SUPERRESVIDEO.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SUPERRESVIDEO.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XmrKy2YvyhkH",
        "colab_type": "text"
      },
      "source": [
        "# **CONCEPT SUPER RESOLUTION VIDEO CONVERTER**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fCctEkW0e-0c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4e487579-557d-4d4e-eb55-348c138a67cd"
      },
      "source": [
        "import os\n",
        "import re\n",
        "from matplotlib import pyplot\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "# np.random.seed(0)\n",
        "\n",
        "from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, Dropout\n",
        "from tensorflow.keras.layers import Conv2DTranspose, UpSampling2D, add\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras import regularizers\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3qsDP_bypMX",
        "colab_type": "text"
      },
      "source": [
        "**MODEL - AUTOENCODER**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bRRpRDcifLDq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_img = Input(shape=(256,256,3))\n",
        "l1 = Conv2D(64,(3,3), padding='same', activation='relu',\n",
        "           activity_regularizer=regularizers.l1(10e-10))(input_img)\n",
        "l2=Conv2D(64,(3,3), padding='same', activation='relu',\n",
        "           activity_regularizer=regularizers.l1(10e-10))(l1)\n",
        "l3=MaxPooling2D(padding='same')(l2)\n",
        "l4=Conv2D(128,(3,3), padding='same', activation='relu',\n",
        "           activity_regularizer=regularizers.l1(10e-10))(l3)\n",
        "l5=Conv2D(128,(3,3), padding='same', activation='relu',\n",
        "           activity_regularizer=regularizers.l1(10e-10))(l4)\n",
        "l6=MaxPooling2D(padding='same')(l5)\n",
        "l7 = Conv2D(256,(3,3), padding='same', activation='relu',\n",
        "           activity_regularizer=regularizers.l1(10e-10))(l6)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zP_cdlhvfNzJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "outputId": "dd27bc39-5d05-46e8-ab8d-ce118b68e30d"
      },
      "source": [
        "l8 = UpSampling2D()(l7)\n",
        "l9 = Conv2D(128,(3,3),padding='same',activation='relu',\n",
        "           activity_regularizer=regularizers.l1(10e-10))(l8)\n",
        "l10 = Conv2D(128,(3,3),padding='same',activation='relu',\n",
        "           activity_regularizer=regularizers.l1(10e-10))(l9)\n",
        "l11 = add([l5,l10])\n",
        "l12=UpSampling2D()(l11)\n",
        "l13 = Conv2D(64,(3,3),padding='same',activation='relu',\n",
        "           activity_regularizer=regularizers.l1(10e-10))(l12)\n",
        "l14 = Conv2D(64,(3,3),padding='same',activation='relu',\n",
        "           activity_regularizer=regularizers.l1(10e-10))(l13)\n",
        "l15=add([l14,l2])\n",
        "decoded = Conv2D(3,(3,3),padding='same',activation='relu',\n",
        "           activity_regularizer=regularizers.l1(10e-10))(l15)\n",
        "autoencoder = Model(input_img, decoded)\n",
        "autoencoder.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 256, 256, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 256, 256, 64) 1792        input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 256, 256, 64) 36928       conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 128, 128, 64) 0           conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 128, 128, 128 73856       max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 128, 128, 128 147584      conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 64, 64, 128)  0           conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 64, 64, 256)  295168      max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d (UpSampling2D)    (None, 128, 128, 256 0           conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 128, 128, 128 295040      up_sampling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 128, 128, 128 147584      conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add (Add)                       (None, 128, 128, 128 0           conv2d_3[0][0]                   \n",
            "                                                                 conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_1 (UpSampling2D)  (None, 256, 256, 128 0           add[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 256, 256, 64) 73792       up_sampling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 256, 256, 64) 36928       conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 256, 256, 64) 0           conv2d_8[0][0]                   \n",
            "                                                                 conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 256, 256, 3)  1731        add_1[0][0]                      \n",
            "==================================================================================================\n",
            "Total params: 1,110,403\n",
            "Trainable params: 1,110,403\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qyAee9ZmfP_n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "autoencoder.compile(optimizer='adadelta',loss='mean_squared_error')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-5wJn-Agkhi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !cd \"/content/frames/\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0bo3NCLHhA-W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1G-iWBxy3vl",
        "colab_type": "text"
      },
      "source": [
        "**FRAMES FROM SAMPLE VIDEO**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cp50Xkr2fSmm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "vidcap = cv2.VideoCapture('video.mp4')\n",
        "def getFrame(sec):\n",
        "    vidcap.set(cv2.CAP_PROP_POS_MSEC,sec*1000)\n",
        "    hasFrames,image = vidcap.read()\n",
        "    if hasFrames:\n",
        "        cv2.imwrite(r\"image%d.jpg\"%count, image)     # save frame as JPG file\n",
        "    return hasFrames\n",
        "sec = 0\n",
        "frameRate = 0.1 #//it will capture image in each 0.1 second\n",
        "count=1\n",
        "success = getFrame(sec)\n",
        "while success:\n",
        "    count = count + 1\n",
        "    sec = sec + frameRate\n",
        "    sec = round(sec, 2)\n",
        "    success = getFrame(sec)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NO8K7FMnzCZ0",
        "colab_type": "text"
      },
      "source": [
        "**DOWNSIZE IMAGES TO CREATE LOW RESOLUTION VIDEO**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IePSYdx5iLcL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from skimage.transform import resize, rescale\n",
        "from cv2 import resize as rs\n",
        "# from numba import jit, cuda\n",
        "# 718 750\n",
        "def train_batches(just_load_dataset=True):\n",
        "\n",
        "    x_train_n = []\n",
        "    x_train_down = []\n",
        "\n",
        "    \n",
        "    data_path = r\"/content/\"\n",
        "    c=0\n",
        "    for root, dirnames, filenames in os.walk(data_path):\n",
        "        filenames = list(filenames)\n",
        "        filenames = [x for x in filenames if x[0]=='i']\n",
        "        filenames.sort(key = lambda x: x[5:-4])\n",
        "        for filename in filenames:\n",
        "            if re.search(\"\\.(jpg|jpeg|JPEG|png|bmp|tiff)$\", filename):\n",
        "                filepath = os.path.join(root, filename)\n",
        "                image = pyplot.imread(filepath)      \n",
        "                image_resized = resize(image, (256, 256)) # Resize the image so that every image is the same size\n",
        "                x_train_n.append(image_resized) # Add this image to the high res dataset\n",
        "                x_train_down.append(np.array(rs(rs(image_resized, (128,128)), (256,256)))) # Rescale it 0.5x and 2x so that it is a low res image but still has 256x256 resolution\n",
        "                print(c,end = \" ,\")\n",
        "                c+=1\n",
        "        break\n",
        "    return x_train_n, x_train_down"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qk3Dxz5HiwbD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "a30930f5-1b83-4db9-aa61-2b92a9769412"
      },
      "source": [
        "x_train_n, x_train_down = train_batches(just_load_dataset=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 ,1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 ,10 ,11 ,12 ,13 ,14 ,15 ,16 ,17 ,18 ,19 ,20 ,21 ,22 ,23 ,24 ,25 ,26 ,27 ,28 ,29 ,30 ,31 ,32 ,33 ,34 ,35 ,36 ,37 ,38 ,39 ,40 ,41 ,42 ,43 ,44 ,45 ,46 ,47 ,48 ,49 ,50 ,51 ,52 ,53 ,54 ,55 ,56 ,57 ,58 ,59 ,60 ,61 ,62 ,63 ,64 ,65 ,66 ,67 ,68 ,69 ,70 ,71 ,72 ,73 ,74 ,75 ,76 ,77 ,78 ,79 ,80 ,81 ,82 ,83 ,84 ,85 ,86 ,87 ,88 ,89 ,90 ,91 ,92 ,93 ,94 ,95 ,96 ,97 ,98 ,99 ,100 ,101 ,102 ,103 ,104 ,105 ,106 ,107 ,108 ,109 ,110 ,111 ,112 ,113 ,114 ,115 ,116 ,117 ,118 ,119 ,120 ,121 ,122 ,123 ,124 ,125 ,126 ,127 ,128 ,129 ,130 ,131 ,132 ,133 ,134 ,135 ,136 ,137 ,138 ,139 ,140 ,141 ,142 ,143 ,144 ,145 ,146 ,147 ,148 ,149 ,150 ,151 ,152 ,153 ,154 ,155 ,156 ,157 ,158 ,159 ,160 ,161 ,162 ,163 ,164 ,165 ,166 ,167 ,168 ,169 ,170 ,171 ,172 ,173 ,174 ,175 ,176 ,177 ,178 ,179 ,180 ,181 ,182 ,183 ,184 ,185 ,186 ,187 ,188 ,189 ,190 ,191 ,"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ABzALkYi7sb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "aad2ee6e-e5db-4a67-d9d8-42e9c2ac03dd"
      },
      "source": [
        "len(x_train_down)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "192"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1gylODKLltPm",
        "colab_type": "text"
      },
      "source": [
        "**LOW RES VIDEO FROM FRAMES**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "693PPxhvoDW8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# x_train_down"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PmCrPuQjRIs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "path_out = 'low.avi'\n",
        "fps = 10\n",
        "\n",
        "image_low = np.array(x_train_down)\n",
        "\n",
        "height, width, layers = image_low[0].shape\n",
        "size = (width,height)\n",
        "\n",
        "out = cv2.VideoWriter(path_out,cv2.VideoWriter_fourcc(*'DIVX'), fps, size)\n",
        "\n",
        "for i in range(len(image_low)):\n",
        "    # writing to a image array\n",
        "    out.write((image_low[i]*255).astype(np.uint8))\n",
        "out.release()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_xfDPcyuPxH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e0c6f6bd-1f9b-4afa-9133-03345755b258"
      },
      "source": [
        "np.array(x_train_down).shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(192, 256, 256, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XChUq0WRzY1A",
        "colab_type": "text"
      },
      "source": [
        "### *A SINGLE LOW RES FRAME*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBi6SWl3nOWB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "6e7daefe-dc30-4882-b5e5-1866e10d1599"
      },
      "source": [
        "plt.imshow((image_low*255).astype(np.uint8))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f257ede89e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EYhhHTAozjnn",
        "colab_type": "text"
      },
      "source": [
        "LOAD MODEL'S WEIGHTS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBdeDRqBp4eE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "autoencoder.load_weights(\"/content/sr.img_net.mse.final_model5.patch.weights.best.hdf5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDnQuN-uzprS",
        "colab_type": "text"
      },
      "source": [
        "USE MODEL TO GENERATE HIGH RESOLUTION FRAMES FROM LOW RESOLUTION FRAMES"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymDwRWY4t63R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = np.clip(autoencoder.predict(np.array(x_train_down)),0.0,1.0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XFqjx3ILz2ei",
        "colab_type": "text"
      },
      "source": [
        "A SINGLE HIGH RES FRAME"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r5DnQlxQuHOG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "f9f6c46d-b5e2-4a39-a836-a235cb6b1e6e"
      },
      "source": [
        "plt.imshow(preds[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff2a5d43240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQYAAAD8CAYAAACVSwr3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOy9W6xkSXYdtnbEeWTeZ1V1V1d31zw5MxyapCyJFkjDNCAZgg3bMMAvE5ZhQBYEzI/1L/4Z8Bd/DRswPAYESx/W40egPgjLBiHCMiwRQ1IWPaQ4w5nh9Ey/u6vrcevezDznRGx/ROyIHeeczJu36lbPbami+1ZmnkeciDix1157x44IYma8TC/Ty/Qy6WR+0gV4mV6ml+nmpZfA8DK9TC/TJL0EhpfpZXqZJuklMLxML9PLNEkvgeFleplepkl6CQwv08v0Mk3SCwMGIvqPieg7RPQ9Ivq1F/Wcl+llepmuP9GLiGMgIgvguwD+QwBvA/gWgL/CzH907Q97mV6ml+na04tiDL8I4HvM/ANm7gD8PQC/8oKe9TK9TC/TNafqBeV7H8CP1e+3AfzStouNMWxMiVEEAEQvomyziWaeNXcM4LkPAIx59hXzeEZm9ix3SalfXEzrTM4sR19G0t68RCBDcIP7mJnv7nPHiwKGSxMRfQPANwDAGMLJ8TGICEQGhkJFpoJJETDSP3s+K98/PRZ+GCKAKJYh/BljijIwM8Ccun4AAgazh3cDnB/ABWIYgE3AhC3AsF2MOP4/QaDRVRxrRgj/k8KiWF6dz1WS3Mejg2AwfPhOsRQMMPv4zHDVi4T10KT71EmVf1eBYl13vatJiv1wXn9tO359KRRzd1kZBHCQsadP3Vv75v2igOEdAJ9Xvz8Xj6XEzN8E8E0AqKqKpSNJlzOpYcvWZVydSMzeQ+V3pnhN/MvX55IV9zCnwwCB5d5xxiCAODxgJpHmHJyfy1zet12w5XxkOKMyiBiHSuoy76PbWaoXyzS6QwEug8FSG7n+0vw/jUT7gSJlQeMrdLDQnHNsc/74dab0Xi+7jgB/xbfxooDhWwC+RkRfRgCE/wLAf7ntYnlxjCALusPNp+tocJXHHDOOmjB8SRwhdx5AaRYWCQz/jDoWxc45qRaXnUfflu/gdMter3amr1ASDlKgMGYCc2W75KFlljsye5Z0Waffs6OrV7RPdlz8s/2G9HaI8lWKmQgGvzBs4NHnNT/nhQADMw9E9DcA/GMAFsDfYuY/vHo+1+lm2J3RRKv4eItCKlbSojvQmKrPCfGsv4IA4t36LAm17oA765HYvXruqA5QZGQf+UqE6TLte530+ZL3xZQY5i4ZfiYT6mV6cT4GZv5NAL951fvEvs/i9fw9bd6JOEpKyINpwSCeM2d4fEuyq+PTZoTtWdyB2XyhlCeX5+Y+KV9CogXHoJKKe4Xy7AEiQp+vCxsSO1MmTH6XnIX+Bdvy/yamn5jzcZqi08+I8y8ce+5cFSjMOSF10h1R24iX4Yo4Iac5c2kbzaloEdoxcxAXQ1Hksf9i7rPMO51R3LbwFezDGqQKEaS2j768aKv6ulTF8zz934x0I4CBIgXVIwLAdZoR2AtodIcXkKDklJrNFcm9t6PPaDzaet3cg4guNTX2TjRmHdeRl04vUmho5tvMo18IYkyMwi3HX9Dj5tJcPbf2q2crxo0ABiCAghFg0JT4mtI+Qzvb79t6NnzQ7NHZKozNmkuH3K4THIp8R4wkFQjzxGTm/mx3vVhNWjbZHOu6/meGmpXtrvslMZUYcR1l2LcZn6W5r3jPjQGGSBni959sUa41pf6zjYIXl86/v2RuXO5s27dMIQ/tPh89XHwbyOeyX0JfyKrg40I9H2BsY3nMmVGWTbrdxLnys9NbG2U1+n5tSoy3fJdnbSMu4+/b0hXLeHOAITrx9nIU7pmuM6+daUu/C444caRi1lwo3rlyEr7ItThJyqKezxogttwkJCGMTuhT88LBew977F1qiHmXnizuG9af2lmZic20gFuOb2kH3W47i3gdSZdt90DQC0k3BxiA7OHfki59Mdvue/FsV56EubeY3vGsQsumQsFM6XKG8SzFC8OU01GK8JEdqGOZSeZRUa7xuyjpNVEc7b/WakQn56Qf5NEZ5nHJc43iFZfkvu2kAD2Kq+Z5yotXSvt5Oyj+f7WXcHOAQbPWiVYNJ5NZt4NKjztMpqP7NQxRbMIZodz+WCrOZNuU1d+OMowE9YX4FOaexRLlFwFBmQNjFk+8W6C09gZkACZyi2uUEc6ZT56f6lH0pfi78Ans6g9bRJpGn5c5RD+VJEJ//enGAMNEfgvzVjTZszTBNs0yvSyNjih81SCR+sYMa2EYQOYPFE9QNvlzM+t5Z+czpzGV2puNRSAs0IwmV/BlgPgM6eq5xTtocmT3LTp9KmzzkjI8d35Xy/DGAMNlBafxm91yVc5u20W7QEEPlQotDWUbg1MxuQoA2EcKKwCxpQajx1/JXHhBNlHyEWhQvsTRSXPgsDNdT7mfOZfrGjn4SaTrwNbP5KiE0tbl4f07XhnINL1p1man/IwMDPHJFJ2HUUBI7NbinnCAmeFhlNAa4d54pkjApOSmIcipbmNz4xk7vrDsqzriQh0vyZskanE/5rDNc3Hd6VIZ2dfxd51pC1suzn+K6UYAgwaFiY8gHNx9/5Z7JQOC2JoT5EmAIPeHaOzgZBINP55wlPwWqvOQR2QW2pTlwm7fP2X6O4EVAYToK0hg96wdJ7KQCQjpZ3OsCwRcrzBvg5Bnis7cleSBMQODn2binT8/lXS9VtdzpRsBDCHNtwoDymu/jUGUZkC+U/IN2mv6PExYgp6roYfFCrKh2EJ4UkAGr4cbgzRdVumtnWHrqMQo4In26k2qnLNnKQn+fIpCLeN+k7bc/lQBh129fk5ZXh972FfSLmnHT8sU2Rdx9732GdMNAobtKTsC9yDm6f2OrxsxEQUGAiz5Or1gS3z2vIyOxs3LLr6vyE6uyYRhNudptfbrIVvnORSyPuXQMryZRlr21mpy3+XlC2ZHfsr1AcSOt0Azl83pljlU2Acodg/iPHv6FADqMwEMV0r7dNoJKOjj8SuV4LG9eyntHQ12YTe8V7een6rMNKXsyXQohhy3M4HiKZTNqW1MRFVpmuMVnJ4CspxGc4xCni2tqIGpaOyrcutSwgPgYPJuoXxGUlZSc0DGwdCX6JntRfmMps8UMDxzO2+xQCagrw5M1oKYRYboWBvNViQxPUgGPkndsYfGJYB4/5WHKNF8Keio3OlrNMkuCZ7Sy8nNfd/XKZxiQmaeMM/gVB3ynPHieomLYB7fHxgHTXwZJUiNDpdlVdGfxBTBubzmWtOnZZ48Q/pMAcNcK4pfANhuasy3ffQBUHmXdubl9eaU54DLPDxnz3sxmkgU/W0jblpEae0o4ayzbr4n8QTFtiQdZUlbBEYXNYJYMiFSNqEOOyeE8fz7SMfGzDyymezOiEbIxFSieC2P7p8Dhfw+ZgMlVdYTxrDF5yLXFGtxaruHRtduSamtbig4fMaAYS5d3qrb+n7qh4ykHZIJwICP8hY6SewziuKGBVDnp1yP2Wq4N2pCnvTLaZnnSzx7RrTdXnmN5mPMAQQRjYR6TsMrh69qg8tYjjaBthaUpaYKacdOh6umS+5L733nc26gBL+g9K8BMACXwy6Prprp8KpjB1u+vIxHHYajCbFrOjepbwEoOJkIzxbFOUOHk0bcnl/yTYyPK1DYev+MWZMAQcwYIsBzaSbJuVjs8Ru4rGr5IM2c1y9k1zF94xbKsKvrTEhduHgnxuxgELP53tD0rwkwXCZkon+UCpcvQhm0H0CYgvToOUU94yqY19klPDCmQjjRsqw02CivifxOzJJpmo3hSOfG5eHp9VrI5RBJeeJxA5CfeQZDxUCYWNa58s408rY5DXPHd13L2575GZDQn1D6bALDM9llkSvMBDnlTPMBiXaUIKV5tlEWaZTh9BkzroCAS9MyicDuFzJ9WWNcRQDmR0lmrxwHo41GNgCOLhVS9RkVa+SCiUX4FNOOtpm8lh0Fu8y/cEN9CdvSZxMYdlG/becS7dUveA+BUcwgLSmvijHJYepXy1lpx4PKYwxOaVhxxuTYa1RjXKQdjsZLnZC6nOWNk/MFuRJGgXxiFhzk/PjlybHRsOLstTvTHNpc3oC7RyCmDC/dpzrAvm2b8rhBwPHZAoa9Gq5s4bFXe++2TxRY3aWynmX0W5PKZexNT//IFdkBViwfpm+gPTvbuBQ7GEhyYI7a60r5A6N9FtSGNWKe6PJvYf+ziYuP/OPSoipESdfnB4sl+aw7dW0vyhXakEefNwAgPlvAgIzCe+01KZ1gcumuTlA6vDRLmF3ua3rXloLLhQpZBAOIC4Cg4p5prppJ7BXcJFkr30Exx0K+Pu84fTFeG36nSWhQXv/0waHunH6Fek2cfuoniy+HoWV+Z7HkEiqPcvR3PAPEls9HWRQafduZzbb0EwaJmw0MWkOruRBzMymzlzydSR/bxXnugfpzj3uieTDvKswFmDo+w3eZUzEzj3Iusyw4W5jLzmLG7xMThfTxfPBZpoTn2Z8i+Eib25A4CEnMKo73cHL4hrrFlpsZZQgkKmRY7CuxpagE9ezCUuH0/FT+K1QVYwDTD9xWEPWdmCa9bDyJ7SeZbg4w0ExHmGkcirHKui8UQ2c6P53JXrR1hoWM8ph45xFMDrA+MzJnZlkLooxQun/rHpekO4+AobCMq3bo+cQ89WdcdXm5MsAr1i0N8SR6FD50IBnGr4eBLW0m7yM5h+P1ORx9xo+i8xIGGDcbfQYDYlSYscdqT6UyUz0B/eudYyp992p53hxgAAqtODduPp5BWYLwDDjEi2juhktSorVzLGTyVUNC6TAj/Tl+RlKgMvpByq8xn1JwngAR6Y6458uf+C5CbdPchlEk6WSpva0Rj+WIhh5ZmUQMUi5xuoVHIDs7iUSVS9qKSzOlLOxcAxRqZeam6TNT35xF1wxXBdJh1z0zj4kOjzErvI501biZGwMMaR6e0MiJ9hKACGfVjcjrOWzrSGU+u0pRfBOguZzI5P6Q1h7IV257KSLYcp+Aw2VJ31cyhv1evsxLSJqaS4HWVDmHBisfwCS/eWYxOa5YRBDsqbbNjsAtddHESrOTfeeWbMv0We8kKu4vVtt+HuG+RlB4lvxuDDAkAVeHClNBBJTUVYph5DyEpU4tOIXpO8qA8oot2j7niPJfvTKyApbtecicCmVOXJIyY+ArvfCiWVRbSzDXbLuO/BllHjnNroM5E1GZWARGIz4CDeJrkGMzw5WTxz83/X4+9ZzcKsLEnnHxnLHv9lrTFfO9OcAgacZUSGaE/LdrcsrOI9IBdtHHbRqYp99Elkf3a/KimcxssSMoyCSg8dLn18wotyviuaHRIiJU0eXRdSVYaAHPczhKkEek/+V7IFBYMpPHeW6rynWs+cTFx/M09th5+swlugo6XHsHCenmAcMoTeqsyAMwQthncC5uPycdWL6OwIRH142i/igdLoGGVDYMHTeQPfNBKLYRd12Aq/WIXVdzGd4ZCzEXBbmd+2RhlpmO8yXNNSOl7QNbKsjCZYXelfYW9BmWNvNzy8p009yeBxTUM2QJvdlnpCKOQE1OXkO6ecCgFyIZxeuXlFwL6nXwr1I7zr8WbTLkY3N+D0CYzuicPh+/iF2a7W8Au4KRgLAfxPPwznGRRuznKptISXmosAPyiAHYFxdLLIJ2ukKzJW26POsr3kdAklka38P4IbEN5l7hC0lFm+8OTZ/sO3rN5btxwJAcYihNien5MUzu03P217La/i0FNPfaArxorN317/mXXAyhK/9C0rzjhlDVkM76fPKi9U5Z/qvmv3X5CAAgA51Tnj+BDA5aDyrmlMBh1yvWoJpGP7a8aZKCiabRTG/as/L16cenki4lHqM+s5XRPmORbxwwjGsypWYjbZ7a45KecykoTM9ls1lrMsonKWt3mi1rFLd4XzmUJy+f8wtOzitk59xckRPdvGwQaj+x3vacRKFL+2JrJqzbBkBhGiStDLWYbaisHtJMi7AkW71kVWN9oMf+dcj11nYh3RNy/oiPG695wzv71qcHFHOpZKPSTkDWHM+ebgww5OHK+JvKtyuOx/A9/xu+7mOQbju3/Z6CygKpR5VdJIND+ijQmqPjdFTkBADI/S7dmzXnrrRbq2y/mXefzvmnfwqLdvoMVtfrLyPzJB2LAkkJTGYogSjDke9Gr2lRXjtaaGVnvXIMxGTFt1GjBrDYpVSuGxz2n906dy+ABJTF8U8zwImIfgjgDIADMDDzXyCiOwD+PoAvAfghgF9l5oeXZ4bJGH4RtKSZ3/jlIa+6dNl7ynpr9kRMIaNJh1A9P6/4FD+jPyENPaayI8ZgIB3Tj2EgTV7K2kmX8Tn9Jzz12wsLndBNXV3OdRAk2cqpEoJwUYeg+TkBN0OckgJ8hQGRyzYurw64SgRLgcMYmGZ+6cM5apJyrIVC7uIVKbOmbJ/dj9medt2QndE7sWhnbiou5lL6tD2Zq98ySf8BM/85Zv4L8fevAfgtZv4agN+Kvy9NmiEUkY4YRz1u655jhjH/N7ln9BHXIVKaZKxS4qN0GU34NPI9/hn5swRjDYwlWBP/rIG1Jl9D6nr1J80SujGrunD+i8BTHJOyy7kETqrKUhfJj8Z5cj6u81AsomxRfT2X18T8pm+oeAXlS6LxNeq3KIz5l7OzA4xjS/JQ+G5eUGSrHnOlNHof5btRdd1Rhbn+PG0DQAL/SOp8hfQiTIlfAfCX4ve/DeC3AfzNXTdkqj2V7Emoc36b4XMPz3wJvkH857pU+NTPofIK3SOkiAkgTPCxqTyERRhjEqjlKgY1RExg9mntyLyOJIO9h8e2ZehpUrOxdCV9XFByFYCldPVWgVD3BfiAik4c3TlHxGT59tmLSJVFTuv1NfMzEiskTD3yk/rvmYiKyUxzFCowOcKWJ+6VSp/GrvI812Pm85Mvn/JcCQbwf1BQBf8zM38TwD1mfi+efx/AvbkbiegbAL4BANZaCJfWIdFjUEjDe2VGE3AYB9pOXwqNPqYwMdFOor2Iwmphcl8EC705jYBFYhBkQMaUKB/5KXkHzwT2PAIFBhsGPCVdXBS6EMoZkEC+jHXZEGiqVxO2MlhNnYVa+4zbKDd7fP6Mo1jnP46EZJYypNZUgLArbZGgffq+evVi9UzrnH+LM/R5wWHbE4p0LaAwo0QmSu7y9LzA8O8z8ztE9BqA/5OI/lifZGam6d5wcu6bAL4JAE3TcMEaCjCIicY/Ukb50Cjmf/rKZxqHpudJVL9oMwGGBA7pgVBogDQ6YQxM+ovAkEwjpOuYGeQNyHuw8fBegQIcvC/Np+SsmzVAdcdVMQQJ4nRFGUYEPgKWZw+O8QZFWxXeVKgJoNkZHHKcAejpIXVc6pDrIgSBR8d1Xyc1grPPtOtJUkwpOz+Lk9eaNFvI5sL1PyenMrhs+7Hd6bmAgZnfiZ8fEtE/BPCLAD4gojeY+T0iegPAh/vkFeTKJLlTXXHuybOmfwKUmbafc1pO7ocWRC1IpO7XAqOOi/ORCMZaWGsTOJApgUEElZnhvYdjD+88iDy892B4eM62vZ5XkMBTt4ae6zyqz0RxRCwzRLCR0TAYgwse5JKFjFkK8ka9pHa7Vp65iUOQtjjuaHRQrAaZ4QmaiE/EfmjPqfCMZ1XoWlbl8bPdRF2T7nuWB6XyjxNfD2bo/GeU3r7pmYGBiA4BGGY+i9//IwD/HYB/BOCvAvj1+Pkbe+YY/52phmIQ054+7b45dmBbHuPj6ski8ONc44vN2hIJDLLj0MBYE0DB2uhctMnHkBgDpOMzvGeQc3A0wDtKJAXs06Sc3CJbOtRYw85dEs8aEzzONREaG9hC7zmwBWZ4o4FxnF+kC6Pt8zh9yuxQVsvtR9FVM0cF6FJ7xKJ7zh71LLE6JU9Dqs8YRpJczaHKrhQzG4OC+Bied1aG1ktzK9+P0y6/z65r5pqNE1vZPz0PY7gH4B9Gm7EC8L8x8/9ORN8C8A+I6K8DeAvAr+6TGWnBHfU40v8UMl/+LoBhjO1bgWEUHZEEX+WqlBqp+4A8mmDJwBqT2IKt4qewB2tQ2SoAhzEgMvDeo+979P2Avu8w9D3cQPBwYPYg9kFIEr0eFR3y0nl3sFOqI8MQ0BjCoTU4shbWGHTe46khnDuHHgQvmlEp//ysoFXNls49mZwtqzMhAooGKWFZIHjPcBQ2+dEGTZF5yj8eUwFUspbB1VQ6TzXQRNy0Jp/jMdsfOQ8k0zwS22F91eWJdvwqzlyZ3jwHMDDzDwD82ZnjDwD85avnqAW4PJQ1dPyu4RfIDsuivUfNtkUDla2moiolTyq1XCqNGo2wxmQAiN8ra1HVFlVVoaoq1HWDtl2gbmrYyoKI4AaHi9Uam80GtquwMRuAOoSxdQ/vXXi+wRb6WdZo1/sX/W+JsagqvLJc4N7hAQ4XLXomPFhv8P75BR6uNtgMQzBzRvlKS4zZQt4aLn9mYIn2rXo3hkK7WiKYKCjOe3S9xxDv1pReuIBE9Y24QS5cWsz28qRjL0Glk1HnkdmCAJtSXgnkRoI+abMxA5sHvUit9quATiN9Ok67l/6ZTzcm8nGatNYPvyfxDOMGGTGNSY4xS06AIF294CAKFCIwJGXHqRxAMA+MMbDGwtoAAMYamMqgqgIoNE0TQKFpULdtAAVjQmckg8r5uBWeSTaug4FzHqABxnAxlJkMXQV0W+1iVScCAO9BBBwtl/jim6/jK3fv4NayAdkKD7oBf/ju+/ijH7+H/tzDscttptJkURdEj32x12MUJq0J43djAlOoyIQ/a2EAdH0Hdg7OxYpOJl+EjHYun3+F/q9Zoj463v8yjUikyV7jTKZT5eXfqYJDGr3Ka4aEH2ESGpR2ewaAmEskMH41Q+gGA4OyJaOAmiiIeYRgcku8YTx2rhLlTsEJ60mUW/ylQEdeJOnOlNmCif6EqqpR1XUwKypCVVu0bYu2XSRwsFUFU4WhWQ8P9j4ABBBmS5oKxnqACdYNGAYb7V4fRypChypMcI4NtK3CqZmihBKhbWqcHC5wetTisK3AICy5QksE6/1IA46ZF+v+jMwIKBUlt+J0+XhLjNpatFWDo8UCbdMAzDhfncOzR+8GOB/z0wpWC9/Mq98a27AtFaMtXJRbp7RZ7hwoTB7IOvQiXSgEV/pxyUikKKxe6nUlrTmvlu+NBIap34iSdtbBQmPGkKimvMytKYbDcgaHZGpM0H2cl5gR4lgUn0JmDFVl0LQ1DpYHODg4QN02sFUNUxlYW8EQoR96eHcORtDOg3Nw7AFDsHUN6xys6fNu2mp3N12coMyCxpmdpj1mEwysz1f48N0PYJ88wrK26Bzjo1WPHz18gs2mK02AmbYTTTk5s5W2RF8CMwwRmqbBnVt38Prd13C4PIB3PT5+8CEG53Cx7gD2uTOX9CRaDFmgExkpLttHCFj9lf/Ol39Gkxegux2b0+WpP6nbkhK7ulbf8aT4vPidrp7zjQEG7bGfOzeOB0gONcpEKYTmy3Lk87llUyFqaol8S29LkcwE9dL5KIKUicBgYqhzFZyOVRWYQlNjsVzg8PAQB4eHaBctmrZF1TZomgbWGGzWK9BDCoDgPMgY1FwDHmAXhgT7zQbeD6Gji6KmUSdOb5xHnznlUI/gWLzoOrz36DFWZwatMRg841E/4PHQY+C5xV9z1vmMdPCxXZ6vyMeVkxBB7r1z6DYbWCL03QYXF2sM/ZDyzh6AEfpDwKE8XLztSxwNRVGJwDG+VGIjdN7bmMS4jvo1zAFqCQpiapF4WpEXu5uqxaumgrU9AygANwgYDKkOMPYrRQ1NJvExOaEaOh1S9K/opjmvqIkSNU+aKb+EaDAk+sdgFcloYciCjE1zHmQkomlaLBYLHB4c4PDwCEcnxzg6PsbtO7dxeusUi+USYMbZ2WPU7zbwHAxv5xyssSAm9OsOru8zM8qkvPAzhPJH25sxzxhUfYDgZOs84cwbOGNReYJzDheesQbBEeU9H5DbVzIpJ71zansBKz0xSkABWsMzsF5v8FH3AI8fPUFtLbx36LoO680a3gtbEOBj/fhkmuxe+H1PUUj55gdk2x+6O4x/5KPik5D7Yv/T9sR47k9+GNJ1KW5rv5KPyjAt57OMROh0I4CBKEwqGiexv4KnP2hprcEntpNG4+QwSpCQhD0DCmJeVICD1kICDiZOlApRjQIMoVxVVcFWwc/QNMGvsDw4xNHxCe6+9ho+9/n7uP/mmzg+OYatavR9hw8/eB+PHz9Gu3iIetPBDAPADNc79EMPNwypbXxBNbMzMrZSEUq9LflUL0LPwIVj9AgC5pnRM6EHwUl7Ub4+jQ5EP0EuS9n70loK0J08TUtL13DwsMJ5oDMDiD2cGzB4D5/sJWAiJqSeONfxZ2aSTq8pMxxr1O3ytI/IilBK/8u9Lv2N6YXqb9nhul+JZksgMiDv7Mo5hHSDgMGiaJRk0uXgITImn+KoNbUXWSbgRCowthvH6xIHp098gXGkQS4gFqdRNGOMMh2MDawhHgvDkXVyMtZNg8XBAe7eu4ef+fpP4ytf/iLu3L4FW9VgGAzDANf1sFTB94y+c3B9j77foFt36NYb9H03aosg+D4OT1wVGCR5MBwDGwf00ZZnZjgAnilGXkqbloyBEiBTNm1SEwqLoaLtM6Ph9F6Cb8fDAmAf2tkxZwDUjsFCCWBrLy+qvkc76L4Wvunf+SGpD81lqYlTYqTahBKCS6ntSrHfFnj0fOo+zzfCBPj2TTcCGAAEYYvftUATckShUGr2cTZivEzsM1kZKAmIRNIpJpF7maJcREBkI0lPmvDNRDNGgpRkFCIwhgBoVVWjWbRoFwvUTYWqqXB6eoyv//RX8W///M/hzq1T1NaCyICZ4Dzj7OgW6qrBZt1jdX6Bbn2BzWaFbrMBe4Z3DjLtOTcHK2aewUGETx/T2lXahKLGdxwiHckHfe7Zh7kSKrRorsMKKAQwoAQG8hDd9iUgzIADCJ7jnI0UGp7f1zZaXfgWRmXk0ae6cuYoPz/fHuWY40AuQbsAACAASURBVCBYHZsOr4/vSypL+9kuKdp4ECO1y0zDFAMwe6YbAwxBEFUjZSIbRyIiW4haM3U4Fnqmeyinz9DR1Ph0MaNQHJlqCDQ5GMOnDJGKc9Fam9gDxWCmuq3RLBaoFw2apsbyaIk3P/8GvvLVL+POK7fRWIuKQijP4BnsgdrUIBBW5+dYn19gtTpH360xDEPwt3gPJfmqaiUzyIKIyCZym2rGKrhigACg3kfqmoEhaz0K1yntlzqudDwugUmmiWfADiYEC+CM31loZThhdxxgKZViPJSS2GGuWw6xHnemfdUkz/8k1WaKuUzy5OmPUjCzw1GvRamLKddx/joLeuNUOBgBRXalD6syE+Dc7vzG6UYAQ3IulkdT5WTEQjSSTD5iL/UudCMQO3me5suQlmMf/AXMao2E+AJl5WKZKm2ErRiJarRpNIIEGCqLqmlQNTVsU6Fatjh55TbuvfkGlkeHuZMTBWEMZB7DsEG3Psfq/DH69Qq+78Heh5WWAXjPccajEjSlfeOhWOvSzNC9KrEL3U7sYRK7yCAKYVRM4AANOfQ50WFSphxHQAoxGT4ykdLMGQFDKhRBYh/yueiph5mYgUWFIVWccoNUe9WZJqMs2J6kjVhWn5qkecQZwVhiXSlAbtezEmOYOa903hgrwzEZJQvKtbIV6qpKE/cQ+9Gmvxoy3AhgACJlx/RVlw0mVJoD3Y7qhckjdwvVyWPnNyaei6jtOShLErYBKnoUwcDIcGSMVTBqQpQAg7EGtm5QtQ1sU6NatGgOlzi+cxvL4xM4JmwGB/KAj6zF+QEPHz7CW299Dx9/8C6GvgO7IYCGl/pFQfOiYUunmjhldWLo67JqKkgEiR3vZ/q8z/cZg7SKkzC5pIUoPUfAyHsH73yOuSjK5CM4cMo/XGGyL2hcv/ieyirKC9JqPX/j+GUigPq2ieCV38cBjEV/TCB6ufmR2olUu81eND6gK50rQ+qfsVMxBdkZgjUWi7rBYrGIzNbC9Q6D6zEMF5eWW6cbAQyhgpHijzssqU/F6pLzUVHTkAqylqyM8XRqgQ4NDmLM6NEHa6vEFsJsSQNT2WRa1G0Ida4X4W9xeICj42NUdYuLTQ/wU7TWooLHanWBhw8+wVs/+hG+/73v4uOPPwRx0I/EnOrFUfMmAVR/RdNI3ba0a4TG1BYc/xVBpKJhM2MgZpBBcvpue4IwN+88nBcAUG9C6gEPLfoynKfrxPrlzqp9+aRZQd/VBvtQc30JlwWbuWK/JDNBwo9E73LZIsBnLBhVKIJoOdJA6VICYKxFXVUwZFDXNW6d3sbp7VO0ixpt1WJ1vsaDxx+jGwZcJd0IYEjIyiG+vmCM0/bURDlT2zmuIWZIAoV0m0IG4SURHCR4KTocq6pCJeAgU6nj8KTRwBDnQiwPD3F4dAJTNVivO2xWG2DosTo7w4cfvI+PP/wQn3zyAJ88/BirTQfnHGSTmQASUhMCG4L3Alyc2kkqMdt3tyatraNJoOh4uRtd1OmUgSH7BzJweQEGYQVJ80fwTaxlVGYN8omszfDkTHWySt93S6hx3fe5qrhMOU3HWdG4vNMUzqoh+KCFkLyqEutQ7PKDsmrplPJTqMYLzJZgyKBtG3zhc2/il3/5L+KrX/8q2sqgaZbo+h6/8/u/iz/89h/jO9/9k73aAbgpwKBTyd2QXZF6iC47KsOv/K98FTqXhosUWygeI7cIOKm/NFOyqlBVdRqZsBEYbFOjjqMRzXKBxXKBg8UBDFmsnq6woRW69QWePnyETz76EJ88eIDz86dYr9dYXTzFetOj7/sQ1BNraqLAytAsbCit8wjTsIVKslB6zj06gqu2S2fss7LeclrRVvG5JKes9GUFDN6Ls7FcrxJA3u5OyjluZyBNGmJ1DPIMMSWUVshTvrcN8T1L4nF3ixZD9nkUGh57QlKgnTtOYvReRi+JSnaAlN2INYgiMxYHx8f46s98Fb/0y7+A1157DW7wMLaBIYNHT57g6eMn+5Q8pRsBDNrTXmh/QVkA41eiISG/hGwHS/sl0FAMNGSbnXni/EmIPFLFASQyfSMwrDFomwaL5QEWBwdolwscLA9wtDiAW2/w0fvvotussTp/itXZGVZPz3B+/hTn5xe4WF1gdXGBi4uLDAxB8lKtgjljYaOvYxgG9NyH9SGTJhuzAETnnZCMbQE/ZXvkY7kdU9umMGFOIOB9ZgxlsFUUbNWGeV1rn94Pq7adlKwYYcpCJLNbrwoKhYCx7l8cSzftd9kfoswfnadqq/niCI/V0/hVjaQRisqo/JJyGuWZqxGBM/OWytY4ODhAYw26boN+IFQV8OTJY7z/o+/h44/e3tZEs+lGAAMA5c0evYJLYDpP9tVgkDs+UDS5ug8jrSAcUWIlglNtGHqAPdxgY0wDJf9Ct+mwuljDPH6Cqm3Qti0evP8R6spGbz4H/7pzGDZrrNYrXFycY7VaY73eoOu64HRkBjiOSESNn5eEM6i8x4Y28Bxs+cifis480bqqDbOui6S9JFejf2OusSg+MQS1JiXr7+oeyUVNi5woRgXPGrYuITYKHJ4tJUsEWt4VQCQ8YqQdGUh5aJQJUVh0KZ/RwyB55Y643fxQYC4MV7MGDSpQbzKWg73H6vwcP/7+W/iX/+LbOH71FXSbDbr1Bh98+AG+++0/wIcf7bXCYko3Axg4DM8JpGetzzGQZiYVZEFplhEoTLu+nJkadHp1ZxCCJh8GeOdG7CH6Iew5GAQHBlUSFUlYtA1Ojg5xfHSExWIBA0a3XuHi4gIX5+dYbzYY+h5+GIA0gUcEhUHIEZXG2BAV6D0GN8AMQxg/kD59adtq+rMDYdWpgFMM7zJY+2Q2CPCw+k+SKSSGiraXL6Tqmk2oIPhqTsi0WJkJ7pMK4VcMSYawtT5IHUaiMvN9Zdm10NJ8c47fyTZQiKbUOJNiXsXo4QWgK5AyxOhWF/jBn3wPH334EZrlMgH4pttgdf4Ufb/Z3lYz6UYAA8eONxFZ6QgzfVrO6yafMoSIMpOxvUjzxEwhhA1iZDLUaB8IeQEl8HiwY3hwiIBkQm0IdV3jYNHicNFiUVeoCHCDQ7/ZYHV+jtX5OfphyBrYK60ZhZ3U9mnhOl+YO3N0uhiWxFgf56PhWkpOv1D90iQBMzyFeAsjJh7rgKvYGHo0KPpx9LY48trykaDiPPJoBRhqb9Zn5QOZMSW2lChIKcBiqrDUIweDhJKmhpwKY+wJEI9Vaq7ykokpdCmYRWRM/rAxMGw1W8I8GhOBdtX36B5+gupJBSA48j17eOfS3Jt9040ABqCkv6N3MT6STsgMSo2sui3FzCiAV3UcIboyiauuLOq6Rl3VMHn3mOj5HXUI4jSGXNVhdKJpGywWDQ6WCyzaFlVlwd6jA6MzJpoKgJHt0URbqvKJALFnDDwEvep9YBecz+fBiRgQHgWhIAjaI1g8hKPFQulSsaxzazPIx9WlgIn2AmQvkGgGgqLhZMAgeAFW9qhibeWd5HdhIGtTq4Pp/aW0Ay/SMLf+FOHWY9+k64p8DctT47+szdOJPi/PxGdEbjuqh1w/PqZ+aXYliif2N9KLauZmUf02H/MxnmTwDh4egx+ylgFAEjNyhXRjgEHTAlbHJstpSYpaRofJCq6S6ntCOag4KJ09CJSxSMCwaBos2kUcpssvUEbzE30zFCdPVajqGk3bhKHLuoqjGBaWAFiCrSvQwRLwDm3dYLPpsFpvsOk6sAtCldeYCDEUTAQPxPUaBjgvS8qHSV1gE6MDyln84h/ZNqlKFDNBtV08yKI1RftH0M1OVxPNLAPEOQFkJE6BAFh4DrMmOS4vH0qpRlOKtxXuNcmRRLobKBYztpumYJA0bDS+KQKWnhNRMKqkMWJdKTCpQsDVcnXpqHbQCmlKZWJVfg0J2ziD7owlW9D105dOcwjg7x0wgBFGlxl6J/YQ5Xo1ZLhBwFCmMLZPRTRa6qQkryGLQ+lbSD1kBA4Ky428iAgOJvxZG9YhDLM91bORmYPsPVnXYVZl3dSomwZVXQeTBBRCUeEAH/4sMZZNHfKJ6y8MfR80K1FcB9LAIASteCLw4MA8hFBnSHlNePlRGwv1lZEDeB+mQilGrX4WraaaT9FYA8BEppAXscntagCyIFuBmGDBcbTegD3Bw8Abi4GDmWW9g4VPQc4GQcMZ9mA2gN8g+Fkyayz6cKL9ZT8YJ03DAYQgLTYFMKRs1GgNRbDlyDQIObDMqxW+ACh/VwQSATsSAdUF2/b9kjoYSiAxuVXeaWEOZb4XTE4KfZtRriP52WUM+yaB1/hV25QFFVXfIZ1G5ZI0RshDbHnvPZxzKg9N+yKV5IKpAdEX0PUdxHgheMD3ID8kmjcMPVzv4Ice3jsg2u2GwuxSSwaVNbBVHWYaUo9BTUwyxoOIgs1IFGMZkEZX9MxGx76MIlV150LYAqjaOB+EyAaN73y2tdXUdIlvMFSDqAKRRVU3WCwO0LQLLJZHqNolPjl7jMdPnoAHB3IDeBii7yRoZecHOLcCDzWYO3jvINOokn+EVZi2TqwY4KRfyAQwikYNJtcyM5gMyIa2BwPWEAx7WIRl+3vP6DzQ++wiNcIwSLYplPmoQQmpOK6i0ScYsJVAJM4bf44upPGPzPjS6Fp0BYkCFRI2i0Q70g0EBqXpMd+Gcw7h0Bg51FdyyZoEZeNQdjCmuReOgyZ3LnQwU07s4sjB5Zj4Hbz3cJsNeucw9ANcFPqaGLUN7IINAO9A8JDp1Bw7fvJx2BptXYehUO/Qe0Y1DJCt45hDiK0jAjmvjpemg4xYSERlotKi4WJ9pTMZQ5H9NCCy6DoH5/tkWtAMNSayANU4OrqF+1/4Er7wUz+FW7duwTYtnq7W+P7b7+KtH7+P9UWHoesxbDZg72J9Dazvgf4MzjwF3BrwHZiHcE28DuzCX/RDFHRYQK2w03NZKQqygETZVwKyG/Iw7NGAsVxUqJnRooIfPM76AWdEYcxo5JeSEYkxO6ftXy5JMn1vZHbsQ/+V9cIRIHIesT/Q2F16ebpRwCAvIE2RzsZtorUUFhWAzCgrWUHKKRMIyuAAEloY116gEE5KiDMpCfPe8VGHkHuJwozJbuix3sTYhL6Pw5uMZVPhcNmgbirY2oKIg2/CEqyN+ysojd22LQ4XizD7c7MJE2OiSSOMgIhgnIEnF02MYGOGmAOfKXAMhMrrZYb6p0lP5MKICHM0i2o0dQPAYBhKh1oMrUhtEQ4S6qbF3dfu4es/87P44ld+ClVlcL7e4KJ3ODh5FXfuH2O1AVwPDF2cLOYGDEOHfn2G7umH4LUFDxXYb0B+APsezA7sHYgHgAcwmwgYPg2VFs4IZUIIKMg7l8l55fsMx4z3aMH42le+gC99+Q0cVoxjW+Ppo6f41h99D8OjNRzJsG1eoDaxfPFlSLto/0gBpNtT6XNR81J4/iq5NndTNXKn3hdMZDny+2q4cLOAYT5REkoiwHsDIoatDCxsmNUXO7j4EXQr6CXnEy7LHplEcRJKEIq2atDUNZqqhrG2cAKZ6AcINn50EDKw2XQ4P3+Ki1WIYmQgTJ8GgMHAwgHUAqYOZoI1qNmiqS1WRgb3GNZaHB8e4vToCJ4H9OzgLxyYXaCwZIJ2B8ETw7NN0521CcEISzn4YMwHn0ecERqccnH1UQZgwr2GbJo5Wqy/AAHrGFClgNezR1VVOD49hm1qfPTgIVabFc7On+LJ+QodHeD45FUc2hM0i2McHh1i0Vq4foP33n4b77/9pwB6gDs4ko0zLeANmIU1VAEcvIP3A5xz8HEmKlFeJ1QCz/KaHmUfmPtljAH8gDsnS/xXf+0/xy/84p9H3/dYVDXOP/wIn/wP/wve/b3vwZA4MI3GofAp+ku638wTL5PHZBAk25aykCsQnlLkmcyS1cWAD/4zNnHs64ob2dxcYIioXjiVCNHxFocJqypMTx5c7CyhYzgXNooN5rBBXuglgIIMmVlrsGwXOFwusWwXaOoa1laAocJ2JAUuCSBMcNCtNxucn6+w2WzgYoeV+QvEQLcB2sbCtA2qKkyFJfKoK4uqMhgGgkGFtmlx69ZtvHLrFjabC/SesRkcutbB2jqAogv+j67rsek26PoOLkVLqnKaAJ5MPoJqmCkKSAc2SGszAqltwhoQcXs8/S44EFQJbiJ4OD9g061xcX6G1focvqrw9Pwcm80GZCwqUwNVDV+H9S+PDw/RVISOCE27RNMeYFgcAJsWA9eAcUGomQD2gTGEJbMDa3BDWBKPKK6cnU0EPQt0AgtCd0b+FmaGJ6A9IHz+tQavHDJWawPjHByAI1OjIg5Lz5EJZaEyDwJgGHCJz44fLmW4pKvr//TmPRPTafy1ZCXl3I74xQOg+aiWXekGAYMMN3H6niY/iec+toMxId7g4OAAh8sDHMToQg9g6D0ePz7D04sLAJw3mzU2ef6liRZti9unJzg+PERbt7DGYOgHrLoOg3fRdCEFCMH0sBFsvPdwnqNAiY0ntQmCPPQ9DBHauoKtKhAZsAcqE7exsxWMqVE3LU5OjnF6eoyHn/RYNg1ev3sXTXuAw6MjeOYwzLla4cGDB/jo44/AmxC8krQ6cfIriL2aiDfLZ+a+ARACqHjPGDDAuZgn1HY84shN+2gyDBzW63O88/ZbuPXKXfz0a3dxevoGDBG8afD+gw3efzTAb9a4cI/RPz2DMYy+W2P15AmMc6jJwNkwwgGycZEaiqaRiz6ZMDQKY2BNBW8NnAvrYSb/MskwqlQWEZjjcG4hLIH+ExjWeaw/Occ//ru/ge9/4fdgrYH3A97/+Am+94MfxdiRCoVZq95vsiz1KMfscMP00Jz7IFrJqhONEa3MMJHj5AJRAMWJf6AYptoz3RhgSCRQ2e/F9vEKGAQwCIST4yPcf+21MBRoK3gmvP3ee3jnvQ+wXm9gCHGD2RBe7JlD5/cehgyGuH9k3w2oqlocuyBj0x6LE2CwVaDdfZ89/JGNyLqSMmoRwGHAZr2BHQaQtRDRrWyFqgoGIXuPR48eoVuv8OTxE2yGPm5pN2CzXmOI+zCsVytsVufoN2sMQ49hiMAQmYqsiyASIppHlmUv5oco8HVOQM6FnaDS/fkNcTQhQukdDPd4+vQxfvTDP8Hnvngff+YXfgFN08A7AuMBPnr0IVarM2z8CkCg/M716DfrOAIAVCTa3gLgMISJsBtXCDGvYKiGd6ENCXWM45Cl6XIdBPjCojfZZyWzH6SP5dBzxtPNBv/sn/9LfOdf/KuwXSAMHnXA+6sBK2eCgjDzrjtZQ8MQwAXd34O2q+0NkgADWciTksz55V9j/U9K7imxmXATXRUTANwYYAhe7uzbIxVnkP/ClSGxZ7hhQGMtjg4WOFy0aOoGDIPzp2d4/PhJXMKN05wDZsIwDPAx+GPoe5ydOTxF8DW0zQJN26KuZXEWoedCxwmGggkDMkAvO0X5+DIoaVeJAayMhRsYm4sOpiLYugGshfeAMRUqy/CesFmv8aMf/TiMjoBha4u6aWEePwUjBDoN/YCu63CxuohTtl1wJEZzIgyfcZx34oVoqb/wxUUbVreo98GE8GnW5PgdBcYgsyNCKAXDD4wP3n0L3/v2/4s/8/P/Fg6PF+i7AQeNR+3X8BuPrrdghGXxvHPgoQM5B+MDOIQoP0JY1SkApSGgqQhNbVDXwXx4/PAhhh6AN6HeIuAmsqMIgI606BA8ZfaUhDCaX2uu8f6Fx1nnYWEweMKF91jBYk0UfDVQzEPIE8QVoJdlI6gfqummRF6cuxoQWCJSC1CIJ5WZlHyJooDyEf2qkm+CI0O6SroRwEAAbLSBleVQaGxJ0gTGGBwsl7h16xgnJwdY1DUsMx4/eYKzRw/Rd2vUce0EIgtmRte7YKcODqGpwnJqTGFcwnoP6xmWARufbdXS8TKKAVmYNnYPjtdyfFtEQZssmgVunRzh6GCJprYgS+idx0XXYXCxW5AFE+A4RAyGyZMGwwB0PjjnwkgCY3AOfd+j6wf0MRLSs8y3yLEMsjQcIlDVdY1Fu0BlK3TdBmdPzwIrkGE3yD0uLroiLwIj2zZqOYYYbgAGnK+f4l/9we/ii1+6j1/6d/891E0NM5zDDufg9YB+Y+FRAVSBnQf5DTCsgGEDEzfP5eR8Q1iNqDJoa4tFW+HwoMbBcoE3770OYoYbejx89BCfPHyI1aYDOO6RrQRQBFdMqoJmJ7VsMFCNczBWHIGLGANRqFmaXan+1WYJZ3Ek5Pdf9uw4EXAEDuNIRMl7dBWg2iU/K35ukXUuLthmjuxONwIYkAQu0yCJSByZdskRJiZHt17h7NFDbCqLbtXhwSeP8OjJGRiE27dfxa1X7qCyFZzzWK9WOD8/D2aDbeC6DVabNbregYS2GpM7UjQkMziFCUDkXRgejPScBMU4R9CxsagXSywOj7BYtKhrCxAwrNfoB4/eMRybFEHHROAqMCGi4PyUdR99HHlwzmOIlJ85T01K+x+KXcm5rQDAEqG2Jg5s5CXYGMJog1Al9sNAGP9AYedmX0XuvibGZTw+f4R/8fu/g1dun+CNN97E+uwcvHkCt+owrAFPDRg14BmGe5A7Bw9rEDukqJxco7AoGgcTbrlY4gv3P4d79+6hri0GN+C9D97Dt//w23j73fcwdC4xprxGJqc6GgqQIEvVizJlGDAZuOClBhAcrB4umGRwmBeqOUalRLDwM9C8Q2Eu1+gQNckBqc2HEui25qE+tQGyh3FTpBsBDATkVW3jAVJUOFUr2tKEYLs/fvgYf/ToE3yPGFXU4lRZvPH5L+Ev/rl/Bz/99Z/FrTuvBBMg+gWsyfH+b7/zLv7v/+ef4Y++811cXKzSlvZhlehpOWUeQtipOtjzoGALh16Yyw8QNr3H4yfnWF2sUVVhi73eOayGISwjDwKbMBmJvawfHeaHkIAFAR6BHbjk5FSgIPal6nvCbGTy4GYT5mU478KQn6zpEHtPWnW7MCEkUCZnHPUfRFuKP8ZWBqAaZ08e44P338bx0RHWF0/hNo/gNyvw2sNTDaACewKzC6zBXYB9F4Ym42rS0bCBGxw2LozY+JMjHB0d4fTWKWxlsdmscefVV3Hr9ineefedwJhUUQsxJDkicTDSjSib3xzbAj6GUnOYsUiq343zHT1CQuuyMJYdaHpkmrJQl6AA2a9iz7xKziHRn5c8fJRuBDAEyhsEVtYhJMSA78yk0rVA2G/Rg9E7h449amPCVvREeP311/ALf/7P4vX7X0C9aBUohJ4Q7PDQaK/fu4t33n0HbhjCNvU2LrKSUEloev4WlFII3TUxhDmE0oY+ZJC11LofMDgH0wNkCA7AwHHLOMq+CzYUJhP5yEA4Ak/ir+EV+zEoIP5WkTbB1MrrNA5DmITloj9CnKneB1teBzLpJIJURMhQqbHS6JG16LseH7z3Lk5PbuPp03OsnnwMt1qDNx7MFowqgqcH8xCjHYMpwMIcYml87MmDCdPWQWGjHvIefdfDDw61rbLHXa2oXXQViKmdwSyhQ2EIhGzS1DkK41x5t42Qh5hZRDOsIQ2NkqLxuSiX8QZSX1LbFzCRTYpLOQgVH1dOlwIDEf0tAP8ZgA+Z+efjsTsA/j6ALwH4IYBfZeaHFFT+fw/gPwVwAeC/Zubfv7QUQsdJRiaQPzVjUC0iQlVZGx1hBt7W4KqGsQsMHhg8o4YN2sEzumFA3/UY3IBh6PH2j36Mj97/EK5zqEwdFniNlDvtVSk0U5UjWA0Gni1sVaNZMNC7MMcgeapl/oGJxfZw4LDoSux4svclyIbIy4pSZ/PMoGGAo7CqvAAEs0tmQzySOyM4CyoCVQ4byZRRkuGuMIJSzg4MJH6skfTkIVLvQmhtABqAvcPjh5/gvbd/jPVqjbNHn2BYDyF4iSmAvSyQyh7gAWCXQEFTf8RNjI0JvoGu67Bar2EMYb1eo+96AHEYGlRutiONKJ48hqqntFsWstQCRJCdpNTc2rgwTmQEIqxcgkPqmsk+oyLzfYyJCRPQrCU9YLeoZ8U6go89zRlJ+zCG/xXA/wjg76hjvwbgt5j514no1+LvvwngPwHwtfj3SwD+p/h5aZqsWKM0U6ZQI4pPMZYdcTKLC5roT7//Fv4Jfht37r6Gw+MTVNaCvUPXd9hswm5Pg3f46OOH+OCDD7HphjCkWVWwVdCiJs5x0EOn4cUzZFestDgIGcC64FD0MpwXZxISilGDMPstL0cv6/8zxRmVcQUh7z2cMegpT5ZyTvbPmNAoaY7gtDUGVVz6HqCwis9mE+Zw6O43YmKB5VDRifSImfhZ1FsKbUEAURglOnvyBB++9w7cwFifX2BweucwSu9WMhfm5UXwTARixNmRFIZIu76DdwMMVQFwTVhQx8b5LHr8vvhE/p2cg6oJUhi+9LUo1CTQGeWrXABfQCHH3mgzQPdYKdlELCkTDHl5WgdO7xAtSeXZhEyF1iwekuTjCulSYGDm/4uIvjQ6/CsA/lL8/rcB/DYCMPwKgL/DobX/ORHdIqI3mPm9y54jVRZQzM6/fEXukrkTeGFtzCDyGDzh7XffxYcffYSqblA1LapKtreLqyFJJ4kxCVW7QNMuwq7VNm6ia2OnS8whLwUuUXa2qkBVBVgL4zxqjxCi7BzY+TCk5l1aTZmiP8LEad1VFYY+ja0AU4NjVCYjxj9E00b86cb5uHq0jvATYYsrIsT5FceHRzg6OgbI4uzsCdyjh+jcALHjadyuxEEgJViKxxIWoC4pMBIHbRD0MKoXYi3Ozp6AUKVt7wJ8q1hStbWcFMNE9iKjB7LaNTiEQ282a/RDH2IYiOH9gMH1afTK55ZQPakEgQyIkUUgswDR9pqwE4XIxpBNBkyBEvHyJqqfrlVtNhopyQ+gSfH0jGBdYrkmvzdV/pGjM7lFFBxdjSuE9Kw+hntK2N8HcC9+vw/gx+q6t+OxS4FhI1GkTwAAIABJREFUnIphythhcjVVY8TWIsQJUWQBY4MtPjAc9/AuOhRNiJcwFoCR1ZdqVE2Nug5CKFGNASBKYMg+AfkONH5A1TVwzgcd4xn9Zo3NeoW+c9lqJkqAYK1BVdswL6NpUdUNbNUCtoodECH0ue+xXq8BhNiDfnAwtgKZPu4SnRdVCVouDLseLg5w/+7ruPPKHTgifPTJAhfdGqvNBgOGon1Tx6MkDtGyFjnIx0lYXPRR6MAz0drBf+NhLVBVNjjx2KRYj+wfQaprGAkQ7R3+wlR0jhrcYbO+wPnZGTZNjb7b4NGjT/D48SNEp0yUtVgeiS2QYxAGKjWB0kJzSXEDSpBYiJpAHkfTQYKodubIJThogM4mmmZso+gDmjk28xwpabKoyirtlZ7b+cjMTLRtRHV7IqJvAPgGANR1g1nqVLAG0QglKOgAqLCQShQ8W6XVnI21MLVsShs3po3mAcm+lNFBaeJuU8IWUsSjbFWX9q4kWEsAPBYLF606Cz94nJ8RvOvhhgGyWasxsgCMQVUFUFgsWiwXC7TtErZegGwDxJiLoe+xjjtfb7oe1vYIu2Ur4SKKsfUmdEo2sLbGyeEx7r9xH7dfeQU+xsm/9/57Yc2BkXs6kC0VWKNPIIZYp/YdR6JmYJDIDkIYpWjbGoZsyovjyAprAYumBTOic9SnQCuDAApEHn3f4ezxwyT8m26NR08e4eGjhxhcmI0JCiMJsj+P+FriLarLzEiLNsXlJ2VmI/VjkujPiCuR1UTCmvVVzHuy2CsVv4p+XZwjaRvBr8QXynsnUiewlREolJdKudkjPSswfCAmAhG9AUDWpn4HwOfVdZ+LxyaJmb8J4JsAcHBwOAorU29XpaIzTn4rcDAGUeVAuGbS9hTiFSRoqQCPuMFMYAQxCyubztSw8T4g7DNRVUBtDZaLJQ4OwkYz61WH994ldF2gvjAE48M+FJUNE6fqqsKibXCwWODw4BAHyyPU7QFM3YJsBWbGZrXG2cUFut5jtepQ2y7SdQ/EOATARBtYbeLLFrVtcLhc4qht4BBnZA6uaLNgDhm13yTlNgaF/UB9JrEUy59MGRq9F3DS+JU1OFi2qOs2+DkIcSn+GB8g8piAIcRo9IOLYOpAYJg4Td0PPc6fPoV3HjCETb/B2fkZum4dFryJ17Js48VUrJlIwFRtRsVchjCFTw8BAjlIE5kkuSZqZh15rFdzSjxC3Z9NEX2PLu/0eZM0iYyMn6pO6RDFV3mF9KzA8I8A/FUAvx4/f0Md/xtE9PcQnI6P9/UvpC+c0T6v1hxPJ4ZgRlQ2vgRD0SEllF32mAzCX1U1qjpQd9H6CRzUHxkBnrzUW1XXMKaGhG5XFjhYNrj76qv40he/iFdefQVN3WDoHL77ne/gn/zTf4rVZg3AAAZhfcjKoqkrLJoKi7bFsl3g6OAIt26/glt3XsXx6S3U7QLeOzz4+GO89eP38OjxeZwWQLBxjD2pJ7LIQVUAkQ/swXmcP3mKihk9O5ydnWFwQ6xPaJOmaVBXTfBxxIllgR3kTuq9w2q1wmq9DmaKDdGB+X1FG11enQ8vsW1qHB8dYHlwhKZuYKwNrMA5DC6YKbJzOEVgGAaPru/RdXFpfVm0hUPwUmUozrD0WG9W6LsOBKCuLWycYh5mn4YVrr0XJaO0u6ABskBnz4cWL0qrWPIYUHjUZxH6ZJ6nlBdK0QMKCgESA+Aij3GuM2kEZDw+WXzmXxoA9037DFf+XQRH46tE9DaA/xYBEP4BEf11AG8B+NV4+W8iDFV+D2G48q/tW5A8KhEEXTz2BfWNnZZMCQp5+m1c/j2aCzaCQhL8tJrzIpoSEUisAEc2M6IFGVY3qmo0zRJ1s0DdtGjbFseHLV65fYzP338Tb77xJhbLZQKso+NDfPcHP8CDjz9GTw6GTFjvoa6xaGu0TVip6eDgEHfvvY7Pf/7zuHfvDZyc3kLV1mB4PH58D0cnJ2iaBYwnPHCMNZ3BwsDICooRKPMEHgOGRd85PPjwY2yenqM3Hk/OzsLy/ERpy71F2+Lw4AgHB0tUtg7BT3FZe/GhOBc092YT9iSQKechaZaBJHCEwLSsIVQWqKqwP0YoZwVmBIdvFVaqIsSYFOcxDC6CQwfnOrihh3MDvBsCSyLA9QEcKxsWtqkrG8yPeL8b4hJ9zGkhmlBcVqtWBTFxxIgBLQEoBDeSGZGnX0keJYXXTuCo0Vi+p9aJFyhkSB8jRpwaUzWxEmnFqZWYzzGHMk1B5PK0z6jEX9ly6i/PXMsA/psrlgGinoOHm9UW9BIqXV6cbP/kFAxnsmmQ2YJsRGuroPWbRYumaRNbEGAIm9faZEpYQ6gri3axwPHRCY5PTnF4eIijw0Ocnpzg9OQIh4cLHC4X0UcSt47xjMOjY7zx+pv44Q++j/PVGsZYtE2DpqmxaBq0lcVyeYD79+/j537253D//n0cHh2iqupo9gB3bp3gtVfv4mtf+Qp++MO38Qf/3x/jW7/zO3j05AyECsbEpd2ND2HFPmw8QghTqBFXkWLvwFGwgg8GABiu77FeXaDbrNMeF865tN4lKAyzhv0IuNDwRRcVhqFAuut6nJ09wXqzCezMVtE/EZhC0zTB6VrVwYwYXAI35z2YWPlwQqwC+xCrUNcWSwrbvHdDBef6UMeYz9DntTtlAR9izjtyi8ZlgpW9PdQ6k2laugwxy1+4KTGN0IqaY8i9GpBKAwFAGnnhiBxp0dZtcrFFovOpHRdBzDwx2/ZPNyTyUe+sjLTiEFHwFeiqJ6dS/EuzMEFJ+ANjMOmvqirUTdhCbrFsUUdTQhyCTd2gbRdYLA+wPDgM6zwcLnF8dIiT4yOcHB1huVwG4a4rNHWc+hvLGgQ0BvnEDtEuFlgeHMQIR4O2bdE2DZZt2HfizTfv4+d/7mfx5S9+Ce1yEcfvRc2EVZWOj5c4Olzi9dfv4s6rr+GTBw/w7nvvw9swR8Fx0HjEeYXpyhpUZFERwfIAS4GGW5OF2vUO68Fj00XnqBdh4bgqVLaLs1/C5AMKHGS0AmxgTBCa9XoTgMieA2RBcSk9ece2qmGrGsZU6fk2gnp4FsMg+FLSqs1xez4vwul68NCDvayJKQxPa+ZsYJORkYUooAwQ2VDm6C1IZkXc6otHf4lZpF6b240B1XY+byQ0kkeJfUoni/acSTOgQcWnYjRb8rm66/HGAANF2z7OYDQ20dk01JTGgymZASYOAVaRIVR1oKjJf0BR6zctFm2LxeIAi8USbbvAcrnEcnmA5XKJw8MQi398fIKTk2McHx/iYLFAuwhLvclcCAaSVuSoYb3zgaEgzJ4kG5YMIzg0VQ1Xe9RVg8PjExyfHOH05AivvnIbX//a1/C5z91H07TBaRYNdXmBbMNzPHtYIty+dRuv3H0DxrQg8mElK3Yg4+HZho1dCFhUBm1Th0VOhgFVU6Gt68BGsAZ7j2EYgkJL051ju6bXkRc3kdWfSBy5afUnysABggwbAkA/DAD5MMIDyo640ICpsixgA0SWKCtcBX8JRWBg70I4t3PiW4RjD8fB8RgUdVwwJ80n4UL76302k5sGyDEt4ORjTAwIot0jGSdNy0sKzxzW2vTs4Y1iE+mFxg9lUgQnL9ImN6VEbE+Fb6IwTUQxlVeXqnW/dCOAIVBME8boiZRg53gBgNJai8ZQMgXqKuztUEW2UEVtTqC4dNsSJyenOD29hdPTWzg+uYXTW7dx585tHB0cBE3eBgCoK4lnyOPigGjUuH9kP6DvB2w2PS4uVnDeo20bnBwHlmGMwdPzp2iqCvffuA+YBqd3XsW91+/i9PQIR4ctTo+PcOfWbVSNHQ0TBoETbQ1wmgq9vrjA5mKDpl5g6B3Y+Ch0QXgMGA0Bi7rCsm2xbCucHC2AxQKdBT549BBPzs+DGcwxGtEhCoCYcXmEB7EMhcmmryteIJDmtgBx890gHBJ4pCoVPsVpWmi7yEwIkKAogouzTF0MmAqXOvZxGTrZeDe6CxhK8HnyKQAPzppWwEYDXSIenG3/bN9rv0N+a9I+4gcrRDHwefkSQSErvrFAa6f7ZUn31VRfOa6eeZV0I4ABoBRjkGx/a7PNb/MmrzbOUiQAZAyapkHbhMVVmrbFYrFAU1eoqgbL5QKnJ6e4c+s2bt+6jVunt3B0fIKjo2MsD5ZhV2oZ+QgSk4UGAHsX1laMO1Ov1iucn19gveqx3oS/fhhQ1xVee/U27t69jbZtsNlscPfVu7h9+gqOj49xcnqKw6MDVLVFXRk0cWMa73zhPIyvVrULw1jC+ZMVfvinP8AH772HylSwpoKjIQpO1LYU1pCoa4ujw0Pcf+1V3Lt7Al83MJ98iLc++iiAKpnol1F7UebHyevIvgNkUw3x3izpZafLwU4i8CZlm7t5qeGyWEWRS07MEOxOYt6g1KqeoRhCnBTHPAKG+AQZiZh85ioXwMBhlIFUTQikQEE1F4tDE+C4AVBiGWNHIpV5FSHmM0AwBgdGHvGYT5SuKh989XQjgIEo2ORVFYChqsRBVaeRAmvDOo9hklNA5spaLNollssF2qbBYrFAu1igid9PT4JpcHhwiMODIxweHKFdhJEFMjYujhIEJHjF44KjcZJVt9ngYnWO9XqFruuw3nRhz4WB0Q9h7N05h66yeLpscXp6hLZpcLBc4v6bb8LaCk3b5IAqG8Bns7pA1/VgIrSLFnXcS0K82kGDAJtNj3fe+RC//7vfxh9++zt48PHHYdjSmiyWLPtbhElJliq8cucOvvbTX8fdV08xwKNvKrTf/a70etUHp50sCzEVAl1MKNMaSfKJgBD6ujIX5LoRpdZingQLiflHDRymQSdhhxJQBmSJ/LQvjc/sQfwBumKMGZNCThdbHYbdxCj9Kouf8i3YBxfPlONzAl/kuvU88jmW9jLbLtfVzAom9aU4zH2FdCOAwViDo+Mj1HUNMkDT1Fgul2iaBpWtglOxCuaCiUOYMoy4WCwCIDRhGLJpwv6Ry8UCR8fHODg4QNsuUDcNYAy8Z/Rdhw5dAIA4PNZtOmy6Lqyj6MKuUf3QxeEyF9eJDBNnPHIAFGwYwSBEbzQCs2lqmZ/BaWy+7zusLs7RbTrAGCwOljg6OcbB8gANIy0nB2acP13hR2+9g9/71h/gd3/vD/D4kzOszjfw3IOsxDN45NmWA4wBTk6P8Oab93Dv9ddwcLjE4HscH52gsnEH5GhLS2fT4l0SXTkmgJCZg+58pUkhKjH4IyZ2c+qc+VMmjTGUiYMIKZw/C8bAHO35MNUJlJlGylc/BsWpJMgCMNDtEZ/lkW3zqd5WtdBAM35WZKHFsyW/q7P71BYzJ1J26b2w4jcMSMTGvulGAIO1Fqenx2HX6KpG24YRhMAWTASHKo0kmBiiXFVV9Pa3waRo27SHZN22aXHYfvDwFytcXKzDAzn6C4YhLagaxr598sz7OPEpvVexNgBI5KFoGfGLDIPD0/ML0AXghrCI62q1xmazwWq1wtnTc3Rdh7Zp8MXP38fprVOADR4/PsPjx2fwngOwWYuzszP86Q/ewnvvv4/BhWnTPQ9g04PIwXgHuLBBy+AHVGC88spd/NSXv4B7d19F09awFQEuRirGUZRyMRYFAAojNDgUQTqEtHeDZ5dUPIdGiE5hTEFBlFeS1qxVi+XYWDT+yBbI2UDMDR/BoZAVEZAIKExKsFOefgSHmBG2Ai124AsjOTZH50nOk7K80r0KFXiLwBKhnCuu2Ycy3xDboHiR5V/ot59BxlBZi9u3b8Xdo2vUVQYCCcqReAWZxBT8DjbavLIsfFyay4XFPBiEzaYLQOCGFOQjDjUwwPrFkBae3JBpfWHldJPpIWH8fMD5+QXcMKDrNth0cWp33+P/p+7dYnXLsvOgb8z1730uVXWquvrmvrk7UXfbTUfYSGCkGFsWPIEIFi8RPACBCPOQCCHlgZAXkKJIeYCgSEiRGgWBJSBEAokIGRAgodjI7SiEBLe7sYidvrr6UtV1Ode9/3/NwcMY3xhjzrX2OXu3HbRrVe2z97/+teaaa84xvvHNMccc83i0JCmXRwOgw7Lg/t0zPHj5Pu7duYdTVzx+9BhvvPEGHj96iuPlCZcXF3j86AneeeddvPP2Q5wuVzy7vMTl6QJdVzSx0OClC7A0dAg++Opr+KM/+3P4/Kc/hQ/evYtDX/Hs3Xfw9HTEO2+/hcvLS9tsZrZamAUwhwHjGAAFKCTWEZiM1kGBjMBQ6Dbz7tDCZlSBhMFmJim27choMtYgrD38+eLjerX1FXFnoQ70ZORvqcVP7bA9NoCCCnPb88P7z98GbZtLkOGN95+v47mBfZAp+D4YsYPWzY5bAQzLsuDBgweRjTnWMZQ8kApfvFLzFYqlO1t7tyxJpxMUQDudcHk8ol1cxL0E3MVzFWSsBGLb+AoMKOBQKaN6wtb1ZJF29mMBPf10xNpPaEvLqVPJ2ZPlsOCle/fw4Q+9jldeeQWH8zPLmIwV/XjEk0eP8N67D/HOW+/YJjaXRzx6/BSPHz/Cs2dPLawZisUdkLI0nPoJIuf49Gc+hZ/6qS/iwd076O++i+9+4xt46wdv4rIpvv3m9/Hk8SNA6dBDBNrEIZN4cugwUIYyfiVATM4DhTkGuWyBS+IxDBEUGVbkd/t1vVxLChxTi0ixr36JTcVZLyUSAcyLOVJuvpZMhuDqo151Mxv8vGME36Rpsr1q79zOBwLIDeOa4rgVwCDiuQ3sQ3hrQyGBZF7RHTLsUM1dqkUsTZpoN6rJ+WiW2RUrOBtAZXcR9ZhYi5jrY8c7E127xfUfjyccjysuLy7x7NlTXDx9guPxEm0R3Ll3F/fu37PZAg8HXg4LDj4cefLkKb71re9Aljfw9NlT/PDNt/GDH7yF9961/JBPnjzD5eXJ0sVfXOB4vETXE5YmuHvnPu7eu4u2NDx68hCPnz5Bax1PnzzDb3/lq3jl/AB9+BDvfPM7uHz8GOt5wzvvvY3Lp088EWtRBv9Nqx9e+JiiLD+gY9GZQ5iqGv/gPaSZcS3Gxe7pk1DqERisqzWiFQNmOAIgWwhRqEOi+cidtqyI0WxXA1tZgM4nitwNN9/GY+gEnc5h+x4vOG4FMChsXrpJi9SF5pGuA3sgvLzdluSS8s8AwfgHgELuNkSSslpxGlaKKwM7d2KKsVq2roUNd0vffmnAcPHsAk+ePMbTJ49wPF7gcHYGaRbpKAAWZ0ACAZqtC/j+99/EN7/+TTx6+B4eP36Ci4uTOdOcjfTV4hdO6xGn09HDfm2zmnv37+Cll+5DtePJU6CvK6QrvvnNb+PN73wHH7xzBx+8c4aX0XDvcMDppDhdXED6iqa5gJegQJCIvR24QK1u9uNxJTGUKkMxvz37Ut1xpxxJF2DgBfB+CxUvQ7jMY1eGCjmDMEYe7tB7ikxdETkxjrnS9TWqheWU4/yM7Uj/Rzn+4PhGFPdc0LrZ824FMABmKaRpPVUoK5B2DuSnoIDN4EC/jWqzjL9hFR0Imn8HF0bG0auxjtPpFCGtHFPTmq3rGmzhdFpxPF7i6CnjLp89w+H8hMPZGV5a76NBbNgSy5WBk6pF7R0vcLx4istnT3B5uaJLQ+8GDroqjpe289Lx8iIWEgk61tMRT588xrOLZ3j69InlfQAgaguIzlRxXwQvn53jTGyX6DvScO/sDO96u4afgIFLhRnY+YztEMndsqvLgcCbXgLEEIF9N6SxL8DAacPY3GYUhdrTA2NULcAwKDCNAOWmfK07KrHRaCWazFiCMgCqtdor5B/KMdfn/6/jVgCDqjmdSFdrk9d9ByMQKU+BW5IlMKwD5QUAbSWkmX6Dbkk91PMEnHwB0fF4xHo6xvqBUAAHIAMO/nSf3rT4h+PxiON6skVTZ+c4bweLO/BAKgiwHo84Xl7g8vKZpSu7vMDpuFry2tVTxK/A6bji4plNoa7rJdZ+hKjg2dPHuHgmuDxd4nS8hPaTpZ33jV6f6QI9CM7vnuPl+/dx0hWPl4677911y9+x+PoFbs4bjl1r5SHwhg7fTduzA+aBfihycfTFUIJMgKDQg60FE5l9FoXz8zFSH4c8GTIj7GsHEk054jvuHRXs9q6rYFM/7w1L/qCOveHNLiTJXJvf33ErgAGAj/ONynJ3T6N2z3G28F5UcEiHGb3gcW/xcluCD0tNfjydzPJfHnE8mqIzgo1z+CbXPQOh1h4OyN5PMOdoRz92PHZH37PHjy3K0fmtOVSBtZ/w5NFDPH74nvkQTh2XJ9spa+WCJgeKS4+n6P0IAHh2YcuiV/UU7GDadWu/o3agLXj51Qd4/dVXsAJ4+k7D+ZvftxiQVXG+LGjLmeWaFAEj9mIWoLAkoPgcgKKpJaoIGOMIfLggcbkObILTwlTyq8J/N8P74YQMqBFdK6lH4/1zaQVZhIOeSdbYDijDhwqCXg2+5x+4+2FoUxTE+4fv6rg1wEDrL74tWHjOqxnw6+JgA/m9MzBAfeOQlpZNAKA1XNqkOU4n2w/y4vLCQMH3YGBH0D8RDrNum6Gc1hV99YVU3ZY4W79ZKrL3HlrGZJvx6PlsMYA5Hi/Qj5deb+C4AqcTlwYDquZcvTwecXm8wOl09Nc/eMp5e5ZIxxKjZ0UTwYNXX8Nnv/BFfPzDH8BpPQJvvIHffvtNLD98C0sH2uEch7ODZaeGQrunmddUDqtD8nDxhqaFVi0LE4aO4dCCYw5v+qD5Rc1mdlj+BQzIY1ZvMIgFFMIXNF6Ts4GKfCrLm0BiY+ql4uN43RXHfPt1DLeWf8ex0VXBUsB2Pwt3CseUy0CNrlX3vePWAEOQ14B+70pmCarTieUeKkT1NVj72Jp+VdukJLkosELQxJbrnk4nXFwYMHCXpjEIqB4+nPDszzZVajkK1XfOOjRLl9Z9yNHXU3rVfeZkPVkOAUH3RDSWZk1EAfEZFt+r8vJ0iePJgEEB28uie05KYbtZKjbtgkNreO311/CxT38Sr73+AVweL/DBtuDV3/0HuPvGd7GqoC1nHlbtMzPNl24j6bM62OZUbypLUu09BjsqVciqAlDxtQ8aJ7fhupL9OhVOyGFXagUFPocITFAI9qLx31b4tvY38Gfuf95SWBGrNZa5fcxcz/ESHf+aqmnseed8Bc4AlF7axtvu6imc3eNWAMMICva7bnISQsLBopZ7kAJi6eHzOxkoBy9OQFG1WYijr5HYAwUyhVBuv6d32zg2U8Orb4C7RJevh46+HkwcfRhiiVMWTy1/8rLUlwaunr5sxamvOPWOdT2i96MNV7Tj1Bu0N+iy2IyHiOWs0A7G0p/fOcPZ/TPomUDaOc7vvYSXXn6Al156BevaIb5HZ6d/RpnFqNh91U1b1Dac4yA2U4Iy9g/9DOaUTBCvC5qmZGcAGHNRGIJ4f09TysUtkkqgnnpWKyDM0LC11FoeN4KBy48mOBpL22mjbZNhKEjHmsh03fwNAbQOrRNAs05Z4wTTH2XYcSuAAdiCw0jjpHTrdMTY2EChliUTw0hQsA+qcMWuPSE5atFs3HFFng6CQ292Y44IrhU45HiaIAKn4N3zC6yrMY+Y8TidIBeXwPHStsDWhoYFi6jnMSToWYgQIPAEADZF2E948vAx3vnhu5BmiZweP3yM9bTizuEc987vAssSCsZMRzVgiEwhlbkoyjR0iFnLcDpmH4QVo9CShbjzMWJJkNcN4OBDCSoEF1QBakyyUujI5MW+rPXZBwVGaaLcFpvjzNrkw6yYEEv+UoTqaqtcwiomYLni0PEPPjbY1cRyCAYz7AVYvB8ZA0BhRx1+jgiL7MBQahdspvaO1qOSxIxCtuMQButliFjS16ZVSOoOSjooUkVhgtDizsWUs5yWsyxLPYRegUgrd/fuPV8Y1jwpq+LZ5SUePnmCR08e28yDs42+dp8BsWEKA7Ns/0nBIg1NgbfffAu//ZX/Bx/+6IeAJvi9t36Id9/5IbR3nJ2dwbKnVlagoaBaFDmXMc+si9cP1GqygD7oUB2Bh+2pVucEIU3HXhF+0TpwYevRKew9ISND4VUxdAillOH77aHe7z1HtNN7U06HIcR1dU71xZfq7p/bc0OfvAAUoJlR6prHLQKGHtmKt0jqKTnVnC80YZ0UQeERj4igmZrGi+MzgkIZxZpfYFmwFAROq6aDUggA7RpOSu0dp+a7N9HXEJYcUc8Qdu1xTVeF7fzUfTstQD1oSwDcPT+D6H2s5+dDfVYP97Zp1dWBzUDx0BruH86guuKdH74FnI44dcUPHr+HJ08eA+hoC62kWeGoHgQaTauFKYxDCqnnssW83aJVwxehvQPNR0pSk5BJxk0X67g1bMGTp7P5Hpk8RtnRg1WvpKE6V/k+w7F5t/odf8t84qrStmWPb7YtGmzHFw8AEvQmYKiGj9+9HxmDQNG0F9Sv/9qRbeVC65bbFMx2nbb1Ig2xM4DkkCJiJBjIwylNNGDRYehhgDDOsUfa9K44HRZTTPdPLAAudcVJ3U/R616BtIA+pairDRucTh89zRoTiM6WeGEMhn+3nNlqUwJGyqTgIA33mmdPXhqaduh6gp6OACwdnFRhifDjVGbqVCWwOWYflWwe7W52eFYqYoJjTIHOfas6/pTvKlDF8yRhfxow5r3FW6eo7zVdvUsPajWqGefCc17DUlmn6c10U8L43lecnH02wzFUlf1X5EbHb3dw9YXHrQAGO3rWXkxh50xAAMdYnQYYgDEFdLGNBotwEQi4mrKJlA1l3MqUiL7BGqpvgl4kSbVDxT34HjkJbbbJxLpAerOt6sSq0ARYxWZWbHsEX8QklnUJkFjApTC/gyUutfcOEuMshwyniW2lN1oB30taMqEqdIX2IwQdZ4tgaYJTZDqizyOBwV5TxjaYhxOaz6sHhTM/I4Gul37x8/WIoLNgVVS08hypM1O83t9zfGjFygEcyDynq+Kb8bham+jWUOT0OqAQzWTeeqNEAAAgAElEQVTz+0r/4qHEtRjDiFMD2AP13sqY3oeMwY666SlCKMS2Oy0U1BEhqClzETJ5iQaUCOgMFN9DomwqE2HAaZFq89pmuZK+Be2RHEXQsYg7wBbBIgectTvoZ82GNy4AqwpOXaHrit6PZr19zNy14aTcJMUckaejJYVh3EQdF7Jzq98jx+r1b5uWOZyd4c7dc2BpuK8n3DvexXLxDLLamolehZtlUKQn6x1Zo6Mi2yPqMFnTDJwiU3AYmtiBxvdIuY4QbQ+2KvEp5QWibPUxYzxDuDYmFSXX4tQXmpSwvmzZKo6/pdxFkLDHMK0brgSBF1F6a6n6hOdfLQPDnK8vEPV+TR8/tCJbPM04Kgqa38mn57qtvxe1HAFNO5o2jwxQhw2PYlS1ZdsdwRgMoXPsD6jLXbiuHDMoyJ7WXDuWZkvGl3aORe7Cc5SEDVpVcFKg9xPQLULRrIrg4rji2fHkvgJPKXc64nQ8RTxFL8OL3tWnFn2YM3xWrLoG81AIzu/fx/3XHuB87Tg9XPDD4wlnj57ieLJ9G7T5zMjMCFAYhQPRKvU7YE9g8x5EGw7+4Ho+FDfvb878LBU+lcP7aGAUBn4qDdp68ZEk56mEniwjIy2rsmDUncoSNEFgX6UqlDqoTSTjShDYJSOjjA8Vu/JIAARS7rblAu/LDE60UqSFbicgcT4bKb9z34SoK74GWBggWBZlm51ovtqvQ1cDEBWJ+fEQaiCQ3oYSyHYVWrkVot33VVxwWA44O7OsS0urUZc227gqrTJ9KFbby+OKCwcB/pxKaLbFVZg/oq89waNc30Uc6BwsjC9gOb+LD3/qE/jYj30ETRpefvNN/PDyiLvvPoxsVQY2SwBLD8ZElmDq27sOdH0YUVRKyzLG0ZexL34IH5EM/ogQZToTAd9AlsAt4etRUcTOtcl5ECngOJMRsQ9bBRMvM16IfR7vz+v2LDDfgX+VtkEOSUKW5oZ67lG58YuunEZaA6eZLtw7/4LjdgCDmnXnFCOVHgAEDL6xYQXpP60zxU99CzP7uIZEShOIZ26ygi0rs0oibXqhK5Rb7oJofU3r0ESBZhmkzs4OlsL+YMDAGln8DcsbbBhUgcNBARygh0PkkljXFcfDAZfLhQ0rOE3Zu6/kPMaPwPwX0s2HwXgJkYazuwc8+OBrePn119Ck4dHTp1ik4eAOyrUBnKZtzkYkAFLNj9Jy6/rMupJNlKDpraIKWZGhz271AyRaYf8cpkVrqQPF4LL1ZxSfQuCJDjMcZCJZGz8bLMVliXETmiHuQ6wKfKgm9f4qEmmcot6T4m8YwxWs4Sqo0NrQN1TmPe+G2pj7pi6G2wEMg6ILqgp528fuBD6MCI8LwlnmOwABiwml736sbl0IBOLgUPerALbNuUHjyO9o1si2UPOt7Q6+R6Zg9AGEUNbuTmdcE8TeCrnzc0PvC6C2QSthsjUPoGqeJdpDmAUWRNWk2WauesLTZ0/wve99P3Z//u73vofHTx6je3k2LWzsSiXZVu/MpZDJz1p2RvxOZaqCa+1M91tgNFvU+63GhUTDx4V5ssN8yb0ssUcZT9dgyKxCAfYYFu5phJU1T0czhXwMl4Z1CQkKtfoxlhnqwUdc9fyRacw3h/O1PHPnMu8hCunOEAkV+G6GDLcCGAAgog6kiFVFYxWXFAAeOkDF1t6hzUOFmcCl10zFVDpYslIfjGyp4k69aoPbrbkLVhPvIGMsq/oiq9XSza1rUvQUVP5J4im+LwL9CrndmsSqyRWKXI/B5LNRrQ4HC8F66nj3vXfxm1/5Ldz7nd/Fup7w5OkzPH76FJdHyxlJmss2rkCc5DyPVv6uTkKOsoPhJR0AYaODG8CVbNCle8WvQtyhoXgF/3ln+cF2UD/21KiUsxH2PiBzqg6P8I349aNTNZ2bI/SVokM/CzDU52sylL3DSdH4eXo/qYVOZUu9MGuy+6yrjlsDDIpKPP0MG1gpQupTk5oWQ+i17tC2oIutMLQpw5pbwJkDwUYEYzDMtqNC5CU7onb+8WRBRqfGXbm5+YkBAzdBGSzX8FugYntfdnc4rt0ySEW+ggicykUg0sxPImixy9yiC7CYY/TUV7z17js4PHxofovVVoOe1tVVUAJ0FYg1E1wF+rw+Gi3yOKdv6zbS8z/2aRVltql3olE7iAp6CPgoC3u1KR0FlLoNTCX+FpIAO+/hi1LfotBELraeLf4IjFMVhue6EaDG6nhBOLaveC82w1VRGmYYNW3nTNGG+r5PhxLpAOpBY2dPNER84K5AW0Zq6+nTTMoBbe5jmGzdQMqCN2YnxnXRl4xxyAVdogBDo0+n1a3byDzICnKIWcTFP9sd5iZVhQMAHYE2fdl9SXds605zILCEuXAnm1s7DjfW1TJQXa625+PJf8K+06cygJQLvLAvtlJWbSXhttpL8Twa0WeqoYljG5ETjuoUdnhLBGajuPlic95eJvovCnaL0jrguxAXUOBRZG+3EpRR1mkEP8rvnKh+j5/ugUPmfKitNN9NWcpnVqPF91bSjxsiw+0ABngn9BSkAAc2vjoASHEKOgXkWNp+xIOdXOnL/G3kkLQHZKdc0W7boB4ABAXODOzOAvlmrfO+AKWopNAtfRKRMKX7su7VhiRlWjLup2NGraQGQJugLQ1nemZ7XvYVl0d7yiIFBOtuzv5O5uPtCT4K9wlYG0b7eLxIxHl4O8me1POFy3SaFlCIs2FZk2nkb2GDpfLvyvionGyjiGXwS2JY0ASLK1eHRh1CzXwWKRXUI1CL36Gzpu4vSsXUsGGs0VXNM1zEOs6vtA97cQ/bRKarb7r7VD1uDTAgFMNea3bcUGxEms13iznGGmcWYurRyugqkI7wYg87Z4v42D8eXR6Swj5OY9o/dQah69xdFPayfXUogtcfBKf0cSQYlLUU3Xe/KtmOwrBTcetageZApIvZdWcSB6MXaMFIkNGWHGN7Dgu+D99ZY0yQVJgJdLjgrCuKAmU7pLu10PeBc7CtE4g0/hs6fWIX4MV7GjS1TfZnfqfxQ0Abg4TqoGfnt7//7L8YwQc7sjvXxltCOEOyd01C1VVsI+5hHbY48/4NiVbYHpIRaQgtUX9jszLCkRbTptUA9OrcEnSGIJe9GkdhmYFhHg7oEEAU55TA0IuweucJ/3YPKSYq63Vg3oR0tnH58xpJTHonyAFzhg4p4LnXmmQCfK/mW9ibHwHQpgEOXIimUDTf8s3I2+hvoA82HIJ8TdZMa4SiAUfGslamsNcD9agS7uBaL45xRhkeDNGJVsHoU7Fe0KFMr1EJnrKQazIXgYivudEWLxyP1Vq3YraLUjZs329cwDV9u2kQmWr7HNYxl5MPBN/optBwO4BBLcVai63DNYaDdhQqKumZZ1saLkh8EIVFFjEVeaF5iPtmHC847VrFUOgMCdYADFMqNvfcZc4ImIG5CFDjEIC+egKhOjHVtJslwNbK051OpsAHKtLql7Ez39nmUxErG3vS7WHn5cJmCnxR5wKnGnWnSK34uDZXsGdYcjhxr7RgxvpM95Jx7bVtvvx+f0p5vtULG5kaWY0OyWeUrIPn/P5x9bL7sJSzSNualm6JGke9hH/J0Ibw9qrYtwcKA16Wt6p/7ZnX6xwvBAYR+c8A/PMAvq+qf8TP/QcA/k0AP/DL/pyq/op/9+8B+JOwLKX/tqr+zy+uhnoKtErxpQhttToWeNOaWH5HrODCJHNfWqReF/eys8O8g2OKs6g0pzRnOkaLuvVMA/CcjF33mlwA35o+MjB772rhnSasUoAAQ2+TbRgdZKWGX7VCAMsp/oPwAwbD4PdUplF0+fiAXW/XVEXNoEVXNPG2irePaeL6BKErI7ltcXqGanlOiqAnYRT8OjHA9UgHCF18urWwvC/BwSMoBz+Thh+igpYxJEfCAkYNdCr2eALqFdTwYZp7z5UbrQUaiPRJyfAio/9mLEVQHef12zBlpVWuf1yHMfznAP4TAL88nf+PVfU/HCop8o8A+JcAfBHAxwH8ryLyeVVdX/gUBtiUCL78KUqiagE6DFxS5ke2RmilC3g+GcYYXg3+5RKhBIwCDOGMKzcIJEOJezZ97gpdS9eQ8Rjr1czKjMZj1uWCCoPAViFjsVoVcrauVTHpq7DpzCpaHB5VJR09AePcQchvFOJ1L8ugC7RHPbKFWXcyA6+jcBij2XNChVdnPYImppitAx30SQHc9bq8WGmJOlYPapM9rj3rLlqmNMv10S52dL6X5nc0QAU3o9UG30rIgQ+NvVyTmxblRTfG+2TdZl2vjumB+filN1spcQ1gUNW/KSKfuWZ5vwjgr6nqBYB/ICJ/H8DPAPj15z8E4HLbAAZ0SFvQGGnol4qIxyo09NAF6w1RhbaO7mNq+FctgEEQAVQByq5AERjh3UkrPIECv+uejo3A0CrtjE7xMkPAvZCuEYlJ5a5WPo4KJi9ovriBZ4RsoZadSXGDZKDwlSuNi24/BiNJTpCcAwUduY5BI1OGnZ0YUhmCxC5Sfj4ssU29oDVPapeezyxVubZjjEFIxc/PCZ1kEK6kkVeUHeBXl9kV86HwfGmYOl6aDkEhFAUcAnxDzpkJjHJEwePj9iViAJ5g2V7CDUnD78fH8KdF5F8F8LcB/BlVfRvAJwB8uVzzbT+3OUTklwD8EgA8ePklo+yiliyVHa4eElv8CRxaqHRfCAVoF/SmluC00dnW0up6Iwdz4A+QHV8XCg011fGXb1N3WjtOpx4BObIwf0SxGIX+19h+E7/uwwgKSLXR9m8I7Q5g1DqNHIjfS9GZyfryHpluGoSpPmO0f/FTgLTeoFpZA9L6I1wb7tyztlfppdGA6hCwdkzlk3g394tM86TpUKz1kul3Pk4gJW5gtvbZdrUtYtZnaCO+5/RcBegMHVtXxtFGuaeCwguPSRam18zPL7Iu0/GjAsNfAfDn/XF/HsB/BODfuEkBqvolAF8CgI995IMaU3arZ12G0yxZxs4XgUhH9zF8g68h0IbebQdrbQppHaDgBAi4PReJaU4/UcrHbiPSojJp6+nkkY2QSCNXp5ZC2TUZildhcPbV8OC958VwJpySSM0sV9f6cy1JhE5DgW5rKWbJz/q48DobyCLTrg4maWgwnq4ARsR0QB7z8JijmcCvALgeZlh1aYoV6fykAI77GsI6lyCeXCylxannZVbm5s+wCEJ/FuFYyhVKpeeQQFA3L0pHK2LGKBo5+t0BfgIULZfJTICuOArHGQBsA1hDP90MGX4kYFDV70V1RP5TAP+Df/wOgE+VSz/p515QHrCuFpnH0GAKq0RrzeCQSVhaF7SmaM29+9ogvSHihYtlMcbPxUHFGkw9MtA35J9kDH11ARGxEGVXgFqOLUNO8kxQEjFC42QgHlJsSQp27OvSvVZ9MuJlpoIU1cfJIpwFaW6AOQ4vL9VsoTrUxuwUOyuaU3UDT4i+kCKdW7ETVzpPPFuyazX1tlH4UnkpZSbrmjQ0W9HbOeM3kMo71SQcjWWokkZdvY6SqzbZtMoeI3MRf08qIKZhnreblPuCQ5Z3KT1tjlzFgCXPOdK3MzIfQJMVg8+pAHbz40cCBhH5mKq+4R//RQBf8b//BoD/SkT+Esz5+DkAf+uFBapiPZ4AQYQAx2yE9OLljgpAIOiSi5kWtRWHxhrUNsj1hVQEFsqRDS8KlQv/QqkSLXaxLOlbABTmGG2N2aFbMhMvtsYJUIJaa77gCUP5o+E1BeprWr0e17p5KR7AuvMWZZJDF1t23hw0ExjqTIOBmxqY+jOaC1vMZvimOlCOf5n+PesbdWCZBAaBARzUl4hrLvPuJPTOuyRjDrQqluRrx+jPn2E1zvbgtOQQ9UjrPgC+3ROMqfTTMAyLGIZC7zS/D6DwZycklIFbVdxSQIHhIgAozKnIR2EYm2Hg5pgcnn/QjEFE/msAvwDgQyLybQD/PoBfEJGf9qd9HcC/BQCq+lsi8tcBfBXACcCfus6MhMIVSHLXalPknpR7rBUgLVY5LirQbvkRWuvo2tB68zUVdEQ6S2j5OxxBU+kUkByrlth4BQAbsjRuCtvKuJDmKHIruiJqFi6dVs+/r4aOv+nY1CJCVXLjYqtUDW8OhyMKQxEHByT74NEalTmBiEMYZoxSHzbVNArZTsj7BnWWAC3DHLvYYL4Hg4LTdesWa8OVwW5axD6LzaX3wTYqKBTzu8WBweiSnZlxmGhAYX91OjRBgj6Q0PvpyO93XgARAqbZDnBmwsatABAm6gXMIq+Wnb+vd1xnVuJf3jn9V59z/V8A8BduVAu13a7R4KsSe23CsG0V/c3KNXQRaDOhb6qW1q0reivz7cVRJRBfzKdpAHhNVieULc/QklnuyLpLNMfRKR1uw8QstnpWk4FBiIz9hunvXsAkX7tcS8vPMOq0VgFgxdHaXDEzgUqhw+qh5uV9VW2NhvSO7mtMzKnofWHTB/a8ns+mZ7++yqysoqTwphBNfaYp+lpyTYIUPHQm5DWMdoxmHyIx9xo1P+9FjUqlJIMSpxEPsAXBQgYZlaK1OkxByMCGHAZBOdmr5l73v/AYUPRmLKEetyPyEYwwRDIG3epNfU0R26zAnFgNTRcHBsVCGg0f/3sewd4sY1HOfYdpKFGFKRAjzlLJjJlUUMiLyA5KyUIrm76AlePqkSXuv6wruP1dFLf8m21S4dTqVSmxtJbKm5wXUp8Xqys7WuwgvqJ3LvZCgAHH5EFvq2UMkKJGj8rMcwEOyAAoCeIlqG+cbVRAoZpxccaiCX7ZK5XZkF3klKlEO5PzVNZRwKE0Uw5lZJATDKVohPrn15IGyWnRYFMGeaq/y3EzAnDj41YAAwCbjRBg7ZrrJAqqb5pGOB6lo65QsOarCRmy2rKQTgs5d0BhDJVdUHEICvmTymclJAUHUoGK1A3vwQCf5Bc7LzmB4yjoBdScsQQcal5pv0t4SwWKQqfnB9MLnz8J2Ma2JIobgEFp8WGsx14235szBkVxaanNWLOtZCv80b5xRVyTOiWgmzZZTILUOMtTDrF6DkDqv40tJGhY0zAOV9CH5f1Z1yobA4DoDjHB/LoaDTswoBsDwnPA5TnHrQAGVUtsAjGh62GWBjsxNopDfZNmNA1qU1+evLT7UKFVxxjKYq2hzKS1Ii12tACtWSNTICA0bLpxoKYSCkM/9xbipHxMCZ7LQflakUt9B+sc2mlPrtNnHP7kddF0jod5n9cgnJ2RS8qnUYQW2RuHtDgV1EGBYO3Pr6AAWubNe46CW8Eq2nAT7VVqLenuC/EBw80LKPiaFN4/9IuzpVn30onpTMLfwa5rWCZms1XGaQjhoJDgkCA7Hpr3XQUIE7hcfdHNjlsBDAA8PRsiA9JmjL+VHYRlgk1XaleodIuIRPOoydFsMCB4GDiIdbm0ZmPxJpDmRINOJymPp1KxduGRp+iwvlJXLJeb/Xm8qYBCr5pEJeQSZ/9R9z+IYgQ5fy+K5na9QmEnQLGOrJbXAWWjGX4pAFrzaMMe1k9L22Yd2C75OUHG2UL0Z8YGhM0XGerWnAXMfsEN4x7aYe+nto54jUo48qRkARE7csd/Y9YF2X7ch2w0BskA9mhCDXXOmnnvTWC4qdRka7bHrvI897gdwKCc80duL18taLRnNpKLkAuIWByEqsfcA7Z5RAekofkmLOYHnp16LvoiaF2hC9x4iCUj6R2ti88+cFrS0se10klR36FUgIKOoMl52Ov0MO29w2ciWCe/30vrAFZXWIIQx7opeLg23aQwjyNkb1PuyVl/erftNtEibR1nMlL2pKAofRAZQ2Ad4Qwg6uHtg+ZRkPzMYps/h21VwHhoa9SbcqZo82V+HtW0shSkn8MRcHjFnXtSRkkFsnKaFwx9cBVXqFONcU29TOsV9fnlAytb/GrXPW4FMCjUwEAwrmbkOFCnRta8T8SmNgHbg6GJ2E51h4ald7duTOjCsa3dbXLDBhPErAUQ6xkgirUDkNW+Fwu3JjAIUgCrlQvvf1KSXcrIaTAa3mrS1QUtZ2WqFUeWVZtnt/93SGqROj47lxkbQIQjsvvUWmsert4twpTAjQSzGsmZTCFBQUNjBRUcEvwlnZHZSOOSc/FYEr/ARiYEJ7e3glCG8KVI8/bLBstb6koOjAa+AjqbZepvzvjUQAvxuifIZF/YZRNz0JEPKMZ7XnjsXraBj2sdtwIYAFjik4bcMj3Ek9ZFEwTrmyqw+owGxNKoLwAgC9AMGGxxlW0zb5F+KTDSWmxhl0lUxBWG43laxeR9hhk2xWaYwtwLPIFYyzE4Mqtjk0oiJQLPMzElYyqvWig2Q3hj3O6PrZYltpCPRCMJQlSBptRHApC3e8zNwVhCbAzhe070nnUpjjEqnorGMMr5j/ddaGlpT4QjTiCW6EGL0SUrcSVV9QC2wp6qQISjLxRFCrMRiPZ4rrWxr9fovuQ7ZlLoUB6xgdWf98Xk80cW4TI8j9tm8hDyVVB1AKQrwOEKMBh/3/y4FcCgalvLY2kxFr3SY65jV9C6nyzriAmANKB16MnSrzVpWBZADuZYpBOxiaAN+1hWBE/HVaewTNSUHMS+HjMeqQMDB8MZT9CwMJntJFcWmiv73Srj5QE+US0qGqEpQYE7b+VDrXROD/JoEMRGEs4gcv7AQAGci3dAC7YKxPkAds/onXUKUmBAiBbDK3WnSesSIKA8D47bo4RZArLOaBDJZdRVfHJDZNZR08Iz6Ezjm235Qa9q7xRKXxoj7pQW4db2nmmURv+r5P0z2ExyF682sZBa/w2S3fC4PcBwsi3leqGzAH0I9ndt8QFskbH3EEDXjo4VrQNt7WgiOD87s2XcPsMgHrnIDW7DWgGBzgpuor3XyB7B5xas915Yjf3u9HcEKDDIpah+utBH0keQkFlEUzHCQTXiGepl6ZZhEBYVn9GcRUFaKohOT4w3lpwdqF5/oDpaU/jFNwGJNRDuXyBDGmIK3HmcBtvqt2qFrxEcopYFL3z1R7RP3mrvac8ojkitzKuWO4MQj3SWTqPcsTNgk6cqBEANACBAsvIpYqOsSfl3Cw5bukB/yLaMmx23AxiQa9vVlYgLlOx7XlURtN4PRDy7wpZE9xPEQWHxZdht6Wi+6vLAxT1UINRnViqoCUQFPEzoySfUIhwLWlOk6GuglYQi1goAANzZCiZo9cLTV5+CxDbQIsB8/8ADVWxFIWfRyUpyeNHdoek2mbHJ/ggD3YpfjH4kKAhiiXt2FrjU2HyYAk7vsX26M5lUDItW7V0iHyXpjFVJRjZC1SzLE6VWfESKHWtsL8Ul3+nizfUiQ6h8KfJaFjnqFXMqZWaLcoKI8KyjiKEYPyE7IPD8Q57z6cXHrQAGAOiSO0fZRioUhLSPM3kbwJHL+0OIFeIORFVFO62AHHHqHUtbsCwnLMtiG9Me7DcguQbCn016bKpAUEjHErxutpAmrR+Qjrc6llZqGgWNoc8cL48mzt47hIYKkYyBjTDOvqcZqzbfnHq+LR2DZ1w5bWs6DMowWOM4gwACNB+vD1bPLWO2VhnhMA7AgrByly4DVXHPf4dPWbvjuKZiQ2kDZR/ECqbZiBbwjHZKGSlfbPwFBPUoYQCF2sqTKhcASaBq2XSwmbNwpibOhSGozPG6gBCu2eKdHnvyZsetAAZVYIWlawsH2+J0s1jouD5UpIwH2Rnh6OMGuObQ6wocTyec1rX4FBYsy4Lz8/MYUtgO1ks4JSHwna2qFXJFm3gkgYpCa9s+uBLvOZ8AG9ZAY2+MIBJRcFUA8CWHzr9aeDSVwZ9rrEHdDLN8/vbvBwUryhLMSqHNhTsq7k7enreJSNR7EFIOjwrYBXBI85mmAp6CWJcw90EWySGOxHuxP8IhqhqJfMlS0qmZP/mUedZE40++pE5VsT7le+2c56t6O2gntpENlmeUft1b21FqkY0+9XZ+utlxO4ABwHHtnr4cPhUmvkQaPjVIBSD0lsYI2fIpRGHMgVPYygCQnmbBavtNAs4YEH4HMoeFP4sE6ESdpHSyHxOJBUk83zP+8HtTqRtr5/Wb7ZFUncoHzL1eTFoYj2JFaMmqcBJ84i1idiUPKX9sfAPanQV0tJKZCv4exkaK+Hr7D5vKBoBxSKVlqbn3Y2EM1QfC9yA4xDYE0RYJAjncK8xB+R5ABcp8eSUCF3uuQ1uTYbA+I14kxEQUZZn1sY8lRBxATWeYY43piPa5qrMwAN1NjtsBDAqcTmukj2/uHGqSswdLa+68S0QeZy7cweehy82XZXNZdI0CrGoL2Jh97U5bO8OzJUBiaQ1trYAgWESwtMwElQpelbAqWPk+hDZPTXbIfpNBk12AcRG67Wkd/wilCIFPGg2XM7ZbPD3ovgRwDVX3dwI0lyGr0fCuHa0DWnwt9BNUUMj60YLrINyM/hyUGKksrOHYANV5lyxl0z4TCA1foVJ4SWMzkBR2hJXPhDi1T6JmMjbbBsnr7ATyfp6M2Rp+xvbQeg/fIjtyBJ6d+5933ApgADQiHwFFbwKcgO75GCILUVs8nyMiA1KLYYGBQYBJG4FhK+kJJhwzV8OqyAxMSvPCu0SwwMGKFli2YpD1WBLwKWCD0qf1GZKeoFLIdEa6bu+2Y8pKMqM5L0QuER4NUbxH8YlIsdo5q1EUU2B0ODqF0ZB23ypjePDAhgYFLQSeTK/INyAREBROWVxhMSfgKCp2xRV+TqQ2/cAUbGbFP86goKVHihJXrMrK0H8x8gn/qoCP95Huv+HuMRGd389xS4DBhYQv1U2gaB+aCLTZjAIdg9KM9stiU5ABCiFYVpRZ+WZx/gEOOSygdkTYrlXG5UECiABLPcc8BCfYDtVemleclsYdaWQvrSdwSBlY+OWxhDeUko7PUSiSWo5ncqxcBNQVc6DQbspI03vXgc6LM4HIN1CpsZAVOfMSPoOnCvAOFraaUY0+UfUhY/G42u7kCi6H7cJ380QxDXA7x1UAACAASURBVOAWhGQaQwKVPabA0knh8w2wb4fz0PIetMRUVCl9bUalAmcpAzo0S8jl4Nn0c8KyqjG44aG7f974uDXAQK0kdQrlhGXz6Qq0rpDWPZ1bw+FgwmWdYwIe6waKspJRDNaIzwECNDRWz0ooagtHgjvZBBDmSFznxUYIBRCnrmvvhaSWTXAICpJ7cB6WBW1ZYr3HuBdAZRGjBe5GbxAZU4JZFAebv5eN+SU3y+11b04kWBUwiB914QXKe+8r4wbUYlpOvcklQDYVIK+R1jx3hloWcBELg+5iuSkbIF0Sjwr4bPx03t9172PoiGUENxMJzhCxftnYNpzJKUg4m6ybIkV5tVo8FR8KqkrK+o3YwfB5oH7jNVdTzCuP2wEMQXclEHOIYQjHVIe4kCwwwWi9Y4VbIF9N2ZpAeiZCpagzuAkA0oKTMWQuf85kxDLr8HKrU1xjFx5giZr4hYEs4pqd1Nnezz7nfpH2YgYeS2sODC3AocgOgMj5g4gfAHw1qj1MfFFW0PAYV6fQKmwXrbXT2anZxuV6OGDRedsENjyLeiVomIc9g66b2AyAga6HukedxHWOAC5hFNheQ+BYK+8imeSGuRrty4GvO0PwfnOFHgy05HkyvNHHoIPusn7p/J13p5Tx+3J+R4utDaZz8DaOdoi709CFznuxcXZK7kBc4603HWHcCmDQ+NHo8Hl6ZmaJFJrT6oLUFUtbIulpa6a50juadog2cK46RHscYIMtyHUTiFNuQznu9WGILDDfR2l2swru6vSkM6l4FsCzrvZ82+LOLfe6etkWjFVBoQqFPd6GRgQGVT7TFbNQ36GRwy5VwZEYO8fYP1Z4ukJ1D+ASyaFGdfLSsor7YzhckoamHV1W71NPQSc5rKkNJzqypMp0CEBcfMTZhxyfj0pq7SH5DhUc6NyT4ij18yjPDM3L1VSIiWSlHBD0J4s/iRfPzTMeqbvcB8XBmmwvrvLrE/FTNnQss94xAsj1j1sBDACi3SmgadCTOUihiYwelE5qrtl3za2qKLCaZV2W9PTGMMGtIVrJd0hQqp6vsoR2tAoSwJH+iwQHCpI4ZbZuXrwsjbE8sISAg3UqY2IglcSwwxyaVHIyrsIldgShTPeJZPAV6KRMC73Gvp8Y2okZuW06mKHk+dLNmQgbK/aXbPDUbZL+Dngi/NKWEacUILb3/gkKWqM0iwUeCHkYUxk+x85jlTHEUKGa6zpFCXA/LQH7FGGR86kVXMbv8u/kA1nzBsjeZnIbGjKWLFLOjXfV+K+bHLcGGExoJPabBPaYgwwobFNbAFR9nQUtZvfxp0CxQsV8FNK4LyAiw3PTDuhisw9uEbszjXFxFa0g60EWYbkCJGsYf3VN+0AK3mrIrVvf1hfoksAUMRh8T39XMgEyhko51eOWacFm+yHx2+o9rnxkRupuGwV32zCYdzBFPtPkC8ELs8OWkMRKSf4ucJV+0mwfOkftNZhrsgwnCqDsHWQ9AHIhWgAIW6G2yKSsBAHNuZ/hSYJCZzhDlKWGwZD5Jj9U4xnsn4GlhPKOYBIcpWJovWBQ+rnX01H6/gUGeDLtwZuUx4aWlZODZYU518StjlqGWSjWgTF0VbSuWBt8eba4UjplJX1FAQfJJhafCTEnoqE2hxpBb8MZKPFcXsdObdKAJZmQDMAwmqKYNqVfBAQNp9I9WcNOY2FgDaXlwq0QhpJ+FFJacQcn3DFoPhwK3jjzUZ2d43e2JERRk9GQXhso+G86RbVn+nqWq7XmhVEOLE5jmJRXjlZ8bhoCSPW3cFWkst0CYOqUcTI5DlkGENzhCnEX/wkl3/ZcMMLNNwUZrjwoPzeFhdsEDKrlBaiAV1gHILz65Uz5G6jTUUGVwa3v7Lpw/vUeBerQmO6s9DG1DRdcybvtvByJYiQVKnwRDvUCp+OCKsH+HkKZAJmIzYSUjXmzkXJMHPRxpM2DHgy3q/8voJhRqbvOQUUEDM48GKh2UWc4PRgDQqm7+yb68HCz/mux+sy5kaHJYP84A0yW0BFDMC1xBDAnXS+03vwWkn0dFprvU+SDikYWBvouSruwKTVuSXvVR9kc+UURzAK4dWSa/VMActDzkWnY91vlHrqbPpX5smAVNwOHWwEM7MyY2yVGlOmjuW/z2IICgGEYUIaXaYnjDomOB62/1o7uqHkDYlwJWCBWff4MDmVc3HkOHeKzEPH+xerYI2T6PhuKqyDrdGBYuSFXZs7ssBATdAKnT2ciFbGXe7Vr0HlAIZ3DpnQ+VmttwNBjYRTZVndgYJ7KZBgowKBxjqwh3lrcWrdsE1N6+gUkmEQCXZatYW1HzYz4CM22j1iQnR/aClFAmxRwKKHMgQAOmIJgGgkyXiZ/R98XwxT1TUSJnqyg48OQ5/EGMqfROfvi41YAAzByg/qibGtais3F8w0AaHmZc8HG96mwXLlJ0OCwIe4OYjAOJ/isncdFH9ObHJWPMmN0PwADn5OgAsTmLANNrha9jjuTYtcgJynP1biObVeHIbnKMZRIzdKvzL8Zrb+WdnM25VLfg+7bQDxWqA67aSFazl6bsSGufFoUFBLvncpT2qFrPDNAwRtLSlvEexeoTQAofURG0jGkhIkfof/B24o+JkShoavwHhDtY9DVHgkOhjACAFvc5MHAL2TFf5mTPMvcqr4iPbrvQ8ZAqzGcIboCSOYQVweYA6Tj84tzpkCigSNqkijsjW5fSvSPSH4nvkflFpSouBk1yS9NcJM6hw1wyRumFNnPUSdBg4JTlmyMtLQODJqKXKcXoakCI5hptllRpFj6PHp13RdQ6D7tIr3/4kMpT8OG8kwysyYNugCiLZ5ZXmoqO8GNCq6Y3kuKv0LcwazlXpcLAQI4xj4j6PBgZklaHwXEEgYpLToZQ2FoKY+lzYKWpoIPvol6/0TtN4aHTKCLp/qb2F88UhLU4dxFRz5MYLoZLNwaYLBjtMTpkIOkx2EiVgg1iD7MGYPgcFTy8tuLBcSi8BBOR7NgYRWJ2v6s6B//Tt1qRh1goNAL+DjKxX2q8IVdYcOS0mpap2o+qCThC+BXMZugWU6hx5jBIbaTy/F0sCMkONqtVzC0wqJGoM32ab7Clc+dywjl11yLMjsaR6X3wY8DVmsNWoDCikuG1cXzgHbN5di0w5ORkfqHB2SRlXB3cS13cPYKQBkuWPbwMuBB+h7sTPNqRN9Vlsr7gmFoyr1XPa4OJjmawxjJFPCl3OrcAS84bhUw5KHQDUsQcLLRFNwDfKjdIkCcy2jHpKRZ0oipEqyB6DxcLvnbLCHrYFTamr5FXyo6vRJFKVgH1pWKWx1jVIBSt1KRARjz8hCEMHhTS8peATGO92ZD8RlUMCob0Y5lcvpy8TwWGU0awOCxGFAUyl/YANmBMnTb8jl030KA5amPx7mOIocZBRSiz6ydPceLXV8m8mleTIGqiREicuJo7+ituy8hjwibkq07T7I0jMvC5360520nlQluCqA5koy+t/mh8U7I4LZY/ekXj8B2vePWAMOm4sUyc1yLEDwKchkaOCDEeYY/u7AzTqJOFfI5RV+CjvLRmzoRgypl1PRsg0JXhs8AYkyaBVVA2OnxnaPCWVh1ANoaEJmy3fZovYnPccWZkqdwsZh4AozZCs3AYOs6DliWgyXT3UprsC2F+r6XGZeQNB3FhKYprYu1WGBuiqtYFjoqu29nWPwIXl/2tZT3CYta3jvfTeOXqvrmOhjRGQAT69LCD30SXGHqTT/B9qhRENtjKwuDQZgauoFzbT1epQ5rf9Tj1gADNdKs4Njcg6CCTq/mHIJX2OeIA2CEHpIV1PURkubfyvXG3G3Oakn5OSpd/tL8QwozsE9RExeSdIzVZ9bPO3IwKGFYLlVLclMCreaQ8mA0Wp18SCUmY4r6uUBSUUp5FXA3zyj31nOA5PKCqyhZswCwHBrkbx0U2P5uEGjzz24h1SkDl+zbloPVMbgNtx/Awd+3d1us11tDBHEABWjy/ZO5beWCVE4cEIMxzHUo7VEHIDNM7DzBJcuiJuP87w8Xbg8wpKOJTbcveLTQQ6NxzOXKTmqczoQi/G7xQ7CjczU0Ian1gkqPfXkhr04jo1knOs3CJM8dROUOzRzfbyh30wQTU4j3B2T2Ohfn5NR4Lqj57mRjVQgrkKRiajCOrrZytLUVdM5yKJIgnKXVV80w9wI2fm1rFRjsKvpPwiKXfgIVsqCRSJajzbNLTd0wDBkLMBiw2P22VYl6Gnz6ABByJbVda6fki0buBmOVTvlVx2X+2eJDOVJCyPdmGwUZjBlh3nOdhquvf9weYKCTCEg6XF7Gxs/Zmb0Kt3qG4xgDjGVTDHMiKrVLHSgY9oxYHORj5+j9Kqx+rhoa1eG7VHApfZ4eBee85jn36+bhfOAQyqO00FB6r8sVYc8IfPRvRPKUYtl1LDdsVZxPX8DoHLR7bI8Oy4+5tDaFTQOyK80u7FWR4VZ0awuc/rPt86ak5T6vUIxFghMD0MeG5OdNEhR+KIBpu1etWX0CIMsiQI0vWf70OgawppwL66Ljo3Xnr00z1uo60GTsd9GUaJebUYhbAQzpiMpQ1hrfbso0Wh2E48k/wvZ/6IpYx8+jA04HEVGF1llu/cWpGJgRqjowy/PiSRoKFzaxX9H0pX4jzxs96qyi+Y5McEu6ghR8F6bq/06W4ipGWkQgkHpfqUZRxARPzrDkUCfKJ3vgGgYImiqaLtRgr3tS5irUY/tosidWoFywBYSxhKuYeMqLR5BqMyPCN6zLqfNBpUApZVV3Yn2+sHl3QKGuqck/Yqga7bqdLZlb6HkHZXFgn9MQvJZ2M75wDWAQkU8B+GUAH/VnfUlV/7KIvA7gvwHwGQBfB/DHVfVtMTj9ywD+OQBPAPwJVf07L3qOFnEfzz/vpVKQuVTAljGbOeT2cXDA8DcyztDcQsdU27jKTaSb5QuNpta6oCqRvlJm5N+yJzQBCWnaiuDUiEBOCPQik9UnUa3KILyzx3SQkKqEVRjpc8FwrraxNwuk1xWA0xteOW6eDkF62t0Q8N58mm6LG95Fy79IKw53xHn7W3LgCVDIWsaissQJKGYrzqCxvdiCq8gDV29yWXWuQq3vRRrnfEgQY4VNUxCIoZ53xK4l+Mc9smWi1zmuwxhOAP6Mqv4dEXkFwP8pIv8LgD8B4H9T1b8oIn8WwJ8F8O8C+GcBfM5//kkAf8V/P+fIngng25HNOl9ux2yP0gp3zyGQ3yrI2i2ZiZS+cBBozcaS2sMXYXXxDeil1BUSnRnZoUqVZlAYPdL1rYuQkhLAQYICyHsqayg6XmcSOP04HzWoaxzbbgHFZljKNFn5pklHbw056qug8ryj+Gqej/bjsKBWUbcKUi9i+9u+JLYDVB16mWLuOB9R2r8OMfy9al1idmzHP1SqOn5QMhUyL4vDiOCy5DPBBGNJQNOYeZkcNSBj4OremLErJpYsvL+4g4bjhcCgqm8AeMP/figiXwPwCQC/COAX/LL/AsD/DgOGXwTwy2qt/2UReU1EPublPO9Bxf4PEF4s2WQZJuXwYoKujRJoJdsct9Fd9Q5oaJboqQt685BlQdEVLgwa18objTMhqWsH+KxBikunWpeWqHjSfrKW8vrJCapvIY1trugsbVGajLkIoiWcecQzo355c1pTjcIiJbu0oY8iCOxFgle+ThoOjOtStreM9kHCWm+LFWhpLTDlH+qPBOvLl61MIV95ZCfsawQI50Bu6OSp/mVY5kMHC5Nu4beJ4SSZE4AIEUcClH09GUK16UoGWdX0cgO4aUjktY8b+RhE5DMA/jEAvwHgo0XZvwsbagAGGt8qt33bzz0XGHIMWzo+OsdRtVjjQMcNnSMqZxFs2fQZlS7dGo+Qnx27WtXTn+PgIMTvNt2xf0hBfD4lJtYq64hi6H/RoYx0nJay45ZUK0JLvHspp75rBdYctlbmFQ8PMKjPv5IMVGQqtSJIDc45lK6vTGPzWUtpLRgFgTJhXDHonpY6xrNHJKhgZ3Ep3JyWYFjbgw+u78b38/ZUrjtJuGtRxwL4yrfiT9/ExPDiWMXiy8zTqCTUTvh27ePawCAiLwP4bwH8O6r63iAMqiq7LujnlvdLAH4JAM7PzgbvdzohCaJOj9Q6ZaZtQ8Te9kH113Yticyl5d91HJldPto3Nvzo+9lR1B0AM8rubxegODcjFcHH3DJONb74IA/zWYVeZxa2XnkbculIc4N+CGI5Ok8VS/r8Oik2DQcM1J6vTuuYLe/PFAuWaq3HuDkBZmYALGemRRPdCMSIE0WwrH5NWs4kQGJR3tiCVnbiaMqQrTr1tlVB10YEsJyhpUG4VNyGE0rPORt7aM+msNT9NBA0aqz+BmSvf1wLGETkDAYK/6Wq/nd++nscIojIxwB8389/B8Cnyu2f9HPDoapfAvAlAHjp/n2l55s0tQoJkC8WDhy1KctGsPaG40wFFUE0aXO1WD/aIYjMpIMlFXAWI1yYpf4b38guS3meVjkrmRUZtElTccWRV61/WK5CYYMtjEAPWm+4MIdF4m9UOZXxd5XGawOYl8BhD8lYFGP1sCDPZpmi6/M11LK2xH5b8+QeUCEZa8RjNL5ziYUpVt7KSpM+DCk9WXHrCm3NRzfcrUt96MqCGB6uYSSZ7W1kqgijScYYM+tJMDZTwjc5rjMrIQD+KoCvqepfKl/9DQD/GoC/6L//+3L+T4vIX4M5Hd99oX8B5WXJHK66LgTaQ2xLtCO3q+/aIWX5bOwNwRRjMRZDRPYRcOwZfPe9uiT1yOEDspeKw27gIBQypRWrOJ5dvufpjhrwHaQCwmQPiswHKMTpAhjxwxtoKb38Qq04n0/HywwMlVrPL77Xk9leeYXALXMwqaLU7oiNKWsRW7GJOjgqb1ZJgQAxLc1/dWyP+jY1ehSobGEctuUwLzjjzkvatTFkUAG6+OpNGjYT+mgFrh3hAjEHlNiZKghO9YWZ36eJQpuOwO+LwNpzDc/2uA5j+FkA/wqA3xSRv+vn/hwMEP66iPxJAN8A8Mf9u1+BTVX+fdh05b/+ogfUcS3dZMov4iA1UzBkNRx/LTMptsHyIRFTOG1VlNOntZoamKALRLg5TAsv9nUOBTstPcuDG77Kq6PgKNDXeMZw2cyl8rSStsapwfTmFOxGoad3dSBoLdlQGNzp8QMh85kACQCc6leuDdUsFrs2VrbZOPK2H47h2dfJNKO8ukTaLTNBIcb7UzvU14uZLS/DZmASGhOWFZWpxfPdN9GaJQqDx1ZwuJCObWurAKFoL8JKakb2uH/H5ish52bwcqryObZ297jOrMSv5Wtujn9m53oF8KduVo1E+rDeAAZvejR/GYcrvG16RD7GFJVavLxqg+ri2Y3dqoel8WHJ0tAMVaLTwyJPPgr7O6linYTsPqPJx6hfS9/Arv5fcboeAkSbVKGdrojyhkVIZfhiu36rbzzLFaET+MpYKhelKTx3AxVUi0NPk10xxVpd9DS8n3qrBbCQPte2yHWn/LH0c57qzRdeaQh/ykYopORzFgCqzUK5fSl2NCSVvMhelY2xXWRoomAPfq9hD1+ivLUzSRsmVYbRaUJSkshuSnvEe0VDz30/PkriHgdmxeS4fPFxKyIfgbRywojCapGugvNySruGd5bWIXwLLkAUAjvlTd6yEPXVdMPiLKHFTW84w37tMBPdxW2qWAKT7uv6B+dWtQY3pHY8ngcKnJq04k050+K64HWLWKRP50VP43RkJ8tRZP5Gl1yRkamJWD7GCpBXHdUnFP4OpJXjD7fUIwjUGZuNosaJpVhYi21ZsaLD/U+1ZlEHzbiB2sayOZOOaXVr7/WX+g6DgaH8+b/EJ+mh9DWGI9uE1alsJ0F0sBiap+yjDc/0hvJ2a4CBJsDAYd8uAuPpGPMNL20WRcNf4CnAVlfymGUorAPppbdOkuxI4U+LZ6v6fi+ROl4SzaGmFF1DMaDF4AwW+UcDh91DC7uSrH8obFifjshroBU4hsJC6GjRFKYwqyd27YzfhgfQiPl4FlWgmTgOK1qLGkphT/OCKf47KgjPlWZ2RGqAMT0kWLAN8tlWbu92j6yWd5MswW92oNON6NU61IaS+ocyFD+HN7FOQpIX1OJjKX54WcfhZSVzz4fwyiiwE5g21PZax60BhhwHpRc9GHEcM21Kxa3dRRo5xwFc1Ti2H6IO1wVLgE9PlbrQJrRQdA4/0lJzIVFQVa9KK8vBM1lDQf8XtM3zDtOXqEmiUS2ollvYhn1O622XZ5uRyjO1ey9WVdz6Vv/Pslgil1bRcb8aGwfvHlgJ4BmhDIhagHZhdOBrpy8g3ky1ACICFOjsM/AuwJLYFcDYoz1qb5ULpfxWiRDl+AwkixuU3hkcq6ZcN7O9dmgQEBCDevjvrFCSiPcpMBAQgFFQXvQ6o1UawcL2fuBOSuZhtieVvqQAEXFZm2Bo7HhzAAU7GKh4jrfB5/nSyBBSfxlFw5KZ18v9pBYSvwA+irT9KmDwGZKwUqnU5pvwN1GNTM6zhUaMr5GWS7N8ppQj+NmmtOksDuqqHV1jcs/yGQw1ff4xvKErlIiBAhXAhi7NQLaCQmmLvL/6DXqyvJqabWxJEB3oJgn/hrVi6dMEBSl15XWr5MI49l/0J0FUUc7DU+9pScG3024BNAncMn6MxszVn+9bYJiPPQuv0RfDbAGtBrAFhdharaUDEunc8psQwBTDCCuYilDZQ5Mlg1yiQ9iLIwVE7TBmUALjL0gxy/sGtaY9ohBlqvMsexKKwI4+vFvIMIWRQlqqV+fOx+qL+00ScJvyxYDwkg0m0LSXIFOt1rDGY+cYuQXy2Vl4Ad8WfSBDCXYddwLzAWrp13IUJbXnNd9BvV6SipsPqsaI75zfWbN4tCMzVwUIzHt5IPqOYBHP9DbwN4jyCwkZRIxNBGBa7/J+B4aNUdxHu4q6GLzgeQeFiqDABKXSysyGVGKo5W+CQuZCrP6GGA54pdnpLFTK9dWi0ZlXnydFRcsC4dIeOqueK/vOdGq1/AJwy7W0bv5JJBdCVbs0aSZzO9Y1ClZ2yQ6tBKLaJ2NH5jvtkdrCeKgAxYmam/V4GULAGl6bjeL/K3JHq6KAUWZzDsFNbazMRRa0tiTQQnPZuthJDisN1nslDtn+3GJvTnMfdelxbRoDTZCu3aBsj9KmgpzBkDpkGG6Kcq877c7j1gBDUNdr1j8a0JevqqSgcAv58aByYxC81AUHAyru4F/gkKACQzFTFRQ0y4TkPVGLlmXHDTWYaK61o32rdZ7eayJPAUaUllrNqC4SBimw/FDgDbHqFHlz5jwU2FROzbpU9p0sT7lqjFv9QHxXikGAagg+M0p7VYKhpeLXIZjVw3fBKm0rImiLAV3TBiylnWUpO3jDM1WttiN5UVeTvRVkB9W/wuC73teyQ5e3hdY9OFJKB542sExshaJeJJLOepkuVZqamx+3BhiAWVDZJsWaxXW0hNhYp/QBZBl2U3qMp+JAQEifQENV6kqjGWU5hjfXB3mJwRZG4Z+uqm+0+70Ju4UCX3mUJmI2zLrkPCwSf4p15tc6FJR1q+NTpSCyvSDpGwkF5fUrmHKgDvPG8rfwzesHtiXwkVoOi4xilD5QHefqOSzqir6m8WgFaPI1s17MUsW61n00s2gtyu/DvK7ouqZzVsfdver07iiEI8uM9prAPlLZbVoqOGC2hVHCuGcwAtc8bgUwKBBbojdwx+tKia/2qZLCh4qJ8m4M1sS5alLO2vJVUKqVKuwhMk+7E3Ng3TlbnkzBPm2NvE46Ue34wCOiOgCKd99vd2uQhMPGk1LuYUGJXSNFHdi+Tm0y1KM4IwUBCmMjUBAtiapKg7RUpm0PlpKvANWa50I8voSBRFQe0uTWltJ2VEpri9Za9E+0GcgqtCiylgazC9LHw5aTaI/wyaiWLfrWERD8ulkthUMD5UK6NA4NLUKbCxIORpK6b4bD5dpn2kMmrujT6xy3AhgA7ogEQHoEOQVrmAyqlH83R9yomUdS06NuTjxP7UZFcws0CO9sxF0Y09mV1E0LMA1/SC2mAEBR6uF5oXkYvwtHHi8dy6jialQVmT/TNYDf2ZhZoqkwPSprMUFUyKR79R2ndPqejEFEYhk5+4t1CNs/kJp8EaltnU1m28cFXddQ6kQ4jXetlj6GHLVJ4en7VDx4quzdGSz0asdvAI2WZ9LZyF26N23r9l3qXE6ZNRIG+Pkiq/peqOzZ71It7K+CQZIGR88dYH7+cTuAQQswqDWAx2sl4jLTL19eys06WeZAa9troYugdYVlJXNYbTqYVhGNuASul2jc4oYhuHVdq9PbvXfhJUEOpIgIBY5VL2WELdJ6xju21LWOrSswAJkkN5+Y7TosqkrJCbEj26kVqspZ24qsan75sKwhoCPYakz7ASN2dcTUqheRi9py/N77inVdcVo71tOKda0AsFXFPX0wBtIg7QBL9orBwhsw9Hgf+k2yr4qCxjBtdrzWjimmrA5Byb40/TC21Np3tSq5LkvvR9uRBQvEtjmauipgZ2/n9BcctwIYiPDmSGlBzSyPY7P0VtrR0dC4ZLKoQ+Rq8NMa6yf4veTS2WEtL8uwbdXRMIzl/YllfDiZ8vKxGvyq7YqK9CZwaekl7p9JQinVrUNa+erZ7tWR4swlgIFlk15SeGd640ODiAvY0SaO8S1KtE73zsyi5iSYrGwZa/M9RrpeqhNh6dRTZwm6Djtx957TfgFxVDjkkK7WkcNDM0g9w61RFbtHX/HZe0eA70BHvL/a1JLVohdLb2y1gODOMMJud2kKUcyVqLYb+V4Nfd22PM9JtT1uBzAoEhiQewL0biHKlntUbCwVswKIsaj1QTrEuKKuRkQyyo17DFpkibnq4GVoE2Cp5Xsnuem3/Vhk/Kl0dyM8abNpm/eMGuqpoQzZXBPKRYUYBNrvcMpN1WVYtNFl5q6Ub0c90QAAIABJREFUfIawfSen5Vy5kicTKLMzm9pqEdIRJOnn6eGwG9Plwd+x97WwKo37gm3BgYpyEKAgBRj22zFgwolBTmnuWP6yt+ZOUZsj/C8yNbM/L8VlZAHDhc5G1RtbpmsMVKwEGhm+91Qb2Apiyur1j9sBDFAcTycc3Fy33lPh3dz1noLbgsqyQRRNW2kcfteweGcvTY2NiAFC0nuLhmtNsKgxCmmeZh7wzUeMRi5NY6ahlS3y/CUw/lEUPAxKJXn5exaQvFPyCpEaQxXfqfRcLYjCYvljUgMAWNeOta871j77YahvUOsxs5S0huWQW9Q1AvJcifIkODtiO2RxtQ6a2iQw0Ks7eZfrrQl0bCM4XmfrjO8RZ8lEYgI0sohr6RsFp2SplHutFjVHPPkKbBXJ95/ZZ7xVXjRhhde8MA2DBok+ktp+cTQv8n0IDFCbUlp9LE320HyGoccUGSC9oS2KShWbx8DXaEQhw4D6slu/TjwoJXb9s1TxvQnUf1prtlu1UzlpVqe1cWcqBk4tEWadzjTvHM50DP1RBLnMMIDIz3fU+UZJ5exkWHXcnQE0fEykZ1NF93ZY19WBoZRc3nOIB4jvfJepUp+Yr5Gsf9azgAsVL9BsVAbeo+Vf1PfAVnVQ2q5g3lxkKUATN12++KyO8jgalHhmHVYx0cF+/WuVQgyQl/P92K6cXszhwHiDDQ+w8WHVZ5Tiva9yc6TaJuHIfF8CA0gfc94YgGWuYfcQHJzaiy9rJrMACAbiulmcY8bBI/BJPfmFeqeLCFpv0Ab0iJAsItKAdV3LLksjMMwRjubrkaJ0VdxSmsvIcrCkEkDB28zCdFXo2oOGr31F52cy3WAM6Uyjgkbbsr2auE2kBUXcY0FAzViBLEjLjGBuNZ5ja/s0lO6q8Xl5weHfGhKcTeAqMGpDAtEVJQ8MhW1TlbfW28u2d+IO3qX+Me7f3Pnck6b/6qTJqVxlBqD1v7K0nTeza+mPaa1HdgdlJKrCgF1sqH2T49YAQ3Sh2p6Iipw4EKHfwJyT0O7Lmnk+VDynICswePniViJBgcDQ0KWjNV8x2TKXAJBKXqMYbfix+GKe+iMRnx5DG1J62KrLpS2DXOSYuzgmXTlotehz4XSYAYMrug6iEqQlFFPo7AOiQn5dVUjeQCyTuEYD0GqMQYsdu2oXxkORNJzK9TyAsHujiI1hHs2lbM5vy0o2lpxsJGLJCSRf2tpdAPNiS/ocaJFHSlLKQ+2I8Z0qm5ruI4+MMxX85sOTr7C6XYFTX9FPJgtmtIDD0tDF0xw687vJcYuAoVg5/yEGukvBMFy7zVQAvtyXefk6evdulgQGTtCp35OGIymiiMbOVF0A6Wux9g5OE9CYciwWQuugsCyLLzf2e2mENLMP5dqDTAZTw2e5K46GQvp79JJBic5HD+LJo1h1JEfZNxYK7qhV3XnxXRec9IQmdAr7EKo1tOWARSaFcyCjk5ClkjEMU4EvPFhhzY+R63K+VDbntHr54h4Z/mafWrY1GZQzljGjtPeVwEYGsP8m3I38ukvs46KN76re0gPATr3jeFqxtAX379zFoQmOFxfoveOwLFhhSYTa+xUY2Jk5VVnGe/5HF2MRpjwlAYb3C9chcFGVleGmufnYzb25kBJH1oDWG1bpaT2qEEoKUh7+DKHzsmFpq9HvJjGcoIfdhkcuflwVaOlM4v1t89RRbGLmhTQYKIxipNylMQGkTNUZAqorVSeUVeq9Kezhy4ENHQ6HMxwIkg4YHCvv0f86NbkXAViPq1RFhs7Il9qwhrkh4hqJlyVDsDqmo7AChsmht5eaAUIFhuDpMFksddukuPQhbOLCNNqvxiZgKWjm5r2NuRlzPD67gGLBH/7Ep/DFL34RP/7pH8d6usRv/dZX8Rt/+//CsyfPcPfemc+m4UbHrQGG3i2UtnuefHVHYXa2+k5RFstgzhlrue5TmlIElnkHZSWaLBhm4kpHwak8PLvR2IgZpTfOQFj59FFIa5EjwH4QQ2BzAMX2IGDquGQt9Cp4HfzJfJ8UZIInoF02oJDKmL9JOe0UQeF56kkrySGKA5UzpMNZx5lHCR6cAVHruBfFqJ9a/sv2nhl3nVpmm9PBuRlV8F/iRVGu6ooIX45TbuFQYGD/XEuAciNfSUwJVQo48Du+UcnACv6qD5ABeAcnbsg3IXo0PLWfpLQJuuJ4WiHtgJ/43Ofwc3/0n8KPfezH8PTiEk0EP/8L/zQevPZRfPk3/g+8/fabOBwEG0/mC45bAQwxdFCAWXbscyYdoYK0zr0TNWgax/INjT4jBNqH8vVwliXN9n9jzrwjvHfA0F+56hIu2O64E5+pUHWAcmCQygYT3EzQPeuvcoPYvVGrn1N+Ip21U73Dp/Jq+VKuGaTf20NDMa/oidJ2BEyNMbe0Hn4Nzooczg7h8NV4xtC5Y9EY1Ghu5qzuQPslL2DXJKIMDCAiBmW4HG34VJ87zkQMVY3NLRJtFPxIQSjDDC331LcVCWZhjDQZyZ6PJDf7qf3o7waftdOOn/z85/HzP/fz+NiPfRy/8/XfxW9/7Wt46aWX8IUv/CR+9md/BmfnHf/T//grOK3H9y9j0AICMabjctnSeB2AuJMllpsq0Fv3oZcMjR1ioxoRbGbwGdxUt7svqbzDyQaLX/diaxivCYFHlRWroC4bYWAovIMDbbSGqRg5FgzhKX4ABf0LbKsCDLF6VHPZ8wAcKcR72FDsrjPnUdEl8jymde++QWv0Eak432rC2FzgVtqxfF9BIRdrlbYbEuqyv8tLhOayXAGHgnSG1uzPe4DgmDgZc2cOpaGkvBWZUc4olTdTl6faCrWg8nCF7V7NmaY6DBSQgCheeuk+fvqn/1Hcu38XX/7yr+Hv/d9/F2+/8w6Wwzne+P538TP/+D+BVx88wKc//Rl8+/e+jfcePcJNjlsDDPPx/CkuHqmpqj4ccDSvFp5xEaqwGQ9VeBQTMumKk0qf86ejLXMvjLQ+Zyf8h/uxMKdjCYdNAde4J7z2EOT82eCbBlVlY4FDX6rNy3NKQe4TCMSYf79tE5zKcKCya1YXtmbheDpCoR7o1DzTkpR7PPa/kpQtgWChoSwJCnx+Vdz0G4xgVOFoNo9SmAVnWObWlgCEkd6o67YUQOFDq/9AAkgFaRQQ75LPZcbuWg6f6uQOHEZZFTRZWe8QWfCJj38SH3r9w/i9738Xv/q3fh2P33sP91++j8vjJb71ra+j6Ql/+DOfxRd+8ifx5OIp3n7vPdzkuEXAMCG/zMqXU2VDfgRUxyAxO8vgHzV8VquiT+Wxe6SN6eBo8edQWwOOJeb1zQnZIgy52DcM/ICKPaSSK+yCR/WxwKwWLUk6yKqQmSCtImOmKiQbG/wP0W4FawbVKrUR2LvV+AVnIMyUJZOnS4NuM0zbW0Ex9XdhBm4ak/1lTUvLBxPIZ21qvPmEIjNRDu8XKYv50lwbNmzchlZWGTmMrzODE9kWeVMBpRrN5KBOQ8VqNBHPCtXxgQ98ED/x+Z/A8fIZnjx8hFdfeR0v330Jd84OOJ5WnJ2fA3LAnfM7+EOf/Sy+/u1v4Hd+93dwk+PWAEPbKH+LSMZBeRmANCn2RhwG8Ghxf1cb4ZtQtZxurMDgz8kySpkb4fDpxxLLQOWJmQMgFLy6uhTmJK1gFgxkr5HCuadxH8iUvHTAfDNLN3/AMAzRVOSrxhJsSU7XBjV2RWaKvBq7cTgcLETaI0Pt3XLow6XS0isoVEpSQMFrMPh09hRyYHCso8ZWbvWl6nCBfwkyepZ9Ec5s7otZAHTGsQCNugpya5HGWjAFwDDU8Ed5k0ROYKSxowFSXwz14Y98FJ/+8U/j7v07eO3DH8BnP/c5SDv45tCKR4+f4umTJ/jUxz+Fj3/y4/j4V7+Ce+e/uanT845bAQzp1ZchUGgIHHKg2IBCW8JC1+hDdpyAAEBg8C4pANQi1LlYnQIMVk4ts9a9QZqDy5IzE9yQJtMFJDAERSQwKIbn1HEl24f35kxEKtEQ/wFTwlPvWNY1HY5kBhUcJktclW0ODuPVy+LxGq2FM7c1i9+oe2+w9urKKlhNOZT5FPKZiLatbZ6srvZp6t7YP+N7jYeGM1AQY3UaAEi2jooFzu3zpaHNQCMyWPurEN1KYlQNUxHlbXSY1vI6RFvIJGMtFIIHr7yC1159gA9+8BXcf/kVCBaoCJbF5PjieImLi0vcvXMPvSlee/AqPvSBD11Vsd3jVgADACyuvK38XlzgWhHEVsb8TcSv8e8r1a/DBNBXkEQuKGUts1B8id9pDaR+Bh9TGUN5ZrMkIBnYwzJS8Y1eu3IOAJR2jt+F9YjyKs3OWRyunzieTljXFXrF6sXyBuWv+t6TjfXHLa15EFeu8effo6/DnwXbiKWLBXLJars/9x3LXu9jdCWzfY+gwDqO/TGsFynj9gTS+rQB3lPnGyA9N8wVBLJjYDfxOccSdQJjcygtP+whlTXFtCiSnvDaAmqqiqUJPvzh13F+7yW8+da7WH/wDu7du4e1d6zrimVZcDgsOD+/g+PxhIePHkIV+MhHPrJTqauPWwEMAuAQYciCRcp+ENLCqnMMywzNEYlXqSUVzBHU5tlbDjckfxiYRIUYx9mYhD33pxgZw+LAkNatQRzh6+KVtPo8o6jbKFZlTA/0AE68K4S/KEXxNfSuWJaG0+m0taJShrpRoxEsCDcJhtw8B1MbSFyYY+ZRgYXfLoLYQnBVCFOql9fir+Azkiym0XdRDXYBMjPeUsoqwycq3dRm0eNFIcXBASqx3D+GDdgeHO6E03HnGlZYvBC+21CFpC1jPUVieKQdkLMFD159Deu64itf/Sreee8RHjx4FX3tuDwe0ZaGO4eGs/M7WFdg7Uc8fvIYr77+2lU12z1uBTBQ+EPoKoUsf+fnYr0F47RxFGnXM7qNdKwtC6QtQ9m0xtY3Lh2BECPojPLJPSYStAgMNeSZ72iHhgoZMBS7LOWOgV1UKcKADRUaeHSPO7BNYWp2I7+Sf3oaNkXdF9JYUyNQSQ6LquHlG9hOTmXtSQBFthGG5mxYRAHp4exLv4cHttVuRIJDKc7/JqTmsCCtuNUvtp93BjAhf8GGVOv41jeT3rgQBstORZeJiY1H6eWUNia82fP7iL9XmcFQaID62aHhEx/7GD70+gmHszsQH0Jenla88+738bWvfhUXF0fcf/AK7tw9x+FwM1W/JcBQLINbn6pS9ajj8XquQyMCIKd2FBEmCbHdx7HETaqWsWmY5wdVpUhD6bDcn9jv4HQp0jpWuqnlnMlkmmyB74oeATkc3xOgBvu7GZKgflPOSQfaIljgacu65g5HKB7xwUKNjCFbJV1gEQ1ZHy4+VmZym9puyKagd51g03xtSvoGvA7q0FkZjJvamcXbr8rd+WYa4MB7zaKP1GBmOMMoQEx0uqS8RZ0GrPf20SxtP0pkeyiSvaUUseDa0S4zCmjvePbsAqfTJZoccPfOAa+89iouL49YLy7xqR//EL75RsOv/tqv49AWLOcHtAVoZ3evVScetwcYrnFm/Fqic4nAXT3CjcIwmVbtlpNAew+ASaclC9ZinUijqz8ihV3gjsOwSM2SyLiKh9OrlkOlBxW1CAGBiUqkCTxxhJUaz1skaNLn+QblfxSweoVO1w4F2GK2UGDRcVdt1Vizsp2i2x7BvhwMm7BeCcsRL0AKjR0gzBLjs6APwEvyYH2qQ8MkYAOk65FJ3MHOUi+yPkmV9mtFZurtW4Aiqjm2BLgPnZYS+VWkF51YyWFZ8N7Dh/h/v/FN/M1f/TLuKPCFn/oj+M533sBb3/kO/tgf+xfw6msfwuVxxal3vNY7zlvzPTCuf9waYIi5eYy0az5m2WPD5/1m0iqKU3Y6LDqyg0MMi2lorZXZM1cegUc8KiJnAylrAQa7zrz/0nw1qA/Iq9OLsytCimi1RnKHrCqNRDoDp7batA6ZABVbImag7msQqzP/v/bOLcbOqorjv/WdM2fajlS52FIpUiqYWsVgJUQRedAEhZigiQ/6oCYa8QGiJvqA+kLik0YwmhgTjCRqVGKiRmI0CkSDGARrhV5oEZSLtJ0p0PuMczvf8mHf1v7Od85MdZxzGr9/cuac+a5rr733Wv+99g0ivQ4FPcUdkiEJz44yhP5140VdnthmR222GYm1cr/XFWFwmqckccyDxsxNU5+pCfIlo0q608horovsrXqBz19vqOKcnFgu7ezLwKvS7TXkK0lhFix2Imr10jpV5Ye8crul0qag0+kwsWYNa8fXMLF2DYsXnI9Km/Xja7li+5tZnFtk44UXMjt/ioOTU/3eVIvRMQyosY6xlNagEo327CDP5aqFDwaipsTG2EV/yQgrPvmh0eG1iu83F+/9u6FgeaNjXUB8XXiX9e52YQ1rEGxAsj6VSZgyTlN3AuXXZVVFU2HV7EH174Gw1F2+1mYGyfOlH3tI8RpNVD/aqNRtFypPYBIajYa/XIw3zmT3qQ35EvVjjIoxrbZ62jEmCLWxq2AAtFZLKR3VYG7VEsU9IcjVn/+WeJ/4l5fA+Pg4r92yhQ9u2MhEZ4zW+Dqu2LYN7S5yerbL7NxJ3v2u6xgfn2CsM87DDz/EkZcO9Mo6ACNiGCxfIuarJW1Eb5cTOdVQGZd4vPFk4umj6xI1XWL+4oovMAvQpn0UffEnDZkuyLO16hXdV299qTOARhE1/Kmu0rl+ccnS6JLp94EoBClz6hs/QtyjwNQLEOJ4knzAGHUJMemr8chUvGpIX2zKGWNm/YIxDtl7BuR4LDumWNV5YHsgMgEMowqVvPLsrOml+WODUVAzPF9Jc2hT0lNQPDiZfB1IIQVRJRq5xe4i0zMzCLBxw6sZa7WYW1ik3V7jAuMnT6FasHZ8LetesZ6xzhjdhQWOH32pr77qsKRhEJGLge8DG73Ud6nqN0TkduCTwIv+0i+q6q/8PV8APgF0gU+r6m+WlKTGAMdM0MQhXFMx7xqKbcWax4VgW0qP+5MNoMoKfE470+wpZwSsYXD3tEBaZFFtzxoMByd0raamhCHrNW7bDjRaTts96Cm8L6Q/eHrnaSXv69civTWMuciMnu/JKZIB7C9LYjl1ddb2/PirKrNjw30a9RbiGlZkCXQhip090R3rxzYt9YoMNWeqKU809dSYuEyI1WSMLPudmm7hZCgLlmXY5kkdEnsS7FT87mKXyakjvDj1su/WV44dO8H8/DydTodWq0VZKi/NnWDTa+CV576S0zPTzP1rul4nfbAcxrAIfE5Vd4nIOcBfROQ+f+7rqvo1e7GIbAc+BLwReA1wv4i8Xt0qJAOgmaIGrZpjo+v9C6rGAGN6cqi4xHEShVQ9YtUwBA7hK0eBGWNhDUOmA38uDQKSOCIyFI1q08kk1TiWbOBTrTaMqKVm1/jJ5n65Obf4bWlUGmcLBvtX8X5JJ0Y/gyr+EvYrZmd4T/SGPlhb2WQl+nGtVHX7Ho0lJt1ly02VTVojoDYPgiKSbuxo0mB303yT9Lycd9SU2T56CWkGqYmZJEMqni4URQtpCS9PHeHA/v2UehknThxl1yM7mZudZ83EOkpK5ua6dBdLrnrrm9n2pjdyauZUvQADsKRhUNXDwGH/+5SI7AcuGnDLTcA9qjoHPCMiTwNXAw8PfA+paRDXFAiZ1yuTvSEqNNYra8EJ0XJ1FUHT+gfBCARDEbxWHAUZCrsvwOIpbBp0E0ZMmhwN58J6iDHmUO2VsAXZSp+jWqZCl2ymlWC8CqOIUC9ixcMvcpP8okuarYihEklMenXgmBUo67wwaxHmKdF4bRiVWdrp7/FBqQnnzhWpLMRuQ//+7BOov+ELxsDWjUSMLDRrpiR2F2XNar2mzDIGpee5JGMe1ZU5L3Oj4JwHpmfHG0LLqNRf02q1oVBmZ2c59MLzbNy0gePHj3Pw5UPMTs/Q7qxDS2V+scvG8y/g5Mlpdu96nBeef2FJo13FGcUYRGQL8BbgEeAdwK0i8lFgJ45VHMMZjT+Z216gxpCIyM3AzQCtVovQXIhKzaxyXz9pqleqFJpbBgO/b2WpbkKK30fCdVGF9/miHSm/td3mmTbzJW6PFa8PK0GF+EOM5rsbUgVSUunNy0xPKvNeisBmjOuy1TLUY5+m8JoedmIh6cbIACTpFQneOE2sih2udeudm/dEoxC2hTcGI3TbEQZiKZQSVm5QP9gjrInhApfOoBtfHTx7UGo0ztXio+l6TUbZZakLMKPpfLolUKrAAHuNg4uh9PbamOxIhtfoO40sMQ4pGKLopHyXqygL8wucmP4X56w/l82bLmTm9AJ7n9jPsePHoFxk6+aLuf6G9yHS5f4H7ufY0ZfpjI315s0ALNswiMgrgJ8Cn1XVkyLybeDLXuovA3cAH1/u81T1LuAugE6noxljIKmoqvw4Nz2Xzl5BirYrlRrmdzR2C72UtnLHtm7hg43Jiod3BIpaIoTetUKUMEQ7sAPEebsirgZlXJj5m83Pr/R11bIfMcvM+TIaxzpUC2l8RqLSTieSPJ5nXFXCIrZ9kRQU/09MzldiyzaqWRHuMG12m9bUhvd9+qUdROVToDmbi70a8X2GZfpvl7+YvDGJDHJprmtNiU9JDofUPVMLjRPjrA/LdVfjnpMXiGXFvS+sLZXKSSwfYprDmvZzbbXbTM/MsHffAXZc8Qauue6dXLZtG5OTk7TbLbZu2cqrzlnPnx99mMOHDtHVknb7f2AYRGQMZxR+qKo/c8rQKXP+O8Av/b8HgYvN7Zv9scGwjCG0EW1bMStMmW80Xst7sdBmxVcsuyGldkPnoCukRYEWZtm3QP/jlG+fRgwvEbfOZOHXoLTtb/F0vShIbXhwe1lUBScYP80Kaq6WdMy1SZNjz3sS1GvCemLisTiWIe7DGGpf3VDevHnhnpcKu5VZK5WrnjQH76zGEAU5zZbxdvh28ArBYFcCoLFZYZqdKdqgmU4sPbPqylIdvXx8sF9AuBrrEreHQ5nStWRsOBWcpB9J+WnNQSq04XojpRsmy5rxDtpdZOeuRzn+0mGuuebtbN68mcsuvYT22DjHjx7lwYd+z57H/8rc/CzSalG0WksImWM5vRICfBfYr6p3muObfPwB4APAXv/7XuBHInInLvh4OfDowHcAoH7b+hD0CUY+LLJiyZjxvL4A2SBSGo2Hu6/0D/MsoCj9jHvvocrYM1EgLTdhK1LX8Ax/vUm/2yEr+/hZh612zFKVQAH9Oo8kmmkrVe5Ja31OMlzhLlM5UyXVWB9UvU595Su7JWXXzptIlarKOKKOe2ixpFWhzb0ab6lKrqlNbwxyNFpeRjsQK/zvjK+fVatuVGky4MRPEs0smCrxl/f2ecoiYTIGJYhvjZw1iMHI2vkdmk6lLzUHclWY96Rp1JE9hCOB9Zi3xvxU6JZK0WoxPj7G5JFJ/viHh7hw4wYm1k+wsNBlcmqK5557junTpykK8bNZe2fZDsJyGMM7gI8Ae0TkMX/si8CHReRKn5pngU8BqOo+EfkJ8ASuR+OWpXokHEnouglFwTirgBaIlq4i+6mwrtloslNLtCySZY2FILX1XJs0BNA8xQwb5kqBSNd7pYKibNFqaVyJKBsurakoREovYYZn4YiJiFuTMkpZEmIM/sbwJ/e2MRiXvDTml4D3mi1PbRMTQMOeE34HL6/DMnjhMi2WEjbCTTDRef9M28OQd1EGTx0m/wSjZJom/hpbQRRcEyG8MbABkkFM0X4nZ9cP4Q2Lwqj/FH4DFbdiliS9elljcyOMHPDyYtKViFtgTWTlQ2KltVDiPhx2wlkP+7N5mechGgKsqTciOI/E/Eq/fkUq44lhOaOhqiwsQknByZkZJg8fYO/+A7TbgmqX+cUuiDLWajPWbjvd121WPADSSyNXHyLyIjANnNkojOHgAs4OOeHskbWRc+VRJ+slqvrq5dw8EoYBQER2qupVw5ZjKZwtcsLZI2sj58rjv5X1zPatatCgwf8FGsPQoEGDHoySYbhr2AIsE2eLnHD2yNrIufL4r2QdmRhDgwYNRgejxBgaNGgwIhi6YRCR94rIkyLytIjcNmx5qhCRZ0Vkj4g8JiI7/bHzROQ+EXnKf587BLnuFpEjIrLXHKuVSxy+6XW8W0R2jICst4vIQa/Xx0TkRnPuC17WJ0XkPaso58Ui8jsReUJE9onIZ/zxkdLrADlXTqfVASar+QFawN+BrUAHeBzYPkyZamR8FrigcuyrwG3+923AV4Yg13XADmDvUnIBNwK/xo21eRvwyAjIejvw+Zprt/tyMA5c6stHa5Xk3ATs8L/PAf7m5RkpvQ6Qc8V0OmzGcDXwtKr+Q1XngXtw07ZHHTcB3/O/vwe8f7UFUNUHgaOVw/3kugn4vjr8CXiViGxaHUn7ytoPcdq+qj4DhGn7/3Oo6mFV3eV/nwLCEgMjpdcBcvbDGet02IbhIuCf5v/aKdpDhgK/FZG/iJsqDrBR0zyRSdzqVqOAfnKNqp5v9RT8btMcGwlZRWQLaYmBkdVrRU5YIZ0O2zCcDbhWVXcANwC3iMh19qQ6rjZyXTujKpfBt4HXAVfiFgK6Y7jiJEhliQF7bpT0WiPniul02IbhP5uivYpQ1YP++wjwcxwFmwqU0X8fGZ6EGfrJNXJ6VtUpVe2q21zzOyRqO1RZpWaJAUZQr3VyrqROh20Y/gxcLiKXikgHt1bkvUOWKUJEJsStc4mITADX46aX3wt8zF/2MeAXw5GwB/3kuhf4qI+ivw04YajxUFBpi1en7X9IRMZF5FKWMW1/BWWqXWKAEdNrPzlXVKerEUVdIsJ6Iy6q+nfgS8OWpyLbVlw093FgX5APOB94AHgKuB84bwiy/RhHFxdwbcZP9JMLFzX/ltfxHuCqEZD1B16W3b7gbjLXf8nL+iRwwyrKeS2umbAbeMx/bhw1vQ6Qc8V02ox8bNCgQQ+G3ZR6bO7lAAAAPUlEQVRo0KDBCKIxDA0aNOhBYxgaNGjQg8YwNGjQoAeNYWjQoEEPGsPQoEGDHjSGoUGDBj1oDEODBg168G/cdHfqw/QaFwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b08RU6IAz9PM",
        "colab_type": "text"
      },
      "source": [
        "GENERATING VIDEO FROM HIGH RES FRAMES"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Gy9pFRevuWX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "path_out = 'high.avi'\n",
        "fps = 10\n",
        "\n",
        "image_high = np.array(preds)\n",
        "\n",
        "height, width, layers = image_high[0].shape\n",
        "size = (width,height)\n",
        "\n",
        "out = cv2.VideoWriter(path_out,cv2.VideoWriter_fourcc(*'DIVX'), fps, size)\n",
        "\n",
        "for i in range(len(image_high)):\n",
        "    # writing to a image array\n",
        "    out.write((image_high[i]*255).astype(np.uint8))\n",
        "out.release()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0DjhyVBwBG4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}