Trained Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copia de transfer_learning.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hmA7B0RSuq4j"
      },
      "source": [
        "# Entrenamiento de un modelo basado en Mobilenet en local"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OT1wGp0XuiZd"
      },
      "source": [
        "*(2021) Lorenzo Rondán Domínguez, Andrés Martínez García. Machine Learning Engineering*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hRTa3Ee15WsJ"
      },
      "source": [
        "# Entrenamiento mediante transfer learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2X4KyhORdSeO"
      },
      "source": [
        "En este notebook, vamos a aprender a clasificar imágenes de gestos usando transfer learning sobre una red pre-entrenada, en este caso la de Mobilenet.\n",
        "\n",
        "Un modelo pre-entrenado es una red que ha sido entrenada anteriormente con un amplio dataset. Se puede usar el modelo tal cual o realizar transfer learning para especializar este modelo para una tarea concreta.\n",
        "\n",
        "La idea detrás del transfer learning para clasificación de imágenes es que si un modelo se entrena en un dataset más grande y general, el modelo podrá servir como modelo genérico del mundo visual. Por lo tanto, se pueden aprovechar los mapas de características aprendidos sin tener que empezar desde cero entrenando un modelo grande con un dataset grande.\n",
        "\n",
        "Primero, vamos a importar los paquetes de Python necesarios.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqOt6Sv7AsMi"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras.preprocessing import image_dataset_from_directory"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v77rlkCKW0IJ"
      },
      "source": [
        "## Preprocesamiento de los datos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0GoKGm1duzgk"
      },
      "source": [
        "### Descarga de los datos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vHP9qMJxt2oz"
      },
      "source": [
        "Vamos a usar un dataset que contiene unas 600 imágenes de 3 gestos que nos interesan que sean detectados en la aplicación. Simplemente descargamos el zip con los datos, lo extraemos y creamos los sets de entrenamiento y validación. Cabe destacar que estamos usando una proporción de 90%/10%."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0yowJ7r79ee",
        "outputId": "33351d5a-d962-48a8-98eb-f7da614534b6"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dT4I-B1h9xJ2"
      },
      "source": [
        "import zipfile\r\n",
        "with zipfile.ZipFile('/content/drive/MyDrive/Universidad/Master/MLE/Trabajo de la asignatura/hand_dataset.zip', 'r') as zip_ref:\r\n",
        "  zip_ref.extractall('/content')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ro4oYaEmxe4r"
      },
      "source": [
        "PATH = os.path.join(os.path.dirname('/content/hand_dataset'), 'hand_dataset')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMh0iqd_Z7Ze"
      },
      "source": [
        "train_dir = os.path.join(PATH, 'train')\r\n",
        "validation_dir = os.path.join(PATH, 'validation')\r\n",
        "\r\n",
        "BATCH_SIZE = 32\r\n",
        "IMG_SIZE = (220, 220)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fFvhn6uJtwZF",
        "outputId": "59edca0a-9874-425a-d5bc-04a2457e7b76"
      },
      "source": [
        "train_dataset = image_dataset_from_directory(train_dir,\r\n",
        "                                             shuffle=True,\r\n",
        "                                             batch_size=BATCH_SIZE,\r\n",
        "                                             image_size=IMG_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 562 files belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cAvtLwi7_J__",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17c53489-88d9-4f19-f084-cc7532d9227d"
      },
      "source": [
        "validation_dataset = image_dataset_from_directory(validation_dir,\n",
        "                                                  shuffle=True,\n",
        "                                                  batch_size=BATCH_SIZE,\n",
        "                                                  image_size=IMG_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 68 files belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yO1Q2JaW5sIy"
      },
      "source": [
        "Vamos a mostrar las clases disponibles del set de entrenamieto y algunas imágenes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VZiqQDABuPPl",
        "outputId": "7a9cc9e8-9e3f-410f-9d2f-a21091b26fa1"
      },
      "source": [
        "class_names = train_dataset.class_names\r\n",
        "class_names"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['backward', 'forward', 'jump']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 273
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5BeQyKThC_Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "outputId": "097aa53c-14b8-4566-fa1a-8ed1fbcd62b3"
      },
      "source": [
        "class_names = train_dataset.class_names\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "for images, labels in train_dataset.take(1):\n",
        "  for i in range(len(class_names)):\n",
        "    ax = plt.subplot(3, 3, i + 1)\n",
        "    plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
        "    plt.title(class_names[labels[i]])\n",
        "    plt.axis(\"off\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZqCX_mpV3Mx"
      },
      "source": [
        "Como el dataset no tiene datos de test, vamos a crearlos. Para ello, vamos a ver cuántos \"batches\" de datos están disponibles en el set de validación usando ```tf.data.experimental.cardinality```, y después mover el 20% de ellos a los datos de prueba."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFFIYrTFV9RO"
      },
      "source": [
        "val_batches = tf.data.experimental.cardinality(validation_dataset)\n",
        "test_dataset = validation_dataset.take(val_batches // 5)\n",
        "validation_dataset = validation_dataset.skip(val_batches // 5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9pFlFWgBKgH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a3584c9-28f0-4ef8-bafc-61b129b92d3f"
      },
      "source": [
        "print('Number of validation batches: %d' % tf.data.experimental.cardinality(validation_dataset))\n",
        "print('Number of test batches: %d' % tf.data.experimental.cardinality(test_dataset))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of validation batches: 3\n",
            "Number of test batches: 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MakSrdd--RKg"
      },
      "source": [
        "### Configurar el dataset para mejorar el rendimiento"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "22XWC7yjkZu4"
      },
      "source": [
        "Vamos a usar la técnica de \"buffered prefecthing\" para reducir los bloqueos de las operaciones de lectura y escritura."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3UUPdm86LNC"
      },
      "source": [
        "AUTOTUNE = tf.data.AUTOTUNE\n",
        "\n",
        "train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)\n",
        "validation_dataset = validation_dataset.prefetch(buffer_size=AUTOTUNE)\n",
        "test_dataset = test_dataset.prefetch(buffer_size=AUTOTUNE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MYfcVwYLiR98"
      },
      "source": [
        "### Incrementando el número de ejemplos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bDWc5Oad1daX"
      },
      "source": [
        "Como el dataset que estamos usando no es muy amplio, vamos a crear nuevos ejemplos de forma artificial aplicando transformaciones realistas pero aleatorias a los datos de entrenamiento, como rotaciones y escalados. Esto ayuda a generalizar los datos de entrenamiento y reducir el overfitting."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3P99QiMGit1A"
      },
      "source": [
        "data_augmentation = tf.keras.Sequential([\n",
        "  #tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),\n",
        "  tf.keras.layers.experimental.preprocessing.RandomTranslation(width_factor=0.2,height_factor=0.2),\n",
        "  tf.keras.layers.experimental.preprocessing.RandomZoom(0.2),\n",
        "  tf.keras.layers.experimental.preprocessing.RandomRotation(0.1),\n",
        "  #tf.keras.layers.experimental.preprocessing.RandomWidth(0.1),\n",
        "  #tf.keras.layers.experimental.preprocessing.RandomHeight(0.1),\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9mD3rE2Lm7-d"
      },
      "source": [
        "Vamos a aplicar estas transformaciones a algunas imágenes y observar los resultados:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQullOUHkm67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "outputId": "15484727-5519-4ab3-952e-15f80fa71b90"
      },
      "source": [
        "for image, _ in train_dataset.take(1):\n",
        "  plt.figure(figsize=(10, 10))\n",
        "  first_image = image[0]\n",
        "  for i in range(9):\n",
        "    ax = plt.subplot(3, 3, i + 1)\n",
        "    augmented_image = data_augmentation(tf.expand_dims(first_image, 0))\n",
        "    plt.imshow(augmented_image[0] / 255)\n",
        "    plt.axis('off')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KZ1xa26BNhdiG9cNvqZ/lFMaxzDWZUmHBQT+PM/YJYw2tNbTWYZz3DOWzGbP5Ap3pRM9JZ7QM+ucuoyitkUt4cMZhd4GbXg9Oe3v26d2ULhiiSi2w6Ux66gWa11NeCjkcThlwCiWa2eKI19//E05ee4vX3v6Au7/+Ned3v2OzOaOtW4q2ZeksSmcYnXWuNVGCwdK4FuMMGKEsKyR4a0qdUWQFVgmrusSIQythtSl5/PQZtrUcHx4ym89RWYYucsTWONuAbsGZAAlUBwL6jrptae2zSNIhq94jBH6ikBfkHtB4BSdKELs9Hye6WG1gM5loYIsLiq5rgl4g9M0yahtIxU3XXknIDqAEoTsu5pAfpPsu1WY9YIl/fpJb5KlUEPbeoVTRXtZU+5clCWC186YkAGIAchLPTf9xUl6GPDRqgWDESzc0ORwG66arJlFLV6mqy2d4I33oC4VCaNwpSy5VOAMBE1Gxi5OcQ5i0DqJ+nhCWUx6/YZYGiLsHOVt82csrl+fc/vBnPHr4nPu//YiDNkOMRVuoK2F265jXfvQ2508fIdaSa41WggpDuIKEVaJg0cwODslms8TS3V2FUW45N1whsq+8g3sliEv4SIU6vaKbeiRNPmXTDMHNRLeYjHMPuLl8xqa+Sz2Bo763fdP/HGGhWJABG4zadKw7AJy1bF6+4N5Hv+PRpx9hnzzi2DUss4I8WcHYezAgWhHOOb84xRqsa1mfvqDIc2azBVkxRwth8Dt8Ejyd1lqapqE0DaWxlAaszpgfn3B08yZZng9KHWHO2Psz9MJPe6nSsg6rZhvAjN9tfcOoficD7Q9xgQdneJ/yy7DDJm1BUlHicLSJdSI4p1HLE17/2Z9zdHiLR19+wd0vPuXlg+/QdY1zLYsiThQE5RwaLziV0uEvCAERlFYYazivNlitqK0hzzUnJ4esztecn51jmgaH41ggz3NAobQmm1mOb99meXyCoOnQaywTfedMmXWfV2fXEI1/6UGOEsGmimrL+kusX+e6+TXjDtMPZe1QqC5pm0F5JpTrltJJ4k29cl0rd1/2mY4fdnGnw1GSPJtKB29lS5rm1SMRQbkEbIw8HePhm9HXPRB1w0G5dEYMeOGbem8iYIn3ykXoGMGEGvCudP11N8AR2K7mCaUyZWWl5ZVRmxOAt1ci4RoRTqcNR7wzAXrG6fcPRtdYGBf6g0Ajmvz2a7z/F3/N2cs167tf0zQWUzvc4oB3f/EnvPPzn/L4a8WzLz5GJ3LGGr8Syzhv8bYODo+OOTg+HhotE/LwsuBtnwxRKKxyfug+8Vz9YEX/n5q67uoG/D21THvrkex7uSO5ffWQgu69kfS36bywKcmTytZ0snFyuzsvgfdtACyb9Yp7X33J/c8/gRfPOHYtN7SQxSkyIsgIHAMYY6ibhqquadq2qwOt/Urhcb/uukgAE601lG3LpmkpW4H5jJuvvs7N26+QZ/kAtPnvhuXf543ZB3h29Ys0bzufe1Q3GW7gJd5Dl5pkTBfVNEJLwU+HulLF6sKM9lhYFC7LOHzrRyxu3OLonXe5849/x+OPfk1VnnEYOrMV7w1RDjL8sIDSillRsFguOVwuyPMMYwzrqqJsDMvZHD1bwIHldDHn9Oycqqp49vwpVVWyWCwBYYOgb73Nqz/+BcuT20NNGyZ/esfwtoDqh1l6INRZK3utDgXO9krLJQAngpqujrts0M1OSCzpmA8XBEmn6AbddUhDRUyfWuTrQRlT4NqDnGEdjKVTBELpSpw0vm6QbVCH8XvfCeyOursalHpseoDTv+3uJkBaBBzOeZDbgRwheIUsNrZnB1qHAEcpD2TicFWMuFeO/fNe1ifiOwG1g1oeAeSLXO+R9rmNnZJtLReD217w2QBO9oKaJA+T73qJDnjHl0jBrbfe4S//y/+Cu3e+4MXjR9im5eZrr/HOn/wEOV6wePMm3DygenZO0QhOlF914vyqk8ZajBMWR8csj48n87Qrb1MW79T9wGoflT2tE637/ZSuHo3aJLRp+iDKmj58z4u9AYbn5/jVXnm6m6aAys6wo6iTXI3CxWcDU2R4HSQqtMqvoFM0tOsV97/6km8/+i3m2ROWtuEkg+NMYbSiEum9SiIJS/t9kaqqYr3ZUBvDYrEgz3OKvCDXmZ8XlniV4tBOt4LKGKq6Zl3VVFbz2lu3eefd9zi5eTN4G7frbZfRPuWZugjYX2b0Y8qQcewHQhfxxcUAJyn5bhSXTp4MORu+TdCuQzsQ/C59Mp9z++13eP7wPi/vfI64imaWI0ohNgh0Y6C1KOcoMsV8nrNczlksF2Ra0bYtddOgXcsyL1gUc9qm8UvQM835+TllWfHy9JSz8xXZ0Qnz197hw1/8FW+8/R7OgWn9BGW07kFLBwJGwC5yQZicFjtl2nhKqQEC7xo4cODAKg/DV71AGJiGHYqN7vFtGk4A7oY/urRGxm6qfGXaekkB3GSKI88FiZdgOnwa3zYQ6B1A++P5Q5PPXeq9Sd4lZRvXWwoUxx2998YoRML8GwQd6qKrkw6DxEmEIZFkyXrq8ZsyZl1a/2n7Sh9w7JGaAtbDsk244vF79Axc+1GR4zy4j3En8Wzn6xKCcDsAKuJxpTh65XU+OD6hbWrEWTKtUIucKjMUNw+58fYbvHj2BaapaVDYsJy2ahuqxiLZgsMbN8jyuNpkf/JpvU3V5dR9l/VQ5inv4FWlC9vDh5poy7hlgf+VeinDg+75FO16nkrRy4KcJNb96XT9Jxrt8af078NDJ4IV7Q2YquXs4X2eff4p9ukDjrWlmOcUkoX+q7yJE2SBDVozen+MtbSt3xwXpTy4KQqKPEerfsi89xgFkG6M/860VE1NVTdkiwXvvvceb771FrP5ops7NyzkxTW3bfRv/54C8/H9VHy9ngzfuX9eP9gPcBKB41PrM59kK/Bh6uFJoxghXEDoJ0FapbxAAjKBvMhwme6SU6LR1pKJoJ2fg6kzIcu1X0Lrwo6g1seRK828KDBaI1r5zQLznPPzFZt1yXldsqnWvHn7Jm+99hovnzzm0f1vmGWKw8Mjbrz2Jvly2bkJPeOmllNYxC3JPgsuQd1Jg04Lt0TzhLKLdTixWzzlEheRjJ93cUbg4DoZMmWBTHTbrV+OXkn7JIZem5hmeu3bf4ppU0unBznp91Hxy6BPKWTHSpk/NIkk829GACICkRRAjttqn9s21EgQ9IIi7Fqd8gzD+TfRy5Mqg4GSoN9M0SX5Exjui5QoGthu5zEvp2W7iFLPRBTCnqclyX/6AQxkjmwLzMuk1ZFSFPMl+WyeJGHJjaXQt3jzvV9w/s1zqiePsHWFEsFYQ9221K1heeuYV954E6WjTAC/t83+iZQpjQX0TqDTraLbrv+r7cEZ0q55GFuKaowduh+7+e4iEpHB/JrLttFQVl2U1shYiTIgCNEE5iOm5fzZYx589TmrB99RVGuWmZDPChTQOPzGj94qCDLdTwyubUvTNpRNTdnUGGMo8ozFfMZiPmOWF6hujxWfrdR744e1KqqqoqprnBNeff0NfvTu+xwcHXV9K1Jv4O7uaxe9S8NcBE4v7NeJXhh/E+XkPrqEB4fgRSDdA2wYxKWBR992cUy9FpxSiM4orEWJX/lkrfV7vSjxXhytQIG1DVoczhrW641XGA6qsqauG68MwtwcrTVz/EaBuc4pJGej1uiN4mm14e6dz2lby9nqjLI+xTjD4ug273zwC975019wfPsWSkegZUceqrQBk6I6/NLdUM7Ui5Pex8Evf96Fv4qAqH4FVVp90SjY65IbKYQhuIhAzSOgsWcmOhd6u6R/PwYk/X2qzENrynYddWm4YZheqI/yH8DO1LL6q0DD1VO7wE0KaKBvFJfwTAcpgUQxC/TTmPvJxV0asu2+TykFN2nevNBX3UPpAOwwrsk4t571wn1MvQdnhMqT9xIEchTEKcDpQFeX+T6bW+Clq8eE010cu99WbBFgAjgraBTYGTfe+IDDn77g/mqFnD5F41eavGxzTJbz4x//lNfffgeldGf0pOAmve5ypU/RZazaGO5fjzcHfojf5J9LY69PmgsZPUgd5Vt46wdWsSSGXkxEcGjXUK9f8uDOZzz8+nPa1QsW2O4IHsRvcKoEJFqrzntgjDNUTc1qs+b07JTz1QathXmhWc5zZoX2q/4sgwGA2K9aY6jqik1Zsi431E1LsTjk6PCITVny8NEj8qMjlsfHYW5qAgrp+S7uDj4FRAZel1F/GHt0xt9NfZ9ed4HTQX+4oL0uMQcnyXTYKThFeWMrPSmRD5NU/DiTDqFVGWiDUY4Wi8EjV38WhMJisRiM+D1tMqXZrDfUjWExX1LkOYRl1vP5HJVpv7xavJrInIAS1HyBstAaw3lVsn76mBfGMs8UWrWcNRUvX6zZPN9Q14b3/+LPufXKq4ANO4rqIE9TRd/XkJ9Ulwhex5ZQkoDsRRTOWcSGOMKQBHbUoCG8r2HXgZxdreSYAEFdHl3ycxcQSZXvdnvBmHnTcL0mmlaSPszYo9G/C6Xw/6Ou6JIRCV6V+Av6sg3BzrAuU+UaW6tz0I1ApCCI67cL3OdF6XGAG4CfYb5ST0n/nbjt+LbKuyWk0rJvbyIw5L1txT9tcbkt5dNZxSYMaY0E4hgZu/Cx7yM2fTVMKQUhzg/nZXnB+x/+hLw847vPPub05Us2BprlDd568w1+9ud/yfLoOLjyYxxpnez2WOwCPTspAXrp/lpXG+CEvj9Z3/61JL93iJjtD3d4AS6qh8majn1sTzNMRTstD2ObJ/kZxSviVzTZpuTJt3e4/9WnbJ4/YmFrMjWUpWNJ51w/LGWMYVNuWG9KjGmZz2bM5zlFrskUKHGoOEPZhdEMoHWWyjaUrff8VHWDM44c4eXTJ3zym1+R37zFyZtv8u4HH3Lj5u2wmkq69tr2tm8DkfR9GiYt306dtRf49/XcPxv+7pfp7KZLeXD6jDJo652em/ByyEzCeEMlJZBhsMriMqjakpmtcZL7JZquxVmDc44syzhcHpKpjFVdc7bZ8OT8OQCzIuPo8ICDgwMQf0yDMwLG7ymhncLpjLwoUEooRCFtyxLD4WyOk4zcOdbGcvbkMV/9/d+jZgWLv1wyny/6Bg0rVSS4aQZtIK6vGkc3JJt6bkSFScbOK0ontv9GFE5Z/zzUX6xuLyCHTKaQ4eoqD4HoJ0WDP9tIOuCVDm4MvQvTHTkVqtuenN0gaHzfVcuI+bfCdMJwe4niVaG494yvmzG4Ga8o8hTBTdcuCbCL70QkAGn6+EfxjdtoCKRSkJG+917NmLfOQ7cVbpt2KxZ/Tb1sngfTwL6uJi0/E6zMvsH7ioq34R9Hv8y6syZj3kdKvwcTlugh8llJ6iz2E+e6c4NEhKObN/nwr/6G4zff5Nnjx5S14ejmK7x6+xVu3L6NVdr3JXaDm7FXZ7xE/FKens4KH9a5P2j1aoL+6AKO9d0N3rv+vUubmYSLJ+zj7tEekHMhpR1t/Gp8s5P/d0aOL2UqpwTCopHIX6apePnkPnc+/hXP733NrDr3uxSrfufgAXAXv6S7rmsqZ6hMzabccH6+pm4a8kyzmM8pwq77nRMheDLiLuTGWWrTsKlLNvWGsq1orWGuM5ZKkW3OqR9WnD27x/NHX9OcP+PHf/ILbr36Jtl8QVyqkHpPp+tn98rHSPvA0C7vzUUkicC7iC0uOGyTDm53QycDqzsVcEkGduZRQLmg4H3OMudPJHfKYbW/R3kh5cJEKRHFfDZnpmcssjlFVZIXM16en3N6tqI8W4elcwpRBUWuKJxCB1CgxG+oJ7nCZYIoIRfxq7EWC4wxSGtQUtFgeP7iIXe/+Ig333+P4vU3/HwcbNibIkV4KQck3Vf6Oog70cYQA2GbxqUgLiMXG+s6sXxjWOtFiehY6dK3FRHI++cutNEUDwy8Jgnwid6FHvj0c0e6L1zqstxmsikmTu9TRemS6hPVC6XLj5v/y9IQcKTAJg3Th029NH1dSHg2NvuCh8NFABJPfN+Vh+l8pfkbvo8rWcaenouVxzBM8EQmpl56PhMdwE7m3KRaRbnOQ2e53J4Y8V0/BLYdvn8/Ag9p1pJ3xgUDw4HRGnN4wo0PFpy8+3WoVOkAACAASURBVL43qpRGi6KNEfTIJuln28Dmomt0+acHg3bxwCDulK6sAyehHphOvEj5LkU8AMh2mB3WeRr3tGyLnnJJwJWbztf3BDe95IpQJnRupxGbeaNVtbRtzdnzR3z32e94ce8bpFpRYMhETeeZno+MMR6gVCXn5yvKqkZnmsODBbNZgTWGzWaDtZbFbEGu+mXi1lo/Z6cq2ZQbNmVJVddkWnOwPODw8ACtNS0WbVecPz/j2082iG3JtebG628hWT9cNQVypgDLriGoMQC6zBDXLhrLs16r7qbLnUV1QTzjfHWeB3rV2W99MbSmhLDawhqWs4I5cz/W3foN/ZTSaJ2RSYbKIc7F1bOCrMgpipyXL8+oa8Pzl+c4PePwwHFQzFiIJvgKA7N7cOPPc+o3VcrzHNfOsA0UeY2uNjy79w0Pvr3D0c2bzGZzj9XFdiidQSmGA0CdhnKxyH1TxJnyY7d7uuJqSvlFC9nJcFWLpAgjdOC4OWCS9DBvyf0YvAwadKQ0Uy/OLl7cN5zSK/sxOBp1ph9isf0L0hSYoPOkxLz3IKRvS5c8m44TFzvvGKCkVxnEIzKdp+2htBHn7qnj6Xej/KSZmgAU0dvlbMJzI+EQy9kBP9UDmPFcmikhOO0Vmd4zIxW60dMTDQlr/XJ1p3PQGSLGnwXkQFnVK6BRmuk1BSy7hqemQNF2eDt4f7WHp4bAPZV9WzQAusnvDuyMhhxGsmv8+a5nw8yRDMVuD9+Ps3mhN3OsoLuIFOL8nE1rLavTpzz+6hNWdz7jqFyjZgW5y9AuxhEnE4P3DHp+j6uf6qamrErqumJe5CyXCw6Wc/KwAKcNe+Fkoil0TpZl3YZ+ddtQVRWbTUlZ+l36l8slx8fHLJdLAOrW76ejTMvZs+c8/O4ur7/1PkevvI7OhrAg3fZj/7DS9zdKdwGkDivsi1OGhuUUXXya+I7EnIt2HAmXTKQmnnl7/nBJfAF1OodqapZ4q6n7BkFUhlK6izvLMuZAhgOtyHTG4eyA07Mzzjclp6dnZFnOvChwWXTPu3BOVr9HS9201HWNbVvm+YwsnyOFYmGEA2t4uV7x8t63VB/8lDzPfTzOH1fvwjyMrh8nGMP/HHX0WJbg1hSBMUyKQ1mpwtpCyaH+LCDhYE5cBDnjzjpEuENrfwI8jZThrqZ0gSEkbtLTvZtWiGOA5fe5iWmmCfXfX9XhKdgGEMNy9+AmXn05o9LdBQ4jrwRPI/0KKkn6Th83pO2aApzdedsGnOP77XJK0uZbOU7eu0F6YaC0DxzObupaNnp1YuiO/4bqaiz8BkOcSZ0MheM2qBgaD0OQ08XvXA/IomUe0nFpeOcGQ4kXeW/G73cBnyT32JF356ofQhvb3gPAZKhqAmSGX9v3k8VLZcI2yNlVGzulxwjcIBfXaGqsdHFMdQhxIC3iaqrz5zz4+gseffU59vkzFliywm+m5xqDa42fKxNkvQ0Tjo0xlE3JpilZl2vKcgMCN24ec3x0SJ5lEACwMRbTGmqpybO88/y0xuu1TVlSliVN03J4eMDh4SGHhwcUxQxjDMa25E4orCIzcPr4GY/vP+SV937MIg6BTdTyPo/LeLj4B9MIIE97M+OEi/0t+D02+pugaNE7uuGQkSZNrkNljQNxmm7b+/kCdXBI/fI5bV2j24aZ5Og4UVGiteq9OhmOhczIJKORBmcsTWso6wrT1ohY0FHwKmzrJ/XqsB123fi9cxRChiA6w+aWWa2ZiUa3jurlc6g2YB0WP1RmrQ2gaVgNQ8dqFJCusyBiGMEDjDT80FLr79NJht11JKD9EQ7jqu+V8D6a9kRMCYherAwNmRhye+gphu0FROQB1YGcPp4xz1zEtn9ICi05CQDTeuzrIHWI9QBxXIcpHk4lbwDoo7hl1Kemhv/8cvuY6xhn3yd31fHY+zPAI923yeyDHZ6hgefP9aC2M3Lc6HvXT6SPWR2WKayoTGrtouGt6SGsbTAS05dxvYT8WGt9/r5HvBc9n/TiWOe344/9+wp7biJ1eewuslUuSZhoXKLtMsrunyKdNBpffVq9gTT21uxCPvvreHue1zi0T98CLaY85endr3ly5wuq50/JTduvhByl7z11YPHDElVdsd6sWZcbVuUKh+PwcMHNm8csFwu00hD2tqmqmqqqqduWrG6wDqw1NK1fOVVuSsqyxlqhKGYUeYFSuu+bzqEt5Cjmotlsak6fvaDcbJgdHOzZc2273vaBmYsAUfS4dkZerNDLkFyMUC+3D87Wc7a9N2MEPgA3dC9THC4oxGYUeskbH/ycIi+49+UnPLvzFapsONaOGf4gTpTye88I3cZImQs7MOaO2aygyDNW5Zq6XOHsIa0VEE2uc7/pn8RToBVxDzLbCRmvcbSzaOuQxlK+XGE3GzAW5ye9ADZsVRGOoE819QDtj+ouWgyOcHjadNUq8UNYSpKztUfC0QYhbANzJOo0SftyArJXZjJ4dtG3qeKeDuuB5ZQF0HFBB8pGAmTSy3FVKLZFf38Zj8j4fSx7KvwFQQeBOp5g3IOq7YnMu4anesUznCsUgXZf/9t5TAFONzwa4oroY2dJJRUGPRicsvAGSj4wVboWYUswhndDO6qfwG1tD0jGgrXzMuwBOVPeonE+L7qm4fcPRW1/b5zttsoY0Fi+XknqgW9a793hkYl42us57EB5Gut0eim59GYCHE85ji6icTanP3NYacBWPH/0Lfe/+IjNw3tkTUUmYbPa0Aed+K1RbPDAG2MwxmCtpSz9vJmqKhElHC6WHJ8cc3R0QJZl6DB/xxgDOFrTUjYttq3QpsEaQ13XnK83nG8qmtYyn89ZzBfk4fvgjkSsQ1nQRqC2IIamrCjLikNjhgZOZ9R9v8nAu+t06vvQR73no7eKGC7MiN8PUcVuuniSMXSxi2OUEh0yjo0XM5Be+/ugjF2YACsGRCOSc3L7LY5uvMLNd97nxtufcu+jj9g8eohtKpT1CDc3Bu0cDQoXPBxaa7+sXCsMYWOjqmK1OqeuczKlmeUzsiz3S9Cd7cCNw9FYQ+ssWTgHSYk/0VyTY2qhWm3ANKA1jnjQnvfmpGjejSy/gcGQWjahDpQSxjLM6w0FYuOK/C1G8Kg/bG/vzUuUG0729fcJMk4g0LA90kZM37uJtkvzsJ+5ozKNeeo/jzeqU0jj+CK4ucqGawrKeuUJzm2Dxf6bCNpkUO6BAu5g6niIKQUJ46sk8afzf8aSOQE7SNdnU+8PJJ8NBNwQcADbc0QDDVdGhRJFhRNCpApuLOzSoaBh3SUghVjvKdN76RLjTS3QXUBjK+9bfa3/bgtwJOGnhqEIA10kRRnHN+nVsXYyr35l2BXuFBM0DYHHE/In5FIAOan864BPKpfGLMu28T/oE2Men0h/+3naX9OEXO/DFIdpS14+uceXH/0TL37/FVl5ziJTqCzzK+Dipq654LTFGj9fpmka6tpv4LfZlGw2JcYZ5gcLbty8wcnJiV81BYPjTfKiIG8NtWuoTet3K64b6qrm9HTNumyYzwpu3Djm5PCIZTEjE+350YI4hXMKGwCWQWjCTsnbvL5Lnu3WA1M6Y7fHx3V1mrrkuvadEmXSX/fRXoATl1F2/0yWR/zKqJjWhLU2HrrwmfeTdp1qYgIonXN0+00+OLzJye23ePTR73h650vOzl+g64pl27BUgsoLTIhTh/isX14EQXhUVYW1llZrf4xDllM7y7quvNcG8WBCKwwOLX4CslKQaSFTGcZYWmMCEItekVCxiRBKG27YMaJU963mRkNTSnmLUwJEddaFFWReKsYDF3s3nucBD3LUhCCM9Z3UddpSI4tURHWdf5cln96PyxvjSSl6w6bjS/MoSX2O83V1BXlaX8N8DsFNX1/+2WUtniGw6X8Ph6R6i6rPUzqMleQtsYhDLknbZxCHSKdDO1meuEt6nDoEaYP8I9t6uO/2JL6g7kUM3tXWyNOyTWGJruu/tRIn8CeJDfLk40pXMMX8T1mmu8BLSnZiKMl1ii8FMPFvCgjR5Sf13myDsavdJzov31ST7TBYpsHNRNywDW7CP+JG4dIh30nNeHnazl+ErRZw3XEKYg3Vi8c8/PITzh58R2FqlnlOkSmiJylS9PoL0oGJJhjlZbmhqirmywVHx8ccHR0xn8/9DtbOYY3pipdnftsTbSx1GXYqrmrK0u95U2QZRwdLDg6WzIqCQmdhmwGHRYFTGKdppKXB0CqHmoeDO4M10nGdfD/+G4OfKWCTel27lWiBXHft9V7UHR3+FYhzWvfRxUNUe+WyDCb7xWeDEBPpxyitOIiLMDtLR8P8iFsf/pybt17j/jvvc/fzT3n+xSdsqhdoZ5hnFn9wpR+mycXvXKyUQrQmy3KKYsZs5ve9aU1LaWoa66jbhrptacIZHc5abOYBknEGUUKRC7MCNs5QtjVWOZwYv/maNx/7FVXjThfvJCiTHcI+so9S/RL8GFW0Pl1oUSc7rNzB7165kXhP9ntDeuE8DDc9rLAfiKT3kTEjY6ffpookVXZ9GldboA+9K2NAMkXRc9NZ9h1g7TvwVioJsBmm0Xf4Li/hQe/IH04wd2mcROGw3a5To6bbYKVPbycY3cNvMV+DnEbwlng60nzFdHpFGjyhSb/wojsolOg9iXElVTflXR6ndVlPz9gTExPxaQ4NjzHAifv5REDknO0ORozzfWIndNbixGKvMPC/kKJVHn7u9JyMAHKiEifC0vMjSZ9gV6+6HO3uz7G/+m1NlAittWyePuLZx7/h9LOPmVUb5rOCmYAOis0mnjmsxVq/WsrRH6ewXq/ZbEryPOPkxgknJycsFguy4AHCOQ/g407/SqGzDK0znCupqprVumRTNjjnODlccHJy7De/jcfLEGSR9frCILRAKxarhfnRktli7vevCgteEOkcGMN6CA1wgeG2z9MzBYS63xPfTDbLBV1iP8BJJd5Ilw32vbgQ1CTKLvnX3znSRwJoB1YU6uiEd37+Zyxv3ebjcsPqyw3GNUim0ckJqBiHGH+8wyzPWB4sODg8YDH3AMcYw7pu2NStXxLuHK0JXh7ToqVARNCZRmmFVpDRYNuSsqqw1nUM0mfc9X+d0CdqD6IQlxB2qFBiV4nbmEVk4+s8Ti4WnPcqIRjTd9uBS3vLKkzD9ZNZU69OD376FT6M8uev20pwMFSQgKM03kGDjmhsLU95g662B6ffXsD/ngY4Oztu8n7omdsPdNLf/TU2VGS93siInhrAT+gafNN/N+i7Q02xm3aABEJpdnl3OkDbKbo+ngEgDysDU++I8xF297ty2NVPJ29cD4rE+bjDeysEW3yqCnyb7To0d9rLElJ0HrD0wlo6gBPDRqMm7gNkrfVKxfhrVB4S5Nx+j9Yflqa8UlsUhZ5cDDJhSnpMtfru35OG9SXrb7d86uW2OH+eYn224v4Xn/Psi89oT18yKwoyrXoPgwsbXiqFs5bW+tVPrTUYZ2mdoW4byqZBtOb4xg1u3rrJ8mAZNqbtF5p0gLcziFVn2Det4XxV0RrL0XLO0eEhBwcH5FkeVgd6b7lfaWX8LskurNjDIVoxWyzJ83zLyPR9MzVIO/NhYDwMWmLCa7OfJIn3sp9crCe+/yoqNR1pr9zSitiRATesj142+BuN8aBRCWQZhyc3yBdLZrMcQdHk2otw4wWQuDBvRiDLhGKWM5sXFLOiW/qp8xydeZfg2XLNauORLqZFnAXRYYjKe09yLMo0lJuKthFmIc1xQWJDd4pIjSbEuVFHjozTy/oYqHfFAqKVHxnDT0bznp5h5xuDm3GnHA+ZTXGj/3z3sNC0EEoVdj8JbDuOfgjqMkNP+9D+1aFtD073ZlS+i920ff33zxxDdpkCfWlbTucx5jOebdWBGYl7bjDk5w7V7ol1T1kHSV+Kdis3XLJCK/SnwcTjkHtHHKrq6ygqhEk+cn4Yy4dVfrd0cTQSJuw7N6jZzg82KvfYo5Q+j+I/HZ7C9avD4l/nwYkWfgjTG04xQRfqI8zDuaJ0Ub+9qP9Lwn897Kd75i9u+IAxsO+/35vGJdTolIHinMNgcNaQOUe9Knn89Tc8+vIO5uycXKnuZO/eYpeOr2LfM87ShsNcq8aDG7Ti8OCAm6/e5uDoiHyWD3k58gA9z/i/eGK4wRiLVorlfMZisSDP/RxUCbxnraWqa6qqoWnD3BvnzwtaHBxycHSE1rsgwQ8zOi8LdDpZGPn/gjhHptpOumCSseuEo4ueic5Cugw6S4dOglDa+Y0b3AoupKkQa8idpcIhWvrJzEpQosnyDF0J4ix5rigKTTHLvUcmVEEOzB1U8znzxRLRKxoTJvOGsonyQ11ZnqPzGcooTAsiHlhtZznxfkhEK/1eO51k7srky0NYFoh0Tpvego0P8HOCxPagazz5KxWWqdLthe4u4JF0wK1rSHsHsBmC1+E8hmH82yuo0rhSQDb17qrSPiC4y5OzDfAS5ee/HhkHqXHiRv19ePTCID62a7wDOQMlIMlXQ69LkkzMWfd7SuGnZUy/S5+l+ew9jGNDIAFyXR/o8zIckuoNhDip2Yn3hkjnkknrclj2+Es5B2HH9DZ+plw3wV+s739TQ7P9ypihsWGj92VC1o09HTbk3hjjvTfJEvEu367/1l7hfgHfx1q/JO3UXgk8HzTtsE9sSbguf6n8u5jS9o27X9u65dk397j7m08oHz9jCcxmM/IsQ4k/h0q74dYC6fyvuq4pyw3nqxV12zBfzDm+ecLiYElW5INpCgP+EofB0RhDYy1Na9lUDWXdgggHi4KD5YJZkQewFXW1T9+0/nTyqm7DCeUVelHw+ltv8cqrr6GzrO+7e4yYGOf4hdc5Q9A/vr9cXBfQJbw3cBkPTqghiSBnkEbItGOL2cY0tlQ7N84WXA9pIVgUNhOUzhBxWPxwk9AvvfNmmXTnVmVakWWZP0U8z30y1vqN8ZSgtSKf5cxmOa2zWARCXA6H0uJdiqJojUPahkxtgwefiQj6thVT541xw87kfyWTNInDX/0E5u5E6QTYjC3GmJfx31D59F08DocMPTppXF6sTylpn7bqvktdlr3nLsbZX8fKPbV80/cp7QI+V5H2eXGmPGlDAdvzS8dSEv/SeKeGwybQiEz87vqu7Ai3u0zjcNHQ2eXVSJMet2sKTCQqfyGZppJAs4hfkrlpu5SRC5axEz/JuBWHblrq8zV1tUYXBTqcQSfRMk3Yvi5LqtOn1JtzytaQLZYsbtykWMzpVmQJiFLd5nsx3cmy41dHOnrLOy5bH/a16JHpvT3RSt9pxEQvwFWmpG6//xAFITxp9+ijHgOZLskhPyZc1/HGPjizrZeSdEbZsEHRCfDy8RO++fgznn99l7ls0AeQaT9JVwkoZ9Gowaq4uCTcGEPTNGzKivWmQkRzdHTE0dFROGeqr7sOFIeyOOe9jd4DZCjrmtWmoqwNea45OVhytJgxz7Rfou6tA4xtu+Epv1dOy6aqKduGk1du8dY773J0cmPCGBvWe19biUz3v3bUcKzny/OBSHAnuIneLxIO0r5QlAEXAZxRLFOMEtVeajQN3eq98Oqex49TV+wY5PiyYJVAkdHYltY12LDFtV857t22rWv8eVbOT8TSYUMjCZOzLIJkCuXEz6/RijzPMKalwVtsCodxhhaDaIvK/EZIbbVGwmZNNtXmKnSeyY6XWKgCfi5MLGucH+EtPXHROu2rJGWu6KYcL3sd32+DnN5a9s+SBkryezEJY2+MZ+z4ve3CfR9Zdnlvx79O2gXS+nKFA1udI67QQ3oA0F/Hk4x3pgiAUx1K8s/U2NIdxjMJVIY4NfD1EOD4xBg8SwXf7nInXX1wCGzg5S6z43lr08OvXu44tK0x5Zr7X37L/a/usHrxBJdpbr76Om9/8AE333gdk2c4Z1Fty/mLM37/2We8+PozTp88ZFO3LG+/xo9+8Rd8+NOfoGczrA4yJOQoHTLovDUjGs/WU+LbJE4M7UB+kAkpi0zxiwc3FuVkMr0rQcqXh46XA0XXYNeuCY1+ux33sC1jJyPY+qafqxVh8hB0RWA09h647uJ7UDTEHQqHwnD2/AlfffxbHnz1OXqzoVhalKiwoZ/n56iESdKMPOMBTku5KbHWcHxyzM2bNzg8PCTLdOcF6cFNkA+uf26MH27abPzKK5zlYLHg+GDBcj4j1xotPv8Ov2dO3dTUbU3VtmwaS9UK2eKQN956lzfefo/ZfLmzXgdyYtBGUS78AC/MiFJdNkockr4v0G+eeAHtXyaeRpCCllQbp8VNDVTSAg8z7Jwb7GHgYiFG8WgcuJZGWowy1KbCKEfmCvyqA4O1YTWU8+5dEfwhedqDHButQAnDT1ooMoXSik1pWG02VIdLXFhtZbEYBUY1HlTZEqHBz43waNjtWDbnUgCUcsHAlHAd6PGoPARMdveNDG79ZjcDwTrpxfFrzcdQd9SGUZgOJwZPeVC6GCYVVR+/z08/CS7mex94SeOesvKuvgcnXV7f0+VASBJL5+Lr+T8FN2OAE9O4FAnd2W8SLYXuRcxvnz5pkD1xTntlxsH2R+QSW6+7c6P0neuGxMVN9zWQMNprwbRsHt3lq1//im8+/hJzdo5qSsqm5Wn+Kavnz/nzf/efkd8+pjVrXt77hrv/9Amn3z7Abc6YtRUYx4vvvuH0vCJrDT/5sz+DLO8yNh41i3UgIp2HNXqzvcPGe0T91hDbRovvLH5CsRLBRoNoIg0m+smVI+kNn8GzeJP+O/L4pbTblzIUrXuzkihCN7pL308Pn/TcCa5fNeu8LlqfPuWbz3/L3S9/jVs/4TDPmOcFhSrIlPizpqLBMjI+4wqotm2pqhJjWhaLOScnxxwcHFAU/pRxD2THc7V83zFh9W/dNFRlzXpTUTctRaE5PvKHafql5arXV85hrPHzdaylsY7KCDWaV159gx998DMOb9wGlfkRDef3aFPKV/q4vrfq9AK2vCzfjr393XP/sguz03s8QZeeZDx0T41fEgEmMVDvMQiPkg+jM2OQtcRyS8VkhtA66+emZMovWVMCxgZFIF2jG+OHoTKt/bbWXd59jCKCVopZppnlGefO0TQVm2qD0cqvYHCOqqmp2orGtdTtBovxy8LJiDaaY6Jyh1KaOJzQWabRw9X9lzaeF6G9yzR4fZBuJcd4XLartugJ2/Li9Pm5vFfk8kp6iiF3pbEP3FxtQDOkf65nyblhXxiWOu280vH3ON19eRiL6SkaG8Hb/TB5N+FBmgRcU3nqeDkJFjf5DEkMubQ3EiS5Z8RbzjmsUhgLplzz5Js7PPjNf+DxV18y2xiEBqdbMms4KxvufnmH93/+p7xycsj5vbt8/h9+SXvvCQetI9OClZxGW6grnj29y70vj3jrvXdZLBeJtyldjjzcL2dQNy705fBIhZ4+OWQT61YplHM4O1HPIp3s+tdAW/kcCfoLyzFiiOngF8uYcfCxmp7yBsb7GJcMOqqlKUseffsN97/8gs3zxxy4DZnK0CJkkpPhJ1aket8CxrlueKgNuw1XVY1oxdHRIYcHfqfi/qvhApLUexMBUt00lFXFqvT7uh0fLjk+OmQ+m4W9bHqyrp/741duQSOQHy556733eOXNNyHLsaO2GvwIfXEopofAMSr2pMtekvq5O/+xdcGFAGdKMUVEiUinpscLvFKv5ODNSLhHA7Pf+yAI9uQEWNsaZuI4mc+Yx4P7xHSzw+PJqsZZMl0E6Jko0jBUo5Sfn1MUGYtCUWRCkWt0pkIZ/CmtbdPSNgZjoKoddWMolC9UXLUhI4E38Gxt4R5JPDcB2nSAZ+DQRqTbw9jnKaycEOnn5OAcJuyVEXHkwIvjIrOAS+pxeEBm7NBpO28LUh/PmPO3GfIyjDnl5ZgCR/9ahqi+jyXR0+46GtTwBLiZAhYu/TDlwfhSotCWCdk1cOf0j5P4vpcHacw7ySDBWBZ0cSWsJeOyTXgtI+gxznL+8gWPPvoN93/995gn9zm0lixXlFgqDEZalFOY9YaXDx8zn+fc/btfY+4/ZSkZ8xxyDVVVAoYZFappeP74HutqzSysaAvOli7fY36d4oM4PDzVdiJxjkE0lybqBbwXWgkSvNFX1QjYm7ddmGOCl1LJNLLft8Kl/JQOR13WPttllHXt5Jw/8BY//6ltWh7dvcc3H33K6v5j8sZQCIAHHE3T+BGCLPNxS78FQWuMByR1zflqxcvzc8q69odgHh1RzGaJ8bpLQmx7gVbrDeWmIs81R4dLDsLKqe7Q2AQQdZsJVg2lUZhZwevvvcPbP/2A4sYRLtMX152baqF47/qf8WmiW/bxbmfMbCW3/Sw1uC4jc7+3Byciuan3wwwlMDblyIACJdz3lmKMPgQW/HbSeGWure0yq7RGKe33ywnKPp/P0EWOU2CcX+cvqs9zjyc8UMi1sCgU81nBwcGBdxPXLVVV0VpD3uSoGurS0pYNytmwe7JORNJIATjnJyx3afVVhow8OYyrsd93I3px/JEQ24DAL433yLyr7dhZI4ByPbhJV7ANwEoYNtu36C4CmSisdzR/l7c0P/sU49SQ1XCoazqNq0rfD5RFK43eO5G83RdPWqfdFNxo6ce2nATaEW5sA4tBvkaKmB1tuAWEGSumQeiuP4zbNs3qcKLi7jk41lo2T5/w6f/3tzz95HccVisOBbI88/VgC7T1e5G0GLK25Oz+Xernj6m+fcCyFWbznEI5FMEz6rxCsk4o25bamK0usUtQ7wI98fkUr0OYhSXiyy0yOIAuysc4HDrej+eq0WUNnPQ6HYg9YUbAPhhaqTgbB9+Xo46norHHkDGlk6OOFy9e8PVnn7O5/5hFbSnyGTPC3E1rqKqqAylK+/MOo3ERQUZZlqzWa9Zrf0r4bD73C2GI50uFcrjhCjDnwFi/q74xhqZtKaua1brCGMvRwYLFfE6eZ5MGqjW2/65pv0hf9wAAIABJREFU2DjN4varvP3TDzh6/TbM8nDW4va3KUgfV26XS+ll2E6cO8EfEgRG9/UPEPoXydxLz8EZdNLgLdiZncR7MxkoQm1vso3SjN95QSUYbGtoEKzSOGdQ2mNrcQ6MQTKFzjWilF/GJ2owKTcq5jQ7mfaHb0bhocOwlso0ZBqjhLUtaVcrNi9OkTdeQ3I1PG5htGeJ7xTJNSmTBwaCc4rUk9KjXLrfsqeTK1F+Nr3qLdxINoC6eMRGb/X29k7KtAL9XKguqf2CZfA0UTrbzHsZZd+DxKGbOIKyq0eXtRwGw4g7hb/vKGoLbPQP9nkJBOnm2vR9ajjJtYsuBopyRHa16jZdTjHFtHdZXtvxdb+TMBGc90aAdELe4eebrU5fcueX/56H//hLjqoVR8sFKvOKwjl/NltuFa0RVNsi1YbVd19ispyldeg8rHQRwdk4WdkfYutEM18eInmyVDc6R3cMw0bQFdPv8n0Jz6QS6XcoFj+vKBpiovyfDofWXnWv5lhfTD3f68ElgNyJYk6WPeiPsRfn+0iOAYge3Af17RynL1/y+zt3eH7/AYvGMpOMPC9Q4nDW4KwvQ13XWOf8/Jc4BzQsCffnI645O1/TNC3LpT+CwYQhK6DfjV+pQU6sC16YtqUOE4vPzs5pmobFPOdwWVDkqus/NuQb5/uLjbsnW0frwOU5b7z/Lm++/y754RKjVYcO+/4ba9JNy66h4vuetT78EhIDyY2H5/559L02+hskOjblEw/CmLriD6TciPG7gJHNwxwU69CtwTUtSmnyMIFYS1jO3YC0Ph6tNVXZ9kNIElY+BdZVIp0bMc8LfxZU2INC6cwLl0xTyIwDZzkqa87aFZsXL5DWonL8KpXOKgOwnZU1MPj2WHt9dY0AUqgpFxTSuE+LCP0xnH7cvhOseI+OWA+g1AB8RMDQe3HSTbMkiT+9Ji0T8ry9ymmXIE9pcuJYNL2ibO/yFpXJ1RTmuwDH91E+2xPoYjyww7WylWb3cdqPZDRfQ8btkICb0Cl78J1kZPRRVCBpPraU9djgSU3QLR7fBZYc/frxIeJvxW98uXrykE///pc8/s0/cqstuVVoREETDJW44MAYQ1XVVHWFaVs2py9YHhwxK+YoJZ0Ca52jaRvWTc2mdTSiefWW32ytr8dxlQz5eKpf7Aqffgf0G5X6JRXBoxABjt84Tsch96vcJ9zQAzJVD2OP3Na9hKHvqfi5JGj6gSBwbBREKVutVtz98kvuf/E56nzFkVLMshzl12B78GAIAMJSV5WfU5V5gOP5sGK9XnN2dsr5ak2eZd2EYhNO7o5ly/PcnzGl9TA/zk/F2JQlq9WazWbDvBBunBxwdLggU2DamibOPQ2d3dZ+eKpsLaXTVFpz9MbbvPH+j1mc3MDleTgoWsbioqOL5lcO63HoXbs0UHED/9mlQI4Xf/vz9L13Mu4STSWjN6+2wU2q8QdCfPdMaCW9kPMHcVqsaaBtyIFZnqO1Gowzau1BT55r8laFCccGEb9pUbpBlgreHaWk8+Io4rkhfhKyDXHOZwWr0xUvnz7ifLXx215rv/OxDQJVq2ExSe8HHgm6+7Qe0jrdZqC0PhMBqlQHUBx0W7s7/C6Z2m17VuKxDV3MY7d5ZJUtcLObgXYx/C6gMyhZUuZYFUNgczU9OGP654Cby8S989mWF0R6xThWzIJ3+aXpu2i0hfrvjgcfddipMozzNumRcSQYehR0V9zJ0ukO58TVhtCuz/n2V//Ai49/xYmrWBzOyXGYMFsCR1g16WjaltVmw6Yqmc29C38WtqHvOCwYCE3bcl5VnNUGN7vBm+9/wOHxCXEouh/gTssxrXSnlHAKfsZejkEPT8IrpVASdsadNDyuDknyz04wN/AayvZ7EnbZoRt2DQVOhf1h5UjaBkdd1Ty5d5enX35O/vQRB7TMc0eRCToAA9MarPKAuiwrWtP6ZfMmDJG2xg9NrVaUZcVilrNcLlkul/18Hee6k8WzPGfhHLNkXo4N55TFE8fX6xIlwsnxITdvHlMUhT8Gom2xYjxoxusFY8Ky8NZx2gju6AavffAzbrzxDpItEBcP11Q9wOkV2HT7jK6TEq0zoIZtNnaSDNjl+8jGcVZ30P6djPclGBWoddMZGwObjruHinbLi5MIE4fFKUPtar86ajbz+9lIH326qV+e52RN07sLA1PYRNkba7BhtZQLWwina+p1CJdpjdaKTCznZ89YVyU8qVg/ewo45jdvcfLKK+jFAsT7sAVBdXlPO973BTaQOO0GlRrdnnF3WqUULa5DUZPIVwjL0aULkypBCea8c9OCY5CrCSt1UnBvAawdq61iMbsOwSAf/5poSsFNDd3tnXAn329irxpInbRjJW2JgPR7GbnekT2wgiSABJHxMFcv4FJhN8iZjIenwhyGtJm3+CbNc+STJD+RRZ3Flad887t/4Nvf/QPz8pRF7g/VNdb5c+iMd8M3tqVqGzZVyWqzxolweHDAwWJJrjPijuQ2bJffNA1lU3O22VC6nA9+8TM+/Nmf+DPrSPOX5nsayKTvdrXzludvbGgMQE6/Y/LVpm0A2L1JDBh/GZdlKP+HcnO6Xne9/6FZj56xuOkqgKtrnt+7y3effcL68X0OXM1RDkWO1wuZBjStazG2RQQypbAGmqaltYa4rHuz8QAH4MaNEw4PDwMocd08yuh1bNu2m0ic5zmi+lXCcXiqqmsWyzm3b9/i6OgIEWiqmqaqaJq2G+qyxtBYQ+UsFVAJvPWjH/HuT37G4Y1bfijNqYEtAmwBnSlv7agKR1oqfHdB1UdNmfaTfZBj25i8uM2//1lUXU7oPDdT6Yx3NtzNfz6C1CKSTsv5NLSpyeoSQbBhDoqWnumLoujcenFC13q9HoxxOvrdjx3OW0c6TApTinQJtlYarTNE5YDm7NF9fv+rv+N8teb04QOUM2S3XuHdv/hLfvLTnzI/XCRKoZ+yFgFFLOe+eSq94le9U8yZbi+c+MyGOQMedPv0FMMJiOmmYh2oxLtddQIoYofuO7ZjZ4OOW23CIk3LcRnqz13qvUf7lMNVoIsE7BjUpdf/JPmBSU8J8XnULpE/OztDb3+/FXFvSWz15135idZ6RKuSOIe6MNuxDNcS4S1KwGFoynPufvZrvvqHv0WdP2Gu+/k2EZrhHNYYatNwuj7jybOnbDYVt24ec+NkybzIyJzyp6Q4sHjPzXq95nx1zqqsuP3OO/zbv/kbjm/eoE2rMNTFLpB+0fyz2E8mLdnO7hiC2iiTBt9eaZyzG+TQt9KuV+E+MX4v6FuTfW8Q4TAvl+mBzjq/MMVayqdPuP/pR5w++BZtS+YzmBUZeSbJ6EFvm+H8JrNKFNbWbDZrmrYNHpwNTd38/9S92ZccR5be+bPF3WPLFQDB2muquvqou6ekI42OdNQP83/Pk3pmpNa01Cp1VXUtJIskCG4ACRC5xOLuts2DmbmbR0QmwOoZNcrIREZ6+GJuy73f/ezavZyenPDowUMWizkAfd/T9100wp3Fpe3fNjkcZ59K66KD8nq9pu976rri9HTFYjGnrmPyaBECwTo6Y4cVDOMMvXNsbGAbNCfvPOZHf/4zLt95hNJT1f9GQ6tcihiuOrJq88ZLqSWwOW4M3GsMvsET3pjBmUzmwsoa6jkMzMwW/JGlADc+/Wetg94QvANRITOLkyhmkRyzkBLr/ABu8jqmtZa26xBCxHgDApyzAxIfBAl57TVGptRVQ9M0XF+94rN//AeUrqicI7Qtt69e8UFrOFGaH/3FzwgzEdFwOc1EoXv2hOGxv+PHPJkD0SEZxhBjhSOuyH5K4kCAxmU5PwAuMqNCVjZJWMvSEz4/4s12Ax1jGO5yqjwcpCOwyX+PJp544zr8c5ZjSgz+6WCm7M+72uDo8f1jJQEoYPCQ2Tt+7H7DeNqb4kwvG78LCSwfs/LEYdWmp2QENcYADsQNBgiHcTu+/uQDPv3V32O+ec65sChZjfdIv61z7PqOnWm5XW/Y7uJywGo5p6lUDKElZLSavcf6QGs6bncbbrcbPIGTkxOub25xX3/N2YMH6KoqhPUhuLlP2R4DQwdG3OQNpnNKCDEwyyPb+XbOifugzZ0X7I+iI2Pn2Di/a95FSZfa965H7n06Vrz39Nstzz/8gKunH6PNlqYOVAp0JdDJxyXqwcMnZfY6+8x0XfQBm88azs9OOVmlgH4hRK8rF1MmuK7Dp3An2TEZAUoqjDPsdjvW6w0hBFarJSerFXVVDe8spUJrhZEyOjX3Ha0ztN6x7iE0NT/+8Y949L3vUDXNoKPvkldH5+wfzZIlHQRZHQ1AKBDuHTt3s3RvVpc3ZnAysBnl5LgTZ9zJcYi0xxvkOo0S9j6KMeARMqCCZNcHelmzIPrGxDg3AYjhFmPDRavH+UDXGxbOoZMnuxIgvSdIOdB9XReR8yCASDsW0rtq6ZlVcenLmp5w/Q2Xp+fMpMQrgWp7Xj19wh9WJzx8/IiTdx8SpENMk1ek97pLGea+z+eWcyZbM/H6kTGJJ9wZ/C/1gw9hyOdRLiv4MCaEn4CcybLQXZTAoQI+RhvfZ9GKPYU3KILhTQ/Zgre5vA6IvKllct99Xv/9649nB+7yO1H0+cFcLKzpgcWYdNzhx2EsvEYOjc8cx24UDeOikMDjzZYvnr7H0//2f1F99SWP5w3SVWBjCIXgISAh7TLZ9S3XN9dsNlsW84bTkyVKKXa7HWouEXX0ybPBsjMdV9sbbto11jpO5wu2z5/x9//x/2D1wx/wL//9X/P4uz9EqBjNeFCeR4DL/eN9usU7fzcwrmI6p0IIyf9mdBh/6xmcOxDOpB3e5BbHgM9d50/unTaRpL9LcqwsQUyBenEzCDGP2M31Nc+efMSXv/st3L5isZDUtUeJQHRFUEO/gCBIjxdu+pzA4Fzcdh21rjg/P+P0NPrLZMfyWldYZdgR2UdCiMyKiMtSpjd47en7jvV6Q9f1LBZzzk5PWS2XVHrMr6akxOsKqaIfUNt1bJ1hEzxGzXj3+9/jBz/7KScXFwilYuTsO8FNnv/3G1p39Mxh+zJi85D9axFDiIQA6XPWY0dusPeEoUavqdsbMTh5bf7OB4qCtTkGcu6xFsdLpoo4VQBhHLSBvlU0kpiOQebzojCVCcjkUmnNfDajriqCswQhMIIYETnEDov5QKI/DiHEtPIiJlSLAibGOMjbSUWAWgoapTDOU2vFvO/5+skTXn7xjJMHF4Q6JggVOYf5AG6mltn43rmNygzCOYdUCY7kcE4EQVOAKKSMk1swcaj23o/e+EUfhLyEwL41Wbb+txnU8ebHAA+Db8+Rq8pz80mDMnk7l6jgOCh43fnHhMkA6vYs97us1INnlizAPnI8Usbl3/T0vfcY7i1GnVVwuMW/6XHFsQmDcwCUD9vqLuWX8+44e8PXH/2ej37xt/ivnzMPeSwLvDU41+McKKGwxrJp11ytb9m1G1bLORfnZ6iUsLPvOnpV01RNdAbtOm43a25ubum6GHDt7PQMpSVbe8XVk2s+qCUnqxXLi4cI1H7O3LvfpZgD9/lfDf0cpvfIgGjYmfmtlMs/T7nXAueuYTnKmZGFEXeSVK9rh+HbUIxdAaIwl8TIY2bzEy+yzA643ZarL7/gxZOPEbtbVpWk0aArGY3ksDdPJIfhSIg6JO546nDWc3625PzsnOVyGX27BuPTo7QaNUTxit57uq5DGEHfd5i+p640pycrTlarwWE+ggJwgFASWVcIrbEBNj3sZMXJ5UN+8hd/yYPvfgdR11FXl2L+GDi9j2EbSpyr4eDYcbwwLlCIkdEpAU6xgjHqzfJGoz4Nbzg3XgtwRBCTVyiFYAmlBBwsTe0rqvLb+5REFAQpH0YI4DxmbXCNJzQ2xocQmZHwQxp6IQR1FcHNarmkqSuCtegEaDAGy5i40lkfAY7zMZ2kyFuq0w4pEagSqFFIGqVp6gZ8wFlLjaff3PDpH/7AD372U6TWKaVDRKgypAw2omiovd+iGNmvZ3IyDhjRyqAkZXRxFn4a/G/4ESFa7OS4EUUfH/TB6wfO/tLaqNvE5D1i/e8SgeU7xTbKfiJv7TbxO9i4b1v2l3xeB5oOjucxlZmWPYDzbeu1//wciXX4fm9xamgHIQ6+K6+661ll/RLUjv+FgLM7nj99n4/+/j/hvvqcpQQldZy3HkiMiLWW3vXs2h03tzdsui3L1YIHlw9YLhYEH3enbDZbuq6jqRusjSHub9e3bDZb5rM5lxeXrFarmJBwZwi7jmdPPuH6L14wP7tAqCkAvWspdv9Y/vtOoCMOtcs+Qzq5/m0GO3cBwDetsxBvInbuKHl5c9RVQYCXIeUNjLeWAWT2aSyu9EISTMf666949fGH2K+/YKYsc63RlUDKnGhTDdHzc7+Umzry+M07moL3zOYzzs/OWC6XNE2DUiqe7z0hJXHWWqO0RvRm2BDjvccYg/cOYw1CwHK54vT0dNhhNbgd5KcLEEohK01Qii5IvF7x7o//gsc//CnV6hRfREwmt4QojdBRjkSRIgaDZ7/Nj35+jV26Pxem6KAgRIbeHMFv1hBByPRbgJDcV+5fovIjPptQz6W+LspwKFsx5RskrXoXoDkoISag8xK2ZoMXW4LZ4dqa3ieH4hBSfo/oWAWRbdFKUVcVdd2g6ppKR+HY9T2985jkpd73BoEYYhhoqUm8d1zTrCoarakBEQJKCKpK432NtZZaKbRpefX1M17d3HC5XDAMtDwR8lKaKFmmUbFHADCm8tsHOSXoiseLASIisxR8nOBlUs6hGQcWLdvGhxGJy50vEyVWdEspxI+DIoqJICb3no7p+xRhqTTf0lIAuf3yJv43E2HItI3vZGr2jx0wJeLeuXXsvuU4OfouwwXj3xnIjLuc9usxvcNdzN2xc72I22tN13L10e/49Bf/FXH9ksu6SkxMTDoogkAR5ct2u03Olxs607E6OeGdx++wXMTI5ISAMQpjDTtjcbs1tjfcrjdcXa8xNvBwtWQxn1PpCu8s2gVmQfHy62tePnvBwx//hEqp1CDyYE7kdrjXGfIO8JMbdp/VESL63wybIsa17DdpzH+2ch9L9yZlYsAzIZ2Pn1/IrTGXcdyN5PBYHE6EZBBLVBDoIFFEB+4ISDyIwPqbF3z2/u949enHiO0VunJoXUdmnyj/xdBXI5BxPkbNtynSsHUxIJ/3nvl8xsXFBaenJ1R1deDrGYgMUNM0QxLMbKzHXVMdxlpC8CyXSy4uIguktR7lyCBPGJZ4YoJOga5nrB68w8/+xV9xdvkQtCZkP1FRAmwxkdsig5wMbvb01vj5OA10CH0Ox+2b+SoemJNDHSKDI18rYF7P4JTCETEsdB4HJWGSB2qkq45vLd5/zsSpGVBIbPDswoYdt8xch24NvelRKlY9hrCOXubG9KlP4v1idOKI9hYLT9009NbSGksAjDFDQKaMmJ13MTkaMWw7UqArjdn1g0NzpTW2quisheC4Wb9is9tw5jxSiYE6jO99n+AbQU4eJKMQ3Ac5MU/VsGwVZ/MQ0C+3aR7gZdtGBkdM4qcdqc3Ro/tj4GhfZQFevNf4DvcBmslV6Z55R9Vd9fznLeKg7rEvvo3qCZmeneCT4y98bN6MNmlRjTe4LlFFjKtT4+61oW6Ti8rri7qOVw7KOIPnLGSjsDzeKgfjNbhoWDjHN0+f8tl//6/4F89Y1TVaKBSj4HcubnD33tMbw+3tmt1uy8nZKZfvPOL09DSBlRjAE0A3Dd7uuFrfst3sWK+39NZzeXHO5dk5M12nqOghcv19QNjAdrvDeY8+zEI4aeP9uXBgpd4jzCcGZHGPQ1Aa7gzE9jaVbwVqCgs4wLBDNgM5QdyZFN8+cMzsyc6+1o3pEPq+x/Y9xkR/lCAFqtIoqalUzXKxYLVaxSXPYPFdx6svn/LNF58Q2jUz6VFplUBkuclY1/zcEELUFcHHsAPO0Zuevu9RSnFycsL5+TnL1YqmjuxNRDdhYNqlUlRVRe0986iUMMbEbOFd1Dmz+YyzszNOTk6G2DmlrM+tUoZFcUHQzBd857vf4/LBA1RV4UprZdoJU+Et8rvnsZh7Y9RT5T0E2a0iTO4XfJYKbyofRwZ/6NvDUwoT6/XltU7Gw9r6CJ7uBit5wo9fDFbfULsR8xy1hoBhG54K4IyLWb6lwgWVKMcx8ZizFmMNXdtGSi9ZejmxphQCKaDGo5xDGIkHrKup6zrGwei6oYbWWqz3eKniNj/nQMV1VWMMwfu4+0rGaMhV8PTthtuvn+O+9/3J9ruAwCefHzFYtPttV7bF9LvBMTSb+mHK0mRHsfLYsYzj3kdgFER5XOz1Qe6n19lNx0qB5MV46D5/hMmYuPN+b2kZxjuTeTGCtPzlsRBxe7c6Ml33Lf7Ssh/uPDlHHFxz7PrioWRq9y4wWdY5C7pc3/L+I4U9WqZjfyfVFPbvmJ6R5rANDmUtV598zof/5e8I37xknlhXSVo+TmPcEuidoXOGXd+yaXfMFwve/e53WJ6dDLl9cl2892hdgYgRYK9vNljjuDw/5cGDC1azBY1UeB+QXmK8ovU9TjioFMEPXNsU2r6GtSnLdM4ncS+OC/AoK8SkzY/1yZ9UGSye4Z/JmMuA34dkXLqo/JWQ6OmpkUmJF2FdSkNgevpdzPP04sVLrq+v8Zsd7KLvivEu+qbMZtSrJY8eP+bHP/4xZ2enBNvzzZdP+fKj9+ivX3CKYVFLqmq6TV8U8WpG9gRCAt55LEdw0lNVFcvlksViwSzlnBLJuEB6CD76zHiFqioq76kBS9w+bvqevjfUdc3JyQnLxXIAN9OmjQokLu3GXFUxc3ng9MEFP/zRD5nNZ/ePnUKvH9NPZBlDaVwVbG6kViPuyWmISnXyxuV+FnScG2IKHu4pr99FJfZ+jjy4/J3+2LtBMajfpF4hINPShvASjGJRnbDQDU3j0Uoi0UOOquBjYD6A3jiMcwOLERkaDVLQdx06BCrnqSuPkpKdtaw3mwhcpMQag/MeLyWd80MMnSCip3uwDiUllZTUQrHQNZ1p2X31JcoZhFjEV0gvHMgWczl4piBCiBH8TJeoxnMkYXQOE/nY4ZAY/ItSCPDYnInVSRGQp8tNd4GcKfA6xuJMKcrx+kEBF0ouL4u9fgCktnjNWW9HyYAuKXIgUzNTYD9aMXdZM/sgpjy+//dw5xJoTWqVQMcewDna/OUzU8VCkh/hyPOPlaz6x2hK++MrMMlqT/Sti+3lCcHw7OOP+eBv/o7+6y84P4XZbIaWEhkcwrvBOrXO0vUdt+s1N5stzXLOd773XU4vzwfqPlL0IVmykfEJATbbHbfrjsuzJe88eshiNo95nkJM2WKtp/fQBYefSR5+912UkgTnIyCcUCiZeTgOUoazyvm8PwrE4WjIFrMo5tybAqm3vtwzlILtMX3HZrths9sREFRKUQkR45Vlp2vn8H1P2+7Y7doYN2Z9Q3d9xXqz5ma9YbPeoTpL3Xu8cRjn8UrRzSv6ecOXXz4A7/jJT3+C7dZ89uHvWX/9OXPfMleCmdaIbByneESSuPPJ2uQOEaI+cEnX+LSS0PU9zjlWqxXL5ZL5fD5lXfLszXI15RtDSqQcQ5Z4H3cMLxcLzk5OYjLNtGoxsKWZnYfBXaPrOtpdC1Lz8PFj3v3Od6mbBrtnyYzGJ4McudegOtKZxSiPEk+EQoYknzqOp1w4NkcOnnRM9oix+d5ENt0PcF5z/Z20OiWL8y1vysBQpkZWCNkwkwvmaGZ1QFcCXIje7wlFN01D1bVJgcdIv1JrZGJUVNAx8Z61KCmotEQqSQge6ww+1DFPeFbMzscIqiHGlPH4IfaOCAGtJFpKtAHpPNsXL/C7LWJ1NkYNTu87GSxH2qwEN/sgZ1hOyApiBM9ASPurIo2as1IJcZh9eLAyRAoKFaLSOaxOKNp/aoXf9w77DNDwqQBqA9V5VGDfxfC8ZUXs/b6vFO8ZQXK8LgOh0Ro58pg3tFAiS3q8muLgvFEdHzdM3rQO91hQogA8YQS2cRyksRGSb1rw4Awvnz3l1//P32C/+IyLRlPrZlgiUEKQ18WNMcMyxNX1LT7Ag4cPOTk9HXYLeh+XC3wIODy9i8HPtruWtrPMmorzs5OoeGR0GnXOsWtb2t7Qe4cNnma54uzBoxg7Z1hqLuZDYmGm8vh4MMCRzTpWxu37QsQQDqOBOoVPfwpA59ibTtjAY9c4R7e+5cWzL/ji8894dX1NIEYG1lJS1zWqqtBKg7N0my3r9S27XXRL0Ls1qt3GYHkIah+YOZgFEEISFHQ+cLMTbDeOr25e8MlMcdII7O6Wm2efUdmOZSVZ6BgA1sMA/uOO2qlCDj4MgNvalO8pLY9VVRVZl+WS2Swm1RzGRmqP7Msz3C+PqVSUktTVjNPVkkUzo9YVSqoI3An4MNbFOY+xnt5Ytrsuhkm5eMTZo3eo5nOCTNnN96TCAKCZGuAlwBmPi9GACYdj/6CD7xiqx9wbxhUg8FJGViy+XHkhmUka/n6D8q0Bzv1KrqhUYX2U62pv8rAYr0Ck2AOCoDRBV1R46kqgK0mQHhv6QXHPg8fMF+wWu8hiiISOc30TOh5+hKDWGq0ks6ZmsVwggqDzbRzUIVBJhVUaPFjj6J1lBBAxmnIlJFWQtLse79KEym8jmNZh7/cxJ8+pUkjnh9Fxeb9f4nbD2GYS0pLYYf+UHv+eMOQNS0vYhQIqx85UUU3fYRTCk0rllxcgkIUFX/S0GEHTvvjbX7p820qJMxEla8GdtOxgs4dDgBFBT9wRkP0Nxga4a66JQfhSnC9E3mkwgozJeUwB7Z1zuejK/e8HFkiMzx1e+whAOhw7AenjmLbG8vLZF7z3X/+W66fvc0lPo0+oRY3OICi1W7ZQ+75ns9lgbM+jhw95/PgdmqaJRs0wxiM0cN7TGcOZWDd/AAAgAElEQVTtdsurm1usD7x7ccaD0xNqJWPMq7Ss0HYdm13HTbtDLmr+xc//FcvlMtV/XzncxcMdCvBc//0ydfIu2jnPqzSxY7vmc99SgJPGfK7dBOQczOVSwcbf2+2G508+4tP3fsvTJx9zs17HJUMCSsi47J9ycwlncSkdT2aJ584yFyk8SF2hpWQuoQGUiONg1xm8MfR9z+2659XHns/kFtl1yKtXNEJQVwql9RB2JIIbUc7wQb8NTKHPeaha2rZFSMHJ6oTT01OWyxjUL++cyg7Q3o/MTxmLLSSw7b2nrmtWy2X0KauqVI88IkZ57TPAMZ6us+xaQ2fhwXe+z9n3v4+dNZGJUinnlEv8zzi5J/P7GNApJcrA6iaQM+SLC2NU/G9TyrkxkiIHgiT+Tnqdcr68pvzxqRqGZ0+F3PBH8ar7DmLHlzfG70YAEGPUSGephEPpgFQprYKUSSHExtVOU+mKuq4RIm0Lh4lPitSRxVFKI4Sl0or5fMZyuYzCLDlsxYCBnlopkIptteM2bNj1HUEKqrrGO4fSBmUjyGl3PTe3G2bvEpOtDW/nGUNQcTCYhiabgJ2yTWLLHluOmjpzFoMygaO7WBwyfViAqfuAxQhkpopqv0wdkst+HO89Re/5vjBFBm/u3/A/uwysFPvtlWb7lMgqBP/h+0wFSb5LaaVQfBYlqixvMr3XG1g4GQjl8w+ATAIvojh+t9U1zv+jzzpi2eThc/3qFR/+8tdsn3zOGZJaS5y3WGOiL42IdbMhxIzMux3Xt7dsdzvOz8948PBBmu8xgCWJbY3jOeaj27UtN7cbbtdbVouGR5dnrBZzdKqDsTHq67bruWktvRf86Mc/5Wd/9a9idPS9tozLDIUlm99SHBoCd43hiVDfO6cEqcM5CVC9rcA/g97Jqwx6SBycm6OpQ8C0LZ8//YSPf/1LXj35kPb6CmEtEpA+glxLDFxKCKgQIwDXdYOqKqSQLIRkUUefSqU1SkAjApq408n7yMZL56isQfUtt18/44vQc6o0CyGRVYWXDRZBLURiEHPAxTBYgrlvhh1Uzg3sjTGGZj5juYrxbsoUQOV4yEtapVMwCJyPAN4YQ11VrJZL5rNZ9CUdcpLFeoygKLI5znucDwQkup5z/vARq4tzkALrPNr5NLbCsAQ9omlxMKeP4tIwPRBvEQXcMRkR6zeCsTceT2EqMUWqU/5113L+sfJPBjih0F6jCgwTU/e4Ur/b3yA3rxcyMjnOIoVDaIEqUiuQ6GPjLELGzLtKSpy1g7CZ5JhSGiEVSsfPla6pdDUOIClZLBfUdYP1KUmnsrTLJdttTNznlUBqCRKEU2ADSgVcMPTtGidiFnOV2yIEEBnkTJVR/jqzQuPfeVAQhWzIg6gAQkP7TTVqjBUStwneNQiSLhgAzxsvhxzrq30ljyAHUyzB1xTAHbI3f2w9/meXKRtROginSRk135TNuGdyT+bG4bf5pKhARsQxfi3261TUSh5vzz0oxGEXHo7TY5+/TX+NfjpRYXebDb/5h3/g9sknnHuo5nMIAu8Efd8jRMzYLJUcUq3c3N5ydX2NVIqzs/MhaJr3aV4MgjFayr0xbLctr67XBCF49PCSs5OTuO0bBuvbOEtnPVsnOH33u/zlv/l3LM4e4kJiFJNGKNsrFG91rHdLBTQAYvaGwhFwkz4Mf98Hht6WMlreR44V3wnicrqTcXer3214+ewZT377jzz78H3CzTdU3qF8lA/S+8g0Czkw4ZqAAhoJSsY2WuiaeT2LUYKVQoiAwuGtHesRYtBYYRzCOsx2R3uzZrVYIOo6blaREqNVCvCachWmnGhpkMXlE2sxfUffdtHnZWBvJMvFguUiMTcy53srmI2CSc8ymBBZpr7v6boOay1Nyja+n2w1szbe+whqXExlZJ3DGEtnLPV8webqG56+9ztmyxVnF5ecXV6Oy1W5SwYkXeiKY4bLndM8TDs6X1rEzos6azQA4rFjLOfenf8/Guv3A5w0KwWHwmxfIU3VrBjkZNgXvncIzeHYRPyK2InBEVTASkdMhhBzcARBzC6e8k7VVU2lNJ01I4VYAB2pFFIpnPPDs2UKGiQTOEKDVBHtx9g6gpPVgs1mwXa7o3eGUEkIAuEV0mqUkeA6THcb43kojXRjGxwXgbG1hvXNkOuUBeN0qWmynTTcDwjiElta9riDxcm0d0mPvl5flTzdnmIfABiDhT42QKIvC5o9n/unVqYgfK/BsoGVPg8gLpJmg0/pBOyFAb/sP+jo7yFx5TE25+h1wz8FGCostPx9cUAU1983xnJL3EtLiyjwBqPFe26vvuHD//Z33P7ml1zIwGqukdUC5yWmj0tX2+0WlWKHGGO4vrnh669f0naGy4szApGhAYasyxDnjbWGru9p25ZX17fsdh0PLk44O1lRaZ0s8Lgrsus6ttsdawfq4oz/9T/8ey6//z3ckHOoaKZ4JB27C9ocCud9MJ+vFkQfo2wkSiGGpYhyGbucp29juZOt2tcZRH3gpCIEz+bVK5598B7XH76H2ryiEj1SJzbOg/CjYSeVRCHQAbQQNJVOxq5mphtm9YyqqpFSEIi5xlxK4WGMwfUWOgudQVhHU1fMhEIXbECMjdYRnJw4GJOYfQEDY7PbtdFvq225vb3FGMPJ6Slnp2eslksWs0XMOH4HOMljIiT2JWcKb9s26iKthpxUWZcJKfFiDMXgU1263rDb7thsNrTbloDk2ZOPefbFM0RVcXZxyQ9++mc8/slPmZ2d7UX+31uiOui3qY6/u2TIz73y4I8BN1nXiTvqdV+5F+BM7pcm4YHHsxCT1xnEXtGA8f9RYQ932Ov8EtzkzyqAdg4tQeiAl46AnrAPGelGkKIg9DGPh3MDfYYgppFXCogAyHmPs/GcSmtUzsANw1qjkJLaWCqtcc7SdS2d6yGMVGMIHuc6bLtBOkvQpdJPFGd695HZgJHVCYAcQM7g3V50xICAB4Zn0jnFfcYJKxHDtt2yHAM5eakq3++QXcvP2S+lor9DGRIH/eiLc7dieOtZHDF912ydCUr27cg8yZ/CGM8kryxmsJRZt/FZe7+PVqeYbyKzPGnOiTsunmLT6b2OGCVHn8e+MbLnELt3XR7T7atv+PiXv+DZ+//IpbacNJJKx3lrjUC4Hm8svbH01hCI22avr67ouo6TVXTchOh03Pc9s/mc2WyWZEvADI7IN9zcrrk4nXF5tqDWEh9yOvG4rdZYy6439NWc7//Fz3j3Zz/Gz2cH/Xz8xQRRE9/z4m9QRmNrCm7+lMvRd0jjSwRLv17z8otPef70Y8zmGo1Dy5BIR4HUAiVUBMciLRd5gQqSWunEkEiUrJhVNY3WMfegFDGckcsAoKfvOmzfg3VI61Ee6mQmy2TcZh0SfMxVlueqc5EJUiLGVcsRtNu2GwBJ27bRZ2YVXR1mzSwC6TQeyyERM4f7BFLibiljYlC/tt3hvaWuZ5HhSgAmhBDjuSmF1ApkjoET/W+6Pibj3O52SBGopaB2FvoWs9tytb7FGwOzGY+bhlna2RWXIQ59RIe++hYlFP9GVREGY2/QTIVcLI2j0kR6PZDPdc3VvL+e3yrZ5mjkJevi4Nn7SnFsOLkvrffqla3Z8nlATHZnPco4qrlCSQ2hUMYJhGQqT0kFHpy1CETcBpoYkhBSniklh617vbVY5+MEUmqkuUP06BZCUNeaZqaptIyhs02PQKZ09/HHKs12ZxBOol1cVpoOnOw0WTIz07DZGWTkZpymK5gOxMOBMLIrQ34Un/7ZY3Fy+2aQk+PkMFjlU7AxBR5hUt9xwB1OknHMhBIPp/44Hh/nmOP121RERg77Si1kIJ/eYQAux6mxA4skgZMISIdDw+1LKJGF0kGblxcKJs89LsTKF9i7x15dj7XD4OBO9k2I9/EiB/4rHxTYbrY8/dU/cPXBbziRLYsFzCo9BKsUWeHk87cb+uTfsNu2PLi84PLyAojxqrxzMTp5UgJ1XeOJCmiz2XBzc8vJsuHx40dUWuGcoctj2TratmPbe7ai4vR7P+QnP/85+uwUJ5NiTW2TLei8eSEL5vxq+65XRxpr/CKM8/eYz0L5e0jQSenF93aWifI6Mu6Gv+OODdYvv+SrT/7A1bNPoN+i8RPyUSlFraoxojMC4QXaK5qqHtgaJRS1lIjgCDYk5W/xtsPbHm8N1sRlH+sM3tnowyMywCFGvk9JMCVxWSx4PwTtMylOmkx94n2g7UeAAzBfzJnPF4NTcS5xHhTLUj4MhqoflqYikxi8Yz5r4g4/Fb0uh5xUxF3BetYgk9OyT0usfdvR7jqsCayWC1aLGbUAvMN4S9sZXj3/gtmzd3n4wx9FsJTlR9KPBzKE/fl7TO8cjvj9IwIKn6t0LJCSa4acOGACcvaf80/RBW8EcEoLXwxwrHgTsV8JMfkkmU5euEcQ5C9FpJF7azC6joMaiQzRu0WIWKcc72XimxMAH/ApEJSQAuci05J3UFH8RHQp0SreIzp+9ZEBUhJZaepZzWzRMJs10VpE4J2j3QmcCxiv2JkYjFADYm/t9FgX5fgg43nZlfj4bqIczXjfuXG8H/gUdXXMVRJ5HFmwU7lPoxXBELNgVFHjObnfRpBTnnF3ieeP7uVZMd+3DvsnUQoGYzwkiikhSDlOKOi2AcUO1vpEyOwBnXzJ8JyIfsQeaCnvNwDUN2Rfjr8b+H3D48hpeUkls1cBsNIPilgG0ncCJ+KWW79d8+nvfsPX7/0jc3tLswjUImZwlkSDwBdjIgr2nvVmQ993XJye8+67j1ksFljTs75ds+la+q4b8vfM53NccKzXG66vb1BK8c6773B5eYHpetrNls52KCVxxrKxPdcW3MlDfvJv/wPn7/4AITUEWUT9PgbY8+ER5BwTaFNrlWFMHFtyymPhkIovnv3Wgv7RQp8cPzIWJR6zueLzP/yWr568D9trZtKhxVRe6sTS6LyjKQGcKmiaqhkMVBlE9JEKIYX78Dhv6M0W17dgLThD71q2tqN1PUJrZhoaJWikpNGaWXIKFskQdW6Md5N32hlj4q4nwJjoF2aMoWkaZs0sbi8vHIgREcRk1Z13UGUWKO+YMjbed5EiLGut43NdBEQ2AXjhHFYIhNJAZn4s3WYHvWFV15wvlyzqBuHBe4dMz7q9XfP1869wxhZjLP6U4GaQ07lfJ0x0oQsOxsDxpeoxwvGR89N9YgqMw3FT/r3PHIt8/ODO0/LmqRqOTPi7J9xUCX7ryZnaREqFRiC9j7Sjd+ig4nqsFIPl54WbRvD1HmtsyufRRz+UEAecMz3W9HhnhneIyj5updMqZvKtXMD6QIymERBKopRCV3rYqSUIYC3WBYxV+F2L73vkfD5YelHvSIRQ5C3kk1cV41LWgR8LU8oufpdBTWyk/XMnwkRKCB68SMHODtmSMDgBZ2R9P4SJzwvF5/0zw7ELDpid3Pb777h/zttY8lLO8K6DNEgDd9IkIjlKjoH2soN4CXBCYSQc/B5uKJIxcbw+w1nHvp8e4FDopG2sQeAAQkCTGdCp7UEIeO/iHHMO4wwWh6zTTsagqFSVcRmma/n6/d/z8re/ZOa2LGcaXSUgFA5BWbRPPF3fsdlsWS7mPH78TgyvL+P27lZJXN9H/wilomOyFBhruLq6whjD5WXMA6SUAq2xWtGmeDit6bntA6Y54X/5+c/54U//DFXXwwQIcBzkkAVrGNuj6Os7TLfxm0RtHRvf94HTt7lkOTyx3Mt3yAYy4PqWb559xrMnf2B39TXzYJinmGTZUA4hoKWiqjR1VSc5BsIJdIhjTCkV54Xz0ZgNIaXasVjf0/VtzOVkTMrpZOi9wQbLoqqYNxXLWjOrNHOtaZSc6BAZwCqFkQojJDZtBe/6Pjqne09vDVLEXFJKR99OayxGJ90yBDViWO5yzmGtm7D/fd+jtWa5WrJarQCwxtK3KdVEGFlE1xuQcWu5NYZ212K6nkpIVqslq1lcfvLW0luL8D4lGfWY9Qa73SL8Oei8u6vos/0DQz9mOV2cd6QMy/X3GK93zZA3kfai+Mk7qP/JS1SJbZ/EYckvPSoohrceKioOGyLcqzrHl0gPidZfCPi+i+PYj8+PW/ii7ww+JtB0zg7oOSbhW+Oco8kJN5XE27gNte96grPDNkIAKXUEMSqS5dZ7rInLNyqtf2qtqdLWVIFHrBqcCOx2HtdtwPaFVT7YtFMUKsSwBXUKavaDou2DAFGwKSWNNl1+yho3Z18Oua57A2+gTVPfZL7lEJ/v33vsqXKp6XCsFQJ7jz3YpyDfZkBzUIaxXYCWkMZ3CXQY58x4rRh/hr9LkFIquWxRpU8TEFTeMlVIlOB3T1YNz9vvwVhCCHG9/uolVzc3BCFZzucs5gtUXUcwknbnOe9pr19xc7vlm6trwq5DOoevNM3pivnJGT/84Q85vzgH1/HVR7/no1/8F6r1N8waotIKIoa/D2lrd0pW6NLW2T751zRNw/e//z3Oz+OuKYLHBZ9yzOV3FtEi3ji6vmW33XJ2dsqjhw+omyYqLCXRVY25XXO7WXPjDFvR8NM//xl/+W//N+r5DF8AD3GkjaZ/T0X1XrdP2pUwZW3yksVBCTHmTl7avBsuvV3lWBtN2i+1gbWWq8+f8vlvfkX74jlNsDQyUGsVx1heAkzUspIqpduJslJJhfIxn1POweRzKh8f758BTt6RFGMctZjE4Cslmc1mLOdz5rMZs1lDXWl0sbsopPlZK42r6hi3xkfGpjM9u12LDzEY32K5pG6iwWsTmIq7rwJSx80vSDFJ6RDCmDerXOLKcXOCj+xNtJ/tIKN9AjXZUdn0hu12i7eOWTNjkd6JAL11xfMAH6gCSGuQ+GhdEOVTieGjPBNTSuVNOn9A7/ujdu8+4i6up7jiNc+Ooq7cxHN3ef0uqnzHApsc0OowVXCDJVbunrm/NqWtmqd3ACweEyxS+EhjxoeN16XJ41wc6HnnVN/3bDdbrDHYuuFktUSmuAlxucaz221pd1tm83msv5RxKzlEIKM0vTWRCs0htAGtVbI4Atb2CNshjOV284p2c8PqwaNIdU+mfhTESsrBibMENwe24J7S3/eHmTI3cekqd1pcvx+vkxlsJV+cQdAWgrf8SXe5g0UqR/RkdBfvdGxsjApd7B3f98d5q+l4kQnSMAAb2AMNqb1DpqcLxZbff5ikYhSoWdIMYIbx72gNFnUYAFAJIvP3kxoX28unCjr3oBBguo4v/vA+H/zqFzx7/hxZVSxmc+rZnNlyRa0VtuvYtB04S727pe16tp1hZgXaSVoHdjWnPzthc/sv+df/5l9z8+o5f/jF36Juv+K00dRVWkpOtS+t05j7zbLrO3bJcfPi4oIHDx5QNzVSKoJz2JB8NGYz2u1umO9932OdYbVa8fDRI2ZNM/SNB0SlQCu21nNtNd/98z/jX/71X7O4uEiJCA/noBB7S4l3jktxIMtzmfhg3MXwFIBWkIZMYDIn3/Yyiv9ijCdGPYRAv77hxYcfcPvkI/R2Q60UlZqhlaSuYrgOoSLAc8YO2/hRxGVMAJLrgYvLwMH6tJST4sp4l+LTjAx+3/X0SWDOZk10BJ7PaJqGpmkmOZ72l19kSrZcu4bZPIKl9WZHZywnJ0tmizlCq8jMhB6nFdQNuo6BCb13OJsTxcYEsN7FJa/tdkvXdaxOViwXyyGPWhTVYoj5VvpPZvDvnMP00VCXMoK2nBbCFeDGJ3bLCznJiZV3Y5ZgdIpMX2d03v1d2D+jIEayf+Ie3ZfG/N33nCyvT5jO+/XE/UtUw+V3rw+H8oXC4fwPqT6jv+yRHTrDT7GTKgRkMCgZkLXE4VCM+44EMX9M33V0CQkb0+OcHS1A08eYOCYGjqqqiiDCsP7ZtR1dmxKypTDwldbxGQmMCB+3ckoZd2B5As47lFYRDOkKWSl0I+iIeUhEYm2G9xPjlsPcKZnqnoKDwzDv+Xh5r2OsSZl2YTxe+uKM65b7LM6QjDOE4Vofkri9C2wE2M8WfXzJavLl8e/23u1Po+y/y9RqyY6EwyrWfe+VBvTxdXEKwCOG80qAcy8YLMBNyU0IEWOSIBXCdHz+8Uf88v/+G64++ZDOGIIQbAN4IRC6okoWrpeSGsGJCOhKc6EUC+LWXSs8L66vefXqcz4MOx4sJFfPP4PrF1zUgnklY46fFJJBBB8t0hTPwyUGpzf9IPgvLy9ZDpmfY9JbpEDXFbP5HAOsN1t2ux296VmtlnE5a7kclENpATvAiJrF5bv8/N/975w8fheX8lHlhhqU3L2AZiKhD74JWeaJODezwRYmYyGZckdYuXLJ509nTuyX5JGW/EBevXjBy8+f4jdr6uAjKy4kUgm0iktPVRWXnzoZcyqZND60UCk/oYxxX6yFwOCDkxloSKl1fCjylzk8UM9nnJ6ecnJyQlNHYHNXrBnIcluhdUXdBOZ+HiNpb1usb1EJGBlrQUiUFjGDtvdIIVCJWezaFpfGofOBXdex3m653WzQSrFYLJgv5mkpLr6FUopKVxgddVU8LgZ5nZfevPPM61nM3aZ1MhpGQ997h/UBLwXLk5MIyGRi6fdDj++VbEgf9ioFCi+ODe23B3+ymtuTiW9aJku3YtRjw73vKW/kZHwM2Ax1Lh4ymaAFXVU6mpb3yZ+iY2IYdpF46wg+0soiONACKokDKoDg6bvo9LVdr2m322FJarfd0qaIw23fpWR6Pm3zdggp8MEhhKTS0Y9+t91xc3MTndrSdsOBTfA+1ccPa80ufSeljBnF7QxdezYeus4gxBj/YAoMBQwUdGqlvXxQ963NHzr9TnHDSJSVwEkMa9gDk7BnFZbUadQEkZ0KniGLs9h7yD5LUJa7GZg0Ju5gcfbv8TaW6fg9RraKRO4khTZk2S36p6BShl4amKFyKTN9KUfbfvDfKZmb9NhJ3UqBLcTRY15IHIFvnnzM7//Tf+Tmye+ZuY4qQHAhxiEREmyHDtFAUHVDLStWsyVV08R0JVi8tbTeUPdbFu2W2ydrPvDXLDycaMVsVkcDQ0bDIYfAz5ZtNjJ2uxjPo6oqHj58OC5NwWj9CZBKoauK2nnCesNu1zKfz3jn0TucnpwOcXGi/130mYiOohZZLfgXf/VzvvvjH4OqGHZlCgEiTNpYyKJvJuwlY9+H0RQMw3cZ6IjiJ25DzsgnLzNPAKsow278abA3B7ohK6HETHvv2W63fPH5Z3zz1XO0H8GNSOM2xwBSUqKVxmuPUWaIOxNwCBQxzpFLSj8ZdrnvAE9cwrTeY5zDekcQUNUxP9T5+TnLVVxWyhtTZLE8lUuORVMRwMV+9d4zm89ZrhZ4orzsug5CQFX1INOstTGxM9WQkLPve7wP2NQWm+2WAKwS4JrPItvosstEEFRNoMm+o87jXcB7k+4fs41XUjFrZsMusLgzaWQMffqRVc3F5QNm83lMeZCM0dIHDvE694SyTNnIkq2JYz/JxgIIDXNDTLHOiHnK8V4aASUJckSn3lNeEwfn0Hdk8j0j0JkePVbFvXtnkR2iP4lVARM8/a7j+qtv2G53zGc1tfRUPsYi2NqYSkML8C4lOEvbRI0x0ZHMGrre4kVPtd3GSMUpSnHewucJSFVxduYwxtF2LddXVwPAqesKJSTWxyin1sSYOqUyykpaKo2UFUpU+N5idhuENwilB8+YEdzsD6Dj4ObYEs9h3+yDnKkFMoAiiLSvB5GYmoH2TMt7gQjkYs5yT5Cx70dGZ9zGW4KT9LBcocNn7zNShWK/+33efhZnsO73j0/qnq1KUfgejxN8wtLsgZZ8r/jngIqK5x9nU4uLJ7/L+k6u8YbNNy/4w//4O66efsCMHq090QNNRutYSoSX1EGM80PPWcxW6FoT8BjjMM7hrIXWQNvTu57ti5cs5nN8XWMrTaUDChGjfCe/G3zaFOBsyhN0i3OOB48ecnZ2xmw2G5mYHNo+71Qh7VIJgdms4cHFBecnp1S6igqIMPgw5EzLu7bj5OKH/ORnf05V12lpamoQJCkax7oc2zqMNBgQJhFb9wU8TA2JqeGawN0gvAv5IIYF0KPW89tajo3F3LLOOV68eMHnn31Kt91QSwZHYUmgktHHUakMOGJss6qqolzvTUrFETedZL8mQoxiLYCQhK0Njs5Zds6wtQbjPKppWJ6uOLs85+T8lNlsRq11imwvB5m2L3eyO4MIaYOJ1jR1HdP6ADalAwJSLB2FIAKcru/wwsfEmClorLEWY6Ozsvc+Aq6zM6q6GtpPFuNOak3dNAilcNbhdj3eOvq2p921SCFZLuYsZzNqpWIuQj8CnAxunIBmseD04gJdj87/Qyfd26+H47DUOQeG6eSm4eCbgWfLdRimXiiec2TgF3Py4Pg95X6AI8XB9ZOBHIr6FWzC61YjBGOSSAAriLsaXl3z7OOnfPbxJ3z14itq4fjOgyXh1TPEdoOQCmdjDiktI43X1DXCJ6WqJLquqeoNuxTyuqoqdFOjq4qqrofokkIq3CrSeFfXV7S7luvra7TWLOYzpBS0XaTKo0OyG9mLBJi893ghkEIxE4rK92yuvsZ3W7SuiTunDpXRXQDmGE36uuP7fizleVlH5sFY7hKIAjvD6OikF5G1nApmH9I4yP+NQzUrztF+Ze/5h++5Dw6OTZK3GeQMM6JgY163c2D/2gh6xOB7cxdQOQakjp5bGDJ5fb30oTpke2Iv9usr3v/v/5nn7/2S2mzQKs5MIQTzqkGrSMELK6hFRVM3KCGZ6Vl0DJUK4x2tNTEA5rYl7Ayit8yrmhkiZQOHvuvAW7RScetvqrIzhna7ZbPZsl6v2aw3nJydcn5xPmRiziXu3DIjyAnR9yZ4z4PLSx5cXKSAnRGIuCTge+vY7QzXNxucrPnBT3/G+TuPQA8JVcY5JMe/J0xaZpwGsJo+hDCydAXc2WdIczeNrgT97GMAACAASURBVMMTOEuk6eJPIKQ4IYzj7D6B+hYX7z1Xr17x6dOnXH3zkkVwSCmiXFYKJQWNEGg5BmwNIQKGpmmiEbvb4fq49O9FSropxGA0hEBa3nR03tGajrbvMc4htGJxuuLswQWnF+csV6vIRAqJEnJwPYi2ROmHGMGLCBIZJBoNVcA2DYu0cy8vHdVVCjbYzAb2v2s7jDMgxRCQsut72i7GvKmqKibkTPURWVBTgOFkfAohILEzzkXnYmscq3nDah63hSsEwkY/VGccPgisFLQiYOqKy3cesbo8B6UiiM4JqbLwntrdRzFGWY7pnlH/H7/4GFiK7xomf93/YAadk/+8r7xxoL+jSrmwEI9wDBPmZ5/aHzBeALPrePnlFzz9zXs8f/8P7F69xJs117tvuJaOWV3xznyOahrqqkIpTa0VlZJ4XdOrir6umfk5zjlWqxXbtsUD82bGcrGIKFlmhSKp64oQ5oPlcHN7gzOGrt0iU6wZY90Q/yAOKjNESA4+0PeW3sQtgbWUzAW43RodLBJPmMR2KBiQguE41rblsdLxdv/c2AV5Cenw+ggoRsrdFwwOISZpC1nkhmO+ONMlmNznB1nNy4m4V44CsIOltuMA7m0sWRbkyVpa62XZf6+cvT1b6flzuTRVKtvx0vz5yLJU/iwYQNaAavO1Q33FUI8QAn275dN//B98+uu/p2qvWNYCpWeDUp1VTTQehEAYQSNr6qpGBFBe4m2PNS3Gt/S7Nbbd0bctrWnBW5Z6SS0lVVWluFHRuXLX95i+R4SATkpis91xu14Py8SPHz/m4uyCWd1AZsJCFO5jnJFA23as12sCcLJa0SS2JwvNEKJDqrGetjPsOsfi3R/w6M9+Rt/U1HLMsBygkA/Tn3jPzOBM4Xwcw54hga0YLdE3YSUnBlD6ieMqmrhhNAd5rfB/S0qeHwRH16559sUnfPHpR9jNFVqkiM25bYWIDrVyT1YKkdj0Gikkve0wPi6r5nMloyyyNsri1ht2fUffdYQQWC6XnF+cc355wfn5OU3TRGYmsZRD+8OQTiWz1S7t3pIIvIyZzb2Ny/h1VcWYYyLF7FGKJm1iyc7OvTG4EJenNpu4jNr2BqU1p2dnrFYr6rpKUY8Z/GbcuENkogdy5OKcamjW1NSVRmYiIoRhR5lHYBBsBXB+yqM//zMW7zzCKz1QJ6FIBD2A90HGMDx36NcjAGXs8Smzk4//U+V5nhbjY9IcSYJNvGZOvNEuqqzkDhXSONkn/x5hLfLxfFUgDvS+7/n62Vc8+dXvePn799E3L3nod/Rux7Xz3G527GRLKzVzFaOeNlVFrWNAPSoZl7mkoKLCh0Bd1THol/dUStPoahi4kIOLKXylCcDqZAUEPB6t5EApdr3BmH7IWWO6Hikku03cmtd2PTZnL3YO6aK/jhekAJFTtkXKrIz22qNo01IYHnM6PmRwoMwmPnRdfte9Z0ShQor4OR2xoy/O+DN55mh8DhbGt7Esj4G0t5mtOVYKGMIA/ybUavwRIrJfUPRMwdgcLvfug5fxKaIEOSMtc9COmb0p71P6gcchGeOFfPPpJ3z5q18w265pqopGxSVcIWX0NwvRyo7OkhpNyl8TAtaaBBx6erdlt13Tdx0707I2W6TWnM40S61Z1nXazRQI3rHZGrq2w5p+2F2z6zrW6w0hBC4fPOBktaLOyiI1bnZUDQGcD3SmZ71Zs9ttWS5XCMAaE/PJBRE3A7iYiqHr4+6XXduzkpLbb16g65rzR4/Qs1nqEwbjp2zP2I5T8AlhiF6c++I+ADMdPcVYKD4fjImCITx6/dtegsfZjlcvv+Crz97HXn/O3K+ZKaiVpEpxzNQemNwvUsqBxbPW4inAEWOE/LwstLM9ne1x3lPXNcvVkuVqxXK5ZLFYRLaEuCtLEJ2C43J9emCRHVsS/aaQ0UcU79FS0eiKSqnBqFNKpS3tKvlpgnUW4y2+97Rtx+3tms12iwfOzi9YrVbM5/NosOv4FuOOqRFYK6US4IlskfeOEGKohflsTqVzgEIxld1JnwmtOXv4kIePHzNbLKL/DQyM45gIGkpj6tuwMG9SRjkffc9KI+Bb6YBxKo5/31PeIA7O8Z09R28spoLhLuUXSCnereXVy5d8+Lvf8OV7v2J5e8WF9ighaIPEB0Uwgq0zdPUWoxS+6akWC6qEuhFi8K2JzmWWuqpZuDmm6/HBjzleGCPIIqIAJ62dyhQkrKpHr/q+N7i01XC32w15q3LwQNN3GJcd2jxeKF5c7bjaGS5OMhoIB22RB1HZvrltXwdujoGcmMdqfFb5XQgBSdw9ItNj89JIdDSdPjN4P4ziyWQRHhlk+QJ5gEQBvVfnY7/3x9Sxcuz8t6lkaw9SO5YAMS9SiGQNElJk6antLYp7DfecWE+HhsJkDGVwI/ZuSFKIYvwsxFjnvGV3fXvLx7/9Ne2L59SArmoqrZg3DU1T431Mq2CtRUuNDilKrA8IF8CGtDulpzXtkAdnZw1BBJbLORdnJ5wt56xmDVpFVsVLQS/jssS277i9vY2Ue3IaPTs7Y57yQEVL1iVnzjCkZnDOY4xls9nStVtms5rFrI5pG9oWay2qrlBVhXM++fWsefnyFV1n2H3zgvf+y3+CquYv/u2/43t/+VfMVqtBJuyzKft9tz8W7jI+jl/AMO+Km4zP2zt1/25v65zYL4FAsIbtzRXPnz7h5vlnqO6KBY65rplVmio5+Gqpoo9kvHCQNwMrDGmpVNLZLgZ2LMCNSKyadTFOTWt7TPDIFO9mNpuPm0eSX09kcGK0audc2lbto64fbEWR/i8gZmJ4JAkIi8xAqYnDshDRh6ZyDdZ5rHFsty1t27M8WXFychK3qs9m1JWOLEQIMal0YMJw5w0i3gesjRnDlRKcnp5yenLCrGqQ+T3S/HDeY73DOk+zmPPu977P+cVljPidZes+1hblh/3xPB2JWeyNsnof9LzBUtMd5Zh+u/sHJi9ypLyGwZkujwwVGG6bB0H+fI8/wZ6C8yGwW1/z/KP3ePXBr9Gbl8w11LqKCllrGilYCoUzLXa9ISwaZPDgHYIxdoFK4byl92mHhiQoj/RgnBlQtlY65qpKMQa0rFHJS1+ktVLEuA7fVDWucrTKDHk/lIqJAVXKX+LaFu8MbW/YeoXa7uh3LYSQEntmhiW2URxcU1+YvUYfzt132D3WlrmbMotzTOAKIZAJscsEavLkKf1tRL5ZPpYBjohbLw8ZJzFed0d5m8HKH12OaB+xLxjSHB+AXz7xiKEw1Xf3t9UgnESxLLV3Xe6VfQEggN4YPnnyhKcffcBZcNRpJ1StYqoSrSsIgVZKuq7DY5FB4xM1LkN0PvZJIBsb/QvatsUQWJ2ccHl5GTMqLxZjjJEQWY9KKpq6oW0a/M0NVzc3eASnJytmi7jE3Hf9oJCEilHIY1boGBxtt9vR9V30YVguwccM5DGzco9yM6o0X9u2Z3O9pu8dF6enLIUg9C2b9Q2f/P43LN79Dt9ZrVKQuZwGZgpwhjJhVfZYzaLl9wfHaECkb0oQLwRIGZ1Ky2js6bxsQPyplJCUqG07Xnz2BV89/Qy73lA5T41ApxABWU7nzwO4CTFNgk8OvN57tFaopsJ1LX2btoeTndWJzufWxZhlSjLTVdxhV9eD4h6WtWTyvaFoaxUYnKsKeSiFwAkQjiElUHQ6FmP3J0Wb3QDGeS2irxAyi1SkVCyXK1arFU3TRD8kPQKcYEm8cBFSgNFZfrvb0nUtSmmWi0WMayNUjORcOODnKMvGGk5Xj/jO977P8uTk/xc5vA9yhNjf+SeJe6Mzaiidie++57Fjh8fFa6fGG/vgjNZHMd+GvhTjRBeHFTyoWAiYdsfnH/+Bz3793+mff8FCaeq6QWtFsCaB6ICUAYHFdT2uX04USbm9T8oUJyPttkBJaEA7jQtxcA5CU6vBe14qhVISY+K9nIsTqJYVofKE2tM3lm1yeIuhuSPtnr3mwdHaDmE9odsStptIaZJkFNmyjk6Ed7ExR5FC+bUYB0hWXiOAmPri7AMLKSE7/ZcoOPvjlKPO+8iilWBHCIHHjwG3jgzS+1ia+/6+i/V5G8uYrTtMhFxetx+Wc7nH8ocowsS4pBQYHe9fa5nszbF9kDPMkRLciIC3PV989oQPfvMP+O0tWkeQXkkRl6iSdYuAuq5jJHDjEaIaLGzvx7jZxht21tBaiwXmywUPHz3i8uIiCt/ErE7AeQqcNp/PmS8WvLpZEwI08zmegO16/KxH+BmE6LfjUqgGZy3bbXRGrpqK8/Nz6rqm73psv0455MD2PV2yaNc3G9yu4/HJKScnKyQxDL6xhturazbrdfRhEDmzcsFSTsZhVoCT0QCM4FWkpcl92r18/9KgyH12tL9F4Yj+J1BKK95Zy/rVFc8/+ZSb51+jekuFpJYx3k3eLTWwoQW4ySzyELjPuWiYaYWTgjbEfo3QAUg+J0KAairm84Y6OadnJuV4hdmbvzBZnpIy6jqpB5ZbKUVVV2CZBN+Ljsqj83MmYbIfmBAiRtwXUNfVJPZO9kcKPkQwhBjCpQRiSBLrXUpbssHZmLNqPp9RVRoRBD7YGPsn5J1TkeX0QnD56BEXDx7SzOaEnI+wME4nJRw/fLy/j8nofaYnG8THgf+byPm7sMQx++NYeS3AGemsvcFyh1B9XQnO0a5v+OT99/j1f/s71k8/4cJ7TpolSz2jURIbPFIEtAqgLOgo4GIsgehgOPiUyDywZFpPTXRjQtRKqZjMzCeQoxQkFkfkxJtVFSMMe49NWYSD91DVeOtYzhzdchkBjozxc5qmofI5E3LcTu6FZeda3PoG5Ry+rhl2R0g5AYQTAThotWnKhcM2HUFTeRaMhl5p8N25vFjUQaY2PJZtfNzoXNYg3NnXx55z3/d/muxO3kkTQc6w9l2ENB4wUPGew9WDJVLMHzEMjQR+RPFb5JsV597dZscYHYDgLFcvvuD3v/g/2T37A+fSMasiuKnSUkEUurEW0blT0LUdQrgxKFsyIow1rF3P7W5L5yzz8xMefecxjx4+ZN7M0YnlFFLGqJEpVYjWFTUBFzyL5ZKTkxW7tsU6i+0NqIrgPMJHn53tbot1kW7fbLdc39wAcHl5GS10GLIjZ+ajt4Zuu8VZx+3tLbWueHgRwVC3azHOgfO4zqCtReCLWEO5T8TgYxFKRTgBOVlLTg2EfG72x/CHXx/pt3jN/jws2Z63da6MpEUUQLvNhq8++5yrL5/DrqVxgVoIaqESS6jHXFIhDI7jGeCUCSuttSmeTQS6Xd9jjY2g5/+l7s2/JMmNM8HPAPgRkZFHHd3V1TfJbl7iaFbamdG8N7uz//7uvDfvzY60mhUpUuLZ7IPdVZWZEeHuAGx/MBgA94jIzBY5yxT6VWdcfgEGs89uFiuLBrKv1yusz8SyYawIc2MkhkYzmZxzME1VTd6k/cVH1hBln+rzaVxMHeogQdI2y8fC50p6uWua3M9w1mgXyPSk7jnZLciWmP04yj6YRnRdg81Zj651IKvxPowRARN7of3JYx8Zly9f4sNPf4izy6sce6MALK/XjPPozSxk+wKI3BVvdhqwnP7uvvi1WiE/fd/Hx/0WHEJhrgdfnUCB6bilgI0xYLi9wWe/+Ef87L/9V/zhl/+ElZ/QNx3OTIMz28KlGiwWDMMRxDH5PBXlSyO+wCy+W01lZQKZRNCVgW/yE5xz4CmVymaGRXkmSv7fxonFZoqUa2ywtei6DjFGbFZr7Pb71CzNg5CqTTYNuq7HWQyAGeCHgK//8AXe9SMMbaCeYtXRDzRvKqrEvOhfMuVRXoKMhE+5q4qJ8BBELP9mgqEiPI9qmpVmdS+p3AFsTllvHqul5uSo9xUXppS/JsogJ+L0s1HSzueupfq11sTASXXllCl3OThGjNfX+Pnf/nd88+tfYuV3OHMtVk0jxcxSHAQYUgI/HWONlcynsBdXAhlYCBjeDXtcT3vsxwGr9QpvvfMCb7/zAhcXF9kFYJBM+qnWDRgIJkpNqqbFqu9xcXkBl+qANM6hcU7Ov9/DsZM04f0eow/45vUb+Bjx/NkzbM7OQERitbHSH06q3jKC91LeYT+AA2NzucF6vZ6Z8X2IsK7FxWYDawlsABOhCHMWXzUDOcfIod47tcWVD6035Xw1bSzOWgu8mYX3kQIcaYQh1jHvsf3mG3z9299g//obuBDQMFLjVpoJd1o8p1rfY4j5vSq0IdVZ0pYcYKCxFuvVCpscRCwAp+u77H6PSXkcpwm7/R5N28I4J9V8CUjItvQkZM12BQ5ssBUGUI+ASYqrguEySiaYS3Qt2SdzHqygnBcgj1lqQw3DgN1uh/1+j7Z12XrDzJiSt8FzgOeAiQPG6LGPAdyv8OTD7+Dy3Y9g+g3iAkwJgJ8rW/VDHpMZulb/My3ud53rEOTfvx/uKfSneuTys9OXKOJa3uQsCD/h9voNPv/nf8JP/+t/wec//Qesxz0uGocVsQQ7MoNCAE8BHKSfCMcI+EnggZGus8Y5kLUgkzREUA4kBpAJxDkHZkZAADsBO957KbG9QObOOsBFsA8I+pxpQkOMWIWAi/MLvL5+g5ubG6zXa/SrHs6xxOKEAM8RbYh4vd3i9TjinADHZY4O1ibvoyI1iyVmmfotM1uDFx1Fwywg6RhRAtKwLnLMDECRvRSJ0vJoRWgbteMcsbbcRdwP3RinxuPVVgujyDEw6fVdx8yApSnWGP2eeKGt6LFqMbrnfuZACaiZ8bDb4bc//Uf89n/8A/phQk8WfSM1oaRyrJVUWI4SyJuK4zXOgZ3BdreDAeBgYCGgYjvssQsTjLO4vLzE1ZOrHBOTAzi1IrkRu5+10vCPAHgymLoeTy4YZ+s1AKB3DVatNMccxwF7L6X6b25vsRskzufZs2d4+vSJVEVOAslYi261AgzhdjfA7/bY7waEccJZL00MCYSYwU3ABMLl8+fYXF2KNRc188wLc4LZ8oLRJXWqKvy3+PoA3NRKhgEK/2I918OUiscwpCQG4Dyw+/prvPnVP2P72T/D7F/BIcIZhqNCZ5qODeDQPaVWjOr5GZzXTdrsSGX6s7MzPH/2LKd/N41D07VoGgEVgFhBhsT3tRaNdQ1MUoxhCCAD8qQMVGQWp6zDau8p8GFII9CmaXKl7MhJ0ilgqax+CnJiSEpLBd6oep8DhVNfNi1Rst1uEbzHZiMByi4pAeMwAKACBJnhOWIyhKuXL/Hu9z5F/+QZkOLqZuZlpMamCcwTihI13welxs0B4KvGXA4Q6kwpmRY+8dvj51q+Pi0P/kgLDh05CVX/skWrWLby2wDAc8S43+HVb36N3/78Z/inv/tbvPndr9HutjhvLC6tQ+8ASxEcpmSq9uCYwBVR6iIbYBoL2zdwqw5N16FtukwgAKSeBauQ1uqSAnIc5O80TanQkxA2OAUVW4JtHJwXwo8cQVFidJgIAQLAx2nC7e0tbm9vU3OzJm++JgQYGvHqy68w3VyD3n65YHKxCLZKCpUFLxaceZM1ecalFSSvRzpXAUcFJOn5VYDOwIteI210dbeUS6b51F46R8zwp9xgDwEyx777U2oD/zNGDWiWQiz/5sRx5Z+R+VbrjH6PCtyotnJkfpYXmAOiNIcUJWMiMl7/7rf4zd//HXB9jVVk9M6hbZqUMisEE6pKwSEx1xADPAFvpj04MhoSa4xhgBzh/PwiB3NqhkrbtLDGgiIwYkQMERQiIkVoZXRLBpEJDVms2x7rFhKb45woGmAM44jb22vc3Nzg1atXmGLE1ZOnePbsGdbrFQyVzBFOD26sAzBi2I+4vb7F5dkGT88vsXIOCAFhSsIjRsS2wzsffIDVZg2JfijW1TmgnGvlSXyBa4anBuPqCAbma5c25oG7EipuqMifI5YeoZsjhPUIRvLWYhr2+Oqzz/Dlb36F6c0f0MQdmlTYsXEWrXNwAAyXjMzIXNYxC35JiQ6cKgGn+i/eSxBh17U432zw9OoJLi8vczC7dXKtOpuJieDSHPpURVhdZDaBIIK0/rAtUjB9OHQVouxRANn1pB4AqkFZDCAvBQJNNKmTvRMrYXKdTdOU4kGluXNIlbbHtO9CqsG23+8xjgP6vseTJ0+wWq1ARBiHEfv9kLIL1ZXnMRCD1iu8+8l38daH74P6DmFJoPmpK8UaS5Cv9Kf8Sek9LvhzzQXLb5lVIT8uM+77bPn9fa9PjXstODOGqie888QJdRMhgLG9foNf/+PP8Mu//W/46hc/x/jN13DTgI0BVobQWYe26QCDjNxDjCCWWBfrGsBKGrdtHFzboO1atF0L5xqYpFFKyfaAOMUKc6Ug4kSs1lqM04j9sEdvesmoQiWENS5HJ9tKjI5pGlDaED5GvL5+g+vra+khsuozc2/GCQ0P8PtbhDevYKNHtA04VgI/a3iFYc2tMvMaAfrmgDwzqlaiqs9V/S4dXDNtA2lemgk6xUmQoVkYUG0uFYBzet2XLqdTZsxTlp1/VYNUA03rmYF9tV53bVjMBeDS1ZsDj/PWqzSH5blO7EkmhuWI8fUb/P5//D/YffYL9HGP1kAEDZnM4rRrc4wRMTWr1Ya1wziKa9Z7WBDWq1WqLSL/xCIlgC3W7jq1TiTXl9Y7MQRMkKwQYwxMLL9zVoQUgeBTPZDb2y3eXN9gc3GRs7PaxkEZLUNiIKy1mCaf08g5Apv1GqtW0mhjCAhjwESEawTYZ0/x4nvfge16aTqa2yvXjL8oDPXK6TOWv0hwN1af4gDQnLJ01u6KWu093BePc59Q2g+3t1v8/rPP8PVXXwDTHi04TyehWNnFgiOMinOG3ARmSff2wc+sIVOiRR8CrGuw6jtcXV7i4uIc/aoKKJ4vnYBeYyQYN2XJTinjz7oGnbrLWJde4nVi6jtYW2OKO4kzvc6scFTulwLBOQ9AYkKlKnMLx07SwklaDflpzNY7jRGa/IRAEs6x3++x3W5hrcPV1RU2mu0H5I7h0zjl1g8TM0YCnrx8gZff+w6ay3MEQxLuUYPlmVV4OWn15zX91yrUcbCk2cL3WXseOuZK4TIG52HjYVlUR6w0h5PC2bwKAAgRfnuDX/3df8ff/Zf/E69+80s0uy366LFyhHXj0LpGgg7bFrZxCGPAOI0pKt7AuhbUDAjJ2mKpSvVzNpn6DaQyOwsgAWV3C5iB1LU2MsOQA8KIYRxgLKEzPaypBCwRTGMRE8XLZiQYSxmxR0hU+3a7xetXr0DmCk3bppQ/i4YibrfXuP38MzTTgMnYdEcVAlnswmJ1YRw036xmuwAEvd1DUJEvAUYdjTTH2aKxRorFimNSwUSeo+kYJWCbjZhhcxYVDgntGLipvztl8Tkdm/D4hoLO+tmkoKlBblZ6wjRbneTQMqNgk+RdzkSlw52m93Fg/dL7SzfhxxG//sUv8Kuf/j3c7nUy3Vt0zsECsIlOplQqAZAswmzuDjFpkCMIhKurS7z1/DnOzs5wthEXLQBpaAhgt9thtVrB2FR63hpw9ECaF8HSJfi/bdus0RKR7On8XAI6go8wxuHps2e5N5U1hBgDxnHKlk9A3BG73R7TNOLp0ys8ubxCa7XhoYePEWOM4L7Hez/+EZ598CHYWVAU+pb6ryaDHAJyHaN66VQpqlcmA11WC49YfHUdWV0E9S5UgV8DnMrSM7fe3BVa/ucdRAYxTPjqiy/xxe9/j93NDc6YYe28LszBEyQ8VzpfJ/dhCMldKp+PPuBmu0NgxubiAqu+R9t1MFbjX1KQf9pXlNLuiQxc+o1JAH7yHtjt0XZ97lwOzHBlZTnlVKyzWLWRwE4dZJyTbwojL8AnAazVqpeCgcbCECP4EWMC+gLKpaCsjxE+WTB3O6nrdLG5wNXVE/RdBx88og+puB+SlWdA5IiRAPf0CT74wad4+sG74FUHwIBizbNElhLZ8qz1cx9dX3n44wAdleJbK+1KuxreoJ8vjsdp2F7vjcPrPgzs3AlwloLz5F0cHghwxPDqG3z9zz/H9ne/RrPfomePtWOsWou+b9DY0tGVmTGxmgY5Gc2QuCKBgzTmG/cDhmHI3VNBBDgrv0+LSIFhNMUwMig1dQuB4ZoW3t9iGIYcEZ8XkFgaUyK5rhhSMwcWZOVB132P8/UZYgi4vb0BI+Ly8gqulc3Stw3WEdjfXCMOIxrXgw2yi0erTtYLpwQwdzHVE6u5S8d9oXOQMF8YyseXT1QLqeM+Muwxos3rMgLK5KVgIJs5+KlT9bOWswAx9XfH3FmFbB43uNEhc1fNaUaPZYZRbcBlzIV8nn5PyOsgn1AqxaBMttacyvVPDXWhIEZ8/cVX+Kef/Qx+d4vemlTDyeTS7sRSrF21xxADRj8mwcPYj2LBadoWTy+v8OKtt3B5eSnWys5lATEFSWPVGAfXtqINWwN4seyAj7v01I1cCwqlIW2j0nVSmVyvZ5OwZxbXcYDEHuyHAa9evUKMEU+uLrFerWAisE81dEY/YQgTnrz3Lr77gx+CrKn2GpclpIW7Frov67svikn5vnxX4kfKmizPp/wt84GTq3qa0T+GMfkJu+trfPa73+H25hqWGM5ZOAsJxCXpEl50PHE7aWyNfgZUVjFOMZMhYBwDfPCZVlwjlj45H6fqGwSyBLIpTjOniZtcEsRaK/2g9jusxhX6FIxMmadW1rcFsc5c0wm4SIaWSZlYAPJeB2JI8Z8hiCs4uaT1mcdxhA8RZKTD/TRNqbZUxOg9trsdvPdYrda4vLoUS5XRbuERBhEu7fNxCpgiw686fPzx9/DOd78Lu1pj1Jo9WPAMBWbfgp4K7R4HOfKb5cQVmXQAbKoPCZQKKM7PPnO9V/z0T2bBmZ2GAUkLqMzyd2oVhP1uj/2rV2jHHZzxaB1j1Tj0bYuuk942zjowpGneNI3gOIFjQIwBwU+Aj7mQ0bAfcPvmFm0izLbrNGjxLwAAIABJREFU0LadBA2nHjSwBhRDAUapzoshJxlZxsK6BsN+J0zUObimhbHJBaObIgXcZmGA0htlveoxhQk3NwFvXr9BCBEXlxcyoU2DsB/x+es3+OZmi6v+LJnn58i+nrdT7pvZ1J+Y5RrYqJaoSjyjrJFoh3q28kc3a4xivZH7MznrBRA/uSLxJUj5Nj7WY+DmQSD6EY0aP+ZV5DL/GZQvwN0M4GRNqvo+vylrotfT7yoMdXq+WABW3O/w65/9A159+Tk6y2jhUoxLiTWRomAp7oGT9ux9CuYMuNnuEUG4vLoS68lK0m/rDttkDCwRWhKgtNvtsnZMJGUZuEF2QcxpERK307YSa8HiDo1Bvjepl1WHWCkjqcig1klhsajuhj1ubm+w32+xOT/H+fkZbGMRRg8fJdV48B4DR/zwk0/x/MXLAs5n2uBSp1yCnMPXBzRCd3+fr5UuxTmjJ1kjUPZyJoq71vzPPMabG3z9+ed49YcvwX5EYwALytY6k8px6N2r4Nd1rMFgLcBCECvdfj/C+1AKuhqp2dR3Hbquy8qubRqYlIpNlYUFKLTkxgHb7RZvXn8jqdb9CkwEo+VBwLnmn95TvRJ6ryb3LcMBYDCp5Ij2jJL4IAF48kwjpskDPgAg2XPa9Z6lDckwDLDW4my9xqrvYdO+NdaAPEAcYAmwtkGgBqO1OHvnI7zzg7/E2bN3weQkVi6Dt1pRmtPRMbo6TbvH1JT693fC9KPflzuqv59Xq6bFfZalvVspvt9FVaHWuwchh8YnYpYuvju0NKFzLAHrltC0TSZMYrHOxOARpjGZtBOj8wFxmqTPk5GKpsOwx+3Nbe410ve9MNTkJjKGEAwAjmCb7otlumy0kkVlHZxrSupg16ExbcksAiOyAafS2WTFpWMMoY1yzSmVjo8x4s3r1wgc0a7WCMbBNi3isANiyJVcs37CXDl5qtk7Yc2YuXnS/2vtsqBnnjPVGtik30XMyWF+HqmBQnqefEV5pZrWEqzUVpy7XEynwM1pN9vjteZQnpZD0/UpS8XSgiOWg9SDZibAClOeweCMi2h2zvqvDus9Xv3yl/jN//1/oR9ucN5ENMbBGIsmWXBAgE8ZG9qLRwHxNE243e6w3UndkH4lqbeusbDOwDoD4yyMc7lQpg2yt7bbLVbrM2mDUgG+bCXJ/blEUcoZJkQpoDQi8ih1RUiKAvZWhFpIgoCYc7yCDx67/R5vrq9xe3ODZ8+e4OLiAgBjCBN89NiFEdtpj9vAePbdT/EXf/OfwOt1shNXE3xsre8E7EID5XtZv4eA/yXoZaByZy13KorF5xGO/Zdf4suf/xTDV79B52+wMh6dMXC2gBujbiNAWHLlhptZB/S3KclkGifc3NzCB8bZ2TpVpLcl7jH9s9ZmgLMc9TXE6s8Yhh222xsQSYkQm6pY52iCJEG18J4U30tuejLVPlyABf2rWiZ0rU1+bqLkYp18qpHEOVjep9ibGGNyBW9ETlZ8UYr6RXGBNg6mX6FfXeCDH/0Eb3/8Cdz6QvhLQLUHC3/hI5aRh437eXKtAOheyHuf75YRcsTifrIicJzX3Xf79wCcCqGm98uRsWH5nziXSFB8YywmY0Dsc82ATDzM8NFLhd3oAZaqlcwRwadYAB9hQVidnWFzcY71Zo02NUwbx1EAy26PpmuxXq/RdS2IY5mqtKhEESZF2LN1aLoOcWDs9juQkYwQ1TqJpB0DMwPGpJgCAmKUVETn0LUtptBnDWS/3WOCRbRWrB+7G+y//hL08l2AbGalMxS6WKG7rTiJUFldTA9FzWW+KRbN+dg19NkJtbWlbKxDhnT6vo+Bl8PbOm3ReayjiMTy13CqZXeHYKtfay8wteTVGop+d+zKD5JwzLh99Qo/+/u/Q9i+xtoGdNahcSaZ+LXjcRDXYwY2AEiZb8Tt7Ra73YSLyws0zmHV91itVui6TtyxCbirm1gtLLfbW+y2N+j7FjZV167T6WPliiCiom2bFKlGBG8SiLKEvu/ABnCWME0j/OTALPE++wRydrsdtrdb9H2Pp0+eomka7HcDxmEnDTdDwI4j2ree48f/6T/DXj2HT24zmVd1n1SxctW63eV6na1rZWI7tkeW55wBnLuYPi1fPK5x/cUXePP5b4HtK/RxRG8JXQr8NiQuIpdeI81jZAZVMU9KH3NextmKo9Ydkwq7AmVO1SL4sLkUy5L3HrvdLrdLEEpQekCWU0wlliqwdAmHlXgtRmmMWfQScV8j7S11diAB+mwVjyxB8Ul+KMCZUip81/cCcFKGmD6nppOPHDBAUsK5a/H03Xfw8sMPsDrbIMu+Y/NABWQu56We07l8qce348+1HCFayApVCE6cs+aP9T3KOj/s+g9wUVH1Rm90KXznw5MBXAObgodBFtY4tK24eKwTEDAMg2h0IYIGqX0Tg4AbP3lM44QxesBZrDYrnF+e4+LiAm3b5nLWQDKNv97h+s0rbDZn2XRpTZMq9QoSNzBwbYPoAWsJTbIIbbdbGGNwdraBc+IvNWRhyaS27JTq7RRTZ9s0aKcGse9hjMXtdotxt4O3TrTi7Q1uP/8Nwnc/hd1cJiZaCf+HCqzlgqTDBA3rBq+1yjrVu7qAugXyV8c1U2Uix5i5bjCdh4eAkruEQn3du0DB4xrVJOZPUnySfnMEuJUfF3Cj39cWHCY6PC5rMcjfnZqfaRzxq1/8HJ/95ldoLKFrHFyKZREQkqp+Q+zXBICsAdiAJwBRNK1hSFkr1uaWJHpdMbm7gzUzhhDChNvbN1ivO7Sul2cjIIW1CcAh5FpMel+wWo1YhMg0yT4726wTUxatG5D9PgwjJg7YTyOur68BAC9evJDCnCFi2I/YbreSehsj4nqFT//dX+PFp9/DWFmWCsBJmi3VdD1n9Mqg81qdADH3gfQlyLnvt8Ud+Dj3xFdffIHrV98AYYKlAEu2stwgZ7mp6yaylC8gQ6AYQdEIL/MstUWYU2YTSVPmEEtMTbIGhOBzZXkF0SCCTT0KT1nEFQiFIPJnHEf03QrGcubxM/dUBci1BEk5t+whALnlgn5sWIL4G2OklxtiKlAL2WNRAozHacp1bAJHTCHAOIfz83NsNhspTKgAW6s7x4B99Lhlj5sQELo1PvrhD/D2u++meB9zkrcrf6rl+IECdgBylkrdHLj8UeME/c8s3nTqXu8//b1BxopKy1mPaP/1hUgY/hQ9TN/AtGLC7lwjPZz6Hut+ncvAT9OE0U8I4wjy4paagsfkJShwG0Y0qx6rVWoulsyRElQm2l/TNDD7Hba7W/zhD39A2zbYbDbouxVW/VrM5USAIek0nDSDNvk2tYEfkqXIWCuWm6RUGA1UA0QbcS4F0YlmglY0grDfSx0DZkyR8foPX4FDkEC0pB0WZaxshlpwLa0hS4ZamOwxYjs97mOkS9P5shGn/mZpxTkFco49y/Jax6xID32eP9eo98Ndwq7+rAaD5eiiocie0Y2M2c7NGxv5hyfuS77Y3d7g1//4M8Rxj03r0DtCW9UGsXauDcYUcxBVsw4RYQrwU0TbOLSNuLViaoJJRCBn0UDKLtSgXXhDxHa7Fe140yIm16dEEErzwkiADx5Ws2Aq7ZtImuJqpuTKrBFZzPbjbof9oAHREVMM2O73CCHi2dOnuLy8lADO/QALxjgMuL7dwjcOH/ybT/DxX/1b8GYFRKnnI8MCZO6gQ9UWl/U/5r+rs62OKQaztaxBiwqeE/tq+fvHOL788gsM+x1WBFhr4DK4ocN7V6mZCV/4MtgA0G7a8p2EL2hyCMFZgjOAS9myMYoyXINVZsA17oR1PBWLNZJYonVmurYH9VXtHDkiW5v0X87MzaO2L6RPOK13FCXWkYWxhBAIgYEQCSZK82MwMI4ThlEU/cgRbAwu1mc435xLRqIp8ZCCjRIQAmPHETtEbJ49wZN33kG/2YBSVpg8/jE+om0q6OD74/zsENzMj7mHOI6MOy361esCcJAVLPlOv7//WncDHMzk7yEqBuflrfN0DBiOvVQwtQDZ1DPEOXSuRescmqZF4xqxvhiDm2SxmaYRQ/DYB49xGhE5YtN1WK3XsMbllEJDJgOPpmnSHTBub6+x3W4FmfcrXJwHrNdnORaGgFJim12KTAcmH7CbJoTtVhoQpiqVqmWCAIZJ1qAIIqBxFj4Ik+/7XlJr93tMAZgAfHWzx5vrWzy5fAbY4ygUOGYTuEfI0zy+pj7nKWD0bcYp5gssm8wVTeEUWNFxDOQcAz8POdefcyiV1xvxPutU/VoNMTVwediTFnBzam6YGdeff47hi8+wNgHOxNQjR/aJSf2mdAT96z3GcYCfJgzDgGka0fdSofjyYoPNeoXWNak2CMA+gI0Ag1LuVWbGGItxHDDsB4RVQNMow+XMoCUF3cPaFlqERORcmkuWooOEErDJzBiGAT7scxr7bpKsrc1mg/OLi1R0jQEEEAcwDHbc4Pzlx/jor/4DuqunmNIzlL1YGuAezPddawFJHJC1PywK95DxUAtO/fcxjtvrNzAk9ZVax1Lcr6oTUxfe0+q9BMxcuvqfggqJt5KEE2cJ677FatWh71v0KUFFO5JT6pGmhS2zKz55HPPuJIK1Ii/G0SEEj2E/YOwnATm6HlysN7nh57JXVgJpCuJ0aC03oePKFJTNQvqdUKr3Hvv9AECKAHZti1XXwRkBimQMpiCWo5BajHhmjCCMZLB+colPfvxjbJ5c5ar+yCEG+bF1ApISVWIm63GfolZ/VpbvFO87JtXmo0Annr3Ph9dvZvdawM19++JugEPlkgfBP2lUtXYLAwbQgNC2FrYxoKaBmKUp+xudi9n/2aQuxrfmGiMxdrcDtuMe4zCga1ppL992cr1kyrOmyQ9NRLDWoe9WABjWSe2ONzc32O9HXF6MOD8/L9H2ydwplYUbtMbAAbkUuN/t0HovNTdAgBUHFwyAVPDPOYeu6wAiTEFqxYhGETDEAWyAN7fXePPN13j+3gcltqLacOqmmukEC3CS10IBQRXPQDxvzncfyFlaW04heH2fi0ppG3IcWnFqgKMBx8xctKEj111erw5UfohL6885GPN9UZjz6XFMAz8ANpTOrsx/uaEeIN9iCHjz+9/BjTt0zYjGBZC1kqWTMgqzSpLuJcYomuxun4N41+sVzi8vcHFxgdVqhXXbgSBuBmuMtGyIAGLMWYqAMPu2bTFNUsNjOvdo2y4LDC0mGIKXvZczUbSrcsI7nuHYAKaB54AYAQ4RwzBiP4zCQ5gx+Amb83OpbrxagSAF0nzwGDnAtx3Wbz/Dp//xf8dbH30fhlo0HlLIM+/H44rHIfkdW2GNK9DvH0aztcC/i0Efs/Q8xsHBo3UW1gZYQ6kcQXEpGTNvMGmMgdfK2Qk4aLE/Hzw4liByawmXl2tcXl7iydUV1qsVuqbNNdFyVWIjfdIMQ0AOiaWwYrZJsZBsK9c08CFIHFdyE7HSBcRNqyAn/0tgB4CkamdrUBWZF0vPw2KBKAKa04lzheZ0TpEnPc7WZ7n1z363BxmLkEDTNAXsp4CtB24mg4FWePnyY3z0/b/A2dVzwEq5hTmwWfD65d/FWFre7x/3Axl57nIv1aeZdzItzpPmjJfYZnGv942HFfo7OLHebNHcarChCN1Eiz4yBghzDSAElt+rm4UgVU77vhMGZQ2244Bwc4PJe6xXJcgqxoDIZnYNJKJUk3ZPK1jnYN2AcLvFbtgjvpJqmZvNJrVXSGnlEUByMzEAGyOCtZi0Z4m1uV6DRLIDMASX+uDkzrD7AdE1QMtgHzB6j32YMO1vcf31FyA/wjRt0pjvF1rHwIY8qoIbzrEax1xJx85zDDgtr3HsusvPs6l2cc4arNwFrnQsgU/uy7K4r0c3skDDvIibGlhqpY0O1YKlsJrNSzbSFGvoqU18bD45RgzffANLEdZps06pMgwjlgomYciIATFMqSJqArRksFqtsFqvsd6coes6GCKsKu2WICDE6FpVsUdMgGkaRDLYj5LaK8GklUss99wpvXhm8VxJWzbpfkyMoAggADFKtVwfPCIz+rM1nj17hrP1mcRAJUG58wNehwm3hvDBD7+PD7//fbikIJl6D1AJKj49z4dgZ26a5+rf4ndprrRSrn5KuB+wLJWPpZvzMY2GA1oLWCrF5GrL/zEQmQU/CsgehgFh8plGGteguezgGuk7dXZ2hq7t0DontEFVIHOC7oYI2lUgQRW5F0pxvwSx9GkLnmRBGVejhD8grXfizxqDOePXRCkNvUpFr607ocQpIt1D5MM1V2UfkHIJ6/Ua6zORdzofZCxM40Bkcp+q0Qdsx4DNs3fwwfd+hPXl24DtKxk8L65XWzy4MJnZeuj91ONfZv0/bdkRo1alHFb/5jipAjeL+zm83t174gFZVA/XHAoDZBhmONOg79bYag6/a0DWiWBQgggRfhwRQwA4wlmD9XqF65sbaW/fNGAwJj/C2E40SHUxza4Nia63FhFAS4TWezAYPgS8efMGwzDg8vISZ5sNXONS/6WEkZAIMgVPcow5+LhPsTomxRxo2waCIHZvPCw8YB36tsNqGNGOA/y0w/76a/j9Fk2/BsjONspdrpglkp6bQZELzR07Lv/uiCWn/q7+bHmNuwDR0i9du6p0LIsALq9fn+9f10hau5olMFMS81DHhQjqZRxC+jNj9gr39fNKdVkwpAzuy4eZZsfr12D2INLSkCXzxBgLyueVYwwM+raHI6n4CwK6VYdVv8qg3lBRKqTInrwOuo8BMCKYUWrUsFRnDSGIG5iSRp1S1LkOMrY2m/S1BH39jDEUYLQfBoQY0HU9Ls7P0bcdrLWF+YeAfYy45gi6uMR3fvITnD95InFwlSKQBfFybaB7oQY299GpnkN+m49LjH4uGynThL6vrbDLPVEDm8cKcLo4onUR1gaQsVLY1CDHehTAs7j/BR/RdY5RgoqbpkHXd+hT9p6miDeuScCmsm5lq0UJpq1hp9KXCllVTCJLQcFxmtCFUAKNFdwYkoKQiBnY5KbPVmMz6zXXgGd9dHmxXDsiadLpvUfbtjjbbCRutJdaU9ocFMbAxTbflw8BwzTBWId3Xr6L995/H/1qhUg2u8eW1zug72OfsYKT+6w3MyRyx2f3j4PrHKPvg3l7WOyNjvuzqESaojwEzb+vr5YmxzKDQ4BrOtDF2/CmRWuiZF6QSUQm4ECJXCnEGYNV3+N8s8E4eZC1GLzHKnis7EriYVhbMSioYolRI9lEEldjsU5dimNqtLcfBsQ3rzFGj7P1GVZ90UzVPEnGwLUtjLXw4yhxCeMoBaQSsLJGwA77KF1lXQNvA4hEI/XeYzvusRsH/PIX/4hP/5f/gGdP3gaTxgjcMeZTLPOs3LYGPRC2wRXbuDN46wg6r4/R93WTz6V5fGnFUc1SGYUw7cLKTgGkY4xaGd1jZeI6jm7vLA3TtxU9LQ9WjZAVw+hJVctagpv6wtXLpdBjAH63xYiIkQM6MAwVNwEZcRWAZT8wMxrXwJ05saZMokWHxORjZDBrBdoZwsogiSnmtWdA4tNY3JPsxaWUATDELA8tamfFVdC0LbTKd9ZoSQP7SdoopOebvPTJ6vseV0+usF6tMez3mLwHGSvp7cOEr0fG2F7g3/zNf8aLj74jQjdNbaEvAlL8xmweZwx+hlQOFwLLPQVoUDIBs/5vukvNYk/lvRV5ZumphdBD3Fl/zmERYRBgKIKMKnGUKvyqa7Skg0tLGAazZM1yCsg1xoCNBZHJFay71Qqr9arERFJylVZxUzmguRLsnJSQbG1VhZBqq6u8UfeYuorqPaWWyUQ8yRoqr42VmlISe5z2QpJFNaxNasYMeEWWpqJEhM1mgydXVzjbnME6h5BCJSKz1ILDlNd/nCbsxxGXb7/Ey/few9nmXBTnNJa8fq5Y6f3UQwGY0j4vaL8+L1A30pyfRT6rPy3y4l4SOhhLev+X0v6DXVRFAzn8XFm7QYk51I1pNlfYuRWa6RptCKDUmsEaacInkyYMUH3TjbNo+w5tinEZQ0BU7TAExP0elPy8jdXgYQLnwqTCmNumhW89xmGAaUtbe7UOhfMLrFerHJOTbkJAknVorMNkpWPrOIzg1N6hcRKcbCDBzlrhuEGK4wkBZ8Me19tbfPPqG/z2d7/Bkw++K5tfnRBc3E0HiGZmYi0zXcOa+rP8rgZAR6w39wGO2m9cv66BzDGQw8xAlMJT5gih5ydYXLf+/F+LNSeDfsy3OOn61POMOUjMheWSNYMSIFeBqGeZ22XL+UHzjX8AcmLAYIDQODCkSmo5+RykuKQNi+UHiE7WcvITYIWZTdMEAwfjbD6PghOpaQIAVWYJiTXCWQvvU1Cw97nfT7l3+b0GHKsVI3JENKniExGILSi5rGK6H+ccnj17hs3FOYwx2O12iCA0bQsQYTdNuN5P+PSv/z2+9+O/hu3PkyGlgJWCcRbgDYf75JBWy6nUFF9/Jp9XKRcLslYLziFgSSJRpzKtea7k+kjBDSAlNxQHk9LaTKhWKfgpNZVCFNIJAm4a2wiPthJQa62FayQ+09kUp2lskjOLPYIj16SySYtlYx7/5KxFtDFXF86xM8szK1hCsTZJPFmd1ZrWnYqyDUOgCBh2ICNxY0ggR7MWm0YyfjebjfR2IxLLTTqfDyEXugUgoRPO4eW77+HFOy/hGu0WXu76mOV8pqCi5h1pmjKdlv1ZtrWCGwXwAnKUo3GZ6j/5uA/Y37cv7gU4y8OPadlU0ZtgP0W3Bk3XYd+eYTW8hkFEQ8LQI3NO9VNxwJBiSlMMonFam3qRTNgNA5yziIlhOtfAtC3ANEuNS6aDZMkBurYD+5A1U5B0Hx/GEdfXbxCCx6qXFvRSvMzlhzKpIaBaZWKMmMYRE6QCqzUWhikx8bLD265Fv+rR7XeIwxavvvwMcdjCuQ6RtMYMjlprlhNLzDPmmTE41TBibjGRQ+/P7vm2VhxjTAmyQ4m3Ed4i7S1kLZTBn47KX7oE6tePmZlrUT8VXpXxcT4q7TLPtQKE2e/kf4xiWldtv2heyODm8DLlt5YY47CDNWqyr831DLIEm6w4zkgWiRQYk++6roNxBgF180Ppipw127QviAgUU5FD3fXJOiSZWyFlZE3o2haApJUTGMHIfhV3BMMYZaix1NozkgpMMSKCMKUO00+ePMFbb70F1zYY9nvsklvKJ0vRdj/g/MlT/Ognf4mzi0uE1AS1Zuj6Oh4BDneDG4294cUxaoFc0nL1S6L0WFWTxlpDBeUiiHdZex7jcE7WmzKhyueze8+KnXyn82KSIsqWwY38QOMeTeoLGIKAcO3nlM6uF8mggdL75TTVgh0VTzLWwlkpTTKOI8ZxRNd1uc0DEZVO5cw5cLqOIyu0W4ECKtYfGEr9oFLiRSpBEhOPbNs2F9BsmgaRo4RhWCvWoSlgChEgCwZhHxjPP3gfH3/vE1xcPUlB/pjxhyW91BYpjYl7yFgC9+qbStnW/x8enK9vIOuHSmZVf5ZH/ynp/UEWHOI50aYXczP7/A7BEMJfdwatmwSZxyhMjFyebECII3DEbhwwTCPGaUQI0oV4P4jWtttLwSP0EX3Xg5ximUSsEOYTULKMAEldjG0rAWxR3GQ2aa6Rgf1eskfE39vDNclszRJDIV3NHUIiDGMiop9ykUKC1DoQ5i1EYZxF2/U4a1tshhG/+un/i//1P/5vaNcbMLcZJBdltridjs5/+ssLm45+e8yKgwe8P8XE6++OmtOTCTMCQAzSkDSdR10SUuPztKXmPnfVYx4JQ0Mdjgog8l2zKJC03BuJ+d4ltI5u6gXzOuDgRCCW5nvsRxiWeDaCmtsp0bFF3/fS3DsSIhEsGSQ1U4oAesLox9z0NoaIeAQEqEDJ7DKBGw1AdolZT8GX3j76zEYz9OYAWwQbZmDAWAmy9AlMX15eYrVegQiYJqmxM4wDIiYwWTTrDf7ir/4dLp8+zxazpak7z+VszjWWgnFXzRtmPbK23CwBjwrU9F8tlCtwM9uL1b3V1gY6IrAf24gxwBoGDNTeVJSiVBVegUWuJcMMYxM9MoMjJ7rk7C7K4IUZPgXDuxT7AiAHoWrMC2tJDwCEKFIVIr84c0/VSuRerbXgUIKc1+u1eBeArNQREQwiopMWEdo3qlg6Fm7N2hpS0b7GFbnG5fmwqe2EFOAsfEMy0QiA9FALRAi2BW2u8OSj7+PinQ9A3RqBSsyQ3rPu+fl7GYZqHjxfx/IMevyhrMmfJeI/9qtj5yNNtgw03xNpS8zOVG6/vK6YKVH1wPeM+7uJZ2G8YMSomFtmGEVjBAhkgb4lrJxoeVMsAYQwlOrbiw80pIwLZUgERkx9OZy1WPUdVl0LawRMSME/cRXpvVlQ8r2qGVAIvG1b0RbHUbKfUiyNdJLlHMUvzww0rZgKtQoykYFlJ+jXWhhmcAyYwiRzYwgIct8hUU3TOPRdh/P9Htfba3z9+e9w8fwFjLFQ/ywv5nWmnFSoWzdN3ivV32wBOAJiTv2drS/m97AENfp9/pdMzHoaqUo6z4DKAgucN9Sh5YYP7vtf0zgGDutnl1haqg9AtUsWe1hByFzjesBN1BfHftij6ztM21jRRtKSuy4VqGwkMH5Ka2aM9EYzDKONdCF1aiR1VjvLcz5ffk1UFSFLwivdk3EWiIT9JEX5pPMyAWxAzsG2beqNVtXS4ir7JGlPKgCUL/R9D5NifeQWGCFOuPEG3HX46Ls/wruf/him7eHzFB0CSgakpH5eS50wXdN6bSu3LXi2B/N84FBgZBGoSCjFH9XZIbV1IdNTohURsI/fguNjgGGuKFtpWC2XyYFb7w9j0NbZcwngxNSgcpomMHGu3qwlBgguV0hWt20SoTJHVuvtiLWVmRGIiiKZFkVZrFobQ4hSpDUGMJqTgltBD3BcERPwr8+YpbOczCGBmRLnY1O5iRFIAAAgAElEQVSVcY0Fq88jQFtS6W/DBN80ePv5S2zefh9YXcCbBjHNgannG+nhsvWhAlqoefFxC42eQuVTcU8V3rTEBrO5uAP11MELBQfOJBpydlo1f6oY6gXm70+Pu4OMqRBsdf3yEAc3PxfSDKBre/TNGfbc5DoxMQRYolTctJzIGYdgIqiVCVuvRmx3A3a7PS4vPVwjsTldYtbaB0fUPUHIzgAhbSQO1WZKIGdMfnwNWMuCh7WQWEAXAtqmgWmcEGmklDIeoXuFQ9o8qbT95KVJaAgBniUTwDmH3jncTCO+fvUa7xPBkQAvDbo7MumzuS2AkWfEpEZCJZMZ4eEI0S3W9b7YnLJR0ueJI5Bq/NW5lwHHBeCkXjOL32RqoQyFM+X8awA8x+ZvGXVEKJo3UYYw+f9a4CFv7fq3NV0onzoFftL7aAzGtoU3DhzFLRPTiaW5rIN1UsGVjEXbihXThyCamAITI8DekmSOaKYHGXHhItPj4WDVzFOBsv1+j2mU2iataeXJrUXT9fCpXsjovbgoCBKEr8I8KSuAMEXdw33fw1knRUBTLRUfGduJsX72FC9/9G/hrt6CNzYpKVV6dX3uCqXPeHPFyIs2u1gO1iyc5TfHBd7s2keACnPqbJTAT1aaqPx71ACHjFgPucSYof5bDQVttuonlUEeScaeCk9OUdf1rIYQQIkecpmOzJ/kN8pVDm6gIFIRGSBEA2nfExkBEaP3aNoISoG7mQaJAGNFwYVYGyUQn2cX0HeFjg0oxbTFGHMWlk3134io2psEafAsKCwrkSRVkMk2+ODj7+DZi3dgmnZhWV3QiJZAIBQVghiVObWekMOFynNYK86F5vN1zPw0B9GX+aDjpUzKz5Y8j6rXWOwD/ej+PXG3i+oY063mxiyIr6Yn4XUG1vRo7RoTd6A4SBNNL/50MhbWUDb72RTA60NA17Y4PzvDsB9xu91J0HDbwjgJPhPBz7lDttTgM1LHAFFSdI1BiAHEGnQsqbAxBMmCoqrbrSwBQozY7baIoYGhHiYRoUn3OU0MJDOoIwMT1ewKcAjJNxvgY4D3ERMM9tOAX/36V/hku8XFVZ+RNBKjzEK/2pNLsME6qfpdJhaaEdUS2HxboJMFtgo93RNE0DSPGsgU90JMmtO8BoSAnEIYChJlsyEJm7KR7nJdPYah8xP19rgs56nfL19nLVefvxJmtcKVDjqgiaNzYwj92VrWjQnMJOCbDGzbgKoyBzOpUWnU+mwhBLF+lvqOItCTAI7FdgvtzyNuBnHt2ARwnHMCtEIEGmS+QcbANQ3GEDB5D9c0ABkY54omHiNCFCuSZJREdF0rNbEIpTCbAcg6rDZn+N73f4h33v8AxjVZQ828i0SkZW1xBmLqxaOFZqvvq2fO3yg4rZh9CtDiI8x+Zgk9soYFDM3XvSaFxziisRKHxcVqpVaqwAyDCJPabzAznJH+aBrLgijBuZqsq/FenoNw8rwYnJVITkC6HtkFRrVLCrPlrXkWoIBLOoxLbOaArmuhjaEFaBMIDrYjNASMw4gAwMcIx+U61VmRtBJkxKfXnFl4SqV8ayxyYHQ6h0lKhfWE3nV4+t77KXNqkzMT9RlmYwF8yhQQcoBwvqUl2CnHFbBfA5s8kRWQrOi2mu9jsmjpLTg27uL9Be88bDPckyZ+P0JSSwKBSjZ5GgyCbTq0zRo7ciB4efjIAjSgqAw5LsYZJ8iYBNBs1isYA6xXqS6HLmyN1lmyo0KKNickk11OYZZ0PDV3j9OUgiFNfgqpTmvgnME4TRinETaZwp1LDDgRpDL4SASTnKacNrdNgdFgIEyMEAjOWEyvv8b111/h/PLq1ESWl7VQxGKzYv67Y9aXo6evfns8ePKQEFk5ViUoaktNDjjmI2njVSxOttkk0ETqqyd1I6qOcT/xP4ZRM3FUmvcCMxQBiENwcsiUjvElKmDnyHH16xgjyHucdx1u0x2SxrBYK6Z7l/rQRADEMM7ARSfNbqvASiIJRp5oKopEspYouEcS+urSkj3IWUMlI2XnY4g5ZqGm0QyAE1AhQ9l0TwqeIW5qH6XfUONcdk8pjQUG2DR4/s67+OSHP0LTNLm2DVCnV1O2FmuW1twEc8jgddpr03zmO5WJXkETMbLClUelBZ5au3y+ap2X1pvHPFRFEYBe7WEA4CiWGVe6gBuyMGSlEjZRbriMlKIsNWYI8ICPoiQVa9o86WI5DkBiBhbV/WY6QAa/7KWz95gKVDaOCzBOdG2TohnU+hg8XPDS380QGAZkHcBcAsR1e6Rni8m6r+7Y3GxWQfHSnEgCvrp+he9+7xM8ffYcpmlStq+ORbkDpfdjJrQTlsbD35RbKCDn2HE50uzuUy72QM0PjsmxpTKgVvDZXT5gX9wJcJYF3Chz28Xv+Lh2YayF63o0bQPLEwgRgQ18iLBECAh5Bq2zgCGEGOEoCUxILMuT/gqbTapi2bapeSUw06UqgavETYaQa1xGEb5qLQre52J9WnQrgkEp7Tt6j+1uB59aNjRNC2Z1C0qAps6HMQbRJquHs+AgEVVt47AaG+z8hN0fvsL2y88Q3/8I5Fqgmkt1OR2lx1otOjHustIc+/4uF1X9WoPLSYFJxWwzCKpAk1pvagGWASHXVkCS2c63xECyvT32kYFNxTc1aC4vU0J0sQIn2lBStUL5+FCIzYUgZp/NzbiHAMlyREMMjl5otYpxyDFnSSFhkv1JjsFGtGS1egiI0KdDLp1vtMClOb72qjRocDBZi2gCvJcec65JdI8SQ6PHWyOFCGtLkGZxyb5mqdeTBEOI0nl57w22psNP/vKvcP7ihbgREjikqg4Q41DbPz1Sg9BEo1Tt02yFQrGcElKV3MV6qABlVHGH2Vq3wFeLcXy/3XHLf9YhAHcef4P8WhWfpmlk3pgk/pcIZCwoVUCmGT0J/7bBVPFg8xot2dJs8ka55y4P3SSaEaj0Nk0e0zii77qjAlTS1zXcQppCG1sKaJKmsmsIRIwC5FivX2jYpir6go/nz6UlFAIDxrW4ePYcb73zDvr1Ct5IyxW1kimAX4J5jXvKz3+Ez9efHx+F2c2OI2BB8ULnrLR9RGEQBHeU8I9auhd7AHQYe3MfyLk/Tfwe4VlzgFrLEeAArNsGfduAEMExZNN55CI4jUa+M4P9BMvC6DQ4UTuIMwCOYvaMiCnKvDDvnLZslAsdokC14gzjCD9OsJ0RdyUos0BjDKhx4CBp0cMwFMJhTW9NulqlxTHEqk/GgkxE01j0TYOVb7CdJuxffY0wjrCuQ10mPhMeyvxVK5BZ6QGBqfb4AKvN6eW7GxjpHeimrd1XS6uRAsyjjEHTGQGpf0Gq9cn/eOHKerzjHrQJzNaPCYgmZS+oVgmtF5R+/oDHXs5p/Y5BMGxhyWI7DPAxgEwzWyM1g1Ny00iMWkygITH+CuDEmO4Zqb9TjNL8kPujDEYtemRttgYh0Ym6bW0DEElMgqlcmrJvFYwVd5dPvbH0/DkZoZHX+2HCrQfo4hne+tFPEPoz0ZyRaFafX5URna8HWj2B4p6aTTqhaKxHDj1lES33g7RvY3b7MdJnyZq5BDePenCEVLJe0CgRrHE5kFZa7hBi4JxBm38rB2SaIaJshQdbRAit+BSjUperaIxJNdBqW/f8XmbBrcwzGlAAZlOJCwXVqqQt19OmOm5akE+LEKpVWo9R+VPiTDnvNUOpiCaKS1itMJEZMQKBLfbBwJxd4PkHH6N/+hScvAlFHxZqnz0uqdSY0/hD5MF94wAcqfWmnnbdGoLqiiJT3+AdPHQGag6/fcBvyrg3yHhpWn7ISYVYGeCIxlmYbo2RWhhM6IyYySMk5U+Bi57VcASnFgva76mxDYgN/Oix5yG7sxorBOJS80vJ8CEQJ2JjZFBTmyW10do0jplYTSpeVvZoMpOm47z3aT6qbIB68ZhTn5NUAygakI1oW4veN7jxHjfbETGmbK9KQ57POTBLs7iDFjgh/29DsrqmS1/0qd/JLcxBln6voLI+VwgB1kol3PpcIfUXIhbNTC1W9TyIUvC4rTiMb3GLM+0Dmbay5S/RGy82610ayuH+S3QtiiKGGDElk76uUS1YhQlrlgKjMRZ+muD9hDjF6hpaEK9ozd57+OBzDJzOB4DcYdkZbQthstASF1UB8EQp7s5ajOOIpm3RE6AuCm1q6L2HjyELBWbGOI0ASXmHNzdbcLfB3/zn/wOri0vE5CvUrspUBRgLmR23Yuq4372rad/Hwc0MdC4F+BFlKy4OJkrwhlILAAVCpILzkQKdE/OmgeHONYUOYZJ8rqwwqRYaHZmz2WXUmo1iNTQJJCLNZ87hpeqYctABDeg/5yQ7SxNh6qrGB9YO3U8VXTJraneVYcXlunq/uSt52lcK4mOMsC5V/I6SOTVFg603WG+e4Oz9DxHPrzAaLWybnwIgk3kKkiL+pxx/PDBS4F62TG0BXV6rKNb6rygo3/bJHmzBUTNrNrfiOCo0pOIQiIaBzqG7vMTUbtCMHowoVlqDRU2alPbZNEJcTYOmCTBk0XcrONOI5h8ZMbVECMmNpQRCxqBppV8OACBCtMVlWp+aGIO4oJSQKfWmUkEgDyQZKaWjdk1ABejUiyOKR0LZDuCGMDHw1atX8MOAZs3is11okumUOAhmSgStm0s2R/n5MfR811ouXy9/U69tfq1EqlrJ4jxZAKTPjbUSkxArV2eiDQMzE/iqudZui8c6NKZIl12nMNbrgUQLC00c+buKvhbWhWOjPv5ANyUAbGAoIkwDGmcwkMrFAnBiMpPDar1x5YWqLYvioTEBakrPVlFG7o/T9V0F8mVdc2FIoKQ2AxIrQFIhuWXAJho1VOIPtByDPCAjhgnDuMc0DdmCY5JiNEwePjKGaQJbh48+/T4++vT78CI6K0BX4hL0vuT0c551ag8c8rbTG0vBGzNr7PzB2tVAs1YSljEXBKQMqrI26hI0j3RzxLwpEmABCl2pa7QC+triQOnSe5/BTa47Y0zm63K6wpvB9Z64b1KqODEU4CHzrLSXAuytRdD+hUkuaF2dxSnzmCkjZi5ruFL+SvXjkoShAGdKGYEmWaZijJi8x3Y/ArbB0xcvcfXiJahfIVIqgFnREJEC+sJXsjg4AhpPgZWl1bH+7PhxxWJ1/JxzgVRk5VxW1PttbrWcA538ZnGPd40HZVGlR7n3ZDR7LX5v0zZYXZ7DrDeIcQdj9nCptLcEWon50iIV/iNC0zRomxZ2LdabxrVZA7XWFmYIIJv4kjuLjACAnEKocSSZWUiwWJOAlB6bg2QTM5cz50skE2JBEkXbqITP4jPmKAGSJoLjhNs3X2N3/QrryyswNeXkQFE59KMs/2qkm65/ktfeR3B/3FDUfcyqIOmNEigYU2dFQpgRrCTHF1eirukS6D1mV1W5Q0U4yB2jefG7ezWQiumXj2j2+uQmrrQ4JoKFWD8tImwFbmbnTfQPZSicNA0tt1DrhYaya8FaC59A/jgMYN4Uhpfur5jeVYtNtGIIxllJHIhBroUSlO6cQ4ght2FApelqA01KjUJBAfsoWTm3Y4C9eo4f/vW/h+1X8DwHlGq9mQPwP3YsXFb3/XrBuFVIK2DM63LkuLnVbc4eHtuIUeLsQkSubp2z4myyRCloW+ztYwK4Bgl1M9/IlKoCp2MhMS0cGWweXgF96Z7K12SGSTQ4jiOIgLbtYF2TeT9pppycYHaPx8CWQiu1dOdabygAb5qmVIqhNNncbnd49XqLy/c/wocff4SLqytEa0Vpr+Yqc5gFr1Bwcxys/2mG7IVT36nSWkBOlmVHRNiptZvbqgoXfeha3w9wDlTGw5uqJ1cAWqrTwhKMuz4/w2plwduYCjSpps4zghbLrwTsBhdADaFxLVonpd4jRwEwpKXyKFlXCkoehwFI/TratoVjB9eUFEWkNELTdYgxYr/fI8YAQ00yJPCCm1BeSQFCYsmxxqasAJ7VRNB/IWUIAIAFY2WA/euvcfPNl3j+zntSspsIGmZPy0Vb3oeCq0p7mZlO9d6/xbiL6A+sdMCsMFpZe4AoQvNHlsLEGAAxlR1nyY4zXAd/pnPkgHaZk8cMcg4Gz7eIAeXAe8r/AfdBtyW4OfKD/Hf2W06puIbgx0F6pFVp3zPmtwRNhBQzJq1MQsrwqMki2zASXddxB2VfCGDJRcuYc9A+A+DoEf0I00hV2zrbaBwnGDvk8ynwMamo5zj5HHC53Y9gADvT4N0f/xXci/fBMLAzwHBoKdH7n1nDFoz/2OvlHpnNywntdhnjoX9rsMuIAor5CMCvAZECUxw36T+OUSrDR5bSHWQqgGOShc6W/oO6aayzAEfpTYUCxJFdpPVemK8dR8bIk1gJTVnvmvb1+Jo3q5XJLHuRzWgjZlAid5vWgQysI3BwCNrxO13DNazIAvlBK0tO7c5XRZGIpB3RNCECGexEFg/H87df4Pnb78A6yQ7UAqKFvu+xClbj2HfHZMl94y5gc+y36VXm7xnk3HOSOZA7ds77x/2tGqiw5btcGsuRGWsENl2DlWVseYJn6auhpkupoyFR6GQMrCU0DHDHufKq2HfS9YyeP6Z6CJrGKsAjhggfAzCO8H4Sa1BspQ1D6tSqBC7F/0KKr9Fu2HM3Q2FyKFYKFq05O1S4mNxi5Bwln4ln8mhjxDjs8PUXv8f73x9Aps0PI9u5FMXTm6g3+IEfeHmPwMLL+acZ+drKLNIVZhqmAYhJOgOn3xWXHgAq8Ts6N7oxTcoIUMH22AcBs91dM6ylABJ3XBFsQNLm0okolRh40FOf1HAAgsSpDHAIcCiLshSYxfxPDJARWlU9MJvTK8FgnYWNBhTk7hkxW1tqCwkR5cJlCs7VlC70yWmPIc9ThOyj4CeMe6BxFham0JoxkoKLQcAVM6ZBXj/96BP84Cd/iabrAd05+ryGkhcuAeYTRs9aINag5LS5vTpnZVWd7c1qHpfX0XPcpYvMrT1zRSL+abf2n2xUoYoZQOdAc/2XLDgKbpIuC9tI1hFMmCvT6WElo49SY0vlEZyBYfCSiauWxuVYWjZqoKGgSMGn3v/8BPl/WcfUGk/e2txWovC1AnIYUstpmkZ4L+2HQpSMwLwPiXIV5cgMHyJCZJB1ePb2E7zz3nvoz85SVlR9L/WerlLEF+MuF9VSrhwDO8d/f8rycUigcqgYIvJvGAe88vBApLJV9fOW53/oeFCQcf0+29zoUPM4eGQGiA36psdqfY43sAhSkS8FAqZCcMyQlE6TmKpDS5QajknZdgOSfh1GMy/KYhhjMPohMVrRaANLZeJxHKUPVYjoVyUDhCAp6BxbcAzwPki9Gzlp9cwinJgoB4MyOEe9F7KrtDYUV5f6Xm1kRO/xhz+8wW4YsToHQLwwuUYQjvh8T61PdZOqZzwE39yF2u9m8nLVQqxIc2CAKmRSj80aizEwmAd8ElkYlMajejl1Pz5asFPNjwKb/NXsh8funxSRVAHH6e+R5z1ptq1ouL72OE6whhCMBeeWILJhY4zQtrYgygH4um6Ear2yFadUEJZ4HGHkPkwIUbo8C48Qe4wxtmJIAFJVWmMM2Jf6HwyaBQ4jRoRpxDQ4GNuCAyN6aYIo7ioBXZ4lw8U6h3c//i7Or65KdmUlVBPunk0QgXI83X375JQWLJ8XcKPCVun3GMidHb9Y06QWzde0BjdQkFwd8AiHMQQOmlJ/XKSotWHxofxJLTyWvIeoABwknmqIELm4xDVdOwSPGKUi8CkZqOBG+2HpXJcAaLlodo+mjuFFrhFA0iTapOrgIYQMNIq+y/oincvPwiEAgnONKO8uIJLBEBiBhMaHEHEbGB++/yHe/uAjNF2PEaR1Vu/lFaeAi97bw4HM8XXMlpgZ2Dl2HEEKCx6sxNFzz+61MqwsQc4StN41HhRkDFSTccJIMJs4VECcDGi1QXfxBMF28DRiZIbjmNE+EGGT20mv0zRNSisEQBI3oy6SWWxAKiLWokkFwUSgIkYESHyOmv0mP6Hr2tSLSipFNo1DCE1mttba1F9HkZzegrhWRMEsAZYS/CjmWZvSa3UerDGSQeBahGkPC4P97YA4BDgmeKOuH521mGYu+XOLkjhfk6QKLS02RHS0zw0W7099fmrMNPV0/WxiZbnf+jd6zjrVUunDGINIIfU9UutQiR1KF/y2QP3/t1HPeAQjAJJVw7qO+KPv/dAkS4UUDsBNJSCjh/Fj6c9GChilrEJkBqeWJia5jqKXlg61+TzGUFk6pfBa0wj4v97dYpxG9NwLs08CwRChbRpYWxi9zpUhcUmpwEDFsBQghBAw7gfYVuheMlkiKKSO5TFiZMZABu+8eB8f//gvYbs+WYskRkdrmiQpILNzRNFk5lR47bgmubTAnFqj8pv0pGK+Oao169/CwAngePJaKshqgfZY3bYcAmIMYJ7XGlKaqoPJtfChWmAIyMojUPOihYBOayo/42qeSy/BuuXBjBfV5yknnVtugGQxAzilgE/TBNd4NE2i2Xw+odnSR6rE4hwA2ArI1+6x+j4CA4OP8IiYfMSbMeCV6fHJe99B++wF2DYprumIQnQEN941ToGj2h17zDWbn+koKMoS59gV558/RAHH4SP9SxXeB3UT1wvMHviUFUcnp7rZ4Br0l09h+jPEKcLziAmMlE8DbbiQz0Gpr5T2qInzDBub696IuU+CvBrAc64ub2IEcURI9z1NI3jLCOzRtq0AKAhxKlCKMcBZK0w6xuxT1icxxsxKseeaG8nEXTdYzNqBdSDXIvAIBI/pzTcYb27RwJT+MwmZs6KdvJ8Os4pqX/xRrEnV744Q6inUvvxOlrhmNnOrVp3jWpvV640MYFZWvWbwTBJyzMxiwYMp1T8fJx8HUCxNnKk2zRLzDOBEVLFL9QloPl/HwMyxMRd0h98xAEcAT3uAq8rD+bxiys7924wEUwYyCCxF9ELSbpXm8+orOLWyRtM0IfgAdi5bdqSIplhhiwaL3G+OqRY6tbUTOWNmGAZQROo+nWIkIK1VfIyYAGBzhZd/8ddon7wFTs9ASXDm+UzCkGdy6VBwHnt7auh+LK/ns38K1Ojrmi+ocqRC/uhxC631MY/JT2hI+jfN9nh6tlz8kQzAxeqdfgQmAzYxW7tZ4xcfuD+YY8pGmuCaeUjDTD6l/ZgpITLIUeY7+RqgbJ3J7qzZ9cQyo7IjsnRAN4ZAXVeC+9NxJZNMZAVxrM7DCAgI0wTLEtzMIHz46Q/w/P0PwU2HyEjNTFEKhgJzej/gNDMizR+dove7rDb/knHsfPN8tuPHpBeHn5VPvtV93OuiWg7O0vjw97VAra05xjlcvPUCfbcGDW/AluE5wsYA9R9mkJMKLlln/z/q3vxJsttIE/wcwIvIzLoP1smzSJEUdVJHd0/3Tq+N7a7Z/NljNms7a7Y7vb3bo5HULZKiKPEm68zMiPcA+PzgcBzviIwqdku5KMuK6x14gMPx+QeHO4wTJiNSFSk1WZVIwuI6J2kfIgHo8jps3/eABgmzBt5HxOBxchLQ9z0ODg5wkJJ2WmsRggelHSiGNFIxcmI9QBSudWWHSAyj+A0EWCsUaoyQQQDAuk4SFW57hONHOP76S/Crr8AcrtpYGBWLw9Cos8Jw1GCDCMh+K7kzEqsCbffdnvO7hLm2ojLtTpVir07NFlX+PAU5WjRujiwrFDfTYkmV/4n+9Qbbv3ZZsrjz7wmhSiTjVvEUtg54npmrVmqTOyfEaYwB7EpSoDADXCIUW+tgrAbgEyUedZKNsYqcWpzF1YDgZHjr9mUBrbIkob5vq9UK1rppv7OMD2tFyfvgQZRYUjXloXKVfCqsyMr64EDigXiPjR8QbIerN+/g/ptvg92qmgQLOK7Zm52l6oKMxxbBPzfvx8fuKvWS3bjvlpaz8vJU1ectSDtfpQfDVk+zG8ArU5KejSFOPIbzck8MkntMElwmwyjohpKS904Z5JgATt/3srGkm8avUZkougkAOCeWBcQoYSD55hhwDLK85AdxaTM26XrO7Ey6qIQzSHPSarXKI16j52vyZccAs4fP9ZKo3NEPEpttGODWh3jrnXdx6/YdiRxONacxJ9sJyY91jcl794thUf3/Xcp0GQzgiomfZ4GmwGEZwI4MBBq/tiB6qewNcGbX5ybm6cJ1jMGVW7dw86U7+Pabz8GGEGyEZw/DBoaNCCkPsJAt4t1qBWcdmAmWkbN0ayAvpTTraJOuU9SdMrISwbJBxw4+iAAOaSAMwwAAOCRkR1DxMQgwlDKVSxCXvEVcqX9WZoerXDokS2gaoTJABqRhST3hnIVzFsPJU5w8/BpD34NXKRw4qVd9bm0U83NZEaI6HJjG4DirjGnJ6UXlr7VYa2aJwRwwzsAzvt4cTavPXW/5J51Iz7HFWpY2gAzQ6gPSBJuXF7LlXh3PyNbk5PqVcbB3nSDyH0EYuiNs+VsccYChEpmYDOVcQHpSWXIsQENVUFHgMV3DwOfP6SgW/wK1ZBuqO59fdowwi1GQNkulpjIg42Adg0MockMC2EOUhJzbEGAOLuJnv/glbt66DW+L6lLrHKm9K8kbtdJcixPU32jyywjkpGqloTlVsPUd6nOJioN3Hm8zRkEBBS1IOO/FWwsfEyipxv4k/hjQDu7cmMDSRFLSBTEYZTcsMwMhSKTwUJapBu+xqtpSrywqstY/JWbbeDmLqnqG4OH9Fs5RHkccKW8y4SRvCvo1cKxVIa+KMqilvw1AshIRfEBk2Sp+5foNXL1xAwcHB2LyxhLMsJGLBOgXZYRqSFH5DM6QO/uWXUbzeJ7Q4/WVUrvr6sLctauqjyaCXQBvuZyRbFOKKlCgmhTRzqZnKebDi5dw683v4eknHyFuHwJMCNEjUEhKTtYio5etg+sUEVj3oUiMGwCxogsTgrcJ6TMj+cjEvMsjsgUT0HGHECU6JbYbbLYbnGnf1xwAACAASURBVJw8Azhg1XUwRkLK++DzFnBjSOIOkEkTryifCGQvfyLKHu6ULWkUi9V7GA5YWYtt1+HRyQbfHJ9gE4GM8zmBnNyG1cSw1KGqFGqwKR2xq0snZScCTg6VohTiVHkr+DpD5uYYHQU4LYvTovbzWCJ4BHJSUQubTJaFsZMcsNxU4/Gjb/dpBsFOjM5a9P0W4hujS6bpulwApNGUDSnHE7jIXjq0BSep1pqeRJYEekTnEPwAZx06BThqBUOvI/+VSQBNlGt9SGMMYojZigeAPgzYDgM20WMLg9XhRbzyxgOQ62TsazvXQIDmlDBXf6XRKMlvAS3ta1vFJcU+tUrrc8agZdRrs8eikpvcJy86I/0ZSl5G49YQJiqpOGJkkKUcZTo/DakRwHnCTtC36Yiznp6ZU/TrATFEUFcciCOzBNFr+sOgjnQ9eqAUXbnw6AQZU1KXxIGUrgI4InrG0G9hDYFcJ9dIETTUzUJekXWnXkt2UAUcrte4dfceDo8upJ1ZCmZMATiVj12tIArTV7fLXGNN3ryAfNW6/Oxph2f6cW5M1eCtHt96n+edFs6Ig6MT8Ly1oVbIHLgZ++YYa3Hn3e/jyR//gM9/9Q/Ybk/QrQmEAMDkLeKgmIVVg/oxCOQIzoozMLws/eQ4HN5LnitwcQbWEnUS1ky2BkcHBwBFDEOPZ8ceB+sDHK4PAUi0VmedDEYiGMMV9Saqu9KlozbJDw/mUkeDAGcYbAn9ao1vnx3j9OQE3aVL5XoVyAEVq7pWBJPyHEI5ZuPmWZtKENMEzrG1NhvBhlLE3PT3eNttfd05JV+zSMRmZjI4P6VWSvWg1QzzBegiW1B6DDXXSa+zhkGjBtpfspyNJkgAK2vAfpCAf6akU9Co2ho91lgLMg7EETSYpDxNHiN1LcTqo8RiAs459H3f+CdYa+CMOC1LlBtZgNRE2iEqiFBHzNIaxS6TgzVFg1l16H3AZujxbBiw7dZ4670fw1y8Bm80o7kqebSfqz6YZZ6rJ5wDkjre531u6uOm39X3Gi/VtP08b8ESqPJFq/1vloHUX7pYQ6C0P2KZdeIyIdfziqLuxCbm3f0JFGlgvAab5klwdAcWX7LBD7DeNsePXyXOUptbT/3F8nkL5kg9dhU2UwL+YRjgjZHAJ0xATKAm7QxElLSvliRWFrPkVhwY6CPw0rWbuPfGmzi6dLnSKBVobHzNpm2gIGc/UNzK1DIzs2wwlPtMgco+oKk16opOoJEs1WPheZjNvRgc1KCmei3HtZPneDLVYp3D/R//FM+++RJff/4R1kGWN4gCTEhbxC2BjEwkIYYUCVPNZVn+Ma6DSzSXxluJgUDJTwfJOVFZBqCMK1lCcohcYuBsNhvEEOHS1r/e9AAYndVJYhQsjRk2ZVxWEYyGwIGqNiodwRwRfA+OHitnMZw+Q9icAj6AOpdGCEEyxslFOZOfqSeWZsjcRdO+WRKwOeAxf6DcOw9kbnyLoWvZMqBaRV7nqarbTu4nqoxg01OOmap61jpfZdy2GchR6cbqaEQCMEosumtwjs8/o9PLW2YMwxZHByv0p5x29InjsA8RNtZ+cfKat2qLfSoO8XEAuCS+zAxPAkZd18kOp5Svh4jEKZjQBGrRHgwcwSy7pwgEa6fJAjOQAHKYerD46W37Hif9gCsv38f3f/w+gu2Kf1yV02yXvNRjA0g6itvWrEFMy1bvsiOkjXR8jO+pr/XfmdT8jGKXcj7BDSAuNGXnqRR1GG7SNGAOltefKmbFGCCxxjGxHTp2NFu9YdlIglCWnWKOim1TDKVSitEly036nYKltt9lBYFTrqgQgvjDzLZA+9zDMMCShTM231PDHXBMrFBSqMyMAYxTJuDwIi6/+hYu3XsDZn2EMAI3Mt5rgKMNp/UuH6di9t2YwH0BS3tsW//9772s/5/X8N0JcMbqVW8wsTr3rDcR4dLt23j5/V/g4f95jNOnX8HEARYRhkLuOB88HFYIYInKSiL86rBFEDBDkYsjmjEgzUlVZerO7IB4/WYEvHIrxIMU/bjv0Q+97Axh2dY4QJzP6two+XFZ13CngpSVWWoY70W5GwbWBFjfY/vsEfzpU9gYwOxqJAlVmERQO7iGjxCH49LmSyzM3EQ87sN9ziu/jSwXtXykZ1CDwBrkzDM3QJXVNAGDGhSfV3gjpQC7XUAywbYEVplUgadk9yTHFDmdu9McuJmOSgbgDaE3hACCIZsBY/ZJqZSgpE3wOZfbmClsmYPqaSrFqrtWVqsVDFUO8SPtOmmjKtBjCdJZYuzENBlsNxv4GLDZbhGMwZsP3sKtO3fRk0l1U20+r/ReXJHr+ePva4CeHyG3jX6eBTBol19re3eO7UEzjnYBrPNRsifKCMhN2Umq5IuSLVfAZs2eqEHFSdm1YJTyRgUmTn5gMi4iix9liKHERRqNF3E3aHd1Tp9J2P5IlGPYcIzJeK4BqTBuLNFOZXt5P8j+YNfJuOBKVvOzJeADRiCgj4xr117C9Zdfh7lwGYFs+8yGZupMwHz1MWVUvrscnXWNM8ccYaefaC3z1Hxu9dHzgJwzt4mPB+L4vVZ8qYwtGbta485bb6PvT/Gb/+s/Y3vyCBS3+RJMBAoBnW7zjiHFGzDQbYhIAkWUKPHIiOk+JlHnZByQdnhwTH9AzhtiyWDdrSTJJ8nWV0SgH/q8E2s7QGJ7mOm2w9wOKXMzMmuTvo8M3w/gGPJ2chMCXBgwnDzBsDmGs0CgZEFnwJtATka+uvhRWKx28bet1+5lp2VGZ47V0Ylc9uqX7yJqOZDN/oFimsCpcaSrw53nazOndrPFkkosDjPn4I7nsbRtxXnAT5nNanJKqSeys22aoJdVa3pXMRPMSyBIxGLgCFqvcHxyigtBokpLXyRQZVSu5IQQA5Di18i9hGlspqN0/zH4cdYhBvGtME2luPpf5agdLwUVFJAoCReD7CKJAUNgnJz2OPFbPNn2uH7/Vbz93g8Bt8qnPw9NPS4ZjI2/J/VhkLZaYqzrp83gkRijLZGtwqbik5LbaQT+s2HQKPViBZ/XEiOLHq5YBi3TfmonLWJh7Jukmmng1MEg6+CTgMizMw5kSm47n3KXDX5AFzoYa2Ap5S6MSH5pDFgJaZCZR9U9Vc1Z9ZixOVCfMrVonkZ6R3fe5lUFBqIPJeSBtVitVgCJYcCc0p7EILsercONW7fw0u27MK5rjJJyp/rjHLjhyXtaBN3IMdOWcMlU7nVcqD4a76Q6g6WZMRrmxkANlOt7v8hw383gjBD4XIWWKj+udK1Iu8MjvPq9d/Hs6y/w6e9+hf7UwwwRkT0CCGQlKJ+xpghVEsQ8tyd6mhgwhgG2IAfERCeKMrZghJISgkWJN4k1DYngQYKLRR9zfhFR4AbkqHkebZtJZ3LaOh5konDOwoeQgRViBMWIE+/xaLOBB4NyfmoqrEyW3dpqrxta2aT5vpkr49/mjh8DkQx0oE61nO/KjRCK3xMTQLZYb6q0auUldL6aZsXK03llliE8R6VmrwoQXDw6vU4nyjI29DgaHV//Pr5e+aj4E2CYGLE2JstUs9QBTuwJw9gE9sHgWMLMU4WiyFDa3SgPGFHyrcn1qMTUSVcDynZyBlIcqelyS82AqZyUbeqMEBmbrcfD7QaPAuPn7/0E1++/LkFDZ3nl3WV+XEj07fYn7aPyzdhqHY+b5v3Eah75S+wohfmt20pB8vkGOWxXiMMpYuFyEiubj8jjO5c0w9L4e5RJWeM4FUOp2qVFAJGBsSh50YYeWz9IYNeVh2OHWocWcJR2Otk6ZtN0QwdBXA8oVsuLecyP0GzdHihyreNE55HIyMFpB+9lObZb4/q1G7h95x4uXroE13XwlWExZsbrJtslFXPgRnRtGsMVm6J9NV6WPRO0oB3Xc7/FGKtemD+3+W70e83gPO+8sHegv71K0j31BDXlGqSSq8tX8fZPf44+DPjsg9+AT5+lVvXoTWFRgJRBXL3ikwKV2hNMFGCg6+DGdaAQZMspJWVPBGNsCgsizmsaIkCSKZuUyVgu672H8QIi+r5vwNkcqs0hw0MQZ+f0/IYIzhr4GEHGwoOwgcHTADwdAgKniM4AAFNIiwQm5hBy09DYLYC72Bx9vwvkAKgYmJHWT/XLsW3Ips3iqoSQGZ36ugXkqMIq2/yzYpx9mvNR5tur7pcpAB0DtjkAVwBQS0Evgb0W8iYvmuBx+egAJ089YhDF4hMbyiwgwhqxmC0BQz8g8gA2DBgGQjWBqIKDAKhYJeCs2ShURq0yFWqlR05O/943yzR6jzpJbkiMEEdGDJKTp48RF2/exCvv/hCri5cksnFSzwatQ/RZZc4SHYPKXdNGy1insTfTr3XJyjklg8xW+ZLFjNZ6PacYf1KMdQCZZK6lRKpJ3iQqtgDhyLobKS23AIKIIwCKefS0xoPK4nTc6TfKGPsYAD/k5VMFPmZkkNSB9+ZUJ6H8QKC8IzeEAIqyvTtWOlENPmTZN1MdpgMJMl4jS+qUCPGFu3nrFu7evYvVeg1OueKAliQYGwr6/iwdvvSdsjHTth3j8fndTrvYTTmmbt9W/88fX/TevuUswPOdAE4zeY2MjJ1WOBGILA5euos3fv43OAkRTz/8ZwzbY2DwIDPkMNgqjADgHERZoPjGgAgUIekQvEe0EdZ1oiy9B7MtaosZERZgn5K3QbU3iKjkMYkswQNB6EPM8W/mhK22Quuli8xaAHDGgMjBwyEgwMEjDlvEEMAa9I4I6l/DCXgpiKjbVBt6X0t2eelp9/c1A9N0HajcNiXZVKtAdlRJ8jtmyTOG6MGmbg9leIrw633Fgp3W+zyVKYNTszJAPRB4pKT0/DnlUINXUTBTZcaVom8ATjKPnbECskvWqbxNVzKAr3J9mcXZMYIh8YgYTTyY3EHlePlUPoO5sWRrn6v05MKTkIE1VlZx0nkCaFJy2ihLCyEtMXgvS1XUdXjwznu4fuOlSl5mlqZqjPKcRRmbOWu3buWmr7R50I6duWvXf4WNXtoeuzSRLd/jPBQnsQeaugMZ3osu4ZS/j1nYd2tAbCrDU/QFx5B2bhYQo/Jc5LDiA6o2yUAnsfiacyqmxMwaZNS5EhCW0q5dTPQskiLWiNqoEn4W3Q5CyoNmZdtEB8mMnuRbt5YDKbULi+N/iOJ6EMiI/831m7hy/TrIOgSGzGvajiP2Jn2Z5X2sT+ZAxER+d/RnYXPk6Cm7UwzcReN65i77SG8G9pOxMD1un/Fwtg/ORAGfdQJyw9cgZ2LFAjDdCldv38MP/urf4Z+HHs/+8AGG0ycg42FtATmaF6RYQyZbumwIxjg4WAT0oBhhbJAkZjEtS0G25QkKB0Ki5gEUmi4xDpYMyLpsmTJLlErnqm23yaFMtgDW4fBV+cei+JVsMgYr69D5QRwqT59KDJG0PMbZDte2UwGZa//nAzZL39dCUn+/CGxQnGXTBaSdsuKhDG4AFnYMFmqOyKQo0suRAFNNlnpvSG75/7+UOqbLHKCZK/q8dUTW0gaqUGqGoZWFgnNVZoT52fqIR9sNbFKuITIkg5RMBAJE1ddG2MWQ6kulcgm4SF8RkLa5KmhKu0DyBNGC/jIGysRWA3OkSSvElB4iOXBqFFfvA05iwBaMW/fv4/XvvYNutU7ArW3XzAjlumPn0CgTl/bTvAW/X6kWY0bWfL541S6z9agKKathKGfebu92PsENADiDnCInGysAMhgdMXvIvxnIUioQYYT94QJs1OGcY+mwxghLraKXNEYkQhJvVjqM63msMo6RdODsU5Wl9AKuNFdgedYiS5SW7U0KjyCpUrRdYAw8R/ExiwF98NjEiA0DvDrAxRu3sLpwGXHsXDynSyrx4Oq4OZCzBATOAjlyzfnxsfx9RXhAR4hOpIT5xJvl3BYc1yxVXevnwCJ4DgYnW9i70OGuVqsFScEOA852uHLrDt7++S/xQRjw5e9+jdAPeVDUDI4IZkoPr8Jk0yBhC9d1OZeOYxkYIQbAWCAOMKZkDyptlJL5sShycUZrnWUBlLg8aYJQoc+gJyVo07aS5QGfrOceHAMcGM5HbGMAnz4VFiceAZr5Wa3kugO1Xcf9ISoAmfacMAtt3+mAbwAO2i5bYm3K8QpsUh25CG+x6kkyxBMJowMIEDQKBij/1eC5ZnHOmKf+okWtmvQJyIxLAaW7LIuzJjtKAUU4K4QCbppTi24VEOE9Hj38Bv12gwMwQgS8F78v9j0odohDD3RermVsATLpichIVCoF53nnYOA8yRgidJqSIU79Fuoi7EzMDZflMxbGk1mcLYfg0fsBG+/xtR9wul7j3R/8Ajdf+x5gXYEPLygYLZCvgCJqp8lWce8CQMpgkVJLzFAfJ53IKyN7p8WZlXtttabzo9oE3+HZ/62LMyWZaggBMRhEW5buLZB2NNUsTxHgAiDQAOw0ujK4U5CRHbU1UjfJ8r5PRLgaCNlnpxqTTd6pSvbreuSftH5cM/Qjv9R04hw7osZxCBLuJBiT2ZshBGy8x4YJN+7cw6Wbt8DrI4nQXRs4alDqPfNr2wdn6Zwxs7o0Uc8ZW8VwWx4P8/Kd5ockyzrH7SqU54daV+rnnafOlhdaoppSxJzR9dTySOeoBhlfiwFjO9x89Q3w0OPJ0yd4/MUnQO9hky+OUt8S/8AAHoINSWJzMAUQJH+Vix08RNlYRlqmSjeKPsVGkHxREp9DGtWwLEsp+KoBDhEVh2OaigZR2raYJgEZ6D4PTe89gu+BwLAMkAfCxksMBzKyxRBoBnK2Tpt9dS1uz2KqVs8OgFO/YuZ1DDbGz1e/z9akjjWWyT3GKO7SeY1aJ0kHpAWRlvGoFVdrmZ1bbY4a5FACA4xxosFy7BkDurK06nMKi1O3V2Ul6XdIyzzDFl99/hk2z55ixb6yoiXUAseAYXsKMKHrVmDnxAcmW6opbxU43z9Heh3K8pOOC91BZUZWtdY/xgQA0Mq1sDoxgxydFPu+R9/32MSAk+Bx7ZU38fp7P8bqwtVq+/A8K5LHQR4eU/nPcl2srPoIKFCdXHNUGjnN7yuQU7V50u1pvtJ7lL8a/Iyfa2yh54udw1Ln6ysMdlm6J0j0bA00KcdlKIiszyrdOqfH9DxAZEpNodpYJQM4Z8AsOjdYA5BFDOK265xNLgPp/pVITNq8uS/P/jrRVBVAVd3PzCBrwc5mmZcdgxF2fYjX3ngTl65dFyAv4fpbGa/ln9p7n1Xm9c9ucKPLttMW2Yf3qS9YDt+HgcxGgSmAv/lDGSP7LE8BLwpw5E4TtKqsjAjfSNGjFYRaqVtYsD3AzVffxus/eoTfDltsH36Oru/TmqlsBzR9L1aNC9kqCMHKXnF4GOrQdR1gAE+ATZRnCAEwLisdAECQHRuJY4A1FmxkW/p4kiJIwghwy7DkAVyNzObcyBI6PDl5WiOMFQ2EYZCrzs/jqizTH80JWrJuGBlYjSeZuYkHQE4OqlboUhkDm6p2qAVdFVWODAoW1oY4pfOqpTQ0t9Rt++M6xucZSH/Gki1NaF3rPqlGIloZX/pu7pj6NwCT4+uWCQlIbI+f4pPff4j+5BliR2lZt8cwAHEtwfk4GsS4QQwBrusA46SviMEIYBMBwzCWoM7fDeNXMarFUXMe4CAFYdOoIYHrbb8KzGQi9CFgu91i229xHBnrC5fx1tvv4PKVq8lhVe/fsh35y3SPXQxJacuxRTq1OmU4c3b0bA7h6Tn189egpUhEmkW5AMl6ozqhPFf9J/PZWN7OYVkdIdCzFA1LxkSZm6mZ9EuZGlEST6b9LrXemYWLpZd9zrz36FxM8lyyfzsrcZsiAEpBOPO4omQLK/TneldRu8zSQlGMPlE+X7eYc/K/0ZxZQwg4OLyAe/fv4+joQoWGx23QXvx5YO4YDMicPQYqU+AyIrkq8CPGbD12JoAjN9g8IJqT4xrMZVkZM2V161Zz8K6yN8CprQxTXVzBTtqUNAtudl4PSMJmgNUFPHjvJ0AY8Lv/579g8+hL2G2fvfTBkueJaAWiIVmhKUCgERBjuw6IVqw+Vmq9hzDlQilL+gUDZpOcLAXgGGPQD9sEWvT40kk6GY07NHqNmsywJDuyxKmth09JPSMZDNEjsIGPBkMAfBxgMYDIAClFgQiGZlrWFhovXaWlCy7IeIkNqb9r3lefCaOBNHrWSd+BwKh9jwBZpkJmxGDEiY6Imoim+nlct8ayrVmic1bqyabQ1iojLRCZK+3yY3PKjnuOlRGy8owhgH2PLz75CF/+8fdwkJ1pMSnWfrvF1jnEyHAdwzm9NYGcxMPhFChKwY5xsgskxojgZYIASa63PHEb0yTYVKCa+7HaUo7KmtffNW9QYGDrI7aDxL95Nnjce3APb737HigtTS0yipVOynKjDGE1CRUgkWz+WWBTnZTqrK+iCmS86W4wbf+zlgaaa+u4U6/afFwOFKE9c17JmtlCR1fRP3oMxC3WMYhuqjaNZzaSK+BT/WvHejHqFOAI4Bw3SKUrKkPDWJN1TMl6X7GPRHl+qk0Srt7DAAgMzwGZjQSl/IRW9HVTnTIfMtc6rbz3wcMHZH/SECNs1+HGSy/h6vUbcKs1Qp4Tz+j8MfrYo9TM7zTY3liHvbjuLe4qNJJxwi73h/2uPZ2nzirPHQdnslwxQnFLyGp+osx3kt0WxmJ94QoevPtD9CdP8NE/HuO0PwX6HuQcQA627/O1XFgh2oAYCdYQQAEwndCh0YFDhxUAcEQ/xOSLI1tWZd3WyhIQpN7OOGFw4FP2ZRWkadvUk7Ao9JRDK72aNAEYY+F9xBAZ2xDACLDWAIYQCQgmgiiKY+0Y3GTAUwkn1eAG2aJcAjb1pDJHj0/6opo0dvWdxhEpdGZSAslx2MBkFmacm0rrpUVZsIYRO6dF/VNa9qZYkGVc79rmn87JABrlWmc+u0od5b599vgRPvqX32A4eYpDYgQkdjIxi957AIAxHdgEMFuR08GX+EzaNxixIcRpacGIrOmSLhkZH+mERr40LH2l6JW9EYqek4Ud4JlwOkQc9wGDZ/CFl/DaOz/BhRs3EdTZthoDpbRy2rR1bvLxwG2NgfzteKKowE19N+bp5ENA9sPJU8QMGzPvuF9dg8TmJ1ZWIY2rdO2zwNRfsthuhUiUt05ThiNiPAq+0d9KrCRDAhYoQFLyeErGbgVgG7akFB1/xSCSYHrr9RrMLCEQ0nKQBttr2L9dTTmrW6PsbNXxyoltYs5Pm8McJMY+cERIpqD3EX3w8DGg9x4BwNHlK7j3yis4OroAjb6fZeLfTAWOjbGlsXVGAy39svDTWYZAw1xizOhonZ6/7M/goBLUisVQ4QK3Cn3XJLX0CxGDbYfDqzfx4Ee/xNPTAZ//+h9g+gHW9un+RVFYt02OkQxnDWIcgOggcTUBWAsCw4UOAQEhyiMLdW4qq7PseFp1HXzO+JYsDTJ5q6CSKkQm5xNRBe9T4KbcBikQWogRIQ2a4LcgYok/Ah3DobYLKyAwZm3Szc8AN3PKcHKM9oMKlKLuqu+WwKu2S/nASAnEmnxEKYEDyBbWRpc5Ggas2k2kVrix5xPkqLLT99UvAArYmxP/sQEgzoOchUoVZqbmeTyOquUKiCId+h5/+P1H+OiDfwH7XpL4aXoRktxtAkwYYdiW1CcpuSbHCMQAqiJyy/bactcCUAFwzH2Y66YyFcXHSmQ4ZvmMqc00Lk/f++yHsA2MTd+jHzwG6vDgvffx5o9+irBeJ2tTp7tWAWLJmku6WQHJi0CCuXPm9HPNhLZghWaj9MwZIWOlXvR68u9KjA9xAaHnrVhrMqgR/FBNWEAzW4sVnp41RacHARQNyBvZnefFb0ZzvxqW7eNZPwBFESP1AxX/MGstvPE5L5Qh08gsJdS1GzCOOeSa70kVqIwTzrJetoH7GOFZrjP4iNONxyb0OPUDnkXG9esv4c6rb2B1dBGcl+nn6zRept63zB9bA5j2fVmCGxleyqydUWoDrz2/rQ/twFATsIN2XDTPdQYS3Gub+BJYaaym9GTjjmi2e6FVBO21AFn5NIi2w4Wbd/Hgx79E2J7i8Ue/wma7FeWwcpCwC5KYU/w3CN5aOAew3wC0FoG2MuyoM3Cw4CGCyaUEbaEoG4IsfRnxPbCwCfxQ2TmVspKX9cEyycu8XpZ+QvAYgnjKCzNEknbCewTv4RxhbQEiCxsk/zLy7pkZS03Bhw5uqFU3dbaaAzvjdAlNu+trygbNmAIbfV+/Si0ShElbv4mQY95kRqZicWp/mzkWR7PDA1yWGs5dmYJIEX3dQaO5kubHTNueSUkqEVKBTAU3mbxoJmtdnvL45qsv8d//6R/x+JuvcL0jXFgfJrAfEMMAdOui3JmBGBCHHoEZICsynaIZS5u3hor4wFkoVR+pWprUEaBAG0XWMnOIBK5YlqVCSsmgSwjbIaLfbuG9x/VXXsf7v/gFLl69io0pD0x5rFU7jEbtu8zKzIyl5yiNrpaWz0ZInmy1PVLftdnAp4zPXJljxsds7IvBtX/7sl51KaN4BGGapqUdK5UeIdKVv+ovQSWiBH446abCAJICWDUq9F01aaqh5AcPa2zJYbgHE9BMwii+J9MzE5uOkaEJjfvD2eaLARiGiGPv8Sx4PCOLe7dfxrVX3oQ5vCARv6Hb0MvEDv2ULdL5MmaLF48rTdSSFPncs9ib5fs275N1oXNiw+pVL/tcvxB60wY4qzf33yY+ulgGPgmlzy1PtRb/6HozwEnQtTSMsQ43bt9D//2f4OPjR3j82cfgbQ8ihqUIayWOh7U9QAZ9UiYWBDIeZB2cka15MRpYFiUdQgA6iQ5imSUYHSHvBgGKx3/xG5ElpRDD5Bm0bYpFKROPH3yO6UDMsAQJGGgsGBadczKAY7EmavZmfI/SC9NPc0tQc5biWJg5fZ8jNSMFYqtZhp1MXBqM1EpHk2RTLjRJV0iNNAAAIABJREFUvKmAp34Yjpwm+/NLx8+17egIjKV93A/jw5TkLtsoRz4YrMATehRiCDh5+gR/+t2v8e0fP8AB91iRQWcJrnNAlCiyzAznHNarFQjIAc6GwcOYxGDGAPULMUQC9NWpTpUsyVQCW/qpBqvjyVgnoxLML2IYemy2PTwDfhC2sw+ydEvW4a3v/xC37t4FrAE45IkMOuFVdRkpo4JExuOnav+p3bBDxipmmpNVq+CmPQ6TcaXnK5tT6ih/uWlRxt8cGBq37XkdE52z4iLAhcURHyuJHxZ8AEyEcbVxgBzZuJEfLkuwtb6bLu8iX0sZGaQ2VBYnQqMpJ8ZV9QqhufZcUd2Y759kUO+Zqtr0zZJuUBYzJuDDRLh64yZu3L6D9dGF5DohPkvFmpnxw6oxyBll9zwyLUtApToiV+BMMFUbF1UfizHMaddd1bdlJpkBkqlN5sDNHqDuuXZRVc0/wXk0NymecfOs42n8APKU64MD3HvwNrA9xgfe49E3XyFsBxhEWCv5RGJkdCkcvYbfNpAlDmMtyEiKe6EAhQWKzokChYCOECvqn9W51ubBIo5lBPh2OSXVNFsOMU0UhgjQiKyJKbJEcLZDZMIGK0S7hqVyz9wmM21GmfeTwIWlrZQanV5jV8c3lLi2/+heZ4Gbtr6j86sinjqyDKigRttPJtcCcjhGCVO+pzXylyitpTbVNXNU8iy9TPm/ZkxVdyoy2Zwr7dX3W/zx4w/x4X//f2FOHuIC9Tgii7U7wGrlkhwqYBffKEOi+EEGNi2iEAAyhBjrPlQ5IJScVsiB+co2ziIrOb9b6uPyV1Ix+MGjHzx8ZPRDj+N+wOPAeMoGr771Lt58/5fg1Qo+BNRrPIXBmbfiKvQ3nbfU2mdMdNZcmUxYevnq3/jWqbegrG7tUF9fsy4N21N9b9Jy+OQ+53Q8AEC3WiW5ap14tURmmFHuJnHcFdYzVDqSjIHXgxRUo+q3he6vfxLQKI7AygQXVqwO1ifbyg1T2TGHpE+j+AIVPVieqmXHUb1vAX1hM9MfM7xsK8VLt+/gpdt3UtiS8YOMgBVGcvscQKeuL1CMpH3LeF7fJYdnymgFfGqdp47oBLWVC+NTg58XKc+3Tby27hdu2oCbmQE+njhnr5MUJxFhdXgBd9/+MbZDxMn/919x8s2ncENA5wYQAOe8DCxjMVgDaxhsrdCaJOv4JQVDzPFpYucAlvXZyG2mWmVjSmBBUVbOuVYBRsnPY1jzrCA7tnEMshSW7HMfIryPYKwQ2YKcAVkGKAI8DnHflrHjbX1kHpA7mIWavZn0U93maJXEPlbAxLKaAUbqi6PHZwVkkFkuPXcMIM9byQlElTWg1tKqn2WOoRwXhUlEqFR5+q4+KrVTTJmNHz9+jH/5za/xzRef4ZAYK2K4tGy7cgJOYggY/BY+rOEigVwHhrS99jYRw8DmQJVTFqJebpHaNvGhNH9VTDGdUJYlvPdZ2XufgsAx5y28QwzYBuDirZfx4Bd/B3v9JgZjkiNz7QifVF1uv3nZmJVPFB0TF8bHPmWWvUl9VS/bZVJT2e3aMh7Xjag5Tul4Ao0Gue7kOp8gh9drsHEgJH+vBtiPng+Vzk8AUn2+pst6aWxVlJ0hkuj11bSnIybXZ9RObeTi8r0apnoPrW+sJvNAEQEaH63kmFIWqh6bi3/J4T6yBH+160O8dOcurt+4IWMoL+cu6+cCyfYvE1lvwM2cLM+9b4/ZB+TsmjcaNltfSdnOwuCofDRkwmj+2mcs7wQ4RO1AWwKOs4zNDPLbixHQ65Nu6TRYX7qCV975IYa+x7/84zNsTh5itfVw1oJSvA9rrXjmG8qJMx1Zof9IPPeNJbiVQ+xlrZitA7GkhPDMAClF3za6loaSj4wIQf4mtUGsws9rO8W0e2obvHgYOYOuO8D68ACBIgxCZccjX3/SLqyol1sWh+vBtlzOAg1zwjP2BdhHqGaPIYifDomCAiWHWiKgigANTMHceSy15WYqpgWM7BiZPs6zN1WplX4GNjK6M/ipFWjf93j08Ft88vHH+OLTPwJhwNoZrGCF0UxtuVp14GgwDB7b7QbOEayT7OBOwYpu2COtW+IqWK3gtDxblQk7gZSIM0rqwBgIkWNKuyBBNkOIOeBZCBGbzQbbvscJR3ha45233sbLr70O6lxR8kq5SCtVDVYmJE4gc6qqWxZmqe3nyr7AfpbRofavkWOqQFIzybb+OvovYuTIzjzRu+elsOvAZIFoEDwjuADnbHWAyBUpFFHjl5AmsdRno8kxezhlIKLMLwGwjVFaA4ldum7MLs0ZfYYIDJsZPCQ5U7Bc90lMwTJjFHaoZi01qn7klIOKGcY5XL35Em6+dAvrw6Ny/T9DKXwkMB01QHEwrmkH7Ts8V13PWkX4c5WzGZyFUVUv0VRfyusORb60zqy/tVabThIGR1eu4a0f/Bg8nOLD//ZfcbJ5gm4zYL2SpY8h+XjEGNGtVlitIsg6GNtl5iaGAAIXa9U4kAkSG4fE78A0FnltichnoaAtYCC5RihFLOZ6cEmuLO8jopcEgqI4BzAHGHuI1XoNQPx6LGS3V91sSyAHQIrsWoGthf5p2nWBTWgUyh7AYh8EP7kOp6TBLH0JAiLNOxzXLM55LDFqmg75zE07AxQrgG7U+XEK9ptSKXAt4wWwGCNiCDh+8giff/gb/OHX/wQ8/gKHNKAjhqva0hBh1XUAHIwx8MGj73sB/obh+5hj2Mi10dDrpFvAE30uh5WsyspSZrYy1PmCOAcz896nfELyFyLjtA84GTz6EHDKwLV7t/Gjn/4MF69cSRmZR01D7ZJH7QuhLVUgzfNZuGeVJSWtwKqZkKnUV8FNzRqI7wWnsBTTYyf3mHmS8zBhzBViFH+pkUEkRSfWiBg8jLUwhvPGS+Ykf2lTNavVGAogWnp2ojYmEWMKcCSeU2gMtp1LiBMyI0/vhUnjwsrUgCZE3S0oGc05MraBccIBJ2CYo4t4/Z3v46V7L8O61ewt/5ylTNnjEVSDIQBUAl/WJZlkZ8rmi8ju3LgYG91nlbMZnKpyY2u0Qb/tiftZ/Yrk9yhkHS5cv4nX3vkBjo+f4U///CscbzbQCJUA4EOAiyXTqwHDGifIXicmmFEXUtqKqrtgxk8TkcNwJatS9FKyuKwBD/Kc1hh4Eu/9yCn7LADDnAWfvYfBANut4cwBiHM6xNyu4/aeDkDWnJWjurbtXUcI3gUwd32/VHYqnAW2Tp0rx7Tj+NgYoyQdPIdF2bJixNXUMjUv9TnActsolBmzDZT0qjI4/TDg6cOH+PrTP+D0y49xyT/Gyhp0xmDlHDpr0FkDSjl6nOvgnEPfbzEMA7bbLTrnEGHgss+agpziK0NkwMbAmLRFW6l5BRss4ykGmTjYB0TN3kySoVyBamRg0Jg3gXCyDXjiI54xYXXtFt7+2V/j2u07iM5V6pSrVpG/3GZT7FzGDs3L5YtYk+Nz6iUHvXMDdBtVlgdn/k2vgfEhBpJdu2JxagaqTK3PP0b/XOVgvZKdewCQAk2WMm73AgjF1iGwBYKPiOxluc9wURaxgMnlS6oU8OS3okNT2ILqgLkI6mowCjs0fdZWJkZ6oAI9IYVEiMw4iQFPOOCZIVy9fgtX7z/A+vJNwHbgxtmshHus6/4iZc6oElmqJWpXqYD4aGQ2s35q9pp5nDWeR5/Hdd31rEvzyT7jei8fnCUkNVLr4wNy79cP3FR2zw7URo2mw8V7r+Bl3+N4s8HjD34Ls9mC4ylijFgnVqQngOIA8g4m7VoSy5RAxsofGAgenPxl5p5TLQIJDqjRK8UxVnxHkmOls4jeS24e5ka9BR+AwYN9ivBJVuhc28HYVaYEGe0gU0t50ha5/RR4pdap5HaJsVnqR73u3Pf7ljETNBbK8e+1w7G+z+0NWdY4lyWWiU0ASD0hxZxXDOC09XNqGNTvZRyJP9KcUpLNTeK3cnpygm++/hLffvU5wvYUh8RwRLBE6KxFZw2sIUQ/gHkF54SlcdZgu92g7/uU+80hAJPwAbrlNRMpzGBEGKjTpkGkIJG7Q1qWUss1518TCyCEgM1mAx9LwkEfO2y9ZFE+dSu8+/5f440fvQ8crBN40pZDqsfMTpJcr9FXqJRdNaba01oAOVfmlOY80EFZ4qtxSwYqrbExATf5tfg56XdcT9SUn26xzn/pcvHwEGvncJrkwHvkHVTD4EHWw6bo7sYYUF69GlElClQS+OGUhqR59tT2OqlmXFMd1zBKmQEsPjby9bJhqMtdY5+tdskz9Wn9XTomZ0FPhu1mGHAyeNDBJdx/8D1cuXUP9uCCzAMtM5DrsgToZ+HJjO6e0+c1OPkubOAEuGAHi6MYoPobVaypY/06PfT5Ad9eDA6AGnjPV3T+Ak3jTxq+mgQz9Tf3EFRZaW6NGy+/jjf7LT44PcbJp38Ab07SdYwE84sB8Bbm8ADGBIQou0SMMXA2gsjL/cIgWooMjOEmcWDxgpfVcGs7mLQnKHDq0qSUNZkhcYQ1Bs4YDCS7qvwwgEOQ0N+9wSZ2QCTE0MMPPWBKMtGqmXeCm9qKbCeBkbVbtXv9unTds0rNDI0t2jm2r75ufU79bGVd3aS0AXqv8wlwiNN2TwCWs+pMQEaOUVZnbklqvHQIjCY0lGs07M12g0fffIU/ffwhjp88wtoQVraDS7l1nNWdUiRy5wcAByAirFYdjCFst1t4P0hmeyvLV6M9PG1ohFwXVWLC2sQQgRARvUccBvjg0z2D+KUZkmSCKebN4D1iiDjZBjzb9vCO8eCdd/Hzv/obXL56DTzSKFQ35hwbUjE1c0xLffDOZaaZ19nrzHxXAx3kdC6EQq0WXaK+D/XkqACoHr9zxkY6c7K8dZ7K4XqN9arDCauxWJZLQxQZsCHADINs4XbSBmJ0IrHfsjGkHvVVqyan/qwcUJaORIVzcnZXUTEwcEanN/GPyaPtjMldWZzF31Kf1mCmLFEF2S3Y9+JMz4whBPgYcfXKVbz6+hu4ePky1FlPqzKro/MQGOnzqi65XpM35bpZ30x/bq7zXKBnsYFmPo4A0bhONW9VHllxQfn3IuVMgJMfeqGxZ06S19JzE4S574RaTij3JxBct8bt1x/Abk7wu36Lx59/DD7dwgfGeh1haC00/WaT2AHA2hViAKLnvJVRWR1jLGyi9QXUBEim8VCs7BjEquDKGZAlvg3rY2elhZRpWZbDNl4CmnnfCe9iCMZExDgIwzSyADDzOrbuBRyWhimof9lKmfuu7otdLMy4z+rfd03gu5gc9YUCyaSY1hiW5eAcFI1nISow7eVIExYbYXPkeaVv5tpoUkYMQO77xJB473H85Ft88dFv8e0nH8Bsn2FNnBIH2gxunLOwTpwvNYqrc05Ad9oBuDk5wWa7SePIoXNdkiek0PmAbg2vnyFyzLFNYgjif5aYmRgkamzf9/AAiAyGYcDGe/gg+aw4AtvAGJhx/fYd/PJv/haXr1wtTFVN39QTecVk5FGS8X15P2nfBeBzFnuz67sxqG9/L2NvooxZmbhavOtjdlD0GSCd32KsBblOPGgUgSIFqQQVRlALUTPxCcto4Su/MEm8mZbsqAjHWB/q981bkvFo1S8tOQBXSH2xr8vEO5qz2rtAWaPxuTEyBj+g73vxQ4OEOlsfHOLWrdu4cvUqjHVJ5lu9OC8Dy/Nko1dGlZuAmR0idKZ+wowBcMY1zypjF4U8a1XsFZHu+NT5VgQpVjD4rLFx5hJVy+JwNj+aya86dnYyq0DPuDHrCWA6WTY1yf8bsnDrC7j91rsY+gH/8H+cYPPNp7gY+3ycHwi9pRRGnODsAGsMDEGcLUm2gGuMG2m7CFlDrv+SACAicvJF0OBRUZ2OCcYaeF+exwjKwUAG28jwHMHGI7ADkAIMmgBoELGR1TBmR2YHQLJYipBUSnaGUVkCPHqvJdp2aRmrYZ5GIGfct+PrAknXFO/jSc6q81hijIh5Ak4WfOoDcNmnUOVonThNT9qzetx6mzzHiL7f4uHDh/j89x/gj7/9J5hnX+GC9bjgHDrrYK3kYZPo3hIbSh2fN5sNVl2HzjlYa2GtgbEGYZuCsAUnO146l8MlxChLTevuAGQsqmh74meTIhDHlAE8BJ/+hMHxIIAitsOA7RCw9XKvIVg86ztcuHUfP/13/xNuv/wGkKJnqz7BeCKU1iptRWpNIAGY1Hg8sk7zb9hLnvay6GeMtCW5np6f/hp6YgEMtWdWy5881zjnopwEYGsstkSwIaILET5E2Bhhq4cXX8e6MaTDJVsJt6A0TXqJ4gFQwKH6zhT9ZOZ1m5nfaFHrrMnSFHPOzaYyxSghBhoGJ59TpTlJICcE2UE1xIDTEHHlzi28/sYDXLx0GcZYSaxZ6fw5ZnfyPKO5tdG5mOINAi0CgF2gZhbMLJTxb+P70cJx+5Q8NkZYtuTvO7vst0RVCd13mnwy87DgbNl8V9+Hmo8GhAgLOriCG2+8g9tffIGPnjzE8eZJUtABvrPoHKFzEjUYDJC1kusEJaqwjB8GOCAmcKOWAmdrBMmK9TCZeo6glEFb44eQmipkMjq3zsE4Bx4GxOgRElUqCdgCHAUg5c7aRZfPtZla2fkbnWir9ppzNNb3S8zLuCxZF7vYmXGpUXlznIIzan1xzmsRpVY+6dvan0jkp45Xm45mbp6VaH5dXJdGN6enePzoET7/7DN89Jtf4+EXn+GqTTFvCLCCoWFJ8kvpHxmTnbS9l90cWnlnHbquAzNLVuMQsMYane0KYGVIJGQGgACDAEMWCAFhGMDBI/iQx5rumArM6JPPUD949N5j28s9TiPBrw/x7i/+Cg9++D7c0RUwWZQtAWmiG+k0aZ+MasqXVYeMlbuyA3PjaPy6BFzGxyz9jct4Ip0eq4YaodopsFCH9jnO67DY9j02YYBn2VzhfcQweFjr0NkI9gOiAdgSQp+SbCL5Q1on80I97hOA1V7l9L6ePOt2JsJEP+Xxldqv/jxnUOc+aq3MJFs1mCnHEZBjPEWW8eIjY4jAECWUhGeDjWfcvXwdt+7eR7c+QKS8Yb661UJ8uJlnq9tgDHJStWcAhX75gkKU5u65wqO+mZxXnTs37+S+XHrOOWS/52PsBjhVJedYl+qL5piGLdgDDe6aQNPtmxoxC3MSyeHg0nXce/0t/PHD3+LJ42/BfkiIewUiB0ss23XTOoJuj6XE2MhyFYNTMCfmymEyAZyQtivGGMVqprIlPYaI6JO/TvLPiRDatnMOKx/gdAt7CJIAcbWG6y7IDqoIYXEwg8r3QPJZRdYDfvS5LjXgqa83tmra9p9falqk1avfx4qkZZKkH+vvzjuLo7m1xk/eKEp5s/M6rTJvzw8hoO97fPv11/jyyy/wyce/x+d//AM638N2gLMWNvluGQI0VYbqCJtyqDGAEAO221MAUaLkWoP1eg1rLU5OThCjxKWJLuLw4LBEKQYExKuKT0u2od9K6IPBw/cD+rRDKsSIACBEzvmmBu+F5YmMcHgRD37yA7zz85+iu3IJbBwwsjAVzIxgH+aonQmgOaOdd8nTHJipvydqHf6XrrVrPCwdv2R05Emdkf/Oa+n7LfrtFgihMbJI+42j/MWI6AcE2oAig60DnBVGIzI4pc1JNDmSuZ71Y7HdRib9qNSTZq1X9L0aUfV3BbgsNfh0HhM7QOYKiXVDknrER2wG2ZG4oQ5H127j5v0HWF+5htg5xCqZs75M2PP9m3+xvOjS5k5jN4OzSj4ViObEwW0tpjfQl+opR32mv+XX5xxbWvbPJr7AumjlmpIAz5x1P0XR+Qggrdu2gIbz7zJ5FGuPAETb4fbLr+P6vdfw8ItPcbp5gnBygoMYYeyRhP8mnxEiJT+JmFI1IEJyY7BGXPVlRw9zCWKW6uwBsToYCbUnxzI/JMfOgmYNAR0BK2vgbIc+SNoCtz7A6vASjHHJUIl5wJzVBzU9W/7arqAE/Wl0LkZLV0tLVrvKmEatr1NbTGc9B6BMXHkWVTo6yZ/HwgBAspN1rqXG0zXQToj6jLIVXjLS6+8aDM97j2fPTvCnP32Grz/9GJ9+8BsMj7/CQcdwJAH9jFGHYGRlXLe9LpnV112t1rDWwboOIIODA/GbOTk5RfQRzrkc6yZP9pFh0rZfXZrSOsYQ4H3EtvcIMWATIwbW2E8G2x7oA6E7PMQbP/4Jfvz3f4uDW9fgjYWN2jalvWSMjmVHPJ7UnV/5nKa1mSuli2KszvXPDICZ+22pLLE49bhcOrYuY3AzxyrlubaOYHEOy+nJMY6fPsWw3SB2hBiTj4n6vaBshiiyAxgry6QSV8wItKXEBxADkBxWzAyy4rsItskQTW2kK12V0VSXWjeN57Dxe5V33VKu38eMeapksow8FvRPfO8ZPkQMIcIHhu86vPbgHbz29nuwFy4hdGJ4SE4mpCmu3eFV1X4e6OyQhdwumJfx2pgan3NWmYN8mb1JfTu7Vpb6U3K6jX6cJzGTTXPGXLTHoNjfyRjtpNbeqUIlCm7KSRMrvj2VM4OlE3MLcupOa2qX70HdAW7fexV//N2vMWyPQcGDUmAzDgaHnQVBUjZYa0AUYSE0fogMDuJPo/fiBKZ89FnolWnQGAe6VOWjxxAGxMT4xJTkcBgkLoghYOUsnFkBZNDHiMuWsOpcsRh20HPa7i0lO2JnZMTkOqZWhFq+eugU8LSAZXcfLbM49etZk0TD5qRV4jlra24X2XkosvSoE+7I6soNvR9IBEdwnLKjg/f49uEjfP7pn/D1Hz7E8ecf42B4hrDuEI+uIgUQ0TNQjIP2OmQIzjj0fS9bxEmsZS2SKdzBdU6szb6XvELGliSFQYMAlu3gMUUm9vrqJUr3ECOGENAPA4ZhhSdbC99dwBs//Cl+9Pf/Adfv30PUkP657nUDzrdTwWoFNNagrrlWmlCbSXCHPO5zzPj4feR8iRWS59ltWMyBnX8dm/7fpjx9/Bgnx09B/RYeFkOSq2EwcBawxuXs8UQsXrdgWGVSrET4BVUpa6qXAnjE1wygnN4BlIAPdGk4VYrrETpit/XCyThg1G3NDQhQYJMN3lRnzTOYA17GCB8Jg/cY/CDHEeHCpcu4c+8+rt24DuMcQgI0ddG4VXU9oYbrhDwYNU79Oi5TbPPnLcpO16CnmbZqt4700ljrZ1x/jwfaI5JxsQTLVzt8cc6YqAugkV1A9WVkrldlnc9Kx2WJzK8qwMZaXLvxEi5fu4nPvvkcLgUbCjGi9yw+CgaIHBCjhzFA17mcWRkhpp0wMnB89HnLX6pBvq0lUf4gQkCAj8l3h0rG5GHwCDH52TBjCJKTBxCnzQgPIl89C6CT5rhVx5bhGOiUVpp2QU31F1ZnCmzGynfM0ujxu47d9V19rTEo0hrNZRs/j0XrbEky109+Rxp3e1CqoqvaiTjGiJPjE3z15Vf46vPP8MWfPgGePoJDj2AOZVwQVUo/jSdwzqmmPl3ajtZKHKi+7/NnTXIqIFucKE83G8nhdsAp7UME+wiq4r2EILunNFLrZrNBPwwIAHrIltjBB2xhEA5WePmdN/GL//V/w8W7r8ryV9Q8QhISIMNFmpP+2QYrH5fASaNTpsfr+yWQsmThzl1rDPrH501A58zYXbrX84Kvv1R59PAbDNsNVg3402i+DEMscZQAGONgDMSBHQCIYJEMByoAAvq3o6gFTxnkUJZnka20C0cNKC5n5skXyABHgc203dvPwtbEnIctJ5MN4tfWJ78zszrA7bv3cP/ll3Hx0mV4ZxAq4WxXKtAocRp/UT11HgdnisXzyc3OeV2v+DyyWLV3zfQ0YEfBja6wjJnNapND6TPOTN9Zj7jfElXNzuSvWou+rsPzlsLYzFsrXAtm9RszAyl2yuGlK7h0/Rb+5A4wBMYFa+GcRWctyIovQhw8fBhkNwnJbqouUfI2DY7BDxj8kAU3MxNRfB+61UpCjRMJFR8CmKOAGE0oCNlKvOWIE9/jdNjCx4AhGvQxoPcebAbIzGGbR6s7d245qm7/8pu2GucL1VvI8zkN4JmJx1KDnlwpNKBojsUbszj7lHwOspdH3jZ+/gGOton6cpX+YUCTkzXnTN4vDJRhGPDo0SN8++23ePjNVzh99hSXEFNoAck1FbxHSCyINWIhBc3Dk/osxgiHDhaEzknMm7732Gw2sCkooPaVdRaBGdvtFsfHz4AQsOpWMin5CA6cAvpJWocQInofcDIMOO1ladYD6JnRe48+AvHiCm98/4f4yd/+Fa7euye7q0CSuTmNdaW0CUhbQOe0CM0qsVk5e06mZg6A7LrPEiszZxDUx9WO87tAzhw7dN7BDQA8+fZrwA+wRqRP8i8lIBAChkG2fUeOcI7hOgNLBGYDcMrj5AOYYtmwkTZjjFpopotrw3feYKt9OvQUlT1l62NibzSfVdvXKe95xWRqnik9V3xwNNWJ3PPg8BC37tzD9Rs30K06eIqzMiq258yOuhrwUAFv+bGlgrN9Iu4wyWjIp+2W9bmyjwEw993O6+8BEpTZyctQdffNLXUtlOdK1TDH5uSb8nS5Yve1C7CpD+XKSpu3cnQJqV7qAOzqAIeXr4PdAbZ+gz4EHMQAt7LonIUlA2vlesYY6J5NnybUCKQgTUPedaIAR52Je2Nghj7XzYJk+YoIwXupnTEYYoBH2k6clltC3MJHApMDCAjRI1IPwwdpjLbLenUfzE6Q4/dQGRiDRK5+YSSvF8xJ2bTf0uzzHQbDXBn3K+Uln6QIAVg6pwAHOkErUKEG4BSrZNlSrxXuuMWGYcDxySmePX2K46ePwWGAS0lgJafOgOCBgQ1iDKDOIluuxiBGyv5LEshP6yD1Ux8zQylFCAMSLkGYnRACjp8dg48YnXWZwRFFb5KfQcRm8HjWexz3PYL3iNHCw2Agg+Ac7rzxBn7w1z/H9VfuwRstaQQAAAAgAElEQVQDxAXGhbV+nMdB3dpySKv8l0CHvi6BhDl2ZFepffGWrlX3qb7W42h87hwrOvdcc5/Pazl99hgmehCJbAwhYBUjAJfZ75wPCh7GuMSuEDgaYQ2hLEr1l8qkLatmIZJQIBqFm3nkj7WjNH05AqP1nyTS1B1TPhnBEo0lpuTKkpoBGGLE1gf0g8f9l27jzv2XsTo4FKbfjE30/BCNfC+xe7neZz7Zwkm8W6bmftvltrDXdzO11Xlfr/88tMg+/VqX3QzOHKCpvpvzy1iq1ugik9/kkqok0i8NYzB3nYraMg6Hl67Crg7hn30LzwOiY8S0n9a4DquVg0k7TJTF0fswR5jBT55Niwi2/PW9gBxLBmGQKK1kTEoLQSLMWbHJXwgD4COMWeP08WP87le/hjEON67fx2p1Ie3mSg+vLIEpOYCa5z2zqbmaK4rFPNt29akKTiGgIzd5Bb72UbpjdmiO7WmOr4a3MVZYuRehAv8MJSLCclpmqQZpntjSeJ34SaGdBKFgaGbCjJHhhx7se1jEIqcx7UZBpYCTVRuCOM3HSA0wB9LyH5HIaAIxJ5sezlpR3KxKhxBDxHbbw4eAo4NDcQZOcT0kOrOwbEMI6L3EuQmB4b1BD4ttZ3Hnjbfxs//lP+LWy/fAnQTapIVJvtEhtZXG5ffiTTH1M6rLEjsz9/0SyNkFhsbn6ve7HIzH7+vjarkZ+53NAauz9exfpmyOjyUQajKhGJC4YmlHngZWBScmcOiBGEAcAA5wvIJxSeskBgcskeE1bEcWiFGXaWZxoMgSx5Tlu9JBc+PxrDIGOTEFtxz6AX2QjSHeB/RDjz5EHEfGs8B44iPM+iLuvvMjXHv5VcSuQwycNlSgDi2F8m5kEM3b9lqxXL8zHuC5nvdfv5S5vchx+XWqH6dX+K4yv3egv12T1Bx7Uyo2tnQKmBHfmvq4JOSzk+OcYitCYZ3D0eERLhwc4BiM6Af0fYSzBpozquuUybFwzuTBpyVYifwqOXVU4cuAEx8RgCiC1utMpUZmbPyQAjul9kqvIUT45GwcQwAFjxUMTr95iP/7P/3v+PqzL/DOD3+Cl19/B5evXodzFgCniLItuKkH6PN3emU35K18ZtwhTZ9xUkgCeHiiWObKWczdLkCsUWNENIwk3TuHhVnZpnZ5LZeF/tLnzt8ZysfmrNwsQcsMGZkwOMBCgqMZY7Ber2TJtTIECLrUNzIbuMrQLjesxiew3fboKYVNgOImQmDCEBnD6QbWOKytSwH+GDESmGX7dz94bLaSxNNHwhaMwRncfONVvP/v/x63Xn4V1qkj/Ri8zBhIO2R6zuqdAx3j3+rfx+eeBZQacIGUlwitQypXzOMSE7M0HubGwFlA6ryWvt/iEMIy5lQfNSzNz5paiyM4IG85jig6iJPzMLPEllEELpsRAKRlsKnOSldP7KkxbdvVY2/crku/FfZm3C+AVkEBleeIPkb0bIDDC7jxylu4+uBt4PIVDNYUY5GBeqegGqHlfVv0mfaRgTlAv1T21dVz91gC7pPf2/+W7yU3xHSUz5fnGQ/7JdvMFcAse9PesICUdhDr93VFCS2Dg/w509bVd3Jc+50OIGcNLhwd4OjwAMfGIrKTSJJpi6yyNpT9GVxWrBobh60VcKKDKNH6IQTxW+gcTIwweVkqwHYdHAjwHn7bY3NyCp9yrhhjk7d/RJd2TRkwbAjYHm/xyX/7Lb790xd47fuf47XvvYPbd27i0o3LODi8CEpbc7OCWLBAJt9xmQ4EpOnnChQmRicvkxgaWQdp7Vb7GNTMUUuW87w8TOs7B3K+q7X1ZyvMEA+raQBFnQDHTIMe0zxbUsRAlbICyeo1hMODFdbOInAAYoRbr3Dh6AirzrWWMQnjg/R+DIIFYGTo2NSh73sYMuhSIsSYEoka1+Hk5AR0egq/PsjsS2RC9ELT9ylCsfcBWxCeUcS1Ozfws//57/Dym2/KxKURnEcKuq5bU0/KcKFqbg22Vstw+U1f9wEVc+VMFgg6L1WTHpeeZizH8Fpie5aYvbln0r9zOx4AkU9rsLIOXUrwKgEmgY4cmM1E/hXTMjil/IjVuGfJJ4jCgqtsZ1d0rk0MKapbIjgnUNbv85hr5p92TNRtXxsdMS2v6o4pBbkhLU9p9OKY6nT56nW8/e73cfHyFcAKq6/3JjUwZ7tzDNZm5EoP4/H3yzL/IiD5RUD15BxWEEjIaigfWz9e6cW95rjnKHsH+qtrtYTslAyQSdXk76qjMO3ZcW/VA3ynYZcmG2R079YHcIeX4M0akYBAsl0PxiAMA3pihEHy5BwcrIWViRFd16UdVbITRYGNWtQCigysdWDJuAJrHCJBQuWnNVrnoiQa9B6bTY/VaoVu5WTHlruAvh8w+IDDaBGwwjYCm6++we8e/2d88s+/xt1X7+PBe+/gtTffxsXLN2HcCsa42cEwZXN0aYkAnO2/0oDWUTdMxk8hdnZ0xfwEpp/nGEBVLHV6gjYi8DksrO3TTkTGGAl8tNBG+ZhUxv3XdR289zBkcLBe4crli7h06SK2pyvYELFer3Hh6AiHK4NkD8LamiVCbrcp21dPoDpGk09P9Nl/LDJL4Mqk3J8dH8P7gNVqBcCgHwK2pxvEwNhsLDaDxQYWvetw/dXX8Yu//zu89v130K2OoGuMmiRxzGbNvZfM7EWe9f/KHmzacxfLMWFhZsDQLqBeG1KFsUFic+JkfOTPRA0Y2jW5zLXJuF5j4+E8FkMMS0Y2daQo2cwRYfAIlsBOppmu64SBpLIsB6Lk2Bty2hwgqaZRs+XfIECiMgFHMh9zjjuD1jjMTFw9486AyhzvJkaEUGKkhcSKhpR7bRgGWa7liCEC5Fa4c/cuXnv9DRweXcA+rMTYuXiJ0axBjAId3oMh+S5lF1uz6/j2S4g+aOpK6Tvap4leuOwf6A+YgJsCaMYPptIpwsfKKpyZDbdVLGPlXF9bUDSgDWaMwerwEKtLV4HVEYYhQPYzBVhjJClmjLJNrxdVpROO98q46AAxOV6IWGyyLdxYI9ZsFBCnikmnLeccVqsVhmGA6QcMg8fh4Rqr1QrGdlitJH9PDITgDbrA6BAR+2Nsvhzw+0cP8eiLL/Hky2/w2vfew93X34I7cogIYANYlBgixSqhDPQgUAyFIG6XebQ9CaYZQLrQIoparkdVH7JOOwusy7jMWaS7QI8qu/OsxLUs1U4UJhZ1TTOJVQbDWJnGGCUSsXNYX7qK+OwhjniFC0cH6JxBZ21KM6LAPsk/SU60CVM0qTQ39/KD7G9yzpVdIckhdLvZSOZwAEQWwxBwstmi7yO2foXjSDihDjdeexPv//v/gAfvfg9kDhFZlyLmoEnbHoX9KlVlbjE1owVJ2t5zAGLJ+BqfM/f7/DnT+42fA8pSUanr+J882zIzuQuINfc6h6VDhCWREdWjxLI9PAPVNMZNWhItYNykZacKoCD5DXIZF+Mih6o+q3XZ9MAGMFS5/zSWjm4U4WpsZLaGy/E6xuUY3SHmMQSPgRl9ZFy6dgmvvvY6Ll+9Cutq43SK2F6oPxXUpNcpc6Njbh7E77z0AnlxVpkbg3PynB+g3HH0tz+Q3xdw7Q1w6tI2hKigMdhRAWwqkQHJnOVS6Mj6Iep7VjUQQdNOzBODxcHFS7CrA6B/Is6ZbFBXwVkHYzUdQIRLW74lYSDyYKKUjLOmiCMDfpAw9LqdMIf9JgITYbVawQ8D/NpndujgYA1rO3RO4otI9HGH4+0AOmV4ZlhIcMCTT7/GP337X/Dphx/j7fd/jlcePMDll66iOzqEsRaCtRLqzZRtApLQNtdWUlfhWmhScvp9kHPdxXO86I6ytIQ5Xp5oJrmZ489bmYPc8kPLPADt8+h7Y4zstUYJ1qfK1HuPZ8cn+Pabb/Hkmy8xPH0IwwHdaoWD9QodlUlSul+AqE0ybNNOKJOyh+d2JcrAdWyh1s+iGE0UvMdms83bXkEOfe9xenqKjSccR+DEGPwP9t6k2ZLjShP7jntE3OFNOSEBJCZiBlFEUWRXd4mtoc2klX6CdjLtZKadtNRaZjLTWv9AZtJOpq02sl6oy7qqWWQ1q6qrCBIkZiCR43vvDhHhfrTw2cPj3psJFvGQFgd4ee+N8PDw4fg53znufvzGK3fwz/6zf4U33nsfzWwGYmHGhllZ4RXMmAePvdAA4DkY4KyVxwRm6TNu80M8NvHv5A8lBbJfaTgp5rYPB+9ONJQy703yzkLZryq4AYC53qLhCjVVqKUJIEmsIVhbAG4Qa4Cy6VRtAH7BeyOsoebXkY0ZUzEwio22wjVPEVhB1O6wXk13tppbf+O2vLv/lAa6XqNVJnJ3z4yt1kA1w4svv4LXXn8Ds/kcmszYSyA8M5CE8BiVKFFxdwP4/DpFr4vvjXkG9+V9KI0ZG/+U8nxf3k8EcIbeG7dQuJQWSBUrEJRkah2Fa6mQir97pU2poWzQtdnltFpdoiKNmeggeQupNSAkeg00ERO5tQuVEKj8nLid7qKwzdZZuUbos0HrXWdW6dsjHLRyUyoEZqNsmtkMfW/i4zRNg7quIUhACsJMSEi2QIoYmmfYtqZ+jWb0mqE2G1x8/Bv88u4X+OTVN/D6++/jpTd+gBvPvQA5OwKENIORNEyYLAdacoE9cLogBpilO3CWUTBdrVU9zkhj3pmSJyd+Jgc0ybV/QrfrtyFBwgeFBCJPmvmxFzT6Qa9VoswAoOt63PvmPr787DN89tFvsP76Uyz0Bp1qwEczs6sucsoQrOdGClQyHN/gtnw7y1Ux+6BkvV07o7W28XAqC3YAth4/H7isVxBCYbttobnDer3Far3FhhqcS4nFczfx0//8P8Ub775np7FinMKe00pWWcIjDC/02WdQttB2CdCSpyYHOPvyLOVT2jrsyl7ymO0CVWE6i73XPkhFDsrX5YPyQuarRDV3qIhQCaCWQF0RwMJHdW/71pxczz1Isz3x3tRZQEQyKgAhisZ/Kl/c0oWwDi0fdA7gl/ohDyTo+8d2guvvEOvGAlXvZQW6jrFpGZcdY60YWxA2YoabL7yCV975IU5vPgc5n6MXEvsOnKHBl6gtIgfBGHgo0R9Kcn5XhuaTeHH20RMBHOelsb+QKktO7gcv/HCxcVquAFfyhV45yDHfywUjMtNHlWoxk4CJE+yEkIB2awK0AjNB2Kkel7cU0u9scVaDUjZipTaWiNYCzJVREJ3ZMqiV9lazEASGQF3XaGYGUs1mMzOtABv5loyFq5TGsrE7CIjQtS0q0miggIqhdYf2ssPXv/kQ97+6h08//Ahv/vA93HnzbZzcvAU5r9ALhmCChAmw4JWG7yFKjAVT1xjau3tZox7AODl4KSmwXWsLduXnSn8VSVLYeZdYiUSHb213ID1qD6UULi4ucO/eN/jm7le4/83XqDZrNKKD1pX32sSTHv43m19mIb2ErKQPMcAM7+Fkttu9mUOQS5APgQBNcIfNatZoZjXm9mBO1ZnrXa/RVkB9fISf/id/jrd+9D6a2cLX42m8DgwNYrMGzhlOsbET5508t8cK3Qk0DsmzAJjyoH0765UppQQIZW3jp0ds7+rCs1eVBIUF7845ZTZySLA9jLLttpCSwHUFqmvjnWHT31IIEIynxIPjCPQwWwA+aO8U3ORGR+B55fvOG602aqXvIzeGI49RbIC4oya0Bnol0GtGy4w1My5BkCdnuPX6O7j+8mvAfA4tnHcqUDAg4yoQ8svB7t/Ns0U+ZAz49hA6GDwdMAYPoXTN1G5d8qRljOmAoxoQmWVFdFH04jhLrDxVMcynZMnnv3dNk5CQaOYLLI5P0RydYvv4ARhtYHY2A0hoBa3iyBrh/WEPh0FqwozUpIxkQ38rpaCQWm/SeoJQVajrGgRg3jRYyApQZssjCwJV5iRl0XZmE7BmdNAGAAmgBaC5gtSMGRTWDx/hd/cf4quPfoPX33kLr/zwT/D8G6/j6OZ1UAUYD46LBRG6zdeGrWXkGSdGPeGrU4ilfilNJ8VpSpQza27hjnlxXFmuIjlXOnmBboGck+xUbo/Ey1Ogtm3x4OFD3L93Hw/vf4N+u8ZCwJ8arvoOfQcINiCGpHmn2fnEEMoZIBaww/IuIZwDBGA2qzGfNd5Lo5WC1gJSCCgy7nbWjKZusJwv0DQNmAkkOrRtj6phABIkJZbHx2iaua0TDUZmLpDGBVkEzNnbrv76GJUATIk/d3tuOJ4dS0BMvCC15A1KvHfZuw5J63jIK1L3fjYeYreUmcH21PjRpvhOqa4rH8fHGYdm2lRCszRT9vZkeSEAriuwXddoADhgRlLoC6c/TPwZ157mfUZemClZZ8HFTRPlBGAIULXWYOU88MouSIYpDwIPdNbTqRl+2opZoFMSSisoNsH+UNV44ZXX8MY77+H0xk2wlAPvHBB7a9IpuWHHMuIlBEXFXhoWESMfYhiU6NB7OX+XeP5pwc8uepK6AHsD/RVfUbiRelec4BgqsCFIGi0g55MUDCDfYpk2oKwaLE6uY3ZyHY+++RpdrzCDiaEgQGBt+CacVB3K40BLvNPFgWHnmTKufwaz2S2glQZVEpUMJzCT1tCdWShaS4l5LVG5KSQAQlYAZNJWFRG2ZL1LMNaPJnOWllIakhmV7qEe38Onf/MAX3/6MV5450d4+4MPcPvlO5gfXwMLrxKsnpWpMrXC2ghUu84o7scMe+5jnBh9x4AlfrYEivZZFe4ZQVdTmrNwAAcguJgfCKarxzFlj5ZPl7WBUgqb9Rar1SU2l+cgVqjsIXxaKaiug6rInNatFQgGJDNrEAQ0iSTAX/D4ACa2jlHaUpg4Oz3s9lfjLwhWat+jqiSWizO/I8ucRStQ1y1mCtj2FR4+usCXn3yGt975AMfHJwcp39F+p8hbYv+JfvlnS2DCfcZypiRsS2Vwq2ycrGHOhDVSpZjGK6JynpkyHatzDO5KSqnwwPi975jcbqi4faSUIQYZjGzr+w7brd0Q0jQ+ojxD+anVodfXBPtTpcB97r/II+bih/ltFswhkmXhjzksJAZFINOtxbHhE9y0rtKEbaew6Tp03EMLxo2bt/DGm2/h9vPPo66bpG2GOiwjJ28jb48rS5Kcze9ghJYRTsxCT+LB2UV/SICS09N4Jg/RI46eapHxOMgZAy/5dvH8fvkN6f3IUxS5LwMaBkgINMsTyNkRtHBxZzQcyzMbTw77wWKqH6/S94cROuHoLHZbJimNIqmqCn3Vg0CoG+NydZaA7E3O87pCVUmY/8L2XRNtFhB1BQmgFgKCTcweEgI1AVtmCK1RCw3UjLoGNluNvm2xvvslfv14i/uff423P/gTvPHDd3F06zaodkKDkJxz5eZE4Ob0AZB23+AblXdvzd61tqbkhRl67g6jp3nmj0WxJ4YQBYok2+wFSx0ISoCcpcjp2g5yW2aVgu5aCFaQIkR/CScO+9d7VzuBwEKDWfh8A8CxykAIqF5htd6ittt243N3tFbo2y2gNY6XC8zqmffyEMw0RF1XmCuJHhVarXD308+xerzC0ZFbZH9g20XkZDms98koHUrSj1mCu36XAE7Ri+NBztAS1ZyvvdEe+OSGwD5rtijI43pn5Y6v7fP+fdfkeM3/RePAbf3WWqNrTWBIB2QqacJYEhv+8uvGIgptkXl9vVHtwnqYRcnmGDhhfTjWU6OUaWvNYGWnpvxfNAUmwrS6ZuPJdB4kt2Bca9hAlx160hB1jVdefRUvvfwKjo9PEmM+GQ8llWlaz9/nZD1BlD5BLQjj2nKvB/YopH8CKhkCTwuSdhkXh3r8/xC0E+D4No6wSzp9lHpukmd9ZZCkTSlXlAXFyAieB2avWGI7zJVLCIHZfI7FfI5KdRC6MwdzaVh3PVnhqQ0qVz1669p3UTidpeatBCF8I5CtkNnS3PvFbFVVWSvQOJXrSkLOGyyXS8zmc0gSIAVjScCenaIZrNyANP/0ShuLgbWZVqoqkHXTml3rDXolARJo+zW++e1HePDl5/jms9/i3Z/+FDdffRNHp9etHLSDzIObYCV4kDOwAsrnVJWmD933uP9yYDIGer7P5MPyOxuRrEvcGpFxILccEHpLk0J7+AB/MOvABDQqYQQ3MUNWFY4WC8zqOvCkm9pgB37SvvD8ywz2EbjNfXPasTmmwcSBAlz8GVHVUF0LAqEiAW1DIRAIi7qBmJsp3q7XmJPG+d0H+Pyzz3Hz+ZdMHClXvyftY2uWhtVjZpyUdtCMTUGJjBcP9uCw9tMZwbAfemGYe3N+HDtjiEBCDt6TLFDNtjcPhbd5oS6UN/49BpqvCtVCQkpzcrcQYdcQEXyAVGZGJQV6e5Cx2ayhUQmzA5ARgl0O+42Sz9hjs4uY7eJ668FhpYwBoZSdbjIHZvZdD2XHCpFZMtD3Cp3W6K0RoGysM6UBxYReM7Qk3HruNt5+94e4ees5owfgbcm06ONzSunPEZAzNqQKbgVf90Pap5R2DOjE13Y6J0ZA/7elMeNhF+0FOIBnq+xqoFiROfTqFSyn6cYph7vumzdzjBJhBqKDGJ2QB4x3RQpg2VSoZAXFwp8LFdK7weHO1zHz2xwpI2a2gf/go7jGA8qNr6Zp0DSNBSEKEuawQxICdWPi31RVZYQvAYKNECCYM38UlBdeDAGQnQtmQk2EHm5QacyJABJQ1l3aMGMuGJerR/jtv/8FHj6+hzd/8hhv/einOD27BkHGMjFtRZHrf2zrdtzbu/vYlBfpMzuAzKEemTG3/1UiV0ZhJqgi72F636fLzymyvODuJR4XQThazLCcN9g+NoO4qiocHS0wayobayR4jRy/xn/xu2JB6+b8pZQ2FpNCZXdQmYX0xovUK43VZgvtvFNsTio3B3QacC9Fj1r1UNsNPv/dR3jz3Xdxeu06iCS8R9UaI66cSRtk35Oxn3lGctoFXBLe2yNcvZVtPQPw4MZ6xjBce2O8BTZ2ii9yWP8We+Ty6alyOcqgzD2ft9VVBTl1ZaajiIzBplUP3QugEv7sP7KGnWgJXdcZMMMMkmTCX2TTkfGnYQqReIb81HDyjDExiSOAaTvVt612fWP7yoZE6JSGJmcEM1obrbtT2spcM/XWqxobRehQY3Z8hDfe/RPcvmMO1LTBHwzlctAanRSV18hRhltcsIvKUhnDmY4DxWYJhCTlHbl2KOVAJL/3h+DlQ8q1E+BoaEhEcWQSaOq+p0LLH44WuRTzAsULrOIs8wLHitR0JMoWIjPYHtymWaMXNVpUYEVohEDPQMUaNIj9YsrYKXOgoZk3DlZZbIGT9eI4ZVRVFSppTipXqrdCjyzaV5jNF5jP56irCsyAkG63k11jIthbCyCyzh1CVRGUNnF2iBmaBBoh0PUK3HboNaORAsLOSwtIXKgedz/5GA9XG8xPb+Dd9z+wbWc8Mg5uWjkBYGzBp0uZbnlN+yMeTNYbUVBm35auqjBnNs7hVD/TQPiay0NPmPPhu3vOLd/3PZaLOc7OTnFycoL6Yo6qB+bzOeazOWaNsADHOyO9kB99Z9LHgBtoWmvoXqGn3vI4/G7Bvu/RbrfQWmM+nwMQ2HY9Vus1VKex2VTYKMIWBK5MBHCtNlBoUdEiBXJxvQvffTpXdm+slMX5LsCgEYwRHaUtCVl/z3Gzkz9Reo3Ye2PWPcWgJajXoYLQ3Nvdmg6khB2bidKg1HtTsk6/D6BfCII7Wo3A0KpH3xNqXQEwp9aDGJWQkPM5hDDR5JnZyk4TKV6Y6JWgDPiBaAACohQAovACbBYFuzUz8bogc7SI6ZvBzipO11wppdErbbw4NrSCYrP+bNURxPIUr779Hl579wPMTm4A1QxMwky5ZSWMqQRlhtdSwDIC9Q/FMuGJkXHwNLTLaH0Sb49Lk8uw0lRl6fs+2u3BYWU7zIEK19p5Q7lrHP0OjZC76gf4ZHCRnG0VOwd85fKB72LSKGVP/CaJLVXoOkZNQCUYQjMa0hAc6kOIImVahaEpbDH088hk5njNY4TeRpttahNVFizsbpQAfmaLOZrZDLWQBgiYg0psPBxzRtV2A2ilUROBSYCUWYgntFkrJCzAIRFOP+/tNl/Vd0bhiQZCNXjUb7F9eB+Pv/4M/dvvmiiavhWjKT2/BTe1qn2b8hC0xm0fRA2HrKxQOoThD/HmHOrx+S7IHyXhD0yA54sUW1DxE2IIRuq6BmuNs9Mj3D+7hvmNF7B+eA8L2uB4UaORhEoIc5YawUaIZc+bUqQxcPxizajcZtyE2E69PZ6hqiqv2N31zWoNZS1WEjbA32aDttXY9HOsiXApBN790Z/igz//lzi9dhuCzKJnuH8LAHUU3HhQBLtVvbx2K50eLxMz+/rEfyWgoK3bxskfZ+GbPDTc4mvNajCNNPpuuyiWEQEcctNghLAuDnAe1VjRxhF0vy8kci8iM3ql0Hc96lpBSuNFdvebpjGgvuvQ9R3azo4pexCy2YElvAonNqtrAqvYsed1g12fxm4XVLx2StsjSAyo6fseSgWg4zyYvQY0zHoFs+uw95s8eqXMdyZslYaqGtx++WW88f77uPniS5CzJbSQMCcNloEHee/NGMRB4XqmaUs84a5lOjL+zL8fQuNez8PAzS4wtct43WUgPg3tPotKMzR5dipOVaVuxCCrclASGoXSdFEengGsKRff29tBNv++76FA6GWDbc+4VFuQNlGJiQg1nDBRqKtwPINmmB1QFI4NABAUhog9IWYKyy0MlVLa+WNTz1kzw6xqzMB33hBpvkuSHuCohqF6BdKMinrbKAzAWPVd36NXGqKSEFSjEgKb7RZKM6RozCGgLKG2DKkY0C14cwHut9B6AQh70BuHxWhRr4y0owOyztsVOjQ8b0BTwglPMIAOUVRX1YNjMbchiqx+MES2SLvswRlaKsLGrDk5Psb1GzdwcnaGi9kCDRHmDdAQkgM4CMaqFf558lBcNBwAACAASURBVHwaRzDWfmoyKGe3zkFI6ftUa6vw7bbxtu3Mui8hAZLYbjsTwVgJXGiBdSXx8rtv46c/+xmef+FFSFn7xnACnvJ6F9rDaCzh24QZJsAzB9B96PinQpr8+YE7HsEOdus03PSF5t4aTuy9MXEeRpHDGA9R+/oAoDqs7bFOu7RcWoOFC1+RLmb+3gEcC6ilDTbpgJ2ynpJ440bMp2Z3VG/WwVjALaU00/rRmWqGHxTILQEgQi1ru37Rrqsks2WcIJJdU2w/gdDHcTvHB2oqNh41pRR6raG0Qtv1aNvWgH8W2FKNk5vX8foP38HtV1+CWDQmYnG0TijWic4zblRfHp8bQORlN2nDjtsxin0MHF8tgJz8+67fY5/7aD/o/3a8nIOjGGDty3snwFFtB9Q1QOXjG/ehtPh38Ii4tIX8YpETeanHEGnqjgeU0thut+i2W5AgqG6D1fYR+rWAoGsANzDwh701UNvzU7ybMrP48jlfAvttjlUljTCzgEfanSouQjKzsQPdcRC+oESgqkZFAlRp9G2H2iq6qjJgyayVEagqtnFzTHnZth0DWK1WUF1nAhwyQL1Gv94CSoNJACSjdg0eGNimNcZWaT7U7dghr8RjoGP9NSlEilzLT4r+n3RAfZckNNv1NkGRG349YCohaqPcDSuIsDw6wrVr13Dz2jU8ms9Q91tIYuv+dwDHdL60vGIs3qAMwiJnu49ERVNPbQtms65H1iI6HTlVSEoxqors4YI9Li83WG1abESDzVzghbdfx5//F/8Kz7/0crrj0FR+FJyWBJUbiH68DTnLp3d55GCFojTucM+9QtxJG5efVX5u7GkoD1jclEa+aNjJKyB4C4w8GIKhsrxLvQ1aK2ju7POWV6LjYq7q+HDnoDnvi5EZZq1S27ZQSqGua8yaJtkppZWRmT2Hk7od6HBAR0oZKW7tAY4mDSaFaJ8hBOw0FwBoDd110F0fYt7o8I7Qpkak+WM1fF+E86bcdFdPFeTpEV559y384P13sLx1HaqSgDc9huTla+EcRi+PyfEmQH4lT7aRJteF0b9jtmqcfux3fP1J5PC35cWd09aFdCVD5RDaCXAe3/sKp7dfgRZy6HFOwAawq5XZauRccMUeASTfysywq3OUUuiscFZ9D6k6bLsW3WaDjgzIUMsFjhqJmTSeG6XswYYyiq9TsDKFHbRucVxV1d7KYKIk+iXBKB9fLhgvC6wHiYQECXNopmAAEGB7RpWUEr3dKs7M0CJMnyHKs1cK27bzv6Vg1KIGeomutbY+FVvR/hvDnCG53UHhNyJ3cII9bRMFT0HeL/HnoWTSX01hDiDhZQb8VF1i3SMDdVZIsdbFU8WdciQiLJoaMylQKYC0gtv+zWwOMCQKnkUphedFYfnYH5rZp7t56rq202HsgUjf9yBlvRY23XI5w2KxBBGhbQNv9yDMT0/wk5/9C7z85huQokkMxwR0ZAAuBrb+u+3nAAAouR8bGWM8ZCz87N6h/BYrDY69Nxoa6W6osUXDzMp6cZQ9wkVFgeny8RA9B/LGkQdH/s9sazabKqRtl90hHL5TikRnUOaG5+MpN2k9NwAAZghBJp5Y1L4hYrD2/S9ImCMd3GGyiKeDfScCFpoSCNAKqu+gut7nq/sOqldmTY1SNsaN2fnaaYaCAbraLixWGjYGlFlC0CyXuPP2W3jzR+/j+Lkb0I2EJkKlY2E7JlPLlPrC42zGjcSx+38IOgQQHQo2Sl6kQ4BNaQ3O09JOgPPJb/8Rbx2dol6egaSLJgOkTFUsIvKO5kgSxtNTyb8j0Rvj32OuNzcwYOdCFQQ2PVCxQEXaLJLUCrRcQC4a1CTATFCKIe0BcaHs1g1a1yaMuOVfowAYVSWtpWzKrJn9ormmaVDVVVI+xQqVqEEkIaL56KquvTJXXW/WUxAAbbaRk1LBKpcSPRmApdsOyrp0mQiKNRQAphq9BnrVoUJvPDgcthS7LnO2MntPjas3YARp2U1K9n2wXhxYQOfzHPFi7JqSGvSzaemiFX8VKFmb5UCnFVHxPQBJGr+VHENeZmZs12s8evQId7/6Cl9+9gm67QpLYb03GdokcodqSsRrbpzV5w4KdMcyCCLUM7vuoVdGyFvech4Up2AWi6VZPF/XZtpW9GjaFq1iSFRo2xaryxUEpCvMaFuVxnAKWgDPQEDgq8Lz8bRxThQxuACghQBlW8zLIGkEhMBZ9lwEODE/O3ACwHt7nKfAl82+K3knwXt6tN3FZsBRtpC5YPlfNSLA819yPRoLLnCe9/LYvo7PjXKLj53XhJnteWk16qqO8jVvTWSMk2ds1296ecMOWfqpQ6XNAuLeGgDmsEzlPUlKaWgt0PYC206iQ42qqXHz5R/gnT/9MW6//BKq2RwdmbHPRKGbsu4yYyxtmrLX3D4cL2xE+kzpe3gOowbmId+fxgjd9xen20dPu+5mX947Ac7f/+KvMD86xStvvQ/MFtaR4KK3Duz4+LWIz5PJC+P9CEllCC7gUnxeUt5AwfILYMi5NQFjpYp6Dl3N0csZNDWYwbhJN5stKikxbyq/rdEcYBg8VGQ9KclaBjsoAIT5YcCcKdX3UL05hBMA6sZYyY6EkNYDRPDx1gkgQahEOBDOASjBNtib0pBk4tUyh3l6UzYJ0RH6Hmg1sOl7cwyF9ShpAfRCgUhBonKmIBCtbQhuVY66zlmKFI2xIKDjLvVXOdVRT0JX1eW+j5LBC0TAfahEdeyx4fR5AD6E/YN73+Drr7/G73/zIb7+/UeY9x3qOXy8Gje9a8agW3MTpqQYjL7v/HQUkUBdN3YKSUPA7JQiaLh1VmYaQRtLt+/R1DVmRzPP90oBSpG53hC6vsZ6tcWnH/4O7/3wJzi9djxumhbabNg+HDFO4M+8nfPvO4WgAzp22llEns9BXjHyT9LY9tkh/N24daB2DAzFac3CY/ixaHRS2KmVr/OJWul7Qd4bWboX30/a2fRVXdfWyCQI0RuPfNeh73roynhl3PSWA7QJkGEr0djxrooC+WUHpupUEWtmKPvXW/CjlMCmE1h1Eno2w9lLL+P1f/bneOntdzE7OgYgzXS13W5A/mCNUt13DxKj80YaDmX+Sx7ek3YXuMnByPhrnpwLy17M8SnsnD9iT/fT0k6A8/Dzj/F3P/8LzGdz3H7lDYh6Fl4cu2GcZep+218DkOM9eeyfHTC9VeYlijvEMQRzCKMthEAzq7FYLrA4PkO1PMV2u8KWFSruzbZrzehhdiaZqSbpuYsAE5SPhLfgXDnZLsSU0gT2I0FgpdH3JnAaAD9IieyOAFFBysocOgf2MZzCBJG11KQwQKTvwRqQIAihwb32QamUUn4ImfY3MUvcwjitWhCZnQBs3cSaFAT7iC0Dr4iDOXBCN+7QLNWT0lCYpX1Y6lvf1oXnriJ5XubQtqNrjAggJs/bzkLt+x6Pzy/xxRdf4evPfo8vf/8bqIuHOG7stGWyM2rcimMOcViklJjN5qis1au1gu57M7a0jeDqFKpWIKUwryUW8wVqqtBrM90LbQ4Xresa876CYomN7vHgy6/x8P4jnJzdtjGeKPLsHd52ZAvujc8YLY49lwnjgM3JawrnJSst8o6VGmC8W2aDowM9AXQdKvwBJODG9cNw3ZDZwizZwUsT1sKs/xhObVEkYxN5dMVIswlp4dYvAQyKwLfzhs9mMzNFymx5MIx1ZywQGW+6b0cbHLWSvTFeRfAuJx4DzTY+QBgHSoVFxM7D7665LePMjJ6H544pAB0BSgqc3X4Ob7z/Q7z8xpuYLY9BJAFNVlc4vyl72ZWa7RSAO+V3EdVl4LNB7qVydS7RIV6TXV6gQ3lrl0fG9WHx3U4+4vDpp3wM5eU8pMw7AU7dXuLu7z/E384W0JC4dedV1LMAcrzStGc8jeAyxEIrnqoaVAJhxwJRBKIwbFjnyTGDIkwZNHWN5dECR6dnWJ7dRHv5GJtNhwYKpM2WPwYBsvZ1iBlwuAAtCqZmTwo3ioP91nCtzYniTdOgqRtzMrkFOa6sGsZ6ZiYTkptdMK/gmiMhIInAgqEUoCkEKTRWiAk81fVmekEKs/5AuzOEJKHmDqptQYrBVW+2n7MLNme7wiqBEBcoZhROvw7uR+3/Lahs0bt7V1eYO0rWbI24n10dNGuQTqcbnFXZdh3ufXMPX376Kb7+3T9i+9VHaPpLKD0HL078Cff+OQRl6eOr2PJIKa0n0YQ1cNM67rXOu9P3Cq2LJCsl5qcnZm2ENod2Cs2QYEhBqJo5aiacs0LX96g1cHH/IT779HPcefUNY3VnJ0HuWzMT/bBtEWoXC/VDLEpy+cTKLnouXs/jP60x48aCEABDBJli11SMrQVINh5kdS2BooG3RwNaaLv93E5PRVNb6XNx21zNMeHqBpiF6kTBk+084bWN+O43dNjn2E5duXU3bqqfma1H2gB9Eqk+yN/vvDghkJ8DlGxDiLgt4Qpd32Gz2XqvfwtGy2ZivOs6KCa0qNBLgeW1E/zgvXfw+nvv4OTmDZAUntUDD/mS+C4KM5McOQPcsgB3swx2yvcOAyUlADA2ng4BQmPPB1DK3vD2YBWI/nhgWJvqjYOcHODvA1W7aCfAqaDQrR7jo3/4Fbaa8PaPtrjz6utYHh2Z8nlXIYro1WEbhsEQeVmKnQHrQiTjkdF7OiEPiV5VFY6PjnD9xnXcfP5FdOsLXN7dYt11AHrQZoNt26GdzzCTBMmuIQlgs6A37lDNwfUMIu/BMYMoeG6qqsKsnpkBPZiuM42ghPJrF+Kw9LGASOb3bRuSRSaq79C2ndkHxibqgiT4beykFe5//Ft8+Itf4qW338aNF5+HmBngxP60cfdmO1U26DRG8cgGzqHqkEoK5hAaWB6u4leQkjUkQBB2O6dN4PkqbhutNS4vLvHVV1/hy88/xd3PPgWd3wdRD1UZx7fzxjGFaEYMpOOCGUKa8APOUm3brVfA6VoP7YP6zZoGy9ncHBFBBN0p6FbZRchmjQwJQEmJSmoI2WHRb4FO45N//Hu8897buPnCCxDUDCyzkvck+R0aLtHd+wRZ/EmAiVvFoV3GrNgE9HjGdzeD4iEAJMy0g8ujtGA6XzNwKO8b9rZblDkEm4t3EcVldjF1NAPQV3ShccTfRGZTRl3XSSR3581x4ENrha5r0XUdttsANmIvpIkSb7aEN01j10O63az7pn14uC2c7c4oC4KUNpGOOzZeHMXG4FBM2Moa8nSJH7z/Ht7+8Z/g+p3nIWYNtAe/QyMt5beyTAj84n4DQdYRBnJ3R/1K3w9J/yT3Dk3Lpf8sQE+e8NUzZkamKZMrefsd4qXKabcHx27RO29X+PjDv0fbKbBWeOUHr2O+PEbtF9PGFYh36LA9QAx+imcMrAQLS0BDoW9bkAgnYrNDUeEhfyBanKeUEicnx7h9+zmsL9fotxtQv0X7UEErQFRmakdrDa1sYD+2rW6BBNuBQGRcx2zrVVVyAECEMKeJN3WDSobmdB4lZpi8XIA3IoDSdQFOsOW7a8IaIAWwO19IQ9som2BGJQhNVaGVNdquxflXn+Nv/vX/i68+/RLv/vgD3HntNSyu3QRk48GNEfBud4ZrVrZCHmC7VdFNIdgSIe+CEh1ifR+Udt/Jjd8RDc6aorRJikregSFLrr5t2+Lhw0d4+OAhHty7i/XqHCdgDxaYzRSldtnY7elKK6A3gIeZUdU1akEgNh5GImFDy298iAL3TlN+HQFq4UPpkwBEZfhWKYbojUVLXW+tNA3BGkIrPPryc3z9xe9w/YUbqMTM99dYrw+EfeSZCrNbbkH8ODiJfxt+jACjsx53WKzuM/GHUDzeyHo8GUTRTh7nDaMQc8hNR+/i9SGwM//GXp38zz2X1sPIoqtImjXAElKYwzUduInXzOTrlJwnMd5lRUR+l5U744/gzriKZGYU8dgA3dTDCaTtztGXxLPj+QXhkwHFgJjN8Pyrr+D1997G2fPPAfMa2pt4417GnYZO4RZz7swY91LF38e8KqUyPQkd4inJgdxoXiPeGzf2nC/VIgU7IxRaIAeQ+8pYop0A52i5RNcrqLbH+eoBvvr134DX52gvz3Hnjbdx49ZzcLshSESD3SO0kYrHVlgBpSmlcXF5ia5XmM1m3s0ZQIMz99K3uLyWyyVuP3cLWpmtiEfzGo++WKA7vw/ut+irBr0mdFpBmPh8XnEJa2UoG++EGQAx6qpC0zT+/WYe1wSxaqoalQzBqZzwM4PZMLC5ZyNdcjo1Je1CUld/rc0J6A4cwQkGZa1ra3mQEJDMaCqJWVOB7S6M/vwhPvnVr3D3k8/x9o/ew9sfvI/rL72Can5kFjz73gmuUycGHMhB4nh0Hq791oWz0lxdYnfjPnfo03qA/piUCGyPi4fWfHwt93y5NmrbFo/Pz/Ho4QNcPH4IqB61gOfBvmvRkwS02XlCtVlY7L0XmqLDOk3wPmMtS4CC63/W1F6ICyFRVeYctb7rsVKXWCwWqIQ0e2K9ZSkgJbznElbJ1HUNpWtcXqzw1cef4u33fwSqI7BSqGuhEVEK8OcUUD7FFxsVYxSUVXmqyqfzaZ0MAmAteufpgrckBQCd1CEGOO5Pu/UkHLw98ff4z86pwcUeiutZUiL5zryrSG1vgqZWTYVmNkPTmLWHLrgqov7z01FRXKF4F1UIGmhlIgdvjQMzJOCn/816RLiu8t4wNy6CwY1Ic1qJ5soVB5YFwCQwOzrC8y+9hOvP30Y1n4XjfuB2gAVKwFTGs9+m3w6Vl6X7h4CdfYZE6X2HlvcgAxdWbERGovdlZTIgfib92/2e3R6cqoIQwLFqjRdk2+LeJ7/BdtvifL3Fuz/6U8zmczM9M1/AhKt3Ut8VJxQ4Xt8SuzXjimit0fU9NtsWDx48wtHREU5OjtE09UBJJpZ0xFhCCANybt+CkAJH8wYPT47xzb1v8Pirz7HpLrFSGrVgEPdoKtO0JAidBmoiQGkI4Vxt2rtbjTxkv/C3riq7E4sGu6/MOgiAE2+EWURtQo8H69C1gQc3mv1hjszwi+MAs3XY/Jn4DLVkzBuz8FkpDc2ETnXY3ruLv/03d3H/q9/hh//xv8QLr7+Hk2s3TPRmckLeMZK5RnCCusRWzsOUCvxDGDtG4yUF9G1Q+h+TgqIij/t2TV+4tCXPhNvK3W034L6DDLGHoZUCtALYRIZlbfpIu4MvYQCOA9zMZq2BFibuuAt7oHqFzbY3J9j7nTtGUXR9j223MdNVdQOhzcJ5k58Aw3iCeuvOZ7YAiStUCvjq95/i4v45FkfP+XaILTDXBnn72S9me20GjMYstpLAc3B7H0geCH1Enp6MHzWbNYCa3WLhdHu4OzNJiMofnBsChw4BTay0DdhN1+3sU0SHrEP4rsnMaBpgLSu7qYKdPCMABtB0ndnlBzaLuysTnwOA6RNBZqodCDxaong6lIzL2QIfc80tHnZrLHsPNlMD1L9HA2THnht/280Wl+fn2G42WDJD7pg6Kumkw8j6MZxsNJntzP+Q3/n1MR7bx3v7AM/TpN01jQ04/D+87sfs4L/dtBPgzGpplqUoAd0ZMMBqi0fffIl//NUv0SqNO3fu4Nq1azi7cQtV3XgGSnwCRUuKUSqdQ+AkJM4vVri4XIEE4UyehCBRWYM4wejztgyzWMxx69ZNzOoKy+MFTm/dxtcnp3j08Yfo2nNsVQfBCkQumquZriICKmi/9dt4cux8IpstiK29J6sKspLmCAZ3fANSQaZ13gbmlO+ks2GAjhQCrDRUpBCDgA2CUhKgCaBKQrAwi4mFNGdVaYVlDWw7jYt2i89++yEeri7x7j9f4/2f/HOcnp6ZMgCIz3VxHrEikDSjD7HnaddAG2P40jPxQPs+eHFyKlnYCcgRwYoFEI5LsMoSrECsUFknuFksX5mAkdZgcNa/8zAED4Rpq9iTw9G7NDM267VdC2F3B1qwpBjYdj00ryGWAhVJaBtDRzNBa6DrerRdj23XmvN5WKAHwMLEcdKaIa0FHtd7l9AnuwswgBsXqn5cKA6ux7/d/QP4MQY38XZhrRTM2VMpuClu+XaK0INXCSIzpZX3vVOmHujYaLs2pFbR+nfPXmWvTUwevEXnrJkFwwb4KdV7j43j+coebDxWfxfN2dlWPplxvQzLkIMe90mhH8iu66zqGgwzDklr9FoBNuCf1IwKFTZtj3tffo1Hd7/B9du3UM+XYC0wora+NTHDg6sYSI15bL6tnHwacFMqR0m/7yvTGF9T9pnk5/VP+o697/o+KZKJJppoookmmmiiQ+iKLsufaKKJJppoookmenqaAM5EE0000UQTTfTM0QRwJppoookmmmiiZ44mgDPRRBNNNNFEEz1zNAGciSaaaKKJJpromaMJ4Ew00UQTTTTRRM8cTQBnookmmmiiiSZ65mgCOBNNNNFEE0000TNHE8CZaKKJJppooomeOZoAzkQTTTTRRBNN9MzRBHAmmmiiiSaaaKJnjiaAM9FEE0000UQTPXM0AZyJJppoookmmuiZowngTDTRRBNNNNFEzxxNAGeiiSaaaKKJJnrmaAI4E0000UQTTTTRM0cTwJloookmmmiiiZ45mgDORBNNNNFEE030zNEEcCaaaKKJJppoomeOJoAz0UQTTTTRRBM9czQBnIkmmmiiiSaa6JmjCeBMNNFEE0000UTPHE0AZ6KJJppoookmeuZoAjgTTTTRRBNNNNEzRxPAmWiiiSaaaKKJnjmaAM5EE0000UQTTfTM0QRwJppoookmmmiiZ44mgDPRRBNNNNFEEz1zNAGciSaaaKKJJpromaMJ4Ew00UQTTTTRRM8cTQBnookmmmiiiSZ65qjadfP/+L/+H2ZoMDOYGWBAMMBk/ogIBAJA9gk2/2kGmEFsLxNMOgKYyCYnkMuDyL+TLObyl+xnnCb+7Z+PfpfS58Qiwna2bmAGu3cW3ktRoZLrbOprsmL0m3N89It/i4/+4l/jWG8xqySEEKb9NIOVRt91uLi4wIMHD3D37j0IIfDa66/i2nM3MJvPQTYvVhp936NXCqu2w4PHF7h79y6+uX+OvmfcvHaCF59/DseLJY5lAwGC1hoXqw3urTb4ZnuJh/0WP/izn+Ff/Jf/FY5PT8AMEFUgivGtrTuzq4rp8+xTa518MjM0KzDr9Jo2v+O2k1KCiCCEgBDC8o8AUfQ7uv/f/7f/9e5O/A7of/5f/zdWWkOxhlLKt40QApWUECTAYPRK+fZwdZdCoBIS0tYXggAZ1ZsIgkTgYUFwXWTSCHjeY4aAs1AoGkdmXNl/zP92nMaN6dLn48/dS8mNKfOd4UZ6nsKMIycfHLk2siW1Yy2678WHY7z07ZwVJ87Pj1cU0lhejNOnidyHkVkEgCzPMhg9AT0xNLvaav8QMSC0ADHCmIjexdG7uXTdylXA3QdYI0nrxpBmhmbz+b/8T//jlRsT//v/+X/zsI7mHoO9eHX37A0rZy1v2fuWg6PcTRs5PrW/zOP+2fAEWRm+Tw/s0w9jNMZLvs8o5clieqcuvJx1l7OBMXxJcjcZV6X6UCwLoneDI96zb87GyS6ZUOLxKHvzSdF3N26cTgVBsPlL+D3TN/m7SvVmZvwP/91/M9qZOwFOTkWeyK85xgWBwLai5BvbASLKedh9JTZP2nyehg13Ma+7lzBKVBfmsXeSBQbxUxS406XiCk11gudefA2f3riF7TdfgNoWdV1DEHnwpy0IAAApBUgICCkgpAFDLp1jeQMazF/b9thseyznM1w7PcFisUAlJJgBzRrbbYu279FbRUxVheXpKeqmCcolqWQ8sMYHcAnwDLRVVN4xCnkNlWuc5iqSUr1RNkpDqd7yhOFqFsJB/HHFagWM6XszDtiCOobpPw86mI3EjHiOyAgFyjMFwGzzczxMDm04BU1uaA4EZQx03G8gH0sB6JRKQH7AciYryJexgIqi38PxtIs4/hJsrKT9mfUoLzm1yK59fDshgzOhuHGRha2kEGIAqFw/ObDvfofCE0BOwUTtmssTy1tEAuKKjokSubo4fiP2JpRNMBRDQ3Dj8orAOAhsgY67Z3SnRVEUgZ3ceN6hF4Y8kuoo/76sXO5ZA75CfV1+g36398lVPtIpTIHDAhCMWi0aH3meJSAQOxbi+lhM4zm9KKZ2AJ4i2HDldHLDySD2rWLr6N4dDIIB+C18L9av8Dun/QDHK3xTMrKFjBnRC4qIgd0zfvx6To/wdgzbXUKOmRceVLh6jPJohD7GBXTIOBK5ofSj4Kb8uiDm2V8TXAGQOLl9B0cvvoTH976EUgpSSoNQWwUwe+u/rmsslwtcXq5wfnGBxekxpAU5cFDfWkTKPrftehABJ0dzHC0XxnNgwVbf99hut9hsW2zaFl3f4+z55/D8iy9BOkFcmpkkoMjpvskKAxVDFM8R03LUaWKHcHHPPa1V9cckbT1VWiuovrdXDZjXBDAJsPXuOAArhDDglshY7s4Tg0w3M5wmAMBgTSDBiSIm0vaN5k8jtlYzleEkmR9vDpTb/JG2e+l7LKjdZ4lLfB28cRO9FgZ8JQUflDH66RsD3sg5VLXzwSlTIARrHLjvDIYm8pZoAv+t/PNpM8s6Hyu5kjXvJDALaGhjCDoIWmj/qz8qDI3JCNN/w3qQA8Mgh/eye6WXYCDbiQBiwlhDHWLwxu1eYNBBwUpAx+cRAZwxIyeuj7sUF9MBt8DRHHShy2afsnegMsnH3XJ8Di9zdo2dksE2BnQG3h842Wb72Cb1AGdXW+1597cGOJYHw8h2BU5+DZ9xdYw7xCNKOGbIEQWlOraAcoeYP6W9ijIpWPSyqCi5S3wXBUYxhWVoo9DnC7z4xlvY/O4f0N/foNIapBm6771VV1UVFosFuq7DdrvF+fk5jlenmM1mpto66nzAK9a+N54DKQSklKElmKG0Qqd6rLcdLlYrbV2JwwAAIABJREFUtBJ498238MKdl6K6w7dtUFjBCnGN4K2wEcEV8kpVECO4G2HL5VjI8EaBZyIhcZWBjgMufd+jtwBHCAGtXRtZAKQUlFIAALYAh8BQUoI1QwgGaWH6TQjPN/CCmkDewk9/ez51n8ww01caRMEbQ1F/G/6I+tcZLDsAzbDPo+mGBMFkg50Sf1G4NYKM0nRB4icCt2xmDsZqWu4nBDsY8q3gcN+9zPGxjr02GBH4e4gQT7ew9wbFnh8QINmA2atKHoyN8FPxGf9smZ3iVPEUbAwE/b/EZT46sOzDUrlyiBhieJbKYVD8OFHaHruBh8vLCWREFw0sJIsOyCpWx2s5v8VewzFKDNGoDKV6jAGeXZ6W+HMA9GNPTgRuDs2vlGYf7QQ45KRZDmI5Yiz/ETcWJc+QF2KmA0W89qPAzCa3FPx4hnJgh53LjbJMEhiciPNB/WL0FaFuihktSV+4ltmXRvhrMIDTs5uQ12/h0Td3wZsNZkJGAE+gqghSCDRNg6ZpsGq3uLi4wHK5BNe1QcPWUuy6Dm3boes6X7S+V14gmz4ha1Gav541RNXghTsvo1ksIxuqgLBHa5RS0UIj907XfsZHxHGCZ4S0Nt6Zvu89gHGKyK0jcgAoXoOUDGgyayoIAGnXVkaQunyYGeR4XCAedP7Tc2uhedPpEJQHAIJgLFuy8NdS17cbN4732DOBw7vBio0URlz4fZQxYckLEoPzULz4nTQqpEs0KjgjL0osh0pCuuRaHxPawW8z/u5YRl3VHSElZZ5f2wd2Ql4hzzj/8Juifg8c9STgZrdHx39L6+eueUbgbFgNOS0HfYMyJPotm6qx/E9RoTj6tzT1lvCjS+vUcgl0FG2GcG/f2Inv7/Luj6V5EnAzNr720cFrcHKlF3eZBZvG01V6ZwRO0+kpyhha+JwNwInVLiy4ScFMtnTSPksJ8yXsZeB/sWKe6cSuARINNo4hAwEsQCzB1KPbrnDv04/QPbiHyqIOVhqECkKYeksQWGssZ0u0sxarrsXDh48xny9wcnyMyi6E7roOl+s12rYDACwXcygNbLYdNpsNZnUDkS3grZsadV+jI8b56gK3QWm1KG5L11qHCKADBFV23zTpcHa9NN/umP4qe3GA4eCMrzkAFAMcB4ZiICGEafPEWgeS72yHgZ+astdFzIeCwlghuAd8eoYBSxwYHU7y5f0Z18splvi3e0VCsXvGYx/3vlxqsJ1Xi3LKMjyk59PpG1t/Y114/MWmlYpAwv12971sigydYv2AwTDZJYxz8GO+2xLZ72DyBkLp2ScBad8NBW/vEz9ZUNS772dwxumRPXk/STmijAF4tsps8YwnLG/H2QxAX2J8D0rg4bgDtYPxwwy2crQkI4PHNjJ6XD5sjQDP//D5hHoeAFhyr09WjyKvWgNobHyM0Vi6gRdqBx2wBicDGQXKIUZSfw73E3dyrFO9sMstyfBwvrzApYnnPd01j4QLZfbiLQZLsbDdQ4nQL/rdFbhb4cvff4iP//2/Ax7ew2klIesK6BRYKWhtFRxJK6QJQkgQSWw3LdarLZqqQS/NYuPtdoPtpoUQEovZDGrZQ/Ua6/UWl5crHC+WoGoGAqGqa9R1A6EYVVODuMfd+w/wOgOSnHBwgmLYMvuujbkNXdvkln/+OfaX37+qVLLK8zLvGrjxPQNk0jx2tUnuufAOEg3v/fECUqTPaTsayA/QaAoyNiqyssYUA68xC5sZYBHKk+TnhSNZPMb57GYQxLB1O5QXRuRcPOUV//Z18zLIrm9iQNv2Ul5wWQWVP4PyODioqA7cwPFEBmqiP7eL62ne9ceh2FBCyqcDz85IDqMem/3AZyzNzhKPpi9cjwDDwJOZPRnbz6beKPgROXxQPACSSZxBWYjI67kxAyupgTNujDUbBAYzhNZhnFneIyYwjRs+cdq8KuZrbhgkhRuUdZcuOfg+7R4T+wFOUYln980bgyAYS4ogXBIG8+sOyu8h+54BxnYDgWL8mzKCN+ciCv0clcOn3z1YEgGfo19SAFo8/OIj/P6Xf4n+3l3MtIKsKggiaGuFM2toxVDo0fUdLlYXuFhfQOkOi8US8/nMbitnKNWjbTsQA4u6Me2ggXbbYbXaYLXeQPXKvl/Y5iRoZig2CyW1A1K5wmEuNU+RxpV2em2s7crXxwXXVQY5++hpFdHYQB4AHiuwCOPghzTMjkQKO7QCy7qpz+AFGYBLGgq5UlldmuAZGV8rQkRmMagzDhJL1WUMf9+DoF1tNcImh0z9JJ4pdlPnZrKwJ7PmSbM1ntj5hU0ZBRNIm8XzGqGfDuv7WHGMlzf24OjR8Xe1aShz/LdRIFM2coKA3icbBiC2UI40Xx6kyZdEB/Aylo+DEk4HRrMIA6s/0znm8QGnU5w2uTisUwq8IsgUtUXCnw60cVRyYr9Y2xk+zihJn03rk64n4qyuBZkWfy+BKJTBfkl/7cQm2LcGh6IdE5wX7ekVUGJVUdbL8ddI+8aouJBjBHKQIO7cV5G+mtKLB5Q5xnNWGnvGVHqLR19/il///P/D+uPf4qwCqK5N/BcGhLQCkxmd6sLi4otzbNotjk6OcOvWLZwcn/hdR7ITaLsOrerQsYbuNVhrL3D7zix27bQCKkKneyjdgXUP1hpUV1ieXPPrQzhrc4eCA1oG/GLKvVZqWSGX2m7wF/XKVffaxGQWFOukzHEMn7H6x5/RnYPfmwgoixE4uj54jxgCn/g+5+PZgf54qisr8wAUlMoZezdKdYgDZLj3uHJFD8UydJz7GMF1619i2sXFZMoFrn8t+TzYg5ehgnEb//PpcgECCfKzbay1748x6zq0HyFMqRVE66CeIY+rSCUbuAQyAIpFz0Hj/hC5MObt2eX5CfcMpw3Kbz/Z/cgM7KHCNYA4HtOhi3OEE5I5fiCKeNIZAMxhnDq2wRBoxeObmRFvKNslu5lduS3IcR4chLHu1nX6xoiGiQc/sY8qBQzwQ3TEgxOSc1EP+7EUpQ3G1LcEOJGUi0qeWnigACp8QXdklVwaqRDFeY0g/PhaLjMLrp7Ibed55YkpKa/d78akDGjRPTb3v8Qnf/NXWH35Kc4qgUUlwFSZDtEMUdVgIf0i1PV6jdVqhdVqjcXJEjdv3sT169chpQQrA2IEEWazHqteYd21WF2u0LUdHp5fAiRwcnKE5WKOiiTcgmStFVgrsAJE1WC2OIawAeXghUo8QNwXRNK2gJYRMRzCM7krcZcg9m+mcv8P2vmKkbA716qqslv5bZC/qvJCwa2Dcu3gABCQC9YhHVr3UqpEiXJ6LX1/9NtJzZGylCys/Ho6LtL8Av6IhDdHac1F8xH9Qy5cRFa2gSWZaKJQTm96jHmgrHD263PsGHVZKQCK4C35pNciceg8uWw/x5SqGzdhl5R9k9OClIOg7xMVFLi7k4Bic82I9TK4iWVT+B7pHsrTPkEpd3qFCjWg4JHJ76U8H3+JZwXIrktNd5c5GveqZ89HitxJ5nz8JZ4ZlxeGxo/RDxpmvauJ9MRJ84Z0sUHl82D49XwUBeqLAVEwPNgVdlDH4hRUVEfvnYn1TZKPAz3fCuBEmcUUez5sJxig45iR00eCRjvsdcmryyDHf6cEVJrrvlMy4URujULpNfuFSgq+AIYGQ0MQ8Oje1/jyr/8Sq9/+GsfEmC3mqK1nRGsNJrPF20Qk1eiVwuXlJS4uLjGbNbh9+zZOrp2haRo/naXtwtS6qSG3FVbnF7i4vMR606HrNW7dOMO1szPM6xlqIQAmCE3QLNGC0JEGGoHja9cgZeUZOFjwMQUUzq7d97VH5L3aCWpyBWjfF98bTJFcUSISIAq7pgCYuEVSmn7T2gDUaODX1ouXRHCOgEbRA/OtCrkvwdBijW75eXhXll3lGSjkbLyGsWkXYLKrNxIec8IzCDmG81QlxYuFImG31bcD4ORpnbcn9mQK654J+N8vA7WyN1iX4PI4GPd6RXGuKCiIPPo3YKLH+3XZV5jKXht/F049E/HgmTQfl15E381nWMdZ1gn7ypanLQLKTF6N5Z4ClFTnBFQQQEP+/pKnb/AOxGvkUl3m8kjSOmyB9L1xeYVw/CsS/h/kNwLMXJ08gAoDJP0elXlMp5QMqEF5YgMoqW05z5h2AhxOvowxrUuSofgM1JBIAc6TuBVLSDcpZGzJZZdyhjACGxEGsyIrZtTSa7LOZtboWUFqhc2Dx/jo53+D8w9/jabvUM9m1pC1ipAImgh936NVHVrVY9O1uNxuIOoaz995ETdu3UTV1DaKcYh/YQS5hJTGE/T4Yo221Tg7XuLmjetYLhdmOosBZg2lNXoNdAwowVicHWF5dGTd6JYDRTxUbD9xdC1jutIgyL0EpXRjNHj7FQc1MQlZgYlQwQlGhhASUth4RAIe/Fhx7oFNCnCE/QtACSgL4Pz3t2mtDH9k95zF5V4W+vaJ+ijmH4oW/XM0xjyAygBB9G/s2TBpIze859dd1uGQH8fkS0nIky2DKxP7Allwb49RiJ8ZE9j5vaTOBRAUt7nbW5oerXKVaAjqAMcGMe/s5u3yvdiYjnJw8jzmFZSAZJpnPq3krhvvA2w04ZDeD4Usy8GYKHl02P5Int1tPIzxSi4zS3Vz45e9AZMCA4p5GOmY8G2QATD3OQaCBmVwgMzpfytwiI2X07ezyagMMKP3O28s3Kd7Pzs09S0Aji9IJvRcZaKi7ERSQ7fjWBr/w77SMMOugZBXcci/6buD+898aNf4Lr8U1RVeaOrqzofh9Raf/4ff4O7ff4gld2hmjZm+gDY4Ispba42u77HerHF+eQmWwkxLPXcTzXwGsh4Av2sCNnqxjZ7bdh3aVkEKgdOTIyyXS9RVDWLzXNu22G636FSPnhUgJY7PrqOZzbxCoMBzaetFVnuo6h7EXVAgMRPutv7TgfS9ATmOfewicAdQhLQxjrSCqAAIaRsadpG5gIy9NzT03pS8Ofl3VwTJu8ZTWOP0T9GuJcsuBR42HeyC40RZ5eUaCvLkGqc8N3DHe0PKF64IbobucG8Dlu8f0AaJwM0+x98dly/o5Z2GgZO/V3aMJKswwlXbN4ZfYtlKo1VJdUUxQfJeD3HCPwPeGrSqu8/xJUrS+XEIFAO/JkAg/paM20ECAJR4SMeoNLVU4pEcEBsAEU25xTZL8t4ARlzP7dLNY0Zs/N68PFkmCe4jwAQ4jQAPlZ7LKPGa7sEdwMFTVBR1dAGpjjyTo9pDrFP7BbFrj0IGyXt3Vc1ZumG4DN/jGzUH2cjbzXae6wxmgDW6zSW++odf47f/7ufgRw9RnwFNXUNWFSQ0Kh/WX/vAcNu2xfnFJS4u17hx8xpuPXcLzWyWMjLc5BejUz22vcJqs8XDx2soBm6dHOHayRHqSkKSKZfSGl3XYbNtsdq2uNyuMb9xhtfeeBPLo6NIKeUCYoiCYzQ/znApczFC6G0fDXaHu6Asu66qAA9UAiJJu9rfzitDRCB7wKYkYQ7QJDKxayzgcQvKvQiIeb8EeNjGFRoDkIRBu5cs1/zrH5IS+ypR8Hm5dgvXQb65YB1Js2vqNFHFEdAZtZ5HfpcUzr60bGFfuI7BIZvuu7YLl60pcTD4+mNTwlmuT7wu9VI2ShPSFvPzuoLSB5J3uoW54XdchkHJIp1BIeHOungvkXt2zHMBByCyMnggXnxs+NaCoZcDhxIl4CYGPLn3yFaeYMIgCH854r3Ms1MqQ6lMJV0x5hFKak7xbjPjbNDMgD/0Ft77MwAzBwyH/Uc1eJPf4XR7jYbpBi7uXXlmvwfelT1gyBWAB+NgRJAfUJ6cUUKHkAU3bAMaKuhujS8++kd8+Iu/gH7wOa43NWZyBkkEETGPIxf8bbvZYLPZYLGc4/bt53BycgIp07lQthadZkavFdbbLc4vL3G53mAxq3Hr+ilO5nPMhLHUtVbGe9Ntsdp2uFi32GqNOy++jNfefBuyqkfqHGZ4A5IetvteyzL6GYe65+gzESxDPfe9IUECkCk/urOmAID0cOur89gYK8Z6VxIvjrAsHwEcYb6QBzuOR80Mo7lSADjR2ByMKwTMGXepiQsYpcmnjf4AwDO8L1V4rjzx2pPwzIhCycZr/swub0zuZygJ9ty7U8rLnSBeEupj1rb33MSAKpfZ8fOwuy5hwY6+mgDHuQni6SJ3thTDrLnZYeskFIDNnsRe1qdTtulMgP8nyS5vxaJDxXkcKNwbGweGo3mYLkkfAQcMgTlF6TlCiG7axz1Q8pCUyhXLbSfbnVuHvZQnPxSD0b9/7Li8ciNjAHR3eCXHQJlrdzdV6MANDfLVsINjJz3RaeLuBa46QUzF38gyBYX0QyCd5JUL4tL5Mum7DcUNGzV9QNzFcucVStPEDZ643gC7U9Qs/r3/5Zf46Je/wPkXv8c1bFCJJSTVqKD9jlINo/B7pdD1Pdquw3q9hhAC166d4fj4GFVV+Q6LBRuzOTizbTusVmucX6wAADevn+D05BizWWMO2ISZwurs2p51z1griaMbt/HOj/4jHJ/dRAuKBAwlfYgie0ZtvEdhxM3oVJfPiSisH8jzNRl+L7w2CWUCAYD1wpg1N245TWzlSGG8N4IsELJ/JWMhzdeNjRDh230T1hvi+zRuZSrlFY+NaIogKqcTpqU+2SVQv20fDgyzknCNgXNU5jGAMf6uQnqkgtKrqgEqz7+Nl895YErvYo7OKWeAfViGfHGlLU0JhH0fyIsXz2yIedb8G5tB4THzSdkde90bCkgNgyyNf3YPew6noOIx6ab1h6A/8CG8oThOsawtgT3K7kVgxIOS8oL1fYZAnG7vtVJWUfc58OHYk2OupCihEW6jbVIqf9GYjqyxtOzG0Nzn1dwfB8cr+HjBFTnO8tE9vbCOAEGocOJA9P+macJtrywLAjpLapBeqiMQ/3IMThRQobchD5DL7r2CAKENmz1++Aj/8Fc/x+rjz3HMwEwSlFZot1vUVWVAiz0XQTFjs93icr3Gg0ePcLFe4+z0DGdnZ6hr41lJrTZYoWdPBm9bPL64xOPLDU6Wc9y6eQPLxcKfDM6K/cGPXa+wBSCOj/DWBx/ghddeR2cbwHiF8ubm0EDeRIBV4EPFs3vKanjfCZ8BQHUCKru+zyq5EuQASgYMMvlVBO+C7NQVkWUox1tpewQPThncu7OfgnB3IChtb5sV3Pjj6KK3EuHGz3B9Wlrt/f2RGwSpgeHe+eT9eogVGF87dKppX97p/TKQ4kj+2RvhOqeQxHlw4IGOva55kKeZnjLPO28uF7xcV4326BtLGdjO7iVgHZGsHvPO5Lnv4tUD5Uqum/aBqPx2ma/KYCkFN5EspKB3aTCi3C87hkvgJfYCFTwmuZellCZ6UVRKYfGXWUbBZEMqWNRjes8pnJBH7vXeBWxGvWXMRQfGGB22i6r4rqCsMCIE3P2EWTwYoQFrx3mnvByJ6fyRggWcK0o/RYDAjGONM14m88Tq8hL/8Hd/h0effI4bLFHN5pAwU4YucJ9SCqKSAJG51rY4Pz/H48fnAID5Yg4i8ucThdwNKbtmp+t7bLctHj2+BDTj5GSJIwtuQlrl33u5bbFCg9uvv4zXP/ghsJiBk50XAVzmwjeoJEY8lorW/B7W2g1M036PB1u8buWqUgrSABLC/LlxoAOv+fQxD3rjIMk1AkI2bzG85wSZiAyL4NIfb3cfu88pBQrfffsjim2BzG2cUcmKdCSl9N65PH1caYPROPn9NHQIuDkE8Ix9N8Uut0HpffkUFUfpzRftYEsSCC7PywMdWEWiNbS62gAnb6W4GT3vjZiiKS/FsDzKgMppA8CIZHz+glCIw2hP2iKY2HHfpSrfK4On2FhwT8dTSUZMO9CEqAMc0IgWNDuZ7j/LoMjnUBqQTu64F5Lx0AtiaILfJeXHtiu/2xCRqpaknjmwCvGihtO9wTjbTwcc1YBg6DuAMeTTJDlASAKMlqzQwrdBXpSoQg9MwjQYvEKI80/dh8M1ETspVu72P4aZamo3W/z2V7/CF3/7K1zTPc6aBqI2O7FUz9CKsd1u0XadOXsKQGvBzf0HD3G53uL62TEAA4Y2mw2ICM1s5pU7A+iV8dysVis8fPgI680WN68d4eRoDikAzVbQaYZWCkoptL3CulPA2RLv/PTHOH7+FvpKuNYyeUfMEk9T+cFJbhE1fFczp+3p2iVfge/yH/Tf4M/mHfXLE/XPd0xxXfLfTn54sEME8lvDIyBE5L03zt3uwQ+cLI8MAttoAYzEY4OG9zPhmLRpBHKQ3XPjKvbm7HMB52m8ciHDHYIRbb11goMjIZUq9VK+uzyH+7yKB5W5ACxC/sbDUgL1Y+uGhh7Z4IkB68Tr7Tw5/tM/l67V8d6fK0qlku3ut935ee6mAGBiBe8ESfG9iL0qh8mTMblziL4Io7JMYwbBPj0d2o+sbE7XhUa52HaK7iW3GX5RFNy5dL5wo+8uVMTLNpOT4edKKxA0ejZagYnMXLqInmOyUcwJFI2bHNyUptFiPnLn9zETGGLv2N8NcGLUh5SxiulsRwQw5P4i8IHgWveufZ+vey5kGytofy0SzkxpZ5TccPHnQUTwqNO9rl2v8Ml/+Dt89td/iZvrRzidCcwqgqzn0FqiJ4UOHVbtCooZ6FowG8Dz6NFjdG2Ls5Mju6jYBILbbDboug6L5RKLxQJSShMI0G75fvz4HKvVGreuLfDC8zdQyQpd34Kdp0Mz+m2LddvjUglc0Axv/eTPcPu1H4CbBhoSIpuHGyiSgeUUb+lM8XY+/QTAnyK/76ycBOQg7a/4M79+1Sj2NPnfvl4wIMacpurvI667EA51pNcRwI6IhoUDBYnniMMiwRK4gSsH4B1yUYGTCwOhe6CrOL/HzNGUr7Xi2AlmY2l6GQGC896415XKMeYhKZWjVO4nAWfjU1VIgEZevlI+A2+Mu+/+oskFD2a8x4bB2oag8MDHXLuqi4zNDpcgK0oWuv3lr41r9pJsCJ4KdyHn6bj7OHomy3LwmsEE2YixNcp3oQjFdLmBHcB9qssYw2I6PWaaa7gRf9e6nKQsvr2NvuxtPkIBpDS4V2C2MZ0iOZXEXbKGT9zQqldQm0uo9TnarsW270FVBTmbo14eoW4asxUcTuTZ8tkQGzobK4NyI/BP8gmAbCTmfXriiRcZu8omBTIlQdx5hWQjF2OQszfx4Fqinvcoy5SZc2YpkBNuWuP+R7/Bx7/6a2D7GMdLgWUjIYSJCqk6NpGKQahIouu3WK3WUEpju91ivVrh+OgIN2/eQNM06PsezGw8L20LZRFt0zQABe/OxcUlqrrCCy/cxtnZKfquQ7/Zou3tVIDW2PYtVr3GuRY4ffU1vPOTP4OolyCW5rycRNoM6+vg6iGiMxVWqQDf+RyQnCqfCrrvH8WDLQVuQwDkdlj57eEmhzK4cX8cDIFgKCC6EgMcV6akgNnXiPMp3B8bL7GBkCvx0T4jA2ZC/TyUsQ+bv5Cduet3bMV8ZAWtK1v+3pJAdOlyL+XTeHeelHZOU0VpmBnQKlES8Tob96njDQccAZw/Ql2ejoIC3QdcKGHEuB2G8sF/jUB5MobiDAs6KWbVhH+8CT4Cbqh8r+SByWHHfpkWxuxQNHNy0XhL3I9sGbMt4y5wUHi1Ka2dPuK+R3uxwvrRY7TtCgoaVT3DbLHAfHGEqq5BUsJZvAZQGd5s2xaP7t/Dw88/weruF1hdnmPTdqiXR5ifXceNO6/ghRfvoJk3dppVeI9OzC35OB+j3Ljm8M94ffGkACfq5IEATEtt7lEmeF1hbZLEGRTD2Sj/MTjjvDe5sB4WOYAbr3biIntmy92d5oZSPe5/8Tl+92//DcTFNzhaaMwqgbqWnuG0ZUQjVOE9MOv1GtvtFk1V484Lz+Ps7AzMjPVqhfV6jbZt0bUttm0LIrMmh8FYrVd4+PAhAMb162c4OzvFbGa2oOu2R7vdou1adH2HddvjsRaY33oeP/7Zz3B67brphv+fvPd+kiS38jw/gMsQKauqFZtkN5tDjuDMnq3N/LT3p5/Z2dmtre3d7c5SdVezRYnu0jozI1wDuB8AuMM9PDKzyLmdpB3KsiLCHQ6HfO/7BB6MGA/JziCYYHCmxiZ/ZV+aIm/Tt3+u73viFmgjppLS2Oyz98X/7kkKe1L71EQVprnfImz3VHsz0eB4ei4mi8QTPJ/fXgs/RaBFEf2aHNVmD7jZJ7leB4T2jH3Cl6aS2M5kDBmJ5e799RHjnwCHfSrsKcgJ67fv+vTZ66Z9mp+5Mvr3mvE6mbax1+BMwY0x6BsNcIZ0dQ0nE2B3gu7m9/hmB9xcr06j+TIDXqZ5RubhS14yrtOQdub9TJoXC8c8zxgT4ED7ZdBgOA2Oox9TLY6f0z6cgbtIhEKohmZb8ObZS14+esrLR4+ot2cYAfl6zeHpKXc+/oTTDz8kPzxAJZF1LTQKupb6ouTF42c8efiQzfNHdG9f0tQVrdIoGWPyA45evKPdlnzyk0/J1yu0BCkjBzx7OAeTtXcZHx+tYaMtnbhiwl0BcOafnicwepppPxF1FHx2B9R0/u/UZwJyrqhbWJRXsfn54h2w/D1flhYSjUBqxdnLlzz4w29Rb19ysDBkOUTCuCbYf1EU0fUTzfrItK3d3i2l5NbtWxweHtpdU0ZDmlIXW7q6QhhDHMe0bevaYzg/P6fYlhweHXB6YrU+GEMkI5Ikpq4qyqJk29VcdIo2XvMf/vE3fPzZz/sjAvoJs9MLQy/OkJJLCdTl9/bf9cx7tz4zc2mKQm9gCpmi3Rnl2qCBkFG6eyMQ1GOWPRocR1JlKKGO/HX6YvrbnvD5C4agLIs0huffE7yMJd/gevi9r9+QZ0f17CZdD3wEwQWXoSd8g7Q61eDoIACYL9gYbLwMYyYUYqx1moKYvqyJxmcXvA+O13+uKSyo7fTC+OssuNEW4NxQE9WVXOayFICEk44lAAAgAElEQVQWH0hWuDk84g0TkDIw/bkir4t8xt9F8MIdYGMm+cJLYhi78P17TUbhy0eTFcTkHLhhrQwv8sLoZeap/i1uzWutwWhEW1O/e8WTe/f58bv7nL98Q3dxQaztppYt8DpJePvpp3zxT//Ih9nPEcmCLtLQlTQXb3h9/xHP/vSA8s07orokUS2ZhA5BqRRnF+c8vf8A2SlyGfPJ558RLxJ7agDDmhSM18/cWpoKIUMKznK7JF3PB8cP/AxXFB5F7XnPPnDTf/pr+/LvpIFShnN8r6YCT1AHjYYxBiUNSliHJYkg1sb5DDiQgmD75jXP7n5J9eg+6wzyNCKKpfVrmfjoDLUzvQanbTtu37rF6ekpi8XCtklrdBwNZgwnzSulKMsSbTRVWZIkCacnJ6xXS6QbYC0F0m1Br5qG86Zmg+SzX/2CX/zD35Ot16g9sHaqLfDvnU/TQR60gX6ojVtmoeBts4aMfFf+MTAExQs+B+Y7B8xuXhrP04HBh1+ksPFxLMix4M3668geHHui2mu3GLc9LDPEBEPPjk1lxjMHV7fQRHkVwNm/5oZxCdj+7BiN+FD4w0wzBb4pAtAhKBd9dPEpYesl2IHzDfmclsR/N8aMKzyIvnsJZ0hkjSNsUx+cOY3S9UCOmfk2/r1z3ey789eRru6XCcqQgRYynPtikn86+dy4XgpuxPz3UPgY/d737PiVw+0rgNVYu2KvjXpHAEY4njoBLoFgDqI/XmgkgIwIsej5HsbuvFRty+bFM55//yceffsdxesz4qYj0y3SKBKjaJWmrErePHrM+uSEw1unrJc50iiqszOe3/+eJ199i3i9Yd1ppFHWp0YKFJAqhWkUZ9UZZ88j3ty+xe2PPiJb5DsdNw9cdtNekHMZ03fp+iaqPfywJ3s7ox08GjIDB2wuAzJXT1IrtSkp7Fk3xiAxRCFz7bVydiIo1dG0HVVV0XUtSnXUXQ2RJE0zFnHOcrF0sWk0bbXh4Ze/4/V3X5FWZ6S5sOc+YRE0gSq5U4pO27+262jbFqUUR0eHfPjhHVZLW64AVNcBkCQJaZbR1NYZues6u8Vcd0RRxMnJCYeHh71Dsu9rE0miLMUkCReFYPHBR/zDv/wnVnfuoAIC7nHOFMzM9+2YkM/PHOsvYYQDN0KgCRiqDyktps8PQNZLHb4e079h/PcP/793mjLDEJgZT2DnNCYe8AmIsCDHB/0LiaMQ4z7aeT/h/Um9evBEX64tbxxX+7L1tXuvp/qzTNmR7JHa3JfTK7ImReHmkOjzGZCi313kG+YdbPv3jZH06HqvzXXMYyBF423vvXSM6HdHGR04a3q6sUeLFBLZOfOZ17hMk803Nhv3DMtrsCY9vCPN7pR6Q9Iw5f/icvovjpEPbg5ifH/Myyf3w5/XIyb7+NHV2qDwvhl97Lu9C9InWedAcyAsjgT2GXOYv69khNYa2dTUmwtePf6Rl9/+gXc/PqB+e8ZCO1fduMMYhRAaITQa2JQVZy9eU7w75+DWCare8Ob7hzy7+x3t23OWChIhSOIYsIc8x0YjjEZFFartqC463rx4xHb7C5LTQ7x6/qqjuC7r8/c1015x2KZTho2I7uhtPqP77X64yTUrzV0Cbvb/3m2U0Yquq6nalqZpiKOITEZELnieUAqjFErb3QjVxTmbs3NevnnL5t05bCu6psHEEcl6SXJ0xOeff87PfvZTjO54/eM9nt/7irw6Y5lAHEcMJgTd18sTNKU1nVLUbUPV1GRZxu3btzk6PibLc2TkDmdUIKQkyzLyxYIWQe3aUFUVMhL2AM6TE5IkGYEbtMZojVIKZQTZ6pgvfv2P3Prkp5goBhNZ6dRtB+y7vGfEDGM5mUNmz49BYzNhNm4wh6MdBIOZz88W6RijJIhct3/MbzCwmaYxOAPf3/MAKAQ7AaiBmc/x2hjGzIye6+8hxmVDENr8amJ99TUPNi4Dnj2l6LVz7BAxM66OGYOhKag2bp6Opd7BUyws289LrbWLnzGYnkLaNQUn/b3hUJ6gqmKoYyAJ99kmDCfsMyml9dMyw7lsUkowGv8y099zfWDETMuC9u3inxuTTD9+/sLwdWqC2JtG+HMIQRFqhEdfJq8c57k+KQmFkPcGN3uEgdD3VYTzeoazhxqaochxffb5jU3rGF7TgELYMwrfveXlve94+u1diqc/ENUFKwGJFGgUpVR0QqGlQqHBxIgOmqKk2RQ0FxvOnz/l7fc/0L05JzcRaSxJBCQRGKMxbYsy7rtWYDRad1RlQdVUdj14QUOA8zyeFR72mYGvBpu76UoTVV+hnniLgWA4wrtrWtxloLtqwPDe/I0AmxLKj8Zoqs05L358yOMnjynqmjzNyLOUbLEkjmNoO8rthm1RUpcFYnNGV5VsypqmqFi2AqGd3TCOOFvlvHr+lDT+X0lkx8O7v4WLV6wlLLMMEUVEUWTNDki0sgH2MHY3lD9U05/ofXh4xPHxMavViiRO3KBqtJTISBI5DU6OoOk6t2W84/TgmOOjY/I87xefMQbjDt7r3JlWrTLc/uBDPv/iC7I8t6apoK8HdSZWqne+INZfxGtdXC/3alHf60Fvm91FOZIaAL8ReGAlw6j1ICcI4mcE1m9jQljCaXBT05QgevAy9B2z9/1fb5wSYqes8B3DZwgh/f3dfL0Gh5mtnZNy97VrcmX3/jXGZu8rJoRLhHXzoNz1occTMCbaxpieEoxIhmOGQ3CwGTAz8xmmHQ2Nq9P4fW6R+Dzut/BlGmOFEBOsiClYDE6h9wJSD7aMNZOHjqG9ZkiAmAk4eCOSGGiAYEwDrs+YLtvUEJYzBsLD44POLvyYzT9ZP9Pv10rDIty9xQDejbO1TrD97rNC7DTBFbBTzykQmIIdPxd1U7F5/YrnX3/Jq+/+RPPqOVlTkgBJHBNJidISjSFC0hpQ0rhAxRpVVlQXF7x58ozn331L/fQlWWtIEkkWJ8TCEEmDVh7Ua6vlB5SxUfxbrWmVQhtNFPbQjK/RFNzMAbz3TVdqcELq6jUY0wnjCY/PN4ChSXliOvkm82OgdzvzJoyDUG1LHt79iu//9f/i2dOnNFoTC+vwaWREkiTEGLqmpdUalOIQTRZJ8iTmWAiWEYhY0rSKd1XHdtvwbPOc+weSZSxonv3IcSpZJxIZRRh/8rOUSGyAPWMMqlM0bdtv7S6KLVEUcXp6wsHBgXUQ9m0Qg9+KcKH6pStXa8Miz7l1csJ6uSSK4n5HhSd4ndLUTUNZVXRG8Ktf/JIPPvrQHYA0ZUgBgZ0uaMdc+tPhHWG1h+KZntl47d1VE8yTnZ2x9lMlJAajv8sIy81EOuFiDDUnQpjg95CXYN66izstuwzk7Msz+8wVz8615fI8w7W5GTDWvgX6Owf4Qn48BzjC1/QmIjcf597R+7z09Rn8EnzcqtBYttNHJmQ8l5uU/Ev21XtO8uz7QUob2lwMZgQPvOzR4Qzm4/51EoN2y8XeCQGbEGYcoO0GpV6YMt7cOmZee59hntbDPDAazHx7QI4rcf98D7+L/sJfBHSuSiOeJqa8fTbvaN2M6mN2tB6+zlMtj65rLp4/5cGXf+DpV39EXLzjMBIs49geBu2DjiLIdYwykljbg3zRmlhViPqc8uVTyncv2Tx+wqJqiOMEKWO3uSIk7U54FgIjpAU6RmJkBHGMiKKePtp1MPY1GnXDHuxgg/zNg6B96QofHDFCxhbweJDjCUXQwQG4GaRyT+SvBjdmcj/Mp5DWwbYsePLgHl/93/+FzQ/fQ9sSGRDOFKWBRkgMhiiKWGUZsYg4iFPyNCNOYlIJqVFWK9I1pKpmURVs6gseffWv3Moykq4jznOQsZ0M0u2Ygd6ZEeNDqNt4Ntvtlk4pTm/f4vjYamGiyOJWq+VRPTDSvr7ue56lHB0esl6uSOIYG2RJY4RwPj2aplWUZUNR1CyPP+LWpz8lXiws+AolIL+QGMZiypDD4+dHngF9/5v+3+547GNUfswHMDxl+pMH+vLEBDffTHhj01QqD7pyFgAJQU9Q+n+T+/vK3yHKTNZXn2HSgTPPXNYWMRqAIVJI0My9YzIU7TPtb9OUcf250lk/YwOp19fZAGGU034OOyAVAp2wbn1jJlqjsK5TQLQjefrnA3ATgh/Tb1AwCC1xUTIRaCQCjXZ9ZzDOFB4UeaPTMBMFlwpGnumP5orrLy8ABG0dz5mR4Wo27YKjXS1I/4or1se+e8Y9O3DC90j9WtrzzqGpQ+k7pNavJ3fTTxJjMEpx8eolj7/8A8/vfgVvXrISmmWckURxP49MQP+lEUht/4TWCFUjatg8+xEpJYtOs0giZCSd9t1YXbEZytIYtIPhBoGQEVGakuYLG1PH00bXxuss/SndeF/6cU0nY0+UJSOCasaLcL5ygdJyhlj77zvVDBshBEpKFIKzJ4+49//8VzY/fk+uC7JIoDVIrLNgFMUIDKmBJBKksSQSMevFiizLkZFAoOjaitad72LqDlG1aNOwffWKg4MDkjS15qY4QgiIjEWgaEfgtEZ3irapadyxCtuiYLlccnJyQpqkRA619ipDpVDK+dA4Ca9pGpqmYbVacXpyQpZlSCF7Ct47MmsLcKq6ozERP/3slxz85BMaKYmli8mjTL/7hMnhi+FfuMtmagKAQXMzXVvzE0vYg0XNzIydopbpEE/mxI5YewPTvAbHp7Gq2H6HgZwNjD8EN7sgxpe7y4RnCfUV4OU6bZpcCW+GH5cUMvnsJ2KQrjBXhMRsx8F27tlAkPf3rX51F5wbxlpJp4e1PAH/53Z2Bcw0XAiejkkpd6TJUCM03wbTqytsXVz/9CYMu+1VOiFSY6yQYzTCCGTPOm5eGtGRfddD2hFmFB7U+Hk/9PMOVw9/ivFlcZlSZ0+dQ4FsdP06z/vqBEC2r8+sVmKuhD31soUEBPLyftBIpxXpUE3D+ds3PLj7R559/SXy7A0nQrOOBZEUNp6NDPzA9BBUUmvt+FOHVh1dYyjO3rJIUpJsQRJZXij9OnGV0c4vtFEtdadoNHRIoixneXBItlwEh2uKvkkh0JrTiO4DL++jZbvCB+fqBeUx7DBpAwk1YKT7Kjgl0mbmvp3AivLsDQ+/+h2vH3xLpmsSaYmMiCWxiC0SlRFSQWIi8iwjiRPiKGWZ5nYnkxS0StME/jJULaJuiaUhJyJG9Kd1t00Nqh0kcK1tsDetbbThomC7LTg/P0cKwa3TWxwdHJJnuTsU0xI0HUwm/1nXtdX6dB3r1Yo4jkdE3j+jtKZt3aGaTYuMU9CK5z8+YHlwyOmdOyTL5UgyEHJglP1i7gFH0L9Y84pXfVr6Pzhzgjdd7Z0AYPxuKv/TvVMOvj87f3jCHoz7lbPtZqSpJkUIb5EYE3Q7Z+hj4fj2DutiWi4M4MZ+99fn1sw4L47YjvvVM42r2hC+b8zWAwFkBrPAQOhGDGMGi4xptf0xlcXnAPe4zeM2XFsD1NPWkBk7DQrWLKsxKOH8H72kabT79CtnPyH2dQxPSg7NU8YBrP63Hnpv2n4EzpQRjOMNBTjg5zVuWC8ft+Chne+j+RA+O2p6sEPPzanR2YdcU+PlMu2jPVfOwUAwD2nmDCSZljxf3qRsP/oeFwyNNP2CMkLQCQkGZNtQvH7F02+/4dV3fyIuzsmE5jCRLCNJFwmaGX4LLjitc7Mom4ZOKVInoCdxTOxNTJO+8jyqU4qqbShUR9EZWhJWB0ecfvARy9U6aPXA499Xexvyxuuma28Tv2wwJnOvZ2A7EmbYO2J+IvtOJHhWYND1BU++/SOPvv4dVGekkZXChBDEUUyeZEQehHSClIRFbpFjImISCVIpe86LqtFtTVeXtHWF6lroFFkWkQtJJAVJkpA4e6V2p3vXdY1qGlK3u8mapQo22y1VVXHngzscHx+zXCxtHga7uUfHBtDa0LYdRVlQlgVRFBFJSdM0CKw5S0TSbsXWmq7tqKqazbZgs9mCjHn+4D6vX71heXjI53/393z8678lzrJeAzAPbq6hTp1dneOLY2llfK0nFmJOWxGOuf0zwoznx/jLDUwDY+7nqktTu7gHJv5PCknvZjzRjEx/T7+HeYbyh3HuuUvwGehOg7pDP5ajF4jRtzlBA7FHOiVkxNNyTQ8OTHDPVlMG1TGDJD6Ih2EpIw1Q+Huf+npKRHfAkxDWsVc4nyEMCRplDNrYuTk6Y0RbVb5A7j1Hx79nh4D78XKChNEGa6Ga9y3AGLQwwblmNxfc7FRNiF1t7ug+A6ARwywdzcawzaNp65kc/We/QbNXjIkh/w7aHpik8PW8Yg7Nt2EGcLt3+nncCwVinOtK4DUqMZhLPb5xa9E10QhAa5rNOW8e3OPtt3eJz16ziAVJlJBg3Bl4cugJM9Y6eh5X1ZUTomMbyiRNSZPUWgkCGm6YPq9oupaiaSk6MPmSkzsf8tGnn9oYcL49HpDOdunVO6nmnrksXe2DwwRVT1IoccyS1B3mFRBLGGyu4dNTxqE1rx/d5+v/9p9p3jzlINYkcdqXGQnJIsutBgSB7ASpSEnT1AZWU2C0olENSrdUbUFdF3RNRdtUXLQljW5ZJgesYskijlmmKUmaIozGGE1VlXRtS11VFNttb2YqyoqyLMnz3Jq2kqSX4LRTensVno9I2ilNWVYW3EjBwXpJFAlU11EphZCSOM+IkqRHx2VZcXF+geoUhwcLFl2NPnvN+avn3N1uSE5vcefjj62WSnjvr12AM3Tq3hENxsOPFz1xNqFUMZ0DO2kMtKZAx2Mxv1tFOD9796obmcSoTYPJVqARYpDc+3zBN3s9XJRzIGcX/DB6ZlKfsCwRPjRei3MA0490f9Dw3kaLfkxm63EpjRkKH6/qsG6DLsf0+QJg4uliAHz8tnlP9z1g6Z342a9hgUHL4pmc1gpVlbTFGUWxpVGaKMvJ1gcsVktX9qAhc3FUXfBqMQI7oQZHOCGlbwgOygnhmOD87hHhus0HBLVeOjczyZ25s2cyjYCaYLwMxnN/uBaCxPmi+zkwTKCAkQ9MNeQrl/G0K9PsWgzG7zJsN7d8JiBsr2nG/W+nrHE9ZpC6pSs2PL//LY++/YryzXOWpmWRxsRxai0R2oA2RM7FwmAj7iuj6Rw4qduGumnRRpOnCYvFgjzLSGPrVDxo253vaSD8t6qzGpyqojYxdz465bMvfskHH31sN+kEMKGfAeLqNvt8f67v3rV2UfXSqghmzhXlz6FgMxr5cBaOVV/9niA3EJuzN9z7r/8nzfMnLKUhjyLSLEdEDrore9xBmqaWTcqIGO//ArrtMNrQti2dbijrgqouqaqKqqlpdIOIYL3MWOcZ6yxlmcREkQQj0EaiopgkiqiBoiypypJOadquQwjBcrVCCGkHu20twRPDZFBdR9dZZ+Su66ibmiiK+OCDO2RZhu4UurNmM2MMERBpA8bQNB3VtkBVDet8yclqjUSgVAttw6unT3j1+jWf/OxnliAKe1b9QCxEL6X0/S2umFRuVHp2IQBh3OIV2NCzl88B/+q5azvaiBBMi7+I/Px/mkYamhGICL4LsIH8pHNMF+7ohRmt5qjcuVaLnf7q33+Z9NJXZfe5HicEt8y+ovxYBGOyQ2ygnynzRcy3a/qrNwv5MkVg9vRAIGCQQ076iaYsXkF2GqEUSnUo3QE2PIOUUQ+EvAzcdR1nb9/w8uH3vHnwLZuLczoDyeEpdz79jJ//4nNWBwdW4xIPW0f6uk36I2y3McH29X7d+DrPS6ROodW327fzKmn1xqRL6UI46QjmafB9RigeLvg/rzE2w6U97wr/Hxd82foZGwTnixcjcD3VWk1Z5XXHL9ReDcDA0w0GLSAGYTQ0BWfPfuDRt19y8eIxWVtjp2m4Rj2PtTwFY1BG0eiWqqkpq5KLoqBqapI0Ic9SFrkNvRITDZpVbJ9bFwp7JFHjzlOsmoa67VgcHvPzz3/BT3/+c7JFTrfTN7tI9TJN7GVanavS5QBHTAhyOJHMkKdXy132YhEAJiEGc6LPa8a/hbGxX9qm4ck3X7N5cI+V6sjSjFgK8sxpaKKItm7o2g6lFJGMrHRlNFXV2IFpNUbbeDWdbmlb63tTliVV22CAxXLJ4cEBq9WCRZ5bm6OtEAKIhSSNU9IsRVYRddtyvtkSxRFHR0ekaYLqWpqqIomt5iWKIxDW5KSUBTllWVIUBW3bcnJ8wsHhAQBVUVK1Ve970zUtprWnjhebgnpbskpSTo6OWGQZXdNSqdaa3VTL2fOXSK0hGjNQr8HpGcaE+AyLP5QgrRltbhC9dD2lzXPmk+ksGDQ4cpBSew3IWFV9UzU4PoXaqDk7vQdAgVFq+BfmEaHK2n8PTV32+lV1MZPf03eN8jPp36sIxSwYC8du0MTudwwcvu/D1b220TActWCfAHYJH0yYizCAwuiO6nzL60ePefvqGW3bsFitODq9xckHd0jXK5QUGBR0Fe+eveDB19/y+uE99LsXqLal0YbXL97w6sVbdNXw63/4B9Kjw/48HXBanEB7M9LWMA98jPFjsq+fRC/gWRcdS3TVpRDy3zdNZ89e80OYP2QYvdZlqsEZeMbwfQiJ0TuOh9y3vzsDaHotjphUepzXsNummQaN2hY4ykzW1uWMeP8tR2eDcReIoJkGjEa3Fe+eP+L+l7/lzY/3SOuNDXgr42FejkrEaSw1Cuc701Scby4422yQAlZJxnqRssxj4kjYXcqui43xYgFuZ68NW1LUJWVdI2TE7Q8+5OOffEqa53RKjba6h2DvulqYfabD6zx/LR+cOWnRmxX6PO7/Ud4QcMPOpBLBxPXnFXrGKIw9AuHlyxfc/9OXoDqrKousj0wkI5LIam0iBBdNS13XJDKy52MonE+LINKD17gyLupw19G0LU3XsTw44M6dOxwcHlqVXJr2RySEquYojsjynFXXUZYVb842JHlMmmd0xoIx1bRIo0mkoFMdnepQSqOVfd9mu6XYbjk4OuDg8IA4jl0E1uF9BqyWxx35UGwKtDYcHaz6s6m6psVojba6RhaANB1GRq7/hr4fRdYMwPTc2pqS3ukUugp8jEAx9pyvyDGukKGLgKBNTSl7K3dDUti+/hrjhTivoZkAIDEsDM/0wsd2TVYhEXf3RqAw7Mdppcc1Fp5WXgFu9mmcdss1O/l2nzGex+yfRAbCLTG2nrvgKTQHeVAhjUbWW949ecKDr77h+ff3UJsLVNdiIsni5Ba//I//C5/8+peYdYbqCjYvHvHj7/9I9fApB3VFJMGkMa0xvG0qzl4/5vE3kju3TvlgvYQ4Grd7j8/G1Gy1v0+Cpk+AsnQdYIRwB67e4EXxnqmnUe5Hby7dow0Lmcl0TtqdvJM1OTO/xuXuvsMEwCcgn6Pf+xs0ENYp/9sH+PZeG5m5A9dlEyOMxAiFpqVtKs5ePeHh3d/z4sG3mOIdGZAEvjYDPXJFOL7adi21UdRNzbYo2W5LjNbky5zlIifLEuJIIIUhioT1FXNKB23crl7TUTY1RV1SVBVN2xIvMoSBly9eUIqI5e3bnNy5Q5oO/qF++e8zOU21Ofu7/Or1cP2zqFwf+9qNkKXTDEzBTa/18c8yXOsbOUXoQ1aKouD+vfu8e/GMAylIk4RYSlIJcRS5XSqSOI6J49geVtl0CNPZ8BJKWzu308dbZU5LpTrKtqXDkC0WnJyecHxyzGKxII7jYcdUIJFFkQ0gqIUhz3NWqyXLVQ4C2rYlEpIstVFKdaecxqilKkuUsoeYbbZbzi8uiOOY4+NjFnnuQlhj25AkdMrVXQ/qv6ZuyJLERkVOErq27SMnK63RIuXk1ilCSudPMdYK+A7vt+ntpF4ffjm42bNYL5sq3nbrpfNwkvvp4cHB6EU3NIUnZ4dd2ffLDkC5/HNXU2NGv8P7Yy+VMcAJO9RKy0EF+2cH05SZmwdBvrCe0+/TNCVW+5MIgFVw1au+e0fKELR53nENf5q24fyHBzz43e949eARUVmRqwbVddRKcbbZ8sNqxfEHd1jnt6nevuPx3a85f/yYvGnJpUCKBK0VsVa0sqHRLdu3T3nz+hnHP/+UJE/6Xvc0b06Dd9kW18tMWnPAOPz7a07ztR/owSjNaHjmb055SFDynv7qx2iyVqcjNtI4BW+zQ2vGdGpQTYRZCap2SZoZ9/Fde8VECB2BNGjVcvHmOa++/5rze9+QlBviSJDiae6kGsEa08oGp61Uw9btAm6VIstSFoucJIl735o0TvpCBjipaXVH1VYUbUnVNTRti9SCXErqs7f88M1doudPOfj4Y74wf8+tOx+SZvlA9PsuHLd7n3Px+64zn67wwbnkRlDurJQSMoAQ3BACmbEHw5DVoFTD4x/ucf+7u8TlBXEsbYTiSJJJQRJFbls1RFFMnucURUFTVggiIuf17fVpyh2Iue1qNlVJpTqSxYKTD27xwYcfcHhwSBLFxDJCRjFS2p0OUgq0VjaCcSSJTESapiyXS05PT6ibBoBISpLEbvOuqgojQAsLUqq6pmk7zi8uQAjufHCH9XpNJO1ZIQqBSgz5coGQkqbrKDalNaMVJbGRrJcr8ixDYIGbUm77uDbIZcbHP/nE+SQNHS+E2F3E4bgBHgFO1d9mOsjs/pwbf2OMOzF7KmlNmPVkioRz4CanHoiIcJWGdM2DtzFY3y1nDjw48NKDm9knZ8oMQZfv66HE8HOo/2wRO7+vA3LeB/juAEGvsZmCBdcWCyTmo7YasLF/jaKtSl48+I4f/9t/5ezRIxaNRgpFl7QQKZJWEzfw+vEzildvWR2sefX197z75iFJp8nihDQSSKFpGoMwCoEGo6jKgvOLC1rVkfi69ZG/GdXrKhBylWQ6fV5KicHGHvn/XepxjAi+m5n70zS/dkb0SOywMfd7CpbG63i0nuY0Nu85TCFtHOa+wPvJjFtlD3AyXcn5m2c8+e4r3n37JdnmHXkkiZZL6y7ukh8AACAASURBVKbQGYyxvl5GG7SwRnKMQWmF8jue6oqiKNBKsVzkrJc5eWYj71dVZd+ZCWIX7NYK1Iqm6yjrim1RWH/UpkYIwcFiyTrPiehoN68oildcvHuM6Ark3/0Ttz76lDhfDr3t+usq7c1lIOiq9XalD04vdYvro6aR8HhJBcSe+0a3nL1+ysM//Svtm4cc0pDFGYmURG4bdx9q2hHGJEmIooiybam1IpLSHasgEMaafKq2YdtWlGVFFEec3Drlzod3OLl1agPsIYmcc64UAqGNA0ed0264DjegFgploFX2dPBFkrJMM+IosmalYosRULmJUFa19bs5OeHk5IQ4isD7Wri+jeKYJDN0BlSnKLcVTd1wenqbo9WKWAhQNjihxtAaTW0Mxx9+xMmtW+hQM9Bz3Uv6X+BMYgPDsOM77Eahv+NCp3nMuDM5/WSUPWNCiH4XyKD52K3TX5NgOgVpAP02Dqxe045pQKxGaUpSp+WHn2J0bfrYSH4IwOwYxEyIw+gdk4Im7RyBm2km4Vs7vHNe4g3f3YeYtDzLWJNcv+11z06JKd3x0rYWghZJd3HO87t/4Iff/Svdix9Za00SS1phnY61tg6RQkWYquHtsxdoo3n25bdkRWu3wqYJsTSgO6IoQqFsDCcpUZ2h6TpwNMX3Yx+ReAawXKU92OmbiYPldf0Tbka6nLP/ZZqnufUTvK9X8c1n3//uAeTYYpxwIcf5r3X8VzhW4ePB90v54HQ9Tse+5z0dQreUZy949t3XvLz3HebsHbkxRJGfmwJNhzF2x64CpJBoKdFKU7c1dVNR1iVFWdB1LctlzuHhAVmaIhi2frdtSyxjsjglcieTN21nD5SurC9pVdUYY1gsrA/rYrEAoNEtRtc0m4In335FnmUs1wccZBkIy/t8J3k8t9svu+sewiNM5sM0hOnqXVR7ro8cn5y6biS1z0mJk8rPgxtNdX7Oo++/5fzZj6y6Lcs4YpHExLEkkhGptIdeGm2RqnHP2aPbBVVTW+dOIRxgga7t2NYVRVfRqY71wZrj42MODg45ODiwB2kiEdpqewZiayOKShkTGxtpVChQcYpc+oijgjiKSOPE+scoRdWULnbNhrOzc+pOcXh0xMnJCVmakibW54a2pQu5DvZcKqU62qYjiRJW+ZI0ihHaWI1Pq1AICgztasFn//QbyFPLPqYRr2xn95Lw7GgGzMaEey29+qsHOMMvZq/svtN/91KKf7L3v/FgbJZr/3WlESghGNI+DXMK5jSfg9ljADcT2XGOgO8IFPs78VJmMyXOlwgpY9+ffW8LLpoBEHl+JIUYZZmCG8/o54COAFCaZnvBk6+/4tH/+O/w+gUrA0kUE0URsZHEOqI1LRUNynTQnrN9fJ/61WPiiwsWUWyDg4opm7ZwzCCJkpTIbWjoQUhA30bA6xoamst2iYRaht5heQ+tvDFpH2P+i4u9HAwGV+hnZGA5uswMuFvTXX7VywmBlm4Qc0eFTis4KesycONpo+++3Zr1cblNS7N5y7P73/Dy3p/QZ29ItLICuT+ux4yftNu5FZ2yQn5RFVRlxUVZ0KmWLI85PnI8UEZod6RQ17lDnZuWNmkRUlgrSNf1jsVlaQ+IXroNOv7sxa7r0K1GKpAdVJuCty9eUVxcsDq9vUvvfZyKPdPnLwH8V54mPideh743YJwEtj/O5lTNvVfCM4auqnj+4CGPv/4GsS1ZIsgi62MTS0nk/oB++7VWHdoYYhc/ZlOWqK4jQhAhQBuU26sv45j1ImexXBAlMXFiy06T1G7l1danRmttt+A5QmNNVj4Whu2a2PvqSEmcxC5gX0Tk4gsUZc35+YZ3785YrNecHB9zcHBAluc29Lq2znEGi0rjOKZtuz4QoJSCo8NDlosc6WKsaOWcvIyhiySnn33GZ3//d+godmpJ0EK5IIjTQzg9YPP9TbCwxmPk70+f9eO/f7qZQXEUqjmmZ5qNZ8j4++XY+EakAZh5Qd5MMyAmbd3HowYGFwKUmT7y4wYghu3KUw3rvs67jrmpb9sMU519ZpY+0BMr4a/MAG5gT4j9YQPDlLj1faU17eacJ1/+gT/95/+D+N1rTrKYPE2CsgWREWgtiJRAqg7akrNH98jTjON0QRZH9qxafGwQQ6c6yrahVJrGCKLFgtXhYR9nK2jeDqjZ5yuwU/8rTFTjMvcLhH99SYy/ehoRzuX3TX7CzczvvXNYXG5NGkSEMT26Vv123jmXZbd+I5AjnODuwJJRDcXFa5589zVP7/6B5tULcqFJoggpol6ziDbIKEILjVJWOLC7eFV/pFBVVyjTsVjkrA8OODo6InMx4/DWjqqyQrbuqJoGZbC+O11LWVVstyVlWSNlxCLPWSwW1griTFnSQKwFKRFNazh/9Y6zN2ec/lT31gs/CKIHOVevj/dN7+dkPE07NP0qpBp84hHywDgFgou3b3n6/X2q12/JO00uI+tcHFsAIQ29N7fR2gbo0pqubemUQguodUfVVAiwh4gZQxInpIsli/XSOhJHUb/ARhKYACGl3cFg95u7nU7DTi8PRlBD23qTmXAsXEaAQHUakBwfn3B4dESe52RJijGarmt3ulEbTVXXFEXBYpFzenrCIl9YabVpLLJWilZ1xFnOL371a/Llgk6pXkVpyxxMJBbZew3NvLw9aA/meLUHRvt3Cc3Ngf5cQTHUyveRlY4HRi76/9zEv6nE3PXRUF9m10HvBCyGIxvGkqWY/L4O8ewfAOl21wivpR98d3qpiCH7GMBeVvT82F6u+ZncCyTYXfwyyTvBZD3YDkBjL3E7J3m7djqe3fueR7/976RnrzmRmoW0dRFOha2cBkRrG6+qbds+TtUiTYnkSJbEx/dQSlO1Ldumo9IRx8enfPDJp2RZHtAsX9+rTUtzpv05sBk+/9dlprqOeWqyaNx3Iei3EV+Hf1xaiyt8oKbgZlrr8Mlw3opRBl/X4Z1/btqroRIa0Giw0ayVot284fWDb3n23V26s7esJGRRZAVvP2cAEQsQcnQskI+9VlUVVVXRdh3pIuPo5JjDw0PyPHeimHH0zWprmq6jahVGNQjVWWf9uuFiW7AtKjoFqywlz3N3QLTvFI3QmkhDYiSyg2ZbUWwKG8olDmHHPNKcNUv/GX19/aMaZq/6l4/rOLcTIwQ2/vfA7DSRAV03vHr4gNcPvkdWBZkxJEL0WhvpuK8JIwMreyp413V0jpDVTUNRlmAMaZSwXq9YHx6yWi9Zr9fISNqDOdPEne/UkCRpf0oqUrh4XCZo4wBi/I6q0K8kcgMshDVR4RZbnMRkeUaWZxakeTu+EWih8RFKlTAoDK3qOD8/oyhKPvn4Q3tGlYhc+Hjr5NUqRVFVHH/0M37+xS/tFjyP4Ef9Ph6HMEaH17R4rcF48pjgvgU3O2bhPYR87s/PkXFmeo8MMEHYgZvtauxrGGpnhDEjDasHQOM2j/vBg4592pLrVqa3bo5e6AFCAG4mQOKq0wmvBbquWWcTgrlxLXZ/eZA72EH7pFwoBVG3vH78iPu/+++o1885loaTWGIiSTPpS2NsIL+iLCjKiji18bPyNOvDz/v57RmC1srSkKpC5Ef87LNf8MGHH9rQEfRdu9NfU1Pa1MQW3gvrt89s5R7Am3PlZI3f1DTLjMQoQ59vpHkMnr/s979pCop28Nldnq9DaGLdV7fxeO9/9Twgs7TXYJAuplnx7i3n33/Nqy9/D+/eso4leZIRu0e1cdYAz696CYwe4Pi4b3XdkOYph8dHHB0dsVqt7LxWdt5jrACvdELcdQhtqJ1Q0DYtVdWwuSioO8VqkXF8fMThak0ep0RIjNY4fGZPEGgNptOIzgsZHUmaBt0/pkNzGtB9/m3XATx/ng9OT39EL1VdWk44iHMDKjSmannzw4+8uPs7os0zEmlPA88Sa5rqmYih3yJt3MCa4ETU1p0Z1bYteZpx69Ypt2/fZrlckOUZ+SIniiK00TRObVdVFVmeI+IYEQkQEUYptNF9+8RMe6SUAdBxYM0TOaflSZOULncBCKXspUt6YmqPdGiVoqore65VWbBe5RwerknSCG0EbadodOdCanfUIuLv/+M/c3jrDjqKfE9efxzYnSCDhsdflwhhAmA0Yx+eLNJ5cCOCe4zu2Snu92w5UHXDQc6QdlldD4CEAOx5YkZIB0JCkDMPbq5LzH2QsylTnH8+qOMO8xi+CP/+PeBmlpgzz/Bn32F/7S3LQ96BpAzzwABKSHvkyptXPP7tf6N78gOnCWRJ7tagtMcGuFgdPhR94yTPTnWs8wNWy6VVyXuQaiyT6Izqo7KWdU3TKT7+4EP+5te/Zrlee4Vt0I55kL8PtFymGbtUC/SXgOD/iWmOOb3Ps1dd38kjxDAK7yHd9/Priur1gkqQUfZCOQEOH5vsPWi+vA67/eQ3fGhj3SOEgmaz4ek33/L27pc0L1+QJwlpZH3GpDAIY5BEGGnBhfJ+msFf09rt4E3TkKUZJ6enHJ0ck/uwKEL2Qoj1azXEUUScJIhW0VYeHLWUpYtWnKUcHR1yeLhmmS1IhY2/poygMwKMpDOSVis63SGNwkTCgajQnDi/Jq6rrblqjr2HicoVZEa/AlVfIJtdMXnCWzbcNBSbDT8+eMDbl89J3U4GGUmSOCLC7miSwgbqa9vW+skYq8nBTQqvwVFKk+c5d27d5s7t2/0ZUXES9aDEGIMWba+6y+uaKLY7sYSwcW9kbHpN0cjDSNh6R5HdMo4QKKN74AK4MqxvTqIsCDKB2tBrnZS2UY6bpqEsSzabDVmWcnh4SJLE1C5YYas6GtNRqYYSuPXZF3z0q99AmkNwBpL1zZj2srs1IyFepV6/Ks1JrtP3eVAzngG72p2p6eYmpzkIdgXMD4DPGOz5333O9+iAfdJO6Cuyv7Thbghu7PS5DIiM6zmNYryfvAdmtD33R6bQyV1pOurNGY+/u8ubH+6xoCOLbbgIpY2lEcoKPVaAsVthL8oN26okiiXLRcZykZJEEagxo9JKU9c1RVlQVRVRknF66zZCSjbbgmS56LU4vv2+rmPt6P60DwSEZY2uwXsx73+PNDea8/PmutfmaclYUHb/CcY7nfatIzGek7sauLl3769DOI2N4w79btQeQV1v3Px8MMYen6CNItKarqh5ee8Hnn/7PertG1IpHX8SiGCXZn9grJQoAZ2xIVE643xoXEDbKE44Ojnm5PSEfLkkSZPRXJNColDO78e6WUTOp6aqaoqyoWkVcRRxeLjm4GBtfVe9cISwY6FBGUmLpBGGRrTkmWB5ZAPb+rZetREp7JtwDN7HbHX1NnH/Ig9aDIwGbyJp7Xt+Lnmbt9GKF0+f8eiHH9BNRRzJ/oRtKfwpPjitR+A01bW0qrWRgo2mbTXboiTNMtbrNScnxyxXSzsppLB/fkCltFKcO8W7KAriOCF3J59KBATRjL3Wyg+MMRbgRFEE0oIK48xnUmsiFzsnSRI0dhtfpzrapkG46MVVXdE0Ta912m63dF3H7du3Wa/XaK2pK2s/VZ2y+TBkt2/xt//yL8THJxgZBYhYYLfg4bQGYyAxBjLD7qnpmE2dOt83eXX6lIkPwGYMfkJQMzWt3bTUEzkxJmEhc5oCu2lrhmUxD252JVUbtt8I7wcW5JkDDMaMT1Ue3bpGrJbdEif17wdrWs1d09KIcUzLCTN4Iu8fGwNdiUY0Bc/v3+XHr/4HZvuGVAbzOnid0d6Ua3eNvLu4oO1a1us1qzwmjQWRtM6YJpB0K9OybUq2VelU6Tmvnjziv/zv/xvLTz7h81/9LR998hNkbBmDGao9u8bmmPT7CBNeEyDM1WDz3zcNK2GfwNO3dk6LNQMkLn1bMPc9OxppHv3/o2Jm6jJTpQHAXK5FDk1Wg4g/HXfbLyacKHNlQW8i9SBJlTVvHjzi0e/vUr58yUpCnucDXzTargkzaI+001wqH8ivrtlWJWVTI5OYo+Mjbn14h/XBuueJAzgf9sRa7eegvNBaU1Yt27IhjiIO1guOjo5YLhYkUdz3k+7XkkFp7EYYozGRIFsvWB8eEUfuCAmNRaa7J7Ve2lf7xmFfup4GJ+A5ASkeK+YcQd2dPJdUwIBAU16c8+jBPYrzdywjSETs/FUEMiBinTvTyUtLfjp0yqqgq6aj7Try5ZLFcomMJNookAYRWedDGUd2u6e0W7tFAHCyfGFNVWErhd1ubpyGJmxP5KIpGyl7B2TtmHsURURxRJ5npHlit4Wrjroq0WlK13YUZUmrFK1qKYqCoig4Pjrm1q1bCCHscQ1VQ1mUFghpRR1FfPa3v+LDv/kFXSTd+dt+KTkn4x4kjE1Blvjg0P94++0U/PQDdI00lULntDX+/cO96XlLU9Bzc9OoeiPrT7g6wkUzQ9TF7uVZhhi+VHhtyWRsd4ZLgJk822uFxYCHJ6kHCjMM+lJNDpOZYiay0Gzb5kvxRgfLE6T7pdFdy5tnj3j41W8pXj7mUHfESR5oTW2/aOdzU3cNRVVwfrGlaVoWWUKeZVZKVh2xiBASjLS+abVqKKqSTV1QtQ1ZFLOME5LtGVV5xrtX96nePSX65//E7Z/8jCjN2UG5o/aNAQ8M8Ts8/ZrmCX+Pu/Nma3Cuo6wQsBN0tL8xd33P9ymwn1cUXUFAJu+8As7PFL8jshACPJ/GmpzJInVrUPqx7l0DDF1T8frHH/nht3/k3cNHZLIkXg/R9KXwm2cE3tXBC+La+ac2TU1RlGw2BUobjo7WHB4fkS9z5y/KYE0wpgdX3kTWuc07SmnqpqNpbSy4RZZwsFqxzHPiJLHWFWfite9tadtueB6DTBJWRyeWtwoxmitjWhQY/MJu2wMQ/2INzqiwvRfHrqGT+l+Zorrh/McfePPwW7L2nEVsSGVEFA0B/awJykVlDMCNJ/Jaa+qm5uKiRGurWYnjiNxtX0vT1A6G28ot/I4ncHljinJLUVywXGTEUeyItBkR6V6ViLEnRTuHYSNl73AspUQnMUpZM9tiuQDhAyc1VnPjnL68Y3ThJmOSpNy+fYvFckHbtGilkEDbNGyLkhpY/+xTfvFPvyE5OcJEEqHoAY0NsucC6126yOcokuivC+EQ/SWTZ678EODMaSK8RgeY+fzrADfh4Zk+iYFmje7saqr6J658T6gK3xmFQDjdwZIhvXSVMn5HuQmeNQyLdSRxMqI6PfDdU+ude+5Zv2tvVNm5Oo/eLPAeWQKB0CkIgzIlF2+e8fzr39M8f2RNU9GcODUIQlVds90WNE3NYpFxsLK7J6uqAgPRMiLJYtCatu1s/mJLWVYIIThYH3B4eIiMJJWq6eoNz+99w8HhKeujE5Ynad+QqUnjKifJ2T6c0fh4bfHN1dzYFILi/tqMus6P2F4N4BygCebPCAT2L56+ZoSWRvdGgP9903W1SgzjP6+xG4COcDwGsFHzUTTtltfPHvHdH/+V8x8fkreKfIHz4XTab6wvkAz604OUruuGLeFlSdO05IsFR0fHNnp+oHEJ+8VgfUE10Dn/1LpTlHXDtqypW02WxBwsF6yW9jgHKRzIcm3u3Aafuu2oO2th0cawWK249cGH5PnC5b1sIALCdQ3h4ap0dRycGQIV6p/nBDHLb+edTP3TAhBaU52d8fibr9HnL1hJxTKRJJENDR3JiCSOHdK0Q+D3y/t6+UnfNh3nZ+cslsv+bKrE2efjOCZJE+IkCXup/2r37iu2WwtwFvmSSLigXpFXnYt+27PSCoR9jqCtXqXsz7OKoog8y0BA01pn5qpuMAjatrVHLSjFtiqRUnJ8dNRrnqJY0taK2MaipFASc3jEL//lP3H8k5/SxIlTXQeEUfrYN39OCsfIL0iDt/XuzCUzd3GXsM0DoennlFHcXInVDvfE6Xx0z4Ig4Qj6SFgcSSrXpLJhHwmCEgdT2VCmq4ARE6I699YJuDEBIxUQvHgsSF1Wc18fz6xtZ1zauP6dEzNTT1u0oty84Ye7v+Xsu7scqIZ4tUQqjWk1Wne0prMCB4JGNfaMnKqgqkryLOXk+NiFoDfujDcrYMTSbTZorf/bdlvQth0H6wOOjo9YLVfWR67qyLSkbFpe/PCYTz5/Q350bENNjGo9luKvEhDm7l8aPPOvMI20kOyCm1nQDIPGcUIjfIY5SnGVxnE8h+f42pBzNNcnmtKd1buD5fb7JUKgsQu1N0Zbzc3zx9z/6rc8/f6PrKqCRZKSJwuyKCYV3t9m2vLBlOTPMKzqmqZpSNKYW7eOOXFnLdrxsPanfodfaOYy3jG5o6xqNkVJUdY2Jtt6wenhmlVmTxWwGij6Xc2dshaUqmkpm5ZNU6LzhE8/+QmffvYLskXu2r5Hhdz33yxrmcl3NQ29AuAEAzuiPm6qzCkBJqO9rxIGUG3Ds8ePeP70CRGGWEAkRG+aip05yarPFMKfkqqlDcLXGdAWCSul6ZQ9OyqSNp+PeSGlRMYxMtI7moVB0oTGbaXLshwpoqEpbtu4Fvav0wpjFAjTq9O8tG60mzCOYAsp8SK+tWXWdueUc4SsOxtf4ODwkIPDwz6IoQV1Co3BJCnROueDv/tHPvnb/4BIVsR6MgvE3DbxoL/fm16OH/CTTmCbIwFtQv5lTXnTc6jC5/1neH/u88/x+/mflXbAeu9YO7BlD4BkD/bDJTSBGZcBwpn15K/0fRhm7zmCGNkDjPBgVfSC0agts3DNGoxcxkEoCStivDp5h8JPi9ptQz8Hhl4ZT1INoqGrLnj+3V1efPcNcbEhw577Zt9tNxgope03YyjKgotiw0VxgYwFpyeHHB8fEkcRRhunuq8o67qnLXXbUpQlRVFhDGRZRpqk1qFYayJtSLUgVYLt63Muzs6503UkUrpFEY3aNSfB9112hQ+Opaxm+GE5EbvE9mamuXbZNomda7MamOBzv29OuH7Yk2eOBrpdmnt40k7+oSK2DQEou0obNB3jnXp5/uDAheo63r16xaM/fcPLew+QVUEiOhdhO3U+NwbJELLDYP1llAMYPtJwXdfUlQUlq4ODPtZN1PuUBmYp7/djAh+azvrwlGVJWZYorVnmKYfrJatFbnc290Nh6LSNbty0LXXbUjWKolEoJOvDY3762eec3L5jeerQC7N9v98cO/YZvS6feA8TlRhsY+DN3uMKB/T1Upu9q1yx3fLjg/tU2w3rJCZPsJFFnYOslNEOkbDOzs5rvTdbGVSnkEKSp9bfJYkjMHbitO6gTAEkaTLUFduh9l2SplGUZclysSLK4/6gO9/t2k0GrS1htUKwcVv2RK+56jREBmIZOa94QBuM26XRdaqfTK3WLNer3mnLgiRt/YpUy9Yoqkhw8ulP+OI3v2F5cEi/HTa03QfM4vK+nwc7w/Wpet1fGy3rYNQH5udNN2Mz1RjchGxyH8i56fE+psRqTmIb5flzy3cd6/mcvTfJ7POMJGIxxEXyD4vJzzkJ1wGmKwwqo/vhnNmRii9hPtMM4R23yuiaLa9//J4nd3+HPH9HKnxMrMi+Vw4Bzbwwsy22FFWBjCWnp/bcN3vOnJ3gUmLjSDUdXV3QNS1lWfH2fEPVdJweH3G4XpPH1r9AGYNQ2JgejaYVlugPpnJGofyvBDB7QE/YdpvRgZpekzpb3I1JV82Z0ccIxM+tl2s2djq/LhEc+msToeDS4mfqCe8HNadtCU8B8HHxtdacn5/x43ff8+r7B0QXBXkUkzlzb6fs3I59MD1XpAs3Y/04m4aqadgUBZuioFMdy6XlK4uF3f03rf3gd+NMVC5gbt00lFXNtqzYljVxJDk6XHF4sGaR5+OI3mY4wNPyLE3ZQaMly5Nb/OwXv+ajTz8nThco1x9eUN439p4n9xt6Lunfv9wHZwpW9gOsIe+eCTRd2Bdv3/Lq8SPSSJBI3FEMLuy0sBoc4XxdlAvqh0OqbWO3eHdtR11VgOb4aMmtWyf20K8ss6eDx+6EcCMQ2iCUAalByGGySXtYp4+JU5YVebbokbY2plf/2XOqhi2hQshx/2gDSiONIJUxnRJ0prO+MhqUMmy2W7v93BgSp0I/ODhw52u5oxiUojId57plg+Czv/mCTz7/rDez+a18tg7Cgb557UkvuYwAzEi5G+QNrw1+FD3h9gx9OvQhoJn+Hv0Nu6vm0hxAuEnJHzbqa9cvMDEQsz+n7lc9IyHQr8/k3bEfXQJKZ6TckPgLgvklhvdNsNIg4faFTS/Mq+mHd7pcPUowNn6rAdVVvHx8j4df/Svty6es0cRpOlQJAZm0tKBt+yit26JAa83h8RG3bt1itVwihEArG9MqyzIapalUydZJqMW2pqpblssFJ8dHrBdLcplYqdZIDAmtaWmo6UQLkehpQzgZrqt5nJPubZ+43Zrzw3Zzk50wu1XuJZswY3BvDvDsMI8dFLP7erGj69m5D8N8DZnr9P1zu0r3AZ2r0hxQGzS9g1b/4vych9/f49m9B8QXJUsRE2d5Hx5Fa0PTNEgpSY1BRM4VQdjnu876kG22W84vNmyLgiRJODg6ZLFY9M7ttn3eNBVUqr9mei1QWdVstiV103HreMXJ0RHr1cqd9zg8pwPtkRXYoQbIc+785Kf8/G9+RXZwjHartieXI4FsVOSot/qPyeS67lp7r6MahJnsnPLXr6nyC6+rruP86RPE+VvypSGOtN22Jt1xCQJwZykJMZ5odkArusbGwxFCcHp6QpplFrHmC7IksSePu51O/lwqof2J2AYfWA4hrAkrjmmc7VJpTRzaJvUQVFD7Qz6N3f7dS8tuwHckNG3cn9UCVVWN0oo0zVjkC9Ikdf4Axm6xU5pW2ZPCSwRHH3/Mp7/8gnS53Flg/XtmtoXDVCW+C24GLU2YQkAUfpodE0eoubCRoOUsERjqNp0XwfPCOUrf4KQdidL973GvSqZGqP3pKjB3Fejp51n/wnCXnKuRwY7HJVLTvnebMH9/fUK4Ly1mD8XIOQAAIABJREFU/80xyHI7QITCSINWiuLVY97e/T361XOO8ozMubsNUVu1PRvODCcfb7dbmqbh4PCAk9MTFi6QGQgkqt9GmyQJUWRV6pttSVla6fjYMQRv4jYGa5o2ghZBJzRyGZMv7U5LrWwkcimCRRFoYEbr5lrJo8vhykiQuJmY32kABGF07GBK7k2j+ej+D2nLdTSKY3Bzue6n79pL1sBlJuO/ROia0+QYbai2Wx7fv8/j77/HnF9wJCJSmSASB/iVRis7z5vWOu36GHEI0TsU186pviisSWmVpr1Jquu64Sgh3wduhKxxQY3MXE3bURQ1ZdkQxxGr1ZLFYnwcQ79jyx/K6cxTtRY0UnJw55Sf/fqXnH78ESaOdkyU486wwtRUuJ7JOHns6nV17Tg48/euS8rH5RljUG1L8eoFqWlJsACHKLLgwznLGiS9ZCzAGGUHXOveNCWlJMsylusVi+WSNE2JpGSRZoOpyxF47x/iAQ74xYntYCFole4dtKwqzuWxdii34Izdci6lO9PKE1p7+Cc4x2WjEYHzpmEIHBhHMYeHB6zXawv2zs8RMsIIuz28aDvOWkmXHfN3v/ln7vz0C0yUuH4wo/73IM121uVj9r47PHbTxA3SERhLaMZg1Jr+hnd5bdDw/p0a8j7z6d8r+cB2XkVsFWOOObl7+1ZG2Df/tmlCQEOQHWr7mCfaIfEb0Zl97/GMxeeZk+AvSSOzs/GzSiOV4eLVK378/e+4eHiPFIiTGCEMkYEIG9Cso6NuW1qjaI2iahuqtmWxXHL7zh2Ojo9JMxsS3mg9TDbXF0JKlNJsiwalDKdHhxwdHZLnmQUvxh3Q29qdjtq1b7FckS+WYyGi7wZPU0a92AsLc5rVSa9MO2lPvpuVek1eqEX0ADmYL/1NMTzX90g4J72QNtH07U9jED53zxfUywMza2BOewO743btNOHTIcjpHLh59eA+T7/8PeLVSw7RrFIbRVjEqT3Cp9NohQUfboeU9AAHK+yXZcnFxQVv3r6jrBqWC3sciQf+QlgzmA1dEo/bY+jj5nStPUjzYrNlW5aA4WCZs1rkpG7XlH1kmJf9CeNNQ1G3FCYmOjjg0199wYef/wy5XqDkNfQok74Pj/HppTMzXkv/Nj44Yvzd2tCur8WZu2+MldK6zcZtB/cKLOFFRBDSmUPsFmxhFOgW3WkiEZGlObFMAEOSpiyXC/LF0m5fw/rv+AGxW7pDphI4WA06s17L0TYtTdOwXC5t9wqnNu5BjtVWREniHBUHzY32gyEFQkvslusBDHg0fXB4yNHREYk7Xn5bFERJSuR2jdVtR60MH//scz77m39AxIvBpCbGxBPA9NqucT9fPRHGqzDU3Mwl20ODc3XfttCjdvqGkLjtlDbU46abp3aSGVGv4TJzvTCf3lt78+f2jWPQl4EbxKCZ6vMEXMuPbs8QBl7Vg77ghZdUZdDaGeOPVFAIrejeXfDkj9/w+sEPZNoQJbEL9qndvB/eorSiaW38mqKpifOU0w9ucXh6TJZnvZTp16XGBh7rtDUBN02H0po8TVmtbDgJKaTVVLszfMqqpmpaqqaGWHLno484ODwc1Pyj8bBr6bI1FDLT0dp0aHGqJfd9frMhziVp1D27IGfugTHwmZYjJr9nf+7NZ6uxuwam32fvz2hhrkyCPlDjoHE1tJsNLx/e54ev/kjz8hnHdKwSQRrbCPhRFKN0REeHQoE2tKpBGwOq64/88ZrLzWaD6lqWecp6tSRxrgx+V5U3cS0WC3sEQyh0T8xcm80GozqOD3OOD5ZkaYRA8/9y96Zdchznne8vInKprTegwU0UCXARJcv23GOfe+b7v7jnnjN3bI9lS7QoieICkAAIggC6uyqXWO6LWDIyK6u7Kc2MwQmcRlXlnrE8z//ZrTOePjgQgYfbkJtOW0dvHbaueOP9d3nvlx9TnW4whcIh9ym/4yApc6Nvbpj/Yvaga9vtE/25gz/HO+LXa5hVDE3T2ysPPORE6o85ZkSMKBqIYqydUReLZDYSUiDwTsUR2OSSQcrCHIiIE7kHOaMcNmVR0mtv04+RUNlL+efBm5KsT8dIBEzWOZx0uEKA8eBMiQLX9Wm/MYblcsn53btsjjbBZNV4hO46pPEVb5quY7k+4v4HH3J0cjJJhz/07dQ+HE1BxCdNlHY6YhORa4qgAwGfI9Rj+TT0ixhMi4cIxWGePd33+pLzpBKf4WIju/JkbPL1MKfBObxecmaQOvjAsTeDw2vDV7PbzLfxvWNfDDNsIPlD11yHlgdJ0OFwWvPd51/x+LefIXZb6nUZNLKCwkWzlE2gxRiTVPNtp7l795Sj4+NxOghI2hcrvENmqzXbtuNy1wKSs6MVx8sFtZIUQoCz6Czsddc0XO62LM7PuP/Bh6w2R3mPDN9FoAWj7yKTPA+r2NOZM7RWzsy1167tEYVh857h6BD2EINw4Iapluj2j3+g8frbo4A/QigX2VtEPd3c2QdHyQ0Ax/QdL795yLeffcrVs29YSs2qlCwqmSKI/b0VDgvCYYWgEIrW9PR9F0oD4QHO5RVN07JYLDg5PqZe1MmpWAiRtCzRr24ByWE59y1tmoarqy1GGzarmrOzY5bLBUoIjOnpnE29IKzDdr3PmKwdjZX0xYLNG2/x7kcfcXx+jisLn/jWytFY+Knsgi9r5pKQeNe+4CgYlkA84jZanFvkwZnbnsXwpwfbPydnBvmlPAq0dH1D77R3nAIEwU4oIlCRqfMBX0+q8En4rPGRU23besdEIbzvSqcpi2ijDIRFeAlTFsGT3IHADk63zquxhfCVwo3x9am6rqMKaFcpX0yMUCdLa43WZnDeiv8EOOk7wkeBKUTRgZQ+IkMINpsNp2enlFVJ3/X+Hayl63uiD0tnLA8+/Jj3H3yAVGUq9JdHGE3V5PlwuJTefU7FHUPuvLwUVcnzHlbTq09uNPo5/Z1vE+G+Ijt1Hyq/7sqbuceLUlncn/sF/NU2/NhPOai5hQQ6aGXC5w2EwMVDxnh5/7qTR5uCnLkXGM8/v96kwNMQ5xDOYvqG7778gj/883+ne/aE45VFySqUa4lMXmIjkwhEu2lbmqZltVqmEifRqXLQ0Ia1GUDRrmm4vPQZjleLkrOTDetFTakkEl8PqO1bmr7lquu5aEHWx7z/0d/ws/c+xBbl4F+T9cPcu1/XNzmBfs3hyy3a7d5gKpDtb8uk9WuAUDxvL3Lv0D1FoHDXCFkHz0eQs+iR0i17Zjfeuv/MDtrdjpePv+HbT/+d7bdfsRI9y4WjLKAsFUoMxZjToozfIfDNjqZtU7DNbrejUIp7d+9ycnKMDPyp6zpiQWqtPViPATOLhS9Qm2t4Li+3bLctZVVy9/wOJyfHCCHQXYfpDRaTamE5Y9C6Y9drrjRc2gJxcsY7v/xb3nj/Q0S5BCTCyaGDJl0TtThp117HzrQ0Oa5PRBvbzdXEI30UbjyHE4M6NAvHCz+fQAowfY9ZLei2kuUIsWUEPJvIRayYHQbbGYeWwXEwJmbB2yQlDimKkWMVUoTEXCA8KPYgJ+Jx60GWUgojNF0o6FlV5aDnkF67pJQa6nckxuaZ3Ej97vWT3qmxLLHOIqVksfAOW9F0ppRESkffdrRWQbmgPLvLJ//XP7I8OqUTgy/SVAOQJBO3Nzgz38OTRnAjPKSRiQn4lt438W1BHjmWxiEdd7156ZD2Rkzn0E+g5VmMRQSFLv7KgcVwTj5mt/G/2QMnw4504etMW+NnCGs4A7zJFBx2x6zg14GbXIK95slJZD6ct28mDcc4FwKnLE53vHjyiM///f/j4rs/cCJhUSwphZdh032DJibWjur7nrZpKErF2dkJ6/WaoigSUc+1tFGd33Ydu12Tkpednmw42myoqioAVU8L4j20BS0UR+f3+Ohv/hbKmlhQwvsAhr4K1NqNOKg78P2GXhTjcPNp0MLr2oYRHubvAAJuAuLDMXvvOuKC8zzl8BMlUWNCFn+84BHdM9IsF5lQE2iAjfwyPYH37ZQOtG559vQpT/7we14+/JJK71iuBYW0KCGTQCiEz6cmhB2tI3Ap700EMH3fo0KS2LvBsR6g71qEMbSdweoeE/hZFOSFEEglM83NFbvdlqKQbDZrjo+PWK1WWGMRDjrdoPs+zUVrNJ01NM7SOIFbLHnz/gPe/8UvWR+fed4by8VMGMuc4DTwHE9Th20TESHOhVu2W5uokh1xH+WMjwsTaSppDs/nmaozPbIsvGNxKOvrsktaZymUSGUTYpEu3euQYc5RFFAvah+D74KOw/m4fNfZRLRmOzIyilDPA+cthU5KysUC5yyt7lk456XHqAoS3kFRKhWiqVx27YG4e78h6TMfG4MqfImIWIFchBIUEa27UP/jsge5WPLxJ39PdedtelVlKm98mQl/k+HdXFbD56CyZUif77I41LzMwPR4fz+RQOVoyPfm3RjkzAOafeA1Pm56k9evZXQYQcjkmfaJlMXYTQHD3LVuy7QmmGkPsh4CO+EZXHzmQ8Bp9kH/gjGI6z+0nDGPQI7w/Sbw0uTFDz/w9aef8t2fP6fur3x6CFcgXI0MavEoIWtjaLsu+Qu0fc96s+H4+Dj5HeRrwAUwFYv0dr3martj27SslgtOT09Y1PWoTpSPCul80jIH6njDR3//t5z97G2MjLJ8/pdeOLtv9i0bv9fd0vQXNQeJ7efAYUZqz1sO5CNNm+6bCgxz4ObgLSY7HAnnM5QEmVUuZCe5MK8HzbnD+aSvIXeDnwUexIhhiuMQGBEguu7YPX/O0z9+xsWjr1jYjqqWVCVIJSlcJjgF/oGU2DzEm1AI09iQ0qRFCsHpyQl37pyxWCySIkAUBbpQdK3FdB1W995sJXwW/Vgoujfeh+fVq1cYYzg6OuLk5MjnjhICQs1GoxQ6gCRtNJ3x5Ri2vaAvV5y98zYf/OqXnN29Cyq3lEyoeiBGY7LvJtti743xxoTlTASn+XZ7J+OJADY6ZKJinH6dTkrvMePAWZR0XlUtJmFsAlShKItgT3cSa2wI9bY+cV6oO9XpDkxw9IU0Kact7k+SEUOaI39aSPqnBH1v2LUNG7NGliFGVUlkVVECereltxbjLFLIlB8FgmO0CIpNkeXMkZKq8h7uhSqwzoyIvhMSVMmdN9/m/i9+hSsq7Ag05NEF4Zk8+kyaBA9gMi1SAjXx92TMRMaI3BTAjsG3H5foazOoe0UMDY9MVMR+yO81MIND4GdsvnrNm1cLAGPgkeODOI9veh0xN8bpNhMm6g8cnTd3jeGBxsTkkPSbP+scyUjbIogRwzseer8pARprg3xemrZpfYKzz/7MqtEsyhIhfEisbBoPIoshUjH6xLy6uODlxSUAq1CeJeWpIgxNeCkfAqs9OGpbLq8anLEcrZcsF/XI7OusS4nVtk3LTpScP/g57/7qI1jXwKBJFpkv3z54H9Z1AjaHOjf1iQfGJrd//STaFCbkE2NYAAdk3nSYP2Z+ft7uGWC/07JUH1IG94i4qrxQK5ybcZJnEOycwwnv0uDzl1msBC0slpg9X1E4SWHHfMwJH6bdPPuOJ599yovPP6NsX7GqoapKpAIhFdKKpETI32PqM2edDT6iLX1vODk+4s6dOwngC+HfRxSFj8YCH0VILC001G4siiL4sF3RNh3LkAdqvdmkfDee7fmSR50QHlh1LY3VbK1lZxSn5+9w/5ef8Oa7P0cWRdKYTkdm9CPTTB3UWN/Q/vooqkjMyLQTRE+NQzM1ENm0Tex9ChylFAgXyr07Q3BcAfxAFEXpq6dWJc6C0R4ei6BBEcHvRkgo8Q5TWmtf3iA4YBlrx8QrTORhw7ijHN5E5QDdeyLXa58jI6IEISVFVVEYQ6+9Kauqa0CCLJKDtPBw24cSB0eumP20LEukFP6dwp19yHlBWSz45a9/zdndu9mzy8kkGEBEzmLHDGWsaYsgB/Lw3CCdzDAidyNFzvxMZJA4AriJeYG883d8gvSUw2tk95v7/lq2v+Dx/vI3EkkjO2f6SkcdAjzhp7vmuPwZI4AVe3vG10lANt9+4CXnDVuewfRa8/jbb3n0pz9TbzsWRe3TRTgN1kuajuAQqbwk23W+btTLVxc0bcdmvQJI4bDTUFiHZ0ptOO/Vqwu6ruVoU7NZVUjhfHRIYGTGaIw29BY6JOXxCe//8hes756hlUTYzNk0rKeo+flLCfV+n5GEjdc9RHzU9l57mLNisn8s/PidP0YQmG7z7UBfuRBFF0p6GGu9Nh3PAL1wJ1K/xyva6HJgLJ3WtG3HrtlhtMYZizY9VkCxqKgXC0pVsihrFrXP9uuwOGPpLl7y5Ms/8fjzP8DVSwrpzUpKqeRALiaAJuWZCSV9Yv6nqGE0RlPXNaenJxwdHY1KMWAtzpoZoTi8l7WY1ufP6bsOrXuqquLk+IjNep0sH4keRPcOKemNT89wZXq2QLE+4p0PHvD2g/vU6xU6vs8B7crIPL43fje1m/z99tvNeXAmEzG3Dc9pbsYS5OThc2QrBQRflkH49/uVKqjrOjg0SaQaJr81ZpCcpfAERwx1kJzw9kvnfB4LX4fK59dJIdwJpA1EJNpWowmoKAtwPmR8US/2FlVZFLQB4BTRFCZ9rWkVzE9ogzEhDNYaf90iTOpwORvV5w4MktPze3zw8Se4us60NzJphSLAyX34cy3NgZEksrD8uDQJ90SXw9L3TSkCRpoEhvkQFbDZkQeg/U+vHVoLk68TLdX8Z37soBHMhYNxExBMAZlD/0SbMAcm9wiLmAFT2fvlz/+XMPLcb8ni6KzhxePHfPE//gX3/XecFpJa1VCCtRqjvT9d2zR0KpqEbQApr7i8uKQoCpbLRRJuYjBCvVh4FXtmdur7nsvLK3a7HUerirvnZyzrGms0jQvgxDlM1/lCgT10i2Pe+vjXnL3zHk7VQQiLnbT/jrmSeyDEYrQnZ+xT5+ucFv1UW25mCRvYcwQONHhO2wj700pM0dGPbM5aTN+y3V5yub1i13YUVUWlCqpCUdULpFKeRlmDMwZrvbbQGIPZ7WhCCYQXLy9odw2y6UH7WoGUJcVyQbFZc3Z+l3fe+Rnn5+dICXp3wfNHf+a7Lz5Dv3rGRlkq5YX4RBWdF/YjYXYhQlebWAIhAKy+80lojWGxWHJ2dpoS3MaEtrghllEIn6G/rCq6pvVBLpCSA1rnNZtKFRwdbTg9PaWu69H6d/joQwqJqksoCloruNQFrNa8/eBjHvzqb9jcuYMLNeLiedMW8cSPoyNx/cwcI26GOzc7GWf3mYKbfSJ56EITgikEu6LALdcY5wmXtg5lnS/XUBRJIyDCAlFBgushOUrJkGwP/KD55HpmuE9k3olZxEcJREQ4788TAU52vaqqAkr3k7woxl0V3z9mN5ZKURRZdWFjMu2NSUBOCkGvfSVxF/Zr69iZgp0s+ft//K/IzREmK2s/a55iCjYOD/aYmEZtTkQ5g8Sw50h+i6vnzzZi4GTMdELgEoofEbSfhiNlbHt5lJgHKX9Nm/b5TWRgf8O+gCImR+eajtFxB6SsH21CyF4i0RNruXj+nD//27/SPfqSc9mxLKEsJVKt0H2PQYOx9MYnILP4DOLb7RUXF5dUVcn5+V0Wi0W6VQwb7/oea70PnsPRax8l8urVBQ54494d7tw5w1lL3/iknkopcI7O9Gyt4crB+o13+OBv/57N6TkSBS7L3H2dbW706mMz5nQNjdbmBPD8ZExUgpF5ZU8wnszBvdNFbu6e7tu/V279cpOdOWuOfdu3O15994THD7/k22+/5XK3QxUli7pmUVUsVmuquqYoSoQ19G3DdtfQdj1O98jtBXq3pTWGbdPTtz2LHgorMYBG0Fcl3WbJ5u4dmt2OxaJmtaz44ckjHv3hd1x994iN61hVvnyQKlQKgskz/0TXCZxLwm8EO13f0YZ8Nmdn3jS12Wy8MkBGvxeLs4DzVoGyqqi0prNemxTBftu2WOejoo6Pjzg7O2O9XnseGuusBf/QyB9jugUtSrSqOX/zfe7/8r9w8ubbuLLyWtB8os8xjRsHeNpyi8R+1v1rMyRzmzDxnEDlE+oA5b1JMo0XUkVBXdVsZYELKmIDCFWgysLXeykkyBCZkJlTCFqWOEFsBnhEbgN13rGwSCYoly2B4QXiQKZrRPAitA/f7tqwL1QYzwjSoAURQUUYJ+lwf+8X4M+JtXNsyGhsjKHtDTsjWNx5k7sff8K2qCjF4EzsX3yIpIo+wjnY3I9WGas741jMfZ/LvzEa2hlaOzfOI03NhMDFPTnBm7nT698O8J0Edva0LGMwB4fXyP5amWEaeX+TkfRrpFyRPdMhQ0BkMlMwM+tDk4GlH8OD4xztry558vvf8fzrP7EQHeuFYlEO4FgJiS+BICmEpNEN22bns7Zud2Atb5yfc+/eOUBK6WC19kUH2xbnnI8oEbBrfKbXXnccHx1xfOxV+rrXOG3RIVVDr3t2uudV73DLE+7/6hPuvf02Igo3btJ/boZej75lVP5AZx3yecv3HVL3v1YtTcOJVibuFvlszebRNdP3ELiJ3+dm8gB+/F5rLc+fPuWbz37HN3/6A0+ePKHpeoSUlIXy0axFhSwK7zrgHLr3+V182ULDsm8onM8gXAjJwjlWTqCcxCDpDFz1gqv2JU9ePUOalvPTI85ONjz58o9cfvcNtW3ZFFArgQgBJ1FAlzi8j+mQr8bakEgvlASyIdtwr3vKsmSz2bBer71prCyJxhtnfZJKguWisAVVVbFA4NqW7Xbrq413vgzDcrnk7OwspVcAkuZTBHpmIYWXG+twQrFcH/Pz9+/z7vsPKOslVkqv4XSTkcgHKQnBGVubHUH/ebsZ/9docAaqGFBl+pnAT5qwEQxxs5Tn8Omol1XJhYjgRSSAEXtASulNWcbnlxFKUC0rXO8zO0atEnhHL+sN6Qn0WGcxrfOprTPgkpsUImOy1iKC7V5kz+IC4i2rCiEKwNeeUoVHyD4fjg7JxQS542FMShYdH4UQAeB0PgW88aXtX11cYssVf/8P/8D69AyLxLlcK5KZqPxDJy3MCKgcIIKHgA23IaBu8jnTYkLGgf8OYGYMfDItlJzAADE8y3Xv8p/ebpI4sjUwVr8THBnHRH6fqh/4HjZE4j1lFINW7IYHu/nR0zXzz73tU2mZfWl6ROQC8O+aHY//8Hue/O43LMwVq6WlLFXKb+WsTabZqG631tJ3PdudT0J2cnLM+d07rJZLcBZpDaZtfLbh4HfXdV0ippcXl7x6dcGirjk/v8NysUAASkrKsqCTkqvtlm2745XVdHLBuw/e4/1PfkFZVxjm1se+b0PooFv0Lmk++DBiHyBghZdQvUdGnDRRA/QaCwAHcPkAysfzxvcdw2+xf42bwM1YL0Y2zQRGeNBg25YX33/Hf/zrv/D4d/9K+/wZputIdbWdoyWUXHGhUn3gH6qsEEqhHJQC6qKgkAVKSSoBC0A5H/3a9j2y6+lbzQujeWGveHx3wW655NXDr6i7HUelYlX4AtI28LjE7MM/G+qlOefQOkQsBf7ShnpTzjnW6zWbzYblchn8OYeEuHmfSykRSiKLgsI5ZN8H81SPNY7lZpmqjc/mVwu8Ofr9aK3pjQVZ8sZb73D/wQesjzYYecMAioFGxTUTAe/+BMopyWF7Vx4cdF37UcU2EQzVbhOxDbvSsw5EOO2ezFbnHKWSLAoFVmONBqoRcxPCx+nn5g0X7yqEB4tB8xJzyQBIKZDW2/k9yDFoXaY07NMWtSzOOVTOkIX3qYke6HHWiMDFC7yqsWka+r5nsVxk9MjhrEFrP0GNNSNQpbVXN8YiZRY4vXvO/Q8/Aql8/aoRQNgHZz8WABw656ZrjVXoQ5/FfbmGJrG9GbqfdeEAjEU+mcX8Oa9Zi3N+2mUDkJsBCtliFqmvmB5J7JhxiLkY9dvMghuQ5eiy+515aEsyEcyAmuma9GcNic+SFdgfBYgUUejS1hDx5zTfPHzIw9/9FnH5A+sNVKXzxSoZojycsmgxEEPrLG3Xsd22HG3W3L1zh9VqFcxKgqosUXGtChBBzd40DQ7HbrdFIDg5PuEoixBxAi/8lAUWuOoMV0hO3nmTB3/za1anx96vwM6tm/h3AOjcIBlMjFEh+EGkpKTOCkYp2MWPW+//W1tO68OXPb8b4lyaAvy5y4m93wO4GTPOvDkEVkqMLLBac/XDcx79/lOe/uFT2udPULplKcBYL6xLa711IDBoaXyMnJSSKgizhZRsytr7uRQ+yV0pHIXzjrzGGJx1YAxK9xR9Q/tDx+M//pZdWSPblkVRUNQVyMKDpijAi5BWIqyWPQfjlMyyYbvb0mvNar3i9PR05Fgcz/F94KOL/ff8L2OiwGpRc3x0xNFqQ10OTsVTP7CYfT8mA9TaUi3XPPjgA87fuJeBcean+hyJyz8RPno48E1PWMMMcWPrQlpRXgq4FcT5cQAnPOSeEj7R3H0Cmb8b6YEEpfLakq7rfC2qDFg451Kcvl8og89J7FBrLTZoRuI9E3gNlDdW/O77DmOqULRvn6FHk5IIWh4ZTGAiZB/ue5+1WBVD50vpHbhi9MaAPP0gGWvoujaBHPDO00oVaAe7TnvCbSy2XPDrf/hHNmd3uCTBhARuIrrO+/WQ78d8NNRf2nIAMkz+Q5q5gb+OGaRfPI6I5IdBGs6aCA2vL9iZJdrZGMwyu/3zsgscODYHLNcQ9bxP83Nvc//RZa9PZb/HdNIo+5wgTgYy6kC6Aeg5BFoosJb2++/57rf/hnz1jONlQVX5EHBl5SRENtd0u8xMDHfunHFy6qVOIQTOaEygFYUq6GyXGETM2aG15vh44x0yY66ceHkpEGUBZUEva2R9yrsf/R333nuAK30OqkRYIhGO1Dnvn5mnH/VnoF3j7VM6xEx7rXU3wOTdxWRDlNyzGeM378/pWb6v+gLOAAAgAElEQVQxEZQdh48lAGtrDd3FC55++We+/exTzA9PWbgWpQwQ/DgcCOO/pwSuzjPEQilK5TX1hSxZVkuqsgrpChwCi9UdBpKm3mkLncb1Gm16Xn73PdVqyVp5FqulRBcFhSOUY/BJXnMfl5jZ21mbikr3vc9U3HUdVVVycuITWi6iaSrQ+OgLaoNjckp0mRJehuSXOKqq5PToiOPNhkVdU4ZnjPXaUikUa9HG0mvjI8h2DU4qTu/e493332O1WdNHTfxk/cbhiZaLkUY725ZyEU3olnAB1AfeTPQPzYHELVjbrQHOX6o12NuGQDiB7h1GFvSFonL96PhRBmIyvxjwYEcV9H2PgRBCF0xbOKSSSCfTvRwuOVWVVYVUQxp3gpYmhQM6FzJKDkxXodDGYK1J0iqQHLGi87HW2mucQj9FzY9zNoE6rxVSaAdXvcZZx8vO4u69xxuf/B1GFKi4+NKkYAQqp+amvE3Bx1Tbs68BCgTXz6B41uxYjtDzzBjn0qyYTNZ4XNL2zDDO/BwhDics/M9vntNN11ckwDedeXinGB3gpoLEISAkMkE/v4fI+/bQnTMTUnaNnI7sneFcwlDOhazCztChQ04QSYmiJFYfdjin2b18wRe/+WcuvvwjG7GlWleU0pulpBukxtxvTVufikFrjRCC8/M73L17h7qqk1OwDXShrmuWK0135eiDSn2326GNZrNZc+/ePdbr9YghxPexoVq4VBXnb/2M+x99wvIoam8CoU2dGzPOyjTXxUhFLxIQ8mB9LCSkHo4S6OxEHzTFeXDAa9/EdK5lusoZBjc9d+9aA/KZPW92XjuD7RpePv6arz/7d54/+gLZbSmE9VrH8IyFkATvl1RgVVr83C0LiqJESkWpFiyr2rsoSInFom1H62zSwOu+x7U9ND3CaIpKUVqHcj6oxJuc/LESm0LElVI+Z03UvliL04Y+mKPatqVpGna7HTjHZrPhaHPkrREjHhlePVs70awU6ZTWXhskgKPNhtOzM1bLpc+VM0P6vQbJ0vWGptFcblu2rWZx55zz+x9QHR/TAT6VtxydmzRukbRMAM50Lozl6MjnHbh4XOAHIos0DN/ntKt5uzlMHPYY5Nwxh35Prji8giOFijs93CN3HPaRTYOk5NLru70OU2GgIuDwCZkcImYw7TqMNZSiHD1jTlgFIoR4B20F+PwbxpubrOmRZTmozvzNvTq8bRBBq+PNZtIXBo0TOSBy63wlY9N2aGNQyw0f/eP/DYslxgW7bk4oEuObsJ0ZujnX9zmgOURMU8+G0JAB/OTHu306PRnVXCsjxE1zYZ8Yzn9//VpIqzjuHTFoLUQGhA92wcFXnBL+OTC4T/jnjh8de01zeD7ugvosJkCLiD7K3gmAWEPfa5pdw3a7o91taZsdxhlUVVHVNZuVj8woygLTXvH9w8959tUfWcieWkVwIEnidHwW5zDOYULkSNv7qKj1es3du3c5OjqirhdIGdNB+AjGsiqpzYKlA7vb0bYtu11DXVecnJyMnChjvwgRkwD6ishClbz78/c4PT0N1ZpnxiUAjzjYQg5CCIFhRuouAlHGDRGKKT3FZHnd1n/utWxzHBIi6r+eR+xN90DrRU7v5245B3Qcrm+4+v4x337+Hzx/+Gfc7hWlMN7vJqxRKYQHMdLzCikkWJBGUKmKqixT0eZalVTK58wRwms5jPOaEt13tG2HbjvoDUJblHMUUrEQCsUQ6SuET2UgrPFuFFFody4Vh7bWelDTtuyaht3Ol1Doe816veb46ITVauUBfoyaim8e+Vjgm3mpEmMsXQBLDlgul6yWS8qqSlYSm9GzaNHw5ikXwJHFipL13Tc4v38f1it64RMcSuGVBAmIxDGZATT72pygxRFhDaR1MVkg+U8xaDtvkoNvpcE5pL25CU3vSYJxwiOpCklVKJR0yd80B1Sxk70aJRYgi9ISIBVIixAyEA+X1GHxWr5cQggV7Hv6rk9x/lJKSFIco4kYHarj9RwOa3qc6VGVT/pnXci5g9ci6dYnbypCsqWIXKUQqKJE9Dq+JNpYrG0x1vLm+x/y4cefUC8WvvRDGvDcCW2/I0XMeZAm1i11dgeayCbNVNMTmfnUr32YqBlzn2hw8gk9Ol6Mr7P/PK8rMedwN2cghzgHXN43hHxMA0/IX/OQ5mRKINLnIYmI/f4T+beRP4dLeTd6a+l6jXCOUgTJNhBh2/desuw6+u2W7YsXPHv+A98/+x57sUU2Hb02aCXRy5rjN9/g13/3t7zz9lv88PQhj37/G7j6no2CxaJEFUWIIAGcSFGF0fQcfRC6rveOxSGhWR18Ifw7Oqz1jpRCKVRZUJoS1XVh/kpOjo85OT4eatllbx79C7y03LFcn/Hggw9ZbTZowUjtnua02CfSUdU+mgdRi8MQ9eiEJa2mCWgZCV0pBn0Qol7nlp4uvksU3W86L9La7PdYzhGjNTH9nH631rB98YxHf/qUh5/9O/byOZvCUQpf9HgI2ReUZUVVlMM4GlBWUasqmX4kghIFzmJitnwMWjeYbofpO0zXorsu5GHqkTgqqaiEr0xfxNI8QiCxOGfR2g0RU9b61Cghz1PX+yzF292O3W5H0zQpod/x0RHr5Yq6rkPfDGJW0kTaqTbUsmsatrtdqK1YDWbanKc7b22Ia8Iar8HRIaFtrw1FWbFab1BSsLu6oKgqavBpXSCrAB/R/5hWDfwhH8mcMvk5L+J6ycZ2LtL3Nlr+WwGcQ/4e021jZD5PcF3YVUhBVSiENeAGH5yoOpYhaVAurQ4LyRendELignOhT2czaHbEUAUvvUP0h0noWQ5qZq+S9M6JUvrSC1G9OLxKlnGSAek6B1ZrumZHqSQK6aVL47NnOhsLC7okoepwn3fvf0i1XDIQzyF8MKl8Z4jFQBwykJP+v12bN1nNaeuuZ56DJjlnuDeBlJk58xNoh8w+Is3Pw6Aj9lOyN2frPD+XKP3nJYwzUn+TYHGb9Zg30zW8ev6Mx4+/5cWrl0ghqYuCsvR+axjD7tUrLi4u2G63cPUKd/GSpm3pe81CQ2n9MxvruJLw7PGXKLNFNJ/w4vFXdM8fc6Qs68pnJSaAfykF0gn6GHEYVOmpUnjbeJ+B01M2mw3Fnt9B7pwZ/7wP38nxhtPjY5bVglIWOByGoMa3lr43NK3matviVMW9n79PebTBKa92F+GaAw3y/ZgDnMFUkPkTzGm8rwPG2VildSi9Hm0IcvhrxJf/ze2W4Gb++MG8sX/JgUmmbS5qyATd5QVP//wHHv/xP+hffs8CTa2gLEo/5yBp6quipCqrYOoEYQXKSEpVhrBrvCRiPOM3wU3BuJ5W7+jbBtNrrGnpTMtWt7RGI4qCZalYKEGpJFVRsKjr8KwWEa7V9L5Ypu77YDoTKXOx93fx4AZgvfFRU4uFz5Cc89LYonnKAxyBtXjfma5j12xpmh3OuVSvre86or+PVD79iRVD1v2u72k7TdN0bHc7X5y2gmePv+XF//v/sDo+4d5bb/Pmz95lc34XGa6bhNckyGZ0cPQp9tdEEir21090NxnPoZtBzo93Mp60QwQ/f4q0z0XMNoAYr84fwMaeOjYnKD6834OgjPEnoBFu6c1DCuMUxmo827doq1MegeQiGVR5KkusRwQ34acvYW8TQg6zNWM5IUOk1vRth1Cll4qNxWqLMybUPBFY5+hwtBbefOsd3v3oE1RZJf8elxFO/zLjfh1s+j+e3O1r4hzja/24a6ZxYV7DkGtscm49N2dGEuzr64Az1pQmAB1B9VhKmdNcDYuc+U/EIAWkw8NMExlwyoSf2+CYMR2JkpInqi+ePuXPv/kn/vTZ77m4uvKaSOH9xRACjMb1PjmltZaF7lnhKKuSlVKsC0GFH7e67bBtS3/Z8vT3Hav2OeLygrprqMsCVZb+usHJUsYejYwqaW46drst1lpOzoaoEV8t3GVaHj0qgxJT2pdlyfFmw2a9RknvN+MQSchwzvkMsb2h7S1yfcr5hx8ij4/ohUTJIhX1TRw3SqJiQrjDYGSzARFMUsLfGEeemmJuVA7MtJyKv7bCQL6Oh2253+A86B4Y15gRTtZYhhHzO8owZ6z1WYAvnnzL09/9G83Dr1laS12WVMIlB2FP/zzNV8hRIIu0PgO9RCaQ6YyFIKBq7dN6aNvS9g1d16bIolZ3dLbHYFhVFetFwaoqWBYFq7JkURSBW3itex+FITfkt4n5b5xztL2maRqMMSyXS5bLJUUIBx+06uBdgselgKKPigfw/jpt04Cz1FVFVXqNlNYaAihSIZKQYJZN+dlCaobLi0uf3LAo6C9e0Wx3XDx5zOXTx+jdFT+v/47l6Wl4vmyMpoQpp/Fz02huo5jfF8fypiXxVwOc/Qc6fMdErK2lcwJdLBHlAmc7EnJnIjEF5iFTOfnhTaWQ6MzeaK1JgEWGwpZxwHUIddOm99WKo3o8aGXK4C8zB9SkktjeS5bWWQRDxmKvevTAJJaep3SZXTKQvki8cbTOYVcnvPtf/ivl+dtYVTKgVjXUcsqQ7pTn7/2OfTeDdMdjEEHOdeBmCq3HGryRpox9gj/uw6km43pwM37G168NVouESJPGCphlgqlv5HiR74HCfPt0Gs5uGm/MuzH5MEzwVHhynPTa01c/POe3//TfePQv/43tq5dDBEWmnVTOUUnBsl4gi4KVLDharKjqCiUFFdZngO17pDUUvaZoW159+5Bv+y3nVcWqrNAs0FVFKR0SFxiUBRtq0hlL33Wp3lTTNNSLmuPjY+qqSn4HIggfg9AR/dsG6VdJyTrU1Ylp8WNUZXSg9DRBYyysq4ru4hVPv/qCzdExR3fOUPWCXGtDlq4hJ9gR/9x2yg6nR8Czf2JuZsimwOvbRIQ0Nwi84djRzxltzpg+JJmSqClNfM9ajNZcXV7wzed/ZPvkW8qupSgVhaxQwtf+q6oyaMZDVeyuTwliVdDWCYIWJUS9oh1O26QdMVbTGz8/29ZnwG67jj7UPywKxXq1ZL1asV4sWCxqFmVBGfhY9N1Des2OLgr/LLpne7X1/AX83NS+1tRyuUSpoZCs1jrVS3SBFxpjMLGsRNCAxmPbrqVQisXJMYt6kZQKzlm6zkchK6Uo3AJVFjjHEDnVNGyvdnSdpq4rjhY1tfTAqus6dk93fA2UZ3d5Z7lksVgEwTw4HjMGt2kwp3PBjX9eN21GXOoWa+5/PsAZPcDM44YHikmuZPDCtkIGh2gHWF+PxiokPj8NcqDYLmiAcLl62iPt5FwVpILodGzbhq7vkh3SwxvP4IWAsixSBkeSuSa+hxyKdEZw4DLCFIiVtZbe9kgbnJ6Bsqqw9MnJubcWi+Ts3pu8/d4DjCjiBYBxJuVcS7Xft+PBnQ7+j2tjAANezZlLY+N7T6SwjCjl5qmB8c9JvfvXep2Bzbi5RNB9AVhg9Lcv7U8X9pzGK7GITEOTtmf9mLYfohti+BwZHsN2IxVOFpjLSx79/j/49ne/wbz6jhVghUuRHb4OnKQEKiGoC59valHWrOsVZVWCcDjb0UdNijbQ9oimRzjpvxdl8oNrmh1GyaF2TgA31lpPULdbrrZbLi99woST4xOO10cs6mUIAIg+ci6o40mgpet6dtstve6pVqsUZhs1oka44GPgVfC7XcvV1Zarqy2d/Y6rf/knitXvOTs/5/7f/Jp7Dz7wdeaysjGxsyPIulXz0ySfPfmoDNszn7qfWssFHdif3wc1lnv0bbJOyABNBgoFLlTacbRty9PH3/Lk0Vforg31/mJyVJ/QUUmftbgsSzopUzFla63PT+O8RkhrH4Uk8BYDEUpEeb4Sil4G8JAADj6Z7GKxYL3ZsFz6WmhVVSWTUoo+xM8FJSVlUaDLElWWOCnYhTIjCJ+Asqp99JZz1gOhvvfz2PqsykKKDLAbbJavJoIway3rzZr1ek1RFB4A9dqb1yJoshYtJSpYJ3Rv6Nqedttgmo66KDlardksFp5rGo0IRWmvXv7A8x+e85Z94HleqMcYAU4+B5yYDvbtIHsCs5Pvt+F6/0sBDuxLp4OmwmeNVLgQiTBeCNY535nOQahmKkWBk2CNwzgTfG/crLTj7xQTBnpi6rRLZRKclN4/xrnkfzNlsFFqINj0feZiQwrGGPnnhGXovJpcOpkAlg736bWm1T310Rn3P/iQO+f3EEVBrJ51SKJ32f9+/zwh/DEgJwdIh4DFTWBjzkQ1PMnwPZdWr7vWT62NQEe+cST1D5vdCLTm0q7/TfyY9JdIYCrOixtA4vyDjr7rvuXZwy95+Om/4V49Y6kMKjy3RCJD6gWJpLCCuqioq4pCFSzKEDpblhhn2BnvS9A2DbrpEK1B9ZZlVVCH4rNFUaCU8qp44+eW0RprDEUQLNqu4/LqisvLS5qm4eT0lJMTHzlSlWUUfYDBQdg5b0ny4awNTbNFCp/LxGhNhzcdy0IhqxKETxyoex9GfnV1hcCxKgSVbtEvWp69+J7eGIrTM+6+8QZlDIUlL3j74+bslEiL7Hzv6+eP+mnCm/mW0/SkbTxAJua0llP5auzl5psxhufPn/PF55/z6tlTVsHPJDqwFzIEkIgoPHqwE+ei7jWIoJV3YgA4zq8DkY2cwxdNjpF32hh6o3FKsVr6rMDHx94RvqzKUZbhfRorhug/XVPXNZdXW5pOU5QF68WCalEHwKHRssNUFa4ogvbGd2ae5sAGU1oML+97H1RzfHzsy5Y46GWP1Sb5vAFB+AhZisHn3tnu6LYNpYOzoyNWqyVFUWC1T99QWIs0hnbbcPnyFabrMtIm0ifxU2Q9OekHz4v2JADGG+aypd+8Bv+nRVFdd7M93BakIR9+5/1aohMXrkhoV+TajEjghcAiwoD6UHBtvZobQYoIGbxyfJSWkCLlB4iq96jtqaoyFPMcM4g55+o4KXI4IYRCKIEqXKpE62TwccDnCtHWI2WDYLU54v4HH1Evl3TJDyEDDKOJQQBSYhbUTDt5zN6iditD0ZNxzE1O/v3GOWim4CrvizkT1VSDk19p3oQ1w6BfYy1O/g7T+TF6v4wo56bGseQ6BvZicm4kCkKkGbInCMyZ+kbzh/3jsD27H77j4e9/w/OHf0KZFhWSDSspqYqSUhVBnS0obMGy9InFlJDUskBajek01pkQUdKgu4a+39HrDmE1laypBBRSsqiDNIsD631smqahb9vkC2CM4SpEjyil2GwGE9MQzecGoh4dk00oINi11HXFchkSs0Ei+EIpSrdCSInuDW3b017ucL3leLPmZLFEOktvDFddx9OHX/Puy5e8+fbbCdRM6VBsEZxEQi0E7MWBXyN9JJ8oCMEIM2M28/t1aTcKL2Rzeti4d/4YwEzWxegE30/WaF69eskXf/6cR19/SdVsUSFVR1EUKHyZhUoNAFsIr12vqirMmSYE0yoKBg2+s2SFm/1DaGforKExmkZrtLUIpViuV5ycnnB6cspqtaaqSorg3zOUURjTCqUkBR6sLBbeJLXdNVxt2wR8nABtNMqBUwphLUoISqV8jqjeBJ8zh9UaYzRd37Pd7Wh2O6q6YnO0SaajnM7nkco2aFdN4G1917Hb7tDasKwXHK3XPvoYaLVPKOgCz7Wdxmx3KGN8PqtRYMR42F32/nHNjI67Zo3sXeSW7S/W4MwR1mzDGMmNd6ZvZaGQSo0zQkZNjJDe8TersxHPHcxSmYkqY8UehFiQwUFYgBAOKUMUlLMQ7PcCfBHNA707mKxicjCTnNDig0XmJqNWKCB85xxaCDqrabWmsZZWKu7dfYOju/cwoahg6kMxYU4ZE5uNzJj06rB1oJJjDdNAgKMJah5MZFfL7pt/jg4d7Z/OjfwZvNnj/5QWCfcU4E2bny3DnEmMcrjQsMBFTuj3NWRT82DYmG2bWXFTiapv+PbzT/nmj7+l6C6pS4kUPgpCSUldVtRVnRKgFbZgUdU+GsXineWtodc9xna07RV940FOq3dcmR1WwEm5YV0oVsHZ0puLHc6q5HhvrWV7dRWqGzsfFm5t8D1QKU08ECKLgk+d9ipybyZo6XpfeypWRXbW0u5a+q4f1uKugVA/rtk2mLbjuK45XW+oy9JXLbcWZSz91ZarH14gnAURnSfH9Gwg2OF3FCSmQkR2rLeuDVvyfdcB+0Nz63VvY8B+S5A24hvjPo8rxzlDs7vg2ZOHPPrqT7SvvmflvBZSiJCeQwoKKVJiPX9vl7LQF0XBzlhMbxBYXOZj5Y/0rghRQ9I6TdN37Pqe1micFNTLNachu/Zmc+S1nFIFf85oFUivlfxDC1WkzMGFCQUxFzXLVR3yTPUeIDmHK4ODtPV+aq6wPltz1/okgw6MczRNy+XVFVdXVwCs1ivW63VKigleWxiBlzEewDlHyLzv53/XdvRd7+tFBt8ab94aHKG9nx7goFYFdaGQCqxyPjWGGwYs+zq7Zvb2ZmsoQv/DvOr69qPDxLOt7MPy4VE5tDe8mZQKWS2x9RHd1SULZ4Ahq6KU8wzDOZcinAb67qe9y57VBRttzCXmsAgB2nT0fYcUYIxGKV9VVgqRysFP33CaayAHOCIeSGQkItmFkRIKlUxTW92jNqe8/eBjxOoILYsgskUJceijObg1B3Su03gMx8S+G2+bO01kEzJhSjc/B+L75kz+cBvPl58isYZBwk6Zw92wWAUTcBe/OJfsz7lGclgnM6N9rWaG8b4wZjHZIDDSCsZToiP+80df8cW//TPm5TM2QrOoCmQMP3VQqcLnm1EKaSWFVSgRo1C8ecca72jY25Zds2XX7GjalrbvMM5SVTWb9YrNasl6UVMrFdatzwtTqYK6qtBG47ZbrnY7tjufLHO1WlEt6uSXUwUfHlV4vwMbhCEbNEHbqy195+vBHR8fUwaw0jVd0tha6+jbFh38J662WwRwtF6zWa3A4ZmFtTgHykKhNcoZnCwG7UrgsDHT9N66yJHMQYl0yNmVwG+cF9nCzK+dS9+vW8vguP82RwtEPuOzzXOA/Jp9DsAZdHvFsydf8fCPv6F59mdWtmGlHFVRUEqJEgIlQq2nXPsW7qCUSv4xfd8jnMHJoSizj3oluRZoo2msZtvuaLsWJwT1csnxnRPOzu+EKL+lBzcEx+XgJyOcS+kMRGAYfrvPul8E36DlcsmR1vQhczcuJJ8NiTF9ZFPrc2kJ7wzdhQKaxjoutzsur67Q1vr8TyenLBc14FIEpLPe6bovY1Rk1IgOCXH7tgMHi3rBarVKyWtj+QhrfQkH40BWNcenp5R1laS0ER0MPCLmjBuYznRsJ5w3xzvha54O4rbtRoBziHm6jLD/pU2oAqvKUK3XEv3MhQj2trBNihBSjgRrQuj0AEaEEHsa4SQ3u8HhWAifTdLGAmkRTcfaU2ENRKCjwnfroq3S5+0YcCVMByoizb7vQ9E8lRzSml5zdu9NfvbefWRZ+zTzImOMYYIcwI37/RfBSnz1iRp0pGk6TG1nWwI3YxXM6PqC+Pw5M872Z4QuJzDXav9u2P6f3SLAkVl/RoklaUoiZMn6YpA9w7bs/0T8r5UM8muRIiiyC+ydHO8oAREIU3Pxkm/+x3+n/foLNoWiVjWLqqSsfZSScw7TBy2li8MvUlIyjMP1kch5tXiv+2RyanWPDOalzXqdHC5TvhgX0yv4JGh1XbNar9g2O15d7ZBCUi5qkIK+67z/y3JFKbyKvQuOk1r7CKhd03C1vaKsKjabzZCkLWiAvVOmX78xqV/XduiuZ1HVrFYr7yfX9SlIQVuLKkvWm43PaCzCGItxz7pJf/s2pMUcb9vPfn1wPR6SPl7TJscUYPaYqZnqpnV/cP077wZw+fw5T778gufffE3ZXbISjpUqWZYlqvROxkUAOjLVS8r6PwBGKX3Osl73WGHS3ImFMCMtb3XPzvQ0XYsxhsViwcnJMad3znzZg9WKIqQaib47UZOEAyE9yDEiaE1wKGcBf04MCbcQonWdj34qKxZV7f1fgnbFOgvKr8m+64L/jGG387luNpsNd+7cYbmoUxFqIQQGgVUOVZZUiwVI6SO22g6jDX3X0zYtVlsWRTD1hiSFo1B0F+pUISmXS87vnVPWVbYe9sFtPr5zU3vQcs/v27vgxLJwqN0AcMYSxOELHtbmHGoCqIrCE19n06TaV8n7e3qv8cL/7gVCDOFoQrgkDcXniQAs2u3LskwIVRtf4RsicDosRfhrZaYz53y4qsiJHCPVXXR8NtbS6cAA+h4rBG+89TZnd+/6quGDqHIr5nZ9bw7PO9ozQyuv93O5Psx8dGR0Do+fEaBNj7uJaP2Empj5lcOXQaMTstuK4U+MjgxX+Ev6ZAIix0CKVCoiAi4R5qPuO7579JAfHn3NwlkvbQbfhEVRUVYlQki2V1u6rsMaSyUKhPVaG6ONdz52g6+CJfrBhMysDjbrNWdnZ6zW3jk4+j/EORcTWRZlSSVgtVpx1LZcbXd0Wqfop9JLG8kZGQd9CM811rLdNVxcXtIby+l6zdF6g1I+PYNwgqo2GOPr8XTWYvVQl07AULAQkfLn+NpaUC1XnJydpgRxOTjP6wpO19dI+5n/7zLTlAirLOxwTGGRy/4mg/4atiSgpQ0iaahufY1bCD0ugBt9teXp1w95+tXX6MtLKmuppaCUyvu2SOkBjlIUsVCz9XM1ukGAXxcx9UCvNb3zJhwpBCrUHHPBbNr0LVvji7eqovBz9viI9WbDarViuVxmed18BfrorxlTQMaoJ6RKSgorPFCwhcVWJgFyF56tUgV1WSCl8q4P1tC0LU5kDsVtS9trus5HCB8fH7PZbLzGk3HwjcPTbVUUVAh6baDp0Z0vvdK1HcuqZrNcsVosKIN5K4J/G3xcrXM4qVgfH3Pn/C6qKHz2b8aavHjP27Q49zPVxXC2Y7I+0qS49po/2gdnrCrNNQbp2+TY8bb4zEJAXRYUSh0adA4AACAASURBVCCwKc9NzDVjnc+TMai6fPZTF/4QkYEohALlJDifPMzmtCFgL5nZYXWv6aVP+Ccz+/qBN07vMurLJA347dYED/tQJNAAnTZctR2d6WmN5fj8Hvc/+ph6uabNVHiz7cdIcUHC5MAp85fKNBDZM0TT3k13HqkgA9EXUSOVaW+m4OYQ2PnJCK3ZmkubIigP3zO1XCL0g21/PxZkrAHL/dcmTsUZNtrzUsi251rBtFKt5eLigi8+/yOXL1+wDBEehRxs8koqX2CwLH2oadviXI9F4pJ6mmBf90C4s5qd9n9GStbrJXfvnXN654zFcjmKUIzNOR+VWOIdKY01rFYrTk9PuNxuQ7kGi6z8eV3XoXY7ZKUSUe96zeXVlrbrOD4+5uT0hKqqAOe1VcoNqeTLCrdr0Zc7tpdbnLGcro/YLFeU0lc6d8ZhHT7TsZTcuXuHo+PjlHV6qqk8OD2iVjXO5zBfBsiST6AgFHkcNwJO8ybhG2//n9rmwPZclx2mAfs0IW/OOrrdjh+++YYnn3/B9rvvqbWlEoo6i9RLUVNhXUTzkLMWrYMZEnDGUpQFoi5p2xajewTetKWCVtYFsN0ZjRUWVXjzbb3wIdy+MKc3MalgnvI+at5x2FqbBI6ccQuC5ktIHBaFoFIlhVRJey5D6Z8i8K14zV3b0Juepmm4uLhk1+zojWORRXJ5R2efCiX6oKX7Z/jBOZfyQXVtDw5W9ZLj1Zq6KBHWgTXYXmO1xQrogVaAXK24+/bbHJ2eeneMSN3ytZJp7vNxPUTrp3pPGI5NeYTCs480cgfa/+Iw8QNq9NABTriQsyCo8hiyMmptUNUALoy1CGeCY9Tgh4MQsfDoyAwSgQfgFT2BWSipksTp1dnhgWa6KnqZD4zaMSZyPl9MnDzWhjIMzqeE77Vjt+t5qRsuheRnP/+Au+/e90nWIvJy+wtZME/gps82PmPviMl2N/l+zfUnM3Au8moWxBx4lqkNPO/P+du/xpQ8rq/EyLKFnPdD9nu/HSDwDKBmxATETJ/MbItTWTAFQI6ua3n06BseP3pIaTRFVVCVJaUUKcpEhuzCVVXRtR3trsH0FscQDSIC0DfGeHDTd2z7js5Z6vWSO2/d4869u2w2R5Sq9ObfYCrCRT8E7+cirUkJOZfLJUfGUtY1zjmqomS1WFBX/veu2YEekmludw27pqUsSx9KvlyhYpbzKWAMWhijDX3bUxYFm+WaZVkgncUYh9EWIwRboFvW3L0fyjYgwUVa4yY05sDohuWTq+OHoRoLSnvEPjDkuWu+rm3sczOz/g/QhPwznZkBfN+C1g8/tpcvXvD0q6+5+O4ZquupnM/RVIU8N3Eex1pg1pIFr+QFKH2Yt3XWl3nA0hrP4D3A8edFjaysS9a1L10iQ5i5EyDkkNMpplWITz4Ae5uqXvt1FPKMRd4RzcGQ+KCU0hdqLhRKxugvB1KiQrRU07RcXFywa1pUWXJ2dsbR0RHL5SoArqGvbfAtE0L4JLfBPOWjkYeCs4uqYlFXlCr6nbkQpWUxFjSCBkdTlRy/9QZvfvAA6gob10WufLmhza2Dw+An4xcinnyzRHxLgPNjRevp8eO3tvi6F61SOFUiUGmwo5QbCbiImhrrsHiA45G52F8coyaGO2d9EyOdYGBSSbYSwwSLzxl9K+LxEfXmDk/OMbJPRsdGYxy9tbTGUp6d8caDj6mPz3AiFF4jQ73X9N5NbR4q3M7zfNZcNdK5DyBzbs6OgU7k6/vgZ3imybgJRt8TWn9dtTmZRJ4efvQOEdhkmpjR+4rZz9lbRXBDdp8pzpmAyynIFAKs6fnh+yd88adP2b38nqVwKbKkkIJCyYwY+9xNVV3Bq1CzBhXKHfi6UQS/hF3fctW3dG0AGmennJ+fc3p25p2UhYreCIFY+j8rLIQipEoqtFBQ+wrmvVkCUBSKOoSrW+el6F3T0LQNL1++5HK7QxYl90KNqrIsKZQn5G3bAYPWVmuTEvw5BHVVU1eVryIdGJk3YUCvBIvzO7zzi48pVivv7+DACRv6VwXaJEdrI/b1dRrU+f1BcJqZIz+5dui5D22eWxf5duFwwiKtQ1norq548fBrvv/qc/TFD5TOUDpHIXyk1NjNIQBFN0S0OuuT9CWA0/f0sZhk33vTj/NUWeKvWZQly5iVOOSCcdYhszWT8wMvt03WdiakeL8fL9AbESqMC+Gv52TKrjyin4JQZFognUOpEmjpO0Pb9FgHx+sNxyFnVF3XlIUvyeCS/2gciqHCeRLKQ+ZkpRSb9ZrVcumz/TO8my8EHQR3AWqz5t79+9x7/z1EXfvsVNGKQFgr0Two4p3jc8yvl7FgEANo4vZMHTpq1zOKawHO2Gt5qhEYHir7NXPzeeatnVcFawTOSow2OFukAfXXdjhnElEZUNuQ6C7BmGzyePtpFudvB/VcJLdSyuzJhk6SkgSgvJQpvJNiuHVKyuW8SnvwQp+kjQ+1qIy1aGd5+613vHOxUsNr7HWnGI/XDXRuDEyG79f52Ewn0cxFfb+GwnBRMZjPxEiExtBlKsnl9wwMfwJm5p7XP59g5jKvRXMwcmiP0TSzWhbmh3BWej0AeJL5ScwfPzou+0zP6wzN1Usefv5bLh79ByvzknWhWJQFhQomqiAh+mH2BDECoM5Ymr5PjEOFOdr3PVftjm3fYgUsV0tv+z868iUSyiqEmXvNibXWq7vTOHtnToTD4k0CpSwopUpScRGyi3stjKIzmrbtefnygrbrufvGZkgEWCjAZdqPjJC7mGdHs1ouuHvnjNVyiXSEGkM25aqq6gXvPviAo9NT+kD0R/0Z7Q1MBaS4bZi/10UrxmeM594mRPy1BT5TQnAb8f0W1ww9g3AWvW14+egR3332Kd13X1H2DaWSFBJKpXzeJpGbgP25uW9kXqojCaHWJif5mPm3EJLFYsFiufSh1qs1i5UHOdGXzDqHLILJtOu8xkQVKVrSCfx355jyYM9/BC5LBuuCCicKyQn0yH0glf9JKalVydHREZvNhqqqAtj3WcKttehQsyqfqc65EAnZ0+watNas10vOzk7ZLNcUmZnNhPVhggM+UnJ27w1+9uAB681mIOZOpPeI/T8IePPRu9drbMb7DwsIh9stNDhu8jm+cGSWozNc9s65Gj8wdYfPSFpKQeEcKoSmxGineLDAo26UI/iCBScnjcHghMOJrMJ3zoTjRMOn146TIXmxu1ikLXujbHFGe3LUsjhpkxrRJxIkhKoGFZ4dslxGdaAxISKjKPnZe+9zdvc8aHx8VEUOcuKyHPXyDAiaI4SDf9D1RHI4fw4Jjy44/j4l4r5HgmCyr9mZI8axb8f7xsn/bpKEX6u2R9QnP+O7xP9EBG0D0B7Mk2LuMsPFDoCndMghbZBz2L7n+bff8t1XXyB2r1iiqVVJVSiKYOMvpQrS3CDhOmspy4KtkuyaBpzXsCgnwFkfcdF3CCWp64qqqlGFSlqToihQQhKLYiVV/WiND7894JnxWUqCSyiA6byAUdV1SKzmHZnLQoXn9vPbZbxFG8N2u6Ntu1QrqCxLnB6k+d74HCeL8zu88/Ofs6gXGQq/ZrBnx2N//kaBy797LOhos/1za+b6369LcxGEA3n/zMm+1wkBoz0OYsii1ZqLly959NVXvPjuMXQ7SmEoZJEKK5dSopwHQ94JniyBrB00OG5IFhmPiRF5xhikkNR1zdnpKaenpyGSqKSqK197LQAc74zumX7XtnRVTVFZhPABKC44Kds+aFEC3fWUd5gcefCON0MFYSNqeqYAOzH7sCaKAlUWlFU59j8S0ueNcgRNzGA58d8dvfHZvLc7nzJhvVqxDDlvZHQmtjb1YWc0vdUUy5o33nmH83tveId+a4fi0HMDm/PUtDAGQeQ29P6QQH7TkrhlFNXwOfadCNSX20nduSJICMGiKllWJVtrMabHmCL53xQJKESVXZwEDmt9em1UWAMhu+LoRlHCjjd10+0emESUn0t7cX/k7bOgwA3qvaGmR6hR0ncYCxdodkpy7+fvcf8Xv6KoF0mrFDphpLG6qUUCabN3zU1seRvUevudMjehcqkg/52uxxhvidFfyMI6eZHrbPM5Y8+P97e8AYD9RNqwOqY/hk35rqQVE9ncYNyvh5jc3HajNdvnP/Dwsz+yffo9CwNLWXhfhYwYAn6JZBFLxhifSbwu2V5q+q5HAsp5h3opBdVmyfFmRVlWqCJEFo5eSow1e2F/XM9SgAnzuV7UiH6Y20oFs1hOOANRL8syZXsd/B9UoAGCCB00lt4amq7l8uIV1ho2mzWL5cJnNDYa7YxPrGYMVijeee8+9956h6IscELOK1pzwSQ8U75u8jGZaniGzglA7KCmdT9T9uvaxo7Y3JqepTbzbjH6VQLtruHpt4959PVX7C5fsXQ25UmLJRhkmGvSkfLXxPQcscq2tSb1qbXG+0uaAdwURclmveL06Jg7d+4k06dSElV60B5z5xhrabWvAt6H+3gAVHhzkvRAVkifzT63OCTxLhvfyBVkcJYWMosKDgQ7Hh6PUUVBUShUWaTK6GnNZNdOrhMQQJmh1d4kd3l1Qde2LJdLvzYWNQqJC4E+SXNjDJ3WaOH4/8l7zzY5biRr9ASAzKpqRzaNKJKiRFF2NJrZWTv77N79///g3ud918uNNKIk2u7KTABxPwQCJiuru6nR7pC74NMslwYJEzhxIhBxeuMm7r/7EIfHJ4imSlBdKWKNee7C0q5JxTw1X6NqUHjJJatyJRPV/LvCBtQVW/6tXKcIv7xdlAnr1UrySmVAwSJ4NHOqc3AQpE2Usr8ag5TvVe5bo5lKiyMQEINEfySAkhaqKSDk/N1Xra9mK24GInNT12yjTIyQ9+K8dh4CfooTcHSCe5/+FsdvvQMmV/iOquNbh9CqK2ft1domC0OwS/nVgLQCeVDxn/qqvtcFpb6X1mOfKWpZEC+Bm93vFUC/9uxNVfaalq64IGUnxkyuLyChS+5b+qU9ZhoGfP/1N/jp6z/AbUesQFgZW3abyOQpWm7avaTRg6cpCe9pEi2PJe3CarXC4fFx2iab2BBmWOdyPBzXdTDOCZOSdnPESeYnARV7leqOFLumS8HWjEl+B+UIDbHfdX0OTbBE38f0N6UdJy/PzsCIuH7tEJtND0bExMCIgIE9zv2AM+9hb9zCvU9/g83pbYCK5iwBFKmq8MV9Xc/Rljm9Wlly6n/ty94q7oLzy56nMBxiRnn65Am++eprPH/yBD1FYWtsMUFaM7tuJZuzA22MGL2EF1C5FyPjfJCEmdY5bPoO169dw/UTMXtmE2ny75kzijmRc4wYhq0AHNfBooB4IgvjSj3mvoxzBVd3ZBERQgIZ0iIERjFfEUkcKdd1sBqkE0jJLiV+G7hyneASumQYR5xvtzg7P4OfRqzXHU5ODrBe9yCr6YUYEzxG9pjChDEEDD5gfeM6Hjz6EDffviubB3R8Lw4CarXiVyz12lWvgUDBFJfNqytHMq4HpYCXcoPlAVvHopHPNZECJvgIjEQYwVhVERWdkGpFGGodIL4BsBZT7mgRQLESck1dY2WXJ5JteMJNipf83kaqGAwFA5UQzY5X1SDyIWCcRvgQsPUTtiHg7p27ePDhJ6Buk7a36/Vn5ppG2FfA44L+I1JKv7TR/FlaFiaJjtyulDvEwOREhjstcUXhnIHrbFtwDebVHvu/odTMBc8YmeI4vKctLlgEqLkWClMCAlMKjjlFbH94jB/+898wPfkBPUf0QAp8loS1EuZpDAuFH1EvDj6krMnDiM5anBwd4+ZNidy6OdxgvZakm6rteR9wvt2iX61hncTVIWeyRi7BOxdGQPrSOQfXdaJxq5DmpM0bA9fJ7q9Q1TsyAyGkbcC+aJwJbJ2dvcRmvcbp6Smc6yRjM4tpwscAzxFx1ePeBx/i+jvvgVab5LWgxebkhjvzFrWCcdk82VUpLgJANcv5OoOcOUu7aG7fOfaCkln3gO2LF3j87bf48fH3oBjQW4IjMUsJSEcGH1k2ow1KJ1cr/3xiKL2PONtu4UPAenOAg8MNVus1rDNVMuwU6oQAZVUV4HS2hDGYvMd2O8B1fbXjUIgcqrZ5L2lvVPWxskQy3jjtWkxO8JFTHQpzqEyOsogxBARjSuwrL2SBJgidUlby7fkW2/MtrLU4OTnBwcEBiMS3jiB1nWLAFD1GP+F8ChiMxY27D3D3o8+wPrmBmBvp6oB86bclQN+asurfl5WMfeXKDE5LlwJikroMnimtRnktzSCHCePk8Xw7YOQIHxiTj/A+ousC2I/g4BC9Q5wmcW60rnR4Bh260PPOAluj3Vx3TdzHaat5JcxrYcNJuFJDI6L5PXILcKZpwvn5OXwIGAjYHBzh4Qcf4vTmLTm2oL12/VJBNgc5XHXuRbKz4vP22Sr3npr+n7NE6UFBM41DjyF9rRbt3WpVvy8Am13NN7+7cv3/HGU+tvb9pYMTANldqEgXS8zB0CtVJp8vbxgUA8YXL/D9v/0rXnz1L+iGJ+isJOlbdx0cAyYmdq9KdKvsTc5OzIyQsiv3XYdrJyd4+84dXL9+HX3aMqtxQECEKW1fHcYRZ+dnsF2XAumlHU19j5gDApZxVY8R58SEFokQ05ymEGCTs6O1Fqv1Cj6GLJCncZDcPiFgO2wlmGeKl6N5eW7fvo2joyOEEHH28gzn5+eYJi9xRRCxuX0D733+Gbprxwiox7wyN/Vf9dvsGSRXHVCrrvJbnAntfd1J1Rykne9fx9KA7eb7ZL6i3bl+2bMYjuDzczz/+it8/+//jPD8MdY0oDfCIhZfk9Y1QoPkhThX9sp7NV2Nk4BgSsC5cx26xNroXIIByBqQNTDpVcGFTX3ivW7b3mK9Wefov1mpr2QrGZIdwZUyWq9d2QfHCKOZFes0J20wAGwK1mck3k3fwabcUmPaECC7hQOGcZT5EL3Mz+RMfXZ2BuaY4uacSN43HzCNY2VJScwXIkZLOLr9Nt751a9xfO8BkNwtgCS3iQCYzOQQ7c6XOasJ7LKdV1+7dpWFpXLFXVQtPQSUNbV+bRfKhZunr2T3XkSYRrx4/hSYJqAXx7DEC0lKhhgQxgEeFvAe1HVgSKI/EFK4+oiM8qiuT/HorgVond1VKcb6GfW9ABzVNiUpmlJ+oiUggZu0BZUZPsS8I+PFOOL6W+/g0Ycfw3U9JqKddmw6O6+HuxpdHXgv94AOCtSU7m7fLTT/zneLQLZhftrBqTtqSlUot3NhBtD+/gZooq9S9j3HvF0MaXDKeiHYbaNfpA5pTeTIePrjj/j6P/8DZ09/Qs8ehlyi80VoahDNKcYMCMQ0XBLqDdOE7TjCWIfDw7X4JRwfCXU/A69kDDrTCdBJwr7vV8VHhkt7SHj8sDOos8kpxeOpi3EWNgY4Z7HZrCWGljWIfsQwGHjjMHmJDTIFodXPz88xbAfcOL2B69evpzbyMESYxgkvX77ExBFhs8HDTz7E6YO7iJ3NQd6kaiYHMcvPurNYl+lSwE3+NR1jkkxUP5A9wnnhpx3Q/JoV2vm/+o12n3Tfc9SQjkLE+Ow5fvrqC5z98St04zNsELHpAUdWwAalnFOmYhJmLLvKaiC5JqTvpsnj7HzAMEzJz0YYob6TpJer1Sr7elnnYGyKd2NtulZxCrbWgocB47jFNI2IcZ3Mv1wp4rvuAvO2UHBDGWBV7WQKsGJbEoYeGtlt5ZwBOCD4EVMCR8F7DNsthnFEoJJr6uXLl7Jr6uAQ169fx3q9BgCMGDGOU05QG0LAGDxGMMzhAd7+4CHefvgejG4LrzsZhEx7UY7dnJ9ht88VoMjrHNzMQU89x16lvFIuqrlNuYAJhmx5K0N0yaylSzGzpLp/+fwZfvzhMXD+Ep7WCH0vodWjbqVO2uV2C+88Oj/BuE6c/9J2TUYETJTdVBbJwUruhVAaSxtR61Q7Fatjo4KfDGIUcaU6KxiQP4BTfI4QGUNgnE+M8ylKzo/+Gm7ff4STm7cRGw/z4ixWU7uCDy8TYFqfCr1eICuvVBaUnH0ougCY9Fm/Q8rf0kQ9afG7AmA0n5fLq7JQf+5CC5O4MDKzYxa0fjGxSpvsLhYXa/H1pzS74McR33z1NX58/Bir6OFUOKsfgR4/YyBD2ikh9H3AdhCAs95scHh0hK7v5C6cYjgRimabwuSblAFcAc56s87BzYCG10j+BaVNcrA0Y8CmfVbnLaKVKMsHB5s8uMShX+LbKFCbYsTZcI7tdsBqvcbpjVOsN2uJLOs9LBgcAs4Hj9EY3Lp7H/c/+QTu+AhRPJ+RCVUSwf2ng4tW46wvl+UBUkC79Krt1c6q17Es1KxaoGk2B5rD9rRr9B4/Pf4e33/zNaazZ+jjiN4CnXGwllJcJpMcayVEQDZJpfg3rNfntEM3XZtZmL+zl2eYfETf92nnn0XXiQ+Mbrc2KVyBOrsvsQ8CfAjb7TlePH+KVd+BVisYNtCRXwKVVOtIdR1VuLP52JjEflGeozo3YkxM5qpHx+KQbAwhBo9xQN7Z672Yo3wICGCM04Tz8y3Ozs6xWa1w48ZNHB8d5fmv66GyPCFGTMwYncONd+7hnU8/xvrGdXiTFKSqq9tPCniW5KLJmAHN86O5WnPlP2E9uDKDs9Sx6VOuSDFbtefrUUBKZBkZYdziD199ge3zZ1hX8WOmccRgJeGmtQbGRJBhuOQj4ECSpFMHM4uHPJNkaNUEaTEKoFRnNFWadFEGlejJWtf6edV3p/muWQzSjpP0fvKM7eSx9RETHG4+/AS//tt/RFivEQxSXBlkZNuaMbC44kvHptbjskBkSrOaJK9SuPrT8cTNFJT6ZIy9gK61DbX+1PyrHqu6axF0yL/odzUbOB9vr2tZNElhLrSpboSliwCopvTS4bQLnuZFwU0YJ/zw/ff47ts/AGFC56zEC3EOzlDWJkMIYEgog5oqB5AAyjnOthOYIY7+nZPt4OnPdQ4uLQw5EzkVR+Bh2OL58ydYrztsNgdgJhH2hGy2EAWl3DszOERZY1XWJ/Y9vPeyCFhhZyWv1IiBR8QoTsUhBowh4OX5OYwxuH7tGjabTV4EiCIMMYx1CG4Dd3oL9//i73Ht7vsw1IMioPqnNn4B6W27L8lcHdu7Wue+mUo7vykAlF1ElIHPawtxFqt1eV2XxzHAIeDlk5/wzZdfiAIcA6wBrClB8tIQSWuEmTElKYQFG/kDg0ORLUhs++RFA9aYT3W0bf2rWcVFVjwtLCojx3HAdnsGIsBZJ0ofESQgdhn4Ej9SRr+aq7JzfWZ8RYnIsXAgFoMQQgI4KwmVwmXHmDEBoxczsKaKiADGIIlpBw3Kef06Tk6Oc1qW7BvHKYjn+RYBDO8MVtdv4OGvf4XbD98DNishM2KjeiX2ZhcE7shCqJyXdhByhICGE9LjloFPvVZfVq7M4MxtZXuOLuahXAGR2FqpEALAEdvnT/DHr78AT1sYIvikQaovi1LgXQe4zuZw2z4EScAXApA84vfVpWZtBLTI142TZdVOGTRUf/r9HOCMky/OaiFi9BIPgSOjO9jg0Ycf4eZbbyFYyaGl/WfyQl9Awo7gWjATtW386rCmOUfbQ/9VoLTS7yvNg1smYs/Cvq/MlgUUZA6Ugdtqt28QgXNpSS7zzefZAVVb0uI5wP4FgdIbYkZ8/hw//ee/Y/v4G/FZsJRAfmVaTP0fKvZSgayymOPk8fz5CwCEzWYDZy36vpM8PKsVXNcJo7MHmJMRjfbly+ew1qCzHSJMkoFJyLNsDojECDGIqUEBEMq4qVknkDiVKjCKMWA7jKJwBDEXD14o9uPjYxwdH+dgfZEZPgU+i51Dd9zj5vsf4O2HH8CtDtthmBcYWnxGqdd8nO4ftAXAlzmjyksx6y6PE7qgDq9fqRB6peEssZzztUTl9DSOePzdt/jjd9/CTwM21qJ3Br016CrgW/+1MjGNcYLEgpF92gCzZIUHISafz75LzKOgo5w0dpqmrHiSNXln1LwoE6O/1OZZZ13pT5WxBmncyziPxAgsKSMa5SZ9KO22CxyscwAhgx7xwfEgmpLDv8/zfPAThu0WAHB0dISjo6PETIlSEoOH4SAZziNjDIwJBHN0hLsffoy7jz6AOzzAYAg2Uq6rrvcyV9pYOFdZG/bL+RoAMQoAmikDl9zjYoBTL7Q6m1+50orQKvQ8jvjumy/x/bdfg/wIkAiekFCsSYGw/BRgyMPQiGgIMRqYKEHJOEaAY3ECZqHOQ6wXSsqBmTJgmS3MVFVYEu5xBlNZ+HOKWsy63S/m6Kyy0yTmlPUMxltv38PHn32G9eEBXrLPfVIW9Nn96ybNQpaVy7y0vV+lKD26+33bbswAUiDFpn4qa3fW6OWFDkDaZrtc9i0Sr6s8r4XpPganPlbepPbRV9ZFS9uyuk5zPq7kcJwFrPc4+/57PPny30Av/ogDmrDpDfrkO+CsgzUWEv5AAnQRc3J8rC4GYXGmcZJtr0m7rYN+zQV7rktqGzk+4MWLF5K1e9MVMwEBLC4DosFChLxAXQ3+hxzrhAPDRIlx4kiyhUdI4E3ZojtgSs7QambbHIp/wcFmI35Qqe5jnPCSJzzngPWtW3j/s09xcnpafPNQ9y0y9b7Ux7tm+h1I2ozvue9NM/ZpdxGr75tz773uZQZudNzvKwpyateH4fwMj7/5BufPn8EZoDcGnWV0zsKluCu6qNrkE5P7LyYtVtscKMk2mbOTe/ABxJLwedV36DorwS+tkbHiQwaeZKSjbMVUal2FOdLvZGfeMAzw3qNzveSDyoO+VhpT4D2OKeO9uFlExBTTR9eqdK4sQHKNqPOICnBKc2Ecp8QIIeVtBDxHbIcBPkhS25PjY0lVoq4TBAillJ7ZdQhmDd4c4vTRx3jvt3+HzY078GxhmGB0fUJVv0uV3X2/XabcVveAsmpKPFxw/9ZtqQAAIABJREFUu1QuMVFJBdJad2F9C/aZCYH0v9KDIUa8fPkC//Fv/4ZnT37EikVzU891HT8S5p3BwSOAYRBB0afEmgQOkkqAo8+DmpqmEgFpjEv1Y0TDecv4XGDVzrU1UyPghhFDogInCc09JWficfISzj4EDCHCGIePPvsc12/cROACG5p5T6b9cqHFwFgQmPI9NUf+PEbnolJxO+0PhES1zhZ3ZemSNlprbq8tUvkTyj4Kdgfw6B+3Qr7gngJ2C7jZBTnYc795mYYB33/7LZ788BgUJzjLsCbNgwQYjDVivkWEOL6ipOXwSONLtN4QGV06lyA7qsZR8jyF5OOgyQfrmDUKTgBgGAacn2/RuxW6rodmT0ZSGBi6HXzWlnUbMECRYWHQ2y6xpgwTCRQJITBenp3lLeLGWRwfHeP4+Bg27SZhyP0mjjjniK0F7t55C7fu30O/WkFZrZZpoFRfar7fnW+786+Yl9vvgHJ+C3yQZWTp+Oo8omZH5+tYdsAZ5uP+clkQQ8D5s2d4+oevgfEcKwtYYhmD2g4K+nTdqEAoK5hV5dN7TInRi8mhfhpHGMM4Puxxcu0Yx8eHWK/WWPcrdAnMW1M8CjO4iFyco+r+MWpGKtnuh+0Wne1hV7YdM6mPY1qz2mC2VXsleVDHmaHIEt+WCGxMBjJyzWL98KHNiehT2IS+73F8fIzDoyP0rloXo6QpmThgIkZwDvbA4ujtd/Dw89/i5r33QK6DZYYJKAO3Ajd1Ry8zXZd2fVWWx8nSNS4bUpcyOOWaimDayakTeRHczCrEzBiHAd98/RW+/vIL+HHAsbNYdzbRZBHBe7DtANS2R7lyDAF+HDLMkxDYumWckxkw2Uq51IdIACpM8tdZ0Bpqm+sc+BRGRzSAaRJHRu9lG97LEPDCM55HwruPPsG7v/0rhK4Tc1rxr9zpkVoA1ODsUs8arl4X2vhPKbVjdf48G0TaHzVDoWYs1esJM+amWTja679JDsV12Sew52yMDEURlgaUrDSJnWguMb/e/tk7BwMxRjx7+hTffP0VtudnOHQWvZOt4TlR5oze17rV445jRPQSHNMZwmbVYb3qsFn16NOWb1EPxVFXfWXAaedHArkaz2YcRpydnWGzPkDfryRCayiCNYSAmBYf9QvSHSplfpawEFmUCmJJmitjGEZM0wTnHNarNVZdl5QyQojiczF4j20AzmFxcOMWHn32axxcuy79sNSfuqCilQm1qb5+bX9PLfoKQ1v9k+pcZ5gB3P/pxY8jXv7wA+JPP2Dlt1hZwJmQFOCkSGUlqh4RnPsKSMp0kG3Pw3bI0bmnaQIZg4ODDYy1uHbtGo4To9FZJ6AmjV8FOhbCWlBSjtkCWUon4GWtQdfJTj4BOSMONiWpcz6jUaA5A5PiNpGuWZ2X51xkCbZJFsYgpYsICBFABDgkBmmaZE6xpFsIzOhWK8kVl/JVGRTZG4JsId9Gj3OOGAhwx8e4+/57uPvee7BdJ2t8YqG0ZJP6nzw+lencN1kYWFrsrlAuZnCq/1XzKL4BJeqsghsxa6RzNTs4AJDSgx7Pn/yAL/75/8PzH75DhwBHJfYFxyAAp5fB1fU9XCWcAZJ8IXkw1A/O1RZwHRhieyciwIgToiTvpOLMVQ04ANlhrRHAMSIEDXwWMtgRSlycGococQJ+9fv/B/b0Jsa0m4sio6W/5gt9YUCg4OIXBi16v9qnSF/nf9VNm7cZyygrsYR69LUWyq8w+N9EoLPkD1D9uLNIJl4gn5uPA2a0BZW2rI9NpfFfIEIMAU9//AHPfvoBliQBoXMma6QqtIlEaQhR8k6Rmm7S9tBpksSD1hJOT49x48YNXL92XXLUGAFLnRNTlwPBSPz3nG1b60zWwnU9hlEcFqdpKrGmKuaCk6Iigl6ex1oLMppbShQJoMzNSG2qEmVbQYTDo0McHx8DAJ4/fy67X4wVU9YU8GJijOYADx5+itsPPoLpD1P/KBipfMMaDbUFLnX/X2XcXizAq37Vg1XK5kX954j3/56yo9xSGePlmP3KQC7M8OOA7ZOfAD/CUYSlCGsiyIr/FhuqlAMDccPW+1dJJblyJ6hAtMRRWqNPW8H7XoB713XobZeBhbW2cpsRxYRqxqR6dgYAY2BcBzIj/DRhnKYcuVieDYkw0PGaXBxCm6RZ5qdtUpRwMktpoECVIQSIT1EEKMh7ZmAYRvEHTSDMdh1O1htcOzkRh3sgpzbK7E0MOI8Bz8KEF0y4c/8uHn32GU6uX79Qhufnr+bIRabctlSqVQOc6mOX5pr45lylXJqLSjFLhV3yJ614eU+589XezIAMjBgxbs/x/Vf/gR+//Fe44Rk2GLEih84CfWdBbGBVeJGamMrWUZCB8jlESRNOWU/r/ExlvagWkkwBik3VJA91DskYk/JS5ag6DciJOaW8OjKqM9kYPIbIiOjx6KOP8eC99xHr6V1rYnlYlorOsUImni4jca7IfOwTwPvOrfutPoa5mB3qay+9XqW8yrGva7nsGep5k8FNpWUSkLMP7wCjfA1qxse+OkzDgJePvwfOnmNl06JAtKjtErWCKATxG5jGAcGLAnH9+rW8PXy1WmHTr2Cp7DaRKK3UUOhlR1R6OmPARBi9x/n5FpvNAVZ6vIGkW7AmbcsWpcNYC6JauBdhbBItTykLs87pmDKgH2w2OD4WIc7MOHv5EkwE14sJagoBU4g4vnELDz/4CIfH1xHz7hgqcmPGcIHmQlt6VoDZ/r5ZKlcH8VW/v2FzZb/H3ey46rmyvPUe08sXII6wzsCYtCNKgWbD4lB+JRJzJUUGR/GxUXcEk8CQtWmX32qF9cEG65RYkki2nXedK4qIsilc5m8N4HO9kZhPJPMYgMASZ2cYR6zW66J4N3+yVmrk8CxDze6uLU6Wg1apma1TysYkM9UwjvJczmLV99is15KDztjswyZErORTnDhiAGMgwuraCd568A6ObpzCpCCe2kc784AASgH+LpWHi8Dl4vkwny5EOvcoD4eLyhV8cJKQ1tQMeqPZcbWG0iyOyVYfQsBPP/2If//n/4vnP/wRPY9YYUJHBp0BVl1KEBYDpjCiDx1i9BIfJN0xD/RUARmMMhDqhHfyc5tvqjY/1bZONVlxQschiCaoQl9QODLdh8TeaI6ewXsMzDi6dRuf/+YvsD44SInWdsu8M2jPlw3oL8tFruvPKXPWZun3xhcgJhYuonR2Ql+XLbr/G8oSuFtkcupZWIObLJhx5QVsrwBhxnR+huGnH9GPW/SbAGsZMFaceQ0BZMBpVxBlSliiFxfHTII1ss374PAQB0eHWK1WMEToXAdni4OnQQI3abHhuTlG51cyFQzjgGma0Pc9MoDWpITGpFfxETJWs0Enk7IB2CY5xLKQIc3pCDmu73vcuHEDp6fXYazFOI4pUSAjiAAToW8t3r5/H6c3bqa8PekZUuXr/tudJTRTGEST1I8CzNTxtOmgnWtdOo0rsPWmlBakX+GYWWFmIAT48zMQGM4ZEInzuSbT1GuowbeAdpM1Cg4sjulk0Lseli1CDADEUbhfdVj1K9np5KxciSjHidJ7SN7CophkMJO0FjHnJ/9SFKARuezGCiEgOpeUGSRQhpRMkwAvY9mkjQACblrgp5YEoLIwxDYmnd47VFaHfrXCwcEGBwcH6JwTH7phAJPJEZ/HyWPrA84DcBYNYr/Bu48+xrsffoz+4DA9tzRsa7HRzhDiYYeVrurfzpllYFOeZe/w+FnlEhPVXGIVIdAwOzNEm//ntG/fe2y3W3z5xRf45ssvwGGSLX8wKQqlOJFZawDuEILHMG5hHWGzXqXgV4wuLQgcC0eY/dJn2pTa8bW+haIvgZPmzsRCIcpVc4bwEMQcVfnhbMeIl2PEixCxZSCsjvCXf/m3uPvgXfiukw0puU0SXs9aKWFpju+ADmq/44oXbZmVyxDw1Y/dOTfmRgaShqJBTK6qpdXlMqbnqnT/n7PMx9TSdwVEU/FJrF4vWrOuROVXJdvRxxFx2Eq8EAIMca6D5sFiXRSMASECPElmcLJY9xv0ZgUGwzmh8Vf9Cqt+Jb4IqkHnZyiKgvqMZGUBnE3AzjlwiBi2A8ZxlJw3qBohLVzMnBcE5vbZomEZe8bAOInp41m+02CBm80Gp6enOEqBy7wXj+lpGhGmCQBhiBG3HryLDz78GEfH1xLwSYtZFchN2zpWY1E1+PnwLJ9LD1MCfLIBUfeOcf5NHz/P6SJaF8d/Blyv6dzYkQU0e9WPtCA16kU6RvjtOeI4QBNLAskBFwLO1dxqSBJg6k3mZnbxobFgx+C+MvEYCIgmm5gbJ7v1lh+sYo/kK6b2Xrr+1f1DoIbxB6plXfvdpIkj6E22ojuJmkzGSN4pebDkAzprx7kile6h4MY5h4PNBkfHx2KWIsL5+TnOzs9hrJNEuMyYfMDgI15OjBeTxdHte3jw0ec4fftdkOuSzK/nQQ3u9b67Zqm6rhf5q+kx2oe/dLlSsk0tDaiBgI407StjVmIAUmVjCHj+7Cn++N13+OqL/8C4PcfGEJwxcCljr+7Xd66DNcA0idlpHAZ0KakaIhB4Sk7H6T4JTavDVoyVkzDzzgQzlLzPK3ATQgAH0WRjGowwVHLyJI/8EDRqMWGcGOc+4llknNkV7r3/Cd79/HfAeg1bxT6Yd5d6YDQV4wIjWxZlBm7mfTEbDBcNjstAzoW+OFVd52Jgl7Jsy6uarlo77Jujue4rNPuTwtWntOjpd9SefJW2BSDgPgQgeKH1bSwspS7gZJJdvwgTAsFZB+pJFoIE5AHAkE1zKpkmq0WrOMOm4HmZeK8qL2otrDFg63J0Yw3WR0DJaB4kn5XG8uBd2rA0aKKmTcqkrAxZ3/cpIq0oTM7Klt/IHmdTBNwGoT/EnQ8+xc277wCu25mK4v9QgRAUkLNLlaNirhf6p6k9LwgDKgdy1htk4atinlHV9pduPvhzFcr/Lf9ctacC2urH5jJhmsRxFhI6ICVGwJz9VF8yXWtqE07XdQKs05rAoTAqkWM2dYYQEazEjZaM5OpsXqrHWn+ST4s9UNXNWQc4uedU+eHouslcX4MbVp71WiRHZ9MTGGy42pCgWkV9zcIcAcDh4SFOrl3DwcFG4uUg5W3zAbbr4JI/XAgBo/cYJo9+c4SPPvkMd+6/C3LrEo8qsZSLfi/UKuwtE/Oq43Vhzl909CXjDniFZJtywVpMl2ifrRklaT8JRAzbLX764x/w1f/9f/Hkq39F78/QmwhHnHd3UKqstSaHlQ/eY5okG7B6s0emHHWyRn3qJxOjOhoTDFlwDIlilLDeUuOYaXmJVpy82esYHyRNrCBrCiUTa4gG24kkpD0Ip2+9jd/+ze9xevsOgtEAZPWERpkxc/pu1taL4KaxV10MaubnX4XducgXZ582eZXyquDmTStzBmfp/c45zaf94GYf1a8LRP1Z5QjHgDCNiFEBAqBEvjIUtkpNwix0/SbFEeHAiCGmEO0BMIQYJWw7RwfqXbK8pnlHyGkamFMAteQsTEhsKJdQ9lOI2A7bvNMpP2cCSSVtigjqxrcnjUOd20xAlwINqk+Q7npBPl4XlYitD9jGiJv37uPkwYcImxMgpZowpMxN2aWT+yMDmF05WIObvWO8ktdcJ5SrKBtGWfDmZBARpbxElBe/17MsjFdaPiK/zp6FiMBGEqwOhjA5i45SDkDlQSvQX3xiBJgzkBNw5usxp3HNidGDxFtKjsoxREzjBHYSjZtNOi/dK1IyjaUEm5EZJHnsd9bgvNPPEIyRwHnqsB9CKGudsoXM4FjAXtnQUiK+6ziOxGKi5cSiR5kzlgg+VIlx0zW6rsPp6SmuXbsG13diihrHpKwHxEmAuzEGU5BdV7AOd+/dx4P3HuLg8FiIgAy+FMTsyrhatan7tAX+hJ0Ge6VSr4ftL5dNiVcCOHJFpIm/G9U4C9t0XggB5y9f4un3f8CTr/8F3bNvcMgjerKwROidy4CGkolIhRV3HawtvjC9c8L4cNnxVNeTuW1crZFia5OEafCS5wQxAjGCffKwTxmVQ4g5D05MOzgmL7ulfAjwPuJ8GzCECf3xIT7+1a/x3sP3RZvMDYRWUW8aqDpkqX0LP7948hLL8nOovfn5+wBWXf4U0PI/B+jsCmZ9bSf4fgCkr2qTL1elVrOf35lUk0WjCRMYZC1GQxjAcAyk/SD5HKQFQU2x1lrZuZiYFrZADDKvfPDiBJxuEEKAn5ICUiWoRVN/ylFiOQEcUqDiLFxaMAY/oedVno+JjoFxLgU+SwuWacdi1tqtkRg9FLJTcpd2wWi+LS1ZIzYWq4ND3HvwLq7fvCW+SEt9l/6yBw1BnoNEjO9qperLNGMe06LASJQ8BAAyqVJYFsgl89Pi4v9fQN//V5cWqF9sxgBrqzM8GNEI2946AWbiQpgbwxWAF/8xIsphPTjFrVGXlhgjDKfdWHq9xP5EQwBMSRWidUvmIwHgERQ4AfjYsHNqYiOIEs5EOSKyAhxtB2UuCYw4A+a7srgCwWm+iWVOEtimQFeIkG3jxhj0fY+TkxMcHokPzZTqIc8qCr5Pu5GnEDDFiKPTU7z/4Yc4uX4DZGwmEZfGZm6fRnZVoLP0VsIIM/+8hVJ+nysT9ZJYz8OrlSsBnJYOL5VYnHhczEbTOOLli+d4+tOPOHv2FCZ6OGK4hIwlMqWBsYToJ3hvsF53QhNbB2PWOSqkAWCcCNG6buX+ZXASmTTuik9A1F1SiblB8kyPISCmQFBBI0Gm60gQPxkAyhBNkTAGYGLg3QcP8PGvfoWjo+NclzwQUdqplKruiogVCKWFBtzi3ayJlMa/UrmqCWsJ3MzBz0X+HzkaJn45EPNmgKGiwWQNG+30nn+xA3LQCopL2NbmEGq+EyHMqw7eOkQKZTgpSOEIa8UsxNFInpwkhGOQMPHGmJRg0MCzzzszAImVAdhd9pRnysZcu2V5PmMtQowYxgEHYQNKYeZhDSz16ACE7RZTjJhiQJcWCQUoWbDGiv1Ic1uVIqtpUVIxhmCtQ9cb3Lz/Dt57/xFWq/Vs55r6MqDKFbSv1Zfms9SnEeJEYEiynjyXLlBil/wp6u9LXV/fso911N/21b45L0YghuQ/loA7dJ4U8M8AIiROkyS6FLcFa8X06b0Hh5D6VK6TxzabFKSylXne+2oc2Z26CZMIGY9c+MF0gRyjxhAhWgOy4goxBTGLVbRMbiNVVIoMXWqlJed9qUMGS2mnkwKczWYj6VScg6x5lIPpMqKES4EkyB1DQH94hAcP38e7770Pt94gVPXYNy7bGta/Sf12l5slFnQpWzilNfCXMci+EoMjZqkSP2apwirvvPc4OzvDkx9/wI+P/4hp3OLIGnTaKUYzwibfGI7wCe2qM3DXdTDGSJbgccS595mSttaWBQIE0+RTqp8hRUfQfB3e55gIfvLwk0cIvop66RFJ8/FM2E5e8nL4CcEzBm9wxh1Obr+Fz//mH3Dzzv0Uj6Fa8DKA2cGzyxP9AuCiGneZS8umo4u+2+9fU0ruvz0szlzQzoHPvvcXHfemlxJ6Du121cwIyDFCdcs5pJg2/V7ATbW45q+oeb9P0Mi4YnRdcZhsx12qrRVfFREgsi1Jtn7HtLgwIlsJUDlB6OzIoOTwuTzGkCNb682yyTqNKeecUOgpyms2U6lUI4LtOnQx5hgiEgvHAJBt4yorEBgRHhogDYklyoHgUMa5bIc16NYb3Ll3Hzdu3wYZm/oJogihKEVJN6+e7eJMxq2Q1seplAMNKa/tgZk2rH3D7TXn93gjGZx6DMub/Npq/Gi/Dx4GDEss0bV1QZ+NfzHfkeRTSkiGUoofw6lv05jm5LpgrcEUCKygIxdVgFuwnn9N7EzjPTOTyWpVoFQ3t+rFJMQCcjqOsGSSk7LeNT1btY28Bu/6KZuctQ2qbLXKxJIxKZ8VoU+m25r9EJYLAKQ+I1vAOcTuEHcffID3P/stNifXMZGtfG+oue/SGhDlzW5/lm5dWN7KdQphUlZQzsdwPvnnjv8rORlnJgeUbclzBKYLsdYj+Akvn/2EP37zBZ79+D0cIjpj0KVYBNZQzg6rSJMhSNpaC2ctbKKd+75H8BKIbBiHjMZVm5R/BtYEGCM5dwQVGyS4nVklHYwx+dRoSvkQxOPdR0ZIAn2aPHyUrLPj5BEi8DJ0cMfX8dFf/BXuPfoEZnUEVkqvnq8s/3H7NWrfpXZIX9D++czLgc1FIGbpmDkYqX+vP18EWv43liK4AWVwzOyYzKCQiM6EgWTqEvI4mOu3u5+rMbSgDRsSe7wzKXNyjEJdV1U0ySmz6zoQGQQvdLVUyIjwIzErSR07+CC7QUIsuw2993Bdl4EamhG+MBYhDsGIEda5HE5+vV7vLFjWOfhJ5uNqJbsnJd9QEtAAmHRXCmfBbwyBo5iYrZWYHCFGeDbYRov1W3dw4733YQ4OwaYODNeC0bplLwI3F82v+Tn79NDc97zviDdHGbjQ9IRlXmJ2YDJnMsABBPHjKr5kzcEylq2wiXm3VNR0Oul6mc2PYm5NTIZsbWOEQOKvlhnHeqdrW2oW7jKwqcqLHuNDwDhNWK0ijEt2XwOADcg5WI6waT0SXxrAzBj09tlVkZK6aswc8T8yqU26vP1d4+yoj1DkAB8jzrzEwDk+vYHrDx6hP72Dya4Q2GRFrXRPYc/mJirDnBlR9deZdS4uW+EoCRMlAOozds+UX4mWlb55uRDg5Mij9eW5kEc7kzlGcBRv9RfPn+Knr/8dT774F9DLn7ChCX3aPWUSuCFSGtkmecM54umq77PjILP4DARjMAwDAGSmp+s62SGSUEyMMWVHIFlUgjgcEwwoBsREX4I5MzlNxOLIAnKC+twEbJPW6dkgbq7hw89/hY9+9xv0J8dgayE5wrmR9aWz5guAeO1fNO1z584n0vzjK5ih5mXO2MyPvQwkXSbU/ieX2mejMXc07I0yNHqS/lUhFtKiKp8X2q1mRubtOvveGsA5zYocwVyEtTFGEl4mRtSQBVmGV9Bfaa6N1jarFbPMC+McLIlDKClzAV3MVdGR8aPxS1QZ4RBzbqDGnyc3TwnjoCYDbUQN+Kf1iEnD1xxDIQS4TpwqQ4wYJsaWHU5u3cXm/gNMroclk+9Dus1YWRLgcm1jT9nHamt969erXKfWmrNf05tQ0jifszP1a2Yw699QxpBYRQXkELfjnki2gPfV+mCtSUxCyetUQEk1wxLT18HBICAgJbvkUFi/tOZZZ3fqNy+sSuesazS9UA41MvkSiqR6ELIWjlYILDnbAjN8CJVjedo5VfuMETVtkpVRyPZ39UdzTlw9FODIsTGzLj4yIlncePsebr/zLsz6EIGsPAq1cqC0QfWekNnXMnF2+6p95AVzVNV4CodrSwdX586vfZVyKYMz12Rkl8T8ZpR/G8cRT58+xeM/focv//n/4OzHb7GOWxxYxso4uJS8z1KVW4a4xK0hEWRT0uRccoSUbX8ltoACIQBwxsn2vKyFUSNUpC0ZHDym7blEuWRGmCb4kJyIfdJQWdLL5yBhKajf5D2iW+GtDx7h13//1zi5fwfeSXZV7Z7sXJmbp0bi8pw6LWSQzoBO5i4XxFlFhy710VyILvnS1P11kalrzuDsM43s8x14lfKmUe87bYGqHeSA/L0CmL3tt3DNhhmalR0RUi0SsesQux6RDTiFbM8yPkcILte2VtI2cEw2eeZmp4fOe2MMaM7c0+y9CjtUjIUCnBTAjEiY2hgCOMpuLb1+FpJ1O6aLE1UpVYDCwMYSAZaIMI4jhmGAdU4iuQ4jnr98ifXpW/jgk09xcHyMELRTCDlAWT1+s/C9OG5H2wXLv9fCffdac622nbuL4+E1LhfVcdlsUQGd9B0TwRNhtA7eWviI4lBcToQyfTJ+te80XIEBEDIAbiYBdjV+ouTLko7J8ZMIMLAlgGXurwJYyzgvY57Tgm+sLf0dU3wn74G0EaUeDzBGnkeTgfoJxtnkL0PigI/qGZkl75QCMpadUSHK89rkdC/zqpXxRBpQEFhtVrj29n08ePg+Tm/eAtkCbnbai/JKnX5XOcLVnJH1rZ0KZZy3cwDVOfWcaHp7fonyGWpivnxuXOqD01Ssff6dxXQcBjx98gRPnjzBl198ga+++Hes/IBDp8HHUmZian0SCJIh1Ziy7TRyxDBswdzn2DfWOazXG0xTymgcArbnW3Sug9kYWFKTVakTMSEmyrMO6Jd3TfmAaRKmJsSA8wRyxPkS2E4Go7dga3B69z5++/u/w60H9xF60SxNpKS0znqB5p3FuUMLv1N+2wEw9cCcX2kvhVm+u4qpaumcfb8B+4HOvnNexaz1pgCdVkACGVKnvs2/JnBT27NrAatAvNGU0C6YS79VN86FjQHWG8TVBh4WIRgEK0ykIwPbdZISwYozsoJxBuehquBMHfU151tdd2FGAiiZkdUZc15qk/UcDOs2Xu8neO/Q9SsBGpZlI4Ex8CkNyubAwEEZnMIMNcE5q3uO4wTXjZIx2nuADE5v3cLb9+6BOcW9rfphCaD/KePwUvNFBW522O995zHA9Gpz+c9WGkX/6rJCz4mGEKwD2w6MlJswCttgGJL00qbs9US7wVzNDEgyZwVknmNQS2EeFyqUcLfEe0ufObGU+fz2mvV4yqwllxyGS2NNQzj4FAdK2Usoh2o0KGGK9k8RMW1Xr+cDCJIjzhX/m7IWpDxdZMDEWK02eO/h+3j73j2Jk9NkAjDL/ZXk3ZxzaQ+ox+rVxmy5VwsilyvAjfi7bFxdyURVBlFNMpQKSLK+ES+ePcN3332Lx99/j//413/B+YvnWK8AZyXbqjWa+A9oE1CqTS15ocvN4f0E5ojOdbCugzEWXV+Sbw7DgGEYEEeGtYnF0YwP6oSoUehj8b3RpJkaxC8mB2TPUXxuYpSM4cHibDATtg2GAAAgAElEQVR4yRvcfOcePvunf8KDTz4DuVWKUloh1Gaa7FL7Ox3BO292WJj8+z7ZdwEguUzQLjE1S78D2Nk6WZel7y8DM0vHvymlfS7pZ0lbUBMqlH9r/wfAFT6h9pr51VQgaM8xs0qh63scHhzCGZtHJQPii2BtToUAkxz7mAFDcH0nTv5B905Q1spk7udbgBmiCPBQclLNQJq+V6FrXNG0qbqQaLttS6r26acpBf9MTqak4zFgmgaM4xbel+Sd1opj9DBNoGHEFAJCZFy7dRsPP/wYq4MDDBAfC61vZqfmYPESYF5/roHoLkM67yc9b67lLsvUN6mU5qGmT+U3WnzffE4vxlr0nZNEsWQkJQcUZMhYNl0H6qyE8rC6B7xyVk2TTNiZIr/qrdg1eI/JIpFZmD3PURRWZW/SH1d/KLKSSDbJaPBYBS5zpWAuY3MstswGmawYk86dfG5K2skpDYWxKe5bUWAYKOscMwIT2HS4duMW7j54F0fXriEam82G0icSN670kYby3FXC6r7cXUeaxluYJ+2Bzc400u6sXWKqa15xfbn6LioCNCZ7jYQ5sR0vX57hhx9+wvff/gHffPmfePLdVziIk7AqoKbjZZ5zhUDnVJr45oREZQME67r8UJT8dlyio0OI2E4DjLPY2DUiIsBIdLhQehLjI9nuQ9mxNU4TtttB0DMzJjB8AkATO4zW4PDGdfzm93+H9z/7NdxqDUSCZdVGy5bcsiwte+PrRNwRfXu1t5Yh23fsPrPU0u/1d/Prvgrr0wLfP81M9aaVesEufy2obcANI3/S84vvDsorlTFUENECAKpPhPSdAeNwvcaqc8BQs4W79eZ8voAcMoTkiSmgAgxjDRwbhMASyTgSAoeUBTlgmsbksLzbPoU+58zMKntDJPPaB2FPcy4/EqDonEQ1XvL/Yy5+cyGKRmyMhXUdKESMERJBNgBPPOPG7fu4/egjwDiYWpRdcbwuHVMzARczNrVWuvTb5fNMzAOXHvb6ldm4XXq/sxim/ztr0BmC4ZjGVgGR6kyrawjVgL1aGI2zcMxACEmhoGZ1LOCdqnmX6pT8ZmxmUfaXuexUsF0D50gkuxNjgPfT8tirQBJV40uAmd25HyrWRsGLMkscOe8K1vuou8UUDc6DATaHuPfRJzh+623Qag1UigwqBSD3W1bYWnb58jmA6tj68xxbpLAKujhmEIPSf9Xzq6y9yjy+FOBoQ4tvbAm6pEhYAc6LF2f44Ycf8fibr/Dkq3+FffYdDHkEt0bsN2lU5SujneDcvDNkYDoLnibJyjoMIDJp+zgwby0mSREfUqCylRNbZ/ABCAwKunATJi+2ShGyukNqElMVGANz8suZsDUduuvH+PRvfocPfvtrrE+OC3ThWhVXBE8gYlR2CdRLYFXpC9v8st8uAywXUd/7gNLStfaVizTc+TXriTA/fqme8+Nf57Jk4tg5BlV7pSFDnH+oFjFlOMqJNZBp7rFzP0ksuOocDAAfA5grIau+ahpULOXA4cRsshWTqSgs80W1ooSzpsp5B6LrLOZCTj/Pv0MChUrHT96jV72QE7hiZC13mibYBHjy/Wl3HqmS5L2HH8REZTZHuP3BJ6DDE4mAnqZlwxqk3TXNAzdq4m5ZGvsXC3iCMgoqg2pfhYY3oHYBKTe64PKvQ1kamjWgWTpl9nwM2aDSEWFlreSnZmEdiNzuXEuAR/orXwbCekjg2CaPEiMzLxkgVUBM2z3EiOgn0GRyOBJ9iGWWYtdfsfkDwCSWg3Ec0zrWVcAhpgUeMxZF3jfRublwR+rArEBfjxHrRAI4KS2RfMcYAmGLDqvjm7j+6CPE4xN465CJL+23feAGy21wFZCz1P875yTg00T9rgBPQwhdcX24sg8OAMkwjbhTMe89njx5isff/xFff/kFnn33B6yGp7AOmBwh4iCnko+IyRYuFRVTEhW7IyetMqHhvu/hvUQz1mNIWSSIMIwMjOMW4zSCAMTVusTJCRHsdascIPiGMYWIs3HCy3HC2TghhoDABhMInoEpEnB0gI/+6nf4/Pd/i9XpicTHiWUxnkdUzsJSt5Y0balfKdjZ1Q6WFvx95qSLTEv7rrcIjOp/9X2wO4iXys+l1S9ipN6EclUGYId9qYF+hXuX8MtFZql2mU+B+4yFB8NzxAqt/0z7Z6DxrIgBthbRpzhR0yT5eogAa8EQx8UMOpM8EEfeAV3fZadKpdVVYAMCoFzOG8Qpk7KBgWiawXuYZHaGauLpeYdxBAM4ODjI22M758B9j8kH+Aj4NAcjM8ZpAoIE67x57z188vlv0K3WgstmfSLdsAuYhJkXv4k8D/7EMarrbDFRVaa//P8usCnvF0DPa1jm41a/pKWDZoUhfpiWILtdg0eMHta2juAqc9XsZKj4ucl11NBRsZVAZgg0LYgq2UTJLYI5uzXEgBwhW7N7108xB7kKIhbne/WMokCkCMiVCciAYG2AJ8o5s4y1cCorquFXW0/UGqHXUbJhmrxEKq4+nw8DzoaI1fEp7r3/CMe33gL3K0SYElU8XYcqoSQArShobVf+afGZ5udz1WCNEzcXhSCzN/O67ilXTrZZaxp1iTHi7OwcT54+wU8//oAfH3+POJxjjaQNAlnz47R9lYHkOCdmJNUK1UYp0VSFyek7cbYaxxHMDGtNE2Je42R0XSdpHbZbsA9Yr9cStt1LsCdpKELkNBBixBgCtiHgPKRdVMFgYoPBEGhzgEe/+R0+++u/xebadYkPwMh2XXms3SB28t2+eApXA55LYOZVy75zmuvqXwKLsmFY/moADVLtYvdaFwndRW104fc3CeRkLQYoAGXh8Vohv/PrnoXs8rJ7rGoKFmx7+K7HlAT2vP21n0m1iyQ8VVOshXSh2Rmcdn+EEGR8BJmr4zTCe4+u7wtwaPq01mRbPwcGJKTEtEXvjOQCIiAqK0OEOE0YOaJzFn3XV/O4+Ail1gQIErPHBxjr8Nbde9gcHIIhWjkU2OkiVKvws/ZVul//atl3NTr+Fcdz1Q/zPiOkBfw1nyOLIPwSYK5FnszAkgNBUgt472ENV/Fs2jlTTEFpgebiWBxjBNl2111eL3Pfah3FHULzEXJijXS3rnW2VUrqeqc5FlIMGw1OOWea5iC6MeHlNczCGpPzV8k29b5qoWoNVfMUlzxUtdIdYnFWHscR4zjg7OwcQzC4d+dtPPr4Y6wPDpLC3sohDZ2gSgBAOVYT0VVZy/1l+dx6vu0rvPzzJaLzygBHgeR8UZqmCc+fP8ezp8/w9MlPmIYtVgA6a9LAiZimEaMJiAl1d24W7p2AGFvvc6BQZERCO8YouaNE+8x6TakjEabJYxxGhBjR2w4mAhxipt9j2kI7TVM2UWkws4kJAxNGa/Hgg4/x+e//Aad37oEMIbYpUUqzVwLpqqYVEf2ts+E+xmb+e33MvB4XgZr9NdkdNzuoOr3jNDn1O7PnWecazr7v6vdL5qzXslRaoRJxCftmWab4wWQcsYeJueKicEFlmvO6rkvabBEWKmzVX44BqEc0M1IsTAkKSCY5bYa2DnmBMSIQZb3ltAPRY5WAkW75puz8azL7wwvPqywKESO7RVKtMbKAnPOtRKdNPjbsGTEpLpRYH2axok+RcePGdTz86JMs+AUASc6eDNxMWxdlVTS43EVl37ysjgAwz2FV99l+Qb4Dci4+/I0p+8d1mhtMQBT5PIIwEMMtjOMafCtwpvl2/8hgCmAbS/R6MDjU10G+dmGt81fZZ6brinmsOa4q+v2SeSpFqZR6JNChRL8+PhHBWYvgXE4KqqC9yGgGI+Yknt6LW0WIyc9UGindS8KsgCgBtQiQQb/Z4N6DB7h15214YyV3XJJPpf1qIFmBtNk4r2X10hy4yIy1fI7Ov6sP9qvIygsBTkGh2FncVWh677HdDtien2N79hJgL7lE0nHBewQ/gTtxvWVmWAPEWDlTkThjlYzChYbUIS6/iTlsGM9zOgdmiOlMaCF4ZslfFSJODo/QkS3UYwQipyyyk5c08eMgqSCYsIXF6AzuffgQf/mP/4Bbd+9K1lWmjLa5mnB1qRfmWnstv9eyquFH9oKbyzp7H1DYd0z9HVeC9yIwtaPtkgZiahftnwtM5vd+rcFNXapq1uxWZnhEqVR3F9i6vWgWIFAvecmz7wIkfZFgZwebFVadQ1hgbtrz2pxNAtQMiC2McYAVhjNWjE/L6iTFhCVoX50hvC4m7ZrUW1B6bpnzhMhIOd4ijCkad82giDlshCUDZ2yWD5pPzkLMbAGMc2aE9RFufvQbnNx7CE8WultT/TXm2rU2TSvDdQWq2+3qwGSfNrqzKKhcnXfPFRSH16n8XDayaKnSEMJKALAWE9L8mTGRlzn+6mVzsDxmCcbKLL4wRDmNQWSG5SqeklaFS0wcYSg7EKrdT8wylowBJ/Ym3RmE5EyPYgZlQ6DkhuFDQId2OaV0PfX5UbNYvp2uGhqA1ieAE2SjDEjycNkUcyoysJ285JqLEVNkdJtDvPXOQ7z9zrtw6zWmXNsWPBYWp37cq8eF+rllAUPNfi9BAl9lvF0KcAqlW76fDzilsqOfQBxRMjOl6yxct76OPllN+eWBbcrV9Fk0CKDSehy5UJRkQa7H2TCA7BabfiVZw5nBkMBmIQQEFserEDiHdT8zEZvTE3z617/D3fffA1m3CDTmnTxnJxQJN6ftMGzL4OSq7M1F3y0NxJ3zsua7zAzVgGWZfkZz3lVZm7rvl8DY6yrE95WmvkQ7n+fHFsC4+/v8mjsgGrtzCSiXctagdw5ns7EVQpAcVM3xSYuNyQeB1IxT7pUgBgCkqMIWka2Eew9Cl4fo4cMEY03aJSVnM9f+DtWNm7ZATpFircS7aTR1MggQbZSYwK7PsqLWqmPktAMSOLh+A3fe/wi+WzdZw5WBatofS8BmuX33sZz1+WXOLWujOwL8EoFeP+N/1cLy31X2jV0teUpwBHQNSX3VrENAAbnMObN2voig5HzPdEJ1n9pSUIBoTIA+BoA45JQiruuwYs4ekxcx5wQhBq2pY00B6kahoUnm615kzqxn2S4u61SWAUmTr+X43AxNIJCxCGQwBIYxwOgZz6cIe+0Ibz36CNduv4UIk6O0VFABtVK+pMDv9tn+MXnV8XoRsJkzPj8HSF8IcJpAX0ZobB1cyrJYa+E6DcbHCeCIJd1ai9WqR+9KYkyVL7mi+uVCxWUNrgen2FydcxKa3XuJfQNkO6hqnJEZL8/OEELMDschsmwJnyZwIJyfE0ZvMaDD4Cw2t97G3/zTP+L9X30Kd3CQ6Phlz/l6IOwiXHkwytWn/KzMXIGd5cmyxMYsgaD5efNrza8xP38JUO0DOXX/KIC7alkCQPtYqjcC5FD72rACS5/rc/Zd8rJnpnyzds4wkIM9GQvXdZUGuUcgZACRNDhO7Y4ynuWv2PsFsBh0fZ+25jJ8ossnP8F14uRs0uJuiGGMQ5eYHQIXOkvbyFiE4Mv9UAk11YBJ6P0xDuAQcyyt1WoFBiW21mMKAbZb487b93Hn3n2JzFq1GWb9saxs8Ox9OWGpd3Ruz8HMMui5eskL+ivMsT93uXDxWRrbpA6sACCOtsYAnbNwBuiMeG0tMQxaWK9jVaHURjcQ86CYZmAkTY+6H8+V04ah5AiCAZJJaZpGcCzpH+r+bGVYAeWGStA81mc0ck0JLjsJCDIm+axVPo+kTvwj1HylLhu6tjXxm6hEb9ZcVjFERO9BRNiOEwbb4/Ttd3H84BHi6gDMwkfp0qTKBqW+WJL5+rxL/XsRyJmft/te+wT5/UXXVplw1XIlBgd679l3evO+79F3DoebNc6tAQ9i83RujYPNAfrOQhhDHUjlfNLsx69QaSKh+0La+aGCQOPpqIf5dhwRksOxtQ4xigPb+XaLaSJsxw5n0eAlHI7u3Menf/v3+Pg3f4XV6gjBUzLT8w7MXAY1WaLJOZWgVv2lIXQuAST7AMq+c5aO23eP/Ln6fh/gqMvOov0K5VW00NdZU82a0oLAnYOK5ry9H5YXhwvngx6j/7HE6p7sGrQ+RrQdAonDbsre1IIIHdNqIq0ELNCOB51Tcm6J9bFarcAjMCanyL7vYY2ITW58cEyz25CyIIMEGiRhSYVqN2h8YLLUTX9lFcjRbBmi3EwhYHP9EB99/BGOjo/hU/Aybcumv+gy4FHN9fypjaC60CkLwnjZ12AuxKk6fvGHfb+/JuVVNevdY0RKWgKcARBDAhrKKhY/shhjdk/Qc6vFScS1fg0DspK8MpoIMikhZx7xcxmblM9q/MtW7DYtiO72rdkYZSvnAKwwV8mLJgYEPwLOwhoJAqUuNLU8Dl5MT87a5EQta4pJCr4kpXZwjBIHp67zOIGZsR1GXL//Hh599mscnd6AZ8CyME15hUpzAvqa5UNukiuvC/r5srVkdoWqxeqxdDF4uspYuxrASQ+/KHxTJxwfHuLayTG2BxsgvIA1jFXfY73useotnCJFtOyHoVb47Fa8fs87g49jhHUWhkym7PS3YTvIQAHgXIcQRePbDgNGb3AWCWcMdKfH+PQv/xK/+s1vcXh8TYIrcaEYcQGrMEelbV3bYy/r8MvAzT7G5aJrLV07nz8bhPNrLyH1fJ0qq/z8mBoA72u3V5sAr1fZGa+QhdsQqpQNtQCu1ubq/Svc8EqHyaJfRW1VQQMGszgkUowwzJITzlhQtLKjMY6ICG3QzXxuA3+kSslhl1F2nEQbM0upWY21jZprJK2V0nV0vhJxUQUIICO53qwTCr+EiYCYmGOEj5I00QPoXY/77z4ErMvaLoAFuVK+nwPvOdOoRy/1WdF7yhHyXcvqlGuqPCna867pGvkc+nmj5fUqs7WC6u+Y8zMSijyS+RSb9UEOr9uzgBplHuRzyxCUnwQsK+ioh2NmRSLlyxR5iexjls2tRM2qRAlwqw8p9HbpDVHZ1UikPv4KmDljd/G0SOFUQoAHY7IWziYPPmV7MqjSQIACZvT5ahcP4xzeunsf9x+8i83BQU7pkOuYlSVqdIk52EA1hnNZGJr1nNJ2vDqLOZ9H9fpas0zt/S4qV3IyTiI8f1ZnKO8loNBm0+Pk5AjXbt7Csx9ugeOAjYlYbzYSdttYOKtChmGqQWiSppfjGmjUU3US08HJChjKIpwA+w6Vq0h/8h7TFFJ9A0IAzs7Pcb4dsEWHZzCImzU++e2v8avf/QWuXT8FcDUgsrdhuQwMrV/135UAyb73+87ZC2YqJklReNbMVZvnNipmk6dl9pxlPCCP/wvbYuEaV3mm11VTbQq1HwwU5FBK4yHf6xiIEKHWsC/4JbTyMpesNegMxB2yWhubRSVragSQARkLZiDCI3DaZsriZKxsigjfEjyAkLI+JFOVClSgjDFjbM7Fo0xLU2suY1M04dQ+TZF6GmPFwTJGwHtQjPAAxuAx+AlnMWBwK9x5+x2441N448Q8RUk21JIxa6m7ZS6Ic70XxuuuIK+/WzylPHteSKhRHHdvoge/oWXpuRYAD6XM4QHABAvYHjAWoJDGaLtVXE0qaVmGrk21sqFgqWFQsvxrwZKuZxERNiWvVDDKkM/ee7jOlbWG63WI8lhPd6pAWDue2sIC85UlqZnUtA18GrbojDgRc2CJwxZYzFDqbF89V4T49ATvwQBOb9zE7XvvYL05KIqP9kMN/LU951VMj0BK+XBZV7TOvwQIvzoIKgpLhcb2lqubqDQ/TqUVKVV4eLDB0ckxrt2+g6+/+QbTy+fo+4iDtUFHsksqEftV/uw0MEwLbupQ17owtKanmM1SznXCtwGVppr+UucrtcnsMU4R2+2AYfAYnAUfdHjno/fx2V//DtdOb7RRI1NZWnAv7IwYZ1tQGRoGv77mUpkvEku/z3/bC3Jy66WJw7sMUNNeXNp4fs+lsPn76r+f0Vp+vvmC8maWmbaTCs3eMZCTWV+VlamPa8DmzmHCpG76DoZEAPoQEAMhxi6ZjapcOQ3DVI2F2UIA6BhI44nEeVgWBiTTcwRHD9aoqsw5CJs+eCvq2/EbYxuWX2pVgKBeJsaIwU8AGbC1svCEgDFEbK5dxzuPPkB0HUIylWk768KnanVbl1cvtVaZa0uXK0btE1bf7QFQIgPfHAbnVedvOTqx7iAE0yPaXvqYODEaCpCLmbAs0uXeyWNyB2DqaI+NKacoc7XptSMB88U3TABOZIkOrhnoNRwDp1xWOif0flo9oABhk3Zd1fmmCqNUMyMZSQiTM3nZ0h1R5VSMOVSCAWU/ughpxylGGGPx1r13cOvufRjrym4vIhDJs7T+PHX/XQS6ayj2S4/QpbkkM1YwWS0PL7/a5QyOXrQCOy2TY3B4cIjT01N473F8cg1PH1s4a+BMyiKuCDsNQECynppkozep8+s/VX3VCUv9aqZJkuy5zuUdVMokaaeoR3zXOXRdj77vEJlgwgRAaMBIBqvDA7z7wfu4decOjOt01uTnv4ghmVNwWejtqG8EcJviYn6N+bWbY/T76reles3rVAZerbXMWZziQMocIIEYK3+JCqy0wKgI3quYqJbabx+AeyNATlXFeZ0XnyEpGpc929LvVH5Mi/2uiqUQxZAk3WQywrhwlaOGJQBl8V1pTajZr6jcbia4GNrz+Z4sn9UZs3MunRTRdz06m8xPRnB/EYsEjYhexkdhh0RyVcIY6m4qyT4jAjg5OI/ThClEnJzewP133k2af5EzVeNe2PY77a5sTFMznh2TnoXb79rPBdwCyEHT/seXBaWw+bx8kiikfqp2UZlkalW5EcEcEKOBYdkSXUfilQjxKvm07MrNmq0WuS0mXmdKzCgfAnyKhxNiBwcHdbEwaaEHGMZSiiTebu+eP6/sVkQDcOYtoXNZx5+ueRJ7PIVWiVX+RlaTnnyemLFlYCSH09PbuP/xr3Fy+w4m61AUsZa90fV9qVOY96sDDeM0lxZXZGOuUnSelc+/IIOTX6v3zYLOQNc5nJycYBgG3Ll1E9PXPSyNIAQJIoaCapFElaSIt+icUNkSBZJkt5bwwghRt5EWYbjZrAFQijrJOVS1DwGUvLU4Mpx16I96rPoexlgJ1AeLcZwQ4DHAYjgf8OPjH7A9H7BZoen/edlnatlZwJmBKpeGtJEi0F3AsHSffG2g7ARZ+L1+nV+zYXAqgAO0YEm/kQieHoyAstsleehLj9W1kMek0qdl2bu83ZaeZw4SXuvC5Wkz8VAjg6a0Wt2+cin4uag6CQTYfoXu+AZCfww+f4IYA1D2SzRVK8O8jM3yCKLhGYrZATLdqRlfsv4Qwii527g62FTXS9kPEFkBTNJEOSRmiGCNSz49BI20hBqQQ7fFphxyHDGMI6bJw6563Ll7FyfXrqHmfuYaX9XYMyVk/rOCG4VJ+4X8VUu+wl6g2t7/jQD6v0ipWTsAMcBySH4qRTGGrsEZ7KQ8VYhgAoxNmeJhAANE78Gesqyr/TNrllKrUC/W1lpRFMYRU5hKPJyug7EmMUDK3qSdxE7835A5mV05YI0BBwE45cnbY/WZjTE5bQNSwmhnbZ4TMoaSfE6sTuAoZj4i2MNruPPx5zi8/wjcHzSskALHGuTkHliYGlczH736HGmZGp797V53l8H5BQBOts/NAI6+V81wvV7j6OgIt2+c4vnxMeLLxxJ/xpCkpI+FxtOKls/tn1CEIvzUSVi0U2FtQqLfvQ9tg7BkGwZJ/prOubTFjuAD4APDdR36aLEKHbbbEY+//hZPH/+E68d3cziFyxrtghZL9UAW4i32WWaE5sAnvxLtpIbYd532moJmdCIDyMEQVXOIOaZCrP50ykWJLEpp2ldgTZK42aThF3ZIh6GuHRcBln3gTn97FXvsf3cR5sHARFkANWae+NgAJi3OC1APF42rX4q9Itf//+y9Sa8kSZIm9omqmbm/LbaMfc29sqqru6rZTRAkhwOQl/kNPPBAEDyR4JF/hwfyQAx4nL9AkDwMhgs4zemqrsrKNTIiI2N9i5upqvAguruZu7/M7K4XAZPAC3e3VRdRkU9ERUXBTQfrPSnkvSwxvsXHO5APMlakBc8a8tan99EQlZtTZlVgZjABummwXC6Fz50sG9dKF2M5d8PX7ZH4gD2DevvVx50Efgup541zsk+cdThZ9TgbVjg1BntXr+Huw0doF0vYKJt+fBtG3hMLIzNYEqAv+/IcvBosjahcE4SSL28/uFn3YFJxvDjL5T3EFgtN0BR4IXkqkPetV9LB8ygHFUg38Ht+wMJIU4eklSPgJhyLxfHHFfnxQGl/KGcd2CekjHl4KM+sXIEDXh/xeV4o9og/eKfiTZWnyTCgMICti+9q2hZMKVmutRJ0b8FwUDi6dBX3Hn2AZrkPw/BGKknmcm/AFOAm8+Bswf+xv7bJ6LHz6/dx9VlfPw64dtXRPwngSINJDoNWaezvH+Ly1WtYHF3CyelzOHLeEitRl1IUvTbF1vKOAbY+FbUBIEviQh6A4Ba0bKOgDjsnsTMw/QrOGuwtFthbLEFEcQkdszBt18pUlOtbGOfw+ukzPPnqMe4++BRdp6NMq6HsTkKH4n9Vp4wDnfz3GFihbFDnXpcpoJT/DvOtHP8r3xWnqcKcLldBxhRU3QZieBAkP8JUAmE9MVNevk2eqwSaL6aQz4dW+J50X4gZyBUgp02r40N2q9t520ARYdG2aLSCCyJtRKnEIR2CaABAK0BpMGmQkmBKCbtkv08VZaIo9VVwy1srG+IuugU6vzdVuKZoJwSlkgu7dYFV86hl2UTUQpJznp0ZPO97vBgsbl6/i1uf/CVosY+wMB7ZhpY1cTWut4Jprq/bHnNTThdXD/N/0XgphUPBH3FcbC7hhaXC4p66Jn5jKJZAX+XbVwxdIx54TolXiVLG30IfQTw83ukRwTez8JBslRBkXLacmcq2DrwNIK7WDVA9NxS1buKmnMAET6M+ujMAACAASURBVGRCOOg8Cbkw8JkKIaEBnGHnMG6DPM2Bt8S/MmQrBmbGYAxWwwDXNNg7OMTtu/dw5959tMslrL9nrMXD89Y7Z4xv02feVgVc2SDb0+umQU4OaHL5EDFsVuS1ao3QDjE4lBAzRgAOyO/VxNBNh8XRVeijaxieP0VvT9CBZVUH1y5wSvdTWt4WFK1SGl23iMvznLMSoOWS5yFcK4iF0SrCol1ib7lES40EWrKB9cXntoNi4AwMMzA6cjh9c4LP/+GP+OBXv8GNGzf9gFFRAG9tn4nrdkHBwLrSL0AKUHpwUIKhKYATLRU/YBgcd0EvvTYoLJoxkBUs7NKlmykoRJtffBd+ef1U242V+a2anloDDF7ZFecqoR50lldetUAoY5YCP+XKlEavr0krwrJr0TUaA4VVhhKjZqxFYy10FmMVgGnghXQ8jMlk4IQiiTXt6+mVi9Yali2sseB2SjCWx4ISiXKAKv7lZMEW3kUWQ8g6hmHG4vAQdx59gG7vIEucFltuvBgbgM8mEA6MA6IEykMXT4Am9tPOHKz0ENMU/qKKRljvEpZPX3Qal4G08bw/kb4qJQtHiGAhXhyKgyvvH5IpKb/ZZn5NULkcN9gs5Zs0u3ggw9QOswTPIwJ/pDELyV0DQjFO8+2EAmiJ3ueseoVMBRes51zwrHLkDXmPFk+L0hJSVlupSLogxKZaZ2GYAaVx7fp1fPjRxzi6dAm2aeFQUhF7g9wUG6PcGOBRVqxBzk+hbfK/5Kntz9u62SZVT1qfHw5ARZZjd4s97B9ewvO2g7GnsAxYLxB18An4CG6ZV/fTIV4pExG6TjIjt+2isOhD1cM+IWFTMSJC02gsu0O0bSsMYTi+U5hWAjAbJoANTocVFmcDDpXDD19/ia/+8HtcvnSIxf6+DJotNlM9YEtXX33v7qssxigCG6AarFMs6RUEEMEIorLwwcXsVwn4WIj6mTnz5xlAo4AO72KGbNYcggKzAVN5ZdbKmSmTKW/PhaQ11simWHe0tcfqnH7W2jiaddl5SqfDlUpBtw2U1j4oUU6G5aQxIRiCdewAP/0DoMhJFR0JhfLIwFdm9SmlwIoRPB0ARMFo5c8DYIK/BI6V97qGybzUamHJeA5w8j/JrMwSb+Acjq5cxd37D6KXN8OFGxo/nd9ocRaK5afx40bgVAMm/x/5c2/XWqofQ9IZSmnobgHu9jFQB3Yr74n206qBP5VkDM51kbfzExhCOZ6mPA7xCt/OYUl6iGFUCtA6gCYLZr+vFQeAsz6luP6ONM2WAxMVgtNQ8oVMDysoH17BRsI1CCkPlAMkANo5DM7CAHBKAbrB1WvXcevOHe+RrQyuUUeFlGOzjtoGPHKdEI4lwF/Xsbx3PXfO+jVSxvPSbh6c9AY5joypMuXVNA20AvaXHRq/JI7Baadwn6AjbMluWYSV88/UWqPr2nLjvsAYVZZi2QHcRUC03y3jaixnLJgcLGQgGOu9EI790lUGyEJhwIKBN69f4E//7v/B3QfXcevwA8kwyamOk20z0lY5wAngYJOi381FPuVZGT/ma5uOVe3nUHvB7FoOnHCveIEsJHmZiJEEOLNVXpx9GeHDbUxeA6uLTjGXyRhNHs/yqpyjnnL5hOmUjcVBKbj9fbimlSkl2d02As9wU8AsAayyc9GjUnihPMAhRVCswEiZhl2whmMhM6HJWRIzr5hky6AQYwcop6GdgyXjlYusXiH/bOfS9KktQI54bywzmsUCt+/ex41bt+VNzoGrxs+F+3lBAkfvST1mOfYJxwUEifXr92zyuE5NQdWGxNtCP2b85vDdOQbrDla1gF2B2QKsvQEV4snSDQGDR5hMiMHuitISiVi23CMU/1/vY3hDMJyzZoAxPbQH/85ZaKXQaA1Nso2Ry/or54O6G4NxkTw4WbsVF6dj7PNTAWLIGAIGZ9HbASsz4MxZnDKh2zvEe3cfYO/yNVhScLktNAZuiIpX7aSP6jpmlU3jvmjKiWeneMux99bFoKoe+bEp2gxwsm9pbYJaOx+AjiKCVoRGK7RaSZZRZhh/ldMa7KPDAQAKMfo87C+zXC69IrZwfisGx052JfdL5obBAMzo2hbdwQGWiyUaShlRlfgfoUnmOpVlWAsoJXvWMImQF0BEUBZ4+tXX+P7xt7jz8H2pYwFS6oZeb9UxgAMiSR0yodzXUPuOQGcM3FQXxnPBSxMUmQAci3yKb8ojFI45diAYOFYgNBG0BXdxqGptGY/Vexydl+15kQW77BAuAbiOED1XlJ0PnxFI/KyYrfbmJP4c2MEQANWAWcVkYKl9/aoTv6GhKAoGKYBtCjaPTw2CCX58K/KxOOwNF+EBpRRs2A8nAOVKqMmwoOx5ofQpRicodTFiSve7JO00MMbilC1Owbh04xYefPwpusVe1g7/2AC59uik9qL6sjCVh/QZpoTXpHdGY1O2Fx/2R8iMH1va0C4Sf2Oj10ZRWpUX+YjZ83AYfz6rL8s0ZfBCImvqAPTXAGhR/vJY4EdjTSErwQzdKLRN2EoBcr8Hv2K0l3Kv9NoGA8FfmfdzVsAQ1wjAG/ZW9lnTWhbaWIveGJwag161uHr9Jq7cvgfXLmH9AgJf+dQGFdFIm9TAY0o/SY9zek68JB1LV6a2kGvHtOtutO6BGqctACeHOKFxSjd8YIQQlCVL7BbQ7QIDKVGmQLSE4rw6GI3S6LoOi8VCpqRivI3zu34brFYrH8Vuo6cBzGh1g73lPhYLidPR3gIcGHAqKB+/R4hjQCPmygn5QJqmATmNpWuxenOKJ3/6Gv2vzrC8dBAbcGy4jnlvxK3oxXRE4zTpzagBwBrzcCYwPeecixWClZKN8ghkwEVQcchEGzw4dblE8REEraXnsmOwShxBkFVEeR3r76lMbzFRafNFkeUHbgA+jBDomq8ezG74GZRC/iQwoyGFZdfhlCb4Cr4/IXvzKB9vMAwGDka8rD47KvLYF2TCK1rAQSiXwisGuHvwkwdgRtDs1nkgnxqKYIllas1YWYp+Zi1e2AHHbYu7Dz7C1fsfgxYH8JF+sYHHBGBhcSKv0IjgD+CdfD25FuBjxNm9QVlJ/hLnx86aTM2Lg5/KBX8u2s7HYWyMnafss9UKWhHYWW+EJvlaxI5EwCOfShFIawBK3mOyAGQKIIHSiziXQwFsBPkofa60Rtu2MW9N0FvhvjhN5p8ZYqcc0u7jEBsX1nuDklGoEgiLEGadX4EEQMK+WOQDpWNuOGPQO4fl0SEevP8hLr93A1Y3fstrjh6uNdfXjtxWy+8Q8xQlQ6Frxu8Zfy68J3QCPHkQlA/NelxvAzlbY3CShR4+xwrqzylCs9zH4c0H0Fe/xemz54A7BcFBB3DDknlRa43lYpkAStwmXpip73v0qxVWq1WheLuukw3HVBOBkeLEbNAAowGTi6CInQWsLJkNloAG/FJEwgKEoTf46h/+iBfPXuDulZuFtTnWTesN69V84cFRMcPkLhQ6OoCfXHGmVIHpWH5PPD5R3nQPR8WUK8CpKa88mA7wK93IpaA676LgqADSvSmeY3P967qcx03656LNAytXjuWYSdN6U0qB4qAvrZ6xsVcDbWDRNjjcW+IlSQ4pYyglC/Ob9MnKxHak1Izo0UHK+hoL4ACCknNWAFA4p8N6eS49GWPtNOYxzK3jYoPPLHt5EOYnfY/uyhW8/+lnOLzyHqCarDmq9yXtk36OGQvrJmw8GALnN9GutuiYIaMC30PaK2V7h1eY40W8KCRdvNaAW25KX5mTcaQg+6Q1ikDGimeGqDDEOMhUClv6ALJruIwZdpCGIwJIiaInDdLw/hDnM+TnU/L5OBQZFEIlgk6yVgLp81igsEw9ZUUeIx8OkMXAKSWbbebTwkXDZECrAPwe9Du/fcSq79EbAwvClWvv4cHD97HcP/R1zpo5AwYJ7KSab6JRWbwrwyPJgF1E+tRsRy1GfhYPToB++dxdDniSZyZeiv39fbx38xb2L19HrzpoN2DBPZwz6I3MU7bdEt1igdYn4csrNQwDrE+slFuAYa+P1m/gp6BiI5PfydU5B620B1IErQV2OMUIrs9WKywUwWiCajQGIrB1WIIxvH6FL//4B9x5/0Ooth2xT8eaKAE/DpZF9OCE5lpf+rxp2ioHN0Bi0vxYsbFb+QD5yBkwA1mbwc80pfv9lBXJBnLEAPlpkPwJ22Juwrmct94WcBOonkoY88zF+kQFO8VNnFkrKTvu2BhOLl65FoBsfgqga1ssFwsZGmwBtFE45kChAWLgIpFs88DZnj8BqAvIQck4jLgVCshnKteNCGXrAF0C57KdcstvLGg9AaAg0I0xODs7w6rvsWKGbhpcv3ET12/cBGmdVonUILACN+l4bLysXhxlduxHjAvlMf78qRxbx0fkwzfGNf3Ed/zTUV3SURNx7SsR4FhymzVaQVmZFgUlBW890FA5OAks6r0hjqvEft7KICgfnlB5gzh/FANMaao54xNrLIw2PrFfk4zqbZSYPj2Tg4GQ7Q+VvS+Ae1kE4pe4+/pIdmXJtDwMA4xzaBZLvHf9Jq5cew9KN7CbypPrIfxI3t1wUynL19+0ScaPGu6RP8Y9s5toS5AxosKO01OZlZQjQWbJJrlQCxzs7+PypSO0SvlOlGklcf8RVNxpWPksxQA7SffunPOxPBq6VejaToRozHDsy8bCmEwcGS2geVIENLILbO962RTQBzqL90dhoTU0AGp8kKUxOBsMvv393+PZZ5/hvft3Ad1AodmglDIllymGqOC8CU/ZYJnqnNChY8ClBJK0dk89HeTCJonhfZQLzmQRxOePMNu4G3BEcGUuijHvzdh0Xu01endAjSDaIGg3gbax58j3UkmvAyY57moeYYBZg6mF0Q3OACjr4Gd0hQ+ldKEEWVnExZ+XjYjAIT2Df2/MWeXWLahcT+TlLQR2+M2yo3nATbY6zwjTolxMURlrcWYs2ktX8MFHH2P/4DCClTFwnRvldXvHa1FgnKxBt1ucW40C/3/4V7+TsIX3w/iQCmwuzIWgXYBNqHnuMfF8oAhGKRivGJX/F6WP10EBL+RjRuIMbZzKAXPh/QpvKX1xXH0i6hTBxSUQclbAtsoMgTHdULKR73v/PAHOwVEQFUNsk8AKwWMkcXQShxZATt9b9NZgsJL7xpLC5es3cP/RIxwcHsH5RTwhwGIj6+RgH9t5ujDY1sDLOlySx5dGbP7aMf4vftM4//9sHpz8QfnzgkCjyGyI7mulFC4fHWBv0aE/MbCuh2sFgHRdB6VlmWhwnYMZ1g5+aXgLrRQ63fo9Rkrh5zKLMpQrR9JEsuIjuA3JgyVWyRvUNDK3CqWgneQHMZbRMPDD48d4/MXnuHTjMrrDSwhpCrZRvuok/a4UAK/Hoox5OpKAToOx9grk39eeQyTTcR7YsZKl+EopsFPRai89c2PdXykx1NdndSzl1Vqd6mNj7fA2AJ1xUBOosLt3ftaux8M5eXw4L54fsWAJ1jFOrcOpc2iNw6o30I1B02g4s4IbFJxWcE0r00pKg7z3Ex5cEPJ+QqxTEOzBg1eMuQyYa61LQFNWIDZRngMmgRu5RtLjWwyOsbKMlWGsLNBTh8tXbuG92/ehFgvY2EwjDCgv2dHKrof5j+PDuG/SCLAZfy0nEFYp1LdlTAQqjN+1sTHRP9lVhoCh0TCk0BuHllX0YGhk8ohK2RSeHDwczjnxi/gNX8eMrPy9Y2XJFbAszaa4WCOXz1v5ZA0EoDCC8wvzDN7B8xS8N4Zlt3XrgGFwOF4NOLU9TgaDoVvg7v1HuPngfah24RP7pXrkdf45SXgz7+cavJZgZhfwFIi5fFYwBs5bj532osq/y0d6KYj99mah0yWN9NHRJewdHKB/AcmJAe2XgXcgSAZG4+clCZJroOs6LDpxryvOLMnsnwqDyAMXAFCqTdYpSxk4F1gVMpPskwzSGoM1fmdiB3CD1emAL3//Bzz4+AMcHF2Fi9s/J8o9D/EzCimk79mYDtMPuwqsCFQwzRg1uAkFSBlSySPQwBjeEheXGABEEFj3dWGlZNYK1SMne+8uVAOcMZD3jzEY/3Fo3Wqpu2rSczACUuU4MN6Y48KUmYprzDBgdXYKZ00mFPxpJ3lv2AwwpyeAsdBNA+hG4oqVKFvZxdlKWnrF4hH1HtNUlrKO9Z+8zhXnY5xNFosQlpvXQj0GwDugN4yzweG0Z+gr13D/41/j4MZt2Fb7kjiEVfT1WJgiPxqquqyD73NR1IqIz4nxbki8zcFwq5b+M+ol/WU9Nk+WXyzKvY15u6b6lSAjeF5C1mBnZJVfyES8BlIQmtuBSPLFEIVs+EEPcNbuyDCWPEsRgX0mfucqGV/UI6TEKPmbI8DJZWteN44PK8AfUhcnI6D0bI/+wWf1tozBWJxai2PHWFy6iqsPP8Lh9TtwWja8pRC1OY0rszr8GAryYHeD7se8Yiwj+Xn0w/ZEf5MPq9xILF8kQVGD5eFltIdX0KsODQxYKVm+CgVjZdk3wGiaBt2iw95yERP8SVwHZ4GtLiLzwAxpLw2FfF+NsIzdGgvSLVRjwWzAZEFxg7EESogoTok510CtGF///o94+sXXuH7rPqCbjR6O+BkGboaHUteXSm5s6mJTp4X4oqln5MfAfpll9m5WPnjZt5uYq6Ed163FAHDy+CfAe8oQwM86xtlGmwbTlDfrolHgQ/FSAGPKfuyeGujkyj95sILVmD5zwSfXh4eg+OIYcNbg7PQEr1++QH96gkE7GONXgVgLblR8dz8MGIxD0zRo2hakmyhEJaSdweRACtCtpA2M1jETtFbiIQx85AsXFYCvQM6rAm5Kz070zPr4CmMshkFSQhhrMFj4shqACNdu3sHDjz7G8tIlOC2MnsBNtPUiIF9j0sLy4epzN96rx+8mT6U8vT7vvVbgEuRQ+gvnONu496Lim+DpSDybjgO7yYkwYalYFqS0WoFcAklJRvl+JgbYgh1kbzyFZGwzAzHtQe5Fq42E8DwVdyxh9pv/uBTzkoy65MUMxkN4bl7OHOQ4D3jJeYBPSe6G9wWg5FxWBp/zKa7eCoaAT9gZti7RiwVuP3yE63fuo1ks/UpeRj4VvRbRuQX0jPZPxeO1aYDqbBAB2RMmvm+m9f7PdC7yPhinHVZR5S+bUPRAWiLtBV63t4+9S1dhVAtDHZgYDhorw9BwaAnouhbLxRJ7ywWaRseN+hodcguUQjAk+MuXmAYkX3ghvMseRML8Wrw11DRonAWzAkEDjrAgAlkDpzWcI2hncXZyjC9+9w949NmvsXdlUTQwUEff120USlUObBH6GGFsxN/huvN6NQqw5DkveGxyJhOB72e1iQCycJRySeTgJoDH3JOjSEPyTfgpwbBZamhroKjjmGv4IoOXXWhsii3QJm/NGCgde7awfbh23SXLSW7FL9HzYS1evXiOly+ew/Y9TAsYYzAMPVYrhiKG1pL8SxGDVFAABBWnubiWTOtE8ODGv98JbylScWVJDiIk8doYuAl/+YopG1etyNJwYPA5sHS3xP2Hj3Dr9h2orkUPDhn2k5cjyKHcTE5FySx7rOObNHx+FG3ijbVrpi7IxGg+fQWse3wuDlH8rEFOcVVt+WW3MuS+ViksmkYUE4cYssCniCvsgFI2Kr9qiojhLMOyAcOAKWzD4Ds/8F/0viDKryA/ZSFFmo4iRYBJ5Q5lqas5NuMBJmgQFDGsbBqUPDrJwRPHAbI6xnpnxn1IgGmcxKR1R0e49/B9XL56VfJRZTmoigLWBZ4Azdtk9JTcqw32n5W2OBg20U5TVKMdh0qII2szIiy6DlcuH6FrG6heBKBjYLXq0SqgWzRoG5mS6tqFJN0L3hgPdNjvMyWi10Gp4M5OQbSgzGIjApFsKS/Jx1J5gvKQwUDQmtCQwoJkz5MGBKUZg12BjcWLL/+IZ99+jbuXLoHaBgy/WiQ+D1GOJu9NhZRRMnruuh5rxynPzhp63qAwyXvR4j25wvQKIRvWayAsghklgFORB5vhXGjrLMcEEBJxSR2nvBhj9dkViV9MKqXGGKgJx8eCx3cBPpuOh0/r80Zxf4rnT77Fyavn0D7LbsgKbIxBv/JKxDLajmJyTOcc2BrxAvk8G0kXBcUQ3pn6MdUxO5bbi36QxK1AnN/F3uVCO3ifUobyJNR91mLZCwSXr17DvQcPsdhbol9r/QzcAJXVmvor9sEGQSxjhJDnARqjMZ7eRcCvXTHK+hcVzIzTJmCTrklyqEA2SOCtVQrLpgGcAdjCec+Hc05WMSkD1TSRV5TEMvgJpIRUmSWZKRP7DPosASwBTMdCoQC7AViWQeHlGLdWshgHg6AcC+sUjF7yOs5zl7zFg60AXIJ+cx7wF8lYo/dG/qAUrt64ibv3H2Jvbz+CshxwViUZ6ZTxw3l9x77/WFr37Iy/C0SAy3L4/Eja4sFRay4iX5R4LBYus9oIhLZpcLi/h/29BczZCwwDQ9EKXdtC6waNbvzS75D1QaaXiBTYESwRiBoQGIocmBWYCUrJqqwkXFR8RvQ++CYZ+l7y6yAEPjo/3RIYS3atVcqjdFgQGbQYcPrDd/ji93+P6w/voV1eAqMBOeU7J1gE45SaJRO2mUdmSslPKch6GmpMKSbhAVlhVllGonwUQCnfRlHGCsQGsCh/Kg7O3MMTrM0IekaetamOU9debAruaFniWbZjKQinAOvUNZNvHLWOSo8LO4vj1y/x5Juv0B+/wQKMwTn01mIPkL5nwBkHg0GAKwBH7JfiShAlfBKxaOkCEZAE0lpD+32m2DEcOa/gSjdyKLtvtvi57oJnvxR8hbNhgGVIUr/B4Ng6vBoMuNnD7U/+Atfu34cBYLPpi9SuUVhlfXJOiuXcLsx/tMXqQWGNtYIiXH9Ret/bSHn9gscwB3AhTpWCJ9BPyTtnAegEIJCWgAvAMWjaVkxgv+SamaOxnC+VJiACZzBHbzMz+2TCFF0pOWgP1wRDNh/v8dsEz0fPJGfvxkgPsx9HTrw21iZgY32aBOdnLlbOYsUOrBvcuH4TH336GQ4vXwEpXeXiKd9S6urz0RiPl8dyvbVt6IiXbeuw8X0gun3aybKNdvbglIMvMGi+1r2MbdGNxnK5QKslXbRzFtYqtPv72Ft2aBq/IsNKwqKQ6E/BexpU7mEI3wVw5UtaJVlStveuR+dhqTS8tyHEkjSN9innHTSUJH9SEhcESrEnw2Dw9Z++xifPXuDapUOZ440aPXQQe6YHdl5uNdK+YzQFhLYL1PqZmXVdKFRflRHhORVgHJ8T61zeH4DOtjrV75vyCl5kShbr2DTSzw/i6mdydpwdwxqD58+e4cnjb2GGFZYol+oXmwIyg00PCwdiSYCpm+RZgTMR6BBzAstce9xkLIhRkrn0MzAb/saO1cLfsYugzVmHwRqcWYZpO1y6/QhXP/kVcHQZg39fGnd5Y2+ANT8Ta23j0SmFUFrD5WegnFWSolw/9/ZQ7lXJO0dcBx5TyDmnMFiHE2Ngmga0Yiw4nJe7iPK/nNfkmcEA01rB1caDH7SpL7IHs0AWZvbblnjZDsj0bUhk6Ult6IwgYyMvu/WxGwBXPFaNBxe8NT65pbUWxjFeO4s3cHCLJa4/+hD3PvoUzWIvGdBSgqIsPzeFtstKH2pU1LIGO+u/N+u3WleM6Yxt9TtHkPG6B6e0/uW70tqniAa6xRIOBMUs00BaSwInosggYcUEIW2lIF6IEGQcgh4TQxRWc1zzL7/FTScuPtJKhLmV78QaWjdgGJDS0MRxt3Eoyc+zWCykDE7jxXfP8P2XX+P63dsgf1zKgfR+pDYpG7u0tsfadZOgnJqaik8fOSeWRsx+4MuUudmDpUSEFEZEWfv5JvUWUAB75Psr38E3KJfIaCOKflews+3ai0mZS2Jkmmon7w0oR0pChYKrrfpS4QUXt7EGr169wpdf/Ak//PA9OmZ0TYNFKwHygxlgG6DVXexLohAz5WDNAGfDnlXiDWKE1SuclsnGInieCRY5y/XRwvWSrAY4of75hrnBixMSewZvzmAMjHUwjrHY38dHn36Gm7fvwFEa6/BTrtHDgcqLSEmhMnOWvK1ozfWe3QJgNo3dbdZuUGJitdev93FXXtHG6ZPs/EWkaQ8kr3/W29fEvhCeWfUGr05P0ZPoAGMdBuOgByu7a7MDrAWcgPOIfjwQjwHnWbkCL5CfVir2lALF4cce8ITNOsMzFQjKB+HDZ3Lf1BPJUxQcEVWYOZeJCINXKr+3OOc9OStn8doMOCWNS+/dxK2P/gKHNx4A7RKykD7Tyxtkaw1FfhQFZFocCO+rp8rqG/23EXCT/xVl3hHQ1HTuGBx/JissRatJBicAkoyRTdcJ0LAW3bLF3mIBTQSC7PobPCvkc3GQ0lBNA6Uan5dDXHWUBVuFlVVZKwkqR2LqooF8LhhoDeXRsWJG1y2gwREQaVJoFMMpBdc0sKbB6fEpvvqHP+LDX36GveuHcJNtu4465Xt+aloR5sfGpnLqaai1txdKNfF2PsDDd+U3OxUFF+aDVZwDF1mQpv3Cc0PAcfytKvA7MT1Vl71WdmPXvi2UW4LJOpmeRszvoyiNE5MEAJH3Y74XS1ACSFcDkKXhTx5/i6+++BxmdYZ9DTSNbGUiS24trFHglmOqhugxVTp6PqOnLlSmwl8JWKiiD8PS3AicyOcMAccl4UHelULM7w7uvbjMslQ4ZC0+cxY9NK5ffQ8PHjzE/uEhklGR2mySa0ZOBHwWrOvqTNFHa/eeA9SE4/V0RTo3VliKoEZ0d6WKfrJW+sekpFxL2VUVugLw8U4PUsywwvHr17BDH00150L+M5Y0B3YArIYzBNtLnCC0EwBECggxaRFocHxZMApymRn+HLsE0ifLzHGMxt9A1bf5sYrH1sAuZzFp2XYMIch+GGCMEYBjDc6Mgdrbx/0P84HNrwAAIABJREFUPsLtB4+gl/uS2K8whHK5vUWmFmCzMraw3RBP1421Rf2ZRm5EEDkTbHjNWF120RfnWkW1C7F/sQKDrUGjJMNx2zTo2gZtI9NEbdeg7Vpo3aJtlzJ9pAPA0Rj6AewMYKU51hq5sGJdnLcNliAF0OOtOCJvPARlrUVxOOcwkEKjFfYAaHbQaABWODU9nj/5Fi+/f4rllfeARvsBUBZkq5CNVm92eMQdFwJRN7ntfiwFpVlyUYh70nHgKd8+VMXbRCUX2hOJwSha9OOgZao+m0DOz1Hnfwyqi5yASBLmNbgZt0bEmpWIhCDKPViv5Axn74ogCsJ5zlqcvXmJH77+I06efoOFW6FTjIZaaEXQbQvFrui/wjOnFJDFsAl8AQAV9/4pC49Rb114duy/WF5x+VtJfwzHMkb7vo8B0NGdD8BYxmAdeuuwcg5quYf3P/wIN2/fAamwO3JuYYd5+gB81mk7L3lreweQs9vzxhXe+DW7gvrp+v35KWzGm/Ot59BaZ+agJnC95wOYAavTE7x5/RK2XyHkIc5BiLSCT4VhrQdCsnkytY3wCPsQhdhkjOgu49p4zJ7vDfTaKKunV2MMYqhSYWBWq7zYL5KpcGrBH8inpkIGbyc5rVYr9EMPx8AAhtINji5fwf0HD3FwdEnuy3DwqFOiYBsvvzm8uaQxXl0z2IICmLi+emJs52gMlk+PbTD23mrZQjq+o0fnJwKcEjEGZS7y2+H0+DUWWqHb28Ny2aHrJN/N3rJB17Vo2w4gDaIGumkE3OhG5jtbAjm/T5U1frcHB2c5e10aVIGxrLVFI0QhrrXkVfCbDkuGSOenxdjnwiFoAjpFMApocIbj59/i+8df4L2Hj9BoWSYNkm3hEqUVWuuNHtAV4rkxhbfrsfjGLR6ecQ8CR+VUI+JUdiXLwamMw8mnp4opDt8B2wKMtwG1Gti8Hd4cGbDsM/uOGECTt3E5ZBCgDjh4gzKFUcpHf4N4R/rVGZ588xUef/57uDc/YJ9XWDLQKo1FA7StBlsGw8LYAc4ZOCfB41ppDxCSR8lDVf/uUrDn/Z9LzaZJuaJioKP30BSAnUMQpY2BlDazWgcDHK8cXg4Ox47Rt/u49eAjPPrlX2D/8mUY3cAge3Utw/Px4d8Xf094Uwqe5PJcfu9oN54DhO8CeIAxvn8bxgGwbn7n9ZXPYHSWV7HfvN5hWJ3h+bPv8fKHZ+hPT2Aahm10Sh1gLbgpZaAxBs6tfJb2NnpxGJTyCBHH5eJMDlB+atMnr0ygXP7VnuYc0DBz3Bcx56FavkVwE+SZV+EOY9NR+ffkzQl5oYyxMMw4dQ7Ly9fw4NH7kipBaRkPmeCZ8vJHqzT7GUZ68ErVXZfhptHH/VwexbwvlLhG4OBKXZX5j1MRfqIHZ7qxOPtcP88A2FmcvHkNmAGNVpJQrGnRLVq0rbjP27YFQ4NZAoC10rJ5XsZ0RH7ZM0lOBLiECCnL4RI9FJywYA00CMl6BRFWQ49hGKLb3kRXuXe3K+CsP8WTp9/hg35At+drx1wKWQpfwjupeGsAOUHoTln3UwCmptqqyI/vKkjz5+Tvza374nzWH8ELFuq/y/RUXr5Nnptdy34RSMoY9nmntb5P11R9Cy5WusmxcVUWhWj+nZ244ZlxfHyMr778Aj98/xRwFo0CtJJAfEXynUjDOcDYAav+DKSBheqk1MwSqI9sVQ/Se5LCDwAu1THUKSSCDOAm332ZrUyRsR8zDgn8WGthHXs3vIVxCsY59Mw4I4XFtRt48Kvf4PDmHRhFa23EebtuQJa5AB8FN15ujLX7rjQFjHYCVghV2G38X2QKvDLWHcFTV/wOHhVncXbyBt8/+Q7Hr19BOwvOloibYcCggUZLLqdhGKCUjvZjmD7XzLLxa5Yqg70Hh8kvG1feU80phxTARcJIYHw6PeoPpD6qs8HXYCVVGPFdObiRexysNQL8ARgHDBYwjmEdBOAMFtcPLuHB+x/i4NLluLlzDubHjN2axr3kXHzUh5P3JhgPo48ub60N9InjU+9TMWXiOsCh8r9J2smDM95Y9WDOjnqGPX7zGq5fQVMAFsn6l98aSndiTWqJw4FqYEP2Rkfe6vMWJMi748LAcMlzwohpvZnZb0WQgR0g7iQbjoeGDntgKa19BlWgZUhSQNfj2++e4uTsDEdXPGMUTO+nqIrmCS5/ZG9H0mJj/TtyfJvXY9s1u0731J6cehojWe1JwU2BmW2Ul3cTILqoHpxxz9j4dcB4H4glWw9+Ktgj2JRjgImZMfQ9VmdnePr0CR5/+w2GfoX9RqGDRqMB9tO1XdehW7QAO9l8dhigNKFrG4RgfMsMrVR8exgbKR4gCHpJucDZiqxY7lBPH4/j/E7IHHKYWBvs47RD+KqPy2CNsRisxqr3CcwO9vHow4/xwUcfo10syp0iYptt7qfid+YGq4da7IsRpbTNe3Neb8/U83cBNxd1TKxTmJrKZVMO6OV84C1mB9gBxy+e4ftvv8JweoyF9yomIOBgzIC+T+NO6xZKU8xCbK0FB8VIWeb7LJqbwdEoE1VAAn4YADG0JkhKkgTWpPTkwRSN9lU+zuO+bm69r9c8Ntk9AvgdLBNW1uHUOJwNTqZtqYHav4Srtx/i6s07oLaFjXJkujx524/J2xqApbHx8wLsUZ7e4RXFJJWv6roZOU1bAE5wTZeIFchyUFRKXPjJwQwrvH75HGZ1BrT+imj9J8StSEFpv/klERx8Nsb4sPWAsBBAJtNRKTiS48oglPd5BKK8axFgGGPj0nFmwEE2aFsul+gHh9PBgHyA8vMXL/H06VNcv3ELTdMm1By6gHKmCt0x0gVhQGVuUATBXQcUhltGPBvbvDtr532dg8AYe244Vg/gUOzco7YLuJkSBFP31CDrotK29gfGyz8FiCKo8SPYycVeaCe4kwtF5xxOT07w5Ltv8ad/+B1ePvsesBZaw692S9cSQaZWSUuAuTFYnZ2BAOzv70OTgmVAZ1tyhPKmXDUB4LAkKuOUA0iSQCrAyYoXWeHCgHHgbDdk56w4X1WWuM1ZWSllRLD3g8PpWQ/bANeu38Anv/gFrl27huCd5aJJqj6IBhYnyy42briZYxK/FO+UwM0m2uSFGfPU1DQ2Lnfx0ox5Ed5O4sDkhXEJZrC1OHv9Et8//hovnj2BcgMUAZYdBuuwCJ4fTgHpANA0QEeNeDI4JK2U6VhG2lUcro77qMqVfabZAD/uPDYiFXg9GXtjbleOOmsdBBcrp4LxwIAxtjAmLMOvHrPojcVgHIZuD7cefYYPf/VbdFeuYWh0LDnF9gFCaEFNY/Uv+a/SPz8S3+zqeay9ecW5qTM0Uo8tw2LLFFVYOhrc8OGJCaEnwyh0puyJ8+rFc7x68RzD6gwDfOZJY2CtTEmJW0+sSuWnnZRuAK0ikwRGyS2v9CZEgCMHxduTby+QK3NmTtl3QQBZv7O4xA+QlUyZYHH+KMVo4NApwvHpCZ49/hbmo0/QNpJYaqpx18FNXer1O/yQn8I4ZR2mPAKbGCu0I9YHXf2cGshsEq6bppqmptGm7ntbqFhJNgFkxsDM2HTkVi9Q5dMJgtEMA87evMTzr/6Al1/8f1icPEGHAQsADSloLfFk+aomrcVLaa3B0AvI6PsejdJolKRTyONn5DOWNqtvKHeyzEMNnHUxWSBbC2cMjDUSXzMYGCfZV51j9MaiNw7GyR9bYHAKhglNt8T7H32M23fuoWnbVKZq1VnZVhOyrrJQN9FPmRYaAy91n09OXUw876eU55+adh3TeY1CHc3Q48XzZ/ju22+wOjn2QfHe2+dHgSKfXgQKcMJrjiycFbDDfoEG1W3sV+Hm7xwDIBwAcDYuY/sHr0EYT6AYpBv1oZOVwey9QYwEytJ7E7iLIN869IMpfhsmH3szRK9Ut3eIew8f4da9e9DLZbYPG4ciApTGYtEfUvjpvkNlX+c8i7L8PztNPHYyvibzRO3CcTvvJl6UKHzLUBgzx1URrj/D46+/xJsXz6GdhfOAZjADViv2cQEKTcNQGlBWGFCzVKx2LfJIK3CGfCLTOiAYobXyyFPlh+eHqRhmhnKAoxC1D7QaONSAdg7Un+D4mz+BT95A7R/CFe2SQJRvtbwFy/ZEVY0M+3D+WdHYoJtSmDXlXrddXN81yBk7Viv3MWU+db4uc/39bRDq5wWCgcaUnjwE3voaAz2l6pbA4hXevHyBl98/Rv/yKZb2BEsCWpI8U4tWQIsmhu1XsK1C18lKxbCKcXV2hqHvgaaBajqQmgK+Zf0iy3ohLR5+CfZk5+Cy+JsQGCpp9g0G58AksqAfJFPx4BhDP8CywknfoFf7ePThL/DJr/89LI+ugMnn1QorpZgR/NWxVWIT5W1VgeriSDlFPSW4pzw1U8em7p86N2awjD1vGxC+KHSeMkZPBjuszs7w5Lvv8Pjbb2D6FZbkPZFhmlyJ178YZwzAOdihBzsDYguwBWGBGE9mvfslTBl5nSGy1hX9TgRZiRXGJyePS6jb6BgPnh5m5Jt7rk9PeU3GJdCN4RicJfZzwKof0PcCcJrlPm7cvY9HH3yIS5cvo9dxSUIse6yHN7BLGT7SLzXuH2HVXLev3fQTiCnMgCTPE2f/Iv0MLL/TVg3rAnedgsCDcxhO3+Dp11/ArY7RKUjwrnPomGGNRb/qpVILQuuX+Dn2jOizNrJzEZ2G98bGyN06gHc4b/cSEFEBbhLAgZ97lakrZgnS1EqWjzfG4cWT77B6c4zlkYHbq5uNE+KM6DKbxoou810YZDtoyZn3vICgtiinqFRqJeAZK8vY77HynQcMXURaL2sp+MbqMgWC6t9jCi+PQxAjweDs7AxvXr/C61cvvUKQOIQwZSSeG3mmcxZmGOAWHbRmkFJYaMmD069WsNbgbHVa7BwfFQgHl3c5VZ0T+zw2zu+bE4DN4AP4JXBS4mqsYzgPcCSJn3fD+yytJw44vHULn/313+Danfuw7VIWGNR9kAGaEBsRRGRt201y+I6gpj6+C42B9SkAE77n+b1qL8/FHxNpS5/1c74t/H+1kSdd52D6M5ydHAPsoAk+Kaz2q6NCXpsRwwJ+pZJzYGMksWyI/woghm0COOyXba/pjBDLk03TZtuQjNUpXlf1bQT/hTcpOQSck/QOsqjFpeudj4lzKWBfKY39w0t4+MEHuH7zBpquQw8zzdi1vJ7AN6lSm8DLFOr5CTqnelT0EIV/YbPE3F8wVv4dx8SOHpwxxRa/iVHlG5aJYK3Bm9cvwdZAQYIYnReQMYOxlQyqIdhLBSDFIpTBLkKExCAl6m6aJjGU5Sr53DqNW1tlUG3+7K7rYIigyeL45BQvXr7C4W2OeGWkxbxSyFqNzsMOEy4crIOFbQJ3HVx4YYDNQcnnEaZTwCS0Z03bANWkh+OCUbKKypVTYyBn+v7qcwPwyXnCefBwenqC/uwUCpK1uNUBkGcB/X7qyVgj26HotHGq1hpt14FX4hEKK0Hato0gh0AApyzWWjcg3abcOSRJIv3gBLJNNEPSshAzYZ2DcYBx8n0wcnx1tsJgDHrq0Bzt41f//l/j7icfwrU+tgIJLMS4GTBobaAwZBEufJn8vUGjrgGX4L0ZBzTxqRNe0SkgVJ+fetZPAVAXj2otVBqg4QoOPF0hHK0VurZB12gYL/fZMbhYnu0ATtPDsuq2zKwuBqoBxazcgAQQh2zunGZr/BRP8KzkoQUUbFG/T1sNNnf1NtfAJ2ym6ZzD0EvA/+CPWSfjoXeM3jJ6a7EyFrppceveA9y5/xDtcgnLYdJOgH4mNRAWAWyl5JpJB6aqMoF7fk7+HfPepFg6T8Foyw3CHXTVzls1jHtxKFpQRLKc21gLkJbNv6yDBtDqRvaACm7GUHLnwGaAI8lJI3GVCmwF4LDzy0tzsOPfrOKqDznoKKHlTYo6eHHW6+K9UDkjSzAOSBOMY3z39Hvc/UWO8YH1TTcrhReKyBxHTpjvHGOJXaeepqZ9pogg/BxVc7DQsytkR/Cqj6p3nhcE1ffXVulPed6fl9IY2ES19ybWuwBFm4ARyb5sPtC3aRpoJQso2Vmws5AYOe2BJUX3ftd1UVAMwwBmxmKxwHKxiNugWCP3hfPWWkmb0HZxiwbHJEtrnQUrBeenlJUksQeYYfseZjXIlJSR+BtrHazNdgXnlHpe3uW8Z8dBH+zh7q9+gY9/+2u0Vw5hiKBCUCqnsV+uNUNxnvKxlsmGGsSsj5fdDYdwzS7GRu2JmXrGmCfvPMbMn5vW+TYH6+XxIAYDqCefZDUkgfUZbHxuHJ95u23RtrK/VDGWlCqmskRVMJhckbuGgvos2juVJ0rFLPdUmGqyYTsIf9mY92YT+Bnz4sSl7z6/DTME3AwDzpzFsQVeG4cTR7h+9QY+/O3f4uq9+3Bakm86tS51codD2QVJtkxONW0BN+S/r8PWH0dTYyISVd9D9yAB5V01xo+IwRkpTMhaFl3EDKU1FDtorbC36NBp7YWTD/ZVCqoRa1CFJeLkV0hZ44PFjFfKzs/D+k39kCkIBK+Zi8ClZrQx4DPuRuToRoyoG7KyxTrg5evXUs6RdsqnF6qTQO6ZCAUOIKfysuzCQjUAqo+NlW3iQcWbKf9HsvHplPLdBCA38c22aa63xYuTaDegk5OXrYUXNv8/9EH9BkCAfRNiaZRC7xysM7JuhBlNyOTqZEduAGhaXXjUknULvwFtA+1TJKxWq7iaqm1aaJXdW3mVoieFOcbfyNYQA6wZMPQGvZFdnwef38aEKSyrsOoVetcAbYd7H36MX//t3+DSzfcwaAFWam2vJmmhohij444L3g7X5Z9F4zJGz9X3jAnmXcHNmJypnzE19fG2GACpnKOmWzSGAR8TKZIdUBpKN3DsdwKHXKaUktV9SkFniUaj7CaCoxRm4F9TeHVkHMk0a4ztLMocihsAV5JfjlyMMwvTcHGmIuiJNeBERR+HwOHcixMADvumitc5h8E5rBzDqAbdlSu48eFnuP7RL8D7BzCKZCViuHGkhct+mCA/dnem/NKfEXif9/7C073jmNgpD84mhF5dCEUEZyXoa9k26DSj6zrZqqHTWCxk6kepBm27hNIN2rbxOXFktRUBPntxWJIaUD8AFZyJKb08ADCbCCTGGC0XhDnzCQNWG/75dPLWWW95EhYH+7h77z5I67Gaj7RFaqZdFPYm0VA28WZAc17KB0WyeGrli7Vr6vL8qHdv8bRdRMqF2S6Kaeya3AeRfwplyKcC8WK1kmx1slii3dsHqQbODcm/mQtX66C0TDstlxJkLGkR5GrHDDMYnw22XGkSxoNqVfTkEJD2mGJJVhi+O2vBnOIImBGt8ODB6Z3FYC2GwWAwLd70GqdY4Pb9R/jsP/znuPnoIwAdtPPqkFNczc7cPnLhtumi0cdMgJtN1ufPMSan3nHRPZ1l2WppNmHUKO/R8Uau8cu6NcFvFtuiUQoquBCysUHIjaFwXpXv8se9jVl4jcvCyH+5URHlrPxY88DmOdeYOZYHKPtQdIxN48CmfDcMzvJAib4xPg+ObjvcevAIH/3yL0DLPdjcQHGAZCAPxU9ujjFVPerv2JVVcz6cMAS20Sb9N2Yw/Jx8/tO2asi1oEfBRIxhdQrTr3DQtdjrJEvwcrnA3qLBYtFhsZAtGgAtwMaDG1kClTwIZADLFs4BFKBr6DAPeArhl6F38QaJgHR+4OQpsuMqj5zprORPGAYJ5FwNA87YoecWn374MT78+FNJDTvZGGO+tZErRzv8XGJ8kqYs1V1c3qNTKRPHpu7Pl1Fve9/UMy6696YGLXkm0ykAWHvd6oEc4gBSPIAclXuzZwQwAQB6AaOXOLMWIdgyJO4jAEprdIsWi8UCWjdQSsZWjI0ZBh8IbGN5lstlSnwJBRUywga3ky8Hh13AfRCx82kWwoqpsFFgPwzynRnGByE7xzAABq1wcO0afvMf/0e4//EnUN0CYIJ2oR1dKWAByQAdFV053sKlhfdrByAzBl6mxsu2cbT2HN+3xT9OoZV1eYtnbTn/VlEBTFLAQfDqCzh3IOegdQZwtAKFNCX59jFawiCIkDZqVrLFTL1dCGEalIZS5dPy+bjcJPMYHuTYsHBF9lsLfRwMB/EcIcWlecDDLFNTq9UKwzBgYMaKGQM09q8c4f0PP8Lde/f9Mzc4LWjiR6hTfr52YG7QE9vovLL6PIbBriBn23U/YauGGin7o8zoV6c4OzlBCwet2ugGbxr5bNsWRBoMyUGj/SabUBrW9hG4hI4F/I7FsVLCTEQS9yNjIwtAzvRDFDaOJV6ByAdqDhiG4LEJCaIgqeONRW+MBD8yY//SVfz6r36D5d6+LBGPVQ+DM3eHlkXYmaIVkVsmW7w+PwEM1Ep6TDFPKe38mvr6qWfl7xx7/kUHNYFq4Fd/HxszUwM4CXkAqPIcVWMs8PEwDDg7Ocbxi2foX3+PxpxgAYMGYukqj5MZktiyaVoJDvbCzrFfsj1IvAwR4hRVGIuATA/I5oNlyvqYboEgsUEacWorb4MAovpeshX37DAwYzAGg3E4Uw26K0f49Ld/ifd/+Sna/T3ZVzerb8038jsXNuvmavJ5jSikc/JYDWY23T92PoEbRFDDgM/tUl1LGRDi9fMXmaaVDBUfIzfKvc7C9Cvx1jQKjUb0Nmql0DThr0HTNiBSaNou8nVYbaV0IxzgvSEyfZow8kbvQKXSQr3y9CLx0tDX0rHxBcEAYSB6QGVWwPctw2f6llusS3Foxlo/PeWArsWdu/fw4OEjLPb2wGthEWO8MSJzJ2xsDpdz+swBd6zLWJ1H6LwgZ3SsnAPknMfDc04PThii8VWIVp3/35kBJ29ew/ZnWCDs3SHBj8IwEn+jdQutu7hlA/s52TjPVm3UR0ohrKLKp5wIHpxkuxJr6GAqRG+Qj2COgEYsSeuX5QVXInDWOxyvnAR5QcEu9/Hbv/5b3H30PmjRYdg4mEdchNiRAbw79edw/9UMVLtOp54TPqfAzrb78s9N5dvkFXobgM4YsNvkvRk7H9zaQAZ01hQ6AE7t50IOnNev8Or5M6zevERje7TEaIigSQIxtRaw42xIrJkH1ScAAkgCNVEaaVPBUoDX9fNgnsJeMR4AgaB8kk6lNLR2fq85YPCpH5S13soVALZ3cICbd++gOzyQ7Jq2HNub3dfpM3pu1rw65b3hc2p85L938fxMfR993sS786owZNowrZPxxymBpbeBaG3hRYLx0fvmtSwRwxoB7Q0BzWKBvYbiXoWLrsGi02hbCTYWAK6gdAulGonTaZroxYmeHmtB1siL/LSpL0hpAPufYXlyDdKneCN4ZlD1qRjdiADf+XeHadtwrXXAqnc4HRzOjMPgGCsGjO5w7eZtfPyLX+L6rVuwuplw3QTDOkH6dd4f9zqBPCYLnxlnFc6ef2JZ/FM8OVO0dasGeXH6vQkAO5946dWLFxhOT3yK9pCLwFt9GQMFgaWUgss2RwvXR6bxQtX5Of8wrxkr7+QqYF1REgmgCgLaGANFshoFkJTYzH4TNgii7p3DqWO8hMJ7tx7g4a9+C+ztg3W5DC+WjqZ8N1xYD5uEbPbAnShXRGuK0/8eG6Bj18V2GuncTQy2CRTtWoexYxcZ5IyBlk3tVl+35v4mD4on2i+4soNFuFqtcHz8Bm9ev4IzBgutZINNrWPuppALRzII+2RhjQNIAjbJZwce+l7S3lsjy8AbsZoDHxIk5ifuG+enAMK5tEFUqIcCEABMsoCV1pLbRFEUsMZoHD9/iW8+/wKPPv0Ue1f2MTWG82PpwHj/jBjik5SPx03nUz9svn68ND8XcQYS3h4KnkpiKpRo2FOQWJJRnhy/BtsBnSK0TSNpOtoWi0UrAKfRWPjVfzLFJWAmpvdQslDFMQTgKDEdvMYAQowYGGQ5cy4FgxoF4+ReyOlp9zRVG2Wx3xR2bAGLCwkxmWEsoffg5pV1OGOgVy32r97AvV/8GrcefYBmuQeoBmaLbI0xOGuXpUptBPTZl9qTM3b9eWkXb82PoV10ztYpKnlQ3jjh4TnwQXS/rU5P8Ozpd1idvMEeD+KmswaDGdBaP/9vLByRbFvjHJqWoZouBTrmFudIY0T0DMBP3UZhmpc7b4jCW1Dsu+NAZMG9RMtrDbSKsSRgCYZ2FstFByIVN6yIA7VwAZYMxjsy11Q9p5hik5CdOraLRRrovHOf264fA1Lnec9FJFlREUBzOBY+z9t+6eZJEJSdA7NfobQC2EkiSi0rq7TyWzQoD+A9S8rOy0qyFodkfoBspcCyZQOAmANH8uX4KaoAOJkQcwv4ggflxd6IgI9BEAWTPLeaZcm3pCEQcawcMLw+weM/fYnnj59gcXgV1LRFXWu+3eR2B5JXrOb0McNi0tNyjuP5s9eu3QButlmqY/Jr0rK8AJTKPlJuZCCiuMqvoXI9zOkxhjevocwATWnHbqVVXMUXttWR1VcKzOI9jNmOtQaUAtvAg9nADIAZQbWUWYBDuIMwVtIhOd+Me6rJO4yULFuEiHHHKTuxxKnJPmzWh0YwA9ZpGMeS7JKBFQDT7eH6+5/g7ie/xOLyVVhJ+T9iVI/x1nSk1i4AXp66nWd30mWb3nHO+6doZzl7ka3lmWaaaaaZZpppph9DU0uCZppppplmmmmmmd5amgHOTDPNNNNMM830ztEMcGaaaaaZZppppneOZoAz00wzzTTTTDO9czQDnJlmmmmmmWaa6Z2jGeDMNNNMM80000zvHM0AZ6aZZppppplmeudoBjgzzTTTTDPNNNM7RzPAmWmmmWaaaaaZ3jmaAc5MM80000wzzfTO0QxwZppppplmmmmmd45mgDPTTDPNNNNMM71zNAOcmWaaaaaZZprpnaMZ4Mw000wzzTTTTO8czQBnpplmmmmmmWZ652gGODPNNNNMM8000ztHM8CvJQPaAAAgAElEQVSZaaaZZpppppneOZoBzkwzzTTTTDPN9M7RDHBmmmmmmWaaaaZ3jmaAM9NMM80000wzvXM0A5yZZppppplmmumdoxngzDTTTDPNNNNM7xzNAGemmWaaaaaZZnrnaAY4M80000wzzTTTO0czwJlppplmmmmmmd45mgHOTDPNNNNMM830ztEMcGaaaaaZZppppneOZoAz00wzzTTTTDO9czQDnJlmmmmmmWaa6Z2jGeDMNNNMM80000zvHM0AZ6aZZppppplmeueo2XTyf/qX/4qJASIqjjMYDIABgNn/OTAzmBkAgRlItzGUUiAiMCmACPIP8ucvrN9THqf8RPxklKfKB4RzhE2XFc8sKwrEOiG+K/wuLvXXMTOIB5y9eIq/+9//D3z9b/8d9PFrKNPDGos3Dti79xC/+ef/DJdvXcXn/+e/xpN/+3dYGsa+VmgVYxgG9GaFF2ev8fRkgNm/hr/8z/4FPvqrv0K7WIB940vTc9buKL4754rz9fH8fLjXOQfLDs45GGvjOSKCVhpaKWjfl3n/hP4Nn+H41N9U2+Xl+u//2/9qY5f9Oeh/+B//Fw4sH9rKomxfAMLfRNAgqKzeSlHk57q9AIAotXfGwCCq2ZkAqPgeVgBUNV4mx1N8SPqQgQhFcl89Jus+yx4EDp/MIJd4CYA3oagafFSWQe4o72OA4leGdQavf3iC3/1v/yu++3//b+z3pzhsFNq2BZihLIOtgzEGg7N40Z/iu++/x7MfXuDoYImrl4/QNA3atsWyW2J/uQ8AOD49wdPnz/Hd9z+AGXh0/y7eu3QFi6aFNRbHp2f44eQUT8+OcawJn/2z/xR/+R/8J9g/OPAyTgMUxgNyyZjqGnou8DZY+p5UXvPinvB/GhMWzjn8d//1f3HhxsT//C//FYMUcp5iz09UyPqcz0PdgFD3xGPpOgYXTcOQZ4toruUwx9spe3cYC+GpTHJt/r5NfE5r/Oufk415C4IBgL6HXZ1i9eoHvP72T/jmT1/g+dOXWB2fgOGwd/09PPj0M9x6+D6Wh0fgoCtFoMgzwRiIMRDAzgFDj+HsFJqBljS0bkGLBVgRCo7jVK6xz7rszrnid14nZi7ktcpkfi3HiTiTSaEQhPRajt/De4OMBwClVKU/kAZ/gRSA7KGCQ5jx3/yX//nkmNgIcDgTVpQJMQQGC+e8xE8Mm5eFo5wN94RuCcM4PD9Xpvl7mWMXyjnOXhBATkaJSTm9sOJQLs6T1CEX+lwKq1jviolSXdNvqzT01ffw8Ld/A4MOT3//9zh5+RxWW3T7B7j94Qc4uH4V2F9gef0y6GAB8/wYlhlKi+J0zLD+k5QWQT6mZLI65+231mfV8brNi2cFgVD3SaYzx4BKXZ5dAc7YvReZAp9PCQ4AXqeXvLmu1Nfurp5Ja12ecWlB4V3jQCS7J8r09CTK2L5+Yc0vkyXPjYD8e/hWGUoydFMZ6qbkKCiDUmuwvHoL93/xa7z69jGGx1/CBUHsy01KgZkx9D2GvocZBiy7FkeHh1gul9BawTnG2eoMlhmkFE5WK5yuehjrsL+3RNu2aJT2uIygSUGRkmPOwK5Owd4AIFKxllHYB8mWyRPO25gAdqmOY0o9NEiQr7VcvWgkIMQJ0As8z/FMkr+xErwmR0seCzIX1ff8nWOUZFSSMxm0GuHzZAwkADYKcjJZOgYgFBgNA6wU1HIPTXsTy0uHOHz4Pk5enOD01TEYDvtXr2D/6DLabgmQQrBW63GvALTOYvXmJX745ms8ffwE/cA4XO7j1s2buHL3Nmh/CShfakcIxvc2+RmuUUqt1WNT3dee43V44PRcviU9H57hx3xm2NUGeGxjhL6ojQX/IF8cBcBtGRibAQ47X6hkpcnx8OrgsWAZ2KMDUQ445wSMZMznWARCztxjijrVrVLIRL4MI4X37SPjJRe4FfjxFeLsuqLkuWVefIyNuMAoBEstjm7cxi/+dh/X797Fy+ffg53D/sEB3rt7B8url2EaxpX79/Dq8Xd49fpzrFYrOOvA7GCdg3XCQodHRzg8Okr19+h4bLCF77XXpr5m7PcY1d6Y3NrJr9kEYqaOTZXhIgOcVN7ETRTGQ87/DPGqUA5KkjKPoL+qfzoeAEC6ryoJariTQEzVP/WXeGAELgWjYULYFcdDRYoxmU6FIVFfnr9qTFWxvznInvhoRXBwMMMZCBYMgnNOBHWm2JgZpu/B7HBwuIcrVy5hb7mAUgRjDE7PVjgZVrDMOD45wclqBQZhb28J3TTRa+Ccg7NGLGtjQQo4XC6gSM5p7UFlLCyBORf5VbvWdaypBpfrV1xICsYfBUCTI7pwHiK3pmq1XTbljDN+OMqYzBArnS8EFB5SORbuKZkT1XXT4CZ+ZmCFSYMXB2gWe7h8xLjSO8CD6vD4+hn58NTOwbx6gSe/+zv8/f/1b/Dq+Uu8GQh7XYMnN2/ig7/6K9z8+GN0BwfiCVTw7ZvKOjV+lTcEcn06Zsisla/ycu0ipSNPhOsLz08CNAWeKDo1G00UB7kfc4he0Cna6sGJLJkxFUMAQRBQHDs2/y2lo+w6wIMm8q5wQJQ1cQRRUxb+mLdhDOMlEIDxHhjpQF/SSYt8GxCIQMh/KrBvWI39K1ewPDrErWEAOyvuuEaLe5EtDo9u4+4nv8HJK8brrz6HPn0NYkbPjJduAdpb4vb993H5yjUopRE8S2NFmipnPTA3DVSpzzpAigN4RythinaxEHYBXn8+ykFapreSfPM/2QPnYLpMA/f0vPLZ9fsCS+eszZT9IO/xyc3V4jVjgGlqoKzTmMDjUMf8DZSLtRzYcAYAEJ+xXr68gl7gg+H6Uzx/8jVOXz/DoetBuvOeFMB534GDw2AH9P0ZlGIcHe3j6NIB2qaBIg+ItII57XF6coLTszOcnK2glMbe3h6atgEDcOxgrZW/YcCwGqD3l7h0cISGyAMgAaFxdobz9i3bOsgG5zgajsGCjt6EvLEr4DgNDf78FOoGBG+9Ly2XdeCJft9khJVvKQ2koM/XFW8BiyvgmKZHY1kzrwB835ZAfBrUTMlVAFCsoAGZHWjEWzMmV4moMFcIgOktvvn8c/zh3/xruOfPcJWAS9rh9PQUTz7/Hiu7gtrfw+0PPoTuFmV981ar9N3UVFwAPduIqrZcfyPFMRunAkNZ/H/Be0PKT2Mxw4FB7P2fREVvx2l4yvoyyDou317TRoAjSpvyIZqKOvIzOjq47q66EJWC3l3GRorCda3IGXcyR49RBD0bnlXTJsAwdSwK//AbgNINWqXBwTft3bWaCQSFyzce4M4vHb52Fi+//ANOj9/gzBL65Xu4c+8+Hn78Syz3D8FQRUOPDa6pY1ODcu05WWeu3RdrtA4QawtgzBKYmuqY8jhdTKKMvbjgbgLB+T6OQssDgHVPDa39LqZWURv0pYrjIJiDco1jf8Rqq47+FIBaPLeq0249V5amLIvUsfDopgsxnB7j1Q/fwQ2nUGS919jBWgfrZc7K9jjtTzEMPRaLFpcuHWKxXED76STNGgygZ8Kq72EdYzVYHCwbaK1F0BPgHMOxg2MLYxwct1juXcZyeQgFBefLJB4BXm9TRiGHynZDeU8us6aICLRFmP+5KPGBA+BjKDgBmnyqdpPndoc3oVSqCZiMKu4NxlQx3iiMp+y+yrCLpfV6ZMwIjBd59UfM0OHnhHyrZSL5d5ycnuDJ4++AszNcaTU6RXDO4JVjmFWP54+/wZe/+x2u3LyFdrGoPMTjYQhjcndMDo3phahvJ54lF1aGTiwTx2+I7C66j+CyZhO8kUCfj7/xVluchi+A7mY+2jJFlXlw1s9W1yZgkxR5KIQPpAIAYj9Qx4VjFGwblCSCJRgHUnxRZJDg2srv9eblaD2n6l/Xoz6+JuRHgEE6l2qbABehaTvcffQIVw6XeHrvDp49fYIz43B04y7uP3iAy5evgHWaL+XsuWNAZpO3ZtNfKKMI9ql74Q2zdUavB9RYu+0i4C4y0Nk0hZpTbC/aFCOWfo9ZRuvX15apfMhQ8EYDBQHASeDsaEDEeslLJ/uzKlx8xXlpHGQlkCOWoPC9dQOOn3+P4fvHuNw12KMG5AC2FtY6ASTO4qw/w9lqBWaHo4NLODg4QNd1UifHsNZCa41F26JtOzRNAwLQNhqNIjQEKCWelsEOsGzhCGDVYO/oMg4vX45GAEOm54MXKXpkxtoWiAIg92rvAjaTIr6YACdQMmxzbihjw8bGzjYeGxsvyXZeBzZFLEft3cnGWgmGEO+JcCbIvniCk/zNAE4BgBz7+5LOYbfZeCumagiAMzh7+RTm+8c4YsZe24IAWCIcLJYYcIYfVid49tUX+OG773B05Yp490k8mflzN+mqMYNzChyNGa5+gEo7c+ZRy98RwS3HE5SdIZZPhsTTEGVTWpVnk7D+ZZvc2QpwRo8jYwKUCknATQ5lFQTZ59WKVciuLa03YL2DynMowQ2814QSp+YGT6GQI/+lUKbkR8vqtaE9Ct8Rl9fEv6qA+e8wMML7dNvi8PYdLN67hlt9D8cM1TToui4VqXj2NMipj+W/p74nEMgRrIa/Ar3XA7pqmzGe2QZmpupwEWlMYARZu+laYINwRQn+5Le3WqoHC1bJr0PwqmcszEkJjDZlKYJiMQmxbwnj42+kkuHk2Ism3z2mkPwDkWI1FIhbgB2G1Sl+ePwV1PEb7GuFRmvAOIC9hHEGwzBgtVqh71cgoghutNa+Xv8/dW/+ZMmN3Hl+HIjjHZlZVSSbzT4ktaSRmVoare3Y/rD//5rtmo3tSts9q1GfbN5kFYt15fVeHAD2BxwBRMTLTLY009kgs957EQgE4HC4f93hACxiMymrFNZ56dQ2NZtKe4ADKX7K57VIU3Pxg/eozragJ2s/V+oukz8LT0LBC5nwmsmoBY3zZx8pwFkan9O96FVMfBSbvmLs3MdrItNIK1bWnAA5kgGg6dr6+Cv0RTYGpjpNMjcHOSE7sT9dXt4DQE2ZLKOMGHfD7c1z6K7QItR1gxIwxuCUZWsNzThye3XJ4d07sH6aNLHTStlrvLUGWk6BnlP5Se8smTlbs1aCnJwGQb7l4SHOOaxM+9fEPs/BTjTk5rJwLd0zRZUp8hVhOXXwmvLMiRHHqbvTyonPn7JoTnkACsQ+VZ3oDpuX4d2HklpQOJTcHDWdBjoLj3Fiansnc4P3kvh62PBKx6gUptlCvUFhESxDaJOyUgAcm9EbSEv+ToGX+67n5cQlqfk9pRTOOqy4NP1212BYu7aW/twADiwFV9KDdyigKBxzJZCwuJsMgFTmfYovFirL+wvLNLc3MgNg0YCELe4BNf/ONNf9xb3MEZ2StRzfveXy5bfYcQjYRCG1IDLFNlhrOR6PdF3P/mzPxfm5996IYJ3DAk4JVsA4g7GW0Tq0VrRNTV1XCRjGPjZmxNiRarNl/95TaGus9jSSIBwcLsm3UwpmhQoPoJNMCHbWL48pza39mJLnXvzUg8+btXxlrJ/it0kBA2HaYvLkLHn/rqmqkzydjDwSyCkNv7y+LuWLz60ZkPe916VA9hG0w1YW40b6wy3WGqSqEF2hlS9PGY0OTgMzHOlvrlE2nzEJfCmBL2dj6S7ZetroyEm0BsJn7557mslngiZryk9ThYmoDDTOQUvEGatg5p5hdDfAOZHW0BiUaNf/9swdEXzhgWECGAtUgpuV59PpQeSWv8V7Cyk8RlFo+Fek6KKcSG5Js5MAZ0EB3yKbGHydmbwFEP7LAI4z2VjMCOMIy+FsXvY6OCh+e8Tln3VLz8zaX9yjIP7FZK0P47QiScEsvDt/RLqrDY81LSwhyDzmJbhwROE+jQNfRva8K8Hi9wEVkv07K5AJsZSXUp1zXlsUsR5LdVIBUXL7qWmHu5omeaY4fmTAmgM3L7/h8PI5FVBVPl5GHChxWGNRWiNaMVqLFWF/cc5mt6Oqqmw8kIKynXOM44C1hqapaDctVV1nwZaTIeGco2pbtk+eoKoGF/YfSuBmhTanvDFLzsnaPgem0XrFMlX+8aW8n5ftjmpWFkwf5RGwyvdr00sR3ExlLQFNkX8GgO6uv0u4hVmb8ktrcurUtbxtxXhwjqHvuH73hqvvvqW/uWZzvufpjz5EVwrVgxoMCp2C+JVSKJQ3dseBcbjGdQfG21v6rgMlVFqhqwqpqkSeNRkRHQ+5dEq+jFlfzD040/dpm4S7U6HUyktCmOCJYz7fPyd/ZjLMogp/iJy8G+DIDDXlBFmJo1kKuXvfv5JWpO08R8Y4q+CGCCJ8eW72rLhJOKWKT+Z0QeAUxjDr3PDCie4Fo9/vwSmARd4mt8IO4cupuBhYenCSkMYGXREFyhLk5OWaUHes85tMBQJ5QznzHoUBguPetq61fc09nYOsx5pWXbRuCjb2KwPmICYI+QQaJuA/L/thKT7rmFhYgpc4RzL5ZxTQOe1XxmhWrbsAzVqKSnoyYsry5i86aTFGnsDhxHLor3j14kvc9RXbSlNrPQluEzyO4r2LRhzNrmWz394pgKLlPBgLSlE1NUrrBRksCiM19f6C7cX7QINyFbnoeDBtIMRLlVM2IoRg8chF4e2Zcoog6rGmpUwWYizmfGl05ItTtFsHK9OGmG6R7wS4geQPXKtrdmUBYpYNnPI9BNzM2xF/u1DGeOz49otP+Oq3v+b1558xHo7UF0/463/4Bz78ix9jOkdjFY1A5aKKUmitqVVF7cANB67fvOSTf/tX3hxuEGU4Ozvn/R/+mGc//BG6bX19XGKwrDLBgMjkRwT+ed3njonY1oL27o7OzNsf+yKOgei9EVAq6iZSP5TkKwXJQ8fdnQBHRK0UtAQ2y1tT43OGW0Xpc3fWf4CR4k7XMNxfMWNddi0TPiXoXoInmXhkBhzsYiDNLZ21z7W8edk5kLmvnOTFCW0+BXCc88v0UnBxBDc2tQxQIC5NreH8PrrxPT7QUrIpyBx5K+Ks6nzALOgaYrgesSyfjI38Wvin4OYANNyMqZObfZbWvCarCCECGUea+vaCwvndjBeWxvp75t+/T1rzYpGUCsUgTMLqBKDxBvlsHMaCrOHmzQtuX3/Ltta0ukLHzc2cw4owjAPGGobRA5a2bdjsdivexYnPBM/nXTdQVxpRYHFp6srakdH0DNbRq4oPnn3A+ZNniFSL1UwRlsyvzQg2XZfMDR+GR5oay56O/CNhQcV90/t/qlTyU7rKZJn5PdCsjSJXUvuTpihkxxLkLGI+sh+neKuI25jVd/n9DoCT5OTDQM2pMWVxGBGwlstX3/HZL37Ju88+hpsrxBquL9/x25sjw9UV3eUVCkctgg5Lqq34crUoGidIf+T5x7/l+uVLBnOgdz0DFR/8+G/5T//r/8YPf/Yzms0m0KCgwASj47AL/yTvyIpnbE0nTauc5lp3ZUSIkGuHCHCU8iyy6KlMD6/KwFjhO9I9AGcOQJiqldXFC/zYsRbnUohQciuuzY1KXvpMQ6wxyIOvhX8T1voP0JanmDYHN+ELeSeVbskJXD0UoOTfS+/MdD2fTipBWAQLXhnGPpp7Sybwc8KrlFke8Z6NXgvJPRNZ3TJ9JVjikR8LxRjy+/qXHqfHmXKBWLY3cLq/O1fUp0pzWfzG3LUfGFgSYshBT1Zsvrmcy/st5FqaQ8v3zXDU3CC5a35esimDqWZRWroC0K1PQ+QvDm0Xi8Ey9rfcfP058uYVtVKgFYJLOw3HMqz1xzQ4a2nbDbvtFq316phBvGywZkDEsdtWCJZuODIGCW+tYRTLIAZptzx57ynb7Wax+2tJwKwJGU2ibJPMGoqr3lKWqFjmXuLU/483lcN1HkORy0XJ2FEyYH6az07dm/ZeOgWac8Xs67EGcuYAJ+aXPJ8r+/s+D/MciKaeFHDKYrpbXnz6e66/+Yp2OKIay+BGnDF0r77hi1+8Y1srqrHH1S3xbIpU52BY16OD63e0lWJfKw5m4Ko78vJ3v2W4HUFX/Ohnf+U3r3QO0MV4y8GNOFZWQU07DhftcXMvTnzKBTGSG7dEIFEQxON65Z8Qb5fZXAhN4ix7NA6YSIf7QozvBTgr4MFFJs1eFgRsBDrRws3BjWQjuWDNCCMjmJIlAp+nXMicQswWh8qERuF6y7ejSRWJaSJp7Kxc8NxFKxdHxv8g3XyXe9QF98w0XPNrLoGGOZCJAMcSd261OBv+sncoFE4s1gqI3z3WOtL3sl4ubIkRiXF6rjYXMKnOJ3P/6VMEI9PvwKszhTefconLxV0U9NwRz5KGSi50c6BDxqaOgmddGI9pDM6Qy1r97kl3jce5lRzBsMyB1VzIFeVmwtaBE8+RSqC7veXVNy+wfYe0LSIKJaBY9oGzlqrSbDbtdPZdAqATcDHjyDiOjEPPpoKLsy1NW/vN/QYD+BUrN0PHremx2x0X7z2haZvVPY2SC0ccoqbVO25BLyF53lY9FJncKegqq0bsY0kuYzHhDvCXgF72R97+KS1/z74USmRJmIW3IaqqmYFZ1C/ed57mZDJtFSifqOvaNRfGe20t480l18+/RHe31MpiK6icRg8ONfQc3lxj6or9ZoupG+pQNxHxU+DaL8mulLDVmrPtBl1pqg7U0HF57Hjz5ed8/pv3uXj/PS6ePJ10JeU5ginkREiBvpGucy+Oc7l+mYGPhArjVZkG9hzcSFZIxhtxV5yAKaciJdZ0/tL7090xOEpNq78iCk7jUNIUhmTgJtY2l2uxAdNnFmudMe7CtXhSAUz358IgvlCFup5UlBnIdMU1VyLHmUKD2NFTT5X3pchXWGSBaebAbM3F+cckt/IjDti8kUuQNAEin7/MU3hnnPWeIOt3pLaZuz5a0klhxUEZePxUv0ZQFL1Oj9Z5k6XJKxEs8cTTbsFzCdgknR95In4vhf70VP45rUg8NcZz8JSPwdONyMqR8lpUPnfTYKmUHmoM5N/n01URhEs3cPXVS65eXbLVNVVV+QBj8buFRxAen7fW+uXem3YC785NM634+CcTvD3DMLDd1Lz37Cln+x1mGOnskaHvOXYd18dbrsaR3a7h6Q/eQ2mFibTKFa2oaWow3pOpLXmbBYXIepxZErNZivbfYx4TBVRwFGB/MnYzvgqKOkltWfLEIsUxI8tr8+enumQ1S2JwhZCZzTDJyoeBm9P1neqX7BTnEAv923f0b1+ycQObqsJqr2eNGUEd6exI1xk2mw1GgUug2RfstGDFgkDd1GkrBDEWpwy9DFwf3vHis9/x0d/8jP3ZuTdCw37fE6NSdp6QvC+Twy3Ty0rhTyLI9UXqANKKrdxiyfomOQDieyOICvwRZwXybnDE61FYTXnul1D3AByVo69MoE8ZCHggd7dKNqizTwlVK8y1qaJSnIR8Qnjm6C92hWQFLUzEjJ6lEbGaHSF5dxK4Se92Rb48eK1AuM4H1y3ATZYeMmjm+efvWfu+YKQ5WRLB7niny1TrPXUr+6O84JgGtgeDU8Fr5U7gxqaiHmeKHspMEKykArQGRpzzb/m3Xkbke3ArYyDlDPfVJASzt+fFSzEA4kUXaxnyRI/Ber0e5P1ZG1snUsnP3m6y1mJvj7z94jnj5RFpw0nDKsqOaarUOsdgDP04IsFz0/d9Aji6qkiepaBg45TWZrtlu91QNw1aKcS5dETDsTccreKj9z9kc3GOTQEE2RiaW6iRbEHe5bxykoZy/4ZlrPXbI0nOhvGdg5TsvkgGbDL5HwNMc0W1ADrhz+V8fB+YOXFvDnBSKbmF6/Kx/XBQM3lEpoKnWQuSYTsOljev3sDxwKYSNlWNw2GsAeW34HCjwcRitECVjUnjktCx1vlNLo2hqWpU3WBqSzsKjR25ef0t3335GT/6i5/RbjZBKhTWLqcURTRQc4AS//WyWoKntZAcHsATabFCoww8JkgkE36IwcelrlDT++e0vSfdO0W1Zm0Xx9U7Px/ohUnJexIRVyjHBUb1nTVZ9nmF7wI4pXKYPuaEjC+X8O5caafnigs5aPM3JwS7kk6Z0Ol+Wb+pDiesh4WADB1/wkWaD7w4R5rHGySPCw4nqhjgrHiNkAmMxDbPgd2qGzbVd/6lzFDEWj0kzcfgI0sT6cpKSi7donXjwClXCuiirFMNzWNzsnJn9chthVKplDy4rOh82qMcvC5aU7LicZwXF9uZcfiSX06DuMXvQLvLd295/c1XcDigalDOopwiToIhggngput7ur6naVrGceR4PKK1ZhxHqrpO++EYaxjHkb7vqbRiv9t64IQfS1XT0FrLYejhMODYcPbkQ9rt3suGcL7SfHVOMe0ymdsrHq3MMDsxXvKCy2cf56BY7uMki3/LO5NMOCUZCjCUgDt3gpsclK973ctKZLo2Der7pqJOKtbQn2tjMI4ZZx3Hm1vevvgWNRhqpVDKU8E5h1bKg2wcZhxxdkApC8qBEpQTGFxYaSsYY+mHAaw/yVzpGltbjqNjMw70w8jx7StMf8A2LUo54qa7c56cQEfGZSEwJwc4E3gJU0nJcAAfLR9LPKE8Bfx0uwvOEQnPhRV3yVsT4eiEtEoMkHfe6fS9AE4u4CQJxOnlczE7eXAkWVYBEnpiudOAZu3a9PsO4b3yboQp7iZlyL9mP1xG1sLVWtIhZC1M+AT8RAVhPw2aiOL9czHexKbz6KySUMUEXct3R95cSTEGprQ8fLD3FBwMzgZlm+UVEb/dvLOpX7xBVgIbzwsq9aNfthnblE8vBopGxJSB0lPCYVXJfy9E9KdKsZ3zPS5i98V7FNOl901Jro+F1dcXKZ82TcI+opgFiefWV+4xyuKDAopaAzcL1ZsN/6KMByTnHCpTEIMZeP78D3z38mOecYsyLWK09+CIpONCnHMMw8Dh0NEPlnYjyQNjA/AxNzdUdU3btiDQHTuOxw6tNfvdlraqwmHMgtWKqm1otxvam46NbdnvzmnrliF5yjIaSVJJhQAAACAASURBVAluCjIU2mJOOCnjtuaeC+Z8Mqf440levMXxLknWeZ2fhyvI4neeFrImlDVFPqy0P9FoMgrzrT1mmRfPxMsPmYYqvW6wHAyTUl941UUYcdxcXXH79i3aQVVXU6wYfupUVX6rgqHvGPoD47Clc5Za19TaHxgb/RnGBVkt3m+iELQSaqB2YA8949UVahjA+fhJEYdSNixsW/Lc1LSZARz4VSGYqNeYHB4EUFJ44zjlfIByd92ICyIdo/6Nb1mfbn+IfPleACcObIWfikmiVMCmQVnyYWLqkNdF7w0sGP1+kDO/L2Q8NcvvK5LAgS4wQ6jrpMQTY4fOXIuPWdSNE46ghL+WG6XlSYWNkhx+/w4n+Dl+5xKC9UBQUNYH+SYMfqJuUzsC8IjSdm7iZ7QKOZNAUjIFSMb6i1JhisB/KhGU+M+59eoDLbOpMxQSjrVfA4olRSeh8xhTPpUyJZeAomSDcRqgkwW5tOiX5U8eAMc8a8HfJA4B5WVGOtlaToCi+OhDdKWvbPp0xfVQn0kKLDwa6+1bf5Xnc48MxTqGmxu+/epz+u4S6waGwSIHcHYzHYjpHH3f0/c9w+A9MrvtNnlrvOVg/fENw+DPoao04zBgxpG6qthuNujsVO/oNdBaU2monOP9p0/BWkRPoc2pi2a8T7bny8mGZsSQ7Pt8OJyysh9dSu7uEujlaXktTkvk01bBqz8vx0V9mCmXmUGRxkMg05oxkd5LLCaAGnvCyGJdB4YvU19mMlfy63kZANZy/e4th3dvOR96qqohbrahxU/5GAyjGxmN9zJeX19TVRVN1dDWLU5gtJbRTifSjy4eNhvktTMwWsQIY2dhGMFanPI7IIdV+wXtp2OO8spHHTTF1kn8HQFhFA+lxyA9nu9iXejBCIpCZyVtF8iXy87YT6cWTNyVvvcqKmJjMoYr40BK4ABx1ZMkJRdBxJ3vOAl6CvS02t6Fez6r2yn37xww3Adu7suXW3n51NFaSwQHxmBN512TgFQaXdUFHVxcrTTzwMSAyrwuPrhS/Px4AEwi5RLHHOAoFDgfOJwfSy+BEeOAUOEvWWJrACe773lFBcV/Ysoz/kVqBKHzGJOIYjpMdgaCo1I9Ne0yA41TmacVQ8ixuDLZShKlFSRhM703KXomYJbGQRqrcbpqAjMeLU0cWnzm4yo1XZJhNnkw1tozF1Iu+wgxCMZy+e1rji/fsVU1Wrxa6vseY/yqPcSvuIgAx5iR/W7Le8+e0TYN4zDQHQ9gDMo5RCmGYaDve8ZxwDnHfr9nsymXfqu8j4Bdo6l15WkUeN/FqGWJcU+Zopi1O+ePsn9X5EH2c130fD/h/j8rpbbPxHEQD+kvpvm0q4vKdsZnq17AXEZPKGYyHkhhsqdBjvjDVEO2VVrn4zHXJ5I3JvKsqPzB1W5SgDYD/eU7OB7YYmmdY4xAz/lVv0iMN5JCd3RDRzcOOFFcd7f04+gNXeUD3EfC9G2onvfo1OA0zhoo9hezqVZRPjiRde7KZEX8LnZOs+lJl0mm7OJMbUeZFMMoMkRKpLufSsv7bhpH5TvvSg86qiFHXklxk2OF3GotB3MOfPLfd31fPh/eP90syjxR6Vg1ogB38XeBDlOmqbw7cM0p13vRsakD707RRd31PZcvvubq2694++YNg3U0T97np3/1M84vLqCuEa2mdkPB/MBiqbb3KCwH2xrN0yJuEZworPKbi7lAsGiZiopeG5WEvYgkN2sObCaBF68tPTgrFJkU8OOU5YtUgGJOV9tln+t51u/MPaJlyhjWkYTUqmeSBGMmvBMN7/i18NhMz0QFVLSy0EUT38efE/g6VecJXOVZBTDjyJuXr1HXA0+rPdtmQKsRrMY5fACxswzGMAw9h8Mtzlgunp3x5PycSmvM0CNmpJeDP7knjrWuo+uOiAhnZ3uqqkoAJxoJAozGYqyj3W8Ym5regVhBlLckkxcgo80pUHdKthXUkIcZVI81CSRvcViMHEgx9fFCpgth9VnOA+vMPunAbNIjw8YuM7bTtUyQLxVlYekGb4OvxMLYmAGb0oAONXZT3vR1ll8hjMcDGodqaqg0Opzx55xDZSeRt23Dbrdjv99T19oHvQ8D3TgyjCPHvmcwYf8z8WPU4rBYRDnqWqiM9jCh0aBs0lGx/cLM8M6ETfw36oXYKkGCk1Km8esmg8BndWnH7qmseYdm600T3+Q58y0Bc8laysm79cn39ODMvRJZXZO7cf7sfR6a+/NMSLYAVXeAnAwM5h+ZMJmBs1RQ7OgAWBxB0U+DZoldpk5GCNNMfjmgWIezhtGMEKzIaH36eAnD2HU8/+ILvvjXX3B4/jm319d0xtFtnvLu29f84z/9E09/9BG2mts3p1PeT+s0Xc/n97aJsQXZ+yKQETXz4JRWzuIaEkDR1I/5++ceLucEv5LKPVqA81Cv3mnLfbXUzLJSRZ/4ctbLmDw0JJQSVwrNik8faTaB6Zm5cFl6l0gDLRkJLLsojiMlZaY1iiUQntfVOQRL3/e8evWaxmrOqw1tWyHa4AyY0eJGQzcauuOBw/HI8Xikrit22206p0orYRM9NtbSWS9OjTH0/cBm29K2rV9em9PaOqwFY2A0jsoathdnXL59A9ZydnGOaltEa7wFXPL+mkA6xTIFMAq0u4u97mWjP1UqAEpszxKwT8bqCq2kfGKuZ6aVNTnizxRz/pneX/LeNHajB26qfq5r8ntThrwNebWz/HN9M5N1Zhwxw8CmUijdYsJ5aoLzB2YaixiLAjZtzf5sx26/pa78ApJ6GJBjzzCOiPgg43EccWb0gEnw4QFKUSmHUganHAaHVd5bInYSFt5zkoVQpLpP8l+YZEaept2HV3paVrw4q6nU4RFfTTgr1eDks/eJ4+8FcFIF5hkXFZueLyy/qedXy8+vTc+u5M2ZcMXyibRxJ97Bwo1WviN5YzKFURi9iea53ya4SQWMWGQcuHnxipdffc7V5RuquuLiyTM++MmPaZ6cMWhwwyWvPv+Mr//lX+mff0M7HmjF0ivHd9cv+frXN5w3mt3TC6rmItEwovBTivOUYj01NZInpcP+krNpMK2Uv6d0cuMn4LPw4Ez3o7dnDfDOgXKoAdH++nNIhRADH4cjSzAX76e/TNiugb75O1YDgNfqk6uYGU50+ZdMYIuExZ2zOk/Cfg6YgrXMJIJyZS3MFX0pGBZ8mGkGM1revnzJ8Po7dpWhrYS2bVEKxmFE7IDVFaPxC2nHcaTres72e3a7HXVdB6tY0bYt+/0eUQrde+vXLw831FXDpm6otV9CbskDTAURD2DM8civ/q//g+Mwopzj2Ycf8uHf/h3v/ehHbLbbyRjIaPcwzl3vw1yO/rk4dCTIpDTJtMJfkPd7/ke6V4BhNxW08LispZPEWlu7OinNKL8y5ZTalJoR/snBz1yBS3wo/i7kQgiKHwekP6LC3FrcU04hAZQrxFmUONpNzWbT0LQNOo5RrUFpjHPsdluubjpcAEdxxbDSKpwbaNC29+etDZbW6snQjPTKBMSEJ6RkzZlOn+756dyyv2BGlnSzAE8zGrnQ1zNbJwM+suCd6fvdfHHPFFV0M+YFRe8DIQ423gtOwYLRZpUJBEwDIXDRhKCz5yKxZ0SZssjEhHMBfFr+h0fnGZYKJVm2uRLOyJCvQUnd5xzaGcztFd999gW//5f/xruvvmC4vcYhNE+e8Df/5b/wl//Lz2Fb8e7FV3z16/+GefmCJ86g68oLYDtgzIHXhwMvvvqUn1z/nKdPzv1qSUd68/ddqRLTPNaoaLtbV3Rx9dQc3Jz6W8uTX5vX488lrYHGdI9MTEopJP0QmI+NtfLL95z6jJldHFMFnTNRkmePYzHijzCGRCIsWntHaCelKClWzay1g+m5eZ40vor8vsRD13H17XOqm3dsK0OzUejK8542/qRwrTWVUlRa46xjGEfqpqFt2zTl5KyhqmsPQpRC6h6OHVVVU9cV26ahUhodWh7lSBT+znkg1B/fYX/7Kx8Qakeunn/F5e2Bn+/3bLdbYmxbVkpq750LFNZEGqWX+I8Y2n+StDQWHzqu3aQ/QqNzqeo9BA8Jxpu29Zi/Ovc4FnUOq0DLe1OoRVRBPm855iT/LMpcGjUAVhQGYbQWY3oMFuMsYkArhRVAhcNizYASy6ZtaTctdVOjEJy1KGeptKaua+qmRVfa0yt6ycNY1kpT6Yq6cuAUwyhgdTr0tCSOS0a8iIAqQUtqTkbfyJ9WXMKVueHmHQJLHph2Y0+kLK6VMTbLMRB/S9Fxdw+S7+HBiYAia1GZu7i4ELhJCmQoDFKnFIg35L1DPEwCev32nWnOgynKe3Y1aSgyZSVLYBGDfMFhbg+8+fi3fPIvv+D6y+dsxo7W9gyj4fbbI1/97mM++PGPaJ9sef37Tzk8/47WQVNXaAXD0GMcaafWm5trbm5ueJpel4OqvE3rjV7z5qwqy+zeKsDJwM2pQ/9OAZm7yn5IfR9TmoOzfDDGCxIsI1Ey3Yj5Fs1a9sVd4KYcJ5MViGRjjcAlMtFSUOTB3glsCQX3L94Zric18z+gW5LJ4xzHq0veffcCsUeqBqpap1VOUtc4Y/1qKK3Ryi8br6uK/W6XTgRXWoEVdF1TOYdxlspa6srQtg24HdtwVlVqa9ic0zmLGQfGwf8JsBdL4wzGjNy8O/Di97/jBz/5C374gw9Ag4iPWctJM8mKSSHcQYCMEpNFW5b2uD06S/4tx/F0P6pCSR/+cgjCf4C3ZjHVxPSztK/X5Vv6TFhUSl1StCV/LtNj/uoSBM2ZAC/LFc6vBjQd1vagFM4pjDUY54/GGZw/NFYh01Sr9hDcAuIUyIgS/NEMlfYyIKxuFUAUaC2I0lhxaNEo0WHF7pwYmWxKGzX6oOkiTMDN+48kNyK/Opegko93i2VFzJ+Dx4KuodTsHeXK45gv3wZltWtX090enBUA4du9umUda8J6VSIm4iyFaom4ZfXxWLf0zFwprr3yFAAg9UV61M0omMDNiefj+TTD7YHnv/sNX/2//zfdNy84dwZTDYzViCiD6h3XL7/jzRdf03xXcfXxl7RHw6auqbSgsFirfTS88iccG+cYjfEMmtMlvn81JmqNUWabta0AicjUawDnVNxNuj+bprrv+qn0WIFNTNPUXf43pSQvXdwAa609OWApf8+57CRglAh0U81m5Upi7OSZyMrLLTJ/Y8nheV6JYIq50M99uxS/70xl5T1dreXw3Uu6Vy9paouqLKiqMJSMDnvhhLdppTk/P2N/tqdtN1R1FZSQ+CXho+dBHbc5EEFXFXVYSu6cwxnj94GKjQvxEljHrm05q2uUhcE5Rms5vHnDmy++oP/7v6N9eh5ofKKZM3CTC3QVd2/PbsYVRn+u3s2SX6freUpBrnPiuGlJ8bzc708Pz2D+fKM1AJbDk3RjpT0hp/j7uUdSWAdFwQ2Y3iBAjUUY6U2HcQOWCuX8bsTW+n2bxnHEGH/kelvVaK3TRq5OAK3QlY8v01qQcHTISDio0jmMM1gMKAvagRuolec1l2tukel4kVjXXPxkAKM4JDanY8ACsQRHBGKh3bkhlXJFmq4b6KXzQNJYcMHT5+/b4Pm5X1c8aBVVFEYJfBSb9EwMGJnBBwTnc5YzxonwZoXpFu9dq86Kgp3fW1lAtMhbkFimq4lt3bSJUQI+K2UYhO5ww3d/+D1f/uJfGF684EwcVaPpcfTO4azzpxPfXvLms4/RSpCrG3aiqcIBgpEXrAvngIuiqmq/1bwqlUnOcHNwMw/gnAetxs+5NyK2VNb6Rso+OwVWTink+fWHBEE//lRakqldBB3LmmCeM6YUfzkYvI/GMIHvqR4zuiPZnSiC8uwzwJO3Lrmb19K8n1zyvOZC806Pj3hhGN91PBx5++IF6nDDtoGqiXLGTRaiREE6eU2bpqGumxB/EJZ8Z9LaOYfWHtyMMQ4nbAI4jqPPowTCAbLOAYNhI4rzdsO2brDGYAYfQK2N4XB5SX840D458zvNRgs0a+89zoRwvYSFcyPkXhn5CNK8WgtrP0uTnpimN+L8UvT8TM7QpXwqy1k37NLv0BkxVmSdlus8Oo3BkCeWOAMA8V4aRhPiKYx4HNhxoNWCblsqrRDjwDiUUhhjGIYh8WdV1VRZAHwcK95o9Adtiojfrbg/0rfVdMyIOIwyGAaUDDjbh9O6Mx0i5fDPa30KShayRDzwkQCapqfimF2hGQEsrdAa8j4tr+cyoJS5/06AM+2+mmnfzOU0vXtqaFmxkijkSk+yKs6+rzJd9s67gE2ef57m3or83fGR+WnpkudfsUQEGI8Hnv/hd3z9i3/GfPucHVBXnjnFCtoKYh3GdozDO959/jGbuuZCVTS6QoW5TOv8mSTHYeAwWgapuHjylPOLJ2E7qOVUwRx0rXlq5nTKAdAS/ISYguyZuLHfGnjJvTTxM3/XKUV9l1X2x1ls/3PSaTA5XZPk3c3GTHooDtdyv4vJezPj0Tv6chomMXaExLsSkI/Lhm7i61zouKU39qQRkL84p8nK3TXhebKoYDAdri65fvuGjTg2uqJWCnEqzFq4ybMaaYZfFdVuNmw2LXVdp+lT4yxuJgSt8dNMzjmGoac7en611vplIXUdtr8fMWakVopt01DXNWMEnw60s/S3N5h+RDm/anIpr0rZsU7JJUjMAepdwPaxpWgD5eMgprUp/enIALLry+fuSnfHOOXAgHAEz7ruyPXNKRm3yHvy+Vg5ki4TJ1gMpu9pjN9g0oNx5//CI0prz7NKIRlYj2OZIJcrrWlrTa2Fwfq4nWN/DB4hv7LqMHQc7RFcjxODE4uPPl7yUDneo8bPZlOityDKDqI+V173S94XDwMecxpPU7mxvDwfgEIpG6bD4ni6Pwb13imq7MdUi7wJobXZfoWpkQmQOAm0zQR5QNUTyUjeEmavJcsTrSQh+8zyxo65wzBN5cyFcK5e1sDM2nczDLz94jO++Of/Svf5JzxVjrapkmvRgyZwYUnf2Hd0t8Jmt0dvalS4T5hjtNbSDQM3xw7Z7fjoxz/l4unTAheXlmEJbNbaOf+d6ja3kCT0WQ5EYxlBic8H/tpf/tz38UbE339s8PT/jFQCjnxQZmAH308KPx89ed2mMZLvAOocpzfaurs2xTS6r198Vb6EfM4jDxRCOWo6nYE816l+LnPnkkKwSmNHw/HVK/o3r9gr67eld5rJsR6B/NQ+YwzOOjabTVryHfeD8l5IFXbV9vxuzIjgqOuKSvt8NkwNWBHsaBjGka7rGMNS3BgLYY1NQFY5i+l7rDF+7DiHw+KIwZ7Tzt1zgX0nf2ekLhV9bPdjHRNQmolTui/QOhM9IX/4jMDk3neflnkTyJmQ16pRLOsAJ37eCfjzMorrLuhghbgKZQ1919GPI3Uot6orVAUYH3RctzVSV/5ZJVjrUOFoHVy4rFTYZVvRaqiUZrttqZsGOxrsODKOI0M/0A8ObfzeZnFbGUl6fFZnkUkGyUrejDddbFrU1W7i6zSllah1d//k3yPwXYIcyT7zHrhfjj1simqqWRLSOc5zIn4HUoT5dspRYabvUNZJZKaw4+daXpbgJiN+AkFMQPUhIiEROZXjU6GYZkrCOXDWcP3mDV//+r8zfPMlT0zPvqrSTsBKKSQIwX4YOB6PWKCqqsmlHhSOtTYIW0MfDg784Cfv8ZOf/pS2bfMJwZnVM3lw1oTJmiV1yvUtbskwkvXdGlCJ1/JYm/zZU8/M67RW38eYSovDFXPBaWSEeI443ZcPSt/eic4TEJmMh+9dJybeXWiMEym9V7L6RaGXCfwFLHmAQpl4ZlbHdG3aXsEBI0LXdbx+/hX2cAM6Htwr6ewdFyxTa23ydI6jQStFHU4L9++R1A7fKm8JW+tjfOq69jE7uz12NPRdn0CKcR7c9F2PscaDrHgQpwhKadB+OqHeVIxKsH6HNayEncRnG5SdUsDzsZILwZzH5uP9sacc9Me0/H3CQxsVaPgn2H2h3EnO5Z95meVn9nKRiS9O5M+JvApusg93gt+LYvIDJZ2gjMUcjlhjUCF4WCnlN6HE+TN6wq7E1oHWVbGYIxrkzrqwV5mfpqqUZrPZstlucKPxwB2o6hrphHFQ2NFzpZkdnknGh2t0SL9jRwRHRjQ3clpbO2fSQquf6K9SKng+mbRvlK9Ttaa8DzXSvh/Aie9IrsRkg4bmrEPuKBjLHVFZEHPJdBSfq8oSmTY9PFHuvFblfOHsuQLdZII/uJtEhFH87pP9zQ1f/+bfuPrk95y5gfONn3LqAzP6WBrHaA394LeUb7YbdtstbdtM28LjD7vsx5HD8cDheMA5x35/xjD0vHn9mv3Tp1R1zcQMszauWIUiko5mODW1UtwrYfNsoE/0WvPgzOu0du8+782pa48plfWLVjpM3g684FiZ+inKOXHdpcIe4pHL/Z98L5CU+kNCOyQHBylTUV93oj/nMV8x/+TBjdPBWUxbdN/iENvT377j8u23VAy0lUaFpa9aa7TgLdOsbGssZhxRomialrqq0aJndCqBgrWW3XbLkydPaJqG/hg8NWOMnXJh+mrAWkvT1mgRvwGbdf44E10z1ha1bVBtgxNV9OUEYJnVZUn/HLysmCbTvQd4Mh5P8of8RnnhsaMt+OakV8c5723wGf1vx4IAJwF18SkJ3PiahH8fAHLW9ZAktCV3lRGySwATiMWpAesGxsM1HDvaqqKpqzTtb0SQcQjqJ65WLfd9SRor7CTvDcpgKohfbk4tiFK4SjOI48LccNv1DDdHGA1S+3HushLTDvNZ3fMGRnHkslmG5FdInptYjk31vT8JsljZVcqUWJU4fiOdy+mwuwfHA49qmPFYfiEQbKrEbFOfDKRJTsYFtni4YitB0h2VzrIUU0xBeOSsvyg7BlA5l9xBEcw6EYwdefvVF7z9+Dc0x0vOtjVKAvq2YIzFOUNvBm66Wy5vrunHjotmz25b0dQqnP/kB7Gxjs703HYHjscOpTVvvvmSXx6PbD/8kL/++T/y0U//kmZ75pm4MHfK+q95auZCZa6QiuDY1b6Q4l4OYO7y7Ky9604hx/fjhT9FmmgaBRGFZ9NbW5K8Xovg9Mz4KFs6gZtTjF0A2e/p9Fn0RxgEqx60VcEti+unvTfh1ONgiRvlMGIx1uKMw/UGsdC0LeJGrl+/oLt+w5kaaXSddvyOr4+7aDvrGEd/EGE/9NRVzbZtqXXl9xQh36TSGw7jOCbQst37wziVUtRNw8b4CsaNA3EdQz9gjaVtGhqlUDa4zZ2fIqi1pq00TV3Ppv2Y+q8Am6WBUF67H71MffA4kU7ptQk6IILXpCqWqzfj9+gN9ePGT/W5eGxGZmPGdMpYmq6H8ZndY+V7+i2Z7z912TRA01RM4U27z7MW+EDAMdLbI8fulqauqGtNpcNUtghonaZC60pjrE7hCnF3+ZxuKqwIjIHz6Xxdj3SoqdlsNuzbDjN2DDe36OgJU4HgxP2bHMUxOgv65P1bgu55+/3y8vtoQhqbJVCcANOa/JvAlCRei0bJXelegLPoxKwCDnBhBYETISz98RWKnpWMUUTyArPrd70vXV8RuGuZM8R+quk5k54sJy8h1tHzK8qN3F6+4uuP/43j6+fsnEGpGkRwFgQLzk85DWbk+nDLzeFAXSnapqJSoJRFlN+ozBpLbwdu+wM3/QEcXDQt+/HI+PorXl9+TXf9Csb/nZ/8p5+j2w0JLmaCcy481oDEOuBZBnat02wCemtCJn/HHPjM3z8HYDHFGIrHGmRc1nsS0It8ZJvH5W0MYzeK4Qlo3w9u1uoyf2u4sTpe4m/Jxshd5S5AqazfPw1Io7JzDMPIzeGaqzdvub264eb6FhHFhz/8kPefXXD5+gWmu6GpxK8wCYLbx6j5pbRiLWbo6buOruswo2G73aFVlRRC9Ia64EFzzgdeHo9HzGjYtJs0paXDvkBKa0ZjGEbD9c0RayzjaP3S8rQTd/DXWYdYy7auaIMVHlOKsMqWcE60OSX8PY3uBvWPcyzkaWrb3FOc35+Y3yWP+Mz4jP2WcadkcjpfyLA0oPxfzt+pZCkBSU7v1Ie5fgrfH055V7w7QwU4cdixg+MNTaVB+/1rlJC8OFVVeZBTaSrjPZFd1/kVVjGWU8VYMr+s3Dm/m7BWynsaY3tCnE7TaHR/5ObqHYeu93vqRKdJBtiSrM5oFZuQUSy0bYqRyZuZ55+Abg7s5/wdvS9Tv0w6SBZ8MVVNMl66P33/Kar0prKq6Vq+WYbKhKLKGCfzPuQ/p/uZ6z0wWt4BDwE584MHU0UXqHP+PQwLN3l6KDrRYbtbXnzya7795Fe03VUQsBYnOs2WGWM4dh23x1uurm5AhN1uCzgOhwPihKr15B/HkdvDgeubG7quZ7fb8v5779G2Ld3YMXQ3vP78cz7bPePpD3/KedjDg4TdSw/Bmgdn6fqTjKEgHLyVCLWgi0RgM6ffujK8z2qae5fm3qQ/n1TSXzKgIsHDEw+fiMN8kjExGNb/fkhw9cIbFOtQ9MN6nxS1jn0kJyxdls/KfPCcKBu8H9c5GIeBt2/f8vblS15/+RWvv/6Gm3eXXHYHOmX54INn/Od/+Duuv/yUujvgKsXooHYWZyyD8dNTZhgYQhxb/LPWoiQe19BNL1fip0XCT2Mth65jMB4QtW0LznlvEl5WMAw4EZq6RusKrS26rqjqCnF+jCL46Son7Dc76mBJTwrZe1ZdFO55/0Ax3u4z5OBug+MxJWvnfDv31sSr4dNNvJ8rRZdry0Arv4FduQ+XL3NJwIV8KsBKNgYXwCjIpJR30mFzHTJVMRvnMvVvacwEcIOl74/Ymxt/yFldI5WiCvLCLwuv0FWFAwbj+TnWbxxHvxdaAC4+ON6GuLBpjycgTY1rpRHxx+4cDu+4PR44vHqJ6zqq7Ybde++zPz9H4ruU1AAAIABJREFU0lxxlF2eCI6SlhMJ5vIq9+zMvJjApKNiYSWYXcqlnM6zPs7Gjt86cVp8cCp9b4CTL3nMLe01uzO3SSNnSH5zrfY5ITIk7rHHfQweFGYhqENRbvnKdXATPsVlUfzid5EErOt49/JrXnz8K9zNWxoMSjQxUDGWYZ2lH3pubm5w1vDs6QVn+w1aay+oOdLoGhGhH73gvr094BycnZ2x3++9e9IMbKzidjC8+eoFV2/fsX/6zO/Wmk8GytJbc5I+LqN1ZPBAbnFxmfg0mL0eDLEFsTNkEhCnBMZDPDhrdX9IWx5dikCYLCZNvEs1qtoZJAFOAZZZ0fcC+nRz9pbymZMet+hSIoK0tVIenqJc6I8db777jk8//j3ffvIp7tvv4OYabQa2tqcbbnn+7R+Qd1+ytwObYaSva8RBU6kk/LvjETMMWGM4HA50xyN93ydrv+uO3l3vnA+udOKXgfcd4zDQ9f6Aws12S9OEs6pCHZWSpDiMsVR1RVVpRBrqtvFxb9bCEKZPrAVd0+yfUG92WFnC1xhAvUrr1Rvr0x2FAfKIvThrPFyO7XANSsYqYikCbZKeuN8LPP8+XcxeVMh4Wf2c5JmvRBy7a+nB40IA8avrlHMMveUwgFI+eFjpyrfbWRxh8gMPlEdjORyOaSdjY4wfA8agw1Ek4ziilFDXVQpYBk935RxKaUTV4A68++ZLfvNf/0/evX6DPd7S7M/46B//ib/5+5+zO9sjYoPcykHI+uKf2LjSiJ5TJt+z5i6+jS3PdcKanso1c5g5WlPoK+legDN3gAT4Pctz4mXOW3KrjD93AT2Qc04F0xa//cXl9Mz0wOx5VvLl1rggtsHJyM3VG57/6pe4F1/ypKlonIbRYe2IswYr/hycQ3/k+nDNsbvl7GzLD3/4PnXlPT1919MNPTfdAa00Xd9zc3vg9tCxaTds2tYfvIagHNRW0L3h8PaSq8tLPrQmeMRi3Mwy7mZt1cEaoJhKmYDqKbqs0pJ1oLPIPxNKp+oJD/Nk/ClTCcAmOk4uSX9ForDONNV/RLvuKqPcrmH5zPLZsl/Wns1zunvq76xh7AcON9e8+fpLXnzye55/9ind69dcDAN7BWjHYTxi+yPvugPPj9f84OkF9WaD1ZphHMAptIAZR47HI/3xyNB7g6Hre45dh9YVCHTHI2IdtdK0Ve1XXA09/fFI1/f0vXf17/d7qqpJRzSAj33T2gEV42gSqBL8Ml6pNdY4jHYYMVhnoN6yefY+UrcZsCVZA6cU86k0OQzK/Mvlso8zTbE02bgNSs+FRQ7eQzLJGZ+/xObRoFLRqkrTUesG1GoK+fMN9+4CObnBVpYxeybqOMn6eq28dMsbNwBuNLjbgeOVZWN8w2MMMvh2Rt6xzvndjCGt4NOAshYTpmGNMX7KNRxZUtd1WSd80HHTtGhd8d3nn3H95o1fFdj3XL98we8ur2lVxd/908+RTRufKiGOTL/WQh2Wcjp+j3At9mmu4DNdMzNq83Kn72U9cCm68UGY4U6AY7NNT4t2BODiqxuhb6hsCoyW8P8cDfl/IgFdzjTkLsDlY1FhlNMsc3Syhu4nAuf7HK27x2SqSJFGzPGKr3/zr3z3h9/SdEeaugbrsGbAWR9Aaa1lGAbe3bzj5njNZtvw4Q8/4OLiDB1WXx204urmwFV3ZDQjx0PHm6sbrBOePLngYntGK5X3AlFhXcVgD3TjAWPDzqsuW5kys/JWz8+doeI0RRVanGPU9SWcpZBZE+Jrnpw5neeMvOat+XPw4KyBHMiH76ytIV9BY1mMjtX0vQDfKWMhOt8W9+YPzOuUMYlEkDPZZgI4a3HOYseRw+U7Xr14zjeff87rT3/H+N23yDhwrjTnrWYjfvrW9LAZHYfOcBh63G6Pa1wQ2A21FnRQdpWuGEMsQtf3XF5f04+G84tzRuevKaA7HPw29pVOy8qPhyPHwwERYbPZpFiHSIdhNL4flEK099KYcfBKQ4MRg8HhFFgFRjua/YaL958xYAGVxXBIOpTW/8xB+3p3rYGbpcHyuEFOvgozrrKJFryfoIhjpTyKITchIcaDpKLC1ci3p/lf4oOiMgC+BDY+33SbFdrDpGdWCe8V1x1yr8xurS/CDAPm2GP6EVeL38gvjiM77YEm+HMJ9/sd5/s9WEt/PHj+j/suAaMZGQfjdbGx6LoB8St3lVJUSqjDSsSxO6DqirPtHhGox5G3z7/m01/+kh999CFPfvoRTlk8lPKNlFlH3OVpdsE1529NwiL1efiUvG9OlLW8vtThAMofbHSvnnjwFNUEPLwITzN2wjT9I6Vwj27GlSbgCbC8PNFIps/UtJJAMWte6lJex0r5gVcweZFtClRL4CG9w2LtkdfffMKL3/wrcrhB67BUD4eqqrBLqqHrOo7HI4fDAa0Uz957xtOnT6mjq9uBcxu60fLu5pabmxtubo70w8jF2Z6zszPqqkKL8gGNTmFEYZTDVc6fteP8oAAbduiMatWxNhe3JjzzfmD2a+7hmSz702ndkpkDyAcq6e+Z90+V1jyTJzKCy4S7c1iRuMP/KqC/K63GVq3UafYUE1j5Pi/MBlSwOi2CxYN5zMh4PHI83HK4uuTV55/x9aef8N3z59jLt1xYw8V2x7Zp2GiFMn4jMpyjMo7WwGgc9KM/MRnYNjWVCh4c0bCxXtoIfssFY+ivbvxxC12H1I62qhjDFJbWmhG/9f3NzQ3X19e0Ifamqmuqyk8P+A2M/Xv9CT7+LJ+qVux2G1wATyZsAGicxSg4e3pGe77FVpJmeCUKwhP9IFPXM5dap8bOKa/sY0sx3qowrYL8tw6i7wZHWngS8/mfuZ7I5A4T3D7Fsf7Z4AFRWa4ZuCm+3wVuYi1yBR+s8CnraRlXxBaGvFYc190V/XhJa24xfcMg1m8g6fw2IsMwYIwJAKdmv92x3+0Q5+i18ptKAr2zxAklY6zXA84F34uA+JVX3tNhUTi0KBql2FRhalYJ7Tjy7puvefXNC55+9CG2tn6343wfp4wv0waaBeAR5srG31ez65M5VOKIdbrFcmJe5/C7OkcrPDhY8mD+U+lOgDO5cjMvgdjpBUFgumgezjxR6atkhMi+ZsA/eGjiQzNB7CQTypF5YphafjXWdkawWKawIMgaWswvOeewbuTt66/49N/+BfvqBWdKUcWTiBUIilHA9Y7j8cjV1RV9P/Dsg/d49uw9NpsNShQubOanK03TNMjtgcOh493VgU1T8/TpBdvNJoAgm4LLjPX7h7TbDU3bTo2MnT9vU7z9AKE4zysSlvcGzoq/S8y4PiW19OpkgyDxQM7ESXZM7PHIgc1DFU3MF2Cod9pmzfc0zYHmehn3vWP6XvqRTj4Zh9KqJeamxQDlnfSvc/7AyeurSy7fvqG7fMf1q1dcvn7F5avvuH7+DcPVJZVz7JVwUW942mzZ1A1awWCN39By6MGOKDui7YAeemrnaAVaUVQheFdpsHWNdRtUpdHBA6O0ZghGBU04QLOqUGHX4mEc6HsPcA6HI/uzixTToCsfOOxskHHjgHGWYRwZxpG2bXjy5ILNZpM23ry9veXYdYxK2D09Z//kDCOg5ufy3dF/c2/M6f71Zf45gBuIx2JMgECSOgih19H6ZcKA8fTrnHGj4pvkSLy+lDP+cwquRabZgIK3cwURMcDK6DhlmLnYpgfQYT5VJ07QCMM4cn18y8Fe0Y43HI8D4zigtV/9Z5z3+nddhzH+tPC60mnH4riqsGlbjsNAH6aphmH03sPgAdJKEWa3UNqPoVoptqLY6opN0zKKP++qkZH+5pKv/vAxf/0Pf49ojRVDPF9qAiGlbJ+I6SXbBELmsj1sYVjw7dz7UNJ/rsvmQCh5wvPDP+/pmTsBTr6TImQdGEBOUVnHSuDPTOEl+vj4kokhJ/eXzCqdgs3I5lUzFO5m+ZOBJGsgJz5eEmUaMLGdvi0OgxVHf/2G17/+/+hefMlF29Aqr5yscxCnppz/3Y8Dh65ju93y3gfvsz/bU1WVn4uWSUnEl/WD35F19+Sc/W5HVWlwfmlr3/lYg67vsNbxg/ff5/z8PAnKef0j6Ew7EbkHKOQw4HOPTfo8AWDWmHRtysrfcLPsE/iZgPGyfo8Z6Jyi6UIhJc/NcrjAbKg88L0n7mT/rivZfBylHCsC5M7kHM6M3L55xae/+TWff/Ixt69fM15dMtzeMB4P1H3HToSzTctet5xXNWdVTatrEO8lwY6IGxnp6N0R50YqsWxqD1K0yuKWxC+hbXEoq9IeIKIUx35AV5rdZst2t6MJxzU4HK4TjqpHSYVCTYAzmxMXkRDrRvLAGmNomoaLiwu/skWNdPrIMAwcjj3j7oyLH/4EXW2Cw9TLvHVgWPLKfdOyzBRLjF9wzmZ5Hl8ajU0nvCczJsneJYyfT39k2KgEF6n9s2eknB1YAJIspViNXGzNgFORP40XyuNTBLCZkX5PiptbOgcyWMbDgJIKqdq0VNs5v6fTOI4c+47j4cAYzqrSYW+cSmucUjhnUZWmGgaO/YgN+0FVVQUiaYsEY0w4r9CPkUqESrxBpbVGEIZqoNYDehy4evuat5dXPN1tg6xyqf4pADiT69POwpEIa8vG57EzsWckfU08kTw+yz7Iy5vuBb0eHR3/vimqHHG57C8oxRNPJZEaGVam30HU4g/P8sPB5QBnxbrJEX1hK2XMmG8INd+vY81aXdR5kcciYnF9z8tPP+H1x7+l6o7+/BD8IjUNWBHGYWCwI904cBh6VFPz9IP3OL84pw6bgcVdja04RmcZrKEbRrrR0DY1T/Zb2rry+xk4y2BGhnHg2A0cjgPV2ZYf/eQv2e3PUg2XqzWSeMmdIqttvUtB30efXJ2eEroLl/NKhULvZ8/kA+TxWqwxrVnoxX2fKRiV622KrnC30punYm9OTn/M3i75t2zsLEqcKxayPp7tyjscDrz89A989st/5tsvv8R1R6qhRzvLTmCrYK8V+0qzqyu2labCBS/JiO077NBhxx5jBiwe+EeBXodVInHMO+fjchDA+OkIrTRKFMM4opSirWuapqEKK0rA66Km6djtdhy7DpNt9heBjnPOxz+E7e/HccQYw/n+jO1um9z/27qiqWuMGKqz99n/4KeI3qCtFJ7mafpj3cN5tzdmbiCE0VEg40c6Jtzk4V/wrExyagLYJdZIKiJem8mOmCHuhH8q9MFTbAbYHwJGTskw58qg+iJb7NN19eJcXE+H39iyg51s2TWaTWP9qj+r/NRSWPLtnGMY/apcEb+Lt6q0P3A2eMEdgnFQh6nW0Ri6vk8vHY3xOsl54OmnlP3eUEDYTLBGV35RzO3hikN/5NxafxRJmkqKO3LnGGDyqk3AowQamV2Cy7ybZRzO5Mg47YGRVF5833R9Get0Kj0A4JQupsTIsBhvxVYqUx2LmYocrcc8UQDnCsF/zQVHlgeKYGFmzz2EqfM0t7Css1hnkWHg3edf8/W//gZ7e2Abl+ThUG5yv1nn6Puem9tbjv3gA4WfPkFVVSo/YmMHjNZy7Hqub48YY3l6vuPJ2Y5N2JbehSXmx7HnOFp6an7447/io5/+Nbpq8I4jfxDbAiTn0CZ61AKNl1Yjiz6cpzWgOeeuk5ZlFEhTh07IO/wboVh+BsmfQ3ooSIwiQIqRWkgBrJTDfMnapYKMqfx9Wpgnj16xzH8pWkrFtLgLOK4uL/n817/i+otPqW9vUM7QOEuthLapaZVmIzX7zZa2bamV9oB9HBgGv6LJ//UMfY+zlnbT+pWDWif+8nP+YdiLQow/wLJSFqdrtBP6oUfE7/mhM3CglKKqNG274fz8nMPxiDEjt7e39H3vPTMQprK8Be2MZRxGEGG333nDJLjh60az223Z2IrNe+9zdv4Ews7KuTdActoWVq/vhbvATf4XlcTUJzPh+ciScy4Y+5O2L3gpxQn6FLOVOwYt2zd5taL8lwTU8zG1StF7NODatFdRzswDe6rM5SVZfHhzuGJT7dmpis0mTL0OBo3gjJ8FaBq/G74jGD7KT71GvWitRRtDU/kdjwGGceD29hZrRsThQbz444S6vme0Iygf2O+M32+nUpqqqmmwDMORw7vXmI8+QjU6ND22vdS5yz6aeLPkdX8vOjTyxRiJy13U9/P88ftUdg4iJyNhHVjO050AZxlUlDUsm29M9ZwDPhcs0+h6yfg4IcR0vURsCwbMir8rrU1JZS26s635d+csh3eXfPXff8v1l8+52Ah1XVNrhcYi1ndcdGvHTcjqquLZs2fs93skMKW3GoGQ3+ftOHYdda15enHGbrv1qB4/p23C1FePozo/48d/89fs33vPs8i8c1Pds+mp4KiMes3ltJmo5YFPFDHO5Rw6y+nJl8nwBfiZvq91QYnWw7AgeZzk5KsfVVoAmDu8OHHKFZcFWWYW7LR6MkQrnMYoq+8qb2Z14LRKLLw4pyzXTHGk2ooXxG9evuTbLz9HdVecVQ4ljkY0tdbUTU0tFa00NE1L07Qo50/pjsclmNEHRhrjY3Fwjs12w3a3o24aH0yIIEr7zd0I1rAolFFYRixCpTXW6CRfRAQlKoEkrTR1VbHdbjnb77ntjgyDj6URgaaq05EPwzD4z3GkaZpQj2BfKoXVgq2BVrM523F2tk9epsWKzlKXF54on9Uvob1fAXu6TzL48Xo171phU17L4mvuyDcptxBAvDYqZPp0+fj5IzGgm33K4sfp+q6lyVPh904yZkRpqCuhqv1Za/6gVkdd11hn2TQtm03rV/lFcBM38NM6rdBTIlThLCpr/dYFLkyVeq+kxTIdThvPqxJrEWv9CisUrWhM1zFeXaJsPCwWP9bT+rdTMqL4VXhwIMpyQdJxoqsU9uVnRlrk+7yDReZOiIQ91zsnSw9aRfWggTUHQKup0MrT7+gOWkHV6VMygJMU6EQsyfKnfRSIzJ+/f7msOt5RQQiJs/TdLZ/95t/45ne/ou1uqLd++kjJ5CKNZ4TE5avOWZ48Oef8/CxbqTH5bhyT1Xh7OHLsBs52Gy7Oz6jrcJaVsX7VxtBz6Do6NE9/+iN++Ld/hd5vpimNyMyTqbcgdVQOxeUVKzIfzHf19MSSEwMuPTyzApHJ3baqbWXKK9m89R8rqf4Eae5VSbznPCJUYVDanN9yq4TcJlibrDqdiv7KhP5cyYYvk4JZoW+yjrIaOQQrgqtqxuORy2++Rh2uqZSjUt613tQVm7oNBkBNTU2lqwTwzTBgRhMOfg1gCbACuq7Yn5+xu9jT7jboukFVFRIsV+XwU0VhYYYf54ambsIY9II5BhAnsKNUCubf73c4cWgRxqHjeIBBa4Zh5Pb2QBc2BRyGgaZpaJpmkqACRmBQjk4Gqn1D3cTTy8tpKL+aR6U25oo67605MApds5qiQM/f9dhSWnWar2rJeCxRYW38r/BpDuqKvJEzM29LMq/vIc2iJ7LpQkl9nY3jLG9uDJwy6tauef+fwowDuj8iMlA1Kq3AjbrEWouxhkpXtE2LqCVwds5PISmtkdEgApVWtE3Nfr+jbVqGrveHxzofPqGVCme3mTB2nL8XlpFXA9ANDJfvUNYkqeOizD6hj1f15+yeRLGeAEt8IgYxl/yRg5y1tJzmfRiYvRPgFO67P8Z6uKcGay73/L1l3qAnJ1ntPxPqm+pcCHRkBnLKMvMf4iEwtu94+fknfPqr/wduvuRJ01DLGRU2rAjwdthoTDgB/Mjt4UBV11xcPPFLwkN7/LgMU18x+r0fuD0cwTl225amqT0OcNN+CKOx9Maizy74i7//O/Y/eB8bApATdHMzD1qgy3QlAIZkCZcMmabO/p2W4ZrCnH7GvS5ixYLnL91b8Tjx5+nNCReJbm0/BT1Fq5VqLoz/GQ+m52dWriy+hcEQZVGk4xqwCdlPu9gnALogu3j+GZ3l9nDD5etvsf2RWknaIl7riqZtaJsWrTSVq/xuwmGJtRu9e9yFeBsrQo9jRNjutpw/vWB3cUa73dJUDZXSiYbiwpk9SuG0wlmNMWOq22jGcHBm7Q+hDW3UWlOH9213O1CgK43g6I5+x/DRWI5dRz/0KT5ns9n4FY4RWIiAUhitqPY73vvoI3TVIBLd+RP/e4ATR13BDhnt5xboKTGZAX9yoPP4UrFtASWfKST9lbEz89GwKJW7fJGF8b5Kv8z/uABWpQyKxnXusYyPKAmLW+btu0O3SVaKxQOMoT+itcNVMnlidIXU0A8Kwm7afiWU8e8O13yVNaqq0GOFrixKNE1VU1Was7OzEBCvAR8TWisFSnOrDjjr6MYeJ3FltEEr8UdFjHA4jlgr6TyruCVDNBROyfa5IVosfnEZBwellGvhFMycyZ85kMq/r2GDh4CcBwGcsgEn9meImjXXtHeUdepa/p70+3QNFwhxXnYk8ITc14kiEA71s1y9fsM3v/09/atX7NyIszD2PZXWSO3BiHU+oOtwPHJ1fcOx673rWvtttFMdwsDxAWQjXVhyOvQdZ7uG3abGOcNogrI33nvTjY6u2vLkJ3/Fs49+CrrF7zQZuYtCO5aDM9+PguXWOLl0vUNoFgg7vcglQ20ObHx7y628ikcXV8oO+LMGNvEeM8dUFI6SjZU5kJkTRmCydSKUWcKd0vMT7pwgcgSYJ5eBR6s4Yp0sm8Ihpud485abd9/x/1P35k9yJFd+58ePODLrAtBoXkMONcORSSZp9///D1am3TXZaCWKIjnNIZvd7BNooCoz4/Bjf3juHh6RWQU0KYkYhxXyisPD/fnz77t1mGmtpWl0CmWV8OzabBODhILP84wKIYWyiqp+9p7ZS1TIbr9nf7Vnt9vR73oa02CUKSkVCFHyQaUNXtaSAAmtNcoLE25sA1oTdVpzcYk+6vtOcIoRIhvHkXGamWZXzFTjPKNUSgbYNCilU2YchUfjsNDu2N9+hGn3KDRxNU7yRqtcm6pm2PVmIIQh9XsKgVygovMZfFq/+ldsNXONKoFShZH4NfSjUrmcvJLmL+0TdfoQpcpLdZH12/K6Xgy1tru+S9y8V5m/beCOSgLzigVvN/jqHJWoJI4TuAllI0E5AhqNFeFSSbFX8RsTkOKCX2uXYBU9qLUp6y7qWLSXXS9+bM55fAzoyXEadjw0B3GRMkZKj2iFmiekRENkmmZm7wUMqGwNiWdkeA5mzj8XOhdTSKrHt5oGYgpFj2X/uqyz3u4tlyIP/8IoqnSRqgOPgRzp+PIkNcq9hMjWmpbqfhnlX/htuUY6cvXbO6Bc/TwX0KAoljXTdOLzf/4DD3/4E3fR0vU7FJ5pnoiAdQ5thIkNw8DD4cDbt/eEGBNxOSmmqTVt04g9X+UU3J7TSc7pGsOLl8/Y73q8dwzeSd+c4+RGHnxA377kZ//m33Pz0Q9QtJTo/NXjXt7RLrHDMq5Zs6REOs85hSoXsGX41fId5f35dRcwpGATeSP7dS01bX/c9FhdIvcPq50B/Po3zsd/AzuXFqv5K+soj/dmvC5N9ZOocdvn+vX9siNns6N2E2+++ZzDm2/obaRvLF3fYW1DYy34KGAGkt+NIjhHDE5MTMnvZpxGpnHEuRmltSTfMxI51TQNVktYd1AiRMCSzCzTrE5VvtHgtS/91EZLSG3V72wmk4NEY+mcI4aRcRyYJqlVNc2z1J4yBknfCbMPOB8JQUmNxK7lxYsXNLbBVzRam8VUWU+V8LGWLgDNefr6881iS1sfromKFYsQcLPOn7XdAxae8dTzLVocdRZVUh+z2kDWr2zCzIskoM4lBJVBzEZWZwEs2Ty2BrdV/6u5zoAnzjNmmmgVGJu0fBmtK9EsZprOeW188p8hRpTKGh8poJl9P2OMGCVaH2sMylopFJvyOYHi6mrPbtcTfCAYhWoNykdwGjUprAc/n/Bxxuucy7gelDT+1b59yTf3zKSoySrs1VTldZFBzvo+rK+xadu9+7Eo07q9n4nqAnPe2i+zkxcb++m7wc2CiFX5XHd6AU8L/awfatn01ZZXnEdbcU6I+ZIheL7505/48ne/oxsmrtsebRUxeoKXisKzm1GpouvxeOT+7T3OObGDpirF8yzJlAZj2O8lIsMHn8DPkXmeubm75cXzZxhtmE4D8zhK7hs38TDPHLXlb/72Z/zwZz+lbXOtkO2Tbx724mRfGgBSun0lACfKq3y5OB7XAx/zX5KQC1h6gr6qmV/oZyNF1STOhc8fWntMarhkal1+XI7Jm+1q4yrMrqL9ih8/hWGKIm/LlTcjelEbt+17Yt7nEnXEnU48fPUnTJhoUpXtrmnpuo6u7fAuFcIcR6ILNA5UyhsSQwSfzbMT4zBKsrG+hxhxSZMSQiBqKYsAGmUj0UNwPtHNYr/PVZW11oRUsFA2Ai0SagIcRIksMXqpPmy1wabfhmHkeDqBgrbr8LPjdDzigmSMLZtMMic3RpdNJzs4L2HttTp/vQFsN/Lvo6l8H0n1r9lEExFTxW/55hLVFpaxYllbjrbhKTX+T4wrrof4rDfAGtSUzxvF90ooWy5YLyetzm/yJI/a/Oi95zjNaBStMlhlE/RL/Ukdy5pVpVRKQhmT9ktXmCwdl9bn7BxoJRFRKR2JgAhDFkb7TsLJT9ORyY04tcMnu68ymqg9IYwQZ6KOhKgxkWUeV/LXmpesgc5yzCXgkf0RnxJdH7WubO63BlV/gQbnKXR0bp56muk/al9TrJ7qKVXl+fdq/TZd61zLcbnVgx1i5Pj2LZ//6pfw7RdcG8e+tZjG4J3DTY7BeUbnmIdBzFPHI6fjkevrKz5++RHW2pJy280z0/GIc47dbkckcjgeOBwOtG3D7c0NXddJD3zDPElU1cN45I1zqOuP+ck//D3Xd3fiW8BmU6y0LKVVFFLJj2fjdDZVhessnENVP1WDK1uMUhkLXeBY56O86t/2vqmPhVTfTbN/9XZJg7laB4lJFQZGwo7DN9eQAAAgAElEQVRh/WC1Wjd9cfHalzsBeW6Faat6+tbvy9t3LIj14SKBxwghMN8/MH/zDY1zGNtKIURS5JLW2E5YSfCeh9MDbgrYtBNJSQdxnp+mCZeceff7HRGYpkkiEJsG9kr8B4xO/roKGyLRp9IQURyVM6jQWhN8SjZWnn3REFijsdoQtRS6DTESlaVRlkZLn51ztF1H13XEGAWoTRPKii+ddw6rFM9vb+lbi0Lq9qw0EblcQFpHqmgO1vNdxvkDBy3fp5UNqPp3fsxjn9WahaRJzJbUSl+ynLu6UHqtcWQWDpJqqdDFxlyyzF0+dSsp5G8qoLS9/2Mt3zRGwjQLTaIgaokQ1EnJkejWaFME7xhiKQGSo+9yHhupRC77gQ8Bk5hmnZQ3IGAfwDZiSj4R8H5mnEeCk8zJ8zxJVnE3oaJUPa+FrfwIl0yI2cS6JKHMY56Oz8OdrldbgOrXhb+9e2Qva82fPufdUVSbDSdrcy7tQ4/tTSpPrloDmExcNUK99FoufgEIXezyE79dPB7FfDjw6X//Jd/9/rfcqpGrvaaxEs6nAkQVhAijhIOfhhPH4xGjNT94+RHPnz+HEDgdD9zfD4R5IkZh3nKTWEo4vHz5EbfXV8WpyzYNpmnw4YH7YeZoWv71P/xrfvjzv0W3jYx2QsCLpJ96XhHfRfgbN3PyDp66VT2mSWNlNnzsvJo3/NntLzr5f3tb1kPSUqllEMqTFJBzbnaQa5y3BdzExeRRBII0yGp97mL/Xy4sy+YcgC40k7/PDE1es3lBhYB3jvE0MN7f02aNhZawVa0WbUnf9yilmKaZ0+meefKiXQmLg/3oJnRruX52w83dbUnQN4xjyQa+21NMu0priahSIYWWA9EXutdaE10Uf51Kv5tDzJUytNqgjMWHIE7JShyjm1z2IZmT97u9mJ+CJ4bAdDpJMEGIKNtw9/wl/dU1aFAqyhjUa0NVms+0cctUPw1s3iXM/YsDQoqUCX5Nf0UrkA5ag798nK6+jxWQXxwlsiC0CF/LNbY8qHzMS6bef6rTI8tcCc6Iy5qg3pClFtsTj17+j1rjIvjxJJGESFJJZcTclFzCCF6ioups2s5JOpEcGRiR/GzeOTH9JvScfXOMteKg7D0hTkzRgdFoa2j7ht0szvPWGlwycU3TzDQHdqon6A4bTdHglvmqfCqXvaauNSVh4MscrkehRgpbkFPmLP+6GdZLAt77mqZyexrgqEc+PrFpnjHOxy9XbdL1V0+AnA1IOrtcvdm/q+Vxj2Ka+u6Pn/LVr39Jx4m+i7SdkerfMaLVkn8A5PjTSez3P3j5krvbW7qmgRiJbuYAECI6qdGHYcAHzziM7Hc7nt3d0VW+OVEpTNugmoaZjubmR/z0H/4D7c0tManSC+uuEHUdlZpYCavNNbKYQdWiJnwU5KyGer3hwaX5OD/t/OIrHedFTd/6rL8YIf2vbekZCrBRIGrzuIx3HuRYnaNAJaZ5MVqwMOc0f4pKlZ6SAdbrZNlH193b0sgWBUUWR+O8gVbXzlRUv48h8HAaOM2SKKxtGlprsbaqBZTMNY21XO33uGHkMB0Y5wmXAINLKvXr6xtun91x9+yOtm3RRuOjmJIeHh7wIbK/uhIN56pDC5CQ0CoFUQp/zn6mCVLmoYx/BkHV+s2RKdYYGiPPYoyRfDnXV3R9zzRNolWaJ4bZMWEJzTVcPSc0O9aTs56MLSh5CqR8H0Hs+wpt/3tbZJtuP5b/zzeockyl4VKrib5wLICKRWu8+qXaJPP7erTre5yNY1qzRXu8uXe9hPNxeR2v+lpeF37pY5R8NGEm4rFanZm8tJLopeA9zrsCcMZh4PDwgE+Z7m1jid4xTyPzNOLdvHpAoy3WGrQ2eB+ZgySrDUSpS9VY+r6n73u89YR5ZjgNMCtQHRELqCKwbJ9r+6RZ85wBTT2XcbU0lnl9HKhvdpDvAWDe1d67mvjSkfeQJlYEuJxZwZcLpyy/POZRnVN118evQhTV+i61RFfJUOtexcCbV9/y2a9+Cd99xc1VpDEh8dGk+rMRNU3l+j6Fl3Ztx4sXL7i6usJoDTHgrKVtW8w4MvsgNXOGgXEaUQo++kiOL9VZUz/zAtOm5eMf/Ywf/81PUdYmYlkhwMLks9Ykq8hFAJGnFZNF3nRToiV1znjr15w9M2kdFzSv18BypZq/PJ1lJlfze9HIGp/8+GE2Gdvs+LgNA3/0LLVWseZHLZJuliABosq4aAGoXBi+fG5NDzVEUYtz5FbLlrDZcu16vsrpCu89b+8fOPnIVVzU5CUJWyRpaTzBORpr2F3tOc0zw3jiNJ4IPmCU4u76jmfPn3Nzc8Nutys5Z3JyS+ccD/dvRf2eig0KkMy+OUpAvweCFxW5Uvi0OZTMr3ls0zMskjBEE1PRTpOkWkvXdex2O5qmQSlwbmaaB46j4+Ai7XVDu7vGK118TQqPSOsvo7HHnTCf1sasNpZqja3yXX2ArZbC625mi9D5hpX5CGy1W6SvasGsnJJTDHFhHCtny5jwVt5bldKrjfvSBv7Y6GagVkc0cuH8xaG6SJOppI8n4IjKYQhFa6MBQpAyPymrt5+FhkOQTPaHwwE3zairKwx9wvWyHsdxYBqTWRcJObfWJoEdJueYcjRvygnV9R1t2+KNg32Hd1dM1kk6hXmWMSoCS6Y/s+wN1aNnjZu86vLcq1Dx6rjHW2Q90e9u38e8+94A5yLyZUkMVlh9XN7n82oazRR6WROwhTbbTxcI9AK4KbJE2U8XtWh+F5TGo3CnA1/90285ffUZt61K5RhAhZohpbo1xJSZ2NNYw8uPXnBzc5OYoiKmnBy73Q4XAv40MKUMx847Xrx4zvPnz1MSQPFNyOtY6uAETNPwd3//C/rdLmeGWUYigZvVZpaYbUwbWyzghko3y6M0tLVn1s7iCxdfagOdSUEKUUerzMRSxgu11jqlwVyLRBV7UZk5qkeVPB9IqxHCRnpUSP2aS0crivQZ47rGTT1Ny0kr8ZYzsFheVXVcvTbU8u+S5FodtVJL51WWN2QVmaaBt29eM8wzV0gSMaskD06p/eQD3s3ir5ZCrk1jCUROwwARnt/e8vLlS+7u7kquGUj5PrQ4Ro7TxGkYePPmNTE6bq6vsTqlR1CKVAAu0biMuVaaeU6J+rpOhBJVEZKS6sooMApxzHQOY5sCbrK2S6eIlCbVxbJGYYNiv+u53u2kOGfKtqz1BjhWY7mN9liNeT33FwSOi+vsA28CwrJZLqYQ4FhoshbUlNq+37SzLyvgcwGKLIarBWDJZp01eMv62Y5zvu6aN240Etufl6tljokiSuQfkTAH3DiL+dN7YpxBU45UMTDP4qc5HI+MJ3F3OJ6klEjOrzaMA36a0VFMsNroouU5HU8cD0eatluEJSXmvVykU9fPqzU6ZRvXXuFdS7MbscHjwoAbDtW+nelPoyrevwJ2agtyHgP2FLpYAFAs++p5O1dYPGaqqunosfYOgJMZZ6xU6MtPKtb+OGrpXLX55oOXDZlHqPqJXihVJM3t95d6rNIuGQGv8mJT6EhJ1EeyQ77+0+e8/t1vsNMDXWew2qCjEGzOWeJDwMeUfM9NhBC5ubnh+Yvn7PY7TJMAS6r42nQtfQjMKKZ5Zp4dXddye3NDm8FQfq4Q8D6kcNWZ67uP+PiHP8BY8b2J9cLcAJycMCpvdGUhbvfefK8YC2E8hYDLb48Q1sWxz3ttBVMF5KgK46SOIaApKSxkxvIun5jj5dSMH0BbMBn1gqhYaHqN5f8axm1peDUP6nyDKwJCXjcF8yyMqNxcUcIvH1thiiX/hFLr/hTaWZ0dcdOR4e0XKH9Po6Ax4ghscrh20jDEmAUAEQJ0AkExQttYrq+v2e12knAvb4RIJJQyAhw6JZFLh8M9r199SwyO6+sbWtuWZ667FxN6FJ+CiV0IhceEbJI1WmhLa3TUqBhpQqB1jnYasdYW00DW2uaihH3XMgC7tuH2ao/RmkBtLqy2ujR2W0a/XSuPORs/CUQvAKUPpV3kw4m2knhY8YRz8La8XUOJ7ac810+ZMPL8Cbih9EFtrgYb7T/fT3lc1ljSlDsdmYLneP/Ad19+y6tvvqVrW17c7VHjCe1mhuABhdUQvORfOiWAczodOTw8cDge8TEye8dpnPDG0M5N0Zr66EuwQvSReZo5nU5c7fdSqFYeTPhpisQqmsAEOHUKKY+NRjWRKY54NyaFgClPmPlLERgo+uUCAB/NB1boVVXv19qdaibK6BeZ5NH5/R6TxHtrcJYdM9shVw5dl89YXguRVSjlEvp6ovMZFDx1TEayOU/A7Gam6JiDqOp0UHS6od/1oBTDm1d8+btfMb3+ghsTJYW2MpRMFlFqSGXnxxAD8ywOXrc3t1xfX9Mm6Y8YiYlJ61QF1qQETm3TcHdzw/XuikZbqfAaY1HLT7PnOMxMXvHjn/8d9vkzZqXEibGOvElq8BrclFW8DFQqdZ+IJCamSw6xTftk7vPjw/nO9pSkWc+X/F7nPVCVClpVppkcAvphMvKlbRBkou/EP5YvoWitJPoslmfeqqlqTWf64vL7vMGrxGIrkLLKV5XufabtzABms0Gfv0/XDJ7T/Wv8w2u6OKG1aEiz9iLGmEC6l7IkBUQrTLL928ay2+/ZXe1TGvpAZAE1yizh1TkfTtO2PDy85fXr14Di+uqathHtTPHDKNKpOG364PEhrH6LAFYRU+VvYfyaru8JMTJ7z8PxgEsAR8LPQ6lu3lqLdUGiUbquSLUy7kb+tAB2xbJpXvSzquahjib5l6SpudTUlj6pogqJ1P45tcliDQJrSZ/N8ZS1lc8vodbVf2KGMlV/tlLe+XjXZUnOt7O1WesMmKVXH0RL+fXnn/P5bz7hq99/yqvvvmWej7x8vuN5A+HhXsCx91LPUJPWSEraZxuarqWZJuZh5Hg8iv/nbofSGttIvqmgAGW4ub7jdBxx08SbN2/o+774lgm2EXOvmx0xCqgp7Co7JTctpnMoa3BR6Dnn2cnAJv9liLg8dVzR+LvMsmut5SVwtOX51byUOfifDXC2dFIQ+dLR0jW1fK/U0r2Scj6j6W0n1VOSy/KQMV0rk2QdYgcIkw0S/jYejrx584Y3r14zvHnL6eGBOQRiY7l+8Zy//8UvuLu75ptPf8t3n3/CPgzselsAiVFJjZhqhPhUcXieZ8ZpxDaW29tb9vt9paIXZL3Yyxfb+dXVvjgWa6WESNNIhhDxPjC7QNSWru85Hu7xwXF1d4tpmhJ9I4y7AjY1E39iDmsrVfk6X68aw3dcZoEnZxqG6rViCgu4oYCr1dUKx6JIvv+SWvFfqbHO5hGypi5LBFoLI7+0AcY8RmX8Kg1OBn+b3+UXdX7j79EU9fyumb+fZw7f3ROGmTZq2ctV9mnRhcaLBicD95SbJqKwTUu324NWhOhRWmGtpWkkgmq79kUoaGlswzRNvH3zBiLc3pjksyPdVEbAkcZiGoubHdM80Ycd1i4b3epV5dB2QwiB3Tyz2+24f3hgnmeIsWLsBlRDNAFzc8vQSJZlXdbg4uixZuDvB1q29P5o6Oz5zvthtloWi2JGzPT7qOY3n1qBm3MQAgXLq5pS1er7M25Yje+yNy3g61JP6n7WodeXm4D1w/0Dn//+93z+X3/Jq08+gdM9+/nA/fCWP30+8apv+OHNNW3f0yqFMZauERNSo438tS1dKjr79uEB5wNN09D1PW3ynclrRSmNu5a8am/v33K4f+BN32NS4kyInMYxpWSYCF7qtelUsBOQhIC6RSuHc5HofYrqzWNaWwaW9zWIX4b5XFirv98CoHqOt/5la/qoQU7N9tbXf6q9nwbn4gaU9QJrieTCqQXYZGIs2hy1faBLTe4RAI9oZlSM6GQ+it4TponhKGq+03evePv113z7zTe8/eYV9jBIQbEQOTYaf7Njun/Nv/r53/DN736NPr1l3ygau4S9aq0XUJBMVDlDsXee2+d3or1pW4wxqVp4xAdfVQ+PyTw103ddQdj5miGZppxzyU9nYpwdf/z1r/jsj59xc3fH3/37/8DLn/8rmr5PIalpTDcApxqqi+OX52HZH1WB0ToTcKV92Er9mbQVi5f91nxS77ub2T8nhlVf1SIb5IWw0jB8YK0C2QvwX8Zz2/XsTC5N/KO0VsQ6J0cF/M9Azvt3bDnlwqml9svZ2MYyt9tTY4jMk2N4eyAeZ5pg0GrxDMuSddaYOucYppE5CQTjPOOjOPSKmlskx6ZpaFL0ktF6RbZ5Y7GNpd/viEc4jSPc32NtK9pRJSUZMsBRGLSxxOTvVq+1DLzL+yz9B4qWJvdlnqWiuLVS1FAAjCFgiKZBdT1Bq1KKIG+sl+r1lDmvcPyf2xZO+2G2TFMxm5EysLmQd2ZxQlWbjW19zbMNMa2JmoVsTaxJrFz5z2yvWc6rrnO2JCqB+7H9KQjhM5yO/PH3/8wn//iPTL//HdfDA532jMyYAN+dJuZxYrQtzjYYrejals5oNGAT77VWMoN3XUe/2zHNM9Y27Pue1jbFn0hpTdNYdrtd6etpODGcTpz6luBnQpQs+9M0MU8zxIBK5R1UAvZBJAQaZTDe4U4P4CYwlhV/r0DOMjzn4OYSBngMFyxA9nxct+B+rfy4DKSeau+V6O8xdWvR0lz4XW2J5AmiudjRmnCDZzoeefX6W169ekWYZ3SQugXBO8b7t7z59hUPD/fEt2/QpwcIgS5GroEGxRQc5n7im+8mfj98g/viR+j7t/QETN+V6q5GgYoBQhCH0ZS0bxgGjqcj1lpub27ou078D8pYZEdkCEGyGZ9Soj+92zEnL3VjDWgtzmhBchEcDkeG01GI8HAPw4nXX/+JeRxgt+PHf/tzYaLIJlEDnKwtyhvc8rne0OI5MVWb8mNkslXHKkh1Zc5VtYtc9Xh7Hxatlq5/uC33sQL+l6SU3DZyusAJXT+o6DqKsEDFBKBouXIRsEWKXavXictv9cZezkmb0GrGl0tcXJfzNHP47i1MniZqTIxiNnUeZeVKLmlOnXO4FO46zhPDMOLmuWh2QtbyhIDzHqaJEG0p2rn0lVTAsyegmO7veTgeMSlSpO97obdkDo5A0zYEL8BqGEaapltVF8/jqpU4waNzMUOpyNy1DfM0cTwcuL69SaBFkvz5yXO737Pr2rRe1oCpHs6VaSz/r2ClYXtv+l6u/fgq/QBaQh01R4ixkOtmfSSNZ6oCD5f5f71/5NQLddq9zT6YF8qZBfgczKgSFRqr35fj1ntV3Z+6hRCYhoGv/vA7/vj//T8cPv0dV9ORm9ZglUF5y4zGec3DMDJ3J0Lfon2gRZz0AZS19IALFhc8bdOy63fM05TcxvTiV4MAa20toY2F6doHEQhi8AzDCec94yjC9TRKaRTTNPjZEbxPNRFFW9kqaIOD6YSJs+xjNbBI93gMwD9mhqqPWY5LfO4dGs6tFidGMYnnPW49oU+399Lg1ASTO5DNQ2eqv00HnwI3jxPRmgNMpxN/+s2v+PV/+y988cUXROcw2YbtA0wjYZ6ByC7CTWPZdR2dNfREVJBqxq2baU5H7v904uvxwMd9T2waJqMxWnwGYhAnxJhC+MZh4Hg48PDwwDTNvHjxnJvrW7q2X6kwY4hEyeqO84FxHJmmAWsURkuF1+i9MOWmQVmL84F5mplOAwbN9W7PtdEE71DTxJsvv+D1V1/zk5/9LdnfYiG8dfRLJTBemLTLqEHVQGg7ySxEuwaqC3NR1VWWa27fPPH5KUb/gZqrsr5pAZPVekiLeLsZZWxCqBf8Wi2fr1NeI+IwXG7GeiOt/mf1fm0qXAEdztfc+fMtl9IxEk4n3MMbmjBgdUgJypQIACGwirJI11cJsORw1nHy7Ha79HkSX4QQCE1DEwKqaaQQpo6pFEje4JKvgDHMs+N4PErFcmNK5GIJ/1aINjcEZifMXbQwSNblKKxOJQ0QIaCNrPmc8O94OvFwf0/Xd5I9PEZi8IR5YtdYrAJ3RreVGama3EUDHCqAek72T2m/61mtQe+H1rQyyexD0ehFtaRCWGtuFmUnsKKfFQ1VTGZtfEr/53QYea3wxGar8jp7Yr3V57/rgWNgHga+/PT3fPpf/l/efvI/aKaZXdvSWAPBo7XCqkiPYvCOcDyg/bUICMTVPa216KAxwcjdQ2REM6dcNyrXqdKmOPw2LaBTJF/ahppGnOXDNEsB2XFkGEbGcaQJkeF0QiuJVHQhCRw+oEOUzNFaCRBXy1wVl4NqbOuxenSILvjkZLSUeee7TU2LFmezEqrPT7fvESa+EOZCoHUHI3X1VpVRX7X2NzS3fgxVE9ZyDe8cX3z6B371n/4vvv7nf2I4nVAhoGNAlM2RJsK+aST9e9Nz218JI9QQw8TsndghJ4ceZrQHNc6orgMkRHuIAa0EmQfnUIhfzek08PDwIFmLjZH8HX0vxc1gJZWGGJJPTfIF6Dv2+73UCUnFCH0IKO9Rxsp9TyNhmLnreu72kk9ndCMmBPzDkeH1a6lE27fVFrYM4jocrx5RObD2k4q8Y/FukHORlliYTg20VmCnbKTVRptpRFV9Kk+QCJ0k6i1QmXUhwg+vnWsyazFyATmrJ05MX62u8YhAkK6ax249Gudg53/GM1Q/CIPDo9zMfP+W6eEN2o9oqwCdzBBC8xJEVTkWa0OIUSojp0SX0zSV4oEheLzzRBPAiDZI+YhSQdTgSxoOtJbrNW1bTGD39/dYKxFZTTZ9EQkYCftugzh9TiO6kerMSi8FNHUKn42Vr00GTHoYOJ5OdA8PkqYhHXN3e8t+t8Nkf4EVI8t/1aTEvKnLAQUMJ6S74n1PMPd6/ah3b7t/taYTYK1bzW8uS/LwPkR8UXyKUIJeeIKW67Py2tsK1Rf68NTVQvBMhwN/+udP+NU//me+/s3/4OZ04Hp/xVXT0xqFn0eIAVQgmJnAhJ8D0bl1zanUF62N4JoQBFBryf4tWsdY6NMkNwqlNaZtUdNYnP198ALYtSF4z5BKmAzjiJsdXdeVNeTmmdE5CXDxkVk1vB0jY9Q0Fa9WKpthq2G/MNY1SN/61LA6592M6zKAymbHsthW+OOp9g4TVb7p8rkGN7WdeVl/+U0uKLbeCOvrZl697adS4JUmorj/9ks++S//mW8/+RV2OnKDxNFrpH6HVZoOxa6TgmO7pmPf9rRNS8Az+JF5mpiGE2GcMbOn04Y2glGKNqFuk0xGw2lgPB1TjQ7xjzkcxNS03++x1pa08wQJuV3SaztBzaOE3OUkgN55jg+H4p/jxwnHxJyurWLk5uqK/W6HS46OBIniMlESRqVhLBJ2MW8XRlqNfmaolcyTfT4UOQQ7hWOna9XS0QqMLDQlZ62Y7XajX/q3lWzP1ZebKyQaWpHSh9iqSVhATCmmQHnuGNfPtmXVBZFuiH9RiQKkkGSVwJFaNDpFCHh/pHOmWaol5vrREO2RD57D8cj94UQbo0jbVV20kM24ISTNjULHDHQ0XdOK/wADxEBjNV0rDpPFD0ebQi5FYGABTcYKc3ezI+AZx5E3b95gG8lhIwtDo4zC9j004pg8eY86nej7vpRjQOUCnAtIyaCsSQk6j8cjDw8HyZMVwaEYtIWrO1AGE1LaiRXTzpImhRFf1O4lkLPWhm/mSIFSlQZCVdGFH+jC0EpB5WuWw/+lali9f1wAK+/TVKLdNNQ6RyMmsPCYiXirGarnfHP5pW1YWxZeYgwE7zi8fcM3n/6BX/3f/5E/ffJPNONA37TcmJYb22E1jH5OSf0CioDWEtXkQ0jmOelLrg6ukbEL3hOUwiDZ8621OO+IRJqmwTYN0Swh232/w1vxmZGSQBFtFJ3t2LeeuXMcUgFcow1tWjMhlSwZ54mjC5y05nSacM7TljErg0jewAvFV7S9BTf12G4jCldi2pbtPQH015putXl9ur2niepdalS1KX++JiT1xINtLgTkjVQxjiNff/p73vzhdzT+RGMkT4VVUkbBGIOJmo6Gfb8TRy3T0CrQwRHCTJgHwjzg55HRn3B+oFE7eq3ojGHfNDSNRZFCy83ICJxSquxpnplmR9O2NG3DPAuIEX8aC5oFuHjPMAxSOLBtpJK4bVBRwFhuIQTGeWYcRqZppGva4hjpprlcj0ax2+3lPufDdJHfLZqcNWSp6aFmOjGfVBjo+VXzfFyiKeE5Sjbi1R2f8hpQVT+rc9SmXx9i0+unUkmMXDagOvFlPmYt/Szm3bQ+0uauQkTpFKK5XP1RuecpH4azYx+ZjQxuzhembKqH04k5KjrTSIHAOj1B2nNU8hPQaVPT3hOigJy2sfRdQ9+3XF3t2e16+rYrzr3i9yaaFCG/BMCj8BOtZO1orVBW1tDkZx5OB2wrQo2UbABlUpVwpZhGSfxXIqKMwRhZR7EKJVdqKeEgUV0zs/OcppmoDXOE2LTYm2eEurazymN3bl7ZCoQr9F7N5XoN5LaZ7SInfLiOxpmGspaq5gNpK8xHluOXz+f+LrDZLNey0jJE9UC/q49cTmh5Bm6q+6TMAvjoccPA/bff8Nknv+XTX/13Xv3Tr7GnI3ddw14reqVpYgQfwHmid3g3E4OHGEuEbjGnoko9N7EKJcEASVMyz2KeEmDkReOYBB0STSulCCYVm/ViCpVaV+Bsw67t2Pc7hmHAJN+1frfDGgMEnJ/RbibMI4wnTPCVliSPq5iK8xhf9LWp6fIREq3Bfzn0Hb44yzGPa/zeNfXvCBPPBLZWMa4jBBZizYJRTTpqRZmsr1PfY8OsVXQcvv2cz3/73xhff0mjIkZJiGfXNvTJiVBHTUvLvtthtMFERQyecRqZ3MBpfGAajkzjicGdcMzctNdc9y3XbZsyF4vt06PomxbXdamGTuC7+weU1uxvrkErpnFgPjX0TQNG4yYnNv9pZpwmDscj8+x49vxZcYYkWtq+wwfPNASK4/sAACAASURBVDtm53GzRGURYLfvaaxFRXFQlkUVabqe69tbtDGpQuxiIsqjnAX6ZYoWE8+CoC9P7Vbb/tj0rySgMy6TNuryMZZDz71RzpvQQAIJtT7kw+Tlq+cpzDsNi1IqVbzeAMu4bFCKXKpubcQqUWwRyWNUSUx5My73fYQx1Ex7G+B65ly8eY7Vc6X+j/PEcTyhrUH5Bq198VWzjS1+KkSRsrSK4qCvVdHwNI3loxfPeP78uSQj61qJPiyFKjNgogC7knU/gRyjpSK4J6BaTYiR4/GINeLwb5MUm/mSMQbbNMzJ+bltW9lI0liSeE+IQXxkkPFs25bZOY6ngdMwEZPvz82LnufP72iaZkkeqPQK7C3gdc02t5Jn1qzWa3ar8fmX1nQaT63XICxWf4tQJo4F9Ub6mOR/qS3s5xzcPAb4F1Bz6UKcL4D0VQBm73j11Zd88+nv+exXv+TL333C8Oob9vPInTHc6pa91VgViN7h/CxCql9SJ6gQ0FrRdA22bzFti2kadBJcVVyy78dEk9baRTMyi/Ado+yBOT2BVlKioW/Eedk7j8eDMXRdhwueG3/F4XREIeuibaReWwhicZiDJziNngesc2n9LfdQ5XUZ22KOrPi9PMdakNvOpdY5LHyZuqeisJ6coEesCNv2XnlwVvJozKx5G86rlyMryaZc6ilC3AwgQBhPfPHJr/jus3+ijyNdt4TKtbah7/rinNVEASkKhXee4Lw477qBcRg4nU6chhPOOZrGcnW152q3o+878aVJIEAyrwpxXMXANE08HAfm4IlKigG2Sos/wSzVWudp5HQamJ3jcDhwHAaur6+4vrqib9tUjkHRdp0wgXlmuj8JIBom7nZXXPc7LIroxRnaoxg07F6+4OrlR6XgJqSSCGnMCsi56IdDeS4ZUtlp1zT0+JzUfhUrNS/ZLlu/1mrNTNBrX4P1tTlrtYEnb7AfZlNkf4s89pl9riQZtQZqKtWuEkCQJ+oSSOGMaT+2lMsaLIIFKQt3vs73f7oihYeIOx2ZD2+w4URnI63VtFZhUjXk4nsRJZ9TZlI+VUIOIdC1LVc3N9w9uxPp0TZVksr0FFmTYoyYTaMUKYxRTGC5JERIxGwQv7nD4YBNfnFbdblOvgreScbYJkaMTQ7Gom5agRud/qwRbdE0TzgnzLw3GjVP6znYgv9qVpa5O6f1DPzZMPZ3g5zz9fShNIUo0YJSq4VbnI3VoumVwrQUmq3bkxL9xd8uA5r83ZaXyQfOJ3G5Sb4VhEgIjjdffcEv/9N/5LPf/Jrx6y9Qhwf2RK6t4soadk0j+c1MNmMFvI9opGJ9zqfUtC3NrqNNf13TlVxMxAheTL1ZaNLGYFK6EWNMKdFgrS3rXvivOMkHYyXDsUhaxBjpfMfOOa6vrpidYxonrlLutrZt2fkeHz3zMDMNB07jCYukRih15jb8P2tsibEkZc04J/sY1vvRpXmpprQSBt4vL05tAn6f9fDeeXDyy2V7KoWwl43uXPWYmfWqaGb1B5RaPodvvuLL3/yScPiOvaXY0pXSqFA5CCqDCRrnZqKPxFnUdd6LE5X3wmynaSIouLu+k5IJXScq8ioSSimFbSytkj7M1zPPno3cHw7inJUlxRAYxoGgotgyE8g5HMQR+dmz53R9nyQaQIWSbdUkInSzgJm+6+iaBh0l4V8IEa8Uc2P40U9/ws0PPiZqA+QEVQHJoJp8QAoxrEHOOSKuGer5d2dT/uR39W/bec5MZ/3+MWKU/p571n+wLdM2q62MzDmzv0QtvQIlW7MMxrKyt/bss7by38jmpAxu8t3VuZBzaRjfT+iBCDqAPg3M332DCSeM9hhly/rO4FYnzUhUgRBEo5OzAiulpIhl3xe/G5vybOTu5qSXKuW10SRGH7OPk/gjGGOYnUtSa0quGQPH04m2a2mblqwRQoFRmiZIVeVhGIgxSh2eEo6+ZGptGkvrG1C+ZIPFz5KnKgTx7WuldMolqfYiUN2sw5WGh2100aUUAxVXLFmSP8y1IVhVJQk+EVme4IznC/9SZxtbPvDM9PEU4EtEmLWSF7U379BQywmbzxFyyRh/PPLNJ7/ls//6jxy//YZ2Hujx7BrDvrU0ppEMxG2L0gY3SpoCcWC3aNsQjCFojWksTSvHtl2LtQ0GveROi57gF46hUCXHmjXiNzPPM7axaJ07LkKCMlJMs/jFKQ9Kqpn3wXO13/Pm7Vvu799ye3tTCsw2TUvnHI2DcZ55M010SnLzmGWIV2NVBLA8zQWt1oc9DvrzgZkGzmnhsVYRVG09eIeq/3uYqJbNKq508BdPXAjtEuiuGFwmUQ1LePY48tWvf8X4x9+zU4qm6+nblr7raNqWeZJS7yEEGiw6eObZEZyX/T/Kg4fomWJg8p6gYHe15+UPXnLz7I62S0nDjHiwizSnhJhiJLZwtb/CPfe0vWhf+rZlt9uLqtoHjscDs3ccHh548/aecXa8/Phjbq6v6dpWCMQ5nPNobbA2MjtJnR1CoOt6rq+uaG2TiFNsqD4G+v2en/zt30om40Q/az+nPEVPJ1jKwGGZMA2ljOf5eecksPUxqOdYb7Q26wXxuAan7puY1OQrdXbsh9hqAXABmZlJ60RLUAPI1dMk7UXZ3Ir2KmtkllUvwxsf0cpcZvzFlEllBLusMFq1+pCIYjqJg74ippwzwkhz3pqSxTuZVV0KZzVGpNfGatq2w5qWGMC7ID3SsdSoyuaNnBwzj9cqMkeJel1XQKBtW5EiY+R0PBI6T9O2GNsC4rvTti1awXA6JUHHlRB2pVSRZEHS7UflsPOMNRbNjCdyioqwu6G/vmPUKd19qk69ngW1Gt8tcFkFZADrctn1yC+vGSCV7/+CbNX/K5tSUucvrtYDRess0v5iJF24Uf3NZeK8CHLUAv4yHjlDM2tiXlscMhCrhJXyQ7XlTcPAd1/8CfXmFVd+oDWBvdV0jaXvWzrTYW0DgEvmqeAdJM0gUDRYRklyyZAc8lFpvLSBKAkrJYO+HEMIkNZYJKKDZpxH9KToVC+mqgzWtSrrUzZSjUby5WhrCUpJAc9p5nA40BSA02DnBqs9h2Hi9OY7tIpEXbpP8YGpGFDidJVWLe8pteZlUYbUoKaey6yFWdP5BrSkeVSVafd8zTzeno6iypdR6ZEqXLPdrC6en4/JUQFJ+svXLONWMfPgPW9ev+bLT/+ADV5s9UaX9OqNbdDoZOIZCWEmRk30KbQuZHATGN3EaZ44eYfd9bz44UuevXzB/vqK1jRoY1Ep3FuFSFCL/4RJzl8xBPquB6Cxlr5taYyoCU/jwDTN3N8/SD2Q/RV3d3fsdjuaRvx6MpPOTDvGmNT3ntvbG66urmiUYRonicYKgWmeuf74I37wwx8W/4Kn5vIpyefck/3y+U+aEFfvEyzNvhNyx/Td9nrnyF1+XyS3BdysEf27kPlfq6nN/5FqI0uy+WOamPVV5LiQxqHe6pY7VOe+A/DVIOrS0leP/HLm/5CO8CHwMIxMzhO1QVlhmCYVMqyvLNFToJUXH7mmpzO95O1IGo/gowghQWq+NWZJ7pfXRUjOv6IoSUAtaXJy4U6XNoic/4aUNFBNU3IkpuQFUdokYWSJcAwxJB8gXcCOThuCipI6vzUGo5X4i5qG5u4jTnNAWVXNw2aNVLT/pMkpZv63JolLjH5rVnm3R9tfp2kliUuzY3yOhAOKyUGl6uLFr4T0PCouWgD+DMEmSX9F9CtrZRlowUPvcd0VhlLMzvPw9h7jR1rrabTsAV0vQSGNbTBI7bJ5nnHzRAyOGLxYEJwruZm88wzHE8fDgaZpoFfYJplr0xpSKJQT+vbIpi6O9QEVDTgkIsoYCRlfOluOy4+hoqLRbUmE6bzj1evXPDwc2O/3dH1XIhENETUeiQ9vMc4ROhGqV/PGmv5WGskqRUKtwbsk0OZz6gGv+f2K5tMhqp7Tqg/vo/F/pwZnUTsmCSJ+z4VWLcw1YFqYcnmNkjPjj3/8lG+/+ZrrJGE1WtM0pkhxuhGP8HEYGIaREKSyMSAAJwRx+J0HTsMASnH34jkvP/6YZy+e07Wt1JRJ4XkCtOQZjRGJI6AIytOZBpvCY0s+ghSq50JEnQbmyeE9XF/fSN6bFHoeQ2BO6sKghPyc95yOR2IIXF3tabsWfMSriIue2Tuiafjxz/+O69tnqFzegcxc10yPajzhcXCTAceaqb4fSL2o/j077rwvl67zmNks9+Nc4/ShtfXzXRrnMgdyQI3alkuo6glrgTex6qcciSOPaEjzOEIJ0SVt3LL4zoHyRT8FJCfh4BwuQGMaopJw1ZwyP6S+5NBxowLaeRorZiip2WQk8VgM6FRQM4fDziEI0Gma6p6SOHABgJDNBUqlsNoQFh1HlmKTKXoaJ4xtMKpZwCdgcmoH53BuxjtH37VL9Er+FyNWGxpjsUqjcRKmbm1Z89lfaCUUPLkuNiHjaQq2oP6xy3zImszcFv4NCqlXFooWU5r44xTS3wDuaoN8YtNa/1YxwMtvpW/V3RbCWfO1AjizdiK9D7NDOYfRSII+pUCrUuIjF2b1cyC4Ge8ncrbX4D1hFl9NnQSZ4TTwcP9AVIrdNLHb72naJeO2aGKWEYoYVFCoKHnfjGmYxkEc7K0VgKTEFKWMQkqQZG2vQmEwWtbg1f6KYRwYx4nvvvuO5y+elySaVkETHON3r9DjCdV21TqU8SnpqYSpbUxLlX9YUqlt95ka1Gynd2umKnghVkgjLZqoVAV43i0IPwlwimd3YRepswUdVw9RSzRrKk7vM3NVy/Eb8BOi5/Wrr/n9J79hOh0EUFhLa41UYDUmDZDY9pumYXBHhnkqZeF1hBhEu3IYjkxOCOn29pb91RW73Y6u7dCJGJxziSjS9hINGNlgixo+LQix2atSsCw7kFnb0HUtXdeWcECtNEEtG5JPGqXjOOD8xNVVT9+3eBWIKjDhmMLM4B3tyx/y8S/+HXZ/jVc5b0cKxFUKENPQGkXnwQ7VxNcIOU3FE2ruLWNR9Ua6Om79fguAngJEj2mb8m8fqubmqXYGbjKpR0ii0LI4Sc+8BTxVe5fT6WqD37TFTSPLXPl1YVaPPUP1IdG4QjlHowEV8Aq8shgFUWe+sJjDjDH0fZ/of3H6d96JUy+LhJc1Ns45Iil1fWQp/qoRqTbXNVKqmKjOHOBTleZpntCDpd+JGS0DzAzCiFI8dPSO6Bxt00jeHkQ7ZLQhGqkLZJRChUDXGPZ9S2s0TilxpN1OBMs2/VhbaCRtpGeRjvHs+HoeP1zAT+JNEUkWAZlHF61m3rSSRmAZtvd1sN7cjkUYWCGblZlKFuHijPr0dWP1GpTGa0M0Ym5WSmG0pW1McigW35g5zhAiegpE54kxpESxKaGec8QQ2e123Nxe0+97lNZSBDNpFNtUe6rv+6QJkwSB0ajUKVlXzrlUS0oxjCPtONL0PdZI5n2ldDk/Ij5tGgVKi0Nx33G1v8J7z+HhgRgjt3e3KKVoGkvXiIDgZkfnA1FL6pc8ctkCs+wp65pfZQ+q0kwtc7s+rp7nRTmT73XOp2I6UCWeWSdMfVd7h4lqETcWdrkw5czMqTutltcyOsuwFDWmqn8EIoF5PPLVZ//Ew9f/TBdHWmNotMZmu3laJJLxMUgmU6MZjuKPoxGAE7xP6ajFLt91gkgzSDFGozEJaaaUVDGU+jNBin9graWlxTsPiuSDYKtJkzTbXQorl3mQwQgxZzcWc5lLfTqdTqWelTWGaRzFmdF7XPSE1vDyb3/G7Y9+RNCmooBMYBlZV2xig5DX5JE2ISVy/aJ2rFWFaZuKpIyvi0oxE/fyx+bay++FBjbfbVH7JZCz1jSt+/YhttogtGxBsoWnvbrMUtZH5fd/DpBbj3+9RWyXepm05XX96+r83LaSs1IKoxS7CMY7tA1Jsq1ixdTyLDnao0nrQ6GIQYQAGwyBnOgsaUijL6GvkSjamUpwyuB626cCEFloUwMqaWmmaZT13bbieBmSVJw0PUopqV8XBoL3NFqiT4wxdF2LTpuT1RodAn6e2PXder5qZl/zuqqf2417BeRZhMYV8Mmb9IcOaLatzMtCn6t5AxHzMth/DxBzZjrd0ue2C9tv84Z6NjdP3KOcF4nRE5Vk2zZa0dmGrm/Z9T1d00EITNOMm2fC5NFzAC+aRD875nlmdDPRKPbXe26e3XJ7eyvRtEr2B5ci/E7DwNXVnr4T06lWpgAKCakPJSqwaTtCjJyGE03Xstst7gJaa1TIQpbULdQobJOiprqOae7x3vNwfw8K2r5HGYtXM9/c3/MwOUnqmWhyEWDVggdYK6XPTFFQEpOeA/fqOJXoPKnQMg8tyyDfp5rH9wU2ub13qYZL7XxT2jjBquW4unOq+i63MM+8+eZLvv79b7DHb7nWgb7paBtDYwyNtoCEoHo3p/BrsH3LeDhwHE5J5SyOylob9jc37K/22FTrJkdUtW2LZMdWYLQk/ioTsVZBa6WJRjJJarNUPg7puXPxv2EaxdaZkDvG4J1jngWtz9PEcDrh5pm721uePXsGwPFw4ng8Mo4jJ+cwz17wk3/zC5pnN5I0LY9T0uCcqccLyMiPUG8Il4BCPPs9b8Q1bEpLK5P1hfvJAlgqKZ8DK1UVI10zlvUmXxP9cu623x9OUxWVR1SJ/Fsfwxn4f0xKfwzsnM81hck8qonZnlt/uwFH21avZaUUu+sb2n6Hv38D6FKWJB1Q5j/7zjRJ5Z1zAUlAk/Q3xMT8U9kUQsDHpMlJG4NK6v96zGr/nPxM0XuyjT87KYOUYZjniWk80RhVfGZqTY82mhCEF7jZ4bI5TUvm2Kgke7K1DdoYnA+cZodTmqg02XFwpan+vm21XDL8JUnFW+EkH3MOpD6UJmRVO4ZnbRssOxWl/5e0tmeb5J/JAOqzspYnwsopnO0xat1BBZjoiXHG49BWYa2mb1p2bc+u7wGFtSPDMHAY75mmiTiLtnL2nsnNHOeRpu/YX1+z319JJHBj5Q5G6FrqRR05Ht6y3++4vrqm63qskQr3ErUtUVgeAVtNFCD/9u1bQJLBLvRtJbO4NmhrUSFiseI71LbsOgE4h5Sxu42KYCQjeBM92rsSPHCJEh+bt+2YrqHoGuQvvHARBrcMrd67LvHW9/G/gfeoJn6J+NY3qyX+ZdE/dvMtwwYk7Ppw4Os//JHj16/ofaRN0RomVfkmMdcYcq4BARIhRuYYOIwDwQes1uz6nqvbW549v+Pq+iqZmRQupZ63bYsyFmWUcGGvCMSVmqzeXHK0hTJafMVjlGJ91mGtoWkbdjtRF3o3M44DsWmZ55lhGEuY+uF4omkanr94Tt/1zPOMUYppnHg4HJms5Sc/+ykvf/4zVNeQoP4yZivH3sfm7GntxzL+SwrsmADFAkTXICmjjQXRryngEuetNQ3L8UkPuEL+oaKrJCl8oNEiQInwzk+dYweKanXJdgXVWD0V6fZ9Wkx9yBcua281B4/RyAJ0VkLJJZpSiv2z5/TXt9y/+RKpQeVE21nunLQvVuxPtmkw2hZNjY5Zak99jlFMwBFi1OLUT6rMXDsAp5xWWzOUTr4KznsRYipfG5BILxSJN8xYa6TOTwZKIYA2YFLm8aRFyvm1VAJLUSuiUUkAsowhiGlKiTSdn74e80cB50YLsZ7JBdys20rfV83eh4lwiumAHBWniGiiCitelDV9WVr/i+/JMnrZ36r40RS6vqRdzmeWgP3q+wRwAB08ioCxktQ1Rw6FkOl0Jz5kER78WwY3c/AzwzwxzROEyO1OQEtjLSHIWtBGC/hIewtEjqcDb9++ZRxH9vsrbq5uJVGsSBHi4J/WW9N1RCJunjkeD2hjaNsOlC5FawXYa5SWvSpHTTVNU4JmjsMo0ci2EavI29fMb1/Dj34C2hQevlZQJAHrCVCqlJLi0/LjCkcswKYKLkmDv8zEElcainlzmff3BTfwHhqcP0vyXG2QT3REybEhRt5++5pv//gnOJzoo6bViyNXDp0LIYjzVsodkKOOsj1Tobi+veXjjz/m9vaWq+s9/a7HWIMPkdkJ0DidTpiUByATmWkQh0jv131PfdRaS8RVlhi9x1qDbyxd14K6wlhD8I5hkPof4zhKSQY3czxKqOrzuzuurq5QWlTlVngyQzCY24/58b/9P9jdPRdrdrVIs8R8aUyfBjTnvy8mtkRkKzCyENCiBVDUzCKDoW0/1v37S4DYh8nIL7WyOGsbcr03PSF4P775PdGqDSJyTgulT/UnVf2g3r0+80bUXe25/ugl3332W2YXUQ3F5KrRhOhRaFqbsopriziZqmUdRfEHMJhyL0cEZQlzLlArAsvsXdLaZNX80rfsD6e0Fn+HlAIfrVbPY4wlesc4TSkoQK6ltcZnpq9E0k3xBPgc6ZieLSZAJDl5HLu+xWqFe2TeLs7BUwC2xvOxBsGXDy/3/DO1Gv+rW2YfusDNFPp/YZ/Im95KO/A9nmur/SkagM0xW6vB4x1f96HwtQDaRXYhEpQiao1n0RbmezRWEscqIljNw6uZ4TgxDgP7fsf19bXsD3kPQ6II8621llQKsg6k1M80zcyT4+7uWfFpK9pNAGswSjEAs/ccTic8SISx0eJXltw6iJGYNJfWGmx67ehxPuCGkdkH5iBVxd3bV+gYCLq9JM2uxusxTdzKD7F8T3mV8av2gO1moNRiqmItTHwfcAPvU2xzTYnlAd6HIZ8jZ7XI+0l610T8MPD6j3/g8OXnNG6mQ6UohsoUkJCzT385b0YGOkpprq/2/ODjj3nx4oVkKW4btJYaNNpElNHM88zxcERrw/VNckZMEqL4BwSij1V/xYafbaBRqTIOoWnw3i+qRyV21fkwEyLiB+Q94zwzjCN933N9c4NpbJphQbK6a7Hs+NG//T/54S/+HdbsRY0PlbRdA4zLAHNRcaszmslD/ui01ZhkNW/LBlkDmMfAzTkN1O/V00ycxyXhD66pNW0uEuUSLp7nYXtM/XnLJB5rl1T3iwFK/un8+2rQl/4++TjVMfm9bVru/uan/PGTW07Da7Qx2BAxOmUbTqUZlELySilD8EsfQ6qno9XCnIV3iHnZJwYcQizh3qfTkRgDvdrRtJVJFooZSXx3ZMMQ85JENxFjCj2Xiso5pFb4WGKuKeFm9lGILkW7pHDekELgtdYYwASJYCHk7e0MPn7/tgK9lzeKf2lNkfhL9WTZZLXSVcVY6MtculDVLvnmrX4HmZOEsIpM8SitnxtP8rek8zSgYyT6gLUdtukxxiYhuBUNIIsQEJwneo/Vmq5t6fuOt/cKH4Ik9mskx5mPAaMiaqOFVyq5OdCjtOI0nDidBl69+Q7nvAjqV2KFyNFWMQRUB50xIuB7LwkvG6kaXgJd0p6mjIZZntQaTWMMEUWfKozP00yMkftx4s3DCR9IOXae1rSda2a2B6S/2hz1GG1v95M8N3Ft3sr3zd+9C+w8rcHZgJsnd6dHWr0ZZsLOmpFIRHvP8dtvePPp7+DhG1rlyiToGNFBiHJ24rg1O/kTYCNFK50P7PZ77u7uxOfGpky/KvExpYRI01gMw8DhqOl3PbppNj4U5yL3os1IOUBUtjc36GkSVbhRhBiYZ8cwjlJvyjl88AzzjFKK6+vr5C0vC8AFz4BnNIrrH7zkp7/4O3a7Xblnfe+4AYv5fY2OgerzpSlTjxJjYUKqksJq0PO/o/0LQDjvktYzuMlmwKzxewzMfB9ppF5HWfOWYiWE1muBYDNxK2ZQaxCWi5d75E1CNQ03P/oR/csf8PqPb+mQTNshyqYlOcVUMfFonXzWYkoA6OaVJosIxsjvSoGNBueliKBPHZvnufTVGCN+bzEWQrbWlkKEeZPJzy0hpKC0Kb8559A6gc30nDGZmrRShNR/P4t/nkc0TCEEjDZcdT0S+uuRGBVdtC5l+LY0e2FKz7SuZR7fD9zU0u+H3lbPkQViFoCTw6a1WhxLt2P4rnXxfdjE5ZFf30dVfzqKaQTT4Hd3eJaCrTELaTEJ3clcGkJAK+i7jr7vmaYJ0zT4GJj9jG0txlisNdU6TNpYLYWbWyUmbx/heDjy9uG+OOLv97ticYhao3QjKRlixDiHmyamtM90XYcOXrSbipJF31pL27YigqWMy845Ruc5BcdsDK+PA/M00fb7dP77Ed2jVh1VO9C/Dyhab1qXjvs+flpPApytnfkvbqq+mmgv3Dzz5Wef8eqrL9BuxFglWpeUnjyDjxxS6pxjcrO8zo7jMBCA3dVV8ghXoEEbhbYmZXMURItSNE3DOA4cHt7Sdw3q6lrupVQKfZXxzRkoQwxLHhHWCFyTa9hUzrTJNnoaRtHmOIeLgdtnz7i5uZHqx0nz5ELghOekIz/48Q94+ZMfY60tTEGGTJadSmjtMS3A5Vfpcxr6d+LTCAvISLvmucyap/KSFkdtXpez86HrfqwQ9Hv380NpuSRDsQ+vqDsjkLhKkJnbOxf6heO3wkKs5knuVQkSsAw6TzODwty386c118+e8/LHP+HV57/DB49HEmqGEIrZSGupE6WRgnpEUur4ViKXWIBzjmKEnM1YE0rdHTEXTdNEJGJSvqssWefMrjpfKwkxWYhXaU5IKnqlVNL0ylNm0FPWlpYQW621mCGQcimjc4yzwwHRWEy/RzdthjfEHPO+GuyqbRbNCgDneUdA6GNS7UXp9INdF4+YKliEppWZKsbyXdzykOqYM1BYCW0q8eMM5B8DMe9jqspgVeV7JgFWW4tvdwy6YRdcqoumio9Y1pZHKBqTrm3Z7XYM04S2jeQ1I2Xi1uKb4pEyJpmOFbJuTNQp5cgSjXUaTvhvPbO74/b2Bts0aRCWZ7KpaOc8jkVAaJqGPpmmVC4V4dskiGgYkIKgXS+VAYJHh8Dh/jv8OGB1Fjqqibkg8D7Gv7Ygtwbzl9ZD/f4xsPTn+DG+dlF5OAAAIABJREFUU4MT38Phsxa83xcO5c33/v6ezz/7jGk4cZWIx5hUAiABgeLYW90xxMgwDByPJ2KMJQlfRtBt29KkcgyoivknyfA0HHj79juMUey6/VKED0U0muDBq4iPErERYkhAKEmtMWIiWDRRW3zwuCAptQkwDiOH4xEfA23XcbXbsev7VABQjp1C5BQ19vY5P/mHf83+9g6ldJL4l2ctZofqGZbF/r6bZJKVLoLmTbTOZmNcGI2qfyq/X/q8fL2OzFlrnGL1e9ZAveMx/sptZQ/O1K5ixfCqVi+MC+37gpwV098CzMyk6++yhLg6TK0WaayYOrCaK4Cm7fjBj/+Gz++e495+hfv/uXvzLjly5E7wZwDcPSIPMsniVcW6ulszPa2VNO/p7ff/ADv7dvftm9WsRtJoWuqj+ihWkUxmRrgDsP3DzAC4h0dksrq3lS3wJSPCDzgch9nPDpipidgAiQX/Y7DGutEe8g7BdeCYgNyCCoJjZ69QzL8AAGUGZfvszQ1MhU8ETYYpPgTMLP4GZe4YI2vs9yqhmlDSdnEzw1WYUedYBuKYcXMb8SFmxMxAtwVCKI7SoPnzSl8uxuuALh4VPv48zVJ3lRY4H7wfs/roFLdgOyz3tjRo9knlunIJV27f6lHl1GH4A/1SNNRLnmU+WN47wBEmBVNB56BopGoYEHtu8U8LHs4HSY8QcxHOaU8az0Z2NTnnJUupr20L3gNmOiKJ7xRzwrsP14ic8OjyEkM/HNBd7z3QD5hG2dmVUoLzsgGGSAB/UIdoToyRJgRyQNfjbNjgdhqxQQLvrjHdvIPLL5B8qAJUQyMOxnkBTKzfl+PYHl/edwwkrd13l+myLXdvE7/nursvsJEqhbHlacKb3/4Gb7/7Fp6AoInuzC8GgO52ELu9PKiqxGOMuLm9rVlbW0ZNVYpbNtR5sS/e3t5is9miC7IdvbywSXkm8RkjbpeY8mcHgicnZJ1VRU4OKWfs9vsCuhw5TPsRcA4pM8YYsYsZN8nh6rPXePb6RwjDOSyxXmmuihclEHrD2FqQczgalbRS6fMqAR1ed1hWCcPJcnjNoVbnmM/BvC33etxDKATJEj4jrY0fjo7fsYV+CP7u9+LHhQo+aMWcNSzquUPCJedw+eQpnjx/iW/ev0HMETFlkMtwQSVxJ4IQmyIz6zxlB6i7CzNrtmRptZh4XXmw199E4tOTc8Z+3MMFhw1Jol2na9d7J353qH4e837l5rs0iJWbtTzR/IESMxIYiVCCc8pmMZKccpCt7BL8kA80NLMxWetIrudR5sX83HJM/pwBzxKczIC34kMCdIed0bdyeqZENmmP1vq8WQAzwIPZj9WFIkqQuUBHjRaUHRA8o3d7eBolMrMG/WPSXbeQOZQyYzeN2Ef545wAaJybacLN7R4EYNCEs9QBgAOCeM4JwBZeYylJek0x4kiihmfOuP7wAcyMy/MLbLfbBqyJ+cw5ibg9jSNSjNjvdhLTqQsCnMhSnki07ug0IW7fYxM6hHHEzds32H1439DkigBP0aelJmY+TEaT5vWcAjvH1sDHCITAPePgrFU1e8gscVw9f6pCYiC/fYff/9PfA+++wbkfsXUOXRANTgihqLUoM8h5kMsSuro8H5jGCLfxcARYThDJOSOrSOLX+IaQU9H23O5u8eH6Gpt+U7IRswKGsmuLs2QoB4qjMSDPYo3NIQGWLIlaBWYAsD07w8XlJQDg/fv3yETwui1vjAkuDPji65/g0dUzOE302fZhW2ftVwM5BvwaMFPFGoAZlfXWsRRED3DmIlVWMamRqgp6L9XPx3gO+Y6OfasBajU6QMvogTYnzcdB5j9dme9AQukvoalzcENsfYnZGC7LfWzKh+aK+U4RJcto2QPNCAsDjSl1Ufnx4wR0j5/g6uuf4Fe/+gXG9xMCMuASvEqw5mMBqImKZHuArAtZvybFZs7ixAmb3644D4OgIEl2PKYkKvri56N544rzZCvQNG0upgOuIQicaXFK/8l12aTx5n2dp5KLriNC7wCwgCRu+nfeV/LfjK/ywjy5puBTiWONwC/LQwc9a0yr5G0kMc8ABlwU3LBFxBWUU82+jEwW1nQdNbKCIzJUuRyWlnat1NNSmxkj13MhOAxe3CVyTnW3HVm7dVPJJJnnbfNLyknDHgB912E39OiDQ/ASCdnSCgnOpZJD1er15NCHDilExCkKwCOJ3XS72xUTcd9LPkXnJbYOESG4Ho4IE4mf57S7BXYsGQF8KJHBJX6bpGQBgPNxxNk44vfX1/iXX/wLXvz0r2Udl6Cw9xO+1sxMhVfZOy/A0DHT06nr7tue0z44c1h84mVQRBeZzPX8QZ2oE/z62zd4++tfwsdrDB7oQ0AIVAAOAKSUIeG9WSTCJNKV5ZABA31wGPogodehCyllcMyAyyDNdsoaBNg5j34YsB9H3Ox2ON9J7IGyDY/QqLktRw6J5kffWTIoc2PHr8SWSGz/m80GT588wcXlJZgZ+3HElBKclw2n0zTh6sUzvHz5CiEE5IZALom3yagGcOZaGDs31840MEL+p8r2ykCoOMWMsuWWVWoqBIKoOJyRo0ZT1mIf+0GlLfO5YsCwtqc52xyba5weYmml0RljYwE3lvKujfrddsfagr6PVHIAHFnt90fXKB353o5X8ypU9T31k+C3Z3jy+Vd4/PI1Pnx4j5T3Am4UtKSUSlJYcg6OZSu2zRGQ+OE4L5G5ZdNUKs80gEOO4IJuZ508YooYxwk3uBGw1A9l7jkzF6tUaoIBNQAzgctuS9Zkva320/r7wAfACcDpyGHoOmyHvtSMRsqfAf8GbNYlqD3I1jLG4dxfJ/JlXD5Cq/cQylJ4EZCjghThgEmJsEuF5hbQ1yyxmS9oy5PIfuuPmZxm9GkBgpdr9ogglRnIieC5g889nIUoSQmWTb7V1nnnEHwAd4y+m9AFj7fvb7AbbsGPzsXBt+slQWcI8M5rdH6NHk+y1rJqIL3z6LseOYkgbXmjjDbe7m4R44RhGNAPTrKS2+s5ibjPMWoOtgl5mjA5h+C8aJyZEXQ3Vew79JsB/W0A7Se8/e5bTLcfMAxnVctVB229z2YyBtXrZmM+9zm7S2szt1Ycgpv7AP6TACdrx55y7jEBtp6bL+GliQJ6eRxH/Oabb3B9/Q4b5xC87LAwZy2T0pwj2UaqxNc0K3GagJxxcT7gyZPHePzoEc63W3ShQ991Yu4igssAESO7hrkSRBr0XgLw3d7gYrxAPwzSPkeAdyD2oCCZkM0TPedqtmJng+bgKMBZNmTtiMvLSzx58gT90GO338MREKcJaZrAcIhw+MmPfownz55VAtkAG2MYOqplcOdjIZqPOnEaKXIpTZpmwc7Nxo0Ovq9Jx8upPdfhkEobtW21IRX4zCd8g/RxqHF6cGUBQG0R26syGK6AnLk+6pSm5r4L9mBM5s2a198w3pYZz7VQs0lSge2sPofLR4/x+usf4Z+/+Vek93ukaPFrZLegbYctc0vrYQKIJQAZF3DOunmgpgSV26iAECICRYeYE8ZxxH6/lzQQBlK0z53+R7lqkohKRrbyZ+CiKJ1VSDEANF8MDHaE5D3680cYzh5BXai1b2jex2VuNyCHZyM/H6wHPcE/vhwFYWSrmUTDTphpqGUe0EyLY9qbcg1VZ2WCOiVbtM1GG9yuCxN8sVgrBKo+pTrX56Sy5VEEZgePHp47EE8SPiQmoaAhwJErW7I9ewSdZ5thg4vzc9zcjMgpoVcAMWw2ElXfubo2iEooA4H8uUTtDl6C86X9HpxzuZe0/UlDIQAO3bApUeVLUMzMcJ7BWeKzxSli4hFek3BakEFzQh76Hv1uj+tvf4fp5h22j58iUVdA5KqZsB1umPatDD/mJkhuzt3DOXnxe02Lc1c5rcFpbNvHSyXtc2R8utx8+IDf/eabklOq60Rt5huAA6Ak0oMS0t1+h2k/YooRm+0Gl1eP8FiD5236QTIB606LoiZfXXuVkO92knU8dJ1Iw4s/ZpbnNSCDwWAPoXks0mFgwOkuKO89ttstNptBAg2miC4EMBI+7CNGt8Hmxad48eOfwZ9dIi6ADdrvMKaky/wAAaMQiDImjGYxr0ycph+OqQmxuEZ/1Ha2hH3GGCtRL5LUChI/eF459XA5gEhcFbLVHCn6pShtGGVg7N4TIOa+Wpw1ZtKaaxYUezEv0Eiwq404+tzQ9Xj1xZd48w/P8bvvfg9HAm4MIBjQMYJPFgkYKEzMAWAnu5Zsh4Y5a87em0yr49HnDh9ubzCOe0x9B+o6SFJH3dHSzMtD866o2Is/HSr4EDU/FzNVUtPCFCPGGHHrInZdAF1cAd0WRF6Db1aQXsb8SLeRMfjZkCpjbr4/3Nn+B5Tm3Qnm0FslbxtvySGoNHpl/rGCUtNwlOqPPbZdACtzfSaiVZyEdulY/VwCQmZ4EqCfM2s0/QQDupJVnOEhaUr60OFsu8Gjiw26vpM4aRaEz1wcYGZR1tyIAEvytgL8TLMZNGK/dJHRXoB03d3ubpByxjD06DvZrQUI6GLOQJCI3xkR05TkyRklxQrnBB8CNv2AM7rG/v33+O63v8bj55/BdU7DL1Ah6zZM3HaWjvks5MpiYkuf0oGwt2aOAhZ0rbnnlHlrWe7U4Cwn5MEDGmDTCOoniXBOCbsP19i9+x6DY3jPxXFYZppTdE1KzKi0J6esMSoczs7OcHF5gbPzc3ShQ+cDOvW3cUR1+zcZIdX3IAFO3nsEHxBjxO3tLbZbDb0NqDqQkUJAnCaM4ySe6a6GpxLNs042jWUwDAN8CAWkHUoYjDFl3DLw+vMfYbh6juSCXtNcp31QSWFR4LZPh7HcGvqa62HUS8p7twCp1DoHM217Z0CmqbYFN4cApwKbw8a0z+GifQIzZi44D7W0Erwt1oUWB6iMdEa0T4CTWv1pIDhvSr2vSEjGTJrmtnT+mPbo5HMYIOdx+fQZHr/6DL/653+ASzv0GrbBYnXEGBFCJ+HhgTLfJIUTITMkJk3jE9f2Gpf3lpkenEPuPMLkME173O4cvDvXOZzLdWUNtgSP53N4NjomoGQFNjFpwtuMxAJ0Uo7Imol56Prm1kPiSrRgnLMXWvRlc2ptHB66n82x0jrZyyfKt5KVmqimMrH7jLcoCC4Mr62cGt+pBb2yh3HxXK50DXxCu3SiGB3uvUPvCR1FIE/I3ImfDYvPpb2tD16an2VzSYI4B19cnOPs4lz4St9JwD2qa1WmOmu6EFfmsHNO3CIgpq8uBOzHUcBgw9/srXLO2O9vAY7wpLHdCDV2DyDBKsEIEBeOnDJYBZSovqbOETZ9hylO+HB9LWERKKte3VUtm/HyMtBzmnWMYzUjU+fDPQTAteP38VsEdGP0sXLqgfMDmNGP1cW+aNx4/R704T16HhFcAjs1IzmSP0ikUXIBRA6cGA4OfTdgO5zhbHuBs+05hmGDvh9KDpsCxcmkhuMqfSIBJQBKwktbZBY50tJFmGmsvEPTF87Va0MnasU2omQLGrz38MHj0dUVvvrRj7E5OyttXP4VRio3n+zTMq2WDBRcFv98XEwKqO+xvJeaRUTLUaXDv3nbUdp/VBuE1py2eP875tC/XWlMcIvfQO0zgZ31z+bbso/WwM3y+7Jwe55k508mkQizEtDW77JtbWnjoo+X7Zm1BQDBg8IWV599ieH5K+zgMKWkCTRT0ebknMpuKSLxWyPfamUFvBcghyqZm3M/Ww4gRwhONh1IGPtbiZHTSnKr807XmvpFBN+JzwNRHQtumKtUBmE6jJQiKIvU3geHLqh9u9XgHtDFFkChbik/NYhHytrYP2TgY9sc2u0OgFIYar+fWNdcAYn8xqIfDxnaUiBexZg2ZuDKWunw/KxOAECSbeE+IFEozrjiJ6PXOom15rsAr8mYqdnVtxkGnKnbhHeynTCxCts6h0z7yZag1uotwTNr7KcUY9vS8s4uSJYqM+VO0yQaU0LJ5ei8h/OdWClCEBcNR8XdI44T4pQQmXA7Rfz2zXfYT7EZmzkQKf24Oh61ieUyzDX57T0fD0Dvf8+9km0uv8+umWGyxaQ+0gjOGTxN8DnJ1k+Ndiiw0yasA9SBlyBOhUO/wRAGsKrXyBPAhBSz1OFrK4SxO1CQgXQ5ASwSGtnigUwAhCBRhcc9tmdbOWbgyHv4rkMGMMUkHusNiLLcNsRUUb3G8xFwU01kBoZC5/Dys9f47PVrkEbHLPUUrZODaVta1AysL8o1Zy1SiF2uL3B6OWrzuuZjVxn2AYgik5wOmXxl/Av0q6yfyBhKXSOz7bMPteg7tWucy3EUQmC7R9o/ALMxXI7nco2VsQRqZnmas4iZkoBK8+a/m2e3Y7dkNnTkE5B4ycyEx5+8wIvXX+Dn3/4KKU1Fg2PmKQMpjutYUgn77pCj5HqCk3hPEqic2kkAMyYZSBp60aDEKLuqOHMROlrNsANXZ3giABKAMGdhItA1IeetfdV3J2uQwXE/IucEFxzOz7YonHYJZFugeGTelvlxz3JKi/NQQU4x/qkW1vqjdBktHO7X5rlqcAqoKZNwTofKscJcW8pU23N4tD13nMjY+mEA2RNos8XUncGnncwp3dHnu9Ck92F4IqScwCy7qYgIIXQILiCnjP04ISQDHfJ8C+oqDI1KlHxZHnWnoIGcpECkH3o1Q1XNmQ9B8yBKDJyzs3PYxg7S9UZKSJwD2Eu/+5BBKYITI06MKUr6iZvvfo/b9++wOTsvPTfTBTfgZknTWH4ULmN67sKGjvCxg7GgufXoY7TbVu4EOGsPPN2q03VZg23nkTMkaouCdIeEqy9HJOGnNwPJXEiyHTyx5KCymBjiFOzrrhJCM2kEDNVOF+91IpmsmRm7cY8xThj6oYCbMGyQyWHc7zHGiKCBk+C8RjJWAJOsX3TCKXKWvDtKzMGIcOjOzvD09ecIZ+dIzjJutf1gU+IQvNxV5tdJLYeMFEXdSPrjfnWvg9ZDcFN+naqtaUtD7MhkrYdJzAtZYWWSOHzLtn+BY2aNY0DHvtd6rIMz1lSu68zVQCawpplrXsYYPuZjd/BOAOAczi4e4fUXX+LX//BfMb3/PXLuDrU4mjiQWFKYmCYWGeBJ/BeYSICP+jKUNapPkzAMAva7INFbzQy24x2GfkAXOqSc4HwF074AGHVOzXXeGpCpqvRKdIt2BraDBuDQIVEAeycZmtt+WXbQjCljHdU0x4+t52PHPlbK/ZOWhtxUNkh1alrfFDC74AcQ52OnccSWfdtqkpujmF+6tgjqcz5KI8w6H/uAcL4Fbc+Rp2s4FxE8yXxrcz0Bkqg1hJISwZHH0G/Q+QGcgBwTIqOmB9HmiJuvKwCElG5Y3YDmYOs6ieQ/TQjegywIH1yzhgM4JQ2VslPHfwMXVaoXExgjOwcmB3by/M57DC6gQ8Ttm99j/PAWnF4Atrv4jj5seevHUu9TfO4UHf2DfHAse2+rmsVaA05JMGsMUf9jADFnhALuK0FuX8LSvINRAI5zDjHH6l/DGTFKXJiu68qW75zzHOQs2kZKaDkljKPE0LGYOABAzokne86YkqRe6LpOJrjG8JBFHOWdNPlgUMfiaZrQ+0FVkYx9DvCPn+H886+wDz08eWUu7dZa2+yK1UE/Ngnm15zWkB+nwk1RClIl4oacNBKs/V62Z+34WpvvC94eQnH3ZDbHwExbRPNDzTjNx2Su3VofsVNNofKfMfn6u+Bp4GDprr4fMQAB/M9efYYnL17hzfs3mFIS35UkGcFjnPQ5DvABHtDI36LFcd4jJw+iJiEguPj7kfZHEW4g2s0QAkIIiByRkzo4s3wGS6i5eBtjrlWDSYCaD0TB1DBZZmQmZHiMCRgTods8xsWTl+CuAzvMlDiE9XlwbFXxfS76cy2t5gYVx0jRl7UJ7IzKayLOpg9bbefatG67rWgGVoCmMPs2VXNbx5wuVS2cvQoXYZiJMGwH+EAIAQieJGEmhM84FwqQpix52vquB20JgQK6IJpHMTn56mCsyDuqeSp0IjQ772uQzCbCt/Gy1PfY7XaYpqm4TkjcKe0z3Z2FnEoIBxEWaly2Zt8JMiS4JTS+W9d7bMcOt3GCGydM1+/BKYF8Xwa09d9s+7HUagNo/d3Q+Pb+9tyxcpc/zn2A/0mAY/4js8c0ks59ihBZKp8AAO/hhh5T34OTQ6/T1UwU9um8SnA+yJbOlMAk0R6dc/BZ/AAkymS1aZamGnJlwrF+KBMLQE4J435E2qYShwdAcUiOqooPitRNBQhNJ2Ghu63Tx3HEftzDd5IBeT9OiJnx6sUrXH7yDOy9SgtOnaxdqXMN3NzHZFjPzUag9Iidq07JcvzY4LV3m5R8OKmoOX8a1Jxqu9X/51BacGBAZdbTzTzIzZiV8+2fOl7eCzhhPlq0+L/xtYQr3JgFcDSg5lQ5dHw2ppMBcugvrvD8y/+A7371C4zjB0wpIeQMnxJSipJqhUVdn3MGey7mJu89svdA45eTIdI7LJoxcgE59nynIIeZkXWLuvn82KaDmQkOdr/8FQbWjICBPquLGUjkMbHE3xqcx6OLx+h8h9H6oNFWLHoNpYMb85wp5VbB7b+TUkxMQJl8M4VWO+m4KEhmUlil3Y2OkuYO83Y/SlbuRRvKBL97ki/neNte06R6JmwdYesmRJ5015NtIGl9SjR2Wwb60CO4IJ9eNqxkFiGbG42vBesjIsQ4gcHwCuKJAFJAxCzbuB1E0I+aJNPu9eqXMaOd5GbmWwM5+nKLtaVJctX01nUB/dThJmbspwyGuUpIL815UBmo8mnGyI8RWo8Juqd+39dc9VEmKj0ILBtjVHdxeSMszupyziEMAzD0yDsn6ugC/hgZjOBJNSkOBAdkGWjkXGJeuERwTnZy5JzU3q7IlcTB0eCY7XTBivlD7KUB2Ym5a5qmGvQPM3qlv1m0N43PgS3Q1m4aNU5B6DrJhD5NCN0Wz1++xGZ7ViafLJSqwcHiWdbGY34b9nt9UrWkXQZKhvC0GFnaQVXKsbY2o3rwN2eMxwnN+qQ+3aaHUNber0CbhqlZUDMWYbCcFhLQFFPTCKJdHUM68QsGaIg0Sukc5ABQH6+PeskyW2aaOJPUuy2uPvsKmyfPsf/mWjQ4GhPHzFS+ZVyZwZRBEGf85DzYhxqdXHuFSHZjIeWaoLEh3E61pkx1nRMIwaci9CwBjvWHLf9M9RxgQRI1Q7pQfom7xYzzocd26NF5j4hUnGjJOh5chRwsHGgboNMOZ6U9tX9OlXaNPFQzlUQoZrO31ELHZy4Xbd36+7d5oopIzfVk8b1au3fFwmAaHyauW57vKI4CtsMWXb/BnjyyhjgwzYplRAeJJiUwoR/kWBc6mavaNWR52lh2AqfSb1AftgS4EZvNIBYL1eAUkGNanCRxoXLOwqN47m9UFCgNL2oo1IFWvxVamRjsGbEj7Jjx4WZX6rG65R5bRJj34wr5Zj3O5dpqmfihWvuPAU93OxkvKiSghhcveK29SV9BEfoqw3Ou5OVIOOx0p066/dCDIIH2mMVu74jU7EjwYDCrDZEZmVNxKHTBl2BfJiyUBykhI9QssM5McczY78WhrO8H8bUhhs8BiWIBP0G3k9uUyjnpdtms7yCLYL8f4bs9ppiQyeHZq0/xfBa5+PDPyhLEzPvIHYCqY7bLFtwYmidqCeYpUNKeQ9NOObfW7mU9a++0LLX9D5OIz4qBSv3arIySNbvoDkyCV7GWbD6iriXpSi7rZZWZMcRvbYU2U70d6yT/D3jPhSYHLLs2rj55gacvP8M3v/tXTJNEHPZgTCHq+shlPYlGltX5PsAjgznBxQSXAVAqOlzLUSXAIRfpMetqdt6BMyNOItg4DaPP7A8EAPOPcAtYUYm97vpiiW2SokSqNbq37QN655BjKgkR56WRLst/dS6fJMGNFH2UyTfnH7IZ14AhZQB+HdS05BdkDuGu3i/fRNg10Misgf5aUGKSMJW11QLxZWlWKg6+rmkHitACZOfgzi7gNheI1CFSQiLAN5YG0zkRxNWh11QMEuFeQK9X7YgFxrRs4iCN6K3WCQlXIC4QQxyw2W7KLl+zDPR9pw7zWeLnnBBqRfgW53pHrYmshkyxeyUWVELKE4CIaYz48O4NeL+D64aSGLV23zGQUwGMsRwud96/lDX0BwAh4K5Af0s1kEkl1gBFxfISh4GvynsvJp8jQt/36LuAnU7qZvaL97nFkiFzGiZMqilpk2qaBiVzLiGoW63KQWma0iLSUl8jicrYCZH3ugXcEpjJAHIhVDFOiFFi5QCC4L0PmDLjZsqYEuOGAp6//AKbq0+goZ1kYTTakkp9rd943tgfVObQeqbBMUlpIVG1tul1EHPol7P65CPAZymZ/rmAG5NlTCtiEooUAzntwUZzhgpAZiCnodRtP89AItBoZg776dh9s+Ytrj/1e/X6Zo0O2y2ef/oa7/7lCvu338r894SUInIOyDmCc0JmB0B8ZCgQQF5ickxet6qqVlZ3Toqw4QHK5i4jxLHRuLCmC4ES8BQTOMyJYVn7zacwYgOeTb658qCsmzgdxszozs4xXFwg5koLq42qtq2Mh4HZuwgyf/xyftAaHOaSOLMVhIF2XjXIXIGEA9QsmcoltsGgneNFK1EKFazTPqxqVPW3rZe1bruD3BTa32/QnT8ChQGJbjFxhGZEg9OdoIV+MRdfMbtfDTwAMAsbklICCIhKF2StAEljS03ThJQTtmdnJUUDCOi6gJwCpikCnMV5v+0KFXIYpOA9i6bIUTEjtsCh5Z/WIR0YPTHGD2+Rpx06XFbQSWKmnvVp25fUjlWDcJqLTWlibfn/s9wr2SZQ27jU6FhZJZhrM0uP+WGA254haQAwSTUv9zkjfHq9axyurNhvCb6n+W+yEPmUM/I4aYZVV0BEywRMarOM42TaIWgG8xSLBEhK9LpSRQvwAAAgAElEQVQQEL3EHCgqbVt+zXciKg5fY8oSeTkldBdP8OTLr0GbLTSnIBoNd12RxuyYAHawoGbFp+gehQqhPSQPiysX9y3GbEWjUzU4R4hHU9eS6d4XkT9QWo5MRroq6G+GrBC2dYZUF3sFOLoFu63vQHsz10ycZHQrxL4F8geX39HR5XkLadf7gKsXn+Lq5ef45bu32MeEzkekJGHhc4rgHMHZ6715/o662yTHKFFhnfSGY1XLZwteWcM6qCoHDg6dRmflVCMqE1H5tLbnBTG3NiwlRJNgpxSxA+PGdaCrZ8jbM0SxL1RtQMEzXMxf8lH92pYAayZizOWNfzeFscB/zGXbfktmiGrOpcKQ+XDNU1NPjWTcgpvFWmgqcWzM3hrWNBKHD1s1BRKhPzvDxdVTvCFJfZCREOFA8CKikmwqUSahcXE6APqOOYt7BVFxe7DqZfcfK6/xBcgnDVcgiTszNtsNQvCljq7rDkIkFCfi8p/TrOdqeuLqTyOXWKoSLjQLJAALUwTFiPffvcE47tA7XuwiXE5eraEVWG08Vq68qywF7bs0nafKyUB/y4ceHFuDwLS4tpksBSQRAWdn8JePkahHSg4xMRKLrdEFL6pOT4CD2P09oRs6hF4CFUFj1ZiDlHMVoHDOmKZRJ0nCWmkZLVG13xu4mqaImCzQkancRJPDmasmR+srgf5C3cFFIKSYiuf7k08+wYtXn6Lr+gZ0VYB1itlUowdwaprMzU5r9yiR0HFqNQJLs9KhBmdOF5Yg56ia+J6MdWn+eohFelTZZHlxFLKx2peo59vXEjKJA7PTEhSu9RVzbQuaNrUjXsfwhxmu1jWgpAAl4PLZKzz70c+QNo9xEzPGFJFSLP44AnTq1vGk521no7MEgk7jXrWCCHTbrEHATODIQBIybSldZlrc5m8OahZSa87FLDCNo/rHqeQcJyTOGC4v8ejpM/HHOVJmfcor4Maua0FtM2f+3ZQGLIKh2pw5Uyo0VgU/kgBoizWPck8x8ZmQeeTRM/MWUMBwOwLUHNdL7w0wXQh48vIVzoYzcAQyS7ybYiFgRtIdtN3QoRs6CfwXxN9MQEbTFo143/eSUqhTjc8wDOi7HsGHkptqmkbc3t7g5uYD9uO+xNmxuDgm+DoiBKchElDnmXOyEcZZXLemL42WVx5QxxIMeHb48GHC9c0IlbOXPT/7WrjOHTzgYynRKVBzF88EPkKD01ZaUdrxoEl3SZmhH3D5+Aq/Cx0wNVEwnasU3ykoIpEEnPfisGgLiM1fxyFr7g5KppYTkDNNoWzDK49vd4dl1ng2FeL74MXGP03iLOZ9WTQEIDFjv9/DOYdhGCA2TplILeE1SSXmDHIez56/wLDZ6rNrvB/Sz7rKq9e69KUd05l0DwfhuX25gun6/XgdM1XvbByXYzqX6te+f1y5H4j7tywnwYd2qZhtjdjLidnbVNEUAKlzcK3nlJbGQI2tP4CQjbBRlZ5aorUc6ZPPWoJcLEfd/PKAbnOGq5ef4fLpc7z5xdvibBw1c7EPHTryCMlAf0JRWRLEl84TyGv7Mqm2ytpIRdh25GbzsXX2XPqjmdbWOqw1WSdto2Rp5gLABPgwiDOIGZs+4PL8DN4t4q1w7f86wPasuaeBXGG9f3rN2lgcO76mNX9IJUNCKCwNTEvhzaEMc7nGWfRfLroEAbdZ/LZQ6lp7skJ7Lrz5JBOdg9IqIBxbE0SExy9e4er1V/j2779DziOSi8ickLLXeG0JYN0a3ndiYs01jk3mMomLc3U7d10BKzpDSL7FOIFzwu3uFlOccLY9wzAMxaE+Z8nJFhyB2ZVdvK2A450DcwUXTtev8B9J8Gnj451EO06YkBMjJAJlwJNs1pHEudbnlkLpblrfgqfSuntO5T903t8L4MxATfuJurXb1jAZPEQlkGszkwi4evwYQ9+DbzI4z/1nSi6nElwdgHqlMyth4qyTRQYy2XY3fUacJMdU13fwwTfPrmDCfHrE4z3LazgHJiBOe+Q+oAuDINhMxRafYsTu9gYOjEAKvFTlRyyTgiATaJcZ54+v8PJHf4EwbCRKMZkEYyCnbVMFIi04Ae5DJme9rPcvTRwL7SHNx3h2v43jCW3C7In3vK69vrap6iB+MEb6Uxcdo3YRk0mvcqCQ/MP9e3YLLcanOqkSqvZQLlbHS50u4nvJDUBvMDAqa73zNVqi3tRzWlBxuHh8hc++/Bofvv0VpvG95HSKUXZKThOc7yS8A2eAI5wLCIGQiBBjBjzggpNEllleqvxTkGNrOmevvgJzxmkmtEKTchaDmAGbmVZHfnNzPcF2skSkaUKOGdvg8eTxIwQizHXAi/5gyM4cA7HcnFjcdTD2zcGWiC+/t1qQh1hmihFDv4Dh09kcXAfN6lullZBpgRwqEVzcIyDIzjVMvbnKhAFu7oEJE6VRRwSJps83l5f49C//Cte//lfs3/9awAESQBGUCd6Tzq/GbOQVwTsgd16i3DcNqSAeoEzFuZiIEDghc4eYOjAz9pOkYBC+KFu55dYMaM4q4x9l9yGA4DwkvD+BWWJV1feC8r35/CbfgV0AeASNNxjfvUPPMjYR3Ixl7V2ygV7StwJijQZWANpuZFsCmPsCmvtcd3oXVW3nHIUtNTgFjrdom8r1s9+wOUs4Pz9H6DrcpIQe7bbsutWa0ORzYgacR04JEQCKH0yQZyuTTCkhExCjbNPe7fYYNpsSwZGaoGJLzUNFv1CinFFwp4E34TpI04Q9ERCGEgabkwEcBUJg3MLhsy/+AmcvPgf7TiXgxjx1sORtEla1d2kfrzN/61Prv/oO6yNLdHpyLNXGVl891/YfaZ0/jAAfb+fDK2SsjG33hMXfKBfUmCBAXSNN15xcmMasy/1NTdRMQVqsy0UppKjV+t0P6awKJOtNJfSbM7z87DW++edP8P43smU8FxOVmKlSmpCT+CxQJ9pMkEPyYqooQc30jzmLskezgc80NQuTEQEV3BVgqSu2MZXIaS6MKGk7jfDmnDFFDVrIjO3lJbaX5/DBNYx72cfQudAIegsGe3BD026hKVRB7GwYfhjR/7csxVcCqFqcNZCDFdqCJtaKXcmY32x0DQJuClBZxjlrvufFoVMze01zyizhSR69+hQvfvqX+Kf/6y2mfAvKGS5lOEo6d+Vem1/kLU2Ck9QMuaYdArP4qJVniunOOKDLDpnEkiCJOAl5l7Hf73ATHDZZTFkEICaJy+apScKp4MnyWaXMujNx7v9IBHgv4UyM1zmSILUEgHfX2H3/Bmk3ApuuEmqj15R1d1uzSYSW/KfCmrIAWmBxYixqO+danI9ZC/fywSFrLYyVNX+LA9a5hipb1t1e6uAR+i3cZosRNRjaTIJlXtyoTNW24Smll2M1bkABRKT5ZSaxswOYxcsgMlumEMJWdeid5LqZpknVp6UnyuRhZux3e+z34mPDzAiapJNARWXohwGvvvgSw9mFeKOTK51V3+FY7y8GU8SQwuQO/+Y27UMw0tTa9On6wLcV1/evp6lctrxtrarjpSF9p9rzAIoH4Fj1ig0BbmDy7PrV97H+1D+zcddEmTTrMHvMmrRvz5gXAWDm1lvvPyQMd/X1UbOJ3d91ePTiFS5fvEKmHuOYEaNsvRZfhVR8bzgn+dO1FjpJ/ld2l6g/jqU/mWkO1RRd/hw1OeWokSJRgE3rhNmC/9nuKb0mM2Fk4JqBm26LzfNPkfsNsmsIs7Sk6Zc1nRzjYM0uzxeyNjflnOzvBwxwWq1t6RdqvQbn/dSIROIeACrahKV5qTiGN+BmVRqy/jsQ3JqVqeuuXMGHa3WtOCJsLi7w/Kc/w6Mvf4zoNhgzIeaaooRZU5SAkZDBTvqASFwrur5H3w/o+l4TXwZJt6D+ZzWeUtNHyo+6ELDdDAjB4ebmGtcfrgu/qbsAufiietf2Z5mxdR2060pBDhGJiTYneBI/oRgn3N5cIzIjZwJlqc2AqHZi/d7y7DW2BZTxNUyxXJszvt+U447Gp9fFvX1w2mV+zC5WmCrmk2XN7MFMcL5Hf/kIyTn16K7uWAYOnKeSmI8yJKw0UEO+Zy4xMgDU7KtsXj1iy9yPe4BQB53E1WcZHMze0yRKCSKYiy3YNC6OHDISpigORJ2Xbe390IOJcHt7K5M/Z7x8+RKff/4FQtdpXIc6eeuz5+akNS/y5Tgc9j01a5/Wx4laJK3Ofit+2IUxmD9UATkLxqMIt/3dDnfLq4sAUD5bok5oNVYPFeSYqaks1LYvgBKgDFzHrmLjaqZikAAQG45MKLvo6kKrD14IQWZapeYcASWXjV3aRk+t5OXjStUaNgvc6ocDLi7x+POv8M3P/yfG73+LKUR0XUCKEdM4ysYBW8MhKoBx8CGIJDxqiHn1vyMvxFTWhzyJGfDwTc43SJ6pJQNr2mym7iW4KbmzlMakzIiJsM8Ot0zAsMWj568QNpsGIhpnrGhW5rDtTgHWtWQLwsyYfzbtbb8/ZECzLORIVCUG2NGa7LiGG4GuCf1BatorPiqlX3k2x0odK6a6mZDhTL869w2d86OWSdnXOj+WJnbXrO+rl6/wo7/8G0y373H7+1/AxxEOSRynnUfIGQGMxBkerNG1fU3pI9uFga4TMB8TECfkPAFwc9ymneTAIO9A1CHlDru9CN0f+ENxSrZ0QZKyZN5+0eo0w0PSP4VjNlpOIokZFRwA7/FuzPhuP+JmihiGjV6vdKo0sjVboZnXK+Z4fSeNrrLKy5Zgpz2+psVZN/rXcu9s4j+0rDMqYYJ93+H84kIjDsuOpRmKq9zObpM2EUoo66wLxi6VxGQB7CTwnwXzGqe9qN5CKEzYAgoG7wqDYNRM0LOt6Nwg18LwRdrEYjBkAjEyiX3z8vETXD19CjatzbJfFuBhCW6WhHutj1tgsOpnU3reJtUxqHS6rAFXatpOCqBaULN81WVrjoOeh1mWRATADGSAFmBVCYoADpGUyhgU4l6J+l1DY460BbYwV62S0R+qstVyBZ5yYp69zOweYUi1gfVK+A6Pnj3H1bMX+P7730vQP79Xx2IBLuZ8zzkJkdfttQQIoHEyh9nLuicvL2BuGK3ZuvS9o4N+mml/WXz17AWYJVlnjLE4RE9xQpxSXdYAzrcDPnl6VbS7DM3YiYY5S8frHbll6Q3Kxax9FeRU0NMS8lNz/iGDHkm3UbaJlLbOtJeMmV8YiIrDavG/WSwnY2Fivjo+Z9ujrb8btetLP6uD+OE9yzqXgpwPHZ5//hV2b7/FP15/j/3N93BTRuaIDELoI7qcZMcf67xWgRgEsBfA7hgQaZ0AzkgxKjtxkuWVSJzgtU9lj43D0A9w3mMcR6SYsM975CARjQ1EWcy4NqifLJVKVMSHrxGAmcv7AgzOERkJ6DrsxhF5HGX9KN82k6wJH+AGZiyHaEnLjgD8+2gwD8y2zSw5Vu7U4Myk0BPXrH0HVkhlg5q70OHi/BxdCGA1IZkPjd1rdZbgfkYMshAdefE5sXDOoXPq6+Ic9tMe+/2+JOE0wuqcQ/A1HDZg4LROaiJS56wIw/oEEoClIT44ptLmTIQpJ0w5YeSMFAZsnz6DO7tAdAakqHL+U3xmvbd10ixBEFC85U+ApGIrXRnRNUBVNDmYM/ZDDY5db/23TpAaWqPtOa65eoilbWM7t60fjAhUus5G0WGiyykN3X1w51KCJQAzzfxHdN1ybtTxm1211opy1DmPx08/wWdff413P/8njPsdQiDEmOBcgrft49OItL8FUtYQ9yRO+QQFNCI0QP1yOOmuHO0fywFU+oAhyXc5lzVlgBKAOv7X3SmEOZ2wsPfTFBEnIE0RDoyrywtcnp3VNy/0r4LQwkgZIGpips+4eO3jH1KWwKfVcD+kYsLi6msavVmAm3Kv/hZ6LldwudCqOASAa1r3JbOxMaL6pOZceVzzIli5cv6iw+UjvPrxf8R337/Br//hv4F215DgTBHjOJYchl2XwPACtRxKnCdbqw4iGFPo4KOEVpBthZKLjQT7iHCku8mC82VDykgjWKP2W0RvR4S+6w5ixtk7FR8cFmAgS9C0wFz90lJCQEZARNxdg8e9BONssoqTrUsCZCO8ClzNGDRs6geXO4H9QbKyeTkNcGYSaoOEcRpxrTInWmJkiXnTb8/huwE83R7iMW5+KxIWdWhd6NYBmZudEkqIvPfoAckCPk0YxxF91xdgFDRWgW2546ZOcNUsiI21jlwZWKqmKpMM2TvEnDGlhDFlDFdP8OzT10i+Q1anSSPEB318ohTwYW1YvWZ9Qs2YqILERsacdXjLsFvTi43fMdAiIMdqPVT1HrbnRBsfcGE0a2pBEKk9TrRY4PN3W66fah9H+VtdW0fI75qEe2pmrQMbO3cIRGf3tBiZACInzsaff4lfPnuO73/xDuMU0Y0TxM/NIRIQkDFyhvcjAFcS1rJJm+C6zvXBlhfK3sgcJ81nIacsjv2NT57sdtSxMkCjdZGCqnHcY9TYNzFG5ARwyvAh4MnVE5yfnRUtG6tOl5WAz8fb6ESDTBsh6WPn9EPW1BwrB3NZfoimzgkzLIy03iT3NX5QcABnM6/WPmiFnzV6wswSxX5lXTrADIhoSPi8/c1BZi5rt322tRk+4OzFp/jsr/8Wt/s9Pvz8nxDH98AUEYLwGCLSpJmErgOcOgCbyd+pOZq1TRSChDcBFOAwnPOVt+UszrKkPjmd7K6KU0ROEhRQWwvvq0P+7J2a79zQf4IGto0TUoowrWnIGRQnxJt3cHkS09us06lIVWbwotall1tusr4OTvnctOfXjpdn3LFWPjoOzrLcR+ou82omeTLYebjtJfxwhnzzFplJIxpLrhpJoFm3bhPrvhWNKMncErHKQhhzXw7vHcYpF2cwJk1gpiamdl20dUDRM9BqPiryL5NW+2CaJmTnEJmRFJWfX1ziydNP0Eoxbd/dt18N3Ejm6ZOs68Q5NMRDUTyz6h2aPtPfM8BzqIs70fb2ae1nS6hQtDcoz25H4IEWsl5piPmJblGsU17L1NVzMZUKYF4CnJnUaqphC4m/ePiMGLdVMzcjMGdI63OwuflYqQtFXocCuosrnL/6DL/9zS+B8RreTyDn0QWP7AkpipSZoviteO8F5PhQcuMI47O+aE2d0jeO6gYBaXtS6XfZPhN8dC2mBBNhRDpm9cGRuFYZhIwA323x+MlLbM8vwRQE3FRuJ8IRTDpdgkSTMBr014xNW46mkjl4jdNM4KEUMx/aLreiWVSQA14AhXqjfGAOZJblLlM9MavFcCZlzHlO+7xj03553Ma3OeVCh09efYb0l3+Nn99+wM2/3oCnPUKYSqqGGKMCGofOSfwnC3IowqC4V+dMku9Q1wKDNfIx9BqG7RYmAMhQh3tfNXsamoGIEKYI6uwdm/6aQUYB+zlJPCgCinnLNJscIyhnjNOECDMnM8xKYFPcPHCIqmeP+disRAc87PIV7dyxa5bXG98/VT4e4BxTE5TTh5O3PT7rcnIIwxbdsMGOnFgcGWDVxmT1TBfNtQMhAOTAkZF4UjCk4dh1+OwekCYm9ATvACJGzoZSPcAZ3nnZwgeV+BavZTu7cs6qYXQN6ZovRHFWzBhjxJgSxmlC6Ht88vw5zs8vCr75IaaXqn0B2iB/rVRzTJN2dOKwScYFJcKMDq1St4KdCubav8Nn2p3tJ5rf9wdKD7O08qVtMtbSvNrMFw/WlwZQrMtFk5Xb8bxj+74RlRpAkBu8uxQ22CKsVx69kIRP+TbIeb3f3quA0/m7M3ugO8PZJy+QN1uMt+80p07EGBy8q/cBHqAAZoYv/QIxU+SkUn3WhIxAu8OEFvPQNhXYO9uukmT0I9dt6wzdWaZ9PqWMMTN2U8KYCLvUY3N+hU8+fY2w2YKdK+840040AHfel2UUcGyOL8HKXdLrfRjAv3kxYQ/tihCCKslkF/3BhYKWTjPgfWhOXzzqlBYHhFkacloRsRSkFu3j8VeaCwXtsxno+w2effEVcHuNv//uW+y+/w1oP5ZdvKbBqfmnXNn9i+BA6OA9IfIOlDp4n5B9gulDxJ8uICUJNSIR+bUlTeoHAhBzFMA+RYzYw7d+as2nvb+sidTUiRINedKAsxGEPRy8C7jJjEfFTdmXTqmKi0baKf83Ut0d9H4JWu4qHwP474yD88dYVscIKBFh6Dtshh57qKosJ3AOB1qh2mktRJp/Asa3VStRJAYAkMjGcRrhuh5QdZ43R0Wioi0CiVQHA0tQlG3rRxmJTZSUs2htAIxjxPV+j/f7Cbi4xCevv0Sn9nzSxs6k7Hvy+gOJZaYJWRIHLm28q57DHqxdRovfa6X1wakmqvZcyxyX966/10MutFgUChFX+6ds++ban4VhogE2aEnBOsipqmUqFxudNv2FM2IFquPH1ma2ioAVdb/R/WO/D9pSvlcWQqHD2eUV2ItzYg+NNwUGVLiQIIAdQifrC5zgchIQEyNYc1ghV6fV+ZxgoAE8zkkG53bbd1VQmvQr63SKkg1aohkrwEkJU0qYEpB8j6evXuLi6RNkb6M074Tab0vq2LLSug5OCRl3gZc/C3ADqHA0n9/tObMXtqYRm5hLULNc/0swuHa+AF7GgXLbAmLqL1AzbkLK5zfMzFX2bmuCHByG80u8/MlP8e1vfoOf/7cdbm/fIoxjSdsDiJuE9x6JIphIfD7V38yRQwidAHtOCDkr9rOYEbn2gbNJbe1C8eXpfCgtNzDf9qdzkkvNAlwe7FZWS4kjiWbMCBjhMCJhA9S8kOb32sz1Zj9W7ZuiBDGotl7uAvrt2lnTZN4H6NztZFyfNkPbd5aTjKoSrq7vsRk2eEtUtm/mXJPnFVU0c0GKMmgaot40CUSzdhX1FdWt4+aQFby8tsVdOOZCXR0cGWCHqnurQCpnFnDDEiAsRsbtLuJ9zNheXuHixWdwwxYtK6T5Q7S/2q475Y9SJ/panx4bnVPved9yTHqaa23s2BrB0vc/ADZKGusUe9BlCfwMrFB7lIzXcjPqrXHreOUtyFn6InDOIJizuoIVvTY3z7k3ctanFLbTAPA17cRswmobGLYRgRD6Aa7rsYsZA0b4EOC900jhDM6M4DOQM9g7IFnuHV/XGlBU9AcAGCg7RWzeEaABAlkJv5heayJOib8T44ioZnBWMJRiwhQjppjht+e4evUcm6tLJC/v5rh9Z2XQ2k6Ri8zR+EjPLtp/TDOzdt3a8YdYatZum/eLdzFNzUIQK2ySlppHve+UtkbvWTrUUjMYZTaxNrINOrcyXsf821bfGYBDQDh7ii9/9p/x/v1bvPnnv8N+3COEgGmalO84TSWURVupgIeRFOAEBeaSOsQxZG0QAXnSaxmUAWbxBXWsSs4k/VrNtjWFQ2syzakB/trHs8jeha+LYsA5D0cBAQk9IjhNMJO4UTO2cYCYp5bjs+zNJU+7j/DdrpX7aD3Xyr1TNYBPs8flRGyB0VIbUCcYYdicob98guh6UI4lENicDdgKqmo+Itt+p4PKjdmkdIo835KZ7Xd7jOMI73xRIc6I5ILD1vrkmjmatFg9ioq57uZKnMGO8MmLl7i8egpyNZhgVWcsOuUePKnUcTAS7TGTToQY1xyz4ruxBnSW8uhdy/yQEMx3Dh0j9mt+RWvnHnpR6DaT9drMLDS7sOnZIznEiqYFNv2WYNUIkPiuSN6atZ0Sc+Zga282tUyddBRqtSEHjjMZa5a1jPSZDIIPPRI5jHGC2wvB74PTyPKMTFk0NajJCBlZCKv6AhggahNnWmeZ5tIi2BIRPBE4SeZmZAZlJd45VR+cnHV7uGwXN82OxLpinF9s8PKLzzFcXCA5gmcGFf+HZixaNYGq9FhpBBUmkMt1a2Dl1Pe7NDoPrSwZGVDnRfNmMLN+O5/u64cI1L5Zmubb68yPZM53bAVoG1aeewzczNp68EmA7/Dk5Wv89G/+Fv/PzVtc/+s/IexHyX/oPWgkTa/QgzQKf0pJbVAOLgR0bpDE0hDfGI5R25rLugLUH1R3DHpIGJTiTkBqsqJqXbAcUu06t7hQpsUpcaFyRkwJ0zTC5YyQGX5KcOMOLo1IOYF8p9NfFz4pL2loVqvFP8bSTs33U8B+KRj8UTQ49yknJ6lcsHqGCPD9Bv3FFaLr4dII5gQhDsq0NVWCI4ILkrqB2AOTBzTXBjmG4wRwRbLlvZll56naRi06ceukZQ01IGoLoNRVXgRFa1MAziLPTWbdreEluebZ2VlBtKt9g3U2c6wvWx5ZNSGH4GYJVkw5S/ZvsXBFIrF6ZxnACph0avxoGfwheLVyept4+9Z/LpoboAbMckBZwawB3rj08rwcXR2KnQvmYElYmNuDbcdwlY64AR8G9MscxXJuNYzG5nmbJBLGpI1Y3qcn6gwzzRJyVq2oKK4TZ+ynCUOS3DrVzFbXY0maqcH/yrqzGCCEsraKfECyNV2YmEaB5Ya52rpMCTGmZqdUQk7iK1fNJQ4ZHskRnrx4iWevPoWjDpR094+2w+ZsawIGDgHgbK2vEPI17c3RHv5IYv4gioFqnvdR+7lOFw6PtdqItpwC3fZUYq40bE34XpDkts7lGqYj363dfnOOl59/jev/+Ff4f99+h9v3b+D2E8gH8W3RSMGSVFNSmMicz8LTSFIz5JTQ6Y7dxAyJFcUaxsSpcGP+N+oQnJu9TbpAHAjIDHZzf6ZWc9YCIdFwynqZphEpZbic4DOBEuCy0hx4pXf1edXdghedYyCnYarLsTqisVk7t3bdHw3g/MELq9VcLIr3DmcXlyDvwZHr9arWLB0koyKqO0fqka6E0TmAHRy7Q2KjFLGYqTiVhH3c1O8ITUZvCd3uMqnkJ7FKTYNSdmDkrPb8hJgSYs6InDER8OT5C7z87HOEvpdnlZfQ1zkhFa/NBgNaJYjbx45J6cbDxVtYVVng8+fafQQquUfm4GZu28aslkM1l7UAACAASURBVLV2VlBm0Ytp9d4HWEj/47pwi9Cyomo3ubE1b+ptyPo3B6MErNi1mwt0ijTKYgYs+JZtj5YM4y2Ap4KBDIjNpd+qap7h/pkU3YLpBlY3ACclzeek8icRIaaI2/0ejsVcRU6iGHvXABrTpoouXt6FqzOkxKLSfnU6H52T69R3oTgSN87FFiHd/HOYGTFnjDFhPyWMk8M+DaDLCzz/+j9ge/EIYNIM72imYiW8x9du6dlZP65JnvUWPvhsNVdr9zzEYkDalsfqNQuty1o50Kw0DLqtY61e/VHo02HlbVv1gK7fJbiZ1XCiPjPRhrNH+Pw//We8+f4dfv13/zv20w5+DFq3tM85j9BNSBo6Ad6BkQBSzWUXQDwI2OAsgIKTaKXISZof3TXgnTj7cpS54TTi/ExuwXz9MovZVjYpVNMWAMQYMU6TbqzJujYHEG3BEBNzLv3eEjKhCbygb03XGSuvbViZz6vH+dCkb2vDvt9V7gA4p7ZhzRfpXc6hx65xzuHy4hxD3yHuhNynlDBNE6Y4gWIP7uXFU8oAVf8cU7e17HVJMGdskxYzvDAjQAh8vY7Yw8Jnk2ZszQ3xyVly7qSUMCWZHGOMeJsnXIeAT7/8Czx+9SXID2DTjTRtk9/145BtzKUPkzjbkOU/pKwaGovWqzJn82taK2tSzJwRWF/W77XJLZM02GfM+s+pLJceKthprpDPusJndyxfmA0OKdjgJcCwx7Cm/KimMbtPyFN15qwEey5BH67H+VpvNW1Va7EsjKpVEmBxux8xxoxMDq7r0PcdiBxiTBgJ6LsgEqM69NsfEVfzAqpvAJdo5JZF3GLSGDHMkr3B2tFyWG1/zkBMjGkSp+JdShhjxH5KmFKH5HpcPXuBV199iX7oJNAyrRPd0nd1ZBfjZ+9UCf4xE9Va/WuE/9i5B1MWZNW0BMu23wfcHAgIK9e22ojl/WvuFEULDx2iRfqS+f3zMzOMu/raOrbOYXj0FF//1d9i//57vP0f/xW73Q5gSbfgnWxoiaOYkaLmVksYJW4TIKlKgofvAzxPSM6B4UE5qTZL46jpmgxeHIKZc233Knk3UxKrtoaLkFR4qLlagDBlxsQZCT2YOsA7ZEoqfLiyNlDmONuD2lECilJAaMmaRvIYsFl+LkH/fcudAEdbvjhWmdSccdHs83By6rkWbDuHfrtFt9kgvqVCqPKKFCM7IsRJsAUKpNKzHRP0yQWNWiybNnx1SqrJMcS/IhXY4Cw4tJ6btzNlATofpj3o7DE+/fon6M8uAfJzomuFMJsQM4LwB9KxcnvbbsLB5CCiefocaiSXO6SgppKT4KSCxtME+hgLfWilMPtFY83sU7MZ6NyUiXKASYlQtQRAJRTtnDCCXxhIXY81dq6afIzccgtIWhv52nuszQl9EptG59CJtt7TMA5mxJRxuxuxHyNSZrAm/eu7AK+S7BQnuJHgg0NgCVom4Yir8dPqK1nJy66PZp1y1b4SJBmu7NYSvxtOCchJo4xnpGSRWrnkuWPOyJTgt2d48cVrPHrxFAgOSPcFFpXFzftnft8xLc1RNfsR6fbhlvm43acsQeMS/CyBzimtl927vMdKbtZNS4/WrpWDdwvsaARAk+W893j6/CW+/tnf4B+//w2uf/+NpikBgge64BDjCOfFvUJ2W0F5hIf3hMwAe8B3XtNfBBGwdR2wJGAT4wUcAnmkZM0R37UZ0NHjyCRmCp3bkq5EnZbVupEBTImxTxkxR4AcfB9AwSP5DKIMV4QwNSHruxsFr2th7ibSrolj83k2hu11a3yrAcynyj19cFoCXcHN2nVNMxrCuLy+qi4SCHm7RdheILNHjgzu244TD3PYwILhLKiQ7JObVW9gB2ygwaQpsedTwwg4cxngpa2yqN3tzRYEqfW/GacJu90Oew0mdvXkKV69+hTknIKsNcA3l1CODtiRrr5zcG3R2ydYM/wegigmnu3aEVNdBX6moWFqnPRKZ9+/nGrzQybfbSEDvbNSgU3pkQMpEs160LUxm7ho5ANCkX7oYH0rbJl7/CyJ/XLOrc2vYwzB2mttOKY0XGoDOWfsbm6wu70BpQi4BGJC53v0XQByQuaE/bgDc8IwdGBkcfgHIWbVPFG7y4PL+xS/ASOw1scsmntiBuUETlHi6djJGVhKZTflNI2YOGPz7Byf/+RrhLMtojluHpmQs35UADgXTg63ti77+2hZSLF/FtobYFW6OQZMTmlxTvpyrszdtbl9DNzY0UOxTRflCVXN7FmlnoMGAgD6zRavfvxTjO/f4L//n/8brt9/D6IRwUsyTEAiaXdc/dDITXBeTK7ZeTAHaTlr9OAQQGB49mDL4aZaF6dxaSSTuPhIMmsEcOOFjQCfUR2Lc0pIbD5p2l8s4D9OE8AJzjMoqDlMwz7Ixbo20Qh9hbfWrm1/HygscAh4DNAcCF5Yn1NraSnachLgtJLax5X6sqdYF0MDb4WAbnMGwKmt3TpPt89xAnNqgoUBExLYZZC3/m7ayoDY0eXT7I5giHYHVGICHFN3otYGgIt5yuBdATrMon6fJuxjQnIeX331I1w8elTvXkGg7efipICx9pX+wGLscK0Nzcovw8aKDm0hzeqqe0LvnBV3SkGlfSvfP3bK/YkKNYMyw/zzNa4MkOrx5T0FgTfHCeJs6ATPF83fgrAzzYHNgd/Zij7s8Jp6/Nj8Xz++bLQxEAY4Ybp5BzfeoOcJPkXIyyQ4CvBdgHMChFJOuN0J2BiGQZJxcs05B6DsUCyaVOQirbILCGV3ImDRf6jtfwU3UIl1miZMKanPnOymyp7w9OULPHnxEs4PyEniCZ2a3aVfjNDXMwC7Q4K9uPcYcaeVBW/C1MPX4hyW+8+pu2nF0uH4lKA0q4mqGEGLTzS/6cjJU7xh/kyj6cBwfonP/5f/Fde3Ef/4f/8X3I5v0fsJwbtqavUBUxCtTfBJtJ2uh/Me/TBIEmnV2GT2AEc4aEybXNcfkQUTJNXgCBgx7Yy9g/GqlrZzlthwk2o0JbYyy87DnOAJON926LoNfPaq7Zc6c9P/zNVJxDZcFH0O02yenwI4y3m+Nk44xTtXyj2djO9V17Ipy1rArUq+qdt7j4vLC7whh5zrls4UxXk3pQQfuEhNRNBYX3V7MisiqE6X2g6GbB+FXBdcDYd9rOM5qxOWEq+8bDBQJcGUMWZgTMA+E/zZFZ5++gXcMCC5+URc87+5uxgBb35jnUl9TJlJQyeYYT2Ihc4ARTq48977FCNEzHOz2QMrLWw/ptmAgRtAekwdqbl8Gkhe3FaeoN9OabwaYD6fC6V1pf6lpNt+X69jAWoPiLxpBev7MmeM+x1uP1yD4wRvz8kJcZoQvUe3GdD3YbaGcs7Y7yX6audDicJatI4twGnWkYMrKVfAQOIEZNTt321uOiWeU5KYNzEBU3LYcw8ezvHk09e4uHqO4IbSZ8s1cby01x2C0Xas2uMH55bnV657qOXYPD1mBl0TKtvfp3x0lvXMnq+a+5Z+LJ1u7TmSodsuKmexpDzH2tI+m+rFut6B7aMn+NHP/gr7D2/x87/7P3A7Rgyd7KqapglhGhG9w+QcqO8AGiEbZnxxwg8hyC4pll1UgMx7met53gZqdiSC4KER+F0DqkjMwLkkobUwCglTEod+B3VgpoDkOgzbc3gfYAoDpnUTE5e+1vmqZEyuPey75dw+5V9ThInF/LjPmrgT4Bw2DjACd59SiSxQJlDLswnoQsDV48f4VRcQxxvE6ItWxE+TTIhu0JQJMohdCEhuVNRKJQW89UN1QM4lWzgRSZZieawGDDshVbGqzPnwuAUlnDKwj4zbKWOPgM9//Jd4/uWPEfsgC83yimBu7pkBhQLIlmCmveDw6x+lLKqurfp4KeuP2ZyHS87Xi/Wd4eryBmQ7pSrIgX2ujXczV1oC1hKpY0ClVrZOCO4CwmQMYgGwiKis+6KhQvtMuX4cJ7z/cIMxZmx9B+elJgvLsBl6OHLifxMkzHyJQ5MSpjhhNNMv56J+9t6LbwEDXiXU1vE4JwmSxomRUiw+Npllu3rMGSMzblPCboxIyWFMHW4JONte4MXr1wjD0NC6+8Ebg7Dc9m1D0O8COAcaHDSrbjFOP0SI+VOVU+aou0xL7f32/Rj4OVaXCQ2ARfM+DGFRaD7rZ3vuENf8QYUhGpqnrz7DT//mb7G7/h7f/fJ/4GY3SfJP5zCN+yLU5ZzQ94zOhRJYkjiBHBCCF01mDiBKcE7BgPYVaXqrqvmUT4ukbP2VU9YAgQxwk74kS/bwnHKxUoAdEm8Q/QbDxSW6LhS6cAxwV7GsPV8R5Nqcv0sr2dbgnKug/yPG4g4T1fK3SYbH2BDBtDQN4FqpQ79rrxCpiarvMe1Ss60zywBoJFJxRkymvqkqq1Yzot1Ssg7n6l9DRDN/m2LTXzCPbM816a+RApMmNjPH4hhF/Z1SxubiDJ9/8SUuHz9G1PDYDU8oX2h2UNtcqeK8Z5Wx0AoSXpY1iXxtAq1OqnZIW/T5A8qfAgg9hFK7qpFa0IJXXlxrV7f9f3pQ78Mg5Fi5Y3Zu1t6FlHygmWHVnnGNpGQMpF1ehdAowMkaTG+KER92eyQmjVFlDnIiMco68fC+E58BEqATQhCBIUaM+x2mONVkhVTz+Tg4UCfEm2FqclmvkiAwF8EjZdldJck05Xtk0pQqjAkAdwFPXr3A40+eFMBEjUljOQ7LvpTukKCLhaDPlvKK0NTcvzpGOg5/TuUu35mPBWfL+tq5asKqYknV3CjCYS4+hy3zkQCnlt9pqdVR/znGOsM61U7cQSFDj6effYkvfvY3uL6+xs2b3yDQpBYIV/IXWl2OCOxCycWWmy6T8CgOpCkVsmuE9JlSfS7cEEh8Jxnio0bmo9M48OtcjDljn0SbA+rg+wHddgt46eOZDLUodXzVtr4AqGWscLgelgLYUrhz5V0gW9X5tFvJbAhOnz6EtrVR7TVAGyRsDdzYsUrcFXkzgdkjuoAxBOTEGKeEMCX0fUKOe+TJIe29oNvQSSr5zI0/SAY7BhyDfO0Qa49zVQJtTUWEQyBgjseterz8ZS4OilkjP8YohBtEuHryFJ++fo2+H5CQZr1nz7t3aQnCCnG4D9G4D7hhBjiTjh+rZE538d0/qNyn7Q8VJFlSVjM3geZwRZywbZu2yfjN/bNF3h67f4evMcuZDHXH4l/VAmnrZaFi1m5Z01J/9T2xZwMpJny4vsb3373BfncLjwyHDAcH54DOe3hipGnEXoOXee9k84iGq/fey7ZYjWRuAkWVNLlosgZs0GkkcmZGTJMQ65jAyZz/FQCVtS4lpYyYE/ZM4M0WT149x+ZiK3FE2vFZ6fNVkNPEJoHaR5bEvP1cfp/Vh0Zo48MYIA+1HPPvakFJmz7AxndNk3OXf45cWp+RM0sQ4BV+04L09t/8Qrt3nT63ba6g4e7CUFCyPcOzv/hP+Oz9O/zP//IOu/01wKk0kiHJoB1HII5wLqBm7HYlBUmhMlkC4RKUxzZAbSk0O3IgJ2+VHCExGsHdrhKTVUoJY5J0Q8xJdzcP2F6eIyLBI2nolHnfLMuaVqwFo/fR3ADVebisB/1c81M7VX7ALqr2xQ4lUaEpyynATR2KOLUHTBqcNOkdsjj/sdkJs2ht4rhHjgyntklyuv1adwYxZQkA5iViJJRAOk/w7OvzWdwRJcgRavTinJW552KjtEWZWfJ5pKSOijFqhNQkAcNyBvcDnn3+BS6vniDmNMsU3y6cNXXsH7vcB/jcPckOifldtvEfCkpaotc+7yGW1n/G7NFWKrg51Lqsv49MSCr1zCWdg6tX+sc+626CdfX+gUq5lZL0f0Z7rb0NZvWWehZtuf3wHu9++0u469/hLH/AlhICSYCwoOkXwIxpigDt/z/23vxLkuNID/zM3SMy6+i70Y0bjYsHQI44o1mNVvt231v967tPequV5tJwJJJDDkmAaKDv6qrKzIhwd9sfzM3dIzIys0ByxILeOpCdlXH6afaZuR0gIoQEbBZtK4zQWjRtm7Mx11Jm13XwIWC12WCIAW3borUOxJQS9Mb8ES0OJ+FDvEVqtbyPEcE53Lh7B/fffht20Y40J99u5sUktFbM+oopGnaVmqjXAtJ1XRO7AM6ua/S6qwBxQLQd6tFag+36WUb7PUvXEw6rN88Ug3mt3WyZSO+zbah+Rhgsb97G+z/8HHzxGo//4W+w3lzIPWQQGTAcQNGD2gVAHjGIBsQ6lz2jKATEIPZllMDMdr+rUK3IJwKwqY4mGx2ZpBVhJonsHRgInNKbpG3e4LFoJLcc2IBjYp6zOGByLO2qaKZThkZnH4PZ+u/cdXNjdOj8nvItvaiKpLkNcuYMiHl+Tk06KcaIbrPGZrWC7Xt4Z5Ka2qSPl0lNDJsInnUN4sgOACNyXJexGr5IClQtACVwGdRUbuAxRS72PqDv+mSsGNANPVZDwIqB5Z03cPvRp6DTUwxRGf6OZTPWKuZjWv25dvyPIW7zEgzwh2lUdhHnXcevqn78U5Qy03bXe07qm16n39vAY5cwMT62C0BNpV1hCPvXhbZJgRlR0ebVtc/graIB2ZDXe/j1ObhfwfIgcW80v3l6j7VWbOCY0fc9vPcS0ZU57/NTIuySv00cDWIKR7/pOpE0VwNW6zWO2gWctWIUyYzgPeIgGtYQGSFAXGCj1NukyOcBADcLvPHuI9x/+B6ImxQr67BgsLV9AiBHqa728eounxLzXWCTgJH2GUhRMK4xuAH2C23MJSHmLqC3S7NTPRQgI9uBtAfsJZBDqj4gbPc7JROKdEve/kgCy9X0M9PX6jv0QPlBAKxxuHHvAT747MdYP3+KZ1/8Etj0iExYLgFLBJN5AoHZwBgHcECklJctCf+yZWPzus4CuX6n1A4GRvQ8nNyzwSOtiCak1bfGGMHeI3gP1fDYxqFpj2CpRS2x76Y3lfBDJjv+XKXs0t7tAkFX0QIBV9LgjEFNUW/Nv0AItCBI3e/T5xTCCQCUbF0Yvu9x9uol1qtLLP0A7yUb62aDlOwuommXEhQJlEK0m+zTD+ZqWmpSsiLZZvVzlHOzW0W8vdBGqvIguTpyeoaU52aIAWiWePjBR7j91ruIrklq/PTQBKZruxua7lFqD1fagXKmquIVB3VXmbv/j0E4r6rd2aeZmJN2r2vJ0H5S57k95Pr86BkzAEXXVj3v5vpmGzzpDKL8KbZw5T07gSaEiWIEYMbMmiiOwU3FJVJ+SwEVzHDGwhgBNDbljFIPD2uT8UCqcwwRm80a3tuSbNMYGGtk69TKdfqsyEKch2GQnD4MNK6BNQYcGBRLjrjAJAbGKZVK8AYeC6zJY3n7Lt796FOc3LgDA5dyY+6eg7u2Thio0qdMzh0AlqMtwlpjo++qxn0XA7gOpa5X7bUDjOfs1KOnBjRTGpL/1n400PwjIGME+E0EBaNbGJobjuuu5ERfOdNi9ZKFXleM6GbLIQGmCPQVgiKIx59b4MZb7+HtH/05np6f4eLp7wD06doI7wl9r8lmLZxtEEhSGVEK3meMBRmbwBAnL0RGjL70OTEITrqKZMUSJJenBLYVbaY1BGsIhlIIhb5PW7wMHxx6GLBt4NqF8POkSDhE+0pnxKQx0hqMx3VuTl9Fmzc3JvvKQQ3O/EKdI7plge+qZ74HlfQXI/r1Bc6eP0UcOjCAPgQskubE+wDTDyCYbCilaJdBgmyTClpnVZ6rrN5U6agRgqqRjcmYtFs2NjTWzsyZVkNJy8BI+5V9j34YMISI9uYS7773Hm7cvLG3s78NeTpkKz7HQPcx1V0S/9y1/3/ZXfaBzDnCd9V+ngoPh+pQ3oPqPjNag+V9uwlzlmqTNMdqsEkai4mALU1kWezMyS4tMvrAYLKAdTCGK+EiCTYcZR2TEFdrZU9f1lNITNCibRfZayLLtURwroAlQGx/Nl2Py8sVmrbFwrXiRguGB2NgRucHrLoNNp1HNxgMbOCtw52Hb+DBwwdwrtmiS7mVM1sRW2CeOQfATBix8LYDoH+fVgekkUUqqHlN1+mWVmuPFmfap6O5COQ0GWrtQVBsk+iyQd4SnAcYE+k+P1t60uQeZXkmKrwNlGCme4Q25NqONZ5SB1Sa+wrosAXaU9x6+xHuvf8x/vnZ18BqnRLALuAbg9YZNI1s2yJGOGthyMGk/G3GJLtRDX4rKW0xMpBPQKZENk/R+llslQxR2siqxCECArMYGPfAECwGY2CchbWMGLyYhFQt1/7GzN9aBPhXs7h001aAvjnQX//eOQp/CMCRB+x+2Bzwqdn4/MvH52MIOD97ibMXzxD9AHBKZIkKbDAk6iJ6GGYEJNsckOzNsoRlR4wCfmxZMJoXO2uP2INSRMbSmckHPxF75hrchJxQM6aIjz5IluTNMKBj4O03HuCtt8W4mEdS9Xab646dQ8NbfZau+32J2yGgMz037pf583+MevzPUuqxm9PgTPuyThS3X2tVwEstPNTv3VYPC4jQvPBUU+7JfXMMtWxKsXhS6f2ZV4y1OvLhlJfNY7NeIwSPBUR4E4KKHP2ZYwBgsw0ApWdGI2tctqLFI9El+wPmmK8zxoirK4tNAVEESHLndKsNOufRuCZrQQMzPEf0fkDX9+iCwToYhLbF3TffwOnN06yF2ergmT7eVWjyQ2hWIdi7nnNo60pBzrepy5+qHNqm0uNz7azpIBFnmxjdPlJfPENGtBBU5sOufhGtT1p7GpUdav6f5jkL8Bn7VMw/a6x5yKwCBeTUrUm8BmVayaUWRzfv4I2338c//+Pf4eL8DMQyh0GtgP6YIvXbpKlSYEMKRoTfcdrZEDoSx+9Ned4k2LekMTE6Hw2N2iSgyGAASYoGBiIklxw1FrY16rSc75vjW9sgN/WR7tyokoG2jbmvqsW5qma1LgcATk1A519ySOKZqWYe+Bgj+q7DN19/jZcvngNgOCc5OnRLCC6lmif1jhIDYD/0iciRTBKUSaYTLw1plhrlnEiKlJOMpfokbZLOkxgjhmEo+/npvA+MdR9xPkS8ZgJOb+Pd73+O2288gGmc7O8f6suZ/psFHpPvQxoA/a4/0/t2EZ99wOcPIaxz82A/Y7/eZa7Ou9qzr6/H91Ces8jaUf3srofeK+9CvrfY8ZRzc1qlEZGbArQJsFEgU0vbmdjHgNCt0L9+CdtdYmEGODKwMLAjwqu2Asrs5F3Ousy0fLK56bqQ62ZT8DLObudJbU8GZBu4RuxzLlZrGNokrywHZkZIXo790GETDDZo0Z4e4eH7b6M9ar+dWnUytvqdt5yzcKAc87AGZ5cw8V0qcxqcublfb1ON5r/O4/QYzTWofFWTsHL1XKbtvh2tK1LmKhxgF1BklDhVhKKsnNN07iaDVAGean1VAj9pZH23wO2Hb+Pk7gM8ffYYtu8lkKwhOGtgyGdeV4JeSloiQzGFC4kTATykOmjeNoYlA5uQCUeWqN0pjEJ2lMjzFiBrAWMQuIePEYMh2LZNGqGQ21j3df1dj/1kVGSdjtbG7u3Wel1dhU/8wRqcvQ+98posuimdyzoZLy7O8fir32F1eYlTIrRNi6ZxGWAsGtmXbBrZZzcp6BejAB8isTSfi4Y4CrAHiIfVBIjlT7IiB1H2oop5q8wnQknoA2MdGWsjWcPf/Ph7oOViEoE3SQo0xauTQUl8jKCIF0mLVAyfd/X/YY3a7nJYg7BddgGVOYL9+9bru1J+377f3e9jKSZvqc9obyZPhEqRApRqgFQDp/F4bRPvMUPKGyQ7tAxizybeS0O/wfr8DMPqHJYDJE8Uod7aUoHG+wDnNHSDPE8kVAneaa1FCIzB+xwjZPAh11Ps65AlcErAyFqLTdxgtZGoyIvFAgBh8GLQ3Pc9fDRA2+LuG/dwcuNU3LuvODVHgKaiJyqdCu2uCbcZ3btP+tynnanB5XUtW3OHaNYWZ8y89DiAyCDDyJw3M+CyKkwC3UwksVCq56K6Nub38O6UG5yMzlGtrx3gZbxOKs1EFhqAsr7k75GjQGZ9CWyRwfL4Bm7fewNP2yWG7gLWe3R9LzZBjQWB0DQBLgYwApiMJPKOGEXgDykdAyehP2awCLnHNSAkL8NQgtPGGBA4ip2OMWisQZPMNWLw4AjY5hjHp7dg7VJCz1XhMK7ML6purQFOBjrVs4jmQc8cbfo2fOtwoD8C1KBF+09Qt76gktDqxpFJE0ElvomLKTOiH3D27Ame/+63oGENZyOcsWgbJ6rtyg235NoQNZupOsvAgCCZxks9E7gwBb8TUQ4qVrughpDiZwS1s0k5spgTqJEgZuIibtAP4mp6eucOvvfDH+LO/fvCCJDn8pi4aw/l42UhZwrL1VrYGof9dh9/jLKLYU/3Rv9YZZ/G6TqXuXrXx6dlexup/D3+TZPj8wwxVsinBjVFSyISXyG82+M3N9a7CEzNZUYanBQMs+87XFyc48WrV7hcrWGZoOFVMxjIa1+9EwkxjrdCTQoxSyAYI4kJQ2rvMMjay7SAKIVtCKOoroYMQvDYrD2GwcM1DiEg2c+ljOMx4OLVGb767Re4d/9NHC9bSHyJseS4BWKqMZj2ldKiMcAZ9/e+9TPVoE3Hpr7mOpZD4EwLM2e7x9n5nY8LiuAM0PMsRt4CrN6heZHyEzltsSavonw9lxWR8GgGyeW1Y6Cm9S7tGcnH6fcYIXFmAvqlC4gBDmjbJW7euQdaHMP3a7GxIYMhRDgCnO2x3jAiezi1yzEGngGEmN+k6UjqtCSUtCORZNfBpLXn2SMEn2K4SSwdpTXEgEtrjoxDZIZdLHF06y6cO8qBPxnbtGMOpI/WB1V2TsDWeO7antrH777NOtgPcFD4dHmmTiauDlYupVSuHyF1lIEXZh+wvjzHky9+hc2Lr3EcN1gYCVe9bEQ9x3HA4Dv0g4VrSIJ7wcECebsqI1YU1Z0ShLi8ngAAIABJREFU/tKI0rESLdKkLSlOoapjFZnYZ0mBWSTJfuhL3JshoOsGRMP44P338f4Hj9A0bUbSU0mgoPwa2FR/HxqA6aGZwZ1jnNOF+m0nzFX2RXdpBHaVP0TjdJ3KrnZcFeTMSSPy9+gulAlQCHhNxCPEzGA7x5vMr13vmdtS2GJEpAxbQdRkDiXwsV6v8fr8HM9fneF8tcZJYMRmbEBYNytyhPfybYMaFptsc6OCgoIF9byK0cqW8TDIM6swr5TaoIzChwDuBjgn2t+2bUSNHxieGWePn+Cv/6//iJOjG/jkBz/B8ug0a05rkDIH+uY0ONpX2/27DZrmxqMGN3PakOte1BZmqmWqNR+Z9ieGPzvnmJH2RMRWZAIcFLSXuDgTtFEXHtPCDGoYyQ6FMzAGVADlETPW2s1rFkqdcttm61JMMtTgl0E4unkX1B6jozMwSR+2jYO1QIgBXR8w+D5F+rai3TRWUj2ktRdCkCjeKYxCTmeSvBFjsu8JHOGjR2CPyCHzuaGXcCcMhiWCIwfQEgM8rHM4PTkpAHKK7HaUrfWB7ExVrS1VOlxtbm8JwVyUKYeecMU4OADyHjryS/QaVQkW4FpcVEk1GyPJVBbDyxcv8PXvvkToN1gawBFDnMoYzsr+eowBXd/BWMCYI1g2iHGsfmQUt1p145ZOTuHdDQMwJZ08GD6yGCoHTtobn9zAJYEmDKVEnxFDYAwhIsSIPlj00WBxeoyPPv0Bbty6jXpZMBeQQ6Pemw7GROrIPXM1xj/HKP8QwDDV2Hzbe+eknV3XzgGE7wrYOQQmp9fsu3f73C4d3mSejJ5DKb7Z/v6r2ISII5PxGl2rzBZZHJltSz8MuLi4wKtXr3B2foFN16OlAB8NPBMa1txApcQYERFhWASN2pOqBlY1c7HWJnfYiD65s8YYcwoHleqttbDWwFlJAdG2Lcg4OCeM1/QeCBbEFuunL/CP//mv0S5u4oOPv4/lcgmqpKEMeFAxwUPazKxdGKvhp/dM18sU3Bza0rquZa6NtTZDeYcaz86aFKR5hwR2BIRgvDQIgIFs2aSx2lpbhLyNOS1bNCqtPXlNAlqmxHAaj81YoBjzfRUs6t8ZSo3AsFscwyyOMTChDx7HYYBbWLjGif2MKXMt+AAwI5iQXOEBH4YcUX8kaEaGsw5N28JaB2tU2ylbWD4G+KQRVWeegSO64OGDl9AMMIABmjZ5IB8AN7vWxy6tcAaq46MHBcXRuen3jnJlG5ztQav/LRNEr6knRc20Y4zww4DNZo3Hj7/CixfPYQhiGGhC9shYLFos2kXOYdN1XX6+NQaBvdjk5NwkCnK4GvDChKgkHJF6hAiElGjMewQ/5DQMm34Ak0GIEd3gsRpkm8r7gI03GNwJPvr0c7zz0Q9gF8eIZIsKNXcLA1TvAceyWPNSmnRuPViTcdvHHPXYIS1Nfc30732l9OH+uAVzhHtfna/y7utaDoGamkldjTEVI9wiCOx/bx7TyIiGYCbXZqKcb0n2AdXT9zFX/bd2m9V3+kGC7Z2fX+Ds+TOsz56D+0sMYYVLbtCcHqN1DJNcVkXE4CyMsIq1k7bJtnIyIK7qpNocIvXuqqXvWttj0S4WOFousVwewRgLH2TdG9PDDhZNaHDJAU9+9Wv8Lf5veB/x6OMPcXzjFNa2qJ0SplL8TiKuRLteH6O2YXz9ZAzr9k/XznehjNokByD9EbfmrGrc9tGPucKENPb6R0I9Ye5igOptXyJEKHiSSyJzssNJUL6WQsuA1a3Kf5dYb5W9TUZhk/XLk/uJYFwD51pQjGDuELzEaWJnYNoGi7YBUUo0a5JYrjwueLDnrMXRNgISOqGnAabvARKtjqUSDsUPA4iFG3kwBjA2IWA9dOh8h4ENusiwlgAXQSaO+kQVFsBYW3MQ1FT3yDcjL+Sq/lP+tItn0eT6XeVgLipVBomUGPNvOTtuwOg3od64KhWMEevVBZ5+/TUef/FrDKsLLIgl6FAV8ZKQVG3OoW0bDCnyadd1aKyDMwZgmyW/0tgiQdXMVjU8YE7uqLUVuk+JAAN8CiAWKCXW1Kzh3mPwARtucfPNh/j4xz/G0e07YOvAMEmNJ72i41uvFQFYQdA/6hgfk4HKA3Y1xv/7aBDmzupYjSYT9j93TtrcJalepd7ftTKnwZljUtO/d7d7ftFymldzgDRG0TaamOKgCfcQMFERpGQJg6xp3AOg5qWuAm5ijOiHAZtNh9VqjcvXZ+heP4dfv8bGr2HiAseLBQZnkTx0ZT07saELIZQYJEQjIYVqhlLxmBrkWCt0QVtkEBE5bXU1DRrXYLlcigYHYtMTQ4Qlg4Uj2A3APmLTrfDiV7/A3wcP9hf4+Eef4cbNN6r3boP5fQC/EPFqOFPbpxqdXeXQmrmupQaEDCSPtxKOY1T3pBGYGiLXJa8TEog9kh1RQG1tYzMH/hXoKG+IVAL8UQqpkAKJFHKctRblO1U5fdd0O4Ej0rEbN7OICYB6DxlDWDQOx43DOUGC3SbeBqQtqMahcRZN41Ji2vLgEPwonUlNh4ZhEJvR4NH3vXhkgcBBBGxDEkjQM2PgINgw5YX2QeK6RW5A1gAUEeFh0KKSzmcBzVW0OGqlSpMFUljfbrvGqeedjO3h9XDQBmf6l/zi0d+FdNZn6sinxRPAe4/V+RmefflLrB7/CsvhNY4ooCFCYy0aayTqYjKEcq6FtTLQQ98LYmUGjAPbbQAFklDW41ZQlhgFAYfsJaVam2Hw8H4QW5sY4SNLMs0Ebrqug48Mc2OB9z/7Hh48eg8hWbxvF+2RqpeqhSH2S9vgZgpyCCgp7Ks27kO5o3GamSxZgq6l6UoTtyVVYbrud3tKHSLMu9rxXSr7tF6HgOZVfs+NrTD73WMdkzGOslBiBps03+pra43qjvaNNE/pX50nACpPDFk7602Pi4sVLi4u0W86WHj0PWHTbdBaABbJ05FAZEuMKi6S75YWQ7nJqF7yrVocZx1I60OUAsSJE8GiabBcLEWjxYAFwS2WiDZgcBFMA/xlAMKAvmM8+/Wv8VOKODpa4qMfnMC2JxI1lsZbVXUfTf+m/E+CkzIQUNfKOb3OQY3Fd2Z9zIE/ASZkBELs86iqC1djHxNvyQIj0YgGZfBkGBTHwlkWMiKl4JUFoETV2MjVKhKXOtT1SXysbud4XKYUckdR4TddZa2FbVpEahBtC6YkSPc9emvhLIHQwBoCkZOsUkbNLABqgGBT0mkgm1eAxGaGIoFokZPQdn2PwfskCNgRKAyaoDZ4sPcgArrLS/z65/+Ek5MbuH//fTi3SCBQPjoWuuU2N5aj3xVCZBTbvizOz9C36bHp57C4cKVIxnMEN4Gc0YRKw6fEkQq4QUXIhr7H5evXOHv6GOHiOY7iBktr0JCFswaNszBghL7H0IgFOZEYWTlrxd1zGLDpNwAkY2reiwcAJhgKIDJiFW4bCZVN4m3FDHCUbSlOCcxiKLY7IUqyTx8jfNC0DAwfALgG73zyMT75Vz/C4s5NBEOaSX67UGnzFhEbCTO7idg+BniV6+eOC3MrTC5izPCKBFZfOw/j9NlzW1FzUtWuNnx3CPlhpjS9Zt+Y7AJ++W/Iciqf/WA2FyrXF2nTZODAzGAzZtJT5lA9GTVhr434mcUIf9X1WG16JGtJxBhxuVqjMQS3dGiSnU1khlXJO2mDd7WHgJQOgnNbQggiudpkkMzVbGbxo100DqdHR1i6JjsPMAjRWQQyMPBoXYBzBiFKdHT2AWdffI2f/j//GYv2FO9+/H24oyajlkPjvb11O2lIYgzZyqOmkQcEhul6upalqloGhQZAVC3OfFuna0QwQJrspLNWtCw1BaqfJRGvE5Xi4mU4WlsMkMSFLXxpqnHSOa0gVUEV9ttFSX1UgJ10RtHhl7ZBrjOugVmeILolQvQ5fpohgINHv2EE30vgzMUCYE6CfpPrTWRArpZhGM5acNPAhwhjtC8A27YIMBj6HqvVJaIvBv6aTTxGhokBC/JYPXuB//of/hNefPMMP/6Lv8I7jz7FyekpLLkirKStr12ana2SK8pQ2z4Zn6Id1j6eG8dtDHJYJ3oA4IxR94gYsRIWnXDyyvGkrfcORdrqOnErvbg4B8cAa8VLwliT1GeUJ6FoVUQdZ1KMjLZtAY7o/IC+7wEIGnbpvFHzYxJ3OguDqLNbF0rwiH4QcJPsb4ZBPKS89+hiRKfuqR5YdxYdHeHuW2/h0z//S9x5+BbINJXaPw9X6XLtpymILb9GwvU+pj9lAv/impCZWTNieTMgZo4YT4/PTVLMPPe6l2l79l0zJYyzfcBcEdnqmihzJIV+mX3+3O+09LLaXJgrF40Obc+hutTbCnXk07rORCXnU2SAjQXIIkKS9fV9j653OG4snEnzIG7XuX63ElxjJDR95RALAGWNEyR4WQgqUMKAYcFYLhdYNg0cJNlfJIANpaSFoulZNBGLhXg+hghQNOg2A57+6rf4x+P/gvboFO88+hBwBCaC3UEm50HHNrObXjbHLGvGMLdurnOJLF44RJSjDkcA0LyEhK1tSP3Wdo9cvRPTA0sW6lofX3Qpst0EIEUqHs9bHQdm2btlIog6aWxHosJeioufj9MkYzkwJ7QVQ+r9W/WUgRYn92yyFu7oBNQswJtLaNwka0zmmTEyhn7I4RmsMRiGoUQ2JpMDWmrfEZHEikt0wxgLQwxrHKwDXIovt+7XGPoh804wYbFoAQAuSm36iw0e/7efY7jY4NmT5/jg40/w8J0HWJ4e512SEaCheWFgGyDWjG83bZvSyt9nHewHOFHDSM9InIkA7ytKT/UTY8R6tcLL589weX4OA1Epu+T5YK3uo7tkWAV0KYMwlkssFou89xhck4Pvqdq8cQ2cdaK+VnWmVDgtFtFF5PgZ6ROzpka0NkOMGGJAHzw6b7Fhh/bOHXz4Z3+Btz76BK5ZAFHVdJK+QTFpGm7pJ6jUpsfGf2cGVPf5FKX+nmBmF+Ldde1Vy1U0GPV1h5491/7rLK3WsWC07AMbwDxDqy4WIUHHqhIIEpkePXduftRERnOvKZgeeRqOa5XftZcopeRAcyA7JgGIkqY0kEGonqvScb6n6g+NRqwRxUf2NeouzsVpwBoJAmqNkfWOINtShkBBvC6b5RLHx8diuxcYMDbV2SAyybMANIlWDGYAIWZDaD+s8eRnP8U/OodFY3Dn3Ycw7QJgt1OHuW+ujk/l1m/dPwXD+5jldSwMSJRhJDFS1PlgMuBkZDwe+3Sfzj09lpCh0mxKauTcPyjvUPFZgY2e0L5LegDZkokGbFDmu0YVTvQ3OWPlamTTCh6vr7kx2b1+9MIy8gyAIzAMHt1mDUsRCxPQ8gDLEYBFiICzyHxOFLCcQU0IA7zXtSieg5QifHPkvLY48SVNVaTURLVAzjl0XQ/fiSt6u2jRLpZomhb9MGARHIbYoPMRr379zzh/+hSP//kX+Piz7+PDH3wf9x68A2OXoskxys92z9MyhgLyan4owly5Tj9zYQe2wP/ON0rZC3BiDCkw07YKiauM3UXSmyH8CUVzZAz9BucvnuDl498iXLzEMXm0huByZtNE6BKx0wkY1IuKdEAJTePAvIS1g7i8BXF/c87heHlcDJZTILAYo+TmqCI6akbwkFBt3/cYgscQWQDO4NEzYG/cwgef/wAf/vgzLE9PM/OQDp6ADirLr/RDdWqGUO7TcBzSdMyBnt33FLi8j1HOlXGL9pd94GYfeNtX/+teDoHPLXV8At3i3JdADlCt2FoKHWcWn7qGFk2NxJehTNbSXEyDl6O8Cs/fC3hrDc7cuBNJXKrlcoGj41M0i2N4svCxSLTOtTBOkgXaBF40SCcnoq1rXW1rRHuTCCaLvRxBtbTirSh0T/tHApq5psGyabMQRJ4lBASLFiCkaKyNtVhYiyNngcaJgNMAPQcEDuhWHb78x/8K5xw+/7f/Cx689x5Mu8htmhvLMZKZ1+qM/94Ncupjc2NyXQsncAIkXYRqZmg78vCWxK5/TNvHZV1t2XegAjpEoi3KLKhAa2YGpy2kiGTfVaSHvFug79ulif52/V9p7nR9T9oYhw7Wb9DSAMMeWnlOKUg40QaTeKNLaySkrPd1oFpU254y18XWVXm0XqeG2c45tIsWfd8jUEDTtDhaLmGsQ9O06LoOHAh+sFj7iGboMbx+jqc/v8TLx4/x9Isv8aO//Cs8eP8jnNy4CcCIFm2ylbiPHnK1DHQ46vH+Y/GDg1tUBLPjhTxe4IxCaCctiSn79vnZS7x4/AW6F1+j9Ssc2YCGHJyRvUdTxcExJKnic+bg4NF1azRNC0MGZCyaVoJ7GWNEJd516PsB1vQwVnJaMVJ8nCAxb2LQ3wJsYixeUn3fw4eAnlm0OMMA7xzuvf0mHn3+fZzcvyu2C9VA1GXfnnrutwxyCtgofTo/wLvH5zCwGT97DMj2fc+NYyYm1XMPSTLfpnwXgM0cuBz116gNY7P72WcAYgdSramYk+ghA5zZuuhYRvEUEg+9JOcSgWOZbgpuMiNi1TlWtZ0bW52mk6E1xmC5XOLGjRu4eesWTm7cQHe2QNisJG8bswAb16BpTDGQTAZrQrjNaHvZOgvjRPUt2llxgW2aRsCRLLwS6MxrHh5guVzieLGUNc8MDapGiVGIa2wEW4ujtgFHhgnJc5JFv0VBvBu7rsMX//0XIGPhyOLB+x+BXJP7KM95lI4Za2vHo4SZM7tstDLDvf5LIZe5qhpCNvyezqut+whZiB5dR+rjpNePwSEBI/tHNRcoqvvyLIYYG8fEX4A6U8fkBszT8i1NW9IgKW0s9+r12/0iQoOBcy3adgHTLBGNAxnNuC7bqBK0NsAYixrA2CQAMIqditqKcuK1KvAHdaYJKQ4UybaetZL6qG1bBBuwWLRo2wbGOFhyaI0FRWDwDGwCYgBsBCwzhvM1vvjpz3Hx6hzf/8m/xsc/+hFu3rsLOAsNVHGID4E52dclOqX8NGL2/jmTh6vsDgAHAE7f92jabXdr1cyUEZSJx3E8mbUSgx/w+vUZvv76Mb787W+wPn+NE0RYwmjvNk9QFFWnusdFjmngItoUxIiMgYWVDktItR88Vt0GgRgnOIIjKyDGR8Br1GIBOiEF8Nv4gHWKd+NDwBAMBhj01GJ57wE++rM/w4P33wc1rTCKmY4dLYhtIS0XWbepv2ZAzT4Nx9z5q2pDVGMwBWf7NDj5Htpu0HSyTd+7q8yBp+8CsNGyt66RRwRXsxjvvlwlSiXCGdHoE9K/5T/5FRE5pMsYCd2AWOzPJHEJiXoeY4k0b91GSLrvSdvmQGotKdfj3rYtbt26ibv37uH1gzcRV6+weboCi7okr2Qisa8jUHIbF5CioMYmYcRQIZDDMCBGeUfbtmLDkYi22hJFjrDG4mi5xMnxCZZNC2KRYEEMMtqdEcELIIITu6G2cQihhfceNvWqjRHGB5gAXL46xy/++u8Q+h7/5t8vcevtd2GcRTAMgoXhmvFqL6l3yJRATzq0ZsqzBJtGnHvK/K9bIa7nSHW80njl2hszmuMZKMwVLp+soaCxnkAVNsQaMoByIs66z2K1lkwCvbqtNur3anD2MVEFoqQgaQfNL+tpLPi4xRLLG7dhj06xuXgJJl82bZJwI+7gBArl0cYaZHfztDWlyWhjjHBOnhJCwGazEfCT7FhljYmWqGkaLJZivLxcLtE6J/3aWMA6cIjoreSsCnEB7z0aFk2o33S4/PK3+EXwWB4f4+adezBsc//u4ikj2k+UNVWpyfWIHuz/q5a9AOfi4hLHJyJBqZFYakVhfrlS20yLWbaXXp+d4cmTJ/jdl1/iyTdfo+170IJy7BuT9lA53ScqeDPqDEnCF3JOmrZFktZKZ2h28X7o0Q894uCxaIQ4iu4ugoPs60ucP/GW6n3AOgSsfYD3EYO32MCAT4/w6Huf471PP8Py6Ba46EC3yohIKciZuZaQFtuBgTsEcurfc/dNn6EMVGWN0s/zz8y/obhoe9LOgZxpf+yq29zv70qZMiRdC1muz8ti3jtpLCjUiAj5XgU+YAU08s0QzcRWRmYkFmvU7yRJ0LkqE2KRQAJ2jN+o0IRZpbobY3B0dIR79+5h/dbboP4Sz0MHvnyBxhGssZDEmyY9ZqyTztqmqhS1uxgVt+0CzokhJRsSbxitFkv+nOPlERZNC2ckbIMlsUdQi1eJf+WlQ5xDiBFtZMSF0BCf8vkgqtYHCBRxuXqNL3/6dzhdNvj+v/s/cPedNxGdA7Gy2BkCvEe40TESWlf6YgRy8nYJ5esZ13t7SufRVslcOWkMU3YFOTevPdS5Vc+zrJhRaR+iAVASS+qKTwZEIc+pYokjvR0ZMCxbVvOg8cDgYbyOZGzK28q/qV5VHcYdAtFsHp2CmqXolJhhILZgYNG+RmjMNluezwxATUeQ1zGlutkqDEPTNAg+gK0tGtQksDInzSkRjhcLSbYZktYYBkjpHgIInoFNt0H0EUweni26ELC+uEjG4crviu1d7tFdNJ8BkNr41n1+WDtzaKekLnsBzuVqDZfc0nSfvK7IaKArNKzHh2HAer3G8xcv8PVXv8Pj332J9fkZGvJJvVcn5UNCrtsfY0TFTVRycPR9L27gqm5n2dO3zsKma1arFbwbsFgshDB6UddF5pTwDxLIL5agfj4yegC9NXjzg3fx8eef4eSm7DPmvFwzStmpJFYW7HjJxIn6eU6Tsk8rM3du65r8jt3gRT9zAZTGDLi0eG47alv6nO+Pq7Rt3/OuS9lVP+kmrrabIBGGaVtNv30f8hzWLZV8ogKmzGIoH1OQypoZAMLPDZDCzKs+dIeEPNO9U+2rfgzJHnLNdHRdtm2D27dvon/4EOw7DMHj1VcGQAeyNoEZHvGO+tn1R/os5jQMKliljgeYs5DjvUi8jXNoXZM1vYYMWBN3pkS7knOugEnRGEXYmIyQCWiZQIbQECFaFhfbjYdfneE3//C3iO0Sn9m/wp133gJMnO0/WW/zWgyFc3KycoAAFZCjnaRE43ouge2S52Fi+FX7WYW9KFqpOW2vzuEtoUHPxSQAR07dV7t6F22XZBwfO2/w5B1TAQ4oWH+7WWmEJuC+1DObI+uTRr8VSOm5POIksWiapoEjwEQPogFgl4QB2SqNHJPxGADPKQWR3m/ydiy03ekNxpTz1lkQAY1rMv+ktG3lDKFtGiwbB8sE8Zom2W4iCxfEkN+HCMQADzFw9gEYIsExoXEmCweqqZuj8dvjLrpmVH05N+enNlHTMTlU9gKc1XqD09MTUePasVuYpm3PpSLsSnz7YcD5xQrPn77As999gfOvfgW+eAbvgKE5RmwXQKVVkDDylAm4ZhAHEcho4rwW3hciJ7lnkiMhyR6/DvzQ9+jWG/R9j0XTwsbkQRVEgxMjMPi0LTWIsXIfgUuyaG/fwg/+8id4+N67IGuL98yEgu3eH0yDmP+uumoHqp0DAFc5NzoGBjjmibb1ibXmJoI5yDfS/GKg1lQxc4pVVojDuBv2a2l2tWHu+usKauoyt9jq/s1Gw/U9O55TA8jRPSMiPAY3U3A6qhdnFokCw+cJQU1LpiBDjym4yGuwukbpgbUWt24CHD2MJcA6RO/hn3+FwAEhgRLdfjKGJlWaEEWIJN62LZqmSQAlIHgvATc3G4QQxHandVg0i8K4JmDSStQdcWMOXuJ8WMBaB+cYLQBrGCEQLBhNZAQT4U1AhISIwBCxOXuJf/qbvwNRg7/43/8dTu/dAxueMHP9I6+kqo0GxZtlZiAU1OSfqgkuj7nOoB/Q+UxFmJ80lQzJNlLe09m+nwxttTNylDUV0vzT3GOGSqR/HQRC0hal7zhGLjWNjjGOBPZdIGdXGWnbRjqjatCqsav5HMAwBrAGaEkAeCBkr8TcGJb0EuwDAklIBNXEaIiGvE6TskDfp7xTvRJ1LXnvAWJYYjTW4Hi5gHMWliV9hKx1gxDkuUcxxYYLTiIkc0BARISDaR1sSyATANgsxxwSYuv+IxXQrtj/+4TFubIX4Gw2m9yJOinyw6eEXMeUS9LLmLQoz58/x9NvHuP1s2/Qbs7Qtxb+eCFgBgzSfDUEYbZBmW8sfvrgpIJETtYWYkT0ASEmlSMACRqENDmAzgd0foXTY0JrXZGAmVIiTUmuGSIhMGEAQCfHePTZD/HORx/BHS1GOU12rYRtdD/fN/V3XfaBgPr4YZAzXkg8un/yHoT0EYmUSU28I5gFJEZEENskMSEzW6qlBt5G1HPam51tHmmctEO3Lr8WZSRZbrVvbLarbZlu743GTMHNHBjleizHZefett6n0hRKV85pTXgCXOrv/A4qW0lTMFQygVPKeGzA/RpPuwuE7hV8iPAk2hJpO+VeUoIoruAxE+imcckbymZngL7vxQnAexBRBkCW7Ki/RBA0Vb0tiFmixiajRssSW8WBoTYzHCFbYKkOLjLaRSsGoRG4PD/HL//uH3Dnzh384M9/AnejQfWa3MvKzMp2nF5Qz4x07WR0p/oAvfS62+Bov0cqzEqB2qjVGXxAPFontI64zK3M7DmFZjDiG6hrCKjwX3osk4whEj/YpREa1bv6u9a2FeF0vGW2zVzTqFGqQPE/33o2J97DUdIDDSGiY0IfCQtrUrA/ncvQINiyNtJ+M9dJZlHiC6nxvvLBPA/TWrHpOsOyBp2TrAFHGn4lQnY5EjkiABEG0TlYM4g21hgwBkQMYphvCORM4hF7hO56zEbqvUShEk+lhAHARYie3vNtt2oPAhzvff6tAIfSAI7VRTSasESEYfA52/Czp08xrNdoEBEjwQeRyBwIbAyMIbgkEcUYQWwkXw1RjmBKJBbo4DSwQMrhMSTVXYpMGlPnkIVpWmzWa5xdXuKoXeS6+ghsktdV1wHdYLDmBn3b4J3vf47P//X/iuOTu4ghvXdPP02ZlqrYuKwyAAAgAElEQVTqZKzGaP4Qw/82AGdOG6ILRMGCgpqssRmdHz9TpRBU50daqwRqc7JDRpZip5qNXVtUdV3lbTJggm+K1mE2OvQ1KCr5zRJPaNfVAGb8Pbpe+34HwAFkTLbm3owUw1xrb8qazP+le7TuRerdBjRTEFPI/vy2krMW1pwIwLEWFhHN+jWe/OoSAwf4CLiEZphl3him3F8hMoCYvEvEVdUlr6WYUqlo5mQVeLJnFU/qQyaFok9xdmCAEEGWYR2SFodhggQZBdI2dwhJw0AwNsAz0ELs+rwPOPKM9csX+Nl/+X+xXDq8//mP0Z7cqjQTZaTqDSg5tHsyT3U92+f2XXE9iopVxKxhVsv8QuqPpF2DhSTHpG16ULQ4yAIPqj8ZnA3zSRFN1hhR3hKqaY9uy+g7as1N5mekmjilYVLrfRoIuY6yOJnHfCZA4Oj+RIe9H9CHiMtAuOgZrgFaAzirOwWm0GNUpgRVlvERgFAarWCB1NNxgTaFQwghgK1BCGKs3LoGi8UCjXOCM7zWDwAkgr+sSYsmgSpVXLA1WJ6eom1PED1S1ORiTD7Hm6ZCWa3FAVVbtDprZpjuLFDaU/YCHN0G2qrcZAAzc6o6PMaIzWaDi/MLXFyco9usYSB2MgDQdz2GhYNji2hSNGNK/gwsegQTC+FS7wlK0qk2ksiA2aPvB2mQsVWE1Zjrd7laoe8HLJNkGAKw7jus1xtseotL73AJwr133sWP/82/xRtvvQtrGuQw0jXSPNC5EiAR4jGQO2i39DA9tktaO6TBwWgcOAPkLDkwS1wg3m1gLBJBBYoig00hDPpMbVb2laGi5doHbrb7oGgpiiYHdaLZa1emUl0NVHQtAPPgZvqMXee1jCRg1bhge6GPAIypVQsEDalebzWV88jH5rQ3BTjstpsBJGLqYrHAnTt34ayF6dd4ffYS61dPAPRABFqwRKWllAAzpkB+YBgr9gBt2yaNrTCgkD4meX4459C4JuXkUfCWPLGMgTEOOYBgarsBwaQ5r1trUmcSD8sYgb4XppkAYNsERACOgGgIC0swncfLr36D//Y3hMWNE7zz6edo0jb7qD8nI0pzvyqtj4rMzDN35jxK13dBCOjYFnqn7c7JNZPxd63tFrBbGaRXEryuNY1jEzPAGdPiKbDJ9ZjQoalmoWzFJl9e3jGOtA16ps/PwiCNr6keAgbQ9T3Wmw08GWxCxMpvYKOBMUuQYbioSaSjGA4nYBSiCNwKznROazuYKNnKp+CXzsFag+BZ4kdx2dpeHh1huVyK3VoUB4boE4Dhos4xkHXQWAnPEhEBY7C8eReL41swsBmAMsY0fpdmvzogIEfnC5VeV7A6Ld9mLewFOFlK+j0WF7O4enZdh77rEEOARbFeV199OFuk2JlnbP3NBeXp5LKao8p7BBSNE8fCfPquR7/pwDHCuQYhihv8puux8hbnHBFPjvDJjz/D2++8k2PogGvPhjHT3tsvVLWJrwZupiCAJufnrp2eV0m+UsSk/tVznCfvlraAiu1Q/sb4XQpCZKJqYPbD5RDAyXWEgKYpebwuZQpu9BiAvLipHh8iVB7F82WHpFLmgcqHiTgYI67N1bipN6JqWDPhTdIzZ5BSnq91nr53VpMzA2rq52RgYBxOToHbbz3CvUfP8fWvArrzZ2jiIHmFUv2iIQSEvLYa02TNjKrdJQnugKxmt1aiFNeCQxJyRDNlRiqvQh9K0E9YNYYsW1oggnNNNtC2Rgg+WNIpeTAcMbwHNr7H11/+Fr/47z/FzYfv4c69+yUo6WhAJz1LKHOa8oFSB565rfq9S+i5NoW314bOW4KkQpDholFqhXo3J3KEUTA36odik5R00CWsyAT81XN3TmsAjDU3clyD5eXXZdpUP3cO3Fy11PeHENF3HTbrNeLQg32H9eYc3Bs4Y2CpQUjekrKeLawV4KUmFrWXsToAmSQAxbSFYbLiQHZJwEUD3bgGy8Ui2+YMwyA+mskmM0ZNNC1ejZaAxlhYahHJIBiH4+NjNM0i9VsdYWi+7NLiZGaVzhlD0MCH/6IA5/TkRHI/TV+Qya3+nieUOb5FCh9Po3NmlAJ+F5H/NiWGCB9DJoK17UOMAd2mBzPQtC1CAFarFdabAStq4JdLvPe9j/Dxj36IxfFRug+jds6VetBG9Y0M1hxYFSjbpXnZdbxoa+e3MOr7OX+r9oazUXFkiZ0in5gn8vRZzDEZ9nlElsSnXKl9s/sqjebkBCTN2JpcAeBcdxoO7Aan+Vv7c3TdFeZxtYimxDmZLIikpQCW6vlUAZv0t0n/odCK6l01iCm/p5qZEeMY3T65HpQ187K+Fji9dRtvv/8h+otXeHr5Cn2/gaWUHsWa7NINACZFKS5BzQDvRTiKMaK1DZqUYHM0r0aCYAWUq7pl4AMgGINohRYZdrAAKFAKhGZSPxGCMRL3IwREQ+BkA7JcGCyJcd4P+OXPf4bTB+/h8z/7c9y6dbusg1ynmioqs6RybkL66rM6MNce1KSS1z7XoBxb37mdCmxIN+C26UQB4FyuYLHzMeDs8n2VshucKN2R2sn3xM5nZunOCThTrQSqdVXT7Bhjyuwd4QeP2G3QdxsMqxWiIzhrEMMRTluLxoqxsBj82upZ5Z3bmlvpL3XIcdaCY4QxDJvmvjUGbdOAjEFI9j0hiulw4Cj2Qf2QovwDgRl9DBiCF+9jODAR2oWFMZxpENT9/sB4zIPDJDDXAlUtrUzG86plL8A5PjkeeU/l/XtOqDeiVIbGmgUiQtM0WC6XWGS1s2gPjGmwXCzhmia7tGXpi7GjWdPu2D5ANGk8o5qESFlTI0wI4u7mBaUG2+Do5ikeffoxbt25mwni7HurwZkbKJVcahVlXdl9AGe0YCbtjDWA2QIl5b5aTVhrsGRrKoEc3cJLn23tkORwUYcHRkTUbLysWwMiQSlT2VLVAqN+mrZ5qnHawgRX1Az9jy67mM5eZrTFvaSU/kqaFdoGNwoCIzTlJEAksFNsNst6U5sTyiME1Mx0CmaAlDdoRjszp8UhM752BIaqBlprcXR0jDt37+H8jQd4/c1vMGzO0SBKHUniT1GSOEWNnmxuYpTYVF0H5ijnnJNEvFXeqrrfNV+VhOSv5hhQwDMgObM4ikcOGERW4o4gCLMkAWoxynsBkZjFoYFB1mADYDNscHn2Ej//+7/BzZMT3PrxT2DaJXyteEgceZ4Ys/5fz4ZKELqec/8qRTX0upVajiMHBJSSQN+O5oqWDSIw1Cd4W4NaurgAyintKbcXLU7J21YfS+uLOZ+b0rXp83Zq9RkphUEpOc4TGUQ2GJiwiQTLBgtmrFZr2WWgI1jTgJgQIsNGBShqp1SMjMlIsmoQYejFXq1p2qwNjd4nvpvseliC715cXoiWByYTX+8HCYbLDPElBNbRYzX06IYNhigu4j5lMidbDeoMnduttSkl6Wpy/+U1PqP6/rZKj70AZ7FYbEtrCX1TTPEmgEz4AOQ4NQDgGoejoyPcuHGKG6cnWK+eg4O4cS+WCyzaVlAqyZ68ENuCQKcfebdJsRU4E7eYbF7E/VPO6bHSqcBi0WC5XEgUZIpwboAL4sE1dD3OXrxCv+nQnu7gSFctI63GeHDr7+nfo2uqR43AwATgbH3XACflAMtgBsX+pgY3dR1q9W0UtQEIIjVJclXWZDP5fYfKbo1TkkYqjZO2+tqSeGWEs1IIxqLq6D5UQGMCkAkS82qauTvdJHKlyroMpJDzYkMSq2cWsDFesxXhqKFIYkK1vdMU5Ew1NVORdtQHRJDteQPXNDg5vYHb9x/g1v238Gx1iVW3woKBNjJo8IjRwIGwgLi5Sg6dZFQ89FguF2haCeBnQGLMXNnQyByWeS5tTHmsUj4fAShAcraVfk7BzoiNBH1LcpUm+FRXfGut/I6cbEcYxjCWQ8BpMLDRY/3VP+OXf93g03ffw9G9N8EuuYKPhPmkUUpjR6B6ms9On6nub4dJyLUqzJw1OPot2b6zT5v0g0xf5DiMFWDN2smsARkLPTWYqAFF1Pg4clW+ti5zWpwxfU5wmBlFlNi+b1qm4CZfy0CWEFPlQjKYBySIpW1bsF0gmgXYtPDcw3iPzQbYtCk+jXp/k7p+J3s6U3ZINJKx0HZxJxfzEjN6vyodGGJbu0ka0sZYGRDIGjIgBDB6MDwiUs5T+ODFOShakF3CLRxgvPQXa0W3tTi7aOWURiGNhtIySvmtkI9tl0Nazr0Ax6XtpfqTnloYsFYmLeG60tZYHB8f4e6dO7i4dx9nmzO4DdAuFmichbMWzooHlWAkmWRyb0nCpwOp2qLAKrnFnHPDWpuCGSHnqAkMAJKsb5Eiogpos+iHgBAiIgy8t3h9scZvf/ZP+OjTH+LokztZnTftvjkpd1eRCb/3koNlCnjmzuXfQAUYFDyMGeYUJM1pg7RviSgFq4tZuwCO4KiGeSyangnD19854elVNU5KKDDWCFyrckXsu6XZo/GC1mtyvxiMovSChOYocSmeR6KpIC5jMqttmXmXPpfT9y5V6dz9eq/wrOlNWf7KdbLW4ujkBLcfvI2HH3c4HwgvfvdreL8C8QAbg+Sfiyl4YWQEaB4djyat1bZtYYjgSG1vyrsNkkSbkilmkJNokg5V5HFAy2xUCkJEAIt/LoiEsJtgYBLAMSbVMUYQRZwESdPQ9sDrbo2zJ1/h4vUZju8+VNvZ0lk186wEkNSJWcszW/Lh3LNXEib+FKUGHnmbqlLOaLLNeukQkXjAoVoHVAQ0U4F0pQ31ehlrXSiPv9K9q6DBba1LEgpZGe5EkNkHTCf1rM4AGNNZiUuTEtXeuInm5CbWmwt00aNBEO0JA5EsTAp4qfxIwDhlM4wMDqNsLymIseqSDwFDxlgk2/3UP154aQrDMPQ9um6QMTKyXax1DlF5QkDwGyA6sB/w8slTPP36Kzx82KJtTwph2UMf99EnFfbkd80DCgBiPgw667IX4PgQsobk8EPnzxEoJd1zsK6BdWJMaLJnhzaEM/GsMwxPNTgyd4v2wRgS0JIaH2MV4RUhM9qT05PsfYGkk2iaBssAMBxCIKyfv8IXv/w1Hr71IU5v3sqAasrAR+2bMIEsSVcDxYmY1c+ZRf1z/fctBnMHDLrivQceS0pgqWiVEmKfa9uUGB3SOMVk1Hldcc2o5LaXUoi0bOVQrMZXweEc4MBEk6PHytmx1iUV9UgZByvb0XlUnU+fvIVA28/Wa7cIUA09qdSq/lfrxsxwbYubt++CrMO693j94gWG12sMzHAhoLFlXXvv4SF5c6y1aBcLOOtAkO1rk/5WCExgkFEGp/OrAhETaV+9QbUtOX5IJCDF1wLJG2ovLt3WHmKU/FnGgMlgAKG3Dkc37yIulmA3LxCNS15Io3m+dc/1xDF7S17PKV4LZRStoI5S06caRZX4xzZ4c/Rxl+Y7RgU342Wp0101+HNaHORjCsfKs6Y0f5RnbkfdtrU45V31u52zODpa4uTGTRzdvIvu4hX6dY8hBhiKIpwbC2PVYzBtI1WBErWOuU5R6uucSwA91RsGxiJrJl3jgCRcEBH84EGugYlA1w8YNhuwJuhM2kufFAIcgiTdXK3ws7/5e1xervG//Z9LPHjrEQwpnBhP8DkBrP5d92q9VYXRU8qYzvGVXWU/wBkGKPocqcIVJTNjPDWAevtjGAacn5/j5fPnePXsKTbnr7AMa3hnsiTDjMrYDCVO0r6KVSetSdIWpwjKOT6UIM4YAhrncLRcwmYXcsAZxqJpJYhdJMkevunwxS9/ibcefYKPPz1Gu1xsMaZxZ8/bLcAo0VR0TVsT/Sogh+SGJO1sy2+1aleup52RQv+g8i1AR00UdoGafG2lvdHfWer4w2r8L1xmxhwVyANSULMCXBjzi5oobTMVETQR/nEfqKGxvmMUdLOuGSkDydOweFdlKWkbaO3U2qCWpAqY0fpRCp5HhZrmOWCMxdHxMRbLJbrNBi+++gJfXzxHP6zhKGBh1XAS8D4gRg9LhLZpsWiWMCSgxsAixrLtJF3K0K2EMq903sVR72l/MXN2bFCoZkzK3UUkRppJG1w0AWGkXYgR6ALhnIFN0+LB3QdwpzfTdkwEksts6lQU0CWGzFWttsZuXCpdEGUF0LUumeHkOGQxRR6mcZco4Ndowxn4jcFHPR93Ckh5rm3P6QJsyvIikm3I/K4YEY2R+D0TOpx/c2K8rPxPjJ2ZtoXeEY1Tuk1lDmqx1uL0xinu37+HszffRn95hvWwxmboYSC8c0i2MAUvGIDTltEMGJT3ybkQvAgGxuZzuu6ttWKfiQhjbDJABpxLKRtDwOVqjaHvQZRSQkAMoBdtAxMiiAPOX53jxTffoBvWkIygqiKqgOYMqAEw0j7NC2YzmjOUds/tPsyVvQBHvRh0wedYNOmlmVErEZ9I6ptNh7OzMzx98gQvXzyH3WzQGI8QPELaaydrYCLBWVEHMwAOQsA00F9tF6Lv0sZ7HxAHnwhX2hJK0oPuyYdkTKz731x1FFUE04LRr9e4OHuFYdigWTZQYy6VWufKvAaH8mKY3jfVCNWAp6D/otES4CdZcKcDO703M0IAbACORhaiMSCOmNpnzLWlnFM2kI5PGN0cxp5j+P8zlWyHUkmh89cJVdL1EqUT5dzMfNBnqwBhEljNki0xKG4T/q335nMKcrbfOXf/rmeV9iBLsNJ8Ku2p1JU5aBdzNoBkjrh//w188oMfYvPiG7z+Zo0uDmgj0AaG9SnkfASahYM1jRBeskD2cOEMXGReRSAZzNfboVm6hW41yLUEkXAj1Eiz0kRVa0xuLjmvQgwlT50PKW6PATPBNQ5379/D8ki9LrlIaBVYV9sbUJzo4/bB+KTXUGZWsNK1LCp+RQ4w0YBNstxjTsEYq2tN0tjEAnKUxuwSALMgkEoN8HN8LgBUg+A9wFAPc6KrUSo/os1ybwJQQOF7aU1OU4Po87TOrHWk7aFzzuHWzZvwb72JzaYD+x7PKaJ/+RgUN2DjEKLMw2hMzkCRQVMC4OL8UdlNQgyFvZe2ixdzAuyx2KrBWHESgob7KBob5xxc4yTEy2aD5XKBxWIBd9zCOofVegPfezhjgRjRdWt0/SWWrQOhCitjiv3MLq3N3HGCdm49UsXcYy7R8K5yMNBfPanqMmaw2+qyECPW6xUuLy9xcXGBYejRkLi6xpQsc3AAYorfAXURZWRwnyaIGi0XaUAJtAGjZBi3KbiXapCk4wwG3yH4UAURM/AhoOt7dF3AZnDYgOGbBu9/8Ahvv/8Ii+VxUguiTFDeRuyzhWb+3jMOuwBPPj+RKvaVDPaJku422RwwC8GPlXqeyt/67PG2ICXpv3ivqAYACQAps9vJ6GdA3L5C+Z4r9POfoEisCWxJJuOSwI3+JGVX4wWdn6njwTrWws1kfZMwTSYY4mxgLxFNzQ4phkZjsou45Kv3gJ/JxlShOTQeofyMKXgDADJYHh/jrfce4emjT3D2+hVWlxENCG0gGM/g6NGK1alIuyEiUKz60ABM2RWWOY7WhW4zqMRd125LIOACUovWQbYF9IlCQ8JIG933A4IHog9AjDheLHDn5g1YkxizvEHlq1EfpNppjYBielt3VEVjBBbhW6z9P23R/i0AQAPzUZIqR55VJPNa5+nctrZctt3mbS2LgFkZf30ekEUw7c8JUNTnxAqIkK5tFtsurVNMTJfSzkPd6jmaNj5WBKK6nW3b4s6d2xKJPwYcOeDF10usz55DIrpZ+OTCTYl3muTQo0EyyYjGzNrkjcwx28yofap6hWlRLU3woTihhCBgB2J72zQNXOMQvET8bhoHIgvvA8AR0Q8whrB6+Qp/+x//E7pVhw8//gzHJ3fGsfOm9GQO0IzQr6p/eDSGqlCZruVDZS/A2RXkb/TYGXQKAMF7bDYbrNZrdN0mo0dArLGHoUcMLuUP0Zg1RcVfN0bVexqIK7+aJJ6OtQabdQefuoNZsy6X/fdu02HRtlgulwBZ9L3Hpttg1UVchogLMrj71pv4wb/6M7zx4CGsdRnBl07fX/LCYhbpK3dQRXyxPTCHBmzfME7BkUjQKJojlTJYmZ7JC3cOTevHkBl/1Mi8kn7rCLlzWompFLYlmUH8W0b3bf1xvQolRqRgpW572WpL105uJNq+Z1yEiOZUGFBwU6RRZaQmEc2IMSAegVDMvWs3eJyt09ytNfOe3jICPgUkOdfi9M49vP3JZ3jy7Bme/HqDnjsMIcAMAwhAa12eH7XQAl3nzkok1aicSgmh2MogxkqS56q/Za4yCbFHtZ2RCEYOqYBJf4YQ0KdEvMPQwyvACYybJ6e4ffMGiMRbq6LF+R/FLUX02O7EiufuHBMuD71+ZbItLgBHNJBRCKLM4QRy8m0HgHem+xXNn9LIsi7kd6z4TD1H9FpinTMz400EjV2aBbdUf53JJUhGhnSHuwfyrGyLV5W2bXH//l04a3ByvMDJnbt4+vgrrL/5ApuwxjIwHAZRDFogxQIHERBikGj/ug3FLLTaJY/AtB5iZPiUADvGiKGXNEli0iGanVHaihRDp21bRB/yegR8vq4xwBEiNheX+OIffoaLZy/x8usX+ORHf4H7bzxAu2xgnJVt5gnYmY79zMCnnk1aTNXojC75I2xRHR0fwdrtS8YLkmYXqGpYSGdgmiTKIJTwFCI4fcNVi0yfyBHBi8eUtTbPeu2Eoe8lsFJkkBGAs15vsPGMFRHM7dt49KMf4M333oVr3Kjjau3NVHqQU2PVJljD4yFrTuoemttemh7f1Qu7EKwwvmKnk3DW3j7bOlozX13wo2PVnco4ZjQF9bOmz59uqY35ZwXIdlX9T1ym2wtz6lY9O23bwfQTRDn0Qn7TzBhSkuDIiGcVcFiaqdfjIU3ANggrQKXugaraQL1njsJ4Mhg0Bs46PHj4Jh598j2sXn6D4eVjbHoPDkFs5FLwsSygxJTahQyMc7DNAjCSVwcxwkSfiPNQAenShhI3x4AoIg5xNJe1MJKnVfIoqddYCAF+8JLYl9PWBAxADY5PbuH09CZgbM5FtR+6VCAnAyLt26mNHY2P7UNBf+IyBfiAgBxNlKNaeeLxlum2JmZ7q37umvq4ejyV63Qbc1sLxAq2tI5T+kmUPPMAoGxt6pzSu7kSeOeGZJ82uxb8FLydnhq0jcPxUYubt2/j7v038HjZYvPNb9D5S9gYQTGiIQNLlRt+tnctPFRjyolNmU19KvVVExNupFHMjN73CIMY4Ivzj5iBWGvRti2GYcBmtUHfDylwYIPjoyPJHuAZLRv0gbD66gl++vo/4NmT5/jgk0/w6JMPcPfNh7DNSRqPcQ68XYB2PC8i4mihlrnxRwE4y8USmjtqRPSYcz4RZXR1JQHZY5RYN5KOnSqY3aScMxpoCXl4RrNhP8orb6waWSzxR1OZJaEfx4CmGQCK6HvJX9VHQlgAb737Fj78/qc4OjmBSr9bb2LeqssU3DCQbBW4gAweX7+1sGYAzrQGSvR3PSMfx/gaRecxT4hYfbYnCLP2Iec2/CGUdY741eeyxkkpeF4D1xTi6HyvpLEtbdWIG6lKYe5Rk4OJyObzo1PbAIJQ5komHJN3jO+j/JmCkr1rbSqIbAGgsq1Sc2LKfFyuM9bi5PQG3n3vAzz/8lf43eun8H6AA2dpM89bMGAMTNPCNi1sswBZBxgLceaGOBRw2dKWjNNa5xT0MNVTw03U67XOWachJ7z38uxqfAMzfGT0geEjwHCIkdFtenSrS7AfJNhaajCTzcLAtCNnBTmd+lRrf2bKAWD6pyr1nKkFLcOUIw9nrccIsAh9nGp6p+BHx2yO9sp1ZR5Kqc0qpp25Te/qsp2AE1nQ0HmRbpTfBltMe45PZKCd+KnOR+Wny+USx0dHuHHjBm7fvo2bR0s8+9URnv72F1htXgORYYhhjVQkMoGqmHPWcpkeHBHDtL84b4U7J/3unJP0RkTZEUHrKq7sDZqmQWd6xBBhrcFy2QLk0PQtuq7HMlgM3mITI/rz13jy07/Fiy9/g6dffogf/vlP8Pb7H+Po9A6Mq7y69szjPB6VLIBEW5Q3jfjdHwJwZN9NGj7armLO7oCZ0KdJGZIqjIhwtDzC8fExbt+6hfU3DXhzCaaINm0VtY2BNfr8osIvth/bH1YOmJCDRjElEv//KV+RPxnWEGzj0LYtmA1CCjhoAIAJXdcnr7H9ZW7y7r621EAkgu3B2KW9KXdVRH8GtY7BDZJ6WMFNDXAkFLcS8trbrS5CXFLkYhCIGJEYQJTQ/yZz1hGpmBKpQ20DCnwqra2et9VT16NkGxwznpdA1W4d7yKm54W6rR2p7ysS7q65UsB0kYhjXg+JkGK7/1ToHNd3fM3cvC7qZfkn2/7tvGd7HBmFaLqmwf2Hb+L9Dz/Gk9/+E+J5BwLDpjYEjnCQyKzGavj5ZGcRxU4AMYC9hxjEiE2AbEmZUZsIGPV30zTZQFHp1HRNSULDiAjJGcSc4hFBaJIzjIWN+P/au74fOW4j/RXJ7p5d7a4kS7IlJfblDo6BIEgC5B4O9/+/JA9+uOCcwEGcOGfFkgwr0kra1Uw3ybqHYpHsHzO7lzvg1kIXsLuzPTNsNlksfvWRrGr9gO+f/AVf/u5zNKe3ce/hj0EahHAG0JcnPB5fKE2XlnxG42vWsjdHqoWEMQDQ55jYrDKN7LeH09f1GMvs7wQI1YC7LrqAZqktME80rJ+rAbGOXiFVpQdiXTBRyqGC+WCq6lQDJgKVTe5UwA6RRPRuuxYnJyc4PTnFyfExAjO++/MXiPFCKsIEwIpeRkBSs+szF5CjAGpSm/yenJyycM6VzdCpr5TFcc6hazv0XY/oA7quk+ScRhLeOuuACHhv0ex22PYeQ++xexHw9ZsLvHz6HX76i1/ip7/6V3zw4UdwXYNIgEFJ07IfoOgCVbWvKvfl9RhrUXMAAA7sSURBVMANcI1km86VHDDVvcv4rQBOrXAxCpA5OzvFvXv3cHnnDuKrLRoKaJsGjXNoGklsRyQbKIk4xcChdHytBPnTOkSUaKP1ZN24RmJUqPFCkBgXEKXabDY5cFhkOXLaDx6hZ+wi4fsnT/HXP36Fx48+xeaow+IsgavYpEPyj5mnMjCX36v/zqzilM2ZDOylAT46scYS/RhJwSSLbwRFfT95uhMPa+px7QVlKIaH86+bCm2KLC2g1SwekEBOoTCWyzmgS9Py9M7MsfrMuJxDA17Bzfi7NCtjyVMeg5yx7PNW8/v5a2nisA7N0S3cf/wxzu5/hPOLcxB8FRRSl4siAnkY6oEQgCinQYwtS1SyhMCzZ6nrMX0uAsmmTVCOqotYmE2orpLE72qcReuMgDBYBB8ABjpPeLvb4q9f/Qm3Pvoxjs4+wMnpaZm86/Ya2csq1kfumNR3xX+82YhmIpQ2hyckmOVqT3vsje7TX7VL01xkajv0nOv0+2NmqfxkjaTC5KmoHgYOVT8R7AQ0qSMhnYfch4ccmH12I79vDCwcrHUw1mHY3sfp2W28gICZEDVlaXpeBVxR8gsCyAFyFcjn8nWOTiVYSxJJPLE4MW06rmPOiUPi0LUtomO0XYumkRh2lgw660CRMfgIpoAQLUyIcEQYhoi3f3uO31/8Bhfnr/Hzf/t3PPynT2AayW1ZhzM51O8gAlebpEsfXk+uWKLqYKwdK1WMk93wmHmm2rCbTYs7d07x5v4DvPzwR7gIHh126LoWLg30wtSk/1PwL2cF3JgqinGeVKs0A9YY2K7LrE4MEpZUkLZ4aW3boDk+zrk5QgBiYHRtJ0oyNIh9wDdf/gmf/ezXePzJac7RU1v1Qx5uUeKqTTAdUPPOnHoh/2fyvyySmeuxJH2q1xTtE0/3Fy6Wc13GScrWI7831LqTeqn7QY6+xuRjs8l2AkzGBrkcVS1Z21OLcdUxKHo1BUNzqWfOMbg5BFKm1w+9N7uGMo6stYABYiAcn97F7Q8e4NWTvyD4Af0wSDZxZ+RkR4iIFBDQA9aC2AE2pvFd8teZFG9ozMJUdH3VEkQkh7E4LXJxxfIYSd1grIMjsXEMBjuHJsXZIikcLRkcw6Efdnjz6iV+/5+/w9mDx/j0s8/QNt2Mw5JNkqlOzIUFm+hG1gMkxq28mZaubuaY0PlgastqlmUelLK2BXqtYicXyqrHSXb8mDOJElkCNU7HgnxP90+Ve+VUNdkx1JNJMsh1lgsJxJg83ibORxqatMDqTvOnjXRxAsDTF4RRgbCZHgYDGxg2CIiIDCgvw8oqpZg2BKQo8zGzNxmwACghDJDGToRzMr8GH0ZtbNIxf91OAiIcdRu0zsFwSvybEtEa8jiOLSIztrseLjKawPAA4ttzfPf1V3j0z/+Ch5/8BDlxR1THds7AjeZDhiStzo7j/0wOApy2azMdlG9MQhwpYs4bD5c6FsBw9w7ePXyI8/NX2L0+h/Ov0TZyKNzod5M3ZVJ+jca53PDGGFm/S54dp+B9SOCmPZIkfT6lZ4iZsZOjbMSMk+NjbNouDTSJFRNcg1ubDazYRwze4/Wz7/DlF1/g9O593L59Gzm76R4XfBGpE4lLUwMynZuqzx0ENcwj4wb9emUw6nJG5U08v3/UJBIRZo99Bbmyb4lq+v6IcZp+Vz2xPZPo/7ewKcBlTD9fr75XAYk5AE6TXW7TKVO2UMescEX/iq6MQ9FP9XeJ9ViqZ1XDqpzps1V1KpUDsdLjsjnXM2Pb97DOoesaqG3JxpbK8rWMKzXasi8BXAL5FbDMYBNyRcr0ImUYYxCtJD0kNrKlkAFjOI/dCGGDm7aFcRFNZPR9D2aDSMBlDNiFHV5++w2++Py3uHvrGI8//gnImpyeQB9e/YJxRwGgMTOA5WFcLt5AmepL3XdLzAtQg5oaoMuP6mtd1pwhlUK0RAkHo31XWDAzajOegWGtTOSYx5NBCklQ2XPKCyZj4KRLytO+mdqGOZO4X5jl5G/vA95FwoUnBA9sHKGJEYZjAdwQR5NjTFnDJXFnfb88b+f5O7VNilLcNE3agJzi5uRko9Jezjk55bXp0MCA09ItjAXgUiBAg8BpS0TwYCPxkN4FQj/4ks8tqls77j997oXWSI1bopjr4LiOzT0IcLSkpYJG9JdZ6EASdub22RmG/gFev/w7Lp7+F3B+AV03LMgZWRMNjTcGUhocIWqeGrmv7vg2pJSzoF5N1gmOsBBa7ajboCGLECICBwQi2IbgQLDkMfgem+DBvceTL/+ARz96jKOf/wydOwGRwz7Lsuj5qsc6Qu3SKTMwUpUzGnTqEaX3YwI8ukmsjl2zr180dgql/hPv82qFuNo7Xx7QV31/L6Dbh8BuprOaZ6lDbJ7KIcpc/597muU9ZmVainFXQ6HGWL5CM71invZZfW8e/b/PEE/rPb9Wf2ea+beqS+U9cwzgKA5KDBIkQI+6hxgxeI+BuNqbVzk6ZjyuklKnH8m3XpYbChugz5rbJ6GNHDLBGNneYBkNNfI5Q/AhJDbZglK7E8mRcM8Rx94AbNAM73D+5z/gm3t38fDeh8DxKeDmmZDLzbmqU9VFuU8X4c2NlVo/9zEv4827FWBFea26rM9el6WMjWqy9EMBUWUuQopzJOxyNNq0PJtQpSBdutdExIltSn1OEAChcai1rPmYoZw4dCnrvT5PffPRiKzAGxEktMluhz4C2whg8LgkhoGDsQxQTFGLI6xsJE0EQNLRKr1SZt9J2zbNtcZktkdXSVrXyHwbAkARIQCWgE3ToLEWjhPMMwaycGdSPiuGjxEcPHY7SW0SooWHQWss2k4SXI90o3r2q3VsbLP09VUg5yDA0R3auUI6Ccex8cpex8hrKbQFpTU7ay107NZLIAxhVXJwuTqJGDP8IFlMvfdwrkHbdil4XcxszihrNkdYY3By65bkqYoAvA6CtL5oCYgRg42wNsLBo4uMi+dP8cf/+BwPHt3Fo08+hUZSBcYmZ5/RJ6IUbbms+S9131UT27Qrp904ZW+KMUiRZdIyTzazLBFlNTjc9L5AoTPr47VGjxzODFMZoocmxatYnb1yg73V60z+I29zAegslVHL0pKTAh6ZFLQ/AXVClpa6xvcc33/JKZk+077rCm4AnZimbFJhJgp7U2xI3/fYDQGRDNjYfKBht+thWTzGNoN1eWyxE4m1yfkA6jtoElh5XYMhte8xRoSUr44MwZIY9oCQQFiaGFKp+eQVA2wYlgxCjNi0wEmwsGxgh4CLyzf4/vlTxBjgiDDZuo+R9eDswyK704vDIhvL8tEbKDWwqV9PWZwapE8dpnpyB8Zjpmb/6g+NwE+2gal8yNIgpWi9dUkM2QAt40eu1QcvjDGQcLAoYQtSn5pU1xrE6Dg41DZzwMOyxFSN2dxeDOz6HpeXl+h37wAO2F2+wfmlB85OpS2DOPEA4JxB4wpLFjX9RAUsM8hJ7adqJ/3iE5B3aBqn9BgYEc4ApnE4PtqgaRtYTkfpIUA8xHSgum3gQ8TWOcTew/OAiADPQGMJpjUgy6PnngKcJftFRSGKLQAOtnktV6ZqaJpmZKiLF1SMyjSGgzbybrfFy5cv8fzZMzx79hRvzl/hFse8kXhJqaf0IQHC7oTxbVWZVWI6/aDRlzvX4qiTDOIIDKaAaCKMiTARCEE8RmPES7PGoAHQ+4gXT77Ft199jQcffgzbugXlrBu56oQK2EA7gKZYfVmmRmEmC0BhWifOFGpanSXK6S4kRJWcMtHkhEsTrQ7e/EMGJmVypsqDJvXel9phcm1fNGxZX+VCNzJG5d1EOQRuph7sdcHcEtgdXwOK5z8+2r1E39cyzflSA9Slex/S87FwrheptZzoOVc2YmQfYsS2H7DtBwyBcWSsbOhtUhwOMIL3GPyAJjpxUIhzXp80zYiWE5JzpBuwebEO2kZ6FHzaZ8YQYooOrdcsOO/bkwMNDM8ekVPUdDIITNiCsHUdunsfAV0HGFZCZtQk8j9Dl/jrtiy/q3ZHZWZ/OITOTGoAInqjkYfLXjtlHEt/aXulybAaU1OdnY6zOo0DUTrAAuRI4RKXWOasEj6jbDaugY46mwKKkv3TZZKZ3St6vgxqtNIF4E7tPrME1fM+YBg84m6L2G+xu7xEhJd5EwzuLNo0h8YARENpDCiTEyWAbvV8dR9A7W2aqpyTE8YECMvKBpEJ1ll0rpFTz20rgDGF/FanIbA4ArpvlqxJ8/EOHk720Fmb5lvt+9J/9d9FW6YDiWrnkADe076VXJmqgZkzRZw7h8dehW4yUyXRTnrz+i3+/uIFnj/9Fi+e/Q3Du7eAC8rX5Aon9QGDRp2hD2xsYR1CiOj7Pq0VFuggJ67k5Jf3Ht4P2KX6SmzqKBsXI4Oj3mfcsEQESwY8BFy8fgvfD7BNnnUXWZTp6wxuRh7K1ZPI9Wi6eefXE5wh2TeQIU6lEIK7HUAhxfiQ/Rh1WQJqJAeQRjBeStOgQJoSeDsEcLTcqQIzS7uSGrbI+k8GTjdZloDhElBZ+uy+cpb+H+tFAQz6V+z//qXPcZkzDT547+U6TEqgbAoOSJnEAJlUdn0vOW28B9sIYoIzhLZxoBgQYoD3A0Jo0TgnepaAjRDI+exMXgJbum/kCJosn2VdrJwBUtpdN7mTBA+11uZNnLJMkrxQZgyBZI8EE/j4FLcePEQwJt0tGeURluFU4wia2IXl9ppSYDdXpuBeZRnsM5hL/iPN94fKligYHBFfozE1L3956ah0AXH5m+eZqv615FNI1ftMlA8YqH3l8oHsnO0b0+NxqmBv7NDEGCU2TWREJvgIbH3ELgCOGNvtFgTA8AZ208LZkrZBSYN6qU0dzJrFUTuu97bWom3EwYjeS0aAKv1Iu+nQdh1cStJpCDkECbOsGJCRjAINybaRGFNYB+tw6+wO2vZE8iJWZMiSXVlalVDHTHGl9v91WBy69pLBKqusssoqq6yyyg9E9u2EW2WVVVZZZZVVVvnBygpwVllllVVWWWWV905WgLPKKqusssoqq7x3sgKcVVZZZZVVVlnlvZMV4KyyyiqrrLLKKu+drABnlVVWWWWVVVZ57+S/AdZpJT36Hm7lAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bAywKtuVn8uK"
      },
      "source": [
        "### Rescale pixel values\n",
        "\n",
        "Vamos a descargar el modelo `tf.keras.applications.MobileNetV2` como base. Este modelo espera valores de píxeles en el rango `[-1,1]`, pero en este momento, los tenemos en el rango `[0-255]`. Por lo tanto, tenemos que hacer un reescalado:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cO0HM9JAQUFq"
      },
      "source": [
        "preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R2NyJn4KQMux"
      },
      "source": [
        "rescale = tf.keras.layers.experimental.preprocessing.Rescaling(1./127.5, offset= -1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OkH-kazQecHB"
      },
      "source": [
        "## Creando el modelo base\n",
        "Vamos a crear el modelo base a partir del modelo de **MobileNet V2**. Primero de todo, vamos a especificar qué capa usar para la extracción de características. En nuestro caso, vamos a usar la última capa antes de la operación de aplanado."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19IQ2gqneqmS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4aaa967b-33b6-4efa-820f-3e4f51ec7e1b"
      },
      "source": [
        "IMG_SHAPE = IMG_SIZE + (3,)\n",
        "base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\n",
        "                                               include_top=False,\n",
        "                                               weights='imagenet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AqcsxoJIEVXZ"
      },
      "source": [
        "Este extractor de características convierte cada imagen en un bloque de características de `7x7x1280`. Vamos a ver cómo se comporta con un \"batch\" de imágenes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-2LJL0EEUcx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb8ec448-8cb4-423e-a101-6db3f613af02"
      },
      "source": [
        "image_batch, label_batch = next(iter(train_dataset))\n",
        "feature_batch = base_model(image_batch)\n",
        "print(feature_batch.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 7, 7, 1280)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlx56nQtfe8Y"
      },
      "source": [
        "## Extracción de características\n",
        "Ahora vamos a congelar la base convolucional creada en pasos anteriores y usarla como un extractor de características."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CnMLieHBCwil"
      },
      "source": [
        "### Congelar la base convolucional"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7fL6upiN3ekS"
      },
      "source": [
        "Esto previene que los pesos de la capa se actualicen durante el entrenamiento."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTCJH4bphOeo"
      },
      "source": [
        "base_model.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KpbzSmPkDa-N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18628698-4ed7-4395-c8ca-ee8eff1583ae"
      },
      "source": [
        "# Veamos la estructura de la red\n",
        "base_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"mobilenetv2_1.00_224\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_9 (InputLayer)            [(None, 220, 220, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, 110, 110, 32) 864         input_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalization)   (None, 110, 110, 32) 128         Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, 110, 110, 32) 0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, 110, 110, 32) 288         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, 110, 110, 32) 128         expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, 110, 110, 32) 0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, 110, 110, 16) 512         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, 110, 110, 16) 64          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, 110, 110, 96) 1536        expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, 110, 110, 96) 384         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, 110, 110, 96) 0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, 111, 111, 96) 0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, 55, 55, 96)   864         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, 55, 55, 96)   384         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, 55, 55, 96)   0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, 55, 55, 24)   2304        block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, 55, 55, 24)   96          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, 55, 55, 144)  3456        block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, 55, 55, 144)  576         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, 55, 55, 144)  0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, 55, 55, 144)  1296        block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, 55, 55, 144)  576         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, 55, 55, 144)  0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, 55, 55, 24)   3456        block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, 55, 55, 24)   96          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, 55, 55, 24)   0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, 55, 55, 144)  3456        block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, 55, 55, 144)  576         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, 55, 55, 144)  0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, 57, 57, 144)  0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, 28, 28, 144)  1296        block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, 28, 28, 144)  576         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, 28, 28, 144)  0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, 28, 28, 32)   4608        block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, 28, 28, 32)   128         block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, 28, 28, 192)  6144        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, 28, 28, 32)   6144        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, 28, 28, 32)   128         block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, 28, 28, 32)   0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, 28, 28, 192)  6144        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, 28, 28, 32)   6144        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, 28, 28, 32)   128         block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, 28, 28, 32)   0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, 28, 28, 192)  6144        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, 29, 29, 192)  0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, 14, 14, 192)  1728        block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, 14, 14, 192)  768         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, 14, 14, 192)  0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, 14, 14, 64)   12288       block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, 14, 14, 64)   256         block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, 14, 14, 384)  24576       block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, 14, 14, 64)   24576       block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, 14, 14, 64)   256         block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, 14, 14, 64)   0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, 14, 14, 384)  24576       block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, 14, 14, 64)   24576       block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, 14, 14, 64)   256         block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, 14, 14, 64)   0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, 14, 14, 384)  24576       block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, 14, 14, 64)   24576       block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, 14, 14, 64)   256         block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, 14, 14, 64)   0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, 14, 14, 384)  24576       block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, 14, 14, 384)  1536        block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, 14, 14, 384)  0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, 14, 14, 384)  3456        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, 14, 14, 384)  1536        block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, 14, 14, 96)   36864       block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, 14, 14, 96)   384         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, 14, 14, 576)  55296       block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, 14, 14, 96)   55296       block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, 14, 14, 96)   384         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, 14, 14, 96)   0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, 14, 14, 576)  55296       block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, 14, 14, 96)   55296       block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, 14, 14, 96)   384         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, 14, 14, 96)   0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, 14, 14, 576)  55296       block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, 15, 15, 576)  0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, 7, 7, 576)    5184        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, 7, 7, 576)    2304        block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)    0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, 7, 7, 160)    92160       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, 7, 7, 160)    640         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, 7, 7, 960)    153600      block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, 7, 7, 160)    153600      block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, 7, 7, 160)    640         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, 7, 7, 160)    0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, 7, 7, 960)    153600      block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, 7, 7, 160)    153600      block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, 7, 7, 160)    640         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, 7, 7, 160)    0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, 7, 7, 960)    153600      block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, 7, 7, 320)    307200      block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, 7, 7, 320)    1280        block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 7, 7, 1280)   409600      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)   5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, 7, 7, 1280)   0           Conv_1_bn[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 2,257,984\n",
            "Trainable params: 0\n",
            "Non-trainable params: 2,257,984\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wdMRM8YModbk"
      },
      "source": [
        "### Añadiendo una capa de clasificación"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QBc31c4tMOdH"
      },
      "source": [
        "Para generar predicciones a partir de las características extraídas, vamos a usar una capa de tipo `tf.keras.layers.GlobalAveragePooling2D`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dLnpMF5KOALm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "825e289a-5eb3-4c0d-f901-7dbab3459ee5"
      },
      "source": [
        "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
        "feature_batch_average = global_average_layer(feature_batch)\n",
        "print(feature_batch_average.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 1280)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O1p0OJBR6dOT"
      },
      "source": [
        "Añadimos una capa `tf.keras.layers.Dense` para convertir las características en una predicción por imagen, con la función de activación \"softmax\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wv4afXKj6cVa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ea4d31b-9778-4be3-bd03-d87987e3aa92"
      },
      "source": [
        "prediction_layer = tf.keras.layers.Dense(5, activation='softmax')\n",
        "prediction_batch = prediction_layer(feature_batch_average)\n",
        "print(prediction_batch.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HXvz-ZkTa9b3"
      },
      "source": [
        "Construyamos el modelo aplicando todos los pasos anteriores:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DgzQX6Veb2WT"
      },
      "source": [
        "inputs = tf.keras.Input(shape=(220, 220, 3))\n",
        "#x = data_augmentation(inputs)\n",
        "x = preprocess_input(inputs)\n",
        "x = base_model(x, training=False)\n",
        "x = global_average_layer(x)\n",
        "x = tf.keras.layers.Dropout(0.2)(x)\n",
        "x = tf.keras.layers.Dense(512, activation='relu')(x)\n",
        "#\n",
        "#outputs = prediction_layer(x)\n",
        "outputs = tf.keras.layers.Dense(5, activation='softmax')(x)\n",
        "\n",
        "model = tf.keras.Model(inputs, outputs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0ylJXE_kRLi"
      },
      "source": [
        "### Compilando el modelo\n",
        "\n",
        "Vamos a compilar el modelo. Como tenemos más de 2 clases, usamos una función de pérdidas \"sparse_categorical_crossentropy\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpR8HdyMhukJ"
      },
      "source": [
        "base_learning_rate = 0.0001\n",
        "model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8ARiyMFsgbH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26091427-93e3-49e4-b41a-e41f85f7233d"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_10 (InputLayer)        [(None, 220, 220, 3)]     0         \n",
            "_________________________________________________________________\n",
            "tf.math.truediv_4 (TFOpLambd (None, 220, 220, 3)       0         \n",
            "_________________________________________________________________\n",
            "tf.math.subtract_4 (TFOpLamb (None, 220, 220, 3)       0         \n",
            "_________________________________________________________________\n",
            "mobilenetv2_1.00_224 (Functi (None, 7, 7, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_4 ( (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 512)               655872    \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 5)                 2565      \n",
            "=================================================================\n",
            "Total params: 2,916,421\n",
            "Trainable params: 658,437\n",
            "Non-trainable params: 2,257,984\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RxvgOYTDSWTx"
      },
      "source": [
        "### Entrenamiento del modelo\n",
        "\n",
        "Vamos a entrenar el modelo durante 10 epochs:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Om4O3EESkab1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "606c6b39-d603-45a2-cd3a-1b83f078eeff"
      },
      "source": [
        "initial_epochs = 5\n",
        "\n",
        "loss0, accuracy0 = model.evaluate(validation_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3/3 [==============================] - 2s 49ms/step - loss: 2.2627 - accuracy: 0.0381\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8cYT1c48CuSd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f901c079-e393-4312-e393-8c32734c219f"
      },
      "source": [
        "print(\"initial loss: {:.2f}\".format(loss0))\n",
        "print(\"initial accuracy: {:.2f}\".format(accuracy0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "initial loss: 2.30\n",
            "initial accuracy: 0.03\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JsaRFlZ9B6WK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3614ed9-6f0d-416a-955a-efb0dd57d40a"
      },
      "source": [
        "history = model.fit(train_dataset,\n",
        "                    epochs=initial_epochs,\n",
        "                    validation_data=validation_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "18/18 [==============================] - 4s 85ms/step - loss: 0.6762 - accuracy: 0.8221 - val_loss: 0.0164 - val_accuracy: 1.0000\n",
            "Epoch 2/5\n",
            "18/18 [==============================] - 2s 81ms/step - loss: 0.0065 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000\n",
            "Epoch 3/5\n",
            "18/18 [==============================] - 2s 82ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000\n",
            "Epoch 4/5\n",
            "18/18 [==============================] - 2s 79ms/step - loss: 7.8001e-04 - accuracy: 1.0000 - val_loss: 9.2585e-04 - val_accuracy: 1.0000\n",
            "Epoch 5/5\n",
            "18/18 [==============================] - 2s 80ms/step - loss: 5.3239e-04 - accuracy: 1.0000 - val_loss: 6.2606e-04 - val_accuracy: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hd94CKImf8vi"
      },
      "source": [
        "### Curvas de aprendizaje"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53OTCh3jnbwV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "22bbc441-d005-4bd5-c8a3-46d77eaf2419"
      },
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(acc, label='Training Accuracy')\n",
        "plt.plot(val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([min(plt.ylim()),1])\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('Cross Entropy')\n",
        "plt.ylim([0,1.0])\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQadCVU3JRNY"
      },
      "source": [
        "# Comprobaciones\n",
        "\n",
        "Vamos a cargar 3 imágenes sacadas desde la webcam como última comprobación de que el modelo funciona correctamente:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMZTea1eWKzz"
      },
      "source": [
        "my_images = ['/WIN_20210214_12_19_35_Pro.jpg','/WIN_20210214_12_19_36_Pro.jpg','/WIN_20210214_12_19_38_Pro.jpg']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "XpgScjefJQ7r",
        "outputId": "08a9255c-4507-42bb-ba07-0fefb21a1cfe"
      },
      "source": [
        "import PIL.Image as Image\r\n",
        "\r\n",
        "IMAGE_SHAPE = (220, 220)\r\n",
        "IMAGE_PATH = my_images[2]\r\n",
        "back = Image.open(IMAGE_PATH).resize(IMAGE_SHAPE)\r\n",
        "back"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=220x220 at 0x7FC1436F6588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 297
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GaG5Nd6IWdGy",
        "outputId": "8f8ee42b-9858-439a-93e1-86e7d449cd05"
      },
      "source": [
        "pix = np.array(back.getdata()).reshape(back.size[0], back.size[1], 3)\r\n",
        "pix.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(220, 220, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 298
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DWQS8c8_JjGA",
        "outputId": "c9cd6957-31f0-45b3-c3e1-d089b6bcba12"
      },
      "source": [
        "result = model.predict(pix[np.newaxis, ...])\r\n",
        "result.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fc142faf488> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 299
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iNhp2BaqJm1r",
        "outputId": "10c54347-9523-467b-9c68-8d012dcb7d1a"
      },
      "source": [
        "print(class_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['backward', 'forward', 'jump']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cg6GxXeMJpsd",
        "outputId": "e07cccfb-2cf8-44a2-d8b8-b41bf1bb6198"
      },
      "source": [
        "predicted_class = np.argmax(result[0], axis=-1)\r\n",
        "predicted_class"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 301
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPssl8ZrJ3LU"
      },
      "source": [
        "# Exportación del modelo a TensorFlow.js"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "zZhUtO-DJ5p8",
        "outputId": "105f591c-5c43-4bbb-ded6-cef8236e822f"
      },
      "source": [
        "export_path = \"/saved_models/gestures\"\r\n",
        "model.save(export_path)\r\n",
        "\r\n",
        "export_path"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /saved_models/gestures/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/saved_models/gestures'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 302
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rTE9EnZ6J-cq",
        "outputId": "23dc4b12-79c6-4003-b3b2-680c552c1b3d"
      },
      "source": [
        "!pip install tensorflowjs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflowjs in /usr/local/lib/python3.6/dist-packages (3.0.0)\n",
            "Requirement already satisfied: tensorflow-hub<0.10,>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflowjs) (0.9.0)\n",
            "Requirement already satisfied: h5py<3,>=2.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorflowjs) (2.10.0)\n",
            "Requirement already satisfied: tensorflow<3,>=2.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflowjs) (2.4.1)\n",
            "Requirement already satisfied: six<2,>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflowjs) (1.15.0)\n",
            "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-hub<0.10,>=0.7.0->tensorflowjs) (3.12.4)\n",
            "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-hub<0.10,>=0.7.0->tensorflowjs) (1.19.5)\n",
            "Requirement already satisfied: grpcio~=1.32.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.32.0)\n",
            "Requirement already satisfied: wheel~=0.35 in /usr/local/lib/python3.6/dist-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.36.2)\n",
            "Requirement already satisfied: tensorboard~=2.4 in /usr/local/lib/python3.6/dist-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (2.4.1)\n",
            "Requirement already satisfied: tensorflow-estimator<2.5.0,>=2.4.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (2.4.0)\n",
            "Requirement already satisfied: opt-einsum~=3.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (3.3.0)\n",
            "Requirement already satisfied: flatbuffers~=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.12)\n",
            "Requirement already satisfied: astunparse~=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.6.3)\n",
            "Requirement already satisfied: wrapt~=1.12.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.12.1)\n",
            "Requirement already satisfied: keras-preprocessing~=1.1.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.1.2)\n",
            "Requirement already satisfied: termcolor~=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.1.0)\n",
            "Requirement already satisfied: absl-py~=0.10 in /usr/local/lib/python3.6/dist-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.10.0)\n",
            "Requirement already satisfied: google-pasta~=0.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.2.0)\n",
            "Requirement already satisfied: typing-extensions~=3.7.4 in /usr/local/lib/python3.6/dist-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (3.7.4.3)\n",
            "Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.3.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.8.0->tensorflow-hub<0.10,>=0.7.0->tensorflowjs) (53.0.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (1.0.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (0.4.2)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (1.25.0)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (2.23.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (3.3.3)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (1.8.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (1.3.0)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (4.2.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (4.7)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (0.2.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (1.24.3)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (3.4.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (3.1.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (0.4.8)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (3.4.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P0RR1-kGKADd",
        "outputId": "ebed3ba6-b213-4508-b9cf-97208ad1730a"
      },
      "source": [
        "!tensorflowjs_converter \\\r\n",
        "    --input_format=tf_saved_model \\\r\n",
        "    --output_format=tfjs_graph_model \\\r\n",
        "    --signature_name=serving_default \\\r\n",
        "    --saved_model_tags=serve \\\r\n",
        "    /saved_models/gestures \\\r\n",
        "    /mobilenet/web_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-02-14 11:59:11.526565: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
            "2021-02-14 11:59:13.329244: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
            "2021-02-14 11:59:13.330056: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n",
            "2021-02-14 11:59:13.335938: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:13.336344: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2021-02-14 11:59:13.336384: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
            "2021-02-14 11:59:13.338091: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10\n",
            "2021-02-14 11:59:13.338165: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10\n",
            "2021-02-14 11:59:13.339796: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
            "2021-02-14 11:59:13.340145: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
            "2021-02-14 11:59:13.341944: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
            "2021-02-14 11:59:13.343061: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10\n",
            "2021-02-14 11:59:13.346642: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7\n",
            "2021-02-14 11:59:13.346757: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:13.347178: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:13.347528: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
            "2021-02-14 11:59:13.347905: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
            "2021-02-14 11:59:13.348023: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:13.348416: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2021-02-14 11:59:13.348456: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
            "2021-02-14 11:59:13.348487: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10\n",
            "2021-02-14 11:59:13.348511: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10\n",
            "2021-02-14 11:59:13.348546: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
            "2021-02-14 11:59:13.348570: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
            "2021-02-14 11:59:13.348592: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
            "2021-02-14 11:59:13.348613: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10\n",
            "2021-02-14 11:59:13.348635: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7\n",
            "2021-02-14 11:59:13.348708: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:13.349092: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:13.349426: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
            "2021-02-14 11:59:13.349473: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
            "2021-02-14 11:59:13.858486: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2021-02-14 11:59:13.858556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n",
            "2021-02-14 11:59:13.858575: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n",
            "2021-02-14 11:59:13.858765: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:13.859218: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:13.859624: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:13.859966: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2021-02-14 11:59:13.860015: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5721 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "2021-02-14 11:59:19.175271: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:19.175735: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\n",
            "2021-02-14 11:59:19.175917: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session\n",
            "2021-02-14 11:59:19.176203: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
            "2021-02-14 11:59:19.176328: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:19.176754: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2021-02-14 11:59:19.176802: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
            "2021-02-14 11:59:19.176844: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10\n",
            "2021-02-14 11:59:19.176868: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10\n",
            "2021-02-14 11:59:19.176891: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
            "2021-02-14 11:59:19.176925: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
            "2021-02-14 11:59:19.176949: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
            "2021-02-14 11:59:19.176972: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10\n",
            "2021-02-14 11:59:19.176994: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7\n",
            "2021-02-14 11:59:19.177067: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:19.177454: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:19.177827: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
            "2021-02-14 11:59:19.177873: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2021-02-14 11:59:19.177890: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n",
            "2021-02-14 11:59:19.177907: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n",
            "2021-02-14 11:59:19.178006: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:19.178404: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-02-14 11:59:19.178781: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5721 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "2021-02-14 11:59:19.179025: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2199995000 Hz\n",
            "2021-02-14 11:59:19.205168: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:928] Optimization results for grappler item: graph_to_optimize\n",
            "  function_optimizer: Graph size after: 1002 nodes (735), 1505 edges (1238), time = 15.133ms.\n",
            "  function_optimizer: function_optimizer did nothing. time = 0.276ms.\n",
            "\n",
            "2021-02-14 11:59:20.256218: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:928] Optimization results for grappler item: graph_to_optimize\n",
            "  debug_stripper: debug_stripper did nothing. time = 0.056ms.\n",
            "  model_pruner: Graph size after: 701 nodes (-267), 1238 edges (-267), time = 4.846ms.\n",
            "  constant_folding: Graph size after: 437 nodes (-264), 974 edges (-264), time = 66.742ms.\n",
            "  arithmetic_optimizer: Graph size after: 437 nodes (0), 974 edges (0), time = 12.575ms.\n",
            "  dependency_optimizer: Graph size after: 435 nodes (-2), 444 edges (-530), time = 6.152ms.\n",
            "  model_pruner: Graph size after: 435 nodes (0), 444 edges (0), time = 3.062ms.\n",
            "  constant_folding: Graph size after: 435 nodes (0), 444 edges (0), time = 19.443ms.\n",
            "  arithmetic_optimizer: Graph size after: 435 nodes (0), 444 edges (0), time = 12.734ms.\n",
            "  dependency_optimizer: Graph size after: 435 nodes (0), 444 edges (0), time = 4.542ms.\n",
            "  debug_stripper: debug_stripper did nothing. time = 0.388ms.\n",
            "  model_pruner: Graph size after: 435 nodes (0), 444 edges (0), time = 2.611ms.\n",
            "  constant_folding: Graph size after: 435 nodes (0), 444 edges (0), time = 16.667ms.\n",
            "  arithmetic_optimizer: Graph size after: 435 nodes (0), 444 edges (0), time = 13.301ms.\n",
            "  dependency_optimizer: Graph size after: 435 nodes (0), 444 edges (0), time = 5.045ms.\n",
            "  model_pruner: Graph size after: 435 nodes (0), 444 edges (0), time = 3.231ms.\n",
            "  constant_folding: Graph size after: 435 nodes (0), 444 edges (0), time = 16.461ms.\n",
            "  arithmetic_optimizer: Graph size after: 435 nodes (0), 444 edges (0), time = 12.9ms.\n",
            "  dependency_optimizer: Graph size after: 435 nodes (0), 444 edges (0), time = 4.743ms.\n",
            "\n",
            "2021-02-14 11:59:26.767273: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:928] Optimization results for grappler item: graph_to_optimize\n",
            "  remapper: Graph size after: 431 nodes (-56), 232 edges (-56), time = 2.365ms.\n",
            "  constant_folding: Graph size after: 223 nodes (-208), 232 edges (0), time = 15.519ms.\n",
            "  arithmetic_optimizer: Graph size after: 223 nodes (0), 232 edges (0), time = 5.833ms.\n",
            "  dependency_optimizer: Graph size after: 223 nodes (0), 232 edges (0), time = 1.62ms.\n",
            "  remapper: Graph size after: 223 nodes (0), 232 edges (0), time = 1.006ms.\n",
            "  constant_folding: Graph size after: 223 nodes (0), 232 edges (0), time = 6.078ms.\n",
            "  arithmetic_optimizer: Graph size after: 223 nodes (0), 232 edges (0), time = 5.233ms.\n",
            "  dependency_optimizer: Graph size after: 223 nodes (0), 232 edges (0), time = 1.608ms.\n",
            "\n",
            "Writing weight file /mobilenet/web_model/model.json...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_OrPjiYav-i"
      },
      "source": [
        "!rm -rf "
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}