek0dHIdLgd3//5mXG4nuRn5OFxqixyw3qPOnGyEEMJU7Taslolb3hng7KIgYp9A1rwH2k6s20o5EZGXNj2MUYqsXQDaXhCR4J6JcE/jfE18/bbxpUULNM2rseyN1YyYOaT+1zD+ilGs+mBDyDu95YttjJg5mLXzNlF4vDg4DUARCNF0C6E6/D4/h7ZmkNAufCVgTnoeuUfyKS+qxFf6IYkDvPWdNBxOGSIDEYTamf0bD7HsjVWUFVbiinQy8arRTLl2PKMvHR5y+Ir31lvar5pKj3VFoITjaSc4vCMTd5SbIVMGEpsYTf6xIg5uSUdVFdr3TKGm8sw1uBaKICImIlhUVcDYOSMZOmUA2YdyiUuKoaKkssnP3+l2MGhCv1YvGTeF0+WwlN4Amu2L+3UhIi5GChU8iwErYXUHuGcDoUVyJzHMZH7vSpBecA5GRFxpTk7PMhe8A1aQVYTqVENeVF3TzbY+DdixYk+IhJQ0JGUF5WxdutM6/1RKug/ugtPt5PD2IyAEhmGg+0K/mLVajf0b0prUqSorLKc4txTde8jy+83pkkizg1GD+3EgSeCFn+ygMLus/tjCrGJe/unbXHLvDCZdPbbRdSpQHUpArDCYzD3H+Nu9/8HQDQZP6s+MWyfj8/h48cdv4fP48Hl8uCJdfPn6KlSHSmVpVf220206a4URGFNxKCR3actDf7sTh1Nl3r/MCtSWJBFLQ5r3dqaqrgX0HNaN9K3B0S7VqTJ02nlUVShiEdEPAhJpeMDzlulI4QelKyLqZlCsBRwBkJXIiqeAGsAAWQmeT5B6NiLq9q/nGS5wCsJUPddUefDW+uqLPo7tz7bMY/VrOlsW77CuYDQkqtvB2MtGsHfdQarLa1AdatgIUH5mAZYtMeoQkHvEzP+858eVIW3MVAcW9gvAxc6Nvfj0P5/W2xCP5mf5W2vZuXwv3/vvgyH2sDA7TDW4hJeeeJPKsirad09h9h3T6NSnPfP++TkHNqejef04nCrL315Dn9G9SN+aga7pCFUBBFKeuTxRoD7KpARaLd39h5vp2r8TmbuzePsPH7UsqV+0LlrZEoZNH8SuVftM57shiqBngzZX5xaBcF8E7ouQUgffWqhdBLICiIHIyxDumU2OIKvfBG079VE032akthcR91sQZ1eE9lvtgFWWVFNeVEFy58Sw69WJ7RMw/KHOkKIqITlMlSXWCuqKqlBT4Qnb5zomIZprv385hmGQk55PdnoOX721JsSIqU4HUbGRTT5TRHQE5YUVOF3hZzohjqBzKPu2j6IoZ7nl8UtfW8mYS0cESWm0SUmob1/UmNpqL55KM5ckdelO0rdnktAuluqy6nrj6PP4zBwJcbKwwOfxmVm6rVShb0pYtWHky/AblOSWcnDzYTr0ase+dQdbXIHapV/HMxqZqq32krU/NHdKGrLF5eFfLwKhREH0Q606S3rXYOa/NPxb8YFvE0RcDoq1HpVN8/h9fvKPFREVF0GblISwx8W3jaMouzhku9PtxOk+aeKry6wjN9KQVBZXhdX4kobk0ntncum9MynNK6OsqIJ3n5yHtybUCYuMjcRTFT4fSghBm3bxlBVUhPXTQuyXiEdz3seCFxdY2oHinFLWzNvE1OvHB21P6daOiqLQyJHf5693WjN3H+O1X73LtBsnms5XwBmqsxv716edPLEumn0KQaZw8hlSyvprGX4DQ5csf3sN9zx5C4te/qrFFZXSkPQ+w8LP27/aY7l9xk2TzqMo/kmEUME93fzXUoxi0LYBDSfpuqmP6F1r5gaeRc5/lbVTQPP6ee+P8/nngy/wxv99wF/ufM5UHLb4Dk/qlEiX/p1QncG+qOpUmXDl6OBjO1p/mfh9fkbNHmppwJwRTgZPHgCYjbA79+vA2DkjiIqLDAkXKw6FGbdMDsy0QnG6HUy9bjzturblyH4XSot+ewqIGNJ35IWPPknY/tXuoE0R0W5GXzwMd7SDgWOqmXx5Ob0G14CQQePomk5FSSUZu7JCx5eEViCeQgRMcag4I52ogbC36lTNLxcL7TWtVmPf+gOcSM8LKzwYhAB3lIurHmtmma2V7FlzwDKPT3WpHN2TFbRN8/rZveYAGxdsJS/zLMpwyFpTcNW3CYzWq5Rb4k8n2HjV4bCT90+DbV/u4k93PMvrv36PZx9/hZd+/HbY3rWzbpsS5GiB6XxNuXYcSgMjkdI92TJypaiCvmN60WtY9xCbJBRB7xEnv9jbtE+gx+CuTLx6LE538PvndDuZduOE8DIFiiA2MYbug7vQvnsyB7dForfEv5Ae8rKa1tTbumRnyLaZt0zG6XKQ0tXHxDnljJxWiTvKopLd62f9p1taXG3ocLYubqE6FKITok7aKyHMz04Rod9JUpqthiTkt1BMW3WpzLxlMnGJMa26r6bwVHrYv/GQ5XdGWmqjBvASsg6cYOOCrWafY4uAxplBgv8QeNeDHtoB5ZTQszALkRqjBWzb2eVbGQFb8N+lpG8/gt/nr59dbFy4lcT2CYycPTTk+Ft/cS0LX1zG3rUHkIYkqVMiVz12abCatISMXda/dEURRMdHcdn9s1jyynJ0v46hS5wRTvqO6hWisaMoCvf98VY+/NsCcg7nIYQgNjGa635wBV0HdGL2HVP58o3VGLpR/wKoTpXJ141n8rXjQMCQKcP49FUP19yfhxCm/EQdDWeQh/e4Wbc4j7xmWiD5LIzPZfeOZOYViwEPDoeB7lcoKXTy6lPt8XpOGnatVmvVrFAIgeJQWiw54VAVbvjxVRzdl0Vaaga65icuKZYThy36zAmBK9JFfFJss22cHG4H4+aMYsp144iKazry2JDC7BLKCyto3yM5rNhqaUGZ5dKn4deDqkpzMvJ5/dfvBwSBDRQF+o3rw+Crxoace1r49yCrXgr8EEjsiLwO0ZrZohVKCnCI0Pwx3VSltmk1x/Zls+h/wdGPnMO5vPOHj3no73eFHD9oUj+8Hi/L3lyDp8qD0+Vg8rXjmNqoE0Pa5nQUVYRMDAxdEpMQxRWPXMyLT7yJ1kA2xeV2cvmDs0OuOf3Gifg8PrYs2g6KAlIy+dpxjJszip7DevDyz97G79VOPoMwC5mu/+EVCEUw+85pfPTXbLr1yyIyxsAdYdQHxhvar/IiB6s/jyNj/8Im7YXP4l3r1Ls93/9XJG5HBkIxMHSFy24t4e1/pHAsLSLoWG8TrZ0aIxRBQkp8I3mL8Oh+g97De9BzaDe2LNpORUkVsYkx5GTko1tE6NXAxDEqJoKaphpuC+gxpBsX3zWdTn3at/j+PVW15BzOIzo+ivbd21na7qqymrA9bksbPLff5+fN337IicN5GLqB6lBxRbqY8Z3LcCZHhJ58qshKZMXfQdZFegWoPRCxj3Fa8hJKEta5r2qgSvzs8q1zwDSvn71rD4ZU4Wi1Guvmb7Z0wFyRLq77/uVc8/hl+DU/LguhzNoaL5VheptpXlNa4JrvzqH7wM7sWLkXr0dj4Pg+RMVF8tbvPyb7UA4xCdFMvWE8w6cPJj45jgf+cjtVZTXomp/4tnH1L8KEK8fQf1wfDmxKBwNzxtijXVBbiLkPX8zGhYm8/vQGeg0qJDpOciDVybG0CPoM83DFXUUc3BHF0veTAo5AE7M7RVgrYXvewh3hoe4PVHUYtG3vY+Z1JSx+++TyrNPtICElgeITxcHG3SqBUwi6D+6KO8rFoa0ZQUuIiqogFBFqaAV06duBpa+toKygAs2rUZJXZhnRdLocjJo9jK79OxERE0FVmIIBBHzvPw/U63EZhkHm7iyqy2roOqATCSmhVTO11V7e/sPH5GbkoThU/D6d0ZcM5fIHZocYsc59O+KKdIVEHBRVpVNdU1wJ7/5xXpA0gA6kbTlMdK/2MLGd9b23EmnkQ9XzoTs888DZH5SWG+/GiIgZSN8Ggo2YCmpnW7z1FNmwIDVk6cnQDQqyiig+UUJSp1DHduRFQxk5ayhejxdnhDMo8lVH2taMsDpVH/51AT9767v84MWH2LlqL3mZpnDwgPF92fBZKrtW7UVKGDK5PzNvm0JEtJtL7pnBzFunUFVqOhR1FcTJnRN54pVH2b8hjYriStp2TqLn0K5BaSBd+nfi+idu49PXl5PQJpMO3X0U5gh2ro0mNkHnstuKaddJ47+/6YivVsXQm3Z2+o+16HOo7SAmKo36v02H+f+3fC+fp7/TFUM/+dLGJMTgqa5tURTMMCSz75jG/H8vCkpxqOvd2zhq5Ix0MWB8X3IO55J7pADNq1GWX26ZMK86VYZOHQgCJl87ji9fXxX2PmbcPIkZt0yu/7kou4QT6bnEJcfSfWAXy/FXf7SB1R9srL/XNu3iufO3N4a002uTEm9dy6UIujaQ6ln7yWay03Prhb11TUfzaqybn8qMn5ypVQWJrHwaZKMggp6B9Hx+eiKrahfT/hknCK4EV09/ctoCvnUOmNcTfiZT3dRsAnMJ0OWw9qadboc507P0lmH36v1c/fhlJHdty8V3TQegMKuIF554C63W/BKurarlk399we7V+7n919ejqErYTvNtUhKYeNUYygsrWPn+eo7sPoY70knnvp3oPaIH/cb2ZuJVYxg7ZyT/evhFKour6l/8Q7sieekPHamtVtG8zYSmhKDf6F506NnI25d+Uym90fM6nJIh46uDHDDFoXLzT6/mzd99iKeyFr/Pj8PlwDAMC8V9ydCpA8g9UhCS3OmKdBER5aa6vNr8AlIETqfK3IcvZtvyPZQWlJ8cr6Gfpwjzerpk2k0T61to3PvkLfz7sZetc86kWQEbnxxHcU4pr/3yPbw1XnPVVDcYcdFQrngo2LH65F+fcyI913QQA/exfdkeUromh1Rh9R/bmzYpCRSdKKmfDDjcDjr2bl+vNZZ3tMAyX0ar1Ti84yidzpADRuW/w+zQkd4tiMgrT3FgCXqB2T/SOE79L8U5CBF19ymOaVNRZD3RU1SFqrIaSwcMCCynh5dWiG1qiUpC+rYMBk7sx7jLTVlpaUhe+vGb5B0trJ8Ubfp8G9uX7+GRf95NUsc2ON0O2lg0bHa6HQybMQhDN9iyeAcv/eRtNK9GSrdkeg3rxsAJ/eg6oBP3PHUnX76xmmUfbKuPGJfkK3z4fArd+3vxehSay3l3RbmYecuU0EfybsBKn0so0KW3tz4K5nQ7mH3XNI7uz2b3yr0YhoGiqGY/SwuHqm3HNrRpnxAi2mzoBv3H9uHwzsx6R84Z4aRL3w60757M+3/+NCgw0HBch8uBUATturRlzgOzAJh09Vi2LdsdtqK0LhJl6AYf//1zDqam1zvesYkx3PPULUHLkoe2HmHNR5uCVoYKs4t556l5PPLPu4PGdrgczLp1MsvfXnsykq+YS6czbppUf9yOr/aE2HhpSErzSvF6fJwJ8VPpXRPo2NEYP3g3wOk4YEahOVk0ijALiVRQ2iCi7gQlubmzT5tvnQMWEx9NdFwkFY2jVYqgx+BT1zNSHWbvr+3Ldlvu1wMvasNZx/L31qF5G+VcSDi8PZOP//45N/4k/Beft9pL+o5MPntuCb5aX/3Lmn+0iJ0r9+KKdHH/H28lP6uI2ipv0MssDYGnyoGU1g6jO9pNRLSbiCg34+aMZOTFJ6OCZfnlLHtrNcf2HeEHf5UoFsvjDqeC6lBACNp2SuL6H84luUsSP3jhIQ5uOUxxbikxCdEs+M+S0JMlbFu2i9wjhchGs3FvjZfuAzvjcHeg8HgxKd3bMfHK0XTs3Z6XfvxWk+2T4tvGce9Tt9TrtgG07ZzIjU9cwYd/XWB5zmu/fo9fffBD3nlynimt0cBR27liDz0GdWbwFDN/z1vtJX17Zkh0TvNqbFy4NcQBU1SF+/9yG2s+2sTu1ftQVIWRFw0xq00DfyKGboRduW2dIr/fVIUW0YSkdRq5IMvCXYWm9XOaRno+NPMx6sdwmssC0Y9gS1CcOn1H9yTvWGHI8pShG7TveepO+YQrRrFv/cEwuaCS2kZJ9Uf2ZFGYXRLyN+/z+PjvD17nBy89RHS89QRSGpKcjDwWv7KCnIy8+ne3LL+ctNQMlr6xmtl3TGXMpSPY/Pm2kOV6zSs4ciDS8l5Vp0pCu3h0v06/0b2YcsOEekfD0A3Wf5rKlsXbueruTHoNCr03h0PF5VZxuB04nA5m3jqZYdMHMWz6IMbPGWnKUES62Lx4h1nV2YiiEyUsemlZiL6jNCSZe7MYc9kIju7JwhXhZOTsoQydOpC9aw+GXdIDMxn/xh9dSf+xfepfHaEI7nnqFp7/7it4KkIFdHeu2MuACX0pzS8jLTU9yD6W5Jby8d8WcO8fb63ftnFhakiETxqSouxiy8jqxKvGkJAcx5qPN1FRUkW3gZ2ZdeuUoFzopuxUyyveJchqEC4sHTbvV02cexqVn/4jyKpnAmPomLlgToh+HNRTXxVoDd86BwwBcx+ezUd/W2CW5Urzy9DpdjD7jmmnNXSfkT3COmBJHdrw7p8+IXN3Frpfp9fw7uQdLQir3XJg8yHKCios1ZbXfLyJVe+vR0ppmfegazoev4d3/zSfodMGmtWGjdC8Zk81Kzr2TOGep24J2V5VVs1/f/g6tdWmQ5eV7qZr39pGyf4O3HET+dlb11GcU0re0QLKCitI7pyE4lAYOLEvAPlHC1Ed1pU/4SqgpCE5uOWwmccV4aSssIJxl48EIDI2fD6BNCRFJ0rIPpRD/3HB4oCDpwzgw78vtEz+1zWDzYt2UF5YHhIl02o1Ni/aweApA5CGJGPX0dBy7ADheni6I13MvnMqs++0rnps37MdDpcjZJnS6XbSc0i3phaNAxhIz2emfg0G4IbIq82uBPWHVGC+5lb5MwLhbL6NjfWlC8C7juClbc1MjvXvBcd50KrkG8r4uaPY9uVuqitq6t8fZ4STmbdMPq0+ou17pITVptN1g+NpJ1jxzloqSqto0y6eLv074fdZ/xVqXo3UxTuYfvOkkH25R/J5+w/m0rqlBIyU6D4/y99eQ7uuyWF9dWmEEaECHvjL7ZZ5mx///XPSUk0pie1rIunSqyZEK9HhcnDLb35MRYmHE4dy0f06VaXVxLSJJqV7cn2D7TXzNlleW1EEOUfyLffVVtWyYf4WHG4HSOjcryOKquCOdjc5JdE1nc1fbDcdsAbEJcbQbWAXDm6yTgj/6q01GLoRsmQtDcnxtBw8FR4i4yIpzS+jOMe65ZSiKmErVwdO7BfUUaExQ6YMYNPn20LsfFxSLBHRbprtbuw/iKx5C4wy82fnCFO+RjSw94Z18Yl5vIWH3UJkzdsER0h181/tJxD96CmP2xq+fQ4Y0H9cH+556lbWzttESW4pXft3Ysp14y3zelrD5i+2h91XfKIkKFScviMTpQlRPIdTpeBYYYgDdnDLYVZbiCWGIKGiqIKouEhcEaG5Rq5IF207JZJ/rDDo5XC6nWYivwWbP9+Gr9ZfP3OZ/3JbHvh1Di43phETbhAJEHkVX76wih0r9iLqhBoNg4iYSCKj3Yy7fBTDZw22FgVUBNXlNU3PjqSsf563f/cRP3nzccZfPoqje4+Hz9GQknef+oRug7ow96HZ9UbU0I0mKy/L8ssQYcpJfbU+ygoqeO1X71FdXmOZ1C8UQZ9RPcM/SxMoisKNP76SD/78MXGJXsqKBIaMoFOvFHqN6M7BZtqbnHS+6n73fvB8BEo0OE3H1VSxDzNLVTqB4xSVs7W0MDu8SN9ehO2AnTKRsZE89sw9rP8slbTUDGISoph41ZhT/jurIy31cNhJkeE32LZ0V/3PpXllVBRXIoS1E1T3Bd8YzevntV+9b9nyqDF+TefIrqNhG3i369qW4hOlQU6c6lTpM6qnpfNVnFvKwS3p9bZz7+ZoBo6uoc8QD063gRBOQCCiH+DY/jze++Mn5rMAuuYnOj4a1aHQZ0QPpt08id4jzAl34/fe1NtqOsJbF43a/MV2egzuSq/h3Zutys7YeZQ/3fFvLrl7OiNnDa3PoW2qMrqypCpIPqghQgi8Xo2lz65i9+r9YSeQUkL7Hqe23Db9xokc2pYBRgk+j0ZNVQSq08HElhQR6TnIqv8QFIXXdiKraxAx3z25zdkLNCtJDAGRN5zSfYPPXB2wQjt4imO2nm+lAwbQuW8Hbvn5Nac9TlVZDfnHCmnTLr7ZHLIgDBlWTgLMypi01MPsWLmXbgM7M2LWENyRLjZ+ltoi4VAAhKDbgM7EJERTrvnrdbtUh0JMQjR3/e5G5v3rCzJ2HsPhUJDAxXdNqzfkBVlFrHx/PSfSc2nbMZLKkqKgHIWyQif/+nFXhk3yMunK7iR1HgrOoexdn87OVftCnETNW0llcSVLXl1B1sFsLn/wIhb+50s0n2ZGIgNtmVojRGEYBkd2Z9FnVE+mXDuOle+vb9J5O7bvOP/76dsMmzGQ3av2W2oVNaTfmN7sWrkvZLvD7WDw5AG896dPKCsoD7MU4sAd6WTWraH5Jy2lR990fvq8GTUVwqC6ZgBxna/nQKb1bPUkeiPnqw4f0vMFos4BE1EQMQdqFzc4VgDREPMDTnmpUInCWsVGBXHmyuEvVCLjIrnojqn1nThOFcNvkH0oB6EIqsurAy2GWoau6WH/POp0oD786wJiE6MZffFwkrskkZaagWym+rgOKc2WbZOvGcu6eZuD7J7T7eDKRy4lOz2HZW+tQQH8ukGfEd257gdzAbNye/2nW9izZj+KCp16GChqA5skBR8+l0yXPl7GXRLJ0KmjwTUany+S9/74XMikta7Iatuy3ezfeIi7/3Az+zccwuvxBjutsv5/mkWr1UhdupM+o3py1+9v4o3ffBA2Yg7gqfDwxYtfcfxADscOHKco2zr/q47kzkl06t2e1KU7Qxzr2MRo0rcdYc+aA9YTeiFwulQuf/CiU27B5nYX8NiTx5F6MdKQ+HyxqHEPk1EQ2Wxyg/QuI7Q4zG/KPxiF9TlYIvJapHYocGzd35YKUfciTlmtXiVsH1xxBqs3m+Fb64CdLtKQfP7Cl2xfvgeHU0XXDaLjosIK6lmha7qp9dLoy1tRBbpfZ/vyPeiazqHUDNbN28zD/7yL6vLQ3ovhiIiOIKVbMg/+9Q4WvbKcAxtMdfyBE/py2X2ziIyN5PZfX09VaTXV5TUkdWxT/6LlZOTzys/fMWdzhgxEgjC/U42TVlfzCnatj2Hi9VeAy8wRSF28o8lqIc2rsW99GtNvnsTdf7iJDZ+lUl5USUVxZdgE46aoMx7Tb57EoIn9eOFHbzQpUOir9bF1ya5mcxDqKnqu/cFcPnz6U3TdwPAbOCOcJLZPoN+YXqx8f53lOK5IFxOuGsP4y0eGzYNpFu828CxE4MUReBPjYg+B5wPg4qbPlQHleSuMYKMtIi4DtRPSu9zU/3IORUTMCuSMhcOP9K4D7yYQCsI1GdzjqXe6nEOwdsAUhHuCxXabr5uMnUd5/y+mcrwAhKrUd41oMWEO1w2Do3uz0Lx+FFWwdclOrv3+XDxVnhbnLzpdDgZN7EeXvh2Jio1gzcebqSmvpl23ZC69byad+3Wgc78OjL54GMU5pUQnRNXLvhh+g5d/9jZF2SX19qE4Rwa0Uht6jYKco1Hk5YxhaMR0ANK3hYvemhi6QW2Nj73rDvD4c/exaeHW+khd/tGCFouj1lFXhNWxd3t++tZ3ePO3H3IkjKQRmPZz27JdYfc3ZPQlw+g/pjcHtxymuqIGrVZDdaooqsp135/Lwhe+tJ7QC0H/cX2Yet14OvdrotNFUxi1yKp/IKSn/rsj0lEG+rNg/ABoxpHR87D+A3OYAql1SfBKB0Tcr5C1i8GfCWo7hPtScDQjPOs/jPR+CXoJOPsj3LMb9IRUwTXW1EUMcgKd4J7R/LOfIWwHLAwf/2Mhe9YcAKh3uKrKqszlqvCpCaFYGDyhKBiaXp+LoXk1dL/O8nfWEZsUG7a1SB2qy2EuXz1xJUIRRMVFcv0P5sIPrI+PaRNdn5zuqaply+IdrJ/fWHhQmNVGjRrhqk6Vzn060LajG7xrwfAQ26Ys5BqRsTq9BnrweRUy9kWiqAon0nIZOn0gN/U3qxKf/+6rrXbAdL9Bz0Czc4Dkrm25/f9u4PVfvd+ksGy4cHtDhBAseXk5Vzx6CY/++162LtlFRXEFfUb2ZPCUAZTkllqW8wPEJ8cx69bJlvtaivQuJrRKSwPfFjCaMQIiBtPAWchsqJ1DtzmHIpyhEixh7gxZ+W/Qj1IXNZOebPDvQdQr5bsQsd9DVj0P0kfdSyGi7walreWoNl8fR/ce543/+zAkt1FxiFZNIq0QikBRRb0jYugSQ/fz6bOLuO6HVzTfEkeA0+Vk5EVD6NKvIwDjLh9VX33ZGIfLUZ9SgIQDm9NZ/dEG8o8WNhKFFiAkiiqDJCYUh4Oxlw03W2z5TxAVVYuqBjuJQpH0HFhLZIzOsbRIKktN3cdZtweikHdMZdvSXSx6uamE8FCcbgdDpp7MU1JUhbt+dxNP3/0c1WUtn2yHY8lrKxkyZQCPP3cfu1bu4+i+LBI7tGH0xcOIT46z1HcE0/m95J7pwVqXrUXbinWJqh/0TKAZe+PoExBCbfy3qIHSMXiTkmxWJrYQ6d0Mnrepd668uUjvJkTcL+u7c4jIm5BGWUBw1WEe6xyJiGhm8nsGsR0wC9JSM9iz9kDIdl0zUJ2Q0K7lInxWWOZg6Ab71h/Er1mHimfdPpn4tnHkHM4nrm0Mw2cMDisCGo6qshr++4PX8FTVhq0oFALi22pUljkAwYBxfbjyoV7Iil8EnE4/19wjufxWcKiSkgIHWekRDJtUhaELJOY7+cHznYltG7wUNXjKAIpzSlvcHgjg4run444OLq3vMbgr/cb04vCOo60aqzGGbrBjxV4ue+Aikjq04ZJ7pgftb9s50TJJXnWaM/fTxgj3NyQwHZ+mckYERF4DnvdpPIM7LV0cAP/+gNJ0w+f2mV9g+jFQA33g1O6I+L+A/wigg6MnTTe9tfk60Lx+3vrdR5byK6qq0n1wVw7vyDzlvqxSmrbQavv8fy+ynJym9GjHxXdMI+tgNn7NYNDEfqcUefn0ucXsWXsgbATe4YDEFI3iPPPvsE37RK7+zkXERz6LrC4G6aNbdwc/+qd5vs8rOLAtmn7Dq3G6zTdPVSWblsWTlzcgaOzeI3u0tosa8clxDJse3PtVKIKrvzOHD5/+tNXRtMboms7BzekMnjKAMZcNZ8xlw4P2D5zQ1zJJPiLGTWIT7a1agjTKsJL5QGpmVWMzCPdMM8qOh5N/NC5wTQAltDit5eiBVYRG7YXwIGsXIaJuq7sBM9fMKAgseXb82lun2Q6YBas/3NBEhEtw0e1T+ey5JU3maglFoDpCm3wHhrAcP1ziqsOp0n9MH1K6JzN8xuBm7z8caz7cQE15Tdgej+a14Lt/zqbWo+CMHow78XJk+U9AnnzRFAUiAjmw7TprJHfSAnm0Jx/q5u+cIKJ9sIGdMHcUu1fvp6ygvEWCh84IJzGB5b2je4+zfv4Wyosq6D2iB3Mfms325XtZ8/HGJuUpmkNKiebx4XCGJvUqisK137ucD/4yP2h5Mi4xlklXjTnla9bj6AXaLkL+GIQr0AS26URm4Z4ESjTS87m57Kh2Mp2v5kLzzWDmW1gVABjmbLHOAQNAOfVEfpuzwr4NB9F16wiXX9PpPrgr2Wk5TeYiIQJ9HVuR9+rz+Mzv0sYogu6DOtNndE/6jD71QoL8o4XsWbO/SadFcUhmXltKr8G1+H0Oorv8EFkzD3z51OX7CHw4nOZk0+mSjJpmRuUb1gKMvaiCKm9C0NjxyXFMuX68ma/m87eop21UXBSqQ6WypJr18zeRsTuLNinxTL5mHLf+8jo+f3FZqM6XRdpKOHS/TmVpeGdn6vXj2bchjeqyGjSvhupQUFSVa793uXWRVCsQjp5I4Q76bjB3OFumIq8kIOJ+jvTMNxPflShwz0Sc7hKgUYh11bcO/tDACko78985wHbALCgvqgi7Lzo+iqFTB1KaX8aqDzeafa8avywCBk/uT97RQgqzi4P21+UXFWYXh+jIhEN1qk23pGghB1MPN+l8OV2S0TMqUB0QHetExMwAfwbNrbdaFTG5IxwI/0FwnayGc0W6eOQfd7F7zQEO78ikOKeE3Azrcm5zXIHfr7P9q9188eJX9Q5vYXYxO1bs5bFn7mHyteNY8soKdizfjV/TSezQhrYdEzmy+2iLZpfR8VFUlFbjqfaS2CEhZH/f0T159Jl7SV28g7LCcnoP78GwGYNDeu+dCiLyKqT/gDljrM/ncpuVPWEqw0JwDkc4h5/2vQTdlxKPxElogqyO1A4i3DOxjs6Zvdqkb6c5u3SPA+UU80tsTpnywoqwbbjM3o7d6TW8O2//7iM81bWWEXmny8nU68ez4p11QRNNRRUkdUpq8SQKAEOGbQTeGg7vONp0DpuQuFySPsM8ptZXzGBzMqOl0jjZuuHrZfWqudwGidG7geAiiBk3T6L38O5sX74Xb7WXvessvtAbUNd67D8/eA2fx4euGeQfLSBjx1Gu+s6lfO+/D7B/QxqLXllOVUkVDpeTQRP7sWdtmMT5RiiqQlLHRPIyC2jbOQmHM1i4MTI2ksf/fS+7Vu4jY/dR2qQkMObSEZa2rtU4BpiV1Ho2J6PlLlC6hS4hhn2AZET0g6d/L0FEY+2AYebAGsWBFkRW+0tNAV9ZgXAObCLX9cxgO2AWdOnfif0b0iz9jqseuxQETLtxImMvG8GO5Xv56u01pmqy38DhcuCMcDL7jmnkZOTz2fOL8fv8Zt6XbjDqoqFMvXECr//6fUrzyltU8aj7dTr2Pv2+VJExEdZLp0KiOmDIBA+zb/QADoi4BJwDmpAbaBqhiNCZEWY+x8iLhjDyoiHkHM7jxSfeDLsUYugGPQZ35bnHXwn6nHRNp7bKw5qPNzHn/lnMfXg2cx6YhV/TcUU4MQyDNR9tYsNnqWZUMUzEUXWqeGs1XvrJW2AYJKQkcOsvrq0XGsw/WsihrRk4I0zpjoYir2cEtQMi9hfI2i9AywC1rZkw7+wPhHdMzzquMeD5zHqfPw3p+QwRGagw1k8gfamAYRpi/2FMY6yaSf+RNwbrktmcdTr37YjT7bR0kLoN7FLf9eKJ1x8lc3cWnz2/hOqy4O4Tcx68iD4je7BvfRq5GXmogQbUcUmx3PV7s7Bm8+fbzUhbM9EaZ4STvqcR+aojMsZt6phZOIyqE5JSNG76TjkOhxPUzoioO07vgtI6Qtilfye6BPJaj+w5Rk2Ywimn28HQ6YNY9cEGvNXek22gpJn3+/kLyxgU0NkaOKEfPq+GM6CIP3zmYBa/styUoAjz8TrcDhwuB+/9aT6OQJu6S++fxahAuz1PVS171x2kptJDzyFdGXPZiDOrjywEIvYHyNoV4NtobnNNQkTMgMLips89myix4OgH/oOEOmJeZOXfEPFPAirIGqRvCxj5gLOBpqKO9G0y/45if8DZcpVsB8yCWbdO4fD2TFPgtK5BrCKYduPEIC2eyNhIJl49hgHj+7Bx4TYKjxeR0q0tYy4byfx/LyI7PRfDkGao2+ngjievr+8D+Pi/72PDglSWv72myUiN0+1k5q2Tm2wx0iz+Y6Bnc9GtHXj/r8VotSevJ1RBp14duOO3NxAZkQtGlZnLowTytxwWPdZagvQHHInwdOzVnnZd25KfVRRixFWng0vvnWGKwlqcq/sNNi3cyqbPt5HSrS03/uRqkjubVZqKojD9pomUF1awY/mekGiAUATdBnYh+1AO3gbLMIXHi3nlF+/yxMuPsOTVFWxbtstskO1QWPbGKq77wRX1QrNnDDUFEX3vmR3zdBGxiJjHkVX/JLTSUjONVOSVyNqvoPZzTCNn0YwbHTwfgmuELU3xNdJzaDfad29HTkbeSWdFCNq0j+eu391Yf5yiKPQa3p3vPHc/O1fs5cCWdNyRLkbNHkppXhn/euglU+dPVdA1P9NunMj0myaBgEvvmcGEuaP418P/Qzeasl8OEju0qe8ocUoYlaDtZ/A4na/eMtAa+UUOl4N7/3QrnXu6QD9hRjccDfqQOkeCbxOtU013gWt0s0fNuHkiX76+KsSGKw6F9j1SGH3xMP792MuWPThrq2r53bV/IyImgtl3TmNMg24aPYZ05cpHL+W1X74XOklXRH3+VnlRObpm1CvsL3rpK5I6tEEIwZu//wiM/2fvvMPkKK6u/6sOMzubtNIq55yzkIQSSAiJnMFkg00w2OBszGvj19kvzmAbG4PBJJMzEiAESCjnnHOOq9XmCR3q+6N6w+x0z86uVnzG5jyPbHamu6dnuvvWrVvnniOxLZt5IYPeI3pw7fcvC2wsahKEiYicB5Hzmu+YzQCRcxuy/NdeYlUPskrxWbXWyPLfo+4LP9GMODj7kfGFiPCpibgH4fMErD4kxKsSjJw6hD2b9lNxspKCNvmc9YXx9B/jz3Vp2b6A1p1asvrj9ezbfJAlM1YiZbIVgx23ef3hd7n7j7eqMrGAQeP7MfvZeakH1AS5LbLp2Ls94y45g17Du/t+bunxMlbMWkPxkRK6D+7KsMmDkkX5pIUs/wu4e0BC796S+x4WLPs4wsblrTh+yKBNp1bc+KOrieRGAJ9ZqjAQOXcgK/6GulH90qFq/Qrb+38DIpfVJnFBEPDFn1zLiw++waGdRxCaQLqSPqN6Mu2ms2jTtTVlxRVpl02RkqN7jvOXe/7BVx/+Mu26eR14EjYt2uq7FKObOkWHi1NL/B4fbMGby1g5e22dLi91jNf++A69Rtx7SmrkpwUyDiS8JKc5prgutdfS77d3kM4hL/lqqIKrgbVZVdU+x6eCyrIqBo3rhxEyOHGwGM3QGHHOYCZeeaavGKgZNug6sDPL3lvF7oMn2bJkG44jUzhOc15cSN8xvenoVdBatMmnXffWHNp+JOWYmqHRsWc7Bp81gNHnjUhZGgMlL7N+vqIjtGiTzxnTh6csjcnYPCUujIYJfPchmy2rs1m/NJ+dG3KRLlzz3cvoXG1wr6cuLYnIFUhrO8gi/JemNFRcE9Q4SujtEeGGu5zHXjiKsqIKFr+zAqFr2HGLwo4tOefGSQw8sy+arpHTIjtt01asIsY7f53FicPFnP+lc2peXzt3g9JQrA9XYoYNju5J9UdURthLObD1EFad5iErZrFj1S7Wf7KZYVOarh5/euCAW+VpC/p43zUFskwl4n4JGA64JxR3loY6UROqK/3zBOz0oORYGas+XEfZiXJ6j+jOpsXb2LpsJ7Zlo5s6AsHUm84KTIJADfSz/jm3weXE4/uK+PPX/sFdf7iFSG4WLdrkM/DMvmxZuq12BiWUhc1dv78lxaG+LvZs2M+zP3sF13FVJ8yyHcx/bQl3/+EWInmKUC5jM8DZRd1BMpQFEy6MM+GCCipiU8nvmIFYrdEfRB7IIFHAHMi+EexVQJYihxt1iNoSDu8+ihW36dSnfY2IIyixwDt+cxOlx8uIVcZp06UwaZDIb5VL1wGd2Ld5v2/nVc1HuJK3/vwud/5OtSrPemou0Up/JXk7blMR95FvAFxXsm35Dt+qpJSwc/XutNYcnypklbLxsDx7LK2VatU2+qTfLx3c48jyP3g6YwE8CpHjtW5nAgGimYLq50iBSmK2sNeTHyhonc9bf31fdSraDoZpMHhSf6ZcNzEwN49XxXnif/7VoHq9dCWPf+9ZbvjhVfQZqRo9pn1xMs//4tWk58UMG5x/29Skik59JKIJ/v69Z2u4ZLqhsXTGSq79/hW1y5XOEYi+St34JQQMGFlOv+ExLNvFaHkfRii1gSYJWg6ER3tixAHIvk1JJ8hShDEUQiNB1A6PZSfKKT5cQutOrZKpCAKm3XI2Z31hHCePlNCidV5N/K3GxCvG8vrDMxvkzC1+awUTrziT3IJsDu86yopZawPmu0KtGgTgxMFqI+xkWHGblR+u/bdKwGT8Y4hWT+Q0CJ+LiFxM0yeSLrLyKbBWE8xd1pBaO3BTnRx8IU5fmvRfnYBtX7WbF//vdVxXeS6u/WRTEqm+urvuxV+/yf3P3puUONTFnJcWZaxeX1ZUxpznF3DhnecCcNU3L+KTV1qy9N3VJGIJug/qygW3nZM2+ULCa3+ckfRAWzGLcqeCT15ezPm3ebOo+CL8KhQCQNjkRT6m9NgIig5B606taNEmoPXX3tJAW3EFEPddSju+r4hnf/4qlWVRqpturvj6RSlLeS3a5NPCxw0jWh5l9PnDiVXGOH7gBI7lBHLGDu5QM/F4ZZylM1dm1KVUHxJJIuBa2gkb+xT0k5obsuIvnlyEd07ucWTFnxH5DzS5q0dWPOqZdwf9diZkXY0glqEUngTj3yfg/ychWhHj7995hvKTlVixBLppJDlZgKqIbFiwhUHj+tF3tD+dYMP8LSruZQDHcnj1d2/z/WfvRdM1eg3rxg0/vIr3/zmHEweLySvM5ZzrJjY4yC+esYKTR0pqqtCO7eLYLq89NIPvP3MPmqYpbk7AJEDTLMKhIpz4h+zaMpRwJESn3h38x20pIf5R4LHAhfgCRN49KYOtYzu89seZbF6yHSOk9NMGT+zP5fdekDRRDEdCtO+R+sy5rkteyxwGTejPhgWbEYi0Y8W+TfsZOL4fc15YEKzVloZzp5s67Xu25cQh/8lyczRzNRdkfAlE3yRp+S8+Gyl0RNaFTTzmfLDWEFyZN1UXt54pJzGECJ0+DmuzJmBCiFbAEygZ7yLgf6SUz/tsd6u3Xd274WIp5dzmPJ90cB2XV//wTtLMzQnqOpGSfZsO0mNoV9+3y05kLi7q2C4bFm6pScA0Q2PK9ROZcn3mop4lx8uoKkstnTqWw4ZFW2oTsAY4D7btsPjVf7ByXiGWZdNjcFdu+tHVqYmme4Lg4AUgoepZpIwjsmq7hlzb5ckHXqSytDJpPH/l92/zta5fprXH2QpCtSm5buq4riS3IIfOfTqwYYG/V1f18mvx0RJ0Q8NuyAvDB516dyBWGVwJyMr59Gwq0sI5CM5+Uq+Lg4zNQWRf2/hjuseVJk7QMrPeFRG5SBltu2Xe0pAfTKqXdUTuXcpD9DOAz1L8Apj70kJKi8pqBur6yVc1rJjFmrkbAxOwkuON6GhExc5DO47W6Hj1Gt6drz38pUad+4b5W3y7/Ky4xbG9RSqZkQkC3R7U1pQcnM3zv9yBdF3CkTA3/e81Pg1Ltm9DUBKcbcjy3yPyvptUsZ39zDy2LtuOY9k1v+/6+ZvJbZXD9C9OTnvIw7uO8uxPXyERsxBCIIBR5w1jyYyVvkmUpmtk5ar4EmT23RCEEHQb1JmNgTHy34g+EZ+Jn5UasQ8g6wKaVAWLf+JzTA+iAEITFWcNE6n3AmcHqfeYTk3nY+gMCPkLBDcHmru/8hHUt28H3Aj8TQgRNBVaLKXMrfNvbjOfS1oc2XO8yWrQJUdLWfjGMj55ZTHH9hXV8g8yRFAlzRcSdq7dwxt/epd3H/uQsqJyjJARaPdRWRqtHT/NoaRbU3ddyeH9OvGqBK7lsnP1Hh68+c8c31+vgyVJ9ykINkRfRrq1Eh471+7Bjlsp47ljOTzxg3+l1SHavmo3n7ysTMnjlXGsaILS42Uc3nUUofubfI++QPkftmid3+RK1cEdhwmF/YOUETKabjvU3HCL8b+2TrDJbEOQ3jKAH7R2iLz7VfIFSigx+2ZUshXy/pkQvhQR+QIi+0ZEi9+oVvXPDj4z8Qtg48KtTYphVtxm7ZyNzHl+AZuXbKdDr/aEGsFrlIBuZj50lBWV88HTc3n9oZlsW7kLCE4E7LhNebGiB4jQMNR9FYySIo1ENIEVt6koqeTRbz+lEpwkmGrwTQvXI1zPrX1JwvL316TQERzLYcGrS9m5Zk/g0Rzb4akfvUjFyUoS0QTxqjhW3GbF+2to09lfAiErJ4vug7sAUNgh/eQ0CBLYumwHZthfELnGUeDfAW5JwBsJGuaWBiFo1m0i8r7jLW+q30bkfMm7L8KoWlQY9F6QfYuKYfk/9NT3m7N1NBnNVgETQuQAVwGDpZQVwAIhxNvAzcD9zfU5zQXD1DOyqwFACLoOVF01K2evY+bfZyOlRDou815exJCzBmBmmaq07B3SDBuEI2EqSlKX7voFkPnro7K0imd+8nKSVtaSGSs565px5LXK8yd2Ssm+LQfpOqCTZ2K61ePzpN6Yxw+a7N6YzFeIV8b5549e5LtP3l3bLWN0Bb0POOk1b8CG0h8gs7+MCI+kqiwaOH+tLIvy1iPvc8W9F/DePz5i3fzNuLZDr2E9uPiuaSx5Z0XKrFy6khOHfEyqNaXYP/VGVUXMzo8waGJ/1vmYbDcEO2EjUa3z9T8/u0WETr3bp+yz5uMNzH1pEeXF5bTt1obzbp1SE0hPG/TO+FclTdDr3V/OHtUq7p4ArRUYvRDmsFTVZ7290k1KqRaYyR1hbhm4JxDmAGjxK0isA1ylmdNkc9z/v/isxS/Al9juBzPLrBFwPnm0hMe/9xyJuEUimiAUCZFXmEtBuxacOHiypspjhA2y8yOUn6hIWfIXmqBDj4ZlcWzLYcmMlXzw1JyauLjm4w207FDA2AtHsn/LQd/91i/YrLrN9T6Ki2Wt8q1gJWKCV/6auuw366m5dOjRjm6DPEsuAUS+AFWPkb6iZkP0NaS9S3XRyfTLhc//6nXue+prbFi4lTkvLKDsRAUt2+Uz7YtTMEK6r86jnbA57sPfyi3I4Uu/uK4m5k65bgJ7NuxrtFuBk7DZs/FAjSxFXZhhg1HThqW8fnxfEe898TF7Nx0gnBNm3MWjmHDlmObtlvSD3smzO6sHkU+So4YsR8Y+UVQYkQV6NxV7jF6kJEfmKIh/SEpsFLl1LNJsJZkjshAtfg7WJlX917v6H/M0ojl/4b6AI6XcVue1tUDQDHKEEKJICLFNCPEjIfyZbkKIO4UQK4QQK0pKfQbfJqJtl9bktcxN+a2FprzShCYwwwZm2OS671+ObuhUlFQy8++zsRM2juXguhIrbrN+3mYuu+d8+o3uTW7LHDr37cC1910e2Hq9YtYaKtKoFx/ff4JHv/00v7nlL75CpfNeXUzLdi189lRl7KJqZWWtBSL/pxC5GvTu1HQoEsJKCF7/exv8bjYrmmD3+v3Jv0ve15RFRIM3pw1V/wS3hG6DugQKQuJKti7dzj9/+AJrP9mEHbdxHcn2Nbt59LvP+CaugXAl51w/MamyeMW9FxDJb4Cc6wcJVsJi9AUjlKZbJEQ4O0R2i2xu/tE1KV9/yYyVvPO3Dyg+fBIrbnNw22GefOAF3vzzu41SEW80tJZeUlR3piuAMCKrtmNHxpepVmtrGTg7lShl9EVk2Y+U/ljyQdWskBC11bUwaK0R4amAg6x8Cln2A2TFw8jSHyCjb0F4HIQnfWaTLw+nJX7B6YthZ5w3HKO+ILAmQBMYYQPNi2FDJg2gryef8+af3qOyrKrGXisRTVBytJQufTsw4fLRtGiTT0G7Fky66kxu/tHVvovR8co4Kz5YE3hetuXw9l9n8cvrHuKDf85JqYCfPFzCtpU7A6toR3YfU/8hQGTfgsj5KpjV93oIMLATOmsW5RKtSD2Gk7BZMmNF0msiPByy76Shihq4YK1Fxj5GaIKOvYITTSEE7z3xUc3KBFJy8kgpbzw8g11r9mRsFwwwcEI/2nSt9VDtPrgLo6anJkuZQBOKZxvOCROOhAhFQughgynXT6Rz3+TVmpJjZTz2vWfZsWY3VtyioriC2c/N4093/4ODPt2tzQllmVa/Umeq8ao60LqlyLKfQfx9tVxob4D4TGTFQ8iy/wOZHGNF1nlqkllzndV4p+KaQCZWIUu+hyx/CFn2S2TZL9TEM3yO5+jx6SVf1WfXXMgF6pdkSgE/Nvk8YDCwFxXgXkIRlv6v/oZSyseAxwAG9B3UNPMyPwi48YGrePKHz2MnbFxXIqVk6FkDOeO84excu4fsnCwGT+xfM5BvXbbT177BStgc2nmEGx+4Kun11x6uP8ApuLbLoreXM/2WySnvxSvjPP795xQPKejbSohHE0ps0WeG1rbOg4wWVrysrLPUslViHQjBSw9to+hwUFejIr8nQRiInFsg8gVk7F1PsC6IY2ZDYiUFbacy5vzhLH57he9Wris5frA4eRnFldhxm/xWypQ80yWW537xKjf84MoakUnd0Lnsq+fz4oNvZLR/NXRTo+fQrpz/pSmcedEo9mzcRyQ3i9adClPU713H5ePnF6ReA1ey6sMNbFu+i7sf+hJ5rZpZwNWDyL4ZqXdS18KNgzkIEblMdawCakb/Av7lfBtis5RgodEb3BJkfLYS3jV6ebNQqWaaXqIno6+DtVLtW33tE0uRWqsmk2b/jXBa4hecvhg2/tLR7N10gF1r9wACoQmy8yNc9z9XsH/zQeJVCfqM6lHzTFhxm72b9qdUVRR3dCs/fP6bynjaw0fPzQskfM/65xxGTRvmGw/f+st7bFq0NZCTBrBn/X6ElipzIjShyPQ1LwBmP4TZT2kLJtaAPMmurfD+C2sJ4qZW+Bhdi/BwCD2MtDZC5eP4220B2BD/GCLncvFXpvOP+5/11fGSrmTDgq0pz78Vt9m6cmd6+Zx6WDZzFZ16d2D4lEE1v+kFt01lzccbGu13a4QM+o/tzX2j7mHH6t1YMYsOvdqh6UoMvG7zwMI3lyr/4bpfz5UUHz7JEz/4F1fceyFDzjpNNAKjLyL3G8jom+Ac8gy3L66lOYCaJPp2ZNvgHkRWvYTIuRXVSLEEmVgAZIPZU+2jtVVyIlor9RlV/yQpHrpHkOUPecKsn27yBc2bgFUA9dvo8oEUhrqUcledP9cLIX4GfI+AAHa60LJdC77zxN3sWL2HypIqug/qTGEntfZef6YAgJT+3UISNi3cxllXnYmm62R55tHpiK17Nx3wfX3dvM0q6WggTGfnZ2NmlWFbdk1A1U2D9j3aBnPStFaQNRmAc27qx64N//INkrbt0m1gF5DVIo51ljq0CCL7KqTWAqKv41/Sl0jnGAIVRA7uOMI+n++b3zrfV0XailuEc8JEcrIyroSVHivj8e8/x/hLR2NmmXTs1Z6SY6Xoph6cxAlluVIdQIUmCGWFmHD5GAAK2ubT+mQhr/z+bSpOVuBKaNOlFdfedzmFHVpSVR4NDo5SUlUR5ZOXF3HxXdMy+g6Nh4YInwvhc/3fdg6TfsklgUwsQmit1Eyw2hS3xhXpIq/qiXo9/gmpyZwFsY8h60KQMWRslmf9YijCa9YUmk3b5/TiMxe/AK6//wqO7S/i4PYjtGiTT89hXdE0rUarqz6CWBfxijiHdh71ZBSy0DQtUMYFIBGziEcTNbGuGtHyKBsWpE++QHUHjjxncIq3oxFWrhO+EAaE1VJ4r1EOrTsd4Gh1tawOzLChNBulRA3cRu3YKgQiNBi0+5AVfwIZoM8lFQ+tc78OXH7vhbz553dTkjBXSpyof4wvKypn7IUjWPSW/+TTD+88OosNCzbTfVAXsvOz6TOye4PJl27qOI5bkyibYYPzvjQFTdfQdI2ew7rx+kMzeePP76Hpyp/4gtvOYcRUleQc2Ho4MFG04zZvPzqLQeP7ofksaTYLjN6q8SEI1gaCG8AcsFYg5S1Q9XewNlIzMXT3gtYGsr9UM37J+CekFg2kutb2DiXfY61WMcwtBaOf4oxprTldaM4EbBtgCCH6SCmrRYKGAZkQcapV8D4VbFuxi5mPzabkWClm2GTshSOZetMkX5HCuti/7VCgf2PJsVIevOnPIKBNl9Zc851L6DqwE7vW7PXdvm0X/4t64nBxw5IWAkZNG8qld01n5uMfsmP1bnRDZ9iUQZx3y+SMfslOfdrz1Ydu5YkfPE9VebT2Ac4ymXJtf3LNfyBLPBsiYyAi56akJSaRda4SN7TX+n+AlqOqMsLkC9+9lL99+2kSUUVENUIGuqFxzvUTmfH3D1J2NcMmnft24PCuo41airTjNvNeXVLj7ZaWPyHgzItH0bV/J+a/vpTK0ip6DevOlOsnkl+oih4VJVU89b8v1izXABzZfZwn/kep5UdysxBp7hnXdtm6fMdpTMAagMgmfQKGSpoq/oSvIGF8JtIciKhxQwgakKOAgyz/jWeE692/sbeQzhZEzj1NOftPG5+Z+FVeXMlbj7zHjlW7kVLSfXBXLr/3fFp66uhBOLr3eNp53aPfekpNSsIhpn3xLPqd0Ytl7672zdp0U08WffZQVlyJYWg1yuxB6Ny3A5d+9TxatM5jycxVxKvidO7bkQvvOLfGCiwdDFPnK7+9mVf/8A6blmyviV9G2CC/TR5jph5Alj6vOim1VojItUm+tBidIOcuqPi1/wcIU1XckAyfMphtq3axddkONakWAjNkMOGKMax4f41vjGrZroDCjoXpJ4D1YMdttq/cxY7VuwMtl+oipyCHL/7kGj55ZTEHth6iRet8Jl+b7Nby6u/eYceaPV4XJ1goC6SCNi3oMbQrbbu15uDOI4GVTtd2Oba/yFdi41OByE6jPwngqK5Jy2ccco9D5WOQe7f6O528jixHxmZD7B1q+NLWUqS1FpH/I29Zs/nRbAmYlLJSCPE68DMhxO3AcOAyYHz9bYUQFwCrpJRHhRD9gR8BQT3tzYp9mw/y0q/fqJl1JaIJlsxYQSwa55K7pgfuFy2LsnZuBrFYKlLjP+7/F1/6xXU8tu5Z30QgiMPVqXcHQpFQ0qBfH537dGDAmX3QNC1l2bMxaNOlkO/8425WfbiWDQu2kpUdZsyFQ+jd6wmwy6kZvO1NyLLfIFr8LFknx+gF9npSB3kN4ouQsfcBnbzIOL7+11tZ/dFmDmw9TNuuhZxx3nByC3JY+u5KjtbpSBUef2XElMF88vKixn8pKTOS/5pw+Vim33I2QhO+XL1YZZyXfvNm6nVwlVr+tpU76Tu6F31H9WDjgvR+mdtX7sKxHXoM6XpqllKNhVaoyPrOHvwDT1iV9900XI/om5D3HUCA1hlcn8qt3hUSqz25krojrwXWNqVVllEn7f8/fFbil2u7PH7fs5SdKKupyOzZsI/Hvvcc3378rrQm8R/9K3hJsQYSrFiCWU/N5dK7ptOuWxuO7kmtMuW1zPFtYmrVviC9YTaqUn/lNy5G0xsvwVMXRsjguvuvYPf6fSyZsZLK0kr6j+3DmVN3oztzqRlI3SJk5eMI7d4kkWKht0Ri4E+l0JAl3wAk6F255ps3s2PtYNbP34wRMhhxzmC69O9Ei9Z5vPv4R0mTZjNsMO2LZ3N419EmdalKV+K46fcr7NyKL//iBvJa5XDd9y/3OQgse28VW1fsSHn0rbjF/NeX0GNoVwZN6M/qjzYEfo4Vsyg/UcGJQydp161Ng9JBzQ2RNRVZ9Tz+NAoBWleIvx18AHudinEiG4zBypUjpSHNRuqdofKpeu9JIIGMvY/IvuHUvkgAmluI9avAk8Ax4ARwt5RyoxCiK7AJGCil3AdMBZ4SQuSiXIefA37V2A9zXZcTB4oxwyYFAQlNfcx5cWFKW7EVt1n94Xqmf/HswAHyyN7jwYRyH9iW7Yn3Gb5LkXNeWsSwyYNSxE8Hju/Lxy8soORYac3DW106LuzcinEXn8GwyYOarUPFDBuMvWgUYy/ytE7iS5HRGMlJlQtUgbVe+fp5EOFxyPi7JJvWes7XsqR238RisswKxl+W6nr/pZ9fx3tPfMz6eZtwbJdew7tz8V3TCOeE6TaoC5sWb2t40GjM980yueyeCxiahtdQUVLFo995mrKiMt/3HcflyO7jzHzsQ6rK01tZlBaV8/JvlbG167hc+rXzGTY5M2FSia24WY3ysKt7ovvAOUpg8mX0Anubz3t1j7ED7K2KK5Z9HVT8PvV4shxpbcO/QibB3v1vn4B5+FTjFygNwWhFnDadWmW0zLN1xU6qyqNJy2HSVeLBGxZsrlla8sP+LRkqf6OI7HNeXECXAZ19E7DS42UsemsFk65KXi40wwaTrj6T+a8tqY17QhHWC9rk02dULyZfN57cgubjRfYY0pUeQzyNRjeOLH2O1AE7gYzOROR9s/YlrQDMQclLV9UnLCupiYHOHqj4HX1G/DypugQwavowNF3jo3/Np6y4gpZtWzD9lskMOLMPoSwTMxJKsgQ6VeimTv+xfbj62xcHyhlJVzLjsQ9ZNTtASR+1YvP6wzM9TcX08fXFX7+hKnKOS+/h3bnWa0prCFJKcMrVSkiTkEDGlxLY7U3I0xhMr1Epo68pWZzwWMVflvWvhxejfHsSnYZj5CmgWRMwKWUxcLnP6/tQJNfqv78LpFn4bRjbV+3m9YdmkohZSOnSpnMh199/RYOJWJBCsKZrlBVX0iYgAduxenejWoJd22Xeq0sCSReu7bBx4VbGX57skefYLlNvnMS6uRvZt/UQmq583CZfOyHt7La5IN1j/qKFMgFOvUCs5SFyv42s/Ke39ASISA1/ohaWSt7ckpROuXB2mMvvvYDL773A+xzYvno3c19cpPi3poFj2zUDjhk2uPDOaexev4+NC7c0aoYpNMH9z349+HeUMOelhcx7eVFaAq0QgvXzN1F2orzhe0JK4lW1D/zbj7xP534dKeyQZplFuhBfCMYxZFUJhErAbAPZjWmRdpDlfkuLAozBiKyzkaINlP+K9IK9Uinum4M9yyOf7+uWgx5DBcX6A59Mlbv4N8WnGb8qS6t46ddvsn/rYXRDQ+iCS74ynaFnD0y7X/Hhk778KiuaoOhA8FJN6fEyEo0QWwXVIVdybJPve64jWTFrTUoChoTew7tTVVrFtpU7qSqP0XVAJ6Z9cXKtT+vphCwDofnnFE5qpVdkfxkZ/RckVqEyxWoZlvo+sY4yZfYxnR4xdUhS4nvySAkfPD2XkqOl5BfmUna8rGbSb4YNug3swpCzB/L2I+83ukJ2wW1TGXPhiMD3D2w7zPO/ep2KYn+rNVBjXV5BDhsXbq1xe0mHugWLHWv28MnLiznnhgaqlvYBqFqIzIoj43GInoSsO0HLrFACIKNvq87t+issojVkTUGExinaQ0NILEM6pZ5wtR/nz4X4XALjYIMack3HZ9KK6OSREl78vzeSyr5Hdh/jyR++wLce+4pvZ041OvRoR8mxspTESLqSgiArHmDzkky97+oeNHhwllKmiIVuXLiV1x+aiVYtNCoE1953We3s7nTCPoiMz/GWq3zK8sIEvWPqfkZXRIsfq0EYHVnxRzXrqQ9heDpUBcHnIOH1h2eycdHWmtmzbugUdipEupKW7Qs4+5pxdB3QiZFTh5CdH2HF+2vQTSUmm8na4/EDJwJbyzcs3MKC15emTb50Q6NL/47s23yw0Ro9oKpna+ds4JwblL3FsX1FzHj0A/ZuOoARMhl17mDOuyGqtGloiUpoLNUaH2/vyUFkAHs7/mV7WVOOF9gZtspbnrdaEOIoor3fYGIj0f4/9Bf9e+NfP39VLVHZbg1f6q2/vEer9i1rFOb90LZbG2U5VM/mIRRghVONLct2oBsN84oag/oNSWUnynn6f1+ipKgcTYBtuYy5YDgX3Db19DPk3CgyPg+sLZ6gcH0ItVReH1pYWahFbgSiyMQGz+Gh/mCcyEjgeOeaPTz/y9eVR6/toJsaRtikXYeWaLrGyGlDGT19OJqhkd8ql1d+/46SNbId9Xs28EAe3hlMF4hHEzz9vy8mTfj8EIqEqCyPNcr9oBq2JyZbnYDZCZsPnvmEVbPXYcUtug7ozGV3j4DYu5CnU7Mi4h5BVvwRkf9jMr4ZAqz0kCcR4XPUcYxekDhGQ44JOMHLrIByFtE6g7vHf//ThNOstHZ6sHzWGlwn+eGXrqSqPMqeDfsD9lKYcv0EzFBy3mmGTcZfMTpthUlLk9Q1Bbqh039srWBmybEyXntoBlbcIl6VUP8q4zz381eJVzW1hJsZZHw1svzXkFikBOpSBlINyEYaafyztDzlZq/3wLfrTdqgpYqY1sW+LQeTki9QQf74viL6j+3Nzf97NV0HKEFcoQkuvH0q33vqa9z6s2u54zc3EsmPpFX01nSNvRvr3R8S9m89xJJ3VjL72U8aDEp9RvXkwjumIUQG94PPNq7tEq1Q17OsuILH73uWPRuVNIAVS7Dig7XYFR+Q+tDbimyaKWSC4EBXLTFiQOQSGtZGagiml1gHuT1/eIrH/89C0YFiju49npLoWwmHRW8vT7tv7+HdadmuAL2OCKtuaGTnR1L8VetC07TmjWFCMHhC/6SXXnzwTY4fLMaKqvjlWDYrZq1h3Tz/KlqzwS1Hlv0UYjM9sWi/LEaH8JnBx9DCoBUgjKDJbtiLbcGQrlQevXGrJjl1LJd4RZxoRYwv/+oGxl44smapudfw7tz39Ne47f9u4K4/3ErfM3phZplp85PdPuNbRUklK95fw8y/z/aVy6iLUCTEV/94a6PoNPVR1yv3xV+/yYr315CIJpCuZO/G/exc/gxS1h9DJLgnwd7TiE8KisUu1QmXyDqfU49fqPErCM5en1Wd5sFnsgJWcqwssEpRfjL9D9W+R1u+9Mvref/Jjzm88wjZ+dlMumosYzwbGz/EKuO0bFfA8QMnmlT1SIEmOPOSM5L0utZ+ssHXPseKWcx5YWEdf8dmhnQg+hyp5EMNFQkc9f+yCkrvR2bfhAgHtIkDIms60lqmltFqAmFYiXVq6XkfW5fvCEyAFr+1gpbtClj14ToO7ThCKMtkjNe92ql3B3Zv2MewswdSWlROZVkVB7YcTAlGuqEncU8qTlbyxA+e5+SRk0jZQNekhz0b99OqQwsiuVkNd6v6VOSMsMGeDfv5zS1/wTANrESyBo9j25ihIGmLRgQBow/+FakwwqxVtRfhc0EUKG6EW0KNFEWjoIHZT2kn+X2m23zio/8JKD9ZgWboUH/5R0pKjgXIIngQmuD2/7uBD579hHWfbAJXMnB8P6bfOjmYlyMhlGU0q5F8izb5nH1tbX9CWVE5h3cdTeFrWnGbd//xIUPPGnjaqmCK11NBbdWq+hw0ahtUNah8CplYgsi5M8V0uwZGVzC6g72L2gRAU6rpoeC4B3Di0EniAXyvsqJy3n7kfcyQwfr5m3Ecl24DOnPlNy+iQ892lB4vo0v/joTCJhLJpsXbkD7JVG7L2vjlui7vPzGHpTNXqhAtZfpCEIqHevzACYZM6s/815Y2WmMMTZBTkM1vv/RXdEOjtKgs5TxbtErgCr+kSIA8CaRPZGtgDvSnPeg9qZnka20Q+fcrLTGruurfWM5dCMLnQ+z9gPd1dX+J3ID3m47PZALWa3h338HadVy69PNZJquHzn07cPuDN/q+J12ZtIRZXlzJ3779FLGKWIMDdCgSwgybVPq0JeumjqZr5LbMYcDYPnTt3wnbcmrsRIoPlwSSzRe9tZwOvdplTN4OhFuOjC9QpXS9ByI0Lo3RtkttxHRqt6l6TpV99QA+h94akft9ZPRVz+g0G7LO9UrG6RHKMhGa8P2dHdthxqMf1LwXr0ow/9Ul7Fi9m1YdWrJtxU6suIVuaCA0hKaDkxxcdF2j/1jVBXV0z3H+/t1nGh2ApCvZvW4fV337Yp77+atqqSHdwCZEbSImwE44vqTm2uMLThwNmNFpnTM/URFR9ivRl1EDk0TN4rummsuGzkB4VkMy+pLin2UUxASIVoic28DoEpC26Z81P8jTjg492/neM0bIoPeIhgencE6YS+6a7tu1XT9+1V3Wbyh+mVkmfUb2YOvyXak8M01ghkzMsE63gV3oM7IHVtwi4plHx6vigZPiqtIoj373ae763S2nloRJF6w1SGsdiFxEeALoHTwrrKDnWKBiWbW0wBZkbJYylQ/aI/ceZGyGtwRmgzlEqbZrWWlPzwwbaX/j9fM9Kzdvk93r9/HHOx/lkq+ex7uPfYjrShzLwYyEiORkEY8mku4TM6tWI82xHZ74n+c5sDXzxgpQS4bL3lvN1d+5hA0LtmZmxK6J2rHJlZQcLU37PXdtCNNlvN+FdjxHlswgItcgrZ0oioOF4pgaqR2JWntEzl3eR+xRzh8ZLxuGIOs8RHgi0tkNiSX4dvVrp8dD8zOZgA09ayAL3liqKmHeAGpmmQydNJCW7QsyPk5FSSUbFmwhHk1QcqyMDQs2E6+M0657Gy6881x6DO7Kx8/Pp6q0KqVkG84O4TguuqcDpekaNz5wNSePlvD2I+8nd1oKQAismMXJwyUsenM5i95USw35bfLo2r8zlT6CpHXx5p/fY9CE/hn7v6XAPois+J3Hj7CBVcjYe0ojJaVcnA4uMrEMEUmjfG50QOTd2+hTHHr2QOa+tCiwBdvvoT+882iSYr5jqfK0burkFORgxS2klOS0yOaGH1xZs8z86h9mNH72B1S7EPQZ1ZOvP3I7K2avY9EbS1M6a2u3l0n7ZlJdmvViG0ZcUfcVgQo81zTqVIWWgxT53qwzAuHJ3sATfA+JyBeQolAtG8pKRUCVx/03Dp+nlPerR9XIFZ44b3XypgNZ3jLB56hGVk6YSVefyYLXl9ZyHU2drNwsxl08qoG9a+HaLluW7+D4/iJs22Xjwi0U7T9BVm4W4y8bzdnXjOPgjiMpy/qgOqtzW+YSq4wjhBJfHnvRSMZfOprd6/9BzHFqnjehixp5CisGmxdvY/Ni1RkWioTo2Ksd3QZ3ScvBPLT9CFtX7KTf6F6B26SFdJDlD4G7zyPJa0pYM/sm0CIBjXB+CWEC4vMhTQKGMBGRK9T93Ai0aJNP226tORRk4ePXv+JI3vrLrKTfzoomwDUo7NCS4iMl6IaG60im3jBJCcwCq2avT8sHS4dYZZxwJMTdf7yFDQu2sOSdlRxKdywfvnQ6rF1SQMexNur5r74Ghpr4NUpPywWjm/KAxFCVr5xb0zf16N0Rud9ERl9TpHuR68U/P+QiCn5F9RKmyLoImViDSviqb6gQRK7hdIlJfyYTMDNscNfvvsjCN5ezceEWzCyTsReOYviUzCtEW5fv5OXfvIlEzQrqPhxH9xznuZ++yu2/vpGty3b4rpfblsu9j9zGiYPFaIZOj0FdQIP8wjwuv/dC5r26hKKDJ2pU7Z2Awb7seDkbjm9O2zgAIF2XvZsO0GtY09r5ZdWzJPtm2apjKPa+Z4q6j9SA5fegudT332outGxXwIV3nMs7f52V+qYIOB3wryaYOpd89TxatWuBrmu06dK6Jk+IlkUpOniiSefoOC49h3bj5NESTh4tZewFIziw9SA7V+9p0vFSzjtk4IoeiOx+oG0FkQC9ACITPK+yzCDjiyFaVz+nCuKzwewLRv80ewpE1rmQpdT1pVsGZff5b2pthsjltXuGJ4PeBhn7QC1nGv1V8vUZ6YL8NDHlugm069aGRW8tp7Ksin6jejHxqjMz9i+tLK3i8fuepaKkSnU31hkkYxUx5r+6hFhFjHBOWC1z14PQBGdeNIruQ7pScbKCzv06kluQQ1VZlJv/92oWvb2Srct3Yics36WwaiSiCfZs2M/+rYfSPqMAqz9e3/QELLHMaxCqvp89HlDVs6rSG3utXvd2upNpPlmI+rju+1fw5689Hjwh84NP4mrFbfIK87j5x9dQUVJJmy6tk4Rv187d0Ciro2pUe4MmogkO7jhC+x5tOfu68bz04BvB/LFGMhJc28QxL0UzdwJ7QLOUK0b2JY04SCmy7EGSKBHOLjXBy7kt/b5GL0RebcySZT8GN9VTGWLgVoLmrThohYj8B1Rhwt4GWksVv9LGy1PDZzIBAyVfcM4NExtuh/VBImbx8m/fSvuQWHGLT15eRChiQknq+45l86evPk44EmLEuUMoLy5n1j/nEKuMI1230YKbDc0qNP0UMnBpeQmWD6z1iBa/QpY/jFIc1uoEMr8H3ESEhmX+2W6lMmy2VgICQmMRWZcq0qsPRp8/HCuWYPaz82osNswsE03XiKexRvGDYei+nWFC1zLqmEw5nmkw5foJvPbHGexevw/d1LETDn1GdkfoIu1AlQ66qeM6LmbYZOS5Q5n2xbPZfuAEItQCkZOHiJSCFrDUIGOqQ1HGwBzgNTpIiL2Bn22QrHodkf+DzE9OVqK4ND73gl9QMwYhck9xqfy/BAPH9WXguGDifDq894+PKDleFujMYcUtlr23minXTUDXdRw3OdY5juTD5z5BM3Q69enA5GvH8+KDb3Bw22EkEMoK4dapgjWETLorT2n1Mb6EQE9TEVaWWfEFdRTsg2K7BubQRnwwyMRCiL2neEB6d0TkKjC6+G5e0Dafr/7pyzz6zaewLRvHdtFNZYzeIGe0HgxDp0Wb/BStSFBito2GJmjTuRDXdfn1F/+MZqi4k986j+z8bCpOZu46knRYQ6tR7u82sDMXfWU6J2yHRHgiInciIrwDzCj+d4BUOoPOIdDbgDEIVd2ci0qU695/FlhrvG76wgzPziI4zTGVCHXdCaLWCpHtT086HfjMJmCngl1r9/iqONfH4d3HGHvhSD56bp5vsubaLtHyGIveSO1cipbHUl5rEHXX2utBNzS6D2oEBygJ1TwIP3jdJPk/Vt0ebplSHg4q25qDQPepxEhbBUlrFWg5iPBkpLSg4hGSAmd8DjKxCEKTEVln+8pSjL98DN0Hd2X5rLVEy6sYOL4/rdq14LHvPduYL03Pof4dTdHyKKHsMNGyxlXyclvlUHSgmF3r9+Ek7JolzJ1r9jDynCFsWb6zhv/Xsn0BjuNQdtxHkqMOzLDJ1JsnMf7S0Wm384W9DVnxiPeHqyaL4YkQviyYsF/dSi8rVODDVIkb9W1lLGTlkx4JNuDeOY0eaZ8jPTYu2haYfFVDSkmXAZ3QNJ8VOleqGpJjs3fDfp7e8FLS27GKJsSvNDBCBsPPCRaJbRhpnlV7ByLnRsg6H+z9SHuLp+vkkxQKE1Gnalv/ODL+iaqKmCPVc1H5uFd5q95mC7L8/8AYgsiaqirK9VDYoSXfevwuls9aw8Hth2nfvS1nnDeMR+59kmiGv6sZNhk13T9RdG2X7Lz0fDT/HSVTbzqLFx/0nGC8Ma340EkK2rWg++Au7Nm4H6SqlPUc2p1d6/akLVRoukZhx1bc++fbkvKrEzv9Kk71IGOKr+UeQ10rHbR8RO59XhOE3+cannF3SxW/3Aq1MuBTZZfxJRB9gWA+mH3auF2Z4r8yAVv7ycYMBegszrxkFId2HGXT4q1oupbWIuhUYYYMRp47hHWfbKpJ4IQmMEyd6+6/IiP1YV8IAwjjr1Suq+qJhlpvB4iaAWVnHbKvT53ISFsJ4jlHvc8QyMRq6rYL18Jbwox/gEx8jMj9luo6qoeOvdtzWe9k2Yop109kzosLMiqJX/WtizHqyY1YcZv9Ww/xwoOvN6iV44eSY2Wsm7cpZbZvxW22r97N95+5Bzthoxs6QhOsm7uJtx55P2nmqxlKJFI3dBAw8YoxjL+kCckXNrLiUVKuaXwh6AMIrjdYyLKfeMK51b+PQOR+LcmmRVa9rMRzA1WmTUT2ZU04789xqlDWXQ3HL8dyaNulNVd/51Je/cM7aJogEU+/pHiqaNO1NTn5kSQ5IN1Uy179zmji8iMoCYigKr7wlm21AggVINzjSDR85XTC5/tO+mTsI4i+TU3Vxd4BURv/yYcL9lpkxRYIn4XITrWDy86PcPY145Jeu+M3N/PXb/4zI+5ph17t6D+mT/KLUgmJv/uPj9i9PuC3aAAfPz8/ZUlaupKK4kquve9y2ndvg5QS3dCJlkX5w1f+ntStKzSBEKKGMtN1QGeu+e4lTSpvyujr4B6i9jrZ4BYjo895Cvd+iCIrn0Zdl+r9HHUdItdQcyLOHoj+i+DkS1Ndlv+fJ5H/dQlYvCrOlqU7Mtq24mQlmtC4+jsXc/LIRDYs3MKcFxdmlLw1CRLOuWESF905jYPbD7NzzV6ycsIMntif7Ay5IYEIj4f4nNTXRTZo9ZbpQhMhNoNkroQGeleElloOl/EldZIv74s0aJ/jKGJt5TOIFv+b0VeYcv0EBo7ry+J3VlB2opx9mw/6JsS6odF1QHK1cMHrS5nzwkJct4GuxTQQGoH6OWUnyjlxsJjCTrUk06GTBxKPxvnwX/NIRC0MU2filWMZf9kYKkuryG2ZcwpNFdsJJBnHZpE2S63xfqz9HWTFXxAFv0URUh2vGyjNNdTagXEqFY3P0VR89K95GW+7ecl2Rk0fyvefuZfd6/fx+kMzmladzwBmlsmEy8cw8twhVJZWqQanqgS9R3SnY73JVGMhsqYgE/NJvedNRKjefRgaCdE3fI6iI/y0wNwq5XmaNFhnMkGLq0pbeCLo/uLOddG6cyvue/oelr+/ml3r9hKrjHFwR6p0h25oamm6TlJzaOdRXnzwDSpKKk9p/Kksi/qGBitusWr2Oi6+a1rNa5H8CLc/eBNv/GkmR3YdQwjoObw7V9x7oUeZMIjkncK4lFhOapLseBO/dKmJT3U/vkBVwkwlJyVjH5O+E9L1xHf//+K/LgErOliMkalDvQRXumhCo2X7AkZMHcLHzy9o1vMRumrvBrju/itq2ro79elApz7BitiN/pysi5GJdSBLUQOrBpiInFtUZpG07VSks10ZKSO99yOQcysyNk89OCKMCJ+l+BSJVfhX1zKAe1QFQC07o83bdW9TY1v07uMfsfz91SnXsnXn1kkJ64YFW5nzwsK0HAwzbDDluon0G9uHP9/zD9+l4KzsMGbYpKzIZ1lRwiu/e5u7/nhr0sujLxjBGecNJ1YVJ5wdqvHwLGgb7LqQGdLcv+5BGhQE8oO1QQUwaaU/PnjyJZ/j/wcO7si8+23/1oOMmj6UUJZJv9G9aNO1DfvqixE3BXU47mbYQEoYevYgRpwzGICcFtmMvShYW7HR0NtDeIrqYKxJjsJgDgG9XqVIK1DdkVXP1cY26UL2DcqYu+plcEvBHIYIn4W0tqO63JqieC6Va0UGCRh4HbBXncmkq87k6J7jPPa9Z1KW+ISmJVW/4tEE/3zghQY5sO17tOXLv7qBx7//HMf3FaW8r5sGvYZ1Z2WAR+TK2WuYeOXYpNjUrltr7vr9LcSjCTRNa2Y7vHS0mMauUCSQ8XkILwFTmobpoCHcIvApKHya+I9JwKorW4logl4jetCqQ4HvdvmF+dhW5oNTZUmUvFZK/C63QGl4KQPTpmP6LZMZd9kZJGIWezbsR9M1eg7tdnq9HrUcRP7/Kv6VvRlEISJrsn/gELpakrL3qVKu1gqp94WKPyrLBu/hkPYWVSqWTSNveh9GU1t8z7lhIttX7aK8uIJENIEZNtAMnau/fXHSdnNeXJARAbbv6F606dyKrz50K3/71lNJSzVG2OCC26cy8++zA/c/vPsYFScr2b/1EId2HKagXQFDJvYnFAnVJNbNBqMvQU0S0MQKR3XzhQiDyPOS9QCcBlHC/2pI2LV+L8f2FdGmcyE9h3YL7IwuaNOC8hOZifLa9eyCplw7nmd/9kqD/LF0KOzYknv+chuuIzm86yhlJ8rp3LdjM0wq0kNErgFzsIph0kWExqgJoM/PJMJjPaPt9eoFcwgysQpZ/mdqBndnnxJxxaJJExYA9DTLZenRrnsbxl48iqUzVmJZjhKbMXXOunpc0vi1Yf5mErH0CYlu6PQc1o2snDD3/vk2Hv7a45w4WFyTaOmmTtf+HRX1Iaib3HbZtGgrvUf2YNPibWiaxqDxfSns1IpwGoeRJsMc5jVn1b1HBSqGNYHq49bhCRoDwElnoi3T2+J9SviPSMB2r9vHc798TVHNXReehHGXnMG0W85O2TavVQ59R/Vk+6pdDa7Fa4bg8K6j5LVSFjzRihg9hnbj+IETHN0ToI1Us7OgVbsWDDlrIEtnriJWESO/TT6X3jWdvl4rdiRXZ8CZfdIf51QgLbU8aK8H0QIRPguRNQWYUmcbFxKrkfZqIBsRnqjUoMFThVb/LeIrkM4hkh8M2+s4aip0FSQDOiIbQlZOmK/96ctsWryN/VsO0qpDAcMnD0oqi0tXUnQgfbVGNzQ69elQ40zQvntbvv7XO/jkpUXs23yAgrYtOPua8YQiZtrmSelKnvjB87UJYZbJB0/P4fYHb6ZN58bo32SCEGTfClX/pJYPEVLLye5hGqxgpcABo9oIWoDISp+AReryv6QiJydWgQghQmeC7t8l9jlSEauMe44MJbiOi6ZrtGiTz+2/usFXlqK6YzETqYO6jSau6+I6LkMmDWDt3E0NdgIbYYNzbzyLNXM3cGTXMcywwchpQ7ngtqlouoZuUGMNdtpgb1fi0TKOMEdBaBTCrCfsax9ExueDLEeYw9QSpDBAy1UOHABuHKKvkRK/GqRKZAAz2By7IUy/ZTKDJ/RXk3ohGHrWgJTO7fXzNzfI29NMnTMv8jTkBHzt4S+zdMZKVn20HgGMPHcoYy8eyR/ueDT4IBLWzd/Eh/+ah2s7IARzX1rItC+exbimNAk1ABG5GmlvB1mFWkEJo5KvpkzoTQjVOnw0uKKidU7WJHOLkPFFIEsQxgB1D50m7a+6+MwnYHbC5vlfvabE6+pgyYyV9B7Vgx6DUzvhrv7OJbz9t1lsmL8FiYsbUBGTElq0Vh5R21fu4sUH31RuCq5EMwS6YQSqCJshg3v+fBtGyGDqjZOoccT4tODGkRW/BucE6ubWkImlkH1jrZWQdJX8hLOHWvL8EohcqapjdSDtDWS+zBgGkeN1UqYJHHoHRPYXG/e96sEwdYaeNYChZ/mrre/fekiJ0ac5hmO7nPelKUmvFXZoyZXfTBZrPLzraIPdsyXHSjwxWGUjZcVtXvvjO9z1+1sa/jKNhAiNBKMbMrEMZBXCHIJ0jnhmwpkmYErklayLQWtR+7JbFryLOREhNEWi1QpVN5K1nuolHBn/GMwxiJxb+XRv+s8m3n/iI4oOnEhaSi8+VMyMx2ZzzXcvTdm+z6ieXHz3ecx68uMUtfT6KOyoBpmqsiiP3/8c5ScqcGwHM6SDpqXEzbq49WfX0nVAZ8ZfPvrTj1+AjL7r8RkVMV7amyGxCJF7b83SoowvgqoXUfeeRFrrIT4HkfedZLsh94DaJ6P+AwMQaoD21Y+qhonIvUsJwZ4COvZun5YjdyiDjsK+o3pQ0K72+TVMnQlXjGHCFWOStmtIb/LIrqN19MAkLi6zn5nHwHH9fOUwTglaPqLFzyCxEukcQGjtkOZQKPsBjZtAhpUlUVadgoubrkCSDdnXIaPvoK5zvhcz1URWJhZD1SuQfz+iUcKxjcdnPgHbtXav7+tWwmL1h+t9EzAzbHDVNy/i0rvPY87zC1j0znLfkrxhGrzy+7eJVcapOFmRIlTnusGzJzthU1laVXvT1rvvY5Vx3n/iI9Yv2IJ0JX1H9+LCO84lv1XzLO3I+Dxwiqid8Xnr6tEXlCKxMCCxUonb1XAfpLfNqxAakzyLEHmoGUFDD0YIsr+ACI1Hln6znjhiXegQPq9Bf8hTRawihh4ycKPplyBn/XMOX/7VDSmvlxdXMv/VxWxduZOq8miDth1O/WReSo7uOU60Itb8y5CgxAOzLqj5U2jtkNFXfDb0Ei0c0PtCaATYOxWXLzQejHoWOFpbcP2erTA4u5CVy1EJuUFqFUGCtRRZGUXk3M3nSVh6rF+wJSWJcmyXTYu3BSY+I84ZzLDJAynaX8xf7n0i8NhHdh/jt1/6K67jEC2L4nrcRgdP4T4Ndq/fV9vMktL5DCtnr2XOSwupKK6ksFMrzv/SFPqM6tnAt80QbgnE3iXp3pJxFa+sdRAarqpaVS+SXNVKgLMXGV+MyJpU+7LIVtX+TGD0Q+TcrvQLE8cJXJ7Uu3gSLqcXdgYdr5sWbUu1oUIVC1Z9uJ5l762itKiMqnQNGMEqSGxeup0zG+HSkDlMCJ1Zc3sJQOo9PRu7+ifjdfKLVhA+G2Q5uCcR5mAIjaZuOiO0jkjfzn8TzMFQ8RDqKZA+nyOBMij7KeT/5LQKSX/mEzAnyNVdktZ81nVcXvvjO2xdvjOQD+HYDsf2ppIZaw8SPJ0yTJ1YVYIWPu9JV/LXb/6TkmNlNcsAmxdv48CWQ3zj0TsDuWBW3Oajf81j1YfrieVk0b5HW86YNozcVj5JTNV2cH1uHBGCrI2K1BpdD05Ahh9eDmYdBWB3MFRtouGSvQ7ZXYDDED/De5ACEDoOoQz0YpqAsqIKKk5WEMoySeRGkNnpk5/9J6vYVG+mGauIMfOxD0nEEkqQMhxS/xoJVxNs2X2ccHbm++4qL+NYQSWtOUEuB4AMhQe1FhC5wVPBh5oRPOtiRNZ5yduGU5foqyGyL0NW/I1kYnJIeUk6u6m9D9LcD9ZGsPekJnefIwlBnbVBr1dj06JtvPHwzOANBOzZuC+w6tPQslY6zaq5Ly1i7ksLa87x+L4iXnzwDW544Oq0bh2bFm9j9rOfUHKkhKwe7Rk2ZZD/Mqa1DeLt8SXGG5shqwPYByDWCt8vKFZATl2NLg2qenvNI+m+twmhwWCWgtMbYluCtxcu5KSPXwN7ZUbOr49YZZz9Ww4SzgnTtmsaeyMP0pU1ndV18dofZ7J5ybbMRGAlvsvS0nWV28KnBJFzq5I1kjFUfDFAa4fI+7aiRmSC8BkQexNkXTFXpTOGtYrMlp7jyNgMRPbNTfgWmeEzn4D1HNrVN1CZWaHAZSmAxW+vYPvKXWnL96dCVDXCRiDv58N/zaPkaDK/RrqSaEWMDQu2MGLqYN/9nv/V6+zduJ9EXoR4myx2l57k4DuLuOSr08mqp7wvo/nglPgcxYTsPISWhawS4Abwr8xSRLjuzd4eGR8J1tqAbwzKtLQtuG8CFZADatYSgFAETIFoIonVD9UOBkUHixGahpWwoLDhxEdmhynJkkRyI+zZsI9tq3ZSXlxJPFcgczJPnITQkPVm2kII3nx7AdNvmUxOfmbdnvsKDjOwXxmDc5bS1XTpGhpPputAIjxOzcytNYCjiMqNFRw0BiJy7lDVNPe4qoCGp3ryJJnyZhyktQlRLbJodM/o/P/b0O+MXmxeuj1JdV5oQhlzB/xcZSfKef2hmcE81jSizpnAzDLpM8pft6u8uIKPX5ifkpdYcZsPn/2EXsP8aQUbF27l9YdmEM+NEO/flnI7wdzF6zizMET3QcmcQWnlQTwL1a1dFxqY+YhwFtIOQyzo2bQR9SrO0r0Uqp4nfQIWArEX5GL1Z16aZ1/kQyThK80DcGDXMdjZ+CRs0dvL+fCZeWiGhhWzMnYjOL6/iNyWORzdc5xPXl3M4R1HKT5yMuP9g+DYLnNeWEBB23yGnjWw4R08SGmBtV11mVpxkF0ya1jQChEtfqkqnW6RqjQa/Wlc7Agh8v4HGX1BdXcjVIe3WwE0ooPb2qhke9xSdR4is/idKT7zCVg4O8xl91zAm395D+m4OJ6lS99RPek3Otg7b/n7awJJrIUdW1F2siItPyIdjLDBZV+9AE2vHzwUEXbJOyt997PiFkf3HAVSE7Cje4vYt8lLvjrkEO1gICodLMNi446XGTi6Uul0hSaBngfWUGT8MMmDpQYiF5FdqLhsuW3ACppZ7UJEhikfQg8ylgA73UMggeoZYSYPyypghRIA1bsjtALQT63cu+jNNeyPFuPmV4vAZvbQJjSbV17+gHZd23B073E1sIXx8sfMH/xQxMRO2CmTgrgWY86ClZx786SAPWuxzz7GgJZ7GBz5gG5mGV2oQFasBHM4IudLmZ2PVgDhyRmfty/MoQhzKNVleln2UxpHWtYh/i4y/h6K2B9B5H4V9O6ndl7/YbjwznPZt/UQ8ao4VszCzDIJZZlcctf0wH02Ltwa+F4oEsK27JrlxsZCN3V6Du1Gr6H+lay5Ly8OzGHSNbx88MxclXzViV9xXJasXkq7kTNVkh8ajzB7gOyGrNRIvdd1RGSg0o92CiEa9CzYEN6EMOskDE4cGQkSpK6GBRz2+Vw/VAAvgiiA0DCEyAG9Yw0/rXVhW+at3NOoJGzPhv189Ox8FX8aOfw894vX6DOyB9tX7say7CYn4LqPTJNjObz5p/foObQ7uQUZJCFuOVTNQGbZYEchsR1ZtgCRd3+GS3pGjaZXk6G1ROR8leqbVcY+AOvtxh1DViDLfk7NRCDrfERWGiP3RuIzn4ABDD17IJ37dmTt3A3Eq+L0G92HHkO6pn2GgmaOumlw0V3n8sKv/IT8qDmmEMJ3ZhGKhLj9wRt9PQgByk9UpF1aaNvVv1JxbO9xnFZ5xAvDVI41MY6WQ1ilGCcSDqJlAtgBHEDk3ASiE8QHKZ/A6m4OEUJEzgNNfXfh9EFG1+AfTcuBlxG5t9TuHw+D5adu31RUX4P1wEYUIbIdInKhWiptJFzHYd+R3ch8t0l1Fgc4VLwPcptWpzFCBqOmD2Dlh+t8uWJFFYewc2KYofSPXVtZxOCsWXSLlNLFrPJeTaiKlr25TrfiKULG1CxTxpJUoaW1FuKfAKiOxtAo9bkNauvUR73qsrSQ5Q8hCn6DEnz9HAD5hXl889E7WT9/M0d2H6N9tzYMOWsAoTSt/4m4heukVu81XePsL4xj2XtrKD0W3MVqhA0VA+s9+kITTL1xkiLeBzwE+zYF64jlpeGwnrDcmuTLbWtjlKpnpAqgZQLBCeBdRNZ0MHogWpyPjM6gNt44EB6HMPMBG4GJrEwngzMXkRWp9W10DaiK0vzx64j3zwQ0ROQiRfEAWg3LZt7aI+xaU8bFwxvueF86c2XDy4UB1U0rZtXyBpuIjn3a03VAZ5bMWJnyGUIItizZxhnnD2/4QPF5kBOj9mRskFFk9GVEzleafoJJkMo02zmsfm+jHyCQ7gmIzlAWR0YfRHiK6oiNvUfju8Orr7G3X+x9lWSfQudrXfxHJGAArToUMOX6zI25B47ry/JZa1Iy/Rat8+g1tDutO7XisE/3ybDJg8hrlUenPu15+5H3ScRsHMv2LIMMbvjhlYHJF0BWGiK2pgsGT/JfNi3TNaJtsqkcrdE2v4ghXQ+ofTRJl74JuhdWt5sbYL6M8Ox9pNsXZLFKaNwqcP6syKtanuJ45Uuwg4Q7DQi9jtA7IJ3DkLXFaxluKuqoNwZiHxiHvMpL42BbNmedseOUS+5NgWZoZOdlM2JSK3JLdvgGUaFp9G65ALMBI11pH6KbUVUn+apGAplYgWiOBMzeiqz4q/dHtY/kOWBtSSLfK1+9+UqzJzB4hSByJURfR11fjeAqgwWJtR5p9nNUI5RlMmpa5vd8vzN6Me/lRbhO8kRSNzT6ntGbrJws3n38oxTLotZdCuk7qhfturVm06Jt7Fq/t2ayYGaZDJ7Yn4lXjk372S3a5AfK8Ey6apzv65t2HoUeLYkWOLQcU87w0K6a90IRSZ/COsmi9jzC4ydK+oBTDDhq+cp+H5znQZhqSTuvBSS2E3hv6i8jQmORbqWaRESOcUoZSkYx7BGInItAJ0pnGJbPgbVVzFizvcEkrCoTf9p08a2JX01oAiOsqq5blm4POLTMrKoq8WyjQiRn8bJWl+1UIauQ5X/wuh1dFP2lEMyxEK9TPHF2IuNzIPfrpK3eZ10JiUV1LNqCtOEsZOxDxOcJ2KlhynUT2LJ8B5WlVVgxC9000A2Nq751EUITDJ8yyDcB27J0Oz984VsgoGv/Trz6+3fYt+Ug0pV07tehwVbdcCTEoAn92biwXueTJrjg9nMJZdU3RVY4maOhD2xBqOgIQ7oewDhkUbVREo5ILrvwAHn5dY4V6oHITg6iMv4xRN8jua69C3K/pVq9bT9ul4GI5CNxIfoRZJ0iEVPrAm4mKtxFiILbaUodav2mzRzf/+mptAtN0KFnW0ZOHcqIc4dghkzKnTJWzlmHU5dDKKBT7w5M6Tih4YNaq5CVc/0+jbSPrL0LGZuhxHJFSzAHquUcYyDJmjaWR7Cv7yM5G1/vTmeXJ/yqkxrEDIhciwhPgPAkdQ5IqPhDwEk6pyjc+zlAqZ6PmjZMVVu9ZN8MmYw+fxjturWmsGMB7/ztg5T9ivaf4Ip7L6RL/44MmzKIOc8vZMmMFSRiFlk5WfQZ2XAX44QrxrBr3d4US5w2XQsD+asAI6cMYs7RtUT2lmHkWFRtBDPkcs5NJxiVX1dY9iSioF4S6BappaBw3Xv2MITOBPNKqHwU3yRMk4jc/mr5PBzl1JIvA1XlaihJCiNysrwltKWspzNdR/Um64RKRNMtRw4Y15cD2w5lpPHWHNB0jdyWOfQf24fxl46mVYcChKax6K3lqecgod/oTP08U+k36V8H3HJkfJZyVhGG8v80Birdt3o8Oxl9NVXv0D2anHzVIA7Rt1AJoc/vqnVRVdes6eqYbgky+j44Acv8bnHwd2gk/msTsEh+hHv+fBvr5m5kz6YDFHYoYNT0YeQXKt2vBW8s890vXpVg9/p99BjalVlPzeXAtkM1idTuDfv5+3ee4d6/3F6jnu+Hy752PrZls3XZTnRDw3VdzrluYoPWHRdcN55Vy5cjtJ1UbYKufRNccstx8grqBp4Q6Mk+iFLGvepE/ZvPgthbiPBkVelIqVpoSL0HVNbvhmsiMkq+QD1UDk25PS+75wKe/vGLOJabtNSrm0oDyDmFxor6KGjXgqu/fTFd+yf/3lNvOosda/ZQcbKSRMzCzDLQDZ0rv3lhZgc2BgW8YSJC/hUGmVjhibJ694IshfgeZFwHIoi8b4HudZtZQU4OQb+No8r5WqGni1R9v2kg8hDh6mqWrkr+KgULhtkv3bufI0NceMe5DBjfl3WfbEIIwdCzBtaQ2Ze8vTJQaPWDp+dw2//dyN4NB1j01rKagbb8RDlvPDwTK24xYmqwz2ePwV256M5pvP/ER0ipusW7DezMtd+/PO359hzalf0nynA2nwQJuS1czrnyBKPOrqfqr6V2/Mqql0mNTwlVtci6gBqJleQDgdEPGf+EGpPtU0Kmwq2yRpW9a04h60sy/4RR04axYtYaTh4pTaqia4aGbugNyuA0BkITTL/lbMZfNiZprtuxVzvGXDiKZe8qhX5NgGboTL1hUmZaYAIwegL1V1b0ZLHUunDLPY6pdy9IVMyxliKjOmRdjsiq9akM9JEMgrMdIld7iVjdIoSZbKaudVD/RBq/VSO4y7ex+K9NwECV/c84f7jvmnYsTQv28QMnaNWhgI2LtuLU5ZK5EitusXTmSs69+azA/c2wwXXfv5zK0ioqTlbSqkPLjGyIzLDBmZeMpldBnAl3jUZU/B+4dW9wAXhK5HVR+TiBgcM+ALlDlbmye5jaRMsEoy/C6ISUmdmeNBu0rjT11uzavxNffejLLHlnBcf2F9G5b0fadW9DVVmUsqJyFr6xtCEB8ORTMTQ0TWAn6j3cAkqPlfLkD56nz8ieXPq188lrqfgvkdwI9/zldjYt2sqhnUcobF/A0LMHEm5ACqP22GFE7p3YJX9n3/YwjiPp1ieO2WJ6gKSD63V3+QUgB6hQZtstfqVOvCnq3yIHkfddNfNMrFSfaQ5BRL5AKp9LgD4AnM0+x2mtAtznOHUIlQz5aR0WHQqepZd6Xqaznp6TUuWw4jazn/6EEecMSVuAHjVtKMOnDKLoQDGR/EjG+oUderZl7HkXMSD7AF2Nw546fV2YiMgVyS/ZG8FeF3BEE+EWISOXeYba1YOrBoQRWRcgq56lWRTvM4Zssj6YGTb4yu9uYfVH69m8ZCvZ+TkMGNubaHkMPaTz1iPvNygfUh9CF6n7CJWAzXpqLsveW81Fd05L0nA770uTGTKpv7Ik0jWGTOxPG88pJCOEJoF4n9LiKiqcOJW5YWjdChG52ndzGZ+Fr8k2AA7E3lZm2zXxr7ETaQMRPkdpg8VmgixTfOPsq7wOy2SI8FikvQ7fmBrwHZqC/+oELB3adG7NoZ3+HYL9Rvfi2L4iZepdj8zvWA77txzM6DNyWmST06Lxba0ClKlz3reR0Ze8AdEBoz8i+3oQdZSZ3eNKqTwIemtAR+R9Bxn/EBLLUIPrMMi6FFVyz8Kjytb/BjTNNqI+qsU8dcBA5KS61Du2w+K3l7Ny9jpcKRl29kAmXjmWkI8uV2GHllx0p5otua6LpglAEI/GWfD60kadmRk2KWxfwLH9RclJmPR6Ax3J9lW7ePy+5/jm3++sMds2DJ2hZw1sVNt2Xezb3oJ//bInrm0DEik1rv52P/r70XPcareDNJBV4OxVHYhGfxpLRhXhiSCylXNBBu4FIucmZNmDqOWaumKaMWRsljebTbMc8TlOCf3G9GbVbP+kpccQNYMPWqqvqogSj8YJ15O2qQ/d0GnXvZHyJh6E0BDhs5HoEHtHVWy1QsXFMYclbSurXk9zJAu01gijD2htkLH31RKR3hkil6pj6p3A9uOJeYr3p1zdrytGHFJyLT4K6kd2H2Pr66t5d38Rnft0YMoNk2jXLTWpMcMGYy4cwZgLR9TwWasFVhe8sYyiRlAszLDBqGlDWTl7fTIvVdbKLBUfLuHFB9/glp9dl6TJ1pBCfzrYls6Hr3XgULyYwrEVvDO/JZtaDuSKb2ThK8af2NDQEZGJRYjqBMwcrBqIMq1qmqMBoeJYOAOuuDlMEe2ttSTfHzpE31BJWDMItH6egAXg0nvP59FvPZ1Sxu8xuAst2uQrqQE7dRDTdI22Pg/VaYGIILJvVZ6AQRpRzmFUYuMfZETkEu8/wghzKDK+UHF04vMhvgRy74DIBRB9h5S+aK09uDtP5QuAORy0TkrcU+/kdazUv7Elz/7sFfZvPojlJbwLXl/K1uU7+crvvliT9NTFzrV7mPnYbIoOFBPODjH+stGc/YXxtGibT+mxNDY79c9QwG3/dyOL31nBitlrlX5bvWfedSRV5VVsWzKb/qOy1RKbFtyI0RDi0QTP/PRlEknq/S4v/+5tvvG322nRup68r8ih4UAkPFFCVIIeuQmiz5BRIhaenrKs3SC0QkTBL5GxOWr2WvM5FRCbiXSLENmpifbnaB4MGNOH/MI8yk6UJ72uGxrnf1nZbhW0yfdNwsywSSjr0+lSrR0Q02jcuceCD2AMrI0XZj+1JOnsBXsHlP8aGZ4MoSkQX0DqvR7Bf2LZGJiQfZMn7hlSXEifisqejQdY8/JyjENVaCfK2XSigu2rd3P7gzfSoWcqJ6yipIoZj85iy9IdSCnpPbIHl959HqPOHcoHT8/NvNFIEww4sx99RvVi3qtLOLj9sK8CgBW3mfvih3zxBz2UZqDRr0ZOoylY8u5qioqLsfNdbMvFtSWbFm+lXbfWTLzyzNQdtBbgpBOblSBruXcich3S2oXq1m8AoiUi5/pGfgOByLkN7J3IyifUBKHac9dahbS3I/J/lrkwbAA+T8AC0LFnO+749U28/vBMig+fxDB1Rk0bRvsebfnLvU8QrYgRzgnjujKJTK+bOuMuSe3wOnH4JGvnbGTbyp2cPFpCdm424y87g9Hnj2gmbcqAg+htCBxkjcHqHwAJZPkfSQ5IcWTFnyHyZci6SM1U61YzTin5AjAgPAEhHQgNCzRw3rf5IPu31iZfAHbC4cTBYrYt30n/scmdRQe2HeL5X75Ws7wSr0qw4PWlRCtinHPdRGb8/YPkpZeAxiahwZBJAzDDJhOvGMOit5YH5jmOlaBo7zzkwDJklWTHxvbMeLqQNl1aM+X6CXTu0zHjX2XL0u2+nyNdl7VzN3HW1fV4YCIbjAFqqSYQMmn5UoTHIq2Fqo07BRqQq1q7I1fUzjobjZASUkz5MglILFEVCpHXxGN/jrQQ8I1H76x1+3BdOvXuwOTrxvPaH2dyaMdhwpEwumkkdUqaYZNJV52ZYmmTiFlsXLiVjYu2cmjnYaQr6T+mD1NvmkRuQXPYiaUJgoGejAbk3F7zl6x62RPdrMPVin+iLIxy74Sql+olcxkM3mlhQmgCQstVmntGH/yGVCldVs5ag7Sc2nelxIpZfPjMPG7+yTVJ27uOyz++/ywlx8pqeKw7Vu3m7997lrt+dzNzXlxIIkONSk3T6DqwE7qhs33Vbg5sOxS4bdH+g8iqlUgE0UqTfz3cAyseYcwFIxl13lDfia4fbMvhwJaDOK1M6l5XK26zZOYq3wRMRKYjK7YRPJEMI0J1rJC0lpBzA1Q+gf/yckhVP0MTEFlTadogK1S3rawkeclTqkp+YrEqGJwC/usTsIqSKpbOWMGudfto2a6A8ZePpqPXpdKlf0e+8bc7arad9c85zHz8w1oipCbQdQ3d1JGuS+vOhVz61fNp1aEg6TNmP/MJi95anpSoRctivP/kHI4fKObCO6Y24zdKqCpWYhVoOYjwZDB6+ZTgQ2q5sma3gPVubIg+ieL5NJe8g1Aq0lo7qPi7WobABa0jIu/rKWrDB7Yd8nUlSMQs9m05mJKAzXlxoS+3Zfn7a/j+M/dw8lgp819drGyspDJO9+s6MsMm596s2uF3b9iP41PxrIZuSNp1rkRKi5IinRYFR2nfOcGWlWXs2bCPG354Fb2Gdc/o14lWxnDd1O/r2C5V5f4dWCLnDmT5rz0eX8o3geyb1f8nnXQn5QeZct1NRO7dzWMhZO8hUOLEOQrG5wnYqUC6kvULtrDqw7VIV3lEDp08EE3TFNf0/lo+1aGdR3ni/n8pdwgJFScr0QyNcHYYK64EYCdddSZnXZU8QB7Ydpinf/wSiWgiqfKy+qP1bF+5i3v/ejvhNJpljYa9FRmfq1TLQyMgfCFEnyO5im9A5HJEjV6gDYmlpFb6LUjMVwm/aB6fXQVTVYkSi5CJJTWvity7UipgVsImEbfqP30A7PdJiLat3EVFSVVSE5F0JfHKGNtX7+GWn13LC796ncrSqprr4SeeCnDFvReiG6oDevXH69M4v0jad4sBCWKVGmXFLmddsIPnH2rP+09+zN5NB7j6Oxen/0k8OAkbGUC0jQcljsYgdZ3jftZaIZXc1luaFlobpC+NQYPQ6OaxELKD6EQJsPemNXrJBP8VCVgimuD4gRPkFuQkdXGUFVfw168/Sawyjuu47N96kM1LtnLNdy9NGdSjZVGWzFiVrKvjStBh3CVnMPm6Cb4SEvs2HWDJOyt9b3wrbrH8/dWc/YVxTeKCpcJClv3Gm+UlVMe/tQn0/qQmTyZJ5GlZRvByVKbdP5lAU8tfbiXE30YFTC9ouvuRVU97Bs61yC/MRzf0lA5GM2xQ4NOVc3y/v3+nYzk88o1/MmRif4SmgXe8oJZvx3Zqrlt6cURJKOwSyXX4y/90ouSEgQCycqr3tXn38dnc+5c70hyjFr2Gd8dvxmZmGT72MBbYuwADkf8jpL1bDURuKcp4u70SWE3MR8ZmQWiEmrWJiOp+TVma8fR0PB25U4beEdwDpN5/duPtkf5bIeHoviKk69KuW5uk6tQrf3iHLUu310hCHNh6kI2LtnLjD69KuYVmPzM35T52bRc9R+e7T95NKCuUUvlyXZfnf/ka8cpUjqHruFSWVbHm4/WMvah5jJplbLZXafcG6ugeXz6Vup8K6vyZID0x2wJ5slnOEa0v5N4EZT+nfsInK/6KaPFg0iTSMAyE8K/A+AnXnjhY7GvAbcVt3vnbLCZdOZZEPYsiv/HFCBuUFpWl3ab2O8HQ8RW89vc2bFyeg65LpATNUI1lm5ZspejAeFoH2OvVRTgnTF7LXKL1KCtCQO/h3ettLZVsjixHZJ2jlozjc5X9j4yppNnoBfZuZNkvQe+oPG31zuqf1h7cgyTHMEW4bxbobfCvnpkqtp0i/uMTsPmvLWHuiwvRdA3Hduk2qDPX3Xc54Zwws56ckyx8J9VN/saf3uX7z96bVHI9suc4RkhPETZ0LIfd6/fRoWc7tq3aRV7LHEZNH0ZhB8VLWPXRejXjDIBjO+zfdoj+aWyTMkZ8aW3yVYMEOH5k3BgyPhsRuVL9afSl6WuhGir4eZWsdJUyYyAiPA5Z9gCps1VHLSHIeJJnWP+xvTEfM0jEraRDa7rGkLNTSe7te7Sl5HiZ72mUHitjwev+EiMppxoyKTleSm5BDt0HdUkjYSGIVmn84+cdcR1F+AewErW/5/H9xV5DQMNl/DadChl57hBWf7weK6buNzPLoOfQ7vQaWtvxJhNrPOkJbw1VhJXVT/YNtdtE3/IkSLx7InZElc7zfqi6gHLvRlY+7fErXNB7IHIa0GBzizxBRQNCw9MuI4qs6UhrNfVbvzGHKN7H50iLQzuP8sKvXqeqPIoQglCWyXXfv5yuAztzYNthNi7YkjQQW3GbHav3sGfjfroPTl7SPxhg6ByvinNwxxG2LNlOIpZg4Pj+9BnZA6EJDm0/mtaI2bEc1s/f3DwJmKzy+IJ1P8/yYlr9h9mB6IsQHomSQ4ko+y23qRqA1TyEuoR6P4QRuTeDtSZYbCWxGsK1en+artFreHcOfrQ7aTMzbHL2NamyMm27tsYwDRJ2arXItV3mv7YUN4NWbjtuc2xf7WS09/DubF2xM5A/9tpf2yI0cCyBY1U//9VNABr7tx7KKAEDOPPiUbw3ezkJTe2vGRpZuSGm3zK5zpcpRVb8yRM/1QAbsi6o5SUDOPuR5b+jRhjVPYS01iJy71Fd+nn3Ko6WvUMdQ2Spyle15I4vEkoMWpZ6NnhpZCWM3mqiWF9zDFN57p4i/qMTsE2LtzH3xUVJFY49G/bz6h9mcOOPrmLTYv/uwGh5jJd/8zZfuO/SmgEzv02e/xKUgOIjJ3njz++qWagmWPzOSs6+ehw9hnRV+6R7ViQc2NI8CZi01pC5gZgD8TlIayOERqkZgznc6/pojAmZCZHLFNlf5CHCE5AVj3uaX/W+uNZVDdbWGqXKH4gEdWu7hmlw+4M38eJv3lQm2whatMnnC9+9lEhOKgly8nUT2LlmzymLGTqWTWFHFXCycrK48I6pvPf4R8pnTdbfViOVSFz73+GcsNeNmRku/so0+o7qyYoP1uE4LsMnD2LQhH61x3SLoOoJkgYrGUeWP4wo+DWKf1XuiavW/R0sJTSYWKSuuTFQzdjdIiCcInhYHzI2yzPk9r5f9GXIvjWZn1EXeifI/RpUPgPyBDW8mezma+X+T0UiZvHPB15Iqj4logme/ukrfOfxu1j4xjLfwdSxbP71q9f46h9vpWW7gprX81vl+srrSOny3M9eVVUXCWs/2UzX/h2ZcMVYkJKA4k0Nju1rJuFjZyf+DUNBAbQKWfZj0Lshsi5AZN+ArPg7jY5f5hnKqkbGEKHhSKcYoq/4HCcbzAHIxErPlssvvjj4CbWOmjaM0DGXg8e3YUZCCJQY+FCfCWSvEd1p0Saf4sMnfatW6azskr5ZlknnPrWSLxfcMZW9Ww5ixa0UEV1cgYMEp/7FFjX/m1+Y+RJu6y6FXHjHuazZuQO37V4Gdsli8nfOJbfOSo+s+JuX2NT5PrFZqrLlLTfK6Cskd3hLIIGseh6R/xM15uR+E2S5mkRqQRUrD84BpaCPQ03nvTkAkXMnyWLVtd9f5H0LWfmkclLABa2nSsKbgb/6H52ALXh9aUrJ3bEctq7YwVuPvJ+W07Nl2Xbmv7aESVeeycEdR0BK2ndrzeFdx5IqIUITxCrjtYHQlTgJm4+fn08oEkII5REY5D0JUHSwmZR1tXwys8qohqXKt7FDaikq7wHVVRSfpUrAgVCeZ6BB9k1q8JXlyNhs9VCJLHVzyrh3Pl4gcw+Auw9prSCw9Vsr9OVqFHZsxdce+jJlxeVI16VF6+rvmoqOPdtzy0+v5eXfvkXZiaZpmJlhg9HnDU9K8M6YPpwufTvy0m/fouhA5tdMMzTGX3pG4Pn6Q9D3jN70PcM/MVdLh36B2FVVRHOk6iz1ndFbEPsIaR8Caxnq+ggwBiCyrwvu4HT2e8lXvWtX9ZTqQPPj2LhFUPUMyApUUl39zPkFu89RF5sWb/VNsKxYgn8+8EIgHxAgXhHnnw+8yLcfu4sTh09SXlzByGlD+fC5eUmDrxE2sC3HkztRcCyb3ev3Ke6lK2koA4tVBmsmNg65NI5n6qrqiXtcVVlz7oHc7yi/Pnt1mv00alTRzZHKOxcNEsuR0Zlq6UtrD+4haqv6DmCBtdKTPwiqCmq+Xq0H5HEKz+nBuOF96VSQTYvW+Rim/zOgaRq3P3gj7/x1FhsWBIkmp4emCyI5WUkrBC3bFfDNR+/k43/NZ/msNT682uDrHMnLoked6ntD2FVeRqyDSbtzOzK44wGGth1Ibnad5Ms97i0d1j+HhGf14/G97N34wj2irlViAcgS1ApAS4hchQgSe0UiKx8ludHMAWszMr4IEZ7kv0/0Fa9ZyVSfQyWqg/bU8R+dgFWUBGhUSUUgTfesu7bLR8/OZ94rS3Adt3YmIgRoAsPQycoNo+kaZcf9u2mqO1UUUd9IWb4EQBONE7hLAxE+G5lYTmpwMFBfNpiAiSyGsm8DIQhNhMRJ/LWlwl5wKlbr8Vo+yjH+F163SPV3NCE0DhCQ+KTeMdLwqbJvIl0gyG+V2ayj64DOXP3tS3j256+mqkcH5ahCXaucghwmXD6GMy9Oreq0696WYZMH8cnLi1LFWQPOu++onpz9hfEZnXda2LuVVpu9w2uL9oOr+HXgzdACbnJ5AqwFdV8AexOy7EFEi5/Wzu7sLaqpA1sd2/faaWpJ0kelX81yi5PPI7FI8To+94NMi8qSKI7tEzMkyouxgXy+5Ggpv7zxIey4heNIxVnVauMXKOHiXWv3+u5fXUHWTSOQ5A00E38VxTvUcpVXbRJ0VNKUjofpQOXD3uZ9wBgJ9ir/TUVbVJUqr0bVXFY975H4q6tepuKehc6G2LuoQbc6WUhzHuaQJG5Q1+zeDClQdkTHyGfR0VI6H82Go/481bqITBuAXVWR1P3dMASGoVHYqRU9h3Xjgy17UraID+lAdP8R30YfPximQbfpw3h3XeZd77EOJm57i8Edl9EuJ5eu2b09q6E53gpIXUeNeqhrVSayg2Nd/J16+52EqqeRuIjQGPWaW6waOpxDis/l+h0roWRKfBIwGZ+nGtrqXnP3OLLyH4i87/qfVyPwH52A9RrWndUfrcP1UQ52bTejYlHK4C0lRshg4Lh+XPXNi/j97X9r8Dy0kM6Ey8Yw79XFqbMOV9ZTsz8F6N0gch1EX0IFLKmqEjm3Q/RVzyAV0geyhJcw5eOfgMVrzZqdrciKXapcnJR8eZ+RmEfjxDZ1RGP1ptKg++AudO7TgQNbD9UEMT2kqyqlD59LN3W+8/hd5BakL7WPmjaU+a8uISmACDBMHYFI+qwOPdpy/f9cmULC3bBgCx89P5/S46W06tCK826ZnKREXR8yvgiiL5CRaGS11Y/RHTVra0CkNQlxZOkv1ERDhL2lyYaqVhKkzz3sHg/g7ySQ8TmIzxOwtOg2qDOaruNYAfGhesU7TQyLV9S79q5U8nshg288eidr5m4ITMCq4Vg2nfq0R0o4tCO1Mu66bloZr8whELnfUNI3bpl3QAeyrkbpx72PugcbqLg521HV1oAfRx6n5p6OvoG01nscovrcs+MQe53giWt96IozVA8T24yl2hOSYfm0NjLXCByaPYylM1Zie8mv0ISS9PMZ03RTZ9jkgQw4M73hN0DBoX0c2XMsaTwSuoYmau3ahCbQDZ1pt00mv3VyTCwvrmDlB+s4uucYumnQe2QPhp41AE1X8X6ffawm+ZrYZiy4JxWJnijpf09DUWGqERoH8fcb/D61sKDqWWT0dZW8uUdR94ALdjp+X8A51dhY1YULzh5F8dBObRnyPzoBm3zteDYt3pq8RFgXEjXQNMabBkVu3LRoK5d97XxyC3IoK0qvJyNdSShsBlbyP3llMWXFFVx4x7mn3M4twhOU35azWxFT9a6AgLzvKUNba6Pi7aR9CBwg044hC6zVaY7XmORSA9GM7ewIbv7JF1jyzgpWfbgOJLRok8+ejft8t55281kNJl8AuQW5fOkX1/P6QzMoPloKUtJ1QGcuv/cCNi7axuqP1iIljJw6hDMvOSMl+Vo7dwNv/3VWTYXh2N7jvPjgG1x7/+X0TelyBEh4SXVDyVdIkX9rlhBdGt+96gCe2GzKY5GmgmoO9nk5TmACLptr2eo/F537dqD38O7sWL0nuAtXqiVuP5mWQEhwHJcdq3an+JgGIRG3A2NTtCzGX+59giu+cSGd6nCOmgStrRK4dPYpTo/evVbsMjwVnMPIiodpMAlLO+moex9b3vKS332abtXADwZC+HMo6yZhgsyI7AA9JnUg0nk0GxZsoaKkipbt8jm086gvDyy3MIt+07oijIaf+Um3j2LpzJXs33KwprljzEUjMQydDQu2UFlSResuhQyZNID8wizqxpFYRYz33/gAO24h8yQOsHXbZsrd4hqNwsHmMka37aYqX4CMvYVa+kt3nxog8j3dLg9NihOWWpaUJfVeD/pdTGXq7gcZdB8J1D32eQIWiBZt8vnan29jxt9msWX5TjX7qw8B+a3zApcRgyAERCui9BzWzXdWWB9d+ndEiOpuwXqQsO6TTRzfX8ydv72pUefhf3JhXzVmtNaI8FmqM+6UFaDronG2Nv4Q3kPgd0u6qnSs5ZDqO5gehqEz8YqxTLxCefg88s0nfSsKZtigQ492zHt1Mbs37KOwY0vOvGgUrTulGgMDdOrTgXsfuYOKkkoMUyfL44pNvGIME68Yk+aMJB88PTdVpyxh88HTn6QmYG4pMv4RDSeyQnUvmnVMlKulKE43si7x72jUO6CuZ/0gZkIQaf9zJOHa+y9n9Yfreeuvs/zjF4ozpIe1RjWdWHGL0qIy+oxqWOtNNw0GjO3DjlW7Arc5tq+IJ3/4Al996EsUdqzvZNFYCP/ONJGltOnCZyuearPBoXmeE10JSvtBljOx9UBgE3CgUUftPQImjtCBPA7tOsKOwt2+LiwderWjMD6TI1uU2Gz7Hm0p7NQqsDA54Etg261wLJdQlokQ6vpWf5aq/KxN2W/3gf1MHHYgZQlT07bTUa8gOy9C15zq5MtWnLnEatLHMAHmaET2Nck6kPaONPs0E7RCRNZk//dCw5UsRv37Q+T6msY3Fv/RCRiozp/r7r+CP9zxqLLlqB/DXElVaZTzv3wOHzw9N+MOEyNskNsyh2FnDwrsRqrebtS5Q+k2UFkYnQgg3DuWw9G9xzi040iT/bcyg4Dce6Hi16fxM5oCqQby+q/GF3mGvV4ZODRWEcWbeOtmBfjbSSl56TdvkYhZ2Amb3ev2svrD9dzwwNX0GhrcptxYFXDHdik/6c9NPHEouZtMxheqVnugweqX3hvMoXVecJHuCRpHam4KdI9g7/+eyLkVWfkYtUTmEGgtEeHmFB/+z4WmaYyaPozio6VKzNmHDyQlnHnJGSx7d1XGCulG2KRjnw5EciPkFeZRfsJ/Aio0QW5BNhMuH012XoTjB04E6+ZZNgvfXMalXz0v8y/YBIjIZcj4EiCIB/n/CeGppIgdu8eQFU94WngwIdwRkXub4tE2ARu2bmbbnB0p0iCaoRHdFmP90U010jXHs4oYNL4/V37joiZ9VhB2z9tLbFVqZSicHabLqI4M6NlXveAWIct/61WxGqJB6IjI5cnJl3ucJF/j0wW3gqBKvci6QKkLuOWoMUinOq41h4XNf4UbrqZr3PZ/N2CG/bSI1dp5u+5tGDltqNpGE+iGBppAM3wEMcMm02+ZgqZptOveJtiQVsCQiQOUMbSAq799iSrjB8gRCCECDXKbBomML0SW/gBZ8jVk2c/B3qysZbLv5N8r/85COPW4KPYGj/dUSY1ga2IpsuqFJn/K2ItGYWYlf28h1IAUq4zXdKu6jsSK27z5p3dpziRGNzSyc/39wwpa11m+cE96yVcdodrgo0K1nhsgrS3Iku9CxV9o3BJwU+B4PngBMIcg8h9QVi3mCETkC4j8H3KqHmr/bZh6w0S6D/K36nIsm+LDxdz4wFWYWSZGSN3fumn4xhrdNGjfrY2aWAg479bJgZ8bzg5zz5++TCQvwpgLR9Cue1v0kH/ccB3Jkd1pfBubAucAsuIPKn6VfBcZfQeQiPz/AdGaf6shzKnfsZfwhLH3UlNlc/cjy36bZmkrPfqO7uU77guhTLWrky8AK2azceEWDu1seIWmMWjXvVCNj/XgOg6FHWqXV5WHYhmZJF+ExtVW0WUVsuzXyLKfKq7VaYcV7EMpchF5P1Lx1RwB4amI/P9VLgjNgH+ju/f0IFoRo/hwCS1a5zP6/OG+CZVtObTp2ppLv3oeX/rl9Zx9zTim3DCJbzxyO5fcfT4derVLSpx0U8dO1IqCDp8yGM3nhjRDhuJEeB/ZqU97vvnYV+g7qidCTz0P6cpmNfKW8Y8Ud0gWox7+g8iKv4K9DREaiWjxOwhNIpVcrYPWfGT4GoiWaY4rUwZlGX0Xf2uRZU3mEA2e0I8zpg9HD+mEIyFCEZMWbfLRNc23+llZWpWxlIVt2Sx7dxVP/vB5nv/la2xfXS8gu6XI8j9y9mWHMEOpqv7n3HhW7QtWQ2309f6uehFkDBlfCpUPoZaY4yQnYKdJ+sEtRVY+FSxdorVDRK5B5HzFM17+dIye/xPg2A4nDp/EillccNs5vsmPGTbo3K8TPYZ05VuP3cW0L57NpKvO5KYfXcXtD97IwHF9ySnIQfNijus4tGidR9STseg5rLvvgApKqT2co6rGRsjg9gdv5OKvTEtRzAc10e3QK9VYusmorqDY21AJTAXEP0BWPgtaAaLFzyH7Dvwnkun17JoGE/SBBD5Hol41PLEG/8mT7XXWNR6hcIibfnQN4Zww4ewQ4ewQRsig3xm9fGWVHNthR/04lAZ7Nu7nxV+/yRM/+BcL31xGIla3oiqR0XcYM3EGej2emW7qdO7TgbbVHf2ywmv6ymTyKlRMt3eoWFL6ALi7UZytur/f6ZKuiat7ylqF7/mKLER4MiLnK0q4XGu+MfrfqQTSrIhHE7z+x5lsW7kTTdcwTJ0p10/EDIeIO4ka4r0ZNhg8cQD5niVE574d6Ny3lkha2KkVmq7x9l/eq+FgxCpifPD0J1gJh4lXjGHwxP58+Nw8XyLswPHJmXJOi2yu+uZFPHT3Y0Qr4jXHFJqgoF0L2nfPvEMmPRyIziS1g8NCRt9E5N3nqQbfgNTbQbRa30mHrOkQPl/pO1krTvE8BISnq/IyAuwdHom2fmCKI0WL5MmdG6S1JZTwXpOqKIILbpvKhMvHsG/LQfJa5tB1QGf+fM8/qCxN5cVJ6WL6WEzVh207/OP+5zi+v3aJZue6PYy/bAxTb5gESGTFH8E9xthzXaSUzHurgGilRk6+w7nX5TJkUl3enoN/9UpD8TPK67yv9Nxk1fNKp8gXIQifB/F3OTW+i19nmQXWUqS1CpH3bUWc/hynjGXvrmb2M3NxXYnrSAZP7EfPIV3ZvX5fTaVWaAIzK8SoaWr5Obcgm3GXJusgFX7tfB6+67GaZmvpSjYv2caRPce59y+3kVuQTad+ndi/+UASlcIMm4y5cETSsTRdY9S0oexev4/Ni7cmLUcKTTDq3KE0F2SsvogwqHttBbhXgNYCERoB+n3eBMDreNP7InK+hLR3qBiWSedwOmjtIfc7CC0PcJGl/+MvjVCfB+mewF8UNo50i5q8gNV9UBe+/8y97F67F8uy6TW0K6vnbGTbyt0pepO6oQfSLupjyYwVzH7mk5premj7EVZ8sJa7/3ALoayQmtDHZ9OiMMGX/ucw7zzVmsN7Qmi6ZMiZcS66u479T9rGtjDJkjbK6k5pSGYTyE/WOqIEpjOXw8gY7m51D4V3ISKfnkh0s1bAhBCthBBvCCEqhRB7hRA3pNn2W0KII0KIUiHEk0KIzO6SDPHyb99i28qdOJaDFbOIlseY/fRcLrlrOn3P6IkZCZHXKpezrx3PZfeeH3ic2U9/whsPv5siW2DFLD55eRGu65JfmMfV37oYM2zWzErMLJMv3Hc5uQWpGjmRvAhf+e0tFLavJatKV1JyrJQXHnwjkE/WKMgqAgNPUrlVgKwmoXp6YdZmBDFEzpcbyVXQqJ2l6EqPJ/8niMgV1JQBjd5eZ6YPqp5O/jvQDFp6Bt5NR35hHoMn9KfbwC4IIRh3ySjMcPJ8RDc0eg7p7qu2Xx8b5m9O4cdYMZsFry+loqRCmV67J3Edl6pyjbFTy7jvL/t44LE9fPfh/YwYv1UZBlfDHIb/46mTnHxVw/ZmcEHJlYPQwkrjrakVqNC5ntK036NarVD9StOO/W+Af6f4tWXZDmb9cw7xqgRWzMKxbDYu2kokL8KEy0eTU5BDKBJi4Ph+3P2HW4gELGvv33qI39/2V6WAX2dQdGyXshPlNdWRL3zvUgo7tiQUCRGKqKrKwAn9GHP+CN/jXvn1Cxk2ObXz9emfvMTJoyWn/gOAV0Hxu5/NetVWoTom0dV7zn5wDymB6KzLGvmhAvXcCRCFEPkiIv/HXvKFei9yrf+u8TmeQr4Hoxv+z5rhcZuaHucNQ6fPqJ4MPLMv4ewshkwaENhlP3iiT0NWPcSjcVVUqBu/EjZlx8tYMWuNeiE6C0hQVaHRuoPFV35yiAf+sYcHHt/LFbcfJuTWid9aXsDYYXgag35jU0N+nVHPQaPhCbEvtC5gjvf296s9JZTsRN1reJrR3BWwR1ApfztgODBTCLFWSrmx7kZCiPOA+4FzgEPAG8BPvddOGWUnytm1bl+KcKAVt1k3bxM3/Sg4w7UTNrs37EcIgeu4LJm5MjCbtxM28Yo4kfwIA8f3o/eIHuxYswchBL2Gd/c1566GYepJRqmgkrqda/awY/XutJpQGUFkowKSn/hrHc6atR5iM0l6IJw9yIrHlE+WezzzzzT6I3JuA2Ei7SMIWYRvFcc56r+/s0ctLXqVLZF1iTITT+EQuFD2Y2TkekR4PDgHkYlFVFuJYAymsQTJM84bwaGdR1kzZyOGqeM6Lm26FHL1ty/OaP8ty3Yk8S+qoRs6ezYeYNAZZcyfkcv8Ge2xbTAMOPvSk4y/oMwLnAlkfB6iWsxUawtZF3rXpvq4QhHtA5cnG+gyMvoiwlORekeIf6TsoPRO4Gyj4cFAgCxG5P9UVRYqfu+/2afC2Tht+LeIXwDzXl6UIj1hx1USdv+z9zL1prP8d5RwaNdRyorKaNe9Lc/9/JVU2xkPju1QdLCYPqN6kt8ql68/cgf7Nh+gtKicTn060KpDQeD5aYbGkT3JscGxHGKOy3tPfMwNP7gyYM9GQO8Mzl58Jxs1UisJZPkfSa6axFU1JfceiL3ViA/MQuR+A4yuSLcM4ezxYlG1z60HZz+BGmPWOgh718YYAFoHT/G97rW0IfYW0t6gPA1lQsUv+wAYXVQMEKkT93TIyc/muvuv4OXfJn/fL9x3Odn5DR/rwPYj6IZGfftJK2Gzeel2xl82hgM7E7z5j04UH1NpQ+8hUS6/rYjsPM//19mv9Ns8OzOR82XPxzFBbSIdUd2Djp8QbQNicsYgMHogcr+tZC3sA+pY7iEyqnK6RxB53wF5kRrfqvUsk6CDswu0kQ0frxnQbAmYECIHuAoYLKWsABYIId4GbiY1MN0CPFEd2IQQPwf+5bNdk1BeXIlhaDg+1d+So8GdM9tW7OLl375VM5Ow4nbarkgzbBLOrZ34hiIhBo7rm9E57ly7B+FjzGzFLDYv3X7qCRg6ZJ3niRcmGyGL7EtrtOEyhAABAABJREFU/pKxD0gtkzvqJnSqyaOZIOxZOejI8j+DsxeJpvbXOkH2dQijGxknRjKOtDeD1s5biqzLw/IqdtEXkLLUU6q2AYlMrACzPyLnLqptkGT0TYgvVtuYAxGRa1LW8YUQXPa1C5hy3UQO7zpKfus8OvTInM+SW5CN0IRv9TKSF2HJrCN88lYuVkJdc8eCOW+0JBR2GT3V+24yeaAUoRHI2Ht1fxQV4LVCn8RYVyV69wi+wcgYVFN5FOGJSuVbViCtHeD4e6ImQ4K1CRAIozeSMP7k2k+ha+k04N8pfgGUBvAONU1QVVblq8lVWVrF0z9+iROHTqJpokEFdd3Qad25Tiu9gK4DM+N+urbLge2HUl6XrmRnIzhH6SCyzkUmlpF8n5lKc07zVg8S6/CPUS5UNUZEFSVcbPRQbhPRt7wqu6S6yUWEx1Bb0fJLwARJ8c3eoipBMpcau5waWGDvVJJA8UWoGKysjmT0PUT+92snytYqFcPcE6C1UXSOumKlHvqM7Mn3n/06ezconcNug7vWOB40hOzcLH9lfKE6vUuLynjqNx2wYrXfb8f6CE//tj13/fSQN2Z6wrnV0Dt6ril1dRejHic5ROq4oxF8vUKIrAvUfxo91DKhvR+J6+laZgJdNUoY/ZXMievHUZM0h8djpmjOJci+gCOl3FbntbXAIJ9tB5EsMLIWaCeESBHWEELcKYRYIYRYUVKamThom86tfBMn3dDoPsR/+auipJKXfv0GiWiCeJX6ly750k2dydeOrzHrbizMcMi3ZKzp4pTFWKshsi7wpB08cqhWCNlfVoNxNWSQ/pmfKW4DMAchoy973UAJlFiiBe4eqPgtsuynygMsNAJfQqXIVxpmJJDlD0L0Te8hCSLBu3W8CasfpDhYWzy/NpDlj0B8PkqB2QJrHbLsQW+JNhX5hXn0G927UckXwOjzh6cGOwGhiEmPwV2Y9+r6muSrGlZC45O3C7y/TAiNTXpfdXylcviUSngOtUuBYTUg5d7uLW3UPQ+hDLdz7wJspLUWWXIfsvIfyKoXwVrciG8pkJX/RJbcg3/yZULWOT6vfyZwWuIXNC2GdR3Qyb+D0dBpUehPMH/ld29zbG8RVsxS8ct2gwVahTK07z28e0bnkwJN2Xb5wQjokmz8Z7RTRstadcwOQXiiqrJXQ1bgX/m1vaQn0y7gECJ8lvIejL6FiinV8glVEH1OdWHGF3uiw36JTQLp8R9l7F1VhbPXe8tqfhVmy9OYqksXsYBK9WwCMrECWflPz1HCUR6IlU8Edh4bhk6v4T3oNbxHxskXQIeebckvzE8Zk8yQwZkXj2L5+6tx7eTr7dgaJ46YHNzljVdaS9AKajewt3ryG3WTKlvpE+qdqI1fBmAqHcNQ3SS3GmHlU6wVIJ2jyPLfI8t/jYy+6HWKZ9pRaiETm5Gl3wdrHr4JtJbr62ZwutCcS5C5pAqzlOIvFVt/2+r/zgOSdBiklI8BjwEM6Dsoo0XzUCTE2ddO4JOXF9VYCVWTVc+6yl/xdsOCLY0SxJ9+y9mMu6TpVir9RvspnqsAO3LqEN/3Gg+ByJoGWdNQD4HPA2kOgngda46k3fP8yaai0Hu99ngi9x7AVN0svorDKnhQ9r8EznRkBTL2IQjTs8BpKAGU+M8h4sjESoTWyksGrXr7xJX5ata5DRw/c7Tr1pZLv3Yeb/9tFpqmIV1Jdn6Em398DZomqCzzT/gqygwgDHoHRFa9ZSVrc8Cn2ZB7H8LZhXSPIfSuXlJrQN4PkbF31dKyyFaq0qEzlXl39FUaZ0tUH1GvKaP+tfNItaEzEVnBfMp/c5yW+AVNi2FTb5jE9pW7sOJWTVXVDBucd+tk347raFmUvZsOZKxj2HVAJ2584CrfbsZMoGkagyb2Z+OCLUlUDyNkMPLcADHSpsDogcj/ASqRqldhAjCCVhzCikJhrSE1juioSm31647qzjVHIKue9tm+GgmIPo2KOQG/c9UTyNzv1KnKNwS/z5KqegYQfcNnGwsZfQNhNucymeCWn3yBZ37yMqUnytA05a4w7ZbJdBvYhUVvL8exU29dIaCkKIvOvYTiDNe5PtLa4nPuAAk1KYxcjrQ2KKmH0BilD2gORuo9FJ9OxiE0XMUUWalkKdyjNF1ax4HEbFITL0Odt9YGkXs3KffYaURzJmAVpPb+5qMYww1tW/3fjZOjT4Ozrj6TVu1bsuCNJVSUVNJraHemXD+R/Nap8bT4cAkHtx/G9mnjBWqMaHVDQ9M1Lrv3Aoaelep43xiYYYObfnQ1//rla4pj5tmDXHjHuc1mzp0M/9mQCJ+nDLxlFbUBI6SIpiLkEePrPkQahIZC6GyEs09tYw5U+2RkfZPO3sOC2CwwupBZ9U3gz3MTqpLmHMT/YbJ8NHtOHcMmD2bguH4c3H4YMytEp97taj6/VfsCig+XpOzTuoOJyLnFI93Xv0ZByZKD0AvB6FTn2yVUgisKENnXA9fXbm5vhOgr+HdkNRb1r53hKVhfrtShP7v4t4pfrTu34q7f38LHLyxg35YDtGidz9nXjKfvGanUhHg0wdaVOwkcOISqVjm2ixkJ0X1gZ2584Koa376m4pI7p1F86CRH9x5HCIF0XboN6sLUGyee0nH9EXCuekcwR3kVoer7OwR6F4hc401i6nYUa2pZLPuLCCqUCKfRq5aSIKMZnEuaBMA9CYkFqKG1oVhoeMfyO563/CkDdCHdhs28G4uCti34+l9v5/DuY0TLY3Tq06FmNabbgM7sWLU7RYTXdTXa95mEaDE5denOTacJZ4PRD1GjpyU9WoWhqCxJxtg2svSnBK+ENAYBMhN531N0l08ZzZmAbQMMIUQfKeV277VhwEafbTd6771cZ7ujUgbdbU3D4In9GDwxWDDNitu8/Ju3PD6W8LX6MMIG02+ZzPH9J8jOy2L4OUOawWpDoftg1U68a80erIRNz2HdAruZmhUyConlSPeEEmXN+6GacVibQG+JCE+rLcNqEWTVW+Du93Z2lXN8fCnkfVcFwBpooPdU/LEmoyKDNXgBGBCeBvGPfd43EKHx1V/W531TEXz9IONqyUAU1MpcyHK11KG1oaFHxgybdB+cusx9wW1Tefk3byXxcsyQwfm3XwFmEN8vTaCvabqTyNhMiH2A+l28SlT2dVQndDL6Ho1LvgIIxr6wgcrPevIF/4bxq3XnVnzhe5em3Wbl7HW8+9hshKbh2H5NIKpS1bJtC6rKY/Qf05veI3o0ufJVF+GcMHf+9mYO7TjCiUMnadutDe2aUccwGBLsTUhrO0LLV/wscyAyPh+w1f0fngAYkP9DRdhOrERN6lxFiq/4HTLrYkRWsmq/MEcirc2c0mRF1qVE1Ed19SysKCF6F086pu61MyA0GjWRbBGwChE0BkkvOTNqeXJY6jWRl8FzKnzpFyPPHcqCN5bh2A6uZwRuhAz6ntGTNj1THUyqjxWIujY+9q46oq14S8931jRayMRaGle5r/7cDGOYrKjT1PHpotkSMCllpRDideBnQojbUV1ElwHjfTZ/BnhKCPEv4DDwAPBUc51Lppj99Bx2rt2Top1SFx17tefMi0+fb51h6krd+NOCcwBZ/gfUA59AxsOq9Jr3XYhc7nOCg8BYCYmD1FY/lDq7rHxCqZrXmZ2K7BuVeCJxmtZmHfZUouvPIL1gFBoLCNVirncBcwCy4pE6n+VA1sWehIX0iOn7Sa7cGIqIngTpEWI/oiZIhsaBUwzOFlQyI1S1zzmizi880TtOw5WEfqN7c8MPr+TD5+Zz4lAxrTvlM/W6AnoN2glufu0M3C1DJhZ7jQd5QJnP0VpSHWRkfIGXfNUZMBJLkIQRof7I+ApwGuun1sjrdrrdjj4FfBbj1+FdR3n3sdlpPSCFpnHujWdR0M7Hq7OZ0LF3+9Nsn1YXNrL8YU+iIo7EVJyt3HtVh1t9aC0RobORKcKnFsTeRppDEXodA/HQGRCf5x2/KfphCa8TOMADNTwZcBBGL0Wklxay/Ci4h71tJGidVKMQKA5v9GWSE8IQZPkk5vZ2ZOWTICupiX3mYIh/6G3geBp9no6i0U8t72kNFxSycrK46w+38NGz89i6fCdm2OCM6YVMvDgE9mbPe9gj4VtrkdbWgDiufgdhdFf/6ZZ5upB1fiv3oOqezP0WJJZCfCGNuxaNDUgmUjoI8enLojb3J34VeBI4huJC3C2l3CiE6IpyIR0opdwnpXxfCPEbYA5qMf414MfNfC7pIWHl7PVpky+AQzuPsG/zQUWKPdWPdGWzzDxP6Rwqn6R+yzbuEWRsFiISoJmTWIPvsqF7EFlyH2TfiDA6IKMzwN6lWq/1VmBtQD18jRH+jINdzW8WqKVNCaIFIu/ryRIaAEYfRMFv1GfJOBgD6wgiCkTeNzyy+QrABb0nIuemlCqbjH/sVdPqPOiJ+dXvUhNE6pBfZeVLFO1+n63rRzLwrMm0apd+9l9NjpWx2RB7G3CRUQHRt9UsXu+KrPgTtSKFfjImJmTXafGP1+9yRe2b+AiZmM+pcb4yhL0aWfkYIucOPk3+xGnAZyd+AcvfX4NtpX+2pJTMeXEhV3zjwlP+vH+L+BVf4CU4dUnrqPuvxa/xu/+ktRr/ipYD5b9AhsZB5Cq1CpBYqh53cxg4B7xltMZwjlR1TkGjhl+EC5HrvMpcHQhddTzae8A5rAzsje4130OEJyKRKl7IctWolHUZIlyPy+yeRFb8Ofl7unshXk9qoc5kTCaOYVcuZNnc4bTrPZZewwcjgsTEgPxWeVzxjYvAOeTJS2wFK4G0QopUn3s3lP/J+82Cky/0HjUd2TKxmNTfVypKTPkvqk868Jwah6DKvgWl90H+D5vFYLsxaNYETEpZDFzu8/o+FHG17mt/AP7QnJ/fGEgpsayGSZJ2wmbRW8ubnoBJWPjmMua9toRoWZTCTq244LapvlyO0w63NGBd3lbk+aAETOhpJhUVUPWk97aSgkAWq+4XvR8421EPWK7aNuVAOv6zRbxtveRHRpHWFkTYz3czBEGEVBFB5HwJuJUUPZ+6iCmRwdTPD4bQHPJbFrNx0UY+fnk7F94+iTPOa4D/4h71kq96M7roa0iRS/LvYFFLPHbVsmjkC4hQne/qBtGOJJ9K8lUNay0klntdTJ9NfJbiF0BVaVWDos2O5bBu3mYuvmt6itBwpti/9RAzHv2Aw7uOEcoyGXPBCM696SzfZoDTjvhifKshMq4SJt3PL9NAPfd+A7mjkq7EOmo6pQGsYm9ZqiVQLZsgUJ2R9RGk3l4db2KAproCQ6NIdfAQqmIfIDxdy4kKaKTCS0wbSU4XuCBcdG0LLzx4nI49F3DLz+9ssHtSVv6DlEm8sx8qnqgng1NHv1BltRAaj8i+qnZXtxj/6lYmDQyNRbpnpQpZ+aTign2K+I/3ggyC0ASd+3RseEOphF2bio9fWMDHzy8gWqbInScOFvPSr99g97p9Dex5OpDmcqeZ+RAaR3r14WrD6Ho6N84Gapciy/F/ABriGyVQgaUCoi8j40vSbJsO1YT9AMjKph1VQOdeceyExruPL1Sq9+mQWI1/oJQBkiCydntZCfGPVKXR2Q8kPlXNmvRwvEHgc3xa6H9mn4xsskASjzaN03R8/wmeeuBFDu88ClKSiCZYOnMlr//p3SYd75QhgmJYUEc0nn5XuqHOQi31W8mvuQdRhdDqyUyQ/2y6Ck2UmsYja5VXpWoq0sQv9wRNSVoMA3oNjGHFNA7tKGHZu4vS7+CeDJjEW95kO6Crs/r/EyuQ0bdreW8iwr9N1dzZEyhPdLrwX5uAAVxy9zRCkRB6moxfDxlNFkW1LYeFby5LUbS24jYfPT8/YK/TCC3P89Oqf8ObXpLlDxG52OMOnI41csVFywwWxN85DedAk83HXUdQXqLuHyFcti7b0sAep0KYssDZCfEZyPLfIEt+6E/Q/f+FJhqkf46mYcikAbTr+v/YO+8wN6rrf79npJFW29y7cbfBxhgbg03vvQQIhEAIgQBppOeXHpJvekjvjQQCBAi9916MAeOCjW1wwb2yrtu00pT7++OOdrXSSKtdr71r+77P48e7mnZHqzk699xzPqcvdry4E1ZRXU5lj/Ypq2eY8cCbuDkrBU7KZfHMJdRv79ikZVeQQq20pDKnICgLa6BeYuxoC5s2KTXS7OrcMm9dp49AVxN2rBtW7XZtv5y0xbzn5nXiqHIJCnZSz6EabkPt+HaQc9tdkkgVSnXWcmdp7NcO2KBRA/jCn69h+jlTGHnIMIYffEArEcGIHaG8KsGR57Stt6J8xcYVm1m/bFOzonBjbSOFxMW2rC/UaHr3IpXXBpUwcfSMKg6REUjZ6UWOiukmyxXXsfs60pfIburTJeWXkG+gM8uj4Shfm44NqwLDFyJTlEfsMMIfOys/v60oDjqquDtC9Rna+bf216Ea/kXn5WwYihGJRrj6F5dz9qdOZfTk4Yw5bCR2PIoVCT6EouVuzv3saSUFGXbW1LLm3fUk61qkGDat+iB0mTNiR9i2aUcn3Uk7iB+VJXsT2C8SgdBw4ZuU+IlI9U+DCWhXEmlDnqGDxA8PRFCz7ZVNc1/LArhpWDK3gowTVGwhBAjEVsMqBm2df1uyk5tCp6TsYqP0orT3u8qH2p/n9Bndvez5tP9uRs/+1Zx5zSnNvy+dvYLXHppFw84Gxh0+mmMvnE6iqnh7lfXLNnLnzx8g1agjOdFYlI9+63yGHTQktN0QQP8D9myyXzNWf6THL3RivdquI1vRMbRtoUWXejdLP2SiVhknJbu7/W5kd5ULR0cjVd9ENT0G7tpAGPVcULW6XZPaqY23twrltahvC4ov/nwdc1+t4um7erNk1mrWvLuZqadNYtj4kHwUa4Cu0mx6DLK1iRIXahHC2p+wR97HNolC4mJI3k/+eCxa8vOy8XUrlfpUIMxbAu4iVOODOjfO6qkTjGOH7+rg9xuidoSpp09i6umTANi+eQev3Pcma99dR+/BvTj+oqMYeuCgoudINznc8+uHWTF/dbPm4bRzDuPMq05i0KgBbFqZ74S5aY/egzpHjqd9WLrNmLcK3GU6Kd2enCXLUuzQHkjFJ4Mq7dbt2TQdee7aI9kCuhKxY9H24thI1bdRTU8Hy3s2xE/Q1YlNT+rke+kBygV/Ha7rEYno+MApF23n8JPruPMPA6jsOYD/3fAA46aOZtIJE7Bj+Q6VVFwbJOF7aEcqrqOPFddA/V+DpchuQHS8nrD7uRHHCNqGhf29t6NqfwbVNyBWRdvX8Hegkvdp4WsiEJ+OlF1Q2ucR44DlMe7wUYw7fBSNtUm2rN+GV6iVR0A6meaW799NqjHV6rXbf3wfX/vXZznu4um8cu8bzYr8oGelpxZqprtHiOpy6w4gifMhMlhX8ql6sCciZWejnDmQzCwPFqt8rKQludwNeoVtID8vKlM9lP2Q2K0TODubyAFIxefyXhY7W9lbQe0N+O5qLAvigW8++dg6ttfYzHxyOYjurHDcRUdy4kePyT9f2RlauT49T+e02JNbol9lH4am+9m9ka0srHHgLw3ZIEhsetBr7cHWmxKX6PL22u8Rms/mLkSlZoRIfeTutxhV/w+a/8Z+DTTeilJp3WTd0G56DejJ+Z8/A9/z2bhiM1E7Sls9jh/529O8//ZqPMdtrgp/64l59BnUi+M+PJ13ZryHk5VDZsejTDxuPJU9O7as2SlERgRpEe097gCk6lvBRGsVWH2RxNkglTp662daRWWLt2Zjo9/QYNJp9Q7yr8K+zHVbtRYHzdadR3bXJFISuk9krpxQxVWtflWpV4k0/A8RHzsOoOgTc/joFzbz9x/YoDazfN5KZj4ym8/8+gpiZTlLvpHBSI+fQ3qunsRHR2pnB4HKz+hWP3sqCm5PA2ce+e9/HLGngD0aVfubYLsLRHUFZuXnof7POqUjDwfqfw3V/0fRB0eldMs8VUvzZyX1KspdhVR9s/ixAcYBy8H3fR7/x7PMfWEhUTuCm/YYf+RYLvjiWYhIXiXR4teX4ocsMypfseDlxZxw8dGUlcd55b43qN/eQP9hfTnz6pNLbnrb/RCITdOtI7JfjZ+qZ1z+VpBqVONtujKu1eywEqp/jKga3c8wOgKUh6r9KTrB1SUjsioV1wJobS5/i47clV/Yuo9lV6DqwV9PbmAzFofJR9cx88keoHSezCv3vc5hp06iuk8V+HWo9Ezwa7QGUOxw3Sw9B4kfhUo9FSTkd7TlRjuIHwPJ1bTOY4npqitJIPGTdUm+E0iD2FMC3aC0Lo8vRNNjur1LEVTjA4S1WaHpIb3U1F2Sc/cyls1ZwX2/fRTP81FKUdGjnMuvv5he/aux43YrKQkn5bLotdbthPTrDjMfeosjzpzMNT+7jCf+9Rzrlm4kXhHjyLOncsJH92IHOTIEqfhM3stS/aPAmbJQ3mZo+AutHQkLqfi0rrT01uqIbeQAXRXoLKCVEr89PoggP6AFriUOseO0s9fVpN9ErNa2xYpAnwEOPfs67KixcZpctm/awawn5nLsh4+kRd/rXS0JFD8awiZJUqltR3OD8d2MH6xM+OtpmbRaOrk/fgQQQ3r+HNLzs1Z8xgKCkiI9l/2t4K0u6uSr9Cx054Ts99IFb4OWY4q2re9pHLAcXntwFm+/uAgv7eIFs8FFr73Hotd0cnXfoX04/7ozmh2ohp1J/BAFajftUr+zEQSmnzOV6efsPjHXXcav02FjKYPogXQ8z8vWES2A2DFBG5CsL3arHPHX6wcgcwkB6fF/qKYXdf8zq6/uXxmUk7eOPnUDVIpCjkG8vLVRE0t4/+1VTDmxOhC/1cu0Kv0WND2BVH0HpCWKoPwGvbRS8SktBhk0FG8tKZF54yz9t3IX7tr9uAuQqq+hGu/T15ZyiJ/auk+m1QfiuU22Y1mGLwS1o+1r+5sLHFsPKl1yGN/Qwo4ParnrhodaFf7saNrJX798M5boTg3HXDCNEy45GrGkaHVkY73OBRs8ZiDX/vLju33sHccDd6n+MoyM1cVGHUKaBZGFKMoaAn52tXoE5a1B7Imtmk5LxTW6ui/9mv49dnSwwmBBiKPX5RSo9PN9IZ5osWFu2mXha+9x7Ienoup+q3XKSAERVOpp3Tcxmt2Sz0O5a8A+HKR30M+xAR01bKR1paqt91FBk/GO4i2H6p/r3pvpWfpc9qRAyDbjYMWCzgI52FOCnpthE0kLvJriUdZADDgfX7fBMw5Y+3n9kdl5VYvZ+Q81a7Zw6//dw+d+fxV9h/ZmxMQDsKwIXk60wk7EGHVIfkua7oZqejrIR2p5MKTqy3pm567WKszeRrCqoexsJNMioxjeemi4kbzohv8Bqv5PSNW3WudBSBWS+BBQvO1Kl+PvRHkfAGXk3pvrwpJ5rZdkLBHiCTsQv81+UFPgb0M1PYEkLgZcVP3fdc/GZqohcSkk78i5lgfSDyo+qZtxu5ledx3EXQXSM1xFvChprb2WKuCAlVJQYPUpkPBaBsVmp4aCzH1uAb4fJpqs8IFUY5pX738TpRQnXXYslT3KqehZTm1NjgSKJYzcC+wX3lqtjN+c1uBB2TktDZyTjwTSL0BsqrYzUjynFxSq/vchEwQHmp5AqSYkkZ0KIRA7IrCN3RkX3JXaqfA/IDfNwfeFmvWtn7t4eRyVeiXoq5vVvBwPVX+TFsEmgkq9DsnbabFFFsQ/DOnng0rtzHeoD0Sh7BxdeV/7nV28Jwv8tbr1Wvllbe/ejIJIf7STVsCJKlRVmyEyKDg+dxJjBedum/26CjKMpoa2S4pdx+W1h98CYMjYgYw7fHQrPR47bjPswMGMPnTE7hpm5+Aug6bH0Q9Wikxliqr/Myo1B+pvCNbIG/UXZePNqIbbQTWhmp5C1f4SVf8X3YoiC5V6jsIJrU6g9r6Hcpw6BR/VeBuq9npo+Bd6NidkHh+lbJJ1EV5+pGfrw0QYO7VvsKyRixf0p0O/p62cL4BaSOY2Qg9QNVD/R5RfKHrRjgim2o6q/R6q4W+ly0h464IS8kz/yVxsKGs7V0873blJvjFInFXgvIa2qNtWj+cUX7p2Ug6vPfQWvueDwPnXnalTK4KlyUjUIp6IcdonTtgTQ94F/EBXq54Wna7ASXLmo3ZeD+mX0RpftZB+EbXz+yi/Hpx3UPV/QtX9EtX0HK2+RN2VgUBo2PvoQepZlLtyt99dp+K8g9rxDd22zZmDdpQyz57gpIVH/tMH32t57uyyqF65Sc2ioFiqt06/X8nbaD0R9CF1n45m572PrhajbnqGwgUMpdowB+r/idr5XT2OknB1+6P6vxF+XzZEx2l1/yJI7EjyY1gRnaLR3GS8OCYCBmzftIM3Hp/DB2u2UNmrgh2bi2srKV/xwZqa5t8/8o3zWPDSYuY8uwDf95ly0kQOO3VSt/8O0c1rQ77EVQqSNxP6cDivo2rfDXKUHPBB1S9Cf/D6I4nzwGujzFrV6pmp1Vs7b1ZvJH5Cy/Jle/FWoVIv6bwye1KQwN15ERTV9KxWeW8WnAV9v/302KMHsmJeL1JNrxIvD+Y0Ilzx/YuxY2X4DSpcP1Ii+nzOW4WuXGRUKUi9THj0K6PAXUpkzNf/nMWoxltDc2Nyx6Qa/km48ndEN9JNXAj2IW1f2j4MEmloejBIZE1A4mwkfkqbhxpacFIub7+4kHffXIaTcojGo7hF+kMCeK5HqiFFojrB2KmjuPaXH+fVB95k2/ptDBs/lGMunEaPftV76A46iPt+kBKQSxoa7kWLoOZSD/V/RfnrabZ93mpU0/1Apc49tIpXjerT/A1VeU2Q6+TpnFh7Eh0y+qpOR5nc5RAZiMRP6txEfX8bKmxFAnTyvNWHZGoq61e9RCyRRETwXI9pZx3GhCPH4u6wCunvA1Hd7LygrSo0EXch/WLh45p7+JaCoyeS9X/QFf5SXnRv1fS8/uyEjq1S9/hNnNv2ZaUcqf6WnkB7ywFLf/+Uf4xSPwf7vQO2dskGbvn+Xfiuh+f6ROzgo2YJFGrzIXDAuJbwpGVZTD55IpNPnrgHRtyJFFT9DZSbQ/GDkLKXs78L/gZUw3+y8siKOACpZ9EzsDRg6aq5ik+X9sXt1wENYPXTyvjJu2hug+QuR6VeRqq+3Xk5RKkXyX9YPfC3INXfByJMPhkOmj6JFe+swY5FGXrgYFa+s4aZj25g6CCbwaN02XcG17GIVh0LfhMdT7YvNFGwgsT5BZQeaXR1KbVqLG7A/A904mvoZQcHzdlLR+JHQnw6Lb0vu/mspZvhpFxu/OZ/2bZhe0vqhCVEolbRCu5YmU28suX5GDRqAJd8vZunAOTRROHPy5bCh/m5kZLM+1Snn3VrIG1PXrQj19yL0lkI9sEl9kN1dGGR9NCrCbU/R0fwHHCXolKvIZVfDJLFdx2VCuu3CGAhsWMgdjg9yuFr/zqI1YvWUr+zkWHjh7Bj807efmkRm5YqTvqQECtr+T5UPiipIhIZrO+lQ/iEv1cRLY2U3kz7Evm10j7xNhQG0gXaWWFr+1VCc/JmrAFB+kamgKx9+dP7vQP20J+fbCUR4TkeiFaPbqhtDHfCRDjirMl7bpC7CYlNRblLyf+Qt2V8im1P6woQyoBiStkq67o6CqMabg1yCgqsjKsGVMNNOuGWSPAvTWsnIw3+FlT6VV2Z2RmosJl0MG7lgNoGKk1ZxSAmHDmOpXPe5zdX/xVLLBzHpbK6L1d/dyOJSh/L0p+n1UvK6DP2cPoMqkS/V4Wu0REsiJ8D3tYgibhUnSJLJ80WnUEWMpqZbR0h03Td0F7mPr+AbRu24WRHvHyFErSqZgEh6OnnTMUqoFG41xAZS7gtsimu6VVMu8vRk4zomCJREoLjs7elwFmo0zqi4wpeWefcPk5zvpr0RNvJzHiC/KrG/+qqzM6YkKh6CkbKVYOedPlbEasPIyYOo6G2kVuuv4ttm3eAUjhp6DewgkOm1+vGaL7gORbvzJ3OUReIlqRJbejAwKTguMSqRMXPgtRjBfYJI43yd5bwjhU7X0dtWMdcqf3aAUsl02wNU6RXOk/CtiOtDVuAJcK9v32UT//qCqzIXmzEYtMgNSNojZGp7osG0ZPZu3DiJqj6HjQ9C87r7TiuHpV8MijVzn+MVP3fwVuJfoCKRXYcnXhbyAHz1qKaHg0EVwdowdXomMKns8cFQns5SB9U3a+0fhUWiE1aLufuX77Q6nNTu83mj984gNETk/To47J+ZZztW6q59Fv19BnUFxIfg+RNRe6nnUgC1NZAX609IpGil4WLYQ3UDlre0k/xdlaG3cPimUtDbZQdtxkwoj9r31sfqmT/2sNvcfDRBzJgRHu6L3QzpExr0iXvoTkCTjxYvlpF4c9+lOIOmqcj8fYkSD5C4R6QuaRRjfdqEWKrR8jmWYHzlTXhVQWiR/7WYDJUmb9NJQPB1dn6XuLH6xSOAtEXsSdoCZyQZHPlLoXkvfo8uBA/jof+ZLFl/dasCKrw8E39eO2JHgw/sImG2gjLFiQYd4TLUYCUnaZTQNotO1HIEbJR0RHQeCd6Ml6qA2aVVHlI7Ehoeoq8z4DVq23718nsxd7DrhO1I600cbKJl8c5/4tnEbHzfVTf86lZt433Zi3f3UPczUSQqq8h5R8HeyrEjkWqvqHF/Ar55tZo2m434UPDfwOnpZ3911JPoRr/G3LKD7QuS6kPYyaKoxp1bljGGLsrtNPkLNC6MO57qPrfopIPEWqw3eXgbc950ULfVxL8jTQXMah6Is5N9Oyb/4WolLD8nXLmvFTNptVx3LTLgOF9g60ebc+FSi2tFyg7Gxpuo/3K3sUEdFvOLxWfRotMZv62cYgMQ8q6e9L2vkeisiz0daXg+IuOpLJXRWgQxUmmeeo/L+zm0e1+JH6cFr2MHQf2YUj5x5Hqrxfp7VpO2z0TfWh6SU9O2/sM+WtRdT8LcmRbo5qeol1OisTQqR3baZlwuqi6X0LqOb3052+C5D26g4aqzz+HakClw4VKsQZmpSloHUaVmsGAQfNCl6+3bIwx56Vq3ptbgRWJMXx88B4rl7btfPGWSK13HQTpd4O2c+15/1WR1YoWpOy0oCVV5nMQA8oC7ck9mwKxX0fAItEIE446kEUz38PP+sDZ8SjTzp7MpOMnsGXdNl6+Z2beLNJJplm1aC0Tjiocbt47iISXUJedrVtY5ElJrAp+CFOqz6BC8ixKxdEJ72VnZUkZeChnCaXPF2IQm46q/SX4q/U4pTdScZVuG5E3ZqUdv9QLkLgMiQeRHHelrpbJmykN1yKjyXvIddpEfCYfs4Nn7+lZcHR2PMrkkyZS2bMScLNy2IqRb9DDUYFyfUca3NrBEkogdquaUOkZesnX6ofET9R/k+hIpMfPIP2WDvnbY4JjTP7Wnmb6OYexbM6K1tI5IpRXJxh3+Gg++9sr+fXVfwtdily3pCPLRt2QyAFB4nMLUnFl0C7HpfWz1Yh2FjJL3mEOka8rjTvaI1o1oppe0gVJzaesyVLZb4sI2JNQycezmlVHtHCz9M5xyDLn34Ta+XWwp+n3QsoATztr/lZaL63FofwT0Phfcm2b4HDkaTt55ZGQCF6AFRHi5XGmnqbbX6mmJ2g7StiOpT1/A6S30H55HQXOLIhNbfndXazzhFFIfDpEJwIxpPpb4CxEuSsQq5fWCWsjeX93sF87YACHn3Eoi2a+1+q1gaMGcNyHj9Q/j+iHHbdJ5wgWRmNRevTpqOBf90diU1DeBnCWor/8s3MUQIuuDgscrc5XbFfOSiTeT/cKrL8ZbXDCKp4iaGOayU3yIH5GYFyy9lc1qPo/UNyqpiF5B4oUEj8R1fQQoQ6mv4lCzoZl+fTok3+MWEKszKa8upyjzssS5vU2tjGmjpBZjmkvgaEHrdxf9/NgVu2gtX5eRSqv0/3lpBLiJxmXq4sZMnYQ1X2q2LqhJZXCjkX4+Pcvbs5ljdqR0KrI8uoubCW0u7H6aoclPSuIUmcTFHzYU7IkGToTV+eDJc7TFY71f9fK+QUnWdGs/z0tQq2A1NNZ+3habDQyjHA7GODMQtVtRKq/q7tX+LkFUwESo5DTlKgI219PHO2YzYHTxnDK5ccRLw+ir87C8Gt0mEzboI7Q4tKoxjsCcVb93a2cd7QWXPknaK5YtCft6mB3if3aAXPTLv/7xYOtol8Am1ZsZvvmnfQZ3ItxR4whakdJN6VbfaeJZXHoSXtZ1WNbeGv1Wr67OjBaisLOlRNEw3bF+SqUDOtA6lFUdChk9wrMwwISUP0DxN+K8mrASgSSEWFGykGHnYs93K7uaRk/AdwCIqO4EBlA+L3HkdjB2PFtOGkXlNbTmXjMQVz4pZDcNqkocJ5dwUM7pQ7tc8QizZVXqunxQBoiY1iD5OCGW3UfOON6dQue/Nfz7NhSm/f6O6++yymXH4cVtZh66iTmPregVa6YHbc59sPT9+RQdz+qUcs5OIsClXLdeSIcR0dLOoRFm1Iv/lpwl6EaHwomqYWeQxvKr0Sio1DeGvDTYJUHbZBy8YJ83cz1C+B/oK/tZnJ7c3EQbwPKGhzkibbG9QcTK7PxPB/P8bDjUcoq4nzu91cFUfscpFKnc3QqiraLKXKJtfSQ9dZC+s2c41P6uyF+AkSGd9pId4X92gFbOnuFFiPMwfN85j7/DqddcTxRO8I1v/gY//vFg+ys2QkiJCrLuOQb53dtM9pORqVnQ2MB4c+CtHfWI7Su+CviHPjboO7PxcdjjUEqrwGrUmvRpN+kOTer4BD6gGpr6aVJi5JG+oIXkleBINHhqNgRuuy5eRnDBqsPh5z8caoHbuLtFxfiuz6HnDCBMZNH0Oy0qCYt/ujMRudi9AyEHztrFhnRGlvukiypkeymwLnoXAipvI7mCJgzP3w8ql4vgezhZFVDPspXvP3SIjyn9YTCSbm89dTbnHL5cQCcec3JpJJpFs54j0jUwvd8jr7gCI44Y3IXjHo34e9E1f1UJ67v1h6q2c5P8Wo6Vf8XikariAcR5QN1vmxDRsy0jQKjNqUOXPDWI5F+KOIhY7Ah0g8pvywQxc7YWAFs4r2u4Et/78ucZ+ZTs24rww4awpRTDiGeyMqdc+ajmp7R9sDqA357naW2KNNFEM48Wt7zQucP0mHix7a0RnIWEf73cXVvTuOAdR47a2rZuaWOfgf0KZiUGkZTQxMqJDfCd32SdS1f4v0O6MOX/nYt2zbuwHM9+g3ts48FAFxo7EjSdnuIB//yZ+vheEBbs6ptYPVANT0ZhJpz8z1CkHJQJRgLKUMS56HyInA2REZpByVxKRIdq2fdKqXD2/FTAJvhEw5g+IQDQk7sBHkZW7LOawMJMn3W9Os+pUWvwqKIFpI4B6wrtRPm16Ia/0e4YyqQuEgLSUrWsyPxApf3TZugTsZJuWxauZny6nL6DC5dg8j3fTwv3AlwmlpSJiLRCB/+yjmcdfXJ1G6rp9eAHsQS+9bfUDU9Epr43nlE0I6ARXGnKpu29lM6euTXFBZKzaMsmLCFtfDK4KGs3og9XjcDV9mTL61VpfwdiD0SqfqmzuHyNkBkKFJ2NkSGUN0bTrr02PBRNz0Z5AcHnzF/C/r9iaJtWcYOdzS1IgJlZyFlZwTRvFV6+TBUsNoC+xBdOGYNaHlZyoIx5Trj0W7VY3avdsDSTQ73/voR3p+/iogdwXM8pp9zGGdcdVJJDtKoScPxQ0q07USMcYfnl7P2HtSzE0bdDWnV56uzKYfYYUh0NMpbFySVdhJ+YHCbXqDk8fsraHuG7KF2fAXsg6DsQt1yR2UcRx+8ZaiGlYAN1V9H2iO/kJ4dRLuyxxv8XPFZRGyUNTQoWb+XYqXaOoclrMIpaIeB6HwtgKaHwQ9zwCJB0UFOFVP8RG28c5OUrX7BsqmhM3jrqbd56uYXsCzB93z6DevL5d+7mKrebb/HkWiEQSP7s/H9nJ6FIowI6eOYqE6QqG6rD+JeSqbfY6cjED0EoiMgOgrq/9SJ546A2olKL6X0qF2qDecroOFfKKuv7rnozNPFNSia9cuSD6KSD0LiwhK6X2ShmnQuWm5/WgTiJ+qcKqsnqBSq/kZdzFAIa3SgoRgy0c60AbL6Q6y/zmVz3ibf1tmIPaW18wW6qj95f8hFPVT0oG4TP9mrZSge+dvTvD9/FW7aJdWQwk27zHpiHnOemV/S8T0H9OCo86a27uNYZnPAgYMYd/io3TXs7ock6PhsJTOTtoE4JK6A+Cm6LDxxBSTORSIDtAErqLzfQaIjgx/aI2LaDkPnzIeme8A+HOLn0ZLz4dHSN/PG0i/trkAlHyV8Zmwhqhai4xCrHCk7Eenx2yKdATItjApUoeb059SaaLlRD1sbqpAScomfELRWycHfikreU2BMhvawauFanrrpBZwmh1RjWkfCVmzmjp/cW/I5PnTdmcQSMSJRvSwVsaPEy2Ocfc3+1s6pI/YrI+ac+RqMabtVdjHY06Hsw5D4OBIdiUSGouMVnRmzcIKE++xcy7Yo9T7dQKLiLvDrofJrtCxdOi3/kg+S33S8AP5OVONdFOwL6QQitFZ/XZna4yf6O6CQu+O/XyB3zAvarLUgsWkFziNatzIXqyqQy8l1cZR2okvte7ub2WsjYE7KZdFr72nl+lavO8x4aBZTzwj5o4Rw+pUnMmrSCGY//TZOymHS8RM45Pjxe79KdHuw+lN678AMuuGyRHpqMT/ppZuTWlVaKDD5ACTvAESbjOQjgVRBWPf4XKLo5cpiSvogiaDhc2QkeMvaMfZ2kn4BnbsWVhFZoyNaVm+9DOlvBqnKa2ehkg8UaGmUtY+3BUm9BvaBuopLysAr1uajWA5dawMj8RNR/geBtlEguhg9MK98P+uIAp8LB1KvQvwssLp5r8BuzsxH3motHwH4nqJm3VZq1m2j39C28+yGjB3IF/50NW88NptNq2oYOm4w08+ZWlIEbZ8iOgbcELHkgsQhMhTKL4X0XMDTkZToSECh0m9B4/8AF4ULxALV+lLJLMUVeUbtySBVSHQCKj2H0pc224m/PojchTkwPio1JxC/VtppUz5EBrfe312Jqv89RW23xLVtiPQPugFkZIraEr4NIXeybvVAKj+Hqv83zWkmUoZUfq7wkmJkIPkFCyrQRnutW/Sc3WsdsFSy8Aehsa49EREYM2UEY6aM2MUR7eXEz4bUo6XtK9VI5Zebw8RiT27Z5i4Nkk9D2hu572jHIm8JLgerD8TPLa4OLz307BGQ8ktQdb+m/VV/peIT3nwaQKH8Gl3OnnwU/cC7OpJV8WntRPkb23S+dHPt51AoSCodzk98WOdAdGS8maXHZgRJfFTru3mb9HvcViK9u5SCrV789cYB20Xqt4dPMKyoRePORijBAQMdyT9zv4t45VD+Maj9HqVFuAUSl+g+pEQgkZWrqZKout8G1YHZ50rp5TRrYND5otizbEHll6D+DxSe1Ea1XA5AbIruGuKvp/3yC0JpavFFevv6W1Duami8sSWtQxK6r2V0DKBQjbfQ5sTZW4lKrtTjsXrpHonuStqf3mJnaXllER2vW9W5a0CsQJKjyGKiuxo9gcx9Tx29JNsNHLC9NsxT2aOcirAqREsYGZL/YCiOJM7WTg822i/PhOdDsPq1rNG3wtWaNwUfVE87FFKl+7hJgYRjf1sgg1Gk2qfs9JafVS0tCbLtwQZ7mp6JElJe3YpCY3H17LI5X0orSuMuQTXerIeXeoXSjFAqOIejQ/DuYu1otkn20qINZefpSGQYUqWlJtqsYnS1sxxq4Nz2Naw1hDJu6iiisZBOG67PoFH9u2BEey9i9UIq/19gUyIUtV9YSOzQ0O0q+WBge8IcOb9FtiEyVusghtocCfKVig24LxLNKMk36r6dHZo8VkD8dF3dXbQ6MpM6kYvSqQz1vwgEW9P6n9qJqv+z1jHzagKnsy0yS5spXVjQeDtEBrUxrkwD64ydiekG1/HwAgAtlTMyqGIslsmlUIVsIJEgut/17LURMAQ+9LkzufuXD+I4HviKSNQiGrc57QrTEqX9CJI4FxKng78DRTXU/SAr+TxDDIkVeDiaEz2LoUDtBIIITF6bH4AoWJUUndnZh+n//R2o+n/S/j5kUbAPRCquIJMDper/CO57Be6h2AwzbJsHzjuond/ViabtJo2q/0+BsWQRO01LYjiz9aw1dmzQD02BswCVfgNAFwrYh9BmdYq/Q7eCct8tcO2InnlaA9t/S4ZWTD93KnOeXUD9zka8tJ6l23GbU684bp+rUtwjREdrjTp/G2Drzhap52k9+RGwDgjvsQiBhmBb0SQHvFWQuDiYeOUuHXramZHKwNaFEJtM5llU9f8KhFrbmQIiCaTyi3olIHE+OHNRDRnR6lyKaYcVmhymUXV/CCJz7cXTObTuGorfVxVUXgfpmaB2ItFJEJ+GjrJvQjW9rNM6ogci8eNKUKtXuqqz6Rn0d4KQXykeQeLHd+CeOp+91wEDxh0+imtu+DgzHniTrRu2MXz8UI65cBo9+pmlkY4TA6u/Ng0Vn8tSj3fRTst4iBcScCw1wd3TTbVjxwb9HXMNRjKoairkLNiIMx/iJ6LSb7Zx3YzhsYP78PQsuezkIAeg5RpS8RltwNwFOedob35cBtVB5ytDmAZZNjbYE4AkkrisVQNg1XBrUCGpvxyUsyhQgb4ya3iNkJ6jtZPsgyBygF7KVdvJf0+D988+uPU5DB0mUVnGdX/8JG88Opsls96nolc5R3/oCEZPHtHVQ9uLEb28DkjZWSjn3aBqMI2OFNtI5dVFji/VhqmQFj9ZuBsDyZsCDpjzNiQu0P0OvRW0bV8yxQKBmLR9qHYAs9MA7MMg0QTJ20PG1RFtNNVB5yv7FG1ICSXOQdRWiB/dOqrlvouq/xv6ffG1sGzqeaTq+qx7VuAuAnetXpWJTUYln4TUs7RMyDOOV/D+WT20/bL60h3Yqx0wgMGjB3DJNz6U97ryFSveWcPOmlqGjhtE/2Hd4w3fq4iORHrcoJNUVZ1OrIyOpKBjFB1L6WH0iM4vSL8avrnNpPrgOqqOwnkT1frBjk2H+r+A2kGzU5R8BOV90DoJXcqQyut0UUHj3YHxiesZptcdG6870PA3VCbPIX6q1sPxVoEzl9ZRwbSWwIifpCNY7rIgVy9wrpueCCqy6sk31jEo+xASPyaomDV0FonKMk667FhOuiw/qrx143bWLF5HZc8KRk8egRXZazNGugaJI9Xf1tFcd7V2zGKTya8GzsKeElQXt+UQBTbH6hvS6ghQm9swhRn71Uh4nhJojavJuoI5/VaWrUzp59tdCtXfbyULI/GjUfZkSN4ZTMB8iIwOnLzdKVDbQZJ3oTJ/D6sfUvVlkEpUQ64upQPKRzU9jpRfpsWs634TLI0GznXyHnQz7pCInjUIqfxCMEntLiIU+4ADFkbtljr+/Z07aKxNopRCKcW4qaO45BvnGyPWXiQB8WNK3DmGVFyNavg3+mEvZsQi0HhnG/sUIUj8F3s8KvUq4RVEtZB6Rc+YVH3OtdKQfh3KTstq+g2gEKsvVH0FpJLabfUsmfEEU49ZrtM0OpWO5n1kk8m7AFIv6OVHbx3hywpeoAI9JBCYzX7P0tpxCzXSab0UbZyvPYOCh//yFPNfXoRYgmUJdtzm6p99jL4lJuYbMohWR88opLe1d+IilLdMSzcUrUqM6eU1v1iVciFsiOlew7qlWaEcKV8r5EfHQXoGrZ00F1Q9qumF1k2/CdLJEhegyj+J8mH+y4uYOOkfRLvlt72i+X32N+pWZ+UfC+x1Lh44C4DLtPCuv4mW9ySVIzibg79N65N1M7rln2RXuec3D7OzphaVJbK6bM4K3nhsDkeff0QXjmw/wD4Uqf4xqu73oApV8Nk6H8mZ286TB2HkxIdbksCjE3RUzn2fcKcjpZ2wAtpbuMshFjhg7nJUw3+CvDefrTUjuPH/ojgplwMnWlT28DvRCSu1eqk9pFGpFxH7EMJLvyO6KtNdSfiMOxBUzCOORAoUtqgkWtF732nL1dUseHkxC15djJtu+Rulkg53/vx+vvTXT3WnCfy+h1WFVP8IlXxIT2hCJyQxPdFxl9H+qsU4RIYgZacGv0e0FEbjfwm3X4FWV3OHjGyymn4DqEZdregsBiwUcW7//YGsfm8nO86s4oQLdnbyJLKNnpTtxtNLj82CsSFkunWkMp1PsikymQ0tGkOfw2/Myjnes+xz4aCGnY2sX7aplfMFLf3RDLsRf5PuEaaSRRSQI0jVt2iu9isZAWsYUv0DJH5Sq9el8otFqgU93bsw9FvLQWXC9/72oOpnazAuj2fvqqOpMYXn+sx4vJRqxGzsAtfMkGk2WyqZaqE2UEktHFuI5vLuQmOL5VwnoisqY5Nb7+ZvQdX9GrXz66id30DV/iJ8KcbQbmY9NRenKefZUIqdNbVsWb8rOYWGoqikjji5K7VUS6hzYWn5gviptO/5Re9fcS1S9Y1Wx0psGpSdW+Q4h4JFRlZL+zBV/4/A+XKBNO8vdFj9bg1Ok8uMJ3q2M9ieqUwsZnPa4z6U7vkJsSCdJffaMYifXMJ1cv8uNpI4P+c1H5W8H7Xja6ja76F2fCNYSdmz7HMRMM/xkAJufvaM0tCZpLX8hLuctnuliY5eRYZqY1fyDFKBvy5cPsGvD/K7ClFoPL7ODbEnBQ9f62jUxtU2KP1ZsiLgexAp9YmxJ0H8NGi8XY87FJdCvRyxBgTLG5beHj04KA4oVhAQ6OdYVUjlZ3R1VXaRQeWnAhmKcsKNZwwSl4C3JqgG83UbqcSFtDYVrs6/UDtbxu6vRtX+WlehZfeUNLSbXHHWDGJZOOnd2a91/0WlXgpa1wQ6fsUi01YPJDIwEGhtDxbSXJmXQ1HZiiJRJnc92jZu0YVNWWPasDKGE5i+eMLHdcEuubi2XGuZpWdC+sUC+yigmvD+vjZavDqlxx8ZHohK56aC5GD1BatSp7LU/THQQQz+JrFpLfIUscNDlmVF9+mNHQ+px3T7oshgLdgdHdN65MmHIPUSLUEAR+eQWRUtFfZ7gH3OAavuU0VV70q2b9rR6vWIHeHgow/smkHt46jGewPnq5QvBxe189voB7S9y2++VpvPWe5S7nI6nEuVnqFD+K3yCTRfvGE9yxeU88CN/Vi7rAzPFSLREq/jLEDKr4Dq72lnxXs/ZCcF1ogg2T97hmvrYgDiuk+nVQ11N1D8/bJ09WqmvDo6Een5Gy2Mq1ydH+EnA82hcqTy00GVUdAbjhjYE4K+kMdA+WVF76156bEVrnbc4scVf28MRTnk2AnUrNuGm2r9eYzYEQaM6FfgKEOH8VYFzlcp9suH5N2o5CPB7+2pkPa0U5CLagSvxHZAeTjgriJsInvMWbVMOqqB+/7Rn7XL4zQ1RrBjpY41hfjroPyjKHsCNPw1ZB8vuG5ud5OYtqmxU3TxklUJDbcDdRRfsowh5Z8ARHcIqP4eeGtR3nYQD1Fp8DYGTtX5KHdJIDmSQndOsZGKq3ROb8FKffSYWzlfGRxU8nHEOGC7gMBFXz2X2354D57r4TkedplNVa8KTvjo0V09un0QBek3aN9yYlbieHuQHi2J4P5WcN7VCs7OG3Q4l0o1orwt4OQ7SNEojDmkkUu+sJn//nogq94rY8T4JmLxUpywiDY+0Yk64TYZJrfh6R6ZkSN16bRfr2dqiQu1eGvqJfRsOSjFLn4jUPUttDFU4CxEOfP0Od130Tkh6PMkPo7EpyM9ftYiQxE9MNAPK2GpwN8aci8AaZRfY1KUdpFp5xzGglcXs3XDdpwmh4htYVkRLv7quftXi7Q9hI5+tyeapWjpjNHOZbjmSExaP6PeB1oFn472JvRR7srQ6r9IFHr29fjE/9vEH789lMdv682HP1ODbWsh+eKkUc5cJHYUYh+IKlgw5CFV/w+VvF8XJVg9IH42EumBqv1eVlV1CTIbiYsCGwT4m1GpGdrBcldoOx3cL/ZBSMVnkOrrdQ6ctzaocJ1K0QrXDH6SgvbUb0M2o5PZ9xwwYNj4IXzp759i7jPz2bpxOyMnDuOQ4ydgx/fJ2+1iMhGUXSGBNkBtOTYuuAtRzipIPRO8tqvXdqDu+oJbozYMH9dEv6ER7vn7EE67RJh2Sh1CpsCgkGOk0PcFYh+MSoa5JTEkNlk7P/ETgDQq+TDU/YL2J/eCiNbqUg1/B2cJBZdek7eDPVqH++Mntvs6Wq8nSr5RjSPREe0/n6EVsTKbz/zmEyx6bQnL562kR98qpp52KD0HtDcP0VASqoGOVyP7tOQqtS1dodKzwXa1LA4eOnK0K5XQDjTdU3QPK6o44uRGXnuyD/f8rZKLr4tSFl+jjy02sWuWt4hp1X9/de4OupgqMhyp/BoAKj0Xkveh2q1/GEWCybVKz4XG/1DQcXPeQzU9jZSdo/XQwppxF8OqRC+PhlRaRg/If203ss96JNW9Kznx0lLlEwwdxwqaYa/chXMk0SHkNprRqtpA9R7adlA6Kp6aTzQW58IvHkN5z/H0HhhUX6oUauf3KCyW6qCI6WiQ1RvKzoKmJ2lp0BvXeWLRcc1H6Dy6jlRWSdD7UbT6dDHnCwAflX4LKTsrf5O3Xrdjcd/Xhip+epB3keVARsfqZr2t5C6iOrevvcbQEEokGmHSCROYdEJp8gmGjiP2ZJSzmPZ308gQ1RIHBSPDGZTuCZt6oY39OpeoDYefOpQxR36IIWMHBVFUhWr8X6AtVsgBbJGdkYordCoFHs0pCxLX+VUBLY5TRybFvhb5xoXGW9s4hwOpGVB2Tsi2NCr5RKCs70Fsik7Al+y2RAKJCyF5V851bKTsgg6MvePssw6YYc8h5R/Lejg7YlgqkaovohruDJll5bLnCykEn6EHHtY6uVyiFFeq96H+9xAkpUvZ2RAdj0q/DrhIbKqW0PDrUOlXtcPjLqFjZd12s6CsSs+lTUcWT+fS5Q35A1Tdr1qO95OQvBflb9UCr80IUvXVwNC9DiiIHY6UnUtJlZoGQ3cidriWqvHW0TEnTEH5JVroOD2L4hO/dlZ+77JOIECcqn5TqBqSLcUgQf5UkfOnXwN7rE5KjwxFqn+CSs/QeanRUbq9mcQgPRvlzA+Kqjq4IlF2gXaS3GWUlAahwv5OClX3p6C7SjCO9Oso912k+oe0qjyNHwNWJSr5qF52jB6gbVxkRMfG30GMA2bYdSIHINU/RKVeDEq425kUX3aGDmNXfweVfg8a/9AJgypkBNsbGYvp6JWUoauNNoHyUNZg9AwxWXwM6bk6f6LpEd2Ww+oDZRfqqkZvvW790zyr7ChKGxGrH0i8hP2DKsvcszQ9Qf4XUFr30ys7M6e6MaYNVivHzGDYG4kiVV+D9Fuo9Bydc9Sc41UC0gOJToDowajEFbDzS3TORDHMhkbbee6IjkzHAmkav053ArH6QaQfuMXsYRqVfAqJDNLRMncZEIPYkUFUPKJ7RXpraHvS18YYvVX6R4lR0iQ0Mpjm9ngZ3BVBT81sW+rpPNj0bIgd1foc9qFIF0fsjQNm6BysXkjiw5CagUqupuSZZHRCligh4L69O0aXRX+gmF6VjX6wLZ0jFcz+VOPdgTRDSm+TuFbjd+ZQ+F7TKHdBoM0T7ONvhcZbUQpIv0DHk2+zcVCpV5HoON2KpOiyAuhKrjtRaruOzGVwVxU4LqJbfkT2bH6EwbDniELsKCR2OGrHl9txnI1UfZ1M1EbU1g7IU7QD6V1E4BpaBJ59oEIv68WmopIvgTNTN7bOVKDbR9CmGLTagar9FS0TzRSkZ6K8Tdqh8dph6wviBaLcn9Rt0vKqKsMOWY2q/YXWVMtMDL21hDtvKZS7SkfsuhnGATN0LhKlZNE9azhS+bmc/UuJnMXpWPKqpYUL/UKh/QiUXwHRYboXpLtcJ/unQgyUSoHzJkQPAXch4UYsBs5S8o2JA8l/t3PsbaACRy4yAh2Za2sG7+ictNi0lsa0kQGBHEcubrds42EwdD5C6aKhtnYArOziiFKP7WCOqtpZ/Fh7OpI4E5VeBE0PBTmhb+XsFBzrvA7WmKCf4o6Qk4lOxM8T1XbAWwLJZXSeEr4f/ItC/EhIPdfG/mmdMtH0HJIIRGwj/QivSrWDlk/dD1PTbGgDL1h2qyttd3sSpTlGEd0kO1e1OHZ8CcemwCrUWqIYvs57KKaan3wAkk8F4XatiF/0fO58sIZrPa9W92IHs7l2LGV0mBiSWWIAiI4q/VBnQfOPEj+TMBVp7Ck5SawGw16Evz0Q9CzFLkXBnkhJuYzSK3jGs7D6ABWhu7feb0Rp1wjDnlz4WGcuylkGTQ9TVEEf0KkLy4B6iOU++wLEg+e+UESvE9sQRUaSiQdJ9EB97TZxWjuX0fG6a0eeW2Mjmd6b3YxOccBEpLeIPCgiDSKyWkQ+VmTfq0TEE5H6rH8ndsY4OpOdNbU8c+tL3PGT+3nl3tdJ1hXL9dk3Uak3dYuG2l+gdn4HVf+XQG+mCFKOVHwK/TAXi4b5SEj+lESHQOzYtgfX0bY3EoWKzwMFeheqhuChbscygr9C7x8/E6xBQGAEvBV0flJ65pHNvK8xsPqg/O3gvgeoYEZYSpsUB+Uuo/mLKToSqfi0/mIhQqZpsFRc2bm30A3Z12yY63jMe/4d7vz5Azz816fZuKKjQp97Mf4WVO3PULU/QNX+BLXzu4FgdHGk/ONBVNim+FdkWPqAQOXnaTMS5r9Px6q0g6XDgmKhaWh6nvYtC7qQfhrKLtKSONID7UQ6wRJjZyj75Z4jYxdtIK4njanXdSTfPjiIKpZgO/3aLHFbC6n8RlARHtH/rOF6ibib9qrtrCXIv6L/4gOAycDjIjJfKbWowP6vK6VK+JbtGtYt3cgt37+rWcj1/QWrmPnwbD73+yvp0a+6q4e3Z3CXa72o7IRG9z1Uwz+Ryq9k7ah07pC3ASL9tdCg1Qf9YBVbJrT1wx6CJC5Gpd+meJVhpnF0O5ch1Q6o/xWFH+4OJsP76yC1XUcA/dntPI+g8x583aJJNQVtiNBLf/Ez9TKnv1Pr7kTHgTMbvG06Uudvg6ZHUUS1SnTVV5HKz6Ma7wF/A1rzpkA0z1mISj7S0ivNPkS3E1LJICF2v8lS2GdsmJt2+fe376Bm3VacJgexhAUvLeScz5zGYadO6urh7SE8VN1vgxZlgY1Q21H1f0Kqf9x6Sd2vBXcROto7UX9ZS29gG4WjPJauYg5BoqNQ9rHg7I7egm4g9RBMkMLsjNrSgfMqrScWPzMoQsict1QnMROxioI9prkhuN50Mnq1YClY/SF+rG4B563UyvZ+DaRe1PYreTdS+WWk6huoxruyWjTZhDu8SVT9r5DqH+lrWz2Qyi8BaVB+t2+LtsvWVUQqgIuAiUqpemCGiDwCXAF8e1fP3xU8/JcnSSdbZhBuysVzPJ6+5SUu+caHunBkew6Veob8h9vVcgn+tqAnYzoo+10TbJfA+fIpvvQW08mhGdXjvGvPoLSqmkxD6/Y4O17Wv84q887QEKjyh52z0DgjYB+G2Ifoyp7IUP2yv12P0eqjxxk/BnBRTc9rnRzl6X+t3mcPvHWo5BNI4gKk+gfBWESrfSfvCLm+o6scE2fTEjWTbjtj3B3sazZs3nPvULN2a3NPSeUrnJTL4/98jonHjidW1t4m0nsh7ru6xU/es+ij0jObi09U6gVIPkirSFfsqKCSu5BdiQJxJFHgu8DfqfOrSqIjuWBt2caMOGwHWr2lniTcfhWzlQlIXIZEyvUyIBE9iVQ7g++JrM+b+55u4+TX6OVNv4ZcZ081/B3p8ctgJSXTa7YGVXsDod8rfr1OpWgVFYx1TuBuN9MZS5DjAE8ptTTrtfnAwUWOmSIiW0RkqYh8X0QKOoIi8mkRmS0is3fs3P1tAlLJNDVrt+a9rnzFsrkrdvv1uw1e/nugiTSHfFXy4awqmDSQ0lU2fqHlDhukv54FURHo7oTgvkfpTlWCwk9asfmFT9HoXIfnJgXOafUBexr5S4Oi+501Pa17m2WWPq1ewTJI5t6U7t3Y9JjOZ1FbCU+cdbTQo59JnA2qs+LHoZdGCxDWo27/YZ+yYQtnvhfa0NuKCGvfW7/br98t8HYQ/iy6gWAqWs8q+SDa1qRa/qVfItz+WCB99XMZHa4no2ERMm8NpduPCAXTITpsg3z0V3tH0h8K2cQYJK4m335FQaoh9SgqPa+llY+UBVI3Lfur9FxU/V/BW6Yjk36uZERmx3rtGOsT6X9W/yCSFkZKR9L2QjrDAasEcq33Tgpb+1eAiWg9gIuAy4BvFDq5UupGpdThSqnDe/bo1QnDLU4kGkGs8C/0WHw/mDlmsDPr6Ll4EBmkf0y9Tv4D5FH4Ifa1Fpa/DpzXUXW/0q05crH6UtpHMxq0lSh0vY6Wg1sQP6+Dx4Yhemmw4kqIn45+ZKI0G15/k27InXxI59m1uh8F7ruohv8EQq2lOKZpVO1Pg7LsLKJFChdaVXLtd+xTNixRGb7sohTEy0tJbt4HiI4k3C7EgyRvUOk3CLcRhSZ0oqM6/iZwF6Ma/6sditzrWD0pPUE9SuGIVin2q4CdjIwG6VniGEo4Hz4SOxSpvA6soehxB7ZXbdYTvvTrqNqfBQUP2YduQSWfLEHhvuVaJB9CJR9q9apEBxOenB9v+U7ay2jzW05EXhIRVeDfDHSiTm5iVDW69XkeSqkVSqmVSilfKfUO8GPg4l29kc4iakc4aPpYInZr58OORznirCldNKo9j8RPR0eXst+HGJSdm7Wu3p4Qt25/0fIABj833pF3Hik7kfzZX8YoZl6PazHBkqpl2hpXCPEjKTyDLPTYRNAf/fxZom77E0ES5yE9f4NUfoH8zgGOFrJ1lwW/e6j6v6Dq/wFOWwrbuaR0DkUWUnZeyNhiED8t5PV9h/3Nhh1x1mHYuZNFgfKqBEPH7p1fVO0mMiSoyM5uzmzrJbFYsFSlCuWoFooe5Qomp3WurJuTJhgZGkT5w86R+btE9djKzqCzWqa1EEPKjg/SF8LIaIWFEXbvMd2rVuIQHY9UX4/0/CP5vRp9oEmryweo9CxU7Y8g9RjtE2sNUiP8HS0v2YcGE+7s8UX0a3tpC7Q2Y5xKqROLbQ/yJ6IiMlYplfnmOBQolLyadwm62Wrt+Z8/k501tWxeXYNlCZ7nM3bqKI6/qHuWsu4WrB5I9fe0OrqzGKxqpOy01uvs9kRw5pE327MOCML8Hvqhiwf7hM1+fL0UGRmedfxApPLTqPpbaG7SHRkJ5R/TY/G3INExEJusZ1apXelDmRl7hOYy6MrPg9UDFRkJ3vvkG+lMiXY8qyo0k+SeQjtVQQ6GNQgpvzRfxNR9n3CDlNIdBZIP6uRgtYMOG2gvZ8k8OjpIzr9XJ+dLJZSdgcRP6dj59xL2Nxs2+tDhHP+RI3n5nteJRK3myNcVP7ykG41y9yMV16BSr+g2QzhgH4GUnU7zcx6bErQGC6kYjIwMIsgptBOXsQG5NiyFSi9AohOzr4xUfhnV8I9A4d0CyqDi4wgK5bwHVs+glU8c1fTgLtxlxn5lOU6xo8GegigP1bgq5P4UevVgUKD7lxFuzeTFQksOWRnET0ESOX0X/e2ER+gUOO+iGv4J7sZANLajchWWdnCbJXYiSOW3Ucm7W5Lz7clI4qN0bLm169nlJHylVIOIPAD8WESuRVcQnQ8cHba/iJwFzFVKbRaRg4DvA/fu6jg6k7KKOJ/+9RVsXLGZ7Zt2MGBEf/oM3v1LB90OqxdSfnnBzZK4WMsYqCb0Q24DNlJ5tZYySM/VS47REajkU3rtPw8fJJHzmoNKvYx2vgJDIHEk0hcip7betewMSD3Frs8iA+NT/qnmBtlS8UlU7S/RgRCVs68HVEPlNdBwm27vgaLFqbIgcSUSnxZ6NZV3zxkk0ObqjFlxXJ/HWagd4sgwiB6IVH+/E86977Av2rATLjmaw8+YzOpF60hUlTH84KFBE+b9CQuJnwjxE8M3Rw8MulnMRz+3AkQh8SEkfqqu2nOXg1QHFXp3hZwkElqwopx3snLBFKAQS+uGiT055xQTwFvc0ZsMCJwcezJS/lF9L7HDdX9GZw759sTRS4UV1+mK6vSsltcz92WNQar/H6Feu1RQeIm0PpiYdwJWhc7Vc5fqCWPsUKTi2s45dzegs2rMrwNuBj4AtgKfy5Rvi8gwYDEwQSm1BjgFuEVEKoHNwO3AzztpHJ3KoFEDGDSqeyrodgusXkj1j3UuhbcarMFI/Cj9oADEW76/pCyNashtWyE6UdPq3+q0KvkwOO+hH/DgIXeXohof0NGk7DNIDFX5Vd34epedFhcabwb7V3pm6ixGt+AokGOmtgMWqNqQfRxIvwgFHDDcNeGvo+gc58sGexpq5/VBNZinxxoZjlR9iX15ybGD7HM2rKJHOROOHtfVw+jGCFLxSXCX6B6QEtOCnZlodfTAZqkcIa0jL3lYSDzHT/fWQ/J/5C5Xqro/Ij1/Se7XrlR9Nsid2lWtNl9PttJztPPlbwkKmgrhaMkJdx35NscDf6WOwoflh5YsbFuIUirQY6jU61nOnAWNdyJVX80XwN1L6RQHTCm1DbigwLY16Gy9zO9fB77eGdc1dAOkTM8y28KerKtYUs/RPCu0euikzlxSM8gP9TuQngk5DhiqEdJvUvraSjVQW2S76JyOyHBI3hMyjmzcII+kwLULVpKyizNEGx0ZbKOPpPM2effqrdINdhOFigw8cBbpL4PIoEDraN+PnBgbtr8iED0IiR7Uxn4xpPJLQZFMZqnOh8TH8yeQ6RmER4dcLY8RPSTrNV+3DfK3lTjc/oHOV6FlvTQqPUP3tKz/d9DBpJijk4IQQWyNh1JJhHwHTKVmtnHeYkT0xNvfUGQf0TqEzlxy30tV/zekxy8oaHf9jdqGUabz/bqxpM5+o7Jo6GoESVygnTBvpS5djo4g/CEq5PRkkmb17Ekl74fUS5Rc7WgN11G55D0UjjIpra/lzKNtA5MpLCikOp2JPBVK6O0oDm1XExXaJ3Bkwxwwvw5V/ys968VF54n0ChreltBexWDYl4mOQnr+uqVNWWRsuNCn30BB2+FnOTvuElTDv3X3jVLzpKq+ArVtpBAoB/w6XW3elg2zBgR6ggXwt0BkYMiGDgpWAzq6Vsz5Am2HC0xgVVLn5+VFwRQqeU8wgVeABcl7kMrPQLSYokzXse9PbQ3dC6taV6xER1JwBlNAoBUiqJ3fQzU9qQVJU6/QLqkJVQvpt2izv6M9HqWcEs5dCQ3/LLI9kl+SncE+mPD73xOPZPj9q+SdwUw8RXNBgV+DSnar9CaDoQuJ6FY30UMKqqxL7FDCq7N1ZbJq+BfKXaElLFQdpSepxwPpn2LEgh67JZ6z6dEglaIA7tLQlyU2lfB7DPLodjdhTqP7HqReQzuHLhl9SlV/I+1rzbTnMA6YodshiUvR7XNyI0eeTnZveiJoNtvOh0p5+dpYLVcFbEhconPYVCkl07UUnwl6LflwGfztqIZbwHk/eCFzjzGgAio+C9KH1uXznUkkq6ooG1Ug+d/TOSUGg6E07MlBdCbsGW7Uy2odyln1gxSOQsfFdZJ/c05aCedXxdIxbCS7ZVMwBpV6LpC4CfJKIfjfhrKLA/mPKLvPEYtCND8HTKUKVLQi4CzZTWPZNcwSpKH7ERmMVP8fKvVSEOXKzVHoaPi7iLGJjEEqLgerd3DdFzt4jeYT6pmyZGl5qjqdbEsjrWao1kCIHx+UpSegxyHgbQyan5eYG1KUTJl5XJe/l51bYL/ObMtkMOyvRJCqr0B6FqrphUDxPRuFjtC093krlnpgIxXXaGkgdzmq8f52njsMC2JHtHpFTx7nZY0jcLzixyKx43Q7NU4Bfycq9SykXi4y5vYSRTfcvpbwtI5iUb+OSmHsXowDZuieWL10U9vUMwV26Mw+jnFd/SQ9UbU/DzTMdtFoRMZqg5iFanoJnTifbQw8rWtmT82S49DK+ZSdHeSrtSfSF/K+2IdpHbfISIhNIfyxF51w7y7KOT4SzGgNBkPpBJHmxjsLbO/kyY41COxJqKbHoOlpdt3pKUcqP9d6AunX5Dhf0Nx3UnoHzldmPD2QsjN0D9qSyeTU5nYWGKQns1ZPJDa9dSP1LCQ2HeUsIN9e+vr4bohZgjR0W1RzMmUYEXQ1YGcoS1oQm6rlL/xN7JrxsrVwYdVX8vXN3KWE55XZQcJsayR+jFagxkYvyQZ90bDQyxsRWt6Hcig7H0IqlnDm6/YrsSPId778oH/nTq35JtW05HbEQaqRxCUl3bnBYMjCfY/iX7GVdI4cTAxJnInyP4Cmx9k1+2XpFkY9fgnRsa03eWsJjzw54RqPUqVFralE25TsLgDR4FwxmnvvRqfkNNQO8LeAqkHKzgh3vvyd4G9GRSfo5d/mpd+oPnf5VVo0uxtiImCGbkwRQyI9oPJLkHoS0u8R3pi6RMovB4lC+uWOnwOACkicqUUcw4gMDJT1c8Phrm6RkocgiYug7Ey9JGn1QvydugrLqm4xVqoR5ddC/R8ILyl3UMkHkNx2Hc47qMZbAykNX0tvVH0DcZehvE1IZEjQtsWYCYOh3Sifon0lK7+gn+X0TO1kdNRxigzXtqDuD+xaZM0KlOUvBQlxDK3eBc4fyEqEER2H9PxVIEorKOmBOG8DbtBaqC/4O7TQbcONWpcsD0fncPkbdTQsg78T1fCvlm4DEoPyTyDxE7UQrpQhsWlgdV8RdWNZDd0WiR2JclcQugQXHQH1v9EdhqnftQu5S7QDtqvGS+LgbUPt+AqQ1r0qyz+OZJT1y05Gpd+k9f1EdMJutmHJRSogOkabcqsPREflbI9D/R8prOeDXj5o9ftGVMONtDL63kqo/ztUX4/sTz1rDIbdQfRACibCRyZA402BLEVb9iuTw1kAbzVK+QU6jbSHCFhDAymaLUAC4iciiQ+h0yKGa5vmb6T1fUWR+AnFxx8ZAQTuaK5upNULUi8EjlShwoEIuOshlrGTClX/p2AswXuj0tDwL6j+LpI4v+S77krMEqSh+xI7AqJjaFkSy/RrTOhcBFVHceMlEDmQNj/m/vZAu2dX+on5upw7/SJaxkFpCYr637XkQViDgryKXjSH4O2JQZh+Vy69PchbK4L0bPWranqRfGPna8PrFVLpNxgMJSNlkLiCFuFkgJiucvaWBZOitpyv7KW7QqRp6UW7KziQeiRwvgCSkHoyKBzS0Typ+nKQTxXYYqsvUvkFHcnaFVKvUzwC6EMkK8rmrQnGmeuYeoFt2zswETBDNyaCVH4R3HdRzkKdEKqSQYViKVUtCt2Edxo4bxTYxwb7ELDHQrJQ1CfTmBa0M5dJFs01eAUiaMk7Ue5CcJaDVQ7xM3VPMykrqCXUPtoqSIhBWY7waqjxCs7l72jdHN1gMHQIiU8DeyQqNQtI6khQ462UvtzoQeV1UP+nwrtYgxGJo6zBBQROLbSNyNgrm3ZVYfrrdLTcXa1FXmOToer/EInrVIjOysMtSAQiQ1taRIHO+wq9rp/lQHZ/TATM0M3R1XmSuAQpOwu8dZRuvCIQOSBoTlugHYXVR/evtPpD7Chaa/dEwRoFlV/VlYDWQIgdA4nLaN/cRQUNfxv0rLfpfq3n0ynOFzox1QpTqwaIQeIj+h6ziY4nfGbtGufLYOhMrH5I4hwkcTEimeKZUrF1myT7aMIdjihSfhkAUv4xtP3K7CdAXNuv+ClgDdbLohXX0m6dQeftQLC1HtKvax0zyRQGdQLxYwqPyZ6KVH6p9WuR4YRH/OzAtu0dmAiYYe8iMkDnbJUUbo8iZadqQyGJoCl1q5NB+SfJPPhSfhnYB+klQ5WG2DRdiUg0WArN4Oly75LEWsNIa32csrM6rcWPVF6Lqv0NLSrQUT1jrPwyEpJQK/FjUakXAiHGzHsZ04awQJm3wWDYRazelK5JFdMFOFgQ6Q+ORWu7J3rSmKlWjI5Bqr+DanoW3A0QHY6UnaaXB6NjIfHh5iNV/FRIPUXHli09UA2o9CwkflwHjs9H4scEqwTvBWPSml9UfgWJhkwIrR4QPy5oO5TJqY2AVCLxYztlTHsC44AZ9iokfhIq9RptGg5rEFJxFVj9UO4iUGG5FkovTTY/4AL2YUhYKXQ2fk2IMydaC6dQ/7I8ouBtzHHsdgFrINLzF5B+G9QOvdQRHUPBGaokkOrvopqeDqJzCaTsJIgd2TnjMRgM+USG6kiUv5biNiwKiQuQ+ElAGprCFPCVbq1W/jGan3NrEFL+iTYG4Qf9LHMouIQZRhrc5doJ6hQiSMXnwF0F3nIth2NPLiofIYmPQGSYFrslCfahWqoiV/6nG2McMMMexmmZ5UQPav8ynNUfqfwCqvE2nXxeyIhFhwOCqv1xoO0VNuv0205eD0E13odOtG/1alBJOQjUxhLOUkh6Ylew85SriyJVSOJiSFzcyeMwGPZVlE4A92ogOrTI0n9hpOqLWv7FWURhJ0zAPgrV8B/duqhgX9omPab2LAU6C8FbnX9tvwZiJ5Qox2O3TorvFET3CI6OLH3/2JFaRHsvxThghj2HuwRV//esFzxIfByJT2/feaLjkOqfoNJvQeMd5DtDgLcFVffb8G3N2Fr9vb2EzR5BG7DoJHA/oM3ZbfTA3eCAGQyG3YZqRNX9MZA+EMAHewJS8Sna9VUqFUjFdaCaUDu/QXhOqweN/y4i3hxgDaa9qdzKWURBuygZYee28mwtJHZMu65ryMck4Rv2DKoJVf839Iwt88+B5O35GlUlIYg9nvDIlh1E1orlWkSD1hZHFdmn0KULhbgF3LCG1jlYA4NEWIPBsLegGm8POlak0Q6MA85iVPKpjp1QyiA6usC2XoHzVcwRspHySztw3WoK9lJ02romQBlUfErnYRl2CeOAGfYMzvwCG3xUqpBERBtIFZSdQevqGVsbBimjoCGRaoifjlR9p2MtKuInEV5BqCgpwdb/AJoeaf91DQZDF+EGNix3cuVA+pUOn1USH0FrfWW+igWwoewUCkfVIlqxvuobEIg8t+ua8aMI/+r3guheW6Sh8b+0r0etIQzjgBn2DCq3CXUGDx0N6xhSdq5ueh09EKyhUHYmUvW9QIU6zLmykcovanVnKSBN0eY1TyugXF9qdVMaUq9qTTODwdD9UUV0s9QuOCKRIUj193Q1ozVEFwFVfTPIawpbeoxA/Hik4rO6g0ZHsHoHUjodxdf2PDV7F85hAJMDZthT2BMKdMqJ5fcobPe5D807h8SmoZqeCAxnlgBh9MDWgn4dwiqyDNlG25BmIlowcJfHYjAYdjtSFlQJrsvdAPbEXTu31R8pvyL/9fiJWq6mOdIkQAyJn75r1wPEKkMRJz8XrFRZihTKX2salu0iJgJm2DNY/bQYYKvlwrg2Xhkdm85E4kj1dyF2tF6qlF46Olb52dLP4a1DNd6Narg5qETKMk6RvoQ/PkJpFUme7utoMBj2CqTiCnRUPRO3sIEKJEtfq1Ovl/gwJD6iRaKp0MuO1d8pvbm0akQ1PYtq+JeejPp1WScvdI6Man5bxBBrSGnjMBTERMAMewxJXAD2BFR6JigXiR2hFeZ31zxKqpDyy4HL232oSr0KyXvItOxQznytvlxxDeIsCnorRmgd7YroBHv/A9pKniV2ZOlLoP52VGomqB268MA+lF3rW5mNR+lG12DYj4kMR6p/iEq9onOlIiO16GcniSnnI1rotCNaW/4WVO0NtBQLLNACrZX/D2EnytsMUqlbC7WyYdGg4fYGCrcqEp07Gzu8xME4kJ6DcpfrrgCxo8Gqav89hZLJu+0se7hnMQ6YYc8SHYd0IHF0j6KSgfOV7USlwHsfar+LIkLLgx8P/lf63io+iUq/A8nbCpw8AfGTkMQ5pY3FXRxId/iAh0rP0qKr1f+PdrcTycZbj2q8A7yVQARihyOJSzuvPZLBsC9i9UIS53f1KNpEJe8FGmhxohz9r/6XKDKK+hYtfSGtQB7jSt1TsvZHQK7YNHq/6HjdNaQUW6EaUXU3BL0bU4CNanoCqfqqFovuMGlU4/2Qnqnvyxqmx1Syhlj3wDhgBkMu7nL0jCo3iuXl/A9gQcWnkOgoPaMEreQcmgtWjfT8JaVHmzxU/U3kOYL+BlTqVSR+SonnycHfiar7NS3FDy6kZ6O8Gl1ZZTAY9m6cxYRHsHJtWhTs6Uji7CAlQhB3JSq0AMCGyq8h7XBy9NLnNloKCvT1VcPNSPWP6GjkXTX8KxD0Du7HX4Oq/wNSfb2O4O0lmBwwgyEXsSkcfs/F1b0pM84XBArXYYn4SfB3lD4Obz3hlVAOpGaVfp4cVOrVkPO64K3V/wwGw15OmExOGC4483S/yIwz5GY5Njn7ivte+4aRnkOoDfO3BVGxDuBvae18ZY1PNT3XsXN2EcYBMxhyiY6ldAOmgkrLLArmdql26o5FKegIyi4Er/21hDt2At7mjp/XYDB0D+JHUboNy5ksSjnhi2PR9ue7FbRTKlDd7wD+B4SPzwMvt0q1e2McMIMhjwhS9UWgAihD53lFCH9cYkhuMmpetac+J/b4Is5ZGrwNoBqyDhlUQG06hsSPL+VGwomMINw4+xAZ3PHzGgyGboEkzg9U9m20/YoRrosYCYp6sohNJXxpUCB2WIErKt3RxN9Cq0lj7FjybY1oGyQdTMS3BlFQI83kgBkM+wCR4Tpfy30X/CaIjkM58yB5P5mEeIhBbBpEx7Q6VOLHoPwNkHqF5iTXyDCk/JOhl1JNT0PT42ij52kxxopPAFGk8jpU7W/R4Xal/8Wm6ut2EIkfqyuiyBaXtHXkzzhgBsM+QAyp/EqQVrABrP4oqYC6X6F1xdJAHKQcSVzU+lCp1Han/kZaomMWUvmZ1qkWGbzVOicrs6Ro9UYqPq1FZstORXnLwVmCtjWZZP9rOn5rVi+wJ4PzNq2XIe2O58V2EcYBMxgKEoXIaIja6If7RLAPQqXeAtKIPSWYceXOFgVJXALxs7Rwo9VLy1OEkZ4VOF9ZatrOPFRjFCn/BFiDkJ436LwyvxbssYXPVSpShVR/R1dKOe8CNsSP3iuquwwGQzuIDNT5XZLQVqrHTyH9FsrfgESGBdGukGrq6EFIz1+DuyL4fRShUg+qEVX3e1p1M/E3o+p+q+0WMaTi89oRdFdBpDdEx7Ori29ScRUq+UQgVNsE0TFI+UdL10jrJhgHzGAIw10WNN/don+3D9OaYtZAJHFeaeewqsAaX3QX1fQU+T3VHEi/BeWXoo1jNH+ZYFex+iEV13XuOQ0GQ/dANaIa/wvOAv27NUCr7UdHQvy4EmsPI22LZKdnU7DFXHoexKYHpzqgk7t+RLQdLtUWd1NMDpjBkIu/GVX/Z/A3o5caPR2Vavj7brhWbZFtplekwWBoLwpV98fA+Qrsl78BVf+HQBKiE6/k7yC8Kbfbvorv/RTjgBkMOaim5wmVaXBXgL+pcy9mjw5/XcrAqu7caxkMhn0fb61W6s/r6+iiUi936qXEHk14cn80KAIwFMM4YAZDLt5GwsPqUfC3duqlJHEB2oBlLwrYkPgo4ZVIBoPBUAR/C+Ff7V7nTyCjEyAyhNaVjrZe6jQOWJuYHDCDIZfo6KBFT24UzAGrk6sErUFI9XdRycfBWwFWX6TsbOju7ZoMBkP3JDKU/OgXgA2RzpZpEKTqq6imFyD9OmDpgp74iZgJZNsYB8xgyEHiJ+mGu3i0yDTEdMXQ7qiysQYgFVd3/nkNBsP+h9Uf7IngLKRFpkGAuG7u3enYSNkZUHbGbjj3vo1ZgjQYcrF6INXfAXsKkADpDWXn6Soig8Fg6OZIxbVQdjZIT6Ac7COQ6u+1X8nesFsxETCDIQyrnxYTNBgMhr2OCFJ2FpSd1dUDMRTBRMAMBoPBYDAY9jAmAmYwtIVqRCXvgfQcQIF9CJL4KFg9u3pkBoPB0AYKlXoVmp4AVQtWf6T8YohO7OqB7feYCJjBUBSFqvuNVqbHAVxw5qPqbiBcgNBgMBi6Dyr1PCTvA7UD8MHfhKr/J7jvdfXQ9ns6xQETkS+IyGwRSYnILSXs/1UR2SQiO0XkZhEJU3IzGLoe991A+yu7rNsHlQwiYoZ9AWPDDPsmPiRzes0C4KCSD3XBeAzZdFYEbAPwU+DmtnYUkTOAbwOnACOAUcCPOmkcBkPn4oUpSgOkUN76PT0aw+7D2DDDvodqpEWKIgdv8x4diiGfTnHAlFIPKKUeAkqRCb8SuEkptUgptR34CXBVZ4zDYOh0IgOBSMiGGNLZoqyGLsPYMMM+iZTTWqU+C6v/Hh2KIZ+uSMI/GHg46/f5wAAR6aOUyjN+IvJpIKMHUH/U6ZOW7IExFqIvsKULr9+V7M/3DqH3/8OuGEdX0NV/++FdeO0w9lYb1tV/x65mf77/kHtfAbzYFWPZ03T1372g/eoKB6wS2Jn1e+bnKkJmn0qpG4Eb98C42kREZiulDu/qcXQF+/O9w/59//vzvRdgr7Rh+/vfcX++f3Pv3fPe21yCFJGXREQV+DejA9esB6qzfs/8XNeBcxkMBkNRjA0zGAzdkTYjYEqpEzv5mouAQ4F7gt8PBTaHhe4NBoNhVzE2zGAwdEc6S4YiKiJl6GzliIiUiUgh5+424BoRmSAivYDrgVs6Yxx7gC5fRuhC9ud7h/37/vf5e99PbNg+/3dsg/35/s29d0NEKbXrJxH5IfB/OS//SCn1QxEZBiwGJiil1gT7fw34FpAA7gc+q5RK7fJADAaDoQMYG2YwGPY0neKAGQwGg8FgMBhKx7QiMhgMBoPBYNjDGAfMYDAYDAaDYQ9jHLB20t6ecXs7ItJbRB4UkQYRWS0iH+vqMe0p9re/dTYiEheRm4K/eZ2IzBORs7p6XIZdZ3/7XBsbtv/8rbPZG2xYVwix7u1kesadgU7A3df5K7qT6wBgMvC4iMxXSi3q0lHtGfa3v3U2UWAtcAKwBjgbuEdEDlFKrerKgRl2mf3tc21s2P7zt86m29swk4TfQUTkp8BQpdRVXT2W3YWIVADbgYlKqaXBa/8F1iulvt2lg9uD7A9/61IQkQXoysD7u3oshl1nf/hcGxum2R/+1qXQ3WyYWYI0FGMc4GUMV8B8dC88w36EiAxAfx72h6iBYd/B2DAD0D1tmHHADMXI7XlH8HtVF4zF0EWIiA3cAdyqlHqvq8djMLQDY8MM3daGGQcsi93QM25vJ7fnHcHvpufdfoKIWMB/0Tk0X+ji4RjawNiwPIwN28/pzjbMJOFnsRt6xu3tLAWiIjJWKbUseO1QulEI17D7EBEBbkInL5+tlHK6eEiGNjA2LA9jw/ZjursNMxGwdtLOnnF7NUqpBuAB4MciUiEixwDno2cT+zz709+6AH8HxgPnKaWSXT0YQ+ewP32ujQ3bf/7WBejWNsw4YO3neiAJfBv4ePDz9V06ot3Ldejy5Q+A/wGf20/Kt2H/+1s3IyLDgc+gy/Y3iUh98O/yrh2ZoRPY3z7XxobtP3/rZvYGG2ZkKAwGg8FgMBj2MCYCZjAYDAaDwbCHMQ6YwWAwGAwGwx7GOGAGg8FgMBgMexjjgBkMBoPBYDDsYYwDZjAYDAaDwbCHMQ6YwWAwGAwGwx7GOGAGg8FgMBgMexjjgBkMBoPBYDDsYYwDZjAYDAaDwbCHMQ6YwWAwGAwGwx7GOGAGg8FgMBgMexjjgBkMBoPBYDDsYYwDZjAYDAaDwbCHMQ6YwWAwGAwGwx7GOGAGg8FgMBgMexjjgBkMBoPBYDDsYYwDth8gIt8VkX939Tjag4gcJyJL9vA1h4lIvYhE9uR1DQZDYUTkFhH5afDzHrcL7UFEFonIiXv4mk+KyJV78pqGzkGUUl09BkM3QkReAm5XSu1Rh01EFDBWKbV8T17XYDB0b0TkFmCdUur6rh5LNt11XIa9BxMBMxgMBoPBYNjDGAdsH0JEviUi60WkTkSWiMgpwes/FJHbs/Y7UkRmisgOEZmfCZmLyM+A44C/BEtxfwleP0hEnhWRbcF5Lykyhh4icpOIbAzG8tPMkp6IjBGRl0Vkp4hsEZG7g9dfCQ6fH1z3oyJyooisyzrvKhH5hogsEJGG4BoDgvB7nYg8JyK9sva/V0Q2Bdd6RUQOztqWEJHfisjqYPuM4LURIqJEJBrsN1hEHgnue7mIfCrrHD8UkXtE5Lbg+otE5PCs7YNF5H4RqRGRlSLypaxt00RktojUishmEfldu/7QBsM+jIhMEZG5wXN1N1CWtS3XLhR7znYE9qQ+sBlKREYUuObVIvKuiGwXkadFZHjwuojI70Xkg8BWLBCRiSLyaeBy4JvB+R8N9l8lIqcGP/8wsEO3B/fyjoiME5HvBOdbKyKnZ43hk8EY6kRkhYh8JmeM54vI24HdeF9Ezgxef0lErg1+tkTk+sC2fRDYpx7Btox9u1JE1gQ2+HtZ57dE5NvBubcG9q13sK0suI+twfv6logM6Nhf2NCMUsr82wf+AQcCa4HBwe8jgNHBzz9ELysCDAG2AmejHfDTgt/7BdtfAq7NOm9FcN5PAlHgMGALcHCBcTwE/DM4rj8wC/hMsO1/wPeC65YBx2Ydp4AxWb+fiA7vZ35fBbwBDAju4QNgLjAFiAMvAP+Xtf/VQFWw7Q/A21nb/hrc5xAgAhwd7DciGEc02O9l4G/BWCcDNcApWe9pU/A+RoBfAG8E2yxgDvADIAaMAlYAZwTbXweuCH6uBI7s6s+P+Wf+dYd/wfOyGvgqYAMXAw7w02B7s11o6znLOe/PgVcAO2TbBcByYHxg464HZgbbzgiu0ROQYJ9BwbZbMuPKOtcq4NTg54yNOCM4723AysAG2sCngJVZx54DjA6ucwLQCBwWbJsG7ETbayuwXQcF214isNmB3VsevBeVwAPAf4NtGfv2LyABHAqkgPHB9q+gbexQtD38J/C/YNtngEeB8sDeTQWqu/rzsrf/6/IBmH+d9IeEMWin5NRcI0NrB+xbmQcya/vTwJXBz80Pc/D7R4FXc/b/J1nOTtbrA4IHOpH12mXAi8HPtwE3AkNDji3FAbs86/f7gb9n/f5F4KEC703P4Pw9AuOVBA4N2S9joKLAAYAHVGVt/wVwS9Z7+lzWtglAMvh5OrAm59zfAf4T/PwK8COgb1d/bsw/8687/QOOBzYQ5CcHr80k3AEr+pxlvfbRwH70K3DNJ4Frsn630M7PcOBkYClwJGDlHHcLbTtgz2ZtOw+oByLB71WBvelZYFwPAV8Ofv4n8PsC+71EiwP2PHBd1rYD0Q5sNMu+Dc3aPgu4NPj5XYIJZvD7oKxjrw7+DpO6+jOyL/0zS5D7CEonr38F/dB/ICJ3icjgkF2HAx8Jwsg7RGQHcCz6YQtjODA9Z//LgYEF9rWBjVn7/hMdCQP4Jnp2NytYsru6nbe5OevnZMjvlQAiEhGRG4JQei3aKAL0Df6VAe+3ca3BwDalVF3Wa6vRM88Mm7J+bgTKguXL4cDgnPfsu2gHFeAaYBzwXhDKP7eNsRgM+wuDgfUq8AACVhfYt63nDBGZAvwFuFApVVPkPH/MOsc2tJ0aopR6ITj+r8BmEblRRKrbcT+5NmqLUsrL+h1a7NZZIvKG6JSHHejoet9gnwNo22aBfv+y36/VaAcqe7kw125VBj8PBx7Meh/eRU9CBwD/RU/U7xKRDSLyKxGxSxiPoQjGAduHUErdqZQ6Fv0gKeCXIbutRUfAemb9q1BK3ZA5Tcj+L+fsX6mU+lyBc6fQkZ3MvtVKqYOD8W1SSn1KKTUYHdL+m4iM6YRbz+VjwPnoaGAP9MwPtFHdgl4WGN3GOTYAvUWkKuu1YcD6Eq6/Fr20kP2eVSmlzgZQSi1TSl2Gdkx/CdwnIhWl3ZrBsE+zERgiIpL12rAC+xZ9zkSkH/Ag8AWl1Lwi11yLTpPIPk9CKTUTQCn1J6XUVOBg9MTpG8FxnSYhICJxdFT/N8AApVRP4Am0zcqMsS2bBdpuDc/6fRjg0toRLMRa4Kyc96FMKbVeKeUopX6klJqATtk4F/hEKfdmKIxxwPYRRORAETk5eJCb0LMrL2TX24HzROSMIFJUJjqxdWiwfTM6fyDDY8A4EblCROzg3xEiMj73xEqpjcAzwG9FpDpI6hwtIicEY/xI1nW2ow1YZoy5190VqtCO4FZ0zsLPs8boAzcDvxOdwBsRkaOC9y37XtaiQ+6/CN6jSejI1R0lXH8WUCu6KCIRXGOiiBwBICIfF5F+wVh2BMeE/a0Mhv2N19EOw5dEJCoiH0bnP4VR8DkLItH3A3cope5u45r/AL4jQaGO6EKijwQ/HyEi04NoTwPatu4OmxVD513VAK6InAWcnrX9JuCTInJKYFeHiMhBIef5H/BVERkpIpVo23e3UsotYQz/AH4mLQUI/UTk/ODnk0TkENEFVbXopUljs3YR44DtO8SBG9ARnk3o6Mp3c3cKHIvzg2016FnPN2j5LPwRuFh0NdCfgiW404FL0bOrTeioTTz33AGfQBuTxWgn6z5aljePAN4UkXrgEXR+w8pg2w+BW4Pwd8EqyxK5DR16Xx+M442c7V8H3gHeQi83/JLwZ+EydPRsA3om/X9KqWfbuniwxHAeOnF/Jfpv8m90NA7gTGBR8D78EZ2D0VTy3RkM+yhKqTTwYeAqtP34KDqRPGzfYs/ZUHRF91ekpRKyXkTyomlKqQfRNuCuIGVhIXBWsLkanbS+HW1TtqKjVKCdogmBzXpoF++7DvgScE9wrY+hbWRm+yx0IdTv0cn4L9M60pXhZvRy4Svo96QJnR9bCn8MrvmMiNSh7eb0YNtAtC2vRS9NvoyezBt2ASPEajAYDAaDwbCHMREwg8FgMBgMhj1MpzpgIvIF0QKTKdFtGgrtd5WIeDmh4RM7cywGg8HQHoz9MhgMe5JoJ59vA/BTtPBcoo19Xw8q9gwGg6E7YOyXwWDYY3SqA6aUegBAdEuWoW3sbjAYDN0GY78MBsOepLMjYO1hiohsQVeh/Rf4RViprOieW58GSJQlpg4/YOSeHeVeTN32euq2N6D8lkILEaGsIk7vgT27bmCGNkmmHJoiiljCwo54lFkOMatQ4em+zdvzlm1RSvXr6nHkUJL9AmPDOornemxes6WV/QIQSxg4oj+WJQWONHQ1yZSDilr4UVDRFD1s39ivELrKAXsFmIgu6z0YuBut/fKL3B2VUjei29cwftzB6j9/vWsPDnPv5jdX/41auy7v9QgRvvu7r2DHu9L/NhRj8fubWVyZ5IBDqhjYcycTKjYwrHx3aNZ2f3pWnF5ICb2rKNl+gbFhHeXle2fy0l0z8ZzWclN2mc25F5/GlFMO6aKRGdpi8fubSfcro6kPNPVazjlDksZ+hdAlVZBKqRVKqZVKKV8p9Q7wY3TTVUMn4jQ5oa8rpfBco6FnMHQEY7/2DKnGdJ7zBaB8RSqZ7oIRGQydS3eRoVC0tFwwdBJjDx+NhITp+wzuTVnF/hkONhh2A8Z+7QbGTR2NXRbSblBg7BSzjGvY++lsGYqoiJQBESDT5iZvnStoOjog+Pkg4PvAw505FgOc/okTKK9KEA2WGiN2BDsR44IvntXGkQbD/oexX92LEQcfwIGHj2nlhNllNtPOmkKfIb27cGQGQ+fQ2UlA1wP/l/X7x4EficjN6JYwE5RSa4BTgFuCXlWb0S0Nfp57MsOuUd23ii/9/VPMeXo+a95bR9+hfZh+9mH06Ffd1UMzGLojxn51JwQ+8vXzWPLWcha8vBgrGmHKKYcwelJYBx6DYe+js2Uofoju6RdGZdZ+X0f34zPsZhKVZRx70XRaWnoZDIYwjP3qfoglHDR9LAdNH9vVQzEYOp3ukgNmMBgMBoPBsN9gHDCDwWAwGAyGPYxxwAwGg8FgMBj2MEaJ01AyvufzxmNzmPXEXNJNDgdNH8vJHzuWyp4VXT00g8FgaJPVi9bx/B2vULNuK32H9OHky49l5MRhXT0sw36KiYAZSub+3z/G87e/wraNO6jf3sDc5xbwj6/eSqox1dVDMxgMhqK8//Yqbvvh3axauJaGHY2sXrSW2390L8vmrOjqoRn2U0wEzFASWzdu5903luGmW9rd+a5Psj7JvOff4cjzDi96fDqZJp1yqOxRsdslK3fW1DLryXnUrNvKsPFDOfz0Q43wrMGwn/PkTc/jpFq363RSLk/e9Dxjp44qeqzv+TTsTJKoKiNqR3bnMHEdj3deWcySt5ZT0bOCaWdOYcCI7tYK1dAZGAfMUBIbl2/CiuQHTJ2Uy8qFaws6YE0NKR780xMsnf0+AFW9K7ngi2cxajdp+axdsoFbfnA3vuvhOR7vz1vJzAdn8dnfX0l1n6rdck2DwdD9qVm7NfT1Leu3Fe1l8MZjc3j+zlfxHA8R4chzpnLqFceHdhnZVZyUy7++9V+2btiO0+QglvD28+9w/hfOYtIJEzr9eoauxSxB7usoqN1SR7K+aZdO06N/j9DXI7ZFn8G9Ch53+0/uY+ns9/Ec7RDt2LyTO35yPzXrtu3SeArx0J+ewEm29JBzUi6NdY08f/uru+V6BoNh95Ksb6J2S512knaBih7loa+XVyUKOl/zX1zEs7e+TKo+hZtycZoc3nhsDi/e9dquDaYAc56dz9b125r7+Cpf4aRcHv7b03nRO8Pej4mA7cO8//YqHvzTEzTWJlFKMWrScC7+6rkkqhPtPtcB4wbTo29V3ixS+XDEmZNDj6lZs4WN72/Ka6jruS5vPPIW5113RrvHUYxkXZJtm3bkve57ivfeWt6p1zIYDLuXhp2N3Pe7R1m1cC1iCRXV5Vz45bM7HD0//uKjePo/L+C5fsuLIgXtF8BLd8/ESTmtXnNSDjMffosTLz0ay+rcGMbCGe+FOlqWwPplGxkx8YBOvZ6hazERsH2UmrVbufNn91O7pQ437eI5HisWrOK2H93TsRMKOtKVM1MUy2LJrHDnZkdNLVYkP1/C9xRbNmzv2DiKELELzydi8ZCmvgaDoXui4Nb/u5tV76zBczzclMvOmlru+Mn9bN3YMdvRb1gfVG4UTSkWv760YHStdltd6Otu2sHdDRGpQrmqvoJ4eazTr2foWowDto/yxqOzcXMjT47PB2u2snlVTbvP56Rcls1dmWeoPMfl9UfnhB4zYGT/vDEARGNRRhw8tN1jaItYmc3Yw0YSsVt/rO14lGlnTen06xkMht3Dhvc3sXXD9tbRKsDzPN58bG6HzvnmY3PwPT/v9R01tWxesyX0mIEj+oe+XtGzglhZ5ztE088+rFXzcQAEKnskGDRyQKdfz9C1GAdsH2Xrxu0oP39aZ0WEnTW17T5fbhg+m6bG8Pyy6t6VTD55InZW9EkswS6zmXb2YW1fVMGG9zezYsFq0sl0SeO88Itn039YP+wym3giRjQeZezhoznmwmklHW8wGLqeHTW1WCFJ7r7rs3VDx/JHG3YmQ1+3LCFZF77tjE+ehB1vHVm341HO+OTJJVVz126r5/23V7E9JDUijLFTR3H0+UcQsaPEEzFiiRhVvau44geX7PbqccOex+SA7WUsnLGEl+6eQe3WegaPHsBpnziRIWMH5u038pBhrHl3fSvZCADX9Rk4uv0zqfKqBFW9K9mxeWer18USxkweWfC4D33udAYM78sbj86hqTHF2MNGcurHjy+YEJth28Yd3PbDe6jbXo9lWfiex5nXnFI0XwMgUZ3gc7+7ivXLN7J9cy2DRvajz5DeJd+nwWDYfWxdv41n//syKxesIVGV4JgLjuCIM6fkORdDxgzEdfOjVXY8yshJHRNOHT99LBtXbs5bOvQ9xeAx+TYUYNj4IVz108t47r+vsGnVB/Qe0JOTP3ZsSbIVD/35Sd559T2isQie4zFy0jAu/daFeQ5dLqdcfhzTzjqMNe+uo7wqwfCJQzs918zQPTAO2F7ErCfm8fR/XmyORq2Yv5qbv3sn1/ziY3kGZNqZU3jzsbk0en5z2N2O20w+6WCqe1e2/+ICF3zxLO74yf24jovyFRE7QqzM5rQrTih8mCUcee5Ujjx3aunXUnDr/93F9s21ZCdtPHXT8wwaOYChBw5qc6xDxg5iyNg29jMYDHuMnTW1/OPrt5FKpsFXJOubeOrmF9m6YTtnXnNyq3179Kvm0BMm8M4r7zbbOytiEa8o4/DTJ3fo+kecPYU5zy6gdmutTnQXsGNRzrzmZGK5y35ZHHDgYD7500vbda1XH3iTRa+9h+e4eI52+FYuWMMT/3qO879wZpvHV/Wu4OBjDmzXNQ17H8at3kvwPZ/nbn8ltCInTGIhUZ3gs3+4iimnTqKqTyX9hvbhzGtO5tzPnt7hMYw6ZDjnfu50Rhw8lMFjBnLMBdP44l+vpeeAcImKjrJu6Ua9XJCTMeukPd58IjzfzGAwdG9efeBNbb+yUiOclMOsJ+eFLgGe//kzOeOTJ9F3aG+q+lQy9bRJXPf7qzosqhxPxLjsuxdy0PRxDBjRj4OPOYgrf3xpm1H1jvDm43PyqhndtMv8lxbh+/mRPcP+iYmA7SXU72honknlsmHFptDXq3tXcv7nzwB2Xe6hblsDN19/J3Vb6wHwfZ+e/au1hk4n01iXRMLyHZSifkdDp1/PYDDsfta+tx4/ZFkxYkeoWbeNYeOHtHpdLGHa2VOYdnYnFNAoePxfzzLnmQVYEQsBGmqTnPKx43b93CGkGsJzVj3Xw3N8rLiJfRhMBGyvoZij07Nf50agwrjvd4+yfeN23VIomcZNuSybs4KZj8xu81jf83nvzWW8fO/rLHptCZ6bXxmZzQEHDc6rfgK9hDp++rgO34PBYOg6+gzprQWtcvAcj579qnfrtRfNXMLc597BTbukk2lSyTT12+u54yf3lSTwWrNmCzMeeJM3HptD3ba2J4EjDjkgNGm+3wF92swBM+w/mE/CXoIVtRhz2KhmVfkMdjzKSZces0vn9l2fl++Zyayn3iadchh96HDOuvpkeg3sCWgl6jXvrsP3cpYEUy5vPTWPY4tUGCZrk9z4rdup21aPk3Kw4zaJyjI+9esrCuaiJSrLOPny43jxzhnNS652PErPAT2Ycsohu3SvBoOhazjk2PG8+/pSPL/FfkVjUUZPHkF1311rE7Zs7kqeve0ltq7fRs/+PTjl48cz4aiWydqsJ+Y2q8s3o6B2ez0frN1C/2F9C577mVte4s3HtYSFRCyeufUlPvzlc5h47EEFjznr6lOCIihHR7wiQiQa5UPXtZ3/Zdh/MA7YXsCOzTu56bt30lifRGXyByyhvCrBGZ88iXFHjG7X+XzfJ1nXRKKiDCtqcc9vHmbZnBXNOQtLZi1n9aK1fOlvn6KiR3ng8IXXQKeTDukmh82rPqC8KpFXcfjUf15kx+YdzRGtdDKNm3a455cP4aRdNq+qobw6wbEfns7RHzqi+TLHXjiNIaMH8sbjc2isTXLw0eM47LRDzezRYNjLUL7igT89waIZ7wECIqB0Ec/Bxx7Ehz7X/hSJpoYUliXEEjGWzVnBXTc82Gy/atZu5f7fPYqb1T8xnet8NQ9OkU6m2byqBiflMmh0fyLRFvHoNe+u583H57bkcwV27IE/PM7iN5ay5M3lKBQHTRvL2Z86hcqeFQD0HdqbL/75GmY+8hbrlm6g/7B+HHP+EaYi29AK8222F3DXLx+idmtdK12vqB3h5MuOZcrJE9t1rjcem8MLd87ASblEohZTTjmEJW+1jqpl+o/NenIeJ116DJU9K+jZtypUgbqhtpEbPv4nIlEL3/Ppd0BfLr/+Yqp6a0O08LX38pYTfU+x5t31zb/Xb2/g+dtfJVmX5JSPH9/8+shJwzpccm4wGLoHc59/h8Uzl7SWxBG9JHnRV85p17k2rfyA+//wOFvWauHUEYcMY0dNXV7Cu5NyeeaWl5odsInHHsTmNTV5EhROyuW2H92L53pad8wSLvryORw0fSwAC15ejJPOz711HY9FM95rtsmLZy5h3ZINfPkfn2p24Kr7VnHm1SfnHWswZDA5YN2c2i11fLBmS56oqptyefbWl5j91NslN2l9+4WFPHvryzTVN+E5Ohdi9tPzCUuCcNMu65Zs0L8IfPhr54bmb+Ar3LRLqjGNk3LZtHIzd/z0vvbeZtBfbbZpOGsw7GO89cS80OW/D9Zs4eG/PFWySGljbZKbvnMHm1d+gOf6eK7PygWr2bpua+j+tdvqm/NNp519GBXV4dqDTfVNOE0OqcY0qfoU9/7mkebJpkIRmiSmVCub7Hs+jXVJ3n19aUn3YjCAccC6PU7aRUJLAiGVTPPkzS/wj/93a6hSvJNyWb9sY7N46ot3zciTsdA6NfkJ71ZE6D+sX/PvB4wbnCcLEYbvKWrWbmHLOq1WPeGocVjR0j5mYgm1W8N7r3WU2m31rFiwukPq/waDYddx0gWW/3zFvOff4S9fuplVC9fmb1d6OXHjis34ns/c59/B8/Kj6YVIVMaJBL1o7Xi0oNhq3rA8nznPzAfg0OMntOrkUYx0Ms3m1eEtjTqK63isXrSO9cs2hXY2MezdmCXIbk6fQb1IVJYVbAXkNDls37yDF++ayWGnTKTv0D6IJVq09ZYXsSzBc30GjxlI7dbSJRx8TzEoWzFfdBVi3kw2bEyOxwv/m8E5nz6Vs645hbXvraduRyNOMk3E1gbRC+kR6aQc7v/9Y4w+dDjTzz2cyp7F1fKLjt/1dd7Ja0ualajHTR3FxV//EFE7v0G4wWDYPUw89iBevf/NvK4coJ0d3/N54A+Pc+l3L6TPoF7EEzFq1m3jzp/eT+3WOsQSInZEq+O3I0Lee2DPVqmrlb0qEEvadGQ812f+S4sYP30swyYM5fDTD2X202/jOl6zPI5YVp4Ni0QtFr72Hls3bueoDx3OAQcOLnmsYSyeuYQH/vhEJmWOsoo4V/zgIwwY0a/tgw17BSYC1t0RuOhr52KX2aG90UAvR7720Cz+8fXb+O01f+eNR+doxfwgrO6mXdYv3YAVaV8zsRfvmtHq96mnH0q0lCR4X7H49SX87Su3IJZw3ufOwHc9InYEpRRK+aFjUUqxbskGZjz4Fn/5wr+p3dLxaNhL98zk3deX4DkuqYYUbtpl6dwVPPOfFzt8ToPB0H6OvmAavQb2zG8yncWOD3Zy83fu5Fef+DPP3vYyN3/3TrZu3IaTckgn0yRrk6x8Z027inA2r9lCzdqW5ckjzphc8uSrbms9t3z/Lpa89T4nX3Ys1X2rsSIWSkEkqu1Yrg3zXJ+t67excMZ73HL9/5j3/DsljzWXrRu2c//vH9OSGY1a+qd2Sx3/uf5/bcr4GPYejAO2FzDykGF86a/XMmhMkR6OSuE0OdRurePJm57PX2p09UwzYpduwLZt2N4qJ+v0T5zAmCkjkQKOYDa+65OsS/LGY3O465cP4TkenuPhuz6+q5BIhB4Z7Z/MtDKYmHqOS1NDEy/8L1/hv1TefGJuvhJ1ymXOswtK0v0xGAydQzwR43O/v4oTP1pcLied1HmkMx9+i6bGVN5zqp0eqyT7Azr4tWpRy9LmwJH9Ofezp5dsA52Uy2P/fIanbn6RHR/sxHO85gIlpRSVPSuxIpa+UPaYlN7niRufww2J9JfCnGfn5y23gl6SXD5vVYfOaeh+GAdsL6G8urzk/AJVIFdLN3T1CylK5BG1I0Tslo9INKZ1bKxIaR8bN+2y6LX3QnPHvLTL8AlD+dq/Posdy5+V+p5i2dyVpQ00hLCcONA5db4yrUAMhj1J1I6weVVNSfvqyVrIcqXrgyK8S0YIlmU1y0JkmHLKIQwZW1ouGEDdtnoWvLo4b7lReYr6nQ384P7/x4Dh/Vu1V2pGYPOqD0q+Vjb1OxpCuwaALkYw7BuYHLC9hB2bdwQOVAkoQnMdnLRTcvQnYkeYfMohedesWbOFaCwamsOVi1hCRY9ydnwQngDvuR5llWX4BXIyEhVloGDmo28x4743aahtpP+wvpx97amh8hTJuiRbN+yg54Bqho0fwsoFa/L2GTx6QOnvo8Fg6DTWLd1Q+s4hJiEStfQEqkjifTZW1GLc4aPyXv9gTemJ8pGIVTBnTPkKQajoEd6lxPd8EpUJ1i3dyBP/eo4NyzcSr4hz1LmHc/wlR+XZId/32byqhkg0wripo1k8c2neRNLzfEYeYqR59hWMA7aX4Hl+wUT8XOwym0g0gpNymh2laCyKUqokxwm08ahZswXf9VtVMfYcUB2aTBtG1I5wwiVHc8sP7s7fFo8y6YSDKauIM2bySJa/vbK1wn+ZzdHnH8EL/5vBaw/Oar73zatquP0n93LVTy9rTnJVvuLJfz/P7GfmE7EjuGmPkZOGEUvEcB1X30NEiNhRzv1Mx5uRGwyGjuGk3NLztwSq+1SRDOQhQFcx+r7Cb0f+k+f6bN2wPU/lvkffKprqm9o8PhqPMvW0Q1nz7no2vJ/fb3f0oSMQSzj6/GmsXbKhVYGSWEL/YX3xXI//XP+/5m3J2iZevf8NarfV86HrWgRo3397Fff+5hFcx0MpRUWPcnoN7Mm2DduaUynsMpsjzjiUnv13b9smw57DhAL2AuY8u4Abv/nf0OCVWEI01mLY7HiUAcP78cW/XstRHzqcASP6MeawkZz32dMKJvGHoXzF+uWbmP/yolav9xrQk0RVWUnn6D2oFysXriUScl07FuWgaWMAXWQwZOwg7HiUeEWciB3liDMO5ZDjJ/Dag2/mOZ5OyuWFO1vyw15/dDZznp2v9cgaUniOy6qFa5hw1FiOOHMyQw8cwmGnHcp1f/gkQw8cVPJ7YDAYdp3tm3fw+0/9I1TIOWJHdE5Xc3WhECuLccUPPsL5153JsAlDGDx6ACd97Dh69m9fz9t0U5oH//hE3uvFWghlo3zFqMnD2bw2PGJ20mU6p23c4aM44ZKjica0/bLjNv2H9eNj37uYl++ZGWq/3n5hIclgKbF2Sx13/uwBGmuTOg+uyWHHBzup3VrH6VedyPCJBzB26ig+8v/O48yrT2nPW2Do5pgI2G5k86oaXrnvDTav/oAhYwZx3EVH0ndo+1pR1O9o5PF/PhsadUpUJzjvs2eQrE8y5+m38TzFoScdzJHnHEY0FuX0K0/k9CtP1DsreOWBN9m6flvJy5BOk8P8lxbl9V8sNYq2deN23nhsdmhj7XSTw5p319FrUC+qe1dy7Q2Xs2XdNnZurWPA8H5U9iynZt3WgsKsm1fWgII3HtcVn2FCtYteW8L37v6qWXI0GDpAqjHFG4/PYdFrSygrj3PkuYfr/ortK6bmoT8/RUNtY94zKpZw2GmTOOTY8bz24Cy2btzG0HGDOeGSo+kzuBcDRvRj0okTmve3LOG5218pXYpCwcYVm0kn08QSseaX002lHe85Hvf/4YnQHNaIHWXpnBWUlcfpO7QPx198JNPOmsL65Zuo7FHeLBWxbO6KUHsrAls37UA27+CuXz6Uv7qhwHc8qvtUc83PP1ba/Rr2OowDtptYvWgdt/3wHlzHRfmKmrVbWfjaEq75xccYPLpINWMOS2e/j4TJR4gw+aSDmXjsgQAccebk4icSOPKcqTz+z2fbcRdgx/JLx+PlcZJ1bYfw3ZRb0FnzHI/bfnQvvqcYNn4Il37zfPoO7d3KQV23dGPBc8cr4zxz20u8+djcgjkaruOxfXMtlujIXXu/OAyG/RUn5fKPr9/Gzg9qmyd/65dvYt2yw1omdSXgOh6rFq4JfUZjZTbnfVanBIyYeECb55p80kSeve3lkq+dQXImYImqOBE7UtJE0mlKh47dc11eve8NZj48m3iZzUVfO5fRk0cw+tDhzfu4aZdkQyr8vGkXN+3y3x/dWzC1xPd9dnywky3rttGjX7Xpg7sPYv6iu4lH//F0qwdL+QqnKc0T/36Oa39xecnn0Sr4IZ6DUmxeWUO6ySFWRF8nm1fvf6Pk64LOOZh6xqEAPPaPZ5n99Nv4nt+yZFBCJK2Y6GEmL2LN4rXc8bP7ufaXH2+1vXFnUpd3h5xj0MgBvPHonKL5aFYkwl+/eBMiuor0I//vPIZNGNr2oA2G/Zx5z79DbU1tq+fLaXJ4/ZE5HP2hI6jsVVHk6BZEtA1TYe3OHI+tG7bTZ3Cvks711tNvo/zSK5jFEkZPGYEdj7Jszgru//1jLRWEJZZSFqpERGVEZNM4TWnu/NkDfPEv19BzQMsyaVNjmkjEwvPzHb1INMLrj8wO7TOZwXU9nrn1JZ6/41WUrzj6wiM45bLjzERyH8KszewGfM8vWGmzZtE6fnbZH3j8xmcLSiVkc9ARo1EhejAAK95ZzW+v/Tv1O9pWuN+6YVu7hE3FEqaeOomDjhjDA79/jFlPzMUPxqF81WEtrTANH8/12bhic16OyNBxg4gUkLzo2b+aSLEWR6JnqW7axUm57Kyp5dYf3UvttvqODdxg2I9YOmdF6PK/57r89pq/cfN372Tjis1tnicSjTQnq+edy/H403X/Yt7zC9sekIKZD80quQKSIOp94ZfOZsP7m/nvj+5tLd9QQlu1QucNw/c95jw7v9VrFdUJ4uXx0P2r+1axYcWmguOQiIDS75HWR3OY+eBbvPnEnI6N29AtMQ7YbsCyrKL9w1INKWY/PV9XB7ZhBxLVCS740tmtEu2bUZCsTfKPr97a7BwVYsYDs0oZOgB2IsbVv/gYZ3/6VHzf5+2XFpd8bFEsIVbgfbEiFg3bWzuSwycMLZi/tW7JBtyQHpYAiaoyvXSa8976rs+85xa0f9wGw35Gjz6V4YKnSk+YVi1cy7+/fUdzz9dinP+FM6nqUxk6YVK+4sE/Pc6mlcX1stYt20iqMXw5Lw/Rzbe//PdPUdmzgsf+/nRpx5VAJBoJXQr0HJ/tOXI7YgnjpubLYIBOvO89oGf4RUSwrHz5Cyfl8Op9b3Zo3IbuiXHAdgcCR5w1peiaved4rFu2kSWz32/zdJOOH8/l119UMOJTu62e995cXvQcxfKpcnGSaW79/t2kmxwadiY7PlvMxVdU96sOVaL2XD+vx5nrerghgowA65dtZNIJ4/PeYzseZdpZU0KP8RyXbZt2dGzsBsN+xLSzD2uzbY/T5PDYP59p81zVfav42j8/S6xANAgFj7WRm5rRxyoJBbMen8s7M94DoGZD205iqSjfDzWHdpnN6ENH5L1ev6Mx9DxRO8LYw0aF2q/Dzzg0VAUfoMGIsO5TGAdsN3H6FScw4ZiDiNjRwktlgX5VKfQe2LPwRqV4/+3CqvGbV9VQv6N9S29u2uXle2ZSUZ3otJyDiB3h0BMPJlERb/We2GU2J156TF64PhKxmpt35xIvj3P+dWdyxJmTsctsxBJ6D+rFpd/5MBOPm0BYaNFOxIyIocFQAgNG9OPCL59LvDJeNJq/YsHqklIbrKhFZY/ygtvDdLYyOCmXZXPfL1gRXYjHA+ewZ9/2yVcUo8/g3hxy3PhWfS2jsSg9+/fgkOPy5S2qelW0blMUoBQMn3gAl3zzAnoO6IFYgl1mM/2cqZz7mdPoPSB8zING9e+0ezF0PSYJv4OsXrSOWU/Oo6mhiYOPOYhDT5zQaoZmRS0u+so5nHHVSbx6/+u8/uic0GTyHTVa76W6T1XR6/Ua2JOqPlXs2Lwzf6MlrFy4lqf/8xLTzp6sK/4y59+8kxu/eRtOiaXX2bz/9ipO+8QJjJ06imWzV7T7+Fw8x+OVe1/n3M+exsYVm1k6ZwUVPco59oJpHDR9bN7+mTy0Oc8tyCs97z2wJwrFmdecwumfPAnP8VvNJsdOHc2yOSuaCyEidpQefaqYeOz4Xb4Pg2Fvp3ZbPbMen8v65RsZOGIAR557WEtv1oCJxx7IhCPHsuKdNdz+43vC869EmP/yIo676Mg2rzn9nKk89o/wiJkA9/32MQ496WDGThnZatJ3+0/uZdXCtaHHFSNTqX32p0/hP9/9X7uPD2Prxu0MGTuIsz91Km89OQ8n5TDxuPEcff4RoWki08+dysIZ7+Y5j77nU1ZRxgEHDubAw0fjpF2iGU004JxPn8ZdNzzYcpxo7cSzrjU6YPsSxgHrADPuf5MX73qtubXPqkVrmfPMfK7++WV5YfLKnuWcdOmx2gELIRKN0NSQatMBA7jyR5fwp+v+nV9Z6Cu2rNvKto3bef3Rt4iXxUg3OQwaPYCqPpUdcr6gJep2+fUX8b+fPciS2ctLS74vULkIukfjo397mi///dOcdU3bxuSMq09my4ZtvJ/TgHbDis3c/4fHueTrH8KyLKx46yjjJd/8EG89OY+3nnwbx3GZdNx4jv3w9DaXVQydjQf+dpAKkPCWLYY9S83ardz4jdtwHQ/P8Vi1cC1vPTWPa2+4nIEjW0dYrKjFmCkjGDhqABuWhUSpfFXystgRZ07mzSfmUhNSoOSkXBa8vIjFry8hloiRamgiUV3OpBMmsGrh2qLV1IXIRM9HThzGhV8+m0f/8UzpGmIF8F2fRa+9x4Dh/fjs765sc//Bowdw7ufO4ME/PdHKJrqOx7+++V+++o9Pk6hO5C1Fjp06iit/fCkv3vUaW9ZtZdCo/px02bEMGlW6hJGhk/BrAQ+snnR2CapxwNpJw85Gnr9zRqtmsU6Tw6ZVH7BoxpJWwoEZyiriTDl5IvOefyfPgYlEI/QdUpo4a5/Bvbnuj1dz2w/vprG2CeX7rZLvMz8ngzYb65a0o/daLgKnfuIEQBcVXP79i1C+4oZP/IlkbRsaYG0YS8fxuPe3j3DBF89uVYKerEsy68l5LJ+3ih79qjn6Q4czeMxAqnpV5vW2dFMu776xjLptDVT1zi+JtyyL6edMZfo5U9tx04bORKXehOQ9gAP4YB+KlH8CpEAukGGP8PiNz5JKppttkRc4Yo/+4xk+lSMFk+H8z5/JP752a54jZJfFdMSqBMQSPvf7q7jnlw+xdO4KIhErLzLkBvpYAPXb6nnj0dkdcr4ADj/90Oafp5xyCFNOOYQX7pzBS3e91qHzZXBSLi/8bwb9hvZh7NRRLcUKCha/sZQ5z8zHcz0mnzSRSSdMoM+gXtixaKtWRSiF57jMef4djr1wWuh1ho0fwpU/umSXxmrYBfwaVP2/wV8PCFi9kYqrITK8zUNLpVNzwETkCyIyW0RSInJLG/t+VUQ2ichOEblZZO+wyqsXrSNq579tTpPD4jeWFDzu9CtPpKp3VUtOkyVE7Ag9+ldz1y8fYulb75cUXRowrC+f+91VXHvD5SQqS2sJ1G4EPvrNC1rlna1etI67f/Uwbrr0XmwF8RWrF6/jb1++maXB0mbDzkb+8qX/8PI9r7N60VoWvLyIf33rdu74yf0sn7sy1AhH7QjbN+/Y9fEYOh93KSRvBxqANOCCswDV+J8uHlhh9gf7BfpZDrM1a5dsKOjsDBo1gMNOndQqUhONRymriPHKfa8z48FZJVUpRu0IF33tXD7/h08y+aRDwistsyiow9UGo6cM5+xPndr8e922Bp655SXdWq0dLdkK4TQ53P3rh7n3N482v5cP/+0p7v/9Yyybs4IV81fz6N+f5s9fuIkX7pwRWqXupFw2r67Z5bEYdgcequ434K8BXMABfzOq7negSpdzaovOjoBtAH4KnAEUXG8QkTOAbwMnB8c8CPwoeK1bE6+Ih7eWsIREVeEk04oe5Xzhz1cz68m5LJuzku2bd9BYm2Tzig/YvOIDls9dydTTJ3HOp08LPT5Zl+S+3z3GsnkrwVe6B2RINWFnYMei9D+gT/Pvbz4+h2duebnkZuAlobQBuv8Pj/Gt277Aqw+8ScPOhhaDG2jgLHlrecGor+t49B5UmohjR9hZU8u2TTvoN7RPycKTBo1qegod+crGAWcR+HVgtb3k3gXs8/YLtOPkhTS1tu1IIPwczvmfP5OxU0cx55n51G6to2bdVuq21lO3tZ41i9fx5mNzuO4PV5GoCnnrFMx85C2ev/0VHfUSIVERx7LChUp3BSsiDBgxoNm5275pB3//2q04KafkNmql4DQ5LJm9nJXvrKGyZzkLXlrUKqLnpFy2rt+m27+FYMdthowZ2GnjyRtfymXD8k2UVcQZMLyfEXBtD85CUE3kf9n7qNSbSNmpYUe1m079BldKPQAgIocDxSTHrwRuUkotCvb/CXAHe4EBGznxAKJxW4fws4jaEY4449ACR2kSlWWc8JGjGTxqIHf+/IFWxsBNu7z52FzSyTSTTpzI6EnDmx8YJ+3yp8/fREOW4KryVec6RFk4KZc3HpvLeZ87nVRTmmdueSm0AiliR1BK6dldB5UqPMejZs1Wlry5vKjqdC52PMqkEw6msmdrp1f5irrt9ZSVx1v1f2sPbtrlnt88wvK5K4nYEdy0x6EnTuBDnz/D9JUsFX9rgQ0RUDuA7ueA7Q/2C/TS3JtPzG2VDxWNRZl8yqTiX9ICE44ax0FHjOHnl/+x1fPquT47a2r5z/fu4oizpzDphAnEs56/J256njcemd1yLqWaUyU6G99TLHzlXY778HQqepTz9K0vkWpM5Uf3RLBjEa1G30H75aQc3n1jKb0H98INcWoLoZuO20w5aWLetmRdEs9TebatPcx9bgGP3/gcVsTC93x69K3iih98hF7FqukNLfjbgLC/p1PEtrWfrvo2ORjIlg2eDwwQkT65O4rIp4Nlgdk7dm7P3bzHsSIWV/34o1T2riSWiBEvjxGNRznzmlMYXOJsZvHMJQVnYvOeX8idP3uA//3iAZSv2LJuG7+5+q+tnK9QOnl28/aL7/DaQ7PYsHwTVgE1+r5D+vCFP11TUK2+FJTvY8ftgnpfuYglVPQs5/iPHMV5153eatvimUv59VV/5Q+fvZFffPzP3Pe7x9pdug7w5E0vsHzuSty0S6ohhee4vPPKYmY8YEQQSyY6hnDz4kNkr08kLtl+QfezYad8/HjGTB5BNBYlXhEnGo8yYuIBnPnJk0o6fvOamoKTv02rPuCpm1/g95/6J9s27sB3fe664cHWzlcBrGKdLdpJ7fZ6bv7unShfsWL+6tClVcsSrvi/j3DgtDEdvo5lWcTLtaOpSlHpFx2BPGj6WD77uyv1ikrAzppa/v2dO/nVVX/lt9f8jT9/4aaSug3ksn7ZJh7/57M4TQ6phhROk8OWDdtLEv42BERGEG6/4kh0dKddpquS8CuBbD2FzM9VQCv3Uil1I3AjwPhxB3fNx0fB5tU1rF68jreeepuatVuo6FnB1NMnMWrSCEYcPLSVhlW6ySHd5GjdmxDHaPOa4uv+TlOa5W+vYvHrS3n6lhfbTnon6LfWDsHUeHkM3/OJxqO6XDvnUCfl8vK9r3P1zy7DL5AX4vt+szP04l0z2y/Yagm9B/X+/+ydd3wc5fX1v8+U3VVvlqvce+82NsbGuGCMwfReQw2EkISEJLzpv/RACL2EEnrHBowr7r03uUiyZFuWZBWrl9XutPePWZXVzq4kYwgEn89HYO3OzsyuZu/c595zzyG5S6KjV5wT+ozs5UhMPXE4n48e/ywo4Tq4KQOtXuP6Ry5v8ymZpsnulftDPCY1n86Wz3Yy9apJbd7XdxnCcxGWfxfQvIzvAs/F9v+/3Whz/IJvRgyrLqulILuQ3Sv3kbUzBxD0G9ObYZMH0bVfZzqkNQ0CmaZJXaUXT6zHcWpY8+mterzqfp1Pn11K7+E9yNjeBgkbSbSL76V6VCzTIi4llqpT1aELWtOi8lQ1Oftz8cS4qXeotpmmieJSmX3rdI7szME4Db6ZpEiMmj6MrF1tk+kRQvCb9x8KuS+YpslLv3iLqtLqxs+2JPcUrzzyNj9+8V6i49s+Qbxt8S40h8+jtrKOE5kFdB/Ytc37+s5C6WUvIvUsmqgUCkgp4Bp15g5zxvbUPtQAzUVnGv595thtZwhFx0/x1h8/pKa8NuimXF1azY6le4lJiGHgeDsj9tX5+OTppRzamgVAXHIs8++fQ99RvZr2d6yEk9mtr2q0eo2NC7dRXda6zyNENr1uCdWjcsENUxh34Wh0TedvNz/pqPFTX+dj75oDYRO7khOn+Ne9L/D9x24jqVMiK95YQ01FHZZJq8mYJAvikuO4IZAcRcVGUVXSlj+/837Xf7jFcaIqc2cONeW1beZwmbrpyI8B2m6FchYgdUDEP4Ll/cwm5It4RNQcUMf8t8/sTOBbE790zeDjf33OoS2ZdoLR7HuZtSObkhOlPPD0HY2PbV+ymxVvrEP36wghmDhvDDNvntrUerdg4VOLWz2uZVoc3Z9LQXZR0MR4WLRz0jGtf2eu+NE8EjrE89pv3yV7z/HQXRom+9YesCv0gtDQYcHLv3yLeffM4r4n7+CTpxeTn1WI2aB238o5yarCvHtmkdItmWMH8lBcSsjCrSUs08I0zZCuQvbuY3hr6kPiuK7bC8Jzw0xKOqG6vNbx3IWwrevOoi0QiNj7sOpXgn8DWCa4xiM8cziTadN/KwE7AIwE3g/8PhIosizrzDVXTwNZO3NY/e4mKoor6NqvM+dfdy5v/v5D6qqc7SQ0n8ba9zdx7mXjkRWZt/+8gNxD+Y2rsYqiSt7+08fc89gtdOzRAYAti3ZitDHY5GUWfDUlY8uiY8+OqG4F1a3QsWcqhTkOXmxW4HzDEVct8FbV88R9LzF4Yn++96cbkRWZp+5/qVV+mpAkbvzVlY2chHGzR7Ds1TURA5isSvQf7eytVhrWYsik8lR1mxMwxaXQIS0lVKtIQI8hkWhBZxECKdUe2/7fwzcyflWWVLHq7Q0c2XMUT4yHc+eP5+TRIg5vy3L8Dhu6SdWpKjJ3ZDNoYn/SNxxm6SurghYyWxftQgjBrIAkTV7WSSpL2+aqYZmWY+XJEUK0q4IeFRvVKBw77NzB5B4uCJZ5AAzdYP+6QxFjiqEZfPL0Ujr16sj06yYzZPJAnvnhKxQdi9ylEJJgzMzhjJk5AoBBE/vx+YuR7ZQAkrskOVI6KoqrHCclDb9OcSsdk5YYOKEvxw/khi5INfNs9atdUBCeC8Fz4Vd2hDMtQ6EIITyADMhCCI8QwinJex24QwgxRAiRBPwK+M+ZPJf2Yu+aA7z71wXkZeRTU15L5o5sXv7FW60mEppfp7aijtKCcvIy8kNWe7qms+mT7Y2/V5RUtn2118bN2sud0Hx60Puafev0ECFAIQkQtGlqyNB00jce4vmf/AdfbX3YlmUwRJC69djZIyO2UIUkiEmIYeysEY7Pp/XvEubcTOqq27fqm3/fhahutXGKSlYk3B5Xm4Rjz+Lbi29z/Kopr+XZH7/KntXpVJfWUJJ7is9fXMH2Jbsjio9qPp3cQ/kAtri0r2XrXWPL57sak4Oa8howT08aIhxkRWo3faG6vKkzMHzaEKJiPEhyU19PUiQsaLUi1YCiY8V8+PgiNn+2g/gO8a1ub5kW2XuONf4ekxAd1OlwgupWuPhu5+m5rv06h628nypoH29wzIwRJHZMCIrpqlvl/GsnEdWOVuZZfPU40yT8XwFe7GmgmwL//pUQoocQokYI0QPAsqylwN+B1cDxwM9vz/C5tBmWaYWs/BpkELTWyuemRW21l4riSiQHs1jLtDjVbAy5Q9e2ia6Gg6zKdOiezPi5o0nqnEiHtOSIo+PhsPb9TY3/7je6Fzf8vyvp3MeuiqV0S8YT62lfW8ACX73G5kU7GDp5gKMtR3MIAVm7c9j86Q7qqryczC5CiWC2K8kS1zw8P4i02hzhEjCwuWDtQY8hadz7+G2MvmAY3fp3YdycUdz/1B2NVcyz+J/FtzJ+gS3x4PdqQS0szac72we1wKl8u3BXdarK8XlTNxqnvrv263xagy3NobgVzrv6HDr36UhylyTEaUwWF2QXNnpQujwq9zx2K8OmDMYV5SIqzkPagK5I7YyLuk9n5ZvrmXTpuJAFqeP2ms7KN9dRcMR2CMjPPBl+Y0kwZuYI+o91ruB369c5bA7aFspKc6huhXsevYUZN06l+6BuDBjfl+sfuYJp10xu137O4qvHmZah+B3wuzBPx7bY9p/AP8/k8U8XddVe6mvD8HtaS0Ikwcu/eIvYxOiQEjjYlZu0AU1l37ZVh5whJMH4OaOZfeu0xgTn/Uc/5VSes85MJJQVBq+q+o7qxX3/ur3x9+d+/Gr7+QKmxfGD+dz3r9sxdIPD27KRFAmthWQH2CvTrB05HN2fy8q31jP71vMjTnIausn6D7Zw46+vdHw+Kj4a1aM6/g2s01ixp6Ylc9kP57b7dWfx7cW3NX4BHEs/4VytjmAL1oCsXcf4+y1Po3pc+OpCv6suj4onMGTkra5HVqTTIqwDJHZO4Hv/dz2JnRKYdfM0ju7P5e0/fYQeetiIUFwKZYUVxHew5UzikmO46qFLGp/fuWIfJ3OKHNt6kWCZJkmp8Vx8zyyWvLQSy7KHqsAK6UhUl9Ww9sMtbPpkO+PnjsGIFGdMi50r9jHjpql4wiwiJUk4JsztnW0CcEW5mHzZeCZfNr79Lz6Lrw1nrYiwrYIkSTiqfnhiPRi64XhjB8C08Hv9lHn9jgmEZVps/Xwn8cmxnHPpWBSX3G6+Q3NsX7qH7Ut20X9sH+bdMxvjNJXpO/VIjfj8ufMn8skzS9utNRabFIPqVrj255dRW1lHTXktR/cfZ9l/1oBlBXTDgi2FANZ9tDnyji2LnPRcwLZaqiypIqljQmNFrP/oXo6fqepRGT5taLvew1mcxbcNyV2SyMs6GZJsybKEUIQt8xIm5BiaTk2FHnYB5K2u592/LWRuwAhaUhWM9mZM2K3G+hof/7znBVK6JDHnexeEeOc6QgJa5Da63wia3GyJYZMHsuSlle0+R00ziEmMYczMEYw8fyin8srwef188OinVJVVI8kyRqCt2VBt1Hw62xfvpt+Y3mRuPxI2OZUViROH8+k7uhclJ8pwR7lI7BhodwoYMK4Ph7dnB/0NJVli0MTTl8k4i282zqpKAqfyykjumhxii6G6VS68fTrxybFt09kKG+AMVry5jo//tZjh5w1BdbXTEDrAx7L3pWPoJhnbs3n+p6/b7b7WyuUtzl11K8y82SbVluSVcXhrFmUnK4K2GTFtCOdcMrZRK0h1K6QN7Mr068+NeKiO3ZvadDEJ0eTsO8aK19diGCaGboatAHqr6pl96/SI78Ud5eKzZ5fxj9ue4eVfvsXfbn2axS+tDIiv1hKfGszdUNwKI6YNsUVtG2DZQodt5YacxVl80+GtqScmISokRMmqTI+BXRk7a0TbaAoR1oSHNmfy3I//Q0xiNFGx7XddklQJs4GUb1qU5pfx/t8Xomt660yHFvmM6lYYdf4QYhNj8NX6yNyezfEDeZjNKlDuGDe3/O4aohOicUe5cEe58MR6mHXLNGIdvGMb963KjRUqWZGRFZm3/mgPYlmGhWk4L3g1TSchJY64lLiw9kqWBSV5pfztlqf598Nv8OT9/+b5n7xGZUkVhm4QHR8dtIhUXApxybFcfFcwb0zz2fqEZ/Htx3e+ArZj2R6WvLQSXTOa+BOSwO1RmXHTVA5tyaKipOpLTyMafp2DmzKYceMUeg3rEdDiaR2yItF9cDfyMwuDqlGWaeGr86NpBr1b258FCDugdOqVyoW32oKLL/z0dYqOl9h2ILpOpx4dGXROf04czicqPoqJF41mymUTKDxWQnxKLCkB0/DMHTnkZznwHQR2shpAdVkty19f17hibDyXMCfZZ1RPHnz2Lt7+88cUHi0O4rOoboXUtGT2rE4PGPbaj+9cthdPtJutn+/EW9MsKEmC2IQYZtx4Hkf2HCMuKYbq8lo+fXYp1WU1gGD41EFccu+FbeJ7nMVZfBNRUVTJcw+9hu4L5n/JikT/sX0YMXUwC55YctqG1s3hr9fYvnQPlz8wl9d+816bX5fYKYHayjo0Lbiarvl01ry7kSsfnMt7f/8k4jkKSSAERMdFM/GSsYycNpSlL69i25LdyIqd3MmKzMS5oykvqsRbU8/QyQP5yUv3UnS0GMuCtAFdkGSJ1B4dePuPHzkep7meI8DCp5fYcSWQGIUTW5WEra34w2fuZN2HW1j3wabgdqIQeGLdfBGQ+GjAyZwiXv3Vu6QN7MqhzRlB8dGyLK7+6aWUFpRTkldKcudEPn1uWSP5P7V7B654cC5d+nzrhY2/s/hO33l8tT4W/3tlSDVEVWXm3TOLQRP6s+zV1WfMP0xWJAqPllBd3rYxbrB7+QPH9+fE4YKQ57R6P8W5Jdz066t47if/cZaSaIRg4Li+nHfVJN7588fUVNSG8CMKsgspyC5s/P3Qpkxm3TqNc+aNDdrusgfm8uyPXgkJmKpLYcD4JpXgrF05YVu7LU6N+NR4UjongYA7/nIj7/1tIUfTc5EV2wpo6LmDOLg503FKa9Mn222eV/MWpGlRVVbNY997FllV7MqhYQVts2dlOjn7jnP/E9/76ozNz+IsvkIsfukL6lvqRwno2q8LNzxyBa/95j1HGoGQRLuTMt2vc/zAicbvaVsWpbKqMOeOC/jgH586Pl9aUMaQyQO58sfz+PCxz8LuxzItZJfC95+4nU+fXsqadzdgaGbgvOxtNDTWvt9EZTiWfoLtS3dzx19vChKUHTShH/Ed4hpJ/A2QFImh5w5s/N3QDU4czm8TXURWZIZPHYLiUrjghinEJcWw9JVVthWQaRGXFEuPwV3Zt/ZgyPuqLq8hfcOhECFaQzN49VfvICsypmk2arM1/N2Kjhbz/E9e4/pHrmDQl1DzP4v/Hr7TLcijB07YI9AtoPl0DmzOxFfvD1u6D1dmjgTTtEjsGE95WM0qpwNBcpdER66E4lJI6ZKCkAR3/OXGyK1IyyIv6yT/+c27VJVWt4mcqvk0lv9nTUi5u1OvDky5YqJ9PIHtqeZRGTt7lL0a0/PAv4fo6JpWg5es2mX+EVOH2J5s2KTfm397NT948g6u+/ll/PjFu7ng+nPxO5D5Afxev+Nkli2qauL3+kNEKBtQVVLNCw+9FtS+OIuz+LbgyO5joYmUBScyCzCN8BIsUoM4aTsgyYIO3VI4kZnf5o6AZRooqhJ2KjqxYwJgUx5aswSSFYmFTy4me++xxuQrEjSfRvGJUvavOxjy3PW/vAJXlAs5kJipHpX4lDguuG4KmDXg34swchCt3CElWbIHrQZ2Q232HsdfNJqHX3+A6355BXf85UZ+8PT3KDxW4jyVahFWgsfQDPxev82VtUIFty3T4t2/fNw4iXkW3y58pxMw1a06PyFsvlF8cpyjBYQkC/qM7IWstv3jkxWJ1O4pdO7V0XHSKBx0v0FdZR0JqfGNwaLxOU1n6SsrWfjUEjSfxg+evIPEzglh9+XyuNo9hSkrEscDOkHNMeuWadz+x+u48v5kfv5MIf/v+RwuunYrVuXvsar/gVX3GgMGvcX82wsQUvhjGoaJoRls+HgrT/3gpaDJy+QuifQd1QtfrY9nHnw17D5a5cC1gqqyGo7sPPql9nEWZ/HfQMuY0Pi4LCGEYPDE/o7Jj6zI7a76yorCpEvGUlFU2frGAZiGxco313H+NZNRPaHx9lR+Gc/+6FVyD+Vz/S8uZ/SM4faQkgN0zSRn37F28Te1eo0DDjI03fp35kcv3M28O4dw/1+reeS5HH70j6N4eBGr8pdYdf9BeJ/hR38/QXKn8INIpmVhmRa5B0/w9AMvk7k9u/E5d5SLviN70rl3Kq/95v2wgqqaX2ubj2S4czAsVr2z4bRffxb/PXynE7Dew7o7VpZUl8r4C0eBsNttzUU5FZdCVGwUlz9wEZc/OI/Y5BibnxChIiZkQf8xfbj1d9cghGhXwqDVaxRkF3L1Q5cwZNJAm2zQAMtuC+xasY9Hb3+W9R9t4UfP3c3QKYOQ1eBjqAHDXVNv31SjZRF2bDqtxwFGjE8nOqYOIXQwcsE8CfjA8iKEzsAxdUya46wvBDRO/Gj1GtVlNax+d2Pw+/fpvP/opxGT1i9LqNd9Op88u4zF/15JZUkVul9n98p0Pv7X56x+Z0NIq+IszuKbgjEzhockYbIqM2zKIIQkmHTJOOKSY5tijiRQ3Qrz7p3FvY/dSu/hPezYFSmGCUjqnMhNv7mKlG7JxKXEtat6VlpQzrApg7jg+ikhSZ+hGRTmFPPSL9/imQdf4fxrJ3PTb64KiV+KW2HAuD60u2wnBNFxzuKjsXF1jJm0jI5dTiFJOsKqBOMQoIHlBaue+CSNmx4qJGzJLxC/DN1E8+l89Pii4O6CBRsXbCMvswBTD7OPM+B2kr37KG/+34dk77UtmU4czmfRCyv47NllHE3PPWvC/Q3Fd5oDJkkSaQO7Bq1awF4dNdjO9Bvdi3v/eSubPt1BaX4pvUf0ZOLcMUTHRzFi6mBGnDcYX72fz19cwZ7VB0LGwNUoF9f9fD79xzQJ8I2bNYIdy/e1KXGQFIldK/axZ9UBEOGpFw2+Z4mdErj6J5ew9NXV7Fi+B8u0iIqLYu6dM0nqlMDe1Xtb52Q1QNjJl6N9haWDbzEQeRrH5baYOKOKTYvDV+YaYGgGBzZnMvHiMRw7kIfm11j9zgbbLDwSzkBwqS6tZvuS3exeuY+ouCibNFyvIasKGxZs4+bfXE2vYd3Dvj4/6yQbP9lOZXEVfUf1YtK8sWdVp8/iK0ffUb3ZvGhni0cFM2+aCtjTgPc9cTs7l+0lY8cR4lLimDRvHN36dwbg9j9dj+7XOZFZwFv/91Fom1/AkEkDuO7nlzfmPuPnjGLv6vQ2C7Lqms4T9/0by7RwRYUxYjctSk6U8p/fvMePX7iH635xGZ89v4zaijqEEIyaPoyL7pjBE99/nqpTbfPHBVBdMuMvGu34nFW/DKzWLNMgLsGkc08/hcdbn/40DJPjh/KpKavB5/Wzb+0Bcg/lRRTEFbL4UhUwsBPAzO3ZHN2XS4/BXck9VGCLiFsWe9YcYNQFw7jk3tlhX19X5WXLop1k7zlGYscEJs8f33iNnMVXh+90ArZx4XYyHaYH8zJPUnqynJQuSQCkdk9h/v1h/KAC7coJF43hwIbDwUFJgCfaTb9RvYNeMnrmCPa24lFmv140EjPbInyo+XS2fraTaVdPYvp1k4lNjOZERgGqW2Xlm2upKq1GkiVkRcfQG1aSgRHJxv83ISrWw62/v9Z5ZWxV2walbYDL03Z+lb/ObjeahtlmEUVZlUMHJRrOuR0tV0M3MHQDX73WtLLVdAwNPnz8M3760n2OC/D96w+x8MnFaH4DLIuTOUXsWLaH+564ndjEtnlQnsVZtBflRRW8+7cFDte4xY5le5jRkIS1IsqpuBR6DelOVKwHf70/aEGjuhQmz58QdN2npqW0a5Lb1M3GOObVIos711bWcSIjnwFj+zDv7ln2olbY8g1/vflJJEkgJCsQepzjVgOEJJh+w5Tw/ofGcUI0LpzO37RwudsWRwzd5M3f2xahmj+89lpzSFJAq61lQtsGEd2W0HxaiDG5Vq+xZ2U6Y2aMcEyqasprefbBV/HW+jA0nRMZBRzemsnlD17MsCmD2nX8s2gfvrMtyJ0r9rHi9TVhL/CsHW0LLg1IG9CFOd+7ANWt4I5244pyEZ8Sx22/vyYogakoruKVR94OrzIfaAVIsmiVAOqE+jofFUWVPPH9f7P2/U1kbDtC+vpDlBaU2/oxdQaGDk2RQbT4fxN0v8GxgPhpECzIPVzFsvfiWb0gkdLC8Hm8ZUHW3rZVgmRVQtd0dL/e5uRLdatMu2Yy7ihXY9tCcSm2hk9L01thT2V17tMx8k4drglvlZfyoorQTXWTz55bbifeASKt7tfx1tSz/qMtbXoPZ3EW7UXVqWqeffBVR59HQzNI39A++y0hCW79/bUkdIjHFeXCHe1CcSnMuvV8egzuFrTtW3/6iJz9DnGh2b4QdvWN0xhWqq2s492/LeSDxxZxcFMGBzdmcDz9BFq9hq/Oj2U132f4/UuyRPr6w47TntVltWz4PIElbyeTsScq4qyQrEB+Ttu0zwzdCHjtti35klWZLr070bVvpyaOnCSQVcWOXy0+P1mVSe3RoV38Y7B1yjK3H3F8bt0Hm/HWeJt8jC0Lzafz2fPL2u0kcBbtw3eyAmbqJsteXR324hKScCSMtobxF41mxLQh5B7KxxPjJm1A15Dq0caFWxun/RxhwdRrJjHtmkn84crH2n0OPQZ3Y8nLq/C2HE0PQtuCoubTWPHGOsZeOBKpwa/NggVPLSZ9/UE0fyySBBsWJ/LLZ4+jqM7H27QscvuxQYdLcSmttxsbIAlcbpXp109hzIxhjDx/CJs/20nR0WKSuyaxZ/WB0KqYZf+n+HhJu8fwTRNkBzJzaUGZ43VkaAaZ23POGnifxVeCdR9tidgCVD3tD+0d0pJ56KXvcyKzgPqaenoM6hbivVp4tJjcg/nB2n4tEB0Xxc9ff4A3/vBBm6tkDdA1HV3TObL7aHj3EQvaEsMMzeBUfhk5+44HGWVn7znG23/6CNPUMbQEdq6N49LbTjFiUmhr07Lg8K7oZh2DUMiqhKIoINn3lra2ZiVZot/o3lxy72wUl8KhrVmkrz9IbFIsR3Yfo7Yi9HwM3aDsZHm7raBkWQo7dJa5M9txf7pmUnqygtQIjgNn8eXwnUzAqkqrwzrPA2DZvIfTgTvaHdZwNXvPMfasSg/Re2kO1aUwcHw/FEXBE+uxlaPbgUET+7PitbVnRHgR7GpOXZUXrV6jpqKWuup60teno/ns0r9pgGlAaaFCp+6hAVPTBCUFYXgf2EnX3LtmkjagKwufXkJ+dQRD22ZQXQqGbrDmvY188foajMD7VVWZExkFYW8QrWq6BQRrm28nJEHnXh2CRGYb4ImNwgiTyDtN0J7FWZwJHEvPDbuAVFwKEy8e6/hcqxCEbdlVllSx9NVVNrcoDGzrnP4ApPZIaXcCFp8Sx9H9J8InX+2EoekUHi0mbUAXinNPEZ0QzfuPLghKkrR6iRPZboaOr0VucUfU/IKDOyIo53tUxs4cQb8xfaitrOWz55a36bwUt4Jlmhw/mMc/73oe07K9JlWXgmlGoF9YbYlhoVZ3Qoiw7cSouCho4YQCdjJ5Oq4HZ9F2fCcTsKj4qPCkSElw3S8uCzuiXVNRy6FNmRiGycDxfUnqnNimY25bvItlr66OuDpSXAq9R/Tg0+eWUphTclp+kTuX70Vxye32cAwHIQTv/GUBJ3OKkBUZSdSj+aDlCnTD4gQuv7McSW4KDn6fYPuqOAxNoLgULMsKCh6qW2Xy/HGMnT0SgJHThlBwpLBNyWNDgG4ZjNq6+gyHfqN744nxkLEtC4RACEFUrIdrf3654/ZxyTH0GNiV3EN5QatI1aMyef63xAjXrARtL6CDOgKkDq2+5Cz+u0jsmEDx8VOOzw2c0I8xM4Y7PmfqJoe3H6G8sIJOvTrSd2TPNmkaVhRV8uyP/4OvzheWtiEkQXRCNPV1Pn5/1WNNLa12oKaiFtO0kGRnY+r2QlYV8o8UsvKt9ciKTXGQJIOW8WvvhjhmXlmBJJuNKhimCT6vRMae6MC+5CA9QSHZE5Zz7rgASZaoqagNq+fVEg2tY0MLXmB/2bitelTm3jWTxS+uQAS6FqZhcsl9F5LYybkTce5lE1jw5OKgpFdWJHoM7vYt4bAaoO0D8xTI3UEZSLunZf9L+E4mYO4oF8POG8SBjYeDOBSyKnPFjy4OW8Hat+4QC59ajMD+ci5/bQ3TrpnEtGsmhz1WSe4plr66uk0rwc69O5Kz77gjr6OtKDpegjvafUYCmOpWiY6PoiDrJIZuovt0UtM0vHVqCHf18O4YTubH0rFLAVgaFrBzTRwr3rfL17IqM+f26ax4Yx2+Oh+yIjPp0nFccP2Uxn2Mu3AUO5bvDXtj+arhinJxw/+7EkWVKTlRSl5mAfEpcfQe0aOpBeuAax6ez1t//IiiY8VIgerZuZdNYOjkgWFf802B5dsG3jcCvxngfT/w71jwzER4LuTbEsy+SzjvinM4uu940IJDku2b5rUPz3d8TVVpNf9++E28NfXomo6iKiR3SeSOP98QYsHTAN2vs+a9TWz6ZHsbhoYgOtbD4a1Zp+0eovsN0tcfPJ21Z+jpSAJZkcjYltVoX6a6GgJX8DXt80p8/mYHLr1TBq0QBJw85uLD51MxNIEa5WLO7dPZvTKdgiN2lb7XsO5c8eDFtqgtEJsYw4W3T2fxv7/4r8k+XPfzy+g/tg/DJg8ka1cOpmnRf0xvu8oVBsPOHUTR8RI2LtiGrMqYukGnXp3CXkffKJjlWNV/B6sO0Gi6MSmgDIKY2xHim5tEficTMID5910IlkX6hgwkRUIStrjo8PMGO27vrfKy8MnFIUFo3QebGTCur6MfV3lRBS/87A17sqgNyMs6+eVvdZZtseSOMtE1gaRImHpg0WrZLdL6CKvYBsiqwphZw9mxbG9QZScuUaeiWEVr8ZYsU9Ch+3BM1w95/bcvc6rAR32tYfPp3DKXfv9Chk8dzJiZI6iv8+GOcjUGrgbk7MulqrTmtGxSzgQM3aC6rJqkTomkdk8htXtKm14XkxDN3f+4mZK8MmrKaujcp2NQBdU0TXx1fjzR7tNyUPjKYFYHki+nVXcN1C/E8u9GxD8MtNNA/iy+UvQcmsb8H1zE5y+uQNcMTMNiwNg+XPGjuWFfs/DJJVSVVjd+t/y6n5ITpXzx5jouvnuW42ve+MOHnDic3ybJHMuwKCus+HLWbZbtcau67filuCSwBLpmYUHjYE1bjtFrWA/qqrwUHWuyaDMMQWyCgeYLjj2q26TfMIGa/FtWvrWU3V/sp6rUjnuqR6Xn4G6Mmz2S8XNG4ff6EZIU4iFbU1HH/nWHkGU5MsXlK4KsyuRlnaT/2D64Y9wMC3MvC4GAGTeex+T54ynMKSYuOZYOLXhfDYvmcI4G/y1Yda+BVUnoNKsOejpUPgzxvwKpy3/j9FrFN+vT/BqhuBSu/PE85t09i5rKOhI7xjuKsjbg8PYjiJZTdYCuGexff8gxAVv/0VZ0v9b21ZBptWlTO6kKzyObemkF582roLRQpTjfRYcuFl0HTsBQ7OrOK796h/zMk2GDqivKxWU/mEOHtBR2r0wPCnYnsqKYOLuCzcsSEQJb5d4SXHN/KZ7EySBFc+sf72Hnir1k7cwhPiWOc+aNbfx8hCQakxPLtDh5tJiiYyWobpkFTyw5Y61T+2C0ayWquBRKcktJ6pR4WodLTUsOJqxasGHBVtZ+sBndr6O6VS64YQrnXDz2m1FU0vbS6omYx7F8GxHuqV/LKZ1F2zFi2hCGTRlEeXEl0bGeiFUOXTPI2Xc8ZGFjaAb71h50TMDyswrJyyxol9BxW1pw7mgXfl949fdO3f1875GT+H2CE1keFFWh73AV4n+KrMSzf/0hPn1maUS6Qc+h3bn9j9fxzzufD3rcNARjplWzeVk8lmlzWIUkGDy2nuHn29X4GTfMocfggexcvhfNrzMy8Dk3LJ4atcwsqKmsJWfvcRRVZt1HWziZU/SlNb2aoz0aYYZmkJcZ6hncVkTFeug9okfQY3mZJ1n41GJO5ZUhJMHA8X2Z/4OLviHeuX7Qs4gsJWJg1byAiP/d13RO7cN3NgFrgDvGjTvGjebTObTlMPU19fQe2bNRA6wBpmk5crIsi7CEyROHC9rVBnTHuPF7/eGrP5Jg+nXn0mNQN97+08eYphHiiZaYqjH1kgpUl0WXnn669AyUqvwbUd2TQOrBLb+9hjXvbWTH8j14q5xJ/gPG9wvjkyk4djiGe39XwrFMN6oLBo32Ed3xNpDsz8wd5WLypeOZfKkzB6qiqJJPnlnSqFcjScL+Cp3hqtc5F48le+8xSk6Utml7QzdI6ZrU+oZtxJZFO1n97sYgvtqK19aiulXGzhpxxo5z+giURVuDbyOcTcC+kZBkyY5Vlq1+XphjTwH3GdGC22WF/1uHsycrPFrc7laaJEsRq1NJnRO4+9FbeeWRt6koqnDUyrr8rhLcUSaeaBg6oWESUAZ9CcJ9PSPPH4o7ysXKtzZQdDyUK6t6VCZePAaAviN7smvl/qCYunFxAtfcX4qvXqKuWqXXQC+d+o5BRAUmlgX0H9Ob/mOC9RsbYBomq9/ZwMaFgbasJOzBnS/pyNESUfFRjDx/KFsX7WxTR0BWZTr1TD1jx68sqeI/v363SZzXgIzt2bz+2/e557FbzthxThtt1KHELLF5rlLrYuBfN77zCRjYWf5rv30Py7SwTBPLsvlIc++c0VggGDiuL5+/sCLktbIiM2yKXeo1dNvTcPuyPWg+HXeUy3EiJRxGTBvCnlXpEcavLUbPGE5iajw/feX77FyxB7e0kkGjTuJyW2Qf8FB0ItzEoY6l7UUoPVDdCrNumcasW6axccE2vnhrva07hsCyLK7/5eW4PCp5mSdDx5OFoPxUIjFpP2Li4FOADnJvEG27lHxePy/87HVqK+saH2uvP2VbIMmCEecPJaVbEsv/s7bVypokC3oO6U5KtzM3cr3m/U0hf0vNp7H63Y3fjARMHdaM83UW31ZoPp3XfvsuJ3OKwbIQkkRccix3/OWGRhK14lLoMTiNYwfzghc6kmDIOU0T39l7jvHFm+soLSgnLimG9mRg8am2d27x8VNhK/RDzx1MTHwU9z95O5nbsynKXkf/oftI6eynskRh3ecJpHbTHOwgDdB2AdcD9rT3oIn9Kc49xSv/7x0Mv97oyzji/KEMO3cQmk8nPzt0qMeyXGjyHYyYlRogbndp18150Qsr2LMqvakyaFpnPPkCGDyxP+dfM5ntS/ZgmK3vX1Zku7p+hrBtye6QVqqhGRSfOEVBdhFd+4Z2fb5WCA/IvcDIbnXTbyq+8wmYaZi8+YcP8dUGW+rsXLGXfqN7B/zHIDYphlm3ns+Sl4IJlpZpknswj7QBXXjvbwvJ3nOssTReX1PfrknGPSv3c8l9F/LJ00sdV5GSJPji9bVc9dAl5GWeJDn+I/oOr8Xlso8xcHQdfYd5w7QBBKCi+XSyduVQXV5Dv1G9OPfyCYyYNpQju4+iuhUGjO1jl9gteO9vC0MmmYQE58wbS2xSHBDn/EacxKkDj+1fexB/fTvasq1ASHbS2HJ/vUf0JG1AF1I6J/LFG+ta3Y/iUrnuF5edmZPCbq/WNUsym6OmrOaMHedLQUqCqCvB+xHOPDAABdzntr4vs8JuB0geUIZwljP29WHl2+spOFIU1CosL6rgk6eXcuOvrmx87LIH5vLMD18Obt1ZFvlHTqJrBtl7jvH+3xcGxy9BmzmZdZVehkwcABYU5hQ7brNt8S7Ov3YyhqZzaP1CLr75aCMxPrWbxiW3lSJEuGMpYEH+kZOcyCggNS2F3iN68LNX7yNrZw61VV56De3eWMXeuHAbp/LLQvYSmxjdxI+SHSZ+W2pUN3vMW1sfnHydCTgs0lW3wvTrziU6Por+Y3uTtcNZq6sRkmD+/XOI7xAmJp8GSk6cCnsfKi+s+O8nYICIuRWr6u/YlnhhYpjUuQ0Jtg7aIbDqQRnwtVXLvvMJ2InDBRh66JdJq9fYsWx3YwIGYBkmiqoEfflMw+SLN9fRfXA3juw5FjTBaJkWsqrg8ih4a3ytJmO6ZpC+4XCIDlXTsSwObMxg/gMXseK1j7jz/9Wiupr2KUkgKxbOA3sSh/fE894/Hg9anSZ3SeLef97K6BnDgrYuySulrjpUrd8yLNLXHyI1LQVDN+k3qlej5+GOZXtY9c5GaspqSOyYwOxbpyGrCsteXU1ZYTmxCTGkpCWfMY0fAJdHxdfMv84V5eK8KyZy3lXnUHCkkNd/936bKmyaTwtVzv8SEJIgsVMCFUWVIc+dySrbl4Vwnw/qECzfdtDSwTza7FkFlAGIVhIwy/sZ+JbRlHSpiLgH7ZHws/jKsWfl/pCEwNRNsnbloGsGSsCs2+VRMVvewy0oL6pk/7qDrH1/cyivygLFo6L7tFaTMN2vs3tNOqYWuTWUtTOHnP3HmTQ7r9lUog2Xy8IKsD2Cq2Aq9fpYnr3rOSqLqxoflRWJGx65slF7rDn2rkp3nCivLq/l4KYM/PUanXt3pHNv2xmj6FgJnz2/nNzD+aiqwqgZwzln3liWvbqKI7tyEJJEnxE9Q4aHvhQa2pea3YoVkqDf6N5cet+FRMdH89pv3+P4wbxWuXWqKof6eH5J9BiUxpHdx0KuLUM36dzrzLU6vxSkjoiEP4J/B5aeBdpOoOF8ZcCNiL0z8j6M41jVT2BzySzAAM/FCM9FX+WZA2cTMPQI0yp6iyTocGCcuSVkReLQlkxHqQJD0+l7Tn/m3TOL9A2H2LP6ALWVdbYYbIv9W6ZFdVl1xC+SYZrkZ56kQ+d6DF0EJWAAckjhQQAyNb55vPePjSGtgbKT5Tzzw1d46OXvt3hZeHJ20YlSFjyxGAs7Ab34nlmYhsnSl1c1tvoqiiv56F+fBz4D+33WVNTira1HkqUzZnHh9wVX0ywLRk4fhmVZvPa798Jy3FrC5VFR1DP4dbAgySEBk2SJObdPP3PHOROQOiKiLoaoi8HyYvl3IKwaUPrZP5GI+vph8K3ADnoN3w0fVs1TiIS/8h12O/vaEE4ImEBLrgHHD+ahqBJGi/WPVq9xeOsRygrLHXej+zR+9d5PyD2cz7bFuyg7WU7RsZIw2+oRExQLe7FzcGMGcy53jnOhoUcBpS+v/r48KPkCOxl44w8f8OBzd4UubMLEMEM3+Ojxzxu9FnsMSeOSe2fz0i/exFdnn5Pm09i1Yi+7VuzFNEz7czQMcvYeC/95nwYkSQS1LxVVoc/IniSkxvP5iys4fiCvTdU2IUkkdDyzVZuUbsmhxxbQd2Svb9QiEuEB9xSEewpwG5Z2EPRjCDkZ1DEgIonJGljVTwEtuhX1SwLxLzSxP5P4n42OJbmnWPjUEl76xVt88cY6ahxsHQB6DOrmWJhSPSojpg0JeiycsrlpmKR0db4gZVUhNS2Z6PgoJswdw93/uJk7/nIDTjc1NcpF31F9mqZsWkJAz8FpKC6FshIVSW5LH08GzyVsWuIKy8uoLKki93B+0GOp3ZKJSYh23qVp4fP68Xv96H6dz19cwYrXQ3lWhmaEJJmGZrQ7+ZJkgdJi5FtWZSRFCpkQMg2dbYt3kbP3eMRJ0eZQ3AqTL5sQMc9oL05kFpCXGarqL4RddfzGQkQh3OeB56JA8In8oVi+9YDDjdTygd4+FfSzaILP62fjgm28/MjbvP+PT8k9lB9228ET+4cmPQK69usaJJUQHRcVsqgEQBLEJscQHe/8fY9NikF1K/Qd2ZPrf3k59z/5PboP6ha6oRD0HtEzIr3ANEz6je6NpEhUlrexTS0SOVV5CyUnKsJussiBnzt6xrAQqQgALDvB0rx+NJ/G8QMn+OjxRSGfTUP8ap7EGrrZ7kp5Q6wKeksBz9+WMUrzaaz70PaQ3fVFaGXTEcK+N/UZ2aP1bdsIy7RY9EKoqr8QgtQebZPn+e9AINSh9oLSNamV5IvAFKVTR8aP5dvwVZxgEP4nE7DsPcd4/qHX2b1yP7kH89i4cBtP3f+SYztIdStc8eBcVLfSaHCqRrnoMTiNEVODEzDN71wtk1WZMTOGk9gxPiQQyorE2AtHBT0WnxLH+ddMsr25Avc31eOi15A0pl9/LtFxUaF6UQFz20vvn0O3fp2pLo8jP8eN7m8ta9ApPJpD4TFnTkYDsncdDX5AwPW/vJxx0+v50WN5/PrlY9z/p3z6jQitKJm62S7LJNtovO3ZjupWGTyhP4pLQQ2YbvcamuZYsTI0k1MFZfjqfG1qPUqKxKR54zg/gpju6SBz+xHHMXkhSWTuaCKNWqbFxgXbePT2Z/m/q//Ja799n6KvRIhWw/Iuwqr8JVblw1h17wXEC78krDCm8ghsXsZZtBe+Oh/P/eg/rHx7PcfTT5C+4RCv/eZddizb47j97NvOJzY5ptG/VnUreGI8XP7D4BaKJAtnfSrTYtzskUy7elKIX6DqVpl2deh349L7LsQdbX8XwSb5e2LcXHz3TCbPHxeyYAJ7MTrrpqnEJsUwZsZw1nySgt/XehyorSwna1d2xBZoQU5hyGOT549n8MR4bn24mF+/dIyfP53L9CvKQxauul+nILuwzfplQpLapYdlaAZ9R/SyE1mPiqzKdOyZSjjRIW+VF8u02swz6zE4jTv/cmNEsej2oqywgvqa0O+vZVoc2pQZ9FjuoXz+/fCb/N/Vj/H43S+we2X6GTuPIOj7sar+glXxEFbN46Afbf01rcHyE36R2T4bwNPB/14L0oJPnlkaVI0xNIP6AFfrqocuCXnJkMkD6dK3E7tXplNb5WXguL70H9M7KEkwdIOcvccdD1lf4yN773Fu/+P1LHhyMdm7j4IQJHdJ4ooHL8ZXU8+uXUeJS46l76ieSJLEtGsn03t4D3au2Iev3s/wKYMZPKk/kiRx96M3s/jFlRzckoFlWiR0iGPUBcOZNG9sI9/qxl9dyVt/epvZVxcyZFw1smxRUy2TvS+KtP71dOisU1Mp88ajnSktqiakxNoCiQ6WSp27ZjDvtlJEYIXQMc3PdQ8U8c4THclOb6oGmqaJ6lbabAOkRrmQhAgx3g5H9jV0k1m3nk+/sX1slWlZcOxAniMpVXUr9BycRq/hPVsVQzz38olccMMU51VyGJw4nM/6j7ZQdrKCnkPTOO/KSSR2jA89D48LWRYYegtPNkng8jRVOD//9xfs/mJ/4/Wavfso/374DS782XyIPVMB1cKq+geYJ2gsT/jXYenpiPjf8mXCgFDHY+lHCK2CmSD3Pe39fpexbfFuqkqrm27Alj3puOTlVYw8P7SqE5sYw4PP3s3+9YfIz7LJ6SOnDwvRatr6+a6wx1zx2lpu+d016JrOuvc3Yxgmsioz7ZrJjJg2mP3rDmE1qKrHR9GpVyo/fPZuti3ZRdHRYrr278L4OaOISYhmxo1TiYqNYt1Hm6mr8uKJ8dB7eA/Ov3Zyox7gtGsm89rv8lj2Lky/7BTRsQamITh62INpQv8RXoSA5e8ls/WLeBR1Q0RJHyfLHEWp4co7D2KZ9QhhoagGky+qJKWzxofPdgzZXlaVNtknSbJE31G9yNyRHZq0ORDqZVUmbVBXLvn+bN7600ecyi+jrKAcSUiYDhpWyV2SEJKg17DuHN2XG/Y8UruncNNvrmqXbmFtZR0bF2wja2cOsckxnHvZRPqN7hWynepWbH9KBzQk+gB5GSd57TfvNsb+8sIKFj2/nKGXTaDPJc52WKcDy7cWvO/RqPmlZ2DV/BMR+yNQvkScUfoDTvcJN0I9cxOlYQ//lR/ha0ZNZR3VZQ6u9qbFkd3hM+akTolccMOUsM9rPh0rgu7I5y+u4MHn7uKmX1+Fv17D0HTcMW4+fHQRGduz7NueCWqUyp1/uZHU7in0GJJGjyFpIfuKTYzhmocvbXpP5bXkZRZQerKCtLioQHuhMz96/odkbMviw5cPkrM3B81nX0iSDNMvLydjVzTF+WogcIUPLLJLYWhL83HLgvpPES1urKrLZMaV5UEJmOpWmTh3DFs/3xWU+IYTEZwyfwLRCdF89tyyoHZFVJwHf73Wwh5KIm1AF2rKa1n03LKISZ4kS7ij3YydPRJPjJsJc0azZdFOx22FJJh181S7NWDBvrUHWb9gK7WVdfQZ0ZMZN0wJ8fk8uCmDjx5f1KhddCq/jH3rDvH9f95GcpfgbUecN5g1722ElkmiZTFkks0r8FZ52bViX8hKV/drHNicSWx3Z/Pc9sLyLgCzZSA3bG0c/y5wTTj9fbvGgW8NmAXYpXwJkCHqepubcRbtxqGtzlxTSRKczC50jBmqW2HMzOGMmRn+plddXhu2PXg0PZeSE6c478pzOPeyCXhr6omK9XB46xH+cdszIAlM3ZbpmXnLVKZcPpG45Bhm3Hhe6M4ETL5sPJMvs3UATd3k6IETVJfVktJVs/mWLoU7/nQDuYfz2bohm53LtuOvN9B8Ji6PRUpnjQkzqtm+Kh5DExgtrTeaH04SnH9N6KCIVb8SLC1oqtLlshg0uo6EFJ3KUvv2J8m2wGj23uP2QFZgc1l1HobyxLq5+J5ZHNl1FKPZzVtIAk+0G29t8MCVJEuMnjGcV375NpWnqrBMK+h1zaG6FS6609Yjm3fPbJ76wcthh7emXz+lMfkqzj3FyjfXcyIjn4QO8Uy7ZlLIYEJtZR3P/PAVvDX1GJpB0fEScg/mM/OWqUy6ZFzQtvEpcXTu2YGC7KKgRXFDrG/AyrfWhsRkzaexf91Bel08lDPRZLPMEvC+4/CMhuX9GBH3s9PfuYgCz3yoX0jTPdJty1u4ziZg7YbLoxIuynhiTv+G4Il2k9AhnvLCCsfnS0+WY+gGsiLb5+BR2bp4F4e3ZwUlFLqm8/QDL3Pn326i+8CukQ9q2X6Tmz/biSQLdL+OBcSnxDLtqkmMnzOaLn078/ETi9H9Jg2lVNOAVR8nBf4dvsQvJIErSuXm31wTxDurLKnieHoWw0bWOfJYU7tqjQRW1aMyeNIAZt96Ph17dmDVWxuoPFVFUqdEqsqq0Y0WNxJJUFvtZfvSPSF/Jp9XY9K8sWxbsjtw7ibdB3Xjup/P57PnlodtASPA5XExbMpAZtw4DU+M3fefe/dMsvcdpyQ3tKVnmRZv/fkjbv7N1ax+dwMbFmxrnM7cv+4gmTuyuf+J20lIjW/c/rPnVwQFG9Mw8Xv9fPHmOq752aVB+0/slMD8++fwyTNLkRtMcS2La382v1GxvCS/DFmVQyfYDItT+aXEOr/b9sGqB9/KME/6sfQcxOkmYGYh1DwPZin2H1MFdRjCMw9kB47QWbQJMWG4WKZhfSkF8oET+nL8UAsdsAAkSZCbUUBqjw5IskRMQjQ1FXV8+PhnIZOEy19dQ9nJCi6978JWj5l7KJ+3/vghhm5i6DqGZqJ6VIZNGcTsW8+nx+BufPrsMmqrGhIfgb9eUJzvYuVHSWiRWpTCPu/J8ycEWcjpfp3MnTl073aQ2LjQRNbQBZ17GFSWKqhuBXe0m7l3zcRX52fxv1dwNP0EqkshsWMCRbmnQj4vzauxbfGukAqRkKDnsDRK88upKK4ESeB2u7jqp5dQdrKcumqvcxtVgCRJdO3XmRk3TaXvyJ6AXeGac8d0lr2y2vF1Hz2+iP5je1NZUs0LP3vDjl+WRU15LR88+hlz7riA8XNGNW6/6ZPtjclX43vxaXzx+jrGzhoZuHc24bpfXM7Lj7xNXZVNMzANk6FTBjKmmYZhYZhhDNO08NbUI6eG4RG3B96Pwz9nnPgSO9axat8EbQf2xKQJUkfwXIpwjeTrkNL5n0zABk7oR8a2I0EXmupWmXTpl8hoBcy69Xze/9tC5+O6VbzV9RzYlIHu1xkwri/bF+92HIO2TIs3f/8BP3/jAceJoboqL5s+2c7+dQepKAmsmJrxBKtKqln6ymoqSqqIivVghcyWg2VKCEnCqbya0jWZeffOQlIkeg5OazoHC5a/voYtn+1ElqH/P8Hj9P1RUhl/4SgMXWfYlCH0G9ULBIyaPoxR0205i8Kjxbz0i7dCX2taHNqcaftRtoDh19n0yXZUj4o7ysWYmSM4/7rJSJJEeVFFeBkPy/5ML75nNmoLbsbdf7uJv976tKNQYtaOHE4eLQpYRgXLh/i9ftZ/vJV599gWLdVlNfi8zpyIY/ud2wQjzx/KwPH9OLL7GJIk6De6V1Cim9gpAd0hqRSSIDG1jRNNVjWWbwuYhQi5L7jHAc2GOIxc7FVomORVDm3FtA06VvVjYFUHP6wdgKhrT3OfZwFwziVjydl3PLiaLAmSOieQ2sNBt6qNGH/hKFa+uR7DdCLi20nXwU2ZlBaUkdqjA5UlVaHbBbD7i30MPXdQY7IQBAv2rT/EtsW77OGelglMvcbe1Qc4ui+X7/3pekoLykIWY4YmqKtWcFpMqx6VK38yD1VV6Nqvc9Cw0InD+bz++w+wTItZ19QyZiooLe5y7miZ/uMn4k7QSBvQhdEXDLPNyFPg1j9c17jdP+983jFZ1XWD7Ut2h8QUU7c4vOUI7lg3skuh1+A05t49k8SOCexZHYEXFZCfmDx/fMjnOfnS8Rw/mBfCu7I/I4PPX/gCX52/MflqgObTWP76GsbOHNE4AJC1K8dZ10sWFB0vCSkIJKTG8+MX7uFY+gmqy2roNrBLiENMUqckaiuc6C0Cd5Q7Qt+lASZo+7G0dBBxCPdkkFpc41pG+JeL05/8tLwLmslWNIjqlgW8Jb8eHcP/uQQM4PIHLuLtP31MXmYBkiKj+w1GXTCMCReNaf3FEZCXkR9elFDA3297JqAob4sjymr4P6Kumxw/mEfv4cGTKzUVdTz74CshK5WW0Hwamz/dwbRrJjmOWwsh7BHnFo/Lqsygif3pO6pXyGuyduWw9fNd6H4dHVj7aSLTLyvH5Wn+fl24E67k3Mt6sOKNtXz42KdExUcxce4YElPjcUW56DW0O9HxUWGnHevrfGENyk3DxFfrw1frY+OCbZQVVnDVT+bRZ2QvCo+VhP1MNJ/Gsw++yrU/n0/nXk1JhTvGHVGleveK/ciKhN7idEzD5PgBe3V1cHMm697f5JhMA8Qkhl/leWLcDJsy0PG5+ORYBk7oS+aO7KB9K6rCkEkDadXVzcjDqn4UO7nSsNgB9YsQ8b8EERBkFFGEJ5lKCNfE1o7iDO1AgMDaEiaWf/PXoqHzv4q+o3ox/YYprHrLjiGmYZKYmsDNv7n6S+236lR1eGkGv8Y7f/m40Q1DdauoLiUsj9LQTXau2OuYgC14cjHpGw9H1PszDZPaai+Htx8h3PWpulV0vx7C9VRdKoMm9AshnRu6wRvNRLU3Lo5l1OQqUJq/XkUoAxk7ZyZe7zY2LtjG2vc3M/ic/gwc3xfLtOg+qBsxCdFEJ0TZlawW0P16RGcNX4C8nrEjm9zD+TzwzJ10H9A14vS3oRl8+M9P8Xn9jJ4+LGhqMlIinLkzx15AOyxOTc2kqqwGQzdY9fZ6TuWFitKCLWMSG2biXUgixB+yOWbcOIW3//RRUGdAdav0Hd8HRZVbScB0W3/LyMUe2JGxfCsQMXeCOrLZSUSFHxg67Thjgm8doROQfqhfAe6vRyrofzIBc0e7uf1P11OaX0ZFSRWdeqYSmxRK0mwvsnbmhJ3E8Qf0Y8wG0rVu2l+4MIbQkmRPO1WWVBGXHNtYhdq4YGuryVcDZEWiU++OCKcETJKYfft0lr+6Gj0wSq24FaLjojjvyqabbkVRJYXHS0jpnMj2pTuCguamJfGYJky7tJKoGBMhJUHUFVRW9uK5h17BX2f7VtZVeVn84hfIqoSiKsiqwi2/vZruA7qQezg/JID66vxtUsLXfBoHN2VQfsMUJl06nl0r9gVZGLVEaX4ZLzz0OkOnDOLiO2dwYGMG5Q4BtDlik2PQneQqBCR1TmTjgm2sentD2ICrelTOu+Kc1t9MGFz1owvI2JSHLAo4dVIhK7075197Kd4YNwWEmzC0YdW+RvCkjg8sHcv7KSL6RvshOQ2kZLtd2BJR14M4ze+FWYmzCa4OprOe1Fm0HVMun8D42SPJyzpJdHwUXXp3+tIyKUfTTyAJ51poA8G9YTLP7/Wja3pEuz3dp1FVWo072m3brgElJ0pJ33CoTQM5mtcfqLYlU5gT7OmouBXGXTiafesO4K2uR/frCEmgqDKXPTCnMfnSfDrHDpxAViQ0Xz1Ws+peRYnKq3/pzLxbS+na24cQKrgmIaKv5p0/LuDovuON57lj6R52LNuDy+PCNEzOu+ocplw2gQVPLQlOJIWwP6E2xC/LtPB5NbZ+vosLbpjCkHMHcmDD4bCx3dQtPn1mKavf2cAdf7mBU/nl5GUWhE2cwG7BJnaIc3TXsCyL+tp6Xv7lW/jrnYV0ZUWia99OIXzXtqLvyF7c8qsh1JWuRfNrpG9LpHO/yXQa3z+st0YjfFvAOE7TAI8BGFi1ryISH6UxPfFMB+8nhCRLUne7YnZa0AnbFbC+PqeS/8kErAEp3ZK/lGBcZUkV6z/ayvGDeXTolhQyot0aTN1sssppAc2n894/PkUSgfHsm6cy/qLRZO50LhM7QddNeg7sxuzbzmf5f9Y0xi8h7NH0iXPH0GNQN7Z8tpOK4kr6jenD+Dmj8MS4MQ2Tjx5fxKEtWciqhKn7kRSD4NKrYMuyBHavT+W6X86n70jbFWDjgi/QHb7QhmYGyLJ+Xvvte9z/1B28/7eFFOQUIcsy/np/YBih7T5EsiKRf6SIYVMG8v0nbueTp5eQtSO8vpShGRzcmMHBjRkISbSquj9m5khyD+WTved40ASU6lKYdMk43vy/Dx2TL0mRkGSJKZdPCNGLazPMKmTvnxgyygv4GTBS5tw5WYg4HwePt6JfY3kDxPeWMMC/GxoSMAQi9gdYNU8GkiYAE6IutfW+IsIAPde+oOSeBGUAYSeP3Aj1zAwPfNfhjnE7VqrbClM32bl8L7tX7UfIEp17dwrQEtr++nBJn+JSOHYgj3/d+yKWaTFoQj8u++FcjqW3nZOjulU6pKUwce5YXvrFW+iaXVlS3Cqde6VywQ1TmHr1OWxbspucPcdI6pTApEvH0ymgwn5gYwYfP/E5kiTshYelB2Jg03ssOObmxd93ZeiUAVz7s8tB2Ir3zZOvRlg0imBv+Hgr1/38MqZcPoH1H21t5Goahtmu+GVoOkfTbYrCFQ/OJa1/F5a8tDLsQt4W467h6R++ioBW1e2HTR1Mn+E9+eAfnwbFKcWtMHLqEDYs3BY++VJlug3oyg2/vLzN7yfkfOteo0fPXdDDrvoNn6iD6wSHCge08kqw/Ftx1BAEW2IiIIIq3BdgGQXg34adshgg90LE3t/6CZrFdtyTu4FoXuVzgZRiG3W3hOJswv5V4H86AfsyOJlTxL8ffhM9YBFRdLwEWZHaPKocEZJASDbnycBOxpa+soqYxJiwYq8toboVhkweSFR8FOfMG4vcJZld2XbwS+mWzKkYN4v2ZNnbTutPKlAJfJGVi2WYZO87xsmKSswBTf120bj+DY66PlnmgK5xKLC/nRUV1AyKzB3SFJmFWw4y+e5ZdI52U1NRy8KnFkdczTnBNC2SOtlE+PjkWG7+zdW8//dPSN9wOOxr2uzTJqCsoIxrfnopC59awqGtWUiSwBXlZt49s4iOi7KDuwPikmL5wdN3NK78TweW99PAassInE7DCvA1sB5o5dURbqSiRetb6oCI/71d6rdq7Qkf0Qo5Vj+MVfNvGrkRwo2I+X5TcJK72W0CbR9NQVS1SazN2wdn8V+BaZq88LPXKTxa3HjzLcwpar8DhRPbQrL1xJp/zw5vO8K7f13AuNmj2mbzKgSKS2bUtKG4Y9w89PL3WbF0D8e9NcQlx5KYmsCyQ4Gp9f4dSO1vx6ntFRWwu4KKkkr2rzuE2a+lKKhFy/glyTJ1Q7qwaK8dvwqPFlMzqBOmEbrQjUq3K8Vavca2xbu58ddXMvnS8RTlnuLo/uOs+8DBrinS25QEHQJFAEmSOGfeWNIGdOXFn74e9jWWaaG10VZI8+kMmtCPuXfNYNl/1tjCsZbFyGlDufieWTz5/X87Jl+qW+HGX19NnwjtxVahH7MN0q3m3Fgf+LeAMQpoZRBHhIudFsGpiYSIvgU8l9iLTinF9neMBKsWq+bZQHtTxrYXuhDhuZiG60NEX4dV8zxNlTUBuBBRV0Xe9xnE2QTMAd4qLy/9/M3gG7llYWiGzSnSjTabbIdb6Rgt/NI0n87qdzY4t9iErSslAM2voSgyY2aOYM4dFwBwMLuIIlknZVoP0tIiq6wf3naEvesO2O/NYcxOCNtPUtfsJFGWZSZeMpLUfsdt3g8a41OjydorkGWLylKF8hKFoRNq6dHPh64LMvdGcexwPK6+MayrLWQqnRkyuBvJXZLanYCldEmiW78uQY9d/uDFZOzIQQvDI2szLFjw9BIefPYurnl4Pr46H/W1PuJSYpEkiZqKurAGuB3Skr9U8gWAth/HMrhZGeA8RJoAc4My0LYCIpjjgsvJu7GhitUGmFV28Gq+OrV8WDVP2PZCAXkJEfM9LN8m8K0HNHBNQHgu4KwR938f7/5lASezi4Ie03w6skvBHa00Wu6cDiyLkPhnaAbHD+SR0CHekSspJIEn1k19jQ8RUOm//MG5uAMTy1l5peT2Vhk9dnTEY1cUVbHuo83UlNdixTpMcyoN5weWaVeCOnZPZuC8KIR/FVgVJA6IwkiU8fssNL8gL9tNWl8fSb0V4k5F07vyOKsXJlFXY1MA3DFuegzuRn1NfbsqiACyIjO5hcRD2oAujJg+lP3rDjrK9LQH+1anM/mScYydPZLRM4ZTXVZDVKyncdgnPjXOkcdmmtClz+kO4ASgHQTLqcNgBBKfyAmYcJ9n+ze2rIKJKFB6hb5ASrJ/2gCr9mUwjtHAjwWgfjnIXaBB30sZioh7CKt+CRiFIPdERM1tPbk7gzibgDlg9fubwq5ydL/O2Nkj2f3FvlZXk+F0ZMIlbyUnSsMoxAt+8OTtJHVMxOf1obrVRs7YwewicqqrqO+iYnbWEEnhV2d5WQXs3bMHI0YPS/0RAiZeWMnJXBee6Cj6jb+AuOiNgV69/V6SknxM6A+WAYbR5D8pBf4/ukc9HYd56TwgEclbx7q9hZANU688x7n0HwZCElz4vemNeUhpQTk15bV07pXKjb+6grf++NGXNvauKKxo1DxyR7vtaagAYhOj6TemN1k7jwa3J90KU6+c9KWOC9hJlOOl0LACjNyKFtG3YtU8CmYVdhImQOmDiPqSBHhtG84nZoG227b4AECy/dfc4fXzzuLrR9GxEjKauS00h2WYjLt4DFsW7Wyd6hCQmglBGF6rZVnsXrnfcVeT54/nwtun21I6FkFisgezi1hXW0jyyOiI8UvXdL54+Qv8lh8SnZcnkmIxeEwdui7w+yS6D5lC1z4gfJ9DtP1+FfyMuNhO0AwdzlFs6Z786niseIWC/O7c+4dMDh+YGrTvfmN644l1o7XBmLwBfUf2apxcra/1UXSshLjkWObdPYvi4yWU5J4Ku8hrCyzLdn5J7Z6CJEuNsjkNmHbVJN49sjC4PelSGHzOgC8laQIEbH4kQrmgcoTqVjOoI8E1Gfwbsf+atm+x3Vr8EoRHswr0TELjpx+rfkWwwKrcy67s/5dwNgFzwMHNEcZegWlXT+Lw1iy81XVh1ZnjUuKor/GGXAKSIiGEcAx+pmE63nPd0S4qimxtreYJQlDy1eMYwzrk0ykmvHpUVdU+poyqDvs8QHScwfhzq+Fcyeb5yJvBt4FIyUBo0R8GdJKQPGuxqgbCyHjW7S1kakxnLvvhXBa9sALdp6NpekQ+heJSqCyuoq7Ky1t//IjCo0VIip3UTrt2Eg+/9gPWf7iFjQu3AfYq3BXlwtBDPSjDQoiIJtxX/XgeC55czOFtR5BkCVmRuejOGREng9oM9/ngXUjwClC2K1uSC1oh4SMlIOL/YFfBjFOgdLfbi18SlllDWH80o6z10Gj5Am3Qs+Hlv4HMnTlhC/RCEnTqlcrQKYM4tCkz7HCJrEr0GNKdo/tzQ76jsixhSWbTwFEAkb5zlSV23Glp4bNoTxZ5Uh3JI6PpmFpFv8SDYfdRdLyEKaOzIzpcSJLFuOnVRMWYdqXW08uOX2ZoFaglNE93iomnHoVVeQO5eEqwfZskS9z5l5t4/x+fcDLHri62FmcqS6vAgpXvrGfjx9uQFQnDMOnWrwu3/9915B7O55OnluKt82H4dVuBPvB5tyWGNYhPh0P/sX2Ye9cMlr66GtMwMQ2LIZMGMP8HZ2BK2TUuQI53QJtcMAQi+jrwXABaJkjRoA4H2se1DkUddhXeIZk3K9rwesOu7H0NQtJnI6QDXBHI9oMn9iexYzz3P3E76z7YzO7V6fi8/qAgpbpVZtwwhcrSajZ8tLUxyAlJ2N5q10xi9dsb2lwJMg0zLDesofI1sWcPhsQo9IjuF3Y/21buwl8cJuAqFpJscdXDhXSP10FEI+IvA+MYVk057ffFciOiYxFSHvtJo8fYfuRsL6ZP12R+/voPqCqp5tC2LJb8O5xIqC2lkdQpkXf/uoCCIyftlWLgM1v3/hY69Uhl5s1TmXDxGPas2k91WS19R/YkNjGG1377HqZhNn3GDqt2WZHo3Lsj7/51AS6PyrgLR4WQnl1RLq79+WXU1/qoq/I6+n2eLoT7fCzjuK1GLxR7SS53RMTcDjibxzvsBZTBZ/SbLNSBWL7VhHo5WuBbBq4RIHdv9rhhP2cUYtW+EbA8EqAOR0TfBOKMSMqeRRuhuhVkRXaUXxFCMGTSQEZOG8ruYT3YsGArp/LLg5XbFYnOvToy766ZPN9M4NPet8q0ayez4eOt+Op8baoESbJt9t0SBwMt0obka3hiHlNSw8uibNywjfQVh8JWjFS3yajzq5jVpRxQIfo2hGssVsVbhCV7t8B+7CSsu1pLxrEihrQwFkjsGM/d/7iZmoo6TN3g0TueDT8RKQk6dEshfcMhNi3cbsv7BE4jL7OADx9bxM2/u5qHXvk+BzZlcuJwHkmdEhk6eSDv/+NTCo+XNNEswkkgmibZe4+xb90BBozty9hZI4K0BgG7PXnBcCpKqoiJj2ps+35pSAmImDux6l6miZNqImLuhrJ2JC9SR3B/yXZo0P5SCUuDsKqw6j8PcMEaHwQ0+xL3vg/+rYABUioi+gZ7QfwV4WwC5oAJc8ew/LU1IVyGqDgPlz9o/+Fik2KYe/dMZt8+nU+eXsKBjRnIioRpmJx7+QQ6pKWwa1U6FhayqqC4ZQaM6cOMG6eS3CWRDt1SWPaf1ZTmh4oQNoeQBEkdE+jUMzXsNj1Tk7Ep9pHRe3gP9qw+EBI0hSQYPzuNc2bmkpQaC8owhGeurSUldaK1VlhYSEn0iBHsr7B/TevTEUrqkSSJxE4JjLlgOMsDxNGWEJIgPiWO5C6J5GWeDAm6mk9j0YsrOH4wn3GzRzD1quCW4E9fuY9l/1nD7i/22a9t9pYlWSApMrIiU3z8VGOCnLkzh3MuHsusW6dRdrKCz19cQc7e48iqzMjpw7jwtvPPWPIVeJN2suW5xFZ0lpJtnpaAtidgXwGUQaD0AT2D0PaCH6v2DUT8I2BWYtW9AXrzqkWz7bX9WNWPI+J/xZfWUDiLNmPYlEEsf22N43PXP3JFo+L5mFkjGDNrBPvWHWLR88sDFRKTbgO6MP++OWxetANJFsiKhCRJdEhLZvp15zJoYn9GnDeYL95az761B1vlw8qKzPgLRzk+l9anI5kU0zsxiR4xkSu+3Qd3Q1KkkFggJEHPwR2YMq+OfkNqQBlkxy8lMIkndQgzMRyMnlE11BgVFJfEAxJI4UU+YwPaf/3H9CFrp/NUtqoqnHflRNubuAVVwtAMjuw5yoInFjNwQl9GnDeYEVOb1Pzv/OuNZGw/wnt//8QxPrqiXFimiWnCvnUHwbTIPZjH1s938f3Hb0V1qax+dwNbF+/G7/WTNrArF989M8Qy7UvDNQKh/sMWSxXCTlaEChS1+tKvDjJE3wB1/yG0CmZC/VJwTQQpxeZ/1a/ALjAo9vMN9zuzCKvmaUTcL74yZ4+zCZgDJswdTV5GAQc3Z9qcLMsiNimWO/56Y4gRrqLKXPnjecy4cSo7lu3hVEEZVaXV/OfX7waR+IUEvYb1aPwCDJrQj6gYD2/+3weOhFg78Ckkd0nkpl9fFfb+ZRgm3movZnwYHoF+FKt+Ieh5zLspCX+NxeGdLhocglS3wty7ZjJ2VpjJNamjHcj0TJzbUo4vsoOX0g/8zlwUsMmts2+dxhdvrA9uhQhbjPKKH11MbUVd2KSnqqSajQu2snHBVgZO6McNj1zRyKFzR7spyApN3Oz9C8bOGsGOZXuDbTnqNTZ/toPh0wbz6v97B29NvV3Y0Q12rdhH8fES7vjLDW38DNoBuYP9841BQLqi4kc4an2Z+VhmLdT8PaD5FY7DYoB5CvRs+1o4i68FsYkxXP3QpXz42GeNwtGmZXLVjy+h/5jQEfsRUwczdPIA9qxKJ3vvcVSPwn9+8y61FbVN3x/VTnQGTbClARI7JXDVT+aRtSsHb5Vz4uSOcmEBlz1wEandW04r2rAsWytL08KVeOqwvJ+BtpPuXWQu/V4CS96Qqau2z0t1KXTu05Hb/3STox4igIiaj1X7Em2PXwAyqKNa3WruXTN5PuM19HotKNbEJsdy+Q/n0qVPp0Yrn5C3Ztqcud0r9+OJ9XDf47eR2MlO+oQk8NbUO3KCZVVm6OSB7Ft/KIibqvl0qkqr2fL5TkpOlHFoc0ZjByD3YB4v//Jt7n/i9tPW/AoL4QLXmTPePhMQrnFY2r4An9UB2gEsswp8K2iqjjpdH7rNG4u57Ss5z7MJGKBrBppPIyrGQ9HxEha9sILcQ3koLoVeQ7sz+bIJ9B3RM2wSVFNRx0u/eBNvTb290hEiZFWo+3Q+e2457mgXI6baulHdB3UNUjxugOpWOO+qSQyZNICOYaxHDN1g++Ld7JTLkT11dEutIfryDvSY2OxGp2dj1fyLhgtLkWq5+vuABdkHE9i5fgjjL5pK31GRdU9E7D1YVY+DeTTMFi4gBqixdy73RsTcQVuqHpMuHU/Xvl3YungXtZV1DBjbl1EzhhETaLlGxXrCDCYEI2PbEda8v5np19nCfCW5pyg4Wuy4rWVabF2825F/JiTBuvc346/XgqpmhqaTf+QkBdlFdO3bqdXz+dpgFtuTiFYtQh0B6jC+dLXJvx2rfikRb1j6YTBrCJ98NT/HEuBsAvaVwQJvjRd3lBvDMFnx2hp2rdyPrumkdk9h7OxRjJk5POLU7qIXv2DfmgNhh1oMzaAgq5A3//gh1z58WeNCdOT5Q9m+ZE+wNI8kSOvfhTnfu4Cu/TqjhHEEOXGogA07D1I+1CT/82Jqx0t0uaJnM71FHav6rwGvUXuhNHxiOcMnQMUpN+s+70mHnudyzryxYZMvwCZ7ey6F+o/CbyN1BwKtS9zgngbe1hdFKV2T+OHTd7H5s+3kZRTQoXsKY2eOsCe3A6fUf0xvdi6PPLRVX1PPiw+/wcP/+QEIW4Nt/UdbHSdKDc1g9+p0Z5skv87+dYcpLSgLqZxpPo0NC7Zxyfdnt/q+vjZY9Vj+7fbEotQF4Z50+uLQDTBP2hI/2oEwG0hYKOD7gtZb0xYYJ7/c+UTAdzoB03w6i19cwZ61dgk3JjGaumpv40Wv1Wsc3Z+LrMrOnmcBrHp7PTUVtbZwIUTwLLRY+NQSomI89B/bB0mWuOGXV/LGHz7AssA0DCRJMGzKYM6/ZnLE++ii55aTsy8Xc0Qs6Dp+r4/ti3fRzdWH/qPthMryfkzLm6gIDJv0G1ZNvxFZiPibWv+gzDow8yJsYED8rxH4bC5Tgw1OACV5pWQuLEQ5qTGgYyr901KCKok9h6bRc2ia455lRWbe3TP59NnlaH4tYrt20yfb7ATMgrf/siAswT8Sb0UIwSmH4AV24CvJPfWNScAs/w6oe42Gsrnl32ZPQcb+gNOVgrBL8ksIH5hkUIcirDKsNji9gWmr8Z/FV4J96w6x9KWVeGvt1n5MQjTV5TWN129x7ilWvb2e4VMGhU3A8rNOsm/1gYj2Og04susoHz72Gdc/Yot3zrjxPI4fOEFpQTm6pqOoCp5YN9f9/DLiO8SF3U/uoXw2fbINb5KCaSiYwiD3cAEfPb6I634REAb17wqQ55u+iyLwn6RUH/NvO4qIvTzU7NEJ2p4IT0r2QIycCGYKwhOD8Hekgfd6Kq+MDQu2Uni0mK79OnPu5ROCPBHjkmOYfev5Yfc+7dpzObAxE5/XF5FYX1tZR+7hfHoM7saWRTtt/9twiDS8pMi2YGwLWKbFiYz88Pv8umFWYFX/xRaVxg+oWPWLEfEPn74UhFmIVfVXQvmrQRuB2g+8bZk+lW06xleE73QC9tHjn5G5I6eRrFp1KnRCUPfrHNl1lPLCirCl20NbspqSr1ag+3RWvb2B/mPtP2rPoWn87NX7OLApk/qaevqM7Enn3pEJiT6vnz1rD2LEBRPzDc1k7XsbGxMwjEhJk4mlV7Hpg1fZs16iS99OnHfVOaR2c2gVaOEnkwIHgppHIfbHIcnXzuV7OXwYinOicJdZ1KUXUbTjCHf97SZkJXKSUFNRx4YFW8namUOHtGRUl0LxiVLqa5wHAhpUo0sLyqg6Fd4/LRKEHD7rtUwL9ctqf50pWD6oe53gBNtnt/v8O8E14fT2GTb5krCFVpMR0TfbnLVWpTJUu/UYRNg/izOFrF1H+eSpxY1tJgMjVPPJshcO25bs5oIbnOVCMrdn2xPJbYBpmGTtyqHqVDXxHeJwR7m497Fbydl3nMKjxSR1TmTQ+H6Olf3mWP/hlsA0Y9MtyNRMMg4coaaihtjEWCw9m0g3UguNsmNv8N4z3fFEuznn4rEMmTyA0JWrDkZ49wwwwfsOSFcgpN4gmq7pvIyTvPrrdzA0HdOwKDxaxN61B7nzLzfQpU8rCzEL9qxJZ9vi3cTER5HaI4Wa8lpH83GwT7s6YCm0Y/netk9yN4PqVug/tjcF2Q7WY4S1Av2vwPJ+BFY1TVV0DdBsjmncz05zn4sIv3h0AxYi5i6QOmCh0npbWkV4Zp7WubQFZ5BRDEKIZCHEAiFErRDiuBDCkTAjhLhNCGEIIWqa/Zx/Js+lNVSX1ZKxPadNqumyKlMSQUBUdbUvjy1rsbJxR7sZM3M4ky8b32ryBdi8qDBfpOITp5p+kRIj7scw/VSW5FB0vIQ9q9J56v6X2PDxFkKig2jDWLBZiFXzTNBDFcWVZO3OCRobN3SDguwiVr+9MeLu6qq8PPujV9m6aCcluac4mV3EyaPFDD6nf9go0iVQmTIM0ybdnQaiYz3huSSS+PLiq2cKxhGcv75+uxJ2OjCLCV92jUXE3o+I/42dZCuDA9NGwYrVdpBz29u4Z7TNLuQbgm9T/AJY897GNk1S63497A0ZbD9TuR3DJbIqU17UlOgJSdB3VC/OvXwCQyYNaDX5gtAY2ADTsJqSSLkjkSQJBCBRTGFOMcfST/Du3xfy71+8SX1tywWavWVk6OBbimUEJ7CfPb8MrV5rlBsyDVul/p0/L4gohwHwydNL+Oy55eRlFFCSV0p+5klbyiaM7I0QgrQBtui04aDS3xYYhomQJKQwC8mYxG/QRLK2D0cKg5GDo4REW6Dn4JzdKrb9WsLfA1IXMkTNw6bPNIcMxAFuexgt/ue28v5XhDNdAXsGO/3sBIwCPhdC7LUsy6kZu9myrP+agmNlSRWKS26TrZChGY12Et6aej7/9xcc2JCBZZr0G9ObYecNYuuincHBMIxYIUCXNiRZDcg9lM+H//yMiuJKhBD0HJLGVT+9JOz2vlof3tp6omI84L4YvG8SbkXgq5PYubZZxcqC5a+vpaayjjm3X9D0uNoWgqUJ5jGsuncQ0VcCLo4fzEM4JUKmxfqPt9C1XyeGTB5IdVktBzYeRvfrDBzfl9QeHZyGWpYAAN7CSURBVNj86Xa81fUhJPk9q9KRJBGivyarChffZa9UUtNScEepp6WUX11eS0KHeFuvp0VVU5Jlx2lUuypwlPKiCrr27UyPQd2+hqE/lbAXmJMIollu2x7JnQl7UxOJhK1oKWlNU2X2xraKtHch+Lfbr1NH2397Eb719A3HtyZ+AZQXVrRpO1lV6NI30NKxYPvS3ax+bxM15bUkd05kyhUT28SzbICuGXRIa5vHbn2tj48eX0TmjmyswDDT5Q9cRPeBXSnaFcoptUyLgiNFpA3ohnCdE6hoOFcpLBNWfZzY7AGbV/b0D1/hgae+hzu6QQohQKjXdrb2zqB+ERiB+GpanMxx5pFWFFfy1h8/5pbfXY2hGxzacoTSglI69UxlwLi+lBdXsnftwaAFvu7XKS+swB3toq7Fwl/IgnEXjmoUUh1x3mDWfrilXb6TYHPHdn2xz1bsb5HEKS6FAWOd22nFuac4ln6C6DgPAyf0Dxk2+2qg4FzhlAgNoH4wikDER5xORUoBw7lYIlyTgrS9hPsCLKLAt9jWB5O62vErKM59tThjn7IQIga4EhhmWVYNsEEI8SlwM/CLM3WcM4WUbkltKvEqLoV+o3qR3CURy7R45ZG3OZXfxBHK2plDftZJ+ozoRfa+Y0gBq4qEDnH0Gtad7Uv2hOzTMi1n9dLGDSD3UB5bF+9m/7qm9p9lWRxLP8FT979Mp94dyS0JbZkqLoWsnTmMmDoE4Z6ARQ14P6OlqKeuwY41cWj1LU7Cgq2LdzH50vHEpwRupMKDiL3PNnRuzenNvwFLP4aI/wXRcVFhS96WabH4pZXousnCJxcHHjNZ9e4Gzrl4LDn7jzsmx5ZpNTpWNr5nt8Idf76Bbv3t1aOQBFf/7FJefeSdyOfqAN2n463x4fK4qG+mc6S6VcbMGt44ft6AypIqXvrFW3hr6jENEyGgQ1oKt/3hWqLinLXbzgiUvtiJVMsA5rLV6Rtg1WHV/hv0rMADOuAG11iEZ35wMJPi7GRbSyf4pqciPAHhRrMYq36l3YJUeiI8syD6+jP85r5+fNviF9gV36ydOZG/kgIUVWLCHNviZ/OiHXzx+rpGvlfZyXKWvPQFE+eOZevinciB+GWaJhfefgGLXlwRkgRYZut0i6qyGnat2MeGj7cGGUrXlNXwxh8+4PzrnOyybOxdd4AJc8eAiLGT/NpXAzISwedRXSmxd1NoRae2opbtS/cw5Ypzmj6G6Buwqo6BVRr5xK0KLP9GLDMeRAyqO/xC7vjBE2TuyOHT55ZSX+ND82mobpW4lFgmzRvnaFtka4GFxrXzr5nM9OuavrdTrjyHrYt3h6VbREJdlZc+I3tybH9uY1FAkiWi4jyMvmBYi/cLC55azP71h8CyE0HBUq59eH4jTeYrg/sc8K0jONbIoI6gOYfVql8G9Z8HfvPbz0lpiOjLbcmcZhBRcwM2asHxC3UMtv+tH8u30aZpSNEI9zSI/7+v4t21CWeyBTkAMCzLymz22F5gaJjtRwshTgkhMoUQvxZCOCaDQoi7hRA7hBA7KirLz9jJRsV6mHjxmGYTN9jakR6Vrv06gyRwRbkYN2cU1zw8H4Cc/bmUF1UGJW6WaeH3+hk0oR/3PnYrl9w7m5t/czUPPH0nxSecv+xH03PJDGMVAvDZC8t57XfvByVfzeGr9UVskzV/TrgvQCQ+CnG/Dnhg2VM+6dviWbMwEacsUJFlcg+14I8pgyH+HwGj5UjcLcMOlvoB+o7qGXYCCqCmvIYFTy5uDEqGbqL7dLZ+viv479IKdJ/OkT3Hgx7rPawHo2ec3mh0dJyHex+/lWFTBhEVH0VylyQuvH06c+8M5QJ8/K/PqSqtxu/1o/t1NJ/Oyewi/nbr02xccJqtwDahwbIjCrvt5wJUm0ysDGncyqp9MSAhotNU1veBfzNW9Z8CBNiGjevAPTXwesXeH7EQfSso/W1R3qo/gX+D3SbwrcOq+r9WuIbfGnwl8Qu+uhg286apqK7g74nqVugxpBuqRwVJ0Gtod+7++83EJcdgmRar39kYQrbXfDrZe47y8//8gPkPXMRlP5zLw68/QHScx5HqYOom7/1tYdjzOpZ+gifufZG1720MSr4aYcGOJXvCt+KaxyQ5DRH/a4j/O3jm2xUQZMpLk3jj0S5ghZ6goZsc3t4ivooYRMIfwXMZdospEnTwbQUB4+eMClsdtCyL5a+tprq0Br/X33gvqCiq5NDWrLA0ESfsXLEPq9nwljvKxS2/u6Zdlcnm53X5D+dy/nVTSOyYQHRCNKNnDOf7/7wtRDU/feNh0jccRvfZMVjzavi9ft74/Qe89tv3qK+NRGb/chBR80HuQWPswg1SR1v8tAHarkDy5aepk2OAedymvGjNra9MEFHgng1EB/apgms8IuYmQMeq+gd4P7YpHNo+rJoXsBqTu68fZ7LOGEuoGmglzlf7OmAYcBw7wL2HfXf4S8sNLct6EXgRYPCAoV/OubQFLrxtOsmdE9mwYBt11V56De3O7FvPtzVrHCpUJSdOOfb9NZ9O1q4cxl44Mkg2Ii8jzPhqoMo0YHyoXUNexkn2rExvdSLJNAxkh+kfy7AYMLalrISMkLtBzF1N7+XUGizL2fPPwiImITrkcSHFQsz3waqxb7xWlePrQcPSjiBFDWPmTVPZ8oZzsikkGUkSIU0vTdOJiY9CcSlt4ugBrHxzHXkZ+Vzzs/mN5fMhkweyZ3V6m33bwE7AJ148hqROiVz900uxTIuM7UfYt+4QeZkFjJ45nN7DbBsiX52P3MP5jvs3dZNV72wgqXMiQyZ9RSVtpTci8W92xcr0gjrQFp1sPIlym5Tv2Fa0wPJi+TYiPDNsrSXfcuyL3rSnkKK/h1C60LBOs2rfIbjiZgAGVt17iLiHArutt6ttQg6U8r81cz5fSfyCry6GdenTie/9+QaWv7aGguxC4pNjmXbtuU2Cni1imN/rx+91vqEWnziFO9rNsClNFYWj6blhrdaOH8xD8+khrSrLtPjwsc9a9WitqajF1TOalvFD9SiOmoRCigPPRfYPYFSforToVRw5RALiU5y4TgLhmQOeOVh1b4F/C+Ham0fqq4mqruKiW6aSs+8YhQ6tSEmSKDlRGvL9NzSDE4fzcUW7bJeUNqDqVDWP3vEsN/36qkZyf9qALsiKHDEGNui8NZ6TLNFzSBqxiTGcd+VEzrtyIqfyyti2eBefPreM/qP7MOqCYY1/tx3L9oT9Wx3dn8t7f1/Irb+/tk3vof1wIeJ+CvoxexEnBzQnm120Vv0ywpPqNSzv+wh1OOg5WLXPBxaUFjbH6waEawy2ZyXg2wRmEcF/c789eOQ6D6R4+7X6UbAqQe7dKo/6y+JMRscaIL7FY/FASJ/MsqzmIyn7hRB/AH5GmAB2pmGaJpnbs8k9nE98Shz3PnZLaLvIYeGhupSwbcuDWzJ54aHXSOmWzNiZI+k9vAeeGDe1Fc5fHs3vfNEf2pqJ1oako8fgNGJ1i61GCZIkkBUZSZW4/EcXN+M+hMfMm87jVH4ph7ceCX5CgCfGQ6/Bkn1TRkK4x4DUpdk2sYj4X2FV/Q2sUzjCspXc4zvEM2B8X8p3FNM8EVDdKn1G9rS95kJeC7FJsXTt15ncg22srlgWWTuz+evNT2JoBokdE5AVKWJ3JjYxBkmRqK/1IQBdNxl/4UiGTBrYeB7v/f0TsnYdtdsQAg5szOCcS8Yy65ZpjZ5t4aDVa2z4eOtXl4AB4LLL606waohc5NbAOILlU2yLoeaJmpkPNY9B4t8D+7DAPO68m8CEmeXfHpjMbKh6CkTsfXb17JuPb038Aig5UUr6xsOYpslFd1zgPLzTIoZJikw4cqqpmzxx379J6pTA4AkDGDl9CAkdWn4cLV7j0IosLSjHG0KCD4UrysXUqyexZPl2ZEVCWALZJdNjZC9GTg9XdGxCavcOXPWTS3jv75+EvB3VpTD50qHgW4NlliGUvgF9vKZqvIi+AUtKhvomL8Puah3H5Wo6plZRjEy9R2VJeg63/v5a/nn3C2jNkikhCTzRbvwRFspz75jB+//4tFWngAZUl9bw/EOvIQmBy+OiU6/UyAtQSdBvdG+O7s9Fku1Yl5gaz1UPNXGED287wgf/+ARDNzANi+zdx9j0yXbufewW3DFu9Ag0HFM3OXYgj6qyGuKTvyryvgClt/3jeBKtTLObJbagas3jBCdWOnhfw5KTEQErIUvfi3MyJweGmnpj1TwekD4RgAHuaYioK/mqSL1nMgHLBBQhRH/LshoIJyOBcGpozRGJEXVGofl0Xv7lW5zKL8Pv9aO6FVa+sY7b/ng93fpH0B6xYO37myM+n59VSH5WIQc2HmbYlMGcd9VElr60ynHzxpt8CyguGUkWIUa3zSHJgknzxlFQU49ulmJKFXTtUMPc8SMZkDoo7OuC9yFzwyNXsmvlPha/+AUIe/WakBrPHb9LtFXOAzdky7cUPPMQngubdiDiQB0M/vVhjiCwvB+C36JzzzGMmJZK1ieHkFUDxa0wfs5Iplw1iX/e8VzIK1WXwoipQzi4MbIpekuYhoVpNHFbIkF2Kdz6h2tJ7ZHCsfQT1FbU0WNwt0YSLMDhbVlkbM9u4qJZtpjh5k93MG72SJI6J9IhLYWiMIKvAJWnKYdxRiC3plcmA27wvhvm+Tos72JE1KWB3104BzC3LbbaKIvRFAitmqcRCX/j6zC2/ZL4VsQvgI0LtrHyrfWYhollWWxasI1Jl45n5s1TI75u06fbQviTzVGaX0ZpfhlH9hxj9XsbuOk3V7Pq7fWOVbDkLkmOE8GKKrep4jzlyol06JbM5Q/OZXN1Bv3ikpk+ZABjB0ygrR/l0MmD+MmLXXj9d+9TUVKJrMiYpsWVD46kW8dnsLwGNucn0NqK+2lTNQSBUIdi1S+i+cJjSrz9XS7yVJOuHMSrCVZnl/O9P17Ph49/RkVxFVgWXfp04pqfXspnzy/nyO6jQe9ZViSGTh5ISV4rfDMHWIaFgYW3pp5j6ScibjvjxvOYdvUkSgvKKThSSEJqfNAAkKEbfPTYZ0HDYZpPo/JUFZs+3c7066cwevow8jIKwovEWha1FbVfYQLWCpSBoG0lPNExFqr/Qlg5iZoXIfFRQATa12Gm40QsVs1zAdHoZs/71oHcyzYe/wpwxhIwy7JqhRAfA38QQtyJPUU0H5jcclshxEXALsuyioQQg4BfAx+cqXOJhI0LtlKce6pxZdFwcb7/6Cf8+Pl7wn73i46XUF3RNn8+QzM5uCmDURcMIz41jioHsvz2pXuYMHd0I2m/ASPOG8L6j7aG1RVzR7m47peXk9gpgYKaemLio+k+vBOdEyvxxLTud9YSY2aMYOS0oZw8WoTb46JDV7/DBW3a00GuMQHpARvCNQYrXBnfv9l+XIsFXzVDJsxnRN95WEcrGTWse2MJ/LIHLmLh00uwTLAMA1mVGX/RKHoM7oY7xk1NGz/ztkKSJWISo7n/ie81Gpz3GREqsrt75X4WPrXE8WYiBBzZfYyoOA++usgcCdOweOoHL2NqBsOmDWHK5RO+RikLNUCq3xHmeTnAD4sA3zqIugQQ4D43QJptsWpW+2P5thJWFV/bd3q6ZF8jvi3xq6KokpVvrQ+qjGg+nc2fbmf4eYPp1Cu8Z+z2pXuxwrQUg2Ba1FTUsuGjrUy7drKjZExFcSUnc4pCtLASOyWQ0jWJouOnnCs/kmDc7JFMu2oSh44W43KrdOnYkWH9u5AaU0B789jEjgn88Nm7KC0oo67aS+deqSi+P9kt+Ub4bJmc+uWIqGYT5HI3bP5kXdA+p8SXsAEJOn9But4Lr5HL7uI8HnzmLqrKa5BlqZGiMf/+Obz4szeor/Ph9/pxRbmIS4xhzu3T2bv2IIoauYXYbgiBLAuufOgShp1rL7ZTuiaR0jUpaLPyogr+/fM3HVugul8nfWMGw88bQuaunIgK/YZmsPSVVZQV2hPeF1w/JeI1dsbhGhWwE3K6bl2g9GrhRdsStVj6UYTSB+GeGuZ+ZWIRD+ZJh+P4sXyrEd/0BCyA+4BXgGKgFPi+ZVkHhBA9gIPAEMuycoEZwH+EELHYrp1vAn9uz4FM3WTfuoPs33AYd7SL8ReOovfwHq2+ruVocANqymooL64gqVOi4+tO5hQ1Cra2BZpP55Onl1JTWuP4fHlhBZk7chg0IdiiJaVbMhfdMYMlL61sJNRbpsncu2bQe3gvkjsnnvG1tqzIpPXvav9SvwQrHGdI2wvuZkR0ZVCzqbmGL3pDmb/hIg+otNevQJZvw50YHcQbGTFtCL2Gdid942E0v86g8f3o1CuV2so6xs4eweq3g0nDsiIRmxxLdWlNxMDhCAHTrz+Xcy4ZFzYJqqmoY/Xb69m+bG/Y1oGQJI4dyOXw1iOtcvVqK2upDSSRGz/eyuEtmdz7z1tbFaHFrMPSjmB5C7Gi3RCdFHl7B1h1bwW0dlpCtrliUfOh9tVW9lKDVf1PiLkduyPncG1oB0CNcn4O0+aFfTvwtcUvsBXoty7eRU15HYMm9GP0jOGtjv8f3n7E8XFdMzi4OSPszdE0TapLQxeCYWHBgU2Hce9x/p4YmsEXb67j5t9cHfLcdb+8nJd/+Taa19/Ypuw3tg8zbjiPxI4JjWbgZxIpXZNJATDLsbxOMgSaXUlpnoAhI2JutwdVGk2YbXHOKfGFnIiyOWrp9MJLCYt2bWHe2HOC9hrfIY4fvXgPhzZnciq/jE49Uxk0oR+6btBrWJrjsJSsyriiXXgrI5uPO6H3sDTm3j2bTj2dbZIs02L/hsN88vSSiDw8WZV4/qHXIrZQG3B0n00TqSyp5siuo3zvz9c3TpyHhWViaVlY3hys2FowE2i3HZl+BGpfJnRhFxCF9lwI/h0Oz7dAzRNYsT9GWGXYVM+W3REL/CsJS9ew6pwfPwM4owmYZVllwGUOj+div/OG338K/PR0j2MaJq/++l0KsgsbL7KMrUc478qJEcebAaSwEy2EVKOaY/+Gw+0+z4pmYoUtoft1CnOKgxIwzaez6dPt7FtzgKROCXTp25l+o3oxaGJ/PDHusPs6Y7CqsaxanDM8JzFDYasK6+lYvp22YKt+JLCSaAkDrBJCaTZ2EJs8fzwARcdKeOaHr3AqvwzLsoiK9WCaJopLwdQNOvfuxE2/uYqa8lpe/Nnr6H6jVUHEptMVTLp0fNgbQFVZDc8++Cream9E3oaFza1oi3VL8wWV7tcpL6rk0JasILLzyZwijuw+hifKxdApg4iOLgfv2+BKAaME/DVQVwxRP7bN0dsCsxz8Wwld7angvhARNQ/MyogtqUYYOVD1K8IHOivwnEKogKKGJff8+vpzXwJfV/wCe+pt8Ysr0DQDTIvjB06wdfEu7nn0logJiiRJjtIuQoiwpvUAR3YetQde2jGQYpkW9TXhK7wns4tCHsvamcO6j7agumQ69exGnxE9GDCuX9iE4cxCx2rZQgqCw+ejDkfE/xrLtwHMMnuKzr8V8NNdrYPkIop9yRQj8MolHMwuYkgLKzJFlRkeGHzQ/TqfPLOU/esPIYTNu1NRGv82QpK47heX0WtYd1791bsUZBW2LY4EMHBi/4if5QePfUbGtqyIAr2qR8XtcdvHbY/OmGWh+TSWvbqa7/25aVLRW+UlfWMG9bX19B3Vi659U8H7KURXgaGDroN3NZbqQ7int/1wdR/g2F0RSYiEPwAyVtAUZDj4oObvWA28rhDoAauqMItiuVsbz7j9+NaMKDXHwc2ZQckX2L3ttR9sYdyFo4hNCm/mOXbWSFa+tS5ENDW5S2IQ/6clSporzJ8ByIpEYqem45mGycuPvGW3RwPnVlFcia/Ox6iW2i1nGlY9Vt0rAcuhcBcpoI52eFCAMhyh2JIPVvWj4Q5Ca6W7+lofLz/ydpD2TW1FHYpb4eK7Z9K1b+fGKdOoWA8/eOoONi7cTn7WSQzdoPjEKUfz2gbIssThLVmMOH+I4/Nr3t1IfU19RA6L4lKYd/csPn9xRcT3Eg5+r5+j6SfsBMyChU8vYf+6Qxi6gaxILH11FQ89WQeWTtPfQQf8WHXvB3we2wDjOHZAaRnANNv4FmwNMCktQK6PFIhbS3ANwBNmHwK03aCEtnm/q7A9aL8I4eZUFFWwY9mexsWIEwafM4Clr4TySiVFamxJOeFUfngnj9NFYsdgQcxti3ex7NU1jQlFZUkV+VknGTHV+ft2JmH51oB3QeA3pxiggiukm2xD6oiIusL+t39HiJNE77gKogw4cap11aYFTy7h0NbMpi6LTwdJMHHmCIZMGkiPId0aq9+3/eFa9qw6wN416ZimRX5WYavC4Ac2ZDD5UufrIz+rsNXkCwFjZ44gY2d2u6bDg45zpMlVIXvPMd7+88dgWRiawZr3NjHn5hhiepYQLKiq2/IProkBPa42wAzjW9lMy024p2LV5dO6qXYrVTLLb09g6g4JnXaYr4rm+a1MwA5vzXIsr8qKxNH9uY2rESdMnDeGrN1HyT2Uh2mYyIqMoipc+/PLIx6zc++OjQTMMwFXlIuhk5sCZsb2bEpOlAYlEJpP58juo+RlFJA2sOsZOa4TrNqXQD9MaOBqWBEYgAvLt9Ye445AqBbu87Dqcgn9QnhApNJgcOuE/WsPOk6Z6j6dJS+v5Mcv3EvuoXxyD+YRmxTDkMkDmXvXjMCbgHUfb2Hjgm14q8KX9uuqg587kZHPkpdXU3SsuNHvLRL6jenNoIn9+PTZZRG3Axz5nopLQZYl9q87RG1VHfvXH2q8YZmGiZAsPO58bDH25rACf6M2QkrCOejIIDfj8cXeZSfNVl1g+7avxpvgBqU7aE7ekJa9uoy67DT2+7+J/KyTjlYxmk/nwKaMiAlYXHIMl3x/Np89txwhApeXBbNumUZKt/Dq9B17piIpcoi7w+lCUgTTm3UbdM1g2X/WBFVzTMOivraelW+t54ofXXxGjusIbZ99cw+JOQ0JU6BCqx+xBYQjeZOqw3BeSMigdIvo8eyt9nJwc2ZoEmVabF2ymwHj+lJXVc/BzRkYusHACf0YO3sEY2ePAGzXk89fXMHJnKKw66HayuB2mK/Wx9JXV3FgUya65izy2hyKqjDt2smU5JdRfrIi4rbhEB0XxYGNGciqzEf/DJYcMX0asbFZWMRg8+uaI8A5VUe17UAiDiynYSoPjX9b1zmgZQRcDgStJ2KOB7IHysLqGfptcn5buw/twLcyAYuOiwrRPwGbHN1aq05WZG793TWcyMjnREYB8SlxDJrQDyWCn+OWRTvJ3n2s9eRLAhCtlnVTe3Tgxv93ZRDf4+j+445JpaEZvPF/H/DTl+8/Q/YQOpi1IMUCsj1yq2fgvGqUaLqJ14LvCyxtHyL+/xG2XOuaYHOCtN3YUUQFFETURYhWNP1KiyrCluN9dX5e+NnrVBZX2UFGCD55Zik3/upKYhKi+fSZpRRkFyHJEr1H9CAv82TI5ylJopEnaJkWnz23nB3L9kQ+qRY4suso3up6hp47kIObMyJW3BoXTc0uB0M32LF8D7u+2Ifm00KuYcsEQw+30moHd0buYQcM8yTBSZGEcJ/f7NcURMKfQD+EZZTbrQPaM7npAnUAqEPAG+bm3tYV73cEnhh3WAmT6Da4J4yeMZz+Y/pwaEsWpmkyaEK/iNX7omMlLP73F623uoTAHe3C14r4puJWmH/fnCCl9NKCcmdOpgV7VqUzYuoQ+o0JIzXQLlhg1th0h8BC0KpfivONt/kX0LCpEtWZtmadHKYiKzyImLsCvDAJ+xapgGsMQiRGPLPq8lrbwszpYzYtFjyxGG+tD9MwsEyLpa+uZvT0Ycy7dzaL//0Fe1YfwDQMOvbsQHlRJZo3eEeyIgVpR+bsO84bv/+gXabdlmWxZdFOpl59DrkHT7TJS7TlOVSXVbPwycWYpuV4TdXVgMdxLWC1bxrac6FDYu0Cz0yaqlECEXMbmHNAy8bSMwL3nrYuJG2xVhF1JVbNi2G2MbGTvjOPb2UCNnb2SHau2Bty8UiKTN+RvVrfgYDug7rRfVBTb9dX6yN9w2Gqy2vpMaQbfYb3BAEHN2Ww4rW1oReaEPYKtNGqRqHv6N5YhkVWi8kSSZGQJAndryPJgoqiCl58+A36jerFlCsnEpsYQ111+MqQt6qelx95i3sfu7X19xYWFpb3E/Ctwg5ICkRdglD6E14rquVFrINZapPxw2lPIRAx37NXE3omSD5E1FCob50D0n1A17Diq6ZuUppf1pTMBErer//uA1S30phsGaZB7qE8FJeK6lEbH1c9KsPOHdRIUl751np2rtjb6jm1hCRL5Ged5LIfzEFWJfatOYSh6+E7eC0et0wrctKGIH1bPK60lgmuDO6J7ThTAbEPQM3TgVK+hG2ofStILatrEihDEQqg9MSqfpwGkVX7J8ybEx1tiQrXGEDCktMCrc/mN2IXwn2B8+u/o+jcqyPxyXGUFpYHLdZUj8o588a2aR+xSTGMv2hU4++WaZG99zh5GQXEpcQybMog3FEufHWhbf0GNF/ESrLAHe1mzvdmsOiF5aGLF7nJf1UAnz2/nK2LdzHliol07dsZRZUiJgINi8i45PD0kFahZ2DVvR7QabJAHYGIvtnmOzrCwnGqzfsxIvbH4Y+jDrelU/x7QBRD1DkIvTsOknBBSOqUGKRm3xIhE92mxe6V+zl+KI+qU9WNlbPiY6eQVcmOhZoBloXiUvDEuJl6lT0EUJx7ijd+/2G7ki+wF/RH9x1nxo3nMf+BuSz+9xfU19aHnboPeX1gu0gCsztXxzHpJqdn1HZpAgr3NCyzKDB5Lewf9zSEx6GaKnUGd2fbfq+6IhCHdOy4Fy4Zk2w+rGcaiDhbkLrubYITPjkgyBpZE+908a1MwDr1SmXu3bNY/OIKm9xo2cKeN//uaiSlbe5Kmk9nxetr2f3FPjS/ZpPwZduc2xXlolu/ztzy+2tY/d4mxyxfViTOvWwCBzYcRlZlxs0ZxYSLRlNXbSdL1aU1mKaJqRuYhtl4gdtaVbZlzb61B9m39qAt6tdK5e5kdhFlJytI7pLY7s8LsE1tfStpuhg18H6MFXU9kfk/LeHD0rMRYROwAOQ0+8c4QmTroiYMntif2KSY8MMLTqdpWSGJuKGZSJLBlCvP4dj+XCRFYuyskY0cGV0z2PzZjtPmQCR0iENxKVz+wFwuvmsWT//gJbs97YQIpuzhsOL9jlz802gap31QQe5qW3e0FWa1LaRqVgb2I4GcBHIr/m5yd0TiX+2bj1WNhWoPBDhBHRqkjyNi7rH9Qs1S7Deu29ZIrrYlFd8ZCLj5t1fz2m/fo7ayDiHsG9u0qyfRd1SvNu/mwMYMVry+lvKiCluI07IwdRPVo7Ls1dXc8ecbyMs86Tik4opyMXb2CE4cLqCmopY+I3oy/bpzSegQT9GxIrYt2WMvMC2bWN68Nd/wfcvLKODdvywAAS6PK+K1bhkW2xbvZMZNkbXKwsIsxKp5mqCbqbYHq6bS5u6ElSpwgB5GULg5RDS4J4NxBOFv281XdStMv+5clr+2pl3f+bKC0ATSsmDQxL4ISaKyuIq+o3pxzryxjdI5Gz7e2vbho2YQkiCpcyIAI6YOZtiUgaz7YAur3gqn59h+FOXHUV3bnSiOYsd+C4hCxN1LW+8FdsHgA/BtoIlLJgL3nUhcLBUR92PbAcQ4jiUSAvqETgmjioi6qOmcXBPtxM23Hjs1Mm1+YMzdbTzn9uNbmYABjJ01gmFTBnH8QB6qR6HnkLSIU4wt8fafPuT4wfygaoth2v/2e/2cyChg2+LdVJc5y0iA3fKcffv0xlL8vrUHydyZw4CxfRDA1s93tcopAnv16o1QAQO78lKSV3qaCZgFvi9wrGh5F9hyBN5PaLpII2UNarDdTVtg+kHLwNIAM7w4qKRI3Pev2/nXPS9Q15LH1XA3cILD45IskZrWIYin0gBvdWSiffgTFMQlx6J6VD549FNKTpTSfWBXhkwewKZPdjqfXxsPIwJuBkLAZT+8HD0mBtxFCLcXoiTw+GhPC9LyvhFIhJoFaeMEVv2niKhQ6YBguJp0u6z68AmYfztEN7MpkRJt3z7jhJ34yT2/spXjtx3JXRL58Qv3cCIjn7rqenoM7EpUfNvN29M3ZLDgiUWNyZBhNv2dtXoNDY33/r6QYVMGO1IbNJ9G+ckKRp4/lCGTBhKXHENe5knWfrAZzacx8+aprP9oK3XVbRjBt3D2fGyBAoepybbCql9FaPyy7Aldz9yA3Eo9bfrCtdNexsIA/RiWUQtGB0L5mU2YcsVESk6UsntVelA8kFW5XdUqUzepqajjjmbThs1RnBtGZ60VKKpt9bTm3Y0c2ppFdHwUI6YOQZKl9sv6NIOsypiGiaIqDJrQn8Qu4/HFTEW4CxCuYoiZEpl71xJ6eiD5Cv6bWzXP2PZrERM5AUo/UPrZdzLfF02DRyHHOWpvG3idiLoW3HPAyLWvEzmNr1Jj+VubgIEtSjpgXPsd24uOnyL3UH5EwqLu19n1xX66D+xKxvYjId9rQzOalPEFxCfHUVvlbXWK5cugQ1p4km1EWD7Cs0drbEd4KdX23bIq7YkQKxwPSEKEFdU0wSzGNlUN6Fb5t2H56mw1ah9QV49VPw3hPgdEqMaQJ8bND56+g/f//gknMgqQJAl3jIs+w3uyb20kwb0WZ2KYYTWRYhKiIo7sh0N8ciwX3DCFl37+ZqN8QHHuKRSXQnLnBMoKK4KuEyeeYgiEIG1AFwaO64s7xvbii02M4WB2EULEg9oNIVcCTiK7BpZ3CfjX2X9jdbBtmyEl2Ty8EEK8Dr4t0GoCFoBZCN6lETYIM/Uo92j7Qve7jAAV4nSw4o01rfJ3yosqSeqUgCvKFZIgWabF4W1HOLztCJ+/sILETglUl9c2ah3uXd0WA4C2Q0iCrn3DJy6tQj8W4bmjiPhHbFNlPQtQA4sPp9aTDO6Lwu/LLAd8AcK1BEYR1CzBqo8BLQW8xVjVqyD6JoSc6LiLy384l/jUODYt2I6QBKZhMvL8Iexc7qTJ5wxZlek+IPzgVZe+nSnILmx3dX3+Axex4InPqS6vbbz/5R7Mp/fInuTsOdbCV1LYXMVWjiGrChfccC6mbtJ3dG/S+nfhYE4RQnhA7QOyCYQZjNIPYdV9bHs0Sik2pUEdjeXbiHPVSrerW0ob7N2seltexAiX+IcpNEgJIA1vff9nAN/qBOx0kbP3WJv8Fn1eHzNvnkbO/tzImikWVLVH5DAMJFlCdSv46vwtHhf0GdmLlC7tF+MEAvYbzQn1QU/aEx7qCIRqT+NYlT8Pv6+YO0E48Di0fVi1DVY0hr3asXygVQCDsfvxlv1/79tY3vfBPQURdQ2I4GQoNjGG7/35Bmoq6vB7/SR1SsAwTDJ35jhyWZzQa3hPUp0SVguWv7YG/TQS5bpqLyvfDJYwMQ0Tf72flK5pzLrlfPatO4Asy0yYO4ZtS3ZzYOPh0CQsUM1TXAqKS+HKH88LUbJuC6zalwIiuIEbjbYXS8uE+EiaXT4s/Yg9mejfBUIB99SAPk+zrEnPwqp5ivBTRfJXZs9xFpFhGiblhRWtbmfoBr2GdqdDWjLFx09FXHBG0ixsD1S34pgYqm6V8XNboS1EghQb/pI2q0BKRUTfZv9evwSr/lPnbZWBCCcupVlpE+8b5VtUeyKyfh/QBZuErds/ejpUPYIl90DE3Alyi46AgBk3nMfUKydRdarq/7N31mFuXFcb/90BwbKZmZkSO04cdpi54aZNA8U05a/MbZoypWnDDTTMcThxHMcxMzPba1jvrnA0c78/7kgrmNFq1+ug3+fZJ440Go2kmTPnnvOe96WyfQWBcAChacydtrC0j6trHHWOd+t+84ptLJm+vMXJF6i2cUNdJM9JwWLT0i18/tdXsujNpdTV1jNwXD869+7IQ7960l/QVROYps7Ua45n8jmtiAWpZcjGO8jEL2cHMnI3hK9WRQDvFyGtVWDvVZQa2QjGUOVwoHVo2kwmkA2/AWcP3gNmoLivLS/gtCU+fQmYhPeem1vSyRupi9ClT0du/N3VvPHQO2xavo1IfbRl4nUtgG7qXP2jS4hFEky76w327tiHGTCZcMpoTv3sCQexZwGiM8idHs9pIPJ8vvRekPIKyEGE6SHxYW9DRv5NzorT3uD+w28CLgWJmUgMRNnFnltU1JRBjXq9oel8/V83cN+PH2X7+l3ouj/pV2iCc248JeexWEOM2dMWsnzmKnZt3F1Sa7jgiC2but0e34uE9Ys3c/WPLmHEMU0en+27t2Pjsi0kogmsuIUZNNAMnXEnjqR+XwPdBnTlyNPGZngdLYJTm5t8pQ8EC+Le/qPup4BG1xsNd3UbewaZWosov6lpT9EHKT7SHW4ZJ+0w2gyzXphX2oZS8bU+/6srmfHU+yx8cykHautLJly3FEITDD96CKd+9kSe/suLrF+8Cdt26DOsJ2ffdOrB+Qma430sZwQYearsek+Ub2l+1T+oqv0FkMjGP6kqDA7qhp0Aaxb+8csBezOy4TZE9a/UQib/kINGjjTIuV88jbLKMO8+rXTGbNvxvpcIOPL0cVR1qMw+RFa8v4YFry1W9kGt/A1Xzl7jOQTkOA71exs454tZfr8SRhw9hGUzV2ElLIQQGKbOiClDSSVsQhVBJpwypriHchHI6JN46hTGn1ASFJ5IQWIaOdV9a7Y7nf+jTHtZJt91q6BFFtrln+fDLtV/6hKwPdv2EakrzVrASqSQjqRz74585rsXsHPDbv797QdaPLpbCsygQY+BXek9rCcIGHLkgDbVfhPllyiz0ZwTUldWQnknuwifh2xYRe7FEVBcC4Ti+AjTnaYTSgSxGbHOzbKMweT7OiZdr8HzPQNYPsKVYW76w7U07o8QORDlyT+/yI51hUmlYRpUZAX7xv0R/nHLPcQjiaJVgLLqMtp3rWHrKm9PTU2A0HSl7pwH20rx9mMzOf7SJrHHqvYV3PLPL7Do7eVsW7ODzn06Mu6kUYQr2mCk2d6Or9Bqcj7NrzCyn7dUy9LeplSfZdxtJReDeVhe4kPCnGkLS65+LHxzKad+9gRO/MwxnPiZY/jD9Xd4LyIOFgICQZMTPnMMle3KufrHbpu7jWKYCE5Cxp4k37cRAoWUCHOEqoY4u2mKd7pqzZsjVVVERkDvDpiK7+Psw6/EVqE3ADUArI+1ZxjplpYDJMFaAgEvkepCTL36OI6/9Gj27djPhiWbefWB6Vjx3IVOIBSgV57u4xN/ep7l760uai+kGRp9hvdiw5JNnueH0ARlVWH1++c976Qcpt3zJsMnD26iZwi48GtnMe7kUSx9dxVm0GDciSPbzgvS8WkNygMgi3WU8u81DpBAJl5DhN3FfHIRxaUoBEJvJaWnDfGpS8CspIXwsSPygkQi3AjSuU9HjKB50AlYuDLEyGOHsWvDbvbvOoARNBg/dRTHnD8pN1i1JffPGAHhyyD2BBldHHMUosxD2kLvhai4VQU8e7MiUgfPBK0CWfdtMu1E0Q5Cp7gK+v4rslE1W1hCL97b3xXN0mAzDOudFcRkvLAKVwQV7cqpaFfO1KuO5ZHfPJXze5hBk0lnjccwm1Y2bzw8g8iBaFE+VrAswEW3nMWg8f257yePsG5B3qSUJhgwph+12/b6tmumPzaLKRdOQjd0pCMRmiAQDnDk6WM58vSxJX++kqB1xDvp1YHW3GCFmg7Te6jkutmTrxlRt8M4ZGhJ/MmXPhhxzFBVgTlIQekjzxzHgdp6arfuxUpY9B3em5OvnFJIk2izGGYiqr6ttJqcWvWQVq1agAWxQ0NUfgsZe9r1CpQQOBKCJytfU3szmem80Glg78SvUtLLjLIpVkFl2T6cLlXEMHhh1TD6h9POAjoEaiGg4lm+TZHnJwkadOnbifbd2vH2ozOxkk30FqEJQmVBhk5qsqjbtmYny95d1azI6qDx/bnyBxexavZaHvzFEwXP66bO8MmD2b7GqxMCkbpGVs9dx9BJg5oSNAH9RvUuyWe5xdDaNf2WuUdK8+4b+bCVIGu6maBVNb8LJ+6vwPQB4RORgNkpm+mPz2LOtAVYiRSDxvXntOtO8BQn7NKnE7qp+3IC87F11Q56D1NEWU3TuPSb53L/Tx4taYpONzSlwexIdYEJQVX7Cm7+83WUt6bt1Co4qhQrwojgsRCcrFaAoiIrcElw6hRXLF3VMPopwcLMbmqR9T8jZ1Uhd0Hsv0XfvZeZXrFugcGwgq5M39a7KQkTYW9OWQkYNKE/533lDKbd9Qaxhhi6aTD5nAmceMWUnO0Wv7282d/LsSV9RvQCAdf8+DKe/utLLH57GULXEEDHnh049boTuOPW+3z3YVkpFr29nOmPvse+HfsJV4U57uKjOOa8iW0/SKP3AL2v2+rNDsx+XL/mIJRMhdp5loirD7xa0TKm3l98AL6lnzCsXbCR1+5/mz3b99G+aw1TrzouR3QzGyOOHszsFxeUNFWXzKuuHH/pZJa+u5IDJVTBhCYwg6YrQ6HOKU0XXHTrOYw61t9tpM3hHAAs0LqoNpOzD7DdRYh7Ycmo4pxqNeoxEUaUXQ5ll2d2I+t/A84W9dp0HIs/S3PXzJSq3cygM/Tc6BpzGywjrYpuQKAjQosR2mHButKSMFCJ2Bduu5qn/voiW1Yo252+I3txwVfPzNgVASx8c0mzyZcZMhlzvLJ8GjJxIFf96BL+99unVAFSCIQQXPad83nz4Xd992FbDusWbWLeK4tZM38dQgiGTBrE2TeequggbY3gOe79I/scNWld/CKXi2eMBGtOkY015d6Rg5RbDCjnUE4+ZuMTkYD977ZnWLdgQ2ZluGzmStYv2cTX/nE94TxlaU3XuOiWs/nfb5/CsWVRLRXd1DOCc6lkijcensH8VxdjmDqOJDMx5AWhCc7/6pkMnTiQpe+s5MDeBnoO7s7A8X1bJJdxMJDJ+ZARlnMUWbH8c0q0Lo3UamTkPnfq0QFjMKLsc6DltiXVVEpLLgyTdJDzT8IMhg09DU934RIx+rjhjD52OIloAjNsFny3jXXRgptQATTBBV87K2OCLDTBBV87k5OumMKO9buo7lRFt35d2LxyG0bA8L/xOZLn//lKJljG6mO88eAMUokUx1/m40N3EBAVX1LCgdZ8lGZNd6VKn3i7hXvSFMnZyPISdIq1AAw1ro2tXmtvQ0buBcdt3er9oPwGhFbdwuP4dGL13PX877dNldydG3bzv9ue5qKvn83wo4cUbH/CpUezYtYaIgeiRVtSQI60wMr31/DaA9OJHIgQLA82q3g/+vjhnHPzaWxZuY1Ny7dSWVPOyOOGtU0LvRQ4+11i/BZUUlWJKL8uV8xTRpCRe1ybLgGiAlF+lar45+xrlytI7HXt+lh24ZAuBU2p2s2WsEpCGtqnW1caaF0RZb3ZVLufOCbrd9S3KAlr362Gz//qisxv7+V2snbBxmb303NwN4Zn8U8HH9Gf7z34NTYs2QxC0G9kL4yAwRN/fL7ofua/upiUlXIXrJKVs9awc/1uvvqP61s1OV4MIjgRSRJiT6NayyEInQzxl1qxNxMRPLXpf/3am2mEP0NT4p1CRh+BZFpLrhzKL0d4eh+3LT72CdierftYu2BDDrFQOpJkPMm8VxYz5aLCiZfBR/TnS3/5PPNeWcSmFVvYumqHpwaKbTs4tsPy91bz/nNz2bJ6R9NKRBO+qu1pjDhmKIapM+G0MZ7PN+yLsGLWahzbYcjEAbTrUtOyD18M9kaI3kNOxSq1Atn4z6bKllPrihtmJSiplcjGPyo9p+xVgLOfksvCWjdE6Axk9B7SASw/CdvdrorpKyfD1j4M917olw4BQR8h221rdqAbzWjwOJL9OwvFEKs7VVHdqYpYQ4w1CzbQuD9SVAEaKDgfrITFO0++z7EXHVWySHDJECGVUHMtab9OUsvdZNnr82qo30OnqcwvQe+rWjmlrvrClyEbb3MnjILufrI+t71OTYlVfAth9G3lh/v0YNrdbxS0Fa1ESnFyPBKwcGWYr/zteha/tYz1SzazZMYK38Gg3sN6suzdVezffYA3H3on8z6pRKpZbaphkwcTCJkMGNvXUyDWTtmsmr2WfTvr6Na/C/1H92kRvaM4JLLxdrfa5X42uQ/Z+FdE1U8zMjey8W+57gtyP7LxDkTl91yOlwunAW/OpBfCEDxWyRekmtwyeplRaL+LTeEIjXalWvCUHYegnhj17A40EKdvi5Mw8E680jhQ27w1WO3WvQWLTyNgMGhCfxzHYevK7UQb44QrQkQP+HOgHcfJ6RY4tkNjXYTV89YzdOJA39e1FiI4BYLHqOqlCAISGX+NouR5gqjf20FV3EMQvhyMbKurNPnQ47owJ6uJ8djD7kFUKE5gJmbWQ+RfyMAJiLLPHOQnLI6PfQK2c+NudF0r+LlSiRSbV/m4qaNWHqOPH86aBet9Beh0TfD47c/iSFm40nRk0eRLM3T2btvnS1hc+OZSnvlbWmNJ8Mp9b3HS5VM8E8ZsxBrjLH9vNclYkq01QcQQVamqYAnQNIYr469SGGxslZg5tUr3K/E2hZNuUo0DpzYisk5oYQ5HWgtpnvsTQJR/QQW/+LSmqgi5SdgSeiEMh+nLd7Y4WLUE5dVlaEUmJtOY9cI8jnVtPrLx5iPvMv3xWaStj1oDx3aINsSoaHcQNixFkU6oAGOYS0DeRVNAUTZEhE5GYCsist7LbTsb3hNHgSMh+Q65gVCA1g1ij9J0bvmdDzY0/gGqb6NF/m+fQuzbsc/z8bpdBzJcwnyYQYNRxw9X2nNF2utvPDwDXdfUwsFDy7AYNi3dwvCjvPWWDtTW869vPUAiEsdO2RgBk4492vO5X15OIFyo75eG4zisW7CR3Zv3EgsHaN+hj/eGqVXK87HgBuogEzOU7IC93eVz5cdvCxl/DlF+Y9NDek9KW0AKCB6DCF+ohotSy8mOo00xrBFogHKX+1UOm2sO8DobW52E+SFcGaahGZmjxn0RkrFkwXe/bc1OHvz54yRiiZK4g17nhG2l2LN1LxyCBExBZMUIAeGzXE/avNZkcCqgIfRuEBjr6lVGQWtPPplLBMYrAdaCe6DpaiQ20JTY+yS4yelKpPUQyu187BOw9l1rPI1tdVOnS29/xfZdG2u589v/xUr4VzTslIOdao27OiDVzd8LtVv38uSfXiwgwr7xyAwGHzmAzj7HvWb+hoz1R+PgTsS6G3Sr6sjw8buASnqXZV0g9h6fA9PVjVfrBKn1/gdvLc5dUQQmQPzF5ku7ogJp71RlZFl4c85JwoyhMKYf0xepJMwLrQlgW1fvYPrj71G7dS+BEocmspNp6Ui2r9vFhsWbmPHErKKt5hz4LLhSKZu1CzYw9qSRJX6CbNjIoqvBwoMQld9UNh5pArI5Wumt5SuAp8VyvfYSPheZWulWIBKoVWdaib9Uo1sbkvPUCvcwfFHRvoL62sIbbHlNuW9FyXEc7v6/h5Qiug+EJkglUi06e9LQTT1XBiEPd/3fQzRmuYQkY0l2barljYdncPrnvL0/Yw0x/vPdBzng+h7KTtVoC1cx4NZRFMgB+Po7pjLEbZnmgnnBWpP7/yIE4XPdIaRiMEDvi4w+7oq6Fp7rTTHMQJTlJiUn99l70ElYrD7GjKdns3L2WjRdI9mciboLy7IJuIybA7X17N6yh0dve6ZAV7IoNFGQ0Nsph03LtnLkGeMIFkmuPSEB2bIzUARPRhJU9xt5QHH/yi4ubCsLw38SW+8FwVMg8SrqHBGApuRMrHmUNkbsIOMvIw4nYP7oPqArnXt1ZOfG3TnZu27oHHm6fw/39QenK3FVj99BN/WMr1proJsa/Uf39q14PHrbM55TSCnLZsk7Kzj5ymMLnrMSKf7326exEhaxkV2JddFwOiQZ2O4detdXc2Q3qZTL0/wucwgktlIYoGxF4IbiMgL2dnJnyA0lWZHYTdGTVx6AqJ+rvEImgGmzWUISRo1h+e4YA8OFCcHzC9dw9tjSDVzXzFuvJiOTRQyyPRCLJHjx368z9qQRPPTLJ4lHElgJq2WWRdJH/d6RPPfPV0jGLSaeWSKvwEkgk/ORjcvBqAMtBcGrm367YhBlarrVa8K1VIgwouoHYC1BprYg9I5IJwbxR1uwE1tVTRMzQOhqCCQwkQ+K4PpxwYmfmcKLd76W4zlrBk2Ov3Sy72vWLdjInm37PKvwesDATtmt9joF5Ys75kTvBcOyd1d5TgLbls2it5b7JmAv/Od19u3YnzF0dpIprLoIa+Yu55jBDgQTQH8UQbof3tysAMJw27JFOYYxl1DdVH0V5mhk7BmKt7dsiN7t8975cJCRfyOCJ4AxKLMAPpgkLB5J8I+v30tjXbTFrioP/fIJzv3i6bzx0DusmbceIYSnj3HxjyRzjNfTWLtwA/f830Pc9IfPlnb5SpDJJcjIHGQ4ApH1SP0MFQOahXAHxkrZtshewueq6pW1CNCQRl9o/BvFf/88OHuRkX+AvR/M4Yjg1AJ+9MHgQx7CbAMIuPZnlzF04kB0U0fTBV36dea6X1xOVUf/L2rrqh2eSZCma0y5YCJmoPW5ad+Rfbj0m+d6PhePJNi9xWfV6kjfoLlu0UaEQCVf3QyczimOP3INgWX1bHl5JTL2NLL+l2r0GtSJQpjclWUAgqc2JV6u8r0nUkuR+atFGaH5rKa0Nl0vM8qUijWMqniHTlVP07P/U6wKvcTqijWsrt6d+Yt3M3l+4Zrmd4g6tOfueEVVvFp673Ekc19ZyL+//SD1expIxpItvoEJTTBoQn9PPoeVsHj9wek4TgmBXQLxZ9xqoyLD4uxGNvyuGXJ8G8KpU9Uvc4xq9xjDIP5Uy/eTWgLOBrDXIqMPKML0YeRgwtTRnHLt8YQrQ+iGRqgixMlXHstRZ/mbmG9ds8N3uGTIEf0xzdbHr1BFiGt/conv5Nv7RYRgiw01LZ+5KpN8ZbZ3HPZsb0QmZ0PiVTVpLaNqIWmORQmqpqGrpCt4JABCVzqEPkeCbPi52lcaToTmaw5pblEpaARrHrLxT8i6byLrf0EvfQe9y9txcp8DOL03Eu9msr6hnuUl+mDOeWkBkQMtT74Atqzazr++cR9r5q0nlUy1PPlCJf7dB3YreNy2bPZs38/6JSUYmYNq8yXfo8mfMw6xx5CJmS0+plZBxt2CRAcInaHkRmLPUnr1Po2o6gY5WyDxCrLhZ+5UbtvgY18BA+UfeNl3zidl2TgpuygHIY3qTlUFGjmgErCJZ47n3Wc8RliFGunNvH5/rqWDbuoMmzSIS7/trxCeiCbQdB3bQ8wTyFFSz8am2gbVduwkMslXaEWE6DKBMxlU4mMrewZzjJKRqPo+Mv6iuhhEBYSmKtmHxFvKKDkwUZlxe2py2GqaLnisK7jqgKimdRot/phStRNwaLQr6Vq9DVgIodMRRm821O1nN1XEMUuqhCWiiYOyhPJSiG4JDFPnlGtO4D/f/S947CsZTxFvTDSvfG+vV5VE8iuCNjI5AxEq4mVXMmxkfJoSwpUWmCMR4QtUWyfyL5cXoblTZZ8v4qcG6iaYrzzuNdqfUuV/+7TSKnmfFgg46uwJTDpzfIbH0xyZvaZTNWYogJU3FBIIBxg2aTCOI1k1e62HDRZomiBYFiQZtwo4P8FwgG/cdXPRVlM86s8D7T/aXy/Kb0Gj1sEOYIHTiIw9iSi7ClF+HTIx3T1HE4oGYY5VfqZaharIm0eB9T6eSZNzABl/BRE+X72PCNDyG3ApsIFGxVmLPUYvcwyUH9+qStia+eublZzwozo0x0tuDmbQYNKZ43GQnmLUdspm54bd9B/tw9vLhjWHptZfGkmIPw/BNpoIT61ARp92E61OiLLzkfpgaPwH2CtRMUhA8GTXYHtjkZ0FaZ7fLEE2IBMvK0pHG+ATkYClYZg6mHrzGwLHX3Y0j/3u2byyv8HYE0dS2b6Cky6fwiv3vpX7IgnjTxnNWTdMxTANXn/oHd59ajZCqD55v5G9ueBrZxV93+qOVZRXhj0ThS79OtOtv/cFGu8cItJVx+mQzEq+wAw6jJrcmLWlhbTmKAK91g5RdqV62NmPbLgdZCMSB2Xh0R8qboHGP1OoMJ3e3XIwy5CNv3Mz/4NJvipR5MdcTKnazRYr6zOI/0HZlQwvhxeg5CTMDCoZCqe1OjKthG7qGEGD8790Bl36dKRd52p2bihUktcNjVBZCRpZtpc4IYCl1OqLwalV/Bm9R1F9NRn5t0tGTftIzkFaS8k9DxyQ9WrSLO2C4AVjOJTfpEj76RWur4aYrbg1hxOwAghN+E7z5mPEMUOYds8bWOkCA4AQGKbB8KOH0Ht4D1bNWVvwumBZkFv+dQPllWVsXrmN/932NLHGONKRhMtDXPWji5vl+Qw7ajC1W/YWEraF4KwbT/V+EcrdY8WsNTmJmBDQvksq68yyFXex7CpAU+294AmAREYfgMY/uh9XA3So+IpSTU8t9XjHFFgLIHye4kQm3qHlpfF8lEOBo0c2kmAtpHfVmUDL25FVHas8eVg5aGsnPE2g6zrDjxnK1KuPY8HrSzBDZsHgmW7odOhegnq8xK08epzLsq6Z18aV7IhWmSuXlI/UEiXKm/GR3Ips/BdqIZj+fdz7QOI1lJenHwxlJ2VvR8ZfcnmvqMTOC8lF0EYJ2Me/BdlKDJ04kDO/cDKhihBmUJkijz1xJGfeMBWADcu2eL5u3iuLVNImYPARA9BdaQFN19i4bAuv3Pdm8QtEKEd6M2iqCw0VeMMVIa7+kbcnIijH+WOmjiYYSWE2pIguV8nX8AkRBo3Oq2B5tFZl439A7kVl+RaQVK7y1hK1QvD0xFIjvjL2X9dX62DVz/3Hn3uZ0aY/Yye9y/oC0K+mHX06tadnT3/CeOZoDY1xU0djFBnpbkvopk7v4T353K+u4Hv3fzVTvTzpymML2pBm0OTo844sTYpC7+6jVB7wN4+VUWT975D1P0Y2/gF54BvIupuRdT9EJmaQc1I6O3OTL7UD/H8fC5x6vE9sExG+ECFMRPAkRNUPEFXfo2ii3gLXg8PwRiBkcv1vrqJb/y7opo5u6nTv34Xrf3MlZtAg1phAevidJiIJ3ntmLgjo2q9zlli1IJFI8ujvnvXsDGRj8jlHUNm+Aj2LpqGbOhd89XQqi0z6nnnDKVS2r8B0EzzdlITLHAaNyj/vPOJXYq6r02S5fwkgCpE7oewK/GsJIUgtUzxELFot8plBKTZ2Aqx19C4bSO/yDnSurqRnz3b07N+52VdOPucIzBKLCAcNoe47F996Dt+462YuuuUsNENj1HHDMAJG5v4ESoC3rCrMoPH9iuwwvV+8J6tBCTz7QMZfRh64Fdn4e2T9T5B1NyHrbkVGHy6gXsjoE3jasHkmx47qDOn9KFxEGhA8Xi1WjUGIiq8iqn4CoqbI52vhIEIRfKIqYC3FhFPHMO7kUTTujxCuDOfcNDcu2ez7uqXvrmLCKaN58OdPFEyYzH9tCYPG9fdVsQYYNL4fN/7+Gt57di77duyn78heTDprQrOtqT4jetJp1Cm0i8bpXFnN4KEv0nNgNE/HNIAIHJnzOmktAcdrzNCC5LuIym8g8w1O0681RkH0AQ4+cOG5f28EaK1J6hmfP4lENMGyd1dhmBqppE2gzCSVSBGqCNGwt7H5nWRBaAIpZcE9QTM0eg3pTr/RfQiVh3ISq6ETB3Lel89g2j1vED0QxQyaHHP+xByfyKIweoHejQIJCBFEBLyJ2TJyt8dvLIFaiP0P6WxvKpunttLitZesV+KFsUfcB1wNntAZHtUsHWVi7HOzChThHh5GyejUsz03//GzRFxdp+yp67kvL/R93dIZK5l69XG88dA77Fy/O9O2si2oSx7gmb9O48ofXuT7+lB5kC/9+TrmvLyINfPWUdmhkslnT6DHoELuUDaq2ldwyx03sOzdVezaVEsyvJouk/eyPpwd93TVasyGjLuK6R7tNRlFyAhS6wlOviRFABE6ydXFa+U0e+EblrCNAL11IsQ9BnXlvK+cyXN3vIx0hcLNoJn5b7QhVrw65nEofodcXh1mzIkjCYRMyqqaKkSBcIAbfnc1T//1JTYv34oQggFj+3L+V84sXYw1cDTwLrm/mVqsecJa4roS5N9nopCc7ppt/7hpqKJZr9p8RBDln0U23OZO5yeBgGpdhs4p3Nzo47YxPRA8roXv7Y9PdQIGqnLlRdbXDR3Lhy9QUVPOlpXbPYmSVtxi7quLiiZgAJ17d+S8L5/e4uMtry6nd5+eTDluEjJRDrGHUFeYA+jq5MipkqQgcneRPbqWHmXXQvR+sm/MouJm0ELINq95F4PpthwK213Sdlgzbz3SkfQd1TujXJ8Nw9S5+NazOePzJ1G/t4H2XWsIum2/rat2cOe3729RCb+8uszTxNtJOWxavpXNK7bxzhOzmHrVcRx9XlPiO/r44Yw+bjjJhIUZMFosUCmDZ7B9yxyWLXDoXGUgJhp0O+EqTOGRpMsIpFYU2ZuleDShM9TKVG+Fma4xFBE8Bszhqq2DrbiGfiva8DnuyH/eNWL6E8sPo3XwkrsJFWllBsvUCn7BG0sLz2vbYc2C9aQsO8dPtXAfQaZcMJEpF0z03cYLRsBgzIlKTmD5ml4kzOdpug2ZoJUX3KRl7CmKVt9lClFxI7LxT2p4BAGklPxJYKIrx/JBQdkgYQwvPEwJ+3bUsXzXAbr170y7rjWeexh93DBGHj2EXZtrCVeEqensVikl/PLyP7ZMVkIIOvRox77t+ws4ePFIgplPzWbutIV06tmez/3qykwRokO3dnz+V1eQSqYQmsixRioJ5gD27JPMe3cFWt9G2nfsxCnHnUCHGu/Fl9Kt9FugS0WdSb6HCJ7ofq5qkH5SJR4QHUHriKj+JSQXKhFpo5crbVEYm0XwBKUDV3DelSPMUaW/bzP41LYgm8MRPur1uqExbNIgUinblxJzMETIlkAEJyOqfooIn4cInYOo/E6TG3zmYNZSNONwb4gicARU3aaSH60zaL3A3odazQ2k8MMK0Fuja5WPKpS+lKszFThKjQ/nYeeGWmY+M4dHf/cMj/3+WX579V9ZPnOV717LKsM01kWZ+cxc5r28iEQ0Qc/B3VocSBKxJBfecjZmyCQQDmRazqBIxY7tkEqkeO2B6dTtzhP0E6pV1Bp18BlPzmX94jj7duvU7dFZMjPFPT94yXuKUpZibGo08cf03i6/otTvIowIugLBWjsInqQ0doq0E0TwBAhfiKqEpaGBtRRZ913XXuYwDhWOOd8/MUpXYv0EqJH+09htCq0Cyq5WNzTzSAie5LZ/8lrUyeY8/XorvmvVTxR3zOirVOpdzUMRmETuNGUaJnCw7XANRJW7fxO0Hq7TSO61lYwlmXb3G7x6/9s89ZcX+OuX7+Kx25/1/Q00QyMYDrL0nRVMf2wWtVv2goCB430oCD4wgyYnXDKZjj3aY4YDOV2eNIdPabjtYeYzswtebwSMlidfwPY1O3n94TVsX68RbdTYuFLnn996j12bfCpXTnNq/xakVjf9b+hMmnQJS0D5Ve4/TCUyHTpD+UX63cS1dup31LIHDgSQRNb/FBl/ofT3LoLDCZgPTrn6eLoPyiUBCk1w5Q8vZt3iTUT2NXoKwCpT1BEFj6dRt7uehW8uZfXc9a3WGcuB1l7dDENnuGrP+SjW9gsgwmc3/W/8MUi8oUr5zhpk7GFFwC67CnUjzT7hJdjFqi6lwITwGYjq3yEqv42ovs0dGsgLXvEkbz86k1TKJhFNkogmsRIWj//xeU+bDiuR4j/ffZD//fZp3nx4Bi/e9Tq3f/6f7Niwi2GTB7dIhqp91xpGThnCdx/4Kr2Gdvf8zRVsVr79R2TdN5CNd7J/x1pWvr+G2q3eKufFsH3dLrav3YVtN/12tuWwe/MeVs/1aCVrHfC+weQen1KMBiXW+jV3qrUZiE5Q9ZMS9l/wQkTwJAhflvVahzR3Rzb+g7ZnEx9GGuXVZZx90ykF5/roE0fQuVdHlkxfQe9hPQtbSkLQY1B3X2scK5Fixaw1LH5rebNcsVIghA5GN0RwIhgD8F4UFIlh5dc1vSa1RlXxU6tVDEu8iaz/GVLvCeZQCs9hd3rxYKB1QVT/FlH1f4iqnyjtPK2wwvzec/Oo21WHbaVIRJOkkilWvr+GmV7T9iipj7995S5ef3A6bzz0Dv+89V7efORdJp4xrqhtUT6clE2voT35yt+u5/TrTvSNX6lkioWvv4Os+zqy/udYjXNYPXc9GxZv9k/U/SBh9ksLSFl2E5XVkSQTFq/c5+NT2ywtQXen8RVEcAoEStEJM6Dsc4hsn9tSofdGVH6dpvNGkuEgxqc1M1VZGj71LUhQK4DarfuobF+eUX8WmuCm31/L1tU7WPLOCmo6VdFvdB8e+OmjJKJJVeS2Umi6htAEtmVjhgP0GdqDUccNy9n/gdp6pt3zpvJ9TDnohoZhKuL/Z39xOV36+Cv2tw62ElIVYde41uuiM1RilZ6Us7dnkVwz34xqbUX+BqGz1AhxzvMHMxEZUK3P1Hpk4g23F39GrtGuiy0rC0eiQVWglkxfUWDf9N6zc9ixYVdGViI9zfPo757lmp9cwroFG5pG8IVQNykfN4WpVx8PQKQuwqZlW30rAwIHoTWCjCCT8wnZC3n57n4c2KfRZ3hPrvi/i0oOnJuXb1W8szwk4xYbl25m6MT870hA+GqI+Qng6qD3za1YiTJVgre8EsQgBE9BBCe7yd1BIDkDT/6NjIK9VSlWH8bBQcKuzXtASjr37pipuE48czyjjx/BrOfnkYwlmXDKaF594G3+8fV70DQN6TgIQWbizQya6KbO+V89M2f3juPw7lNzmPHULGL1cYSmpi2llJx85bEc08I2ZElw9qjKrt5dSU9YcymIN3p/REbLUCKj/6UwPsWg4dcQmAzmCFeUM51QHMwC2AB0MAYr7TI0ZWEUPJ78JDKVstm+diem4+QsYa1EivdfXMCUC3Pj14Hael6+562cTopjO7zzxCyGTx7CgLF9WbdoUyau6aaGY3tULQUMGNuP9t1qAMULLGY/pYmk+s7tbTjRe1g1szOL361GNw2u/vEl9BhUZCoxC/FognhjDIJ5Ca+EzSu8p7hF6BQlOULcZ68GIod7JRCBEcjku3i2p7U+iPLLldzSwQg/W0t9Xp9CJmYh3GGx1uJTn4BNf+w93n50JpqukUo59B/Vm0u/fV5mFLvn4G70HNwNJPzhhjto2NeYk8/oQZ3hk4cQrggxeEJ/Bo7rl9NyikcS3PGN+4nURzM3+bTFUSKW5L8/f5xv/PumNhMHl8mFLpfLBhzl3Rc6B+KP526odcq1WEj5t/Nwal0RzjbS/9K6QWCSa220AyU0WotsXANl1yEC43M2txKW23rL/ZLslE0sUnjxLXxzqaem174d+1m/aDPX/vQy7vnBwzi2gwQ0Dbw6e4ZpMHiCKvlvX7cL3dDwc6ZyJAwdF2XN4jALZ1TgONB7YB0L3qli07KtTLv7Dc652X9EPxsV7crR9MITwgjoVLb3ni4SwfFI/RaI3JfHjdCV92PwFFUhMPqQXtEJcxzSWklhAHMQoROLyliUjmZUxw/joLB97U4e/vVTipwtIFQW5DPfOZ9eQ9VgRKg8yAmXqZbj9MfeUyKdWdeGpgvad29Hn2E96dijPWNPHFnAH3vu7y+zaPryzOukIzPyPa8/9A59R/Yu+ebcLJw6ZOM/XQ9ZV2oidKGbOGWfL7oyYE5Dxt1JbS8kXF/TtjrfdHU9WUshOZNM0hd7GmktRVR8hexY5TWNmoaXmO6K972Fp1OJFNPuep2Lv3kuj/zmKTYt3+o+I5AeAUzTBJPPa4rxuzf521bppmTccQ3s220w+/Uq9u406dEvTiIWgliS+3/8P75135eL8gLTMIOGCqoe8B00E5VQ9QvFV7ZX0pQca6pyH75EJeWIJgs1fQDeSXQAglPUovOg4ZekS9rifPrEJ2Cx+hizXpzPukWbaN+lmsnnHpHR2lo6YxVvPvJuzqpg/aKNPPmnF7j8exfk7GfHhl1E6mMFxaRUIkU8Eufir59N44EoKcvOqXTMf3UxyVjCd3KlYV8D29fvontbmFHb2yB6FzmrQGcLxHdQIIzp7FFSBGlCoSineEe6rQQMTUTFV5GxhyisjFhqyi4wjuwA1m1AF4QoTBDNoMmQI7w4Ed7ZrHQkL/77VYJlQRJxK/Ob+InTSynZtak2M67v335Ujz9/X0fWLw9hJdT3aATU46lkigVvLOGcm04tKdEeOnEg2kMzCj+VpuXZwySR8bdV5VKYyr6j+lfusTsIWY90GtU50fgXJJo61vBliODRyqYjMV1VojK/RQDC57VR8oVKtGPbKPytDcVFO4zikLBs5irmv7YYx3EYd+IoRh43FE1TBtt3f/9hkllirFbM4t4f/49v/udmwpW5N7s50xYW+KI6tmTP5j184TdXKfu1vApJ4/4IC99a7qvMnkqkmD1tPhcMOtPz+RZ/3MY/uRNuWRdl/GE8L5zEc2B8Uf1bmN7bZNBWyZeA8EUIvZOq3Bd0DNZCakPOIJQZNChvV04yjy4hNJFZ4OU8nq7Ke2DDkk38+aY7VXUsvaB3vD+b48CqWWsY4AqnlleX+QhVS2wLNq8J8eZT7bBTAscWbFgeykxROo7D2vnrGTqpeVs43dAZMLoPy3coke3M9xAyCqp9pFYoAr5Tp6x+yj8LWhVgg0wgZQJiz0Hk30hMIAXmCCUOLUIqMYs9ph5Hojor3RDBo5o9zpJgDsMvyRP507qtwCc6AWvcH+Eft9yTmWLbsmIry95dyUVfP4fhRw9m2t2vF5Rk7ZTDilmr2bZmR85YdTJm4cel3r/rALdf/w8idWocfMTRQzj3S6cTCJlsWb29qBm0k3LYumpbmyRgMvE2hYEm3bfOh4WMPY8QQUVaNccAD7XiXQ1URcW9CehDXW6YV1AwofwGZQpt+RiBy6hSlM7y26ruWMXAcf3Y8maTNIgZMhkycSC9hxXy3sZPHcWbD8/w/N6tRKokc25QCViaI9NjYFfad21H7dY9Htw9gW3BqgVhsiNnKpn1b8vGkQ6aaJ52aQQMTrnmeJ5csYQUcfSAQbgyxBU/uZCKzMSbjWz4g0usV7+vjG4FayWi/DrFrRE10Hi7KyyYlTzGHgajB+h9EJW3QnIu0poPogwROD7XhD0fzgFlUWUtQlUCJiFC55HtuZfzzQSnIJPzwN6MqrQZgIaouJ7DFNTm8eSfX2DZzFWZdtPmFdtYMmMFV37/Iua8uCAn+UrDilm8+sB0zv3iaTmP+5k6S+DBnz/O5hWqotK+e3suuuUsug/sSu2WvRgBvag1zoYikj0tQlpEuOCG5xVLbLCWIa0FCL2vqoqYE1yj5ZYOQYXIxEitE2CAs9V708CJEDgOGX8e78lMG+x1BXp9k88+gpl/nY6mqfcxggbBUIBTXIpDNoYdNYiX73nT8+0dWxKPxEuiTwpBjjzOcZccxcv3vOVhUaTi1Mr5ZSCbYpaVdBdsqAnOYg4I+Zhw6hgaZ+msrt2GYRropsYR5xzBkVnDbTLxljsp7R5PYhcy+T6i8ocqCRNlyo3FmoP6Td3f1VqGjD6OKLtctSWN3qp9KRsQxjgITsQ/tZEqcY6/qqbH9X6Iskv8qRCiUomuxh5FnZc2EFCL17Qn6UHgE52Avf3YTGINsYz/mCqdp3jqLy/Qe3gPf9saCXd//2Fu+v21dOqlODA9BnX11MbUAzp7t+/LuSkvnr6clbPX0qlXByrblWMEjKKTkdvW+CjuthTOPlrEa3A2IRv/jqqKXImo+JoyHpVRSlsxmipgVX4XIRNIHISzA2l1doUPs28ObgUucgdSa49/RU2A1nQzr2AJMIUjTh1DdbKMUHU1jm0z5oQRDDlyoOdK8ahzjmD13HVsWbW9KOehOZRXl9G5V8fMYX3255fx+B+eY/2iTS2eEus1uDuaT1neC9WdqpjYbzw13aFLVR2T+zbSuywrSFiLwN5BwQrcmu9a/XRXK3GnkcJobSHjbyDKLkMmF6jkSO+PCIxR7WFfJJENv3FtitzvNfEO0tqIqPo23st2Q03Wxp5Wr9O6QNnlxZO8wwBgx/pdLHt3Zc6CwYpbrF2wkUVvL2PpTB+dIpRgdE3nao67uKkSMOSIASx+e1mB0bIQgg1Lt2TEf2s37+GOb95Ph27t6D2sB6lk8WuoYU8b+ZM6EVrGxbAhci8SW1Xyw1e6qvirKZ6EpbsBGmAgKr4GejektBDOHqS9Sy1SSNJ07bjt0OQ7kHw3hxCeC8NzuKVTr/acdeMpbHppOXL3AfoM78kRp44uqFICVHWo5KwbpvL8v17zTnxLDD2GqTPmhKaBsIlnjCfWGGf6Y+95L0Kl13evHnNspzQLIheaoXHMBROpCW2nqryWCwcPZ0BNNhE+6drg5XH2ZBSZeKVpmj/xJoX3CqVfKcOXqt86tRxEJcIcq3h+Raa7ZewJVfVP35vs1ciG25V4tI/yvjAHI60hrqJAOYRORoROpS14Q5/oBGzVnHUF5q8AiWiS33/+nwghPMnOoALdf3/xOJPPOYLFby8nWh+jvKYMK2mpOOVIzJCJpmsk8nlIUhH7t63egRk0mp0isUsxaC4F5nCXy9WSdqF77LF7kOYENU1JOcTu8dnecEe842COdacoA8jkdIg9g8QAbNXCEp3cBCDh/rmf0/Gz2hGqZeVSVXuXDWRzZC+V5jSW2hMJj+tIqGs1/SurcIAVHnY/aRz1uZMxX1vE6jnrWpQspTVvNE3jqKuPY/n6XA/EI685gdSzc9iw2GvVL/G6KDVdZ8T5E0s25AWYHtlJ+4FlmB3q6VGzld5lbune2Y90dkJsGr7aSKl1rpp+kRua9T7ywGyyo7mMPw/B4xDhS3Jf59QBKaS1yiM5Tym+TmodGAML3kbGX4b4C2QCnrMFGv8CVT/Mmso8DC9sWLzZs+1tWyme/NMLRfk40pG89sB0yipCrFmwgd2b9qBpAqHp6JrEtmx0U88MEBWsLh3J3m37qNtdB1I5cfhVwXxCaMuhdwHPdloRNdH0NWAtgNQ2RHASMng8RB/x14nSOqOmgjsrqRS9h6okN/4NKaPu+zmg9VX7kAD15FI7/Kp+OgTG5jwyqmYrrx9oQAv2pXpSH/pXKl2vDbvrIV+6xkV4QFdO/da5vHDnay38gkWm6jVkylD2S9ifFXc6TRjAcZ1qeOPhGS0w/BYMPnYoW+uiUFeKEwCsb6hnc80OOnaq4+g+BzAD7jCPTCDtHa6WoNf7q8om6QTMV2InBQduIfs3kQkdtPaIym/nKvHLOMgDSMKqolZwf0wiYy8hyq8rfBtnF7L+VzQl43GIv4AU4byhgNbhE52AlVWGqdvl7VxeSmVk/446XvzP67n8LU2gaYI+I3ox9qRRvP7g9MIELAtWIoUe0OnYrQN7tnqTRHs2oyBdKkTgaFVedW+YCgGl42WvodnEzJoH1kLUKq4DyrooH7ZLkDXUmHdqgzL5zjjNu+8hk4AJQlcXQKnI0zGb0mkSM3gfus9mKROJY5IMF/P1asKg88eybMt2Unm/tV84Nwyd/qP70rlPR3oO7o5h6p762V2PH8TaPXuxSghgmtA47XMnUtatXc6+HEeyY/0u6vc20K5LNV36dM44GqyN7af9wDI6d6pnVM1WpnSapIQIG+9U7Y2i1UmtqX1r9KX4b57/LaRURUvriNBqkKJa3cicHahvTcc76XMUjyw/AZOJ3OQr855JZPxlRNnlHIY/wpUhNF33jFXSkVjNxTApefYfLxc8rBsa7bvVMOrYYWiGzpsefMM0bMtB0wUdutcoLSqPZKDJzuggIcLKFi3xBk3njKFupjKJmpAr8pnlbmT8OcBUtjO2TwLm7AYCyt6m4ffIipug8U4KJCmcrW5bc1bpnyF0GtlyF73L1DWR7QlZavwKdwrRflJvdm7YnaP/pxs6dsr7e+jQtR39x/Slx6CuVNSUe8avivZdsd4IFbSkhRuAsosSQmgMntCPoaePK9hX/d4GdqzfjRk06TmkG4Fg04zn5podDB9mM6rmAL3LO9C7bAAy/iLEX6KJr+UDrabp3/oAf0X6gthmg7MXGbkPETwaKaogMRus92iqenoVOyTYmzzfQcaeJ7cSCpnqXfAYWuvYksYnOgE7+rwjeebv0wpMRbMhNFG8QpL/nCNxHEn93kbGnTSStx+d2exxCCGYeMY4Xr73Tc9gOvulBQw/eghV7Q9SFFCEEJX/h4y/rlpRogwROgnM8ZBah0y8BqltbmLlV3Wz1Z8sokqc3Y+316ubtKfUQEvtIjSEx4U5pdMkNpevBVQStpoS91sNfb88gmXvrcrUcxxHZkilORAQrghTfUYPkhqsx2+iCuQ4DTPWgcZdddiWjdAEmqbRqXdHarfsybyXlDB88mD29rDYm3XMyZjFgjeWkownkY5ENAjKdoUZc+IIVdWoJjf5AjUZZm+k+dawqaYeQd24tHbu9FCpsCD2uEt4zU+2/K4jDXQPUVZnN948L7v41O1hAOrceeHO1/w3aKVYqp1yaNjXyKjjhrNrk181OuttXImDEccMYdmMwpth3e4DrJm3nkHj+x90V0aEz1OcnvjrqtpqjkOEpoK0kInXwVoNzsZm9mK5CxU/OOTIHTT+Fe/z1GpZ8oWJ8PAJzE/CVidL71B0ua4/O9+Kk2iIIYSKX0II7JRHpd3Q6Xxif0SHcrYToZhpeM+bhipZJEciHQfd0AmVBxFCEG+MZ96rU6+OVE3swWotK+ZKWLtgI9vX7wIp1dT/7kWMOnZYRrV/eCebUTVb3eRroBoUik+j+e5MwG3vuTBHFknAvOBAaiky04Yusbuk+0zx+gqZO2rqtogYdSn4RCdgo48bzs6Nu5n5zBxf0VPpyObd5z1QV1tPY12Ezr07sG+Hz0rLhaZpaLr680rA9mzZy59u/BcX3XJ2xtC51RBliPA5ygomG8ZAhDFQkajrv1fCjkotT9sts4QoBq2DL5m7d9lA6AxdypfTSAusIAZ14JSjJ7JrUy1Swpp569i+dqeHsL/gjMtPproDNNZFCJUHCYb97VwmDR7GtrU72Lx8G0ZAp/+YPnTs3oGU1V/d1CR06dsJwyy8xN5+bDYdozuaRscd0Op1zI2SiWcqCY4KmpIvnFqXxN5M8qV1QlR8kcxlLSMuqbmlSAumlgitBoxhhY+LKnzPo4PVF/sUIFgW5JqfXMqDv3yCWL2/20Gx9qAfHMdh9dx1jJxSmkBlZfsKajd7J/LSkTz8m6cYOK4fn/nu+S3iOhZCgDkeYY4veFiEL4QwyPrflJCEtYT7adNy4r4PfIjZ2UlYIy2Z/tU5ZuQ49m3fT+RABCthMfeVxaTswkSm/8i+HHVUFZE6Vckrr6nwz4cHdeTY0UHWLNhArCFG9wFd6TO8J5qhZ96rfbd2VNQUTkNvX7eLDZuW0N12vzM3jB2YWcvUW89G13UqyEq+ABmfRvN+nKZqCWfHEmtRM6/xQ0u8P02lQekFrZ1PJdXG13C8BfhEJ2AIOPXaExh17DD+9c37fZOwYMikqmMltZv9qx6FkBgBg2FHDWbl+2uLbmmYBqOOHcq0u9/w3SaVSPHkn55n4Ph+GQ2yQwKt2i2rF7P3+JAQvpLCzCgJiTnI1Gp6aZ2grBcIb2HWoqhR/7HeX0e78kLehRE0CK97j+WPb1SrP9uh19AeTD57ApqPFceIscDYtBF1Atie8154VOqkdFi0fR7V4cKE39i4k+HlaiXWu2ySIqTGnvIQyPVCGaLqZ+R8f9LioEsSzUIonTmv99Gq1Ri3tYICU97QaYXbH0YBeg/rwXfu+zJ//cpd7PVxVdA0wciTRrDojWUl71fTNAKhABXtKopTrFByL1MunMS8Vxay26cNmUqkWLdwI4vfXs7YHKmUQ4Cyy6Hx1224w7bi4I5U3Mt8pNYik7PphYRQL9BbEb8GAWismb+NaNlWbLPwmHuZGuvvX0K0PooEyqvKOPaio6jpXDgUAEA5HNU7WwR8V857wQH3Lxf71r7PUHNrgROQETTotGcxXft3Ady2Y2ImJF4soQpvQsWXChXrZQsWgq2F3scVbC2ECJ2BjPyb3ITOVF0lL1/eFuKTnYC56Na/C8ecP5EZT77v22485ZoTeOKPz5OIJpqdMtF0QZ/hPQmVBxk5ZRjP/fMV3ynHippyPvuLzxCuDDP+lNEseG2xrwyCpmmsnb/h4KtgOUiBtViVS/VeYAxBlH8OeWAjyObbDx8cTISWZyoso8iGX4NzAHUBGPRCQ1R81ZPwXQp6la1n16pFBcm4bjgsXbYx57dZs2I7wdpqLvzaWa16Ly84jkN0GUiPmG+GmlbK4CAbbgdnFyW1HXMMzG1IzEIm3uPQJ2BSEVuzRX2zIMo+j4zeB9YS0lNnhC8DY/AhPq5PDjRd45JvnMt/vvNfzzhjJSykA8OPHqIGj0qohknU9oapM+rYYSyZ7mMrpglOufZ4Bo3vR1llmDXz1vvGLytuseC1JW2fgNlbVMtalKuWpNEHGTwHEi/QZslTW0AUcuFk7PGcqbteBMA4BhG+rFVvYXavZOaKJViJ3M9thgzWr92V89s00sjz897hm3d/kUCo7Rb1Ystioh65frBMoybRld5lAwCQiVeVhlcp1ShRhsiuHqY2KMqM00CBhmVbw96opme9KlrmKFdr7Eky4ubmeET5lW3y1p8aIZ5Trjmebj5aW1JCeVWYG267mt5De7iTcBp9R/YiVBFCZKmSa4ZGeU05539ZlSzNoJEnjtkEoQmOOvcIOrurjDOvP5kjzxxf1JxZtOX90tmLPPADZOQ+ZOxpZOM/kQ23gUwiqr4PwVPxNzQ9FDdu/5aeMs7NLfXK+IvuY+kLOAUkkdF7KHkWOw/HXnQUZtDM+Z7NoEG4KlxwY0klbZbMWOGpVu0Hx3HYuXE3+3Z6t/40uZW+wwMFv7Oma7kWQ9YyV9m7lFaKDqYb9KSFrP8lMvZfcNbSdgK6ReDscgc/PCBCiPIblc9n1Q8RNb9rMvY+jJLRfUAXzrrxFHTTI2QLRay/+BvncORpozGDBkITdOzZni59O+doQaVj21lfmEpFjVrwHHfJ0b4xqapdBUedrQQnewzqyme+ewEV7YuI9LZpAJPIyN3IhtuQsaeQ0UeQB74LqQ2I8FmI8uuVndkHBoOicTF/utvZ4U7dZcePpJLoaaUZfbf+Xeg3qk+O2LduKuNur0NzbIdl77aMb1m3+wA71u/yJvrLBkYdncT0COWO7dBvZLq9akMsfwCnCMyxpJMsGXsJ2fg7V9PNSxeuraGpCW+fe4oIHououV3Fr+rfudOSbZPQfqIrYLVb9zFn2gLq99QzaPwARcr/WyEp3wya9BjcDU3TuP63V2GnmojVju2wcdkWnvrzizTWRbAtm3gkwZ3feoAv/O4q2nWpYdjEgSyZvrxAFNEMmvQY2ETu03SN0687kUHj+vHQL58sEMRzHIcB49pOH0lG7gV5gKYTywZ7KzL+IiJ8geJUBI9H1v8aRUpNoaY6AsrmI3YvbXPyByF4LELvg3T2uJNx+avoJFLvlRtDkvM9tgOcepWYtULGoLpjFV/842d546EZrF+8iYqaco69aJKadvWAJgSxxlhJK8hVc9by5J9fwE7ZSFvSvns7rvz+RZkWgEy8AbGnOPda+PfPumIlNayEhhmCcEWIMz5/UtPOctTp8xEgl2Aah8Y71DRX9FG3albwSUDrrWQg2toCSEaQ9T8AcwKi/Bo8J4NEmfo7jJKRiCVZ8PoSNizZTPuu7Rh38kh0w8C2CuPM2JNGYpg6Z95wCmdcPxXbdjIyFft21PHmIzNYMn2FmnATQpH7hWDCKaPp2KMdgZBJIpp3vmmCnkNzW2qDJvTnm3d9kd9e+7cCXpoZMhl/cgv4mc0hOdedyk7HSXXeysg/ENW/dbli45CRO13PvvR2BoTOVomObMkAih90MMcgjP5IfRA03kbhNSRca5wsWEvwjp+2Ot5W+qBe8X8XMPPZecx7ZSEpy2bklKHohsb0xwsHBqyERf2+0nTaGusaeehXT7JzQy26roGAs286lTHHu1pizm5k/W8YPspiycga1i4tw0oKdB00TefCW87CTE9COllagZ7Iq2olZyJlTNkHJZ7xeUmnrG5IWyIJ0fuR8WcQFV/20XjTPU3WDxaf2ARs5ftreOz2Z7FTNo4tWTN/A9UdKxk2eTArZq5CSpUQCQFXfP/CHOKonsX5adjXyHP/fIUDWTYSVtwilUzxxJ9e4PpfX8nACf3o2L0du7fszbQIjIBB594dGTCmb8GxDRjTlwmnjGbuK4tyWgrB8iC1W/Yq78mDhYyrCUUPEU6SsyDsWi1pHaDqOxB9GFKblQJx2YUIYySY/ZENfz64VqU+yLWXUKRrIaPI+Gt4JlbWPAhObfp/EfBZlDj4V+6aR7suNVz09bNzHls6cxXLZ64qeD8zYPj6L2Zjz7a9PHrbM1hZv+fuTbXc/f2H+PqdNyGIuGXsFO07w9dv38qSWeXs3hagW58kIyamCFTFAHcSVu+MSrTyORABMjpFObAg8hDIOp8jFBCYAIn9blLeGqS/c4/xbwBrPjLWUQ2BHMZBIVof459fv5dofQwrYaEbOrNfms+Jlx/Dmw+9C5pAuvqCk84an1V5UFUuQ1MxzHEcZr84n0VvLcuc22nrmhf+9SqDJwygsn05p1xzfK5KuhAEgiYnX3FswbFpusYV37uQB372GCkrlWnnp5Ip6vYc8JPDazGU0bLXdHUSUptcMV8BZTdA/GkV1wACRyNCp0DoZGTkabC8F1eloQzKP5sx/RaANCeDlS/dIRWvNnwmTR/eRCUa+YmI5lontQ6arjPlgolMyTJBX7doI+89N8+zuNBrSI/8XXji/p8+xu5NtTi2zETnZ/8+jY7d29NjUDdk9GEgitDgsq/UsnFlkNWLygiVOYw5OklNr62Ay+HSKvA/CaopkPzAUpP71rwiRyjV/SHxKq2v7Lt2RgU3liQ4tciGPyGqf8Whp24otGkLUgjRXgjxlBAiIoTYJIS4osi2XxdC7BRCHBBC3C2EKNafahGclMNTf34RK5HKqD5bcYu63fV06tmBG26/llOvPYFzbz6Nb93zJXoN8SBO0hQE924rJL9KR7J11XaSsSSapvG5X1/JMecfSXWnKmo6VzPlwklc94vLvX9HAadffxKhylBOyb5xX4R7f/gIjXX+48Olo1iLLuvmLRug4XeuenSj0vhq/Bcy8b4iQEpv4q83dDCPg+CJEDoPUfVrROU3cifeRFmTmWr+McXyOB3B4/BOtKQSDc0W6ZMJV3i0dZh65bEEQmZOK8YMGpx+/cklTXXNmbYQO09wV0p1Dm1avhVSq4jHDN5/rZJn7u7AoncrGDkpwhlX7mPslEbMQNKdFEq/+Ri3YpR9Agmaql8ekPvwX3VqCK1CrfAoR7WDA5SuY6ND+GJVftf8prgsSLxV4v4+evioxC+Atx55l8a6SCYhslM2Vtzi/Rfm8817vsjZN0zl1M+ewJf++jlOvfYE3/089ZeXmPX8PM9wIASsnK2MnyeeOZ6Lv3EO3Qd2paJdOcOPGsSNv7s64wSSjz4jejLxzPEZ7ShQMXH6o7OY+8rCVn/uHPhK4eQtQGIPKMV0eUD9JV5HNvxFzQrYxQekcqEp/bDg6RA8GVFxK6Lm95nkKwOjP561C2e/UmVPI3+SM4MUMjFXOVVkYLt8p9ZVp/uP7kO3/l0wA03HZQQMug3oSv/RzU9d7tpcy95t+wpcEqxkipnPzgVAWqtYuyTEC/d34LXH2lFR7XDaZ/Zz/LkHqOkYc/le6diU5qXmdw5MNznzE9wtEmtFBSJ8BhgD3P0GUHGsxBim9UBU/VANofkt4GUUUt5m6IcCbV0B+ztqydIFGAu8IIRYJKXMoewJIU4DvguchBodewr4qfvYQWPX5tqCmyGoFdrSGSs5/pLJdOlTyB1IWTbvPz+PBW8sAaC6YyXJIhpi0BTXAiGTk686jpOvKk0dd/2izSSjyYKJIsd2mP/qYo67ZHJJ+/GFCIPWE5x8gTkDzCbCtIy/7CYu2ReEMsWWacXokmBA6EKlO5Zaj4y/gEy8A0ZPROjsXONlx2/a1FIXgHCrQIEpaoAgtZbcpMOB5Axkag1UfA2iD0DKJRFrnRHl16pSduZDRiC5EEioSSUP7ZaOPTpw8x+v481HZrB5xTbada7muEsme1YwvXCgtt7b8UAoT9J9u1Pc+Z0uWAnlsbYk6PDm0zXc8OPt1HR0yZ05AdmAym9Bw225VS1RATKF9+/iVTHLOhBznOJk1fzW5ZhFkc4uSLxC861mGxKvQdXPVdun7maf7VoguvvRw0cifgEsf3+1p2RN5ECUZCzJOJ9W3/rFm5j+2HvU7a6n24AurHx/TXEnjqzwM+yoQQw7qnmz5fTr5rw0v+AYrYTF24++xxGnjS1tP0UgAkcjY5sorIJprsgwimeVnEOBJZe9UVXFnBZMHGqdEZXfAdmAjL+MjN4PhBDBkyA4mcxiyNmOr4q7vRMMt2WnVUPoIog/jvqis74rZyOy8Q9Q8U1IrXQFSm1Ah9AZeVY3DqSWgb0L9G5gDCd/ZS+E4LM/u4xZz81l/utLEALGnTyao845omBbLzTuj6hp7/wBCwkH9tYjpeSRv3Zm3dIgVkKg6ZL3X63izKv3Mv64rGqWU5fh5Ynw+UinDqzZuTvU24Gzk8IY5uCfTGmqqomJqLhFyfKktiJFCKJ3Nfv51O5rQVqI8uuRkTvc9rYHZH517tChzRIwIUQ5cBEwUkrZCMwQQjwLXE1hYLoWuCsd2IQQPwce9NiuVQiEAk0aS3nwk3iQjuS+H/2P7Wt3ZMjYu7fs9dcHE9BzcPdWS0YcqK33nMhMJVPs21nXqn3mQ1Rch6y/jTR5HYKgtcttEVlL8b6ZO0XsNjzfDWEOgdRyJRqaDojWPqS1GLQ+yrbIHKUSoILEEJTqteIJyeRciD6ojsOzmqdUj5sSFPczODuQDX9EVP1EVdpSS5SCfHrVHHtKrW7D5xfssUO3dlz89da1zwaO68eaBeux4rkBTMlZdOeZvy8i2ihI+61ZCY2UBS/+twNX3OLKVeh53ANrWV5VT6p2sGjvjlHmjUYHT3GTqfwbloCKW1yNNYlMzFJlfBlVk2Wl8vycA2qVb80FqlD2LHnQ+xc+9jHARyl+AQSD/nHKDHqv3he8sZTn//lyJn7t21VXVN9QShgyqXXTxKmUTcJncRo5UJpdTbMITlRtvdQ61MLCBASi4gtkbtSWH8E8AckFlN6q0pWPoIwr65ksv1MZe0DRB0JTEcETEFp3JEEKFztak6Cns0/FQWenetyTt2RB5F5XGDvZ9Fj8BaQIIoInqGSw4fYsdxPDtdv5pnvtNsEwDaZceBRTLjyKlqL7wK6e07NGwGDQ+P6smr2WdcvCWO5HdmyBY8MLD3Rg+BERQmUScPIsgCKu5VA2UmCtpLA1a6rKVsqH+2oe1VRRtDcjY0+7lJlKj335QSCdvQh7m7uI1T1eZxdy+Q4h2rIFORiwpZSrsx5bBIzw2HaE+1z2dl2EEAX1biHEDUKIuUKIuXUHShOV7NC9He261iiB1SyYITMz0ZOP9Ys3sX197hhvseAVrghx0S2tlyfoMagrXomFGQrQZ3jPVu83B1pXRPWvlLdf8DRE+bWqBJtNhk5XmwpgU/z0yF6pmErvSe+BjP4Pz6DnbEJG/o1svMMtQfsopDt71IRQ9D4ghgpyfhdXIjf5ytqPTMxQwbTx3+7xJFEBzFJWJ6mWtCaax5gTRlDdoQoj0PS9mCGDCaeMobpjJesWbiLf7FY6GmuXpLVkTETo9NydenIdbNVqDJ4ClKH4JFUQvkIluKHTUOuqdIuxHCp/gNC7IxNvIA/8AGIPKaK+bHBvEKVCIut/iIw9Q2HypQNBRNmlLdjfRwqHJH5B62LYxLPGFSRami7oPbQHZVWF+kOO7TDtrtdLjl9CE5z5hZNb7b5hmDo1PhZEXfq0FVlZR1R8BVFxMwRPR4TPQ1T9PFeoU/jFEgN8u8I6ubUHAQQRwamKdyYbyY0pEmiE+PPI+p8jU17cWoAkMrULkMjGPykro0zs8YHc4fF8EuIvqqejj7gaWuk4mFBk+Ohj/vtsBcLlIY67eDJmKHu6UqesKsykM8axeMYKLI/itq7B+uVhwITA0TnfuUzOwzt2S0Uv0XqgvnsDAkciym9GVH6FJoqE214MnocovxpSK5CNf1bSRKnlKMrMDkpPshMQuQ8ZfRBSXoWHgFIG0Hx00w4B2rIFWUGhatsBwIvBnL9t+t+VkOsBI6W8E7gTYNjgESVrD1z5g4u45/uPEG1QPCE75TDhlDGMPMZb+Xnzym1YJcgNCE3QrksNX/rL53JGgVuKrv06039MX9YvatKe0k2dynZljJzioSreWoiQmkD0ffoUZGQzuUFAVy08rVK1AHNOVEO1BnHc1U0AglPckrntM4GXRhJSC4s8byGjD7nTjaUoU6dvUPkXUkolFqnleJffk8jk+8oZoI1gBk1u/P01vPfcXJbNWEWwLMCksyYw6lh1vmm6wPa4IWoaINojyi8vFAP0VbF3EMGjIXw2KvioygCACJ0FgWOV96coB2OQSkQbfu5OJh2MLIXXazUQncAcqixjDsGk0AeEQxK/oHUx7MgzxrF1zU6WzViJpmtIoKZjJZd881zP7Q/sqS/wPPWDbupc+9PL6DuydVN4aZx5/ck8dvuzOUmfGTQ4/XMnFXlVSyHAGFoo0JlGYDREvRIwAaEzIPpfChOcMgidC8nXwImAOURVxLV2qh3oe42kVLWqgICfhfiTSL2LO63XOqkcQC2OkG6bzKNKY80DPtv6/XvghMuOoWu/zsx8Zg7R+hhDJw3kmPMmEioPKW6Zj2CvYRgQPBqRHuzKHOYWfKdAMVUxgCQqIXYXrnofRM1tSvdNxlX8EpXI2KNqqvWgJyC9eMJlYPRHhI4How2neEtAWyZgjai+RDaqAK8Z2Pxt0/8ubV62BLTrUsPX77yRjcu3ENkfodfQHp6msTs37Gb6Y++xcdkWhBA5RqSg1H1DZUFijSr9H3LEAM7/6pkHlXylcfl3L2Dms3OY+7IaJx5xzBBOuPToNtl385DqzxwDodNdDoJbktV6ICpuBASyYVeWHpXrAZl8D0KnIqpvIzfBkUCIg+IBpVaBPoTmg5datXpzngKqnO2ldppBG0sxAMFwkBMuPYYTLj2m4LmRU4ax9J0V2FkCsLqhMer44Yjqs/FOFP2SUIkU7RBpUr5sVHw7e7367YLHu0RTd+v4q24Lo+0/Mzhg9P0kGGt/pOKXpmlcdMtZnHjZMWxfu4OqjlX0Htqj4DRJRBPMfGYOS2es9BVIDVWEMmT+mk7VnP+VM+gz4uCr7EMnDeKqH17C6w/PYN+2fXTp14mTrziOnkPaYIq7JKhpaFF5C7LxH+qG7RK5RcXnwBiJtLdDIj0FmZ5+i0BqIaLi1sJqh9YR79ZUqdBdPmopU3SmWiR5TS5npBD84uBBJHdFMHTioFw9QhcTpo5i6TsrCs8xLUT/o3+GCHhYyBUdikpXygKoRHO+op1gIoLHuDw3F85OSLzDodM0TCAqvsQHNfmYjba8068GDCHEIClleoxgDODlj7HMfe7RrO12SSlb4gXULIQmcsaz87Fp2Vbu/8n/sCzbt1yvGzpfu+MLOCkHI2C2aXKkGRpTLpzElAs/SGFKqabt4q8AMeUfWHYpVN+mVixaFWhN2mWi6ofI+EuudlcaCYi/rKbNQ9ltWAHBk93WWWtXKjoYQ10zXZ+KC4AxCFF2jVoZWcuzttVBhBGBo1EB2isJCyACRxY+7DQgk9MVgVfrhQgepzSuEm+Ds08R+M0jEc5W9zj7UeoEzllfmMquTbXs274/o8XUqWd7zvh8Ntk2cyBq1B7D5zsQTdNnzh7ljUfC3XYFMvEmovJWVVGTUXfleCiSr08UPnLxC6B9txrad6vxfM5KpLjjG/dzoLbe14nDCBqc/+UzGDShP6mkRbgi3Kb3mX6je3P9aN9h0UOD1BolieBsR2kMHg9Vv0A429TkpNGX9HUpwucjA5Og4dc0JS2O2866TbU0s1qYIniCoi8czPWidcI/drlfvqiC8CUIYSods5ztTUTZRWpbY4TbLpO5+zG9KjWqK6ESmSAiOEXRQhIz3IVtJ5dXllLTonovSvUz7D2sF8dcMIkZT85CaG78kXDl9y/CLEi+pJpK9yweA+gIo3NmW0WIX0l6MS2tBRA8yeXpWsj4oUy+Ply0WTYhpYwIIZ4EfiaEuB41RXQecLTH5vcD9wohHgR2AD8A7m2rYykVz//rVd9VI6DyibIgIAhXHrzv00cBMvaUKxXgJkhOLbLxTtfex8seRihBxIKAlHTJojWusrnh2h31UFU1a77Ha0pBChJP01SWTu8j4F6UaZ0d1X4U5Tcg468ouw9pQWA0InQeGVPv8FUQ+y8qEbPVfgITVJKXDVdkUF3o6UTmVZqml6RqT8QecQm4qGOouBlh9Ke5u1qoPMQX//hZNi7bSu3mrXTqvJDe/eciEvOQzjhE2SUgytzf5233PdPK2/nBd0zm/WTsCVRZPb2NqlLKyN3q+3N2csiVpK1FyNQ2hFGa3tBHER/H+LXwzaXU72nwTb5AccNCFUHMoPEBVdYPMextyMa/0rTASygJCqceyj/r/ZrUao8HHXD2IyN3IcIXu9I4ScW3Cp0J8TdoXUEzAbGHKaRHGOo9Kr6HEJrLlXJpAxVfRMaedaccuyDC52Xikyi7QnGeZELtm6BaYBZYGUlk49+yBhZAWvNoEjy1IKUhE2+qY8FUjwWnKsmgEhwMTrp8ChNOGcXaBRsxjVUMGT6XQPA3yPruahFvDFZDT9FHXPqElvX+2dCaJkVTK3KSL4UkJF5DOjuKDIm1JWxk/DV3yvKDRVtfkV8E7ka5EO8FbpZSLhNC9AaWA8OllJullNOEELcBbwJh4Angx218LEUhHcmujYVmybkbQbQ+ypyXFhxUlSoZt1j27irqag/QY2A3Bo7vW5K2VNsjmZt8ZWAh48+pkrwXfGUjHIj9T13Ueg+Xl2Cox7XuiiybfNPd1pVa8IQbDHKQTkDcFkFwnAqMBboyunKy93GzF8FJYA5AJueATChNn7SAYxZk9FEge3rL66aWfiyR+W+89nb++I3eDB7fmTNvvIKyoom6oO+IbvTpdUduS9Cah2zYqPgOOcbb6d/JQCVYBmhViHBWxcFajmc7oigXr60Rh8bfQNWPP2BrmDbHxyZ+AayZv77ATSMfTsrh6b++xK133tT6ypeEjcu2sHHpFsqryxh57FDCFR4tpw8ASisv/zNbajJXXuRd0XF24l2RdytG1lKlnB9/nqYvSULwAki+5U5Eah7vm0bAfS77OkwL2nZQyZPeA8ouLfS7BTCGISp9eL9aO0T1LyA5D2lvR+jd1QIyPw5aS3KSr5xjyP68QIZKAiTrp/HU7+exb3cNZ95wLn1HFpchqe5YzfgpG3P5WM5WlRSHPwOxR7Le16Gpuqhn/i3Kv5BZIMvkYrxpJK6H8SFqtRYg/hRS6Epy5ANEmyZgUsp9wPkej28mI/GdeewPwB/a8v1bAqEJgmWBQvuNPKQSKZa/t7rVCdierfv493f/i23ZJONJAqEAHbq34/O/uoJAKyUsWg3HQzYgDbvINJzeHewNPk8m1SSKs4MmnhjgbIPkFtRFl5bG9kq0DBDtQHolwylUdScFifcguQgqv9vyKRWto0rSiiG1smX7dCGAQaOjLHtvNzs33smX/vrV4qvJ5EJw8qesbEXaTc7Ce7XnjkyLEJTd7E6RpmHir/31QSKFjL/6seaCfZziF0BNxyo0XRSIZ+ajYV8jkfoo5dUtt4JybIeHfvUkG5ZsxkqkMAM6L9/7Jtf85FJ6D/sQKp72NrxvyibYe8AoTMCE3tdHNgJUkpCA+BOFTyWeQiUOaSmccgpJ3DqIziC3eh9vuittr4WG3yDLPosIeE/i+yMAgclF82dpLaQ1cSAQhMmn1XPXLyp44KdPcP1tn6Vbv65FXpF0OXWFi3hiT+Gf9Ln/Dp2f2z4VYfzNtj+g5AvU+8eeU+3sksWpDx6fGjNuLxx19oSSyvIVrQhcaTz+++eINcSVT6SEZCzJ7s17efux91q9z1ZDq8F3Gaz7B1MRvpDi1j823qJ66f+6GjGeK8iUT/KV9TygJCcOIGNtO37dhNYlw5omCVfY2CmNutoo6xf5JaoK0t6Bd6BsjjNnK95G48+QdTcjG/+CTExHSXV8FCAhtfHDPohPFY48c3yObVox+GmHNYeFbyxlw+LNyuJGSqxEimQsySO/fspTx/CQw+iN920rBbrPBG5gvLtoac2N1aUf4OA9QWf7J18F+7GUvI48BAsm4UrStAJlFSp2Wxa8/b+Xi2/s1OFfSi2m/+beI+JPqPhV/301OGQt4JBTJEpGyl0cf3D4VCdgJ14+hdEnjMAIGATLAp7nlRk0meSjHdYcovUxdm2uJV/t3rZSypvtA4cBoVPxsocQIe/xdvWyQYojpnlbNn1wcMBa1PxmrUHwaFrjLymB9UtV29GxoXbrjqLbC70rTRNA2Uh7PJaI1HKl5/VRItfLxhbqih3GwaBTz/Zc8q3zCFeGCIQDaIYGedVX3dQZMnEggVDrErD5ry/2bHMmExY71n+QbW4FpZWX/1lMCEwuomloIiq/C4EPctjJD5oPJ+3gIAKTaU2CmbJg5QK3wCAFuzY1o1On1eCfMBXzf8yDsxdiD37AVInmYCFTq/ggK2+fAFZm66HpGud96XROveZ49u+uR9c1Hrntaer3NqIJpR124uXHMGBs36L7kY5kyYyVzH9lEY7jMO7kUYw5YUTxU/GDn3hVbxs6EynKIT5Nac1o3RUB3OhX/IXGIDURWf9DVxgwG+k24wdx4h6aNYMIn6eqU6nVNMlx9FTfkUyTcRNkDwY4jlKEPuaMA7z9bA3RRp3y6hrV7qnyqZoGxilVbWnRFMh0tUI3j4LES3xkVoRaV5f/V+IEktyLrP8FVNzqDiaU9CL1ndvbVAXDGMGnfF3YIgydOJDv3P8Vdm/egxkymfHkbBa9uRTd1HFSNj0GdeOCLzfTfgd2bazl3adns2fbPnoP78nR5x3pirQWCVQlELfbHFpXROWtirNpb0Rpep2ECJ1W/HWiUk1Na53die7sc/qDjF+AOATnt94ji4OVTsQ0MAa6shg66jPbuEPYOLY6lF4DEwwaE2XN4jCde1VTt/sAVR0rfXjKAeX1W8AlNqHsYmUL16KJxUP5nYdIf+aSEb0Paa1BlF9Z+mucve6wgAGBMUUWAoX4VCdgaYQrw5kpx6/9/QtsX7+LaH2MnoO7ESpv3mP3iT8+z4r312Sc6Let3cnSGSu5+keX0KVvJ7av3ZVTBTMCBmNPGHloPkyzEGoUOXhC615b8WVkwx9dzR0AG4LHqklJGUed8D6Kfe4+mp7TaSrveyGfG6CDl3xEm8BEVHxFcdnsHUqHR++hji+1AWQ9Uu+NSK3BiT6DdPahaRAqk4w5ppHhR0Z44PfdePLPL4KE7oO6cuk3z6O6Y760lIGo/A4y9rB70QLmaET4ctAqkdYacNp+hewPA++BgwCUXw+Rf+ZpiOlAOzD7uEMX+UhB4++h6hc+putZkHF1LmV84XQ1ZFDxzQ9UjfrjDk3X6NpPjfWf96XTOPHyY9i1sZZ2navp2LN9s69fu2AjD//qCVKWjXQkO9bvYv6ri7n5D9cyfupodqzflYltaQRCJt36FXqqfiDQ+yAqv9Wql4rQKUh7k3vtuQmG1kkl/9ZyWs6jSic7Xjd5P+XSIS18j9IggseodmtqNQjTfR9dSejYm0Frp5KvxIs4iUUglJJ9n8EJun1pF28+1Y73X9NY++X/YIZMzrnpVEYcXSiAK8IXIEUY4q8CUZUUl10GxjAkmuvN+EFVkYKoe07+PUQotX1MD1kkE4InqenZAuqHDdY7yERPpafYDGT8ZXd4w33P2CNQ9nlEYGxJR384AcuHgLLKMAtfX8IbD71Dlz6dOOb8ib6BbPu6XayYtTpHzsKKW2xctpX1SzZxya3n8O/vPkgqmcJKWJhBk4492x+82faHBa0rovrX6iKXEeWbpdVA6GzV00+tVgEt+S6FN3YNQuep6RZZr0at9R6qIlSgxD8E5B5XUTp9c+6ICF90iD9fN/WXgQC3miMAAkcgov/LWcTqBghNMvGkAzx5p0rYt67awV3fe5Bb/nVj1krSVvwPrRpRfjNNQaqpkiDKL0Q2/J4PTPcmeD4knqUgEGlVCL0HVHzX1VpbqB43xyPKLgYRRNb58TdsZOwR9zP6Q8afUcMamfMkpTz0Yg8iyr94MJ/qU43yqjB1uw8w/fH3ME2DI04fy/CjBnsXsyQ88/eXcuKXbdk4tsOr90/n4m+czcr3V7Nu0SZSyRRGwEAIweXfuwChfUhl/IOCjii/UbW+UltA69Bk7G0tRiZnqX87DYo4nw9jhHIISa1Tjh3BkyByD4UJmAmBo9zBGshMAFbczCG97YqwK1OTBa296y7ipoRWe4SmI7KOORCEEy+oY87rVVgJxfV78k8vUN2xip6D09QTqRbZwsyaPE8PWLlvH5iAjD0Jct+h+4zZCB7ryvbkxyFDaT3qPZFaGcRfVvcrrVtGMkPam/yHr2KPqpZ1Ws7IC/YWN/nKi9XRu8D8LTmWfz44nIDlYffmPfz7Ww+QslLYKYdta3aw+K1ljD15JGYowMCx/Rgwpk8m+GxcshnHw/jbiidZv3gTU686jm/852aWz1QyFN0HdmPg2L4f0+CVhlaooyXKXX6G8jOUgQnQ+BdykjBzorIsymkXSKSMqJZour1nDEKU3wAioLhO9i41iWkM5UPr3abh7MOrYqRp0HdYkwOAdCTRhjgbFm9iwNjeyj8x8RZgK/Xr8KWIwBGF+9f7us4E09JvSJMvZ3abRLh/B9uudMAc4poap6U/3JsUArRKRPnnvV+XmRDzQLq6VwyJ9yn8Lm1lQp5Oug+jRXBsh3t+8Ag71u3KcLc2Ld/CnOE96dy7Ix27d2D0ccMIupX9SH2Uxv2F5HLpSNYt3oima1zxfxexeeU2NizdTEVVGSOnDM28/mMLrQsEuuQ+Zo5BpJMXGUc2/Nad7nYh2kHZVYj8ym7F15SYaLorIIIqfhkDITRVnc8iDOa44jf0DwrWypzkKw3HgY7dLHZsUr+tlUwx48n3+cx3L4DUcuWh6NQBGgQmub6v+Xxigai4WVW2M1Px6Ws5/z0PxnHARWIOlF0D0fvdB9yEMHSGEpoFRPBkJRCeD61YdVhX9560AbgHZHI23t0DTRUZAs2boh9OwPLw0l2vk4gnM/c56UisZIo50xaChDnTFtJnaA+u+tHFaLpGuDKMpuvYVu6NyAgYGR6QGTQYc6KXp+9HAE4DMv6iIreLMARPVKXstNCnU6e4FnpnRMkk/JQ7kpyXLFlzkY11iIov03TqCaWmHzzZbf1V514Yxkj191GBKMcv6WjYn5swSMfhwJ4GJZaabaUh69U0lFaWsdyQ9m41Cp/a4KpkX4oaj39Drdxy+Gc6mKNVshZ/jtJ8M31gzUFU/Z+aYLTXgqiGwFianwrVVIk/Y/Pi8Xyz8Au+HyAf5xOGlbPXsmPD7hzivJVIsW7BRtYt2IgZMnntgbe5/rdX0alXBwIh/985XO4mCwJ6D+vx4chONAuJTMyExBsgY6qdHzpLVakAZZC9HhAIYwCl3vJkchY4tXkP1kH9z6HyG7lT40Y/RPVvwHanIfWeZGKf1gWCeYnehw2tXW5i6ULXJY31WTFMwr5ddWBvQTb+k6ZKjw3J95Ey4i7UAJLI2CvKpg4JwWPU0Ja1wPXkleRQSrSuyo8zehcHl4RFlKdv1S/diUpbyVyU4Ekrgici08db+CzNLgBl2trK67nSPtPhBCwPm5Zt9f5O3cesWJJNK7ay6K3ljDt5JMOPHsyL/361YHMhBKOOa0NT7UMBGUU2/NIlmdvqM8YeRdpbVJ+/4c/gNMkqSFGlLG60rihl5a0qidByA4xMvOcGo/w2WgpSq5Cx512biSyIUPODAB8mnFqluJ/aoMQeZT3ZgSOZELzzfE3uayT0HNTBx8fMUt9D5XBkag00/pFMcJL1akJI6+m+TzrBspv+q3WgiUN3MJ9rNzL6XyVAGCxVCVoiE69Dco7P87pa7TcHcxxYc8gNwEK1tQ+HplZhzfwNWDF/SRMrbmEJiyf//AI33n4NZtBg2OTBrHhvNXaWmbcZNDn6vEPFt2w7yOgj7k3f/czJd5DWAkTVjxUlIv406WtEokHoYkToJPWYsxOcOBi9yDnfZARiT1C4sJFAFNn4V0XDyFlgikzF5aOJlJKtScymyQC76fdOWbBpVYiG/U3fg25o9B3ZyxW/zf8uLCX+6hwAEULW/zS37Zh4FaWblvR4LUDAjWEHCxsZuRcRPNblfJXYIUltcCWNivCVzeL3bxGYgEy+SyGPzFG2dSXgcJRzkYgkqN/XSDAcKGrtASqILXxzCeNOHkkwHOCan17Gw798kmTCUlawhsal3z6fipryD+bgWwmZeMetrmTfAJOQfBeZ2pqTfKkX1CPrfwvhiyBz8tqqYhU4GmH0UcTP5Fz8OUwSEtMUT8HorVp6orykfnlROPUoQmgn2rx1ZW9DNtxGLtkzvUIykEhmvdqJNYtDmefNoMGAcf3o3CuMUy+8w4KzB5AQuQvPqppTRF8o8bYraOi10soWj2wOCUi+h0zORlTcAIaXx1wuVEVvOoWBR30faO0UKbcZiPBFyDSXkASq6hZAlF9dwnEfRjYcx2Hf9jrMoIFuaDmm7wWQsGP9LhLRBMGyIOd96XTiDXE2LtuCburYls2EU8cw8YwSkugPE85+D66prRaW0UfBmpX/Aog/pgjkiVfcCV+3lR+YpCop5kjlm5iZGvSArENGHkCUX+NyVJ3iGoslIamOR1QffCwsgKNagvZmcjxz09crNgf2d+apf4dJxyGhCQKhAFMumAT2LLxjiaE4m6m1PpyvImbczmZo/DuqKuYVwyopzQpKgr0KGd0A1gJE+fU0+zvYW5CNf6Dw99VQEidSxcLmugDGACV/klkAuGr/4YtKHiL61CdgTsrhuTteYeGby9ANjVQyhaZrOHZxbo1uNH11vYZ055v3fpHta3chHYceg7qh6R+DUfrUKryDjA7OOp8XxVyvs6yLxtmlrBwIqPJ2CSsbGblH6UaRQq0YxiDKrnE90loA2YiM/AdSa8kElbIrW6E2XeQtYk9QOB0lQbRDVNyM0Dsx8QKHmDWTZe+uwgjoHHnaWI465wgcJ4WVsAnkfSwpQRi91HcgD7TiqKwmYrwXRA3IZjR9MlCm5TLyAKL6txQNYDLukl49zhutO6LsfLetWsLNSKtEVP8UkvOR9iaE1k1NuX4UeDIfIyx/bzXP/H0aqWQKx3awSxRIFe5wiFpEXsr+XXXU1dbTuVfHVqnmf+Cwt+I9xWv5TOgCSFVdzgisuki+3dSOCp5Ks4sX631k/SZXx0qA1h5R/jnQ+7TwQ0hVYYq/RGZBax7pyiC00e3ZWuLRkbBR0hHXIYz+dKyp5PTrlzPjqdlEDkQZMKYvJ10xhar2ldSurqJdezVslAsb9C4QfbCVBxbDl0NqDnatiEodRkq6dkwbMkNTfpDx53z2KyB8keLmlpQEC+X8EThKGYhjIoJHuh2i0vCpT8BevvdNFr+9DNtKYaftuzSB0ISv0rPQBBNOGZ3zmKZp9BzczXP7jyy0TnjbQDTXv/Z73jWzJYS37VAW8tXvrcXI6D2I8puaeestqpQu61W1Jvmuu7LLskGK3gt6B8WRagukfJJRuc9NNgOEy+H0607i9OtOImWlePfp2fz55n+TiMQZc0w1J12wj0BWXmElBXr4bIxWiL82oYh8R+hMNcnTkmlKGVO/XzH+hFOLf3Ug2WSyWzJMVX3goyCS+fHDjvW7eOIPz+VMMQpNQJH4BdBneM8CcdZ2XWpo16XmUB1q28NXFFRQ/Lz363C4Fd3EKzR/a7TdCV4Xzi5kwx+Vb2MxHSgZVZ0He62ibohqN/nKqiZbc5FRE1F2he9uWgJZRF5DyDrQlFTO6ONGMPo4df2uWbCBx37/LPt31GEE6rn5ZwJNk5np72RC0BgZRYeasoOo2BXhUGkdFSdZpoeQSkFS8c2a0yBMbfF5wkCYQ1r+eYx+iFbSZz4GZZpDB8d2mPvywpzgBYp4X15ThhHwvgillEp1+mMOETqRwkCjH2QJ3Fats0BLPbUUp0A6/tUgmZitWoHJGWpoIPY/16My/wK1kPHXir+ds0uRNu1txbeDIt+HjkytQ0buRDb+RbXyZIoHfvoYbz/6Hvt31hFtiPPetGqevbcjtTtM4jHBhuVBHvpjb7audf0d9bbWBZKqPWkMbOHrnOarT1p7fG9geukrv8NoG8x6bh4pK/f8l45EN3XCVf6/Zbgi7Dm9/bGC3hO0zhTGmeaSp+bikuZ6Ara0Emsh4+/4P+3UIet/oqQLrCVKhyr+BJ6+isn3KDpcI+NuxWcFzSYoWg3e34mGRCBjjyMb/6TkI5z9LJ6+jEd+/SRbVmynsS5K3W6T//ysO2sWh4lHBft2G7z6v3a8/qTi/orwqcXf3xd+RPcAQusIwdNpWVu3xHuXb5xyVEL8AeJTXQGzEha2T6sxGbPo2rcTOzbsziGnAiDh8duf5av/+ALVnfKFNj9G0LoiKm5ERu4HGQWk6mubR7lleq8AUIFaTRVbYUolNaF3U0lSsz6HadhQ/zOovMWD0Gq5x2TlPuYHZ6/PEylk5N+u6GJa8b4HVHwNoYW9P0tgrMt5yj4PTKUXFvkX6c8nU2uJJ95k+7pgAY9wyXuVLHmvySg4EDbRTTf4lF+hpqvaRPtLV8lX7H9uW7YlCKoBg2IQ5RCY6BLws4/XRITObOH7HcbBYv+uOs9Kl27ojDtxFHOmLfS0Elozbx0zn5rDlIs+zpVHgaj4GjJ6l3uuC1U1KbsKIv+m9deTo87zyu+q/TglLNIAsCHxLFIIV5InFzL+rEu7cJq294VUFWmP61Em3nNtyNJDOBpU3ITwE3g1R2SJhWZDuBqMrlREai0yMZ3Zz/fASuTeF2u3B3jwj11zXjr+ZLeCaowErT8464t8HvdFORUvvwWAUG4tsXtp2YS3jTQGNpuyidBZyMa15McvglM+cPrDx7+McxAIhoNUtPMmyvcY1I1rfnpZRmE6H44jWfhWCVpHH3UYIxDVv0FU/QhR/WtExdcRwYlFbsRR1GlTLHd3oP5bEHuA0pOvNCJq+jI/OKU2UfpqyABjGNhbkbFHkdH71WoRiYw97yZfFuAq9zsbof6byMRree+bQjb+GRLvUqDIbwwBZ3ve50ti6NsZOKq+2SMMhANNAoeR/+IfjNIotVXpylRYS2j5DcjyGE+3Vdsx43wAouxK10khiOK/dEFUfLEV/JfDOFgMGNvXs1KfStoce/FRnHz1sZ6vsxIpZr3gx5P6GEGrRFTcomJX1Y8Q1b9FmKNd3Se/ayZc5DkAS1WmGn7SguQrDQnxF93KVB6SiylZt0+UIaWGjL+GjNyjKvoyqiY3Yw/RFL8SQAwa/4hs+LsSkM0+msQ70PA7cm/1OogOLn0iQVPMSyGJc+L525s9PDNgMH6qS8Ox5hUfGGp6VQnboHjA8Zdp+b1Dg+TswoedOjWwkYYxEFFxo0u1EEAQglMR4Uta+H4Hj091BQwBZ15/Co/d/nSOjpcZNDj9cycSKg8y9sSR7Ny4GzuZmxDYlk20PvZBH/Ehgsjj/eiIym8hG+9yJyGzg4YDpBTJW2iFWjltAksFMGMkanx6lluB8rMJybM3EmXgxJANv8hsIZOzwRwO1nq8ExMbYo8j468jKr+tbDti01z+V/b2ArTeiMCRSj4ib4VmGCmGjY+x7P3CUrhuaOimga4LrvrhxQghVGDwbKN6fCelwlpE6yaydLBrM04AMvE2xJ52j80B8wiXHGwqR4LwhTSJtx7Gh4GJZ4xj5rNzSVmpzCVgBA2OOnsC5dVlTJg6mpfvftOzSpaItvQG9xGGqMxZNIrwuUhpQ/ItCq+dRtS0bl83wWpr14kkMvEmwnBlDFLr3cVdvMhrsmOYCYGpUP8dMvHFmqP0GgMT8E3i7CXI+u8qYn3gCBWbPXmgAiq+CQ3f8zyKPoN97mtC3RsdWzLlwqPoPawngNL/KilZKvF8k41uR6alcMDOSh6dHcjGfzfdo7ROSlRa7wHGSESVur+oSuKHI/D9qY+cy2etQohsgThBVacqOvfuCED/MX3xEhEwwwEGTxjwAR7pBwTZgLT3KVNoZ5PPRrYSJZSHqoCaQFprEcZwVQ2zN+F/8ZrKHDa1Wl24xkjFfYr+J2+7lKuw3gzkfmT0fkTF19wR9/zgJcHZjJTeF61EIxEzC6rtgXCAqVcdS1XHKgYfMQDDcNuPspG2L0Sn/ThbClu1jcHl2OXxU6x5yIhAlF/rPiA4HEI+XGxf74quZp1r0pGMOFq1o4JlQTp0b8+erXkteU0wYOwnsWKZAmcPMrlUcUWb2a71ThLp68uHRJ7aAtjIxPvu1LhfkqeD3l/JFtgb1EI4dDo0/pPcxZ0DRCG5hOKLNVuJPJtDIDkf3yGF1CKUzEJhXHUcEzNo5HCjzaDBhNPG0rVPJwaO60dVh6wOiWy+4t8yOO5fMU9hLxigu2R4mUDW/w7VsUnvdjuy4feI6l9ltRo/3Pj1qY6etVv2smLmqly+jiOp39vA8pmrGXXcMDr1bM+4qaNY+MbSjCGtGTLpO7wn/Ud/ggKYjCkuhVUCqRPcbQ7SRqIYEq8jscFeX+R9dAieggifq4Ju/DXVqvQqQ4O7nyqgmYCRWo23wWsWzIEQKySQCnQGTb6a7jNmsWtTLSBo37WGS755Ll37Zrezk5BahzxUKy/R0ZW3KLXKYeYoSMv8ySxADUrMAXnZYamIjwie/9crpPKGiGzLZtrdb/L5X6spuvO/fDr3/fhR7JTyeNRNHTNocuq1J34Yh3zIIBOvQ+xZmuxvmkNjK9+phMRA1it5HGsJ/jwmHbQeqh0mQsjEu4pbGX0c/2p/Hc1b+AhILkb6KrVLBClkYDIkZ5LPhTLKj2fc1Crmv7oEIZTZ+0lXHsvks4/I2Qf2ZnfROwisuRy8LVreZyBA6eboavuMgbY1H+/vyFaJafDotjjIg8anOgHbvGIbXpUCK2axbtHGjJL9OTeeyqDx/Zn/6iJSlsPYE0Yw8tihH3M/x1zIxn+B7WNM2mYwafI2bA6Wq6ZcBMZIlXxlhFJLCLx6b7Cb4+65IqaB8R7ke9TwglYDlbcgG/8KMgFpX8bwFdTUDOWm3w+l8UAU6ThUtssdS5eJ2RD7L03+jtCsbEeLYELobIQQSmNINoBM4h/MKiB4PCKcRaLP5kzkQFNB93AC9qHDTtns3e79O21b08Tl6z28J1/883W89+xcarfsofewHkw6a4Iv//VjCWu+Syg/hItCDNRkZCmJm+3q9BVL1ARU3ArCRDb8Xpk7NxsDNBDtQO4pso3SVhSBMcjENAoTI6Esm4I1SGevqwfp6qmZw9HKz+fsGwxOvfYEovUxKttVoBtZi01nr5r6TvtCYtHUxmuj71/vjyi7Ehl/BlLr1f7Tji0FMMAcjghfnJHUUMfmtfhMtEAf8dDjY5+A7dpYy7tPz2bPtn30Gd6TyecdSVX7IjosWahsV47mkUTppp473Shg6MSBDJ3Y0rH+jwmc/WCvPoRvIIBy5V9ozWy73aZWASBjj1LySsleQfMrSE21DsyjVNvSOeDu3wAcZd9z4P9Uq6Dq1wh7PZAEfWCOkGyFl5ils8MdTsgPtEElYOocUJZMyfnklM8zEKhkTVM6Qs5ulKBh9iblShAQDRGYCICsu9Xns+qI6p8UahcZA1xvtfwbiFK6P4y2QSKSYPZLC1g5Zy0VNeVMPucI+o4szdJG13XMgJGpzGcjXJk70duhezvOvqlUq6mPH2TsOQ5t8uX6rxYTPy5Ac+0zA+FsADvmyuGUsgATJQg3O0hnB0J2VPSMxJs0VeFU9U7W/woC4xWdQMbA3qXkGbK4wIFggECnfDV4iWz8uxt3sj+fqVwE7D2gtwcnAfYavL8DUx2HqFLTpl5Ul/BFoHdHlN+s3jX2pKvPlg8DEb6g0Gzb6It3BS3gPvfRwMc6AVu7YCMP/+oJUpaNdCQ71u9i3quLufkP19Kua02zrx84rh9mOEAibilpchearjEhPeHxaYBTR9uUjw3USR9DKcVXKu+1wBgQQWTkvjZ4jyykkx0/oVRPlNhejf0PkCqpCp8KqZVuQuLyE+Q+iD2uEqbwOSW/u4z7qMgjEMETXXshoOxSZPQJSL5D7m8jydheON4VXGREyXBkD1aYg1xyfv7bVqsgmP9w+FyktYzCAGaDvTPXjPgwWoVEJMHfb7mHxrpIpo24dv4GTrvuBCaeOb75HQiYeMZ4Zr84L4+vY3LMBR99H8c2ha/sTEuRrvS408Tm0RA+B6GVQWoT0mrBJGOzkCDCyOQcSm+1lUj9SLylBKsJQfgzanoy8R4qNqfUX/J9ZGo1ouonrp5aCUec2uqS2vMTKwukjaj6YXpL9V1F7kV9Nid3W3B5xF4WRoZa+GYJqgpziBoKKvieBBiDPXYxVMUoezO5LeAkMrUd0WLB6EODj68MhYRn/v4SViKVmfCxLZtENMGrD0wvaReaofH5X11Bp14dMIMGZihARU05V/7gIqo6NqOH9ElCmnjdshepUWaRNoU2IXgsouY2RM0/EdV/UmbLsYeQB76JjNzhtq1KPeVK2C7d72+xYGIpcNuZ9iqVjDm55tsKSbdNWgLPyqlVpNDkWz4bSJU4ZRBQNhfmkXgT6tPK/17JnKYsObIgwhegvqf0DUZV0tRUo8f+ta4QvtTjuQSy8W+0jBx7GF6Y9eJ8GvdHcjhcVsLi5XveIulR1fLCKVcfx8jjhqGbBsGyAEbA4Mgzx+XxdT4FaE6/zhOmWmClPfz0AYiqHyJq/o6ouQPC14C9HOq/h6z/BdLeTunDLaWIUAeVdIuoKnH7lsCdVqdR0R2cOlSsyI5htpKtSC4sYX82MvY4NP4G30qdk92aFQhzDFR8Gf/vzLWh83o8tTz3IWO4oo/k+DMGVFXS0wRd+L93/NmC+Phh4WNbAWs8EKVxf6HZp3Qk6xZtLHk/Hbq34yt/+zz7dtSRslJ06tnhE8XtKgki5LazdpX+GmMIouw60CoUB0qYpFt7MvacmqLMvrisxUAZTUbRxRCEwHGQLMYB0xGh09Q/QydCfBptP06ehuWW070gUIbilWqFZq0EvQMieFJWYLCQDb9rZlooiYw9A9HHwByBCJ+vVOeb5ZH4QKvJ+/+uiKofIuOvqIqh3kV9f3pv/32kFnu/t4xCaqNqlR5Gq7Fq9toCwV4ATRfsWLeLPiN6NrsPzdC44CtncvpnT6Sutp72XWoIlrfQT/WTgNBprj5WqWiHqLja9S21UOe5e3NPLUNG/pvLFXJ2KkkHrYPbfmsmhpkT3Ip5kZgUPh9V+T5GDRAcshaqLOKNmUDaWxBMAGshMjETsBGBya7khVoIy9jjkJhR5BgVR0vW3QpaB8XNNUe5gs0t/VxCWRHlPSYqv4ZMzIDk+4CGCE6BwFH+e7E3ID1t01LI5IxW2we1JT62CVgw7O9UXlbe8opI+241B3E0H3+Iii8h639BSdUcrSei4qtZL276vpsmf/IDlAQiLoHUbVH6ThpK0NtRlKtlDCOzagxOheRSV7OspQjTVKJuRt3fEwlk5DFXKsN1CLAFMjkXUX49mGPUCjNLyNQboingW3OQ1nJE9Y9pebtDU0RUY5DHUx1UVa1UOH7HLCi9ZXIYfvDkCAKOLSmrbFkMC1eGC3hfnyaI4LGqlWevoyRR46pvZ3EZswRCU8uQjXfgHQss1X4T7V0elsA7Xrp8KGtO0WMQgbHqn1on5d0af66EY89HmvyuU/yadPDuKgiwFiBTa90hgCZXD6y5rjev5SZfxeKjDdJtAztRZOROKLuG0pTs86dKDURoqsd2BiJ4gisCXQKk3/chS4jHHww+ti1IM2gw7KjBTXYumcdNJp/3KeM/tAW0zojqX0LoHLVyCZ6Ed1lcA8OnauLUgrWU4h5m+wEbgqeA5ifjobnE9yJl+fAF6R1C499boVgNYCIqvoio+j6EL6e4UnORqqi9DDUZlQ5QKrmUkfuQycXI6KM0n9jm87wSSrVfa64KUq1EcQmgdHD6ICpuJRPUnD0t58c4O1QVUxh4fydO84a3h9EsJp97JGYw9/sVmqB9t3Z06p1fATiM4hCIyq+7i55xaoBG8+MpagjNu2Upo0/RfKJxALTuEDoL5QaRD8vlWxa5vQaOJu1bKBOzlHp+i5OvgDKyr75dken1IRRvkfosdp3dbuKa6+qBtRyZnINsvJtWuWpEH1ML7qLtVc3V7jJQ32UFlF3X5KqRVv9viSWRjEFiOtJa5XPcQUSgBI7lB4CPbQUM4Lwvn06sMcam5VvRDZ1U0mbCKaOZeMa4D/vQPp4QlYjQWZn/lU7ULV3ne/6d5v16ezvqlGruYrUg8TrofkK2jkvy9itdmwi5H+gBqaXNCLV6QQdtGKL8nMyFLoJdkaIMov/2ed/WcJ6iEP2Xz/6agwXJ6eRyHvIhEJVfBb27S4wNNLUe7a3K89JxSa5aB0T5F5olz8v4q4ojkVa/B5oqka7wavjKZo7rMEpBv9G9mXrNcbx2/9toho6TcmjfrYarf3Txh31oH1NoYI5HmO7NNbVGySXkxKOAalf63fpKomFYblJQTPBYA32Qv7RPpr2Zcgd+WpjgiC4QOgURPEYdgzkeoQ9CNvxSkdvbBBZE76H1fM96n8nFLARPVXQLpwGIuoNDGpBUbWBrPpkELnyeonYUg7PDFWBNkRlWApqqbEFFnTDHtu4jtTE+1glYMBzg2p9exv6dddTV1tO5d0fKfcr6h9FyiPKrkbF2kHgLiIPeF1F2meKLeUHvTOkrFQcCoyDmtUpJQvRe/FeEDtg7wBjprnKKld4NtRJ29gEN6v/RQO4gv7ojAmORxo+g4S9ugHRomXaZFw6G16EqYf7QkJG7VTndHN5khi3jyIY/kKsCvTNPBdpWFbb0ZJHeX8lqxJ/F82ZgDFeej8Ephycg2xCTzzmC8VNHs33dTsoqy+jS53Dlq81gDEJU3ISMPqYSJlEJodOL38S1di7HqzkkirexrDkUvb2mXO9EexfFK1+iKSlxat1tA+70YJKcBFCrRFT/DBl5BKz33AclKob5ibI2h4MdtikWv5RnrUy+D1pHpUPo3ltk5EGXQ+dObIKyRdPaNyVPqWXI6OPq9xJVqiKZeJtc+R53mlXromQtAuMVLaTNhx5ah491ApZGu641BbITy2eu5q1H36V+byM9B3fnlKuPo0vfTt47OAwf6IjweRA+r7TNtW6qnGyvp/lEzIZUMe2bYkHJaEoCRSUZEcHCA4LACcrlvuGX7mPudnIvsuEPiJrfkn0xCq0LVP8CUiuR1hqEVomViKHbzyI+crMZdlPrNTkTmVyAqPqRy8HzUYG2FkBgMjJyj9smcb9/ew1E/Dh0Duj9EC2Q2ziM0hEMB+g3Mretv3f7fl7773Q2Lt1MWVUZUy6YxLiTRn5YlnUfXxgjEFUtkBwInausfJqtSJmo5KTYAqtIDExPnouKIvsQrtH9N5CNf3bfL2tRFntaVb+NoVmvCSDKrwHnTEjOQ0oLqQ+EyJ8/2vHL3o9s/AeUXa3M1K15FH5/SWT8JYQ5VsXnbK6e3F+kkmiDbFDt6Y8YPrYcsGJ4/4V5PPHH59m5fjfRA1FWz13Lnd/+L7s2FVMPPoy2gKj4IgSUCKg/DNB6gzWrFe+gK5K5qYKqCBxV5L0c1caLP4V3QmfljTs7ipAauRuZnA/mKF59VPDnry05RKoLbbkKs4G4mqaSdXivPJNqHN3Zn5t85ezD63vS2vhYD6MY6nYd4I5v3MeymauI1EWp3byH5+94hdcffufDPrRPPETgCJcP2pyYtwaJWbRu8tpEhM52d1OtvGs9ry+p5Gsid7it0fxrM4mMv577kLMDGXsUGXsKtCo2rR/J7Te+xt6dRrbU5UcUFsQeQzoN+MZ0pw4AGXuawu++2G/x0Ux1PppHdRBwUg6vPTBdGdSmIZW+zhsPHg5ghxwihCi7BiU54QNjCOgdaBlvCxTHYzSi4ttkApZWrZI+XwJ9EqwNeK8yHXDSUiYS2fgPZORe1T5IzmDOs/9i1nOzqd8HC2aUH4IA1pbeaaAMx1e6BHkvcnBA8R+cnXgXv6XPMWmI4ITCh+1NKthHH4aUn+r1YbQU7zwxS6nbO03fp5WwePfJ2SSih6dPDzVE8GjXK9Bn0SGqlFJ7S4jhaWhdVbzKkkAQ5V9QC1JP2Mqk2zchaZK2kcl5SuE+8RZY82jc+T8e+OkjNO6L8OAfDkX3p9jQUish4+7wj1d8Em6yihKDLhkmBCZ7vFcDMv4yMno/JN6l5fejg8cnogWZjQN763EcjxuBlGxds/2DP6BPC+xNyPiLLhG/Bn+/NBNR8RXVAmspRGdE+Y2FjxuDKL6W8NPfspCiTHV1rMVuEpG+wUnefakCK6HOpdrtARwngl5SIUhDEeJ7uNIYfolWiAIboQyEux+bjPUQ0Kz8g97RVYHu6apApxciptIlM4a4fDivm4fuEofznAXCFxTw/mT8BVd7TXFLZPI9CExumczFYXhi0/KtOHbhOaMbGnu27aPHoNYIJx9GUcio0sizFgCmW2nxWrSZEL4SoZUhW9wPNhEVNymR42yIcjD7QqIIBcD3uk8HpBRE7ye7CrRoZgApJSBIxHVsG4yS7/jpWLAW/0QzhYpLfvEt7W/rDu9QATTvwyi0cmT4Yog9nPV5XLPt8Lnqf7VO4GzxeHUQJS0UI8P/0nvk+twC2FuU/yY2YCGZC/EXEVXfbaWob+vwiUvAyqvKMsr4+ajuWP0BH82nBKlVrjp6+mKpLbKxpcrHWhUtNqCWtWrEWOTpHdk7aXX1JfE6mKORyQXkB7kpZ9Yx69VqarcFiEc1bEug6yW+j9YRUfUtZGoFNP4N3wocFRQmqwEIn6tWt/Y20PuqG0Oz5GATEZyKmo68RbUnku8BQiVHoZPUv7UOqoVrLSP3+zdcZfyg26J0lNJ0vvejU+shfJuE5ExVOUiPkB9Gq9C+ezt2b9lTcEqnLIeqDlXeLzqM1kPG1fSgc4Dmq1oWJKYhA1NoVQXbWgHBroWPJxa3fF8AzmY1QejsIp8g2G9YjMGjg6yYV04y0dJml4Yo/wyIcmTDn8HZ6rGNdHUd68n93gRovRHBE5Cp+SrBFBWQaM6hxnSFVQOqCqnXIGMvqQWjMQARPjtjmSTKzkM2/ovCCdczlYaYtUTJ7+i93apZ7ncjI/cC2UMUSZB1yNiziLIrS/qG2gKfuBZkIBxgzIkjMYO5uaUZNDjxM0d/SEf1yYaMPkKLEqnENHeyshUTgiKv7C0jrhl3K8vHqY3ufgqrZOOPb+SGH29j+BGNLJ9b6JfoD8fV1tmCMIYpzTNPJMAcgkrCgqiE1FRTPuaRyutSBFUgcfZQ/OYgoOyzWa0NU42oV3xTcfIS05EHfqy4ITKOKP+8Gk5Iy0lofRCVt6qVpVYFwWOV1ZOX8ba1xOcYUsiSbE0OoxiOvegozEBu/DICBoMm9KOyfUvOw8MoBTI5y5VBKLGlaG9wVffTGXKplTCR0f7Kendk4lXAyxOxFJjgbEGSIDehgG59LC74wh4u/9ourKRgzaIyUiWHaRsZf0tVgyq+5r+ZbFRVdQLqWAgqXcKKz7ux2o1ribdpNkabw9WUfRr6AET5DVB2ueLCNdyuFvr2FjBGQtnn1GISgAolUxE6FeXhOVYZdBuDKPh9MtpihZ+Z5ILix9jG+MRVwADOvnEqmiZY8MYSVfwMmpx+3YkMmnBYPLLt4YCzoxWvS0cC3f0rRYG/n9rW2a9se6zFFBq9thQJZMNv3XZd3ttpEAjCeZ/fw21f6c1Df+rCZV/ZTSgsEFpzyaOm2hh6L4QxAJkIUthGMJW0R/l16rM4DUi9h1ol1/+AJvmLYmX+NCQi4OrfpTYgo/e5Y+tp/S73ZpF4HWktRVT9HyJ8mev3CC0brzN9ttcKE+TDaDF6DenORbeew/P/eoV4YwIpYfjRQzj3iz76e4dxcEitoOULuGxTbI3SFpM20hiMQCITb0D8DXfhZ9N6/mQMGXkApPf1GwhJ+g2LM3BkjKf+04mrv7GLbn2TGEZz8cRxF31uSxCv+AVoVYiKrygtxtQGpNYOpA2Nf0U6+1FJbb7SveeRqulGDFWRjD7QVIXPjn2ppciG1YjKb6p4Fxjn7rsl8atI3Ul8sCnRJzIB0w2dc24+ldM/dxKxSJyKmjI07RNX7PuIQKC4TK21dkgHrgDNBkFnE7I+3SqI0GYkdru4hZFu6Aw9sgxHDmFf9Eh6dIlAcj4k38U/8FpIvbcKC+ZwtfKVSXIDkYYIHgUYYI6H1DJo/AuF30Mpn7MClfTtRzb+idxgmf2eKRVYrcVKMdwrcDk7wVqrJrTM4RSQkQNjlS9eATRE4LALRVtg+OTBDJs0iIa6CKGyIIHQ4cT2kEFrT2mLHD8YqOuouQqahPqfII3BkFpFm5G+ZXFeVSAkGX+CQyzeg9r9Z9JzbAewV0H8TZB+ygAaGAOa/h08SdE1co45AEFXuFvvo6pRDbe7zhvZpbZSkssm/0cZ+ZfLx/X7PpPI2FOITGUuv8IVd6v0KTBGuHSX7LcKQeY3yP7NTQhMKeFY2w6fyAQsDTNoYAabGyc+jIODcC/OV2m9GbatLpRUc6KqFjjpQYoS/N7ayJzbDGhc9u3LcsmzxhBk8t3iL4w/4/qh6YjKbyEjd4G9kXSwEeXXuYRPC+nsg8Z/0bqgbEL4DABlVtvsajyBTK1HmPmOEdKdAp2vjhEBIoio/EYuCV9UqvJ/9G6aVpM2hC9zRSMPoy0gNEFV+8Px61BDBI93r5uDWNAFjoVk+torIiBNAlJ+LfxDBY0Rx4xj5NQLmx4yeiJTW8DyS8AcSLwJgWNUlSt8DhLLbSW6sSF0truAlOAcQMYey6q6t+z40DoorpZTC6lixH8XqU3ej1tLlANIJilzIHyR8pDMgij7LLLxdneK1E0QjQGI8OktPPaDwyc6ATuMDwYifBZSRtyKkLKRaBGMwYiKmyG1Gpl4E6yFRTYuNUi2TfIFqOCQP7mUXxovgA3Juco4Vu8O9kaEORIZOBbMIQitHSDVQELiDXdfrRhrVweovO+gRN80E6F5KK4nZrlTYNkSLglk4z8RVT/J2VQExoH5W9f701bmwx/g9NBhHEabQeuKKL8BGbkPFbta3o4UoTMhdAoy8R4knjsEB5lG2hasZa8RQQ8ZhhzFeA/ICDL+gppstrchRAUyfL6K13o3wFAxO3Kf20ptbcyVLk9WuBPaJdjZaR4DdTLqJl95v1/sCcW11bKmh7VqRNXP1KLf3gNGrw9leOhwAnYYbQBdXaTh85DJuRB7nNKDmI4ouwKl8TJEWXMUTcAOBgKlTxZpbsPMsSFqXJ0xCdZid6WcQgQmgdbXlZnwQ0oJuibvABlBfScBSFRD5beQiXfc5OtgWxEOJN9RVkLGQPf7KxakLWRynlKcziLZy8Rb3sfi7FVDBe4EUgaiDAITD/LYD+MwPgIwRyFqbkPaO6DhNpqVe8lG6Gxwzb1F6GTkIU3ASuWbpWEqYVmtGzi71GS0vUNpBRrDlG6gb/yxwVqEjNqQfJ+MrANAxY2gdXan3w82fklIPA/ByWqxWsr+nHqktQhhjml6zFqMNxfMRibeV56TORBKsudDzIIOJ2CH0XYQZYpgWjKhVIeKr+RWl1LrD8mhNb1lF9cqyQuhppFlUaWSDmc/suF3BVOSMrUOtJ6oKR8L72qYroKCrMt6PqEmeur/6PIv2oIHYkFqNXA6IjBZtQKag70W2fA7RPXPaeJ4+a06NZBtWFE8jMP4SEJDaB1Uq63E7THHI0JZbatm+FhNaE0lC1WNL9bmM8YqDpRWo/7r7IT4c8jYQ+RUxu0NgKlir7MT/zgkIDmbptig4phs/CfoA2h91T4PaW1CUal8ae01zbwgApH/qAnNtDirtPDmmzm0aUekDXGYmX4YbQu9FyWfVqHzEDk+ZoDRt4QXGmTkE1oEqUaQfRWcbRAViNCJSg3fXqOSJA+JCmXrs17ty5yE7xSOs4PC5EyC3E7bKS/rWd6YoazR7GJwVFXOWtr0UGAint+NCLgr08M4jE84RND1ZywBWmfF48x5vYdsi+drPbTASoGzByU06oPUaii/VsWu1FKVXMn0NGLOhkBM6YiZ4wAv+kBALUQ945QN9ur/b+/MwyWpq7v/OdVVvdx7586ALCowyDKswybgsKksiYCCoKBGNBFeDUElyavRaB5I4pIEI0+e5H3donlxQxPFgKKiKMaILCoMqw6bgWFVRhiYmbv0Ut113j9+1bf7dlf17du35/Zdzud5+pnp7uqqX93qOn1+53fO99C/bh45piaC2ZfSVviTSOjEv+sEa0l2wOoVlguPvjhgIrKjiHxTRCZE5DEROa/DtueLSE1ExpseJ/ZjHP2mUqzw3G+3UA17mKksCSpo+afoxKddu5naUzN/xN8rdsJmrtqShFmJ5I7r4rNVXOVlD91lteSWDBIJIbwDnfgq3TtH4xD+DPJvwBmRPFPaN8G62Y+vJzwkf2LjababvyFA6JZcYiR/cuzI1dsYOUdXht/OUu8CvRRtmEbK85u2UNyW1m1hGVB9CJ34gqusC+9gZodBIP96urp/dJI2R0HyrqJ5JqLf0dvPrw9Db8TZmSSqrkI5ahVH7UD4C8js3xTRz+Mmdbv1OMbZkoW8E5AG4mXFLo/bZL/wdohte7NMThayRzWiZAuMfi1Bfgr3i7UrcDhwnYjco6obUrb/marOb73nLIhqEd/7f//FnTfci3gCIpz0xuM44fXrlvrvUAMtoWMfi0PDFUDQyq0wdD6STegLOIW4ZcXxT7tS51SyKUnbWRj9G9h2GZ2TRNPaC82AVtxNKytBtyZsUAPtQRSxdBXkXwfk4ubfXo/Nxr34UTeesTgrIVPLFlPLEK49B5JBt30UvBchQ+e6PJTw3rgtVJn05Y4ISt+H7GFxgmrWteKo3O0U/GUHJ+aaJMa69FhSNuzB2x/m2k98n3KxTBQpe61dzRv+4kwKox2iJ0sMLX4nrs52kykN7wP/Z8jIu+lkyCV3DKpbofRdOi5dpUTKZPiP0fFPuQhUKvUWPbOlBOWbIDgMwvW039cVCH/NrJfcaushsy8MXwjFa+L0iyd6GKOPc3IlHltdokOZsmOyY2x7496zMgylH6DlGyF3klvSLbwOit+kUeyUktaiW9Dyj+IOICD5UyE4yInratX9ViWJsS4Q5uyAicgwcA6wVlXHgZtF5NvAHwIfnOv+B8EPv3wjd/3oXqqVxgziv792C8OrhjnilLUDHNn8oeX/btFzUff/ya+4H+z6V6f6P04UtfYbyOyMFM50GlIzaGu59jhHJb8Tjc8iD2O2TLoZX+oNOYeQeuk7uNljUgJvp5yPIRDXs4z8axGKaPlWIEKyL3OqztEzEI2j3i5I9CgaTULtSddRQGNHNXoCHf8kMvLnyIq/hOp9aPiISxAu/yi+nq2GrIyOX4GMXho/9yF7FJJybZYiS82GbXr0Ga76+LcIyw37tfGXj3HlR6/mwsvfOsCRzSPRc1D+IdMdkbLTl6pucErqADruHLXwTsB33SGCQ6F0HZ2dmGysup5CN6sFvYqv1n4NNb/D59P68M603/+BySuduv2sc7tGwcs7SSL/IKjc6Aqq/H2Q3MsBz7WM81a56sraJiCCya828uY0hNL30ehZZOiP3JJi5S4gQqMtrtVZ2zVRKF7rHNK6BE5mD6SwR29/g3mmHxGw/YCaqj7U9No9wCs7fOYIEXkW13/hSuAyVU284iJyIXAhwAt32f5NaKNaxO3fu2ua8QIIyyE//cbPlo0DRuVOkg1Q5H74My9x4f3xTzS2q02g4/9KQ8G9lThiQ+DyFNracji0cmPKsZNI6qU4E0pn45ehO2HFVqodxrIKF7VrPa+sm/UF+8fCh25JQ/yDp2/m7QLR87Dt0rjIQUgWvw3R4tXIig+Af3BjP9mj0K2XkOgcRk87cduk0u7lwZKyYbdee3tb2kStGrHp0U088/iz7Lw6QYJkqRE+QPIkq4xW7kH8tUAFHbtseuPt0vVQ6mR/cm7b7LFosC55Gld9pDEpmpE87mc4yW74dG6E3YkehWV1S8obGbdEGT3Rst+47+PQmeDt0bAhhTck7OKF6Nj/jbUc63JFrWMMXcVl/mxn8/KnxkdRVMdiR7mVyFV+p7Z8W7j0Y4F3BGhdy9lKclYfwE+BtcAuuFnnm4H3p+1cVT+nqkep6lGrVm7/pZCwFBLVkiMVY8/3OLNYjLQ2vJ5CXZ4DoJNX026oQtIjPdnGNhOfRSc+5Zprtx2iW5kI4rGkRbN6/XpnnDHpJ95QrNyco5Fw6gM1KP8IHf80uuX9UH0g8eMajbtlDUqQ0PdtGrWN6ORXmWbcZKT75OLlx5KyYc9t2oJG7RMMz8+wdfMysWFeml3INGxbeX3cA7LZXoW4iVJyMjcS9+Ks3ArbLolFQ1spphw7iTDePoluJoApx5EX0F0ie7d4MHJBU55YJv7Xg+g36PgV6LZL0OLXSf7bqWuZFD2Bc7xKpDuINdceLnqm6TVB/LgV3RJixl8oEfmJiGjK42ac696i9c8oMJa0P1V9RFU3qmqkqr8EPgKcO9cT6Re5oRzDK5Mb3u62psfKlUWI5E+ivdIwbhdRr+CZUqXvlgrOeSgDVQjvj4XzWo7ivzTh2EkEsQOWFs3qZgaYFARWGDo75b2ZSLqlAsgdA/6+yMqPIYU3Qu40GnkSdYdqEh3/dLwEEFPdiG77O9j2PmZVNVn5OVq8bvpr2WNITC72Xriko1/LzYbtfchq/Gz7d7ca1njhXrskfGIJEqwlrV+pK/QBrT1M8j2V5jxV4vzQKhCCPudWAKIW3z2zD91JTASQ2Yu5VRIm2b4MZA8Fr5fK5Xpv3ha8HRHvhcjoXyEjFyH518ZLfnFqCkWgCuVb4vSJ+vBKrghiy8VQvYuupTd0Mzr2z0w7v+Bwku2rF7+3+JjRAVPVE1VVUh4nAA8BvoisafrYYUBa8mrbIVhIGXICp7/jFIJckwETIcgFvOptJw1uXPNNcLhTccdnqrLP2zEWJY1p7bE1RYZE563NWFRdTkbUop2TOzqWPWjeRwCZ/eLXMi6JfuiCLiUXOtH69QsgOADx9yW9EkqAHVNeL9CIbgHkILMaycWrWVKA3PGIlx6N0sp653hV1qPj/wzRk7M6I0foWok0j65walzZVK9yzAHDyMjbe9j/4mG52bB1rzmS3FAOL9Mw70E+YN2rj2BkVfKy/9Ij65pEM4SzX3mcKOl5jQmktwvJ93hA++QrxTGh5oqTmpECFM5p2XfWOSxTkanATYgK59J7I+6kcbk0D8n9nhNbTYsYyc60f2UF5xL4TWMPgDwy/I7GNv6BLlcuUWKn4npGRs9C9UHnRIV30JPmmU7E+oYx3s5QOCseU/28A/faIm2BNuccMFWdEJFrgI+IyDtwFURnAcclbS8ipwN3quomETkA+GugC+XI+ePg4/ensCLPT752C889vYUX77MrJ5/38uUzewRAkMLrIXcK1B52ejD+Pky7aXOvgeLXaG/QeiqIB6UbgEln8DQE3ZxwnExcEdO8NOM79ebJf3dRNhlxyem5Y3FViuV4GUFQbwWM3zOH82w2DDnInYIUXu3GVXgNFL9N+yxZgTGX+Fnb3NT+R3Eq+3ElY/YVSHAIBAfTNtfRIsl5JiEUvxmb5Co9Ga4pWpc2ssjoB6B6n+ul5u3gSuYll/jp5cJSs2FDowXe9S8X8JOv38yD6x+mMFLguNcezREnL5P81Tr+Psiqj7sfcQ1dpw1pyDdI7rjpOlKA60s46nq4Tl4VT35igebqg7Tfj9W4Unw6kj0m7rV4B67g6Ago/AEigbv3JYv7+VWULL1rAjaPx4PMfsjweeDtgOR/31UDJtkQfRYYgWANhBtwUfh6NAsgA/5BrltG9mXt+bpaIlWVP/oduu3D8fuz6CiQRDS92l1yvxdXgN7txhscsWidL+ifDMW7gM8DvwM2A++sl2+LyGrgPuAgVX0cOAX4ooiMAJuArwD/0Kdx9I29D92TvQ+d/95QCw5vJXjJujaSOxZlEorX4ZwFr8mBEcifTj04oMWvQ/mntN+wtXZRwurDMP4vTDk1+jwU/x0yO7mS4iZjIP4a1D8gNXdqdkRQe4yp2yI4Fir3pKgyhxDeB6P/ANsuZXq+Rr0MWyA4JPFI2vRD0PIOczZadbzVaPk21+Yj2gKZ3ZHCOa7xeWuSv7GkbNiKHYc5852ncuagBzJwfFeVl4SsQFa8zzWgj56ON98XGbrAVeuNXspUcLP2pMtLaiOH+GtaXqvEEj5NVeSV9W4/Q3/Y4sxI3Nj+s8wtEgbOfj3KlP3yXuAS08vfp93uKlBCgkOcLar8nOnRrBpUN8LIu0mKoilDpOeo1Zj7uQBE8QT7k26lRIbc70v+lEWZcJ9EXxwwVX0OODvlvcdxSa715+8D3teP4xqDRtyMJHcSROPgDdP+lYrF9XKvcs4ARRo3ehbyr5k2KwXQyatISu7Xya8ho3/dPozCm2HsMjompndF3NKn9iRkXgTjl7ckgrYSpSThguuj1mEFq3zTHMYpuDB8p1mzD/5qKH6lsV3tEXT8X5CR9zrBXGMKs2HLlMwezqboGOAnFB/FEf/M7i6vLNxA474LwNsxVm5vonx7HBVrtmEV19Inf1pbxEayh6Ll/aHWj0lkiJZvQApvQks/jKU40qLoIVp7CsIHSc5Dqzr7l6TaH/4C55glOWEzOV8ejer1tPy3jOvTOP6vTNl1LUPpWjR6xvUeXgJYL0ijD2RmTuL2dkBGL3Uh//A+8Eadjk5wRPu20RPJ+4ieYlq6TbQFLf/YaWF1FcIfgeAgCG/rNFDngEWb49y0TkuAtVjANOXYnUrRo00zDzcVTT8mMLUEWkly8kK09C1k5D3JHw3vRIvfdjkc3s5uGTolimcYS4ZEUeiWTYbfgZZviiP5IQQvQ/K/T+vPqFbvJ/n+9JxERbbugNWgcjtaur57e5A/D0rfIF0mowbhRihUofS9lHE0n1TBPRJ9phBVTZfa6LkPZMTMQtqeS5Vos78hVG6BwhnJ1yx6Bi3+p/uNIQu5E5w25QJ1dRbmqIylibcDMvSWLjYcIlkXJ3Br/8HBzvnadhnOwHSZJ+WtcrkcHR0wXKVn9SFmXgrMQfFLpM/4yi6HIalYQUY7aO7MlYiOhreanNSvldth8stMGffot+jE51wCbnBY30dpGIuLDJI7MS5O6oBXT7RvtUsRWnsKiTaDtyM6/kmXbtF1/pcHwb5Q6hRhEsi8MK7M7KK6Uoudq9nDDW41oJXMrhB20imbK510IAMn6uq3OGA6hm77GK6DSpzPVv4xGj2FDF+8ncY5N6wZt7HwyJ9CsgxFhE58Ed3yl+jEl3DLmbNIUtetUPzPDhtknPH090G7ujUqUHu8w/s+ie2Uoi1xXkrr/CcL3p701mh8FngpQpzFa0hc+i1es33HYxhLCMmdQHL1YQjlG9FtH0In/nWWzhdAFiaupPMSn++S72WErsSsy/9FR0dNk5RYqnE1Z2tsLINrRp5NeK+fVBNtmJZvwv09m/8+oVtijX7btv1CwCJgxoJD8qehug3KN8ev1A1JjSmHq/bw7HesSrrDJhCsRYbeilNW7tTHcmqHHfYHzqGbnu+hxW/H/em86dvhO9Xn3GlIeCdauSWOwvW7EXzgVKvb0EZLkFY65sEZhjENb2dk5CJ04gtxtWBLOySA8FfM/t6uQPQoqQ6Yt7NbYcjsFif9d6OG38mZyyHBgdNfqj3q1OynCo1gSr7CPwAZOt9Foso3QHj/dojyBy6NJKk/be0xkp3OjGuV523/LhSzxRwwYwEiLok0+0oY+2gf99tBBTz3GqRwhtPgmvx3OjcC74YAhs5j2ky4ep/rydhqJGQYRv/elajDVC9GnfgchHcxt4qiurYPTs6jcG6jD17rdjIKmpCbsTyacRtG//APQlZ+3C2JRY8lbNDLxKqTMzWKjH4Eoudcakb05Azbz0TGyQ75+ze9VkPHPkG7bfRh5F2IX3fWViBDb4PqA+j4Z5h7VXcGZwMzkF2HDL0xeTNvN5x0X+uyaBRrvi08zAEzFi6VW0k3VHXHph8RoiySXYtWN8LkF/qwzyFkxZ9DZrqMiZZ/SuKSg5aR2hOxcGIDKbwODesJvR0idyhTPTbrXQaa8XaFkT9FvFV0XBrInxEv0TaPMYCciRkYxqyJNvUoojxbPMgeBig69vFYV3GOFF7rKtyb7UWiDhq4xPjbnEBrM/7+zqZ1tdSaYap4qG0Z0YPhC5HgIDq1IpLcK9DyfzHd/vmQ2cM9FiDmgBkLl+ojnd8PDofwXtxNOQcJiuBQ11x86yX05nzVE25djzQZfX9yuFvTxigkzhK9nZHRv0HLP4Tw1+CNuKT+6GlgCOKWKkTPQmaNy9coX996UPdDUPqOE5ecRtVVYYV3uyhc9uVQeD0UvwtMuCqj/FlI7phu/xCGYdSpPcVUv9c2BIIT4oIgIbkxdbfkkfxrXEV4z86Xx1S0vHBW7Hy1oB2cqETbJsjIxWj5Z24yrQrix1pl6vJg/X3dc29HyKyG4rec3MQ0Qpi4AlZdTpsDVt2IVm4CnUCCw2HkPU4cvPaY2zY4Chn6gy7/BvOPOWDGwiXz4jjXK2EJLnsi4u+J+vu5m7b3g7iZWrQpRam/W8RVChbOjrWCfuUMTGa3xhbB0Wj1Edpng1HcZSABbwek8CbX4agDWrwmluNIMuI1lxMyzQGromP/FP9IVNz4K7dD4Rxk1eW4WaTPAuqwYxiLi6leiUmMQvZgFyGq/AxqD6Vs181xVrlH8YYedxA7NbIKhi9CdAuUfuAqHYNDGu/7+5HsTOaQ4MjUfUvuBNe6qBO1J9Cxy+lcvf0A+A1JHC3/BIpXUxfs1vB+yOyGrKhL9NUjagsXc8CMBYvkT4lbaTTflAKsgsqNaCVDe7i6dScrZ5gV1qB6L41cqV5mobFRCu+A8A6UKB7T1U7EcOQDiFeA3DqnYVN7Chfx8nAtj97C3CofK3Hfx05VTy3nVb6tyfmCqbLt4tWQXZcgSGkYxqzI7OEaYkdPMN1xyeCiOl/A3Zed7ttc/NkOcg/Rb1AtA71Gv+Kx6VYY/2eUctN4M3F+18FOib7wBih+g0brtZwTdc4m6DnOAi1dz4xVm9r0N9Ri7Hy1iN3WnnJCuLlj5zSe+WJhu4fG8sbbFRn5s1iJOW4S6x+IS6av0uhf1mkfu9BoPp2CjLrlvdTm262kN+Zta8MRPQ3j/4jWnnERptypMPQWCI6G3InIir9Ccuu6PG4K0VY638pem5iqhneSPNvMdFD3NwyjewRZ8Wexhl6c4+TthrNHkzj7NZNURBj3ku1E0PToblzJVONxNTuLNRj/ZKyYfyciOdeeKPtKJ0Q7fL6z0R1ys7qiVhfZTt3AKeNPDfV/Uo5ZQcP1cxvLPGIRMGNh4++LjH7I5RiIH+t/daFvU0cyMHJx3FsyKXweOHHFzItoJLE3kwFv91isMBYezKyO9b+6zBeLnoaxD6H1202yyIr39q8s2ltJuvES1/Ou0FI51Npcdwptaw1lGEaPyBAyfCFQBa1C7XF0/FOz2QEU3gbh35BcxZ2B7PGIeGj2eKjczPRomefyQ2uP4OxX5OxF9CzdR/sVxv6hYb+I0vPEeiWzOu4GkGTHgriHZpNdknzKtrh81kWCOWDG4mDq5ptNsn0WyR4L/ho0s3dyU+3CG6cqZGTFe5xx1In4zQwyfIGLHkW/de2JvN2BCN32t7M8gSYNMy2j459CRj9Kf3KssnHT3RuYHtXynK5Y4TRalzgl9wqXfN8aBZN8ej6aYRg94rsE9Hq+Zbdk9kS8IXToTTB5RcL7eyND5wAgQ+eiujXWGPOBELLHIEPnuQlsbaOTosms7iCPkUaT/QIoXutyZ/tUXSiF0xPsUQa8PZCRd7QLr/r7OFvVlrCfRXKv6MuY5gNzwIxFhQRHoeGDzFzW7ENwAGSPdtWBtY0J22QRaXJMMrshK//e9YLUqmtmXQ9zey9qilipk5ioNS/V1eUguiTaFjf97pcBOwP1hqF0vXMgvRc7vRx/TfIH/H2h8FpnSOs6O5KLlxMsM8EwtguZfem+0noEGT7f/bdya8o2VRpLjz4y/Ceuh230rEvd8OJ2PTIEftNSZu4VULyyZV8B3a8uVNHyrcjQm7rcfga8FyEj70GLV8WrCwXIn4LkTyXZHnnIyJ+h4/8ndsIEqEHhTGfbFgnmgBmLi+xRUL4lLjNOE/gTl6NQONeJAZZuINk5qqDhnUh23fTPzuAUafnHCS2IMu7Grz7Q5YmIc/L6hiC5kyF3cvefyP0eZI9x+RRSiJ01c74MY7sheSi8GYr/gXPE0pYBVyIrP+JU5YvfSbcrtY3xPpruW2+HzuLJ0fNQ/HrCG3VHrhsnTJm7wGoL/l7Iig90v31mN2Tlx5z90klnf2Wkv2PazpgDZswjNbT0Y1cJSBWCo90MZ1Y5Rxknchreg5ZujKNQrTNKhegpmLwCDe8lPVomvd2wpR8l7LPqBAdzZ0P5W13sxIsjbANGRpyemmEYM1N9CC1dB7Xfgb8ayZ8x6yi25I4Df0+0cjOUbyTZCRtDq/fBxOfj99Oi6wGzTWNwleUpcjWFN0LxKmZ2wnJI9qWzOu72wYvlMRYnNt015g2d+AyUvu2S0qNnoXxDrP0yW/HTDAQvRQpnkzqHUI1FWjstVfq95QtM5Yi1UovlIJKoG0kPCOKlhTlWDhmGMW9o5W50/BNOEV6fd5PAscvjaPwsyezm9P1SJ4Be3Hg7JN0+BpA9llnnkUZbSJa1iGJJnplSKeJ+jP5M1ZnGTJgDZswPtccgfIjpM6uqc8TCu3vbp/+SOMeh1QAF4OVJd77iku3CG9raBXV33LQkdSW932TdqAkuufQFsz+uYRgDQuNlu9bIUAUtXt37bnMn0S4fETgR547txwLw1yBD5876kBLsT7I0TwjhY6Rrjkn8iMBbjYk0zx1zwIz5obqR5JlVGQ0TqhO7QpCRP48rZHJAninHytuRZAMRQO4UZOXHe66WkcI5JGvuKDNH82pACZ34LHNrsm0Yxryh5eRG9QDVHiJgMZI/FbJH4iL5BZxjtR/kTyfVPngvQkYviQtmehBwDg6LtRWTGOvwQWXKxpW/54qIjDlhOWDG/ODtQLK/H7SXGM9qvzsjox+B2hMuETPzEpdTVnscLd9M+4zVQ/Kng8wgztqJzO6Q2Q9qG3rfR7QFomdioVjDMBY0EtDo+dqCNzqHHXvI0PmQP8ulZng7TbUwUlmR0B4ti+Rf3cGB6oaMqxac+AyJ+bNdUUUr65HC7nMYh2ERMGN+CNbGTk9rVMqbuxI84oT8/AMaCf2Z1U5mAR8XHXMPGblods6XlmKl+RbDJGmRrm7zuuJQvmEYi4CMk25oi3xnIXfa3Hfv7eC6fHg7xy8IMvJuYARnu7Lu2Nl1ccSsW2qu6rFlWVGo0b1yfhpmv+aKRcCMeSKDrHg/Ov45J2qKgLcKGfpfICu2yxEl9/sQvAzC+9wMNljbfcWljqMTX4Tq/fFYVyJDb2tU3ARHQmJjbXDGcgadMhkBb9euzwVqoJV4/JZ7YRjzjRReh2oYa3J5gEL+dFfVuD3IvBhZ9TEIN0A0DsGaWUTMFS39EErfwzlKArmTkcJZ7v9+mh5ZljbR1UR8ZFaOoLr+jZLDio8amANmzB/ezsjoJXEVTq1DnlY/j7myh8asio59AqInmTJE0WZXAZVdB5W749cDpppYI4DvtMfKt8YNeJNmiAHgIcN/THfnHqKTX4fKL9z+vFXI0FviJN25oFB9FA03IF4OgqM6awcZxrIngwy9GQqvc/lg3g7MPYo0E37cS3J2aPkWKF3HtIlg+cdo9JzL3Yo2u0mgbqMhc5F1khrBkVD6JknpG2473xUPdFnApJX1TtpCJ3CRxBNjR3COjli0DcL1aDSBBAfETuXimpyaA2bMP96qQY+gM7Un4yhd6ywwjDXM6suRGSDnWnJ4q1xSf2Y1KjmYbFWZzoC3C5J7JWRf1qEX43R04vNxa5HYGEab0fHPICve75ZZe0LRyS9BxTXkVnwofhuGLlgg2j6GsYCR/MLvl1r6Hu1R+AqEtzWeahnw3dKnBEhwpFve1Apa+lbCTgVyr0KyR7k82G6o3g+Tzf17nVSPUm3vTzsbqvej45+hPgHW8g0QHOg6ASyizKrFM1LDmC+izaTfGs25YHGoPjgMGXprwyEq/Yh2563mku6zx3TtfBFthfCXtM9EQ7T0g+72kUR1A1TuomGgq+4Yk190OW+GYSxu0io226hCtA0ZfqebGJKByh0p22YQb+funS9Ai98lyX5RvomZ28mlUUXH/y3+fH3fFQjv7zD2hYk5YIbRSmYPuheHLUPt0ekvpRo/cZWa3RI9R+oSR+3p7vfTgpZvI7mNiDiRScMwFjfei7vfNto0/bluI1kJP5yFY1ff9zMpb4jLa+uFar39UisVNLVn5sLEHDDDaMV7QVxp1Kyxk5ZbELSXhPv7pWyfm93ya2ZXkkURPfD37n4/baSdi3R4zzCMxYITaO0yP63VJvn7kqwvlu0gQp1Capsmbw7yHUK6XMbicmkW12gNY56QoT+CwtmuLFxGITg2LhFvTRwNkJYkfym8FicK603bjqE3kXjL6SRauh4d/yRa/EZj1ihDkHsl7cYwQPKv6v3ccsck7BNAITig5/0ahrFA8PdDRv53PBkcgczeELycJFtC7syWz64Bfy+mO3CBe81fk3AwdW2ZJj6LTvwbVH9F3UFyyfYJ0h2FM+g5Bd3fK+WzWSR3fG/7HBCWhG8YiXhI7mTIndx4KRpDi1e6snAUMqudo9baz83bBRm9xOVpVX8N3guQ/GnJxit6Hh27zJVoE7rk0vJNyMifulYjhXNQbyco3wDRBPj7IkPnNOkF9YB/AOSOh/LNuFB+BlBk5I/pSVnbMIyFh78PMvLephcULe0Epetx+VMFKJyF5F7W8kFBRi5Gyz91Fd0I5I5Dci+nPUKu6MQXILyHelqDhr+E7DpXrZ1ZjYy8Fy1e48SyvZWQOz2eBPZKBhm5CB3/JI3uIx4ER7jHIsIcMMNIRKG6Aa38CmTEGQxvJ2T4XbhlwYiOzoq3kzNAMx2l9B3QcRo5DS6xXye/7BT+ESR3IuROnNvpTENcI+Dsy11CPnnIHtGhMbBhGIuO6Hdo+eegRSR7KPgHuNZH+Ve5CshEYew6fvsENInqxriXb3NCfQUqP3c2K7Mb+HshK/6iH2fUNLw1yMrL4mKiCTep7LkqfHCYA2YYbdTc7Kr6CG5Wl0HLP4ChtyPZw+nrbRP+ksSE0uh5iMbm2OZkBjIvdg/DMJYUWv4FFL+Csy01l5weHIAMXwRI32Q0NNxAcjVjBNX7nAO2vZAhF8lfxFgOmGG0Urkdqg/TqBSs4WQavkByddAcSG2LpCC2HGgYxizRUux8hTSqucsQPhBHq/qHeAWSJ6QZXB6s0QlzwAyjBa3cRvKsTmLHrI/kTqQ9STUDwcELX+zRMIyFR/VBkn/ay06Vvp8ER5O6jGmizjNiDphhtCJpS4xxG45+Hip3MgQvxTlheSAL3u4uud8wDGO2SIaOsjn9xFuJDL8D1zA8P/WQkXeCDPf3WEsQywEzjBYkewIaPkB7FCyYo/5WEh4yfAFEZ8ZVQjt10M4xDMOYAT9NSia7fRqHB4chqy6H6kO4Rt/7Y65Fd1gEzDBaCQ6JkzuD+JEDCsjIu9lut4y3kyuhNufLMIw54SMj78LZrRxTdix3cqwLtj3Igr8W/IMx56t77C9lGG3EMg25k1ziqgxB9lBMI8swjEWBvx+y8h9dlbWWIDjQTfKMBYU5YIaRhrcL5HYZ9CgMwzBmj+Qhe/SgR2F0wBwww+iG6HkI7wIiCA6bmxK9YRjGvFKFyt0QbQZ/D/APxPq+Dh5zwAxjBrR8KxT/o/4MitdC/gynKm0YhrGQiZ5Fxy53S5GEQODapa34C5O6GTCWhG8YnYi2xs5XGD+q7t/Sd6H2m8GOzTAMYwZ04kug23DC0pH7N/otWvrugEdm9MUBE5GLRWS9iJRF5ItdbP8eEXlaRLaKyOdFUuXADWOwhPeSHKqv9V/U0BgYZsOMJYmWoPYITsOwmSpUfjGIERlN9CsC9hvg74DPz7ShiJwKfBA4BXgJsDfw4T6NwzD6jNJuvOqvJ/RwNBYrZsOM5YUm2TVjPumLA6aq16jqt4DNXWz+NuAKVd2gqs8DHwXO78c4DKPvBIekvOEj2SPndSjG9sNsmLEkkTxk9qQ9ip+xCskFwCCS8A8Grm16fg+wq4i8QFXbjJ+IXAhcGD8dP/ZVhz44D2NMYyfg2QEef5As53OHxPN/+0AGMgAGfe33HOCxk1isNmzQ13HQLOfzTzj3XwNXDGIs882gr3uq/RqEAzYCbG16Xv//ChJmn6r6OeBz8zCuGRGR9ap61KDHMQiW87nD8j7/5XzuKSxKG7bcr+NyPn8794V57jMuQYrIT0REUx4393DMcWC06Xn9/2M97MswDKMjZsMMw1iIzBgBU9UT+3zMDcBhwFXx88OATUmhe8MwjLliNswwjIVIv2QofBHJAxkgIyJ5EUlz7r4MvF1EDhKRHYBLgS/2YxzzwMCXEQbIcj53WN7nv+TPfZnYsCV/HWdgOZ+/nfsCRLQPpagi8iHgb1te/rCqfkhEVgP3AQep6uPx9u8FPgAUgKuBi1S1POeBGIZh9IDZMMMw5pu+OGCGYRiGYRhG91grIsMwDMMwjHnGHDDDMAzDMIx5xhywWTLbnnGLHRHZUUS+KSITIvKYiJw36DHNF8vtWjcjIjkRuSK+5mMicpeInD7ocRlzZ7l9r82GLZ9r3cxisGGDEGJd7NR7xp2KS8Bd6nwKqAC7AocD14nIPaq6YaCjmh+W27VuxgeeAF4JPA68GrhKRA5R1UcHOTBjziy377XZsOVzrZtZ8DbMkvB7RET+DthdVc8f9Fi2FyIyDDwPrFXVh+LXrgSeUtUPDnRw88hyuNbdICL34ioDrx70WIy5sxy+12bDHMvhWnfDQrNhtgRpdGI/oFY3XDH34HrhGcsIEdkV931YDlEDY+lgNswAFqYNMwfM6ERrzzvi5ysGMBZjQIhIAHwV+JKqPjDo8RjGLDAbZixYG2YOWBPboWfcYqe15x3xc+t5t0wQEQ+4EpdDc/GAh2PMgNmwNsyGLXMWsg2zJPwmtkPPuMXOQ4AvImtU9dfxa4exgEK4xvZDRAS4Ape8/GpVDQc8JGMGzIa1YTZsGbPQbZhFwGbJLHvGLWpUdQK4BviIiAyLyPHAWbjZxJJnOV3rFD4DHAicqarFQQ/G6A/L6XttNmz5XOsUFrQNMwds9lwKFIEPAm+N/3/pQEe0fXkXrnz5d8B/AO9cJuXbsPyu9RQisifwJ7iy/adFZDx+vGWwIzP6wHL7XpsNWz7XeorFYMNMhsIwDMMwDGOesQiYYRiGYRjGPGMOmGEYhmEYxjxjDphhGIZhGMY8Yw6YYRiGYRjGPGMOmGEYhmEYxjxjDphhGIZhGMY8Yw6YYRiGYRjGPGMOmGEYhmEYxjzz/wGcPfrddI6D3QAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig,aux = plt.subplots(2,2, figsize=(10,10))\n", "\n", "ada = AdaBoostClassifier(clf, n_estimators=2, learning_rate=0.1)\n", "ada.fit(X,Y)\n", "xx,yy = np.meshgrid(np.linspace(-1.5,2.5,50),np.linspace(-1,1.5,50))\n", "Z = ada.predict(np.c_[xx.ravel(), yy.ravel()])\n", "Z = Z.reshape(xx.shape)\n", "aux[0,0].scatter(X[:,0],X[:,1], c = Y)\n", "aux[0,0].contourf(xx,yy,Z,alpha=0.3)\n", "aux[0,0].title.set_text(\"Dos estimaciones\")\n", "\n", "ada = AdaBoostClassifier(clf, n_estimators=5, learning_rate=0.1)\n", "ada.fit(X,Y)\n", "xx,yy = np.meshgrid(np.linspace(-1.5,2.5,50),np.linspace(-1,1.5,50))\n", "Z = ada.predict(np.c_[xx.ravel(), yy.ravel()])\n", "Z = Z.reshape(xx.shape)\n", "aux[0,1].scatter(X[:,0],X[:,1], c = Y)\n", "aux[0,1].contourf(xx,yy,Z,alpha=0.3)\n", "aux[0,1].title.set_text(\"cinco estimaciones\")\n", "\n", "ada = AdaBoostClassifier(clf, n_estimators=7, learning_rate=0.1)\n", "ada.fit(X,Y)\n", "xx,yy = np.meshgrid(np.linspace(-1.5,2.5,50),np.linspace(-1,1.5,50))\n", "Z = ada.predict(np.c_[xx.ravel(), yy.ravel()])\n", "Z = Z.reshape(xx.shape)\n", "aux[1,0].scatter(X[:,0],X[:,1], c = Y)\n", "aux[1,0].contourf(xx,yy,Z,alpha=0.3)\n", "aux[1,0].title.set_text(\"siete estimaciones\")\n", "\n", "ada = AdaBoostClassifier(clf, n_estimators=10, learning_rate=0.1)\n", "ada.fit(X,Y)\n", "xx,yy = np.meshgrid(np.linspace(-1.5,2.5,50),np.linspace(-1,1.5,50))\n", "Z = ada.predict(np.c_[xx.ravel(), yy.ravel()])\n", "Z = Z.reshape(xx.shape)\n", "aux[1,1].scatter(X[:,0],X[:,1], c = Y)\n", "aux[1,1].contourf(xx,yy,Z,alpha=0.3)\n", "aux[1,1].title.set_text(\"diez estimaciones\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Es evidente que al ir incrementando el número de etapas del algoritmo el número de puntos mal clasificados va reduciéndose y por lo tanto mejorando el ajuste. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Método de ajuste.\n", "\n", "Supongamos que tenemos m instancias, es decir m observaciones o m filas en nuestro dataset. Entonces a cada instancia le damos un peso $w^{(i)}$. Observar que con la notación i nos estamos refiriendo a la instancia i-ésima. En una primera pasada, todas instancias tendrán el mismo peso igual a $\\frac{1}{m}$.\n", "\n", "Entonces con estos condicionantes, un primer predictor es entrenado, y con los errores que se cometan en este predictor, se calculan los porcentajes de errores cometidos, ponderados con los pesos que se tengan para ese predictor. Es decir se calculará para cada predictor la denominada tasa de error $t_j$ de la siguiente manera:\n", "\n", "$$r_{j}=\\frac{\\sum_{i=1,\\hat{y}_{j}^{(i)}\\neq y^{(i)}}^{m}W^{(i)}}{\\sum_{i=1}^{m}W^{(i)}}$$\n", "\n", "Observemos que con la notación empleada con el índice j nos estamos refiriendo a la predicción en la etapa j y con el índice i a la instancia i-ésima. \n", "\n", "En la fórmula anterior, $\\hat{y}_{j}^{(i)}$ hace referencia a la predicción en la etapa j-ésima, es decir la que se obtiene con el j-ésimo predictor para la instancia i-ésima. Entonces de acuerdo con esta notación, en el numerador de la fórmula anterior lo que estamos sumando son los pesos de las instancias en las que el predictor se equivoca, y lo dividimos por la suma total de los pesos.\n", "\n", "Una vez calculado este valor para la etapa j-ésima, se calcularía el valor de $\\alpha_j$ mediante la siguiente fórmula.\n", "\n", "$$\\alpha_{j}=\\eta\\cdot log\\frac{1-r_{j}}{r_{j}}$$\n", "\n", "```{index} tasa de aprendizaje, learning rate\n", "```\n", "\n", "Donde $\\eta$ se denomina tasa de aprendizaje (learning rate en terminología anglosajona)\n", "\n", "Veamos ante de seguir adelante cual es la representación gráfica de esta función:\n", "\n", "![grafica](figuras/grafica.PNG)\n", "\n", "De acuerdo con la figura anterior podemos concluir que cuando la tasa de error es alta, es decir cercana a uno entonces, el valor de $\\alpha_{j}$ toma valores negativos y tiende hacia menos infinito cuanto más cerca de uno se encuentre. Por el lado contrario, si hay pocos errores, entonces la tasa de error estará cercana a cero y en consecuencia $\\alpha_{j}$ tomará valores positivos, más altos cuanto más se acerque a cero la tasa de error.\n", "\n", "De acuerdo con esta consideraciones, ahora lo que procede es calcular los nuevos pesos para cada instancia i (i=1,2,....m), de tal manera que si en la etapa j-ésima no ha habido error de clasificación entonces se asigna el mismo pesos que tenía anteriormente, en caso contrario, el peso se incrementa de la siguiente manera:$W^{(i)}\\cdot exp(\\alpha_j)$.\n", "\n", "Es decir los nuevos pesos se asignan según lo indicado a continuación.\n", "\n", "$$\n", "W^{(i)}\\longleftarrow\\begin{cases}\n", "W^{(i)} & si\\ \\hat{y}_{j}^{(i)}=y^{(i)}\\\\\n", "W^{(i)}\\cdot exp(\\alpha_{j}) & si\\ \\hat{y}_{j}^{(i)}\\neq y^{(i)}\n", "\\end{cases}\n", "$$\n", "\n", "Pero como estamos hablando de pesos, todos los valores anteriores son normalizados, es decir se dividen por $\\sum_{i=1}^{m}w^{(i)}$.\n", "\n", "Finalmente, un nuevo predictor es obtenido utilizando estos pesos nuevos, y se vuelve a repetir de nuevo todo el proceso, hasta que finalice el número de ciclos que se le haya indicado o cuando se tenga una cierta convergencia del proceso.\n", "\n", "Para hacer la predicción final, el algoritmo AdaBoost computa todas las predicciones hechas con este procedimiento indicado anteriormente y la predicción final la hace vía voto mayoritario, es decir:\n", "\n", "$$\\hat{y}(x)=argmax_{k}\\sum_{j=1,\\hat{y}_{j}(x)=k}^{N}\\alpha_{j}$$\n", "\n", "Donde N es el número total de predictores empleados.\n", "\n", "### Adabost en Scickit Learn.\n", "\n", "Como es habitual en Scickit Learn, se pueden utilizar dos versiones de esta metodología, una para clasificación y otra para regresión: [AdaBoostClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html) y [AdaBoostRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostRegressor.html) respectivamente. \n", "\n", "En la relación anterior se han añadido los link correspondientes donde se pueden ver los parámetros y atributos que poseen ambas clases para trabajar con ellas, por lo que no se cree oportuno profundizar más en esta cuestión, ahora bien, si se cree oportuno comentar que Scikit Learn usa uan versión de AdaBoost denominada SAMME, y además con el parámetro *algorithm* existe también la posibilidad de utilizar una variante de SAMME denominada *SAMME.R* que se basa en probabilidades de clase en lugar de predicciones y generalmente tiene un mejor rendimiento\n", "\n", "### Ejemplos con Scikit Learn.\n", "\n", "A continuación vamos a ir mostrando una serie de ejemplos que ayudan a entender cómo utilizar este algoritmo con Scikit Learn.\n", "\n", "El primer ejemplo va a consistir en hacer una predicción con un arbol de decisión de profundidad 1 (modelo stump definido anteriormente), y calculamos la acuracidad obtenida. Después utilizaremos este mismo modelo como base dentro de un modelo AdaBoost y veremos cuanto mejoramos esta acuracidad." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Modelo test Score: 0.916, Modelo training Score: 0.915\n" ] } ], "source": [ "from sklearn import datasets\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.svm import SVC\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.ensemble import AdaBoostClassifier\n", "#\n", "# Cargamos el conjunto de datos breast cancer \n", "#\n", "bc = datasets.load_breast_cancer()\n", "X = bc.data\n", "y = bc.target\n", "#\n", "# Creamos los conjuntos de emtrenamiento y test\n", "#\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1, stratify=y)\n", "#\n", "# Creamos el pipeline\n", "#\n", "pipeline = make_pipeline(StandardScaler(),\n", " DecisionTreeClassifier(criterion='entropy', max_depth=1, random_state=1))\n", "#\n", "# ajuste del modelo\n", "#\n", "pipeline.fit(X_train, y_train)\n", "#\n", "# Model scores on test and training data\n", "#\n", "print('Modelo test Score: %.3f, ' %pipeline.score(X_test, y_test),\n", " 'Modelo training Score: %.3f' %pipeline.score(X_train, y_train))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como podemos ver el modelo obtenido ofrece una buena acuracidad y además los valores de la misma son muy similares tanto para el conjunto de entrenamiento como de test, por lo que se podría concluir que puede ser un buen modelo con buenas perspectivas para ser generalizado a cualquier conjunto de datos.\n", "\n", "Veamos no obstante si ahora esas tasas de acierto las podemos mejorar utilizando un modelo de tipo AdaBoost." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Modelo test Score: 0.937, Modelo training Score: 0.923\n" ] } ], "source": [ "#\n", "# Estandarizamos el conjunto de datos\n", "#\n", "sc = StandardScaler()\n", "X_train_std = sc.fit_transform(X_train)\n", "X_test_std = sc.transform(X_test)\n", "#\n", "# Creamos el clasificador anterior que será la base de AdaBoost\n", "#\n", "dtree = DecisionTreeClassifier(criterion='entropy', max_depth=1, random_state=1)\n", "#\n", "# Creamos el clasificador AdaBoost\n", "#\n", "adbclassifier = AdaBoostClassifier(base_estimator=dtree,\n", " n_estimators=100,\n", " learning_rate=0.0005,\n", " algorithm = 'SAMME',\n", " random_state=1)\n", "#\n", "# Ajustamos el modelo\n", "#\n", "adbclassifier.fit(X_train, y_train)\n", "#\n", "# Sacamos la acuracidad del modelo\n", "#\n", "print('Modelo test Score: %.3f, ' %adbclassifier.score(X_test, y_test),\n", " 'Modelo training Score: %.3f' %adbclassifier.score(X_train, y_train))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como podemos observar, se ha obtenido una mayor acuracidad tanto para el conjunto de datos de entrenamiento como de test, y además la generalización del modelo también es bastante aceptable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gradient Boosting.\n", "\n", "```{index} Gradient Boosting\n", "```\n", "Otro popular método boosting es el denominado *Gradient Boosting*, el cual se genera mediante un conjunto de árboles de decisión individuales, que son entrenado de forma secuencial, de tal manera que cada nuevo árbol creado trata de mejorar los errores de los árboles anteriores. La predicción de una observación se obtiene agregando las predicciones de todos los árboles individuales que conforman el modelo.\n", "\n", "
\n", "https://www.cienciadedatos.net/documentos/py09_gradient_boosting_python.html\n", "
\n", "\n", "Gradient Boosting es muy parecido al algoritmo AdaBoost ya que permite emplear cualquier función de coste, siempre que esta sea diferenciable. La flexibilidad de este algoritmo ha hecho posible aplicar boosting a multitud de problemas (regresión, clasificación múltiple...) convirtiéndolo en uno de los métodos de machine learning de mayor éxito. Si bien existen varias adaptaciones, la idea general de todas ellas es la misma: entrenar modelos de forma secuencial, de forma que cada modelo ajusta los residuos (errores) de los modelos anteriores.\n", "\n", "Se ajusta un primer *weak learner* $f_{1}$ con el que se predice\n", "la variable respuesta y , y se calculan los residuos $y-f_{1}(x)$\n", ". A continuación, se ajusta un nuevo modelo $f_{2}$ , que intenta\n", "predecir los residuos del modelo anterior, en otras palabras, trata\n", "de corregir los errores que ha hecho el modelo $f_{1}$ .\n", "\n", "$$\n", "f_{1}(x)\\approx y\n", "$$\n", "\n", "$$\n", "f_{2}(x)\\thickapprox y-f_{1}(x)\n", "$$\n", "\n", "En la siguiente iteración, se calculan los residuos de los dos modelos\n", "de forma conjunta $y-f_{1}(x)-f_{2}(x)$ , los errores cometidos por\n", "$f_{1}$ y que $f_{2}$ que no ha sido capaz de corregir, y se ajusta\n", "un tercer modelo $f_{3}$ para tratar de corregirlos.\n", "\n", "$$\n", "f_{3}(x)\\thickapprox y-f_{1}(x)-f_{2}(x)\n", "$$\n", "\n", "Este proceso se repite M veces, de forma que cada nuevo modelo minimiza\n", "los residuos (errores) del anterior.\n", "\n", "Dado que el objetivo de Gradient Boosting es ir minimizando los residuos\n", "iteración a iteración, es susceptible de overfitting. Una forma de\n", "evitar este problema es empleando un valor de regularización, también\n", "conocido como *learning rate* ( $\\lambda$ ), que limite la\n", "influencia de cada modelo en el conjunto del ensemble. Como consecuencia\n", "de esta regularización, se necesitan más modelos para formar el ensemble\n", "pero se consiguen mejores resultados.\n", "\n", "$$\n", "f_{1}(x)\\approx y\n", "$$\n", "\n", "$$\n", "f_{2}(x)\\thickapprox y-\\lambda\\cdot f_{1}(x)\n", "$$\n", "\n", "$$\n", "f_{3}(x)\\thickapprox y-\\lambda\\cdot f_{1}(x)-\\lambda\\cdot f_{2}(x)\n", "$$\n", "\n", "$$\n", "y\\thickapprox\\lambda f_{1}(x)+\\lambda f_{2}(x)+.....+\\lambda f_{m}(x)\n", "$$\n", "\n", "Para clarificar esta idea, a continuación procedemos a mostrar un ejemplo, en el que se puede ver en acción el fundamento del algoritmo Gradient Boosting" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import scipy\n", "import matplotlib.pyplot as plt\n", "from sklearn import tree\n", "from IPython.display import Image\n", "%matplotlib inline\n", "from sklearn import preprocessing\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Generamos los números aleatorios\n", "np.random.seed(42)\n", "X = np.random.rand(100, 1) - 0.5\n", "y = 3*X[:, 0]**2 + 0.05 * np.random.randn(100)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeRegressor(max_depth=2, random_state=42)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "\n", "# Ajustamos mediante un árbol de decisión\n", "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n", "tree_reg1.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeRegressor(max_depth=2, random_state=42)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Obtenemos los residuos de la prdicción anterior\n", "y2 = y - tree_reg1.predict(X)\n", "# Ajustamos de nuevo los residuos\n", "tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\n", "tree_reg2.fit(X, y2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "repetimos el proceso anterior" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeRegressor(max_depth=2, random_state=42)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y3 = y2 - tree_reg2.predict(X)\n", "tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\n", "tree_reg3.fit(X, y3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lo que hemos obtenido es un conjunto que contiene tres árboles. Se Pueden hacer predicciones sobre una nueva instancia simplemente sumando las predicciones de todos los árboles obtenidos con este procedimiento:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "X_new = np.array([[0.8]])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.75026781])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))\n", "y_pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La figura siguiente representa las predicciones de estos tres árboles en la columna de la izquierda, y las predicciones del conjunto en la columna de la derecha. \n", "\n", "En la primera fila, el conjunto sólo tiene un árbol, por lo que sus predicciones son exactamente las mismas que las del primer árbol. \n", "\n", "En la segunda fila, se entrena un nuevo árbol con los errores residuales del primer árbol. A la derecha puede ver que las predicciones del conjunto son iguales a la suma de las predicciones de los dos primeros árboles.\n", "\n", "Del mismo modo, en la tercera fila se entrena otro árbol con los errores residuales del segundo árbol. Puede ver que las predicciones del conjunto mejoran gradualmente a medida que se añaden árboles al conjunto.\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_predictions(regressors, X, y, axes, label=None, style=\"r-\", data_style=\"b.\", data_label=None):\n", " x1 = np.linspace(axes[0], axes[1], 500)\n", " y_pred = sum(regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)\n", " plt.plot(X[:, 0], y, data_style, label=data_label)\n", " plt.plot(x1, y_pred, style, linewidth=2, label=label)\n", " if label or data_label:\n", " plt.legend(loc=\"upper center\", fontsize=16)\n", " plt.axis(axes)\n", "\n", "plt.figure(figsize=(11,11))\n", "\n", "plt.subplot(321)\n", "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h_1(x_1)$\", style=\"g-\", data_label=\"Training set\")\n", "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", "plt.title(\"Residuales y predicciones del árbol\", fontsize=16)\n", "\n", "plt.subplot(322)\n", "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1)$\", data_label=\"Training set\")\n", "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", "plt.title(\"Ensemble predicciones\", fontsize=16)\n", "\n", "plt.subplot(323)\n", "plot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_2(x_1)$\", style=\"g-\", data_style=\"k+\", data_label=\"Residuales\")\n", "plt.ylabel(\"$y - h_1(x_1)$\", fontsize=16)\n", "\n", "plt.subplot(324)\n", "plot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1)$\")\n", "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", "\n", "plt.subplot(325)\n", "plot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_3(x_1)$\", style=\"g-\", data_style=\"k+\")\n", "plt.ylabel(\"$y - h_1(x_1) - h_2(x_1)$\", fontsize=16)\n", "plt.xlabel(\"$x_1$\", fontsize=16)\n", "\n", "plt.subplot(326)\n", "plot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$\")\n", "plt.xlabel(\"$x_1$\", fontsize=16)\n", "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Veamos a continuación, cómo aplicar esta técnica de forma directa utilizando la clase *GradientBoostingClassifier* al conjunto de datos titanic.csv " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
101113Sandstrom, Miss. Marguerite Rutfemale4.011PP 954916.7000G6S
111211Bonnell, Miss. Elizabethfemale58.00011378326.5500C103S
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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "1 2 1 1 \n", "3 4 1 1 \n", "6 7 0 1 \n", "10 11 1 3 \n", "11 12 1 1 \n", "\n", " Name Sex Age SibSp \\\n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "6 McCarthy, Mr. Timothy J male 54.0 0 \n", "10 Sandstrom, Miss. Marguerite Rut female 4.0 1 \n", "11 Bonnell, Miss. Elizabeth female 58.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "1 0 PC 17599 71.2833 C85 C \n", "3 0 113803 53.1000 C123 S \n", "6 0 17463 51.8625 E46 S \n", "10 1 PP 9549 16.7000 G6 S \n", "11 0 113783 26.5500 C103 S " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('data/titanic.csv').dropna()\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sacamos los niveles de cada categoría usando para ello *'select_dtypes'*." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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VarNameLevelsCount
0PassengerId183
1Survived2
2Pclass3
3Name183
4Sex2
5SibSp4
6Parch4
7Ticket127
8Cabin133
9Embarked3
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" ], "text/plain": [ " VarName LevelsCount\n", "0 PassengerId 183\n", "1 Survived 2\n", "2 Pclass 3\n", "3 Name 183\n", "4 Sex 2\n", "5 SibSp 4\n", "6 Parch 4\n", "7 Ticket 127\n", "8 Cabin 133\n", "9 Embarked 3" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfo = df.select_dtypes(include=['int64', 'object'])\n", "vn = pd.DataFrame(dfo.nunique()).reset_index()\n", "vn.columns = ['VarName', 'LevelsCount']\n", "vn.sort_values(by='LevelsCount', ascending=False)\n", "vn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creamos variables dummies y no nos olvidamos de borrar las variables originales que corresponden" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(183, 11)\n" ] }, { "data": { "text/html": [ "
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AgeFareSurvivedPclassSibSpParchSex_femaleSex_maleEmbarked_CEmbarked_QEmbarked_S
138.071.2833111010100
335.053.1000111010001
654.051.8625010001001
104.016.7000131110001
1158.026.5500110010001
\n", "
" ], "text/plain": [ " Age Fare Survived Pclass SibSp Parch Sex_female Sex_male \\\n", "1 38.0 71.2833 1 1 1 0 1 0 \n", "3 35.0 53.1000 1 1 1 0 1 0 \n", "6 54.0 51.8625 0 1 0 0 0 1 \n", "10 4.0 16.7000 1 3 1 1 1 0 \n", "11 58.0 26.5500 1 1 0 0 1 0 \n", "\n", " Embarked_C Embarked_Q Embarked_S \n", "1 1 0 0 \n", "3 0 0 1 \n", "6 0 0 1 \n", "10 0 0 1 \n", "11 0 0 1 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(df.drop(dfo.columns,axis =1)).merge(pd.get_dummies(dfo.drop(['Name','Cabin','Ticket'],axis =1)),left_index=True,right_index=True).drop(['PassengerId'],axis =1)\n", "print(df.shape)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Age 0\n", "Fare 0\n", "Survived 0\n", "Pclass 0\n", "SibSp 0\n", "Parch 0\n", "Sex_female 0\n", "Sex_male 0\n", "Embarked_C 0\n", "Embarked_Q 0\n", "Embarked_S 0\n", "dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# veamos cuántos valores null tenemos\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Creamos las variables dependientes e independientes\n", "\n", "X = df.drop('Survived', axis=1)\n", "y = df['Survived']" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# Estandarizamos los datos\n", "scaler = preprocessing.StandardScaler().fit(X)\n", "X_scaled = scaler.transform(X)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Obtenemos train y test datos\n", "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Learning rate: 0.05\n", "Accuracidad score (training): 0.737\n", "Accuracidad score (validation): 0.609\n", "\n", "Learning rate: 0.1\n", "Accuracidad score (training): 0.810\n", "Accuracidad score (validation): 0.696\n", "\n", "Learning rate: 0.25\n", "Accuracidad score (training): 0.839\n", "Accuracidad score (validation): 0.717\n", "\n", "Learning rate: 0.5\n", "Accuracidad score (training): 0.861\n", "Accuracidad score (validation): 0.783\n", "\n", "Learning rate: 0.75\n", "Accuracidad score (training): 0.883\n", "Accuracidad score (validation): 0.696\n", "\n", "Learning rate: 1\n", "Accuracidad score (training): 0.920\n", "Accuracidad score (validation): 0.783\n", "\n" ] } ], "source": [ "learning_rates = [0.05, 0.1, 0.25, 0.5, 0.75, 1]\n", "for learning_rate in learning_rates:\n", " gb = GradientBoostingClassifier(n_estimators=20, learning_rate = learning_rate, max_features=2, max_depth = 2, random_state = 0)\n", " gb.fit(X_train, y_train)\n", " print(\"Learning rate: \", learning_rate)\n", " print(\"Accuracidad score (training): {0:.3f}\".format(gb.score(X_train, y_train)))\n", " print(\"Accuracidad score (validation): {0:.3f}\".format(gb.score(X_test, y_test)))\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observamos que el major valor de acuracidad se obtiene para un learning rate de 1. Entonces trabajamos sobre este valor" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[12, 6],\n", " [ 4, 24]], dtype=int64)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb = GradientBoostingClassifier(n_estimators=20, learning_rate = 1,\n", " max_features=2, max_depth = 2, random_state = 0)\n", "gb.fit(X_train, y_train)\n", "y_pred = gb.predict(X_test)\n", "confusion_matrix(y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.75 0.67 0.71 18\n", " 1 0.80 0.86 0.83 28\n", "\n", " accuracy 0.78 46\n", " macro avg 0.78 0.76 0.77 46\n", "weighted avg 0.78 0.78 0.78 46\n", "\n" ] } ], "source": [ "print(classification_report(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "false_positive, true_positive, threshold = roc_curve(y_test, y_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En este tipo de problemas, la cuestión está en determinar el número de árboles de decisión óptimo para realizar bien la clasificación o la regresión que queremos ajustar. Para poder obtener este tipo de resultados, se hace interesante utilizar el método *staged_predict()* el cual devuelve un iterador sobre las predicciones hechas para uno, dos, tres... árboles de decisión. Veamos cómo poder conseguir esto " ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# Generamos los números aleatorios\n", "np.random.seed(42)\n", "X = np.random.rand(100, 1) - 0.5\n", "y = 3*X[:, 0]**2 + 0.05 * np.random.randn(100)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GradientBoostingRegressor(max_depth=2, n_estimators=56, random_state=42)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import GradientBoostingRegressor\n", "from sklearn.metrics import mean_squared_error\n", "\n", "\n", "X_train, X_val, y_train, y_val = train_test_split(X, y, random_state=49)\n", "\n", "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=120, random_state=42)\n", "gbrt.fit(X_train, y_train)\n", "\n", "errors = [mean_squared_error(y_val, y_pred)\n", " for y_pred in gbrt.staged_predict(X_val)]\n", "bst_n_estimators = np.argmin(errors) + 1\n", "\n", "# Utilizamos el número de árboles que se ha obtenido anteriormente\n", "gbrt_best = GradientBoostingRegressor(max_depth=2, n_estimators=bst_n_estimators, random_state=42)\n", "gbrt_best.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Número óptimo de árboles decisión: 56\n" ] } ], "source": [ "print(\"Número óptimo de árboles decisión: {}\".format(bst_n_estimators))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Veamos estos resultados de una forma gráfica" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "min_error = np.min(errors)\n", "\n", "plt.figure(figsize=(10, 4))\n", "\n", "plt.subplot(121)\n", "plt.plot(np.arange(1, len(errors) + 1), errors, \"b.-\")\n", "plt.plot([bst_n_estimators, bst_n_estimators], [0, min_error], \"k--\")\n", "plt.plot([0, 120], [min_error, min_error], \"k--\")\n", "plt.plot(bst_n_estimators, min_error, \"ko\")\n", "plt.text(bst_n_estimators, min_error*1.2, \"Minimum\", ha=\"center\", fontsize=14)\n", "plt.axis([0, 120, 0, 0.01])\n", "plt.xlabel(\"Numbero de árboles\")\n", "plt.ylabel(\"Error\", fontsize=16)\n", "plt.title(\"error validación\", fontsize=14)\n", "\n", "plt.subplot(122)\n", "plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n", "plt.title(\"Mejor modelo (%d trees)\" % bst_n_estimators, fontsize=14)\n", "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", "plt.xlabel(\"$x_1$\", fontsize=16)\n", "\n", "\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La clase *GradientBoostingRegressor* también admite un hiperparámetro para trabajar con submuestras, que especifica\n", "la fracción de instancias de entrenamiento que se utilizará para el entrenamiento de cada árbol. Por ejemplo, si submuestra=0,25, entonces cada árbol se entrena con el 25% de las instancias de entrenamiento, seleccionadas al azar. \n", "\n", "Como fácilmente puede adivinarse ahora, esto cambia un sesgo más alto por una varianza más baja. También acelera considerablemente el entrenamiento. Esta técnica se denomina *refuerzo de gradiente estocástico*.\n", "\n", "### Binning\n", "\n", "```{index} thresholds\n", "```\n", "\n", "Uno de los grandes inconvenientes que genera una ralentización de *Gradient Boosting* es la búsqueda de los puntos de corte (*thresholds*). En este sentido para encontrar el threshold óptimo en cada división, en cada árbol, es necesario iterar sobre todos los valores observados de cada uno de los predictores. El problema no es que haya muchos valores que comparar, sino que requiere ordenar cada vez las observaciones acorde a cada predictor, proceso computacionalmente muy costoso.\n", "\n", "La estrategia de binning trata de reducir este cuello de botella agilizando el ordenamiento de las observaciones mediante una discretización de sus valores. Normalmente se emplean los cuantiles para hacer una discretización homogénea en cuanto al número de observaciones que cae en cada intervalo. Como resultado de este proceso, el ordenamiento es varios órdenes de magnitud más rápido. El potencial inconveniente de la discretización es que se pierde parte de la información, ya que únicamente se contemplan como posibles thresholds los límites de cada bin.\n", "\n", "Esta es la principal característica que hace que las implementaciones de XGBoost ( [se desarrolla más adelante](xgboost)), LightGBM e HistGradientBoosting (verlo en scikit learn) sean mucho más rápida que la implementación original de scikitlearn GradientBoosting.\n", "\n", "\n", "\n", "### Estrategia parada temprana (early stopping)\n", "\n", "```{index} para temprana, early stopping\n", "```\n", "\n", "Una de las características de los modelos Gradient Boosting es que, con el número suficiente de weak learners, el modelo final tiende a ajustarse perfectamente a los datos de entrenamiento causando overfitting. Este comportamiento implica que el analista tiene que encontrar el número adecuado de árboles y, para ello, suele tener que entrenar el modelo con cientos o miles de árboles hasta identificar el momento en el que empieza el overfitting. Esto suele ser poco eficiente en términos de tiempo, ya que, posiblemente, se estén ajustando muchos árboles innecesarios.\n", "\n", "Para evitar este problema, la mayoría de implementaciones incluyen toda una serie de estrategias para detener el proceso de ajuste del modelo a partir del momento en el que este deja de mejorar. Por defecto, la métrica empleada se calcula utilizando un conjunto de validación. En la scikit-learn la estrategia de parada está controlada por los argumentos *validation_fraction*, *n_iter_no_change* y *tol*.\n", "\n", "\n", "## Stacking.\n", "\n", "```{index} Stacking\n", "```\n", "\n", "Podemos pensar en *Stacking* (stacked generalization) como una evolución al sistema de Voting. Para conseguir este tipo de modelos, se va a crear un nuevo modelo al que llamamos *Meta Model*. El grupo de modelos iniciales serán los *Base Models*. El Meta Model usa como entradas las predicciones de los Base Models. Al final el Meta Model nos da la predicción final.\n", "\n", "De forma esquemática se puede representar de la siguiente manera:\n", "\n", "![grafico stacking](figuras/stacking.PNG)\n", "\n", "Stacking presenta un problema importante y es que es propenso a overfitting. Para evitar tener overfitting hay dos estrategias que se pueden tomar: Blending o K-Folds.\n", "\n", "### Blending.\n", "\n", "```{index} blending\n", "```\n", "\n", "Cuando hacemos uso de blending dividimos el dataset en dos partes. La primera parte es usada exclusivamente para entrenar los Base Models. Una vez entrenados pasamos los datos de la segunda parte por ellos para obtener predicciones con las que se entrena el Meta Model.\n", "\n", "### K-Folds.\n", "\n", "Esta es una técnica muy conocido que se aplica en este contexto. Dividimos el dataset en k partes. Los Base Models se entrenan con k-1 partes. Después de ser entrenados la parte faltante pasa por los Base Models para obtener predicciones con las que se entrena el Meta Model.\n", "\n", "La arquitectura de un modelo stacking, está formada por dos o más modelos base, que suelen ser llamados **modelos de nivel 0**, y a mayores un meta-modelo que combina las predicciones hechas por los modelos base, siendo denominado este meta-modelo como **modelo de nivel 1**. \n", "\n", "Los resultados de los modelos de base utilizados como entrada al meta-modelo pueden ser valores reales en el caso\n", "de una regresión, y valores de probabilidad, valores similares a la probabilidad, o etiquetas de clase en el caso de la\n", "de la clasificación.\n", "\n", "Los modelos base suelen ser complejos y diversos. Como tal, a menudo es una buena idea utilizar una gama\n", "de modelos que hacen suposiciones muy diferentes sobre cómo resolver la tarea que queremos resolver, \n", "como pueden ser los modelos lineales, los árboles de decisión, las máquinas de vectores de apoyo, las redes neuronales, etc.\n", "\n", "También se pueden utilizar otros algoritmos de conjunto como modelos base, como los bosques aleatorios. El meta-modelo\n", "suele ser sencillo y proporciona una interpretación suave de las predicciones realizadas por los\n", "modelos de base. Por ello, a menudo se utilizan modelos lineales como meta-modelo, como la regresión lineal\n", "para tareas de regresión (predicción de un valor numérico) o bien la regresión logística para tareas de clasificación\n", "(predicción de una etiqueta de clase). Aunque esto es lo más habitual, no necesariamente tiene que ser así.\n", "\n", "Veamos a continuación de una forma práctica y utilizando scikit learn (StackingRegressor ó StackingClassifier), cómo podemos utilizar este método para una serie de datos con los que vamos a trabajar. \n", "\n", "De acuerdo con las especificaciones de uso de estos modelos, se necesitan proporcionar una lista de estimadores (los modelos base o de nivel 0), y un estimador final que sería el meta-modelo (si se utiliza StackingClassifier el estimador por defecto es LogisticRegression, y si se usa StackingRegressor, el estimador final por defecto sería RidgeCV).\n", "\n", "Como un ejemplo de lista de modelos, podría ser el siguiente código:\n", "\n", "
\n",
    "models = [('lr',LogisticRegression()),('svm',SVC())\n",
    "stacking = StackingClassifier(estimators=models)\n",
    "
\n", "\n", "Cada modelo en la lista también puede tener una instrucción *pipeline* incluyendo la preparación de los datos, antes de proceder al ajuste del modelo. Veamos un ejemplo de este caso.\n", "\n", "
\n",
    "models = [('lr',LogisticRegression()),('svm',make_pipeline(StandardScaler(),SVC()))\n",
    "stacking = StackingClassifier(estimators=models)\n",
    "
\n", "\n", "### Ejemplo de uso de Stacking \n", "\n", "A continuación vamos a genera una serie de datos artificiales, que nos ayudarán a comprender el funcionamiento de los métodos stacking." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1000, 20) (1000,)\n" ] } ], "source": [ "# Importamos la clase para generar datos de clasificación\n", "from sklearn.datasets import make_classification\n", "# obtenemos los ddatos\n", "X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5,\n", "random_state=1)\n", "# resumen de los datos generados\n", "print(X.shape, y.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A continuación vamos a hacer una evaluación o clasificación de los datos utilizando una serie de modelos que los integramos todos ellos dentro de una función denominada *get_models():" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "# definimos la función con los modelos a usar\n", "def get_models():\n", " models = dict()\n", " models['lr'] = LogisticRegression()\n", " models['knn'] = KNeighborsClassifier()\n", " models['cart'] = DecisionTreeClassifier()\n", " models['svm'] = SVC()\n", " models['bayes'] = GaussianNB()\n", " return models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora vamos a evaluar de forma independiente cada modelo utilizando la función RepeatedStratifiedKFold(). Esto lo vamos a hacer integrando esta funcionalidad dentro de una función denominada evaluate_model()." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "# evaluamos un modelo utilizzando cross-validation\n", "def evaluate_model(model, X, y):\n", " # definimos el procedimiento de evaluación\n", " cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n", " # evaluamos y devolvemos resultados\n", " scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n", " return scores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una vez tenemos todo lo anterior definido y organizado, procedemos a utilizarlo y obtener resultados " ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# importamos previamente las clases que van a ser necesarias\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import RepeatedStratifiedKFold\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.naive_bayes import GaussianNB\n", "from matplotlib import pyplot\n", "from numpy import std, mean" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">lr 0.866 (0.029)\n", ">knn 0.931 (0.025)\n", ">cart 0.824 (0.044)\n", ">svm 0.957 (0.020)\n", ">bayes 0.833 (0.031)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# obtenemos el dataset\n", "# get the dataset\n", "def get_dataset():\n", " X, y = make_classification(n_samples=1000, n_features=20, n_informative=15,\n", " n_redundant=5, random_state=1)\n", " return X, y\n", "\n", "\n", "X, y = get_dataset()\n", "# obtenemos los modelos\n", "models = get_models()\n", "\n", "# Creamos la lista de resultados y de nombres\n", "results, names = list(), list()\n", "for name, model in models.items():\n", " # Para cada modelo lo evaluamos\n", " scores = evaluate_model(model, X, y)\n", " # almacenamos los resultados\n", " results.append(scores)\n", " names.append(name)\n", " # imprimimos un resumen de los resultados\n", " print('>%s %.3f (%.3f)' % (name, mean(scores), std(scores)))\n", "# Para comparar obtenemos un gráfico box-plot\n", "pyplot.boxplot(results, labels=names, showmeans=True)\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observando el gráfico anterior, se puede concluir que los métodos Knn y svm son los que ofrecen mejores resultados.\n", "\n", "En el ejemplo anterior, cada método ha trabajado de una forma totalmente independiente y cada uno ha sacado sus predicciones. A continuación de lo que se trata es de combinar esos modelos en uno único que permita generar también un resultado único. Para obtener esto vamos a definir un función denominada *get_stacking()* que nos ayudará a obtener el resultado que buscamos.\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "# definimos la función antes refernciada\n", "def get_stacking():\n", " # definimos los modelos base\n", " level0 = list()\n", " level0.append(('lr', LogisticRegression()))\n", " level0.append(('knn', KNeighborsClassifier()))\n", " level0.append(('cart', DecisionTreeClassifier()))\n", " level0.append(('svm', SVC()))\n", " level0.append(('bayes', GaussianNB()))\n", " # definimos el meta-modelo\n", " level1 = LogisticRegression()\n", " # definimos el ensamble stacking\n", " model = StackingClassifier(estimators=level0, final_estimator=level1, cv=5)\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Modificamos la función *get_models()* definida previamente para incluir el meta-modelo que se ha construido anteriormente" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "def get_models():\n", " models = dict()\n", " models['lr'] = LogisticRegression()\n", " models['knn'] = KNeighborsClassifier()\n", " models['cart'] = DecisionTreeClassifier()\n", " models['svm'] = SVC()\n", " models['bayes'] = GaussianNB()\n", " models['stacking'] = get_stacking()\n", " return models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora obtenemos los resultados, pero añadiendo el modelo de tipo stacking definido anteriormente." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">lr 0.866 (0.029)\n", ">knn 0.931 (0.025)\n", ">cart 0.828 (0.047)\n", ">svm 0.957 (0.020)\n", ">bayes 0.833 (0.031)\n", ">stacking 0.964 (0.017)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from numpy import mean\n", "from numpy import std\n", "from sklearn.datasets import make_classification\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import RepeatedStratifiedKFold\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.ensemble import StackingClassifier\n", "from matplotlib import pyplot\n", "\n", "# obtenemos el dataset\n", "# get the dataset\n", "def get_dataset():\n", " X, y = make_classification(n_samples=1000, n_features=20, n_informative=15,\n", " n_redundant=5, random_state=1)\n", " return X, y\n", "\n", "\n", "X, y = get_dataset()\n", "# obtenemos los modelos\n", "models = get_models()\n", "\n", "# Creamos la lista de resultados y de nombres\n", "results, names = list(), list()\n", "for name, model in models.items():\n", " # Para cada modelo lo evaluamos\n", " scores = evaluate_model(model, X, y)\n", " # almacenamos los resultados\n", " results.append(scores)\n", " names.append(name)\n", " # imprimimos un resumen de los resultados\n", " print('>%s %.3f (%.3f)' % (name, mean(scores), std(scores)))\n", "# Para comparar obtenemos un gráfico box-plot\n", "pyplot.boxplot(results, labels=names, showmeans=True)\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora ya tendríamos entrenado nuestro modelo, para poder hacer predicciones con él.\n", "\n", "```{index} XGBoost\n", "```\n", "\n", "(xgboost)=\n", "## XGBoost.\n", "\n", "Extreme Gradient Boosting (su acrónimo XCBoost) es una librería de tipo open-source que implementa de forma bastante eficiente el algoritmo denominado *gradient boosting* .\n", "\n", "Los modelos van a ser ajustados usando una determinada función de pérdida, que tiene que ser diferenciable, para poder aplicar el algoritmo del gradiente descendente.\n", "\n", "En este algoritmo, se necesitan al menos los siguientes elementos:\n", " \n", "1.- La **función de pérdida** que se va a optimizar. Esta función depende del tipo de problema que se quiera resolver, pero ha de cumplir el requisito de que debe ser diferenciable. Por ejemplo si estamos en un problema de regresión, se podría utilizar MSE (mean square error) o MAE (mean absolute error).\n", "\n", "2.- **Modelos de aprendizaje débil** (Weak Learner). En gradient boosting se utilizan los árboles de decisión como aprendiz débil. En concreto, se utilizan árboles de regresión que producen valores reales para las divisiones realizadas y que además tienen la ventaja de que pueden sumarse lo que permite que las salidas de los modelos posteriores se sumen y corrijan los residuos en las predicciones.\n", "\n", "3.- **Modelos aditivos**. Los árboles se añaden de uno en uno, y los existentes en el modelo no se modifican. Se utiliza un procedimiento de gradiente descendente para minimizar la pérdida al añadir árboles. Tradicionalmente, el descenso de gradiente\n", "se utiliza para minimizar un conjunto de parámetros, como los coeficientes de una ecuación de regresión o los pesos de una red neuronal. Tras calcular el error o la pérdida, los pesos se actualizan para minimizar ese error.\n", "\n", "La librería de XGBoost se puede encontrar en este enlace y ahí se pueden ver todas sus características, y en concreto el formato a seguir para su instalación en Python, que es el siguiente.\n", "\n", "
\n",
    "sudo pip install xgboost\n",
    "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se puede confirmar que se ha instalado XGBoost, así como la versión con la que se trabaja, mediante el siguiente código." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1.5.2'" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import xgboost\n", "xgboost.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Al igual que ocurre con los métodos de ensemble en scikit learn, también existen dos clases denominadas XGBRegressor /a> y XGBClassifier . Ambos modelos funcionan de manera muy similar y toman los mismos argumentos que influyen en la creación de los árboles de decisión y su incorporación al conjunto.\n", " \n", "Se utiliza la aleatoriedad en la construcción del modelo. Esto significa que cada vez que el algoritmo se ejecuta en los mismos datos, producirá un modelo ligeramente diferente.\n", " \n", "A continuación construimos un modelo para clasificación. Primero construimos un conjunto de datos artificiales con *make_classification()*." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1000, 20) (1000,)\n" ] } ], "source": [ "# importamos la clase correspondiente\n", "from sklearn.datasets import make_classification\n", "# generamos el dataset\n", "X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, \n", " n_redundant=5, random_state=7)\n", "# imprimimos un resumen de los datos\n", "print(X.shape, y.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora construimos el modelo y lo evaluamos usando una validación cruzada utilizando la clase *RepeatedStratifiedKFold*." ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracidad: 0.925 (0.028)\n" ] } ], "source": [ "# Ejemplo de uso de XGBClassifier\n", "from numpy import mean\n", "from numpy import std\n", "from sklearn.datasets import make_classification\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import RepeatedStratifiedKFold\n", "from xgboost import XGBClassifier\n", "# definimos dataset\n", "X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7)\n", "# definimos el modelo con todos parámetros por defecto\n", "model = XGBClassifier()\n", "# evaluación del modelo\n", "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n", "n_scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n", "# report performance\n", "print('Accuracidad: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se puden hacer predicciones de la siguiente manera" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[12:16:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", "Clase Predicha: 1\n" ] } ], "source": [ "\n", "from numpy import asarray\n", "from sklearn.datasets import make_classification\n", "from xgboost import XGBClassifier\n", "# definimos conjunto de datos\n", "X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7)\n", "# definimos el modelo\n", "model = XGBClassifier(use_label_encoder=False)\n", "# ajustamos al modelo\n", "model.fit(X, y)\n", "# Hacemos una predicción\n", "row = [0.2929949,-4.21223056,-1.288332,-2.17849815,-0.64527665,2.58097719,0.28422388,-7.1827928,-1.91211104,2.73729512,0.81395695,3.96973717,-2.66939799,3.34692332,4.19791821,0.99990998,-0.30201875,-4.43170633,-2.82646737,0.44916808]\n", "row = asarray([row])\n", "yhat = model.predict(row)\n", "print('Clase Predicha: %d' % yhat[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un ejemplo de regresión puede ser el siguiente:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE: -76.447 (3.859)\n" ] } ], "source": [ "# modelo de regresión para xgboost\n", "from numpy import mean\n", "from numpy import std\n", "from sklearn.datasets import make_regression\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import RepeatedKFold\n", "from xgboost import XGBRegressor\n", "# generamos los datos\n", "X, y = make_regression(n_samples=1000, n_features=20, n_informative=15, noise=0.1, random_state=7)\n", "# definimos el modelo\n", "model = XGBRegressor()\n", "# evaluamos el modelo\n", "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n", "n_scores = cross_val_score(model, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1, error_score='raise')\n", "# report performance\n", "print('MAE: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Las predicciones las obtendríamos utilizando el siguiente formato" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "La predicción obtenida es : 50\n" ] } ], "source": [ "\n", "from numpy import asarray\n", "from sklearn.datasets import make_regression\n", "from xgboost import XGBRegressor\n", "# generación dataset\n", "X, y = make_regression(n_samples=1000, n_features=20, n_informative=15, noise=0.1, random_state=7)\n", "# definición del modelo\n", "model = XGBRegressor()\n", "# ajsute del modelo\n", "model.fit(X, y)\n", "# obtención de una predicción\n", "row = [0.20543991,-0.97049844,-0.81403429,-0.23842689,-0.60704084,-0.48541492,0.53113006,2.01834338,-0.90745243,-1.85859731,-1.02334791,-0.6877744,0.60984819,-0.70630121,-1.29161497,1.32385441,1.42150747,1.26567231,2.56569098,-0.11154792]\n", "row = asarray([row])\n", "yhat = model.predict(row)\n", "print('La predicción obtenida es : %d' % yhat[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parámetros importantes del XGBoost.\n", "\n", "En este modelo se pueden utilizar para un mejor ajuste del modelo una buena cantidad de hiperparámetros y en lo que sigue se va a proceder a mostrar los más importantes.\n", "\n", "#### Número de árboles.\n", "\n", "Un importante hiperparámetro a utilizar en este modelo, es el número de árboles de decisión a tener en cuenta en el ajuste. Esto se consigue utilizando el hiperparámetro *n_estimators*. Veamos el efecto del mismo, gracias al siguiente ejemplo." ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">10 0.885 (0.029)\n", ">50 0.915 (0.029)\n", ">100 0.925 (0.028)\n", ">500 0.927 (0.028)\n", ">1000 0.926 (0.028)\n", ">5000 0.925 (0.027)\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# cambiamos el número de árboles de ddecisión en xhboost\n", "from numpy import mean\n", "from numpy import std\n", "from sklearn.datasets import make_classification\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import RepeatedStratifiedKFold\n", "from xgboost import XGBClassifier\n", "from matplotlib import pyplot\n", "\n", "# función para obtener los datos\n", "def get_dataset():\n", "\tX, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7)\n", "\treturn X, y\n", "\n", "# definimos varios modelo, cambiando n_estimators\n", "def get_models():\n", "\tmodels = dict()\n", "\ttrees = [10, 50, 100, 500, 1000, 5000]\n", "\tfor n in trees:\n", "\t\tmodels[str(n)] = XGBClassifier(n_estimators=n)\n", " # Devuelve un diccionario con los distintos modelos\n", "\treturn models\n", "\n", "# función para evaluar el modelo utilizando cross-validation\n", "def evaluate_model(model):\n", "\tcv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n", "\tscores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n", "\treturn scores\n", "\n", "# obtenemos los datos\n", "X, y = get_dataset()\n", "# obtenemos los modelos\n", "models = get_models()\n", "# evaluamos el modelo y alamacenamos los resultados\n", "results, names = list(), list()\n", "for name, model in models.items():\n", "\tscores = evaluate_model(model)\n", "\tresults.append(scores)\n", "\tnames.append(name)\n", "\tprint('>%s %.3f (%.3f)' % (name, mean(scores), std(scores)))\n", "# para poder comparar, hacemos un bosplot de cada modelo\n", "pyplot.boxplot(results, labels=names, showmeans=True)\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Profundidad del árbol.\n", "\n", "Otro hiperparámetro importante es *max_depth* para indicar el nivel de profundidad de los árboles de decisión que se debe considerar en el modelo." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">1 0.849 (0.028)\n", ">2 0.906 (0.032)\n", ">3 0.926 (0.027)\n", ">4 0.930 (0.027)\n", ">5 0.924 (0.031)\n", ">6 0.925 (0.028)\n", ">7 0.926 (0.030)\n", ">8 0.926 (0.029)\n", ">9 0.921 (0.032)\n", ">10 0.923 (0.035)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# nivel de profundidad de los árboles de decisión\n", "from numpy import mean\n", "from numpy import std\n", "from sklearn.datasets import make_classification\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import RepeatedStratifiedKFold\n", "from xgboost import XGBClassifier\n", "from matplotlib import pyplot\n", "\n", "# fnción para generar los datos\n", "def get_dataset():\n", "\tX, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7)\n", "\treturn X, y\n", "\n", "# generación diccionario con los modelo a usar \n", "def get_models():\n", "\tmodels = dict()\n", "\tfor i in range(1,11):\n", "\t\tmodels[str(i)] = XGBClassifier(max_depth=i)\n", "\treturn models\n", "\n", "# evaluación de llos modelos\n", "def evaluate_model(model):\n", "\tcv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n", "\tscores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n", "\treturn scores\n", "\n", "# obtenemos los datos\n", "X, y = get_dataset()\n", "# sacamos los modelos\n", "models = get_models()\n", "# evaluación y almacenamiento de los resultados\n", "results, names = list(), list()\n", "for name, model in models.items():\n", "\tscores = evaluate_model(model)\n", "\tresults.append(scores)\n", "\tnames.append(name)\n", "\tprint('>%s %.3f (%.3f)' % (name, mean(scores), std(scores)))\n", "# plot de los modelos\n", "pyplot.boxplot(results, labels=names, showmeans=True)\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Podemos ver la tendencia general de aumentar el rendimiento del modelo con la profundidad del árbol hasta un punto, después del cual el rendimiento empieza a ser plano o incluso se degrada con los árboles sobreespecializados.\n", "\n", "#### Learning rate (tasa de aprendizaje).\n", "\n", "La tasa de aprendizaje controla la cantidad de contribución que cada modelo tiene en la predicción del conjunto.\n", "\n", "Las tasas más pequeñas pueden requerir más árboles de decisión en el conjunto.\n", "\n", "La tasa de aprendizaje puede controlarse mediante el argumento *eta* y su valor predeterminado es 0,3." ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">0.0001 0.804 (0.039)\n", ">0.0010 0.814 (0.037)\n", ">0.0100 0.867 (0.027)\n", ">0.1000 0.923 (0.030)\n", ">1.0000 0.913 (0.030)\n" ] }, { "data": { "image/png": "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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# tasa de aprendizaje en xgboost \n", "from numpy import mean\n", "from numpy import std\n", "from sklearn.datasets import make_classification\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import RepeatedStratifiedKFold\n", "from xgboost import XGBClassifier\n", "from matplotlib import pyplot\n", "\n", "def get_dataset():\n", "\tX, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7)\n", "\treturn X, y\n", "\n", "# generamos modelos con diferentes tasas de aprendizaje\n", "def get_models():\n", "\tmodels = dict()\n", "\trates = [0.0001, 0.001, 0.01, 0.1, 1.0]\n", "\tfor r in rates:\n", "\t\tkey = '%.4f' % r\n", "\t\tmodels[key] = XGBClassifier(eta=r)\n", "\treturn models\n", "\n", "\n", "def evaluate_model(model):\n", "\tcv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n", "\tscores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n", "\treturn scores\n", "\n", "\n", "X, y = get_dataset()\n", "\n", "models = get_models()\n", "\n", "results, names = list(), list()\n", "for name, model in models.items():\n", "\tscores = evaluate_model(model)\n", "\tresults.append(scores)\n", "\tnames.append(name)\n", "\tprint('>%s %.3f (%.3f)' % (name, mean(scores), std(scores)))\n", "# gráfico con los resultados obtenidos\n", "pyplot.boxplot(results, labels=names, showmeans=True)\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Podemos ver la tendencia general de aumentar el rendimiento del modelo con el incremento de la tasa de aprendizaje de 0,1, después de lo cual el rendimiento se degrada.\n", "\n", "#### Número de muestras.\n", "\n", "El número de muestras utilizadas para ajustar cada árbol se puede variar y ajustar a las necesidades de cada modelo. Esto significa que cada árbol se ajusta a un subconjunto seleccionado al azar del conjunto de datos de entrenamiento.\n", "\n", "Utilizar menos muestras introduce más varianza para cada árbol, aunque puede mejorar el rendimiento general del modelo.\n", "\n", "El número de muestras utilizadas para ajustar cada árbol se especifica con el argumento *subsample* y puede establecerse como una fracción del tamaño del conjunto de datos de entrenamiento. Por defecto, se fija en 1,0 para utilizar todo el conjunto de datos de entrenamiento.\n", "\n", "El ejemplo siguiente demuestra el efecto del tamaño de la muestra en el rendimiento del modelo con proporciones que varían del 10 al 100 por ciento en incrementos del 10 por ciento.\n", "\n" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">0.1 0.876 (0.027)\n", ">0.2 0.912 (0.033)\n", ">0.3 0.917 (0.032)\n", ">0.4 0.925 (0.026)\n", ">0.5 0.928 (0.027)\n", ">0.6 0.926 (0.024)\n", ">0.7 0.925 (0.031)\n", ">0.8 0.928 (0.028)\n", ">0.9 0.928 (0.025)\n", ">1.0 0.925 (0.028)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from numpy import arange\n", "\n", "def get_dataset():\n", "\tX, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7)\n", "\treturn X, y\n", "\n", "# función para generar los modelos\n", "def get_models():\n", "\tmodels = dict()\n", "\tfor i in arange(0.1, 1.1, 0.1):\n", "\t\tkey = '%.1f' % i\n", "\t\tmodels[key] = XGBClassifier(subsample=i)\n", "\treturn models\n", "\n", "\n", "def evaluate_model(model):\n", "\tcv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n", "\tscores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n", "\treturn scores\n", "\n", "#\n", "X, y = get_dataset()\n", "\n", "models = get_models()\n", "\n", "results, names = list(), list()\n", "for name, model in models.items():\n", "\tscores = evaluate_model(model)\n", "\tresults.append(scores)\n", "\tnames.append(name)\n", "\tprint('>%s %.3f (%.3f)' % (name, mean(scores), std(scores)))\n", "# plot modelos\n", "pyplot.boxplot(results, labels=names, showmeans=True)\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Número de features.\n", "\n", "Se puede variar el número de features utilizados para ajustar cada árbol de decisión.\n", "\n", "Al igual que el cambio del número de muestras, el cambio del número de features introduce una varianza adicional en el modelo, lo que puede mejorar el rendimiento, aunque podría requerir un aumento del número de árboles.\n", "\n", "El número de features utilizadas por cada árbol se toma como una muestra aleatoria y se especifica mediante el argumento *colsample_bytree* y, por defecto, todas las características del conjunto de datos de entrenamiento, por ejemplo, el 100% o un valor de 1,0. También se pueden muestrear columnas para cada división, y esto se controla con el argumento *colsample_bylevel*.\n", "\n" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">0.1 0.849 (0.032)\n", ">0.2 0.913 (0.028)\n", ">0.3 0.913 (0.033)\n", ">0.4 0.922 (0.027)\n", ">0.5 0.925 (0.028)\n", ">0.6 0.926 (0.029)\n", ">0.7 0.930 (0.029)\n", ">0.8 0.928 (0.026)\n", ">0.9 0.931 (0.030)\n", ">1.0 0.925 (0.028)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "\n", "def get_dataset():\n", "\tX, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7)\n", "\treturn X, y\n", "\n", "def get_models():\n", "\tmodels = dict()\n", "\tfor i in arange(0.1, 1.1, 0.1):\n", "\t\tkey = '%.1f' % i\n", "\t\tmodels[key] = XGBClassifier(colsample_bytree=i)\n", "\treturn models\n", "\n", "\n", "def evaluate_model(model):\n", "\tcv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n", "\tscores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n", "\treturn scores\n", "\n", "\n", "X, y = get_dataset()\n", "\n", "models = get_models()\n", "\n", "results, names = list(), list()\n", "for name, model in models.items():\n", "\tscores = evaluate_model(model)\n", "\tresults.append(scores)\n", "\tnames.append(name)\n", "\tprint('>%s %.3f (%.3f)' % (name, mean(scores), std(scores)))\n", "# plot de los modelos\n", "pyplot.boxplot(results, labels=names, showmeans=True)\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Forest (bosques aleatorios).\n", "\n", "El algoritmo Random Forest es un ensamble de árboles de decisión, y constituye una extensión de los métodos bootstrap aggregation (bagging) de árboles de decisión que puede ser utilizado tanto para regresión como para clasificación. Debido a que el método base es detipo bagging, se hace una serie de muestras con reemplazamiento que servirá cada una de estas muestras para entrenar el árbol de decisión correspondiente.\n", "\n", "Por lo tanto, en este tipo de modelos, tendremos tantas predicciones como muestras bootstrap tengamos, y entonce la predicción última o final depende del tipo de modelo que estemos ajustando.\n", "\n", "Si el modelo es de regresión, entonces la predicción final se hace en base a la media de todas las medias que se hayan obtenido. Si el modelo es de clasificación, entonces la predicción final se hace en base al voto mayoritario que se obtenga en los modelos base.\n", "\n", "El modelo de bosque aleatorio también implica la posibilidad de hacer selección de un subconjunto de características de entrada (features, columnas o variables) en cada punto de división en la construcción de los árboles. \n", "\n", "Al reducir las características o features a un subconjunto aleatorio que puede considerarse en cada punto de división obliga a cada árbol de decisión del conjunto a ser más diferente que los obtenidos en otros pasos.\n", "\n", "El efecto obtenido con este procedimiento de selección de features o columnas es que las predicciones, y a su vez, los errores de predicción, realizados por cada árbol del conjunto son más diferentes o están menos correlacionados.\n", "Cuando las predicciones de estos árboles menos correlacionados se promedian para hacer una predicción, a menudo se obtiene una acuracidad mejor. Por este motivo, tal vez el hiperparámetro más importante que hay que ajustar para el bosque aleatorio es el número de características aleatorias a considerar en cada punto de división.\n", "\n", "En este sentido, y aunque sólo es una recomendación, el número de features a seleccionar se sugiere sean las siguientes:\n", "\n", "* Para problemas de regresión, elegir el número total de features o columnas dividido entre tres.\n", "\n", "\n", "* Para problemas de clasificación, elegir como valor la raíz cuadrada del total de features.\n", "\n", "Otro hiperparámetro importante a ajustar en estos modelos es la profundidad de los árboles de decisión. Los árboles más profundos suelen ser más abiertos a los datos de entrenamiento, pero también menos correlacionados, lo que a su vez puede mejorar el rendimiento del conjunto. Los niveles de profundidad de 1 a 10 niveles pueden ser eficaces. Por último, para decidir el número de árboles de decisión a tener en cuenta en el conjunto, a menudo lo que se hace es aumentarlo hasta que no se observa ninguna no se observe ninguna mejora en el resultado final, o esta sea mínima.\n", "\n", "En scikit learn, existen dos clases para utilizar este tipo de ensambles: RandomForestRegressor y RandomForestClassifier. Ambos modelos tienen una serie de hiperparámetros muy similares y dada la naturaleza estocástica del procedimiento, no siempre se obtienen resultados iguales.\n", "\n", "### Ejemplo de clasificación.\n", "\n", "Veamos a continuación un ejemplo de uso de clasificación de este modelo en base a una serie de datos artificiales obtenidos con la función *make_clasification()* " ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Acuracidad media: 0.902 (0.023)\n" ] } ], "source": [ "from numpy import mean\n", "from numpy import std\n", "from sklearn.datasets import make_classification\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import RepeatedStratifiedKFold\n", "from sklearn.ensemble import RandomForestClassifier\n", "# obtenemos el conjunto de datos\n", "X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5,\n", "random_state=3)\n", "# definimos el modelo con los parámetros por defecto\n", "model = RandomForestClassifier()\n", "# hacemos una evaluación el modelo\n", "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n", "#\n", "n_scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n", "# sacamos la acuracidad media\n", "print('Acuracidad media: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para hacer una predicción lo haríamos de la siguiente manera" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predicted Class: 0\n" ] } ], "source": [ "\n", "X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5,\n", "random_state=3)\n", "\n", "model = RandomForestClassifier()\n", "# Ajustamos el modelo\n", "model.fit(X, y)\n", "# Hacemos una simple predicción\n", "row = [-8.52381793, 5.24451077, -12.14967704, -2.92949242, 0.99314133, 0.67326595,\n", "-0.38657932, 1.27955683, -0.60712621, 3.20807316, 0.60504151, -1.38706415, 8.92444588,\n", "-7.43027595, -2.33653219, 1.10358169, 0.21547782, 1.05057966, 0.6975331, 0.26076035]\n", "yhat = model.predict([row])\n", "# imprimimos la prediccióon\n", "print('La clase predicha es: %d' % yhat[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ejemplo de regresión.\n", "\n", "Para este ejemplo, también vamos a obtener una serie de datos artificiales mediante la función make_regression()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE: -90.329 (7.945)\n" ] } ], "source": [ "from numpy import mean\n", "from numpy import std\n", "from sklearn.datasets import make_regression\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import RepeatedKFold\n", "from sklearn.ensemble import RandomForestRegressor\n", "# obtenemos los datos artificiales\n", "X, y = make_regression(n_samples=1000, n_features=20, n_informative=15, noise=0.1,\n", "random_state=2)\n", "# definimos el modelo con parámetros por defecto\n", "model = RandomForestRegressor()\n", "# definimos la validación \n", "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n", "# evaluamos el modelo\n", "n_scores = cross_val_score(model, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n", "# imprimimos el resultado de la evaluación del modelo\n", "print('MAE: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En este caso también se pueden hacer predicciones utilizando para ello el método *predict()*" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predicción obtenida: -164\n" ] } ], "source": [ "# ajustamos el modelo a los datos \n", "model.fit(X, y)\n", "# datos para hacer la prediccón\n", "row = [-0.89483109, -1.0670149, -0.25448694, -0.53850126, 0.21082105, 1.37435592,\n", "0.71203659, 0.73093031, -1.25878104, -2.01656886, 0.51906798, 0.62767387, 0.96250155,\n", "1.31410617, -1.25527295, -0.85079036, 0.24129757, -0.17571721, -1.11454339, 0.36268268]\n", "yhat = model.predict([row])\n", "# imprimimos la predicción.\n", "print('Predicción obtenida: %d' % yhat[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hiperparámetros de Random Forest.\n", "\n", "En estas clases existen una serie importante de hiperparámetros que sirven para mejorar los ajustes del modelo. En este apartado vamos a ver los más importantes.\n", "\n", "#### Tamaño de las muestras.\n", "\n", "Se puede configurar esta clase para que no se haga ningún tipo de submuestreo, y en este caso, los árboles de decisión se ajustarán en base a la información que proporcionan todos los datos de entrenamiento con los que se cuente. Esto se consigue con el hiperparámetro *bootstrap* que admite un valor booleano, de tal manera que si su valor es false, entonces no se realiza submuestreo.\n", "\n", "Igualmente se pueden definir los tamaños de las muestras, en el caso de realizar submuestreo. Esto se configura con el hiperparámetro *max_samples* que es un valor entre 0 y 1 y mediante el cual se controla el porcentaje de muestra con reemplazamiento que se desea obtener.\n", "\n", "Veamos con el siguiente ejemplo, el efecto en la acuracidad que se obtiene en el modelo, cambiando este parámetro." ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">0.1 0.860 (0.028)\n", ">0.2 0.881 (0.025)\n", ">0.3 0.889 (0.026)\n", ">0.4 0.890 (0.027)\n", ">0.5 0.892 (0.025)\n", ">0.6 0.900 (0.028)\n", ">0.7 0.899 (0.027)\n", ">0.8 0.906 (0.027)\n", ">0.9 0.899 (0.026)\n", ">1.0 0.902 (0.025)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from numpy import arange\n", "from sklearn.datasets import make_classification\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import RepeatedStratifiedKFold\n", "from sklearn.ensemble import RandomForestClassifier\n", "from matplotlib import pyplot\n", "# función para generar los datos\n", "def get_dataset():\n", " X, y = make_classification(n_samples=1000, n_features=20, n_informative=15,\n", " n_redundant=5, random_state=3)\n", " return X, y\n", "# función para generar un diccionario de modelos\n", "def get_models():\n", " models = dict()\n", " # definimos porcentajes desde 10% to 100% incrementados en un 10% \n", " for i in arange(0.1, 1.1, 0.1):\n", " key = '%.1f' % i\n", " # poner max_samples=None para usar el 100% de los datos\n", " if i == 1.0:\n", " i = None\n", " models[key] = RandomForestClassifier(max_samples=i)\n", " return models\n", "\n", "# evaluamos el modelo usando cross-validation\n", "def evaluate_model(model, X, y):\n", " cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n", " \n", " scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n", " return scores\n", "\n", "\n", "# obtenemos los datos\n", "X, y = get_dataset()\n", "# Obtenemos los modelos\n", "models = get_models()\n", "# evaluamos modelo y almacenamos resultado\n", "results, names = list(), list()\n", "for name, model in models.items():\n", " # evaluación del modelo\n", " scores = evaluate_model(model, X, y)\n", " # almacenamos resultados\n", " results.append(scores)\n", " names.append(name)\n", " # imprimimos la acuracidad para cada paso\n", " print('>%s %.3f (%.3f)' % (name, mean(scores), std(scores)))\n", "# plot los modelos\n", "pyplot.boxplot(results, labels=names, showmeans=True)\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Configurando el número de features.\n", "\n", "El número de features a tener en cuenta, se configura mediante el hiperparámetro *max_features* que por defecto tiene un valor es igual a la raíz cuadrada del número total de features que tiene el dataset.\n", "\n", "Los valores que puede tomar este hiperparámetro son:\n", "\n", "* Un valor entero. En este caso se elige el número de features indicadas en ese valor.\n", "\n", "* Un valor de tipo float. En este caso se indican el porcentaje de features a considerar.\n", "\n", "* Un valor igual a \"auto\". En este caso max_features = n_features.\n", "\n", "* Un valor igual a \"sqrt\". En este caso max_features = sqrt(n_features). Es el valor por defecto.\n", "\n", "* Un valor igual a \"log2\". En este caso max_features = log2(n_features)\n", "\n", "* Un valor igual a None o 1.0. En este caso max_features = n_features.\n", "\n", "#### Configurando el número de árboles de dicisión.\n", "\n", "Otro factor muy influyente a la hora de trabajar con bosques aleatorios, es el número de árboles de decisión que deben tenerse en cuenta. Para configurar este valor se dispone del parámetro *n_estimatros* que por defecto tiene un valor igual a 100.\n", "\n", "#### Configurando la profundidad de los árboles de decisión.\n", "\n", "Se configura con el hiperparámetros *max_depth* que por defecto tiene un valor de None, que indica que los nodos se expanden hasta que todas las hojas sean puras o hasta que todas las hojas contengan menos muestras que min_samples_split. Un valor concreto para este hiperparámetro indica la máxima profundidad que se permite alcanzar cuando se construyen los árboles de decisión." ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">1 0.767 (0.041)\n", ">2 0.806 (0.034)\n", ">3 0.839 (0.035)\n", ">4 0.856 (0.033)\n", ">5 0.874 (0.027)\n", ">6 0.882 (0.020)\n", ">7 0.888 (0.025)\n", ">None 0.903 (0.020)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def get_dataset():\n", " X, y = make_classification(n_samples=1000, n_features=20, n_informative=15,\n", " n_redundant=5, random_state=3)\n", " return X, y\n", "\n", "# generar un diccionario de modelos\n", "def get_models():\n", " models = dict()\n", " # definimos la profundidad de los modelos\n", " depths = [i for i in range(1,8)] + [None]\n", " for n in depths:\n", " models[str(n)] = RandomForestClassifier(max_depth=n)\n", " return models\n", "\n", "# evaluación del modelo con cross-validation\n", "def evaluate_model(model, X, y):\n", " cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n", " scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n", " return scores\n", "\n", "\n", "X, y = get_dataset()\n", "\n", "models = get_models()\n", "\n", "results, names = list(), list()\n", "for name, model in models.items():\n", "\n", " scores = evaluate_model(model, X, y)\n", " # almacenamos resultados\n", " results.append(scores)\n", " names.append(name)\n", " # resumen de resultados\n", " print('>%s %.3f (%.3f)' % (name, mean(scores), std(scores)))\n", "# plot modelos para comparación\n", "pyplot.boxplot(results, labels=names, showmeans=True)\n", "pyplot.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }