Aerial Image Semantic Segmentation using PyTorch Pretrained InceptionV4 model. Fine Tuned on bulentsiyah/semantic-drone-dataset Kaggle dataset.

aerial_drone_image_segmentation.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zlzmG4yYlpgr",
        "outputId": "820eb5cb-54fd-4139-afd0-27fa0531bb7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9YkvYft3m-st",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a950556-bd05-4327-c11b-3a0c4c342778"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.7/106.7 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m75.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m28.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m82.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q segmentation-models-pytorch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "cdW4WVk7lMGx"
      },
      "outputs": [],
      "source": [
        "import time\n",
        "import os\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import imageio.v2 as imageio\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms as T\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from PIL import Image\n",
        "import cv2\n",
        "import albumentations as A\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "from re import I"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "A3YLC1melMGz"
      },
      "outputs": [],
      "source": [
        "# Path in Kaggle\n",
        "# root_img_path = '../input/semantic-drone-dataset/dataset/semantic_drone_dataset/original_images/'\n",
        "# root_mask_path = '../input/semantic-drone-dataset/dataset/semantic_drone_dataset/label_images_semantic/'\n",
        "\n",
        "\n",
        "# Path in Local Machine\n",
        "root_img_path = './drive/MyDrive/Datasets/semantic_drone_dataset/dataset/semantic_drone_dataset/original_images/'\n",
        "root_mask_path = './drive/MyDrive/Datasets/semantic_drone_dataset/dataset/semantic_drone_dataset/label_images_semantic/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJzbi1iqlMGz",
        "outputId": "e45565be-4baf-43fc-9421-0b8e577dfa6b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of images are  400\n",
            "./drive/MyDrive/Datasets/semantic_drone_dataset/dataset/semantic_drone_dataset/original_images/000.jpg\n",
            "./drive/MyDrive/Datasets/semantic_drone_dataset/dataset/semantic_drone_dataset/label_images_semantic/000.png\n"
          ]
        }
      ],
      "source": [
        "image_list = os.listdir(root_img_path)\n",
        "mask_list = os.listdir(root_mask_path)\n",
        "\n",
        "image_list = [root_img_path + i for i in image_list ] \n",
        "mask_list = [root_mask_path + i for i in mask_list ] \n",
        "\n",
        "image_list = sorted(image_list)\n",
        "mask_list = sorted(mask_list)\n",
        "\n",
        "print('Number of images are ', len(image_list))\n",
        "\n",
        "print(image_list[0])\n",
        "print(mask_list[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "k4BveRHllMGz",
        "outputId": "68bc2661-8165-4dbf-cbc4-335e9ae34f33"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4000, 6000, 3)\n",
            "(4000, 6000)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Masked Image')"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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dj8QY+aVf+iX+4A/+4GZ/f9e73vVWP2eHDgcbXSHeocMB4Mwzz+T9738/X//611edz2+Kf/u3f+Oqq67i7LPPPqDnP+6444gxcuWVV+7Vtb788ssPeM03h09+8pM88pGP5AMf+MBety8sLOwzqb6t8MlPfpITTzyRT3/603tNEV772tfudb/jjjuO8847j/F4vNdU/H9us5U4Oeccj370ow/hyjt06NChwx0NT3nKUzj77LP51re+xSc+8YlbvN+nPvUpiqLgn//5n/fKGP/gBz+4z3211jzqUY/iUY96FG9/+9t505vexKte9SrOO++8vc5TT37yk3nMYx7DWWedxfT09F6u4ieddBJf+cpXeOhDH/oTG/7HHXccIBPjm8ar7tixYx939cOFz372s1RVxWc+85m9pur/Mx1lZe2XX375Xoy4Xbt27bP2k046ieFw2J3nOxzR6DTiHTocAF7+8pfT6/U4++yz96FR7969m9/6rd+i3+/z8pe//ICef2VS+573vGev29/5znce2IJvAcaYfZzbzz333CNKI73SSb/pOv/jP/6Db37zm3vd74wzzqBpGt7//vev3hZj5N3vfvde99uwYQO/+Iu/yPve9z5uuOGGfV5vx44dB3P5HTp06NDhDoSpqSn+8i//kte97nU84QlPuMX7GWNQSu0l+brqqqv4h3/4h73ut3v37n0eu6L9/p8RXyA67L/4i7/gve99L//n//yf1duf/vSnE0Lgj/7oj/Z5jPeehYUFAB796EfjnOOd73znXufV29JF/ObO84uLi/s0LR71qEdhrd0n1uxd73rXPs/59Kc/nW9+85v88z//8z6/W1hY2Euf3qHDbYVuIt6hwwHg5JNP5m/+5m941rOexb3udS+e//znc8IJJ3DVVVfxgQ98gJ07d/Lxj3+ck0466YCe/9RTT+VXfuVXeMc73sGuXbtW48suvfRSgH0yxw8UZ555Jm94wxt43vOex0Me8hD++7//m49+9KN7dclva5x55pl8+tOf5ilPeQqPf/zjufLKK3nve9/LKaecwnA4XL3fk5/8ZB74wAfy+7//+1x++eXc/e535zOf+czqRc5Nt9m73/1uHvawh3Gve92LF7zgBZx44ols27aNb37zm1x77bUHNUe9Q4cOHTrcsXBzsZj/E49//ON5+9vfzi//8i/zzGc+k+3bt/Pud7+bu9zlLntJ297whjfwta99jcc//vEcd9xxbN++nfe85z0cffTRN8u4A3jRi17E0tISr3rVq5idneWcc87htNNO4+yzz+bNb34z3/3ud3nMYx6Dc47LLruMc889lz//8z/nqU99KuvXr+dlL3sZb37zmznzzDN53OMexwUXXMAXvvCF24wJ95jHPIYsy3jCE57A2WefzXA45P3vfz8bNmzYq2G+ceNGfu/3fo+3ve1tPPGJT+SXf/mXufDCC1fXftPz/Mtf/nI+85nPcOaZZ65Gt45GI/77v/+bT37yk1x11VVHDPOvw50XXSHeocMB4mlPexp3v/vdefOb37xafK9du5ZHPvKRnHPOOdzznvf8mZ7/wx/+MEcddRQf//jH+fu//3se/ehH84lPfIK73e1uFEVxUN7DOeecw2g04mMf+xif+MQnuN/97sfnP/95XvGKVxyU5z8YOOuss9i6dSvve9/7+Od//mdOOeUUPvKRj3DuuefyL//yL6v3M8bw+c9/nt/7vd/jb/7mb9Ba85SnPIXXvva1PPShD91rm51yyin813/9F69//ev50Ic+xK5du9iwYQP3ve9998o17dChQ4cOHQ4Ep59+Oh/4wAd4y1vewkte8hJOOOEE3vrWt3LVVVftVYg/8YlP5KqrruKv//qv2blzJ+vWreO0007j9a9//arZ2c3hnHPOYXFxcbUYf+ELX8h73/teTj31VN73vvdxzjnnYK3l+OOP59nPfjYPfehDVx/7xje+kaIoeO9738t5553Hgx70IL70pS+tZpsfbtztbnfjk5/8JH/4h3/Iy172Mo466ih++7d/m/Xr1+/juP7Wt76Vfr/P+9//fr7yla/w4Ac/mC996Us87GEP2+s83+/3+dd//Vfe9KY3ce655/LhD3+YmZkZ7nrXu/7Ubduhw+GCSv+Tl9qhQ4cjFt/97ne5733vy0c+8hGe9axn3dbLuV3gH/7hH3jKU57C17/+9b0uRDp06NChQ4cOt38sLCwwPz/PG9/4Rl71qlfd1svp0GG/0WnEO3Q4QjGZTPa57R3veAdaax7xiEfcBis68vE/t1kIgXe+853MzMxwv/vd7zZaVYcOHTp06NDhYOCWro0AfvEXf/HwLqZDh58RHTW9Q4cjFH/8x3/M+eefzyMf+UistXzhC1/gC1/4Ar/5m7+5TyZnB8GLX/xiJpMJD37wg6mqik9/+tN84xvf4E1vetNhi43r0KFDhw4dOhwafOITn+BDH/oQj3vc45iamuLrX/86H//4x3nMYx7Tsd463O7QUdM7dDhC8eUvf5nXv/71XHzxxQyHQ4499lie85zn8KpXvQprux7azeFjH/sYb3vb27j88sspy5K73OUu/PZv/zYvetGLbuuldejQoUOHDh1+RnznO9/hD/7gD/jud7/L0tISGzdu5Fd+5Vd44xvfyNTU1G29vA4dbhWO6EL83e9+N3/yJ3/C1q1bufe978073/lOHvjAB97Wy+rQoUOHDh06HER05/sOHTp06HBnwxGrEf/EJz7BS1/6Ul772tfyne98h3vf+96cccYZbN++/bZeWocOHTp06NDhIKE733fo0KFDhzsjjtiJ+IMe9CAe8IAH8K53vQuAGCPHHHMML37xi282WqmqKqqqWv13jJHdu3ezdu3ag5a53KFDhw4dOvwsSCmxvLzM5s2b0fqI7YUfVtya8313ru/QoUOHDkc69vdcf0QKTeu65vzzz+eVr3zl6m1aax796EfzzW9+82Yf8+Y3v5nXv/71h2uJHTp06NChwwHjmmuu4eijj76tl3Gb49ae77tzfYcOHTp0uL3gp53rj8hCfOfOnYQQ2Lhx4163b9y4kUsuueRmH/PKV76Sl770pav/Xlxc5Nhjj2Xzz53OaDKkKXfSVAvE4NEpQ5lZtJ2n6GX09CL1cAcxNmhlUFqhSVhl0EYRgRhBG4VSkJQiRYVBYY2CBBEIJCChaLvyxpBbg1OQktwaSfiY8CEQQkChCDGiSG03X5MXPdZtWEv0FTu2baOqKowyKOPIMofTieADEUABIRB8xKeAjwEfIqAoXIazFm2kExNjwPtAiFF+VCT6SMJQ9HoUucE3NeWkJoQISpNljl6/QCVovKeqSvIswxoNSYFSpBhJRBofyJ3BKEVKQJJ1RCKJhjpVBOVRCTSaXq5lWxsl20JpYox4D4UzWKOoU0MTomzgBDFBDGCsQpHwQbbBIM/IrKWJDbFdSx3kMbnVFC5HA3X0JCIGed06BEJIKKAJiQRYDZkxxBiZ1AljIijZ1FprFGCtRas1PPART+KB970vP77yKqbn1hCaiuXlXYyXh0zPrWPt+qMohwtcceVl1HViZmaWqy45n6WlJVIAnxIxJnyMkEAphTFATMQUCSGQ2vdNknXmRpFQkBIxJYKCiMIgHbgQIgr5eAIKrSAmJZ9piqt6FK0SKiZCCKv7zfTMRh77q7/Bps1HYbRCaS3fSw0pwczsNP3Bivv4jdMnBSQSP/zeRXzuI+8nVSNCVTOVZ8z3C5arisYnBoUjNxbQFC7DWE3TNFRNICmFyyxlXct+kSJNU6GVZjrPsU6zWJaopJjJHCFERt7Tz3MGuWXX8phMG6YLw87hhOVJw/x0H6OgDpHpPGMqc5S1xycoipzF0RAfEzPOEoHSB7Q1+BDYszxkzaBPL3OMG4+1jrlBwWhSEnxiJncMm4bKw1S/YOIrytqTa8OonOBjYn5qQOU9o6piLi+IJBYmNXODPnmmWC5r+tZhVGLnqKQJgappCDGxbnqAj5HFcc26mSlmcosGgo/0MkNmDTtGY74XItv7A+wNW5lWmtxpUuOpYsVgdgPeB3bt+DG+qdFaYUwiM4oYNIGMwaACPEpprNUUzskHqsAHTx0ipITs8gmtQWuFNWC1wypDVIoUIiklFBqXTzM3cxyTssYzwOWO5Mcs7Vli3ebj2HzURnZsvZbR0hLHHnMcvcLw42uuwxtH8g2+qrBZj8H8HMs7dhCVJpuaRvllwuRKvC/R2tDvFTgLqEDjPVrBpGogKbSNGGUJsWZUlXgPOmnZBs7idE4KYKwmqYRvIsvjknFVUzUB7yFp6GWG+YEjN3KMaKK8x1wbQkqEFDDWMnAFaE0IiaquqEMi1IkmBZoYZSdOidpHrryiZHp6+qecCe8cuLXn+1s61x//8lfDVMF9HnIZ7z32q+TKAfCDesLTv3Y22XWOYpdi05e3wa7dh/ZN7Q+04dL/cxfOf/L7OevKR1P9P/Nww7aD9/xKc8Xv3ZXTf/FC3rrp2xi1f+yLy5sRx9mC79WB17/gLOwlP96/1zMWNT0g9ntHsOjxwKD3LJOKnGbD/u+zpg7oq7ZC8IdwZQcGNTXF1U87Bj914IRY5RW6PoiL6vATkQy88tf+jv81tcQrtt6XTHu+dsNd2H3lHPmmER+574fYaOBJb3kx685fOGzr0jsWSFV5SJ5bzUwTZwaH5LkPNfSeZcrrrubr/NNPPdcfkYX4gSDPc/I83+f2EzYN2FNtYMfuecrl66jLHURfkeJuYr1MVRm8anAmSXGlFRYpPpKSQtFpjVJg2urGp0hSCq00VkMCVAKnlRSWSg5uTYAmgrFGiquVYipFFBqlE4a2mCVBAmscc3N9dJwwXBxSGEvW0yQUVltxy1YRozUqJpKCZAzRJVKMVL4hhITVll6RYawUGKQoRa72+BiJKYHROGuIbbFXjSfUTSChKPpTTA0KVApMJjWN92R5TpYVGKvItCHFRCSC0YDBKtNerCtICqUCpAYfK7T2EAIa2UaZk0LYaEWTwIN0O1IiKYUnYqxCxwQhEaJaLcRT0vScoecMPnnKOlLWgcwZrJXns2h8lOdKWuNTkOcmgUpoZQghkpLCOg0xEWIkAVmmKaymaiBLsS1QaF9fk2eazGhiaLjuih+wddNR9KdnaKqaNWvW4qxiZm49hctJMRGSYu2GY+nnlqosZTtai5KPXt5vhCYEYgzy2bTfL5UUKkZoG0K5MRitSSQaLw2HoBSx/S7kFqw1WGsJIVIn+V7pmGTbK0sCNAlCwOpI0lpoM9qgLVTlhKzo4aw0pVbonjZzzM/P4tzKYUORVv8mhf/dT7knFx59DDuuuBTV69EzliKzGKtYmng0mqmeY1xHsjwjKbAqUfrApPYElRiWY4zSrBv0ccUUZYgYpZkuejReGk1aafq5xUsHgNzm5K6h8YHl0qNSYrqXM5UXFJlhUjeAouec7K8hkbuM6V6f5bIiKkUvM4zqmuGoZqYomJ8akDvLVJZjtMFHTW5yUpGoKo8xhhmjWawDoW2/jcclRX/AhplpJr4hs47CObyXY0Y/c4ybSAT6LqfxEYCq8dSNZ7Y/QA8Ue4ZjtDJM5Y5J7QGFywu0jxQmEZECufYNZenR82uw/Qy/sItURuoYKdGs3TzHzq0/xjiPMbJfxqTw0YDqsX7dgKbeSUxtU1ElfPQk5HinkO+osQZrElFJQa5UwGhD9ImGgLUGl1l8SOQupz+9iUk1RmcFs4M1FIVjz/aGoteHumbz5mPYsukoLr/yMnbv2k5d1WRZTmYSZekxLqOYnSVMxljnKGbnUSTqsAftKpzRZL2cXp6RWU0IJQGDxmKtp25KjNXopAGLtT2C1zRVJCpPiLIvRw/GgrMWayDEQNMEvJEvdUiJzGp5z0Av12QKnM4ITUTHSK4zjFOoJJ8LCvLcoprEJDWYpNBBEZOW/a5tYHU06gPDLZ3rp3cVTOYK/vv8e/FsM8en7vZJpnTBgxjw17/8cc553W+y9ks/gqoCld0GK98bw9Puxud/7c95xbZH41+5Bbv1uoO+rrv+xVVc+7cn8id/63nTxotu8X4hRT4/nuL3vvJsTvxkoPfq6/nHkz/Pj391jru94Yaf/CLWoqYGpEGPZNQdrQYHQM052LWADo6Qm/17kAW7bgNs33loF3cgGHvWXe3Yfl990376rUMCo+W6rMPhwVeq+3PWpn8hGzq+/9L7sXGhZKNq2PGA9Vz5C1v4tp9h/eURY/Y9Ph4KqLKBhkNyPFW9HnF2jturekvrGos0g3/auf6IfIvr1q3DGMO2bXt3h7dt28ZRRx11q54rLu9g3i5w1MY1zKw/id70ZkxWkAxEJsS4REolKclUMKGpU6JJiagAZTDaYIyVjq815M6SGycHIaTAcdaQZY7CGhxgY8SRUCRCW1wqrdviXZNnhtw5VHu1q1BopdA6Mh4OWVock5TCWIfWFrSG1cJIk7SRAlgZtNY4Y8lczlR/ivm5OdasmaUoemQuI3cOax3WWjKXUThH4SwutRMt6yjLmhCg15ti3fq1zEz1qCZjloYTIhqXy46WZTlKG5S1MnFN4GMiAsoYktYkIoES9ATtStA1SUfyXJM5aWr4EGlCpAyROgSaBmof8TGhdSKkSPQJg8GgiVEKQucMRQ+sVlhrccaRtWtZLmuatmjrOYez0kghJZoQaKKn9tJowETUyvk0JJRKRBIhBurGS2ELGCPMB6J80lnbefEhElPJdddcysXf/x7V8jJ17VEalLbk1mFdxtLiLnpTsxyz5SgikBU9jj3hZEKIeBJVTDIVTxFiRKWEVuC0ptCanrVYY1BKpvRKKeoYqEKkSolkjHxvlMK2jZWsvRiyzmGsw1mL0fI9ke+PND6iVqC13Mfl2MzhmwmXfO/bDIdDUkxt80j+KIoco4W1sEIR0O0P7fk4y3M2bzmWfpaRW4NxhiZEes7ijLA/fPA4k6jrmhgCKiUSnmE9JgTPmkGf2X4PrTWZNWTtkbjyHuekkVDHhDIapzVNDAyriiY01E1N4Sxrpwf0MkuIkcxYrIKYpEB1WgPSeMlcRiKxXNXsWBoyLCsKlzE/JZNwkuzjVisSkTpECpdhjSIlJSyM0LBncYmyasgzh7HQa9ftYyCzFmcMIchnmxtF7QNNTPjGszyeYIxlul9gjaKfZxhjKH3AGI0zhrKpSTGhVSKQGFYNe4YTmqDIicQQCJkjpAbfMimO2rgBXy0Rmj30nCJzUGRQWPA+oHRFDDswBrQRRk+KECKEIMydRGq/UxarDVqDc5o8s2htsE5jraGqIyEoNIamSQwnJaPxMnVVkWeKZjICFP2iQPmSxd3bKAY9ZqdnCNaSrCYRCE2DTwFX5BjtacoR2pm2abVE8HuYmuoxN99nqmfRKjEzN8VgqiCFyHA0YVyOqH2DUa5lsVh6Lscah3EarWR/9tEzrCcsjSuaxhPbRmG2UsAryJyiKExbjGs0GosRVo2JZJlCawiNfK9DiPggDJeQPCF6fBPlHJACVRPkvNJhFQfrfL/xq9fT26ZQAa78j2N4+Plnca0fAvCoXuDU372AtHHNQV37z4L+NSN+5cMv5ZrfOR511XWH5kVShG07+Mo7HsoN7ba4KUKKfHR5LXf72At5z689hZ875zLy8y+nfuUG3rlwIjMnLYC7hYtsY1CzM7BxHXGmTzJ33C92shpV5JidS6hbUXiGNQO4mabRbY4UmTn/etzwZ/jMlExpOxw+/Oc1xwLwhLkLqOYdsee49KwZ/uZVb+fpU4u847uPwowOH01BjSYt8/Ugw1rS/MwRWqEefByRbzPLMk499VS++tWvrt4WY+SrX/0qD37wg2/Vc9W+wg93UoyuZCZsJ/Nj8gi5tRhrCdbiUfgk/HLXFsokhVWK3BmsMSRkkhSVAmXIMk2WGbQ16EwmsSYFmTxqS1Ryse+0UCFXCizbFt4RUCqhlSYzhn5m6ReO3Fm0ENVJShO1xuaZTH6MQimhJ2dGo41BWy1rMBaTWVze0tGVxhopijWgjcE5S5Y7nHPtYy3ee7TJyKemmF+3hpmZgmZSMh5PiCisc9h2GxhrEBK0UM+1NTgtxZBRCYVHqQalJhgzIaqKSV1Lkalkfqp0InNS4AYSXub1GANGKVRShKgIHqo6YJCiNLM30rONVqADta9IMaKVPD6l1BYrQFL0jCPTSra/BqWlWM2MRsQHsp1rDyEqrIZBoXFaMZp4QhPJWlq/1jKFJ0W8T0LBNTVGD7n80u9RzMyxefN6xuMxo+UhS8tDRuMxxuVEX7OwuNBKBTJcMUU2PUeIQdaVZEeU92Gk8dKyApoEXmmSMsJaCJ7SR2KS7WJbpkbPKfrOCJsDQ2r3GaM0SpnVrncClDJYI80CZR1Ba+oUMUphnOO6qy/lqssuwYdASkKntVpT5Fn7PKqlILP6b6VkImxczgn3vA8my7BWrygnUCh6Tr6XPsj7nTQ1lW8YB5kGF9bSs47Z/oAsdy39XktDBWiCJ2vz05sY2qaNYlJVLI3HWKPJM4dSQq/uOYdKsrOJRESYA9YYFJo6eKJKLI9LRpOKIsuZm+6TOyPPYa0wD2LEGo1W4BuZhBtjqHzDzuGQpdEEazTzUwP6eSayDKVx2rSfqyJzlpASMcnfq6Zi5/IydYhkzjHIHD1rpEBO4IwixohKUFgp4usQZGJfVoQI62am2TA1YK0x6LpGD6bBDVB2mpm5zdBE9my/EmdqCgf9XFNYcDox6MH8dIPSAbTHaDBaY5zGWsvs1BSZ020jCiZlzaQOGKXxPlCWkboSynXUMimvQyP7ipulripCkO+tUpGmrFHe44xG4fnh975LOS455tjjGPSnIBpCaEkx2uAGOeOlXZjMkQ9m0aqkWrqKQWYx0eHrVv4SE0u7l1ne0xC8RiXwdUMMHl9XIqepoamlW6TTCkNJjs+DgTQqVUo0viSkBmXB5ZoiswwKS2YVPnrKpqGOnip4UorYdlKeUiSkQB08TWiIShpyziqc0XgfqesoDY4E7Wbq0OJgne/T4hKbv7iV/vWKDd+JbHpF4PF/9gdcVAtt8j1bvkX99jGsOzKKcf2jaznx7Rejf3TtIX+tdf90OY/81m+v/rtJgXfuOY5T/vqFfORXfom7vukSWUd7UW1/cBVffPZD2fyiITT/48LeGNT0tBTgs4M7dAF+U6SpPmk8QY/3fweOVsPaOdhPWcDhRFpcYv2Fzc800Y53GE7t7QP++j47w4j7Zctsu79BvWkX33nqn3GNn+OuX/vfbPmoEw3hYYDykVQeAkq60jA3Q7JH3j5zqHDE7kYvfelLee5zn8v9739/HvjAB/KOd7yD0WjE8573vFv3RFqKVJcCSntiXzNMGd43VEozUV70yS2112hNphUK0UiHGEEnjFKgNEkpQpL6w1mNDjJBUkouWrVSBGPQKSM1HpOiaG2VWqVsi1IctDLklr0KmoBcrIEUq1ZL8ZdCgnYavnLaUyqh0k2LepmEKiC170cBMQVIQrdXClJbiNdVhQ+RFAKDXs5oaRnvIy5z5HmG1lLQoRQx+la3jOjLQ5T3bzQ6RnysibESqrOSC1NtoCgU3kvnPaAwRpOiTNGNllGraumaMn9TJB+JKZK0omy86OuVwijRP1ujiCTGPmCMwhlDbhQ6ky3T1EIRNUoRoky6VZKpn3NyAV7XEd9OerVO+HajKzTGym0GKeKzTNOESO2FNaF0IgTwIaF0hdMjfnjpRdzjlHszXlqiNxhQ9GdYu24d27dvJcXAutmjqMfLDJfHFP0ZHv3LT+I/vvYl9uzYKlrvGEnIe5EPLdIETa0TKUkxFGIS3X7bkEEpMiUthRjlM49KJnmhLY6dVvjgsUS0ljYKWmHaCbzWMumLKjJparyPBDQXfudbHHvCXVizdg2RRC/P5XvcMmsVrGra0wo7Tcl+sPHYE5hedxQLW3/cTg4VMcEgd9Q+4ZOse1SNaIaRNVN91kz1GdWelAJWg/ZRijiX4VJN2QQaH7FaNO91DOweTQixIaVEL8uZ7eUsl6V83iiZSIdI5T2Zs9AkKp8YZBqlEgvLQ6wz9PIMDfSzDHSkbuR7nmWOSePxMYkcQcuks2oaFkcjRmXD/FSP+UEfn5IcO4yhahrqIFTwGCMhRfLcMKqlMTWuKiZ1w3y/h9OaifeEGGXy7eXY0MsyKl/SxESeWZbKEbsWl8iNYc2gT4gRqw0ug2ljyaoa35sl2T6qqhgOd5Nnnn4WqaNCG9F2J61wVmFcBBXaz83Q+NabodAYHZn4EU30WISJMSgyAgZ8IqSaqAKzgxwfA8REkWtUVOR5j97MFqpJiQ+O9Rs34Zymv8bBgiHVI3Itx5JdO3Zx3wecytFH78RXDdVoSFQwM7+GVNfUk8D8MRtRRrN8wyVEPyHr9UiqZjyqaIYBrQ2ZczhjhYnjA2UdmZl22MxA0sRgSdETfYPRBmcsKgViUhidYYyi8TUxSZMmonFWEQjUTUPdRLwXyvmUEyZUCIFGIYwjomjya09Kicw48RaJkRgSykCmFNYqVA1VODwXSrcnHLTz/Y5dbDp3GRH4R7Z8aCe/vvz/8KevfB+/2It86ef+gXu86bmc9NKaNNx3QnyHRVNz/J/A391nljP6W3nA//tS7vLX2zhpx8W3+BB95bV712hKowZ90nSf6O58o9BkFLrfw+xcJPbXyaXcfsDP9XCLPdJodGgXeADoX3gNxc+dSLn2wI5JSadWX3eQF9bhZpEtaC6sZ/jFouFuD7+SY/t7eMD/+1KOPq/hpG0jSId3Gp5u4i900J530Cf2j0AWySHEEVuI/+qv/io7duzgNa95DVu3buU+97kPX/ziF/cxdPnpkAt3qyy2sLgso5fnjMdjJlWNqSsmyEQoMxnKGNAKrYxQclPEBnDaEG0iKAPyf1TSKGNEi2gMuqUQK0SDajCtNlmm0gHRh1trsG2RqFbMs5ToM5XSGCXPr7UW/XgSM6HVAmiVGqzEPCmuqn4RGyGZAMeYWkM1TUoQoqcKQYpZazFGtzVfIAWLdY4QKlo2NyqJFr1pJ6MKpBnQTklTjEQViLHC2IAjoW3LV2r1pjFGnAMV5P2HJKZkVn6ND1KkrxjhrWiNU5LCvGnkPYq2W2GMEjq/VugYiVGmcavtiWRIOGKKGONxmRYteJSJuxTXQhluolCOcydTvsoL5dy2FO4QkzRBiCQSdZBmh3iXyTq0SRAnbL/+ak467i6sO2oLa+dnWNizh2uuuoxJOUYbS6grlDLMbziKsO1adu5e4H6/cBpf+/JnKSclLVu6/cyluWKSaNTrEPFJikuzsv21UNJDEgqxVgmNQRstsoooTSVUwigIxrQFasJpRe2jNDe0ovINdePRSklBT2T79Vdy2Q+/z/0f+GCyzGGtEcYBKw2L1rSQVV+vld2NqZlZjr77PVncejWZlYZT4xO9vnglVLUUNyjNVL/AGUvPCY288pFx1WCMpg6J0WRMYYUWXvlEIlCHwPKoZK7fY930FKPKk2IUqrozeC8eCLkzTEKkSZGeyUimoQyBXaMxe8YTjDbM9/s0MTApa0iJwlhilOnmdJavNo5MEgry0mTCqIKYEr3c4bTFmsio8YQgr1l6+XuWWRSeOgY0MC4rvLH084wmyrQ/c5aJ99Q+kjv5TH0IFJlBjaFsPIpI1XgGvUKaBkqTZeITkFvLwFn6TcMSCuUCabSnbW7JdwGtaBqFcwm7wqrRSSb7OiNEaJpGWCohoZQhRoPSwuBJURou3nsyl5MDYz9mUjUU2YCAF35JcuT5erJ8hjD2ZJmFZsy2bbuY3XAU6zevZ/d1NdWkwifYvWsnoJmdX8PcukX2hIqgHdOzc+y69lqK6bXkeR8/2UFmK4zLiDGgCMTgKasG46Rx1fiAijAaNyitcU78NOo6oY1MwcVwUJpyRmuiF9q9kOI11sjpcKYQb4fF8ZBE28xLCkPE1xGTCUPIe401oLV8r42OrFyXhBBookhAnFVkRsztVLqx6dfhRhy88z17T3BTZMOnLuGc4W/y9Fd/ke31DMEbwlHz6MvvRIU4oC+/ljf89bP4pRf+KW5ZwY5d+/dApVH9HmmqT8yPsEvGFaOVeAjosTeDOOihduzGLJf4mWK/HpO0Iq6bRY0PEY33Z0CaTNjwnQnXPKogHcgAUslUXHcsn8MCXcM/LdybR236Dpn2XPIH9+CkXXsO+zpUSKTR+OA/sbWk26k528+CI+youjde9KIX8aIXvehneo6sLbyCUigU1hj6xrQXazVToSDEQGgCaC36axJOJwyJ5GWSnZzow60ygJaJqTGr7s4rU94VrEyN0UqK+yTmWEnp1YvbSEJrjV2ZmJNuZJUomR4rrYUi3JpBKRJJS7EdW/GubmlPYqwkU9zUFvYpJlLyMplrnXsNYj3urBOtfIrEKM6/1nhZHxBDXNVy02qQMy1FckgNxnqsa0XEiFGbdRqrFFVdodBgNT5GUgo0jehbjVFkWpoEMYBqi5zcapnqtn+i5CJaJrxAUrj2ZJFI5EZTNWLwZq1G64K1Wx7M/R70GKb6Pa664rtcfMFnWR7uAp2k4PBSaButQInpl7GGmCI6JOpmpSGCvN9cU1WRpmVwJS1mVTEJ+yGaRO2HbL/hcj7/ud2c/pgnMDd1T2bXbEQZx6AqMUYxHi1T9AaMlnZTVmOchWuuvZpJXbVTeQspUEfZ1loprLO4GLEqErxfZTik9rucojjHByXfjBXmhdGKlKJMy5UGK4V3JIgGOCXR9yqom4YUV7aJJhmZamsV+eFF/8HxJ5zE0cdskdcJjTQhtBEjw7YZvvLdW+E2WOs4/pSf54ffOA+d6lVWwqTxVE3F0qRmzaDPptmeGJEphcKQWUsVakKM5M5Rt8KFlIQpUI0n1HVNbg3T/YLcOZw2FC5SNommCeTa4qlpYqSfZQxsYrnVY8cQWBpOWDPosX5mQBOSTMKdpaoa6hDpKYtB3PRTTOTGUUbPctmwVE4YN56101MUrqBsGhSQa0OJ7CfThSMzniZCXwktfXE0lgJQaTLrmCpySi/TdZdlOO+JvpUGaIVvNfUpBspJoCgcmXOAas0aFbkx1D6grGEmK5ipl1kOAT0YoJZ3onUi6STmkVqaUDEIC0TrhCK2BnQ1IYLLWnlBctKAave32odWC59azbMUubFWNCFiQyP7r3LkxSz51EZiMyFGz2B6DfghhQ6Md+9ipp8xNbeGUmvKSUmMnslkQr+X45zIZKbWr6eZDAmpZn79FowJVOW1TPcsjfeMyyEhQd0ktHUYm5NSJEaPwpAXGSZTTMqSyagCZRj0e9h2+1WlF0lE5dHGUqmKlFr2kRL/h9gkYhLdOErh2wZcUTgy7TDGYgzEJtLU4leRYsIqjbbyPJKiIfuDs3JMruqI+O7deSh3twYH43x/s0iRuS/+gC/9131Rowknlz8+4gqiw4IUOe4jP+bXfunpTD9sO7w/25d2flMo0UWn6QGxcIdvnfsDpajX9tn6CzmTTYFiq2HqusT8JSP0+BBOBTUwM4XesYCa2kjS+zcWD1M5bmaKtLh06NZ2gHAXX0vvHicz3nyAU3GboLlzyBOOBJx33cmw6Ts8cf13+cDUibj97KcdTKhxSfIHOQ3gTkhJX8ERXYgfDEQthmaGtEqhNcZQFAU2JYKHJmga44ghorUCzY2TattGOBkLWszS2tlwq58V/etKsbh6DRsjIQqtmggWKYC1SlJ8akNuRQ2eUiIkMGgx/knyk0LExpY+raRgBZnGRkVrrCRGcFrrVeqxuKSLO7lKXt5XSjLdM7rVNCInlQg6yoQvxiC0Sx9JOuFWntdIbFIiQqpJ1NjMo41EuhllxGFbRRrfMPF+tfCgnYLHNn5MtKitJh+hy6JkUhRCQmnZdkml1tROJv2TyhNiogka5yAFaTr0cytTcq1Zt/m+/NITfoOjNqwn1DVz07Ms7byCH/7g62S5NE8mZZCJYEykKNuxCZGoxMUdJZFoqqWHly1d3jnZ1uLzpERP21LajfGUzQLzM+voD6aoI0xZQ4yJolewZ9curMsIPjA1Nc3ycJnF4R60tRy16ShuuG4rdTvxJsaVVDgaIiqCTlIIxJvMn+VCX26xSqOVzKqblYgza3BoYoo0SRgDJimUFdYGTYP3EiWXaYPWppU+iDt/IrGw63ouvfgiNm85ihg8KYhHgtK6VdgL1GoZ3jZ/lGLD5mOY3Xwsw2suJ4TIpK7xjabvcsxADPYypfFavBBqH8itZYin8oFebrE6UXvFJHrKpiI2DTP9PvPTPZYmE1JL6S+sofGepokU/RzdNMR2vzQGxqOSsqkA6OcZuXEMrGYYPT6IYZu1mrqRiaY1knZQh0hUkV3LyxTW0s/tarMud4baC219xZitToFEhtWGEBOjsmFxNAEUa2YGaDVpI+QSmdPSHAmJ3FjK6Ent92pY1kyqkiYGejZnqnDCZPER1+r9m5jwKeJjpJ9ZZo3m+hhQvQFZ35J81TaxFEZLs66qI5FIlkecbnX2KUIy5DZDGbvKHFFE6pCoK5GIoGog0sSSplZEs1JoRpy15MbSG8ySF32Wd2yl6E8xPT3FcE9JwhJCYjysmF07Tz0akfWFFeSrksw66kmJyXoQKhZ37qA/v55ef4rRzh9QTnZiciPFttaUo4qEwWqoyorMRGIKJCzTgxmMTozqhvGkptc3rSO6oWkipRc6vssgqdDGUlqyrCB6OX6lBHUT0G2yhbCUDLk2FFaaujEEDMJIqT1tRCPiYaCgrmqaOqBVbFlMYuagUsto6XD4sXP3nZ5Bm4ZDzO8exRofbrkIVxqVZ1KA9257d/mbw+6fn2P5Scusm25j6O4KISkWP7KB+YsOLT039TLUcIzdM6ZZu3/Tu6QgrJtBD0dwCOi8PxOamo3/ucyPHz9NtLd+D0laftSdsLd1W2DPtbMM71fy0N5V/Nkxlv7V6rDpwgExLh4e/Gm46vfudJT0FdzhWw+h1cRKxJZuacVJ4m+yHG0zdJaTZxlZa/RklaXvcoosw1qLMg6lDcZajM3QbfTUSkHUOoHJVNGIw7BzFmcNJkWUl3gwtJQvK8erlecwSpO1+dFKabSWXN6IJqaEkvhnJGt8JcNX03eWqSyjcOKILTT8FYp2ILWmQkrJ+08r8WpG1qZSRKW4miueEjijGVhhDThrMcaSOYPWDU1YoGz20KgJyQQxqFMa3Y5qY0tdjilJ0ewD4zKsZmNr026qFMXkiJU4JAVtaSfRSuCjIgQxbothRZgsrAHfiLlaE2QSbrVGq4yNm06h3xu0F9qOXm/AYGqdZLC3hmOzUzlT/YwiF8mCQmLAJmVk0ogGuygsvcJirSZE2maEIreazEmjRmtxOtcp4VBY4ymHW/nXL3+GK664jN27dpNljuuvuY7+1DxZbwpXDFDK0sREv9ejqmvuds/7sW7dPCG0ZXVrTEYUyUGKYiimjUYZ3VLj2wi9tmiyCgptMCiamGiiFFmTEBiHQB1iu62lAaS1wVqH1mJK5luHeB+kQRNiAF+jY8OOay9nuLSAb8QROsbYsizku3vTK9tVWQbQm5ri2FN+nlHtWZqM8TFhnWGmn7UGWAaU5FkblVYN/ZwRE7XSC3V9cTJmOKmYygrWTk/hrMFqQ985UpJcZ6stVima4PFBmkk+rjiLjxhOSgyatYMBhbNEItZIIyy1DZDcCnW/DgGrRLqwa3nI0niCNYbCOQZZjjPi0o6SWMOYxBFbNMHSCGlSYOfyEqOmZtDLyXPRIfcyR0zCnCmsIcTApG6wSgGJygfRj1cNRV4w0+vJ/qIMhRXqfh08lW8YVTVGafZMSrYvL7M4WqScTAhZASZDK3F11+3nUtUNykS0jWAijVfU3lBHsxofqMnkNXxDTDU+hvZYJY7fCVZjGlcYQ6SIMw6tM2y+lnIyJCTNzLoNjEZDyiZAbwqMYby8TF3V2Cwj1DWT8TJ7du5E25ze9AzT69ZTjyYoVzC3YRN+dC2TpR+hlZivOafpFQ7rRBKiVMSaSIwih4kxUU5qfBMITcKaDGsNk7Jm554RS+Maow2DIqeX52Q2w1pHbg25SjRVQ1U3q9n1M1M5awZ9NsxNs25mlp7rEzH4mCg91EGK9KIQi0Qt9CJSaJtUKaCSHFcjGoOkF3Szow63Ka7bCtt23Pzv8hzWzRPXzZOO0CI8Fo7tpzWsm95bc21UolxzeC5p0/QAdi+i/f5Xn7Fw4jJ/BEJfcR1TVx94MZfu8CO9Iwf5TsPFjeFY22PprhI/ezihyuonM2kOBNaSZqcO7nPe1jD7/7nc4QvxmDQBjU8tXRuFbnWCyWgwUphoY3BZxmBQMDPoMegX9LKCwmXisNsapRmjJc5MmVVjNIXocBGVrlBBtUKh8UGKy4RQTtGa3Dm0VmgkbsoaKXgTBo8iKE1SN+Zgq/YHJfFAoGhSom6nnQpFriFXYKInNjXe14QoF89RGYLSsi2ipkmKpAy5EcqrcxkqJUwCpS3JOlye4zJDUiU+LdKkRbA1Jk/kmaJwBqsVITRUTUMIQS7WU2inxomJD5QhUjZRpskGcqswSqj1VsnfG59oAlRNpGw1nZlTRA2NT9QBYlTtNpaJNVphnJLiPwEt1b/xNxo45f0+m485BaV7NI1o4bVKGBXR7aQwxSSFvmoZo62KoPaJSSMadq2VFPzOoLXCGSXboDDYTAnDwhm0GrNnz1auvfZatErUk2WOOeFE+oVjsrzE4vICymRs3riZdWu38AsPOJ3U1CRdYbRHJ5FSrOTRoyUaymlFUGp1YpprTaENzqhVrX2MEUWiUOCUTAcqRAqgFExWiuwkuvomQdBtQW8MVYo0wRNjoKwrRiHiteW663/Mjy75HnVd4dvGTkzy50oVvuJNIE0poc1rYzn+7vekNztPr1eImzgKD2RW41Oi9pHMtsVsTDQh0cvEBGjPcMzyZIzRin6R088zityiNHgv1HVpSMjkO7OGpKSBY7RhYTRhx3CMsobpXnGjEaM1Iq2ICqflw258pMgKjDaMfWCprFo6OcwPBvSLXKK9UiIzpl2vSAeU1sQEudaE6Nm9vMywLMUUzLrWKV5R+0Qvy1BKKN156ydR+0hKidGkZFRWZNZS5I7cWqYK6Q77kHDW4lNsNeOKKngmdcWu4ZCJj8zkPUxVEY1F9aZJweCrtkANmpQsIYlpXtYev3xQxCgu/VUT2TUaUjYt3UyLljzLDBZDiBplNUm3DbeocTrDKkeoE3mxnnwww2h5GZtNYTPHaGmBWHvWrl/H/NoZSWvAE4LIIVRSND7gsoy1a9dikyfUY/rzc8RmyNK2H5FrKIzBaCm6faxIqaYJDVUThGWTCVNJK0l2qJtIVGAzQ2gijRfZwHA8AZUojCN3Wfu9cpBkEu6jJ/gEOpGUMGesTmgVQXt8EoZTDPIdU1qjtKGqE1ErjAVlpEknaZNG0g6UIiRN4yUeLu2vy1OHDocLeQ7r1pDWr5EC/Ai+MqzWFaw76uYp3svHR8Lg0E/VUuFQ1mJ27b/PQFIQ1k7fchzcbYkQWPftPZjqwI5NydzZuSaHD2ai+PzSfXDKsP6uO4n9wysbUQd7Gn4npqSv4A7/zo2SmJuYwCdoMPgkWkBnFJl1ZNbQy3OmezmDPCdzMrmwzpI5i9NGCvcko2mDUKglBAn5r9YYa9FapmwxAEqT5zl5keGsIW9N2rTScjGsNSlJRFlsdew+yTQztT5rTUwE1U5ldUsrVje+tjFiYAbi7Nv4ICvSkvOrVTtFRab2WuuWeipNBHFcjxS2jYmyGp1Z0DWTeieN303jRygdGOSafm5wRmjzIQRCDDSpZlSNGVXiWO0yRZ5rmZhrJRe3tJR64QwDMqmTaCfInBinSZSXFHMxSESS91KohQh1A3WrAZDsdU1EUfqKq6/4D3bv2UntA1FJw6QopvFVzsLuyPIw4psEUbUkCSmordU4J7FlK3SFzBqyXIORBorRCtXq2kNboDtjKYxDGykmYwpoNWa0uI3xaEgMifHyIjt3bGcwM8O6tesxWgzTqsmIK664Cp8MW475OY7auLmdMIqe2hiJznPG4CNUXgppD60busTi+SQNiBuLYKH5GxKmLbpDaI3ejDRxfIjE4MmUop9lTBW5UMWNFamFsbgspzCW0NRceP632LVzJ76WqXgKgRRDy4BoodLqdFy17v3rN25i8wl3afOnRcIQfCAzBqMNISqUFoqxsPKjRGNVFSkG1g4GzA96GKOJEQpjMUpTx4Bt89JT25gpnAMiC+Mhi5MxSiWc1Qyso+fEaM4HcQAHKH2D0WrVjDAqKJuKPcMRHpib6lNYi9NOpuhJGAOFNWiEjZG5DKWgDJ7lqpLpOYYNM1MUuRTObkXCEBPWWKyWx6KkKTAqJ+wejohJUTjHVCEZ5XWbP66Vkvz01sywCdKca7ynDsIaKDLHXJYzaBp8TGTT61gzP0+ey3fbYOhZK2vXWmLF0Bgr5myjiWdciQleE1q6OmJsiJLoQ5CIvMwgx0YtjB1rexT9aabnt+CbmhgVg7lZmqqCID4LqRpigNn1M1RVxWQyFimM0rg8Z2HXVrZedxV7tm2l8YHJ0h4Wt/6QXla2MW4yeU4x0TQNIQUSgTI0lF6o384ajNOMyoo9w5Lai+SkicLisFrTz4WphIWy9pSVJ3jf5st7il7O3FyPmX4h2ztJ4kRMNXVd0/iE9xGjDJnRxKgIUY6pOkBdtxN1n4gxSaPVWLSybYygNPk6ZnqHIwY3LcD7+e3iijBbqFkc3rxR2qZTtnPZ8x07HjBPmMpvNDE5BEgzA1hYxlT7TzUPuYE1s4dsTT8L1HXbmP3RgfHLk6bLFD+M+MK1pwDw2C0XU80fvsaOKhtSdXCn4arI5dhzJ8bt4LD7s0GonSvFshRuyjq0s1iX0evnDPo5mdaoGElBiu2UoEmR1OZ8O61bwyqhYxqlhCpstGR5a7WqCw9Bcp6NVWSZwrX06cxIzI42QnPXxhDbCVOISbTAJJKP+BAAmYzHVhPeqrRZYQWjhM4dQmq14pLtnTkpqjRSqK3Q803rBq6UQqfWIbt1YFfWYWwkpWWWRzewa+kGxn5EIKJVwOoo26eRKV7ZeMq6IRDQLhFSoGoCVZCfpCPOiVPwyo+G9uL1RlM5t5JBqsDYRFFI8ZkSpKhWGQHGyr+1Udg2KjGGFUp9BCILu7eyuLSHqqwZlzVl02BdwVGza8hqy55dlj0LiaqWojHPhNVAEody2xaQMbVu5CGu6vKDT3ifUEox6Bn6eQZoolI47cisuFnHVHHdNRdz6WUXs3vXDVx/w/VkeY+6qiF5FnbvAAWbNm9mbqYPRNatWUuvcNRJXO21ElMupTQB8Cgyo2W7KCnSGmgj3YQpEWm/R6nV2Lea+xTFkMwaLX8qRW7aAkFrAolJXVMnCHmB6fWZmRowyDOslon7NdddzQ/++wKqqiQEL0VUy35YqSoUN0otEtJEyno9TrjHfVGYVTM4oiY3jr7TRCJV3aAVlFXNruEQEqyZ6jOV52g0/cy2DQfJi3dataZ7YK3Q/MfeM/GepdGYpvHM9nv08qxlkmjyVkpSh4BzYrQVohglKg3LZcmOhWVCiEwVOYMsZzrPsUbiz3qZTOKrVt5gtZE4q6SY1A0LowkRmOkX0iCwDmcMoWUhOKPbzwSyzBBipKxrxpMJjffM9gdM9zKiBqsNmTH4EFo3bpEq9JyjMJbheMxoMhHH+KkB00WOURqrLTMEqGuKNWuZXz+HsgkfPdomXBFxmWGmNwAsShtU0hAgBWl8Oa2wRpE5hbVgHWiTSMZjbCKzSMZ6ChJVph2FzegX82TZFH6yJHIel1HXDc5YjNVU4xFLC4uEqJhfuwbf1FRNI3TtzLFtxw4WF5dpmsjshqMpXKIZXU9hE1pHlEn42lNOKjSyfa3ROAtJeZrYUPmG4TCwNK4ZNQ2TUFM2oqGvY0VIgV6ekztN8A0xeWlmRE+R95idmqPIhN5eh4hScgwSD4gM5zJpPGkxtyybijzX5E4aodZluKzA2ByjNVpbjLPynU/CmghJXP+N7sSUHW5j3A4L8BXoiadZuPlCXKvEMcfsIn/aNq54keLqx7UF+SFAcgZV5JgdixLruZ8I831UsX+O64cVKTL/n9ux4wOcinf09MOGHT+eZxhLzpj+b0abzCFtON0UanSQnf+VJs3cwSjpB4Db0eH3wKBIqFa7bADrZApqDDLV82LYE4iEkPAhUDWRcS2FZR0CoZ2empTQKRJim/WtJb5MIxTnFKQIN0bT72cUhRN39Bjb6S0ycYziUu6jTHNSSqQQZHIDZEbh1KoCnRU7rJREQ442UiwG0SOK67oUKVn7IklyqMR8C1qztRVzOfEOSlq3bu0BH0csTvawON5NFYYoE9BKLjqF1hlYGouRVmjd1L1knKFibKfzEglU14ngE0Yl8lzRy6WQtK0LPMibsVpm3ykqMZdDLmyFMWAZ5IZerts8dYXVYNFYpJCq20k5aFQwLOyYcMOVlzFcWmD3rl3s3rGNH//wfJZ37yBog1YZS4uaHbsSo1FiaamhLBO6rRytMThnSDFStzpT02bIey/GbGqlIRIkospoTVIRH72wJFTEN4uc/+1/5T/Ov5CpmbXMr5mlacZCu+0PJAwtVOR5j6OPOYnJcETQA445/mSSUtKkafPAtdL0M0c/d2RW44wmaN26Xxuc0dJcUisGbqIFD0kKc4ireafyXZBJuRLqAYpIL3NM9/s4awkJyjqgUlqNSUPBxRedz86tW/GNmP/FGAm+ab9nN36vVn40Cq0NW06+G1Nr15Mbg9YtKyVEnBYDukkTGE1qSl/TyzJ6WcZUUaC1oQxCQbetC3xM4kAtEWaRzDkqX7N7eZlxVcl9jRSlvTwjofERcmtFe954oZRraWiUPrIwHrE4HlNYw5rpAbm1pJjItGoL9iieD7rNcU+K3ComTc2OpUVCDBRZxnRWMJVlq+vst/TD2EaipSSNHKMUo2pCHTxTRS4+DNqQOdOyKpLo1aN4KxSZxcfAuK6o6opEonAZvSynsDKtT0p8GWa0gXHJzuUxV96wFa3lezkpG0rvScRWgqJIMZCUxK31CyPtEyMNNNtKLfJcQZTpc+4MVmVkxpHbjF6vJ8dQrekN1pK0wbdO5L0iY3pmivVbttAf9EnI93bP1m1cf9UVWC1a78wmvnfBf3HdtdcwHi6TzczRnxoQqh1kVlzwfWuyqZ0F7dDaYbRDpURuFVO9DK2SmLDphMkSRSE7gIa2AWrbY5ynqT3jqpKpv07YtiGa8JR13Rq1RcoaQIsDuk+kJMcvQqSuS5og7KMQG4loLAy93LWv1RbeAYKnbRYGMckMqWWmdOhwG+B2XICvYOmu06zZsvAT72NUYvPaReZP28oNDxscskIlTfdJowl6vP/5XdFq0to5jsj0hJ17WPODsJf3y/4imhvZjh0OLfIdlu/Vjp/LapZO4LDoxFUTSOPJwX3OIicdaZGItwHu+FsgRaxWkvVNJPiaJgRxr1VSHItBVVo1GwNxA9cYovATMcqQtDiApyS6b3RiJTgsKdCZYaCtTMu16Jetay/AaVnP7bQ9aZkcmiQRVFGtmJhLznOuNVUbZeWMAdIqFdggEVZeqTbPXIoywo0HUKXAKI1JiajTKg2dlvptjEKryLgeMhwvUfuKmCJZhtBpVzKpQ2yntCsBapA71VJ9IyoojJMLbee0aIybgG8S1rYZ7pnBANQSnxZoC6qkCGGlzSAxS7E1pZNiP2GsJndtljmpvfgXunvVyPbU/UTTaOqq5vyv/C1Ll36DrJezZ2GRbTt3Mq4DbcsDpTSTSULrRK/fusWnRGYlr1y1zAGtJYs7JCkElFFkVj6hECMxRRIBh9iF1k3AqAxQWNNQ5J7NG9ehmTAeDcm0Q6EYDxeY1J6612d2dp7du24gJCh6A2JsMKrBBysGcyjJp0cJ7Z1E3Rq0JZ1oFOikxIk/SaxdpjRV6wwtFHGJUkpR6HNSWEpEn0mK6d4AZS0RRagbyqpi3DT4TAzu6ij+B6M9O/j+d/+TNRs2thIMYQ+kFKWR0mqeQajxSYk79Oz8Wo6+6ylc/q3zKFo39rLx9DIYTSaMK8/a6T7zA4fGtHpo0SfX3uNDhtGGJgWaIEVqSp5RVWLqSFM1GGtb/bVMQWsf6WUZTeMJyaO1IzOKcQg0QZpie4bLOGOw1jLTk4ZGP3cEH2Ufx2BR1FH+nruCsvE0KTIqS5YnNevmphlkjsZ7KWozS+lrvA/kTpOqtOqqroCF0ZCQApl19LIe1oAfllTeU+SGqonEEMmMmC76JEaGy5OSFHOMsyTfNp6QBlxmNClakkvM+Yy8mtDoWabyPno4QjtDCNIMRENQ4u8dQ8RYhbaKtk2IsYYi0xROEhAUkLd53D1nMSojRjlW5TbDB4/SFje1nqYaEZOmVxQsL+xkMhyzecsmptasZWn3HnwYYlBUIeFjg29KMIap2R5LCwsMl8f0osKwRFMuYi3ElrnRt5JoMZqMSUm0+YVKNE1EKU1MNdYqXGGJUWQIjZbPBG2wtocPNahE5X0r12iw1oleHzGl62W55IUbiagMsaZpJiid8L6hqQPGRpSW/a32Hu/bY7KvscbhdE5ELsq1Fnd1reSYr43CWEX0HTe9w2FGnsP0gFTcPovvFYRBzg2P8RyT7V/hq1Wiuu8I/80Cu3hwiwiAZDV6qo/ZsUDsr2d/7R/8bIFb7JOGR1iWfYrMfOd6Fk4+hnrmVh6nxJIEdZBTrTrsC1PCZxbvyy9svAh9lyFh4LBLh9aNXw0nLfv0YD2hFtPDDrfnQ/L+QQrOhPaeWHtCE4ghrRaSdRPwYUXvKtOyzN5IpRVTHkPStJNKhW5psiqJxjnPHL1eJlm/SklRHxJaQWEN/UwK6ToEoQ4jGm+zWtqKeZluBeBNhElc0f1KcZ5ErCm6b6ux1pI7i7aaoMQ0SFmDV5rY0tkDiaCSUNuTvKYzCnSg8SPG1S5G1R58KlE6tTpecX6P2uCTogkKMGRWptXWSCHojCF3miKT6DJtJEt3haislJLs7QShaWOQlGifNYrGt/FrTaKqwK/KTmTK7tWK0Zj8O5EwVij2wctkMrNijNfUieHI4NyAZHr86LptfO+HV3Dl9dsZNgFthObtQ5CiKcGkbDXKyPOEEAl1a3yXWaxZ+WQSdSP5yz4mYtvAaUKiatqiLcq0XDS2Hh8a6mYPP7jkQq7fthPrehx34knMr11Pk8BaaWQs79lJWVXkvT7HHHMcD3rIadz/4Y8hGcuo8RIV1la3MsVsp81KjOvsimGa0vj2+7TqZq40ts25X9GMqzZkKQLGOvKikOxuH8VZPQRATKrEuC3htBwkTIpcevEFXHf1j2nqupVfSORdapsZqY1OS61WHaVwWc5J97ovyWSi546BxXHJnuWKmKDIM/o2Y+CcNJdiRCnR6McUmTQNbsXcLQVCgElVM5pMKJxjfmZA0TqoTxU5mTOUvm6zs7U0zKLCGUtMkT3DIbtHQ0JKOGOZLnJyK3Fj2ohpY5NEQiHbT7aF0YrhZMKe0RhaAzlSW6hq+W4ZwLQGbForrDJUTWBY1YxKyZNfNztFL3P44LEI26MOjTQFtCQGOCNNm4WlJRaGE7SxFFnGbK+AVjNeB9mvM2OJKaGTaM6tF514k80Qk6wrENumknwuAXGWt0YRVSQC/d4UawZz5FlGEwONT2iMTK6NwVfQeI/RQq/PXM7MzBz9/iyjpQWGiwsY1yNzGXu2Xku1vMCO67fiXMbc/CwpGZQyGOuwzmKzjOk168kKR1V7TG+AMZrRrqvoZxGrxd9gpj+FU5q6aYQlEhUGizOtkZ1vWglJguAJTWJc1lTBU9aeUSnbf2E8ZtvyMgvlkHEzJqSwmj4RWvaQ0YmEp8gdM31HL7fkeSE0dVWjTRLDzNZPQauED55qZcre+mbUTRAzQGNwmSPLxb2dFIjETiTe4fDhDjABX4VS3PDwAVu27L5VD9swv8z2U3uHbCoeBz1S02CWy/1+TNKKuG4WjkB2TFpcYt1FzYFNxe/4o70jBv987d0BeOixVzLedGg11iok0vjgmrSpIicVh9do7kjF7fmwvF8ITUPTNC0VUQpta03rSo3oxlE4RMvptCY3hty0tO1WdxvbEHKj5MBpUiJ3ml5uZVIaxc05NA2hLSZa0TZoS0DcygOsmseZdiIfUfj2B6PbmDTRGIIiJNEMF0XWFijy3kT7rlap5sq2Tr5KtNVt+Y5ti+ukE6iGmJaomxUTtkjmoMhFEyoFf0JpKf+zXNHvS8Et0zHpQCijcJkmKilGlWlzjQM3idXSkj0dpYitfSKkhLWGIhMKsMRaSV5vUycmk0hZrlD224izGMWQrnWc9i0dv7BgdaIsFUoV9IoexkoTQbmMotcnsy1dOMrCQvBiMhcSZSWu7E0jWcpZ7ihyJ+vysTUCa6O/Wh2/9zLdCjGQlLAtrNEUzpJZRa/nyHJDSA2+2cPi7m0s7N7O9dddy84dO7Gmz6bNx2KNYjQZ8qNLv8/i4h5mZgcUmePnT/l5Nm3aJIVFElaFT8IiADFrC6TWcE8yvYXipqhjEhdxrVuKtmqT9cTcrbCW3IpPwcquH5I0J8pKdLsGMQ1T2oore4rotgOwuLjARRd8m9FwiPeS675i2EergaYtxm/kqSs2HHs8s5uOZlTW7B6OmDQNWZaxdrpPL3eUocEidEKtxZQvM5qkUuviLTF+S5OS3ZMxzogTutOOqbxAm0QTA0ZpiSSLkRDEkT2kRBlFTjEuRZM91+sz1StQWr6jri3EvU8UTowcq+BbSnxi92jMnrEYjDljGGQ5mTUiDWmbTiQxRcysaT8vTUqR5cmEiGKm18NYizOO3NnWVFHo1T4EYtRk1hJIjKqGYVkSE8xODeT2FLFWo5WYOUYkhaDIxDBuz2jMsKzxVYOvA3XWx+OIUZG8wmqH0zdKHtAKH8SSzTlpPmbGkrxGBwtRE6NGJ4dK4kiflDi1177Gp4jCMJjaQrIFkybQn5mjNzUFyoEWl/xd27eCgfk1U8z0c7J6GUtiem4O5zTLi8sU03Ns2nIMg14iM3XLfBFGxnA0ZjgaMy4nhCga+rKuqb2nV+TkhWxTknzuMTaUVc3uUcnO5ZptyyVX71hi+3LNQhkZTjSjiVwIa2Pp5X0Km6FSxIeapqmYVBNG45G4xlvalAUN7SScm8g6dHsMzzJp5lW1FwmS0iQlkprCKAwixbC6fXyHDocSd6QCvEXMLeN7TdC3cv/RKjE8dULsHaKLfg1MD9A7FlBh/9cWBhlq+sjUx/YvvIZi161vXMg15iFYUId9sOeqeRbjhCes/S7DzYZ0KJs6VS2M24MFpUlT/YP3fLdz3OH7V3UTiEkRnW3zqmmHrmpF2orRQosOMhgRTbcSTXJqJ5FWiVGXthqXGWofMG0wdmyLD6NUGxMmNHCrW9q1suSZQtGQolAVdVuoqKSwRDBCibZGgdb41oBNW9F+K2KbT6vbzGShU6p2Clo3UaJ22iJ/xSk9AJFISDWGmpRKYqhIystEKBqCEi20c1A3qTXCEnkwLT02eUUySvLHlRR3UiTDuEwUvYRVUkyHIEZoLlPUTaBq2km5VhRWkzkpsFPd6t6TUL9jlMx03U7rikIurr2X3OYUI8R2okySHF8UTSNa8xgDMQZCm9uuNfjQ0Hjp7iYV0U4KRmOlGR1SEh12r40xqiuhlylF9LEtdCXiKiVF7mQ6m5RuJ1vy2kUmxnnWGlKIUgymIZf/8EI2bzmJE0+6K5OyouiV7N65kxChl2cctfl46nLENddczTf+7WuMRhPWrJnGxBodFUkbMR1ryfVtjDmJttmi2qZHW4hoJcZ+RoMOEa3FUM6g2hz7iE8KQ8TGG13YtZYpulKJ3GjGIbLsAzpGpjILSqFU5PIf/jd3v8fP0x/IBBMF+LYdpKQxoNtKvHU2oD89w13u+yC+duVlFFlGkcnEt7CGJoqhXFCp1ZBLQVM4MUgsfWSpnDAajwHNoMjJLG20XKBwFoWYuRHFFb3ynnET6TlLPSmpmobCyBTbaIuzjqhi6zEQybWiJFA1jRj46YYmJlTj2TVcRmnLupkprCql6YTCKWl8JGRyXsVAimLMuFRX7FqqKOsGbVbYNYpRS4sunKWsvZjHGcOoiTRRstQXhkNmej0GRU6IULiM3FXUIdDDYK2i9p4QYVRVVHXDpKyZ7uVYr5mtS0ZVSbF+I2vWT6iWrmdSN6AiKEPSAWcNDQGVJOHB1x5FRZ5ZlLbkTjPxYzm2UYmsZ8UUMCm0sjS+wrgBtr8Gv7CLLM+ZnZ9FxcRxJ9+VPTt2kAJMlktggfn5ORaWthGiIShFGJf4JuDrCmWgGu9ksucqMh0IMWJtK2Ooq3ZflsaM1hoaiaBMLRslRQheoZw402vtCURc7jAxMm5aPXibmY6JpOTIbQ9nC1IsqXygLBt8zIU+rsIKYR9rNHWsiUFYHCohxwalMSZhrW41/hlWW8o6yZSdCGiaJqG0oZdpGu8J6tBSCDvciXEHoaDfHKp1BXNzi/t9/5gU49rRyxqyH/VQzf4/9tYiFTlqOMYujGnW7h/dNikI62bQwxH4I4vPnSYTNpw/4ZpfKki35nukZCqu918y3+EAke8yXFj3uH++lcmmhJ/LcbsOcrRYCzWaHAhB4pafL8+IvSMwxu82wh2+EE8pSYRNE8mdbotx1dKUxNUYJFNYIfTaEMXsTK/ohRWSKWsdyhkiGtV4vA+o1dxwjVKBoKRMEtdtoScaY2QaohJE0Q3KJVpLfzZiCpS1U8wEaCsFHimQgugypeBv+ckJah+w2rRFsTQMjGk150ko7d6PafwQo2qwEskTYhD38va5YhIzMm1AGVmjarOdJW7H4JSY1SnAWXEQnoy9xCW5hGvj2FxfU1YygRTXd9nOPkDfacmQ9onKC102L4zk3q80Dtr3qjCrk/DowTexbXpEVDtxV0r00ylaFImqLomtqzcATRIydkI2GInBQJHnkFnktZCC35iAbyLOiVN1WTfEJGZsMYmBXuMTkzqSWZnyG3WjxtwoTS/PRDveGvkRIjEscf63vkI5KbnrXU8GFOs3bkKRWFrczSw5Tkca78ms47+//31mZtczXj9iefs2slzyslPbjMhaKq1s1zaKbsX4TiU0SorFNvJJaU1o6QUJaNqvTz+32LZU7huJzzPGEJuaumlIKIx1DGyGU5qlliofx0MuvuDbbD72WKxtJ+tWxBWgUFYM5touE0LyMBx94l2YnZsljJcwWiLIAvKdr2IkBMisoq4hKHk+3U5EMzVgzdQA38o9TNuYqEIUWUiWUdYNkxjoW4PWgXFVEoMhhEDPWQZ5jvVSfPsQKZzD+5rGS8GeKfm+G22EPj8smWnjDH3rfF44zagSo7rMGDHsCoG+tdQhUPpAWZcsDcfMTfWZm+ozLhtSSjin0VGaB3lm0BP5RvaNYzFV7FxaxCqJ4cqsxWnL0qQixkhuLE1TkaKicJaliWfsG8rat3ICxVRW4LRiQy9nen4T9733vbn6e2N2jbdTaDmuOaNIaIyK4pPRRkpoLZ4BzokeX2uwZGgl8gM5TEQSlp7roZMmpEC/txYFVEuLZHmP6GuWF5cJTU1vehprLAs7d1KOK9hQYHs90qQCZVizbh3VaIl6EqjrZUYL16PVAkUvbyMnxTQuJYWPQYwgScSg6Gc9qqZkXJX41kQtyxIxSnZ7ZiYYxA+h9pGkAoUFlQJBNRRGY5SGGKiqCdEHxuNEHRzGanzToGyE4PAxUTUVJC/GbQqUiiRriEDuMiwGdJACPybyzFFVDT6C0RYfq7aBkVb9OTp0OKi4AxfgK1g61tJ3+1+w7licYsPf9hhuMhz7X8uo5hA2wDTi/rx7ET3bI+5nJnIoLHp+FnbsOnRrO0C4S66lf8+7MNpy6x6XDNAV4occuoLPLtyXt2w8H7+pYnRUxtzuyUGXPqmQSPVBjCzrtOH74A5fiBsjEWApRnxkdWKnYlqdbgh1WYnZBEJbVCtFpBb3Z2PFzTspBUmhncNpLe7RSejYEY3WMpWUmluLmYdRqAAq+LaoFsprQkn+okIoviRUWinPW2037X6V2tdBqNDGGnpt0W61xkQxevIpiqY6eRI1So1AlWAiIWlCtRJ1JtFOTRMk31Ypqlom0gCNTmRGTNUkfkxctOuQaGIlmdBIYSzZWYoQ5HK9rhNNTJgohbCYI0kha41s99wZkmm3d1LyPF7YCdZJjJsPkrmdZW2GcpMIUS5oez1N7iyTsbiuB3xLO/diXNc2AGiL8JQiSjmy3NEvSpl2mVYXr2Uq3MszMVyyBqVKtG2LnSiT/xgRo6YoBaHWGu8jzmlC9NS1bFMfI2XtcdaSGLO0tJU61Hjv8U3JeLzcxkA1LG6/iuFoGWsLdu7cyfqjjmYwM8uJvbtzydICKd5YMKEUyhiCkqm8TgmrRMceNehkSCmu+g3UEaIW48AUAh5Et63FxyBlYiyokRguF4VW6/IMnBO9a91QtVKLnhEX8ssvu5iTLr2E/n3uj9K6/QybVY9/jcTNCQtBPrt1R21h4zEncf0lFwrDxBhqH1sjPkXlEz2tUMpTBYUvPaX39HL5PGZ6BcOyJiXZTzOjiEGYIbl1jCtPEyRCpa5rxnVgZm4G2+uJwVgUEzifJMasV+QY01D5RKHAGsWwahjXFQuTCcpYiqKHCQ3LrY+EcxmqifiYxPdBKxofIbNM6pqq8fQzy6DI0drQyyTGq/aBvnNYJU0UozKM0VQhEmLJsKzoFwXz0wNGZSnmgUoaao1PFJljVDUkEj1n2b08ZmE0ZP1gwEyvYFzVWGswOmNgNFde+0P+0S9x3KDHYHY9y3t2oG0gRi8meI2nDmB1IHMODeTaklJAqUDdNGSZxWCo69jKWZBsep1kH3MF2fRGlkZLeB+Yme3RlGPqyVjkCTGS5ZrcOpoUacqSOgRqIC8ceaYY7Z6Q9wbMz80z3rHAZLxEEz1JGTKTif9D07JbjEFFkfokxG9CjkUBnzxWKVLMGI0rxlWkMDmkhLeezEkjQWmNM4p+ZlBGUYUSmoivNdrmrJk2bRoAQj1vWRpGK8pSmkUiMxV2jFEOTR+URCgqr/BenNdDiqtGbilJ8R2DyAFCPLgXSh3uxLgTFOAAKMXy8TB1K2jp4eoBU1csMPWjw7O/pcKhRxaza0jcOLPfjwtrpzBLQ6iqQ7i6A0BTs+E/l/nxxhmi3f9tmLQYx65cS3Y4dPiX60/GHHUBJx+9nW3rj2XWGVR9kNkVB5uWnjlS3k3Db4o78qEbaCm8rXlVjMjFUVuBW20kPzyt6LalEtdK8qrRCm0NNmvpr42X+KYoZkdZ7lBGt7pBKZBtluGsJdeaQoMjoULAIWZvtNmypqUMJyVFSe7s6oS8SYkmJEKSCZZvKdu2Ne9SWgoyZw3OKGL0kpMTPaEZUVcLhLAEYUxoPLbNtSWKS3LeMzi3onEE6zSDwpIZg8bgcok0csYILT1KtJoyUhw3IaGsxARlueSzeyTqrK4DAVBKU9WJuhaS8koOeIwRQ+vyrrREqBmwtp2sKvkMovIkAnUjk0pn9Wpub56J/rYJAR/kaB9jpPGNmIyl2DY6bvwfKEw2wOU9koJJExhORCtutZEIqUzhXCSlBtpINp3kGBRVQhn5iYjmPcRIDEmaLEnhoyelGpIwFSRyLWD1iMsv/g6TsmL39m0QE85adu1ZYt2m47jHve7Pqb9wGve4930YFJrdu67jqqsvYvt4J8MQKaOiQtMgJnxomWRbFMlHgo/oJLpxK/QNAloyqJVsm4gU31aL634IUXLgYyL4QGg8KXqcUeQamRbWtTRIFNIYaWpijPiq5Lvf/iZ7duygKkt8U9M0NU3T7PUZrGjFkoKs3+e4e96HpDRGS655jOJfoLXQ7ENKNE3NwmgIMbFhesB0L2/NFZEcdWin5+JEXjZCIVZK9Ni7hxO00vRyh9aafp61ztpK9nUl2dqgyNs86tQ6YO8aLhMSrJ0akBtNUzcU1mG1pvJJ8qu1NFo0MiUf1zU7l4aUTUNmDFN5Rs+JflyhyTInWfApoY14HDRBWBTL4xEhwXRRYLVup+FOzNdU20wJnsxlKK0YVSWL4zEkKKzFWYMxlsxaGu/RSjOdZShfUtYNx598bx70kMfSGwzEGd1A6UtxPc8cRWZRRlgARitCaNCId8O4rCnrSrLGkwM0VeOZ+Io61JhsQG9qHnyg15uiNyioy7E4hDeehMgdAom8P5AGTyZRYS7vMV4eEqNhduNmFCNC2E2RS9MSI1IViYtjVYctueqKqq7F06BOEAqapmW25BZaD/g8i/QzxUxPMzdlOGo+Y/2046i5AXP9nFwbcjugnxVMT+Wsne0LW6hpiMHjfUlsW6HBB7wPJCXsphASkzLig2mPQzW1j5RVI5FrPlHXgcY3hOSFvq4SViesAac7g5oOPyNchlozf4fSgP8kJKvxa/e/wIhJMXsZh90YMU31YWEZU96KtVoNR2icmb7yeqauPgD39Dv8iO/IwJ7FASFFTlt/Gc0Ayo29g/4aanSQ0wamB3f449WtxR1+d/FKY1VLt9RaprtKprJJSYGmUKv0YmVNq3GVCK3gg3gbaZmsEyUiLNBgnSU3iuQT2lhxaVaRGDVpxdgnhFZfLvnfyohxWAwyKdQrlHgADU2MQk3XWvSQWooplUSTmGmZlJGiFDw+EJInJYm+MromhKZtOAg10ykttPgk70kG/fIcbb8BazTTfdFp55nD2kRZNqI/D1L8a6XJXCKy0rnQZEbeR+sch1OGHNm2o3GkrCJ1gLwQ2nyKiqCFip+0UPt9aDBaXJ+FuSAH/pTk04lN63tnwFlEP48YhWmbMLbBlwq0EyZDWqHSCjSaqAzZ9HpC2E6oPGUjBXxIN+qTa+9bUzehvUuBEnEm4nIpAn1s8+bbJkpM0pyQaXXEGYUzEWMiRkmhF4MnNjvYdu2PuOsp92Z+fg1Li8scfdzJVMM97Nx2HcPlRckm94ENRx3HhqOOwjjHj6+6HnQ7qddtDBXg2k2u20g120oDfEx47yG2BXdrhBBbSrJBtl0d4up3LSRIWty6Gx/QStz1e5kjeC+NmBQJKExekFLihuuu5gff+y73m3o4EYW1CRtb5/SkJG2gNeEDoT8fc/LP0ZtZSxzuQJwLxM8gs4rl8YTlsafILPP9gWS6K0NhI+O4Qic3NCHQJEQqoaFqAktlZFzVND6xpt8js5qlqqYOkUHh0KqW/T1pMm2YBNkvssyyazhiaSh08UGvwFlDv5BIMh8DSTmhz7d+BNZq6iYy8Q3jSY0PMD87wFm96keRWUtVBrwXWnlVVzQxkhvDUjOmrCVC0ZmMvnM4bRg2nhBE4lJ5j8JSWMO4iZRNQ1mXEDWzg5ymNT3UiPlXoxXDqqLvJAKupy0LAFHz4Iefzo9/9B/84AffkwYYBlconLYoNHX0sqdokYAYZxkUA/IwIcSIcwUx1tQ+EbW8t9xlzM4fj1MGW5VkvRwVPdOz6/BBsbDteqpRydzsNMYoxssL9AuLNYZ+0Wf9+nXsuOEaKPrkxYDJwsXS2LSWECR2rGqa9rgraQ8akVhUTc3iaIiyDZmJTOplgo9kNsM5S+ECTRBGTK+XY5RhqhDTtbKKOJWRNEQVoc1M900gxAqfPMlIbKOOCqMsdV2SEvSLPtYaGl+TUk7WFwo9eGk6VhWVj0z3+jgDk6Yh+ESRSRRcE4L4NhhpWHbocEBwGWp6QOzfSu3u7RzKR2YvcsRj1H6ZtdXBMHX94fdiSLlFFzlm5xJxy5r9jzOb6+EWe6TR6NAu8NYiBNb/x25GW9YS8lsxFTdy3u9waBGGFk/g4uEmQNi8BxPKx4NLS89zYe902At3+ELcGYOxBk1qc7ohrGjEV+OfJDtWGyl+tQJa6nrtA3WI5M6JJtao1owr0TRBpp4KvK9RbUSSammc4lklgu6GSOMDqWmkUBE+NKJYF6qzT4kQG1QSyrRqGwJWrNWJwUMQV2kfxYZNqxLiiBQ90YhuWGmDUZE6NiiniFrcyVMQt/M8M4Tg8TYRk8bZdlukdhtFj9XikD4p5WQWVzLNraWphUbfNL69uDQQFak1VJpUEa0TxiYKrXAZ5E6jkqKqxHTOaKGXxlQTifgmEptE8gmbJ5yViW+MUBSSW900gYQid+BTWDUfHkwnkkpk+dEQLPXwWlLy6LTi4GlwU8dipo7Bj0fQ7MZo2Q7WiNu9VRqjnbjpJ4U1MoVM0eM9JC+aZ3EqX4nFE+p1TUKlgA6BRomWWFtunKjjqevdXHDBN5lbv4lqNKQsS2bWrEUpxWDuKObm59i9Yxun3OvnufSH32LP4gL3O/V0TjxuO9/9zrfJtCa2JnStZx3Waaxu45cAbSxWS0xVlRqadppolWontOKZUMe0ysowWqHa+L6oLDqzOKBuI7sCiaqWIgKjVwskpzQXX/hfHH3sCazffDR5URCjOM6rVttvjBbmA4CC2XXr2Hzyz3Hl+TvR0gljcSwu3ONKaN1zeU7d7qc+QaYNo+SZVA2F66GVwceIj6JKH07GDLKcuV6P0gWSgtwaiiANnph6ZJmlaYSyn7fF7cK4xOjAqKqZynJmilx8H1LCosmtlTz1KM2Vqi3erdYslBN0ljFd5DRtxmBuLXUj8XWFtUx0pA6B6SJD6YYqRHzwNE1gtt8HDcOywsdEbjUTLzT3zGl0owkJnFGMh0N8aLDakrQYw2XGikN3CMQkDUCpWhXjEGnyPs4Z7GiBT/3Nn/LD73+f4Bt0LhR8o01rKjahbjxKG5ZDwGlLHRuJJXRGjlqpBqVxWY6OEGNAa0evP0s92oPGMz0zSzkuKavI1NyANZvXsmfHAldfebUwNdoc7nI8pujPsHvnDWzftZPj7vbzqDgmjnegVGDSNG2EosK0xpMG+Q5pJcycpBqMi+IPYQw+llhn6fcyjHb0ehrPkBA0fZOzZm4GFUeSO28N1onBYvSBSVVicifxlEBmc5TW1HXFuBwxKscoY7BGivzgPUo7pqf6OJMLDZ1IirC7nlCGRJ57nHU4Z6jrADGQVBJmShIGTxO7C9QOtxJ30gJ8FSmx4b9GXHa/eY7ZvG982Yoxm1KJQdbgTMD3b5sNlab7sGMPetwQBvvHflmJM1PjSXtdegTh+u3MXj7P7nvs/3EradGKd76UhxbHnbCDJgW+ecUJTJVQ7Nj/CL39Qt0cVFq6GvTvnMevn4I7fCG+Uoi0tmhiYJZE96e0RrejaMkwbk18TOs6DdB46qomNJ5+XyKPfAjEAEb5/5+9Pw+2Lb/uOsHP+k1773POHd78clIOSs1KyzZgSQUmysZDG5fBbRsoQzB0QFc3GKILBzRBBBHY0A0dRFVQPVHR1dDQQAGFoZjsxuUBgcGWNdmapVQqlcr5jfe9e+85Zw+/qf9Y+76UQEOm9FJKp96KSCnzvXvP3fecffbZa63v9/OlzPTwmiupTHN8mMKvvBFqTmxjYkoKVQrGA+rR1vzlMguOFXBlxFJKnl+YefNcMzFmYkzanFtwZsLIQGFCaqLWxDgxE8BbKpaYJyQr9bzUgvUqn09FfeHWupO1M6D55WIKORZqjlRTsM4SxBDHTKqGtrVIoxJbMbpZVbh6wVRt+GJSOXhoBD/7zIOzt37vnKBgsWIpqWjja4TsihK+nVVvNJVGddLkqJRn54VcwM8xVbFUjCl0S0tpdzHNQ7jVBcbDRynjdaBSZIXbew0hLDF1h1IdzkNJmWkqtI2a1VNOOG9IuZDioFFxpRK8AqH6qCoE6zUirsyyd2d0gyxVrQIUpUuLSaSqFPMxHSPxEp/48Ht5zWsexIV97nnNkiuXj2jaFTVnpEaeeeIJKrucO3eGaXOEk0gQ7b4FVQZYK3S+wTmjsnRRC0apqB8a5RBYY7GzRL+1OkzZjvmWT5ySiFE93aVCTBHvNBc7lqoAmgrGqWpjShMk9dtPxnDlynN85IPv57esdqlV8AVqLlALzhu8d7eGW1BvZYp/9oPvIeWJw+2WzjpO77Q4W2YTiUatxcqtLbizOhjQCC/YDJmpHyi50DhH6z07nSf3vQ6cgM5bjsbMEBNd8Exp1C2yge3Ykwpc3N9lf9HN9z0qXY8x6bbcO7YxMaTCMlhqzRz1G2qtWGtYtCpBPxz0cbvgMGMkFlhaVd6kWqhWSCky5sr53SUslWAfgmVIUYd8zmMN9Gmibbr5GCfKbHFYdR2pJA63gzIJrGGbMkNKjKmhNY5aKx9//hKfqsJ0/ixcucIHjn6JRbqq1hZrmNJI03jGFMlSibFiXcBaS2NbqIUxDjjj2dYJZy3MCQgYMEnwwdO0e5Q0sD64jrGBYpzS249vsF1fB7+gYnRwmSNN60BUMt4uG5557JNU37JY7LI9eIwcJ7IUtZTMkWrJJASl5nujZHvma2Xbujk+L2OdDk2kFlJUH32wjjhn3FP18QSh8x5jFL5WRR8rxoEQmpl4Lyozjz3bsWeqBe9aWmfBZpx1WISUI3km7hsL235iM+gmv2SICZZNq5fVWRprxOjPsKg16k7dqRdT3+gN+OeU3Yxc+MWOwx9p2eteaDhyFY5/7iJnPh659A7P8p3PY6Wy3fv6DLyqM5hlh716k7I496K34nnV4Pd2qDcPX94DfKlVC6fed5mjB+8iLV7CVtzdacRfzto+EPm7b/z7/N7Hfi+LD3ec+/UBs72N22tuMy3dB8qivV2P9qqqV30jLieQdNSLamYptkilZmb6tObknsQtkTWrGSAEj6GQcqbWjBWvJPWaoDJvHbV5zylhqFRbsLWSxqoZy7XeoptrDNBMmGbeNNfECQzdSEWkzn+ufvaTmB6xUBlxTFibyCWqVDcnbQxLYt1rs+icxm5p9m0hUwg24K3exG77AUzBO4MpFeOh86Le9FwY0vyc2Tofk0HQBjQ4lW0LBUwixUKMsFxa2kYQE9n22lQZEUqGiULjLcEatmPGiA4DjD3J5lboVxEwVlUFpQglafOeZv9+MALmVkI6pWamKeumTyq22cW3LWl8ClN1w5ZKpcQBMS3DsMXlQjZQZq9uMoUkSSF3c1MqBqooEG6MCWdgEYQ6zVRyUfhSNbplLgWChZqFJOC9vXW+xXHSht5seOqpT7Bz+h6+53d8E5vNEV3bIQI3D65x/eA6xtR5ALEC13Dl6me5cO/DXHrms8Q04I3QOU/FUpKqNnJRyTRVZbAWRziJwDuJuRPmTHTRc7QknIZDk0Ql4k4MrlaGqDF7JVe891hJTHGikUrwMGXDcczkKjz20fdzZs+zWC1wi9M88PA3ISyI3pB8QJybz3l9n1x88LU05+/i0uOfwBmH9R4jltYLfcqMubJ0MKZCrIJNQmM9U0rc6AcMleP1hkXTcGa1oJ+SxvU5Txsy/RBJWZsxI4UhJhZNS6ZwYxhxIgTrCE5BYs4YtpNuvhtnGLIw5syy8RirNHSXYLPd0jaB0ztLtpOS0K2IpgCUghih8ZaYCmXeZh/0g/rmc6ULnuAMIp6pgBVLcIYxllt8ie1U6GNiOwx44zm16tTxXIXWeY5lJOZKcIYSMzEXbvRb1inz6NVrPH90CHt7dMOaM21kx90k5QgmE3PURjcmFk2j7yUHoWkos1KgFsFKUDCjdZg5NcH7oHT1WvHWs1jdjbiGYh3t8jRiArEYbNOqxB1DSQMBgwme5alTeFcZNhPDsGY7jtx1972k7VX6G0/QNgYxSyASS56hi2qRqVWvbycRZlPRjbbG9Kn6pBZDqgXnK5agahSbaUNmiEdqyZBKqRGqoZRKTehwIFhK0YHtMAz4oI299Q7GzDSMTDaypMMFr0qlqraOfhrwVrkD3ut7qSRBvEPQgUmMiVKExge8FzWGpNt7s3SnXoV1pwH/gnXqI4fAHtf/14YzK41q6ifPmY9NtM+vMdPpW1877n/9lCd12cGVA+zhQNp/cc3HKzbObPauVwPhLYeIVNaHHf7pLy0xLrbe4h7dqZehXOVPPvEjXP5H93Px4z3u6PZuw283LV1Wd7bhX6xe/Y347LU2ViXnNRdKVlhVmSXgKjM26tWuEHPRqBnRiCqZCelGKuSE1DpvRFHg2Cw/Ryo5TZgMJ97zVKFagxHzgrdJVLpeawW0yaYWlaSXTKqVqeiGmVqpplDKSMo9xkZs4/DBIrFQagaser+twtNKLeRaqFKwXoFCTgolT8SsG72mcUxJPeBFqlK1s1406wwfqxWcn6XFVW5R3MdxYhwLzhlSykxx9n9TyEUIXj3BqSruKI0KJHEKmqZprW6SrUqjrTNK/JaTQUDGGFR9UDSayzkDxVIzIBmDkPIs21esPCkOtEHtCIe5J8/DDWtG4nDA8SEwjBgLjYBphJwtMRZy1m1a21ra4PDOacRbLrPE2tF4i3WGKSW2o/qWpUCi4q2lCCSpc4PnKKUyxAw4Vo2dvdYbnnr84zz+0MPcd99rWIrj6PiQmCunztzD0599HG8Mb3rjI5w/d4bGN5TUEzcHHF1NiLUUMeT5HFSOgA48lOhv1DaRuLWNq8Itwrpmw4M3uu03nBD8lfRsMjgBV9WHzAnsLivgbFtgGxPGgJdEiQc88aGfJxWILGhcx32vfQMheFKO+BzA6kayCix3dnjwDW/m+LknoWaVmidYBMeQCtucCVVwBqaiUXatt1zbbOmnyulVx86i1bg8DI0zbGNhSpVVCPRjZEiZzgWcMfQ5ctzDph8xwHLREpxuu6eYWbWOISZSLbRGBxjjycayZI76kbBcsLvoELF462mdxs2VqpnrpRSkCl0IxNzTx8RmHJmmxO5Ox04nDFG/3lshFm3kvHUM40gt4I3QD1sF2bkw8wksjVPPeBuamXZfEOPwRn/v52Pkqc2Wo5Swe/uc3m/Y5yZm2BBlfu2NYMXgZ995ilUBlRTGskVwbONErRUHNLZhiD2lFHYWO6oGGUc2/UgTFpxf7TPGxDgkmpVaUs6cO01/fMzO3g5HR2u2cSSslqz2dmiC8Nwzz2L9kvXRDbrQ4aUy3vw0wpb1sKUaR+sdjQ3E2hOzws+cPfFVywtpBSWRikrC6wzWbMRjATEJMWpDqGTGKelVSwpSC41rZv5DVeieMSxbvVHua9bri3U402LsQK6VWjLjNJBSwrmA0Z9E01hiTBg8bQgaaxc0UrBUQ7AeKRY8pCmRhVsQ0Dt1p75g3WnAv3TVyqmPHDKt9ik/2GOkst60nDucG4YZumukMu3X2YL4tVegVCuYnSVy7Qayc5H6IiMLX2lxZtM3v5Yr/3XPj7/xF/ndy89yyi4A+OA48sP//P+A23yJ32uGtskraKbwaqrdjwSO/uf7OP/sofoVb3fdTlq6c9RvtG24efEX8Fd/Iz6DzWqpiFilR1NubcA1akalv3aWaJ94s9XDrVnipWgWNuYk/3tucqhUCqZUrBQFPFWoRvXtMlOsjQgiL0DI6ufIcAUDNSE5M1ZtjIwYjMlMaUuJG1KJFIEgllwLcTqRRTvdbnpHGwK5ScRcVDZd9fhvDSWrequxha61hGyY5m26sdqwpwhpAtcY/Ay4QwyhCzgnpDIxDIXjTcE6HVjkotv3ND9BJ7L/NBYFS0WZs70TPliCs8SicntEBcmplJl8Ds6pvziVE7BYRKLQONGBCpod7qxodntN5FxIw5bDq88SOiHFYY5NK1izQepTs3dZyc7jdBKLVmhbQaxyAuz85qlVPcxGdIM2xkwTWlat5Xg74s1EkoqYirEWa3W7WkrGEgihnTd72tDEKVFNxrJh21/l5uEBO8sVTeNYLTt2d/cR43jd61/PpeeeZbtZc+Asp06d5fLzz3Lq3Hn642ONiWOOCDMq6Y8534pWM0aQPFsxjFodpGZq0dgkM+eOj6nM+dh6Jp/kkddciFVfVymFGpVoP1rDZtLUgNWy5czKcenyAU1jOX9mh7EfuXk8sT06ZhoHUmx1Gz8T7EXfVFjveegtb+PRd/874rC+9Z5w1uCsepzHWHBOLR+bKWoaQMo0wbMKDd4mYlHVincOkyI5RWzT4K2QamEqhUrheL3F767YWXTEpFLlzjti0WGXOE0fyLM6whsdNBxsElMutE1g2Qagso2ZWgtNcEw5MeU0y8ozMekg7LgfcMaybAK5KjMhWKd2lmIIXqBmSqnzlnvgsO9BKsF7drpOOQvDyJRUmr7OkSnnOfIsEYvluBaeGXoOp4kEtGf2uWvpMcfPU1OijxljdNARGh0saWtaqCWR0SGHKdA4Q62ZQsY0jiFPxAKdW7LTLSkpU7L+7ovVRUJ3mqvXPokxlmUXGG4cYK3h7NnTnD6zj1A4f/EulouGm1ee5+knLlNtw6m9Xa48e8SFcw+yu2MYb3wWqZVUNd7RNw2Nrxpf1jgddM1QxqmotUesUKO+/72zjHnUkacooK2WSjEVgyEmo19fNPFCrJ5TUpPerFuvgzorjJM+3zlnpjSSivbL2YCpeg2MKZGrXnN90MGhmi4Ki8bq++vEziEG5zThIaWJbY7kUkjJkfMdafqd+o/qTgP+4qtWTj02cXVo2O8G4o0GM6wBuOtXB25eucD2otB+nblnZdEimy3uYEM8t3rR3/dKiTMrr72Xd/x37+P/dP4j858sbv3dBRup7ssD2aq904jf7krLSm4rd/+rEXfjNhPNP6dupyxd2nZmWN2pL1Sv+kY8TREcGG9vbaSNCDiLFTSCapaAZwFEKeV1Nsgqt61SxGpzVuqczTzfcFUNmFbf3wwzS4lCUVq7041yKXP2LMzNh8qWbdUGqsSqMWQVNKd2S8oblcCbClk3rW1wWCtQZhKvFRadZuZa66jScLzdMgwjJ5dJMYKzDjNvqkWEcSpzc2xJSTehxpnZD11nwJA2+9ZofJjBUDA47zA2Moz65+o3N1hXNZ4pnTDqtCH0XrBOm+qSVa5fim6t2tZx4tGvprLsHIXEkCIZoYhofFipyCynrpU5pssioo17qYVSjijThlwEIeG8DlucAcwRJn4CO2+ErTFYK4TgZnicRqfVIjAT1I0xWGspWYcxNWfEasRSLhVr1a9esjClrDTmVHEm0+REiaqe0O2permtHSjxEh947y+y9zt+gNeceoBpKEBR2vL6iL29XcZxoHHClcMrnD57CmsLN69fZzw6VL9p1a2mUt9n7n6upDlj2ZoXvKnqzSi3JLUyx5+Zqo1ErIU0vwcWos+tQchiiLWQh4FhSsQpIzXTWZgmbVbatsWkDHHk7Nl72T17ViPMUpptFSeChXrLJnL+nvs4c+/9XH784wqxQqGIjTUMkhlzxhjDeqMws7v3dvDOso2ZMWVab8mzNDtYT3DCkBNtCvjg2ax7pjFipNJ4S+McO53nYL1lKgXvPU5gmCXQ3lmmFBkzjDFyuO3ZXy44veyYpkQulWVwxKKpBovgcaJbUWcEyBxuB23qxBCcY+G9yutzwtmOYLOyGYwneMt20kHBNEWCC+wvWo6HOLMoGgYZ2U4JZ/Ua1U8JjOGwFJ48XnM4TvSlYBcNbUrkS89z3GWaptJ4h/cGY6D1DaHtWG+PySXhvVCs2h/8fC4vvEWMZUg9wTms8TjJBOMVolgK3gdWtmV37y5tRquwPHWabrlgc7yhjJGD557n5pVLhKZlGhPXntswjSOhaen2TrE+PqQfK2fv22dz/eOsj29gvQUKzlm8z1Qp5JpI8YXrRyzqvS6AFKXfO2+J6IAiYJjqRLABkcIJC8SonAlMUh6GMfo4Jc3vH42tO1xvmHKEaklUnO/Yb4WjjQ4PTzgd3nk6v2AecxLHNF83MuSIsxZj9HPC2ZO0C42VW7aNJlwkwbpXGIzpTn39ygdktaAsuzsN+EsofzBw/PwO+w8NtFfcra23v77l3HWVrH+9tuG3ygC7Kzg4xOx1lGBf1LcVZ7Bn9uH5q19XcNujf6LjZ2814Z9fj6UVdvvlT9hidfh/+4zG37hVPIyvHdjZ69n/mzsvbxN+O2XpYr7xtuEvsV71jbiINnOmQHWzt/iEgVYrphqkqCNcleDa4GiDoBMcYwzBGjKFkjXyScTOkWLqn1ZQjzaw2BmuNd/oaiNiEIo+4Wb2Keao/uis9PZSCylviXmgyhYk0fhAsC3WZbwTnK2krJuyxgaESixxVkFUcgZj5s2MEQUVCdr8CrodrYVSYEq69WXe5DTGUsiMMZOLKivaoGTulCeVp1d9ToM3eK+by74v5JJJeQbOOTMD8vT5MkaBT94Lw5QYp4IYQ3AqN219h2+CNkQxkVAvbCoJK4IPRuFwSl5CEMacyDGp17ZR20GpR2R5hjoZDD3OqrrAepW/e2exRsnPoJteayyl6savJLBWs+FTjjRGN2xVwBqFlQ1J9e7WwJQKOUNNBVMNxmdSqQwp0uTZc2sX7LcLjCkz5GnChUy/foaPfezD+LDiwsWLSC1st0c03Q5jv+bGwRVKOc+p02cwJrBcnaFdneaJj7yXg8vPE4v66UsFijZWuZY5Tk097CrwUCGtF0OWgklKxnfOMKbClHVcU6vCsrIAtRAR1jHreVwquRowhThlnr16wJQywVmOtpFnrhdCLTx0YTG/5playmy94CTZjhMuQrtc8cBbv4XLn3mU4CzeiEaNNdpsr4eBmARvLe1JHrgLTGVU/3brMSlTZqCbM0JJheNhIOXEuh84tWg53XUcTRNjSuzZFu+EnIRaUIDbGOljVvn3NNKXyrJx7C87nDF0oVElTMkYE3CidghQT37KlX5KHG0HCnBmtcI7zZTOc4zZNhZKFVqvkWiCbsSPNhsWi5ZV1yGz8sJbHSi4ZMm1MqXMwnqsNRzFketT5LlhZEgZ0wRWTUM9vAHjFm8z1oKdKfWCWjRKzYyjSjirXgT1mjdHd0Fhyr0mPRQHSZUiWEsuRWPuYmF3tctitcfO3nmm/gYOWO3sYo3hzF33ITVja+L61UtstiN92mIElqsdTp8+w3pzzLWba8KioVsEjp5bk0tkHAayFKwTCokcdSttRDPDSykYMTSNoWQhAQ6DCZY+Kl9iyplsT7bQBmWt67OQxlFVSlRMKRgrGCfEqEqpPm5JudVEBNEBk/Od2iaMqhDqvMFunEes2jpSiuRqCNZSYiYlo8+nqJqkHydSycoGsAFnAoUJYyKbVxoV+U597etOA/5VlZRCOLDwEHSX6xduuL+eTfjJIXQBs7a4a8dMd++/6O9L+x3+RkPtX75m60uWtdibX7w9eM/2tZgXs+mWO1vxr7oE+rsz3/xNn+FU6Pnkf/sWFk8dvrw/83bL0sOLSw/4Rq1XfSN+srXWZZyZV7UqDQfBikK5Uq1U5i2G8AJgqtZZ3i0E66lW/aGUOoN6gKI3204UrAYqU0w5KwtdZJa9q/y1TjMBvWhGea6JVEZiWpPqpE2jtQiWxnvNozYBMRmpE7VOjNGwXDSkFFUSbsBQyKWSS6Eaq1sxLEOcsPPWNEbdUjprcPPW1HttgGNW769x6suuSSN3hqjbfGNFo3mcxTeZmtWPrPJyzdh2YohjAucI3uNdniWf2pB7I4TWzfAlfS3GOJFKZcyVFLVJdyYw1Iw3SiJ36I22NUabzTGqJ9ro8y2iRGLi08RUCU7wwSlZ2RpCo014cAp6Us2DWg6MMwwpU3JR/3kxNC5wK6pMlOCeSoUKbRdw3mKHkXFSCfOYKziL80LMieO+Z6dpaUKLMZDKwJQz3llKTcAxB9ef49z5s1Ai3gcWZy7QLZZcuzSScmF9fIMmBCoT7WKHuy7eSx433Dy4gjOiW1IADLmkWZpbkLl5k1oUzFZVkeHFYahsh8hQi+YzG5Wkk2HpDQbYJqXUOyMU61TOnCNiLCJ2JphDHzPXj3qmyXNqITx//Rrt4Q26nX10NHDyvydvu1kJYi2vecNb+PDOKRgOmWZAIFT6fiDlwpm9Bc4IQzHEnFl59XxPqZAB59TbPdVKaw3j2LMpld0usNd1iLGaHe8cfcxMMdOFlk2eyEVp7BA56nucwJgyy6Zhr2tgnJgyOGtvNdCp6rmTUlZwI3BjvWG3adhbdExFB3at90xpIqZCYw2bmBhyYhkcR5stx/1Anb3JwXmsVMYxabyaGGKZ2EwRK5apZLYxcXUaeHI7sYkRrGH/zBnuOn2WlW15fPNRQkgYm6k1YashTRlMwRhV0CBJz2PjNJUhZ1V/5AnnteE2GGUICExJB4Q+aJyXdw5rPavVKbJYjm7ewFsPxrA5OiTGwvm77yWlnrDo8HstZ0NDrYXXPPAmVqvAf/h3P09whtX+KbYHT7PdHGBtQOqobIlUGMaBkjPGivq8RW0XahMytI1XiKYYYonUscy2GkGsAjMLmTSDNKXUmcugyRE5FwqZOFaCWxK8Y0qZMfYUlAXRxwlXK8smEHygViGLwjbzzAXAVHLKOHTDrvGXAqKS92mqTDEiRthtPY0zaJhAUTvOi8Uo36lXX91pwG9P1Up3Sbj6y3dxzyduc2TTba66u4LrN7CnluTuJcSZnT+FPDV+zbfi5eH7ePS/6vifvvv/Dnzh4z1Mixe95S4O7J1G/Cuqaa9y9m1X+JG7P8nCTFyLK+KXieWrwSGpfFW+8dsqS79jt/my9apvxE/CppXMnXFeJYkm63a1inrCHSqNnleu2vjNdGzm6K1pHBWKZlSqrL2FUIzmlEvK5JlynoE8TVhn6WbqcNGMKPXOFt0g5dozZm3AQ3B0ocEaBXvVKrQhqO+RSsqJ7TDNgLZK34/EnJimip191c44UokKPnKWSqFkzZrO0UARrC80TSDFTD+NOK/e4ClWwrxFNqHoTXrWzS9AwCJeZdQWlWVbawg+U5hj0nImZgjB4L2w7YvKyo1Rab6tt4YC1hjGqTCNiVQizQw6EuPwTlgEp8ddQIxC1GQOeyvFEE2m1Kxguiyaky7QtoIx0AVLqdpse4MSwDWJCRGVTG23OgxpgiUng4jFecOy65hGjTmSYig5AxlndZMfvMfYxOALY6rEpNvSlDQjPZbCNum5ZEWffztrs8UU2gbS9ml+8X/5Z3zX7/wv2T9zhuef/izr9TGCYWe1y8HBVTY14RenWa4MOQ/s7u5jw4LU97P0rmDN/PplKKLSZ2/l1qAhz95mJ0KsloqS/I01BDNH+hlhOyVy0a9z1jLmAk5IGVUGxKiNvggi6o5tg2PROcYqfOa5q9hPf5LzF+7Vc12Xjrfy3ivoZEyEU+cucPGh1/P0h351ztdOSNdwatUxJLVMWKMSqYTmyXfOMcaJcUp0jWc7JY7GgU0ts7LCs9t2bM00534nghVGETZj4vSuUtZnpTz9OLCdMned2adxjlx1kNU5Ry6zBzxoZrfm1zvWU+JgvaGWjHPqU+68o8REKqi0WyYSlYVzeCNsx0jJieNtz7IJrNqGPE3ElAiNZteXWiii6hhxOgC6ESPPHk8cxolJDLvn7+J02/JNDz5Eno6458E38y1veys//3P/hBsHl+ha9SWPo26VXXHknDneZEpdsVpOdE3CWU+qCW+tXu4kIBRyLkQjiCR9vzIRs8W7jmHqiVEorqfkSHUrvGs4eO6zDL1mzu+fPkVjWn7LO347IVjlb8SJRz/5CVa7Z5k2W1zrObryPNiM9R5fG7IL5JTY9hPOKzCvAEMc1XtvLFLA+xn26C02VaamocpInpJG71kBbylJmQgWO6c6JFKelUtJ/eQ73Q7eOQ43hyTR1IaUskI769xIIzjrcXX+cwMxZRKqHgn2ZGBbMTXjrMXaQOMg+ELOCuscUkKqIVGpPrC4sxz4xqs7Dfhtr7t+6Yb+yytg8/2lqjYO07bYa0eUe8+8tDiz3RX18OjlPcDPKTl9im/9f3+Yn73wYb5YEw7wy1cfwiShuC//3FdTVZ15Rwj0oiu3UN+45jsffIzD2OHnHLizfs3mdx1x+uMeGeML3yBCXjUcPdhy7ftGLv6zwM6nvrKtuaRye2Xp7Zem69+pb4BGvFTIMenWBFVbiGgjjaj3GFAwWz0BtaE3qCfwrnnLjKjEuTJTyRGcGIIYpEKat7XqFRXISkafSlKPccpIyUCkykCiJ5eeQsYbleI6seSUiSkiIqxLArS5TyVzvI1YI+ztNro1S5BKIifw6PanCuSU1QNbkpLSjcU63Yrlop7lk+a4HzNIpUohFSHNN5DLRr3TTTDa5DshJ836Dl6lyyULbaM5xrlkTOMJraoDQDO4Q9Ates3a6Hlv5q39nF+dT3zLotuuOcvZ+6qe7pPns2RELI3rkNbgcySDNsClqnS/AgbsDPvyVv3nMWZyHmkbRzbcks3HUul8owg/q8CsNhimccswJYwRmuBUrl61OUxpItekw5JclXotRocCMwsg50opeqFsrDZElYw3RnPVvYG65vlnPsLHP/JGdt/+DqpUrl+/wrlTZ2jbJfunhZ2FZb2J9H3PYrHk+PgQ0y6xcWTXVDZ9ZJwgSUC84AW81Qz7xsCUYJTMVOE4VaqYGcyX5uxzIc3wNowqMBAl42cRTNXkgH6aKFXlzKp/qLfgdofrAe+ExhqefOJxHn7kN7N35rzaL2Y/7cl76wRxGJqGh3/TO3j0g+8lT5FF0Gi9zhsKmVz1XA2+6O+QNP7OGNjEhLGG9XpLqZULuwu6UBgTpFppvWNII2PKrEJDY7Upz6niDByte45F1QBdCBj0ORtSVd+7t4w5M02RdqEfIttxJJrK8XaLdZ4zO0uGKUEVrAittcSinAbvLLEAaFN3Y33M3qqjbQIiZmYPGJX3e68/O+pwBAvX+p4r08SlfmDI0O7t8dD5Mzx8/hzPPvFxPvnBJzheH/GZxz5Gt9rj+GhD0+4R/EApqmqRYkmT4fqmJUvD7qrg/EiqFciIqWTA1kDwjpIVJuiMQ0zRzGtajAssugWrxS7S7HPj8qdoxHPm/EW61rEIlXGbqFOk5MK5uy6wPT7gOE0cHBxw+cpljF2Qpi3iA847ct7gjOCNpQueoU6MxeCaVlUHNSk0MOqAxYlBqjCOieAdOAiNI6RESmo5iWNU6GbOIJByvrXJNnN2tzF2ZlQIY5oY4kTMBW+V4h7ThGsqNVWGacA7ixOHKaoMMiI456FmUk76GeEc1hhcjWhSpUIrKzr0nCYhiXrSG2tZWE1puFPfIHWnAX/56hXegH9u1Z0FXD3ArleknRfXmHxenNntkgl/mUrnd/nB/Q/wpZrwx+Oa8n+9wOpc5ei1L+JBT+Tpdy57X7aqhf7+yBte+xyPfvYufu2//2b2Pjvyt3/g9fy+7/gV9lzP737oI7zrdf8Ze584ZjrXcfCGwOEjkbe8/hm+a/8ZPnp4N8c37v2Kj0GGiXq7zrfgqc2rvs38quu2fzT8xE/8xLwxe+GfN77xjbf+fhgGfuzHfowzZ86wWq344R/+YS5fvvx5j/HUU0/x/d///SwWC86fP8+f/bN/lvQV5ipKVfL3yYZYjEpj3Qwz0mgfhS7N/CXdeFuh5kycot7Unfhd5y2f2pX196tUBXUZMwOuqjYhYqBCnPRxap4oeU3KN6jlJsiIWIMYD2JJuZBzRYzHOEuhkHKiH0fW24l+0OOIpXJ4OHC8HbFOaFtL09g5TiyxWUf6oeg2uBpiKkwpkksEGRATGdPIWAaado52Q+PBrLXsrAzBGTIWZJazFpmjLQ1ujlYKXgiN0DXaXHpvybFARWWfsdBvVaLfjxPbKbMdK4frRD8Wtn1knCrOBpadp2mcbrLEELwlZo3XsijwbIyJRGKMW/ppoo+TDj7EYD3gMsbqtrtUhWFVqRgLM6sJO4Oq1EU7b7eDyvO7xlFqYZwSuaqvlDJ7T43Twcf8dylFSipQDFbcfN5ACELjDM4aCpXNNHE8DmzixDpm+ljICMMkjFNmmq7z1JOf5Nr1a0wRzp+7yKUrl3nyqSc5PFxzcGPg7IX7WC5XhGaHt7zt2/iu7/6drE6d5XiIbBIY7wnBzp5f9Y5HFHI1ezLwIgQDnVRaZ+gaT0Hoc9Fca+toZ7CZtxbrHMZ5sj4FOK9AHCVZC411WIHNoJ7ocRJubg1XDg747GceI+XphQSBkxi1E8Jq1ffYubvu5tTZcyy6BuvUF401NN7MMYJZ7RNVvcrqZa8crjds+oFFG1i2LbVC45zSxlPGOqvnwBzpFqwllcL19Zqb/ZYpZxpn2V90eC9MMeGcQ0SIVT+ARDRmLFdhGyOXDo+YUmVvsWDhNZ4uOKvKlzlWq8x58k1wjDlxY7tlSokmeHaalmUbKKLvX28MJReGMc2vkzAVuDSMfOzwmM8ebxiN5a4HXsd3/dbfSn7+U7zvl36GK5eeZNv3QOX6wWUuXX0aZzMXLpzHiGO7hbFvGceWo02Ha06xXAYWywls0Su+y7gZHFSM0tK7zrHa8fhg5thEjexzRih5oNgVTbvAisO5FeI7VXx05wjLFWO/4eq1azz1xGf45Ec+zKcefZRnn32Wg4NDxBr69THWCDcvPcbYH1IpUDO2MXhnsFRyrPRjVFVAUStA2wQa7zTi0Lo5waIy9RkpsGgCq9CxbDpW7RJnPCVXSoWStSlOad6GV5hyZsyJsVTEBHLW8844q0MCKhjDarFk2S1U+WJAXU0WbyzBKmm/5EqKGSseMSe+fr225gzGOQRhmDLOW5atpZTENr6ypbQvtl5pn/WvqPIBObVPvXCGstO9DHdad+o3UlVvkeUCc+0QKS9+gJA7D6f2XsYj+/yyjz3D7/uFP/Elv+Ynn/udLN/zBOfee4AdX9x6v76Izfk3eg3nC/d8x9PgCuv/5728/n+YWD2fcOvI6/7HY37ub/xW/v6nfws/+/SboMKn/0zgtX/5E/zI//bf8L9/57/l2898mptxwRP/82sJ17Zf+YFsbx+XQLo7kLYXUy/LqOItb3kLv/ALv/DCD3Ev/Jg//af/ND/zMz/DT/3UT7G3t8ef/JN/kh/6oR/il3/5lwHIOfP93//9XLx4kV/5lV/h+eef5w/9oT+E956/8lf+yks+lqbxeGc4ycHWXZ5OG0WgnHjGq26BUpm/yrzgHz9pJBza5Ahyy69sZlN5njfCuWT1Js4xZNoHRUoeqKVHykCVNMcIVfVtGt1gj6kypkwIKhXV6CeVfRunPzcXIabCJlWYCnsr/f1SLhpNJgZQorQvoFtphxjdTqasPkbvlIBe0Y1nQX3jbQiEUEm2ME4FjIOq/krnHeQ6S53rLd9jrUqfrog2QcZQKgSvWdVlpmc7Z7HWEGPEGkec0iyRLpRiMd7hmogFUk04DGLcHKWVmFImTQNU3fR6b6HqDbFgb3kxUy3kVJUJYNTTKabOUXL6nBSEWDSvPOaEm7dlpszbdW9ZtI5SCuM0qa/XzVC+Yiglzc2hxYpyBIIzakuY1RPewBjLLcp7ypWJgisJM1PUjc08/+yHeeLpR7j73DmuHiTe8ta38ZGPfJQQHGfPXZzPx5Z20RHaBYvVPm/95m/jfdst48EBGd1sV3RoYKo23aFUjscT+BWzdaAwzcOkUiqTPh1oHLvMyQCFYA1OCmM1JDKh8UxxouSCmPk9gNHNn9GfXUQoqfLpT3yctz7yrZzeP61KgFqVeI02uFRViuzun+beh9/EY9cvzV9TmWZ4mhiVE3dOlSuJwtHQz+eOoQkBb4T1GIkFWm9orCotqArWGrLyCYTK8WZL1wT2ly2DqJ++sY5gsrINjKHzjiklSlGLxnoYuZYSKWcWbUMXgv75FLWZdlalyrMao1LYThMxRfphYK9rObVccHOIpFJZhIaYMjFnVQeIghYzhue3PZ/dbDmalH3Q7e+wH3Z44/m7ePJDv8zhzQOCMyyW++zuXeDq858m58R4fBMqXH72CQoth2swVt83i8UCYxKLZqILhpw9U0xQoGRomxYrylGQrAoEbwVsyxgTuRYcCsJzzS65VsqYKUYfP49HtMsdQtPC5ohtHBlTYVhvQYSxJnb3zmDRCDd84MrBoxT0OS5SKLNqp1AYYiI4Q7EVJ5au6WiagJjKOGn0nFghTjqQtGKY4onSwZCnqD75mRkBCryzxuDm4U4jFpsKNWVyjRpVJpkqnlIiORdCcASrthhB8C4wpTR/DkyzxQJiyYxjxjlU1o4OEY3VoWrJWQcJncNK1Vg4U+FVdFP6Svqsf0XUnQ34nfoiVVcLuHwNd7Mnnl58+W+YK59eYQ/XEG+TXPhL/rDMG/8fa/7Ot5/nj+xe+YJfcjpsuW476nNX2Pv0KQ7e8uWb8Wr0nztb8f+00qrSvuUmv+/+j/GuS6/j4b+VcUcbnvsdp3nHj/46H/gfvplz773BuffcoP6aBfE8+f2GP/LIu29J1gE+tr6Lj/5/38Ld777xFatFJGbqeBtl6d0dWfqLqZelEXfOcfHixf/kzw8PD/lbf+tv8Q/+wT/gO7/zOwH423/7b/OmN72JX/3VX+Ud73gHP/dzP8fHP/5xfuEXfoELFy7wzd/8zfzlv/yX+XN/7s/xEz/xE4QQXuLRaNNlECUDU6FkyueA2aqIEs9PIpaUXTVTx9FIK1EQmUeoVZubXDQGyxklVscUkaKQoURGmDB1S81bSun1hhfBOIczevNGFZzTza0vBucsYgrjFJFa6IdELsKi0ZzcnOYtvoFhKvSjbrdMFUqxhGDpGt1kTlEjpELrCcYyxkxFJaQ5VcQqJKsksE5VAzlWDscEZqY9t4bWNaQ8EbM2+CVryK6zKtPXm+iK947gBalKUQeNXopzFnXMmVITzqpXvGsc/RAZ0kRTNa/ZBL25tuJowkzzniO3rEUl9FVfJIGZzq5byWr0750Ymq7O0lKhZqGIo6CxaUoWr0pbn71LKWfNGhZ9DfL82uRcyTXThIYQLMa21JwZJ928x6K//4kNoc4p89aAYgiMMpwFgldFRT9lWg/OG6Wdp5v82nvexb0/9Ec4u1qRcuWeu+8i58zh9ct88pOf5L77H0bM/fTrYyqG3b3T3Hf/w9w8/hC3YvZmKnkpmSqWMlOkc7UnBm0Syj5wUslGmwkRFNRldNCQkVtNyNw9E5ylaTzDoFCxSp0hX/MUR1SqbqpwcP0yn/zoh7j73vvIKSp5/YV9+K1GxjeBB97yNh7/wK9AmjBiyVmwweBEc+QrhlwHbhyP7C1a9lYtY6xIroTGIVNS+0lR6e+QKzEr/Xx7NHEwTtqkB5WMr9oW6nALDtd4w3YqDLkQnGHKSnCfUiTGxM7uDk1w9GMklaxKAISaC8Y79XcjGGPp45ZhyuwuOlZtS0FuDeu2U9LYtVKIqbBoAlaEo2ni6X7kUt8TK/idJUvneeiuhzm72/CR9/9rhqHHW4exsDm+weHNHu80ek6KUMUQa8s4FXy7x/kz59j2h3Rhg7UTjfV48RQmrFXPniYpVIpEXFFVQKlC2wS8czjbYMQSSya0++ztX+T6zWcxzZJ27zyUhDUOnMctLKMRuHGdVBLGNhhrsFNh/9QpmDY0bUN1DpFMSRVaTWjYbjPFFqxzOGdwoWAsBOeh6gdULJlaMqlAKgq3tLPaSPkDhRQnhpipAo1xGNGhXOcbrNFoxlorwTlKKJQYSSlhveBE8CbgnCPlqNeqNJFiofUdFkPMI2a+5jmruedT1a3+uo96PRXHzmKHlIVKwnuHC56A6LBBPIbpBfvTq6BeWZ/1X8e6kwN+p75MVSuYnRX1+k3MbktxL+5EKcFiz+7D81+4Mb7dJU8+x3/zt3+E3/un/jsW5oX34IengT/4wf8NO39vl93xU1ALp953maMH7iItX4RX3IF8DWYJv1GqeJhe1/O9b/wE9zY3+JWDh+DvncMd3dS/t3B/e8AvvKVy7n26wJBU6O9e8Zbv+hReMgdpyT/9+LcQPt5x7sOJC0/d/KosG7IdZuD0bajgqf7FRfZ9o9fL8pHx2GOPcffdd/PQQw/xB/7AH+Cpp54C4AMf+AAxRr7ru77r1te+8Y1v5DWveQ3vfve7AXj3u9/NI488woULF259zfd+7/dydHTExz72sS/6M8dx5Ojo6PP+AW3XjAjOi3qIZ3L3LfktAll3w0LFzLLZSoUqGCmanl3Vp11Q2WMqSpIucaQfBvph0pzYkkh5Q00H1PF50niVXNbq1U6RKSdtFq1T77GUOZPa45yhGs3RRTQmyzWWsHAYp5tJcZosIMYQWlGqeT9qpJlT2aYS2xNTjMRoqEVvSikwTpVhglKEkiBGyDMRPU6J43UkRcGKboStrYxRI5OqQK6ZWLLmSmf1tUqFRWdoPUrJrhWpShS2Xp/7NHs+axamWDnqI0OM6ketEFNiOw5MU8RZoWnmLX6ts/x83uZOhZjUi5uLRoIVErEkbf5LwRSwqh8gpcSQEkOMmu1unUbCOasZ0KUyJoXgxVwwpiIyR1ZZoW0sq0XHogtYI1hmJYQxxJTJMZFTxEhh2TpWjWXVOJoZhOaNUGXOYjZ2jleCKSf6aWKKE33ecPPwszz7zJOc2t9nmgb6COtNT7d7llNnz/Ps85eJccKFoLwDEd7ytm/jwQcfVAmvN5iaGYeRfkpsY2aTi3qtraUY3VzbWWab58a7m2Fjwau0W1lqCrLTf0Cqqim6xtM4hxWDRQhGWIRKZyutETpnWDWeIPCJj/06ly89S4wTMSddwZ/gGHgBuHrxNQ+we+4izhrsvFlPGbog5Jw5OOpJqbLqWoLzLLyncdrEUZVSLvP3WauDmz5GjseB475nyoVV27LXNjrIAtqgPvhSK43zt2js1Qjrsed4nFi2Hed3l3gDXXB4YzSRgPn5Q9UsYoTDYcuV42OFr3nLbtfSBacb5FkNMSWloSPKkdimzHObno8cHPLUegNty97pfS52Z/htb/8eSv8c73/3T9MPvQICgyXnyjjBMCb6KMRcidlSWTFEx+6ZB7jnNfcz9lfx9pAQEk2j45cpjRg0jnARHJ0LNLZj1a3oupZF0+GsQ0QTDZqmpesCUFmsLmBdoAzHtLbBB0e/vkF/fJOxPyZOW9rlkp1z59k9e45mucC3Htc1TOOag0uXGVJlmo5IcZgR+oWSCsOUMcaRiyEnHdg40Ti5KSfGKbLdZmq1eOsUDhmcMjNyphr1/TtjsAKuGgweKR5TdWhSSlUlh2h0X3COtg34AK07YXPoJddZqzR7DFYMgqGfJmQmrxtjMRisE5ZtYNG0GOMRPNYEwONdoGtaGt9gKhoBlyulCmDnQeKro15Jn/Vf8xIDTQNnT6sEfdXekaDfqS9ZZdkBYK+vX9L3pf0F0nUvxyF9wbr/HzzFH3vye8m18M83Kx78l/8VP/5H/gT3/LEr7P7iJ18guV+/yZlPpBdFUH8xYLdviBLoLxbe8t2f4o++7Vc47Tf8zV/7bWz+6j3sf+wFwNr5Xxv54NG9nHvDNWpwpL2OJ/+LUyz+j8/yrXtPsy2Bf/7T7+Th/2bi/n91wOLJo6+Om1Cgbr4KSft/VHdk6S++bvtG/O1vfzt/5+/8Hd7whjfw/PPP85M/+ZN8+7d/Ox/96Ee5dOkSIQT29/c/73suXLjApUuXALh06dLnfTCf/P3J332x+qt/9a/ykz/5k//Jn1tj8MZijJKXqygl+sSHKCK4GaY1xUQpYJzgZ3o4YjSWbCZNpc+VqefEWLTZ0SXtSMkbPFsgYq02MjlXRCo2WMQaulal7f0UiaXgQ6fNUo4q/zWVTAYMYislZYZZPhwaoYj6bpeNw9TCplcSsqEoMCwDqF/VGt3ebQYFh1URlSDP0Vy68dQb1VwqoRW6xuq2u2bW6zjT0WVuUkWzis382FW33sG5eTOtmcBTTIyD/rsRo+RtCtZWjOjdSs51hqEZchamVGk9lCpstnHO4IVIpkrBeqv+KjGYahgnlXoHb/CO2aeoN/Pe6/PRF/WhI7PPGVUUGCPEqWCsVfW6QJFMHxOuWKypxBRxTYNUoaQMCFOJ5JJ0aCMQpd6KNLIGGu/JObPeZnIWvNcNfwVi1q3wVIRFN3uMSySbDBzynnf/ax546PWc3euQvJk3Z4kHH3qY/f1T5By5cfUKxlq61Q5SE/fddx9XLj1Djgrxa4PDGDM/F/NrVArWQUq66WaW31trVQVRKnFWUFCrUtMFpBZiySqlRzevwQfGaaILjtZXYiqz4sQq3ZxKEcPBjRt86Nffx733PkDbLcjeYecBAqJKAGplubPHvW98hI8//xTeagJ0P0VSiRxsejofOLtYMOWTU1UIThinOm8sLWOMTBk6qxCudR85s7Nkt+uIRQn2nbeMU9KNdRsYR830bp1hI8LhZs2UVJK841tWjacXIRa1HbTeMI5VFSbW0qfMuh843PZshokzywVNq7nhY8x455Ax0seIiF43pjnf/eo48OSNiXWcMN2Ss6d2ON8I07Zy8eJZPv3Bn+XZZ57AOLBi57VvmX3o4Ntd2ral394A4wjtKU5duIfOR/qjp1gte00lcELXLvS9Ere0XYPzQmaCUqk1Umtl0VisW5DjiBghpgySkVo5s3+avdP3Eac1ZZho2x2cM9w8OCRYYTi6AasVZ17zIKvlkmEcySly5cpVVqtdjdBrAoudFZsb1zBksslMU6XO52nNYKWyWArOCnnOiDfOMGYFNwZUXZJrnm0mdbbUCNY4WlGricXj8Po8iccH0fx0yaQ06bUBQW3w7lZzTh4oYmidx2BxtuCsn6Xpjjp7zQ0qqddLmCYHdL7FNl75EanQNrpZT0mHllX0PeLV/0Gwr44twSvts/5rVmJU3rS7ojbhTvN9p158GWBnCTePsXsLcvvibsGrFcq5feTpr02cWT0+5tn/yxt45G1v4v5/ecCbnn7sC//cWtj5wHPceP1rmHa/TBN4Am372nDnXpFVLUz7BVzl3sVN3nX19Vz9F/fx+l85QqbPZ2OE61s+8N7X8f2//QP869/zm3noNz3N7z/3aQDee+MBPvnvHuLBf3mM3Cawmgwj3C4+xx1Z+kuq296If9/3fd+tf/+mb/om3v72t3P//ffzj//xP6Z7GSd6f/7P/3l+/Md//NZ/Hx0dcd999yEzt5miGzCVxc5wGSqZF/KhrbOYXBXOg8ZzkbRZPMkORph9wPo9pRZy7iFvoWzIZSJWvVFzztFY3bzqPhZKLmy2I2L0qS8JNnGco68mnIe29bQukHNhGBLjTNkNQZuZ4JQmHJzTG36ZVFJc6yw51y1cwJGyArXUby5KbDZW/clzYy0wy1Er1oHmTmWcN3hONkUVsqj82AtT0o2xmRs9qQbrHLlmnKskMcQ4ok+3DiIEBTN5Z2aYWsVaIZVMER2alFxIU56jtzIxZTbDRONlbuo1HrGSwVTMHHWEvhxzfJRClWIquk30eZaJa2NqjRBF5fPGOMQYLEXJ3sbMBHnBed2rN74hkygl6nZMRDdw1hFMVfl6KcScbkHTaoo461h1LVvb00/qeTZG5mM0bIdEpeCcoTOF3D/Hv/qp/57f/p2/h/3dXXZWhm0/cO899xGniSKG0+fvppZKSluuXrmGaVbsnzrHzavP472hOEuqlcYo9zTnSrGGhJCMUEwCIwgK5kMg54lhjqgzs0y9VvW4Ksnbkmb/uGk8+4uASxN4R5GMq5WpVEoGJ1Y3k9bwmU9+lCff+jaWOyu8V2K4FQWp6UsmiLXc/8ZHePRX3kVMGw77AWvg9GrBqZUS3a0YWgtjUWtE4xyTmcgl0zhVKmxnGGCcPb6N8wTvONwOxAxLb/GSWI8DC68y6FzUw78derYxs79asdM4yCofd86Qx0xOheA921H94MEJR5utNm7BI52lVGiMbno308iya6giDDFr7J7A5fWaa8PA5X5kMJb29EV2nGNn3LDoduiWwuWnP8GlK09TUPWIZmlXcoWcBaSS4jGyWuHbPdrlae66+yzT+km2Ny5Rquali2gD6bDKWzAeslpu3Aw9SyXhgyUEh9SEeKsE9X5gGhJFoFnsYpsFm/UlnDGcvngffrHQxIHhmL2mMGx7nnv6SbrVPk3b0nQr7n9ojxuHW46OboJrMM5y8+gqsUTcrEbxQVUlwzjRhoZVaDFSSWSmlDBFI9BqmZjGzIgyMHYWDQm9piHKA5hKVQtIEZJkSoauUUBnqhOxlFntYUD0U2HKGVOt8g2Kxj+Sq2aYI5RqMMbSeIf3jm0cKAYEhzMttmSyS2p+TMrD8N6oFUOBDRiDDr3mpA4xgZQ+J3bmN3C90j7rX/YSgzSBurO804Dfqa+4attg1lvs9WPK3adefJzZTotfLanHxy/vAc61/PePsvz3X/7r6vEx5z448ey3e/gyv0t135iNuMyQnNRU7CDYQfjZn/427vs3PXfd/CKe7lq555cK/Hb4Y9/3C2xL4KefeSvrf3eei+8ZePDGzduaHHA7s8PvyNJfWr3sXPn9/X1e//rX8+lPf5rv/u7vZpombt68+XmT8suXL9/ymV28eJH3vve9n/cYJ6TVL+RFO6mmaWia/3QCUwVSySpVNrMpXAXUelNWtOksVWXpWN2YKtStkOIMJxLIed4M1gK1YGTE1g2lrCllJJeskmbAGYuTSilJ/eZVG+BYMzEnbCk0wdN1hvV2ZEqTNvgDUBO1seSo2+OdpWGYTgBwug33XsFnehxKJlfftkK3pBqMWISsW9qqW6gYK9UXjINci8YYJVHpdDAqNZdKG7RJG6M2r85ozu40aWNhLRirG0JBM3ZT1ptjY7QRbp0nFd3QGxH12s9ydWMsuSoISy3ngrH6coAhNAHvBVuEmDNDzAQqzhmMUQCcszBFlap7awjezDfamqntnHlh2z3HZ02TxsFt+kQVwYj+Pt5Z+nFkKgp3gnn4UlBJPQpgU9pAVa95KXohrLoZS2luRp1hZ9dhTKNDDjFU76lFb8oV6uSwM8zOmULrG7wY0jTSLJbs7a+4fu061gZsWDBOE+PQMwwTp/Z2ODjecvHe1zANPad29/gPP/8vGdZrrNFsdyv6muq5UBGUXWDFkQUKhTEmpqq2jJWzxCqUWYIbU6UvhSIGsAo3pOJqRayjADVXEEuthcZZlmH266Pxd8eHh7zv3b/MPa95CB8C1uoG1Ni5GZoHY2fvuY/TD7yexz/4q0iFVetprcM2wvGYyKXSemGY6q1z5WQrLUNkOw4c95H9RceZRcPRFJlKZqdr6AdlG+Rs8daQYmXMOrU5WK9ZNp5lG6hGuQ/WBqQyvy56zihPoKGagavrrWa0W0tjPctFw9F2SyoFjV2obKaID2EmoRfGYeTZ4w3PDAN9Ab+7z8J6wvoYG6CnsN02dJ3HhgXGt6TYazQeFWdVzZNLQQUhlThuWSz36XzixjPvh9rDPFwIfknNCYNlmiK5JnwQsiTIFWtatIuIUDNIw6LtqLno71EdqVRWOzsYG+j7I46u3YTUkgXSdk3TepzbY9xaihuQIvoaO7XcxJQRKUzrQ0Lb0B9fJQ6Ht7LXrYWpjKSq0MoSJ6YkrLqgthQs1nhyqjTe6lAuK8AypTkLnjpnrpc51ULPr1sRjiT6lMg5YowqWCroz6ui0vOqiRHVCCIaX6csj5M+y5BxmideMilHjNWBSPANYxFySoxpS0xQ8fjOEbwHDNM06pHWgvGBYAzx1UAF/wL19f6sf9lKDNI21J0lpb0TAn+nvsoyUHdXcHATu1mSVi+OhVAF8vk9zHb7NYsze7HVfeQZ2jc/xHD2S7dyxart7fZ1fL8xKjdQfP28QYUdwIz5izbTZRG48i2Oxujnxd/9tXfw+v9X5NT24LYfn8R8+7LDUVn6N9hL/FXVyz7TXa/XPP7449x11138pt/0m/De84u/+Iu3/v7RRx/lqaee4p3vfCcA73znO/nIRz7ClSsvgCl+/ud/nt3dXd785je/5J+fYiSnSKn5FsXczB7YWrShgkpjhUZTxMhUas2QM7Xojd9J9nipiVwGajmCcoRzA00DbeMIwc5bcpVRihhyhVRhzIV+iowxsR0y6yFyvB0ZY6Rt/K0tadPp1nscI/1YSCcAuaJgtRPs+xQT237LZjswjInNkNluE2WWpFaBWBPGa4Or22f99nHMjH0hjScgOqVSZ/IMJBLGlFn36gv3Xk+TanTrOyWFwNWkb7UyS5ZEDP2QubkeWI/DLAkVZl4XJlSyFIYY9aa6yAyrs7TB48QQp0LJIHNzm0lYj8r5reDnbbTM1zRnNHouOAXaGWCcm21nwwyBsrci6UAjuBadZ9EqsR6pTJNK2GtRuJo3lpQzRQrV6M13Gxq8d9hgSKJe/BgrOUFwqjKYpkSaEpIF78DUjFTL0rcsQsMiBMzsPe1Cy07bsWiWLLslbeco6YgPf+CXOTrecursRfb29shpZLvt6ZY77Ozu0k+RU/v73Lh+A+dVOVGrQgPd7OmmqmpCToiEoLnhooMmb40OL5xGxYmbN4K1EmNmyuqZxlhkVlAI+ngxV6oEgvP4Wd4uIkygFHajG0eRylOfeZTHP/VJpmlSgnopM2n/5DJd8W3HPQ+/nsWixQdLRRhSZhHUDz7ESCkFK5CoRD2tONysubFeswieVddgjKF1Sv4eU741sMq1EmdJcKFwuFmzHScF0DnPXtfSeksqaj848cZb0ffClBNHw5abmw1jmlg2np024J0OOdw8zxtmQv44RQ63PWNOPLXe8J6rBzy+6YlNx+rcGcJ4iDl4GpOPuTkGDutZ+nHEGkNwntVyn1PLFcE4KnWe9Vis7cjZk7NhHEbSuGEcNoh43BzP2AZLzFvEQ3EJG0BcJVdlRgzDxPHmmH7c4JwneMv+3j47e3usdlaaUmAadle7dO0u1e/zzFOPsj7sKSYQx4m7L17krosX8CHQ7eyzd+osjTMcPvcEVz77GFeeeZYUM04KpkzEccPRjeepucxsDlXJpKTnWipCP04c9wPDlBgiZBHGWEi5QrWkaEhJSBmmVKnV4G3AYBlHfS9OaWKII7VmnDkZ2CVqLcQpUYsODDMwpUiKBes8q92l+uGrma/z4Jy+9o23+jqbpHBPU6h1opaBIR4ypp5UR8Y8kuuEWLVVpJQZx0mf8zGrRzwXclFK/Kuxvt6f9be9xCDLJZw/Qzm3T73ThN+p21S19UjTfEVxZrK3+zIe2VdWte85//7tl6eiz/L0b4SqBkoDaVEp4fObcNA/P35gcev+7Nb3Wcv64T2e+K/hR3/w33LWK0+gWU6UGdp8u0u2w+0b7tyRpb/kuu0b8T/zZ/4MP/ADP8D999/Pc889x1/8i38Ray0/+qM/yt7eHn/0j/5RfvzHf5zTp0+zu7vLn/pTf4p3vvOdvOMd7wDge77ne3jzm9/MH/yDf5C/9tf+GpcuXeIv/IW/wI/92I99ZVPwonnQKRWMJIy1c3a3pVT1W3Miu60VMzcJJw1MpZJzJk0RayMlHeIlEZzKvKsVpHptKqdKUxwxJWLJUDTeqc7ebcHMTfQsAZ8KUxznRlk39TFmilGppHMKIBrGEyl81QVWBWolZ7BW/ZSNF7yzYKpmWJfKEDMlJZy1eGcwweBDZRxPBhIFsWiIWUF9mzbN/ngd1AkyR4G90GyKVSvJOKpc3ziLNWkGbVXGeDLgUM/0qd2GPk3ElCigHnNOvPN6URmGiBFh0bTUUohxIJZMikV/htWmJ5pyS45eZylo12oAvEKtPDL/vupHAGbSvRhH1zrW24FaMyF4jaejkrTlubXVDr7SdYHgAsM0MY4abZRzxNlAFxqiRHLUQYlUbYAV5JfxLlCz1Y1xHkglUq2+ZkpnLmSbacKCJjhKTpQaCa7wzBMf5OYj38a9993HRz70UR5+3UOcOXOKT3/qk5y/5wFOnznD1A94JxzdPOLs+bv4nh/8/bz/F/4J169epR8Txlha51C0tA6GSqm01hJEt6tSDVkqdfb6p2HSLeFJ1FvKs2y3MKUMteCd1/PCGt3uVajOIlWjoYxxCIW4XWOtI8XIhz/wPl77+jcSmlZfe1E//wl3QYCLD7yOsNiDzYHGqmW1Liy8ZTMDCK1RS8TBRsnTajvw7LQLUh3JuZBKpZmHKGOct+IxMqREY6DvB7qmYW9nwXYcAQXwNcawTYUxZVorVAXhk2vm6uERy27BbtfSj3nOLTeMqTJMCW8dh31PomjjXjNXjo+5MozcGCYILbsXzxHGNZvLT6uEujnHdnEXNl6Do4+zaVfsLFratmF3tcPB9WOMUT9zylCzxTlNDqgF2pAxtce4fXb37mLqn0VMxgH9MDKMI3t7K9oQoC+zjFuwwQOJbDJFZl5DLWw3WzCCb1p8NgQX6PZeQ21OEW4eUiWxOLtH0zRcOHdB7QxDxLpAHHsuH12jxqowyU1PMdfZrI8p1bKzu8ulpz5NkYS1HgMYa6neaKTjbCORXBn7RE0WjJLzvdGBHFVf71RU3ZJypRbBegclk1OkFgVthmBpW1Un5DJfC2uZtSyaL16ybsNTLmy2G1KMGLF4sXhrEQNTHuhTIkaY0jRD33RDb1ujiQ2lMkz6PIp11FKZUo8zqrgSC8RCiYZkMsWUE9nPb/h6xX3W364SgywX1GVHaV520eCd+gaturuEK9exRyNp/8WDrdLZHezx5msTZ/YSyj/6LItHHmZzz5f+uuLAvjpnkVqiRPTi/tPm+z/+usMHDTuf9ZghMVxYcO1tgfibj/muBz/Ife3nb75/9A0f4Nf/0n1c+789wM5jh1/kQb+Cw8319kLamkC5I0t/SXXbP2WeeeYZfvRHf5Tr169z7tw5fttv+2386q/+KufOnQPgr//1v44xhh/+4R9mHEe+93u/l7/xN/7Gre+31vLTP/3T/PE//sd55zvfyXK55A//4T/MX/pLf+krOh4Rg2TdVGnTXahVt87VKEgMQRujmYxes0Z3IXPgbunpx2Nq6YmTytP39hsWXQOo1DaXTIxxBrwJuRYsGvUUk5Kgp5hICXxj6BqPMUbjw0ompoJ3jlwipVSsNbrdjSoXDV5zqqmCqZbgRF3ntRJao17xImzHke2UECk4Y7Fefz9jBGccjYVgC2OKxKkSx4oYVQnkAslB8S9kpJeqck1jLE4c3jFvNjPGqb9S86lhM2peMlaJ6U0wWCsUEoZyC1ZUKApq05aEPGXiqJFordf8cZHKVApu3u6rL9yQqyFHiFnjiOa+Du3uKjlN5KgNbhsc/TBipWJNwPuAMZWuemoWutAwTBObMRG8xzuhxDSD6TQTOzErAKRinaUJgVot1gjZCMaZOaKt0jQNwVlC8OqFVoc9vknEaYQiWAk4q5u5mgvTNCFG/f62gAuWIR7xSz/3jyjT7+K+++4npcrQH/HkU8+yf/Yu/N4KQuCuu+/hxvXrbDeHHB8ekPyCVHVI4UQVAM5YatWYNXJmKJWIbnuNhVg1JsxUZRBEEWJMKh+jKhm+6PvBGqegPBGCNeAdq7bFuoY2eIJvySLcvHmAnUalTNfKlWef4OknHmNvfw9nLc5oNrRUbWxSSrQ7e+yevYsbm0PE6DZ/nPItpsCQC0YKh+seaywXdpd4a8k6Q9IYspIZcmbhHX1RD/DCBFJJbIaRc8uOvWWLiMNbx8IreTvXSusdmzgxTBPdsmMsiX7oKVWHKssm0DWelDcMqeDn1z3VSrCegjClREqFZ463XJomohh2zp3lrgv3YWWP46d/jdicp/fnMGaDP/p1JCaS7LI193K0jayWga5Z4MKKmIVhjGrtMBbjLCEIlEzTREpSyOGw3ULVIYAzjuXC4Jxn1bWkOGCDnms16nnsvccZTxwToxWOjo8JrsOHFmc8wQm+28WE0xwfX4ec2Tl1jiY0XD+4wnt+9Vd45JFHOH/2NJevHqjyYOcUB0dbll4YxjW0DlMzi90VtRwjdqJzLaWMCvZLE8M0Umc4m4jFh0AVYcqRxi3omoYcB2IpDGmaJeEz+DFnfKhYsRplhqNpGm3enRDjyJQy3nul1wMYvS6VVMmpYIyQswLWnECVzDTHEOaqPAC9thiMOKqptD7QNRpvVo0lpwRZ32dimEkgojGIxtM6hynzoNVkxEB8lWzEX2mf9V91WYt0HXVncedG8k697FW9xSwWyLUbmNWFlxZndnoPLl99mY/wJVacOP++Y568sPslCenVVUoQzCtrjnBbqjrIX2D7/cUqLSoHb15y/ZsL3/FtH+O7F1exX0RW0JrIA6vr3BxecxuPePaG30671OJrR/d/RddLUC5IfUEj+qqqo6Mj9vb2eMe3vR0vEPMMUDMGEYs4q7nLonfyOamcsFTVfgsFW0dEJkQSKQ/Umii1kqNmL3etZs/GlIh5nLeGFeesbqBmiXrMRbepRvDe4qySzLdD1k2f0w2zEaW1g26VaxV8o1A5ZyxWRLPHEUIwc443GGtw1mKNbt9TysQxkotgnWGaVKarJHSw1lGkaHzWpFtcKwoSW3aeNliGmDRmKhfGSW8sQ7CExmOsqNQ4ZbomYIxhipGjftTjMQJZ/dzOzzA4ozTvKY3EpLnjVoQmaGZ3TJUQPEYq8IJvxjpRZUFVL3oRvekdpvnGnDJvdxXO1DXqzTVGJe7eeYyBGBNDyhpf5yy5Fqi6YY1Jfbcqvy4YNOrLiMxU8ELNVYcRxlDnyLM2BBqvgLqSk/5cMXNTrw23MULM4xwnVzHiscYyxkQ/RHxj2OkaVk2Dn5UMpVSO+wWn73qE7/yuH+DChXvYP7VPvzkiponrVy9z5vy97Cxbrl+9yi//259huXc3b3rz63nfu/41zz37NME6/fliKKXo+U9hM0WFjFlDnxRidyJjr1SGVMgxEVPS4VTOpFxuvR4a/Se01mCbluXOHssQGMYJFzqyVDabDcPxTXKckJnH8MDr38z/6gd/H6fPX6BpO0Q0MDClzDCMPPb4J/m5f/j/YXfc0AWdDwYjnOoCR+PIteMNjXUYpxnRe5366rZjpAmBrmu4frTBIOwExyZFjoaIt0I/Rry1nF12+t+p0HpPsIbtOM7Nm+VaP4AYFo1jve5Z+MDuIrCdIrnCatFyY71lOyVW8zGuYyL4wMFmzRNHx1wdJsZS8asFu51nWQznz15g68/z/JVDpu1NyvpxTD4Gt4PsvJFiLRw+SieFi3fdw2LRcO3GEUfHW4Z+i7WWNPW4xrHsMuQ0gw8sm1FoQsOZPUsbJsb5eJxYnIXMCKbST1ucOATRa5MVgmvY6Vq61uOcpwlLgm9pFkt89xqKWfHkYx+jaXbYPXeRUiLHB1fZ3T/N+XMXec39r6HvtxzcuK5+4MuXOL5+QO2WNG3LtN2ye+4C1698mJtXn9Dhp4wYA7kI1cAU44lxG2c93jpqzreI/t46Yi6st1tSqTjnyTlRcsZ7R9u1WCrbOBHziFBwVuF5Oc8qAJmvH8ZiqqVkvX5ZZzCi9HVh5ng4HcKlkkjJ4qzKkTVpoqqVxlTGGDHWA4UpJuJMevdem/a26ci1YgrkWnHGAKp62U4T//6Xr3N4eMju7itPZvobrU4+63/H/h/EyVeQPW4tslhQV90dwNCd+pqWpAJXrsOZU8Rzqxf/fbnin7hEHYaX8ei+grKWq7/rDRy99ov8vcDpb71C6xJX/s2XWZ3/BikpsHyukls4vv+lf3/uKr/7P38v58MLEL6heP7uh99O+LQ2tdOpwumHD/itd32Gd/29b+Pud30RwNtLPfZc4fK120dLtxYunKW+yKHSq7lkOzI+9in+Lf/iy37Wv/p1V7UgzqtAvGjTbUwll4xmjGvjbaTiJDPlSMojcdyS8khoBB8EM8sJQwCsxSCEIOSifumcVf7unUPlrpY6P7a3IMbijcE6NeYPRbdxMYEp4IOZj4UZGHSyadbsbGOK5l5LJUclKK+WntA4lWRWIUf1oDtjZ++1bs2rqNw+IwrxIpFrxnud2JxIxJ2x1Cz0fZrl+zOHzGRyyfPNLfRDJOU6byQLJSeM0S1rRf8/Umfas4LfrBPGaSLl8sLXVo38so0SlKUq4b7xuuYuReWkCJqNXsqtaDbvlW4es4KznAip6nDBoA18zTpk8M7OmcSJvhSCM7gg5ARTUkl+0+qQpGRDnaWvqSoF3WLVHw23NmUxVoJTwr4UQ56HIqlkTBbilDHW4oNmXueYGcZI1+qNecppzizWvGLNmS+AwVtLFyLT9oCnnvg0p/ZO8fwzN2jbDue9esJLIhc4dfYC3/Sbv30ePhiq9QSrTUMfMyWOICcyWSXbW1GZvxfwVu0SKuvWRrvM0nEjQjVGh1eAFVg6S8qJXME3C1zbQc34plFlyTgRBGg7NinhjLIYnn78UT71iY/yLaud+Vi8KgmmyGc+8yj/5F/9FNdu3uB1Vmhmz3kqheNh4GDdQ7Ws2gZjhD5XUq7stIEhqmJhR1qc1ddtykn9uVNif3+PnbbVzXKttGJ0MJcKjdcN/+xewNbMzU1PY3c4s7tUeCNCcJb1lOinjDGWWEb6bGicsB0jnzlc8+x2w3FMuK7jwqk9VjlBTNScmPotnR84ff48h49+jFgscecRaHZw28ep6yvkalmHu7i5KXQL2N1ZcvPmkQ6Dpp4qEe96Sk4IHi8O4wxNI1QixZ7CNANODik1ErOepz5YLBmpggY1KICydYGuDYhxer2JESMJZyKpGLr2FEfHB1QMi509louWzWZip+sYtxuev3INjON1r3stx+s1KSa22zXrfuTC3Q9Qxs0M54tsj69SmajVYLyhSKT1K6Y4knOGouyC6iClNMfcKTQtRR0QFVQVYE1lSplMhZJxNRNTYpgGxGSY4+aMcRhEI+PmlICSmSGGWe1IZVbbiCo3mOPUDEBxGlfnhK5rCAGmaSIVhX7aOaUh54x3jQIix56SqoIwi8L/plQQMWo3qgXvLSHcyVf9upcYsJ8jQb9z43invg5VncHsrKg3j7C7Hbl5cYOgaoVy9hTy7GW+FnFmL7py5tx7rrO59yy5+fxGsRo48y1X+NlH/j7vH1f8yV/+32HGr9Nx3qZqDypnP7TBfvpZuHCWzV1n1A/+Esr2wr997mF+7wO/DsBxbvlHv/hbefh/2mCGWZ4uQvWB/+X7v41HfuRRnr78OvY/dvOrPn7px9u6DZe2vXMt/QrqVd+Irw8v49ugvq9qyNYqTCrq9hMqUnVjbagYW/Eh0TRQS6u3gDVSyEDG1PkiWGCMukVVGbnF1TLHU9lZ1q0xSLmqVLyayhgLYi3VCm1rCRVKEkquIBUThMYrfMy5OcJr3o4bsTDLYSsQU70VA1Qpt3zhRsqtrbSd877F6NYv58pUIqXqDasVoe2c3vSmQp7jgKw9gdNlDIUpKaXcGIP3QaXPos2uGJWTFtFGOecZ6DVLy701pKQANeMMOVWcQy3cpVBqncFi80DCzdvoUSPnqJVMmcnGQkH99lI1Os0YJSc7a2i8ZxqT5k2HQK6aibxoW2rpOe6z+tdLmIcxFeMrxhqCb2C+0Tcl05xI0UulWs2dN7XOzYJu+8sciwRCTJEiwpiSbrYnQ5sDItpMaBRV0jx4b3UoIZXNOFElYFTVD9UwjVtKfJaDG1fpFoF+UIuCr5mzZ8+Sxg1Xj445df48XespGdqm4aE3fhPPfvZxvJ3ZG6J2gImCKaLbXzFUMlVEn4NSsQjO6Gs3VKHMcEBjhMYZrLXYktUjMFOmsV4VEdOIFdSGEYVoLGampKsVJDOMiV9/369y7/2v5fzdFmMzY0x86tEP8Q//+T/iYDPxhte+lcX6kLi+Qi6RcUycX3Wc3VkypIIAnRPGqtngFYX1Tbky5kzwnsPNEclauuBnP7JmgMeo8XHl5NwqdY67s6yHkSFGwLBatAqhcxZRPwTeGPopMcZKE1RhcDxMXC+Jxw+PuDpGqnecuXCWM97RpkLOCbHqdd6uj9ltWhZ7p1iffQvTMEH/JHLwMcZkyM392J37cfWQOB0wjoG9vSWr1YLNMJI2xzQ2YU2lYqBmsghNcHhXqWR29ipDf0wqEwaDtZ5qMsY5nDEsXaPQQjEsNQSb4Foa63SkL56Y0hzJd5oxJY5vHrBY7RO6jloybWhodne5cXjE+uiYK5cj58+dpsSeYRxx3YqxHBHjyLBZ0672yGlgGNeUonGIet7rNaltPGOKjDGyaALOCjHOAMiSiXp5pgq0jccawzhoykLMFUxiSj05zzcSM9dDxKl6Jqdb1w4oWCvkorGFIlajG0VwLmBtVWYGhYoheN2Md60C7QyCVDB1HjZiZwAh+NZRcqEW/d1SjWAdXhyZDTFXTBEEIRmFK96pl6FOn1I7UCnKt0hJL4K1KnTS2TlWx+nmxlmKvf3gozt1p15KlWWHbHvMjTXlwt6LjzPba/E3F9T1+uU9wJdaz19l/7HTXH/rC79INXD/25/hn73hp1iZjnN2w7RXaK/8xmzaXA+nPzGx+PAz1L7XP7x0ld3Pnubm61/64918/DTX7llRqvD/+xfv4OGfOUTi58DTakWmxP0/fcijh29g53P/7iutAvV489U/zudUXdwZMn8l9apvxKtbM+ai+dQJEDPn2Fq8s3oDZQSHpcx+2Zoy3dIiLhNjotaEoGCzsUDfJ/UYt7pl8q7SGEfwDkrFz1vHXAq1JqaSdRs/b3DzVPAe3cxaR8mGmCfNO69AgtZbnDfEJOyu9KYwR+i3hbazOIcC4WIiAk4UQNe0DlPLrcc6Aa7VKuQMU8w6XcsQo26EQjBYq95Gg94sn2R/dy7QOMc4RcacSVGBXtbNG6UyxwLVqo2qKBHZVTBOBwCpVCRpZjsV0qhNlYghmReacGtkhsipd36KKt2PpWCs0oxLUfmnVM1kd8ZgnW4njTFgDKFR+FlMCvUSHM55lp3+nClltmPU5wTdWCmwTyO+QlDve80wbiNTqrSNnaPBLKtF0E2Y0WPItTDGSKoq7zezINs5g5jENGScMwTXEBqVB08x4UTp3sH6OY7N0DSWNGZijghHPP2ZX+djH389j7ztW1i2gZs3rkOF0CwouWfcHrO7u8vVS5fJOXPz4BoH28jeMrBs1KPeT2lmF1i8c7QWjqbMWPT5LLNHFlHInoiSuq2zNM7gDZB10GOsIxiIOSEp6gbyhLHgPabp8DaStxOGSpz5DBXh2uVn+MiHfo3fsvjtGO/46Ec/wD/91/+cTTZ8z3f8Lr7jHW/n5vNP8a5/+DeZppHgPIih8ZZYIM6KCYOmEGwnVRUMKXK43pCLQrMWTcNO61iPkVISUnWwpGoKQ3AwTIntFBWSlzPLeXDQp0zJGcFpY1c0CquQ6aeEuIZNyjxxvOb6OJIxLPb3OLfq2DdOI8eMoeSBkkdCu6BMI32/wYfrdLvn4Oav0I/HDO4ezP4D2HyTZvgUaewZpKUfEru7cNf5Mzz+mU9RYtTNfWZuvg3eW0QKIg21JjaHl/DB0jg3U+kTFkOteW58LcEqGd+Inn91ztcGg3cVI4FSd2gW54jjGt8f43YuYEikKDRdi9TKctHQb44xxjJNiZ398xyunyC0hjN3n2J94wrgWezs0N/4lDIanGap29qQ8sB62GA8DFHPTR2oGVKcmLKwaIMOFZzDVoPUgmCwrtJg8d5QysxvqAU/R4MpxK3CbDXx1lNKplIwVd9n1nhV+lCV9+BnCMWcZkBVqkXwdravaBxlIjPFqDFCKTOmgZwLMka1rQRDETBiCc5jiqXxC+o40A+TphUgjOOrwyP+Squ6aCj2P4K8FYVoVpH5Nb9Td+oVVgbYXcGNQ8zOgrx8cXT+KpDP7WP6/pUVZ1YL+++/zOGDd5GW9VYT/i/e8E8Bwx996rfxrve/hebwN94b0iTY/Wxm/wOXqQc3Pj+iK2fOfOCAowde+lbcrYV/8p7fwpkPWF7z4WO1LHyBkpi5+9/cnvgyGcbbC/xzjtp8BdagO/Xqb8SNnW/KLEoqr0WzbI3Ded2YTSnNm++EFEHcicQarC3EsVJEfbGUTNsYclIg2zRkJiMsOwUFFalMU6KK+ZyYHhBjKLlorrXTxpkKw5AAleGCQtMs2oiebHKcM7oxB0wA49S3LAacK0p4NkLnBRcM09zoOuNu5WDHmNUX2qpEfhwLItrgzgy1OWtb2G5GiikIllVrECu44CF5JTLnSentxuCDkJLC5Zp2brSTfkgYI4gFZ4VcKjmprL4UMAacBRELNWNQCXSwKlFPSWFtmrmtTbY2iBVjqt4kU8hRCMaRUyaTSSbjXUWs8upzNS80m9bThkSd4WMVJfiJ0U1xLoUpFvWKG5VrVwyd9xjR3PCUE9bpUMX7hmIz4zCibQLKG6gauRYah7MzAd+qP9VblQIY6xnGCY8QPEjVoYOhwThVT6RpovE3+fhH3sOia7nrwgV80zAOPXEz0HWBcRxxboltl1Dhta9/E91yyQfe8x9oXGEYCyJCazQtoAr0qahiAZm3vpWxzHyRqltiay2mVn1eY5w9tJVpUo6CFUONE5v1EV3XcDwMuAoe2PY9wzBSxOCCgZzxUjkcJz70gfeyd/o8l29e4Wd/6efwYcXv+f4f4h3f+q2sdnZYdS133fcQV558VKPASqUrhdYZplrJWWjcibe/0HpYDwPOWM7sLhl9ws4qgMYZhggVy6JpOO5HUim03jLELcf9wJmdBauuxQLWGLzJOrir+vumXEEK1lgOpw2fPj7mUj8yFFidOc/KV8Iw0UwV03m6nQZK5ZjCNAwY0Zi48fiYdrFktXOW9eIeWATccEC6+n5yrtRmD7v7JrL3HG2fZWc7sre3YH93BVNPQgdo1gniG4J3xLglFm3wrJk3vFiMqzhrNVpvKhjvqBIxxuG9xRSv17Ogm8GSdVsbmgWrMw9zvO0Z+xHac+zsnyPFHpFCLoZNvyYOa8b1Dco4sdmMvPFNr+O5Z57k8PAmY7xBGS1tt0OJh1y78ji55Dne0eG9IdbK2I+YEmidR1BVzFG/IZaCFUeskUKi5gzikGJJVRteY06QaMr88DYQGo9DiLEwxUgpYL0jljS/ljDliGDnRIxKrhErKEEdvbZUEWLJTClhfYc1Kj8fp5nI7ipTSXMsmr7PU5kw1VFQ9YTGWhRwDhM9zmbEREotGjcZf+PdgP6GLXOSSHKn7tQrt2oXMNsGe+2Q0p1RxdmLqLLw2N0d6o2bL+8BvtS6fpMznzjH8/+Z5Q3f9ln+8cP/gkLhv/j4f8lz778LX6Cayoummn2dSyp0VypnP3ATeeayfi59ofoqtuLdM47iKtfftmL1bKJ7dn1bfOBfqCRmuHl0ex+z6+4MO7/CetU34v2YyFKwpmCl4q2jbRxNCIAhTpFSJuoMlRJ065lLIk6JnCKmygxYy6SSSVmbNyNmjmFSunRoDU4cvairMdeqUKJZBh6zyn2Dl3mvZ0g1k1OmVvVCW2PAgLO6lVUCuCF4wzZHgjO3NtDGQsy6UW2boLLKMjf9qVLmuLNcC7mCqToImNIcXeT0Qtj3mWEoWGv0xrMUmka/boo9bWvomgaytps5aWMtFEyZG24RwkzoLrkgThQEVmBMWXO4vSUKxFJogjbY+RYETTd846Rydt1GoVtIo7AkEaEJOsRo5gi643GiT3H2ZFfazqpM2RqMVcl/QYnbtVZtYErBVY2L66eJVJRoX5h9+mJwweNyoZ8ymURjA9g8nyGFnFXWbUylaR11gDyVmZysr3cpKottWkMtmZzVN2+NwTlDGzzbfmIoCSo0wVNrpvGGvZ2O9WZkiIdcvfQox+tv4eyZM5qxPU60rWccR/b29hCBZRtYHx/imwUX77qX7/ju38lH3/0uYtpQkzAVlTAjQFJQXjEwVohVs9jnoCiC6LGnlNXnaxx+jjzrkw4xjID3DnENQyrqzz+8gcmZWgsNKkXOFFKFPhtSTVy9fo2f+6Vf5Pq0oV2d4nd/7w/xm9/6VlarHdq2Zdl1PPjWb+H6M5/GoNvoGCvLxlJzZYgJ66DUxM0+slcDu22rz6loSkAqhVygdY4xRYaY2O0CyMjBsGUb1daw02i2u1oVtBkU0fixzRTxRoi1sJ0mnjne8OmjY45SwbUNZ/d2OWUNdb0mJY3t8t5Q56g31ywxrsWJpiyk7YZxs6bxVwg757DXPsTx4XVKaZD91xF297GbJ7BH11ntLJnGkVIW7O2fZ7PdUMYBkcJ2GoFEFaXspxLpVg4XNGXBGodQMdZScsaIoUom5cwUBe89TdPiXCB4hzNCHyNjFnZOP8jO3h43b1xnPfbsnjpNCJblznlqGpnGkVOnT3Pj+oh1jrZbEWZqfmgD4zbhzC6mESDx/Gd/nWE8YtG1BGsxtjCkLSllnNXNvkFm330mIfhiWLYtDkcfM0Z08BZrpB9GWh80F75kmuB024lQiqEYp+oYPLmonceZgg3abJMgo5nqboYxNt4RvAbBD2m4BbNsnL9lmbHO4pwl55EpKeSuFlVJVDGqViiVtg2YKkxpYMoDkifIQqpJr0FWaBpL95UAxe7UnbpTr+qqu0u4eoA7HIinXhx5ugrks7uY4/XtA27dhpK24fABx//5B/9Hfnh5g6OS+L6P/CEOPnjuVtZ4NVAtyCtomf+FKhzB2Q/3+E8+8+U3yF/FVjx3lWlPcD0cPuSRvKS9tLn9zXgBuXF0e0npYu7I0r+KetU34ou2nRuQAiScNVgLJWnudaGQq0oXLYY4ZbITGuOoNesWvWRKEd0MW6M3YaIZ4nlMpFIZiQgV7zxg8UYj0qzoY5eq0KBNTqQMzgCmavMaZG42zXyDehKfVRjFUItggmW5smy3A6UorVzmQUBovMZpJZBSqXM8T7HMv3ehCeqI1818ZpoyGP19Uioz1MzozS0qk+4amHKipkpfem0vReiCNrO56LBhioVCZhW8Rhmh8V5TzJpBfgIHc4ZFsBpHNEWNsBJmCFulMQbvwDuh8YHWgfOTyuSL5pYbRKnQXjdVi6VnnPSG+CSCzs0S95wMRfIsAY/kWpBaGKeINY4mOJrGUKeMoDRwQfDG441ljAUvwmq5UGheSbRNo9nm5kSmmjHiCMbilg1QSOjWPSdU9prrHAGmEXEKgzPEkoilELynnW/ut9uJRddixbFsDf2UmcZD/sO7fpru+38/9919ka5rcD6w03TUNHFw4xpXr13He4+1G3KGM2cvsH/xPq499nFSVYm4VB3igKoS8hzpZ41Klr2TW0MWRNUVthamCFmY9RkoUd4YmqadY/SUF5DjiAVSyppPb60qMrBMquEgLXY4mCKrbsX3/effwyNvej2r5YK2CXjnMNbw4Fse4aO/9HOk/kgfr+p5YgQF5U2FOGWsdaxCMw+kdO35/2fvX2Nly8/zTuz3/i9rrara93Pre7N5FUlZoixLtOyRrMS0NfLEduxBAg2EILGM6EOAiQFnksCB7YG/xLBgO4YdYBx/MGDD9qcEI2QmhmyNZUszEC2LF5ESr032vfuc0+e2b1W11v/25sO7zqFoUdRpskk2W/slCPTZu3bt2lWrVq33fZ/n98QuUEazeUzFPuGnUjgZDfw2psyws8P+0nz1tVpDN05KqhWlMuVMU6Onv3JywudPTrk7ZgiBg6uHHAWP24zUnB8MVbyz955zsFgsif0ALVPHNWc5A8r5yTFxWHLYOa6XgOy9lz7uIOsXcDc/w+7eEVu/xz2epJbEbirs7R9w7+6KaTMRWNCax5WC6yoxDgSNOBrbnKApIQSG0OFaYLnoqHXEchYi4jE7CaZoCDbqgOo5OnoXh1ffxbi5Q9uMLJxDpi11uSKfnTBuz436WRJXrzzKzmrFZjty787r5KefouTCYmeHph7nhdXOgu2Lr7DoA32nqGTUeYIMFFnjRVgERx88zkMl0peOXCoy+8L72FNbQh3UMkceDiZJ577vuumD/G+HvacUo6uDQm4WPSge5zzqLGt+CB7vPE4sRm8zTWxyRsWYAgaMtGGpawrekjK6aConxBGkp6KItzi2nGZPumTKtpK1EXxASyO1hlfPInYshu9gRvZFXdRFvSVLo8etlnDnGLfbPzT0qvYed3QAr9/+1j7Ahy3v+ex//RS/8b/4O+y7BdfLhv/0E/97xs8cIP9RT6nhrduI+wSHXyzs/MZr6NnZ7/0D9+vGLXZfPOLkPW/8d9YF+BFw1ozXxS6L17a4qbxpDbk7OX/zafv32RsX9Q3V2/6Zy3liWHgEh7bwwFssYt7e2maQ2gxaC72bJbrmNw0S0GCS5DyDwWIXUAnmme0CWiqlwXqquFFxwZv8PTqCU2IUavWA0HeWW5xLRdXRBbvgu690EcyfGrwwlWYTTxpoNaBWcKC23RSx4UBtGdFgOeWtWgOf7WI1OEHUQWfNAjS8CDvLzjbuCjpA7CwzPKU0E81tCxRDQDo4XydKbnRDwAsMnW0brbm3ZnqaLPIsJ8XhkIBtm7BtKg2qKK1CEPPUi4cuVqZUydW8nqgwjoq4ZtLo1ui8ZzUEIyiXzHRf1WRx1iw6zzQ5G3iIKRjA49Q2vaUW2/AGz6LvbHigRg33vSe4SPAGX4sSmPKENqWLNuRopc3HUCPVyX65Y5YtC95FA/c5A5nVVkzmjnlGl8OCVrOB3kJHakprjr6Lc9xZoFVrYrfbiRDs+SleyXkksuaFL32O97//A5zeu0urlfX5OcPQ8cSTT7G3f8DJ+chquZh95IW9S48iX/oC0dvxGZzMGcmKqknt3X2/P7Nf3Mz6LL35cqesVIUpW4PjvbeGXBzn45auTPRdYNUF4mqHsh1xnae2PNOuHc0JWTNn2hgWEZ1OuLZ3lScvH7HoB2LXEaLF2zkR+uWCIp42xz6VpoylkmrleL1h1fdc2lkwzl/fix2q8/uzmseducn0Itw9PyU6T99H6IVUK7t0Bk+slU5sy1rUNsqQuH56ws1p4sZ2Iqmwc3TI5WXHqkBQZfIWWxi9Ed69NhvuOW80bwpDFLaTaW0QR5q23Lr+MvAa2l3FTfdwp19AW4P+KsfxvZRQyHc/R2rnhPIY165d4fKla4zna9ImIy4gw5KctrQwMXTmJ2xaLf7QKR5TDLQWbSOupmwIzo61ITpaaaSa8a6jWx5xcO195DLy2ssv04V9dvZXnN66wXR6QvYd+5cu2bmyKKqV8+NjttuJ5XBk9P/OogNPT8/p94+QukH0nBALXe/NWqPBoI8uMqx6FrHRzQqSszERfGQRFmy2GxQDIk4ZpjRRjDz4QKlhypuCc4pqphRn8Y6zDt2sKbOdYqp00YZNpv6YlUytUsWTamJdEuIci9gTJM5JCRhjwBljwKLU7kelmf+mqiVuiFRqMyBl0zpnhXsCkeqUSIHmcOrhLXrheVEXdVHf2dKdJWxG/J1z9OreQ4PbyqUd4um3oMH6Bqq983H+yZ/8R+y7Bf9+rPwXv/yX6F7r4Gv0kc0bxPZrfe87Va7C6tXG0cduw+t30DdKpa+Vyx+7y9nTb3wr3oJSB9uKtyicPhlYX9theauxfGWDG/Mbeyz/UclmQs/fXEAbgCyGC1n6N1Fv+0Z8yhl1jaGzC/1W7UJOolJanbdg5jlsreHV4Xywk4PMMTjeLgA3U0JbY+iVrhPbQtdmoCFkjvSyTWEfPKlObMdkxGlxKAaXYiY+m6wdVG1LE3ykUmk108RTtZg0U4TaxEBkwZNGu7DfJvNCRudY9Cbv9t5RZ5+riBGxRRqtWj65gg0ZZk92rUr0goueXGEzFnwwoJ13HlW7j+XQQ+dIuTBOmRDVIsZU6aKdSGuxfzdVVBqughdP3wemXGapesM7x6rvKHP+NzjGZieYWitNHDEExrGQaqU1g8wte+ijZ6oZqSbz1GbPXXTmeR0nyykvRfBSGDqPukAQa+qMKm8Z2ylnew0UCNVkveLohwiibLdbnDfZdQhGiks10Wav6qLrEAy+VrJtvTpnaojgI6qWXx3DvK0vjs1WiNHyydU1VovA0FnWeew7PJ6cy7xp9QxAzhUvW66//Hm++LlP8/RTz9BqBVd57stf5MqVR9g9OOTa1SXjZksXAydna9713vfx0nNf5LVXn6d3jtoqMdhzURXUmUrCKPGKM2IbIAYYLEb9z2q0/CEIKTem2vAelqIsg0NTRrXQOaWKEMXku7lUJDhis4i52grl7C6LWnj9pQ23r1/nyaffgxOPiG2TnRP2Di/z1Ac+xHMf/R8oUW1IkwuxcywWHV3wdNEzTWV+35rcX1HjD3jP+ZiYUprl5hCiPc86KaUZfyA4s4tM2eK+zlMmaePl8zUvnG3ZNqVbrXj0cI9LqnQqD+TIEjoigsVDV8ZS2W42+Glib3eXRe8YU2Y7JdRFhsUCsMHBYrVi9/AKt6+PHG+OkMOncBTarU9St8eI86Tlo5yUPQ6zslit8P2C7XrE+wpZ8NIRK2zJhAhOCwSH1mBAO2eJCzHagMc7YWdYsjP0BB+YysQmbei055En30ffL3ntlS8xJc/BpUP2D3dIx9fZZMAHvIM8bRn2LuODf/C+LVr54hc/wysvv8zJnbumgqgT43gHdVt7TZs1tE2V4DsO9i7TR8/d49eppcB8HebmuMDNtpBKMrI5Zl2JTqEopWVidMRmAy5RU5tozYhWA2Q7k+m3pjgX2FsNeGfxhyUnyqQU7wnB0XuhloR4nZMg7PwtCptxayyFXpGqNG2MNRvPQ4XO95RpJNUGYnYIszCZSlSksCWZb19M5ZRyIuW3joT0oi7qot46pV5wuyv03glub0FdPCS47S0UZ+ZONvzMv//f8Vd+4Bf4R//3P8e7n9/y8p8Q1H+NplRmefpb4JQoCv1d5conz3HPv/bNSf2/Ca/4/a24zMum2gtnj3vULdn94sk3/JAkV7h38uYfH+LQxYXK65upt30jjgi1KC1YRJXvZhltNeCOs4BavBiRuVaTKjofUCpVEyUrpTbbiDQjsOc8UQuMyWS8XpRaIThFqORqudGpVnqYgUSW5+txqDOpuirkYtJ3fCP6CJiEOrpAVqNolVYpcxPrgrd86ybUalnoMUAXI2NOlAJdnJt3bOOUcqEVYdHNxPGmpKYm9xZrDp0Iy8EuJj3W5J6PE4gwDBYLZn5vTAqeqxGltRGjPQeCScsR20gVLYRmELdcGhOFpuYN9zO5uNIQp0SxQYNFlDWWqwVdCUaUn+O0ind0MdKKUksx4IcIKsKUyhxp5RAn8yZVCdj2dwj31QyCpXVanJNzDjeDQ5wo27QhFwjDrAhozYjvYN5z4gyjMxr1ertlTI0YAzjzxgdn2+7grGnposf7uWlolaEXcoVSGuM42UbcJ4J4GkrsjOR8dr6laiOnc6re4OMf+zX2Dy5x7dqj3Lv9Oo888iSr1cDp8T2OawVtjFPi4GCPUivf9wd/iPXxHWTaMDaDDhanSLMhEk4YnHsAbfPYxrzpvN0P4LwQUabaKA6G4NkdOjpxTFPCsrYDYB5ki6ozD3qa4/Z2uo6n9vbIxfLsgyrPfvZTvP/7f4jlageUeZMNfd/zof/kf8YLn/kk58fXqRn6Rc/RcsG2FFOPqA29qkJqyiKYH32slalmbp6cEXzgyt4O4kaKytw8BWuac6Hzwjo3MkprhRdPTnh+M7LOBdf3XLtyxJHz9LXhxRQiU1OmXOi63i5+SiV4JbY2R3ElRu/oDfUArmPoTYUCDV8bceio+Yxw+Q8w+OeY7nwOSeeoBtrq3cRL7yLKCWX9AufnB8S4y2pnl+35CTF4QrB0g53lEVVOqFj0WoyAs0FEcN4gbj6Y7cA7WhXGVIhOGVMBDewcPMNq/1Hu3HiRe7eOcd0CF+C1V15kTA2/2CWGwHY7stzbpbRG2mzZTIWiymZ9ypePb3F+tqGJZ7Wzz3K55PbLr+JpdBEbGul96Xjm7ultPJGc6xwT1vAC6+3EZsyUlsilIE5BHWICcIIL3JdutKrUWQWAKN55e47nYaSlJSh+jkRstRA8BBWmKePFzVD8CjSCOIKPiDNSQlOLQ7THkKlFZmq/x/to3nS1eLumWATgbC+qDcTbe7vUESdCwOFwlDanO1zURV3URX2NassBt9nib5/Snrj03Rdn9vpt3vuXTvn/XPkxLl3/PITA8nvfzfqJr33zFsB/hxvxsIFLn5lY/NYrb46q4Jvwiv/2rfiDEiiDPIijfaMlVeHuyZtH158jUNGGDD0t+jfnfn+f1tu+EW/V/IW0SuxNipu9XRQ71OJmEIv9cSYZz2niLCe6mZZd5wbYO7sIzqlyvrboLLAtinON2Fn22Hb2HItTBu+hKQWTWN9PLzf5tCd4QUSptRKCxfi0WinMsV7O0Q0z2bzpLJU0r67DUWqxJjQXlHlDL5UQBK9+9n8bjCs4R+fE5OtzA90Pkc4F28qrNWMxdDiNbMaRUq2Rr2uDVITOwERFLc27tTmKbD43iID3Qq7Wujpnj1HwuBm6NkShCyYeFu9oKpbPrALVseg7aslEL3RdTynmay21mMS1Gc284QjO7ocK0XnbVjpQGqlWumCvNaKo83TeY3HYtoEttczy8EApNmApzYYFMXi7WBcjZ9+HxtXSEJSclU2pTMk2xw98+j5yPm1BAstuAVS0KLFzrHYd201iTNmaJudJ8++1psDR9xElIGqbNkew1zadcvfml7l16zYBo9Avj3bB9+wd9aTNKdv1mktHh2zHxHZ9yiNPvpsf/eM/wb//1/9fQqsUHFosTUDFzTT0hqjQxLaGjkbvhZTsmO1nGF8Ux6J3BAyYV5qpDFQcc2+MFyEED6rmCVd73zgyGgLilF4igw/cfOUlnv/CZzm49GMWcVYt0x2EJ55+mu/5wT/Mp/7df2+KDW9S9d47RlVSg94LZ1NlK46dITKmkdtnW0KILPtufkyNIXiOtxOTF2L0pKlStbG36Egl8dr6nJe2I/fGRHWeg2tXeGI1sCxlVksEHEIVR3MWb+WoiPOoN9J28Ka4qa0RRa35jNGy2nNBROlCJLjGOI10LnKw3JDdxKSOsnofunONPt+mO/n3tLQlE1gv94hhy+HRZfZ6z41XX0IqxOg5Pj5jWDmWuzs4VwizMsXo6bOKoVsQpDH0EeeFnM0y4Lzn0tF76Hcf4+z0Dsc3b+FSRuLA6d0zrlx9jMWT7+TmjVdpePb291C1qyXn/Mw8gPPzU5Nib05xoWNv/4Ca7lLbxM5qxWpw3Ds+B+9wKogP+NpYj2dMqdIVz6J3iJh9QptS1OIWjUxrgMzgzEvuxZl9Yh5o1dZw3iOIAeT6SNNm3IimiPOzdL5jKhlfG4vFgKKmbpljHzvvGUJv5wY1sKN4QWZAJ05w4gltTpiYz0Gx65GSmUpB8UQnqI/gHM5ltFREo6kQcsKLJ/qLRvyiLuqifpdyoHs7cOce/mxF2Xs4CNZbKs4sJ3jt5oP/vvrrp7z4yD4t/M4m8v4y5TshT3cF9p+v7H/8Bnrv+M19CG/WVnyu9aOO2h1w+WP33tB9SVXk+Aydpjf+QH63CsFeY3Ho7urNu9/fp/X2b8S1oSLgLLYs+EBpDa33QVUeh/mjtVmOdN/1yLxBK8VipcRjJ0gRXAj0NCOdN/OXhxAAi92qTWxb00wS77tAy1umXEnz+bHzQnOFrKDNmtbtaKT2EGfYWMO2MThWQzc3/JinPBdqKaQ8g7GaozaZf9aahfaAHBwIzgBsrTW2M0io6xwL74nRk6dGSbb1b6ky1kKj4nyjVTtZOjFPJN4aruiUVBy1KWWCIkZDl+DoO09rthUC81gjzoYPYptnxbb+zkVKK+TSCN5TstHTGwXvMRCWCL4L0MSaZ4HcnF0kIwTx9NHywrUVSpsYx0xuhcUysHiQ7V5p9T71HWqrbNMW1cW82nJ4r3RBDMqn5cGx4tTUEM6DqEfVEYPHSUWk0LKn+kDKBpjrYmNMG4B5O+4IoacPHZvtRC6FSn1AVJ+yefsXOSIsqLmSiz2Wpg6RTJ3u8IXPfoLdD/84l44O6PpIPwzkNBJ2d8k5MxXl4PCIvYNLTNszbt66SV2sqOen4MSk3d4im1Jt9pxQZgUDZLV8ZxFHNzeXXR9ZYMeqF0/DQH9VHVkwYFudLRrNGpllH6lTJmhFnWMVbIIkobPN+Tjy8V//H3nX+7+XK1ev2XBkHj6FGHjP934/X/joL1HzCALrVFl17kHgSXQeKJxPI7TCtmT6EFn0PVUbp9vEtjQWwdEUTrcTe25BbQZyG1W5fn7Oq5uJjcJqf59H93fYKxU3FtT72b9mx67zEKUDCnilj5Ga1YZD4tGo9Co4LxQEXwutNFqxzL4ueBoBTYnWEm57g7C4ir+yT7r3PPmVz9FEqd0+7vAP0O1cpsotWl3jcOxevcrJ8W3OTtesp4R6QV1kZyfSdwF1DS890iwXvh86VsNA55npkEJKFue0u/80l5/6ATbnx5zdu8Pe0RHDzj6rvQOefPxprl7eZ7VacufWdUoeOTs/4/qN1zk+O2a73uBbo4qzBnd7zs7Cs7O7i3cTt29/wSLGSqDVnspow8hWWfQR3zmc62ltNJtAmkFsEnFBCC1SagLX6EOcYXM2yBAxeF9xlSlP1GoDkfvZgU2NEK+quODofKCp2VtqK6bEme0oPnYsYiSXBCIPIJVNLR6ytEprClgmOa0hEm34KAGRSMk2UFv2A7VVxpzYpPncK4onsOgHeudxapak+p2+SL6oi7qot3TpEHGLBXLrGFldQ/3DrcXrKuL399C7b6xZ+1aXe+E6uy/scfLur/FNsa24++bsz2+oRGF5Q7n0sXvIqzffuA/8YepN3oq7DHf/gHL54w+/FZfS4M7xm9+E37//1ZI2PJx94qJ+93rbN+LB24WxBJNjBuwCLfhghNvSUDGis4gBf4KPaG2MtbBJzY55VWtynX/QnNaiJh1X6MC27a1Qm0mEnYAj0ffBwD9VKanhgofeAGYO81pbFrBtrsvs5fazbHqIkRA6SpoBbc4k5UaOtu3KgyVLm7PLq5KSZZj30TKxc7AN4ZSUKiYF9lSkTfPm3BqO3MrcJCreC85DSg11MPQRnTf4bc6edvOWkGaS87E2hjlP17bKjjJvqWL0loUs4CTgfcei79mOI/jKOCXLNO4sZk61UmsjBjf78cWaIakG6EoNEU+ISpVKaEIpdnHcd8EI4LhZ7u5sq+Wb6RJaQ5uy3mToHV0X5wGKQzTStHJytka8SZpj50CCSeJboe8sM7okYREH+iEgTthuC60KXjzqzcOeioGcYjPeAOIZ+o6T7RptQqv24eBn2WqpJnNtLdswoAVcDNDWHN95gXt3b3Cwv8N2c0bsBoZhwZS2vPbadQ4O9kArw6Kn5DWr3X2efOZ7uPXilzg5P6UKNojxjiie0EzypNUGA6VBwbMTHR7Lie/ml/i+lF+dQ7APhCDzRYI4VJmjnio6D0xcZ3FNqTZ8mDejtTL0HTevv8JvffxX+aN//D+jHzrjNIhjO058+dU1Y+lYyIQ4oWqzZAFnmfdTrZSauXc2Ijs77C47DJRux23VwmZs+CGiKNvSCCmRadyctrxymhhzpV8seezSAUcidLki4me//Mw8qErohOg93kNgIDezumixjWotDbxH5oHcffXx4M2fnls1PgSARM6nwu5uR5qU8c518vom3fIKbfkOdLmDbF9mOH6Ow/3Ibn8JccKw2OGJp9/BS899mWm9po+eSzs7BN8BlS44Y1E0YbVYMXSB0AVqbsTgoTSUjp3L7+PqEx9guz3j5O49hmGHg8N9at5y5+arvPrlZ9ldLum6jnFzyuOPX+Md7/1erjz6DM8990VeevElnARyKixXO2jNEI+ooWd9/Ao1ncz52sW80ji2U0JEWC2dbZlrIUTY7XrGsRjWzBsUMLiIEKgOfOgQCjklu1hynqCO0jLITFyvhU0uhGiqnlqasRAApzZcS7WymbLB/2oxq4oPOCrRB3zoLNqxNnIuqHjED0Tf8ARUlCkV+zxx3hIhWmZKE8F7i2ts2awYVdmWQnOw9EKTYlwLGrlBrt8d2bkXdVEX9Z0r3VvB63cId9fkKzsP/XPl8i7+9OwtFWdGrVz+D3c4f/Iytf8aW/GgkL8958XuFC5/akP8wqu/dxzZN1tvAkH9gdq0gR+F1oeHgrbJVODOvTf9OJAQ0CmB93aMXtQ3XW/7RrzrOprWGeSklFzpFLpgQK0qhVortRhVuglUqYhz3EdXtVZI1ZNqJXSeGJi3JnPuePQ0rdRmsmzRGfKGNQMpZRQYFhFiYQb7WhPfbNvbB4cLziBdao1b7L6y2W7FGsdtSjSx5qAp0JTVoqMLtqERUWqqOByLzpGcGrSptwZhypZnS4Pg4uxbNBlmFIfKLIm3ZT/znADnhSCORQe1KZuscyyWfb/vbIsv4vBYE+nF7sciu23QgCpaGy56i4ZzlSaVopmxVHy0bOCuD/RdpBbzgqo6nJP58dlrlqvO0VlKqhmK0qSnlEKphWlqxOiJEmjFssK1VRYLaxRry2gzP3zFYq5yzngCOzGYRWAmtiOKUzFoFUabz8n884jFtQ3Bk0vBqScGTyuVMWdijGxTRTuj0dsWcEutCs6a16bCctGznSZKqqzbxKq3TWcXIkOwWKhtWzOevcKNF36Tpx57kuB32KxPWJ+ecnjlEX7wh36Ymzeus9lsySWRxy2r3X0effQpPnZ6h5Oze0TvSU3JJm4wYCDKWIpJ08V84L0HbQ6JHa1VgrfcZxB0luZ6JzOF3QBhThVt5p9PDdr92CijBFJSQmtFfaCKYxE9v/WxX+Xd7/teHn/mXSBw584d/tUvf4LPfvzfETf3WC4CQ4zQGrXCogtsc+Hu2YYqytB39l5UG/CkWumceaPPUyYWsUFOqrx8vuV2ShYp1i959JFDrnkhpII6Rx87/MxuMM2G0KSCM2k8rRHEhgIlZTuWBfoYac4UFlUFcmHRB4IaWK6Pjp2oSJmoTdiOlTyN7EbP7X4fOfhe4hCoJ1+m3L2HdJG2c5VXxsc46ndZldssV0u6YcnOzoLb67XFZUngcG+HsZ5TSmI7FRb9gj4GYgjUWelS1ZGK4+jR7+fao+9kGu/y/Oc/xZUr7+WH/+gf5Qu/9evceOU1fDcQnTKNa6a0ZXt+Ttnc5exky4d//I/zvne/mzSuuX18TshK3wUSoGGgpLuUzauslgZRaxS0jfTOcZYLLjrGcY2fVThD7JhSQ9XTtDCmSi6NJEoXA84pU95SWmEInal14EE8mdNGLsIidKYMEkvH8DP0DzF1hseGAlNKc6wZOBHSzJSIMaKtEWKkOEd0QvTzkE0TNPON+7mJz7mQqyl9RJWmmTEbI0DVAxkRpQ+OPjjsJA/OO3oXCBd9+EVd1EX9HqXB4XZW6J17+L0FtX84H27rPP7SIdy89S1+hG+wrt/i4Nkj7nzv7zwB+q1tf8vDxad/Q+UnOPpCZvWp1759PvpaufTxe5w9ffSmecU1evg9GnHZTAZme7PVV7FDazNv+O7+Q0fsXdTXr7d9Iz6OlWGYLShe5m24R7WRS0Jbpdn1NanaBZ5UkzzXanTxXM0XHDvzROciaBOLKPOOGE0WmnKmZLs/wWLMnLcDt3PWTIRpYlsSrVrjULFs3+g8VEHVvMYyN53348pyzZRq4CtUcVHoQ2QW1jPlTNc7nCg+etKYTa2pJj+eSjKfeOeJnYHY+mjRS9tcKNU2wUPfIdLM6+3aA9pvFwdkhi0JRmHGC8PCo1h2eVO7bZy3glrNizvzlFh0EW2wngpTV7DkJZM6K+YH77wzuSfKmBOqlZznaLPBZNK5VNsupWYkdgelNhuCSKMLlo3uvCKirMcRj5gCwQWC75FBOD5LFDU5qgV4KYr57X1SnKuECDL760sr5GpNgg0qTLlQW6U0x2bKlFrpuzh7+D0dpoJoDep9EFo1L/ZUM4uuo3MDLTtyMg98qYILylSyxbCpQLPc7D72CBP3bn6J55//It/3oQ/x2ksvkqunXyxZnxyjtbG3f0AMyp1blb29Qw6PDnjqmfdyfPs2m83ZPDSojA0QA+HVagyF3T4Sg9CmQqMRvG3InRgEr1Xb8ht2zUB5qjrHmxkme9oUUm242KHVJL41GYSr98Gi1EpBQ+D2vbt85pMf5eDSETdu3uMX/od/wytf+DXa5oSl82wTDH3Giec8J3IT7m1GnPPs9JFNtuNUERyWJT+mTOcDVRPrXNgKvJoT96ZEQTg4OuKR/V12akNzpanQOYf3NvBhJpY6EYKD3BopGZU7tIr3HucC1QkZ4yS0KSMOovf0wRrNsWISbheweFClC6YUmdbnDDsQyy385hi/LbSiuNVTuKN3UkMjrF9AT7+M7OxxfBLZ212xPHyMw6lQx5Erj72DYdnQqeKrw1OIIXC6PsU56ENHrcJyscPRo+/h0Sfew9m961x/9SVEB65du0KZzrjx+h1CN+AdbKdCv+hBhdj3nE0Cp6f823/1L3ns6XfwyLXHuHHjN3j99TsEJwzDgsPLlzm+9QrVjXjUmBzOLD9VCnF+z1RVi3ss4MSk2vu7A9OUgcLpekNp9+FsjhB7nAwEiUQvbEtCvGPVx1lqbjFyHmewP7X3SXRiFofQkVPBu8AQIqlVQgh4MYVNQ0g5g1SD/ZWGR3Fq6RrOC0PoUHGgjikV2pyI0KiEfmZolIp4B8nUEBExv7g2NqnQe0/nA06dgUsu6qIu6qJ+j2o7S2S9xd86oT1+9PBxZkcr4snZWyLO7EFp4/A/3ODkmccoK2tKpQp7zyuXf/Um0xMH3Pjwm0/fdhV2Xm4cfvwW3Lr7rZGhf52S66+z98LhN+UVB1j/8JY/+d7P8aX/7nu+7s+4sy16fPotoaNLF9H1GvqetrN8c+//93G97Rtxz7xNnX3gqVa2k/luvbem1oi7jlwbm5RB5sivpgYB8sE85GrNVXAO1znb9DWTgdt62u6zNUgT1FLpe51BaZhc3Rk1nfuDpHmzUq3VwXlPjA43bxAd5jnPU5l97AbOotkF/+A7ci1sU2J9NuFCo+89wVtk1v34ptqU3BTN1kTH6Gi1korFOVW1/V9teaaoG/3XecvilVbI2R5uE6FVoescffTkopQZZuZnqqPz9nxI9ixXHTE6SirkWsxTLCAusOoHQgwELYhXptHigTRP8yAi0lRoZLR48gTq2gxpM9tBynUmRFvmulRP3w1QCk6tcRQHw9DhRBnTNG90hRg6YnAzSK7RBSWLIN5I79K8SeLDfbn/vN1yUMocQ6aOQTy1WXRboRJ9xHsDwCGF2Ct9H+hDYNMme/58R+86DnZ3CBJIIxyv76GMiCqCpxaIvXnEIwJV6Dphmo557tlPcunyVY4eeYIQO9b37oDvuXzlsvmqVRiGjjFNvPrSC2zPz3jq6Xfz6rOfYsyJqp6pzNYIYLXoWQR3/0VnrPY3a8Pyw0VIxSwJKkItBVd1/lsdTiCnke2Y7Oec+flrsyznpgY4LNrYjta42EbS87lPf4zj03Oe//KXWd+7jq9lBnR5xlIZUyGVLVMuHOws2N9Zzs1UpQum0DifEl00GXJtSvCeJI7XxolbU7IEg90dnjnc46oKdcx0fUfrhJwyCCYzBpitIsgc8ZYTJVeC2GDGz8+R2Nuaoo3ozWcsTvAxmK9czGtcx0pCWXgbBPbBWxa1Czz6yCWmzYbsrqB7l/DtjHDycaJmui4Q+iUiAW1KDIG9a4+S8pYbr93kXR/4g+TNK7z00jHBB/zQ2ExrfLP4OFY9eweP8cS7fojzszNefOE3IRWkeVwIvPjSi7z02itMKXPl8iV293dAhdBFrt+8RfMbdheNTuD09ITNF7/Mo08+jkpH30Wm9cjuI9fwsmZz8gohZFwIhGBpB2NuKN7sDK3RiiDe2/MqxmPQVvG+kQsshg6cZ9GbNUTVEUVwKGmOOAwh4JzQih233nsiNkbLLSMl4zXSd44ggbgwH3hKjt47gheyVqZWGHyg8xEfIt5BbIpWGyy1+zGTUag0Ui2UmhEcw2JgmkZaS3PGufER/Ex6j8G85ikVqhOWIdB3S2MKuItG/KIu6qIeohywv4PeO8Gfryi7D9eoqhfa1UPk5e98nNlvL713wqXPXuXmH/L094RHfm1D+MLLUApDLvgPPU59k3pxURjuKJc/eWZxZN8pNsc36RWXJpQV/NKP/gP+u/P38+X8u+jcG7iTc8sJ/xa85tJ35jUXB/u7X+lhLuqbrrd9I+5CmLe8Sp23ytoauVpDGZwHPFWt6dbaKFTUWQOy6jyxE3KLhJnALQjRR4vyyXmWo4KPgpdAvL9hbDDN4UK1V7yzjVqpRt11XqlZzQ88Dwqib5Ss5Npm2q/FX4kIWsxf3HWBWpWahbGZ7N27gPiK9wJY8+28NRYue7QVaqkzdM0hOMapWLMdbDggTgle5sZUmCZIc4xQrpCbec7NEw7TVNFqg4emAl6Z90P0wbaLXmx75bxwmhJjsQtZG4yYKmE83ZKbbf9rdkAktWQS+lRxTi06Khm8bDH0ltcbE10IuLEa8b2YLNmJYzn0s1+44sUyxhexo9ZCwTzyfecpBWvU5wgi54Whs2FCq4IP0JqzxlIVnJCb/d3ShJKV5cpAamUGmdEgt2y3BySAF0WLPZ8Bz8HuLjE4ttPIer3haHeHGIWdZU/XOWIw24C2Rhc94jyL2IFTgjhaK0zrGzz35S+y2j3k6tNXWMYAAkJhypmdg0sslytu3b7N6vAKlx9/ijSOnJ7d5e6Xn6U4ZXfocN5AUr04k5h7h7ZqfnAsn9vk8xVU6YOnihJmVQD33xfNtv0qDom20TQfd8UHS1wvpeHUXs8mjuCMu5DHLc9/9hOgcLB/QC6Zab2mecc6V2Q90kXHshtwIux0gav7B5ycr7mz3tBq4rxU9n2PE+GsZG6PiRvbifM04YaBJx5/ow3ZaQABAABJREFUlGt9JGwm8w47h6jF5rnOKOtNIIo8yLDOtZGKDRBidBZf5yzFIM9xcM4LrSipKjEYSbsqlHGc2Q/mq8+lkufziohDtJE254Qu0PUD2/N7tLMXCE5gMbDcv8wjV6+w2ttDxHP18iEHOwu8C4Rg2/rt2XXS5h5HewekfMrdkzXeB1wRdleHXLr0JI+/8/tZLlec3btNLdGGCZq49uiT/MEf+sO89OKXOD09Y2cVuXP7Jlrs75aw4OjSATUnSsq4zTmn56ecPrvl4HAHqRUfPNq23H3t82gdSa2x6DuWi57jk7u05hjTSCqZJsr+3h5X9vbYbvMcFzmRcqa1iniPd95sQc5sEYpj0oJr8wWMmurBLDPW+NbWZnCf4l2wYZHauVJaYTV0jHhG7GdC8Gw2iVQz3hu8MGDvZ68OfMAFGxSqVhoKDbogOI3kquQy0aQgztOqvfeddPTBUbxxLcaxMRboOsdqWBLEFBrtYddaF3VRF/X7vnTZ49Yd8vo9A7e5hwS37Q7EnRV6dvYtfoRvoLSx+/FXcfkxVr95/asemx6fsnz9Uc6e/OY7vLiGS781MXz2TYoj+2brmySo513lUb/gE6dPI+13NvNSFbl7gm63X+Me3oQSZ5L0UpDd3QtA28OUcw+dBvC2b8TXm0xryjB48OZnNeiTMHhP33nS/cY3eIbOUZszf+McH1VnQJRHcD6SS2UzbeliZOg7Sq2kkmdhM3jvWK6EXO/TdyHMGzNrYj05Q6PRdYKfo7Scmxvo4B54h3PWOZcbXAAaRrj2Dgn3IVtGJHZeib2fm0rbxjkP3inOd0xTtq26YBfazjEEa7Rqq2Q1n3ATj1RsA1xsQFEddF2YIWkQexhHZTNWGgbFG4JJ+EubSedeCME2YJvNyHprecIxWpPTSmFT52g4EVpWWk3kluh7z5RANbPoAqkWEMfu7oIuLqBVnNjWuguehetAlFRsQxUElr1nyg31SnWV03GNqCPMW8lWC67NxOahY7EMsyxf2E6ZUm1IIUXpQk9ukxHuVVgOHqr9boc9R8FZRrGI4pzakEIFp+DDnEvfPIu+o4uRlJN5VUvhzumGWoWdZWS/c9QqpGL50IJYQ6CNDseUCzvLnnE65eUXPse73/8D3Lt3wv7eLmjj9PScXJS7t24w9AO7uweIE07v3jZewDAwLAbG7ZaehsOTKowGLGAIs8zbO2phpkTbsRvE4sx8CCTnmUoxxYD3RC8snKNOmapzQKAowSnaTNkRRIzUHwISPalUpApdtFQAsAY5BI8T2KYEeEIrLLpA9N6YCVXJ9/PK5/fPZmqcpcK9WnhpM3E8jah4jq5d5Ym9HfbFZMHFB6iVqVZwjt1oML/TKZNqQ/qO4JXT9ZbSTE3ROfN6I7bpHIulJeg8FnY+oFLNalIKUu39XovSRc8QHVEFh1HpLV9euXf7Fv3OipbOCG1Lt1zxyONPceXaVS5dusSy7yh54vprN5imFfFwj1Ybezt7XD7a46XnPkPvKnFQ4jCwXO2hxaBgjzz+QR554j3E4Hj95S+RRhvGVYQQOtZnJ9y6foP3fc8Heem5L/DJT3yCVtMcGVgp9YS0iKx299ndP2JKiR1/zrCzz2Jw5Bi5/Nhj3L3+aU7vPs/QWZY2JZGmRk6ZUirBQ25CSZlaJ07Wx5ycJ7RFOueRmcJfdMLmefashmgqk1IbqVScOlSEMVU6bwBMhNlS4QnioLUHLAcbktrr1DnPqu9pYuoFwRFcwElg2S+hNVKu9KFDxRtzQAQnQquZbbLcdJGICx5JghZFvUUw1lxAlBBsAFWrMrVKaoprcLw5N9VVbt9IDOxFXdRF/T4u3duB23cJ9zbkSw8HyFKBenUft9l85+PMflvp+TnLX/3i7+xPtLH35S3nT6weWoL/H5fLcPBcZe8T19Hjk+9EItrXrm9iK552lb0vw3/+pf+M1//ROzhMx1/1fckV7p68uWT0r1U5mUf8AtD2ptfbvhEfJ0Wk4qP5sFsRWoYQ521KKeSCyaELxL7H2VKTmjO1Cjil0VCBiBIDTNoY05bgZ9hWbTgLhn7QiFned6BZDhnTlBBxD2BtQ+/pukjOlSllwiyHdS7MssxGrpXaZt+qs9xmEdvKjmlk0fX0vUeKowmMUyKI5WJvpkRNGXFGvq7FIGPeKy4IKgYoE73vdbYL6UYjiqe1Ri2Kjyb73G4aqGPobIs5aUPEPNbe23Y0BGscWzKUvAtCLhOhcyyb+cmjD4y5ULNYfrpTdpYREJKaFD7NW+6+d4RO6CQQpCO4jlKz2QnCQC6Zrms45xEaXewM0OSFXG3AULTSyZxNDJTc8MXkp3iTlTZ15KKzLzXiZI6RawbZG/rA+dYygcVVWmnovCVX8agquShTgr6z17tqxjuTvZZS8F6IkTlGrnJyvmFnZ0nfO5wG8qSkqZCoxgQoZY5H6xEq2cG90y3BDyz7yGazZj09zxc+8zH67/sQd25cZ3dvl9At2N1d4QROTs7oV3tQE90wQMk89uQz3HztJaRMRG/S677rKKXStCJNjf6ss+f7PowtiMXk0WjNmnVRsQ23mF7fO6Vrjs1UQRoBk/xXoI9+ptbb1nOaEqpKvxhse6lKAyiV1hohdvRiwybGiVobXTB+wtmUGO+dWU59M1L7+TbzwlnidppItbF7eMDTlw/ZV6EX8wznUhk6T81Cc7CIns6bymMP4TRlmji2OeO9weRKMTI/3lsUYhNoBjWMzrbbVQ3GNZWE5kwnQi4z7rE06jaBF7QaddtHixUUVU7uHaPqeOKZ7+Fd73kv165cpoueVjLTZs3ZdsK5Dh+XxGHF+fFdxjGxu3PEdHaCyxM+RuMc1EqQBe/+vh/m8Wfex/npKa+//Dx3T07t/ZwqYVjSrXZZ7ix55bVXuPLoYzz1zHt47rkXGUuilkL0SpfW1PGcIrAGrj72OLdvvk7XB26/doPl0SOsdiJ3yx0WUQkeXFBSHdmcGR+iacVLoHOeMPQ4Vc42meP1xDJG20hHIZdCVhuoeAGczEBGO6/UalK74IN5v51ZhKZsCQaus+PEi9i5C+i9RQamVKhS8cFep2myaL5lt8DhZym6HQOpJirO5PQKnsI2bck4kEjAov78zI1QL5Tqac2jVHIpsyWDmZkAwUMqmdzcA67ERV3URV3Uw5b2AbdcorePcbsDrXs4cFtdvDXjzH63ii/fJmxW5DfY60mzOLLLH7sLr73+bfeBP1TduMXq1SPOnnmDPycwXhY2//VjHN47+epvjRnuHn/rCflzohIHuw8dpXdRD19v+0Z8sfSsBs8QAk4i0jmCtyZBm8nB0UYfHX2wTSUI620mz5mzzjugwpxHi84Z0w1Ssws8F6yR8372z1bbtnfBM6aJXDLOB7y3+KtNKajDNtqYb1uaGIXcWdyQeo9zFeccIvMFpHe2yY5hbq6V6hQ/30cLgZwrZdoiGtBmgKFpsr/XRUi5EZr5GEOwfGSHY5MSjoaoQdHME2sU4TQDt7p4HyLn6ftgYCURa7IAxYBfWkGcn+PLLBpu0Xk228SYCnghxmDNTHQPvN6qEANIMymMQ6kpQ3BUn2i1UksjmyOTlCYOdndw/r5E1OjvTYs9Vo0MLrBcePPyZuV0nchSCEHoQ8eUypwhHxEXmKaJnItFW0WPCGymkYoSgg0q0kyxvx831pqAelxUk65WWAyQWkZSJUSLZIsS5pzz2U/ekpGikzVmMXjQjA0H5tgzZ43pQhrDIrAzDHTR0w2BxsTzz36Cw6NrfM8Hvo8hCviO1167wc7uPleuXaOUwmZMjNs1IhC7jvd+7w/w/G99nFaKqQPmbWLLJvXVZoOZMsPYUMB5vFNyafiGDQlQiigexav57r1ziFRaNVtA9Z7OCbGLeO/JY+J0M+K1IU44mQqqakMuJ6YAcMLuasGBX6BF2bZCXieGaDT0bWl41zgdR+5sJ44Fbo5bzlIm7u7wzquXOFQhlvvAQ9iORmzHOSqA2Pvbu2D2jJYIwY6pVCqrGC22LohtddUUNWZMdoh628iq2v09IKpbTrmfKfNjVjQ3nHPEEOg6R6mVLgZ2d3YJDZ557/t5/InHEaCkLdvTLeId3nd0ixU7Ejg5O+XkdGHpBiq40HPl6iVef+WEu7dG9iqsdi+zs/84h1ee4O6tG9x+7TrjdqKLPV5gW7eshoDUhJcdtttznv3sZ/jwj/0xvu8P/gCf/dTHKEHJUyLEhkrl5O5tujEjvuPy5UNuvvoaVSIqlZsvf4aWzjnYXdA0c55H4yLgcOKIAYboKRUjoYtwNlYOFjtEF4087wK1WVJFdHEeRhpXoDZLluhiR+cCXWcSdASoFrnoxZry1iwyUTB4n0VR+hnKZvnd22xMjuADMdpgptRC6Dpca6S6JeWM94P5/0VnZUgluoio4r3R+1UyLVdLFAgduVpEm/cB1cIi9kQfQBrrqbDJldxAHu4a+qIu6qIu6kHp3gq2W8LtM/KjBw8Pbru8iz9bf+ujut6E0vWGndcq997z8CfJ/li5/KkN4dnX3tJ/o3Qd/ht8eGlXGS9F4t2NfaGBW2/R07Nvm9pBVkvaovu2/K7fb/W2b8SHzqTQrQmtJZrahkKAUgppJqN77+j7QNdFWq6EGOmHYB7yVjGmbjMJOYoPjVSr5QAnuzBrVeamU8m5UKpHu46m9z2nRu5uajnHgpJLMs+p87Rm8CpULQoNR82N5mfQV2+AOEsBU/vbpMNHR0pb20jHCFopuVHnxsKHDiQx5UpD0DzbF2YLgxHAK85UwXTRJMk+2uavGJ3LMntRuuBABXF2gSsqdCHQB8eYkt1mEenjHKtGs9ifprho0nuVQKoJFxz9orOBRzDfuKpB4IITk4LDg3zrZT/QDZ4xT2w3G1JtrFpiGXqWw0BFOVtvWE9bQnDErjOAnipTygbxitZ89TFYU+ntMQTxgFAE8OCamD9UYNH36HbC+Uj0jq1MbMZELdac0xpNZyheUmq1xjsEi/Raxo5l5ymlMmYbRpRWOd9Ucha8RJpvlDJx4Btd7Jm6HnUGHiul2guGMI0j0TU63yi+kfPrvPLCZ/nQD/4wDthutzz6+GNs1mvSNCLayGmiNsfOzmBS280pLfR4NW5AbkoWi31qVUGcZciLUF2hoxKl0Sq4YJBCJ2p+ZJU5gx5yKbRmvts0e85jNFhXFNt8FlUWXcDVyrpWcjYSfxMB7H246ANSCmlqD1QHWS3Krla1+LXtls+++irXU0OXS6TveOypJ3hqd4UbM2frLW7omKpt5oMz/684oQeDh7XG2Brj1NhOGTPZC103kLWxcMIiOgqCa0op9jqL84TOQTELi1N7L7j7NG69b9OwFAQ/+4W2yZIagsCUE1WU3f09Ll/e4+z4Npv1lloT6805R4eX2dvf5+z4HuutUeLv3D1hf3cg55FSJrpuxVSF2nkOH3k/73jX97I+u8trL38BJxhZ3DlWOwe8/4MfYnNygxeefZazkzt0Q8fu/i7Xb77E66/fYndnxSI2Xr93TD8D0W6dmNVCh57l/g43Xn6JKVd2Lx1yevuLjKcvsuxNuROCMHSedbIs7z5Gwpyf7p0ND2KMLOOERymlgFY2YyH0ni7qPOxUUhIDCpY2KwgszqzWascn0EXPordBiYpZKDZjRhCGOAAwVWVbG50XUlFqhb4PjCmZJacqNSoRS8rQBq0qriWaMxtTE8GLp5ZK10e8h3UeKc3k6D4oRUZElE56+jCQfWOXiBNh1C1nU6bN9qLa3oLbmou6qIv6tpV2gVf++B7DXeXwiyPheOT38qyoF9zeLnpyittfUVcP59NtncdfPoDrr78Jj/xbXNrY+fI5x+/at0XV16kwwuHnM6tPv2ok7++C2lwzhtMbLoGTd3l2Xgy4sxH5dhPxY4fuP3yW/UW9sXrbN+Iyg5eMaG2bb4BcFbyw6Bx5akwpEztPF6JtnPs4X+zOXlBploMwb3tbmenjnVBGxQXb5EZvfm37HY1NaXbhqJbfbdAro/H2PuBkbmqCNZ9N5wGXszitfgg4cXiZ4XJAbgXVQPDWTIbgaDUwlYmSGmmsNNE519suikUb3t2nnoM6owJH7+m9I1MJveCDSbidN/m+qtKKbZfUmEWU2pjynOPtLY6ptkpu5r0vueFoeBqdt+9tp2YXvqqUNiIYmGlYdBYZp0KoFQmNw+UBi25guz3jdDy1YDFpEMQUAl6N/BwgegM62RbKSJnY9TyblIihzQT4hhfLzQ4hsBwWeAdnOhHEQG3rVPASKNUk/MMQCRFUTO3gvJ8bhWZKgeAtT1gbteksIw+0VmzDnyqeRqqN3kXynOnetDElJU0wLC2ZO02JgnJU4PtrYGdQnt2Fmx3ztq9RS8a7RgvKybilKgTXQV0znr7Mxz76y3zvh36I4AM1TcQYSVPi+O4dDo6u0PeFcUx4F1jtXuIDP/BHePXZT3N27xYOYRm8Nd6lUkWxGHs7nmupJGSO9zLVh3fOJNpzDnptzSIAZ8VGo+JjJKCzx1dx2tgJNpQ6VzvMXVFTlTih7yNDFEIzj73Oigu859wHZKx0sXF9O/LS6Rl31xNuteLyI1d55vCQHRy9BNrg8Sp4MfS/ziwB16yRU6A6ZxLomVdQ1ZQbXhyhi4g2aq2ol/mjUwjikOAM0Jhtm6oKeE9uDadzLruDWo0dEIMpHVSEAuRcWKcN6ylbpnw54eT4xIZG/YJ8PtF1S+KwYFisePSJFSkXNpsz7t09hrrEu5lGHgYOH3sXB1ee4l3vfC+dm7h3/Q5tu0G6BU48u4uApg2PXLnE4fvfQ1Xhs5/6GOebkbhYMqyWvPLiCzz9zie4dfec4+MNyyEiwO7uPnFvj2uPPcat115hHBMHjz5FcBtubW7S9w1CY2qNabKtc1EjxyepDJ2db71z5NKorSAOgqEyCFFo1QaLzJFhiqPVRlNvCopUKU4pXaPVRq6CF7X3czCAXsrFgHIYa2PMG3IN9N3A4CNTmdBmlpBx2j6YRLrQqKUw6oQXZpCkUmthShXxgb6PaEs4qWgTxlLYbke6bkH0gSlvKNKYSqV6pWpBJNC0MOXCpkxGj1dTBV3A2i7qon7/Vt3p+fL/wfGJH/87APyrzSP8X3/lf8X7/uGIpK8vMW7LAVlv8K/foz195aHBbeVwSTxefOtgXm9i+eu3iet90u7X/r6rsPtS4+BjN+HO8VtThv41SrdbLn0uc/0PG1T3jVbaVcZrC5bPv2bKvm9XeQ+HexeS9G9hvf0bcV9IxRpOy7eGUg3KpAqhOrrQ0XXmXW1q3m5RyyMutdJKIRclDsJebxu1cUrk5IhRGPr7AB61HG31Bv1ytmmWGXyVsjXjXTSnYQgyy2atGbfG6it+cC/mF7eItGr0ZRGDW3WB4HRe4AW8BLyz7Vu3EwhBZoltsc2dE3SsVFV8MG9vKXZBDB5Vo2RrAQ0ecYKTWYKtziKcZnO7qEm8xUOck9hKK+TRInLFmQx16CJgoKXaMkqjVMVLtK10MNnodky4ucnx4umHDodazvVkQ4UuehYu0mrlPCW2uaDVOu9N3tBahWrxcEqhVKU188ZPOROjSYadE7pgctOmyrjNjKkRB5Nat2a+6Bg62+R54wOIVPogRogvJk8PAdQ7amr4AJVi0U9i8lMnlTFDrUIfMul8O1PJhVwr4mCcCmR4tDkeSfBk8zwSPN2khDSRV43XV7YdzNvGzk7PNBnJ2XcOdRWcsl7f4NWXPsvT7/og7/ue97I5PUGCZ7s+Z7GzDwL37tyhtcz+wRFXrj3GY9Fx/YUv4URYOAgxmvd/vUFLsgg179BglooO2/jTGlGE0BqpWDxZbY3c2oPPl4Zj6B2DCOdTYWr31RMGy0NhiJEueEowT38CkJlQj/0ebffj9xprLxyvJ9L5xM1xZGqNvaef5B3XrnDVBWpWQidE7xDn6Ry0nNFqEnijtQfSDBzsRchqypaKM7igc3jnDEyokMeR7dxgizjzbqs17lIsH7oCzQlhPgeod9AcYY5HrDOwz83wr20uTNmGF6owpYlbN27w7vd9gBA6TrQgThgWA9479nYPOD094eTeLTabc9K45erlA4auI8QlTz11lauPXuP0zsvceu010nZDw9OvljhRFoOw3Wz41V/5N/zgD/8o73rvB7n+8pc43xjXABWcOA4P9jnc3aWNG8bcWOweMCwHrjxyheuvvcpmPbE6ukLXwa3nfgPPyKKzAUQqs3Kh2YCgakW8Dai0maJFEXLGYvnqZINCD9H5GVLZjKFRLUoyBuh6T/CVLoBXR602TOuCxwc3E9Lbg3Osn1VLMUSKmpamExDvafMy4jwlO9arATirWBpEb29oHMpUCqoGDWy5EbCBbmumFhIXcb4zyXtzaA02tKnGPYg+k6uhFoOD4Ixzsd8NFCrw1r8gvqiLuqg3t7ZP7rL3f3mZz7/7/0eUBQD/650T/sRP/H3+yCv/Fe/4b+99/c24A/Z30Vt3CScj+XDxUL9XndCuHCAvT2+pOLOvVZozYaOk3a9u/ERhuK1c/vgp7qXrbykA3cPW6tPX6T74FGnvG9uK33tvZPWJ7ts3UBGH7F1Q0r/V9fZvxBGLInOFcSqkSe3i0BmYzDuHOCH6QBc8tVbMRuoYoqc6oQXPgEmYO9cDgpOMd9YwOOdwBMRZ81Dm5sEJiFfbhFU1v+EsBw84arWYKB+Y/dJCyoWq1SLEYpy32nZuVhpdjCyj0GphGhujZKKfiF3HInRoK7aBb5YPnvJMib+fd67zi14teqmUxuRMsl2r/a5SizW2KKmaFBc1/7rzsCkz1XiexgoeV+F8W4lRWPQdrSnjuMF5j4hCE3yILPvOKNxOyblB8+RmwLbQB5xznI1neBGKa/gYoSkuujkT3TbSfQwkTaSqtFqJsbEcetJY2OaKumbRZk7ovLnXvfMGF3OOqWZyLlTX8AtFpTHmQh/MjlCrI+fKYnCznFTJqnQ+MPSRvhmAqdfG2TQxTffp7w4nFl/kQrDtcW20aps+lUrNzKv7eaCRYLkRDouwtzBqNE3YRXjniflUXw5GFm9N2ORMDJFeIoPvqK1xtr5L5XlefuFzHB2sWC53LKILx86OUebzVrnz+g3ED6TtbXzwfPBDP8infuUmebNBgTwT2qNzFk2Fo+lXoti8YvTvVu04VyXcP65VcMGRFEqDHhi1EkUYRKg6b0G9NeOaza8tucygQE9Ri/JTJ/TBkVOhFqWg7AwDz6XM3ZMNi4NLfPDJxzmkElJlHBMxBGptc1PnKaVQGkibQSNRSCJYC6icT5nczLvuIgwI4sD7aNYLUdR7xlroQrD3xLbgmOPM+g7vHGPJ5BkEWFUJztQxTkzyX1WZcrHH9YC0fj9uqxJD5Pz4lO3mjOVySa0F73t8iDjniV1kGAYDsjnPyekZXdfx6JVLLIeOWs55+bnjGUy5YHm0S8uJ9TaxjGZFGLoOr5lbN17lmfe/j9XeJaZ2Rtf3KIXjk9e5e+cK6hyFwNFeQIMQ+o7bt++Qi7J76SrLZcft1z7F5vw6y53A2CZSLsafMC4dqtniFifIHmJQFItjpGY2U6G1hg8W/abNfNu1Ko5gMMNq580QPMELC3Fmz3AwRIMC1qpmixHBREQ2UADbzHc+4LFhgBdBvLIInq7fZZsmxnFi2Q0MMdrGvtox7MXjfIA5KaDVTNMCEi1yEsuRLymTcqE1I7fToIlStOA14tTRGpRidodUG3c22wfS+ou6qIv6fVQivPxfFL78nl/Azv9fqUO/5L/53/xD/m+f/1n2P3v8de9Gh4hbLtDb93C7PS08XNxX3R2Iezvoyek3+Ad8m6pWurPG5tpXnqN4Dpd/c6T73CvwraaDfwtLz8649JuZ63/kG9uKj0dKevejxN987s1/cF+jZLWk7T7csOeivvF62zfiThqhc5Ta2CQlFcyfrULLjaqZRqNJpc+eRqXM+u0QggGxankQCRSd0nVCPzhIzUjIsx80OgidhwItF9RSxxBRvBNygzFZ1rZFmRlMKHiL0emiNSjTlGaA230wkTAse0ouNBrTNrEZE0PfzV5IA0F58WRtFE2UqTDNvuKcC6pGmw5+3lqqDSSmIsQuoFVJqbJY2GPIpVHqLNUUwKlRhMV86k0sj70heHH0XSTXyaTvKuRayC3hm6PzkSF2VG1MU7Y8dnXEGCm5sjt0LIfAdpuBQC22YXfCLBO22CvnlNIE5z2rrif7wDonvIOAEp1HvbAuIw0YU6bvAtE5gg82CHbCWCp5nB40xK6z4Yn3bt7SQzPBMtvJEZojlWQbffG0zoB23gdaSaRZno4IMTqi84xzjFkMDgmRRd/jqoG+1Amb1CipsNvgvc3zvYvIgRhV2eFItXGWK1Ucl0fh9k5gEgUxcngfHLUokxZyzhTJnG9v8sXPf4zdvUP2Dw65+ugTHF0+4vT4hM1my/s/9AHu3nmE1e4eThqv33yN115+mXVb0fWN0/MzBMdysQAniOrsl/ZMuWAOdXvvpNni4LyfrRYVgmPTGkWFg+iJAmluOozMr4Q5qsq60sY4FtQFmhNEG0EsSrA2MY+6DxYLJbDvhHcc7HN0dMDjOyvcHMe1TpnO29AllcJ6o/RDj1ezo+SmlFaQpkgwGrZDqC4w9AZ/q01nBYQ8eLy5WX468wZctTE48OLR2QPR2txgqkHt7PWbt8MqxHkTX7SQa7UWXGyIZWoXQZuiLXHv1i36J540voQqeZpIzrNZn5OmkXu37tAqdP2CaZwQJ9QyWiRhE/b3d7l69RKtZu4dn+I3Ca2N480a1zI7i4b3gb3dXcr6lPH0hEtHB/gQmc63pM0a55Tdnd4GJd3AdnNGLcJiZ4flquf45mc4u/1FYhRay5RaZ5UH0MQSHqLQOUcXzFLRmpJbpeZmcDxvcEbFpOXTaJBF75QgDS9K7BQnjiCCqKNzHd1QqW1Da83sCjgQs61oMx5Ibc3SD4Kjn60kla/YdMxq5CkZYowshkhwsxrHObwEptSos2qiqRozwoG0TM4OrZ6q3oZrWmlqaqbaQJwphnKpdqmtdv7vnTd1zgxCvKiLuqi3YYmgXeDsHUvOnvI89u9OkFypq56X/8SKv/Phf/q7/uiPLxp/4C9/iuf+T99DOPn6G0/dW8Hrd/C3z2iP7D/UQ1OBenkPd75+y2+TD/7Da4TxUU7eEdh7ubD7yetv/QHCQ9bq06/SffAdpIM3PpDtToR44+T3vuGbUX1/4Qv/NtXDjdJ+W/3Kr/wKf/pP/2kee+wxRISf//mf/6rvqyp//a//dR599FEWiwUf+chHePbZZ7/qNnfv3uWnf/qn2dvb4+DggL/4F/8i5+fnX3WbT3/60/zoj/4owzDw5JNP8nM/93Nv/K8D8+xmmBIggngsI9apZcoWZUqFTcpsSqbO/r1cTd4sYluyogYtSzWTS8VJIEaLCYte0LmBb1WJLrIcFuwtB4Y+mow4NHaWwnLAtrpaGWuiiW3QRBtNM7WaLDU4TymZaUrkUhjHLaoF7xyr1YLd3R7VypQSggGiSi00nT3UYiR0bcowBBaLwM4qsrcMrJaOfiG4AE0hz0TnxXB/G2xQrvtDhOBh6J3Jr+dmUkSoVYjBUVwh1Ym+c/igRBFWccDpQJkirRh1fj1uaaUSvSOKJ2CN52az5Xy9QdVyhpsacTrnMmeqNyqF9Tgy5cZ2KpytR1prLDtPFEGbWOazh51hySIuGOLSIGjZsd0WtmPhfD2x3RqpvGFwsjwpOSulQS6z57kqisVsaTPYVy2NqkIu1liUmhBgtQjmLW7OcpS5P1wRqDCUxsHJxKM3ttR7W/KYuITyA8XzZ9rAj/qePbXceZyQpfHamPncWeUzm8iUIssWDBzXhN4PRN/Tinmvt6kwpsZ23PDyy5/jxRe/zND3RGfRT6vdXYIPnJ+d03UBaZnjO7cRdTz++JN8+Md/nNX+Eb2zfPXsAq4b6IYFYZalR2e+bp1jy2Kwv08U1DlG55lm+e4qBnb7wOAdS+fwQBRh4Yysn1rl9fXE8baS8RSEMRVyqWgxpkEtypRN5j30Hau+YwiBR5dL3rdaEbOSFfLMB4hDByEQY0dW4WzMJIXV0NF3Eedt2w5iQEbvWQ6RztkxEILHebOnBGcb1gAsHHgUaZWFd7YhxbwHRR3jDGaLwSKzqtrxo+Jw3tPFYMOkYDwIjwEd+/m5nVIl5Yy0yr27tyhp4uBwn3G94fat29y+fZtXX32NO3fv8Mhjj/Oud72Dp59+gp29XZo6un6JjwOHly7x2KNH3Lt3zPHpxNHREY8/8Q4+8qf+DM+8693z74vsHV3i5PWXmbYn1sg3i6vTWlEtnJ1tyClZ3FbJuAyUQqsT67vPcnLry/gAwZvKowu2nS7FNtzLhceLw7tgFgFgO1XWY3lg+3HqcOpYhEBo+uAcY/MZs7w0KaScCGJN9X1lzqKPxmLQQBcjfTT/vzalVbMgLTrbqtc20/iDgSXrPCQS71kNC1bdYEkZsw1lEcJMU490oSf4wFRtgCIyUNVTiw0Zm2ZUCqVmpjySayWGwNAPRImmepjVVl7MrtI5Zb8L7H+XgGe/2z7rL+qivlNVd3pu/fAhn/8vV/zBf/Qp/tnf/jv8y7/0c7z+w3tc/2NHvPfvf56P/+zf43+5Ov+69/P/fPx/4st/0c4dX680emR3B+6d4Lf54R/nIsLhwzXu38nSk1NW/9OzPP7/fo6dX372bdOEA+h6zeXfTG+Y2RbWwhP/+i7cuvOteWC/vbyHgwtf+Ler3vBGfL1e8/3f//38zM/8DH/+z//53/H9n/u5n+Pv//2/zz/5J/+EZ555hr/21/4aP/ETP8FnP/tZhsFItj/90z/N9evX+cVf/EVyzvyFv/AX+Nmf/Vn+xb/4FwCcnp7yJ//kn+QjH/kI//Af/kN+8zd/k5/5mZ/h4OCAn/3Zn31Dj1e8kBKUJPhg0ur7RPLeO7SqZXZXyNJo0gwcFj19J7RWbYuCUXmnZLLj1tQa+mwXgK01nAS0ecTPMCJs8KjqjJ4sioj5yXOZm/zOMmtzqSZjd6DZZL/OC6VZhFgIfiYMj7Yh6jqqEzbjRKPRtwgeaimIU9QJFduKB+/mzGL7223b7gz8VWBKjX5wCMpmNPqxOPcgmgxvsuLYOZxTnDMolzhQMW95UYuEEw+5ZpxC30VCjITgCM78uE5sk+uDwdtaswvkMVWcCMPQ0wVHLoluCARvcWCpJEBJ2Sa5FdiMhdgLNI+I53xj1OuDnSXRT5TW2KwTtcFysSCViTxZtFI0gDYueOq2Uir0naO2TGuwXHR0vjM4F6Ba8WXE4WnVBjgZtY3qDBorM7AqBui8J/qBa9vG5ZtbumLb2ZDhaFIeZ8FSIJVC0kZ0cwRchHQ58eQzCTcu+NUXD7m5PSaqWQ5qK3gXycXo0s5ZrJ06pYsBaed88XO/SgyRD3zw+3jyqWcIwfPok09w89UXuX3rDoeXrphNYLXi5OQUVaFb7bPcPWE95847N9shLBLdYGNNDKY1qytoBjwM3hFLYTNlVGFQRet9OF8z8jQQgrBJlW1pOAQnntQaWjLaGpNAiNbAKwbiq6WhHjQERlWkKW7eVgfn6IPFxqVUEDF/eM7ZZPVF2aI0hBA7hhjgvt/be1yzpl/xuGgQuoBSVOmwVIVakm3QQ0DnwUPwlnfvnLP3lHN0TllGzwZoWDSfV1ONqED0fgYJNlMllkrLFpvVivmKt+drXr/5Ou9497vZPdjn9PgEHzuOdvY4unTE3s4KEeX85B7HdyOeSk0jB4cH9DFw8/pNzjYTedyQtnu8890fJMaORRdYDB1VO57//CdZn9xi2k4s4ooyjUzniWmz4Zf+7S+jObPT9ywOjqjpjNwmFoePoNtXObn1eZxkQjQeRe+CWS2aqQWUBqqmQBElTwrO0wehVJOr2znFBjI0ZTMZt6LMNpOdrmdKlSlnljGQU8ZLx5jOEefZGzqcK2ZBqNbcM2+8c2mAJ3pYBEhFaSoUbRRVVGxDn0i0Ysd09AbSE8zOA0ArOK+MJTOmLXOuBiU3plpAFVWz3DSBEO1zRGzMQtcvWPWRbRrJaeK+M2IIppgRIt8NHvHvts/6i7qo70iJ8KWfDXzyI3+XfXdfxmubxL/3f/5v+EBcc9mvgN97Ape14mJDvSC/h3JGVwtYb/E3j2lPXX5ocFu9vIs/OX9LR30BoO27Ai73jdTyt16j/+A7mI6+fjcuVYhrWF5Xjn7zGHnp+rf+wYlD9vdo/dteMP2WqTf8TP/kT/4kP/mTP/k1v6eq/L2/9/f4q3/1r/Jn/+yfBeCf/tN/yrVr1/j5n/95fuqnforPfe5z/MIv/AK//uu/zh/6Q38IgH/wD/4Bf+pP/Sn+9t/+2zz22GP883/+z0kp8Y//8T+m6zo++MEP8hu/8Rv83b/7d994I+6UrnNMpVBQohe66Czzuio+GgCtzKAlGgjmr65tzghuYGtgg32ZZLyhtdDm2yEmb+07xUfbZrf7ctwgtCbUBN57a2YFgvOIg1yyRScJqDZyqsToWQyRLgRCMBBQa0Z7nnKhzdkO3tvGsRYDnrXakGYbuz4YMKq0QimFhklnjQwtxACtVkpRWsG8uKo0qThxlolrvRYEYXCRUROtGrE4eEFwM3ldmEoh+I69YQcV2GzXaGl0fmF+bYFSKtucWCytgR16T65KLYU2vwSlZASl5kSbZeJeIDpHKoVSIC6EEIRSPNICfehBrFHNpXB6viaXymrRg3iD5EkkhEDO5g8vpSGYd99HoY9Cq86o6aVRtZDK7C91RigvFWswm9LEmusuOPZXA5utAfVk/jmthXaqDBX2vGe/7+3vSg3RykmxaLZl9CZp7x3nj55Tv2/D7pWO8PLAcGPDneM1PR7nIqUmvBQWfSCnyul5omhlsQrE4NBaGTc3eOWlL/CBP/BD7OysmMaR4zt3uH39FqfrLYdXHyeN59y78zoHl67hveN7vv8Huf78Li8++1lU3Eyeb9Y4zU0nTi3fXRsFGPqOvvNMKpS1svTtwftoqo1pHmDV4ME5kgBdYKczcng2DwA++Nk6MNsmxFGrEgWC94w4xlSNxwD0ETrnid58t5NCFaHz1lAtgke9AQcRs+P3PszHWjUa9rxFFXSO77Mc9KL22F1rLDw41xmgDjG1gypplux7J0QfyTMEsJe5yQwdovaeaqJEmIdQUBACRjh03rNyjlGY5eWZ12++xsHRPnuHR3jxHB1eZnd3xfb8mN969tOc375O8D1XHn+K3UuHHB0eIBROztaMpdENS+q4wVWl5MzlK4/x7ve+lxsvvsA2b0nrM1NWLA5syCb2WmxSJvQrcrpHBroust0Kvt/lYG+HO2dnOIHohegVHwJabVhBjgRnDIT1prLsBR8dTcyy0byyuwgsu46xFja5oFVo3lHVogS1NCiN5k09E7yzOEMapWw5K1uGMJBrnNMbKqO9GWdfuOCjR6lscsUVU27gDBbXnClOaq6Ir+a990JrM4Ngji6rTZlyNZk5nlW3JJViLIRgKozaHI6Kd96GScVeW4P4OZor1OBw4nEhEKQhWYlDDypsp7f4BfBc322f9Rd1Ud+J0uD4Ex/47G9rwr9SPzYArB7qfq6Xc/7of/tf8d5/tkby7y0dVy/I/g565/gNgdtacPgrh/DazYe6/UW9+aXrNVc+NfHKj3e/wyvuktAfw+4rldXz5/gbd9Dx2wfZk76jLYdvy++6KKs3deTx/PPPc+PGDT7ykY88+Nr+/j4f/vCH+ehHP8pP/dRP8dGPfpSDg4MHH8wAH/nIR3DO8Wu/9mv8uT/35/joRz/Kj/3Yj9F1X5kg/sRP/AR/62/9Le7du8fh4eHv+N3TNDH9NojD6alJWRwN1wlLhNIgYFLGYt01ffT0IQL3Y5oaMRhRrQ/e4EtYrFPOmVLbV7bXsxc0eCOEO4cB4LzDSceUEk7ub+K7mSpsBOhaIEhHHyKtFYKH7VQe+CYFYcwVNOF8Nikw84XwokNwjLkSgkOC0oV5i4ltu4OPbKctaohvarEtdgzWQJRsvkYfrMGqGKltteyBSi7F8tBLpamwwFPItGq08FXnkZki52YZfO+7eZu9MWJ5bUSnlDaRWyF2gVIz6hvbNNEzZ41LYDvD7LbjZETrzlt8FZah7sRel8Uwb77EGcxq3r42JqKLBBfQolA9OSmyChwd7LNNE6enp3bRXSH0npwbNWfbbM7/66IFGIkahXssBY8aSTt0aKmkWs2XSmWTCn5uXLve40Wh2FCmNUcOld0ucBgCvTepKjONX8Qi8IYu4AKUJyb0gyMb3edf/+t38uLdR1gM5zjZgNrtuy6Y17UlUktsi3l0l2qwrDiDp87vvcznf+tjHOztWNPRDxw+co3dtGE8P2Wx7HCpt5ipKbFcrTg4uMqr4Uvme20W3eVcsPhylKoO34MraR7oKNspW4xeCLRcKLUh2NbZ2At27Fv0W7H4vdJoToguoEHnGLjKlMxXbVJxiyxLzRrjUnVOExCygmgl11lC7h0xYE2Z9+Q5ps6LecGNxeDmKDYjvPdzxvy2NFJTnFa0FcZU5027DYo6caScyE3IKE0cVawxL/NuU0RI9lcjziLQxmLnCntYDi+NjMm5QRn6nq6L5GmyBs0HxmnLZnPO9es36IYFUhPPfv4T3HztZc5OzxkWA49fu8pjTz/Bk08/CTROz87JqZCyRXdVrSwPrtD1C0LXcXpyzHZqpOaIi55pu2UA6HtCF8h5wjtHPwys1+fkWlktd2glo03YvXSZVs4ZN3fIdURRfHNQKt4LNdvwq4+mLV/2Du+VMZtlplRjIoiqZXx7RVUp2qi1oWJWFVFHGCx6zLiADRFvwyUvrNixrbcWaq3kYgyBqiap9+oJ0VPyDEr0QsOuXdwMgqtFZyhlJXhLmMjVAJM0ZVsK2kxt0SqIN8tJ8AYNqtrwweBt0jxOZwDhzANQFQQl58rGV7xTHMYA8N7T+Q60sXkbmMTfip/1F3VR34mqOx1/5uiT3/T9RBGOPi3I9PBScx163NC/YXBbOVgQ7313xJm9XWv47Kv03/9OpkMlbIXFLWX3pczihWO4e/JAsfBtRXuKQ/d3vwHT8kV9M/WmNuI3btwA4Nq1a1/19WvXrj343o0bN7h69epXP4gQODo6+qrbPPPMM7/jPu5/72t9OP/Nv/k3+Rt/42/8jq+rmi8zBpN8uxl+Fl2w7bT3ICbprtW8qSk1Fn1EvW1hmyjTmCwruzRyrXRe5obXEwKkMhFjYIgDwQdGnagqqAbENWvIvaJN2Y7W9PqodjHqhDhDo3Kx2yLmZa94NimjY2LoA330iGZwYjJ6cYTY44OnlYwPClpRAR+hZANPeZlBYQJFbYDgxS5QwfJ8lzse74SzdX1AGnbiHlw436exuyZ0Yk1HU2WcLHe67wKNZvJ3tdgkVBnzFnSwCCwRxAcWw4B3ZguYaiIEez6nlOcGb24wF3MjaPhxXLDc8qHv6EIkTQWiI6VE1UaMHSF07LKitA3T1Bi3duFWqw0elEqb4zqNkq0IBg6ziDjIUwLMG+3E4uIcwjRlBE+qhRAC4zaTSsEhdL6yt+qM9CyONBWKM3sEIg+ypUutjLOq4XDPk69M6Lsn8pXGF19+lP/xt57hOB3hY2DwnsXOHnm6Z1F4TWneMRWT03fBIaFRW2W9nTjc6bh0sMfmfOKlL32MS5eO+MAHf4Dzk2M7Ppf77OzucnbvFru7u1y5+ggHe/vcu3vME+9+H6+//jK3X30e7yDBnNFp28SxFBbe03eRkgul6INNoHNC6AItFbrWGFVZF0WdZ68zQJtToZRsUvj72ve5UbEBT+ArX3ZUYKoNcZ5FdPPARw10JuCcJzgHWkjVkZvZJaK9y/EhIMwsCFEGy0F78PPB2VAs58rRruJq46xYzFXnhC54slqTrbPEvYqQqzWTgpKdGuVe7VyQtNlzk8ucIe4sV1s8ixneaPtUs0ss+p4inlwt3k/EsV2v+dwnP0YaJ/quJ8aBnUXjsUePeOLJx3jk2hHjZjRAWK3kXO15SxN97CFntqlyevcO52dr1us1BM8w9EiZ8KVCCLQ8IjPMTlFWOzscHl3i4HCfs9uvU7Uxbu9yfO9LbM7PUGlUZsjgordBCxA6h/MOKiz7SK0ZFUHFzcPFRvSQnSDVFEfht+Wsyyxn997TilAF+78Wcg4MwdOjTLWQMFXRMDhqzaRSieINKFmtiW+itAa5VkqtlmrhPU4CgpBKskGYC/SxMCUDU9aaDeyXdeYfmIIoF0vDaAi+8xbDVgu5TnO0XrA4zFbpw8AwGL9DKpQm5jd3niCBKSeC++6X/L0VP+sv6qK+EyVFeW66BssXv6n7uexX3P1jE1c+Jl8/wuy3lwPd23nj4DYntKuHyEtv/Tizt2vpdstjv3xCGyLxlTvoWwCiJ6vlhST9O1Bvm2f8r/yVv8Jf/st/+cG/T09PefLJJ4k+WEOpinMVaATncVh2N62Yj7A1VC37W5pJpTV68IKzQCUj6OZZih7ECLnevNDdnJFdtdF7JTToYqQ1UMoci6YMMbBcCG2WzIqzjd6YMpWGjwFmv/V9SnUYAilXUi6kZD7cMJvQpXkG35AgqDTLXsa2Nzk3vAsGVmqNYkHOgOVyG47MZMch2AZpCN42tMU2omA+ZOeUPnqaOnJunG2LecCDAdo2m0xD2N/p6WZKeS2JVBI+OkrN5HFimxo4z2q1YDV0jCnTmloEEg0zRlkMlrbGVBUVywpuueCd4KNdNG9yJhfFhQjzq+Sd5cWX0pBOUTJ37h0TY6QP3ojLajRvBQM5NR5EFWm1rWvfObQJY8q2rYvWJPrOouparohzliVeK4ve03UGqVrEDkTYridiE6o4coi44PFiMvuFEza58HyXOL0aOb23z3OfvcLLdw8prsc726qnHFmGfcrmLqIVkUadYMqCCMSoRByr2CPOE3yktEIjo5vrvPTSszz29Hu4dOkyy+WS89MTXnnxRULn2ds7JE8jZ6cn+M5z6/ZNziYYt4ll58jbjTXQXceU67wBzBR1Jm8Wm9YaAN3gA+IdmwITgbiIRGfqE+8tRszdf55bs0z72ebRe28e7OCZSps5DuapDSGwio5cKlNttDkizNkOmlQqubb5fq25a86j83HdO0dTSLlYBnW0hmyTK6UUXCucnlpjLyJEscFJVUtJwDmaCFtV+xvUov9i9HROkGo+eFFYhGgqCu8otVo8mnMPnqvFHMPXh8CqX+C8sJmyRRr6QBlsSJbTGtcqlw4vcT5NlJPbLBC869hsCqHzTKnNlo3GuNkQY4fz0aLt+p4QPQqcnh6jqrSSCNIYlh25JTQ4tmPGdwOLAOdnG0IX2Zwfs9mOHD7yFHm8zvrsFiqNKVdSyyz7wFQSijCVjA923RjEhpytKF2IqBMWPXMzLIivFG30GlCVOerLogwXc2QcHYxV58xwIbWGo4GDoJ5CpZaK95ZE4WWOG7N5jCUtOJO8o0qrBbB8c/HQDYGsgZIr2holG+RyysY00ApaDZhoqYueEDpEE1OBnO31q61SWwUc4juGOF/PVqXVbF5w9fbvFo0b0AXGaXpgK7qob6x+t8/6i7qo70T584n/x7/9T/kv//z/65u+r//jD/4S/3L1x/DnDx/RpX3A7azQeyf4/aUB2R6i6k7/3RFn9jYu9+VXzCb7nX4gYNvw1UVU2Xei3tRG/JFHHgHg5s2bPProow++fvPmTT70oQ89uM3rr7/+VT9XSuHu3bsPfv6RRx7h5s2v9q/c//f92/zH1fc9fd//jq97sYuuqvqA9KxVybUgzuE9NG043xAn5MLcEDj6XghRyFND5sY91Yp3Qhf9DLAy73YXIq0mttstNWfTqKsQvUddsGxyBFVPmxtwcYK0RpoaTas1yc2csLU1nLNGulWL/VFxNBUbBKgg0oiDo7XKyVnCe6ON912HqjN4WgOZo4VaMQ9u3we880yp2Ja4dxazpWJ+ymYboDDHOnnTGaOiM/nYugqLTFOqhzhvw8+3IzF6olRCuK9wqaQG21IpTTjY3wMHmynRmqMWo1ErjRCi5Y6LkiaLOdumhldHFyMxKDh7/VJutObYGxaIV/Jk0t8pZ0IXWLSe7TQy1cI6FaIPDDHMkdJtzqoO5pHHaNfBecQrXhxdH+aL7RkM1kVrklqj7wdKrQQEosM7gdZIteC5zyCA2w4+pYXpDHaOLvGORyLu3pbFWWKnz9w7WfJvP/4Ep2GBiMOJHaNNG0EdU/asVoeE8LLpI5yQmxHUHILTwO5yxdHuLts0EqMdY+Itrunejef4wm/9Bn/4P/mfs1osaDnBoeJDh/dGhXdeOT25Qxd73vc97+bz0ynbO69RRKjqkKmSi8G28B4n5o8vRU22H8QacwRxkWHV0WNgLpptUacCM06cODdLijXuGGMLLxaXV4NF4tUmODt0mebjLYjQikG6ptKIYtnS3jlrmmJk8I4qWOSdKqJ2bDWBGEws3oBOlK3aFlrEuAoGzbP3gm+NTWnkatF2u51nqso6Wyxe571teKNl2Vdlvh+Z79MyxIM3v7A6R62F3tvApqK0Zt8X5ym5GGROBDTQuxUxdjy2v0fZHXjuhZfYmypDaYQIQ9/TSgIiq4MjpDXz2juHH3r2Lz3C0eVDrr+gBBrr41M6p2xLRUKkjVvE99RaWWeFEEEr02ZD7JcMi4Hx5C65bihNac6GmqXqnM1uDXIXFuwOS2iZXLZ4L7b9Tdli3sQ21SVVEsVI9RoMbRYcjWYk8tmLXxWcdHTem+Im59mfHmZgoaULdM4xhEDEId5RnSLVttheTCkQnBBiQNTGmvd5ByqVMTeDcWpCReljMDaGcwTXyDrHTmZ7bKnYICxnpagzPofzDN0K7w2cKWIAylTmpPgmtFrJopyUrQ0l/qMM4e/Geit+1l/URX1HSpWn//vGj7zrP+ePXH2eP7jzIt/TXecH+zcej/C/3fss/+z9P8mVX39jWdm6u4TtFv/6iYHbHoLbpgL1yv53RZzZRX0bKgQ0vm12s99V9aaO5p955hkeeeQR/s2/+TcPvnZ6esqv/dqv8SM/8iMA/MiP/AjHx8d8/OMff3CbX/qlX6K1xoc//OEHt/mVX/kVcv6KV+YXf/EXed/73vc1pWpfr5oWVAtCwzNvjG0pTM6VXKzxbBlQbwAemKdUHtSDC/RdZLXo2dvpWQ49i26gjz0hdjhpbNOWzZiYcmOqmSlNpFRQqjV36pmSNTAqtm0V9ZYlrEJJdlFeSTjfcA7zcDtHFwPB3YdRwaKLHCx3GLpAF4Ntv0udJbOO2qCUBs0a59bAh0jXWcObU2NKhZwbpRgae9zkeduv4MEFYegju8vOqOBeqJgawDzUjmlMpNxYxYFVH/FOcF5AK2PacL7d0LQRQz+TuE3Kvt2MnK/XnJyfst6eUstEcI4YgjVPM4l7OVgEksO2ajE4vI94CUiDKH7O7N4y5UwujZPTxPk6MW0KC7dgcAuTpAaLmKutseh7lsPAoutpxbKDUYdrgUU/EF0kTZXzTTJPeaqMKTHmZJv7CjFEFnEJ6iijsl4X7p5kTtaFs23mbEyoCOsAzy3gN1rlN3LkN7r38P9n78+jJsvTuz7w8/y2e2N5l1wrs6qytq7qrupWb2pJYJCxkFiENjQIYVmDBzRiPZJ9sD1CzAwDBw9zzthjDkcMgwX2GHmMjLEAAwIjJISsDSHRoJaQ1N3qpaq79lzfJSLuvb91/nhuZrX2zEa9VefzT1VmvvHGjYgbEfd5nu/38/2HPM3/wqP8q3qZm/aQ5JZgPIg2JblWCiofS6US6xLj1hisRqcVtNmxRrOWnaVIJOXIZjeSUsRIobXI5uglbt18GUTYbneIsVy/ccQYI8tlT4oDu91EQ5uQvlvwyBueRsIhnXUEa3Rb6y1NYCqZTdacbwGaMcTayEXz0oOzdCEoAR6FXzXrEWex3t2JEks0JZeLNt1GLKkZlTqL3LGNCEbXnHMcWKltlgPrz7Q5dqz3jj441p0ndB0+6EZ2LIWYIjllStKN7G2uAU1YhcDCq39/aaEzgkPjpiqQEIIzdLMH3TtLWCxAHLWAcQbjHMY6xGqzLXMmtfPqnXfGUERzxp3zdM5TaEy5YEU39p3RqLNgDC1Hgu1Y7u/RL1cc7O1z7sJFDs7sM+5GutATU4J5hlGbwdSq7/HFglIbwXvWe2cYNifcuvaqRivWGbqHo9XGhKfre5wYaq50ocMaKBnW+4cYkxg2x2zHzOkwEfqexUK95a0ZpgTDrnF8ktnthClpmoTzhonCVDKZTJ0HZ3km8O9yZGiRsUWGmJhyYsgKX7uxGTg6ndTL39RnXaWQyEwlI2iySiyVGJP640vWvHLR1Ik68z6q3rsyCRqkEtlNgw65nL5GYxo53g2cjCPbSVU9xjnEWIKztFb0dzVNnjBiqDNPw5kAzavdJBek6aA21vnY0m3Im4ApTDmSaplJ75/Z9en4XX+/7tcnu5q3DFf26F7dcfinAj/7R57hv/+Gr+Cb/vR/zE/H8Z5/3xm75OiLR5WZ3ctxOIPsrWmbLfb47u+39A7OHt7jUd6v12NJ3933hv961j28h+95/LHZbPjgBz9458/PPvss73nPezh79iyPPPIIf+JP/An+/J//8zz11FN3Ik0efPBBvvqrvxqAZ555hi/90i/lD//hP8y3f/u3k1Lim7/5m/m6r/s6HnzwQQC+/uu/nj/35/4c3/iN38i3fuu38jM/8zN827d9G3/xL/7Fez1crFVPYY7qcXbW4bxQpBFEL5RLLYw5Y5rVi6YKMSXGyUJRqS+iELa9pdNld21KDBbd0NSqF3feOfpe5ga4Ucna5BmFC9VasVYwqF810zBUQhAwlZxhmhIF3caMk8qxnTWMpeGcRUxhM50ipmFqA6xufUQ97ikO5KwRbEjFlKbDAG9nPbxQC1Ag9BbjVMpeamOIEetU4j3lpNAiUZiRbvAbhUzvPItuwaLvdGtsFXDkjErym7PEosMBkwRjAtZlXEsaI5bVq33bR5tiYbEIKq82ohC6IgQLwRZql9UzmhvBWFSYDEMshGBZLyxTFnwIWITcVEodgqWvlpMhYQyI02NtQCsGH5Q27rNu0mhp3pAL1lR8EHaj0FLGOctUlLI8TZFWDTFVxljVay+Gcxcf4ck3vIUbr3yAq1c/TC66XbeucevoBpcvP8L6YJ+Xo+Gf1zOINLIIMkddGWPx4tHtXSOXSq6BcwcPcvXVI3IBaYJfKMgqeIMxlTFlCpBr4drJTRYh0JnANEZe/OgH+OHv/4e86/O/kAcuX+LJJx8HEdI0UKtw7vwDSrnfnEDtODz3AGfOnONf/8A/Ig1b5QpUIVUoqKLDtEZsFes6fb6cbpWDqIUhZ9iVRghBIVq1UqaENIhNgWjOWKVQU6mtEivUuSEVMTProCLNUEqZYYsaD4YVgvcKZGuaP650bIM4gxSNG4ylYEvVAYETpilSjKETbbhVJWxws/88gcLb5rxwT0WcIYsOA9KcPNBZYeW8bmmNqmxaSeSU7gzHAgpJrArvxgJr5zCi2/xYK7YJvVUrRy+NaUxYF1gdrFkEr3n01uKt5+GHHubW0TG5ZIyzGCP0qzXTdkPnYEobbo4T+wf7hL7n0uXLjNubjMMEVe0suQklFwqJMVWkNoJVq0AIDlMTvbfktOH46gucnt5U+433lDxQ0JhBNWNXhpQxIrjxBGcqiwCu6LCKpn79SmMgK43equqgKUUNEUttmiNfasPbnlXfU8jkoiCHWNWa0hur9hgaLVdSljk2TQPMMoU0D1pzTfqkFyHmRKaSSwGEYDsdVqIZ8sEaxDgohlotmFn5I/p5TVN7jnWOXKJGMs7KlZQbuQyqiMhmHqwmHYSaQjWqQipSEStIEVL7zNg+faZ919+v+/VJKRHi+SUv/6aOt33p+/iDD/wo//n/9Rs4eO8Rkgo2FQ5/LvOffehr+b5nvvuef/3/6Z3fy99d/3bs6b018nW1QHYDcu0mZn3prsFt5dwae7KB6d628Pfr9VVtcV9l9Ota8wLpbnwH99yIv/vd7+a3/tbfeufPt71af+AP/AG+4zu+gz/5J/8k2+2WP/JH/ghHR0d84Rd+Id/zPd9zJ1cU4Du/8zv55m/+Zr7kS74EYwxf8zVfw1/6S3/pzr8fHBzwvd/7vXzTN30T73rXuzh//jx/5s/8mY8rziTmiojGy1jrkRmilVKj78FYoQsL1itDLZVxHKhzjnZtmTzvx4VKqwkxjVyhlkZDMCJ4p8ReK1YlsEX9gymp5NkvHF0XcEYvOsUYjCRSzuSSyPX2xa3MlOdKyg0ngFEfYsPMfnKle5cCxlaNRcsF46z6QEUBSsaqt9pZSxCjoKVRL1q7zmnuuRVyzLQm+M5i7UwPb4ZEpTWlsi86T6uZXAvZqheyUnAWvDOYqgiq9UIUoDQqNGqvXzBNSkEPziDB4a0+ztKgVktp6s1MreBSYRE8rWpWtbeav15zZhx1m+St4FcWcQZyo++MHoc1LA56WlKQUiyZbRrpjDY+ndem+XSbGKMQgse6yv6yw0oljoUmht0YVaI85wvHmFl6TwXGadJNZmlMQ6ZmvZCvdDzw8FO85c2fz4OXr6is3zlevfZRUh7JGqREHE+5cfMVLj94yM3Ok5Nu6V1rYDULvc0qhnY7g7plTrbClUtX6I6fo00n9NYqod3o5m8cE6vFgs5ZvFUCOWKoAjYUdicv8KEPLnnw0SfJpXD+gYc4d7hm0QduHW/ZbjZYr77izekWXybGaaKFFXGzoWX15UprOC9U37GLWYdETWjG0dk5ysooGNFZw2IR9FxE8MaSbZ432oaKY6qN0jT/W0FrBuNULm9FqFYoqYLoRtWK0HWOZv2djaY3opFUkhmLEthFNNas1MzKWpw3OGcwzbDugjb1RSFunoaRprF5rc3NITQxdMGynqXim9SYGrRa8dIIs0y/GEuVhrOCd4E0w+0alqFksogS3M2cve48Y2s0k/BWIXYFITiPbQ3fGUK/wDhHFQNGm2cKnD1zFmMacdwRVmcouSJ1YhF6ahlpdkXXdRyeOcfFB5+k0njfe95NjFGZC1koVSnjq96z7DTucDMOlFwxckDOiYSlt5bx+AZOCt7qoC5GbSatVKZc5kSDgBXLlCfG0tiNKpvXiESjUu5W2aXClDJdaDixmAYxVYwLtFY1MtEaVpqpSK5KWC/MtHFEJegIkiHFBKgawniPsYWWoA9e2RbJqHwcgVZJJTFOBWs9RhTUFoyj5ELve4wBsGoBqo3ShFJmGXpJ2BIx3mo+vNPIROc8vkVKKUwx6cBFdHBpOx1W1ZpJDf3OaOov3+XPDDjSZ9p3/f26X5/Iqr3n5lvWHP/OLf/Vu/4mv2t5ihe1mXzTV0wcvO81yJqUwqv/6Aq7N0WW5t4k6v/B/gf5q+/43Vz64XvcqBtgfw3Xb2FvbKgP7N/Vzaoz2HOH8PK1++C2z9bS5uJTfRSftSWtvQ50cr9MnZyccHBwwJvfsp63appJ7KxXD6E1eO9o6LZaalV54Zy7XYpuZYPzeKcXZ7kkalF675QzwVpWiw4raCOBeiEbDWmV0mDKhW6W5NKYGxGLFcglK4AtZ70NtyneQs568X5b3rAIQYnKo344FzVS6rY/Z2pF/Yk0louAFSHGpNsqow3tNKmcNSeZ89A1zskY6HulfRsM3nbUWtnFidYa3ipwq4Fmd6dMrRrvtgg9S99rHnFJjFNimEb6haXvAzklUlZJeC6GRe8RUZp8SjqwCN5hXSNl9UUPKeKDpXOWMSVSVHmpdbptDZ1liIkyb4cNwpnVCuc8MTfiFBELuWZSKwgV7y0xFTanURtUY7DecLhShUOuKtNvs0eVbNhfBt1oNiHlrKRkwIllOK1Us+SRx9/OW9/8+Zw5cxbvPS0najPshmN+6Ae+ixee/xDWOlppbIdG6PZ44k3v4ni3x+lW14YiAohuAGvWeLuSaDNJ3PcLzh4ILv0cR7c+QghCF6xCBwWWXY+gmfDGNz2vq9WIvqpu8v39h3nmbV/M2z73N2BN4MLly+QUaRU2p0dYF/DBszs9ZRw3dP2KG9de5V//0D/l5PQEikrCU2uMpSHG4buANfqcLb3BzJYPY7XhyKUwzVntFIVi9aHTWLLSiDnrEElgO+wwLujvFME4bcLTFOeIQB3M9P0Msmvq4e2NobNGqe5TwjqLMSopHoaRIMLCW4LV97L1+l442U6kVKitEEu7E4O2DhZjjQLAvKWg0v9WYarqBc9xBhOueqwRNsOEOMdeHxiHgVIS3gWMwJgb2znWzRjBzMDAEvNswfCUJmo9cUpnt51Str2ziLF0zhCswwdPjBttbiMsFgtEIM7WE+Mc+weHXDj/EI8/+QQf+Lmf4qPPPouzjXGcOB0TDvV3l5pn1Usj1oKvhUeeeoJbN65RClx88AGOXvo3vPiR93KSdiCVKRa8tzirZHIx+hnVqkeqodQJZxviKjlXnAhjjkw5z1nsOtQLWAx6v8Z6BWVWoKqKpYnRWDFn6L3D205TCJLS2INR/kBqmkKx6DzONKWwWyHlwpgSuczJ80WIOZMqLEKP8wrnpFlinKhUjHFY0aSIRmFIBYrBSKde+BaptiLidbgqDms057xWIabIsjvA0NjtBpqpQGU3jUzocTRFJDClynM/veP4+Jj9/bu7WL5fv3Ld/q7/4s/5Fpy9v9W5X79+1YJjfGDBi7/F8VW/7cf51os/zEX7S3PB/952zf/7j/37hGvbO3+X93u+5q99H3/s8MV7vt8vfd+Xw7cc3lWe+C8uc+OENo7Uxy7fNbhNasN/5Bptu/21f/h+vf7KWrh0gWbvzRJxv37lklSY3vd+/rf6d3/N7/rXvTN/jAXrZpIyusXR3OcKTf2fdoYnxaREZusCMUcoVSFVqE/WiGZu2yY4q3ncpSr0J+XG3qpXWWdLKlmeMtNQ2LWMc4nVwt6RRXfOEbOSzQWDtXO8meg2KXiVts8BT6ScMaLEcmv1mEs15NQQLE4qqVb1R06VsAgsFoZSszarohfBxkL1aMOalRLvO5XNe2/JuTIl9VHWqhfUNSvpWkRnNpVZet+E0ipDGem6DmmWvhNCZ2kkprhTHzEqi23SGKYR7xxiGn3nSEVp1Z0RcpmoBpaLnttXrZ3v8VIYzUSMkWotNIO3jlYKtViKVIpkYsw405GL5pD31pB2G/IcqbYdIlNu+A6cb+wte4KzbMcNIkJn3eyldWDnnHXvoVQFR8VKqw4ve9zaFR458xAuL1kulnfyzHPJpFRYrc7whiffwcsvPa/NW64Ya4hxy83rL7E4eAqh6Ea06ga8lESJI5SK9T1+bx9nlFsQM3TdZUp9kSHqpf1q2ShVJbl2plBLrdSijzfGwnq5pO8C3k6892d/gn51yFvf8bkcHx1RS2V/f62kcCd4aUzjjs1my6uvvMKwG9gNOxwNv7BskzDlCs6oD9s7TNVNeWctubyW4Szo8zelQqDhLAQf1NONvm+M0TgwtXQElRWXgnhPilm3tAa8tex1AeNu0xuYpevMwy2lsp9ZB7YpMwyDeqZbpfceJ+otT6WQRePkut5rIzffB8A6OBadByNMVcCoR940BSt6p7T6Yb44qrXSiWEVHGK9njsC1XZE67E5s3QGFzqGUqBkaq4Y62gLlet75+itkGIkFqFW8C1henOHZVGbQsZcLaz7BcfDlsP9Bd45TqZKHzyh9zhvsK3y8CNXOLr2UV587kPEKVOlEnPCCeqVzxHvHblWrLVQC8054pR0P19BmkUMLFeeaTQMsVCrJeVCQ5kNzkMWME046A9wrudkOCGPlTEWcBVnwIqS+Owc4SWm3hk8VpXX4JyFJtimU/lUbucLqh2m6y1CVe84Gje4cGH24neQRyoTaY5EUwCeEON8T8bjTcNbZRuUqjR15ww5i2aMk3QLXrNGXEqHdSonLziFWTaodWIi451XS8kMTey9xxuD5EYujSlOUB1Iwng0MaAZtQXdr/t1vz5tq6w7rr5rxcNf8yx/4dG/zzuC0+tGfmkTDvDly2O+9YsCT3zXa42sO534L374y/hjX/nf3PP9/7Un/xa/93O+hXM/eeueb9v2VzCO9wZuM0K9cIjshvtb8ft1vz7J9bpvxJedXlCWWpEZMPWxm+vXvNvaVNY5UzelzBgzxsAiaJPqvcdjMNnr7xH9vU0a3qOZ2LnRmlCKJ05KXA7eKK2dhq2vwYQa4DuPVN0O1ipKvS71zgbNezs3wZr5LaK3F8wc9YVucqXRqja1t7eRvXPESb3swTkNezKGzhn6rpEx2jiWxDAphXzhPdY2ajWE3rCbMinrdsfZ2QfsDVluQ7MyYyyznNpgDASn8szS5k1UhlXvoGjEmnNBm3yyeicrxOzJWTBO/b7OyOxNZ4YlNYxTGXowuikrtjIM+vhjBJHCmEaMWI0cE8vKHbJJxxxtI7s4y/bb7EGnMoyRaSzk1DhYWfpFYGU8t463DE0bFYOF2vDhPJ//zt+FNyv+yT/5Xt770kvsTSOPvOFxHrz8AMuuo+vWnBwdUXLikcee4fx7/xWvvPwCucodVcHR8VXOnn8Maw0xFVrJlJKoFbr+gK7rNVMZ9fNb0fisLBc4OLzIdvMii+AwNIpVUFTKZs62BttFqgjWaUNysF5Qs7Db3OAjH/o3PPaGp7l06Ty3btxke6ry0NAFXn3lVQ7OnqdfHbB3uGXabrjx6mWufuSjiOtw3rOePb+6Z1f4lxSVFUMjxUQvFoxhTIXeW5VxG8B6rLNMY5wjoJpuhY3Q9z2mJHLKKlUulc4Yln2gX3iCMUypaV69NbTS8E1f8xIzxlttcFLC1aoWhpnyrY5/baKkNaTAMlhS9WzQt9HKC9YaTjMgCmXzxrLywhAzu9TITeF5oIqShtCsIwQDBlKudHZF56zmURtD5zTizJdKTRZ8I83JA+vVAlsS02bHILezrnXQ5QBT1C5ha2NpKx0NK46+67l2/SqmO6BfLlnurzl7sOLWjRtcv/4yP/6D/4RpHIjjTkH1TuF5KTe8W4IsqDlzenLKoluw7lWlYL3Fir4m07Rl2G4xVuXatEq3NJSsXaRxjVLUErLuAwfrJTlFlnbJ8XRCyhGq0KzFGUfXObXiVOVfeG8wzdOqJg4svKeZQk1CygnvHN45hfJ5g1S1Dyi5UUizSiVYR2Wg5IgSOTThAmMpuRBLoZTZqyWVsSS8cRj8Hc+4xVNbJmVhyuozN2aOgyRhjUGsvt40zSWvtTHFmZTfNBIvxpGpodanrMnzZn6feCNYLDlBy/c78ft1vz5tS4QP/GHHe377X+DALIBfW1ruxfK7vuxf8t7vfgYzzuDB1njo+wzv/Z07ngnLezqER9ya9rU34Kcc1HtrjJu3Gmd2ssEer8mH/a99I6CsAv5gj3Z0fE/3d7/u1/36t6vXfSMut6OSqmZ8kwxtblBkNoQ2dNtr0M1RngFAIegWxorMsu+sF/Si8luj6GWFpNXCdtqQs0GKwsSc081sFwKIeq7FQG8NzQjGqK9W/bGggC4BgUXn5uxdlbJWwHslHk8xz7E72hSIrUoyN5otLU2bs22p5FS0ka4FsWAxBC/kXGlRSdLeC83oRjfWEeOVYk02BOuBhMw+2M75uelOKjePlSklppzpQk/fefLcBCN2llk3xlwpuTIMhVYci95DLpiqjxtT1E8ZK84Jxro5Yq7NPvJK8IKxlUIiF4hRY7FqtaSp0XdWG/epUepEdCqNbQb131shZjjdaSSbNeqhN8HhjFKseyy5TITOUGrRoUeDViyrhWd/tYeRjvPnz1LFsN2ccPPmLS5dOE9OEYNG6wxTZG//DE8//Q5u3nhZPeJZY5ByzqzWnvWucLzZYm2gWx7iXU9wgSbQatE352w7a7mxxbG3eBCzuUZtlVaF4DrIEHOh7y2lNcTqcEeY4/OqUGrCm8zxjY9w9eVnWa8Ci8WCNEauXr3Bore88uKLpNp44olHqSWwd/ECjz75VjabgpSBgxCoDXYx0VrFO0X7n6ZC3E14KyydpU0T29LAuZkELoy1Iq0yTPocKBhP5cuUhqkF13SoFVOmCxpvpXaJWakRE8FZnFd/MUVfH2O0yT7dDTgjLJybaeKaiGDR90mx2jXVVjF4em9n64Yw5sKuonFURnTTOvvDg4chR0qFphM8vMw56l5p6WTd4GOEXOdsdeeoc0wb0mhGaLliKfRdR0+hxEhwFt9ZjWZrjSFlppRoxuALECrbKgxTQ7wH79g/c5HdMJFihlZ59epVGg7XBY0N8x15nPS96y2uZc2Xdx5n1bTcSmVKmb1lmFUT0KphHAbwp+A6djHSjOCMAwEXeqxrtNbY7TLgOVgcME07mnFcOHOWo+2x0vxnbkbXdRhTaNWTW6ahaTm1ZihCLQYfhHEYkaqDnlIrFKF3jtwysagyQCnpCeOUam5cBQqlFWJTRZMxQsm6lZfmNUvcFs2Jx0EWzqwPwURevXad1CYdlLWi1iF0yFBtRqpQq9WkNqt58AajAxNrdQNuNHZyKGkm5BcyBYxyNaZYNenAVLVBlPsbp/t1vz5tqzXO/Whg9yWFg3ugSP/piz/I73zn53Pxx17bYq8/tOFbP/J7+AdPfc89H8b/73O+gz/0xv+U/fcd3fNt23oJuxG5dhPZu3xXkuMmUM7va5xZzvd8n/frM7hqm/kG96Xpn4p63cPqW1OPqrWaNSuiTbJgKLmx3QzsdltyzUwxcbqdmFLW2JqcmWJhO2V2QyGO+ucxqYSx1nYnukZhPJaSNb4slYQXYdX1dM6rnNwrkTfXAqVpjm5tiKk4p/5rY2YZuDPk2hhjYkrqvc6l6tavyZx57eisBYTatMmMWQnDpWo2b5N2xy8NjVYTm93IMBYm/SV0Xc/BaoH1joLVDG1r1BNqLYerNcuFeitbaxoJhz6vnQ94byhzTJwTgerYDpnjU4VALTtP5yxd5+h6hwtC7wM5GmJs5Jqoktk/03G43+G8MNUEtmEcWCd0ndVc8wKlKLxtypXSdJASUyZNagfY5VG3b7my6pTCvF549pceZyFmYTMUhpjJtdJ1gRAMpTViKpRW6TuHwRKHOsuJG+PuiA9/6L2I0c3gqy+/zGaz4+jolNIgpUiME2IM3llojccefzMH+xcYk0qxg7NIi9y69hyHB56zZy6yd3CePiw1Cm/Ouhdj5qgpUR9znkgJxF+kWxwyTIlpKkxDYRiSEsYrOPGYYomxMKTIqye3uLXd4K1wsPQszCkffO+7GYeRWzducunKw5w5XPPq9WPOXTjHG5+6AjVjrOXVl15BxLBaGjanp5iWcQILZ+iMgdqIJRPr/JyZpn57UZ+2m7eBuUBLVYF7U2aMiVwKqRTGqDFXtc7H7wPnD/a4uF7O9PV6R5q9sIbOwBQz05ztPCZ9P6YpauNurW4uRRUvrTXG2iiC+sZTItbGODdDJRd2YyZVq1vYuZGeim7lc2k6rJkVKRUdQFjncH2PCx27mLh+OhJzpdWMtKIDQIFSCqYkpOQZ4ifs9x1rZxnHxFiFTRO2WQn5QQx9CPQOemOxBobWGBsMOJrt8F2PtMqyCywWPTlllosl69WSg7Pn2DtzgHOOg/NnWe6fIWPYJQWfGQMlJfbXgTN7S/rgGWPidMiUAs45BMvh2YucOXeFYHu8BLz1BGvZX3UcLJZY0cZy0XXUJrgQ6HvDzeMbjFMF5wmhU2sHmsiAQPCWxQyJM1U/n5tpDFMk5sx2Hngs+oUONWCW/Bf6hcNaHXx6o7kJOWtzexvwZ0QoGaZJH09tDWs8tSntnxmchgQdyFgHRqgipAZVhFIdDafXolW32fM7ESPaSMepQrW6XW+igwNUlYSgnyXqRiAl2KXGUAqVOg9d79f9ul+frnX+Paf88We/5t5uY1eYr7zBHa8TCm179h8+wabee5TZM2HJtd+zu+coM4BmBdlbQUy466d3fbvSOzhzcM/3d78+w6tVSPeHL5+qet1fEhjbMLYqQdwJNuh2e8yRzbhjN0yMU6LWxHLhObO/ZBEc3np6H1gugkaDWfWzplrvKIVqnSNxqrDfr3j4wgM8dPEsh3uB/T3PchUIwRKCpZszgp3RrWWTqvFdTmFptVXEFEIQnDcqm7eCGI0sW3ROt/hOME4967Vq5vRtOb0TlT8aa+lDYNkFQudZrjpC7yizHDiVyi4mTk8nTjaR7RCpFXof6H1gmgrDlBEjTClR5iYvp8Z2Soyp6BCiFcYyIQLBWkyppJRxs3TTWIedZeJLv2R/sebs/pr9ZUBMw3e3vZ+G0vTiXGODDL7TocUUE1MuGHFQnG61xMwbbn0+nANnbg8IGutloOvVjz/dBmXZOkPxoAoMCY52hTEmdlNkyBVMo5mCdWpf6IJl0Xly08d1stnw8x94D+O45fHHn2BvvYcPPRn19o7DSEoqrw5dRy2J5eKANzzxFhbBE5zBGWh54kMffC/Ht55nb2kQrOZQ64uqwyIRSmtKXZ9GMA6wDHHBweHj1GSZpsZuiKQZhNVMBcns0oAPGiVXsw4IXt3c4uawJeWR4+sf4t0//kPsHR7y/HMfIhbh3LkDfL/kxo1jbtzc0GrlzMVLPPHGp3n4ybfwhs95Ow4IBqW8t8oYM8M4YXIixcSYYMKSRIilMOaqgDMjFKOvlwL2rWaIG0tvRcnnYihW5chBdCAic2yUiA6qCnAcM0MuWGsYS4FasLwWM9WqNmpU9avfzmzOtTLmRq0CzTCkws3NxEkqpAbeKEhxN+dS0wopJ1pNjDFphFVL7HWO9dLjnKdkODrdcToWklgyKoHeDpEUM9MUOd4N3NxObGPBOUcfAmIcuyxEoBhDHxwhBLJRC4KC+3SQVlGIYcPNPvpCHDJiehbLBbRKTJWj4w3Xrl5le/Mau5Pj2SKiyh/fKiVFQtdhlaZHzLPqZByIpyPGWnKO2iC3yvbkGOeXLMKStQ90XoA8x4Xpc7i/WrHuO0qJ8xa5gO+4ePYc5/b2sQ1qU/igYDRwsEGLnpoMpYqSxl3FWYc3HTRNDyi1ImI0bVF36MS8o5hMCCrk2sXIVHSoo/J2PX+qgPVOKfVkUspMQybHzG4a2Y4T22HDbjcp5G/MtNJw4jm7vsyF/Sss/IrWNPs7lzYHtmdqKtRSaKaqisrY+fOuMk2FnEVVOjNHoxlVYAkN21Cbgrm/dbhf9+vTuSRmnvvbb+BW2d3T7b79LX+D7WPrX/B3l39ky5999Td9XMfx17/gO9g9svdx3bYue6QLcPMIO959k1XOraG7Dz38bCu534h/yur1L003GktW67xpNjJTy8t8EagQt+0w0rswg9K02dOYpQaiF6C5NGot2ki3jHVKNe+6DmcDrnpEGp0fNIs25ztU9jjluanVnNuUksbcWIUZWWuprcxbbJVWikAQlUGmmshFt7/OGQzavNem9HQzw6uM0W3Mbkx0naVSmcaKF8NqEUi5kmsiV83erUWHYaYpsGtqFe8NuWnkm5HA8fGEC6K05FzugJREDM6pXN0Zp823aHb5qu/piqXUNMOdMq3e9tiCsZFSNZsbo9FRYyzkFJmToVRQ3AxeDNKEbDLOiGZzW5U9T1PBG48tkHJhFyOpNZadJ9eC9w2kILYSHKzRx1GyxlZNqdFSmX3+BnKla5FgHQvX0dnAZpc43U3EXEjDDT744ffyznd+IYdnznG6Gzl37hzL1Yo4Gqz3Gts0W1pzKVx5/Gne9/Pv4fj4OiXrlgwqRzde4vKVy3S+J9fZB2sNjUxLE2nczhFkKz1vMcTckNVllsvznJ5exc/QQDFAazgniFHy9y5XzaJPZc79HrBdZelOOb7xHO/92Z/hsSee4vT4FmfOHFJK5eyjTxJ3p8Q0YYxjvV7whsce4sa65+duXKWlkVwrw5QYUqLWSh+CZjEjTAjGKp085gS5UI2QG9gGy7lZyhRKbuQ4MdU5fk5AamEzVqRUus6r1SJnCI7mNK7MiZDTiGkVZ+xMvFdbSagWZ91Mi29qKWk61MAYppxIRSGI1nvEWsZSaSTEGJzRlhE0FnAb8xwC7nDOUXCMSbe0hglrlO6fW6GzgrMOsY6CwiDXnUesSrsFQxIYGwrmm7fteyEw1UoqjmwVSLeY4wibcUgV8FaHdxV9Ipu6m5cL3WjnNDEOg8anhRX7vtOhjzO0lvBW4xgFlYxjPcY2rLdMMbO3v+Lw7D5bZzg93rGQgHUB4zoClrXpGdNsaWmZ3gesdfTeshsmchb6bsHh2T2cbdRWuHHjZW5sj/UztjVaMcSYZ2m2DmZySUi1PHTxMsEIz137KLUmLAFvAjRteFO7HfcIve0UVtluy9gh10awohYA1JJTitXBG4UQDP1sWWhNYY01QZOGNY7dNKm0fr1mJZ6jzVVKbVSjCpxmwAf9UBIMUiqxJo3vywlawlnHGHfsYqZJAYFawDSDswYjGaERy+syqOR+3a/XVT3w46f8qZe/hL/68I/d9W3e1QWe/53w9Le/FmVmtxPf87d/I//3b/oX9xxl9pt7w0e/qvH0X37t9911GWgHe3D1BvbVo7sGt1VnsOfPwEtX74PbPptqisC9sQzu169Pvf4bcRy1ldkL6hBRkrj3Bu891EIsGW+ViF5naFaDO5CeVAq1Vs14zo3atMGnzhm1OdJZS0qNIY5MOUETYi5MNrPCq9wYzT5uUrEzeMgajeBKKSvFmUprQk6NvrfgZqp2VQCaEci5zBL0Rj9vbUutDFMht6YbmlYo1WKdw0gCY2ko9TgmZhq7wVuVcW5o82Ch6eNvCrlCknrZaybWSkwKilstwgwRUxl8qVGBdLUSamOxWFCrY5gMu3Hg+nSqcnqrgw6qsNlOSm23FodaB1LWfPSkTzLSKrRMcwnnLLm85qFv1uJFaDkTp8wwFZzxGg81R16JyXjXsFXorKeUinWBXUzc2mRatRjTFNA0nzOpNGpN5NiwBAVWOSGP2vS+9Pz7efPTb+PSA+c5mwveeWop9IslcRxoxs5RU4lUIgcH53nDG57hfe/9CcZdVFJ0a5yc3mR/9wqL5R7byVNMpeVImnY0LL5faxRXVd4/AqUKm6nn8NwT7IZbWDdnxqMNZ85CE6E1w7JbYLF4Z1guOnLWbW+oE+nkJXan16n5CvtnznF66wZjnBiHY8ZhpOsXLJdrXn7xRU63A+cvPcL68FlefeE5muuIdcQKrFdLbYZzoUrFIwTnyAJjTpSsMvIsDYNQY2WvDzhvFHSIYen0vNafLTBnQ8vsyR1LY2EdUiN1mthNUSXD1jLmRG6avZ5bIw0TwWaNOKsVi75Xp/mcGGvDhIAzUFLEVQcISRwO9YtLK0rHNjI3fDMvxwXSnCXeWUMwhs5axgrNQGfQCC5RGXMT0aa8gr8Ts1bUu991FGsoU6I13eob68FZUmvE1AizxWFlLdbre7zSCKaRU2IywvpgRckTtIwPAecXDOPEyckx0+hZLDq6/X3KbqA0S2POXq+VZAErLPqOPOw4ulZYrJeErmdzckTOllzAisakGVOoOTNE8DaQiWxHjRGccuVoGDgYJ1Z9IKWJMUUMhuA9pQjrbg+RxrXjm8RcaFVT5qvAjdNb5BxJSa0sfdczxYSIxTjHZlMoCK5TdUOwGutWKEg1GodWKtuUmJKCOVVpJPignn9ByKOeK8OQCU7J9LUZGo4xF24e3SC6BViDx+LFEIuyJBxzo18LpzGREHY5sewMxoGzcxJFVUuNOJhSw9imA8F5+NnuEb50v+7X/frklxkTP/p33snuP/rBe2qgv/Xf+0f8nf/xd+COhzt/99AP7fh/fd07+LMXfu6ej+MvftHf5C9919fRvbK559u2zmFWS9pme0/gtnzQ448W9+PMPouqpaTbr9e9TvrTr17/jXhzuKD0ZCtWtyqtgTi8CRjfICv0KKZ0R0YpRpm3UmEXMxVUG9sgzBFHmIqUxpQnYp4wxqj73FhirowRnEt4r9Jta1XmXKvG9wSrHsPdGImlEoJV6SyaE21EY55ERAcITTfStVSsMfTe4gwYqXrh6KA09YjHXKEVerH03tMaxJjxDnIxGDGIA0FYdAZrLcOUsE60UZ79l4t+RVcTp9OOUtTTiTX0vufswZqcE8e7Hd46vDWkNFIK0PTxJau5vEibAVjClCZK0ogfi8O4gHUw5BEMeGNppinNvqiUPuZKKRA6g8WhlttEE6VQx1rJQB8spkHKIyOZ0BSA5wQahZwgTgO5gTPClBPBNfb6JQvvCa4xlWn2JoMhk/JEzLehf5Xt9jovvPAh3vjUOzg9OiL0S7pugbOGJoLrAsZZXGuM40iWytPPvIuXX/oAMV2ld0JMlWGKnBy9xOXVZWrtSamCWHy/p/JtGq1WmhX10wLQGEY4OLzA3mqfGI+JqSJZAEOiYp2qJmxxdMGRa2E7RKwTlosFfeg4Od3y0kd+mtXBec5fOE+/9Jw5d5bdOBK6Nc42drstZ85foFsOnNx6hdPTG9w4OubcuXMcLDt9joylcDv7uUKrGOZIM4Fta9Q7KgjBd47S4OR4oHOO3sgd5UmtYLseaw1VGqU1Avoa7TYbhu1AjpMOwsQoiK5BqpU0DxlKKSwMBONgzhsvJTPURrNOvcQ5goWcKtZWbPBMMRGJRNH3sHOW4AxTKSAKbitFidrBGGJVX/lYNBiRVhlbRYogzuCtUK2lzK+jGENsgnOBJpqrblrFOpkHP5XmjKo6UqaJ0Wi3CoVGsKrE8cbMQDGgNmyt+NAhtWLjxBgjNCHfBgM2Q2crXecIoQNjqbkwTonl6oC2Xqq9ZoY8eiOEVc8wZVLzLM48ynjrA7SikMFSMiKBvlvRJECNBFvZDIkpJq6NA8Oio6QEBoy1TJNmeIspnNvbI6XEyTCR00TnHWIKqW5Jpehn9Rwj2PmeVb+HSGGz21IySALvBSvgg1MZuGjDnZmtB1U/37xXi0wqatOgGsQ2ap3IMdGqxbmlDmTtMduTiaPtdTaiYvjDxfKOBcdbS0mVKlW93sbjsQxxYtHNw9CcVMpu5I5CJISC9erNb61RYsOb+9T0+3W/PhPqwR/a8l98/Tv5cxd+9q5v8w0Hz/GXf/OCK//ra424PR35n/7OF/Gn/uhP0cndZXvfrq9cnvCf/S7HG7/j49iK81qcmbl+C9m7dHfgtvtxZp99lTMyTrTlfVvCJ7te94248wGRohnHZFLNxFboisYFOQdDGhlTQsPNNGO8zRe73lnWXaDNvs0xZaDgnKWWRp7l2HZ+Js28NTEO3Awi3I2JZBtdZ2hJG4YGuGIYS9UNjgi1CN5ZrDg9DqnEpvnYiG4A7Qxn02VhZYoVY9VD3q0g5czpkPDesew7vBdoMAyJNMuxg7gZBAYxZWKu2NpwpsMJ1JYIxtMvLNYK49Q4WKwRazjabigVhpg53e4QafQ+4I2nVlgvlJBcqUy5gAh96GgpqdTcaCazDYbeWULrWXRrtuWYqVTNw7YN2xpmzlfP8wapUmb4VyLGBFaHHhhL8BpzJlIQoyR3bxs5NVoVqhP6LqiPuGmDk2qmeVh0loOFpeRMaYJzns5BkEBDqcc2NYLT17e2xnPP/hseeugpzp47r7aF2jDWc/ZwxTBsqFlYLVd4q9Lpg4OHeeSRZ7h29dV5sCIY0zg5usHh4TX68BjQIQJNlBFA0ya1AU20uW0CuTROx571/uO8/Py/QayeD4bAoncUCi5YSIVhagohCxZjhUXomXZKIB92V7n20gfYPvUky+WChqHrlljrOTm6oaTycaLvAxye59wDj/HDP/pu9pcrFsslkULFEHMl53wHjJij6u9TrsRUsAYlgpcGpZFqowseTyNXlZcb0abNW6G2SqoaOVZqpqSJzTZSYtbBDIKRwlhGUsrz5lnAOkQsuTWCVz92RTDzUKDlrNL+1qhZYWypRVV6tKZ51c4SnKOZRsxGGQfSGErEuUDnHYWmGecObKsImdIaU4aFN/Sd1e32TOUy3mFuv29bxbRGKirRLjQ2MeO9qjWqiG7ag6MhxKay/hoLIup/l9oITn3xJVe6vqPEiVQKrTUMDbtYUmMiDjvwTiF5ZsJ6ld2DYdxNBG85Ob6FiEd8TzIObKDELYtFz/rMQ7x68iLDdI0hT/qeDIL3jeA7aI7rR7eIddRYxqZxfGu/nDfNOyqZ3OB4u2E3bTHiOLd/hqOTm5zutoRec8Rr1YQHEaX97/drVn3H0ekJTjw4jYFMqbBtlWWvlhjnhCqFPBaCW+CMPlfDFCml0nfqPccIgUpnOvWdSyaIxpxJg6UPGAPWqX1kiBkvhuAdDoW9xZTUQkOBJhQ0FcIgTNKIAtmqFcSL0vC9V7BhigljK4v+vkf8ft2vz4Sy24m/9ff+Pf4vf+g9d91Ad+L5nK98H0f/9EEkvua7ffgHBv7rf/8p/sSZ5+7tGMTwrV/6D/if//6X4m/em2cd1J5k1iva0THuxoZ08e4852XpccHDNN3zfX5CSgwEjywXlMM18VzPdMZpokVpmEm/w+1UMVPB7iIyJiQmiImWs9Lg58SW+/XL1OkW+u7+VvyTXK/7RrzWOlNsdYltb9OcDQxxpE6FmDNiwVmwRpu7FLWJiLmxDI5gZfZ360X4UJRy3AeDt2aOElOJZiuNEIQuQCkGaY2YC7tpB0bUxyva9quvWn3PJWdabayX6jGdUgJpCm8TZvCUxnDdzsD1wdM7p1FSDbCWzjX1JtbGtC00C6VofJQ12ux3TuWfMU+MaVSvq3eUprRhRAFkxgjLRUfNhSkVbPUs3QJxsBs2WAveOMYSscbhbEfnvYLcsvrCF2FBa6Iy4knp4c0ItRlqVdVBm/PXjRhSaUzSmIZ0B9jlHDiBMZcZlqcZ48Yahas1oWuNmCK1aByc7wRpDWfUFy6tsV7vMU4DYx6xGR1+mEYsA6lAZx2ddbTcsKIb7vXSsCyWXBKlZEoyHN98hQ9/+Of4nM/5DbSidPBxt8U7Qy0KCEw5EbqONg3EaeThR57mZ/7NuxmmU5xvLJowpsTm+GXOP3iZVL2qMJp6mNUXzh1bgmazV1otbCfh4Mxl1vvPkdOpRst1Hu9AqkFo+N7RSmWcIqkKe6sFJRaub7ekAqs6MJx8hH/2ff+QN7/tN/Loo49wenzKen+Pg7PnOb55lQ/+/Ad48k1PkbPw5JvezFf/vt/P9ec/QB42GFGbQHBgmyFNmdQqu1KxM0iwoRA0cQ7jHa1Vem8IRh9LTUoad85hneF0mkAEcZYxRnYpUnOmFm06pamFoNRKcJb9M4fs7++xWi5ZLdcEb5hi1Pgso4MchyquSi7kWkmlMOVKrRMxZrZj5GizZZwSMWdiKXS1KhzRKi+iYJgKdEBwSm+PMWI6x3IeyqWqTaSxmpIwDAnrPb1TwndrjVY0Pm2qlVwqLgRsH2giUDImF6Xui5Lip1JpRs/zgpBrw1vD2BT/vZwSi2XP+vCQk91EjAN76yXLvQWnR5kJSyyZNIwq1w6BSqXGRG2N9WpBSw1hpM2p14v1mnF7yjQM7J05z/rco7x69dmZuaBJDd41ZS00jzGBRsJjEEnklDhKO6zzmFaVFC6C8RNjruyFA6gNaZacNEO+loI0h6GRUoRquH56k5OdxxrPol+SaqKSacUSgtfoOtHNc0o6uHOukZImXtRSKBSmZOhsIEeFdK4Wao1IqerQp2QahdWiw1kh18RUy53PHSMGYx016cZ7ao3YqvIrjA7BMFCixuPdft3ECJ23OGlkqYit5KnogPJ+3a/79RlRV/7ZwF/7vU/yH535yF3f5r+68g/4mme+hbM/9VqUmTsZ+a//3u/im/7gX8aLvadj+Ib95/kLX9LxxHfdeyMOUNdLZBgV3La/UDr6r1HNCOytPrWNuLVw7gzbN55je9kS94W8hOqbxtL+StVA2hKpghSQDCaBjeA3jb2PRvrnbsKtY83SvF9aMSFTpC3ujWVwv/7t6nXfiA/jiPe3YTkqgQ0+4KzmU9dWUbKW0ca4FFKsULlzIW+NNqWtaP5xqo1UM95aEMhNtyIxq0zWz8TrmeVGznMkUq70wRKsp+bKNKlfsVQdFOTSyFTaELX5AvpFQFplKoVWG03Uo+6dwxmHtYJBG3mH0sS9daSqW1qxELzHNkuuRTOERTf++8ueUj23tpVas/qi5xipLnglt4uw8D1DHml1Yr3ocRLIJIqxlJqRVvHWEUtkiCAzTGoaE01Ec7rF4US9zCklqhigYkxhWzc0k+lnj7pGWzWqKHjLi2BItAal6OtBUwBfTQ1pmVYqnSl0vSFnIbVCK43Oi8r3jcVaKHVAjMpp/cJScQTXaE2lzt46qMJuLEwMGGvwxuKcZsaXEtlFfXFe+Mj7ePyJt7Be7lFKpLbEydERXdcxTQNTnFj2S5xxxJo4PHOeK488xoc//NPkopnvwQmb05uciTfxbsWY5tgtaXc24EY0rq7mSC0Z4zylWk6nntXeA1x/5QTpNJ5LRG0Y0xgZAItGzk1R6PvAGCOrheVgvQQK0/YVgizZbU8ZxoFz586Qa2VvvSDHfR5/asHm5JgmHm/hDY9c4vpH36sqDR8U+lfBGIPzjjROeKDMx9x5h/cWI4Zm9LUppVCq4GiIm5tLGqcxsx0TofN0teGojMBQtIluJSOtcW5/jwcvXeLCmUO8s8QUwShpvuZEd6fpLVjjMNZpxGBrYIVgPYuu0qYNrVvhL14k5cw4JU6GHcenG042O6ZSMUYbKmsdTqCvBilGm1hnsFSGWFl4z9L5edggOvizQsmJbS04I3irn0OdU6DhNEd8dV6p27Vo7n1plVQUpOhmSwpFI9GcMarAaAJSOdpNVOfol47lakEIDmmN7W5kTJEuOG7cOMUbT7/w1Bo5PtlwsD7k8OAse4cHLFZ7nLt4mRef/yCb3Y6DvZ7dqeXkZOD05Jj13iX6xRnKLuFsw5hCqQUmy7XTm2zHgdLgcLGHIXNtc5Mxj0icWARHIWNtj2sQrDClHcMwYI3j3Jl9jK2IsZTiiCXpULJUYspMptD7ghFVPhhj8KFnEZTHkFqj2MKULVPOpJywvuIstCBI0Ti2VAoYw3rR05A5qk2w0sh5ZnOQEAJGBB8anZshmMWRsiZOBBvwWZVQ89yTlCq7KZGUgIlBM+EXweOMKmqMkTk7vaky5H7dr/v1GVHuaOTb/tcv4499/d030A+7NUdfseXsT3+MnLw1rnzfxF//PVf4Iwcv3dMxeLF8/Vf9ID/6jz8fu/k4GmMD7K/h+i3szQ3lwcO7ullbL5Ab5pO+QZb1munJB7j1xo7hAaF0tz8z7/KzUzQXvZmmXc7HqK2Hi3DyeMD+O5fpr19i/7nM8sO34Mat+/nprSKb3f1G/JNcr/tGXCE6BmuETAVRmJQzBoeQ4qh+S6Oe6impp5AZamTEzNurxtQ0D7c1aM0wJghe6INFTMFYJSWDYMQirVAb2GCwzZBpGGPovUecRtwMaWKaGp13rBeWMWbGqBFgpgk1VaaskWHLztMFR0lKnG6SycnQLTydc5SciamQa8M6q+xngxKVUyZYR+87Fp1DpLEZjom56IZ/1JzeFDPDLjGGga5z7C8DtSppfm+11s3vOJFz1Ixko5LxIY4zjE0BT601ttNEa0LKEJylpqhy5dowRvdvqUaMQ0FfTsnh1lt8q6Q0k+qtIBS1AhRYdR5n1YdfcsZgNZc8T7PqqOGNAvJqVWVBCIlWDakmHTCgr5M3SmSvc2Y73nK4t8abxGY7gkDKCmhyVjCmY9GrX3a7eYXnP/rzvPGNb8c6hwDjuMMGr1vUnIl5woaeRd/B2Hjoyht59tn3kfM4D1QMzmbydJ3lwQWmGHQz2eoM1IOShvk8VY+xzH7eXXSsV4+wWr8IMlFLY7uZNA++6aCjuYoxAde0UcU6Dnq1a+gWdyQev0qpidVyQawQhy0/9a+e45EnnuRgveJgf02KylG4enKD5d4BU0yMc/Y1WXPEa614K+QCY4HQL/Q1LgVDoxPdhpOVyJ+yDqIQjSWrpbDyllQLQ6xYVEkypYTUTB863vTQgzxw4Yxu0Oe4N6q+L2uDWuf33DzAaA3GlKk56rFar1nUdWBMEReWtHFgGkeaMex5ODi3x2ZvxW7SuKvdMDLVRNcattNc7D5YWlXi9n7fsXAKDKsCmyFSnKXrAtMYVc1iRL3e0tilQirQG8PKNcasCQoNiLWpTaVCsJqCoDYFlW87Y8BZTGna7BuhlYyplmDg9HRLSZlu0eO9JQ8DvqHy/qTblIO9s7zpbZ/Hm55+huCFG9de4fToJnG34XBvj2GzY7k+R4zHDLuBg7PnuXTlzdx4YQK3ZUqJbczkJKSqShakkeqOVhvGCJ2fEyoM5Ai5RPCeRdeRE+SWOez3WAbLrd0xKb32GopYmmQKGWd6et8T06gxioAT5VmUbHGdJdVRc+yDxwVDcEvGMZLjgLM60MgtzWZyhwWyoADG0iixanqEVIacMG7mWZhGLBlTPaUoANDZjPMVIpRaSEXI1TAMlZILHsFJI/SWRVD/v7NNM+izDsWkGRQ4cr/u1/36tK/WePR7In/nq87zdXu3fu2fn+uvfN538ucv/x/pX3otx9vfGvgv/+Hv5hvuoam/Xd9y7j1817/7RVz5xx/fhrp1AbGG5u/+sr8sPgXy9Acu8PyXX2Q6o9cGv7j5lqJbbm47fOb/6qX3r7Ipb2BHQZr+fzMwXBR2lzzm8y4Sji5y+OHE6mdeoR2ffCIe2WdEtXFCpkzrXvft4adNve6f6Voa1TX10Lbb5HNBgsEbQ7N+3ppBq4WcRoaU0TQwIZZMnd+4pTQyTS/y53TbmFUqXDOIVSliZyzOovm/M2SrtIo3QqswTUkvEJtus51oEye14KRRDHeyu6fYtDmKFe8ya+twxhKjPqYKWKsy3NIMgqP3uo2zVsnt0jKL3tGK5hxPSaO4gg00A2OtuL6j6xx9ZwnO46TDWGaS9kjogjY+GKZc2YyRViqLztH3luo9rTS804vtcRpp4sg546z6tFsVeucUelQE69QLa1whF4czHucrqSpwytHIVhAyVqDa29Ajpbo7pzL23CAPIwajF/EtU3KlpkIfGmIaxjiKArkREYJZaENQIjE1Glazyo0wpS05F6xrCEKzhlYyzTg6t6BVOBpPqK3wwvMf5OErT7EIHQ2Lc4GUEkYMpTVqKUxxwocFzggPPPAYDz/8BC++9D5q1hisUjInRy9z5uyj9CGwnXTNXCnUOCGACwuMvT3mVQlszZXUDjg8fJhbN55jVwtGKutulmNXgxODEUvovP4eq5vxVhLbNBF8T1idB9szjpGuK7QyIlR2mw3HccJ3gTRtMLZjsViyv7fkpecnmlhqnqn5peKNxrUNpWGc0sgRIRhtln2zrK0hYkm10dtGFUMSjRAMaNTXNhdyLcRU2U2RkkYuHx7y1JUHWS9XtBxBGuI8tRbGpCCzzlvG3SlDKtCSDjOULUgat+Qm7K33tZFtBaSQxyOmSW0frTZMGRHnefEosl6s6J1jFMFrPhybcWDhPYXGqusIIYDltn8Aqg4ZplIhFZz3WBGCgZYTm6HQrL7HxAq7VChV368VwXc91Ma681AyuWQKDRFHbZq7XWvDNB3qyWxTySnjvJsHR0DO2GmHlIQLHlMd3q1Z7x3yBV/8pQQb+fDP/QQfeN/7OLp1RO8dU25Y7zl77gKXHr1C2m052QxMww4f9qA54mA5GkeqwDosZ/CYwQGpZGKO+lkaoRVhajrwqS1DVcp8EKFIZcwjYEmlkmvGiKNVi74NLQtrWXrP/nrBZls5HXYY21guhGGI+G5BpeFsJeeJMRa8X3C4t8+OU8Y0kOZz05ignzdimMbM1BJSFeSYm1I4rZ3FUa2xGwu7qWCNJZiC1UBzqmlk07DOkCfNEZemGyNroObbg5PMkGbmhG000/Bdow4KN7zfiN+v+/WZU+H6jv/z938tX/u7vx0rd2eg/aI+8Ue/xPHU//Axf9kaj/7jyP/8VRf53+/duKdjWJrAb/6an+S5H3gSM6Z7ui2AaBzOPW07mxVktaR9shrxC+d48csuMp395TffUiGcqMz8zjF+DHKjWaF6sBNUD6V/TcZuRyGc/tLf2WZsSg1w4xnPrTde4eDDmb33vPzZ2ZDf3op3+5/qI/msqdd9Iz7mTJPKlAVrdHsTvMFVS8mQU8EHzaTOpeBMo3PQTCUlSEXAFJwYsmgmeasN5yvBGyV2T2BFWATDIjhqqeoVnyWqDo06qjTKHDuWq3png/c0rzC5cQZUhTlDmNpoCM40lgtPCIZcBG9htXR4H+YYNSgl3rZNI8VinVdJvqsYu2K3G4hxwphEbSqV9MGxXPTsF80Htt5Qa2ac2pxi0DANXGepLbPdTRgxrHtH8L0Swq2oX7IWUo5M40iuI6VWNuMITb3y1lq60LGZthixdJ3XKLY2UcaGEYd1SYFtRrPTa2sEqw2lMY5aVO7f5qEIYhWI5IVExWDY69ZMNVJoDOMOIeKMIWlWE6ZVMAkxFWPV/7mbMp03LDsHZErSBjqneXcuBW+9AvIkcTpOtGYJVrh57XleeeUFHnv0SUpOmjVdG6kW4jhqbjCRGCNdMHgXePTRp7l+7aNkiXR9Y8qFOJ1ycvQ8y70F28nBvM01fomxomOfNtP75y1pro2T0XJp/wrm1ktIHek7R0qNvg+E3iOipGdIdIuexdJysttwvJ1o2ZLSjkuPePYWHULh9OiISmB1cJZhHPDesTk+JixWnHvgYdJwzMn1l/DBULNgg6VmiLWoNLgowbqmAlJZdw5rNc/ZAEOFYhwlRT1XrRDjRCnCYtETW9FkgFwYpok0TTx6/gJPPvQA3nuVnIulIaScuHXrBu//6Ec5PjkmjSOb3YAR2E4TmcreQn3KN8dC11kWwTNOGWNgsWdZ+I7DxRlWyw5nexbOcHT9mJQSp2nD9U0i1cbheo9HLl1QBUiqOOtpYpnzr8ho1Fmas+BDU8vKGAu0yi5ngrUE71QdYrUJFGDhhQqMueKdJefMlCZyqeQGqULwM+TQaEa9MULnhAnIaKSXq5YzZ89wywyMcSAWj63aWIauY//wIm9+61s5uvoh/vWP/wtOTgascxCW5HxLaeXSc/XqVaqBbmFpN4+59eqWahx0h5S0pTV9ryENZx0pVvplYBy3OiOqmuPe906TImaauFiY8o5Cx7Lr2MYtYzbkrPL/RiHVjMuqZKq1cjqe0oUV3gfqcMSUDbc2x+Qk7C8F0IhA54TSBCuVFEd2s4Kk1UJpqlZwTo+5Wj2/bLN426gl40PGWEsrhd1U2U6VTKPzjWojwXp6a8mpMaXGlLPaMYyZ5y+anY6BWox+J5hKNQoptEZtTc4GbLcP3Js09X7dr/v1KazWePS7G9//Ozp+x/LummArhv/wd/wgP/p3Px+7fa2RDTd2/N++52v5ut/7V+66qb9d/8/L388Xf8E7ufRDN+/pdgDkoqkZd+EP/9iqewvk1vEnXJ4uZ8/wwpdfYjz3yzfhdhTs9AubcNDh6Z3/zxBOGuffc4o5GairnnSmZ7jo2V385SGZMj8smWejJcCrn2e5+cwVzv1sZv3Tn30NedsNyP6K5u9NtXG/PqaMwfQB7gLr8LpvxEXm7NYyewGbQpJaK3TeYVwjxUJrleAti97S8Jr366rGmFUzQ7MaLau3FxGqgs0RD05Ubk2rNCrGKvV6yplUG87UO37dVAumGd2GtkbfqQY15sY4aeb52i1YhUCWzDYOOKP+0BgLEgTnHAZL1y3JMbMbRgplJm4bduNEbYE+eAyw7Basu57adNs/TYVWBdcMzSltqNVCLRCcY9GpN71hqVLZ7kZyKZgK3nQaE2YNwwzTarVSasJKwBr10DoMrVlM86guIOEdLF1HcEZl+TGx6Bb0rmO728xeykbGKo24trnhserJb43SMq0aerfEW8suTQRrWYQArWCBWqB3S3ZZdOQpGWuV0i0NokTUMuzoO4eVSimR0gq2tfk7p2KMbtBbgzFmqqicPs/nwjg0fv7n38OFi5cJLpBzxhg9Vrdc6EClqTc+Rt28nTn3MGfOXubVV5/FGoMXRzNwdPNlLvQX8PYsMVmVoJt5JCIKpDNG891bVSBdSo2ToQe3QuoOYwzWK3W95EzXG/WsOovUyslmwzRVrOkYc6M1zzBGXn7pedbrNd4HvHekaSBmBXINu2Ns13Pj6kucHt+ihbMcnr/MjVde0sFUSkrhlw4xFVdVJXIQHPveMOXCkCuxGXxplKrb6lgzU9SM+847WsmkmIgxMg47Upx48tJFrly+ROcNKdc5D7ry4ovP89M///M8/+pNrINV73C2ETrNUSfCOljWa8fxrREDXFh3iDOMo0ZtpePGidlx6ndc2F+xWnR89HjCGPCmsSmWKXWk5jixI5sh8tRDD2ConAyjch+qDqym2nANOm+RBrYUyjRiSgMqIXQ6kDDC2ECqXj0IqO3EW5ZWGKfENIxMzmIwGGt0+HRbhT9HK+Y6Kz66QEHYDQNeoNtbYX3FZIiaGog3hr3987zj3/lCxqOP8uM/8kPcujWw3t8ndJ7cwIrHhRWCY5gmbl27xuGFQxaLJcPpKWcefpSzZ89y44VINzjGpJni4zhRayX5TKkNYzt6Z6gWgg9YDNmAWMMYdSDVrxYKMfNrxjRBa0w5Mk4q0e+WAWtU1l1ojHmLN41UE1ICrVSOhx1jHVkFT6mJzjmMEVLJbMcBEYM1Hi9C6CxGGmMZsMazXBhcFkpstKab8GADYjy7Einz/RbRWEQnlc5bStXhoJnz5r0Reqtxj41Kikr8d14BhjSI833EWhFr6e0al+/77+7Xv101b6mzdNRu48cVa/WZVM1a8mFHWjlKL+ROyL1uPy+8Z4vZxU/4MSxe2vDHfvQ/5MO//b+769v8iXP/iv/l876Iyz/4MRvl1njsuxPf/WX7fPXq3rLBz9glV772w8QfO4eku1fVSK7IdoAu0Ow9IrHlE5/yIAf7vPRlDzKeb9hRk36aRcODMhx8SNNCdhfNa5L0X6HyUrj2rj3csEfx0BxUza/9NW8L2uibIqR145UvsISnr3DlH12Hl1799XionxnVKrIZaGfWn+oj+cwsYzh+ep/V8AB86Nf+8dd9Ix4jTEmlscaA90ou3k2FXBKd9YSuxxuvMvIGtEZKjYogRggWjUiiYqURrNAE3X7Vdgc6JWjGcJXKZqrEotnIGsckeAEqeCzLoEA1jX5KeO9xrsNQqLlRU2MsiSwZjCG4jlIiuRZMMUgsUDJCVE+s6zlz8CAPPfIWnLF89KPv59rVD3Gy3WFEM8eXvVdQUBUoQmWOSauFcZiIOWPn7esYK6VmVougtOkSqU0vjntR6fNuGtjGSfPABSiN3DLVBrxxnNmzrLoVVgzbYUOl4L2FdjtmyRFYYGsjjjuF1SXd1jsHLjhKyuRcqKZgMBR0U7buViycp81wvFYGyLBaLBDpOB4ncsks8YwpY41nzBnvPA7NkG5NlCEgloYCxMrsQzAO7JxHXKtQqVgRhl2e4+KEZHQzf/3qRzk6usljj7yBOE3qX0+Jvu/Z7bbQIKeIiHDmcJ9cKg8+/DQ3r78EJZGSeoDHYcO4vcFyvU/OSi1tCNIqIhaxmpFeRAdKzNL3XQycOfMYmxu3sAE6Z6lFMM7QmvIHkMI4FnKrpKnSuTXd+iJdf8hjT7yVd77zXWw2J0y7HUe7LWfPX6QLQoyF3Tixriq9PThzhmG7JW5PKSkrjLCJSrSBlAulCBj14Y4z8b1aRxW1SlgRxDmohv1OM8il1tnrnBiGgc12y6PnzvL45QdIrTLGxOb0FGrhxVef54VXr6nPeL8npwxzBrVY9d1XlBGwv/SkIZOGzCs3d+wtVYlhZjp97wz7veHVm7eYciPN5H5qY71YaMa91XN2ihMZWDqLR4doDcMmqsolVstY1V/ccoEqLHs/Z9yrVaRZIc2RbVaESmNMVZtxCy1FQt/jQoBacUZ9+KlBagWK4K1oZnsF2gg1MeTMIniat/TOcrTZaKzfwpNi5eHHn0bKjp9890+wi5m9CxewRih5Yu/wDIvVQ+ytV+xOjxgPR6bdQE2VvcNzlJgZtyece+A8u/VZat2RyoCznoGJWCfi6cRi0fPoAw/S8si105sgsIsDU8qIWFIq1FIZ446UI3v9EqnqB78N1lEFTKVGIc1DrVubW6oOwZJb4dZ2RxaDSYl171i4oPFwc2rCFDO9tXgDodcoOiOGGr022bXegUEaW7Ci9h5weFOwttJKIWdwohaLVCY9P4oqHDpnWAWDdw0xiVIzRpRHUlEFQ61CHDO7QVUS+2GPZX+J1D7LgUD365eWzlt/7RLh5tsPefyPvp+vPP9TjNXzbe//rZjvP8vlHz5ijhT5zCmZibbzwPpjq1nL+OCSa2/3LH/TdX7fY/+CK/4mvUn0kvCSKc3wx7/7G3njf58/8Y+9NR76e56f+C2JL+juLsrswCw481Uv0n5kjXwMnbu7tuM/+f6v5yu/8u6l7rfrrzz+t/nfvf1bOP/uu/CrVzCbHe10Q6sNLp77BVLuu6paP7HbcOe4+sUPs7vUkAp+89qWuqkglbQUykLuqpEGKJ1QPs44bGlgJqizuy6vPr7f85lebbdD9pbzou5+3XWJcPLGfW69ybB6z90pCl73jfi5wzXjGOeIGqWI11oZh8YolYO1sDBuzpXV6DIxGtHTXMPYhmlJt9ezL7zzQsyNKc6ZvU7BSt40pjipPJfbcVwKBUO0KSpFY8BCUFBYacIUNXKnDwpya96wWl6iCwuu3/goto3EmDSyqXcYM8OEnDZ8w5S59NBb+Xd/2+/lwiNP4r0jHt/gX/7w3+dfv/ufklPRDU8AsKRcqc1wsFwwpYR1hfWqYzfC6Wag67xeMKcJTMWJpVYdJy4XS5bdgjGecLzZ0aSxWgT9+Vy14cuRZd+x1wdqTdzaDlgx9MFSWmG7zWxyZtEH+s6QamXI6vOtTZvI4IWlD+yq+mCdNKQWRAxDKogISEZEL94zgnHCGCeaNPa6QGsd0zTijf6cxII3BiOeJgqHkuLnZkdY9PtMaWSKO1wr5KbqBG8133tMiWoLxqrqwHvNPq5T5Lln38sjV56g6+atuIjG0YG+VqGjDx2r1RoxnsefeIYXnvs5rl3/CNZAbgaRyunxK5xfX8SaFalaBPPahYoYasvUtCOngnUd1gVSgrq4wN7eBYxsyHPs3sWDc/QmM8VME49II+4g1n3C+iEuPvxG3v6Od/E5b34zOQ1MKXJrc8rV6zfpF0u6fo8Lly7hbAb0d7ZWObp1g93xMYsuMORGNBkzX0PFetuLVskYaqvq1RbRjHon+BD0e71ValEuAqK33cTMbprY73ueevgSlsRuuyGmzKs3bvD8yy8yxIG+77DZ0Ch0naFWEKPxV52zrA4Dt44HdruEdUKrGjl1axtZecOFw44hRR1IlYbDspkyWMOi95wOlVsnEWuP6ZcVMSs248g4TixXHRijkVk5s7IabwWG4KC0RnaG6gxJDFWUFD8VhRE6azVXWvR89s5S06RKBRfUcoI2dYp0FAxtpq6DtErNiZwKm5QQq2T1MY0cCjRjaM7jDOy2J/T9AX1f+Zl3/wg3b96iW6zIacQv91islix7y/HVlzh5pSFd4NKDD5P2tmyONzS/Zn2+kbanxDHSLy+wO36RVe+4ebrVaDKncLzWsnrVxaAx20KaGlNMhL4RnMOHDmcrtRio5o5CxxrBitFH2hS05+xyTp1ImBmsaKSyCJYhR5ztlL5vhJILdiZ3lFJoxtJbh4iQTaZUWC86hjEBGYtX8F0pDKmQqVgq1lqCt4SUqaZiaNASU4Jl6BFnSUT9GeeQpkqA3aixeh0zxqHOr3FqlGrpQ0cwZzHZEnefXTLH+/WrVAWzG2nbHZw9+DWloNMDK/7kn/5Ovmb92jn0jV/wN/mfnjnD//cnfzfuZPxEH/G/XYlQloHtlQU3PkcIbztiGSK3TpfEkw5zagknhmob5z//Vf7Aoz/A091LWH6FRlDg//Flf4s/M30ddic018jLplvQvtK/7Nj7SGP1ciLcmjBT/rdSD6yfPeUP/qtv4Od+09+469v8laf+Jn/oyf+E/fcfv/aXrfHY32t8z29b8uXLe3vNHnZr3Ndepf1k/wua+19cMkQ4OqXljKyW1PMHlNW9q3HkE5zyML39MU4f1/83Se404XAn0Ii8+sRv5T+2TOLOBt1tQW4e3y2v/VNf1iKLBThLWy3IBz3xMBD3LXsf3mJ//qN393tKQbYD7eCzdBLx8ZQIJ2864MZbzC+wTPxa9bpvxIP3LBcdKaY55xdyzrQykRKUCjFNGLGYZkm3c3x7KC2RSiHnhjVC51SySGuIgHeCEeg8dA4606hN7vC0cmnkLKz7noPlPkfjKUfTFih02YAD6y1MjWGXiAOs1msef/xtPPLok3hjeP4jB3zkxZ8lphErsOwtpSamFJEmWKNxO+uDQ1JYM4gjLFb01vLGZz6fFz/8M9w6uk4lYVpluVxChePtjiaJaYqYXOiCJQTH3v6SnBOtqK51mCJkjXpb9A5D4mR3RK4R78AZS2cd2ymzmzLWNUpLhCqMIwqZKjPhXdejDKlQgb4J2ykRS6LUhjOOZacUc28sOUWcs1jjqXFEvKcPS8y0AQqpqFfbSGAR1lhxjHXEGwUwjeMWxGKtg1pwCKYlvShfrVTpkDVPuJnCmLbknAkmYLxBWqGVSikTYt1MiNesOWM0vsh7fZ1ffun9vPLq23nkoUeZpgljDbthS0yakS1z1N1muyWnSJm2HKyWvPyyRYzBmUpzjXE8Ztq9SvAPEVOHWFSm1Qo5bkg5Y63D+qCbW1Hi+8nOcW75MHX3fsaYsF4oZeBojOymgjEB8Wfp98+yFw753M//Ap549Apnzpzj2ovP8uKLr1AonBxvCN0CG5Z0yxUheHKcOD7ZcXDuIs4aXn31FaaUWXqPmFmaHRPGObyzFKqqLlolVZVRZ1HPnK1KL3TeUUqmGYV8pdw4nWb/fZx48spjOCscnZ6y2WzYbre8fPUVYop4Z2mlspsyMU4sOkOeN+/L3jNMGZsbZvYoSKvsrzxnvLDZqUXh5skADVJppJxZ+MCyN5wOhZgthweHjNOEdT2r1T5d6Dizf8iq61RVUNq8zGmzTBvEVjbp9kDPUGrFGENwht7DovPkKjrQawoCNKbRUsGFHuPVMiOtzNLqGRhpRIFjTQ3jrWTKNNCLoUllLAovjDFxdbuj31sjRkh5RGrh7KXLHJ9+iMMLz/MwAz/7viNOt8Ljj7+JwzNn2N58iRoHtkPBlsLLH3mOvg8sD/cpaQdYxtQo24G9g0uYm2eJpy/QyOSW6J2l94ZU4WR7kyZQpOAEfOewYYExjmA9JU+0ol59WsVbSyuJqLpvzeNmjsMToVaB5qil0HzDeUvAk0tmERzOKa2/VEGsoTOBvoOF7wChSuEkKvDOUTFS5s8aHRLFXLDGkFOmAKvgsSSNqrNCsELnOkqBIY4UMj4IwahSBVHInwDOGRadYUqJmNJMSdcM83V/iT4cEDcbtrvhU/RteL8+nUrGhJxsaKM2YnJ0Srtw+Kve5uo7A1+1uoUSIl+rp8Kr5JXDfbrNeERozjBeXHLzGc/mHSO//en38jsOf5ZL7ujX5S6u+Bv89d/3//kV//20LngunufHT57gX770COOH91h/xLD/fKa/FjG79Ks2tL+gauXwb+/z85+/5Y3+7hqUZ8KSl76ksfdhi+R6ZxDQv7zhm3/o9/PlX/rf3t19f0z9d8/8D3zjm/5TDn7u6Jf8m6Si51KMyGJBffA8ed3d+yb89u/bDp+4JvTieV75gk5jxgDziXcY3FWZjMI8bWNxrdGmT5MD+9gSQ33sQY7evIdUVQ7kXjh6Cr76K36M73v+TWx/6uz8s/qf4dwel58NkO7u8bTtDlkvafaTOwj5jCwRNm/Y5+abDc02JN/9c/a6b8RVSm7wXU8VjUsax4lFKKw6lWFPuXB2vydYx8lpJU5KZY65KNhMhNoKImWWYAu2wcLO0maDXpCJboZ8a5SCejlbZTNOtHbKWBJthjohlioWiyFYoFvzwANv4Ok3vpXD/TPIfL8PXX4Dp9tbHJ28gLUNWqFWpW2PbaLzMxyrxJkUrdviEByd0/ggpGAN7PLI7njAisNbRyPTJClsrVQaEzFHhphw1uJnFlWSphedTf3utUGjYK0ll8rJbgIMq4VX/3JWqnGWRnDagLXa1LsslUUfYFYhSE3QLK3o5tgi5Kq57NZ7+mBoNZGdwdqKtMIqrOiD53Q8JTcwZo4zixFjA1A1l702Up5o+JkHoGT0YCydcdSskVm5qbTeG91yx1SZxoy1ViOwRCi1UEpDmj5m6/W59cYhXccujnzwA+/hwUsP473j5OSUKSaWixXrvT2MCHEauXXzJT74vp/khec/xNmDc7zlsbfxIz/9PlJJSr93ld3xK1y8uKKaA8bS0YxVqr5Yuq7DGKNKi9qoVQdHY2ykxVkuHF7k6ORlijROx4E6gfH7+O4B3vDMb2S9WNB3HW98/FFShmm3JRbhTW99Jye3rhOeWnPh/Bk+8L73cnrrGnE8ZbsdGIct/vSYvb0VCycaQwUUhEUI+FrYzRFbzSgEkQKNqkT/UqlkPEKJE14qrVRarUy1MVVVUkzDwLn1iv1F4OT0lGmKSK1cO7rFbopYo7FluxhBDMvlkpIzuynhbGE3VvX+W5hiZYt6eBedobaim1drWC0tOVfSttBby7Iz7JIyAlKOLID13lm870Aqq+WCxy5fpOs7hnFgmypThSLKjwi26WNrSj/XeYP6iJ1VZUPKhUVwiLPEWBimhMlQRM8z7wMxR2rK1GbJCBilbNcG05hotWDJuFqJJTNWiDkT08SwPaVfLjBLz8nxTWKuXHzgMuu9l7l09ibvfd+SD35E4xE/912/kYvn9rlx6xa73Hj1+k0unLtAHE8ZY0fcbqhNeOixJ8hp4PTkJnHYwME+/foi164/S65Z8xFxNDEEJ+RW5+vMxpAizjpabhix7PcduzGTRQeknV2zK1uKTJhi8E3l+reHnJhGTQlnBLEWa2cFEmg8Gvo5UUrBEshNJfDeoZ+ToJFxTqBAyZlSVQ1krcXUhHFCyo0kql5qtuFCYFkNMadZhaADEylKgl96jxOYJt2GOwt7K42LTDmzGws5ayrHctHj7XnWizPsTk853p4y3M8R/6wuyRU52dK2O7UYzdXGCbMZqev+V7ztryS39VJp5tPkYtkY0mHP0Rs6br698vRbn+frL/0Abwyv4OWTb8vYMwNv7Z/nrf3z/KGLwDsgNccr+YCf3l3hR66+gZv/7DIP/cDpXdHI999/yu//mT/IT7zzu37Vnyut8j3Dkv/4x/4D+pcd7/+mBebI0d0w+I1Sut21xneenuNzu+d5Jizv+jE9E5bc/D1bDt5n7kjypTQ9r3Y7WCxoVy6RV+HjbsABTCy0zfbj/wW/WnUdr37RRfJq5qVUsJ8m/W7puDMcWL8QP+Ggunst6XtOf+OjXHubo4bXvk/i2cK3/fa/wfcevZXjD5/B/aLXfrgI9dEHMB98/u7uKGdkN9L2Fr+OR/86LBE2T+xz/a2W6u79+/1134jnLAzjyBgn+s7inSWWhMz04eA8q1WPNYYpaWRSbRovo5JasEEv/pyx1HmLphst9WlWNZYzNZWfQ6WJ5umaOdIplhFnDXsLzR+mCKFz1OY5XJ7joQef5qEHH6XzHc6oP7Kkic4Hrjz4BmrZkfOOlHbUYjCA8xZjGzUXXv7IT/Pg5cc4WPRM6ZTt6TX+5T//bl6+9gK+0wvJ093EbogYYNEF9rpuTphqVDSveSqZVCopNqwYumCxFrIIpTUlndei/mMRStJmKniHNKPbKWPZTiOjTPTZEqyj1EzN4Fygs5VgPbuxYjGsXKZYodTEMCX6bkG/CHPcW6a1ikglp4rzC7quZ7PdkFqg1EIzEYvmvUutZODM3j6WntPtFkzB2EYvFmM9YnRDZqyjlESuBitC31laK9AynWmkOkFVGbqVBrYCutl23rEI6k8uJVNz5sUX3s8LL76NRx9+nMVyzZmzPZ3zIIWrL3+Y9/3sj/PhZ3+e7RhZLDyXzp9lPC3ErDFHpQo5C4fjMV+4/CDT2cf4satnuZmXSmB0YW7IhYYOfsSof6eJ5XgMuNwxDbAdC96vOTzzMPtnr/DGN7+Dt7zlaXa3XsEtDkhpQy2FV14ZoWamYUNOE4Ub2JY4c/YMz334A8ixoesXLBcBjGez2fCef/1TrJZrLEJnFIZnBCRFXBOcs9RW2ZbKrjRsq4wp49DBzDZqPjPiyEab11IqpIQtmUcuPkTMkdPTHYbC9Vs32e5GxFhC6HXoNGd5C42UKsvOQhViavR9x/46sB1GxjEzTolWBfHMEDtwoeJEWPeW02oZJ4O4Jeu1QCmcX2qMYLZwfGvDO974ZvaWC7w13JwmjoeEeEvn9Nwx6Hd1sAY1wXCHim5FYw4zDeOFMqlX2liLdQ5vBEGJ6M47tlUZ81YMKSs/wAoa65cLzRg2uZBzJTZVRHTesbzwAKv9nutXn0dM4PKVh7l8+SLnzn2Io51w9djhun1CL1y/9iqGotHyfkm3d8gYJwyWlDK2s7huSectxzdPWCyX3Lp6g5tXr2JsT21LYtqysB5pgtRGLoWpFby1xDgRSyH4DhrUMnJrq1nuzhrG1NgMI5lCrYWcdEjZRGF3pWrIV4wZKzM7o+m5763CD3PJYC2l6nt24TuMNJw1iDRKVjBmNQUvBisWrCFXHQyW2sAExFSsyXjraeLxoan/31S8GIQEIkg1LIIOdiyFKUGOGRCcNaQC2zGTElgcvVlgU4dMhaPTV5Sl4QIPXTrkIy+88kn+Jrxfn/KqYLaD+nXzL9OQtko7OUX68Cv6Mi/9eOT7/w9LvnT5GvyrtMp//vxX0N36JGY9/zI1Xl7z8m/2LN5+i695/J/zectn2TOfnuoPL5kr/gZXDm7w5QfvYfuGjj/7RV9F+84LnPmZk1/Vby6lwHed54W3bnjY/VKY1aaO/KWbb+evf88Xc+WfJt54dccHfn/HlYduwEO/9Pf91ed+C0sf+S+f+Du8LfzKQ5hfXN/xed/Bn3z0j7P60InaG063CmP7dWjAb5c9HuCXO1d/HSq++WG2D3/MfQ1yh1z+613VwXCpUS5Gzl844eJqwwd+5DEWV3/pk5RWkJdtlqULiw9d/7SSpct6zctf8Qjbh/hFyHj4Pb/h3eybke/53z4X98t8HDTbuPG2PS48a+EuVSBts4PV4u4YFp+NJcLukT1twv3Hd6a87htxJ4LtLSIOMbCNiZgrwVqMGEQcVgxxzGyHiSkWTJhlrUY0X1jmZiGrtN046F1TundpOA+dUyllM0KqhZIqzglrK0wj1GpZdoG9hYMq1GLpwzkeuPQGDvbOYhDSNFFLI7hCG24xHV0jFiEcXODM4cNcvf5hanPEGIm54LzBuEYxmZsnr/CjP/g/8t6f+n7NVh5P2Y4b+kVH33mGcSJPmZgrtTbEFNYdUCspZ1pTz3kqmZoagqFfatMZYyGXimmCdQFntGFdhB5Polar2cpRyCLklultnb8IKlPdYcTiWdK7Bbt4wnaaAIub5beZ/z97fx5sa3qVd4K/d/qmPZ3xzkPOqVRqHkFIgAwWErbCBbTLYKoMCBsbg8tumsLGAxUE7ibaduDGhG0iygVNd4OriiobUwVlwAUyICSQhFJSashU3hxu5h3PuKdveqf+4915lSnlcHO4ypRCKyIj8tyzzx6/vfe31nqe32NBBtbLASDwfYPWEq0NvRcoaa75+HvfYoMnMwatC0LosM4T0ORZhQiWZdsgY0YmdIrvCpEYNEiFWGVrW2uJQlGVCqIlREf0YIRZRa15PB4jAkEIhDCUpqRta7yLdKFHSgUyUhaa3rU8ev4+Tp+6mbXRkK5d8Mj5T/LZT32UC5ceRpmIUpLJKEMpOH/5fvb2+1XuOUlCLw2fnQuqnYy337bD2zf3+MDVO9gLZZL5pThrhEggQbH6dNQCQpQs3Ahnc/LqOJP147z+ze/g1pvPcuzIFgKLWwjGoxwlNZ/++D2ofIgxhnq+T1EOiL7hkfv/lM4Jto+f5nB/j4O9Q7zt8f4q9336U0iZ4sOETNvg4D02BoLzuADoSNNbGpcGN4HUSIYAXRQEEel8BBmQUidQnnO4vmWYZ1SZwvYdOgYWTc3lw0N6ZxO5PtQYLRgNCgjJX1yWOTF4ZoseicC7yKyOCC/IjUFryazu6JeBCFSZRKMwo4LNrTWiTp8F3kUOdlu6peewlXR2DqLj+NETTAZl2vYHx7LpaZ2lUBGBThvvFQvCs7KnkB67XL1eSmlC8ETnMUIgVPpMiXJFiBXgCYQAuRL0PmKtS38rkzonUf7TEyljeu9qXXLi5lspyoxLF89TjUrkwQjbp22xtZJzjx5l44ijrDqEynA+cjibMR6vURUZeVawfeQYB3s7CO/JjGY4HjGcVDzwuftoF0vG28cYrG0yO9xnuL7JrXe9nQc/+58RomFZtyAUUqYIr75PA81BOWJcDYl4nO9oGk+628kOEILA+p4+9HgRKHUOURBIg6noAyHaZAFR6ViXQqJVBiLQe0v0EYWiynXybIdkI7LR0fqA0pJMmRRFGD0xCpRIA7gYoMxzpJHMabDeE9tAXigyJdEix/cBJyLOr9I3iDRNjwKMAKsAwTUVgJQCYxR5zMjjgEoNcCpgfY0PMCqHDAfXf7L91frKKNH0MFs8ex6zc4jpgrj51Bm+xeUlP/GP38d/cyfYo5Zi1NF3mpt+QZLVN2hzeZ114RsNv/iXnl4i/nKugez4Z3f+Kuf+4RF+6tf+Irf928Nn9JJvfHLGD5z7L/nNO3/z2r+ddwv+zsPfxgP/4XaOfXDJbcvVdUjJ+AHB7k0DtkZP/RrVNuPnrnwTP3vy96jks/u4z9kFf+vT72Myc8jdA9Ca+AIl6F9YIkTEdH5jmlClOLg9T+czgHQCfQNmNj6DxZ2Wv/E17+cH1j7Ouvq86uBDZzzf9Xt/nck9q+dbpE344004wPr9nnj4MvJ7mIywOcaOBF9kQo7wfzz0Sv7hW/8QcbKBB596i704Jdg+unX9FHjbI9qOWD1P+t1XcglBfXrE1dc/WZkASVERs+trsb/iG3FTGIwCFQUuOESmyIwkU5JoAyF4+s7jQ9qeBwSKmLKqA4keTkzk3RioNOSFQiuN8wFsSLFeNiCUhChSzBSJnouCqpIYmU4WtTAos8761q1sbZ9kWBQE51g2NV27INMRmj0WO5dx9QKtJTmO7Y0T9F27gnulDa3tLVpqSqNRBNpmwfl6yeMbmqrKqHJDbhSukwiS19LHmEBPTQ8+UpUZgzKj7XrCivQuVcAGn66nyGi6ntZ5UA4tkjey7WsyNSEXOXVzSGN7RJDkumSYVwQh6Jyls55C5xiZIURqeozOUra0TFnvPiawWeM6jEz+2cZaMlxq+kKBs5ZlP0WrjMyURC9pbcusnhElCYLlIpk2BBHJdMCJFBsnyTFZjo0tCJPiq3qPySRt04JIWb8yagSBulkAEmMM3jl656nyPDVCMlG3XYj4vk+0ZK1ABK5eeYC9qw8x27/AvZ/8MIezA3yAPEsS5egURhmkCsznniv7ENFoLZmsr3HnTSeolzV7e49yvNnj7KZiuVzj9xanCTIdX6wk86w+i6PwFGZJKQ4QrufoTW/m1Jk7eM1rX82Jo2sE17Osl+RFTjnaTsd617K2sY3tG3auXuT4qZvo+45z938aYUqOHj9B09SsbR3ntu0tLl54lE987KP0bc/W1iY+rprPkEBsnQNtMowStD7Rx0eZpg8BFUBnBh/SltwLhRIRKZLUGAKuX9LXS04dO4ZRMsEJY+CwXhBDSB7klRLFSIG1LbOmQ0tBkScJt0gogOSP7jsIgcwIXJ8aNikkpswYbQ44cmyT0XqFzgwxRNqmpV62rG/nzFRPPQ8InVMWAzbGI3SmiQjaumHe1OTGpAGeDyChyFQi37uk4Mi0SqwCl7LfM52SEkaFSdt/Ir1PBHWkTIO/GMgQBCRKBoRKMnrvAzIIFl7gokB7n8BgRcbJW17Lm976Zq5cvsjhwZSIoBpOePTRSyzPX2Tnyj55KXjT1nEm65HmSmQ8HnHoD2naFmMkAkE1HGBdx2K2RClNMSyZ7l1hOl8Q+45B15FXBq42lFnBaOME4/VjHO6fS3GFzhMVBB8wRlHKio3hBkYZiJ5p54HHc9ItTecpTEGuCwZZiYuOcT4m04JFN6XzHmc9UvqktkAQfMCtpP+9FyANfe8Yl5pJWaCkoPeJVRB7g9EB610Cq5mA957CZCiZY1UkdC3WdolJYVKkY9tGjB5QZSVRwLSv6Vq7gm96AunEOjOSTABIhFBkZjXstAIlBYUaMDBryAiLxZym8WhTMi5LquskLn+1vvxLWL+SCzfXLW+NdYPMs6eWqMfI5p8esPmnT7yRFwYge7HqxWoAX8q6NbvKTW9+jPg/jZ8ZhGY9l371Jq7++JLP2gF/40//K0b/YcT6p2ectF+Q8R0j5X7Eqmd+/T833eZ/npzie8dXn/Yy5+yC77r3+zC/tMmRDz4G1IRjm/gXaQP+xNIH9Q3zRovJmOaIIE2hV0C0F1H97QvI3rHL37vjd/i24VVyYYAnS/+/plB8+F0/yzdt/lWyX18jCujWPk9m10vB5GNXnmQfeclKSMRkhDsyIWSK8cOevVfLa4OMx8s+MOLf3X0Lf/XVH+B/ePibn/I5DVnk8DUbrF3aue7PJLGov9qIf2EJQXNiyM5TNOFPvMz11Fd8Ix46RycC01lD8LC5OWJjMiZEz/7BYQIdiXTCPqwGtF3LrKmxNmBd8pQLGZE+kEmPkasmKAT8CryRiNqB3gakEqsM2bQKVCJtyhWAL5hs3sHJM69gNJxA8ATvkcJTqEi0U+LeHtPdPZq2QQRPJwVN/wgTmbG9uUWz2KG3LYKAycEFR/SOtCYFQsBkOgGUtEZG6DtHDIJBlicwUXRoBVqBylSKmvLpDSmjQIiUud6leHGUkkijyESCKEVi2jwJSbNcUNsWY3KMNAjlQXnQCh1zojAoBijDiu7sCVFhtCTGNBQwMsfHQFxtwkIAIxINurMKokAjMHJAkD3egjQlvQPnAm0DxmhMUSUZqxJkRiaSsoSu7YlRUJUG5yPB9RQ6pMeFxAuFyRUCDz5ldAfvaZ1FB4VzHqRAiVVDJSr61uKlxIUWrQ04UCiCnfJHv/8/sli0qVFWkSITGCNRMkG8Ah0CxXQO1oOUOjUbvkM4y6ntdQ6EZW7nVEXDKyeX+ePlCWZRo5RCSQ0EjHKUZkbsLuOXNWp0hDd9/Xt529e+hUwEQrBEb9FKYJcHPPjpR9k6eZZTJ7c5rBdsH9lib++Qu153K1VVMjvc5zXDIg0M1Ii2rYnRc+4zn2A2n2JnB5zc3MCR/PEiBDqXvLjaCDIhECFgvCXE5JlWIhAEaJnk6zEk8nouBPge53uc94TeIYRkezJBK0mUgs421F1NiNC0adutFFgXkVIxKHKWrcc5QVYKfBuxTqA0LOoORcqy7voUXVgMK7bPbLF1ZEKep+fSmIwQPFoKpBIo1ZNliuXQ0zeezeGIm04fY5AbtIgs6yURiRDJsuBcQAifSKURfBQIFJkQDIzAIXHBo4LES0nvE+UbBCpE2t4ln7lORPWhkWgtsUBpNCFEmhCwn2f80EbI1za4887Xcuttr2B/f5fto8d5+9dP+NAHf5/hcMRwcIApR1TlAOdadq8ETp/JuXhxiVAj8moAImKdZTQcMxgUxGApB2OMkgS3ZDpdgrUUmSH4nsn6cfr5lK6eU03W2Ny+hf29h0jtaVi9PhqjJbkqKbICpeBgNqXxLRYPNl1uWBVImfLt1wYl88WMtmupW4dU8dpZfQLaJVq6FhqFIkYoTIlUAoGjVJIs08nz7wV6xbGSBKSI+JWaQia0O9a2+NAjpMLZgJKKvg/0LpLlBu8dnZWreDlHEIm74X1SO/gQED4NaQGInraHfqUkMn6I0hOarmU6XTDtHGU5Ym0yRDnHzu7LnGz91XrBJXxMkKv5IoFPn0tdh0T9yZd/6Zvwr6QamZZajhM18hnq6IfnvPNf/bdsf8Jy9sISwlPHidVnRux8e82x6plXviEKfv3qa/m24a8zkU/eZj7egOv/zybbf/QYxEcJm2P8i7gBf2Lpwxau7t0wb3R78yY+T8etagXqRXRV2AG8/ds+xr86+YFVNNzTDz631ICPv+Xf8st3bfLf/e9/EfWEl2j9fk88mD7t336pShQF4ejGk9QO1dWexUFJu/nk97508P/8+Lv4T2/7V/z8kW8gu/zUj316s2R9bUw8OLyu+xC7HtFaYvHVITJwrQm/+kZz7Th+IfUV34g3Xc+ib5k3FukF+jA1BCGZbSlMlqjNNiKEAjRKaISJSOXpfUDIgLMrSFnw9F1EqeRFlDLJFnk8alGADxIlI7lZBepEw2BwM2fPvp6Tx09jtCZGQQgOSwISFXYfF2uatsbZFhVdAkdZCL2HS4+ydvZmNtePYH1H2wuEcjgXiDESRCJQaylXMWhZyiju2pX8N20etZTkOsNkKT5ICYlWkkypFf1XURZpkxdlyq+2Lk0rtVZoaVaSWY2UFZn0eONRSlJkJcRA11vmiw4tk9RbiBxjsuQTlxFtBIumI4QeITRFnpNJwaLr0MKkXOhcYkSB8JIYFKacMBlXOCexVkD0ZCI1OVoPyLMSIQU+KJTxTKc71HXDuFJIKTFGI4RHSMgyQ2d7nE+DCq1CkmFJCUpS5IbQtJiYGmijNL3ribFByIAOkiADPnhynTajQksyqehcR9t3K2q5oCp1yo7XAmNEOrmPmktXPftz2F7b4M++5Q3k9Nx//mFmyzlKSdbWxnhy+rZl3uW0LiJzCUKilGNoDnGLB+nnPXk1Ybh2irte87W8/W1vxXYtoiwwWiSaePRk1ZCTt72SU6eOsXf1IllZcOXiebqu52D3CqONU5w8c4qrF+YcTA+ZLy5x4uxtLGb7XLx0hf2L5zGANBrnIlony5CWyQcd48ryEALCR3JARZAk6JYUAiuShN4oTWkifQe4QNc7CJ5cSbSKLJc1zluarsH2PgEIZVJmaAnOeXy0xOAYVZoYkxxZa5niqqyl7hxVpggh4JRmvD2iHFXo3GByk7b3JlsN0iJKa8oyx7tA8JZqECkHEmUWzLsdJoPTWGeZNomIz6qJ9C4QHpeICUEk+cajEgSlqDQgcqJIDWQfQoIIriIO3eNwMiFQRiJ1+uzQItH0JYHcSKogkVJjigknT9/K8bM3UeQ50bcURcFsusds3nDm7K2cf+R+8rxkPl8SgiIvMuqlQIiCY8cj01lBnmc0ywUxChaLOXW9pCxzsiz5+x95+BLNskFIhdnawvnIlUcfJh9MWEyntE1LUW2T52NitAzzNawPNF1NCAEbLQfLA3KV0ztP7x1CCYgp8s9jqYoSJeBgOqX3HZ21BGRqhl3AOU8MCYgpSJ4gqZL8XAq1YlYkTsRiafE+cROikGijEF16TazzRHxiGUiP0QIRVKKaK0dvPT4GtEngTaWSfSUICyqgdBr4pel2IqRbKxIQ0nqUAmEAD7moKNQGvg/UTcvcpySM0XCAyjSzuk3H+1frK7ZE3cF0QbxOMvFT1rNI1L9aN65eO7nA7x25ifLC/BkvJzrLmd/Yf8bL+GHOo9/hObX+zNf1xNseis9vHp/UgH/oAvQPEzbX8OP8hsH59KJHXN65bg/xcy6lmN6cpUWFF5j6xblan8HyrOd7vv4P+O+2P81zMTV/92iPN3zHz/DeD/wQ6qEC1QjGH7/60kLatIaNNezm8Iup5T4yecjRrWmi+oJG8OEB/+HVd/Ou13yK9195HU/lLXBVZHH3UQZ/eHh99yWGNFj8aiMOQtBtV1x9g8EXL84Q9Cu+Edc6QzpHbjw6V3gFV+dTIpJJOSSTihgCREkMEut6QggoCdpIMm3oPESdgG2BSIKeCyIB59IWDgRKpaxfJVODaH1kWGxx7Pir2N46zWgwwSiZTv5J7zOtDNEMQY4J1qJGhuVUYFcZ5CJ4QOLqBXZ6wGTjCPN6jnOrODYyfOywPiBWROiklfZ471kserTWjIdDlJTUdYcIAt+n6CCtU5O+XHb01uIjmEyhtMDjcN4SbJLbCw1N15BAWSUxeNIyXqHzjDzTdH3EBY+QkSAlWmc41+FCOnHu+xqtDVKW9EEghUZLSSQjeOiFTf/GGlvjk2wfOcZgOKBr53TtkoPpjOB6bN9TFhUiRKJS2OAhGIpByZHNdTY3tui8o5/ts3+wjw8OrX06SXeB9LRm+CBpuzllHjFaoRW03ZKulwSXct21BkTABU/vSTnJUWOUYJCXLNuauuuRMoGztMwJyoGMqw32CgSHRSooVY6SHbmW/Lk3vZFveN1dPHrhMdarkvM7l7nv8j7royPMwyYP7Ao+ttwgaonRkcrMEM0jzPYOUNmE2+76Gm699WaOH9lic3OT4Duy3OD6BV3Xs751jHq2T9cFbr7lJg7391Eq49JjD9G2DcbknLn9NeR5wYVHHkGZnNM338H5h86xd/k85+67l/39GUbnmEGZrBsyIkm2AhkhIgkx4kjHvAwRA9eSX0OIyacvBKPCrPT0gSwvWIYe7zu88xipsQjqtqVe1vRdItfrkOTfIUZClAgFwqe0q0oJ8jyjtjZJ5O0qH15LqvUhwyNjBlrS9Q3L5YLh+gCjMrTSPP4NpaRKYDWpKKqwGiqEaxaEK4sLxGgZqTHOBbQyCK1Q2qCjJQMKk+QjRSYRMpJrkcBtcSWrJpAbnWjq3qG1SFR0ItL7NBBasResTfL6LE/EfgQM84LjZ29nffsIw9EaQimKsiTLMg73d6mGa0SRcf78Hk1r0ZlB1h3pVtL7crnQnD6tmH2yITNjrG7p+57JZJTkgVLRdzXL2Q7BLSgHQ/JqgPc9h7ND8jwjq4YUozXadsF4fYvjJ1/FxfMfRgtN42qUlrBKp+j7jl70dK7H+7Thl0qsfNqSLJfIGGi6iIzqWhSgQGI0CMLq54gQBqXEKjJO4leDH6kUUhZELL2zyRo00Ggt6LrEUgghXdZotVKkrAY3QlE7T+s9IgqKVeSZ9w6dq+TFXyk5tIp4wJAjQqDpmmQxCBIlwDlJwYhhvkGIghaJqMaMC09VFGRaYLQhltB0L/2W5av14te1OLLuxaEsx7pBlPlXJaFf4vrm0b18+O+eZfnTJykuLV7QdS1PlRw9unNdl7VB8p7xx1HCfL4B//9usv3BC9A9RNyY4E5s3NAoKdVYxIWdGwZoAxDjEfXR1Xef4wUB2qKE5khk7bW7/Ojtv823VlcYyufH4Lgrq/idt/8c3yJ/iLVfK2Hv8PnfsRdSQiKGA9zJDS6+fUg+jUzO9cj+yU9UvmcpdzT1sS/4cwc/e8+f4X9528/zO5PXYA6fYiAhYP8VmuHHBsTl9bElYtsi3PD6VDpfwdVtV1x5S44vr6MJv0610nN+Rn//93+f9773vZw4cQIhBL/2a7/2pN9/7/d+bwJJPeG/d7/73U+6zP7+Pt/93d/NeDxmbW2N7//+72exePIH3ic+8Qne8Y53UBQFp0+f5p/8k3/yXO8qAGVWsDVaY3M4ocpyYkwbqVwbtJJAyonuXUvdtjSNp+sEy97T9h4lNEOTM8kVpVEp9kZpiBK/agaETB7gUWUoM5m243HI5ubreONbvp2ve9u7uP222xgMBqvNtCcGi4gRoxQmK1HjE+hskwIY5dkqFkhglCJXMNAB5ofQdWxtn2E82oSYIphQCUqVaU3wAlbkYdtbogggAm1T0/UdZZEzHlSUeQ7C43wDpOisPFeMBjml0WgBiuRP7ZwHInoVxZTnWZIZY5EynQzHAF0TCE6gVEmhJwyrdUxmiBKkylF6QK7LdDIbBcJHMilRJlIIQegU9bwgz85y8vTrOXHyVjbW1sk1BNuxuzNjsWzxNsmU8SIpBoJF0JNJR1zuMd15GCVhOJywceQmbr71lRhTET2MTEYmDW1rWRxOcW2btvwhbcMjEmNyRoOSPM8RAqJIsWVK5JiYISnIs4oYBMuuT1J6bch0hpKaKs8x2lAoQwwBF13y3wtQRFywFDm46HjgygUu7++yc7iL0RLXt9gQUHnJQ2GL/985+PThZUbFFSr7SfrdP6Ve9py+9Wv5c9/2Pbz9Hd/AG17/eljRqL3tkdFh8pL17SNE76iGI46dOELwFu8dWVEyHK9x/MRJxuMNjBJcPP8w+3tX+eB//l0OpgvysuTKxUe4enUHgiAisDFggwMliFKu5NJJcl8pkRQEWhGkog4ws4GlDdgQkEaRVwV5kVNVQ7Isp+9Sjn0Ijt45hExDCyMiPnpa62l7h1YqHWMkWXAuJUYlZUcmBAURHUCjqYqMo6fWuOnu42yf2UBXGZHAYlajjCHLDJGIdT1Nu6R3Hd77VfJBAgAOBiXDYUmeZ5jMYHLFXrvLpdkltBYUWjPSikpJSmMQAYKPOAARKIWgVBqt0+1JKQje0zlLFCQPM2mIUWlDVZoUYehTvJZ1K9/4Sodmyk1uf+M384pX3cWnPvaHfOgD/4kHP3c/mck5PNhDKMX0YBfvek6eOEHftuTFgK3tDQaFQa4SIHavNAihWV8P9H1qwqezKYt5najf1uJdZDmvVxtjTzObcri7R99ZXFQpFqxfsNi/RLM4pKiOESnYX0zT8CIGSlUyyAoCkd73dM4Sgk8KoL4jhkSR3zucceVgSrvK8C6ynGFeUJqMQZVTFhlSSmyICCESOd35a9elVEDLJBn3TmJUTp4bjHIE15GbSCB93ncBotbozBBEoLU+Hb+kkzkpBdY7OpciHJd1zbJt6fs00HQ+4G1AxaQwiQKMSvaf2CtwJVpMkC7SLhqcj+RFsj+EmJRIOpKGAS/SFP1G15fbd/1LVcJ65P4cdvZTJviLtUWLAQ5nKXv6y6BeLtFTL0b9+JnfwP/IHv3W9WWFP135DOQXQrWepvb/9Ah/5cPv4y0f+4v813/3R9n+my1rv/cgMc9wNx3FbtzYPGfVeeRjO9edMf18qz+5fm2TGPTzZwu0W5F3f88f8Z//q3/Kn7z+V/kvh9Pn3YQ/XjebIf/H2/4l7cZLBDzIc+LJo7R3HOXcfyP5vb/9T/nf/tE/5eHve4rPgBiZPGSR9ovvqzxf8Jvz1/CKu58+pqyfRLo7jj3t77+ovEcsX54pCF+qspsVV96cJ6Dfs1UU192IP+eN+HK55LWvfS3ve9/7+PZv//anvMy73/1ufvEXf/Haz3n+5Inud3/3d3Pp0iV+53d+B2st3/d938cP/MAP8Cu/8isAzGYz3vWud/HN3/zN/PzP/zyf/OQned/73sfa2ho/8AM/8Jzu76xr0CoSQ0BhkEahZUtrlyymS1RMG77GB7wVjIuK8aiis206gVIJhBbxyFzR9w7rPF5GEIosIzUqIdJbMLJgff0m7rrrbWxubtO3Dmct40FJ33uWywatBFon6nSIAfzKZanHiMFRxl7ipGG5WCBjACReadAFUhWM144RhWa2nOO9S/E9UqEE6FyRa41WklwHnGvxPjVDeZYhVxLeQVbhbKDpG7y3SdYbBHVtkSriQ2DRemyUaJlk+EoIUBIpVxRsVlPTIBFKIIwAF6CJSGmIViJFhSLgY49WBVk5wdkG29fI1QBBOc184amqM9x24ha2tjZRsaWvpyxDwWIxo26XSZkQNU2XNmWjFQ2dALPplMFgTFVVtF1AzhuYN/RdT4yerrFkecG0dggRgIy8yFNcGRIlDd2yo3ceKTXjoaQq0ra17R3EBqKisx6pWgpt0iAg+hV5PccHhxKarMgJeLy3yBiSpUGkTWqmk/0hRouPng/fdz+PPPYIVZFT5Rl7dUvMSjrvEDpnP0Zit0fcbYliwGTrNt741nfypje9ntA3KJNz9NhxylzTtQ1KBOrlAhE9eVkyGE5om5qiGnK4t0NRDRAIhuN1zn3qY1y5dJnh2hpHjp/mFXe/iq3jZxCh4+N/8kdc3TlgY7CWcuqlRMYk4fZKE4RExbQNVNbR9T3Re4wxCAm9iFgEmRaMioIoBa1P20wVAzEIbIz44K/5n41SyBAIPmKUSQRyH7Ai+Xi1SiBF61r66IheEPMCnWVo39HXLT7AZm5wasUyiIGubhJdWxsyk6GMpOtalk0PoWNYFWgjVxnWKx9/FOiVL1gqjc4ESitC48lDkqZHISjzDE/A9h1FWVEZg4gRqWSKlwsRQkRECC4QFUitEDE1lSK69DsAkXK0dZYGDVIVnL3t1QzXxnR9hw8QYuB/+7Vf49TZ2zhy7BhZUVGUGZONI0QCs+mUza0T7O9fQcTAwXQfIRSLxZyDPUk+3GB7O2d3H4TOEcpdu22TJ/VH3Xh6mz6jnPNU+QA9qBivj/DWMhqvYW3P7PAwDRTUBMQsPWavMDrDaFi6Bms9MSaiuvciKRy0JlMl3nbY0NFbT25yRoMKEWFWN4SoU8wYkuAsXewRRqfM8NVzG+VqCKTVtfgzQaBpW5yP1F2k7j1eJO5EaBs6m2IrF61DaZHSIkKGEpEoepxL9gEb07HjXDpmXXDECJ1v8D6SS42OkmXtiSFHxhHReuauZukkk5FmUuR0StP2HkfKPbf9gqq6/rzgl7K+3L7rv9QlfEwQo8XyufvAr7ecS7C3jdGNuf4XsVT7FUBre0L949v/PT/+Y9+O/OkJ+uD5NSDjcw0P7k04sXX4rJetLgiO/i8BddARl+eIkyH+6BohU8/rtq+3pAuo/SUczG54Ew5Phn1HGZOC83m8fVQjuLW4yvGniJB7IXWrGfJz/+2/5G/3P8SRf/fZL408XSlYn+C3RgQt2X1Nzj1f/zMMZRoE/d/f8mv843u/i+N/1DypuTMzR3VJszjz5KsTHv7Nx76On/u6X+Fvl9+Lap76venz53ZsxWWNGN7YgdDLtexmxeW3Ftdy75+pRID1zwbE5We2rjxez7kRf8973sN73vOeZ7xMnuccO/bUk5bPfOYz/Mf/+B/58Ic/zJve9CYAfu7nfo5v/dZv5Z/9s3/GiRMn+OVf/mX6vucXfuEXyLKMu+++m3vuuYef+Zmfedov567r6J4QDTKbpcgBpRTWWXxwEAUyKGJUaevRumvZv1mhKcvkIXTOoWWWSMbB0a+irVQEGxLUR4h0ohpjSBJFITC64Pjx13LnHW9lNBimCCYFMjj2dvdpr/kMQWmNEuBt2hgJKdCmwA+2EHjGQiK1pu89HRo12WKwdYJyvEVeDCiyjG4x5YHz9xKCQ8uVj5JE1u47CD5nVGnqukfJBFEymUTrVWQZAq00IUJvHdY6hMyQgHWOMheMdCITZ0agJFibsr+VEDgrsF6QG4V3NcErpKySjFP6JOUUGi0UTUd6foXAC4sQPWuDTUbVBEHO1qkTbEy2EijNdrRNi5CC5fKQRy8+hkMgST7z6C19Z1m6hmAdRVEyGAzJtGRvd5cQLaNugQ0rGJvt8V4SyOijhtBSZAYRAjYaTHoh6GzabJvC4EPayvplg3URnRlcAKVEyj3uAkpoWpsy6Xtr6V2fVAm2Qcnk7Q1RE5HXiMttdGghGI+h2he43nG2cMyF4kqb44VBeY+WiQif5SOkWeP4mbt5xV2v57Wvfx2TQcZyPqUaDCjKisO9fXResF4WTPd3GY9HLJdzQpRY2zMaj6gGaxiT0bYt3lvyvODsK97AK15n+PAH/jPLtqfrex6+7166ZoHOK269/QiQ8uMDkGUao1SiZAcPIeBsT1cvCcEjfUD5HiUEyihyo8nLnBAS/C8zAmLaPPoIQSh8CEjiKnEgYHuLI2VYD6qkUtHq89FRQqbhi4ySxkemy5beORpS89R2kUtXlqwfH6KFwDpHXTcgQStBVWZJ5bAij7sQmE4XDEYVVZkyr6UyCDzSC8CvgGGJFyCVp29qMsaptQoebQyZkhRm5QOPEW89mUzcAbmihwWfiOpEcS15QJLgiNqYFT/AI6WiGB7h7F2v59Y7b6deLlFaUi8OePTR8wQh2Nl5lF/473+GP/ut38Y7vv5bWCx2+fRnP82pk7dx5yteyWfviyzme5RtR9t15FnBaLKGd2OGg0BmpuxeaZFSsVjO0UqQqUg1KNnc3mI+a/ExovsaVWSsb23SNTNmiwapC4bjda5euko52ebYqVezd6Ghb+c4Io21uBAxQrFRTZgua5x0SCkBj+0tPSHZEbLUnCulMULSe0dvA7G3+BivWV98AB8jQoKUiWURtUh2CJeAmZJkH6mXHWhFEzwHdYeNCQ6HbWm6NNQJQhN9j7MOITVSpijLFK3mCdaBShF0yXYk6PuIs+DDSnrvAtYrTDZG6iGd7+i9Yn19jSLP0SYjN6BVj/WWg/09QoTto5vP/kX7Mqgvt+/6L3WJw/l1yzpfSMVlncBtL3OJuq7BI1F8eWzwn60UgZ++49/xve97H3f8nEY8D7aDqnv0A+vwLI345f0xt3xwinz0MoyGhLNH8cWNdY7K3qMOlnA4/5I04Ndut7aImFIpEGkrrp5HI26W8E9//S/wNX/pnz+n/PXrqa8rJL/wj/4531P8CMd/5TM3rhkXElGV+CPr+MHn/dfLU+FJ2/3vHB3wU2+Zwh99QbRdjIwfsTRHsy+ChukLOR+Y38H49gOWn9h4ituGxXFNqa4/UxznEE1HfKpEh6/gcpOSy28psMPra8I3PhVZ+91zuP76KIQ3ROz//ve/nyNHjnDnnXfygz/4g+zt7V373Qc/+EHW1taufTEDfPM3fzNSSv74j//42mW+/uu/niz7/EH3Ld/yLdx3330cHDw1mfKnf/qnmUwm1/47ffo0AFpEMqURKIzJybRGRk0mNZkSGAlVITgyzDmxVpFpSWNbbOxw0dK6ns46mj5y2DiW1mOJ2BiT9zCGFG1DkmpPDx9lOtshxIBAkGvJYrmk6XqyoqQajpAqeUVTC5L8qXG1eZPlBJmNkVlFPt4kO3KWyS2vYXTyFWTDI2TZIIGdoqAarlPkY4KDpvG4ECmNpspLiiwjy2Tyb+sCIQyH85b5sqZ1DiHDqomQqXkMEGMk0xIl0wmyFAHX2+SpFSUhBjItMVISo0AqhZEaozWZLAk+p7c1XVzSuoZmFS3W9B3RO+b1IXvzS7SdQ4gRMmS0raTaOM3a+iYupGfSu54o4ODgCntXH0N6hyCiYs9AQi4j22sTxuMxeZEjhKCqRlgX6IPHR1g2Lb1tWDRzpvMlAYOQhmFu0EKgtaEoc8aVhuBwvSNTJWVeYaTGWY+WmhgTmd7anuBdynmXCpMZiqKiNArpemToUVLSWJsGK6zk/CuSvZQaKRXOQ7N6fLlJW+JL8iin7rib1951C3ffepbXv+puRlXBvNNsbr+SE8dv4x1f9w18zRvuplAB17Vsb28nGa5J8V0ypI3gePMYXW9ZW99kMBxxcDAjRplUBfUS2zfUy5rBYEQIgd2rlxmORxxceYwP/P77ccGTlxV5WaYMbBHJC8PapGKQp5i7KjcMy5y1UcloUFGM1zh+6+0cu+1Wio0j6GqI947YdbS9S9tAkxGFXqkpBIKIVppcZ6lZNSY11H3AywwhswQR1JbcpM1xoQVKS5zUFEXFkfUho6qgzA0DmWRAPnjm+3N2Lu7T1h31osH71ExrLTnY3WX/6h7LxZK+acFZvE1NeQwgpEJIRQgChECI1DyG4Oi7js46ln1LFwIuBLRSlEVBmWUpzi2maDJPUo8IEWnbHmf7BASMgegdzkYyo0GBUGo1SEsN5tlXfi1v/jPfghQRaxMJcrmo+eiH/5hzDz5IkSWI22h8gvXNE3z8ox/A9pHxYIyRiq3tDdbXhhzs7zMejzl2ZIvJxjYxKi6fv8Qn77nC1ctX2Lmyw0PnHub8Y1POPXrIA4/uEaLgyLHjHL/5JqoqI0YYr2+RGUO9bKmnh1w6/xDL+ZzCKJS3FNUaw8lpqnKIVhkalb5YokAKKHJBWShcsLS9o3eBvrXJVoMkU5ooAvNlj7caQ4kIkeAd3oXVkECvuARiRS4XyF5ifHqdeged8zg8nRfEIBAigfSkktjeMW8C0zYyrXt62+Jdyhqfd45ZZ1l2ns56nPcpgUKAVIpMS3RMQEshAz5C0wYaLzDFOsNygnA97bKlKkdsrI2TAsrHpBxRgvnhHrPZksGgwnwFkVleTt/1X+qKwyptsm74DQWYLhD+5W1pUF1ieHwllSLwr77ul3nkz4+vO4roiRUzTXf6mZtcHwVrv1Wh9meEs8ewJ9ZvaBOuOo+5PEU9eBF29r6kTTiAbHtE+PxzGZ4H/0v2EbOITB6A/+LX/zb3dC8idn1Vr8kKvuF9f4IY3CAFk8ng+Db27PaTmnAA2T/5WDvvFpT/cfyUUmezcGx8xmMW4klgNhHgf/z4m/krt/4x8Wk+phZnBPHkked0t+Oy5itk1nZd5SYll7+2xI6e/fNXOsH2PYH1D19+Trfxor/b3/3ud/Pt3/7t3HzzzZw7d46///f/Pu95z3v44Ac/iFKKy5cvc+TIk194rTUbGxtcvpzu/OXLl7n55pufdJmjR49e+936+voX3e6P//iP8yM/8iPXfp7NZpw+fZqma9LJcpVRZlmKokqBS6g8ka+lkHSdgxBp+55F59HRUuZJch5IWxvhEzndGImIkSgCQiVljRRpozedXeH8I59kfW2TqhjQ947OBaoiRwmoyhJvFE29ACmJUiOkS95LH/FRE8sjBDVGC4lCIFSBNgWZNmgpQXia9oDdnceQ0lFVOVJGlJKEAHXT0HQ9QgiMMYlYvopaa2qHQpCNDFJGut7Sdw6kB+Fo7AIlU2audx6JQMuAkWEV3+MJpOcFUiyU6wMiKkIARHlt+yuVxhiNcx0mCIIY4EUGIdHY+2g4eeJWjp08TnQdB4tDZgdXubpzCWd7ZvMp4zIjzzNC39OGwKJzIDNGOr1uRTmEkKwHUQiUyRHCpvsZNHlWUOUSby2H+1cZjoZImeF9YDjcwLoWWzcErxBCIxOiGZMrIh1BSPKipO1XCgLSdi56h5UW6y15oVhYR922uOjxSqbIMq0xSqN0krsCZErR+8BiITiYOYKHg7pn3lqObW8wLAvqrmevzvm6d30Xt5w9ie8tCInzIbEAshwpIq5vqQYDYpTkSiNVwgn2WvHAfQ/wqje+meNZjrUercHZnsP9PUaTTfp2zmzvMg/cdx87Bwesra0xHo0oc4W1HmcdWksCgswoog8ptomYclFzjVGSTiRStRQg85y1I0dwUlG0Pd18RlMvCM6jddqs+5go/0oZ8hwEFc1hiyRt3SdrG2AyJJGskBwcXqbuenIjMFmgd4LMRNq+xdkUG7g1yJnkhoOFZRk8vY/Uly3dsme4WSGQaG3QSjFbNMwOF1SDEmPScGR9a40sz1A6bUZjDHi3gsOFgA8pBs3IgtgNGRcDCqXxPmDUKh9epk2qlFApTR9iyhR3ASMgz8xqu+rp+gT1kz4miTvQe8nJW17D0dMnEVLS1jPyqkJK0CbHBM/WsbO8+89/B+//j7+OF5Lb7riJMpcMNs/S1TXj8RZdM+Ph+x/m0Qc/y2LvMsvdRymrivH2WR6aT+j6EaVVnD2xBvUSXUFUgjr2jKzjys4OZ06exC32qWdzTFGytjFgd2cP3wciiqa1HMkGZHnJYjqnHI0Yr52km11EKJ0AdSFS2yUy9MQIsZfkpmAwHCBCYN7U+OiYLxuKrGArH1G3HdO+xWQaLyK99ysFSkg2AScoyhKjDcumIRLoHQQJresIeAaZYTAwtJ1j2QUiEr0aGDrb03SJ5K+VwAfPsnH4KK9Zb6JP8Xg6TwR/LQUKhYmahW3pY8BHgcxKtsbHGOQD+qZFKMXRm07jbKKw59ogo6WuW+azPRZNQyCyXC5ZX39hvtOXS73cvuu/1BVzjRwOiNMv0UbeeVAv3ymO6pJ+6CuNqzySDf/Xv/Rr/PeP/AW2PvrUw6GnK19oBmvPLGu/+Mgmt99XY09t3tAsdtU61P6COJvfUBjbs5XobAK0rQ5lX0REEOjrpKeLAJufajGXDkEIjrwffuBP/w4//Pd+lb8y3n3B98/HwIe7yA996ruofmGd0eKzL/g6n1RfkAn+VDX5HPzcwVm+b3Ifv9ts8Hd+669zy+e+eNigOo+6esj4s0smHxly/v9yin7t8w1jbBXvHd7Lz278WbKdL/7sCCay+8Z1ti9cvf6teG8RXU8ss2e/7Jd5+VHB5beW9OPraMKt4OhHLINPX01A7iIHe30H9Yv+qf6d3/md1/7/1a9+Na95zWu49dZbef/73883fdM3vdg3d63yPP8ifxpAZ3uEMGRKYUOLDR5PkqnnqsDhQUSM1gQC1keK1ZZR60hS4EZ6G3FBYEyKYBIxEkKaZkZEcpHHJI/cP3iEK1fOcdcr3ozPcvrZIsGufMSHSJ4XxOBomxoRwORFaviDTxFiKkealFcuiLgoUFJitIToONi7zIMPfobLexcIocEYSZ4lX3jb9iybGg9olWBWeRaxIjCShrZ3WBdpbY+U4H2SGEfhccEnyJCW+CQETlLNvk9b45CaJZ0phBC0fUemM/JqhO8ddDZlAwsQoUHKQO+WzNuOgVljkJUQBC52DMsNNjZOk0vNpQcfpG4bnDAcHuzz8MWLGARaSvb7jsz05CYHFEEYpJT4oNBGYfuWRV2nLPZizDgvOZi1dK4nzwUeQRSKw8UhIXiE0mxMJquccIHKRkRj8WFKkaeINhcczvbJ2x11GobICMHhY0yZ5IXBR4+RhrbviT6gYxpQaBPITI5WBqUkzqdtdYxxFY+VPMZFWdF2jumi4UMf+xRKKra2jvPWd/wZ3vPOb2B9lPHw5+5nuLbB1uYEbQqKQvHY+UcZTdYZDIa0yxld13LkxE0UmeDKpfOEoJhsHeWxhx+iaRqOnzqNEZrlbMpo/QiuW3Du4Uf4k498HJ1lrK2NGQ4KhoMCJSJGO4JzGK1wIaZjPaZBDyIdw03bEfIk4RdA1/cr9oFGaUWWjwjDirZpWEwPqaeHq+M5Ad0EYKSGoMiUwgmBdZau7xmuIHmT8Rp5XrA/n+FCvhrq9Mw6S4gS5yLBRXan3eodmMwZSoDREFvL4sqcalyQj3O6rmexWFIORmyf2KCZLahGFYNhgZKSNGPwKUUBCF4SQkRFRWhKeluyVhTo1YAl5pqma6l7x7hMkEMlUuyW9J5eSqJW5DKuyNsS6wWshnfJh67ROuPWV7+F2++6g3pZg9TkmcJ7T9ssQEh8FNzxilckGFvvOX78DHfc/QquXr5CmeeMj6zR1Af8xr/7X7nw2AWMEmTaEX1P10a8XXDy1M346RWoH+XuM0cJuwumzhC8Q0uJD7C3t+DodmBtY52Dg0OyvGAxPaRb1GgREALO3nQXr3vDq7n/M59gutuxd/kiWoPJBii7QBmF7QO987SNBRSDvKTMM6SIdN6hM010ns5bZFD4YIkqkhUCoQLRCYo8AxdYJU0m+Jttsa5PzbGS9DbF30UFQiiKUtP0llnfsuwcREWMkTb0NM4RHEip6XqBiIEgROJbKIWMBQJPVD169dlrBFjvaLpA03r6KFCqYnvtOEMzJLQNGYLt49usr1dcujxl2fS4EMiEZ3pwlWXTYrRGCkHTdMxmL4zE/HKpl9t3/UtRYTRAtB3cgI0cAEohRkPioHzZ+zKDAR9lUpN8hdXd+WO84Qfv4f6feBXFxeuLIoMkTe8/O6Z/XUf2BfrrvUVF+OSEMx9xN+y1FRFka6814DeMZfBcqrcIB6zewlGCHURUKzCLiC8EzySsyGYRc+kQ4T7/WLbff4FfuPJtfOSn7uFfnPjw87pbV/2Sf3zlG/nt33gzZ39jztFzF8BfeV7X9XQlypJwZP1JmeBPVWv3N/yvP/ou/s2df47xec+tV5/8+SJCRB82cHWP6ByYjGg0k4c9O6/9/HtQlI5TOufO2y/y0M6Zp7iltBXfOnkEcf7S9T2IGBIb4yu8EfejgstfUz1psPF0pTrBsT/uKe+/+oR/lIjr/J664ePVW265ha2tLR544AG+6Zu+iWPHjnH16tUnXcY5x/7+/jWv2bFjx7hy5clvgMd/fjo/2tOV847OQmwCWqd8WHnN321RJDBUkWmESr7grnNEBzaItOkmZQ2XGYToaduAjCHJSRUgwspHqtFSE4LnkYc/w9rkKBvrx+g6R9c6jHEJfjQYpOitxgJu5X8FpEpBRxFMpil0ajqXywWuW1D3PXs7F7h48TwHi32yXOCdxgXLrO6QGryLK0q1wIaO/cOe8SAnNwKFoDI5NgZmsyVCQFnmSK1wzlNkA0JwhOjAhxTFZpJv0q+23yEEmn5JiAKTSYTyBD8HoSnHJc4HYu+woSOi2JxMiMLh/SFeQlmM6dqScrSNiJ7ZdJf5cknbtizrGYvFIYMqRwaFiKkZh4ANIGRACElUEi/gcFljBEghWNQ1HslkOGY0GOEXM4T3yXuf5QyGawgswXuarkVJRaUi/XJKkUV0VjHUmvXxgKvTOW1URGGJscajETKmvGCSP9p6h+s9rXUUmQGhiMZjlERrgZYZWg7oeqi9IM8GVIMJWVaCyNgMihOnAg8++BC9U5y++XbO3nQLb3/H2ziyOUIGS71YsLa+xcH+HsNBxSRTHE6XbG4fJcs0wVmsc0zWN8h05GD3CtYGTJYzKMHans3t08QIbdtSDobsXXmEBx44x3zZklcDtBSsT4aMx0OKLENET9u09LrHhZiyPiVkMk1u03ZYEYOnc4HWBpQSEB1KZoQIKgYyCUJnlHnBcDymXt9gtr+HXS6QISSJtuuTh0Zr0IY+LJgtFgwGZRr4KM3W5iZXDnZonWd3v0GoBFDrnUcKQZ5rOmdxUaxsBFBlEiGg7dPWeXnYIuScydEJOiuYTCp83zIcV1TDEilSpJYQAW/ToGpiNmnqyNXDGVpljIqKUZlhlEkSdNIwTq6itNoQCFJRCYVUkKvHo91WydMrwCFSXMtWj9Zz4vbXcdNttzGbT7l48TKDakieeZbTmrwsUxQYkXa5RDEghsC73vNelE4JDtVNJUppPvvxP+QPf+93wXsqDXV7SNe3q5i2QFtPUdOLXPHbaCqCMYRxwaOXO4yMCC3QIrIhlljXceL4MRbzGZ2Fq5cuEF1axq1tnuRNb30Du5ceAqCoCg73DjHDMYPJGZbNPpkSZIOC3fkBu7MOqQRIhckCtfUIoai0xsaQMr2F53A5A0wCvSlJ3QWctxQ6R8kE7YvRE6PECIkUEms9TWcxJmdcZoBNn1tCUhjBrGEVMSjoI3ROIHJBITVCJNhmngeiCIQ+DT08EVQCwWkEdWdZNhEXJH2QaFWyOdpGRsPh9BCiYLK2wWiyxmQ8YDQa8eiFffYODjic7tG0HVIbTKbIQoqKvHhpj6/Eeqm/61+SksBkBDv2xfWRCokYVMTxgPBlEBe0uG3C1/7VP2Ugb9BA4mVQ3731Qb73O1/Jnf8vCeELXusAIgRwHmFdUi84RwyB2//lAQfvOMNj39onEO7cUFzUbH/cke83Tyk3fqElYvJiq4M5cb54eTTgq4ptSzYH90RhkACfw4nfPcAPctojOc2GwlXii2TV0sU0aXdPfkyDz1zhs3/rlXzLPz7Kb77i11Hi+t43dej5sUvv4GP/9PWsfeA8tyw/9QIf4VPUM2WCP02ZueXIR+wX/btqHeryQZKIA2I4JE7S9Q4fqTm8dXRNRh1XFoD3HvsE/0KcecpM8eezFY9th+gcMX/5KnReSPlBzpU3V3Tr19GEN4KTf9iQPfIU3+vq+o7BG/4sPvbYY+zt7XH8+HEAvvZrv5bDw0M++tGP8sY3vhGA3/3d3yWEwFvf+tZrl/kH/+AfYK3FmCR0+p3f+R3uvPPOp5SqPWMpQd1bOusYVjl5BnmR472jbn2CboeIcwEZYpKH+ySfzYxKmyshUELgfUgSZiJSp4YrRZGFFN8S0mm3MhLnppz73EeIt70FH3JilHh6amtpesvG+hpZWdLVc2KIq2Y8eVIzoyiMRiCxvifQcfXip9nbv4T1NhHE+4bZ0lENDAKb8nWjwvYph3iYG6KMSCIxRrreIaQgNxFp08aGmPw6mTE4DzKEVdRGSCCH3mF0upwISQkQSVJ9gCAVuIClQ+caFxqEVJhckJkBdVezaJcYLeltx3x5SPQZ47XT5OUAZx17s0O6dsHe3h5Nb8nKHEMkzzTeWvpg0UpTFIaiyGgWbZKpu0gUCqUVUWiybISQhiAERVWxpSV9Z8mKFF8lupoYAxFN7yzaL3GLPmUVB4WTgr5NUKVF2+CixEeJ0goZE1Ar0xoRFZ0P+D6gpaEQAucjptBocopiDaVHeJmhVMYASe4cMh9x5pbbKbTi5Nk7GI9HZFoxPTxkvHGUySjFpaVmI5LnI7I8Y7lsGW9sUeQK5zru+ZMPsn3iDK961d3ooqDrEwjQO0sQOVtHN9jf3eG+e+8FnXHTLZLJaMCVCw/z0EPn6J0jak05GpJZR5lpJmsTMqMQwdO1PdZ5kBJr06Y0+iS7BjBGE7yndynCSam04a67FJVnjFrR6FNWeBQJIFiWQ/ITY1xfs//Y56in9QpQkXgDMaQhx8H0kOPbmyAT8md94zhldZFp3THYOs32seN89p6PIKWn9x4pc4rKpOGZ9IxHkdgnoGJmfDpShcDVPaF3TDYmSKPJi4ys0AiRotGkkPjeY8KIo9U2xgXuO7iIjJpRVbBWZChliCopVWTizlEYjW07ms7iMgheMNZg08gGxOPRW7DsPBGB0Rnbx85y/OwtbB87CgKOVMcxRhFDYDgc0WQtTdOilGQwqNKQwAekMoQYaZZTmrpjOB5wz5/8AZ/4+EeR0eFdzbKdo03yqy/rJSbTtPWCyWDBqY2juPkc7QOnhvtc7PdwbYuPkqysaHPD7l7JyeNH2dja5NOfuZ/ZdI6WhuFkjTd9zdvo2yn7B1P6tmZ9+wj1cgkIyuEmVbVODFN8cDgf8ULgYsQ6i7MCpRIjQCvJ+nhE1WcgAk1vkYA2nkzm5CIj4pDRI1BYn4ZfmTQ4FSB4hIgUhWE8GGBkpOsgeEtroXdJzeQINJ1jUOaYUtD3EuckIch0HzUgJLGPDHXBwnXp/vpAZz146L2AqBhUA44UY6KNHNaH9FYw2dhkMKxSTJySyCgJtqWrZ/S2TxA4JZ/wveJXtp6vvHrJv+tfooqFSRL1+fVvSp+2hEQUOXE8JHyZnOTWZ8ec/dH7+K7ND77Ud+WG1TyU/Ni9386Z/0kiOgvWpY2sdeA90QeQKQmHzBCLDLIBIdNEIxmeb7j5l1JWl7QW4o3xZYsIatkjD+bE2SLBQV9u5RxbH2949EjxpM23G0TcpCR7dA9zCYZGEyYV7ZGSeltjR2lT3mxK3NdssfGpBfrq9ElXbS4dEP5vY177E/81H33r/5tcPL1RwsfAb9RDfuRPvo+bfx4mn/zMU/WpL6xWmeD+6NoL9v2LENF7S9g7uLYFZ21EeMJmWnSWyYOe3dekrbhYaqah5xX5xaQaexpHwvPaii8bYv7yTXMQnUtDMSDKJJOM5tmZHqHKuPKWAe3Wsx8Neik4+f4l5uL+82JIXLue5/oHi8WCBx544NrPDz30EPfccw8bGxtsbGzwkz/5k3zHd3wHx44d49y5c/zYj/0Yt912G9/yLd8CwF133cW73/1u/tpf+2v8/M//PNZafviHf5jv/M7v5MSJEwD85b/8l/nJn/xJvv/7v5+/+3f/Lvfeey8/+7M/yz//5//8uT/CCIUxOG+xwWJCTE3niiBue4fUAWMMIfjUkAhBCI5MJ39siBLrAkLkiJgovSF4RAQhk9QUPMGDtZGIAwW7Bw/hPpdz/MTdZNkQ3zkiEdc7QvBsTCoWiz2iyBiNthBSJUlJSFuw4Gp2rz7II498lr2DHeq2STFjMtGeo1xRhDEQPYXJsH1Aa0mWadrWrmKQNN5GjBREL/FBMhxolNTEAE3T0zpHmYkUQRQNQjlc7LF9uo5CSgLpWNMi+Yij77EioNWI0HeYXCJVpO5b6rpBSsUoy1m2AYGiUAPWhkdY3zjG4cE+UgaqTNJMG7q2QyJQAbY2xxgRWSwDwkWatiV4l3wXRBSB5bzGE1kfjdje2sK6wGIxJ9h+laMuGY7X6es508U0wfrygjxLlHihJU0fMFlBQBF9xBE56CwyKogSLR1aKJQK6aTeOmz0ZEZTmAwtM1yRtmciCpAlo/ERvPX0fcuRo6eYbB5FZhW3v/JVVBlMd69y4sxJlosZkchtd95B37Zkedr8PfjpP+WBc4/yzve8F+csSkaUcNi+J8ty7njlaygHY7TOgMBoNMAWGiE1Kkb6roUouP3uV9Mu9nn43KcJwVO3HVEIikGKrVMrmf5oUCFVslQs645l3eJcWOVqS4zSiVa9iuLyfU9rHSEKMiUoyxKdGaxPm/MQPG3bE2EFZFOYGNEStARkJDt6hH1tWEznqN5jgidTglYqDucLZnVLMRhSxMgoL9kYbxNUzWRc4vqGYrRBURYEZ4nOonJBkbfYvkf6jo50P4gCaSTjQlO3ntmVfaQQDIdHyHJNrnNQAuElWRiyka2Tmwxsw2NXriKEYKPI0UpjgyBTCmMMPgScD4QQQUhKKWljQK7o6r2I9D4QnUMCSiscKRJQ6YIzt7+OW+66k8xI+rZDKsVobZ2+74jC0bQtB/s7hCiYTNZS5JvKIHqs71BKsmgbvG34w9/7ffb2D8kyxXK6i1EwGmQp1tBIOufpnMPNryDzIeNyk5k4QSd7jt58jPhwiwyS3ChMVnD02HFu2hL4cJXJeExVFFyyDlMVnDp9C5sbJfd+/F72Ll9GoAiyoBqvszg8YLof0OUR5of7dJ1PJPRcEb3AC4U0OblKppeiBCmhyBOvoeuSwsEITd00KakiBMJqM10aTaUFQia7hF3ZdVAKH0EERdcH+jbQuEjdKqQURAJaR0olcXZCnm0jyzK9J9weDQts34L02ODoXIo1I31igIhJSVSOOVINEAvHQe/xPjIerTMYFFRVTh8ssldE33Bl5xLzZU3i9UmIcfV+WBHiB18CwNeLUF923/UvYcXxANruhYGv8hzGwyedWL+sSwjmt4255Uc/w18/+v6X+t7csPq9xSv51V/4M5z+948S693UcGudGu5hRczTCX40Kp3wC/GUsmPZ3bittIigFh1yf57i9F6ODfgTytx/geL1t9EceYKfWcLO60qGR48TVNqER8m1/7/WJQuwI8HVN43YuC+jOLfzpOuWhwvO/MOCu7//b/H/eO+/5TsGB1+0Hf9MX/PDD3wn5z9yktO/ZzH3nnvxH2SeE7c3cJPiBXv/1dKirh6klAYhEZNxOvaeYrs+erhmekvaistWsOMlp/UMn0W0e+o78ry24k2DsNV1NbdfyhIupMjHZf3k94GQiMykQeegJD6F0igUhqtvGtJuP3sTns0EJ//PKWp39oKacHgejfhHPvIR3vnOd177+XFoyvd8z/fwr//1v+YTn/gEv/RLv8Th4SEnTpzgXe96Fz/1Uz/1JE/XL//yL/PDP/zDfNM3fRNSSr7jO76Df/Ev/sW1308mE377t3+bH/qhH+KNb3wjW1tb/MRP/MTzyhWNPklptYKut+w3DmUEgyJlxybGcdqAWBfQUlLmKhHNg0VKyLXCaEXfeaz3xCBxQSLxGARCpg/eqNIWpXUBHz1aRtz+OXQ2Zm3rFqTQDIqcrc01siJnOMiJsePixYvobMhwMEAAfeiYLXa4evE+Hn30QRZ9hwBchK7ryTPNoMzRRjCoSgaZYdEsqZuObCW/xwskCq00WmkyLZFY6tYSo0BJjZSCTOdIJJnvCMJhgyXiafoet8p7ViQgkpESY9J2J2jDoCggJnK8QkOE3lpi8Ai5at6cQFMi6AkyZ+PIGQbDAb5rWCwP6buO2lpUlpNpAyKwqDsGeQbKMKpyTK5paouSht42gCfEmCKNUHRtlzzceJZ9ZG1SUS9qDq5eJVhLZy1SS4TSKYNYOYRMub79sqare4QUHDl6DCGGLBd7SC3QMg1nlEtwpyAMVTLAo3QF5EzGE6rxOlpmbB49QzVaA++Z7T6KBIbjgle96e2MRiWPnnsQqXP2rjxGIHmc86JACcmiXeLaBR/+4z9mdOIuhsMB3hcsDvephiOC71Em4+Spk2TGoJRgerjAWks1GGDbmmXTMxpWgOfRhx5g//CAPDcpWi/LsNbiQyCGQN925EbTd0k6nOXZalMnCFJgdI5Z+cO1SK8lItL1AZ1pcqlQWlINChCSumuS4iIzaTtpHcKn5srHgCfSrcQWAc1wbQNdVLSXL+OamigkRZ7hmpbpdEZRlixtT2iWFCanNJ7DnR1UVjIoNUJKos6QRtN1Dd6DKUoOD2v6zqIiBAQ2BJzrkDESnGTnsV36ZUM2LFnfWGN76xgb5VEGeUlmFN42PHrxMruNo8pTtruVgj6AcJGxjqttP8QYCAK0VGQhYIQnEwrrEmsCn9QNKkbcant/8+2vY317g6sXH6Eajlnf2Eob62bBbDpDSUVelRw5dpzFfEGeF5g8S8BI7VFSsLdzkfs/9ac89PBDKAXLw8fouzlGrbIbQsQFaLq0kdUSFs0Sv/cYebnG1unT5HnO8ck+r92ekpdjtM45e9NNHD95HO96Pn3vBSSeo9ubXDh/Aesc28dPcHC4w+Gi5vLelEzAuskZTca4+QzfBYZHTuHsnLo+jxAZKlqiDAyLjDxTVEozbZbM65ZCCSIaosT7iPWRTAsQHilSZr1UIkUmSjAmS0NU8Xl4pHOevqtRytF2DikkzgtE0GjhQaXBYb0csbZ2JydP3URb18xnM9bUNtLA3uXHOFjuIGNHKSKd8/Q2DXKMUhTZgK3BGNVF9jrPtPfkRYXJDaMiQ8aAtYL54RUeeeRhDg4WZHmWqAUyDUWJnr6PKKme0f/4cqovt+/6l7KiEoi1EewePHeJutaI0ZAwKG9Qjs0NKCHYf+0aX/93PsR/sfbRl/re3JBqo+HvfeY7GPzrCScf2iWsDYnbE4JJkI8bCVa73hIhfr4B/8LG4+VcXcf2xxoe/bNP2IpH8AUsTlxfYxc1HN6acfRSgajbJ/1O1C13/txF/odf/Qv8oz8/4Ie/7Tf5G2sPsggd/+jyO/nNP3kdZl9y9OOB8iMPvri2ki/IBH8hJXxE785h/zDZC/IcJiNC8fSb/iduxaUVPOzWeVu+TygD1E//3D7nrbj3iHlN3Hj5bMVFa9Nz9VQwwhig64hdB4tlUjE9YZgRCsPVt4yojz57E57vC07+9j5yvnzBTTg8j0b8G7/xG9OJ6NPUb/3Wbz3rdWxsbPArv/Irz3iZ17zmNfzBH/zBc717T1GRzne4EOhtoE9wcHrfUuXJMz3IqwRE8i29d2RSURWGzgpaa6nbhhhBRIk2hqLKQHjqpsXaiIziGsgskBbkgpRh3oeaq7ufQ5kR6+vHU6RO9BR5hjEZmxvH6Lueg/2L2H6MxPLo+U+yt3+VECxeRAalWclhBa0VmExR5BqlIHSWxiaed5WD8x7rktTTeU+mNNokEnTfB5ARoxR5JokxUrc1MUZCDEluEmMC2oW04fOAj4DQZJkBYRFCU2YF1rZINC5aatcghSaXKb6oMJoQJDEIDIpCjjhz9tVsro2ZHx4mmXkj8AGMKZmYihACzll6F1ewPE3ftSnD2hic61nbOoGWgr2rVwhRo43BhYjrenoXKAqzshBEposFRiqG4zWqqkQqT9v19F1Pnhs0gnnTUg4GrA1zhPKAoBoOcX5JmSVPsHc1MTp0NSI3GmkMeZYjpGZt4xhlOSJEQV4UED3r29sMRhOEkOxeucLh/g7N0jCfXiUKweTkreRa0VuLEmL1GAWjjaO85i3vxKJZLpZoJTB5idYamWVoJdi5ukNRDchzQ1mVlLFAEJk3S3YvXeQzOzv0zqLyYhWXFhmMhmRBMJ8dEEPEe09VFhgtk4c5RJwPKC2vRdJpLRDBo5VAxgQXa/qOLFdkRkJIm96Uyd1TagFK4PuO/dbinUsnCZlB6iS5lgKCTxAvqSRS5xw9cRKdF+xduUrbW6xSXDnYZ7Q2RmnFsrfUFhwZJk/HgzQGoyRRRfrePmHjKDEqo4+Ozifvukp7alxMue4uBuaHNdlScsfWFqeLbbTO0RJC13Dh6g7TxrJeluS5QSqDIm05lEwEbyXACYGIAhEjToQEEEQwd+kYc95ifIcW0AvJba/5Oja31oneUpRZ2roKge07ZvtX8QG2jp0kRodA0tQdg+GIpl5inSPEiJaKT3zkD/j4PfcwGBh8e5lFcwjCYrKYBlVdRCqBUQJERApFlitGOhB8S1tfYVBf5OLuNkfGFXuUVGqbjXHFB+67zNUPPYhtOwq3w+vv3GJrfYMTJ46zu7tkY2ONw8MLdHWXUiNiYLq/z2CyxvjE6US6EILx+in6dn/VRKe4RLwlS2NL9CpqLoaIFgopFBJNluUE54gxpTYgUo64CDqB2lwCA9oUz0AI6bGF6JnXDa0NFCYHISizEmtDot53GYERznp2d/cZDoeoPKe3ATrLqFwjTjKWywOGXoKtWbYRJ0AVBYPBGr3X1IAcZKwPk62zazvoPaqA2XzOlUsX2NtbUBY5kYC1HiM13jvapicA1smV2uTlX19+3/UvbcUyQw4q4uI6YXxKIVYng+FlDmJ7UgnB5bev85d/8Ld4W/W5G3ITishRlWjjOyHHPl3+0g2sv/aB7+HmXxLIzmI3blCE1fMsESJq1iEPZsS6+fJpwJ9QX7gVl14gn6OgxOeC+uYJg0+1T/l7ffmQW/7nnp9f/jn+5evn9I1BX8gxHiYPwPgPHrp+Wviz1Yrr4I+u4YcrVUvgeTEArikcruwT2zZ9VqxNCMPquoZ117bi48h97QneVS6RQwt7T/8+et5bcTd4yu3yl7pE08P+4fXdd+9T2kXdItZGhEnFzhuGqQl/lo/iYldw8jevIJruuj3gz1ZfHiakF1DjgcY7Tx8UykiMC0iVIE+lUcgg6XuHURm5zlExkZdlzBjkOcSGpW2SbFqLBFwKDqUk47LAGp/81n3AhYgSEa0SR73uA957+vYyWt5HmZfkZhMiKBUhRJRSbG9tMd19hHP3f5LFYp+26/ExrEjLAmc9PjqMyTAyZe4KsTopdZYeu/IfptuzsNraCZZNT4wRQdp2Z0bioqduPJleyW5dR4gpq1sKgRIK+fhWauXpTHFgEiEUPkRCaCAIfFAgwRiFEYmCHQEtclyIxJUXsyhGnDp2CklkuaxpmiXL+QFaG4zKmC7nZEZTZoYuerzwiBjpXcBaRwyR9e2beM+3/RUeu+/DfHQ6Zblc0llYHO5Ttx1lNaRuOtYmSWp74sgWeZavJK6R6CBXijIbUGQCoSLr4yJt1myfnufQEHH0/Ryp1iiGQ0QvMVrgbMNo/QhFOUHoAilztMq49ND9FFVBlg0Qrufyg5/llW/5Rmy7IB+USCnYuXgeFzVGAiHQdz3loOJwP9Hcx+sb5JnhtjvvYDQa0SxntG3HaDRECFBK0rUNZVVijGI2nSKloF0cct+9nyRIxXRRU+QZRV5gMpOo3JmmLHK8h2kQdJ1DCyiLLA0BXMA7z6KzdF2/ioxSdL0jE5HBJKk0nPVUZUWZJWm2Dyv5epSAJCLoW4/tHW1v0zEukprChxTTJUl/o7UikppnlRVsHTtBXg5xj11gb+cqi77j6tUdjm1voZREhICOfkXAl1y5fAlBikLK8gKCTfR2adBZydhU9G1N37cMCsPGKKPtHbuzBPQ7efoUd545xdpgDWcj0bVYIjvTGTvLjirLKDKD0BkoTaXlCgQX6BxoKVP8lXN0AZTWSUWwiu7pYqDrWuZNQy4FJ87ezGzvUR645wMc7O2T50Uajgw2eddf+HaKwSRlXXctRVlgbU9dNyglGI4nCKG4/NjDfPbeP+WxRx4iNw27O/cSQ89gqOl7j3ORrovYzuOBum0QKlJVA1IIomTeLFnM9zkZpoyzk+Sm53h5Fd9NmZ2b0u5PiYsa5QNRKa5egWF1gs3tDZpWMBgMmE0NZZmeHxcDXmgW85qiMMwPD9DZgMnGFpO1bWw3ZX1YEIUhyzR9aHFBIqTASJUGb6JPvIEURob3Hh8gWBAyqQ+kCkgpqIqMzBhmdU3XpWGhIUcAbedBJgig9xKT5diuR/oSGzL2ru5RTwSvfcstbGyMiLuB5XyXxXIGzlNWFVk1ZP+Sw81qTDSsDYdM1tYhKuq6oe0cRVEhlUQLhQuBncMp1vXozLM2zhFhzGy2xFq/GoQFPBCFJIYE7hMv0pf3V+vlV0mi3j5zPNTjILZRlbarX2Z18OoJP/jDv8bd+WM37Da2VcPNZsgitFzxL837xZzPkd0zR499qUuEiJ62iP0pse2+LBvwa/UFW3GRZq/PrQTMT2qqh754K44QdGc3ufLmAjeIcP/wWrTe4CJs/6dH0nb0xarNNerbtjj/3Y5vvjNFnn3kymkWn9hkfA5G53tU++xNouo8amdKnM6JMSCKgjgZPSdmxBO34g80RzAbDzIctbQUz/h3z2krLuTnt+Lrw+u+bzeqxKJ+7lBC2yMWNbtff5TlSZ61CS+vCE7+xkXiwRSq8quN+PWW9x4fA4MixxhJ7ywhCHKjybKUaa2EQevUYOqQ4VxP2zTJR64040FF5xxCpJ23tQ4fDGolwbUuopQmkzLF8OAxmaZU0LaRrgvUi8vYfp/B8ETKxnUeGzumB4/y2Xv/mPMXHsHh6XuLc4Gq0ulyUdL7Htc7KCRayOTj9mlDZF3argsky7pP9PJKAR4CNI0FESiLgixohEz52/NlR9d1rI9LlNHYZQshYF2SghZG42WiLmopSVleiUwtVxM+HyFgkVEykBUhgEMgpMZ6S4gC7zpsjNx8+iZG6+ss5nP64Lm0dwXXt+RZSVFkwAAhAnme4RdL5rOGzBhEVIgQcULzDe/5iwg/5zP33oN3FhEildI0SuK0pMoMdtUUSqkI9MynO1R5iY+S3nVooyi1AScIAZahp208UkasXSKkT2C4fITrG/Z2OsaDMdVgg2q8hdAFWb6e4o4E2E5w4pZXJs++73HkvOpt78K1SxZt5I67X0+VawaDAV1Tg1Q0832uXniEs7ffzdb2BtODGZkx9F3KJK9n+/goWV9f52B/HyEgyzKyvEQpqBdLBmXGpz7+UXZXkDtjDLlJ0nstAmKlzXAuMF+svqBiJHqHygxN06aBiTagBKbQVOMyyapdoG07MjyZltSNS4DS4FdU/mQNiEhykzavQWiabokQYpVnHxPg0DpklqGEwnaOCJRllpgAcgUUiZLhZJ0zWmOU4GB3l535nGGRU6yGMz4GOiTRedY2thnkWUowaJbMDpdk1ZBqOMa5Dts2EFK8XJSCvXnDonEImfPKm89y9+13UJgseZKLjLbv2dk/ZNoFclOgC01vNLlWaFL2e0AQvMeKlC/eWktvA1GapGqLaRAnY8A5h7ceZQa88eveydXz9/Lh3/1tmtqlLHEh8cFTDdc4e9PN3H73q5isryMIXLl4kc2jx1jf2ORwb49Ljz3Mow8/yO7lx+jaA+r5BfbnF5jXDUjDwOuEqIg6beKDx7kU0VjkGikjhATWc97STC/zyCOfZbhWYe44zqEfcfGwoO8Lpm6AKxS5MQgCRduztWjZ2FhnuQyEIOi6mq7rUXnBeLKGUZGdi5fQx4+zWM7o9w+oRrdTDk+hdh9OYDajUZpkx/CRTKj02ksIBNoufVYIJ7FuNaIRYLKkfJCkzx7nHVJJskzhgsVbjxCCQmfkMtkVtDA44XCuAZFRlMdRMqOpL2IyDW7JYuZBQlkUrG8fJdjAdH6I0ZpqOKCZHeBkxubmEULbsz/dpXeerKhW7yEIKl7LIhc6JEBnm6Idh4OcxaKl8zFtxYXEaJ0GECtlz1frK7OilsjxKJ2ofWGTpBSiLL9sG/DHa+eN3NAmvBCOM7oE4KL3eL70nnmPRNcvH5WC8BE9bVYNePviQ8VeojKffYz107eyf5fAZxFXCvRznH34XNCcHlPd9/lGPGaGw9dvc/AKSTBPfrbyfcHx37qY/NYvUonBgMM3HOVVP/px/tPJD3zek37qg/g3BPZCw9v/8G9y9l/Jp92QixDRBzXs7CdptVKI8drztqw8vhX/3GwbTsLJyZRzrD3j3zznrbiQxLpGjKqXdCsufCTaLybMP+vfFQU777qZ+U08axNeXRac+I0LxINDAGLdIIaDF6UZ/4pvxIssbSSVSt7eTGukyDDGoJUiOE9nOxZNDUqhhQAfUUrg8QifKOilyrCuh5givwKCxdLRtAGtIfhIZhKcRylQUuD7QIyCLANEw2OPfZKt7aMc3d6gXexw7oF7eOjh+5nVywRCkxEbEuE91B3DQqWGT0JZ6kTh1TkyBlCKGBRSDOg7ixBpuy51QMsU6+OdIMS0KbfOYp3HO49RAtd7YoTpvKWqcmLQ+NAhpEAZhZQpBigaUka20sgoaH2PIPmzQRJDIEiNFRGtJERLCEklEIUmYtH5GuO1Y8yXDfuHMxCKyWSDxeyQ5WLGbOYYjycMBiPmdY/MJmxPjrFczrHNnLwseNVrv54jGxP+6Dd+Bd/35EYiyVEKrIDMlESg61qsc1TbG7hlGkIE0WF0ThSe3CSvuxQpX9h1PSZLudV97xBREkVGFAbnA0VW0bQ9Qe4zjiN839F1nqMnbyEzGevHNqjn+2TaUOYFy2VNWZb0WAauZ/fKBYajNZQI7O/usHnsJMdOnUWElENv+0BWZOzv7bG+vka9bNDK4L3lcL8nhEhe5Ozt7jJe20SKwP7eJc6ff5j9vT2yLKPME8kcJMRV1FvbgpAEAc4KQm/xbcuozKiqHJPnDEYTsjxPMWKr4VPT2ZVdQNO3PdO6p+08IQSMMsTo8QQECcwXBciV79XHdNw7F1AiSXCDEMje4/qAC56i0GnI4ywESRQKGwKBiNGGzc1jVMMRV69c5uJsxrHJhNwolm0AlTEcZFhbI4koIVGDiuB7rHPYrkUbjVcykdtDoLctWhvOnj7La+96JaUKVEWFEJIQA3XdMF/UuACjYUWRFyluS0BwYUWYA2JSAfQ+pogrIci1QcrkgScEetfj3eNZ146bbrmV8w98kk996AME51Ai+cWFEkkZMjvgA//n73D29juwfcdkbY0TZ25iNj1k9+oFBmXJfZ/6GLPpIX1zlfnsUZb1jP35gr22J1MO53Kq3DAoNHkmUdZgAO8twVt6PEooYvTkucHZjuXiMrbex6hjnN20uMNd5u2Mbu8Kg0GBsobNE6dYG5fs7x6wPjnF2bk+tkIAAQAASURBVJvP4EPKwF7O5kQEzXwPW5TowrC3cxnvA1pFFrMZVVWBGqPlEhd6ok8DGhE9Wkkyk1QGyy4QQuINyJhUQjGIZL0oSgqjsLZm2SSIoJOeYMGnyRDBBbx0DIsCIQ1tG/CuT0INoagGA+rFAhEFRTlEqIrx+jp5XtIeFZw9e4bD3fN86EMfZufSw0z3d+m8YnNzQNctaVqLl1CNCvACERMzRGhN286p5zPKch1PiiaTWjEYD9KAtnV0vcN5SZFDZlJsmrjOWJ2v1pdnhWGBbFpis+oolEJUqw34y0DC+UIqKsXJV77wbOVCODZWOuQ2SrqoaGM6Hb1FO4zIqEPPvn/mDd6NKkWgPuNYnioo9h2q8TckZuzZSrqAmjawP03+1i/5PbjBZXs2fvchgr6Fw9sTOV14gXous0oBwaT3VTQatz1i5w2Dlcz4yc9Ydig49Vt7xP2DF+8xaI0/ts7l93Z84NQH+cKuWQnJETXgF9/6i/zYv/+bVBe/wM/+OOl+5/DzMLayJK6NXtDA7vGt+OVZ8nDfNtrhHGef9e+ueyu+6kPwPhHUJ4NnvvyNrBBYNSXXXaIo2PmWW5jexrM24YPH4NjvXSXOn2A7iiFJ8wfVlx7W9uVWWkSyLGX99s7TW0dmFCrkiUxOivbqrMOYkKi2WqOVwseezjmMNAgR8SHSWo8RpBNHsTq5khEpVZJwB9DSpGY19sgUQ47SARcOOP/Ih1js3c/hzlVq26WmR+c0XUPbNVjvCV6sIHIBpQJKa4a5piwVfpVhrVXGuBxArjicHyYYlIYQHXXn6B3IqMFr+g4KI8iNorGOzkWklEgFRSkRuOR9jgoXeqzwWB9WFOzkvRVC0/mVtDSmWCYjBJEkV4dVFJRUhKgJcYHzSwgGlSm80OxP5yzqGrTE9TWL+ZTpcsm4GnL6zKt553v+Aq981atYWxuhtWI2m3Puc5/jU5+8l9M338yj9/0J84MdEIK9ZY0uhmwOhmyXFc1qwx+8Jzc5IUbysqCvLW3XMxpKFIGu1zjnkdIitSHPKzINiABotBRkxYAoQETN5voxunaKczVtUzMYKqR2gEWaIVolX+5wUtI3Mw4vPMju9gZSZXROsXFkgpKB5WLJ8TM3I4Wgnu8z3tzGe4eQ0C07BqMREJhPZ2itMVnyKMcQEUKxdfQ49Xyf+z59L4u6wXmL0hl939PVPkmjlSIPGpkWfkijyVZKDSuhGJYMhgPy4ZiIxgUJNmJtYDGvWSyX+FV2e5Ur+mVk0XUImQCAfgVzS95whZCSIFJAtncps937iDEmwdEAqVIMnxRpk5hnhkFVYb3DNg7rfSK+i4gIisnahCpOyEZrHFzdYe/gKpO8YFh5RGexUeCkxgdHdIG6brFBgslBKJQuEH1P13d4FMeO3sQdZ89w6823IJVgPpviQ0Arie8te9NDei8YDgboIlv5l9P7rwHmztN5TxYTJT3TBqRKcEYJ3iegHSJgiBAthMgdd72BY0fW+cBv/e+ErkFEaG2HEILMa7ROVO/9K+f59D2f5K7X3U1elgwHJR/8z3/Ixz/2cdbGY0RskOIA4iEm63HLjqYPFLGkQDNrWgIBJRWZzgitxfYelQFElABve2y0KA3aR+p+xtX9A+o2YPOjXPYVTE5i/TF2bCSvKtBDHr24h+lajhypOXVijRharLU4Z9N7yGgcPZONDfq2obeWul5ycHCIySsmG7dRL6/Q9j2ZVFgEGqg7y7wPOO8JTlJmGq0SNFPrlGoQY0TiadsWGxwBQHuaxiF4XPlREJxgsewwQqfPXMAGAdFTGo9wNdWwZOzW2N7eYlJl6Jg+/5T03PfpD/PQ5+7n8HCRJPwiYzwukVHTeYcpC4qqQOLpugTutbanW06ZTqdoLdjb32M0GSGUYG1tQpUZcqXZmy7Z3V8QAG1S2gUBlPzy3YZ+ta6v4toIQrhG6P1yb8AfL7te8N6TH3ref18IxwnVs6VKjPi8nNXHgMPTRsdEJj/2Jd/jeem82b/4Z/8NF965zh/Pb+EPLtzK7Nwa4wcl40cc2cGNVbVIF1AHdWrAXwiJ/8ugYtuy9TsP4U3aTNoRiBnI57Dg7MeSg7eeYHFSYscQ9P+fvT+Puiy9zjrB3zuec+7wTTFHZOQspSRLsiwh2zQ2NhhsQAUGG9pAA65qhsUqu4ppQVXRVb16NM2iV1cV1asxQxcuehlo20BB42IwxrbwIHmUB0kppVI5x/yNdzjnvNPuP94bmakpMyItKaV0bK1YuRRx73eH75x7z977eX7PpzbgOim2P1E49ZMvIEfHn+OnvIZSGs7skSeed9x/5RVv+ltazZVvLDz6MmSGDhl78wQ5Oqk2A2tRW3PK7PMzgJo/vebqlTmjRN42ucK/UXzWLPGXV3HC/rt3OX03W/HlCvU5SORfktU07H/TQ6/ehAtMX4Dz//4GKkToOmS5ekntlFLNVO8+x+/rDod3b/hGvE+RLKkSwKVS1EUrfGew2nCyXlWJaanZsto1GGsYQ08fArmADBGUQqNRCsYSkSgvbgCtthit0KbmXze+bttQhhgFq+yGOg3H+9c4uXWDKAWtLN53OAdDFEQ0Rhu2uynoQs49k87TtHUbrYohplitGZLox3WFHDmLcw3GCusBxkFjVAIyrvEVyiVs4Gt1+GBdzUPuh0jKiZQNW90EEU1Kdepzm6KdizAyYpyiUY5SNAVBVMQoi0bI0iPJ4d2UJDVTk9RQRjh1333s7Z5h7Bd4Iwxjz/UXRpRxXDj/EO/7tu/kD/yhP8Dep03UdnbmXLp0jre/8yt44mMf52ee/STLVLe1Z8+cJ2lDKJl+dQLjwMlJT0qCNY6hH9ElMm07claEPjCMCd95vINQYNI5rNL0fW2sO21oOofoAqrDNpHV4hrOWbZmO9i2wdoWbbYQ3RJCT9+3XHzgIQ5vPM+1555kdbzk1s1j3vZV7yWnSNe2PP6hnyGkQgqB+bzj+PAWzWSHnd3dGq/XeI72r1ME7rt8mRgCJUdWiwWz7W1uvvAkL1y9Qh8iOWdK2fyRSGM1u3tzvHXVC18qmbkUIStBCaSQCKkSvtM6Mm8Eo4VSYmUMhJF+CMRSI6GMqsMV3zQsU483pjY5KdXzQDxN04KBIoWYBLTFN548puqNLTUvWUrBWIPzntyvEaHGfqEr0VtplFJ1e42mnU7pnCM7TzGeZdPQH+2zNd1Cs2TVj2jvybQMQ89826NKIZaMZEFKwjctD1x6kDc/cJlJ2+CU4C2ELMy6ln61YFiPHJwsSAKzrkEbjSqbOECjsbqC6076SJDCgMIZS+MMXm+2/SiykfpeFyFloZTCzt45zpza5pMf+zUWi5NKeM+FPtXhhYjUXHqnMRae/eQTnLl8Ed+0/Oz7f45f/eVf5szpbfqT54h5ibUZrQOQiKkgorHZsgwDqc2sQqbkgDcKxFJQSEhYa/BGo6xBi2E1DJvnHTjYv0k/DJw/f45zT95guPUsXgJFa07P9giHn0QdnSBiuHG95fTeLuuhB2NJKMxkjtKK3TNnMVoxrNaE9Zq2m7FcrVgcH9A1oOyUEhfEUghBamZ8rt/v2tfPTZsESYVJ60BB1oUxRQ5OVhhtq0VF18/fGBLWCo1riDEzSERpSyiFMa456Uec13gm5NiitMeawrhecHx0yHza4JxhurXFcnGL1ckR6+WS5dEhq9WKra0pO3u79XNxfYxVCu0MKdbhq+SaiT4OPa2vW3BJwuL4mFNnTuG1Yss6Zqd26fuR1tcc8ZwKymuss6T4Cv7he/WGKHEGzux92RDy77SOH/Z8Zfvsa7rvjh551Fka9Zl+UqM0Bv1i7vO6BG7m7tf1XH+95VTiQXeTB/du8h17H4R3wKo0/I/Xv57r/+dHsMu7l8K+WumQMYcrOFq84Rvwl5f0Pef+9TPI+x5geVkIWwp/h824GDh6VFN8TQ359PInigs/tcY+/uzde4hfpdRsStytw6KJffXf12/+yie4+f99EBUL9qiHm4dIDBtuxBTZnn1eh3ZqjOz8qmbx+wJvaq69Ypb4y2txv+L0+TPwwrVXvmHOL3nFX++t+J1W03D4TQ9z+Bb1qk34/Gk4+xPXUGlz3Bhdh6v9y/wT41izWJvXbqF5wzfiWukqW82pbvuMr1m/MTJKPcu9M4QhQylVzilx4wEVNFWOi66SVFHgjME6KthNNCEmYgx0raOdWSSXuqVSmlgMWSmc8yijCCESQyLlim+fakXjPM4rVBJS1ohR5BwJMdF6Qxqhsb7KGkWRoib0iVlnETegDBuPc6YkAV3wGGIuZFO3jl5pOudoW8tiNeKtx9q6OVIqoXRiMQyAqa/PVN60UhXONaa+RqKZgm8akELIESmFFGukmVKBmAaylio3FYtpPM47htUxjff4+Q7r9Zr1ose5lm/9w3+GP/iHvpVJ6zZQOV7M3BWEoe8R4O1f+U5Qf5p/9g//DvvXrtTt7NizXi/px54UE4oKlZvMOrrGcny4JFtD27asV5GUNJ32G6qyw5pJ9bDHVW0ojcX6QoiOYCaodIIeFjhrWK6XeO8x1jHfiZhbkb7vOX/pQcbFs/TrwH1v+kqs7Zh2LR/78M8zrk64+MBjXHr4sTpBE81iOXC0zJyygf1b++zunWJ5cswwjhzu32JYr9jd2+Xk+BDrPTeuP0tImdl8zrbRpDhwfHzMcl1fb+MsBsUwVEuDVooigRgTzpm6YaSS0otWSBHGMWA3hPgXz4VU45pKKqhGoZRCG1u3hKXGl9WMeTDWIQiq3EYHaIwxFO0QlbBGIcqgVCGkAqkQSgStN+oKjbUONWScNeRcCDHim462bchGMZ1VP643ZwmzGePRAY1raP2SmBNrASttjQ4UQceAOE3TeLamUx48f4FTWzNSSZWiHwMlZdbjwNFixSoklKk+ceNd3fprgzOWIGBROKvwVqNEU6xFqOR0ZaqM2poKqiulIDkRVCG7jq/+Ld/E1s6U555/hpwyWmtWY0BUzVOIOWEt2GI4d+lRumnDEx/9JX72/f+W3XnH1jSxPHycYTykKCi91N+zEkJO9GnFGKucvomaVtffRynVD16b1nouhVS/5C0KbRRT7ynR0i8OuXVzn60dz/Www0pZ7Kz+Lg6OAqXM6bY8YxjhaM3FkyWndmdoMjkklApMT+0S+wXrDCX0eD+hlMTqaJ/h+IgLDzzG7pm3UW4tGdOqDv7GQAhCLJpWNTS6MEoBXyX+SkGR+qXnvNl4xKGkTEoFbTRZoB8zQw6sx1htQCWTYpW2p5hBBSR1SE7EMdCvexaLJ0lpRVgccO2F57ixf4uj/ZsMqzXL1cB8Z4/drTlpGFkvluRcSGNBK0/MpQ5bdSaOS4Y40nqPUoo+JHSAYTHQTWaUmNmPgXbLM4uOk2UEFCVm1mX48omoule/vnoD/p4Pv0KY6rsHXBmEB6x6sdF+1dsrRasSK7mz23+xaqpH/vT5n+A//6q3cPE//PobcSWACDoW9OESjk5eGfT3Bi5ZrTj/w8+weuclDt7qiHNwy1duxourG/RP34ADqKzYflI4/R9e8vV+XmsjSZdNCsjHDs7AQ698l28/8wt8Lw/inrmJrPt6XWgt7GxRJs0r3/k11s4nAh+L3atmib+8ihf237PHqSs3Xj3abfPvslqjPke2+Re8lKrLv1ebs2ya8IO3vnoTvvUUnHn/y5rw2+UdpPwpgzIZRpQxYF+b2u0N34hXUm1tFLRStE5hTGGMAylkrLdYrRkkkItgUFhla36vh3EVGKOgncKYOvjQVldqs5JK7tUW33q0KqSYEQ0xJxrv2d5qCEPdJBqlwW4ae+FFL3YuAzEnxpQYxsgwRoyHxmrEOCR7ljFtNmMKpxWd92gjpByIsbBSYLUipYyxFiWKkmvDX1TdRNXoQbXZ5GRKqbcJ8XZ8mcVYgzHVF56koHSoWx+BUAqUkTEknK7yaQCMp+gRrRxFMjlXenGhQoxct8VsNkdy4OToFtdf+CTeWR56x9fyO37X76R7WSbi7Y9TEWEMPcvVivnWFpO25au+6j1ce+Z38r/8//4JQ9/XfHDl8DawWPVI1kxbj8REtHVbFdAQwfkJ2kWMN2hjyZgq6x6WHK/7DfHeMMbI7haU4QhtW5KekMKINqBUJpeRvjecvXCO1mfWi6uM6yVFPNunL/LxD72fyew01jsW1z5JKYrJ5J184kPvp+m20N025+9/lP0bN2i6tr6fY2Dol9y48gyhP+H46BqT2Q5To+mmU6ZKkUJkjCPrfmBICqUsvnWkHFmsR/p1AKWYTtpKI7emUvatIeZEcfZFqEQqCtEao4Q4DuSYMNpSKFhVGw5uN8u6StGd1RSqFDunREkR6z3WO0RbcgHjHLokUqoecmctzoFSijFVO4V3DlBoa2sDrIRxiJsNvAWJpLFeiHXe4rUiGIOzHSVDXp3QDYfo9Zotl9AlMSrNiWto24Zp47mwt8f2bEJjDa4oeimEGDg5OuLqSQ+2pZvMNx73+jwboxGl0ChIkVWCIGoDYbRoAyIKrQRnq4VBaU3IUu0pOBq/y2z3HN3Us3/jGo+97e0cvfAcV5/6BF3bMp4sQZnN5rogwJkLl7h+7SmM7zi7OyOOVwmx2i+iBBahSqZjntJ60FrRNZYhjyircd7TNAZHZQ5YpRnDUD8DNjFfSheGCN1kGyHRS2QIJ9zav8q5M3s8cOk8x9cXmP4F9m9cI44jXdthh8xiPXL5Hb+JmIUQM9POoUqEYjnYv8nuqTMc71+nhIybTNk+fYqT5YKjm7dYLW+yc/oM2/PzHB89zRBGhlUgorHe0q9HaA1YoTOGoquyYNJM6ERYjT3e1sizlOsQFCWUrEglMcbMqs8oQv2M15XfEWKhlKoDP14dYjE4Z8hSWA1rnr+ZSWNitVqSS6bExLndPbZ3txlEavyfog6uciDHSmHPeWS5XFJyVRTlUiP/JtOWPAZigucOjpnNJ3TTlgvtNnYsrJaHJKny2yxSmQL36l59uZXWnH/rjdd01209MtN3LjNvlONRN/CJGBnEkl/NxPlFrFZFTn3jVeRndlHpzs5lJUARdCqokOqfMUKIFTKV0ucvRuvLuGS1YvIzH2f6oY7w5gscvKUlteqz0tRTVz3ln646UQXcQnH+Z3vch5/97JnSn4dSsym5eanxOriyDe955ft873PfgHv8hUprVxo1myHbsy9o8+qWkV/sH+KPbX30VbPEX14nDypOXTgDV+6QCZFS3YpvffHtJKI1SptXPofuognfeQJO/4crn11arlTdipf80uNJQYbhNfvF3/CNuNUW4xTGaqwVSk6MQSBb2qbBaMgq4b0lpoRWum5hjEEkEZ282FRabelaR8yZmKBpLI3TeFUP7JASaRMbljPEWDc8KUdyTmhrMKZu2VrXABWkJptNvNFgvapkaevonMUbD6JrszKpTXKfR5yOWLHVWyUZkUJKdZccQ8EZi9GGEDO5KLQyjLr6zsdQ8N7UC/WNSmBMCSn178ZSM4tzKTVJXMomcqjSsB0eYzROabIIoiNO6aoeyC2UnpBHck5YYzi1tUXXtUgxxDAhhUDXTvgtv/V3cnpn/rJz4iUDS0qRxWJJ201omwalFd2k42t+6+/gaDQ89+wzrBYHPP/kr5IHRy4OazXbk4bVOqC0wk9mNE4jopnMbc1VdoY0BqQIy8UxxihSAacUe1sTklLEImgV8c4SM7hmTtt07O5tcbT/HGUcuXXtBbxVDMtDxiTsnX+Ew1vP4hqNb1usDOScGWLg8PCAqy+8wOLko+ycucR6taBtHMNJ4cZzCe8bBJhNGlKKdG6LLHB0fFIHOM5TSqJfnbBcjxQBqxTWWARYDwGsoXGVzOxtlfzW2DJhFQLG+5p1XQTvqtQ8hghlsxlXlSGgla5NsrEYpWkaizemStK1plBIqdB4j20alBJCzhRM9U5TKdmiNGbzeIvlqv5MDTEVVutAkBpNFUIgFqHrJhhnq3TaAIW6cS7CpOswKhCLJnlLjjPM8oj++rPM24YLkymXvcc1HusbrDWUkjg4PiKKcLJcsdpQ4sV7mrbBeYc2iiIQlEJKVWA4DaKq593aCnTUmymnCFWinwWtNEaExmropky3dlDGsL19iulkStd4Vuue7VNnuPn0J9AGtqcdUgoilfYtkvjoL36A85cvsDPXHB5+nKu3nmdnZ0Lnp6ToIFuGGFnFY7QrtK3Dj8LW1FVYnFI4VXA4MELOBWMszliSBJz2ZAFtFMMQGNJAn0aGUDg8OqAxiW54gadljyiOcfc0cQgsRLCuZffBi0zuv8DVFz6EyIL5xDPf2aLkhO+mDItDTg6P2dre5fS5M2hVuO/S/ey2hpQGnPLMtx6kP7nGpFEshshqGZnIbZ5EzVMvBWLO5FKTILyxleiPwjqPi4VQCloEpWuKQhkiIopMZepZ7SBRc8kby7gYOTkBqz1ox/buHtPplBQDzawjl8Ty5JBJ09LsbKHmU8ryhFU/Voq70vWYEUHJSOwXIIJtHHkoZAqNsUgpjEnhJXFwuCKVxM60Y7vxHFhDM21RYyKMFZip9BsOuXSvfgOUGMWl2d37aw3CRXv3F6cz3fIOXxglcVACz6fuS6IhfyKc5+bPXOC+/Jl4byWgckHdbrjHhBoDjAFJuX7fbi7e730KfO6Svsf98ic59xGPXDrD0du2GHZ1lVYXiFNIE+pwVsAEhT+G2ZXM7Jk15sr+55WK/tmqfFrD2T3niJJx6rM3uqNEbvyLy1waPwzOw86c0n3hEwH0kPi5kwf4szufRM9fOUv85VW34qc4dfXmq2/FN/WiV/yLvRXXoLyrMv/PVnfYhKsCOx8XTv3kK/v9q0S9fUnVAC/5xdvmrpvxN34jbg2TSYM1mhhHcqpxN77zZNnAgERhbfWJGhTjEIGIoIipMA4F7xXeWJRoLBUAZ41GJBNLhSUprasJHYX3rk5OMBi9iWsSRet8pVmLYLVFSiYk2fy9MKF6Zp1WKHSVaE0884kjS6igtFgYctrEN0VSSjTeURIs1xHvLWaqNqA1y6RVUBTrYazvRzOnaQ2KoQ5iS0FpwzAIpShEC2oTQ+adRwvoqqFCaY221XupRVCSAEMmE8NAzlWuG0vdHhrtQAqlJEqpHlrftaxH4b4H78d+ih9mE4uWMyfHR6AM00ml3stmMrW9s82ZvR0ObzxPWGUkR4ZQo+as9xws16gIrUtMJxMkC8Y3aKspKZDGQM6QUiDEQhwzMSuUdSxGYT73GJVReiSlSoDuvKMMC8blDSaTFp0LcXWMmp8i6RndTBNXVzleW05ffDPDcs0LN4+IZovF8SE3nnuCU+cvc/qSBWWQFOjjSBaN9S1601CjHUWbChXMI4pK+88l0PdLTpYrkigsUn3MrSWJIQt4bZBSG13rXM31zjDpWtpcsK0H61gtligRlFakWB8jlgocM6bS16VATgWzyS8HIQmUmOrW1Rqm8zndtGO9WpHIlNrtUUQwWuOdq41/rJ5xazZ+ZaNrxJxS5DwCmm7SVtp5zihjaI0h5TpA8q1HckY7mGqBokF5lqZD4ilCiCxtgxMoyzWlLMilMIRILOCcA62ZNA2Nr/7pgkaKEHONyVKqNnNeK4w2lfheYF0EYxyiFVYK3tToQOsbfNvgvK/+a+ORzTm9u7OLlsC6H0kxUoylmW3THx+glVBUZUPUAV1CeYf1Iy/ceIoQe5IdydoQSgfSYokMKTLmHqM1MhS0KWx5j6HGC6YoJJNqlnoG6xTagc0arRVWDOvcM6SRbDJGFNbC8fES0YIuSy7vXWA8OmTVX+HwZB+K8Ja3v5vf+jvewa0bT/PBj17jykpz/6Wz7Jze48b1feKq59atA5p2ytaZ06RxxbNPPcP26Yu0zYzDwxs0O4HJ/CKuPc+4eoIi4L2j7SzuNnEeR0FIRQgxkdJIZ2rU3u1PhT7FCjnLEHIdZBhra5a8cUgpNI3dNOLCGAJhLKSYmM47zp6/jxjWjMOyUjKUYRh6FHVwNvQrxnFZgXZWkbOh0RrRmpwTh7eOKt3W1xg+ow1t45FSORnOKdar2yqrjFKa/fWAmc042ziuPX+NdapJFSHe24jfqy+/EqPZdsOr3/DTyqnMTL022a1RmonyjPKlIdf+6fWb+P/8j9/C5Q+uUTHXpnvMqDFUmNMY6gY2l5cku6/zc/6yrhhQT7/A7jNXwTuUteAs0nik8+TOIUbhrp/A4fGLsv4v+HvuPHn6qcf09pOFf3Byif9468pL8WWbWpfAu37yT/Gmf/48fBG24J9SIvzazQu4Bwxb8571q2SJv7xOHlKcunj21b3it+t13IrTePhswxfn77gJ3/2osPfTL9xZI+1slam/PIv+NfrF3/CNeMqJ1SqhlIJNY2oNaJUJIWxyxAUMeCpkKsSIMYbZbMJEtcy6RC4ZbdTGv2wpohhCQkrGGoNBo3TdSGk01mzIyCgigvUNzoJSlQDcWL8hbt/e5ghdY9DW412ziVXbxHSZjM5Qcq4X00XQojClelaxCq2Fbuqw3iICTeswCEMEYwyqwLpkYspYHVBBI5IYw4hIwTvNbOJIJVMAZyrQS0RISWFxdahgPKY4jLJYY1mGkT6NFF0HEqUIjXN4mRDjQJLI0eEt+n6NNYo49hztH+LbHWaN36wZ60GvqNun9WpBiJm9UzubRvD2NrJeZD//iV/mQx/4MUAYw8CNo2OKCKf2TmF9gxBrbFsudfOpFSUHrCRCXDP0CeUauskWnTZsSWL/6LhyBJIiloxCgR6ZdJYSR9areqGvSIiAzYYHzp9iZxuGEIihp/GOPGbMZJvdS1vklPAUwpjRumEcEyL1WCza0LUtItVjqpRsaNxCkp6mmdTGWCCFnnW/ZswCJeGtrp5h7bh9Sa+MwgBt0zCZTOtm2lpijPXY3xDutW2JMeI2UUqlVNuBNtUjbbV5yeaia0yTxIBRhiIAQtu07Ozu1O2eMrSNrgOrMVUQoDFkkUrWVorJtKlgNgwJYCOLLyUz6Rqs98QkKGNQahMDtYncC6k2Z9YUtpoOUPTjiLWe7Z1TdSPcVEVByYUYAzpFnHagBVsSISS8FrxTINBH2UjzobEKQSGpYEwdftnGooxGIjRtQ9s6uqahaRu0bWi6Bl0SOUWGmCCPFG0BTQojJ0PPdOcMky3FN/zO38PVx97Cz//Ej3PlycdRwibjvrC1d5aLD54jpWsUiSibmGhDzAMxHpAShDTW9IQNcVu00Fa9P7kolFGUkEghIaIwYuqAcKxxa0UlRGWGPJBIlFQheY2Dk8WaGFesls/SM+OWeoDYOMLp+zl//jJ7j1zg/R96iqeuHLJYX8Svn2bSHTCfOObbO1y98gJJDNvzOc4arj73PDELB4cHnL9wnj5lblx5jgfe/Db2zr+Ng6MXmDaJtq3AuxgTIWYwBrOxCBln6uuUTB8zrTeIKBrlCUHIJEqu8vGkoPEeh2bIhXFIaFE1MqwkxiiEIJhhxXq95tTZc+xfe5Z+GFifLGiM4fTeDrrUxn0sisnWhLZzHB8voRiMNaz7VR1WKpCsKKVgtCaNhSwKo+sQS2sFaNbLNR9/6nmm0wnOOQ5ODlmtBopAEXWvEb9XX56lFY2++4bYbKImX0tlKbyQ11zLzeu+Df++m1/P4/+3t3P5559FQqhNX5F7DfcXo6TU+LaXNT2K16+B+Wxb39lzA9/3V7+V/+vvj/zj3/q3eZON3CzCr44X+Cs//Ed5y//92ar+2pl/cfkRIhxe3QJqlvgT7N7xXYsX9t99h17x2w/3Om3Fy6RFj9NPVUJYy/E3vjqYTRXY+7Cw+9PP31UuuGqbCgB8mf3htfjF3/CNuFJCSrXZQNXIGp3rptFaC0qTSyKnCv5Bq5pxWwrjOOK9p7EeQcg5bjKSHcYYktiaTe0N3hliyRixm+xukGIw1jOfezRVQhzjgHUalCBSN9m2GJahh5LpVMUaphSB2hSXCBpDSEJKt+m9pXq1nUVnoWk8ra3bXJEKBqpE38x6TMRQ0LaS3dFS484QWtfV4YHOQMYqIYqqBHksGg2SMMpjdVMzpbXCWEeWWDdvxqNpcUYxDAuGccC6lkk3wfoJZ06fxVnP1avP8fwLz5OlemQXqyVlAwJTqg45QhgZxsDW9vbGe1tBGIp6IuUUOTm+Rd+POGshCefm20QpWKWIIaJKomsmeFe3Xsv1GgNVWl0KVkEeVijf4nyDtp6trR2cKcSwIqY102mHNw0JxWLMGNfh5y1N07K9e575qfMYDcZ6drYnxHFJHJcsh5H1sm7Zp60nUo87JRVgpzVkFM76CrpTNdZISfVFZxRhzBQiRtf9bU4jIRS8tbTGYqgAMKgba2Mr1EpJVTeEWOkmzlm0NXS2q5v2UmFr4xgwZpOlTkHrKsNVqloSatXBQMqQxkw3aVAloxU45zk5WdbGXFW7RUqhemaUwllHSpVBcDvf3jtHEU0OCXIkpZEhjLSto6Aw3mC0BtGUkhFjWaWekqo1IufagKOqsgMKja+JAWIc2mo0Qop1wGCcZzabQsl18wwYo1itIyZmusailalQwBTxzuGbBm1rQ22MxvgKGqzvhSblSnuXHMkxvChfL6IoJXPq1Hm2trZouim+8bRNx+HhPnLpAX73d/xv+Lmf+iBGweHhVZ74+IeZn55j1YLIimR6cinkKBAVWteNitUGiQWrdP2CEEgx1zjECvtHlRpdR2IT01iwVEgfJkGpgMnWWUQXxlEYkmJYDjUGTh1R+pucufQWlusBmw/5mkcfY7X4OMvnnmEvRZp4gu3g1q1D/PlT7O7uMo5rptPI9vacfnFcI/FSwTCyOlkx2z3L0c2rHNy8xpkLF9nauo9y/AmKSTROU4Krx60WlC6ojTXAWU3MCm1BtEHrBiWVpYGudoKSFcY4chH60mO1x4hDQqYzlnUJaB1QFFaLNSIvYKxl+8wlTj75YcowMtvdZTXWc2WiNUYSw2JgsjVnazphtVqzPFmCrseOVtXmNMby4oBQa8P2vENRY9mMy5RUWCwHQsx0rUVJoZ06cj+SA8w6y/4X+ovvXt2rz3OJ0ey49V3fT382g+8d1n7peS69fhFmt+sHD7+aZ/7Co8w/8vi9hvtefYYs/Xa1N0ce+XuKP/ev/zPiVNEeFbY+esRj1z4OXffFb8I31Vyvrd6lyRFP1G3iHddr2oqvB2T+RU490JvYyJTqZtpaTr7xUfbfoV8xvUIVOP0rhe0P3F0TXu+sqkR9tX5pUHE7X3w2ZbO9etV6wzfiIRREKWaTKd4YUkkoqu/TaDCpRiclDEgmS6EUKpgqVjm10gVvDVZ5tBW0togSWutoXYfVdSuaU6l/qk0cpQupRMgw9IF+PdK0ZtPoKxIFZxPa6Bfzxoexpx/HKgF2t8nlmpIz63WibVsaZ8hxIMVI5x2Tjc9kiCNKavxUEcUYR2JKDGMhF0FS3QB65WqskxVy2VCHEWTzulVdTaOVfpHM7RtLSQlXIJfAmGpUG5JxxVByqc2O6ohKA9V3W2LiZLli0kHOEasEJRnvFNeff47wtsdodYW1lZJZr5Y0bUfXtXWTC7z4qSGwXhxz69YtmumEjWgaxsLq6Jg4jGhv2Z22aKMJY1/zsde1sSYltK4npbK+xmyFgUKmbedMJhP6ZaQ1DnJCa8Fpw+7Oaba3TuNbQ86JjGYYeoxr0TliwiEimhwcfQxgFM5qFIWC1Ox0MfjGozL4xlX5bI4Y2YC2tELqtABVNgOkHJBUGwXtHHvbHa0Wlss1IVTAmdWK1hmcNghSFR3LVW1WUJzanZNKYUgFKRltNDGFjSy9QgwVG3n2Rs1hTLUlFADjSNIzxlLtCqqCsiRRYysU5KQoIjirwRm0AbKiSKrxZUBKQsLVTXOKiGiadgqmw7iuUtilpg8oCghYY1HO0UAFy8URq+rxKJtINaMNtmnqh3COOOPQFKxXzKeOOFZWA1LjBovStK2nay2iXc0wz4H5tK0DBHQ9D3Ku741kRNXs5/ruVG99LqUCOouQN6A7peF4seSUb1Cq4ejoEN90zOaFnIXf823fymp5xI//yL/g4sVzbM0hq5P6mothYh1BVfhiCZmUEqnUc1dZhVYvyR1JoIohlGohcMqhnaLPiZILQw4oK+gCSjkmTVVC9GNPKqpGsPWZ0Edal3j62WeY29Msu8uMo/DPf/JXEOXw7XmUhtTFOoRZPcOpALu7Fu8arPUMy2OWBycoZZjP5sxmc/ZOnyVSmJiR1B+Rhm2aZgelLVYXcqzv13xuq8wcjQWcrXagIWXGWMg5ospITIUsmZKr9afxjlIMJQeaxuNUQwkQXQGVUFEwG4DbEEb290dCSpw5dx7JQjeZsYqFtrEY35BKRmehA2RMdI0njktGxWbIU4cZRTLO2ZqCkQtCYRg2ct0iWAzZKHJIpBjQncEpxaybcmpnh6OjNcv+N04s0b1645SoKjO/23pJt3X3dS2/Ngrx57veMXmeXzzzbj4zeO1e/YYraymTV5AeizB7rn4nmFWEKzdAK2Rr+rolKXTXFKNEGp2wa0Wa3vk46a4I6puS5Ro1ab/oW3ExCrW3jTo84ejrH3z1Jjwrzv5SYv6BZ6qc/DWA1rAG1XhkeJltRxuOf9NFJr/43J39iLt/1C+vMq7BWkXTVoCRjlL5VFLqhtLYCsRqPDjLehhAV5LumAoSNd6BNQarNZRCloQ1Gl1qLJQ1Buc0uagKTkOQXDdpIY51+6zAt6ZuZZOwDrHKiW3BGUdru9rUlEjIAYnCOAhRQePr5t13GtcotMm4BqZGk3LcwI4UqIJ34LymEY33LSUXGhfp+0wohc5ZduZTJt2UdX9MKbfjwgzG6w00KVCAEBKlWGZdQ85pk0cMShRGVR8yCqwplE18mjMN3m/Th5EoAZU1J8dLTDNj5/R5xphYDyMpRH72P/wI73zXV3L58iUAhn698YVP0ZsT4vbHhVKQU+Ijv/bz3NzfJ+Qq122mM45Wa1YhMfEGbxxoy2KxoEiNhlNzS5FMKSPaOF4UkShN41uKBEoMrNdCzo5xvcIZ2cjFEymuWI0W7BylCqqsQc9rHFXpSWmkJMV6TOCnsImzGrKmcbpKuEXqxq9pMFptwFSqJi4UQStVBzKmPlejNDGNkCJoS8qZoQ+YzpNFkUpBxpHpBgyhNiT/nKpEG60QY4hUa4IpgrF1QJNUjbbSm+bduqbyDXJApIC2YKvqYzIR4nqNSN1sN12Ds6ZGmSlFTImUMimOlBwIYWQcc821NxatNV3n8E1HKIoS63tVo7YKEU1RBqczSkCswW48vyGVut3e5KJPrIaSalyYqu9Z9ZrX4Rqi8Na8uIHRqkalVXZDYRzyJmpNSCmhnMZIbeCHPhOsw1iPUZqQMloiVitM05KLpqRMkVQlysqgrK6qGhS5VPsHMbG7d4oYxupf9i0prolhYHlSbS/WGuZTgzVr1sMRi/UI1MGCNhmV6mdMUYpQMujMxDs0ilQq2b0IIBanoOhIcZlSoH7vabIkzIZ4H0NhGCNFIiFlMorWKFCRvh+YTzSLkxc4e/Z+pqcuc7QKrBfP0/qONjokB5puiiZyGPc5PCmcO3ea7e0tPvrhDyOp4JsO7Vu0slx+01t508OX+eVf+lnGLEjoa2b5/e+gj9c5OnwORHBOYzdwQcmFnekMkcz+yRqlClBISTNKQFRlV1A2QKSqFUIAS4tJlhQrfDBJZlgnQigoUTTWom1V9wxHx2ilCTHSdpbGuGppEIU1jt3Wss6VDzKdtZQijDEwloCQmbZTTIrkEtBKkzKVtC+CKYJ1QgqZlAuShcXJikxiTIrJpCXnXOPV7tW9+jIrsRqj7r6pXot9RYjV56rj0rMuX3iY1Z3UW5ornP5LTzE8fgau33y9n869eh1LNQ1yh5tTFauiD2UR/frlGXb7hYM88tjkGj/7XOHwsVchh39a3TVBPYbXZysOiDPc+paHOXmEV23Cz/18YvrTn0A23JnXnAXe+LqJTwms5ei3P8LhWzSTD93Z7/wN34gXicSg6NG4XVtBUCm/CGXKWQhFKDmilTBpGkJOxFLo+0RIYGJgTImJNRilKKJq5FGjkKyQQaNUveDNCVIpFJ3xnavbHq2w1tBZj0GTUqFtFNoqxhRqTJhEjNIoUTjlsR7EVjlkzoKmkt+lpOop1hrvDSmVKs8VSFlIkhlXA2aTLTz2CSm2NihSqcutc0gZ0IqaO64EY6v8Mqq6+aQopq3DmYYUhTFqCDXCyBqN04E4ZoKs8bqS3bVyGCDnkdY5xljoh2PEJDpvKVh2zlxmcbIirJc89cSH+fF/+8P8vj/0HcynU0qB2WxWAW4b+Yx6UUYjPPfJx/kn/+gfcmt/nzEGnHPsbM+xzjCfdAxjZAtF6z3Wepx1WKXpQ2AcVmQllJTRxrK9swsITgnoluV6RQiB6WxK1wglBJZjxDYeaxv6vqcUwTYtjQK13qdzJ0RlKTkS+8gQFDJmtG1ANNNZV38vm4gkxGKNZhgEZS3Ot5TqYUDr2kgaU7Ock9xulnWl9IfM2tYNOCJYVeXIRRmUVTjnKoxVZzSKSdcwnXjCRtWRCmjb4F0h9lIfT1WYlzJ101tVHNUX7r1DgGQtxhla5+laR86FYRzqfbSpYEGrccYheLp2Shgixti6QbGGWdeAtaR+RKhkbKVdjSsLEe9qpFcYSx0qqFJhhrZaRJTSNT4uV7+3NVUiHFMdZohIlcWXjDeaVAoxZ/wY0QKlVDlxH+pjUzSTSYc2DTGNyNiTgkbbCDpincN5i0JA2aoYoIAk4lAtJGI81nmMrrT1kCMpRra25hwd3GQ228G3HW3nuXXtGJGCdlPmW1uEYYXRhRwHctH4xtB4yxgCKhksmoSABVMAXendRcA6T4k1qtCajnXuQW3I/xqcskhJWKs3ZPaMtXU4Uz37mpwyqzgSk7BYDkymnmF9i6ODF9hqznPYPIKeeYqCw1yIMZF7RT9ErHqQ7f19Tp86ZnveVJCidWA0w+qE85ce5v7LF/j5D76fj338SabbeygscYg0zZxTp9/K/vVraBdxRtEYV+MjI/R9wnmNt5qSLa2t/vAQMjFnGucRVYhlxBhNSoLZDAclF6QIYcgsF7EeFygKhununFM7M0qfGYfAqh/puinGWZal2nh847CtY53XmIlnMukIMTPf2ubqC9fplyNKFxbLFc45Ui5Yq9DOQFEoKcQsDDluVFSVy5BRRGXJRRjGTD8khvD6k5/v1b2623JHAz/0d387w590/O6tX77j+w1iuZ7X3GfvfJ+cpfBMUq+7L/zl9efv+xG+88/9Gd76vz/8DZv1fa+4q5g5Fetxoqx9xabwC13tYeK53PCu9hl+8CDjlo44v9ut+N0S1Ncw7b6oKoA8bbj+1VOG0/KKgwadFOd+NjL52U++lGIwjBUGeLfydKjXrV2HDCMn3/AwB1+huJuZ5Ru+EW+Mw7dNvaiPheN+xbIf8NbQ+AYtDm1qLFelNmuMVehYvwQy9SJ2NeaNpNpilCJKIY+C2Vz8GmsQKVhj6RqP1mCdxVpFSZmYbkeYNSgDCk1IAQz1QlqEoa9AH41Gq0qeNtpAKZSiyFkoKlEo5FAly94ZlCoY7dAa1n1GikFnRRWLa4Y4Mukce5O6jR3CakNtrn5i6zS3/+d19bZ33YSJd6w3AK7WKtbjiLctJdfmNueE0pUMH0vG+AqMGkNPUQOYlmKFxXBCM50ym01ZnxwxHm9z7fklOUU+8CM/zN605Wu/6Xexe+oUZuPjhpeyI0uOfORDH+R7/+bf4BNPPEmMmYJCTSekdWDoe46OTzAojnPN5G58y3w2xU4ntE5BcWANOeear20iSmoEW5GINYLojFFLYgkcDyPKWCRK9YjaunkNq31ELIY5Mc+wrlCyYkyQdd2cyhBovCYXR4qJnBKShaJgPSi0L7RotE4UVUn9JgspF0iVgq8UeGMwzlGGEaWrN9s7zRAFrTWunaCspeRCyFLHf0qYdJZu0iKlNpJQUJvs6pIi3misMShjiaVs4uk0rmmrnF4KMfSkJORSP6RKyfQDgGEym9JOOrxvCGFgvVzWwZBriCmzWq4rPd0Ypl2Hs5p1jBjDhhheN+qNsxRJWKOYTLrKvUGwRipduxSKVhS1sXMMgag0rfeIgoxGpQxisE6hSj1vUAojijDWYU1BwWZgoUVwzm5izmqO+XK1AcXJpvFnQ/dvPOp29nocSeNQbSy5bmN1ilWmr+rjTqZzdnZPUVKopPa2YbVcYN2UlBPOeI4Pb7E+SezMdhjWJ2g8pmRKLuQkWO3IRKxRWAdWuQpBpLwYIYdVtGqC1oY+DNjSYq3awMQEQVOEenwbS5GRpHKF5FG/aLSqWduHx0vuPz+ltcL+rWewfpfJmVMsV4fYtGTbKcLQs1wtSeue6bTFesv+gWdnfo5zF87wySeeRdYDW9u7PPzgRR7/0E/x0Y98AtdMcNZyeHTAev08e2fPs7tzma2tM/Trq/X7TxRgaH0FIeZscXaHk8UtJElNGVAFFHUIOSbYRDMmMuvliLQVXInSTGc7GF8lZkcHN2kmE7ZnHV63BDOwDoGm6zh3bpechWxa2smU+dRzfPAM167fYm5OYVzD1rQjkZltdfTjQIwZ76nxY4nK4kgV3OaMQjLkrFFkpNRM9JI1rrHYRhj6REr1WIeDL96X4L26V5+PKoUL7z/k/c/+Zn7yux7h//DIv8CpO2tIn08dsOSCmdwRuO2FvGZRXn9v+MvLUHj4zddQzr2UTb3hDAG1QbnXoL/hS3KpU/E7kF2rmKqq076+rZZdRH51uMy3TD9BnGq2P5m59U79hfWKx4BeD5TZ56C0F9CrHjEamby2VAUVc928N57+4T1ufJV7Vdm9TorzH4hMfu0KvPxcvu3tfo1Z4FjD8W97hP23q3opfq8Rf6ms13TekHPkZN1TsmLSTDCuSgqlJEKsQKNMzYCMKTLGTOcMRgurUCAJqnW0ra/Zwbpu4UKu8tjGGyaNR0RvGopUJbLF1AglqpBSKGhbN1xOV/n6S4eNIsWEMRllGrTym41lopDQkkAgJ0Gb6ssuohExxFIQMiElYhIaa5GkyDmzPZ/SdRYNWK3JMVZZccgorbGuSpSHUD9gCoW+9CAJZxum1rEeh5qrG0eU8vimBerGLYUepRusbTHK0Gy28THB1J1G+lTjyEpidXzEajnUmDQUQ7/gn33//5uf+LF/x2/7Pb+Xd7zr3Zw6cxajNWO/4vrzT/OB9/8I/+7f/BuOVku8NbicGaVGN+WSCeNIZxWts1gtNTokFXJMxGGNkKEonPe0mw/EOAZCjKic8F7wHhwKpVqayZzzs8zRyRGUgjMDbddUf3R25LVwa3HEdBaZTTu0UhRpWKxXHB8tGMfKJdC6NryN18zahumkxVMp+ZFAzhnjHLZtUNogm6cuAhrBGE3OtTE2pnICxiFXRYZ3GGfxTUPJibEfNvYJg2+b2qjmgqhcY7pQGCVoCrHUIUzjFM5YciloXfeHSmus0tXL2zik1Mil3I841+AaR9dYrBH69Ql9P9I4jbUt1jr6/oRcMtY5JpOG2ax6k9u2qgFSkhqtFiOomvRgVa6b202EWZJSG2dtcLZu7ltviDJWCRGFkKRu7zeRY9rUwRc50zU1mhCkqgeMqoqVKiKmbRqssbBRfxjjkap6xhoD3B5UaEQUoqCUgpLapBsKw7iGoiko4hgQAecUJUe6+R5d13BysM90ewelFUe3bmGcZ3v3DGfOnqMMN2teNYUokX4ZaJyp8v/G4IoipkCRgjWmql2yYKiKiV5GGq9xTkEGIZMCIPWckKRAl3qOqxpPB9A4D07ox4Ekwq39Yx66eAl0Ygj73LjxEdTKs548RFhcwYtHzxx0gomF4DxDHGiOD9ne3ydnwdkaEeecIYYVV164QgyJ0+fmaKM4c+4s43JgeXxIc/4Ce2fu5+pzt7BKqoIoJJQWtPLsnHqM0+cus/yVf89iuFXfe1EYUxMw0JrG1OMyjrlm1acageaabc6efxDrJ4SwxhrYaz26CFIU2Ib5dMp82iE5slonzlw8zfmLF7j6zOM89/Tz4IT10NN1MxbrNV3b0RqD95bWe3KpkMzbEgURoaAYpTbeMdZUBdkMOjV1UDv0iZA1W1vzF0Fv9+pefdmVCNOnTsj/xx3+kz/+p/hvv+kfcsacvOrdMopn0oRFGXjYORrlPuvtomQOy8CV9MWXtL5arUrD0Q9c4tzwOJw9zTPffo53/EePc9rX1//8eoeP/dhjXP43K+wTz99ryt+oVWpsnZg7sFpsNuK417fV0kPil5YP8Efmz9Kf1pz9+SXHD8/vfiv+7j1OXbt5x6oAWaxg0n7GVlwNEXWyRIahws7ushFXMaMWa6TvkZwJ73qEa1/rKPZVmvCguPCBQPex65VqbjSkDLfzx1OCMUB7l4MBpTj5qvMvNuFAvUaQe7A2AHKMDMVQpDAONcLIuRqTJVKJ1SlmJAvWGRQFKRXOpFxtnBsLBgNS/X9WIKtct2auxasKVLPaVDmkCNo4Sq6ZyqIyxtWIMxFBqyohz/k2AEjox0gsmaIgpoSJ4IwFDCGGqtG2oAVA0ViPazRFQSqRfkxorV487xFouwlK5brhSxnbaJyGEKsMve0cKoBWEGMkxiol1lhyhqgg5chQqtR41s2IOjOMCZ37epGsMtZbUJk+HJKioIzD+ymNtwzrFce3eq40W6z3zgCF3bOXMabhaP8GY04s1muu/Nov8fhHf4XtrV0u3ncZiYnV4oB+tWCxXpOpwC6UxU5bGmA6nZKVYvANOWxAcAhtN0WZuknUCbQklDGkNDKOfeUCiMY3NRPKKkMFsCd8V/OhkYToc+Ts6fsDbI6kFLBujjEK7wwhWY6OM9Zajlcrbh6uMa7D+paUEkUKqShKUIxhZDUm9naEia6/48Z5OmvQViFkpGi0VCF0GAOUmsVtrKv8emvIknGNZzabMZlOMcbQrwdGKnBtOplirCLG2+yABKoed0UyOYU6vOjXYAzddF7jZZDKPTC2eq+LJosQCsSQiWPAeo9zroLoSkZKoes8qkDB0A+B1bqvqgOt2Zq2lFIbf2M1JdWBlyhDSkLMIykGrNFYayqFXCu0MrUZb5qN4kLwRiFGkYzefAdUeb13GmMcvtFYCqoIrrEs1gOI4K0BVbhxPFSZ8mSC8+0mYi3hvaO0DavFCi2CUhrjhBBi5RAYAyHWTXiIpFiBf35DqXem+ifGVBjHiG0meAfD6pAwLlldWXLu4iWaC+dJWVBmwpu/4i18+BdeYNmvSZxwtK4wvL15izNVgaNN9YJ75xhzZEiJMWawCrM5PwuCdYpQqixbW6AknK5qmKq8caSUGFMCpHrcdZX+awqxH/Fdi280x+PIenmVrncwC8zdigcv3Ad5xTiMhJjphxWhRFSGg+PCmb1ttrbnHB8fE4eeWdcxm8+Zr8Ebz30PPsjB0T7Xl89ycPN52knH1vYl8uIqNq7oUyLphLVT5jtvwnV7oBuQhihClkBOgkoKEQMalMqEUFUzbdMwm+1x9uJb2d07T9t1eK05ObxFCiOyXKBDqtJ242lOTcixR1uF85Z+ecIzT+xz9YXnCSGzM9nl4qX7KWlg5g3EkU5F9rYcY2pYrQey1GOgyEYarzXeaUIP4xBJUJU0RtF1ljgmUlFsbW8zm7YM4z1Y27368i6zGHjsb0f+m0/8Cf7Yd/4IXzf92B3d76C0rENiTy9xCrxSZBEWohjEMIihSPslJUm/XX/z+d/BuZ/c58m/9Bb+82/7l/yZ7ac/w/eeHy386ndG/tKTf4hb//I+Lv3wNbh5LyPhDVVFUHdAw1bCS7nmdxFl9YUoVQq/tH+JySVPf06hxviat+L+G97E1i9eRY6OX/0OMaCG8cVGW2VBnayQ5aryiAAZAypmxL36e6RSqQ34el1jwzZlVxHRr9w8v9iEf/zGSxtvpWoEWUovxRCOocaP3enwZNOE3/xKUzk2t0tA3eEw7g3fiJcCprWonGg2m+jj5ZqQMo2zdE0DuW4bs04MMYAxTBpXgViq5j47BY1Tm/zx/JIHMBay21wYK1BGkFQYh8iYI52f4DZxY2wSiGIoVE162Ww8M0KmbTzaVtJ4LoWsUiUiu5pJjkqEGDBG6Ic1h6FsJLEt064jxMSks+QYN3CzgDMW26hqexAq1VsLqaQKCsuRmAreNVVhVepje+swSiGiCTnjJOFajW8sqYQKs1INWjm0MhVo52ClMkq1KJUY+wU5NSgsx4c3MEZjjWI6n6Ebw5gTpIhSmoygSRzs32IYR4wxdJMJxbX4Wc0C1xRKSOScaSZTcooIhVljaGSObXw90b3bgPUimoKoqhqw2qB0bf5864CIqEwyDTEqimpwUHOBRbM1a8hZM+l28V5TYiQXMKpg/YQxWZbrxNEycOM40PgZWRLrMOKbCaSR5eIEjKbtJqSgGW8t2ZqMzGcTtLY4UVXGWpHztQGVzDD0WGvRCpRztNbhtMKqRNc0tK2nbSw56ypvV+pF2N+wGsk5bZQZmnYyrVAxMRTraSaGOKwx1lG0qiA5KWilMKqglCFRyBs7hTGGqASRQkqBrC3KSB3aaE2I9TgexxG0pW0bus5RSiGNmVBAwoA1inHMjLnC0owG30wpZEKs8CuQGh0YAm3XoZTgrcNbQawn6kJICW0s1llENNpUQn3KhdaZSttG0zrFZNpy62hBLDCdeKazCU3ToAGlPMYoFmXg6DDSWouIkMJY6fXULaeIoEpVIijj8I2v72caqkWk1BXobLpFSQMnx0OV3k+32emmG35DwbiG5ckxn/z4k8RBmLYN+6uCiOBMw2LI9DHiVEbpwsy36FJYxYzGMvNNHSBmixTLECLWQCGjaHAiRBMwxddhk04gpjIqdM3B1krIqqCNwbmGOEaMclWa3Wty2SKGgXLzcdpH3oRxnm5rm5nUXPIUAzFF1ssl+zdvVNtNTHSTCWEMHJ4saee7mIOeK1deIOWRk+US0Zaw7Jke7rO9u4NxZwmr52l8i3Uzpnv3c+vmVZ77tY/x4IMPo9wOVt1ElZFSIlklWm/RSggpoLVnOrUotcM7f9P7OHP2NEPfo0rGDCNuOiddvI/rn3icGNeoGKuFwZ8CY4nDQNt4To5ucnS4j3EZa4UigZPFdU6Ojjk97djRhbkVBg3H4wmL1QhS2QjWmI0lo15AiFJoY1CpgIFuYkkxEzJMZ1POn97i4XNznr56+Lp8F96re/X5LJUz9/3IIf/8+W/il/+z+/iuCz96R/cbxHIlf3ldei5Kx9V/9CAn3wm/9sf+5maj/5mNg1GadzUNP/q2f0F8a+a7/+jX8eRfeSv+Q09+8Z/0vfrClJT6GX8n9ToC2j6lRLhydRfeCcOFuqTRqQ4L5C4a8eKFG1+lgQvMf2xxZ37xxQrV+LppPjqpTW/T1CFFrjA7NQTEvbIKRvUBDo4+pQG/XebKPnb1uTf8ZlBc+JmR9snPAlo0ujbjfV//v5S6qdeTV/eLK8XJuzZNuHntwYZfXp+Gr6FCTtgwEEKgT5EcK8jp9lbLKEtREaHKgJXoDZxJgIzRCt86KiNrs50RDcnSNB5vXI1D0xpnLSEnxpgwG/CbNdXzHHOsPkYNQ8gYZWh9U2OsCpiSsdrRKM+oRhJVtupczVbOJZNzYtpOMNqwXEeKTqgihKH6xa3STJqGZB39WOFis4lna+YYUiLlRM66RjnlkbZp0FQid+MdZZPXXJKg0HS+wWhLsCMxJ0LOWO0rZEwrVDFobXBOGMOIUpbWGYpYcgqgd2gmM7Z2dnGuRlf1yxOuXHmWMYxY7XAKZo3HI6QQcPMO4xyhH+hLYTrpsKZmIpcCzWyOVjDGzSo/G+Zbe6zVghQSummwBlzj8WZCkcw49oRxpNgKtWsmE0Qiupnh25Y0BpISGltIpfqTs2owdoYjYbWlKAXeQ+pRWEo25KIZYuLWyYixjmFc068HftPXfzP/xV/+cxgyH/vox3n/j/8Ev/CLP8dqvUKc4+BkYAiJS5cqMCpjcMZjnMcaBSWDVjhnMc7VnHWt0dSLf6EwjKHCyrIwDgOrxQk72zukqFHK4ryvm33j0MZsYtA0frNhHkvGKUHlXBtipzFa18isFF+UtmulmXSOONa4v1IKIWUsoCSTs8boKikXga5riUERixBytV2UAuTMmBVjLmSpII2mmdB2HkpkGEeQgtGqAtmsRRuF5Aq+iqkQkmJMGVFms4mEGtIm5FKQnBm5HcWXSLGw6hXFOM6fnTHpOpxzFQwXRsYQyGMhhxFrqrc85oI2rp5HVKuAUxmRRNNUH7vRis4rRBtCkY2MPvP0E7/K2QsPsnfmFN1khogmxoB1nm4yI6VEDAPHR4coZep3WNFY4ymjAyN1EGIikAklE2MmR6rKvrq/KyW9REx2FF0oGJwyhJgQY2s0mS4YUwmgpdRoQjZNu0WjjcM6zTCM5CJsbU9QN9acO/dmtnfOcny4j9KK5WrF4eEBi+WSfj3Q9wNFEjkEGmeZzabMph4wND5VX7xVxI2K59aN6xjf4CeebDTHBzcwStg58wALbTFNhzaaj3z4F3jyyeeYzM/ypul5JlunODm5gk6CKYWU68AqSEGJxWlPKZnV+ohbN66xtz1HxUCjbaX+60geFtw6OWLsa3IFQHruKURrvDV4D8eLE0LJmADONigvPHflBeIK+uNI2O6YbbXozmFWK5SKhCDIJj0CySipA4JSMiKFrKpSZAyJEBXeN2xNZ+zNtphYzfndLw0S9L26V7/uysLuB65w+PgF/qu//u38tTf9k9f7GX1eK6P518fv5J//4NexdzPzd/7L/4FG3dn565Thb9/3M/yD732Cv/V/+oPs/quP3THo6nWt2553/Wkd2u0t8JfDa/gCl7qD5AtRVPkzbKIuX99y1+pxu3fxGDD0e68c7fU5S8HBY4atX9pCDo9e/fbjCDcOXlQHqOm0Usb74cXmV9Y9vBph/RWGGrLumb1QOHzLS8esXSmmV4RuP9Nd7zE3X2GD792nStRzrvC2Sfu5/eJKsXz7OW79Optw+A3QiGsFIUWGFEmVhIbd5CF767BWk5PQx6FeYDeGogrrYaToKnUuUiXqAIraZGhdNyDWGbx1sIkU8tYhkshRqjRFav4tG8l5loKzbrPlVpQEjXa4SYVFxRRr9E0CMYX10JOzEEvGW0XjPYJBK0ejDWNcs1wHJlQlxbofmU0aJk1T83hDohzXi0RtM846GutYpkIYVZWS67phFyUvQrwUMISIsRml6oeIUbc9thOsLYzrhFaKGAs5GLQuuGaKIKwGT9PtYBDSemT7/Clm8xnH1rMeA6Vs5JzLJat+wLddlRibCpdTpkKRpGSMVVjrKCmxPjlErKPzLf2wQlmLbeaocURjUCUiKRBSpLQTrO9oph7r1+SSMMbSzLZIcYV1Da7bApNpGXBa10izTYxdiic426GNw1hdY9qMJyVFyJr1uObgaAXKIzHy8GPv5jd/zbtZ9IX5fMrOvOXC+TN8zde+h6efeZ5/929/hJ/49z/K8fKYZS88/fxVLknh7LkLWF0bS1Vy9ZWo2syIqe95DBHXVLtDpnp+Va7HWBoj1jga36GsQyTX41OkRjJIwWy2oEppwjiiVP2OcJYaFTamF5vmkjeQLNG4xqPchN1ze3STOdZ6lAatMhIDUgLjsCaNC5Q2G9+33kD3MrFsSPUKsigU1SOfpPoBGWKVuUulwRcBqw2z+Qwo5BgpKbA4WZMFsgjOV4VEDKH+PFeTAGzTMKZEzPVcXY+Rxlgaa2hUJqyOCKJYbgAqznuUdjR+gncRdI0Qc656zzWKxmkMmmw12pjNAKRQXM1gx2SmbUPfB85cfJCveNe7SSmwXi1JKYDrGFcnLI/BeYP3HVs7u6z2e7puizYtGPojVFN9/05slTwri2xi77yFUSorwBmq5xtTL4iKwogl6QF0obOuxg6S0aZ23ylLfX+LoIyQkMoLKIUhBPox07WedX8VDp5mNttBSeHmlefpt7fQKJTWTLqGrmtIMbBaLpnPJ5zaaVkeHzDfOkszdzzz7AsVeKnh9OndmmdvGsKwRFJE+wmT6RbWtcxOXeSpJz/Mxz78UY5Wa0TBar3i6PiQC2emWOfpY2YYIyIQRSNa0TaOxinGWAcqv/pLH+T09hYXdraQHCqEUin8dEY7m7HcQP6MtogUVIqshp7RaMYxk9E89OD9ODvyzLXrhDHhG4fSsEwZSQGWI76dYFdCCBkohFChfMMqkkUqQT6XzeAqk4pha3tO13q2t+aIbni+19y4MXCv7tWXe6ksqKNF3R55x7VfOA9ver2f1eevnoun+N/9q+/gzX//mAePn2f7+xe85zXEG/2JrVu863v+O/7IA3+RB/7eEy9d7H8plNI1A3k2ocwnxN2WOK/f46LVpzRqOgkqQXMwYm+cwPHyS+u1fDEr3aFH+ra8ubz+w4vumiJK5t1nX+A5/SDbTwdWl5tX9VV/tkpT4eSrzjP/sZM7G8xslMaq6+qFZym1+Q2hbsVDfFV5ujQWvTX/7M2/FOZPDxw+1r0otdcRTv/cPmp9B9+3n0WiTgww6s/uF9804TfeY1/5/btDRcQbvhEXMeQMXTNhahT9ODAMkVIU/RDqBSoF7TSSEiFoutainWKIgZBrI6pFbyZalRLeOce09ThXIWh9HwgG2sZU/7FvKKUwjlXabrUlaYVRCmertFGIpJJRpfpfS65ecodiOYwsQsRqXYnIumZE53XG6wZwhCSEJFhtaYxl2tYmtx8i8+kEY4Rx7Fn3NdJr6isg5eh4ySoMiKia4+shp0LMhelUARHJBk3B+RaKJaQVOlf4Y84bUJKf4lRLW2ClB0YJmGIQJjz02Nu4dPECN65f4dL9D3LlmSc43D9AaY1RBq9ApeqzbxpP6z3gKhxNGbRRNJMWoxQGYbU4YblcobRlujXFOIdxlUqeYqxy6jTgiFUunSKkAlbjmyrptVIwfkI7nZOKQYqj7yPWZLzXGFWhXVmEIhGFxhtL2fiWc2lIylS/bhi4ebCgTxpr4J1f89v4i3/xu9jqDJ946gpKQUwFa2uM2dve+igPP3SZ3/aN38A/+6f/jA988Gfo14Gnn77GMGQefPgBJBvIgZICw1BVCHMNxnraborxDSWFGhfmLEpk41k2hBJZjyPbXcM4BnIqNG0HKPzGV2ycrxv0vicXIaeCc4UUIykVlJ3ipzvMt08x2zlDN53hG4exFqPrRlZJ2UwI1cZ7DjkmhqGnX56wPrrF8uA6w/KIdYgoBJULRQlJFHbT0CqtaZyj8Q39eknOBatqDFTbNghCShlyIoyRLArvNNa0WN9Usry2lFIl7loJQ86EUrCqEEsixsiUCa2uTIAYInEY6zHn3AadGGoWeilYI2ija+qBqVGC1tTmSpSpgzwlxHGkN4pcKkTPlNqY3bj6LO10zuUH7mc2n/HCM08xmc7xs222fMOwXnL1ytMbj7vdPI+WKRPGGNBUVUHJm8x3VX36WgxWNjqyVDexFrMhulfVQE4FZwuQ0FYw1PgMY4XJtDb59fMQEE0Iiagy2kRCyGxtTdHWEFZHrBYLptNd1stAHOsAQCuFsYkQYj02QyDHxJlTe+yeuoi1Lc4aDg6e5+atfcYhIVozn084unENZVv2Tl/i9JldYgocHwXieMDHPvxh1mPEGEURTZHCM88+SVhtI9kSByGljSKoCG1r8MbjG0cmYHRgeXSLx3/1I5z+6nfjrCI5i9Mt580e6YHLrFYrdMiUIphmQlFLyjAQEdq2YXtnB0vk1v4xQynYiUf6qnYZxsiQPGfP7RBCYGtrC63WhBA3YMVCTFXlYa2h7SrQLYpma3uXs3szLp+bMJlMuXkYODlZcOPgnmf0Xn15lxoiHJ7UZu2+c6R5gxm+9Hzdi9LhVKJV8a7u9+HxPr73b34rj/3Ax6AIH/8v3sKvPfg3gc8OmXu1eqdv+Yn/9G/w3gf/PG/7nmvI8asD7r5gpaoUt5zdpb80Y9wxZK/ueDu6PjtBvXmCWwvNQaS5sULvn9St5m+QbbmKdwjiu92I32Hj/oWs7lbhuAy8d+spnmkfxe/3tLca1udf28+7q62486j5FGk9qh8Razh55xlufaVm96NCt59I3cavLuCPE3YZNjnsLzW6Zdqhh/ElGfnLyj9/gBnuI3f19mGnRq6d/skrdwZN+3SJOhu/uLUvKhsAUIrVW86+ahPeHHHH2fFv+EYcZarCphRQlmk7ZWtqkVIl40YlrHfMyRwvV2gcrW+gJIzSTBSV9pwTMQvO1qxkZyyNq41IDAW0RttMkYxzrjYGY650NSlQLLrSlAgh1C2vNuQSCSljaHHKMIaB9TgQSq7yZ9GbPGOhay1KW076QIkRYzWTLY/HMo4Dh4vhxe19m8EoyzBmlPI0viXGwBADKdUYqUlXqdRCYZDMzqRh1nmGEBCvUUTGuEAweFfz1632KEk1Yir1rEKgsR05Z3LxDKJ49C1v421vfwerxQm7OzssTg4oMXL2/AWOj/dfAqalhNcVVod1qBLx3hNDBbjZxqO0YVz35JRrZnprGNdLrFG0jWVIuUrLU0ZZQ9u2DOs1QxFmxmIolGGN8y3dZIKfTGqmc3cJJFNWC6waMKrglCVJREumdS2iWmw3A+dIQyYVS1GOxJqTxYrFOmObCV/3jb+bP/0n/wiXzu8SQuD++84iJZNz/RDRpnq4u0nH27/ybdz/4GW+9gP/K37gH/8jnnnuOW5cu4nWhfvvv8S07fBdh3WBrm2ZTTpEW9Qmys7YBmfBWU0KibKRPNUtcYUTgmLStVjvq2pDV5+1Mo5SYvUHe1+PZ1qmZx5k68wlZrt7NN6gJaLTgEpLdCwQKv255mPJp4AuqlpN0zrH1pkd5Pw5kryDYd1zdPMqJ9efJa8PKTmgcpWkGw3OObquwzlPiiNGVcm80QpjLSiFMxoKJF35DGbDeAh5JIaElIJ3lrZxuMYzdRZnLTknjo4NjbM4bQghcrIeCCGistC1DVHV/OmSU6W8G4W3lt2tOV3rNgwEBSWRMoRUmE5blBFOFmvWhyc465hMO6DQjyNtN2FndxtBIaXgmob1asXeqT2WywVGWx54+E0oCh/80X+FswJhoNUGpR3GCDnHGtcFaF1j58acUVo2nIeq0qEkRGxVJpiELlR6P3kzI1HEkkB5UogY6/BYhpgZY6gyajLDACcnK2aTGRPrKClycvQM2/N3cOm+S1jv6mOqOjwMMTLGwHp5wurohMPDFWf3TqGVwjeebtJSRHPhvgtIWHPr2k3QDQ898Ainzp4ixpGJajg5uspicRM9mWJ0QFJkyI6hWI6u3uTW8ZJOR9IIfXCgG4zT+GjYEzhtCyGO6C5hg+Lm/hWeP3iEy5cv4pCqCmk7Jtt77Oye4ujoiPVQOQTKOpZlwFrLo/c9RBp7nnzhOsnV7wuDxXog1mHQ2CcO9o/ZO7WD0YLMO/RasVqMlAxt50lSKsAxZlKuYLbptEMZx/5xYLHKXLt1yK3r+xR99xuIe3WvvlRKL3rkZIGaTEgXdim+XqTaoUq5DV8azVgUy3f/8/8Ed/+Kv/ee/+mO7/fT6zfxQ3/9mzn/Lx8HKay+7jH+8Xf893csSf9cddpMefw/+n/xtef/OBf/04ysVr+un3fX5Tyc2WW4b4v+tCU16q5AXS8v0RBmijDzLC57TNyhOS5Mnl2grtx642/K75Qa7kx9i+8ie/wLVe1B5ko2vLN5jn88tZjVyPZTkf7sa8s4v5utuLKG1WNn+Jrv+Tn+3fNv5qGdA773gX/A/XbGugTWEinAqlTt8RPxFD968jZ+/MqbOPj4HpfeX5g+vQQKsjWDYfyMx5TFku6WsLz80t+dPKKYP3+K5qlbd/aivIMYX0o8uB1pNtn4xTdN+PWvfmVCe3OouPD+owoovoN6wzfiq/UaazRNa5m4jWcyF6wF54QwRoYh4b1lZz7H2waKEDM4k1EYrDGYbNBJ8LbBa0shs1iOaKXoGgtKKOIouW5hnVYUV2OWEKleV0XNcpaAJChiGWKNSjOhsBZNpsKbQFFUIeVMQmEVpAg1ZEnwvm5ih/WI2Iw1psK7Nv/e+aZeUG45BIWzBeMcU9VydLzk8CTgrKFIJuWA0aYOHErGe0OKiZjrplBbhzGOxmvCUDb5vpbOtzTWULTGqimrZeKBBx7jq97zNYgkbg2Bptsip4FmurWBailyGEkx4pSiMQbMJlrLOoZxJEtBIwyLE4xvUdYSUsE0Hc43lBQRqXAwQQhhRCmhs4o09njvaOc7NQ4rZrIYjHYkHG2zg9YwFiCvKClQSo9q5xTTIuLIJQEe33qM63BNy6qMqKyIoTCGgZPjFU0z5Zt/3x/iT37nt7O7M6tScKBtPGMYGRAa7zEiiNYY0Wit2NnZ4nf8zm/k7W9/C3/3b/0/+cDP/wr7h0suXgJlGthIj7Wp2eFJmQraImOtBqlS8pQyKWe0VlhjMUajtabzjum0I+WN31s0RWtyCoRQG/Vmfobdi49y6sJ9TDqPTmtMPEStYoVnbM4frauNQ+n6u1PKwEYbIlSQmZSExEAJy8oM0JV/MHvgftIDD9MvTjh4/pMc3XyekkesrVt+awxKBK00OUe0tqiNT9spS+s0qWisrn7xojTr1QmNgr2dCb7xGNeAsWQUpQjDGDg5WTGMmc41hH4kbWRhVhmMp5K3TQUYSr4NqYNJ65lO2/rqSvXPhzFgrKnL6BwxWuGso89CEhjHkaggjAmtB4Z+ZGdPc3DzBuMQaLs5fR+YTmdobcklc+P6C8QUcK1jS0/IWUg5MYwDw8aYHqUOJRqvUVLBjqiI1o5GtyyHgT6OWK1QOKwX0JkYK02+FIX3GkmVgO+0IeVIIiGqYKyhFE0RxWo1cu70nNZq+iwsFze4cuVZnJkQ0kjXtkxnW2xv7zKbdbAWcp5QUmY9DNw6OOHB+y8x6Tzz+ZvZ3jvLlWeeZnEycO7ig9z34IN1qNevECUslwfcvHmNxbJnGHqOV5ExG2LZDBEQ9oeAKIXC1iGqgBiFXhUOjgJXb2r2tht2Z5rUrjg6usb+wS0eevg+iCPFGIZRmE7OsLN3xHK5xJlEzoIoRzudICZxsjrmZH9VlSIpowzMpi0aSCnSdR0pRZaLgLFrrGvpphOW6yOyjkipnxdd66uCQwzz2YTGeZytXvSihKs3bnH9+i2ylJrAca/u1ZdZqSyo42XdGJ3aJZ6eIS/zEZseohjM3QTofgHr//H8N/Pmv3WdvDfjX/2dr+R3b/3yq97nfz56Dx/4v3w1p36sNuFqd4dH/+uPvCZJ+merRjn+/jv/J/7Ls/9b9FNfnEZctS35vjMsH5wyzu+Okn1nDwDZK9ZnDOvTOzSPbjH/xAnqhRtv3Ai3lO8IdCYbO6B8CUjT/Unk8XCOr29fYNxzNDegvbbGnWwTdl7bcPhOt+KSMs3+wO/e+hX++ns+tPnbGQAT7ZmwOb82X42PuJHfNfklOP9L8G740B8Y+dMf/uP4f7DH1uNHKO+q9/zllTPzZxPLyy+1tcUK19/bcvlGh1p95hb9M0qpGqe2Wr/U6N/2i0871o9tmnD3ud8vf6K4+OPH6KNl7QfvoN7wjbhxIDoRk2IMYUN6rvE3WUqNwdFCImPN5k0rhhiFTN1K1eZY0XqDMwrvFEq3NCaTU6QINLal4DbxSy95Bt3molhMjY+KEsFUefcwZkoWWu+xxtaL6FJznxsHlkJK1KiEYhnH2vi03iGxkGJGK421GmsFSqGU2tiMoeANdI2v+cNaUEVhreLU9py2aehai0iiHzX9GFmHgS3b0lrL4TIyZMDUWDaj3CYKsNCPwtAPrDuYzU4huWX7zCXe9bWPMp1OiCmxtb3D/fcbnn7y4xweHzOfb5PCyLBcMJtNoAhT75EUqLOK6gdWUuFSzWQLbWuMWBzX5JJpJi0IWO+JsVSFgSmUFFCiUNagrUPZBre1u9nkBYzxFOvodi/g51MKQlOEEh2TkpDU1kxI04Iq6FLw3QzXenKBMWkyjiyFknsWx8cYO+Nbv+OP8Uf/8O9le2tSadupymcBlscH/PTP/DxveutX8MgjD9G1DVJKpe0D/fKEf/lPvp8PfPDnybr6eg+Pluxt72G0YG2VfhcsojRUvAEaAaXRkmucm7UoSpVn60ptNloxjBtfTIGiNWPIxKLQzQ7nH3kTuxcu4Ym4uESvDutFh1KbxtveVgjVDyOlNttw2FDDKnVT1eiuomqmt1CHTUUKklaYuMJrTzOdMH3bu9g9eYSbzz7BuLhGSYFVH2mcrX7vGMkqY/E4bygpcLROoByT7V3azjMMI6mAJ4KxDENAQkJZT1aWEBLr9ZrVao1ShsYbkkiVlG+ee8mCMjUqrahCzkKr66BCkSmSEGWQIozjABTWfSBTie7GGKRUOnuNM0woEUpMuC5y89pVTp89xdHRIU07xRiHcZ5hWHN0cIB1hic/9qv0ZWTq5nhXENMTY2AcAkVq/ryIIKpC+XLRGAVoqkQ7rdFojFS1Tn3Pa1SiqQtsCoUUCkb5KnOnYJzCKkUqUuPOdB2A9euera3TzCYTUu4JYWQYjmh2tijjwGK5ol+PxCzMZhOGdc8wDIxjIOXEyXKNbzrGsKJfrRlXS5qm48KDpzh34RKKRL+OGGc4uPksBweHjGMmjFXR4NVIQQjFbprvzRWOEkQSojRaSc2QF83EwI7RtFhKVBQZESU88ZFf4aH7L3D58n0M0XB6Z5v10QGXzp3i5vOfYF1q5FmnFdPJDtHZypnQBq0KDk03bWichQxWW1pvSclxfLxguQicu7DL8uSYxWKJwVLEYrVhHOvAazqZ4Jq2AjGLEFMmDguODw/JuQ5MtXoN64d7da9ex1JjqsRjeFGK/ull10IUe9cy8C9EPR3PcPA/PMDWrccxtw744b/39Xzdn/84c/3ZL8afjmf4r9//bbz57w7MP/54/Utj+Nh3X+B/vvxP+WyE9Nda50wk7rU0T33efuRnLTWdEh84w/JyR5y89u333T0ojNua8d07dA/NmX34Ftw6fMNJ1quX+GXqwM9VVm+gwrnu0F7Hj349RH5h9RDfOr3F+oxm6/EKndt+OnPzK1/bgOaOt+IlY47W/NjyrXxj9+G7fpx3NQ0/9+4f4KfeVviPf+i7eNP3Fbhy/TNuN3n6CP3eMxT/UqMctoRb793jzPuv3plX35rKTRhe5i1PifHB01x/7ys34W6puPRjC8z+4q5e310dFn/tr/013vve9zKfzzl79iy///f/fj72sU/NjxyGge/6ru/i1KlTzGYzvv3bv53r1z/1DXv22Wd53/vex2Qy4ezZs/zlv/yXSZ82OfvxH/9x3v3ud9M0DY8++ijf933fd1cv7HYpJTS++lxDzKz6nmXfM+S6eXXe0rqOxnR0rq2NtC54r2lsi8IyJo3SBm8VqEzaSGInra8RSlCl0RTQhVQyYxw2r0nQumCtICqRUkIXjTcOrw3e1f8aVWOxnNFMfMu0aWiNp9mAj4wRkgh9yvQxESlEqTT2IQz0QyZtLuK1qVRpdCaXAiKcLAdOlj3jEAkhMOsME1c3krKRsXtjAM2qT0QyxqmNxDUTysDRcsEqrFFWaKdbNFuX0c1F3vTO3843/a7fz9ve+S5On7nAdNIyrE9YrVd0sx3OnbvAbL7DxQffTFaZLJrt7R1mswnWWaytjUccIzEFuvkW22cv0u2cpZnMaP2ExjUgMAw9qpnSzLbRfo5pt5nOd+maFm0NShlMN6sUe+9RrkGswbUtWimCCLFAFBBjcd02yszIxUJRSDG0W3t0OzsoW0nmRWuy8WQR1qsFy1Xim37vt/NH/8jvY2trQilCiIkYEjFErjz3LP/iX/4vfOBDv8I//IEf4gd/6J/xyU8+wxgCMUaWi2P+6Q98P3/n7/19bh0dk3OBIqyWK0pJxJhQ1pFFE0UhSqOU1CZcMmojey8FtKlbdkqGkmvDIkIuUIrCuOp775Nl9/538sh7v5Hz508zDQc0/T4mjxijXozI07rKb0RpUIaiPAnPKJ6BlrW0rFVLj2cQxyiWXDS5nmwAGK03G3qDLgE7HNEOt9iZGO5/21dx4c1fg+9OkVNgtTxmve7BduyeOc/5C5eYb+2h/ZRiJhjfgVKMY2DZB5Joxljoh8h6CPRDYIylEqpDQEqhcR7vXeURWFupDsbQth1+MkW0YYyZmEr1uCuN9U319sbEGDKpCMo4tJ9gncfa+hkQU6KxMDGa1jq6psV7T8mFlITp1haHB4fsnDpP0zhOjvdZHB3Q9yONt0gW3vO1v52t3fswbqtaOpKwWq9ZhxEh44yh9Q5vNc46tIA2FcCnVR3qoQW7gSoqXWnzSgnOKDQaj0dZUA6UWEoCSYbUQ+gLQwz0Y2AYBk4WC6yzm8GckCnk4QSVR6aTCUYZUowc7t/kcP+g5n+WgpRMv1xx89Y+V69dY7VckAvsnTrD/Q8/woXzZ1ElkFIEIqvjGywXK9Z9T0g1Xs9aixZojMLpug0XqdL6ytyXzVBIUChap9meKXyjmXcOI455d4YLl99Etoonn77CySrjmkm9UNKKm9df4Or1m6QC88ax6xRN48mp1ISIjR3CG4uMQjgZGJYDcQyUXPDeMp9P0Uq4deMGw2rNhCm6dBhrGEJkCIWmaWkaX5Up1lBiYnF4i+vXrxBJGFcVK0V96UvTvxy/6+/VF6b0coBbByjvyQ989iYcoD0qvBB3v8jP7jMro/lv/s0fZOvHP/Hi3138oSf57g/+0c+47ao0/KXH/9f87b/y7bz1v/ok5uPPvvhvy697lH/67f/dZ2SF/3prW3uGU6/Na35H1TTEtz/EwTfcz+FbJsTpF6kJf3kp6PcM+7/5HOFdD1VS9hupcr6jLPFi9YuWPnUnPuUvYKlU+MWDyzTKsT7/0nOZPrvGrl/7AXLwmEHtbL3yjYqg+pF/d/Wxu/75Hw1rvn9xip8dIw/YNT/6h/8GT/+B02A+87xUizVuxW3R5ot18pCif/T0nT9o4ysQC0Bp4lc8wJWva8nN5/4d2pXi4vtXr0xn/1z3vZsb/8RP/ATf9V3fxXvf+15SSvzVv/pX+eZv/mY+8pGPMN2caH/hL/wFfviHf5gf/MEfZHt7m+/+7u/m277t2/ipn/opoG6C3/e+93H+/Hl++qd/mqtXr/In/sSfwDnH93zP9wDw1FNP8b73vY8/+2f/LN///d/Pj/7oj/Kn/tSf4sKFC3zLt3zLXb3AknWVpBpL2AB/jKkAMFSFoCGFnDVZKaQE2GQpW2tAII8FciErjaARk1j3q9oAKVOBTVphdN16FwqIQhmDVrnKbpXHGQdOYbRGilB0QEnNRTba1Pxqq7HGIeIRkdp0F40RW6OrJNEPEWcNrTdISkgR0AWtwFqHlIyoTMibBqTpGFNmsVwRYmR7XsmCJ+uBUgrOGJzNm9gmR8yJiGLVD5WwbArGWmxTUBkkT2mbS2zvXOQ9X/1befjRR5jNOj75xMfIObNaLBnGka3tXaZTzY3r12g7h6bmJSuqXGc9rOmHkaZpCGPAtpP/P3t/HnbbeZZ3gr93XGvt4dvfcL7vjNLRbEm25UHGWJjZDlSokKSKojLUlXIqQwVi6CRkcFFNdaiQXOnQlVBJh0B1J3FS3aGBSgpIDAEM2IDBTLaDB1m2bM1n/uY9rfWO/ce7ztEsnSPJlmX0XJfsM+yzvz2stdd+nue+fzdCSMxwFUyDQRIIBdJkLfPFAqFrlu2CREbrImcJaILMBYRnS+O5mE3RpiKjUJVF2wEYS8rly37xsSWEbhhsrJCXC6JzKC1RpiI4j+tl3+SIzpkuLrl4aRdtBgwbWWTJtSXFWLykruNTH/8Yv/yB32QZMsNmRAyBT917H8PhhM0jExSRhz7/AB/76CcQyiJzyc3OGbrO0S4XVFpTNTWC4mEeWF0wAzmV9zqW0WppvgAEUpZotpxKjroCcor4kKnXr+PUTXcwrDXG76FCV7ahRpfGLucrIMKQBQ5LUhVJVWSpQRVpennl+o3llcrlohQ9Mjp06jDZISny9st2hJQiud2nljP0ZAX7urdy/oHPsn/+AYwpG+vRcEBKnsViiY+CiMRIyDkym7X4UIZKIWVkLnAvpWwZrASPiJFKS7zU/bO5fPETVMYgpMDFVOwKIZKlZjAwNJWha1uWiw4hDVInSBGhCt1Ta4USks4FkivnarfsuLxolwK64JHLBY89+DnufONXMmgquqXD2hFSWqqmwdpV5tN9br39Ti5ePM/ZR2a9jDxzuJyVIZqQWGNJCULOaJHxovARkJJKF6m5S4lMLmDBmJEIbCpwtxyLn96oqmcnRMiSznXknKiMJvbKk5g889myDG1UJvafHUt/wGK5y/HTr2cSI7PpISkW+r3SimY0AikQSuO95/MPPsLpU1tMVka4boZP5bkIKcgpcLB3kcWiZdl1+JD641YhhUJKQYwZI4u/urTfl608okj7Sl4YWZS0hdVRJolDfOz6KNIDoo/Mp1Ny6FVB7Zzp7JAjW6fZOHKG8+fOMcojtoXAEek6TwyCFBNCFAuRlpnZ0iOkYmQMPgSW3bInz0NaOhKJjEQZWawNymBNjdaGEMt7Nhw1LBeH7Oxto41kbTJAo9nZnhHjl34j/kq81r9aL3ElkAcz8mLxjFL0p5Y9CHxsdprbq7PP+PcRySJVPOI3+OT8JB/dvo7XrF7kz2392kv2kPfTgL/xsW/n9h/eeZIsOrctt/6DJX/xW7+L/ITv7+NHMpvvf5g8f3K+sFhf49bvvZe7bP2SPbbLZYSiW/kCdMZCwpE1pq/bpF39AkjQX0AlDQc3Vpit61n92CW4eJVe3S/1in2WuH6+nGlKw+h82cY+Q/P4RauceejCBtwB7tjj54bsAqPHMvvX3iMDEAaZ5a2b1L+z/xw/O0EInL/vFPH1CXWVqrDfaBPf/YN/nSMfXxBGBrei8APJ9fceIqx9GrQtty3jRxNhIJ/UNGeVufhmy3UXhsiDq7CEXJaoL5e4u27gzNfUT9qyP7XUUnDiQ0vM2b2rel5PrWtqxH/+53/+Sb//V//qX7G1tcVHPvIRvvZrv5aDgwP+xb/4F/zYj/0Y3/iN3wjAe9/7Xu644w5+67d+i7e97W384i/+Ivfeey+/9Eu/xNGjR3njG9/ID/zAD/Ce97yH7//+78day4/+6I9y44038g//4T8E4I477uBDH/oQP/RDP/SsF+eu6+ie4Bk4PCxUyhRzyeJFlJxuawiEPve1bCKlLN5ERJGySjSVrjBaI4InyEwMHhcFxkiUojRQQYBUKKkRRFzoSqyQEOQsaNsAuSOJhFKCWhepYwZSKF5BjSGFVIBRRqFQuDbioqNLrvfrKnISpJzwqUiFgxO0uchdlFA0gwolBDJLpLYlGo3AbNYRbCKEwPpkSF0pfHC0bSILSRaRFANcJk+7kv8rpcLqASK6In314KMmpk0mKzdz6+1v4uvf8U4qnfHesbd9AaUUi55EPxqPWVvfYD6b0rYLEJrBaMh4NGIxPcR1HYvZFNlPm4er62jbEJNA6YpMwrfz8iVcaYIrsUSiP2hT8KScsT0JPImaqi554yJ5go+EJBkM6yIzHwyIIiGvSIxBJIExFSJn5qm468fjISjVU5ArQgyAwhrB3v4+WjakHPjJH/vfuXjpAn/yT/7XbG1u4JYLPvwbH+LDH/k4WSiM7jeyQrCyus7db3kDk/EIKeGO172e//Fv/89cvLTD5++7l3/53n+N60FlQhTwmtIGRUYridaqeE1i2biDoK51+TCLEWSRS4cY8Slhc8a7jiQqtm59I1vXn6YKM1S7gySXLaQsoLWi41B02RDNEGxNlgVkGGMqgDNAmxKrBYX8X0gEvWodkKpC2YYkBC56hFtiwwydfVF7qBL3FUJALHcZ2xHV7a+jmayx/eAnIDhiDCzb8j4v21i2hxkWbcSFEpVnrQZVl6gooYhZEF0sMXdSlEGZkBhZGtXOe4zRWC3pfCTFhEQgjUH2m3ujJU5kEpKlj+iUaaxGS8r5pjRKQZi3yAwBQZAK0b8+CNDG9lNYyZGjW7hugTIjBqMhUioO9vdYLAW+cyyWLTvb2yilCSRiLNF7CkXV1MgMLnfFMhMjRitc3yymnAp3EnqJcy4qGyXJZHwoFpW5WzJUhWSeo8eIonhw0hGjIIREkJ4sFTkYlLIMxhU+xl4WHjk4uIQ59zDjwYjK1hxOl+QcSHFKCI75bFGOU2vZPzjg+NFNbGUgW3y7KJJvCQf7u8RQrA/GXP686wehQmGkJIRE8qlA6GJC2KpE6OQedkk/rAqRkBWIcnwYXYaTWXqkijz6yP08duoUN0qIyrA2WacSkpMnjvPoo49xYbbA1uW8FlkyGNSEoFkspgQSGoHRkowoDIaQ8CkSiJAEvsugoK4kIoNAMhoOqOqanAU5AjnTdXP2Dy8hRUBFTYrgcihpJv5LvxF/JV7rX62XroSPsHvwnFL0p5bqEr936Tr+1MaHgdJ4398d4z9cfAO//9nraR42rN8XGX1+hto9ZGUx5Vf/8S0vSSMekfx/Lr2dj/6Lu7jp3z/wZFnp5ef0yDlu+OFzT/vzZzobF7dt8q6tn37Rj+uZygiFm7zEXbKxhFtPcHDzgPQFXLa/0PJDwe5XbLL2+wZx5sIrX6qeUzlH6udpoYRAaE3uurJAeY54ri9G5XNlsLR5Yh+ELqqxnJk8sKRbHRCG4EfXfn2S7vnfzxwit/6bOf/Fm76Fn7rl566qGT+mFiyOC+RvB6pZR3X+Cfc3bOCp9PQQWP21B5kdv5m0Lp6U7+1HmUtfscrRX1lenUTdaJavvYnzX2mfuwnvBCd+s8U++sLTUF6UY+HgoKzg19fXAfjIRz6C9553vvOdV25z++23c/311/PhD5cP5w9/+MO8/vWv5+jRo1du883f/M0cHh7yqU996sptnngfl29z+T6eqf7+3//7TCaTK/9dd11B51XVkEEzYdAM0VoRY6bShqY2aAUxdbTeARItKyozoDYVStBvm1PZ7skic0wpEaLHBY+PiZQCS7fgcDbH+ViIyRG6LuBcxAdRFpg59zRjgxQaa6sip5YKZGmgC6DLUNWauq6wqsJIQybS5ZY2digUIzVhtVlDSYuQEmPLlqgM28o2yCiN0RVNU5ctr5AFzOYTORYAlKLIl0PI/YasxK15n4qfvFfVtPPE4UFNyjdy6vqv5Jv/yH/NN3zDVxPdopDYFzNsPWLQNJw6eZK3fNXXYY3msUcfJcREM1hh69gx9rcvsb+3z8pkg+PX38aJ629H12Ps+AijyREwQ5KsSNUKsh4itWV2eMDezh4LFxHKEpLmYBHZ3T1kMZ/Tdh0IsLUlCUmSghAFQll0PSRLQ+gCzofioU4BkyMyJqTIkFtEaknBowcryHqVpAegbWlsYyTlkiW/urHB6saEtlsymx/yM//2x/np9/0iZx97jF/4jz/Pb/7ep1C6prZV8VqnhJCSN7zxjZw4uo6SEiEkdVNz7PgWb7zrNWQ/pe2b69FwSKULsCz3MJBizy7ANIlASkVlDKqPRZBKYbRGKkXOghACy2VLkGNOvuHrOHH6OgbdDsYdYqTEGHOFdu7RzOWYebNFHG8StaXzHt91iBSpJUwGlpXG0GjJQEuGWlz5daMklRTIXJq/4ANt6+iiJNRjuvFxFs0WnWwo3PXSiFmjoN3HLi5x7PgxTt15D4OVdRaLGTH23qucMKbQ02MCIQu1WymF7OmVQpteuJzIKeF9YuFTke33cv/LEvnUe3YRAmMsVVWjrSamTAhlKNX6WBQFCEKkNPpJFU5A50ihDCLIgsrWKGtLjJ66HEeYWcx2eeyRh4s0XmQunb/E4f4eIXjqZsjq+gajlXVsNSDHWOT0MbAytEiR8b5l4aclbUEJfKbYVaqKxtoS56eLz1jKhNKUAYToeRJItMlYI4jJsXSe4EX5vEq+RCmaEn+ndFHm7O4t8SEyHlhEztSmwOtEmjPb/hwHOw+hZWb9ujuJJ+7hsLqBaXUbc3ML+/kES7YQ1VF255mYKg72d/HeIwlsn3+Mw9mUmCMpBIIL5f0IEe89Shm01ayODIPalG/FKZO9Lz68GMjBk0OvSgqRS/PMmQPFoi3QPa01cQkqSHy34NzONk4YRuMVIOPaGcvdc2iVWbRdiTZEoYwGIWnqAYPRsCRQjC1CiSvvjestDMEn2kXEGM1oWKFU4SbUw5pmUKN1Gb5pqwnecen8Bbq5I7Qls925Obt7B6ASvAKk6U+tV8K1/tV6aUosOrj0/FL0p1XOXHxgg5/ev5u/8JF38R3/8i/zL//2H2f2/Sd5zf+25Mb3PsTKL92HfPCxEt8lBbefeLrP84XU/+1zf5wzf+4UR3/y3mdswq+1mt/+HN/7nr/Ej+6ffAke3dMrNC/dfYnhkPlX3sjea740m/DLFSvB7pvXSDedKNv7V3gJ//wk9Cx4PPrqSwDY1lyQxJx4w5GzT4rWUrOO47+2x9Hf7ZD+GodEAnburAuV/7kqRdTFfbq/ucm3f/7q1E43mxH/8c/+IHuvewbpe2WfUWGQjq7j1ijQ46e85LPrYX775vP/4D4n/Pw9z92ESyc49uHu2ansV/mev2BYW0qJv/pX/ypvf/vbed3rXgfA+fPnsdayurr6pNsePXqU8+fPX7nNEy/Ml//+8t89120ODw9ZLpc0zdM/xb73e7+X7/me77ny+8PDQ6677jp8F4h1RVVpbG3xIdCGQAgKJD2FunijYwIjIaUCWzO2kKbbPuRdm4DWtnxXjGVb47wn5oBRGpElznmCi4QUMUYhhUGqsskiKzKSmFIhsWuBjJnGWKSWCJHIouhdB1XVyzYhy/j455aPZNGBzlRYpJAMqwqZwPuycckiYJNiNGiwxhB8h2s8LgTIYJSi8x6RMyJHtNXliz8RqSQudIQc0FrQ1EO0OcKwOcHJm97A13/DO2gPLtC1gWMnjrO/dxGfJGvDGtdpFotDzp/L2GbM0cGIi+cfYz6b8+jD97OyusnNd95N9C1aCtr5nGqwiq4qKimpLcyjKuCqxYxLF8+zmLeofnsrVPH8WikQtpfQLzrabkpdW4ZNgzGmkLh1kcUKJTFWUZlMI0veL7k0M0pKUipbU601InpC6IAygIm5yFFlb1+wRnH86AY6e86dy2Q75pGHH+Tf/PhFYgZTVWjVD1YEkDJ1PeKWm65HS0nKGa2K5cEoyaXzF/jVX/sw2lZIrRmPR4je368oFgZyQiqBSIJEvpJBn1LGaI3q4WilMVM4F2jWjnLjm97O2thiF5dK3Jw2V87bJDRLOSTVY4RUEDzZd0gEdT99udL4i7IBVlIWGXsvTpeCIs3OmcoUb7FPGR8zLiZ8m9BKYHQNwwbv5lRuikkOqSSDQUMIAe322Fhbw9ZvYfv+j5LafZIR2KhoKknnA0qIIoPutXaZXD7fpASRCN5BKnyEBD1TQSABY8vAqXWB2DMUtDYkCmjNaIU1FS4GUgz44Akh09QZW+krW9ngAlaX36dY8sMFlqgzKXiELD55KSSz6SGXzkvWj2zgvWO5mHKwN2U0WsV5h3OOuqm5eKYoQqJSWFnjQkuMCZFrciwDtUprdNb47Igpk2I5jkLKkIr9IIiAVhIiJb4tle1zCOBCLrnolCGDkgrZN/ghFl95G/dZtEuG47q35UisplcLBTp3wPb2I1w3OcrNN7yG3c88zM6ZB1kbrXPdrXfSDBv2DqZ0AT5/bob2CzY3JkynhyzbjhAiWUtmi5bWOZz3hBTKtk0oEIoYWgZWsugEy1QUHoIedEOBAZb3X7BoBVJXDHWHiLlEjZkCGtQR5vt7xLYjDSPtfEo3v8jG2pCNyRC3M2UxXbKyMsFaCylTVZbx6iYH80vMFi0hJJyP5RwQRYkUIlS1oa4lIUdySFR1w6AekEW5jgwHA6aHM/bnOyyXLSILJisVXUj4ZSSSsEONbQyc2b2Kq+yXRr1SrvWv1ousBHI6L8Tg9dXnlaI/U51+X+L33vcWTs0D5AUig95fwoXtMlh7QgkhqNWLB7vd746R/8kWnPnM89/4aisnJu+/j//z7B/iN/+XB3jv9R+8ajnt89WPT9c49csvDTFdrK+xf/dR3PiV0dgmDbuvW2FdCOTnz7yyN+NXmSWe+yxxEeIzqi++mNVczMxydyVLXM37YULvX7fbC4ZnLdPT13a/y6OQbjiGvP+RZ79RjBAT+uIBF//JTZz7RzOO69Hz3veNZsSld3asf0I+qbHNSiCfQZ4udw4xh2t06zxN7pIlXHqDoT43Ru09C1BNCKZ3HeXSm547J1wGwdHf9TSff/F2ixfciL/73e/mk5/8JB/60Ide9IN4KaqqKqrq6ZNboTyzxT7zpaCpzRVPrZA1KZVYoKauIQpcFwFJVRlSTiy64mFGiPJ5EUDLRMrFJypFRmSFFgX0I0h0zoOQaK2xRnG5dSEKXMgI4VFSsuxaBAnvEjkrbP+FU1cFXpRCxLsiTR/XA4aNYdnNSwZtDsQsWR0MaeyY5XJJEoL1lQlaCVx0kCM5OpQRJeIqC4bW4rxCyIjSmc45gihDJS0ySQiWXYtPni5EqjyhGt6MqUdoNeS1b7gbt1wg7YRjJ47TdUuawQiJ4GBvn+F4Qj1Y4cLZxziydZQYAiFJ1o9EptM97vvkx7F1zXR3h4ODeYExCYGfLQjWIkn4VLzXB7uXiDGXeKbsWc4XCK0YNRaVBFkIKmtwqQDlui6wmO+jtaSpLIOhoGlGPVHb4CLktiuUeaVIrnwoCkCmiBYZ55ekZIu3WAqsVoS2bIi1rYkpQo6MhgNuOH2KpGoqq9FVjRWPN6eXkeMpJY6fOMnG2kpvfRDFMy0geM9vfehDfP7MBYQQbKyvsj4ZokpYNEJkfEjImEghFoVGOXRL0y0KJb1EJmUSZYM6mpzkxjd/DasNqPmlInXXpiyZhaATNa5eRZiKGALSO0xPWxdXJOeF2B4RRARZlH8rnvClTObyP1KA7H3jKWWyzP2AowwyOh/wQmLNkGwaQndAFeYFTKhNATe1u6xUq+Sb7+L8Zz5K6PaKBL2PTpMKiqC8MBhizkihyVnguw7vIk1dgVLoLIuSQxa5epb9cCyWBpOciMkTYySHSFYSHzOZ8nfeeaQUOC9octHdt8uWyhhSLtaALCSpgOzRypBECUcXKTEYDrAms729y8bWcciClckRjp04zWK5YOlSySGMjhw11qxAdOzu7xBoydmSIoiYsLVARUg5kHImZ4HWCh8iSsTyOETC6v64UsUSEyJoDFFEOh8wKlPw+WXQJ4UkZYlAMmgMMcN8Nmc8HNGYAq0r3rdiiVFCEMIe88MzDAbH6DbupjGnGK+MmGdYTh0KzaSK1KOaxUFN630R2ysNGXwvnydmUiywQYQg+lCabCkZykyoMy4qYsq9GkmClEhpED2xPsVIWDpSY4BISB2mUVhpyNFx7tJ5zl48B6Il+oCpJ0zWNllfPWC59MyXDoJDRIvUqh/yKKwck9qOkCJKlugx7yMpSUZji7WC0AUkMFpdQ6sy3Cp09QrCkosXzuJTICOoK82x9cIK2FkEUlMRIqT0Rc4PfpH1SrnWv1ovvERIiN2Dwgs5dZQwemFxXXr2eGMtQ0JfOCDvPzNROcfEufnzQJ6uov7uR7+F2373oRd9P89U+lMPsv2uTW55z3/Pr/6h/5Xrr6JxeK66GOf8g3/8lzn+6WsnRz+phCSfPMreXauE+kvADH4NlRXs3TlmPRxHPHzuFduMC3eVQ6S+Eecqo6y+kNVsRy7E9KQs8SdVzqzd17I41jwnmOyplVXm0ptHHH1QP3dkXYygFSv37vGje1/J/7x5defBr33DP+EPf/JvceoX964MDcoTqp8mT8+zGeuf8Zy7Rz8jJyEMM5feusLRX14gwlNUDUJw+KZjXHqDepKs/aklg2DzY4HhfRef/HheYL2gMdp3fdd38b73vY8PfOADnDp16sqfHzt2DOcc+/v7T7r9hQsXOHbs2JXbPJWsevn3z3eblZWVZ5yQP1fFFPov34GDxZz95Yw2daTskQKEUCy6jqVvkSZS1IoVORt8LFtYiYIsyUQWbcfSJ7KUSK37fOVCNRfCkwhoJbBGXYn9UkISU2Z2uGR/OmPv8IAQPEppBsOKuoGYHCEFgoslgkxBVRkGtsZKjU6RoalQQhGjREbJfL7kcHZI27Z43+G8Q4iASJGcEp13HEznzNtIiAkXIjF3hNjR+sCi83Q+EVLC5YhAYnXFqBmzMbme0fgOYqp53Rvu4b/8E3+GtZURx06eZOPIhEvnHmH7wllm0xnVYMTa+gbRd8z2L2Eqw97uDspUrKwMQWTW149wZGuLw4MDlCykdFtVDMerjFbWkKYh6AFBGPZnU/amS9zSMRxu8prXvY1TJ08ztBXtfImbz2inh7SLtpDWUyZjsKZBm4aAYffgkL29HabTHZyfE0LoPeYFTtaFgA+hj6EIpBTK8CLHAkXzrmw6c0YpUzZjPqMQWGsxVUUOLfOLj+Gmu0gJ2pgr22yBQBvLDTdeR2XUlS3yZcLXww98nv/z3/8sMQtGoyG33HiK4cCitUEJjXcBFyI+RGROKIoHWva53TEmvOv9tIiijFg9wY13fzWrTUYtt5EiF395LpCrhVrBj7bIyuA7j0gljksojVQalCZJQ5KGgCzHhfe4ztG1LYtFy3xR/n/RdSxdYOETy5hYpuI3V0phjMLawkOwugwWOudxEXyzRlutEXKJOysgtIh2+6w0hqO3vhHbrBSGQ/DkFIne90C78qGZs8Rai9USt2z75y8QUqFVycYWQlJXFVIVgNZlurwQxRdujaWpLZnMvO3wIZSmkSLfV9oWgvpyRlXZ3uJhqQfVFUl3JiG1oq4riqYGUop0beDU6dNoo1nbOMLKyhhrC5TN2pqtYyeYrB3B2gqkIgQBUZK8wrWpVyE4Ou9JKRNI5T2MkEM/+BAFaqaNYFhZdFTkBLLOdDExXTgOZx3RF7l6yhAiOBdZdoFMKkA3aTAoZrOWUf84Cz3fFNp6jCz9AkJkOd3Dhl1OHKm57bhhU50j73+aNHsE0oIuRR7d6Tg/k8znXVGw9N71mCJCSupBXZQLCXq5AVJoUoa2K9DDTILeslMGEEWFoeoBalAYDm2E84eZizNJFppKKYwyVJWG7JnPtwnLBVUzRNdDli4gTY2tKmxtWXQdnfMYJZHIEh0XJErWVNKijMDHwMIHBgPDZKTQIlBXcGRzwNBaBk3dZ8QKgmv5zGcf5HDWoq1i2Evtt3c6Lu47rNWsjhq0BPVyZthcY72SrvWv1gsrsXRwaQ+qXor+ApvwJ5aae/RDF0q+8LM1Wt5x9sEjBZz7Qn4GmbF0/MxX/QgX/vkqB990B6J+6eFqXNzmjvc8yJ/4H/4G/+OFu4gvsHGMOfG2D3w3x/+P+1/8Yzq+ye6b1l5xTfjlShp275qQrz/2cj+UF17OX5XLKJsvHWm6nXru9xuc1stnJffrw5bxw9d+Ti6OC9Lp534/c9/4ikXL//7bX3XV931Kj/iHf+n/zf5rJ0++v/qZ5enmwCPis58b85Mwv+MpEnUpObj7+ZtwEQUbn4iMP37hJWnC4Ro34jlnvvu7v5uf+qmf4oMf/CA33njjk/7+7rvvxhjDL//yL/Nt3/ZtAHzmM5/hkUce4Z577gHgnnvu4e/9vb/HxYsX2draAuD9738/Kysr3HnnnVdu83M/93NPuu/3v//9V+7jmh6zgEjuadkSkTPOBURqGdYjKl0VKFWItNERXKbJhsoMGdSKdr7Ap4itIYuEyBIjyn8SSUgZF1t8ktRaY7Tpt5qFrFtXmoQki0wSitjLR4WEJBJaKlwosUnWVuUxiozWRQKZElSm0LXb0DEaWKQwpAAhFUK70BBDpu08iMigtuSYy98LQRcc0UdU7yPXuXjGbd84Chw+AUKRqUhpA1MdASxvfdvbufnmmzBGsHrkCCIHlDaMVtdoF0symcP9PdY2Nrh4bhfnE01T45zjcH8HZWpWJit8+uMfZT5fsLGxSbeYY7Ug2zGqbsg50rYtufMkN2V6OCfFzHC4yn/5rr/IG95wO9vnz3HvJz/OmYcfw3VLunaJsg2TtWNsHd9ifWOdSxfP8vM//x9pu46MZn/WsXQeW9eIyiCyJaYCikpkjCy5yqnzvf+4bF2loBCaQ0Aqhbaib/4gdqVhU0lQmYSWme7gEsv5nPHmUZp6QJYKUmRQDzi6uVEiwS43FELg2iX/9id+gofObpdm/fQpJuNhgbQpjSTSdWCshhQJwfePqUjqjSxM8nKYlftVzRonXv82JrVALXf7zb8spGehWehVaEYEH5A5l39L8V5HKUlCliz3EEkxloiqDFr2G/EMWhY5NALi5c1kzj0duzRdBQRXjmELWCnwKhFiwoeES5lsi3e/bndRuUP1r5fwMybjNcJNd3H+M79LcG15Hlr1+dKi315Hgu9IMZGyKE1yVZO1IsaIzJm6tmglkCEgpMRWlhRzOTdzLqC0kFl0iRgCglDUBbK3BlhYtEtiiAxGmkjfzAtTPOSuwAKFVKTkSMSSUhI9OULTNOzv7kDONINBGTpISdst6FrBcDxB64qulSwXHVFGaj1A4Wljh5QKIRKRjBYakRI+BRLlOQQ8XnQ4lwlRETtFEpFaCRSCLDNGFWl+TBHnI1KJ0vQmTxIgKa9XliUWsKlXsVbgQonDu/z5kkTCxZbZ/jaH22ep1BHOijXStMOlBoNm0GViN8PgaIRjOu+oKsNosg6H+wjRlkx0Ug9LK595GYnShhgElRW0HnIunn3oxSU5Ef0S0WnUcISRCpk8o0YTo2B3mrB1RomEtpKwP+XsIw9y/cYK3aFnMZ9j5JAjR07RdpmwfYloEm4+xZlyvPpQ0hnWVyfERnLmwi5tF8txZODwsByLk7UJUiiUEohY/O4xBO5/8CzTRYcWkm7qWZ1YBnUBHC5iZhkhdxkhDOnlj1l+3nolXutfrWusBHK2IM/mhYq+MbxmKfpTS6SM3pnD9i45PreHVoxG/Df3fJijasmF+PjQRT7B2KnIVxp1KcqvByIylIKRMFTCooTkI3f/JBffOOftP/Y3uPXv3/uinsMzVgis/dyn+U8fuY1b/trb+KU//I+42Vzbdvx/uvhGbv+/z8C7F/VQxPoau29c/5L2g19NJQ17r5uwsT8rzIBXWOVejfh8WeJZlZSgp1ozXo5Si8DHFjfwzmbKclMy+fQz3ChnJvcvmJ0cEoZX32gmndl+0wpbD6my+X7GGz3+mtkL1ybI/qaB58Pv+TV+9W9+FdX5WXmoWiKNedpnjbnvUUZ33PasEvss4dJdmubcCmr7EIRg/y3H2Hld+e70bCUSbHwqMfnYS9eEwzU24u9+97v5sR/7MX7mZ36G8Xh8xec1mUxomobJZMKf//N/nu/5nu9hfX2dlZUVvvu7v5t77rmHt73tbQB80zd9E3feeSd/5s/8GX7wB3+Q8+fP833f9328+93vviI3+47v+A7+6T/9p/ytv/W3+HN/7s/xK7/yK/zkT/4kP/uzP3vNT3BgCnwnxlSihpQo8TM5E5JHyRI1Ri4bqCAFInlIZVOodI0ykUxLTIJaKQa1Zhk9U9dB1n1kjSi5vrKg+ZXSiAyLbo5SfUattUCJ2Yq5yOBdCOScMEaRRUaZ8oU5JMhJomQ5iRGBTMYoXRoFUzztCFlyoLXBu1zkkiljpMFqS8oJJVWR9BIKNVsp2i5CkigyKStIiiRrEkeohid401vu4eabbmUyHhJiZDAa084POTw4YLK2zuVs5radk1Ni+/wZkDWmgt3dS2ijmB1OefDBB+i6wGRlFa0MPjhMMyA3DUKAj56UAlpDkxK7sz1CymhreMNbv463vOUNVFbC0S2+YuXt3PM15fl3rcPWDcPxkEFdIYAH7v8Mv/LLv8DB3COlQSrFbD6jWywYr4zQMpOjL4MRKH7/6HoiN4WOHSNRUGLCyKQYCl1dFbq61n2+dszUg0GJ5VISt5iy/ciM0frxXraqEArOn32Y7OasHznC2mRMzpnf+NUP8IFf/10kkqPHNjmyMUZIyuZWZkRO1LUBBKlkM1EPBsVfLiCkjPcBTSbmhKpGbNz+ZlZGpm/CKUAsIGJY2nWErQk+lC82qgwdslCFmp4T0XfkEFBSYHuonLgcP9Xre6S43BjlK/7wmOm3rQUE6H0sHnhT1CK6B7qVoUekCwnncjkXBhtUix1UatGqeJutm7K6vkF78jVc/Nx/QmiBoCg8EsXTn1NmtphBzIyGA4bjEcJqupRQQaJi8Wx3ISGloWpskZDLEjEXQyRHz6Itx8KgqRHK0gzA1gPGkwk+eNplV5reEMuFtOc7xCRISmGUQirwy0jXtSipaV1gMB4xmx5ALuqW4Iq8ejia0AwbchZUlSTE8oXMqEjV1Bipmc4y85lHopBS0aYlPicIl9U5EHIoKQtSFRq39yAzWQjmS4dIFbU2hRmRNNE6kijne1Upslcs2g6FJadI3UgOD6doez2V0SyFI+mEkUUOL4JGZklKLdO9cwwmW6yureH3DyEcQHLs7B9Sj1dJ1QBpNohuj8XCcWRtFZEje67FaE0InhwDMThyymU7HsB7SecSIZWLHSJdudAJ+q26WyKqClnXkDROZhol6UJme3vGysAhkZhBYm+2x/a0Y2NcPgdzlVEhkEKgFhJqy3Tecng4peAGirTfh0AMRUFgKk3TmEKYT5K1yRgtFC5kgojIlHHRc+HiNm0MNGODEZKRNVQjzXTR0WjFykpFMxljhcG3nr1XADX9lXitf7WuvkRIiL3DMiB+EVL0J5Z0EX1hn3w4uyq58fz1J/iO9Z/guBpwVJUmRQnRJ5v099lfe67Gn72lhtj9L/CG+MIl7vi/7vJnPvg3uOdv/g4/eOz3ruqxfdy1/NrfvYeVs/e9qB8vRiP27z76it2EP7ViJVjccYzmd+fP3rx9qVaKiJDI9nlI6LpnnfRcl5dTECV85Hf2bqA6ct+TssSfWmrhmDzQsPO6a8ugn58U5JNbiEeenk4AXPGJ52HDV/2hT17jo4e/vXkv7/hbtxD/p7XHZfV1BU8FNIaAmV32iT7LQ2kyF966wvFfbtl78xH27hDk53pvMqzdl1n9vfMvubrhmhrxH/mRHwHg67/+65/05+9973v5s3/2zwLwQz/0Q0gp+bZv+za6ruObv/mb+Wf/7J9dua1Sive9731853d+J/fccw/D4ZB3vetd/J2/83eu3ObGG2/kZ3/2Z/lrf+2v8Y//8T/m1KlT/PN//s9fUK5o8JF+HY5WitpUkHNpYlPCuwLkElIwqGpSLg2ujw5iJmcPsvgGpbC45JkeHJKEoqkHmKxQuqef9w2Dyx68L1nhuTT6iUKCFn12ue/6/GiZaOygNMoioWQBNHnvmbkWJYp0tbss6ZCiyHX7DZwQkhghdIHGWipdIE2IiFX0pO3+dUDQ2LpAzIQnxEIPJmmEGCPMSbaO38Tdb/kK1o9M2DyyikLgQ8ldrpoRRwcjomvJfX76zs4lFrMDXLfAVg1KV7TLDj9znHvsYbSUrG1tApkQIkrqAqwKGVJpiISQGFXAeLPZEp8yqlnl7e/4RlZXx6QYkFpTDxqUUmWoQUYqXSBVotDChbYIAYd7O9h6QM4ZazXGaKLzOCnQUiPIKFUI0YvFghQ9QmqUSCTvyFIW8nYGckYLiadF5IyxBqkkRpcNcOcSWmsGdYVzAbfY5SB0DFfWyTnyC7/4K5x9+AE2trb4S9/xnRxdrfnpn34fbRKsrIw5fWqLymhSlkUeTi5b2Z7yXeToCaMkOQYOliXTWGtJlJqQJRvX3cnG5hFsu4MCtCqbuCgsi3oDoQ2u6zBSoXVpjoMoEW3Bl8ZIS4Exupdvl3MnC9kr6TMJ0Q8ye992/4Elc+7jMUuT7GMkOI/3AWNNkafLAv+SPQG+ixnvAsIYaDao2x0UocBFg6eOc46cPM107xKLnUcLk00ooPefC4FrHUZImklV4sdSid1SUtIoixAJ7xw5OObzedn0J9CmwlQDfHboumJ1NKAygtZ1dKL4pYOPtIsFWijIkXYxp6oHKKnJqmxQCR0xeXKW+FTytEMIOO/ZuXSR8coEKQRKaubzJaaqUcsWbS3L5SG/81u/yXS+ZKVq0GqEFIGUWoSQDKuaROgHZwqfIjmB0YIUM8GBELpA6QxEJfoEh/LnssrEHr6HCChhCJ0qA8kcofe3++SwWpGTZDnzxSoyNOzPOhSghCalAgGUUpFloFvusH/hAWRaYTa8kcXiUXybEdVxApmawCAsaazEucjhrGVQVxhrcG5eeAdKYrRFkIjJIXUZXPqYmbaJlPUVsngucHKk0gilCN0SJQSmqekQODIRwHlWBwEpEnWli4JCFHoBUoMRKAuTzaO4dokOkbrKzJ1nsWwZj0cEV6LuBuMRg+WMzpUYRKkUGxsrWG3wLhR/phQsfMv2zg5dGzCNpBlkRMhUlSL6yGzmkOOG01sTqsEQ7xMXDmbPu0H5UqhX4rX+1bq6Eq2HvUMY1ISjq6TnaySe7/5yoS6L8zslpukq69E/pDjVe64H4sUPArbjnM2PfxHkJjGy+vOf5tMfuYFb/8pX8PPf+o+4zQyf9eZd9nzbT/w1bv3gi4TJVRXTu0++YsBsV1vzY5rm2CacOf/8N/5SqpQRMQHPff4kXa6hpIjI+cpi42WpnLn/4ibcCu74c5wrObPywJzp6TFu5Rq24iaz8+Y1jpy5+OyDlRBoT6/yl7Z+gud77Z6pfu6Of8ftf/47ec0PB0SM5KaCA/m04d/a711kduoYbvLsj39xPHPmW7Zo1/NzDxwyrN4Pa79z4QtiMbhmafrzVV3X/PAP/zA//MM//Ky3OX369NPkaE+tr//6r+djH/vYtTy8ZyytNVVtgLJ5TrFD6wqjKJnZ0qC1oguxbGaVxaiMTx0+OHxoiTEwqCuqSpASJCGx2jDUltoYYiryXSUgJoUi4cKSLnWkBDFnaqNJlEilmCVV1ZQGOXtU7ydOOWGVRpHZn88IAbQV+JRwXcbYQoJuu46U+y/nStPUBmSRrJMyRhXY16L1PRwooU1xsS5dYlzXZCnwocRfhbxKFGvcdvtX8g3f+A5qY+jcksUysra+TlzOQRrW11c5PDggCEnbLvoYKXoZqcZWA5xb4LsZDz/4Oeq6pqksjQEhNaKxLFtFPAyFziwlwSekApDMlx3zzpMTvO5N93DHnbcwGNR0zpGdBzLOObAWrS43jaJsr4VgvDLhTV9xD9v780Jmdi2j8RCpZIlOajNJFS+8Eh7nWpxrUUZTScNQa+btnCgN0feZ3UKjZMS1SwQwmoxRKGLIiCBJKWCywdY1lRVEAUlm2sUunavwnefMow/xiY//J3Z2dzi+MeG+B8+gjeb06ZOsjJrSZOgaoyVa5F4enxGxyMQPDqfEBFIrlK4YjAdYa/ABBpMTHLvxJiq3h0weZUyhUUtDW60jjCV0DiMlWkukUjgkIQSi92hxWU4Osm8QShRyJsYi17182pegsPIrpWQvQS+Nu6RESSkJaIkLia7rCqW9MmQlMQpqIVCyxIw5H8Eaunod2e6iCUWGHlsaYzl60508eHCR5BfYShMSRO/xzhXQnFJ435JExAtV4HYp0smysbe2QhiDXCyBhLUWKTTGmhLfJRVSJFwIiCxKxriCFD3JB2xlkELRLg5BJbJpkKI03L4rQMQUfJHtAy4kzNAyHDbUjSEnsFUNbe+BbltGRiHQ3P2VX8uv/9LP4fwCaTQpeJxzJTYtJ+YLhzSCyiiiBF3LK+R8ISBlUT7PMmhhyUkU+0vKCJVpqvJlbdkKYpBUVhN9IvY52I3V9J07icyybel8pKp1yQqn96BLiakzIQVAINWCtttj1W8zGmzQzs/TtIdYm8lBYuWEqAfsxZoqS+aLBVU1wtQNctmWVICoMVUmy4DMgegiSuuSX4/o5bHlA1WKMuQQUhXJenTEeYLoyYMhQmmy1MyjpSUx1AmZBIvFPg899AArr72LarBCJaAJ26zUQ7abMWE6Qw8tQ+vIwSNz4sjWSVLqWC4OaYYrSNmSQmB1MsFoQ0yJ3A/yYuzYPdxh7hxKK1YGg2JVUxG0oFtkqtriiMxcpGrAtUvmy5Z59/JLFJ+vXonX+lfr+UtOl+Tp7KWVol+awvbeNUG3RNPwh7/mpX3Pf2V5gsH9Ly6NQNQ18eQR9u8YU+9GBvfvwKVnyQe+tMPtf3uf/+5Xv4e73/NR/uHx38KIpzcW/8Vn/xi3/dNHn1eq/9wPTOLuOMVy4+XNof5CVJZweMcqK9t7cA2DnJe9LmeJN8/jEejTewqP6Lm3tF/wypnu3ACAo8f3QZhnlViLzrN6f+TSm+Rzb4qfUtPrBUeeY7CSY6L53Dbf/QPfxZ//m/+e71g9c01PoRKG973z/8l3vu+vMHzgsEj/rXn6sbO9x/DsFm7yHK+3gHbjea51GVYehI0PX3g63O0lqhdMTX+llFDFOwiGLEIh8IrL2yuFEgJNBJFJJBbdjGhqjFYkQonZyZBixoVAiI4UAzEIgs5gwRhBTJmYI0aXbFqXBEpVDOsSLRZ5fAvfVHXZ0iaHziWbnBRpO8f+NJFlpjKSQW1KzrGLRQouNSkUD3NKmcpWWGMRonz5FsrjUkJmja1NgVxlTdt1COeYjEsDsrvfsb9wrEzGKLnFyvp13Pn6t3L6+pPs7WyztbWJMRW582xfvETdWCor2bl0EakkezvbuBBBKKSqsbplmVsWywWz2ZTpwR6DwQBB8dMu5i05SzrvyRSKO6k0DQmNlRqpJIezBS5kqmaFd/6hd7I6rgvFtYc1pRh5+IEHOH7d9ayurHBZ4yMoJ/egqfmKr3grB9M5u4dzlvN9RJjT1BU5JpJP5BDR2hCSx7fLQgKPiaoRhOg53D8kSOgWLb5vFheLGYfTGVJZhqMhm2srrIzGVFXVk8QzdVWjrYU+4znGRAiBNmeObG4wnS/47Gc+y/nRBCklJ08cZ2tjUp5Xioi4xIVElKo05lIhtMHYAUYJmpUxVW1RUiByUSmgRmzeeBuDtAS/ROnyoZqEZGlWwVQk76m1LMezVDgE0ZUteKWL9SEhCBQSes6p2DhSyU7vZRd9jFPf6ImibihHNRhdssy17CPPyFRaYb1k6SNtm0mVJWlJpRJWSHLOLHwihITQFa1doen2USKhBGg/Y7Kyxvp1d3D46Cf6XPayeZeUaCnbNNiqIoqyKc65SJ2lkUg0xmq6LiKEQstMXVcFBhZLskFKkRACOQVSvMxr6JjPOoyUhK7D+Y7ZfIaZSSq7QNsaZRVGlSSFIIpSIOSiHhAps5gdcObRM2xuHSWEGSlLskikLNnd3eWxh85yy2tO0zRD5EDjD6dE2bHsEqayCGGxNmFMxFYC5QvTwcdEFg5FIrt+Y4wi+NRrFgRKRlLIuATaSLTURAFaKLIuUnPZ8wC47IVKgnbZ4XxiZWVIDDsIBZVRSGQZLurC04+hZbZ/lmqwia6PYa7/CvYfvBdpKmxdEa2mkpFGOUxs2TtYYK0oMDVrkUtHyn32d4rF6pEUyVpWVwYY03LQwtyXZAQunx85XYmvQ3hCW+LmdDNCVJaoGy7NAzQOo8HWmkX0iMEq1XhEdA5lDLKxDFbHzGYzZCqD0c55Qphi6pquKxaluraMmwrnS9Sk84GYQUlJiEt29rZZtAGyoLEamRLSSoROZCFQSaFcoraKGD3z5YK2Cygpqc2X35fpV+tLu0TMiP0pxPiSSdFVG1Dndsnza08BWL72JH99698CL45C/sT6Hz78bdx+6cGr/wdCIgYN/vQml944ZP8tjm+96/d518Z/4HVWME2O95z5Zs5/+yp5+ixRRzEyef99fO5jp7j9u97NT/3x/5W77OPAuH8/H+C//yhm+sCLe3Kb60yv//JNCujGknjjMdR9D7/cD+WaSlzFUDULwBjoXCGn65dX0dCcV32W+BkeljchnmNANHxkxuENk+dvVp9QyWZ23rzOxtmLzzycCwFiZOsDZ/hfXvutfMef/tFrfg63m4rzb1Xc/ACgBKKunq7GkQI/epFDjwyjR2HzQxcRVxlX96RKV/e6ffk34kSQFUbVGGORUpNiYpkXWCNB5JLfm0CrEm3lQr+ZorzwQkq6FAlLX4iZOZN1Ytkt6UJHJiJRCFlaQ2Msdd1gGkUICe89SiiENAiKRL5zLYjSuMec8CKBKhs/JSRSampTIVWRovvgyUkRVULKEtWjpCB6T5cky85hDDSVoTYKFRJSZkztsbUlR7BWEmKiixKp18n6BJsnbuGuu97M1uY6w/EqKXl2LlxibXOLpqmoMdR1VQBVwZPJjFdXcS6gTUVVWz736W1WJ2Meuv9edi7uoIxG2xprLFJSvJ/R4X0ocWUxlTitlDHaUA8qFos5+9MlicQtt97Ja15zQ5FQh0KOTjmzu7PNJz7xSXRT6MNcboJcx3Q2Zdm2TGf7rKyuEZD4+TYhOoKT5Fho1NbWpOBYzKeFwq00wS3Z2z8gxcjhfIGLFKm4LD5n7wUuSEbNELJmNnesjHvvTCrPxVYWXVXY3vsYQiTESDNseOPkzaxO1nn4sXOAoGkqTh5bJQbHImZqW2FUzxWQCmGakoksBJW25XH2su6yqQYXBRvX38JkWKGWFwvkTQpSFizEkGwHJOeolKCyiiQUvt8oqxTQRpUNeP9fjoWsT2/TEFpd8YRf/ijrlfqPS9dzLjFl/b81PTFdS4kSuRyHEuY+4roOgQUlqRVUukRtLUPCR4EwA1zyVH5aNuw5UIUF61vHODx3P97NSamQ57W2jCcTJmvrGGtovcd1HVpJaiUIPuCjxzlfYqmshVTO9RQ9ShlGgwbvOnxMhB5OZ7RgtlgSgFyVTPq6MsQUcF0ooDgfMFV5XglJ1YxASOqcmU8PicGTgmfQNDTNAKUVUhuU1lRG07ZLjh47wSMP3IfrWgbDqsRyhTIo8CkhRKCpMlILYg6kLMoQL0eSB6sN0iQiofArsiA4hZUKlz1SgjKamDO29+v7qMAnWi+oTY3Inumy2EJEzjjvkWRW14YoK4HyWaFlYWmkWAB/ISWkELQH55kMzjI5epRWepidownlOBKDMa0cspQ1lQzMp3OqtRVMVSP1kpwj8/miDEGuSMglCcm4yjQVPLKb6VI5+spxV2B9oo8GzEISXQH26TBANBVzxhxRBquXtJ1jdrjDwe5FqhwIKaGqhoEVbB45QghwcOFcAfAZQ7tccvb8BQbNAKUMAtUzG0wfV5fQQiBEYnq4j8gwqoqlRGWYLYt1JavEWCbGusYOBjRWUFUGZDnWpdTUg1c4ZenVekWV6ALsH8KgIRw9QnqRjcBzZYNfbT32DsuN1wg7e656JMy4/ifUc2/lhUSsjGhv2eLSGyu6t87407f/Hn9q9f/gRl0/YZtdhhSVMrzn+C/wXde/G/2pZ2nEL9f2Lq/5gQP+yn/8Lh76NsH3fs3P8i3Dz/J9P/KXOfXxZyJiXUMZy/S1R0hfzt/WBRzeOGD90eELGuy8XHXVzZnR5dj8EiCnNxczy+x46/hBHqxvezxL/BlK+MjafZ7zbzPPSRJ/ak1vEGxsbcCFS0//y5z6gYRi4/cFsz/ZMpLXlnaghOTr3vFxPijvYvUzsPkbDsRT5OkxsvaZjtmp6gWLEIZn4Oivb199VN1T6yqBbl/Op3ZfGoVGiCKDDr4lpoQQGWMEKQW64AlZIJNBKlCqbOpCgBAFyBJrlnIs22bdXOEtlC+GpsC8BEgR6KOg+y156POfM1oE5suORYolf7oHtLU+lCzkSjEcWqRUGGFojEFKRRiAEC0+u97LPgQhadslbSjyVKXLBmyx7AgOFiqjDdTWEnxAC0VlxsSYqFbWOLpxK6+54018xdu+ktXJiP39g+Lj7Bxr6+ss2wXL+YzlsmN9YxWpFF3nMVXFcGWNumt57KH7iUAIjgc/80k6t8Q2DcaWeCilC8hOal8k80bTeV/o0jmSUmJ1ZQVjNBcuLlh0HTkJ3nT3W1mfDBECYr/pDNGzt7PDbHrAz/y7f4eyNcootKkKPTulsouVskRy5YBzC0i5SImdozKWGD3T6QHz+QzvSxOYU8kpltqAGmNNyfoWud8Kp4ySc3wIWGNJKQIZiWAwqKiqEjtWaYXtpeEpJpTISCHQOvOa265nY23Eo49d4sTJo6wf2SwxWSL3EudQGo0ezidV+YKfciZ4RxVqQoyIlIkxoYdH2TpxAuMOEDmhTJ+rrQYwWCPHgFWyhwBKulSGNiZHrC1Ne8yCmDPRl2PUKFHi+EQBtF2Woeuemp4pjyf1LqecypCKlHEx0fkO7SRNbamNwujiExVCMO0CrvNQWaQQ1EpSI/Ap0sZYItuqMTo5dGpRUkJsGVZDjpy8mcOz95fjIGRqZZlNp7RtC0oQY0SkWHLVfSRE0ZPYi6cfPWA+m5F8RkjJaNhQ1xbnOrIodHo/X9AlSTWa0GiF6pUOMQSEUqyuWpSuenVMpHWRZtCgqwEhJcipDKiWc0Cwdew4Sgtmsym2HlEJQTKKlGE2nbK6cZxbb3895848QDMYkvMhzi3QKpKrgAySEATOgdYKSMQsyAp8BB8i2hZ1REoCqwVaZYysEUpQqaZwJXJ5bD62SCWoRbHAdM6DTMQUkUITPRwedIyGQxpZ0UWPVhpEQkbQWpBFD3vUic7v0s0uUm90bLz2a7n0md/igIpsJFZIGpNpckslBG2X0KbGVIrZ9BDdR74tF5GQEjkHhMwIVRpylTOVvqw06y9kOff0/n4vLvqJUEpEEgRHrhsWA4MKM6SC+eEun73/fgZ3vZn1Y8eRqQO3S+en5JxQgwEjqTCLeYk1FAKpNFVj8b4cT1pJhoMhQznkcPcS8/khGoHRGi8yMWRiLPDH3JaMVF8JJlsjJkbjIlT1AG0kwWV0NSRfTebNq/VqvQQlZy15voAja/jVpmznXsz9PU82+NWUqGve9vUvMkf7KfWXH/h2Bh95yjZVSMTahPlrNrl4t8G8ZY+/cOtv8sfGn+KkGjwBsvbs/u4bdc3uHQ1bV/NwY6T57fu543ckP33kbfx/X/tHOPXhz73g1+lyhVtO0K5+efnCn6lCI+huPYb9/Qdf9Gv2RaurbNByVYavIsQXGNb30lWzk7gUA6+vH+XfjM3Ts8Sfevtzc5qLqyyOX/3PiFVm7+4jrP3HnWffimvF6v1zfrsb8o7m2iXfP3rq13noT/0C/+3f+OtkaxBlU/Ck29gz+0h/jGSv/VUfnBcc+/UdxPILb5f4sm/EZU/m9iESc4GnpQRGS6w0IAzWWrJ3LLvllc2LEsX3LXW/CfSKjEBXEmQixAwhkzxFBjusqEz58rzwHVVOiP6qZ5VBitxLMQVZ6DIcC4kQMlqVLUmWGZkFla4YmApiZrpckChNUnaSpmmojC6NUWVpvSMFR1NpVMqErGk7jzVFJuxk6GOXFC7WyPoIKla85W1fx623nKad7XFxsc/O3owQI+PJ6pVIHmUVq80EYytmB7vs7u6xemSL6cEBIgd2Lp3l3k/fi9EVuqzhqJoaLXJPp88IkYCEUrpITKWktorQdUhboY2hC4G9gxkxJKp6xO133oo1hVCdUiKnRLuYM1lb4xvf+U7uu/ezfPbBB+mcJ2WFtgZrqwLR6umU82X5oiCFIPpIiokudTzy6KPsH0yphxOGzQhjbSHTy7J5K1/Iyx5OQInwMqCVIcYIouRrt23HoK6wSpCio21nSDlAioRSikFtqO0AqSXtck7bRTaPbrGxNmFQW1CCyhgQmZyKPxkpi1e2HwJYawg+kFLCx0AtKkAQs+To6dtoZER0LUopcspEaXDVKpCxMmN08XEvs8QHj84BoxVSSmIu7IIUSz65Vpcb8H54KMsWNtL7wgWPX0AShJyhf39iiCTvSTHihCKkjBhUCCOxsvicAWZdJLiAExqJLHJxA21KdDEX/7oZoTqHlJmUAjZ1DDeOs3f+YcLikNSHHCAEzjtSZ4ipbORlKrL8uqrK8wGUVixmXbEcS4kxDUqWgVVGoK3BL5bl2LF1aYpFRlGeGzn29yUZNuUYW3ae+WxJ13mk9qRcZOOV0ZiqIsaO/b2dIoGXFeOxwnWFRzAcjjBaIaQk5Q7nPSYXi0DdNHjv6EJxaBsj8BFidMQIShoGlcG5AD1JPIRiS6lshcoCqYpVIMXy/iglSVGSsyf4AoDLSeFTKHFo4XF1wsFiwamtCVpJIhohCsDESIHPjpgTUigqU6Bly/k2g8PPIzfehJAKMztD9nPqWqFChVcNwY6QJHYOpmxtrLKyMmG5bOlc4QcUCnwBV8YM3mV8iKRYDrh0+YC8winowZdQvmj3K3IZAsk5zjmN2KxZlx1aO3zsqBpNoxPdokNXE/SgYrTiCudBFtVAionOO1IO5KgLU8FIspB43+LcnMVst6j5Q2LuAkorrNYMBjXL6JguXAFXScnO7pSZzCg9oEkaYxTSVBgNs8XiC3S1e7VerVIiZsRBifhJ120R6xf/VU/NPercNvmphOJrLPeaE3zfiX/KczXA11KL5DjzkzdyvPtUuT7fcT1nv3aIuWeX77rtg3zr6PNsqSf+rKvfxBuh2H19ZuvfXsMDygku7TD64LN4y6+hxOqEw5ubl9VW/MWs2SnLxueHz24F+FKrEBApPy9rIRtV3sIvgY14te95IEy40xzQrmmq52PkpcT6fR3tkYpkrr6hPbhRsnZk7RkZCzkEBBVqe8p7L3wN77jhg9f2JChb8f/fwVsYPbwAWQZ8eTZ70m3EoqXehsWJa7vveltw7IO7iPnymh/X4z9cXDWY9cu+EQ8xk0REXIa1ZYFShoRg2bXIDD4XGXGixAskMqn/Ip+TQGSNvEyZTmVbHh0MhcRWCiEgxI5ZezkDNxNEIIfSCOhKEoMvWxhRAGdal2YoxUxlVdkQ58hKU9PYGoUmxUiVDJWAzhlSMrStZz5vUaZI4dvgcNGBNwzskGJTrrDaEGKHEhKRLYu4SsrHObJ+A/e88W5uvOEEs8MZTW2o64brbtiEXKjvUsBgOCbHgHcty8WM6eE+3kWM0eTouHTuMR577DHGa5sljsg5tBT0HUNpIIUiB08IBRjXOVegbZdRyELQ+Ui3XLJcduSUWN/c5PjWOhJwMZJypm0XtO2StfU1lNJsbR3l9jtu57777ufhx86UCC/Vb8KBZbdkvr+H1YYYAvPZnOmsI2U4nDm8z1SRktUsFSjZ+1Hp5a+ZjCy+2wRSy9KwtZ7UZ7O3XYuRY2xtqJualcmE8XilxFwJgRCanBNdO6frHClKRMq4LrC2ukISRe2Q+0Yv9o9BSUmSEiMV1ppePRBIMZbkhxCpVo6yurGG9vtICmQtI/BqRFYaYkBpkELisySGiIoB25PeU79pzylhtS552+LyxlsUKFVK5NxvhVN/XPfqgJQLwE0J0AIwikrL0pRn6GKmdQEjLUkKlMzURhJyZukiMUic0AgFQSiSKLncVgqiromhRqYWKSU6BcbDCaP1k+xO94mhcB6MsWRRXo8sQGld4uWkoakqfIx9k9Znrveb+ZJFnoixAPG8K/JolO4bO9BVVSTwlFSMdrkgIEAqTF2TpSKGiBSKFIo6ojKGGDyuixidkErRjMbUzQrjlQEHBweQ+xjFHCF4lLSELiBlJPZQyBQyxhT/ekoRSVEVRF2GAVoqpJEsc8SlhFJFsVNk1CUjPIRETiV6LoRM23liLLaFEMrgp0AIM9qCzhqPYzo7YHjzDagq4aaOnARGSwQaSUPKoX+ZJFlFZosL6O0HaEY3sHnjnVz49JxQHSHXgwLCi5FB6JBkdvdmTEYrrEzW2d7ZwRjLcFCOuTg9oAueFAo1fd4GIqIMqK58bymy9Cwlsv8MLedaH3CUIEWHMBYXB0TlEDqzc+lhHrxvhcGdtyOqVYQdY9rI2uoaBzsXCW1AaUkzaIhL8MuOhZ+xtrrO+sY6rVuyc+k8h4e7FN6lYOnLsM1YiDLQipalCygryAGkUHQ+Udc1w0HNwWxOSBmVEi5m/gBcdl+tl7FE6xGHM/J4SDgyJquXAMh2ldngV1OPfV3Dzbp5/hteZf3o/u2c+PmzZMC98Ub++//t3/Ftw72r2nhfTd32+kdBPUc28heqhKS9eZNo/4B04UC0Anf6COaTr4xGvADYruJ2po8h/gLBvq6l1Nzz+8vTfHX9mWfPEn9K2e0FwzOW6Q1X/3Nikzl40xaT9z8DyLF8qUSEyIcfuBFeQCMO8A2je/n567+O8WcPCj39KY14ns85/oFLPPRfbRKrqxsiVLuCEx/YQ85e3MB8cdsm5sEMVxEG8GX/jUApAzmU5jpHstAoJYg+kFIg50DMGYGmkoIsY1E3KFHywJNGZg0iIil+TWMUtQKrFSIplDU0A1skzBQQFSKRZSwZ30ogtSb3m9mcFS5EFl2LEIqUQpGqS8VsGQpduanIKYAUKKUxMUKtWHaBw7aFWFrFmIsufe4iAse4GZQv5CITyDhXYaoTjDdO83Xv+M85eWKT9fVVFvM52gYeefQRjhw9zqDxV/KfL5x5hM45to4eQ6iazmfGq0eQds65M4+Rk2N7Z5tmPGHhPF3XIXKmNgYhSy51iEV9kIFEIpBRtiqwMVLpw2XCamhFIqTig65ry/7uOf7D7/4yn7r3XjY2T/Lmt7yVUyePY01plqwxnL7hFMeOb3Hp4i6PnT3Lw489Rtt6ssgcHuyW+CZjSYVVT1YKhURrQyc6fAxEUcB3+bIX+rL3uR8/Zy4TwQXWGBZt128FDQjFeDRi0AxRVpOVJcmqfFHJqTRX0eM6T0oCpQQyFwq11hofy89TIkOMBTKmNUopfBbIfsIqhUAqQ+4l9lJqJsdO06iI6ErONQiisMRqRIoRK/vmTSh8zBA9VpfjNuRC4U+xDC8ijw9pS5/dN+EplQi0TLnQiMdfC9VHnEkhUL2JPKVEzpKMQIZEGyMLF5DSlHNJQmMUMVKaZFU8wa3zLBdFip6zImSJN0N055AyIVPCJM9k6wSLS4+wnB/iYy/FpwDQBCXOTWTJymgFqxVxsUTKRMoJ54ukWcleiu8CgkzuI88EErSlbizGSAQJrQ05hnLuVU1JF9D2SmpC1halCrVfZYixZblc4lwsqQtdS9PUzGd7CJFwbcdgtILSClCkGLjptjv4/P2fRqHJqgIUIXQ45zFKo1VCm8Si7Wi9Y9hUhcwuFVZXDLRGq8ysW6ClBkE5jzJEPHXVMJ87FmmJxKCEJipPkplBrhDKIqTGBQ9RsphFhLAMKsPBbEkWGe8VRiu0Voi+gSdJZE7I4FkeXqDa/gRhchfCTpCLS4zVPpUM5VqralI1JIfA3t4Og5PHWV9fo1s6qsrigkNcPvYJDKqKWsHussQtXk5m6B3ilzPw6DvxK5/zOafyZVkI9mYtdhAw0pOR7MwC01AzbgZY02ErQzpcoLUkz+YwczTaYCtDqwSdz/gc6ELLdG+HxfSAmCJGaZQ2WJsYmBIxGUJG14LGGIwUdCLTtom61uzNHcuUqCpL6jqcj+i6Qb5YffCr9Wo9UyWQ0zl0jnR0nTiqXrwU/Rqzwa+m2hvcVWVvX239yM9+M7fu3gvA/i0Vf2y4jRIvHYfhT5/4bX5i8tXk3b2X7D6vpkRdsTj6B48nMT9Zs3p/9cogqMeEDImonhvAmY0q164vgax06SIfPbyeav2B58wSf1LlzNpnWhbHm6tuaAH2b5as/t7k6efOE3zim+svfOjy9lrS/dk9xt+nwZpnHJjFcX3Vm3y7LzjxgQPkwYvjFLjrNrjwFsvJR64OzPpl34inFIg5IKUqsB0JxEhIsfi3c0IqSaUhxEBMiVoYpJTkWBrq4B0+erLMBUglLcPBAKMVWRTacnblza8qhRCKkGLfiJZNsFaGSlaky95Gihw1xAJsk6J4gnNPZz+MsWQPCpDBY7VGJYkPASM1h+2SECOVsqCLvHPhHUZbmlpBVqS8TjO6HqEGvOWed3DHHbfSLhd0zqPNgI3RCsPhEOc8VVWxffEiCRhNjnKksexvn8MMYDweMz3Yw7WOrptx6cI5fMw4FNoomgbicoGSosg8M70Pv2/MhMT0vkglBVpKkuvw0aOlgpzR/Xb60Qfv4z3f839hZ/sSUhj+27/4Vzl67Bi2qooY9zK0SSpsXbG5tU7daA4P9zjT7hKc43BvG2s1uqpQUuLbls6lsqzXphDAgyvSYzIi5x7AXvynl7fDOQsyxRytlEIiSCGhjMR1kaX3VDFgksa7QNsuSbFIycv6LuFDIOWMloLOB5phTWUtsXWIHPu+QqFMibhAFDq4kLI04cagbU3XtuScqIarrG9tof2sH2ZIMhJvRmSpkMmj++PLJ4jBY0UBBGYK3b/tiidcpYwQsfd/5yugNiXKhlyKMqUoMWX9qyPKJlz1FPXLAwwlJKnfnkuZkDGXYZMTDCtdzjsh0KZkLAfnygkaI42WxMsbz5zxqsIKg84JKUFnx2A4ohqvQnIIX2jbISaCVD2ENGCtKZaGGKkrQ86e6cwhhOqhcwmRIXjXR7ZJyJlIDyNMUElTqPBdR4yxkNiFQKsS/+ciCGHQA03wAVLAkLG6yJld9pBhPj1kvLKCrQeknBhNxnRdx+7ukvF4iK0sJ06d4sTRoxzuLBB5iJvvFxq8MvjQoqXgcBpYeEcikXXGSI0SihATKfseyhnJwhfIYJR40aH6GLaqUozzAO8yWEnwkugiSWqaakByCefKuTvdWQASUynIEiHBSlViuUSJljPKEmIBylWVIqVdlntnaCY3c/Km05y99wLTsMJyMCILicqCEQFFYLYs97+5eYy9nX0WizneF4m66P3fVdOwnJb3TKr+/OPxxlsYjcglNaJYR9IV74QyNQQPywxNxbCStMGznO2T2w7PPjF5LJGxlfj5AdsHU7TUDAcGoxPtYspy2WK0wE23CdHj+gx6n4sMcVxbNBmVdWEphISmWJQmowpHubbMp0tcVAxSoFsGKmNQUrG+sQ68SIryq/VqPaFEFxAHU6gs4fqtly0b/Krue/bSpQb8Rpu46ace/9K8+bsH/F6nePu1sZ+es75x8BD/+oY/RvVFbsTjsQ1C9QdvaOcHgnxsA/Hw2Zf7oTx/XY4wq56nES+UWXLsr1cvp+U/Zz5x8TjcAO7E1UPI9GHL+OGa/duu/keFYebgTUdZ+ZWDpw/yQiA3FW/deviZ//FV1CI5Dj65wZG8T1YCaS15+WQ5uT67iz0Y0609uRmXXrDyQMYsE6rLiJCpLy5Qey9OjRE3J5z96oZkrx7m92XfiJfYI030GS8EyoAQESnKl79ERppABITKWFXyeUUu4Uw5F3Z6iBIjNJU2rI5HNFVDigEhMlJofA4oJUgio0WiNpIkqrI1iWUva7Wiriw+BYIvuca5C0xnLaYSNMYSPIg+KmjedkipGNZ12X6LhNIRqyUkQYhFRq0psVI+JRatQ4oaYzcZjE9wyx1v4tbbbufUyaMFJEfiwoVtbrntVgiOZSrRYDnDymSCNhXD0YjF7BBpGmaHUz537yexlWZ3d5eQIlIVCrCREqEVSSnabonqpdVd65/ATMglZ1zkAsITAq0ltrL45ZKY+yiqPvbLIVi2gZW1Y/yp/+Yv8Ie/5R2srTS9yoACbep/GUPk4Yce4Fd//cN0AapBzfTwEN8uGY5GmGpAVC3kQIiZ6SKgtO59s4EUIsLyhI3b5Yd8uQHISMoGVGlTNsixZEG6mEhCY6oapQ3eOUxVNtcpJUTOJYfeF3+8lgrVWCYrI1Iuvnn6LPgC8ysNMKLAwJISpCwLCbL/+UJomo2TNJVEzMsWWSBI0pLskBQDlSr8A99Tr0WKxcYgBD5m5su20OtTJApB09TFr9xv7bWW5XGQezBbD7HqZd0hFwXBZRVBTJmQMuTy/yFGnHMsW1eOk1yXWDNbBjRSQKOLnDjlTJIQUqa7HD+WC0TO6xrluyInjw5rR5jxERb721gr+szzkp1dhgyajKTrHIPGUleG5VKgdcSaxNKVaDNELsDBwYCcEgeHc5KqMLLkm7fOo6XoFQix5Kv3MLAkJUhbjr9+YJGlxAWPSrkHI3o61xJiZjhe4dzZC2xsHcW7iFKK4bBBG42xlsVsSgwghaH1oWciFDJ3jB4fBNokailoYzmXZO7f114JEYKHbAgyYbQp7xuaDCxcICVJjBJlIcgSG+LxQMIEW7gVKqMVBD/HWMt43KDUsgyCip6kT4YQuFAI6jlFpAhAJvgd1PRhpvIu/Ph6mF2gWS6oaTFG0jQjWqmBir2DPY5tHWF1fcJsOiPFVCj9iAKibBrcYg7ZIaUkZdEbxTPiCVsHIftmvCD2kdUAYTU1mdNbIyajVYa155x/gJ3d8zz2yEPcfNNNKKuQMdPNDtm9tEtGoqwp2/uQqIcjIBdLSXTIJFARrJIMagVCkkVmd7FAZU1lJZ3L6FojdaZLczLlWB+MKmoKqd82NZtbR9jaOsZg8OUbQ/RqfZErgZwtyPMl+cgqYX34orfgLzQb/GrLTF+6LuQvfORd3Hj/Y1d+Lx67wN97+I/wc6957uz6a6njasDOaytOfPQlu8vnL6VYXD/8A+MNf1IJWJ4cMXhEfkGOv5e6hL+KLbcQCK3JbZ+u8XK+sTlzeH4FgBMndkFUV0f3zpnJ/QtmJ4eE4dVvxfduU0w+MibvHzz57kJAhMh/+N038UN/9LdfkErmH+y8iRt/ev74429qeEojnr1HPMNblHRpwicfOX/VdPPnq7Qy4OzXjAnDjAziqpkAX/6NeL+pE7Jst1MqUUXaFo+1CyUTvDYGo0tkTQygpSnNdM6EIBC17K0gmbbroJdiK6VIKeN8pFYGITVdaMuWJBcKrzEGciLFQrwWOSOFIgRHDEWC2bkEOaBQWCUJ0WOMRkmNVhJkxreJFCQpJ4RKqJzIBIIHqy2VNgzrLdbXb0KqFV735rdz5523MxoO6ZZT9vc8o/GYlVFkf7tkgi/mc7QxeO9YdgGbFW7nEmcefhBT12ht8SFy9txjBfqmLRpNUgLRb4mFVMWj28upY8oUekLuG4vS1sYAKXuy0OjaIrqO5bIrIKxhg9k7JGfDoBlQNwOaOuC6KYhBUSj0m+aUIjElptMZH/nIR3nw0XNsHNlEesf+zsWysZUJEZdYmZBW0NWSeVs24kIZcmzx3pf77Bu0grcrHaZEXPGfypyx2VIZy9J7IhkRYdYFjkhTjjFR3lN0RkRJdI7gC8ndGo0C7KAugK0Ye9p4IomiGNBKolSJJ7usGuiP4McvRsIwXjuCDl2PLC9b7iBrkihyYS1lD5QTpOCLTF1CyNB1Ld51PT/AMRgMsH2TDhR5vKD8vieqi55WTQ9yg3Jfvh84xODpXEfXLlgu5hxsX2J/f5dUTzh6/Hq0sXSh5CkbBVU/YMj9VjMmATE9AcBVPhOjrHoufUDkjEqOZmWdfaHQCqwSiByIKOijAWOMIAu4LyePD54UIkqANQYpIsZqqqopsWfLjrpZIUpF9B05RlLKBAmQ8M6VBAQEXesY2ppBbQCByJngHN5HYvB4ike+soYcPXt7F1ksb+HI0aN9VrlEa4WtShZ9TpnRyirX3XQLn/rIw4X8jgYCOUuUKj71ofHokFBe9ucaZCJCZKLLPUdBoBG0bolRJUYuJUOSAecyRkHMjhzprQsSITM+LvrPF0EIgWXb0TnHZGUFm6egQ+/TjmUwGQVKKLRJhCTKUCBFYjwkLi+xfqSlOb7Ghfs+i7erRH0MYQcceonNHcpndvZa1lcTR44cZW9nl5A8IeYySDCSruuIKTOsBMnD0hVKv1S6F5mUgU3O+QqoTWiDHA4QMiOTQ7CLUR2HsxkRhzZNiWwSoDIsly05N4w3trh44QImFkUbMWFUZm0y4pDM4WKBlIq6KZ+5CbBGMVu0qKwYWMsyeVKlUEoTBJAFyUcOu2XJs7eSSktEFixmnkeX5+DVHPFX6yUo4SNi77BcE6/fIjYvXsb8YrLBr7aqXYHP8QlxYS+sLsY5Gz8+eLIUNQQe+LXXwGte5IN8QikhObjLc428pxdVYjyim/zB/ZzoVhXDyr5oMOAXpZwHnpt5ULLENSxy3/S9vBOW+nxp/d60cYbP6VuubpgAqIVj8kDDzuvEVT+FMMhM33iM0Qef3IgTS5zbqV8U7PyR5VOAildX//6h13Ns+fhWP9f26fJ059n8/ZYzX1eT9BMabgE7r1VUe0eoP/8MMWvXWLmynP/qdbr1a2/qv+wbce9D8duKhBCi5DWb0jj5KEhR4X1CREimJytriVa2+J29K7gnAUKVpknInuidA5oSn9QFhwyQCYUUnhPLrrijh01DZRUxOeaztsQr2Z7mHiPGKpQ2kMqmT4oCUFOSAkfLJeu4rmukB5ck62qAT4HOBULKGCFpzFG2tu7kxMnXcNsdr2M8GhA6R6pL7NLB3jaPPHaWm2+8juADK4MVfNXSti3D4Sob60Nc1xK9YGVtC20yDz/wWQ6nu1RGE1NG9Vsp0cO+gihedZQucvxUPM1KgvOFPK16OnXuc3S10j3UTuI6j1CC4XDExuqCi9sHYGtc1/Gv/8W/5Gd+6qf4z/7zb+Hrv+Eb2DqyWV5/UbLVP/+ZT/Erv/R+RDVEaUnbGpYHlxjWkjwPCGMYTVbwekjKkoPZLilljNYEVyTKib4P74Fnpfkum3AhQKSyAUsyY40p2dQ5Y02FkBahDFJppHTEVDzc5UMgo7TB6EK6NjIXz22EylbEHIufWOteSdBvoCMYa1BalUZUljzvJBKqHjIYDZDh8MqGOqMIuiGlhFUFSpaEJKaIEhmjZE+fznQuYIyhspZBU6H7rHApHh8+QFFORSBmAbL8Wx8CPgQ65/DO03UtrlvSLebEWM4xqRSmskzWN+lUfcVpH1MmhIStJKqcikWBIkv8llICS8I/LnogSk2SBp2KPF3lwGA4pB406LRE5ICMCW0UQkliSAipcD4wm86REnIUjMdDpIgcHpTtcF1XaFMkYiGAqSyNknQy0i0jSmW8b1GynOtA8fjGAq9TSISICCKZSJYCnxOS0sTPFwskibx/2MPRWgaDMYv5DKlrlosFly5cZGV1lYtnH+W+T3+cmDJCCqxVICH4TOoJ4sWnnTicObRWDCqN0BnvIl2XqCqFVTCdO7RORMAKgTTFRkPKSJVZLCNR5AIERJKIOBao2JRBkikRgfOFYzCuwRR/vE8tiYAWBqEyPrTU6P64UmQyOXZM98+Q7KeZH3krYe310O6h/JxBvogSEaE0HsNi2XL+wgVuvOEUK5MRB7NZGfCpAuyLMTEc1QyzJ88DrUvlnL88oUnF9y9l/7MBWTUIrRDRk8qBTI4OKRLDZkDIgZ0Lj3LD0aPIukFXY9Y2JDf11pHoPFoplMzMpnsMmhH1oCH4gNECHwq/IrrEInUsvEcCs7CEXCwni7mnMgplMsEHgktEl7AYNiZD1tZXCcFyMF0i8x/cL9iv1ktQCeR8SZ7OYGWMPzp58UC2/OKzwa+2Tv3MOd5wz7v42D3vpXoRXu73nPnPmPzmw09jZZ349Y5H/rsZ1+sXn1PeZc9Bcpy4bge0LrFLX4TyJ9e/vHPDn6dCJcjrEzj7pd+IX638ONv+WI+J3k/3slVzIdNlz1vGD/LZ6nbUVTbi5MzKA3Om149xk6tsOAXsvkYz/sj4yTT83ic+fGTGb7Wb/NHhtcPR9i+MOZYfv8+sJdKYJ0Mlc6L67Hnk22542jmVTOb8WyuuO1hBbR9e889//Ocqtu/ZZHH8hW3Wv+xP9Swgy4TpJbcpCYIvm8j+axyVlVe831paUhbkGBEklDBkAlqVjbjMCiMUuo+GUkKhRNngCCI5SZq6BhmR0pMzeN8yb33JLlcGqUoucE7F0+m8A+kw0vSQpvIlt3WOzpXopTYvC/Xdlwxbo2AZICSBRrM6Os2RzTu4+bY38qa3vIX19QluuWQxX1JXFbbSKKPYNIbkHDELQozMDmdsX7rEses19fAI2mh86Ihhxufvf4CYypfoklVetrs+FkgWPdk59mCv6XyBlhbb1AhyURfEhNKaYWXo8CUfWsqyXZMWcChtGCjD8eNHiRF2Dw4RQqKVYTFr+Yl/8+P83H/4Wd56z1fxX337t3Ns6wjt4YJfev8v8PnPfQapDefPPMRwMOCmU0dZn6yRhEIqhaosgQ5tIlYbWp+LP1hIUigeYLK8An4SVyTwQC4wN2s0dmDZWh9Sm4rReMD6kU2a0aA07VqSkoFcYHA+lk2ituaKHQEKM0AKhdKSGMrgzhhDTpGQMkkoEiAi2MqgjelJ3yDIDFc3qbSAhbuiPkhCE5Upm18rkVLgUhkOGVGGDAhJcr4fMmm0Un0jXwYQieLPDbmoGTKZGCPLri1Mga6jc76QxrVGqiLvt/UAY+veEy+vvGzGOVRIRQnRB5B3PlEZidJFIVFc7ICCHHsPVcy4WDzZCUGSBlJX1AopYO2QwXAEyxbXBQ5mM/RII3KNc5HKWoQKpCQgS5QxV+TVxhpSFBht0Fqy7FqEMoSYepUKSNUPX1Bl+BUySE2IEqUtSikWiwUpBYiO4HzJbvdlwGFyYOkdqqoxRnGwfZbReJXhYIW6HiBypGlq6sEArQTawPkzZ1gZW+pmQrvYIceMEDU5B6RItMEjJayNLcELRJIsZoGuS7jskbp4kysjETKhVXmPU8osXcDnjE0aLWqcd4QY0QJUMijtaMOMGBRSZDqZWS461tZGheSfIjnLPv5QIISkqg1WCpJIOB/wqfdQx23M4Rk2js1QE8ls52FoVliIDVQ1IZLRuUXlljZEQsgcP3E927v7dK0rEYKokjjhS/yezL0kXcrCh0jFXyekAjI5ZaSprlx4cypRZANjUEKSZWF96DpzMNvlwTNnuPGGmxkOa2LoiN4TvaNzAW00KoMQFTlLBuMhEtjd2SOkwqgTJiOEYmUwYNk6lj5iLZgsWV2tWMw7lsuAo9/iJ4GIojwOJfCLlsN5i5KvAAjRq/UlWcJHxP6UHCL52CZhUn9JZINfU23vcuNfddzxvd/F7/zRf8SRF7AJ67LnIz/5ek4tnx7w3Xz6HP+v3Xv4u1ufuOr7izlxMS741eV1/Pj5t/L7nzrN6AHN4EJmeM4xOTstDdQXo7RmcfwPuH1FQHdsTHX2wsv9SJ6/fED0lr3nLKP75jPycrdezXZiN3a8vnoMPzao2dVfk0TnWbs/cPHNinyV8wQ/yszvOs7gN57ivw4BebDgxy9+JX/0xg9cwzMopabPMNRu6hJ389TH/Synbxhmzr19hZO/2L2wzHAhOHjzUQ5v4skqgb6HuJr6sm/ESzpVoTsLCplZih6kliVGVgiV+qYkkWIgozDGIHUBSCkl8a5k+QrhSS5SS4OUFiuhrutCfU4lBUnp4kFubEVK0OWISrLEFokSCVRpw8DUdKY0FF2IWCt6EnPJKs4psuwcti7DASEEjVWQBYvQkRMM9SqDwfVsnbyTN9z9VWxMhhgFi+khs9mM8WSN4FvOPPIw1WiVQdNQDQbUShJC5PRNN7C2vsbB4QEXzz7KYjHnoYc+T9c5qmpwRb6dMogYS6auD3hRooVEL1/ulnPa+QxjakL25JRRuqKuFKNBTd3LMY2x+BiJKaBMIcLXlS0xXDJz+vQJJvsDzl+4xLwLaGWR2rBsPb/6Kx/gwvkL/Mk/9SewMvK5Bx9EKoPI0C1bjm8eY+PIMZrxAK0NiLJ1h+KBHowWTFtf5OmyyP9jDFiq0oCT0UoyaAxWQ20sK4MB9WBA7Lf9g6amri31yqC3DhTQWMqPN7hKZKIo75eVkJJH9QS4ovqWCCVRlDixLgSczwhVts9VJTG2KsTolBAkpFaM1jbQOUCOSFEI8kFV0FPMM7IMWBKInK7kiMeUC2lca7IQJUJJZEQfSxZjJHhPiLEAymLk8GAPFzPa9Bt/U2FrjbWGSutCIE8RF8oQAQGV1mghmXeB3Dq8c7RtR20N4TKBnbIJlymXoUO+3PyWX0+7EoG2UksaqUsjDwVsR0aaGnfQMZ0uSt5026Fri3elqRpUmpVmhDIWBLhuiRACKQ3WDlhZ36DrPNZvo1MiZsli6QmpxLOFmAtdPHRIEQvvICZS6EjRIxA4F1AiY2WJzGusIaVE25bH7kJgICnS/7rBB1+UD1KgjMG5jo///se5/sbb+Npv+lY+f+9HyN0CJQ3GSnLWuLaj9b4MDKO4cpX3yZWG20IlNYhMSCULXmlFrTXzruvfE4XrAnagsBZcEjiXSTr3sYYKrQtzIXpBiIlF23L98RWM6mXigEyQ+smJyhCTICZBCoLoJd4Hlu6AvH+G4cVPI7a+inBUktsDdO6o3EWGtihGoqzIQjNbLNjaWGV9dUK76Iq8NmfCIvZRcZJBI1GLci6lXEj/oo+hSz0MUlZ9xm4qcShFBOA5bAOSTFVXGKto24TRFmKkbVuQkc3NFQ4Xx3jg/s8zmwZWRiOG4xGZzLJ1VPUAqWYYA4pI12V0pdGVQCEYxKKeiTlBiCh6O4cvx6xsFLrRBJdxHUxnDh8yXX61EX+1rr3krCUfTqFpSKc2ic8DiLqaerZscNE05LWVq7uTmODizjU18Xk24/bvf4CvPf83+X+86//P3p9HW5af5Zng8xv33me498aNOSMjZ6WUKTSCEDJVlGxjZExhuzxVtd3YXpQHKNm1wN2YZS/cy0MZul3GU2Ob9mK1odrgso2hcQvKRtiFMIbCIEASGlLKeYo57nCGPfym/uPb90amcoqIzFSmpPzWuisibpx77hn2OWd/3/e+z/v/5hua1Q1J1X/vZ/4bzv6rx583OaqsVvzor34N/9M3vXAjfjmt+LV+mx+79F7+42fexMbHKo5+aqB++Ars7nNffOC6b8srXaqqCM1ruzF9PVR/xFLdTGycMZIpPZugup6yd/ObzuupIrEsh8ucF6rsZQGkYrqexLNXtaqdyGOx4S7X0W1b6nM39vOTJ1ZUd2zSHbv+rfiV+xzTj82elfV94BP/5QfuhptoxO3yua+TUnuByDzj/aisVmw9mLj0rud/XXXHCpfed4zjv3AedYMRc6v7T3D1fk3Rn/dYFFDXqaD5km/EFZIXXIqiZNniCpRNU5L4SytXk4jElEb4k6aUTNuJSLeUyBDkbNRbybFdrlvWepAtowmEHNFFyYasCyORvaCzw2hLU9fyvZywxtMNHTENDIMQra21xBxph44hyIl7ZT0b0ynWW3K29KFniFHgXbpBm+NMt27jzrvu5763vZPjx06I7DsPXL50Gd/MUFqxt7vk7N1vEeK0cZw8dZLF3h7nnnqKy+efpvINdTNltVryyGOPc3V3l6aZ0vcdKQSGIbCzvwAC73rnWzi6ORfZMnrMTu5JYSCEMEpqA3u7+yzbgdpOaCp7uE0tKstmPwdSiBidKLGjDx1D1zKpK46d3ea2U1tc3lty8cqCnZ0FfQCwPPjA5/i7//PfQanIlatL6smcGCOnjh/n3rvuYDqdSYNsLGkEu1lvqJVhPu+4fGWB0UYeJwreGTZnNZuzmklTMZ3U1JUlhEhOhaauJA4uJoz1VNMKN3qMjTEYZ1EhyvElyPCxGddI8tIYd0aRiKURjFeKkscqJEA22c4LT8AYi1ZgAG2Fwl+KoZ7O0HkYt5Oyyc62EqsNWvK4i/hZnVY4a2RDnjJtlDg5RSHFNV3f03UtYZB8dWs9s+kE7x3GOraOnsAYI97+nNBKCbnfSMSaVozcg0gaP4OMMagClbNYa7h4tSf2HaVMJX88ZRg96frAYjRGyBmlGMUStKHQhsLcG9wItFeAIWOqKe0QKdpiTEIXmFQea8Sz7b3BWQ3GomzFZHIUN5mz4SdY76nrWl67j/06sVvTDllirEIm5UzfrikpUlJgvRqlUkZjjKWyBmcts8mUtuvk+DUWrTJaa6qqwfeZdhA/tnY1VS3esdVyRU6R+eY23nve/ZVfTYiBvfmmWDj6wnK/wzpNzC1QqBtH6KFTvfAschBYYrE4K/GJhVG1YRyZNMa0ebGNZPFghZDGGEQtYLsCzgFoQlBU3oIv5BzZ3d3jzts2RX6vhI9QgDQOExOBjKZ2XuwVMVJGOGHf77K4/CDV7DYmPpEuP4KrPL2bMqQTYGtK7OiWa5zt2d6Es2fv4NLFq7R9f9hQK6VJ45BPDpFCSWItErZCEcl95dGV3NccE9pqnFVMrKUgr0+vDIOKDHHB+YsXuPXsnWxsHyOsL6NZMfWapvKs1mt2dhObWxsYIGtN03g2jh5h5+oVvLPMqoo4RIYuopSl0obFomU1RIY6SHxlAu0Mtmi2fM3cT1A4Qh/F3pAiQ3ijEX+jrr9UzKi9pTTLx7YJR6eiIHo51/lC2eDG8NiffQu/8w/8Kn/gyM+iX2iN9Iz6t3tv52N/8K4bj/cKA3f8wKf5gZ/6/fzl377N7BvP8/33/iveU6kXBTf9y+Um5f92jLJ45AUvc/LnDctv7Jhpwad/Nqz4R5f/Kz70wFdQfWLCsU9Epg/twOVd7gsP3tjtfpWrTGrecK9Atuolm1uslaZ7PiFtNgybnjA3xFqRnGLrwQ73iVe3EScnVMyUl0oqsHqMMHvtAXR2Fflkf4Z3VU/QHtds3eDPq5A48kDg/BFHMdfXjA+bhfVbT9P8yueufTOJPH32KU/6QL4hYFsqGfc8KItiDMq758TfuaWcaz9vKVjcAfXOCTZ+4/qnEv0dx7j4bvds7/nBVcrJ83Vdz5d8Ix5jEpmqBqWj0G2V0Hhr7yhkQlxTksVmQ8wSH5ZKBFNQJUMxVMZha8O0dhg0664jKdhbLTBa0VSerBIkJXnKRRF6KCUyn1Z4J7CgdZdJqVCSYt0GQspY60EVhhiw2o7QuPEEVxUIQpJWSoFyeL9JVZ9h4+jdvPn+d3HHnWeZNDXT6RSjM8u9Nc43bB89yqSuadcr1ut9tIKYW9qHl1jnmM2nQm6ncPHCEzz51JOEkvG+ghTJwNAPdCGwu99inKaezNk6enyUIosep6iRNA8opckp8dDnHmDnMw+SpxVd19J2vXiNh06aOVVwFExlZGOVFMpXTCYS7WQbzS2zDbaP9uwv1ly8fJXlYiXyz1VLUdBMN/FVgJy56/az1JXHjj5pZWRgorI0tKUk5rMpvhL/9bHj25w4foTKe6a1p3IWX1WihNBI1FeMGGUopZCdoqoqmkmN9hWUTFN7aYDz6C8fY+aSajA2o5Gsbuvs6B+vMNZREHXEEAJJabz3GANV45k0E4G3jRLyUgolB2LW5NCTdMCOn00FQ9aOdoikrChGUSnk9qKIRWGyYrVe8/iTT5LiAZxOZO/GaIxxVM4zaRomlRcSu5LHUOK9hJqttRZLhRr120WGDM5YHJkUk5BAlXAWppXlyMZcPO6ISiekA+J9Ofx8lTxyyBpMUUzswZCikDCjoqFIOoAGN5mh7ARXAkpZcoLKWybTCcpNaeZb1NM51XSGcwZNwqQBlQOODk2mNZYr+z3DYkFRihg6VI44A8ZBtpqChxQYotC6Q0y0fcS6iso7qsoSQ09KGpUTQ9+SojAUSsr0fUe3bvG+YjKbUYisV/v07ZpqusHm1gY5FU7ccoZjJ09w8Ylz+NpgrEHlhhAGUkgMeSCVJIoMlVHKjsP3g6g7jXWgTKRdB7QWuOQwlEOyfDZZ3ldQ9DYLST0LOR0F2hS6PpATLPc6jPXoGmiVAND06AdPhT4GGCFtORYUAr2cNg6rNUN7FX3uYwwbX0nZvo8YOjyJSVkx5RLKBLoIOVTs7O6yfWTG0aNb7O3vE4cor6OS2GsTy3UgZ+F5UEAZPR56osBQ1ovKKUkKgFIK5zRxjDSrKo1GNunN3GMmlo1jx6kqDUGxN0SUq6nmG4Sc6NqedrWibiZMKmE3zGYzVI7s7u6QhwCIlz/1UZQ/yOMcU8Y0BjUIX6LZaKjqhlgcfZ/phoGkEvUko8OXIwr5jbqZUusedvcl+ujsKeLMv+zrfLFscFXX/PH/04f57qOfe4Gffm6dj4/xMe66uRtTMurxc5z5kXPwo5a/esef4KnfvsEtv/tx/h93/Wve5t2zTs7XeeB7f+CPccsnnytJf2Zt/9plfmJ5K79v9gR/5IE/TP/9p5n++uPc271w8/56qbzRXLfk90u5ikH8e+PyCaMPm+64NSFseoa5JtZavL/P87YaZpZXPYk9F1TKyJnmi1zMypLmC8UZeLHSXeQ/L+7kv988f/1Z4p9XzbkVzYUt1tdLMVRw5a2esx9vrkWMFVkWTM9l2jIwU9efO9iXiN99ntuuQdXVc2IXm4euYL7qNKl5/vtbNFx+m8Dbqkcvv+Tvjyc2Ofe++vlz1QtMnyqUdfvc/3ue+pJvxAWArCjKUHKgxIi1Qmzuci907GIly1srKqOJUbHqAv0QRlm7+CVRRbbkY6NCDnQx4q2ldAPkTBnp2VoZyELCNjoT00DIEYXCWUXO0tgoImn0Tk9q2YIfELvbPjB0A0UV2cj5GVV1AuWPc/Ls/bzvv/gvqb3h4vmnaG6/gzC0rPoWVQzT2ZT1Yp/QdzR1zTD0KG2xKuOsIcXEYrlPCB0PPvQ5Uik473E50WnDerkUT4U20mymgpHFLgWovJP4qpRk2DESwCWWKjEMPd1qyU4cANkWK22Yzmc4W1GyAp3p+pakCvVsTl0KxhqyUmRG37YStcCtp49jzpxkb2/N3v6Svf0lbT8QlOLYkQ2ObB9BG0nzlWwtyWlXhkOPqXc173rXxuhPlpN+ZzRWKayzeF+htMZXQtBf7klzkJPCeMmdTkVhlcG60Q+ewVo1xpXJhn1Q0pQf+lm1EXq+EVBdH8Qa4X0lt8VqKAmVE6FrUSj6nFDGkYs0uH7jNFVTY0qQJkxrOaaVoluvCSEReitgOCNQtKAVRcFysWB/f49mMsG5CldNcFWFNQqvFVXlZPuuxLMux6XESaHUNd88AhMsBYqSHXUZJUDKavFJlQw6Y4pi3liGWA7j2VIWIN0zlzlKHTTjCq0K3oAzChEKaFCyfTcaiQD0DdOt09hqgm1muGZGPW1EVaIyJg+oPKDLHjYqKMJjl6zzcSCiDO2QCCExn08xZSAOQgJ3lceNJ6fOOUKfSMDe/lI84whh3BiNtdV4fxU5GBIS52VUYWhbdnd2mG9uobXcpwtPPsWZu97M5uYGIJvolHpcVRGiDE8r40TmX3mG1Eo2exI5+DAYFBVKDSTBkuG9RKHkrHG+UIKWQWCOmFphtAFdUFpUDdZqVKogR7o4yLlDEEI5BtbLNdY4Jo1n7+oaCgyhAAdRbqJcyONQZdrUFCIhBrGR0JIWj5BayZ8/Ppf4slU0LPQRQvKEFGn3WxbLc9yVjlE3E4wRGwXIe0ztDNFr+l6RR+uD0iNtvwDKoJwFCiolShE+R+4Da3qmjSUF0NbhrWbd9zz55Kf43Ce3uefO20hkjJtwZLPl/CWPrjdoTEu/XlNVQkhfdTK4qnzDfCOTw0AqmZAT2SrWqx5bFI3TrFXBeivvi6XgUTjlSBhSSWQd0L6n5MRrL0x8o17vpVKRLXjbojbmxJObQv5/OddZriMb3BpOur3n/78XqEtxA64TWPWiFSP6wSc4+yDww57vvvtP88TvmnH/N3yWv3H233Cfn/C/dxvc8uHrIBxfvMIP/7nfxw8ecxz5j49jVp/9onnVxfmXuT98rGwU5fQx0sTJpnv24k3381Vq9HMp2q90HWSJv1RqgR4jzK4z0urVLJUzH7t8Bm6FcHq4uSvJmc1HA+0pe92Do/5Iobv/DNVHn6FCSYluW98wuPHJFJide/7ntdQV7H9e/N1ihekhvQjgPvvC+a+puXVv/qJ54nljwrkxpuzzS2XYeAi2P75Pvs7j7ku+ER9SwemC0kEikkKiD+IltAjQpzIVRiuJ4ilp3EaXUSorsuESIyFlYi54O1KwS6ZkTR8K2ot/OOfIet2Sk2E+aagqRyISYocznmkzwSqLpjBk0MXJhjzDkBIp9zSuEliZsgQVQYG1R9jcvJ0hO5aLhKs3qZsJuV/TNJtoJY3kZDIjJ6EPhxBYLZfim4yFerbBdDZhtVjgnGe9WnLu/NMUpfHeEmOiGxIlG2IUSb+3jhwiIScaLZ340PcYpXDeS5NkNEWbcZM5QsnQbMw3sc6RlaYEqJuKXATIZbQWUBdK5O+I1MSDyIq1EeCRtXjnJDc4FSYbc2YbU06HIwz9wN6yZXtrE29lU6u1RmmFdV6gd0DKGecc3jtmCBl+CJGcpfHzztLUDmsrrHM4L/7rru3JUbLotTWyQVYKcsIoTz9kydgeo9UKmaIM2hp0zGgrHnhpPkVWq4yhDJI/PwwD5IQ29djgSmyVd0bo58CqXdN2GWcaka0HjR5hZ2i5nbUz1EZhtcKrQmWEU3Bgk+mHAWedRNxVFb6qMM5SO4tXmULBuhrrZSNulPDYQQjqSl2zQJVnZGBKepQMPfQYJVWKIifQqmBkX4kefyLnsYlCjdczlhpZDKNkvSAqrqI02nl07oWQnSPNxgaT+96BzhGdAkYFdFmIfBM4nBSpg22tDA6sNTJ8jQFTDLONDfbX+wxdwGiPtpkYA2mUlJeS0CUwncoxG4eePgRCCFAsGlj3AzEL2yAVhdKWqoK+XWOMoXKWmAJd29K2gTe//Z34usHowmq1QmlDKoXVssWYmtn8CKoMpNRhTMEZsdOkGCUrW0MuHdpkgX/lxGo/MW08k8ahiiMhcN/KTklEMpFhyISSUcqgigGTqazDpEIYCmmwOC1qmUtX9hhiYdpM6OLuOMSSjbhRBm0iJRuUFeVLzGtCzKRkUEah9ARl5vRXHkapilSfYXas4tRszOdu1+zvtwwhkYbA3u4es+mEpqlZLNeHUXqpQBhD69V4rKhxaqOQIYC8N2sZSoSItgqvE742DCHQh8jWTI3qDwUl8MS5c5w6dZKNqadympQNKhtMiqAsqa5Zhw7VG0ARh0ivJdVhHfdYDR1OaXTMlCgRjvL+OhD7yMQ1VK4iZUXXF6xXWB/o0x4SYqjx7sWjbt6oL+9S7QC7CxnynTpO2Gq+cNngxlCr8ML//4WqMGA+8xh3fAbW/6Tif3zLB3nk91boM2vuufTUS/98ydS/9iA1fNE04AAoTZh9yZ+WX1dlB5fftXndTffzVayUfCC+mo04oPqXHkRJhJmTnOvMC6qkvyBVChcubAJwy+kdrjtL/JlXUTl27nXP9Ua/WCm4/LaKWz9ZH3IpSox0x8sNxxn+ancb1dXnHyIUZ1FGQ3zGe90QmFwsDFsvfr1hVjj/X2xyy8/2qP65118qz/n/cpv+yPM34ZsPwpFP7KNu4Jj7kn/Fy8axYD0oDEMoGMlzos+JkkfXo6oAhfWS0e2twoxycI1CV27MIpcGpE+BbsjEqKm8wRppJrSGpvHEXk4eQ4yUPoMxOKMJKdGFfpSh1+Qy0Mf1KJ83OCNZ5nlIIns0FVVznGp6mi5VHDtxJ3/oWz7AkY2GkjPtAK7SDKHH9pb9/SUxRLaObJJSYjareeKhz6LdlCPG8PTjD3Lh4gWm0ym7uztjBrghpcwQEhSLMQptLf26JSXx3pITxo1Ed7JsanO6doJcoJBg3NR1Xc/eYkXVNJQM8+kU4wyh7Q/zqn3lyNnTrltUKVht0NaBNkRRrJBCHDdhCbShskKpzwY2ZhOOHtkU8B6jnNtZbDXBuAprpAmHRB63yM6InNdoyxCy3A7vRim4wjsjYDql8N5DTCLH1uK3NloixdIoydVaEWIRyF/OdF2P8x5jRZJeciaGQB8CcaS0G2OwrsFox8bWJsePHRV6dwY7QtBCjCwX++wtO4pybI4xZrJmlzY5C0saN94ukeWLCsOO2+ykJBqrrrx458eBgreyQdZKY32Ncf7Qu31AVC8HTS0jDE6p0a+tDl9b0hupcRt+kFKg0YApBUs6bLQLRfzshmtNM/J5JFnl15p+kD8TGlsKlIwvAdVdGsF34xvs+FwJRX40nosy+TCuM2cZdJUiMuvKeprpJiv1FNbKz2njqJzDWI/znn7oiRniMKAUOKtloGIsKBhiwFhDGCIpJBl8xSRkeCNU79Vyj9tmb8I5y2w6Y39/j3a5x+Wrlzl25jaa2QYxBo4c2eLq+UwpAylG+k629SEUUJmqEmidc4UQBQxjlQGVcJUwAfbXa9zBsYv8WVAYVVOTiWqgT4mcDNoWUoSYNDFGEhHvDTobDizYk7k0k+RxkJIV3hqMgiEnKAo7WhVyKWg0oYvY+Ywzd34FR052XL10hd3dfS5evIRShWY6ZXv7CFtbW0yNJg6W5f4u2kDdTFF6l1Ik7s5oYREcZNqVXMhFYawl5zQOvoqcZGlLyR2eSGUzJXAYtbjuW4gCnfOVYrnYocQOlWFIhhxrjs23YL1HiZnKOPbXA6vFkqZuyLlgvMc6x8Z8zrrriSFhjGVzaoklkqLm+KxBlUJVeUIegYk5YX2iqBZX1SitmE22mEyOAY+/Kp93b9QXb6lUUPsrynKFmk5Ip46Q6pd/inYj2eDFWbbN8iUv98zKr7aOuu9xH3uYez+uRXL6OpD2vmplNLF5w7pyWC/zochOobx/YQXIK1TXG2GGNVAKKmeKfi07cXDnxObylcee4LP63htqHEHO2eJNzJT77UJ/3xn8bzwk30iZydOShHAjW/H/sHMfev0CQ0MtNptnguEoma3PLNm7Z/aSG/z2ROHKe49z7BfPSSNycBXWcOm3HX9eOb/KcOQzha1P7bO6fYYOBX+dTpgv+Ua8qS1aJ4kn0uIzzUo8liVKYrT1HoPAu4qCnAK5JJxV0lwmUGhUkaiqkoV2XfmCNYVCZtWtaIPGaYPTDq0sOSm0EZl5odC2A8XLbVBaJNxpjPKx2uJVwxADIYus2doJ8607qOotChPe/7t/H6eObxJDwvmapx57kHZQ3HbHbRgN1lqcdcQYCSFilOLiU08Rg+b0LSdp230osF6vaPsOY4xI7gc5+S0lo3Mk9mtyvyT2a2bNBvNJw9WdPYw+yNwGikRcKa3ksdFKZBhFIsv6GIiAL1B5j68sIQZpmlMWankscnvdQAGhc1tLLiMlOQa01iIRjgmMkeglBUOKQt9GkWIZI7Us1nl8M8FqLU3FkLCjhFU8vRmjFH0GbTS1N1R2jN4qhZgkrs0YSzOZ0Ldy25zRGGvGxi6RYpCmIBRiDKjUkWJkCAuclUbMOgMFnLU0TSMbb6uY1BUpG9ZdImZFN4TRY63pU2Hddwz9mq4LJBRTJ422NK7lGU2n5Cs7o0bqvLrWGI+bxVIkWk4Zh9ZGGnYNXmWssVRVc833LdcuyhGk4ZZm+6Axv9Z8w6Fa/RCmVhTkotDaUkiSIHC4D5fGOKaMN7Ktv7bmecab2jP+mgtk5INLGzNmu0sEnHiJ1QjAGx8Z6ddJ4s4nGU/SDmU0TdzHqoRCYRU0szlHjm5ReUNOSXLRowDB4tAT+gFrLEPM5JzoYmGIGdMPkKJs2Z0hpkzlHJPaM/SBNHQUCiEX1vtLuq5ntdwXVYepqCeOCxfOUa97UllQVZ6mmkCU95W2W6OtNIQxWia+IuVIJEEBUxwZ8W6botElk2KAoshBjl2tC5RIiTIw6MKAqxTWa3JSqKIxygnhWyeUKoQ0UHvNMHQsVy3zeY1SGYUMzazNZNXTR/FAUxQKiy5yZKQSCTlS1hfY27nAbXfex6lbTuGrmtX+gkuXLrG3WHDh/EUeefARco4C0jSGrY1NtCkcP3Ga8089xjBITrdzCj2MagMK5IjCobRBpUwOEac1xTq0daLi0QXjCqVTxDSAU+S+MBTAGLp2n8eevET1pnvk88Gu2VcJXIUqLaZb0yhNW6DteibNBOsMi1WL04ojG5u0yyUhwSoMVM4xncnJg9IW7yoIiaQi87lnOlUsukIsme3NMxw7dpwnHv7UTX+evVFfmqW6AHsLaTJPHH1lgGzlJrLBraHWN7YRPzdsQvgCbNFLvuYt/VKtUgTy9Ea9IpUNlKaCxQvLjF+Rut4scWflxOYGt8+vRk3OK8kSnz3CZ5r7Mcsba8R1H5lcKBLbdQNVNFx6Z82tn53KcLBkjn18zW8Nha+8AVfG5/aOM30RmX9pKlg+e6hon7qCW1xHBrqCvXsE3jb7xPnxe4rdrzzJ4k6eMyBSSXHkgczWp/bZvX+DP/yXf5Z/9L99gLv+4/Xdly/5RpwskkqlRo+UVoiYseCMRjmFM9JM5KyEAG3k5FMjXsFioIuBUjIma7zxqKIhI3m1pmCtxyrIKTMMgY3JFOcsIQ6klCE7YhrYW6wxFlJM9DmjLRStKGSyieITHjJaH2Vjfie3v+nd3HXXm7njrjtxNrGzu+Suu+8kdh3N7Dgb1mCtYzad0HdLKJBCy87VXUrJTLe2wCounHuUVOCppx6nGzrxGOeMKUIqFH+uIaeMNaAnNbW3zDamtK1sc4XdFcfttwwXVDnwOGdUlmbQKNnszpqaynmsVgKDAnJMKC2ee2sN1jmGYCmIbDunItAlBdkaGDfPeczi9YjftoRBNqlW44zDGNlqG+shF4mBQvzbxopnNg4JbcRzqrVkx08n/jCjOMkCUoBbyoinWQnVXprfsU0thRKzNEghktKARSKVYkjMN+ZYZzDW4pzFKcYcbpHTLtcBpSVHPoXMat2N6UuFlCIxBIY+oJXCaUXtNJWzY66ykiZIya0piHfWKPmXNONKnhslEnCj9SGEjcONfIW1nqLMYaN9sFAGrm3COfjMKOPGXC6knhHXcSAzL+MWViHSEI3YFmK+9qaX80FDrygjhKscwN/UmCGey6HCPGs7AtvktQXiey9F4vOS4NhI2pJNTTKWrB1F6VEKL1J9RYtBFBKKhPMVznuUHgcOxoiMqcj/e6vISSLgYhFv+wjrFql0zvRdwleVeJtTIqVATJFUJJt8iAP9uqWZ1iwWC46dkobzlltvZzqdEmKk73umswlV1RDaiqEfMCZQ1ZbJrKF0UAYFqcjgThWs16RY6LskEYBaYHI5aywGozIxRbRSdGkAlQBHCoqcNEYXQpH3s1SiSNaNIZaEIbC319LUGzg86Zo2gpBFgwFjTnZBMuSTbKzFDrPPzsUHqScNcchoV+Odp64Nk+lx9KkT7Ozs0K47Qj8wxMDR7W2aRjOsVywm0xHsKK9Dq5IMVZRMWXKKWOuJOVGGDhUztq7H2L21wN2KuPXJkJPCGodGozC0UbEYIus2oVMAAs5o6tkmO5d6cpL7ZrQhlsKQInQI4NMqch8wJrMeAsFmrIY0DBgnIMY+im1gc+YptmdnHaldxamTb6KuFE88/jH221eZ4vtGffFUBr0Yt+Dek247TZq+fLzUzWaD59qypTvg+qFJoZjDmMk36hWoNx7KV64U5Gn96qvAQ7ixLPGYwL22aPzJxcxeHnh79RRhw99QljgApTB/MrC43V83Of2guu3C6p1nmPynzwLgnrjCj1z5Wr7yll+97uu4dbbLjjr9wkMN757DByirFUc/FTj3NfYl1RYyMLD43aP4J66wfOvJ540pU0lx9JOZjc/uc/ndm3zbd/0k3/tr38g9/3p13S/lL/lGXCnL5nxO7TzLbsG6X8tJXAGtLHrsPhQSW+b1uE3LsolJEWKIIr+0oJGcX6UcMXSkXNjamFN7S4odXWnRRhFzRAUljasSuarS1dgcBZSFxtpDeWtIkVXoKdFh7Rk2ZieZ1xu8611fyW23ncV6z3qxx6SqWOzvYm3FbFZx5fIVYhqIQye5ze1yzNuFKxcvsLur+Piv/xJXruzzpjffS11VTKyh7zrJ8AMSUKwlx0zOmVIgRCH9lv1C2wbxM5NR2qK0G2neoEym5DEWQPS/5CIZvo2zeCc+2Fykz1HK4L3BGj0+5obaVwwxoY1sbFMp5FSISeErizZjY6cNpkhmspzoh1EbLUoA6xxV04wSbmlQVAVdP6BKwqgisVJG8pOtFeL5QYa0MVoau6SIq4GcpfmJIZGiNLVKi+vZOYu1Fm8VRVdCmo4DpbQ479BakWLG6ExAmpSUZSOslMZqSzO1rNYrwhDQxhGj+OtTHMglY7RIgYvIBUZ4WpEGdvSJl/FLHZirxz8QRe8I+asxvTRmexcvUN16O3oi8DmjhHp/TWx+QDa/diUHWd/yuI6501l+c1GSqaxGMNzhXr2IznxUYhFykUZ4jJTLz3jzPNiyW63IWvLGFXLMFGMxzpHjQCqapDSpOJJ2ROtJylD0+HpNhRQzOSXKaJEwGpw2ZKVlIFZkhOSqGrSoNGJIdH1hGBLaSIOptUGVRBrktUWOpCgRZ01d48b3DOc8fd+LLN1ohhxp256YM8v9XWKIrJcr5ltHMTpz9fIO0+mUvu9Z7O8zmc2JcZBGTxWUShQiXZ/pOkku6IZEGyPWFiqnsNowmCKviwI6e2qtKUrury4CjJT0h0IIGe8LtVMUI49MzmIFaVRFKoFcFCUJp2GxWHLyaIMyBYNBIcMcqwxGF/oQ5BgoWeBlpZCVpnKOdR9YLC9SXXqCW269jy5EQhjY39/DGcNkOkUZidbzborpV9QTx3xWscgd09mcvb19UbxkhZZbC1rLe1MMaCPsgDxEQt9TbcxxzYTcDhQd0TpiKkjJUoLG1hW1d2InUoFLTz/Gidkm2xsTSAONcziXoWqoqpq8XrBqW7R2hK5D+5r5rGbd96xWHVNToCS8gtrA9uYGXVL02RCGgLIBW2AYCpXf4o4730xOuzz12Kdp+578cg2/b9SXRKk+wu6+bKuPbBKOb1DMyz82Xigb/HqqGI3jtYdJfdlWLuj0Rif+Slaaule9ES8xypT+JV6/xRk530n5NZ+3VDuRJ6Ljdhvotg310zd+Hf5Ki1v4l/RdP6cUXHq7445PbVF2dilty4c++Tb+wQ004ndMrnDVnkENz/9+VYxC1/VzLDmTBy5j3nWaVL/0M5Cqwrnf1nD0k6e4+G77nIGDjortTyY2Prfg/Ndu8Z1//l/yVz/8B7n3R1aoIXK9GoMv+UbcW03jneRVq5ZcOnLKaFNh7cGmRHyx1kizl1Ih54QqQgg2zuCSE2AShTAMxBTQaGqnMUS6viOTJWWhFMjiySwovPPkpOjjQEwDKYE1momvmFSWVVyz2O0ozDl26h6OH7uNMyfO8o6v+iq2TxxDaYhDh6s8JQ0s9xZsHnE4Yzl67ChXL19l+9gJjh117O95nnz0QYybMJls8vSTj9I0G9z3lluo64o05gpnJIO46wZWXUcsCm0Q70qKIlXP4J1h1bas1iti3KKPUajRuiLGSBx6kYPaetysOYwpxCiNUXGAMmhtcHrchRbxfmMV0YGxDqss1slGPo9y/aqq5TYjzbMxGpUC1ii0qQjDgLMeY51snqtqbEjl5D1myerWRb6Xx3gpMNJdokYPuWz4M4UcAsqUQyK6QuOdRJ4xNpyyrbMSSVcgqgNytcjGQ4hoCWomxURG7qMpjBtqyTgeoviAXaWpJw1p6FkuF/T9II2uzljvhJKOGhtkjcYcDo9k5nDNWK251pAffN85K3n31nLLbbfRNBO0Node88Ome5zo6vH1cNB0H/x50DwLtfqAai955vZgSz5+vORxuKEYFQkxEVMhpmduz6VKLuQs/ud+CAwhU5RFWUtUlpWakZwieUsq0IdAu2rJOcrltBb2iTYjf0BdyyYfH46MvTaoSANKCcxD5YirEB9wl4ghQhHribaKFA1hSPR9pMRISQN1PUWV8TaHXt4rjGEIPQXLZLZBzokhDPR9R+kyxjVAwlpLyYnleo2xNdY4br39bh765G+xXCgSmZwKKUT61DLEMG51QRWRlZcDaZuWD36VwRSLNgVrFOsukLIhBYctlj6tIBaKFTl/yhmKpuiAdgLXIymsrlh3a5aLljfffYZJY+iHIgkHBzYArfBO0XWRMCiykoY+pELMhVIK7bBmf3GFUyUxn81Ytx2z2Zz1ei12COdY7i4YhoEYA5fKZVJoyCEymW+wubnk0pUrhFwIKRPSIFFl4/GchgFtvQy71gvKdELV1ITc0HZXmFYJZywqKWrv5dyoFIgFjQAlh5KhRJx1pIlnXtb0RxSrKxdorKJuPH1WdBi6GFisGe+fIYxe+k0r0Wm7ex3FOExd4WpJfCja4bTj7O230a6e4tLFJ1i1a4q2OPfyI6jeqC/iyqCXa8piiXKOfOvJV4SUrXLB7qzh4pVnZ4MflPPsfP3ddEdeuC1pTxWO3+B2azdMDiicb9QrUOrV5Yp92VWcuVc/wixldMwk8+Jb7mK1nM+9yvC46ym3CHyiv5W3+3OsbyJLHECFxOypzNWtGx8gxmlh56tPs/Wz+5Aym/+5Jnx9um5o2931RX7NGczwIraAysPnszGu7DI5d0ok5tdRYV648NXmOZvw7ODYV53n0ltmXDWG73n7v+Cv//gf4d4fvzFQG3wZNOLaJC7uX4KcGVIkJaEbG+PkpDcnoZiP8vQUMrEgkURRCTHbACqKDcoopnOLypn9ZaDPitQHnB0hTdFgRq9uij0Fx5AGujiQTQSVSQq8q9BWsW47Vm2hqs+ytX0LpMgtJ2/hLW99C66yxBCw1rJ79Sqr5YrJdEoznQIF39TYnDlztmG92me1n1BkUjEMfct6/yqbc8+kvoWrV/foeiGFt22g5ESMA1d39nns/FXZJFJGQnEZ/bayVawmU4xxXHz6Ep/+1APc/5a72ZhNxrgrKxLhEjFWYVSRKLMkub5plFOLV97grSWkwDAkfFHUWguxvgwiac5plARbJo3H1Z42jbnTSja0IoW2GOtQ5oBobjBKSexk0mRl0NritMbWhTgk0mDRxpJKQRuNUiIFL1nkpDEWCoammTCZTFBkgjcYCt0wiE993GY5azG6MAxCW1ZkkYYrTYgZ77TQ+o3GaocaM7lTTKBFIp1iwjkLI4k8q9FnrzTGglUaa40Q0MetoNJa4qjQYwxGGRvNa17tAw/3QSPsncMaw2RSM/FWIqyM0KgPGnlV5DooZQSdlWdJzo3iUOKuxwZeLiNyZV3Ek5/y2NYrTSqisMhxQKVEpbWQvZPIoGWQM9APAzEmQsqkXA4j0mzVoFVDWyD0PSEsCXG0A4RI1o7sGpz3hKJpnKLR17b3B9FouUA6oPCP+VfWGIp2lCJDDa1ku814n9Q40MgFlKtp6kLbJrrVioXRskFGjZyBmqqu8LnBh8B63dF3a1RRVHXD9omjVL4iJokpTCFgXIXzhq7tKGjQmZxBq5rFsqfyGm89WhmUM7R9i7YHAMiESg5bNDFEhlLwOqI09DnS5UQOipQK2IyrrSgNyAKCLBmI+OIoKRGDwRiRzBWVWa9WWF/RVDXrZU/Rwn5w1qF1IaQidhGEH5DHBl0XURXlHAndHsvFLtPZJqoUjhzZpvIWpWBja5OmmbK7u8fVy1foh0JOnqquqZVhMdmHK1dRCD0/xkQeqeZoJTL4MGCMJ4fIsFhgjh2jqieosiLGNZlMHMBNwmhZKXTrnlgg9/uce/xhTt5/H9WR0+iYGIbHmVbQTWriOlKlglGJHDPGWaICQyFqS3EW7+T48NM5IRaGXHC6pZSO2juqBpxN9LuPsL/Ypes78HIMlvDqgoPeqNdvqZBQO/uUfkBtbbwisWTw4tngAFQVD37n3fziH/vbHNUvTlgyanpDv3tnaJBp3hv1sqtkdHhjqPFKVpx8ASLMkNc21Us34hgt0vTXuHQb+bXFnfzJjYu0p2/+mJs92bP7pobsb/w6du/VbP3WCXjqPMc+tubTIfB2f32N+B3+Erk2mBdhUJbaP/e5L1kk9Xde/3jm8+FuelBsfq6wt3OKcqTQbUX+1j/6b7nrI7vPgrtdb33JN+I5G5TWWKcxJrFqAzkncg4YLbnOWktONg6cN6iYCMngrUMVhfUeZwzL9Uo8uAXaIRJLgqzohoy2mtlEti+V89TeU1WeUjRtN2C0UH6Lki29UpnQdhg3ZXv7FmKeMp0d531f+36Ob29IJFDRVJMpJSeM9Rw/tYmvLJBp1/u4oWK13KcPPaDZOnqCkiLtesGF808xm04x1tCFwroLkLvx/sq22nvHydMnmG9vE6P4m4fQ067X7C9XxJQhFdrlDm27Yjbd4Fd+6aN89Nc+gXU12micMwIjqzyucnjncDrjlKH2DRGFsxKzpQ+/jGRvKy2wKy908RCiHMPKUE0qlDWEdEDdVpScsaiRol6IOaNiQlmP90agZXqUnrsKazXDIEOHPOqtV6t9lHbynFYVzqpxuatJuaC0AzJ936KKQLysgpgSB7tjrYU5kFIWyXiR2x5iRmtFzpLZLLdZiTwlZyiJFDMqJ/GpK0tVNwyxp+uFmK2UkqitlEfPtHhjYxhI41ZaFQ0KjBobxnFTWJ4hMy+HoDWofMXGbEblzQh9k2amHBDWkrxx5FJG8r+866hnbpfHDGkOlu9FvLR5BMdFCjkkidrKSZrfnEcKeIs1hqoW3+HV3U6I+zmNzX4ZZfRGkglKQSkDJbFaLEZitmzYGcnnxWqKUqyGItRerVmGwlYNG5XCas1BBFvJhaKNbHWRGDCVC7Fo2tVaLjdK/bUxQgPPmX6IhBzohp6+j+SiUdrRR4UzlqoSkr6vBEZYitgH7BCgqolhYHfnMtrAbL7BbGM+Dn08JkeUstQzzSMPP8FnH3qI2hWyymQ1sN8lgaRpR+0l/xsTMVlTMCQlCoOcxEOfSHTrAWM1RlcoZYlFOApTpyTjvGgoRULNQiZkMFrsF30n0DbjLIvVipQNxiiKEqgaulBUZoiZfsgMMVE5j9OakDoMQjQvJJS2aALLnac4evQ0i8WeJCMoxf5ijyPbR5k0DXXl2Nias1q1ZO2ZbczQBo60R9jd3aXre4E6kmAEQ8rQQp7blAYwhrhe0K8avNPsLwfCKlB5y8aGo6hEVoaQM6VoKmXoS2LRD/R2k/l8k3DpKVJc0w891lu64CkYHIqNUuiHlmUqpBDQ2jGbzzkyOc7Q9yQNqu9RcY1VUUCctuAqA6kwrNe0XUexipwDMYpV4o36MqsMetVS9hcS9XnmJHGzftmxZAB28VLZ4JYHvvsufv2P/B029Y012ddTH33gDu7LD73i1/vlWqZPfBmcmn/BKlYK9BcoS/ylLiZ5v6+LjbjKmY9evhXO/Arx9M0Ph+2ip77asD514z+bqsKl925z/N9cxj9xhf9156t5+8mPX9fPnjJL4sTgrrzwZYrVaOeeoxBqHt1Fv/fETQ0P9KA48RuRyeMLjv7GKzM0+5J/tQ8xM59WeCsRX85qtDVUVkEREFOKCTOeqA99og9RNmEmC71XBSDirUIpQygRVQxOK9mkjRvOnDKTpsJ5ae67LrJuW5TWeO/xuhByovY1OkOIU+rpbWxtnuX2e97KPffejtaFad2A8TRNRbda4SsdPUqgAAEAAElEQVTLfC4gpzQk+q7l6pVdTt5yC2kIXLlwHpQlxkzlNF27ZDqZkHJm6BIhlTHvWuKtKOAaz9nbTvGme2/D2ZEYnwMXnnqMX/7FX+OpbkBVcuJeplP0wQd4Qby6rsH5GSFl+iEQhoJaBVJqmTeau28/jSrih3FVBYhXNY7b8hwz1mp0VqQovuySpemu6orKOtmoHzaG4mbXRkjhpWS0kYY1I552SiHnSEah+1Y2wkqPHlfH1ubWmC/u0VZI6iVH2dqWJMA9XwlpPAVykDx2YxSV88Qs/mVtFKUkUkpo40g50o9+dW2stOuj9Fsk3rLp1sqIr5ci1w8CbQuJbAIldIdeJqXAOcmiNlqRhzVhCCJZN2O2ecmokg4bbsW1jbgejd2KMSe9qg5jvkRtXjigpR/kM5si12sVI3m9HOaHM7a1Ocv+vSSJWEsxQY7jY3nwOwW8p1FgLX4yIcXEOkrueklFyOlm9KaXMl6HQqsRKqfNKMWXLW0aJdm6lBHcZYQ3YGBQMkAIGXbaTBcLmzXMvR5lyUhUiBIIYwoRQ0EbR9uusdaRSiJ27WEzSs6krDFuSjOrMH5gaFu898xnM1arNcvFCuscvm5kljGyELTVzKqG0CmsytR1Rd1McNaw3A8oXWTYUiLr5UBT1xw5cgJYcumppwWUFjvCIDGHlbU45UQFAaTcs+4jVhmKEnWEUTWpQCkBq6BYiKmMrzvQzlBSYdUGiXNUFqMdIQeUTZSSiLlgsma9GMhFUU8r4rkF3ounO5dCyZZS5PkOMVOU/JxzFmfE0R2ywQCrxdNcvvQ40+lxdnd2Mc4RQ+Lqpcv08znGOLStUGrg6s4OVkdIiclsg80jW+zuXkXlAjGN7wMS+yIvED2+jpI897s7lKYi9wnrDLWSrXkLxAJqyKRuZGBoGPpdLp5/jCou6Ne7KHq2G8Mq15gUiK6iMh4depraY4eefQ1D27Pc2+XI5CQbsxk7+3ugI5Eeqy1VVWG8hSGxXveSquFF6VOSYuYr5NF5o75cSsUsW/CuR23MiCe3yNe59XnR673ObHA1nfDd3/hv2HyJTfgL1eNxyQ9e+W2c8PsYCifdLl4ltvSan957B2/5By+whX+jbqp0/9o3aV9KlZ1CVZ4Snj9z+hWrIQAv8RpT4yIpptdFlvj5p4/AO+DWUzug6hcGn71Y5cz8yUh7Qt7TbjTNcHmbYvuuWzAPP82Pf+ZdfO91NuK3W8W532Y585EZ/kr7woOQpobP52VcvsqRzx7jylsPwUbXVaZTnPiNQPPk8uYeqxeoG3rIvu/7vo/3vOc9zOdzTpw4we///b+fBx544FmXef/7338Yn3Tw9W3f9m3Puszjjz/ON33TNzGZTDhx4gTf9V3fRfy8eI2f//mf593vfjdVVXHPPffwwz/8wzd1B1UpxJQZUiarjHUZoxKxjET0LJu9YhSxBIbUSkSSkSYtpshqJXFfWotfeF5POb55hBNH5mxMPbV3TOua+aShch6FJiuNso56Ohkpx1FkjlFTggd1C8adYrJ1G3/4W/8sX/f+9/LgJz/B7t4a7Twp9bRdh7GKc48/zu5ey8bmFlVVsbG5xfHTJ7BOc+T4Ce6+922cOnuGKxee5JEHH2AIiX7IdP1ANwRiHuWVqdD2SXzGSjY0EHHeEIcVv/4rv8T/9jMf4fLVNU3TUFVO/NiqjDFRsvGtvOH08U3uuusM977lLt7xzrfwlV/1Fn7be+/nK99xL3fdcQu1d7hnkMMV6jD2DDSVdxLvNcrXtZE8ZIkgcygrkDalNdbK7RDJv8RhGSPwpsVyxWJvj+X+Puu2w7qK+WyD2WyT2XyTZjLB+4a6aQ7p4E3jqSoHSrK4MQ7jxGuei6gFYpBNmrZ+hCspacq1GVUF4v1v6kqUFc7hfYWvqnFBLRvtEiM5yDbdGoP3FSiD9x5nxwHDSK23RstjlKVRq7xDaUVKkaFdMAwBZdzhFkWVhD4EyMG12LKDr/FyWraVShuB3hkrcWBao0eauh5haQfKKatlQ5xzoY+ZVRfYX3WsVmvW+/usFnsM7YqSwpizbWWA4j3OS968Mpo+w6KPLPrIEGWbbZ/xu7USub3WQsc3Yxa6SMvLqFaQBl0bsTHYAwp8ScxsZsPDxBY2PVRWMSS4vE5cXCXWQYYnGSP4tiI+fq0K2npWbcdysUCjmMw2aGZbNJNNqukW1WSKthatDdYamkmF8xWT2ZStIxvU04aYMv1auBN53NZqayWVQCn6oaddr2jblq7rCENHShHr5Drnmxs4K4260QZvPY2tqYzHuSIDnzGCDgVd7IhJhlq96klaKN5WS3i61Z6cFeuuJRexXRg/KlGsoa40lTEYJcoYrRQmNzS2YVZVNL6i7wIhwPFjc2rnxFceR7+/FntJU1XUlaEoTeVrnJFYFqcts8ZS1aDouHjuE1y98Gk2NmpOnbmVe+57O5tHjqIy9H1L27aiSCqw7gLWOVIszOdbzOZz4S6UgipZAIBwTUlx8H6SM7lviasVKSuWbeTc5Y6LO5F2Vej3YOgi2QIOlFPEsubJ84+zWF3B+sT2dsOxIxUVu3TLi+j+CmbYoaSBPkMzqTk+azg9n6Jj5MLVq1hnqJrEetin63t2V/vsLoVSnYaEItGHxNQ11LpiVk3Zns9p6pfvB36164vxs/71WHrZiWc7BDh9nHDr0VekCTddxD12CS5deekmWGum+uY3Xj+zfDO/+Yfv5sMfeCv/9gNfwY/87t/OP/nG38Xf+uY/xCf/yB2ox8/d9HW/Uc8t3X8J56S/BlU0EmX1Ktf1ZIkX2YxATiNT57Utf07k2e8+9oTI5m+yJk+suOUXBk7/UsSubkzmk23h4lfPUdZS/fqUdJ1DvZmu+cbf/atcuc9z5e0bhO3JNV/mM+pQnv7MSokj//FxTv5aYuMhqC8p7Eqho7oGL/q8Mp3ixEdf+SYcbnAj/pGPfIQPfvCDvOc97yHGyF/+y3+Zb/iGb+BTn/oU0+k1ydOf/tN/mr/+1//64b8nk8nh31NKfNM3fROnTp3il37plzh37hx//I//cZxzfO/3fi8AjzzyCN/0Td/Et33bt/GjP/qj/Pt//+/5U3/qT3H69Gk+8IEP3NAdjFkRIihdKFmI1ykFKBmjR1ms1oTY0uWIKgqnPKpAZQSqkwUnTESifrwSyfSQCqU45jOD91ZkvEaal9p6tDesQ2K9KnRDSyqKSXOc+dadGDcjp4rNjaNcvfQkagjc9ea3sbU9ZbV3hXq2iTUW5ytOnDnLdDIlhJ6+72WjnDM7V66wv7sghhVXr1wkpwMSeCKkKFs6rUEZnK/IQ8BYg3eWlALKGtr1kt/69V/nU5/4LMu2lw2yAsikEIipHHqfDxu9IttPpRXWOqSjDZScmc8ntGsIfUdBmklfOXLKlJIoFHmsKKANpm7IFGwJxL4XubvVWJ1JSppUq8sonR9EmqwMCY1xFdO5ZvvIFt46QDPbmJJyYQhBfM3K4KoGZxVtKRSl6WOmaipKGcilYOz43FlDSlkyzJXGW4u3nm61FCD8GDGWcySPzXRImZjB+wrrrFgOQsLI+piUpRF21siwIUPlLdaokdadpDFVkq0+ZIkJa0YVQRof/xKDRI05iyodB2h0UxLpGVLXgw229G1q9D8rjFKEfG0Eq4pItI3mcHAQg8iwU4ZhSHRRGtim9lRujPRTGm09DsuoYEcb84zjIstjnDLrIRNShJFI76yR3OdRcm8oh170gzdQbcaM8BH+czhg4MBHL355EXYolIHGIbRzpUlFsQ6wCpk2FoZ1YuLk8S/KAEKP90ZT1VO2jhwjdC1VM8UbNcaUycZfKSWQQJswtsLomq4LtDFglGI2mzOZZFIY6LtOoIIUcs70ow0lhIEYZchSckIh+fLGWNZtx3q1pprOueOeN/HwAws2NmsWu/tkwGtL0OBdIehCFwLaKmzJklZQNJVyeK3pYksyCR0d1itc0gw9dG0kpoFSoJo4lDJYpUl5lJ0XSYJwNqOMl8c9QQiZrc0Zk6qiCwOpJFLKaKNFJq8KRlmUihilUSWTVMFbg1ZCcJeB1prdK4/TrjNtnGJv/2rcpGXSdGSl2Nvdpe86Ygjs7y05ur3NpBL5dwwrnnjySTAcwuKUEkWP2D/MoQJD8BKJISm8n1Jyx86ipx8Kp7YrqpFfkQvkMaauXVymHbbZPrINJbC/usyj555gvnWGsHeVq/2Ke+69h8XeAl8UyReiikyjJabEpZ3zxLymUhrfeBahZZ0HbLtiXk3IQQgOsQx4ZzFKE0qQSLTXeX0xfta/nkrFjNpbUtYtajohnTpCql++APFmssGLdxw3Nx+Z9+7mUX5q+nVw+epNX8cbdf2l18M14Oob9bKraCjTGnXpVf5FIV5XhNlhlnjOz20Qv8B1kCX+ntkjfKp6K+Y65PXPVyokqosrUIrNzU2ufMWNbZrbE4XhntMc/9jA43HNnW52XT/3PSc+wodufQ/qCcXVt1RsPaypLqye1SgXZ+DsKdSqk8ElgDGUSc30kX2mjwBakyaOz31LBXWifqyiuVAIU0WqBaB44tcH6vOrG2vC9fU9CDf0yfBv/+2/fda/f/iHf5gTJ07w0Y9+lK/7uq87/P5kMuHUqec3DPzsz/4sn/rUp/i5n/s5Tp48yTvf+U7+xt/4G3z3d383f/Wv/lW89/zgD/4gd955J9///d8PwH333ccv/uIv8nf/7t+94Q9npTQ5ZVI0aOUxaNAQS6LrBsoBxVlD4+UE3SqDVg5nK0qJtGGJHjeatbeSjUxhYquRAmzIiLzTKcnbHdpIGzraEAmxoO2MyeQEx07ez133vJX77r+fzc0JMQk4arlYMqs9e+efpiRQ2qMm0rzFmOn6lpIC+/tLjDHs7FzGOo8xiSsXL48eiEwYhCodRzo3aIrWaOPJuVD6nohid+8q555+mF9arxEbthrfQIpsnJLIOA+I3WIOVmQlTVw/9ALWkhw4rLM0kwptDEOGtu2wRmOsle1yATuepHtvBaxW1WhjyDmjk6JTiqoyQKLtxdedMWTn8M4wmc+k+Y2KPhZa3eKqyHRSy4Y/iX8/dJJP7J1BK4syipgK3RBBGRkiKBkiaK0p47b1UMquFXqU2+axeXXWEpJ8OpYMvqowRtO2A8pKzrexlpwSCRlmKO0wtshApbJYrdEU+iHSD5k8esophZISzlUo3WMtIsEviNRdSTMf+o40nUlzqAT+NpQIqpLn5xCixuGb4PiUChAvi3QeCjH2rPcusXPxca5efoJFt2bdZ/z0JGfO3Mt8tomyntobJpWhcSIVzxTZLReRqKvDrTqkGOhTpE8Qklg+JBbOYLVmXjlSgaS0yOpHwF0RY/rYi48AOG1Efixh4PIH44Z8zKvX4x3UFNyhr12xaRQTr1gN0MZMGzJDlpizkkafewzYqmIynbBKSXgFOcpQIEdKipQstghjtDxPSlH0QCiGXBIKRT2pUMnR9QPtuqOkiNYG30xBGfZ2LjCZbzHrtpjOt3F9Lx50CtoamrpiuVzQtWsoUFcT2Ah0baELidkMUeaEJBnoSvzZ1oLVFqc0SSUq3UAY0NoK18IUcA5vG0Ia6EOCbEh0KDWyApSmlIRWSaCHOo+DuEzoA7P5jJAFRKkyIx0+CkUdxTAUXOVQSYBtTW1w1hBCAlWovMJGyDhy3GO98xg0t7JaTHCLp3G0aJUw1tE0DSFG1u3A1sYEEwM5CMSvKGlAKJkSZQAIiLzPGIqRzHplHdo4QkmUKP74NkQu7qzYmFhmlSPGgrJyDCSjefLyHke2NshpxZPnL4hcvgwUFMkkzp2/yO1nztKllieeOo8yUG07Ztqy7PZHpYLCOSvvhcbhK08KUWB+laKkxEBk0lTE0NGHcEOfYa9FfTF+1r9eSq17iSUrBY5vE47NxBv6MutmssHVfM6T33iCu90OcH0nt59fd9mB9pYZk8du6sffqBsstbvADEdJ1SvciZfxszMV9IgMifWXR7cfZ/5VJ6eXGK9tQl6sDrLEU37Ns8SbS5mrqeet/umbyxL//CqF+SMr9u6aE6fX37CqrLDLAXd+jw8t38qfP3J9bzZHdEM6GuAJT/aKnXs8m07RPLU6hKbFIw3VXzvPf33i43z/h34vd/34ErO7ptTPPiIe/gMNv/nNf5dFTvyOH/suTv7ynlgpKyMxvevhxjfh6vpUBi9rRLu3twfA9vb2s77/oz/6o/yzf/bPOHXqFN/8zd/MX/krf+VwUv7Lv/zLvO1tb+PkyZOHl//ABz7At3/7t/PJT36Sd73rXfzyL/8yX//1X/+s6/zABz7Ad3zHd7zgbel72RYf1P6+TIC1gsrVY5OSKSWTSAzD6OsdCekayFGBV5TSk4qCJJRgazxulGTnLL7NMibcphxRqialSMoD61RQ2mBsTcqJFDTazNk4civoCm0b7rz3LZw8dZzF/h7T2Zz5fIomsnN1l/mxU0xmU1KIKCtRYO2qZbHb0kympKHn/NXLtKslu5cvMG0mFF1o246cC85JpnJV1xKVVETenUpBO0u3WLB79QJ7e7tAEajVKPnWZOLYgBsRoo7+YaAoaVo1Iu9FYaxDHxC5KwG3rbrEEArGeWpvD/O08wgHk34pE5WmWy9JOWORoUjOkaEf8GaKdU7gT6OU2WjZ2rZ9ACRf22pN6gdWy0zVzGTL2PW0fUsKkdBrrK9Qw8DQS8NsnRHKcxGPekyQkgCwSinkovBOHpcQM8OQUcpRjMV68SIbYzAGQgwYY6jcGK2mFQWDdR6KQmknsn5E6q69ou8HyRLn4LEo9H0UObczOOfkkDSWykkTnjHoqpZmV1uBqpGhgM6SBT9ax59R6ll/LTnx2Od+iyce/iwXn3qUi08+Sswd2axxrqc6ejvbx9/EVn2UlKM8Ps7irGzTrRGYHUWNALcxzooyyuHFArLshjHJRpFiGK3oVmwKSjKsxeN/cFhd86FrY+RkQcm2WyPb2lySbNF5hiJDa3QePb/XgtBExqygMop6An00LPpEypC1hXQAn1N47yVWzxpSCCjymFnvsNrgyTIAseLxDyGi8xiTZiwlJboYKWOudlVVaFXRd500rUpj3YR6MqOqLcvFAudqUhJon3OWvmvZuXyF+976TobFZXYvnj+0X2Ql/u6cMikliXXLoJRl0hjCoDCInx0b8NaJ4ieOwz+ToGQqD67SFJ0xY2BjzkmObQ2+kaGPMxqlDDkr9nYXbG9OcN4RYwadD3PFi5JtvLZQogxknLdYrShJoYthyAGtICuNpRByx3L3As4/TH3Pb2d4dEkIY+rAag+bdrB0XI0d3XqH06dv5c573sQnPvMZ+p19lJLmXtwFBWGYi9dOJS2WDQZ53nQmG0OMgr0PIZNiIXvAZoKCza0z3HX7m1kt93n4wlWG1RXWi5YhFuzWlPe872089LnPsH3iTpxv+cRvfYb9bo3OFm88tTVoY5jUWuCJBqKbM6kmVBr61KOdx1WGIQ1UztPFNevYX/eH8+upvhg+61/rUqnIFny1RtUV6dRR0vSVOf2/0WxwNZ9z7r8+y9d862/wL07/ODN9c004QCiFMH+Da/AFKSVcHrcuL68RL6CTHJM6go7l8Ovg4zJV+sunEZ/aVz/CLEZULi85dCtG2Ek3Q9d+paveiTyRKu6yw01niX9+6T6y+XC+oa14toW9N2+w9ZsDf/9jvx39zn/P+yef5U5rmOjnj/u8nFYcM1P+wns/zD967JuodoSNs3enI9sZ00cXpKmn/Ut7/MybPkQm86f/2D/kP/0hzZ/96Lew9ZNTNh9YoGLm6f9qi5/5w/8ze7nwu37ku7jrJ/ZhTI1S61ef2XDTjXjOme/4ju/ga7/2a/mKr/iKw+//0T/6R7n99tu55ZZb+PjHP853f/d388ADD/ATP/ETAJw/f/5ZH8zA4b/Pnz//opfZ39+nbVua5rlAhO/7vu/jr/21v/ac7zvT0IV+lGIXKImixHMZC8QiLbVJSJZ1EMI6uoAKWKvwWHTR5CQ/WzRYZXDGARJXVgqsukjbQ11pzNCyXA5ke4ztY2fxs2N8zdf+Ds6cOMXG1pzF/g6lyBZ2f2+PVCwnzpylW+7LNtkaYtcy9ANgOH32LOvFPn3vuHrxPHu7V9ja3ACdJQKql02XNmCMEzI7EiuktCJ1a/YuPc2VixdGn+61HGhVFJmMzuVQqpqV0MQN4uFVSmOtxdU1zWTK5vGTVBsb5KJQo+R23UfaPkkjGvsxDi3Rh56uG1AlgXfEKFFmaI+3FoVhc6sR8qSpUEboyNaJ+gCAInAwsmyvoVA5Sx80wxDxVWaIPSElkdHGgNaeSiuGYSDlRGXM6Lk+8LwjMVTaSoNpNSomSlYyANCA1xJplyPWWWljjDSHWoH2FmvtKJ2WuCXtJIteBqRjYrvR9J3A31JINI2jqR0xCRQsj+R1YyxKW1zd4CYbTDaOMd0+TjVtZJsdVrKVNqJisCqPzec1b7gawXzy9Mq28sqFp/jn/6+/Q7tacPz0SWK/At2T4hpdMjOnMUqylTUFoxVGjaT0sYxW6MLYhBfxth+SyRMhC+1cj55mpdUIElPUZhSXlwOeu4DElJLrzKNE/cCzXrJch1yqHELoxh5fOCdGobLI1hVyu9J4OTVy4xur8Fqi1cpBPqUS3oCvG5xvoOoJIaC0lii0IcjzmSHHgRwjMQqcbxQeiLIsZYmnzho1vmlTpMEm9fIeo2B/d4djx44RY092Dd5bYhhQquB9zdGjx4ljPrnWDqMVk0nDsFjSxgGNQmuHc0o89EozxAFtwSpF3xlyGqMWbSaWRFUrYlTkWIhDofIeY6NA14KlzwHnZKhElvevGGXAU5Jib3efUydvofbi89fJEjIMMZGLOjz+cmKM2nNYLEVllNFMtSfFwDonvIZSEstuF7t8gnjxcUJ1miY+xNQuGQis2GaRJwzrDrfYA7PLnbdtc9ttZ7m69ymi5CmijRwvhXio/igRcu4pURPLwNEjU06dPcvV3css9q6ScqHrI95rlIbNjVPccfIMV899hgtXd5jMjzDfPMqGmxO6HqcsF59+jJwUpSz5+Kc/wSqsmTQNcTVQwkAxFdY3cDC8jNA4jyuFHMXu1K1WDIiFZRjEBlOZ+ouuD/9i+ax/LUu1A+wuBDh6dItwfINiXn6T85LZ4J9/+emUC7/7dt71Zz7Ovzjz48x0DdQ3/ft/dHGUv/0P/gdO/++fu+nreKNeoJQG71CThrwxIW7VDBuOYaZf9jbcrQt+mV7Q7wqgQ0HlG4drfTFWnIwKu1cTKpgLKmY5CXyRKs6I8iymF3t6viBlF4Hf7G7nXRtPsD5xc1niz6lS2Hh4xf4dc8L8+u/h3t2a5I8z+z8U/+RXv5l/1EB3IuPPrLjlyB5nprvcWu9yW3WFW9wO3/u5P8R/d/uv8ee3Hsb9tz/FP/jnvw+/J8fz4ozFriY89icyn3nbv+L/cu5r+LmffA/v/eZP8HfO/Ds+/bX/Hx756iXf9uB/x2O/cBvf93/+XzhpNF/5z/8Cb/rXe1/wIclNN+If/OAH+a3f+i1+8Rd/8Vnf/zN/5s8c/v1tb3sbp0+f5nf+zt/JQw89xN13333zt/Ql6i/9pb/EX/gLf+Hw3/v7+5w9e5YhdcQSKApMKiQk+9o7TRcy5Iw3iokz6Gwp2ZCKSGILgRQl/iyWSEyyqYtEjEpMKs2k8lTO0w+Wqbd4l8ilp+80zeadbB6/mzff/y5OnjzJ9pEtto5uUEphMt2griu6bsUQEptHtujblq4fMMbQd4GqchRbePTBB2m700xqx9DuUHnxpLddJyfQadQZKQgp451iCAmjC/1ywcXzT3P+wjkoEo3FgYdY62sbppEILnUg+9WgDc46MIZFO3DrqePccuYUWTuGomRLWJI0LSGggBgDi70dUor4ykMRMNWkmVJXXjy42oAahxgpo4xhGAJpGKiaWnzbsgaXhiwJZVIaoHjYnKAd3hUabxhCIsQyTrIUvhKieSp5zBJ2YCDncEgKl2c0j00XGCX+5PW6R6uEs47KqVEpIFF2B4FbagSNlVJQxoikf3xrDSFgdcSYMu6AD7bH4ss2WracMRuq6SbaemZHjtDMtphsHqGqPEYXdA6YPKDjHiZmIOGcx2iBx2kSmnFDrEGrTNeu2LtymXPnnuDJxx/lkUce5OknH2bdrtg8uo2yYgeI/Zp6UqgrI8oPayg5EHKSAQ4HOeJl3FCXa02wGht9FJREOwTafpBm2srbSgmFnDLejg37+LxoNTbkYwxVNuM2abRFUA6abzkZMYdKDA6lwCDZ5lGPQ9dx+FCyeo6/TimxLBRtDv9LlSyDBuPE84ym6wOlBKrKkFNh1bXEEBDVQi+ReQlcijjvGBDpesqDyMZBwGop04wpCd16yWqxTz8MXLn4FBtbJzl6/BgpB4w29H2H1gqnJH+76zr6oQcyVlmmVSWRo9lAGbAaUgCnG5SSQYDWMnZIeqBkjcWNaXkRikFlOX5L0WSdCWmgEHHOkmJmCJlZXeGcIhdFHyLrZc901mCMGtMCMjkpYtIoXWjbIMofY9DZMISWoA3TeopFE3JiiJkhJfoYiblgtSKFXZr2Kao7v4b44EOs85zBVOTS4bvLmLhPBK5e6Tm+PeEt976ZRx59jJ3dFUmNgxuVhSOAkP2xSEQdCcvA7iIQUi+JCBRCioRcCPuJ7dmcWeW5eOlz7K92cd7Rri+y3L3CbOs0x46fxJSBK93AoAYufvZjrEOHMw4XFdp5hpQwdYNW0LYDMSp8bbEYUJZSBrqhIyst1qc2kClEFUQp80pkVn0B64vls/41qQx6sZL8bu8ot5wkzapXJJbsJbPBn1EHDfi93/oZfuz2vzMS0m++AQd426/8Uc78Tc3pBz/5sq7njeLZTffmlLBVM2xawkSTvKLcrOCgjEKhPG6/07j9DuVFm3AQi+GXSyOeKgXWwqtJTj+IMHspFoTVItV9PWSJd5Ff2buLP7P5NO2pV24soPrA5sOJy2/XzzoXe7GKk8LyVoVbiSfbLcEtNTw85wpzrnArH2f0/Iu7jx/yv4cf+uo9VpcmzJ4hFioWLn5VxY9+7f+Tn1xt8+v/93dz+2evcu5n7+B3vOf/ypk/8gg/eNe/4t/d9yHCW+R5+Ipf/LPc8y8Wr4lS4aYa8T/35/4cH/rQh/iFX/gFbr311he97Hvf+14AHnzwQe6++25OnTrFf/7P//lZl7lw4QLAodfs1KlTh9975mU2Njaed0IOIgutqueSEVdDSz9IhJdSUNdqjIUybE48Q4xYrfBKoaxseqw2VL6hslaizMbMY4VGqQo7bvucsxgDbWzFy5iyZN/aIxw59SYmzVFO3foW3v3ud8tm3Vm6dctkOsNWboRkaarKceHpJ6knU+Zbm7KhjgOL3UtcvrTDbHObbr3Ho599HOs17TAQcIShH6nZ6jDfXGmRo8cQyWmAVGiaho3pjMV6Tcqy2cyqoFMapesHDZU0WUrJFlM7y5HtYxw9cYKPffJhLu92nL5d0Q6BWAL9MJBigCgNtNKapplgrMf4CToFfFXhrMNqgbCh1Eghl9+hFChjiGnMBdeKoo3I+rswguIOXslayNS5oK2mqi0pWQxRmnslG02TNMoLJTrEiDZWtutjI2a1bPBT1rJ5zRFtnGQ72nFrnjPGgnMKqzR9F4WIrkWmbI0Vr+rYuKcsAwM9Zlpbk6kqkeqWJJNPbS11M6GZbTHd2sRP5ijrMcZQVw6nMioOaBboQaLzDppdAeNZ2QCOSQBaSzyXLpkLFy7yH//dT/DkYw9z9eknWV69Qp8TeSTTGwpVVbO4epnl1YL1kkNuGo82sqG1RkMWb67SwicoY+N9QO0+3LsfqI4UhJhYD4GUxO8r3XoWdYXWMvwBGJ/LMurPlRob+pGaL8+yQlktMWklA7J9TzEgtHzxuJPS4YadLCcdh2T+8XoOfqcc2oWihPKtOAAQgnYVbRhYLlZ0faSqHIoJ3lsqX2O0IQSBGA4hMAwBPwSxMcSIVeCtYujj+PsLy74jh57KaKySODjxuFfMN7bk8Rh5BIv9fZrJjBQCOzv7uGpCP+yQkiJny9AXEhFrMuhAyDJkq3yFN43IxlXGKugHI74/NQik0FkUkqGZKeIBRxFROOtRyrFqV1gsMSZCygK1VNC1a6z3WGcIqaUgnvLKGVAydDDaYJUl5UhEwIgpZDKJ5boTebxWJKXIucfZihz36K88QDW/g35+L/nKQ0zKHikvGGxFmt1FiomduOSp8zvce/ct3HvPHfzqb3ySlBB2hR7V3SPQT2uN9iK3V6P5IQ2DqE9ywmPoYwCjsVpxefecDCK8IoVEKRpTKa7uPMZi/zxbm0fQtrDudjBaMasblouOdUk084bKWIyOlAFyUjgnCQ6lJKx2JDSVb/ATQ58SIWaqytB1K3IqtMPrH9Z2UF9Mn/Vf6FJ9FC/4EFBbG8STm+SXQR5+Zr1kNvgzq6p48h9u8gtfddCA31xM2TNrnQfm/+sG+sFPv+zr+rIrpVGVh3HT/Yo13QdVZON9IDUX185LN97Pdz06FrL94hoM3kylL1CEmYov3cRlK6lFr5cs8Y9fvgVug3Tm+mwv11vzR9fs3XVjW/FUg21lsPRCpfK1/9cR+PlNNp/ncnYNf+w//Sk2/o+GU5/bkZ/tA6d+8SrxV7b5b97+XVx+bwKbUSvL3f+qQw2vcsTdC9QNNeKlFP78n//z/ORP/iQ///M/z5133vmSP/Obv/mbAJw+fRqA973vffzNv/k3uXjxIidOnADgwx/+MBsbG9x///2Hl/mZn/mZZ13Phz/8Yd73vvfdyM0FoO3FW+j9eFJIYt0nVPFU1lBbLfFeWiJ9QI1e2IzSEWcT7SDwnoMTUKsb6tph1MCQBhQwxMjeqsdNbufsne/k7nu+gnvffC++sqTQg/Wslmuqpma5f5W8mySzuWpGiatlGAJVVbHc38X4mo1jJxmKY+hXPPrQEwwhYIshZiV60FyIQ8BQ8F5IzNo6jGRgYbRF126MynJw4QJt26LGjruMDR4FytgYWW3QxrGxtcH9b30rb3nbm9nd2eMTn34cjWa1WIpE2VhKSRI3VtV439A0U5q6QlIcPN16D+/MuOEUv7YxihwCMSfKEMSj62u0yViEWl5yls+UIiRsrJUBYpDtn8oSFVVKwlmDSomQBNZkx2YapUVufHCf9Cix16CyALiMtQIUO4hzO5BFp0RdVXhbxtsv4DbrFGUEepVSRoCUNKmqFEpSDFkiuKxvqJo51eQIzXyTyXyDejYXUF2JlNhDWENcYgq4KC9FxThU0QctrzRsB8qFUaQte/ykKUaTKVzZ2ecj/+HDDN2a2miyM+QoJHQDZKNJOeKypSjF0BemsxqUB2UoKZLDGl1tUUqW+3fthS8bXz16w2GE2Mkt6ca89YO325zjSNXXeKPxxozAOfE+K5n2jH57Pf6KkaCe5fnXSpELdOslOSaMFY96NZnLhjwX1BjZlUu+Jl/nOQvx0Y9VyGrs4JTYCpwGW9UMfaBte/l9Q2GIiSObM6w1olpI4wa2aJQ20vwWuZ0xB9Ig/nKByiXKkChaEY0hxIHpBnjvaOqGoV9RVULJTzlRVZ79vT2OHj/Bm+//Cj7+q0+TUqZtA6GPaB3RejzeDAxtAJ3JRY5dbz3eJvoQqa2h6IJKRqjlRaB8ldF4I8dMDAJ5i6EwxA7IKJsYSoKkscZSiKzWLSkZmkmNUR1KFVJQpJzACPFeF1AmyWuraIYugBO9iLU109rSDrLJdsaSMwxpDXmXsnyU2S3vIO51hKQZ7C1iGWl3sN1lYuxp6wlxCNxz15v4zGcfZndvffgEyzEix1PJmVSURNopIEEiozVkFEMpKGPJuXBld0FdGeZTR9FyTFmnRgsJxNCxWF7GOwc2M6RATKAbg46WEArOabSzMA5xYxmEI5Hk+K0qTzOF1bKjxERTCysj+wptFc69/hvxL8bP+i9YZdCrlrK/AG0oZ04SN+tXZAt+mA1+dfe6T9TT7Sf5kXf80E3nhD9f7eWB+srrHyr4mtczmu60OSVuVc9uul+lbbPtsmy9X2bp1/9b0StSxSARZsvlq/uLriPCDAXKWoGNvtZVCpef3oR3w22nroKa3DiQ7AVK9YGthxKX3nH9W/FiCqlW2PUrchPY+JWG7c/0z7lPKiSOfXSHYx99ZX7Py60basQ/+MEP8mM/9mP81E/9FPP5/NDntbm5SdM0PPTQQ/zYj/0Yv+f3/B6OHj3Kxz/+cb7zO7+Tr/u6r+Ptb387AN/wDd/A/fffz7d8y7fwt/7W3+L8+fN8z/d8Dx/84AcPp9zf9m3fxg/8wA/wF//iX+Rbv/Vb+Q//4T/wL//lv+Snf/qnb/gO5lKIOaGyQhuBE5UANibsxGK9xtiCKg6nPTlHUhnowpqULBorknQlflTUgHaKia+gGNZDIMREzHOOnn47TbPNW976Xu655yx7u/tUocFXXppNerr1EqM1fbum7QMnTk2ZTBvWZR+tDXVdU/Imxlp2rp7n6Sc+x/7urngMUdIYxEQqSD60d5QY6UJm4hRNU4nsOnlS6EevtGHryFFq13B55zLLxT4xRokDQo3UaoXVjqqZsH38OPe+5W6OHt1ksb/H5Ss7bG7MOH78GPONKa5pyEpO7K1SjGFiNE0zes8L0VmydzjrSBGaaUPTVOScyChMTgxhAGOE2IzBaUXOHapkoYVrh2SeF2KSTZ1kiStKifRtxhhDyAqrDFaPedHKkHIGI8+fSGjFW641hAAxJFzlED5YwRiFN46h6ylIxBglEYPQ1a1rxig4hbUKZySbuZRCSAXjJ/hmi+nWUWZbW9STKXVTY3XGpIDOA6Q9aGUbf+CR1s6iR5q8lEQsHTampVDIQm20hqwsUTuyqcja0ofA/pUrPP3EZ0elfaGNgYxQvY0yqJKxdcV0Pmdz4wh7ly+z2N1nc2ODqmrw3lHCQA49qgTSsBoVIIzDhvKszMsDWrlEuSXWqxa0xhgrg4ok92FaOWovbzF6JKOXUsgjqR7Stc75oI1XYotIcSCFgLMWnNDoRUIiRHVjIYc0+sYLlEJMUbLSuXb2I77tIlt9JP4MMihhQ9TNlNl0yt7uPlBIAbKG1brDu9FTViDEzGrdMfRr2q6nrkSabJX4yEXxYQ55A0YpvPXoqsFVnjj0HD1+HGtlKPErv/TzXLrc8Tt+19dT1fJYPPn4o6QYacMgcvkikupUkOsOSWLQkkWjMKYQYkfMmQQYIw2vs44hR6ri6VLPEAKpaCpfocmEEhAFv8Y7izUycNBahlbGWLq2Z7VqqRpJhNAYSka23CVReSPDvCIvi7aNZCAOPcZ45k2hG9YkHfDGUYonhAFHpu12Sec/jdt4M+HYu0mXHkanfXTYBaPpZ7cSB82lbsWVRc+J7S1uu+UWFouHyJTDIVEBZIw2Zt4XMVAIByKNsZXqUMmhFTKwCxk3RJTR0nBTSCT01GCzYlJPqZWmD7AMEVd5XDHoyoNWlD7QrgZSKZL1rjRej4MAK8dWCmaMfMyEMICypCHSlUy+3rOS17C+GD/rvxClYkbt7FO6HrUxI53YIlWvDMjMdBFz7ipltbqhn7NPX+UPfOR/4ONf/w9HT/j1VSqZ/9RrrqYZW3rNtllTq8Tfu/g7+fmfejd3fvrR19zD+roqY1DOUWYT8nzyBWu6n1WjDP2VeguRZv71/370cqtoKJPqVb+n15MlLtshC7EbfX6v8o16iarOyznau7ef4JP2PpHXv0I1e3TF3p0bDJs3thU37Xju+TKraNi/3XN0r0e9jpVoN9SI/+N//I8BeP/73/+s7//Tf/pP+ZN/8k/ivefnfu7n+Ht/7++xWq04e/Ysf/AP/kG+53u+5/Cyxhg+9KEP8e3f/u28733vYzqd8if+xJ94VhbpnXfeyU//9E/znd/5nfz9v//3ufXWW/mhH/qhm4oz8c5Io5wKTism2lAp2aiFkNEmk4YsPmfbSjSVbdiaT/HO03U9uc+gxIsdgyJoy167IIfE1d0BPznN6Vvv4/53fA1nbzlF165Y7u9jtGYyrdBK063WWGeYzzfQ2nDyltN07ZqhH1gu9kgxMpvXPPTpj3Ph6adpNuacP/ckISa0kRzwlMvoWR393SiUzkTnoBSGDOu2o6prtLFIy2coacBQaGZTzk4bhiGwt3uVK5evMMQASuNdzWxrm42jxzG+4fFzOzz82EWGkBlCpJpM2JhPsFWFck7okKWgx+2p5CGqUXITqLyGaAlDpK4bvLOEmEayeyWbWl8E7Kb1qEBQGKNHirY0M8KS0hL1QBm3z0qkyyhKAjnTLsRhwLh6hIRJ5rWomzUJRYoRq4RSHlPPhq0w1kKO1N5SKAzI9nsycQxtQRuLr2oKUaTnrqaZzKinc6rZJvXsCNVkcujpNjmgU4/KK1S/jxqHHWqE36nRoy8qgdGbPoLJ1Ji/DIasNFk7snEk7cE6Ukrs717lqScf5JHPPcBjD32Oc48/Rtstsc6hrUDfUoqyMRzznl1t8N5Q4kC33mMysyimpDIwDPIcWJeIsRNLQAryfIw2+oI0OQZNLuM+/hn/Z70njSqGUhTGarzWNJU97NTESy93vMidFiBiEql6eYYvPAwdOSasc+KuzwmlDSVnulasHUqbUSmQYIS6iWz9mo/8sMcfSesZBNiWx9dQyVhfMZttMJ/v0w2RlBV9HxliZr1qCX1HDB1h6MkxUVvLMPS4psEaQ98NpBjpU8ZZix2TAqrKU9cTvLVUVngI28e26do1KSXOnL2Xe968QeU9TVPx0AOf4OJFic+a1FP6VcdyGXBVhfVJLBSx0GdJDZgYSwiRtotkBVVd0VjhWRit8dkSY2SVxX4ShswQWqxWaBROF2LJ9DFRMKhicVpjjSeXxNAPhJCZz6fEIBtwpSVK0ZiE1p7aO4YYBZCnx9SJMed91Q1obQROhjSr1lpiN2DUgNYr9NXP0Jx6G0rtE1XHujpGyA2qW6DWTzGEJZcuzthoznD27G088sSTrNYdRY1khzzaJcaceadE8VIoQvp3B8oJacxTHpMWSqEdMq7S1MqQVSIrhS0y4IjDwG7bESLUs4ZSAkNIWKPw1oGy1E1NymJRMMoQh0jwAy46Vl2hoHFOY6xhiIWiEgmEQZJfB5uQl6gvxs/6V7sOY8kATh0jbE9fmS14ufFs8GdWWS657y89xTt2voNf+UPfzzEzfdHLp5L5/662+O7/3x/lnh9boPfW4B25cuTa4h6/xB2rT355N+HPbLo3J4RNkZfHRn3Bm26dCjqACWWkoN+EDP0F6vC6vvR7cdKsenkxUddT15ElXhSi2kwZlTNFv7ad+OQch1nin6jfftNZ4s9XaohsPZS4+K7r34pnW8iVwrxCSvkwVazPTJg+unjFtv2vdN2wNP3F6uzZs3zkIx95yeu5/fbbnyNH+/x6//vfz2/8xm/cyM173mq8Q9tCToXaO+ZNQ4mBVdeSVUZph0XR9YEQEqooNiYi181FyQbIVVgDJhhqr1BYUsoMecLRW97MZHqS+9/xX/DWt91Lu79LMznBqVtO0PcduRjqyrJz9Srr5ZrBOarKs9jfw3g/AowyKSlWyyUXL17lkcceYWNrUxpUrUljzrF3jspZQpJs5INxmhqzvJ1zKKNouwFUj61qMFak96mndoba1dS1yDbbroO2YzLfZOPIEVw9xfhaMuiLohjHxHuqlOjW++PJtEcrTVBjZuL4iWBNweqMUoWmFol/7DtSks2z5GDH0W88EEMYN83SQIYYkX5eGnsh0R9stRyUTAoZrcs137SSxpAiADarzOGJuDVWAFPjF9lIM6cKaEcJ0mxYq/GVRavCshszjWsv/mbrmG5uU03nzHxNNdtkY2sLZyXqzZSILQGVl6hubO6UgKMAAc4peyj/z+PQQnze4xearAzZeLA1OE8xlhACuztXufDUgzz58CM8+ugDnHvsUdZ7VwjrlvUwIAgoRV1PcN7Qdy0pjsTvkim5oCuPsxWkQj+02KHHThvqrSkp9oQ+EXyH05ocOmIYWC136LoVzWQqOc2HG2v5UmOzVUpmiMJQUGOjKznUMK2sPIeZMSNcX4s7K5AQ6XlR144hSqHv18S+x9fNoQddnufE0HYo4w9J6+KRzyNRu4xKg/FJGOuZUvVcxgizoka1QEFZxzAOa8IQZNg2BIgZpxRuZBjY2o9pA5blUpppShHwnveEWKQpDVmI865QaU0IPbpV5BzY3bnC1UsX2TpygulkwmQ+YzqbMAw9R46eZPv4Ma6ev0rXDTgH85khlUIOAltTWkFSeGPFljBaUoacMSqJQmWE7KELefTHx5QEVqgMmiAb2WTRGKxWOJcxWcBr1srWuwApJI4c3UApRdsFnLdMpgcKFsWQe1at8BK8NViT2F+14Dx5MDLMqQTUaLSAHdGZqk504Qrh8mdwJ9+GOflOuvMPU0JHNZxjSCvUZE6fTnJuFZhfXTBvGk4eP85Djz4hr309ntCImIdS5HgypUgEHfJ6S3kEEo2k/5wTeuRkxBQZUkYZhbEWl+U461UgukLMkbaPWKfQWTH3DZOpZ7laj9nlmto3Yo2xFlcrptaziEvakqCMgEkOgJNZBi+v38H8YX0xfta/WvWsWLLphHTqCOmlgEzXWTeTDf58VdqWN3/vQ3zd1e/iX//3f5v7/OR5L/fT65r/8d/8Se79X/a597EHnvU79fj1+jxVfRXrddh0P1/c2KtRKgvgLb/qHeprX2FmX/1G/DqzxIsbb0l6acr6q12TS5lLqef+6hzDhqNZvLJe8enjK/zdN7YVjzXo/pXZiqNgedrQnLPo7vVpt/mSf/lpramNpTjw2pJjIuYo4LCiKckwxMxyyCilmE89W5szJr5m3fVyopdgGJL4YzX0occ3pzl68h5uv/sdvPPdX8nGvGG9XqKM0L4vXbyCUgpXOXIyVFWN954cBy489SRHTp5hMmnQWuHrijD0XLl4nnpacdeb3kRKUYBHQxAYWAj0OeHGuK/Kydb0INNbaT1KnMX8OqTIan8XFbJkcitF6hOlDFROU8j4uqHe2GYym4LSZAUqi686RdliqZJQKgtMSynxs8PYRSZKAWucgJjEPIyxDqUMvq6xVrG1McP5inU30MdIGgas9Xjn0VYAU9pakSSnQBwCKanx5FoDAyXJSXUsUI9EdYU0vRnoupF2nRWmqtClUKK0qgZ1KK/V2jOdWIJlbNAljiwkzWS+yZHjJ5lsbOEnM6pmgrUaFXtKWGNUwqcdkSeqkeqtZPOuzUHm+rh9HU9kcy5jY67IaLKyFOMpxoP1ZGUYQmBv9wrnnvwcTzz0OZ546EGeevJRrl69QuxFql80xBgxCqqi8MhGUxuHswZTEilFbCVE9b5tx9uE0Ny1EaDgZEbyU0LXU3mH9YYYB9YhouwCWy/p14XuzJKctkcqeCHnZ0SVFQGdSaZ3GjOzx+EIUFmLM5JnrY0MZa7VSLoeFRVCZ1egIQ0Dw7rF1zVaP2OCqoU03UwnGC+S1gPg2wHs7WArfs0nPt7Wgysp43MxWgAURabROJ46f4n17g66ZLw11LNGtq0x0jjNuit0fSYVed2ILD9TkAGD1oqp16TkiaOUPZciA7Qij//e1Sv03Zqnn/z/s/fnsbal6Xkf9vumNezhjHeusaurqyf2yElsOZJsKVKiTqyEAizYcWTDUSI5FBTTgQIIcBDDiBnFUBALUSwgCiIRseQAUhIntkQ7lmTKokhRpEiR7Knm8c5n3sNa65ve/PGtfe6tHqu67+26XV0vUF1d956z9z57r7XOet5nusnWzgWmWztUdcXB3btIztx56zpkMLoiZj/2t9co8eBAjQsdrQwhBnzq0ZT6P+1D8eybjNUGUQ6nHTl7po3BGD8efdBFRRqXGFpZtEQkJsRYROy4BNNI0qwWK5qqJYVMCWqryvNIwscIRgoTTgmzDFHTuJrEqKJxjhyLFNs4Q+NmJDy9HzDaQz4l3/hnmCufomFJTmf01QSaK+huDWevsfYH3DU77H74ST7xsY9w884dus6fn1+Myxyji7IiisJKOdtSFowUZjaPx0vOELOgUybESD8U24qRwDplnNU4U7NdVZzKmkgu/fWmBNVJSNSNI6aIrQwpBZaLDldVzNoZWSfWEkswYCjqlJCgboq1RSch/TDQT++TUX2A47PS5nBxj3Bh9h17gt/pvNtu8O84wfP0f/Q1/uRv/1v0/7Nj/tan/ipP2tIf/v9bO/7Uf/lv8NxfXfHRl5//nkD/D/QYg6oqZNqSd6aE7VFe3iqye3+D7m/5WpIUj9X7fL4fFWYS4zv7DJ0tryMl3msYVh9HXo8TPuo6+n1De/3BPr7ykZ2XEnc+/y5YcSfkSmHeQVblO3s8RdipqW99AMTfk2mdK3JaDRqh82tAMJQArywNjd4lckrSPVpr+jhgUBjtaOuKfkjEXhiCR5mW2d6H2Nl9nA8/+2k+9smP0dSwWpwVoJBXBYxMpgWkaUXOkdPjY6q6AIzLTzyN0Yqju7c4unuHl199AaxjPptjdEnQrp2hGxKDBmVL+FqIgeAjWlvqpsbqsYps7JBWQEqRPPq+rdJklUhhIGchjiFdIpG+GxBV07aTEuSmdJFOxkDIlDA2rYm51HtprUgpkiSixRTZuLVFVmcKs1Yk1pREdgMimhjTeeK2IMVTq0AbzeBDCaepbfn7sd5qEwRW2FcpkmZlMErQtni2RxRO5SxUFj8MoB3KFCYu5UTKCWOKLLpSCqUtxlicq1E7uyg3YbpVQJGra1ztqLWgxI++7mPUkMgxFVm5MqWrWY3hYyPLu2G3s+SiJFCMVVEGMRXZFrZbjCGEyNnJIddf/yqvvfg8r7/0Ajffep314i7rriclhUEjSs7r26CAyDjWoxmlMUpjUyZLoFud0DoZ3+9IHkFJzonoI5I8MSiqqkY5i3aO2XRG2zia2pLSCc4m3MTinKGdzUhxIKeE2NFfnXMBgwg5F1VC6ePWOK2pnBkXE5q2ssjo5bbWnneEbxhurThPwi91VIx5BULd1GUps/HMjzYDpCw70BsVhBQGR6vSHLB5Dq3LzQXqnCGXjVRMKJ+JqI35HecMs51dCCtiKPLz5D0b7f2QIaPHzAIQiYQYULbUFkIuuQeq9MfXMvrgKRJQ62pECWcnJ0zne3zsRz7B7etv8thTz1A6x2u2trbY3tlh8Gcsjm7RNnNU6lG6BC4qnckS8dGTSRhdYVJEaU1TW6zRNLVDi0K0kFMkpcwQBiQZVDZoawm+Kyw+hpg91lXEJGQxGAEtarTxlAXbycmCZ56e01aWISuUaEIMpCxUpgQKOlOuPTlpalt6zvsYMbYcBzpnUIa6alDAOgxYo8Yk9x7X3cC1P4G//FHC7beQkHD+EPGnpOkWYXKN4+hZ95md7V2euHaZl159c6ySvI95GOXpkgVGYF7y8YUsGTNey7QCMaUVYRgSVW2wTWkjaKoG1zpqpwn9QNXUmCFQ2dGeYhKeRD94nLGELtB5D1YRTGIZV+QuobUlpEDKgZgCPgBEUoRM/KGQgf7Az321ZKqpSVcukabugTz0u+0Gf1eTErN/8DyzX674V3/3v83Jv75geX2L5/7amo+99MIPFwDfgO5zpvs9Bt2xeLLfM9D9LUZHSO99CcFDn+9PhZmgYyaZb58bIc4U0uBR6BJflS7xn6hfo7uo2X0IzzF9Y0n9zDbD7jv8adXoFX9AQBwFw7ahvq0eSXn6+x6Id0OPmIz4OLI5GmsM2tkClqLGKMNWNWMQBSkzeIHssWOXb201sTaIvczW/lO4epdnnv0RPvzskxgtpJip6wZrLNFY1v1AGpO6Xe0IPjDb3i03qUPP4uQABPyw5s7tN7l58y5XH79aAqlSHn3KBi0ZRUKNwFOLJmdfJJcxIDkVMKwU8ZzxKwuHShuoa6KC4EORyGqFdRUpKpLSWO2onGVSVwyp1DFJLqFWxpSwoTSCcGMrQvRoESpdqtgqZxE2qdcUn6lRZDKrZceqDxhRZBFWq45uiJAjaQQRRluSQEyC1nIO5EU0GoOyegS4iqxNCcca+7NTBmtNCVLaJG9re48RlVKnpLShmsxp5zu08z0mW7tFDq8LmDDi0WnASIfqU+knNxum06CNIY1ya4CcE+mcyaWwbCgylmwc2BpxNUo7hjBwenzI3Zsv8OqLz/Pqyy9y6403OTm6S993kBJOBZ6Zd3xqD146crxyWgK/tCqg2ozhVEmKgklRAvKsVihbpMtDSpymkuheurlzqasqGIisxiC1KPh1YGtqaOsaYzK+WzIMSyYzS153VJOOdgYh9KQUcFIjSpPyRkWlzmlnpSh92XYsdRyneMhL/RlKjewxjMb4cxxSoHDhsUXKtsy6qvwMSo8Kg80SQKGN2TRWnbPgmxVUkf8WaaU6f+x7wvRyTBRGPFOYUnIuXe22QlcWQwn/88NAipGqKuedcVVJKk+ZnATr6qIgEYUxriS85zFQT2tUjuM5IaANumnY3bvAZNqyOD0iJU1VV/ihxxjFMHSs1x3K1AgaiRalA81Ek5Lj7KwrywwNOStCyiCGHCPdUNLD/bA5FzNaCT5kJCvWIaCtYujXGKdobF1k9MkTUwI0VlkMClQm5+KvTgiLxRnK7uAaUN4BJTdAVMRLptGOSlcIQsixXLOUwo/qFaMFazVaFDl5kh6rFhUYLEp5fH+H4cVfgUufgeixw12issj8KfQQUMub9KsDjk4vcHHvGp/42Me4efsOJ4uufK7jyR5juYaUA6YcA3rMMtgcbRsVR5ZMiFJIiQi+Lyy32DHxXVuiiaS4JmUhBEE5jTFFpeO0RuVS26cqS9VUaKMQlUkqFzuOBZUVOUHTGFpX0w+RECPfhQ34g/k+jgqpBLKFAPs7hItbiHkw25N30w3+PU3wzH7xeWb/0EC+9f4H4F8Huv1OTdh6D0C3bBjue6BbpbJ8eVRHxx8Ok3hyCjUG0z60yQIxw3cIcBRb7nEehS5xNSR+/expfmbnTdYPsEv8bc8REjsvR+583rzjczFVQnYK/YBI7GFLI1qjHoHauK+f9z0Qz1qXG+issFQjMwspC33oWfkVU1ux21bU1lHVLU2jMToRg7DuIjFq6ullrj3+MZ54+uM889GP4iSglGV7d5e6dpyenBBSZr6zQzw+4I3Xv8Z0NieGTFVPmLQ1IkLTzKhczSsvfI3rN97gbLHk8tVL5JQ5PTvDOcdMGqxtUBaUT6hUEJXWGqoKRRp7a3Nhy5KQ0GRsCXVCUdWOuoJlt8IZQzOdFrY0gzWaiW2p67q8rhFYVVXpE1Z5BFDIyARrbGWBIjvX1qHH/DSjS6p5ASFF5tn3YfQLQ0pC1/XFCx0TWVLp4VYgKpFHmberNX5TXWVMCU9Suty0y2ZxAimMsFcr6qpiOik1UzFmQhaqeko932W2fYFmvkU1mVBVDiMRkz1WIiot0JIhbi46my52W6qYzpluIY1haqKK/F+UK55uWyOmRrkajMUHz+nxITfeeIFXX/oqr7/0Ejevv8bZySGWRLf2pWtZ1OidLlaHWaXYMoHlaeSpbc3tzpGUxadYjlPJ93wyUj6TgUwQjXIVGkUVw9izPTLLCpy1SEqkKEUhkEJh/GctunJoFTm7e5ujW0fU84a6nVOZ4m9WCDl6Yhignd4Dt+OiB8pyREmJzVYbvMtY7TwuMfJ9PvgNcJcxzV2Nvt5ztppyLDHe8Go1qj0EjOaeHHSU/qux/kw2UH7jP2cDwr/JrYUUdru8noRSglWZpp3QGw1G41NAFFRNjVaKGFPpREdQpLLAsZBM6TuPsVShWaMo6faGpEw5fpRCVw5l4ODOdba2d7hw8SqXrtZoVSTsi9MTfvPXfoO9K9fO078nbYv3d0q4oRjaqcHHwNl6oB8EhYFUesFjLEsVnwagSMtJQh7BeM4Z35ecBJV1qUyraog1PgspW0AX9t2W792I94c+MplOcM5yfNKV80wyIhqykILgTSx1bhoaZREV0RpaW+wgKWtC7hliIiWDHSsCs0RM5TBpRVq+xt6zn2eQCyxve6K0ZL9GD3dRZOLkaW4t4MoycOniZZ64dpXT518p4YlyX3f8eGxs5A9GjSGJlGuVNkWtEJNgjCamzKoPNCimk4pKJaTvWfY9i7BGlKZxDdOmonKWPnpWviT5xzigs8aNy1ttLSlmhtiTGCBYFJYQNZUy1NYiEohSlhAfzCM499WSibXI45dJs/qBBLLBu+wGf1DzCN50fs9jDKppirx8e4Lfroqnu3kPQfem0zs+2qD7m80PS2DbeYXZu2wleHdPkkeg9+3VM2J0kQY+AuenypnfulO6xPO1B+sPv38mb66oPvwuWfGaBwbEU6NIU4c9e+/f86+f9/0dQaUc2lY4W0BMN3QMo/wy5YSVjCbQeaiVptaASJGFd7BaVzTzx/jQx38vX/hv/RQqeWzdMplfYL044eaNm7R1w3o9cOFyQ86Z5ekxb738CvPdC0yne8wf20UpqKqWnCOHd25z5+A6ojLbu9vklOlCJIVQvJsiOFeAjqQiUdZEhLE2y9RIzoRU0os3gMiSCVKk6RpLUzty22C0Kz3YplT7LBYLzhZrFELvAzlTGGpbEpdFqcKWqQKIzvu3dRF7Om3QZFJWJBGsKldxt2GwcyZLSY4WDet+oGmaAt5DCfty1hbG+T6QpXJhw1GQYkbrCq1K/7sxmhjBVoXJr+uGejpnMtvGTabMn2ypZ3MqZzEqU6mMyRHiKbIaUFkwtjDc2hR/bDHwjvfuUhLMk+TzX0iFPTUk4xBbo6sWsZYQPCdHh9x883lee/EF3njla9x+601Ojw5Zr3vSmOTN6CNXQMy5LDd0cesaZzHasBQ4Cw17yvPyHYWPQmIo8m1dOuML0CiAWqHAWJrpFvOtOcF7FkdHGBXP2WPRBQgnKRVdVo1hU1s7bLctLghm0jD0RZa8e/Ealc1YrUHWSBrIyY9S+xIImERIY3K6bODvfWF9jEJwpfSY6H/Pq71ho++NnH/9+XcX78h9xPq9r8/jZ6PHdgDFptat/P+S3G82qH9kycfP9r67iyIZH+Hu+DhKMnUzQY9Sd0bZ/SbFvkjDhRB6yCWYzsdMThHXaIwrL3qTGq9V8cBrrUkpE4YBhyH4UGrEzhY0beDwzm32Ll4CNL//D38RgDdeep66nRKCosLSD4EheOpKUzvNtHKQAj4J2imUMiCGYSgKiO2ppR8GVjFircM4TcyJymqUrpBMaRxICbJDkzC2gGJRIHn8bJVFUmZxtsYPkZ3tlrOzwCoMhJhRGWpr8EMJV6zriso5FImYi5VEK0VMgSENRc2g6xJKSUQRCSmCLgF5SSq613+b7vLniPUZdnUXozKLeo/sHKY/Y3V0izfqCdNnr/Chp5/gldffYt37zYmKUkLeNNxRlAPJKNyYI6DHcyhLpqls6SxnvBY5hU8dIWW0GGZtze5kShdyUU/psqQhFDVKCAGtLMY5XF0zmdhyDJBJuSL7RNYKnROVrphUE0iJHAWyxekHI3H+YB7cvK2WbHtOvLJDfkAhSt9NN/gHM86G6Z5PC+jerfFzQ2wV2X5/QbdKJbn8Bxl0f7PRcQy0fDAtfI/siAaZNqiDh/s876T+qwBxU7rE3+sKMxGOb2wDD75L/P5RIbH7QuT2j78LVrwR7FqVyr7vccTA2TMtu18OD7Si7UHM+x6IB9/jbI2ylsF7upCpnKF2jn6IxNjTDYlOhDaCGyXry3VG2GX7wuN8+vO/j5/6PV+gMsJ6VWq6jg9u8+LzX2ZxOvDjv+sLXH3sCnbs9H7syQ/zxNMf4ezkjHVfbuCDzxzdeZUXvvYl5ts7aKWpqpqcMyEEVCw3fcpYggjL3qNG9lhyHjFj6aU1uoRjbZjVqq6o6pqUMovFkhQC3g8sJZKToHSm8wMxKbS1YKbYiWPoVnRDSUNWSkHUaO3IWqO1lBAyxchg6yIBL2gLtEULMEqCjSk+1BwToRg4cc6SlWW96qmsxRlHIBUvryrAvbaOkDODD+Prq4GMhJH50wrnaqpmwvZ8i2a2TTvbLqC7clgt6Owx0WNYQwgoScXTO3rRxVT3sbBy7kPfhFtmKTVvGUPSDrENqmpQtoQynR4fc+Olr/LaS1/j9Ze/xq03Xuf44C7ely7hWhepePSFrUfyGHy3WV4UgFbeLqEZFwZp8MQIr3TwskzIKKJEckroqvzsZoNWcy4M37hIyCEwrDt63zGkwBDTCAJLWFWOsdRpOcGkjK4qprs77Fy8RlPXeL/i0pVrPPHMx7CNol/eYhgC6uyYxu5STbtiZ8gZZTYvoRSYbvYCZUb0rDb7C33PHjD+vJLlnjx9rJzaMJcyAtgNq1mS1e/z8egSEKhGmwA5kyQXECUUGbVW91LoN5kC3P8i7v1HZqzDG79GkTF1g0hJJk/ji5EsZdGlFJWRshTRmaHvUWSUykiKiDEYpUfvennttjLEkIgx4/2AGhPJb12/xTMffQ5nYGt7G6M1O7v75FSWbBevXqVpak4OKvxQZNBKgVEWcjlCa5dRNmNwhFjsCNgMSVh3PTkXT7O2Gp8S6HJsbFUzVEr4lBl8wlpH48r1ZIhqZLUDfQoYMtaV5UHKjul0ijUrbHZIFGJKeKWZVA5jiojBqEwm45wbj3+FlQalEhFPlgGUIuWydKnMFESjdSL2J8TV6+xOf4z1pGXoHb2Zo1KH7W4hMRDrC9w6S1w6WrGzvc/VSxd59c0biCrWF705v1VZGG1ung0FhCtd1DlJxiwLSje8HgJ1XTz2bVUz5EBQwtTU1EaVWrIU6bzHmJKjARZtXbF3WEixpzGWlCh960pIKaOto3EOyZHjoSPlANlQNx8A8UdpzmvJlIZrlwg77QNjwb/bbvAfytkw3bMJabsl7Ly3oFtHOQfe7xfQ/U1Hioc9PSD7xaM8aVo9dNCjhndA4Y7WQvG+5Nu8x3KE5kZ5V35s/w1+x37ioQHV9saK+nibfv+dnUuii1fcrnggio1hW7H80IzZy8tHSqL+vgfixjiSRFa9J0nEWoPVhqbSzOqarjH0PpETOGuIIqShxjUX2N19ms/8xO/jueeepjs74qDz7O7vIzlRVy3PPPMRfDRs7WzRr1ZIp4tv2TlCCNiqpsme9SpROcUrL3yN3gfMuivAYwwU0lpjSCAZI4rQ9YSYMEqQEEip9N/augYFISSsMxgghUDIka5bgdKEmEg5j/3MihQzCx9KBRgapzRO51G6vsUw9PTrNVVVUdWOpEzxuW5qkHIegXpJnMxj+rK1ozc2CVkpKmtBYAgJZRyVg8oYsJphXfzhwRRwb63CuBIQ50MkJAFlaRqHqxtMPcG6htnuBdqtbZrJpDDdOuPIOBI6r8H7UTLMeXiasvo8YRq5L0CNwrLHnLFWk5Uja4e4pvi6benCXiyOufPyi7zx0vO8+tLXeOv1Vzg8PGC9XpNzwmlFrQwpZSIluTzlAk6VlFql4l0tYCBJRjtLbSqG4NHoApREMFVFM5tiRTGfbnG6OOLo6GBUARQpv6WkgieE4ugtLG7KgeXSly7oVCqkrNXknDBKj7YChWka7PgzywjQc13TTLaZznfI0XO2uM3hzVvEELjyRIO6YpEU8MGX1w9sUsn1WAMFjKn6Izj/urqODbjevCeSR8CuFeo+GVxM5fUq1EagMIL2wmTKyLBuOsXHNcDYy65Ail+XkSHfVK3JuBC4H4yXvAFKovU5c56x1o1ZBakgtjyqQbRitViyyInppMW5inYCi8UpOQ7UTUQ7O24R7r3+wXtijOX1eyl2k8mEq09cA4mcnXW4ekIzmZAl4wdPO5kQtUZhaV1Lt3BgI84CoulDRCjhcE4LwSdELIhBicK6gCbj45gQHjzaFs+7ToohRKxSZJ1onMNpISXGBHUhxcDgwdnyHBtLSZLM7l6DMgq6YlWxjaPRtrQv6JKgnnIqChANwWdEZ4x2hQ3PqoBUZbC6nPdqXPBNq5rVasHq9DrNm79FvvRjpJWgV3dx4ZhcTcnNFtr3DMu3uH7LsP/xp/joRz/M9dt3Snq7cJ6irpXCWuFefsBm4VOWNqJk9K+XtoQYhNXCk7pM1xia+QQqQxcTrTGkVGrMomgysViXdFHFJFmgooXkGLQg1hC7AS0tjSvPYVF0gyeHiLYa7SyR9+kN/Q/YvK2WbDYttWTfwdv5jh9bvrdu8Pf93A+6d4u8PMwNsVEkpx7ITfd3nB9G0P0tRkdI1Xv9Kh7+xJl7+KDnHQC80iVuoR9G0uG9BeKT28I6e350+hq/1XzmgXaJ3z8qJHZejNzefTesONj1g3oBsLpiSG7O1surh19n9h1q7DbzvgfiMQ8FkBhNo6vSqa1LsI+zmrqekJIuXsqUSXmGsfs8/vSP8bt/3z/P/t6cMASsq2lFSNGzvb1Fvz5DYbly7Sq10+RkGPold24f005mIzByrE4PeeuN11h1HVjD1taMnCDFCNqgjMFog1RCDAHIxW8qqTB7WfDrDq+glrShcIl5I+/W5AAxFRaraipa48hZyFmhrMXkALkkBZMjSRIqjxLnZoJSlhQ8idJLrLUh5YwShamrkak3KMn0fY82FqtLMrZzhaF1WuNj8YcWKaxi2pYozn69ZvCBelIVcI7CmgrjHNa2bE33mOzu0862qJoWVzmczqjkMTlg8hpCuWAZbTC2JHRnXRgoNSY3I3msPCteVyisYkKTlEOqBnEV2TVkYL1ecHz7Bm+98hqvvvgVbrz1Ggd3rnN69+A87f08v11KwFelNZIFL8WHaxVjcNj4dTFix58xqpLi3TZzmrbGrJali9o6mqahXywgZGZbO4QwwMh2SxZUjIxacyLFm22NJadEEgrQMYYUA1qEurYorQlDKH5gXY6P2tXUVcV8a4fd/UvkHAn9mnprl8pVJK1R2bA1v4RpIkMc8N2Stu1I0Y8M9Si5Pveqj4z0hgNXI0BWI9DlvvdjhENq9ESDBiXFkpAZfeKjNEErlBjI6dzqGwaPrepynOexE557SgNJucigC4Ivf8eYoH0/s76ZDFmNCe+6XACruqFuJkTfkyRSOVcWLTGRJdP3gZShqgxK4ugRLs+bfV/CElPp7VamKq0G1oLOaG1xznLr+qucnKz5xKc+zdb2NtqUBPKS+ycc3TlivjthMptyclvTTqf4IbFcZSatpm1alt0ZPg+EoMlJo40gGRpdU9m6+KmT4my1whpFY8oyzNSalBXVGABZWUuKRZkiAspQagQFRJe+a5003geWZyvqtsYPAa0d9dgY0Dalym2xDqUeMBtyzphKFYVJVGibwOZRBl5q3pTKxEw5TpXQGEPbWlbxgOXh7zDdfYpID3lJqHZBHKo/xaVTUj3nuI+cnC65sH+Rx69e5rXrN8sSYDzk8lgPZougZDxGi6wkSlnqOKXJUnziYqCdONrGlgq9wdOR2dvdorWa0CeMtjjn8CmQhkCvIqgOkUSOEwxAVRQjVWNptMZiSDlwdjygbEZXNVmEsBai/QCYvddzXksmGa5cIOxOHlgt2YPqBn/fjLUFdM8L0/2egG5GhVq65+c24YcTdH+z+WEJbIsTzcOuMMOHspT/Tm/npsIspve8S7w9yBxk/9C6xN/2XDffHSuerZA2VWYP4BCtTqWAcP99YMQ/AOJllDalkqlyVNaQcmHOtFJYXdjrpKH3Bme2kewIYcKHPvwJ9vd2mM8aQtXTdT3TyZQ+BM5OjjHasHfhEkYLZ6crknT801/9JV7+6kt8/nf/C/zIpz/NwY1X+e1/9k9RrkaZ4r1OeZTPKkWOkRQixthSs2UtKefRH11EvzEnlCoHTAo9Vd3QzGdUdVUYriSEGFGm8CySI0kEZRzaKpLPRVpuDRhNZTRq7JROcfQQVxXaOaIfWC1XTCYNdVVhTKkDk1HOGWMi55GD1gaVC0MeQqSP5X0tTL5iNqmZTVskw8I1ZGVpty6ys38JN93BNA3tfJumqbAqj4A7oPMZeijS3/MMJq1Rzo2hYAqtRz9yHqusUrnRFlSRnipHNjXYFlXViFKs1ysO797irdde4rUXX+D66y+yOrqDEDlbJ5IUkBD6nigQJKNk0xU+gkxgSKlIGI2mdhaJAelDSWPUxWtVYDhFVeAcxpTvN0ZhtWU+36KpalK/IqVMPd9GDSvC4QKApmnIPqBTJqoCZq2ASUV2K7mw1CIZialkZefifrbWIICzjkldQwrEAL5fc3TrJvuXrrJedTTTHSZNi5GKvUuPE/yK22++yPJ4xZXHBcmB5D0pJlRVTEwyVoRtJNhIGgHx6AaXe8B7hONvPxfhfLGhRcjnevZR2j4Ga4kuIWi+H6iqGuNc8WALZUlx3+OxeabNa6OEdL39ye/R4lmErDWSNiqGAshsM0WtztAplKRzBaRi23B1VTzeg4ccymsLEfSStm0LwGSsUAsDulal/g+NsZYUI7t7V3n6I59i6Hqm0yk5hbHWDba25pwcH7FeKp7+yCc4vPkmPlpCKO0AMQnD0LEaAiEWsMmYoF5XDhAGPwLLHGmcG/3tRYHS+Y5uSFTOorOh7xPel880Z0FXGlKiMpa6cXTLAVUphMS679je20Ybgx8ikgeyKEKyNJXFqhIQqbSibRxRyqIvxLKEkphQY1VkiL6cI8pgnCH4gWWXwEDAc7J4HXv7RdSVz7PuEmnoUP4ukMjNJVJSeH/I7aOO2XSP5z7yHIcnCxbLFSGU69+m9S6LLuFbGkp/+Lh0YFyYAJITSim6LmCrsjg0OZMksTg9pXM14hOurXBalwBBDKiMshXWlAyJ3g8Qi6Um5ozyEaUUPkZ8CkCidi05FKXLA9M9fzDvfjLo5RpZLFFNU1jw9sFZBd6TQLZHaTage9oWpnunxs81sdFky3sGujcp5h+A7m8+PyyBbbHWD73CTOImI+jbv5niCvwqFWbvrV2pPo68Fmd8wq3o9x58l/j9o0Ji9/nArd9l3zErHidgHtBH5rcUcV5R3X107ELveyC+O52yNatJsYRlKRRd8Hg/kIyh1o5IS9YVx4dLwqD44r/0R/jUZ55FycByOZRKrxTpBaZtw8nhAe3WLtopTg7u0g0Lbr31Cl/6rd8CKm7ffIMwHDP4iJvuEHOmjwktttSNZY+kWICa0iVhXDIojWhT5KLYEkCkBNe2tE1VmLcMq8HTh0xlS7BbluJ5FK1QSWE0VBasK9LdPmayJJTSxAzOVDRtPdYxjcyr0hjXUpniO40hoJ1FmxIoIQjWWth0U9cJpQsoJCtsVVK6rbU0Tct0NqOa76CrOU899iPUszntpMFIAdwmeSwLtM8F9GzqqEbJsRo70tV403ouWZayRMyiSGJKWryuENegXANa4wfP0dEt3nrtN3jjha/w+isvcfvuHVZnZyTvEYSmrorsXxRYi6nqUqumFNZaYogY0WN3dJGFiwh5VDpYY1BSoqeq2QxlNKaqIWfW6wUpRJQoRAXC8oRqXZQPttacHtyim0xZdz2D7wlvPE+MpZ86hoAM42VZK6zWZG1xdUtGcCmSUirAgnK3UWT5BVykMRRuOpszmW2RYyLGnsWdO8x3LjGdb9FcvEQ7naONKuBSa4ytufz0c1x7JuDa0tetJJbu8g3byBg6l2UEOOU9iV8HyDeAW74OjI+C9lHaPrLXSm2ocTZJ/cWrLbTTWamPG+uwxgLxc8D+9n+kKCHkfG/CvWXAvVdRQt6KfFtLRmJEksKHzHod0MaglCL5gZwCSgkpJxSlG1vhMM6CNShr8GMPaFVVKJXx3hNjwFqLsa5kBwi4GobuhOl8l5wjJ0eHGNuwtb1NP3i2dy/hnCX6JUaVDnhXO1ol+LDmbL0k4ZFcktGNKksoYzVWl5R8HwPOwXTS0vtEVRWlioimMp6UAiknUjRUzmGsIgQhS8TqsgwMQ0eWhNMGY2EYOmZX9mhqjU+ZnCxqXMqEFJEcWXWBtq2J45+lDM5qkITkYr0hQWUrsoxhd8qjFAzBkwJom+njGd3JV2nm13BacHKMNzXBzlBxoAoHJEkcLeBaP3Bx7yI/8snPcvPmDW7cus4QAjJ2oIuoUt23ocrHQDcptH/5/FVZ7AQRlguPU0I9cdR1sUGQIqZxVJZS3WgMVeNACkuesxBTQEImq7E+TQMmceoH1j6jWo3CIEnRti1+iOSHrIb7YL75vL2WbJdwYfbAWHCVBHvw9kA2NZ8j6/X7M6BtXEZvmO64MyFsV+8d6M4j6A4fgO7vZlTknbG4P+CTqu9HhVlCZfmO1xZxpW6X/N6rZuzS8xvd0/xU/TLdpYfTJX7/NDfX1Ifb9BffISteCf2+Gpdr5R8Vy7JNvcvTXAycPlOxvwro9UM8Dt7FvO+BeIie3isUGmcNmUROEW1qsrSsuxlRZsy2rvHjX/gcj127TFNZlqcHpJSYTbeZzGccH3V0K49RiZde/C1CNmzN59T1Lo8/+RhPfXhKRvEr//CXuHNwF9e0pUKKREoZZWdMH/sI1WTKcOsl+sM38T6gtSoJ3sZQVTUOKdJNnwgxA5bKlG7plEpHcEUmpEAvBlEG8lgfpgzGWNCaIUEaQRSq1CuZ0fO4YVKLWqA+r4GKWWFtBZIJvmd11lE3ibZpcNahYGTQzTkot8Zhp1NMM8E2W7Tbu7SzbVztsDrjckCnHiMrjF+gxnotoZxAJcF89A+PXu4Nu4oUP2fp6jYkDGkE3dgGMZYUBk4O7vLWG7/NGy+/yOsvv8CN69c5Ozsieo8CjDZUzQSjweeINY6YFWhH8B5DROVE7wdiChhjaZqaYWQ1NILNiWQMxlls3VAZQ1qvadtp8bXbima2zfLoNlpbki5+YyOCTgmVYkmQ94HkIzuuKYA1RhZnp1hdTkWLwapSl6W1xtR1QXGjFLuqKnJK1FVFjp7V8oyUpSxVxqT22dY+l649xaSdMPRrcugYZnNcO6Pvl5AjMUdSCqgcsMainUMlCKnHSg0ocg6kFDkH11nudYnDOf2oGBPHxw3w16vBRXLpXw8eUOep6qOD9/xxNsFt1jmcNucd0Ru2OW829vL2aBM1SpAl5/u8wfd86iLqHJiXw6zI3DdHm3MVqmoJIZB9JEeNs66k/udMypFoSrVf8B053wuKq1yp9UNSqQccK++CJFRMhORxWuPsFn3XM9/SpJTYvXCFyaTBDz2LsxV1O6FxlhvXr3N0dkZOJQXeVQJUtJVlHSNdyGjRVKVhniF0uLolpVSuAzmx7roCOE1FkCLnTyljrEIbqIymrg0+enyXxyWlYLSiMg7bjnVzKM5O11TV1dLWgCnZBxT/vJcOax3TmUXrcvwpobDHQM4GaxTaKIYcIGWsdmXBmEvo29pHbF0VK00MrE9fxVa/jJk8ix/moFuMP4awJFczhAmn4ZRXrx/z+IUJW7MZ+fJVbFUCGRerBYP35BBYdwtESh1i0xiGYbjv2FRkiiJQpEj8g89onWlcCVzLktA5Q9YlWCdBkoRGSpe7Lr2ktnK0TYs1iWhSWXT2FrqeKAqnHLWbMGlqetcTh/chMHuU575aMqqK/ORV0vTBMVCmC5gbh0jXAaDalhv/w2f47B//Hb70f/kRLv6/v/aDLVH/JqDb71SE2SMCuuMIuj/A3d/1qFz6zr8vgXjv4XxfKsyyoGL+znJzO8rkH4Euce0T//jkGf6t3ddYX374J5JKid0XA7f23zkrLkYQDXmTZSCgkzoH5DqUzzdbsF0B699qYqNYPDVh+2vhG29Y34N53wPxGD0paoyp6L1nue7IydBOtsjqIokZz3zkM3zmRz/LZFKVPmbJxBjZ3tlhOpmjjGX/wj76kuLg7i2yWPou8vjjV7l87VJhmmLk8M4RO/tXuPrY09imoVuesup6mv2neOpjn+XZKzO6bsXL8jT9qicPB2OfrRTZp1JYo5EEqDHBHY3Tkaqu8T4Q+h4lgjOlpznmMcRLG6yxJVVbAUqVgC+BrC0JhXMV2ozJ3uONJlIk+kqVvltXuVJl5hw5BiQNBD/QTqaAQpkaN9ml2rlANZnRzHaYbG1RGbASsOIx6QztE+Y++5fadPqe9wjn8+M/3bcRFFFkpUlYkrIkVxe22zpCDJwe3+XWm1/jtRe/xhuvvMjdm29xePcuXbc+7w1O4/OqsYIKFP3qjDj+vCkLWsoSwah7NXFOa9q6LFRC9GRVWFiHImtNVVWIVqS+Yxify2jDfPcCKXi01mxt71JVjrQ4ZBBDM9shDh3r1ZpmvoVOkcGGAgSNodYaCRnjSnUZIkRVkr1nszmz6RYphBIyhWLo1zTVpPh8FQxVTeg6UpTSi105JpOGOKyo5jOSUwy9x6JRoogxcLI8I2VP7NfsX3qC7WsXSST61YBiw2RGUvDEWGrM9OiXl5wRNTLTUnzCiJB18WPn+1y5559pToS+K53g1p4D9g2yziKoXA768vcjMJcNe1586Gi5l2ty7kGHjejdEKm1I2O4j3N/22vJAuOao9gfxiVV00xomposDmcUSCSm8tNo46iNBenBOHDQ9wM5RsS6YvvIRZZcGTXKkhODj8Sgqa1moU5p211mW4GLly9grML3HX3vMc7hfY+cBQRHVA5bVRA1/ZDRWjGtp6XOLwd8DGQiTV2jROh6j1NFARNTUSdorThb91TOEHMiScJqQ+tqUtBA8adpPZCtwmg7Esclh0EEvI8c3j0lREM7c6x8ZvCRoU9Yowkeok4oA21tyuMp0GMOgNGCT544lAR6qzRJxoWhNogRppMi4ZYkJA9nccG6+23mu6d85MMfZz6vuHW0w+3jbZbriHQHZH/KUcrMJ4aLe1us12vapqWuG3Z39xCgbaecHh+yWixoa00YFhyeHNB1nnKalWPJjwoLPd4LKQFyJuuMWIsSTfRSkuqNwwo0tWVIvoD1WGrY+rTGZEHnhO8K404q1qh6OkEJpBwhJuT9yJA+oqNCQp0sCgu+t0O4MEceUDq0yoI9XsOdw/KZKs3w+Wc4+9kF//GP/B/Y1om//7/6Kv/Xw59m9g+efyDP+X0Za1FtO4LudgTdhlir7yvoLjfavE1a/gHofnijo5Dd+5sSFw0yqR/uISy5APHv9GXOlNfxKIQ5ivDlO1fgQ5Afe3j+8PunPuhxZ3P8zjs/mY1XpHr8elX8498UxSpFdfbtH8sOj85y9H0PxNu2xlYGUgHXKVVktkA/yad/4g/w5NNP0VaWlAI5KybzLbzvuP3WW9x49S2e/dSngIhWhtlsymy2zWc+++MYa8eu4fJLwVjDhSuPYybFT9mvO5SZ8NRHPoG9+jT11hZnORGz5fLeNqwuczd7nJRu3hwT63UgJcGYIlm31lLXptSIIVTOkIMt0mTJKMlUxqJ0YfoNAaTcEGilC7toDFqVSiGlC+OtpIAGowpLrtEjKC6p71pBjqV72FYOyYGgt7n83GfY2p5TWwrLHQecClTpEFtQ2jngRo+SVCgspGw8uhQ/78hbhqRJShN1VXzdrgVrCDFydnzI7esv8cZLL/D6S1/j1htvcHh8m6HvS2WRKj7QLAmTS3jZpvIpU2Sp1igMGqMVwcciL7UOVCYPHVC83k4lgkDyBegEySilmbUNEhJL7zHe01hTfLuyAYWWk+Mjcgi4esWYxIckwUwmmMqizIRZO+HSpceQMDD4QAgdwfcomSHRo3Rme2dGrTLrRYdqZ0x39mirlhQ92lpi8pwcRrZ2d+lXC+rJHBlDzKITUvBYV6GyxxphsTwmec/exScZhjV37twa66J6rLZcuPI0Oxeu4KoW8WuqekIc68VSCqQwEHxfmHABOwLgUsV2nlfP19+ZbSTpZR8khVUfLQfamOLxzyUJHqXJKaJUxo5J+hvWm43venzce8+y2bSoc0wPBWCHBGq8WSzdk+dN5udS+iTFAqJgrN8SJrMZk6ZiGEJJxB6PJ61HPYAkXF1hq4rgB5QaSHkM9EvFHuCcKcdZiLROUxlHSArjLD4Ktqmwlebs9IydvR0ETdVMcFUFklgu1zz59JP8zm9skbszqspibEvvOxBNkgQ6U1kFxlBbQ84JSUIfBJ01YGhUqXoLZl1+elOUGc5WaEpFXtf3xU+vFSFGslZY02C0IqaI1SVs7ORkTc5QVQ4lfclAMBprFNk61n2p/0pJyBm0siiKRB0sRjsMUmoac0kgRyLKFCuDRtCiCSmAqtnefoJJO6eqJ6y7nqaZ8KFrM56+kjk67bh5cIG7pxfwYU0XPFXVsLU9o+sDCNRVXewBvmc6n1M3DVttw2TScuv267z4wvNISsSckVxUJ1opklLo1mErg0imURXJ5PGzzSg0KSioHUElfErkULzoSkPYpNSLQUTwIZFyCSIc+g6VNTGBTwH//QiJ+WGfDHrdFxa8rslPXHmwLPiQMLeOkeWqsN2XLvDC//QSf+F/8H/nx+pb51/3L0xe49V/75f4pZ/5CdyXXn1gz//QxhjWP/4M3UVL3oSdfD/mA9D9ns8PS2BbmtYPv8LsHXWJKzAaSfmR6BJf3pwDD7dL/PzpjOH2T84JW+/yOTKY4T4w/i0m1UK26luy4ipDc9c/Emw4/BAA8aHLaBI+ZKLMabYeYz69ymc+9wU+/blPsF4ec3ZWks6NSixOzxAS7WyL3f3L9OslrmppWstquQSE4Ae6s5MRbkROjo84PjoulURty+ATenqRz3z2s+zvzXj1xHN0fIfXbl1nxyq251P0/mNM3IzVm8+D6chJGGIii8KgIOWSSm40UpX6HWcrcqsIYz1SDgEjgnWC0waLoEfpaAkSUmStiMlCKgnOxlmQIrUuvdAa6yya4se2uoQsWccYTlR+Gaq6Zj6bcrEacHFJSrHIyk2pNTMaRDZS5dFTnMaNk1JkgSSKhCVqO3Z1tzAy3Wcnx9x66yu8/tJXef2l57n5xuscHx4wDD33UrpHMI+gNunogJISjuFzYeNEqdLF7Sq01sXjqyl+41yOBWfLDbN1BtGKrB06CVECSpVeaqMURqsxhdlixpTtOHq0rbYQPf3iBLSmbmqSCLaq8aEn9StWqUhfjLbUdcXe1Sc4Ozvk9vXX0JJoasdZ8CSfGfIambbM9uZE1SA5oSVQtQ1HB3fouhUpZ44Ob+P9wGQyQ5OorcPFQHBFYt6FSNV7qpDwi4Fnnt3n+DBz6cIVnB047UsIm6tbtLV4H4ihHA+umgI9OQXC0JN9R0wRZSvuWe5KN/PGO755v8pSqpSsKXLx1KdMTiUjofeZuhKa2uKKQbewt9ZRiPL7JBTnJoUx/3zsYb9Xkybj/x8d4iPIDgLV5vjgPkn6+K/CnSvQtihDRNAkjC3e66rSWG0IqXR/p5zQalRxpFzOGclo44q02VkkRyQnJGeMtVRNVULgQkSLABnvI7dv3mRn+xLb25az40NWywVVO6eqLHfvHHDl2jXETLl85Sq337yLonjZht6TJeBjj0ZjrEGUout72saeLwsmkwZSJOaBFAsDnvOYS2ESFodIwEskBAGbyQhDilTasAkbyxhC1JAUsS8d6m3rWHURpRJN49AU777RRYqdc0mAz3hSSGAMoka7BKksRXIqSxgBIeNDICvoc2bhM3qIVHqgrSdEvyYODcuu4/T0iOQD7bTl6aszPvJkxfEpLA5XLM8WTGYN7bRi6APdel3YaOVK1kPO+Ky4sr2Ls3DrxpscHp+e99mfL40E0pBpphOy8iTdE1NGpMKHTK2KzF5iJno19s7HEjyHo6oMWWJRpghYpfEpEnNPiIa6qtFK09Y1tX00fvm/X+dtLPj+LmF/+uC84DLWkt05Kj7TuubOf+/DfOHf/HX+zxd/nm9GKP5Pdv8JL/4Hlzj8U1dRb9x8IK/joU1K6CSF+X5YM4JulQQT7snMPwDd7+18Oynv+2ni1D584BO+85spuizUifGR6BJvb1iSZD6/9yZfth9/aF3iACpnVH73VohcC+5Mlaq9b/d2qRLw9i1ZcQViH52l0/seiC9PBnycYOp9JpPH+dHf9Qf49Od+BEVx/deTll2tWSxXGF1z4eI2q9MjurOOQUpftuRIig5lLJKEk8Mb/Po//C84Oj5m78qTPPHUh6iqmpQDQSxm/2l2n3qOk7rm5p0lX/nlX+TNF19m7+I1bs3ndF1Ps73L489+hMmVD7N660VQA00NIRSZtLYGjGGIJenZqETd1jS2wiiFN5ZsHEJCsjCEQPAJV1uqGqx2aGPJAj4JkZKqjRQpuGRBy9gNPtZIGRgDqjRaZZRElC5e1NAvGYae3JSObmfvO3SK0bd0PlPk4EkgiiErh5giLxdbkyRzfHyHGy/8Dq+88Nu8+tLzHF6/zunxMd4PheGT8qCjc3xkV2WUNI8N0aIoEXWFV4u6ADY/MoRajx3XophPa3wUFl03Vo2pAhYkk5LHBzBajWAhI6r8fNYYeh9IMZHJSFIEMWhlSESyJBoRJCuiqlifHmOMIRrN0A9YBcPgQSmmkymHB7dp2wkHN9/kbHFG5arS+y2ReaOhmlFVFSF6BI8RWKeIy6mE7cVA9gPdMOAqx+pkoKmnuKoGZ0tati7Hq/ieqCxNq7jz5kvMLl3FpyW3b7zBfO8Ks8kMvz4mTCZYU6GtQ4tCdCLHRHYJrYWYBmIMuCojI9snsulJL6qB8qmMS5ExAEBk7LiPieUQ8KmwoqkfGEKgMqV73laWsQZ8XLSo88AYRfmLjcJi0xcOG1W7LiF1Y8J+ynKeRH4vbWB8LLl3LImUCrPN8apFqOqGLKaEFKqioCgheAVwai0oZdEKrBMYwj0gZhSiNNqU5YNWJdgw5AyxVF6dnq6wbsr23h6VzQxdx/bODiELddNw5eoVlIJ+veDmjVeByGq1QCThnONsMZSKtqQRYwm5R2lwyRBzsSSEOIxhZYrOZ2qnmdTFbpKzKUoGpVl7KfVsRFKQkkWRI7NN3aDP5ByJSaglMQyB7Z2WLJ4QExnFfFLT1AmlKzTFGoPJFNF8sX6U+jeNNQajSxMBWaF0RgiEnEkqsR4gJ4PEzOrwLv1igWhL2x6xs7fHfHsbZyuODo5467XXqZqGCxcvsndpl9XihLzO9F3PzYOO+XzOtDH0XU84WxJSgqzpvGcy3ebCxcc4OlmgTfnchXLO56xYrQa0FWazGuUMiopJVZGqhKiS/B59RKIUtURTshvi4GlqQxZLVKkEfEqDawxksMqhUXS+LE7PlUIfzIOdDHrdIYslNA352v4DTUTXMWNvnSCnpd0ifOYZbvwZz1///H/IBfPtE/h+7vH/nD/2v//X2PuZXeTo+IG9pocx1fEAjz+g903uebo/AN2P9hQFwvvfJx6n5b73YeY2qHcAxFGlXlh8KIFtxjy01/NOZnJbGCTy47NX+Z3m0w+tS1yMYbjU3vN6v5vv1SAWTK9I7TtgxZ1Cf5NLsygIU4t9RC7F73sgnpo96sklXL3Hxz/5Yzz11OOcnR4RhoG2bmgnLSkHJAWU8qxPT+i7gcvXnqJuLAd3b3B4+w6T+Q4XLj2OncDrL90mk/nQc59ivrNLTonlconYKebCE+j5pVKZNQw8/2v/mEWn+d1/7F8jrY5YD57JfJ+TW3e48+ZbXHvmQzQ5snrrJZwpTHbpbtYgujCCWhOTLxJmVfyfzphyIxuL77qylhQi61WpWgutZzabo40ba5Ui/XpF3TSgDVYZlNHEMTTJbjzlQUh9Sd52VqNTpMQPD3TdGr+1Q5ULU1zC6GAYEl2AgCGZGlNN0HUDxpFE8CFytlhz59Zb/Mrf/zv86n/9n7BYHJKGkixem5amatBVqVhTI5KTfC9FPbNJPFYjyBrTwrVC4ihjVhqnS9e22QSJibBedkQpoWsFVCec0TijUVLC8UzVlCVAjCjraJ2lbiqiD0STGEI/KgjAjZVISRQehVEaHQdqY/A+kHTpd3fWFjSZBImZvu9489UX8IPHaUNImclsi+3pFCeZVQJTt8R1LAz/ume+f5GhW0ISGleRV2tSElQYX+d8m6adMHQLQKOtRrmKK/sXUURuv/E6bxwccmHwrNcnYCa4uqEfVoSTNWcnR8y2L7F74SKz7W1sZVivPZJTkeXmRIxhDM0r75XNUiq79L1fHOp+37YUY4BSsOp7+ijnoDrlREqKIScu7e2NwF3uexDOgTyMn+9obVCUC6jAGLSeR8uDKh7yUdJ+7iO/b+6Xt2cRstIji69QIljnqJoZ3bpHIaVxIJf+a5RCskJpsM6gtKKqIsvlCUOIVFVJUhdySUzVkEQw1jJvGtLoH1fJj4L+UhmYc2YymWE0DL2nshUpZ6btFL9YcXI4UNcGdCSkjNauJLGbEvCYY2Y9BCpXMa0MISXiqCc1KpwvI4YhIGSs1YjO5WcICZ1GpY02GKvJqXj4jQklo6KBZbfmbLmindUYK+RcY5XDh4GuTxhlUUbAKGKOrIcCzo0qn01tHUkiGaidIWU/pq0XEJzEgIBNhqqaYWvH4Ae65Zr1ak23XrNarGjbltl8znxvj2655M033sT3a6wxzHZ3MFjmjeGll17FtnMuX9xmb2ubNkf6vuf0+Izp1Yvs7F1k0r7B6XKB0aVPXDPK06Pgu0hwFZU1aFN67FtXGgtyzojKLHxHZSta4+iCxzpNDBktFZWqmE7KNTxKRFEhQcAIbeUQL6yH748H74dt9NEpktRDYcHN8l4tmWpbXv43P8S/+8f+H/xz7Zvv7LUBf+MTP8/v/3f/DB//cx55mGFR3+PokxUqz75nQGYGoT7LH4DuH5BR+YcEiDclfJB34OP+ruedMOKKe13iKYN7b4F4e5C5nR5ul3jcarj7uQn9RUH0d3dRCFPB9JvE52/zhQpiC9U325Eq8FuG9v77z/dw3vdA/OLVj/Dcc5/jyaefoqo0wZ+hjGEy2WJ7ewcf1rz+lReZ7Vzk4tWt4r/UBkmeoQ9orUvydIycHd/gt37tV1n1kac+9CHm2zMgk2Kkmezy8c/8KJ1YXjmJpBRZHR3w5pf/GdWFJ1mcnHL7xRd444Wv8bk/+C9y7cNP8+pXvsR61XP5qWdw0bO68xqa0rtbUn0VoMmpJBvn4M/Bj6bUlGWrMcbgRoamGjx951mtevyQqeqqdJTr8VFTLJ5inUtKdijMnlEj4NAWo22RuCvBGk3lDDon+rNjhgs7TJTCMLJqWXOmdxnqcuOjEbwPrO8uWPeeXLAzq1XPa28ccGdFuRFPcURThSHLZBTl54jel+RJKR5LcfckyhsWq3g7ddm4mxL+hgKVFLVxZJMoktjCdpVqK6GCElSXM9naImOXSBi6QrNaR103zHcucvHyVULoWRzf5fTooCQ7+4EhBbIotluLFljHhDWlbq2uW5J15OypXMXWfAujGipX4SWhVcZaS7dekrwHMlJNEVMxN4rULxmiZ7a9j7Y12lnWq0w1mTNtKvquw8RM42raC5fYu/w409m8hOwh3Lz5Cr5bs16d4VcLjhcr2vkOog1HR0ckL6ToUbHH9IFma5d6/xpV0+IqR0yexckRSsFstl/SzlM4Xw5lKaFxpujEz6+DInIv5XysUvM54ZOgx3C+kmiukJxoq4q6tgTRm8PgXH6+oa1lw66fn833XXnHrpXzEEDFqJQo7Pzm2Ngw4PcHyBVGXJ8/miZjbcX2zjbEgXVfli45R2qbsbXDh1CSyVVJgNdmXFQpRUrl3NGqZEVYMwbWxUA/DKSY2ZrN2dreJYYeo1xJXteWoVsSQ2K+u48f1hzeuU3OGsTQtA1al0VY5Rqy9igjVMqiVcvC94gEUhSSVdRVhY8DMcdSLaih8wOSy89aSWHHnVFUVSamjNWKWueS0q6kAHUrDGuPDwlnHf3KM91qqYxDWQ2qXC+MEjKh+O4jo09c471HNxZnqmL5z2pk6hPGCEMsVpUomRg1lanZ2tpjNtkjxcBqeUbtBtadp++WxV7iPTEEJvMtmqpmaz4nxhn9umdWV0xqy972hNM+8/qbR5wcH1A7xXznCtvbW6ijJXt7e2xvTdEjQItj1Z8oSGS0KoGMrjIkKQnvTVUWbQZhOXh8DmAcWSnWMVJZjdMKHxQ6CdZlbKUYfOlLr7QmSEmM9ylSuYbaNg/619wHA2A0+fHLD5QFV0mwd8/g8OQeg6Y19adP+MI7BOGbcQr+i9/7f+IP/9v/S5778y8/1C7j72XUco0OQqq/x0WGKu/fB/MDMjIGtj1Ckt2HMdmBqmvkYYakxfiO6uAerS7xwIthl8/XJw++S1wp+qsz7nzOEaff4zVBQWoEHRXZfYfH+jbvf7+jmLfukagwe98DcQIsD17jK3deZf/KNaZ1y9aFy1hTcev2DazWfPjTn0WS5/DOXVCWC5cu4ZywWPbs7l3i9ltvsRyEvf19rnzoo3TrjhgTi+MztLWAxpglL37p1wiTK9RPfIyqthycnhJ1w7XHn+Hi49doJjXXPvZpLj9+hbuvfJU3v/zrPPVjv5+9+RM89fFneSUtuHX3NkoV+bXVGus0KWtyX5gta9K5BDcJKGVRtrCTxha/d900BB/IsSCRoeuJo4+1nrS4qsFoVwKTlME5U2BuLrL1TCzp1TmTNcUDrkD5jpDHi4eEAp60wlrH8RB548YhjTMY1HllEtYQlWYZFMsBXDPH6voe2BIhItSVvScnVxprCoDbpMLLiKpENhJ1MKiSuK7L14oIxkqRl6eMc5qG0g6RM0iKRXJsDKpy4Fxh5sRjQqnlCimSvWc6mzJtpwTnyEPH0A3E4IkxoYAoiWW/8faU5w/1hK2dfZrphNXilGk7ZfviVS7sX2Lo15ydnbFaL3DVBD906JhQITPZ32FrvkM7relO7mKsYbK1R9etkD5Q11Nm23NMpbHThtAPqKZhtneRrZ1tXNUyn05YnN2hW3cIioOjI/rlihSF6WwbyZ48RCRrmmbO7s4V8In53lXml68gSjhbnBD9Gt9H2p2a3q/p+o5mbBIQKWxyURiPeQS6hKvpc6g7qhkkM8Qx/n+Eu0IGDTkKbdsWNjwVOJzlXg/4RkJ+P40tm79T3/gF5RnyqKKwiNjzZ93svN92uRZBVFGbbGLnzOhVn0xqco6su44UhFXIzCalSm3oB7KEsYscqrrIsq2BFD0xRyJStu0pkQW0tlRWUVe2ZA5Yy3p5Qj8EmskW2lomWzv03ZqcA4899WEmbc2v/dd/B1NBipHaVcynikUfMdqw6jskF6WA05bKlfMl5YDVpdoQrRlCYr2O1FVdlgYCJo3yexSNqRBdFiYQCSmRYyT4REoa52qctiyXHZcvbTGfNAwhl4yEBNqCJEXOmcpZvI8QDS5bampy9vhcHrcIVGJJVRcQpVDZsjW9SG33mM0vYJTi7OyUuk5UrmE6LdVxIYzKiJTwQ18SCKTUULq64sL+XllKZs/lHcvNG5kBR8yRo6Njjo+OaBoDqeOxy7vMd3Y4XizLYkg4b3CwWgghEjqFbivqRqPJmJxYx0RlNFnKAtAqjUnQKE1MkeAjxipS0IhPxX5RtWglxM4zpIS2jkYZ2vf3fe57NuGxfUz98GrJNiOrFdf+nS3+nb/yh/m5x/7Ou3rMiRL+1k//Rf7Vuz/Lk3/la49kx7h4j1t/70A8G4Vo9UGf9w/Q/DD4xLNVUFcPtcJMYizEzndqaBiB+KNwHbCrwG92T/PPt1974F3i4gwHn/7WIFxl0F6N9WPynTMDVVFufEcFx7e59GSn8NsVzQdA/OGPX1zn+uqMJ579KEcH13np5m3a7W22t/Z5/EMfZTrfYrVYUrUT6tqwWpyxWjacnhyRYsb7Y1788u9gpnPuHjqyCD7GchOoQAWPQuP9gDo9BnvMtXbGldmTLGVg/8Mf5fGPPsvi7g1+/Rf+P7T7j7FaPMNL//gXWRytmUxm7E0q5lXFizmUm2RjEKNKz7YkrLUkZxDyeEMq5WY5Z7QFYzWSi7RUch6BgiC2BEzFlImxeMkbUTSuJitNlDFUK5WfRyig2o7pz5JLN7HRGltpclgyrFbEuaZxxTNJFoxEtGowxmBN6RAuKcGZsz4jzuFNhakcly9f4M6lSyyPr8MoK88xl0oxMj4X0B0VpVZMFTZcnYOyTT2WkJSQNGjRKFueV1JCxYgzFpUNw9iDLWiUbQhqlFULbDKTktHneFEh4HtOb77FZDJl/+JllosjjKuomwmxnrBaHpNXC0Iq701SCq8cFZqh70ko2tkOSiWcs3i/RqHZ2d2jqivWqwVNYyHXVNOWunG0k5ad3V2MJIxr8CnhkkcUaFUY2bjuMa6FuiNpxenBbayruHCpYfBLjg5vs16c4WqDM7BKCbQhhIixNY8//WG65SnaabJu2Lm8z3R7H2McQkZiQJSmmc9Ae0K/JIeB6PtS02SrcX8yerkBzuHsPdl3jIFl5+ljPpfzb2TjKQSM1rR1BeMC4+2kyX2s91iZpsY/FsaMrREIi1KjR3zzBKUzO49AT952FZZ7/ytCRp+rNXJOGA2uaQnLYtVyVpfHSkKKJW3fOkMMCWV0kWavV8SYmU4akIyhSFBT6On6gChLXTu0EkTVxEXkxhuvsrd/gbqdYa2mquvin86BbrGkbie88uLzdJ1n0s5ZrQZiTAxDxFnDEDxRFZl59JGcNaGLhJxwugS51arFiOLYLxCb6XyHUI/BYqks0GRk8TXkFFl3A9raopbxGrTQWIVRwvJsTV1dRhsh9B4o8nyLQYkQoi7XF1EYO9bfkchE+lSYAasNSjuUyiRJ9BGM2aVtrrI122cynbI4PS1Lx6zKc6hI363xPuCqSNPsYUw5UbUuyfDD0HPn4ICd2QQhM59OubI34627K0TVKCJgGGLDm3c7tuYzrl17ijt37zL0w6j0KBkRCMQg+CHTtrnUr8WMlyLn10kwIqQU6BmYuIpl7JEo1LZFVQ6/CoQQiZKIak2XBYzBOkvrHM6WwLcP5sHPA60lO1zBwdG3rJpTb93kxp97lv/kL32Gf3nrt97V4++ZwGf/xa9w9Ld24eDoQbzkBzspUZ1G+t3vwsR532z8nOq9v8/9YN7h/DAkp4uCPGlQD/PUE0HHTPoOvu97FWbvPRBXIfFrJ0/h9l9kfeXBLs/6Sy1h9o2PqTLYtcL0jC03ZVGSK0jNCMq/xYgprPi3k7h/2/R0Bbl6NHwY73sgfnay5NqT11guTzg5vEV3tmbHTNnfn9KvFxirUeJ57cu/zGrl2bv6NN16yXxnh7rKHN49YeviRTKJs8WSYRiIMZeqHqOojMEZjU4ludtoYdsEtq1na95i8wHXX36Ji489xse+8AdZHx/xyq/+I+6++hpP/Ojv59K1y7x56yZ3vvJPODu4W+rG9OiCThuvbQHgMSZ0yEUCmmJhKVMmDmOwWY5IzuRUGEZtDMY4JvPiR82hLwx0vCdxT3H02ep7AEtDScXWhbEOoUfJGB43DPRtQ03E2OLvNCoyazQXtmq8LzfeSUoK/KqHxtZUlWV3e4qtLvDW/Ao3nUFiKMsDKdJ02aS0jx7hUhtVtmObVEmtKfJ1CrPtyISQyGFMtxYQU27SldHkUBhy40yRCSlV/KpVqbCIkrBSgr6wBkvxDC8XJxzcehNF4vjoEMnQbm3hdgwp92TfF0mzgmwM2Vgmkzmyef25gMjTo9sceM90e49JM2U2ndJUFhOWNNaw6JYc3X6L1eKUk5MtnK2IMZBzYHF6zNAtsfWUyXRKHNYYp5nOp0gS6smEYX3Gyd2IKLh94zrd6hhnt7GzLbYvNEgQtvcu8fgTT3B2+Ba3uwNWhzdZH51gn66Z713GWEuMHq0NlW3RxjEMRyjryDmQkj/36CdhZHr1Pb81sjFt40PP6bIoJ3IsSexam6JiUGC0YX93G2tLWMom3O2cNFEb7486r7zbWHiEEhQn5YAoC4D7gt0U9ywI933T1wHy+6TpSgMJrQrgjGI4OVsTQsAow6Qpi6qYEn5Y0S3XxBBQWrBaU2mN1YqmbjBGs1qc0Q0DPkTI5bzNOWKdwVYV0Q8c3rnBuo88+9xztG0Fkgl+wFqHq2uMq/jEZ36C1HW88dp1lKFkCpiKqAayCJUaAyQl4UNgUk9RmNKP7SOuqqiMpQ4Vvh/AFNk4Rp0rC6zWgCPnRPAlBR7J+JCx2iIqF6CP5uBgiTKWqjakU0uWArpRGWOgMpaUI5WULIcsQlKJGMFgsNZgtC61gpLRJlMJmGoHaydU7ZT1es1ytWboB5QuFqLerxhyJEpAIqw7TZaIahWTdoeqLa95ue4xqiiIKue4cGGHO4dH9FEQU45nUsdy2XN43PCx557g2tUrvPTya/fsELpcFzCaqrZoU4IfYwJyBIGUxnT+XMI0cyo1ZSXtP0BHCQxzCpMsVteYCrQq12IVMqIz8kFq+iM731BL9m2m+mcv85/+e/9tPvrv3+Tz9Z139Pin2fDTv/oneeZ/F1EHD1L7+WDHHffw9PcGxFHlplr7D473H5TRQd62C39fjoI0f8gVZlmKB71+B0Bc6Ycrk3+nI8JX71yGD0O+9mD94cur9huZawG3LCD8/tGx/GN6CFNFar41Qy76O+QabHzii2/+16l6NA709z0Qn+8/xqd+4iepnOLVF2a4asaFyxe5ePVJZrOa1772O6QI1575Ebz3hFgSj6+/+gJD9AhS6oiA4Ed/depL73jSJJPolUbpkrRsWfOb/+SXePGFHerHPsmTH/sEN155hed/7ZeZzWrC6pj1ySFPf/IL/OQ/9wXC3Zd467WvkH2Hc7og4NF/GROEnMhDghwYVsvSu6vGCjJnaZoGV9U4W1HZ4uGNIaFUAa4xjTVO1pBjYbqNKUdtigklxbMsShWGURIhZHJiXCwYcgJVWawWJGXEVIgaO8O1xqpEZaCqDD5ElFLEBGfrQMiaHZVonDDZaeh0y8UPPUvz2i+TT04QLUXumyJYCxu/cGZk7BQaTZI8yo+LxzwDWo+gwljiyFxkpUGDV5SEYgFtLY0rYHvwGaksIGhb4bIiDwFTOUQrhuhpjMNaw3q14PjogBg9zfQCxjlyCkzmu2g0MQaC99RtSzOZARSZalxT9WuWvWfwnjx4QnyVvctXmc1n1I3DtltUbsIs3MS1E3QzLX3SMaL8ktXilMXxATJEUp0KsKonGDyxaWmnu5iqYnF6xLLrcXXNZLZNzgNaW1anp9TTbbYvX2K2s0XfnbHqFlRNy3I5sL2/x9b+BVKOmFACAHvfsVwcM5m3+H5BCLC//wQ6e2KK1HCutig1eWVpoShKjBA9x2erkoEiRVYtOYMFVTq2qKoSNrapHitseknxl3NVwr0FzP05GmoMZdvI5GWUo+e8+W+KLWH8JrXxkXMP1N9HtlPWVyX0zygBV9H1viweYsCakp/S9z1KEm2lGMSQyRgFlTUMPtB1HdpoYkrl+EKhTTl2DUWlEULEkGnaKZcu7bE4PcLZfaw1dOslk9kcYxTr1YrJ1hZKB/pVR/KlJi9LsUQoEVIWUtYMAfyQIPXkylD2WAqNwWuFVYZ5Vbrhq0oVaXXKWOvKORUUWWW0syhJdKtM8IKbKAwVaOi6wN3DU6IYrKtIeQAxWMeomoGcStZMCTQr722SRM5lURNULkAchcFQWTDKYquaZjLFOkO3DORcrltVMyHmjmGIBJ0RC8kGDk/uoOWUixcik/mUOlusVviUGUJErKHSlquXdrhzMOWtO0eIVEgq1oKQHQeLnm6IXLv2OG9dv8kwDGitytJTFfm7Ga/DPpfrh4+CT5lKWxSatqqwSqHFoIwhq0QKhhA8OQhJMvWkpXEGJOGHhLLleJBcXusH82jNN9SSvcPZ/q+e53997V/nr/wv/iLXzPBtv/aXuif43/7f/mWe/fmXkf7RDuwzJ0tU2kK+x/yo5NT7/ybzfTTngW3vbW7YQ5+HXmEmGZUSfAfft9jxnj+lR6JLvLte7mMfu3QCqnn7Ddh3OyXA5xvGdt8Iwt/2bbkA6JAVcfLNwbjokgt1HgT0TSbXgqwV6puIDlKtRvLnvV0Wvu+vkULi7p077Oxuc+HyNeq6IadItz7kjZde5/Tuis9+4XeBJHCOGliujrl1+ya6qtFGM1dQ1Q1N05JRaO3o9Jo0DKQYxkRuTTAGqzU+LlmuO5oOLn/qp3jux36M9ekZZ3fuIjuP8fEf/YN86sOXSAev8Cu/8asMoXiyN73eWglJEpIyShlwFa5yTGc7OEkIihA8KCHFjFa+AORkzrubESHkXPhtpXB1BdaWG+QR0BijCjOY0rn3OkjpEfe5hGc5AzGtQWVqlzH9kii7JCnREgpQOaJHQG/wiFKcrT2LLjKb1uy3lkljWWmI3Zqdi4/RTLfpl6fgpYDvLGOScpGN541M/DycS5GVKr3MMWGMLQoBZbBGmLWWwSf6mNDKlW18CEV6rzXdhspFkBjxklFtufHeGFdTDOSU8FlQWWNS5HDoMK7GugW3r9+mmcxKqJkxGOdopzOi7+gXJ7h2QjuZ0WTFE1uRV28kTs6WWOuY7OyQVOLw6IDZfE7dChcvXeXKlSs0kzkhZ05O7rLsVgiKxdEJdbvNlY8+w3QyRULApxVnd95Ehx47maPdlKqekJLgTMX04lWuPvlhjg9ucuO1l1gdX6c7WxJ8YQy1SkQxTGcX2LlwFVGjzDZ0oODw1usc3LzBYx95Fuscrq3p+o7KByaxLKEY08ZFij188/nElDhbdYRRZ55jIsZYoLaUG926rtnfmWM2bPfYz62yOvddb2Tu5xbwcysC48VytF7kUYK+eRwNKpW++TyC8Y1P/H5GXMYHzkDWBnKJ1FSSMc6xXJwgKdC2E6q6RuUB6yyVcRhtmE4yZ4sV/TDgQ2CIguqqkuUQBW0tkgUo1pE8JocbrckhMnQDdTstHfHW0fdr6naGMRptaxotXH/9Zd586wbObXH79k1cY9AmoRxYUxNTZEiRrBLzWY1VNVqBzwMazRAGlARsZaisZmIdUBYlCUEiOGfRTpfO5ZRYrDw5a5rKYUrRIT55bKUxOiIIdVOC9YzKkDMxKyRosiqtC8oYgg7ENALyvFGzJLwvj+FcDVnISaGoSMHTj149ZxVD8izOPH7IGBwpRbLWZEnEmEl+xe3b1zk7O2R//wJXH3uMvQs7BB+QGBFTGOlLezW3D8q1S6QnG4vKDWdreOP6HT78xAUuX7rA62++BXlcCSlFSsJy1ZMwtEZRG0ttNDmVLJDixonEnDHK4GwFWZNMRmkILhCDBqPGkLaIaRSrRQe6LKdC/vZVVx/M93feVkv2biuNJPPYf/w8/8pjf4a/8y/9BSbqG2/ogsDPvvZHOfv3n+DJX/vquR3iUR5ZdxgvxO8x0CBbxb0L+AfzyI8UeXp6QDaPR3W+PxVm31luLloVAupR6RK/WYJKP3/hTb5mP/pgusTPWZK3zzvNI3Ar0FERJ3zTcDYx3/7iIhpiUx7n6yd+AMS/PzPZ3uPKY0/T1Jb14pQUzrhz84CTox2293aQfc3J6TH7+3vceulLDBFsVWFdTRBFP0S07kg5kzJFzmoMxjq0gEoJnTJZlfAwJBOTIEYId64TfutXeexzX2D/2jV2L15AlLC6e52/+//9GyyP72AogWwmGhQabSxiDXU1RRlQolDGwsjqKS04rUDMKF9PeO9Hdq/cTFqji9xl9DNKFiZKFa95BC+K2pjShZsSMXhSTGitaeqauqnwvS/AS6cSPBU02lXkYYGPieQ2YKrEcGkSzhm0hvUQuXtaVl3zxpReZU3xSLqK+fYF5tsXOTu4gVKeLBnvB2pXY7UmI2O0lyKJkEho1NhZLWirEa2RnBBV6teUKrVVRaKfUUaXC5yxBdQLaGcQm6kpctGcEzEnIkITAlOtSNqgncOHiMSESCbExLBek3MirxfYuqHPmcmkJYklCkzmMxDFenmGnm7h956mCTeY+7doZ9top1mcnVI3U4a+x4eANpbZ40+wWi9Zr1ecHB+iXYtR0LQN7dY2VlGkvW1NPhvYvniBWrekqFgPmb39KyjJqDwwhKIUSCmiEogkusUZi9ox29tn3rZIUuxcukg7maCNxVUN5MRqccBbL75MPSkgsarnGFVsAxrIo/UCKFLyfJ7ThuTMct3RDb6woTEShtIJXzWTMagLdmZTamtGgArkVGTr43VaKMnj5E0FWYHlsqkk28x9/z1C8+Il1xoS51+v7+9UU/d+F0iGLApR5vzPtGTqqqaqGvp1yWoYuozWpdILhCwZaxRbWy12DYsU8SmMTPS4RlCUxQ4KrWQMB9Slm74uwHu5XNG2E1JKDN6TUk8HaFfhh4SrGqazOdl2zE5bun6sSDNCJCEuoZPgrMUYRYwDAmO4YVl+TNsG5zQ6G/qhZA10XUTQVFVCkuD7SEiB3scSQFYrnDP44PEx0bimLE3GQL2dvRlKn5BF6MNoZdA1mkyUBKJxxhRVXsoYq3CuZE30PhMzuMqRGFj0a0I+RKRiWA906zUxeXKMWKXZvXyVmPcJPiFEzs7u0s4Np8dHQCaFgZvX36Rf3ObS/i57+1cwkx1y7HnlhRe5fvuE1hhw5XrsqUhA33XcuevZ355y8cJlDu7cxvuANQarhbqCqjboqiZJCbyrlWVWTYghEFIox652VHWLUcIw9PjeE0MkK5g2U2aTinW3JidF9IXsUCmRo6Cr9/2v3fdkuks183fh+/z6WrLvemLko//h6/zhx36G/+p3/yXcfffSr8cJ/+P/7H/Ox//iLZqjF7/75/h+jw9Uy0xsvzdqVEwBGx+kp//gjI6Q6vf6VTzceVQqzB7FLvFOPD8+e5WvND/ywLrEq8U38Tu8i0uCGUB7SK0iNaNtdsO0vINJDdjung99M7FVxFmFPXtvFUrv+zsC64T18oBuEfjKb/5T5luP83v+O1+kbgyvv/wVFitDVVf8+n/zX3L9jRtsX3qM6dYUawwhF0+qV2kMNYJuGJCsNuQqUICy1qAz5BzRqrAlutGo5S3OXvgNHm8S2xPFV770m7zy/IsQE66qCrjVhqpyuKY5Z/5EF5ZaY8atnSFpXQ4+DZjCfuvR9xxj6dx2lSvy7fECkwVSTvhhwBqF0YKWgM6lB3vjxdZWA4YQI/5sIKVEVTlICq0tOWlizITlgskQSE6XGielAUGniDENWYTbpx39kNiaOKaVKQyAKjf5lbO07Zydvce49fqXUcajoiCSigd4DN7K43mrzoPBNuBMY01JiI9Zk2OpUctS4LtRikoXqTLaoE0JrEMbkgh6tBDonAgxEEMsbJ7W5Wa5bgmAEMtSxBhi9DilMa4mSiZHD0rTd33xmMaesFoUhZG2tHXN7VvX0QjKGTKCM4at3X2qpqVfnOK9J8dMiIVNDv0a5zt0M6Vqa9xj1zi6fYvbx3e58OSzXLz2GFU9JRrHpcc/hBLh7t0D2qalblv8sIazI86O7xJ9YLK9TQgeycL69BTfdZgLc2a714jDGmkbTPbkaDGuRmvLEx/+GJPtGbap0EaTYsT7AR8GUg4js7xJ7Bf0aOweYmQ9hFLvNi52vB+wVY0eF1bb21tM2mq8bqqiVBiXS5LzOQte/N+A5POu6Y1p7X5iRasR3AglG0BJSVQf+6uzyLndnPHrzh9JqeLh12PXvFKQE9Yo2klL6FcoyUQvuErRdwFtLXU1qlaywrqayaz0gzut0Oa+86EcfCilsXWLnWzhmppwep1KK9p2xnxrTgoDTdOilSbG8lnZSUXoBb9eYigVWPN5hTNCEk+QiLGa2lpyiGgMogPoAnq1VJQ6c0WXA04nnLX4lMs7rkq/eUpxBJUKZyqcLedxCJHsiwRbiRo/U8XZScdsNt1ciuiGiFGCMqOyRseiUEgap3TpFpfCjCul0NZiNmEsSshEhrhmqsr7FGMg5kA73UFSJPkFEnqsqWmbshQ6Pb1F02h2tutihRgSOQbOjk+pU1/UQziij1RVTcyCNQ5VU+wj0wlRaoaQuHP3mP3tlgt7Fzk6uD02AJTraOp9URRVhnbmyDoz5BKQaI1FpfLvxlkkBaI1bM3nDMPA2GRH9GuUydTO0K9K2n2tLVJF4vtd9/kezfFHDfVXLdXJd5aWf9Nasu9hZLXiuf/NGX/iL/8r/Pxzf4MM/NXjn+Dv/IXfy8d+4YVHwwP6bkYy1VlgffF7BOK6sOLmAyD+AzM/DIFt2YFy7sGel3UNF3ZZP73F9Ct33nmXuB3v8WN677vEDxO3U+ST1Q3C3GEeUJf45I7nKDVvZ6/f5SVBCdg1mI7zzKFsIVV8Wx85FNY8NQq7/ro/t7B4qmH3y74sQh70vMPFyrtyJPzlv/yX+fSnP83W1hZbW1v81E/9FL/wC79w/vd93/MzP/Mz7O/vM5vN+KN/9I9y+/bttz3GG2+8wRe/+EUmkwmXLl3iz/7ZP0v8upPhF3/xF/n85z9PXdc8++yz/LW/9tfezct823TLU778W7/Kf/N3f4Gbr7+GH0558Uu/yq/9g79HCJrnPvost956hdWguPDEczTTHWJUrLuB1XLJarVk3Q0Mg2fohwL6UiBHT5KEogBgY2uoapSrUcqVcLQYyWlgcfNlvvQrf5+/+5//p7z0pa9gUTRtQ+VKoBgC0Qd835OCJ6dQEstHPy4qo1VEqUxIo+x2DMeCsbpMFUY8J0UIiRSLzMU6S11XGG3G+qmMCh6nM21V0s+rqqZup7i6Ign4JISU6TvPMARiDPjgSxqwXxN6T8SQUhoZ0wLujVYsfeJsGXBGc3G7xZkR9IiUlOjKUlcNu3tPYG3pGS5BcWn0wZazUyuwiiI73rDhyqC1wcfIuh8IvoRiaa2KF9gYrDEoramdKWAqhiLx12UD6iqH0ooh51J9NiaFGUlUxjCdTWgrS+McxlWgDVZpstKElBhioB88vh+QnIh+IHSeGDxWoLY1/XrN4cEdlqfH6CxU1qFURUqZ07s3WR3epXI1KZa6q2YyZbpzmd0nPkIa1ty9/jrHhwcEP+BXaypbQ0o0zZSd3YtMmoambZm0EzKZIQxILl3zjFaDnf0LTOYz2u05Fy7tM60d1s5L0JSGMKzp+p71aoWkhKtadi5fJuSBGPuyGBE590unMIyft5RKuSzk4ipg6AdiLD3bIQx4PwCqpP9LpqobtmYFcOax2kwAUVJUDZJK6Fsutgo593yfb7uK5Hz83s2NsxqvvXqURSs12hqksOhqvFjftzMbr9Ujy64KU10eqxzHTdvS1BZtShuBGSv1chgIQ48PkbVPLIfAsotEKcn5lTVlUZXBbV1m78Of5dqP/yEe+8J/n4/+5O/n9/2e38vFC5dIYWBYnSFpKFkK3tOtF+RYSvq69Zp2doGPfeYncbZlPpmxu7dF07qxLkzj+0RI5f0SSjVYbWsq1VDZAqpzToQe8piO7myRzscIY1odk3pGYxpC9qyHNV3wo79fqG1JRI+x5EUslx2zWYMzGYtmVk+pdIVShqwzWhlyUpDK4i9LxtlyHg8xgggqC8vliq4LJciOniGsMNZibUUMgeODW8QsaO2omhpDYLm4xeLkBuvlgpQzMQy0Vekkr51je1aWXVZnKuv52ONTnrm2zd7uNnsXdplOJzijcTqz02aubCuS74gps3fhEtbVxKwIEZQ1uImlahxbkykNNSppUhas02irsE7RuExjFEZBDJF1WIJKJYAyRwIQfWK18mitsZJZd2vWcSD+AICSH8Tf9bkWDj9pSZNvzy+YVcC9drsklj9IaerdQ9yf2+bnbv8B/ru/9if5lX/jc+z/Z1+BHzQQPo497h6IpDy79zeoe7+Njt9cSvx+mmwVtM13/wCugssXWX/hOd78E5/kq//HZ5n9PxP/o//X3+U/+Ev/ESc/fhV84Js4Vb5h7u8Sf6+nOo28GPZ53Eb6vQe3FLBnA9XpfX8g38hOv9MZ+RZUKky5W/GO3ufU8E173cNUPbDGja8fpd7Z474rRvzxxx/nz//5P89HPvIRRISf//mf54/8kT/Cb/7mb/LJT36Sn/3Zn+Vv/+2/zd/8m3+T7e1t/vSf/tP89E//NP/oH/0joMiov/jFL3LlyhV++Zd/mZs3b/LH//gfxznHz/3czwHw6quv8sUvfpE/9af+FH/9r/91/t7f+3v8iT/xJ7h69Sp/6A/9oXf5NsC665i0jsm0ZbJ7ictXLzN0p7zx6iu89MLv0HthvnORqp2QUsDCCGoSIWaECNFjSFRVBVmIIZJSRAtEJYhEJAxjYJvFOYexDlfZIrte95yeHJJTZjqdM51NsYaSyByFvgus+w5RBldb6qbClN4qRCkyhqyKszXGgM9gjCmvM49Msii0dQjp3G9rjKVpbGGAden7DT4zpAGMo6mLV7ypXJEUK4XYEnxmXF1YohFw+ZAReugjk9WSuDcr/pby7KgcCoOnC1N/cbdl3jpE0kYLjDaGuq5wzrGzfwWdGyTqUX6cCTnjxudMUqwAMQ7kLFhTYeq69KabElanEESb8/R4Raml9jETlUUrjUNwRhXPfdUSg0dJQvmIyZlsDaJNWXSQiUNfrAVAjgGNYCrHkHKRMqLKYyiNyRbxQwmhMhXiWqrprOQNhA6NZmd/D2tbEhprNTYN6GZCPZ/ih57T4wPM/kVsVSEh0rQ1zfQaVdNweOc2h8vXSx6Atkync1xVsVguWK07tDYMQ6BWBlu3TOc79Ksjdna2qOqGdPEaKSWsVcx3L9FMp3R9j4oDOazZ2tnDVi3GWdCCX5dU7vXykJxq2maCtGVBEnyxZ9gRfOcRDwuZIfiRZU703ZoQIk3dcvv1VzBVy6c+8zkUQkxxPFY36ob7GeoSpmfMBjRvbgbGr70PXG+S1uXcflg+r8TmteXzFPZ73Dv33VwUxUUaK8zUmEfgrKadzfCrGu8DSQSfhKxKbkNOxaYRYiSlRMqg3BZ26zKTvcvMn2y5eHEPme3Sq3JpXS9WvHVywOLMcHy2InQrDu7cYTJrOb77Fq++8iaf/cnfw2x7m269Yndvv0jgbVmcWaPJqUiijXU4p4o1JEcmdYvWwuB9CT3MRQYqlHA0MbokotdFFVLVm4C1knYeZSBowRpDygltxjR8UxLxlS4ssbWwXHRcfnobZzR9EFpryTayHgZSyLSNo8aSk5AJKCP0seRZWKPRypFVWf6Hocc6RZSBxfIW0UeUcgiu2HR0yV8YVgtWy1NEBZomYytDzpZKKdoWLuw6unWirizTpkISoCoyU1wlzLUUxt9ZUoIYE74/LVacLBwdKXb3tnn8yf8/e38ea+l533eCn2d9l7PdrVYWq0iKoiRq99IWnaWddGyP0XGQHneS6el23JNgBnFnEiQGJoEHwSBLGwGCCRxj4iQDxAgwSNwNJ5gsEyutKLGdwIlkeZGszVpIikuxllt3Pcu7Pdv88by3SMqSSKlEiqT4kwRSVeece+4573nP+/19t7dw5/ZTbDYNISbaNrAo8gJGoXPdmJSI8b0vVYkQmj5B6zxJjOfIzkMIaKNBQt8lkhT4NBBFwE4LJOC61/5V7uvxux7AzRKHj1rO/XZEuBdf6b2cWrJ7HfnFZ3jqf7zGA0e/u3/89TbydIMM28R71E0G81KRVW/Oa2mkf+MHtn1dFWbGwvac9sFtjt5uWb574P2PPM1/fe5X+c7yaXaUexGjGYH1JcniLLn0pcDYWZf4awCI643jt5oH+K+qhua8ZOub9LjCBfY+2XPrsYJQ5NqxV7uzPuqEmwvM8sXAXeRyqm/pfF2n2B/+4R9+0f//qZ/6Kf7+3//7fPSjH+XKlSv83M/9HD//8z/PH/yDfxCAf/SP/hHveMc7+OhHP8oHPvAB/u2//bd89rOf5d/9u3/HhQsXeN/73sff+Bt/g7/8l/8yf/Wv/lWstfyDf/APePDBB/nbf/tvA/COd7yDX/3VX+Wnf/qnv6Ev594JUuyoyhInPJ/+zf9E3zuwNaZesH3xElJLhr4ntC1CJqSSY72PRAhNAIbB4YPDDR4XxhA0KbPkUorsBZUSESND8GilKK2hVApjCmLw+H7IjJ3rSWi0He8rBM5JBpf7gokJVTEysgEdJYyJvUkqgusIzhFlBobBp8zsSYmUMrN5RlJXCqUkMSacD+M5QRKDYOh9Dj/ynrKwTCY1VVlQWIsdgXmMo/96TLUOzhEJtMtjApcR0pLr1QQyBRT5gnxSaXbnuR85RjFKmXMFmzUabQxbi3PU5RYreYcUBCGC6zas+g1u6EkpoaTGDQPKWBYLiyThY5bhKp1lzlnaLEZ21aNEfu9kCFm2rEBqycZHRN9SWItJkKwlCUFhCqSUDK6lCwHV90iZWXVI2LJEmhIxDHTNBmJAAS5Gmq5DG5vZ6emMEBI+DPihw0ggaSbzcyQfiQiK0hCqGmMNzfKYhCY4x+nylEk1QYSBrcUuHklUmqsPbbE926GYzXOYl5R07Yrbt/ZzHZKUpCTwbpXrl4JHljNmVQ6RS9qwOjmmrGpicNx49nGGLrC7u4OIEqksMeauZm0zeFDaIERJSpEgIZBw3hOjH5PT093sjTS+/iEGQvB4l/+X89Mku5evsphvMa3Lu6nl8QX3vfv1NALzHL4mXvy9dQbCvRvTN/X4x/mBzlQeMSZEDNk/IkS2KqTM6p5h+rOHPTtuMgcusscesprCVDkfgHy8SjKo6F0OCCyKElvV2MUF6u2LFIsdtC2BxEJFLm1p7mwSfQdd1/L0F54gSU2/t00fFUZZds6dp2vXKDXhfd/1vcxnE4LLx65zPUprlIaIxA2OvmtAAVEiYkIlh+vCCKgjIQqSH9AqIYMhRklIHiE1UkTaMCARKCnpu4CyitKmrHDwjOcMidGGwmhIedmktMivoYDV6RpjdplOCtyqw0UPMmV3fgLnIvhIWRmgYBg6utHBLwVIkdUaQmvMoGhDzOeS5oTmNFBWUyaTKYWt2CxPaJYbytIym00JbIjRoaNiGALWZAk5EfbmJUqVKBxDFJiqRlXbWAt6tcq2O2OyXDzmrAchBCaBtQWltSz2FsjU8cRTT45+B4FzEWUdKTnqak5RWjabNRqN1hpHZGhXDM2AUgllNbJQCGEoZIXUIFKbO9F1Ps8aJMOQiPG10V36teb1+F1/Nt1u4vitBTuf7/JmlhfUkq2+So/NN3Oeu/WGIBRT06H63MN7L/NmYNvrbBLI8AYPbBMQp5bftWvQGrbm9Nd2OHp7wck7Pe94x3X+yIWP8t3VlzinhpeUEUuguZRyAFtMI2H11edul/grIY3+Oke4wEePH8TsfZ72m9wlXhy0TJ6zLB/ieSLlVT4nhCIhavGi4DbVg/gWv/bf8K4zhMA//af/lM1mw2OPPcZv/uZv4pzjD/2hP3T3Nm9/+9u5evUqH/nIR/jABz7ARz7yEd797ndz4cKFu7f5wR/8QX78x3+cz3zmM7z//e/nIx/5yIse4+w2f+Ev/IWv+Xz6vqd/QeDKcrnMz3N6ldh8KfdUJ4FZbFMpy+AjejInyYCPkSgTPgXSmO4rBSgl0VJnn3KQzBZziskUHwMu5B7f7KP0hJTlmUrafJHve4ZOEbTNvdxaI7zPNV1ACh7XR4yxVJOKqVS4wXG6bOg6R0NPkchBDkKiYQSIGqFy37YfPIF8Ic2YhK61QCRFFOmulPjMuyuVyrd9QUczKAYXKVPEKHDRo5QYk10TMkQGH9CmoDQWW1isjDgf6UVAi8yoyVHePa0tF7dqCplZxLt+XJ9/njEaazT1fMHe/Vc5OPoS3mV61Xc9pOzdRkAQHsgMniaCG3KiuVKIMr8e1hYIssS/azek4JlaQxg8QmSw5URmxbz3pBDwSjNIDyligHoyo28lfdcglM1hcF1LEpLVuiWKBqs1SoISMvtelcgX+FKitUUrm0O3To5xm5ZyNmN23wIfwdYVyQciku1zl7l48SKb9QmrzYZm0+TADgFow+HJMZvNhtn2HltXHmA6mdG2G4ZmTbs5pd2sCUkTk6CssuVAKUXTrDi+cxspJNP6An3nGfpAPZlTWsntZ54hbFYsdi8x3d7FFpN8fCudFyV9n+s2IiTnSYVkvWkYxJp6PkAMOVU+pZzuOaaSxxSJIQMN7/ICxeiCqp4ymc3ZnU9Q8qzfG85Q/Bm7ffY4iUgaZdRC5CXQWX56Ci7XoCmd/0xkO8cLHeTPa8/FXZl7SmlUknD3IvCFgDz/BMkZWNRSYuspuaM82yVCTOjJDsX0ApPdi1TzHaQtcm1VDJzcvM6zX/gsR/u3QGrO3f8g1972TqaLBcYWPPDo2zDWIlLkdHkOsQ5M5zPmizlaK06O9hn6lul8gZQFfdczDJ4QNZtmwJYlKTmUMjSrPqsIlEAVghB6hBQorbI0PGYWP4TI4D3KeKzQKJMVAIMPuAAiWkIEKRRCDHTDQAqSmARoiVaC4AWDjxiVrTDNZoOtLNrCENwYqJjfPykFzrkckDfkBBXnElKNHfNASB4lS7wPtBtPKsgssyqo7BQirE9P6M0GbQzlZEGSktWwJLqO7anl/ELx9MGKtlMUWnPiPWmqqY1j0zukUBSmgdiwvXuVarHL8vgICAglGNqWgYS1mmk9Z2d7Too9bXPC4Db5tU2giwIxZoLEpBjEQBoSyUuiUEgraNYdwxBIEpKI2JBQuiQS6IYGhnx8SpkT9zfBsVx11NWM2n7rL7i+nnm9fNffHQGbK6DbkvkzHfqwRdw+/Lpqyd4cwHvsKuAm90aJn/nEpXsTib9eRvrsvX0jj5+YFwFx/+gDNP+PFf/t/b/F99Zf5Jzs+UZdFfFKBzEhfAT9taH7a6pLPEa+cPscvBX8pXsIsPyySUZx8vYZ6/vHqz07era/GaKhRG7ekS/v/PIipUeC8uQFQcTfovm6z7Cf+tSneOyxx+i6jul0yj//5/+cRx99lE984hNYa9na2nrR7S9cuMCtW7cAuHXr1ou+mM/+/uzvvtZtlsslbdtSVdVXfF5/82/+Tf7aX/trv+vPe7lA6h1kd0QSoKQhEnP10Okxrp6itRnfCA8EosvhYgQJMiAQGFOhjUWaAjH0EByEkCWqWRMJErRwSAHJQ7cJKK0xOodWQU4VFyrXCcXo6bs21zzJnJg+mZSUpWUIHiEFmly3FcgyVVXYTApGnyXYzqNiQmmFVhIjFSEkiJEhDCipcsyVGAOlyPLVEHwGfykRo+D46JTNegNCIUQOqhNCYq1Fa01ZWuqyRCmwwuGDJxhFTJ6UIgKBDI5pWdNN9Ji0nEcwen9TDi2zxlAWNVvzi8QmIcKo3pGAjyMjmuvMyroiRoHzgtJYCiVJMi8NvA8ZAIQ+s+RCZUno4BHOo7UkJIEbcq62GkO8ghqjv0LElJoUBqTvkSknPQefg8ekijn52xpm8wWu6+ja/BopqSDlRU1RTimKGucck6091JbAlhXldE5ZVqAklSnZ3T3HzmLB0C0Z+hYVB5rlEV2KpJCYLhasjo/Yf+4ZimefY1rP2Dt/AaUVR3cOWR0/x+AE0k5Y7F5AiJLZrM5hXF2DLSpicqxP9plt7VJNZmip2Jzu0ztHdIGjW7eprGVx3xYuDBAcpp5R1BVxkwjNMc63uDRgyhotM5TyweNcN9a9Zc9sZq8FCEnwjqHvsbaini0o6klO7tZnFooRNN894WVwmGIGxmcZB0KKu7VjwXui63IOgbKcebvv/vN59XoG32OvWorh+WT2MybmBVJ2xvskII395jkYMaJtiY+5e1wKRZKCYvtB3v62t7KOinZ8kPXJIb/+oX/JF3/7txj6AR/cuMCTbF28nw/80I/wlkffSV2XWR4fwVQ13enAE7/zGa6+5a0UheLpJ5/h4UffRd87lJL44BBScfHyZeqypg0FcViBjKgioaOmdyXeb7BWoo0geUbvbVZJSA2FKmj6Hic8tczANUkBBQgZ8V4w9B1JaKy2eBJaK2KIOXgPMMaSGIguslyfZuuAkXTO5c+WGzMrAlgjEVES4hjCJyVKZCJfK0lCM3hH7yOhjGhTUnMBYwWr0xVVVWFMkVl8bVFKcGf/Ofq+ZXuhODppODgSTGc59K3tI4XR9ANE56lrQ98HehSucRzf/jRN0py7cJ40CJrTJRLBdDrDaEVdFgg6Tg5vcuPGTdZdl60xVmKlYlKUWGMwWhGTwwiJqbKE/+R0DWi25guc71lt1uSVYcBFR9QCPziEligRab0HNIv5jFIL2u6bd4HzSs7r7bv+hZMkLN8iaC5UnPuEpLx9iJhOSbMav1Uz7FhcLbHLgD3uUUfrb1pw2xtmUsSeDGwu3qM2XbwJxF9v8+0Q2OZrRfFlFWb/81v/BQ+b5de418ub+88fg5IZiL/EJPUt6hIXglhohDvrPM/jbkwAuHLxmG9Gl3gqDLf/ixnNxfQiCtxP8jEmXfZ7f6PsuEj5/i+FZkUE3Ygc9vaCPyuOvvUL2q/7DPu2t72NT3ziE5yenvLP/tk/48d+7Mf4D//hP7wSz+3rmp/8yZ/kJ37iJ+7+/+Vyyf33389ksQNGsLm9YmtecOnieWL0HB4ecnq6pB96ynqC1bk/VyJJjL23KbOvRgtkL2mbFlMlondjKE8kEcD7DEyUIMUsIba2xChBDA4foZASrRVD6xmSyJLx0edscWit8DEglcYWBRaLcx4XE0plcNE7QVVYjNXIJOma7HVE5r/3PiCVRGmBSmr0yWaG0PnM4PsYaNYbkpAURUVR1UzrCq1l9p2nnLie+syMx5Sl2H5wNDFiC4PSfa6nmswAN7KPMXvpy5w4P5xJicnMY0gZHFmjsdYgrWWxexllC4ILY72JAGURyRGDByLtZk1Z1miRWT8pwEVwfUQhSNEjjWRwjqQySycRoBQxySzftSVGJmJIKG0xRuOaDeVkzn0PPMTqZJ+T5QkxBtbrNUpIVAwkF9FCQNCkJCnrWT5RlopiOkMlMNZipgtsYZiqLHXWxmCMoSgqqkmNlpLNZoOSYEuLZMJ6dUKzWTKsVzTeYYsKIxXT+Zz1co6Oiq5v2WzWSAlVPc995cs1t558ks3pCRfvf4BCScx8QVWVHB7kRYGczFDWIsMmJ3krzXS2QxMSiwuXmc4nbJb7iLXEllNUSBSlwYdAjI4oHIQGgceYXWJyODcwODcC3uznjvkduxuip7WlqGtMWWGNZVIa4HmAfDZ3g9ji+E+RgW/moSUxeoZug+vaLGvWNv+smI/vM+byLEn/ReD8rPJsDAg86yX/SpNSln+TxsC3lNBFjRCaGF1WkCCYFlCVkmULIglOjw/55f/1H7F/e593/f4fpp5N+dV/9Y+zhDwFDm88xa/8s3+M/O/+NA8/8tbMyguBKiqkVGzv7DJbzPHDwIMPP8jQrim2z1FYS9uuEQK0rSgnc06O4Oi0ZT61RJfPBTFFpDLEFMaubgHj50Op0YgdBVM9nkN8JIYw2jayjSaIQFIS33lQoJXAxRbnYLrQFFXJ8jQniUotca3n6GiDMnJc+En66EAljBwrCq0gRIFShhQiKWU7z+gmwQ2JJBS2UGgMxs6YziaU1YzNyWk+TudbDN2GpmnQyiILOdp1BL3LVXGC3B7hU2Ddwc6kIoaCnorrtyOnpzfxombQM67feZbdsmdSlpTFhGJSsJhZSJ6nnn6cW7f2afoAQiJlPi8rlW0KVVlQGk3vIpWt6EMidpucCaEtWkvaPlLWNWnISzsjNEJLpC0YfK6FLAqLHyKSyDAE2v41wHy8jHm9fdd/+USdGLbg5vcW2He9FV9BLCCq9AKMISEZVD/j/Mf3qD/2FLzJnN8dfdxAqu8Zk0Uj4PVtmf+2mruBbW9gLO6rF1eYmZsn/Fb7AA+bT97zY79n5zmeKM7By+kSVzJ7KGNAxJjJgVdhDt+/xQ/8hV/lk6f38eS/eYhLH2nRJx3VLfnN6xIXgn6noD3P7zqWksyZHtkKIRAOlOMuMH+pSSKn34cSgv3aKF46gVnnx37hqD4hG/eV7/QqztcNxK21PPzwwwB853d+J7/+67/Oz/zMz/An/sSfYBgGTk5OXrQpv337NhcvXgTg4sWLfOxjH3vR450lrb7wNl+evnr79m3m8/lX3ZADFEVBUfzu8sN+/4tU822uPHCNcwtDCAOrZYsQGRT2XU/sVb4gH4OLbGmRwtP0Hb53+CFfpBeNIjEGNTlPjBEhMq0nSJAkMWTqLV8s5mozI232f/aOtmmROmKMIYVh3EpZJkZlCW8M9F2DURolJYisDzqT4bqYmbuJVszmE0xviDFASGN6eMS5HLB15jlxSeCDIvmIlIpyuoW2msVsnnuCU0CKfBEKWdarZUSksZdZCnyMECU6CAY/0G9WDIspQwoUJAQSKXJgmzIK+gHOQJCAFBPehwzEjcYqzc75S+xevY/jW0/RNRlwESKpJ9dnhbPXOG8MAx7CyMKGDLyUVUShsIXBGoM6oz4BRUAJjUsRHwaMktm37j3WluzsXUBJjU/ZX58CFEqMrP2UvuuRwrNVG0IKdG3HkAJlVWOrKVVd4p3n3IXLBNeBVNnv3yw5Pb1DYQzy3H3MtnYwRUE/dHT9gFUahMGUM2bnE7qPLGYzysrSSDh/6RJ+6OiHnsOTA6wpqOoJe/VbmOw0TIo5PgzMFguG9oSnbj9DOZkzrE9QSbK3u0NoljgXMbaknMxAKarpDKkMzkuakyNIguleQbe8xfbuVg5s69cE32PKzAbH5InJ40LI8vMIMiaCzCoHKXNUWgiBJEAqjVaKaTV2wo+BJWeZJXeD186oaiGQQiJTZlNDCLhmydC1YxtBTq5PISBVjvyJY5vA3dC3EehnL3L+3KcUieOfne0A8k8Uoxg920ejkHeBuiRSFJayrunbdQ4wTJHDTYc6zYyxdz2//qF/xfHRmu//0f+Jqw9d485zz/HI+38vp3dus3/9cVy7oTnd5+O//GEuXr5CNa2IIRCkRUjFcnnIZR6krGqWxwdsWsdkEVmvjhFSs1hscXiwTxISqQucjxyftGxNS7wHoysWs4CPLZBBto8CFx0JRWk0XRxom0Aikk0RHhkNEolQjlRkGYrWGqGy3zw4QWLg3KWBtz9a8MXP9TzxRUcMEtFJIoLJrKBtB3yhUEZiAliZZe4hJEIQCJl7ykOElCR+zBEgSaQZJd6pIA0eURY89OjDrA/usDpdsjo95vDOHbS1TCYTtC6QMiFlpGk2ORXeSLRJ+D4rfsxkQURw484dDhqJK65gooPNMxSTgmm94Mp9F5nPLDefe4Zbtz37t2+wf7zKKgkBYlQG5aZFidFZdt95h9aGIQUG51j3HltahAxs1mtc6zHWomVukQgEkk8YKQhtousTqcjtEvl8HLlXhuHVmtfbd/1Xm2gT3d7XuIHI9Te3vkez2H2YvV9+9tXxkr8ORqwapM8XvPcy9xr49ua8uiP8mJf6BgbiwfKiCrO0bvilg7fxx+f3DsTfO3mWxydXX16XuBQIrUidh/DSUvZv1kyvD1yxR/zPb/0U/cOOf/gnH+Lv/Ks/TH0L1qnPXeLFO7/xLnEp2VybcviofnFt2ZePyEtTNISKDMy9+IqA/Iw1j+orLVW/7LYRSALV59qzr5TSbpr0LfeHwzehRzzGSN/3fOd3fifGGP79v//3/MiP/AgAn//853nmmWd47LHHAHjsscf4qZ/6Kfb39zl//jwAH/7wh5nP5zz66KN3b/PBD37wRT/jwx/+8N3H+Hpnu2y5dmkLkywnx3c4uHObYYhM57ts78xz+nVZUVYzlMoVRJtNR1QNpa0QfhhTdvPvqVW+jgrOZ3B6xqYJkVlBpVBaZ9l571FCIQpJITSF0QRrCCSkTGO4lyd0ubarKAuM0kijRh+uyG+QyuBDi0QKkc45CpErzILLNV4RkKPcXAg1StEFRVkwNRapDUpIfIg5FTwGjEpopfE+4UIgDsMoj83APIo49nhLCCIL97XCDYluvcTFSySpgZyIrlJEpYixmo2Au5+9EYgHn8GRMhplNJPtXRa7lzg6eAppFFJkKbkqa6zUiDEgTJkSLzN4UlLAyPIKIYhSYkzBYr5F125QKJCSar6g25yikRQSTk+OiCExqQ1t26GtZugbjg5a2rbJUlxrkFJTT2fUk21SjBzsP8vaRZToICV08rSrE0BQmAu5kssPhH6gNCW2UCz7FcL34CPT6Yzkcx3dcnlMVU/x2lBM5iBhtTqkXx2xDB0hbZFiZHV8QLtcUa7WbJ2/wuzSfViT/eDaWqrC0HYtYeh49gufwkXFxfsfYGt7D+ciQWpcGojSYooyqyRMiZKJdr1iszxhtncpy7djwGqJ1ppAQJuaEDt82KBiwvueYdjgg4MUIGYJdIjgksSKbKkwZUVKCW0s1mhKq+9mEbwoqG3czGSxhBix+MiIp0Sz3iBjwNjybkJ6SmdVfObu48Wx4izvXdLIkic484aHmLvO4Swz/Qz3j0uBsX5NKsYjHJkiRht8FLRNl5UnWiPT898Gzz3xRZ7+3Of5wB/9H7j/2n186j/+O5596gbf98f+BKVWfOGTn+S3f/WXmZ2/xI3Pf5L9W7e58sBV6Fp2SsONCG27Yf/2bXZ3d7BVSTGZYmSk6xybpqXve2IUuRrMB4KHss7SfCGzJcZ7jxol3zH2KBEzGE8JNS4Jkwq4waNUZsgjkSTJi8MQieN7oMkLLaUTPljWmxknJ5bvfuwCly4/xxOfP8Z1nhgc5/fmlIUkaZ1tB3hiCuN7m98X5wQqyVz1KLLv32qN1gYXDTpZpuUezN7CZnqF5fIZrj1wP13nuHPzBkVZsl4u8T6gdVaxOOfReoIyOltsgkcpTT2ZMNk+x52DO6zEDqEokM0+WvYstra5uDfjvku7KO34zGd+nRs3jsZ8goSWkiGM60KZUFJQ1wXEwOA8qNwXLkWRmwN8ZDKZIAGVJF3f0PYdUSkuby9oug6Epu86vPMMQ0DFxNA4hFGk4FFG5WDE1+G81r/r73WShJO3Qrd9lfv+3SE8d+tb8jxeS5O6DtNGenNv4CBqQZICEV8fS6hv9xExWwbTaz9X8hueqASUBZy1G7iBzz51FR6698d+i93H704xN49f8ra5S1xDal/VwLbVVcsH77ybP7P1HIUw/NmtZ/mx/+Gn+c/djFrY3CU+t6j112+liqXh8N1T1lfF1wbhX2kERJO+4eXfXQl6R77m+6qSSCiPvvX+cPg6gfhP/uRP8kM/9ENcvXqV1WrFz//8z/Mrv/IrfOhDH2KxWPCn//Sf5id+4ifY2dlhPp/z5/7cn+Oxxx7jAx/4AAA/8AM/wKOPPsqP/uiP8rf+1t/i1q1b/JW/8lf4s3/2z97dcP+ZP/Nn+Lt/9+/yl/7SX+JP/ak/xS/90i/xC7/wC/ziL/7iN/QLvu3tDyFCz+0bNzi4fQchLFU9o6ynLBbz7N22BcbW2U879HTJgphgpMSIDt8uGZoVnXOoXqClIo3McRjZaIFEa6DMjEpCIGIkiIQQii712EJjCoXwzyc5S8AIgZESOTJ4wYcsZVWClFz2rJPZSe97mq5h6VrKssDaAlPVSCVzyvNdH2xOQoYspRcpkbRCKY2yGjfkCytUpB96+iH3gAsEYeyWEkojpCSkDKSJjmAkgki7OqZpOuaTM3kxyJRrzIw2Y0hbzIFOYmQgY05fN0pm6XY1YbF7CWlKUrPOFWMJlLZIpVEYZIwjK3oGyMAUBWeid20NQiv6vsMHz2xrjnMun9dSTj3XSuFrx2Z1yvL0lEIrCqsgdHRdVg6ElCi0hJToXWRWVGglmbvzrA5vI7seM5sRbEUaAts759ClpV2d0rdLqrLCaLj55O+wbjYE79G7F3GuY7GYQw/7tw8piprt7R2EEDSbDZuTQ5qDW4TphuQbhC6ZLbapyxI/RGb1FCEEQ99n64CAkLKE++j4Dm3fMlvsMZ3NqesJ6/WSzekhiUBRLcbQPUnf93R9Q2kKFjvnWK+OaLsGAcx3LhBHhlmpAiFUbmDre9rNMapYE+oB4pDVFzHmWr2YRjm0vPu+aWOYViVG68xajwA58cKAwCxDF3CXwk4xkGIgxog2BUabnNw+es+rySwvXlIc/5dB91h7TYqZGY4xkJIkxIAJkSizReMM95/99JRyiGCSzzPiQiS0kmxt7WDx+WcIgZQRP/SQIk9++reZX3yQa4+8leuf+ywf+Tf/gmr7MkPXM9lZ8Lb3v48H3/lOurbhX19/gqP9fe67dj/n5gXn611WN6f4tuP0+Ij9W7d54C0PIYXn5o1nufrAWwgpklDUdcFsd4uDOxMunttGyJ623dAMHd4JjNY477Fj1/ngw3hCEfQu4segxjT68JXSY9d5wIWADAqlIAgQSiNFoI+e7Qszft8fuMLpsaMoJhzf+TyagYNjx9NPH3Fh26CUwKeIjAYXIs6DVIGUdIb1IdGHHhdCDldMiq2qQooFWk24dOEK5tr3cnvdcvrUE6xOb7Dev86DDz/I5Sv3UVUVxyc5R+Ho4A7r5RJbVxTW5iWFUUyrkq3tPbQWeOcoyppS3MT1tyiqOee3Zly5dIGtrZKbN5/i8SeeoGnygiNHZkgkUJq8DJJKMV8UbM0t48qCSCQMgQ0bEjFL5cnLBp88vXAU04JSCk6Xa6JMJN9DkPQhe8QJEVsaEAJVGjyBOHzrv/hfal6P3/XflBHQnUs8/Ud2ufyfa8wnn/r29o17j1kF+vm9IbIkIGkQb6r+XzcjfXpDd8AnCaku4QVYuXy8gD947499Tm3oLhSYZ8PLUxa8oEv81fp2WF0TfPThDwISlwL/99vfxQf/6WP8gf/9b/ID9a9xRXd0O4ryxtfxoELQX5hw5z2WYWtkSl7F+WoS9N81Ccw6UdxpXpXn9VLzdQHx/f19/uSf/JPcvHmTxWLBe97zHj70oQ/x/d///QD89E//NFJKfuRHfoS+7/nBH/xB/t7f+3t376+U4l//63/Nj//4j/PYY48xmUz4sR/7Mf76X//rd2/z4IMP8ou/+Iv8xb/4F/mZn/kZrly5wj/8h//wG68zGTacnJzSNj2z+RYpahyaJC2bAVJIyOAwbo1UiuAFPhiENCSdENIyree4usF3R/TNCUMaUEqTI78EUWSP9OAHYpchhh7BgCBm2XdhR+YvA21UZgWVVjlVWSXEGBQnpcxi0pDGUKzM6jkU2pRUhSIYy2Q+QYkzny7IuxVqZ8FYcZTx5uRbAKkSkoBMgcENpJSBeAyZoYcxsVpIlBAIYe6yllKCGzxeRnRaMfSONKuBflQGJHT0GF1gpMQFP1abibuMZUr59zNGY23B1s5FyqJm0C3BZ2AnUySkmKXe2qJk9tOnBELnnuM0el69G4jOEZTGlCVtt8792l1DjJFms6SoJmidu4lVjJh6QjHdxhpDODlisz4lRAjKZqY2eLp2zaSsmc636TYrtNbYokJIjZoZqrpCaUMbYb3aEIaO1Z0bGN/nyiylcd2azWrJfHsXYwUXL16k6wb6fqAoS6azLc5dvkZyA0KAkeA2pwzKoEzNQ488wmyxQ9Nu6Ls+A1PnCd6z2SxpVyvCqmUwfe5+FpLtrS2ODlqWy1X25M4XBNfTro5JQjPZmzKZTuiHlih17qV3DhtyDV0SCoQGNG7TcPj0La6+5wpxq8P7HucGirK66xMn5bRxpRTWlpSFpSxMfr/DGNKXD8bRcybuhqa5mHuZc3Ba7q5WxpJLADLQd0POIIgpQcgMforxLjN+9tk4Y9oRuXawc4kgIxOp7qq9chzc8wx5TlWXY91aZmukFBSTCcNaMriIiBG3PuG8WzKkkqObz3HxHd+FUZIvfvoTtF2LHjqWR0e0B89x86nHOf/we9g5t0thC1zfo5Tk1MPpkWPdJyoJV689yNbONt67DKjLCu89Uijq6SR71ImU5RSdLOvNiqZraboBISxKB5QGKRNaVoSgCb7PFWZjgnoMirrIFYOF0flcIkS20Ph41xYQhoiwuXXh+PCU/X2HkYEPffC3uP50R4wa5wtu3GxYLx2DB+ccFCJ72xRYIyBavE/42CFMAiIyGQq7IMktYpqxde27ac69ixvPfonhS79GWN8mTO5n3+3QfOGYvdktzp8/x3znGseHx3jnaW2DGwba1RqpFeW05toDD7O7O+fk5JiQoAg98zsHaL3Hpe0pV66cQwnH5z7/SZ5+9gbe5yWLUBBCzNkRQmCQlKWinmqsEYgkKGxNXVlSCgwukcZjxwfH4B3KgDUGazRFYYku0HUBrTXGWKQV9KvcOZ90zk+oKktMYLynk9/6vtiXmtfld/03cfwkcf37Kq7EBzCfevJb/XS+pVMc9azvu0dtuoBgBPJ1sIR6c/K84QPbBITJiyvMth6PNElQ32Ov1kwG1hcVU+fOJHhf8/bpW9AlHouEEpLf7Af+2L/687zln3ZcXR7xyc++l0/8nf/IO21Jc+7ld4knozh9ZMbJWyXxJTzb3/RJoLpcS/aVJOgvHOFhsh+YPNPcm//9mzhfFxD/uZ/7ua/592VZ8rM/+7P87M/+7Fe9zbVr136XHO3L5/u+7/v4+Mc//vU8ta86169fzzLVEHAp4SN4IfAbh3URqcUom+5IURDlFKknSFtAUHihsFKye26X9brkMGj86hDZd6izLmcyJNdqlGumiPPZb57JYgEiEYPDuZz0HUZ/bSTlBPNuTIyWekwPVpmtEwVVWVIYiVISWxRZop482gr84Ok6l5nK0UkupRxhjTj7L2IMdMusdWbyU2gRskZrjcdnKelZaJoQEHz2r0hFijl3wqUIyWGiZuh6HLOxGzp3q8s4oMQUbTRu8KNs+Hkg7kLKoWVWo4xhvnUBW06Q4iBfIEtFEtlrbYylsCXWFgTXZ4Zd27xdx5OERBGQMaG1ZlJPcUPPulnjg6euamSCZnWMCAFblEymc5QpWGztQvT061PKYsZsd4eua7DGEr1jd+cie+fOY61Gi8R6s2SzOgVjkQn6vqEoa3RRMAwtCkFdT+h7hU2a6WyXsi6opltEn193h2Y6myBEBs1JCKaLc+gHFKvVMZPpAqEKvA9U0wlSePZvP4MfAn3f0TQnmXkvppzcvk7oW7AGU1aEEDg+uoMtC4QyLM5dYn16TL9ZZ8vFas3i3CW2tvYQIuBCRJqSbnmC0TYDCECICcNwQjdkdnyxO0OrRNd39H3H4Ia81JECH/KiR0uJMrmTeasu0SoHoOUu7zSGlmdWXIzHwarZcHR8zPr0FOcii70LbG8tcnq90XkZlREyk8UMKUU+Ps7k7YkXA3Ehx5/xfD3fURvZILIX3iSmljMkzpm1PKRcYZbIfmpJPsa88yNDn7ClQRlDP0S6rmcynQGJ3YvX+IP/7Z/m/LUHeObzn+GjH/wFpCr5oavvwrsB17dUkwlCivzIwhJkQd+uaTbLvIQzioPbt1jsnGNwA33nqKbTsS5sAUKNVcgGoyu8d3gRUF4Rh0QrWqxxJCExVoDyeJ/bGqTSaAEkRRJjL3pUFFITpCMKjwueGKALue6vnBikVtx4Zp87t0JeygiBsYGjwztodYn5tGa53uBjTonXIjPkInmSDMSQcD6RYs1scpEYLcXsMubB7+M2c7rf/jCnz/4OcvYAavc7MMMNap7BTq9yKhasbh6xZW+xs7fggbc+zM3rN1ifnoINFGXJ5Qs7iOhIPqBswTNPPcGztzdsVZK3XTzHpYu7HJ08xyd++zMcna5zBqQcw/nG05GSCaMV86nBFILCCqqyorAGqQRD6JAi95gTPet+g9KaSkl6N2RZv4AYIkVZoWQg9gNx8ESr0EYSw9gGIELOHFCKQlvUq12e+g3M6/G7/uWMiCB7ATLLH7+W9DaaxI3fV3F1eRnx9NdDC72xRp5sEHF6zzLlNzK7+kYc+frIlLyn8V/WJT57quFZP+dt5vSeHlcCzWUghJfXJV7ovPJ4FaXpcoCbfs3/8WP/E1c/FNCrLEGvbrZ8aP0u3rf7RdoLL+O7Sgj8vOTO+yra819LC/7KjIigNy+jCi1BcZqYf6lDL7vXhCT9bF6fZrWvYworcF7ivKDpBlxQCAUxrbNH2/VsNitc02GKCcVsh2LaI/wUJUv0vKSqTA5eKybUu1dpZE1/ep3Qb5A691kbW2CszhVjMsPgECKJ7L/eNG1m4NLIcvtRoyUMpigpy5qiKFBKI5GEFJAy4YMk+IhXAmQG7SiVP7BdRClFYTVd53A+IMYEYxcjamRJc86wHBnPzI57H4mAthFlDZU19GMYWZbj5t+DGIk+IpQmpSwNJiV0dLTrU4Z4njgCLwQQPSIFlFbZ051/yeer0nwArTBaY7RmNtthMllwLFP2ppPl6SJJUAJjJhgjcxq9scy3zkPyNOslPgR8H1FFhdCS1fIUZQ0pRpzPPs+ynFAKQdesaLqOGCJ7l+7HDS2ha0AZdi/tMakrXF/Re4ctdlHG4gAS7F66n7R/izB4gkjEoUUMETtK5H1SDM5TVxO2ti5S1hVCCIIP6KJAjeCeEBFS0bZr+q7DlgVSCo5u36TtW6QpufbQg0Tfcbh/g5s3n6LtBiY7lzBlwcH1Z6inc7bO7aJVQE8XNJshh74tdgh9nz25JIZhYL5zCRE6QojM3/F+yrrGFhWnRzcgRObTipPOwVyihMSWJbSe6PNSRtlEMVEI6Qmhpes29N2GONtGyXQXZJeFZVsIrNEYYwAx+s8jKYlR2pmXOy54TpbrXK01OHwMGGuRKgcC1kWFsSovjxLU8zovsXy4y4Ln5PZ4F4iPhxggRol6REZG3zS4lOiaXLE3LSSKM3n6CL2FhLPAwhTR2mJNgfcDPnmid5w0DmcnSK3pNiuEkjz6gQ9gyyyzPd6/gUhw/trbOH/fRe488zhd59i5ePH5kDglEabk9PYJXe/YLQqa9RpbbZMQVPWcyRRCGPA+snNuj2ceL3FO0XWewli2Fzuctks23Qapxgo9nXJVmNGkZFA6L/vA40JESRDJElzCJ0+SCWsFKiqkBqdDBs4hsVy1/MovfZzf83vexaPvfpSP/PLHuHlrjRSSi+ctf/SPv59f+CdPstoYZJQ5syHpnJIeB3rviVGR4jba7hGDZXr53XT3fS+rk+ucfPZDbOxDmL3vRDdfoujuYLYeRNZ7lGKJCs+wjIYb7SWOn2vYrdZcuLhNaeHwzjFlWdzNwDg8OeB3Hr/O0arh/Lzi6v33s7Nb8uQTv8PvfP5xmj63WUiR2y+0ypYIpRVVUWAF1IWgmmu0sRS6RMmIDz2JnD1hZKTxDT44dMznRFVktc+krMA5XD+grWXTQqEFse9puh5tLFWhkUbSdCtiTAwpn0ffnFd3RMyMieqeBxhJCXwJvv7qgT++Tlz//m3u/xcdHBy9ek/4NTRi3SBdIhT3dtxGLe4uQt+c1/5InxDxje0T95MXV5jpWyf8RvMQb1vc+3Kwv5zZcBHSSyKtpHKXOOHVY2gf+Fcr/uJ/9Uf4lcf+Hj9+8Uc4/DsPMH3iFNl7fvHGu/jLu1/EXXoJL4mUrB+ccfQOlc+jr/J8PVL06jCy+MIaMbz2NkxveCAuUq7ein4g+YHgIKpAiAqXPO36hM1qRUoR41p8CAxDpJpGylmgtgq8ZO007aBIomSyexkhJZuDp4ntCYXRKKkyWI6SJHMVTjy7+CaSUk42FspSlgWJhC1qkBalwBiFUbl8KyUgirspu12IiCDxMTI4T2FBkUjCZ/AvBFplmalQKntnY8gX/wIgEQjEsa87IVBG5ZMsuQsb8sWqQoyd3DLLVkMgEiHm7OXoI0pEBJ6hWTL4hFcSk2K+4I0eGX0OVRq/dCXZyxtTwoexKkIpjDaU9Yzp/DzaVki5JjqHNBKjDVpqQt8yjJ51W04oS8Pq6DhLz8egvBQdbRvy62AkdT2BzYaynLK1d4HUreg3K3Sm5XHesbW7B5MZpmkpCiiKEmkKrFC4YaB3jtIH6rIi6oLtnQuU0xn98W3ao4bJ7gUWuxdAaZquoVmdMAwDq9NnQGi2zl9AK0l7uMINuxT1hKKoCMGx2bTs7GxTWIsQmt37rnF05zonzzzB1vY55mN2QTXbopgZZtvnWZ/cQY9hgAjJ9vn7iVHiB0ExmzAMPa5vQVmKwtIPLZPpHKEnDCeHxDIDVB88wkXqaptqvuDBt78zp50zXiMJgbZTGE4Zhg3GGrxviWHADz1DvyHEgE4qZySE3L1cFWasfsrhZzFGQshSaCFywuGm6zhZrrIcfuhpmhXBe5TUhBgZQmRaFXmJ4z3WFuNjnR0/gpjOas7E3eT0F9oeGBdFSiRmNhFUACTrmDhqsvx+WkjECIZiNo/cTXFTAsp6SlmWhKRxQ08IkdBvsLMdts5d5MlP/wbLg6fo+8hjP/RH2N3bY7q1zX2PfAcf+MP/DVpGPvvR/8DW5YfYOb/H2ZVnEqBtSWUtkqzSmS7mNOtj7ty+xdUH3kpKcbRBFDzyjvewPHiO3zr8NE3vkQJWzQafeqrCELxAaEmInhAF3gWsFQiZEGoMrouBzku0Cng8ykpEHF89KRAGkksMQ8C7rJSJ0vLZT3+J31x9hr4LxKCROrJerzBacnF3weHtQAhk+83YHNEMjiFoptP7kMyxxRb62u9hWFzm9FP/gZN4Cbf1GG7/E7iTLzK7772EyQ6Fv0nVfR5nL3EarxH759irbjDZvsjG1SxvPo5NPffffz/GGuqyYr055uOffpIwtFw4f45HHnmAFDf8+sc+xrM3j8ZKxfHYGGWBApiUmumsYGexYD4pOV2dElMYF0gBFyEJg9KKwujR5w7aWmzM56UUI8lD7D29y9ajSmQF1OA8nRvoYsCkATlEGCIIOSqkHN596+tSvl3mKwHwu38XyFLGJHCTrw7Gh63EzR+4xOV/2ZM2m1f+Sb/GJg0Dpv0mAHFFPueEN5H462FE/DYA4qV4UYVZWm/4pf238d9/E4D43sWxj9xHKNTXvG0yuRkohfiqdYkv3zLlb933Qb73F3+CB/5lQqeA367Qxy3XHz8P74bLl45BFF+RPY615fBdE9b3fwOBbPc6CXQrvmoa+otu1yQm+4Hq+uZFfemvpXnDA/HVps2+xRCRMmJ0ZPAdKSSGGOm7Dd71Y5BUJPhcxWR0IJTQrhWDFiRlQZlcb1bU2PseghQ4fW7N4AfEkIFBVAadKTeU1ihp8AhikBTVhKous5QjpJzSzQgGIgwxkGKWmSopUcoQQkAmTwgZhGhJZsCUBCFJKeYKH2vp+wHITDgp9y1LApLst00IpFaZSRPgyeAlEbN8WmqMAKGy11wKifT+bh+zC5FAIjiPQ9CtT+j7AW8VITqE0Fme6geUqhAyp7SljPByaEXILKaUgsIoqrJksXUBJXT2GZcVtSkwIqsMtM2VTzFFYug5uP0cYeyzNtJglMFFx87ueVLwIBXR9UymU/p+Q7dZM5tOMbpAKsn2hauUVcWsrqmN4sRq6qqgsDWnm4beBQqj8MOAIjGfb8N8wWa1xHYVzlj2di+ye/EyxmiafoBjqArF5vSY2C1pTg459i0RSRgGds573vLoe9neO49Mkao6QkhFPZmgpCQlgS0qhsUFympCSJJquosu51n6KxO4OdvnLxKR2HIbtMT3HZGe61/8JJvV/Vy4fJXFrGZoGkgCPzik1ugq2w9sNaEoLMXla1hTcHR4g+tPfJZqOmHr3KXcI05C6ZKimOEGR0rgfY/3A0McMived+gxtT7EM+k5+Xgky3/jeOJWSgGJk+WSw5MTgne4oWPoe5x3I6jPn9V+GOicQQSH1VkSHULMXvMIKWWbhCCnZotEZssRd9nxJARS5B5xTaQyuaGgUIKTNnHYeKTQ1Ebm+yeyrD2lMc8goYsSXdWEzRIlFUpLUhzQSvHAO9/HU7/wc9x+8jNIqbjzzONcuv8Bdq88wg/86P8JoxK/8aF/ye1nb/AH/rv/C1Vpxu+wHO4odUmpLdEnBImha1GmpqqmOTwvhtzHXlRs1qcc79/GakNVGpbtCk+gshNkTCByKJtWEKLDBY8MilLZrKoRkaQF0Sd89PgUsMrgwpDBulD4PtK3ObdhOhOA5nt+/8N0q47F1pR20/PFz15ndRrwneTkpKGaWLqhy++HgOAHQoj4VFMUewRXMrnwMOZt38/J6pSbv/MsLddob36c7s4XUHvvQsy/g/7Zz7I9E+hL7yToK9TDLbbUPsNil07PKf0R1XAHKRMXzt+HkIGbN2+yP/TEFDg3s+zs3sf5C1scnTzNZz75OdZtj8qOBmQSSCmQCYyRlFYzrUsKWyBEIqSOvZ0JPkaa3tN5hzUmh7H1PclFNBprS4yUbLoO5wZSciih8TFRlgV1XeG7ASUltp6QGg8SCm3wncenSFIalaBSkkLZV/Mr8NtngkC658GiCLm25qUktroB+NpgvLmUOP69V9n6t5//9gtvCwF74um27vG4FVmert4E4q+PSWNgm37jKniCFQit71aY4T1feOoivPXeH/s9eze4ZSajD/lrZyy8qEv8br3rKzhScvrHV7zHlvy/v/8fEb9fck6t+POf/z8w+8mK2eMKlwLv332Ox+XDLwawQtBdnHDwXsswf4UC2RKoQeR+8C97KUQEsx5T0b/GmHVi9syAPe5eM17wrzZveCB+fHqUAakI+YJx8CgjEErRNx7Xd8QYKMrMCsfQg1sT2prWFISosWWBKTPLpLTGVAXOOYrpHpPt87QnNxFSg9TZ850i1hZUVYktCqRSpJTZQikBKdEhM8gJ8C5mqagSuBQZXMBoAyIzdFLpXJM1Ao+UAjElBj+gZKAqq7FDOZG58pwEHEL2UXsyMJFS5tAycup2iENOmg4Jf9almNIIljPoiXGsigo+P9ehw3lP7zROHNI0DXG+GDOwM5hRwaH1BKkUPgYUGRxFmb3kMWX2SBuDNiVbO5eQxo61Uz7XexmDNhpVlmNlWU5dFyiEyAyWTIGQHFoZXNtg6glKCY5P1gTvKYsKJUEby+7VB+hdwE6m3HflKqVRdG1HPTvHtK7wYSDRslkvWcymlFZmH2p0KK0ROlcwFYttRAoc3LmR3xetkX7DdLZgMZ3zbN8wnW2jyykxRmbzLYpymln6EOiGDkFCppDT91PAaI2YLdjau8S0rnHe0zRr+i7bGYLv8DGwOH+JvutYnt7h9OAWQiqG9RK/aViFZzl/4RLLowOazQZZzPKmsHeUdkI9XeBdz9B3zGZbkAJD1xCiysDbFCStIQqMNrRADNnbivAIuU1R7eF9TwgeKcZjLKYXLUvTGMomhECqvEA5Xp5ycnySWXs/4JzDeccZhI8k+rahaQ5J8TznZzVlUZBCDma7W1WWjeEj03nGYkcY0/YFZ6EoI1se4t2MBqtgq5QctZGDTWBvApUWOd9AqLyFFrlLXBtLVVaEvmG5aRBawOaU2rc8/Na38Ox7v4fPfOyXSTFwsn+T4zs3uXC6YrZd84WP/UduPfMc3/2H/3uuPHT/+PMzqz8hMl9MePLxgaY9pe/3UFrTrU9JwVNYCynRND0zazi6c4fDO0coVSIRFLbMy47oCSlQFpoYI0MciEKMny2FSPn3Cj6RYv5nN3iUhkHk5HDXJ5A9KWY7DVqQcNhK8paHd1ifLBl6wYWLU/puwec+fUgKgeOjFVVdkRI0vRsVC4KyuEBtF0imzN7y+4lX3scTTz7N8s4Bm2d+je7wKfTuu1C778btfwpI6IvfwYEynD75WfZmiX7xINJcYD7sY3mKFsHWYs7ubsVms+L2rZt0bY9AobXk3O6C2ULyud/5DZ585hZ+TJzVUqAQCAVaS0ojqUtNURRYa9FGE1XEixyomXVBEaU1hbL0sScFjZS5UlGkROsbggwgFAmD1jqrGoaBJgRmky2GfkXfrulcR4gJhcBUJZXRuL4n+qxmiS+VJvPmfENTnID+BjPFdAMkgZt+FTAu4PQhyfZiRjq5N//o63HMcQcP3PsCKZiXvoB+c14780b3iUctoCqhe/6grB+3xO+/d1L63bPr3Krfh3i5XeJK5oVAeuVD8pIQvOP8bQB+oD5TaFl+9m3/C3/+6p9j9kzgZmj5rtmX+EL1dtQ6A9lkNSdvm3L6sCSaV2ahJmKWnMech/2ikcMoRX+plzTB9Ian2H99KJje8EBcEBAi+/i8zxfyQ5ty8FQfCS5frPetR4z+wRg8MfbgHSl4nOuQqscUFiQMnUMoOHf5HIVoORg2CJGBhRAqX6QJiMETBsAUFIVB68x/5zyGiB8CKI1QMISESQIRIkbmSihNIhhFCIKh9egiX4ASE56ICIEYBSkFBKC1QQjwIZKEBJVZxBgjMQBC5Q5xkeWkMSbi4BnI1V85kTrLjX3whDjWpkWQCLQ2KG0x1uaqs+Rolif05xbUEmTMezwZPYoMtn3vQZ7VWI2p8TGSksw+caOZb59jMtmjPeryc0XSx0Rs1tA1VNMFk9kWlcqS5a7vUVIQ+5YYBSE4WtexXJ0gAFuUFNN5BuumYL3ZUE7mTCwMfcvx4T6T2ZyiqDBaYwtLc3ia+7vXG6L3TIvE+vSAvuvZ3tulaVpSysnxB7dvQNcwDD3Vzg5biwXFZMHQdejJLl3XMbUldVGy2D2PtgVSKMqywvsBa7K3fBhafHA452jXS5xt2Np6mKIocb5jebSkaRtc33N8dICZTemaFrc+pTk5pZzNKOd7yFlAFyU+OtaHR6RkWUy2ECSUVqDh4GgfHSKTxQ4iCTrnKWc7nL/2EFYJbDUhCMngOgLgYxxrvyIhBZTxxJADyLpuDYudUdmRj+fcaZ0Z8gyEJb3ruHN4wHq1xgdHSIlwly0XY3ybxPUdxhR47+nbHr27RYxhZNvPaOuxsuxM5j1+vhO5bi9LkQXeZ5aYGHO/9VlqOwKrJTu14riNHDaRnUqiTbrbJS5SQqSI0ZaymuD7FasluMGhaXhoS+NNSf9DP4wyBZ/79f/IsFkSEhx86bP8b0/9DjvnHuD3//H/M29559tR4ux3zAuDQgwUxlFVBWFwDINDDB3O9ayXS7Z2L7DY3mI+TxwfHrDebDhdH6NFhyfmEL9S0bY9Ukac8yRy9d7QkWvbHLSpx6eAGxJKqjFlngzEk8PoklJphpDPG8Jo2qEh+QiD5vDWEdeuTblzx1PVFd/ze9/JrWd/FedmzGYPYuUxXT8wOInVM6blLt4p7OQqO+/7YW5sem587GOsnv4Y7fEziK23o3bfg7/zKRAJfeF9IBRx/+M5Vfz8+7idEurZT7E3gX46Q+rElcvnmE3g2eee4fb+koRiPp0zn1XE2ONcw9PP3OKZ67eJ43EnxiVkElBVit2dkrLUCAyVLkDm6rckIkkE+uTx3uOSpK4rpEsMmwFsiS40hZT4GBjaHk8+Z5XawGilEFKiVEGzWdG1PbaSzNWEVeOwKEprKGcVm8NI0Hm51Q1ffy/rm/PSc6/7Dd1mxjaUX/kC09eJ9uFzlL/x7QfE1ekGEeakr62wfcmJb/grzjfWvNGT05OEVBUvqjDbejywiZKZvLcTygP2gLA9QR2/NBhMAjAG+oG7NS+v4IgQ+Pz/7xGaP/9Bavn8gu0RI1hfUux+uuWj3X28u7iOmxnUZsBvley/r6Lbe2UD2ZKAULz48aXPCzzVfo0fnUWCuf3GgTl9/XQlvuFPi0o5kkgkYg6HCiND1HpIYmwWyAyxEAmREsEPrI/v4L2gTBVTmTtgpRJ4H7BKsTW3KBkYphPKeobvTrE6y6uNzbJaOQZUxRByMroCEfPmK6ZIAAolUVrgncc7DyID8xQETonxOUq0kWgR8T6HzAgfx1owhTYWgSemzFae1TEpclibDIIgM4hxg8MNWc47OMcwDCitCSkiQiQmeVe+nkQOopOK7FcXeVEhxZkH2NGcntC5+/GGnAYsQESHIGKMojvz8Y6ceVYd5M5hrRXWaqazbbZ3LtGsVng/IBBobZHOg1Aooek3Dc4YCluircyhcLrIjFTbZgm/0QilmC12mcwXrA/3WR7eYLZ1kflizunJEUkkXPA0bU+IkKInRsfp6pR2vWZSzbl45SqllVx/6knWm4ZkCrzrMLZkXky5cu1hqqrg9tOPgyooqy3admBwjpP9Gwwe5rt71NPZmLqdA72aZkVMCWU1yTmGockX8Js1SWr84Dg9PmQynSJSzD3eJyvKrQW2qjLDpmDVrmk2K/q+xZYlbnBMF9vsntthsthB6oK+WdK1a2w1IZFoVi33XX0LypQ5SC0JpNY50M+UBGmRWiCDI8ZI2zb4doPxAaEFfd9gXYfzjqHviN4jjSYkmdk/nYF4jIkYBk5XpxwcHjI4jyCrKnKyP/l7XQhSSPjg0abKtXtCMalrUkz4FEbvd/Zup7P7JXFXCh9JY42aIIR8XIUkQGi0taDtyNDnM3dMiUJJdivFcZ84bCNKC2Z1fu5CgEwJKch93jEwmZRZARIEn73TUkwN1XzG7/vhP8oj3/kBbnzx86xPjjC2YO/+a1y4/yoX5wVRBpphoF8e0R3eoF/uc7NdM60LCqPQUlJXUyBSTbbQ5YTDg9t0fcekKmjbgZgcp+sVO/Ms7x9ijxxKjCpIJtB3WQKdXCb0lZIEOTD4SEqGsjDIlEheoI3AudyPLWSP8ynL/CN47xAiIkj44Pjkp27w9JOep7+04bE/8F52ZiuW64gLJ/zOZ3+Ndz86wRjLdPogStX0TWDvocfor30Xn3/2aW7+xv/G+ugmZu9R7MXvYdj/bWIcMJfeDTERDj5BEhJ74T35fHDn1xHSsHP5Qfb0MYtJweVLu7jY8enPPoEPhu2dPQqrc46GSpwcHHPnzi1WTc8wRJQUSJXVEHWhmUwM860SqyUp5guHbmhQRmPNGNgnFM5DSiorpnpPjAkzqUBrUkw0Q0fb90ijmJgKkSQyZS9fpXPKetsO9INHmMSAJ7hEaTRSKzb9QNv6nFhPxCfH8HIYkjfnWzNf6/pSwPHbCi59QoP/9noP06ZBDQlfvRnY9u000qXng3jfiCMgTIuvkJy+4FF7/FXv9pXGJfis2+Of3H6Mj378Ec79mmTvxpdAv0yYpVVW+L1KyemX/lPD/+tH38lf3v3i3T/7jcGy+JJDbRz/+vC9/D+v/Bu6XY2fzDl8hyJUr8IH9+xYSxmA6xZk/xUAeMrHp2mgOA3YpUcOEeECIkSEfw0oz9TLW6i84YF4jC7LrWPupTaS3H89Sl3zBVKuDsu3D4Qhs+hxfYytdjBliUCRooAkmcwEVnkEkulsxno6Y9WvCCT0GIamZZaaa5Ul3hnN5kRrSa5N6UNChOzxzoFqCUUcU9cDMWbPrZQSIzIjGGMa+7YDUuWfJaTGltn33XaJRJZbMlY6hRTwI7sdfKBvOzZtS9f1dEPPbLagntRjZ7dBqMyiO++JrskVZCikdFShpypyD1QMkc3ykKZ3OCUwwiOFhOQRPku6ERkwCcS48MjSd9LYoW4M9WRCvdhC2OxtN8pSFTWilkiV/bt+cLkKSDpS9HTLY9LgwFi0tegY0cagTIlWhm69ol2viG5gnQSmLJhOFlQ7U7x3Y7q2o29OicEhk2B7Z5fJbMru7gKjNKc7e+zffBa1kkzmC+p6wmQ2YzqdoaSgPX+ZxXwbKSWbzZK+bbDlLMvLh47BebSBZrUmOIdUmrKq8kJnaLjxpS8QkmA6n+Ccp/eewzs3WK/KnE4uFTv3XSFGj/NzQt/SuURVzdjo41xJ5zrcZiDUU0wxx5SWgzsHRNfjNmtETFSzbe67/wLT+RSSxweBlImTw5tsmjXTmUWpbM2QSiNRiKTwSaCEYWgjp/EQZc9jzZyhX+OjvxvYFkeUHGKibTfsH9zi9OiQIBVGZfl0GllhpCKlXBmYkkCqAmuLLCkPidlkchdoP7+6ycf5mff8TKmRUqILgs2gCRHO1RGkxiVNdmDnJduZf1yQEDJhtWBbZpn6sk/s1TlvgfFzIwQsm56D2wf03QZiIClL3DTIIlsutFZcuXaFK9eu3L1OcX2L7U7YEyu++JnPc3J8RNc0eUkkcrCaLwyF1aTkcW5DWU/RxqL1lJRgGDzL4xOEiLimYTGZoMSAcyF/tkRCSM/gBgY/fsxVgTVkFUuAFCRJ5EBKqRRlKWCIuJBl1i4GogTlBDEGXAxoLbJGIQZu39pQXJ3y2O9/N488NOd//Sf/icN1pHMtn3/iWd713u/i3MWHaVY1tj6HffS/ZJ8tjv7zB7nx+KcQO29DX3kL/vYncbHBnHs3Wkr80acgeuT5d4NzhP3fRBlLufcwl8s1e7OOq/c9hFaOg6Nb7N8+pZ6eoygKcrBtz/7Bc9y5s48bBtKokhDjd50SgsLmJemsLqlqjQ8RlxKFMbjosYXG+YHgA1KrfA5KeWkiVGI+LaEPGGkgBrroUMZmdc8wUJc1KXrSaLtw3jEMm9zyliS5dCIRZaTrB4TQGKURCJIQKKVJ4s2wttfr9DsgZlPS8cm3+qm8ujM47Driq3ujxJPMYFy6N5H462HuBrbdoxLitTxfXmGm9k/4WPMWHrW/8TXv5xI86Rf8wuF/wb/97Xex+zHN3sdXqOt3eHv3OWC8jqmqnI/0EsuMsy5xMVqsXulR655//P/5fv6b/+snOCcFP/7MH+bz/+TtXLx9Ainxn7/0ENtXS2793khxoEjyVfrMjv5w1eY09K/EgIsIiy85yv0up6C/hqrIvpF5wwNxoRSlMVS1pG97jo83GCtzErKLueJa5oun3FOcSCMzjmvohw3eO2xIhJRYLDTTSlIYiVEKKQ3KFLRdz9BHUllSCgCV7apRIJMk0ROTyem8PuB93tpgCpRUuftbqNxmHDO7LGRWqAgRc2dvGnuU8y8GCdrOYU2DMRaQSKmIzhFiXkL0bY/zIf9uMaKkBqEoqhlClUjZIpJg6AawAuKQQeHg6PqBrvE0rcMHSVVZdnYs58/NKKzJP/90n/XJKb5eEGNCyJgXG6FHK5t71mMct1z5yfsYCTGilcCY3IM929pFSEVRlATn6fsWY3On9tBsoHeoskTXNcRIGgYQGmlM7uWOgcqWnLv6MGjF8e2b1PUWsXfsnL/G3uX7RmuAIGwCdV3TdT3VbA+8R5WW0/0DYghM59soqSiUodCWru8pIyAkQ/AcHx/kPndlQEI/rOm7NT4Grj38VlbrDSFCVdZMJxO6ocMnmBTlaB9IDClRzRa5n7pteO6pL3J8eMjOxctUhUUKwWT7Ah5YHR8Qhp7JYoetcsrpkWL3UiDFjuXpKboumJ+7TFlVCN+TXI+Siu1rD5FQlPWEkBLL1RKjNVJCe3qL09vXmZ+7RjWZIpXEx2xHkMZSzbZxfoPrl6QoUMYTQk/fb2g2J7kje9z0hpATwE/Wa55+8nOsjg6ot3ZRRhFclqRLIZFKPx9CMlb8GVVgTAFIrj10LTOYKeJTlqXfDWG7e57Ni6ezCishBb1LIKFFUGuJHcF3SIkY4qgAyBVtISVkglIJditB6xIhytxwQD5MtRJ4FIMfQEhsofA+wuqUhx64xKmDEwfRD4R2jVsecPDs4/SrI2RyfDHG7LsWEqkEUhlSzK0EIQYCCu9aTo/ucHzwHAGNLWpMMWFSltTlLqenKx59/++jmu7w7z/4jyltRewTg+tzSjkKrRIhCJTKSfEiSTQWYT0+QPSJkDyorMyxOqeARw8yZk95CI4kBEnovPAJgXYV+MxnBq4/veSXPxxYN4KQwKiSodVIUbK32KLZfi+H576L2889wfX//L/Q20uIc+8nHj9OosNcfjcpafzt30LqiL7wHeB6wsEnkFqhz72DhTjh4uyE87s7zCuF9yueefoGylRcuHAeH3sODp7ldLni8PCYwYdxUTkuZAQYJakLTWEFxiqqWmNKhQ+B4HIYZZKGWTEhhUCTOorCkND46EkuIqVl8I517zBS49JA12erTFFYkLkNw7mOwTm0KignNcEFQsjHm1IancAph3MRIw1S5M/V0AesKfP3yuv8ouENPS91sawg2W/QiP56nhSxS0dz7h4RmXgTiL+uZgxsC+qNSomDr19cYZY2Df/+9tv4H7deDMQj8Kyv+Rcn38n/9zPvY/axivO/ucE8fYd3NI8/f/8ve/zkQ06wfYnXMFmTTz+vVpd4Slz58Al/zP3fkB4u/NqKi/1Y0SgE4tkKieDctWNOj/Ze8acjIsheoLuXriPTTaK80bxmU9C/3nnDA3FCZEg93iW6PlBVlqH3DJ0gCJFBHBlMkFIOoRISKSVJSELoiTHL0Obbgp1FxMhEqTVSShwgZcqhaBG6wRMRKK2xKqJNAhlR2qAN9C77NrUxSB0IKeBclrtLqQkxEaPPKbvSEFNEppAxrMySSqklEo21BpV61stThMwso/c+S/DJ13spCoQyaJHlwRKJC5EkIlEpRGHp+o7QdjTDmuXK4wbQRWbZlSrZ2Z1SWg3aEFzPctOypSVGQN+vaLqWgT18bO8GdQk/IAxoJXEh3cXhjLLfECJG6Vzbpg2LxTmEyL3HVT0j9j1932F0QV1PSaojSAkRbDlBmiK/5sFjdEHwHc3ylKNbN6m3trD1BLvYZXvnHJPphPXqlFXbEBJYUyKnir3dHYIPKKOJfsC5SwitOT5ecvHSRRbbW/gwsNl0FNqgtSKGgHMDQ/DI4Lhz84TT/et06wZZz9hUlqJeUFcFKTia9THr1ZrJfBtrDFobpBQEW2G05ODOHfquYb1aU1Y1dVGSUqKcbjOtJww+UNQzBgTL4zsIIRi6FlGWDKcNKSlmuwuUkZwe30GZCeV8B1JktX8LNZln8Od6lK2JpaVtVrjlMZs7d5huXWRwG1y/GX3mIVeOxZS/eZIgBU/bNNj1KdZuMXRruq5hazIDEiEmBh/Yv3ObZ77wGcp6hpktxg1wVpwkOXbJI5BCIKVGGksxmWWZc10xsSoDw8SowhjT+8clVRLPs9xn/5lqiFKwCZJWgBURKyJhtDElkdNI77ZrJO52S5dKYERuLXhhSqlMgaKeoAtLGAaiAKUE3jU8dfuAbn1MtzzELY+IfUeKjs3qlGFwTKa5+owXLPYkIJVCao2QMoP8mOiaNWU9IUaBtXXu+xa5uUHLxOr0gJPj23g/IJVFyIYhuDGwLmC1QciAC56AwJYGQkJpgw8J58l96yErCAY/4HMZ4fgZDPTO4ROoELFmVCAIRUqwXka0njKratbNEqlm4Lc5OZbsPvKHuHFymSf/0z9n/2Ag2auk1XUiz6J3HkGoknD4eZII6CvfnS0Pdz6OUAJz/v0of8jV6pDdrRl757Y5t7vg9OiAw6NjpvMJXbviiaee4M7BCc4FhMy2IO8jKLBGIqSiKjVVYdmaVRQGnA/UVYmUgZjUXaVHchGfPFopKlUihaB3ESHBGEMSikJYINAFlwPtQkQrhQgJLQRBRJq2IUqNKmBoW0BQFIq2czg/oJD4wROlyvJ2IhKFJWePdK3DFPUr+IX35rySk0Rmrt64sOSrjz5uIZX3LFOORkD7zXlOb84rP9JDKL7Vz+KVG1+9uMIM73nqS+eJb4M7wfJvVu/hH3/uuzG/PuPCb3QUj+/ztvVTXxV4/66JAekjQb1UhdnYJe5fRXAZI5d/6XnwHWYlxMTpwzV+29Mnz9X5MZ/ilQPiImT/t+5AvMSvLiLY08TiyfYNA8Lh2wCIt5tuTHbO7JQtSmbbU+ppz8H+CjfkAKmzvmM51i1pazG2pDYFoDCFYFELDJ4UEm6ICOkh9dRForKa3jsiCZ+Nq3gpiSEgRwllTBGlAslWeTkWQQSPsjbX7CiTe38HRxg6km+QUjCZTdBK5l7uswAolYixZ3V8jOvXTOsJKEUKEaU0ldIoYxj6nrbvCN4TvKfrHJsh0XTQ9YluAB8SVW2oast8e8JsYplWmTkVIveTh5QYQmIVBV2zYSUChVDYMtEuT+n9FYIUeWkgIIUOyog0hjSEuyxQrhzOC4NoVE6SN4bJbDtXeA1rUvQ47yhMSVVP0VLgrSX1A7aqIQXc0KBMyWxrFxkCRm/RVS1JwDA45ls7GFNgqxKlFM71bJqW85fuZz6dU9clweUU7/l8gtEz6qoihEjvHJPpDBE9SiVuPHczs8GDpKxqyqLM6cy2Yr7YZefcRY7u3OT6k1+g3SSmO4JeQbuSTKqC5fGSsp4jpKSYVCgifS9BGgKe/eeu45oNQQm6usbWNWU1QWqF71q0LuhCx62nHyd5z+L8fbgYkOWE7ekWg2tp14f4EJhsK+azBXVZcBIDq6N92raACNNzOXV7PsuVbIvFgvXqmP6Wp5rOKUIHuiJFmMwvMHRrlifLvHzYeFAHlPU21bBN27fEmNnJmCIxCpZHt1keH2LqCYQEOtfwSaXzwkvIbB0wi/ztpRTTekJtVQ6/8iEDbpXBt5CZVc7OXzE2A5xZHMiWhRgxUpBcogsJERJbRb5Pkgk5Skji+G2ZyP8eUpagG3XWcy9HpUlWnhRliVEKVK6B00pSDPscfu46m9VyhO4pL8/GFHljC7QxGKMQKdJ6ny1f42eWlEGwVDksrNmc4n2DSyXlZIESuYe6bxq6riOkxHPPPoPVirK2eCdxpsgBfz7L6I3S+OhQWkIKBBGQUVIajdaCGBJDcIgYx+crc7ND8HgSUSvwkuihj4yhdwGrNUJVLLYeoiynzBYDbdtTzh7gyL+Fk9U+v/Gh36BbN/jVPqookDtX80Xb8VMII9Dn3kEKkXj7txHGYi99N0NzwGTzWe6/tMP5rV0uXLzApLY0mzVKa7TxPP30kxmA+3wuTSRUkkgExqjc9W4Vs9pSThVbswWzeoLvGzbrlq5tMKVFitziEBJIa0g6oJRBeMnQDyQSciQ3UwyIZBgGR9CRsiypTYkxBaXVDEPH6aphkPmY1aPNJqRI7wKt9xTKMASPE4JFXZKCo+0DWud0dq0tZSleyZybN+ce5yUD30TKF8yvyrN5bY083SDD9j0HrgXz7fjqvX7njR7Y9rsqzIDLH5b8757+S1z4TUf9uds8dHr95QPvL5+YXlaXODp3ifMtAJjJKE7ePmP5UPZ5BZuQG8XNMPDIdJ9PfTP63L5s7gawdS9x3k2ghkR5nKj2B8xJ9+qpBl6lecMD8SigGyJFpSjqgqouR0DaYwqNMQJlFEIJXOPZrHuE0qiU64KUTqRhRaWnSJ9wSeG9whtJqSOlykA7jHVOKSWIHmM1k7LAWp1bllJm2oUQOdE5BpSSOcRq8FArtBbIpLDTGj8MhAhKgVaRFAJRCIQ2GD36uEPCznKKcGkMIUaGYWAYcppyPzScLNcsmy6HwUWBjwoXLFEY6plmt7aUVmZAohOafDJI4kwCn88hMeTzUIoJ13uM0QgpEGqgWR7QDJ6tUpHwiJQBuQyZgRIineUZ3wU7IeTlhFIKKxXVZIvpdIfNakXXrvE+UhUTiqJCaskwOKQtc92a99h6i+g9Q7tG2YJzFy6BFLSrDYtz59nd2qYoLMvlKQcHdxi8p+/XHB89x/b2FpP5nOQGINGtV3Ta0LYtgxuw5QQBLE+OeOpzv4Ustrh4/zW6vkFpzWKxRQoRP/SsN6cE51mtjhHGsL01pwsRIUvqSYWylgsPnMvLA9fTrgUCz9BvsIVlUs+48uAjbNZLonNUkyn1YotyMkXGgVIbXOw5vnGdbrWhnkxR2iCjwU5qrFXs31qTUmQ4PaTdrBm2d6jqGUIpotZsVse0J0uwBXHomUymuTpPS8p6jrQ1Ydiw7iPzczNqbfCxR9mSlAzRZwAjY0+zvENVLlhvTnDhPozJSyeUwnWOajJDa4swJnufTYFWBagcZFhVU6qyQJ+VeI+QVmgxqlHOGHOJOvv3jMRzv2bKmQPpDFwLiZUCIxJtSDQhM921Edn3FfM5IKR87k5n942ZCZcytwIEkRUlmcFPGFtitIagQOfPq+tbtBBUpWGzbhicQymyVFoklNQobQkp3H18IcktCkpCEiObD37I8Z8nx4ds7d6H7wYmuzvE6FBWU+uamCLT+YRn+oFCjmnwKYdNWiNQGlJyxOSRUY9KG0UKibYPGXSHAanywiIQ8S5mVjzkRH1pRsuAC7SjGkFJiZYF1fQ+Frv3UZUVk+mE3sENt80XnnyS9vZn2Tx3QCrPo3YeANfhD58GW6H3HiG5jnDwGVRRYy++l7A5Jt3+GFcu7nH/lftZTAr29hYYIzk8eI79/Rs89cx1jk9Wd3MHEtlGIJVECkmhJKZQGCOZTWx+DZTCIAl9nz3ZpcJSUSiDDx4XB7xQlCliRV4ySUBrxRD7nEEpMxMRwzDWC2qC88iUX+Ohbei9Q5mCaZGtRGIE+c2mwUVBVRSQ8mtLhLZrmRQlKQ0MQ7ZmhGGA6HHpDf+1+7qdl5JEiihe8520r9SkpkP1994rnRRZIRXf3Ei9Hkb6N3ZgW9QCyuJFFWaLD3+OxYfzv9/zUZriy+oSj1oihRgVbK/ehGnBnfdNaC6lFxmyVSf41HCRt1c3SPLeWymAMWBtlJ9/pQC2L7utXSbqfU958Mbwgn+1ecNfESTAVrn31SiFELl3eDYvmRFpG0/fBzYnA10XUCpXh1mjkSLg+hVVf5vl7QG3rhHSIIttFvMZO6UmxkDfdJASWqrMbMscLOWCR0WBEhJrLWVZIhIM3qNkBqcukOu9fMDFLl/MDQM+JsqyzJJWIZGVQavs75YjIxiIGZzEgJICbQqUNgg9sFm17B9vON54EpaymFBqiVZ67O+WWDUGYklQIoOJLFsVBA8QUWPoFUojZQ6W64aeNICqJoShZ320T7vp8FajycsDoie5AamqkcVnTMHOy4rcDZ3G562p6wmzrfPcuX0d74bMnEtJiLkCThmNb3tMUWDKGiVg3TWUdk5Vz7hz6zpRSuaL8xRFidSKtms4OTpk/+YzeD/QLE9ob99kYkqMVqQw4GOkHxw6gguRISRUCJwulxhtmE53WHcDbbPCp8TJ0REpRLYXC0L0+JBolidEl6imC9adp54v2N09hyXkkDmZpb7G5J7oru/xLtJtOtyq4crDb6coC5r1mnIypdksQWoUJUq2bE4adDFj95xk+9IVetfS9D1utSQUGqU0Upm8YNqsWQpIKVLPRu931xO8J/Y9xd4FTFmQXMfm8ART1rTrQ0iCarZDYQxtt+H29ScZiNh6l25zijYlfedRTU/XrFivjln3LYXVSJnjAWfb57M0eGuXyWyBLWuKosQai1ECLc5CEeXdQDZSBttKjuB7/PzIF2QKnIHuMLYcnIW3pVFQriVsl9mnu3aw7BNaCSqVJfEypbsMeRiB+RkrrsgRbXFsDRWASPk8EMlBgUIKXO/oXJu77EXOlDBa52qwGCHFDIpjRBuN1eRAwEQOQVRqzEYIbJolhEBdVQhdsOkCD+4saNanefHleu489yy23qNZdbStw+MIQeXFnA4oqZERhgAxSNzQ40TBtC5QRpDSQNcP+OhIMaGTHsPqIAaBFClbJFKkD/34eRdINKXd5cLFt2dLSEx03QbnWqQ0XCx6pNtwwgK9t8Cd7hOOHkfMLqN2rpHaJeHwceR0C733DmJzQLj5Mar5NpevnOfiXHDp4jm2tyecnt7h05/5DE89c52mGbJyAe5ahAQSIfNiwGpJXSguX94leMfgHba0GKkZoqMdAkZrVN56IoCyKLDJ4lMkpIGYAlokoCbWPAAA4ApJREFUhuBJCCayzP3eMjKdVHQhoaXGSEttS9IQCNExDD1ITWGy1D1ER/CRwhSURUHsB4J39N4RRK7Aa0LCaIPVBcEPaJXDDFd9GFVXb85rcaTPTE1U6SsCD7MCeXD87Rn67T12FXCTe7tsTDLL01X/bfkqvu5GBN7QgW1JQqpLOHnlaglfVpe44HlmPnLvReYv+fME/bmaO99RMMx/92dRBPhPq7fylnL/3pcwCVQ/MuAvs1XMrBM7n15+Wyw+3/BAvKpr6kmN6wdcN9Btetzg6NoMurSWuWtZSKSKKJFhgncDTYxUVYnrNqyOA5sTgbATpluC3akkhoLTVcPxyRJiwhjNWWCaNJI4sseyLHHO49xq9HjrMSROIIKjDwPeGYQyCAVaKmyh73pKIfOGMWUQEJMg+UgQZJAuJEFmoJME+Jhl5EVpuVjmDmuZdb05/IqI1gItZa5+iuDHi1epFEpnQCREHD3d+XbeRYRQhADtps3BbwnU8R1OlyvaxS4Gj0DnBYFrkWWVt3wx3ZUUAzAGaUmjMNZgy5qt+R5SSurZFoWdsrW9i1st6fuGFARlWeX0ZDewHlpcSsih4+hoyMFpQiLlIYSOYXuHcjpj3S5pm4ayKrPXWCk2mzVtc8rRnQO6IbDY2mGrrLFJsNjeQ4vE8dEhw9DjjaU5PKB7qmXrwmWUyez86ckRbduwWi9pDveZzLeQwdOHkP3mQ089mRGFwgnouhbb9xTW4ENO955u77F17iLz7V2kTHjvCEOPbzsa55FK0LUdqIILb30UERPWKI72b3J6dIT3A9FXXLj/QVbrE9arFaoq6IcGsYKEp1CKop5RFBNm0xpcg6hKdDWlPn+F5ck+66Zl69x9lIvd7DW2FYu9i6zaU3qd8DGgy4rm5A7Ht+4QKai3T9g0LVaXJBnQnWK6vUe12KYsa6Z1NYZqnS1fxredvPw5A956BN5n6eZ3jw1ElnWf3Z8Xt96Mqx3OeroLJdgus4Wi85GTLqIriR0DUmTMnxspEj7T6rlCMEhiEAQpxyrDzLxrrVEmf/ZTEhidPb6EwKZxBO+pC4vSgq4PueJKKmzlcMkTBDn4Tiq8H0hJ4bwne4pLtncmGAlSKKbzrRwIWdasT4+YTOa893v+S4ahZ2t3weHhTZYHX6BWmuVpj5R52eVHQCpGZYlWIgeuDR4XEknkiroQPEFkb7VICRGy51okCA4IEqsUCMvW/EEuXnmEvd1dRILT01MOD29BCng3UNUTkIFpZfivf5/m1z93H7duD8T1bWJ/gpxdxEyuEDe3iZtPUM7Os33+AhcmG87vzrl0YZu2P+W3P/FbfPGJp1ltsvIHUj5HJBBKjEtNsj1GSi6cn1NZiUyeqrY4rxiGgBMRRE6ZFTIytD4vGDTIKEEoZmXNuvcMwWOUojAG7yK1KQhKseo7Vl1DqRRb8ymFlhwts1R+6DYIrZgZjRs81cTQx2zjW3crIlAag0sDUQgKUyCURMVRIeU8bRyw0mJUwc7WDKHfBCCv1REB7PHYJ16QO8Vf8LU1uRFJL2DOvq0mRezJwObivV82Rg2q/yY8pzfnlRuRlQvBvkGp8LMRECfFK4p7xfAymjKEyFVn/YCIkSRfwWckJauHZxw+qog24eYRe7HBPztBtc+/3//sY9+NbCT6G8XCKTPrus1Lzpdze+kSZgOzZ/tvCxAO3w5AfFoxnVl8r9goAZuWts3gUsiE1Iai0IQhgc1sszWGosjeYj0m9Ho3kISmNIlC9qjY0PeCW3eO6IcBqbK8MXcza7QSKKHAFASpkDhSCLiUEEkQkRQmkERCJYG1Jieep0RMuc/Xp8x8aZVrz3SMRCUzUElpZLNV9jV3jmQMzkf6bsCH7J0VcqzNkTn4ipQ9qwjwKeKRSCnQQiN1vm2M2TceRims8w5Crj3yPmKKKSlu8METfaRZL1mdHNFd3mEiBBIPSITvM7slc6haTu9KI6OZfweEQGuNtZbZ9l72sYq8BDg+uJmZ2mKCMgZjLV2zoekahr4HJItz50FFQu/H+jhNQuIiLG/fZHl6gvcD+7fukFJiNpmiZKLvBurJHFRPUVZIYyikhug4PjlmtV7Rdx2D61H1nOlsm/nuHt4NDK7HDxHnBkBTb13AGMnpjacRxlBoQdc2eN///9n782Db8rO+G/v8xrXWHs94zz136Faru9VqjciAoQkWBDAEE16/jutNJU4ZXI4HKCcVQ6VMUSU7HgrjuOKyqXICrtip8vsCrrIdOxTGr0GAkRFIIKGp1ZK61ZN6uOMZ97SG35Q/fuuce2+r1YN0m+6W7lPVfe7ZZ++11157Td/n+Q7IU2d3TYg5v7xpG4QQDKoRQklmB1cRUhJcw3LVsjie5Vg2XVBWA6TSjEZDUnAcHh6iygFrWzvM5kcUg4rl4RX2rl/HVGM0CdVv6xgEwZaYQrO2s8tgOMjO2V3DfO86K98xnx0jEyzmR5TWUiuFURIXPF3dIJWmmg6RQrB+7zsYjXZY377IufP3cWZtA2sNiy5Re4HWQ6aVxqreAPHELF8ItMqgW0l5g2pO/pnoaePpBvDmFGifTKlP/3eKxvPedGOfKrRgrYTrS6h94qhJbA1yjOAJwBZJ5G3TU9S7GEkBKinyMSX666FS6KKChcSHDqUMRZk16p3z1LWja10fEZqIIZEiNKsVuo/cCwlsUVGUJT5FktBIKW78TSrecv+7mM2PCN5hdOrZHAWuq1kuatY3NthY2+Lo6pO0wYHQxJRYNU1m7iiDI6GMwCpD3XhiirSxI5HXV1pNSA7vc96Z0InoE9lsXSCFYlCss7H1NrZ3LyBiZH3tLGsbG2xsX2R5fMRTT36B5555jL2955jNV5w7v8uF9RHf9S7PxxQ8c3Q3fnlIOn4KbyzFeIfpuODscMbaZMjadIe1acmjj32CRx59nLrxOcYR0ZvJCmL/HUiRiMmjUUwGBZO1EeNBgekjw6KALiSSlETtsWQTR+86Ir28IRmEEBgj6WJDionKFIzKIUt3RB1btDiJkxQ4n2ilQ8bAog6sQoOmYDQYoKTAmJLCgus8y7ZBKkU0Ah8FKXpa15GiohT2lPlT2QJtEqq1IATRRRSJUL/CkcCdel1KpDy1kQ6SEcS+cSKdYPqF2eu8dq9v6cMVpMHtMWy7U2+sugl4ByuIWvQygtd7xV778iODfS3fwPmXjTBLAugjzAgxU/1eg0qFYe89IxZ3Q9SJzfdd4//z4P/EfabgI63iA1/8czz7/CZ0EuEEqv3qjlURBGbx6hpuIsH6Fzvs3urrlob+YvV1D8SvXdpHnt9kPBnkyawTWCNIyZOQaK3wXUKge4CQbw6d95lyqjU4h7IKYy3leMRkWiGE5Lm9JUeHh0hiHt2oXueaIiEIQnSo4Egqa9ALW1BWFaYo0aXFIGnqhma1oqvzRE0bhZaC6BNRgjZ5qqZEHzfU+UzfFCI7cKfeaCoEutBHlXWepoUgFErGXvvdH9cy53+HJLIpVg+afIyEVSQSiD7mqW3sskO496Q6smgDpiwZVprkFJ0LCAG+azneu8SqeQtrBSgSUqYcpeUdKEl0QD9ckAiIWeNOTCgls3P2cIpJEtc0NIsV0QVG0w2MUri2plktehO9AiE1WlukESyPj7NRllREGanGU6pBxfHxHiKC0rr3dhAoU1FWa1gtqVdLJoOSnTPbtPWKg9khx7NDZocHdO0K36wIZHf39emY6XRKW684PD7E+YTSBYPSAgktJefvn9Csjjk42EeIOdoWWRphBwwmU+aLY1y7oqlXWFMyGk6IIdCuFhTDCUkYqoGh7Xx+X22oqgFts2T/8rM431Ev50QhSUoxnK4jkufoymVCV+O6jkE1ACFRItA5RzXWrG2dQStN06zwJhtPrZaHrBYHLI5nlKMpejRgf/95uqZBFAXN/hKjBtz/7nez+5a3sr51jsl0k6ocoIzJZmpZuM2QwGErWTiBUQJTZbaF1XlKq6Q8jW1KQpxOyD0ZvGYGejxhonNKnEjcMGa7CbXneEH6WXh+/OScPdCCjVKyX0eWXURJ2KjkaXKI7IF2QiBCYt5F5k1ktJb3TCmyOZhIkYCibeosMbEWozXeB8qyICZomkxJDt7jfMwL1wXj8QjbeyOQEm3b5Om0lDkKTuYPGiJ436GkYP/6FYpyxMbWFlVVklJgOBpiC4O2FcaMMGaVtdddhCRIUVJoQ0qJtvNE4Ugi0YWICwkXPEIESlsgevaMjz6npceIEBKNRKoJa5tvY3t7B4VnPD7He7/luxhWBluVlOW9vPeb38dyvuDqlcf4n3/ll9k/XHHpec25C7t8y/0F46f3eSqWtMU5RvGQneoag9GAUTXm4l1nqRd7/N7v/z7PXT3AuYgQIrPv+nsNQW6CqL5pWBiFlgkhIlr0zxPZZM61+ZxaKEPykqK0+OCRWJSK1K6lMBqlJN55hI45OpACROB4tcILydL53NwkIpXCp4SJicqWqKSJIuXUB+cIKuBjQCiD1IKmaZAxGwpG7/Eh5n+nRAoxG/jVnk5KvBfoMn9XvnE0X0dur1/XJbglO3fyZEI+c/l1XKHXv8R8hfQQv8YEt6jFrRSnO/XHXyJ/D9EIgsk/o+LrVgv+UuUHCntThNntf4Ne3yxeeuMmo/M6vFZmZFJy9U+OWZ1NIGHzfdf44Lt/iZHMSR7vL+G/vfs/0r7L0STPb652+Fu/9hfQy1e3U6g2g/CXc0F/YSUJ3VRj917d697s9XUPxLe2phQ64eoVRhmqKlOYqy4RY6JtAz6BLiK0DqlMpqqnSBKShMKHiEyZxj4ygUomFnXk2qUvsTo+pCgKEgoZA11qkCJhlaYsCgqrKIqC0pqe7qoJ3pEagRoPqeSIkCLRB4zVSCERRJxPRB+IfZcyKQVC4VzWnw8HFiVy80DIRAqOGAJd22W9dihwmAxV2kz3tCZhbZ5QJyD5lG8yfcgRV97hu466dswWAdclkAJZaAqtqYYV1bhEJo+WOtMwBRitaefHLBuPszJPZBEQHdG1IIq+u5VIIt90J5EdmrO2XqC1YjzeZjg9y9HxHtHX+LZhduUSYbGkmk5IJOrlDCkVZVXh65rm+ICBVTjnEFZj7YDxeALeIYVlsrVL1S5YLWfEztHMjti7/BT7lwOhc7z1HX8C51qOj4+Zz465dulLRO9JMWvKY4hU1rD3zKPI2BGNpXEghKQSFdONzWxkkWB//xor5yFFZtcuQ1GQYiCh2di9QGE1s0tfwo43ERNN1zmqqqQYbSKUwgjFfDEjEpjPjlkKxbnzF3DOUSPo6pr58QHFaA1Jh9AWW44YbZzF7V1FCJmlCe0M182ZnH8bGANSsFgecrB/hWI4JCLwMdC6iHOa8WSD4doW08kWm2s7jIbrrG2eZW1zm0E/ETwdFYt+nN1PHo2C8UgTtcGLrI0elgKrb7ibx5gjzsJJJng/jYYTivmNk/zJ9DvlEfZpV1SIm+7Z0k2v6qePJ3NxKQTjIjMijprIrE1IGVkv+ru+dLL2efIaYiRFgQuSQEISib0OXNoBziWEkIQQSCkbr0FCu+xUrlRBUeWLmA8OkqBrO4LOmuJsAOYJISCVpCgqVLI479ES9q5dphpNma6fZTSqmB3v8chn/pB7H/xWhsMKt6xzBJooAY00CVxuzpESIbo+IhCEyj4VmVETc1JChC54IGYWQLaEIOhA2yU0Gh86jg72KGzBcDjlO97/v2B3dyv7KGSHRtroWVsf8+SjV5kd10wnY2KSPP3UsxijODOCItR0bWQwWccKx8Z0zGBgufTc5/j0w19gtuhyXJg6+QZSz5jJhn1awHRUMhrk86YxAucidVOTRGJtOkQkRSEUwedzHSmhg2ZQVj31P1Lp/vwiYqbkd4lUZAr7YtVhpEVH8K7fL0NCW7BCIcju7Er1zZ4YCQEWi5xgYVXCUPTJfpEgMxMnn/8hJg8ejNLEEPuGVG5+eiIpBQKvgKZ4p173ivrGBEuvBJsfvfK6OBq/kSo1DaaOtOZrm9bFE8O2cAeJ/7GV4BbQHYzI0+5vQOD9wnKDE8fy12b5yXtETCR5Y2OHUnH1WwtEgs3PeuxRdzoRFyG+Jj2qUBn8kCw5vafmV9/1PzKSwy97XiEMx7Hjpz72518VCBcxnytV/TJGbC9W/fPrDcnwS/IbhpYO3wBAvG0WLOYBbTXD4ZBhVaJLmanFbUQiqOsAQjE5s4Y1ktnhjBAlpiizuVadzXqGKWCVpA1w+colrj/3FCBQrGGMRcg8YTdKYq3BGo1WioHVCKXwPuLcCucD1lYYa1C2RGpLiC67tSuIziNSpOidphUiT3h0gbGZcq5kIqaQncxlpo1H54jBE6NHJUUQ4ILK5mgOlnVECo9VEaUiIXrquqFZBlYNOJ8wpcIaRVEYpmuG8VBRFpLKaqKAWSuI3YmWPiCUJQmFXy5YLVe0wwpDnpSnmEiuRRYlOck40/BPwFPs45zydtMU4wmT9R2ODq8Ro88O8VKRjOD44Do+BKrhkBQ6YvQMqwEk6JBU0wnDyQa2tHhf47uG4WjEYnZEs1xQFBWdc3jXcP3aJbQxjCfbtEJxfDzDRc+ynrGcHdAu50gE2limG5t0y2OeefoZrlw/ZGN7h2KyyXR9E+89TdcAibaps7Fcu0QkD1rSrJa0y2Okj0ilmW5tUkzPsHP+bmxhadslzrdoZRBa0TUruq7DhUDbLBAh4NptMmNJ4m3WuNfLYwqlEGUithAIqLKkmR2xmh3jfUMxGFP6JYs51N2C1cFlllevs/W2BxlunmFUXWRQjJmu77Jz9iJr61sMhyOsMUghb1DHe6h8ou2WSJTq93OtkErmXO5CIGKOxopkn4IYszN+iifkcTg5295gmZ+8y41KkE3SuPXBE+B9+m9ugPBTKnvK6zktBM7DooscrwQ6wciQp9RCgJJYqzhXaVQIKLc6jesTgCJSDoaYwkIMCJFZIimCEBapA7bIk3+lsqlYvYig8j6LTKTkcSHLOUKM+fiMgRgsRalJQSKEYbq+Q9vULBcHfPGLn+Pf/OK/4Z3v/QT/6x/+79jYmHCwv0cIER8SXZcn6M53/booUgCIJJEIPuCc653nBTEqtFI5WjHlKEJJwkVBSoLEmGF1FjPc4SiuoRL84R/8FusbUy6ePwcyy2xsYbl+9Ql+/3f/KyEklnXL9s4mYhGZHRzhOof3jvX1NcZDyWSygxQrPv2Zj/LUM5fxMW/7mBISmbd1ygaRSYA1mYYuiVSFYTKpqJuOopSUVUmKgbbzGK3RRDohqMYjmq5DW4nrVjmGUksKXaCSIKVIkAKhFYGcBa+kQfqWLkSiT0ihqcoCawXee0IMNI3vae0GEQS4hMMhokKrkhgcKQWSkZDyBENLhUgC1wWkTEhRoFRBFltEhBKEGPob3zvg481QoeT0RLP+WIC9w9d1fd4Q5T1mHmgnXyNttgeF6g4Qf80qSUHSOS7ulGp+B3i/aAUrEMaQXqtGW4i56aQhWsXlhwp+6M9/hP/bmY8gkbzrV/7P3PtvuJElfrvXQwjanSHX32tPjdnSlZJ/dfRN/F83HkWJW4/nNjn+t5//P6CeLl/xW0iXp+AvlzzxwtJ1YnA9YI+ziDwa+bLMga+3+roH4l2TcjxR57HSocdDysrSNS1LOoIXhFCwtbnJaFCwt38A2jAsSoSUeOcw1iKJWC1AKA6PZvhmgdEa5xLGWKzNCpM8MBQ5/9Z7UoLGK1QE30d2xZAIocUWHbYs+omJJIqE9yHLQ7TKbs2dw4us7TQ2A+8mZvRhehffFCGEDHyQCmM1JkKlIx2JZuk5XnV0XUtIASnB2Ji1vEmiTcW00lSVYjxSFDrfKJvegdiHiIwxa837aWUg68iNUkhlESJSL+Y0WwMq6HOLIXU1sRjn6WgCZAZfEpFdpUNEqV4nXpYU5YB6NadtWspySDGdQPD5s3lPu1xgq5IUYT5bUI2n6CSpl3Pq1QxSNrEqbAFSI5PAuZbV7BBcS4hg+/dezPbZv/I87dqExdEe155/lnaxwCpNURbIoqIYDnDtMp/I5nOOY+TMZAupBG1Ts2oWHO7v4RrPYDRCJkVT17RtQwiS0fZ5RNcBkcXRAUU1YlUvUVqyXMzwCYwtKYzEuw6foO0aktQMB0OMFbgY6BZHLI6u0a2OCUmixuuYlJhdv0TbNRCyYzcIossmVYv9PabnBwwGA0bF/Yzf/qe46753srV9jsl4QlmWGKVByt6JX9yQYPfUbyUVWkuMzj91b6ymMmolxoRPGdTM25P9G9YKOHGvzlPYGyfWdPM/Msc8P3b6FHEDaL/wgE6ns9QXPH5j2g5ZHjG1ic7FPglAIYzGaoEioINDhxYdHVZCUnlCnnpqmkoBqS1WaUKKeXLqQz7O+om+0gVayZ4Bk5CDgtaDMQatsma58x4tsvO6RxIirOoGJY/xg4KN6TnKasC1S0/xBx/9PXyKCGM5PDhGKUNKEqVE9mOIERccMvWAULj8HRUa4WDZrggpEUOg8R2BLF/xDogGrUQ2jfSOKCwDu8lkdA+DC9/KYngXsyd/D+lrJuMRv/Fffo3//s/97zh3bgspBW274Pf+63/h6tWjPDSoPZeebZEisVjMIQnO7GwzqjRGwpVLX+SzX/g8i2WLkFkNfiI7SKe9EEFRFKyNS7QWTIdVbgw2DaVRjIcDlEg5OkwXKCVzbGBMKGNIRKzKEqDks2GdFBLhwYeAKRXCCWLK04Wu65BaYYwBlfBNyMkTMp+XoggIJEoWCJGT5bvgQSZMUiAFbdNkdoRIKC9oMz0GrRSF0bR1i1YCnxwDayHCfNnhksseHAHcHezxulcSL90PiQZCceMJzVQyUTJneX6Dlz1s4fzXyE0nA0T1Dep791pUkj293AiChajuAO9XWlELKOwtEWa3u4QLUCie/B8Un/rhf8JUVkDJb6wMux/qxwlG5a/rNkaYJaM4fmDM0f2SaG6c0/RK8P/+L99H8/2GD2x99hYw/oGrf5JLn9h9ZT3jBLoW6BUvmwd+8/JEyKZs0y+uUMtvbOfGr3sgvrE9RUVYHnfQCZbHjuByLnAKiqKs2NndBmB+PEdJzdp42GdyZ7q30YLhsMQWhuXyiM4lbKGpBhUVksIYlNS9MVrWFKseSCPA+YgXode0ykxpFOCiI/qWFFpS9IhoMcaiZOhplgmhVY4QqhvqGmIKiNRnL8sMYF0I4B1VYVA6Gx65ztPUM/ZmHV0UtDEhlKIUmkElGBWaymrKqkBpievpwyJmd/YUM0tHCPKEpwdeSkSCSNRNx8rDYGQYF1WeWrma2sNYASEQQ8C7OWmwmU1AfOQkT/x0Kh4TSmVXamuyYVtRFLjO4VzHYnaUt6c1eXuIbDpXVMOcx+5afLIE1xGizxRvXdA0DUJZtnbOoZdzlqoguNBPPfMkrtCCg71LHO1fwnc1pS4oJoIUA4PRmOn2eVzb0ArN2toWMUaq9S1cu+S5p/fx3Yqj6yOGG7uMN9YInWNy5gzCCPYuXaYYTbMBX1EQRSJ2DRJovafwDh8DHsnacMx4WHF9/xr1coZvV9kVm8T161eZzY84uPQUi6MjzpzbxSQwoxGKhDaK1gtWyxpbDdl4y91IYG3zLBu7b+Xive9g58x5puMpRVlhjD4F2je72KckoDdTOwHdRiu0kjdo4YlTozHvY5/3nCUeViREiLQ+cuShkJpSZWCbMb4inXid3wTG6CfYJ8u/+abhxBH95LeTP55EXGVjt5Td1k8XcEJrF5Sl5uIgZ4yb5FBhjm4dmsAJQyylQOrdaELogXifXa6UpCgsSSeOZo62y7T1RH88nIL/3JiSSiNjRCqRUwmUQQKrpgUfEDIfWz4kmi4gVcBWWb5w7q77kB//Pa49f5mt9TXeeu99pOjp2pbhcIy1AteBVAIrLUoaOp8jEpODRe3oUiDJLr+PciiZJ8MxQmUMbdeyajq0GjKpzjJdf5Dy/h/g2Nd88eMf5XBvgWiucXRc8953vo1PfOIjNN2D3HXXBR575GM8++xl1je3uHptj+VyTtc0lGXJdDrBWoNvA40AXXoOjw+ZL9vciImBGPr9pz+GU4LBQLO5VrE2HqGNYT6fYQpNUSqCizjX0EZAJtzKEQMoq5FKIJPIZnlCoguNKvMEPMa8XyIDKQqMlLgmOwEqpVFolEi4FJBGIlTeZ/IUXCGkxJQS7wIuiixL8C0hpDy973Xh3gXaFEhElDIok2ntoHA+m1wuQk0C1MAQfYKYM9F1uHNn/HpVKPJ/SYPwYJYvrmP0Fbeci1ZnBWIyJh3cmYqrwyUijr5mE6+vVWf+jV5J3azv5sbE+0696jqNMDt+jcwYT7PEYf0ziuMfCkwlhBT567/1l7jvcgaiSWW23m2ZiAuBXyu5/t6Keju9aOdRtfCLv/5dHH7vgL+387uMRMGvrib8hw99G/qVuJwDIgr08uWJXrpJrD3eIruM1mUX8jZ5rfTwb6L6ugfiUkDnA1IrUhC0q0hCMBpZjJYIaWmaFW0DxhSsbwwhZSDctT4DupSjwLq2JSVJORyxnM9ZLWuMMYTCZZ1jyDfjpDwp1EIhZe+mriQk0Wf35nUL3rOYzQnBEREYa7FGEGSmAPuYJ2lRgNQWYqJrIyHEfsKUb7IToIRCKEU/3KFzicOFpHYGW0h2KsnQGqrKUhgJxB7AJHzbnW6rE902CVw/exQp69AVEmLIusmY3aNtFEQyuOsWM5rW09mEkIEUAjJC8A4hFJ6ETv2NOAkJ/QRSonq3+rPn38Laxg4BQfQBATRd24OiAq00SUoWiyMqW+FDYH1zl+l0g+Vsn+XiGG0srq6xRYWra7pmSebSa5QEW5R0oaOJibCaZUBRWJQB71uma5ts7t6DNgXTjW3G69u07Yp6uWC5WnDt6Sfx3iN6Hen4+IjhZB1dDBlNJphyyOTcRZQxNKt51r/arOdPAtr6mCuHV2iaGlNmTXvlDUYbuqahXhzTujZPPo1huTwkOo8uSjoM480NkLDY36NdNti1HZLc4sJ97+DBd72Ts2fOs7a2manVWufmxQno7ve91Jt36Z5mbo3KMogTGXhfOe8dQt80eaHG+2SRRsLICBqXaFxiWTeUckFlBYcHx4zWzxLt4FR0eXMzIN0MwPs/pJuXLk6k4nlnj6nXm8fec6A3QsuJBRJFREWHDg3GdWjiqVkbMp2+d44vy58PqWh8wosCrwtWdUM9P+gj2sAWhtbH3ngu4nxE9leeELMLuUgSoXKTzHtPCjmCpKoqihQzU6JuKa2B6FitEstlw7Ba4vwxj37+cb703GXG4xGFHnJw/VkGgwkxOjrncR6iB1Vk2UokT+s9ESkTKWb6sxARjUWSZQZaSxQJ1ymq4hzj4QXO3vd+1MVv5guf/zTPfu5hFtefRQ7WENO38Njze1zb/yOu719n7/qnePB+x+c/M6N1WeZSFZZ6kZs2g6pkc22d0XhEDJ42dDzx5Gd58ulnM5WfbL5mNRATUSqqwlBYhUwhN1RipK1XGCvRVqGSAZtIoo9Zi5K2DUiRAXoI2eHPdTAalbk5GbMGvjQFQghWdSDGAEZQFDY7xidYtUtCiGilsEbjnMPFFqMM1igCCUWgCwHXQegS0ipESvgugPBYayiLkmaVqewiCEIXSMKTCH3so0YalZMzUqRzgSQlpZKvlQzxTr1MJQF+wI2pkM70aF1zqmkMNlPSb56GAyQD/swEdQeIIxYrpEuE4iUaSi9oqr5Y5YmtQMQ7FJGXrd4rKNib9N3fII7mfxwlIqTCvLbkgc4BFRufb/k/fvF/zwcf/FV+t9Fc+HV5coOTjwets1H011JSsrgnx5OF6qWPL9nBf/7gt/Ir6++j2qyprw+wr1AXLr1AL14chIvY3+71i/KlYLVjc/LEHfB9S33dA/G2rtFGE8nmY0kmVKlBCFzrWDWOFCuG00021kZYkyfYMeaIq8VyiXc+O6YPRhhjWMxzfuxgOMz05NWCwWCEUgZBBBkRKHwIOaILUDFhlEFqhVV5uktMdM7hXCCkkGPPjM43+wZkP3EUUaCNydRMAfhIjD5HjKWUp3sysVousgmVKlhbn7K2IXExmza5mCeXPkQg5BlkSoToIeZuWUwnamAyqBcSHzMYI0AgZTZB0gQUvo9EalpP3QVMvaTpPK5QmOiy6Z3rWO0/x6p1OCdYP3uRwhpE6tWTfWyVUBIlFYPhFG2zKz0iYW2BFB3GGKRSNE1DiAFjChhYjBYYpRhWBd3KEOqGen4EUiEkrGbXWR4cYlBMt88SfItUieZwxarOum6BpPYBBoKqLFHVmCgF8+NDlNliNd+jblqikHTNClKeEvvWE10gpus0wTEZrbGYT0lCYmyBtJah0bi2xvmWlHLW/Op4nxA85XgNaSyXLj3Ncn2LUVURUqAjUdc1sQd+QQbM+joDoSmGY+xwnenaNvc98BDj9V3CYAfMiM1xwYX1qqePw+kZMOV8eSklRmc9vtYZoEkh+oZIbu2ElE6/k3ACvFO6mUV+Sje/6Ryb49isoGgldQxZTuBbVrWjKgdoW2S37hMe+okb+okZW7/gm2nr6ea/9+A/9s7rUiq0VUglskt/7NC+Rrs88VYCpOydyQR5en3aSIAkJE4UOKNwSVKvVhwf7DPfv0K3miNCQCqBkIn5YoXRhkFh8CEQAkiVTo1dUuKUZRNTQlZF3rYykUKgaRqIAa0VTuVjYr6skbaka2Y0bcFgaLGF5k98059CysQD73gbUnq2z54nkHj2qccRPkd9iRBoYiDJDHRd6ghElLCEFAjJA4oUoQsRq6CLhsHwHJvbDzC6/3u4noZc+f0P8vTnPo4rd9Cb9xNmzxG6Z9CT88zikIc/9SjabzEt5zz7bM3hbBtbSLzvWFufMqw0RhnGkwnVoGS52uMLj3ycZ569CuQEidPvUGhGI0NhYDgcMCgLAJZ1S13XFNYwsBVGK+q2I+CRUpN8YDbvsjliAVInOh/wHqwp0SonSkhNH6eYECafswSRViakyi7/ISRCFCSpaUNEpoYQU86IJ9B2Wc/tfWYuESVKRaQwmAI61SFUNsAMKYGIeb+ViaQipHzcBxxaK2L0tHWHOuGHhkTtO9ruTnzZa1lJ5GmresFmFinfdN48iU0q4UacZiVHk14UQEaduPSdI+66ukY6PHrtVv5NUKnrMPULgHjKOcF2GSkOHPq45eid45fMoE69xYK4czh8eYne0VzfAN/fqI7mr0VJnye0xZHHHjSo/TlpvnhN31O4PGJ2I30alfZEdwYzu2n0fJIl3jT5BvmraLTEgWX/XUMWFwVJvbIml/Bgr2vC9fEri3FLNzmjfwVMLXxi8nygG0naad5xq+uOW6Y4dwr4BgDiITjmywUGS1mM6FKBVCWLRWC1iigtkTISfQsMkXpAaWU2PQuJshxQbQwZDYcEOeDa0tKqPYriOlZrENC1DU3bMCgkUUiCiCQtkOQJUopArxsMjSNog9F5Uq60wUdBaCNN6xGyQZAwUmBLg1KCmATGCKSEVkFoHE2XAfTJpEcriZARqQzjQYXRMtPvcQgVUSkRdQYiPvTYJEKIghBSnnSTOJk6ap2n8TdPP0M6mcJHXAik5FF0iNjRNh3tYs5i7wrXFo60OGS5WmY6rcxZ7Z2a0m3sUpi+EZE4NfQSQqCUohqMmW6c4fKVL+HaGhkjIgnUoMR1K6L3VIMJZTXEWEtoW+YHV/PEM3mcb0h9s2F53KK1ZWPnPIPBGO9rlnVH065YrZaZhloMKbXEu4gdrHPmrnsojSYET9MuePKL1/CrGd616HIMQqKNxK0cyhhijDRNDVqzRLJ3/Trr22cZTTf75s8C1yZCsyS2S0I3pBhvIMqKoHK2+HI2Y354nRRcpr2mCEqipKZbtmy/5V7O3vVWNtd3OHPmHNtnzmU3c6WISfD8zLPoIkFkAy4hZa/BzdPuwmqslqc0c+AU1IaYcCk3RGJP8z7VWqdTb7RbxuTppv+lU115QgsYWUHrBcfeUgx32BjkdWxeeO7tAfXphJ4boB9O9s90GlsmdZ7YW50n3jJ0qFCjvEMnn40Apch6336xKYacXy8VQiiCsHhhaZOg6TqWR/ss9q9Sz67Trea4rs3Gc1JmB3oEzgeiECzrBiUlSfaskwTIbAB2gxqf3dVb5xHSYLXo98VsXqcEjCcjvOtQ5YCyGpKi52jvMr/zq79FvRC88127nL94kdnRc2xuXaDuImVZEZynMENWzT5L19L11LWQPIu2YdX2vghA8IkQIqUukMLio8XaTbYv/En0Pd/Jo196kuc//ovMwhpy8gDp6AlCe4hZewvSzRjUj7N+9l4G0/djyiWzo2uU9mmMkazmK8bjCTs726TY0rYdQgSeffpzfOKzn+Fo0eTjVeVIP6EkhVGMS83aZEQIHgHY3kG+sIbgs0+ESNlHoxhUVMbgfWDuF0QfKUuJMhIpFFYJpIgUhQDpCCqhZAE+4UOW7vgughKoGAmyo7IltioQTTZX7GIgJklhLVpKfOzy+TRIRJQIFEVpc3PKOZSKjMyASMxRcZ0jeCisxhhJSBJCojAFdQc6abpY41xAlhqVcqMABELe4eS+FiUiJEUPrBNmmXWLN5deQSxuZIOfVLQvf3PYrSWuffc5tv/TCtw3MHoMAXvk6Yb2FHjb60vE8YJU15lWqxT6raOXBOInYFN2d27MT4H3nSix219ZqYRZReyRp9irkYdz0nKVY8W4aQDwWlafJX79fZo/fODf8XuN5f/+K3+Ou9sbuvQkAJ0TUUTMjLpXXELQ7I7Ye4/pDdlem08lIuhlZhK9WOlVvnEMhaBZU6x/ft4bB0rUsvvGAuKv8Pv7+gfiPkEUdAhIhuFwhFaZkmiKiG89dbvAu0hVVihd4P0K7xwpSc7snGcwLjm6fp3L1w7oRvcS1YTQHjE0kel0xHIh6dqO1rfY00mhAE1PTfSEEJEnPI3kUAKK0qK0BmGQUuG9Y7msM6ggoVtNWVq01jQusOqjyRofkEozLAeMJuMckWMkXZvdgIWQCE70u3k7qN7NPZslZROtEBNN50mdy+7OMdP4RUroEHuDNZ/dh73Ljs3GklSZL8ZKoqVkWGnWBobxQCGPnmO/q4k+oLVmOB6jlcyT7LDEu44kqnzGOQHiMebJrJbosmBQjdFIotTElCi1xdULvGsZDUZMNrZQxhJToKuXhCSo6wXN4piua3Fti3NtbqTYMlNRrUbUh/jFMctlh5YmT1m9x2HhJENSwGB9jaPrlzk42EcqiymHOX7IdYQYaJslzgdUkihpM6AYjRDWEhPooiC6muXBdQ4ufwlhKmJwrA5nDDYKJuemeNewnB/3zY9Es5gjhGbz7HkGk022di8yXdthff0M21vbDAcDtNZIIfrvsJ9yJxiYhE+KqtCY0jKq9KmxmhQCLfN3HlMihJ5KnW6advfAO2uvb27CitPJ94kmO+PiDM9PpuOib9AkBJWFohPUHq7XCakFpeqXdhNdMUse0unE9BTL9vprqRSqn3jLU6r5CtW4U6q5EJBEPKW33wDzEIXEo1kGifMKn6CeH7I4uEY320elluA9x7Nljh2jNzwUIFLM+efa9B4JClNInGvxTXYt18bgXcyNj36bCECIhA+JRe0YVpoUs6Y+KUXjPV3nSFFgiwJbWpxrca3kfd/xv+H7z5/n+tU9zu5u8eTjDetbZymsxjcF040z1PNnQCR88Pm7VznLvPMSJQqkikQXSZ2EOCTJMdaOGE/OMb73u5lVu1z9+K/w5FMLvL5InD8B7TF26z50t8eg/jwbuw8w2fg+Lk4XaA747KXEH3x+STi8ysULFiMqquEIKQXaDJjNjvn8o5/i2ecu4UJCkQ8lqzSVVRSlxhrBaDikGlQ5IcaH3PAQCas1ZmhyZGDIzKDQBIIQWVsPmKFAFwqlcuNLasOwUBgpsvFl9HSugagwylJVFUbazE4QAu8dkURbt/gY+xx3SWE1wUea0KILTRQSoUEEgRKKFFpcT4P3SWb6ex/nJiVoHXF4SBYtNV3b4boVVVnQecei8SipcrNUgjKWyipU9wrFd3fqVdXOR46Y/Ykdms38uxvmZp9e3aBORsMt2eCvtub3wPBb38LgI4/zmmUOvwmqfPQy5WPiBvDmBbf9IWDngXb60jeiwYiv/5vQFytxJ0rsNauenWFWkeLQY/eWyMO+SfTHCbxfuFp9lvjOH3Z804M/xvpvVtz9xebLgGmyJrNUXwVgTVZz9MCI4/tuNWS73SWdwMzz9n2xUk1i45EVsvOEgcVNNH5sMfurF3/B13vJV3ZAf92fA/0qoa1BFpqqkFRVFpxGAcGBkIqiyNPpxXLBarVCSEFVDTl7/hzj8YDLzz3FFx95hMZZ9EbB8Nz9FOMJ8eALSLlkMBohWOA6BxGijqQYEEkhVIGUEEPAhT4vVymk0hgSVklKrZA6EZd5shxTzp9tnad1EaEUKSpCUmg7YVBlUF8YiZICY2TONA4SGbNju5K9NZyQCKVvuGIDiEAe6EUKo5BorFJZQxciTdNS1y0xRrS1WFtiTIXSAi0V48mYu3crBlWB0QKpNGvTMdZaBJIVjjoEYsqGbXXb0nWecjxC+C4Pw0Vev5Ty5BNFduhWmtH6FtP1TebHx32MkKZZrRBKM9jYwdqCw8MDhuMJ2lpiSDR1jetajAQfO6LzSG2yYUXb0LYNhVKAxijB+rnzGGVznna3Yn58SOda9i89zWzvOVbzQ+p5zWBtEy8UshoR25quqQmNQxUFZZXzF/VgyPr2Lu3ykKY+Yv+6ZDU7ylPJ8WYGW80SVZXY8QSkYjCZUo6mWDtgMJgynW6xubXL1tYO0+kaRVFkXwGRv0Og3275p1B5PzJaoyrBujAoKSllwOiY9egx4VPCwQ1jtZtArzxZXr/sG1D4lgdvVE81Fqch4OJ0nVL/ei0EIytofMKFxKIJmIHqpRAn9PYbF5lEpswrozL4VhIlIip0SL/Ked3Jo2T2VxCq3w7cRDUXiZgEURi8MnQBVk3Hcn7M4uAa7fEB84OrzGfHSKUZDwu0VrRdpG4cQmZGSR7D5yiyhMCHSOcaUrrhyG00dG1NXddIafFJEXzsj6+EdwHvVkgSoelBmzYIKbNuWCmEFkipQWhCcmhjGW+sIaXgHe9+G/PZEbu7W6TgKKsh48lFntg+y2NXn8Q7sgkknigEQUi0Fll/HRzOadbGOyAskSE7d78HcfHbOZ5d4ZHf/hUOG4mfPYswI+z22zHNVarlZ1m/+F7K4QPcNZoh5LN89vmCo6vPcnzls9jJWzh/9p1srBWc3d5BGQkpMJtd4uFHPsb1gyVaglLkuMbCsDkdYYzEakPnW0gBqxTtapGTHQpLFInWOYRUGKVz0kSSFANL51ZZfhMD5chgpUUAKkEUHqsLfO1A5fg2IRVVaQkhsWgWOdvbZwlOoQqIEkxC+vy9IjwhCExhkEFBzBP8iEMZRWkMq4VHSIXDEbuGoGJ2ZzcKqXMnyKKJSEL0KK16On5EpEBhNcOq6pMwIlJqlBB8jRHMd+orlJwt2fjEIcvdDdw4T2X8MBFtD8YDuDFfk642Sbj2Jwx3XT6HfOq527fyb7JKy+XLPscctXDhpdkfUX9jIM/TKDErCTcbq31jfPzXthIolzDLRHH4IuwMXh/g/WUVAiIm9NJzz88LSF9hpGz0CV0VjHrpZb4CQ7bbUim7rN/c1DxdhQhmkSiPAuX1DrXKU299XKOPudV06E69aL0qIP7zP//z/PzP/zxPP/00AO985zv5O3/n7/CDP/iDAHz3d383H/rQh255zV//63+dX/iFXzj9/ZlnnuHHf/zH+a//9b8yGo340R/9UX72Z38WrW+syu/8zu/wkz/5kzzyyCNcvHiRD3zgA/ylv/SXvqoP6JcR4yVaga0iMjq8BxEjw4FFajg6zDFDbjZHS0VZDdk8M8VoeP6pL/DUF75A1wjSaAf8ISosEKO7wO2ils8iaFFa03ZdzusV5MzYZLKTspL9vDBraWOMrFYrvPOkcURISV2vWC1XRGFRpkRaS1VobGERKjcKnPcZTPlECBnoCBVxLuKCzJrznFWUOXoJEgoZwYlsJpdSPAUeid4sRWlSaOm6hq7p8DHrrkeTNTY2thgMR1hjMSabYYWQJ3uj6QauXlE3q35KG6B3hhYym88JIRlP1hBSIJUhdKs81VIyE+F77XoGWRKhDeP1M9hqBEcHtPWKVfCQBEVR0i1nJNdmWrZ31ItFjiVSEiUkVg3QVULFBT4EfOwytbQ2tFrRSYWqDEkpqrV1uq7BLwLVeEgMgfliTvSOtlmRlCa4lhQ1UuYYsRAjqqowgyGucXSuoRCJxdF12rZGKMHyuTm6rBhPphhrwbWgNJNxdmLf2rzA1uYZNrd3WV/bZjgaYo3NmuYbI+NMnuh/lT3NXBuFUSpnIvcnOOkTXZuoXTbu0iexYSe6aMhU8NPQbXGq8z7RYp/yucUp4fz08ZO1OnU67+t0ynTqe55fVWlBoaD1MG8DViYqk9G66r9nqTRK54xqeUI19zXauxwdJrL3wYn52ennSSk3i4UkSIOXljbAfLWkWR6xOt5jdXiVxdE+3rWZ5JAibdvQNI6ykrgQ8RGcz67zQqbeaTufEDOlPW8F73Jed1QJJaFQAoxk1TTgGqQvEcrmrkbw1M2S6BOjyRQtFSk6umZFCBFlDYLcYOlCRCkNfe63tpaUPM88/QxaF0w3doHI0eERgsBzzzxJ40D2zSStLJ1vUXiU0nQx4oJhMFjDFgOKcpvx3d+BPPs2PvOx3+HZJ/dYXnkEYdcpzrwXUz/DaPlpNu/+ZkZr7+Gt6wsOji/zuWtDDi8/Q3vwOGrtfqa772anXHL3+QkXLp5DseJjH/9vHB7MmS3mLJoAJ87shWZzbUB0nkRkMMxU9EIWOUu9i0itoU9JyNSLmE3tUnab79qaxjcoDaZvRGVDwSzjaJoWZSyuawhCYnTAJEtKgdZ7CAnXuxEEIhqT5T/OZa8PXeRzde0JMaGFRImEKQ3IgPMSQuTo+IgYDWagkG1OrQrC99tforWh0AXBOZwPeJl6OwKNsYLkFBUCKxVGJVYxEYOnCyc+HW/sejNe6wHkYsX2p8dc/g59CrijSXSTvgl4G5ogoUxc/q4pFw5mpNfKZfnroNRxDWn0kmAzG459/Rm2nUSJZX13D7xvNlW5U199JVBdwi4ixX6HOVhl4N00byzg/cIKEeEjPUXxKz7tJEtc+EDiJRpZUrJ465j9B1/ekO2rqh58i5SbmOorJIypOrHx8OzUFf7Ll/OG/DbeUPWqgPiFCxf4R//oH3H//feTUuJf/+t/zZ/9s3+WT37yk7zzne8E4K/+1b/K3//7f//0NYPB4PTfIQR+6Id+iLNnz/L7v//7XL58mR/5kR/BGMM//If/EICnnnqKH/qhH+LHfuzH+KVf+iV+67d+i7/yV/4Ku7u7/MAP/MCr/4QCCNAc50gZMRUoJdFKI7SkdSHrOhuHlApVFggRWM73me0/w9HlS0QxRG2cQywuIVYrViHStJL17fNYo1hdexRSoigLfOtzPrYQeB9YdRnkZ0Mhhe1drEPKkUe1c32ersZ5i9JDbFWiZDb6DsEhSSitkScmQQkSkighhJhvO3vgEmMAl3NzVT/lkyKbPEUBMcr8mgiN8zR1S+c6lEgMB1M2dtdZW1tjPBkzHY8xSrJczmiahkSe9IcUGAwH7O6scfn5Jc4LQoxUZkhZWAptWTYNwWfX4xhz9jFIYtsQfUBJmeOnSL2LcXZX1kpRVSNcW1Mv57jgM22/rUlJYIoGoSSubWlqUMpgjCRFT1GWCCVoly1KK1xwNE2NlopOz7N7vikoiorF8hhbDRApYbTESYGyY5LvmB8fE0kUlSUhMMWAlDzSWmgkvuvAOdquJQSPdo7lYoG0GkJEC81gPKGarjMZbTCebrO+cZbtnbNsrG8zHA6xOhtIAdl87GRILDIXXGmZJ95GYbXKjAchIMU+xzqDx5AypXq+Csy7gBFQjjRWnZDeezidbkSECXFCCe8PkZso4/TPOaFan/ya1/PGIZVu/svpuud3lAKGGjqfemMrgbIGazRKCSQB5TtUP/HWPdVXkIH3yZudatYRJJGlCh5NGxKrpmW1nDHbu8LRledx9Tx/LyISQiCSjfFiylr5rPnONOYYM+gNAbSRxBDy8/pM8CjB9YZaznU5M5zsit6FSBIwLg2yksxWNW27ZDAcY8oSYzR1XROio2k76npFCgFrNDIG2qbNk39t8cFRSJhMx/i2wbVQViNmx3s8/+yjjCdnKQcDhuMhZy+8hevXLtN6h1aO4COeDu8CnY9oNaAsRpR2zHTjQcYPfB8H3YrHP/RBnnz4v+HlFHv2WyhWT7MevsDavd/CeFJxprjG3tFjfPjxNfaffYK0fB65/k7s9jexJa+ye3bA9uY9nNmacrD/BB/72Me4erDIbBspqKxle3MdqyFGjxKJqBRlVeKcoypyooLR0PkaZRWFVmiZG4dRGFx0BBRaCLRVBJEgQO09QoOKKk+0k8Qom00kQyCmjtBKJAFJjh100TMeDREk2hDxLtD4NkcyKkWhBW3tsLqgTZ4YE8IFuhSoSoPWJVF6QhWpioquaXEyUham910w+OjxzhFioosOqQyi8+jCYKQmRlBSE/qGjpYWRU3Xr4fQb/yR+JvyWt9X9fgeg7ecZXnhpgdvamrejmo2E/t/6jwbv958Y+vFX6LEYoX0Lx1TlmSmaKv2zX2zfidK7LUrEXvgPYsUBy16f4k4npPa7lQe8qbYe04izMqXhl1Jy3zv9RLO4mFYsP/uAcsLX5vU5ivViRv6Cw0vVZsQHvygHwK5RHXYNxju1FddrwqI//AP//Atv//Mz/wMP//zP89HP/rR04vzYDDg7NmzL/r63/iN3+Bzn/scv/mbv8nOzg7f9E3fxD/4B/+An/qpn+Lv/t2/i7WWX/iFX+Cee+7hn/yTfwLAgw8+yIc//GH+6T/9p1/VxdkUEilVzkn2HbGzJCsRSaIimUo7LEhC0DUum3Q1K7rVEW6xINktQrVNOn4a0RxDuUEnpwzaBWp0H1EOWF16EpkkRWkRosN1HqlznFgMERchGk1lS4rBMFMaBaB01hqqbMQ2GEoEWd8be0vm2JthBZ9ytnhvciVEhJCy/3kCZfKEW0mV9b4xIYgZ/BjFiao3hEgXc0jPxmSK3SqxRcFoNGI0HhB9djUcDAZobQmuxfuOpqtzJJs0KKUgdFx6/nm0sWxWQ4w1rE3XIUWWCIoQiMbmJkfrAIfWnpTmOO/RVnOSK33izi3IoKkoh5hyAIr+Zj2hq+yk3jYLomuyW7yyaGNJ3hFTYLE4Apnd6l3XEkMg+YikpfY10QwZVmOM1pmVsDgmBEdbrxDasr1zhtXhFcqywAeHShElLdoYWh8wtkCvbSNSwPncuIkeyukUZQ3loGK8scP2zt3snrub7a0d1tc2GFSDHCMmc4zYyXeaIFPPpTw1VsvAO0sO8vS5h9Op349iIsRs/pVin6UdE9F3tLXHSWgKMtC5qU5gOSeAXNyYaKf+fUS/Uqf3q+KWH6f73gmCj70gO/b0dNU3EaRSbEwta+sarcCIgA4tyi9RXdZ4S9HLZ+RJsnzWW8eYlegRQZQWLw0uSdrOsZwfM79+hcXBVVbzI9q6JonsMH8S05ZEyoBJ5Pi71BuoJTLoDjFmjXUUeb8jN2K0zPuh7zPGvXfElKnmShsKY1EiEEPqHbw1MWRXdGMMnfN0YYmSqnfU7qnL2tKFms55ku+Bn5CQlmgNhZYcH+4zOzokpcjmzkWU9MyPW4qypq1XKJlYLfaz+WFsEbpFAi55jB6ALBFpiFVT1u/6drjwPp740pO0T/03ZN2wfve30ew/wSZPsPmub2dzojlT7PHF567wB/MRy+e+QHDH6K1vIRYb6MUXObd7jt2Ni5w5s0lZwuc+91E++4XHabuIlvl7rSrL+qhCio618RhtRsQUWM5XpBjRNt+BG61YtDVCC6w1CCGJRJKPhJQyE0ZmJ3oFRJddyweFyUwaCSlEQookFfDJE2Om6GtlkELgXEs5qKiEQCWFLQ2i7ahDi9SG6HIjsek6TFVgpUJ0DdF7tDaYwmYWj5LEINFRZko9eVIfpES4hCPfmYSoMFaiHP26ZOO+E/6GKS0udixnq76plBuYujCI+MYfi70Zr/UnJXzgzB8teGZrTChfo1t0AUf3S4aX76L4xJN8I+vFv1KltkU3ke5ltBgZiP8xrdTtKMGdKLHXsETMjuZ2FigOWtT+AjFfvvmA94vUV5wa31RJy5wl7l/kuV9myHabK30FN/SUmZfrj7boeUuzM4AExX6DbP2dqffXWF+1RjyEwL/7d/+O5XLJQw89dPr4L/3SL/GLv/iLnD17lh/+4R/mb//tv33aKf/IRz7Cu9/9bnZ2dk6f/wM/8AP8+I//OI888gjve9/7+MhHPsL3fd/33fJeP/ADP8Df/Jt/8yXXp21b2vbG2Xw2y5SxGLIplJCJrqvpDiNrmxusbU4IXYcLCVtalHUsFzUxQqhXtHWNHJ4jlFPCwWOkdoEsd2B6N3L+OO3RoxwkT1y7gFl/gLT/eWIzz1O4EGldSxQlthgwGoywpcGWJZUtCESc63JGc5S4lNCVwSJZNQ7XZRquFNlNXPhA1wVSTAiRQEpCzGDLaIlSOedZofow8EQgZ1yHlEghIkS/HYRgMl5nc3ObtekUKRLLeoWSgtnBdTrnGFRVdmsPHu9zRJK1VR97pfpJfZ7Gaq0RUmaNbsrmSdm1WxCcw4dIPMlqFhIR+3xlMqATvcY3xIgggzhtC6rhpDeb84gYCQhcSnipiMYglEFqgU+eLnYZoAbPZDRCKVjMArosUTER6iVCaUxVooxGWE23XBKWx1mjrA22qljVxyxXKyBhy5KimjDZ2EGKCE6CFJSjCb5e4rzHVhPK8VmqtbOsb25xz4Udtja3GQ+HvaY4A+8TSYLozc+UlH2OcQbeWklUfxE/aZik1APukIFu7CfgJxTzeDOITgLTa8JdgtpHBoU81WXnJ54Muk9o5xk0i9N3pW8OiF4Dnvp/n6xPv5D+/yeRZlKKvE+YHL0npcjU8tChwhwdPCrdAN5CZoB6YtAmyEyNhMIlgZM5r3tZN7SrY5r5IX55TGxXLJcLmrYjhkjXdYQQEVrTdlnLXZYZlAklT00AUwJiyCZpCFIkm6yRt6/q6fJK9c2AGEknrBXn0aaf5AuH71qSkGiZj6e6yU23qhyijKVrW6LvENFRSEPSiqQERgUWiwXzZUMMCWUtIXmic4wGmo21MxzPZqxtnmE2P6AsB2yc3WL/+hWUrVAmMByfY2P7CpevXsY3LQmJlAOKwRYpGYbTc4zv/x6OxZDuU7/K/nPPcCTeglw+y9m1ywzf9RCDCs4M9njs+QWfnRccPf3xHAO3/c3o1fOw91HWzj7Azs79bE4GXDy/y2z2PB/+8Me5ev0IpGQ6GbA2GlIVktDfKAzLCmMsWkk6F9BG0wVPjJ6gFKGLWGUobIHRimVT03UOLSykgBYaVWh81+JcBLIEI6VA5xLKJIL32WRSg0T2ABysLcCFTMcflMQoIPRyCa1IbcK5JktyhEIZi+glA6qX0ESRqewIQdc2RCC4gI8RERS2sATvaHyXaXpRoGzOK1dCUVgFyhJEyg2XBFpInHN0Lr+HFCCQWS/u3vhA/OZ6s1zrby51/ZiNLwy4/l75mtGBk0pc+ZMFd13dQTx3+fYuXEjEdJzP97PFmxbom1WkG7+cYRsvRcB9/UvciRJ7LUuEG8C73GuQhwuYL3Pu9psceH9Zde7W328abJxUkiIbroRbgXiymqO3jzi+97UxZHuhG7qI2XzNLhJ2HjDHDj1vISWq5+a3/f2/ketVA/GHH36Yhx56iKZpGI1G/Mf/+B95xzveAcBf+At/gbvvvptz587xmc98hp/6qZ/i0Ucf5T/8h/8AwJUrV265MAOnv1+5cuUlnzObzajrmqqqXnS9fvZnf5a/9/f+3pd/QC0hQtd6fABlQJBwXSAlhbU6OxgXFQI4vHqVbulIw/P55urKw4SuRQ7vIk0vomaPkroFXp/Br1qqjYqwdS+KBNc+gVISW1hiyjpmowQklyc6XuBUIoZI9KkHJAFlDRKJjwHvT4Bqghgg5ilkSgnvA8vjQ+q6oRhOGK6tMTIGLwVIRTKizzT2SEWeFqeEa/OUO/Y0dSk7tLFsn91hdniN5iCDz6ZpEUIjRDaICCEHSmttEFKdXndczECwLAdsbW1xsH8V1zYslzOMMQTvcnZ55xFaM5muUY3XKM7eQ9RVf35J/Rw0NwtiyhpcqQRKFwwHa0Qf+ylsJPqYs7u9J3Q5lizESNOtekpy1kzvX30eACGyUZQiIJIhxEQgG7N5l/ODZTVAIRCy177XK0bTTcpzb8kRTNZm+nhKTNa3Kcoh4+k6k8kma+vbjKfrHMcBQRrGheKujQKr5KkxXpZ8S6SSGK2wJkcdaSX7uKZet51iHx+W/TlCvGGudhKNdYKDk0g9zfwGLRwBpb7hiN+4mKnWJyTyEwr6TXcOov/91um3uEE7zwL+GzT4/n20lOgedCut8ncWPTJ0yG6BTh5F/i77oXBezZTXLfZT+SgUQSp8UplqXtcsZ0fUx3ss9q9xtL+HFJKyyDryEBK18/guIKUkJvApIZxH5ZFplkIoSUg9PT1ElFJ5OislSYBLkdbli2FRFBijcK7XhPcNCCFjP9nMkgktBdG5HEVlNKBAC2JqKYsy7z9ao7yja1csF8sM3mWWgcQYURK0SHTRI3wkuI6mdTRN4uqlq0wmu0ynEy5dcSxiZDQasrW+weH+c3Rece8D9/LMkx/HJEvjLUqPSAzxYcDWhffQXfh2VvMv8ewf/juO0y6jUPD2sw51/3dSqJbd0ZyPP77PwzPN8vmPkewYffY78AePwOXfodp6J1t3vYvdjYqzO1tUleL5S5/lE5/8NE0bGZSWqjRsrU/QIic+DAcDVvMVq7rFO4FLAiXBWAW9q6pWFqEy7X+1aLEYBIoUPVH37uOFIIY2g9eQiDIRZEQJiVYxnyOVRpO9BSQGYxVNyIAseZBGEpcNKfV+HEoQgkcmycZoxLxpMrgJEQmZqk6WgDgHyiiUDNQuM42U0lijqQpLjIHZyiG0wruUzeW0IKTQU54z3Tznmnu87+iCo/N5OYPSsmprYoqoVGBeoZPq611vtmv9C2vy8D7zC2dotl6723g/TFz5rg3O/cqctLh9OcTNt97LM38pe6O89f+5gf7c07dt2a9ZCYkYVKS1Md32kGbT0I1efkyclLhZ7/T61wnwPtF46zvA+3bWSYa3Pc5RYupgQVr0UWJv0obTKyqRb4rcxNBOFe1U4EaZDWKWCTuPVHsO0eUBQ+od3hECt15x/b1lPpe9BoZssstTcOly/NhgL1AcONSqy7TzOxPv17ReNRB/4IEH+NSnPsXx8TH//t//e370R3+UD33oQ7zjHe/gr/21v3b6vHe/+93s7u7yvd/7vTzxxBPce++9t3XFX1g//dM/zU/+5E+e/j6bzbh48SLO5xzqSKY4FlZQL+ekJKgmE6rBBGMNh8dz5rMFyzoSyl2sFsS9RwkuIsf3IiZn4OCzJNfA4DxpfAY1+zx86ZjAnyDJkunmW1HdVWJcobXCuw6vc+RO8JEQPK7pjaqURgqVp8kEvJdIKZAyU9BTyhRaQaayphgIXYtoV8TVklWIVJXFU4EyKK2IPqJUvrCdADghempnjEQXcD7iHRwe7BFdg1SKsqzQSjIeTRCA8wHnYu8CnLPOUwg5FipGQshacSkTy+UBq9UyT4F8x6qZUTcwufB23rpWMbFQWEOXJO14ncZLlosV8nQWm7LTfI79zUBPa+xoE2kKYmyIvblRj5NASAIJXy+QShNjQJuCRMS1K2QCUw6IocmSBKURRiGlIEJ2Zh+NqKoh3juktFSjCYtrzyELTTFZx5YDhqMpk8k669Nt1rfPMJ2sURXFqTty56E96mhC6vXF+XHVm6oVRmNtdmnPOeexB9cB78VNwPuGqVr6MvB9qvI+vVnJ8XN9fFjvXG5kBkHBC2of8SHdGmHYv1zc7FZPnqiLXjCe0olqPz9XkifFQmuUMdks7gR4+wbVdqjkUCJlMCzSqe49ppRNzk6AOAonFW0QtD6yWi1YzY5ZHu2xOt7Dtdm8UJF12U3T9STfkk5A50IvZZAokddSS0hErBW0TSLGQJKJGHI8lhDZrT2llKeSKeJFbnwgBEJkFkX+mU0GlRKEkH0jUs/SiDEQfSBiIOXGSt05XEiEmJtjKImQksoWbG0PiCl/Dte2NE2bGwhCkhLUq5qmXrFc1YRUcfbs3bzzwXvYXB8zGAkeeTZwZSk57CKj8hyuFlz+w0dY1ILCDnGpQOgSbdaYvu396J37ef6Tv8nVKzMqr3n3Bcng7PtZqyJTO+exyys+/MUV1x7/A5zcRJ17P/Hg07grv4vaeA+j4Yjzax07W1tsba6TwpJPfPITPPX0Fcqi4tx2wag0VFWFLQdoDcRIaGuETJSlxRhFEpHSWFKKdMHTtdkbQilFXbeYwpC8J8TsEVEWJ1Njj9UalyJFqXCefE4QEa0kISiiz+fwFAPLukVaQTHIIF8IiSTve1HkxqFUibDy2QMgZl+4pm4ZjcZUVrNYNv3zBCE4ulUeAxSlwQwsweXGZ900dM7j2kiSkXE5yFn1MkGUdMHj2oC2Fhk6fOey3Cbk6LJCKnxPR/QR0G+eG5o327X+hSXajjOfrHnuf1l9WW747azV2cTRd1xk+ttfPI1HelUlMtvq5gnY0VsN5zb3AZi9dYeNz92utb3NVRQwHeO3xzRblm4sCUa8KtAaVZ8n7l6fY+PU0fxOlNjtr9RHidV9lNj+65Th/XpWUdC8+yKLC5Z6W+CH3HQ+yj9ryB4oy5LRc5H1+QR5/YgkBPP7Jhw8qF4bmU264YZulonRJU95dfWKKPR36vbVqwbi1lruu+8+AL75m7+Zj33sY/zcz/0c/+Jf/Isve+63fdu3AfD4449z7733cvbsWf7wD//wludcvXoV4FRrdvbs2dPHbn7OZDL5ih1yyNOtoii+7PEQwFSSWPusxU6RdrkkhJzluxBzhLKURcHa+gYuFTSrOen4AO+A6QOE4Zh47ZMI16Em96EnO8iDh1HR4/QGcrFgcu/bMXKb5oszEjXWGkLrWNV1No4KOk9CRUIpQTKQRJ54q04SrMXqrGf1AWZH+yxmxwwmE4ZVlamyIeZ88+CIrsP7QCxE70Au+ogciZCa5B0+JETIN71CSYyQSOkpbMFkMmVQlVir6TqHD56YekM7caKrBx/ydFv2ADkJ8L6la1uuXl1StwsWs2Oa1tE5hxCS0eYF3n7vmGoyxqZFfm3KEyRjLKcxWIKsCybhY8yNCJGtl1K5zmBtg/n+ghTa7DqtLdIIoguEvksXvcfYktFkCtHTLeYQZQaEIruxKq0xZYU2BaYY5lzossIYQwEYM2SwtsHuxftY39pl++x51tc3GI/6SLZ+tJvjvvLVOQJBJJRWTEvD2tAynRiqfoKbGdhZr5xioovxNMM7JDLojfQT55O9tafqv+ge3lO5U9ZBp77JclJaCQqVY8NaH7PkQsp+mTcLwvthNwnRTwVPHP2T6DOztcbY7C2gBMjokL5Gty67dJP6fO2+N9AzNmJMJAJISRSGKBVOaNoITd1wfHCF/UvP0swO6VZzYswXYiFAKdUnAcRe4w3LVYM1GiHSqSbYGIFzLu8PIjeuUi9sVyIhUtZxn9xFZc156oF7QvqstbeFPaWuxxROGxPRpwzmU8pmXF2HCB6hFLYqswt6l9k11pZoG4nLFV0ixwTKHB2YmxCKNkSkLbCmpFvO6dojmrrB+YQZvg01fDuD3Qo7SigNm4XmLTvwhcsdxwvPYiUoRMIWE0Zv+ZOIaxM4ukI12cW89bvZP7rO4//l/0d3/VG217c48/Zv4+5dgw1XefiZJXuHNXtPfZjankdvfRdy72P4yx9Gbb0PrZ5nKz3DztYW0/GAM1sT9g+e4uHPfIbjhWdYVZzb2aQymiQjRgvq5QxtFV3d4XykLFSmaEuDlIqu9SQJ3uft51YtiUCMEpskIXakILFWEbtMFxeJU92+UBJTKmLyvVwlERGoUuO77IquNAyHJdpqlBoQukDnO6QSWfvfa/dJYE1B02ZJwXg4RimNDyCNIYWIiiJLHRQYkUH7YrnK+v8IRmtSCAgFw6LCak0gN0lJAoJAoJFJ4F2eZEiVEC7SuQhSIgUYpbBlidKS5erNYe71ZrvWv+hneOaAzc+eZX5RkSS0a+n2AywBBw9KBpfvwnz26Vc11UsXdnnqf9ggmsS9//YY8XRmdLXrglH/nG78xkWEJwDja9JHiz9eIH7iaJ5B950osdtaJ8B7FSkPHGZ/9YbI8H7dylgOv+etHLzjpDn10p/ejRKHDwjmd21gjzZAQL2TXlNDNjtLjC4HqssrRPdVNBLv1NdcX3OOeIzxFr3WzfWpT30KgN3dXQAeeughfuZnfoZr165x5swZAD74wQ8ymUxOKW8PPfQQ//k//+dblvPBD37wFm3aq1q/JKhXEauywZAgEXykmx/RLOdUowkbZ3aZTkfYYp3oHQfNIY0sSev3krTC7H8aHTxu9DbicB32PoNODib3kKoBeu/jiJFkOdqgDkOorzMoIiIGQufoSMiiwJSDnG1N1v6GGAjeIwQUPhCtpnUd9WpFd7RPO5vReU9cX8daS1fXHB8e4LuOar0ARI7+8iBURBuTwUAKOO/z5FqUGJ2d4pPSyD6jqW5XGGtQqSSSJ+UxxgzEpUBG30/yswu8EAIXPLPZMUfHcxbLlqPDfep2gVKSqhpQVSWDqkJGR9s0RLmBTBmc6CQI0eHtKDump5SdjnuAGfvJcABaH1HlhMF4g9XRc3jyOiV6Sj85zzc5gbQSXSja5iib1kmNqQYMxhOUNRCzU/pgss1osp5pwsWQwWDE2sYZNjfOsLW1zdraOoNqgFaZpSBODdVuonNLmXOutUJqzUApBmuGpDRGBArt0ApIGQjHKIgpZuDd67tPgPfpOflWbnifi90/dPO59/R5+UU3tN65pBAMtGTeegjQushA5yl1Eqk33sjfbxQnM3aJ1jLvB9acZnjL4JBuiVw2KBHRvcZVyp7+jugzvDNIgkw1j9oQpKFLglVTMzu8zuz6ZZYH16jnx3jXsVwusTYb4GWZRP4vTxEDWmm0Srg2ZPpwv62cc0AiBNV7JeR1oX/OCb1fCo1UsWcY5H0Fsu+f0gpcoCyy4WDe73rtfd9ASEhEzE0KYiB2EalVzv2OkdKWSALBBVyIWeIg8nI0kZQUISZizHemQdgcr5ccTdOyalraLrB29u3cdf976LzmeN7y2GXBe+4xjERkZ5yYrTqeXjXUc0cdFEJahmNNOb6XyfAsx4PzXP/cH7F86o84u7bN9Nu+n+3BitnBZ/mjR9a4vr/P7NLH0JP7Yfv9cOUPcN0B1da3IOePo44/wd0XH2BnfYfpwCJ05LOf/UOeee4SVWHZ3RwzHg0YjQqmwwExRo5n8+xNkQRFOWBsDUoE2i4idCI4j7WaRV0TkyIl2dO3E7aEFH3240DlRo6AtulYuQ6tNUpZkCBjRCdJSom2y83FFLss7xCSWZeYLzoQDdPJAIIgehgMh6fTaB8EWlpCcH23KOVGYXS4tkErQ9s6dJEn/aI/Z7q+ISCRRBFomw6UIKTMnPCupQ0RrRXR+xzFJ+WpuZ9VloQEFdFoFBpjEsuuw2pJ6iJfGWK+seuNfq1/0UqJ6ccvM/2EJGnFle89w/L87Vv8SUWTuPwdFXftbcHla6/4dVe/c50z35715c8d7HLxXz5/+1fuNSy98iRpv+blRCP6seDtrxeNErtjrHZ7KuUMb7uIFIcOs7d6Y2Z4vx6lNbPveiuHD748Q0SEPJX2w5Qb2YOEH7z0a77qSqAaQXEIo8uBwfOrO4Zrr3O9KiD+0z/90/zgD/4gd911F/P5nF/+5V/md37nd/j1X/91nnjiCX75l3+ZP/Nn/gybm5t85jOf4Sd+4id4//vfz3ve8x4Avv/7v593vOMd/MW/+Bf5x//4H3PlyhU+8IEP8Df+xt847XD/2I/9GP/8n/9z/tbf+lv85b/8l/nt3/5t/u2//bf82q/92lf1AUNMVIVC3BT3dHIDbrUgdjXL+RHD8YDJZMzaxjoxBPZmmhiXFPWlbAo2eRdeFYSrn8CniN58AFGNMLPH8Sly8MzjlPd8G+O3fyfu2RGrS3+E65rsku0lyZoMWVLMUWLZgQshIPhIHZpMwYwBV69wTUvXeeJygSkK6CcuSQ9wASx52hlTIIlAUoIgQsYd0eO6ps8mBp/y5FmITNENIdG0jrU1y3RjjatXLtF1GejkyXjEu462bVgsVixXDV3b4dOJsjhTw5MsGG2sIeIC71xPuVeQHK5e4vuItjy5VChCjq/SktD5UwFxIut9Q8hGY4suUZRDtjbPcXD580jTkXxApl4foyWqEChRIJQCGfMUX2mG584xWd9hUAxxvsOnyGC0xXhrl/XNHTY3z7CxscV0MqUqCnRPM891k6hZCISUGKUwJxRzo1Ayw98IxChwXWLpPLX36CLhVBZ7x55C3vPNTz9nIlO+bwbbSXDrSTCeTJpP6OQnfxC3APde0Zz3BAGFUUjp8RFWLjApJJITnX0GvNootDUZfGuJIgNv4Rco51DJZ5qvAKESJ3FqJG4CxpIgFBFNmwRtlL1/QdZ3Lw6vsjg6IMUsx4gh9k7/eftG8rYJqaeQ99s9m2hFGtfhfHYvz92VvO4hZvp47EF2TCBlBGQ/8xYkKZAxsyxSdAQEQkiMzN4J9arB2ERRVmhjMEbQuT4mTeTTYQwdKQYgorWlGI4oqwEpBpRMBJ9BW+ccggjJZ8q6zDKIJHLTqOkczidan1gczVjOjlgtV4zW7uX8/e9me7tECUnnLdevdzy357hrMyJ8Q9E+z0iAroYsFiuaxrDnh8QuMCwj5f7vM16uOPOOh7iw6bny7B/xu4+PmM0OCQd/gNl6N+LMd+KvfpTUHCPOPEQ6/BT+yu+ytvtOzk3WuXB2k63tCYf7X+LhT32SrvWsjyrGowFVUZCkoioKVqsFTefQWjO1JXXrUSbRdSsQkmE1JOFJEdra0zYJbXsWghFYa0FGFJLgEp13BJ9QNtPNjR2irUKnzHQJfYRciiL7RGiBlllz74FCK2SSoAUpeLraYwdVb6KnCKpAdDk20Yd8foxJo6QHFLo0EASqyNR2mbJBnBGReVMjhERLlU3fXCJZwbisKJSmbhzaaEiZxRNiQCRJjNk93/uAj4LSFkiVPRes7vetJGhCwPk3/g3Pm/Fa/5IVI6KLnPnojGf/V1P88PZ/B26cuPqntjj7a0vScvnyL1CK2X3pdPK9Oh9BKURZ3uJYLN2LvvoNUfqohjT4mqfJ8WseCd2o0ygxe8fR/LZXekGG9/4SMVu+8TO8X4fy919g793qZafZqhaYVTatS0rgB6/dFhRBYI9h8kxg+FyNrN0dAP4GqFd1+rt27Ro/8iM/wuXLl5lOp7znPe/h13/91/nTf/pP8+yzz/Kbv/mb/LN/9s9YLpdcvHiRP//n/zwf+MAHTl+vlOI//af/xI//+I/z0EMPMRwO+dEf/dFbskjvuecefu3Xfo2f+Imf4Od+7ue4cOEC//Jf/suvOs6kNAKr8k185xInpwopRAbCBNr5jNlBQWkt6+tTtNa4eJnl4TV8hDh5kKA1Zu9hjEzojXcgyiHV4ikQhtru5gzsgy8S18akwRa62sbaGVJGuqbFuw5nOgQKW2lMyjm6SYk+FzfSOJcdwn2gblu6zhFjom1aClshhaIcjtFFxWA8whiNFAnf1QQvKYqKpDKdcjIeAtB2nsYljE29SVg2rWrbjuPZAfPZITEFXNMwX8xZLBc0TYdzoY8IMyAUSdlsSCWzo3RMkSQMxiRCo4jeIbRGFwXaaHw7p0siT+FlylFfBDQJawxt509P2CKB95FlFxBaU5Yl2xtrhHMX+dLjFd6tSCHHeUlrM9iXhsIOsyEdKRuzCUMxmDLePM90fZvBeIPNrV02t88wHY8prOld32XW5eV3z+BSCpRSaKOztvvEWK2f1tJrjVNK+JjwEZxPHM8DR21AkRiiESdH1C0nN3ELsE6nAvD+qXDTGPyFvjWih5g3/V3ceIZI6RTYGpnjn0JKtC6QUqaYG2uwhcHorKUVoUX4ObLr+oi2dDqdTmTH6NTnvOfPLUBqgtC0UdD6xHI5ZzU7ZHWwx/HBNXzbkYJDikj0nrrLdO6+FZBNBIPH+0RMHSlGSDkaSvYAXYibzNW07jXrnhQy0BYCUoj4EHJUWQIXAlIonHNEqRHCQRKIlJszCIUia/O7ztM6T+MipiiBSNs5QgiQIp5s/CYlWKVInUOQPQXaZkUMoacsRNo2u6grlSnxhc1NqJgSMSRWnaNpU45dW6xY1Q11syKpMVv3fhNVYXAugBZUhWR9PXJtL6CPYCw7ZosVhUjItMQrSMrSLFYsa8XioOXu3THvedc2z37pc/zPvweL2TFx8TB6873oM9+M3/8Eanw3ave7iHsfJ1z7PcqNd7Kpjrlr23DXXbsUBi49+whPPPEE0XvGo4KizEybIBKVNdR1nZX6paHpHFIWFEblGLCiyCwBlXAInIw479AGYswu9aU1OOeyT0VKhJCFEMYqirLEdR0hZo8DmVSeiOf2CahIqbIxX4wBnwJKGaoix8VpqRkOhlRlogueVd0RomDQpxaEmCMGpZAInwjkhqxzkRjIhoNIpLQQQ282p1FaYqUipIipZGYXrTy1CiQRcV1EKU1ZWtouZP8CkWMWU8j59VqexJol2q6jCwGtLLrQmbv5Bq8347X+lZQ6nHPmkwMuP2RI6vbfgC4vwPxbLzD68BM39OLiJhTYp5pk7UpC+hvn9eruOe7Bu3jqz1ZsvTNP1Z+/tsYDv79/29fzdpWYLZF+8yWzwl9J5Sl1lpK9uhXgTpTYa1gvmuE9W5Ca9lSCcQfGvUhpzd43DV72HCMipyAcQDXgK26/TKLXgk++FBk/XaOW3R0A/gaqVwXE/9W/+ldf8W8XL17kQx/60Msu4+677/4yOtoL67u/+7v55Cc/+WpW7StWVeRIJXzCqj5iqwdUXfDUK7AqkK7vYXRBUZVMJxNccDzbdCzUBOEaytnTKC1YDt6Bs5bR8jGUUCzt3UThMfMvApvUT1v02bcxedufQu99hsXhszjf0rkuv3dZ4n3ClEO2zp7DFiWha7l+5WmWdQ1C0CGhHFHIEheyu3vwOYZMKk1ZWEyhgKynNbpAFxZjizyvVoLoNTJ6Qop0XcIHgZAp3+RKQXAr2q6laTq8c7Rtl/W5KtNghU7ZqM0YtMjUdmUMsr+pCCkQg0dFh1SSoHPsWGHL/PtqjvMBUeRsdakEOiR0ClijkClTpiGfyJ0LdElmzbbVVNMh3dZZqsEAVx8hY5YZGFthbIW2Q5QdYYsh1aBibe0sG5u7nNm9i82tLYbjCdZqlMgu5kL2P/uJvhASpRS2B6mFzm7mQp44isfMDAg5Gi6ErHk9yTwnZXlB07Ssao+WgjAQpD66LUEfzZY/4UksWLrlDHsju/uFXPVbzsMnCJ4TEJ9umVCfnFC1FKyPCsqqYH1kGZf9xNt3SD9DdQ6ZAlr2YF5xehVNKRJOmgHaEKXGo2ijoO4cTV1Tzw5ZHe1Rzw9YzeZIIYgxsFwuAcWwKhBkU6qUIIWA1NnELqVIFDnFwHuPlyG3GHyOoEuAUYYo8tTbKIlC0nZtduMl5zTHmHJEmhSk5E+zwENKEDwxGpQEF3LUX+gdy0UMNE0D0dF2iXpV0zWZHz2oShAxcwuCR6VAiD5PdIsKHyP1cpXd03sPBSElUQpEcggC0bd4ASkpIiJPzZ1ntaxpFwtCt6LrOtYufBObmyN8iKQksTbHHWqtGBYNn/7kp7BpTvIdznu0KkAbNje2sNrjVjVlFRkVgnoF69tvYfTkR1maDVh7gLD3aeza/bD9HXTX/gC5OsRsv4u19mm2Bgfcdf4iZ3e3SP6ILz3xBY4OZ0zHQ0LMjYHBaEj0gRCypr4LHkluwBmdG1WrekGKgZQCMXqu7NcIKSjKgmJk6TpPchqrLC7UaKHxwZFiIiSBUCmns8SsH1fW5B1R5FQLqVTW7ztwURBFRAqB1iUqh2DkY9YWSK3wbQs+UbsuN16aJsfRBYGQAhdDb4aVUNqgpMAFQVWVaCHoujbvg0qhY5YxpZgwRUEkYqQkOUcXPMoaNAnfdbTB5+m5klilIYFT2XCyXtW0XaSsLEoLRBIIstGluO13Wbe/3ozX+ldag8euM9nZ5fj+27/sJGHv3Zry6gX0F56BrXUe/fEzbD6wjw+S7eGSJ65s88Dfukpa1ajVjX1hY7jiS/+nMWfXriJFPlbO/OcCLj15+1f0NlVqmleUFf6yyxGQNIiXs08QNzmamztRYre7RADd3prhzWzx9Rkl9hqWmIxpNm8dqXzFuukpIt1833h7SnrB5MnE9ItL1OoOAH8j1m0kBL0xy/mQ9XyJ0wmfECCVPOGog4o4V7N/7Soow1vufQub6+vMFw3+2j7d0XWEsrjJ2wneoa5+ikYqVmsP0rkaO3+cVI2oxYSiO6BINXrtLKzOEI6vEoIkpYALHToamtaxf+kqjz76BFIpUugYDCxnNtcREoxUaFMwGK2hlSClDBqjCCQpGAwsVVVSFBXKagiJFDzB54mOD4LoAyRHQqGkIsmAEDpDQimovaOedSTRA+/CYFJCG5PzoEVCCYHSso/hEsQQiK6jdZ7QNaiYMFIQtMGiKJQhekeQ4No5zjmSrCC5DH8lKDzGFP1Es9dhp8SyC6xSoiwVm9MSV0dG4w0mk22a5SEyOrwTjCfnGG3dRTW9wHjzAtPpGud31piMRwyqMjshi8wyyDne2YBMKYkxGmM0hTUYq7Enn60HtynlGKsbpmrZsCkz4rOB1wmVXEDPsvAZMBl1g0Pen+ji6eQ7ywJutB1u3IrH02n3ifabm0zhMuLPhmI3JvKChCJ/N1rrnmqezQB3k0f6FhGOkSuHIqJkP3VW+Z1PcspTb2ompCQoTRSGThg6H1keH3N07TKr432axSG+qUkpYkyWT4gE2ijqpqFpG8Dkhlc/zZR9cHgGw5nZoQRE2RPqexZCCCG7pUuJKSQiBaTIsVbBRepVl02uJMQkUSLvy7F3mY8xEonIm5oY3nm8DyA1JI9rOnzn+wSAgoHWFINB1r9rSKHDOXAJNFkb7lzA+zztVD1FPsU8SaV3Xtcia/ND53PKgLQIKfAu4DtH17Us50ua5Qrva6IaMdraRfe80+g989kc71uOrj7Oo5/5NItlSxIhxzjVNUYHquGQrj5moDUXzgRIHa6JXO9a3nr/2/jT3/PdfPjDH+JLxxVx8z34o4dRYU61++2Iw8+wEb/EuYu77O5ss7k24PjoKb742GdRyTAYVXRtC0lgB/2E21hEAU3Xpw6IvG1Lo3GpI5BwAfABoyVlWVIWJptNxuylIEtDXTdEZNbxC0EUgqIwRJeysWJMDKoRInmQghADXfDYJDBS07qALitGlcFqjRaClCKu97LwUdDOV3gXKasC2csgnHdobUFA13mQ2VFdkJs4beeR0uC9pwsBITXIhG8bYoSUJF3TgXIkny8R2mTDHAk4l0gyO/JLItHntDatJJXWpOAISWCtzser0Dl6MdEzP27/de5OvYqKka2PH9BsbdKu3/6b0lAkrnzHkAuzM7Q/V/PYg/+vzMLq66n7FvyVB/4vAKR33ZrHe3b9xu+Xr67x4O8//8YGPt5jZ+Fls8JftkR2LZdd+rLHo7kpw/tOlNhtrZMM7+I4UOw3qP3e0fwm4H2nXn1159cJxcsfuSKIW2QoSd5eEG7mgq3POKpLizsA/A1cX/dAPEmQPeUpkfC9PlzEE8pjr53VgtDVrI73uHbZMF5fZ2ttQmxq9psxzl7AuxXq8DFSlNTjt4PvsMePobTFVfdgwhIxv0T7dMts8W62du9j00jwf8RyPkOkRNvW1AuHb0CZEkh452hqx3JRY0tL2ziqcoTW0HaOlCJCKrQxFKWiLAuMKfLNZUw5w1brrNGWMmvLU0CGmCmWoY8kVwmERJHNp6JMKKGQRqLI2lZjDFLKfgLc4dqa1ORoKefC6XRcyt4l2mhS1AwKmbOsU562pq7D1Q1Bjkk+R0tJqVDRo02FVgrfx5JFIrXzGKWYqAzKlkpRVCPWt9+KVEOm6xdY37rI1oW7EWrIrFEko9iYGLbGecqllepd4yVGawpjKKzBFjrTsgVIcRJnFUkh0sWUI8QSpwZkJ5nX2dssT+puuJnf0LWTsrb9BAJKbujA6W+6T2TdJ3PudLKIU4B+889+nxUn0+50evLM6y7R1lCUNssSVI4SI7SoboGO7rSBIiSgMmBJpFNdtUCSpCJJjVcGh8KFRDOfM9t/juXRHu38iMX8mBAiRqs8/QS0zvnPJDBG4buatm76zO5ALE2OO0ORfCSFhBQ5TgyR6eVKKaQMef36C72QgsJqSIEosnlcBsOO2IO900g3lQF6Sr0hW5/T7TuHD5HgW7zzxCSyW77SlLZClrkh0/kO5yIiBbSWJO9o2o6Ayk0x8vemlMKUInsmOBAhEsjO96g+bi4EtMiMk+xir4kh4TpH0waausV1DV3X0LU147NvZXOtIgSHFrBqsiv77Ogyzz78Sbq2wWqYLY84PI4koVgt5nTJAQEtMhAERRehqBTPPf0lNrfWed/7vhk++TGe2a8J6+/FHz3MRAV277qP7WnBxsYYrRqeeuJhlrM5VVURQ8QHh1IKZTQJSaEMyEDrV4QAlR0R8bjOI1Q+DwWXHeF9SBTWYqQkhkCbyKBWKBarBauly80flTBCUZUGHxPKKCIOIw1CRLqQjS2lAmJi4QJKeLJyOxA9CKkQWoIS2GRRJGarJUSVj28jiUHhVQb7nfOIENFkGYrMuYVoKQnKIJLEdx0xgFG5QaRtQXKBetWyWnWMJoNMode5gRmbhuQS2iqMMqSUzcvyuVhAyLKMLiSsUbjoEWQneasVUoEw+jSO8U69fiWWNTt/sOK57xkQ7e2/QW3XEk//95v8f+/7f6DEra5L95gRV/5Gg/eKnemXZ4+7KLn62Db3/vuWdDy77et2u8setnD+a+SmkwH3zVFiJ8D7jqP57SvpQdeR4jhg9788w/sOVLsNJSTzu4sX3WelF6gGxIlq5QWXgqi5Lfu6iDB8HjY/s0QtX9xg8069cerrHoiLGHGdQ0qZQZlIuC47/SIy3bVQOaZp0bY01/dR1qILQ2ErhuMJy0YyP7hCXF5DiwK58U4KEzGzJ3Ha0o3fjklLdH0NMdikSSXDUCOLCcm8hXLyDFarHNeULO//3v+Ob/mWb0UIx+UrVyj0ACESTzz2aX7nN3/1/8/en8TakmVpeti3dmdm55zbvN6b8PBoMyqj2sxqs6rEKkJEEdSABKiRhhpoRggQNNBAA4kDQRAgCAKhEYcUwIFGAliSIInFkkgVWZWVfUZkRkbnvb/uvtudxsx2q8Gy+9wjMzKjzUh3r7sAd3+4ft9tjp1jZ6+1/v/7lWZtdQsKBhcC/eA/Fhel1G4TAsFaWMjTwSkJ2khFLFhxGIRkhLrIaa31GB9wvlu8a+2jTrFk4uGaPM+UNFKzylistTQc3jmVlS/6UOscNnhyKZhWaFl9wRaLd5YSR3I11CYfxajVjDfggiUfbmjslThnTk48FvCgclDp+frf/icMoWPoVyzYe86uMjVXnDMcrTr64HDO0YdA33m64NQLfhMj1lRmXsvNIEab/1qVtP1yzb1ssOVGLb502O1jZLUmH5OTv2zW9QH8Adn58kdp8tHXg0Ua/9H/1y/TPiLW33x0ySP33hE6T/BOQXE1IWnGzNfYljVeTBpWDOL4aHu+SOgVHGap1lHEkzDqk97umLcfEreXHK7P2V5dEGOk5IoLlnmKpJRpveama4NdqaWQUlT5MaoKsNZSsg6MatFhxssYMiyggwVr2rItN0o+B1qteGsRlGfgnG69a81KJV9irGoFpGBqpjXdcOp4YLFKlMLV+TV951lv1gzrAWsdXbCYVpYteSLOidoqhozBkEpZcqhlGcLoNr4ZQy0QYyEYjegqWa+ToZJzUauBCKkaxKsiIZbMHAtTjOz3O9K8J81XjPuRB5tTjV/LjSwKTRznmd35U/p14OzyCUPw9N4xTVfIusd0Pa1F5vFAc1ap7b6nxkTLkWoa5y8uWa06vvqlr9LyH/Lhxbus7n+J108sjx6suHf3hJouefvbf8DF9SV37xyrqsI68uLJtLUSukAz6oMuC/E/14gxwv6wp1YF0nlv8V7oOx145blSq8E7vQ/5qpaArtPnb6mZ4D3Be0qKGKNxdKZBTFEj6hBSjtAq3gi1NL3fVbVMzCkyx8Z6c0Spiev9jik3NpseZ4XtfmQ8jPSrNSllconkONOq4FyH8wZvhHGcAb12ZS76vCsNMY5KpUS17nRDR0yVRKXkhnEV6x0WiLkgoh5fnZMVwCzDRodbWAwkoev0e6da2I0HTX+ot434J6HCBxfc/cOOs79mfv6NnkD5azt+yfc/9H//3t/7P/M/f/J3+K2zH8w//+DZKY/+accv/9dvK3n6U1D28oDUzc/sy86dUO7Z28b751XtNsP7F13S95RX77N7/WYt87Fq4K//bESInfXMWB1UD822n/h1ZUfh7rcKm+/vkHKbB/5pqM98I54ShGCQBdhmBZwXaLL4SIWUC4esEC7rCvNuy/WLjtURDEcrTtPEdMjs5oG6+TLGTLTz75GMp935Oh17uulDxG842Hu4+QnzBx+S84gcv8b67leR9PvsLp7gDLz73d/lnW//Btvdnn51zMnJCUYK3/rW7/LBh+/jved4c0Q/bHChwxk1Kot4CkYjl0rB5UqzjjRnQMgWSqnUUtWXa4o2Dtbjlubb+oBznkpjnhJxHKnzAcqMmIozFicWL5bqOs3AXbawwXuctQp3ywpzqyURnKMLA8fHR3inIDbvAjZH9Qtb3XBKA6kJQ6Pzjkk0WmhMhYrQe6s+5VqJFQ7JMLcNfeiwnQMRSoUqcHTsOT3qeXTHs+kdwRq8Myoj1haRkoVU9dBf2k0Od3uZGf1xqY7cdN1Ll/xSRr5slPWjH3N3ihKfb47UN/nirX1MaP6xZvsmDu3m+9e2RGS1j27Xzlu65bEW77DSoCQkT5hpxtWCM22h0As3eexKIK8vv1A1jtoMxQbN8Y4z4/U112ePuX5xRo2R3lVymqlN2B8Oi/JCkAYpVnKupJwJ1dNSoRahGGi1MM26VQ7eYa0hiCelyDzPy9DB0Bo4p1R5sWZp0ivW6XXMuUCtOG+wBkrVzOXWhBTV+51yJcVCjAVj1dCeclqI5jfTo4Zpjc5ZuqCe69DpZt6aSs0qVY5LPqZ3CkbMWb3ypeo9gNZoudCo2pxafa5nk3CLxzfmjIiQY1ZgXF3gcmKpBaZYiAVscIwvPuDi6bsY6xiGjqO797lzvGLeT/jekWNhjpnt9Tnzfs/l9poSI4ec2Kx7JhLXZ1d4eu6ePORoOOX0zgOCtwTviCmx3e7Y7XbU2ji/ACjcu/uQzUrjwO7eu8vpycB++w5XF09oRDrrSSnju8BqGOi8Zb87sJ8iYW2oTfOxBQPG6Fa3CrG0xaLS8J0OAMfDltgiiEO8hWoxNTPOERHBe6OqDeMQ07je75br3+jCik4gtoZzhnmcGaPeI6wD4+zLQaIYYa4JK479eNDXUhXWfU9vPSUlahWasUrpr8rRtyZQrcUYq8qHqi1zThVpBm8dc8raYBtHmies73BOFTytCBiL9R2HcY+VShaDNUbjHhv0w4pakqo3Zmi+KvRQwHWelCfGokNgby0x6j3xtj4B1Ronv3vG4eEjDq/+/FuSf/jm939Akv7Hq35Mg5qq4fw3HvK1//wMHn//U9UgyXaPSY3S/Yzds6ji6bZ+yloa77BfosRuMrwPh1ui+Z9jSd9T3njI9ZfX7F43xGN+KKRNz78/4mtVBbZZdDueV6IS9x/npdWgPxMe/O6EvxhvpeifhDI/3hTlM9+Ih+BwXqhFY4/EWloTDqmACH55wah/GaiFi4sr9ofE/dfhfrjHelhzdHqXZCpxfkG9eIq4gXrydSg76uV3Odg1sv4c5voD6nyOXb9K3O24+3Cge/A5xvESLs84dp70wXu88+wpz8YtuVRW3Yrj0FHyRIqJ3f7Afn/gzt3Kw4f38N5hjdXMZTFYGlIbJWeVBYtgxFKlYaUhVlSumzLBGoLXd7dWZuZx5CpmUpwpucANKdg6vA1U72nOYmwjWM1eDgZaTsQUmWbdLiMWYz1hNXCyWb2UfrdWliztRBp35FqRfoWvTRuolrG10AfH1qhvMqWG7zydNcy5cjVl5qwk5iaOoe8Y+kAIniKCP3IYa+msMNhMsLr1Tqkqqb3JR/Lym3vRjQT84x5t+ViDfNM+t499+sc/B/hh5k6VpOtnyNIbtvZxmXpb/MUf/Vmfb9oIhM7jO4/1FisVcsSUPeYQsa1gpS2wORUw3Pxe5SZgW5QgX3AkccQqTPPEuN8yX58vGd6X1By5vtzSrOd4s2ZOlZjL0pQuWc2aNUerDecttQi16daQKssAKNMweG9xVocBkisGhQFLa6jltlFKoYjRTGjTlptSo2T1jHtnlaG1bD9vRh216dbRGN2Uj4eDRgjGCAirdY91ai1ppSJSVTIPdH2v9oRWMFXIWUnppaK+bmtppZJSxOCobXkMGyAGsVBywVuDAVwRnFQqmVrVaz4eRupCbu+HgVoLu7Egx56cI2m84sXzJ5zef8jJ8ZoQHGNs9L3BkAgWaJndbmK8Piftzpl2l2z3W9Y+sMuJ6hsmCVUicY6kOSLSME43zEYq3sE0Fy7Or/R3Dx33Hjzg82+ckMuBWkb2V28xbi+wzjNsjjg6Xl4BopC568MBYyzro55pPCxsBZWYOwMpZ7x39J3C2MRAHhPZNpzv1KffGjnllxGR1hiqGDo/0MikFGnV4m2HseqtpimEcug6UorkktVm0AQXtDGmQabS0hKZ2DIxJoLTgWLwDjH6WjrqO/rOUBrkpH5+awwhBKZpJJUEVJwxgCV0Xu9VYjCmkXKmiluo+YbgA9vdiPNCqRFnNOaw6zyGgmkwR/X8WHEkKr6DUjPOeqxzHMaJ0gTn4DBHKJq08OcA676tn7IkJh79q2ve+yfHP9fYoHTUuEo9//Hzr/NvH/0B/1YPqRX++djzXrrHt8ZX+eblqy8///n5Mb/0n5/9RBnkn5RqMeLHn0Mjfls/WTXN8Pb7Rn8ebzO8/yKq69j/yud58mtu2Vz/7I94CVAGKOHHbMABk4ST71VOv7VF4ic/leO2frA+8434DWxsThUceJoCenpwzSDto85JMLQK1ghxnthfXbJZd3TDiqHvGaYL0vU5uXoOw1cw+Qq//R5V1pSTr+Gn58j0ghpOKd1d+v1btPcT2/lLXO0yJ7Li137pS+wuznn6/H0O+y2r4Yi//5Uv8vU3P8fT86f8l7/zO7x3dkVMme1uy527J3R90GiPG3822iyUWvVgKwJmkVAvfviSk0aBOEHqSN5fknOjSKAaixPwfU8Tj7UdWPW31toosRBJWInYFvVwbjy+V2+lc5YQekIYPpJhNoOzDjGi8mLnMPVAmibKarXkpmsvVvJM8GusMUyxMlUYOo8zQsyGXFU2vj5esRoCdzeGYQG0pCbMtTGmymEumB6VlbebzXPFfOzupaT0jxpuuInL+siqfSMsv/mzNip/rPl+uUWHG6zlokxfgHBLK9+qPv4oFJAlXsyI+vhD7wldUIk/BXKEssMcIpIjzhqct4gTaLpJbm3Z3AJNDG2JEsvGkZphHA+Ml08YL864vnjGfNiT87zkeBdyKvrY5QS5kDpPbEpwblWjvRp12YgbEN3WNwrz3AidR9CNcE5ZiebO0YWAMUKmMjqYUqTzHmc+GoK0qsoJi1WQWUykFBEMxlv1ibdKIyBiaBRy1ug+im6oY8wcDRv6EBAyIo2cs0aXtYYTXvrZjYWUZkoSqkGfS6Wp9Hz5XY00yhxJgFjVQdaq2DwNJ7fUKoiFFA/kqTEf9sxTpuTCnLJ+fqmkOCGtsJsmDtcvSOMBWtPXifXs9zuuL2fccMKcC51N7Lc7hs7Syci0P+fi7BklTgiGzSpwmGfmWe9HWIuUytXVU5DCF7/4FVIqTOPI+fkF0xQJoaPve443G/ouYI1gjeXq+jlx3lMaeKPXeC6RzvTa2C6qg1bLS398zZXcIPQd3qPDPRpd32Gd0uBjirRmqFKJOdKqYD20aui7gKGRc6LlTJJMW+wJpoFtkExDKmzjjjo2ej/QTKAfBKpeP4zFBkPnewbvaMCUCqvQ4a2w3e2Y9gnvLLZa+j7gZIGsEXQYKNDqjOssMjtSFhBD31lVPuTCatUj0kj7EW/13jVNIyVXaI4glukw0aSRKVRJlKxKCLCY5S20856aMsZ6Uk7s9xNGLCZASwmqghmdMcz5Vi74SSp7ds2D3xl48nd/PpFmedO49+Vznh2O+GeHr/E8HvFvvf6v+PVZ+N++9T+g1D+5JXnl3hUv/tYj7v0Xn75GnFIIV5npNPxF/ySf6ZIKJv2xDO/LLS3G28b7F1XWIkcb0mt32L45sH/VkI75kVnhsBxTzY/eiiM/WRNOg/u/X9h87/p2C/4prc98Ix5zphZ9ATgxxLlRjeCrw4rQRLShNUKwhpgy0zyTm8D5BSJwfO8Bq/UAHNNK42oaiOkcPz6m2hXmzi/Ttwu69IzkT6hHb+Dn96EkLrdXWPMB49O3SYdzfvObe7b7Ld95+iFzbhi2nF+e8eFK+IN33+XZ9UG3jA2c83jnKaVodFkwhM5inUPEkLNmlCOoXHK2iNXhQioFS8F5IYjVxk9UAmecQ6xC2WozSEvkeKCUjDMZa8FZq1sxGTSCx1pqqYgRQucZ+hVD3ymRvhZaNVSjhGSWTX3KZ3T7Hfn+MbiAkxtZeAUreGeYD4mGYR3skmWt2+RSKsZpUz7OGesMGEdswovtzHYuDE44dR35pumlfbThFm24m7nZZ3+0+f5YL43+LT4GUdPPuxGj33yCvOzEFy+46MG6tZtevZJzXjzk+vgFr4131wW8M9AK5BmTrzAp41pRJYYx4AScAm/aDaEdAbFUYymiG+9UYb+94ursPQ5XF2xfPGM87LBo011rBtEtYa3aaE+x0ppKqVsrHKYDzriXg4Umjdr0cTG16OZ1aaZTzUgCcUEHNaVSBExtGGeoJelWEqGWDN6SUn15DWpt1Jp1851185hiIueq37xmjDFYb2hUpGZaKbRcEetZrXS76pxgUFlxKU0j5VrDWt2yO+dIVYdTOWVMayTRAUhbnhk0jY6qtTGlguk6LILUog0pTZ8TRn30IrDb7Wm5UnPR9AGrcVVlTqQ6c727JtWGuI5119OdPKILntoqY9R4t8uzJ0icqHnPXCfm3RWzi9SSuTsU4nrN1HZYB7tDZLVydJJw3uJ94MXlBYjl8mpL6AKvv/6merYbdF3PenXE3bunsEDVai3E+YLt9oJu8NRc2I0T3gdyKXgKLniMqbjqyAm8s+SYEKMDNe8NNVeCNdRUSC0xjkkjGDHkmlkNgz7fRdU63sBghN3uQI6FQx6x3iiHowtIKfp5reHEUkzAWENwnpYTc5zU1kBjWDtMa/TeYVDvfect0jJCgCY4HOvQLc/DxjhFdvsDwWemVgg+0A+BmtJiRbBQwARDnhLGNMZpVJhkq9SWF5CaIQw9ZZqYxgPNOlwwgKfUius6ugXWVkok5abcDGM1Gi2z3CcdU5xJUaP6xMgSqXYbcvxJq/W3nnP06FWuv/yzfZ3qYfOFKwafPvoYwj8bLb8/feGHNuEAh+h58Ed/Etz2aSl/PsGbt434z7Okgp0bYbtkeJ/tNMN7VpYG3Dbef+4lBuk76sM7HD5/xPZzlumuUPqbRvknuAICpQfzI17mdgY3yo+t0DFJ8Lty24R/iusz34jPc6aiksJWG6XUZTOrJOZSCxjDatUrMfwwU8eJlhslRfZXW7qhJ3Seo6NjEEf+8ClyOMO6Febe1yjTU/LuPbI7oRx/jrB/l5q3zOE1bZjf/U1evLggp8wHZ43QH3H6yle46xytZL7x5Dn/8jvf1l2uUfK3NZb79+/Qd5qxa5ZN88uYJqMNgREdsdWlCTPi9OZBe0la36wH/JQZDxMpKriNNFHKTBc81hsCBTqHtQEXeox11JJxBkLwKr8OQeWe3iFWd8hStaMNQ4cT3SZN06T7ZTPSXVySPvc6WIcR3X6aEhEa3jnmPBKcoff2ZTPchUBpBmctg9dD/twEqcIYC7vDTMqNoxDw9gaVJhiMNsVGMDeb74+btf/EiPHjnu/lP8IPQNfk4/9aoGo3MWI5FZU7oz//qg+sh0DXe6w1SMuYMmPSJTJFLE0zvI1Zor5umkRUvo5u7JqxZOPJOOZS2F9fsb++4HB+xv7ynGl/TU6ac11KZp5mbThq0aEIDVOE0pZs7KY0Z2vdyw25CcvzvzTEGMRbbNMYvLbQU0urSJOlmf1IdmyNwUhhv9vqc7I2YiqUrLaFG5hdM0tzL8pnMEYwVj3C4373ki/g+xVO7DI80a9lnMc5B5glQkwHDTRtlmvVCDSLDjKK1TCpGCNW1AteqkrQxbqXSQDe6uBNFQYNaY2cI/M4McesBHHrEKPS5TRFrDhWqwGRSj4ciOOo73nWsTp9qLnXRYFlxd7hed1ghlNWx5n75grQzbmZLzGtspJCHCemKeKc8MUvvcGLJwfmfCDNmXGq3LnTcbzxHA8dZa5k6RjHA7//e79Niok7dx8yDGvunp7SSuGw3xE6h0jmcPUOL56/jwRAPBWNY6tpYj2sCM5ymPZUFGroEPbbEXFgXaM1wVWh73pEConGPB3A9hgSZU74YBEyQ9DDd/AdOU1sx4lYNFJuWK3w3pLiRENjJJGFnC+OI98xl4nreSSEgRC0+UY0cs8ZrxIaEcb9hHMe16A5HQJZgXGaaMWQamRKM61CLZm5RJy3zNMEovfBEIJG4o0jroJBY8XEC2WeqFWwUvU+sqgx5pqBQpudPjecRaSouqg0MjqcHHoPpVJaY3A9c4y00rA4il1i9gqA2n5u6xNWtXL/X18w3b9LPPnprk8zwBsjJ8P0Ax//xvmrfOP83/8z/+72D+7yylvf/am+7yeh7NUeqcc/M7Dt3+SSqlFiH8/wlu3+tvH+RZcPcHrE/MYdtm8ExkdCWn/c9/3TX4XSN0wUbPyzP8/toVn5syPQFk/4/d+fCS8OP/XPdFt/8fWZb8RzbgRnaLkQiz6pg7d4Z4iLFxlTMS2Su0a/snS+Y05Fl6Mtk8YDMQz0w4APns3RQE7HpPCINj7G7B7jwh3m469Sdu8T4zmsX8dv7sL2bc7Oz4kx0krFhw7nLa1pTmOpI7vDNTFnjA3Yplvd9XpN3wUaVeOoAFrFGqvANKBZbdxbq0qaVnKXbgRNQ3CkaWbfMtfXI/txoh/W3Omh1UprDtsFvLNQhWK1ebViMVao3pFnzQ1fr1acHq1prRLTDBVKtuA91hn6EDAG5rnqIVTAG8fh6gVjzNRhsQA0aCUrTMlbYml0QancVAVCbbpAH6yqAkSbJW1bYYpJGzXRLbp6PBd2tshLSbnIR1L0l39c6iOrt1Kvb84O7ebzl1WxDmoWjze8/K9uvA3r0PNLR0ds1oEhCI6sQ4a4w9aEoeKMKO3cyfId+ejrGkMTS7mRmmMZ48x+e814dc50+YJpd0k87ClVoWC69YxM8/zSs51zohs8rS6wvqb+WGOXrXjTjZyRhjUoaCzfDBY00oz5ZmtdsQ59XMQQa6GUqlYHBGuU0q4U80bJlTFmcqkYK5qnvTQs1hhKS0DTZljUc965QF2t8KFjWK2wzlPLTFsAgNZ5ctWN+c31bLUibfGyL7YMKRWxllLaQsou5DTTOYNYQ0wJmsEipJSXR9/o1lOg1UzMmcurHakIw7Bm6AOixD/SNFGrbmmvdwdqFVzXszkZaLXoxmLzgPPomYbXmLH0TnjNX1Nr4ysPLa3cxXvHh++9y/76DKHRBc9qvSalwvV2z4qeN77wV7h391X+6Nvf4HAopCyUVGhBuH/seHK+I84T0hpnj9/CUHjw8A2GVc/u8mr5ffa0+IJ52i8KDsvgBtbHHReXWwyCt579YeQwJ7yF5jMtC6G3tGUAqP5qKPlAnivOeVISXKeBYqG3iDRircowoFBzYneYsdZjnU5IjM3kqtF1g18hJrOdDjgCzoOYynyYsGIYvCesAt4oSd4YQywFgyWnRCvCnBPRCCsXsIBxQpNCyhM5Z1brFZ131GoYWgUyUsH7QGv1JUldjMOvBnKMNBquWTo30NtKaYXWIJVEbAXnPd46Sm5463HBMs4TkivH6zXYxiHGJa9c7SdpmpTfkXXo5pxGlimmQO8rt/XJK7M78MqvD7z/jweq/8mvUbqfef3uTx43tp06vvBPR0g/4nT+Ca62P2DnRh5ufeI/bv1AhvfZEiW23b+MEoPbxvsXUtYiqxX51Tvs3lyxe80ST9B7wE+69f4R1QzEk4adBH/Q58APK2lgRyjdn/IjT8Kdb1eOvneNpFur06e9PvONOEAqhd5a1iuvmbQOcm1UA94KTRrjnIi1kKJQSsVZwXpBxBEPI7mfuby4Zn204c7dO8xFyJdPkfkKzBHz5oswP8HHF9A9JPUP4eptLl484zDNtJI5dmuolcsXT7UbFKPNSdFIployoD7rnBLjONP3ncrCWwUEcRofJqgk9CafOee2bGAEYwoYjWMa88ycK2OslGrwViWkFaMk9dCpVNxAZ5cQQzEK6RKDLAC4oV/8wEmjshqGruvouoBxDgTmaWKaZhBh6AK1wby7ZJoTdeWAggg4Z0gtY41FEFbeosJkzUmuVMzSfNN0o2pEKMBhbpo17S2rTu0FN422Nt86tDA/BKwGvPz/N39eEGpadZGo1htpuJYRle52nfq7XXB4A60kpMyYvMMcMm6JETMGxAlt8XiXKh/5yo0hNqimo7nAnDJX5y+4evoh+/PnXJ8/Z9zvGFYbNusV1MQ8J7Ben4+1IouaY9HMU2qkpKzy2Nxe+txTKjpwWfzlpRaNF6Mo0Gx5CGpRSa5zDow2v60pubqMM8XIS7l7LBVT9REMXgcJMUW8V7p6roILRrfMc8Y7h3P+5XXqQmDVKUW/0jQ7uulgpt5YF5YBQREdeFAyLLFoDR1sSFOAXV6kwak1aivEecZUg1hLTgXnvMayVXDGKl2+VuI0sm+VmCupCpvNmuAhzRdMhwOlZnJVsrGzVnPYBZrpuMod4eQNJrdB1qec2ES3v+T19ZapBE7W4JzjajI8OnG8+mqg5MR777xFKZnQLNYFHr6yxp9fcXV5yYU4Hj76Ig+2M++/812cNILvGeeJ3WGktISVxt2N52hlSOMZl2dCcG+wWltaumK/e8G4H6nGcHx6sgyo6pJPbrHG4aylH1bYENQbHTM2GKyzlJppNdOHDpHKdJgp1XDIVaMDF9je0PXqxZaGmEbNjallcJUQhIajVME0R84aXVaJOlgzjpqT8h0ceAkYMQy+Y9UFtvsr5hgBg/edDm2wlFKIuXJycox3OgCaboCTGDbDBgFq1kg0a4U+9MxlYn/Y0UqlGcGFnoClTImCaC464J0QYyHlmWxVZeGswTaBAn0fVD0xJdKcsWK5ng4vKfzWiULfDiO5gVhPFwzUTJo1eSLmTMmNXG6P15/UCu+ec+dbr/Lir/yx6e2PqNI37r92hZGf/Nr2IXHxtTs8/MZP/Fc/ORUT/lDJwy32/E+r2wzvT0iJQbpAu3fK4c1jtp9zTPeEMrSfG3Dtz/7+UIZG7TRqzB3gh902TFaVhBTBRMgrVQEOT4T73xxxl9OtHP0zUp/5RtxYWZoA3Q6KEaQ1bBPKDWwLwVmFtLWqTW0qDV8NfRA2px3j7hxTEkebFev1wN00USc45DVz9waMT+jLC/zRfab+dcz+XS4vz7g+TNRS+MrRG/zD177Cf/nub3KdZ+qNh1YF3vpPrSAF28yy2YRcF4xYayCF+OIK4wzHR0e4YHTzUsuyoXT4oce5AXGOkhNltjQaoR8wor8nGIW+GW17qY1UCnNM5ArzXLBGOD5e67YcgVqZ5xkjDWMsxgW6Xsd1uRRqUrJzylUzmK0+zjUd9HB6evJSJS6i3uDge446R3DmZdPdlk20NonLtlsMYnRbO5aKRRi8ZXAq8TZLoyhNH9OPWb316t403x87W91suGvTRrUub4QGbTh77wjB0fUdXjHXmBJpaQ+7GWcrDsFY9bXf+D7V361SVBFDk0C1nmw8qRmmeeLi7Anj9QVxe8m0veSw25GXN+OUKzEmjJ0QA3UhjNeWsS5pvnytWG8XYnihtUoqiWD8y+dSypWWy42jXYFxLCDCnHHe4rxuh9UmoFFTIualZMA5TwhOM84F/RkBaxzGQC2NUhIxJWrOXG13NOMwbPDe0vUrOm/JJTPHrH2cwJQzY8yELixXomquc6nE1KjoNW62kovodjwVrLUIVl8bS+b0JOoXb7XQamGeZrwElXW1SkmJVKAPQTf4TZ97Mc1IdjRpWMmM1weuSyG3RpwTIQwYKxwOmdofMzz4MjtzRNcP2HSFNMNffhTYp4mHd3v2hxPG2XB/NXF+6Lh/ol/3ybXjlePKK6/c4/r6kmdPHjPFiNnuODpasxo8Ma64utxRcuHB/VdxLXN9+R6yeKnv3Tnmzh3Dbs7M40xwgXunG0rdcbh+j6ET9vsLtruk6QqiA8ZcEuNYEOvJZWKc9qyHI5BKH3Q4QHD6+FNJh4zgOMREJS3b26p8DbNcd4SSkqorvFDIRNHIM2c7jatzFm8cLVWMoJGCBaaccNYw+B7XWeZWyHMkN8M4T+RS2B0mjLe0osDJcb5BMBZC5/CmkWOhtsp0iDSx9KEjzolmZYmLasxxZp5HWlMSPkCeG1DwDlKO5OUV34xQrKUYQUJHkEaKkVY0UrAZq1FTrSGmEkLQQZaB6TBhrVOIYmMZHlkMVVVVVeg7hQEOwTLmwn789G4+P/PVGqe//ZzDw0eMj37MQ66AvDb9gC/8JylvKlP/Kd8kt0p3mRjv3TbigC4QCvjDbYb3J6KcQ46PiK/fYftmx/4VQ95AdT+73PynrWYgrxvNCX77JyFuUmH1GO5+a8LtEvs3VkhtrN7Z3+aDf8bqM9+IWyd4Y0EglYo3hs6rbHfBhtGaxptRG7nq4UmM3i9l7bi+2hNTpq+Ny/OOk7t3OdpsSPceMrOn7T9k1fZgTtjZVwm7d5ivn7M7TNTW6E3P33jwBQ7jBS/SiHVeQWJV88H1Z7gRPassuAI03RDrsU6jhEpshL5nipkyJZQCHOiC0pKNqIzYtIr3FtsCtTVSK6So3kbvBe8acRpJBeYUqalB6BEX8DfQNAFjDTlV9LBuEes1Izg4vHfM08w0zxhgjpFUKm4BxgE4qcTDjlhOqHZplgEpiS6suLsOxFJ+YLAn6CHXLNFd+rMIKat/1VhhvTTwZhloLH9xabhFP94++n8NVXvVVl9uk+uN5F0MnXd0XdCtd7BY06AkyHtkTNiWsbJ4pHujTfYCVSutQdEmtoqjWkcyntSEGGf252dcPv+Q6fqScXupG9einlljIC0U7pKT5mlT2e/2lFqwsnwvaUgrpNSW31OWGDnBOafS7WXrXGvDYMAoKE2saPwScJgTcy4cHa/VGlGaPtZLlBhGw9gqDS9wqDqg8c7r81UUkhZjYixFUwacw3llKFjnFhWHbghLTqRcSA0cllgy85QwYnAuYJ2jxImcMjnrQEmbnPpySFBLIWNorWCWQUTKCRHRx5DKnPOyMYdYM3kfGeesudKtscPgjFBbppWG69c4HN5aXG+VsTCO1FSYZUM7/iKxv8f6Sw9pJeLrxC9vKod55N6JIdYO1/U83AjPt40HR5ZYO/al8uCkcTEG7vSZfRTOtpb7a8+bX/g8u+2e66tzaI1xUqk5xjL0luvLC6a9w/kNw+ohwU6UFCnV0vUDr98ZOOwPzNPMbrul6xxuOJCzDg+NNKiJKWbOa1sI6mYZ1oAxAeOEKWXmpRk0oiA09FaE71HOQDOIqDy+D3Z5fSbSzdCvwhACOScMFhcsgsHeqI1qwToh7QtzLngb6LseZ0FsY54L3jjmOuM6Q46JEiNWOry1VBpi9D6ZS8JIj7OW3W6icwFjLEPfc7W/IsYZZyzOu4XirjwBZ1WtY5wmSaw7S6mN3XjQe5uDPGaq9ZRalJVARYDOdbhe3x5luRc28ZQKOUdqsbRFKRBCUMtHK6SSyXnGFq+sgYBC4XaVQiXVRtf9KXrD2/pElMTEK/9yy7v/5Igy/OgDejqpvPpTSNJvqjZhePHpD5d3FyO0/idSEnxmqv0pGd7jeNt4/6LKB7hzTHz9BKmN8N2nlFfusP/8ht3rlvmUj2Vyf3KuhvrAhXD1gx+XCne+HfGL/3vz3as/+Zdv6zNRn/lGvGlUNtYIpWoznkb1XZubXObSSLXiDB/5ao1hs+nIKVJzUarwHEnjnjiuOL5zhwf3HXGeKPuRMXeU49fwh3eReM4hJloDbz0P3ZqjwzV/4Cpf/tV/zGrV8f1v/gaXl+dgLVSWzaVZ5OBC33d0fSDlSCIh4rTpCkEBSIsU2WCotZFyojmHE6uRTbngWyA4i2+FYD3RCvOUGOeom6ZaKU1IpSHiWIWezdCBqXiUCk6pShtGlam1av56zjPjQQFdBgFRyJx3lhC6lw2htYa4vyLm12nWgBQMgm1K594MjstdJi/N38tt+AJbM0a0mUCIudGMwYmw6f0Pys+X+2ozep/VaLHFk161qbsRoTtrlmxkT9cHvDcYKi1HJG8xY8I1lU07MRinv19r6vHMtS5bd0vDkI0liyM3YZompt05h6vnmuG93zIfDozjiA0eUMtALgpbk1aZZ/1zW2jzxsgika/YJZJLvd7mZoe3WBM0pqr4oD7uojA5sBjTMNZSclvI1sqLb9JwpiEtYYyjGKG1qtfVGFpKpFIx3ikMLifmaSIyUUpePMCBLjhc51962XIpBC/ElMhZwWspJkptlKrXo2SlVyMGH5RtkGKiRCWll1ppy0az1bp48hWKZwWssQv0qmCsU4m6EVKM1FKZ08Q4bpkOOohwYeD4ziscn54AmXffeguRgZMHd9ise8bDlnE/0cyKtn6Fw5179Mf3OMLiTeWNTaOReOVux/NLQ+/hFHhxPfHK/Q3X0WGlcWdVeLHNnHYTL3aqAHh4VHi+Hbg77LkcO67mjvv3Gl/92lf53d/8ba6ut3hrmeZxyVM36r+3hvX6lDuvvkK8/B7nFxdsNqsltSACqoZw1nG66sHBeEgLvb/RMAyrDRgh5pmSM6qAEcQU5jQSXKCgcv9xyjSvA8vOLaBIa8lVqLmwOgqquFmuSVuGg6ZTcrrF45xK/kstql4oCsHMueL8QBcC66GnpkjnHbvDTKtKGQ/9QN85xsOBXAGJpFE5AYP3OFNJUyYEpfZjvfIWjGCa4EyHc4aKYb3aEMeR/XaP9T1ehJwyKRdEHFXAW0MzkGRRXThLcQK5EYyhFn2tOdeYpxFjVBVTSqVKZo6ROGW8ha53lNbIDVrR1IRqDFIrxgkheEqDOGfEWHqnyo4xfXIOgbf1w8s+v+LB7w48/Tv2zwSQNQvr17fYn0KSflMfvHWfr/+L9z9BrcFPV+Zqjyl3qJ/5UyXQwMb2UeN9m+H9iy/nXkaJ7d7o2b9iiMfq7bazYH7tC+TVL0hu/jNWCY3qBLNEgEuFo/cy4ewWwvZvQn3mb5lWhL5Tj+dNaZawQYxmZpcCxmt+c54zYgzeG+bDjBihH5z6bFtlf3mhcTVGCENPtxroj06Z5o56eBfiFROGWHXr7p1l7Qbee/1V+s+/xqP794mHM777zUpr9eX02CzUaBEhhMBm3dNaoUpP3w80qbSUaS0RU1PCsBhoetBM1eJzU6mNMRQRWspQheAM62GFzZmad3qoLiqJr7VhfM/Q9dw7WeOdx/eWOM0c9nuV61ur3u9hwFilZpdcyXFaGiKLGHDBgzXkpFnRy5SD7dU5YyqkzuCaNvLSKkIhBMtNXJt6m9XnbRbImdKTDak1xqoebOcMfTBLRvhNe63/bUsW+s2N14j6PfshaOPdebwTTCu0NEO+xM4JKzqcsdYgThv/tnzVXNTAoFFijmQcBUesjWkcGa+fk3YX5MMVadxzfX3JYT8yx4wPnlobh2nClUzo+iXurVGlQNMtfamNknUcQXPqGava/MTSFkp+o9Wi0m2rjVulgcgi6a/L76wN7M3wQUTp2DlGnNXBS0oJkUrKGTFCzTogmGMmlqoNS4qMC917NaxYbzY4pxR2Q6Vk3eSrf1ohg7lUAoGUMiVmSgOspS32gK5zVBFqE90sitODS9XXIabqc70WclHJc20VqdDE0Voht6LRalloLbPfXTNNiVYrR8cPuffgEUfHa5y1OGfZ767YH0buv/KINO5JOXIZ77LrXmP1xpuM80xrmUc+4tzE179wh/Nd5WSt5PZnFxOnxz3PryvHQ+HkZMOhBB4eNZ6dV05W0NnC2c5wd9hxPm0QG3Et8eS645XjibNdwBjPg/srvvRLX+V3fuM3iS3RhcDx8TGb9RprYL3a0K96ghe2aeAoTiCN7e6A9Y5h1VGbDqoO88R4HREbEOtwzalcOu1pFYLzDEO/bLOrgh1rJWV9bRpn8Q2aNJqpOHEateWFUrW5j7FBbRRpOO/IJdFKxotjE9YUJiwWZ0Vj7bxwdbgk5YJ3A8ZaTC20nDX9YIqIUbr7HHc06zhMiWmcMcERvMO17iVcrRWHsY6YEyH0hLBsz+dCE8Ow7hXAllXmLhV81yEUjHF4I9CUZVFK1MlszWynPV2/4ihsMFbwg0NQYCQl6XDSBoxVxYsLDmLENH0OG2topTKse80ePxyWht8gzWGMUwYHDWzDpEJKUI2n638R73y39bPW+lvP2Tx6he0X//TPKV3j+I9R0n+S2kfPm/9Fo223P/XX+KRUO0zYWRuKz1pJ1cbb728zvP/CSgyyWVMenbL//Jrda3/6lrv0jU+TeFuq4A76ezQjrJ8W1u/sbj3g/4bUZ74RFyOk1LAWuk6916lqgyaIellX2gDnWJSIbUBK0wOatxqNXcBSibES6zU+BE7u3uXk+BhBKI+fMKZrmjtidvfw7YJWJlopfFD2vPOt/5b2B42j07ukNLLfb1UifSPVNroJPzq6w9f+0lep6cDl9pqSZ1IU9ce2CkY9tbU0CAYrZsmAVgNurDcRZ6AkLFnisizeG9arFS4XSivkOdHmGREYbKOVhO0dcTqw3e5IMTGs1jjnyLkoBdiKxva0os18mrALeC7OE3NKi7fbEbqBZgJxt2UaZ/K6p9Kwon7lVgudV+DashPGiPq+3fKPXSLa6gICwxgGb+mNejY1amvJt0abwWAtffD0fSB0XunQZUbKjMQdZspYVPZsncV6Adzy9VjAeIZmDEUcxWp+eayV6XrH/vKMq2eP2V5eUOYRZzICzDEzx6Qy86bqgZQLrQlzTGhPmhcoX6UsEUgpJZzVjR24lx5tldfaj0B0RT3f9aZ5aho9ZqxFUlIZ9kK8rqVSlkY8+LI8Oyo05SXsdiPGWwyy2DH0ehbA+YAVYXV0TN91XFxc45yS/ksE65aYuCWzW+nWgl0y5WtOqm8QbWxK0mxz8Y45JjAWFzwi9uXAoxRtFIVMjolaMillfAhKY4+JPlh2h5H9GLHOY03l6uIF680D/tJX3sQuUvhWC6EzDH1HLSBHxwjgfeYQTnh8FbDuDrvH3+bJe+8gF99kePjX+St/96/Qbwa2s/DwTs+Ty8IbDwPeRK52keOucLX3fO6+53xf2Y5w70R4vrUc9zDOwsXOcH9z4Gy34u6QgMzFzvNgM/H4ojLWC0w6Z73uKEkI3iKt4a3hwf0HrDcD8zxTy8zV7sDl1RYHFGMJgyfOI9fjDFUHia1Cy5F+pRGDqWQqlpV3bIaOmArSLNXMxAVsZo1FSkMoC8hOwAvVKkPjsJuJecIPHR0GYxpNLCJGExW80/x2k5TE7mAqRfPhK3S2w/aqYLBikVqAghiYS8KYglhHLiBkDMKw7vAuLJYP9VtTGmHlKSkxl0rOExUFRg5hoJTEPE/M6Sb/W++j1gutWDCNlBv94JnjrLaPOdJ3AYz+Hn3nNdM8JwXP0ZizKjmMs1RjgMK4DGWts7QbG4pr5BRJsTLOcYm+MwplbGqbMaJKnlzTcu9rzLf+vk9FSS48+NcXzPfuEo9/+IG4uY+HXf5ktY+ei7fuIK8b1sOg29RPc+VM2BXS+tN/rLxpvMP1bYb3J6bu3+Gd//AV9VV/CrbcP0nZCR78lgaMN2cwY0Y9YLf1b0J9+u+YP6KaqKdYY7eUON153XiKEawuZJn2mZy1Kw7uBmSlBy/rHDZG0lywBqRW5v2eabUiDD3H6zXp7jGNxmheoZsO1DKTcqZKY+0tj9YbLndbdpdn4JaIIKvZ2eprNty99yp/69f+HqdHHe+/+z22uyvSNGGNR+zih25CKQqussZoO1mFKkqddk59n60urbk4WLLFgwOpHhsCFZjYk+JMKzP77cw0GrZbu8iHwbmOgpBKoZUK0wEbdZN5WOTUVpRE7Lz6NrvBEZzDWL+QuSs1z8zTSJbNckhVaXUrCec0LqomzZ82ywb8phE3CwRtztqAGqC3ArWQl7gzZw39qmPoA0Pn8E6QmmlppE5XkCbG60uOT+/Q9d2y8TZKRm8qnQYUyiSOJJ4sjjlnxv014+VzxvPnHHZXlJiQmtnt96RSCV59pzFl5jkpJO3Gq10qRW4QcMofqK0u5G7VIKUYKUWQqhtdcUZljqaRUlQYHIbgLDex6K1VUmovvauANuGlYK0naWAxxikbQYzC2+asoL3WItvdDjFqnXBdz6ofEGMWewBQCylOzNOMsRAWGbqUBFUjy1ptzElzx8U59cznQk4Z50SJ+xZSarrd97pdbDVDFqqI0vGNDpowjTwnYpqpMRFzwhkdK+22W+J84BCT5kdfP2fab5nGwnp9lzRt2c2RlDL9alA4obF457R5agUzjliXad/5Hf7wm5Av3qE7+QKr4R5H6Sm7+jd55c7AxbZyiJWTlfD4+YFB9lzPjlXfcbqGZ9eVV0+EJ5fQOeHOCp6cN+50I2ezJTV4dBR5fAGn/pwPn17z/K1zaq6kLDjreXT/AVfbHathzWbd4cTQWuLq4oLt9oxpe8326hKcwXY9XdBh4TSpRaHWxma1IseZWApznLGlI3SBzlVyTuwPEeugxIm4qChqLrhgmUtkuk5gUZ+9tTjDEttY6JZYw7pkGVgr1JzxRjBeJd41aT79lIQMxJJoy/WSqrLxlBNQaEmwGYwzUBoW6F3QhASBlCpZ1KljBJwLGFsoZSIWxapJcBz2IyAvuQo5F8RYCJYiOqSb40Tfd+ScVbaeZ+Z5xjpBOkuu2iDbaqi54ntPyZAWWaANPaYtPncDXfAk0cci54zxljD0lJyZp5ncMuItplqkWYw3kCs1ReaifI6S9TVrUCXLbX06ylwfePSvV3zwj/qPgZ0+VpafipS+nTquv3+K3wu7N2D1dz/P5r/57svt6qeyWiVcJvaPPn3Hyj+R4X22he0eYrptvP+iSgyyGqgPTjm8seH68460udl+f7rrhoTu9xCuGqvnGTPn2w34v6H16btj/oRlgx4Mc1ZJenAstNyGLYJpUHKhpIaIeZlpXXLDdR2bzaBbo2ywwdEy0ITdfqSYS5wPbI7XHB+fEFOD6zPmNOnBXwxffvUB//CXvgxl5psffMjvvfMhhxR1Ey6CoJTq09N7/K2/8ys8ur8mjgc2R0eMh1MuX1yQ04RzcsOXVg9xLcT8kXfciALmaIUmFuctGJUE5xyZx4IZOoXUNVk2p2aJBav4YUXXB23mXMOHsAwKLDSIMXLYjxijDZYYh3EB6x3iPcYZgtUYpOA9xnoVi9dGZWbcbkn1AdUYzI1oqCp9OVhhv0isjTU4Y9T/K9p0xtrYzonSKl5g3RlWK8+qDwydx9kGeUbKCOMMNSGLH9R6S3MdfbiPsXYhpQMI5SbD+4ZoHiPj7pLx6pztiyfsry5J0wFnRbOUs4L8nGuUHBkPEzl4rDWUZYhjl+tUakPsjWJBhyJljsSFtlyz5nO3sgxkFrWDs02hYkm3bc45bO/RqCiopSiMz+o2WWX4SvsXER0uiNHhkfVIK9SSmHOkpIVo38D5jn4YGLqgcvGSSWXx1BfdapdFGm+NUErGtExJaaH5V6zTSLplPIARpW/nnGjto1g59RVbWtWfrTUwON2wl0aQRqFQUmS/35NjIieVzM/TgZwTh92Oua5ZrTaErqNfnXD3kae1xPb6mqvtltOTE/pB8N4vJH0B0/BimND3OGsNX/ziG7x//m2ujr9AaJe0/m+wvmMIHHh63nN/Yzi7arx6N5A8XF5Z7p0IL/bw4LhhUuXF3nL/qPHkvHLvVBh85flV4the8f67I0Pbst0nDs4Sx5nQD5zcOWG96dldn3N5fsX+6hornu35M1Ka+PD7W+YYybXQdY6uswy+xxjDPGkzWauw3hzhnXCYR7UEGKFECEGoqRDzrOoEmZnnDC3gg6PMGeeCbns7z5EPpFQ0fmxKGCts+kALQpwa4zjj+wErjfEwYsTSrMOaxjQeGLqB0hqm6mtXUCZFyQkfAq1lrAMrQSXhRoF7iNNUA2uxAoJlcJb9NCmV3TSsa8ScSSVCMVQKZSoKhxOPWL1Hias42xjnSdVCxmON4bA7kIHBecRanBFiyQp1sxaqAh1bK1xeXlNodM5SpOHxJDKpVHJpxLkyDD3eeMhqdbHN6MASQ+dVa15bJpfKyjuagcM8kVvDLYyL3hnGMXMb+/rpqu7tF5y+8irnX/+TkWZ2b5iyo3f5x/56F4eB8XvHuGkBiRo4+6uO4cnnsH/03svG79NY7uIAbfjEN0s/kOH9YsK+WIjmH2u8b+sXXB+LFRs/d8Tudc/4QMgraPYju+GnqppmfrtR4cHhqtFdKUnf7SImlsXC+Sn83W7r51af+UacVJZcV0MfNM84l0KKlSpKuixVm9vQ6za1lgbOUGrm8mL7MsantkapgjjBU4mHSy7PlFq9OjrinmiW9jgFwvo+9wf4W288ZG0quxhZe8sqeA5Jac4ihs3miPv3H/DFr32J+/ePSdOBJtAPA+vNmumwZxxn4qSDAOesboCWHGi7bExFNP9cxOKtp5lCsJngNP6p5I/8xKVqHqF1ns3pPYx1OKse3lILKUVSTqQ50hCc9/TdgF0HxDSCUxlBjJm5ZPI8YbKlDR3WKKW9FfVL0pT2Hccr5gwlGNySJy6o1zhYo9tgY5eYJG1cW2sL4RxO1z2vPei4s/JsOnAtafM9XVNm9bKHEBbZtP8oj7osHnLrqcZRsSSxRCxTjByuz7k+e8ruxVOuzp7irDbOJRclKaPeWFe0cQYhJ5X05hxJJdF5v2S63+gbVDavMLmKMfomUmpZgHdVm/qFMH8TEVZqoZWs/lJnKYdEE/XJp1RUOrtgBW5i1gpCLYlcCrS6NB4CrZLSSElRfa8iWK9+9VYb1gh90GaPVpbft73cuN9EnuVWKSUTc8RSMEXhVDolqUhVv3acC9Kc+sZbw5hON+wlk2PGexDxdJ1m1RuB+aDZ0lOdOewO+rk5qyfcaKRcbAYTetb3eo6OT/De03lPKZWr3cjzrbCdj2n7xNGmsFqtEOOw1lJKIudGnCcO+z2lFKaUOT29w9e/dJ/feyvRr17DpveQzd/n/qnn/DBzcB0nK8uTq8KrdwwX50LMcLpuPL+uPDqBs+uIr4W1G/nedz6g7B7z9OkZQWAY1vjjNUfrgHMwUqh5y7MP3ufq4hnXV1tKrUgT5u0ZskSwGRpNKlhhdadHamJ3PaotRSy1WRC3eLlHKk1zths0U9jvtvr6HywuaMa7xzHNiTgWnFdKv3eGVvT1W0tWa0rfI2I4WjumeSZJYwhrlXwbQarBeofxhkrCWrfE1+nzMVdUBbEcKvKclm16I3QO0wXNjk+OYNTuY4zGL1ZTmVPCdwqKS9NMqZ6pjLRW6LuBFIWSNb+7Nb2fWVOxdgEFmkBuiVpHnDUcqjZNBVUt2Co4LJ5A33eUXJmnyGEaaWIJwVJo2GaoC7DCG6vxY4OnlEypjSyVlDKSCmKFTehoUqlSiAkFxI1RX0ut0buASCVSqSKEztPdnrk+XdUad37rjMPDh0wPfvDimQiX1yte+TGo6WPyXG0HyuPhZRN+U6VrPP4HGz53dgpn5z/Pn/4XWnK9x+R7VP8X/ZP8YP1AhvfzEXt5m+H9Q8u5hXTbfjEDCR/g9Ij4+im7zwUOjwxpo8A1rU/ZlWlgkuB3MJw1hueZ7mJG5vLyvfG26b6tP16f+Ub8MGacs/Sel549udkei2bI5lrwIlAasWp+d22CE91cpqx+cXEd/cohNRFjI6fKdBiZ9zu63jP0K45P7xDbgVWe6cQyY3m2i2zHAxfbLWOcKTVTSqLfHPPGV77Aq68+5OH9e9RcQCyd9zjjqCeJFCPz9JQ5jgxujYjHO491CnczorLSJga7ZFfXUjCtkVEyPEbjz2ouyyYVPSQvK8uaE8YElXGWijEB6Xu8OHzvFzBRQWrT72M1X9q7hIyVmEFp4ItvG4FWXj7OAtTDlUq3veAXL7i0irSK80Zl7gYsCtgLi38zLBtvqQnJE5IuMTEr4d5YrBFYD0j76P1DYemGaizVeBKOqVTmaSZunzNdnTPvrkjjjnEZdJQSOewPdMNKG/haaVW3ssRGXlzs+r6ghO9yE7clAqbqz2Pd0jgVJYGXosMS72gpUpYNs7X6wHinz0dnLbRK55V4nucMCLk2Yq5IK1DBWME6h1m20jkXcpqJKS5wtowLvfqvxWiEkkc7Eho5J33+1YJzgqEprZy2DAMqzgrWCrEWpnHPs6dPSCWR58TQ9xyfHOOte7nhro1Fbl8o5UZ+a3AGtvuJMS4y4lJZ9arKyAg5J9J0gBwpORJTVnuADXSh52izJudI6DriGHF+Tervc9aO8cenvBjf5tn2O6Snv8/Jm29yvQ045/FdY7vdYjDEeSTGSOg7+mEDLnM4HLh/9y6fv3qPdy4Ddv8h73/3t3nyxj/i3mrk+dby2l3Bt8LZhePuETy+1lgyU3a8/c5zZLrg+4/PMG0kxcTR5piHd085OlpjTCXNB148f8L+6oL99hpa5XK7J3ivMnRplFIgJoK3rPpALLN62QnkKVFLZUqRjoGjlcX5AARyK1Q8NFSJUSpzK4S1w+AQZyhNaEskmbWOZjNiGkkKaaoMviNLIteGWLAlAo4pCXPS11BMiVoNwQvVVKptlHnCOQe2kVpVJZEfGPqASGPOM9J065xKYbXuMAi1wBA22FaRFmmm0XvDxXWluUqz0BuPEUMS0QFB6/HWUJZ7cmsQgsUbS8xqfzAitOYJnWM3V1Ju1BLxTgdj47xnjIZN31NSw3aGeZw1arEVRCxD6Gk1gTHMWRVBxurr0jhVcpRasU4tFNXoQIMKc8kKGGzyEhKZSlUQphjlKBQl1ItdmAC30vRPXckceeVf7Xjv39lQ+h88SNf2o9e/pQkvvn8Hf23+1EPXfNp48WuvcP//faBNPz0A7i+y2jThpkr0f4FP8qaNtx+XDO+zA+Zyd5vh/SNKTo55+/9wzNcePOPty7uc/p+O6H/9Oz/fb3JDOn/llN3nB/avGNKxUsM/abFiP25JEdwB+vPG6nmhO4vYfbrN+r6tH7s+8434jfw654oRwXUgpVJzo9RMXbziGP2c2hrGO5zRQ65xHmMqRRzeG0qccQIWwYaB0oSzF5cUsZzcOWGzWjHPE+NV5vOP7vPg2PD47Izfeesd3r/YLtvpjDGee/cecnpywunJKWWeSAjDagVOv/+wXrMZ11z3gXh9UKDQMCDeqqcWVA7deazpkCUjO9eyUNgNc65YlERdg9BqZZxnUlSvqThLP/SI9/pn31FvMqmto4ke6C3qDS0pkfOMXwjewRqCc/q41UqaVRbrfXiZQ24QUtwzjQfyZqONOlUnhCXTdYF7xyuGvmPoPdYKtISJBzhM2Kq+YGMVTGacfRlRVmrT+DdjqKJNXBLPVBvj4cDh+gnz1Qvm63NIIyVnYtTNq8awZUrO5Ko+VeuTwvdaVa99a6SiJH0rRiPvSlusAEa3maVRkuYmGyeaSV2LNvMouK3UQimJ3dU1oe8UflYLOS9Ap1IXX38lxsQcM7VCigm3RO0ti3ZohjlFplk3b0Yg2EA1hc4HxHvNW2/L4jprg12LSnsbYI3GiRVRy4bSzws5FSiQW2aeZ95++3v8/h98h81mzTBYxDjeqK+yGXrAgBg67/FGaCWTSyM4j3OGOU5c7UfEeFa+xwdlHdSWGA8jMc2M0x6wDP0Jq2MdMhljlsdPONQ1W/MG7nOf44PrHX5+xgff+3Wmq0tefP+3aXf+BvliR+fe4WRzxIsX59y5e4daGuuVw7ojxE7L7zZTponLyyusNdw53fD4xXMu3Ru85vfs9iPBdGzCgScXA6/esZxfjGRG0ovHfONbT6lpT04RbxyrvufRK18kOEOMO959+zu8973H7LdbcozERUHReUsIDmedSvaD8h5qNVhnmEmaXx4r676nzIn9FoythC5wsu7IKdOMgvKsGA6HAwH12R8mbSqP1it9zsWKpTHViRDckkog0Cw1FrzrqKYx9AOmzYxjJlLwHcRocViqLQQsrVkyCXGGmvS+JUY5DyShmUbwDdrEXDLFQUUj+IwxeKONcyqFedxpZB5QS6PmiHcOCYY5FrYHbfKd0deWns0MKTc676hWsAipNVIuVNEki1oNV4eJvvekNJNbwVDoOsvGbfC+J84zDb0nNpPANiTryrzEgu8DTrJakpzDGkOm4q3yFXItC8fCqjUoQS4Z5w0Oi2kVYw0p6ftMUZkCEnQoEceZWiuRxhxvD2ifxnJPLrn/+z3P/ubHIs0E+j79yL8bs8NOP6I5Fbj+knD0y68Tfu/tT6dfPGfCdSEe/QIb8QY2Nfyh/WCG9+FwSzT/ccsH/ug/+hzf/Hv/CZ2onOGL/8P/Cb/82+GlauCnKmuRYaA8OuXwxprta475DtTuUwxcW7be/TmsnlWG5zNuG5FUbrfdt/UD1cyPdx/8zDfiXSdUNBZrjoVpyjirB2AqGCdYWXzTjeUAqf7b1VFHzoU4TkittCXS55AbFYMfNGs5TxOHq0v6wdOvNtxtp7x7PfLPfu8P+H8WzWWmNkSWOLX1Ka9//gs8fHif+/cfUHPm8vyczfEJIVSsXQ7wXYdznuOTO1jbMY4jcYoKQmui3mwBcqLahrUqzXZNfee1NXLWpjDXjFBwTvO3QwgY52nG4p1FaLrVxC2S8iWiqqqPXZxRn6fo75FLIk6RlDPGWd0G+2Wzi1Bv1nHiMU7IaWTcbyn3j1Va2nSbXE2hHzwP7gikEfbnmJp0K3sTJ+adbrkXynxBfaXNOLJRsFpswnjYc7h6wnT9gqtnH3L+7AnGBtargAVqK8SsfvdamxLxm0ZvGWtxzqgs23dIzpQSNZe7NqxzeK8y8YVlh7ONOVVq1S1XK5E0gXFOr4tUSinUrPJujfsCjKjsd4n7ii0vB/dKbsI4TaTSqEWgJEo15FSoLS+Z6rrxF3F0QbdzzlgO+y0YpcznnNW+UNSznbLC1UAlUuovL5SyBMC1pp711rRxiIU//NY3+MYffpfDmNntRuy9NTY4Lr/1Xd589RFHRxs2/YpqoTWnwyljyClyOOzZT5kwbDhaD3hTmectV9uZnCvWdYj1BL+i6wdCcIgJZFkxb17jPPfge9L4gsfvfsDhv/2/cz175jIxn7+Ne/A3ycnjr99j/ejrXJ//DpeX56xOTtikmS4EDoc9IgbnHKVCTDNzHGll5ux8x/roiNfvOurFjN18jiN7xdPLI147KRwuP+Qb3z+DNJOSQlQEON0M9P0J87zn+bMnvHj+PV48e8bZ+RUpZwbnVOlhDM4LeS7k2RH7ytHRGoeh6wPWwmSjzjKKoZXG6qgn50pa/AdWFOKWSiYWsM0QgjCniSnOjKXS9Y4QPKYYUiwErwoZY41GAspiOSjQXCXFQjzs2ZwMGAy5NmzQuLy6DLaaNGLJSLN4L3TGk1tZmAuOnGbmlCkRxEJqjd4anPM6rNIvh7cO7wwpVgyN3Cr7uMXjacZhfaC2iqmJkotyJUohVbUlNGkYUUZFSoJplSSaRqCbrYALnQI5e6twQOfxS3QYpdFSZc4HDEJnRXkfAs46nEBB6NyS4d4s3lVNoaiVeZooToee+i6i98a0xBIGY8kxM9em1Gsi3himmEm5cXTcQU3MuWC8ckfsApe8rU9nbb75nP2jV9h9/qOP/Wln79KEmB3nF2t43uHij/761Tee/WrP557eh8fPP5V+5XAxw+t/jtr0pfEOu49leF9uVUVw23j/5PXgHt/6X97h1//7/3s6Wb/88D/9d/8T/nd/7d/lv3vrS3z1f72FZ2c/+muJQfqOeu+U8Y0jdq87pvtCHj7FPm8U5idZMAlMAjvD3T+cCc/3f9E/2m19kkt+PFjGZ74RF2vonNXtaS6kBk2zy15mMYsoaVwHdLrpbcYQ40zNGYNuHXMpzKmSitAPvXpZUwQa427L/qpjPRxxcrxmfbTCnF3pIR6VWt8/WtFvjnjw5heQVjm5e4pxwovHT0gx03cDbVX1XuUcrVaGzQlHx6cc9lvee+ddtrsDrhvwnaOhB0tXHaG3WAciKsEuVWhVN5ZiHGFYcXTUYdoSqYVKLyttoYcXTAVjskKMaqHGTGvqR08VnCjtvJZCLOp5Rm4kmZUKeK9bz1wLtaj0s5oGZKbdNXN+jWIEhw49ujaRrnfYVpcts2CsW7a/ClaTChhLtZ4ijmwDscJh3DNeP2W+fM54dUbcb4nzDAJxmpgPe/qVJaYCLUMtxKyH/JtmtaGPtw9OY8xawztDqSrRVfaZUqpTbVTqIlvXLPCUEt5bgnO0xuLh14N8BbBmIcQvv1crSihHPfvOWcQ6yOrXPUyJOWm++DSO9Aay0cmaetF1GFAQcmnUlpGqfIBSin7M6OGttrzQ1Bs0wThDQzfXLWfNdO4BQR+LBs6qZPz3v/EH/NHbTzBffIXNlJifXHH8K1+AFNn+5nu89/gpb1qLtML+AD54rPWcHJ8yTVuup3MiHhs823zAhoGjo1NO7gTK4gVPaYbNA+ZwyrW7x9H9h7z9wWOmx+9z8f3fZYwdL773L4ibr2KkUHZv0732d3DdU+L1+3QP/yo8/y0MX2YOb/DO29/lq3/5r/P06VPu3r9LyxrpFoKn1EyKE2fPz7m+3hNTI+eIFXjYbYnbCx6/s2W3m5m6jr7zxFS4/+A+Jc3s9juury55560nPD97zvVux2FaHjNjMFYI1uClYgwEKxhZoIdOI/mm/bhEgnlqYcnUNsyTxrWFg0bBVWtYb9b0wVJbQarH2UxMiRgtOQnOeN0o2EVabgzBen2xGH1+eyAV6PuVPm9LxZjCZBKXuwnP0mSK4L0h5sg8qyRerKG1rDFnYggu4KURa2afZqbcGEKP7xuYRMPTh445FjxOn9+mErN263PU/4aweck3yKmQ6kyrReXzpbDuLVPMlLp45ikYHKlo4kCaZ4wzquQomZaNDj2MWnKcsZQUSTEydJ7mHbtDpguGlGb2ORJcAIQ+BCQnSq0Y6zBW6IOn1sp+nGgYutVK1R57aCLEvAzNYlreOCylmGXgqQkTqZSFL6L0eVmGvc02VV3dxtJ8akty4eG/umS+c4d0pIPhw9XAvc3h5edM2fH88Qlm6zBJfqwG/OM1nzbOf/Uu9/4/e9pu93P+Df78y14ekLr5SDXwM9bLKLFtJVx8LMN7mm+J5j9r+cB3/uNj/ugf/af4jzXhAH85DPxnb/7XpM//c/7B3/+PuP9/XRpx55Bh0Mc/ZwgeTo+ZXz9h+0ZQwNqGj6UMfAqvTgOzkM1NUquD/DGByo/hSLmt2/qx6jPfiHfBv8yZdsYiviq4yi5QLRFS1kxdmvK22uJrLikpXddAToW56NbIBQ8tM+9mrNGscVrlcL1lv77m9ME9Xnv1Vaap8PjxU3LJ3FkFet9xcu8e837H0fEJzln219eY4PC1Eksi14xrHlM1G/zk9ARpjRA8Z88fc3H5gpIGUnLgLFLVq5lpTHGiNs3v9SEQug4rMCfVKDc93VKLgoPsQgMXXbHSqiEWbbhvqOWIoS0bcm36dHNaqzZ4YgxirXois2YJh069nuoWX6LKqEzbCz1kDwZbWWTeCYuSn2UhjJfWAEMVT7UdxQamUjkcDkzXZ4yXZ1yfPWHeXunm3pslyiiRc9FYt5wprZFywlc9eJekVOPS2sd+PnBeV3jWKoitVW1gGyqvbbWRawWKmthRQnnfe8YpLvFbhlLNSyVAqU3p8iywuNaw1uKMRpGVEolxxvqgUUdlJs+RcVZYGyL40OG8x/igICwaLSdyFgqGRMUu9PJG1eYmF6wUTCuUltUzLwaMSuBz1ddCzdrk1K4pqbpWUipKl44TLgwMRwPz66f0p56TcoJ945jtsy33j46xv9+gZsbZsFpvGDbHWGf54MO36fPE0ck9VncfcnRyl773i2KgkatwPvfY0zfI/pjzKcL2XR6/+ztcv/ubXF6N1NWrtOffoHvlV4j9a6SLd9i8+Q8p+8ek6w/Z3P/r1Pf+BebOL7F++GXk8F3s3V/l7P33OH3+jJPjU+KHjwneMh0OSiIvGkc1HiZqbdw9vcNms+bOnbuICI8/fEyeDKvQ0XUe7xr73RXf+aP3ePrsKc/OLtltD8x6m8BYWYAEehaUqrR83wSXwInQjEIgbyLopKpCIZbEquswzlFTJudCqYLvPMfHx3qdl+ebC8pOkOZUIWEEP4Crllog5bzI1SsimhNfcoIgjHEm2A5yZZ4TlEqzBmt00w7LYCm1hcrvNLIs5SWBQTfrwQW9v8VIoeIIbLzlaOjpOkcrjXFS+b/vAsELh8NETAUnTZUBLzPlNQlgQKgUVaa4oJss18hNvdTLWHRhJ4CtjTEmUjH4blGvJCWrOwspakpBEIM0zRIfU8KIoXOOw34i1sJq1WGWAWRrSvxvVcGMfTcQnOHyekfwGqtoWiOlAlYQqdicqRhSVVDd4D1lTpwdZvre0wfw1dFQW4u1js4L8zxSs0Y0WmN/Ye9/t/XzL3O159FvrPjwv9dRXcO+8DzuTzg+OpCLZff2CWH3M5zSBbafN9wLnzDi2Y9Zst1jUqN0P91jIBXsrI1392LGvbjN8P5zq5wJ31hR/1FlOdz8iTII430h/sqX2b8SGO8b0hFs3mvY1Ni/YojHC2DtU+rzpul7+M3G2yRtvP/MZMKbZJZbOfpt/Yz1mW/Er3czBsEtsmnntAFrTRb6cqHWjAFqYSFWu4VerVvjgsKEWtHtMGJ0w5Mb4oRc9UUb4wT+nH5Yc//RPbqh4+io4/zZc8gVe3zMfhpZrdZYZ9mdX2KDxVmPWXmsN7SF1A1oDJgoe3ieDjx9+j7b6wusCHGOWNexWq/pbafQtmW2AFBzJsuskWBNM9HnVOncTQOhHp2SZz30NhBT9ZAoogd8Ed2MiW5Ray1QG7kUBI0iyqlgqjaZZdmcS8ogFSsaE5SSNrXj7opxipTBaZ714ktuohzwZhzFdmQTiE09xuPFC/bnz7g+e5/x+prVak0tif3hoJFd1Jd09hijysCNUtj1OivxvpXMnLNu1jRJSVUQRkjTRDLaOKeUEWORZnDO6qZYhOAMwVtgGcYIzDGTcsENLMOJxZS9kOOsgbzIvTVaDGquXO/2lJyotSBJSevGCGIDwyrQinpSrfO0mrFGbQYtRUqKtGZo1pAr0BqpahPexFBqw92oHJTvpnA4IwpzyyqVn0vCSmMjBrGioLtaSaVwfn5GzoW16Rm/f8X8tQ1f+QcdH/x/32L85kh7rbCRY+6/9ib37tzj+PQIZxWedzOYWQ090mB3fU3kERy/xhk9w527vPjwXabvfovde7/FRT7l8p3fQrqH2NUdxsu3GdZfoTt9lcPF2/QPf4W6/6/Ih3Pcvb9MOf8m9tW/zOuvf4laP2A6+SqX3/1/MPgnyN2v8ezF2xyfnHJxeUVJkWkcAcsw9Jwen/Do3gOsgRAcQ+/o+oCxjunemnk+cHVxwTvffcazFxdcbQ86fENnGdaJZrwvKUa56LXLVKSqf/mGND8VoSSDdwXThCqVECypNHxRW0AXhCSWo7CiFUe/6jC24MSqd3yK5FwwHo6GjhRgPx04XM344HUYUPQ51UrGb3qmGLXpzRWpjlQzc1IwY2kNB/RhiWdcYsSKLTgTyHOhGFWIpBpxtl9gfhFZIIyxwsYP9J3HWogp0uKNFN9S6sz1NmMsSq93VodETa021gpTzswlkUpSm8mSe++MwXlPEMOUItNhIjVZeBfL1n9lMaYyTRnvrUrhVYrCmPZsD4l1N6iMpOg9yErDWs/pqqdIVUl6rpQiiHHEKVHTxL4W9tZBE7xTdY/eLxpSG2NKTHPC2YBpDlsbh13GWs/R2mFkid2zjt4bjFE/eSyVORW2+xnfe4L9zL/tfuar//4ZJ6++ysXXBDsD3x+4dgMAP0GS2Z9a6w8a7epHk9g/idVixI8/fiMuBdz8sQzvFzu43t1meP95lxgu/r2v8b/5H/9nHGqis3/64Gf+WzveO1n/QKN9+TVgMcp9GuvjcnMbQfKPaLx/4C/D5Zc93b0TVk8T/mpCcr1tym/rp6rP/InA6DJQdSStURK6qQmCCaKRNbr6xQezbIgbiGYkz3NbNCie0BvyDQkbdEtiLYIhi2J095cXnPUdw9GG0+Njprt3GA8jc8zkmhjMwLAKzNNBvenJ451meEtNzNOIsZZhWNNMo4pOKa8uLyjF4fyGZnqs6+hC99LfTilURP2S0jROqzUwggtWt0Algxis6MYtRwGsNt9NlsNz0aasZKo1WAdGMqbp1rrKAoRLmZQzgkbyiFRqSowlU7zFiSVLBok0a2kF5gjX11vS6T2aEWIzVDeQTCBiiSmyv3rB9sVT9i+e0HJk6Dqmcc9uvyXmRMxRYWq10GpRYnnUn6O2qlF1JS+xXRDzjEsOli1jKYXWBCsO6xwpzSqxRxvxOEdcCAxBc8Oh4cRgbaPWrFRra2k1E7PCz0oqWAyVtsDpdFhaGy8tAEpRz0zTiHOBvu/RZbUOhAT1kbfamErGefWZT3Mi5aR0+MZHjX6ttKwNVhOjW9olI5zlOd8qyM1BpuqmNMWo2/0GeY7UppFNpRbmOFFixoUTvvzVVzg5PcG+f8mz717xnbcOlAthLSfYC40GOz495vhkTWuJacys1mvu3Tvh++8846Le5eTNvw7divPLK/zFW7z/zodcP/4OL549Qe7+JaYP/wh/+lW6e1+jXXwXt/5LmONHyPiY4zd+lf7xf8NoKvb1v0q9fht77x9jy4fI9bdxD75O/fCfU/xD7J1fIl98E/PKr7HbCU/e/x7ro7uklOl8j7WW05MNd45VJZISWAu1OWKaOFxc8e1v/j7vffCMac6kWik0BTlagwg4Z6A0QmdIMUGD0DuN9KqVWoV+sMypEWMm1IqrQlcs25i4f7ym6xeIHpndNFGbw6Ak7tA3Yj1Qp4Y1ng5HFUMplWQy1/tKIlGbLNFhwn434Tuv/mbjiDUzlUyrGiOWc1oUMgHvBRcCRQopV1oVjK2Uql3DlJQfAQ0vlmDWqoapRfkCNIwJHPme3jswytsoDaRWjFjCqme3uwbrGdYdVhrSLOM0Y4xVCKR1tJqZZvWguyWSMZdEFcg5QTOkVqimYXGIaLJFrqroaXiMX8LRasaIocRJ7R/GKfm/NawJBOdItdKvDK1UgrNIKaRWmWJCM84EPwRErDIpJHO9mzg9touiSO1F0ixdJzgMViw5FuZWGQZD3xn224XBYQxTnBFpbI5WNDKlqmqmlsJUbg9rn/pqjbu/+YLxwQOme3o9zc+hAQelMB+/M4Gxn05gWymEq8x0Gn7o/36Z4X2ZNcP7fEfb7n82KNht/eQVPE3g//g//R/xP/sPhLf+/f/0T/1UY9pnRoptouZ6m7TYHn/Kql4YHwjTvQ47BcK24fcVdyjYMWMP8bYxv60fqz7zjXjJgNUmTMRS6kK5jolc6kL2dciy5hIg57JsGlVijAhiwbSbBtzoNsYKtek/GtSdyRnOzs5xXU955RHd0LPZrJheXBGsw3nD2bPnBGvxwbM+uoMtGfaZvg8YY5jnSKuVfljRDwO73YFYhLv3H3J1fokxlr7vVNIpaAySNFy1YA11aQalNUQcxnuF02XdEHehg1xouVJF/c8N3YhKRu9OtS7e8UjFELoOsUralrZI+8VSRRapvwLfxnEkzZoH3Iyl6wc619GtHD501HlkpKNIxz6PHM7PGS/OmLZn5P2Ww35PzJpNLU44iCEusV9pkfAOXaBkzTdurUEpNNC891qpRX2upUGZI951SkivjZwbwRvECDHNpEWKLstQpbXGNGuMVM1Ft3SOZattKCkDlRwTcc7UUpinGTH6fBDFmlNKJFcB55T4biy2H8ipIqIxTOo/ByOVFCOINtMheKW1p6SRYDlpM5KSSviXDXajgTWUVlWVYRytFewyoU6tLGyErJC7VrTZbypTbyJM00iphecvrhBjGPoVnRNy3FPmPeSZ9uyaeT5gvUZLHa4iRgzXV1sePbgP4tis1xjnCP2aR+4B7zw+551v/v+YPvgd9u0RF8++Qy4Oe+8LpOm7+FzxJ28ihw+xD/8xh8u3qbvHHL/617nfvocdBuKDL8L191k//DuMh7dIV28z3PllzPlvcLl9g+3+GHv92/Dob1N3b+F232d9/ApdGClxwruOzaqD1nDWU1kR+mO6LpPygTjvOPvwCU+ePuH9D5+rN99DiwCC7dSjIouiwXgFSvi+xxiFoYU+MAwOYxsimcsXIxXDHBsxN2bbGPrAnBLjbFRhMyeMMVhXF8J9A+kJ1oDVbXmVRqojcY6Y7BiGXq8xQjE6QHGdQUzBOKBW4mRoRTfLOWUKTb3WFoVUBkOtia5bKyV/UbZgDK4paK9VVXeUHKEKrhOccRhjGFPW13WZSQkaFg9MJbP2gemwX6CQmVq8Jg0YYXCW8+mAd45CYdpP1EVZZFulFlSpIY0ZPmJYAIr+h64PrOyamhIWw5RG5U/Qln8Mxln6JowpIoNn1QeccZRx1kSCJTesLPc2sXrf6ryqUGot+GqpzjIcdaSqg7FmjCqonCqFsHo/CJ3Hd4ZSIikmjEP5GUkBnc0U5LCwMIaglidRn/htffpLxplH/3LP+//OmvJzDIdvpnH+yx0PDq9g33kK8/xz+9q/qPKXE7wZFq/tH8vwvtjS9iMkNc/ftip/QTXP3P2//SGI4QvtK7z17+34ot/8iU+7rhPztuOHj1U+HSVVB1wmgRv/pN/7Z6lmIK+EvFJlJ81hcuDuH1r8+eFH/v3buq3PfCMOKkG23i45x5W8bMYHqlKGjSwHVkO2QLPkVMhl2WrWiqkFZx3ShNaURByzvNwwl6Sy6CqWNk08e/8Dcq4cnZ7iuxUP7gegcbndst/tOEhltT6lW+uB2Awb/HqFoTIfduyjYXN0h4LnyXVgyo6jzRE1Z6YxkXPGOo8zFuc9VrQxM4uEU2jarC1ycmstLVtKzbRFqlyMqJwdHdy1hfStWeDq6a21YjxUMqZqs16byrBTSeRYONRC6LQ5QTx9GPB9oPMB79Rn6p3BWzicvc876YrdxVOuz55hjWHowtKYai72HLMevsUQSyZX3WK3WtWHbpeYuaJRba0UjclolVKSNuataY53rETXY5yl1Mpq3evHc9KNt9pudZgglmIscZ40rk2g1UyOCt3CVEqr1FyX+DMIwZNzJCbBFtEtNAu0LgRElka4ZErKxHnGWkMuGlWm8vyqMmdjELJu7WuF0vAGakkk1Ktub/zGAs0smfCL8uEmSF0beL2OZjE6OGt1S+7V7+sE5pTY766ZSsM4cGT2V8+U5J4Sc2rUlujXx5SaGA9XmvUeHBhHLIV+GDBUxsOO3fnI8eldpqff5q3/7tcZV19hOnvC6d1AOPki9fkfYNwRpbtHvniX/nN/m/LBf8Xu+jn27i8Rz7+FffR1Vqxp0ztshy9x+cH/C9wzwunX4Pk3SSevYqWjvvg9wsnnWO+vOPbPOfnaV+m9oe97ahkwiuHDG00IiPPMOF5jzJ4nH7zD+0+ec7nfMx10uFFbo3lVHdhgkNLwvVXLQQFrKr43iNE4qoajlkKTRKqRVjPjVSHOBqqqFKDRxFIKXBwiuzmzCoZN1xG6AevVTtIMiHMLybuy306EfkXFM6WEsFgmvFmekwKxYk3T9AOEWDJYR28rMc3q7xaPd0KTQtfbpck2pGnC34D7mqXzHSklnHXErJ51HxzON1JTdZCRineCk0KrQhBPaYVYCtZbppYJBNZ+RSmFw37EGIc1DZJG+Y2HCessc230oWPoekqOOtQL2pRf7XUT6EQba2nqg68pEpzDGzjEqMszsg4JLFTJ+rm1cXS8wVtDyonSioItl/vgYXdAxOFdR/CGrrOaLtESqRZiSZC0WW6tUUUHYwrcs6QGmIa3BkqiLoT3NOtQTUQQZ9mshwWWWElFN+ree81H57YR/6yUf3zBvW/0PPtV89IW9jOXwP51mE+PePjbPavfefdTlytuz645frsnvLjN8P7ElRgFrB2tyQ+OmR517F6x/F+uf4X/xb0/mRv+v3r6j/BPP2W8ggbmhnKeP/J8/0JKdFu++1zgzuWkZ7Hbuq0/oz6zjXhbJCGpNoIT5pSIc1GiNUt2c67M24gR8EF90rWoGky8+kFzLNSsWxckYazVQ7Y1ZMCi9GxKozalfqeskpTz52fMKXO0WbFaqRQZEUqOHA4T0zwzj3u6EHDeMk0TcdqTpgNH916h1Mz3v/0233/SOL17Hz++QyoKDCt5JM6CBN2ii/MLwbxCilRrEcmkWhisIFMhz4k5z9SSKLkyl6y5zdYixr70CddFcq0/rsXMhlYCxsB2t2NOlZygiaUfBvywBiu6+XWO2jI5Qs2JyTi8tYROmFslnj1HjGVatv7Dak2OE0b0Mc6pUUWlw+Mky8+weLDbkoe9SNOpkZoqzYjuxfLSwN4g0mqiFpimPSF0OK/RWillzfXOhbo04pIbYiIlZUpOeKP085ozrTUO80xrFdAGN+VCLf//9u42NoryXQP4NTO7sy/AdlsLWwotL4JwAEEBqVXRDzRWJL7FD4Q0hqjRoCWBSFDUKH4r0cRECRJPjPCNRv0LGgViw0sRU0BqCxQ4FaRajqFUgb7R3Z2dnft8mDKwdCtwhN2le/2SJrBzZ/vMzbQXz+7s81iIRA2YlkCH/c6X1+2GosTtRekMw170zp4BwYja24jFRUXECEO1LJjRsP1ijtsLRbXsFZ9Ne1VxRfom69EYNJfed1eDCrjsBfMM015pXtVc9meCFQtxK2avgN/3+Xh7FwB7/2LDMOy72k0TInFEjR5c6OyBAcDj8cPrGwKX7oPLMmCKwOtWEMz1QtV6oCg+RMOdEM3eA1RTFEQiEfz9VxsUCHp6e3CxOwwLGoYMy4XPpyMci0AbMhxG9xlIqAjRuAat43/hz5+E+LlfADEhwwphdTbDPfIBwNWMSOcJnAuE0HX6F0Rzg4goubDa6uEqKoFLEcQv/A+CwRHwK73wDonA652AYX4/3LrLvttBVxG+GMH5jr8RiZoYnpuH3osXcOZsO3oivfC5PNBMBWKYsOIAPCpgWX27IthXDjTA69PhclmIxsVe6M0ATMOCKIZ9Z415EZpLAzT794uIwDBgb7sFAKJAMe1Xt8IxC26fG9G4vSikz6dCFPtFJFUswCWIRO0XjWKxOHSXDiVmX+dev9f++AQsIGYhZto/D5rmRtyMI9xr78UNS+DSVJhxA71RA5qqwuvxwK27Ee6NoNfohWXZOzF4PV4oHh0xw77WIlrM3qPcMBCJC/weHZaqIhyLQnO7YFgG4qoLgILecAxu1Qe3ZiFqGHB5vfbnxAHEY71wu/0QURAJm1DdgGbF7RcpFRWa4oIJFZrbDZem2i+aKUAsbkExLcQVe2lHM2zAUhQougtW3EJM7Fu6LSsCDRYsy75LIR6zEFYMaC7NvvNHc8GvKxAzDo/bjbDE0RuJwmvad65YAhhRwdBh9qKJYSMM09Tg0nWIosKIR2GYMajQoIoKl0uH3veiZW80gkjM3uXAo6owTctek0L61hHpWxcCcXt7RyMeh71jhb2He8ywf6YURUG07x3xSxlF/86lPprR3pt3f/jV+u78Ssbf0IocJdT3jhhgudB/xXAFsDQl6UrifWuJ9v3lcn3cDZyZAtyBEIbUnbAXW71dnItAPdeOW/SvQTdIURQgPw+xwhxcLPAgkqvC9AOWGxDFBGDiv//zEPbMGY0H835DQOtFnnYR35+fjrp9/wUtEsnYF1CUmALNsN9IUk3YK53HYe/4kcZBRzUgBgPahdtzrQfHP/zuu12JqqbknOJhe9eLa2W9IoP0fwOnTp3CnXfeme5hEBER9XP69GmMHj063cO47THriYgoU10r6wftO+J5eXkAgNbWVuTk5KR5NJmjq6sLRUVFOH36NAKBQLqHk1HYm+TYl4GxN8mxLwMTEXR3d6OwsDDdQxkUmPUD489hcuzLwNib5NiX5NiXgV1v1g/aibiq2veA5eTk8OJIIhAIsC8DYG+SY18Gxt4kx74kxwnjzcOsvzb+HCbHvgyMvUmOfUmOfUnuerI+ySeWiIiIiIiIiOhW4USciIiIiIiIKIUG7UTc4/Fg9erV8Hg86R5KRmFfBsbeJMe+DIy9SY59oVThtTYw9iY59mVg7E1y7Ety7Mu/N2hXTSciIiIiIiLKRIP2HXEiIiIiIiKiTMSJOBEREREREVEKcSJORERERERElEKciBMRERERERGlECfiRERERERERCk0KCfi69atw9ixY+H1elFSUoIDBw6ke0g31Z49e/DEE0+gsLAQiqJgy5YtCcdFBO+++y5GjhwJn8+HsrIynDhxIqHm/PnzqKioQCAQQDAYxIsvvoienp6EmsOHD2Pu3Lnwer0oKirC+++/f6tP7V+pqqrCfffdh2HDhmHEiBF4+umn0dzcnFATiURQWVmJO+64A0OHDsWzzz6Ls2fPJtS0trZiwYIF8Pv9GDFiBFauXAnTNBNqdu/ejZkzZ8Lj8WDChAnYuHHjrT69f2X9+vWYPn06AoEAAoEASktLsW3bNud4tvblamvWrIGiKFi+fLnzWLb25r333oOiKAlfkydPdo5na18oszDvmffM+8uY9deHWX8Zsz7NZJCprq4WXdfl888/l6NHj8pLL70kwWBQzp49m+6h3TRbt26Vt99+W77++msBIJs3b044vmbNGsnJyZEtW7bIoUOH5Mknn5Rx48ZJOBx2ah577DGZMWOG7Nu3T3788UeZMGGCLFq0yDne2dkpoVBIKioqpKmpSTZt2iQ+n08+/fTTVJ3mDSsvL5cNGzZIU1OTNDY2yuOPPy7FxcXS09Pj1CxZskSKiopkx44dcvDgQbn//vvlgQcecI6bpinTpk2TsrIyaWhokK1bt0p+fr68+eabTs2pU6fE7/fLa6+9JseOHZO1a9eKpmmyffv2lJ7vjfj222/l+++/l19//VWam5vlrbfeErfbLU1NTSKSvX250oEDB2Ts2LEyffp0WbZsmfN4tvZm9erVMnXqVDlz5ozz9ddffznHs7UvlDmY98x75n0iZv21MesTMevTa9BNxOfMmSOVlZXO3+PxuBQWFkpVVVUaR3XrXB3MlmVJQUGBfPDBB85jHR0d4vF4ZNOmTSIicuzYMQEgP//8s1Ozbds2URRF/vzzTxER+eSTTyQ3N1ei0ahT88Ybb8ikSZNu8RndPO3t7QJAamtrRcTug9vtli+//NKpOX78uACQuro6EbH/06OqqrS1tTk169evl0Ag4PTi9ddfl6lTpyZ8r4ULF0p5efmtPqWbKjc3Vz777DP2RUS6u7tl4sSJUlNTI4888ogTztncm9WrV8uMGTOSHsvmvlDmYN4z7y9h3g+MWX8Zs74/Zn16Dapb0w3DQH19PcrKypzHVFVFWVkZ6urq0jiy1GlpaUFbW1tCD3JyclBSUuL0oK6uDsFgELNnz3ZqysrKoKoq9u/f79Q8/PDD0HXdqSkvL0dzczMuXLiQorP5dzo7OwEAeXl5AID6+nrEYrGE3kyePBnFxcUJvbn77rsRCoWcmvLycnR1deHo0aNOzZXPcanmdrnG4vE4qqurcfHiRZSWlrIvACorK7FgwYJ+48/23pw4cQKFhYUYP348Kioq0NraCoB9ofRj3jPvr8S8749Z3x+zPjlmffoMqon433//jXg8nnAxAEAoFEJbW1uaRpVal87zn3rQ1taGESNGJBx3uVzIy8tLqEn2HFd+j0xmWRaWL1+OBx98ENOmTQNgj1vXdQSDwYTaq3tzrfMeqKarqwvhcPhWnM5NceTIEQwdOhQejwdLlizB5s2bMWXKlKzvS3V1NX755RdUVVX1O5bNvSkpKcHGjRuxfft2rF+/Hi0tLZg7dy66u7uzui+UGZj3zPtLmPeJmPXJMeuTY9anlyvdAyC6FSorK9HU1IS9e/emeygZY9KkSWhsbERnZye++uorLF68GLW1tekeVlqdPn0ay5YtQ01NDbxeb7qHk1Hmz5/v/Hn69OkoKSnBmDFj8MUXX8Dn86VxZERElzHvEzHr+2PWD4xZn16D6h3x/Px8aJrWbzW/s2fPoqCgIE2jSq1L5/lPPSgoKEB7e3vCcdM0cf78+YSaZM9x5ffIVEuXLsV3332HXbt2YfTo0c7jBQUFMAwDHR0dCfVX9+Za5z1QTSAQyOhfWrquY8KECZg1axaqqqowY8YMfPTRR1ndl/r6erS3t2PmzJlwuVxwuVyora3Fxx9/DJfLhVAolLW9uVowGMRdd92FkydPZvU1Q5mBec+8B5j3yTDr+2PWXz9mfWoNqom4ruuYNWsWduzY4TxmWRZ27NiB0tLSNI4sdcaNG4eCgoKEHnR1dWH//v1OD0pLS9HR0YH6+nqnZufOnbAsCyUlJU7Nnj17EIvFnJqamhpMmjQJubm5KTqbGyMiWLp0KTZv3oydO3di3LhxCcdnzZoFt9ud0Jvm5ma0trYm9ObIkSMJ/3GpqalBIBDAlClTnJorn+NSze12jVmWhWg0mtV9mTdvHo4cOYLGxkbna/bs2aioqHD+nK29uVpPTw9+++03jBw5MquvGcoMzHvmPfP++jDrmfU3glmfYuleLe5mq66uFo/HIxs3bpRjx47Jyy+/LMFgMGE1v9tdd3e3NDQ0SENDgwCQDz/8UBoaGuSPP/4QEXs7k2AwKN98840cPnxYnnrqqaTbmdx7772yf/9+2bt3r0ycODFhO5OOjg4JhULy3HPPSVNTk1RXV4vf78/o7UxeeeUVycnJkd27dydsw9Db2+vULFmyRIqLi2Xnzp1y8OBBKS0tldLSUuf4pW0YHn30UWlsbJTt27fL8OHDk27DsHLlSjl+/LisW7cu47dhWLVqldTW1kpLS4scPnxYVq1aJYqiyA8//CAi2duXZK5cSVUke3uzYsUK2b17t7S0tMhPP/0kZWVlkp+fL+3t7SKSvX2hzMG8Z94z7xMx668fs97GrE+vQTcRFxFZu3atFBcXi67rMmfOHNm3b1+6h3RT7dq1SwD0+1q8eLGI2FuavPPOOxIKhcTj8ci8efOkubk54TnOnTsnixYtkqFDh0ogEJDnn39euru7E2oOHTokDz30kHg8Hhk1apSsWbMmVaf4/5KsJwBkw4YNTk04HJZXX31VcnNzxe/3yzPPPCNnzpxJeJ7ff/9d5s+fLz6fT/Lz82XFihUSi8USanbt2iX33HOP6Lou48ePT/gemeiFF16QMWPGiK7rMnz4cJk3b54TzCLZ25dkrg7nbO3NwoULZeTIkaLruowaNUoWLlwoJ0+edI5na18oszDvmffM+8uY9dePWW9j1qeXIiKSuvffiYiIiIiIiLLboPqMOBEREREREVGm40SciIiIiIiIKIU4ESciIiIiIiJKIU7EiYiIiIiIiFKIE3EiIiIiIiKiFOJEnIiIiIiIiCiFOBEnIiIiIiIiSiFOxImIiIiIiIhSiBNxIiIiIiIiohTiRJyIiIiIiIgohTgRJyIiIiIiIkqh/wNT3jMfJMBDFAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "idx = 11\n",
        "img = imageio.imread(image_list[idx])\n",
        "print(img.shape)\n",
        "\n",
        "mask = imageio.imread(mask_list[idx])\n",
        "\n",
        "print(mask.shape)\n",
        "\n",
        "fig, arr = plt.subplots(1, 2, figsize=(12, 10))\n",
        "arr[0].imshow(img)\n",
        "arr[0].set_title('Original Image')\n",
        "arr[1].imshow(mask)\n",
        "arr[1].set_title('Masked Image')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1t91coXylMG0"
      },
      "source": [
        "## For Extracting Filenames or full path from Directory "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from os.path import isfile, join\n",
        "from os import listdir\n",
        "\n",
        "\n",
        "class DataGen(Dataset):\n",
        "    def __init__(self, img_path, mask_path, X, mean, std, transform=None, patch=False):\n",
        "        \"\"\"\n",
        "        Custom dataset for image segmentation.\n",
        "\n",
        "        Args:\n",
        "            img_path (str): Path to the directory containing the images.\n",
        "            mask_path (str): Path to the directory containing the masks.\n",
        "            X (list): List of file names (without extensions) of the images/masks.\n",
        "            mean (tuple): Mean values for image normalization.\n",
        "            std (tuple): Standard deviation values for image normalization.\n",
        "            transform (albumentations.Compose, optional): Augmentation transform to apply to the images/masks.\n",
        "            patch (bool, optional): Whether to extract patches from images and masks.\n",
        "\n",
        "        \"\"\"\n",
        "        self.img_path = img_path\n",
        "        self.mask_path = mask_path\n",
        "        self.X = X\n",
        "        self.mean = mean\n",
        "        self.std = std\n",
        "        self.transform = transform\n",
        "        self.patches = patch\n",
        "\n",
        "    def __len__(self):\n",
        "        \"\"\"\n",
        "        Get the total number of samples in the dataset.\n",
        "\n",
        "        Returns:\n",
        "            int: The number of samples in the dataset.\n",
        "\n",
        "        \"\"\"\n",
        "        return len(self.X)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        \"\"\"\n",
        "        Get a sample from the dataset.\n",
        "\n",
        "        Args:\n",
        "            idx (int): Index of the sample to retrieve.\n",
        "\n",
        "        Returns:\n",
        "            tuple: A tuple containing the image and mask tensors.\n",
        "\n",
        "        \"\"\"\n",
        "        img = cv2.imread(join(self.img_path, self.X[idx] + \".jpg\"))\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "        mask = cv2.imread(join(self.mask_path, self.X[idx] + \".png\"), cv2.IMREAD_GRAYSCALE)\n",
        "\n",
        "        if self.transform is not None:\n",
        "            # Apply the augmentation transform\n",
        "            augmented = self.transform(image=img, mask=mask)\n",
        "            img = Image.fromarray(augmented[\"image\"])\n",
        "            mask = augmented[\"mask\"]\n",
        "\n",
        "        if self.transform is None:\n",
        "            img = Image.fromarray(img)\n",
        "\n",
        "        # Convert image to tensor and normalize\n",
        "        t = T.Compose([T.ToTensor(), T.Normalize(self.mean, self.std)])\n",
        "        img = t(img)\n",
        "        mask = torch.from_numpy(mask).long()\n",
        "\n",
        "        if self.patches:\n",
        "            img, mask = self.get_img_patches(img, mask)\n",
        "\n",
        "        return img, mask\n",
        "\n",
        "    def get_img_patches(self, img, mask):\n",
        "        \"\"\"\n",
        "        Split images into patches of size (512, 768).\n",
        "\n",
        "        Args:\n",
        "            img (torch.Tensor): Input image tensor.\n",
        "            mask (torch.Tensor): Input mask tensor.\n",
        "\n",
        "        Returns:\n",
        "            tuple: A tuple containing the image patches tensor and mask patches tensor.\n",
        "\n",
        "        \"\"\"\n",
        "        kh, kw = 512, 768  # Kernel size\n",
        "        dh, dw = 512, 768  # Strides\n",
        "\n",
        "        # Unfold the image into patches\n",
        "        img_patches = img.unfold(1, kh, dh).unfold(2, kw, dw)\n",
        "        img_patches = img_patches.contiguous().view(3, -1, kh, kw)\n",
        "        img_patches = img_patches.permute(1, 0, 2, 3)\n",
        "\n",
        "        # Unfold the mask into patches\n",
        "        mask_patches = mask.unfold(0, kh, dh).unfold(1, kw, dw)\n",
        "        mask_patches = mask_patches.contiguous().view(-1, kh, kw)\n",
        "\n",
        "        return img_patches, mask_patches\n",
        "\n",
        "\n",
        "\n",
        "class TestDataGen(Dataset):\n",
        "    \"\"\"\n",
        "    Custom dataset class for loading test data.\n",
        "\n",
        "    Args:\n",
        "        img_path (str): Path to the directory containing the input images.\n",
        "        mask_path (str): Path to the directory containing the corresponding masks.\n",
        "        X (list): List of file names (without extensions) of the images and masks.\n",
        "        transform (callable, optional): Optional transformations to apply to the images and masks.\n",
        "\n",
        "    Returns:\n",
        "        img (PIL.Image): Input image.\n",
        "        mask (torch.Tensor): Corresponding mask.\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, img_path, mask_path, X, transform=None):\n",
        "        self.img_path = img_path\n",
        "        self.mask_path = mask_path\n",
        "        self.X = X\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        \"\"\"\n",
        "        Returns the total number of samples in the dataset.\n",
        "\n",
        "        Returns:\n",
        "            int: Number of samples in the dataset.\n",
        "\n",
        "        \"\"\"\n",
        "        return len(self.X)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        \"\"\"\n",
        "        Retrieves the image and mask at the given index.\n",
        "\n",
        "        Args:\n",
        "            idx (int): Index of the sample to retrieve.\n",
        "\n",
        "        Returns:\n",
        "            img (PIL.Image): Input image.\n",
        "            mask (torch.Tensor): Corresponding mask.\n",
        "\n",
        "        \"\"\"\n",
        "        img = cv2.imread(self.img_path + self.X[idx] + \".jpg\")\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "        mask = cv2.imread(self.mask_path + self.X[idx] + \".png\", cv2.IMREAD_GRAYSCALE)\n",
        "\n",
        "        if self.transform is not None:\n",
        "            aug = self.transform(image=img, mask=mask)\n",
        "            img = Image.fromarray(aug[\"image\"])\n",
        "            mask = aug[\"mask\"]\n",
        "\n",
        "        if self.transform is None:\n",
        "            img = Image.fromarray(img)\n",
        "\n",
        "        mask = torch.from_numpy(mask).long()\n",
        "\n",
        "        return img, mask"
      ],
      "metadata": {
        "id": "Y-hN72uQNBGz"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_image_id_df(root_img_path):\n",
        "    \"\"\"\n",
        "    Generate a DataFrame containing the image IDs.\n",
        "\n",
        "    Args:\n",
        "        root_img_path (str): Path to the directory containing the images.\n",
        "\n",
        "    Returns:\n",
        "        pd.DataFrame: A DataFrame containing the image IDs.\n",
        "\n",
        "    \"\"\"\n",
        "    name = []\n",
        "    filenames = [f for f in listdir(root_img_path) if isfile(join(root_img_path, f))]\n",
        "    for filename in filenames:\n",
        "        name.append(filename.split(\".\")[0])\n",
        "    return pd.DataFrame({\"id\": name}, index=np.arange(0, len(name)))"
      ],
      "metadata": {
        "id": "uE8hRhpxMzn9"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DGzMfwxalMG0",
        "outputId": "5057e738-0337-4873-bd37-b75123bf99fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "400\n"
          ]
        }
      ],
      "source": [
        "df = get_image_id_df(root_img_path)\n",
        "print(len(df))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "3E2Lmp7olMG0",
        "outputId": "fa004b54-1768-4056-fa21-ede2ce7f3897"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    id\n",
              "0  000\n",
              "1  001"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec92a2d7-88b8-499d-9a24-40c1b81b128b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec92a2d7-88b8-499d-9a24-40c1b81b128b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ec92a2d7-88b8-499d-9a24-40c1b81b128b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ec92a2d7-88b8-499d-9a24-40c1b81b128b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "df.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zX_UIG12lMG0",
        "outputId": "0b22a571-ecdd-4fff-adec-39ed51ab0d9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Size  306\n",
            "Test Size  40\n",
            "Val Size  54\n"
          ]
        }
      ],
      "source": [
        "X_train_and_val, X_test = train_test_split(df[\"id\"].values, test_size = 0.1, random_state=19)\n",
        "X_train, X_val = train_test_split(X_train_and_val, test_size=0.15, random_state=19)\n",
        "\n",
        "print('Train Size ', len(X_train))\n",
        "print('Test Size ', len(X_test))\n",
        "print('Val Size ', len(X_val))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTpiRs8_lMG1"
      },
      "source": [
        "## Execute all DataLoader methods"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "mtXXppbGlMG1"
      },
      "outputs": [],
      "source": [
        "mean=[0.485, 0.456, 0.406]\n",
        "std=[0.229, 0.224, 0.225]\n",
        "\n",
        "t_train = A.Compose([A.Resize(704, 1056, interpolation=cv2.INTER_NEAREST), A.HorizontalFlip(), A.GridDistortion(p=0.2) ])\n",
        "t_val = A.Compose([A.Resize(704, 1056, interpolation=cv2.INTER_NEAREST), A.HorizontalFlip(), A.GridDistortion(p=0.2) ])\n",
        "\n",
        "# Bring Datagenerator\n",
        "train_set = DataGen(root_img_path, root_mask_path, X_train, mean, std, t_train, patch=False )\n",
        "val_set = DataGen(root_img_path, root_mask_path, X_val, mean, std, t_val, patch=False )\n",
        "\n",
        "batch_size = 3\n",
        "\n",
        "train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n",
        "val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Download Pretrained Model"
      ],
      "metadata": {
        "id": "1C2f2gUrhnOp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eLUSFMnYbfrq"
      },
      "outputs": [],
      "source": [
        "import ssl\n",
        "ssl._create_default_https_context = ssl._create_unverified_context"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DgT5V5XMlMG1",
        "outputId": "333621ac-4806-473b-8ceb-a6e86ea5e81f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth\" to /root/.cache/torch/hub/checkpoints/inceptionv4-8e4777a0.pth\n",
            "100%|██████████| 163M/163M [08:00<00:00, 356kB/s]\n"
          ]
        }
      ],
      "source": [
        "model = smp.Unet('inceptionv4', encoder_weights = 'imagenet', classes=23, activation = None, encoder_depth=5, decoder_channels=[256, 128, 64, 32, 16] )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4hyG0mVklMG1",
        "outputId": "4a66a0ef-fa60-4d17-8751-b0de1e99e629",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unet(\n",
              "  (encoder): InceptionV4Encoder(\n",
              "    (features): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "      )\n",
              "      (2): BasicConv2d(\n",
              "        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "      )\n",
              "      (3): Mixed_3a(\n",
              "        (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=(1, 1), dilation=1, ceil_mode=False)\n",
              "        (conv): BasicConv2d(\n",
              "          (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (4): Mixed_4a(\n",
              "        (branch0): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(160, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(160, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 64, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(64, 64, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (5): Mixed_5a(\n",
              "        (conv): BasicConv2d(\n",
              "          (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=(1, 1), dilation=1, ceil_mode=False)\n",
              "      )\n",
              "      (6): Inception_A(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (7): Inception_A(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (8): Inception_A(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (9): Inception_A(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (10): Reduction_A(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): MaxPool2d(kernel_size=3, stride=2, padding=(1, 1), dilation=1, ceil_mode=False)\n",
              "      )\n",
              "      (11): Inception_B(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (4): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (12): Inception_B(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (4): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (13): Inception_B(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (4): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (14): Inception_B(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (4): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (15): Inception_B(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (4): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (16): Inception_B(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (4): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (17): Inception_B(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (4): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (18): Reduction_B(\n",
              "        (branch0): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(256, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(256, 320, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): MaxPool2d(kernel_size=3, stride=2, padding=(1, 1), dilation=1, ceil_mode=False)\n",
              "      )\n",
              "      (19): Inception_C(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_1a): BasicConv2d(\n",
              "          (conv): Conv2d(384, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_1b): BasicConv2d(\n",
              "          (conv): Conv2d(384, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_1): BasicConv2d(\n",
              "          (conv): Conv2d(384, 448, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_2): BasicConv2d(\n",
              "          (conv): Conv2d(448, 512, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_3a): BasicConv2d(\n",
              "          (conv): Conv2d(512, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_3b): BasicConv2d(\n",
              "          (conv): Conv2d(512, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (20): Inception_C(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_1a): BasicConv2d(\n",
              "          (conv): Conv2d(384, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_1b): BasicConv2d(\n",
              "          (conv): Conv2d(384, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_1): BasicConv2d(\n",
              "          (conv): Conv2d(384, 448, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_2): BasicConv2d(\n",
              "          (conv): Conv2d(448, 512, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_3a): BasicConv2d(\n",
              "          (conv): Conv2d(512, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_3b): BasicConv2d(\n",
              "          (conv): Conv2d(512, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (21): Inception_C(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_1a): BasicConv2d(\n",
              "          (conv): Conv2d(384, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_1b): BasicConv2d(\n",
              "          (conv): Conv2d(384, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_1): BasicConv2d(\n",
              "          (conv): Conv2d(384, 448, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_2): BasicConv2d(\n",
              "          (conv): Conv2d(448, 512, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_3a): BasicConv2d(\n",
              "          (conv): Conv2d(512, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_3b): BasicConv2d(\n",
              "          (conv): Conv2d(512, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (avg_pool): AvgPool2d(kernel_size=8, stride=8, padding=0)\n",
              "  )\n",
              "  (decoder): UnetDecoder(\n",
              "    (center): Identity()\n",
              "    (blocks): ModuleList(\n",
              "      (0): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(2560, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "      (1): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(640, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "      (2): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(320, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "      (3): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "      (4): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (segmentation_head): SegmentationHead(\n",
              "    (0): Conv2d(16, 23, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): Identity()\n",
              "    (2): Activation(\n",
              "      (activation): Identity()\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "model"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the previously saved Model"
      ],
      "metadata": {
        "id": "b0zhHEY1htGB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = torch.load(\"./drive/MyDrive/Pretrained_Models/aerial-drone-image-segmentation.pt\")\n",
        "model.eval()"
      ],
      "metadata": {
        "id": "Jnat-Fc0hwqJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "998eb5a5-4fdd-4b55-b26c-215aaf3fa7c6"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unet(\n",
              "  (encoder): InceptionV4Encoder(\n",
              "    (features): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "      )\n",
              "      (2): BasicConv2d(\n",
              "        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "      )\n",
              "      (3): Mixed_3a(\n",
              "        (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=(1, 1), dilation=1, ceil_mode=False)\n",
              "        (conv): BasicConv2d(\n",
              "          (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (4): Mixed_4a(\n",
              "        (branch0): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(160, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(160, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 64, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(64, 64, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (5): Mixed_5a(\n",
              "        (conv): BasicConv2d(\n",
              "          (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=(1, 1), dilation=1, ceil_mode=False)\n",
              "      )\n",
              "      (6): Inception_A(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (7): Inception_A(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (8): Inception_A(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (9): Inception_A(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (10): Reduction_A(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): MaxPool2d(kernel_size=3, stride=2, padding=(1, 1), dilation=1, ceil_mode=False)\n",
              "      )\n",
              "      (11): Inception_B(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (4): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (12): Inception_B(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (4): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (13): Inception_B(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (4): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (14): Inception_B(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (4): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (15): Inception_B(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (4): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (16): Inception_B(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (4): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (17): Inception_B(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (4): BasicConv2d(\n",
              "            (conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (18): Reduction_B(\n",
              "        (branch0): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch1): Sequential(\n",
              "          (0): BasicConv2d(\n",
              "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(256, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): BasicConv2d(\n",
              "            (conv): Conv2d(256, 320, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "            (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (3): BasicConv2d(\n",
              "            (conv): Conv2d(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (branch2): MaxPool2d(kernel_size=3, stride=2, padding=(1, 1), dilation=1, ceil_mode=False)\n",
              "      )\n",
              "      (19): Inception_C(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_1a): BasicConv2d(\n",
              "          (conv): Conv2d(384, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_1b): BasicConv2d(\n",
              "          (conv): Conv2d(384, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_1): BasicConv2d(\n",
              "          (conv): Conv2d(384, 448, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_2): BasicConv2d(\n",
              "          (conv): Conv2d(448, 512, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_3a): BasicConv2d(\n",
              "          (conv): Conv2d(512, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_3b): BasicConv2d(\n",
              "          (conv): Conv2d(512, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (20): Inception_C(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_1a): BasicConv2d(\n",
              "          (conv): Conv2d(384, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_1b): BasicConv2d(\n",
              "          (conv): Conv2d(384, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_1): BasicConv2d(\n",
              "          (conv): Conv2d(384, 448, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_2): BasicConv2d(\n",
              "          (conv): Conv2d(448, 512, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_3a): BasicConv2d(\n",
              "          (conv): Conv2d(512, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_3b): BasicConv2d(\n",
              "          (conv): Conv2d(512, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (21): Inception_C(\n",
              "        (branch0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_1a): BasicConv2d(\n",
              "          (conv): Conv2d(384, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch1_1b): BasicConv2d(\n",
              "          (conv): Conv2d(384, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_0): BasicConv2d(\n",
              "          (conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_1): BasicConv2d(\n",
              "          (conv): Conv2d(384, 448, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_2): BasicConv2d(\n",
              "          (conv): Conv2d(448, 512, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_3a): BasicConv2d(\n",
              "          (conv): Conv2d(512, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch2_3b): BasicConv2d(\n",
              "          (conv): Conv2d(512, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (branch3): Sequential(\n",
              "          (0): AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
              "          (1): BasicConv2d(\n",
              "            (conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (avg_pool): AvgPool2d(kernel_size=8, stride=8, padding=0)\n",
              "  )\n",
              "  (decoder): UnetDecoder(\n",
              "    (center): Identity()\n",
              "    (blocks): ModuleList(\n",
              "      (0): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(2560, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "      (1): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(640, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "      (2): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(320, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "      (3): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "      (4): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (segmentation_head): SegmentationHead(\n",
              "    (0): Conv2d(16, 23, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): Identity()\n",
              "    (2): Activation(\n",
              "      (activation): Identity()\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bZusCg9_lMG2"
      },
      "source": [
        "<div style=\"background: linear-gradient(45deg, #FFC300, #FF5733, #C70039, #900C3F); padding: 10px; border-radius: 5px; display: flex; align-items: center;\">\n",
        "    <h1 style=\"font-weight: bold; color: white; margin: 0 auto;\"> Training  </h1>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_lr(optimizer):\n",
        "    for param_group in optimizer.param_groups:\n",
        "        return param_group[\"lr\"]\n",
        "\n",
        "\n",
        "def train(\n",
        "    epochs,\n",
        "    model,\n",
        "    train_loader,\n",
        "    val_loader,\n",
        "    criterion,\n",
        "    optimizer,\n",
        "    scheduler,\n",
        "    patch=False,\n",
        "):\n",
        "    torch.cuda.empty_cache()\n",
        "    losses_train = []\n",
        "    losses_test = []\n",
        "    val_iou = []\n",
        "    val_acc = []\n",
        "    train_iou = []\n",
        "    train_acc = []\n",
        "    lrs = []\n",
        "    min_loss = np.inf\n",
        "    decreases = 1\n",
        "    num_of_times_loss_not_improving = 0\n",
        "\n",
        "    model.to(device)\n",
        "    fit_time = time.time()\n",
        "    for epoch in range(epochs):\n",
        "        start_time = time.time()\n",
        "        running_loss = 0\n",
        "        iou_score = 0\n",
        "        accuracy = 0\n",
        "        # training Loop\n",
        "        model.train()\n",
        "        for i, data in enumerate(tqdm(train_loader)):\n",
        "            image_tiles, mask_tiles = data\n",
        "            if patch:\n",
        "                batch_size, n_tiles, channel, height, width = image_tiles.size()\n",
        "                image_tiles = image_tiles.view(-1, channel, height, width)\n",
        "                mask_tiles = mask_tiles.view(-1, channel, height, width)\n",
        "\n",
        "            image = image_tiles.to(device)\n",
        "            mask = mask_tiles.to(device)\n",
        "\n",
        "            # Forward Propagation\n",
        "            predicted_image = model(image)\n",
        "            loss = criterion(predicted_image, mask)\n",
        "\n",
        "            # Metric to do Evaluation\n",
        "            iou_score += mean_iou(predicted_image, mask)\n",
        "            accuracy += pixel_accuracy(predicted_image, mask)\n",
        "\n",
        "            # Backward Propagation\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            lrs.append(get_lr(optimizer))\n",
        "            scheduler.step()\n",
        "\n",
        "            running_loss += loss.item()\n",
        "\n",
        "        else:\n",
        "            model.eval()\n",
        "            test_loss = 0\n",
        "            test_accuracy = 0\n",
        "            val_iou_score = 0\n",
        "\n",
        "            with torch.no_grad():\n",
        "                for i, data in enumerate(tqdm(val_loader)):\n",
        "                    image_tiles, mask_tiles = data\n",
        "\n",
        "                    if patch:\n",
        "                        batch_size, n_tiles, channel, height, width = image_tiles.size()\n",
        "                        image_tiles = image_tiles.view(-1, channel, height, width)\n",
        "                        mask_tiles = mask_tiles.view(-1, height, width)\n",
        "\n",
        "                    image = image_tiles.to(device)\n",
        "                    mask = mask_tiles.to(device)\n",
        "\n",
        "                    # Forward Propagation\n",
        "                    predicted_image = model(image)\n",
        "\n",
        "                    # Metric to do Evaluation\n",
        "                    val_iou_score += mean_iou(predicted_image, mask)\n",
        "                    test_accuracy += pixel_accuracy(predicted_image, mask)\n",
        "\n",
        "                    loss = criterion(predicted_image, mask)\n",
        "                    test_loss += loss.item()\n",
        "\n",
        "            # Mean IoU for each batch calculation\n",
        "            losses_train.append(running_loss / len(train_loader))\n",
        "            losses_test.append(test_loss / len(val_loader))\n",
        "\n",
        "            # Checking for Loss Decreases\n",
        "            if min_loss > (test_loss / len(val_loader)):\n",
        "                print(\n",
        "                    \"Loss Decreasing... {:.3f} >> {:.3f} \".format(\n",
        "                        min_loss, (test_loss / len(val_loader))\n",
        "                    )\n",
        "                )\n",
        "                min_loss = test_loss / len(val_loader)\n",
        "                decreases += 1\n",
        "                if decreases % 5 == 0:\n",
        "                    print(\"Saving Model as loss is decreasing..\")\n",
        "                    torch.save(\n",
        "                        model,\n",
        "                        \"Inception-v4_mIoU-{:.3f}.pt\".format(\n",
        "                            val_iou_score / len(val_loader)\n",
        "                        ),\n",
        "                    )\n",
        "\n",
        "                # If the Loss is NOT decreasing\n",
        "                if (test_loss / len(val_loader)) > min_loss:\n",
        "                    min_loss = test_loss / len(val_loader)\n",
        "                    print(\n",
        "                        f\"Loss Not Decreasing for {num_of_times_loss_not_improving} time\"\n",
        "                    )\n",
        "                    if num_of_times_loss_not_improving == 6:\n",
        "                        print(\n",
        "                            \"Loss not decreasing for 6 times, hence stopping Training\"\n",
        "                        )\n",
        "                        break\n",
        "\n",
        "                # Updating IoU and and Accuracy\n",
        "                train_iou.append(iou_score / len(train_loader))\n",
        "                train_acc.append(accuracy / len(train_loader))\n",
        "                val_iou.append(val_iou_score / len(val_loader))\n",
        "                val_acc.append(test_accuracy / len(val_loader))\n",
        "\n",
        "                print(\n",
        "                    \"Epoch:{}/{}..\".format(epoch + 1, epochs),\n",
        "                    \"Train Loss:{:.3f}..\".format(running_loss / len(train_loader)),\n",
        "                    \"Validation Loss: {:.3f}..\".format(test_loss / len(val_loader)),\n",
        "                    \"Train mean_iou:{:.3f}..\".format(iou_score / len(train_loader)),\n",
        "                    \"Validation mean_iou: {:.3f}..\".format(\n",
        "                        val_iou_score / len(val_loader)\n",
        "                    ),\n",
        "                    \"Train Acc:{:.3f}..\".format(accuracy / len(train_loader)),\n",
        "                    \"Val Acc:{:.3f}..\".format(test_accuracy / len(val_loader)),\n",
        "                    \"Time: {:.2f}m\".format((time.time() - start_time) / 60),\n",
        "                )\n",
        "\n",
        "    history = {\n",
        "        \"train_loss\": losses_train,\n",
        "        \"val_loss\": losses_test,\n",
        "        \"train_miou\": train_iou,\n",
        "        \"val_iou\": val_iou,\n",
        "        \"train_acc\": train_acc,\n",
        "        \"val_acc\": val_acc,\n",
        "        \"lrs\": lrs,\n",
        "    }\n",
        "    print(\"Total time: {:.2f} m\".format((time.time() - fit_time) / 60))\n",
        "    return history"
      ],
      "metadata": {
        "id": "0Y4s2-8ZNW8w"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cFL1FCWJlMG2"
      },
      "outputs": [],
      "source": [
        "max_lr = 1e-3\n",
        "epochs = 15\n",
        "weight_decay = 1e-4\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=max_lr, weight_decay=weight_decay )\n",
        "scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr, epochs=epochs, steps_per_epoch=len(train_loader))\n",
        "\n",
        "history = train(epochs, model, train_loader, val_loader, criterion, optimizer, scheduler)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "SafQ1BuGlMG2"
      },
      "outputs": [],
      "source": [
        "torch.save(model, \"./drive/MyDrive/Pretrained_Models/aerial-drone-image-segmentation.pt\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3k_NRexblMG2"
      },
      "source": [
        "## Execute Plotting Methods"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#### Some Plotting Function ####\n",
        "\n",
        "def plot_loss_vs_epoch(history):\n",
        "    \"\"\"\n",
        "    Plot the training and validation loss versus epochs.\n",
        "\n",
        "    Args:\n",
        "        history (dict): Dictionary containing the training history with keys 'val_loss' and 'train_loss'.\n",
        "\n",
        "    \"\"\"\n",
        "    plt.plot(history[\"val_loss\"], label=\"val_loss\", marker=\"o\")\n",
        "    plt.plot(history[\"train_loss\"], label=\"Train loss\", marker=\"o\")\n",
        "    plt.title(\"Loss per epoch\")\n",
        "    plt.ylabel(\"Loss\")\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.legend()\n",
        "    plt.grid()\n",
        "    plt.show()\n",
        "\n",
        "def plot_iou_score_vs_epoch(history):\n",
        "    \"\"\"\n",
        "    Plot the training and validation mean IoU scores versus epochs.\n",
        "\n",
        "    Args:\n",
        "        history (dict): Dictionary containing the training history with keys 'train_miou' and 'val_miou'.\n",
        "\n",
        "    \"\"\"\n",
        "    plt.plot(history[\"train_miou\"], label=\"Train mIoU\", marker=\"*\")\n",
        "    plt.plot(history[\"val_iou\"], label=\"Val IoU\", marker=\"*\")\n",
        "    plt.title(\"mIoU Score per Epoch \")\n",
        "    plt.ylabel(\"mean IoU\")\n",
        "    plt.xlabel(\"epoch\")\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def plot_accuracy_vs_epoch(history):\n",
        "    \"\"\"\n",
        "    Plot the training and validation accuracy versus epochs.\n",
        "\n",
        "    Args:\n",
        "        history (dict): Dictionary containing the training history with keys 'train_acc' and 'val_acc'.\n",
        "\n",
        "    \"\"\"\n",
        "    plt.plot(history[\"train_acc\"], label=\"Train Accuracy\", marker=\"*\")\n",
        "    plt.plot(history[\"val_acc\"], label=\"Val Accuracy\", marker=\"*\")\n",
        "    plt.title(\"Accuracy vs Epoch\")\n",
        "    plt.ylabel(\"Accuracy\")\n",
        "    plt.xlabel(\"epoch\")\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "m7noezqYJZu9"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "OY_8gK4SlMG2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "50559b07-79e7-493f-989d-d4ab4c0f869d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_loss_vs_epoch(history)\n",
        "plot_iou_score_vs_epoch(history)\n",
        "plot_accuracy_vs_epoch(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TT_xFjEwlMG2"
      },
      "source": [
        "## Evaluation on Test Dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t_test = A.Resize(768, 1152, interpolation=cv2.INTER_NEAREST)\n",
        "\n",
        "test_set = TestDataGen(root_img_path, root_mask_path, X_test, transform=t_test )"
      ],
      "metadata": {
        "id": "3ZNyXzH-smZb"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def mean_iou(predicted_label, label, eps=1e-10, num_classes=10):\n",
        "    \"\"\"\n",
        "    Calculate the mean Intersection over Union (IoU) between the predicted labels and the ground truth labels.\n",
        "\n",
        "    Args:\n",
        "        predicted_label (torch.Tensor): Predicted label tensor of shape (N, C, H, W).\n",
        "        label (torch.Tensor): Ground truth label tensor of shape (N, H, W).\n",
        "        eps (float, optional): Epsilon value for numerical stability.\n",
        "        num_classes (int, optional): Number of classes.\n",
        "\n",
        "    Returns:\n",
        "        float: Mean IoU value.\n",
        "\n",
        "    \"\"\"\n",
        "    with torch.no_grad():\n",
        "        # Convert predicted_label to class predictions\n",
        "        predicted_label = F.softmax(predicted_label, dim=1)\n",
        "        predicted_label = torch.argmax(predicted_label, dim=1)\n",
        "\n",
        "        # Reshape predicted_label and label for easier computation\n",
        "        predicted_label = predicted_label.contiguous().view(-1)\n",
        "        label = label.contiguous().view(-1)\n",
        "\n",
        "        iou_single_class = []\n",
        "        for class_number in range(0, num_classes):\n",
        "            true_predicted_class = predicted_label == class_number\n",
        "            true_label = label == class_number\n",
        "\n",
        "            if true_label.long().sum().item() == 0:\n",
        "                iou_single_class.append(np.nan)\n",
        "            else:\n",
        "                # Calculate intersection and union\n",
        "                intersection = (\n",
        "                    torch.logical_and(true_predicted_class, true_label)\n",
        "                    .sum()\n",
        "                    .float()\n",
        "                    .item()\n",
        "                )\n",
        "                union = (\n",
        "                    torch.logical_or(true_predicted_class, true_label)\n",
        "                    .sum()\n",
        "                    .float()\n",
        "                    .item()\n",
        "                )\n",
        "\n",
        "                # Calculate IoU for the current class\n",
        "                iou = (intersection + eps) / (union + eps)\n",
        "                iou_single_class.append(iou)\n",
        "\n",
        "        # Calculate mean IoU across all classes\n",
        "        return np.nanmean(iou_single_class)\n",
        "\n",
        "\n",
        "def predict_image_mask_miou(\n",
        "    model, image, mask, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]\n",
        "):\n",
        "    \"\"\"\n",
        "    Predict the mask for an input image using a trained model and calculate the mean IoU score.\n",
        "\n",
        "    Args:\n",
        "        model (torch.nn.Module): Trained model.\n",
        "        image (PIL.Image.Image): Input image.\n",
        "        mask (torch.Tensor): Ground truth mask.\n",
        "        mean (list, optional): Mean values for image normalization.\n",
        "        std (list, optional): Standard deviation values for image normalization.\n",
        "\n",
        "    Returns:\n",
        "        torch.Tensor: Predicted mask.\n",
        "        float: Mean IoU score.\n",
        "\n",
        "    \"\"\"\n",
        "    model.eval()\n",
        "    t = T.Compose([T.ToTensor(), T.Normalize(mean, std)])\n",
        "    image = t(image)\n",
        "    model.to(device)\n",
        "    image = image.to(device)\n",
        "    mask = mask.to(device)\n",
        "    with torch.no_grad():\n",
        "        image = image.unsqueeze(0)\n",
        "        mask = mask.unsqueeze(0)\n",
        "\n",
        "        predicted_image = model(image)\n",
        "        mean_iou_score = mean_iou(predicted_image, mask)\n",
        "        masked = torch.argmax(predicted_image, dim=1)\n",
        "        masked = masked.cpu().squeeze(0)\n",
        "    return masked, mean_iou_score"
      ],
      "metadata": {
        "id": "4RmHFLw9LoBc"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "xNyeF4bVlMG2"
      },
      "outputs": [],
      "source": [
        "image, mask = test_set[3]\n",
        "\n",
        "pred_mask, score = predict_image_mask_miou(model, image, mask)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def pixel_accuracy_from_trained_model(model, test_set):\n",
        "    \"\"\"\n",
        "    Calculate the pixel accuracy for a trained model on a test dataset.\n",
        "\n",
        "    Args:\n",
        "        model (torch.nn.Module): Trained model.\n",
        "        test_set (torch.utils.data.Dataset): Test dataset.\n",
        "\n",
        "    Returns:\n",
        "        list: List of pixel accuracy values for each sample in the test dataset.\n",
        "\n",
        "    \"\"\"\n",
        "    accuracy = []\n",
        "    for i in tqdm(range(len(test_set))):\n",
        "        img, mask = test_set[i]\n",
        "        pred_mask, acc = predict_iamge_mask_pixel_accuracy(model, img, mask)\n",
        "        accuracy.append(accuracy)\n",
        "    return accuracy"
      ],
      "metadata": {
        "id": "ND7NT-IvLgLB"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def miou_score_from_trained_model(model, test_set):\n",
        "    \"\"\"\n",
        "    Calculate the mean IoU scores for a trained model on a test dataset.\n",
        "\n",
        "    Args:\n",
        "        model (torch.nn.Module): Trained model.\n",
        "        test_set (torch.utils.data.Dataset): Test dataset.\n",
        "\n",
        "    Returns:\n",
        "        list: List of mean IoU scores for each sample in the test dataset.\n",
        "\n",
        "    \"\"\"\n",
        "    score_iou = []\n",
        "    for i in tqdm(range(len(test_set))):\n",
        "        img, mask = test_set[i]\n",
        "        pred_mask, score = predict_image_mask_miou(model, img, mask)\n",
        "        score_iou.append(score)\n",
        "    return score_iou\n"
      ],
      "metadata": {
        "id": "YvH3cbx3MTTY"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "fdtLGWM9lMG3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "6ca8d46445bf496491949fc3c5f0909b",
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            "484ff92b17a0495197a3cec05d1523d8",
            "51f8282a6b71447bbc7eeef11b22c529",
            "77ae9b2f06104d22b80c3bdde83bd211",
            "38acdc2b97a8493abe6b9bdd88d46b4e",
            "b52a09ef524a49ed98834803f7cd3e01",
            "c6a00d05d06c4d11a4a8c17fb939fe8f",
            "778746317343453e87af22f2c5fdbb51",
            "03f5436e3c994aca91d9284d0b8fcfe0",
            "1e966d76f8c9451ea2bbf4eedbe4514e"
          ]
        },
        "outputId": "5d391313-e990-4a0b-9cfd-cd45cfed6953"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/40 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6ca8d46445bf496491949fc3c5f0909b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "test_set_miou = miou_score_from_trained_model(model, test_set)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def pixel_accuracy(predicted_image, mask):\n",
        "    \"\"\"\n",
        "    Calculate the pixel accuracy between the predicted image and the ground truth mask.\n",
        "\n",
        "    Args:\n",
        "        predicted_image (torch.Tensor): Predicted image tensor of shape (N, C, H, W).\n",
        "        mask (torch.Tensor): Ground truth mask tensor of shape (N, H, W).\n",
        "\n",
        "    Returns:\n",
        "        float: Pixel accuracy between the predicted image and the ground truth mask.\n",
        "\n",
        "    \"\"\"\n",
        "    with torch.no_grad():\n",
        "        # Convert predicted_image to class predictions\n",
        "        predicted_image = torch.argmax(F.softmax(predicted_image, dim=1), dim=1)\n",
        "\n",
        "        # Compare predicted_image with mask to get pixel-wise correctness\n",
        "        correct = torch.eq(predicted_image, mask).int()\n",
        "\n",
        "        # Calculate pixel accuracy\n",
        "        accuracy = float(correct.sum()) / float(correct.numel())\n",
        "\n",
        "    return accuracy\n",
        "\n",
        "\n",
        "def mean_iou(predicted_label, label, eps=1e-10, num_classes=10):\n",
        "    \"\"\"\n",
        "    Calculate the mean Intersection over Union (IoU) between the predicted labels and the ground truth labels.\n",
        "\n",
        "    Args:\n",
        "        predicted_label (torch.Tensor): Predicted label tensor of shape (N, C, H, W).\n",
        "        label (torch.Tensor): Ground truth label tensor of shape (N, H, W).\n",
        "        eps (float, optional): Epsilon value for numerical stability.\n",
        "        num_classes (int, optional): Number of classes.\n",
        "\n",
        "    Returns:\n",
        "        float: Mean IoU value.\n",
        "\n",
        "    \"\"\"\n",
        "    with torch.no_grad():\n",
        "        # Convert predicted_label to class predictions\n",
        "        predicted_label = F.softmax(predicted_label, dim=1)\n",
        "        predicted_label = torch.argmax(predicted_label, dim=1)\n",
        "\n",
        "        # Reshape predicted_label and label for easier computation\n",
        "        predicted_label = predicted_label.contiguous().view(-1)\n",
        "        label = label.contiguous().view(-1)\n",
        "\n",
        "        iou_single_class = []\n",
        "        for class_number in range(0, num_classes):\n",
        "            true_predicted_class = predicted_label == class_number\n",
        "            true_label = label == class_number\n",
        "\n",
        "            if true_label.long().sum().item() == 0:\n",
        "                iou_single_class.append(np.nan)\n",
        "            else:\n",
        "                # Calculate intersection and union\n",
        "                intersection = (\n",
        "                    torch.logical_and(true_predicted_class, true_label)\n",
        "                    .sum()\n",
        "                    .float()\n",
        "                    .item()\n",
        "                )\n",
        "                union = (\n",
        "                    torch.logical_or(true_predicted_class, true_label)\n",
        "                    .sum()\n",
        "                    .float()\n",
        "                    .item()\n",
        "                )\n",
        "\n",
        "                # Calculate IoU for the current class\n",
        "                iou = (intersection + eps) / (union + eps)\n",
        "                iou_single_class.append(iou)\n",
        "\n",
        "        # Calculate mean IoU across all classes\n",
        "        return np.nanmean(iou_single_class)"
      ],
      "metadata": {
        "id": "113sEbzSK9xH"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_iamge_mask_pixel_accuracy(\n",
        "    model, image, mask, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]\n",
        "):\n",
        "    \"\"\"\n",
        "    Predict the mask for an input image using a trained model and calculate the pixel accuracy.\n",
        "\n",
        "    Args:\n",
        "        model (torch.nn.Module): Trained model.\n",
        "        image (PIL.Image.Image): Input image.\n",
        "        mask (torch.Tensor): Ground truth mask.\n",
        "        mean (list, optional): Mean values for image normalization.\n",
        "        std (list, optional): Standard deviation values for image normalization.\n",
        "\n",
        "    Returns:\n",
        "        torch.Tensor: Predicted mask.\n",
        "        float: Pixel accuracy.\n",
        "\n",
        "    \"\"\"\n",
        "    model.eval()\n",
        "    t = T.Compose([T.ToTensor(), T.Normalize(mean, std)])\n",
        "    image = t(image)\n",
        "    model.to(device)\n",
        "    image = image.to(device)\n",
        "    mask = mask.to(device)\n",
        "    with torch.no_grad():\n",
        "        image = image.unsqueeze(0)\n",
        "        mask = mask.unsqueeze(0)\n",
        "        predicted_image = model(image)\n",
        "        pixel_acc = pixel_accuracy(predicted_image, mask)\n",
        "        masked = torch.argmax(predicted_image, dim=1)\n",
        "        masked = masked.cpu().squeeze(0)\n",
        "    return masked, pixel_acc"
      ],
      "metadata": {
        "id": "5GBVlZ4SKy5V"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def pixel_accuracy_from_trained_model(model, test_set):\n",
        "    \"\"\"\n",
        "    Calculate the pixel accuracy for a trained model on a test dataset.\n",
        "\n",
        "    Args:\n",
        "        model (torch.nn.Module): Trained model.\n",
        "        test_set (torch.utils.data.Dataset): Test dataset.\n",
        "\n",
        "    Returns:\n",
        "        list: List of pixel accuracy values for each sample in the test dataset.\n",
        "\n",
        "    \"\"\"\n",
        "    accuracy = []\n",
        "    for i in tqdm(range(len(test_set))):\n",
        "        img, mask = test_set[i]\n",
        "        pred_mask, acc = predict_iamge_mask_pixel_accuracy(model, img, mask)\n",
        "        accuracy.append(accuracy)\n",
        "    return accuracy"
      ],
      "metadata": {
        "id": "ElNTbH5MLS7X"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "BwTtaarslMG3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "4f7fb6b3fa52451e9defdf8bdd0e75b3",
            "0d3542dceed449c0be33ba891aea4e60",
            "7f734c3d85f04602a3813d7578a27e95",
            "de8747088bc04dee98e3fa54ca2a7683",
            "33946e9bbdfd49dfb9c8d4be481828ff",
            "d6364931b18546baa6896b39301ab999",
            "49f795826fa343a3b1430abdcb21cff0",
            "1358eabfe52c4e47ae3d7ac7b9fb985c",
            "780297c6fdd642278defe894cbeb6005"
          ]
        },
        "outputId": "662afa25-2bd2-467c-c40c-c5a6ed30ec1a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/40 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "85f1f9dd62f24951a969f0301a45f848"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "test_set_accuracy = pixel_accuracy_from_trained_model(model, test_set)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "DT6pArY8lMG3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "outputId": "a9b594e8-7b9b-4afd-c072-d18629f000ff"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 3 Axes>"
            ],
            "image/png": 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