RCNN_BBX.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "RCNN-BBX.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1_q1dhCbgL-a1oq4ExiFaS14Iz9sotYLm",
      "authorship_tag": "ABX9TyM4BxoghMfmEj/Ch+ZmjRLU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hoaileba/AI-doc/blob/master/RCNN_BBX.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLBxNpdcl1Vn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbUnNC_tDusE",
        "colab_type": "code",
        "outputId": "d8448382-7372-4f9c-aa9a-3a02e84b656f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "from zipfile import ZipFile\n",
        "!apt install unzip\n",
        "fileimg = '/content/drive/My Drive/Colab Notebooks/data/dog.zip'\n",
        "# filelabel = '/content/drive/My Drive/AI_Gen2/Object Detection/RCNN/Airplanes_Annotations.zip'\n",
        "with ZipFile(fileimg, 'r') as zip:\n",
        "    # zip.printdir()\n",
        "    zip.extractall()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "unzip is already the newest version (6.0-21ubuntu1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 31 not upgraded.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9HujcHpHlOkZ",
        "colab_type": "code",
        "outputId": "1de7c852-bec8-4e4e-911a-cbff6ef97910",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import os,cv2,keras,glob\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from google.colab.patches import cv2_imshow"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3Njo0EMwSu6",
        "colab_type": "code",
        "outputId": "83ba4894-4938-4cee-bf84-aae7fc9e712d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator  \n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "from tensorflow.keras.applications.vgg16 import VGG16\n",
        "\n",
        "vggmodel = VGG16(weights = 'imagenet', include_top = False)\n",
        "\n",
        "# for layers in (vggmodel.layers)[:15]:\n",
        "#     print (layers)\n",
        "#     layers.trainable = False\n",
        "input_layer = tf.keras.layers.Input(shape = (224,224,3),name = 'input')\n",
        "flatten = tf.keras.layers.Flatten()\n",
        "\n",
        "dens1 = tf.keras.layers.Dense(2048,activation = 'relu')\n",
        "drop = tf.keras.layers.Dropout(0.5)\n",
        "dens2 = tf.keras.layers.Dense(1024,activation = 'relu')\n",
        "\n",
        "\n",
        "X = vggmodel(input_layer)\n",
        "X1 = flatten(X)\n",
        "X1 = dens1(X1)\n",
        "X1 = drop(X1)\n",
        "X1 = dens2(X1) \n",
        "\n",
        "X2 = flatten(X)\n",
        "\n",
        "# X = vggmodel.layers[-2].output    \n",
        "predictions = Dense(2, activation = \"softmax\",name = 'out1')(X1)\n",
        "bbox = Dense(4,kernel_regularizer=tf.keras.regularizers.l2(1000),name = 'out2')(X2)\n",
        "model_final = Model(inputs = input_layer, outputs = [predictions,bbox])\n",
        "opt = Adam(lr = 0.0001)\n",
        "model_final.compile(loss ={'out1':tf.keras.losses.categorical_crossentropy,'out2':'mse'}, optimizer = opt, metrics = {'out1': \"accuracy\",'out2':'mse'})\n",
        "model_final.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 1s 0us/step\n",
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input (InputLayer)              [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "vgg16 (Model)                   multiple             14714688    input[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 25088)        0           vgg16[1][0]                      \n",
            "                                                                 vgg16[1][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 2048)         51382272    flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 2048)         0           dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 1024)         2098176     dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "out1 (Dense)                    (None, 2)            2050        dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "out2 (Dense)                    (None, 4)            100356      flatten[1][0]                    \n",
            "==================================================================================================\n",
            "Total params: 68,297,542\n",
            "Trainable params: 68,297,542\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXoOtDrve8jr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def get_iou(bb1, bb2):\n",
        "\n",
        "\n",
        "    x_left = max(bb1['x1'], bb2['x1'])\n",
        "    y_top = max(bb1['y1'], bb2['y1'])\n",
        "    x_right = min(bb1['x2'], bb2['x2'])\n",
        "    y_bottom = min(bb1['y2'], bb2['y2'])\n",
        "\n",
        "    if x_right < x_left or y_bottom < y_top:\n",
        "        return 0.0\n",
        "    intersection_area = (x_right - x_left) * (y_bottom - y_top)\n",
        "\n",
        "    bb1_area = (bb1['x2'] - bb1['x1']) * (bb1['y2'] - bb1['y1'])\n",
        "    bb2_area = (bb2['x2'] - bb2['x1']) * (bb2['y2'] - bb2['y1'])\n",
        "\n",
        "    iou = intersection_area / float(bb1_area + bb2_area - intersection_area)\n",
        "    # assert iou <= 1.0\n",
        "    # assert iou >= 0.0\n",
        "    return iou"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KRHaWtUT-ah5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# path = \"/content/data_dog\"\n",
        "# annot = \"/content/label_dog\"\n",
        "import os\n",
        "\n",
        "dirVal = '/content/boundingBox'\n",
        "dirN = '/content/pre'\n",
        "dirValImage = '/content/pre/0'\n",
        "dirValLabel = '/content/pre/1'\n",
        "dirTrue = dirVal+'/True'\n",
        "dirPred = dirVal+'/Pred'\n",
        "os.mkdir(dirVal)\n",
        "os.mkdir(dirN)\n",
        "os.mkdir(dirValImage)\n",
        "os.mkdir(dirValLabel)\n",
        "os.mkdir(dirTrue)\n",
        "os.mkdir(dirPred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WNSTcJDuMyQ1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path_image = '/content/data_dog'\n",
        "path_label = '/content/label_dog'\n",
        "\n",
        "ss = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()\n",
        "\n",
        "def read_txt(path_label):\n",
        "\n",
        "      # print(fileI, fileL)\n",
        "      # img = cv2.imread(i)\n",
        "\n",
        "      grTrue = []\n",
        "      f = open(path_label, \"r\")\n",
        "      data = []\n",
        "      for ind in f:\n",
        "        data.append(ind)\n",
        "\n",
        "      num_ob = int(data[0])\n",
        "      for id in range(num_ob):\n",
        "        co = data[id+1].split(' ')\n",
        "        # print(co[0])\n",
        "        num = []\n",
        "        for fi in co:\n",
        "          if fi != '\\n':\n",
        "            n = float(fi)\n",
        "            num.append(float(fi))\n",
        "        x1 = int(num[0]*224)\n",
        "        y1 = int(num[1]*224)\n",
        "        x2 = int(num[2]*224)\n",
        "        y2 = int(num[3]*224)\n",
        "        # print(x1,y1,x2,y2)\n",
        "        grTrue.append({'x1':x1,'y1':y1, 'x2':x2 , 'y2' : y2}) \n",
        "      return grTrue    \n",
        "def get_data(path_image, path_label):\n",
        "    X_train = []\n",
        "    y_train = []\n",
        "    dem = 0\n",
        "    fi = 0\n",
        "    bb_path = '/content/boundingBox/Pred/'\n",
        "    True_path = '/content/boundingBox/True/'\n",
        "    for i in (os.listdir(path_image)):\n",
        "        # if i.startswith(\"airplane\"):\n",
        "            dem += 1\n",
        "            print(dem,end=' ')\n",
        "            # if dem >= 600:\n",
        "            #     break\n",
        "            print(i)\n",
        "            # break\n",
        "            img = cv2.imread(os.path.join(path_image, i))\n",
        "            ss.setBaseImage(img)\n",
        "            ss.switchToSelectiveSearchQuality()\n",
        "            result = ss.process()\n",
        "            #read_label_csv\n",
        "            gtvalue = read_txt(path_label+'/'+(i.split(\".\")[0]+\".txt\"))\n",
        "            #select region to train\n",
        "            tflag = 0\n",
        "            fflag = 0\n",
        "            flag = 0\n",
        "            count = 0\n",
        "            fcount = 0\n",
        "            # print (len(result))\n",
        "            imgtmp = img.copy()\n",
        "            # if dem == 20 :\n",
        "            #   break\n",
        "            file_txt = i.split('.')[0]+'.txt'\n",
        "            print(file_txt)\n",
        "            # if dem == 2 :\n",
        "            #   break\n",
        "            file_bb = open(bb_path+file_txt,'w+')\n",
        "            for  e,region in enumerate(result):\n",
        "              if e < 2000:\n",
        "                for gt in gtvalue:\n",
        "                    x, y, w, h = region\n",
        "                    IoU = get_iou(gt, {\"x1\":x,\"x2\":x+w,\"y1\":y,\"y2\":y+h})\n",
        "                    # print (IoU)\n",
        "                    if count <  30:\n",
        "                        if IoU >= 0.75:\n",
        "                            # cv2.rectangle(imgtmp, (x, y), (x+w, y+h), (0, 255, 0), 1, cv2.LINE_AA)\n",
        "                            \n",
        "                            img_train = img[y:y+h , x:x+w]\n",
        "                            resize = cv2.resize(img_train, (224, 224), interpolation = cv2.INTER_AREA)\n",
        "                            # X_train.append(resize)\n",
        "                            # y_train.append(1)\n",
        "                            count += 1\n",
        "                            fi+=1\n",
        "                            file_image_name = '/content/pre/1/'+ str(fi)+'.jpg'\n",
        "                            cv2.imwrite(file_image_name,resize)\n",
        "                            # file_bb = open(bb_path+file_txt,'w+')\n",
        "                            file_bb.write(str(x)+\" \")\n",
        "                            file_bb.write(str(y)+\" \")\n",
        "                            file_bb.write(str(w)+\" \")\n",
        "                            file_bb.write(str(h)+\" \")\n",
        "                            file_bb.write(str(IoU)+\" \")\n",
        "                            file_bb.write(\"1\\n\")\n",
        "                            # file_bb.close()\n",
        "                            # file_bb = open(bb_path+str(fi)+'.txt','w+')\n",
        "                            # file_bb = open(True_path+str(fi)+'.txt','w+')\n",
        "                            # file_bb.write(str(gt['x1'])+\" \")\n",
        "                            # file_bb.write(str(gt['y1'])+\" \")\n",
        "                            # file_bb.write(str(gt['x2'])+\" \")\n",
        "                            # file_bb.write(str(gt['y2']))\n",
        "                            # file_bb.close()\n",
        "                            \n",
        "                    else:\n",
        "                        tflag = 1\n",
        "                    if fcount < 30:\n",
        "                        if IoU <= 0.4:\n",
        "                            img_train = img[y:y+h , x:x+w]\n",
        "                            resize = cv2.resize(img_train, (224, 224), interpolation = cv2.INTER_AREA)\n",
        "                            # X_train.append(resize)\n",
        "                            # y_train.append(0)\n",
        "                            # count += 1\n",
        "                            fi+=1\n",
        "                            fcount+=1\n",
        "                            file_image_name = '/content/pre/0/'+ str(fi)+'.jpg'\n",
        "                            cv2.imwrite(file_image_name,resize)\n",
        "                            file_bb.write(str(x)+\" \")\n",
        "                            file_bb.write(str(y)+\" \")\n",
        "                            file_bb.write(str(w)+\" \")\n",
        "                            file_bb.write(str(h)+\" \")\n",
        "                            file_bb.write(str(IoU)+\" \")\n",
        "                            file_bb.write(\"0\\n\")\n",
        "                            # file_bb = open(True_path+str(fi)+'.txt','w+')\n",
        "                            # file_bb.write(str(gt['x1'])+\" \")\n",
        "                            # file_bb.write(str(gt['y1'])+\" \")\n",
        "                            # file_bb.write(str(gt['x2'])+\" \")\n",
        "                            # file_bb.write(str(gt['y2']))\n",
        "                            # file_bb.close()\n",
        "                    else:\n",
        "                        fflag = 1\n",
        "                    # break\n",
        "                if tflag == 1 and fflag == 1:\n",
        "                    # print (\"perfect\")\n",
        "                    break\n",
        "            print (fi)\n",
        "            file_bb.close()\n",
        "            if fi == 20 :\n",
        "              break\n",
        "            # break   \n",
        "           # cv2_imshow(imgtmp)\n",
        "\n",
        "       \n",
        "# get_d\n",
        "get_data(path_image, path_label)\n",
        "# print (len(X_train))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kjf1UBB4BXUZ",
        "colab_type": "code",
        "outputId": "2dfd477f-5d35-4315-f7ab-826a54dafed6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "f = open('/content/boundingBox/Pred/2484.txt','r')\n",
        "# print(f.read())\n",
        "ar = []\n",
        "\n",
        "for fi in f:\n",
        "  ar.append(fi)\n",
        "  stri = ar[0].split(' ')\n",
        "print(stri)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['0', '16', '163', '211', '1']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cvdwEP3nj6MF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!zip -r /content/file.zip /content/pre"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLFJv69xj84f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!zip -r /content/file1.zip /content/boundingBox/Pred\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aBK5py-1g3lq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def non_max_suppression_fast(boxes, overlapThresh):\n",
        "\t# if there are no boxes, return an empty list\n",
        "\tif len(boxes) == 0:\n",
        "\t\treturn []\n",
        "\n",
        "\tif boxes.dtype.kind == \"i\":\n",
        "\t\tboxes = boxes.astype(\"float\")\n",
        "\tpick = []\n",
        "\tx1 = boxes[:,0]\n",
        "\ty1 = boxes[:,1]\n",
        "\tx2 = boxes[:,2]\n",
        "\ty2 = boxes[:,3]\n",
        "\n",
        "\tarea = (x2 - x1 + 1) * (y2 - y1 + 1)\n",
        "\tidxs = np.argsort(y2)\n",
        "\twhile len(idxs) > 0:\n",
        "\t\tlast = len(idxs) - 1\n",
        "\t\ti = idxs[last]\n",
        "\t\tpick.append(i)\n",
        "\t\txx1 = np.maximum(x1[i], x1[idxs[:last]])\n",
        "\t\tyy1 = np.maximum(y1[i], y1[idxs[:last]])\n",
        "\t\txx2 = np.minimum(x2[i], x2[idxs[:last]])\n",
        "\t\tyy2 = np.minimum(y2[i], y2[idxs[:last]])\n",
        "\t\tw = np.maximum(0, xx2 - xx1 + 1)\n",
        "\t\th = np.maximum(0, yy2 - yy1 + 1)\n",
        "\t\toverlap = (w * h) / area[idxs[:last]]\n",
        "\t\tidxs = np.delete(idxs, np.concatenate(([last],\n",
        "\t\t\tnp.where(overlap > overlapThresh)[0])))\n",
        "\treturn boxes[pick].astype(\"int\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Xl_QX9yNmT9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "file1 = glob.glob(\"/content/pre/0/*.jpg\")\n",
        "file0 = glob.glob(\"/content/pre/1/*.jpg\")\n",
        "from keras.utils import to_categorical\n",
        "class_= np.array(['0','1'])\n",
        "\n",
        "import os\n",
        "files = []\n",
        "for f in file1:\n",
        "  files.append(f)\n",
        "for f in file0:\n",
        "  files.append(f)\n",
        "# print()\n",
        "import random\n",
        "random.shuffle(files)\n",
        "# sep = tf.strings.split(files[100],'/')[-2]\n",
        "# print(len(files))\n",
        "# print(int(sep))\n",
        "def read_txt(path_label):\n",
        "\n",
        "      path_label = path_label.numpy().decode('utf-8')\n",
        "      path_region = path_label.split('/')[-1]\n",
        "      path_region = '/content/boundingBox/Pred/'+path_region\n",
        "      # print(path_label)\n",
        "      # grTrue = []\n",
        "\n",
        "      f = open(path_label, \"r\")\n",
        "      ar = []\n",
        "      for fi in f:\n",
        "        ar.append(fi)\n",
        "        num = ar[0].split(' ')\n",
        "      f.close()\n",
        "\n",
        "\n",
        "      fr = open(path_region, \"r\")\n",
        "      ar = []\n",
        "      for fi in fr:\n",
        "        ar.append(fi)\n",
        "        num_r = ar[0].split(' ')\n",
        "      \n",
        "      x = np.array([float(i) for i in num ])\n",
        "      x1 = np.array([float(i) for i  in num_r  ])\n",
        "      x[2] = x[2] - x[0]\n",
        "      x[3] = x[3] - x[1]\n",
        "      t = [0,0,0,0]\n",
        "      t[0] = (x[0] -  x1[0])/x[2]\n",
        "      t[1] = (x[1] - x1[1])/x[3]\n",
        "      t[2] = np.math.log(x[2]/x1[2])\n",
        "      t[3] = np.math.log(x[3]/x1[3])\n",
        "      # x1 = int(num[0])\n",
        "      # y1 = int(num[1])\n",
        "      # x2 = int(num[2])\n",
        "      # y2 = int(num[3])\n",
        "      # # print(x1,y1,x2,y2)\n",
        "      # grTrue.append([x1,y1,x2,y2])\n",
        "\n",
        "      # print(x,x1)\n",
        "      f.close()\n",
        "      return np.array(t)\n",
        "\n",
        "def Process(img_path):\n",
        "  image = tf.io.read_file(img_path)\n",
        "  image = tf.image.decode_jpeg(image,channels = 3)\n",
        "  # image = tf.cast(image,tf.float32)/255.0\n",
        "  image = tf.image.resize(image,(224,224))\n",
        "  parts = tf.strings.split(img_path, os.path.sep)\n",
        "  y=parts[-2]==class_\n",
        "  filename = parts[-1]\n",
        "  name = tf.strings.split(filename,'.')\n",
        "  \n",
        "  true_ = '/content/boundingBox/True/'+ name[0] +'.txt'\n",
        "\n",
        "  x1 = tf.py_function(read_txt,[true_],tf.float32)\n",
        "\n",
        "  return image,float(y),x1\n",
        "\n",
        "\n",
        "def gen(data):\n",
        "  data = data.batch(100).repeat()\n",
        "  for b in data:\n",
        "    yield {'input':b[0]},{'out1':b[1],'out2':b[2]}\n",
        "\n",
        "data_list=tf.data.Dataset.from_tensor_slices(files).map(Process,num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "# data_list = data_list.shuffle(100)\n",
        "print(len(files))\n",
        "data = gen(data_list)\n",
        "for img,lab ,t in data_list.take(1):\n",
        "  print(t.numpy())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09VkR_n5uacf",
        "colab_type": "code",
        "outputId": "35937363-af6f-48a6-e787-f8d66caa7690",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "from keras.utils import np_utils\n",
        "X_train = np.array(X_train)\n",
        "y_train = np.array(y_train)\n",
        "y_train = np_utils.to_categorical(y_train, 2)\n",
        "print (X_train.shape)\n",
        "print (y_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(78352, 224, 224, 3)\n",
            "(78352, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DiQ2DuRrvFju",
        "colab_type": "code",
        "outputId": "868383d4-513f-49b2-fd56-62eb8083f854",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "# print(y_train[0]\n",
        "# model_checkpoint = ModelCheckpoint('vanhocvp.hdf5', monitor='loss',verbose=1, save_best_only=True)\n",
        "# checkpoint = ModelCheckpoint(\"/content/drive/rcnn_vgg16_v4.hdf5\", monitor='loss',verbose=1, save_best_only=True)\n",
        "# early = EarlyStopping(monitor='val_loss', min_delta=0, patience=100, verbose=1, mode='auto')\n",
        "hist = model_final.fit(data, steps_per_epoch=78384//100, epochs= 4, verbose= 1,shuffle = True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-5a55db3b0a42>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# checkpoint = ModelCheckpoint(\"/content/drive/rcnn_vgg16_v4.hdf5\", monitor='loss',verbose=1, save_best_only=True)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# early = EarlyStopping(monitor='val_loss', min_delta=0, patience=100, verbose=1, mode='auto')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mhist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_final\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m78384\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mshuffle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9oz38KXJyA9O",
        "colab_type": "code",
        "outputId": "56aca641-2ead-44b4-bb0f-ffa7370615cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "\n",
        "model_final.save(\"model.h5\")\n",
        "img = cv2.imread(\"/content/85bcf3cd6540ec7d.jpg\")\n",
        "# print (i)\n",
        "ss = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()\n",
        "\n",
        "ss.setBaseImage(img)\n",
        "ss.switchToSelectiveSearchQuality()\n",
        "ssresults = ss.process()\n",
        "imout = img.copy()\n",
        "rects = []\n",
        "for e,result in enumerate(ssresults):\n",
        "    if e < 2000:\n",
        "        x,y,w,h = result\n",
        "        timage = imout[y:y+h,x:x+w]\n",
        "        resized = cv2.resize(timage, (224,224), interpolation = cv2.INTER_AREA)\n",
        "        img = np.expand_dims(resized, axis=0)\n",
        "        out= model_final.predict(img)\n",
        "        \n",
        "        if out[0][0][1] >=0.98:\n",
        "            # print (out)\n",
        "            # cv2.rectangle(imout, (x, y), (x+w, y+h), (0,0, 255),2)\n",
        "            x_predict,y_predict,w_predict,h_predict=x+out[1][0][0]*w,y+out[1][0][1]*h,w*np.math.exp(out[1][0][2]),h*np.math.exp(out[1][0][3])\n",
        "            rects.append([x_predict,y_predict,w_predict+x_predict,y_predict+h_predict])\n",
        "\n",
        "            # cv2_imshow(imout)\n",
        "            # cv2.waitkey(200)\n",
        "all_rect = np.array(rects).reshape(-1,4)\n",
        "box = non_max_suppression_fast(all_rect,0.46)\n",
        "for e in box:\n",
        "  x ,y, xx,yy = e\n",
        "  cv2.rectangle(imout, (x, y), (xx, yy), (0,0, 255),2)\n",
        "cv2_imshow(imout)\n",
        "# plt.figure()\n",
        "           "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1xdqqYiPtDbZP7eS93nq5fOmFrw4T2VaLnY3t62+/yukHV4vDPNVaaU8x7fX3zp7bO3NuIEw9P3LOcQHoQQmOKHwgZgNFFglCoK44aesGUXDOrXWRoLaOERMol41pbQhMlXUVKDrnIhCxiIITA8Y4oEAuG1NnWQaR1VUzGOQySW3wRKzXG4RArnVCs9lkKhIBgAAaWPG+b/3Orb2zoE9BFJEAGKBIwBsQ6eXHn1GhPvfwY8A1IH7fH/9TR0dHP/od33nxifeALsg7wRMAC2Rff+15a5r3feApCKvjB28/8sjDs+PbnPPBeNNVQaYJNdOf+Zc//af+5A/eePuF9fTarWtfWJeT937gm7792afe+6HvgLaqV/d/4ec+s5zdn89uPPvNH/rVr37i+ee+OF1WP/sz/+Bv/dQ/gKg2t/pEgUK7u7f1wte/9MEPfgsQPfLw+UHqxawJ0FgXYgiBKEoBPoYIEGPUWntrkUsgcs5ppRhj1rmuJWqahnOe5zlj2C16IUQwDgA4xxgD5/hOOQCcIyICntwTAJRSXabknHMpQggIFGMMzimlCMB7L5V+p96AbrNTSqHgpTV5TBlH772MkYiQ80SqumljjDEQA4yBpFAAACEiY1KI4Dxm5Kpqa2OwmExSKRPFL168+ODBg36/zykgsOOD200Tl3fvCJ2+9ToLFIOLkkOCUcqkqRZ7O4Ny8YB8BSarGT995qHJ8f7WxvCRyxce2shuvPaCSBIhWdu2SgwRGEREKQFICaW48j5KCShk3XblLIYQIjCIMRCEADaSj1GlSVeCW2vfuYYUKboQudKhbarGADKRpChV3Zica2u9jOQEqysrEp2IvGIeUEFMAQZbF54FAogSQDAQkSiSR44EmiDfe/i9oLlnHIElp8785f/h74HgwBkwCJwE86ZdX73y9YcfPvvVL/7+i186xGgVAIj11Su/l2XJW7/9diLyj377R770+c+89/Gt61e+8se//9n7d295WL/+jS9ePLd1bveRV77wu5/6L18YbY5Onx3atn7p+bu33r73k//tX/vFf/9LT7zviWc/9F6ACbACeMIoRtvsbm8KcnZ1R2nxq7/4f/6pv/iT4mBWeZ7EGKu6TbSKMTIijiwCIyKKLMYIRPGdbpQhRfIIKIQw1nVIZwhBCMUFkQuISDG+WxIY74QQCEhE8QThIsZYt+MbY6SU1gaOLFWaCc4Ys9YaxC5YCZl1PstSrqQrSy5UVxV0z9A0Tb/oBWs4sLZtu7Duuj0kYIy1dZPmWdM0zrksK2KMCFEydnb3VNOa9z711MFkGoVYl8tom7ps2rqtq9Y5koptDMfz+VxyBiG2xvSKPAYXQ/uZT/z6e55674c+9GxTN6nCN956Qybyzq2rI9rrpUpIYWwlWGQQU500spFclO1aEGNJwhkG562z4/EQgU2nUy5QKdU0xvnIItgYPZAPIRDzRATovfdEQgjBeFVVOkmEThhEYNz5ECLU1q8bijC23syWDcOssVVvc9woDyoBQAABqDocHt6px4AFAAaEyHSQBTFgwAEYqCSQ4DoJFBC4YCGG5mtf/v2NgVrNDhkFLfOvfemFj37s2aPrb7p2vrn7yFM/8kOvP//CJz/xf50/e+a3//Nv/NHv/yO/95lPHR3t/5kf+aF7tx6Yxh4dHMyOV48/dnkyOfJu/V3f+W1nt8/84i/8n7/0737+g9/89Ld+7ENH08XNNz8nxBBw8MLz35jsHw5Hxd3bd+7dv/W+p95z9a3rO5/4TeERFlUVnddaG2NUmgCg8R4Qu6oxMmQUCIBY7BBdIiIW3m3POzTg3VqKMdb1OidQALB3C1bOudYarLPGhRAQMU1yLlhtyzRJY9eze9/r9aL3nHPORWMNY+zg4GC0uSmEGA6HaVas29oGz7m0xgkhQlNzFGVZgeDe+w4IIyIpZZIkaZq6GCRyZMRikIk8PNzf296JzlbLdVuueZIzDVEkxvjFYs1Q9LJMJ3K+mHDBY+O0kJUHY0yaam+rJGX16vB//n//7R/4gR8CgBs3b964ddO25Ruvv2JNq3OVJbqNYdzPnW9tWyFkAmBQpOAdQhTISXBEFJwInOCUarValdZaIRAYp649AHDOOc67/lJIqZC3bQuCt85mOrHWghB1a9H7mWvWtWUaOfBF5chXe1WIUgIDQENoCTRARxsRQQCIAJ7gBLGRqIEAIzLOgSLXSQRFDH0IkjMEtjjef/PFq6Px4K03rzoXhoON42n7qU9/+tTu5r2bLyXJW/sHN3q5jjF54slnP/P7z02nx4888sg//5l/90N/5q8eTsxg1Fy9cf3smcu/99nPfdd3f9vP/Mt/9aN/9od//C//yfFmyqVEjL/9m79WDMVf+8n//j/88u+8deVOpvgLX7p7/qGHb127HgNev37z1Td/WnAhIlGSJIjQ6/VaZ711iOhjjBEYYyyGdxGo7goKIaTWEShhybuRwThKLklHKSV7JwEjomlMlmVJljjnbPAAIKXsmiHnHBEFD0QEDIXgUkoTQl3Xo17PGBMZOOck56dO7/kQG+uKQR8AiqLwzvWKvrOecYwxBiBvDAcVOuILADgaYxhHIlJKScmllNY1eT9L9Wa/399fPyhXK84wBkeeW2sZYxvDkfcxH/QdxTYAo7i7syG5SPMiEDXVWjAvMUyP7r73scv371z9nd/5HRthf3JoVosf/ZM/MNm/qQUP3g3ylAUXjckUd03Vz2VsS8ZYnvAIpJTmCAKBUZCcSGEIznkvpe7qURt8t9pjjITMGmO866VZkmc2+ggUQtA6qUMQQjamIaBV2zoT8kxHSzzy2/eOt/JdAAHY8X8xAkeIABEAAYCAGLAIxAGAKbCegDFEYBCBEfAQQHEFFCH4erG4/tY1ztnB0ex7v+/7f//3v4Dkg/PXF3eTZHMyu4OivdeslRwwlkdKhRwl6c5wFO7dX16/df3ZD7//yltX37hy+7/+Cz/xL/75P7546Uxj3JvXrl8WZ7Z2dn/+X/2ikqpIhamnqXLRrSaLemtj44XnX1zV9uat+5N52etngoEHimmaAWCWZbakSF0lKjnnLBLrkE4OEJEYU5x3m6xzjiISMsZYr9dr6joEG0JgiGmWcs6r1bptW5VoLkWXTVno4FiGjCmZcCa6BMyKnAmukyQi0+/UBt0mleW5s9YYEwkYY8PhsGobHj0CnEDrESgCAASg4ANE8D4C4LulBec8UogxKqVUpspmnSXKWM+FStPMrsos1b3B0LtI6F20W6Ph6bNnj5fL/atHANBXavf8BUKeplrtblb1EpGKVIXgkOx3ffuzk8XqzWtivUg3N0ZKC6CgBC+U4NHtbg4f7B9yH2xdccGzLMsTbSMFxgWSNUZxpgUKZAIZMhaBXAwBo4+htSbE2MZQpD3JORE1bRuJdJaU1UpK6ZxXWhvrI6Dxfm1sC54Ub3xU0ZuD2c7Tl4EnQJIxzgAYEEEEiAwQAAlUBAwQsOt6nWNSAZME3sYgMXJGECMEd3zn9rU33j6+Pz082v/od33ncjqr5tPpftpPxMbm1mtX3m6c+faPfvill77+6U99dvvU3v37+w9ffvTqtfsh+DTvp2n+a7/6m1qnb791/bu/y//wn/sRxPhv//1v5nn+lZc/9773ve/67cUzTz0WnP+3/+bXXvj6K9/9nT/wud//emDeU7Kzu/fgaBqBR0KxvTmE2NV2bLEufYiAjIgDwxACJ3DeImIMwQMhIjAWiJQQaZoaF2KM8/nceycFAkTv/fHxMcXAGFNcSCmVEuv1uizXIYTIwHtPBCEEa+YxxqIoqqoiCGmaNmVJyFpjlZbT2TFH2TonpEYGDHkvK2arddEbOOdCsHmSzOdzzrkN3lMUXTcWnZTSBe9iiEBlXeks9cYywZRS6/Xy/NnTdbOcTaa4IZrWrdeVUkpIrKq1tV4xmUi+3e9v5Pl4OFytq+t3bty/tz8aDM5dvHDp0qVBkZarxf37dxeLeb8/mM0O6/VqOlue2RpfObp3/eobly+eOz48VFJmmp/a3pxMJoq5+we3z5870y+yNE0D8VXZtN7pLK2rlUTWy3XdesmRc9bRLiiAMaa11Fq3Td22LeNCamWDYYIT63APSwS2bdMib5wRRMvaZJKxNpBvuTGbiUx7Y+AaQAEIIM6AgEUAYABA3cJgkbEAJCj6GDgSADgggYJ39yMPnL70+c+t5gtjwqC/cXQ4u3Xj5kNnz7720jcuXbyQZf35fD0cj37p3/7a2fN7W9tnW2MfeeTJ5bJK80Ev1Z/70nM6YU+855lXXnnt1Pb5X/2139o9P/7Yd3xbf3Dq+77/4//b/+9f/P7vv+TKZvyR06+9+jXr6o987Ht03j88Kp0znqGJq6aqueDrZS0yyXq9flWa6XINIq3aJqDksnupDChAJERGCJxxFLypS2SCqGYchRDr9SrvF1LI4K2UPMsyKTky8N53OBdwZABCcBaCbVofrBBSCznsDTs0oKqqEJ0QonE2S1KdpFIJjIExlgbYObVXrpetMUKoItLNmzfPnX/INytnzao65o5pqQCAGI+RIgOtNeeciLjgi8Vi0O+1bZsWqUxkMHR0dFQk6d3rN8f9rVVVJ/nARLtu6izLiiJBF3ppMs7VQztblQuPX7wcvL99++brV67cn+zHaAXDcb+3vblFMdim2hwWAmUhoD/uv94uTDXL9VBwBt4lhc4TwTeH68XRE48+9J4nHp8fH+R5HpiYzSbW0ebmptGqWS9ObY73j6ecEQARxXcJ5xCClFInwwjQGEPWen/C+3cVVF03MstijChkDKFx3ljTRs8pcmPHPFFpAVwDIAEHJoGIgQeI0HE0AAQEAB0jGplH7gmIg2TAonXI4m/92i99/OPf89Uvf2FdV5UxxNhzX38Bon360UdOn9kpzfo//dZvpMnwypWbg8FgvbLPfODyJz/zycjUSy++ppP+k08+0ettvH31jbt3DuvK/Pk/8xc/+/nP1xX8/M//OxHw5/7Fz7W1Fyg4T+/efHDzxoO9c2d/6d9/+uKF98hku2rXra3b+UQJCV7UZYP7h/NVbebrNReqqpsQIcZYlU33ZYwzxhhnjfPOOWstRdaVmB1uv7+/XxRFiI4z4jGAt75tg3Xe+xjBe1/XlTHGWmeMcc5RjBSDM7Ysy7Ztb9y40dWdIYTBYDgYDDvAfLUsKTJEXCwWSqnRcDgY9JbzaZZlr732GgAul8sT6pUB48gFY4yUwDTVMpEoGFcyMkiSpDfsAcRg3aMPX67WK6DgnKvaZr4uj5arVdW2PhjTcIo8+r2N4YW9HV+tco7f/OR7PvCeJ5985DHJVdOYr73w9da4u/ce3Lx5++L5C0WekneJYCw4QUFAuPjQWY5QZIoxQgSikKVKK6YFrReHo2GepWpna5Rqrjmz9Tp6ImKbm5tKSWAeoodIHFiMsW2bEF3T1jFGBJBSIOcoGOPMB8s569gpAkApiAgIHZEHHhhfVk1tXdLvZeMd4AkAJ8AIEFgXmAjEgJABQwAE6MrYwCJjSEARIgJDwdty+fEf+KM//uf+DDKaTqd5b+AjZUWe57lK9H/1p3/49LmzDz38sM56p8+cH25uq7T325/8jM4GX3/xVZ33e8PB0XQyX5Rb26ciQd2Yf/LP/rdr125YA3kyQOQPPfQQIxQiWa0qFMkf+WM/vLF5oejt6mxDyOzCQw9vbm6lSR4CWWt7vT62TN24P503cbo2NjIfyBjHOafICNBGMkTAePBExCAyKTURA4DgoqlbRN6UFVnf16qv+CBRCSLFqIWCQJ2cinxgjCGxLE0Vx0LrVKtenpVlORwOrbXrxdIb27btdDpdr1bOuBipadqqbI4Pj/b392/evHHtrTcSJUebmzJJXn311bu37hw+OOgAXaUUEgDFYa93Zne3KHJC6Bgd5JwxNuoPtOC+bVItmqYRiW6jn6zXDZBh6ANTQpuyPDXqjYok4bS5UZw+tfHIud3v+bYPf8+3feTxy485A1VLL195W2TF8fHxV7705bOnz2mtTYy1dXnek1xdefXKqN+/eOECgZeJnq2WSuF42NvZ6j/6yIUsTwL54bAvBR9k6XgwIBcpcpFkSZ5vb28VvYQz1sHYZVlqLYhcDEYINKbJ8hQkqkQmSgqBJJA4ehaMaVgIAqUPDDExbXDOBfRR8nTrLLCCmAzgPRgAD8CJOACHSBAiAlPAWYwEkQkeiTGQHFgkT9EAsuhoe+tMva43R+P7+w8ihF6eheDfvHWrBVw39vU3rx3PZ4PNMShuIe6ePd86OH3u4UhoTPVg//Zw1D88OJBSPPO+J3/sJ37Ehno+OxoPR1kv/8Yrrzz55JOrdZUUvU/+7uc++4Xnjo6rB/emt2/cddHMV8fH0zkxhTJrmhCJiVXjQiAIHvFE9mGspdaGAAyBcyCCxbomAokeAEIArREAPMVIaOqGI1pve8mwl6mjg0OFopcP5stFf9BfLpd12/b7fYosz/PVcjHMc2BRCaG1TtO0blvkMCj6LjihUu99W9WR4Xi86ZyL1uZFSkRNU+Y6ma3W1to8z89ubQZTRx86GcB4PBoMBsvlcrwx5Izaurxw4ULH7y5nMykQiDZGg8P9BxRjawIXqqrb2ljFJCJmEr3x3NuMi1EvSyTbGAwAgdnm0ukzu1s7WmuVpS9fef3o4HBxZm84HHIKX/zyF5555pn5fPnkU09cef2NjY0RFyzLsvli+tgTj9d1vbWx8+RTj3zg/U8hufNnTz/34kvWxdFo+F3f8RFkajIvGUq6A4HY+YuXRNq/aPwrL785iDBvrHXt4088OsyVN96GKBN5+tx5JnhVVYlgD+7dT5OiNxqt20oIgYGCt0VR5FpJFr3PH7t87qlv/oDIt1zUHEEABMAIMcQogHEGgAgUoQPIARgiomDAAZABI/BMCCF5vSgX08VqcQAQhZTeewA5HI5A8c9+4YuLshFaGxMOJsd122S9HCQnJm/f3W/rMtFwanvj2tvXpdDGtPv7+7dv7W9sjLwNk8nk8HCfc8lQeUdNY4ajLYY6Bi54enh4fO7cmVu3bhGIEAIQDcYbxhiRpukJkYMIgFprbS0hQ0RjTIixQ+y11olUzjlrWyU7rFP5yGKMvV6vJj8YDDZHPQSy1qNKnTPDPGcxzJarTiQ1HG5Vy2W/3/fBGuutd4FoMBgwpODCarXa2e177zsZofe+bVt4R3swGAxSqbL+YLVaxRgPDw81BwqxqqqiX4QQVsulta01TWtNmuSRQZKkUsqqqvIsqbzLtXAxDIbj6XSa5/3aRQ7YVG2apsWgBzFKlJJLzYUg1q5Wm9u7kmK7mPU3t5967HKMbWyWb12/dvWNl5984vFEcZ2q119/9Vu/9VuPj49Pn9meTPfPXziDHBrnB2kmCEbbm8Ax66Xl/NhE+/QzT63KWindHxc3b9zVOX7gQ09/04c/cDidrVbNfLEUKtvc3m4jTW/fDSE453Z3dznw4+PjM8PTOi8IoJelq9Xq4qVHBSprfWELYgQx7OzsCCEUJxb8aJh/20c+uPv+D0BIpMwBIJCjKDgXJxgUeaAIiMSIiIBxZMARkQFBJEBkSOSR8WJn6//513/yp//h31+WK2uaj37Hx77+3Fe3tjYefvTy0eT4wf19Atzd262qand398HhftHP67K0TZsm+XCYzeeLzfHGel0lmVot28Fw4+rVq888/d6yWkqVta398pe+1u8Pp/PJ8WQuRHbj+ush4HS6jvFOkmS9YnR8fOyCnc3niChijCfIEaK1vqs+tZAB6F3ViFJKa43AYvSCsxMsWogYYtM0o+Fg6dxqtcp0B1OzpirJWdeWELzi2NQ1Q9kBhJxzLnRZNc76EKIQojW1YNg0TVVVgWIMnsMJ1VQ1jfc8km+a+vzpPRTy8PAwy7Lx5sbNq28WWR5CSJKkKIpekXXi5clkopQKIXhjh8NhqpR3RgDNlytnnBjq+XJ1+swFM5kJIQgRGfPGlq7dGPcHvX6idJbqRHByDtDKNLpykbHwnrOnC/yWjVS9fu3N+3duXn7kkjdeK/mFL37+4UsXd3e21vNJ3N3hnLvIXnzljUcfffiNq7eq9eLSuVP9VCmdcq1MiIeHR1qleS/jCUzmsyiS1ofD6VwkOUd57sKF6XIVbtySWmxsjUXwo/4wTdN1Wwdn11W5WK5t8IonkikkBB6FEpHTui3H/cGqLDNkvd42UwiSA0kgJAicCURJkYhFgMgYAgfqxPkn4D/v1EEMIMYIyBkTLE3C9PjJZ5/13jdNAwCr1QpR5Hnvq199Ls2zfn+cpUPvKRJrrd/c2Lp/fz8GUiqhQOt5qaQ2ddCqRzFYY17+xuvD4fD4eL5eL8+dO/PGG2+YlpT2Te2MccHvX770yPapM9/4xivAfNu2q1VpjPPRp2myXpedcI7e4T8jUacQJR98F5HW2kwrZBCDF5wzYkIg54yidy5wIQeDwb1b1yPQYrEwTX3uzJnW2tu3l7mWIbjF0kVEoHBCKHiPHADA+ljWTW/Qb9tWcWGDt951anadpMaYjmswxgiJly9f3hoNX3zl1dVqdXx8PDx7Jsa4Wq02Nzfn83mMMXirlOouqJLyxddeO3v2nF4sOTLGyDvbtm2i1WK19JGYVIGAS12XDQIyxskTAuukBcvZ3KcmGDfe2Flag0IKlaTGne/1/MOXD+7eLJ2LPmRJygWaqgymvn/7xoUzuwIjRGKo++PddRs56OmiObPHR5t7k3l5/2B/a2tLpz2GIkATkRXDNIBY29Xm1m5ZNTGCD8FYn/WKw8NDpdStt25cWb2R53lZ11yKxXoVIjAUi/VCRiWEQAlccpGr2NRtVQ8yLTOtNdvY7EOoQY+BAjAiiuQ8CgUsEPkInoHoGn84ERZ3nA4DAo7cOiuEwBiuXn3r5/75PymbmjH8/u/9vpdffaU1/vqN262pALCOxhi/LusY46qq+/0iT/KqahaLNQdME7mYrE6d0tv98Usvv/j4448WuR0Oxzdv3bXW7JzaG452vA+mdf3eqBR1iHDz9v1Vaeu6ds5475OEnTlz5q2rb+tME8JJpMI7gr3ue4eVdFjGu6YI5xxATHTivEFE5BiCjQRpmhKxuq4dEsbQtg0KKZApyU1VEYQ87zWtM8Z0pEuIoaO7mqaRUjIUxruOo+2UUyEEIuj+R6XUYjE7PDxgBEJJa23TNK+88gp4o6W6f/8+l3y1WiVadmmeiJbL9Xg0CiHMZrM00amS5WptnRmPxw8ePAgMs6Ln435rHOMYKRhjdkb97sk7gqDI01TLajULxKRKE9XKCPPptAd4aXfv5VvXD+89OHV6Tyu1vb29Wq0eOn8WQti/9+DCxSc2T52+fzQ7nKxyLWZH8yzVguH9e7f2D+6PN7bOnTtnTTw6mpatqwzcOzgabGz7QChVjHFzuEEwa5pmOBweLY8ee+yx2zduzWYzY0y9XDRNg0IxLgRqZ1zTNCoVrrbMqKKXbfeGR4cHrYAscYcHO2c2C4KE6U0AzphkQnQECVFny4gRGAOOwAg6LVvo6KoYopKaIK6Wy6PDA4JAxJIs/cynPlU1Rkg9Ho/f//73v/DCC0olx0eHWdHr9fLj6dQ598QTj73yymuCCYisLts870+ni/W6unTx0cl0ba2fTpbOhosXLzWNPXPm7Guvvuli2D11utnfn8+miE1ZNs45712e521rbty4MRoNNrY3GOOCKHQupxDCu7FCFEJwHUskJe+EzzFGxkhr3ZpaSpmm6aqxLgYuJDAmdNKsFpniIUTOY/C2ratBv1ibZfc8zrnOtSckeu8RpZTSB7LWZqnmQjSmxU6VrVgIvlvpIYTRaCQEHw/7B8czY8x4PGamBW8Ecilla9udnZ2madbrdbVcc87zrEDGBfJEJxKxqwqsta01PoamtSZ4JgXjQCEyROcMUdIJZ+u2iRQflKsze3uJTtu65cADscXxEUeE1p7d3Hnrxu1bb9/Z2tqxxp86s/vSi88NevlLL33jwtlz+4eHX3vrbh35C889v73Rv3BqvJxOrrzymuR0+/ZdoVSvN6gbB8Qni3JZh9b7waBoTNsfjafTaVs2wMXG3hnv/ec+97lCinu375brWmiVZVme5wLx3v173kMwkKapLrSJtnG2qdbb/RE05eXTY7wwCM10dXzt69e+RHp7a/uRC+cf72VjiAFCQC4JwEQnUBEQARIQgCPwAAroxC9kvcvzfHd3d39/f7VaKa07rRyiePDgQCrujHfWICoGfLEqvY9CqNdefs0ZJxgj4pyptvFKqXJdI19Yb72POkNPcPP23ccff7xq3KqsB4OBc6HoD9ZVXRTFxQsXr1+/XpZr7z0S6/X6xpr79/fzvCfeFTvHd5ylfziPdmp/a+07ag8dfKfblVKrjvYUWnkCY11e9AUL66riNUuSJEuUcaFTPEmh3q0l+oPxsqoVqtj1nhyN9SEEJRMfbLckOtF011ptbe+s16u6ahljx8fHMQB5LwC6a9cZBzr5AQDUVSO43Nra6mqYEIKUatHMicj72LR2tlwa72OM1hghRJ5qQTHVijESgldNyZUa5PliMdMyYQy1zGbHq4QzothLk2Wj9ja312375htvPfVNT0/m883trdlssXPqFCKOx+NHH80mpVnOZ8E2jz76uCAz7hfemd5gq+gNjo8nfN0QcKaGvSAeHB08dOmh1WrhXNgcjWtZN85LyRlBkeUaIUuLQX9UNu3u7k4IpFO1Xq+DJ8jFeGPD87Bq1jok4/5gu9dPIVw+Mzy7uznORa+H3/GR90K2F+EUQg4xAGPAZSRPDCTKd+pSgEiAgTECQGAYKQCAEsqa6mtf+ao3tlMGxwCBqKzKSHDn9oOdna3VqrbOm/U6xtjvDSP5GEHJZNgfl2UdXETE1aqUWswXpYsuzZQ1Psuypmnu3r3vXTx79uxitb5z724nmR+Px6vVCgCGw2EIoSkraw1yHG9sWmvROee9l4oThD8csl1Kk1oBMiaYD9Z765wDZJEYAXoXYyQfCLkkYMR4Y2zdWh+pbtskzYloY2MjSZIu5uq6ds7pNPEhdP4WRGya5l2tdLf5EoCnECAILTqPCr2jOazrerWuGEeldZJnjKMLXghR17W1jjFkyIXSgcA4/66cijEmlNzY3AwxGmOkFBDCaNgvEs0pSoCdjXGiZb/oee+VUhFIJTpN0874UNZrB56n0oKvmrJX5Bu9QT/NE65eeuml1pjtvdOtd2fPn0t7BVeIjExbBmdCcKvVwjm3WCzKsqyMra0tW7OqahNiRFbbNisK55wzljOQHNu6lsgoxhhjXbfehbpum8Yw4AQYIiATXXm2ubkBjGL0aaI0ogKgGJumSdPU1I1bLmB+7Fb74BcIjScDCBEIgDHGGWFHQFKMjACBxRC6bRPesWuH4D/5yU9+/ON/VEqe53midb/XgwAclVKJc4FzWVcNESMALpRzIXjyLlKMh/sH0QcfQtWU1pvWmndNb+PNEUNUWi9X5cbWJiBbLufW2uHGuDcYXnnr7bv3784Ws729vX6/7yl2Iubr1263jUEpJb6z9XeWOs454TsqxneKV3rH8MQYZ+zE90zIOOdpmjMurQ/EubG+aoxQGhFdIBdi27ZSSuecjz5JEsZY27Y6Tax3RFQ2tfWeiLhUQoiuMrbWdvzTiX6FMWtd9x8aY7Ise1fbWlWVMaYsqxCCcyEEQiE4yizLiGE3UiDP86qqOJdt2xrjlFJKylRKckYzGObpKM+LRCNEKSUDXhQ946z1USUpMJYkKi/SVbOOGPM8lcjObm9f2DsjGRMMX3jhhfl8XlXVwcGBUmpnZ2e1nHIijgwJqqqq69o5P1+sjifT4+ksRCBkPgaulQsB8ERFKYSoq0pyHkPIsqyqKkR0IRLjEYRxvmlMVVUASIxLKQP5AIEgtFW5mE5ynZzwggGMcWbd2OmS1zX4BsAi1QCRcd454RljndcIETsw9QTkYfEkWwEILn7w4z/wK7/yKxBp/979qqoYcO+i97FtrODy5q07xKCTxhMSMWhbOx5vZGnuoyMWCQIBoGCExAQPIXCJVVV1bnjnXFVVh4eHMtGjzY26rgeDgVIqTdPRaHTlypXVasU5m87n586d29oa93q9EytfIBboRK0I/J2XjowoIMK7hp7O/wSM+RhcDAAEAEJJIgoxykQzLnygJCukTojhuy0aIrZtq7Vu29YTSKFDCN2EixOannPGWJcz3v0hIEvzLElz573QykfyJ7pVXlXVO6+Kee99BKGSNO9lRY8rbVzw3pdl2TTNcrkcDocdN8w5T7jEEAqlmKONPO8rnQkhCRiB4DxGSLI+Ct1Yv26a7hUixiRJ0iyz1ipk2xvj91x6aHdjI5e6LetyVa2X5eH+waDfa6pSIvfG5kmaKF2XDaIQKrEhNMYGYiZ64ujJ++i4AuTAOVdSImP9fl9rTZEBYQjkfIwBiKGxzoVYt9a4MFsuIjHiAgCVUlmStlU9LHq+bRgjrmTr6cGD2bW37ty7fr8+XofDY7AzWx/70AZg8aTDZwiMdUwjRCBC4kQMkAACUaQYQ7BtW1+5cmWxWLz//e9bLBaT2bxzbmilfAzehxCotU1EGo2HndcoBJrOZ/3hYF2VEUJEv3vmlE7lfDlrTAsAQmJd191nfe/eg/5omOc5ABjr3EmmYUxwxtE4yzl/5JFL8/l8vDECRnhiNGXsD9tHGWMR6A8XrB0CfyL7Z53qxzPGQnAx+m6GBRAyLlyIPpD1oayaujFpmkrJO8to2dRdpuyev5P2IZ7MYnm3peve9rvIA+c8hCCE7P7Zfe9gYKUUIkqpAZALJaR2PjbW3H9woNPcx7C5vdM0DWMsy7LNzU0KUSslgGGgcZH0tB4XOdkWopfII0EEXFWtI0SV1oZmy7LzaSVJUtf1cDhMkqTI0n7RO7u7J5APeoPowvbGdlU1tmlda7x1kovgQuc3bI2PEaRKkCnkMgIGisBYp6P15PM8B8Ymk4l3UXCplBJCxABEjCuNQgIXSZb6GKWU67K23iGi1Hpra2u1KvOsByHmed75eJdV8+Bwdu3t+w9uHS0OZsv7B9DMFJQCHYMI7MQ/DH/gJYyRPGOMRUJAAEAGBIFzTPKsQ6+vvvU2+RADOBv4ieaTcSV9jFIJpVCnwgaLgk/n862trfV6zRV3IVhvN7aHSSqklFLKLMsQcTAYIIosK7Je0ba2bW1VN/v7Rw8eHKRpenR0tFgsKMJsutI6vXbtuoshSdTh4SH6SHQy3wTZu5L+d2yoXcP+LnTF3nH0dkNOJPKufESEk3hSkitJDBjnhCwASa0AIEkSYwx7Z5rQu4qhrjQCgCzL3l0GgIwhCimFEG3bdoaWLpta74SSNvi8KJjgAQhRdMNUiKhxrm6Ncd4FX9YVV7KTb1vrHzw4yLIiRpBSOmOzRHGEjXFfSSzSTAmJUq3L2jEMXJJMLUrQWeVi2RrvY9OYPB+s68b40DrbmHY0Gp0/c947WMxLLhMls7Y1586ehRhnk2mWZXmaN611NpRVG4n7yOrWSZHEwPK8l+RFlxRs8D6SdWFVlkKIEEgpvVyvrQ/WBedC1y82bduY1jmDKKz13vv9B4dapd6DtR4RGVJj2tW6ato4WzST4+ro1uHBjZuwOECogVmCBiAw1kUqAGMhdvtohBgZnWyoRBERozfg3I/92I81TVOVJWfIGBLD4CIRWe+VTl2wAHG5KtflPEafZRkAVo2RiZZaBvK9Ya9sSp1pT9H6eHQ0uXvnPhFbr8vj6byuGqVUmmVCKCDWtHa5qilC8DFG3Nzccj6kaTYeD4+PD4nCSe//h+35Xcv8zpCI0AXBu3nXWsuQACgExwVDC+VqqaRkjLXWCCDOeQiEggGxbliNTGUIATgSUb/fZ4x1jtYuYXPOne3WAAMOEEFrLbj8w0BEN2II3pm2UnLuvUFEKWV0MRADhlXTVI1xwccYteKRSAptvavrukizra2t1WrFOW/b1keHkGZaScQsSQVCV2kczma1DTvb25ubm01ZZUmacg7Gct5omQBS1h81rZktZlFwqfOd06fvzGeLsiaBa2N7o3Fe9LVWyCG6AMCEEMYHrFvg6Hw0rgMoUKokOC+EcDYcHR4vl0sTAhIGxawLyGVZt4yLziZFQHVbDYb9pq601nW5zrd2jLNEZIwlwECMOMZA3lvjHYRYGecCWy/Lqlnaw12V5FBUMQjkyEABQGc9IiKODBha0yqdASARQ8bItygEuNZaC4BKSbKhcZGBcM5pof4gl5GVGi5efIhdSL/2lReLLDPOAlBZljHQZrFJjLIit/6IInofzp+/oLXe2Nhclc1iUd2+c7dpfKI151IpHUIQnNI0xSgYYFmtt7Y27t27t7u7o5T4g6kT0Mnm4Q+Ckoi8D+/m1C6UOxdHB8t3iGZZrYXgUnEh8N19vNP41VXjvbfWdk1bx8p2EtLuSnXB2rH83fI4KZVPQioAMuMsIHPecyk7ZL4DDZRSRVF0lXgIoTHGx+BCsN5HAmMcIJvNZv1+v1ML3Lh2/YSFA0LBu3lYPjoUQigFQiZFD5RcNPWt/YO3bt/58osvf+XFl+Z1M11VNpD17N7h9N5sPm1aHAz254tlY3iSJ8PNmw+OHJdrF+Z1uXd2rywXQiBj0dpWCPQUI1BkYKwvq8YHaqrW1IYDd87tnj6TZDkKqbM0RADOhVBNY7yLxrhA0cfuwgTnTYgu0dLZljHGhTTWb23v8jQzIUYIIRoksNZ6YCJNlVJkzerwASwPIawFtwAhQjyR/xMwjgRAFJbLKbAAhECCYmScu6bx3n74wx9Wie52ziTJWmu7flcptSyXSsvNrfG589uRrDENsNihN93Hffrs6VW5fHBwf75YdF07Mlyvq8VilaW5VunW5hgAlOLGeS6k80EqnaR5JFYbg1JyLm/cuH/x/IU81b08FZwhxxMrSLdWOpTUx5MJFH94QB9jzAcrVcqA+RC4JCHQ2pZz5r3XghvvBUcictYj8og8EgUXPEUpVYhRJVoIYV2QUqJw3gZEwYVAJrjCEJxKtGkbZLyzZsUA1vgs7VsfiqJ/vCiV4B13pVUqmLDkQ4jz5bJqTERmnM3TQiVpZDAcDqeH+6aqRqPR5ubmG2+8gYj9/maWZSZ4naerqhwUOQux18vTvLh+/bqxrj/eyIq+LPqzw8nhau3q8tK501nWYxq8kt94+fWj1YrefFskaW0saHXt5nUhIDC03oXo+v0CWEQOyDkLEgA4IyVVK71tjdQqhFC3jZS62yWIMeNOLL7OxhCj0KqTLEagzjTBOdV12Tl8vHXdDiOkQBSrspZKWRdSjdbaEJz3NjBtiawHb8PxvX01GPZPzzDvR0gIEFBBAAJAxgEoRlitVlunTsWTnRMhuJ/72Z/92pe/8ON/+k81TeOD55y3wXPOBUI3NTHLM6nEcDRQKUSi+XJGLNam7GaNSa7OnDlzeCyWq+OHH350PnmFYmQIq6o0xmiV1JXlUrgTbD7hnFvrm6pmkFKIVVWPxyNjTJIw58wH3/f+X//130KI3tvW21YgBGeCM92OLDkGZyXnnKHkDCFCDJKjEMgYNU3DCDjnBGFdLnUitZaxI+Cb1nsvlPTAmJAolTEGmSBA451KMkAWgFwgFyhJ0663sJFMCCJJCUEoXpa1EMoZL1B6T0Lo1hAwkaW6l6YIMUvyRCbBk7HNslq21qd5JhMuFG9aN5uu8zxt2hKInGkp+AsXzjmiyti6bXSaWIir1hDnZVnmyPeGo51B/0//wMf7eVGt6+lyuarq5WK9vb29risuRN22lXOv3rz2jRs35i3p3hboIh9v3D3cDwyyXp8xFq1x5co1NXnP2QljQsFRCOCtZBCDoxC9j84F40/GJrRtSxC6JjJyRgKFlEQOmLemVpIxiIIjZwKZEFwhiu7OSgnO2Xo5t8YAUXQQHDCIQqJUSVm7m/sTB2o1LcFGYDbAUoBDCABduYQxECOJTB8dTQAgshjYCTX4kz/53/2rn/tX3/X939vr5Z7o/R96NgQHEHwM4/Gwe2uHR8dcqreu3uRKz9bTS4+cHW/1PTlAQg7Xr18/PpxMjurFtHLOJKkwzq3LxrpYt4Yr7A+KJEn6/b5ATiHa1tjWrZdrUzeCsaODB8HZD37g/YeHh03TfPCD70UlpBISgXUNGiAqpbrbJ8hUCEolXZHgY+hWfwcuMMY64IlxFCopiqJ7LGMkhOj1+50xP8aYZVm/39dpopSgEJEAERBikaeCgVJCSGSMbW9v93q9siyNs4KroiiklEQxxpimqdZ6kOemrcfDQZ7n3rq2tat1UzUOuQ7EYyDnQmtC3utb66WULIbRaMQYu3r1OuMyyfLxeLxcrYBjBw9ba4GCb1sFkEnxfd/+sc1+v16VxweH2+ONRy9dPnd6DxGLfm8wGr7+1psg5N7Zc08/8/5HHnsiLXpV1aRp5o0FH3KtkEGR5488/DARmcYKIQKRdw7e8YgwxpumcTFUdeOc61rpblQqMG68E0pyweq2ebdP9d7zzv4MPBAAY8AYAJNSaq2VUt3zhMA4Sh+DdWFZluvaz1fNdNG0TSRP4D2AD2AAXITAOogcIlCHona2yUgnTRUDqXV/+Nf/yl/pkKNA3tpWKi6QWdsSUar05ubG/f3D3d29w6NJr1fIRJ67cDYCdD4R75xWKlXqxrUbTz31lBC4tbWplOBCCCmV1sbaqmr6/X4IIS+yU9s73RhJAFaWLkYvJHLOsiyr2qZqG8GlICIfSHAJCm3wiEgMhNI6zbRUUmpEdM7oNFNKeR+VSgTy1WqVZDnGsK7KM6fHCRMs+KIoEqU3tje896mSWZYZY+q6fuyRy8tyPRz2FrOJRiE429oYxRj7WbpybZGqotcPxEe9IiT8+OBIpCorUtVZn227rsC50O8ltgXOQoyRIzSmrRprLYQokammMYGAo3ZAUui6ao4Oj9vWJkqfu3D+G6+8TsTatj04OBr0+pxLDEzlyjZNoBCCu3fvztmz5y/tnpuff2h3a7vXH24Ph6FtthKeKHFm72xvY+Ph8w9N1s2br7x268btsxfP/JE/8cc+//nPahQUrCAWjINIB/cfIMGlS5eu37g1m651IhFY3baNCY6AfOBSATFEZowRSnXeEq1SKaVZroTWWqVNu+act9ZolTEUznlELlUyOZ5qJYlCltFkMkEUNngCbI1jwaMQjQlN6xBYIGxtnM7XiQ9V4waBcVARAkAAigABWEQGAAEYCM6AgDMkQAAiLgHQGZP1ByiFlPJLX/qSTmQILsbgLFMC0yRpgxWcHx4eF0V2dDR7+OF0upw+/vjDRwfHq9UKEbXWTd3Wdb1er6WUe3t7jLHDo0mWFcY0zrmNjdFkMoFuskHbdmrZuraDQVI3bZoAl+LcxXOvvfHqcrk+oYUQsQNdOYILPrqITAghpEo6xjwEYigAkUtpnMv72XhjCxAhBsZYJOZiJOPSJBPIbty4MRwOrRRN02xvb3Olj44OAWC/KQe9vC3rxpq1cXmq00RVS9ja3lqvK2SwmE2EEGmaJzpDRGMaAo8c8kwvl+si0StrGIE1bQgRuGiMtZ4ZjxLRO4gMkAvE0LYtCAeRFUWhtT579vyL33g1xphlhbUt5xwJsjRlkTjnnqLUChFtWa7YwXd86EO39/d1nivOwdr5EWOMmbrZ3FEXzz6UHE1FkG302xtDTpTqhAKQ9YTQtNVqtbp8+dKd/cmbb77JUHChiPHGGgnMRnI+BgwxgjGOS2F9zIidmPo9ddC3QhRa1bO2akzKWYzQTUVALoVWOes1VckY40Ks16ssKxARhbDWeWshYtM6EyABLBtvGh95PdYQQoSAAIIBixDYCeF/wgAAYJqmXa6GSMRYx+xEhv/LT//TH//hPzGZTBiAbU2a5q13g0FvtlxPJhOVKMaTVGnnAiLcuXNnY7jhnBNCPPnkk+tlOZ1OOxd7WZac86OjozTNiaYdax1CqOtaCJHodD6fmzrkeUoh9Hpp2zad2aRt26a1ZdVkaSZaewKzR2uAEBHrqnUxCCGJoo8NB+goNyIyznbt+WQ6jxGIo2cklMyyrFmup5PjQqkiz5Ikcc6ZuuKcE7HIoGnnaZoGCKvFTBADLogxiHRwsK+FPNzf51ITGQIUQmiZhBCapvGhtc4wRp1i4+jwoNfrXb921TmvZDKbr1rjCPNIwGXe04nztXNGSw4UJGez2WxcJFLx2WxS17WU0nsvAkFktrHeeCwypdjR8fG5vV2lhWlqkEXi3KOnT3sC7+10tRqotG7tarE+j/KRC49cOO1u3ryte/nWhVPHR4euscOiFxgy8JPJxFp79vJ2Va1j9MgFIPOBYqBOH1EZN8oHdWOkYrZpiKjDNzp2s2uwgicuVGu9TrNoWxdDIGatS5IEmdBpR7Qy4Jj3CgBUiW4rY12IMXIuAhNKZyD0wfGMAfAcev1+kmhgAiIAoxhBcEmguvkJQIgsJmkORBCJUSRkCAgcuda/+R/+w6lTe7ev3RLgkU4mg/sYApAPIRdqOV/JlBf9/uZIMsbm0wUimtYpmUyOJlXVco6j0ejOnYMkEUKIPM/H4/FyuVRKKKU4Ytu2yFSapt5UnHNjbTdBJkuTtm1ffvnVrVNbbe0HWwMMIfgYIlA3VT4ycDF2lnwA5pxrnTPGeB+c8x2n1xgXgAmtOh1TlmVKJc65zc1NY1xd1yGQUsoa06Ghw+FwPN4gAik15zItetg5rDlyLox3ad7rpqVqrRExTVNEESNIqROdQmTBhraqU5WMikGeZL1er67b1lhiIkTBQBEwAIwRQgiMUV2X1tokVVJygLhcLk+4n3dGtxJRonQIAYUcjsd106zLcjAYJEooznMhCsk1MsmAuUAmVMu1rUwvKc5s733nsx969r3v4UDNusqSVKBUgpu6YYircu2tEZyNRqMQwmi0YX1onF9WdWVM01rjQ0c/cSk7hW4IgUuBUgDHSFSbtpsHyDlfrNZ1YzrIRSUaBSei1poQKdAJm9j1D401PpBOcuQShGZCrGoDTBKXw929ZDAE1BC6yfQBAAgYMUbACGKkmPRyOBFWA0VH4D0ET/Hq29cXi1WMMBxuSql8iOuqtpGsc4PxqGxaILa1ubO5ubleVYnONjc3N8Zbo9Ho7t17xhiluPfx4GCqFBKR1nq5XBpj+v3+I488VlU1ERnjrbVZliVJ0uGY3UTEU6dOJYlaLf3t6/sQQfMMu8l+PkIEDADO+RiAAJ2PIUL3xwVqrTcuhBBDJM6Fs74qa8YwBlrMl1mWWWuXy2W3d3TiqQ71RMSqqqIPxpimaZfr6ngym68rH6m1PhJDrpZlXbXWnbBjgSikaSqlUjLrZtNlWS9Pe3larOYrRmhq4wMBk0KmXCRKp4yjcS1REBIZhkguSbQxrdLimWeeds6FQDHGwWCAUmxubwXnvffGuLppZKLLpjbeATIb/NHkeL1eL6az1WKJgdrWIkEudLVcaZHWy9KsVma5pLadHB5J5IhIxHSa163xIUynx1W1ruq1lFJnaZJnrXU8SYihSLWQshud3Im+AVme50miiEK3kCKQ1CpQRM65lEzwQJDmBefSGCN1GgG51BHQR+iWHxcnQqI8z4kLJlUA0bjAtRZ5b+vyo+nuOdB9EgkBcs4YRBY9h8AhMPBEPu0VkXejVRgixWgEgBDib/zd//GP/cAf39zcXi5WPiKA4CqtapP2+7PlChgPBJPJ7Oa1m888801E7OhwEiM554fDIaJo26CVzlIdAxR5PwboXNFa69Vi2S/6QojBoNjY2PgDJtJ18mi6cePW3t7eYJAMBv16BffuHIpuNn4nVyACa30n/cJ3NMgAgMA6eQAxiBAYgWYcETMlQwhlXSml6qrl4JHg6OhoIeXGcGBNOxqNVus1qzki+hDLdbO5udWUldCqMcYYt5jOiqIwnYKQqF9kRZ5MjiZK9VSS5HneVHVdlcNeUSRpP+875/rD8XRZrstG6L4n5QLF6BXDTtfifOuc7fBd65vR6GKWZXXdxhi1zlarVZFIRAwUhZK2rgXnZdUYRsNecf/4eDsfEfB1a48nh9fevnpqa3t3vKuEzvuD3a3dSWWv37p5cWdgyGGq3n77ba4kEQ1Gw6Y8TvM8MlBK/dE/8v2/++Xnb90/PHXmrA/h1JnTbWuisaMkO3fh/P7+/UyrxWLBOCBCnqe9Xo8CpHlG+9ThJHme62CODu7JqIkxpVS/XxAyIYTUSqdJt1MlSnlrVoslIvT7/V6/D0Bpqr0zddtEZPnG1s7lJ8TeJUjGwDJPtQR1Eo/kOxkAQ0qylIBHIsYIKAoUAWL0XiL75V/+5el8MdzYjKuqNq1gvDYtObexvUPWV/XaOe+cvX37dllWjOF6ve7m4vR6va2tZDqZG2OQ43w+R0TnIjFwziHQ1tZWJG+Nb9t2Z/vUffOgQ5EZAmNMcHnv3oPxeLyYr9NEmtYKRCGl6rC9qm6E5EJ0wIfUWr+rfGG8U0ZCIA8AHFByIaVsaouISZK0zqYSO3s067StUgrkgkGMzrRWJakA6ud5W9Wcc9/WACSl7NQC3vtu6JBSgnMI0XnLDbbdSSMxxl4vXyxmkcGbb79FXEtdqHQ4PZgXg43Nzc3lYpaCWlcT652U0jtTNS2LLhCbTJdtZU3lekpoobu3oJKkXC1BoA+hahvw1gYf+gAkZ2XNInv7rbdSKS5efmR2MMmSnIxjPuosfeXNNzN+IRvk6zZevXUr6Q0S1Mv1ql8Uxvm2betyxYVMkiTLst5wkBbFarUcDIa3bt9NsnRnZ0trqSXb3tmsmrJz9u6d3t0YjwfD8f17h6U1SouNjRH29Xx2FBz0+33yARG3d08xDvce3O+efGM04AwTNVwvlgghSRQwGoyGSvAQdSCSWXL24iPjM49AtkUwtMQFKgIkEAw4dPo/ihFIqYw6BR2Bj8i5IGCADCj+q3/9f3z8+/7I4fFB6+n7/tjHP/WZT1ZlneU9IcRqWVnjheJCyHv37g/6I6USKdVwOJ5MZkXerxsTKLoQFRe93qAsyyxLl+umbY0SOJvNOkWoUknws25rqo3TGqOPUoa86He51riQpZn4w2qpDpx718zU6Z66O3QQgQu+G+BB/mSaX3hHFk0UQiAfwVqrlYoxMiDTVJpjYHFra2O2WjJy3pngHXlRZOl8uUi0VCqprUFExkAgo+illEAYgieKMcREYZrJqq1CIKFk0R8dLyqUeVVDlm81tb975wgoIDgUSsnCNHWeFRSNFIKIrddrIVFwEhwsRetcXdcxxiRLV8vWxcA5b2rXtPOmNWZInInFZHrh4sV6sWhDGI42TG0uP/RwXZYzFIeL2e0j3ff9Bdet96kUjmKapiFagbwpq+VywWT68KWHSCRKKS5oU8lTe7vWubzXV1pkqR7087qun3ryyclsMt4YcYHeegI8d+HsbFV2Z1kpSp9++un1skySzBs73hgKpXqD/u3bd3d3tiP5nc3NaB0CDfr5th6j4KPRMEmyPE9jcERhb3cHhfSkZJCEXLHEQcsAOWAIxLkACETEkbvoY3QJV4yh4Np5i0IRYTTmR3/szwMHQrC2/c//+TcC+TzNIoVqtS7LFSImOpstZkBsMBhUZTmdTquqGo1GR4eTDvGVUjoXAKDX600miyJT1jvvo7VNmkrvvRDROVeWJREbDntVVXXdRbmuOxpWaXH27FnRjSzsDk0BAgYQQxTIGWMCWRCd5wnfIegpRkLO3tU9IWNCnpxqErwzFLrbxhgpkEgM8gRZ6OeJd01VVQKCRGIQlRAcohDCB0PBIecsesHzVOnjYLVSArCpV0qLna2x1nq9XntiBwezLBuXld89vTmZeQZJlqUsMinAh8q5uY8gFRdSQKAiz8pyladcJxDBpIkXSq9WTQjOeiO0YoghRIYiy3uNNau6qe0hAp7Z2W0ZPZhMD2fzi7tn27qdTubZzk5MyEl66/7tcdx02YglSRtcKhIXnNY8uliXdaI16nRal6dPn143tmmas2dOSykffvhhxtG2pji1HbyTkl++fPn5559frVZ5nuuh9oG+7du+7RtXrgDi1qkdbqrp4UGnv2BIx8fHxFivXJ47c3pjY2NY5NG7XqaJ6NTO1s7OTt02WuvZfAkQxxvD97zn8V6R9QY9NB4coQwUGyW7AT+ACASBQkDOrW2Icy1SJOyOHhNcE0Dw/i/9xR/Tmhu75ioKhUwiGQrWuEjReWf95ubQey+5tNYdHhwYY0KIu7u7d+/ePXP6XIxxuVwjYltXpjEhxI3xcL1eI2OADBG6yU7VuhRCxhhDgBBICAEUtNYheMZY09Sbm5vz6UQwxjovaIyRTqTg/EReHQLEzisRT1xWQM4HZCCIccaiPxGUdOdDZGkqgJxlHLlEppWg4LOklymhBI77xapuKPphr4gMVZrYPMuyojXOz+dSCi54U605C1poROQclU4lZ9a2NlgbaTKdM8wWazvePDdfufH47GrthNDemOAjYsJACK6ByaZeJ4qFCNaRaaySfNBP6mp9am83WOO9d9YSheliOsxSzrlgTGfpclWWVcO5qJzTOt08vfuZz3/uA+95emuwMQy+Wa8MZpVpPPlqOnv1+VcdkKRYFEU/1dXiqK7bfi+9fevOqXMX7t65f7BY1zY67+/euZ0kCQHz3k+n09FwUK2XeZ6nee/RRx89ONo/Oppwxi88dOnCQ5estcfHxw/2740Tce3aNUYoEBlBjFEl8uj4oMj71Xr55mqFLGRKaimklLduXHMhrKtaCJEXRfXyGoFtjIdFkd+7e/Nbv/2j+d4lphQAg2ggWIYCIDJgEEFxIi5NdJwpQIbEyFuUIknkxqCAjL/3qSfqpv2N3/rUclUVeV7VNRJIyTc3+01bSpV2uTNN07Zt0zS5e/cuY+zevXsxxn5/2OnmnIv9ft7UdZqmxhgf3Gg0CsGFEKyFfj8Dao1xAGCM29ncWCxnWZaUZc0RZtNpL8tFkWWMMY7ynZAlzkU3MDoyUCRS0l2lKISIRDZYJbhmUgtJyFCgg+iM7ff7koEAGo+GGIm8Uxxz3eVsyLKMCT5uTeNdrxi6EBnCxsYGhBhCOLWzkyQ6EVwpYb3b2uEsIgAxJERYLudl61oTCVMftQvqweGyKE61hi8Ws60tCczG6BUjqVAyvVyvpdKAEYAt5tUwK4LnW6OdZVkJUJubm3VTNk09Ho87dqTjVEf5aHx2fPv+g6PD4zqtNgZDlejNvVMvvvXaub3zATUvl/euTpNMM62dUB6ZQs2AF0VRpLpeHvd6RQg0HI7r1lZte/f+A0LNGFtGD0BZlrdtG4K/OZ8lSoQQWuufe+65qqkBQEu9v7+/Luv9/YOPfvfO9Ph4//4dJWVTNjaANY3Wel2tkiRZWlevOVmrJGNO8CxdzKa9Xi8wPp1OI7BOKFwUmZK8bZt6Nc8T/aFv4aLXhyKHGIAAOIfWUdOwIgWVMZ4nqCC2QBwYsmggSnDlT//0P/zcf/ntf/mzP/P6m9dVPs77oi4bxmSSYH/QJwy1KbMsmUxm29vbnPMsy3q93uHhYYwxeACAxWJx/vx5IhKCjUebx8fHJ3ZRJeq69t4CQJJgJz7OMtm2VkrZqde7mc2p0mVpIA2iG9zHgNM7FmrEExk10UmdHd45UQ0QTs6NCM64EIGIo1SybWsuGAf0pl2tDAs+VZJFLF0bBNg6JkoEYMYFF2i9riJQ4yywqFABFyH4ug5ciXJpAqL1UXEppXC25oKtmxZANW00VljHA8g03VTJuDVxONpazI53To1sXUdqY/StqYxxkksAPDw+sml2+fwZZKSU2t0uDifHDz96ebmcd3Pd8jxnznrrBmkmGPby4vT2qbOnTn/iE58lZx++eGlzdztyuDM9rL3YOrVzY//GubO7Vsivv/6ac2E0zr11iUoTzY0xSbYjkPYPjrLRRrmu0yS3ESaTiRQ4Ho+qqgohFEVu6sY07Xg8bqq6sSbJ0rIsG9as1tXB4bT1ngP7/Oc/j22ZKDS1KdIeQnRkbLBbW1tXrlzJs3SYJYKphy6dv3v37mo+Oz48Gm9vt3U9W6x8DJ3QcXo82dnaeHD/zpXXX33p+ee/8eorxPlf+At/QSHcv3v73p27x8fH73366e/5wT85vPQECL04ng63tsF5AIByBWA/8au/PBj018sVEc2XpRCZkmkIoTfole367IW9wcYwWADA4+PjEIJzNBwOH3300aOjo8V8lSTJel3dunWrm69vjPHe94teVVX9Qa9t26LIjo/nqZZKqca7qqqEUDs7O9OjQyGEECpLWZ5nIbiqbvFEmMJiiH9wPmeHNnPOBUOJHELgADG+cwJgN98zOqLAETgD7/1wOOwsiN1ju+BGRKF0YDwQEmJrHEplQzQBOOeCKyLW0QedpNV7b42PkZxzpm058Lax89naOrZeB+QDqceM9dN0g2MWA0cmV6tVx1mE4LuT35IkiTH66CKFGP1wPKRgLl86L0UcDfL9+/fyPO+cC3t7e8aYJEmctcE6zfje5vbWYPR/+/E/CyHWTXXvcD9q0dvd8XlyZ3r4nicf3doYBYKqsUmSVMuVbx0yduXKldNndm/ff2BC3N09HUJEFIi8rltE4V10rW2ahoii811Sqetaay2lioG8C97F7nIxxhF559TtcDfvT462YoxV61IrKZEVWeKdLVdLZ1rXNlmq27qGSJxzRhg9JUqnKq2qhqJYr8zVt66Wy/Vkf/rcl57/F//on/7ef/6EmS7337q6unf3X//0P/qFf/aPv/Lp3/7f/8k/ePF3f/P4rRd/9//6N/XR7f/mT//xX/mlX/imp544s7vXK0YcE9NSIOkj7h8fr+pqbaudM6eqqpxOp4yxNE2Vwlu37ldV5b0fj8fdW+iMnDHAwcGBc67b4kMInSJZypOz+JxzMRIAdKRJd26McXZndy8yBOSi00Z1F0LJpDNVd1rVd/TUMYTAlWSREUTrWq0K4CRQErJA0VTVarEsq1V0XiEL3mmOTHChE4YMtSaOkOi6blvvoTE6KSgElMq6VnIZIgXvOOeoVfCBc26bBpX2PjAp82KUWVyWXiU7xiWtFUVv7IMAACUTRj5JVF2t8jz1QXiHgJx8ZMittf1+n0V/5e23NnqpC04lenN76+rNW/v7+3mWeOdkmjDGqqp69MKF6WSyWi1Sled5Xi/m3/SeJ27ff/D0E0/N1muZFFXtesUu49EFf+Xtm84DAkvTlDOsqipJkqpuO45b6nR9OKnr2rRNkaXOGSDI8wwAnLORPLCIqBE4Y0xK4ZwTHIXgPpBSyoN3zrHYWZqEi44JSvKsaSryRKxrJ0IIAUIsqxVnMUkVY0QxvnukbZcyIgOtEh9ISmFMaGvnPd24eiNLstlkSsZ9y7Mfvnvr5uFyzYenPvfpz5iq/M1f/ZXVbGKtvfn2mwqh8eG/+pN/CmSuhObQ5llmjQPGvQu9fq5Veu3ajdliefr06cPDw+Ap0dlwkFRlU67rJIG6tl3ukEI2vhESnYtVVQmBi/mSIYRA/f5wNpkHT9Z6KQUAvP3220zwqjGplnVrXnjxNZVwzhgmWZbmed22rbVNWz108Xyvn5fVypNfVavWmaptXAzG2cVq3V2mpqlW6xUhEYuNbUIIq9WqLMtVVd49Olq3bdnY2ap6MJ0/WM5fv3X9YLV8487dt+7eqYOdLKfrtl7V9fFsPl+tp+u5IxcYLFer2WptY2h9zTAAC9Z5lNnRvFq1sQkZT87q9FxvcIYwR0gFSzkTbV0NR8VyNQmxDtAmWRE8Ryac88PheF1WPtLNW7dVmlXGXbz8yNs3bnYS1aaqyTstdJrkWb+YrefHy/l8tez1UuabWK83i+yDjz0VFu7Bm/dTLz701HskOhv98bq8cu2GB2SMe29n66kjax21TTh9apdzfjSfOR9PbY9H/fzRy2eLVCjNkYPWSisVg0k0V0pJoRKtU82zBLWkbvR+RNa51AVnMkk7BiuQV4nobwxFqhtruBBMSJnIosgSKXdPbUuFDCOB29oYjkeD8aCf52lv0PcU015abOaj7Q2l0/HG9mA0CiEUo0FvY6N3antm/Nv3J/emZeNjJAyMOw91bRDFrVu3Nja2UGZPf9M3n9o987/+w39YaBltpRVwjAK5b8LVN242a9vLekfHx61xKkmNcW1rg4deMeSMn9rellxCJEAo+oVO5fapDSnQWWdNsG1AwLZuGaC1bjAYSClHG0NisTvR0wUCRJ0mIWLVeNE0BgA2N7fv3buXqvTBgwez2SxN004Syhnr9wd1XXvvN0Z9YLFXjKJ3Ihd1U4HkXEkt5Gq9kFImieISnXWCc8GYRzDGSpWsrfN1MxgMgvNVC22Ybm/vsCirelXV9bqptU4hsnrVckZasNGgl6XJuacuXrlye7asTBRcZMBy5LmxVZLoRCUMSDIP0MbQOl9Zr5JEzWZzzpUSrFyXCDgYjGxd9nv9l15945n3vifJi3PnH3r97ber1qECnWQuhsr46bqCXk5cjLY3Y4x1VUoB1XJZN4vLF5946MxDlvzRwb3Iokr7z3/xayREJCYTycC7ytngAbmUUivlXOu9397d/tJXvsqkMrY6f/70ZDI5tbNxsH+UJFrr3PsQvEzTFMCPR73WVMjJBtH6OFksCTB4q7V0LdOJiD70koxhQMayTAshGPlhkWohtkdFkQpvzZkzp1CqyWSVZYn1ISPoDUZpL13dWPfG/Xyjx3zMhBaT+WQxP33xoenh/sXHHm2see6112TWc36l0iwwuPDQubffeCMvikSrra2tpjW7qHWen86Gf+Nv/s0Pf/hDn/zU7xEDY1yMMfjofGxrF50PgZRKQgh1ZSiCF4SInOH+5Gg47DVNbUwzHA6B+fF4qIS8d+9ACCCCcl13CmYheKcM7AqJE8cUsjzPYwAfglRKOB8YY3XTXrr88I3r1yPLBFfOeuTAASRHZxrJGWecguGcZ4lumugogESZaeTCGysSoYWMFDgxXSTRed3PO/VgZKr2Pi+Gy9axpNg+O2gb44GMsyrNdJZKyZVSEGLbtov5HKJcrMqk4G9ff2W+tEqldQnj8abxkbFYFBlAtH7JkYVgewPUIkvUFpd2Pj3YGJ1tGgPQltVs59TuYlbvnjplmrK19tUrbxKDO3fube+euXb16nD7VHCeaV1ae2v/uNcfV3ZOXB0vZn2prHGr2ZGQvZdeeY55BC1ZX559/NFbx6sHBxNADNFopSGgUjr4GGMgwjNn9m7dvr69vVEb++Fv++YA6IEPxxumqWNwTz/5KCIa74AJwZOmaRLNGUYOxIRkIvVR3D7Y//JXnnex+eYPvQ9Cy8j1dIqMtOBKqe5AGKnQ1vWZ7Y3oG1OtijQtBoONnVOLZdO07ni69CHWrRWJeuTxx0AwriURycidCzaSkvyhRx/WUvSGvYeffPru7XuvvPpaUuSnz+7lWu5ufaupq25+snGWQDCuvv7iK1s7p9586+rjTz35wksvIZeATGiFks9nS4HIGCuKouMR5/Nqc1NbaxyATuRiuS56aRepBwcHh4eH3oY8T5RSy+Wqta61bjQaGGMY413Z2dWiXa7s3HiIOB5tCq3z9Xo9GBTz+XI42mibSqVpaOpEyxAcSO6dkVIO+4PFYiG1Wjc1AABjUivOuVTSM9BCLpdLzlAlMhKVVZtizoWykZzzUikbgtbaGBt9SLLUGIOC++Cl4mVZaq2RwHlz5swZxWSIZrTRd9YT4I1bcwoZkFZKras1mUjgemlSFHmzrjkTRNFaC86UqwrCZDQalVU7HPU6I6u1XquMQZwtprfu3HvPU0//zu/83oULF1Tar2E9Wy0/8K3f8vnPfEHd2d/qZ3fuH2wV+ahITu/t3njraoy+V6SC+NFiPtg7szTuP3/y054LFMA5IEJdN97HLMssBWCBGCSJWpdzknxZV72N7eODaeCMA0H0db1qW8tQBAQpdFmWrpszwFFKLXRR+7goKx+aql5OZvvRVr08Obh7U0kOELWQTEjOueS0t7s9W/nYtkWqbGzbIF957RvHk3llIgM0PiCXXGLaL2pTJ3nGCDjhcDBu2toFZOCr1u8f39rc3NzaKdZfOQBYzae25hCdl0I/2K+lVs89//Wvv/TKQxcf2T+ertbm0Sfe+6WvPK+ThIgxQu+9a41gXGvVtm2nYGSR9Xq6I8AZY61pgFFHtpcnHvHQtqaTTSWJXq6MUqxpml6vJ4Tq5tB0js6uLesK7hBCWa3YkxcvSdlN74lSsRh9a5pIlnFwznqKeZ7XTZMXaWMM6+yMwLpzKyFEznmaakIWQliV6zRNfYwd8pUohcAQ8cS/5nyWFW1b51lmrQ2u08VippW1baKlUopFci7s7m0NR7mQvU9+4oXW9JLsdGt5JB45894yjApJK47RKSE4as7Z8WS/KLK2bTc2h4gtQ8uYm0+Oh4MB+XB6b/vunZsI7lu+/WO//hufYiieeOIiYuzl6fXrNy9eeOrKq69v9qSO5tLOzqXd3dOj8Va/L5HnSVpVlUtUXRRfu3r9V37r04FzJSmRMM5S07SO2N7eqULzlNMTD19A7mpoVD9/MJvP6ha4tq1LRULWL6az7e3tiLE/6h/PZ5ubm+VqxXyUgnEmqsYPNzYPp9PFYvEtH/72gwf3BLNtU3YT4oXANE0jofMWWeDkz57aHhUFiw4gTuez4/nCuBgiap0gl84Z4MglU1qkWrV1nete8JQP8sV6RsFW5WpzvOGMicYBh90zO9a2ZH2vN2gcpflgtS5DREfsha+/Npmvn3r6A7/xm//FR24dNbUVQgglq3UJyNJUd9K56XSqtBRCjEaj+/cf6JOhdKJDgToh32QyIc+cc1VlhAClkm7mQ9MYLlEI0Z1+miVpWZYdtuWiDYFiBGGMsR60lFxhY9o8TxXzMbKmrTxFrTVy3uv1alNrnSqlXPBSyuA85yAElwJDdCEQ41gMMgA0jU3SBBWyiMioqlYdcpQOR9ZaCXKyPD6zu1dVFUSW5z0l+PGk5RKtd5zzjVObraNVGW/euk6sL+VgNDwzXzaLckHOMQkQjacIlgXTcs7Ho1PGhSQbcsmprX1oBnkyn882x4PQ6yEhF9jLi+h83s/u3bmLnBvv7+zvj4Y9zvm6dq+8eYdEf1o1O/3+tfuTVOU9lfazHCEaINQclJrX9jc+8WmPiFJYX+VJmiSJt+54Mjl3eq/f75t6aYMXnJtAd27doTSJDO/v39/a2j6YHm4Uo7oxB4fHbWj6VWHJHRw/SJNcCpEp7r0XPL199wZXUir4+gtfThTPM0HkTGt1Lpdts7840P9/9v7z17Ytzc/DRp55rhx2OnuffG6ue6uqb4VmdVcV2VQ32c0mRVItirbahE3TZoDlQIIQBJi2JAKmmASYEAHDlmVathmlZpNQs9XswK5wq27Vzffkc3beK68188jDH9apW/wXCHB+2thfNgbm3HOO8b6/93mCRPI69qmRnDJ7fsmHvdZ6vbQA1bzZ3Tu8vLy6mFzt7Ow0ot4/3CvrYrOZVRD2u72mnAdBoJUEttps5pQgKQgjFCKcpulqtYiC0Gg5m0y5Ih9++l0htcN4Ns9qoUfja7/6z34NQlzkFUR0G81UjUIASylypdpt5JyLoqiqq9FoVFUVhABjiDGRUvb7/S2fa7PZWAuAc/v7+48fP30xlKIMAKDX6yzXa2MkcHA7QP9igM/3tDCMAa0scUixwNNKQEvjOKzqst/taS0pZkqLOI6LIgujSNaNqYW2jnkkiv1GcCkaSr0w9FebNcKwbpooCqBDoYeELJU1COGQeSTGcRxrZUtZaGMow/29Xs43jmBowWR5Fcfh+NrOermUWqVhtCkrj8Xzi2o61VpH1vhSeZ1OR2ibVQsGnLEaYgchBg5BQKuqGo7GRVZaJ6LIs046QMIwDIJwNpklEYHIhVEwHo+lqPI839ntH5+fI4LXWX6ws8v8dFP7QkCGIKxhz28/n2W9ThctFl1GgiCAUZwb9XS5sSzECCjNkzC01swWS4rR7niUxEFTVv1er27UTneMXHuaGZ+Fj5592mjueRnCblMsd/f3ry4mcRLXZXV469r9h596nhfGUeATAMDJ8cXh4XXnXJIMnXYAWOc4IcHxySloUBAFYZRssjKIGPRI3dSX88mo172aT4oyD6O40fxqdgEw3N/fSdJIq9Kq2qp6POxsVkUYhhg6rXmR5dq49aY8PDx0GM/XOaPx+fSqKHmeH2PrAMB16ZLWqJH1crUxIJleXUUhqCsHASQ4qKqKMcYILfM8DENjFCJYa2uMYoz5vrdcLh1yDoG8EoyBKIoX6xWChDC/akQUBMihR4+eUorCMORSCWWsBZs8I1vKGibGGKF0GHpa662lkStpnCMGKKVAEATtdlvyZgua833WSmOtGSEEpRGEbn9nqCWvZB2kpFGbMA4gdozYmq8w0a1ux6+gxxjDtJYCWxvSwPOC1WpBKeGK+15IfZrnpYEW+6jKyyROoUM73Z0sWxukK13HrbZwYG/v+uQ8n81zqUPrGKWhVKDhotffhxhXfOYggsgJJX0vANZBaOpq02mlXGjC4qpcbVZrQtHDB09ff+2V85PjVhozRvqDtpLB5WwGIGQe5ZzvjnccQIT6pXCEtTSSp+upaNECSnJ8/MrBLul3DSRVKa+a+p/+5m8Li5STDlqpeBiGUiiK8LaVf/PwmlK6NRpi2gppK99cNquN73W8SFGKV8tZJ24/Pzu9e/PWaj1Nwmi5mh8eHq6zfLVZAqfD0G/3EqkbIURZZ0mUWqfrJmOMxO2orGrsodls3RsO67JqJSEiOIwjZbQDsOGiPx6zMGoqLgSv8sLZbhgEs8k0SaJ8U/BGnJxeGC3W6/XO3m6WF1q76WQVeKEzZLMuPv3kCaVRGMZ1WWFIJUeXl+d+EEESB5T5rD45nWHq1VwaZwlhWts8zxEAvG4YJbVUNRe+zzwPMsbqunQIUori2NuW/bcoky2RfL5cH+7uYQyUslJKCBEhEEIMIdxO2EqhCMG+T5tGQAjCMGik3GZ2idYKQlDX1vc9YJzVJvB8o0XsR1oqZyXBem9/EMdRVeaX8/LgRhd4YLaYd/px6ofQIYhR4Ef5JudcllkZhV4SBkXTiKbGCHmeF8fxZDofDAYOAgBBI5rOoNtUPA5DxEC7l1Qy3zvaVZrUGTg+yS5OMmOYsRgR6LCrpSAoUQqn6dhCU9cLpUrfo1LayGNxAH0q8+LMD5jRyllrDPD9gGBOSSCUk8oow08unt2+fZtm2KMhAisI6fXrtzUvwzggOXSU1EJIQs/yMiZ60+SldcfLotvuOer/8P6jecZR5DsIgNHM9+B28BhhnzEpBEJIG+iQJ43PvP1e981KzywpuNlMFyd+nMTdNmWqMo1ytUf9IIyVs8z3IaL9bqsqc2NRp9965513vvGNb5ydXna77bLeOAgggP1+X4jmYGd3tcyAM6v5ouWHRVbEgwELGB2x6cXSj0JgXK81nE+mlqOk24aKQOWODm88Pz2bzxeLyez27dtp1FnMil57VJViNisoDcpaxOHAOlBXCrpQyW3P3DWirgu1KUqhlXIwbbepxxRvEMFGWeegQ8hap5WhlAIDW61Wlq3TTosYBgCA0G3RjkIIxlhVNtZaCJHve0VRGAO0BkqZKAkopVxqjPGLUwvakns0AFtxDrR2S+XRZDzsZ9k6iaN8s0iTqNWOinxz5+6N09PnQUoJBWkrZb41KO8OGYeewxWh9PbdfSVkk1WRFxkAJ1envh/WdYExUkohRTqt9kZvPEKjKFosFr1uuyzLMIjmq0Wbes65OAlDL3z29NnNmzcRAY3QRlNC06t5aV0MIHbYOQgVMNgq38dagyiKGGlx1BgrjXWIYOMsANhYySgEQCNEAKQIsdks67S7s/lqOBxmm0lVNXVd+z5rt9O8lgghitj773345psv7x0cPp1e1FI2SsdJV9Qmk1kpm9WjJy0/jLywrG2jlEYowchuSQUOvGgSOnR4eGilQAgJKaOkUwvyve88CFuDTz/5QINNZxQNR/eiDsryRdhpESsPevtNlS8Wi3a/1xu2pVGL9SIOwiSMpdY379x8dnychPF8Ph+NB1absuBNURJCytVm0Oq22+2nTx6m/cGG6yovW8MxMCiN21yIMIybRjhArqZLBJkDgDE6X6y5VK1Oh2AWp60HDx9ZC9qt8PjpcwbCbiugMEyjUBktkVbCIAoAksRZh3CVb7QxBjhMmbamrBtEyJY+5PuelBIhiDBCCAEDyrLcznVtq0tRFJRlub+///z5SVVVEOItt7/KpQs0Y9has+0OQgijpLWdUwUAxHG8De9j7Ky1SimMIUIAY4iAk/1+urfXe+XVQ8/Td+4OD2+1Z6vHu4fBwY24OyaVurrx0rDQ83VztXO976eEBej87FjzmteF0ZyXBbBm+/YO4mi+XCAAs+WKAFSts/OTYyOFqnkaRlKIg729JEqMshhgj3r7e3u8FkXOCYrqEl2cl0UJHPSFssZZTClASDmpgSqbvKibVjJsp7vOMAiIUFJbqxwGKFAWLpaZ0BTidL6q8kpDHFaN8QIWxd79Tx9/4fNf3ixXO+M+QnAwGJRlmRXFwydPBzu7AFqKSSduQ+0oZkZDDhDH3kK784qvDTBeiBgFzlilEcAIQo9SA5x1uiwr3/fLKq+FhMg7u1wJjRfrerES6zX89OOs13mDkH3iDySwJPEr3hA/aA86DhlpxP7hyIv9SvJNVWw2G0KI5HVdVcgBXjS8rPb6Q11xKA0xIGb+8mr6+kuvIICbivc7o2ZrvXMIIVLWnHrBar3BHm2sijqtxqhNXW6aorHcYFHJzA/9/YPD9aYa71y7fu1mHKYU+R4JYpa2o3YSRgBYY7RD8PnJaVlzZbSxLmmlxlkDDEAOU0QDJq1plNIOSGWU0T/KbLg8z7fk8aZptvPJt27dopRSjBWXGGDfx9tBzjgOONeUepR6q9VqsVi3221rbavb4spA5JTWwFnPp1EcEIogcqjT8dKUIFQnCTg8TD++/+26ubpzd3T7zrg/9Me7rZdfvVnU2b17d4I4Oju9qEoBAasLLrhtKk1JpJVbztePH5yUWZFl2dG1A14WdZZHgUcp7rXbvu95PvE86pyrywohAgBsai64Qg4rZQj2raGXZ0W2MgQlGPlKWwgwxBRCBIBWukrSsKoK4BiCXhR2tSWEEKE0AH4jmDKhQe3FigMcJe0dZenlZL7KMoSQNQAh2oo6Vrs4iH0/OLh2WFW1FHq8e80PI+RsOwmsahTnFCJGKILUIaoh1dR3HhPOOGAUr6BzW4H5FhfV6XQ6rZbv+51eD0D49NlJWTbHZ2fMi+/cenN6VTsTf/T+mRJDq8bd3r28hKu8qRtutBsNxwSB9XLVbrfTTjvwI631/s5ut90ZdfsM4Wy5ujy56Ld7X/vJr2EHeFkha3qtVEuNMaXEq6om8CNrAWFe0upoa06vLjS0ytnL2fTRyfN1XT45fu4g8AIfUTKZTS2A0/nKD9MnT8/WWa00IIRprZVSVVlao7vtJE1TAN3e3k6n0/bCoNVqLRazLRgPIocQor4HMcAUG2AstMYYjCHGkBColKLE63Q6VVWVZXlycuL7fuBHHvOjKCoKLoTZ39/vdrt13QQBBQAwxoyxjOHZbLWdiO50YgghIRAAoJSwShujIHSk03WEoLTlPX36SVOLVpf4AT84aN2/f39nZyfwQgDQ8cmFVk5J5+H2cso3i6WWwWxSSwk+fnjspPVZK92hV1dXP/P7v/rRRx/FPiXYTWfnQRylaUwpLerqYnLuB+1utzudXmAEtDJVw0M/WF7NWu1eXTUYtiBJoUPGWIyx5wXOEqMBQggiS5BO46CuqjBoOV0qUTootLFc0zTpawXj1Exmz1Ath4Pr03mR14vBaHh+uei1k7qoP3jvwyhEdaleefnNi9nK8wLO5Ts/eO9LX/sZa2S+nsRRFDKWrZaGNx72MCAIEwsAYcQImcSelTVGGEKnpLUAEELSNK6b0hl6fPF8vHN4//7D0e7r3U57s9nwxo0GRxfzx/v1tdkpjVv38nm2nF5EYXh+eXH76M7F2azi67za3H31Zd8La1l3Wq0yrygkyMCb1244q5qmmk0mzrmqqu7evRuG4en5BSiL+XxupWqnqTJ6sVz7SSBLpaHxAtbtH8xmEz8OuOS1qb04Ng49f3ZeZYXP2LDXWi3zqW2Kik8mK8lVnEZBHCJgCXXQuiLfQOalaWodqvkEAygaTglRgktnsMPIQ06bVjupK17XnBGqpTTKOAeOjg7X+Wa1yrSR1gLnYL8/fO/d927fvl3l2ygmcA4sZnNjDNvy9ZWCcIuTMYyhfr8vFYfIKaWsBa0kLOu6aQxmIIoYAiYfdIPIhy+9dP2Nz9185eU7N69fL/Oi1+s7i+pKPnlyuliUGEcA+st1mSaDKOyWG+EsbYSjXiKUw9hjNGQ0fOed71PqKSHTIBoN+7KpV/NZlq2zzSpOwnaaPH78OApDYKzVJl+ttBT7uzvQOF4IaD0jodFACIEAxphC65xzhGIAQFVVlLJtGpGSkJKQc4kQMQZSL2VeH8FO2t4rarfK6t5ol3hxUaui5kJaythyvUCQ1o1czJcff/Tp/u6ektJo994HH167dk2pZjq9WG0W1iqMIUHIox4lmGLgLAdAehg4JbV+kcRDiHied35xppRIWzEASAp17eBoNptJVRujKIva7WEQJGXBtQjztb+csVb6KsGH3d5LnGMEvXbS3R3tTk6ndVGHfthUzeX5JUak1xt8ev/+02fPLi4np5cXp+fnk/lkU2wup5dFmWtrvMDvdDoQk/lyEcZRXdfbWdBtgT1K0jRtF1VjLAr8yCkELd2sK+Yli+UGsSCvODeg5EI5RKOoPxol7Var3Y6SVivt+CwIqO8h0k5Shgmvhay0szZkzCe0lSQEIYqJ0RICKxW3Vnse3RKbt+16KaXv+/1+f+troITN50ullOcxqV7whHd2dpqmIYRsieEIgW1finNeFBVCgJAttBSEISOYUuqhQb8b+IxiMuqPr+0dPX50hmD0wx8+nM85Yp2reVUr7Kf9yabYSDnLM+IxZaTvs+2rO2Thvdv3Dq9dz4tqONrpdIeXV/PQT1aL7NrOgU+8N177nBXm9tGtiIWxH9y8dtTkZbfVDig52t+zvG42m5du3wqZZ2oT0shps62rAQcBdBg6DBC0kOGIV4oiXGY5JT4lAYJsOxVUZjkEmJIo9EeEpBUv2r2U+q3pvHbIu5hOuBHC6ulqcXJ28eTZ09n0am9nFIeRdfjBw+e3794L4iTutbiTlgIDjXbaalFXWVHMQ+YiBhAEjFK61ZVjrLXVWgNgrx3upmmMCUOU3bhxC0JY1bXDKK+EcphLUwltHLSOapOuV2FVHYbBm358GKU9zws6SaeTtKank2y+Pj0+2x2NIYSfPHpQSakgiHvtV9/6nMbuzst3KlFx2TCfSqOSdjraHW/JVpxzSjzG/GydB144nywNt88fnQKFeC4vj2eG40F398aNW0qZxXrTSCmMURAi3+dWf/Lk2e9+/wfrkpeNXRcNwj60gCKMHEjD6HB3/wufe+NgZ+Ck44WUNedlwcuiyDZaSI9gAgEjWGtlLajrOgzDJImVdJt19fjR+Wy6NBp8+umn22B+6Ac+BVJqQtjJycn2kZXSAADSNA4j377AtODtb8qyIQQmcctasFmX6M0335xOp0qZ9ap474efYhg9enKxf3jv/HJ9/8HpfF1Faa/R1iJcCb5zcK2oK8655EpWwkd+mrayqlLOvfzqq+12b3I173UGVrl21OJ5fTTe6wRBKwyKZVYvszorfISSwNtMJ9d3d5jRr964fudgtx9HB6NBwDzJBYSQIuyxAL0QJVoIIQRka3YxRvX6LaUUIQw4bDTY0mWVFhBgYD2GQ143ZVkmUT9OBo0ClbKrouEGNhYRz0cIEQTyzYYxJqWZTZftzkhIXVUFAEY5gD0/iJOKNwCaNAwwtAho2dQQbsUHhhCCMTRKWCWtNdoZZY3vhe++++5rb7zKQqqtNk5b5yCkXhAAAKy2AU0p7kI3ztexliMpe2n74GqytAZYYxhjUkrqB8bhMI539vakMw7jZyenLIz9uBWn7UroKG5ZAz3s80YQxhj1pvMFQvRyMj87naxWZdOIqmqiODUalrlwDte8WW3WEJG8qJrGCO6KvAmDCDLSGEU8VnGxyovjy8k6q6bLjdCmrrk1ADpAIFJN0+t2PvfqS6Necm00IhYgC5C1YeATihHFkBEFAAvw1WyWFTlhdDwedTqtIMAYQEIIAhghVGQ5pd5oNB71B91ue0sgL8vajyh6QVB7ISvFGG4FT16AAQDEY9u6FTq9nAIclKU0mjgYXs2KvHTHpwvK2lVtWu3+5dXcWtvtdltR3I4j2fD1cpN6ySgdzC7mJ8cXF/P5s8uL9z746OT47Mb+DVMbWSpiYYLY2y+/2veD3VbH1+zm7i1ZFNjKUTf5ud/301TxW6OBWMx7Pi2mVx6BdZMDgpxzENMX09sAEkSdBhgSjDGlmOs6K9dhFGBMrCHOUoCIsgoAg6Bx2nVbPYqoKHkraXs4LAoRJSPuokzg0iKFWeD5++PRbD6599JLBPs1N+9/+HC8c81pwTCAEEMSWEJYHDJMEIBWG4QIJBARjBACzgGnMXEYOQB1nCZcS8/zijJDHlZWEoKclRg5AA2jAUW+1dJKXhe5rIVsLAY9p24w/MrTZ7KU/myTp/028fHB0cFkOudcNo2QUqZxSzSyLNVm09x/cHx8tsgrm1dW12A5zatc1JVcZmVWysfPLs4v13kpSq4sITQKHAR5UWV5pSwsdHW5ucxFaREdDa8ZSXVjCcBlVRmntbMOuUo1hag0xeeLecFVu9dPu+12t4ORjUOWhh4Genc0pAgSayOPtuPIWk08EqShsIqEBDB8cPOwETKMk/OLqZSaQObREDvsDFBCQ4hXq816sZxOJtDoKEGYAhYCrbfITqS4VlxBCBqhe4N+wWsWhMRjV1cXhOEoCdHV5QJBTyu0yZoPP37qh+20MywreX52Jbn66hd/sszKVpQ2RV2tMllwLXQv6WGLKKK93oAx1kiRV2VZV1EcGMUxNISCy8sL7EDoscuz58vpVNalhwEGqh2x1+7cuHz6IKH21t5g1A4ZcLevHwWeT30qtNiqsLbIF+ccAGirnt8C1ylF2oiiWHse63Z7jRRSSkgsgHKdTSDkWvFepyelrvIyjmOlHIAsjkYOtgGKL6erphGU0iDwjk+eadVYpZ8dn8dp22pFgPNp6CxW6sUsOLRGNpxiAgAghFBMCERWG2idtdrzvIury+l85Rx0gDx49OTTB4/a7bQqN0ZzgnAUhBg6a0uIKmuKMl+eHT9/74cffvTB8ccfztP4ThRch7CV5fxqOnPObeHi0+l8cjGZXk6LvJlcTpxxBHuBHwd++PTJ8+Uya2ohlLEACa0gRmeXF+Pd3bCVLLIl8ylArtF8utyk7fZ8Nat5ZZFrhCLYOz65Wi0z0WhrwDrPlDV+EAAEldFJO5FaWeBWeX7/8ZOr6XRvb68/HERRAIAlCBIA0zAa9wcHox1kXex7vCywsx7BqtHIOiOkaMSzJ6cEQwIJhijfVFJqa22r1dFaK62FVkEYeoGXJHHSRmFEEAaMEc+nSTtmjPg+i0O2WCy22T/OhYPAWm2MIqqxx89PgyBCzPv5X/yFX/uN31AEJe3WV99++7VXXtVKvXL97tnpyXg8bg9jSmmxKEuRIwOtXS2WcxaHNw4PuahRqlqRj5EZt/qffPLR2z/5hciLPn70QEEXt7zd9tABd+Nw6IwU+bqXeH/ij/z8Rx98eP36qFF0XjaVkBYiiyDawmedcw5aZyB44cdyzlmrEAZGy6ouIfbTVgDpaLlc54WDzk1mU4zowcEBI76SjoOmn6QAACmAT9M48BEy0+kUKHHv1mHVXHzujXsPnl+eTkqjRFNWGAHorE997gywwhptnILAEYyBdcxncRiulwsMASGkrrlSJgjii6vZS/deq5p5XkkvaF9MF3v+IG0lvCJC8rLKopQ+eno1HOwSHF1eTCmlGFNeb5rafu936yiFSSs4vN45PXnQ3e/xMl8tZ1oYCOFg2Dt9flrXDa9rAEC+WY/Hw298/WvPHj9vp62T02dB4u3s77RH7f3rB5PZNPH9g+7QWWmtTTrh0Y1hFKb7h7uVzCnDVS6ltIIrwLx2p7dYzPwoVEohCNM4oZgIIayCNW8AAFIKIZr/8V/+yzj0lVKD8QgA1GmnWsrNak0Y9SirBO+mLaFU3RQpgUDpzWTquS350hkloQOEAK0NpmCxWWjg/Mgvy6ZUfFnkLKQBjqTUvV6Qr+uyybdoktj3fd9fzFaIIUCAA9AZZ4B1RpGTs2na6SNMAcEPnjy5cfc2FyJbrb/zne+s5otBt7dZLOMwWc5XZVl2u92yzltp2k06TcPdUmGgN/Pp/u5w2B5VWTYc9C4vz3/uG79nNls8uzpXgn/5J78oTe/qavq5N14rq+LRpx/94h/8hXI1my+e/KE/9FPvvvfppiGLT2ePnj3RJoXIdw5s9WzOOWcxhAgC9CPrkNSm1qZRulmu8iCgcTu2trXONsC6vf3h2dnJxdWz8Xjc6SbrxXK+qH3fzzZlNBpixP7YH/3Zv/W3/7PVplKODEZDY9Vw0H707EKazYI4jKE1CkPkYais1hYo5yByTukkDjGEwBqfMav19hgnhbEWRVH30dNTY2nZyEqYIIpn0wUhKQKp4PXe3s5sfhHE/mR2HAV9hB2lOI7jpuRaOYS8bqcz7u6vZ88YHhrFkiS5e+tmVTQ/+MEPOp3Oq6++enp6Op/PHDBpGu/ujCiD8/Vklc97g5ZyYj5bU48pqdNOPF/OpW2AM5zz/b1rXug1srKVRQjMJ6vQiyezudDqYH+IMfV9/3J6+SPrrAvDMM9zACBCyEEXp4moqzRNKSPS6KurqziOLy8mrSR57ZVXed0QRsu6KEWjjGn7IWNsk5cAweVqs8oqBwAvm93dMeccACelQQQADKqmcRBIaC5XK5yDo2t9aOFykRPCjDDKWAAAhgJDEoUR51JqWQvnY2Cts9YgEkW1MfNsg3zPi2LrYLfbd85tM61XsytAodDCMrhzfV97rrXXGt7or5vZ9dv7n3vtzvX94RdfvdMLadejb798b4jJjbSFNptXD8Y39vtf/Pydq4sH1w+7eX4eRWY4cL2uOjn73tnVBxW/uFo/QYHojaPdo+Hl1al1AkMNgXHOAmghxGBrpkIEQmitdsAIWTe8RNgBoMsqm82mnk/b7W7dCG0N87DS5fnFU20qxIxFOo7jWtbWWqXk//LP/GlrrXbsh+/fdxZThFeLGYHA8xEECljlrIRWIGAwwJT4GBHnIMYYGLsFi/u+vw1N1hV3DmLiBVFqLA6T7nxVpZ0xFxYC5vuhlGKTLYwx1jqjHYWUN9pjIWNhUyttnRcG49H+cqY2y+5q2rl35+vj8e27d+4xDxBP/8SXP2eBnUznmDDqkZu39vcPBsYobVXa8aMOtZAvVpOr6XlRF34SQoLbna5xUBsXJ61HT55mRd4Ivl6vs1XmkWCzyZI4xYReTicOQT8Kh72+kYpAhBzgdQ0trEWjjMGMIoq2oEwHUJK2mRfkRcWlCqJksVhprcu8oJiMuv39weD2wbVh2ro2GHb84O71659/7d7Lt66P++3p1eRgZ+wHxBhgDEAIQAS8CLZ7YdxCAIFer7Na5UqBppaMegABDYCFZFNwwR0XhivlBwgRDCECABJlQBTF3f7oajohvie1apqq1Wm/8dLLv/Ub/+PR4QFATkNTlFWURko3no+uLk+lEN//4bdeuXPv7t0bZZ7lVTYrNyGw1XLd67Q/9/orv/pr/3Rw0L922P1Tf/rnP/zkg729ZHc/GQxHaSIiFu68dqepCuLTH97/AJLxnVd+FpMAUyykAgC7H1m8gEMQ4B9JXYyxCiEA4AsjMLSOSzmfrZO0fXBwyDlP4u4mmytdVo3RBgilCLI+xQ3fdDqtX/mV/67hKu4Nq3w1X+YO4TRqK3XpI2CsIBhqZbgoGUsRQtYgbR0BUAhpAz9JEsZIvlkLIQBjzgKpDEZUG7jJqjDdcYDklSxKgYmSknPR9Prx2cVpksQIE+AIIYxg3/eSrCl8P261OgBAIeB6BRYr+Ju/+ajVqV5/vS9VLWVpjG61h3leYoL9mAlVV402mkCMBrv9PN8kUUx9r9XtPT8+ZSwWSmqtHUCEBX4Yt1vG8wKKydXFLM+qKDRFURHqAABlXRVlSQlhzN8qFTyPCo6EEJwLyghEaJu1gxiFUdw0DaA4W9aB7wslnxw/3xmOriYX167t55Py9o3rPmUh8xql0zTNihIhJKXe29kdD8cAwSAInjx7piy4eXT46YOTwxu7tWwc0N1ufHE1b2pAMWAs0NY4B+PY3xnvyUoUmyxAQV1VBFoMidtKTymi68UaOYQAhg5UVRHF/mqzzOpNf6fXHrTmyzkxbjzsJAHlG9OLWp39fUds2gmFrDnIS7nqjpLV1XJ0bW8BcBD7vX5rb9y5d/cGh+vj43cQrQ9v9N7/8NvrbOJRxjN468bdr3zlS+u8GB7c/O9+5buT1d5q4zotjBx0CG6psz/SCm1lDAYhBLcWd0icM9ZCSiNCUynlZlNUVfPyS6/NphnnwIKqrJaMBQA6hA1X64KrFkZ/7a/9NUZDKYFDwXxZX01Wg+GIkmiLSAg8v1a1EI3nJRZCba3WGiGLAVDWLNer0Pd8z8MQVWXpnIMEX79187vvvMuC1oNHzxygxoKq5rt7gTa85muIcacbF3nT7Y2VRAhShGiRN3HU6XaHEMK6yQBxabenoa5KIkValqFFtdS1g6YSxWCnK1XjxUlZFEnS4rX+9NHDJPXny9mNo9tSmrqS14/u7e7uPj8+1VoihLTTvJEQUqXUlhE76I+tAqQdPD++NI4gyPKqwi+8E+iVl+8tl8uT01MIYdnw0XgcBgHG4Wa5IoRssoJSmhcVIGRVFGmnE3c7EjkB7IPnT6uq8gLGN0W31fajmCvJfL/crG/cuLFNSQspm6YZffGLyuinTx8PEgKlwZZA602vNsA5jIAUIPEIQkhYiYBbLGZGKlHJJEmSAAMItTRbCBWBgIyGu6vlwg+9RmvgnLWWEPTo8aejfj8v1nHkOau73TYFpHXtGrVEVHXQo4vlxcHRzvnZyb2bdz796JPrd47SPvsTf/JPA4p/51f+8e5R34tJuzWab07jdjocja5m06++8eUqr3w8Wsyr/88/+OfzLP/8V742PLj3f/t//uMoGgCEgQEYUYM0QshZ+Nnjuo3eQAScdgBA3wudAwT7mHkgAmVdKSXu33+8v3tThLVQjNBQKQ2AU1oSArhc/1//zj/89//4f2gdgpgIaRDuiFqvs4bQMK9k4EcMUuanmkNpsXIWU2I01lYigLhQAFhCkBCCYqSNcc6Nd4ZXkxnzPUpp1O4tVo1zziEnZJMVBfVZ3XCl3GC4U5XWY0GrPUziDsEBY6EUJss2AEkLjDDSj9rG2qpaSNlF1HgBLoqzRVPUqsEEVEXebneLhnvUX6zWYbz70r3Xl/PVV7/6Uw8fPK7r5offf78/HGgL11l2fn6ulB4Muu1WSyp1uH+YzYvAj4jnX1yuuHAOblmUTgqBHFgsFlEU3bp1azJfAEyqqmKUWiUxQlrrMAzzsrAAamuMA8LYrFgpwV999dXjkyf7R9euppOUBbP10i1XwuokbWtnv/vd777xxhsIoW6n0/g+r+o0DKKX7hljpHWbslpsNlWTxXHotbz1clNlBQnIoN2O2snV1ZWW2lkgRYWB09ZAiAACwDg0n6/rWla1YH60Xq9baQdj2EpjVTe7w4GsK9M0e4OBj5AV3AiOsCvrjHhwnc2SiM4uTs+ePXz95RtvvnHt6fPf/We//ne0/vRrf+DVvVeGv/3+d3G7P5kLL9jNS3h045XR7t7HD+4vsvWjp8cPn8+0a3/6cPHpw0nJgbBAW7Cdx9qSlZRS26+8UmK7AQAAaG0p8SgJGY0h8BEIPNrqtncpjiFAl5OncUI7nT1nU+CYc0BppC0Kw/Cf/JN/xKXAGFtoHHTaEUgSaZDDtJUOm9p6Xsf3ewB2IE4NwNIaAC3GGBIstbYAllwIq0vOFbB+GHR7g7OL8zAMqceybO2AreocIrNYz4xVeV5A4EuO840JvHavvx9HbecwIrSquXFGGXlxdd7vd8M4iNOoOxgkyWg+x7vjL5RZRPCwqu0m53XjkmTAhVbGLNara0dHnfZINBYB7713P3j88PHJ8+PRYFDmxeTqane80+/2bhxdr4qmzPju6NrZ6aSpdVWJPKuTuA0AoJhA66qqapoGALBcLq+urvK8TJJky5bCGCtler0ehshZC6yrqspa6yDsDQa9QV8a/cmD+8z3tbWD4RASOt4/6I93DETnk+nl1bQoqw8/+jjb5OdnFx6h7bTVTVuJH969cWucJndHwzcO9n76tVfeunHz5b2DQRj7CAOpq/XGcr43GhKw7QhiBywAAGKkrSOUkSSKA49tMZQ74/2KF9m68DActAfTs6uEBD5jw27n8vJycnn27/z+n3326NlLr969WD2/e++6VM3Xvvblbtx++vh+GGnIxN1X9t/5wW98+MGDn/iJn771ykuV0Neu3/vdb3//gw+f/PKf+p/8i9/47TuvvPnk4eLx6ZV2fjbbrJ4sCRs5sHAQAQQhgPaFFAlA6BBEWzw2hA5jWOa1MS4IQuAQIRSBQGjjrI3TZDjwq3qzWJ0tl6tutxv4ydXV86JaEAoJodePbv43/83fs8aHmCIEHIIOEKUBYggipLRD2DfWUBIiagBKpWl86hlNMAay4RSjRhmCnHPYGhV6vhdF73/4UavV2uRlpxvWdR0mYZY1mOCyypV0nc6OkqbbGwPrdXojQihCCGPaNA1G9Pnx4yjyteGUwSBk89km9KPAbzMC3v/BMVd2UyyJhxu5PNgfFPlayKbVSozUcRjNJvPhcKSVKWSFAe72uoz6r7y0/93vfvfZk2dpmk6n8353tFysL88mAUuAQVJooSTGdMtwFkggABkmTdNQgqxzEBGAUVGWSqnZbNZttauqSuOkLMu6rhljZV0Z88IbutXj1EVNEIKUXDs6fPLkCUDEC3wL0XK59MOAemy2mId+8PHHc4+S/d1x6Adlttkfj6HRBAAppda2yOsUI0cp9tinTz+Vir9+724+mdbSEIgwIlJqYw2ByBhDeu1INGUU+svZMkjiplQYwuH+vm+NysvX33jl4urs6z/5tV/7l7++szf65NEnnaT96Omjg9vDXi8q8lXS7bCQvfTGa2dnj19+6dX7D59oXreS1sXZRVOAJ48eX7sxvHnzers/KJoiq+APPrq4PCtLzZJuSgHjyM7ntbaAAugM0M5iRF+US6zbwjEdsA4AbdQ2bmct8Bi11iGMILSUYWMcwVEcMgjRcnWZZ3W7nV4/ev3Rk4+0mUOk11kNUWi0s9BJqQEAmDKIiYVKO2OcCYNIG8El1DZ2xkOkLfQaOeKsA4gZp63UUafVNCUjpFGaL1ZKG+r5SinM6MHB3vPTqbG25g0EJIxaxhiMPK3scNBVssYwWJeF54XWkCzLPM+U5WzQb4URhUZCo/N1nsbJajatqrw3HH3u5Zur7FlRn7VTb51dMgQ8HDau2iZjVsuFFrLd7jjlnTw/RgD+7b/9t3/pl37J933f9wlkjPlJkM7nS95ILV1dCUKDLVSBQBQF4WazsVpDCJTWfhBACKVQ1lqt9ZtvvgkBWE5my+XaI1QKUdW1NsZac3l+2k1bDOFup00I2qwWUbc7ny2l0Nppt4XvQaSUaXtBVeZa61YU1WWxVa0+uf/o5vXrvXa4uLo4Go3vHh0WWVn2E+hR5eyt/S+fnJ0Wk8s7w/HFIl+WVWmtRYgh5LQhhJBXbh29/8FHSGkCkTMg9MLQD0Qlh73e4Y073SS2Sr773e9Do2M/QqPez/ze3/vf//P/fpOtpS2KfDUajU4uzq1ROzv7eSWePz0Z9ru7g2uSw3/5G79+4+bN88uz8cHO1WTx9Hh6Oc/9qLssDI3TdSN8j7V7408+/tgjTHPjkAt8X1voLASfnf6d2+5WtzF755yU3KMQY2qdgBA2TUm0TaIIOBYFPZ3qvJhXtEni9r07bzx4+H2p7OXFwvd9rXUQRwAZ58A2bwYhNM4KVZsC9JJUWYdZhJAfhLiphdMUIkAJVBpAY7K8jOPQGu22cDhC87o5Ojp6+OBxFPcIoUoVlPrOIqm41jYKqVJZzf3VapXnZeBHUdKlMGx4006DeJDmm/XTx48Ef5Qmw153rERTV4VzsCnsZgmGw5c67WEtz3vtSPKSYpvxWvsQIRol0enyeGdnV3H98suvPnt2/I2f/n1vf/lLk8kkz0sp5XQ69bygaarVMg+DFkAYAFiWVRzHVZnXdU0QaowJAh9jSDxmLWwEN8BZCD59+MDD5HB3f9DvIwfIiiFMNnmmlEYAlmVptZEND9tthr3QDxouiccYIkKrsm4scJgSC5wfhgg4afRwPIIE5/nGGDObT0TNgBJNnX/y8XtJkkpllMCL1TLw2e29cZVyC1ny/PxqXSw5h5QZLY1RTSOILfM/+e/+4re++/3nlxMc0OvXr5+dnGxt3i3m7Y4GR/tHv/pr/+QLX/q8rGpk1ZOHn1w/2v3h/VlW8Chun15MgNCvvfryer30w6Q9uBl20m/94OP98cE3v/Ez2aa6mM9v3dl///13u4Pxyfl673oXR3HZCEiYKLRussVkbXXoDGaYAQOhhdoabKG1juDtxx9jRIR44bsBwK43M8/zCPaoFxGEjRXGCoR8rWngdYTMhWg8FlMaXdt79dnxR9aslBIOA2u1tQ5BRhAOPMYNF6IBzLcIlk1DWIwJhZgYpSiJRI0dsoxS2UhHkIMkK5pWEgEMkLFacoiokNqLkrPLC0oj3pikFUKMMIZNs16t1xCQdX4CHI7jtu8Zo3MATDtNGMXr5RI6J7X2/TZjiCBTVcuiXHoMLpZ8Ojt3wFw73Gm1d3lZeEG0XF5G0cBojiyEyKdelG2q9SqfzzZGA0ajTz5+MB4Pp4sp5+XRjaPVakUl6Y/7k8lGCtAKMUKo3+vtDPqbTTGdTreT+EEcW+C00UIZqThCyBirnDk+PUmjuJ3EcRx2ut11tnl2ckwo5lXDmwY66zNvOBxqpRljYRh6XjBbLrTWdV13u11CtoB21EpahBEhRFUVt+7cgkA/vv9xvxVN54oQIgmZ5+X5Ypnn+bXhoNfr7Q17RpjkzlFWNWeT+dVy2SBAvUiGDLW7rYur88G4N97px3HgB5hSGCdef9CByJyfHn/68cftpA2UPX16Nr+c/+AH700mMw/5t6+/9PTB80EyjEj8zm9+L5835VJfP3i1KalHelcX+ZNHZ6tloRT9tV/7VwcHLzGvc3h0h3ObF1VeCiVIK977wTsPgA6dAgRhpZTdllB/9DaF28iNeyHJds5RihtealMvlhfrzaTmS0w181zDcwcUhMg5hJFnDNxsVnVdR2H75ZfedA4TwrZqDYyptUA03FlNEPK8wA9TTANpnbB6+6cJZgwxjChGxBpIaRCyqK45pV4jTNUoqQ32wvPJFCA6mc0J9SB54dninJdlWTclgNJBHoaolUaU0rpsCKJJEhEMy7LEkGyDcINelzLY8PV8cQphPV+c13XeNAKjQElvvUJNma4XQUBuCN6+OK/rGpyeXGkDtAXG2V/+5V8ej3eZ51HizZaLJEna7XZZln4YWQOUtkEYOwC1BUqpi9MzSkin1YrDCGNsgBNKYkox9bbBPGudVIYwv+ZivdkoazabzXw+r3izv78/n88hhNRjRzdvVE0NEIySuCzLJEmU1pxzCxxmFBIcJjHnXEpJPUIIarWS2zdvbDYrrXUUhaPRyCJYS1la9+GTJyez9bISy6xYzFcnJ8fPntwHqo6wPRq23rx9+Pr1Pd82R90ElRR+ePJk1hQwItzWWTlXoIgSMNqJ82q2fzR+/6P3+oPRdLL+5MPH9z85zjN3dV4tr6on7z/70stvR8rHBbg9urMfX3v+3tlv/sq3fuuffvvB+yezi+Kle29V3GVV0x3t7h8cffTxJ3VZp0kCNLy2e5OY5F/86rcRj4iNPJJARB1BFoN/3dC+5bluE7UYb1nEmouaeRARJXW2XJ9fXD0UeqltIVVlraLEC4OOR2Ol5Wx+UTYbocTB/o2q5NYgCLExikBAIDJSiYZLqaQkiMaOIKmFNJLzGjiljdRGbgsRBFOlDIWsKrlz0FrQCJOX3ALy4OFTTD3mBwgSz/PKstRaE0Ai1jYCIYuass6WdZW5cfdWO961GhplKWbAOt/zoijabFbGyuX6wuEyqy5ooCzkccunHpot5ufns8XcFJtWuR4YfphGr1Da94Ok1x20Wi3P8/7BP/4HVVMul0shxBazU3MBESMkTNMxpYnUgPoeQBBjWlXFYjYvi2IwGGyP+duZOwC25h9knQMIbvJsC4ZomgYgiClijDRKcq0mi3l/PLr/6CH0qMH4cjZX1uR5fjm5UFY1km+7eVLKbreT53ldVUoppWVRZGmchL73pbffBgAc3bgBPc8QzNIuDBJAIwCDspKT6ayW/NHzR9KKxexy1Im/+fbnP3/76N7ekHzy+GFRFAq6yWTSGw0bnv3xf+8Xn95/cPv6UczAJw8/fvn1lyeTySuvvPLNb35zupxrhhGCr738xtXlqarNpycPRdlc26fTq+Niw7HHAtYyxng0yXOhDe71dwn1eWP/2B/5E99+51tP7j/tj/YDFAkcyAKZRgZ+iBExxlngnDMOvChIvfDXQQQB3BKNAYCEsDRpl8UsDBJjTNnUWueT6UkrGfu+jyDzvBDhVGspFdemWa+XvX6qlUXQfzEy4F7kXZRSjlqCmXaQEAYd9VjggKekq3kVMrgd6zUa+n7YNE5BR5CnNTJWKaUIxgiRrKzCMGwagTAAUPeHA2CQ4gYjShmG0BiHkYl7/SMKEmlsGMTWyTzPtZbGAojqOGlzUXK5TlKPr2rmtyH0/CBerovATxFlUjOMo6KEjEHmAWCJ57m8mCqlgihOoxgjT2pAGDPWGoeIF3e6o9OTyzKvncVxGOZZoYQkCFDqIUytA1mWeZ6nea2tbRpR1QIhJIWmHjPGxGHglPYIEUrGcVzVdSkabQGmpCj5xWzSHwz8KGykqJoaI7BF8GohnXNWmySOgyDgVTUaDYQQmleDmzeIc2HgdZL48f1P9vdGwNqDg70fPjkpOW+4pg56gd9qJdfuHn7rd34jSVMHddqKvvPt355cHHVaSbaeIQR1FFIMQafTKstyPp//o3/0D68mFx998AM/YN2dgaWw4LnQ5etvvmQgH4zivWsDh0yr162twlFcORsPBygJ7rz+2s7hNWVhf7SbVc3jk9PJfPO9d97PN/Xl2eLpw9OAtnZ6B0c71ztB91/9xnechgA56iGICSTUao2Rdc5iAp2FEOJtK1UZbZyW2hDmGYspTVutaxD0PG+YRH2EqDV8Nn8ynT1wrlKCU+rFUc+naei1nQEEY2Dt9cNDYABFHnLEGWCt1c5Kox20BEHV1MBCj/pSOYSZhaCRDQBgK/ESCgBMACQOeAQHxpitVm/rRbLGWeCE4o2pyjqzDhMa+WFSNwagIG0Nu4MhIhAx54fMQiU1F7rSjnsBowwb06xWl1GIp7PLOOw0HA93blqQ7OzdSdu73f4u9X1MEUBQabZYueXSm86gtv7F5cw6fDVdX10uNutKO8gNAiwK4u6Hnzy+d+elt159LcIg9Um/03rp7i2jtIPIOOfHSdHwrCr3j47u3XsZEUwxqcuKEKaU+ezkqrWOokhKCQCimGltrHFe4Amt5uvV8dmZsXZvb29/dy9JolaaUoQJgFpY2fBikznnPEK3fMnFdCal5FU96HVeunlzr9drilxzni+XQGsCgM+wx+Ct24eSF2997tWD/X3eNJTCW7duIAI3RU4pJjdv3zo/PVuvVtdv3Hr49AljfhiGuzv7N4aDveH4gwefbvJstVqkcRyEbDzsaiM9HG02WZymg8FgOp3bxbJQcrZeN9o+f35ycXWZ9Abd8e5kNhPOfeP3/t7lIgOYXV5OpovZwf61q/MrjNtWA5/52jhrtZDAOYyQ01oRgpR0EFBnobUAIUAI2cpfCGbWAoKZH3QEa7SWcdTyvCDLZ440Rbma4+OdnTubTdVpD1rpIC9mCJGtfatpFMPMKguhA9Bap6VWxGMW+xhR6xCGyFpAkWcggdSDhkDojFXMi4CF1mpCiLHIOGCtIdhihLcP6zaTYJ0xUjnt2skYOcoY9jXNy3y92SDoB36aJj3PCzjnRVEgBHyfbVsbRb7CyEJkjJLc8es3XiXIxz4p81pqvR0KbRAKvRAAjGGsJY0SVpT17sHd1fKCYOcQbHXS1WrpJ8Fyaeu6SaLk0aNHzXp9sLf75PSUC/34yVwZHcfJKi9KLgCCUtinT5/u7O52Oz3XspTS9TpDCCEAsizzME473W0xVXLhMMQYS6VarVbTND5DWqmz01NoTSdtDQa9XqfbardXqyyJ8+2JglEKoAs9D1nlnAvDUNbV8fFxN07u37+fJn6n0wp8f8NrCJHS4smTZ8tXbqQ+TTrDdZZ3Oq3J5fnu3vjZs2eDXm80GJCT04lHwzy7nExmu7v7ZV195UtfzubzR4/PlrNsuVpiB+7cuoUQ/rX/4dd/4u0vLBaTKq9U2XCATp+fAkzysr6azvwwsQACQo9u3iFBNFnOc61rrU+uLngjtUIOo1avW4p60N+tawshsNBZa6uqoiQGFjhreF22W54BFgCLsWctAA4jCKXiW3+LcxAASKmPMeO89hjjHO2M+8v1eVVvltmZAnp/59W8yqPAi+K2hdwBw6XE1nX7g+VybpFwRFlsHADGMuYlDnjOQa3rNE3nCwgcgYBBRADWjjiIgHV2a2QFykBgIDKEImcNAAg4CyBCCBHgjNNG19PJCUIUIQgAkFpAiIGDZTNdZ+cEh/3uEALrLOa1GI12OK8dNHEaFMUcQSdlqXhW8RpABgCCWnuUGiMDP4VAdzodxlhZbmoOOq07hBTEm+/uJMDC6XR649ZouVxbLZgDildR2qKddLKe3bh7c7XOlvNVII3TCAKCEILGMMaUFrPp9ODgwDm3RUIvVktK8Va9V5ZlEsXOwiAIVkXGpXTObSswSqk4inxCmiKv6/rZs4wxH1Hi+1Gr1RJKzpYLhPpSNO04gUAPOp2maYzRXJsHz56OBgNZZVKr69evn7zzHqYRwYRCc3x2Oog80GtJJROfdQe9IPTiyEdAewyhxphVXiTtjnWuEbyoq/Umny9Xk/nqyfEJ5/za/sG4P5SCv/zSS9l68/TJk6qojJCiFrvjvflsubu/z4WiQTjPCgVQY8zlYlVJE7e6RcNXZZ43BfbZ+GAPe7Q3GlxMrz78+CMLjNZye2xKktjzKSZQqlqqxrkfW5YBAMZsAyFMa40xsQZoZSGgGAW8gR4dINfd373b6+wa4Nab2Xx57gVACB6Gse/FWZZT6m0ZnwZYgCB8oXb2pQIYR9a9OMPt7+9jjDFmCDIHiYMAAGuc3iqcIdymELb6A+SAAcACBAGECCFKqUcwwUDbSulSyFyojdKlsTXzHWU6iJ12Wd2sHVAQgm63mxervFgDqK1TWbaB0DIK8nyBHIdOQseB40aXvgec08aqvFissxUXJk1HPtu5d/ftw8O7kBJDzOGt/YvpcbcXhyEbDjrUI7UsaysK3eQib3QNqBvvDgnDykghxGaTSymNdtaA2Wy2WCyquhCycVbXdR3HsVKaEOJ5nlKqauqtJVopZbWJghACIOomX2/29vaiKGq325RSCOEmzxvBlTWU0svLy929A2PtdDKfTmaL1XqTFRDTdVXX2mLPny1WhGCEEMZEa8sYu3nrTrfbPTg4sFbfuHH0hS+8xTxycLgfpVGUhPDrv3DDY2Sv2wMArKtCGk0gEXWz1x0MkqBcz9/+/Jt3jo5+/V/8Dwihn/9Dv/Bb3/4dgNGD+4/e/vJXvvvuD46u35guN9P5suJSGRenrbKphTTj8Vgq0/AyTEnDuU9bz58fdzpt3w+Mxr/+a+9iSIENgKZawTTuAIC2TG7jAKMxhhEjLY+lEFBtAYB2qykKwoQ3Oo5T4LASWkmHScQCYuHGuLKslpts2dR1t7e3M7jhHLROXE6eBr6czk7Go9FyNWc+bprG6Avwb69/Q67/9j/6g7vjHUQxCQhbzhdPnz4uq81kcrnZrBCB880s6bbuvnz33r070nLGSMC8d99995vf/H39wYgwj1H/m1//Zq83IIQNd3aCJM2lmOV5xkUl1XyVh2EshcryclPkj4+fjHZGmCILXBSmGIPtcLoxxgFblOvtDCqh2DkNoXHObYsn2/fZtkxNMNsqqzGixjilDKaUMcaY7yxRAsXhYDS4Fqdpls/rpuCch0GSxF1rQRRHq/XMOWW1+7eP6b9Z15/4m7/KfI90sc8ghh5wvh+lLYax53m9dmc2mT47e/r1t9/+1ne/1Uniw+uHR9eO/uk/++e/873v9UZj3siqqifPzyGhAOHTs7OHx6dHd+/WQrSiZLPcVEodn543vExo0Gp3s/zy9OI0SRLemF4aGAOhIwAAjDEh2BkAkbEaG2MIxs5q5wzCwDkLnIEQbFvSjPrOWOiAkI2zyDmHEDS2zAtlrBRCa42MBf3OrozkbDa7e+fVKmtiP63RZp01WpWEUvMZHeHfXv/mXM+ePCYHO7tNU11dnje82U0OIHJWq/29kbHN0cHBg0f3D3d2Pnn4KI3jOG49evKsv7cnpMqqZpmVjVSX5xdekjTGeFGYlUWr058t5hiSVpI0WRGE4cXFZbff6fe7y+Vaay2F++jDB8AxxgIlLYRASoURquvKYzHBVGmulEJIIYSc+1F33ijgMEIYAISJrusy8BPGEHAKUyyltlZHcYAxdIA0vKAk7LTj+WzZa/eFklrb7fATxthq/dn6/d7vo8FAuxQzWlczALXHIgKBFjmQC5E/CX0DIXSQIMusdR6jXFYAKgMUpgS8yM8CCCy0zhngrEUEOmesBRBguOUVAkwpsxYgSAikadLNNpW1llIaRj4kcDabOMMRtoRiZxEmKYQB82JnMcbY9yhBsMxWyNlG1RADP4yNAHGr3W6n7QFMxisDN6tlVVQiiiKrNEagqgrmEaklJC4IAp/6cZhkq3yzzJwEyCAE8HK+SqO43x8URQEhnC0XQggHkLU29j2MMYIgDsIoihshqqZeLBZx2gbWpnEkhEjDUCkVBp61GmJincvKyjpQ8WZrl/R9P/J9oyVyAAIrRUMRJtZdP9ilwIQBAwD0B6NGqU8++qgVh3/oD/zcfHI1n01/z1d/8rd/8ze++vYXfv4//a9f3CnfR0VVX0xnT8/Pk15vspynaeyMPD99Opte3r//iXLg2q07lrDpOv+tb323MxyXXH7wyYP9G7c+fvLkX/zu72hGP376hAbh53/i7dAPPEJD6u2OhkW2kUausg3BXuhHs8nUaiUbiQFaTDfAEKPhFt9FGTZOKcOZ7xlnlVIAWGs1hA5CZ4y0zkAIt73QrS+TUShloU0DkTbGxEHYbcc+c3k5mc3PNpsNwV7stz3iKyV93wMGA0MYbSPoRVHw2ZOKsHXOQOAoomnUJTCw2jmjEFBGcwSws6ipJcbUOWAtcNA6oDGxmABrjXPWIaCNtMA45CwwDkEHgLEAAswYs8AIVVMGtzRxhAGlaJPNhS0gNQAD4xwAwAFjkXUISCMRRRYo7bhzFiPgtOLF2tbzGBb7LfvaYft6P/R0Efp2s5pMpvPJdI4cMkIjTVOW2lpFNJgdTzFHjDtQCVFKxnznYNM0V1cX/UEXIhMkPsK2P2gzj1RlgRDabDZJ3AIQR1EURREAIM9zCGEt+GK9VFpGUbS3t2eUoBRLKTHGW1WilHLbqQYA7OzsQAi2loftKF6r1QrDcKtU1cZ0Oh1CSFFUy9UmL6ogCE6On8QefuXO0aCXfus7v/vhw8ezvPnVX/+t1976idPL+Wd3yvMiwp1I+q2O7FsCEATtTpJ66NWX7/2r73xba/vxhx9d29l/fnx24+j67Pw8TdtSmbqRp+dXw9EojNvUD8aj3XWZk8msHafZZu0jcv70ebZaW2t7vV4txcnJWaeTFEWhtI3DNkYeJRF8oe9GSm1NcKCqKmeh7/tVyZ0jCCGjHQAQAGCMQhAbs5W5GoSgA8pYA40jNKibxgG+XE+EbuI4TrwkCBl2EEp7NX0SJSRt+euMAAgQsg6az9bPGNaOY1ApoRgmBEDgLEHQYawccsAzGgVRZDVyFlMCd3Z6T56uiI+MVJ4XbDk+hBAArJASI48SAoxGCAG7jWttWXYGOAgADoPQWg2pQ8AYy8MwBtt/Sq0wcRABSphQglBMCSPYGGVCTI2sGGy++Xs+/7l7t3u9DvKSX/kXv/m9jx4J4uIoAEBs8jKKmDMNI8QLQt2Inf64zHJCaOKFfrcFILmaXUEAWq3WfD7t9ntAuVF3L1tnshJKGc6FFwbrzcY5V5ZlEHiE0RZJt18hCKExRjeN53mdTufk5DQOwyRJhJJ1XY+GfQihsRZjvFmuCMZWaWut7/uMUaUUpRQ644dBq9WqqgpY22q1PNbTsq6aOgi9ulh1O2mrc81C8n//f/0T6tG3v/DF3/ru94eR96/dqZjMiunuwd7d4d1nTx+uJlcBETvdzvHj+1/9/FvPn5/c2bnxyXsfjXqjQbvvWdg0zfR08uabX9zd3aWeZwycTeaQUKaRKTnxXAxpnmWwrK91+9/8+jd+51vfllL3O73hqJNGaRx0843V+gw6gwEghDDG6lpp54A1UlVp0tZK+KGPYSyE2M79EAi1VoGPESLWSgghwhZABwCASEuV+QFbrtYA2MhPGKbL1eVsfmakQRggbErBd8Y9TKWRCkBYi+az9df1ipDYmMz3UmA9H2JtnGoUgjaJ+17ahU5s1qvxeFyV0gF5NV0gwqA1DDOnHYEQOGAtRAhTEmBMOG8IhgQDgKC1ADgIIXIO+p7vLCWECak4rzFGBpiyzrrdPnDGWu0RonRjrPW8wAGjZKW5poD4SQqQ+Km37t4ZhS20CaUNQvq/+pN/7NX3Hv7dv/cPG150OnFZ5VI3nu+Xee5hFPlBo0Wrk1ojBRfrixnAyDoThj5jrNNqX11cJkE8mV5WRd0KWs65JEmk0UVZYkydc03TAN9HCPqERUlolFVKWQeklGVZU8pu3bpFCNlka+Z7QslWkpZ1JaR0bgtBRx4hi+my127HUcQYOXn+tCiK0aBflSUjZJVtxuMhDXyCoUdpt5v4AeOKP35+KSBotP3WD97/3L3bvfEeAO9u79R0maPhfvfB448UbG7cuvaFz7+axMHP/v5vYgxn06vp1eS9D354en7+83/oF954683B7rg25rXXP7e7s98fjs/Orox2nhdcu3at1+ltEa/T6dT3/Z//+Z/XWn/84YcY4zRNgUNaa98Pq1J++MGnjPoQYkq9rdp1K9slFANgjVWYAAfMX/gLf4FSD0ECITbaMuZDiD5LVFmrrTMAWAAsRK4sSwSpNbjX2R30d3d29q4dHly/fv3mzZu7O/tSipPTZ0o3hDqEnLY/fqdqWYt6rfmizi9kuWLA9tKkm7TaUaeT9IOgw7xeq7VrVdhKdg8PX64Ki1FkFLWKIAuhddgh4hAw0GprLcCYbIsY20Vte5LOQoQIpbgsc2MUQFbZGkDFZbZYXnFRYOKUFgghB4y1WivugMJQEyi12Fw/GAw6jLgKig3iG5tNicx+5mtfONztMwQIIUEQpa0OIhAh4HneaHcnSD3kgbiV9sc71/au7Qx2ru0dhn4kuVrOV9f2rgkhtt9lP/T80AMYOOcOru2nraTf70MElJJZlgFoIYRBEERRtIUDKKWEkFmWTafTVqt1dHTk+37TNIvFAiMkpeSca6UQQhiDTbYKAs8YFYZhmsZKKWtNxStMSBhH2tmDo4PDw4P1ZllUJYD4crGAHquVnW3qj+4/enZ29tmdKusGbdazXj9GWFnQhAH6ypc//+3vf2d0sPfe/fsvf/5N53t/+E/+0l/+P/2nf/Gv/J9XvOoe7AhgHz599ujxU+J5Uav1+bffXmaZBICE4aqpw37PRcG//P47t998Q3lMIFcJ7oWBMu5yMq1qvd5UGGNCkAFGWaOt3b6CrLUAmobnkLjx3ni8tyu1JcTHiBmLCA2AI8Ch7ajqCyW2ddYCaxBEPibp7u5dY/26gRimkhMhmZKR0d3Pv/WzlMbGWkBsUWwo/fE3hWJCoWXIMmjSgHmYRiyMvBADbJS1GoRBq5WMfb/bbu84F49H9yDoQNshoIUcwxZibaAyWEOfeMA6ZyxCyDintYYQbQUvyuiiyhzSccvnqhSyhMhaIKlvG7FQOvcDaIywVmMIpOJgq0cCilArZN5qe8VmKeuCl1mdL5GukciZqf7q//EvMmyslM4Zh5wwVW/cMRBtygIHDoXOBlRD3Io7UNr1dMmLBjrk+yGlXhJ36kYgiOua13U9Gg/SVtjtpUkr9nw2Go3iON7iHDebjVBcSMk5T5IEQhgE/nK9Wq5XZ+fn6zwjhHS73Zs3b8ZxzBijlBpjeMPTJDRKX56dU4xv37wlGt5K020o0Q+9LM/39vY++egjKeVgMEiT9vH5RSVUoxTxgzgJjLNS/bhKUxQ5iQJmgGHUScX39q41TUP84J33PsBB+LvvvPvK5z73zrvf/8mf+vJ8Ont+cZEmHeBYzZu8LLmUjqBS8YvJtOKNHwQWQe2sFpwG4aOT51ZZoRWkBGIEIDo6upGvEUZezRuCLCU+hJBSyiCz26aUh621DS//xt/4Ly7PeFXVURACByAkziKCKYTQAeMcsg4CB5yDRjtMiTGw0+5JYTACSgkAodUeBJjguCw261XR6Qxmsw13xGMdLX989kcOAeecFoQxDAjDjGKGgNGAK62gc047jCimYd1Y5tFr116Now6yMk3gg49/m1FojaDUk8ZYBZxTmCJjNCFoy65BCBCKjFHMQ7fv7j148LGDpRdha0UQBEdH+7dv355eTH7w7gcYgq01HX9W67AYACJ0NVtOop5fN6h0OKSeU0JVGSo3JKFf+fzLp9ksaEMNcmNMWZYIsOVySXwdpwlAPm+K6dUVdnDYGSAPr7MNY2yL4cWYVKppR0kcx6enp4jAqBU5oMIwQQiEvuecgRD2+32lVLfXquvm4uJCSg0hJOSFwnw2m4WMTq8mX//mNzjn09miqqr5arler5VSg8GAIPDkyRMl+N7e3uXlxc7OaLOqekFncjW5tj/ud3sEoXbak9aNxnsfP5tAxIy1zPOQkQ78+Ot3en6Gjg72Z5cXktevv/rKaDj8+OOPn59f9faudUYHkIUPHj6nXuRRv9vuOwN5LUI/SqJknWXSGUjo8/MLjXEYJYx4+6O9NEigBlroxWK1zHIHQNJKPd+nHgvCeL5YKWshdIhAa7VSgnPOOXcWYIwBNMZKofgv/pFflMqMd/YcQAizOGpDSCEgzsHt29QZAy1wDlrrHAJpO9rmrbRV2hipnHXYAc8BaGA1WRzPFovdg5cGvZfb8a2IDT5bf6fVw5hR4kNHGY1CP0YOIYcQAE4bBCCvK6m4MUoZqzUqSzDo3kyivWpj/9z/+s//ws/9/n4n9rEmyBCoCbYYGuccAIRSCoAFwEGkCVO3740tXL3+5rUv/eQrP/2NL37z937l9bfu7h11qSfv3juixEIHPEoRcA5sqx8AUKyAtthJq65W68mmyrgVBkGM4jDwIe5E/l/63/4p1ZyrZmW02BnuYEB2x/1+u0UBXc82quGU4NgPPUy0UgxSgqho5PRq5nuB4Krd6q7XmVLKOZOmycnJM0pJ3ZShHzRNMxj2/MiveNUIcTmZSKMhwYgQZQxX0kLQSKmNEUIghH7nd37nwYMHy+VcKfHm66998a030zTdSnu3Odq0lezv72/jsM65l+7dKbM8jZP1bLGYbx4/eh4GrelkSRAlEOXr7GB/vJzPflxPjGMEjX37rZ8YpL33vv/+++99BBGbzJYPnxyvNuVgZ3+ZZQ1Xx+eXDmGuXM7lw+dPDcPAI6WUGsMgSYMwFFohhJQQPvUZZoHn7452fd9vdbos8OerZZ5VJyfnq1UGHHLOIeAIhZ7PtiJDpYySWkqJEEAIWKv//t///1JK07RDiC+4ocTbqjh/BAAEEEKMISagyJab9cJYsR0JdM5RwihhURQV+XrQT7SqgbWiQZ3uQRLvQhT9+J0KMAUIOuSxwPNjTHwuNcSYEs/qLS3VCtFI1XgMGa0oJptNPp/mvpecHp/94Z//g//V3/i//Of/8f/ha2+9HlPgOQ2V8jCB1gGAtLbGaeMkQPbw+l7S86Bn+uOWBkIj2R2kRbUueTZbno92eygAyhlppDaGUGpfIKKA1rrgddHwZVE0xpVCYc9XSjmrdV04vvnf/9n/hQesqEpjVH/QnU6vHDAUsICEvKyLLEcIEUoRQkI2ALmkk/ZGfQfdeGfoB2wwGmFGEcGbTQYdklw45/Iq004ZZ6um5lII2QRRGIYBQMAPWBB6rVbLORcEgUc94FBd185YgnAahfl6Nbm64JwPuj0p5aMnz6qmaYRcLdfPT44Z87chAa2t7/vW2sFg1Erj8Xh8fHbqEFRaI4SG/RRZszMcfXanLqYz5GMv9qNsVjiBfuk/+FN5ra1FsR8ShHw/GO3tN8BNi3LO+WWeT+tSeiS3kmOkKLKE1Eo9evI0y0suVdWIzWajtb66uqqqCjpolG7KCmNKSYhAODmbIusQoNYCpYQxAsAXMHKECIRYKWWtxgS+871vG6sghJT4QRBDSLYTVFsslIPQIZs307y8AqBGUGjZKMHVi8gLttYiKK0pmnpttQTAC4MWsBRTH2L02fp5UQEjITIAQQeZhb5DxCFSChG1WsARa4F1pswWPF95UM4vHy+XZzTAQqvzk7OE0j5FX7yx/xd++d/7m3/lLw99P3IQG8MwAhYYYyGhkDKDLAxIf3cXMJLzGgc0iIMgDqI0Kni+UevBjb7ylGTCMgcoVMpgiCnyZaMZ8RAijZJFw/OmWZXlYpNPFsuiWCtRwoYf9sc3x0ctPy2rLO3FNKJFU7fSXuKnVqp2kuacoygc7+wopw2Sy2q+rBfAt8vNDGOw3iyttQgyjLyIRQwyIWWYJlEcM592eu1WJ/UC5qCNW4EXMIghxFApKaXgVZ1EEYJwPBhGzC/Wq/n5+bDVKpbLpiw459ZBTLyr+ao72Fnlhcf8+XzO6yZbZb3+cJOXq83aC73T85OS58SngBEFrIX2+tG+NVIa+9mdiloR2qwzaGE7ahtpv/W773hh2u70Gy4RYZPptKyrJ8fPhdGLLB/s7LS63VJx4cxsvbycTyeLZVXXRzdv+GHAfP9ycmUhiNIkiqKjoyNCSF03AKB23GkqyWsZBAEE2CrttEEYQOS2myEI4VYJDgBgjEHoHFBVvWr4RulSqoKLrOZLpUula+u4sZKLUqpK6RJh44ACSAEoAdTGagCssbLI5/vXRhfnJ+PhwGcRRr4xzkEAMfts/YoLY4w1KAzaGHlCagcpwAQj6pwLojCIQoRAkkRaNZPLs93d8eHhoTZOGmcBC1hKrfWBaTN8NBz+3b/11/f7ndRnsq4IAIxQApHW0kJQ1fzk9LSWSjmwybPzq8vZclFUZVlVAAODzb037raHKaTWD6kByjhDKYUQC2msQ42ywrmsqsuGL1cbbU3dFJKXFDrm4Jc///a4OwyZP51dCaMdQO++/16jlOeHrW6HxAH0aMHrr3/964BiafXO/i6iaP9gN0mjIGTamiAIQj8I/cjzPMbYarWCBCZJ0ul0tsrMXr/DhXjttVfCOIDQMcacNqEfFEXR6w+ysmp3O0dHR512u9fvbn1PjPlJkpY1p8wDAN68eXu8uxdFcVHWiJLzq8v5crXJqrOrKZd6Pp9/73vfgxA6aDGBu3tj32fr9fqzOyWEQIRG73zv/eUiQ5A9e3oynS4qriH1z2cTbhQkeFtp8j3KebPZrAPG6qraHe/IhiMHOOfGGITQcr0CCF3OplyKy8nV2cWFMtoBMJ/PsyxrGmEtKPJqm+UjhFgDjHYvjhEYQui2v6eUQgisazb5ZVGdNvKsEWdVc1I2F1zNhFoZVxlXS1UZyxF2FriqKhE2QuXWSohUI3LPh1JVjx8/VJZDiDHywjDecgKTuPPZ+jGBGDGjmTYMQLINGSqlPM+TUpZl3jQVJLCsK8/3O91+WbmscGlnGIRda4NaIEJ9Y5zVzgpl6+o/+Yv/u69+8fMhowxBAqxVkiEcB6GUEiHaNGI6nRuIWr2+gYgboAGua355eam0GA57r7x2i8u1BRUm2gCBCFQQnk2WXtxerEupnJAqr8rVaqG1zPNNkeXQupu7R3xVW2GhwQR7lqL2znAtK5B6x/MLGMBNvcIYvvvDHxgL4jSti8pwfXZ8tphNoySM4yCJ/VYSAQqRRzudTredjofDMi/Wy1VT1QjgumzCIJhNp71O99aNmwDYJI2M1YQQYbQXhlldcq2E0RfTaWcwLOuqbOqyqR0EnHOl1CeffJJlmef7+9cPWRwuV5umFmlnqBRquAn81Kc+ssajzGjnLDw8vI7+tXpiEobo4moBHOaNGQ/2x4PdUW+n2+03QmoDSi4gIsPhsN/pAgC2mOAoCK/t7SMHbt+8RQmhlG42m2obliY4bbdK3ty+e3eTZ4PhUGsthXIOIoADP/LoNuiOlFJbmfa27rid8ttSRgAg29p+1SyqZlrzSSMnQi+U2UidOcAdEM4pBxQA1vO8rZkTAG2tEbKWkmvTGC0AAGma7u8cLlf50eFtCGheZlEUUBJ+tv7d3R1EfAgjowiEeFsBNcZQz6eeb4DjSmZZYYzhouZShGGaJp0sq5QGxkXf/u4HwmLgsVZ/BClNWu0gCP7dP/KL/9Gf/7NGc+wsAhY447OgzKu64lGYxlELI5bn1eV0biEuKx74yd54jyDsjILQfP4Lr7/1+VeEKSDRBhkWBsK6rJYGkZorre1yNhe8rpvSGp2vliIvTcn/7C//6RQlKlfVpqSY1by5nE+Pz49Zwtb1+nJ+4Sgs6opSb73IRaOV0FVRtlotreXu3vibX//p8d6Y+ORydmWsQghdnJ1vO6LdbhdjvM2ySSmtNpzzQa/XbXeskoRgKaUFQAqNEOr0+nEcQwi5UMvVinOOIaKEeB4Lgmg+XVxdTeeLRVXzsql3Dw/zWjw7mwIcOECO9g+G7a6ua6jk2fEJRdDaH1dpeF2iPOdKAlWbfF7Mzub5Ml9OlhcXV2EYa22Ncet1BiGM43i5WSiltNbZeiOEKIoCIZTEcRhEgR8OBgNMSavTjtMkjGPPD5bLtXZgOBwDgJwFeV40Dd8WRCHEzsEfPZ0vGvpbVrrTEAFCCQSOa5spuzIuQ6SBWBrbQGQBtNAZ5MAW/ae1JoTUdU0IgRA755IorOvaY1GvOyYk1ApCwIwG2+5LnlU/3qdPJoPBaHd8OBoeEPIicQIcUlJ7LEqTDqWeT31gnZINo3A1n60Wi153EEStUrhvv/sxStva8xe8AWFSOwRZmLZat2/f/Jt//b+4fnQtjaNOq70zHDHs1aVAjnCuMfIOr90cdsfdtB8FaVOJKEh0ozBECMO8WLMA3by1n+ULzIBGQCAwLwsJkLbGatNOW9C6KtvouqTALC8uI0j0uvljP/MLO2GvDcMUe6pqWnGUdlLCsLbST8OnVyfrplyvsr3xXl3U1oAoiYsyc0rm2fK//f/9vRt3rvtRsLO3QylBAPS6nVs3b8RhVGR54PlGaZ95jNBOt2WdphRjDG/eut5up8AazUUSx3VZ1nWttc7zPI6iTruNMQLQKqWUUmmabqc2moafnp5lWa6NLYQ8un03q2WWV1aq/V6/x3zENdRKaz0cD3/89VeKREFqcEAgfu977xNCBrtj59O93q7iGkDAG2mtvZrNHbSj4c7z0xNjLLDQGm2k4a5upIijpK5rCFAcx1VZb2sT1oK8yp1z68WynfaQY6fPT30/lAJgTITgxCM/YvlCAKBzDkHonDPGQYgBsJgYa4F11jpHILFGGo21lhjTbU4FAmytq8uq1e4LaSFAaRJDhK3lANogCK3BeSZaaV9bhDF2zpRl2WkNwOrF+p2Fm3URR6kxDiJLKLXaWguNdghhAAhBAfEwrytGGIbIi5iyoOFZFNFGVyfzxdWmaCVs5+6tTn+stU7aLUABwLjOqsOb1y3Cb37hTT8OCl6++967f+Nv/fXxaO+LX/ximqbvvPv9Tz/6dNAZ8rrQ0t26cWc+n8cRhq225/lRkN699coP3v2wbBTQSDi9yrNgp+/7vud5g36fBp4SzSxvxjuHj9//eOfwsA38P/cf/Knvvv/ub3/vW7vtjmaQG0MQ3mKmCSRGgTCJr66mEfODIEA+aYr8zc997vT0FGLwz3/tny822Y2jo+Vk1krT9XrNGIMIhGFIKeYSEEYhBmcXp/1uT0nRHbTrstJAt1qJ0dpaSTzCq0oa3XBhG1hLRSlFBFlrs7IIPT9J02yzoZhpqVgQPHl2HCXpDz74eKfb6vd6HoGLxeqbX/qJJ8+emqYuNusg+vHXjzCPMOzRMDx9+ARbTDWaPLswDB/du/39Dz44vHmdxZRzuTPun12dSwv6/UFVVVEQWwkIYRBCp6xzEGO6WG8oxQiRKG0BY5fLdcCCVqv18P6D8e3dTz56WFWNlgghqrVmjFlgwY+0Ez8a/ADWbGtD23wqRABYax0wAFvoEHROch74eItR2LZDnHO+7xtlMA085jvn5pscY+wgNZZy4Q4P96x1ZZkTQrio4Y+P/sBatN0oO+eEqBjwEUAIIcx8CKEz0Pec05XHnDMAAoqRc0hCbJUrwpb5A7//Z1/90peS1BMOWo9SDGsjQ4oBQmEr+srXfxpAZK01Rid++rUvf+32rbvrzfL69esQwp/88k8Oe8PLy/Nr1/Yp9YAjzjkA5dXl2Xfe+fb9Tx4sp5s/++f+9O17r3znO+88vv9xdXnRayeJR2/fuL1Zz48Gg1W2oYRullMf0xBhDJGcb75y9/WvvvnWf/X//n8cLy9FI0VRhcwrm7qRjTHQYxBQaKwSsvEwGo3HO4cHj0+etzopwAQTqKxKO6mzFmKwXC+SJNHKEkZAA9J28uzZ0529HQhdrmo/8oWoyrqJQyKtQggjBk2jmUeEU73hsG74Jis6YXcymUetaD6dGdAWRq/XGXTo8PDw+fHp6vz8cP/AAjedTtuh343C1Wr+1ku3mqaYzKdX8x8nNK4f3SLrVdbvdAeDUYA9aB2taxD4H737wY2bR8Bi6FASt9frbDjYMcA556qqwZCVZd00PEnTXm+wlQd7LFiu5rs7+1VVEYTiOKaUNk3z1ltvzSbzTtqZnK2tBYRATLC1llCGENLqs3i/dQ4aqxGi1mqllUc8iIEx2hiltfSYDwGUQjrgO2e1VkHgcyUh3CoA3KDfVdbUdUm2FS2gHSDbHJBz1vdZowACoKmyz9bve5F1UBlDjLLWFmXu+77HfKUccthjsbVWQ+EFiWoEIlgBKU2tGv3lt17/y//xfz7o+e2EOWeFFNSjEDkGiAMKaA0pc9YYqzFmGFADDAJk3BvtDnYAsNC5O9duOufat19xwFkAgUMYYgfI/t7hv/9Hb5g/aohDCHoSgJ/7uX/n7Pj5X/1Lf7FP8SgKe8PBzRtHRZUbqZbFetAdeAR5EMZx3CiBjItY8D//47/0X/+Tv3/5/g/Hw9H5YooYiuNWVuTrcpMEoVGGUlxkm9HO8On58Y2Xbj97+KSu+HhnZ7Vep2FgtPZCzxrgoNVWFbx0yJ1cnJGATpbTosh2RuNlNt9ka4RQ0gvNkh9d2//ggw/29na01o4AbUXNyyDx253+5WS+2az29vZmk+nOYNgUJYYIWuh71BgjmgJFLPC9s7OzXhoT7FohoRCnt46WwoCPvvvineIwefjkaTEoe0lLA+hRiqjGmCkBlrNlOuj5MfK8ACFitaurqt1ulwAHYSyVZTSg1KtrvlptPM/DyMhGOu2yVcYo3WYglFL5ptgZ73703gOEMCQEGGehBQBgtN0XvkA/O+esMsgBjKy1BlqDgTPGOKO1cwhSgqVzBGHnnNJGYeKsk9ZqALdNdgwAcEbU1drzMEIOIGGdwwQg7LQ2XIg0jqtyLdWP96kOAuC2wTxnjSMIN3WOIWAklMIa4xgLuWwwxsjDCrqar5St/s7f/S/vvXTY7qEogs4BhBCGGCLogLIAIIAgQQ5AgAACaEv+xuDF7hwCAAAGwAAHECTOuS3EHkGy3c8w5ANgEYAQvPj/xQhLLl556eVWue4xNhiOCMa/87v/6t69O9qBpqnSftv3iR/7kLvNatluRS3i/4d/+JfKSnz86HFvPPDTcJlvGKF+4LqDNl9l1upWv1PJ5uz57O7du1GrjWlzdTWF0FW8ghB6nueHXq/Xm80WAAA/CVxRhaEfx/FqvTBKz1aLwbB3++bNq7PzuB2cXT43QHghPXn0vDcYNU1pjYhYQIB96fbBcrrgZdFvt4CxvV7PauP7vqr5jd0dLUUr8pE14/2ddhyE1DX1BhgJMFivy8/uVCEM6nQ6yuiTy3MaBWmvH6StTZHfuHOz0+ldv34zCCJGfSl0VdTVpgqoHwTxer1BkBjjsqxgxKOYWe08xsIgfnD//qDfj+M4jmMptOCqqqrnz5+HYQwsRA5tx6fgC8fmi7TRthSwFaQoJQCwjDH7owu8AFMqhA1ERhvhgCbkhVCFYCaEjOO4rksha4SssZKLYjY/W64vjRUIIWMMhNgYAyHU5sfflG2HDCFirUMQGmOM4lzkWnEEDYDGOReFYcVLgG0pV7Va/5W/+pdHe23sW+pZCC1ETlknJVdGAOcw2GI0kdsS4AEAYFu+Rj/a4LwAGEEAfvQb8Nl+BAIAAYIAQUCcxQAgijBwttfp9TodycVsNju7mEZxG5Nwsy6TMIEQYwQ32QJjyBjJV8tivYmZX06Xf+F/9mf+y//sr0eGhs4r5xue17207bRpRONFfllXq/W6Nxz88KMPuFaLzWpbSGmUcBgMdkZJJ1HOGGg00Kt85ScBCeiqWJeissiGaSyd/uDTDzOeLYtl2Al7u73pej6+tgupIz7uD3vr9dLz8Xg0SFsJhC4Ofa0E5xxCuF4vheQeQ8NeYrVQmh+fPpdaeB71GfY9LAQvK/7ZnfrBR5+gMEqiVpsb++zyAsY+iLz+tX0SBuuqOr24yItSKZ1GSch8D9LF1SwK4jCIlVJlXlhtqk0JlGOQVkWtpWmnLWfsZrVmhFpjGKWr+cIZc/3waJvbJ4RYa9ALgi+CGFngrDMO2B8l+qxzDmO8rTw4BwC01kpthHUSALNV6G6fP2NcECQE+xQj4FRdZhg5JWsuSmXKolwAZC1w1kBrIIHUp8xZ+dn6IbSe50HrgAHQQquNM5LzXMkMI42hZMQQYBmy2m1qMf0zf/5/unPQEbrwfEgwBQA5ZyAyBEMPowBS7CByCL545gAEDgIAHYAObB/Qz/4wgD8mb0LgALAAaPDiB7SVxgCAgcXA4uFwx2dBEIVBGEFMgKNagMePjuezFa9FscmKPLucnpV1MZ1PZtMrVXOoTD1dtqH/n/xv/tJPvfWV6+PDftQG3Fil0zQN4ihOEsyoUgohdHxx1giltEWY7h7sS+hqxddlltfFYrO0/3/O/jzYtjS7CwPXN+7pzOfc8U35MiuHysyqrIGSqqSSkIQ1oO4I07gFjYF/MOBoaMAOQwemBwg30RDYbtptOsKW3dCmjY1t5qGxEciDGhBSlagqVVZWTm9+dzrjnvc3rv7jO+fcm1lVUnTvOHHfffeee87Ze69vfWv91m/9FsV0kGk0lapb2yGjrdH90cBYK3upHGcvfeZ16MfRbBhNesm0n84Gg6NJbqps2icSynqT9eM3P/XJKI0Hoz6lUNZFXhWT2dih5ZFoVOXB/Ohv+hFKSVVV0+n04OCgadrn55f7S3SxXNPxbHp0enL/pRc//bnPpoO+Bp+NBumgPzmcLTfrLMuyOOkaNewNX3zhPniS5/lqtWrrzmnnOlsVpVfmcHbQlBUarbVO07Su67IsOedS8tFo1HXd1dUidDIExxYwdgCglAaqGNnduQAIOAfeIwAJ4JHzxtoOwe7LWpQRazXxSIAhovO6bjaM2q7ZMOKc1YMsxR2MBUCDwG8ggO7P31iVpJGxKnAKOAXOiDNN2+RKb7TeWFtQUoMvVsvHs4PevRdPHNHDUeLDFJdtQoiMCQ6CAgl63wSvrXDvW3diW7h3ruGHOzt2AEggKMcSQEYoR++dsYwyr/QnX39Tpn2IEtVZ48jd+6+88vqnv/HtD9dVU9TVarWy1m42K2vMZrNpmyYiLKORL9vD3vDLn/vCn/njf1J0mNHI1ZaBuLqaN0oZY9fr9ec///npdNppRSkVkWy0mh7MkFHK+SrfyCSuuzZvKhmLVnUOPRKQSXx2cT5frz948GFr9Dsfvt8/mHhORS+9+8onju7c6o2HR7eP415ctTWNGCE4X82jRGpnDo5maS85PD7wFJ8vLlb1Jhn0gNEPPvhgenjogZzPl0+enn/qrc9vimZ/EfvjCV3Ml4v5sqrq9999X0TRZDKtunZVbIx3P/qjPzro95uiLDarWIrn58+4ZE1ZeecYwv07d3XTCkK9c4uLy+lwAIjDXr9pKuN01ZSdbvv9fpZlSRS/9863wcGeMBbH8TbpJyQo1AGA934XFTDvgNGIgGBU7BX/guUxJrbPR0coAkAaR0bV6DvvFBBnbEcIRFEmRcKYFCIKL+K99d45f6M31Tt0TnU5IR1nXnCQnKDX1pRdt6rri7J8enn5dtM+ty7/vb//d1FKB4NekvI4JQQcB0pAOMucpdoRH7ZuoDtD9dsH8bsYYPtzBPCwlTAGgN0TPvI05xxQFBF3uqOMv/rqq1fr3FFhHRZl/ZkvfOl8U6+Ue7rcFK0y1lFKD2dH+aYknqDF0+Nb68tlRBjTrrxY2Hn+7/3v/h1f6ERkm2WhOjcZTsIbPXv2zHQqiWKgpDMaCFPanF9cWu89oIgkE5xxnhcFF4JxuVkX2ri67TiXL37ilbPzy7Jpnp6dLVarvKq//d77z88vf/Erv9wpBZRyyWZHB6PJMOunST9iAvIm5xE7X87f+sLn7738sgGSjkbTo2OH+Oz580Ve1RoXlfr5/+mfcHEN0yRC0rosu6Y5mR1WefELP/8/tG27WiwHg8FoNPqVX/mVxw8eSi5iGZ1fXvQG/aptDg8PExkNst7ian7r5LRYriVhl+dns8n04GB6cDg9Pz+fzWac8zSN1/nKOn18fNw0TeiEDhYZPOvOrRJC0Hsb0gvvPaWEEBbIU37b5AEe7b5BAACcM84pBK1NxYRXukDomnYdR6TtCkrBO5AyZVQAgHWaMUSwggODa4fHGDO2bbpVWV+ibwhqAOCEUmYoaY0tOrWwvijriy//6BfjfiriKE6TXj9NpKCABLbhCqEcCHVIkMAuQv245e3Xm/POWes8AgABGsZr7dwvBaSAFAmljACA0hUTCAw8gd/wAz8ge/2y7WqlXBT/7j/yb/7Bf/tPmDgttfWUda3uum48HBpjrhYLoKRt23K9yVfLu4eH7dVqgPL3/au/h2uWyD4lsq5aBgyct9qg813XySQGLpbLZajpdEoFYcogr26tXS6X3vv+cFAURRz1AGjbaiESwZOm1q1yDkinzMV8MZ5NHYVFvtxUZaObdVt6Aa0zohcZsJrY4Wz087/wPxZdR2V8dn6pjamaGhkvO/t8ufEyOZuvj46uuVTD4ZCOhxNi4Wtf/drp7DimcnO58tZabXTdpiLSbXtxfkYJRGlMYrFpyqapTo9PxsORNeq9b719eDAl4N98/fXF1dx0SggxGg+m0/FkNhYRHw6HoR0xSMcE2l4URbutcKtAtkXyCQFA723g/oUpFAiOMeK9DYfRznvvnLGuA6o8NnW7WK6eGVdU1YJLp0xBmSbUeccIxpzLPF+3XWF9bV2NoG/Eh0AIcV5puy6bs1atnO0IgmCcUe9941zdmaJzpeP2d/zu36G9i9JkOjtM4hQRATwB750xxngCDlCjM+gdAQ/BXn14ACAEOi0C2f4dAY/gCWDABsIXCsB2D9DeWOikRKAOsIvH4+np6flyKbKsN5tilsHB4Qu/4fO/7ff/3k5w5JFx3hg3Gk6KqlFGW8DjW6fvvvdevlxSZbDVH/7qu194/bP/hz/6J4bpSIhYdY4A40DaTcEYY4IrZzdF7oEA496DUsYa7x3UdVvXLSEsSTLGRL8/nExmSZIN+iMCLI16XqN3ZDScNk2X1w1wIbPMEcgGfYjZqqnkIGvQ0H6kBUaTHs1ka7tXXv9k3XQIHAm9Wixnh8dXq5wkqaLR+bJ0RKC9rvufX57R58+fdl3DOc2ypGxKbZWUwjojI7HJ12+89slPvf7JF198sS7z4Xhw+/bpZDJ5/vx5Xm2U1jIWRZUr0y3Wi+M7hyCQSBiMBw6tTOTZ5VnT1ZuiaLoWGO20Dhl9UOEKDhI87uqo22BuGwAwCB0BlFLOt9Ip3lvnlfcGQQM4SoFQL5gHVB4NFxBFrFM1EOfRcEEZJ2kS1dUGQRHUHrUxxuJ1nArOe+8IaIKdNjnQjglLuXG+adWm6ZZ1s2pt86M/8WMPnnxQ1RspmGo7SoFSTgABwKFvu66u66qpO2X8duPHnZ+k+3WBAEAYoYyGlgCghOD1J9kBADu79ZwGhIsgWAAAjq+99dYLn3wdk4T2slYSyGQ0Ht159bV0dqAZc0iyrD+7dbKqCg9ore+PhsqazXJdbIo33vx03Bs8fvr85Vff+IP/6z/0fZ/70vHh6dHBqVe03DScx0xEHsl0OrV2u3et12vCqEPPGJtMJpzzfr8fWBZdp2UcFXVFKS/LMkt6nPCqagSXiNB1XSSTplM8SYiUZdeUqq2NWpeFp0Sh0cT2JiPkQCOh0Sl0HeKyLlq0pTUb1WmkSX8QcbG/OpIz/sM//kNt23Igz548Pbp3stysEUmu61dfffWYk69/8+v37txiVNy9fStfr1Ip8mJzcut4fnV1cve0bdvLy8tPvfVm1Tbrrogn2YPnD3u9VIgoz/PBaNCYNhVZ0bTKWSFS59ADSCkRkRECBCmlbrvvE/TovfcELXrKhLWWAIuSzFrNZEQIMbZD5xj33hEhe1L066ohzIfGQSHjVjUkkF3Qo28H/YE1DaNaq1pkibOk1x8V5XJ//h4tRSCeaK+MuVBmIySnxNVN3qkS0DniD2/feem1V1TTEO9tWxJvKAAgRSIceIO+7JrO4XR60HYdZ4xJ5EAcekoYADXWCE4AiCdAgLRKRVFEGCvzfDgahfAAdwZNAJAECQJCgCLyEKogJYRQH4kf+Mkff+9rb9/6vs8DeKCeEMgG/Zffeuvtf/qLr93/hLNYOPPtJ49nxyfHXXdwcms0nloFT55eJrPj7/uX/2cwHYEgP3H75F/68Z/0xCvV/oX/+3/4y1/5Ck/Eumid83VdRxHLEqms6Q171tq011tu1lnaZ4yB9WWZR1GExDNBiSDWK0lZWxaEszhNqqomhBDErmyElEY7zrmxPs8X905v56sV5zzp9ygV6zqXQkSp1NZago7RZZkPhv2yqKeDcbFYMSTDYW9/p7JY0GeXz68Wl3lTJKMsGaT3Xro3no2zfkoIOTk5+bF/6TdVTVfX9fLyan126RuTMbm5mBPr27rsDbJX33zt9v27d+7ftcRtivVkMmp1G2cx4aQ/6lNK1nnZKeU9BALhVo+cc7Jj8AMA7pT8Q6qEiMGIQxoEAEKIEA94NJ2qEGxAAIK9WdOG31prPFpC0XmzyeeU6KJYAGpCVdvlzrnQ33LDjxFGKGOCAiHU1u2iaVeN2nCBQJxHBcz/ph//TYQRyjx67W3H0Let8x4JUIugnHfoCWGbzcZ70MZ57x06TkQYqC24QEDrXWe09lZGKQKxDh16cAa8B7zOGxAwBOVb+wRGQBAQ1jkEpDKenJ5gmmyKFSQSGHp00aD3iU9/imapto4JEfcHb33x+5V2m3Uuo+g3/dRPzctqUdWXy1WxWgKnDp21jnJJkWfZ5A//b/7Y//Mv/ZU/9kf/+OnR7X42SOIslgnnvFjlAZBRRveHg6otAaBpmpAKE8Y6HQYE8MFggN4H5mfgbTLGpJSq00YZY4yUst/vrzbr4I8ppQ49csrjaFmua9tCzGu0YtRbdbUYJL2D4bouDMfeZHTtU4inWdZPev2qbp0HJPTRoyfW+CzprfPi4mpx9959wvhyuY5EnPFEL8vVB0/HNDrpT6Vn3NPnT55+8xu/+vjx47aqjyYzYr0Ani82FKm1djab9Xq9cBo3M6quC2M6HGxl/LcBwJbSH/pPaHiuDx7XWhtyFOcsorNOBdyKMud8x5kDNB41oZYxRLRNWyxXV84pIJYSUF0rOI2lAH9zz4VwWcMMoEC2stYGnxGn0Ze//AOj8UB3LWeEUNRaK9U6b+qutegJoQCglNK6S5Iky5IsjQmlhDCLjhBuHSJs+Tdd1zkPxlsPnjHW6/WAMaAfRbS+x8GYQCQAwKT80o/80OjwABj1gMA5MN4/mMl+r6gaAqw3mnz6s5998OTpumweP35etO3v/Lf/2KP14tnDp1cPn7eLNWUCJEdCgDJATNI0TdPPfeqz/9V//l/+B/+X/3A8nFqN3tDBYKS1jaKEIBilBaNpLK3uKHij2ljKNI4no1EUJQ5wcnhEKM+LyjkUcZINhlRIIUSSJIgghHTWW+McgkMIrSxa63W+AUYN+lJ3HbrOWxKJFu37zx6/9oXPdgKe1tdM6rN8Tquq6rquMxoJW6/X9194iRDW749Upwllf/1v/+3PfPZzR0fH5ap88dZ9W3b3D0/nj589/+Dh6uKqK+vXX/mk01Ywfvv0TrEpdWd00zpt+lmPU3p5eTWZTIKQBqU0sMEppUEoZudQr7v+QxNVeOz7pRC89zYUt8LCtU5r3WnTUuYJRQSD4I3tgDjGCKFAKDJGlWopw9CRbK3WurPW7qHc/SIJAhnD4VCISEpprVXKSClns9lnP/tZRgJVACMhinxdlnmY4wiEaGe0NYQxKUTX1vOryzzPrTEOPSUUADjjoQJAmVws14HxSYACoYyHwgHCdztwB7t69AjbWYfOO+cdkxFyqq3xAOFdkn7vjc9+tlG6Vc57+Oz3f/nVT7314PETa5zxCMPB7/jXfk/jnEOfjEZaa2WMQYcEgFDrLCUckUgavXjnhf/yL//VL3z2i5xGFCOwdNQfRVGUSJHIaLVaZGlsjaKwDbBXyw06YFwKKYUQYW5b27bGmF6vxwkF5yVhqmooAgDIOAqlVE4ZQTDhQgneWY2MEME1+ChLo3724Ozp+PZRcjDcX5DXP/cmtQ4HwzEX0XK9uX33BeO9ZOLJ42da282mSNPe177xrbpSTsOzB8+uHl9dPr+0rTFNZ5ru+eMn0+FYdd1msVxcXKZJ4qwdDSeMCcZEWdT37t0vy1Ip1XWd915KSSlVSllrr5kp+5IpJdrZwPzTWoenBZ9qrRVCcM49WkrBGm1sa2zLOfHeEALWamNUsOPA0fZWZ0nUdU3Q5k2z2FjVdjVj4oZJUOe2OJj3kGUZ5ZLLmBDWNN2nP/2ZNI4360WWRqvl5auvvfTSSy8wTvN8TSl13iESpZRzjnO6Xszff/e9zXKxXC7LTU6AGmsRiHVOOdfqTkq5yjeEEA/eg0dEjz7IlX1Xe90bK+IWICCEMMqsswjAeESAeeec8yJJb917odSGct6P+wD8R378J3Oldecy2YO6vv3ZT//2P/ZHooPpg/fe9dqAdYKwUCTjTFhjPBAESj2PQP7f/txf+LEf/smD6ZEz6C1ywo3WjBJGyWa16poGvY0kb6o6klJrvVgsuk5XVYNIGGPEYxrFTx4+imUS8QiVQ+UYEHDeWivjiBFi2m42nkgu2ratqkoIgZSE/rZVkcssYWmcqzbX13XveNqn/X7/7OwMkTRN861vvZMkSZb1V4vF1cU8z0vO5dXVgnNpOtPV3f0X7iNlRdc0RuWl4px/8O57x9MjZ5zRVoo4mGmWZabVx4fHDz98QBFevH+/1+vtb0kwprCn75n/IeUPyzFJIs45YyTEPZxzIaJ9dYAyAIIATutWmyY0BjqvgPgQ/jpnvLcIRpuWC3BeUYrj8TCOhdatxxvVVGoRNIJG6rgARJekklJwTlEKbVvVdVHmyzhib735RiLFerXYrJaDXtY1NSFMa+0JWGsvzp6vV8uv/cpXPnz/g8uzc2tt3dQhQiWMcSYQMell5+fniBgk4gjdesSPWSqBbZi64+DeKLoSAkA54xQIAgJ6yhhjjAAZHxwqgFKptjGgcfbSi7M7d88vrpz1XVECIcDxhS985sU3PimEYIRqrQDAAwIi58I7dIiCR4RwRPqH/sAf+Ys/+5+99sobDIQzfjycMMJ6aYaIRqnBYLBcLo3SjFAh2OnpqdVmNBhWm0K1XRLFZV4IytqyUkXVk/EwyWb94f2797quy/Pce2+Mqaoq3OvBYOCsN50K46ySJOn1+9a7pmsdXmcU58tLenZ2EejMaZoeHh4+f/48y7IvfOELL7zwwng4efjho4PxwWqxRiSjyewTn3pz9uKdt37oi7nR8SBqlXn27EyKeDwYx3G8XK5apZ49Pzs/uwCgv/q1Xx0Pxo8ePWrbNkD9W7ukxOK2yh96rUJuxBgLt2fPTQkdEYEZGKrhQClhjHECxCM4pTpjFKKz1gCgt8Y7g+gI9VHEjWl7aYROW9dpU1vXKl0Zvdmfv7a5cYXxpTKbullV3Xq9ueh0TrmREeblpfW1VVWdry+ePzl/9vjpwwfz87P1cqG1Xi6XiMRpZ5QSjHdt/fDDD54/ffz0yaOry8urqyvnncUApXqlVFuVpms5o1xQAsQ5Z521VsO+UnVdcYWwnhFwu/VvzZfuqgaO7soKoZTQGw1ffON1xeh4MtOtgl7vR3/qJ883q1WZG2NAK0ikAuMoIoNYyEhIRDTedcYCYYQwTjh48MYhklik/XTwl//iX/7spz87Hc/W83w2ngnKZ+PpoDdQTRsLGSeSEKRA5pdXbV0y9MRZNLYtK+oxFZFAPJ5MBGICZJT2uqK6e/tO0zR11SRpFqruUsRxFFljYhkRhLqsCMD86srobjjotfU16007ywEgBF6Hs4P1ekM8rlYr733X6Zdeetm05uGDB7/xSz/4/OHj5bqQZTY6PbpYXpI06k8mZ2fn2Xj49OlTEExEPMkEF0IIMZnMrLW3bt1p6u7NN95oG+uciUW/1Zozvr8rYVunlFAKezJAyGwQCaWcAA3uVggRBncHNavw5yHfN0Z75p1DIYTVllJKOSMEnW/7/VjpAghq03WrEhEQEd01Q6eq50A85dRabdFyzuNEatMiorHKOU2JI2CXlxfDLP2lp0/6g3Ge5/1+nzE2GI2bpgEA3akiX58/eXIwHv3SL/5zIP74+PgHfvAHu667e//FOIqVVkEw//Bo5pz23nEhOA0ahrgdFBfc5Db93zpXj55sqX9+v/sHRAsBCQHjLGeMIAFOv/Abv/xX/pO/eHD7trXdoK1vv/rSq5/9zFe+9au/5Te8qbxljjIuNRjGKWBQTHKd1ZGIO604ZYgECFAhEdChY4QRIH/6T/6Zf/CP/t5f/s/+UlltPvHiq4hOCPHee+/N53MuxXCYVm1zMB0DgKqbXpqKKOq67t6du6vFotqUk94gi2KapMSDt66XpIPBADy2XUcZ1VoPBmnXdINef51vEH2WZWVVDYfDLMv2biscShnuEJ1zb77+hjf2hTv3Li4u5vNlUVWb5WY1X7xw936VR+++++54MHSCZrPxu48/PDg+8JyyJLp1/wVjTOfMejV/8eUXF8vl0clxvz8siurk5GSzyq1Tv/RLv/TSi6/tkncIo+WCpD8iSilCI5RzznRKMm6tAQDBI+fQessYC9ASIgIBzjmlVDUNFyI4X8qoR0MINaYDis4F4h0l4PLChHps+HMkjFLKuNmryHhoCRDnkYUuQ0Y8GkqBEnAeO9WkabxZzAmwarN+/Ow5pfStz33++PhYJjFjrDcYlkUlpSw3eRrHq+Xy5//xP66q4vD46OmzsyiK/uAf/kOvv/566HEIVQPJmDLKE0BvQ/CN6BBp8J1AABEp2XrTbQF2983OjhEACAISD4xqQEEIAIhR/9Xv//yvPHjvt/zW/0VhuiRJfuS3/8uLs7O5qg6nPc44AuK2voDe+0Z1hFHnnGScM+69R0IpACJywhCQAiDgj/3Ij4PBP/9//fefPjyLEzGdTqfTA631wdFstVodTMbe+/l8Pur1peSc836WXJw9IwjonDfWOxfHcV5sCIXVerlcLqIoJoTozmZZtryaJ0lCGGWERmmirYniqNOKEbrZbI6PTgG2ImqJTPhwOBz2++fn513dlHkZJTFhPI7j7TyQKOr1erdOb3/r3W+fnt663KzefOvTi8XV3Rfuzefz4XhqAQ9PjmgsHz95FscSna/rOonSfL0JLOP79+8TBArAOe+aFgC6rgumGRhFjBMChNLA2iABX3QOnXNBEYBzRilzzqAHIFRrDcACFAAQRKsBg8MEABImATnv9ba6E24wASAOCQRobLfDYhiOFnZbay2jHIAa03HOoigq86JrWqXU48dPAwWq3+//hs9/oSkLAAjpFGOMAlFt9/DhQ29slmWLxeIf/sN/OJpOfsu/8luzLLt7714w1iSSRnfoLSDxVqu2TeOxB7o3Tfj/4fA3vlIAAMG/9GO/8eHDh2flejQaEcGB0NkLty3xIVkhQCigAwTClDMOvbPOAxIepsEEqMGx3dUMdMWUZz/5kz99enr7z/25P8s4effb70+moziOu7btD3oAMD+f37t3Tyul87xs6lhIa20SxYPBYLPZ3Ll9K89zGceL5VVE/P379611m9WaeV9vCvC+WK2XRvXHozROBVqtNaKtmraXZYura7WfttG0rusHDx5cPD/LN5vALFksr2aH05dfezlO5NOnj5M0+ie/9E+ppE6Q+Wa1upwT66eDEWNMxpEDnOd5rbovfP/39XqDoqgkjxjhlPLhYNy1uqlqQkiWZXuEKEmSAK8G86KwnZd3M7fYp1mhz+n6fiDR2oY/h0AR3D223Ozrsau4p4lQCkEaiBCEGxoqJGBaQHyoCXmKyAhwAgI8EzQuy3q9Xs9ms/F4rDrdNur82dN3vvU2ozTMUx0Oh6PRKKjfaK211mVZG+16vcHv/Fd/96uvfLJTZm8Ho9EIEZVSXVOB90Yp3Snn3E2psF/32JIHts0DngUOAQGP3nh3+8WXZqenaZYhAeDUMiSMIxBEC+ioBYncI9ZKecqAME44IayzxqAH2E2rJ54QQgn1HoxzgiafevMz//6/++evzpeDwXi5WJ+fXy7mq/nVgjDWHw2p4BZwMp0uVyseybTf84BV08T9LMrSvK0fnT/jceQBlFLFJqceiXHEuL5MfKuJIwJJW9VOmaZprPWz2WFdtb2sf+O8Kc2yXr/fD9Xe9Xp9eHgYXGleFlzKq8X87W+/c3RyXKvug0cPHThGARC/9a1v3bpzx1jLpKiaejydPXny5Goxr8sq9EMS3Hqv0E11zehjRCnVdVvq4VZkekf43yNWwXYJDSnwXhaAIyJYD0AJMEAayKkhIyRBiQa2D8YYZaF/78ZXSgOGEA5EhLBWCNk5aUBETln4SG1dbjYbb+wXv/jFk5OjNBVNWy0XV5LRMH2vruu6ruM4DiOZx7PpyenpCy++9G/+W//W//K3/faqaYHxVhvBBQAIRr0znAI6p5SyWltrlWq3FIhdF8Cv4VzJR39DAa5PhhDKuEGnvfOAjDHlLRBmvWVAkBAEJIyDB6UUIEUCQFhgsmnnLYbrjt452CEPggnOJAKhVJ6e3P19v+/3ewdt2x0f3b51527a75VlZb27WsyttcvN+tatW8baQEWKkvjg6GhVFMpbT4kj4AEHg8EgzSLK0ziJGPfacCBgndVO1V1Xtwyp5CLP83Av9odzjtZFXZX17dt3gFBldGf0cDQ6v7oMGCzPEgV+Va7jLEZv0bqiLN//8L3TO7erqnLOcSnG42Fdl0KI0Wg0m82appnP58vlcrlc1nX92muvEY9HB4eMUOKRAeGEckJvJrwUge5w+N1n80FYPNCRCMHgF733sAMKgtMFoLsSASWEEsIJ2fVnA0PKkDBPqN8S6Ym/YQeehKYuT2ggPXkA670F4oFYGdGiyBmj8/ncGPNjP/Zjb7755mgwLNYr6wxjDBjlUeycD1DaW5/53OmtO//Kz/y2/+0f/xM/+OUfSbK+SNLDo5NIJq0xeZ6HgCfcA4IoufDGhnLA9zLN73JgCF7DCVDwlGDY3LdtMTGTzlpKqKQcwUeUEwAP1BGGiKrrukYxpM4S51AZq63fb1w7NiYBAOusRwQgiCHwIT/zM799NjuMoiRJ0suLeVm03sNmXZRFXTddlvW9B2c8A6Za3TTdarNGRqIsOzw9dYjG2A/e+8Abg941VVHX9WazGQxGs+nhar6QXNAg0UiFrrvToxNrP8KZpF3XHR+erPMy7fc453meX11dIQEPWLT1i6+9cuv+vePbt7quQ+sW88tKtemgvypyLsWmLAaDATrvrdNaA0BRldbaNE3TNFVKzabTr331VxaLhdY63JKwce9vT7DXgG8D+DDdD3Z4jXMutJ4yvrVsH0apbrUtdhZKd7nHdxw7X3vjK1wvVkqvX5ZSiiHsI+icIQRGg17XNc65sqmfPns2mU7v3r374ov3pZQAEPoauq5L0tSh77SaHR78r37H77z7wot37r5AhbDeJ2lfyKjRWil1dnYWSnQAAN4HOnkobWyDlW2sjRjy8O9lpQSQELxuvtr9ypOA6yH4UNAC2JJxnQ39DkQ7a4EQYNZ48IQxwQjfCnEiBaCEModhBChjjAXElxJKgXrvjbV/8S/+JSai5XoVwBmjHadCKyU4X6/XYRfdrNZZkiZJYj06JLXq6rYxxoDHg8nUOWe9a5WaHs3640lrdJwm/dF4tVq1rQrMkF6v9/jZ07q7Rv6FEHzQGxZFRSk9Oz+LZaQ99kdjrSwRcjydrMvq9v17D959fzge9Q+S9XpZdE026kn0rdFRFLV1463tZ1nAtDnn04PZZlMYZwFRa+29Pzg4WFxWjDGLJhT9heAetq7ROYMI1tmw91FKgXhETyn1xgohnDOIHIFYZ/f5UDDTnTPY2yiF62jLE7K/5dvgNVjvjbsLcPOHSHDb+wTe+ziOlIo6rTxAkiTf+ta3elly9+7dey+8NBqN+sNB2ar+YCAIvPHGG+vlYjAYWWvrui3Lisdp3an7L71MuPBGUybmi9VkNDTGBQEOpRQ4QAI2cB0BETzAR7a872KpAI5ACFMpUKB+v0I9IDAWMaltF3MGAA5Qqy7lMaPcAdHoCWVEMDTovCOERkxKAAJoLYJHR5BRyoGi90CRAHHgYH9xKZFUFvXmb/7Nv/0zv/23EorGOQQPrsvibLPcMMb6cVrk+TDrUaSf+8znzy8ullUOQBfz1cFsNh2NtWpHB9OyLEEIT+mgN9tsNrluSSQOJwNljJSCcy7iZFPVVXvdndJU5RbC3BQ55zLr94x3g8EAEY+OjrTWZVl+7RtfD+zrKIqYjIyzddOlvYwQMh2NKcJ0cqDarqlaguCM3Ww2nPMsyw4ODoK8P+d8Mhkp3UrJg9ar97DPfkKaH2DUfVF+D6/uq6OILjiMHeLtEBHBebTXjy1nwAfaQKC0biGqXRyMN7QO96Wy4OkDdYugZ4zpznuPvd7AOKgapaybz+dKdYgok5gQsl6uCMJ6sxRx1BsOvv9LP3B6ejoaT+/cvZcN+kKy6XQcRhN0xi4Wq81mQwhhgFabruu8ddqoumu1CbssBcLCWv01bXUbqdKd4eI2HCBAqfPhWrpwvs67KIqAABBinFFKOSCdVnlVb9cnUmutNQ6ABv0l6xGA7ONmCLWWnf/2AP1smKW9/+DP/wXVOeKJ6bT3QIH1ssFkNO66LkmSumvLpn748OGzs+dlWQoeCSavrq4QSb4p3/32e502DrDuFHJYl4VDPxj1Z4eHp6enSZpOJpOtPrC4VmUcDAY0jiVjpKoKxgiT4uDgIM/zJEmePX7W1Yo7OkoGH7734Wg0ObuaJ2k67I9iFkVUplGabzab9Xp+dXV6fGs0GF+cXXIqOBXFJrfaOLS9XpplGWWgTCciZkFbtEgJYRydI4jEYyIjQgggJZTacI2QAlJrvKACLUrGVdOC82gNOEvQMwqBN892Yyp299ED8RQ98cGGQ9MphgdFYLBtuQ/HdgxGKGsRRLAUHKPeWs0YPPjwSV52CAKBLZfLwXiQlxsQLErioqryzaqpykgIIbjzMJrMhqPJnTt3RZRMJpPDg8lklBG01upAxbi4PLNaadUyROKct+b88qzp2kYbT6kBNN4hAAGkBPCa7n3d6EqQkF1LwO6nFIAG8/UY/KvjO50vTikFghQcWuccJ9Rpg4iMcyaFYNxY5TnV4EUcWe+8B06FCwONCCI4CgihFRGBIGXAKTBO5Cdffv0HvvADw3R0fHjy8ksvv/Lqq4LzpukIFxdXc+Mx6fXf/eBDHsW9bGCM6/eHkUwePXnmKRsOp1XeCBqlUZxG8XjYH4+HaZpqa5TRy+X6/PwSrZGU8BvhnPOGZlkWp8nx8XFvOCjLMi+KKI6dc13XgfdNWXEgB7PZxdn5eDa1CM7YcpOXRVFXFec8TdPXXvnks2dn68XyU69/qqubcpMHcknTVJ1WwYlOpuOsFzFG9nFhwP9DiXWf6AV3u81CA7162+XHnDfOm2B6O5fpbvhOv/ed35mg3Nj6P3I4Z4Ivh113F3rrnAFESkGIKN+UWhtj3OPHj4zVSImMIm1MWVZN02xWy/VyWeZ5eIXAHpxMJkcnx70k5pzGcRzU3dbr9dXVlZCMEPDe6q4rikIpNV8uKeeOUADqARw6Bx6AfpcwNMSvuLXRj/xq94T91rTdnXy4FMR6Ryk1WjdNs17nMon3dAtPwLNtA/s+Z9i/I9wgHlBCjTXWeU4Zp+L//Kf/zHQ87Sf9fr//4MGjIq9CA9Kbb33aE4iyNO33irJcr9dJkgwGAwAqIuk9PHn8zHsvuYiiaLlcBs6k1t3l5WXTNCEuKvNKdR29ccuyJKWbIi/Lsm3bpmmiKLLWbjZF0ylOWVWUt2/dYozFMpFMXp6dl5u8KIq2bcMrCh4RYA8ePmyaRmv9zjvvOIdSxrdu3To8PGxbFbi3WuvNZhW4p4xtp/XBLtkP1/cmMxV2OdYeZN0/E3al1N1vP2qUSEPhgJA9uko/9l43a3SwS6rwRi/X/pskSeoyJwiMgLdQ13Ucp4gwOTgkjEoplVLoLCMA4K3TQjJKSTjfOI6jKKWUbqq2qmut9cMHj50H4xwSGseyrPLFauOR3bl9jzHRdRo9oYT5oNdFKO6Q09Cvste02D0+fuxCKbc3rN36JOipc2i0s9Z3rRZCAJBAxhCCJ0lCWYim3I0WRXpDMSM0nHnBJaVb0gJn/Gd/9mebpjs7u2iaJuv3ZJR4xMurK8JgU26G48FwMpwdzdq2fvr06XA8IsCSLH35tZeHkyFhAAzKpswGmSfQahWliSeQZf04joOyCb8hIZbFCUXEzmjCmXXu2fPnnHMZRyETIoScn5/XZVXlxWq1CixjSmkw6KqqyrI0xjRNEydJnKVHR0ec8yRJjDFlWWZZNh6PnXNKqaZpZrOJ9y44vxB9huiQUqq1Di0r+08W3h12oq3XznKfJt+w6Y8ddKfGetNS9y8SlsTNe4BbftP2foeRQ96jEKJpOkYAHRCASMZRnBDKCbD1Kl8sFpcXF+vVSqvWWQ0AYRhBnCaEEKQMgTQWtHFFXYXJjlJKymXTdXlVlmVpLI6ns0ZpdEGOjmnngTICTDsb+iBufFK/M92PH9ucEa6rHj6wpBCCuQsRWeO9B6MdIiqlETFoRHZdZ50On01yjnANFG4RwN2qYJSFkq/zyBl33jEm/ugf/aNt2yqlORfr9dpaW3dtbzBw6Ou21VoHj7bON0GRIHi0JImullfr9Xo8Hj979qwoCkRs27brOimlMU5rK0UcRddzQxcXl3yVb0KAGDRaHfrlYsk5b+tmOp064znnQbE/9OttNhskcHx06h0wwYuqjGVMKbXaAABBr5RK+722Vcp0hBCOzHufJEkAZRiXgIjg9038gR8NAEA8Y9Qayzm31gWInnO+dZ83btseSb32AaHHg1zDXnv0B3auOtxIc2PKkb0x7TdYsycUEQmA92GkXYUeAEEZODg4Kcr89BZfXM6B8cvzqziOmyTWbceACBE75x0S6yzjvCqbOMuaziw2m+Vyaeq8KDecS+/BOR9FkXXoKbv34suesK7rBGVxLEPflSeeMmEdEEoIIEUIPhW2pSwKQPcWu28Z8OAJIUCYRzTokFLwnlNqrfdArfVKmaZphJBhxXZdF8dxuPjWWkRCBadAnXcBpQrOexcUbPmyCJ5S6tExyjz6L33fD3zus7/hG9/8uvEu7fXzYkMYtd5Rzjz4si4cei7FaDTSWt+9fS9N49Vm3qquVV3G07IupwfTg4PD8/Nzb5FRUWObJAlBsNbrG3iqUZYyzsONtNbODg/CzkUI6bpus9l478MYFyllEIGPoijLsocPH2ZZRggJ/lVKmaZbtcumaZbLpTEGPVFKAaNa64DUEor79mjG2D5GFEIopW6GSnv6H4YxeTeKAntP87F49GYk+p2h6t4Nf8w6b/pg4tF5H2yaUijLsqlbpBQYFYLkReUsCiEIIEN/cjipi1W9WUqKocNbCAqUcSk9oXWny1rVXTufzweDnhDMW+fMlhpb1pUFMj44ipO+4JEUMQC0rVJKEcI8YKO0Q49AAdh+4/+u53XtZJ0HAMb2krSUUg5ACWNd1xnjmrbV2kRRFLThjTHee+c8IN2qMCG0ujPe3YiHP35Jt0Ew4YGh4RD/7J/5c4E0OBwOh6NRlmVN187ncyRAOWOMhZLQ3bt3Ly8vV6sV7tScQrNrmqaPHj3Ksqwqm36/r6xxFrU1SZYmybVPTaKYZklPd4Yx4T0wwpuqdcZGQk4ms7Ksu65jjCElIpJN1yqlpIyNdsPxyKFXSgNcx53r9doD8UCmk5lzzjkneKS1RkIIwU61/WHPOOPRwm7Xxl0T1d4c93YTLsqe9r+NwHbpwnX4jzT0bsJ3O3a3dlvxslYH3D4cgdi1DTMQtq6dEo9IGC2qyqL3SIz1QNg777xzcnLS76WM+vn506/98j/jaAaprKvcGqWtqTVoj02nOmU88KpTxhhOEZ1+5+2vt2Wu6ooRZ62xQCaHh8PpsXJYt01VF43qHKBFqNqubFqPRDtvvLMAAXgmH0uliN8+dseeKYGIhHIgZNtDQYj33u4aLR2g0c5ZBMJkHIUwLISwAJRzCUgdoPfeo9+1sxEAEjIqQijdhY/hcjHgf+pP/Tuc882myLJeyI+Pj48JIUdHR9ZqpdQrn3iZAvnkK69uNhv0pKnqJIqttm3b5XkR/FSSpYRRh7YzHRJinEuyeH92RyeH1DmXpqnkwhiz2WwODw/DLd1sNmmaEkbjNFksFtrZpJchJUmaHh4ehlmacRyHDsZAzgjfCyGKoojiJDhdQkiv1wtPCMxOzrm1GtHtY9Og40V2zMC9pe6/XrMVdwa6t9T9Wt+b5v7YAwL7EBYAQi1t64Z2yXJw8Ptbbq1HT7pOEUaBEsqZcfZqMe/1U4K+KjbLxWVXFcv5RVcXqmuaplKqZQKarkPCuIwdIR6gqUoCdn7xLF8vXrh3pyw2tusYY9oj8gQJD5iZMaquy6oqjFHbSp1z2hhjvXNoESyi36qwfSTR2ZrsjXO5kf4TZ1F1JozbM8bEcSziyBjTtm1Qt+UsRALAuZBCABJjHABFTxAJ4n6FACJ69IILAsQjGmuCjCYnggD5/Gc+/7nPfF41Kl9vGBXotpSPEPINh8O33357Np7EcTwbT6qiQCQHk4PRaOStm0wmlFJAOptNAv9TGc2lKOuivIH8O/TUas0p5ZQKxlXbofNhILuQ0iMSSuumyXo9LgQQ4hGV0a3qAuENCWmV8ki0tkVRpWlvvc4ZE63qgpEYY6uyDq1Ug8GAMWKsVqoFAGstIRjC0GCIwaGGRqtr97DLn24iTXv7g22W7IF4j9Z/tNPjpu3urfkmQ4WgI+jQOm/szQkZ4U0vrq6895SCJz5K5XCYnZ4eJ2msVLdrBEdE13VNVefWdUVZWKe995RzZdE6RKuWF2fvf/tb8/NnvVhenj8zugHiq8Z4HiHj67xp2/r8/Pl6vey6Ji832nTOOeMs5UIZ6zx4DwQEACPAr6sWO5RjeyKwzfoppZxLRBJyYgBAApSTgPEp1QkhHPpWdVEUdV0HhHXKeKCtdt4Do4Jx2Spdt23oy/DoyS5r3bNmOefXvG8kkso//af+T5GIwcHTx0+NMU1VM8bm83kUJartiMezs7MHH3xIkB5MD08OT9q6uzq/6veHVrtQByaMiogj8Vk/bVWjrdJW7U/wYnlFLy8vgxK5YIwxFrSHgrMM+mdSyv5wICJ5Ob8K0utd1wXR067rAKBtW2PtaDJu2zZN0zzPw6sbZ5FAlmWhYybMnpRSAIDzZq8zRW64yY85yO90ltsbc+P42NM+9syb0VU4bvJTb6Zc+zXjXNAX8pQSa8F7zwkopQGgbrr5Yv30+UVZtauievj4bLEp3n77ncV8pdqOIVL0hGKnFJURkyJfL58+fO/i7EkkRN2U3/7WO1rrpu5a45hMtfN5lT9+8rCsNk1brjdLpdqgP2eMqevaaGutbVpVt522HoFyJj4Op+J1Vc8YowPfxPswoCbsFWVZxnGcVyVjLIpkmqYBAwksnwACANKAzhljnUVCGGeSEk4ICcYK3+NARKMtA/4n//f/Rwrs7q27/WyglAFP0jg7f34WMmmtNSEEnB8MBlcX86Kohv1RvsqttQF5IIQURUEFt+h7w55IZGuuuzM6a7gUMWdSK8uZ1Nb3+0PCqBAiSYISH1utVp7QsiwnBzMmRbnJpZRRmiilvMOqqg6nh6vVChBFJMu82AcDYT45ISSO41YrQhljrN/PNroECMiihTAxNUScOycK3ztt+k4LDtdwa7J7/BFg3551s5sOEdF/R1JCERG1sjKKjFXeOUcoYdRaLyXVyjNG0jRmLFosNk8eP92syhC+p2n6K1977+VXP3E6Xx0d32nLmoSRQ1QIIdHjV7/yz3VdT4cDo6Jf+OpXBlnPOmeQtsbP1/ns4Gi1Wjx79ogSHxrNT09vD4YjBIpIHJJgTATQArceqScEIVytkEntWFQEQ+XCO8ZFoCOGHd85V5bldDrNqzqKIgKs6VpGt2PZnLPWes65tZ5ScB65oA4QGNXWkAaTSHDGgbjvQUSkAMAoY1J4tD/05d/41luf/dW3v6Fd188GnIrF1Xw2mwWQoaoqSsiqrpumWa7mAJBlyXQ6pZyfXTwPHC4pJRJCKe31elfzy17vWkOFCU4552VTA4CyBhEDl6MsS+99nudKqel0Gpq467quqirEBsEzhURyDxGEAGg8Hg+Hw7DJBqobAAScGcjWEIM3DdlSOPY5+NZ6dv8lH0VP92Z6Mwz9Xnb8XTLlG24YboCshBDKmDGGEMKuJTLBGi8YJ4TpzgCyr/zyv3j65LJujLKsbNB4cbko58uyLNqqqAWBiAu0ruu6KCJZFklG+4Msksnjx4+XeXGxWH74+JkDBiwqq0ZrbVTXqVabrmmrssovLs6KfFMWG++tYGQLQBqLBIByoIQy7rdx6o2sHBAAA8BCcMuUIIQ455qm6ff7XacDOtFp5ZwPtbSd691erm3rufeMCsY4JZxLQZjYXopfw6d6RI+UcO/cv/tn/1wSpZPhJE0yxsRoNBkOx3EcB2O1zkkpy6a+e/eulPLq6iqM3ZNCMMaUUqPRKIsT731ZlsPh8KYyg3aWJr3MA1Zt02mFZEuhNdYGsFpru1isggMPNsojabwL+FSwwmzQn0wmSZL0+33CKBOcUu49IOJsNnMO67rdIyPW6qyXBJTqprl8zLyCSd2sMH3njg8f3dY/dtw02e99+FCb8d5yQREtIcRZe3x8jB48AhJwuJ2eFUXJk8dnV4v1alOt1hXhSauRi+zps8uqavI8b6taty1BiCNpjauKfHIwOzg6eXZ+8avffGc4nmWjyTfeef9iVVSdbpVu6jLfrLTuVqtFuVlXebFer9frJVn8FgAAkz1JREFU9XI5X60XSimj2rprOtUqo613nXUaEJEELUGPu3aereq88d7XdY3Od02LzhtjlFJV1bTKOIuRTDgXlNKQZQNFLpkQbEs6I0g4IYwZY3pplKZpWZbz+dz5AJ26kGR9F0tFJJQ679K4l0a9n/3Znz0/vxRULBYLa+3l5eXZ2YUDd3L7xIEjDLTplpv16Z3bwQDyYp2HcrQn5+eXm00hGe/qLl9u4AaXiFHOm67lQlR1zThXSnVKDQaDEJ7med7PekKIsixHo1HIeDabTfjj0GsVUuZVvjmazuq6TtN0Mpm0bXt1dWWcyfO8l/aDoZMdJVcIgdiGFUP8NSvP78qnNy/E3q3uo1i4AZ0S8j3Vcm4GEuTXg1oBQCslo8gYw4V4/vxsNB4q1baN5oKHd0J0Hi06Qq1lPGKMeSCL1RqI84jr9Rq8nR0eD48zBFjOL7quYkyYrnvw8HHcGw6nI09Fqdw3333A4t5gNF4srppysVqttKrTKO66ulFd27aTyWxmbZFXSZLJJDYmCTtj4BY59Ftcbefowv/SNG2bNkhvR1EUBBWRUN1pKuDq6urg4LDX61FKo0gqpQPmzwjVWlMgQIJqO3DOrQcCMByOjeosWnCEMwbfY81TxgCAUebROrTHRye/83f8rr/2N/6byXgqJA+3L03j58+fN00jpVSmw8plWXJwNIsiUdRFkqZa6zTtzeK4qqrlcn0wnTln6vK6i9ppQ421ndEeUTvb6/dD9t22bVmW4/HYKt3VDfGYxQknNBayqipCSLdlFigZR2VZ3r17NyD/6/V6uVxWVRUCgF6vl2VZb9Bv2zbsR9bakNrfdHvhTXHHFPnYN3DD0d40348dN66eD01U+1p2+CYQBT92ocmu7iqjKCwS5xxjdLPO21aLRDpwIIgDg2C1qZ2p63LRNnmnyrrKz6+eV/VGSHY1P/NeA8UyX3tnis368aMHSutN1XbWx2l/MJ7ROGktsUBHoyl4jKWg4Akhu9qVc9pUVZXn+Xw+t047Z5RSdVMu1+vVZlNUudL6JiUvmGvIzQf9gVIKALTuQglaax3KN8vl6ujoeNAfJXEWRykAhAhYCI7gGCeUAedsSwCixDkfYtywZ5IbDTM3Pes+J7DOOu8ooYwyTvm/8Yf/jX6/r5Spq8YjIoHVZj2eTobjAZdsPB4enx45tMPJuKir6XQcIO0tY7BuvXXgkVE63qlmA0CW9GiYMsWk4Jx3RsdZGs6TMbbZFCF8SZJktVohojGGAMWgCY7EauOMF5Q9fvCwrutQkAiXcrFY9NJ+sSknk4lSSggBEKp2KASbTMbOm73+1NZxegJIb1rnx+pS37mnfyz33/182yGIAdHfHd4H/eiPAwsBrzVGWWsoBUTvnCcUOCdWacqJ9zYgGAShbVqPtq2LZ08ePHv2qGvLXi+ru7ZTJu71gXEqOCf02++8ffHsWVPXplOIZDAaOoS0N/CUikjWdVVXG9U1VVGWeUH8tmPCWuu8aZqmqss8zzf5KrQVaK3btm1bFQzIOgz8aw/oARHQI0HAVpm6ro2zRVE49PP5cn61bLU+Ob096I96gz5s3a0O+h6ChTkLQkohI44YQnOFuxoSIURQweivxe923nHGGeXWeYDQgk5/+qf/52mUci7Rewpk0Ot7b0PoOZ5NCSEijoSUQsqirK21o9Gol2be+/v37gZXqJW9yVDhnHO0uB3v0rYUaRzFSqm2Vb1eTwhM03S9XpdllaZpkqSLxUIyiRb7ad9am8UZAGWESh6pVjvndWfAEyklI9xbPJgejgbj5XxFCPPeiyiKEtpWjZQMwAHxwJl3ngHzHtiu4xQoceAYZX5beSMAu4oJMO8c5dx7TwnZ9k2TfZaBIb8glHgfsFYK3hPO0VpCKUHw9mYX9VZ2lxCiHYCD4D6SVHZKBy1fKQQhBDHMBPCcMXQeEVMZK2sk44zxtjP9fr9oLAjV42lRFOV6ZXW7Xi2yVAoOcSw3ed513XTGweoojdfFJnf140cP0SlGZZb2vPcWvVLKGtDWlFU7nU6HA+KNddpJJnpxaoxnhFtjGYuAEERPCSVIkDAEkvb6y+USnFddQzizDsbTw9t37zHOrXHegxARpZQjA/SUEPSEU0oZEI/OmjSW1mpGCKfICAXv+lkCAM5bSil+BzlmRwekoTWNMk4gCMDbP/Cv/4Gf/0f/uGry8+fPGCMHh9PVenXr1q04EoISkcVNq1rVIQEktJf227pLY7mqcqX17bu3gCJhELaIcJR1xff5byCVBkc1m82qqg65cBRFbdtqrbtWRzIJ+3hVNbPZrC4rpdrRcOisjaKoLMtAc1FKcc6LokiS5O233w6z0DnnzmJRFABAKYzGg3xTOmsJpd4jeE6AcUo8OAd4I+0Df7PTCPEjZSpCMNCJ8SNudYcAEqAECCeEAKWMUOcdpbBvWk7TrayBsibjFBEpgBVWRgLBSy6sD/kWJjLinJ+cnMyvllprQlmvl2LTWmtfuPfixflVmg2BRY5wpZQx6vHDB/lmoeri/PxsNBoxTo1R8/nl7ODo8vLc6GZ5NX/78rxtykEvjsSoaRpKKSI4ixbVfLG8+8L9Xjbo9XqTySyOkizLCDDBI6CEUuYJUKCMEATUzgrOuk4t5isiqLPYtI1S+vTkbpL1rAOPnlKqtI4joZRijHmPjBBgjFCkQCkFxCAW5rz1nBLgHIj3ngB4Sq4lXr7z+I40gRDCGLKf/umf/k//H//RsD8YjPqUQSykNzoWUilVd60DYsBHPFovN4mQk9GYcxr0QCkFJnixWDh1XU2M45gPBgMhBCKGAmnbtohQ180eBg+7v3POeaNN1+v1ELGqmsvLyyxJ0zRdbzb9fj8gDqGgd3FxdXx8GJK7/Q7etm0v6aVxLLhcr9eCxd4jQeCEegIIDtE767gQLBggQpi0BgT3EYKz1yIAHpHuOFMhWYNtUAUQpugBEEoDL5VsLyEDirAD/+M4Lpu6UW1YjVESO49AKRLKhBRColFUUEopY9QRm/aGZLWwnZeczReXQOXR8cnFxcXB0XGW9q1FpdRo0P/v/sHf/eV//otNneuubtrqtrv9ielrWZIul8vz8/ODg4Pz80tv9Gq1KTbrtpesVpssy9I0pZRaD4yxwWAQSpGHh4dCREEvI+1lnVbC0ySJEYlxmlFARKuMNd451KZTjWvbFii9fetuEKQI7Mrw1VoLgNZavmOvEopIYFenRUSym0tjCRDKGAEC2x6v737swtadxRIABELYv/Z7fu9//V//1fOrZ2kvWc0X9168t1zOEb3WRkqZ9PoXFxeVJ6PB8Pzps1fuv+S8AeKlk63qADxl8OT52f5dBJM8KAWTnSqOtTaKYq11r9cLMEegDIf8PdT0Qx0/aL5lWXZyclLXtXMuy7LVakUpHY0G/X7/wYMHBwcHgVoWfHbg/xNC4jiORNq2rWktBLoaUEIp59zuovg9RfUjByLZ5V7bqHT7D8EwOG/3JxyIRR8qgGTHEEB0H0H+GfXeZ1nGOQdKGGNt3YQ76r2nEZVxrHRLKbRtJ4SIInHr1snV1aJuO8EjLiOZxNqauq6Dfp2UYnF19v/6y3/p+bNHklNKPCI+fvjozr37WZa5qjZaa61V1znnri4XVV0URYHOhKCw1+tNJpM0TQWPz56dTydHi8Wq3xukaS/LeCB/BIkTigCUB7lZADCqffr8vCzLump5FB0dH0kZF0XFRcTguhwdItE9kEop9c5574Ph7gHscG0opeS6nwC/y734yH0BCGA8AhBCkTDCX3rppeVqHkXJaDRSShljDg8PLi4uEdEonUQxBZbG8Z3jU2f1gw/eF0nE48g7l8QxZezV116Bx++Fl5+OJ7TrlLXOexRClmUlZRTI0cvlMkmSpmmqqjo5OaGUZlnmnKvrOkmSwaAX+JxKqefPnyPiaDQKL2qM+eEf/mFjzMnJSSjPOufQul6SImLgvoTqWZAwsDZQpYwxKtSW9xz9HZq61aEApKHETyiGcj+hSPf01t1F3aOs4L33lhCklHLOvbfOIbuhodK2DZfceqetCcVh46yMIxlHQInxLgSyXDLCwKGtu1om8XA8TtM0SaM0jZXqENxsNpkvLq1V88X5f/VX/8pqeSkYQW/qunTOKNV+8xtfY5QeHh72+33BeRyleZ4fHBzcuX3v1q07J7fuxVHqLG5W+cMHjx89fPLtb387yF5cXV2ti7wzWlkT3K2U0lpntAuDZ512VZk/f/78gw8+oJRWVZ2lfURWNx1lHMJ8RK0JIdsR4N4Hx7EPk5xF74ASyigPT9sSMAj16IN4GyUfpcV85PAfY3kTJIQwY+2f//f+/Esvvqy1GfRHR7ODWMg9U7mpa8F5L02dsaNhf3E1b6rKKF2VxWw2LYr8Ey+/1KprhsrZ2Rmfz+fD4VAppbVOkqQoin6/r5Q6ODjouu7w8JBSulwuCSFBimI4HAohgnPNsmy9XgfGqvc+VJD7/f6TJ0+01owxY0z4VXh9REIpTdPUe19VmzhO+YgXeek9cM4dXgupwg0clNzQrCTbZv8bBFbY6jLcXPcEwCNSChhYJN6DdwDQ68dJlsDFOjwtydKqqgDAORfg4S0LzmxJSd47pEAdRlGitc7zPIp0JJPhcEAYy9L+YDxBJI+fPHglffXr/+KXnz1/+j/843/obeesiiLBKaAzADT08xCERIq6rq3SofluMplEUhZFQTxKGVdlvlwu16vVweGxM15rO50kUZQkSUKASSk7bUNQ6QCs8d5qpbrNZvPBB+95699///3XXn0jznqMCwfAGa3aJpERY8y54DWJEBEhSCgNeSF8hPwA3vvQMLhNAvZMjF9DM4tsfSreiFkJguQx5/T1V1//+f/+v+UMzp+XztpqkzPGO6XiOHbaeOvSKPbe6bYLlSON9p133vn+L32xqMrhcAjwLLzgyckJz5LerZPb77//vhCiKhsp5Wq5GY+moaQUrHa1Wmmth8PhZDJBT4QQaZo2TRNFMav4YDAAgPVmAwAnp6eLxaIoS22MAJBSWuOtslnW977c21yIFgAAKLXovAHKgHK2G9V4XfoPezoAME7QIoUtD9D7IKxCnQtPC5crMFZDhBrGPIPzFj0AIyH+0/Y6Tg+tY2HX22w2SZKE1YWIhIAHb4xL09QaxzkTPPGO1FWruAmytEqp55fPsyx7+PiDX/zlXwiuq63LNIqklFVVxpIbY6yHqqo++OD923deMMblRRFJGWIbZzPHWOj5CaFkkkSLxSqoPITZOgDQKRPFpOlUlsTGmEhyQojRtmvasthcXV0tFoum6V7+5JsAtK7boUgJMEZFHCXotxRVIQRuObieMbqbo+AC/1g7H2pCUkqPFhGBwK6r29/oyryh2QYAEEQLCAnNs7sfIgLxQCn/kS//xv/27/8d2vNFmd+9e/v5xXMuI1vpfj8hTPT7w3KTg7MijnjEKaVW2TRNv/nNb9594d7R0RHA2+EF83JDGWNBwdp7f/v27el0ulgsg5s5ODgI+tYAMJvNsiwL8F7XdYg4HA6TJHn11VdPTk6yLAtQV1C2CmpNANApBTuafRiYxhgriiLMEHTOaNMJIbgIBoY72bOPHDs01O9hisDJF0JIySmFj7VMAQSnA95bawN8IeJEUgaEQJpe83PTOAliwt5vuY58d4T0q9/va220NkppQqi1Lvja8HZFufZWbTbLslg29YaAQa+kFIyT0OroLIZyujN2NV88efxwcXX53jvf+sbX/sX88mKQJGjNfD4PEL01yhttlQbwdV0LIYy2VEjGJaV8Pysh3BqtrFJqtdq8++673/72twMhjnOurU+TzDtAJB7Q7JjUnHNrLaUQmnO8AykZuUEJ2uP8NwsuYfcncF36/vit2Rkv3nhs3SqliPATP/ETB5MDyWQapYurq9FgOL+4nE4mqu2UUomMKKWrzfr7v/TFKEusd71ez1ubRHG+3jx98mT/RtZamqZp27ZCiMVicfb8uXdOSiFl6LemSZIkSRKIt2EspIijzugwq1dr3TRNUZXaGkQUQvR6g/U6936rxmOM0Vo7RA9Q1nWSZavNJswtWG3WURLPZjMuqEwipCAEU50mhIRiEiHEOocAXLKA30spg0CVRWu8I5xRwcF74rdeljIA4h1awqi3jhFKECjBo+MDKYX1znp3ExZ03nBGvHVG6X29I9yiXppxynSnu0ZxKghSo3RYLcYYIZl3RghmrQZv+1nkbEfAoreMEaDUele3DY8kEkop11o3VX3+7NnXfuUrq8XVZrV48MF73/zVr9fVhqJ3zjivGAPKCOPbkDqK0+FwiECTrE8Yo1xwJtpW7cfM1nX99ttvXy3mF+dXH3zwwSuvvAIAaZqGlsaARtV1HdAYY4y1ttNGxiFUpco4h55zEZh+QfMw9MkE9bkQ739XA715fJS8srVVsu2JJWjxhRdeWK83UsbOotH29u27nPBbx7fQ4tVyUbVN0h88fv7s2flZozpCWb83GGT9fJ2n8XV3imCSMsa01iGFT5KkLMs7d+6EVQgAFxcXoYTDGAujeW6uubDEw/SVfYNyuDShuBpGagR4NbR9hVAhVLO01nVdT6fTUNwz1nBBtnbmLYJDxCgW+9lAALAfaZmmcVs3zhncUUsJRa3UFsxyJkyyDOWVpqmbrh2NhpzTpq1urtQAz4Wh8wFbDV+D70H0URTau12apl3XRlGEEM7UW228t3EstdbgnWpaIRjj29g6jtO9eGDIVJyxzhhnjLfWtO3Vxfk773yrLPOurpwxYSBMOCPnXBRFVdv1BiNjbJKkocQdvH74kEzwey/eH/RHv/SVX07TXqets9hp1WmlrAmJVGiJq+uaUmaMMdo55wNwG+7Cvvax7w66jr6u+ZPf02R3bbE39DNu/JYyRjj/1//AH1ysNg4xr0ptzWqxlFJ+9atfresarRNC1G1ztVgSyo21UkpGKSDeOTmt8+KGUwE66PXHwxEFksZJFEUHBwdNXTd1nabparWKo5RRwUTEZdy2Ssq4aRrGmCfQGR3yJAhj+whFQh36/nAg4yhK4qw/AMrWeT6eTrU1XG7HbnuCPBJMMhELEXGHNs6ibJAmvZhwSjjzxHtwVDLvjbZWRBFQihQt2qZpozQZjQZHR0fD8aCp2jRNDw6n1umjo6MoTT3xSRLtU7EsS4fDQdXU0+nYGEUpDIfD/elLKaWUSZKMx2MACCYb5CyjWAjJ2q72aPqDGIgTkjJJCYdASu8N+kxwwWQoy/XTvuSR6YzRzjjbGwxapbR1QNlW1hi3DwqeUyDo67I4e/bs6ZOHF5fPyypXTb2vZIYmbARqjAXCjHGh/StJEkRET5xz/X7/8PCwNxwo49767OeBMgcQktdgpkHc3RgXNjHYtZL5XeOaDxIKOwjPbwMwBkj31vkd4NTH22OC+hrZyRPsH9ZZBLTOv/Hmpz7//V/ojH3plVeZkOPpZLlcZkkKAJPJ7MmTZ+jJcr0+PD4dDsdN1SYymY1nxJN+eq2fWpctzfP88vIydNxLKReLxenp6f379wNPu9frMcYePXpU13W/3weAwWAQSq+hmh+8prU2z/OwLuu6DvemLMvVajWdTgOcVlXVdDoN3iK0eEsphRB7HaskSdI0DcV3YHQ4HIpEEorbLkpjCSFJLyUEx+Px8+fPtVZMUGPUcrnknDtnjk8OKaVd13FO0yweDPqTydg5k2UJMLBogdGiLPfnH4RdgxpPiOTCzh6Ajq7rkiQSgnkwaSqrpoxjyTklhARNg+B3QzLkHAZnDMR768IZhd6ygEZ/hH2703lEdPP5fLVYVnkRSMAhwOAiooIzEVmH4VPptmOUqLbmlIdtRMSR8/7nfu7n6rodjifhyUi2bT8hDbLaOOeiKHEOGeOEEO+CtW21ARExyFgFWXqk22T/Jnv1/48DyXYWlweCjL/56c/8wA99+fxqvtpsHKK19oUXXuil2ZNHj0+Pjp1znHCrLAVWrAujdLEqVvP14vJ6bujV+RW1VidJpFQb0A9r9dXVRVUVwfJCz3vQqg4fPdjivgE6RKJt2x4cHCBimImR5/lwOPTeHx8fe++vrq6U0tPpLOQi1ru0l8VpQhj1xEdp5MARTpTRvWE2no6MM0zQospv3TllgnprKCeEM21MlEgRyc1mMxoNwyLhnDujGSPz+bzf7yO6Xi8djUZJEntvEd14MqQU1uvl3RfuaKtC+BGOgLgxsRugaE02yFqlRCSTLCYMKGfDSd+iAwYiFjKRjWqKuqCCVm2T9DIZJzJOOm24jKSMwSMDguAIRQQXLhrjEpCKLX8AEdF659AjoUhYKIgURVEVdddq5xCBnp7eHg6mjPEs60dREoR0brJ1GSdlWaxWq1/4J/+f45NbUdIjVGjr2kYZb7XphGSMUGstIK2qKkkSckP1e1tVQRrKkyG1D/qK+43+5o7/a8H+SK5FB4gH4nHXeWGc4Yxzyu/efQEo3eSliGLGhHdgrU+iNF8XddlkMtVK5ZuNZNJ21rbm2eNnDOn584v9m/SGAxrWfch8ww0bDAZN0xwcHAR8vuu6NE1DJKd2gWC4cIyxvRS6ECLsUJvNJlBaA8y7C/jQez+bzfr9ftM0IeoPfxUA1/DWwQOdnJwYYwDw4uICEbNh33uP1oXBa8YYpbZFXUJAcBq8ThRFT548GgwGp6enWS9lbNsPKCJunR5NR2VTxVnC5HUaq7UmjDZNQwUnnIXT5Jxr3QUNmJPbJ0DpaDwASrVWy+UqdKlba8OUzbquw1k0TRO6IAnBNJJVviEeQ0dokiRCCB7J3WwtIYQgTHiAENwbpYpNmed5XddaGUr5m5/6NGGcMlHWFRJAgKZpQkDp/VZeoG3bv/W3/8bVYnX3/n1jPZcRIK271ntf1xVi0LTV1lpGhbWWEBYQU2OMtT4k+5R+hPDPBUW82SD08f6C73KQrfz91m53P/aAgkntLAHy1ltvOYvee9WZTisHmOe5MWaQ9XSn1qvVvdO7ulZtWUvKdK1U1aDzJ4cn+zd54cV7dDgaNG0dZqQ3Ve2ts9YyxtabZX+QhcbWkCQFG63rOooiAOCcN6rZlJtgJeHWDgaDAFcBQJQk8+VSG9O0LaGUULopcsrZZDZtVWet8d4ZZ5XRYZEkvcSBG01Hq3wFDKigSCHtZ84byiDuxQhutVoNh0MkIOOo10+DCNTt27cphSxLQg2mbqpQ3gw1tp0OpgeA9Xq9Z4IDAKPCWez3+0GeqFUdlwIJGO+OTk/KptxsNk3TrDcFUBIl8WgyEpFsO00oD6OJmGTKKiooMJCx4FLEkTRahS6Rfr+PiBRIHMcEGFAeIn4mIi4l5Xyr647EGNN1umm6qmrqTr300kthdsBoNAosOEKhrUvJmdbaGKeUevLkyd/9f/99Y0ya9AAoZzIUh63V3tuu67yxFCDUwAOwuI9Qg3U654wJtxUQr2lAhJDQ67d3ruQ7FN2uXerOq4YVFTTE9/YqGKNA+2nPKDubzJxzVVWFIT7onFbKayMJd8YmXKq6G2SDrlHOeN1qY641RPK6pNba8XgcesRCHUhKyRg7ODhomiaI/oVzKIoitDgbYy4uriilUsZtq4JTqaoqCFcF4w7Be5qmoc319PQ0vFooxjLGRqPR3lXHsQysYeccEn94fDA9GMdpIgTz3h4cHw7Gw14vS9NESrnZrEPlk1LaNI1HfH7+bDwe51WZpmldV9Z7B65VXZTIrJ8Sxo5vndy/fz/LMgCQcbQ/f4d+NBppZ40xSCBN07APhtyfc16WJeUsEHTyPI+iKM9zyrYq41vESoht8KCU1rpVnbYm7FFhPVdV1TTNrqBAwvt677YZjEcSCDkErPON6jplxtMj6/Hw8Dist1CrB4CtWAvjRZH/jb/510LAMxyOoyjxHtE6BtuqabibUnLGSVnl4f567xnjjInQbE0IcRYppVJG27q/I0i26muh6xW/V0/F3lKJx4APbB1qsGm/JbUgMc7cvfVClRfvvvuuYLKpu8v5VdO1VdNEUaTbThBCjSPGDZKsLkopxGQyuXPnju6uWX/L9YL2kox4oIRJEVlv4zReLhfG6PPzc0JI3dXj2TiKIsZYHKdCRGFKsbPImAAHo/4oEjFBOh1PGNkOj2y6jgmRpmkIcymloRLby/oE6OX55WQ0CV1Vg8EgTdO8LLTVSHyn2yiRIuJ1W/UGWZxGw/HAoaWcGG9G0xGTvDfsxVlcNmVRlZPpyKP7iZ/88SB0RSkBhps6t4CynzXe1FbLLOFx/M677wIl0+k0qBZvV7xkeZUHMxr0slgKANBaO+8RQMZR2susdTvCEGnbRkrhvYsjIQUTnIb5xQSYFLH3wJggwAnjytiwVjnnMhGeOMLBonFgk17cqQbBScY4Z24rrQ/GOeS08348OUjS/mx6zLmkQBigczaOpUPigXDKVFvn6+Xf+ut/raub27fvHBwcxSJO42TQ7ydxzCmt8qqrm3WxyavCgbNoLdrOdA6wqCuljfMgpPBArHfBvwjKBWWISIAZ74M0TkBhIYhShaQePPloaWbnT7d6rhS2cwIIbv00Z9IhfvOb37p9clsIMZlMCCHGG0fc5HA6m00kQL3cZFTU6zxwZbz3VxeXB+Px/l3SOKOU0tVq1TTNHkMNYWi/34/jOMRhe5fgnKvKRnXm+PjYGh+2pzATVSlVlmUoaN2/fx8AqqpCxPF4HMexlHI0GgXfnGXZ5eVlgE7IVg/aaa3jOBqPR5QRi45zjug8uMGoPx4POaeE0XW+mczG+8C6PxwORkNg8N/93M8dn54enR4i9ffu3+dSTA8mXDIuWVBhWKyWcZqGcPamenySZcaYED2vVqv5csEYC9IvIZEPnuxmKB9FUSwjSkkIygMAQiktioIzyRhrOo1IeCS1s4FlQndchf22G5olERwluA+lQlwBhH3pB38o7Q8IYXEcHx0cpmkcS0kpTZLM6CCcWH344YfBQ986vZPEqffQ1t1kPA0xcbi2zhkpOWPUORvIxwE3tdYaY5tGWeMp5YwKo50xBhEEJwEfCNVASq9jgO99bIfbbF0sXPcS062aEzLCGONHR0enp6eHBweDwUBEUsQRl6LuWsJZWeZhK1gX+Wg87g8HV4v52dk1609ySdfr9cnJSVVVg8Fg0Bt464fD4WazKcuyaZrJaBQJseVBOxfqfmG2YkBhHj16ZL0r6yrJ0slsGjxEXZaMkKODgyxJKCHj0QgQL87Pg6VWdS2jKFBbjNFJEr/44ouTycQZG0XRZDJy3syODgbjoXa2aptNsaaciIj3BlnT1Vk/PTiaaauQ+LzKT+/eefHl+8p2MpHZIGtVMxj0rhaXcRpdzedRHCujRqMhAIYiU0AZw2Gs4oJWTcklk3EkhEiSSFvVHw4Iox7RIwopESBgaohYlmXTtcbZW7duBcJkHMejweBwNgPitdZpHEc8ssoyYKGzudfrhfLyPuIPghFSylA92i1XDwSEiL74xS8GDoDeHXvJCa21MrpR3X/8n/4nyhqg/JNvvC4iKSLZG/SRgIwiESVMSiq4tXY+n3d1A86jtbptndZOa2N0UeTeOdV1TVUH/BUpIYRYt03z/Y2+9m2CtY1Ev4sKMXwHOLCNiSHMK/TWWwAo6iqUzaqilFyMRqNOq0+89mo07PlEZIcjTZ3IIpaKVbk+uXuaDrP9C85GUxo0Niilm83m6dOnw+EwfLiAmw4GA7cbFhWS/dlsFsYFtW0bEhpjTOhcVUq9/PLLWZbN5/M9JLlvAxJChGHMYVkfHR2FwC68ddM0QWfl0aNHAYHnnIfOG0KI2K0WIUTQNFSmkxH34Lhkreka3WpjirL04IzTSZIE8QFCodfrFUUxHA4ZYyFh35//HhWuqirgGGVdbbXfdqHqvixHdqIJs9msruvAbpFSdl23Wq0uLy+D7wydZCFSDzB+XddZluFuPkfwbcPh0BgTSLGUUus9AOEy7g0G4+ksjuMwjlBrLYIl7oTfuq57++23nz17hohZv3dycosxYR0KGWvrZZL0ev0QgzEmqqpq2srtJGzzPN/r3wTysdY6qAEY7YKqe3gXSmmYNrPntf16nvUjVksI4YwjotKKUw4AQvA0jceToWrqO6e3glTP+dXlO++/++TiLBn2F3VB0khkSdk2RHBk9MVXXt6/5tOnT2nYwftZFgkxHA7X63WaZv3+wFtHELpWMyoEZeA8J+CNbapqNBgMej3JuTIm6/d5xAknTLKqrc7OzkLv9dXFpdVmNpneOjmJhHDOAqCMhLF6NB4KyZFgnMaDwUBrzSl7+aVPTEaD+dVVlCaL1dx603T17HC2KTaEs87oRjXAwIETsaCCDidj572QMhircRooVk1pvANGeSSY5LODqfeeU0aBtHUTS+mMybJ0f/5CsCgSnNPRaODBtaoJGZLxRjstk7gzOigaMc6tczKOQs/QrVu3Li8v67rWurNWd12TJJGUMvQ+BJcZx3FZ19ratNfzACKKsn5fxrGMY4dY1jXlfDabBbkbQqhDAkh/5md+2927d0fDSZqmQXAumFTXqoD3qbb7B3/v719dXeV5+X3f9/3j6URE0gFqZ5MsRaBRHO/lFwaDQdu2qmu6tibgndXOaq2UC1wb54LVEsK20hVd53eS3Ndm92uiVDdYKddYASI67xAxkpFDBxSSXtZ17cXF2eHJMY/k4eFh0HKTaXJ8+9a6q6NRP52O0tGg87Y2iqfx5WZ+/TacUCHEer0ORGlrbZIkIWcP8VPIeUNmgIhFsQlVuPV63bZtCOkC3zRQMBGx3+9LKcPmWO0OZyxjjO4kLEejUUC7wngWSunl5eVoNDo+Ps5XyxDUNk1zdXUVkKZQwg3BVggl27alguVVYbzrDQfGO6QkNNYCYOAhIGIso6CGvN9M2Y19ajweDwaDrutCzYxzPplMwi4fmhcCvhvQgIB87REfSunJyUnotbhz547WOozdImTb4BGgkr2WICEkFALDNiKlbNt2s9nEWeIJBDl2QsgP/uAPAZJQ0WCUx3GaxFmvNwAAa23b1cvl/Od+7h/qtuOc37p1yzlnvTfGdFqFE9yjMVp3BJ0zWqnWGBXceQiXwzNDNRW3MguhiGOd+4jG/F7079dBVT9qpojIKMMgwEaIR69Um2XZcrn89nvvUs7iOH769Cnj3HhnCVr0yhqkJBsNhpPxYDxyFFp3rck8PphQzmTXamt8VTaTycxaXxXlxdn5YrGy1o+GQylEaNInhIzH42F/EMtoOBx2Xff87Gl/kOVFERZWmqZad8aoYFJd00ZCfvLVlwnF0WgkpeScT0bjg4MDpVTYhry3USQ61YwGvcXVnHP+0ksvxzJRXdfLMqvNZrWOkvjOvbtpL6vbhjCqjH7/w/e11UGvpVXdfLmQccQEZ4J58E3Xhn0zcJ8ZY5PJZDmfUyD9fl+Ia+S/bRtrjYz4eDLM0jiJ5Wq1qOuSeBz1B7rtKALhrGxqBzg7OmSUa2W8R6X09GC2KfLRaBjHMQBkWdbrpcaoe/fuIWLTdYSx27dvc877w4H1TsZiPB15cMp0TVt5tP1+f9sSTCml1Dj/8quvTScHIekMiUFA7pxznDGKoNrmb/71/6as8tBKMJtNrbVCsrLMjVFad1EkAHzTNBTBqNY5IyNujbJGdU3d1pVRHToL3pX5Bt22hT1EaJ22lPPgnkIC7QGMcw79R6Z2/Xr2ujVVQM54kM7K8zUQ/+jJQyReWztfLoGS+y+9ODuYGKOcc4PBIJScLi/PAXySxcp0nb/GU1vd0hD8hfQ2EACstXthqcViEcpRYSF67xFdsIBQqQ+Kk1VVSSmD10HELMuyLPvNv/k3h6UzHgwR0VsXdKy2LBBK9xDjqD9Yr9dCiLaqQ0CslFqv1wE6sNauVqtQUQy7Umg22nOfg18vyzK89ZapmKYB9M6SdDoez2azzWZTl9XNcnbgoTrngrmE/25hXcRQ0QiqMJvNJiRPIQD13j958iTIvkZRdHl5eXx8HKLDBw8eBK/f7/efPn1qnL26ugoAZ/BqIeIM4axxTkSSctZpO+iPftfv/N3j6YxzGbx41kuFEJLzrmkpJUq3l1fn/+P/9PPeGgLeG922LYBfXl1VddHUpUfXNo01Bq0Lp9A2TZgO7r0XknHOu65rmibczdD7Fe44vR6aTNz2rzGMPAhudycq+OsYKyEkBLgA4LyjQD34n/u5nyOUJkmSV+Xp7VsyjjxA2FIQMexaq3wjhOhnvWAkeZ7fzNK8Bx5kukKF8+joaLPZeM6MqXppRghpSRuk0cKGG3b5NE2ZoIwxKqi1NklT51y+2fT7/V0JUa5Wq7/5t/76yclJ1xhKKRBvnTaVpnRAGC3LnHNelnkSxaPRCKwzxhinmRQUSF3UWZopokQUh4YZpRQjHB0wzrz3m02+j/C2Yg3WhjqtMdZbJ4SghBFKkiiZTaaL+ZIBk0xEURQo3uFgnHAhgtFba9q2AcQsTYMORZami+VSWXN8fDydTpVSQauLcR4Y4mF4sXGWcLJYL6igRrkky6IkSbKsKIrj06NQpLDehEqKEMJaa6wV3ksuAEBbrbWOoqRt1Usvveycy7KBcy5Nt+L0uquEZEGz5u/9nb/z+OEDCtY5gwSKfK26xlizXl5xilIyyWSjtdZacMoZAc6ds/00Be85IYCOgndGGXTOuVjy0MRmra2qKo7jsiwjMQqxTcAonHNBX99fq2KTjwooEr8bq+i9p5QwysJQBkaZRee9e/tb32zaSkb89OieR7LZrA5nR86YUACy3p2cHgHx3/jVrx2Mp+Hip3Hi6bWlZjymcRxPp9MQXIYV3zRN2KyVUoeHhwECfP78eUBbQjIbVK/220TXdcPhMNBToii6uLgIaWNVVftgaB8srlargN2GAnpVlq1WgWQohMjzMuyGSZLked40Tcj9Q+9rwI1DMenw8JCQbUgXx3Ge5+H7UFIKiBJ4DGBw2GLCqrveU9o21IFDABq6EoLvDOnLaDQ6mExDnhHOKMTrQYI+eN9APQkp1NHRAZAgP7NRprtz544QojfoDwaDy8vLsKeHETawY8cSQngkCaM/8ZM/lWR9axxSwhhjjFituqZyzmmtGHGPHz34+f/+H1ndEcAkkpKLzXKxmF8QsHVZzK/Ozp8/nV9dNHWNodtBqTiR3my5RO5aCpjv0fEoiva0lRBPIyIhNPAEbiRb25FV33vXJwR2QAHgnorFCANG27YdjUba2sAGOT46qooiRIOHh4evvvrq5eWltXY6nlBK8/V6fnnpjCU3FNSK1ZouN7myjkdx1XbOYRynEY8effjo6mpBCFsu1lLEWZYFs4vjmAleNXWIwEK9gBJijXHaUISQJwXj2AYVcTQYjxhjcRyPx2MpJacsMOpHg+Gg3xdCGKOiNLLoA5Y0GU+bugUkiYy8savVZivBIkRVlFmS9nq90ONaluXiak4Q0PlIyCxJvXVB/zpoY3nvA4BsnC2qKmgO78+/1+s9evQoydIoiUPYEDpsl8vl7du39z3yIeze8xK36yFN4yQJAzl6g771zgNa74y1lBMmaL/f/+pXv0o5GY0GcbztVkh7GeXbKdqBoRaliXPOWfypn/rpwWA8HI7Ho6mU0igNxEvJo0hQCmdnz/7u3/1bq/lF6MMNDJ58s7KqixhRXV3kq/nVeZlv6rLQnWKEWqUDmTB0rRljnLOEhNYdTwjWdWl11zXVTiCW4G6C/e6bUPtF9506S7uQNJT6A+2aAAmlLEZp0AgyzoRIiVL6mc98ZrPZgDX9NBGMGqNms0lebj548CFhjHLeafX06dO6rjer9bg/2FwtrldCaJcNkXsAjNbrdYgIAaAsyyiKzs/PnXMvv/xy4MuFSn2QUWiaJjiwLMvCMKEQYt6/fz+KoslkEvDLYCuHh4fhvh4eHhZFobUuiiLUgabTaQBlr+Zz612AGgJHOwSLbCuvbkNBqOs6SslwOMyybDQaBSym67rLy8uwY9Z1PZvNlFKUs+l0Wjb18fGxtmY8ncj4GqVCgKzXC4FaVVXB6x8cHKRpulwuQwQciCYBnweAxWIRdpXw29AxEapxoSNtMpmEMLpuq9nh1Fq7WCwIIcPhsCiKUE8JbWchEA9s1MlkNprMBI/6/WGwFS5oXZdKtevV/NmTR//sn/7CL/6zX6ibMoyaJgQZIWdnz9qmckZLDuNBv20qY5VzxnsvBEPE8DWwNPdHCFvD8iMEvbcBCHNuO3kBd6IklNIwqWLPq7qmrcC+H3PLa0FA9NtZTIQQ552xRjBBKc3zfL1eN01zcniklXr86NFyuRwMBp3RhJDT01PnnGpbQsjJyclwODw4OFjNF+PBNeedSqDO4sHsyDvgTJZ1S5io67aqmiztHx4cO+eGw2HTdM+fnxNCkiShgvdHw4v51WK9OpodoHWCkkSKyXiYJlEaJ4JttX2Wq5V1ziPKKLLOffDhh0Wed227XCwIQBxF/V4PALIsi5JYW2P9djWv8rVFV7U1MEo46/cz7y0SjwSMs3VbO2ejSHRds68ASSlnsxkSGE3Gh8eH1luLTkaiVW2nO+stk5wK1ip1sbjan/86L6IkNc6KSDLBHfrRZLTO19oYj1jVdVEUgXDICSUeN5vNeDwOcsfz+dxaGycJEDKejuM0BgqUU4d2vry6uDo/ODgAgMF4KJPIgVfWAKNlmcexRET0XnIeGpaNMT/4g1+eTg8QEZByzgOYJaVE8Np0Dx588JWv/tJ6s0hiERpOQgL09NHjqirarg7E4uODmdMmjWMp5XK5REo2ZeEAHWyNdR9KhoGHAGitQfSdaqq6MFYFeW5GKCUMcDuG3e0mICDiDfR01zwcJt4CCQyqbXXKE0qp4AIAiqIIY/vef//9R48enZycpGlycnpUljmlcPvOKTqbJdFoMNSdqpq66brFahmlyc02Q0sMHQ6HIcnI87woiqZpAuYccsPxeBz4o8fHx3meIyVSyl6vt1qtgp8LTiXwpMIWGSDY4GgRkTCKBKSUk8kkiqKjo6M8zwNCfnBw4L0PsUsoqXtwnvgoikKboVIqFLJDLgyAUSSjSEjJT05Oer3eYnHFOQuC15eXl6enp2VZXF5eBhphnKZcymC+xtlWdTKOohtTjuI0bZUKpxBSzuD8gsp2qBuH9GI4HIaa3DZRXa0ODg7oTv15P+swbDLh83PO77xwN0mS+XweCqHhFebzeZHniBiC+IC0/NAP/3AURUmSBeKi0q0xSkqRr1df/5WvXl1eLOdXTVU4Z3q9lOwY2UVR5Ku11h2jcDW/8N6Ox6M4Snu93mg0IYRkWT8g5fuGAkTcsg4Qm6ZZrVZFUQQGZtjBnLEhht6fXYhZvfc3sNWt4wwgsb+GsWBfgCUIHr31djAYfPpTbxnthNgiP0op5/18tXz69OmTJ0+qsuynWVWUFAillHK2WK3jXvbKG5/cv9drr79G87Ko2yYkJZ/61KcPDg49EMrF48fPm6bpOr1e53fu3LHWBv3KKIoQYDqd9no9AJ+mcS/LKCHgsanqs/NnvX4aeOyhqHj79m1lzabIhRBhMUjGOaFZnKzm20Dk2bNnWuv5YvHJT35SSrlcL+bLqyiRXHJtdd3WxpmmqbuuI5wxKYaTcaMbHvMQj2ZZSggMBv3FYo6IxjsmWVkX2irjdH/UL+qirIvJbNx0dZxeW6pDTxhdbTbGOW1tUVXGOev97Xt3jXezo8PQOhfC8UCADAs41FeiKNJGpVkSrC38JOulnWpffu0Vwul8Pl8sFgdHhw79nTu3gyhdSHg5k3Ec71qdTJJkaZpFURJKG5zzrms3+apTTVkV/8V/8Z8/efooTWNKwXsvIilEFOj63/jGNy4vL3q9THfderXYU90BwHpHGQtAW5hNWte11lrp1jrddnXdlG1X58W6aZq2bYO97mut+yo6flRm+eaxp91A6Bb0CAiBuQuEIgIlzHv/2muvLRYLpZSIow8fPEAC3/7wvayfHR0dHc4OYiZc071w+w543zSNtiYb9ubr5aa6RmnOLy9oHEsATygeHx+fn58Feh6X4v5Ldwmji9WyP+xZa6XkkvHAHw3hNiEklI62RTnroyT23jdtG6fJwcHBeDw2zs5XSwAQQmhr+oOBjKKXX3v1Yn6VV2XdtYg4HI0451dXV4SQDz/8cL1eHxwcHB4ehlAmcOYBILAHAXwcx+vNMsACVV0wxqbTaYhrgwMe9Xtot7zvNE21Mbdu3TLeAUDa7wW2VzhC4bs/HKS9zFo7nU7zfEvlBIBwX8M3bdsGEkIgwmVZFozs5OQEAJxzQcPQOXd+fm6M2axWJycnBwcHvV6va1pOWb/f7/czY3UcRUdHR4ezyWw2OTk5oZx98vU3j4+PjTEqqB9w1jYKAFbzqwcP3n/vvW/FCa/rKkoTJGC8kYkMWTHn/Otf//qjR48og7op5/Ortqms097oJEmiKK7r1jkXkl2j2roprdNtW0vJCdlSqpumMZ3SbWc6pZptY1mAk0NkEhz/bnQyI8DgBmGKEEIpBwLgvPc+KLEAInrHKHXWcsKn0wMAKqXU2i7WK8IYOhwkvc1y8/zRs0Gvd3R0dHl5PhwOkyh2zt2/f3+13FxcXHendF1HkywWEaeclHUBxHvvhuMhYYRFclFsWMSRw3I5V7ol3rVl0bat8ybYzf37L9VlQwkf9EeUC0eoTLOyacum9oBlXVAKAZwHRtN+T0TSAxZ1JZPYE1gX+WA80tY4D7ODo67TXafjONZKASJnTOvO+y01ezabSMmJR9OpWEhBWejBDYhVMClvndXGW0yitJf2vcX1cuOMP3t+URY1YWKzLm5yqZDiaDrq9dK2rbmglEEkxNXFRQCqGBNSyqOjo8l0nPSysqmTXmbRiziKs3R6eNBqZT2u82IwGnEpsyzr9Xr5esUpaevqV7/+L5IoMkoZpULBNmB5USRHg4yAU3VlVMcof/m1TwKjbddVVWGsaivltCMezs6fnV88fvf9bzRtLmOxWq/jLC27Eontj3pAqQNstXr88OF6sTyczsp83VSbpt4A8VEUpclg0B9LJuqqEpx2Ta1V3TZFXW2c7bwz6GzXtC4MaPO+bZq6rJumcw5V2zpjurqxqkMkWlvjURmHEIjTlBBKEAhgFCXeOvCecaa6zlnrET0gEo/gAJAT9sXPfwktcdptNpvJbLrZ5MJxboSvcZAM13n5rffelXFUFJv79+/34t7qatVPs+6GenpRFBTBVXXBhUizmHFOOQnjLsNYqSzLgtxfFEVJlg4GA2d0oB2pZlvop5QrY5umIcAIIYRSKWVRV6GzKpTdg1Osu9Z4N18tB+NR2dRRmgTkK7Qyh+6Aw8PDUFsaj8cBSQh1o8DeCjFx0F8PJNoQpE6n09ARHwpmo9EogKBxHAccg+waVMY3+LkB+l2tVqenp9baoiiklLdv35ZSlmUZmr3arlFKhYsghAgCsdPpdL1ee++DOl9oywGA9Xo9HI9EJEPifHZ2tlwu33jjDcn5wcHBCy+8gM5zRjabzXq9Bu+rqqrr9uj/29R/PUuW5PmdmKujZeiIq1OV1q170IMFMMQA5NrOzBIGwEgajOK/4AONfwKNfIDZcpc08oXGhXF2YUZiObM7HPSInp6uFiWyslLdvDJ0xNHS/bg7H/xWVqX1Q5lVduU9GSfcf+L7/XzHM9NyGGO2bXZdpwxkaZo+e/b08y9+DUDXH/hCUgDFaDL2fKdsKoyhF/jq+/m73/1ut9v5rldkebTfbtfLjjWMtZZlAYDqsuoHIRTSMgwdk+XtjVoxUkqV7xdC2JQVp6zMcvSNtUsjBoR3a9XuG781QqRuWgmhVFmuCAnB1QBECgGkVAUJ47wTHEBIOzUwgbZtH85meV4M+iPJxX6xmQX97dX10HZ6jmcgotafTUPPn5+bmqkhbdwfmOTb1HDPcRGXEhHCBC/KMity3bzTnmIJBl5Q54VvOYosWbbNardNkoQ2NYHA85yqqlpGW9a4rq1Uw2VZYoTUJIsJnpXFfr8PgkCtW1U1PB6P67pWjqvRaLTf79M0Xa1WShyjbEBJktR1DQGQQiikD/yGWmOaJoIkz8oiy9M4EULs93sVDmaaphqIqq+H6mPUiktt1/b7/XdzU23bHQxGEOKmoYKDjgnfC8qiUv+pljWMd1jTdNNU0tIkSVT3uV6vfd+XUt7c3DRNs91u67ruhMCaluZlUTWaYTIuTcdkgtW0hgTO53Pl0gESEaxVZW0YJueSaFYQDizLUaWIIiANhsF+t95uVvF++/XTJxijR48eahjXZRl6IQLANE3KGowRxqiu6c3VNW1bx3HSNOk6WpZ501Zd150cHR+dnAoADcPinazrWtf1Ki/KLAdcaAgTiHRMAEZIIwJA/M0Wo5OCibs6FfCO1pV6meA3ZkDFAIQQCtEJ2XHOBGcQgrZtm5oiqElApIQa0TvGLNP6gz/4g7qsdIRF2Ux935Fi6toHoeciGa1XvGJ5VNCa94J+VZTKc/rdT8rQDPR6SYg0YrkOMfTNZhNFkVrwWLrB2o62LYIEYzydTgdBiCXQCFEbf3XMAAAopaaux7u9Wnuocd3R0VG/31dTJPUFVaf1YDBQ65Dnz58TQk5OTtQBnOf5azUn+ibk1/O8w8NDtbhSBb4q+TXNCMO+OhpV+a82C2+88cbrwYpSLyjRbVVVg8GAfic3VZ0oqisHAKgyQ7leVW+u/l/f3UErSa46cZUWzPO8s7Mztd4zDKPX63EpOiFmhwcQwh/84Aer1UrdFaZpCiEJ0Silvd5gs93zTt679yAMh4x233RszWazIBp0XGO7XX7xxWcPHpxatmbZ2sHhuKW1aZqz6UEc7xlrDVMjGsIQ/PKXv7q4uAh8v2mqJIm6jpqaZpuWrpuB3wvDfhj2BoMBQoQQvaqqOI7zPK/r2jA1wzBc1/0mpAroukmIDr6JP5DfAGCQBMq7RmmHMEaQSCDFd2h238wTGs4lQqSsa42YXHCVdfFv/s2/mUwmtmH2LPuo3/vhu2/98N23Rq5V7ndtXgrKad31/DCNEk3ThRCe74vvKFQ456gsy6OjI6yRrMhNy8qLYjge+77/0UcfqUl+4PuspbzrdKRZmkkANInmu45l6m1b65b+6upSQHDv3qltm0dHR0Waqdtc0819lKj1rNJeqUFgPwgF62zDRBKoKz7LMuX+C8MQcGEbpuvaAIg4jl3XNQ2jyPMizzVCFGCaIA1DUldt27DXRB0lauGcq12uojyrd05NRhTa7TtEOqBWtRiTum7aloZhL47joiiE4IxR13UIwU3TfPzxx8rv1R8OdNOQELz1ztuO50KMwn44mU1UXXEn9isKLkFeFjVtWdftoq3tWi1rijLb7/eqJ4vjlNIOY4118h/83j/shX2ECEKo46ylVZ7FEHQ3V692m2W/5//4R9+3TaOtKwylqZEw6N3c3HSUdbRV6ljDIFLKq8sbVRqxjlLW1E3ZNFWSJJzz4XjkBUFvOBqPppZlWaYJAWibirVUDUHVYWnalsrhxoSoYZOSsHSMcc7rumRNrVayyt8nJMBIQxDdQT04hRgJCAzDKotWUAAAwkhT0Y2jwciyHMghKFsfYEdKnqdVtL0+v8JCBJ7v2PZ+G/m9fpKlcZ4Zrk3lt5r3eBcj9RmLjkspFWVcMT8uLi4wgYJ1ZZYKymTb8rpusgwwPukPt+s1xlhNXt96662maRaLxcXFRUeZazsG0RQ/UJmY1SzWsixK6cuXL5U9Vc0mVQkbx7Ga3RiGocaZbdu6rqu8XFEUqW+zAjOpWbTafqlfpmmqYGwppRrIq72zuuyUW0uJrVRe4evnV7XvcDiU34jfXr48V4JadVcoj+52u1UPqy4f13Vvbm42m416QZfLZa/XU2WGaVumbQMAkEYQQg2jQb+n0ut6vaAXeADIw8ND07Q7JngHXDf84P1PbNvFGGs6iaLNYnl9enZ4O7948vVnQlLL1n7597/gotvtt+qRkyTxfR8ThAnyPEfxv7qO/vrXv57P5xLwPE3yJAaCG5pmmrqSJXheoOt6EARKdaDOIABFVRUdp6y5k5KJOxpA97rlV0qJjjE1M1adtBBCSAAhFgCwjgMA86psGAUImZalG1aeF4ZhIYjUtJVgggB888Gbt5c3tKh1CferjUG0o6OD//Q//YMf/fiHnmOxqtGJFkU7y3Ecz76dz7/b+0KJkGu5nHLZcd/1aN30ej2ioW207QTb7jd+z+9YaxHSJtnEC87G0//Fv/xXbzx6iCFiLcU65rJjnKrMvslkoh5yt9uZppkkiXJ279abwPVY0zqmFXq+qm+SJDk4ODBNc71eP3z4EGO8XC4vLi5U26QMwcpFXdclxtC27dlsVletrpkKl0mIhhBmvGtoWzV1nCZlXemmQXStoa1qaDuFWybYtC3asV30LUAGACA7rmMNSqQTQzAReqHrOrZtM8am06kCXwoh2pYuVsusyFWdo5Ca7777Dued4zhFUSxWS8dzFSgqCH1iEKJrRNfU3K3X60EpOtp0rC2LjNbN7fWNEHI8PfrBj34KCQkH/bwqLdeyHeO//ff/7uLy+dXV+XqzkIJVZSE6tluvkAScd4NhzzKNtm6EEJZljUYjCKWmYyE5RPLp0yeSizRNW9rEcVxVOUGAA6lpmrIAGIbBWYcApE3D2hZK2bSlELTjtG1rJRoWELzuoiCEGiF1XVLaqBl5WdZd10GEAUSdFEp53bE7sIVa9ygnkmEQAICuE8WzFgBwJkO3Zxp2UdFGAgox14yad7eLeddRguRbbzywDaPX91erlWFq77/73utPKgz7qG1btZvhlBFCABeCdUez6Xg0AIJz1hoYw65DvPvHP/npH/3zf/7+2+/8xX//PygBhzoR1RpT7UIc29Y0bTwcqWW9CgVwXVf1W9vttt/vq0V527a3t7eqtdrv98pR+eDBA/CNdlMJVYUQqrIej8fn5+fqt6nITVXIqg5d7RgHg4EagnrfRMC1bfvakqBm4N9VPWqa3nWd2k4NBoMkSZRERgih5rtpGmdZrm5VNfZXzyKEeP78uWEY8/lc4WDTNP3GPQHyPG3buuu6NM1Nw1Yr2fVy5bsOp+1ytfB937bdqqJ/+E//J9PZoRCCEFTXpZC0aYrb+eVnv/002q8pq8bjoVqaK6tgnudxHFu2adv2ycmJUvqatkEp6Pf95XLNGBOcz2/mTVVkaZxlCSGI6DoiUE1slH7NNE0AhaZjAARtm7ousywRvLNtEwHBulaITgghxd3QpmkaNVR2HAcAxIVs2oYxZugGwTrnwrIcRHAnOCEkTVPf9yEEHb9zYHMhESB/9Ed/gpBeM5E2LdftqGXPr6+/fPasZezm8uLByeEbZ4cfvP0G5J3rWKEfXL26eP1JvXjxAgEBm6rVsG5opqHprKUIyqosRccNTMo4oVUpaP2//J/9a8maQeBfXr6qqmq5XCqBlWEYn3/+edu2dV2WZa6uY1Uvqu+WekWyLBsOh0r5oXx/qqpTy8M4jtXbkOc5wbjf6ynlgO/abV1S2tzczBeL2/v3zzCBEnD1rVD/BfVeCiHU8aYseEq6KoTwfV+tW1Q11jSNRsjr51e+gP1+bxjGfn+3oVCiPl3Xi6I4OTkp60ptgwkhCMEsSzFGtm01zR1xaDwe//rT3+R5PhyPkiSK99vRMHz04KFONF3X25YRQobDoWWZy9WiLEsogZCQMv7g4Zv9wThN009//WtK2ySJnj59PByEL549jpP1cDh45523Tk6OAAB13bYtXa3WumZ2HR2Pxw8ePPgn//gPwjA0TbMocqIDwzIEAL/5zW+klOv1ej6ft22926+zPFF9mDLbqDWv6v+yLFFJglxQLloIedPUlDZKyKK+nEJ0EMLFYtF1Xa830DVDfsMjUzIUICBQKBikSSn3ceS6NgCybuq2rSWQAkiEEADo3bc+ILrFNX1RVHHHU4lWRWN4PUPDgWmcjgbvP7h3OOztl3NL0yXr3nv7ndefVM8PiHqNCCHqtlJs/3iz65oac/HG2dn3PvxgGPSSeK8bpBPs//B/+j9KTVNaKqWE0DQNAagKpiRKe72eYd1BeF6+fCmF6PmBslxDCHVdHwwGyrClDjz1Jql/ppTKb0A0GOO8SNu25VI+fHgGAFKDkt1u57lB27aEYNM0ueTqJVM3g0ptTZJEOaKUX0+R95IkUcIoANbq+RUtJt7HaoihXuW2bY/PThaLhW5qUZQMh0O10x8Oh3Ec2ba92WxM0/zpT3/6+PFj9UQAgslksl6vJ5PJZrvo+UEa7xva2Zbr2laeJfHu7qtrWZbgONnlCMs/+p/+Sy8IvcFwNJv+zd/+5XzxCvDi8uql7zoQwuur86apCBaapmmeQYjme+Hh4SEEoN8PKe2++uqrwWBgO5bkVAI+nY27rttv4+1m74dhtNvnk5gxNhpOuq6jtFVifvXUnHPDxJxLwzCqutB13dBInsauH+Zta1tOx4Brmayji8Uiy7Jef+h5QU3bEEIEsRAqEUDnnGGAoZAE6whzoplVnWk6btsaE4gQYJxpWIMAdoyN+sPZ9JAcjltaWp5flsWjN96BoBNV/vE/+offe/9Dw7GW+61nWbuqIsT55d///es3lSBMFIwSYsSB7AR3fW+1WL55dn+3WPz0P/nHD85ONQS3223TNkE/+N/+7/93/emxRIjyztAcWVMoAQKQc65p+nK5PD46KoqCEJ0Dud/vh8NhVZbqms6yTDmo1CRfdVTRbi+E0DDRiUYpdSzbsszdbqdk3Xd2DtN0XXe73RqGoUJBKaX37t3bbndFUSByl0ur3k4lqxuNRmotrPotJW2eTCZCiM1q/fr5le/l3un9ly9feraj2rXBYJBlGeccY7s3DLI4VaJEVY0oZzml9Pr6WpE627Y9Pj5S61aMcVPVRVFMJpPNelfmOUFQ9SVZmt47u79d7cuCGpb34Yc/mkwPNdOllDLRnJ2dDEfO//P/8V8hzLfbItpvh/2BZWqOY+12uyTOd5vd0dERpXQymVRVhYl5fHY8n8/TIj48PZxOx0Va9AbhZhM/fvLVBx98eHV1NRwPJgekpXVR5EbY0zStBcCwbE03JQRZlknJMYZVU0MILeeuSnGdsK0LjHEFurqsGGNE19SUGgIURclg1EdYcxyHILRe72fjWUMF6ygiWErIJVTjNo0A09KxqXPJMcSE6IzSB/fu/ez3fwwhd12f0aYt86HvJstr0VZQduvlaj2/NXX9jaOjpy/P7cAD4A72e3RwSAghg8Fgs9tGi3kYhoQQThmGSMPkh9/7/uX5K03Do+nkz/7iv//z/9+vP/7Bo+0uARqxXVeZUVU5qN6VMAyLsgQQEkNPdjuiabZl6Zg0TcMYU3/Fik+NMVZOTiDka7WrOlwt27BtO80yy7LOX704PDzsDQaLxUK1aBhrQjDL9jebjRDSsqyyLhXAVYF3OFfw6LtZgRoySCnDMDw/P1et2Os3VclJt9vta8RLkkR5nnMgwn5PjVQNw7i+vlZmV8exJ5PJ1dVVr9dTrgcl/1MFgxJiDgYjzro8z7uuQwgWRYEADIKe7wZV2dWtqBqu6d4//Cf/FGBDIkQ7LiQvy/Jvf/HXWZZA1LVl9eabj9bLVZLG4/EQANDv9x89enO1WqVF7rs253y+uPy93/vJfH6TJNFw0KuqihCyXq91nXDO1HQlTqL+aLjZbKYHR5RSCIBlOVJKNdNgFOi6lqSx2o8YhgERSSDO85xz2esFFrVevTxnjB+fnBHDDPsDhIhuGur3axrebxe9MOxYizFmAgoJ8rJmjHPaIIRUk40ABhAACTlnEML/zf/qf60b0DJ0CGFbl8l2ne6WkPFhGG73mzTPnj5/xgxzeX4+nU4BAADM1SeVxjGibd3S2rKMs+Mj1zKB5I7jvHr16t69B8vVGmLy7PzVn/6//8Pj5y+mJ0EjgNQ1JgHRjTQreCc55cNeX5H2lbDScp2yLHu9HoKQUuo4jtpdqbMwTdO6rKLdPvB8KIEiX7ium+e5uqybmtZVq7qH8Xi8Wq0AlJS1EAHP83TTMG0LIsRlxzjrRKfYcaenp99tsJSxU5Hh1A+gVJJRFHnet/xU13YkF5y2oeduNyv17JqmaZpeltV+HzHatW3bD3uOYzNGVfVsWUbTVEWRQXi3jVRszeV8ASVCEu12UVVVlDWWZYxGA9u2B4MR58Cyg7rmnjf85Ac/Pbn/kEkpIEAIZWmh/guDQd8wtMlk9OLFC2VPCMPQsiyAYJpnAnDPc+Io7fX7k8no00//3vEdAUVW5FLK0Wj09tvvGoZmGMaL8xdFVWw2m/PzcyG6Is2kEEBChLBlObpphb1+Vbdcgrwom7ZVI+0kim+urzbr5fz2Mtpvv/j8d7/4xS9e2+yWy3UUpwoU7Domp6ytGyA5gJwL2jLaNFTX7TAYYKRrWOs6YegWF7LjAkJYZnlT5ZZJeo6DecfylFe5S8DE9we9oKK06Lrffv0V1zSBoZpRfndK5doOklJCCc5OT9UlBQBQGqiHb71pBUFSVb/58vH51VUn4Uff/6HhOLbnCwTLqhoMBmrnq6blUkoBwTbaK/G8EMLzvLIs1XRjMpkoqbly6g0Gg6ZpZrOZ6qYVxIsQ0uv1MMZq+2Xb9s9+9rMHD+6XZalAV0ka53mqDLFqj6AYtmpjlOe50norzYrv+0p5qHq1wWCgztrvkn6VYeHg4EBpMaWUJycnEEL1A9+psUxLLbGU41Q1i7ZtDwaD0WgUx7Hv+2pIruadFxfX49GB7wYQwiD0WlprOv78y8e2E3z1+PnpyaM//hf/+qOPv98fjKBOJIIQYYTQcrlUYel1XRdFfnh4oP4gCOF6vd7v97vdxjTNoBcijbiuW9WFBB0mABPEGENYaxk/ODj40Y9+pEK+lTUoT1LR8aat8qwAACCEGOtcxw/DviJm0ruQLUPTsO2Yvu8CwDnvnj17qmnk6PhAKYrSLGe8831fnUdV0cZxrOu6lKKpyrouheB13QIJMdYcyx0MAtO0q6oR4s4mEMX7rmt0AtuygKIzCHQ0gDqGkcCaBnTy5PLyydV1I0SWF6LrFpcXSHw7pdmtNwRKjjFezhdVWaqiO8kL0/X+5le/appmu1r7vn86m6V59uTpS8t1bper/mCAdaL8UhohVVWNx2PHcdqOBUHAeEcIIgQJ1vX8ACGkoOlqPmfbtk40zrka7wVBoCbnvu8rx58KZEvzREr5X//X/+6nP/3xerddrRa9/tB2nCRJ6TeJdSosqixL5adTgyo1VVAeDFVUSCnff//9pmleD8heP7/CYWR5qhv6w4cPGGNZnkIEFCxoMp7lVam+ciqESFULypy+Wq1c13VduygynRi7zXY2m0kp33/vw4vrC6WnrmnNAYcS+354/vLyX/6r//mf/PG/bpjY7tOWi5Z3uhRZkkkJdd0sixoh5AdB1zRqDh0EgZJDNC2DELWMPn36lDYs8gNiaEhDm83q4GCKkV6WpWt7ggDHc07Ojl+dX9Z1idGUc3l7e+t5gQwgY2wymWHN4Fx6XqAb5j7KOJdSAtq0lmFqmGCM66qIo12e55ZhGobB+d0iwLKsmrYIIU3HWArDMKoy7bpOipa2TdEKLnBVNRhJLAHvgI7NfbINEGFdaxu6pmGENNqUnmEITtN8p0EuQVs2eVylP//0V5+9epUJbokOIpzvdv/oJ//g4tXN609qOBggRjmtKWMMShD6AUGYUioRfPr8WUNb0/NKSrOqLqqm41JKee/ePddxNE3bbDYaIQr/tFqt+DfgjUGv3+v1ZuMJ/w6y2fO829tbNTMCAJimuVqtqrLc73bj0QhIud/tHNs+PjrCGDuupet6S+uf/vQnm/2urmsA0MXFxc3NrerGVM/+2hWuXnrFJlHzPCWhcl231+spZfsujlSx+903tdfrqZaoLMu8LC6vr7bbLe06NQeo6sLQsJIrKOUAZ91mtW7qushzKUSaJFIIgu9IaVJKjMnFxYWChQwHY6X7VGOHT77/w7feeZ9L2BuODo+PMIampdVN8fzF17Qtd7tVkUXqvOdSZEWONUwZo5SGYagILm3baIY+Ho8pp23btoyWZd4JLoRoGNVNg+hYt/TJwfjR2w8BFsTESR7vo/Xjrz5brq51Ayvxiq4bGjEsy2lbppQYi9UySuI7hYOQ29US8C5O9gBIw9CCINA0slwuaVMhKOsqJwRqBFgaYW3NGJMQAMk1DWfJfrdZ7KJtmhW7aLdZrVfLZRZH+/0mjjZVmwLCqzrtePP8+ddVXdwsbrfpPm6r690WO5YVhutdMhyMf/zjn1Z5hb6Dv3zw8CGRCOdVbemGZ3uAg45109G4KIp7p2dXN9dHR0ec60jDEMmjg4PValVstq5nY4w1QqqqcBzbdq13Z28/efKk1+u5lk9bKgSIu2Q0GCIATdtK8kwzjfc/+nCzXA0GgzROgJQ/+fGPizTjnEdRZBiGQbSmrG6vrkfDfifYw/79LMtWm40iXVqO3e8PdN2kTSs62YqGEFIVZT/sQQifP30W9nuO47CWZm3quS6XopNisV6Nwn5T1aZjCyBns1meZqPJCFzdhVJgDTuew1hIKaVdp4hR48EgyzLD0LuOYYzKvB4MBnVZAiGaqgIAJLu94gIFQYA1IiHcbLaW41RN0+/3IQGDQb9t26pq8rzsh4EU3Xg8/MM//EMuyBdff/nW2+++evXqnffeXW03dV3n+TpOAeCVoeM4KrMysh2raisqGdExbNHVzfVoOCmKIgx7hBAEANGJ49lFWUohgtDhTOim1vC6FQ1BOKvTydG4qqqX10+nk4PN9qZpc9czBkNfAsY7mOdlnue0EwDhLM+zPOk6xnhXh4wxlu4jSqnr2VIy1tWUtRjDjrFe6CEgCeI6gaIrZUdNDdRtWZS1IjK1bfzkq6fjyUFvMFxvLzvKyrwwt6QqMwApJuLe/YOqKrWuu726lIBvojiuy32e/ObJl4t4s01z03bfvP8WkPjLL77+p/+jf/Ts5bPXb+rT8xcEfoOwU1f/vXv3Nqt1U9W0aVWCVFEUEkFCyM3NjRAijna2dai8TdPp8WK1bGOqrK11XdO20zRtNpkpRIDa9ygKVfcNxttxHCDlxctz3/dFx8u8mIzGygHbiKqmNdaQbhqmaZq2DSHEmu667n6/NwxLCBCGIWNsv9/3g36WJJPZ7MGDB3lZqHpI0bWEEEzyXq/XD8K6LKWUg16/TDJN03TjW9XjnZEoSXu9nnK9bjab8XhMKS3LkiCcxonaAtyBljvuOA7k8uDgQCUgR1GUFHkY9pIsDft9StvZwWSzXXVMnt47tQxTqZA0jK5vLm9udv/0n/3zvIiTdPOn/68vhBCHhwe2hTqapdkuiTZCdK7rGqbesgYgiXWcZpkC0N57cJ9z8fz8xaP796q6wIS0VaVmtAAggMDLVy9nsxlrKcSwbiuApO0am+2iC/p5lTuO5Xi2bdvT2VEcx7vdZrfb1XWV5FmWxBjjJ0+e9MKt6GSR7Hv9wA8cV/cAEL7vCyEQIrZpahjxjgKC26rhrHEsuy4LzkHVUk7bxe3lenXluNp8+aqsmmgTDXo9AuTTp48n0/7v/YMflk22X8/zaB9vdm+98XaUxKtkf71aPL26WMfxYHwApLbbRKET5HX9i199ajvf0sPLukZqI3d5c912LEmSV69ezZcLCQFAEEJ4e3srEaS0sSzD8x0ABWPCMCzatGq8pchKWVE4nke7zg38sqm//OqLfbxT3CjN1Moyd13btk3GWk3DStMFMEqLfB9HWCPb/Q4g2DKqUI+EkOl02nXdy5cv5/M5Zx1BWBnrAAAvX14oRZ+Kytjtdmok1LatZuhxHNOmMTSNVY1rWFVWzEaTdBfVRSm6brfZ2Oa3VM7QDzjrgiBYr9erxaKta991o93OMox4H7G2beva0DRD09bLlYZJXVambiiJllqtAQDyPM/zzLLMlt5pt1W1cHt9AwU0iHF1fdF1dD6/Ni305OvP/6//l397dDR89vyL+eIySXdX1+ePv/oySaO6qTCGyiw5Go2U41yF0jRNkyRJUeR3wQEYL5dLhNDr/G/lzC6K4nYxlxBUVbXb7b7++uu8LDTTqJrm88ef/xf/1X/x53/xZ59/9VlZZ0xURZ20XQWwKJsSYJSX5e1i9fjJl1e3V23X5FV6u7i5vLxcrVZq89e2beD5tmlhCE1NDzxfCio6dseTw6Auy+lkGPgm70rJq/HAPX/+5bOnn5smevXqKSbyT//03/3Hv/351XJ+vri53q7m0fbxixfz/T6v6v54ohlG3TaHx8dpWYwOpv3xsKi+1fxvdnuivEeqoWFtSwhp21a9gkVRPHzzjevra8syyrJ89/33rq+vDc00DGO32xwG/mq9pZRqhqGYeG3bbrdbTdPcwCeGXlNGDB0AqQRNr7EAg95wsVj4npdlGdY1tfxc77ah5wf9nmboEMrVaqUWpEKIO/Ogrum6XmTlyckhhHAymSxvlyqmp+s6CYFS4EMJRNcZmg6EqNPcN6wsik8ODtOyoM3duuHbOn04VPEEOiH9fl/Bh03THPT6ig2h9FxN00ynU/WHqiZMIbFGo9HLly9d28YYq91jv9+/vFq5tqfKZQ2HQTBVPIEnXz/WiCEAHIxG/7f/+38phLi9ufQ9EwFxfv7CwKjraJpWlm0mSaSbmmnayqtzfn5+eHBc1pXqF+fz+eHhobqd1PAhy1JFnNU07fDwcLVaccYVtQBC2LSt49mGrm+i/c//5uez46Nhf/TFV1/uo3WRJk1blnWhftqiWMuOI9jZrjVf3pq2Q7BJri+8oF+W+WQ42u03B+OBkJ2UXdGUkrO2ZRJpRDM0BD/53keMtVG8HY+CKIpWN8sHD4/zJBkO++++9/Cv/vavoii6nl8PR/1+EP7u68f7OL6Z3+ZlwaSo43g6c+q2GRDs9cPby4t7987SNH79SR0fHyM/6FV1u9lsHMchhp6VBRO8kwJpyHKtKNodHs4QwRCjL774YrvdKnaa6wVd1223Wymgermrpp4ezBjv/DDQTYN2rKWUEJLnuRDdbrdp21pd0MvVXNOxhAIgud5sPN+3XKc/GLiB3zKqPNCMMdu2e35Px7oK/WlrCgRUsPY8SWXHR6ORIquptgAS/OLFC9d1bdMq88K3XdDxo9kBoB0Q0redMAw1TFz7WzLFfrsFQpi6bmh6GicdZXVZxftou940VR3t9o5lR1EUx/GdymS9fueddzjnygCz2+1e72DLsnRd13Gc2ewwTpPxeDzs9xGAGCEl1uloLQFDkO53t00VG5o4OhyuV1efff5rIRmA3HGs/iDcbDY//OEP1RP1egNNMzomzs/P1VohDEMvDIihCwh0y3z64iXSdN0yJYJ+L0Qa2ccJ7bgAsqFtnCYtow0rLVcvqrw38Llkv/zV3273K9rVn3/52T6JGtZEabLdbySSnDOsY8ez1f3Wdm1WZmm2WyyvEGZVHRFNFGVaFFkcx0VRbte7eJcCSBDWv3r6zHF9zplhaI5j6TrSdCgBhZDv493T51//9d/+zfnlFRUiznLk2C9vbzXHaYQ4ODmFRJuMZ01NXdu7XczjOA77/cVquV5/u03s9QeIUtrv9yeTiVq+Y4ybplGEf8/z1ExRvZTKZ6KU+ZxzNVWhlDLGVYO/2+0++eQTdXwuFgtCSJxGmoYVb1oJYdXwXE1M1V4nSZKyqiilEgDTNI+OjhBCavejyO7KMvWa8qU4o2maKu2LuMsDl0mSGIahfRN5yhmzDdMzbQ1h2jRZlqnbQ/3b1/NUCGG02yvgoUIjYYg4577rmaapymh1gHVd1+v1bm5u1PZ8sVgsFovBaKiCPt5/9z01+3z64rmUcr1c9MMeY4xR/vLlSyFEUWSubTRtkaXbwdBnXb1eLzabpWlqGEPGWgCFrmsPHjx4dXG1mK94J+fzudp1K7c++iYvoCxLNQpVfuWqqoqiWK1WCtgIAAiCQI0sXNepqvzJky9bVqvs47Iqfv7Xf/XZF5/7vr/arCmlb731luM4VZV5vj2eDABGlutYriOAXKxvGlp++eR3Xz/9nItG08Fms1iu5ovF7Xa7rauGcWHZLofgd198vtptX91c/fyv/+Mu3n3620+/ev64pnXRFK8uz796+pUfhk1LJcR1y758/GSXxpt4T3n3288/E0xgTBRS8NGDh0o3mKapF37reMMYI+WZpG3X1FThwH/wgx/s9/umaeI49gL//OKV77vT6fjgaFY3DYBwH0VhGAZBUBSVEkw1DTU0E0Py2W9/5zkubdrxcKRr2NSNqigMjZi6rmHs+Q4XrKW0PxhAhLgQ04PZcDwiugYQdDwXIqQbRkPbsqhdxy+KQnErFAE9z/PdbpemqXJQKKAaZ0y9yhhjw7Zu5rdVVUkp1XIlzeIg8JTHNcsS0zaw9m1HpW6DrusIwkWWSy6KLANC2KZpaFpTFYHn6LoOJRgMBgcHB1gjmqHbtm1ZFpfirbfeUtAex7VW60XTVKap+757794phqDfCwxdJ4S8//6HlutAgrfRVjewbmDdIEWZDgdBL/Q1HesGUbOzruts216v114YjscT2rLVag0AnE5nFxeXuq7HcRyniRLdKvJ6mqae75uWtdtHUZyovnC93WJNQ4S8urosmoxDlld5lMYNa3772WcAgPv37z96463DwyMAkHLMDkd93QCL5fVg1I+TZBftb5fzoB8+e/ksr9KbxdXl7fku2szXt7/53afL9Wq72wmEMdHDQf/q9ua9jz8safXXv/ibz77+4t/+n//tn/3Fnz89f/53n/7y2fnLzX4HII7T7Orm1vcDhMhyuc7SomlZSzvPDQgiyTY2kG5r1tXV1ZMnT95++21N0yH/dvIfpTFxHCfPc0XOMExDNS69Xo8QxDmvqur4+NhxLJXEoBua6KRi/anptHIyKQuK7/sawmWWc8osPyiKwnSJ67oYo67jSsOxXC4d29vv9+qULYpqNBpZlqXSUzHW4jguq9wynSzLTk9PlY5JAQnX663rMtu2GWaqCIvjOAgC23UZ79RMAEJkIMJZ5zgOFsAwjLZt0Z3xHLx4df7wwRuvn3+5XOZpNhmNOeenp6ee560WC3WlKDCi4zjZ+cX4jfGzZ8+UslMNcfv9/nA4zLJMM/S33347SRMVW2AYxsQ0pRDb5WI2HAx7/dVy2R8Nb+fzMAzbtlytbyaT0YsXzwzDWiwWvh92gldVpRskSZIg8CnvEMKKVwcxMQ07ipL79+8fHR1VVd1JjvHd/OTy8vL05J4yweZ5PplMoihSDwshVGJ2G9uMMV0nlDYIEYL1to0eP378z/7ZP1sul/P53Hfd4SgUrCOEnBwdzWaV5Ti6gR3frdoaAAARWC7nCIEsS7748jPPdGeTaZQnru3KaEOF/Kv/8lPLty0D/9mf/zrLdjc316ZjdlBanpvXxRe/feb75vHx8WK1hBinaarkRy1jd5Qs1ulQz/Nks9y9+/6HjIvxeKx2iqaug1/fDf91U0OGpouO102pTOJxHHcdTdM4SmIJQdvWvV6w3e+IrilMJNE1iBHEaLVZe55XFMVsPAlcDwpparpCbPqex7uurqqOsm9EfY2ua2ma9no9yzb8wFWwLjdwt9E2zlIm+C6OsK4BAAK/pyR8ZVnudjvGeNsyAIDvu0o9SLuublvdNO/AjlIubudI3rEFqqZWJtXJZMK6DmDUAUkFlxh5vs+/k0jBWgoAUOtWtQRWsCOMoZSc8a5s6unBJCtS23W4FFVTZ0V+/+GDwWgYJXEnOKV0vV7nSar0zrRt26aCvHv70UNJKeQdBrBtWX8w2my3jIvJZKZiq4BEhOh1XVdVtd2uFWpY03TeifF42u8P4yzXiFEU1XA0AgCoYkzTNM91y7xibTcdz9q2VbYcVTq7rltUZdXUjuNR2nle0LZtW9VIItF1pqanaXpwcDAc9jebVZGnCtOiDLeSi5vLq47TzWZ5cDiuqiIIgpvb28Fw5Hj++fnF18+fYULeePfNOI9X+3Xe5D//xc9/+8Wn8+XL25sX/92f/bdlGb84f4p1qJvmYDJ0w4ADee/BQb8fMtZ+//uf2KYuWNe1lLXs7Pg02kUY4rPjMwIJlIiz7td//ytD08bDwc38luj67XdSfvIqR5yxwWCgelsF+1ULSUV+9DxPIenkN4hNhEC/31eni/qbury8TJKkY0zXtO12CwCoipIgDIT03TsqRhAEw+FQlZuDwUCpRu5AshirBkVNB1WGIABAaaD6/b7y9Kltu/Loqd/DOedSNlXNOT86OnJdV+lFbNtmvAMIFXXVMEp0TTcNyjsmuGYa1ne4VN03ofae56VputlsNMMgut62bU3b4+PjLMts1wnD8OTkRNmhHMdRCzmFAFKWDErpfrOVUvb8wMCkiKK2yHbL+ZMvX/RC33GcOE4ODo7UfRWEfcEBpYwQHUkEhAyCoOs6hXdVSiUhBO/k5GCmmYbnBUVR1FXbUJommfqY1F1XFoXSiynQouIKqp2WmmkQrEkJi6Kqykb9/Jvt6ub20rS0PE/u3z87Pj5umgZBqOu64zj7zXbQD+M45pSt12shZJ7nEEovcIPAy8qMy265WT978fTf/3/+m989/u2Tp19+9vmnz5597tt6kux4RznnECNNMyhlpmU7ng8AAhxURQ06AARkjHdMaJqBMX7w4NHLl68ERFVTu15g2/ZqtfR8dzKZJFlaf2dK4wcuytOENlW03fmOW6SJqalMFq3rujzPVeOizM1JkriuKyBggnmh5/hOUWSe5yRx7HteLwjTOKmK0tSNnh/0/CD0/NVqZZqm5DJLsu16WxVlVZQXFxcqcEdKqSZQmobzPOWyo11LeXc9n7cd00yDA1jUjUoQBQAIIWjbAXnnrq7r2vM8z/OzNFc0MtswJb9Lwby8voA62kb7vK4ARtODmZpzfbejdH1vOB51giOCbdeiXeuHHiKwqKuze/fiLIYYxFnadqymdZzFpm3nZVk1zXy5rJrm8vo6TVPbti3Lmc0Oh71+XRU6AIej4U+/9/GDw9npzM3j/eWrC51o8/ncMh3eia++enJ4eNR1gtbUtl0lzblT6yZJnCaaqe32+8Pjo+vraxUmto+is7MzIcRsNpMdf/uNN23DTKO45weM0jROTd2cjieWYXqOW5cVhFAFNA8GA9qK0Btst9F+H7V12dZ5r+/95re/zIsIwM62TWUcME07CEKCdQXRtiwniVPLspbL5Xa7adumNwzmi+s//ff/TV5m62hTtc12v1usFr3AE4ymSbRZLAEAlmk3VcOZ9L1+XVLbcAbh4PjwJNnGo3CoYd3SbbVNMAzr6vLG8bzNdouI3nZd1TaapjmO1XUszVMuv92mzpe3CAAQ+kEvCNXVXJaljolCQisBURAEal+vDElKi5llmTpTlehJxR0RQmazma7rXhhs9jvTsT3P+8M//EPlY1ETAzVk2O/3yuBr27aySqrDVU2YZ7OpUqMq7786p5UsOggCxdNTHXGe52VVwW8iHNRYQAhRt43re0maSgRVhxsEAQLQMa3hcPj6+bfbrSoG1CRVtSO0Y/3B4PziFeWd47pBGAopiaaZlqXruqLNUUrVMbbf7+fzuVIOIIQ469q8OJtOh47zP/4n/8m/+pM/yqK9SfBsNr13eialdBwv9IMnT54eHRwP+4PpeHLv3j1lZFDhCL7vd12nG+Ty8pXrO6ZlGYZmWRbSUFGVhBAowdMnX2uYIISqogRcFFmmSj7FJMzz/PLyUumGq6qpy3a7jQ4Pjgf9kRDi/v17tm10vEnSXdOWaRpTSuum2W63URQNBoPJZLZZ7g3DNIjh2o6UAiJ5du+oKJKsSHe7zdXtNUKwEzzohzWrGWv3uw1nHee8LmoEcJmVvuPLTiKJ5jer+c1CQ8ZuvauqpszK8Xgcx6lKCvE8bzgcn57eaxklhLz1zttlmR8eHp6eHbdNxfi3fn+MNCQ5a6qiLvPhoLfbbpVPV3GUBoOBEkwpG5268fuhH++3nDIopLpAR6MRxtjx3KAXJllKO1bWVVlXWCMto59//nme5+otV+8rbdqmqsfjcZqmtG44ZaoqrcvK0HTPCyjt9vvYcTwFhniNR1T4SClllMSMd0VVqnYwTdM8z6MoEh1XQDWMcSdFQ9v+ZBT0e13XxfvIsQza1vA7LvLBoEcIUtGsKm1a7TDzMmu71rCtuqOsa4lJ5uu5ZmlYQ6PpKEpiy7GJrhNdH4xGddsqh0+RlViCOskO+qFLkC6Yp+Pf+8H3CJa5gsSYJuCi67o0TuPdvq4KCISyCjqOnaaJkLxq65a1ECGsoSxPuGBxGiENRVE0m806ziCEUoi2abIkzdO8zEsEoKnr++3WsawkyU5P7z148GA8Hiu9r6ZpvJO27XIui6qxXCev8iRPXN9ZrOau77qunSRJr9+Ps/Ti+mq92mma+ezp+eHhMaXd/Xv3bNts2oKy2jAVwp+1bWuahhBCoZM03bRsl3cy2qebzc42naqonz99gQDuBz3P9na7fVU1ZVl3Aqy3u16v9+Tx12WWN1V1c3nFgWRc0I5dXFxYpkmrMtltz05OvwNmAEIAxFqq+mjZ8TAMTcPAGEMhCcJK3iulDIJgOp0qiV2apgphRyn95JNPCCGKJEopTbOMEEIZc1z36OwUG7qm6/v9vus6hXhmjCltddd1FxcXhmEcHx+/tvmr5l2dnaoqUGsqRUxR1XMcx4eHh5PJJAzD0Wg0nk7eevcdvxcy3qkueD6fK2h8kqbE0JVPstfr7dYb2rQG0b5LT1cBPaZpqmqYCZ7kWRzHveFAJQnWtA36PQXdhRAu1ivDMBzfy6tSkS+UHFYhBTzPu72a06KLl2tPx0R2kLVIdhpCtKltU1fRh7Zp9oOA0cbUyOPPP9MJQgj5vVAiaFi67ZgQStZRTSdhGDi+U1WlEMKyTU0jRVG0TdO1FEmgEfLo0aMiy6EEouMqI8W17CdPnhTFXW48xng4GpmmeX19rYbZm81GN4hlGdvtejjsIwxa1t5/dF/TsW7gIPTatk2S5OzszLbdrhN5VigzRV3XhODlcrnZbNq2lpITDeV5WjZ1URdcCss1p7MRhlINwj/+8H2E0MnJMSR4vlpMDw9My2oZdWyP1vSNBw9Xi2VVlIZhZHF6dHCgaZqpGxjAKs1XN/OLl+dN/a2SGiGCZpNpP+yFfkAp/YYyAu+sxkJmWcG5bBsGJKqrlmBdJ5plmIv5jW0Z69VCcLZcrTAhZVU1jOZFQXn3+eMvF6ulALKmbZLmjuOZpt3vDwnR1T8Ph+MP3n/f97yXL192XSdYp2OisFPj8bjX66m4ErXJVBiBQX+038We511dXamfMM/zzWbz9OlTlRCpdhYnR0ezyVSxLJWNe7FYMNbOpmPQcYxQkeWvn58D2UlRtc022rcdE1LWbeOHwdXNtRcGhm3optayZhdtHz56xIWwHLusK1UXEUKiKEqSNAhCVXhUVeW7tgbBwXCIeNfmqeza49mYd3S5mlPajAf96WQ0Ho+ns3FVFZ5rf/zRB5zzfr+fpunp6SnG2DA017O7jtq2yTnTdW046o/GQ8exkjRS5mZD0zvKQs+PdrsPPvhAjZMVNBNjfHxwqPj3Sl2kYJeKVaOupjQreoPh0enZYDSJk4Rz3rS1YeumrQ1GYZzsdJ1sNps8zzsm0rQEAif7vBcMOirGgyECAAJhWxprc8vWkmzfnwygjnRL74/6WMcIgTB0H71x7wc//HCxvH7nvTeH40FFa46ECncDQt5e3zi6fTQ70gixbVsKCIT8+IMPDybj+dVlXeT/8o//eDz4tk578PAN1HUdaynnnLPud7/+jRCirqo8zwFA2+22qak6z8qyVI62JEujJH733XfzstjtdqZtKQie4gC/9+EHyrusLA0SgjhLMSGmaZZ54TmuouoRQlzbGQ8H09GQ0YYxZnuuF/gCSJVx2rVUoSHbtl2tVsPhsCgzLhghxDA1xzabuhz0w0GvP5mM67peLpc60dTZFkXRZrPhXVeWZUvp0fFxU1ZHswMoAW3auv3uNxUpUpeqK9T3sygKtcSH+G4Rahn2Yj7vGANCJkkiIcAacV3XsizGu6qqoiTJkpQWBWia+4ehAXnouRAjjLUkzoostww9iePtbj2fz5uqvr686gXherGYz2+zLLu6ugr8MIoijGGVF2mcGDoxNF1K2ZTVo0eP2rZWE7G2ZbPxRMkVLs4vttttU1Vty9q6qapKJ5rnutvtti7K2WSkY/Lw/lmaxUJ2CublmNagP6QNE0wWebVer7tOXN/eaLretjXWcdvWk9n462fPg8BbLG6VPf3m+vb44Hi/2Zua2TQUAbhYLExdV+6M/mhYNfU22raUqptWcn56fPyXf/E/vPfeO1mZvffeO0Vd9IcDTHS1ANtHiWk7AsC2bee3S9u0EJSy44+//DIIAomgphm/+MUvvkumqOsaub53u5grAESRV67jm4YdBD3TsCHElHZpkiNEdN2EEGOMCcFVW++iHTE0omsCgLIpq7ZS3+zNcoUBLNLCImaT11Cisq620b6jPPRCKOByvlJQu89++2vIuWuageMEoVfW1fTwACCCEKjyDAgpO57FmY7106NTTpmp6eNB37XNcb8XODaWomvq1eLW1HWEoO96atbTMub63unpqaVbSCKI0e3tLQDoqy+fVHXb0LZuvlXohP2gP+zlea4s3Vma2qbDu7skc9W0iU5iANuiomXtWIbn2ojAIAyrplGMoKzIAYIaQR4mPgQ/eu/R8awnMKRIX0Zph3U/7HHKLcvQNA1i1HXswf17UAq1S9N1TXLBuczSXCeaZeq0bnhDIRe+5ZVlRbCuGXrbthhp905OfddzLbttKaVdvzdkXDYN7fUGBGl12ey3G0MnZ6fHOiGCM4KwqRs60Tar9W6zzZJ8cbPwHV/HuoY0x/QePnjTdYIoSQzLblqm2w5AZHY4i9Lo7ffe/pM//ueSt4ezabSLCdKgREVWblfbk8OT7Soq0rZrhPIUMcY9r7ffpaPe7NH9N148fXk4O7q+vPm9H/+kqpqT47MXL86lBHle5XXNMHDDHpOgrFvDMG5vbwiEtm1JKOIsJ4YpIFpHEfwW8w/apkK0YxIC3TSiJP7kk0+apmGMK/uLadgY4+Fw3NaNskFvNhvDMExT98IAQogIUTJ7tZKOokitp0ejUZ7nGCJO2eHhoZQSA9jWDUFaso+m06nr2rquX726mE1GnuMq9MjtzaKuawJRXVWWZaVxorKBbm5uVDvFORecdV3XVKVOcBLF777zVhj6Ki9gt9u5rnt8fKzaXjWmgRBOZlPbtFQ72HE+GI2+O0/tuk6FAtiGaRANQmgaBufcMa08zSQHvOVNTS3d0hBWNT1CqKzyzXatnAW9YV/XCZKCCPF73/8kDBzbs+I86xBqId5G8fHRSb/fV8lHdV1BCFXx4Ab+Po46ygxNMzVdTTmklJx1GJKqKPe7DQAgju+yahnrNpvtfL7QNN007Sxv9kla121VVV9++ZJz2bZtEPSODg4Jwp/97ndqvZckKef88Gh2enraNE0cRfvtfrPahmFf0/Rnz54pUEOaFY7nb3ZbiEivHw4Ggw8/eu/v/u4XmkZUvdT3+4NgELqhoVtVUVdVs1pubdultIMQSgF1zTR0J9onrusfHR7Gu/2nv/z7LMs2+81qtRoMBhBiXdd105hMZ5R3H374oerJPNsBUMTJnnNOCAGYWK5T1/V//Mu/fP1JrZcr1DDKBG8YPTo9idIEEWxYOsB39k7XsUxThxBmSaoTzfM8SlmVV7/5+ye0bhzLAkIgAGjTWLrh2XfAW8lFR5maJW1W69OjY51ognV5Eg96fdFxpUCdHR0+efp8tVn3w57vegRAxzAt0xz0+spxr6ZXylml8JFVU2oGyYoyL6vZbKbK0CyJbFMnCCAANqvVfr9XcYS9Xg9j7fr69vnLF4PR8ObmRl30351SIUgYY/1+n3NpWY7suHqlKKWE6N+wGkWaphhrg8HAIJrnuFVRWrpRVeV2u8nihHUtB5x29OHbb+qO2wqwSZJtHJ9fXZ1fXT958RRg0B8ODMtSYz5lJT86OjYM45OPP56Ox2mSdG2nY32/jintVOiFYRhSirIsWEtp0xZZrmumkDAtyjfeeMM0NdfxMdIs2w177vXVbVFUlHaaZrx6dblabjeb3WazCXqBF/hFWe6irRe4RVlnRZ6X5Xa70zWzaRrP807v3bNcJ8myqmo225VuarqpP37yZU3r0/unEoqwHwACkiLxfd913aIskzT1Aw8AYBvmbrOHUq7X6yAIxuNx0zSPHz/uui4IPQl427anZ8eTyQhC2bZ1WzdSCsVrghCO+oOjw0NTt3pBHwjYDwe3l7eO4RjYwN9JvvRMF6VlQUxDt61dEgsMq7bhQAIA1IAwTVMpxH6/VzskCEDg+aZpnp4NFdleOeVf9+mvp5KKJic6HrjecrHwbEcKkWeZ79pVXrx8/uLRo0dJlmqGfnJyst1uQSefPX7y9sM3OspGo1EYBLZtP3rwkHM+nU6jKLIsSyFIZ7OZmrer0Wng+VVViY7rut7WjdIgK56AIgAjhA4PD3Vdf/DgQdM0391RjYYTVVWvViu192qaRrAOAUwp7Vra1DRJsizJVWT19cUlpXS73ih8tqkbvuudnBxJKTWdnJwd75K4gSCj7TrNP3/y7PHXT6ngRNd6g/6dhsYwhuNREAQAoydPvz45OX3y5ReDft82rTAIWNtpmqZpRtNQjDFjraZpEnD1zXn06M3Ly5vFelU3zZePn5zcO9tsNnlZbDZbz/NPTk4gwK/OLxnjRVEFQXB8fCwEmIxnhJCDg4O2bdfr9dHRwdnpfd7Jumqrqjo6Otru1rfzGwBA14mwPyzKMo7jqi6UkDfP84cPH/b7/aIooij61a++UG453/cty0qT/NmzF01NaXuXr1LX9cuXL1Uo3M3Nzfn5+b17p51gBwfTrmO+7zVtrbLHFEJhs9m8eP5yv4uauiWILOdLAlEaxY5pHc+OX39STV4hxjuskY5LISHRtIPDQ9txNF23HdO0dBV6a1sWBIBRqvBGlmG6tkObVu2c5jcLgjQAQBzHWZqahqFyfG5vb/v9PpBSgyDabvq+R4A0NN3Q9UGv9/Tp095g6Ie9lnW+G2iYHE1nt5dXTVmtVisFi6yqKtrtFapNHXUPHjxYLpdq37jd74qigEhapi44H/T608kIAenaTp5mbd14jmvqxmwyZYxfXFxFSWpZznb7Le7vDoAjEWtY4PlASAyJAtKGfo9SahumYRiPHj2qqgpDBKSUXOzWGw2TftjTMHZtk9HGNPWyqsJBuMuSdZKeLzbbstQ8lyEkMKxpu93vXN93XVfxDeqqybMi8HvX19ec8/1m61p2VdRN047H07ahjuNohl5UZcdYRxmnDAqYJzlCuKqa1XZj2FZeFNODme24HQdN286XS8Y55wJBgiDBWIv2iRLO8k62tP7o4w9MS1ccO9f106LcRvtdHCFCDMOo67rXG2ia5vieAPz49Miw9PF0ZDnmq6tXN4sbiFBRlg/fPMG6plY/rOMHx0cQYNXG6LrB+Z2bRdO06eFBkmXE0H77299uNqssT0yLJGmUpvE+2ip7T103eV5ICSUHeVrMxjMNaQRiSzfPjk985zvzRN1Bnu83bSshMG0rTVPV6av1qWEY0+lUgckVu0onWpkX6mw4PT1Ve5HZbGYYRpLcSc5UsJiCVG62K1PX2roJwzCJYt/1kJCSUkc31WgWIfT9739/OBwWabFdbMqksE3n7PjkcDo7Pjr61S//3nEcICRjjNLm7OzsxYsXu93u+9//vspo7ff7HWXL+aKu6ySO0zTdbjbj4Ug50xWoQvlnpJS+7xNCFt/RPWRZVpWl5BwAoGmakj0cTA+hBF1LfddTyDG1uVXfUtnxwPd1TMo8r+t6tVpdvbqoilLT8RdffwV0soijy83mF7/53SbLiW3GeXFweKRWWQopILl4nVnAOR/0+0pgGYah8r2MJyNMiGEYum4KIXTdVFOwfn/o2u7s4KBltKrr4XgEEOz1erPZOAgCZbeiVGRZ7nkeo3y9Xr/5xttJkjTtnf7m3r17pmm6rl8UhWNaaZrneWo7ltL9tG0bRYllGXlZqlSc6+vr7Xar6ApRFCnMKEKo67o7LDOAo9EkipIkSdM42a7W+90OAazgJkoa8erq1U9+8hPXtXv9wLbNf/Ev/nPR8cvzl67rOqY1GgwlF+PhcNgb2qbz7Ovnod+7f3xPdMIxvr39drsIdZRDiTrKqqJUacQKIa30Y0rdpz5vhdDp9Xo60UM/hBKOBmPWdopQEgYB77q6agUHhqmZlu56NkJIyZquri+Gw/7hZNw1dd/1Besk7VjblmX9H/7D/xdjTXJhmxZnHQbw8999Ee+T+fX8g/fec22nF/qGpgMp0yRxbef48ChLUgSg5GK32XYtvX96NhkOPNuyDbNpmpubm81q7Vh2mRfKMNNUtWWYCsgvvpOa3tS1ohD//u///s3NjYaJbVpRFIVhmGUZa1qVVa/e1yzLCMK0bTGAtG1V60OQNhyOj46OPN9/+OjR1y+fr5KsksAZDEvGDk5OEcaX11embQPBOaPRbr/dbtWurmqbzW7/u8+e5nlOeZdk8cHRwaurV3lROL63iyNN0yBAgkvbtg1Nf/XyfDIZu647mU7Dfm+xWLiuvdqu7927p6bLGGNdR0mSqHRJhSFJk3wyGhuaJjmvitKx7ePDE8fx6ro1NV3DON7tWdMiCTBAsuOmaU5nY4TQarVSPLkwDJUKWQ2Su64jRHv06A2Mtd1uJ4T0XL+u67Zlai9zfHz8xRdfFFUpgGwZPTw8/I8//8vbxW3TVL2+V+bpRx+8p2LklQTs6Ogoz8pe0L+9uqU1ZQ2TQoius81v39T+MECC86qq8iwDAKjsQYyQRohqPlQKAACgaRpFfLh4eVHmufI57XY7xthqtZrNZq+PYeWgV65Uy7LUNa1sPRom6/mSAHg4nqpEHlWsZHEym80+/PBDz/Pqqur5gYZwlqTRbm+bZhYnjm3bprXb7Var1WKxKIri7bffVtUzIeTy8lKB2UzT7AchAIAxlmWZOvIVEuvk5ETxsNQESv1SXMuyLK8uLhGAKl6iKkoEoWs7k8nEts0iTwEUamxuEE1Jt/pBX/1dP3jw4OZmiTEmRL9dL00vrBhfRDEyTYHw5c3t937wfdt1LcPwPC+JYgghkHK1Wo1GozAMGQMCAoCRZugSCt3SR5MhIODi8tx13V2UWJYT72IokambEIgyL5RVpizz6XRc03a5mlPWXlxccM6Hw2EYhl3HoigajgeO4ziudXw8Xa2WEMk0i/M8K8tyPp/naX7v9EzxULuuq+uy1+uNx2OMSeAGpmGr6bJ6TXVd1zRD5SOoWN22bSeTCSEEIaI+YtZ2CKG33njz4OCgqipMyH4XB0GvKus0TU/PznRd57xzTHO5uB2NBvcfnPGO6vpduk6aphrGaZpOx5NoF99czwnS4jh9/UlNp2PUtTSOItswEYS0bizTDFxPyUcU90Fh7oQQyghACMmyjNaU1nQ0GpVlqRNNJ5rr+J4bKKakkl0qxlhRlRDCoizbtm1pgyHQAcr38b3jM9awKq/iOFWT9qfPn6mbWhVzR0dHUsrdbuf7/na79TwvjZO7jZrnX19c+o5fZiWQcjIeDwYDdQOu12vXtptvxIGe43aUNU0bRXGZlxhrKm7h7k3FumU6x8fHSiv4ve997/nTp0oBo5hQr7FWAACiIcMwHNPqBSHGuGPccdzFYjnohZR2tOOG42uuCw0LIB0QbXZ8MhiPdlFMm8ayLNu0Pnj/fQiAImhwzltGAQLEgA1jt6tlzagT2h988l6UbAejvm3bvV7fMKymobpurNfrf/T7/3A06BVl5vt+J8V8NUcIBIH34sWzjz/5yHFsIThCkBAymY07TgHs0jSaTMaz6fhwdlCXFYLQ0PS2blzbWc4XbS06Snuh3zG23+6uL29sy726url3cq+j/OH9RxDiqmpc12eMSwkfPHiU54Vl2ffvP7y5mc9vl1JKhcCBUDLWXlxc/PKXvwQIKuFRXha9QX86O7AsqyxLRqmUMo7jJIqzJA2CwLIsJdRU/FqCMG1aUzc460aD4XK5fP1JLW6XKM/zYa9fFAWByHEcApGu64J1SpwqpVRIaMMwDg4OFDhTQRmU7uT+/ftq6a/uGrV5VwG1yksZx7Hr+4hg2rEsy3zX61raD8KqKA9nB2peo84nQsh+v3/48CEE4ObmZn5907YtQRghNB4MMcYff/yxclOprCkFt1IZAfP5/PHjx8+ePfvoo4+yLLt//75hGOrAkFKqpU7TNDrRPPtbF7UQIk/Tm6vrJ0+eBEHw7NmzXq/XVEXbtheX57vtdrVaGYYGkBxOhv1+f7lcKmG/QgRTxhVSuOsExpqAMIrT1W7PAWxaphuGRnQVEEApHfT7q9UqcL3BYPDWW29xKTCGmgE7IdOyKHMGMLQcM07j0WgwHA5fvXolJWxq6jjefh9zzh8//sJ1XdUSeJ5jWRZCwPOcwaCXJFGvF2gankwmQc83Ld0PPYCAZhDW1YahnZ+/UJW6cjTkeSaEuH9vpkQL/X4/iiLHcbI01TD59NNPu6578uSJysVMkiwMQ6VUfu+9916fgne6orpumsa0rSRJlHaWsZbohut7UkrX9W9ubrIsVxKLwPdtwyQaCgKvqgqioeF4CBA8PDwkBBmGYVimrhPXdT//4rEinqtfyT5BBGmb1dZzXCBkU9VAyJubmziOVeGslOQAQi6EBAApGUdW3CzmxNBvbm6+/vpriMh8sVKmXmWVNnVD/U/DZDSaHB4eG7bVSYEwVslmGKEyy48ODn3Xc2zv+voWIWJZRl5m5y8vXp1fKqCuehdVseuY1tWriySKMISz2axt26ooEYCUMd0wwjBU4WnPnz9HhECM4zg+ODhgjGmaVhXFdDzuBcFr9vTdlM5xoijq9wfD4YhzbhmGYWjj8ZhzrmBBUnJNw4QgShsJRX/UJ4ZOO5ZkWdU2imjuOM5yuazbBiC1ETKFBADAly/PVRcihJhOp1VZQ4AARlLK5y+eBYHfsnZ6cHB0ctQfjUdH4WDUZx21XWs8Hc9mk+998gPXdV3X07U7AAJC6Or2yvO8MAxNXeeMSiAQhgCKpqnqpuw41QzyzrtvDQZhVeWmRSirWFc3bdHrByp+0nEc17PH4zEXzDAMRmnghXXZTMZj5YBomqYX9MOg3+8NXcfvmFDvd5IkSmNUFMV6t7U9l0mW5flwPIYIDIcDw9At27y8vCwqFeokpwdHDe1cP4yiJAx7o+E4zwqE0G69AQAMBoNdHCGEDEMLByHAwAvcsswtxxZActn1Rt/u/aHUEIRQDdsU/l1C0LbtbDZTvFaFzFVDov1+rz74oihUc21ZVhAEnucp0K5yEpumeYeryDIhhKkbZVkSXSOaVlSlhIBz/uWXXwZBsFyuOZfKB6uAFIQQx3OH49FoNBIQNE0jIVAHtkL5ffjhh0pppvYZiODJZCIgsCzLsiz1UymU2ng8dl03SZLj42Olo10ul0pk/fr567pW2dj7/d7UddU+C9Fxzvr9cL1dGYZxcXGhbn+lEOj1AgCE6zsto6ZzF9RmWRYAQgBRN41hma7rqmbZMIztdqsEk9e3Nwok8/irl1yIltGGUdu1bc9lrPXD4PLyUtNwkkQaIYSQq+uL0POjKGoYreq6ZdSwrcFgoOu6BBxC+Pbbb1u6ZtumYegScGXw4oKdn79YrOYff/KhgF1vFPq+9+rylRBdv9/f7XZJkqRpSllzcnK02202m01RFFmWZWlelnlTlUmSqtMhTdPxeHx2dqZMv9PDg6urK6yRw+Mj2zb38Q4AYNnGarPSDQMAMJlMxpPJ4eGhSn+2XIcxHscpY5xgXcM6Z8JzXMey1bS7rkvD0KAGi6qI08i0DWKQN99+o2pK2rVeGETJt35/3/dRWVeMdw1lXhCWdYV1zXRsAYEQXRB4292urCoAgNqXcs47wYmuCQnrhtYNrepW7ZMIIQpDmef506eXakikbka1YpUEYUMvmjrOM4iQZhqzw4M33nrTtm3btler1XQ6VS992dTr7S6Jq9f43yTPirpKi/yrr78GCH39/BnSiGbo2+02ShPTtm4WyzjL2465ga9k3UpYOB4O27ou8zze7999++1Br69/J+OQd2K1XKs6SU2RlEs7iiLlVI6TSO0yqqayPSdOkzTPlAKLGJrt2nVbt6zp9TyEYJzsHdfSNLKcL2aT6eHsIEnjljZqpvPWW2/lZVE3rWGAo3unAkvbtbIq6/VCTSN5mlqm/rvf/JazTnTsz//sv/voow+aphqNhm3bAADqttlF+yhOPc8r8+Ly1e319bWyAHSUASEobYoiK4osjqOmrfb7res5UrKWVYNRv6FtXhYtZYZlYg2dnB17nuN6jtqGYESUslEIUZZN1TSd4GG/t1wu4zg2TJMyFsUx1rWaNkVdZkUWZ0lD66qtwzAsq5xz1usHlDW0a8uybJomibPhaLLdRVGUxHG6Xm2jKFEGT4yRbhCE0GDUh1CGPb/jrN/vtW29WC80UzNc894b971e+O2ZigHCmpZkmWYYeVm6ga/iTVTskFLpqzpP4U8YY7tdPJvNCCGnp6emaY5GI03T+v2+53mLxUIVr7p+F/5eVVVdlBjgF+cXRNd2caTZpuN7fi+Mk2y1Xq/X6yzLVMG63++zLGOCq6CVoOcIIE3bNmwLE6JAlqpds207DMM8z7GuJVm63W57/X6W50VRqLA1JchSyOzFYhGGoRIT1XWtED3qVxAEp6enagQzHo+F6NbrdU1b33d930MIKrawGrWqE9F2HaJrjNOG1pDArqNNUxVlHvZcJtqbxZUUIgxDNdL3PK/X6/3sZz979epVlmVqKNsy8PXTp5ppjKYjQtCLl89UZA0hZDoeAynzJA89/29+/ldC8KatqrogBiG6DiDWTePli1emaR8dTaqivHj1SiG0lN4vCIKmqYmGjo6O8irnsovTSCBxcnK03e3Kunr73XcODg7G43Ec7ylrgyBQSkuFv83z/ODocDjuJ0mi3OrfeG8s27YNwxhNhkTT9vEuHPQRgY7vSig6wZI0xbq2j6K0yO86b0YZ75bL5U9+8hMuQFm3AKC2YRjAxe0cIzQeDFta53le1kWcxVhDTDAu+WA0GIyG89UyTpKWfZvGCDBEjucallnWVVGVURxLAIiO+8Me7Zhhmb1+MBj2XitWKaUHs9mTr56qi/U1xqfrujRNXddVGMqzsxM1fkMSACEHvf5sNqvr+j/7kz8GEKZ5FmXJaru5nt9CjF3Xk1Kamn55caEYEJR1hmXanls1LSJYqJx4jVRNHfTCm/mtCqAihj4ajX7w/R8JCbMiPzo5zouC3+3osTopFVVdtTVpnBiaJuV3XORRNJlMVKdfVdXNzc29e/fU+CIIAkLI0dFRWZYcSAGBputBL0iSWIjOsqzxeBgne0IQwsCyjOls/P4H7ziuZdsmAmB2MG1pY5qm7/tfffVVr9ebHswczy3r6uTewen9e47rth37+HuftFVZZIllmkWaEaQ1ZQMBYC01DS2J9xjLH//4hwghAeR4Nk2yousEZyLeRyoLhVHqeraaJyhUPGPs8vLVZDrWdYIx7jqW1zmHoqzrzW73/MWL28XN7HBGGRNS5kUhASRENwwDIZRlaZ5nZVMhjSCNDEbDxWpZt1XLGi67rEiJjjvBTNs4Ojm0XWt6MLn/4Gww7GkaxgSWZd7vh6Zjq8zOKEn/8ud/pRKT9/u95zj7fbxcLk9OTuq6DsOQ0sa27ePjQ8PQEIHDyXA8m2ZlbpjmarPW7G+nNABCVFWVigBVPb7q95W5TEGalMuRMTYcDhUTbzabVVWlkowVYz/LMuV4Vl9u5b/DGJdlbRiGYWrDXt807L/7u1+2XZdVTVm3lFIMcLyLkARRFDHBx5OZ6/qmYUVRJCCoqkoFxBRVmSQJ6wSAsCiqBw8e6bo+nU4N00yzLCtyXdclF0kUG4YRhmFW5MpdU9c1gng+XyZJst/vEcFY077LpMYYX11dvfPOO0II07Yx1i4uLljTGpYpISAGefXqOeVsv9+VZanwJFjXAEZVXajdVdDv9UcDRCAk0HEcJIECae22+zRNF8ulbhiAIMOxojS2A1u3zbTKLFurm3w6G+d5quu6oelN0/hu8PLFRbpNm6J1LRcKSRDgnF9dXXDJwzDYRdvZwUQzSJplluVACTHSxqMpazsl5KWUOo7b7w09L+AdoK2wbT9J0qpsjqaH77/zvtIBAwCur6+ns/E+2qqUDtM2NENXE/Rerx/4oe94GKA7jJJi10lJsE4IsW17H+8Gg4FuakmWxWkKIayasm1bznlalG3bllUFELYs67133m6q+mA63m63DaUY4+PjU8ElpdT3/SwrBARJlhZ1VbeN47lfP3uqmYbanH/X8eb6DhoN+xAIjSCNoDAIkjjmnYyjVFmZt7udpuuMU9d3iI5d3+sN+o7nHp8ccs7apiry1NB1hEgQ9LzAlxAI2dm23XWdjvXpdNp21eX1Kym608OjwOunWdV0oKISSdQ1zMIalIAL2VK2jxNENLU1CIJeGIaM0YaWXUc1Dddl47u9Km8ff/nEcRyiacQyLM9/dXld120SpVCi49OzTsg333wLAEiINh6MKO2ABMk+tywLaqRmNaPNt7OPPFMHCcYoL0tEtCzJpYRCQt222q6ioLVsLez5UAAEEAccYlCWZS/oV2k5DIdV3RJNnx0dUkpZQ3Vi5Emu0lqaliIdxUXSCnr04KTihde3W1lpBrpZXDRtKTjd77ePHj0ihFi6uV5sHCMI3HES5YHXd+0AQ2JoZLFcGKbW0vLeowMBq/uPzrjsMICe7bV1SxuqY010gjEehv0oStq6LdLm2Vfnu3WGof3o0ftSYiiJRkhHaRYnGiaB5z9/8cJx3SdfP0YECCCwRoIgsE1bwzqBZL+NiqysqlpK2dS157qyg47lua4vOIAQ7nYbrGmmbamGhNJOAAAR2e52HIJOCs7ldrV9+eLZdBI++/oJIag/GvfHk9vbRZlXhma+ePoCABRHKTbMlgvDdvKiokDERa5phuN4jH7reKvaBhGIkAQG0eqiVJe4Wvakadq27dnZmQobUSLOON5vdhHA6PPPPwcAZFmiZGx3nH8hTNM0LUuFd12+umKMMUZPT49Dz6+K8vHjx6PhJOyFvTDkXOiE5GkW7XZd1/m9EBGsCDac8/ViabsOxtA0TYik2otwLtTxDyRarFeGbnWCB0GgjlJK6Wq1ahk7Pz+HGJmaaVkOxljT9H/w0x8BACBGEkG1xHpdp5ZNrSbEeVHs43jUH2pYZ4KvdisJYb/fG02GCN1lCEZJrLaUlmUVea6WFGVT7+OIMbbbRR0TaqhMiD6cjA+PjvzQGx1MdUufHE2opKZtWI7BWNvv+2ka8465rts0VErpe2FbM8OwbcPN0wJKoNZpZ2fHgeeenh1vd6uDk2mSJcpEzrtOSrhcruuqRRJhjOeLxdnJ6XR6IDnoqHBsXwq82ew6JqEEz5++sAxzOp0eHR3leW5ZKvhO03TMBM/Lsq5bhEgURRBC1naKo6hET5IDCOF8Pr++vjEd+/T4BGOc5hkxdNW8quFP1wkOpGmaLaOcSwjhdDSM97uw5+umsd/viWb4QZBlGQTYMp3BYHBwdFjXLUDw7PR+lCZ5WU1mM8MwOspWq9W3b2pTo6Io1JwsCAI1hFJDGc55HMfK3wMhVG67IAjUlBhjrGAKYRgqzo+imSq8AsZ4Op2qgL8f/ehHt7e3i8UiiqKW0tFkXFXVRx99ZBiGoscHQQAAODg4UPwc9WdhjPf7vZRSoX5ub28BAGrMrj6/oigU5U+tEJWZ2zRN27Ynk4kyJyoRjG3bahnR6/UUquTb56+qLMuU2ej9D95Tlat61cIwfI0cTJIECJllmTKxAAA2m83JycloNOo6EUWJmtBRSuu6VV9dITpNI5qO77w9daVWl+rv58233xJArtdrhYL72c9+ppwegd9T8NTb21sBpOM4aRqHYXh1dZUkyWAw2EdRUiRu4NqOWVWFwm+9thwCALbb7W63U+5cWtM8y9T+L03T4XCopiu73U5VdEoZCABQjnAp5c3NXNnZVRIYxlghnU3TVJF6Clyi7ISqElDiwLIsVQXi+76yR//0pz9VayAlrTo6OiKEfPzxx1AizqUQQlXGqs1VSnllC1Vh9V3XGd9RqFiu8/8HMr7VSKHqn0YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=227x227 at 0x7F0F2FAACB00>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}