Diabetic_Retinopathy_Detection(ResNet152).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Necessary Imports"
      ],
      "metadata": {
        "id": "_bswPFSgDSRV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ghM1Oa5OBWEI"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D , SeparableConv2D, MaxPooling2D , Flatten , Dropout , BatchNormalization, Activation\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.utils import img_to_array,array_to_img\n",
        "from keras.callbacks import ReduceLROnPlateau \n",
        "from keras import backend as K\n",
        "from keras import optimizers\n",
        "from sklearn.metrics import classification_report, recall_score, precision_score, confusion_matrix, f1_score, accuracy_score\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from IPython.display import Image"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset Loading"
      ],
      "metadata": {
        "id": "w2-pJjt9DWC-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_dir = '/content/drive/MyDrive/Diabetic Rentinopathy/Final_Dataset/'"
      ],
      "metadata": {
        "id": "ruCzV-TPBfih"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "        rescale=1./255,\n",
        "        horizontal_flip = True,\n",
        "        validation_split=0.2\n",
        "        )\n",
        "\n",
        "train_gen = train_datagen.flow_from_directory(\n",
        "        directory = train_data_dir,\n",
        "        target_size=(224, 224),\n",
        "        batch_size=16,\n",
        "        class_mode='categorical',\n",
        "        subset='training'\n",
        "        )\n",
        "val_gen = train_datagen.flow_from_directory(\n",
        "        directory = train_data_dir,\n",
        "        target_size=(224, 224),\n",
        "        batch_size=16,\n",
        "        class_mode='categorical',\n",
        "        subset='validation'\n",
        "        )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oC-cTdoCXDWn",
        "outputId": "0ed15113-180e-4e4b-820f-829bb75d133d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2931 images belonging to 5 classes.\n",
            "Found 730 images belonging to 5 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_size = (224,224,3)"
      ],
      "metadata": {
        "id": "uoQIa5ItXuDI"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Creation"
      ],
      "metadata": {
        "id": "VEwWvZ_DDayj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = tf.keras.applications.ResNet152V2(input_shape=image_size,\n",
        "                                               include_top=False,\n",
        "                                               weights=\"imagenet\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7X8azSOeL-2",
        "outputId": "93abfa7b-a59f-4bbb-af93-9a31d16341d1"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet152v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "234545216/234545216 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in base_model.layers[:-10]:\n",
        "    layer.trainable=False"
      ],
      "metadata": {
        "id": "mkUdfVZNeUMc"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential()\n",
        "model.add(base_model)\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Flatten())\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(256,kernel_initializer='he_uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(128,kernel_initializer='he_uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(32,kernel_initializer='he_uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dense(5,activation='softmax'))"
      ],
      "metadata": {
        "id": "O26jKmfeeX5o"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJxtihSPYqtJ",
        "outputId": "e78f9ac4-7374-4951-f5e9-c6d984c7e3bb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " resnet152v2 (Functional)    (None, 7, 7, 2048)        58331648  \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 7, 7, 2048)        0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 100352)            0         \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 100352)           401408    \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               25690368  \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 256)              1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation (Activation)     (None, 256)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 128)              512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 32)                4128      \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 32)               128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 32)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 5)                 165       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 84,462,277\n",
            "Trainable params: 29,345,157\n",
            "Non-trainable params: 55,117,120\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plotting the Model"
      ],
      "metadata": {
        "id": "k1EQeUd6D7c5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model, to_file='ResNet.png', show_shapes=True,show_layer_names=True)\n",
        "Image(filename='ResNet.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Oaqz470OepVm",
        "outputId": "b2af4db6-7cac-4c0a-d28d-d558b6e3846b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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d2s06r6Z7mAlF6PEm9Prry1TFKD8/n6ysrJ56uytz0tDQQKNGjaIdO3YInYrGs5DT3bt3SSqV0saNG3WWdaqp3azzMOSCdlfUVde/uroaR44cQX5+vuZCtI+PD2JjYxEbG2vwneGF0NDQgPT0dFRUVCAkJETodAA8OzmtXLkSgwYN0sz+JSIUFhYiKysLV65cMclzPI6LkRm7dOlSsz9l8eSfuRwQzLzcu3cPr732Gvr06YO3335b0x4dHY3g4GCEhIQYPJmho2VmZmL//v3IyMjQ+/tR7e1ZyGnTpk3Izs7G4cOHIRaLAQAHDhyAq6srRo0ahe+//77Nz6HDgI9RrIuJjo4ma2trAkCenp60d+/eDs9ByPFmDuuvLxh4mk4fR44coaioKJPGZJ1feno6rV27Vuc2ZqbQ2mk6EZH2HfBSU1MxY8aMjrkxHnvm8XjTj0gkQkpKCqZPny50KowZrZXjfS+fpmOMMSY4LkaMMcYEx8WIMcaY4LgYMcYYExwXI8YYY4KzammBSCTqyDzYM47H29PNmDEDM2bMEDoNxtpFi8XIVL9Bz1hrTp06hc2bN/N4e4oZM2YgPDwcfn5+QqfCmNGajvfmtFiM+PsMrKNs3ryZx9tTzJgxA35+frydWKfXUjHia0aMMcYEx8WIMcaY4LgYMcYYExwXI8YYY4LjYsQYY0xwnaoY/fzzz+jbty8sLCwgEong5OSEuLg4odPC/v374e3trfl9IWdnZ8yaNUvotNgzbN68eVq/edXceDx69Ciio6N1xu/s2bN1+gYEBECpVMLS0hL9+/fH2bNnO2I1DBIbG4t+/fpBpVJBIpHAx8cHS5Ys0foRQX36PKmmpga+vr5YtmxZl8gpPj4evr6+kMlkUCgU8PX1xfLly1FeXg4AOHjwIOLj43V+dDI9PV1rTHXv3t3g526VAb83YTZeffVVAkD3798XOhUtarWabG1thU6jU+kM480cwIifHXdwcKCMjAzKy8ujmpoareUrVqygiRMnUnl5uaZNrVZTt27dCAAdOnRIJ2ZGRgZNmjTJ+JVoZ6NHj6akpCQqKSmh8vJySklJIbFYTK+99ppBfZ60aNEiAkAxMTFdIqcJEybQxo0bqaioiCoqKig1NZXEYjG98sormj6bN2+m0aNHa73GNjY2UkFBAZ04cYLGjx/PPztuDqqrq+Hv7y90GqyN2nM/msMYkclkml96lUgkmvZ169Zhz549SE1NhVKp1HrMli1bYGFhgdDQULP/Fdgn2djYIDQ0FA4ODlAqlZg+fTqCgoLwww8/4ObNm3r3edzJkydx4cKFLpWTtbU1FixYAEdHR9jY2CA4OBiTJ0/G//3f/+GPP/4AACxcuBAvvPACxo8fj/r6egCP7pLS9EuvvXv3Nvr5W8LFyAg7duxAUVGR0GmwNmrP/WiuY+TKlStYvnw5Vq1aBalUqrPc398f4eHhuHXrFhYvXixAhsY7dOgQLC0ttdqaTiVVVVXp3adJdXU1IiMjW/ySZmfNKS0tTWffu7q6AoDWqcGVK1ciOzu7Tc9liC5RjLZu3QqFQgG5XI4DBw5g3LhxUKlUcHNzw+7duwE8escnlUrRo0cPzJs3Dy4uLpBKpfD398fp06cBAGFhYbC2toazs7Mm9oIFC6BQKCASiXD37l2Eh4cjIiICV69ehUgkgo+Pj8H5/utf/0K/fv1ga2sLqVSKgQMH4siRIwCAuXPnas7JqtVqnDt3DgAwZ84cyOVy2Nra4uDBg2hoaMCKFSvg4eEBmUyG559/XnNLnfXr10Mul0OpVKKoqAgRERFwdXVFXl5em7azuSAibNq0CX379oVEIoG9vT0mT56MS5cuATB+P7b3GPnhhx+gUqmwZs2aDtxa2rZs2QIiQmBgYIt94uLi0KdPH2zfvh1Hjx5tsd/T9oM+xyWAVsdyW926dQsymQxeXl4G94mJidF8gjAlc8wpPz8fdnZ26NWrl6bN3t4eo0ePxubNmzvml5gNOKdnNpq7ZhQTE0MA6NixY1RWVkZFRUU0atQoUigUVFtbS0SPzqMrFAq6ePEi1dTUUG5uLg0bNoyUSiXduHGDiIhmzpxJTk5OWs+3YcMGAkDFxcVERDR16lRSq9U6eel7zWjv3r20cuVKunfvHpWUlNDw4cO1zr9OnTqVLC0t6datW1qPe+ONN+jgwYNERLR48WKSSCS0b98+un//Pi1dupQsLCzozJkzWttj4cKFlJiYSFOmTKH//Oc/T82toxkz3lasWEHW1ta0c+dOKi0tpfPnz9OQIUOoe/fudPv2bSIyfj+25xg5dOgQKZVKio2NNWh9iYy7ZuTq6qrT7u3tTf369Wv2MWq1mn7//XciIjp58iRZWFiQp6cnVVZWEpHuNSN99oM+x+XTxrKxHjx4QEqlksLCwgzuk5WVRYGBgUREVFxcbPT1GXPOqba2lgoKCigxMZEkEgnt3LlTp090dDQBoHPnzmm1L1y4kK8ZPY2/vz9UKhUcHR0REhKCBw8e4MaNG5rlVlZWmndy/fr1w9atW1FRUYHk5OQOy3HatGn4+OOPYW9vDwcHBwQGBqKkpATFxcUAgPnz56OhoUErp/Lycpw5cwbjx49HTU0Ntm7diqCgIEydOhV2dnZYtmwZxGKxznqsW7cO77//Pvbv3w9fX98OW8f2Ul1djU2bNmHKlCmYNWsWbG1tMXDgQHz++ee4e/cutm3b1ubnaK8xMmHCBJSXl2P58uVtztEYDx48wO+//w61Wv3Uvn5+fvjwww9x7do1fPTRRzrLDd0PLR2XhoxlQ61duxYuLi6tzrhtrk91dTXCw8OxdevWNj2/uefk7u4ONzc3rFy5EuvXr2/2jvBN14ZycnJM9rwt6XLF6HHW1tYAgLq6uhb7DB06FHK5XHNqQQhisRgANFMpX375ZfTp0wdffvml5uPxnj17EBISAktLS+Tl5aGqqgoDBgzQxJDJZHB2dhZ0PTpCbm4uKisrMXToUK32YcOGwdraWnM6zZTMYYyYQlFREYgIcrlcr/5xcXF47rnnkJSUhKysLK1lbdkPjx+X7TWW09LSkJqaiiNHjuhM0nhan6VLl+Ldd9/VXEcxFXPL6ebNmygqKsK3336Lf/zjHxg8eLDOdc6msXLnzh2TPW9LunQx0pdEItF8KukI33//PV566SU4OjpCIpFgyZIlWstFIhHmzZuH3377DceOHQMAfP311/jrX/8K4NE7XABYtmyZ1rz/69ev61zw7GpKS0sBPJqB9CQ7OztUVFS0y/N29BhpDzU1NQCgNbOuNVKpFMnJyRCJRHj77bdRXV2tWWaq/dAeY3nPnj1Yt24dMjMz4enpaVCfrKws5OTkYO7cuUY9d2fKSSwWw9HREQEBAdizZw9yc3Oxdu1arT4ymQzAf8dOe3rmi1FdXR1KS0vh5ubWrs9z4sQJJCQk4MaNGwgKCoKzszNOnz6NsrIyxMfH6/R/6623IJVKsX37duTl5UGlUmkuLjZdvExISAARaf2dOnWqXddDaHZ2dgDQ7Itde+3Hjhoj7a3pheXJLzO2xs/PD4sWLUJ+fj5Wr16taTfVfjD1WE5MTMSuXbtw/Phx9OzZ0+A+O3bswLFjxzRfrBeJRJoc16xZA5FIhF9//bXT5/QkHx8fWFpaIjc3V6u9trYWwH/HTnt65otRZmYmiAjDhw8H8Oh6QWun9Yz1//7f/4NCoUBOTg7q6urw3nvvwdvbG1KptNlfObW3t8eMGTOQnp6OjRs34p133tEsc3d3h1QqRXZ2tsnzNHcDBgyAjY2NzsF3+vRp1NbW4sUXXwRg2v3YUWOkvfXo0QMikcjg7w+tXr0avr6+mpmdgP774WlMNZaJCFFRUcjJyUF6enqzn9j06ZOcnKxTFJs+EcfExICIdE5NdqacSkpK8MYbb+i05+fno6GhAe7u7lrtTWPFyclJr/ht8cwVo8bGRty/fx/19fU4f/48wsPD4eHhgbfeegvAo3cI9+7dQ3p6Ourq6lBcXIzr169rxXBwcEBhYSGuXbuGioqKVl+Y6urqcOfOHWRmZkKhUMDDwwPAo1ux1NTUID8/v8Xz6/Pnz8fDhw9x6NAhTJw4UdMulUoxZ84c7N69G1u3bkV5eTkaGhpQUFCg+dJaVyWVShEREYG0tDTs2rUL5eXlyMnJwfz58+Hi4oLQ0FAAbduP7TVGMjIyBJ3aLZfL4e3tjYKCAoMe13S67vHvwui7H/SJ/bSxHBISAicnp1ZvQXTx4kWsX78eX3zxBcRisdYpP5FIhI0bN+rVR1+dNSeFQoEff/wRx48fR3l5Oerq6nDu3Dm8+eabUCgUWLRokVb/prEycOBAvfMwmgFT7wT3888/U//+/cnCwoIAkLOzM61Zs4aSkpJILpcTAOrduzddvXqVtm3bRiqVigBQr1696PLlyxQaGkpisZhcXV3JysqKVCoVTZ48ma5evap5jpKSEhozZgxJpVLy8vKiDz74gCIjIwkA+fj40I0bN+js2bPUq1cvkslkNHLkSPrss89IrVYTgFb/0tLSiIgoKiqKHBwcyM7OjoKDg+nTTz8lAKRWqzXTh5sMHjyYoqOjdbbFw4cPKSoqijw8PMjKyoocHR1p6tSplJubS/Hx8SSTyQgAubu7Nztl01wYM94aGxtpw4YN1Lt3bxKLxWRvb09BQUGUl5en6WPMfrx9+3a7jZHbt2/T4cOHSalUUlxcnMHbCSaa2h0WFkZisZiqqqo0bWlpaZrx2717d3r//febjRkZGak1tftp+0Hf47K1sUxEFBQURABoxYoVLa5vTk5Oq8fehg0b9OrTnOamUXfWnIiIAgMDycvLi2xsbEgikZBaraaQkBDKycnR6TthwgRydXWlxsZGrfb2mNrdqYpRWzXdr6szGT9+PP32229Cp9FuzG28mesYMVUxys/PJysrK7N+g/KkhoYGGjVqFO3YsUPoVDSehZzu3r1LUqmUNm7cqLOMv2dkAoZcvBXC46f8zp8/D6lU2uo3tZnpmfsY0Vd1dTWOHDmC/Px8zYVoHx8fxMbGIjY2ttW7QpuLhoYGpKeno6KiAiEhIUKnA+DZyWnlypUYNGgQwsLCADy6vlVYWIisrCxcuXLFJM/xuGeuGJm7qKgo5Ofn4/Lly5gzZ47WDCbGDHHv3j3NjVLffvttTXt0dDSCg4MREhJi9jdDzczMxP79+5GRkaH396Pa27OQ06ZNm5CdnY3Dhw9rvgd54MABzY1Sv//++zY/hw4DPkZ1atHR0WRtbU0AyNPTk/bu3St0Ss2KiYkhCwsLcnd319z6pyszp/FmzmMEBp6m08eRI0coKirKpDFZ55eenk5r166l+vp6k8du7TSdiEj7DnipqamYMWNGx9wYjz3zeLzpRyQSISUlBdOnTxc6FcaM1srxvpdP0zHGGBMcFyPGGGOC42LEGGNMcFyMGGOMCc6qpQWpqakdmQd7RjXdDJPH29N19Zvgsq6vtTHc4mw6xhhjrD00N5tOpxgxxvTHU9MZMwme2s0YY0x4XIwYY4wJjosRY4wxwXExYowxJjguRowxxgTHxYgxxpjguBgxxhgTHBcjxhhjguNixBhjTHBcjBhjjAmOixFjjDHBcTFijDEmOC5GjDHGBMfFiDHGmOC4GDHGGBMcFyPGGGOC42LEGGNMcFyMGGOMCY6LEWOMMcFxMWKMMSY4LkaMMcYEx8WIMcaY4LgYMcYYExwXI8YYY4LjYsQYY0xwXIwYY4wJjosRY4wxwXExYowxJjguRowxxgTHxYgxxpjguBgxxhgTHBcjxhhjguNixBhjTHBWQifAWGdRVFSE5ORkrbbz588DAOLj47XaHRwc8M4773RYbox1diIiIqGTYKwzqK+vh7OzM+7fvw+xWNxiv4cPHyI0NBSff/55B2bHWKe2l0/TMaYnKysrvP7667C0tMTDhw9b/AOAN954Q+BsGetcuBgxZoDXX38ddXV1rfZxdnbGyJEjOygjxroGLkaMGcDPzwis0mMAACAASURBVA9ubm4tLre2tsbs2bNhYcGHFmOG4COGMQOIRCLMmjWrxWtGtbW1eP311zs4K8Y6Py5GjBmotVN13t7eGDx4cAdnxFjnx8WIMQM9//zzeO6553Tara2t8eabbwqQEWOdHxcjxowwe/ZsnVN1tbW1CAkJESgjxjo3LkaMGWHWrFmor6/X/C8SifDCCy+gT58+AmbFWOfFxYgxI/Tq1QtDhgyBSCQCAFhaWvIpOsbagIsRY0b6y1/+AktLSwBAQ0MDpk+fLnBGjHVeXIwYM9L06dPR2NgIkUiEESNGwNXVVeiUGOu0uBgxZiRnZ2eMHj0aRMSn6BhrI7O4UWpwcDD27dsndBqMMfbMSUlJMYdTzHvN5ickhg8fjg8//FDoNJiBZsyYgfDwcPj5+QmdiiCqq6uxbds2LFy4sMU+CQkJAMDjm5mdGTNmCJ2ChtkUIzc3N3OozsxAM2bMgJ+f3zO971555RX07NmzxeV79+4FgGd6GzHzZE7FiK8ZMdZGrRUixph+uBgxxhgTHBcjxhhjguNixBhjTHBcjBhjjAmOi1EHamxsREJCAvz9/XWWxcXFQSQS6fwNGDBA0yc2Nhb9+vWDSqWCRCKBj48PlixZgsrKSoPyMFUcUzl8+DBsbW3x3XffCfL85u7o0aOIjo7G/v374e3trRkbs2fP1ukbEBAApVIJS0tL9O/fH2fPnhUg49bpM/6MGaM1NTXw9fXFsmXLukRO8fHx8PX1hUwmg0KhgK+vL5YvX47y8nIAwMGDBxEfH4+GhgaDY5slMgPTpk2jadOmCZ1Gu7p8+TKNGDGCANALL7ygs3z16tUEQOevf//+mj6jR4+mpKQkKikpofLyckpJSSGxWEyvvfaaQbmYKg4REQBKSUkx+HGPO3ToEKlUKjp48GCb4pirtozvFStW0MSJE6m8vFzTplarqVu3bgSADh06pPOYjIwMmjRpktH5tjd9xp8xY3TRokUEgGJiYrpEThMmTKCNGzdSUVERVVRUUGpqKonFYnrllVc0fTZv3kyjR4+m+/fvGxyfyDTHr4mkcjEysaqqKvLz89Nqy87OpilTptCuXbto0KBBLRajnTt3thp7woQJVF9fr9U2ffp0AkA3btzQO0dTxSEyq8FstOb2mSkZO74/+eQT6tOnD1VXV2u1q9Vq+uabb8jCwoJcXV2ptLRUa7m5FyN9xp+hY/Tf//43BQQEtOmF39xyCgoK0tn3wcHBBIAKCws1bWFhYeTn50d1dXUGP4cZHb+pfJrOxHbs2IGioiKtthdeeAH79+/HzJkzIZFIjI596NAhzV2im3Tv3h0AUFVV1eFxuorm9pnQrly5guXLl2PVqlWQSqU6y/39/REeHo5bt25h8eLFAmRoPH3GnyFjtLq6GpGRkdi8eXOXyiktLU1n3zfdjPfxU4MrV65EdnZ2m57LHHTKYrR+/XrI5XIolUoUFRUhIiICrq6uyMvLQ0NDA1asWAEPDw/IZDI8//zzSElJ0Tz2p59+wp/+9CfI5XKoVCoMHDgQ5eXl2Lp1KxQKBeRyOQ4cOIBx48ZBpVLBzc0Nu3fv1jy+tfjh4eGIiIjA1atXIRKJ4OPj0+7b4tatW5DJZPDy8kLfvn0hEolgYWGBF198UXOALFmyBLa2tpBKpfjqq6+eGqcjZWVlwcPDAyKRCJ9++ikA6LUvtmzZAqlUih49emDevHlwcXGBVCqFv78/Tp8+DQAICwuDtbU1nJ2dNc+3YMECKBQKiEQi3L17t8V99sMPP0ClUmHNmjUduj2abNmyBUSEwMDAFvvExcWhT58+2L59O44ePdpiPyLCpk2b0LdvX0gkEtjb22Py5Mm4dOkSAP22N9D62G8rfcZfS31iYmKwYMECODo6miQXc84pPz8fdnZ26NWrl6bN3t4eo0ePxubNm0HC32rUeAJ/NCMi405jxMTEEABauHAhJSYm0pQpU+g///kPLV68mCQSCe3bt4/u379PS5cuJQsLCzpz5gxVVlaSSqWi+Ph4qq6uptu3b9OUKVOouLhYK+axY8eorKyMioqKaNSoUaRQKKi2tpaIqNX4RERTp04ltVrdYt5//vOfWzxN5+bmRnZ2diQWi8nT05MmTZpEv/zyS4uxHjx4QEqlksLCwoiIqL6+njw9PcnDw0PndMKHH35ICQkJesUxBEzwMf/mzZsEgBITEzVt+uyL0NBQUigUdPHiRaqpqaHc3FwaNmwYKZVKzWmTmTNnkpOTk9bzbdiwgQBo9ntz++zQoUOkVCopNja2TetGZNz49vb2pn79+jW7TK1W0++//05ERCdPniQLCwvy9PSkyspKItI9TbdixQqytramnTt3UmlpKZ0/f56GDBlC3bt3p9u3bxORaca+sfQZfy31ycrKosDAQCIiKi4uNvqUmDnnVFtbSwUFBZSYmEgSiaTZ0/nR0dEEgM6dO2dQbFMcvybSea8ZNR08j59Tra6uJrlcTiEhIZq2qqoqkkgk9N5779GFCxdavOjbUsykpCQCQFeuXHlqfCLji9GNGzfo7NmzVFFRQQ8fPqRTp07R4MGDSSaT0YULF1rMt0+fPloXtxMSEggApaamatoePHhAHh4eVFZWpnccfbV3MWppXxA9Kka2trZasc6cOUMAaNWqVURkfDEyJUPHd2VlJYlEIpo4cWKzyx8vRkREERERBIDef/99ItIuRlVVVWRjY6M1ZomIfvnlFwKgKbamGPvG0mf8NdenqqqKhg4dSgUFBURk2mJkTjk5OTkRAOrWrRv97W9/07w5eNyXX35JAOjrr782KLY5FaNOeZquJXl5eaiqqtKaDi2TyeDs7IxLly7B29sbPXr0wKxZs7By5Upcu3btqTGtra0BAHV1dU+N3xbu7u4YPHgwbGxsYG1tjeHDhyM5ORnV1dVISkrS6Z+WlobU1FQcOXIESqVS0z537lzY2tpqnT/etWsXJk+eDJVKpXccc/T4vmjJ0KFDIZfL27w/hFRUVAQiglwu16t/XFwcnnvuOSQlJSErK0trWW5uLiorKzF06FCt9mHDhsHa2lpzSrM5HTH29Rl/LfVZunQp3n33XZP/qKG55XTz5k0UFRXh22+/xT/+8Q8MHjxY5xpn01i5c+eOyZ63o3WpYvTgwQMAwLJly7S+q3P9+nVUVVVBJpPh+PHjGDlyJNasWQNvb2+EhISgurraJPFNbeDAgbC0tMTly5e12vfs2YN169YhMzMTnp6eWstsbGzw7rvv4uTJk/jll18AAJ999hnCwsJ04rcWpzOTSCQoLi4WOg2j1dTUAIDek12kUimSk5MhEonw9ttva43n0tJSAI/GxZPs7OxQUVGh13O0x9jXZ/y11CcrKws5OTmYO3euUc/dmXISi8VwdHREQEAA9uzZg9zcXKxdu1arj0wmA/DfsdMZdali1HSxMCEhAUSk9Xfq1CkAQP/+/fHdd9+hsLAQUVFRSElJwcaNG00W35QaGxvR2Nio9aKUmJiIXbt24fjx4y3eLTosLAxisRgJCQk4ceIE3N3doVartfroE6czqqurQ2lpKdzc3IROxWhNLyyGfJnRz88PixYtQn5+PlavXq1pt7OzA4Bmi44h28nUY1+f8ddanx07duDYsWOwsLDQFMamHNesWQORSIRff/210+f0JB8fH1haWiI3N1ervba2FsB/x05n1KWKkbu7O6RSKbKzs5tdXlhYiIsXLwJ4dHB98sknGDJkiKatrfHb4tVXX9VpO3PmDIgIfn5+ICJERUUhJycH6enpzb7TbdL021D79u3D8uXLER4erllmSJzOKDMzE0SE4cOHAwCsrKxaPa1njnr06AGRSISysjKDHrd69Wr4+vri3LlzmrYBAwbAxsZG50Xw9OnTqK2txYsvvqhXbFONfX3Gnz59kpOTdYpi06fhmJgYEJHOqcnOlFNJSQneeOMNnfb8/Hw0NDTA3d1dq71prDg5OekV3xx1qWIklUoxZ84c7N69G1u3bkV5eTkaGhpQUFCAP/74A4WFhZg3bx4uXbqE2tpanDt3DtevX9e8cLU1PgA4ODigsLAQ165dQ0VFhd4vhLdu3cKePXtQWlqKuro6nDp1CnPnzoWHhwfmz5+PixcvYv369fjiiy8gFot1bhv05Ke7iIgI1NfX4/79+3j55Zc17YbGMXeNjY24f/8+6uvrcf78eYSHh8PDwwNvvfUWgEfvJO/du4f09HTU1dWhuLgY169f14rR3D7LyMgQbGq3XC6Ht7c3CgoKDHpc0+m6x78LI5VKERERgbS0NOzatQvl5eXIycnB/Pnz4eLigtDQUL1jP23sh4SEwMnJqdVbEOkz/kw5RjtrTgqFAj/++COOHz+O8vJy1NXV4dy5c3jzzTehUCiwaNEirf5NY2XgwIF652F2OmCWxFMZOtsoPj6eZDIZASB3d3etqY4PHz6kqKgo8vDwICsrK3J0dKSpU6dSbm4uXbt2jfz9/cne3p4sLS2pZ8+eFBMTQ/X19ZSUlERyuZwAUO/evenq1au0bds2UqlUBIB69epFly9fbjU+EdHZs2epV69eJJPJaOTIkXT79m06deoUjRgxglxcXDS3+XF2diZ/f3/66aefiOjRjCi1Wk0KhYKsrKzIzc2N3nnnHc03rXNycpq9XVDT34YNG3S205gxY2j79u1abcbEaQ3aOBsnMTGRnJ2dCQDJ5XIKDAzUe1+EhoaSWCwmV1dXsrKyIpVKRZMnT6arV69q4peUlNCYMWNIKpWSl5cXffDBBxQZGUkAyMfHRzOL8cl9dvjwYVIqlRQXF2f0ujUxZrZoWFgYicViqqqq0rSlpaWRWq0mANS9e3fN7LknRUZGak3tbmxspA0bNlDv3r1JLBaTvb09BQUFUV5eHhGRycZ+UFAQAaAVK1a0uF76jD9jx2hzM9c6a05ERIGBgeTl5UU2NjYkkUhIrVZTSEgI5eTk6PSdMGECubq6UmNjY6sxn9TW49eEOu/UbmYehBzMoaGh5ODgIMhzG8KY8Z2fn09WVlZPvUWUOWloaKBRo0bRjh07hE5F41nI6e7duySVSmnjxo0GP9acilGXOk3Hnj1d5o7FT/Dx8UFsbCxiY2MFu5u6IRoaGpCeno6KigqEhIQInQ6AZyenlStXYtCgQc3OmO1MuBgxZqaio6MRHByMkJAQgyczdLTMzEzs378fGRkZen8/qr09Czlt2rQJ2dnZOHz4MMRisQkyFA4XI9YpLV26FMnJySgrK4OXlxf27dsndErtYs2aNQgLC8Mnn3widCqtGjt2LL755hut+wAKravndODAATx8+BCZmZmwt7c3QXbCshI6AcaMsXbtWp0v/nVVAQEBCAgIEDoNZmYmTZqESZMmCZ2GyfAnI8YYY4LjYsQYY0xwXIwYY4wJjosRY4wxwZnNBIaCggKkpqYKnQYzQnvcJLYrabpVC49vxlomIhL+d2qDg4O77NRcxhgzZykpKZg+fbrQaew1m09G06ZNw969e4VOgxlIJBKZy2A2W8HBwQDA45uZHZFIJHQKGnzNiDHGmOC4GDHGGBMcFyPGGGOC42LEGGNMcFyMGGOMCY6LEWOMMcF1qWK0f/9+eHt76/w+/eN/np6eQqeJw4cPw9bWFt99912HPu/GjRvRo0cPiEQifP755x363Mx0jh49iujoaJ3xPnv2bJ2+AQEBUCqVsLS0RP/+/XH27FkBMm7dSy+91OLxamNjI1is2NhY9OvXDyqVChKJBD4+PliyZInOjx1mZWVhxIgRkMvlcHFxQVRUFB4+fNhi3JqaGvj6+mLZsmVa7d9++y2GDRsGpVKJXr16Yc6cObh9+zYA4ODBg4iPj++yPyYJdLFiNHXqVPz2229Qq9WwtbUFEYGIUF9fj6qqKty5c8csfmRLqO8ZL168GCdPnhTkuZlpfPzxx9iyZQuWLl2qNd67deuGXbt24fvvv9fq/+OPP2Lv3r2YOHEicnNzMWTIEIEyN87IkSMFi3X8+HG8//77uHbtGu7evYu1a9di8+bNmu+NAUBubi4CAgIwduxYFBcXIy0tDV9++SXmz5/fYtyYmBjk5eVptaWkpGDmzJkIDg5GQUEBDhw4gBMnTmDcuHGor69HYGAgpFIpxo4di9LSUsNWvJPoUsWoJZaWlpDJZOjRowf69OnToc9dXV0Nf39/rbYJEyagrKwMEydO7NBcupLmtmtniN0W69atw549e5CamgqlUqm1bMuWLbCwsEBoaKjZ/yrsk6RSKcrLyzVvHpv+QkNDsWTJEsFi2djYIDQ0FA4ODlAqlZg+fTqCgoLwww8/4ObNmwCA1atXw9nZGatWrYJCoYCfnx+ioqLw1Vdf4dKlSzoxT548iQsXLui0//3vf0fPnj0RGRkJW1tbDBo0CIsWLUJ2djZOnz4NAFi4cCFeeOEFjB8/HvX19QatS2fwTBSjx6Wnp3fo8+3YsQNFRUUd+pzPgvbcrua4z65cuYLly5dj1apVkEqlOsv9/f0RHh6OW7duYfHixQJkaLwffvhBp7jevHkTFy5cwMsvvyxYrEOHDsHS0lKrrXv37gCAqqoq1NfX4/vvv8fo0aO17mQwbtw4EBEOHDig9djq6mpERkZi8+bNOs918+ZNuLi4aMVxd3cHAFy/fl3TtnLlSmRnZzcbo7N75ooRAISFhcHa2lrrp38XLFgAhUIBkUiEu3fvYuvWrVAoFJDL5Thw4ADGjRsHlUoFNzc37N69Wyvezp07MXToUEilUigUCnh6emL16tUIDw9HREQErl69CpFIBB8fH2RlZcHDwwMikQiffvqpJgYRYdOmTejbty8kEgns7e0xefJkzbsrffP517/+hX79+sHW1hZSqRQDBw7EkSNH2nmL6u9p66nPvmluu27ZsgVSqRQ9evTAvHnz4OLiAqlUCn9/f807S2NjA49e5FQqFdasWdOBW+u/tmzZAiJCYGBgi33i4uLQp08fbN++HUePHm2xn6nGWkNDA1asWAEPDw/IZDI8//zzSElJMcn6rlu3DgsXLjS7WLdu3YJMJoOXlxd+++03VFZWwsPDQ6uPWq0GAJw/f16rPSYmBgsWLICjo6NOXG9vb503QE3Xi7y9vTVt9vb2GD16NDZv3izY6f52Q2Zg2rRpNG3aNJPFU6vVZGtrq9V27Ngx2rBhg+b/mTNnkpOTk1afDRs2EAAqLi4mIqKYmBgCQMeOHaOysjIqKiqiUaNGkUKhoNraWiIiSkhIIAD0ySefUElJCd27d4/+/ve/08yZM4mIaOrUqaRWq7We5+bNmwSAEhMTNW0rVqwga2tr2rlzJ5WWltL58+dpyJAh1L17d7p9+7be+ezdu5dWrlxJ9+7do5KSEho+fDh169ZN8zz5+fkEgD777LM2beMmACglJUXv/vqspz77prntGhoaSgqFgi5evEg1NTWUm5tLw4YNI6VSSTdu3GhT7EOHDpFSqaTY2Fi917WJKca3t7c39evXr9llarWafv/9dyIiOnnyJFlYWJCnpydVVlYSEVFGRgZNmjRJ099UY23x4sUkkUho3759dP/+fVq6dClZWFjQmTNn2rSuBQUF1K9fP2poaGhTHFPHevDgASmVSgoLCyMiop9++okAaL2uNJHJZDR27FjN/1lZWRQYGEhERMXFxQSAYmJiNMszMzNJLBbTli1bqLy8nC5cuEB9+/alV199VSd2dHQ0AaBz5861eZ0MPX7bUWqX/WRUVlamNZNm7NixRsfy9/eHSqWCo6MjQkJC8ODBA9y4cQN1dXVYtWoVxowZg48++ggODg6wt7fHX//6VwwbNkzv+NXV1di0aROmTJmCWbNmwdbWFgMHDsTnn3+Ou3fvYtu2bXrlAzy64ezHH38Me3t7ODg4IDAwECUlJSguLjZ6/U3F0PU0hpWVleYdf79+/bB161ZUVFQgOTm5TXEnTJiA8vJyLF++vM05GurBgwf4/fffNe+4W+Pn54cPP/wQ165dw0cffaSz3FRjraamBlu3bkVQUBCmTp0KOzs7LFu2DGKxuM3bet26dfjggw9gYdH2lydTxlq7di1cXFwQFxcHAJoZc0+eygMAsViM6upqAI+2eXh4OLZu3dpi7NGjRyMqKgphYWFQqVQYMGAAKioqsH37dp2+vXv3BgDk5OS0eZ3MSZctRo/PpiMi/POf/zRJXGtrawBAXV0dzp8/j9LSUrz66qtafSwtLQ06LZCbm4vKykoMHTpUq33YsGGwtrbWnGZ6Wj7NEYvFAGAWU0Lbsp7GGjp0KORyebMXkzuLoqIiEJHeM0Hj4uLw3HPPISkpCVlZWVrLTDXW8vLyUFVVhQEDBmiWy2QyODs7t2lbFxYW4uDBg3jrrbeMjtEesdLS0pCamoojR45orkk1XbtrbjJBbW0tZDIZAGDp0qV499134erq2mL8mJgYbNu2DceOHUNlZSV+++03+Pv7w8/PTzNZoknTOLhz506b18ucdNli9KSXXnrJ5Bd2y8vLAQB2dnZtitM0VbO570HY2dmhoqJC71jff/89XnrpJTg6OkIikRg8g6g9mXI9DSGRSMzik6GxampqADxaD31IpVIkJydDJBLh7bff1rxDB0y3Dx48eAAAWLZsmdYZiOvXr6OqqkqvGM2Jj4/HO++80+wkDaFi7dmzB+vWrUNmZqbW9xSbrj02vQ40qaqqQk1NDVxcXJCVlYWcnBzMnTu3xfh//PEH4uPj8e677+Lll1+GQqGAl5cXvvjiCxQWFmLDhg1a/ZuKXNO46CqemWLUHnr27AkAuHv3bpviNBWz5l4ISktL4ebmplecGzduICgoCM7Ozjh9+jTKysoQHx/fptxMyVTraYi6urp2i91Rml58DPl06+fnh0WLFiE/Px+rV6/WtJtqHzRdhE9ISNCZRm3sL//evn0b3377Ld577z2jHt8esRITE7Fr1y4cP35cc7w38fLyglKp1JrtBjya+QgAzz//PHbs2IFjx47BwsJCU7Cbtt2aNWsgEomwe/duNDQ06MRXqVRwcHBAbm6uVnttbS2A/46LruKZLUZWVlYtntrSl6enJxwcHPDjjz+2Kc6AAQNgY2ODX3/9Vav99OnTqK2txYsvvqhXnJycHNTV1eG9996Dt7c3pFKpWf14lr7raYp90yQzMxNEhOHDh5s8dkdpumuGod8fWr16NXx9fXHu3DlNm6nGmru7O6RSKbKzsw3KqTXx8fGYNWsWHBwcBI9FRIiKikJOTg7S09Ob/SRpZWWF8ePH48SJE2hsbNS0Z2RkQCQSITAwEMnJyTrFuulTekxMDIgIkydPBvDoE9LjKioqcO/ePc0U7yZN48DJycmodTNXz2wx8vHxwb1795Ceno66ujoUFxfrvMN5GolEgqVLl+LEiRMICwvDrVu30NjYiIqKCly8eBEA4ODggMLCQly7dg0VFRXNvhBKpVJEREQgLS0Nu3btQnl5OXJycjB//ny4uLggNDRUr3yappgePXoUNTU1yM/Pb5frMMbSdz312TctbdfGxkbcv38f9fX1OH/+PMLDw+Hh4aG5bmBs7IyMDMGmdsvlcnh7e6OgoMCgxzWdrnv8ArupxppUKsWcOXOwe/dubN26FeXl5WhoaEBBQYHmRTUkJAROTk563YLozp07+PLLL/Hhhx82u7yjY128eBHr16/HF198AbFYrHNroY0bNwIAli9fjjt37uDjjz/GgwcPcOrUKWzYsAFvvfUWnnvuuafmCjz6hDVmzBh88cUXOHHiBKqrq3Hz5k3NvvjrX/+q1b9pHAwcOFCv+J2GAFP4dJhqave///1v6tOnDwEgAOTs7Kw1vfJxJSUlNGbMGJJKpeTl5UUffPABRUZGEgDy8fGhjz76iORyOQGg3r1709WrV2nbtm2kUqkIAPXq1YsuX75MRESffvopDRw4kKRSKUmlUho8eDAlJSUREdHZs2epV69eJJPJaOTIkbRs2TJydnYmACSXyzXTPRsbG2nDhg3Uu3dvEovFZG9vT0FBQZSXl0dERElJSXrlExUVRQ4ODmRnZ0fBwcH06aefEgBSq9UUHh5OTk5OBIAUCgVNmTKlzdscBk4Nfdp66rNvbty4obNdb9++TaGhoSQWi8nV1ZWsrKxIpVLR5MmT6erVq22OffjwYVIqlRQXF2fwNjLF+A4LCyOxWExVVVWatrS0NFKr1QSAunfvTu+//36zj42MjNSa2m2qsfbw4UOKiooiDw8PsrKyIkdHR5o6dSrl5uYSEVFQUBABoBUrVjx1/RYtWkSzZs1qcXlHx8rJydG8jjT39/h07p9++on+9Kc/kUQiIRcXF4qMjKSampoWYzc3tfvu3bsUHh5OPj4+JJFIyMbGhkaMGEH/+7//q/P4CRMmkKurKzU2Nj5tUzyVocdvO0rtUsWIdTwzGswUGhpKDg4OQqehwxTjOz8/n6ysrGjnzp0myqr9NTQ00KhRo2jHjh1dNlZHu3v3LkmlUtq4caNJ4pnR8dt1v2fEnk3mMIW9Pfj4+CA2NhaxsbE6d402Rw0NDUhPT0dFRQVCQkK6ZCwhrFy5EoMGDUJYWJjQqZgcFyPGOono6GgEBwcjJCTE7G+GmpmZif379yMjI6PNd8o311gdbdOmTcjOzsbhw4c13x/sSrgYsS5h6dKlSE5ORllZGby8vLBv3z6hU2oXa9asQVhYGD755BOhU2nV2LFj8c0332jdB7CrxepIBw4cwMOHD5GZmQl7e3uh02kXVkInwJgprF27FmvXrhU6jQ4REBCAgIAAodNgHWjSpEmYNGmS0Gm0K/5kxBhjTHBcjBhjjAmOixFjjDHBcTFijDEmOLOZwPDzzz8jODhY6DSYERISErB3716h0zBbP//8MwDw+GasFWZRjPz8/IROgRlp2rRpQqcgqDt37uDChQut/nhj001aGTM306ZN07kRq1BERF3th9QZ6zipqamYMWMGT4N9pQAAIABJREFU+DBirE328jUjxhhjguNixBhjTHBcjBhjjAmOixFjjDHBcTFijDEmOC5GjDHGBMfFiDHGmOC4GDHGGBMcFyPGGGOC42LEGGNMcFyMGGOMCY6LEWOMMcFxMWKMMSY4LkaMMcYEx8WIMcaY4LgYMcYYExwXI8YYY4LjYsQYY0xwXIwYY4wJjosRY4wxwXExYowxJjguRowxxgTHxYgxxpjguBgxxhgTHBcjxhhjguNixBhjTHBcjBhjjAmOixFjjDHBcTFijDEmOC5GjDHGBMfFiDHGmOC4GDHGGBMcFyPGGGOCsxI6AcY6i8LCQvzP//wP6urqNG1VVVWwtbXFwIEDtfoOHjwYX3/9dUenyFinxcWIMT317NkTtbW1yM3N1VlWVlam9X9ISEhHpcVYl8Cn6RgzwF/+8hdYWbX+Hk4kEuGNN97ooIwY6xq4GDFmgNdffx0NDQ0tLheJRHjxxRfh5eXVgVkx1vlxMWLMAO7u7hg+fDgsLJo/dCwtLfGXv/ylg7NirPPjYsSYgWbPng2RSNTsssbGRkyfPr2DM2Ks8+NixJiBgoODm223tLTESy+9BCcnpw7OiLHOj4sRYwbq3r07xo4dC0tLS51ls2fPFiAjxjo/LkaMGWHWrFkgIq02CwsLBAUFCZQRY50bFyPGjDB58mSIxWLN/1ZWVpgwYQJsbW0FzIqxzouLEWNGUCqVmDhxoqYgNTQ0YNasWQJnxVjnxcWIMSPNnDkT9fX1AACZTIbx48cLnBFjnRcXI8aMNG7cOCgUCgDAtGnTIJPJBM6Isc6ry92bLjU1VegU2DNk2LBh+Oc//wl3d3cee6zDuLu7w8/PT+g0TEpET04J6uRa+jIiY4x1FdOmTcPevXuFTsOU9na5T0YAkJKSwt+Cb6PU1FTMmDFDZ/oy0yYSifD666/j22+/FToV9oxo6UvXnR1fM2KsjSZOnCh0Cox1elyMGGuj5u7EwBgzDBcjxhhjguNixBhjTHBcjBhjjAmOixFjjDHBcTFqwdy5c6FUKiESiZCdnS10Op3W4cOHYWtri++++07oVMzO0aNHER0djf3798Pb2xsikQgikajZn6EICAiAUqmEpaUl+vfvj7NnzwqQceteeuklzTo8+WdjYyNYrNjYWPTr1w8qlQoSiQQ+Pj5YsmQJKisrtfplZWVhxIgRkMvlcHFxQVRUFB4+fNhi3JqaGvj6+mLZsmVa7d9++y2GDRsGpVKJXr16Yc6cObh9+zYA4ODBg4iPj2/1p+ufVVyMWrB9+3Z88cUXQqfR6fH3lJr38ccfY8uWLVi6dCmmTp2K3377DWq1Gt26dcOuXbvw/fffa/X/8ccfsXfvXkycOBG5ubkYMmSIQJkbZ+TIkYLFOn78ON5//31cu3YNd+/exdq1a7F582at7+vk5uYiICAAY8eORXFxMdLS0vDll19i/vz5LcaNiYlBXl6eVltKSgpmzpyJ4OBgFBQU4MCBAzhx4gTGjRuH+vp6BAYGQiqVYuzYsSgtLTVsxbs4LkZdUHV1Nfz9/YVOAwAwYcIElJWVCfZdHHPaFk3WrVuHPXv2IDU1FUqlUmvZli1bYGFhgdDQUJSVlQmUoXGkUinKy8tBRFp/oaGhWLJkiWCxbGxsEBoaCgcHByiVSkyfPh1BQUH44YcfcPPmTQDA6tWr4ezsjFWrVkGhUMDPzw9RUVH46quvcOnSJZ2YJ0+exIULF3Ta//73v6Nnz56IjIyEra0tBg0ahEWLFiE7Oxv/n707j4riTPcH/m2g6Y1uFkHogCCLG3GL0YygXjTMmFFPUASVRL3RTBJiNEhUJIqgAhoQoxyMjOMSZo4mCiojjorJqBcNV+NNTvSCmChxAzQKqMgu2/P7wx997TRIN1uxPJ9z+IO33nr6qbcLnq6qt6suXrwIAFi6dClGjBiBqVOnam60y7gYvVB3vbXQnj17UFhYKHQaXUJXG4tff/0V4eHhWL9+PaRSqc5yT09PBAcH4+7du1ixYoUAGbbeyZMndYprfn4+rly5gtdff12wWMeOHdP5Lpi1tTUAoLKyEnV1dTh+/Di8vLy0/uanTJkCIkJaWprWulVVVQgJCUF8fLzOa+Xn50OtVmvF6devHwDgzp07mrZ169bh8uXLTcborbgY/X9EhLi4OAwaNAgSiQTm5uYICQnRLN+0aRPkcjmUSiUKCwuxfPly2Nvb49q1ayAibNmyBUOGDIFEIoGlpSVmzJih+USVkJAAqVSKvn374sMPP4RarYZUKoWnp6fm01JjDi+KExQUBFNTU9jZ2WnWWbx4MRQKBUQiEYqLixEcHIzly5fjxo0bEIlEcHNz66QR1JWZmQlHR0eIRCJ88cUXAIDExEQoFArI5XKkpaVhypQpUKlUcHBwwP79+wHoN15tGYuTJ09CpVJhw4YNnTwiz7aNiODj49Nsn+joaAwcOBC7d+/GqVOnmu3X0v6iz1gDz57FFBERAUdHR8hkMgwfPhzJycntsr0xMTFYunRpl4t19+5dyGQyODs74+bNmygvL4ejo6NWH1dXVwBAVlaWVntYWBgWL14MGxsbnbguLi46H34arxe5uLho2iwtLeHl5YX4+Hg+ld2IehgAlJycbPB6YWFhJBKJ6PPPP6fHjx9TZWUlbd++nQDQpUuXNH0A0NKlS2nbtm00c+ZM+vnnnykiIoJMTU1p7969VFJSQllZWTRq1Ciytram+/fvExFRYGAgKRQKunr1KlVXV1NOTg6NGTOGlEol5eXlERHpFWfu3Llka2urlXtcXBwBoKKiIiIi8vPzI1dX11aPIRFRcnIytcfukZ+fTwBo27ZtmrbGcTx9+jQ9efKECgsLacKECaRQKKimpoaI9Buv1o7FsWPHSKlUUmRkZJu3z9D9zcXFhdzd3Ztc5urqSrdu3SIiovPnz5ORkRH179+fysvLiYgoPT2dpk+frumvz/6iz1ivWLGCJBIJHTp0iB4/fkyrV68mIyMj+uGHH1ozJBoFBQXk7u5O9fX1bYrT3rEqKipIqVRSUFAQERGdPXuWAFBcXJxOX5lMRt7e3prfMzMzycfHh4iIioqKCACFhYVplmdkZJBYLKaEhAQqLS2lK1eu0JAhQ+iNN97Qib1q1Sqt/y/68vf3J39/f4PW6QZS+MgIzw67t27dij/+8Y9YtmwZLCwsIJPJYGVl1WT/mJgYLFmyBIcPH4aTkxO2bNmCmTNnYt68eTA3N8ewYcOwY8cOFBcXY+fOnZr1TExMNJ9i3d3dkZiYiLKyMiQlJaGqqkrvOD2Fp6cnVCoVbGxsEBAQgIqKCuTl5WmWv2i82mLatGkoLS1FeHh4WzfBIBUVFbh165bmE/eLeHh44JNPPsHt27fx6aef6iw3dH9pbqyrq6uRmJgIX19f+Pn5wcLCAmvWrIFYLG7zOMfExODjjz+GkVHb/820Z6yNGzdCrVYjOjoaADQz5pq6rZNYLEZVVRWAZ2MeHByMxMTEZmN7eXkhNDQUQUFBUKlUGDp0KMrKyrB7926dvgMGDAAAZGdnt3mbegIuRnh2Hr+yshLe3t4Gr5uTk4Py8nKMHj1aq33MmDEwNTXVOg33e6NHj4ZcLscvv/zSpjg9gampKQCgtra22T7Pj1d3VFhYCCKCXC7Xq390dDQGDRqE7du3IzMzU2tZW/aX58f62rVrqKysxNChQzXLZTIZ7Ozs2jTO9+7dw9GjR7FgwYJWx+iIWKmpqUhJScE333yjuSbVeO2uqckENTU1mocmrl69Gh988AHs7e2bjR8WFoadO3fi9OnTKC8vx82bN+Hp6QkPDw/NZIlGjfvBgwcP2rxdPQEXIwAFBQUA0OQ54JY0Ts9s6rsPFhYWKCsre+H6EokERUVFbY7TWzSOV3dUXV0N4Nk26EMqlSIpKQkikQjvvvuu5hM60Pb9rlFFRQUAYM2aNVrf5blz5w4qKyv1itGU2NhYvP/++01O0hAq1oEDBxATE4OMjAz0799f09543bG0tFSrf2VlJaqrq6FWq5GZmYns7Gy89957zcb/7bffEBsbiw8++ACvv/46FAoFnJ2dsWvXLty7dw9xcXFa/RuLXON+0dtxMcL/fTJ60RfcmmNhYQEATf7xl5SUwMHBodl1a2trNX3aEqe3eH68uqPGfz6GfOHRw8MDy5YtQ25uLqKiojTt7bW/NH4A27p1q8406gsXLuid5/Pu37+Pr7/+Gh999FGr1u+IWNu2bcO+fftw5swZvPTSS1rLnJ2doVQqtWa7Ac/OmADA8OHDsWfPHpw+fRpGRkaagt04dhs2bIBIJML+/ftRX1+vE1+lUsHKygo5OTla7TU1NQDAj6v//7gYARg6dCiMjIxw9uzZVq1rZmaGH3/8Uav94sWLqKmpwauvvtrsuhkZGSAijB07Vu84JiYmLzyV1ZM9P15A9xuLvn37QiQSGfz9oaioKAwePBiXLl3StLVlv3tev379IJVK2/UuI7GxsZg3b16z11w7MxYRITQ0FNnZ2Thy5EiTR5ImJiaYOnUqzp07h4aGBk17eno6RCIRfHx8kJSUpFOsG4/Qw8LCQESYMWMGgGdHSM8rKyvDo0ePNFO8GzXuB7a2tq3atp6GixGefTr08/PDoUOHsGfPHpSWliIrK0uvSQNSqRTLly9Hamoq9u3bh9LSUmRnZ2PRokVQq9UIDAzU9G1oaMDjx49RV1eHrKwsBAcHw9HREQsWLNA7jpubGx49eoQjR46gtrYWRUVFOp/orKyscO/ePdy+fRtlZWXd6h/28140XkDrxyI9PV2Qqd1yuRwuLi6a08L6ajxd9/wFdkP2u5ZiL1y4EPv370diYiJKS0tRX1+PgoICzT/VgIAA2Nra6nULogcPHuDLL7/EJ5980uTyzo519epVbNq0Cbt27YJYLNa5tdDmzZsBAOHh4Xjw4AHWrl2LiooKXLhwAXFxcViwYAEGDRrUYq7AsyOsSZMmYdeuXTh37hyqqqqQn5+veS/+8pe/aPVv3A+GDRumV/weT5BJfB0IrZzaXVZWRu+99x716dOHzMzMaPz48RQREUEAyMHBgebOnUsymYwAUL9+/Wjv3r2adRsaGiguLo4GDBhAYrGYLC0tydfXl65du6bpExgYSGKxmOzt7cnExIRUKhXNmDGDbty4YVCchw8f0qRJk0gqlZKzszN9/PHHFBISQgDIzc2N8vLy6KeffiInJyeSyWQ0fvx4zTRfQ7TH1O5t27aRnZ0dASC5XE4+Pj60fft2ksvlBIAGDBhAN27coJ07d5JKpSIA5OTkRNevX9drvFo7FidOnCClUknR0dFt2j4iw/e3oKAgEovFVFlZqWlLTU0lV1dXAkDW1ta0ZMmSJtcNCQnRmtrd0v6i71g/ffqUQkNDydHRkUxMTMjGxob8/PwoJyeHiIh8fX0JAEVERLS4fcuWLaN58+Y1u7yzY2VnZxOAZn+en8599uxZeu2110gikZBaraaQkBCqrq5uNnZTU7uLi4spODiY3NzcSCKRkJmZGY0bN47++c9/6qw/bdo0sre3p4aGhpaGQktPndrNxaiTBAYGkpWVldBp6K29vmfUWt1lvAzd33Jzc8nExETrw0xXV19fTxMmTKA9e/b02Fidrbi4mKRSKW3evNngdXtqMeLTdJ2I79RrmJ44Xm5uboiMjERkZKTOXaO7ovr6ehw5cgRlZWUICAjokbGEsG7dOowcORJBQUFCp9JlcDFirJOtWrUKs2bNQkBAQJe/GWpGRgYOHz6M9PR0vb8f1d1idbYtW7bg8uXLOHHiBMRisdDpdBlcjDrB6tWrkZSUhCdPnsDZ2RmHDh0SOqUurTeM14YNGxAUFITPPvtM6FReyNvbG1999ZXWPQB7WqzOlJaWhqdPnyIjIwOWlpZCp9OliIh61l36RCIRkpOTMXv2bKFT6dZSUlIwZ84cvoljC3h/Y52t8TlMBw8eFDiTdnWQj4wYY4wJjosRY4wxwXExYowxJjguRowxxgRnInQCHWHr1q097eJep2u8VUnjxVLWPN7fWGf6/vvvNfdn7En4yIgxxpjgeGo3axJP7dYP72+ss/HUbsYYY6yDcDFijDEmOC5GjDHGBMfFiDHGmOC4GDHGGBNcry5Ghw8fhouLi86jiE1NTdG3b19MnDgRcXFxePz4sdCpsh7o1KlTWLVqlc5+OH/+fJ2+kydPhlKphLGxMV5++WW9Htvd2SZOnKjzt9T4Y2ZmJlisyMhIuLu7Q6VSQSKRwM3NDStXrtR5nlRmZibGjRsHuVwOtVqN0NBQPH36tNm41dXVGDx4MNasWaPV/vXXX2PMmDFQKpVwcnLCwoULcf/+fQDA0aNHERsb2yOf1dVWvboY+fn54ebNm3B1dYW5uTmICA0NDSgsLERKSgqcnZ0RGhqKl19+GT/++KPQ6bIeZO3atUhISMDq1au19sM+ffpg3759OH78uFb/b7/9FgcPHsSbb76JnJwcjBo1SqDMW2f8+PGCxTpz5gyWLFmC27dvo7i4GBs3bkR8fLzWF7pzcnIwefJkeHt7o6ioCKmpqfjyyy+xaNGiZuOGhYXh2rVrWm3JycmYO3cuZs2ahYKCAqSlpeHcuXOYMmUK6urq4OPjA6lUCm9vb5SUlBi24T1cry5GTRGJRLCwsMDEiRORlJSElJQUPHjwANOmTevyD0L7vaqqKnh6egqdhsE6Mu+uMCYxMTE4cOAAUlJSoFQqtZYlJCTAyMgIgYGB3W5/k0qlKC0tBRFp/QQGBmLlypWCxTIzM0NgYCCsrKygVCoxe/Zs+Pr64uTJk8jPzwcAREVFwc7ODuvXr4dCoYCHhwdCQ0Px97//Hb/88otOzPPnz+PKlSs67X/729/w0ksvISQkBObm5hg5ciSWLVuGy5cv4+LFiwCApUuXYsSIEZg6dSrq6uoM2paejItRC/z9/bFgwQIUFhZix44dQqdjkD179qCwsFDoNAzWkXkLPSa//vorwsPDsX79ekilUp3lnp6eCA4Oxt27d7FixQoBMmy9kydP6hTX/Px8XLlyBa+//rpgsY4dOwZjY2OtNmtrawBAZWUl6urqcPz4cXh5eUEkEmn6TJkyBUSEtLQ0rXWrqqoQEhKC+Ph4ndfKz8+HWq3WitOvXz8AwJ07dzRt69atw+XLl5uM0VtxMdLDggULAADp6enYtGkT5HI5lEolCgsLsXz5ctjb2+PatWsgImzZsgVDhgyBRCKBpaUlZsyYoflklZCQAKlUir59++LDDz+EWq2GVCqFp6en5lMTgBbjBAUFwdTUVOspl4sXL4ZCoYBIJEJxcTGCg4OxfPly3LhxAyKRCG5ubh0+Th2Vtz7j1pYxOXnyJFQqFTZs2NDhY5SQkAAigo+PT7N9oqOjMXDgQOzevRunTp1qtl9L452YmAiFQgG5XI60tDRMmTIFKpUKDg4O2L9/vyZOfX09IiIi4OjoCJlMhuHDhyM5ObldtjcmJgZLly7tcrHu3r0LmUwGZ2dn3Lx5E+Xl5XB0dNTq4+rqCgDIysrSag8LC8PixYthY2OjE9fFxUXnw07j9SIXFxdNm6WlJby8vBAfH893OWlEPQwASk5ONmgdV1dXMjc3b3Z5aWkpAaB+/foREVFYWBgBoKVLl9K2bdto5syZ9PPPP1NERASZmprS3r17qaSkhLKysmjUqFFkbW1N9+/fJyKiwMBAUigUdPXqVaqurqacnBwaM2YMKZVKysvLIyLSK87cuXPJ1tZWK8+4uDgCQEVFRURE5OfnR66urgaNRaPk5GQydPfoyLz1GbfWxj527BgplUqKjIw0aHuJDN/fXFxcyN3dvcllrq6udOvWLSIiOn/+PBkZGVH//v2pvLyciIjS09Np+vTpmv76jHfjvnr69Gl68uQJFRYW0oQJE0ihUFBNTQ0REa1YsYIkEgkdOnSIHj9+TKtXryYjIyP64YcfDB6P5xUUFJC7uzvV19e3KU57x6qoqCClUklBQUFERHT27FkCQHFxcTp9ZTIZeXt7a37PzMwkHx8fIiIqKioiABQWFqZZnpGRQWKxmBISEqi0tJSuXLlCQ4YMoTfeeEMn9qpVqwgAXbp0yaD8/f39yd/f36B1uoEUPjLSg1KphEgkQllZmVZ7TEwMlixZgsOHD8PJyQlbtmzBzJkzMW/ePJibm2PYsGHYsWMHiouLsXPnTs16JiYmmk+z7u7uSExMRFlZGZKSklBVVaV3nK6kM/J+0bi1xbRp01BaWorw8PA25/giFRUVuHXrluYT94t4eHjgk08+we3bt/Hpp5/qLDd0vD09PaFSqWBjY4OAgABUVFQgLy8P1dXVSExMhK+vL/z8/GBhYYE1a9ZALBa3eVxjYmLw8ccfw8io7f9m2jPWxo0boVarER0dDQCaGXO/P5UHAGKxGFVVVQCejXlwcDASExObje3l5YXQ0FAEBQVBpVJh6NChKCsrw+7du3X6DhgwAACQnZ3d5m3qCbgY6aGiogJEBJVK1WyfnJwclJeXY/To0VrtY8aMgampqdZpuN8bPXo05HI5fvnllzbFEZIQeT8/bt1BYWEhiAhyuVyv/tHR0Rg0aBC2b9+OzMxMrWVtGW9TU1MAQG1tLa5du4bKykoMHTpUs1wmk8HOzq5N43rv3j0cPXpUc4q7LdozVmpqKlJSUvDNN99orkk1XrtrajJBTU0NZDIZAGD16tX44IMPYG9v32z8sLAw7Ny5E6dPn0Z5eTlu3rwJT09PeHh4aCZLNGrcDx48eNDm7eoJuBjp4fr16wCAwYMHN9uncZpmU9+BsLCw0Dmq+j2JRIKioqI2xxGKUHk3jlt3UF1dDeBZzvqQSqVISkqCSCTCu+++q/mEDrTfeFdUVAAA1qxZo/Vdnjt37qCyslKvGE2JjY3F+++/3+QkDaFiHThwADExMcjIyED//v017Y3XGUtLS7X6V1ZWorq6Gmq1GpmZmcjOzsZ7773XbPzffvsNsbGx+OCDD/D6669DoVDA2dkZu3btwr179xAXF6fVv7HINe4XvR0XIz2cPHkSwLPZNc2xsLAAgCb/CZSUlMDBwaHZdWtrazV92hJHSELk/fy4dQeN/3wM+cKjh4cHli1bhtzcXERFRWna22u8Gy/Cb926VWca9YULF/TO83n379/H119/jY8++qhV63dErG3btmHfvn04c+YMXnrpJa1lzs7OUCqVWrPdgGczHwFg+PDh2LNnD06fPg0jIyNNwW4cuw0bNkAkEmH//v2or6/Xia9SqWBlZYWcnByt9pqaGgD/t1/0dlyMWnD//n1s3boVDg4OePfdd5vtN3ToUJiZmel8OfbixYuoqanBq6++2uy6GRkZICKMHTtW7zgmJiaora1tw5a1LyHyfn7c2jt2R+jbty9EIpHB3x+KiorC4MGDcenSJU1bW/a35/Xr1w9SqRSXL182KKcXiY2Nxbx582BlZSV4LCJCaGgosrOzceTIkSaPJE1MTDB16lScO3cODQ0Nmvb09HSIRCL4+PggKSlJp1g3HpGHhYWBiDBjxgwAz46QnldWVoZHjx5ppng3atwPbG1tW7VtPQ0Xo/+PiFBeXo6GhgbNjpacnIxx48bB2NgYR44ceeE1I6lUiuXLlyM1NRX79u1DaWkpsrOzsWjRIqjVagQGBmr6NjQ04PHjx6irq0NWVhaCg4Ph6OiIBQsW6B3Hzc0Njx49wpEjR1BbW4uioiKdT3ZWVla4d+8ebt++jbKysg79R90Zeb9o3NoSOz09vVOmdsvlcri4uGge6a6vxtN1z19gN2R/ayn2woULsX//fiQmJqK0tBT19fUoKCjQ/FMNCAiAra2tXrcgevDgAb788kt88sknTS7v7FhXr17Fpk2bsGvXLojFYp1bC23evBkAEB4ejgcPHmDt2rWoqKjAhQsXEBcXhwULFmDQoEEt5go8O8KaNGkSdu3ahXPnzqGqqgr5+fma9+Ivf/mLVv/G/WDYsGF6xe/xOn8GX8eCAVNtjx49SsOHDye5XE6mpqZkZGREAEgkEpGFhQW99tprFBkZSQ8fPtSsExsbSzKZTDPVe+/evZplDQ0NFBcXRwMGDCCxWEyWlpbk6+tL165d0/QJDAwksVhM9vb2ZGJiQiqVimbMmEE3btwwKM7Dhw9p0qRJJJVKydnZmT7++GMKCQkhAOTm5kZ5eXn0008/kZOTE8lkMho/frxmuq8+WjO1uyPz1mfcWhv7xIkTpFQqKTo62qDtJTJ8andQUBCJxWKqrKzUtKWmppKrqysBIGtra1qyZEmT64aEhGhN7W5pvLdv305yuZwA0IABA+jGjRu0c+dOUqlUBICcnJzo+vXr9PTpUwoNDSVHR0cyMTEhGxsb8vPzo5ycHCIi8vX1JQAUERHR4vYtW7aM5s2b1+zyzo6VnZ1NAJr9eX4699mzZ+m1114jiURCarWaQkJCqLq6utnYTU3tLi4upuDgYHJzcyOJREJmZmY0btw4+uc//6mz/rRp08je3p4aGhpaGgotPXVqd68uRkIIDAwkKysrodNoUWuKUUfqquNm6P6Wm5tLJiYmWh9iurr6+nqaMGEC7dmzp8fG6mzFxcUklUpp8+bNBq/bU4sRn6YTAN+xt3V6wri5ubkhMjISkZGROneN7orq6+tx5MgRlJWVISAgoEfGEsK6deswcuRIBAUFCZ1Kl8HFiLFOtmrVKsyaNQsBAQFd/maoGRkZOHz4MNLT0/X+flR3i9XZtmzZgsuXL+PEiRMQi8VCp9NlcDHqRKtXr0ZSUhKePHkCZ2dnHDp0SOiUuoWeOG4bNmxAUFAQPvvsM6FTeSFvb2989dVXWvf862mxOlNaWhqePn2KjIwMWFpaCp1OlyIi6ll36ROJREhOTsbs2bOFTqVbS0lJwZw5c/gmji3g/Y11tsbnMB08eFDgTNrVQT4yYowxJjguRowxxgTHxYgxxpgbHRstAAAgAElEQVTguBgxxhgTHBcjxhhjguuRs+kYY6wn8/f373Gz6UyEzqC9JScnC50C60UuXLiA+Ph43u9Yp/r9HcB7gh53ZMRYZ+LvYzHWLvh7RowxxoTHxYgxxpjguBgxxhgTHBcjxhhjguNixBhjTHBcjBhjjAmOixFjjDHBcTFijDEmOC5GjDHGBMfFiDHGmOC4GDHGGBMcFyPGGGOC42LEGGNMcFyMGGOMCY6LEWOMMcFxMWKMMSY4LkaMMcYEx8WIMcaY4LgYMcYYExwXI8YYY4LjYsQYY0xwXIwYY4wJjosRY4wxwXExYowxJjguRowxxgTHxYgxxpjguBgxxhgTHBcjxhhjguNixBhjTHBcjBhjjAmOixFjjDHBcTFijDEmOBOhE2Csu6iursa9e/e02h48eAAAuHnzpla7sbExnJycOi03xro7ERGR0Ekw1h08fvwYtra2qK2tbbHv1KlTcfz48U7IirEe4SCfpmNMT5aWlpg8eTKMjFr+swkICOiEjBjrObgYMWaAefPmoaWTCRKJBL6+vp2UEWM9Axcjxgzg4+MDqVTa7HITExP4+PjAzMysE7NirPvjYsSYAeRyOXx9fSEWi5tcXl9fj7lz53ZyVox1f1yMGDPQ22+/3ewkBoVCgT//+c+dnBFj3R8XI8YMNHnyZJibm+u0i8VizJkzBxKJRICsGOveuBgxZiCxWIyAgACYmppqtdfW1uLtt98WKCvGujcuRoy1wltvvYWamhqtNmtra3h5eQmUEWPdGxcjxlphwoQJsLW11fwuFosxf/58GBsbC5gVY90XFyPGWsHIyAjz58/XnKqrra3FW2+9JXBWjHVfXIwYa6WAgADNqbp+/fph9OjRAmfEWPfFxYixVnr11Vfh5uYGAFiwYAFEIpHAGTHWffWKu3Zv2bIFFy5cEDoN1gM1nqa7ePEiZs2aJXA2rCdatmwZPDw8hE6jw/WKI6MLFy7g+++/FzqNLuv777/n8WlBQUEBDh06pNPu6OgICwsLqFQqAbJiPd2hQ4eQn58vdBqdolccGQHA2LFjcfDgQaHT6JIaP9Hz+DQvJSUFc+bMaXKMTp06hT/+8Y8CZMV6ut506rdXHBkx1pG4EDHWdlyMGGOMCY6LEWOMMcFxMWKMMSY4LkaMMcYEx8VID0+fPsXSpUthZ2cHuVyOP/7xj+jbty9EIhF27NghdHpdwokTJ2Bubo5//etfQqfSJZ06dQqrVq3C4cOH4eLiApFIBJFIhPnz5+v0nTx5MpRKJYyNjfHyyy/jp59+EiDjF5s4caJmG37/Y+hTbtszVmRkJNzd3aFSqSCRSODm5oaVK1eivLxcq19mZibGjRsHuVwOtVqN0NBQPH36tNm41dXVGDx4MNasWaPV/vXXX2PMmDFQKpVwcnLCwoULcf/+fQDA0aNHERsbi/r6eoO2obfiYqSHzz//HCdPnsQvv/yC+Ph4fPjhhzh//rzQaXUpRCR0Cl3W2rVrkZCQgNWrV8PPzw83b96Eq6sr+vTpg3379uH48eNa/b/99lscPHgQb775JnJycjBq1CiBMm+d8ePHCxbrzJkzWLJkCW7fvo3i4mJs3LgR8fHxWl9IzsnJweTJk+Ht7Y2ioiKkpqbiyy+/xKJFi5qNGxYWhmvXrmm1JScnY+7cuZg1axYKCgqQlpaGc+fOYcqUKairq9M8ot7b2xslJSWGbXgvxMVID0eOHMHo0aNhYWGBDz74AP7+/q2KU1VVBU9PzxbbuqNp06bhyZMnePPNNwV5/a46jjExMThw4ABSUlKgVCq1liUkJMDIyAiBgYF48uSJQBm2jlQqRWlpKYhI6ycwMBArV64ULJaZmRkCAwNhZWUFpVKJ2bNnw9fXFydPntR8eTQqKgp2dnZYv349FAoFPDw8EBoair///e/45ZdfdGKeP38eV65c0Wn/29/+hpdeegkhISEwNzfHyJEjsWzZMly+fBkXL14EACxduhQjRozA1KlTUVdXZ9C29DZcjPRQUFAAsVjc5jh79uxBYWFhi23McF1xHH/99VeEh4dj/fr1kEqlOss9PT0RHByMu3fvYsWKFQJk2HonT57UKa75+fm4cuUKXn/9dcFiHTt2TOcxHtbW1gCAyspK1NXV4fjx4/Dy8tL6QumUKVNAREhLS9Nat6qqCiEhIYiPj9d5rfz8fKjVaq04/fr1AwDcuXNH07Zu3Tpcvny5yRjs/3AxeoF///vfcHNzw2+//YZ//OMfLZ7D/u677+Du7g5zc3NIpVIMGzYM33zzDQAgODgYy5cvx40bNyASieDm5tZkGwDU19cjIiICjo6OkMlkGD58OJKTkwEAiYmJUCgUkMvlSEtLw5QpU6BSqeDg4ID9+/d3/KA0ITMzE46OjhCJRPjiiy/0zjMhIQFSqRR9+/bFhx9+CLVaDalUCk9PT80ny6CgIJiamsLOzk7zeosXL4ZCoYBIJEJxcXGz43jy5EmoVCps2LChk0cEmu0jIvj4+DTbJzo6GgMHDsTu3btx6tSpZvsREbZs2YIhQ4ZAIpHA0tISM2bM0HyS13e/eNG+1VYxMTFYunRpl4t19+5dyGQyODs74+bNmygvL4ejo6NWH1dXVwBAVlaWVntYWBgWL14MGxsbnbguLi46H4Aarxe5uLho2iwtLeHl5YX4+Hg+nf0i1Av4+/uTv79/q9e3tbWld955R6stNzeXANBf//pXTdvBgwdp3bp19OjRI3r48CGNHTuW+vTpo1nu5+dHrq6uWnGaaluxYgVJJBI6dOgQPX78mFavXk1GRkb0ww8/EBFRWFgYAaDTp0/TkydPqLCwkCZMmEAKhYJqamoM3r62jg8RUX5+PgGgbdu2adr0yTMwMJAUCgVdvXqVqqurKScnh8aMGUNKpZLy8vKIiGju3Llka2ur9XpxcXEEgIqKioio6XE8duwYKZVKioyMbNO2ERElJyeToX8uLi4u5O7u3uQyV1dXunXrFhERnT9/noyMjKh///5UXl5ORETp6ek0ffp0Tf+IiAgyNTWlvXv3UklJCWVlZdGoUaPI2tqa7t+/T0T6jXdL+1ZrFRQUkLu7O9XX17cpTnvHqqioIKVSSUFBQUREdPbsWQJAcXFxOn1lMhl5e3trfs/MzCQfHx8iIioqKiIAFBYWplmekZFBYrGYEhISqLS0lK5cuUJDhgyhN954Qyf2qlWrCABdunTJoPwBUHJyskHrdFMpfGTUjvz9/bF27VpYWlrCysoKPj4+ePjwIYqKivSOUV1djcTERPj6+sLPzw8WFhZYs2YNxGIxkpKStPp6enpCpVLBxsYGAQEBqKioQF5eXntvVpu1lKeJiYnmE7+7uzsSExNRVlams72GmjZtGkpLSxEeHt7WTTBYRUUFbt26pfnE/SIeHh745JNPcPv2bXz66ac6y6uqqrBlyxbMnDkT8+bNg7m5OYYNG4YdO3aguLgYO3fu1Orf3Hgbsm8ZKiYmBh9//DGMjNr+L6U9Y23cuBFqtRrR0dEAoJkx19QTecViMaqqqgA8G/Pg4GAkJiY2G9vLywuhoaEICgqCSqXC0KFDUVZWht27d+v0HTBgAAAgOzu7zdvUU3Ex6kCN15kMmdp57do1VFZWYujQoZo2mUwGOzu7Ji+uNnr+iaNdmT55jh49GnK5/IXb29UVFhaCiCCXy/XqHx0djUGDBmH79u3IzMzUWpaTk4Py8nKdh/eNGTMGpqammlOaTXl+vFu7b7Xk3r17OHr0KBYsWNDqGB0RKzU1FSkpKfjmm28016Qar901NZmgpqYGMpkMALB69Wp88MEHsLe3bzZ+WFgYdu7cidOnT6O8vBw3b96Ep6cnPDw8dO603bgfPHjwoM3b1VNxMWpHx48fx8SJE2FjYwOJRGLwTCDg2SdqAFizZo3W9y3u3LmDysrK9k65y5JIJAYdUXY11dXVAJ5thz6kUimSkpIgEonw7rvvaj6hA9BMC27qeqWFhQXKysr0eo2O2rdiY2Px/vvvNzlJQ6hYBw4cQExMDDIyMtC/f39Ne+O1x9LSUq3+lZWVqK6uhlqtRmZmJrKzs/Hee+81G/+3335DbGwsPvjgA7z++utQKBRwdnbGrl27cO/ePcTFxWn1byxyjfsF08XFqJ3k5eXB19cXdnZ2uHjxIp48eYLY2FiD4zReKN26davOVNfe8oDA2tpalJSUwMHBQehUWq3xn48hR8UeHh5YtmwZcnNzERUVpWm3sLAAgCaLjiHj1BH71v379/H111/jo48+atX6HRFr27Zt2LdvH86cOYOXXnpJa5mzszOUSqXWbDfg2cxHABg+fDj27NmD06dPw8jISFOwG8duw4YNEIlE2L9/P+rr63Xiq1QqWFlZIScnR6u98fH0jfsF08XFqJ1kZ2ejtrYWH330EVxcXCCVSlv1LJJ+/fpBKpXi8uXLHZBl95CRkQEiwtixYwE8u6bU1U8//l7jHToM/f5QVFQUBg8ejEuXLmnahg4dCjMzM/z4449afS9evIiamhq8+uqresXuiH0rNjYW8+bNg5WVleCxiAihoaHIzs7GkSNHmjySNDExwdSpU3Hu3Dk0NDRo2tPT0yESieDj44OkpCSdYt14lB4WFgYiwowZMwA8O0J6XllZGR49eqSZ4t2ocT+wtbVt1bb1BlyM2knjVNFTp06huroaubm5OufyrayscO/ePdy+fRtlZWWora3VaTM2NsbChQuxf/9+JCYmorS0FPX19SgoKNDZ8XuKhoYGPH78GHV1dcjKykJwcDAcHR011w3c3Nzw6NEjHDlyBLW1tSgqKtL5ZNvU2Kanpws2tVsul8PFxQUFBQUGrdd4uu75C+xSqRTLly9Hamoq9u3bh9LSUmRnZ2PRokVQq9UIDAzUO3ZL+1ZAQABsbW31ugXRgwcP8OWXX+KTTz5pcnlnx7p69So2bdqEXbt2QSwW69xaaPPmzQCA8PBwPHjwAGvXrkVFRQUuXLiAuLg4LFiwAIMGDWoxV+DZEdakSZOwa9cunDt3DlVVVcjPz9e8F3/5y1+0+jfuB8OGDdMrfq/U+TP4Ol9rpy7fvn2bXnnlFQJAJiYmNGrUKDp06BB9/vnnZGtrSwBIoVDQzJkziYgoNDSUrKysyMLCgmbNmkVffPEFASBXV1fKy8ujn376iZycnEgmk9H48ePp/v37TbY9ffqUQkNDydHRkUxMTMjGxob8/PwoJyeHtm/fTnK5nADQgAED6MaNG7Rz505SqVQEgJycnOj69eudMj6Ntm3bRnZ2dgSA5HI5+fj46J1nYGAgicVisre3JxMTE1KpVDRjxgy6ceOGJv7Dhw9p0qRJJJVKydnZmT7++GMKCQkhAOTm5tbs2J44cYKUSiVFR0e3etsatWZqd1BQEInFYqqsrNS0paamkqurKwEga2trWrJkSZPrhoSEaE3tbmhooLi4OBowYACJxWKytLQkX19funbtGhGR3uP9on2LiMjX15cAUERERIvbt2zZMpo3b16zyzs7VnZ2NgFo9uf56dxnz56l1157jSQSCanVagoJCaHq6upmYzc1tbu4uJiCg4PJzc2NJBIJmZmZ0bhx4+if//ynzvrTpk0je3t7amhoaGkotKAXTe3mYsQEHZ/AwECysrIS5LUN0ZpilJubSyYmJrR3794Oyqr91dfX04QJE2jPnj09NlZnKy4uJqlUSps3bzZ43d5UjPg0HRNcT72rsZubGyIjIxEZGalz1+iuqL6+HkeOHEFZWRkCAgJ6ZCwhrFu3DiNHjkRQUJDQqXRpXIwY60CrVq3CrFmzEBAQ0OVvhpqRkYHDhw8jPT1d7+9HdbdYnW3Lli24fPkyTpw40S73t+zJuBgxwaxevRpJSUl48uQJnJ2dcejQIaFT6hAbNmxAUFAQPvvsM6FTeSFvb2989dVXWvcB7GmxOlNaWhqePn2KjIwMWFpaCp1Ol2cidAKs99q4cSM2btwodBqdYvLkyZg8ebLQabBONH36dEyfPl3oNLoNPjJijDEmOC5GjDHGBMfFiDHGmOC4GDHGGBMcFyPGGGOC6zWz6Q4dOtSqG5f2Jjw+LeMxYqxj9JpiNHbs2GZvwtjbbd26FQB4fF7gwoULiI+PR3JystCpsF5kzpw5QqfQaXpNMXJwcMDs2bOFTqNLOnjwIADw+LQgPj6ex4h1qt5UjPiaEWOMMcFxMWKMMSY4LkaMMcYEx8WIMcaY4LgYMcYYExwXoyYcPnwYLi4uEIlEzf70798fmzdvRt++fSESibBjxw6h02Y9wKlTp7Bq1SqdfXD+/Pk6fSdPngylUgljY2O8/PLL+OmnnwTIWD8NDQ3YunUrPD09m1yemZmJcePGQS6XQ61WIzQ0FE+fPjWoT2xsLAYPHgyZTAaFQoHBgwcjPDwcpaWlmj7R0dFN/j0PHTpU0ycyMhLu7u5QqVSQSCRwc3PDypUrNQ9IPHr0KGJjY3vsQyEFI/SzZjtDax+r7erqSubm5prf6+rqqLKykh48eEBDhgwhomePlgZAf/3rX9st387Gj2VvWWseO26oiIgIevPNN6m0tFTT5urqSn369CEAdOzYMZ110tPTafr06R2aV1tdv36dxo0bRwBoxIgROsuvXLlCMpmMwsPDqby8nM6fP0/W1ta0cOFCg/pMmzaNNm/eTIWFhVRWVkYpKSkkFovpT3/6k6ZPVFQUAdD5efnllzV9vLy8aPv27fTw4UMqLS2l5ORkEovF9Oc//1nTJz4+nry8vOjx48ftPVxawI8dZ00xNjaGTCZD3759MXDgwFbHqaqq0vmE2FRbb9CR292dxjQmJgYHDhxASkoKlEql1rKEhAQYGRkhMDCwyz8t9vf+93//F59++ikWLVqEkSNHNtknKioKdnZ2WL9+PRQKBTw8PBAaGoq///3v+OWXX/TuY2pqisWLF8PGxgZmZmaYNWsWZsyYgX//+9/47bffNK+3d+9eEJHWz5UrVzTLzczMEBgYCCsrKyiVSsyePRu+vr44efIk8vPzAQBLly7FiBEjMHXqVNTV1XXU8PUqXIxa6ciRI61ed8+ePSgsLGyxrTfoyO3uLmP666+/Ijw8HOvXr4dUKtVZ7unpieDgYNy9excrVqwQIMPWGzFiBA4fPoy5c+dCIpHoLK+rq8Px48fh5eWldaulKVOmgIiQlpamVx8ASE1N1Rk/e3t7ANCcYtPHsWPHYGxsrNVmbW0NAKisrNS0rVu3DpcvX0Z8fLzesVnzuBh1gO+++w7u7u4wNzeHVCrFsGHD8M033wAAgoODsXz5cty4cQMikQhubm5NtgFAfX09IiIi4OjoCJlMhuHDh2tuR5OYmAiFQgG5XI60tDRMmTIFKpUKDg4O2L9/f4dvIxFhy5YtGDJkCCQSCSwtLTFjxgzNp9SgoCCYmppqPSp68eLFUCgUEIlEKC4ubnK7ExISIJVK0bdvX3z44YdQq9WQSqXw9PTExYsX2xQbAE6ePAmVSoUNGzZ0+BjpKyEhAUQEHx+fZvtER0dj4MCB2L17N06dOtVsv5beF333mxfte+3p5s2bKC8vh6Ojo1a7q6srACArK0uvPs3Jzc2FhYUFnJyc2pTn3bt3IZPJ4OzsrGmztLSEl5cX4uPjQURtis/A14xe5PfXjIiITp8+TXFxcZrfm7pmdPDgQVq3bh09evSIHj58SGPHjqU+ffpolvv5+ZGrq6tW3KbaVqxYQRKJhA4dOkSPHz+m1atXk5GREf3www9ERBQWFkYA6PTp0/TkyRMqLCykCRMmkEKhoJqaGr23szXjExERQaamprR3714qKSmhrKwsGjVqFFlbW9P9+/eJiGju3Llka2urtV5cXBwBoKKioma3OzAwkBQKBV29epWqq6spJyeHxowZQ0qlkvLy8toU+9ixY6RUKikyMtKg7e3Ia0YuLi7k7u7e5DJXV1e6desWERGdP3+ejIyMqH///lReXk5EuteM9Hlf9NlvWtr3WuMPf/iDzjWjs2fPEgCtv6lGMpmMvL299erzvJqaGiooKKBt27aRRCKhvXv3apZFRUWRg4MDWVhYkFgspv79+9P06dPpf/7nf5rNu6KigpRKJQUFBeksW7VqFQGgS5cutbj9rQG+ZsQaPXnyRGvWjbe3d4vr+Pv7Y+3atbC0tISVlRV8fHzw8OFDFBUV6f261dXVSExMhK+vL/z8/GBhYYE1a9ZALBYjKSlJq6+npydUKhVsbGwQEBCAiooK5OXlGbyt+qqqqsKWLVswc+ZMzJs3D+bm5hg2bBh27NiB4uJi7Ny5s82vYWJiovl07+7ujsTERJSVlelsu6GmTZuG0tJShIeHtznH9lBRUYFbt25pPuW/iIeHBz755BPcvn0bn376qc5yQ9+X5vYbQ/a9tmqcDff702IAIBaLUVVVpVef5/Xr1w8ODg5Yt24dNm3apHV/t3feeQdHjx5Ffn4+ysvLsX//fuTl5cHLyws5OTlN5rhx40ao1WpER0frLBswYAAAIDs7W88tZs3hYtQCc3NzrQud//Vf/2VwDLFYDAAGTQW9du0aKisrtaacymQy2NnZaU65NMXU1BQAUFtba3Ce+srJyUF5eTlGjx6t1T5mzBiYmppqTqe1p9GjR0Mul79w27ujwsJCEBHkcrle/aOjozFo0CBs374dmZmZWsva8r48v9+0dt9rjcZrPE1NAqipqYFMJtOrz/Py8/NRWFiIr7/+Gv/4xz/wyiuvaK4d9uvXD6+88grMzMxgamqKsWPHIikpCVVVVdi+fbtO/NTUVKSkpOCbb77RmVgCQPO+PXjwwMAtZ7/HxchAEydObPEi8vHjxzFx4kTY2NhAIpFg5cqVBr9ORUUFAGDNmjVaR2Z37tzRuogqhJKSEgDPZh39noWFBcrKyjrkdSUSiUFHl91BdXU1ADR5cb8pUqkUSUlJEIlEePfdd7WOCtrrfenMfa/xut/z3wUCnk0UqK6uhlqt1qvP88RiMWxsbDB58mQcOHAAOTk52LhxY7M5DBs2DMbGxrh+/bpW+4EDBxATE4OMjAz079+/yXUbC2Hj+8haj4tRO8vLy4Ovry/s7Oxw8eJFPHnyBLGxsQbHsbGxAfDsWUP0u2moFy5caO+0DWJhYQEATf5zKykpgYODQ7u/Zm1tbYfFFlLjPzNDjpo9PDywbNky5ObmIioqStPeXu9LZ+57zs7OUCqVuHPnjlb7r7/+CgAYPny4Xn2a4+bmBmNj42ZPwQHPvpDb0NCg9YFg27Zt2LdvH86cOYOXXnqp2XVramoAQOfojBmOi1E7y87ORm1tLT766CO4uLhAKpW26umg/fr1g1QqxeXLlzsgy7YZOnQozMzM8OOPP2q1X7x4ETU1NXj11VcBPLvu016nCzMyMkBEGDt2bLvHFlLjHTwM/f5QVFQUBg8ejEuXLmna9H1fWtKZ+56JiQmmTp2Kc+fOoaGhQdOenp4OkUgEHx8fvfo8fPgQb7/9tk783Nxc1NfXo1+/fgCAN954Q6fPDz/8ACKCh4cHiAihoaHIzs7GkSNHmjzKfF7j+2Zra9uq7Wf/h4tRO2ucfnrq1ClUV1cjNzdX51y9lZUV7t27h9u3b6OsrAy1tbU6bcbGxli4cCH279+PxMRElJaWor6+HgUFBVpf4BOCVCrF8uXLkZqain379qG0tBTZ2dlYtGgR1Go1AgMDATz7VPro0SMcOXIEtbW1KCoq0vl029RYAM8+rT5+/Bh1dXXIyspCcHAwHB0dsWDBgjbFTk9P71JTu+VyOVxcXFBQUGDQeo2n656/qK/v+6JP7Jb2vYCAANja2rbLLYjCw8Px4MEDrF27FhUVFbhw4QLi4uKwYMECDBo0SK8+CoUC3377Lc6cOYPS0lLU1tbi0qVLeOedd6BQKLBs2TIAz6ZoHzhwACUlJaitrcWFCxfw3nvvwdHREYsWLcLVq1exadMm7Nq1C2KxWOe2QZs3b9bKvfF9GzZsWJvHodcTYApfpzN06vJ///d/08CBAzW3CrGzs9OZPkpE9Pnnn5OtrS0BIIVCQTNnziQiotDQULKysiILCwuaNWsWffHFFwSAXF1dKS8vj3766SdycnIimUxG48ePp/v37zfZ9vTpUwoNDSVHR0cyMTEhGxsb8vPzo5ycHNq+fTvJ5XICQAMGDKAbN27Qzp07SaVSEQBycnKi69evd8j4EBE1NDRQXFwcDRgwgMRiMVlaWpKvry9du3ZN0+fhw4c0adIkkkql5OzsTB9//DGFhIQQAHJzc2t2LAIDA0ksFpO9vT2ZmJiQSqWiGTNm0I0bN9oc+8SJE6RUKik6Otqg7e3Iqd1BQUEkFoupsrJS05aamkqurq4EgKytrWnJkiVNrhsSEqI1tbul90Xf/eZF+x4Rka+vLwGgiIiIF27bhQsXaNy4caRWq7X+njw9Pens2bOafmfPnqXXXnuNJBIJqdVqCgkJoerqaq1YLfXx8fEhZ2dnMjMzI4lEQq6urhQQEEDZ2dmaPsuXLydXV1dSKBRkYmJCDg4O9P7779O9e/eIiCg7O7vJ2wU1/vx+evm0adPI3t6eGhoaXjgOrYVeNLWbixHrcuMTGBhIVlZWQqehpSOLUW5uLpmYmGh9H6arq6+vpwkTJtCePXuETkUwxcXFJJVKafPmzR32Gr2pGPFpOtYl9aY7Iru5uSEyMhKRkZEG3bZGKPX19Thy5AjKysoQEBAgdDqCWbduHUaOHImgoCChU+kRuBgx1gWsWrUKs2bNQkBAQJe/GWpGRgYOHz6M9PR0vb8f1dNs2bIFly9fxokTJzTfI2Rtw8WIdSmrV69GUlISnjx5AmdnZxw6dEjolDrNhg0bEBQUhM8++0zoVF7I29sbX331lda9AXuTtLQ0PH36FBkZGbC0tBQ6nR7DROgEGHvexo0bX/gFxZ5u8uTJmDx5stBpsBeYPn06pk+fLnQaPQ4fGTHGGHPvju0AACAASURBVBMcFyPGGGOC42LEGGNMcFyMGGOMCa7XTGAoKChASkqK0Gl0SY23NOHxaV7jDUJ5jBjrGL2mGH3//fdaD9liunh8WsZjxFjHEBHxw9sZa62UlBTMmTMH/GfEWJsc5GtGjDHGBMfFiDHGmOC4GDHGGBMcFyPGGGOC42LEGGNMcFyMGGOMCY6LEWOMMcFxMWKMMSY4LkaMMcYEx8WIMcaY4LgYMcYYExwXI8YYY4LjYsQYY0xwXIwYY4wJjosRY4wxwXExYowxJjguRowxxgTHxYgxxpjguBgxxhgTHBcjxhhjguNixBhjTHBcjBhjjAmOixFjjDHBcTFijDEmOC5GjDHGBMfFiDHGmOC4GDHGGBMcFyPGGGOC42LEGGNMcFyMGGOMCY6LEWOMMcFxMWKMMSY4LkaMMcYEZyJ0Aox1F4WFhUhKStJqy8rKAgDExsZqtVtZWeH999/vtNwY6+5ERERCJ8FYd1BXVwc7Ozs8fvwYYrG42X5Pnz5FYGAgduzY0YnZMdatHeTTdIzpycTEBG+99RaMjY3x9OnTZn8A4O233xY4W8a6Fy5GjBngrbfeQm1t7Qv72NnZYfz48Z2UEWM9Axcjxgzg4eEBBweHZpebmppi/vz5MDLiPy3GDMF/MYwZQCQSYd68ec1eM6qpqcFbb73VyVkx1v1xMWLMQC86Vefi4oJXXnmlkzNirPvjYsSYgYYPH45BgwbptJuamuKdd94RICPGuj8uRoy1wvz583VO1dXU1CAgIECgjBjr3rgYMdYK8+bNQ11dneZ3kUiEESNGYODAgQJmxVj3xcWIsVZwcnLCqFGjIBKJAADGxsZ8io6xNuBixFgr/ed//ieMjY0BAPX19Zg9e7bAGTHWfXExYqyVZs+ejYaGBohEIowbNw729vZCp8RYt8XFiLFWsrOzg5eXF4iIT9Ex1kbtdqPUxnPnjDHGegd/f38cPHiwPUIdbNdHSAQHB8PDw6M9QzLWri5cuID4+HgkJye3S7yqqirs3LkTS5cubZd4XcWcOXP475m90NatW9s1XrsWIw8PD76Iy7q8+Pj4dt1P//SnP+Gll15qt3hdwZw5c/jvmb1QOx0RafA1I8baqKcVIsaEwMWIMcaY4LgYMcYYExwXI8YYY4LjYsQYY0xwghSjMWPGwNjYGCNHjuzU1124cCGkUilEIhGqq6s79bW7is2bN6Nv374QiUTYsWOHpv3EiRMwNzfHv/71rw59/c56nY7WU7ajI5w6dQqrVq3C4cOH4eLiApFIBJFIhPnz5+v0nTx5MpRKJYyNjfHyyy/jp59+EiBj/TQ0NGDr1q3w9PRscnlmZibGjRsHuVwOtVqN0NBQPH361KA+sbGxGDx4MGQyGRQKBQYPHozw8HCUlpZq+kRHR2vG9PmfoUOHavpERkbC3d0dKpUKEokEbm5uWLlyJcrLywEAR48eRWxsLOrr69tziNpEkGL0ww8/YNKkSZ3+uklJSVixYkWnv25XsmLFCpw/f16nvZ2++9yiznqdjtZTtqO9rV27FgkJCVi9ejX8/Pxw8+ZNuLq6ok+fPti3bx+OHz+u1f/bb7/FwYMH8eabbyInJwejRo0SKPMXy83NxX/8x39g2bJlqKys1Fmek5ODyZMnw9vbG0VFRUhNTcWXX36JRYsWGdTnu+++w/vvv4+8vDw8ePAAUVFRiI2Nhb+/v0H5njlzBkuWLMHt27dRXFyMjRs3Ij4+HrNmzQIA+Pj4QCqVwtvbGyUlJa0clXZG7QQAJScn693f29ubRo4c2abXrKysJA8PD4PWCQsLIwBUVVXVptfuznJzcwkA/fWvf+3Q12nN+9PRkpOTqR13e8F09Nga+vdMRPTZZ5/RwIEDdf62XF1d6auvviIjIyOyt7enkpISreXp6ek0ffr0NufcUS5fvkwzZ86kffv20ciRI2nEiBE6febMmUPOzs7U0NCgaYuLiyORSEQ///yz3n18fX11xm/WrFkEgO7du0dERFFRUbR3794X5jxt2jSqq6vTaps9ezYBoLy8PE1bUFAQeXh4UG1trT5DocXf35/8/f0NXq8ZKYJeM/r9w8kMtWfPHhQWFrZqXb59Ucdry/vDXqyrje2vv/6K8PBwrF+/HlKpVGe5p6cngoODcffu3W53dmLEiBE4fPgw5s6dC4lEorO8rq4Ox48fh5eXl9b/lSlTpoCIkJaWplcfAEhNTdUZv8Yb8DaeYtPHsWPHNHeUb2RtbQ0AWkd269atw+XLlxEfH6937I4iaDH69ddfMXjwYCgUCshkMkyYMAGZmZma5d999x3c3d1hbm4OqVSKYcOG4ZtvvgHw7NZDy5cvx40bNyASieDm5qZZb+/evRg9ejSkUikUCgX69++PqKgozXIjIyMcP34cU6ZMgbm5OdRqNb788ku9805MTIRCoYBcLkdaWhqmTJkClUoFBwcH7N+/X9OPiLBlyxYMGTIEEokElpaWmDFjBn755RcAwKZNmyCXy6FUKlFYWIjly5fD3t4eixYtgkKhgJGREV599VXY2tpCLBZDoVBg1KhRmDBhAvr16wepVAoLCwusXLlSK78XjVtTMjMz4ejoCJFIhC+++ELz3jR1XlokEuHf//53q96fpl5Hn3HSd7w7S1PboU+OCQkJkEql6Nu3Lz788EOo1WpIpVJ4enri4sWLAICgoCCYmprCzs5O83qLFy+GQqGASCRCcXFxs/v+yZMnoVKpsGHDhk4ekWfbRkTw8fFptk90dDQGDhyI3bt349SpU832a6/9ob6+HhEREXB0dIRMJsPw4cPb7TZQz7t58ybKy8vh6Oio1e7q6goAyMrK0qtPc3Jzc2FhYQEnJ6c25Xn37l3IZDI4Oztr2iwtLeHl5YX4+HjhTz231zEWWnGazsXFhW7dukW1tbV05coV+sMf/kBSqZSuX79OREQHDx6kdevW0aNHj+jhw4c0duxY6tOnjyaGn58fubq6asXdunUrAaDPPvuMHj58SI8ePaK//e1vNHfuXCL6v9N0p0+fppKSEnr06BFNnTqVJBIJVVRU6J3/83GePHlChYWFNGHCBFIoFFRTU0NERBEREWRqakp79+6lkpISysrKolGjRpG1tTXdv39fK87SpUtp27ZtNHPmTPr5559p7dq1BIAuXrxIFRUVVFxcTH/+858JAB0//v/Yu/OoKK60f+Dfhm56o5tFEIjs4BIV45oIiWM8ZMioryCuJCYTzUkOmoUY0eAeBcQFXmVUnEwSw4niyKIMJiqJMQ4mviHGvMqIEBVNXImyKDtIQz+/P/zRL22zNNBQYJ7POfxB1e1bT92q6qfr1q2qI1RcXEzV1dUUFhZGACgnJ0cXW3vt1lI33c2bNwkA7dixQ1dm+fLlujb5/fffycbGhvz8/KixsbHT2+fR5XS0ndpqb2OYqpuupfUwJsbQ0FBSKpWUn59PdXV1lJeXR+PGjSOVSqXrPpk3bx45ODjoLS82NpYAUHFxMRG13LaHDx8mlUpFkZGRXV6/jh7Pnp6eNHTo0BbneXl50W+//UZERD/88AOZmZmRu7s7VVVVEZFhN52p9oelS5eSVCqlAwcO0P3792nlypVkZmZGZ86c6UyTEBHRM888Y9BNd/LkSQJAsbGxBuXlcjn5+/sbVaa5+vp6unXrFu3YsYOkUqlet1xUVBQ5OzuTtbU1SSQScnd3p6CgIPrpp59ajbu6uppUKhWFhYUZzFuxYgUBoHPnzrW7/s2ZuptO0GT06EY9f/48AaClS5e2+JmYmBgCQEVFRURkeEDW19eTtbU1TZo0Se9zDQ0NFB8fT0QtXzPas2cPAaALFy4YHX9L9SQkJBAAunLlCtXU1JClpSWFhITofe6nn34iALovjNauYTUlo8rKSt20zz//nABQbm6uQX3JycmtxvpouxmTjB4VHBxMMpmMLl68aPRyjElGXWmn5u1trJ5IRm3FGBoaSlZWVnp1nTlzhgDQ+vXriajzyciUOnI8V1VVkUgkomnTprU4v3kyIiIKDw8nAPTOO+8QkX4yMtX+UFtbSwqFQq+empoakkql9NZbbxm1Xi1pKRkdO3aMANDWrVsNyqvVavLz8zOqTHMODg4EgPr160d/+9vf9H5w3bhxg86ePUuVlZX04MEDys7OplGjRpFcLm/1O2zVqlU0aNAgqqioMJj32WefEQDas2ePUW3Q5LG6ZvQoHx8fWFlZtXrK2nSNqbXhiOfPn0dZWRlefPFFvenm5uZtPlW5qV6NRtOZsHUsLCx09eTl5aGqqgpjx47VKzNu3DhYWFjoumU6U39DQ4NumjGxt9du7UlNTcW//vUvrF+/HoMHDzbpcrrSTs3bu7cyJsaxY8dCoVDouqH6mqKiIhARFAqFUeWjo6MxePBgJCQk6HXLA6bbHy5duoSamhq94c5yuRyOjo4mb+emazzNj8sm9fX1kMvlRpVp7ubNmygqKsI///lPfP755xg1apTuGqGLiwtGjRoFS0tLWFhYYPz48UhMTERtbS0SEhIM6k9PT0dqaiq+/vprqFQqg/lN2+3u3bsdXHPT6lXJCHj4hdZ04B45cgTPP/887O3tIZVKDa6NPKppLL61tXW3x9mepuGSlpaWBvOsra1RWVnZbcvuaLu1pbS0FO+++y7GjRuH8PBwky9HyHbqTaRSKYqLi4UOo1Oa7tlr6eJ+S2QyGRITEyESifD666+jtrZWN89U+0N1dTUAYPXq1XrXO69fv97i0OyuaLq+1/xeIODhQIG6ujo4OTkZVaY5iUQCe3t7BAQEIDk5GXl5eYiJiWk1Bh8fH5ibm+Py5ct605OTk7Fp0yZkZWXB3d29xc82JUKh773sVcmooaEB9+7dg6urK27cuIHg4GA4Ojri9OnTKC8vx+bNm9v8fNPTk0tKSnoi3DY1JcSWDp6ysjI4Ozt3y3I7025tee+991BWVobExES90TmmWo5Q7dSbaDSaPr2uTV9mHTkj9vX1xZIlS1BQUKA3uMhU+4O9vT2Ah+/cISK9v+zsbKPjNIaHhwdUKhWuX7+uN/3KlSsAgBEjRhhVpjXe3t4wNzdHXl5eq2W0Wi20Wq3eD4IdO3YgKSkJJ06caPPJ8vX19QBgcHbW03pVMvr3v/8NrVaL0aNHIzc3FxqNBm+99RY8PT11T05oi7u7O2xtbXHs2LEeirh1w4cPh6WlJX7++We96adPn0Z9fT3GjBnTLcvtTLu15siRI9i3bx/WrFmDYcOG6aYvW7bMZMsRqp16k6ysLBARxo8fDwAQi8W9uuvxUU1P9CgvL+/Q56KiojBkyBCcO3dON81U+0PTaNOcnJwOxdQZYrEYU6ZMwXfffQetVqubnpmZCZFIhMDAQKPKlJaW4uWXXzaov6CgAI2NjXBxcQEAg8sQwMMHCRARfH19QUSIiIhAbm4uMjIyWjzLbK5puzk4OHRq/U1F0GRUX1+P8vJyNDQ04OzZswgLC4Obmxvmz5+vGwJ5/Phx1NXVoaCgwKC/2NbWFoWFhbh27RoqKythZmaGlStX4rvvvkNYWBhu374NrVaLyspK5Ofn9+i6yWQyhIeHIz09HUlJSaioqEBubi4WLVoEJycnhIaGdstyjWk3Y1RUVGDhwoUYOXIkli9fDuDhafzPP/+MnJycTm2flr5ghWonIWm1Wty/fx8NDQ04f/48Fi9eDFdXV8yfPx/Aw1/C9+7dQ0ZGBjQaDYqLiw1+UbfUtpmZmYIM7VYoFPD09MStW7c69Lmm7rrmZ9ym2h9kMhkWLFiA/fv3Y9euXaioqEBjYyNu3bqF33//HQAQEhICBwcHkzyCaM2aNbh79y4+/PBDVFdXIzs7G7GxsZg/f77uOmt7ZZRKJY4dO4YTJ06goqICGo0G586dw2uvvQalUoklS5YAeDhEOzk5GWVlZdBoNMjOzsYbb7wBV1dXLFq0CPn5+diyZQs++eQTSCQSg1sz4uLi9GJv2m4+Pj5dbocuMdVQCHRwNF1iYiJNmjSJ+vfvT2KxmPr160cvvfQSXb9+XVcmIiKCbG1tydrammbPnk07d+4kAOTl5aUbUeLm5kZyuZyee+453bDPnTt3ko+PD8lkMpLJZDRq1ChKSEigzZs3k1wuJwA0cOBAunr1KiUlJZGNjQ0BIGdnZ6NG1CUkJJBCodCr5+OPPya1Wk0AyM3NjS5fvkxarZZiY2Np4MCBJJFIyMbGhoKDg+nSpUtERHrxuLi46IZvxsfH6+p3d3en77//njZt2kRWVlYEgBwcHGjfvn2UnJysG3VjY2ND+/fvb7fdFi9erPuMUqmkGTNm0I4dO8jR0ZEAkEKhoMDAQIqLiyMALf5NmTKlU9tn9erVBsshonbbydj2NoYpRtO11F7GxhgaGkoSiYQGDBhAYrGY1Go1TZ8+na5evaqrv7S0lCZNmkQymYw8PDzo3XffpWXLlhEA8vb2bnXfP3r0KKlUKoqOju7S+hF1/HgOCwsjiURCNTU1umnp6enk5eVFAMjOzk43eu5Ry5Yt0xvabar94cGDBxQREUGurq4kFovJ3t6eZs6cSXl5eUT0cIQoAFq7dm2b65adnU3PPvssOTk56Y4BR0dH8vPzo5MnT+rKnTx5kp5++mmSSqXk5OREy5Yto7q6Or262isTGBhIHh4eZGlpSVKplLy8vCgkJERvBG14eDh5eXmRUqkksVhMzs7O9Oabb+qe0JCbm9vqsYsWhpdPnTqVBgwYoPdkCGM8NkO7GROC0I8DCg0NJVtbW8GWb6yOHs8FBQUkFovbfUxNb9LY2EgTJkyg3bt3Cx2KYEpKSkgmk1FcXFyHP/tYD+1m7I+gNz0p2VS8vb0RGRmJyMjIDj22RiiNjY3IyMhAZWUlQkJChA5HMOvWrcPIkSMRFhYmdCi9awCD0C5evNjqI3Ca//2Rd17GWrNixQrMnj0bISEhHR7M0NOysrJw8OBBZGZmGn1/1ONm69atyMnJwdGjR7v8nFBT4GTUzJAhQwyGgbb0l5ycLHSorA9auXIlEhMTUV5eDg8PDxw4cEDokExuw4YNCAsLw8aNG4UOpU3+/v7Yt2+f3jMA/0gOHTqEBw8eICsrCzY2NkKHAwAQCx0AY38UMTExbd64+LgICAhAQECA0GGwNgQFBSEoKEjoMPTwmRFjjDHBcTJijDEmOE5GjDHGBMfJiDHGmOBMOoDB1A8gZMzUmvbR1NRUgSPp/fh4Zm25deuWSR/uKyIyzbtmO/swTsYYY33TrFmzkJaWZoqq0kx6ZpSSkoI5c+aYskrGTCo1NRVz586FiX6DPbZEIhEfz6xNs2fPNml9fM2IMcaY4DgZMcYYExwnI8YYY4LjZMQYY0xwnIwYY4wJjpMRY4wxwQmWjA4ePAhPT0+DdwWJxWLY2dnhhRdeQHp6erfGsGDBAshkMohEItTV1RkV56uvvmpQJiAgACqVCubm5hg2bBjOnj3brXF3VlxcHPr37w+RSISPPvpIN/3o0aOwsrLCl19+2a3L76nlMGEdP34cK1aseKyOHQDQarXYtm0b/Pz8Wpx/6tQpPPvss1AoFHByckJERAQePHjQoTKbN2/GkCFDIJfLoVQqMWTIEKxZswYVFRW6MtHR0S2+Z2348OG6MpGRkRg6dCjUajWkUim8vb3xwQcf6F58+MUXX2Dz5s2960WPpnpnLDr52nEvLy+ysrLS/X/v3j06fvw4DRkyhABQcnKyqUJs0apVqwgA1dbWthtnv379CAAdPnzYYH5mZiYFBQV1V5gmU1BQQADo73//u27a4cOHSa1W0xdffNGty+6p5bRF6NeO9xWdPZ7Xrl1L06ZNo4qKCt20x+HYuXz5Mj377LMEgJ566imD+RcuXCC5XE5r1qyhqqoq+uGHH8jOzo4WLFjQoTJTp06luLg4KioqosrKSkpNTSWJREJ//vOfdWWioqIIgMHfsGHDdGUmTpxICQkJVFpaShUVFZSSkkISiYT+8pe/6MrEx8fTxIkT6f79+51qE1O/drzXJaMmX3/9NQGgGTNmGF1XTU0N+fr6dmj5HUlG+/btIzMzMxowYACVlZXpze8LBxRRy8moO3RmW/QEIZNRd7aJqevuzPG8ceNGGjRokMGx1NePnZycHJoxYwYlJSXRyJEjW0xGc+fOJQ8PD9JqtbppsbGxJBKJ6JdffjG6THBwsEH7zZ49mwBQYWEhET1MRnv37m0z5qlTp1JDQ4PetDlz5hAAunHjhm5aWFgY+fr6kkajMaYp9Jg6GfXaa0bu7u4AgLKyMqM/s3v3bhQVFXVqecY8zsjPzw+LFy/G7du3sXTp0k4t54+iK9vicdWdbSJ0e1+5cgVr1qzB+vXrIZPJDOb35WPnqaeewsGDBzFv3jxIpVKD+Q0NDThy5AgmTpyo9z0yefJkEBEOHTpkVBkASE9PN2i/AQMGAICui80Yhw8fhrm5ud40Ozs7AEBNTY1u2rp165CTk4P4+Hij6+4uvTYZnT9/HgAwceJE3bTvv/8eQ4cOhZWVFWQyGXx8fPD1118DABYvXozw8HBcvXoVIpEI3t7eus/t3bsXY8eOhUwmg1KphLu7O6KionTzzczMcOTIEUyePBlWVlZwcnLCZ5991mJc0dHRGDRoED799FMcP3681fiJCFu3bsWTTz4JqVQKGxsbTJ8+HRcvXgQAbNmyBQqFAiqVCkVFRQgPD8eAAQOwaNEiKJVKmJmZYcyYMXBwcIBEIoFSqcTo0aMxYcIEuLi4QCaTwdraGh988IHecttqo5acOnUKrq6uEIlE2LlzJ4CHXywt9UmLRCJ88803ndoWLS3HmHbatWsXlEolFAoFDh06hMmTJ0OtVsPZ2Rn79+9vdb1Mqb0Yw8LCYGFhofcK67fffhtKpRIikQglJSUttsn27dshk8nQv39/LFy4EE5OTpDJZPDz88Pp06e7VDcAfPXVV1Cr1diwYUO3t9H27dtBRAgMDGy1jKmOHWP3icbGRqxduxaurq6Qy+UYMWIEUlJSTLfS/9+vv/6KqqoquLq66k338vIC8PC7zJgyrSkoKIC1tTXc3Ny6FOft27chl8vh4eGhm2ZjY4OJEyciPj5e+EdkmeocCybqpqupqaHMzExyc3OjgIAAqqqq0s1LS0ujdevW0b1796i0tJTGjx9P/fr1082fOXMmeXl56dW/bds2AkAbN26k0tJSunfvHv3jH/+gefPmEdH/ddN9++23VFZWRvfu3aMpU6aQVCql6upqvTh/++03IiL64YcfyMzMjNzd3XXxPdrVsHbtWrKwsKC9e/dSWVkZnT9/nkaPHk12dnZ0584dvWW/9957tGPHDpoxYwb98ssv9OGHHxIAOn36NFVXV1NJSQn95S9/IQB05MgRKi4upurqagoLCyMAlJOTY3QbtdRNd/PmTQJAO3bs0JVZvny5bv1///13srGxIT8/P2psbOz0tnh0OR1tp2+//ZbKy8upqKiIJkyYQEqlkurr66kjOtNNZ0yM8+bNIwcHB73PxcbGEgAqLi5utU1CQ0NJqVRSfn4+1dXVUV5eHo0bN45UKpWuO6WzdR8+fJhUKhVFRkZ2aH2JOn48e3p60tChQ1uc153HTlv7xNKlS0kqldKBAwfo/v37tHLlSjIzM6MzZ850uD2aPPPMMwbddCdPniQAFBsba1BeLpeTv7+/UWWaq6+vp1u3btGOHTtIKpXqdctFRUWRs7MzWVtbk0QiIXd3dwoKCqKffvqp1birq6tJpVJRWFiYwbwVK1YQADp37ly769/cY3nNCC1cjPPx8aHPP/+cHjx40OpnY2JiCAAVFRURkeEBWV9fT9bW1jRp0iS9zzU0NFB8fDwRtXzNaM+ePQSALly4oBdn0wFFRBQeHk4A6J133iEi/QOqpqaGLC0tKSQkRG+5P/30EwHQfTm0dr2qKRlVVlbqpn3++ecEgHJzcw3qa2uQx6NtZEwyelRwcDDJZDK6ePGi0csxJhl1pZ0SEhIIAF25cqXVmFrS0WRkbIxdSUaPXjM9c+YMAaD169d3qe6u6MjxXFVVRSKRiKZNm9bi/J46dprvE7W1taRQKPTqqampIalUSm+99ZZR69WSlpLRsWPHCABt3brVoLxarSY/Pz+jyjTn4OBAAKhfv370t7/9Te9H140bN+js2bNUWVlJDx48oOzsbBo1ahTJ5XK976zmVq1aRYMGDdIbWNLks88+IwC0Z88eo9qgyWN5zcjKygpEBCKCRqPBrVu38P777yMsLAwjRoxASUlJi5+TSCQA0OrwxPPnz6OsrAwvvvii3nRzc3O89957rcbTVK9Go2m1THR0NAYPHoyEhAScOnVKb15eXh6qqqowduxYvenjxo2DhYWFrgumIywsLAA87J/uSJzttVF7UlNT8a9//Qvr16/H4MGDTbqcrrRTU3u0te6m0B3bsj1jx46FQqHQdUv1dkVFRSAiKBQKo8p317HTfJ+4dOkSampq9IY7y+VyODo6mrxdm67xND82m9TX10MulxtVprmbN2+iqKgI//znP/H5559j1KhRumuCLi4uGDVqFCwtLWFhYYHx48cjMTERtbW1SEhIMKg/PT0dqamp+Prrr6FSqQzmN223u3fvdnDNTatXJKPmxGIxBgwYgAULFiAuLg6XLl3Cxo0bAQBHjhzB888/D3t7e0ilUoPrJY9qGptvbW1t8jhlMhkSExMhEonw+uuvo7a2VjevadCFpaWlweesra1RWVlp8niadLSN2lJaWop3330X48aNQ3h4uMmXI2Q7GUuoGKVSKYqLi7ulblNrukevpYv7LemJY6e6uhoAsHr1ar1rntevX9e7gG8KTdfzmt8LBDwcKFBXVwcnJyejyjQnkUhgb2+PgIAAJCcnIy8vDzExMa3G4OPjA3Nzc1y+fFlvenJyMjZt2oSsrCzdoLBHNSXC1u617Cm9Lhk15+PjAwDIz8/HjRs3iLj8UQAAIABJREFUEBwcDEdHR5w+fRrl5eXYvHlzm59/4oknAKDVM6uu8vX1xZIlS1BQUKA3IKIp+bV04JSVlZn07YjNdaaN2vLee++hrKwMiYmJeiNzTLUcodqpI4SIUaPR9Jr1N0bTl1lHzoq7+9ixt7cHAGzbtk3X69L0Z+o32Hp4eEClUuH69et6069cuQIAGDFihFFlWuPt7Q1zc3Pk5eW1Wkar1UKr1er9INixYweSkpJw4sQJ3XdhS+rr6wHA4Oysp/XqZPS///u/AIDBgwcjNzcXGo0Gb731Fjw9PXVPTmiLu7s7bG1tcezYsW6LMSoqCkOGDMG5c+d004YPHw5LS0v8/PPPemVPnz6N+vp6jBkzplti6UwbtebIkSPYt28f1qxZg2HDhummL1u2zGTLEaqdOsLYGMViscm6DLOyskBEGD9+vMnr7g5NT/UoLy/v0Oe689hpGnGak5PToZg6QywWY8qUKfjuu++g1Wp10zMzMyESiRAYGGhUmdLSUrz88ssG9RcUFKCxsREuLi4AYHDZAQDOnDkDIoKvry+ICBEREcjNzUVGRkaLZ5nNNW03BweHTq2/qfSaZFRbWwutVgsiQmFhIRITE7F69WrY2dnh/fff1w2JPH78OOrq6lBQUGDQf2xra4vCwkJcu3YNlZWVMDMzw8qVK/Hdd98hLCwMt2/fhlarRWVlJfLz800Sd1OXQ/MzB5lMhvDwcKSnpyMpKQkVFRXIzc3FokWL4OTkhNDQUJMs+1HGtJExKioqsHDhQowcORLLly8H8PAU/ueff0ZOTk6ntkVLX6ZCtVNHGBujt7c37t27h4yMDGg0GhQXFxv8Cm6tTbRaLe7fv4+GhgacP38eixcvhqurK+bPn9+lujMzM3tkaLdCoYCnpydu3brVoc9157Ejk8mwYMEC7N+/H7t27UJFRQUaGxtx69Yt/P777wCAkJAQODg4mOQRRGvWrMHdu3fx4Ycforq6GtnZ2YiNjcX8+fN111rbK6NUKnHs2DGcOHECFRUV0Gg0OHfuHF577TUolUosWbIEwMMh2snJySgrK4NGo0F2djbeeOMNuLq6YtGiRcjPz8eWLVvwySefQCKRGNyeERcXpxd703Zr6okSjKmGQqCDo+nS09NbHUknlUpp4MCB9NZbb+ndLRwREUG2trZkbW1Ns2fPpp07dxIA8vLy0o0wcXNzI7lcTs8995xuGOjOnTvJx8eHZDIZyWQyGjVqFCUkJNDmzZtJLpcTABo4cCBdvXqVkpKSyMbGhgCQs7MzRUVF6eK0s7PTjQB61LJly/SGp2q1WoqNjaWBAweSRCIhGxsbCg4OpkuXLhER6S3bxcVFN3QzPj6eFAoFASB3d3f6/vvvadOmTWRlZUUAyMHBgfbt20fJycm6ETc2Nja0f//+dtto8eLFus8olUqaMWMG7dixgxwdHQkAKRQKCgwMpLi4uBa3CwCaMmVKp7bF6tWrDZZjTDslJCTo2qNpG3388cekVqsJALm5udHly5eN3u86M7S7vRiJiEpLS2nSpEkkk8nIw8OD3n33XVq2bBkBIG9v71b3z9DQUJJIJDRgwAASi8WkVqtp+vTpdPXq1S7XffToUVKpVBQdHd2h9SXq+PEcFhZGEomEampqdNOaH+OmPHaM3ScePHhAERER5OrqSmKxmOzt7WnmzJmUl5dHRA9HiQKgtWvXtrlu2dnZ9Oyzz5KTk5PuOHB0dCQ/Pz86efKkrtzJkyfp6aefJqlUSk5OTrRs2TKqq6vTq6u9MoGBgeTh4UGWlpYklUrJy8uLQkJC9EbRhoeHk5eXFymVShKLxeTs7Exvvvmm7gkNubm5rR6/aGF4+dSpU2nAgAF6T4YwxmM3tJuxntTbnk0XGhpKtra2QodhoKPHc0FBAYnF4nYfU9ObNDY20oQJE2j37t1ChyKYkpISkslkFBcX1+HPPpZDuxn7I+tVT07uJG9vb0RGRiIyMrJDj60RSmNjIzIyMlBZWYmQkBChwxHMunXrMHLkSISFhQkdSu+5ZsQY69tWrFiB2bNnIyQkpMODGXpaVlYWDh48iMzMTKPvj3rcbN26FTk5OTh69KjuPkEhcTJiTCArV65EYmIiysvL4eHhgQMHDggdUpdt2LABYWFhunsDeyt/f3/s27dP75l/fySHDh3CgwcPkJWVBRsbG6HDAQCIhQ6AsT+qmJiYNm9k7KsCAgIQEBAgdBisDUFBQQgKChI6DD18ZsQYY0xwnIwYY4wJjpMRY4wxwXEyYowxJjiTDmDYtm0b0tLSTFklYybV9OiT2bNnCxxJ78fHM2vLjz/+qHt+oimIiEzzrlk+uNkf0d27d3HhwgX4+/sLHQpjPa7p6esmkGayZMTYH1Fqairmzp0LPowY65I0vmbEGGNMcJyMGGOMCY6TEWOMMcFxMmKMMSY4TkaMMcYEx8mIMcaY4DgZMcYYExwnI8YYY4LjZMQYY0xwnIwYY4wJjpMRY4wxwXEyYowxJjhORowxxgTHyYgxxpjgOBkxxhgTHCcjxhhjguNkxBhjTHCcjBhjjAmOkxFjjDHBcTJijDEmOE5GjDHGBMfJiDHGmOA4GTHGGBMcJyPGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcFxMmKMMSY4TkaMMcYEx8mIMcaY4DgZMcYYExwnI8YYY4LjZMQYY0xwYqEDYKyvKCwsxH/9139Bo9HoptXU1MDKygo+Pj56ZUeNGoU9e/b0dIiM9VmcjBgz0hNPPIH6+nrk5eUZzCsvL9f7PyQkpKfCYuyxwN10jHXAX//6V4jFbf+GE4lEePnll3soIsYeD5yMGOuAl156CY2Nja3OF4lEGDNmDDw8PHowKsb6Pk5GjHWAi4sLxo8fDzOzlg8dc3Nz/PWvf+3hqBjr+zgZMdZBr776KkQiUYvztFot5syZ08MRMdb3cTJirINmz57d4nRzc3M8//zzcHBw6OGIGOv7OBkx1kF2dnbw9/eHubm5wbxXX31VgIgY6/s4GTHWCa+88gqISG+amZkZgoODBYqIsb6NkxFjnTB9+nRIJBLd/2KxGFOnToWVlZWAUTHWd3EyYqwTVCoVpk2bpktIjY2NeOWVVwSOirG+i5MRY500b948NDQ0AADkcjmmTJkicESM9V2cjBjrpMmTJ0OpVAIAZs2aBblcLnBEjPVd/Gy6VqSmpgodAusDxo0bh3//+99wcXHhfYa1y8XFBb6+vkKH0SuJ6NEhQQwAWr2pkTHGOmvWrFlIS0sTOozeKI276dqQkpICIvpD/6WkpACA4HH01r/GxkbExMTw/sJ/7f7NmjVLyK+zXo+TEWNdYGZmhmXLlgkdBmN9HicjxrqovVdKMMbax8mIMcaY4DgZMcYYExwnI8YYY4LjZMQYY0xwnIy62RtvvAGVSgWRSIScnByhwxHM0aNHYWVlhS+//FLoUHqd48ePY8WKFTh48CA8PT0hEokgEolafB1FQEAAVCoVzM3NMWzYMJw9e1aAiI2j1Wqxbds2+Pn5tTj/1KlTePbZZ6FQKODk5ISIiAg8ePCgQ2U2b96MIUOGQC6XQ6lUYsiQIVizZg0qKip0ZaKjo3Vt2vxv+PDhujKRkZEYOnQo1Go1pFIpvL298cEHH6CqqgoA8MUXX2Dz5s1tvnKedQ0no2726aef4pNPPhE6DMER8b3VLfnwww+xfft2rFy5EjNnzsSvv/4KLy8v9OvXD0lJSThy5Ihe+WPHjiEtLQ3Tpk1DXl4eRo8eLVDkbSsoKMCf/vQnLFmyBDU1NQbz8/LyEBAQAH9/fxQXFyM9PR2fffYZFi1a1KEy33//Pd58803cuHEDd+/eRVRUFDZv3tzhe3pOnDiBd955B9euXUNJSQliYmIQHx+ve5FiYGAgZDIZ/P39UVZW1slWYW0i1iIAlJKSYpK69u/fTwDo3LlzJqmvJ6WkpNDjsJvU1NSQr69vt9Xfmf1l48aNNGjQIKqtrdWb7uXlRfv27SMzMzMaMGAAlZWV6c3PzMykoKCgLsfcXXJycmjGjBmUlJREI0eOpKeeesqgzNy5c8nDw4O0Wq1uWmxsLIlEIvrll1+MLhMcHGzQfrNnzyYAVFhYSEREUVFRtHfv3jZjnjp1KjU0NOhNmzNnDgGgGzdu6KaFhYWRr68vaTQaY5pCz6xZs2jWrFkd/twfRCqfGfUAfrSQ8Hbv3o2ioiKhw9C5cuUK1qxZg/Xr10MmkxnM9/Pzw+LFi3H79m0sXbpUgAg776mnnsLBgwcxb948SKVSg/kNDQ04cuQIJk6cqHdsTJ48GUSEQ4cOGVUGANLT0w3ab8CAAQCg62IzxuHDhw3e3GtnZwcAemd269atQ05ODuLj442umxmHk5GJERFiY2MxePBgSKVSWFlZGdyh39jYiLVr18LV1RVyuRwjRozQPXZn165dUCqVUCgUOHToECZPngy1Wg1nZ2fs379fV8fJkyfx9NNPQ6FQQK1Ww8fHR9dP3lb9Qjh16hRcXV0hEomwc+dOAMat5/bt2yGTydC/f38sXLgQTk5OkMlk8PPzw+nTpwEAYWFhsLCwgKOjo255b7/9NpRKJUQiEUpKSrB48WKEh4fj6tWrEIlE8Pb2BgB89dVXUKvV2LBhQw+3yMN1IyIEBga2WiY6OhqDBg3Cp59+iuPHj7dajoiwdetWPPnkk5BKpbCxscH06dNx8eJFAMbvUz213/z666+oqqqCq6ur3nQvLy8AwPnz540q05qCggJYW1vDzc2tS3Hevn0bcrkcHh4eumk2NjaYOHEi4uPjuevZ1IQ8L+vN0MluulWrVpFIJKL//u//pvv371NNTQ0lJCToddMtXbqUpFIpHThwgO7fv08rV64kMzMzOnPmjK4OAPTtt99SeXk5FRUV0YQJE0ipVFJ9fT1VVVWRWq2mzZs3U21tLd25c4dmzJhBxcXFRtXfEabqprt58yYBoB07dui1VVvrSUQUGhpKSqWS8vPzqa6ujvLy8mjcuHGkUql03Sfz5s0jBwcHveXFxsYSAF2bzJw5k7y8vPTKHD58mFQqFUVGRnZ5/Tq6v3h6etLQoUNbnOfl5UW//fYbERH98MMPZGZmRu7u7lRVVUVEht10a9euJQsLC9q7dy+VlZXR+fPnafTo0WRnZ0d37twhIuPa2pT7TZNnnnnGoJvu5MmTBIBiY2MNysvlcvL39zeqTHP19fV069Yt2rFjB0mlUr1uuaioKHJ2diZra2uSSCTk7u5OQUFB9NNPP7Uad3V1NalUKgoLCzOYt2LFik51u3M3XZu4m86UamtrsW3bNrzwwgtYsmQJrK2tIZfLYWtrqytTV1eHXbt2ITg4GDNnzoS1tTVWr14NiUSCxMREvfr8/PygVqthb2+PkJAQVFdX48aNG7h27RoqKiowbNgwyGQyODg44ODBg7Czs+tQ/b1Fa+vZRCwW6371Dx06FLt27UJlZWWX12fq1KmoqKjAmjVruroKHVJdXY3ffvtN9yu/Lb6+vnj//fdx7do1LF++3GB+bW0ttm7dihkzZuCVV16BlZUVfHx88NFHH6GkpAQff/yxXvnW2ron95um0XCPdosBgEQiQW1trVFlmnNxcYGzszPWrVuHLVu2YO7cubp5r732Gr744gvcvHkTVVVV2L9/P27cuIGJEyciLy+vxRhjYmLg5OSE6Ohog3kDBw4EAOTm5hq5xswYnIxM6MqVK6ipqYG/v3+rZS5duoSamhq9YaVyuRyOjo66bpWWWFhYAAA0Gg08PT3Rv39/vPLKK1i3bh2uXbvW5fp7i+br2ZqxY8dCoVD0ifVpSVFREYgICoXCqPLR0dEYPHgwEhIScOrUKb15eXl5qKqqwtixY/Wmjxs3DhYWFrruzJY0b+ue3G+arvE0vSW3ufr6esjlcqPKNHfz5k0UFRXhn//8Jz7//HOMGjVKd43QxcUFo0aNgqWlJSwsLDB+/HgkJiaitrYWCQkJBvWnp6cjNTUVX3/9NVQqlcH8pu129+7dDq45awsnIxO6desWAMDe3r7VMtXV1QCA1atX693zcP369RaHwLZELpfjxIkTeO6557BhwwZ4enoiJCQEtbW1Jqm/L5BKpSguLhY6jE6pq6sDgBYv7rdEJpMhMTERIpEIr7/+ut5ZQdMwY0tLS4PPWVtbo7Ky0qhl9OR+03R9r/m9QMDDgQJ1dXVwcnIyqkxzEokE9vb2CAgIQHJyMvLy8nSv9miJj48PzM3NcfnyZb3pycnJ2LRpE7KysuDu7t7iZ5sSYdN2ZKbByciEmn7NPXrjXnNNiWrbtm0G7zvJzs42elnDhg3Dl19+icLCQkRERCAlJQVxcXEmq78302g0KCsrg7Ozs9ChdErTl1lHbqD09fXFkiVLUFBQgKioKN10a2trAGgx6XSkjXpyv/Hw8IBKpcL169f1pl+5cgUAMGLECKPKtMbb2xvm5uatdsEBD2/I1Wq1ej8IduzYgaSkJJw4cQJPPPFEq5+tr68HAH7NvIlxMjKh4cOHw8zMDCdPnmy1jIuLC2QyWZeexlBYWIj8/HwAD79ENm7ciNGjRyM/P98k9fd2WVlZICKMHz8ewMNrSm116/U2/fv3h0gkQnl5eYc+FxUVhSFDhuDcuXO6acOHD4elpSV+/vlnvbKnT59GfX09xowZY1TdPbnfiMViTJkyBd999x20Wq1uemZmJkQiEQIDA40qU1paipdfftmg/oKCAjQ2NsLFxQUA8OKLLxqUOXPmDIgIvr6+ICJEREQgNzcXGRkZLZ5lNte03RwcHDq1/qxlnIxMyN7eHjNnzsSBAwewe/duVFRU4Pz583oXkWUyGRYsWID9+/dj165dqKioQGNjI27duoXff//dqOUUFhZi4cKFuHjxIurr63Hu3Dlcv34d48ePN0n9vY1Wq8X9+/fR0NCA8+fPY/HixXB1dcX8+fMBPPwlfO/ePWRkZECj0aC4uNjgF7WtrS0KCwtx7do1VFZWQqPRIDMzU5Ch3QqFAp6enrpuXWM1ddc1v6gvk8kQHh6O9PR0JCUloaKiArm5uVi0aBGcnJwQGhpqdN3t7TchISFwcHAwySOI1qxZg7t37+LDDz9EdXU1srOzERsbi/nz52Pw4MFGlVEqlTh27BhOnDiBiooKaDQanDt3Dq+99hqUSiWWLFkC4OEQ7eTkZJSVlUGj0SA7OxtvvPEGXF1dsWjRIuTn52PLli345JNPIJFIDB4bFBcXpxd703bz8fHpcjuwZnp+BF/fgE4O7a6srKQ33niD+vXrR5aWlvTcc8/R2rVrCQA5OzvTf/7zH3rw4AFFRESQq6sricVisre3p5kzZ1JeXh4lJCSQQqEgADRw4EC6evUqffzxx6RWqwkAubm50TfffEN+fn5kY2ND5ubm9MQTT9CqVat0d5C3VX9HmWJo944dO8jR0ZEAkEKhoMDAQKPW8/LlyxQaGkoSiYQGDBhAYrGY1Go1TZ8+na5evaqrv7S0lCZNmkQymYw8PDzo3XffpWXLlhEA8vb2phs3btDZs2fJzc2N5HI5Pffcc3Tnzh06evQoqVQqio6O7tL6EXV8fwkLCyOJREI1NTW6aenp6eTl5UUAyM7Ojt55550WP7ts2TK9od1arZZiY2Np4MCBJJFIyMbGhoKDg+nSpUtEREa3dXv7TXBwMAGgtWvXtrlu2dnZ9Oyzz5KTkxMBIADk6OhIfn5+dPLkSV25kydP0tNPP01SqZScnJxo2bJlVFdXp1dXe2UCAwPJw8ODLC0tSSqVkpeXF4WEhFBubq6uTHh4OHl5eZFSqSSxWEzOzs705ptv6p7QkJubq4uzpb9Hh5dPnTqVBgwYoPdkCGPw0O42pXIyakVnk9HjRujHAYWGhpKtra1gyzdWR/eXgoICEovF7T6mpjdpbGykCRMm0O7du4UORTAlJSUkk8koLi6uw5/lZNQmvs+I9X6P45OSvb29ERkZicjIyA49tkYojY2NyMjIQGVlJUJCQoQORzDr1q3DyJEjERYWJnQojx1ORowJZMWKFZg9ezZCQkI6PJihp2VlZeHgwYPIzMw0+v6ox83WrVuRk5ODo0ePQiKRCB3OY4eTEeu1Vq5cicTERJSXl8PDwwMHDhwQOiST27BhA8LCwrBx40ahQ2mTv78/9u3bp/cMwD+SQ4cO4cGDB8jKyoKNjY3Q4TyWxEIHwFhrYmJi2rxx8XEREBCAgIAAocNgbQgKCkJQUJDQYTzW+MyIMcaY4DgZMcYYExwnI8YYY4LjZMQYY0xwPIChDdu2bUNaWprQYQiq6dEns2fPFjiS3o/3F9aWH3/8Ufc8RWaIz4wYY4wJTkTEL3JviUgkQkpKCubMmSN0KIJKTU3F3LlzwbtJ23h/Ye1p6l3gs+cWpfGZEWOMMcFxMmKMMSY4TkaMMcYEx8mIMcaY4DgZMcYYExwnIxM4ePAgPD09DV5XbGFhgf79++P5559HbGws7t+/L3So7DFy/PhxrFixwmD/e/XVVw3KBgQEQKVSwdzcHMOGDTPJq8NNLTIyEkOHDoVarYZUKoW3tzc++OADvfc9RUdHGxxnIpEIw4cP16tLo9EgJiYG3t7esLCwgLW1NYYPH45r167hiy++wObNmx/L92T1ZZyMTGDmzJn49ddf4eXlBSsrKxARtFotioqKkJqaCg8PD0RERGDYsGH4+eefhQ6XPQY+/PBDbN++HStXrtTb//r164ekpCQcOXJEr/yxY8eQlpaGadOmIS8vD6NHjxYo8tadOHEC77zzDq5du4aSkhLExMQgPj6+Uzdcz507F3v27MG+fftQU1ODX375BV5eXqiqqkJgYCBkMhn8/f1RVlbWDWvCOoOTUTcRiUSwtrbG888/j8TERKSmpuLu3buYOnVqr3+RWm9RW1sLPz+/Pld3d9u0aROSk5ORmpoKlUqlN2/79u0wMzNDaGhon9vPLC0tERoaCltbW6hUKsyZMwfBwcH46quvcPPmTV25vXv3goj0/i5cuKCbn5ycjIyMDKSlpeGZZ56BWCyGk5MTDh06pDuDeu+99/DUU09hypQpaGho6PF1ZYY4GfWQWbNmYf78+SgqKsJHH30kdDh9wu7du1FUVNTn6u5OV65cwZo1a7B+/XrIZDKD+X5+fli8eDFu376NpUuXChBh5x0+fBjm5uZ60+zs7AAANTU1Rtfz97//HaNHj4aPj0+b5datW4ecnBzEx8d3PFhmcpyMetD8+fMBAJmZmQCAxsZGrF27Fq6urpDL5RgxYgRSUlIAALt27YJSqYRCocChQ4cwefJkqNVqODs7Y//+/bo6T548iaeffhoKhQJqtRo+Pj6oqKhot/7uRETYunUrnnzySUilUtjY2GD69Om4ePEiACAsLAwWFhZ6bw19++23oVQqIRKJUFJSgsWLFyM8PBxXr16FSCSCt7c3tm/fDplMhv79+2PhwoVwcnKCTCaDn58fTp8+3aW6AeCrr76CWq3Ghg0bur2NOmv79u0gIgQGBrZaJjo6GoMGDcKnn36K48ePt1quve1k7D7YnfvZ7du3IZfL4eHhYVT5+vp6/Pjjjxg5cmS7ZW1sbDBx4kTEx8fzE0Z6A2ItAkApKSkd+oyXlxdZWVm1Or+iooIAkIuLCxERLV26lKRSKR04cIDu379PK1euJDMzMzpz5gwREa1atYoA0Lfffkvl5eVUVFREEyZMIKVSSfX19VRVVUVqtZo2b95MtbW1dOfOHZoxYwYVFxcbVb8xUlJSqKO7ydq1a8nCwoL27t1LZWVldP78eRo9ejTZ2dnRnTt3iIho3rx55ODgoPe52NhYAqCLf+bMmeTl5aVXJjQ0lJRKJeXn51NdXR3l5eXRuHHjSKVS0Y0bN7pU9+HDh0mlUlFkZGSH1peoc/tLZ3h6etLQoUNbnOfl5UW//fYbERH98MMPZGZmRu7u7lRVVUVERJmZmRQUFKQrb8x2am8fJDLNftaS6upqUqlUFBYWppsWFRVFzs7OZG1tTRKJhNzd3SkoKIh++uknIiL67bffCACNHDmSnn/+eXJ0dCSpVEpDhgyhnTt3klar1VvGihUrCACdO3euS7EaY9asWTRr1qxuX04flcrJqBXdkYyIiEQiEVlbW1NtbS0pFAoKCQnRzaupqSGpVEpvvfUWEf3fF0Ftba2uTEJCAgGgK1eu0IULFwgAHT582GA5xtRvjI4mo5qaGrK0tNRbLhHRTz/9RAB0X/RdSUaPtvGZM2cIAK1fv75LdXdFTySjqqoqEolENG3atBbnN09GRETh4eEEgN555x0i0k9Gxm6n9vZBU+1nLVm1ahUNGjSIKioqdNNu3LhBZ8+epcrKSnrw4AFlZ2fTqFGjSC6X04ULFyg3N5cA0J///Gf6n//5HyotLaWysjJavnw5AaCkpCS9ZXz22WcEgPbs2dOlWI3ByahNqdxN14Oqq6tBRFCr1bh06RJqamr0hqTK5XI4OjrquklaYmFhAeDh0FVPT0/0798fr7zyCtatW4dr167pynW2/q7Ky8tDVVUVxo4dqzd93LhxsLCw0HWnmdLYsWOhUCi6db16g6KiIhARFAqFUeWjo6MxePBgJCQk4NSpU3rzurKdmu+D3bWfpaenIzU1FV9//bXeIA0XFxeMGjUKlpaWsLCwwPjx45GYmIja2lokJCRAKpUCAIYNGwY/Pz/Y2trCysoK69evh5WVFT7++GO95TS15d27dzsdKzMNTkY96PLlywCAIUOGoLq6GgCwevVqvfslrl+/bvTFWrlcjhMnTuC5557Dhg0b4OnpiZCQENTW1pqk/s5oGipraWlpMM/a2hqVlZXdslypVIri4uJuqbu3qKurAwDdF257ZDIZEhMTIRKJ8Prrr6O2tlY3z1TbqTv2s+TkZGzatAlZWVlwd3dvt7yPjw/Mzc1x+fJlODk5AQBKSkr0ylhYWMDNzQ1Xr17Vmy4j7HroAAAgAElEQVSXywH8X9sy4XAy6kFfffUVAGDy5Mmwt7cH8PCFbPTIMNXs7Gyj6xw2bBi+/PJLFBYWIiIiAikpKYiLizNZ/R1lbW0NAC1+mZWVlcHZ2dnky9RoNN1Wd2/S9MXZkZs1fX19sWTJEhQUFCAqKko33VTbydT72Y4dO5CUlIQTJ07giSeeMOozWq0WWq0WUqkUlpaWGDhwIPLz8w3KNTQ0wMrKSm9afX09gP9rWyYcTkY95M6dO9i2bRucnZ3x+uuvw8XFBTKZDDk5OZ2us7CwUHfQ2dvbY+PGjRg9ejTy8/NNUn9nDB8+HJaWlgY3954+fRr19fUYM2YMAEAsFkOj0ZhkmVlZWSAi3Vs0TVl3b9K/f3+IRKIO3z8UFRWFIUOG4Ny5c7ppxm6n9phqPyMiREREIDc3FxkZGS2esQHAiy++aDDtzJkzICL4+voCeHjD67lz5/Drr7/qytTU1OD69esGw72b2tLBwaFL8bOu42RkYkSEqqoqaLVaEBGKi4uRkpKCZ599Fubm5sjIyIBarYZMJsOCBQuwf/9+7Nq1CxUVFWhsbMStW7fw+++/G7WswsJCLFy4EBcvXkR9fT3OnTuH69evY/z48SapvzNkMhnCw8ORnp6OpKQkVFRUIDc3F4sWLYKTkxNCQ0MBAN7e3rh37x4yMjKg0WhQXFyM69ev69Vla2uLwsJCXLt2DZWVlboEo9Vqcf/+fTQ0NOD8+fNYvHgxXF1ddUPnO1t3ZmZmrx7arVAo4OnpqXsVvLGauuua38Nj7HYypu729rOQkBA4ODi0+Qii/Px8bNmyBZ988gkkEonB437i4uIAPBzqnZycjLKyMmg0GmRnZ+ONN96Aq6srFi1aBABYsmQJ3NzcMH/+fNy4cQOlpaWIiIhAbW0tli9frrfcprZs754k1gN6eMREn4EOjI764osvaMSIEaRQKMjCwoLMzMwIgG7k3NNPP02RkZFUWlqq97kHDx5QREQEubq6klgsJnt7e5o5cybl5eVRQkICKRQKAkADBw6kq1ev0scff0xqtZoAkJubG33zzTfk5+dHNjY2ZG5uTk888QStWrWKGhoa2q3fWJ0Z2q3Vaik2NpYGDhxIEomEbGxsKDg4mC5duqQrU1paSpMmTSKZTEYeHh707rvv0rJlywgAeXt760ZNubm5kVwup+eee47u3LlDoaGhJJFIaMCAASQWi0mtVtP06dPp6tWrXa776NGjpFKpKDo6ukPrS9RzQ7vDwsJIIpFQTU2Nblp6ejp5eXkRALKzs9ONnnvUsmXL9IZ2t7edjNkHL1++3O5+FhwcTABo7dq1ra5X0yi41v5iY2OJ6OEIQS8vL1IqlSQWi8nZ2ZnefPNNKiws1Kvv5s2b9NJLL5GNjQ1JpVJ6+umnKTMz02C5U6dOpQEDBhgM+e4OPJquTTy0uzU99eXS23UmGXWn0NBQsrW1FToMAz21vxQUFJBYLKa9e/d2+7JMpbGxkSZMmEC7d+8WOhQ9JSUlJJPJKC4urkeWx8moTTy0m/U9f+SnLXt7eyMyMhKRkZF6T7PurRobG5GRkYHKykqEhIQIHY6edevWYeTIkQgLCxM6FAa+ZsRYn7NixQrMnj0bISEhvf5hqFlZWTh48CAyMzONvj+qJ2zduhU5OTk4evQoJBKJ0OEwcDJifcjKlSuRmJiI8vJyeHh44MCBA0KHJJgNGzYgLCwMGzduFDqUNvn7+2Pfvn16zwoU2qFDh/DgwQNkZWXBxsZG6HDY/ycWOgDGjBUTE4OYmBihw+g1AgICEBAQIHQYfU5QUBCCgoKEDoM9gs+MGGOMCY6TEWOMMcFxMmKMMSY4TkaMMcYEx8mIMcaY4ERE/L7dlohEIqFDYIw9ZmbNmoW0tDShw+iN0nhodytSUlKEDoH1AdnZ2YiPj+f9hRnFxcVF6BB6LT4zYqwLUlNTMXfuXPBhxFiXpPE1I8YYY4LjZMQYY0xwnIwYY4wJjpMRY4wxwXEyYowxJjhORowxxgTHyYgxxpjgOBkxxhgTHCcjxhhjguNkxBhjTHCcjBhjjAmOkxFjjDHBcTJijDEmOE5GjDHGBMfJiDHGmOA4GTHGGBMcJyPGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcFxMmKMMSY4TkaMMcYEx8mIMcaY4DgZMcYYExwnI8YYY4LjZMQYY0xwnIwYY4wJjpMRY4wxwXEyYowxJjhORowxxgTHyYgxxpjgOBkxxhgTnFjoABjrK+rq6lBYWKg37e7duwCAX3/9VW+6ubk53Nzceiw2xvo6ERGR0EEw1hfcv38fDg4O0Gg07ZadMmUKjhw50gNRMfZYSONuOsaMZGNjg4CAAJiZtX/YhISE9EBEjD0+OBkx1gGvvPIK2utMkEqlCA4O7qGIGHs8cDJirAMCAwMhk8lanS8WixEYGAhLS8sejIqxvo+TEWMdoFAoEBwcDIlE0uL8xsZGzJs3r4ejYqzv42TEWAe9/PLLrQ5iUCqV+Mtf/tLDETHW93EyYqyDAgICYGVlZTBdIpFg7ty5kEqlAkTFWN/GyYixDpJIJAgJCYGFhYXedI1Gg5dfflmgqBjr2zgZMdYJL730Eurr6/Wm2dnZYeLEiQJFxFjfxsmIsU6YMGECHBwcdP9LJBK8+uqrMDc3FzAqxvouTkaMdYKZmRleffVVXVedRqPBSy+9JHBUjPVdnIwY66SQkBBdV52LiwvGjh0rcESM9V2cjBjrpDFjxsDb2xsAMH/+fIhEIoEjYqzv6rands+ePbu7qmas12jqpjt9+jTv8+yx5+vriyVLlnRL3d12ZnTgwAHcunWru6pnrFdwdXWFtbU11Go1fvzxR/z4449Ch9Sr3bp1CwcOHBA6DNYJP/74I7Kzs7ut/m59n9H777+POXPmdOciGBPc8ePH8cILL+jOjNLS0gSOqPdKTU3F3LlzuY36oO4+8+drRox10QsvvCB0CIz1eZyMGGOMCY6TEWOMMcFxMmKMMSY4TkaMMcYE1yuS0bhx42Bubo6RI0f26HIXLFgAmUwGkUiEurq6Hl12bxEXF4f+/ftDJBLho48+0k0/evQorKys8OWXX3br8ntqOe3RarXYtm0b/Pz8BI2jt7RHb3X8+HGsWLECBw8ehKenJ0QiEUQiEV599VWDsgEBAVCpVDA3N8ewYcNw9uxZASJuW2RkJIYOHQq1Wg2pVApvb2988MEHqKqq0pWJjo7WrWfzv+HDh+vVpdFoEBMTA29vb1hYWMDa2hrDhw/HtWvX8MUXX2Dz5s1obGzs6VU0Wq9IRmfOnMGkSZN6fLmJiYlYunRpjy+3N1m6dCl++OEHg+lE1CPL76nltKWgoAB/+tOfsGTJEtTU1AgaS29oj97qww8/xPbt27Fy5UrMnDkTv/76K7y8vNCvXz8kJSXhyJEjeuWPHTuGtLQ0TJs2DXl5eRg9erRAkbfuxIkTeOedd3Dt2jWUlJQgJiYG8fHxnRpGPXfuXOzZswf79u1DTU0NfvnlF3h5eaGqqgqBgYGQyWTw9/dHWVlZN6xJ1/WKZNSkq49Tqa2tFfyX7eNi6tSpKC8vx7Rp00xWZ0vbpzuW0xH/+c9/sHz5cixatKjHz8xbInR79NZjaNOmTUhOTkZqaipUKpXevO3bt8PMzAyhoaEoLy8XKMLOsbS0RGhoKGxtbaFSqTBnzhwEBwfjq6++ws2bN3Xl9u7dCyLS+7tw4YJufnJyMjIyMpCWloZnnnkGYrEYTk5OOHTokO4M6r333sNTTz2FKVOmoKGhocfXtT29KhlJJJIufX737t0oKirq1Gf5uWLdryvbp7s89dRTOHjwIObNm8dvaEXv3EZXrlzBmjVrsH79eshkMoP5fn5+WLx4MW7fvt3nejoOHz5s8NoROzs7AOjQWfrf//53jB49Gj4+Pm2WW7duHXJychAfH9/xYLtZr0pGV65cwZAhQ6BUKiGXyzFhwgScOnVKN//777/H0KFDYWVlBZlMBh8fH3z99dcAgMWLFyM8PBxXr16FSCTSPcASePirYuzYsZDJZFAqlXB3d0dUVJRuvpmZGY4cOYLJkyfDysoKTk5O+Oyzz4yOe9euXVAqlVAoFDh06BAmT54MtVoNZ2dn7N+/X1eOiLB161Y8+eSTkEqlsLGxwfTp03Hx4kUAwJYtW6BQKKBSqVBUVITw8HAMGDAAixYtglKphJmZGcaMGQMHBwdIJBIolUqMHj0aEyZMgIuLC2QyGaytrfHBBx/oxddWu7Xk1KlTcHV1hUgkws6dO3XbpqV+a5FIhG+++aZT26el5RjTTsa2d1/TUnsYs67bt2+HTCZD//79sXDhQjg5OUEmk8HPzw+nT58GAISFhcHCwgKOjo665b399ttQKpUQiUQoKSlp9Rj66quvoFarsWHDhh5uEejWj4gQGBjYapno6GgMGjQIn376KY4fP95qOVPtW42NjVi7di1cXV0hl8sxYsQIpKSkmGR9b9++DblcDg8PD6PK19fX48cffzTqzN7GxgYTJ05EfHx87+sSpm4CgFJSUowu7+/vT56envTbb7+RRqOhCxcu0DPPPEMymYwuX75MRERpaWm0bt06unfvHpWWltL48eOpX79+ujpmzpxJXl5eevVu27aNANDGjRuptLSU7t27R//4xz9o3rx5RES0atUqAkDffvstlZWV0b1792jKlCkklUqp+v+xd+9hTV3p/sC/gUDCJYQgCFQEJKBWpSpqq0z9qeMZOq0jiIDi2M7Y21BbiygqSr1xEbV4lCNgO7YOM4IVQRlsVVqPdThtR6v2KAeEVi2KeKnlolyDcnt/f/iQGrklIbDBvp/nyR/svfZa715ZOy/ZWXvv+nqt43+0nurqaiorK6OpU6eShYUFNTY2EhHR+vXrydTUlFJTU6mqqory8/PJy8uLbG1t6c6dOxr1LF26lBITE2nu3Ln0/fff04YNGwgAnTlzhurr66miooJ+//vfEwA6evQolZeXU319PYWGhhIAysvLU8fWXb9duXKFANAHH3ygXnbjxg0CQImJieoyq1evVvfJTz/9RAqFgry9vamlpUXv9+fxdnTtp676W1fPPfccjR07Vq9tiYgCAwMpMDBQ7+2JOu4PbfY1JCSELCwsqKioiO7fv0+FhYU0adIkkslkVFpaSkRECxcuJHt7e4324uPjCQCVl5cTUcfv0ZEjR0gmk1F0dHSP9o2I6MCBA6Trx46bmxuNGjWqw3VKpZKuXbtGRESnTp0iIyMjcnV1pbq6OiIiysnJIT8/P3V5Q42tFStWkEQioYMHD9K9e/coMjKSjIyM6Ny5c7p2iYb6+nqSyWQUGhqqXhYTE0NOTk5kbW1NJiYm5OrqSn5+fnT27FkiIrp27RoBoHHjxtH06dPJwcGBJBIJjRw5kpKSkqi1tVWjjTVr1hAAunDhgk6xGWJ8dyGjXyWjxz8I8vPzCQCtWLGiw23i4uIIAJWVlRFR+wOpsbGRrK2tacaMGRrbNTc3U0JCAhH9MvAaGhrU6/fu3UsA6OLFi1rH31E9ycnJBIB+/PFHUqlUZGlpScHBwRrbnT17lgCoD/SO6iEidTKqra1VL/vHP/5BAKigoKBdfenp6Z3G+ni/aZOMHufv709SqZR++OEHrdvRJhn1pJ8e7W999Pdk1NW+hoSEkFwu16jr3LlzBICioqKISP9kZEi6JqO6ujoSiUQ0e/bsDtc/moyIiMLDwwkALVmyhIg0k5GhxlZDQwOZm5tr1KNSqUgikdDbb7+t9b515L333qPhw4dTTU2NellpaSmdP3+eamtr6cGDB3T69GkaP348mZmZ0cWLF6mgoIAA0O9+9zv697//TZWVlVRVVUWrV68mAJSWlqbRxt/+9jcCQHv37tUptt5ORv3qNN3jPD09IZfLkZ+f3+H6tt+YOpuumJ+fj6qqKrzwwgsay42NjbF06dJO222rt6mpSZ+w1R59CmhhYSHq6uraPYBt0qRJMDU1VZ9O0af+R3+M1Cb27vqtOxkZGfjnP/+JqKgojBgxwqDt9KSfHu3vJ502+zpx4kSYm5urT0ENRGVlZSAimJuba1U+NjYWI0aMQHJyssYpfsBwY+vSpUtQqVQaU6vNzMzg4ODQo77OyspCRkYGvvjiC41JGkOHDsX48eNhaWkJU1NTTJ48GSkpKWhoaEBycrL6t87Ro0fD29sbNjY2kMvliIqKglwux+7duzXaaevLn3/+We9Ye0O/TkbAww+0tgPu6NGjmD59Ouzs7CCRSNr9NvK4mpoaAIC1tXWvx9mdtumUlpaW7dZZW1ujtra219rWtd+6UllZiXfffReTJk1CeHi4wdsRsp+eRBKJBOXl5UKHobe26/+0nVwilUqRkpICkUiE1157DQ0NDep1hhpb9fX1AIC1a9dq/HZ6/fp1vS8NSE9Px5YtW5CbmwtXV9duy3t6esLY2BiXL1+Go6MjAKCiokKjjKmpKVxcXFBcXKyx3MzMDAD63bWV/ToZNTc34+7du3B2dkZpaSn8/f3h4OCAM2fOoLq6Glu3bu1y+6eeegpA+zdJCG0JsaMBX1VVBScnp15pV59+68rSpUtRVVWFlJQUjVlAhmpHqH56EjU1NQ34Pmv74NTl23XbA+CuXLmiMVHJUGPLzs4OALBjx4520631ed5PYmIi0tLScPLkSfVnVndaW1vR2toKiUQCS0tLeHh4oKioqF255uZmyOVyjWWNjY0Afunb/qJfJ6N//etfaG1thZeXFwoKCtDU1IS3334bbm5u6jsndMXV1RU2NjY4fvx4H0XcuTFjxsDS0hLfffedxvIzZ86gsbEREyZM6JV29em3zhw9ehT79u3DunXrMHr0aPXylStXGqwdofrpSZSbmwsiwuTJkwEAYrF4wJ3CbLs7iK7XD8XExGDkyJG4cOGCepmhxlbbzNW8vDydYnocESEiIgIFBQXIzs7u8BsbgHY/MwAPbxRARJgyZQqAhxe8XrhwAVevXlWXUalUuH79ervp3m19aW9v36P4Da1fJaPGxkZUV1ejubkZ58+fR2hoKFxcXLBo0SI4OzsDeHg7kPv37+PKlSvtzvHa2Njg9u3bKCkpQW1tLYyMjBAZGYmvvvoKoaGhuHXrFlpbW1FbW9vhfxG9SSqVIjw8HFlZWUhLS0NNTQ0KCgqwePFiODo6IiQkpFfa1abftFFTU4O33noL48aNw+rVqwE8/Jr/3XffIS8vT6/3p6MPRqH66UnQ2tqKe/fuobm5Gfn5+QgLC4OzszMWLVoEAHB3d8fdu3eRnZ2NpqYmlJeX4/r16xp1dPQe5eTkCDa129zcHG5ubjo/NbrtdN2j394NNbakUileffVV7N+/H7t27UJNTQ1aWlpw8+ZN/PTTTwCA4OBg2Nvbd3kLoqKiIrz//vv46KOPYGJi0u6SiW3btgF4ONU7PT0dVVVVaGpqwunTp/HGG2/A2dkZixcvBgAsX75c/VlZWlqKyspKREREoKGhQX28tmnry+6uSepzvTU1AjrOpktJSaEZM2bQ4MGDSSwW06BBg2jBggV0/fp1dZmIiAiysbEha2trCgoKoqSkJAJASqVSPePExcWFzMzM6Pnnn1dP1UxKSiJPT0+SSqUklUpp/PjxlJycTFu3biUzMzMCQB4eHlRcXExpaWmkUCgIADk5OWk1oy45OZnMzc016tm9ezdZWVkRAHJxcaHLly9Ta2srxcfHk4eHB5mYmJBCoSB/f3+6dOkSEZFGPEOHDqXU1FQiIkpISFDX7+rqSl9//TVt2bKF5HI5ASB7e3vat28fpaenk729PQEghUJB+/fv77bfwsLC1NtYWFjQ3LlzKTExkRwcHAgAmZubk6+vL23bto0AdPh66aWX9Hp/1q5d264dIuq2n7Ttb22cPn2afvOb35Cjo6N6fxwcHMjb25v+53/+R6s62vR0tlFH/a7tvoaEhJCJiQkNGTKExGIxWVlZ0Zw5c6i4uFhdf2VlJc2YMYOkUikNGzaM3n33XVq5ciUBIHd3906PoWPHjpFMJqPY2Fi9962NPlO7Q0NDycTEhFQqlXpZVlYWKZVKAkC2trbq2XOPW7lypcbUbkONrQcPHlBERAQ5OzuTWCwmOzs7CggIoMLCQiJ6ONsUAK1fv77T/WqbBdfZKz4+nogezhBUKpVkYWFBYrGYnJyc6M0336Tbt29r1Hfjxg1asGABKRQKkkgk9Oyzz1JOTk67dmfNmkVDhgxpN+W7O7+aqd2MDXS9fLB2KSQkhGxsbARpWxf6JKMrV66QWCxW/3M2ELS0tNDUqVNpz549QoeioaKigqRSKW3btk3nbX/VU7sZY9rrz3dk7gl3d3dER0cjOjpa427W/VVLSwuys7NRW1uL4OBgocPRsHHjRowbNw6hoaFCh9IOJ6Mu/PDDD53eAufRV38bcL92/L49edasWYOgoCAEBwf3+5uh5ubm4tChQ8jJydH6+qi+sH37duTl5eHYsWM9vg9obxALHUB/NnLkyP53/ybWrV/b+xYZGYmUlBQ0NjZi2LBhiI+PR2BgoNBhGdymTZtw/PhxbN68GVu2bBE6nE7NnDkTM2fOFDoMDYcPH8aDBw+Qm5vb7sas/QUnI8YGuLi4OMTFxQkdRp/w8fGBj4+P0GEMOH5+fvDz8xM6jC7xaTrGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcFxMmKMMSY4EfXSHFh9b8bJGGOsfwoMDERmZmZvVJ3Zq1O7w8LC1HeVZexJt2PHDgDAsmXLBI6k/zp9+jQSEhJw4MABoUNhOmob372lV5PRlClTMG/evN5sgrF+o+0/Rh7zXUtISOA+GoB66RuRGv9mxBhjTHCcjBhjjAmOkxFjjDHBcTJijDEmOE5GjDHGBDegktGhQ4fg5ubW7rk0YrEYtra2+I//+A9kZWX1agyvvvoqpFIpRCIR7t+/r1Wcr7zySrsyPj4+kMlkMDY2xujRo3H+/PlejVtf27Ztw+DBgyESifDhhx+qlx87dgxyuRyfffZZr7bfV+2wgeHEiRNYs2bNE3OMRUdHY9SoUbCysoJEIoG7uztWrVql8RDB2NjYDp/HNWbMGI26mpqaEBcXB3d3d5iamsLa2hpjxoxBSUkJPv30U2zdurVfP4BxQCWjgIAAXL16FUqlEnK5HEQEIkJ5eTkOHDiAW7duISAgoFevYUhJScGKFSu0jnPQoEFIS0vD0aNHNcocP34cmZmZmD17NgoLC+Hl5dVrMffEihUrcOrUqXbL++p5Qb+m5xKxrm3YsAE7d+5EZGTkE3OMnTx5EkuWLEFJSQkqKioQFxeHhIQEBAUF6VzX/PnzsXfvXuzbtw8qlQrff/89lEol6urq4OvrC6lUipkzZ6KqqqoX9qTnBlQy6oxCocDMmTPxX//1XwCAjIwMrbdtaGiAt7d3b4WGnTt3wsjICCEhIf3+CZW6mDVrFqqrqzF79myD1dnRe9Eb7TyJenMc9/Yxoo0tW7YgPT0dGRkZkMlkGusG8jFmaWmJkJAQ2NjYQCaTYd68efD398fnn3+OGzduqMulpqaq//lue128eFG9Pj09HdnZ2cjMzMRzzz0HsVgMR0dHHD58WP0NaunSpRg7dixeeuklNDc39/m+dueJSEZtXF1dAUCnzL9nzx6UlZXp1Z42tzzy9vZGWFgYbt261e03ql+7nrwXv3a92XdCvy8//vgj1q1bh6ioKEil0nbrB/IxduTIkXZPXrW1tQUAqFQqrev54IMP4OXlBU9Pzy7Lbdy4EXl5eUhISNA92F72RCWj/Px8AMC0adPUy77++muMGjUKcrkcUqkUnp6e+OKLLwA8vF1ReHg4iouLIRKJ4O7urt4uNTUVEydOhFQqhYWFBVxdXRETE6Neb2RkhKNHj+LFF1+EXC6Ho6Mj/va3v3UYV2xsLIYPH46PP/4YJ06c6DR+IsL27dvx9NNPQyKRQKFQYM6cOfjhhx8AAO+//z7Mzc0hk8lQVlaG8PBwDBkyBIsXL4aFhQWMjIwwYcIE2Nvbw8TEBBYWFvDy8sLUqVMxdOhQSKVSWFtbY9WqVRrtdtVHHfnmm2/g7OwMkUiEpKQkAA8/MDo6ry0SifDf//3fer0XHbWjTT/t2rULFhYWMDc3x+HDh/Hiiy/CysoKTk5O2L9/f6f7JYTu9iU0NBSmpqZwcHBQb/POO+/AwsICIpEIFRUVHfbdzp07IZVKMXjwYLz11ltwdHSEVCqFt7c3zpw506O6AeDzzz+HlZUVNm3a1Ot9tHPnThARfH19Oy1jqGNM27HT0tKC9evXw9nZGWZmZnjmmWcM9vPArVu3YGZmhmHDhmlVvrGxEd9++y3GjRvXbVmFQoFp06YhISGh/50Cp14CgA4cONArdSuVSpLL5eq/VSoV5eTkkIuLC/n4+FBdXZ16XWZmJm3cuJHu3r1LlZWVNHnyZBo0aJB6fUBAACmVSo36d+zYQQBo8+bNVFlZSXfv3qW//vWvtHDhQiIieu+99wgAffnll1RVVUV3796ll156iSQSCdXX12vEee3aNSIiOnXqFBkZGZGrq6s6vpycHPLz81OXX79+PZmamlJqaipVVVVRfn4+eXl5ka2tLd25c0ej7aVLl1JiYiLNnTuXvv/+e9qwYQMBoDNnzlB9fT1VVFTQ73//ewJAR48epfLycqqvr6fQ0FACQHl5eVr30ZUrVwgAffDBB+plN27cIACUmJioLrN69Wr1/v/000+kUCjI29ubWlpa9H4vHm9H13768ssvqbq6msrKymjq1KlkYWFBjY2N1BsCAwMpMDBQp2202ZeFCxeSvb29xnbx8fEEgMrLy4mo474LCQkhCwsLKioqovv371NhYSFNmjSJZDIZlZaW9qjuI0eOkEwmo+joaJ3298CBA6Trx46bmxuNGjWqw3W9eYx1NXZWrFhBEomEDh48SPfu3QVZFM4AACAASURBVKPIyEgyMjKic+fO6bRvj6uvryeZTEahoaHqZTExMeTk5ETW1tZkYmJCrq6u5OfnR2fPniUiomvXrhEAGjduHE2fPp0cHBxIIpHQyJEjKSkpiVpbWzXaWLNmDQGgCxcu6BSbPuNbBxkDNhkBaPfy9PSkf/zjH/TgwYNOt42LiyMAVFZWRkTtD7TGxkaytramGTNmaGzX3NxMCQkJRPTLYG1oaFCv37t3LwGgixcvasTZdqAQEYWHhxMAWrJkCRFpHigqlYosLS0pODhYo92zZ88SAPVB31HbRKRORrW1tepl//jHPwgAFRQUtKsvPT1d6z7SJhk9zt/fn6RSKf3www9at6NNMupJPyUnJxMA+vHHHzuNqSd0PVi13ZeeJKNH/2kjIjp37hwBoKioqB7VrS9dk1FdXR2JRCKaPXt2h+v76hh7dOw0NDSQubm5Rj0qlYokEgm9/fbbWu9bR9577z0aPnw41dTUqJeVlpbS+fPnqba2lh48eECnT5+m8ePHk5mZGV28eJEKCgoIAP3ud7+jf//731RZWUlVVVW0evVqAkBpaWkabfztb38jALR3716dYuvtZDRgT9M9OpuuqakJN2/exLJlyxAaGopnnnkGFRUVHW5nYmICAJ1OcczPz0dVVRVeeOEFjeXGxsZYunRpp/G01dvU1NRpmdjYWIwYMQLJycn45ptvNNYVFhairq4OEydO1Fg+adIkmJqaqk+t6MLU1BQANH6s1CbO7vqoOxkZGfjnP/+JqKgojBgxwqDt9KSf2vqjq33vS73xnndn4sSJMDc3V5+W6u/KyspARDA3N9eqfG8dY4+OnUuXLkGlUmlMrTYzM4ODg0OP+jUrKwsZGRn44osvNCZpDB06FOPHj4elpSVMTU0xefJkpKSkoKGhAcnJyZBIJACA0aNHw9vbGzY2NpDL5YiKioJcLsfu3bs12mnry59//lnvWHvDgE1GjxKLxRgyZAheffVVbNu2DZcuXcLmzZsBAEePHsX06dNhZ2cHiUTS7veSx9XU1AAArK2tDR6nVCpFSkoKRCIRXnvtNTQ0NKjXtU26sLS0bLedtbU1amtrDR5PG137qCuVlZV49913MWnSJISHhxu8HSH7ydCE2heJRILy8vJeqdvQ2q7la/vA7U5fHGP19fUAgLVr12r8Nnr9+nWdJh08Kj09HVu2bEFubq56IlZXPD09YWxsjMuXL8PR0REA2v0DbmpqChcXFxQXF2ssNzMzA4BOr5MUyhORjB7VNpukqKgIpaWl8Pf3h4ODA86cOYPq6mps3bq1y+2feuopAO3fWEOZMmUKli9fjitXrmhMiGhLfh0dEFVVVXBycuqVePTpo64sXboUVVVVSElJ0ZglZKh2hOqn3iDEvjQ1NQ2ofmr74NTl23NvH2N2dnYAHj7fhx6bbn369Gmt42yTmJiItLQ0nDx5Uv35053W1la0trZCIpHA0tISHh4eKCoqaleuubkZcrlcY1ljYyOAX/q2v3jiktH//u//AgBGjBiBgoICNDU14e2334abm5v6zgldcXV1hY2NDY4fP95rMcbExGDkyJG4cOGCetmYMWNgaWmJ7777TqPsmTNn0NjYiAkTJvRKLPr0UWeOHj2Kffv2Yd26dRg9erR6+cqVKw3WjlD91Bu03RexWGywU4u5ubkgIkyePNngdfeGtrt/6Hr9UG8eY20zU/Py8nSK6XFEhIiICBQUFCA7O7vDb2wA2v1kAADnzp0DEakfXjp//nxcuHABV69eVZdRqVS4fv16u+nebX1pb2/fo/gNbUAno4aGBrS2toKIcPv2baSkpGDt2rWwtbXFsmXL4OzsDODhLUTu37+PK1eutDsvbGNjg9u3b6OkpAS1tbUwMjJCZGQkvvrqK4SGhuLWrVtobW1FbW1th/956KPtVMKj3xykUinCw8ORlZWFtLQ01NTUoKCgAIsXL4ajoyNCQkIM0vbjtOkjbdTU1OCtt97CuHHjsHr1agAPTwN89913yMvL0+u96OhDUqh+6g3a7ou7uzvu3r2L7OxsNDU1oby8HNevX9eoq7O+a21txb1799Dc3Iz8/HyEhYXB2dkZixYt6lHdOTk5fTK129zcHG5ubrh586ZO2/XmMSaVSvHqq69i//792LVrF2pqatDS0oKbN2/ip59+AgAEBwfD3t6+y1sQFRUV4f3338dHH30EExOTdpdEbNu2DcDDqd7p6emoqqpCU1MTTp8+jTfeeAPOzs5YvHgxAGD58uVwcXHBokWLUFpaisrKSkRERKChoUF9PLZp68vurknqc701NQK9MJsuKyur05l0EomEPDw86O2331ZPWyUiioiIIBsbG7K2tqagoCBKSkoiAKRUKtWzVFxcXMjMzIyef/559fTOpKQk8vT0JKlUSlKplMaPH0/Jycm0detWMjMzIwDk4eFBxcXFlJaWRgqFggCQk5MTxcTEqOO0tbVVz+x53MqVKzWmnba2tlJ8fDx5eHiQiYkJKRQK8vf3p0uXLhERabQ9dOhQSk1NJSKihIQEMjc3JwDk6upKX3/9NW3ZsoXkcjkBIHt7e9q3bx+lp6eTvb09ASCFQkH79+/vto/CwsLU21hYWNDcuXMpMTGRHBwcCACZm5uTr68vbdu2rcP3BQC99NJLer0Xa9eubdeONv2UnJys7o+292j37t1kZWVFAMjFxYUuX75syKFJRPrNNupuX4iIKisracaMGSSVSmnYsGH07rvv0sqVKwkAubu7dzqOQ0JCyMTEhIYMGUJisZisrKxozpw5VFxc3OO6jx07RjKZjGJjY3XaX32mdoeGhpKJiQmpVCr1skc/Cwx5jGk7dh48eEARERHk7OxMYrGY7OzsKCAggAoLC4no4WxSALR+/fpO96ttFlxnr/j4eCJ6OENQqVSShYUFicVicnJyojfffJNu376tUd+NGzdowYIFpFAoSCKR0LPPPks5OTnt2p01axYNGTKk3ZTv7vDUbsYGiF4+WHUWEhJCNjY2QoehQZ9kdOXKFRKLxep/vgaClpYWmjp1Ku3Zs0foUDRUVFSQVCqlbdu26bwtT+1mjOmtP9+lWVvu7u6Ijo5GdHS0xt2s+6uWlhZkZ2ejtrYWwcHBQoejYePGjRg3bhxCQ0OFDqUdTkaMsX5vzZo1CAoKQnBwcL+/GWpubi4OHTqEnJwcra+P6gvbt29HXl4ejh07pr7Grz/hZMTYEygyMhIpKSmorq7GsGHDcPDgQaFD6rFNmzYhNDRUfQ1hfzVz5kzs27dP455/Qjt8+DAePHiA3NxcKBQKocPpkFjoABhjhhcXF4e4uDihwzA4Hx8f+Pj4CB3GgOPn5wc/Pz+hw+gSfzNijDEmOE5GjDHGBMfJiDHGmOA4GTHGGBNcr05g0OemgYwNVG23WcnIyBA4kv6r7TOB+2jguXnzZq/eYFdE1DvPntX3ZpuMMcb6p8DAQGRmZvZG1Zm99s2ol3IcY/1KRkYG5s+fz+OdsR7i34wYY4wJjpMRY4wxwXEyYowxJjhORowxxgTHyYgxxpjgOBkxxhgTHCcjxhhjguNkxBhjTHCcjBhjjAmOkxFjjDHBcTJijDEmOE5GjDHGBMfJiDHGmOA4GTHGGBMcJyPGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcFxMmKMMSY4TkaMMcYEx8mIMcaY4DgZMcYYExwnI8YYY4LjZMQYY0xwnIwYY4wJjpMRY4wxwXEyYowxJjhORowxxgTHyYgxxpjgOBkxxhgTHCcjxhhjguNkxBhjTHCcjBhjjAlOLHQAjA0UZWVlSElJ0ViWn58PANi6davGchsbG7z55pt9FhtjA52IiEjoIBgbCJqbm+Hg4IB79+7BxMSk03IPHjxASEgIPvzwwz6MjrEBLZNP0zGmJbFYjAULFsDY2BgPHjzo9AUAf/zjHwWOlrGBhZMRYzpYsGABmpqauizj4OCA559/vo8iYuzJwMmIMR1MmTIFTk5Ona43NTXFK6+8AiMjPrQY0wUfMYzpQCQS4eWXX+70N6PGxkYsWLCgj6NibODjZMSYjro6Vefm5obx48f3cUSMDXycjBjT0TPPPIMRI0a0W25qaoo///nPAkTE2MDHyYgxPbzyyivtTtU1NjYiODhYoIgYG9g4GTGmh5dffhnNzc3qv0UiEcaOHYvhw4cLGBVjAxcnI8b04OLiAi8vL4hEIgCAsbExn6JjrAc4GTGmpz/96U8wNjYGALS0tGDevHkCR8TYwMXJiDE9zZs3D62trRCJRPjNb36DIUOGCB0SYwMWJyPG9OTg4IBp06aBiPgUHWM9xDdKfURQUBAOHjwodBiMsV+BAwcO8KndX2TyIyQeM3nyZCxbtkzoMH715s+fj7CwMEyZMkXoULrU0NCA3bt3Y+nSpX3e9o4dOwCAx+sANH/+fKFD6Hc4GT3GycmJ/1vpB+bPn48pU6YMiPfid7/7HZ566qk+bzczMxMABkQfMU2cjNrj34wY6yEhEhFjTxpORowxxgTHyYgxxpjgOBkxxhgTHCcjxhhjguNk1I8dO3YMcrkcn3322RPVVl95EvfJkE6cOIE1a9bg0KFDcHNzg0gkgkgkwiuvvNKurI+PD2QyGYyNjTF69GicP39egIi7Fh0djVGjRsHKygoSiQTu7u5YtWoV6urq1GViY2PV+/noa8yYMRp1NTU1IS4uDu7u7jA1NYW1tTXGjBmDkpISfPrpp9i6dStaWlr6ehefaJyM+rG+vB75Sbz2+UncJ0PZsGEDdu7cicjISAQEBODq1atQKpUYNGgQ0tLScPToUY3yx48fR2ZmJmbPno3CwkJ4eXkJFHnnTp48iSVLlqCkpAQVFRWIi4tDQkICgoKCdK5r/vz52Lt3L/bt2weVSoXvv/8eSqUSdXV18PX1hVQqxcyZM1FVVdULe/IrRUwtMDCQAgMDBWlbpVLRlClTnri29AWADhw4IHQYPdLb/azveN28eTMNHz6cGhoaNJYrlUrat28fGRkZ0ZAhQ6iqqkpjfU5ODvn5+fUo5t40a9Ysam5u1lg2b948AkClpaVERBQTE0Opqald1rN//34SiUSUn5/fZbnQ0FCaMmUKNTU16RzrkzC+DSyDvxn1E3v27EFZWdkT19avWX/s5x9//BHr1q1DVFQUpFJpu/Xe3t4ICwvDrVu3sGLFCgEi1N+RI0fUd1FvY2trCwBQqVRa1/PBBx/Ay8sLnp6eXZbbuHEj8vLykJCQoHuwrB1ORgby9ddfY9SoUZDL5ZBKpfD09MQXX3yhUSY1NRUTJ06EVCqFhYUFXF1dERMTg7CwMISHh6O4uBgikQju7u745ptv4OzsDJFIhKSkJADA008/DZFIBCMjI0yYMEF9gK1atUrd7t///vcuY9G2LeDhaa7t27fj6aefhkQigUKhwJw5c/DDDz8AAHbt2gULCwuYm5vj8OHDePHFF2FlZQUnJyfs37+/L7q9Ux3tkzbx7ty5E1KpFIMHD8Zbb70FR0dHSKVSeHt748yZMwCA0NBQmJqawsHBQd3eO++8AwsLC4hEIlRUVHTYzwDw+eefw8rKCps2berjHoF6/4gIvr6+nZaJjY3F8OHD8fHHH+PEiROdljPU+GhpacH69evh7OwMMzMzPPPMMzhw4IBB9vfWrVswMzPDsGHDtCrf2NiIb7/9FuPGjeu2rEKhwLRp05CQkMCnhA1B2G9m/UtPTtNlZmbSxo0b6e7du1RZWUmTJ0+mQYMGqdfv2LGDANDmzZupsrKS7t69S3/9619p4cKFREQUEBBASqVSo84bN24QAEpMTCQioubmZnJ1dSVnZ+d2pyOWLVtGO3bs0CoWbdoiIlq/fj2ZmppSamoqVVVVUX5+Pnl5eZGtrS3duXOHiIjee+89AkBffvklVVdXU1lZGU2dOpUsLCyosbFRr74kMsxpjI72SZt4Q0JCyMLCgoqKiuj+/ftUWFhIkyZNIplMpj7ds3DhQrK3t9doLz4+ngBQeXk5EXXcz0eOHCGZTEbR0dE92jci/carm5sbjRo1qsN1SqWSrl27RkREp06dIiMjI3J1daW6ujoian+azlDjY8WKFSSRSOjgwYN07949ioyMJCMjIzp37pyuXaKhvr6eZDIZhYaGqpfFxMSQk5MTWVtbk4mJCbm6upKfnx+dPXuWiIiuXbtGAGjcuHE0ffp0cnBwIIlEQiNHjqSkpCRqbW3VaGPNmjUEgC5cuKBTbIYY308YPk1nKIGBgdiwYQMUCgVsbGzg6+uLyspKlJeXo6mpCVFRUZgxYwZWr14NGxsbKBQKvP7665g0aZLWbRgbG2Pp0qUoLS1FVlaWerlKpcKhQ4fw2muvdRuLthoaGrB9+3bMnTsXL7/8MuRyOTw9PfHhhx+ioqICu3fv1ijv7e0NKysr2NnZITg4GPX19SgtLdW6vb7WXbxisVj9H/+oUaOwa9cu1NbWIiUlpUftzpo1CzU1NVi3bl1Pd0Fn9fX1uHbtGpRKZbdlp0yZgmXLlqGkpASrV69ut95Q4+P+/fvYtWsX/P39ERAQAGtra6xduxYmJiY97uu4uDg4OjoiNjZWvezPf/4zPv30U9y4cQN1dXXYv38/SktLMW3aNBQWFqpn3tnZ2WHTpk0oLCzEzz//jDlz5mDJkiX45JNPNNrw8PAAABQUFPQoVsan6XqNiYkJgIenIPLz81FVVYUXXnhBo0xbctHFG2+8AblcrnGeOi0tDXPmzIGVlVW3sWir7cCcOHGixvJJkybB1NRUfcqqI6ampgAeTo8dCLSJd+LEiTA3N1efghqIysrKQEQwNzfXqnxsbCxGjBiB5ORkfPPNNxrrDDU+Ll26BJVKpTG12szMDA4ODj3q66ysLGRkZOCLL76ATCZTLx86dCjGjx8PS0tLmJqaYvLkyUhJSUFDQwOSk5MhkUgAAKNHj4a3tzdsbGwgl8sRFRUFuVzeLsm29eXPP/+sd6zsIU5GBnL06FFMnz4ddnZ2kEgkWLVqlXpdTU0NAMDa2rrH7VhaWuIvf/kLTp06hbNnzwJ4+INraGioVrFoq23KqqWlZbt11tbWqK2t1XMPBi6JRKLTt8v+5v79+wCg/sDtjlQqRUpKCkQiEV577TU0NDSo1xlqfNTX1wMA1q5dq3Hdz/Xr13WadPCo9PR0bNmyBbm5uXB1de22vKenJ4yNjXH58mU4OjoCACoqKjTKmJqawsXFBcXFxRrLzczMAPzSt0x/nIwMoLS0FP7+/nBwcMCZM2dQXV2NrVu3qte33dX58QGur9DQUJiYmGDHjh346quvMHToUPWpl+5i0VZb4uzoQ6WqqgpOTk4924kBpqmpacDvd9sHpy7fkKdMmYLly5fjypUriImJUS831Piws7MD8PDZTESk8Tp9+rTWcbZJTExEWloaTp48qfXd1FtbW9Ha2gqJRAJLS0t4eHigqKioXbnm5mbI5XKNZY2NjQB+6VumP05GBlBQUICmpia8/fbbcHNzg1QqhUgkUq93dXWFjY0Njh8/bpD22p65dPDgQaxbtw5hYWFax6KtMWPGwNLSEt99953G8jNnzqCxsRETJkzo8X4MJLm5uSAiTJ48GcDD35QGymnINoMHD4ZIJEJ1dbVO28XExGDkyJG4cOGCepmhxsfQoUMhlUqRl5enU0yPIyJERESgoKAA2dnZHX5jA9DuVDkAnDt3DkSkfpDj/PnzceHCBVy9elVdRqVS4fr16+2me7f1pb29fY/iZ5yMDMLZ2RnAw9ur3L9/H1euXNE4Zy6RSBAZGYmvvvoKoaGhuHXrFlpbW1FbW6v+D8zGxga3b99GSUkJamtru/2gCw8PR3NzM+7du4ff/va3WseibVtSqRTh4eHIyspCWloaampqUFBQgMWLF8PR0REhISH6ddYA0drainv37qG5uRn5+fkICwuDs7MzFi1aBABwd3fH3bt3kZ2djaamJpSXl+P69esadXTUzzk5OYJN7TY3N4ebmxtu3ryp03Ztp+sevYbHUONDKpXi1Vdfxf79+7Fr1y7U1NSgpaUFN2/exE8//QQACA4Ohr29fZe3ICoqKsL777+Pjz76CCYmJu1u97Nt2zYAD6d6p6eno6qqCk1NTTh9+jTeeOMNODs7Y/HixQCA5cuXw8XFBYsWLUJpaSkqKysRERGBhoaGdpM52vqyu2uSmBaEm8nX//RkandERATZ2NiQtbU1BQUFUVJSEgEgpVKpng6clJREnp6eJJVKSSqV0vjx4yk5OZmIiM6fP08uLi5kZmZGzz//PK1du5YcHBwIAJmbm5Ovr2+7NmfMmEEff/yxzrFo21ZrayvFx8eTh4cHmZiYkEKhIH9/f7p06RIRESUnJ5O5uTkBIA8PDyouLqbdu3eTlZUVASAXFxe6fPmyXv2JHk59TUxMbLdP2sYbEhJCJiYmNGTIEBKLxWRlZUVz5syh4uJidf2VlZU0Y8YMkkqlNGzYMHr33Xdp5cqVBIDc3d077Oc7d+7QsWPHSCaTUWxsrN771kaf8RoaGkomJiakUqnUy7KyskipVBIAsrW1pSVLlnS47cqVKzWmdhtqfDx48IAiIiLI2dmZxGIx2dnZUUBAABUWFhIRkb+/PwGg9evXd7pfBQUFBKDTV3x8PBERhYeHk1KpJAsLCxKLxeTk5ERvvvkm3b59W6O+Gzdu0IIFC0ihUJBEIqFnn32WcnJy2rU7a9YsGjJkSLsp393p6fh+AmVwMnqEkLcDYpqEPFhDQkLIxsZGkLZ1oc94vXLlConF4m5vidOftLS00NSpU2nPnj1Ch6KhoqKCpFIpbdu2TedtORm1w9cZMdaRJ/WOzO7u7oiOjkZ0dLTG3az7q5aWFmRnZ6O2thbBwcFCh6Nh48aNGDdunMZMVqY/TkaM/cqsWbMGQUFBCA4O1nkyQ1/Lzc3FoUOHkJOTo/X1UX1h+/btyMvLw7Fjx9TX8bGe4WTE2CMiIyORkpKC6upqDBs2DAcPHhQ6pF6xadMmhIaGYvPmzUKH0qWZM2di3759GvcBFNrhw4fx4MED5ObmQqFQCB3OE0MsdACM9SdxcXGIi4sTOow+4ePjAx8fH6HDGHD8/Pzg5+cndBhPHP5mxBhjTHCcjBhjjAmOkxFjjDHBcTJijDEmOJ7A8JibN28iIyND6DAYoNeNMn9N2m5Fw+OVPQlERPy83DZBQUFP7FRexlj/cuDAAcybN0/oMPqLTP5m9JjAwEBkZmYKHcavnkgk4oO1G0FBQQDA43UA0udO+k86/s2IMcaY4DgZMcYYExwnI8YYY4LjZMQYY0xwnIwYY4wJjpMRY4wxwXEyMrBPPvkEIpEI3t7eBq332LFjkMvl+Oyzzwxar9BtMeGcOHECa9aswaFDh+Dm5gaRSASRSIRXXnmlXVkfHx/IZDIYGxtj9OjROH/+vAARdy06OhqjRo2ClZUVJBIJ3N3dsWrVKo2HCMbGxqr389HXmDFjNOpqampCXFwc3N3dYWpqCmtra4wZMwYlJSX49NNPsXXr1if2AYxC4WRkYJ988gmUSiVOnz6NH3/80WD19uW1yXwd9JNvw4YN2LlzJyIjIxEQEICrV69CqVRi0KBBSEtLw9GjRzXKHz9+HJmZmZg9ezYKCwvh5eUlUOSdO3nyJJYsWYKSkhJUVFQgLi4OCQkJ6uuxdDF//nzs3bsX+/btg0qlwvfffw+lUom6ujr4+vpCKpVi5syZqKqq6oU9+XXiZGRAlZWVKCoqQlRUFABg7969etXT0NDQ7pvVrFmzUF1djdmzZ/c4TqHaGgg66o+BULcutmzZgvT0dGRkZEAmk2ms27lzJ4yMjBASEtLvnwL7OEtLS4SEhMDGxgYymQzz5s2Dv78/Pv/8c9y4cUNdLjU1FUSk8bp48aJ6fXp6OrKzs5GZmYnnnnsOYrEYjo6OOHz4sPob1NKlSzF27Fi89NJLaG5u7vN9fRJxMjKgjIwMzJo1S/2fU9ug19WePXtQVlbWCxEK29ZA0Jv90R/6+scff8S6desQFRUFqVTabr23tzfCwsJw69YtrFixQoAI9XfkyBEYGxtrLLO1tQUAqFQqrev54IMP4OXlBU9Pzy7Lbdy4EXl5eUhISNA9WNYOJyMD+uSTTzB37lzIZDL4+PigpKQEX3/9dYdlU1NTMXHiREilUlhYWMDV1RUxMTEICwtDeHg4iouLIRKJ4O7ujm+++QbOzs4QiURISkoCADz99NMQiUQwMjLChAkT1AfbqlWrIJfLIZVK8fe//x1ff/01Ro0apV7m6emJL774AgC0bgt4eOpu+/btePrppyGRSKBQKDBnzhz88MMPAIBdu3bBwsIC5ubmOHz4MF588UVYWVnByckJ+/fv781u1yq+0NBQmJqaajy++p133oGFhQVEIhEqKio67I+dO3dCKpVi8ODBeOutt+Do6AipVApvb2+cOXOmR3UDwOeffw4rKyts2rSp1/sIePjNh4jg6+vbaZnY2FgMHz4cH3/8MU6cONFpOUONiZaWFqxfvx7Ozs4wMzPDM888gwMHDhhkf2/dugUzMzMMGzZMq/KNjY349ttvMW7cuG7LKhQKTJs2DQkJCXxq2xCIqQUGBlJgYKBe216/fp3s7OyoubmZiIhSU1MJAL3++uvtyu7YsYMA0ObNm6myspLu3r1Lf/3rX2nhwoVERBQQEEBKpVJjmxs3bhAASkxMJCKi5uZmcnV1JWdnZ3WbbZYtW0Y7duwgIqLMzEzauHEj3b17lyorK2ny5Mk0aNAgdVlt2iIiWr9+PZmamlJqaipVVVVRfn4+eXl5ka2tLd25c4eIiN577z0CQF9++SVVV1dTWVkZTZ06lSwsLKixsVGn/gRABw4c0Lq8NvEtXLiQ7O3tNbaLj48nAFReXt5pf4SEhJCFhQUVFRXR/fv3qbCwkCZNmkQymYxKS0t7VPeRI0dIJpNRd7PayAAAIABJREFUdHS01vvaRp/x6ubmRqNGjepwnVKppGvXrhER0alTp8jIyIhcXV2prq6OiIhycnLIz89PXd5QY2LFihUkkUjo4MGDdO/ePYqMjCQjIyM6d+6crl2iob6+nmQyGYWGhqqXxcTEkJOTE1lbW5OJiQm5urqSn58fnT17loiIrl27RgBo3LhxNH36dHJwcCCJREIjR46kpKQkam1t1WhjzZo1BIAuXLigU2y6ju9fgQz+ZmQgn3zyCf7whz+oTxP4+vpCIpEgMzMTDQ0N6nJNTU2IiorCjBkzsHr1atjY2EChUOD111/HpEmTtG7P2NgYS5cuRWlpKbKystTLVSoVDh06hNdeew3Awxu/btiwAQqFAjY2NvD19UVlZSXKy8u1bquhoQHbt2/H3Llz8fLLL0Mul8PT0xMffvghKioqsHv3bo3y3t7esLKygp2dHYKDg1FfX4/S0lKt29OVrvHpQywWq78BjBo1Crt27UJtbS1SUlJ6VO+sWbNQU1ODdevW9TjG7tTX1+PatWtQKpXdlp0yZQqWLVuGkpISrF69ut16Q42J+/fvY9euXfD390dAQACsra2xdu1amJiY9Lhv4+Li4OjoiNjYWPWyP//5z/j0009x48YN1NXVYf/+/SgtLcW0adNQWFionnlnZ2eHTZs2obCwED///DPmzJmDJUuW4JNPPtFow8PDAwBQUFDQo1gZn6YzmLZTdG2srKzg4+ODmpoaHD58WL08Pz8fVVVVeOGFFzS2b0suunjjjTcgl8s1zlmnpaVhzpw5sLKy6nAbExMTANBpWmrbQTpx4kSN5ZMmTYKpqan6dFVHTE1NATxMwr2lJ/Hpa+LEiTA3N1efkhoIysrKQEQwNzfXqnxsbCxGjBiB5ORkfPPNNxrrDDUmLl26BJVKpTG12szMDA4ODj3q26ysLGRkZOCLL77QmKQxdOhQjB8/HpaWljA1NcXkyZORkpKChoYGJCcnQyKRAABGjx4Nb29v2NjYQC6XIyoqCnK5vF2SbevLn3/+We9Y2UOcjAzg4sWLKCgowOzZszWuXWi7TufRWXU1NTUAAGtr6x63a2lpib/85S84deoUzp49C+Dhj6+hoaHqMkePHsX06dNhZ2cHiUSCVatW6dxO2/RVS0vLduusra1RW1ur5x4YhlDxSSQSnb5hCu3+/fsAoP7A7Y5UKkVKSgpEIhFee+01jW/4hurz+vp6AMDatWs1jp3r16/rNOngUenp6diyZQtyc3Ph6urabXlPT08YGxvj8uXLcHR0BABUVFRolDE1NYWLiwuKi4s1lpuZmQH4pW+Z/jgZGcC+ffuwYMGCdtNF7969CzMzMxw/fhx37twBADz11FMA2g92fYWGhsLExAQ7duzAV199haFDh6pPw5SWlsLf3x8ODg44c+YMqqursXXrVp3baEucHX3AVFVVwcnJqWc70UNCxNfU1NQv9l0XbR+cunwrnjJlCpYvX44rV64gJiZGvdxQfW5nZwcA2LFjR7vjR58n/SYmJiItLQ0nT55UH2vdaW1tRWtrKyQSCSwtLeHh4YGioqJ25ZqbmyGXyzWWNTY2Avilb5n+OBn1EBEhPT0d77zzTrt1CoUCQUFBaGlpUZ9rdnV1hY2NDY4fP26Q9p2cnDBv3jwcPHgQ69atQ1hYmHpdQUEBmpqa8Pbbb8PNzQ1SqVSvh3qNGTMGlpaW+O677zSWnzlzBo2NjZgwYUKP96MntI1PLBYb7HRhbm4uiAiTJ082eN29ZfDgwRCJRDpfPxQTE4ORI0fiwoUL6mWGGhNDhw6FVCpFXl6eTjE9jogQERGBgoICZGdnd/iNDUC70+MAcO7cORARpkyZAuDhBa8XLlzA1atX1WVUKhWuX7/ebrp3W1/a29v3KH7GyajHTp06BSsrK/zmN7/pcP3ixYsB/HKqTiKRIDIyEl999RVCQ0Nx69YttLa2ora2Vv3fmI2NDW7fvo2SkhLU1tZ2+yEXHh6O5uZm3Lt3D7/97W/Vy52dnQE8vO3L/fv3ceXKlXbn8rVpSyqVIjw8HFlZWUhLS0NNTQ0KCgqwePFiODo6IiQkRMve6h3axufu7o67d+8iOzsbTU1NKC8vx/Xr1zXq6qw/Wltbce/ePTQ3NyM/Px9hYWFwdnbGokWLelR3Tk5On03tNjc3h5ubG27evKnTdm2n6x69hsdQY0IqleLVV1/F/v37sWvXLtTU1KClpQU3b97ETz/9BAAIDg6Gvb19l7cgKioqwvvvv4+PPvoIJiYm7W73s23bNgAPp3qnp6ejqqoKTU1NOH36NN544w04Ozurj9Xly5fDxcUFixYtQmlpKSorKxEREYGGhoZ2kzna+rK7a5KYFgSZxNdP6TpV9vXXXycLCwsSi8U0duxYOn/+vMb6mJgYcnR0JAAEgIYMGULJyclERJSUlESenp4klUpJKpXS+PHj1evOnz9PLi4uZGZmRs8//zytXbuWHBwcCACZm5uTr69vu1hmzJhBH3/8cbvlERERZGNjQ9bW1hQUFERJSUkEgJRKJZWWlmrdVmtrK8XHx5OHhweZmJiQQqEgf39/unTpEhERJScnk7m5OQEgDw8PKi4upt27d5OVlRUBIBcXF7p8+bLWfQsdp752Fx8RUWVlJc2YMYOkUikNGzaM3n33XVq5ciUBIHd39w77486dOxQSEkImJiY0ZMgQEovFZGVlRXPmzKHi4uIe133s2DGSyWQUGxur9b620Wdqd2hoKJmYmJBKpVIvy8rKIqVSSQDI1taWlixZ0uG2K1eu1Jjabagx8eDBA4qIiCBnZ2cSi8VkZ2dHAQEBVFhYSERE/v7+BIDWr1/f6X4VFBSoj7OOXvHx8UREFB4eTkqlUn3cOjk50Ztvvkm3b9/WqO/GjRu0YMECUigUJJFI6Nlnn6WcnJx27c6aNYuGDBnSbsp3d3Qd378CGZyMHtGT64yYYfWngzUkJIRsbGyEDqMdfcbrlStXSCwWU2pqai9FZXgtLS00depU2rNnj9ChaKioqCCpVErbtm3Tedv+NL77Cb7OiDFtPCl3aHZ3d0d0dDSio6M17mbdX7W0tCA7Oxu1tbUIDg4WOhwNGzduxLhx4zRmrzL9cTJi7FdmzZo1CAoKQnBwcL+/GWpubi4OHTqEnJwcra+P6gvbt29HXl4ejh07pr52j/UMJyPGuhAZGYmUlBRUV1dj2LBhOHjwoNAhGcSmTZsQGhqKzZs3Cx1Kl2bOnIl9+/Zp3PdPaIcPH8aDBw+Qm5sLhUIhdDhPDLHQATDWn8XFxSEuLk7oMHqFj48PfHx8hA5jwPHz84Ofn5/QYTxx+JsRY4wxwXEyYowxJjhORowxxgTHyYgxxpjgeALDY7799lsEBQUJHQbDw5tnZmZmCh1Gv/Xtt98CAI9X9kTgZPSIthslMuEFBgYKHYJWfv75Z1y8eBEzZ87s87bbbtLKBp7AwEAMHTpU6DD6FRERP7ydMX1lZGRg/vz54MOIsR7J5N+MGGOMCY6TEWOMMcFxMmKMMSY4TkaMMcYEx8mIMcaY4DgZMcYYExwnI8YYY4LjZMQYY0xwnIwYY4wJjpMRY4wxwXEyYowxJjhORowxxgTHyYgxxpjgOBkxxhgTHCcjxhhjguNkxBhjTHCcjBhjjAmOkxFjjDHBcTJijDEmOE5GjDHGBMfJiDHGmOA4GTHGGBMcJyPGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcFxMmKMMSY4TkaMMcYEx8mIMcaY4DgZMcYYExwnI8YYY4LjZMQYY0xwnIwYY4wJTix0AIwNFLdv38Yf/vAHNDU1qZepVCrI5XJ4enpqlB0/fjz27t3b1yEyNmBxMmJMS0899RQaGxtRWFjYbl11dbXG38HBwX0VFmNPBD5Nx5gO/vSnP0Es7vp/OJFIhD/+8Y99FBFjTwZORozpYMGCBWhpael0vUgkwoQJEzBs2LA+jIqxgY+TEWM6GDp0KCZPngwjo44PHWNjY/zpT3/q46gYG/g4GTGmo1deeQUikajDda2trZg3b14fR8TYwMfJiDEdBQUFdbjc2NgY06dPh729fR9HxNjAx8mIMR3Z2tpi5syZMDY2brfulVdeESAixgY+TkaM6eHll18GEWksMzIygr+/v0ARMTawcTJiTA9z5syBiYmJ+m+xWIxZs2ZBLpcLGBVjAxcnI8b0IJPJMHv2bHVCamlpwcsvvyxwVIwNXJyMGNPTwoUL0dzcDAAwMzPDSy+9JHBEjA1cnIwY09OLL74ICwsLAEBgYCDMzMwEjoixgYvvTfeYmzdv4tSpU0KHwQaISZMm4V//+heGDh2KjIwMocNhAwRfi9aeiB6fEvQrl5GRgfnz5wsdBmPsCcYfu+1k8mm6ThARvwR4HThwYED1f0tLC+Li4vq8XQA4cOCA4PvPL91ebeObtcfJiLEeMDIywsqVK4UOg7EBj5MRYz3U3SMlGGPd42TEGGNMcJyMGGOMCY6TEWOMMcFxMmKMMSY4Tka94I033oBMJoNIJEJeXp7Q4fSp1tZW7NixA97e3oLGcezYMcjlcnz22WeCxtEfnThxAmvWrMGhQ4fg5uYGkUgEkUjU4eMvfHx8IJPJYGxsjNGjR+P8+fMCRNy16OhojBo1ClZWVpBIJHB3d8eqVatQV1enLhMbG6vez0dfY8aM0airqakJcXFxcHd3h6mpKaytrTFmzBiUlJTg008/xdatW7t87DzTHyejXvDxxx/jo48+EjqMPnflyhX8v//3/7B8+XKoVCpBY2m7Fodp2rBhA3bu3InIyEgEBATg6tWrUCqVGDRoENLS0nD06FGN8sePH0dmZiZmz56NwsJCeHl5CRR5506ePIklS5agpKQEFRUViIuLQ0JCQqcPQezK/PnzsXfvXuzbtw8qlQrff/89lEol6urq4OvrC6lUipkzZ6KqqqoX9uTXjZMR09DQ0KDXt5r/+7//w+rVq7F48WKMGzeuFyLTzaxZs1BdXY3Zs2cL0r6+/dibtmzZgvT0dGRkZEAmk2ms27lzJ4yMjBASEoLq6mqBItSPpaUlQkJCYGNjA5lMhnnz5sHf3x+ff/45bty4oS6Xmpra7iLUixcvqtenp6cjOzsbmZmZeO655yAWi+Ho6IjDhw+rv0EtXboUY8eOxUsvvaS+SS4zDE5GvUQkEgkdgl727NmDsrIynbcbO3YsDh06hIULF0IikfRCZAOLvv3YW3788UesW7cOUVFRkEql7dZ7e3sjLCwMt27dwooVKwSIUH9Hjhxp99RdW1tbANDpG/oHH3wALy8veHp6dllu48aNyMvLQ0JCgu7Bsk5xMjIAIkJ8fDxGjBgBiUQCuVyucVX++++/D3Nzc8hkMpSVlSE8PBxDhgzBpUuXQETYvn07nn76aUgkEigUCsyZMwc//PADgIf/sUqlUgwePBhvvfUWHB0dIZVK4e3tjTNnzmjE0FU9oaGhMDU1hYODg3qbd955BxYWFhCJRKioqEBYWBjCw8NRXFwMkUgEd3f3PupBw/rmm2/g7OwMkUiEpKQkAMCuXbtgYWEBc3NzHD58GC+++CKsrKzg5OSE/fv3A9Cur3vSj59//jmsrKywadOmPu6Rh/tGRPD19e20TGxsLIYPH46PP/4YJ06c6LRcd2NNm74GHj4Dav369XB2doaZmRmeeeYZg90u59atWzAzM8OwYcO0Kt/Y2Ihvv/1Wq2/1CoUC06ZNQ0JCAp8ONiRiGg4cOEC6dst7771HIpGI/vM//5Pu3btHKpWKkpOTCQBduHBBXQYALV26lBITE2nu3Ln0/fff0/r168nU1JRSU1OpqqqK8vPzycvLi2xtbenOnTtERBQSEkIWFhZUVFRE9+/fp8LCQpo0aRLJZDIqLS0lItKqnoULF5K9vb1G7PHx8QSAysvLiYgoICCAlEplj/rwueeeo7Fjx+q1rT7935EbN24QAEpMTFQva3sPvvzyS6qurqaysjKaOnUqWVhY0P9n786jojiz/oF/G3qHbhZBQNkbolGJxGgiqK86zmvGeKIiLjjqG80kQ5wYRNHgShRxC77KwcA4GofzRhMBxUGjYjLGIfnlxBhzhEAgKqIsigooyC5L398fHjq2gHSzFej9nMMfVj311K3r0327q5+qqq+vJyLDct3RPJ48eZJUKhWFh4d3+vgAUEJCgsHt3d3daciQIa2u02g0dOPGDSIi+uGHH8jExIRcXV2pqqqKiIhSUlJo+vTpuvaGjDVDcr1y5UqSyWR09OhRKisro7Vr15KJiQldvHixIynRqa6uJpVKRUFBQbplmzdvJkdHR7K0tCSJREKurq40ffp0+umnn4iI6MaNGwSAvL29acKECWRvb08ymYwGDx5Mn3zyCWm1Wr19rFmzRu/1baiuGt/PoET+ZtRJtbW12L17N/74xz9ixYoVsLS0hEKhgLW1davtt2/fjqVLlyIpKQkuLi7YtWsXZs6ciQULFsDCwgJeXl7Yu3cvSktLsW/fPt12YrFY90l0yJAhiI2NRWVlJeLi4lBbW2twP+zRKSm1Wg1bW1sEBASguroaBQUFuvVPy3VnTJ06FRUVFdiwYUNnD8Eo1dXVuHHjBjQaTbttfXx8sHz5cuTl5WH16tUt1hs71trKdV1dHWJjY+Hn5wd/f39YWlpi/fr1kEgknc7z1q1b4eDggIiICN2yt956CydOnEBhYSGqqqpw+PBhFBQUYPz48cjKytLNvLO1tcWWLVuQlZWFu3fvYsaMGVi6dCm++OILvX14enoCADIzMzsVK/sdF6NOunbtGmpqajBp0iSjt21+EYwcOVJv+ahRoyCVSvVOwz1p5MiRUCqVuHz5cqf6ed5JpVIAj6b0tuXxXPdFxcXFICIolUqD2kdERGDQoEGIiYnB999/r7euM2Pt8VxfuXIFNTU1elOrFQoF7O3tO5XnY8eOITExEV999ZXeJA0nJye8/PLLMDc3h1QqxejRo3Uf5GJiYnS/cw4dOhS+vr6wtraGhYUFNm3aBAsLixZFtjmXd+/e7XCsTB8Xo066efMmgEefqIzVPD3U3Ny8xTpLS0tUVlY+dXuZTIaSkpJO98Pa15zrvqiurg4ADJ5YIpfLERcXB5FIhLfffhu1tbW6dV011qqrqwEA69ev17vuJz8/v8OXBcTHx2P79u1ITU2Fq6tru+29vLxgamqKq1evwsHBAQBQWlqq10YqlcLFxQW5ubl6y5uf6tucW9Z5XIw6qXlm0sOHD43e1tLSEgBafQGXl5fD0dGxzW0bGhp0bTrTD2vf47nui5rfOI25WNPHxwcrVqxATk4ONm/erFveVWOt+cPb7t27W0y3Pn/+vMFxNtuzZw8OHTqEc+fOYcCAAQZto9VqodVqIZPJYG5uDk9PT2RnZ7do19jYCAsLC71l9fX1AMCPmu9CXIw6adiwYTAxMcG3337boW3Nzc3x888/6y2/cOEC6uvr8corr7S5bWpqKogIo0ePNrgfsVj81NNRrHWP5xroe3ns378/RCKR0dcPbd68GYMHD0ZaWppuWWfG7OOcnJwgl8s7fYcSIkJoaCgyMzORnJzc6jc2AHj99ddbLLt48SKICD4+PgAeXfCalpaG69ev69rU1NQgPz+/xXTv5lza2dl1Kn72Oy5GnWRrawt/f38cPXoUBw4cQEVFBTIyMgyaNCCXyxESEoJjx47h0KFDqKioQGZmJpYsWQIHBwcEBgbq2mq1WpSVlaGxsREZGRkIDg6Gs7MzFi1aZHA/Hh4euH//PpKTk9HQ0ICSkhLk5+frxWRtbY2ioiLk5eWhsrKyT73pdpWn5RroeB5TUlIEmdqtVCrh7u6uO6VsqObTdY9fw2PMmG2v78WLF+Pw4cOIjY1FRUUFmpqacPPmTdy+fRsAEBAQADs7u6fegig7Oxsff/wx9u/fD4lE0uJ2Pzt37gTwaKp3fHw8ysvL0dDQgPPnz+Odd96Bs7MzlixZAgBYsWIFXFxcsGjRIhQUFODevXsIDQ1FbW1ti8kczbls75okZgTBJvL1Uh2ZellZWUnvvPMO9evXj8zNzWns2LEUFhZGAMjR0ZHmz59PCoWCAJCTkxMdPHhQt61Wq6XIyEjy9PQkiURCVlZW5OfnR1euXNG1CQwMJIlEQgMHDiSxWExqtZpmzJhBubm5RvVz7949mjhxIsnlcnJzc6MPPviAVq1aRQDIw8ODCgoK6NKlS+Ti4kIKhYLGjh2rm6rbnvPnz9OYMWPIwcGBABAAsre3J19fX/r2228NzmVXTH3ds2cP2dvbEwBSKpU0bdo0iomJIaVSSQDI09OTcnNzad++faRWqwkAubi40NWrVw3KdUfzePr0aVKpVBQREdGp4yMyfmp3UFAQSSQSqqmp0S07duwYaTQaAkA2Nja0dOnSVrddtWqV3tTu9saaobl++PAhhYaGkrOzM4nFYrK1tSV/f3/KysoiIiI/Pz8CQGFhYW0eV2Zmpm68tfYXGRlJREQhISGk0WjIzMyMxGIxOTo60rvvvktFRUV6/RUWFtK8efPIysqKZDIZvfrqq5SSktJiv1OnTqWBAwe2mPLdHp7a3aZEzsoTeuNgCQwMJGtra6HD6BFC57+v5NrYYpSTk0NisVjvg1Bv19TUROPGjaMDBw4IHYqe0tJSksvltHPnTqO3FXp892J8nVFfwXcK7jnPYq49PDwQHh6O8PBwvbtZ91ZNTU1ITk5GZWUlAgIChA5Hz8aNG+Ht7Y2goCChQ3mmcDFibbp8+XKrt91/8q+3vVmw1q1ZswazZ89GQEBAr78ZampqKpKSkpCSkmLw9VE9YdeuXUhPT8fp06chkUiEDueZwsWol1u7di3i4uLw4MEDuLm54ejRoz2278GDB7eYdtvaX3x8fI/F1J2EzHVP2bJlC4KCgrBt2zahQ3mqSZMm4fPPP9e7B6DQjh8/jocPHyI1NRVWVlZCh/PMERHxnf4el5iYiLlz5/INEAXC+TeMSCRCQkIC5syZI3QozAg8vtt0hL8ZMcYYExwXI8YYY4LjYsQYY0xwXIwYY4wJTix0AL3V7NmzhQ7hudR8mxXOf/t2796NI0eOCB0GM4Kxt2R6nvA3I8YYY4Ljb0Zt4E+cwmie+sr5fzqRSITly5fz1O4+pnl8s5b4mxFjjDHBcTFijDEmOC5GjDHGBMfFiDHGmOC4GDHGGBMcF6NulJSUBHd39xaPXJBKpejfvz8mTJiAyMhIlJWVCR0qe46cPXsWa9asaTE+Fy5c2KLt5MmToVKpYGpqiqFDhz71EeBCCQ8Px5AhQ6BWqyGTyeDh4YEPP/xQ77lNERERrT7+ZNiwYXp9NTQ0YOvWrfDw8IBUKoWlpSWGDRuGvLw8nDhxAjt27Hgmn3fVG3Ax6kb+/v64fv06NBoNLCwsQETQarUoLi5GYmIi3NzcEBoaiqFDh+Lnn38WOlz2HPjoo48QHR2NtWvX6o3Pfv364dChQzh16pRe+6+//hpHjhzBm2++iaysLIwYMUKgyNt27tw5LF26FHl5eSgtLcXWrVsRFRXVoQun586di88++wyff/45ampq8Ntvv0Gj0aCqqgrTpk2DXC7HpEmTUF5e3g1H8nzjYtTDRCIRLC0tMWHCBMTFxSExMRF3797F1KlTe/0Dz55UW1sLX19focPoMt15PL0hV9u3b0d8fDwSExOhUqn01kVHR8PExASBgYF9bhyam5sjMDAQ1tbWUKlUmDNnDvz8/HDmzBkUFhbq2h08eLDFs7h+/fVX3fr4+HgkJyfjyJEjeO211yAWi+Hg4IDjx4/rvkEtW7YMw4cPxxtvvIHGxsYeP9ZnGRcjgc2aNQuLFi1CcXEx9u7dK3Q4Rjlw4ACKi4uFDqPLdOfxCJ2ra9euYcOGDdi0aRPkcnmL9b6+vggODsatW7ewcuVKASLsuJMnT8LU1FRvmY2NDQCgpqbG4H7+/ve/Y8SIEfDy8npqu40bNyI9PR1RUVHGB8vaxMWoF1i0aBEAICUlBR9//DGUSiVUKhWKi4sREhKCgQMH4sqVKyAi7Nq1Cy+++CJkMhmsrKwwY8YMXL58GcCjT7dyuRz9+/fHe++9BwcHB8jlcvj6+uLChQu6/bXXT1BQEKRSqd5TNt9//32YmZlBJBKhtLQUwcHBCAkJQW5uLkQiETw8PHouYU/oruMxJJ+dydWZM2egVquxZcuWbs9RdHQ0iAjTpk1rs01ERAReeOEFfPrppzh79myb7drLd2xsLMzMzKBUKnH8+HFMmTIFarUajo6OOHz4sK6fpqYmhIWFwdnZGQqFAi+99BISEhK65Hhv3boFhUIBNzc3g9rX19fjxx9/hLe3d7ttraysMH78eERFRfFD8roSMT0JCQnU1WnRaDRkYWHR5vqKigoCQE5OTkREtG7dOgJAy5Ytoz179tDMmTPpt99+o7CwMJJKpXTw4EEqLy+njIwMGjFiBNnY2NCdO3eIiCgwMJDMzMwoOzub6urqKCsri0aNGkUqlYoKCgqIiAzqZ/78+WRnZ6cXZ2RkJAGgkpISIiLy9/cnjUbTpbnqSP6783gMyWdH+z558iSpVCoKDw836niJiABQQkKCwe3d3d1pyJAhra7TaDR048YNIiL64YcfyMTEhFxdXamqqoqIiFJSUmj69Om69obku3kMf/PNN/TgwQMqLi6mcePGkZmZGdXX1xMR0cqVK0kmk9HRo0eprKyM1q5dSyYmJnTx4kWj8/G46upqUqlUFBQUpFu2efNmcnR0JEtLS5JIJOTq6krTp0+nn376iYiIbty4QQDI29ubJkyYQPb29iSTyWjw4MH0ySefkFar1dvHmjVrCAClpaUZFVt3vL88IxL5m1EvoFKpIBKJUFlZqbd8+/btWLp0KZKSkuDi4oJdu3Zh5syZWLBgASwsLODl5YW9e/eitLQU+/bt020nFot1n1qHDBmC2NhYVFat2ZdCAAAgAElEQVRWIi4uDrW1tQb30xf0xPE8LZ+dMXXqVFRUVGDDhg2djvFpqqurcePGDWg0mnbb+vj4YPny5cjLy8Pq1atbrDc2376+vlCr1bC1tUVAQACqq6tRUFCAuro6xMbGws/PD/7+/rC0tMT69eshkUg6ndetW7fCwcEBERERumVvvfUWTpw4gcLCQlRVVeHw4cMoKCjA+PHjkZWVpZt5Z2triy1btiArKwt3797FjBkzsHTpUnzxxRd6+/D09AQAZGZmdipW9jsuRr1AdXU1iAhqtbrNNs0vmJEjR+otHzVqFKRSqd5puCeNHDkSSqUSly9f7lQ/vZEQx/N4PvuC4uJiEBGUSqVB7SMiIjBo0CDExMTg+++/11vXmXxLpVIAj6ZPX7lyBTU1NXpTqxUKBezt7TuV12PHjiExMRFfffWV3iQNJycnvPzyyzA3N4dUKsXo0aN1H85iYmIgk8kAAEOHDoWvry+sra1hYWGBTZs2wcLCokWRbc7l3bt3Oxwr08fFqBe4evUqAGDw4MFttmmeSmpubt5inaWlZYtvVU+SyWQoKSnpdD+9jVDH05zPvqCurg4AdG+47ZHL5YiLi4NIJMLbb7+N2tpa3bquynd1dTUAYP369XrX/eTn5xs16eBx8fHx2L59O1JTU+Hq6tpuey8vL5iamuLq1atwcHAAAJSWluq1kUqlcHFxQW5urt5yhUIB4Pfcss7jYtQLnDlzBgAwZcqUNttYWloCQKsv9vLycjg6Ora5bUNDg65NZ/rpjYQ4nsfz2Rc0v3Eac7Gmj48PVqxYgZycHGzevFm3vKvybWtrC+DRAwLpienW58+fNzjOZnv27MGhQ4dw7tw5DBgwwKBttFottFotZDIZzM3N4enpiezs7BbtGhsbYWFhobesvr4ewO+5ZZ3HxUhgd+7cwe7du+Ho6Ii33367zXbDhg2Dubl5i4tjL1y4gPr6erzyyittbpuamgoiwujRow3uRywWo6GhoRNH1jOEOJ7H89nVfXeH/v37QyQSGX390ObNmzF48GCkpaXplnVmHD7OyckJcrkc6enpRsX0JCJCaGgoMjMzkZyc3Oo3NgB4/fXXWyy7ePEiiAg+Pj4AHl3wmpaWhuvXr+va1NTUID8/v8V07+Zc2tnZdSp+9jsuRj2EiFBVVQWtVgsiQklJCRISEjBmzBiYmpoiOTn5qb8ZyeVyhISE4NixYzh06BAqKiqQmZmJJUuWwMHBAYGBgbq2Wq0WZWVlaGxsREZGBoKDg+Hs7IxFixYZ3I+Hhwfu37+P5ORkNDQ0oKSkBPn5+XoxWVtbo6ioCHl5eaisrBTkDbknjudp+exM3ykpKT0ytVupVMLd3d3oR143n657/BoeY8Zhe30vXrwYhw8fRmxsLCoqKtDU1ISbN2/i9u3bAICAgADY2dk99RZE2dnZ+Pjjj7F//35IJJIWt/vZuXMngEdTvePj41FeXo6GhgacP38e77zzDpydnbFkyRIAwIoVK+Di4oJFixahoKAA9+7dQ2hoKGpra1tM5mjOZXvXJDEjCDOLr/fqyqmXJ06coJdeeomUSiVJpVIyMTEhACQSicjS0pJeffVVCg8Pp3v37um22bFjBykUCt1U74MHD+rWabVaioyMJE9PT5JIJGRlZUV+fn505coVXZvAwECSSCQ0cOBAEovFpFaracaMGZSbm2tUP/fu3aOJEyeSXC4nNzc3+uCDD2jVqlUEgDw8PKigoIAuXbpELi4upFAoaOzYsbppvZ3Rkfx35/EYks+O9n369GlSqVQUERFhdJ5g5NTuoKAgkkgkVFNTo1t27Ngx0mg0BIBsbGxo6dKlrW67atUqvand7eU7JiaGlEolASBPT0/Kzc2lffv2kVqtJgDk4uJCV69epYcPH1JoaCg5OzuTWCwmW1tb8vf3p6ysLCIi8vPzIwAUFhbW5nFlZmYSgDb/IiMjiYgoJCSENBoNmZmZkVgsJkdHR3r33XepqKhIr7/CwkKaN28eWVlZkUwmo1dffZVSUlJa7Hfq1Kk0cODAFlO+28NTu9uUyFl5Ql8fLIGBgWRtbS10GB3W2/LfW/NpbDHKyckhsVis9+Gmt2tqaqJx48bRgQMHhA5FT2lpKcnlctq5c6fR2/a28d2L8HVGzyK+q3DXehby6eHhgfDwcISHh+vdzbq3ampqQnJyMiorKxEQECB0OHo2btwIb29vBAUFCR3KM4WLEWPPiTVr1mD27NkICAjo9TdDTU1NRVJSElJSUgy+Pqon7Nq1C+np6Th9+jQkEonQ4TxTuBg9Q9auXYu4uDg8ePAAbm5uOHr0qNAh9WnPYj63bNmCoKAgbNu2TehQnmrSpEn4/PPP9e75J7Tjx4/j4cOHSE1NhZWVldDhPHNERHynv8clJiZi7ty5fANEgXD+DSMSiZCQkIA5c+YIHQozAo/vNh3hb0aMMcYEx8WIMcaY4LgYMcYYExwXI8YYY4LjYsQYY0xwYqED6K1EIpHQITzXOP/tmzt3LubOnSt0GIx1CS5GT/D19UVCQoLQYbA+4vz584iKiuIxw1gn8XVGjHUCXzfCWJfg64wYY4wJj4sRY4wxwXExYowxJjguRowxxgTHxYgxxpjguBgxxhgTHBcjxhhjguNixBhjTHBcjBhjjAmOixFjjDHBcTFijDEmOC5GjDHGBMfFiDHGmOC4GDHGGBMcFyPGGGOC42LEGGNMcFyMGGOMCY6LEWOMMcFxMWKMMSY4LkaMMcYEx8WIMcaY4LgYMcYYExwXI8YYY4LjYsQYY0xwXIwYY4wJjosRY4wxwXExYowxJjguRowxxgTHxYgxxpjguBgxxhgTHBcjxhhjguNixBhjTHBioQNgrK+oq6tDUVGR3rK7d+8CAK5fv6633NTUFC4uLj0WG2N9nYiISOggGOsLysrKYGdnh4aGhnbbvvHGGzh16lQPRMXYM+EIn6ZjzEBWVlaYPHkyTEzaf9kEBAT0QESMPTu4GDFmhAULFqC9kwkymQx+fn49FBFjzwYuRowZYdq0aZDL5W2uF4vFmDZtGszNzXswKsb6Pi5GjBlBqVTCz88PEomk1fVNTU2YP39+D0fFWN/HxYgxI/35z39ucxKDmZkZ/vSnP/VwRIz1fVyMGDPS5MmTYWFh0WK5RCLB3LlzIZPJBIiKsb6NixFjRpJIJAgICIBUKtVb3tDQgD//+c8CRcVY38bFiLEOmDdvHurr6/WW2djYYPz48QJFxFjfxsWIsQ4YN24c7OzsdP+WSCRYuHAhTE1NBYyKsb6LixFjHWBiYoKFCxfqTtU1NDRg3rx5AkfFWN/FxYixDgoICNCdqnNycsLIkSMFjoixvouLEWMd9Morr8DDwwMAsGjRIohEIoEjYqzv4rt2G2n27NlCh8B6kebTdBcuXOCxwXR8fHywYsUKocPoU/ibkZGOHj2KmzdvCh1Gn/bjjz/ixx9/FDqMLuHs7AxLS0uo1eou7ffmzZs4evRol/bJesaPP/6I8+fPCx1Gn8PfjDpg+fLlmDNnjtBh9FnN3yCOHDkicCRd4+zZs/jjH//YpX0mJiZi7ty5z0yOnif8Dblj+JsRY53U1YWIsecRFyPGGGOC42LEGGNMcFyMGGOMCY6LEWOMMcFxMeph77zzDlQqFUQiEdLT04UOx2harRa7d++Gr6+voHGcPn0aFhYW+PLLLwWNo7c6e/Ys1qxZg6SkJLi7u0MkEkEkEmHhwoUt2k6ePBkqlQqmpqYYOnQoLl26JEDETxceHo4hQ4ZArVZDJpPBw8MDH374IaqqqnRtIiIidMf5+N+wYcP0+mpoaMDWrVvh4eEBqVQKS0tLDBs2DHl5eThx4gR27NiBpqamnj7E5x4Xox726aefYv/+/UKH0SE5OTn4r//6L6xYsQI1NTWCxkJEgu6/N/voo48QHR2NtWvXwt/fH9evX4dGo0G/fv1w6NAhnDp1Sq/9119/jSNHjuDNN99EVlYWRowYIVDkbTt37hyWLl2KvLw8lJaWYuvWrYiKiurQNOq5c+fis88+w+eff46amhr89ttv0Gg0qKqq0j1WftKkSSgvL++GI2Ft4WLEDPLLL79g9erVWLJkCby9vYUOB1OnTsWDBw/w5ptvCrL/2tpawb8dtmb79u2Ij49HYmIiVCqV3rro6GiYmJggMDAQDx48ECjCjjE3N0dgYCCsra2hUqkwZ84c+Pn54cyZMygsLNS1O3jwIIhI7+/XX3/VrY+Pj0dycjKOHDmC1157DWKxGA4ODjh+/LjuG9SyZcswfPhwvPHGG2hsbOzxY31ecTESQF+8h9nw4cORlJSE+fPn85NMARw4cADFxcVCh6Hn2rVr2LBhAzZt2gS5XN5iva+vL4KDg3Hr1i2sXLlSgAg77uTJky0ez2FjYwMARn1L//vf/44RI0bAy8vrqe02btyI9PR0REVFGR8s6xAuRt2MiBAZGYlBgwZBJpPBwsICq1at0mvT1NSEsLAwODs7Q6FQ4KWXXkJCQgIAIDY2FmZmZlAqlTh+/DimTJkCtVoNR0dHHD58WNfHt99+i1dffRVKpRJqtRpeXl6oqKhot/++6Pvvv4ezszNEIhE++eQTAIblKTo6GnK5HP3798d7770HBwcHyOVy+Pr64sKFCwCAoKAgSKVS2Nvb6/b3/vvvw8zMDCKRCKWlpQgODkZISAhyc3MhEol0N0s9c+YM1Go1tmzZ0sMZge74iAjTpk1rs01ERAReeOEFfPrppzh79myb7YgIu3btwosvvgiZTAYrKyvMmDEDly9fBmD4uOzOsXfr1i0oFAq4ubkZ1L6+vh4//vijQd/sraysMH78eERFRfEp4Z5CzCgAKCEhweD269atI5FIRP/7v/9LZWVlVFNTQzExMQSA0tLSiIho5cqVJJPJ6OjRo1RWVkZr164lExMTunjxoq4PAPTNN9/QgwcPqLi4mMaNG0dmZmZUX19PVVVVpFaraceOHVRbW0t37tyhmTNnUklJiUH9G+u1116j4cOHd2hbIqJZs2bRrFmzOrw9EVFhYSEBoD179uiWtZcnIqLAwEAyMzOj7Oxsqquro6ysLBo1ahSpVCoqKCggIqL58+eTnZ2d3v4iIyMJgC6n/v7+pNFo9NqcPHmSVCoVhYeHd+rYiIgSEhLI2Jenu7s7DRkypNV1Go2Gbty4QUREP/zwA5mYmJCrqytVVVUREVFKSgpNnz5d1z4sLIykUikdPHiQysvLKSMjg0aMGEE2NjZ0584dIjIs31099ppVV1eTSqWioKAg3bLNmzeTo6MjWVpakkQiIVdXV5o+fTr99NNPRER048YNAkDe3t40YcIEsre3J5lMRoMHD6ZPPvmEtFqt3j7WrFmj9zo1VFeM7+dQIn8z6ka1tbXYvXs3/vjHP2LFihWwtLSEQqGAtbW1rk1dXR1iY2Ph5+cHf39/WFpaYv369ZBIJIiLi9Prz9fXF2q1Gra2tggICEB1dTUKCgqQl5eHiooKDB06FHK5HHZ2dkhKSoKNjY1R/T8r2spTM7FYrPvEP2TIEMTGxqKysrLT+Zg6dSoqKiqwYcOGzh6C0aqrq3Hjxg1oNJp22/r4+GD58uXIy8vD6tWrW6yvra3Frl27MHPmTCxYsAAWFhbw8vLC3r17UVpain379um1byvf3Tn2tm7dCgcHB0REROiWvfXWWzhx4gQKCwtRVVWFw4cPo6CgAOPHj0dWVpZu5p2trS22bNmCrKws3L17FzNmzMDSpUvxxRdf6O3D09MTAJCZmdmpWJlhuBh1o2vXrqGmpgaTJk1qs82VK1dQU1OjN/1UoVDA3t5ed0qkNY8/YdTd3R39+/fHggULsHHjRuTl5XW6/2fF43lqy8iRI6FUKvt0PoqLi0FEUCqVBrWPiIjAoEGDEBMTg++//15vXfMb95MPCxw1ahSkUqnulGZrHs93d429Y8eOITExEV999ZXeJA0nJye8/PLLMDc3h1QqxejRoxEXF4fa2lrExMTofuscOnQofH19YW1tDQsLC2zatAkWFhYtimxzLu/evdvhWJnhuBh1o+ZHTdja2rbZprq6GgCwfv16vWsj8vPzDf5hVqFQ4Ny5cxg7diy2bNkCd3d3BAQEoLa2tkv6fx7IZDKUlJQIHUaH1dXVAYDBk0vkcjni4uIgEonw9ttvo7a2VreueUqzubl5i+0sLS1RWVlp0D66Y+zFx8dj+/btSE1Nhaura7vtvby8YGpqiqtXr8LBwQEAUFpaqtdGKpXCxcUFubm5essVCgWA33PLuhcXo27UPKPp4cOHbbZpLlS7d+9uMSXVmGeiDB06FF9++SWKiooQGhqKhIQE7Ny5s8v6f5Y1NDSgvLwcjo6OQofSYc1vnMZcrNn8ALicnBxs3rxZt9zS0hIAWi06xuSpq8fenj17cOjQIZw7dw4DBgwwaButVgutVguZTAZzc3N4enoiOzu7RbvGxkZYWFjoLWt+pHxzbln34mLUjYYNGwYTExN8++23bbZxcnKCXC7v1N0YioqKdC8wW1tbbNu2DSNGjEB2dnaX9P+sS01NBRFh9OjRAB79pvS003q9Uf/+/SESiYy+fmjz5s0YPHgw0tLSdMuGDRsGc3Nz/Pzzz3ptL1y4gPr6erzyyisG9d1VY4+IEBoaiszMTCQnJ7f6jQ0AXn/99RbLLl68CCKCj48PgEcXvKalpeH69eu6NjU1NcjPz28x3bs5l3Z2dp2KnxmGi1E3srW1hb+/P44ePYoDBw6goqICGRkZeuem5XI5Fi9ejMOHDyM2NhYVFRVoamrCzZs3cfv2bYP2U1RUhPfeew+XL19GfX090tLSkJ+fj9GjR3dJ/88arVaLsrIyNDY2IiMjA8HBwXB2dsaiRYsAAB4eHrh//z6Sk5PR0NCAkpIS5Ofn6/VhbW2NoqIi5OXlobKyEg0NDUhJSRFsardSqYS7u7vRTyFuPl33+DU8crkcISEhOHbsGA4dOoSKigpkZmZiyZIlcHBwQGBgoMF9tzf2AgICYGdn99RbEGVnZ+Pjjz/G/v37IZFIWtzuZ+fOnQAeTfWOj49HeXk5GhoacP78ebzzzjtwdnbGkiVLAAArVqyAi4sLFi1ahIKCAty7dw+hoaGora1tMZmjOZftXZPEukjPz+Dr22Dk1O7Kykp65513qF+/fmRubk5jx46lsLAwAkCOjo70yy+/0MOHDyk0NJScnZ1JLBaTra0t+fv7U1ZWFsXExJBSqSQA5OnpSbm5ubRv3z5Sq9UEgFxcXOjf//43+fr6kpWVFZmamtKAAQNo3bp11NjYSET01P4Ndf78eRozZgw5ODgQAAJA9vb25OvrS99++61ROezs1Nc9e/aQvb09ASClUknTpk0zKE9Xr16lwMBAkkgkNHDgQBKLxaRWq2nGjBmUm5ur6//evXs0ceJEksvl5ObmRh988AGtWrWKAJCHhwcVFBTQpUuXyMXFhRQKBY0dO5bu3LlDp0+fJpVKRRERER0+tmYdmdodFBREEomEampqdMuOHTtGGo2GAJCNjQ0tXbq01W1XrVqlN7Vbq9VSZGQkeXp6kkQiISsrK/Lz86MrV64QERmc7/bGnp+fHwGgsLCwNo8rMzNTN+Za+4uMjCQiopCQENJoNGRmZkZisZgcHR3p3XffpaKiIr3+CgsLad68eWRlZUUymYxeffVVSklJabHfqVOn0sCBA1tM+W4PT+3ukEQuRkYythixloR8sQYGBpK1tbUg+zZGR4pRTk4OicViOnjwYDdF1fWamppo3LhxdODAAaFD0VNaWkpyuZx27txp9LZcjDqErzNiz59n9Y7MHh4eCA8PR3h4uN7drHurpqYmJCcno7KyEgEBAUKHo2fjxo3w9vZGUFCQ0KE8N7gYPccuX77c6i33n/zrbW8UrG1r1qzB7NmzERAQ0OtvhpqamoqkpCSkpKQYfH1UT9i1axfS09Nx+vRpSCQSocN5bnAxeo4NHjy4xZTb1v7i4+OFDrVLrF27FnFxcXjw4AHc3Nxw9OhRoUPqFlu2bEFQUBC2bdsmdChPNWnSJHz++ed69wEU2vHjx/Hw4UOkpqbCyspK6HCeK2KhA2Csp2zduhVbt24VOoweMXnyZEyePFnoMPqc6dOnY/r06UKH8Vzib0aMMcYEx8WIMcaY4LgYMcYYExwXI8YYY4LjYsQYY0xwIiJ+pq4xRCKR0CEwxnq5WbNm4ciRI0KH0Zcc4andHRAcHKy7CzAz3u7duwEAy5cvFziS3uv8+fOIiopCQkKC0KEwIzWPb2YcLkYd4OPjgzlz5ggdRp/V/ImRc/h0UVFRnKM+iL8RdQz/ZsQYY0xwXIwYY4wJjosRY4wxwXExYowxJjguRowxxgTHxUhASUlJcHd3b/H8IKlUiv79+2PChAmIjIxEWVmZ0KGyPubs2bNYs2ZNizG2cOHCFm0nT54MlUoFU1NTDB06FJcuXRIgYsNotVrs3r0bvr6+LdaFh4djyJAhUKvVkMlk8PDwwIcfftjiQYNffPEFRo0aBZVKBRcXFyxevBh37twBAJw4cQI7dux4Zh/A2KsJ9IjZPgvd8NhxjUZDFhYWRESk1WqprKyM/vOf/9CiRYtIJBKRg4MDXbx4sUv3KSR+LHP7OvLY8WZhYWH05ptvUkVFhW6ZRqOhfv36EQA6efJki21SUlJo+vTpHY63J1y9epXGjBlDAGj48OEt1o8fP55iYmLo3r17VFFRQQkJCSSRSOhPf/qTrk18fDwBoB07dlB5eTmlpaWRu7s7eXt7U0NDAxERRUVF0fjx46msrKxDcfL47hB+7HhvIxKJYGlpiQkTJiAuLg6JiYm4e/cupk6d2uuf3NkX1NbWtvqpurf3bajt27cjPj4eiYmJUKlUeuuio6NhYmKCwMDAPjeWfvnlF6xevRpLliyBt7d3q23Mzc0RGBgIa2trqFQqzJkzB35+fjhz5gwKCwsBAP/4xz8wYMAArFq1ChYWFvD29saKFSuQnp6OCxcuAACWLVuG4cOH44033kBjY2OPHePzjotRLzdr1iwsWrQIxcXF2Lt3r9Dh9HkHDhxAcXFxn+vbENeuXcOGDRuwadMmyOXyFut9fX0RHByMW7duYeXKlQJE2HHDhw9HUlIS5s+fD5lM1mqbkydPwtTUVG+ZjY0NAKCmpgYAUFhYCAcHB73bejk5OQEA8vPzdcs2btyI9PR0REVFdelxsLZxMeoDFi1aBABISUkBADQ1NSEsLAzOzs5QKBR46aWXdLeNiY2NhZmZGZRKJY4fP44pU6ZArVbD0dERhw8f1vX57bff4tVXX4VSqYRarYaXlxcqKira7V8oRIRdu3bhxRdfhEwmg5WVFWbMmIHLly8DAIKCgiCVSvUeYf3+++/DzMwMIpEIpaWlCA4ORkhICHJzcyESieDh4YHo6GjI5XL0798f7733HhwcHCCXy+Hr66v7pNzRvgHgzJkzUKvV2LJlS7fnKDo6GkSEadOmtdkmIiICL7zwAj799FOcPXu2zXbt5dvQcSb0WLp16xYUCgXc3NwAAO7u7i0+MDT/XuTu7q5bZmVlhfHjxyMqKgrEt+/sGcKeJux70M2/GbWmoqKCAJCTkxMREa1cuZJkMhkdPXqUysrKaO3atWRiYqL7XWndunUEgL755ht68OABFRcX07hx48jMzIzq6+upqqqK1Go17dixg2pra+nOnTs0c+ZMKikpMaj/zurIOfWwsDCSSqV08OBBKi8vp4yMDBoxYgTZ2NjQnTt3iIho/vz5ZGdnp7ddZGQkAdAdm7+/P2k0Gr02gYGBZGZmRtnZ2VRXV0dZWVk0atQoUqlUVFBQ0Km+T548SSqVisLDw4063o78ZuTu7k5DhgxpdZ1Go6EbN24QEdEPP/xAJiYm5OrqSlVVVUTU8jcjQ/Ld3jgj6p6x9Nprr7X6m9GTqqurSaVSUVBQkG5ZamoqSSQSio6OpoqKCvr111/pxRdfpNdff73F9mvWrCEAlJaWZlR8/JtRh/BvRn2BSqWCSCRCZWUl6urqEBsbCz8/P/j7+8PS0hLr16+HRCJBXFyc3na+vr5Qq9WwtbVFQEAAqqurUVBQgLy8PFRUVGDo0KGQy+Wws7NDUlISbGxsjOq/p9TW1mLXrl2YOXMmFixYAAsLC3h5eWHv3r0oLS3Fvn37Or0PsVis+xYwZMgQxMbGorKystPHPHXqVFRUVGDDhg2djvFpqqurcePGDWg0mnbb+vj4YPny5cjLy8Pq1atbrDc2322NM6HH0tatW+Hg4ICIiAjdsvHjxyM0NBRBQUFQq9UYNmwYKisr8emnn7bY3tPTEwCQmZnZ7bEyPk3XJ1RXV4OIoFarceXKFdTU1GDYsGG69QqFAvb29rpTKK2RSqUAgIaGBri7u6N///5YsGABNm7ciLy8PF27jvbfnbKyslBVVYWRI0fqLR81ahSkUqnudFpXGjlyJJRKpWDHbKzi4mIQEZRKpUHtIyIiMGjQIMTExOD777/XW9eZfD8+zoQcS8eOHUNiYiK++uorvYkc69atw759+/DNN9+gqqoK169fh6+vL3x8fHSTHJo15/Lu3bvdGit7hItRH3D16lUAwODBg1FdXQ0AWL9+vd61Sfn5+bofadujUChw7tw5jB07Flu2bIG7uzsCAgJQW1vbJf13tfLycgCPZks9ydLSEpWVld2yX5lMhpKSkm7pu6vV1dUBQJs/7j9JLpcjLi4OIpEIb7/9Nmpra3XruirfQo2l+Ph4bN++HampqXB1ddUtv337Nnbs2IG//vWv+MMf/gAzMzO4ublh//79KCoqQmRkpF4/CoUCwO+5Zd2Li1EfcObMGQDAlClTYGtrC+DRM1OISO/v/PnzBvc5dOhQfPnllygqKkJoaCgSEhKwc+fOLuu/K1laWgJAq2+C5eXlcHR07PJ9NjQ0dFvf3aH5jdOYizV9fCCKAjwAACAASURBVHywYsUK5OTkYPPmzbrlXZVvIcbSnj17cOjQIZw7dw4DBgzQW5eTk4OmpqYWy9VqNaytrZGVlaW3vL6+HsDvuWXdi4tRL3fnzh3s3r0bjo6OePvtt+Hk5AS5XI709PQO91lUVITs7GwAj94wtm3bhhEjRiA7O7tL+u9qw4YNg7m5OX7++We95RcuXEB9fT1eeeUVAI9+92loaOiSfaampoKIMHr06C7vuzv0798fIpHI6OuHNm/ejMGDByMtLU23zNB8t6cnxxIRITQ0FJmZmUhOTm71W11zEb19+7be8srKSty/f183xbtZcy7t7Oy6KWr2OC5GvQQRoaqqClqtFkSEkpISJCQkYMyYMTA1NUVycjLUajXkcjkWL16Mw4cPIzY2FhUVFWhqasLNmzdbvMjaUlRUhPfeew+XL19GfX090tLSkJ+fj9GjR3dJ/11NLpcjJCQEx44dw6FDh1BRUYHMzEwsWbIEDg4OCAwMBAB4eHjg/v37SE5ORkNDA0pKSvSuHQEAa2trFBUVIS8vD5WVlboCo9VqUVZWhsbGRmRkZCA4OBjOzs66afUd7TslJaVHpnYrlUq4u7vj5s2bRm3XfLru8etzDM23IX23N5YCAgJgZ2fX6VsQZWdn4+OPP8b+/fshkUha3GJr586dcHNzw8SJE7F//3589913qK2tRWFhoe54/vKXv+j12ZxLLy+vTsXGDNTT8/f6OnTh1O4TJ07QSy+9REqlkqRSKZmYmBAAEolEZGlpSa+++iqFh4fTvXv39LZ7+PAhhYaGkrOzM4nFYrK1tSV/f3/KysqimJgYUiqVBIA8PT0pNzeX9u3bR2q1mgCQi4sL/fvf/yZfX1+ysrIiU1NTGjBgAK1bt44aGxvb7b8rdGTqq1arpcjISPL09CSJREJWVlbk5+dHV65c0bW5d+8eTZw4keRyObm5udEHH3xAq1atIgDk4eFBBQUFdOnSJXJxcSGFQkFjx46lO3fuUGBgIEkkEho4cCCJxWJSq9U0Y8YMys3N7XTfp0+fJpVKRREREUYdb0emdgcFBZFEIqGamhrdsmPHjpFGoyEAZGNjQ0uXLm1121WrVulN7W4v34aMs6tXr7Y7lvz8/AgAhYWFPfXYzp8/T2PGjCEHBwcCQADI3t6efH196dtvv6XMzEzd8tb+IiMjiYiotLSUgoODycPDg2QyGZmbm9OYMWPoX//6V4t9Tp06lQYOHEhardao/wee2t0hiVyMjNSVxeh51dterIGBgWRtbS10GHo6UoxycnJILBbTwYMHuymqrtfU1ETjxo2jAwcOCB2KntLSUpLL5bRz506jt+1t47uP4OuMGAOM++G/t/Lw8EB4eDjCw8Nb3Km6N2pqakJycjIqKysREBAgdDh6Nm7cCG9vbwQFBQkdynODixFjz5A1a9Zg9uzZCAgI6PU3Q01NTUVSUhJSUlIMvj6qJ+zatQvp6ek4ffo0JBKJ0OE8N7gYsefa2rVrERcXhwcPHsDNzQ1Hjx4VOqRO27JlC4KCgrBt2zahQ3mqSZMm4fPPP9e755/Qjh8/jocPHyI1NRVWVlZCh/NcEQsdAGNC2rp1K7Zu3Sp0GF1u8uTJmDx5stBh9DnTp0/H9OnThQ7jucTfjBhjjAmOixFjjDHBcTFijDEmOC5GjDHGBMcTGDpAqBuGPiuab7OSmJgocCS9V/MY4xz1PTdv3uwzN9jtTURE/ExdY4hEIqFDYIz1crNmzcKRI0eEDqMvOcLfjIzEtZs9LjExEXPnzuVxwVgn8W9GjDHGBMfFiDHGmOC4GDHGGBMcFyPGGGOC42LEGGNMcFyMGGOMCY6LEWOMMcFxMWKMMSY4LkaMMcYEx8WIMcaY4LgYMcYYExwXI8YYY4LjYsQYY0xwXIwYY4wJjosRY4wxwXExYowxJjguRowxxgTHxYgxxpjguBgxxhgTHBcjxhhjguNixBhjTHBcjBhjjAmOixFjjDHBcTFijDEmOC5GjDHGBMfFiDHGmOC4GDHGGBMcFyPGGGOC42LEGGNMcFyMGGOMCY6LEWOMMcFxMWKMMSY4LkaMMcYEJxY6AMb6iuLiYsTFxekty8jIAADs2LFDb7m1tTXefffdHouNsb5OREQkdBCM9QWNjY2wt7dHWVkZJBJJm+0ePnyIwMBA7N27twejY6xPO8Kn6RgzkFgsxrx582BqaoqHDx+2+QcAf/7znwWOlrG+hYsRY0aYN28eGhoantrG3t4eY8eO7aGIGHs2cDFizAg+Pj5wdHRsc71UKsXChQthYsIvLcaMwa8YxowgEomwYMGCNn8zqq+vx7x583o4Ksb6Pi5GjBnpaafq3N3d8fLLL/dwRIz1fVyMGDPSSy+9hEGDBrVYLpVK8dZbbwkQEWN9Hxcjxjpg4cKFLU7V1dfXIyAgQKCIGOvbuBgx1gELFixAY2Oj7t8ikQjDhw/HCy+8IGBUjPVdXIwY6wAXFxeMGDECIpEIAGBqasqn6BjrBC5GjHXQ//zP/8DU1BQA0NTUhDlz5ggcEWN9Fxcjxjpozpw50Gq1EIlEGDNmDAYOHCh0SIz1WVyMGOsge3t7jB8/HkTEp+gY66Ruu1Fq87l0xhhjz4ZZs2bhyJEj3dH1kW59hERwcDB8fHy6cxeMCaq2thb79u3DsmXLsHv3bgDA8uXLBY6q9zp//jyioqKQkJAgdCjMSM3ju7t0azHy8fHhH3XZM++///u/MWDAAN0nRh7zTxcVFcU56oO66RuRDv9mxFgnDRgwQOgQGOvzuBgxxhgTHBcjxhhjguNixBhjTHBcjBhjjAmuVxSjUaNGwdTUFN7e3j2638WLF0Mul0MkEqGurq5H991b7Ny5E/3794dIJMLevXt1y0+fPg0LCwt8+eWX3br/ntpPW8LDwzFkyBCo1WrIZDJ4eHjgww8/RFVVlSDxCJ2P3u7s2bNYs2YNkpKS4O7uDpFIBJFIhIULF7ZoO3nyZKhUKpiammLo0KG4dOmSABEbRqvVYvfu3fD19W2xztAx+sUXX2DUqFFQqVRwcXHB4sWLcefOHQDAiRMnsGPHDjQ1NfXI8XREryhGFy9exMSJE3t8v3FxcVi5cmWP77c3WblyJX744YcWy7vpWmjB9tOWc+fOYenSpcjLy0NpaSm2bt2KqKgozJ49W5B4hM5Hb/bRRx8hOjoaa9euhb+/P65fvw6NRoN+/frh0KFDOHXqlF77r7/+GkeOHMGbb76JrKwsjBgxQqDIny4nJwf/9V//hRUrVqCmpqbFekPGaEJCAubPn4/Zs2fj5s2bOH78OL777jtMmTIFjY2NmDZtGuRyOSZNmoTy8vKePDyD9Ypi1Kyzd22ora1t9ZMFM97UqVPx4MEDvPnmm13WZ2v/P92xH2OYm5sjMDAQ1tbWUKlUmDNnDvz8/HDmzBkUFhb2eDxC56O3voa2b9+O+Ph4JCYmQqVS6a2Ljo6GiYkJAgMD8eDBA4Ei7JhffvkFq1evxpIlS9o8M2TIGP3HP/6BAQMGYNWqVbCwsIC3tzdWrFiB9PR0XLhwAQCwbNkyDB8+HG+88Ybe4096i15VjJ58WJmxDhw4gOLi4g5ty7cv6n6d+f/pLidPntTdebuZjY0NALT6KfVZ1xv/j65du4YNGzZg06ZNkMvlLdb7+voiODgYt27d6nNnOoYPH46kpCTMnz8fMpms1TaGjNHCwkI4ODjovY85OTkBAPLz83XLNm7ciPT0dERFRXXpcXSFXlWMrl27hsGDB8PMzAwKhQLjxo3D999/r1v///7f/8OQIUNgYWEBuVwOLy8vfPXVVwAe3XooJCQEubm5EIlE8PDw0G138OBBjBw5EnK5HGZmZnB1dcXmzZt1601MTHDq1ClMmTIFFhYWcHBwwD//+U+D446NjYWZmRmUSiWOHz+OKVOmQK1Ww9HREYcPH9a1IyLs2rULL774ImQyGaysrDBjxgxcvnwZAPDxxx9DqVRCpVKhuLgYISEhGDhwIJYsWQIzMzOYmJjglVdegZ2dHSQSCczMzDBixAiMGzcOTk5OkMvlsLS0xIcffqgX39Py1prvv/8ezs7OEIlE+OSTT3T/N83n55/8+/e//92h/5/W9mNIngzNd0fdunULCoUCbm5une7LGK3lw5BjjY6OhlwuR//+/fHee+/BwcEBcrkcvr6+uk/FQUFBkEqlsLe31+3v/fffh5mZGUQiEUpLS9t8DZ05cwZqtRpbtmzp0Xw0i46OBhFh2rRpbbaJiIjACy+8gE8//RRnz55ts11Xja2mpiaEhYXB2dkZCoUCL730Uo/e4ujJMeru7t7iQ0Tz70Xu7u66ZVZWVhg/fjyioqJ63ylh6iYAKCEhweD2kyZNInd3d7px4wY1NDTQr7/+Sq+99hrJ5XK6evUqEREdOXKENm7cSPfv36d79+7R6NGjqV+/fro+/P39SaPR6PW7e/duAkDbtm2je/fu0f379+kf//gHzZ8/n4iI1q1bRwDom2++ofLycrp//z698cYbJJPJqLq62uD4H+/nwYMHVFxcTOPGjSMzMzOqr68nIqKwsDCSSqV08OBBKi8vp4yMDBoxYgTZ2NjQnTt39PpZtmwZ7dmzh2bOnEm//fYbffTRRwSALly4QNXV1VRaWkp/+tOfCACdOnWKSkpKqLq6moKCgggApaen62JrL285OTkEgP7+97/rlhUWFhIA2rNnj67N6tWrdTm5ffs2WVlZka+vLzU1NXX4/+fJ/Ribp6fluyOqq6tJpVJRUFCQ0dvOmjWLZs2a1eF9E7WeD0OONTAwkMzMzCg7O5vq6uooKyuLRo0aRSqVigoKCoiIaP78+WRnZ6e3v8jISAJAJSUlRNT6/9HJkydJpVJReHh4p46NiCghIYGMfdtxd3enIUOGtLpOo9HQjRs3iIjohx9+IBMTE3J1daWqqioiIkpJSaHp06fr2nfV2Fq5ciXJZDI6evQolZWV0dq1a8nExIQuXrxobEp0XnvtNRo+fHi77Vobo6mpqSSRSCg6OpoqKiro119/pRdffJFef/31FtuvWbOGAFBaWppR8XXF+H6KxF5VjJ78j8jIyCAAtHLlyla32bp1KwGg4uJiImr5QqqvrydLS0uaOHGi3naNjY0UFRVFRL8PvNraWt36zz77jADQr7/+anD8rfUTExNDAOjatWtUU1ND5ubmFBAQoLfdTz/9RAB0L/TW+iEiXTGqrKzULfu///s/AkCZmZkt+ouPj28z1ifzZkgxepKfnx/J5XK6fPmywfsxpBh1Jk+P57uj1q1bRy+88AJVVFQYvW13F6OnHWtgYCBZWFjo9XXx4kUCQJs2bSKijhejrmRsMaqqqiKRSERvvvlmq+sfL0ZERCEhIQSAli5dSkT6xairxlZtbS0plUq9fmpqakgmk9Hf/vY3g4/tSYYWo7bG6Pr16wmA7s/R0ZEKCwtbbP/Pf/6TANBnn31mVHzdXYx61Wm6J3l5ecHCwgIZGRmtrm/+jamt6YoZGRkoLy/H66+/rrfc1NQUy5Yta3O/zf02NDR0JGwdqVSq6ycrKwtVVVUYOXKkXptRo0ZBKpXqTqd0pP/Hf4w0JPb28taexMRE/Otf/8KmTZswaNCgLt1PZ/L0eL474tixY0hMTMRXX33V4kfy3saQYx05ciSUSqXuFFRfVFxcDCKCUqk0qH1ERAQGDRqEmJgYvVP8QNeNrStXrqCmpgbDhg3TrVcoFLC3t+/2XLc1RtetW4d9+/bhm2++QVVVFa5fvw5fX1/4+Pi0mIjTnMu7d+92a6zG6tXFCHj0htb8gjt16hQmTJgAW1tbyGSyFr+NPKmiogIAYGlp2e1xtqd5OqW5uXmLdZaWlqisrOy2fRubt6e5d+8ePvjgA4waNQohISFdvh+h8hQfH4/t27cjNTUVrq6u3bIPIchkMpSUlAgdRoc1X//X1o/7T5LL5YiLi4NIJMLbb7+N2tpa3bquGlvV1dUAgPXr1+v9dpqfn9+tk17aGqO3b9/Gjh078Ne//hV/+MMfYGZmBjc3N+zfvx9FRUWIjIzU60ehUABAr7u2slcXo8bGRty/fx/Ozs4oKCiAn58f7O3tceHCBTx48AA7dux46vbNd1MuLS3tiXCfqrkgtjbgy8vL4ejo2C377UjenmbZsmUoLy9HXFyc3gyfrtqPEHnas2cPDh06hHPnzj1Td+BuaGjo1rHVE5rfOI35du3j44MVK1YgJydHb6JSV40tW1tbAI+e70NEen/nz583OE5jPG2M5uTkoKmpqcVytVoNa2trZGVl6S2vr68H8Htue4teXYz+85//QKvVYsSIEcjMzERDQwP+9re/wd3dXXfnhKdxdXWFtbU1vv766x6KuG3Dhg2Dubk5fv75Z73lFy5cQH19PV555ZVu2W9H8taWU6dO4fPPP8eGDRswdOhQ3fJVq1Z12X56Mk9EhNDQUGRmZiI5ObnVT8x9WWpqKogIo0ePBgCIxeJOn3ruac13BzH2+qHNmzdj8ODBSEtL0y3rqrHVPHM1PT3dqJg6wpAx2lxEb9++rbe8srIS9+/f103xbtacSzs7u26KumN6VTGqr6/HgwcP0NjYiEuXLiEoKAguLi5YtGgRnJ2dATy6HUhdXR1ycnJanOO1trZGUVER8vLyUFlZCRMTE6xduxbfffcdgoKCcOvWLWi1WlRWViI7O7tHj00ulyMkJATHjh3DoUOHUFFRgczMTCxZsgQODg4IDAzslv0akjdDVFRU4L333oO3tzdWr14N4NHX/J9//hnp6ekd+v9p7Y2xJ/OUnZ2Njz/+GPv374dEImkxZX3nzp1dtq+eoNVqUVZWhsbGRmRkZCA4OBjOzs5YtGgRAMDDwwP3799HcnIyGhoaUFJSoncNCtD6/1FKSopgU7uVSiXc3d1x8+ZNo7ZrPl33+Lf3rhpbcrkcixcvxuHDhxEbG4uKigo0NTXh5s2buoIQEBAAOzu7Tt+CyJAx6ubmhokTJ2L//v347rvvUFtbi8LCQt3x/OUvf9HrszmXXl5enYqty3XX1AgYOZsuLi6OJk6cSP379yexWEz9+vWjefPmUX5+vq5NaGgoWVtbk6WlJc2ePZs++eQTAkAajYYKCgro0qVL5OLiQgqFgsaOHaubqvnJJ5+Ql5cXyeVyksvl9PLLL1NMTAzt2LGDFAoFASBPT0/Kzc2lQ4cOkZWVlW42iiEz6mJiYkipVOr1s2/fPlKr1QSAXFxc6OrVq6TVaikyMpI8PT1JIpGQlZUV+fn50ZUrV4iI9OJxcnKigwcPEhFRVNT/Z+/eo5q60v6Bf4OEhISEiyAgcsdWrVZaZY1ifS11Xlp1RFFROtRZtXaV2ktEEe9SRbxQfIUXxZl6GWfqDRCotiq2r3aoddXa9qdUiqNFWxVvRe5XBcLz+6MrGWNAEkg4BJ/PWvmDfS77OTv78OScs885qdr1+/j40Ndff00bN24ke3t7AkCurq60b98+ysjIIFdXVwJAjo6OdODAgQ7bLSYmRruMXC6nadOm0ZYtW8jNzY0AkEwmo7CwMNq0aZPOSJ2HPxMnTuzU97Ny5Uq9eoiow3YytL07UlhY2O42AaDk5GRDuy8RdX20UVvtbui2RkdHk1gsJg8PD7K2tialUklTp06lq1evatdfXl5OISEhJJVKydfXl95//32Ki4sjABQQENDuPnTs2DFSKBSUmJjY6W3T6MzQbpVKRWKxmBoaGrRlubm55O/vTwDI2dlZO3ruUXFxcTpDu03Vtx48eEBLliwhLy8vsra2JhcXF5o+fToVFRUR0e+jTQFQfHz8Y7ftzJkzNGbMGHJ3d9f2Ozc3NwoODqavvvrK4D5aVlZGMTExFBAQQBKJhOzs7GjMmDH0ySef6NU5adIk8vDwoNbWVqO+hydmaDdjls7MO+tjRUdHk5OTkyB1G6Mzyai4uJisra21P84sgVqtprFjx9KuXbuEDkVHWVkZSaVS2rRpk9HLPtFDuxljhuvJT2TuioCAACQkJCAhIUGwp6kbQ61W49ChQ6itrUVkZKTQ4ehYvXo1AgMDoVKphA5FDyejx7h06VK7j8B5+NPTOtyTjr+33mfZsmWIiIhAZGRkj38Yan5+PnJycpCXl2fw/VHdYfPmzSgoKMCxY8e6/BxQc7AWOoCebNCgQT3v+U2sQ0/a97Z8+XLs3r0bTU1N8PX1RXJyMmbMmCF0WCa3bt06fPHFF9iwYQM2btwodDjtGj9+PMaPHy90GDoOHz6MBw8eID8/X++hqz0FJyPGLNz69euxfv16ocPoFqGhoQgNDRU6DIszZcoUTJkyRegwHotP0zHGGBMcJyPGGGOC42TEGGNMcJyMGGOMCc6sAxjM9dBAxnoizWNWsrKyBI6k59L8T+A2sjw3b94060N3RWSmMbCdfRgnY4yxnmnGjBk4ePCgOVZ90KxHRpmZmZg5c6Y5q2Csx4iIiAAAc+2svUJWVhZmzZr1RN0H1lto+re58DUjxhhjguNkxBhjTHCcjBhjjAmOkxFjjDHBcTJijDEmOE5GjDHGBGdRySgnJwd+fn5676WxtraGs7Mz/vjHPyI3N9esMcyZMwdSqRQikQj37983KM7Zs2frzRMaGgqFQoE+ffrgmWeewblz58wad2dt2rQJ/fr1g0gkwt/+9jdt+bFjx2Bvb4/PPvvMrPV3Vz3MMpw4cQLLli3rVfsYALS2tiIlJQXBwcF60xISEjBkyBAolUpIJBIEBARg8eLFei8a3L9/P4KCgqBQKODt7Y05c+bg7t27AIBPP/0USUlJPfsFjOZ6hyzM+Npxf39/sre31/5dUVFBJ06coEGDBhEAysjIMEu9GitWrCAA1NjY2GGcffv2JQB05MgRvel5eXk0ZcoUc4VpMsXFxQSA/vrXv2rLjhw5Qkqlkj799FOz1t1d9ZiCkK8dtxSdee24Rnx8PE2ePJlqamq0Zb1hH/v5559pzJgxBICGDx+uN33cuHGUnp5O5eXlVFNTQ5mZmSQWi+mVV17RzpORkUEAKCkpiaqqquj8+fPk5+dHgYGB1NzcTEREqampNG7cOKqsrOxUnPzacQM4Ojpi/Pjx+N///V8Axj1qpLGxsc1fI6aSlpYGKysrREdH9/g3VBpj0qRJqK6uxuTJk022zra+C3PU0xuZsx+bex8xxMaNG5GRkYGsrCwoFAqdaZa8j/34449YunQp5s2bh8DAwDbnsbOzQ3R0NJycnKBQKDBz5kyEh4fj+PHjKCkpAQB89NFH6N+/P+Li4mBvb4/AwEAsXLgQBQUFOHv2LABg/vz5GD58OCZOnIiWlpZu20ZD9YpkpOHj4wMAqKqqMniZXbt2obS0tFP1GfLIo+DgYMTExODWrVtYtGhRp+p5UnTlu3jSmbPthP5erly5glWrVmHNmjWQSqV60y15Hxs+fDhycnIQFRUFiUTS5jxHjhzRezurs7MzAKChoQEAUFJSAnd3d53/SZ6engCA69eva8tWr16NgoICpKammnQ7TKFXJaMLFy4AAMaNG6ct+/rrrzFkyBDY29tDKpVi2LBh+PzzzwEAMTExiI2NxdWrVyESiRAQEKBdbs+ePRg5ciSkUinkcjl8fHywdu1a7XQrKyscPXoUEyZMgL29Pdzd3fH3v/+9zbgSExPx1FNPYefOnThx4kS78RMRNm/ejMGDB0MikcDR0RFTp07FpUuXAAAffvghZDIZFAoFSktLERsbCw8PD8ybNw9yuRxWVlYYMWIEXF1dIRaLIZfL8fzzz2Ps2LHw9PSEVCqFg4MDFi9erFPv49qoLadPn4aXlxdEIhG2bt0K4Pd/GI9ey9N8/u///q9T30Vb9RjSTtu2bYNcLodMJsPhw4cxYcIEKJVKDBgwAAcOHGh3u4TQ0baoVCrY2NjAzc1Nu8y7774LuVwOkUiEsrKyNtsuLS0NUqkU/fr1w9tvvw13d3dIpVIEBwdrfyl3dt0AcPz4cSiVSqxbt87sbZSWlgYiQlhYWLvzmGofM7TvqNVqxMfHw8vLC7a2tnj22WeRmZlpuo3uwK1bt2BrawtfX18AgJ+fn94PBs31Ij8/P22Zo6Mjxo0bh9TU1J73SCZznQBEN14zamhooLy8PPL29qbQ0FCqq6vTTjt48CCtXr2aKioqqLy8nEaNGkV9+/bVTp8+fTr5+/vrrD8lJYUA0IYNG6i8vJwqKiroo48+oqioKCL6zzWjkydPUlVVFVVUVNDEiRNJIpFQfX29Tpy//vorERF98803ZGVlRT4+Ptr4Hj2fHR8fTzY2NrRnzx6qqqqiCxcu0PPPP0/Ozs509+5dnbrnz59PW7ZsoWnTptG///1v+uCDDwgAnT17lurr66msrIxeeeUVAkBHjx6le/fuUX19PalUKgJABQUFBrdRW9eMSkpKCABt2bJFO8/SpUu123/nzh1ydHSk4OBgUqvVnf4uHq3H2HY6efIkVVdXU2lpKY0dO5bkcjk1NTWROXTmnLoh2xIVFUWurq46yyUnJxMAunfvHhG13XbR0dEkl8vp4sWLdP/+fSoqKqKgoCBSKBR048aNLq37yJEjpFAoKCEhwajt7cw1Iz8/PxoyZEib08y5jz2u7yxatIgkEgllZ2dTZWUlLV++nKysrOj77783atse9oc//KHNa0aPqq+vJ4VCQSqVSluWn59PYrGY0tLSqKamhn766ScaPHgwvfzyy3rLL1u2jADQ+fPnjYrP3NeMLDYZAdD7DBs2jP75z3/SgwcP2l12/fr1BIBKS0uJSH9Ha2pqIgcHBwoJCdFZrqWlhVJTU4mo7QEMH3/8MQGgn376SSdOzY5CRBQbG0sA6L333iMi3R2loaGB7OzsKDIyUqfe7777jgBod/r2Bk9oklFtba227J///CcBoMLCQr31PW6Qx6NtZEgyelR4eDhJpVK6dOmSwfUYkoy60k7p6ekEgK5cZHgk8QAAIABJREFUudJuTF1h7M5q6LZ0JRk9/KONiOj7778nALRmzZourbuzjE1GdXV1JBKJaPLkyW1O76597OG+09jYSDKZTGc9DQ0NJJFI6J133jF42x5laDJasWIFPfXUUzoDOYiIVq5cqfP/cMCAAVRSUqK3/N///ncCQB9//LFR8fEAhnbY29uDiEBEaG5uxs2bN7FgwQKoVCo8++yzKCsra3M5sVgMAO0Ocbxw4QKqqqrw8ssv65T36dMH8+fPbzcezXqbm5vbnScxMRFPP/000tPTcfr0aZ1pRUVFqKurw8iRI3XKg4KCYGNjoz21YgwbGxsA0LlYaUicHbVRR7KysvDJJ59gzZo1ePrpp01aT1faSdMej9v27mSO77wjI0eOhEwm056W6ulKS0tBRJDJZAbNb6597OG+c/nyZTQ0NGDo0KHa6ba2tnBzczN7u+bm5iIrKwuff/65zkCOFStWYPv27Th58iTq6urwyy+/IDg4GKNHj9YOctDQtOVvv/1m1liNZbHJ6GHW1tbw8PDAnDlzsGnTJly+fBkbNmwAABw9ehQvvvgiXFxcIJFI9K6XPKqmpgYA4ODgYPI4pVIpdu/eDZFIhDfeeAONjY3aaZpBF3Z2dnrLOTg4oLa21uTxaBjbRo9TXl6O999/H0FBQYiNjTV5PUK2k6kJtS0SiQT37t0zy7pNTXMvX3sX9x/VHftYfX09AGDlypU610avX7+uHVBgDhkZGdi4cSPy8/O1g7UA4M6dO0hKSsJbb72Fl156CXK5HL6+vtixYwdu376N5ORknfXY2toCQLv3SQqlVySjhw0bNgwAcPHiRdy4cQPh4eFwc3PD2bNnUV1djaSkpMcu379/fwBo98iqq0aPHo2FCxeiuLhYZ0CEJvm1tUNUVVWZ7Q2LnWmjx5k/fz6qqqqwe/dunRFApqpHqHYyByG2pbm52aLaSfOP05ijZ3PvYy4uLgCAlJQU7dkZzcdcb7fesmUL9u7diy+//FL7P0qjuLgYarVar1ypVMLJyQlFRUU65U1NTQD+07Y9Ra9LRv/v//0/AMDTTz+NwsJCNDc345133oGfn5/2yQmP4+PjAycnJ3zxxRdmi3Ht2rUYNGgQzp8/ry0bOnQo7Ozs8MMPP+jMe/bsWTQ1NWHEiBFmiaUzbdSeo0ePYt++fVi1ahWeeeYZbXlcXJzJ6hGqnczB0G2xtrY22anF/Px8EBFGjRpl8nWbg+bpH8beP2TOfUwzMrWgoMComDqDiLBkyRIUFhbi0KFDbR7VaZLonTt3dMpra2tRUVGhHeKtoWlLV1dXM0XdORadjBobG9Ha2goiwu3bt7F7926sXLkSzs7OWLBgAby8vAD8/giR+/fvo7i4WO+8sJOTE27fvo1r166htrYWVlZWWL58OU6dOgWVSoVbt26htbUVtbW1uHjxokni1pxKePjIQSqVIjY2Frm5udi7dy9qampQWFiIefPmwd3dHdHR0Sap+1GGtJEhampq8PbbbyMwMBBLly4F8PtpgB9++AEFBQWd+i7a+icpVDuZg6HbEhAQgIqKChw6dAjNzc24d++ezr0jQPtt19raisrKSrS0tODChQuIiYmBl5cXXn/99S6tOy8vr1uGdstkMvj5+eHmzZtGLWfOfUwqlWLOnDk4cOAAtm3bhpqaGqjVaty8eVObECIjI+Hq6trlRxBdvHgRH374IXbs2AGxWKx328SmTZvg6+uLkJAQ7NixA6dOnUJjYyNKSkq02zN37lyddWraUnMWqccw19AImGE0XW5ubrsj6SQSCQ0cOJDeeecd7bBVIqIlS5aQk5MTOTg4UEREBG3dupUAkL+/P924cYPOnTtH3t7eZGtrSy+88IJ2eOfWrVtp2LBhJJVKSSqV0nPPPUfp6emUlJREtra2BIAGDhxIV69epb1795Kjo6N2BMvatWu1cTo7O2tH9jwqLi5OZ9hpa2srJScn08CBA0ksFpOjoyOFh4fT5cuXiYh06vb09KQ9e/YQ0e+P+ZDJZASAfHx86Ouvv6aNGzeSvb09ASBXV1fat28fZWRkkKurKwEgR0dHOnDgQIdtFBMTo11GLpfTtGnTaMuWLeTm5kYASCaTUVhYGG3atKnN7wUATZw4sVPfxcqVK/XqMaSd0tPTte2h+Y62b99OSqWSAJC3tzf9/PPPpuyaRNS50UYdbQsRUXl5OYWEhJBUKiVfX196//33KS4ujgBQQEBAu/04OjqaxGIxeXh4kLW1NSmVSpo6dSpdvXq1y+s+duwYKRQKSkxMNGp7OzO0W6VSkVgspoaGBm3Zw/8LTLmPGdp3Hjx4QEuWLCEvLy+ytrYmFxcXmj59OhUVFRHR76NJAVB8fPxjt+3MmTM0ZswYcnd31+4vbm5uFBwcTF999RUVFha2u18BoOTkZCIiKisro5iYGAoICCCJREJ2dnY0ZswY+uSTT/TqnDRpEnl4eFBra6tR3wMP7WbMQvS0Z9NFR0eTk5OT0GHo6EwyKi4uJmtra+2PL0ugVqtp7NixtGvXLqFD0VFWVkZSqZQ2bdpk9LI8tJsx1mk9+inNBgoICEBCQgISEhL0nlTdE6nVahw6dAi1tbWIjIwUOhwdq1evRmBgIFQqldCh6OFkxBjr8ZYtW4aIiAhERkb2+Ieh5ufnIycnB3l5eQbfH9UdNm/ejIKCAhw7dkx7j19PwsmIsV5o+fLl2L17N6qrq+Hr64vs7GyhQ+qydevWQaVSae8h7KnGjx+Pffv26TzzT2iHDx/GgwcPkJ+fD0dHR6HDaZO10AEwxkxv/fr1WL9+vdBhmFxoaChCQ0OFDsPiTJkyBVOmTBE6jMfiIyPGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcGZdQBDSkoKDh48aM4qGOsxvv32WwBARESEwJH0XJpH0XAbWZ5vv/1W+0xDcxARmefds9zZ2JPgt99+w08//YTx48cLHQpjZqd5IroZHDRbMmLsSZCVlYVZs2aBdyPGuuQgXzNijDEmOE5GjDHGBMfJiDHGmOA4GTHGGBMcJyPGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcFxMmKMMSY4TkaMMcYEx8mIMcaY4DgZMcYYExwnI8YYY4LjZMQYY0xwnIwYY4wJjpMRY4wxwXEyYowxJjhORowxxgTHyYgxxpjgOBkxxhgTHCcjxhhjguNkxBhjTHCcjBhjjAmOkxFjjDHBcTJijDEmOE5GjDHGBMfJiDHGmOA4GTHGGBMcJyPGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcFxMmKMMSY4a6EDYMxS3L59G3/605/Q3NysLWtoaIC9vT2GDRumM+9zzz2Hjz/+uLtDZMxicTJizED9+/dHU1MTioqK9KZVV1fr/B0ZGdldYTHWK/BpOsaM8Je//AXW1o//DScSifDnP/+5myJirHfgZMSYEV599VWo1ep2p4tEIowYMQK+vr7dGBVjlo+TEWNG8PT0xKhRo2Bl1fau06dPH/zlL3/p5qgYs3ycjBgz0uzZsyESidqc1traipkzZ3ZzRIxZPk5GjBkpIiKizfI+ffrgxRdfhKurazdHxJjl42TEmJGcnZ0xfvx49OnTR2/a7NmzBYiIMcvHyYixTnjttddARDplVlZWCA8PFygixiwbJyPGOmHq1KkQi8Xav62trTFp0iTY29sLGBVjlouTEWOdoFAoMHnyZG1CUqvVeO211wSOijHLxcmIsU6KiopCS0sLAMDW1hYTJ04UOCLGLBcnI8Y6acKECZDL5QCAGTNmwNbWVuCIGLNcT+Sz6c6cOYOSkhKhw2C9QFBQEP71r3/B09MTWVlZQofDeoHg4GAMGDBA6DC6nYgeHRL0BIiIiEB2drbQYTDGmJ7MzMwn8cbpg0/saboZM2aAiPhjYR/g951V6Dg0H7VajfXr1wsex8OfGTNmcP+20M+T7IlNRoyZgpWVFeLi4oQOgzGLx8mIsS7q6JUSjLGOcTJijDEmOE5GjDHGBMfJiDHGmOA4GTHGGBMcJyMBHTt2DPb29vjss896VV2P09raipSUFAQHBwsWQ09pi57qxIkTWLZsGXJycuDn5weRSASRSNTm6zFCQ0OhUCjQp08fPPPMMzh37pwAERvmcX0vISEBQ4YMgVKphEQiQUBAABYvXoy6ujqd+fbv34+goCAoFAp4e3tjzpw5uHv3LgDg008/RVJS0mNfS8/ax8lIQN15X0FPuIehuLgY//Vf/4WFCxeioaFBsDh6Qlv0VB988AHS0tKwfPlyTJ8+Hb/88gv8/f3Rt29f7N27F0ePHtWZ/4svvsDBgwcxefJkFBUV4fnnnxco8sfrqO99+eWXeO+993Dt2jWUlZVh/fr1SE1N1XmRYmZmJqKiohAREYGbN2/i8OHDOHXqFCZMmICWlhaEhYVBKpVi/PjxqKqq6s7N6x3oCTRjxgyaMWNGt9bZ0NBAo0eP7nV1GaqgoICmTZtGe/fupcDAQBo+fHin1gOAMjMzTRxd9zL399PZ/r1hwwZ66qmnqLGxUafc39+f9u3bR1ZWVuTh4UFVVVU60/Py8mjKlClditmcDOl7kyZNopaWFp2ymTNnEgC6ceMGERGFhIRQ//79qbW1VTvP1q1bCQCdPn1aW6ZSqWj06NHU3NxsdKy9oX93UhYfGXWTXbt2obS0tNfVZajhw4cjJycHUVFRkEgkQocjqJ74/Vy5cgWrVq3CmjVrIJVK9aYHBwcjJiYGt27dwqJFiwSIsPMM6XtHjhzRe3Ovs7MzAGiPpEpKSuDu7g6RSKSdx9PTEwBw/fp1bdnq1atRUFCA1NRUk25Hb8fJyEBff/01hgwZAnt7e0ilUgwbNgyff/65zjx79uzByJEjIZVKIZfL4ePjg7Vr1yImJgaxsbG4evUqRCIRAgICcPr0aXh5eUEkEmHr1q0AgMGDB0MkEsHKygojRozQ7gSLFy/W1vuPf/zjsbEYWhfw++mqzZs3Y/DgwZBIJHB0dMTUqVNx6dIlAMC2bdsgl8shk8lw+PBhTJgwAUqlEgMGDMCBAwe6o9lNrq22MGQ709LSIJVK0a9fP7z99ttwd3eHVCpFcHAwzp49CwBQqVSwsbGBm5ubtr53330XcrkcIpEIZWVlbX4/AHD8+HEolUqsW7eum1sE2u0jIoSFhbU7T2JiIp566ins3LkTJ06caHc+U/UrtVqN+Ph4eHl5wdbWFs8++ywyMzNNt9EduHXrFmxtbeHr6wsA8PPz0/sRoble5Ofnpy1zdHTEuHHjkJqayqeEjSHskZkwOnMa4+DBg7R69WqqqKig8vJyGjVqFPXt21c7PSUlhQDQhg0bqLy8nCoqKuijjz6iqKgoIiKaPn06+fv766yzpKSEANCWLVuIiKilpYV8fHzIy8tL75TBggULKCUlxaBYDKmLiCg+Pp5sbGxoz549VFVVRRcuXKDnn3+enJ2d6e7du0REtGLFCgJAJ0+epOrqaiotLaWxY8eSXC6npqYmo9pQ4w9/+IOgp+naagtDtjM6OprkcjldvHiR7t+/T0VFRRQUFEQKhUJ7KicqKopcXV116ktOTiYAdO/ePSJq+/s5cuQIKRQKSkhI6NK2EXWuf/v5+dGQIUPanObv70+//vorERF98803ZGVlRT4+PlRXV0dE+qfpTNWvFi1aRBKJhLKzs6myspKWL19OVlZW9P333xvbJFqG9r36+npSKBSkUqm0Zfn5+SQWiyktLY1qamrop59+osGDB9PLL7+st/yyZcsIAJ0/f96o+EzRvy0Un6Yz1IwZM/DBBx/A0dERTk5OCAsLQ3l5Oe7du4fm5masWbMGISEhWLp0KZycnODo6Ii5c+ciKCjI4Dr69OmD+fPn48aNG8jNzdWWNzQ0ICcnB2+88UaHsRiqsbERmzdvxrRp0/Daa6/B3t4ew4YNw9/+9jeUlZVh+/btOvMHBwdDqVTCxcUFkZGRqK+vx40bNwyuz1J0tJ3W1tbaX/xDhgzBtm3bUFtbi927d3ep3kmTJqGmpgarVq3q6iYYrb6+Hr/++iv8/f07nHf06NFYsGABrl27hqVLl+pNN1W/un//PrZt24bw8HBMnz4dDg4OWLlyJcRicZfb2hDr16+Hu7s7EhMTtWXjxo3DkiVLoFKpoFQqMXToUNTW1mLnzp16yw8cOBAAUFhYaPZYewtORp308OumL1y4gKqqKrz88ss682iSizHefPNN2Nvb65xv3rt3L6ZOnQqlUtlhLIYqKipCXV0dRo4cqVMeFBQEGxsb7amnttjY2AAAmpubDa7PEhmynSNHjoRMJtOegrJEpaWlICLIZDKD5k9MTMTTTz+N9PR0nD59WmeaqfrV5cuX0dDQgKFDh2qn29raws3NzextnZubi6ysLHz++edQKBTa8hUrVmD79u04efIk6urq8MsvvyA4OBijR4/Wez+api1/++03s8bam3AyMtDRo0fx4osvwsXFBRKJBIsXL9ZOq6mpAQA4ODh0uR47Ozu89dZb+Oabb/Ddd98BAP76179CpVIZFIuhNENP7ezs9KY5ODigtra2k1vw5JFIJEYdlfY09+/fBwCDB5ZIpVLs3r0bIpEIb7zxBhobG7XTTNWv6uvrAQArV67U3uckEolw/fp1s94WkJGRgY0bNyI/Px8+Pj7a8jt37iApKQlvvfUWXnrpJcjlcvj6+mLHjh24ffs2kpOTddajeeuvpm1ZxzgZGeDGjRsIDw+Hm5sbzp49i+rqaiQlJWmn9+/fHwBQVlZmkvpUKhXEYjFSUlJw6tQpeHp6ak+hdBSLoTSJs61/DlVVVU/kmyY7o7m52eLbS/OP05gj69GjR2PhwoUoLi7G2rVrteWm6lcuLi4AgJSUFL13/pw5c8bgOI2xZcsW7N27F19++aV2n9YoLi6GWq3WK1cqlXByckJRUZFOeVNTEwDwq+iNwMnIAIWFhWhubsY777wDPz8/SKVSneGdPj4+cHJywhdffGGS+gYMGICZM2ciOzsbq1atQkxMjMGxGGro0KGws7PDDz/8oFN+9uxZNDU1YcSIEV3ejidBfn4+iAijRo0C8Ps1JUs7fdmvXz+IRCJUV1cbtdzatWsxaNAgnD9/Xltmqn7l6ekJqVSKgoICo2LqDCLCkiVLUFhYiEOHDrV5VKdJonfu3NEpr62tRUVFhXaIt4amLV1dXc0Ude/DycgAXl5eAH5/TMr9+/dRXFysc+5bIpFg+fLlOHXqFFQqFW7duoXW1lbU1tbi4sWLAAAnJyfcvn0b165dQ21tbYf/sGJjY9HS0oLKykq89NJLBsdiaF1SqRSxsbHIzc3F3r17UVNTg8LCQsybNw/u7u6Ijo7uXGP1cq2traisrERLSwsuXLiAmJgYeHl54fXXXwcABAQEoKKiAocOHUJzczPu3buncw8K0Pb3k5eXJ9jQbplMBj8/P9y8edOo5TSn6x6+P8dU/UoqlWLOnDk4cOAAtm3bhpqaGqjVaty8eVObECIjI+Hq6trlRxBdvHgRH374IXbs2AGxWKxzWlAkEmHTpk3w9fVFSEgIduzYgVOnTqGxsRElJSXa7Zk7d67OOjVtOWzYsC7F9kQRcCifYDoz9HXJkiXk5OREDg4OFBERob3z2t/fXzusd+vWrTRs2DCSSqUklUrpueeeo/T0dCIiOnfuHHl7e5OtrS298MILtHLlSnJzcyMAJJPJKCwsTK/OkJAQ2rlzp9GxGFpXa2srJScn08CBA0ksFpOjoyOFh4fT5cuXiYgoPT2dZDIZAaCBAwfS1atXafv27aRUKgkAeXt7088//2xQ+505c4bGjBlD7u7uBIAAkJubGwUHB9NXX31l8PeALg593bJli15bGLqd0dHRJBaLycPDg6ytrUmpVNLUqVPp6tWr2vWXl5dTSEgISaVS8vX1pffff5/i4uIIAAUEBLT5/dy9e5eOHTtGCoWCEhMTO71tGp3p3yqVisRiMTU0NGjLcnNzyd/fnwCQs7Mzvffee20uGxcXpzO021T96sGDB7RkyRLy8vIia2trcnFxoenTp1NRUREREYWHhxMAio+Pf+y2ddT3CgsLteVtfZKTk4mIqKysjGJiYiggIIAkEgnZ2dnRmDFj6JNPPtGrc9KkSeTh4aHztAZDdLV/W7AsTkbMogi5s0ZHR5OTk5MgdRujM/27uLiYrK2tac+ePWaKyvTUajWNHTuWdu3aJXQoOsrKykgqldKmTZuMXvZJTkZ8mo4xI/TWJzIHBAQgISEBCQkJek+q7onUajUOHTqE2tpaREZGCh2OjtWrVyMwMFBnBCzrGCcj1mmXLl3SO7/e1qen/bNgbVu2bBkiIiIQGRlp9GCG7pafn4+cnBzk5eUZfH9Ud9i8eTMKCgpw7Ngx7f1/zDCcjFinDRo0SG/YbVufjIwMoUPtsuXLl2P37t2orq6Gr68vsrOzhQ7JLNatWweVSoUNGzYIHcpjjR8/Hvv27dN5DqDQDh8+jAcPHiA/Px+Ojo5Ch2NxrIUOgDFLsH79eqxfv17oMLpFaGgoQkNDhQ7D4kyZMgVTpkwROgyLxUdGjDHGBMfJiDHGmOA4GTHGGBMcJyPGGGOCe2IHMHz77beIiIgQOgzWCSkpKTh48KDQYfRY3377LQBw/2YWhY+MGGOMCe6JPTIaNWoU/7q2QCKRCAsWLMDMmTOFDqXH0hwRcf+2PJ15An9vwUdGjDHGBMfJiDHGmOA4GTHGGBMcJyPGGGOC42TEGGNMcJyMTGz//v0QiUQIDg426XqPHTsGe3t7fPbZZyZdr9B1sZ7nxIkTWLZsGXJycuDn56d9Fcjs2bP15g0NDYVCoUCfPn3wzDPPdPkV4ObU2tqKlJSUNvfNhIQEDBkyBEqlEhKJBAEBAVi8eLHeu53279+PoKAgKBQKeHt7Y86cObh79y4A4NNPP0VSUlKvfeeVuXEyMrH9+/fD398fZ86cwZUrV0y2XiIy2bp6Ul2sZ/nggw+QlpaG5cuXY/r06fjll1/g7++Pvn37Yu/evTh69KjO/F988QUOHjyIyZMno6ioCM8//7xAkT9ecXEx/uu//gsLFy5EQ0OD3vQvv/wS7733Hq5du4aysjKsX78eqampOjcOZ2ZmIioqChEREbh58yYOHz6MU6dOYcKECWhpaUFYWBikUinGjx+Pqqqq7ty8XoGTkQmVl5fj4sWLWLNmDQDg448/7tR6Ghsb9X69TZo0CdXV1Zg8eXKX4xSqLkvWVjtZwrqNsXHjRmRkZCArKwsKhUJnWlpaGqysrBAdHd3jX7z3qB9//BFLly7FvHnzEBgY2OY8dnZ2iI6OhpOTExQKBWbOnInw8HAcP34cJSUlAICPPvoI/fv3R1xcHOzt7REYGIiFCxeioKAAZ8+eBQDMnz8fw4cPx8SJE9HS0tJt29gbcDIyoaysLEyaNEn7C2nPnj2dOsrYtWsXSktLzRChsHVZMnO2U0/4Dq5cuYJVq1ZhzZo1kEqletODg4MRExODW7duYdGiRQJE2HnDhw9HTk4OoqKiIJFI2pznyJEj6NOnj06Zs7MzAGiPpEpKSuDu7q5zY6qnpycA4Pr169qy1atXo6CgAKmpqSbdjt6Ok5EJ7d+/H9OmTYNCoUBoaCiuXbuGr7/+us159+zZg5EjR0IqlUIul8PHxwdr165FTEwMYmNjcfXqVYhEIgQEBOD06dPw8vKCSCTC1q1bAQCDBw+GSCSClZUVRowYod1hFi9eDHt7e0ilUvzjH//A119/jSFDhmjLhg0bhs8//xwADK4L+P3U3ebNmzF48GBIJBI4Ojpi6tSpuHTpEgBg27ZtkMvlkMlkOHz4MCZMmAClUokBAwbgwIED5mz2x+oobpVKBRsbG503hr777ruQy+UQiUQoKytrs53S0tIglUrRr18/vP3223B3d4dUKkVwcLD2V3Jn1w0Ax48fh1KpxLp167qlndLS0kBECAsLa3eexMREPPXUU9i5cydOnDjR7nym6itqtRrx8fHw8vKCra0tnn32WWRmZppuoztw69Yt2NrawtfXFwDg5+en96NBc73Iz89PW+bo6Ihx48YhNTWVT3kbg55AM2bMoBkzZph0ndevXycXFxdqaWkhIqI9e/YQAJo7d67evCkpKQSANmzYQOXl5VRRUUEfffQRRUVFERHR9OnTyd/fX2eZkpISAkBbtmwhIqKWlhby8fEhLy8vbZ0aCxYsoJSUFCIiOnjwIK1evZoqKiqovLycRo0aRX379tXOa0hdRETx8fFkY2NDe/bsoaqqKrpw4QI9//zz5OzsTHfv3iUiohUrVhAAOnnyJFVXV1NpaSmNHTuW5HI5NTU1dapdHwWAMjMzDZ7fkLijoqLI1dVVZ7nk5GQCQPfu3SOittspOjqa5HI5Xbx4ke7fv09FRUUUFBRECoWCbty40aV1HzlyhBQKBSUkJBi8rRqd6d9+fn40ZMiQNqf5+/vTr7/+SkRE33zzDVlZWZGPjw/V1dUREVFeXh5NmTJFO7+p+sqiRYtIIpFQdnY2VVZW0vLly8nKyoq+//57Y5tE6w9/+AMNHz68w/nq6+tJoVCQSqXSluXn55NYLKa0tDSqqamhn376iQYPHkwvv/yy3vLLli0jAHT+/Hmj4jO2f/ciWXxkZCL79+/Hn/70J+2hflhYGCQSCQ4ePIjGxkbtfM3NzVizZg1CQkKwdOlSODk5wdHREXPnzkVQUJDB9fXp0wfz58/HjRs3kJubqy1vaGhATk4O3njjDQDAjBkz8MEHH8DR0RFOTk4ICwtDeXk57t27Z3BdjY2N2Lx5M6ZNm4bXXnsN9vb2GDZsGP72t7+hrKwM27dv15k/ODgYSqUSLi4uiIyMRH19PW7cuGFwfaZibNydYW1trT0CGDJkCLZt24ba2lrs3r27S+udNGkSampqsGrVqi7H2JH6+nr8+uuv8Pf373De0aOAaoGYAAAgAElEQVRHY8GCBbh27RqWLl2qN91UfeX+/fvYtm0bwsPDMX36dDg4OGDlypUQi8VdbltDrF+/Hu7u7khMTNSWjRs3DkuWLIFKpYJSqcTQoUNRW1uLnTt36i0/cOBAAEBhYaHZY+0tOBmZiOYUnYZSqURoaChqampw+PBhbfmFCxdQVVWFl19+WWd5TXIxxptvvgl7e3udc9N79+7F1KlToVQq21xGLBYDgFHDT4uKilBXV4eRI0fqlAcFBcHGxkZ7WqotNjY2AH5Pwt2tK3F31siRIyGTybSnpCxBaWkpiAgymcyg+RMTE/H0008jPT0dp0+f1plmqr5y+fJlNDQ0YOjQodrptra2cHNzM3vb5ubmIisrC59//rnOQI4VK1Zg+/btOHnyJOrq6vDLL78gODgYo0eP1g5y0NC05W+//WbWWHsTTkYm8NNPP6GwsBCTJ0/W3pMhEom09+k8PKqupqYGAODg4NDleu3s7PDWW2/hm2++wXfffQcA+Otf/wqVSqWd5+jRo3jxxRfh4uICiUSCxYsXG12PZpiqnZ2d3jQHBwfU1tZ2cgvMS6i4JRKJUUeeQrt//z4AtHtx/1FSqRS7d++GSCTCG2+8oXPkb6o2r6+vBwCsXLlSZ5+6fv16m0OzTSUjIwMbN25Efn4+fHx8tOV37txBUlIS3nrrLbz00kuQy+Xw9fXFjh07cPv2bSQnJ+usx9bWFsB/2pZ1jJORCezbtw+vvvoqiEjnU1FRAVtbW3zxxRfaC539+/cHAJSVlZmkbpVKBbFYjJSUFJw6dQqenp7a0y03btxAeHg43NzccPbsWVRXVyMpKcnoOjSJs61/JFVVVRgwYEDXNsJMhIi7ubm5R7dJWzT/OI05Wh49ejQWLlyI4uJirF27VltuqjZ3cXEB8PuLFB/dr86cOWNwnMbYsmUL9u7diy+//FK7n2oUFxdDrVbrlSuVSjg5OaGoqEinvKmpCcB/2pZ1jJNRFxERMjIy8O677+pNc3R0REREBNRqNfbv3w8A8PHxgZOTE7744guT1D9gwADMnDkT2dnZWLVqFWJiYrTTCgsL0dzcjHfeeQd+fn6QSqWdel/K0KFDYWdnhx9++EGn/OzZs2hqasKIESO6vB3mYGjc1tbWJjuNmJ+fDyLCqFGjTL5uc+nXrx9EIpHR9w+tXbsWgwYNwvnz57Vlpuornp6ekEqlKCgoMCqmziAiLFmyBIWFhTh06FCbR3WaJHrnzh2d8traWlRUVGiHeGto2tLV1dVMUfc+nIy66JtvvoFSqcSYMWPanD5v3jwA/zlVJ5FIsHz5cpw6dQoqlQq3bt1Ca2sramtrcfHiRQCAk5MTbt++jWvXrqG2trbDf2axsbFoaWlBZWUlXnrpJW25l5cXgN8f73L//n0UFxfrnbM3pC6pVIrY2Fjk5uZi7969qKmpQWFhIebNmwd3d3dER0cb2Frdy9C4AwICUFFRgUOHDqG5uRn37t3TuW8EaL+dWltbUVlZiZaWFly4cAExMTHw8vLC66+/3qV15+XlddvQbplMBj8/P9y8edOo5TSn6x6+P8dUfUUqlWLOnDk4cOAAtm3bhpqaGqjVaty8eVObECIjI+Hq6trlRxBdvHgRH374IXbs2AGxWKxzWlAkEmHTpk3w9fVFSEgIduzYgVOnTqGxsRElJSXa7Zk7d67OOjVtOWzYsC7F9kQRZhSfsEw1tHvu3Lkkl8vJ2tqahg8fTufOndOZvnbtWnJ3dycABIA8PDwoPT2diIi2bt1Kw4YNI6lUSlKplJ577jnttHPnzpG3tzfZ2trSCy+8QCtXriQ3NzcCQDKZjMLCwvRiCQkJoZ07d+qVL1myhJycnMjBwYEiIiJo69atBID8/f3pxo0bBtfV2tpKycnJNHDgQBKLxeTo6Ejh4eF0+fJlIiJKT08nmUxGAGjgwIF09epV2r59OymVSgJA3t7e9PPPP3e5zWHk0NeO4iYiKi8vp5CQEJJKpeTr60vvv/8+xcXFEQAKCAhos53u3r1L0dHRJBaLycPDg6ytrUmpVNLUqVPp6tWrXV73sWPHSKFQUGJiotFt1Jn+rVKpSCwWU0NDg7YsNzeX/P39CQA5OzvTe++91+aycXFxOkO7TdVXHjx4QEuWLCEvLy+ytrYmFxcXmj59OhUVFRERUXh4OAGg+Pj4x27bmTNnaMyYMTr7opubGwUHB9NXX31FhYWF2vK2PsnJyUREVFZWRjExMRQQEEASiYTs7OxozJgx9Mknn+jVOWnSJPLw8KDW1lajvgdj+3cvksXJiFmUnrSzRkdHk5OTk9Bh6OlM/y4uLiZra2vas2ePmaIyPbVaTWPHjqVdu3YJHYqOsrIykkqltGnTJqOX7Un9u5vxfUaMdUVveUJzQEAAEhISkJCQoPek6p5IrVbj0KFDqK2tRWRkpNDh6Fi9ejUCAwN1RrWyjnEyYowBAJYtW4aIiAhERkb2+Ieh5ufnIycnB3l5eQbfH9UdNm/ejIKCAhw7dkx7Tx8zDCcjxjph+fLl2L17N6qrq+Hr64vs7GyhQzKJdevWQaVSYcOGDUKH8ljjx4/Hvn37dJ77J7TDhw/jwYMHyM/Ph6Ojo9DhWBxroQNgzBKtX78e69evFzoMswgNDUVoaKjQYVicKVOmYMqUKUKHYbH4yIgxxpjgOBkxxhgTHCcjxhhjguNkxBhjTHCcjBhjjAnuiR1Nl52d3amHhjLhzZo1C7NmzRI6jB6P+zezJCKiJ+8l7WfOnNF7GRZjnXHmzBmkpqYiMzNT6FBYLxEcHGxRryAxkYNPZDJizFSysrIwa9Ys8G7EWJcc5GtGjDHGBMfJiDHGmOA4GTHGGBMcJyPGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcFxMmKMMSY4TkaMMcYEx8mIMcaY4DgZMcYYExwnI8YYY4LjZMQYY0xwnIwYY4wJjpMRY4wxwXEyYowxJjhORowxxgTHyYgxxpjgOBkxxhgTHCcjxhhjguNkxBhjTHCcjBhjjAmOkxFjjDHBcTJijDEmOE5GjDHGBMfJiDHGmOA4GTHGGBMcJyPGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcFxMmKMMSY4a6EDYMxS3L9/H7dv39Yp++233wAAv/zyi055nz594O3t3W2xMWbpREREQgfBmCWorKyEq6srmpubO5x34sSJOHr0aDdExVivcJBP0zFmIEdHR4SGhsLKquPdJjIyshsiYqz34GTEmBFee+01dHQyQSKRIDw8vJsiYqx34GTEmBHCwsIglUrbnW5tbY2wsDDY2dl1Y1SMWT5ORowZQSaTITw8HGKxuM3parUaUVFR3RwVY5aPkxFjRvrzn//c7iAGuVyOV155pZsjYszycTJizEihoaGwt7fXKxeLxZg1axYkEokAUTFm2TgZMWYksViMyMhI2NjY6JQ3Nzfjz3/+s0BRMWbZOBkx1gmvvvoqmpqadMqcnZ0xbtw4gSJizLJxMmKsE8aOHQtXV1ft32KxGLNnz0afPn0EjIoxy8XJiLFOsLKywuzZs7Wn6pqbm/Hqq68KHBVjlouTEWOdFBkZqT1V5+npiZEjRwocEWOWi5MRY500YsQIBAQEAABef/11iEQigSNizHLxU7sfcebMGWzevFnoMJiF0JymO3v2LCIiIgSOhlmKgwcPCh1Cj8NHRo8oKSlBdna20GE8sW7evGlR7e/l5QUHBwcolcpurTc7Oxs3b97s1jpZ11la/+5OfGTUDv7lIoysrCzMmjXLotr/xIkT+OMf/9itdYpEIixYsAAzZ87s1npZ12j6N9PHR0aMdVF3JyLGeiNORowxxgTHyYgxxpjgOBkxxhgTHCcjxhhjguNkZAZvvvkmFAoFRCIRCgoKhA6nWyQkJGDIkCFQKpWQSCQICAjA4sWLUVdXJ0g8x44dg729PT777DNB6u/JTpw4gWXLliEnJwd+fn4QiUQQiUSYPXu23ryhoaFQKBTo06cPnnnmGZw7d06AiA3T2tqKlJQUBAcH600ztH/u378fQUFBUCgU8Pb2xpw5c3D37l0AwKeffoqkpCSo1epu2Z4nDjEdmZmZZIpmOXDgAAGg8+fPmyCqnm/cuHGUnp5O5eXlVFNTQ5mZmSQWi+mVV14xaj2mav8jR46QUqmkTz/9tMvr6okAUGZmptHLxcfH0+TJk6mmpkZb5u/vT3379iUAdOTIEb1l8vLyaMqUKV2K19x+/vlnGjNmDAGg4cOH6003pH9mZGQQAEpKSqKqqio6f/48+fn5UWBgIDU3NxMRUWpqKo0bN44qKys7Faep+ncvlMVHRkxHY2Njm78sO2JnZ4fo6Gg4OTlBoVBg5syZCA8Px/Hjx1FSUmKGSB9v0qRJqK6uxuTJk7u9bqDz7WhOGzduREZGBrKysqBQKHSmpaWlwcrKCtHR0aiurhYows758ccfsXTpUsybNw+BgYFtzmNI//zoo4/Qv39/xMXFwd7eHoGBgVi4cCEKCgpw9uxZAMD8+fMxfPhwTJw4ES0tLd22jU8CTkZmYqnPKdu1axdKS0uNXu7IkSN6r09wdnYGADQ0NJgkNkvS2XY0lytXrmDVqlVYs2YNpFKp3vTg4GDExMTg1q1bWLRokQARdt7w4cORk5ODqKiodt+ya0j/LCkpgbu7u86+6+npCQC4fv26tmz16tUoKChAamqqSbfjScfJyASICMnJyXj66achkUhgb2+PuLg47fQPP/wQMpkMCoUCpaWliI2NhYeHBy5fvgwiwubNmzF48GBIJBI4Ojpi6tSpuHTpEoDff7FKpVL069cPb7/9Ntzd3SGVShEcHKz9taaJ4XHrUalUsLGxgZubm3aZd999F3K5HCKRCGVlZYiJiUFsbCyuXr0KkUikfQhoZ926dQu2trbw9fXt0nqMdfr0aXh5eUEkEmHr1q0AgG3btkEul0Mmk+Hw4cOYMGEClEolBgwYgAMHDgAwrK270o7Hjx+HUqnEunXrurU9NNtGRAgLC2t3nsTERDz11FPYuXMnTpw40e58HfU1Q9oaANRqNeLj4+Hl5QVbW1s8++yzyMzMNN1Gd+DR/unn56f3A0JzvcjPz09b5ujoiHHjxiE1NRVE1G3x9npCniTsiTpzTnfFihUkEonof/7nf6iyspIaGhooPT1d55rRihUrCADNnz+ftmzZQtOmTaN///vfFB8fTzY2NrRnzx6qqqqiCxcu0PPPP0/Ozs509+5dIiKKjo4muVxOFy9epPv371NRUREFBQWRQqGgGzduEBEZtJ6oqChydXXViT05OZkA0L1794iIaPr06eTv79+lNiQiqq+vJ4VCQSqVyqjlTHVOvaSkhADQli1btGWa7+DkyZNUXV1NpaWlNHbsWJLL5dTU1EREhrV1Z9vxyJEjpFAoKCEhocvbByOvGfn5+dGQIUPanObv70+//vorERF98803ZGVlRT4+PlRXV0dE+teMDOlrhrT1okWLSCKRUHZ2NlVWVtLy5cvJysqKvv/++840CRER/eEPf2jzmtGj2uqf+fn5JBaLKS0tjWpqauinn36iwYMH08svv6y3/LJlyzp1TZivGbUri1vlEcZ2loaGBpLJZPTf//3fOuWPDmDQ7JyNjY06y9rZ2VFkZKTOst999x0B0P7Tio6OJnt7e515vv/+ewJAa9asMXg93ZmMVqxYQU899ZTOhXJDdEcyevg70PxouHLlChF13NZE3duO7TEmGdXV1ZFIJKLJkye3Of3hZEREFBsbSwDovffeIyLdZGRoX+uorRsbG0kmk+msp6GhgSQSCb3zzjsGbVdbDE1G7fXPlStXEgDtZ8CAAVRSUqK3/N///ncCQB9//LFR8XEyahcPYOiqK1euoKGhAePHjzd62aKiItTV1em9lC0oKAg2NjY6p+EeNXLkSMhkMly6dKlL6zGH3NxcZGVl4fPPP9e7UN7TPPym1vY83NaWqLS0FEQEmUxm0PyJiYl4+umnkZ6ejtOnT+tM60pfe7itL1++jIaGBgwdOlQ73dbWFm5ubmZv5/b654oVK7B9+3acPHkSdXV1+OWXXxAcHIzRo0frDcLRtOVvv/1m1lifJJyMukjzGH8XFxejl62qqgLw+0ifRzk4OKC2tvaxy0skEty7d6/L6zGljIwMbNy4Efn5+fDx8em2es1N09aW6P79+wDQ7sX9R0mlUuzevRsikQhvvPEGGhsbtdNM1dfq6+sBACtXrtTe5yQSiXD9+nWzDnhpr3/euXMHSUlJeOutt/DSSy9BLpfD19cXO3bswO3bt5GcnKyzHltbWwD/aVvWdZyMukgzMunBgwdGL+vg4AAAbe7AVVVVGDBgQLvLNjc3a+fpynpMacuWLdi7dy++/PJL9O/fv1vq7A4Pt7Ul0vzjNOZmzdGjR2PhwoUoLi7G2rVrteWm6muaH28pKSkgIp3PmTNnDI7TGI/rn8XFxVCr1XrlSqUSTk5OKCoq0inXvG5e07as6zgZddHQoUNhZWWFr776qlPL2tnZ4YcfftApP3v2LJqamjBixIh2l83PzwcRYdSoUQavx9ra+rGnozqLiLBkyRIUFhbi0KFDbf5qtmQPtzVgvnY0l379+kEkEhl9/9DatWsxaNAgnD9/XlvWlT77ME9PT0il0m55Qokh/VOTRO/cuaNTXltbi4qKCu0Qbw1NW7q6upop6icPJ6MucnFxwfTp05GdnY1du3ahpqYGFy5cwPbt2ztcViqVIjY2Frm5udi7dy9qampQWFiIefPmwd3dHdHR0dp5W1tbUVlZiZaWFly4cAExMTHw8vLC66+/bvB6AgICUFFRgUOHDqG5uRn37t3TuX8CAJycnHD79m1cu3YNtbW1Bv3TvXjxIj788EPs2LEDYrFY57SLSCTCpk2bjGxVYT2urYHOt2NeXp4gQ7tlMhn8/PyMfjOs5nTdw/fnGNNnO1r3nDlzcODAAWzbtg01NTVQq9W4efOmNiFERkbC1dW1y48gMqR/+vr6IiQkBDt27MCpU6fQ2NiIkpIS7fbMnTtXZ52athw2bFiXYmMPEWzsRA/VmdEutbW19Oabb1Lfvn3Jzs6OXnjhBYqPj9eOxomKiiJbW1sCQJ6enrRnzx7tsq2trZScnEwDBw4ksVhMjo6OFB4eTpcvX9bOEx0dTWKxmDw8PMja2pqUSiVNnTqVrl69atR6ysvLKSQkhKRSKfn6+tL7779PcXFxBIACAgLoxo0bdO7cOfL29iZbW1t64YUXtEN1H6ewsFBnBNKjn+TkZIPb0hSjjbZs2UJubm4EgGQyGYWFhVF6ejrJZDICQAMHDqSrV6/S9u3bSalUEgDy9vamn3/+2aC27mw7Hjt2jBQKBSUmJnZp+4iMH9qtUqlILBZTQ0ODtiw3N5f8/f0JADk7O2tHzz0qLi5OZ2h3R33N0LZ+8OABLVmyhLy8vMja2ppcXFxo+vTpVFRURERE4eHhBIDi4+Mfu21nzpyhMWPGkLu7u7bPubm5UXBwMH311VcG98+ysjKKiYmhgIAAkkgkZGdnR2PGjKFPPvlEr85JkyaRh4cHtba2GvwdEPFousfgod2P6omdJTo6mpycnIQOo1sI3f6W0tbGJqPi4mKytrbW+SHU06nVaho7dizt2rVL6FB0lJWVkVQqpU2bNhm9rND9uwfjod2Wgp8U3H16Y1sHBAQgISEBCQkJgj1J3RhqtRqHDh1CbW0tIiMjhQ5Hx+rVqxEYGAiVSiV0KL0KJyPWrkuXLumdX2/r09P+WbC2LVu2DBEREYiMjOzxD0PNz89HTk4O8vLyDL4/qjts3rwZBQUFOHbsGMRisdDh9CqcjHq45cuXY/fu3aiuroavry+ys7O7re5BgwbpDbtt65ORkdFtMZmTkG3dXdatWweVSoUNGzYIHcpjjR8/Hvv27dN5BqDQDh8+jAcPHiA/Px+Ojo5Ch9PriIj4SX8Py8rKwqxZs/gBiALh9jeMSCRCZmYmZs6cKXQozAjcv9t1kI+MGGOMCY6TEWOMMcFxMmKMMSY4TkaMMcYEx8mIMcaY4KyFDqCnEolEQofwROP279isWbMwa9YsocNgzCQ4GbUjMzNT6BCeSGfOnEFqaiq3fwdmzZqFmJgYjB49WuhQmBE0/Zvp42TUDr5/Qzipqanc/h2YNWsWRo8eze1kgTgZtY2vGTHGGBMcJyPGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcFxMjKjnJwc+Pn56b3/x8bGBv369cOLL76I5ORkVFZWCh0qe4KcOHECy5Yt0+ufs2fP1ps3NDQUCoUCffr0wTPPPINz584JELFhWltbkZKSguDgYL1pCQkJGDJkCJRKJSQSCQICArB48WK9Fw3u378fQUFBUCgU8Pb2xpw5c3D37l0AwKeffoqkpKRe+fLFHkGI98v2ZOZ4LbC/vz/Z29sTEVFraytVVlbSv/71L3r99ddJJBKRu7s7ff/99yat01Lxa5kNAyNfO64RHx9PkydPppqaGm2Zv78/9e3blwDQkSNH9JbJy8ujKVOmdClec/v5559pzJgxBICGDx+uN33cuHGUnp5O5eXlVFNTQ5mZmSQWi+mVV17RzpORkUEAKCkpiaqqquj8+fPk5+dHgYGB1NzcTEREqampNG7cOKqsrOxUnNy/28WvHe9uIpEIDg4OePHFF7F7925kZWXht99+w6RJk3r82zcf1djY2OavUEtlzu3pCW21ceNGZGRkICsrCwqFQmdaWloarKysEB0dbXH98Mcff8TSpUsxb948BAYGtjmPnZ0doqOj4eTkBIVCgZkzZyI8PBzHjx9HSUkJAOCjjz5C//79ERcXB3t7ewQGBmLhwoUoKCjA2bNnAQDz58/H8OH/n707D4viSvcH/m2g6Y1uFkFA9kUlKmpUMkr0ZxzvmFGvC6KGRJ3RzIJGg0Q0xo0o4oJwlYHoZDSGJ9FEQCWaqBijDkl84jjmKhcCo6JRATcWZUeB5v39kYeOLYvdTXcX6Pt5Hv6g6tQ5b50+1W931anqQZgwYQKamprMto/PA05GAps+fTrmzp2LkpISfPjhh0KHo5fdu3ejpKRE6DCMxpT7I3RfXb16FWvWrMG6desglUpbrQ8ODkZkZCRu3bqFpUuXChCh4QYNGoSDBw9i1qxZkEgkbZY5cuQILC0ttZY5OjoCAOrq6gAARUVFcHV11XoUlYeHBwDg5s2bmmVr165FdnY237xqZJyMuoC5c+cCADIzM7FlyxbI5XIolUqUlJQgKioKbm5uuHz5MogIW7duxQsvvACJRAJ7e3tMnToVly5dAvDLp1upVIqePXti/vz5cHV1hVQqRXBwsOaTHYCn1hMREQFra2utn3xeuHAhFAoFRCIRysrKEBkZiaioKFy7dg0ikQj+/v7m67AnmGp/dOnPzvTV8ePHoVKpsGHDBpP3UVJSEogIkydPbrdMbGws+vTpg48++ggnT55st9zT+nvHjh1QKBSQy+U4fPgwxo8fD5VKBXd3d+zbt09Tj1qtRnR0NDw9PSGTyTBw4ECzPgbq1q1bkMlk8PHxAQD4+vq2+sDQcr3I19dXs8ze3h6jR49GYmIi/2KrMQl5krArMvU1o7ZUVVURAPLw8CAiolWrVhEAWrx4MSUnJ9O0adPoP//5D0VHR5O1tTXt2bOHKioqKCcnh4YMGUKOjo509+5dIiIKDw8nhUJB+fn59PDhQ8rLy6OgoCBSKpVUWFhIRKRTPbNmzSJnZ2etOOPj4wkAlZaWEhFRaGgo+fn5GbWvDOl/U+6PLv1paN1HjhwhpVJJMTExeu0vkf7XjHx9falfv35trvPz86Pr168TEdEPP/xAFhYW5O3tTTU1NUTU+pqRLv3dMoZPnTpFlZWVVFJSQqNGjSKFQkENDQ1ERLR06VKSSCR04MABevDgAa1cuZIsLCw6df30N7/5TZvXjJ5UW1tLSqWSIiIiNMuysrJILBZTUlISVVVV0U8//UQvvPACvfrqq622X7FiBQGgixcv6hUfXzNqF18z6gqUSiVEIhGqq6u1lm/evBmLFi3CwYMH4eXlha1bt2LatGmYPXs2bG1tERgYiA8//BBlZWXYuXOnZjsrKyvNp9Z+/fphx44dqK6uRkpKCurr63Wupzswx/501J+dMXHiRFRVVWHNmjWdjrEjtbW1uH79Ovz8/J5adsSIEXjnnXdw48YNvPfee63W69vfwcHBUKlUcHJyQlhYGGpra1FYWIiHDx9ix44dCAkJQWhoKOzs7LB69WqIxeJO96suNm7cCFdXV8TGxmqWjR49GsuXL0dERARUKhUGDBiA6upqfPTRR6227927NwAgNzfX5LE+LzgZdQG1tbUgIqhUqnbL5OXloaamBsOGDdNaHhQUBGtra63TcE8aNmwY5HI5Ll261Kl6uiIh9ufx/uwOSkpKQESQy+U6lY+NjUXfvn2xfft2nDlzRmtdZ/rb2toaANDY2IjLly+jrq4OAwYM0KyXyWRwcXExeb9mZGQgPT0dX3/9tdZEjlWrVmHnzp04deoUampq8PPPPyM4OBgjRozQTHJo0dKX9+7dM2mszxNORl3AlStXAAABAQHtlqmoqADwy6ygJ9nZ2bX6VvUkiUSC0tLSTtfT1Qi1Py392R08fPgQANq9uP8kqVSKlJQUiEQivPnmm6ivr9esM1Z/19bWAgBWr16tdQ/ezZs3NRMKTCE1NRWbN29GVlYWvL29Ncvv3LmDuLg4/PWvf8Vvf/tbKBQK+Pj4YNeuXbh9+zbi4+O16pHJZAB+7VvWeZyMuoDjx48DAMaPH99uGTs7OwBo82CvqKiAu7t7u9s2NjZqynSmnq5IiP15vD+7g5Y3Tn1u1hwxYgSWLFmCgoICrF+/XrPcWP3t5OQEANi2bRuISOvv7NmzOsepj+TkZOzduxenT59Gr169tNYVFBRArVa3Wq5SqY+MlksAACAASURBVODg4IC8vDyt5Q0NDQB+7VvWeZyMBHb37l1s27YN7u7uePPNN9stN2DAANjY2ODHH3/UWn7u3Dk0NDRg6NCh7W6blZUFIsLw4cN1rsfKygqNjY2d2DPzEGJ/Hu9PY9dtCj179oRIJNL7/qH169cjICAAFy9e1CzrzDh8nIeHB6RSKbKzs/WKyRBEhOXLlyM3NxeHDh1q81tdSxK9c+eO1vLq6mrcv39fM8W7RUtfOjs7myjq5w8nIzMhItTU1KC5uRlEhNLSUqSlpeHll1+GpaUlDh061OE1I6lUiqioKGRkZGDv3r2oqqpCbm4uFixYAFdXV4SHh2vKNjc348GDB2hqakJOTg4iIyPh6emJuXPn6lyPv78/7t+/j0OHDqGxsRGlpaVa91oAgIODA27fvo0bN26gurpakDdkc+xPR/3ZmbozMzPNMrVbLpfD19cXxcXFem3Xcrru8ftz9BmHT6t73rx52LdvH3bs2IGqqiqo1WoUFxdrEkJYWBicnZ07/Qii/Px8bNmyBbt27YJYLG71eK6EhAT4+PhgzJgx2LVrF7777jvU19ejqKhIsz9/+tOftOps6cvAwMBOxcYeI9A0vi7LmFMvv/zySxo4cCDJ5XKytrYmCwsLAkAikYjs7OzopZdeopiYGCovL9dsExcXRzKZTDPVe8+ePZp1zc3NFB8fT7179yaxWEz29vYUEhJCly9f1pQJDw8nsVhMbm5uZGVlRSqViqZOnUrXrl3Tq57y8nIaM2YMSaVS8vHxobfffpuWLVtGAMjf358KCwvpwoUL5OXlRTKZjEaOHKmZ1tsZhvS/KfdHl/40tO5jx46RUqmk2NhYvfsJek7tjoiIILFYTHV1dZplGRkZ5OfnRwDI0dGRFi1a1Oa2y5Yt05ra/bT+3r59O8nlcgJAvXv3pmvXrtHOnTtJpVIRAPLy8qIrV67Qo0ePaPny5eTp6UlWVlbk5OREoaGhlJeXR0REISEhBICio6M73LezZ8/Syy+/TK6urgSAAJCLiwsFBwfTt99+S7m5uZrlbf3Fx8cTEVFZWRlFRkaSv78/SSQSsrGxoZdffpm++OKLVm1OnDiR3NzcqLm5WefXgIindncgnXvlCd19sISHh5ODg4PQYRisq/V/V+1PfZNRQUEBWVlZaX246erUajWNGjWKdu/eLXQoWsrKykgqlVJCQoLe23a18d2F8H1GzyJ+qrBxPQv96e/vj5iYGMTExLR6UnVXpFarcejQIVRXVyMsLEzocLSsXbsWgwcPRkREhNChPFM4GTH2nFixYgVmzJiBsLCwLv8w1KysLBw8eBCZmZk63x9lDlu3bkV2djaOHTsGsVgsdDjPFE5Gz5CVK1ciJSUFlZWV8PHxwYEDB4QOqVt7Fvtzw4YNiIiIwKZNm4QOpUNjx47FZ599pvXMP6EdPnwYjx49QlZWFuzt7YUO55kjIuIn/T0uPT0dr732Gj8AUSDc/7oRiURIS0vDzJkzhQ6F6YHHd7v28zcjxhhjguNkxBhjTHCcjBhjjAmOkxFjjDHBWQkdQFeVnp4udAjPpZaHZHL/P52pHijKTIdfs/bxbLontMx2YYwxU+G33Vb2czJirBN4qi5jRsFTuxljjAmPkxFjjDHBcTJijDEmOE5GjDHGBMfJiDHGmOA4GTHGGBMcJyPGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcFxMmKMMSY4TkaMMcYEx8mIMcaY4DgZMcYYExwnI8YYY4LjZMQYY0xwnIwYY4wJjpMRY4wxwXEyYowxJjhORowxxgTHyYgxxpjgOBkxxhgTHCcjxhhjguNkxBhjTHCcjBhjjAmOkxFjjDHBcTJijDEmOE5GjDHGBMfJiDHGmOA4GTHGGBMcJyPGGGOC42TEGGNMcJyMGGOMCc5K6AAY6y5KSkqQkpKitSwnJwcAEBcXp7XcwcEBf/nLX8wWG2PdnYiISOggGOsOmpqa4OLiggcPHkAsFrdb7tGjRwgPD8eHH35oxugY69b282k6xnRkZWWF119/HZaWlnj06FG7fwDwxhtvCBwtY90LJyPG9PD666+jsbGxwzIuLi4YOXKkmSJi7NnAyYgxPYwYMQLu7u7trre2tsacOXNgYcGHFmP64COGMT2IRCLMnj273WtGDQ0NeP31180cFWPdHycjxvTU0ak6X19fvPjii2aOiLHuj5MRY3oaOHAg+vbt22q5tbU1/vjHPwoQEWPdHycjxgwwZ86cVqfqGhoaEBYWJlBEjHVvnIwYM8Ds2bPR1NSk+V8kEmHQoEHo06ePgFEx1n1xMmLMAF5eXhgyZAhEIhEAwNLSkk/RMdYJnIwYM9Af/vAHWFpaAgDUajVmzpwpcESMdV+cjBgz0MyZM9Hc3AyRSISXX34Zbm5uQofEWLfFyYgxA7m4uGD06NEgIj5Fx1gn8YNS9dRyjYAxxtozffp07N+/X+gwupP9/BMSBoiMjMSIESOEDqPb2rZtGwDgnXfeETiSzquvr8fOnTuxePFio9Z79uxZJCYmIi0tzaj1MtNrGd9MP5yMDDBixAi+WN0JLZ8Yn5U+/N3vfodevXoZvd7ExMRnpo+eJ/yNyDB8zYixTjJFImLsecPJiDHGmOA4GTHGGBMcJyPGGGOC42TEGGNMcJyMzOzPf/4zlEolRCIRsrOzhQ5HZzExMejXrx9UKhUkEgn8/f3x7rvvoqamRpB4jh07BltbW3z11VeCtN/VnTx5EitWrMDBgwfh6+sLkUgEkUiEOXPmtCo7btw4KJVKWFpaon///rhw4YIAEeumubkZ27ZtQ3BwcKt1uo7Rzz//HEFBQVAqlfDy8sK8efNw9+5dAMCXX36JuLg4qNVqs+wP+xUnIzP76KOPsGvXLqHD0Nvp06exaNEi3LhxA2VlZdi4cSMSExMxY8YMQeLhe7Xb9/777yMpKQkrV65EaGgofv75Z/j5+aFHjx7Yu3cvjh49qlX+xIkT2L9/PyZNmoS8vDwMGTJEoMg7VlBQgP/3//4flixZgrq6ulbrdRmjaWlpmDVrFmbMmIHi4mIcPnwY3333HcaPH4+mpiZMnjwZUqkUY8eORUVFhTl377nHyYjpxMbGBuHh4XBwcIBSqcTMmTMREhKC48ePo6ioyOzxTJw4EZWVlZg0aZLZ2wZ+udm1rU/nQtu8eTNSU1ORnp4OpVKptS4pKQkWFhYIDw9HZWWlQBEa5v/+7//w3nvvYcGCBRg8eHCbZXQZo//4xz/Qq1cvLFu2DLa2thg8eDCWLFmC7OxsnDt3DgCwePFiDBo0CBMmTND6mRBmWpyMBNAdHyl05MgRzROqWzg6OgJAm59Sn3W7d+9GSUmJ0GFouXr1KtasWYN169ZBKpW2Wh8cHIzIyEjcunULS5cuFSBCww0aNAgHDx7ErFmzIJFI2iyjyxgtKiqCq6ur1jHo4eEBALh586Zm2dq1a5GdnY3ExESj7gdrHycjEyMixMfHo2/fvpBIJLC1tcWyZcu0yqjVakRHR8PT0xMymQwDBw7UPAZmx44dUCgUkMvlOHz4MMaPHw+VSgV3d3fs27dPU8e3336Ll156CXK5HCqVCoGBgaiqqnpq/Z1x69YtyGQy+Pj4dLoufZw5cwaenp4QiUT44IMPAOjWT0lJSZBKpejZsyfmz58PV1dXSKVSBAcHaz4VR0REwNraGi4uLpr2Fi5cCIVCAZFIhLKyMkRGRiIqKgrXrl2DSCSCv78/AOD48eNQqVTYsGGDWfujRVJSEogIkydPbrdMbGws+vTpg48++ggnT55stxwRYevWrXjhhRcgkUhgb2+PqVOn4tKlSwB0H5emGnu6enKM+vr6tvoQ0XK9yNfXV7PM3t4eo0ePRmJiIp8SNhdiegFAaWlpOpdftWoViUQi+p//+R968OAB1dXV0fbt2wkAXbx4kYiIli5dShKJhA4cOEAPHjyglStXkoWFBZ0/f15TBwA6deoUVVZWUklJCY0aNYoUCgU1NDRQTU0NqVQqiouLo/r6erp79y5NmzaNSktLdarfELW1taRUKikiIkLvbadPn07Tp083uG0ioqKiIgJAycnJmmVP6yciovDwcFIoFJSfn08PHz6kvLw8CgoKIqVSSYWFhURENGvWLHJ2dtZqLz4+ngBo+jQ0NJT8/Py0yhw5coSUSiXFxMR0at+IiNLS0kjfw9PX15f69evX5jo/Pz+6fv06ERH98MMPZGFhQd7e3lRTU0NERJmZmTRlyhRN+ejoaLK2tqY9e/ZQRUUF5eTk0JAhQ8jR0ZHu3r1LRLr1tynG3m9+8xsaNGjQU8u1NUazsrJILBZTUlISVVVV0U8//UQvvPACvfrqq622X7FihdZxqitjjO/nUDonIz3pk4zq6upILpfT7373O63l+/bt0wzy+vp6ksvlFBYWprWdRCKht956i4h+Pejr6+s1ZVoS2tWrV+mnn34iAHTkyJFWMehSvyFWrVpFffr0oaqqKr23NXUyaq+fiH5JRra2tlp1nT9/ngDQunXriMjwZGRM+iajmpoaEolENGnSpDbXP56MiIiioqIIAC1atIiItJNRXV0d2djYaI0ZIqJ///vfBECTbJ/W36Yae7omo/bG6OrVqwmA5s/d3Z2Kiopabf/xxx8TAPr000/1io+TkUHS+TSdCV29ehV1dXUYO3Zsu2UuX76Muro6DBgwQLNMJpPBxcVFc0qkLdbW1gCAxsZG+Pr6omfPnpg9ezbWrl2LGzdudLr+jmRkZCA9PR1ff/11q4vkXc3j/dSeYcOGQS6XG9wfXUFJSQmICHK5XKfysbGx6Nu3L7Zv344zZ85orcvLy0NNTQ2GDRumtTwoKAjW1taaU5pteby/TTH2dNXeGF21ahV27tyJU6dOoaamBj///DOCg4MxYsSIVhNxWvry3r17Jo2V/YKTkQkVFxcDAJycnNotU1tbCwBYvXq15l4QkUiEmzdv6jwxQCaT4fTp0xg5ciQ2bNgAX19fhIWFob6+3ij1Py41NRWbN29GVlYWvL299d6+q5JIJCgtLRU6DIM9fPgQANq9uP8kqVSKlJQUiEQivPnmm6ivr9esa5nSbGNj02o7Ozs7VFdX69SGsceertobo3fu3EFcXBz++te/4re//S0UCgV8fHywa9cu3L59G/Hx8Vr1yGQyAL/2LTMtTkYm1DKj6dGjR+2WaUlU27ZtAxFp/Z09e1bntvr374+vvvoKt2/fxvLly5GWloaEhASj1Q8AycnJ2Lt3L06fPv1MPam6sbERFRUVcHd3FzoUg7W8cepzs+aIESOwZMkSFBQUYP369ZrldnZ2ANBm0tGnn4w59nTV0RgtKCiAWq1utVylUsHBwQF5eXlayxsaGgD82rfMtDgZmdCAAQNgYWGBb7/9tt0yHh4ekEqlnXoaw+3bt5Gfnw/glzeATZs2YciQIcjPzzdK/USE5cuXIzc3F4cOHWrzE3N3lpWVBSLC8OHDAQBWVlYdntbrinr27AmRSKT3/UPr169HQEAALl68qFk2YMAA2NjY4Mcff9Qqe+7cOTQ0NGDo0KE61W2MsacrXcZoSxK9c+eO1vLq6mrcv39fM8W7RUtfOjs7myhq9jhORibk5OSE0NBQHDhwALt370ZVVRVycnKwc+dOTRmpVIp58+Zh37592LFjB6qqqqBWq1FcXNzqoGnP7du3MX/+fFy6dAkNDQ24ePEibt68ieHDhxul/vz8fGzZsgW7du2CWCzWOuUiEomQkJBgUP8Ipbm5GQ8ePEBTUxNycnIQGRkJT09PzJ07FwDg7++P+/fv49ChQ2hsbERpaanWPSgA4ODggNu3b+PGjRuorq5GY2MjMjMzBZvaLZfL4evrqzk1rKuW03WP358jlUoRFRWFjIwM7N27F1VVVcjNzcWCBQvg6uqK8PBwnet+2tgLCwuDs7Nzpx9BpMsY9fHxwZgxY7Br1y589913qK+vR1FRkWZ//vSnP2nV2dKXgYGBnYqN6cj8kya6N+g5tbu6upr+/Oc/U48ePcjGxoZGjhxJ0dHRmlk8//d//0ePHj2i5cuXk6enJ1lZWZGTkxOFhoZSXl4ebd++neRyOQGg3r1707Vr12jnzp2kUqkIAHl5edE333xDwcHBZG9vT5aWltSrVy9atWoVNTU1ERF1WL8ucnNztWYfPfkXHx+vVx92drZRcnIyubi4EACSy+U0efJknfrpypUrFB4eTmKxmNzc3MjKyopUKhVNnTqVrl27pqm/vLycxowZQ1KplHx8fOjtt9+mZcuWEQDy9/enwsJCunDhAnl5eZFMJqORI0fS3bt36dixY6RUKik2NtbgfWthyNTuiIgIEovFVFdXp1mWkZFBfn5+BIAcHR01s+eetGzZMq2p3c3NzRQfH0+9e/cmsVhM9vb2FBISQpcvXyYi0rm/nzb2QkJCCABFR0d3uG9nz56ll19+mVxdXTXjzsXFhYKDg+nbb7/VeYyWlZVRZGQk+fv7k0QiIRsbG3r55Zfpiy++aNXmxIkTyc3NjZqbm/V6HXg2nUF4are+9E1GrDUhD9bw8HBycHAQpG19GJKMCgoKyMrKivbs2WOiqIxPrVbTqFGjaPfu3UKHoqWsrIykUiklJCTovS0nI4Pw1G72/HlWn8js7++PmJgYxMTECPY0dX2o1WocOnQI1dXVCAsLEzocLWvXrsXgwYMREREhdCjPDU5Gz7FLly61Orfe1l9Xe6Ng7VuxYgVmzJiBsLCwLv8w1KysLBw8eBCZmZk63x9lDlu3bkV2djaOHTsGsVgsdDjPDU5Gz7GAgIBWU27b+ktNTRU6VKNYuXIlUlJSUFlZCR8fHxw4cEDokExiw4YNiIiIwKZNm4QOpUNjx47FZ599pvUcQKEdPnwYjx49QlZWFuzt7YUO57liJXQAjJnLxo0bsXHjRqHDMItx48Zh3LhxQofR7UyZMgVTpkwROoznEn8zYowxJjhORowxxgTHyYgxxpjgOBkxxhgTHE9gMICpHvL4vGh5zEp6errAkXRdLWOM+6j7KS4u7tYP3RWKiIh/U1cfIpFI6BAYY13c9OnTsX//fqHD6E728zcjA6SlpWHmzJlCh9FtzZgxAwD4YO1Aeno6XnvtNfBnxe6nZXwz/fA1I8YYY4LjZMQYY0xwnIwYY4wJjpMRY4wxwXEyYowxJjhORowxxgTHyUhABw8ehK+vb6vfD7K2tkbPnj3xyiuvID4+Hg8ePBA6VPYMOHnyJFasWNFq3M2ZM6dV2XHjxkGpVMLS0hL9+/fHhQsXBIi4Y3FxcQgICIBMJoNCoUBAQADWrFmDqqoqTZmYmBj069cPKpUKEokE/v7+ePfddzU/Pvjll18iLi7umf3Bxe6Ek5GAQkND8fPPP8PPzw+2trYgIjQ3N6OkpATp6enw8fHB8uXL0b9/f/z4449Ch8u6sffffx9JSUlYuXKl1rjr0aMH9u7di6NHj2qVP3HiBPbv349JkyYhLy8PQ4YMESjy9n3//ff4y1/+gsLCQty7dw/r169HXFwcpk+frilz+vRpLFq0CDdu3EBZWRk2btyIxMREzb1AkydPhlQqxdixY1FRUSHUrjBwMupyRCIR7Ozs8MorryAlJQXp6em4d+8eJk6c2OV/ubM7qK+vR3BwcLeruzM2b96M1NRUpKenQ6lUaq1LSkqChYUFwsPDu934sra2xsKFC+Hk5AQbGxvMmDEDU6dOxTfffIM7d+4AAGxsbBAeHg4HBwcolUrMnDkTISEhOH78OIqKigAAixcvxqBBgzBhwgQ0NTUJuUvPNU5GXdz06dMxd+5clJSU4MMPPxQ6nG5v9+7dKCkp6XZ1G+rq1atYs2YN1q1bB6lU2mp9cHAwIiMjcevWLSxdulSACA2XkZHRap/c3NwAQHMa7siRI7C0tNQq4+joCACoq6vTLFu7di2ys7ORmJhoypBZBzgZdQNz584FAGRmZgIA1Go1oqOj4enpCZlMhoEDByItLQ0AsGPHDigUCsjlchw+fBjjx4+HSqWCu7s79u3bp6nz22+/xUsvvQS5XA6VSoXAwEDNufaO6hcKEWHr1q144YUXIJFIYG9vj6lTp+LSpUsAgIiICFhbW2v9hPXChQuhUCggEolQVlaGyMhIREVF4dq1axCJRPD390dSUhKkUil69uyJ+fPnw9XVFVKpFMHBwTh37lyn6gaA48ePQ6VSYcOGDWbsrV8lJSWBiDB58uR2y8TGxqJPnz746KOPcPLkyXbLPe010HXsmXJ8FRQUwM7ODl5eXu2WuXXrFmQyGXx8fDTL7O3tMXr0aCQmJvIjmIRCTC8AKC0tzah1+vn5ka2tbbvrq6qqCAB5eHgQEdHSpUtJIpHQgQMH6MGDB7Ry5UqysLCg8+fPExHRqlWrCACdOnWKKisrqaSkhEaNGkUKhYIaGhqopqaGVCoVxcXFUX19Pd29e5emTZtGpaWlOtXfWdOnT6fp06frtU10dDRZW1vTnj17qKKignJycmjIkCHk6OhId+/eJSKiWbNmkbOzs9Z28fHxBECzb6GhoeTn56dVJjw8nBQKBeXn59PDhw8pLy+PgoKCSKlUUmFhYafqPnLkCCmVSoqJidFrf9PS0sgYh6evry/169evzXV+fn50/fp1IiL64YcfyMLCgry9vammpoaIiDIzM2nKlCma8rq8Bk8be0TGH18NDQ1UXFxMycnJJJFIaM+ePe2Wra2tJaVSSREREa3WrVixggDQxYsXDYqjhSHjm1E6fzPqBpRKJUQiEaqrq/Hw4UPs2LEDISEhCA0NhZ2dHVavXg2xWIyUlBSt7YKDg6FSqeDk5ISwsDDU1taisLAQN27cQFVVFfr37w+pVApnZ2ccPHgQjo6OetVvLvX19di6dSumTZuG2bNnw9bWFoGBgfjwww9RVlaGnTt3droNKysrzSf+fv36YceOHaiuru70Pk+cOBFVVVVYs2ZNp2PUV21tLa5fvw4/P7+nlh0xYgTeeecd3LhxA++9916r9fq+Bu2NPVOMLw8PD7i7u2Pt2rXYsmULXnvttXbLbty4Ea6uroiNjW21rnfv3gCA3Nxcg+JgncPJqBuora0FEUGlUuHy5cuoq6vDgAEDNOtlMhlcXFw0p0vaYm1tDQBobGyEr68vevbsidmzZ2Pt2rW4ceOGppyh9ZtSXl4eampqMGzYMK3lQUFBsLa21pxOM6Zhw4ZBLpcLts/GUFJSAiKCXC7XqXxsbCz69u2L7du348yZM1rrOvMaPD72TDG+ioqKUFJSgs8//xyffPIJXnzxxTav3WVkZCA9PR1ff/11q4kcADT9dO/ePYPiYJ3DyagbuHLlCgAgICAAtbW1AIDVq1dr3Zt08+ZNrQuyHZHJZDh9+jRGjhyJDRs2wNfXF2FhYaivrzdK/cbWMuXWxsam1To7OztUV1ebpF2JRILS0lKT1G0ODx8+BPDLfuhCKpUiJSUFIpEIb775Jurr6zXrjPUamGJ8icViODk5Ydy4cUhNTUVeXh42btyoVSY1NRWbN29GVlYWvL2926xHJpMB+LXfmHlxMuoGjh8/DgAYP348nJycAADbtm0DEWn96fMLtP3798dXX32F27dvY/ny5UhLS0NCQoLR6jcmOzs7AGjzDa+iosIkv6rZ2NhosrrNpeXNVZ8bOkeMGIElS5agoKAA69ev1yw31mtg6vHl7+8PS0tL5OXlaZYlJydj7969OH36NHr16tXutg0NDQB+7TdmXpyMuri7d+9i27ZtcHd3x5tvvgkPDw9IpVJkZ2cbXOft27eRn58P4Jc3h02bNmHIkCHIz883Sv3GNmDAANjY2LS68ffcuXNoaGjA0KFDAfxy3aexsdEobWZlZYGIMHz4cKPXbS49e/aESCTS+/6h9evXIyAgABcvXtQs0/U1eBpjja/y8nK88cYbrZYXFBRArVbDw8MDRITly5cjNzcXhw4davNb3eNa+snZ2blTsTHDcDLqIogINTU1aG5uBhGhtLQUaWlpePnll2FpaYlDhw5BpVJBKpVi3rx52LdvH3bs2IGqqiqo1WoUFxdrbvR7mtu3b2P+/Pm4dOkSGhoacPHiRdy8eRPDhw83Sv3GJpVKERUVhYyMDOzduxdVVVXIzc3FggUL4OrqivDwcAC/fCq+f/8+Dh06hMbGRpSWluLmzZtadTk4OOD27du4ceMGqqurNQmmubkZDx48QFNTE3JychAZGQlPT0/NtHpD687MzBRsardcLoevry+Ki4v12q7ldN3j9+fo+hroUvfTxldYWBicnZ07fASRQqHAiRMncPr0aVRVVaGxsREXL17EH//4RygUCixZsgT5+fnYsmULdu3aBbFY3OqxWwkJCVp1tvRTYGCgXv3FjMTc8/e6OxhxaveXX35JAwcOJLlcTtbW1mRhYUEASCQSkZ2dHb300ksUExND5eXlWts9evSIli9fTp6enmRlZUVOTk4UGhpKeXl5tH37dpLL5QSAevfuTdeuXaOdO3eSSqUiAOTl5UXffPMNBQcHk729PVlaWlKvXr1o1apV1NTU9NT6jcGQqa/Nzc0UHx9PvXv3JrFYTPb29hQSEkKXL1/WlCkvL6cxY8aQVColHx8fevvtt2nZsmUEgPz9/amwsJAuXLhAXl5eJJPJaOTIkXT37l0KDw8nsVhMbm5uZGVlRSqViqZOnUrXrl3rdN3Hjh0jpVJJsbGxeu2vsaZ2R0REkFgsprq6Os2yjIwM8vPzIwDk6OhIixYtanPbZcuWaU3tftproMvYu3LlylPHV0hICAGg6OjoDvdt8uTJ5OPjQzY2NiSRSMjPz4/CwsIoNzeXiIhyc3MJQLt/8fHxWvVNnDiR3NzcqLm5Wf+OfgxP7TZIOicjPRkzGT2vutrBGh4eTg4ODkKHocVYyaigoICsrKw6vPemq1Gr1TRq1CjavXu32dosKysjqVRK1fwpCwAAIABJREFUCQkJna6rq43vboLvM2IM0O8if3fi7++PmJgYxMTEaB6R05Wp1WocOnQI1dXVCAsLM1u7a9euxeDBgxEREWG2Npk2TkaMPeNWrFiBGTNmICwsrMs/DDUrKwsHDx5EZmamzvdHddbWrVuRnZ2NY8eOQSwWm6VN1honI/ZcW7lyJVJSUlBZWQkfHx8cOHBA6JBMYsOGDYiIiMCmTZuEDqVDY8eOxWeffab1HEBTOnz4MB49eoSsrCzY29ubpU3WNiuhA2BMSBs3bmx1g+Szaty4cRg3bpzQYXQpU6ZMwZQpU4QOg4G/GTHGGOsCOBkxxhgTHCcjxhhjguNkxBhjTHA8gcEA27Ztw/79+4UOo9v617/+BQCYMWOGwJF0XS2PpuE+6n7+9a9/aZ5pyHQnIuLf2NUHvzmwx927dw8//fQTxo4dK3QorAtpefo509l+TkaMdUJ6ejpee+018GHEWKfs52tGjDHGBMfJiDHGmOA4GTHGGBMcJyPGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcFxMmKMMSY4TkaMMcYEx8mIMcaY4DgZMcYYExwnI8YYY4LjZMQYY0xwnIwYY4wJjpMRY4wxwXEyYowxJjhORowxxgTHyYgxxpjgOBkxxhgTHCcjxhhjguNkxBhjTHCcjBhjjAmOkxFjjDHBcTJijDEmOE5GjDHGBMfJiDHGmOA4GTHGGBMcJyPGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcFxMmKMMSY4TkaMMcYEZyV0AIx1F7dv38Z///d/o7GxUbOsrq4Otra2CAwM1Cr74osv4tNPPzV3iIx1W5yMGNNRr1690NDQgLy8vFbrKisrtf4PCwszV1iMPRP4NB1jevjDH/4AK6uOP8OJRCK88cYbZoqIsWcDJyPG9PD6669DrVa3u14kEmHo0KHw8fExY1SMdX+cjBjTg4eHB4YPHw4Li7YPHUtLS/zhD38wc1SMdX+cjBjT05w5cyASidpc19zcjJkzZ5o5Isa6P05GjOlpxowZbS63tLTEK6+8AmdnZzNHxFj3x8mIMT05Ojpi7NixsLS0bLVuzpw5AkTEWPfHyYgxA8yePRtEpLXMwsICISEhAkXEWPfGyYgxA0ydOhVisVjzv5WVFSZOnAhbW1sBo2Ks++JkxJgBlEolJk2apElIarUas2fPFjgqxrovTkaMGWjWrFloamoCAMhkMkyYMEHgiBjrvjgZMWag8ePHQ6FQAACmT58OmUwmcESMdV9mezZdcXExfvjhB3M1x5hZBAUF4Z///Cc8PDyQnp4udDiMGZU575kT0ZNTgkwkPT0dr732mjmaYowxZgRmSg8AsN/sT+02484xZnLNzc2Ii4vDihUr2lwvEomQlpbGT2XoQMtNxPv37xc4EtZCiC8PfM2IsU6wsLDAsmXLhA6DsW6PkxFjnfS0n5RgjD0dJyPGGGOC42TEGGNMcJyMGGOMCY6TEWOMMcF12WQUFBQES0tLDB482Kztzps3D1KpFCKRCA8fPjRr211FQkICevbsCZFIhA8//FCz/NixY7C1tcVXX31l0vbN1U574uLiEBAQAJlMBoVCgYCAAKxZswZVVVWCxCN0f3R1J0+exIoVK3Dw4EH4+vpCJBJBJBK1+XMe48aNg1KphKWlJfr3748LFy4IEHHHdBl/MTEx6NevH1QqFSQSCfz9/fHuu++ipqYGAPDll18iLi4OarVaqN3QW5dNRufPn8eYMWPM3m5KSgqWLl1q9na7kqVLl7b5tAxz3SMm9L1o33//Pf7yl7+gsLAQ9+7dw/r16xEXF4fp06cLEo/Q/dGVvf/++0hKSsLKlSsRGhqKn3/+GX5+fujRowf27t2Lo0ePapU/ceIE9u/fj0mTJiEvLw9DhgwRKPL26TL+Tp8+jUWLFuHGjRsoKyvDxo0bkZiYqLlna/LkyZBKpRg7diwqKiqE2hW9dNlk1KK9n3fWVX19PYKDg40UzfNt4sSJqKysxKRJk4xWZ1uvjyna0Ye1tTUWLlwIJycn2NjYYMaMGZg6dSq++eYb3Llzx+zxCN0fXfUY2rx5M1JTU5Geng6lUqm1LikpCRYWFggPD0dlZaVAERpGl/FnY2OD8PBwODg4QKlUYubMmQgJCcHx48dRVFQEAFi8eDEGDRqECRMmaB7o25V1+WT0+G/GGGL37t0oKSkxaNvOJkL2dJ15fUwlIyMDUqlUa5mbmxsAaE6DPE+64mt09epVrFmzBuvWrWv1WgFAcHAwIiMjcevWrW53pkOX8XfkyJFWvzTs6OgIAKirq9MsW7t2LbKzs5GYmGjKkI2iyyejq1evIiAgAAqFAjKZDKNGjcKZM2c067///nv069cPtra2kEqlCAwMxNdffw0AiIyMRFRUFK5duwaRSAR/f3/Ndnv27MGwYcMglUqhUCjg7e2N9evXa9ZbWFjg6NGjGD9+PGxtbeHq6oqPP/5Y57h37NgBhUIBuVyOw4cPY/z48VCpVHB3d8e+ffs05YgIW7duxQsvvACJRAJ7e3tMnToVly5dAgBs2bIFcrkcSqUSJSUliIqKgpubGxYsWACFQgELCwsMHToUzs7OEIvFUCgUGDJkCEaNGgUPDw9IpVLY2dnh3Xff1Yqvo35ry5kzZ+Dp6QmRSIQPPvhA89q0nJ9/8u+bb74x6PVpqx1d+knX/jZUQUEB7Ozs4OXl1em69NFWf+iyr0lJSZBKpejZsyfmz58PV1dXSKVSBAcH49y5cwCAiIgIWFtbw8XFRdPewoULoVAoIBKJUFZW1u4xdPz4cahUKmzYsMGs/dEiKSkJRITJkye3WyY2NhZ9+vTBRx99hJMnT7ZbzlhjS61WIzo6Gp6enpDJZBg4cCDS0tKMsr+6jL9bt25BJpPBx8dHs8ze3h6jR49GYmJi1z/dS2aSlpZG+jY3duxY8vX1pevXr1NjYyP99NNP9Jvf/IakUilduXKFiIj2799Pa9eupfv371N5eTkNHz6cevTooakjNDSU/Pz8tOrdtm0bAaBNmzZReXk53b9/n/7xj3/QrFmziIho1apVBIBOnTpFFRUVdP/+fZowYQJJJBKqra3VOf7H66msrKSSkhIaNWoUKRQKamhoICKi6Ohosra2pj179lBFRQXl5OTQkCFDyNHRke7evatVz+LFiyk5OZmmTZtG//nPf+j9998nAHTu3Dmqra2lsrIy+v3vf08A6OjRo1RaWkq1tbUUERFBACg7O1sT29P6raCggADQ3//+d82yoqIiAkDJycmaMu+9956mT+7cuUP29vYUHBxMarXa4NfnyXb07aeO+lsfDQ0NVFxcTMnJySSRSGjPnj161wGA0tLS9N7ucW31hy77Gh4eTgqFgvLz8+nhw4eUl5dHQUFBpFQqqbCwkIiIZs2aRc7OzlrtxcfHEwAqLS0lorZfoyNHjpBSqaSYmJhO7RsR0fTp02n69Ol6bePr60v9+vVrc52fnx9dv36diIh++OEHsrCwIG9vb6qpqSEioszMTJoyZYqmvLHG1tKlS0kikdCBAwfowYMHtHLlSrKwsKDz58/r2yVEpN/4q62tJaVSSREREa3WrVixggDQxYsXdW7bkPfrTkrv8slo0KBBWstycnIIAC1durTNbTZu3EgAqKSkhIhaH0gNDQ1kZ2dHY8aM0dquqamJEhMTiejXgVdfX69Z/+mnnxIA+umnn3SOv616tm/fTgDo6tWrVFdXRzY2NhQWFqa13b///W8CoDnQ26qHiDTJqLq6WrPsk08+IQCUm5vbqr7U1NR2Y32y33RJRk8KCQkhqVRKly5d0rkdXZJRZ/rp8f7Wl7OzMwGgHj160N/+9jeDEpqpk1FH+xoeHk62trZadZ0/f54A0Lp164jI8GRkTPomo5qaGhKJRDRp0qQ21z+ejIiIoqKiCAAtWrSIiLSTkbHGVn19Pcnlcq166urqSCKR0FtvvaXzvj1On/G3atUq6tOnD1VVVbVa9/HHHxMA+vTTT3VuW4hk1OVP0z0pMDAQtra2yMnJaXP94z8D3ZacnBxUVFTg1Vdf1VpuaWmJxYsXt9tuS72NjY2GhK1hbW2tqScvLw81NTUYNmyYVpmgoCBYW1trTqcYUv/jFyx1if1p/fY06enp+OKLL7Bu3Tr07dvXqO10pp8e7299FRUVoaSkBJ9//jk++eQTvPjii13u2snjdNnXYcOGQS6Xa05BdUclJSUgIsjlcp3Kx8bGom/fvti+fbvWKX7AeGPr8uXLqKurw4ABAzTrZTIZXFxcDO5rXcdfRkYG0tPT8fXXX7eayAFA00/37t0zKA5z6XbJCPjlDa3lgDt69CheeeUVODk5QSKRtLo28qSWufp2dnYmj/NpWqZc2tjYtFpnZ2eH6upqk7Wtb791pLy8HG+//TaCgoIQFRVl9HaE6iexWAwnJyeMGzcOqampyMvLw8aNG03SljlJJBKUlpYKHYbBWu7/k0gkOpWXSqVISUmBSCTCm2++ifr6es06Y42t2tpaAMDq1au1rp3evHlTa0KBPnQZf6mpqdi8eTOysrLg7e3dZj0tv0Dc1e+b7HbJqKmpCffv34enpycKCwsREhICFxcXnDt3DpWVlYiLi+tw+169egEAysrKzBFuh1oSYlsDvqKiAu7u7iZp15B+68jixYtRUVGBlJQUrRk+xmpHqH56nL+/PywtLZGXl2fytkypsbHRbH1mKi1vrvp8ux4xYgSWLFmCgoICrYlKxhpbTk5OAIBt27aBiLT+zp49q3Oc7Wlr/CUnJ2Pv3r04ffq05n2tLQ0NDQB+7beuqtslo3/+859obm7GkCFDkJubi8bGRrz11lvw9fXVPDmhI97e3nBwcMCJEyfMFHH7BgwYABsbG/z4449ay8+dO4eGhgYMHTrUJO0a0m/tOXr0KD777DOsWbMG/fv31yxftmyZ0doxZz+Vl5fjjTfeaLW8oKAAarUaHh4eRmtLCFlZWSAiDB8+HMAvP3/R2VPP5tbydBB97x9av349AgICcPHiRc0yY42tlpmr2dnZesX0JF3GHxFh+fLlyM3NxaFDh9r8Vve4ln5ydnbuVGym1uWTUUNDAyorK9HU1IQLFy4gIiICXl5emDt3Ljw9PQH88jiQhw8foqCgoNU5XgcHB9y+fRs3btxAdXU1LCwssHLlSnz33XeIiIjArVu30NzcjOrqauTn55t136RSKaKiopCRkYG9e/eiqqoKubm5WLBgAVxdXREeHm6SdnXpN11UVVVh/vz5GDx4MN577z0Av5wK+PHHH5GdnW3Q69PWG6M5+0mhUODEiRM4ffo0qqqq0NjYiIsXL+KPf/wjFAoFlixZYrS2zKG5uRkPHjxAU1MTcnJyEBkZCU9PT8ydOxfAL5+479+/j0OHDqGxsRGlpaW4efOmVh1tvUaZmZmCTe2Wy+Xw9fVFcXGxXtu1nK57/Nu7scaWVCrFvHnzsG/fPuzYsQNVVVVQq9UoLi7W3KgaFhYGZ2fnDh9BpMv4y8/Px5YtW7Br1y6IxeJWt1UkJCRo1dnST4GBgXr1l9mZa6qEIbMzUlJSaMyYMdSzZ0+ysrKiHj160Ouvv043b97UlFm+fDk5ODiQnZ0dzZgxgz744AMCQH5+flRYWEgXLlwgLy8vkslkNHLkSM1UzQ8++IACAwNJKpWSVCqlF198kbZv305xcXEkk8kIAPXu3ZuuXbtGe/fuJXt7ewJA7u7uOs2o2759O8nlcq16du7cSSqVigCQl5cXXblyhZqbmyk+Pp569+5NYrGY7O3tKSQkhC5fvkxEpBWPh4eHZnpnYmKipn5vb2/6/vvvafPmzWRra0sAyNnZmT777DNKTU3VzMqxt7enffv2PbXfIiMjNdsoFAqaNm0aJScnk4uLCwEguVxOkydPpoSEBALQ5t+ECRMMen1Wr17dqh0iemo/6drfupg8eTL5+PiQjY0NSSQS8vPzo7CwMK0ZirpCJ2fTtdXvuu5reHg4icVicnNzIysrK1KpVDR16lS6du2apv7y8nIaM2YMSaVS8vHxobfffpuWLVtGAMjf37/dY+jYsWOkVCopNjbW4H1rYcjU7oiICBKLxVRXV6dZlpGRQX5+fgSAHB0dNbPnnrRs2TKtqd3GGluPHj2i5cuXk6enJ1lZWZGTkxOFhoZSXl4eEf0y2xQARUdHd7hvTxt/ubm57R53ACg+Pl6rvokTJ5Kbmxs1Nzfr3L88tZuxZ0xnk1FnhIeHk4ODgyBt68OQZFRQUEBWVlYG3fslFLVaTaNGjaLdu3ebrc2ysjKSSqWUkJCg13Y8tZsxZlTd6anN+vD390dMTAxiYmK6xSOa1Go1Dh06hOrqaoSFhZmt3bVr12Lw4MGIiIgwW5uG4mSkp0uXLrX7CJzH/8w54NjT8ev27FmxYgVmzJiBsLCwLv8w1KysLBw8eBCZmZk63x/VWVu3bkV2djaOHTvW6Wd8moOV0AF0NwEBAV3/GU+sleftdVu5ciVSUlLQ0NAAHx8fxMfHC/YTGKa0YcMGnDhxAps2bcLmzZuFDqddY8eOxdixY83W3uHDh/Ho0SNkZWW1eqBqV8XJiLFn0MaNG5+JG3R1MW7cOIwbN07oMLqUKVOmYMqUKUKHoRc+TccYY0xwnIwYY4wJjpMRY4wxwXEyYowxJjizT2CYMWOGuZtkTFDbtm3D/v37hQ6jy/rXv/4FgN8buhJ9H7VkDPzNiDHGmODM/s2IPyGy54lIJMI777yDmTNnCh1Kl9XyjYjfG7qO9PR0vPbaa2Ztk78ZMcYYExwnI8YYY4LjZMQYY0xwnIwYY4wJjpMRY4wxwXX7ZHTw4EH4+vq2+ikAKysrODo64r/+67+QkZFh0hjmzZsHqVQKkUiEhw8f6hTnnDlzWpUZN24clEolLC0t0b9//w5/nlhICQkJ6NmzJ0QiET788EPN8mPHjsHW1hZfffWVSds3Vzusezh58iRWrFjxzBxjcXFxCAgIgEwmg0KhQEBAANasWYOqqipNmZiYGPTr1w8qlQoSiQT+/v549913Nb/t9OWXXyIuLq57/Z6VuX7Gz9S/HOjn50e2traa/+/fv08nT56kgIAAAkCpqakma5uIaNWqVQSA6uvrnxpnjx49CAAdOXKk1frMzEytn0TuqgoKCggA/f3vf9csO3LkCKlUKvryyy9N2ra52jEGCPhLr92FIb/02iI6OpomTZpEVVVVmmXd/RibOHEiJSQkUElJCVVXV1N6ejqJxWL63e9+pykzevRo2r59O5WXl1NVVRWlpaWRWCym3//+95oyiYmJNHr0aHrw4IHeMfAvvRqRvb09xo4di7/97W8Afpk3r6v6+noEBwebKjQkJSXBwsIC4eHhXf5HwfQxceJEVFZWYtKkSUars63XwhTtPGtMOYZNfXzoavPmzUhNTUV6ejqUSqXWuu58jFlbW2PhwoVwcnKCjY0NZsyYgalTp+Kbb77BnTt3AAA2NjYIDw+Hg4MDlEolZs6ciZCQEBw/fhxFRUUAgMWLF2PQoEGYMGECmpqahNwlnTyzyaiFt7c3AKCiokLnbXbv3o2SkhKD2hOJRE8tExwcjMjISNy6dQtLly41qJ3nRWdei+eZKfutK7wmV69exZo1a7Bu3TpIpdJW67vzMZaRkdFqn9zc3ABAcxruyJEjrX40z9HREQBQV1enWbZ27VpkZ2cjMTHRlCEbxTOfjHJycgAAo0eP1iz7/vvv0a9fP9ja2kIqlSIwMBBff/01ACAyMhJRUVG4du0aRCIR/P39Ndvt2bMHw4YNg1QqhUKhgLe3N9avX69Zb2FhgaNHj2L8+PGwtbWFq6srPv744zbjio2NRZ8+ffDRRx/h5MmT7cZPRNi6dSteeOEFSCQS2NvbY+rUqbh06RIAYMuWLZDL5VAqlSgpKUFUVBTc3NywYMECKBQKWFhYYOjQoXB2doZYLIZCocCQIUMwatQoeHh4QCqVws7ODu+++65Wux31UVvOnDkDT09PiEQifPDBBwB+ecNo7+e9v/nmG4Nei7ba0aWfduzYAYVCAblcjsOHD2P8+PFQqVRwd3fHvn372t0vc3vafkRERMDa2houLi6abRYuXAiFQgGRSISysrI2+y0pKQlSqRQ9e/bE/Pnz4erqCqlUiuDgYJw7d65TdQPA8ePHoVKpsGHDBrP0U1JSEogIkydPbreMsY4xXceOWq1GdHQ0PD09IZPJMHDgQKSlpRllfwsKCmBnZwcvL692y9y6dQsymQw+Pj6aZfb29hg9ejQSExO7/i8dm+uEoLmvGdXV1VFmZiZ5eXnRuHHjqKamRrNu//79tHbtWrp//z6Vl5fT8OHDqUePHpr1oaGh5Ofnp1X/tm3bCABt2rSJysvL6f79+/SPf/yDZs2aRUS/XjM6deoUVVRU0P3792nChAkkkUiotrZWK87r168TEdEPP/xAFhYW5O3trYnvyfPZ0dHRZG1tTXv27KGKigrKycmhIUOGkKOjI929e1er7cWLF1NycjJNmzaN/vOf/9D7779PAOjcuXNUW1tLZWVl9Pvf/54A0NGjR6m0tJRqa2spIiKCAFB2drbOfdTWNaOioiICQMnJyZoy7733nmb/79y5Q/b29hQcHExqtdrg1+LJdvTtp1OnTlFlZSWVlJTQqFGjSKFQUENDA5kC9LxmpMt+zJo1i5ydnbW2i4+PJwBUWlpKRG33W3h4OCkUCsrPz6eHDx9SXl4eBQUFkVKppMLCwk7VfeTIEVIqlRQTE6PzvrYw5JqRr68v9evXr811pjzGOho7S5cuJYlEQgcOHKAHDx7QypUrycLCgs6fP69vlxARUUNDAxUXF1NycjJJJBLas2dPu2Vra2tJqVRSREREq3UrVqwgAHTx4kWd2xbimtEzlYwAtPoLDAykTz75hB49etTuths3biQAVFJSQkStD7aGhgays7OjMWPGaG3X1NREiYmJRNT2BIZPP/2UANBPP/2kFWfLgUJEFBUVRQBo0aJFRKR9oNTV1ZGNjQ2FhYVptfvvf/+bAGgO/PYmT7Qko+rqas2yTz75hABQbm5uq/o6muTxZB/pkoyeFBISQlKplC5duqRzO7oko8700/bt2wkAXb16td2YOkOfZKTrfnQmGT3+gY2I6Pz58wSA1q1b16m6O0PfZFRTU0MikYgmTZrU5npzHWOPj536+nqSy+Va9dTV1ZFEIqG33npL5317nLOzMwGgHj160N/+9rcOPzCtWrWK+vTpozWRo8XHH39MAOjTTz/VuW2ewNBJtra2ICIQERobG1FcXIx33nkHERERGDhwIMrKytrcTiwWA0C70yBzcnJQUVGBV199VWu5paUlFi9e3G48LfU2Nja2WyY2NhZ9+/bF9u3bcebMGa11eXl5qKmpwbBhw7SWBwUFwdraWnN6RR/W1tYAoHVBU5c4n9ZHT5Oeno4vvvgC69atQ9++fY3aTmf6qaU/Otp3czHF6/00w4YNg1wu15yS6g5KSkpARJDL5TqVN9Ux9vjYuXz5Murq6jBgwADNeplMBhcXF4P7tqioCCUlJfj888/xySef4MUXX2zzWl1GRgbS09Px9ddft5rIAUDTT/fu3TMoDnN5ppLR46ysrODm5oZ58+YhISEBly9fxqZNmwAAR48exSuvvAInJydIJJJW10ue1DK/387OzuhxSqVSpKSkQCQS4c0330R9fb1mXcukCxsbm1bb2dnZobq62ujxtNC3jzpSXl6Ot99+G0FBQYiKijJ6O0L2kzEJtR8SiQSlpaUmqdsUWu7lk0gkOpU3xzFWW1sLAFi9erXWtdGbN29qTSjQh1gshpOTE8aNG4fU1FTk5eVh48aNWmVSU1OxefNmZGVlaSZrPUkmkwFAu/dAdhXPbDJ6XGBgIAAgPz8fhYWFCAkJgYuLC86dO4fKykrExcV1uH2vXr0AoN1vVp01YsQILFmyBAUFBVoTIlqSX1sHREVFBdzd3U0SjyF91JHFixejoqICKSkpWjOAjNWOUP1kbELsR2NjY7fqI+DXN1d9vj2b+hhzcnIC8MsPKbacnWn5O3v2rM5xtsff3x+WlpbIy8vTLEtOTsbevXtx+vRpzXtUWxoaGgD82m9d1XORjP73f/8XANC3b1/k5uaisbERb731Fnx9fTVPTuiIt7c3HBwccOLECZPFuH79egQEBODixYuaZQMGDICNjQ1+/PFHrbLnzp1DQ0MDhg4dapJYDOmj9hw9ehSfffYZ1qxZg/79+2uWL1u2zGjtCNVPxqbrflhZWRnttGJWVhaICMOHDzd63abS8vQPfe8fMuUx1jIzNTs7W6+YnlReXo433nij1fKCggKo1Wp4/P/27j2qqSvtH/g3QMghIeEiCJY7QWu9tPU2SxldreM7zNv6ClJA06pvbacdtFpKvVFRKQKiFF9lUJyO1WFmROUmC1sVa62LqS4da5fyk4GlRVpEpMpFrgJye35/OGRMAU0gcMA+n7X4w3N29n7Ozk4ez84+57i4gIgQFhaG/Px8ZGdn93hW96iufnJwcOhXbAPtqUtGzc3N6OzsBBGhvLwcycnJ2LhxI+zs7PDhhx/C1dUVwMNbiLS0tKCoqKjbvLCtrS3Ky8tRUlKChoYGmJiYIDw8HN988w1CQkJw+/ZtdHZ2oqGhAYWFhUaJu2sq4dEzB0EQsHr1amRlZSElJQX19fXIz8/H8uXLMWrUKAQHBxul7Z/Tp4/0UV9fj2XLluHFF1/ERx99BODhVMF3332HvLy8Pr0XPX1RitVPxqbvcXh5eeHevXvIzs5GW1sbKisrcfPmTZ26euu3zs5O1NTUoL29HVevXkVoaChcXV2xdOnSftWdk5MzaEu75XI5PD09DX409kB+xgRBwFtvvYXDhw9jz549qK+vR0dHB8rKyrQXqmo0Gjg4ODz2FkQKhQKnTp3CmTNnUF9fj7a2NlwRIraBAAAgAElEQVS5cgVvvvkmFAoFVq1ahcLCQnzyySf47LPPIJVKu102sX37dp06u/qpa4ZoyBqspRIDtTojKyur15V0MpmMRo8eTe+995526SoRUVhYGNna2pK1tTUFBQXR7t27CQCp1WoqLS2ly5cvk5ubG1lYWNDMmTO1yzt3795NEydOJEEQSBAEmjRpEiUlJVFcXBxZWFgQABo9ejQVFxdTSkoK2djYEABydnam6OhobZx2dnbalT0/t3btWp1lp52dnRQfH0+jR48mqVRKNjY25O/vT9evXyci0mnbxcVFu/wzISGB5HI5ASB3d3c6e/Ysbdu2jaysrAgAOTg40MGDByk1NVW7asfGxoYOHz78xD4KDQ3VvkahUNBrr71Gu3btIkdHRwJAcrmcfH19afv27T2+LwDo1Vdf7dN7sXHjxm7t6NNPSUlJ2v7oeo/27t1LKpWKAJCbmxt9//33xhyaRGT40u4nHQcRUXV1Nc2ePZsEQSAPDw96//33ae3atQSAvLy8eh3DwcHBJJVKycnJiczMzEilUtH8+fOpuLi433WfOHGClEolxcTEGNxHfVnaHRISQlKplJqamrTbHv0uMOZnTN+x8+DBAwoLCyNXV1cyMzMje3t7CggIoIKCAiJ6uJoUAEVERDz22Hx9fcnDw4MsLS1JJpORWq0mjUajXQGbn5/f6+cKAMXHx+vUN3fuXHJycqLOzk69+5eXdjP2lDE0GQ2k4OBgsrW1FTuMbvqSjIqKisjMzOyx194MNR0dHTRr1izav3//oLVZVVVFgiDQ9u3bDXodL+1mjA2oYXUX58fw8vJCVFQUoqKitLfIGco6OjqQnZ2NhoYGaDSaQWs3MjISL774IkJCQgatzb7iZMQYG5bWr1+PoKAgaDSaIX8z1NzcXBw5cgQ5OTl6Xx/VXzt27EBeXh5OnDihvX5vKONkxNgvQHh4OJKTk1FXVwcPDw9kZmaKHZJRbNmyBSEhIdprCIeqOXPm4ODBgzr3/RtIR48exYMHD5CbmwsbG5tBabO/zMQOgDE28GJjY7tdMPm08PHxgY+Pj9hhDCl+fn7w8/MTOwyD8JkRY4wx0XEyYowxJjpORowxxkTHyYgxxpjoOBkxxhgT3aCvpuvrDTcZG64WLlyIhQsXih3GkMffDb9sg5aMvL29jfY8eMaGigsXLiAhIYHHNmP9JCEiEjsIxoar9PR0LFy4EPwxYqxfMvg3I8YYY6LjZMQYY0x0nIwYY4yJjpMRY4wx0XEyYowxJjpORowxxkTHyYgxxpjoOBkxxhgTHScjxhhjouNkxBhjTHScjBhjjImOkxFjjDHRcTJijDEmOk5GjDHGRMfJiDHGmOg4GTHGGBMdJyPGGGOi42TEGGNMdJyMGGOMiY6TEWOMMdFxMmKMMSY6TkaMMcZEx8mIMcaY6DgZMcYYEx0nI8YYY6LjZMQYY0x0nIwYY4yJjpMRY4wx0XEyYowxJjpORowxxkTHyYgxxpjoOBkxxhgTnZnYATA2XLS0tKC8vFxn2927dwEAP/zwg852U1NTuLm5DVpsjA13EiIisYNgbDioqamBg4MD2tranlj21VdfxfHjxwchKsaeChk8TceYnmxsbODj4wMTkyd/bDQazSBExNjTg5MRYwZYvHgxnjSZIJPJ4O/vP0gRMfZ04GTEmAF8fX0hCEKv+83MzODr6wtLS8tBjIqx4Y+TEWMGkMvl8Pf3h1Qq7XF/R0cHFi1aNMhRMTb8cTJizEBvvPFGr4sYFAoF/vu//3uQI2Js+ONkxJiBfHx8YGVl1W27VCrFwoULIZPJRIiKseGNkxFjBpJKpdBoNDA3N9fZ3tbWhjfeeEOkqBgb3jgZMdYHr7/+OlpbW3W22dnZ4aWXXhIpIsaGN05GjPXBrFmz4ODgoP23VCrFkiVLYGpqKmJUjA1fnIwY6wMTExMsWbJEO1XX1taG119/XeSoGBu+OBkx1kcajUY7Vefi4oKpU6eKHBFjwxcnI8b6aMqUKfDy8gIALF26FBKJROSIGBu++K7d/xYUFCR2CGwY6pqmu3jxIo8hZrAZM2Zg1apVYocxJPCZ0b9lZmairKxM7DCYgcrKypCZmSla+66urrC2toZKpRItBn3w+B56/vnPf+LChQtihzFk8CMk/k0ikSAtLQ0LFiwQOxRmgPT0dCxcuPCJNy8dSKdPn8Z//dd/ida+Pnh8Dz1dZ9IZGRkiRzIk8CMkGOuvoZ6IGBsOOBkxxhgTHScjxhhjouNkxBhjTHScjBhjjImOk9EQcuLECVhZWeGLL754qtrqSVRUFMaNGweVSgWZTAYvLy+sW7cOjY2NosQjdn8MZadPn8b69etx5MgReHp6QiKRQCKRYMmSJd3K+vj4QKlUwtTUFOPHj8fly5dFiPjx4uLiMHbsWFhYWEChUGDs2LHYtGkT6uvrtWWeND4///xzxMXFoaOjQ6zDeOpwMhpCBnN5stgr+s+cOYOVK1eipKQEVVVViI2NRUJCgmgXjordH0PVxx9/jMTERISHhyMgIAA//PAD1Go1RowYgZSUFBw/flyn/KlTp5CRkYF58+ahoKAAkydPFiny3p09exbvvvsuSktLcffuXURHRyMuLg6BgYHaMk8an12Pn58zZw5qa2vFOpSnCzEiIgJAaWlpg9ZeU1MTzZgx46lrS19z586l9vZ2nW0LFiwgAFRaWqp3PWlpafQ0DOOBfo/6Mr63bt1KY8aMoebmZp3tarWaDh48SCYmJuTk5ES1tbU6+3NycsjPz6/fMQ8Uf3//bscUFBREAKi8vJyI9B+fISEhNGPGDGprazM4jsDAQAoMDOzDETyV0vnMSCT79+9HRUXFU9eWvo4dO9btcQt2dnYAgKamJjFCEtVQe49u3LiBTZs2YfPmzRAEodt+b29vhIaG4vbt21izZo0IEfZdVlZWt2NycnICAO00nL7jMzIyEnl5eUhISBjIkH8ROBn10dmzZzFu3DhYWVlBEARMnDgRX375pU6ZAwcOYOrUqRAEAQqFAu7u7oiOjkZoaChWr16N4uJiSCQSeHl54dy5c3B1dYVEIsHu3bsBAM899xwkEglMTEwwZcoU7Ydg3bp12nb/+te/PjYWfdsCHk5V7dixA8899xxkMhlsbGwwf/58XLt2DQCwZ88eKBQKyOVyHD16FK+88gpUKhWcnZ1x+PDhfvfp7du3YWFhAQ8Pj37XZYie+kOfY01MTIQgCBg5ciSWLVuGUaNGQRAEeHt74+LFiwCAkJAQmJubw9HRUdveihUroFAoIJFIUFVV1eN7BAAnT56ESqXCli1bBrU/uo6NiODr69trmZiYGIwZMwb79u3D6dOney1nrHHV0dGBiIgIuLq6wsLCAs8//zzS0tKMcrxFRUWwtraGm5tbr2V6Gp82NjZ46aWXkJCQwFO9/SXumdnQAQOnMTIyMigyMpLu3btH1dXVNH36dBoxYoR2/86dOwkAbd26laqrq+nevXv05z//mRYtWkRERAEBAaRWq3XqvHXrFgGgXbt2ERFRe3s7ubu7k6ura7cpgw8//JB27typVyz6tEVEFBERQebm5nTgwAGqra2lq1ev0uTJk8nOzo7u3LlDREQbNmwgAPT1119TXV0dVVRU0KxZs0ihUFBra6ve/fdz9+/fJ6VSSSEhIQa9zljTdD31hz7HGhwcTAqFggoLC6mlpYUKCgpo2rRppFQqtdM5ixYtIgcHB5324uPjCQBVVlYSUc/v0bFjx0ipVFJUVFS/j8/Q8e3p6Unjxo3rcZ9araYff/yRiIjOnz9PJiYm5O7uTo2NjUTUfZrOWONqzZo1JJPJKDMzk2pqaig8PJxMTEzo0qVLfekSam1tpbKyMtq1axfJZDI6cOBAr2UfNz7Xr19PAOjKlSsGtc/TdDrSORn9m6Ef1p+LjY0lAFRRUUGtra1kbW1Ns2fP1inT3t5OCQkJRKR/guhKaunp6dpt9+/fJ1dXV6qrq3tiLPq21dTURJaWlqTRaHTKffvttwRA+4XY9aXx6Jx7UlISAaAbN248uaN6sWHDBhozZgzV19cb9LrBSEaPO9bg4GCysrLSqevSpUsEgDZv3kxEfU9GxmTI+G5sbCSJRELz5s3rcf+jyYiIaPXq1QSAVq5cSUS6ychY46q5uZnkcrlOPU1NTSSTyei9997T67h+zsHBgQDQiBEj6I9//ONj/zP1uPH5l7/8hQDQ3//+d4Pa52Skg38zMhapVArg4VTC1atXUVtbi9/97nc6ZUxNTfHBBx8YVO8777wDKysrnTnplJQUzJ8/v9c7RT8ai74KCgrQ2NjY7QFx06ZNg7m5uXbaqSePPu20L7KyspCeno4vv/wSSqWyT3UMFn2OderUqZDL5dppqOGmoqICRAS5XK5X+ZiYGDz77LNISkrCuXPndPYZa1xdv34dTU1NmDBhgna/hYUFHB0d+9zPt27dQkVFBQ4dOoS//e1vmDRpUo+/2z1pfHb10927d/sUB3uIk1EfHT9+HC+//DLs7e0hk8mwbt067b6u6xWsra373Y6lpSX+8Ic/4Pz58/j2228BAH/6058QEhKiVyz66lqeamlp2W2ftbU1Ghoa+ngEj5eamopt27YhNzcX7u7uA9KGGGQyGSorK8UOo09aWloAPDwGfQiCgOTkZEgkErz99ttobm7W7jPWuLp//z4AYOPGjdrrnCQSCW7evNnnBS9SqRT29vbw8fFBamoqCgoKEBsbq1NGn/FpYWEB4D/9xvqGk1EflJaWwt/fH46Ojrh48SLq6uoQFxen3f/MM88AAKqqqozSXkhICKRSKXbu3IlvvvkGLi4uUKvVesWir67E2dOXQ21tLZydnft3ED3YtWsXUlJScObMGW2fPQ3a2toGrM8GQ9eXqyFn1l0PiSsqKkJ0dLR2u7HGlb29PQBg586dICKdP2M8E8jLywumpqYoKCjQbtN3fHY9er6r31jfcDLqg/z8fLS1teG9996Dp6cnBEHQeeS0u7s7bG1tcerUKaO05+zsjAULFiAzMxObNm1CaGio3rHoa8KECbC0tMR3332ns/3ixYtobW3FlClT+n0cXYgIYWFhyM/PR3Z2do//ax7OcnNzQUSYPn06AMDMzKzPU5hiGDlyJCQSCerq6gx6XXR0NMaOHYsrV65otxlrXLm4uEAQBOTl5RkU089VV1fjjTfe6La9qKgIHR0dcHFxMXh8dvWTg4NDv2L7peNk1Aeurq4AHt4mpaWlBUVFRTpz3zKZDOHh4fjmm28QEhKC27dvo7OzEw0NDSgsLAQA2Nraory8HCUlJWhoaHjil9Xq1avR3t6Ompoa/OY3v9E7Fn3bEgQBq1evRlZWFlJSUlBfX4/8/HwsX74co0aNQnBwcN86qweFhYX45JNP8Nlnn0EqlepMu0gkEmzfvt1obQ2Gzs5O1NTUoL29HVevXkVoaChcXV2xdOlSAA//133v3j1kZ2ejra0NlZWVuHnzpk4dPb1HOTk5oiztlsvl8PT0NPjJsF3TdY9en2OscSUIAt566y0cPnwYe/bsQX19PTo6OlBWVoaffvoJAKDRaODg4PDYWxApFAqcOnUKZ86cQX19Pdra2nDlyhW8+eabUCgUWLVqlcHjs6ufJk6caFB/sZ8Rb/HE0AIDV9OFhYWRra0tWVtbU1BQEO3evZsAkFqt1i7p3b17N02cOJEEQSBBEGjSpEmUlJRERESXL18mNzc3srCwoJkzZ9LGjRvJ0dGRAJBcLidfX99ubc6ePZv27dtncCz6ttXZ2Unx8fE0evRokkqlZGNjQ/7+/nT9+nUieri6SS6XEwAaPXo0FRcX0969e0mlUhEAcnNzo++///6JfZefn08Aev2Lj4/X+30wxmq6Xbt2desPfY81ODiYpFIpOTk5kZmZGalUKpo/fz4VFxdr66+urqbZs2eTIAjk4eFB77//Pq1du5YAkJeXV4/v0Z07d+jEiROkVCopJiamX8dHZPj4DgkJIalUSk1NTdptWVlZpFarCQDZ2dlpV8/93Nq1a3WWdhtrXD148IDCwsLI1dWVzMzMyN7engICAqigoICIHt5ZAQBFREQ89th8fX3Jw8ODLC0tSSaTkVqtJo1GQ/n5+URk+PicO3cuOTk5UWdnp979S8Sr6X6Gl3Z3MfTDyoYGsW8HFBwcTLa2tqK1ry9Dx3dRURGZmZk99tqboaajo4NmzZpF+/fvH7Q2q6qqSBAE2r59u8Gv5WSkg5d2M9ZfT+Odm728vBAVFYWoqCjR7qRuiI6ODmRnZ6OhoQEajWbQ2o2MjMSLL76os7qV9Q0nI2Y0165d6za/3tPfYH5ZsL5bv349goKCoNFoDF7MMNhyc3Nx5MgR5OTk6H19VH/t2LEDeXl5OHHihPbaPtZ3nIyY0YwdO7bbstue/lJTU8UO1SjCw8ORnJyMuro6eHh4IDMzU+yQjG7Lli0ICQnB1q1bxQ7lsebMmYODBw/q3ANwIB09ehQPHjxAbm4ubGxsBqXNp52Z2AEwNlzFxsZ2u0jyaeTj4wMfHx+xwxhS/Pz84OfnJ3YYTxU+M2KMMSY6TkaMMcZEx8mIMcaY6DgZMcYYEx0nI8YYY6KTEPGzcgH06eaijDHWH4GBgcjIyBA7jKEgg5d2PyI0NBQzZswQOwxmgAsXLiAhIQFpaWlihzKkLVy4kMf3ELNz506xQxhSOBk9YsaMGViwYIHYYTADJSQk8Pv2BAsXLuTxPcTwGZEu/s2IMcaY6DgZMcYYEx0nI8YYY6LjZMQYY0x0nIwYY4yJjpPRADt06BAkEgm8vb2NWu+JEydgZWWFL774wqj1it0WG3pOnz6N9evX48iRI/D09NQ+l2rJkiXdyvr4+ECpVMLU1BTjx4/H5cuXRYj48eLi4jB27FhYWFhAoVBg7Nix2LRpE+rr67VloqKiMG7cOKhUKshkMnh5eWHdunXaBw1+/vnniIuLeyofrCgWTkYD7NChQ1Cr1bhw4QJu3LhhtHoH81plvi76l+vjjz9GYmIiwsPDERAQgB9++AFqtRojRoxASkoKjh8/rlP+1KlTyMjIwLx581BQUIDJkyeLFHnvzp49i3fffRelpaW4e/cuoqOjERcXh8DAQG2ZM2fOYOXKlSgpKUFVVRViY2ORkJCAoKAgAICvry8EQcCcOXNQW1sr1qE8VTgZDaDq6moUFhZi8+bNAIC///3vfaqnubm525nV3LlzUVdXh3nz5vU7TrHaGu566qvhULe+tm3bhtTUVKSnp0OpVOrsS0xMhImJCYKDg4f8U2B/ztzcHCtWrIC9vT0sLS0RFBSE+fPn46uvvsJPP/0EALC0tERwcDBsbW2hVCqxYMEC+Pv74+TJk7h16xYA4IMPPsALL7yAV199Fe3t7WIe0lOBk9EASk9Px9y5c7X/izpw4ECfzjL279+PioqKAYhQ3LaGu4HsK7Hfhxs3bmDTpk3YvHkzBEHott/b2xuhoaG4ffs21qxZI0KEfZeVldXtmJycnABAOw137NgxmJqa6pSxs7MDADQ1NWm3RUZGIi8vDwkJCQMZ8i8CJ6MBdOjQIbz22mtQKpXw8fFBSUkJzp4922PZAwcOYOrUqRAEAQqFAu7u7oiOjkZoaChWr16N4uJiSCQSeHl54dy5c3B1dYVEIsHu3bsBAM899xwkEglMTEwwZcoU7Qdm3bp1sLKygiAI+Otf/4qzZ89i3Lhx2m0TJ07El19+CQB6twU8nLrbsWMHnnvuOchkMtjY2GD+/Pm4du0aAGDPnj1QKBSQy+U4evQoXnnlFahUKjg7O+Pw4cMD2e1P9KTYQ0JCYG5urvMI6xUrVkChUEAikaCqqqrHvkpMTIQgCBg5ciSWLVuGUaNGQRAEeHt74+LFi/2qGwBOnjwJlUqFLVu2DHgfJSYmgojg6+vba5mYmBiMGTMG+/btw+nTp3stZ6yx0tHRgYiICLi6usLCwgLPP/+80W4DVVRUBGtra7i5ufVa5vbt27CwsICHh4d2m42NDV566SUkJCTwdHZ/ESMiIgCUlpZmtPpu3rxJ9vb21N7eTkREBw4cIAD0+9//vlvZnTt3EgDaunUrVVdX07179+jPf/4zLVq0iIiIAgICSK1W67zm1q1bBIB27dpFRETt7e3k7u5Orq6u2ja7fPjhh7Rz504iIsrIyKDIyEi6d+8eVVdX0/Tp02nEiBHasvq0RUQUERFB5ubmdODAAaqtraWrV6/S5MmTyc7Oju7cuUNERBs2bCAA9PXXX1NdXR1VVFTQrFmzSKFQUGtra5/69efS0tLI0GGsT+yLFi0iBwcHndfFx8cTAKqsrCSinvsqODiYFAoFFRYWUktLCxUUFNC0adNIqVRSaWlpv+o+duwYKZVKioqKMuh4iQwf356enjRu3Lge96nVavrxxx+JiOj8+fNkYmJC7u7u1NjYSEREOTk55Ofnpy1vrLGyZs0akslklJmZSTU1NRQeHk4mJiZ06dIlg/uDiKi1tZXKyspo165dJJPJ6MCBA72WvX//PimVSgoJCem2b/369QSArly5YlD7gYGBFBgYaHDcT6l0PjMaIIcOHcL//M//aE/1fX19IZPJkJGRgebmZm25trY2bN68GbNnz8ZHH30EW1tb2NjY4Pe//z2mTZumd3umpqb44IMPUFpaiqysLO32pqYmHDlyBG+//TaAh3cJ/vjjj2FjYwNbW1v4+vqiuroalZWVerfV3NyMHTt24LXXXsPixYthZWWFiRMn4tNPP0VVVRX27t2rU97b2xsqlQr29vbQaDS4f/8+SktL9W7PmAyNvS/MzMy0ZwHjxo3Dnj170NDQgOTk5H7VO3fuXNTX12PTpk39jvFx7t+/jx9//BFqtfqJZWfMmIEPP/wQJSUl+Oijj7rtN9ZYaWlpwZ49e+Dv74+AgABYW1tj48aNkEqlfe5XFxcXODs7IzIyEp988gkWLlzYa9nY2FiMGjUKMTEx3faNHj0aAJCfn9+nONhDnIwGSNcUXReVSgUfHx/U19fj6NGj2u1Xr15FbW0tfve73+m8viu5GOKdd96BlZWVzvx1SkoK5s+fD5VK1eNrpFIpABi0RLWgoACNjY2YOnWqzvZp06bB3NxcOyXVE3NzcwAPk7AY+hN7X02dOhVyuVw7LTXUVVRUgIggl8v1Kh8TE4Nnn30WSUlJOHfunM4+Y42V69evo6mpCRMmTNDut7CwgKOjY5/79datW6ioqMChQ4fwt7/9DZMmTerxd7qsrCykp6fjyy+/7LaQA4C2n+7evdunONhDnIwGwL/+9S/k5+dj3rx52msyJBKJ9jqdR1fVdV3bYG1t3e92LS0t8Yc//AHnz5/Ht99+CwD405/+hJCQEG2Z48eP4+WXX4a9vT1kMhnWrVtncDtdS1ktLS277bO2tkZDQ0Mfj2DgiRW7TCYz6OxTTC0tLQAexqwPQRCQnJwMiUSCt99+W+fM31j9ff/+fQDAxo0bdT5TN2/e1FlQYAipVAp7e3v4+PggNTUVBQUFiI2N1SmTmpqKbdu2ITc3F+7u7j3WY2FhAeA//cb6hpPRADh48CBef/11EJHO371792BhYYFTp07hzp07AIBnnnkGAFBVVWWUtkNCQiCVSrFz50588803cHFx0U63lJaWwt/fH46Ojrh48SLq6uoQFxdncBtdibOnL5La2lo4Ozv37yAGkBixt7W1Dfl+eVTXl6shZ8szZszAqlWrUFRUhOjoaO12Y/W3vb09gIfPAPr55+rChQt6x9kbLy8vmJqaoqCgQLtt165dSElJwZkzZ7Sf0560trYC+E+/sb7hZGRkRITU1FSsWLGi2z4bGxsEBQWho6MDhw4dAgC4u7vD1tYWp06dMkr7zs7OWLBgATIzM7Fp0yaEhoZq9+Xn56OtrQ3vvfcePD09IQhCn55wO2HCBFhaWuK7777T2X7x4kW0trZiypQp/T6OgaJv7GZmZkabSszNzQURYfr06UaveyCMHDkSEonE4OuHoqOjMXbsWFy5ckW7zVhjxcXFBYIgIC8vz6CYfq66uhpvvPFGt+1FRUXo6OiAi4sLiAhhYWHIz89HdnZ2j2d1j+rqJwcHh37F9kvHycjIzp8/D5VKhV//+tc97l++fDmA/0zVyWQyhIeH45tvvkFISAhu376Nzs5ONDQ0oLCwEABga2uL8vJylJSUoKGh4YlfZKtXr0Z7eztqamrwm9/8Rrvd1dUVwMPbu7S0tKCoqKjbnL0+bQmCgNWrVyMrKwspKSmor69Hfn4+li9fjlGjRiE4OFjP3hp8+sbu5eWFe/fuITs7G21tbaisrMTNmzd16uqtrzo7O1FTU4P29nZcvXoVoaGhcHV1xdKlS/tVd05OzqAs7ZbL5fD09ERZWZlBr+uarnv0+hxjjRVBEPDWW2/h8OHD2LNnD+rr69HR0YGysjLthaoajQYODg6PvQWRQqHAqVOncObMGdTX16OtrQ1XrlzBm2++CYVCgVWrVqGwsBCffPIJPvvsM0ilUp1pQYlEgu3bt+vU2dVPEydONKi/2M+Is4pv6IERlnb//ve/J4VCQWZmZvTCCy/Q5cuXdfZHR0fTqFGjCAABICcnJ0pKSiIiot27d9PEiRNJEAQSBIEmTZqk3Xf58mVyc3MjCwsLmjlzJm3cuJEcHR0JAMnlcvL19e0Wy+zZs2nfvn3dtoeFhZGtrS1ZW1tTUFAQ7d69mwCQWq2m0tJSvdvq7Oyk+Ph4Gj16NEmlUrKxsSF/f3+6fv06ERElJSWRXC4nADR69GgqLi6mvXv3kkqlIgDk5uZG33//fb/6m6hvS7ufFDsRUXV1Nc2ePZsEQSAPDw96//33ae3atQSAvLy8euyrO3fuUHBwMEmlUnJyciIzMzNSqVQ0f/58Ki4u7nfdJ06cIKVSSTExMQb3k6HjOyQkhKRSKTU1NWm3ZWVlkVqtJtqnd7kAABzfSURBVABkZ2dHK1eu7PG1a9eu1Vnabayx8uDBAwoLCyNXV1cyMzMje3t7CggIoIKCAiIi8vf3JwAUERHx2GPz9fUlDw8PsrS0JJlMRmq1mjQaDeXn5xMRUX5+vvYz2tNffHy8Tn1z584lJycn6uzs1Lt/iXhp98+kczL6N2MkIzb4+pKMBlJwcDDZ2tqKHUY3ho7voqIiMjMze+y1N0NNR0cHzZo1i/bv3z9obVZVVZEgCLR9+3aDX8vJSAdfZ8SYsT0Nd3L28vJCVFQUoqKitLfIGco6OjqQnZ2NhoYGaDSaQWs3MjISL774os6KVdY3nIwYYz1av349goKCoNFohvzNUHNzc3HkyBHk5OTofX1Uf+3YsQN5eXk4ceKE9no91necjBgzkvDwcCQnJ6Ourg4eHh7IzMwUO6R+27JlC0JCQrB161axQ3msOXPm4ODBgzr3/BtIR48exYMHD5CbmwsbG5tBafNpZyZ2AIw9LWJjY7tdNPk08PHxgY+Pj9hhDCl+fn7w8/MTO4ynCp8ZMcYYEx0nI8YYY6LjZMQYY0x0nIwYY4yJjhcwPMIYN1xkg6vrPUtPTxc5kqGPx/fQUlZWNmxunjsYJET8rFwAfbphKGOM9UdgYCAyMjLEDmMoyOAzo3/jnMz6Ij09HQsXLuTxw1g/8W9GjDHGRMfJiDHGmOg4GTHGGBMdJyPGGGOi42TEGGNMdJyMGGOMiY6TEWOMMdFxMmKMMSY6TkaMMcZEx8mIMcaY6DgZMcYYEx0nI8YYY6LjZMQYY0x0nIwYY4yJjpMRY4wx0XEyYowxJjpORowxxkTHyYgxxpjoOBkxxhgTHScjxhhjouNkxBhjTHScjBhjjImOkxFjjDHRcTJijDEmOk5GjDHGRMfJiDHGmOg4GTHGGBMdJyPGGGOi42TEGGNMdJyMGGOMiY6TEWOMMdFxMmKMMSY6TkaMMcZEZyZ2AIwNFxUVFUhOTtbZdvXqVQBAXFycznZbW1u8++67gxYbY8OdhIhI7CAYGw7a29vh6OiImpoaSKXSXss9ePAAwcHB+PTTTwcxOsaGtQyepmNMT2ZmZnj99ddhamqKBw8e9PoHAG+88YbI0TI2vHAyYswAr7/+Otra2h5bxtHRETNnzhykiBh7OnAyYswAM2bMgLOzc6/7zc3NsWTJEpiY8EeLMUPwJ4YxA0gkEixevLjX34xaW1vx+uuvD3JUjA1/nIwYM9Djpuo8PT0xadKkQY6IseGPkxFjBnr++efx7LPPdttubm6ON998U4SIGBv+OBkx1gdLlizpNlXX2toKjUYjUkSMDW+cjBjrg8WLF6O9vV37b4lEghdeeAFjxowRMSrGhi9ORoz1gZubGyZPngyJRAIAMDU15Sk6xvqBkxFjffS///u/MDU1BQB0dHRgwYIFIkfE2PDFyYixPlqwYAE6OzshkUjw61//Gk5OTmKHxNiwxcmIsT5ydHTESy+9BCLiKTrG+olvlNpP6enpWLhwodhhMMZExF+j/ZbBj5AwkrS0NLFDGNYWLlyI0NBQzJgxQ+xQDNLc3Iy9e/figw8+GPC2du7cCQD48MMPB7wtpp8LFy4gISFB7DCeCpyMjIR/vO6fhQsXYsaMGcOyH3/729/imWeeGfB2MjIyAPBYG2o4GRkH/2bEWD8NRiJi7GnHyYgxxpjoOBkxxhgTHScjxhhjouNkxBhjTHScjIaAd955B0qlEhKJBHl5eWKHo7e4uDiMHTsWFhYWUCgUGDt2LDZt2oT6+npR4jlx4gSsrKzwxRdfiNL+UHf69GmsX78eR44cgaenJyQSCSQSCZYsWdKtrI+PD5RKJUxNTTF+/HhcvnxZhIgfT5/xFxUVhXHjxkGlUkEmk8HLywvr1q1DY2MjAODzzz9HXFwcOjo6xDoM9m+cjIaAffv24bPPPhM7DIOdPXsW7777LkpLS3H37l1ER0cjLi4OgYGBosTDFx727uOPP0ZiYiLCw8MREBCAH374AWq1GiNGjEBKSgqOHz+uU/7UqVPIyMjAvHnzUFBQgMmTJ4sUee/0GX9nzpzBypUrUVJSgqqqKsTGxiIhIQFBQUEAAF9fXwiCgDlz5qC2tlasQ2HgZMT6wdzcHCtWrIC9vT0sLS0RFBSE+fPn46uvvsJPP/006PHMnTsXdXV1mDdv3qC3DTy8ANbb21uUth9n27ZtSE1NRXp6OpRKpc6+xMREmJiYIDg4GHV1dSJF2Df6jD9LS0sEBwfD1tYWSqUSCxYsgL+/P06ePIlbt24BAD744AO88MILePXVV3UeC8IGFyejIaLrUQTDSVZWFgRB0NnWdbPQrmmQX5L9+/ejoqJC7DB03LhxA5s2bcLmzZu7vVcA4O3tjdDQUNy+fRtr1qwRIcK+02f8HTt2THtn9S52dnYAgKamJu22yMhI5OXl8QWsIuJkJAIiQnx8PJ599lnIZDJYWVlh7dq1OmU6OjoQEREBV1dXWFhY4Pnnn9fecmjPnj1QKBSQy+U4evQoXnnlFahUKjg7O+Pw4cPaOv7xj3/gV7/6FeRyOVQqFSZOnKidT39c/f1RVFQEa2truLm59bsuQ5w7dw6urq6QSCTYvXs3AP36KTExEYIgYOTIkVi2bBlGjRoFQRDg7e2NixcvAgBCQkJgbm4OR0dHbXsrVqyAQqGARCJBVVUVQkNDsXr1ahQXF0MikcDLywsAcPLkSahUKmzZsmVQ+6NLYmIiiAi+vr69lomJicGYMWOwb98+nD59utdyRIQdO3bgueeeg0wmg42NDebPn49r164B0H9cDtTYA/Qbf7dv34aFhQU8PDy022xsbPDSSy8hISGBp3vFQqxf0tLSyNBu3LBhA0kkEvq///s/qqmpoaamJkpKSiIAdOXKFSIiWrNmDclkMsrMzKSamhoKDw8nExMTunTpkrYOAPT1119TXV0dVVRU0KxZs0ihUFBrays1NjaSSqWiuLg4am5upjt37tBrr71GlZWVetVviNbWViorK6Ndu3aRTCajAwcOGFwHAEpLSzP4dY+6desWAaBdu3Zptz2pn4iIgoODSaFQUGFhIbW0tFBBQQFNmzaNlEollZaWEhHRokWLyMHBQae9+Ph4AqDt04CAAFKr1Tpljh07RkqlkqKiovp1bEREgYGBFBgYaNBrPD09ady4cT3uU6vV9OOPPxIR0fnz58nExITc3d2psbGRiIhycnLIz89PWz4iIoLMzc3pwIEDVFtbS1evXqXJkyeTnZ0d3blzh4j0629jjj0iw8bf/fv3SalUUkhISLd969ev1/kM6qMvn3/Wo3TuxX4ydDA2NTWRXC6n3/72tzrbDx8+rP0gNDc3k1wuJ41Go/M6mUxG7733HhH950Pf3NysLdOV0G7cuEH/+te/CAAdO3asWwz61G8IBwcHAkAjRoygP/7xj9ovHUMMdDLqrZ+IHiYjKysrnbouXbpEAGjz5s1E1PdkZEyGJqPGxkaSSCQ0b968Hvc/moyIiFavXk0AaOXKlUSkm4yamprI0tJSZ8wQEX377bcEQJtsn9Tfxh57RIaNvw0bNtCYMWOovr6+276//OUvBID+/ve/6902JyOjSedpukF248YNNDU1Yc6cOb2WuX79OpqamjBhwgTtNgsLCzg6OmqnRHpibm4OAGhra4OnpydGjhyJxYsXIzIyEiUlJf2uvze3bt1CRUUFDh06hL/97W+YNGnSkPvt5FGP9lNvpk6dCrlc3qf+GCoqKipARJDL5XqVj4mJwbPPPoukpCScO3dOZ19BQQEaGxsxdepUne3Tpk2Dubm5dkqzJ4/2t7HHHqD/+MvKykJ6ejq+/PLLbgs5AGj76e7du32Kg/UPJ6NBVlZWBgCwt7fvtcz9+/cBABs3btReCyKRSHDz5k2dH10fx8LCAmfOnMHMmTOxZcsWeHp6QqPRoLm52Sj1P0oqlcLe3h4+Pj5ITU1FQUEBYmNjDa5nqJHJZKisrBQ7jD5raWkB8PA49CEIApKTkyGRSPD222+jublZu69r2bOlpWW311lbW6OhoUGvNow99gD9xl9qaiq2bduG3NxcuLu791iPhYUFgP/0GxtcnIwGWdfqnwcPHvRapitR7dy5E0Sk83fhwgW92xo/fjy++OILlJeXIywsDGlpadi+fbvR6u+Jl5cXTE1NUVBQ0K96xNbW1oba2lo4OzuLHUqfdX25GnJB54wZM7Bq1SoUFRUhOjpau93a2hoAekw6hvTTQI49oOfxt2vXLqSkpODMmTOPvcN6a2srgP/0GxtcnIwG2YQJE2BiYoJ//OMfvZZxcXGBIAj9uhtDeXk5CgsLATz8Ati6dSsmT56MwsJCo9RfXV2NN954o9v2oqIidHR0wMXFpc91DwW5ubkgIkyfPh0AYGZm9thpvaFo5MiRkEgkBl8/FB0djbFjx+LKlSvabRMmTIClpSW+++47nbIXL15Ea2srpkyZolfdxhh7gH7jj4gQFhaG/Px8ZGdn93hW96iufnJwcOhXbKxvOBkNMnt7ewQEBCAzMxP79+9HfX09rl69ir1792rLCIKAt956C4cPH8aePXtQX1+Pjo4OlJWV6X0xaXl5OZYtW4Zr166htbUVV65cwc2bNzF9+nSj1K9QKHDq1CmcOXMG9fX1aGtrw5UrV/Dmm29CoVBg1apVfeofsXR2dqKmpgbt7e24evUqQkND4erqiqVLlwJ4+D/ue/fuITs7G21tbaisrMTNmzd16rC1tUV5eTlKSkrQ0NCAtrY25OTkiLa0Wy6Xw9PTUzs1rK+u6bpHr88RBAGrV69GVlYWUlJSUF9fj/z8fCxfvhyjRo1CcHCw3nU/aexpNBo4ODg89hZE+oy/wsJCfPLJJ/jss88glUp1pgUlEgm2b9+uU2dXP02cONGg/mJGIsKqiadKX1bTNDQ00DvvvEMjRowgS0tLmjlzJkVERBAAcnZ2pv/3//4fPXjwgMLCwsjV1ZXMzMzI3t6eAgICqKCggJKSkkgulxMAGj16NBUXF9PevXtJpVIRAHJzc6OvvvqKvL29ycbGhkxNTemZZ56hDRs2UHt7OxHRY+vXl6+vL3l4eJClpSXJZDJSq9Wk0WgoPz/foP4g6v9qul27dpGjoyMBILlcTr6+vnr10/fff0/BwcEklUrJycmJzMzMSKVS0fz586m4uFhbf3V1Nc2ePZsEQSAPDw96//33ae3atQSAvLy8qLS0lC5fvkxubm5kYWFBM2fOpDt37tCJEydIqVRSTExMn4+tS1+WdoeEhJBUKqWmpibttqysLFKr1QSA7OzstKvnfm7t2rU6S7s7OzspPj6eRo8eTVKplGxsbMjf35+uX79ORKR3fz9p7Pn7+xMAioiIeOyxPWn85efnE4Be/+Lj43Xqmzt3Ljk5OVFnZ6fe/cur6YyGl3b3Fw9G4+hvMuqP4OBgsrW1FaVtQ/QlGRUVFZGZmVmfrv0SS0dHB82aNYv2798/aG1WVVWRIAi0fft2g17Hn3+j4aXdjAGG/cg/nHh5eSEqKgpRUVHD4hZNHR0dyM7ORkNDAzQazaC1GxkZiRdffBEhISGD1ibTxcmI6bh27Vq3ufWe/gbzi4L1z/r16xEUFASNRjPkb4aam5uLI0eOICcnR+/ro/prx44dyMvLw4kTJyCVSgelTdYdJyOmY+zYsd2W3Pb0l5qaKnaoRhEeHo7k5GTU1dXBw8MDmZmZYoc0ILZs2YKQkBBs3bpV7FAea86cOTh48KDOfQAH0tGjR/HgwQPk5ubCxsZmUNpkPTMTOwDGxBQbG/tUXKCrDx8fH/j4+IgdxpDi5+cHPz8/scNg4DMjxhhjQwAnI8YYY6LjZMQYY0x0nIwYY4yJjhcwGEl6errYIQx7xrhR5tOs63Y1PNaGDh6zxiMh4mfs9kd6ejoWLlwodhiMMRHx12i/ZfCZkZHwYOwfiUSCtLQ0LFiwQOxQhqygoCAAQEZGhsiRsC78n1Hj4d+MGGOMiY6TEWOMMdFxMmKMMSY6TkaMMcZEx8mIMcaY6DgZMcYYEx0noyHkyJEj8PT07PbsIHNzc4wcORIvv/wy4uPjUVNTI3ao7Clw+vRprF+/vtu4W7JkSbeyPj4+UCqVMDU1xfjx43H58mURIn68mJiYHp+9NWHCBADA559/jri4uKf2QYrDHSejISQgIAA//PAD1Go1rKysQETo7OxERUUF0tPT4eHhgbCwMIwfPx7fffed2OGyYezjjz9GYmIiwsPDdcbdiBEjkJKSguPHj+uUP3XqFDIyMjBv3jwUFBRg8uTJIkXed76+vhAEAXPmzEFtba3Y4bCf4WQ0xEkkElhbW+Pll19GcnIy0tPTcffuXcydO3fIP7VzOGhuboa3t/ewq7s/tm3bhtTUVKSnp0OpVOrsS0xMhImJCYKDg4fl+Dpw4EC3B0H+61//0u7/4IMP8MILL+DVV19Fe3u7iJGyn+NkNMwEBgZi6dKlqKiowKeffip2OMPe/v37UVFRMezq7qsbN25g06ZN2Lx5MwRB6Lbf29sboaGhuH37NtasWSNChAMvMjISeXl5SEhIEDsU9ghORsPQ0qVLAQA5OTkAgI6ODkRERMDV1RUWFhZ4/vnnkZaWBgDYs2cPFAoF5HI5jh49ildeeQUqlQrOzs44fPiwts5//OMf+NWvfgW5XA6VSoWJEyeivr7+ifWLhYiwY8cOPPfcc5DJZLCxscH8+fNx7do1AEBISAjMzc11Hl+9YsUKKBQKSCQSVFVVITQ0FKtXr0ZxcTEkEgm8vLyQmJgIQRAwcuRILFu2DKNGjYIgCPD29sbFixf7VTcAnDx5EiqVClu2bBnE3vqPxMREEBF8fX17LRMTE4MxY8Zg3759OH36dK/lnvQe6Dv2Bnt82djY4KWXXkJCQgLfxmsoIdYvaWlpZOxuVKvVZGVl1ev++vp6AkAuLi5ERLRmzRqSyWSUmZlJNTU1FB4eTiYmJnTp0iUiItqwYQMBoK+//prq6uqooqKCZs2aRQqFglpbW6mxsZFUKhXFxcVRc3Mz3blzh1577TWqrKzUq35jAEBpaWl6l4+IiCBzc3M6cOAA1dbW0tWrV2ny5MlkZ2dHd+7cISKiRYsWkYODg87r4uPjCYD22AICAkitVuuUCQ4OJoVCQYWFhdTS0kIFBQU0bdo0UiqVVFpa2q+6jx07RkqlkqKiovQ+1i6BgYEUGBho8Ose5enpSePGjetxn1qtph9//JGIiM6fP08mJibk7u5OjY2NRESUk5NDfn5+2vL6vAdPGntExhtf0dHR5OzsTNbW1iSVSsnd3Z38/Pzo22+/7VZ2/fr1BICuXLliUBs/NxCf/1+odD4zGoaUSiUkEgkaGhrQ0tKCPXv2wN/fHwEBAbC2tsbGjRshlUqRnJys8zpvb2+oVCrY29tDo9Hg/v37KC0tRUlJCerr6zF+/HgIggAHBwccOXIEdnZ2BtU/WJqbm7Fjxw689tprWLx4MaysrDBx4kR8+umnqKqqwt69e/vdhpmZmfZ//OPGjcOePXvQ0NDQ72OeO3cu6uvrsWnTpn7HaKj79+/jxx9/hFqtfmLZGTNm4MMPP0RJSQk++uijbvsNfQ96G3vGHF9vvvkmPv/8c9y6dQuNjY04fPgwSktL8dJLL6GgoECn7OjRowEA+fn5BrXBBg4no2Ho/v37ICKoVCpcv34dTU1N2uWrAGBhYQFHR0ftdElPzM3NAQBtbW3w9PTEyJEjsXjxYkRGRqKkpERbrq/1D6SCggI0NjZi6tSpOtunTZsGc3Nz7XSaMU2dOhVyuVy0YzaGiooKEBHkcrle5WNiYvDss88iKSkJ586d09nXn/fg0bFnzPHl4uKCSZMmwdLSEubm5pg+fTqSk5PR3NyMpKQknbJdfXD37l2D2mADh5PRMPT9998DAMaOHYv79+8DADZu3KhzbcXNmzfR1NSkV30WFhY4c+YMZs6ciS1btsDT0xMajQbNzc1Gqd/YupblWlpadttnbW2NhoaGAWlXJpOhsrJyQOoeDC0tLQAeHoc+BEFAcnIyJBIJ3n77bTQ3N2v3Ges9GOjxNXHiRJiammo/M10sLCwA/KdPmPg4GQ1DJ0+eBAC88sorsLe3BwDs3Lmz25JWQ55COX78eHzxxRcoLy9HWFgY0tLSsH37dqPVb0zW1tYA0OMXXm1tLZydnY3eZltb24DVPVi6voANuehzxowZWLVqFYqKihAdHa3dbqz3YKDHV2dnJzo7O7sl4NbWVgD/6RMmPk5Gw8ydO3ewc+dOODs74+2334aLiwsEQUBeXl6f6ywvL0dhYSGAh18OW7duxeTJk1FYWGiU+o1twoQJsLS07Hbh78WLF9Ha2oopU6YAePi7T1tbm1HazM3NBRFh+vTpRq97sIwcORISicTg64eio6MxduxYXLlyRbtN3/fgSYw5vn73u99123bp0iUQEWbMmKGzvasPHBwc+t0uMw5ORkMUEaGxsRGdnZ0gIlRWViItLQ2//vWvYWpqiuzsbKhUKgiCgLfeeguHDx/Gnj17UF9fj46ODpSVleGnn37Sq63y8nIsW7YM165dQ2trK65cuYKbN29i+vTpRqnf2ARBwOrVq5GVlYWUlBTU19cjPz8fy5cvx6hRoxAcHAwA8PLywr1795CdnY22tjZUVlbi5s2bOnXZ2tqivLwcJSUlaGho0CaYzs5O1NTUoL29HVevXkVoaChcXV21y+r7WndOTo5oS7vlcjk8PT1RVlZm0Ou6putMTU11tunzHuhT95PGl0ajgYODwxNvQXT79m2kpqaitrYWbW1tuHDhAt555x24urpi+fLlOmW7+mDixImGdAUbSIO9fu9pY8ylnZ9//jk9//zzJJfLydzcnExMTAgASSQSsra2pl/96lcUFRVF1dXVOq978OABhYWFkaurK5mZmZG9vT0FBARQQUEBJSUlkVwuJwA0evRoKi4upr1795JKpSIA5ObmRl999RV5e3uTjY0NmZqa0jPPPEMbNmyg9vb2J9ZvLDBwaXdnZyfFx8fT6NGjSSqVko2NDfn7+9P169e1Zaqrq2n27NkkCAJ5eHjQ+++/T2vXriUA5OXlRaWlpXT58mVyc3MjCwsLmjlzJt25c4eCg4NJKpWSk5MTmZmZkUqlovnz51NxcXG/6z5x4gQplUqKiYkxuI+MsbQ7JCSEpFIpNTU1abdlZWWRWq0mAGRnZ0crV67s8bVr167VWdr9pPdAn7H3/fffP3F8+fv7EwCKiIh47LGtXr2a1Go1KRQKMjMzI2dnZ3r33XepvLy8W9m5c+eSk5MTdXZ2GtyHj+Kl3UaTzr3YTzwYjcPQZDSQgoODydbWVuwwujFGMioqKiIzMzM6cOCAkaIaeB0dHTRr1izav3+/UeqrqqoiQRBo+/bt/a6LP/9Gw9cZMdaTp/XOzl5eXoiKikJUVBQaGxvFDueJOjo6kJ2djYaGBmg0GqPUGRkZiRdffBEhISFGqY8ZBycjxn5h1q9fj6CgIGg0miF/M9Tc3FwcOXIEOTk5el8f9Tg7duxAXl4eTpw4AalUaoQImbFwMmLsEeHh4UhOTkZdXR08PDyQmZkpdkgDYsuWLQgJCcHWrVvFDuWx5syZg4MHD+rcB7Cvjh49igcPHiA3Nxc2NjZGiI4Zk5nYATA2lMTGxiI2NlbsMAaFj48PfHx8xA5j0Pj5+cHPz0/sMFgv+MyIMcaY6DgZMcYYEx0nI8YYY6LjZMQYY0x0vIDBSIKCgsQOYdjbuXMnMjIyxA5jyPrnP/8JgMfaUGLorZVY7yRE/Nzd/rhw4QJ27NghdhiMMRHxf6L6LYOTEWOMMbFl8G9GjDHGRMfJiDHGmOg4GTHGGBMdJyPGGGOi+//w8VinibPegAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def f1_score(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + K.epsilon())\n",
        "    recall = true_positives / (possible_positives + K.epsilon())\n",
        "    f1_val = 2*(precision*recall)/(precision+recall+K.epsilon())\n",
        "    return f1_val"
      ],
      "metadata": {
        "id": "kqwf8GlmfrK3"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "METRICS = [\n",
        "      tf.keras.metrics.BinaryAccuracy(name='accuracy'),\n",
        "      tf.keras.metrics.Precision(name='precision'),\n",
        "      tf.keras.metrics.Recall(name='recall'),  \n",
        "      tf.keras.metrics.AUC(name='auc'),\n",
        "      f1_score,\n",
        "]"
      ],
      "metadata": {
        "id": "kdbatRSYfs__"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=METRICS)"
      ],
      "metadata": {
        "id": "NoVlg60LYsl4"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Declaring Callbacks"
      ],
      "metadata": {
        "id": "lObfMNoaDyW_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS = 30\n",
        "earlystop = keras.callbacks.EarlyStopping(verbose = 1, patience=5)\n",
        "learning_rate_reduction = keras.callbacks.ReduceLROnPlateau(monitor = 'val_loss',\n",
        "                                                            patience = 2,\n",
        "                                                            verbose = 1,\n",
        "                                                            factor = 0.8, \n",
        "                                                            min_lr = 1e-6)\n",
        "\n",
        "checkpoint = keras.callbacks.ModelCheckpoint('/content/drive/MyDrive/Diabetic Rentinopathy/Models/ResNet52.h5', monitor='val_loss', verbose=1, \n",
        "                             save_best_only=True, mode='min', save_weights_only = False)\n",
        "mycallbacks = [earlystop, learning_rate_reduction,checkpoint]"
      ],
      "metadata": {
        "id": "5zV_26fSZB9S"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_gen,\n",
        "                    batch_size = 16, \n",
        "                    validation_data=val_gen,\n",
        "                    epochs=EPOCHS, \n",
        "                    callbacks=mycallbacks\n",
        "                    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7NXuTxiKaFbU",
        "outputId": "b23fd44d-14c3-4c85-f06f-2a29b137d153"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 1.0741 - accuracy: 0.8642 - precision: 0.8160 - recall: 0.4145 - auc: 0.8524 - f1_score: 0.5392\n",
            "Epoch 1: val_loss improved from inf to 0.99353, saving model to /content/drive/MyDrive/Diabetic Rentinopathy/Models/ResNet52.h5\n",
            "184/184 [==============================] - 1365s 7s/step - loss: 1.0741 - accuracy: 0.8642 - precision: 0.8160 - recall: 0.4145 - auc: 0.8524 - f1_score: 0.5392 - val_loss: 0.9935 - val_accuracy: 0.8877 - val_precision: 0.7888 - val_recall: 0.5986 - val_auc: 0.8945 - val_f1_score: 0.6761 - lr: 0.0010\n",
            "Epoch 2/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.8648 - accuracy: 0.8865 - precision: 0.8167 - recall: 0.5578 - auc: 0.9052 - f1_score: 0.6578\n",
            "Epoch 2: val_loss improved from 0.99353 to 0.74089, saving model to /content/drive/MyDrive/Diabetic Rentinopathy/Models/ResNet52.h5\n",
            "184/184 [==============================] - 327s 2s/step - loss: 0.8648 - accuracy: 0.8865 - precision: 0.8167 - recall: 0.5578 - auc: 0.9052 - f1_score: 0.6578 - val_loss: 0.7409 - val_accuracy: 0.8975 - val_precision: 0.8101 - val_recall: 0.6370 - val_auc: 0.9310 - val_f1_score: 0.7103 - lr: 0.0010\n",
            "Epoch 3/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.7756 - accuracy: 0.8939 - precision: 0.8074 - recall: 0.6165 - auc: 0.9243 - f1_score: 0.6970\n",
            "Epoch 3: val_loss improved from 0.74089 to 0.66638, saving model to /content/drive/MyDrive/Diabetic Rentinopathy/Models/ResNet52.h5\n",
            "184/184 [==============================] - 287s 2s/step - loss: 0.7756 - accuracy: 0.8939 - precision: 0.8074 - recall: 0.6165 - auc: 0.9243 - f1_score: 0.6970 - val_loss: 0.6664 - val_accuracy: 0.9063 - val_precision: 0.8170 - val_recall: 0.6849 - val_auc: 0.9444 - val_f1_score: 0.7439 - lr: 0.0010\n",
            "Epoch 4/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.7420 - accuracy: 0.8968 - precision: 0.8155 - recall: 0.6257 - auc: 0.9305 - f1_score: 0.7055\n",
            "Epoch 4: val_loss did not improve from 0.66638\n",
            "184/184 [==============================] - 260s 1s/step - loss: 0.7420 - accuracy: 0.8968 - precision: 0.8155 - recall: 0.6257 - auc: 0.9305 - f1_score: 0.7055 - val_loss: 0.7020 - val_accuracy: 0.9041 - val_precision: 0.8055 - val_recall: 0.6863 - val_auc: 0.9377 - val_f1_score: 0.7413 - lr: 0.0010\n",
            "Epoch 5/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.7197 - accuracy: 0.8997 - precision: 0.8177 - recall: 0.6414 - auc: 0.9350 - f1_score: 0.7138\n",
            "Epoch 5: val_loss improved from 0.66638 to 0.64523, saving model to /content/drive/MyDrive/Diabetic Rentinopathy/Models/ResNet52.h5\n",
            "184/184 [==============================] - 298s 2s/step - loss: 0.7197 - accuracy: 0.8997 - precision: 0.8177 - recall: 0.6414 - auc: 0.9350 - f1_score: 0.7138 - val_loss: 0.6452 - val_accuracy: 0.9101 - val_precision: 0.8350 - val_recall: 0.6863 - val_auc: 0.9474 - val_f1_score: 0.7493 - lr: 0.0010\n",
            "Epoch 6/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.6988 - accuracy: 0.8991 - precision: 0.8196 - recall: 0.6356 - auc: 0.9380 - f1_score: 0.7130\n",
            "Epoch 6: val_loss improved from 0.64523 to 0.64420, saving model to /content/drive/MyDrive/Diabetic Rentinopathy/Models/ResNet52.h5\n",
            "184/184 [==============================] - 295s 2s/step - loss: 0.6988 - accuracy: 0.8991 - precision: 0.8196 - recall: 0.6356 - auc: 0.9380 - f1_score: 0.7130 - val_loss: 0.6442 - val_accuracy: 0.9079 - val_precision: 0.8745 - val_recall: 0.6301 - val_auc: 0.9495 - val_f1_score: 0.7291 - lr: 0.0010\n",
            "Epoch 7/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.6889 - accuracy: 0.9016 - precision: 0.8191 - recall: 0.6520 - auc: 0.9404 - f1_score: 0.7224\n",
            "Epoch 7: val_loss improved from 0.64420 to 0.62655, saving model to /content/drive/MyDrive/Diabetic Rentinopathy/Models/ResNet52.h5\n",
            "184/184 [==============================] - 307s 2s/step - loss: 0.6889 - accuracy: 0.9016 - precision: 0.8191 - recall: 0.6520 - auc: 0.9404 - f1_score: 0.7224 - val_loss: 0.6265 - val_accuracy: 0.9107 - val_precision: 0.7962 - val_recall: 0.7438 - val_auc: 0.9518 - val_f1_score: 0.7702 - lr: 0.0010\n",
            "Epoch 8/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.6609 - accuracy: 0.9033 - precision: 0.8213 - recall: 0.6602 - auc: 0.9451 - f1_score: 0.7268\n",
            "Epoch 8: val_loss did not improve from 0.62655\n",
            "184/184 [==============================] - 259s 1s/step - loss: 0.6609 - accuracy: 0.9033 - precision: 0.8213 - recall: 0.6602 - auc: 0.9451 - f1_score: 0.7268 - val_loss: 0.6603 - val_accuracy: 0.9063 - val_precision: 0.8277 - val_recall: 0.6712 - val_auc: 0.9446 - val_f1_score: 0.7357 - lr: 0.0010\n",
            "Epoch 9/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.6449 - accuracy: 0.9051 - precision: 0.8302 - recall: 0.6605 - auc: 0.9473 - f1_score: 0.7325\n",
            "Epoch 9: val_loss improved from 0.62655 to 0.62541, saving model to /content/drive/MyDrive/Diabetic Rentinopathy/Models/ResNet52.h5\n",
            "184/184 [==============================] - 299s 2s/step - loss: 0.6449 - accuracy: 0.9051 - precision: 0.8302 - recall: 0.6605 - auc: 0.9473 - f1_score: 0.7325 - val_loss: 0.6254 - val_accuracy: 0.9066 - val_precision: 0.8112 - val_recall: 0.6945 - val_auc: 0.9511 - val_f1_score: 0.7449 - lr: 0.0010\n",
            "Epoch 10/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.6191 - accuracy: 0.9113 - precision: 0.8475 - recall: 0.6786 - auc: 0.9515 - f1_score: 0.7512\n",
            "Epoch 10: val_loss did not improve from 0.62541\n",
            "184/184 [==============================] - 262s 1s/step - loss: 0.6191 - accuracy: 0.9113 - precision: 0.8475 - recall: 0.6786 - auc: 0.9515 - f1_score: 0.7512 - val_loss: 0.6775 - val_accuracy: 0.9085 - val_precision: 0.8300 - val_recall: 0.6822 - val_auc: 0.9457 - val_f1_score: 0.7445 - lr: 0.0010\n",
            "Epoch 11/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.6322 - accuracy: 0.9099 - precision: 0.8377 - recall: 0.6817 - auc: 0.9492 - f1_score: 0.7500\n",
            "Epoch 11: ReduceLROnPlateau reducing learning rate to 0.000800000037997961.\n",
            "\n",
            "Epoch 11: val_loss did not improve from 0.62541\n",
            "184/184 [==============================] - 260s 1s/step - loss: 0.6322 - accuracy: 0.9099 - precision: 0.8377 - recall: 0.6817 - auc: 0.9492 - f1_score: 0.7500 - val_loss: 0.6972 - val_accuracy: 0.9099 - val_precision: 0.8033 - val_recall: 0.7274 - val_auc: 0.9444 - val_f1_score: 0.7625 - lr: 0.0010\n",
            "Epoch 12/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.5985 - accuracy: 0.9129 - precision: 0.8457 - recall: 0.6902 - auc: 0.9546 - f1_score: 0.7571\n",
            "Epoch 12: val_loss did not improve from 0.62541\n",
            "184/184 [==============================] - 258s 1s/step - loss: 0.5985 - accuracy: 0.9129 - precision: 0.8457 - recall: 0.6902 - auc: 0.9546 - f1_score: 0.7571 - val_loss: 0.6476 - val_accuracy: 0.9129 - val_precision: 0.8640 - val_recall: 0.6699 - val_auc: 0.9522 - val_f1_score: 0.7534 - lr: 8.0000e-04\n",
            "Epoch 13/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.5734 - accuracy: 0.9189 - precision: 0.8581 - recall: 0.7120 - auc: 0.9586 - f1_score: 0.7744\n",
            "Epoch 13: val_loss improved from 0.62541 to 0.59593, saving model to /content/drive/MyDrive/Diabetic Rentinopathy/Models/ResNet52.h5\n",
            "184/184 [==============================] - 299s 2s/step - loss: 0.5734 - accuracy: 0.9189 - precision: 0.8581 - recall: 0.7120 - auc: 0.9586 - f1_score: 0.7744 - val_loss: 0.5959 - val_accuracy: 0.9132 - val_precision: 0.8124 - val_recall: 0.7356 - val_auc: 0.9555 - val_f1_score: 0.7705 - lr: 8.0000e-04\n",
            "Epoch 14/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.5718 - accuracy: 0.9160 - precision: 0.8475 - recall: 0.7073 - auc: 0.9585 - f1_score: 0.7681\n",
            "Epoch 14: val_loss did not improve from 0.59593\n",
            "184/184 [==============================] - 258s 1s/step - loss: 0.5718 - accuracy: 0.9160 - precision: 0.8475 - recall: 0.7073 - auc: 0.9585 - f1_score: 0.7681 - val_loss: 0.6956 - val_accuracy: 0.9041 - val_precision: 0.8369 - val_recall: 0.6466 - val_auc: 0.9409 - val_f1_score: 0.7268 - lr: 8.0000e-04\n",
            "Epoch 15/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.5444 - accuracy: 0.9188 - precision: 0.8614 - recall: 0.7079 - auc: 0.9623 - f1_score: 0.7754\n",
            "Epoch 15: ReduceLROnPlateau reducing learning rate to 0.0006400000303983689.\n",
            "\n",
            "Epoch 15: val_loss did not improve from 0.59593\n",
            "184/184 [==============================] - 255s 1s/step - loss: 0.5444 - accuracy: 0.9188 - precision: 0.8614 - recall: 0.7079 - auc: 0.9623 - f1_score: 0.7754 - val_loss: 0.7773 - val_accuracy: 0.8997 - val_precision: 0.7766 - val_recall: 0.7000 - val_auc: 0.9317 - val_f1_score: 0.7349 - lr: 8.0000e-04\n",
            "Epoch 16/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.5360 - accuracy: 0.9208 - precision: 0.8598 - recall: 0.7219 - auc: 0.9637 - f1_score: 0.7815\n",
            "Epoch 16: val_loss did not improve from 0.59593\n",
            "184/184 [==============================] - 255s 1s/step - loss: 0.5360 - accuracy: 0.9208 - precision: 0.8598 - recall: 0.7219 - auc: 0.9637 - f1_score: 0.7815 - val_loss: 0.7206 - val_accuracy: 0.9005 - val_precision: 0.8509 - val_recall: 0.6096 - val_auc: 0.9392 - val_f1_score: 0.7058 - lr: 6.4000e-04\n",
            "Epoch 17/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.5280 - accuracy: 0.9218 - precision: 0.8606 - recall: 0.7267 - auc: 0.9649 - f1_score: 0.7854\n",
            "Epoch 17: ReduceLROnPlateau reducing learning rate to 0.0005120000336319208.\n",
            "\n",
            "Epoch 17: val_loss did not improve from 0.59593\n",
            "184/184 [==============================] - 282s 2s/step - loss: 0.5280 - accuracy: 0.9218 - precision: 0.8606 - recall: 0.7267 - auc: 0.9649 - f1_score: 0.7854 - val_loss: 0.6702 - val_accuracy: 0.9088 - val_precision: 0.8669 - val_recall: 0.6425 - val_auc: 0.9457 - val_f1_score: 0.7333 - lr: 6.4000e-04\n",
            "Epoch 18/30\n",
            "184/184 [==============================] - ETA: 0s - loss: 0.5038 - accuracy: 0.9268 - precision: 0.8798 - recall: 0.7342 - auc: 0.9679 - f1_score: 0.7990\n",
            "Epoch 18: val_loss did not improve from 0.59593\n",
            "184/184 [==============================] - 253s 1s/step - loss: 0.5038 - accuracy: 0.9268 - precision: 0.8798 - recall: 0.7342 - auc: 0.9679 - f1_score: 0.7990 - val_loss: 0.6528 - val_accuracy: 0.9123 - val_precision: 0.8522 - val_recall: 0.6795 - val_auc: 0.9468 - val_f1_score: 0.7513 - lr: 5.1200e-04\n",
            "Epoch 18: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing Curves"
      ],
      "metadata": {
        "id": "wrGVah-dDg-p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "fG7OWdYPbEpE",
        "outputId": "e0500731-a615-4671-f2b9-b3095539f490"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "apPjaGOUwIgf",
        "outputId": "1d534251-6145-4a08-bd8d-9d859b9a76de"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prediction"
      ],
      "metadata": {
        "id": "i9EUCi_UDm9L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "complete_datagen = ImageDataGenerator(\n",
        "        rescale=1./255,\n",
        "        horizontal_flip = True,\n",
        "        )\n",
        "\n",
        "complete_generator = complete_datagen.flow_from_directory(\n",
        "        directory = train_data_dir,\n",
        "        target_size=(224, 224),\n",
        "        batch_size=1,\n",
        "        class_mode=None,\n",
        "        )"
      ],
      "metadata": {
        "id": "_C5cks1txR1M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d3422f7-8d69-461f-95ab-ca19e08d4a77"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 3661 images belonging to 5 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "STEP_SIZE_COMPLETE = complete_generator.n//complete_generator.batch_size\n",
        "train_preds = model.predict(complete_generator, steps=STEP_SIZE_COMPLETE)\n",
        "train_preds = [np.argmax(pred) for pred in train_preds]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dnPwFs1M606l",
        "outputId": "8aedbf1d-d0e1-4295-ed53-9148d47dc3b4"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3661/3661 [==============================] - 324s 88ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(set(train_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVkTJfNi8IG4",
        "outputId": "345c6d79-2b34-4bfb-9bde-f909640eb531"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0, 1, 2, 3, 4}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.unique(complete_generator.classes, return_counts=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9FS1ixsu-wxE",
        "outputId": "74ed5136-ce8b-4402-b53b-50f43423bac3"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4], dtype=int32), array([1805,  370,  999,  193,  294]))"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(5):\n",
        "    value = train_preds.count(i)\n",
        "    print(f'Class {i} -> {value}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dL2I5jwX-uWw",
        "outputId": "58de3716-24f2-47e2-92a3-cd0d386d7665"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class 0 -> 1790\n",
            "Class 1 -> 218\n",
            "Class 2 -> 1175\n",
            "Class 3 -> 401\n",
            "Class 4 -> 77\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2LAHSTYIAB46"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}