Object Detection using Yolo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Object Detection using Yolov3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Object Detection can be defined as the task of object classification and localization.Object Detection plays an important role in many Deep Learning projects and IoT Applications such as we need object detection for Autonomous Vehicles, we need it for security camera surveillance, and even for applications used by us for generating PDFs nowadays uses Object Detection for detecting documents.In short, Object Detection has a wide range of appliactions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T15:08:44.322241Z",
     "iopub.status.busy": "2020-10-14T15:08:44.322024Z",
     "iopub.status.idle": "2020-10-14T15:08:44.524634Z",
     "shell.execute_reply": "2020-10-14T15:08:44.523818Z",
     "shell.execute_reply.started": "2020-10-14T15:08:44.322216Z"
    }
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the Yolov3 pretrained model and some other necessary files to our workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T15:08:48.102079Z",
     "iopub.status.busy": "2020-10-14T15:08:48.101825Z",
     "iopub.status.idle": "2020-10-14T15:08:59.032156Z",
     "shell.execute_reply": "2020-10-14T15:08:59.031175Z",
     "shell.execute_reply.started": "2020-10-14T15:08:48.102053Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-10-14 15:08:48--  https://cainvas-static.s3.amazonaws.com/media/user_data/arya.ashish/Yolo_data.zip\n",
      "Resolving cainvas-static.s3.amazonaws.com (cainvas-static.s3.amazonaws.com)... 52.219.66.120\n",
      "Connecting to cainvas-static.s3.amazonaws.com (cainvas-static.s3.amazonaws.com)|52.219.66.120|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 462457774 (441M) [application/x-zip-compressed]\n",
      "Saving to: ‘Yolo_data.zip’\n",
      "\n",
      "Yolo_data.zip       100%[===================>] 441.03M  81.7MB/s    in 5.4s    \n",
      "\n",
      "2020-10-14 15:08:53 (80.9 MB/s) - ‘Yolo_data.zip’ saved [462457774/462457774]\n",
      "\n",
      "Archive:  Yolo_data.zip\n",
      "  inflating: yolov3.weights          \n",
      "  inflating: images3.jpg             \n",
      "  inflating: images1.jpg             \n",
      "   creating: cfg/\n",
      "  inflating: cfg/yolov3.cfg          \n",
      "  inflating: coco.names              \n",
      "  inflating: model.h5                \n",
      "  inflating: test.jpg                \n"
     ]
    }
   ],
   "source": [
    "!wget -N \"https://cainvas-static.s3.amazonaws.com/media/user_data/arya.ashish/Yolo_data.zip\"\n",
    "!unzip -o Yolo_data.zip \n",
    "!rm Yolo_data.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the pretrained Yolov3 Model\n",
    "#### Since Yolov3 model has been trained on the MS COCO dataset for thousands of epochs and hours of training, so there is no need for explicit training rather we can just use the pretrained model and its weights provided by [Yolo](https://pjreddie.com/darknet/yolo/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T15:10:32.046595Z",
     "iopub.status.busy": "2020-10-14T15:10:32.046348Z",
     "iopub.status.idle": "2020-10-14T15:10:34.801030Z",
     "shell.execute_reply": "2020-10-14T15:10:34.800049Z",
     "shell.execute_reply.started": "2020-10-14T15:10:32.046569Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n",
      "Model: \"functional_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, None, None,  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv_0 (Conv2D)                 (None, None, None, 3 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_0 (BatchNormalization)    (None, None, None, 3 128         conv_0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_0 (LeakyReLU)             (None, None, None, 3 0           bnorm_0[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d (ZeroPadding2D)  (None, None, None, 3 0           leaky_0[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_1 (Conv2D)                 (None, None, None, 6 18432       zero_padding2d[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_1 (BatchNormalization)    (None, None, None, 6 256         conv_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_1 (LeakyReLU)             (None, None, None, 6 0           bnorm_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_2 (Conv2D)                 (None, None, None, 3 2048        leaky_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_2 (BatchNormalization)    (None, None, None, 3 128         conv_2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_2 (LeakyReLU)             (None, None, None, 3 0           bnorm_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_3 (Conv2D)                 (None, None, None, 6 18432       leaky_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_3 (BatchNormalization)    (None, None, None, 6 256         conv_3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_3 (LeakyReLU)             (None, None, None, 6 0           bnorm_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, None, None, 6 0           leaky_1[0][0]                    \n",
      "                                                                 leaky_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_1 (ZeroPadding2D (None, None, None, 6 0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv_5 (Conv2D)                 (None, None, None, 1 73728       zero_padding2d_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_5 (BatchNormalization)    (None, None, None, 1 512         conv_5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_5 (LeakyReLU)             (None, None, None, 1 0           bnorm_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_6 (Conv2D)                 (None, None, None, 6 8192        leaky_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_6 (BatchNormalization)    (None, None, None, 6 256         conv_6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_6 (LeakyReLU)             (None, None, None, 6 0           bnorm_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_7 (Conv2D)                 (None, None, None, 1 73728       leaky_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_7 (BatchNormalization)    (None, None, None, 1 512         conv_7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_7 (LeakyReLU)             (None, None, None, 1 0           bnorm_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, None, None, 1 0           leaky_5[0][0]                    \n",
      "                                                                 leaky_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_9 (Conv2D)                 (None, None, None, 6 8192        add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_9 (BatchNormalization)    (None, None, None, 6 256         conv_9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_9 (LeakyReLU)             (None, None, None, 6 0           bnorm_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_10 (Conv2D)                (None, None, None, 1 73728       leaky_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_10 (BatchNormalization)   (None, None, None, 1 512         conv_10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_10 (LeakyReLU)            (None, None, None, 1 0           bnorm_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, None, None, 1 0           add_1[0][0]                      \n",
      "                                                                 leaky_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_2 (ZeroPadding2D (None, None, None, 1 0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv_12 (Conv2D)                (None, None, None, 2 294912      zero_padding2d_2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_12 (BatchNormalization)   (None, None, None, 2 1024        conv_12[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_12 (LeakyReLU)            (None, None, None, 2 0           bnorm_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_13 (Conv2D)                (None, None, None, 1 32768       leaky_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_13 (BatchNormalization)   (None, None, None, 1 512         conv_13[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_13 (LeakyReLU)            (None, None, None, 1 0           bnorm_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_14 (Conv2D)                (None, None, None, 2 294912      leaky_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_14 (BatchNormalization)   (None, None, None, 2 1024        conv_14[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_14 (LeakyReLU)            (None, None, None, 2 0           bnorm_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, None, None, 2 0           leaky_12[0][0]                   \n",
      "                                                                 leaky_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_16 (Conv2D)                (None, None, None, 1 32768       add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_16 (BatchNormalization)   (None, None, None, 1 512         conv_16[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_16 (LeakyReLU)            (None, None, None, 1 0           bnorm_16[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_17 (Conv2D)                (None, None, None, 2 294912      leaky_16[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_17 (BatchNormalization)   (None, None, None, 2 1024        conv_17[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_17 (LeakyReLU)            (None, None, None, 2 0           bnorm_17[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, None, None, 2 0           add_3[0][0]                      \n",
      "                                                                 leaky_17[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_19 (Conv2D)                (None, None, None, 1 32768       add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_19 (BatchNormalization)   (None, None, None, 1 512         conv_19[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_19 (LeakyReLU)            (None, None, None, 1 0           bnorm_19[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_20 (Conv2D)                (None, None, None, 2 294912      leaky_19[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_20 (BatchNormalization)   (None, None, None, 2 1024        conv_20[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_20 (LeakyReLU)            (None, None, None, 2 0           bnorm_20[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, None, None, 2 0           add_4[0][0]                      \n",
      "                                                                 leaky_20[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_22 (Conv2D)                (None, None, None, 1 32768       add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_22 (BatchNormalization)   (None, None, None, 1 512         conv_22[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_22 (LeakyReLU)            (None, None, None, 1 0           bnorm_22[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_23 (Conv2D)                (None, None, None, 2 294912      leaky_22[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_23 (BatchNormalization)   (None, None, None, 2 1024        conv_23[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_23 (LeakyReLU)            (None, None, None, 2 0           bnorm_23[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, None, None, 2 0           add_5[0][0]                      \n",
      "                                                                 leaky_23[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_25 (Conv2D)                (None, None, None, 1 32768       add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_25 (BatchNormalization)   (None, None, None, 1 512         conv_25[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_25 (LeakyReLU)            (None, None, None, 1 0           bnorm_25[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_26 (Conv2D)                (None, None, None, 2 294912      leaky_25[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_26 (BatchNormalization)   (None, None, None, 2 1024        conv_26[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_26 (LeakyReLU)            (None, None, None, 2 0           bnorm_26[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, None, None, 2 0           add_6[0][0]                      \n",
      "                                                                 leaky_26[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_28 (Conv2D)                (None, None, None, 1 32768       add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_28 (BatchNormalization)   (None, None, None, 1 512         conv_28[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_28 (LeakyReLU)            (None, None, None, 1 0           bnorm_28[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_29 (Conv2D)                (None, None, None, 2 294912      leaky_28[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_29 (BatchNormalization)   (None, None, None, 2 1024        conv_29[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_29 (LeakyReLU)            (None, None, None, 2 0           bnorm_29[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, None, None, 2 0           add_7[0][0]                      \n",
      "                                                                 leaky_29[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_31 (Conv2D)                (None, None, None, 1 32768       add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_31 (BatchNormalization)   (None, None, None, 1 512         conv_31[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_31 (LeakyReLU)            (None, None, None, 1 0           bnorm_31[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_32 (Conv2D)                (None, None, None, 2 294912      leaky_31[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_32 (BatchNormalization)   (None, None, None, 2 1024        conv_32[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_32 (LeakyReLU)            (None, None, None, 2 0           bnorm_32[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, None, None, 2 0           add_8[0][0]                      \n",
      "                                                                 leaky_32[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_34 (Conv2D)                (None, None, None, 1 32768       add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_34 (BatchNormalization)   (None, None, None, 1 512         conv_34[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_34 (LeakyReLU)            (None, None, None, 1 0           bnorm_34[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_35 (Conv2D)                (None, None, None, 2 294912      leaky_34[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_35 (BatchNormalization)   (None, None, None, 2 1024        conv_35[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_35 (LeakyReLU)            (None, None, None, 2 0           bnorm_35[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, None, None, 2 0           add_9[0][0]                      \n",
      "                                                                 leaky_35[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_3 (ZeroPadding2D (None, None, None, 2 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_37 (Conv2D)                (None, None, None, 5 1179648     zero_padding2d_3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_37 (BatchNormalization)   (None, None, None, 5 2048        conv_37[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_37 (LeakyReLU)            (None, None, None, 5 0           bnorm_37[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_38 (Conv2D)                (None, None, None, 2 131072      leaky_37[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_38 (BatchNormalization)   (None, None, None, 2 1024        conv_38[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_38 (LeakyReLU)            (None, None, None, 2 0           bnorm_38[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_39 (Conv2D)                (None, None, None, 5 1179648     leaky_38[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_39 (BatchNormalization)   (None, None, None, 5 2048        conv_39[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_39 (LeakyReLU)            (None, None, None, 5 0           bnorm_39[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, None, None, 5 0           leaky_37[0][0]                   \n",
      "                                                                 leaky_39[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_41 (Conv2D)                (None, None, None, 2 131072      add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_41 (BatchNormalization)   (None, None, None, 2 1024        conv_41[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_41 (LeakyReLU)            (None, None, None, 2 0           bnorm_41[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_42 (Conv2D)                (None, None, None, 5 1179648     leaky_41[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_42 (BatchNormalization)   (None, None, None, 5 2048        conv_42[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_42 (LeakyReLU)            (None, None, None, 5 0           bnorm_42[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, None, None, 5 0           add_11[0][0]                     \n",
      "                                                                 leaky_42[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_44 (Conv2D)                (None, None, None, 2 131072      add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_44 (BatchNormalization)   (None, None, None, 2 1024        conv_44[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_44 (LeakyReLU)            (None, None, None, 2 0           bnorm_44[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_45 (Conv2D)                (None, None, None, 5 1179648     leaky_44[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_45 (BatchNormalization)   (None, None, None, 5 2048        conv_45[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_45 (LeakyReLU)            (None, None, None, 5 0           bnorm_45[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, None, None, 5 0           add_12[0][0]                     \n",
      "                                                                 leaky_45[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_47 (Conv2D)                (None, None, None, 2 131072      add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_47 (BatchNormalization)   (None, None, None, 2 1024        conv_47[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_47 (LeakyReLU)            (None, None, None, 2 0           bnorm_47[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_48 (Conv2D)                (None, None, None, 5 1179648     leaky_47[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_48 (BatchNormalization)   (None, None, None, 5 2048        conv_48[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_48 (LeakyReLU)            (None, None, None, 5 0           bnorm_48[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, None, None, 5 0           add_13[0][0]                     \n",
      "                                                                 leaky_48[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_50 (Conv2D)                (None, None, None, 2 131072      add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_50 (BatchNormalization)   (None, None, None, 2 1024        conv_50[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_50 (LeakyReLU)            (None, None, None, 2 0           bnorm_50[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_51 (Conv2D)                (None, None, None, 5 1179648     leaky_50[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_51 (BatchNormalization)   (None, None, None, 5 2048        conv_51[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_51 (LeakyReLU)            (None, None, None, 5 0           bnorm_51[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, None, None, 5 0           add_14[0][0]                     \n",
      "                                                                 leaky_51[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_53 (Conv2D)                (None, None, None, 2 131072      add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_53 (BatchNormalization)   (None, None, None, 2 1024        conv_53[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_53 (LeakyReLU)            (None, None, None, 2 0           bnorm_53[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_54 (Conv2D)                (None, None, None, 5 1179648     leaky_53[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_54 (BatchNormalization)   (None, None, None, 5 2048        conv_54[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_54 (LeakyReLU)            (None, None, None, 5 0           bnorm_54[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, None, None, 5 0           add_15[0][0]                     \n",
      "                                                                 leaky_54[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_56 (Conv2D)                (None, None, None, 2 131072      add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_56 (BatchNormalization)   (None, None, None, 2 1024        conv_56[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_56 (LeakyReLU)            (None, None, None, 2 0           bnorm_56[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_57 (Conv2D)                (None, None, None, 5 1179648     leaky_56[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_57 (BatchNormalization)   (None, None, None, 5 2048        conv_57[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_57 (LeakyReLU)            (None, None, None, 5 0           bnorm_57[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_17 (Add)                    (None, None, None, 5 0           add_16[0][0]                     \n",
      "                                                                 leaky_57[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_59 (Conv2D)                (None, None, None, 2 131072      add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_59 (BatchNormalization)   (None, None, None, 2 1024        conv_59[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_59 (LeakyReLU)            (None, None, None, 2 0           bnorm_59[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_60 (Conv2D)                (None, None, None, 5 1179648     leaky_59[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_60 (BatchNormalization)   (None, None, None, 5 2048        conv_60[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_60 (LeakyReLU)            (None, None, None, 5 0           bnorm_60[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_18 (Add)                    (None, None, None, 5 0           add_17[0][0]                     \n",
      "                                                                 leaky_60[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_4 (ZeroPadding2D (None, None, None, 5 0           add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_62 (Conv2D)                (None, None, None, 1 4718592     zero_padding2d_4[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_62 (BatchNormalization)   (None, None, None, 1 4096        conv_62[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_62 (LeakyReLU)            (None, None, None, 1 0           bnorm_62[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_63 (Conv2D)                (None, None, None, 5 524288      leaky_62[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_63 (BatchNormalization)   (None, None, None, 5 2048        conv_63[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_63 (LeakyReLU)            (None, None, None, 5 0           bnorm_63[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_64 (Conv2D)                (None, None, None, 1 4718592     leaky_63[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_64 (BatchNormalization)   (None, None, None, 1 4096        conv_64[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_64 (LeakyReLU)            (None, None, None, 1 0           bnorm_64[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_19 (Add)                    (None, None, None, 1 0           leaky_62[0][0]                   \n",
      "                                                                 leaky_64[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_66 (Conv2D)                (None, None, None, 5 524288      add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_66 (BatchNormalization)   (None, None, None, 5 2048        conv_66[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_66 (LeakyReLU)            (None, None, None, 5 0           bnorm_66[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_67 (Conv2D)                (None, None, None, 1 4718592     leaky_66[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_67 (BatchNormalization)   (None, None, None, 1 4096        conv_67[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_67 (LeakyReLU)            (None, None, None, 1 0           bnorm_67[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_20 (Add)                    (None, None, None, 1 0           add_19[0][0]                     \n",
      "                                                                 leaky_67[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_69 (Conv2D)                (None, None, None, 5 524288      add_20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_69 (BatchNormalization)   (None, None, None, 5 2048        conv_69[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_69 (LeakyReLU)            (None, None, None, 5 0           bnorm_69[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_70 (Conv2D)                (None, None, None, 1 4718592     leaky_69[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_70 (BatchNormalization)   (None, None, None, 1 4096        conv_70[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_70 (LeakyReLU)            (None, None, None, 1 0           bnorm_70[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_21 (Add)                    (None, None, None, 1 0           add_20[0][0]                     \n",
      "                                                                 leaky_70[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_72 (Conv2D)                (None, None, None, 5 524288      add_21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_72 (BatchNormalization)   (None, None, None, 5 2048        conv_72[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_72 (LeakyReLU)            (None, None, None, 5 0           bnorm_72[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_73 (Conv2D)                (None, None, None, 1 4718592     leaky_72[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_73 (BatchNormalization)   (None, None, None, 1 4096        conv_73[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_73 (LeakyReLU)            (None, None, None, 1 0           bnorm_73[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_22 (Add)                    (None, None, None, 1 0           add_21[0][0]                     \n",
      "                                                                 leaky_73[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_75 (Conv2D)                (None, None, None, 5 524288      add_22[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_75 (BatchNormalization)   (None, None, None, 5 2048        conv_75[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_75 (LeakyReLU)            (None, None, None, 5 0           bnorm_75[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_76 (Conv2D)                (None, None, None, 1 4718592     leaky_75[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_76 (BatchNormalization)   (None, None, None, 1 4096        conv_76[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_76 (LeakyReLU)            (None, None, None, 1 0           bnorm_76[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_77 (Conv2D)                (None, None, None, 5 524288      leaky_76[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_77 (BatchNormalization)   (None, None, None, 5 2048        conv_77[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_77 (LeakyReLU)            (None, None, None, 5 0           bnorm_77[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_78 (Conv2D)                (None, None, None, 1 4718592     leaky_77[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_78 (BatchNormalization)   (None, None, None, 1 4096        conv_78[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_78 (LeakyReLU)            (None, None, None, 1 0           bnorm_78[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_79 (Conv2D)                (None, None, None, 5 524288      leaky_78[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_79 (BatchNormalization)   (None, None, None, 5 2048        conv_79[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_79 (LeakyReLU)            (None, None, None, 5 0           bnorm_79[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_84 (Conv2D)                (None, None, None, 2 131072      leaky_79[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_84 (BatchNormalization)   (None, None, None, 2 1024        conv_84[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_84 (LeakyReLU)            (None, None, None, 2 0           bnorm_84[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d (UpSampling2D)    (None, None, None, 2 0           leaky_84[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, None, None, 7 0           up_sampling2d[0][0]              \n",
      "                                                                 add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_87 (Conv2D)                (None, None, None, 2 196608      concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_87 (BatchNormalization)   (None, None, None, 2 1024        conv_87[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_87 (LeakyReLU)            (None, None, None, 2 0           bnorm_87[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_88 (Conv2D)                (None, None, None, 5 1179648     leaky_87[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_88 (BatchNormalization)   (None, None, None, 5 2048        conv_88[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_88 (LeakyReLU)            (None, None, None, 5 0           bnorm_88[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_89 (Conv2D)                (None, None, None, 2 131072      leaky_88[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_89 (BatchNormalization)   (None, None, None, 2 1024        conv_89[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_89 (LeakyReLU)            (None, None, None, 2 0           bnorm_89[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_90 (Conv2D)                (None, None, None, 5 1179648     leaky_89[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_90 (BatchNormalization)   (None, None, None, 5 2048        conv_90[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_90 (LeakyReLU)            (None, None, None, 5 0           bnorm_90[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_91 (Conv2D)                (None, None, None, 2 131072      leaky_90[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_91 (BatchNormalization)   (None, None, None, 2 1024        conv_91[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_91 (LeakyReLU)            (None, None, None, 2 0           bnorm_91[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_96 (Conv2D)                (None, None, None, 1 32768       leaky_91[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_96 (BatchNormalization)   (None, None, None, 1 512         conv_96[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_96 (LeakyReLU)            (None, None, None, 1 0           bnorm_96[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, None, None, 1 0           leaky_96[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, None, None, 3 0           up_sampling2d_1[0][0]            \n",
      "                                                                 add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_99 (Conv2D)                (None, None, None, 1 49152       concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_99 (BatchNormalization)   (None, None, None, 1 512         conv_99[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_99 (LeakyReLU)            (None, None, None, 1 0           bnorm_99[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_100 (Conv2D)               (None, None, None, 2 294912      leaky_99[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_100 (BatchNormalization)  (None, None, None, 2 1024        conv_100[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_100 (LeakyReLU)           (None, None, None, 2 0           bnorm_100[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_101 (Conv2D)               (None, None, None, 1 32768       leaky_100[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_101 (BatchNormalization)  (None, None, None, 1 512         conv_101[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_101 (LeakyReLU)           (None, None, None, 1 0           bnorm_101[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_102 (Conv2D)               (None, None, None, 2 294912      leaky_101[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_102 (BatchNormalization)  (None, None, None, 2 1024        conv_102[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_102 (LeakyReLU)           (None, None, None, 2 0           bnorm_102[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_103 (Conv2D)               (None, None, None, 1 32768       leaky_102[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_103 (BatchNormalization)  (None, None, None, 1 512         conv_103[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_103 (LeakyReLU)           (None, None, None, 1 0           bnorm_103[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_80 (Conv2D)                (None, None, None, 1 4718592     leaky_79[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_92 (Conv2D)                (None, None, None, 5 1179648     leaky_91[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_104 (Conv2D)               (None, None, None, 2 294912      leaky_103[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_80 (BatchNormalization)   (None, None, None, 1 4096        conv_80[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_92 (BatchNormalization)   (None, None, None, 5 2048        conv_92[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bnorm_104 (BatchNormalization)  (None, None, None, 2 1024        conv_104[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_80 (LeakyReLU)            (None, None, None, 1 0           bnorm_80[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_92 (LeakyReLU)            (None, None, None, 5 0           bnorm_92[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_104 (LeakyReLU)           (None, None, None, 2 0           bnorm_104[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_81 (Conv2D)                (None, None, None, 2 261375      leaky_80[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_93 (Conv2D)                (None, None, None, 2 130815      leaky_92[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv_105 (Conv2D)               (None, None, None, 2 65535       leaky_104[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 62,001,757\n",
      "Trainable params: 61,949,149\n",
      "Non-trainable params: 52,608\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.load_model('model.h5')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the model and its configuration for our object detection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T15:11:47.819490Z",
     "iopub.status.busy": "2020-10-14T15:11:47.819232Z",
     "iopub.status.idle": "2020-10-14T15:11:47.919461Z",
     "shell.execute_reply": "2020-10-14T15:11:47.918712Z",
     "shell.execute_reply.started": "2020-10-14T15:11:47.819464Z"
    }
   },
   "outputs": [],
   "source": [
    "# Loading Yolov3\n",
    "net = cv2.dnn.readNet(\"weights/yolov3.weights\", \"cfg/yolov3.cfg\")\n",
    "classes = []\n",
    "# Reading the class names which we are focussed on predicting\n",
    "with open(\"coco.names\", \"r\") as f:\n",
    "    classes = [line.strip() for line in f.readlines()]\n",
    "layer_names = net.getLayerNames()\n",
    "output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n",
    "# Defining font and bounding box colour\n",
    "colors = np.random.uniform(0, 255, size=(len(classes), 3))\n",
    "font = cv2.FONT_HERSHEY_PLAIN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T15:12:39.352004Z",
     "iopub.status.busy": "2020-10-14T15:12:39.351748Z",
     "iopub.status.idle": "2020-10-14T15:12:40.252262Z",
     "shell.execute_reply": "2020-10-14T15:12:40.251635Z",
     "shell.execute_reply.started": "2020-10-14T15:12:39.351978Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected object in Image\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f30585c23c8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loading image\n",
    "img = cv2.imread(\"test.jpg\")\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "frame = cv2.resize(img, None, fx=0.4, fy=0.4)\n",
    "\n",
    "height, width, channels = frame.shape\n",
    "\n",
    "# Detecting objects\n",
    "blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\n",
    "\n",
    "net.setInput(blob)\n",
    "outs = net.forward(output_layers)\n",
    "\n",
    "# Showing boxes on the images whose confidence of predicting an object is higher than a particular threshold\n",
    "class_ids = []\n",
    "confidences = []\n",
    "boxes = []\n",
    "for out in outs:\n",
    "    for detection in out:\n",
    "        scores = detection[5:]\n",
    "        class_id = np.argmax(scores)\n",
    "        confidence = scores[class_id]\n",
    "        if confidence > 0.2:\n",
    "            # Object detected\n",
    "            center_x = int(detection[0] * width)\n",
    "            center_y = int(detection[1] * height)\n",
    "            w = int(detection[2] * width)\n",
    "            h = int(detection[3] * height)\n",
    "\n",
    "            # Rectangle coordinates\n",
    "            x = int(center_x - w / 2)\n",
    "            y = int(center_y - h / 2)\n",
    "\n",
    "            boxes.append([x, y, w, h])\n",
    "            confidences.append(float(confidence))\n",
    "            class_ids.append(class_id)\n",
    "\n",
    "indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.8, 0.3)\n",
    "\n",
    "# Building the boxes on top of the image\n",
    "for i in range(len(boxes)):\n",
    "    if i in indexes:\n",
    "        x, y, w, h = boxes[i]\n",
    "        label = str(classes[class_ids[i]])\n",
    "        confidence = confidences[i]\n",
    "        color = colors[class_ids[i]]\n",
    "        cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)\n",
    "\n",
    "print(\"Detected object in Image\")\n",
    "plt.imshow(frame)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T15:13:14.350903Z",
     "iopub.status.busy": "2020-10-14T15:13:14.350508Z",
     "iopub.status.idle": "2020-10-14T15:13:15.254993Z",
     "shell.execute_reply": "2020-10-14T15:13:15.254162Z",
     "shell.execute_reply.started": "2020-10-14T15:13:14.350876Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected object in Image\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f30585ad390>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loading image\n",
    "img = cv2.imread(\"test.jpg\")\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "frame = cv2.resize(img, None, fx=0.4, fy=0.4)\n",
    "\n",
    "height, width, channels = frame.shape\n",
    "\n",
    "# Detecting objects\n",
    "blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\n",
    "\n",
    "net.setInput(blob)\n",
    "outs = net.forward(output_layers)\n",
    "\n",
    "# Showing boxes on the images whose confidence of predicting an object is higher than a particular threshold\n",
    "class_ids = []\n",
    "confidences = []\n",
    "boxes = []\n",
    "for out in outs:\n",
    "    for detection in out:\n",
    "        scores = detection[5:]\n",
    "        class_id = np.argmax(scores)\n",
    "        confidence = scores[class_id]\n",
    "        if confidence > 0.2:\n",
    "            # Object detected\n",
    "            center_x = int(detection[0] * width)\n",
    "            center_y = int(detection[1] * height)\n",
    "            w = int(detection[2] * width)\n",
    "            h = int(detection[3] * height)\n",
    "\n",
    "            # Rectangle coordinates\n",
    "            x = int(center_x - w / 2)\n",
    "            y = int(center_y - h / 2)\n",
    "\n",
    "            boxes.append([x, y, w, h])\n",
    "            confidences.append(float(confidence))\n",
    "            class_ids.append(class_id)\n",
    "\n",
    "indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.8, 0.3)\n",
    "\n",
    "# Building the boxes on top of the image\n",
    "for i in range(len(boxes)):\n",
    "    if i in indexes:\n",
    "        x, y, w, h = boxes[i]\n",
    "        label = str(classes[class_ids[i]])\n",
    "        confidence = confidences[i]\n",
    "        color = colors[class_ids[i]]\n",
    "        cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)\n",
    "\n",
    "print(\"Detected object in Image\")\n",
    "plt.imshow(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T15:13:41.351563Z",
     "iopub.status.busy": "2020-10-14T15:13:41.351305Z",
     "iopub.status.idle": "2020-10-14T15:13:42.277798Z",
     "shell.execute_reply": "2020-10-14T15:13:42.276841Z",
     "shell.execute_reply.started": "2020-10-14T15:13:41.351537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected object in Image\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3058514438>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9d5Rl2X3fh372yTfHyqGrOndPnsEkzAAYZIAJBEmRFCVKlO1HJdvLz5Is2s+2/JappWR52U/RXCRFkRJJESABMCAQeTCYPNMz09M5VHflqpvjyWe/P869lbq6p2cASMOl/q3VXffes9PZZ5/v/u3v77d/W0gpuSN35I7ckTvyZ0eU/9QNuCN35I7ckTvy9uQOcN+RO3JH7sifMbkD3HfkjtyRO/JnTO4A9x25I3fkjvwZkzvAfUfuyB25I3/G5A5w35E7ckfuyJ8x+YEBtxDiE0KIC0KIy0KIX/pB1XNH7sgduSP/uYn4QfhxCyFU4CLwUWAZeAn481LKs9/3yu7IHbkjd+Q/M/lBadyPAJellFellB7wu8CnfkB13ZE7ckfuyH9Wov2Ayp0ClnZ8XwYevVli3TBkKp3E9z2CIEDVVBgsBIQQ8T8EiqKQTmdwvTgdCIIwwNB1dN0AIIq2VxCWlUDTdYRQCSOJaVlEUYTn+0RhiGnGeVRFJYwC7H6Peq1K4LukU0nCIMAPfIIgQFFUVFUFFAzDwLQsmo0WxWKBVquFYRhkc3l0w8C2+yBDLMtEIFhdW2dsfBIhFBRFQ1FVFEVBAkHoowCO7dCo1cjlc2iGEbfTdbHtPoahMzo6jpTKoFsEUkoQ37fndfuyt84dCzYh9k+081ucZu8q723cyDu451tn2XlVxi2TN7m8V265WI0vyh3j+K1kmOZWq+C91269Xr7Jw5JbrbtJut3p5Y0/3d5zeFuduSPb4B5v1mdy+wZuLbsf7e3U/LYLuC3CQrB/N7xF3sr6Cp1Wfd9O+EEB91uKEOIXgV8ESCST3PfIA2xsrtFqNdA0hVy+QKvR4fix4xycm8cyTTYrFWzXw/E8IikxTYtjx48SuBFCqGi6SSKRIJ3Ns3BtEUXTSacz5PIlcsURxqYPkEhncF2PIPDJZlKcO3OGyfEJMkmLc2de5x/88t9Deh3Spko2mwQZ0Ot1ufvue6nUWkipkkhlSSQynDt3kRPHDnP58hU0w+LwsRN86KMfx+m3+dMv/yEz02NMTIzzp19/nn/8T/8ZKBZCMYmkguP7qLpGQJ/1xWtcP3+ehfMXuOvBeynNTpGyUqwtL/Pma68xNTPO0WMPUi4dwEzkQNEICIiIQEj2ju/bAYktkYJbvrhi9+jaW/ZOIFEUZWui3fqNwcQrIwSgKqAOyoy2S71JHfHktLPKW93bfoC3tz37XR9KFEU3LWP/CmHnojWKtu8oDMOtNm0pH4NybtUmIeJJOf43TDP8LSIMI7YAVYk/DdND/Ay2RWXYt9tlbv8LZXTL9gz7Y2efyO2ZCKnsyCPlLhxSJMg9+YfjY5A8frxC2dWmnXXcCrjjNNG+14eyM/9bUcL71b+zL/fLv1//7Pd+7LyfnX29c7zsd6//n1/89E3b+4MC7hVgZsf36cFvWyKl/BXgVwAyuazc3NhA03RGy2M0Ww36HY/pyYPoapLqZgsEpNJJwkhgJRJoho6VMBEKHDx0EN2w2Nyssra2wYGDR7CSOS5fuUoQRui6QiKh02xUkSjouoGlm/iOC2HA0vUr2P02lfVVHrz/XhYunUOJAmZn5okilzNn3qBWa2D3XY4cO4luJGk2uzz55PvI51Ksrq9jJdOMjI1RrzfJZhJ0ul1On65y5coC1c067XaXbC6BQEFVNVKGQavTpt2vcm3hEutri+iGpNnc4NA9R3H6LgnL4tixY1xeuETffoP7708zMpZAN1WEIojCEEXcCnhvR+SeT3uA+nso+T+23OzlvNnvQ5DcmW7ny/tWoH877Xm7NqTdIDasf9imCCkHwC2IJ9132j4BinjnTKlg95wuuWGOf9vy/ejz/xjyVmA9TPODlB8UcL8EHBFCzBMD9s8CP3ezxGEQYPd6TExMkM1mcWyXXscllymhCI1u1yUIPLLZArlcAjOho5s6iirY3NxgZuoA6UyaRrNFu9OhWmtQKo+Tz5cwLZNiaYRUKk2r5xGFEqEpCCnwHYdsKsOrr36XleXrRIHHofk5dCJcu8fo6ATdToMoglarjWYkGB0ZI5cv0yr0GBmZoFjK8MJLL5FMp5mcmiKdSSNUKJXHuHrpPGurG2Sy47RbHTQtjapLFM1AN02CIMB3+2xurLCxsUw+abK8cpX7/IcI/IBkwiJtTfLCyy+QSo0RhCFhFKGJ+MWVRMRa1ba87UG/g7oQ7Dfg3l55e+u/ZXukRA7WkTfTjvZm36uN3Y4mdav0t6uN347sTD+cFG7Vzrd+4fd+3qll3pj3dtt7s1XTrfpyv+ezq5T9sr7l/e2eOHauSN7que5lH76fcqtV5c3S7pfmZhPR9wPUfyDALaUMhBD/NfAVYmT5dSnlmVvlKZdKFPJ5TNNEV3RUJeLsmbM8+MBDnLjnOKqm0Gw1CSMPXTdQVIVOp82lS5dYW9tkauoAxdIYUzOzfO5zf8jUzDwf+MCHmJmdJZFI4nkh+WIO00wR+iGe4yIEHJia5fd++1fo9ztYpsHCtS4f/8iHuHr5CpcunGdzfZVCocjM9AGSqSyeFzA2NsFTH7yHX/u13+DRxx6g3+/Sc10azRaf/qmf5FvfeJoPfeijJEyDq1eu8MM/9jNk0hls2ybsu/hhSN91mJyZ5OjYAU6/qOG5HSIr4vLla7z04nO899GnyKULrK2sMTY+yS/8lb9EqxPh+QIUgRQBqq6goAxeoHeipcgd/2LZOca2l+o3B4W9y7/90sZfRayl7VDL5JADvU3AudkSeqgBvdVLIkRsJ7mZJvy9anu7KKIBLbCXBrhdqmcXXbT1G6iqug20Srzauh2Nb++97aVKdi7b98uzc3kvpdw7bPY2dd8691ILQ41/v36/GW0Fg5EuQKBsDfu30oJvhzbZe6970+4E6dsp/63G0m5aa78G3fzSD4zjllJ+Efji7aQNgoC5mQN0um2uLl3HsXtEQcj8/FGk9FhdWyadyZBKpXju+VN87BMfZ2JqksXFRT74wUkWlxdZXllhaXmdyakD/O3/4ZewEhlq9TZRpGJZGVQ1JAgl/V4PVQh0NUTRAvIpjcjzaFZrJJMJRkpF/sE/+PskTQMhIJ1JMTIyxiuvnKI8Mkk2V0UoFiOjU/zYj/4Yf/LlzxPIiJMnTnD/gw/y+umLlEcnGCvnWF5e4urCdRr1Kiuryzz63ifp9mx6jsPRsYPUmlU0PcEP/fAPUc4k+Le/+i8JNHj2mWc4dvBeyvlRstkkU9Oz1Jt9DDOLoSp4UUhENNC4Bd+bc9B+b+DOa+pNrsXyg1zW3pRavgm9sR843WxiuV2N/e3ITvAbAs9wstipRd5+n+0AUyHjmU7sfF4K7xYyS9yGvfDdToF8P+UHTZ/8JzNO7hRN1XjzzTcxDBXD1MgX0tRqdTYr10hnTPr9kLPnTvOpH/80uXyey1cWuHDxCsvLy8wcmOVnf/6nWFpcodf3yeRKJNMpNDVBNqMQyZBafYNOt0uuWMRzbbJpk163wRd+/zP0ux1+4S//Fd5843Xa7QYf/ND7uXLpLP1+B89z6W20WF9dJZPOYpomhUIJwzDY2Nik17X52Z/9WS6cf5Nuu4XnB/j9DqdPv0Fgd+l26hw8dBjT0rlw4Sxe4DE1M4lQBV/+yvM89MiDvPjCGyQVSa/bxXM9JqZm+IX/4r/iwNRBNtbqvPbaaS4vX+WxJz5KBERbxqGYKpFS/R5e3VuB9o1yK637Vtffjuwu4+bLz73L+52a7V5Q3gng+2lL3y+5mbb/dmia7TQSRLQ9eUlARNtWvR8AYL/dJb2yh8nZj9jZ28xdxsk/w/JWK7u98v0ed+8K4JZI+o5NIpXHSibZ2NhAKAIvcGi0NjBdi4g+9eY6mi6IopAwBFU1iCKV69dW2KhU6fYc1EqTTsflvvsfxbBMwiAginwK+Ry+53D5whmSCYFjN3jzzPOMj43Raq+TyZn4oc4rr75IPp+n3W4RhgLQUVSFfHEU3w+pNxuo168RScHJk3eRSqVRFI10MsV4ucj6epX5mQkWLl8gCgMEEkWAokQ06puYpoJu6oRul8Dp0WlustGqsbSygJHQyGTSJCyDXr9Do90iFPD4e59AVVWCICCUggiJUEFGsRYmhdz3Nb7Zu7E1gCQIBppc/Gnwsm1r8IIIMaA54vQDY6gQMPBqiS+og6WvJIpCIEIoAoiX9iL2BSCSoKmCMAwQQkUo8T9FEQSBOyg6bl8QRijKjvoEyDAeLwJla8k+dA+LIjlYQsdtklvt3Q34N6NE9tOK31pL2k0xbPtWDNsgB5q4GLRbDGBX3ABeu43DA/Z/x9wq5ZB6GSbZyXkDYuvpEMHAkAliQHlFew3PcjvtVl23scqJM3ODt99Ov5K919hRx9ZcJKNBj9xY7/bjGd7vjt4ZPvPt3LvaeSuOfOcEfjO5YWU0YPOGQ31ITm41cg9NM2zp3hrEjv8ioh0z22B879Sj3sLS++4A7sESUFFVhKLguh6KEqBoCq1OFd01MEyTtfXr6KaGpqskk0kyuQL5fJFu16PZ6OIFAcmUxvr6Gkf6XSQ6RKAqgmQyRbfbIgw8Fq4uU6ksghIihcebZ08hI0m/16fdauP7AUgFRdEBBYRCGIEUCr1+D1GrYJgmd911nE67jSI0VEVFRAHFXJLK2nXazRrJhEGxVEJRBN1mE1UROLk0ikiiEtCsbGCoEUvVNSq1DVK5NCBZW1kmlfZod1ykIjl85DCGqeF5EYN3EYWBxnPL57vP4NkJToOXaLjMlWIAF0NvBQGqDAcfxVZdYmsExq99/DLFL5EYrgQI9nDvMchEUYRUVSAEoRL7tquoagzUAyIcUFBVhVAGiNipkBinxbZ2twVgYquG7f+2X/Sd4L3z7+3wzW9tmNrnhZXxJDVEqmif+uQtteZh3Wz14XYz4slxu9pB2bvaNPwegYynijj/tmFTIFCkMjDyDSfut77nYR1SSnaYQOIe33lLw7bvLErsmCB21boXfnekZU8hw77d+np7muzeift2ZAtD2X5XhvcV7UxxQ3dJdrdy+14E8QwgCAf9IBAixpjdj3WoUO0v74ogU1JGKKrA8x0cp0c2n8ALbRJJFS/o0es3QQlYWVkgnbZQVUmxnOd973uCRx97lIcefJSJsUmOHz3Gxz7yEcojRer1Cmuri1Rrm/T6Nn3bI5Mp8MADD+O6IRcvXuXhR95Lo9nm1Vdf5qtf/VNeevklEokEy8ur6JqFoScQQsVxPVZXVklnMiSTSTRNxQ9cLl2+wLWFBVRVpVGrc/b0G4yWCnzu93+Pb33r6xTyeT7x8Y+jajpnzpymUa9gGRq5TIpeu8W1K5fJZ7OEYYDne+SLRdrtFq+depWFa1eoN6vYbodarYKha+iaiqaAIiIUBLqqxYNwC1BvbwBvfWagxW2BsAIoxPqxQAoFKZRYkxtqqUIgB5OZEEoMqhJEJCGSqEKgCdBEhCrCmOMVMfQy0BgDP0AR8Sar4aohDHx8zyMKIqJQIFBJJtLIKHZ/C4IQ3wti3+Aozid3vVk7MH/rft7Z0nRIuew0pu39d5OcO67H/vVCiTnu4T+hbPelGM6PN/wbbjpTiG0MMZcd96Iy+H0boLdAe8c/hSHYDCexaPB0t5+wOlidDH9XFbGlREa3MOAO/woh4vtRxFt2d3w/OxOIHfd6s0w76rmNsX0zO8ZQbmcy3i27Vz9i680AIokM5WAcDpSW4YS44ykN/4kdZShIVHxU4Q7+eWj4qMJHFQEKHor0Bs9uf3lXaNymaaIoAlWDQinNRCKHvNri+IlZ+j2HdquP64asri6TyeYZHZvBtptcunSa2QMHufeek5w/l0TTNExDpVZd48EH7ue1106TSBUYn5zCMJOgqmTzo/zUT/8lfuhHPkW/32Bzs8PZN96gut7Gcx3aDZu7776Hl196GRlFpNIZpqenmZufZ2xsjJWVFarVKsvLy+TzeS5dukyj0cTzfF584QWefvppMpkc+b5Dv++wuLhCv+cwMjKK67pomoqMQt584w3e+8R7Wbq2wtK1NerVNuVilvGxcTrtBoYumJiaJJnKMjU5xrWrFymWxzHMJEoIvu+i6CqKiIfEzgG+7a4ltjwHhrJrMAuBFPrg87bmNtQWhYi2lsSRGLyZMtYQ4qGqosiBZ8DgpRcE6LpAKCqxhi2QUYSqCAzdRNMSOL0+pqlj2y6aLrGsiKtXl/nqV/+Uj33sY0xMzKAqBkoUe82EfogQCpqqogiNbYSIduk1u1/UdwbacPMX+614zJvJO+Y290zKe7XFt2Ps2ksPiIH6GFMAw0oGoD9Q6nep1HDLuhVF2e2ZIth3tb9rRTDwqthvMlR2XNubf6fHyV5D8zDfXqprLw22X7m7fttaLgwpmfjHYb8pe5mNXV21RdZt/TJcZcQTmETBJ7ZZgDKcoMVg/TpcCd/YfVvyrgDuRCLJ9PQ0iZRKKq1Tqa5gOw6qClEY0O926XYcBFCvVVhdXSeVzjI/f4h6fYM//Nyf8PAjD3PirmPIwGFmapRf/X/+Oe9//4eYmRlHtzSEBu12l0I+i5XIYFoJSqUyf/kv/1X+3v/4d1F1Cx2FdqvHpYuXSSYS9Hs9et0OiqKSy+dRFIWN9XUUVWV+fp5SqcS3v/0t+r0+nbZBKmnwN/+b/5b/+m/+tzz++BNUqw2e+e6z/Be/+Is06nWqlU1SKQvfszlx4hi5dIovfflZNjeqKKjICCprm8wePEYYBLhODyEEX/zjP8L3Qj7y0Y8zNj5JJEN818FSUkih7us1AfHA2bvJYldaOZj/pUQSDgZvgFCiwdDb9lrZ4pVRkDJEUw2EHGhqAlQ1AhEQSheFEGRAFHlAH8syEAiiIMDuBxTzWZ5++hscPnIQoZhcvLTMZz7ze3zsYx+j014C2aVYGCWdziGkh2XoEEnCKCAIXBSG3DgoSsybAyhKDOo77Xt7l9U7++FWxsJbgfdtL7X31Pf2AXx3e96ZZ8qO0vYbI3IwPwy584HWrwyX7nL//Hvlhn4ZlnWr9gxcM99O+2/V//vZMHam3el+t9cVb9sWsX0Du1q2a1GxzcvvJL+2EjI0KostwN5bVoS+9VkOCorfw4EShIK8RX+/K4Db9wMM3aJRr1Cr9cnkLJIJi2qlSbfVh1BhYmyKZqsNkcB1bHq9Hnavy8TENEQJOq1NLp5zCaKQUEqeePxxCrksnXaLRtemWBqlXq9D5GIaOoqI8P0+3/3udwhCDythgWVgaBrdTgdVUTh+8iTl8ghSKFy+coUgCBifmGBkZITx8XHGx8cp5HNks2lAoOnxw1AVlfc+/gSlchk/iujZDisrqywsXMZxejj9Lq1GjXI+x8PveYiFq3nW15Zx7A6aqlOt1BgZnaHf6WP3fQ7PzXLm7Flcu0V1I2Jjs0a90eb+Bx5G0TW41Quw59nvSicH26qFQBEKYeghlABFRiiKRFFEvGFJKEShJJKDpaBQUVQFTQEViSoiFDWiVl9jbLTEpcvnqVTXSaYsvvH0V/jQUx+k0+4QBSHZTJbqZgXf89msXEC3NAxT58iRMkeOlHn9tTdptgxcdwIhDEbHDqCrCVRNxVQ0VNUgHNIrEvwgJPR8kqkUgR8iiTdXxSAk9r4yRFG0pbXtJ29Nh9yYdt9ufwtD6NuRW/mdvx3ZqYXGbYNtPnbIyg4BL6bR9qMbhm0Z9uXtule+ndXC3uu363u9t70366Nb1z+kugb2GzFQYITYsjsMQXZQSbyDWRBTewKQETIKUVUtHmtbK6e4jyNhbdtCYCvtwJCzg77cX94VwK0qKtlMgU63jt33yGRTZDJFWg0XpxegoiGkClLBcz0Cz8PzPFqBD0jy2VHarQqO26LX65NIZnj04SdxnAA7iBD49LoNfK9P4BuowiMKPZqtKteuXSCR0HGTOp7noamCUqlIu9ViZGSEw0eOoqg6r546hWmazM3Noes6a2trRFHE4SOHWV1dIQgjsrkcQigUSyXS6Sz5fBHH96hUazRaLRCCRr3O+uoSnVaDw/PT5AtjjI2MIGRArSJpNpvoqkHgSwIvwrQUVCRJS6PTqtFuNVhd26TX94kCD6Fve5TsPxjFrj+7QGBguIqHSIQqIgQBqog/Sxlh9x3SmSwIQRTG/LKqiAEXF+I6HRy7jeO0WVy+jKYcZ231ElcXrhCEIade+Q7Hj0zTbrUJg5BsOsupV15jcmIS2+2BIkmmLLK5FI36Mt3uBv2eQ7W6hO9LZmeqzM8fRFVUPM8nDCWe56MIhWJ5nEQqRxhIZBRsrV2ljCmdOJbHjX1ys5d2COZ7fcP3k53gtdXTe+iq/TTc71Vup223X9aw7cPVFXuw4tYa9vDvXtDcpureqv7b17aH5d/uxHA79dyy/oE1UmxZWWGLw5Bs80A7FxlSEslokEzGFJSMUMS2G68yoFukFChSEMmIaBDHR4gIGYZx2kHem8m7ArgTySSjo5O4Xg9FBbvvk0qV6DbrhL7ED0NWlzdiRlNIXM9F1VQSVoJGo0a30aRUTJEvFAmDgFYzIPB9FDSKuTRq0uL69cXYmBCa+F6E63Rp1pZJJmB0PIfnd7Arbfo+3H/PA1y+dBVFqKiKRiqdxvM8kskUiUSC9bU1nnvuOUrlMj/+6R9hcekaUSQpFgpk0mmmpmZYXl6hWmsgVYXCxDi6afLgQw9xRleobqzSqNdYX1vlysJ1xkZGGB0pYWqS69euce8Dj2OZaRJWhvGxMV5//TkSKYNadQ3XDeh0bDLZkXh230PL7QNTu/7s/SKERMgIpI+lQxj6hF6fUPqEoU+90iRhKFhmAqEryAiECFBVge91Wd+4xNLiedbWl1jfWKLdWaLdarO6usSlS5fJZ1K0GlVURUPKiM31dVaWlnD6fXL5HJ7vcG2hTipt0GnVKBSKNBoNlpZWCEI48+ZpPvbRTxBFko2NDSqVGnbfxjQTvPeJD3P0+H1xKAA7RNcSA2O8AKmywy1jS7Z3Tt7QKfvwwPtrcfD2Qed7lf0Ma9+bb/AOPmmP18Zuwvb27nPfyWpv1oGWuqVpyhs56luVv7uene0TOz5v5Rik54brN12c3mAjGfTFFk0iYFC32MMDxYGvooF2HRt+NU0hDANUVUWIwXpGSJAC3Q9QFYUoCvADD1VVIAzxPI8oCOJJVYY37Y93BXDbfRvbtnFdn2ajS7fbQtPbFPPj+G4Xx2kTBh5WIkGn3ULRFDzfp9urMjE5iqXD1StnCUMFoSQoj00wWh5BkMT2fRqtFrXNDZ7+9jfxvT75fIpSIYOuwcrSlTi+iBKi6QqjI+M899yz/ORP/gzlYpnNzU2+/JUvA/DBpz7IU099kIWFq1SqVZaWrvPvfus3KRQKpFIZqtVNNtbW8V2fqakZjp+4i3y5RKgr/Kt/+c84NDfL3IED9FpVeq0q3/ja18hkc1THR0inUwSex8ZmlUKxzKEj9zA9PUc+l2Fh4TSvv/4Kd91zN2EI7VabubljA7/SaGswIaIdHgfKQNsMGb6c8cARW4NOAtJ3UZAYuoDIJnA6fO1rf4xjd5ianCSRzPPGq6+QSqU4duw4R48exfdckqbGZqvG2TPP8fLLTxMEHuWRAn/0hc/Qbvfx/QjTSDB/z0P8h9/5Aw4fPIyqqFy8cAlV0Snlxzl/dgHPdymWszxw391sbKyQMLMkjCTTEzNkswVeePF5/vQrn0NTdcIwotPtAwLfD+j1XZ757otsbFb5H/7OLyGlh0BDDAyYknh1MJQhaGx9gV0vnxC33hK/n+zcgg4/aEDfDd7fuxa/bXxDDlZusTo4rOmmbdjv2i7w3tUPO/p8qE0OaBpxEyPkVs6bTKRDz52byY0c/nZbhnb2ndi/eyIflD3MNxxDwzlNxiC9l/8XQqJpAiFj6lFRYuN9r9tB1zQUBYIwIAg88kKgIolkiBr6yCgknU7Q8dsEvoeMfGTg3fT+3hXALZSQ9fVLLC4u0un0KY+O0+jUUcwAt9UFXXLo8EEuX74CioLrhCSSCcbGStRrNQwrxLJ0dEMDRbK+eZ0//uIXeOD+95EvjlMqTPCxjx4i9Fxmp0dZunYFu9fhUz/6Y/zS//x3ubp+jWajRb/ncK17jYnpSb71nW+QTCQJ/JB6pYGGihIptJtd7L5LOp2iWMrxnve8h/NnzlOrVHE6fU6/9hoP3PcgBw8dI1soIVEIfZf3P/5esqkUdlvn4Nw8xw/O8Q/+/v8OkUO1skqtrhAJBSOf5ivf/CqP9Fxsz2VmapxHH3sPjXqFuckDXLu+zOrSBp/85CyqbqEpCqomEUpEFAbE5kKd2CNAGZhR/HghLGKrdRRFA8OQj1AcFAKC0OPKlTfQlJDLl15FERJNcfncH3wF3TD5O3/rv2dyPEOvu0IQeHz3u69Qra8QRn1O3n0Yz5XUq13Gxw5RzPtEUYSm6cyMH2Z2chVDS6KqCjPT87TbfWp1G8NKkx8pk8kmeeXV13HdLk7f59DcYUZyJX773/0e7/3A+7h48TyWZZDJpFGljmEkGS3kWb66Srd/jQOHZllbP8s3n/42ipLk0PzdPPrIh7H7AYYpUFUFIWIPnHDACsSKz5AD383Z3nKs3oL+uBlv/s5lSF9Ihlve5dZvt+ZAh0l24+E2MA3j1m+5kw5d6AceSnFAhfBGaNyiAkAQ++FvAbIQA/4XtvbzbmnZ27ciITYoC7EdaUcobIdr3VY1ds6vOznlIY8cb/K6cRaO9oZ8vaGr9ixVhw3fKn6g/IjYhXOoHCkDCiQM/AGQx7YgTREEvgcyREQKRCoyjCDysaKA9voqShRQyKWwDJWEEdHvdIg8FxEGiCii0/AQQpLSNTRNQeVdrnHruk6pXKTV7iAUFd3QSCYTNFt1pPBRVMn6xiqIkEOHD7C2tkHftun1O2QyKRqtDfzAx0qYpFIapVKJhYWrTE0exg9BqAYlP8/999/L+Gie6voKr738Ev+2XuOTH/skn/n9zxCFAsNIYOgmzUYLiULgSxJWioOHjvHwex5D0xNsbFRZWl6lslnH6Tt8+tM/yW80fh3LMCnk83zjG1/noYefZH19ja5tY3sutcYm6YTFyy+9jNNv0+s2WV9ZZHR8HEWJSGWS6JaJH0la3SXmDx4EoNFokEolaDfX8X3J4tIqum7xwAPvYXlplYcmDhIw0D6i7Zdye+egQiRNUEyiKESICCFDVOmjKqCbGlcvL7C5cZ1Op8bp0y8xOz3G2vp1ojDEdrrYbgPbVXC8Fu1OhW6vS6NR59tPf5PRsSKO06XVamLbPo1al2QyzejoKOl0Ct93OfXqKSYmJpEyIJlMMDd3kBeefwXXdcgWsriuR+XKGr7r8OlP/zD1aoNsrkC5OIpmWBRLUzz++ATVWoV2p8XI2BjXFpZ55JGHGJtosbi8RLNZ5Qtf+ByO75FJjxAEPqap4Xux/QRivAnlEJjlFjDAbsrh7WjMOz09fhCa9l4vh21+Nf7ldhTut0oTN3sb/IYbrbbB60ZjbdyFYvhhq53bE9lgChjQA0PON/4Y9/wArgea625NV0q2PBG3DYFyX7pvp33nxi0vN7v5G/lpIfduKBqsPGSIiEAo8WQUK99RvLdJRkShTyTD2BMk9OIVXuijhAG6iAhCj06rSkIJUVVJ6PUQmoXdldj9HlEYoCkqhqbiux6WqSNEhOcF8R6Hm8i7Arg9z8XQ9Ris22067TZmSqfVbscbTjSB59kgJIapYSV1PN/BdWx0TUERGr4fgvAxTUmxWMQPPDY2V5GKRiqTZ32ty9yBcaqVDer1Kq1Wk0a9yv33P4SMVMIgdoFKJFKk01mq1Sa6nsQwkkRCY+7gEWq1Gr2+TbfTw+67GHqCfs9GIFC12I+11WrS73fxPBvXs3BdlyAIMAyDRrOF7/ZoNBq8ceYsuXQaQ48HcxhKTMNkanySUqHExOQk2VyevuNSq7Uo5Mt0ujajoznmDx0jxIg3xwyokht1kwgpIBwsRyPpoqsRMnTodaqEgY0f2jTrS1QriywuXeHq1bN4bo1ur4Hv+fiBy8hYhn6vz7mLr1FtrOK5PpubVVZXVkilEkgpCHyFerVDr+cSRQLLahGGHp7nsL7eRFEFlhUbf1vNJqZpkEyYIEBFJZ8tkEomWF+rsb62TsLMYJkZhKIwPnWAYiFHs9mg1W5imknCKMXRY/exuraMqqsY5hyb1RXq9Qahr9HrdhBCousqUioDX/fh0nYIRMO9b28dQvSt5PtpMNxb7n862W0x2eU3vYNniN3owu1rYrgTcJh9SDvsAOcdQB0zJzdGAtzerDME893Uy26j4TbA71/GW97edlUMVyoSooGR3g+2wXmo4bs+ChEy8JGhRyRDdBEiCGMAlz66GhB6DorXwtC1eMXnR/ieB5GB7znIKELRNUIJuqmimzoScPwAPwhu2vx3BXB3u7HWFoUhvufRaHWYSI4CsUO9oalEqsDzfVrt+IScVCqBY/vYTg/TTNB3+nhugO+F5HIFwlBns7pKOpujUCpy/dpVxkdSPP/sM1xbuIKuqVjJJJvrFfK5Efp9D9e1UVSV97znUZ5//iWmp+YwrQTVehM/jDATsVuaEAJdNymXCzz//AsEgY/neay1V+N2OX10QyGdtkhmkhRLOXRNoOs63Y5Pv++AopErljENwerqEm7LZXRsnJnJGUI/YG5ujnQ2z9LSEppukR3Ns7y0TDKV4cDcHMlMmWqzh2qoW5yiGCyfY00wBAKkEiI1CcImkg69XoWV5QvYvTqVyga5fJkwaLOxtoDvdeh0dZJJA1tE+L7N7IFJ2u02r556DtNIxpNV30VKSafdJ5PJkEmVaOgOMmHg+zbrG2soyoBfD5MsLV1ndLQMQK1SZ3ximmy2SKVSIZvOMzd/gGTC4rOf/SytZhPflUipY1hJDh4+QiqVYv7wEYQQ9HouxfIBZuePYLs2ui555NH7OPXay6yu/CGdZotOu0kU+hi6hettA44iBJEMGR5GsFNneztubO90E8x+cqv8eyeDG1zkeCuy5J21Jz5Wb3/Qjj8MQVFs9+WQ/70BEWWsee9VhneA+nAijcuIqa0Y0IeeGTcC91Br3zUf7A7bcuvVxn79vvWbQJERggiNANtuQhTEnLeiIBQV2WtjqAoiCpCBR+jZmLqC7/UQ0kcRIVKNiDwXIwpRAgUUFVVRCFwbXdfjvRqeQ78fEckAyzRwfZdQCpqtLo7r3rT57wrgllLy/PMvkMnmyGVzJFIpur0uum6QsIw4xofdJwx9Go0GpVKZQqEIUtBud1hf30RKMbDeaiSTKTY2q6QzaZIpDcOAM2df58BMgbNnT7Oxukohk+Xxxx6l7wT8L//r/8bVhas8/fQ3+NY3v85P/bmfYnLiAI888gRz84do2zZnz57mgfvv5cqli1Sq68zPzzNSKnHk0FFmpia4fOkCp159hYVrV8gWijSam4xMjFAqllAUHd+zWdtY59UXX6BUzPNP/un/TRh4nHrtBfznItrNBqV8kReff4npQ8e598FHMBIpJAr33Hs/X/jc5xgbGWFkdDSOfmiodPptsloGTdORgx2N8ZFZElVRUFVAhASRRzKp8Porr3H6ledZWjjH7GSJbqeNnHVZ39jg/NlTTM9O8SM/9Ek67S6NRptms0W1tszszAEq1Sqddo8gCLnr5P2cP3+J5eVV2q0Ohm5y3/0PsLS0iNPskjA1UikLw9Sxu5JOu8Xa+gqZTIa5Q7NMTR7g/LnLjI+NMT83T7FY4vc+8xl+8if+Al/5ypfwQwUUnR/51I9x193Habfb6JqGqmpoWo+pyTnazT5Hjxym38/yzDPf5Nq1BU4cP06351Mq5hAiIJWCRGTR6fYJwxBVUwi9GLhVVQWh7Hv41e16luyUm7nE3e74v1WZwzT/MTVwVVGAiEjuvrftzVsgB4bv2IYwdIMcHq0Wa+V7TjcDhhvHJeHQRU4BUAYBxQZpw4ihb7kYlDOkueKNY2y1Q2yh9DYnfEuiRN7kGRP798f2HYmuhAjfZXX5IpOjZVRVwfdCIhREc4VEwoyP4Qt9ZOhjKQqe16Nv2zgR6GYS3bRwnB5CUzCTCZKpDK4fgKLRtvtcvXqVS5cuDvZ42CRSSfwgou94tDqdmz6fdwVwa5rB1OQBglDieRLHC6isNRkZz+P7kkhEqIpBMpEhnc6iqRateo/KZp1MLomuWbiuQyAlgR/R6/VpNOpsbKwPDt11CN0e/+gf/jKEPqVcAQF859vf4f1PfRRD13nzzdd5/oVnCaXHP/pHv0y5NMPExCypbB4tbTF3+AC/+5nfIvQ8nJ6NrsOXv/xFjp84SLNZZWN9jWazRqGQodtrsrq2SGmsTDqbIZUw+OMvf5nnnv4OXhhw5MRJ0tk8ly5fwAsFUaTSbvZwOgvMTB3grpN3ce7cOartNidOnkQZBFtaXl0kEiEeAUdO3E0qY6DrcWQ9KeOoCJqmousS37fjQ441C+HrvPDiS6wtbWDIMscPPsHv/e6/wzQF2fwCI2N5Pvqhj2GYOl/64p9SKI7g+5L19QpL169x6HDIieN3Ua3UuXDxMl/96jf54FMf5bnnv4umCVJpg8rmKooab9zxPY8ooZPLlQjdHifvOoHrOhiGTj5f5OzZs1y6uEDSTKKrOikrQ6/t8+YbF3nyiQ9z3wP3Uhop8K/+9T+n1ljlhz7xSVRVodezSSYLGIpkZnKMlZWrrC4vYHfbVDbXabf75AojBL5LvbqOnc4h1FhDUg0NRejgB2hqfEj03kBP3w9N+lYeJjfbzPJOaJbvt6Z9y7r2858esCVC3YZIKXfy0DdfD+x3m3t/E4rYisYnd7oo7qW6dzfppt921yUG4L8dY4QBPaKqKgoSTToIt4Pd3ODkVJFucxMhIKHp9B2fvBWh0EMVCigSL/QJ0ZGGjqGZCDWJnshRqVV58/IKVxYWWFtfo9aoE4QhfhSSzmQRisB2+9QabU6cPMb45ASNepPF68tI6je9h3cM3EKIGeA3gTHivvwVKeX/LYQoAv8BmAOuAT8tpWzcqixFUZg7cIhWu0ul2iAM4sOA8/kyrt2l02kho4Dx8Unsvo2MBKpmkEylyGbzuG6FSIo4wl/f4fXXX8d2bQ4cmKE0kiebSyGUiPm5A1w6f57NzQ2cvk25NIYQgs989vd4483X8DybKPKp1SocO3ofzVaDi5cvki7nmZ+bolrfpNNskrYSTE6Pcez4EZ599rtYCY31jTWq1SqpZALH7bG8cp10LodEpZgf5cEHH8R2bA4cmGZufo71SpWe7XLXXfeyvHCdRqVC2rRQFJWpqSmS5RFyIyMoqspn/+CzWJZBFCmoeuwNEoYuFy9d5NiRu0hYaWQUD7og9Fnf2CCdNklnTCLZoVVfJZvxKN09TbfV4vKFC/zd//G/59KFc9RqK4TSAynodPpoisW1KytIdAwjQTZTJmkVOXP6Mu12F6cfgtTYWK+CFKSzSUrlLMVCkYXLixw8OD84pUilXB6hmJtkcfEa1eomqVSKTDrDgQMzBIFkZGSEUrFMqVCi2+7xV3/xr5NMp0hlLCLp8eSTT/BHn/8d5mbLmKZJr2tjmRk6bY/HHnkfPbvBxsYKr7/+GoqqUijkOXL0KCMjZa5fu8LsoYMsLF4hnc5RyJfJ58soijoIKavtVgd5e8C5n+wE4P005Fu5vL3Vdu4b23dLBvf7IkJs17HfpBPTIDvj4+zksm9waWFP9IW93c9OSFZ21CkjQAyjQw7olR1eMcMq94Z32Fn9cGfjVjWKQjg0qg7yDr1aotBHVSJSOihKgF3fRAudONAZCpl0lmQmixTQ7napNOosr66zshbH/e92Hfq2h+OF6JpOu9sml8+RKhZRkwkuXb6MUDT8CDzPo9O3SRfL5EbHyRRHyBRGGJ+eY2Otud9jAb43jTsA/paU8lUhRAZ4RQjxVeAXgK9LKf+hEOKXgF8C/u6tCgrDiEy2SHl0kly+xvMvvkAykSL0JHbPh1BndGScVCKLKixUTcM0QNcNbNsh1jQ1wjAg8D1q1QpBGNIrFmjU6vRGejz26BM0anUqa00atSquH2GYOq+9/jLLq5vYTo+UZRIFNqVShlZnjXPnm+jXkmRLZZzOcSprK+QyWSzTQEYhR48eZmnxGvlcGUO3iEKQkSDwfGqba1wgolmvcuL4/SxcW2Bx8SphaMeRC9cr3HPfvcggxNQ0TF0ninwSSZPl1WUemj/E+MgEYRhx5OARri9coNmsUSxmyaQTrC4uooQRmhKiKrHfZxhIVEVC1GJzvc3Kkk0U9um0qrQbDbKZNLqmMjmZJQy7TEyV6HtVOl2XMIrw/IB2s0fox9Z0x3MRGPQ63iDQV4CuG+TzeRRF8ugjD7NZWaPRqmGZPaT0SKVM+nZAEAR4rs/JE0e4eOkMYRhiGCbpTA4ZSXzfoVrfpNGuMT07zXvf9whjEwUSyQSu77C+vsHm5jqWmWLx+gqpVBI/8Ol1l7j/voeJcKjXa2xu1rAdl2QqQb6QIYxcqpUVXDfATGqcfuMUMzMHMHWDUrGMH/kEoYKhD2wDRMhIxq5tMn65hRhEx9vhdbL91t8imt1W+u3P2/lvLTvBez+5AdCHIHYTrX6/HaPbiWL6IYp2e4Js1z0IGSAHRkApB8Fk5Zb30jDI0nYaYI9v81vNLDvpjBsnOrHHi2avcRJAASn2reeGbhy6PW5dj+LJAEEg4h3EmghRIxdN8RBeF9tpEIYOaCq9nker1abT6RNE4ABu4OH6Hrbj0un2OHfhMkEY0e979HoOdt9hamoSx+0zMj5GoZjHdW3G7TGymRHSmQyRjHA8l0TSYmx0lHQmg6aqCGKb2M3kHQO3lHINWBt87gghzgFTwKeApwbJ/i3wLd4KuIMITUswMTFJMpnkpZdf4MihI1QrNTxHkkxmGCnPUCoXcRybXq+DEIJE0uTChYtk0jlA4jghUeQTeJIoUug0O6wtrzJSnuGHP/kplhbXWby6ztWrl3CcNo7v8Ppzp1CVBKVSgWwqied2KBczrG9cod1uE0UK2WwZr71Jq7rBaDGPIiStZoPpqVnWVhQ0VcPULUwjiSJ0ElaCXrtOt1OlXlkjm8rwmd/7Hbq9PrquI1QVKeHhR97DxsoqmoBsOkGzWSWdG2V1bZmHhUI6mSHwQj745Af4F2+8xNrqIlNTo6Qsg5deOs2xo/diaRJkB9/r4Ad9fLeHiqRdX2Xp+iK16gbZtMnG+iqppMn4+BhT01M8990XGJ+coOd26bk9AhniOD61ao1MuoCMoNdtg2pQqzbRNBXDiKmZfC6LrsPdd9/Nyy+7XLp4Bc/xCCOHIHBw7D79vouQKtnHEqiqJJ1Ok8nkUIRGs9OkUlkjDH02alMcl0d56sOPceXam4yNjdG3+1y5eoWl69d45NEn2dhYpt3ugRJRqWzyqU9/Gtft0e62cVwfw0qQTCXJZBOsr12n1eoghE4qneT82XMEfkQmVaBcGgdhxpqVkCiDXaORlAihbR04EMdLHkYe3M1ZDw3A+8nO3Xx7geNWgPyONP3YjeIGgBZDrVPKmwLnlg1xZ/odk06cVQIRw5AIUgwOqhj2A7v5bjGgSZStOvaNsr1LttMOD37YNhfvpbH2+sgP+fLt57E9GcGN/T00fA6vRZEkUhUioQ4iykfoSkjYr+H1G/Rq69jNKsVyiXa3y9palcpGldpGlc2NCrW+Tc/tkc6kyOdzJJIWK2ubpDNZHDfA9SIQGrl8Eb2vUygWKRRyOK6JlTAYLc+RTKXQNC1enEQBUgxsL4CMolv23/eF4xZCzAEPAC8AYwNQB1gnplJunV9RURSdy5cWWFtfZXpqjl/8xb/GP/5H/wfpVAFNU7lw4Qq/8rf+Di+//CKnT7+OJOLgwYOYRoJ0OsOzz32HXq8TB98PfIrFEUqFItlMDoDz5y8yUp7iZ//8z/H6a6/wrae/xne+8xVSCZ1S3kId8LO6ClcvnWdsfIRO4OA6Lp6ug3TI55OcfuMVVFVndGSMXq/Fa6+9FMfvUFVSqQSpVJL77jvJ5Svn0DWFZELn2tXLdDttSuUSvZ5Nt9cllcowUi5x6tI5RkdLJCy4eNFldm6a8amDWKZKr9NE1wxePfUyi4vXsN0ett2lsrnKpQtnOX74BCohjeYm65vX0HSPL3/5T3j80ac4dOAkuVSJf/Nrv8rHPvoBNtYqNJt9Op0Fri4sUSqWefGlUxgpHT8MWF1bIgokQRiwtrqKpmokUymshEGr3UXXVQxDIwgCLl28SLGUZ3Pz87GHDAobG5tIPEZGyti2S6/Xp9e1+dOvfJVicQTXDmg2Wnz7+reBCD+wmZgaoVBK02xXuHD5Im++eYaTJ+9G13WiSPLTP/PTfOD97+eX//7f47U3XsZ1u0xOjvObv/UrWFaK++59iGMnD7K6cY0HHryHer3G4tI1Fhauo6kW0zOHaVY7vFQ7xcpSnfX1Np/+8T+H7UR4fogMI1RVoKkaYSAJiQbL+YiIkOEml3dKoezebv0uEzGcNHaD2s02GMW3sjtC9M47k+ymVm6j+hu+K/v08147AAwmkL2MthADA/3OAFo3lLarwiiKiJTY8qkqcRz5K9eu8OzXv8jKwiVMTeHHf+qn+D//f/8C00yCFHh2bOMqT06iaSZTU9PMzs4gCTnz5gVy2SxOv0oYeGQzOQzDIJcbp1QqUyoVBpNLiMBiuMoRSAzDRFEgGrhGyrfoy+8ZuIUQaeD3gf9OStneY+CRQtwQsWCY7xeBX4Q4rKskQEofz+vz5ptv8M1vfoPJqQkmJyaZnJokkbD49V//DWy7x7333osQcOrUq3S7bRhX0DUD07SQMqBcLCFQqFbrTM8eJpVK80d//IccPXyCuZlZ5g/Nkc7+EKsrlyiVc9i9Hq7XI3R8DEPg2l1WrnVARqhCpd2q8KUvfYEPffBDyMil1+vR61Wx+zaqGmF7Np4vcFyFZqtCKPvoBritPv1+nw88dTd/82/8NVbXNigUi0xNTTE6PsGli+dRNInj9KjUN1nbWOfhxx/l209/g8LIeQqlMXKZHJNjZSQeDz10P2Ho8Xuf+V0y6QLZvEm32+DawjnOXngRx6+QLWjMzk2RypRot3UOHLiPD3/402Ryo2xsrHL92lVee/01RkdtfurP/RXeOPMaQejz1JOz/P7vf5b77z/G9evXiYKAbDZLGMX+9EKodHtdbLuPrmv0eg6qauC6Lrqucfz4Q3i+Ta1WBQTZTJ5sNseZN88zOTnN6uoamqZx4sQxDh0+wBtvnGJicpRqdZ1Lly5w4sRJut02Lzz/PIaRIJPJ0u30+PVf/9fcddcxfu7nfgbDVHnmmW8jZUgmq7OyegkrkeSBh47xm7/1qzz11IfJZtMkk0nqtRaO3eXAgTkWl1ZpNpo067U4DGzokEqliMII33NRNQ3P9WPviAFPu7VK/55473cpaA9ky7tPSBRlm14Quw4k3pZhzO3/mN4tO0F7OxRrNNCsh2GHd5EuN7ofsjNtnAZizxlVDAKUBT6202RzZRlL08ikMvi+R63RRjcT6Mk0gR8S+aAlVYIoRNVUCoUik5MT+IHHBz7wAaampkilshi6gZSSbreDBAzDQFOHYYcjhFB3t01INF0FVKIoIgzDW/bz9wTcQgidGLT/vZTyDwY/bwghJqSUa0KICWBzv7xSyl8BfgVgfGJMVutXqTeqdO02I+MWG5tXSCQsXK9Ds1kjkZjk5MmTrK+vUigUsG2bKIxwHJ9isczU1DRShtTqmzTqLaan41gXppVAUVR+5md+OjYuOA4bm6ssLy2QzWa5dGkBofjIKEBBkLAMdKFRyKXp93o4fgBCUCrkee3Uq0gZkUwmyWZz2D2Pg4dncR0Px/bwvADDMMnmYtpD1wx0zeDq1SscPaqTMHWqm+vUalWmanUuXLxEpbLIkUMHMJMmfujz4gsvkszmuHL5LFNunyPv+wArK9d5z8MPYdtdXNclmUwQhh4L1y6SL2Q4d/Y0z7/4LLlyxFMf+hCaoeOHgmx+nE/+6J/DiwxO3P0wlcY3Uc08n/rxn+PxJ95PNpvj2F2P0ml3WF1ZYXbmXlKpBIV8SL/fHXCgkpN3Hafb6eL7Pr7v02o2kRF0Ol0syySbzZJMZjjz8jmymQyWlUDXU+haEkWYrK1ukEhYpNOpwaacNTzP5+yZM6iahhAqr732Op7rk0xmMAyHTrfDpUsXKZULrKxUuXzlOo1GDdvu8IEPPMn1a9dod+ooqqBcLuF6sU95Pp9nbm6Ou+5K88D9dzE+UaHT26TZqlCtXcUPayxcv8j09CzJVA5N1+n326TT+UFY2GGsjR0Hfd0meN/4om1rfjs3/+x4f96ixFuf4n5DNL53NMnsjm54szreqbyVZ82tru3MOzxBaMvPXA4DrO0t/0bvoOEmn6HL4dCrJIgCkBGaApYmELrK/SdOILotItuh79ggQdfNGKSLZUrFESbGJsmmEyACLEvfmiSOHj2KoiiYhoWmaUhCoiiBUMQgyNrA3VCJV64Sies62P0efuAhZUSn08Z1XTzPo9/v3bSvvhevEgH8GnBOSvl/7rj0h8BfBv7h4O8X3qosz7Pp9qv0+jV6dhtEhJmAIPBw3A6NpkRKn+npWSYmx5AyxPMcstksiqpRq9QYGRklDH2arQaqqtJstlBVk067Q7/XZ3x8nM3Ndaqbda5cvsiVKxfp2316/R5mIjYGSAmuFyJ0jWbbQaCSSCRJ5vLYtk0Q+Agg9EP63R6e61IqGfR7fQzTIJPJUtmsUyrnSVgpgtBDSkmzXqdeq9K3baQUJNNpAt8jCDzWN9YZGyuSSphYVpJqpUZRKMgoRFMAGVCvV1lcXMT3PeYOHODuu2d55ulnmJ+fpdNpYPc6OE6PmewESSvLysomuWyWbCrD6MQ0PacLQufQkbuYnJ6jkC8wNnmAKAQrqWKaeQQmn/jEpzB0hWajzuL1a6ytLXP8xAG+88x3KBbzOI5LrVYnCELsvo2VtFBVhX6/x/r6BkgN3wcZeYRhDxlBsVgiCDwQIYmESTabptls0W73KBaLaJpO37ZJJlPoeoBlmehaHFDKcXvIqEQ6lcfQE7hOSKPe4s03LzE1NYaiSvzAxjQN7r33ATY3KnQ6XaIowHVtLl0+S6vTIgjaGEZARJcXX/oGa2sbXFs6x9TkPLOzR1GEQYQDQxc0MXAT+54US7n778DdTG5PBze4tm1vM2c7za6/N8qtwO6t2rdzMfx28Pl2Af7taOa3M0HcWF407LRtn++h9r21ZNryKWTolw4QRrF9QxGSyHPo2h2SSkDkOqgSEoaBqoCp6Rw/fhTdSpFMZciksxTyJVKWHnt3RQFSBliahaaZJJNJzp69wPLSCoah8eSTT9JoNhD4BEGI48Ruup7n4rou7U6bdqtJJCP8wI1dl6OIIAhwnB/MBpwngJ8HTgshXhv89j8RA/bvCSH+S+A68NNvVZDne7TbDXr9DrbdRaAShB6OE6EqAbZjU6utE0mfiYlJVtfq9Ht9kukkyVSSC+cvMndwZrBsmaZa3aBWa1AsjCIQg9mrz9LSdVYWr3P58kWuXbsSB2WSEQJrsOEA/DCOvRBGAclEgpSVo1QYY6W/goJARiGhL7EDF88NEBJc18U0FDTNoNXqMDU9iaZbBGGE6wYEfou1tRXqtSbFUolMJoOiQC6bQVEVPD/ANEySiTStRot2q43QdXQtPm2m1+tx5fJVJiYmyOdLTE3OkEymGB8bo93axPdtDE1jbuYoMjS5fn2V2ZlRctn4CDHHj4CIuUPHMAyNXr+PF0Ac/jRC1TRGx8aYmhpHyoBep0M2m8UwNA4fPsjXvv6nZLKTRFIShgHl8giNWp1cLg7L2my26HRsDD2J54Z4hLiuj+e5ZLNJpqZmaDSrJJMWo2MjtFod7J5HZraAqqr0+x5jY+PYdn9rmet5AYoSxhuKJORyBRRFY3Njk4sXLnH02EFSaR3H7WKZCdLpMmfePA9AGAbUapt0uw2C0I9XBbksApfXTj1PvdnC9XxmZpbo2x3yuRHmDhxHYIAYBOYaGL+G8s7ogd1Hqw1cL7Y+31jiHg+Jt6hyrxfKfm28JSDe1sz07qB7bnSV3Ol6uMP7R8TjesDYs7XK2RE3JbajxnFHdBXsbp/6+go96dOvrxN6DqamomsmmoBjRw6jJ1JEUuB5Aa1WnUxynDASW/ySqqk4joOuGywtLnH69Bksy+Tee+9nbXWdMAzxfZdur0uv1yMMYxBvNJo0Gg1MUyeMPFKpFKoaH5geBP5N++N78Sp5hps/1Q+/nbIMw2RlpUG/10UzFCzT5OUX32Rqco5EMsLzbCqVDa5cvUS5PILdc0mlMhw6fIhUMkur3eGlF1/mrruO80Of/CS/+mu/QuCH3Hff/czOHULRE5w9e4Y/+ZM/REY+nXYTKUMMQ0OgIQMVNB1F1UATVGt1xqcniRC4kUEUGqhKgoUrl7ASJvl8hkwmRadjI6VCOpWj13NYWV4DBGGg0Gk52E6fIPCRUZdWq0Wj2WJichpV00gkE5RLRQ4dOoxpJXG9ECl0hNDw3YhOo8bkRJdMOo0QCqXSCI888jgA3/rWMzzy6OO8/sZp1tYuUatUEdLgxJEHWVyrUKv2mZ0VJNIW3U6PCIViaQRERKdn0+87mFZmoHHZCCU+/25tfYNet0O1WsGxbbL5BJ/57O8xN3eAVCpJvd7ASiT5sR/9FOfPXySTSXDu3JssXFsgmylSq3YIwxDLMlEUhX7PYWNjkfc++RBrayqqqjA2Nkaz0WVpcZNWy0ZRFFw3xLYb5PIp/MDFcfp0Oi2a7QrZbJ7nnv822VyBTCaDZWmUjRzLy1cpFPOoisbKSpWV5XO4jsfE5DhCkTRbdUxLQ9csHrz/Qfwg4Oz5i2Qyec6eOcvU9BQXLr7BK6eeJZ8f43/6u78MUQJVsRBCx/MCNMPcO+bfcizvDCe69WHrLRmqgbeQtzE/fO/hZN8qz7sDtIcyvMc4iuMQrCOGOyGH3iIQUys7/el3+9YLDCtB5LtoQhL5Lo3qButXL5EzNAhDUpaJlAFur4uh61iGSqvdZWV5lUuXrvAX/vxfgEDiujaO24d+RKdt8+abZzAMiwfufwjPD/jKV75Or9fEcfsEgYfvezRbDZKJDIlEgl6vR7PZYmy8iGmaHDp0EJBUKjVUdfGmfSH+0wayiSWZsuTMgTKZTAZdN6lsNkimCzzw4COEoUOztUmlukatWqfX7aMNvDpOnjjJo4+/l8/+wZdwnD6eb+N5fUxdZ2ZmlnK5jGZY6KbFzMw0zWaNL3z+s2TSSSbGRmm3GnQ7Dk998GOcPnOO9Y0KR4/fxXe++13uf+hBJibHiRyftauLFPI5CsUc5869yeLidWynz/33343t9tnY2EBKgaYZVKsNkBLX8VBVFXVwIjwipg2mpqaYnJmhXB7jq1/7OkIJ6fd72L0+hJIH7rsfkOiWwcm7TvLoE4/x3HPP0Wo6XLy4gKabHDhwgEOHDyNUydVLpzhz+hRCUfgn/+Sf8o//r3/Go+99iqMn7qc8NoMMDVQ1Qc/u4ro9hJBks0mSlo4Mbd588xkWFs6zsrzGs999no9/9BNYlkUUhLRaTV579VWqazX+u7/1/8bxXE6/cRpV0fnyn3yRH/nxH2V5eYmzZ8/geSGeKzl8+DCe7+LYPXRD5Yc/9WF++7f/A55rk8vlGCmPs3BtiXbLplwuAxFB4PH+D7yXpeUrnDx5lFTaotVu8PLLr9BuO0SBRFHUwWHQOomERSplYpgJHNdnZXmN2kab2YMz2E4fwxBMzZSxDAtLz9Pvu7RaHeqNFn3bIZdP8/iTj6IZCqvrq6ysbBL6Cf7mX/vbzM0dQ1UTgEY4DGR/kw01e+XGnZeD7d+D46qG4efk0A95h2+x2PJV3mloU/Z831/2xoa+WZt25xm275Z3dEPdb8c4uV+fvR3OXMqIMNx9MPCw3GHbh9di0N6+nyEnPmzvztXJ8FR6BYkpIlqVNRbOn6G+vEDYaaJEPkKRaKZBaXyc1VqNQCj0HRffDygUilQ2a/R6HZrtOt1ufLpTs9khCiHwIQzjNhSKWVbWrnH40DyjY2XC0OeFF55nanIeXdcIoxBFERw+fIhUKkkmk0bTNIQQ/NZvfpb1tc19O+xdseUdIAxCZiZnGB+b4tRrp7nr3gcxUxl0I0+hXKI0Ms7ayhrFfB5d06hX67zxxhnuvudB/urf+G+oViv0Oh0C38V1+rz44nNMTc1Qq1c5d/Y1qpVlHLvL+594lBPHj1LIZ3n5lRd58oknWVnbpNYoMD09wcc//mN4tkcoJetLK7QaLSqrFZLpDLbn40URoZD4kc/5yxdRFEmxkB8YFjp4fodkIsODDz5Oo97m2vXrBJGLqigomkar26Z78Txnz55jYmIcEdhUoxAtCNE1AyEDRkfKHDt+HCEEf/pHf4zj+fiByuZ6hUNHj3P3fe+h2qijqpJEMk8mOwJSkkxkeOKxJ0mkC0SRgu9FCBFiWqD5GlaiGFM6ik+jco3pyTzL189RWV9mfuYA1YOH+dM/+gon7z6JbuhUK5uEgeSe++/ld37nd4iDaxl0Oh3uvvdurl29SjaX5aH3vIcrV65w9913ce7cecYnxiiXDuO4Dl/84y8TSUkilccPddY3Ozz5vo/w0ksv0+u0MHSVVCrJt7/1bQrFDG+8doZIukQyYHpqlsvuMulCgonxMfK5HJ1OD0tPsbq8jh1ETMxM8SM/9uNcX1jklVdfwkwKFDWka7c4fOQAdjui3e5i2x6aMPnIU+9jffM63/3W0/RsG1U3KRbH6Ld6vPzSS2xuNBkZnebg/BGEquxLaAxlrw/2zu9xzBh1T4Zdf3Z8GQJL/Hm37O/hMUw7jA8y1CRvBMbvr2K29553Aubb4b6HLnu3xvHhvUcDf/MhgMOwF/eLDSOE2NK4FUXB92PKYfgbSFQtDmzlBz6pXJ65w0f5/H/4bY5OjVJIJxG6ihsJ8nqC5199HT+SeK6H57iISKAYgk6nxdj4yJYLrO9L0qk8K8tr2LbN+PgYB+Ym2axeY3S8yPz8LKqiMD4+RjKZ2TKyRpHEsiwAdF1DGRwAvncn6E55VwC3punMzx8GVFZW1mg02jz22GOcuXCZTr9Lv9+n1eqRL05Qr9cpFopkC2NMYnLm7CXmjz9AJpMjm8mQTiVRheTzn/99pJRMTo6DEmDb8cnhRAHXrl9mYSFgbW2JF198mr7rEoUh6VSCVnMT09TIF8oEYYAIFTZXN+l0O3iBjeu6W/6sfdtGVaCr97ZihggBqVSKjY1N7L6LEAqqqmHo8Uvc63YxTZPZ2Rk8P0BGHuVCirFyBsuyGB3JEYY23c4aqqaTMBQOzJ3kuedfYfbALPPzB0ilDFbXu2zWNhgrZsgVSniuC4rGiRP34EmDZCaHbuggVPzIQzfjwRBFAZ5vc/XKRZ751kVefuVlgiAg8kw2Vqs88fgTeJ5LvV5FegE/9IlP8J1nnqFS2aRcLpPLZVhfX8X3Xebm5unbNu1OG4i4cuUy3V4TpRJiO21Asr6xwsTUFIEv6XVdbKfPyvIi3XY9nsxEbIPwvQCBSiaTw/McGs0a1xaW6PdcAs/DMg0Ekl6vx2ZnnShUIBRsrK3zwnMv4LouvW6PsfES6bSJF/TxXI8wilBVKBXzJBN5wiA2cPa6NqpmkE4UaNZ7HD92L6VimfJImbHRMpqu4ofRLpR9O14Xb88TZajZ3uiffIucO/Jv/3a79e71Mb9dLfpWkRJvlfbt1rMjx56/N69jpy/6/m6EbE0YYRQfgi0UHd2K3xfbi2i2e6iKgi9DQk3j0F13s16pohkWSPC9EKfbZ3SihO/7JJNJJicmCcKIKBT4XoSm6+TzBtMz0xQKed73vvcxMTlGJpNGSgZGzNSu1cBwgtE0DSkhCm89Ft4VwC0UhUKhTKfVYXOjgut6aLoRH7wpVHQzTa6YYKxcRtVSGLqGADJS5+Kla2ysbyCJSCUT6LqK5zr4vkulssn45Ai5XJpOt4ZhWixev0633cS2+/R6XZaXL5HNp0km80Qy5PyF1xkbK3HkyEk2N2v0O32ymTSarg46Od4pqWsGnu+i6Bq9Xp9kwkLXdRQhmJqaYmO9huv5GKZOpJiDuOEKjuugKrHboWO3Edjk80kyqSRWwqBYsqhsrtPrLaFpFqqWIpdL0ek2OHbiHubmpkiYOpl0AsdNYyaSKLpB5PogdMYnprF9iBQdIQSqFh+yq+kWURQSBLHPauSHvPDci1xfXKKQLyKmdHKpAu954D28fuoV1rt9ZOBTKhVYXVlBVQWmqQMhUvrU6i2mZyZpNFtUKhUsy2Bzs4WqQbfXpNdvgoDZAxNks1majS6+LlDQWFleoN9rDjZHafi+j64bhEFEFAmiSOC5EZVqHYmKTMRppAzRNAXH7ZG0UnhBSGWjw8LV61iJBGEQQCgQUiXwItZW15GhAEwmpyaZmpjjpRdfYXrqAEEQkc0VmZyaZ2VlnYceepS5+UOMjU+Ry5cAFS8Mb7l77XZ2Pb6TaIHvHrmdNt8aWPdPv3MV8U5dLXcbZ3fz37vLHAL3dtp4E4+iqvFORUUgNJPZQ4dprlwDAX7og2lgmBaRFLHRWlFAifDCkEQiSTqdJpfNUigWECi4TkCn3UfTLAzdYGpqkkRS58jRWYQYOD0EEcLQ0DQ9jmMuY/wTQgxCLwwOEH4LSupdAdye61GpNgm9AEXRyReKfOlLX6E0PsbsgXkmpg8wOTVDZWOD+dk5Tp06xRuvv8baxiLNdo+rC1dIJiySSYtGbYOLF84wNz/HuXNnuHxVQTOgUlkjn8uwsb4yAFHB2toGo6MWUvSoN2qItTU2Nzf5+Z//mxw7ej9f/OJXsft9xsZGmJyaQNc1fN/B8x0kku5ml3QyTRQGSKmiqgYguOeee8ikV6hU67Q7HVRXQUZRHIPXV+h327zx+itMT5fJZSSZlI9p9VCVPomERirTx7J8+naNzfU+XdfB9yuUSjrlcoZCIY+VvI+PTM3wze98mXqrjWs7aLqF5wRoqsAJQyLfiU+ACeK9gEIKRKSgYPDw/Y9w+qWXaFbaTI5O86EPfITw8QC73SFyfaTv02s3+eIf/xH9Xo/5wwfwA5srV9fQTZWsZnH5ynmiCBRFBSRz81N0e+24nwKfZrPJz/3cz/P008/hOi7JhEU6neXSxUuYCZDCI5IKiqIBasxD15sxSBORzRZpNZoUJsvMzh5gZmaCKPKZmpigsrFJtdZCAJZh4TkemqqzsVZlbcWj3WugGwJBxPzcMSYnxzhx4hi//9nP8b/8r3+Pz3/+C2QyBd7zyJN4XkC+WCaVziKlQhgMzg78Hsf129G6v1d/6R+EbAWO4kZKY4gp+/HNQ7l1TJe3s7q4Vfu2P0dRtEXbqKq65fd9Qx5B/Jyj+FgyLwgJUfjRn/gp/vk/+t9xXBtNU8gaOlEE4+MTuGGI5/kEfoiiqFimyeTEBPlCIY4pIgXHjx9HETpBQLyKjQI0DfSBT7ci4nDLShAR+PEhwhJJ4IeAIIrAtvtEUbxaCN7tBylomoFpZZg+OI3v+fz+5/6Ae9/zCH/pF36B85eucvHKNdY3axw5fJhnnnuWsZExHn74MYqFMuVykZde/i4/8iM/RL/X5aUXX+AnfvxH+Pznf5+LF86TSCYZGysDAcVCllIpS2Vjg431dQwjfmF8P8QwDExDxfc6/NP/45fJZsep1+JNKMVigbNna1SrFebn5xkfm2DJ90kk0gipIqSk13WwbZtEIsm3vvU062sVVE1HNw1anQaGZiCDAN+3ESIglTAYKyV49NGjVKqrdDpNDFOn1lik3W2wutEjnc1y8NgkFy+/yid/+H00WmtUqnnyhRHWllrMTN7N5MQUkxPj1Cs1Ql/y7/7Nv2V6bo6T997N9OwBfD8kiALUSEMVCooEJZAUUwl+9EMf4+F77sYLPCK7zYP3PoSuahhKgCp8avU0//o3/w3Pvvg8r7z6IhcvXaDVynL06CHePHOaKJK0Wl3CMGJ6ZhrHsVHsiMnJUcbGx1BVwde++lXuu+9BnnjvKHbf5eLFK1xfvM4Dh+5D1w2azTZrq5vousH46Dytdpt+30FRFKZnprmuX8P1PE6dep1Tr75Cwkrw4AP3U6u3MM0ExfI4umawsVEjiqBWrxBFHqPlSSanRnDcHrlsnvPnz3H58jX+1f/zL/mt3/pN/uJf/EuURyZotntomkk4iNSmagZCaLTbLfREcmuM7qfd3Ur2AvHNAki9nTJvVc8PBvhvpRnLLWCPTxfajti3zSXvRy0x0D5vDdw7efPb7ZthHl3Xd1El+256igZtFwpCaKSzOY5/4Cn+8f/3fyaZT5HPZyjkc/S6LSYnR0mls6SSaVLJFPl0Fte3CQIH09KxLAtN1Qeuv3KgOcf9EK9SQVN1IiFx3XizXhB69Ho9bNserDh1qtUq3W4X1/XwPJ9e7wewAef7Kal0mvmDRzl88BCubROFkgNzB/nc579Ao90hCEKKxSKKOMCJYwfRVA3XcZDhJBcvXeTjH/8oFy6cJ51O8rGPf4Tnn3+en/5zP8WffPGPeePN13jjjdPcd989aCpsrLdpt7p4rkc+X6DT7KAoAtPy6GshftBACJORkRHuve8Q7bbN2TMXuPe++1hYuMrmZsz1zkzPsrK8Ri6Vp+/1SCSTJJMGC9eukc+PUCoVCSNAEaRz04gAet0OrutRLCR44rEHWFu9wJUriwgVGq2IhYWrOG6bw4cOI0mwsd7h+tIZjp2Y5/zFU0xNH6PbXeX8+ed5+D2fIAhqNOprtFtV1laW+O3f/A3wXUK7S2N9iWZtmbNn3+SHPvnD5JJlCEGXoIUu/+Kf/gMmR/M02jVCAaOjUzQmyly4HAPr+GSJu+8/xree/iYXrlzE8x1UTRBGLu1unR//9A9z9epVavU6vV4Pz/dIpU02NnxWVtaoVGv4XsDs9BSbqxVefu41LCvJQ+95iI986P2cOfsG3X6P5RWFdrtNsVjk6pWL6JpFMpnGspKsra7heh4zMwfwnD6Neo1O1+GF516nVqlTKOTI5hxURePAgYO89NIr6LpGwsrg9X0uX1rEtFSITITQCPw2X/rSH1FvVPjW019n/uAhMtk8CwuLPPXUx+j1/XgsmAkUVccbaDxb0PU2wPGdb4x5++Xf2nPk7ZcXS3xc8La7yp7rW14wDOLBx2c2xpS93HV9RyU7AkjFOxlvJjvpjVvZEaSUhGG4RZEMPTJuGWkRgabF11UFhIwIPJ9WM+SjH/swKV2gygCI0AkopJMk0xaGoWKICEIPXY3PpFVVFUPXAYFpmghCwsDHD3yCwKPVrm5ttnEcl17Xod3u0bebg12S8Y5JwzABST5fwNANLFPdxc3vlXcFcKuqimUlyeUKeFYSVdEYHx/HkyGZXAfXi70yzr55igMzs/R7ffrdHr7rMX9gmlK5RCqVRBES09S57757WV5Z4dq1a9RrdfzA5bVTrzM/N0uj0cK2HXw/oNtxCQKF0JcoKGDEx6Ol0ibNZoP77ruP9773CB/60EdQFY1SsUy326VarXH9+hKaZpDOZJmZncFxemxurqMoClOTUwQD40IkI5q9Fu1am4mJCVw7RTFvMX9gBlW0SGVH6PYcwihEN/LYjmB9w8GyTMIwhesELF2vIoXAMFZJZz2yYUijdZmvfe0ZIgKa9Qr9bodOq8kbp17n/akniSbyIEPajQXOn/0WnbbL8cPHyFgJrpx5ExlUkUFIr72G54doMuD5ZwPOXrqC7TkUS0XUpMa155+l1oxBUtdUDEPH81y+9e1vMDU1QamcZXSsQKFQotXqkcsVWVleYXOzSrfTg1Bi2w5O30PXDXq9PouLU+QLGVQFCvk08wcn8dyQw0cOomsWvhd7gjQaDRIZE0M3iIKAXK7IPXfP8c2vfBtVTWL3fFyngqKqTE3OUi6VabfadNs9NENh7tABrly9TL+3QqlUZGRkhGef/Q6tVoeRkRKdbpNGu8XmRg1F1Thy9G5yOZ0g8FFFzOfv3BOzDQQ7AeHdR3F8X+Vmmzf3BHISQhlovDu09P08XIYukLfRbTefeOSAp462NOttLX9b9hold9/WMARyTGFoauzhkUomUAI7PlSbCOm7WJqCqSloQiKigNCz8RWFIAypVmtshhsoiopt+zh2gN13sW0b2+3S6bZAxl4x6oASrNWaIGJ34UQiDjZVKBSo1WoUCgVSyRTwAwrr+v2UKJIougZKzMWqqoahaWQzKSxTsrnRZWVxmVQ6zRu1DXw/pN+z6ff6HDl6FBSYm5vDSlpEUcDM7BR/+PkvsLi4igwF5UKZy1cuYRkW/V6HMJQIxaDbdchnc0hdQVXjs+4KiSSKpuD0enhOH8tQGRmZpFarUyqVsfsuqqKRyxZoNtpEKKQzeVzfo9PpoQ/AvFprxG5wiSSHx8Y513sTy9TRMEmnLJKmhSZ0LCtFp+fHJ/1IQRCotLsugVRRVQ0hUtjdAJQ+7fo6hiIhYbK08DJvvvFVCsVRnG4TRbpI30YlRJUOgVMD38YSNdobZ6hWWowkXWwrxeLSaYojCbSEQCo+gQxwfJuzF8/z5sWrpHNZSFjIeoOz584TBD5TUxODLfyQSmW4ePFCvEQcbFxSRBLXkUyMT1OvdfDcjXgDTtCk1+sNNAqPzUqdtY0Vjh47jGEKTEtnZLRAdbOJqgoCP47f0O11cVybRC4G+3iFFREGEs8Nsax4d5kUAaZpUKnWYm8fER9eG0UQ+pLAl/T7HVRFJZFIUKvVSKaS9HotWp0qq+vL9HoOb7z5PLbTZXr6MGNjBygUJpBRiFDVHa56bIUARcANsZj2RnST26i3Df7synQzXByk5gatdeuK2H1pbzIBuw5hfNsyCLS1g27YSX/sbO9Qu91979t7j4ZNU7b81mOfdmVHB+7nCblV/7DzBxUMaZnh9vWdoL3lSSIGkQ8HcQX295IcrCiEAEUlQOIGPmYUoIQ+EBGFIUIoBH44oEJCwiDAi+TgvMgunu+haTqdro3nhoSBxA9i7y3H6aMIgWHopJJpkgmLwPcojWS2KB2I6Vjf9ygU8qRSqa2DUW4m7wrg9oMAqUS0e01atQaaqtKq1FA1C7dbZ23xIs9++7t8+id+gs99/o/JFUr4QcS1heucPvMmc7PTPPG+93H85F1MTE8hdI1W16HTdpkYK3H86CFWl1dZX62iqhGWZaEmTNptl1yhRNJK0Ol0UIXgwOwUlc0Koexw5exZGpUGaqKI50XUa20atSazM7M8+fj7+Z3f/h38UNLqObQ6Dn3bJ5kw6fVsrl67Tq/vMD09y8c/8sM0NzeoV9cIvQ4pK0ur2aK20UZLxnFG+v029UaVVqdPrpin6/XQNY2kblFIZ2g0zxD1QvSsRSJMsXxxifF8F9vuoYYBWSuBDDo8/sh95DPQa10lDOqUzBYlVSVTFti1i3SkRjINVmqEMBKo2TSaERAlE6yvrrJSbzOVKZIMNJxGn0uXryGjgEazRSplUS4XmZqcpVatU9lss7nRoFHvEgUwMTnDAw88QOipyEgljAS2raEoWSSCMPTxA0ml1qL18huUR9PMHphkZmaKRNLk8uVFNjcauLZPFMUTqec7rK2vEYUS1/a4eO4qmmLS79uomsBKWiRTGVbX1vB8D8NQSGQsFCG4dOEyVjKBjAT1WpNmo8n8/Dz33Xcv7U6Lnt3CsmLNsVK5zIULr3P3Xe/hfU9+jHK5gIw8UBKx25hQ4oOGlQGFICQiYteu8fgIrCG6ADLePr8VrW4AbsPdfXGe7b/bGuPwenRTzTR+4bcP1d0nBWLHGYzvSOK94bs2rmwD4G6PmjAKIZIoQ1e8rZAsSgygkRweLglI4p7ZGYTrxtjiW/chBhMH2wc8RFEIQtvaaBO7um7z4ooyONlSDs+tFDvAHSKhEMkojg6Igi8hlCq+VEhpChoQBTFoO05Iv9Om57j0+n263Q5ur4/j2qiqQDc0VN2g23OQUsEwE5iGQSqRo1AsIhAoiiCbzpDP5WjUqhw6dBDf93BdlyAI0XWV8fFRcrkMhmEQhuG7nyoRCOq1NrW1DZrVGrlMgeeefZHDJ6bp2zU0VfATP/kp/t1v/Sadvsvy2hqKqmEYFpXKBoQhtUaL9UqFD3/soywvL8c7knSNWqPO9esa09PTQESz2UAi0TSFyYmxrSBUvuejKoLV9Q2cfh/LMmm1W6AoTMxYdPtdHnvkIQqlMaan55mfP8xTH/k4r5x6jXq9yqWL5xBRwPVrV7h6ZYFsJo+UHZYWF/nn/+L/Igz6zEyUCBWDbrfLmXNneeK9j/Evfv3fk86mSaSS6FoCTZU0612ECoau4xs+dDqMjFo4tk2ruUGhEDAzV2Z6foblxR6L1xr02j75rMcLL36RE3fNkUyDprmkDIWuL7l46QqeL9GNFLlimdFEAtNMoOkK7VqHxdUG65stkskUa6ubrK3XyeYyfOITn+CZ73wbTdOYm5vn+PFjIBUOzh9B0zUuXLzEVa6jCJ1CscBXv/ZVkkmLVCqF67noSpZ2u4XreEgCVFUjk80yPTPB4cOz5IsZVFVB11JoqkW/9zph4GEYJpNTU1xfXcLzHBJmEj2Vol1tEeBSLI7ihx6hDAijkEcfe5SXX3qJdrtJImkyPTPFXXffy8LVRZKeT7vTotFoUK81uX59Gd936Ttdev023a6NleyQThXiAFzjI0SRS7xSDYgX1tu4I0V8kG58kswt1vwiYnsTzl5QenvucPu9M8PgszfddPN9Nljeyv1RiPiw7iF1sZObFoh4tyK3thPsmgjCeNLZ6RmykxpRNQ056NsbNwDdJrcvlUE+lUiqNFtd/vyf/3me/fJnaax3SA1cfJ959rs0O31QFRKpJMVCAcdxmJyaIJ/PYZoGXhjStz1cN9bMVVUjm80S+B5CRIS+g6JIDFPlgYfuJZvLbPXXkJ/PZDKEYbjF2b/rNe6RcpkPPPEQy9cvcTVqoRsOvX6dS5e76HqEY3d4/rlnCUKPKApif2lNQyiQNBNsrm8yOT2NYZjUqjWy2Szvf9+TvPnaKTy7TzabZ2GhSq/Xod1uIxTI5bIcPnwoPv9NCsxEAiEltXoD17HJZ/McPjyPIkJefeHraEaCK1fPMXfwBLMHjnHl2jVK5Wnuuuce6tUK3VaTM0LBdz18z2fu4CSqskG71cC2A+6/9y42N1YIAw/LEnR6Xb727a8zNTPG8kqV5ZUaqqLh+SG6YaAokLHSjBXytNaucvLoUZLJAEUNcTyfl54/xfF757BEwOxoCjujsLZ8lve//34cv0/XbuB0e9QEPHfqAoZhcmB2jsmJSdLJDGvLK6QyaTIpi4nREjKqc/16F4UU/W6PVCZPqTBKFEpM06RULDI3N0+hUOI3fuPf8Hf+zt/m1379V7l8+SpBGDE2Os7LL75IrpDFdnqYpsnMzCxLlzeYm5+nWt2k02mh6YLR0TGymSIvPH+KXq9LvpDlvU88xrlz52m3mygqpDIqVgJMzUREIMOIYrHARz/yUb761a9T3ayiGzqGYeA4Nl/72tfIZjNEoaTd6rHMGpfOX2d6Zo6PfOTDbGys8fnP/wHXr6/Q73t4nkfge6iqYHR8DF3V6XUdnn/2eVrNLh//xA8xMXWYIAwGIKShoGxDwoA2eGtsHMSBFjuX/NEObHnn4LrTCLdb696px79zo+jb2Wyz05g4DOrEjh2Mu0RuETFvWd5Qkx7en6qqW3y63JP27RmABfGkGq9cVNVgfGyGhFvHNNIIoeP6IZ1un4mJCbJFH6EopFJJZmamuHjuIslkEtOySCQTZE2DhYUlHNdBoBNFglazRzqVYHZ2gnNn3wACxseP4nsOqpa4gd7x/RBd11BVjSi6dX+/K4C7123z6svfpN+r0O3W0NQ2jruJ5mVod/q0mg2q1Rrtdp8gkmgSlAgUVZLN5Ln78fsojY5Q2azy9a9/k7vvOYmGxHXsQdAkgabp+H6AaVqomoJhmEgJpmnGR4oFsaGgNDKK0+/RabXZ3KygqRGuYxPJgAiDXrfGxsYithvQaPZIJ1O8dupVrly+SOD7pBIJfvH/9V+xsLiM7/vYTgcZ9Lhw4SKO3WV+fpqp6RGQDpXKEm6gogiFkXKZ0dFJ3njjLJ1mD1VTkV6A5tscnSvR73YwNYtKw+HaYpMgSvLSs5sUTINMKoOumRC5KMRb3Rv1Ho12AzQV25f0PQ/74lU2N+rMTEyxsrzMIw8/SrMTnxo0OlKi17uIYgpOnjxCJltGovD666doNOuEYcB3n3kWwzQJ/IjP/cEfglQYHx9HIslmc6RSKdbWNggG7pWqooKmsLGxRhD4mKZJMpkg8CWLi6vU613C0EfKPt/65nPMzE4wOjpCImmi6wpXrl5FkfDoY49RqVRZXl7m5Vdewnb6KHrsG++5LpI4DKaUEYZlkkqmODB3ANcNqFebPPOdZ0imEjzy6GOcO3dmwLkbjJTHKJfKpDNpLl2+jBd6KELj6tVLfO2rf8Jf+oW/RhhIEDqKYqKp8bmiAz0X2K3U7gKOIQc+jFUy9LZgAKsC4uiMu/PvAssBP7tTboiQx36a7M52vPOJYdiet+tqOIwRMuSob4zhciPADusZas9DTTsM401jqqpulxPzSuw8Pm2Yf6ipx/G35a7yhulUVaHvBBgCgihEkaCpgjCICEMZH9IgVIQCXhCgaTpZ3SKVSZPLprF0jfn5eUojJQxDj0MjDOo2dINcvkwykUFGoAiJbdvouoZh6PiBj6apSASKog6oru1DE2zbHdyz/+7343acPvXKIkHQwHc7JJMBYCOlSRQGeH5Av+/gez6KbhDPkDqJRJpyaYRsJs/4+CSGZWB7Nhsb62hEhKGPrqvomk43lIRhPGgCP8CxHbrdLoqiEgQRCSuJaSYI/BBVNwjCENfzCRUZx/xQI1Q9wnU61GvrtLsOqmKwvLTA6vJ1NjbWaLcaaJpCwtJJpy1MS0VTBYqqEyiS9zz0AeYPzpJIKFy7dh5nJUDRDUIJBBEyisik0ihS4HkuqoRMOsH8wSm6vVWazZBa1aVRi5CqRagIEiWTZMJCFRaKIqk3WtSbTWw7AmnhOj4yUpFSYNs+Nb+J9AUKCisrGximjqFpICRTU2XWqhXyOQsrqVGrt6k3aqiqQq/fp9+3URSVdDrF0tIKYRSSTCVIpZNYlonMwGYlDrglRHwIdC6fY21tHWBw9FlIp9PHD3x8L9Zko1BhdWWTfD5DKmXEIW5TFuVSmWxCQ1c1NDWOLthsNSgUc+g9Hc+ODZaabtFutQcBieKDfh3HIwxCkqkktVoNlCKHDh3k+vWr9HoRR48eQRUqvU4XR9MRxDtig8CnXtvktNPm2We/yfyhk2SzZVRVBxmwFe95C3tuoeWJ3Qa77fjXcstwd8PR5+9Abro783tkSm4E3NvPJ3YcAnHjhLT9/07Z63e912NkaxKROyZA2HdyiScNtiiH4bby4WQghBxQXYPj6gS4nosZyXhCRUEKSRBKVFVDNQzSqSSWZdBuNclki2Sz2VjBkpJgcMCK44YkLGugoIT0e20azSaapmNZCWzbIWmZuJ6z1b4wiCcn13VwHBfXc3EdB8dxbtrH7wrgDoOApKFiRyFuZJNO64Shh+e6KIqOrlp4bmwo0DUTRTOwzBSFQpmpyVkcx2N8bIJjJ4+hmRq/+x/+PTJwkSIinU6Ry+Xil1cKHMclCH0cx0EbUi5CJ5XOYpgay4uL5PNZII4poCBxPQVUgaGEOE4P1wtRVJvZiWmuXj5Hr9ug12lSr22SSSf41re+yuj4BL7Xx7Y7ZNNJTp64m7/+1/8GqgpXrp5naeU6th9SyFh4fptuq47neuRyedIJg2ajTi6TYGZqgnJ5hE5vjUqtQ7cHhpGk1lYojI5hJAVSVfEiBbSIWr3O+kaFYqlMoThKvVml2W5gajpmwkCGklqtxfyBOU6fPsfhwwfJFXIEvs3dd03ReH4Dx6nTd3pUqy1MU8ey0rSabYSikkwmMQyLRCLJwsI1xidGGBlNoKoKvu+haQLDiLf82rZNPl9kY2NzEH/YxxlETUyn0gi02HAjDHTd5MK5qxRKabq9AuNjIxw7chK7FXD2wgWCMCSbyeL5HiNjY1RqFbqtDkSQz+YJ/ZAokvi+T61Wp92KQ8w+/PDDBKGHrqsEgYtp6uh6locffpDV5RW+eeY0lpmkPDqOUCGUHna/S7NV4d//+9/gL/zF/5Ljx634YIzAR8EgGmh7ch+vj12bPXZfuSHt90JjvNtk7y5GocSeI3tDq95O/r1R/lRV3bWVfevQ4j2rnSEoK4oCirJlnNxZVhAE9G0bM2FBFCIUCYQEgYfrR2TNAT3DwFMmCNF0A8M00XSVKPRp1qtk0gWQA2OsECgSRkbKrK/X8H0P2+7h+z6NRp1mq8pIqYCqqDTqTdxEklarN4i5HQxA242D1Hne1vkB/X7/pv31rgBugQqeTuQKIl9AqHLxwhJ6soOVSOJ6LnbfI58v0bUdEkKBUMFzIzKZIu/9yHvREzpnz57l6vUrZDIZXnj2FM1mg82+w/rKOidOHKfT6eB6DvlMEcMwWF+rYKVMxicnsV2berOPFArJZIr11TVanQ6GbiGVHO1OG9Hroesm4+MFxsenSOohlxcucHXhKvV6hVRKxTIFZ8+c4qVXnyeZSlPIZ9FVk0cefpzF62tcuHiOi5fP0O5s0ur02axcwNATFEopdM1gc30VwpBcNg1RyOuvn8Xr10C0CcKAIDJQTI3CWIZI8eh4Gl3fRRCQSJjMzU+j6jAzM0e5PMLK8hLnL9ioSsyb6ZpJfqTE6dMXOXrkMK+8cgndVDh59xRvnj/L5GSBRnOFTs/DC6BcGKfbtfH9AMOIDcLFwginTr3G6GgZw4iNraomsCyNIHSZn55F05K89MIpDKMxOK5JAQlhKFGEjueFKIpOEAT0ei75QolWs0apMI7v+ly+uIxpZHjtxVP07T6FchlVU1nbWKPV75BMJUln0hRzBY4fO8YHnvoAf/xHX8K2Yy1FUeLIftevL+B6Lo5roGoRBw/Noigqm5VVEkmNJ977MF/50je59/57sV0XP3CwXYVur40QId/8xlcwjQQPPlSOwUPRd3HdW3TGfhs+5J5jFG7Q0vfyvDu/70Mn3JB3+/uNZbOLnrlBhm55t5Cdrd9q2X5U+t58chDAaYcb5U0bsef6MJIfbIN2EAS7qA5FiU9zv2EalNvatRgYjuODp2MqLVYeHFqtFqOJEVQ1ot/r0en06dgBqXSR0ZE0fuDh+R4yBK/vk0ynMUyNhGWRSSUYLxeJsLYooYh4e3oqlSaMNmk0G6hKG8PQGZ8YIZU26bSaLNRq9Dtdmo0GnV58DOHwbEkhBPl8nkwmQzqd3jow+2by/TgsWAVeBlaklD8ihJgHfhcoAa8APy+l9G5VRrvd4jtPv0g2bzAyWuDokTkUY5NavYv9/2fuz4Msy/L7Puxzzrn721/utXVVb9XL7INZAGIGAAECXCCKFEnZQdGCLEUwbCoUDCtsWXLoD4dtOeTwHwJlhWUz7KDECNkkJYviapAERUAEZp+e7ll6reraK9e3v3fXs/iPczOretANCABpzY2oJTPfe3nz5b2/8zvf33cpS0xtCFTouY1SMRoN6Xb7hFHK9s6YKAsII0WWxnTimAcP71NtNgx7A+j0CZUiikNUIGgazWKxoNvt8sILz3H/wSPWyxXaNEgB49GAs8kZcZoSRBFZp0uS9HjvvRVbvR3ms4qqPCRfV/Q6h3Q6Az736eep6+ucnk5447vfYzOvqa3BqhAZViQB/MzP/gF+9Vf/EXfuvcnZ2SNE0NDJOuztbFPkJVVRU5ea0fYQjKXIC7I04dLBLvP5kuHONo3JmZcbluspaQ+qWmNlF2ctWRLzyZc/xT/5R7+KxPLowRECQRJFvHTzZd55513CKCYKEqqiJApCppMZ21t9ev0uSdjn5Rc+xiIvWK2nxKHvqlcbAyREsd8+rtY1VXVKnAw4Olpy+GjCzZvP82//r/4X/MW/+G/S1Jrb1RFhmJJEQ8qqwGjbWlVKlJQ4C1WhsU4gCLDGMTldMt7a4dHDqad6Yflv/+FXCAKLMXB6siDJGvZ2rvLowX2auiFLQrA133+z4plnbpB1Yz5349OURcV3v/s94iTFCYdxmjBKGYy7aN3Q62W89c5bKCHZ39nlT//ZP86t99/jF/+FX+Ttt9/hq1/9OqPBgMs7e+SrBQ/uvM3+zhbXrt+kqAogBWQra/bey6EMMZh2aOY7Qm3OkRDHeRrORTbLOefb/XBKjviQ/33YJ87FLm1JdedD0I/Aun8vxw+dlvitdfYphrovnOcQ9oc11xd49MVw8klnfV5cpfDzKBUEF59XSnk7AqUIwxClFMvlkjRJ2pg7gzEarOW9927x8U98jOOjYxbzGQcHB+xs7/D++7dpWsy4rmt+7b99nU++/BKhVIx7fS5f7vJ3/v6v8PIf+yMkWYckyjC6wqDZ29mmMQZjDfPlBoGjLFdESch8OWU2O2W9XiOEZLZYMZ+vyIsKZ71XjjUGZx3j0YirVy4zny2IoqQ1uvLmaY2uGI4yOp2M7e0dpIz4znfe+MhfzT+LjvsvAm8B/fbj/xPwHznn/poQ4v8G/BvAf/rbvYD3LxAYI5kvKxZv3eLBo2OEDCnLmqqqPae3rglUwHKxYGtrmy988YtUVU6332Ew6JN1YoyumJ6e8B/87/6P/Jf/5d/ge999g/liwWDYxxhNHIcURclisbxI9Q6VAutvOt1Y5tMFo9GIKEzQjWazznn22ec4PT7BaIHKYqIw4fjkmJvP90gjhRKSQb/Pxz/xaT77+S/y7ddf496D+yyXK4K+5Bvf+AqPHt/F2oJePyaKMnSzYT6bEwYRvW6K1payrIjCiCCOcUKyXBdYvaHHEBVJur2EKHUEUYZuBFYKVOCpYd/65g8YDvc5O35MliR0uxlKKt5+9w6d7pC6aSiKmjAM6Xa7IBS93pAgCDg9XZBmIUrEhCKFOCLOBkhRUOQF1giflVcZSqk9z1pGCBlyfDznP/mP/+/gIsq8QjcrlKr9cEZJpMR3YHgHtHPxiB/O+KLmu28feWatJQgkSdpFUlFYX+SNgaYypFnH0zkvXaLf63B6cso7775FliU0TUWnm/Izf/CnmU7nlEXJah3Q6WYoJXjrrXeJopjFfEWgAi+aenCPa9ev8M67b3P/wUOqsqEpl7yzeYd/8U/+CY4eP+Rv/s2/wR/9xT/Bzt514kSiVIxzjqapiaKUpq7aa1l4oYn0BdU65+1+pcKYhid9c8tIsQ5xwWH+rUyLj5SzX0Avov3Ifyz/GSo5P4yp8sMimwsw+ZwGaH8I1/+h/59DGn734l/EGL8IBRfdMq1xm//JjDZe0SgV1lnKomAyOeNgb49QSerKX6MOR7+b8fabP8DoGiUEi+mELIkJlKDX7ZNlKc4Z3r/zLsIKis2G06NjzlZzbt16k035JWTgd5bGGcq6Rkp46wdvMpsvaLTGaotuBEEoQBiiWDEa9VksFhT5GiUhTWKqylJXJaKlj6ZJwu7uLrPJFFlG4MC6ABk4IGF7e4ss69HpZC22/s+JDiiEuAL8MeA/AP5t4a+yPwj82fYh/znwv+V3LNyC3nBAkkZIJSkq7743Hg2xFrT2QoSyqAnjhKr22yljNEmcMplOmExPKTYbptMJRZ5zenrKcrmkKAqM0X76+xQ9yYdxejOjzXqD1hoh3MWKH0UxvV4PgPV6Q1GWaOMQQqGNY7XKKYoSpRSbTc4mr1itK3CKIi8Jw5Cd7W0O9va4dvkyb775A+7duwMYgjafrixr4jAhCCKUColjwXq9oao0URgiQoWTgmwwoDcasV6v2BQryqIitorxcI/pesV6U9BUNbYx9LIO+abBau8FEccZy1WJxcelhXGAbhpq7YNQ87JBNRqlLKEOCWToB7aloVlvyDdV29FYtPa0LCsFSoWoQGGNIS9y7t69w3g8AgtV7X0ahFBtIXFPYAP3VHG66DYtUgq0abDOoJRXmoVxgGsapJBY59C6oaorZOBv7jhOSJIU17IHiqagKAu0MaRZRtM0rXo1pdvtkqYZONCNLwp13VCqmitXruMcPHz4mNVyTRCEFOuSz3/u8zRVw3Q6Zble861vfoXh1h0+9rHPMd7aIwxTfz3xROxyznQQQrYqYA8XOGOQyAu13wVBQpzXv3Oo5XdbeH8IWvlnzN3+yO/61IDQPVW0n3zeXfxsT5+jP71zUY29wK7PoZXzheqcUSGE4OTkmDD0Zk5N4+dTSkrOTk/Y398/J5kQhRFzXXN2ekqgBHEYUOQb8nyNlIIoUOBinxurNbrRJHFC09Q0deWTbIwmDCPiKKI2BUIKwiDg+PiYyXSGDAKwIEVE0BbvJO2wtbVF05IowlBhjAQaL+KREuksaZoyHA555plnsEi0rnCuAemvk+2tbYQICKMIhyPNko98/3+/HfcvA/8O0Gs/3gLmzrlzHstD4PKHPVEI8eeBPw/e9rA76BOGCusMBku/12U8HqOkt1a0zjKfL8l6ffKiwBjD+++/z8uvvsLtW7d8TmKZ46xhtVrxX//N/5oHD+5TVSVh4OXOQRAQhiFBUHOeGuIxNNuavRiEEJ4yqAKSJEEIQRAqDo9OUCjCQFKUFXVdksQSoUJWyxXzxYbNpkYGMa+99hrGaUZjvzV65to1vvbVr3B2NmF72+Prp6cnVEXD9tYWUgY45+lESZwwna6QIsDGApRitLNFtz9gtV75jLrpkn5Hc2X3CnPnWC2WLOcrBt0+h/NThG0oc0ueV3QHDoviZLrgaqdPlnbZ5FM0ilCGLNY5SsJwkNHUGhX5n71pNqzna8ran5MfxIDF0/BkIBBKYo3f6iVpwng8IIoCJpMZm3VxcXP6emQ900CcF3LRfs6BNNBSwaS0BGFAGAuUctRVe5Nbg9aOuhYIZRDCY4C6MVjj6Ha7zGd+uGOta9Pn11RVw3g8YNAf0Ov02NvbZ73K0VpQFRVSBjz//E2+/4PvYq237Ox1e6Rhh5/+6T/Ia699GyUVSRzyzW9+FW0VYRDw8U98lq3ty4QyxJoaZ+sn7AcnLni4UihMe55BGCHdE4jjvLv83Tjg/SgdPzxQPOdbezzaPYVvP5Vsf/E8d/H4D7Ngda2/h7OWs7MzyrKg3+ujtaZpGq5fv8a777xDt9shUAFCQK/X5datBbqukaGidoY8zzk5OabT6RAGAQJHVVc4Y2mahu3BgE43oxaaIAiQQvoaEQaYSrQGdDFFUVA3Nd0kJpABSdwlCAVKeU3I7u4eTeMH8E3jaDRoI9vC7WPSkiSh08l4/vnniNMMIR3ONhjr2W8gKMsGB6hA0smeuFP+8PF7LtxCiF8ETpxz3xZC/PTv9vnOub8M/GWAbrfjirribLZms1lRVyVIxeHR69S17/YQwrsG3niWKE7QynF4eEhjNN1Bz0+inUVJQb/fJ89XjIZDnNHkmzXHx8ftG9dBCMF8Pr+4yL74xS9w69Z7PHjwAKUilssVcZzy8OEh1hnyMqff73N6PGn9zCxKOa5e2Ucb6A3GJNkIYwSDwYijkxNOJ6dMTk+ZnJ7wza99lSxN+firH+Pq1cvUdcXZyYyPv3qDyeSMy5evUFXeuvRjH/sER0cnzGZz1qsli9WcwVbGw8NDlvM5RmvSJCKNIpbzCUnkedy2EQyH25RRyfTshChKiZOUvNTULQ3y5GzGZLYkzwt+7Mc+x4MH9+gO+jR1zfHpnEE3JUyhk3awNsC5FdY1dDsZa1sgI4WUAcvlChze3yUJcUBerFlvYvYP9p8ENjjpd0sYX6Da3721nnUQxREqAOsMVVUiJUSxomkqlsucnZ0dtLbUTUUQBQShoCzXZL2UTqeDlIqqqlmvC9K0wxe+8HkePXpE02iuXrmKUgF/92//fZ67cZXdnS1UIHjlpZf5+3/vV1AqBitYLlb86q/+Y5xzfO5zn2OxWFKWBT/zM3+QW++/z5e//GXWmxU/eOv7/Mo/+hUQkl/5lb/FaNhne7SFLg3G1kxnJwwGPdIkwVqYzRZs7e16frmVBEHwhFgi/CL4Ow/vPvS++aj78bf9+oc8g9/NN/+o1/1hNsn5v+ed9fmXz6X5Wmu00VhjSNIIpdSFWvBpTnYSx9BCUfu725wcH9PvdWiahs16w3I+487tW0SBvMC+B70OpmnoZDHDgU+AmkwmANy7e5fxaESv26UqK7ZGI2S78046KZcuXWZ//4Awivx5tKxPpSSdLCGOw3Y4H5IlHZwTRFFAmsWMRkNGoyGbTc50ssCYEiG8T1E3S2l0hcCQdWPSNCAKA5A+jcpbBRicsMRR2kJqDmMtv81s8vfVcf8B4I8LIf4okOAx7r8EDIUQQdt1XwEe/U4vVJYlVgqSTobBIgOJQKJrR1NZcIIojhkNx4zHYzrdHjdfepnPff6L/L/++l/zRkDQGs9YNpsNgVJUVUWe51RVhZSSGzducPfuXb+Ftrb1wtV89atfwzlHEEQ0jWZ7e8ezjYT0PHBbc3w8YTgaslouffcUhFgnKCrDZrZiONji4NIV6rqh3x+wvb2FNjXT6Rnfe+MN8iThq7/5Fb4ZBASBIklinrv+PK+8/HG++a1v8vZb7zCbLfiNf/pVwjBivd54KCgJmZxMePHFGwRjSKKYfJ1jteXk9Ji8cWSdPlcu7bNZV5RFSRDFJJ0Og/GQbn/Au7dvU5Ylbun9fTebDV/7+jeoqoowDpGAKTWTozNuvvwKnU6Gc5KiKKgqy/HhMXWlEeJ8OBQSCMnJyTFbO0N2dncwtub+/bsURc7O9j793ogHDx6SJOJiTlE3NdZ69VuvOyDrpjgsRbGhLEv+6B/9w3z9G19rzXZGPP/88zy884DT6QnPPn+dZ65fYzI54/D4iJPTM6anEwb9PgcHl3j7rXfb75dgGsNb33+XX/ijP4PWazqZ4tLBFkLAX/rl/4Sf/Mkv8/7tuyzXKywB8/mM/f1L3L1zz4t44oh7995HCMU/+e9+DecsTVPx8Vdf5fKlqzx4eMTRw8ccbT3myuWXePjwMffvvs3p2TEHly7xysuv8tf/i7/KF7/0E0Rphywd0O9tMRxstw5xpmU98IR29t8T4vhn1pn/nmCZD57HD/OuzyXcUkpfiCU+NcY5jNE0jfGBz3EIhBdQCfxWLL+pa7CGsix46wffRylFliYkSUKvm1GWBcI0BALKfENpLUmgODs+4sUXX2TQ75GlKTeuXeGN19/g2evPMOh2sLpG1+XF90yzjDiOyHXJcDigKAu/E7DG2yLkK4w1fPmnvoRxnvXy+muvs15v+PJP/WHG4wFSthAsijyvqGpNGMWM+33Goz69QUaaKHrdjN4goq5KtKtAGYTUOK2pm4rprKapHcZIdCN8TOJHHL/nwu2c+/eAf699A34a+F865/4VIcR/CfxpPLPkl4C/9Tu9llQSGQYUm5KqronjmNlkRhwlaA2iaQiU73XH4zHT+YKvfPWrvPHd77N/6ZKnYrWvJfAXizY+D248HrNZr5jNZjx69KgdgvoJfJomXtjTaDqdDjjH2dmE1WrNj332s4CgqHNqVzMcDVhM16Rphg4aD8ugqGrN88+/RLfbxxooK83O1jZa1zRaEmxv8wd+/MeJ44T1ek1eFJRFQVU3vPP2LU7OTsjLguvXb/BH/sirPHz4mB98/02uXL5O05ScHj+mn/TopV0eTs6YTqcURYG1gsuXrhDmJU1ds1pOsUaRZDFm05AXBW7mEFKyuzXm6PgYXVd0u12uX71JWZYsl0u081P2sixJwph8XRAlKbquWS3mVJUE63yKh2uNd5ykpqLX61GVNY8eHhInAdbC2emUIm8Q+MFfkoQ4HCqQhGFKEETkeYGxXsyitaUsaobDMd/61neoypowiMAJNpuco6Nj6rpkMZ/z4KHl9u33iJIOSskWuopI0y7rzZrh6ID+oEscRQRBQFms+bmf+xKjYZ/J5DF5vuELX/gUAk23m+KEpqhLqgpmsyllWTIaDwlCwQ/efJ1ed8znP/8Fbt++xa1btxA43v7BLf7Nv/AX+dirn2E43KPIC77/vTd479YbvPDis0TKceudt/iDP/Ml+lsDnAiJwpQsTghlQBD4oubZeK4VqvChxftHGT757eAda+2FMMVaz6gQUpCEyYcyaD5s0YrCAImiLDa8/vpr/NzP/hy9bqedVSTEUcyd996l1+1e5JZuNhs2mw1JHJOmqU9RN15GHsdx65cNVVVxfHzE/s6uZ3zgiOOIXreH0YYw8I+vIz/HiZOILEuoGk0YBXzms59BCMl4PCSOQ78AW7hx4zmGg12qpkHbhuYcwxYaKR2b9YTp9CFJHCDDGjBoU/sGLc1wQrMu1kzOVkwna6ry/7887v818NeEEP8H4DvA//N3esJwOOKzn/sx7t+9w4N7dynzHCklddNgjPZsD2NomoZPfOITvP7GG7x/95432b92lYanKVXiYlt+7merdUOSdAHYbHKcc0RRRF3XnmxvDEY/2aqFQeipa0oRuohEJIQqZrMsMa2dpJS+aB8fnzEa7RBFGUmc0e8HhIGkaUoiE5BlCa7vCTdJklKW5YWpum4MZ6cTojjCGMvx8QlN07C7u8tzzz2PkvDoQZ+zk7tMTyb0un1qrVnnJRbH2XzJqNfFNCWbPCeOe+A8k8F3cw6jG5QQFOsShMPGDcI5ep0Oee4TOBzQHwzBWiaTGYOtMUkcE0Uhs0XehoWIC+GDVL7bCsOQWnvWxHC41bIsNM6d85wFplWGWedwQgC6HTxV1E3TUv88Fpxvinae4cjzkpPjUxrd8MyNZ4izgHUrz2/qiiBMyRKv2KyqnF4vI4pCBoM+g0EfpSSHhw/Z3eqDq6mqhtVqTq+XUJWGui4AQ6+Xsb21xWy6pmk0UkKShJSlQQhLnm9YLpeURcloNGI1L3n84JgXntOEQUzhClarBcZUGF0SBtDJupwezxnuDsg6GWkyIIm6YAXatEISca6eFL9N4/s0DHH+8Tk+7t/fDzz6d13ofyu57+nPfaCgfpA2/tGveN6Fn9MicR4aktJjvUI+YZX8trsMRxAGxHHEndu3CX/hF8BZwkDR6XSoqxJrDUniAwics6xXKwIlfbCDNTgrSeLYw6e9Lp0s9QKXqmQymfDGd77DO1FMnEQQKR4dP2b94hoh5cXCI4QgDoKLHz8MQpJBgjGWzSanabyi11pHUzsfoNDUFHXOKp+xXEwJI4gCSJOALItZrUuyPhjrvZecc8wXS5SMQVQkqWM4jgjCf87ugM65XwN+rf3/+8DnfzfPT9KE3Z09Nqs5q8WE4yInCBSbokYbg2sNaxpjSdKEIJQEoSBNPQ4mnjLw8bQzz1Lw3U1DXuQeYul02GwKVBsSOp/nxHGKMZaqqjhP0Oh0OtR1TZIkPt1CpthA0O10Wa3WNE2DkoqqrMnDkoePHiNVxDNX/aDDGN3yUDXOaYxuqOqaLAtJ0wzT5tdt1hu6nS5pliKc4N6duwRB6NWTnYw4CtHVHocPbuGsYjgY01jB8ekMYxrOZku2+kMEEqMtQSdkuSpwCIIwQAW+46jLirpsCCNFU3s119bONkI6Gl0jZUjaydBVw/R06i1TQ0kSJTTNCicU8ilINAgDnPU0rizr0OlkpEnGJsxR0qBU4IU20j/Os1HA4LfLRmtAUpblBdPn3I/CGI3RlqZuOGsmBGHAs8/fYLGc8fDxlCztUDcaqSSdLCJJFWW1ZG9/TNiGSHsPZ8g3a+aippMFbVdUE0UZeV6gTYkKBaNRj62tPcLwlOOjM7SuMKZhOOrT63Y4PX1EWW3o9TscHOxja8l3v/t99vaukSQ90qxLEIC1FUW+xOiKThpxazbh4MYBaRKTJCFKgq41jTFEkQ+OtoCz58XyqT2j4IIyef75DyoQvSz7wzxAfovq/aNq4w99/oPPcx/2kCfP+52Kd0uDPH+glOfn+mTR+Z3gIet8mG8YRpwcHeGMxhlzwSBZLRdY3dDrpMRRgGlq8s0aXVfMJmcsplMCpdje3mI+nRGqACUl1licsWAtx8fHvrEJA6IsIa8L6rpBRREyCDHGohwEQqKbhqbRWATUNZtNQa29UjhoWU6r5Yb1pqRparSr0a5gMT9jMMwIAgcypTsYUeZzYhehXePhJCBfrUjiDiqA0Thhe7fP977z+CPfnx8J5eTJ8Sn/2V/5zxiPO/R7Cd1+xmy2JIozUDVIQdrpoRvJL//H/xHdXkqv10HKGOs8XuisQSAJVYhuKpRwhElAGILWFavVAiEEOzs7FEXBarUmjlLiKL6ACs69fc8ltv7/IVjBKl8x7PfJ12tK3eCkpNGGF164ydHhMatVjgxipEpAWFQQ43tejTUVKvTUIJzzwcGqopNkJGlKFEWEYUBd19y+/T7r5ZzXvvUNqrJE65rnXniVP/Tzf4jXXv82y/kRUnSoqjnDYZ+mdmgtCVSHKOyxWM7pjXqEkWqHhhWToxPfhYYB1hmOTs8obU2cKFQoKMuC04lm0NtCxinvvX+fKFT+QncC46DbzS7sJvujIUdHxwBcf/YGw+GI3/iN30BKSafToWmKC+mxNiDwXRGcF5iAOEqoK8+RDQLfRSulsMaiZECgAg9d7Hg2UFlWTM9mWGd55dWXOD4+QsiCIPIFYjQaUVeGO3duk+cVO1tbfOaTH2N2+gBT10gFaRyyszfm6PgxO3s9osQPcKsqZ2d3zGx+xvHJIav1gs/+2Kvs7Ax4771bdPsJ167fYHt8iZOTOXlT8Ou/+U9Y5Sv+xT/xx+n2Q95+57tsjVNOjx/x8M49Xn3l0/R7PZwzzOanlJVl2N9GqAgZxN6W1ErqwmCd/9NSTEB4hoEU9ocqqgNM+ymvDnzC1Pjw0ITfrgn/YWOqH+7YrXtiMiUuvs9T2DZPvvZk4fBEgg9zBTwfUMLTHP6PODcVggzRxtIUG6rNmnB7jDMN6/kCXTd004jtkWeauKaiaUree/sHnB0/ZjKZkW9Ktra3wXkb2OeefZZLly7R6XT42Asv8vzNF+kO+gRRSN00vPXmm2xv71GsFqzmE6wLSFyArLSHWzcFtXbMj2YURUNd5xTFEuNKur0IhOHRo1OkgvF2nxvP7XPp0gHjrR5B6NjkS04mt4ljSd5kJG2mqdGa4XhAvs4REsJYkSTiiX35hxw/EoW73+9z9eo1jo4ecHh4iMJjRk1dU+sGbTVlXXHz5qvcu7ei2+1z6dIlLh1cQhuHxhDFIc5wwdlWQiGkQQh14RhW15rNZuWlptoPSqqqwjlP1ZFStvhxG7bQCgKCwFMDy7J86sKTrFYr7t69yxe/+EVGo23q2nd0SZLgHb8MjgCtBUGQIBw+ncVYktjS1DUyjLHWtLin4sqVa6hAsVgsfOqLsyxWc/763/ivyIucvf09/uBPf4YoCfnBmz/gwe1bWANhmDKdTRlvDf3kvtFteKulPxqRFxXFZuMzMHtdOt0OZ5NjNnkFKMIoZj6f4pwgjWOc1WyK3EcrqRBjdGv6rjk6OmR7e5vFYsl8MSOOIz772U/z5ptvUpQbQLSLnkDr9mbmCfVNCMFg0Gdvf5coCijLgsPDQ+bz+cWAyxhLHEcomXDr9l1WqyVhmDEY9BGETM6WVGWO1n163S6TfMVouM0zV5/FGA9BvPHGW3z64y8QRgInLBEWoyVf/tLPMh7v8f7dO7z+3TfoZH26nT5R2OHqlSHDYY/Hj44wRlMUNVVl0c0xm1XBL/7xn+O7b3yfZ288xysfu4Y2K+7cfZvnn3+Gqt5w994tlvM19+/f43/0r/45Ht67zXS2omlgMNzmp3/6Z/inX/kNkiTlypXrhKqDCgIUCmsd5inmyYcdH5Zc/lHH7/S4H+7Yf7vH/7Dx09PP16a5oPYFQfBBJ7/f45EkCbppODk+JogiDo+O+Dt/9++wWm+IYg85Xtrf5vD4hK2tLQ4ODnjxxRf51Kc/S1VVpFmP07MJR8dH/PiP/wRf+tKXGI1HBEp5P5vTM3+/hiHGOc5OT+l2OlRliUAQhRFRFNE4R5Z1ePMHb/LobIIMYvS64erVG/QHGasNKJXy8seepdeLWa5mZJ2EMJJU9QZtGvJyBqUlCCVXrhygjaZqHI8fH1HXNdIJVosVW6NRy6SqOT6e0jQfLTj/kSjc3W6X/nDMarMiimOsMczef8wv/dK/zhuvv8F3v/9dnNDM5oe89NILHBxcIQxDptMpVVMw2t4jiSMfa1XWKBWAa4jjlCiMcNaxWq1I0xQpvX+BlJIiL326exxfXGiDwYDd3V2SJCGOz9VxDUIINptN+xp+wBQEAZ1+n9e+8z1u3nyFz33ui0gR+EQfK3Gt369UIcIJr5Br+a7WGKLQ0pGauq5pmpq4qTGZxVpDHKXkRc5ms2Jnd4uiqDibnlGUmrfefI/pdMLe/h5f+MJPorXmbDLj3r0HLJYzmqam1+sRhym6LonjlHw6ByGJ4xgVhAihsEISRAFGe2bHoDtmvd5gbeDP1Xnll7ElUiniICCJQqqyZHJ6grWO46PHLBYzup0uYeRdBv1cQtM4A6iLbf6590IQKIqi4P79+21X4Tu70WhEEATkec5ms0FK75G9s71PoBoEfmG8d/cRnaxPFEpsE+JsgtGak5M5xaYEJFmaMZ9vcC7k7GzKplgjWu/tt995jVde/Tij0Q5f/PxP8oM33yIMU/7kn/hTVHXJ4dFD7ty5ha4VupFkWUyv12c0GvLm26+xWE84ngjM99Z8/81vkXUkd++e0ukEhCpEKsN4q8ff/W/+OpcuXaWsDJPJgvVom7/6n71JkkUEYcxydswnP/MT1JVFBal/r1opuPgQoOKDA8EPL4y/u4L5pEsXH+Vp8tRxIbS5OIcnNL4gUMiLsIT/nufx23zL+WzOYj7l7t07OAEvvfQSP//zP4tDMp0tuXX7NrPJKZ/45CcJggBjNNo4sl6fL/3UZ3j//fd586236PZ7pJ0MpGC5WpHnOYvFnFAo0m6nzXqN2NrebmcoikDFqEBhrCFOEpyzDAZ9CuewLmC2mbCzu422OU6lRHFEpx+i3YYwNcxWj2maAhkK6rokz4vWnC1infvhqLEBUiQEUqGbhu3tfaLA51EmaUh/0CMITj/y/fmRKNxVXXNw6QraaCanJ5hGYy1cvnSZ27fv+IEVljhRNLpie3sXJQMePXqICjXL5YKDgw6upQJK4OjwIUo6Ts8m2Jbk7xxUZY1UnlcrpSfb17WXgXu/6Oyi+/ZqSt9xR1F0Yah0TnnK85zNOufS5SuMt7cRSuGcRMoAlGy7botAYbWX+EoJyjmstKAsTmiEDAij+MkQtm6IrCaIYsIo9hiqU+zuHrBcLZlNp0gkxaZgNl9TVSWr9YatnTEOx2q1xDQNVelVX2VVI2Tg3RCloqwqWDk2mwLnDIEK6HS6RHFIlw5VUSKwRFEIeUkn67TcW48/W2sQzuCcwGhxMR+oKg93BEFwIcgIggDdyoStNRe/i7ouCaOQpjE0Te0tMcuSOI5bb+22SBiYnE2p6gpjLOv1htVyQ5xEOGvbjEqP7cZRQpr2L36HaRzz8OERQhnCOKbTzQjCBKkiHj464vh06sVEImJn+4CrV64zmZxxdHhEVWrqxDDob5Gkntt7Njmh108RsmYyfURRLMjSAWEksbZhcnZCHCUEMmRTLDh8/IB+N0XIkEA0XL28xdHpCcv5EWnaIU0C7t59i93dq0SRl3SbVtEZxWHL2Psg8+Jp57wPClqe7Gb++x3uA139OXb8gUe4D/LMn144XLuwy4s4syd49nl4xIfx1J/mmwt7zvc+l5E+/b2dpwCPRpimIUliev0+YRgTRCmzxYL1yltTOOfI85wojun3++zu7XF8ctKGkCd0uh3qpkZZRd3UbPKc+WTKjWef9U2TMazXa69+zEukUO0Q9ckOZ3t7B5l1cIT0oy4ygJ3RFlakCFnihKFqcpTygjKDV+dqq0nShMZodGEJghBtoMhbzNx4+LQpc65c3kMpH+5hjf2h3/UHjx+Jwl3XNds7u+3gAKZnE1QYESUpg8GI0WjMJp8RJ961LVCh9xHRDWFqMaam08loAsOJ1sznC6aTGcbUGNswGAwvsvB6vR5101wUGfDy2jAMCUOfqAJcQCZhGNLr9Vr+acy5dST4eKWyKtne2WU03vKwjAWkanFyccGKEdJ7WIh26CTEeRCpQCrVzqQEjdFIVWGtIYwS4jSjqUuMFXTDAOccq8UMFyqKzZrVetPyPR0Hl3ZJ0oimCmlqTVP5BUlbr9oTQmKMQzc1RhiqqibLIrI0JYk8/znLUqqyABxhqAiUJIpCbGnQrVRYCT+5ty3E6pylriuapkaImDj276GfG7QdWDuEwfjhmjaawMkLCCYMwwsoxpvdtxiokCyWS8Ah5ZPfS13VNK0vSFHUxFGCkjH9/oB+fwBAN0s5PXpAd5CSpBlRklI3ml5/wGyxpsgLJILr15/j4OAKQZBQlprNusJoDz9FcYRSAmNK8k3OcJSRZiGLxZzVas5o6GmRDsts5imso8GQpilxrsG5miSMCIcZw2HK2UyDLdDasslDzBGA5NJBSpKqtmh4VsTTdLnzovl08RRPDYx/9wIc+EDL+1vq/fl1+lGdvf83CJ7ElZ0PH58MULn4+PwcP3B+7mmGzAdPJ1DKR3nt7fvdaRS18XcKFQRIpUiSlDCMfGNwcV96JlPTaOqmQRvDaDxGBQFhGJC4lCzLmJydtSpMz6PO85xhv48xxlP8pGgXde8A2O/3kGmGDBJG6QArNcNxHxWlaLuhrudUdYFqdVZCKhqtaRpHmobkRY6xDXHkhYT5pqEsK5SUhGHAerMmeCbGWWjq0vvo/3MS4PwzO6SUJGmHy9euE0UZZ6ffIO30sU5w47kXKOuSW++/jmONEB3W65w0kaRpipBLtrZGXLp0QF1qJqdT3vrBO4wGI7SpGQ47pFnId77zBlkW8ulPf4Z3373Fm2++xaDfo6oq4gvep09dLoqC2WyGMYZ+v89gMLjA8KLI+0wXRUG/3yfrdHzggnPEaUpRNP7CbYuxbmrWqzn9fr91RWuHODgsliiKLgb1AqBVTtH6N3hpsCFaLahrz4vudGIeTg+REk5nNQhJlsacnZ2yXs5wDpT00q+6bsg6XRrt2OQFxjYEkSIUIUmacPXqJaIwYnIyYz5fsrt7QNZJsbrBWUsSx5R5jm40UkAYxyipfPcsJLXRLRYX+E7BaYIwRUlJVRVoXbdDX78beRJD5bukc1pYWZZ0Oh3yPL9YJLXWrVkTLRNIYNoOpq4Lz8hoA2O73V77Wn6ntFwu6HZSrly7QhBJal1yOjmlKEoOLl0lCEOiyJHGGTeuv8Clg6tMJzMOD085PZuRJB329y9zdnZGXVUEoWBnZ5eyyOl1e0ynZ8ymMzabnCtXrmK0oW40RhdEQcTBwSWicL+FWTKUirh3/33efOsNXnr5BRarFXfvnfDyK5/lW9/6Cp/5pGJ37xrd/ohOlrJaz5HKL4A/3El/pPHU76ZoXzCxPuo5gqfDiJ9Olzk/Hz+APD+f1pOllfSfhxF85Hk5Lladc8vW89cHLoQ6ptul0x8wHA4vvnbeXF2+chWpAmzjYZLjkzOOTk55fHjMbLEgLwsvCNvbZTweE4YhRmu63S77e3tMJlO01gSRb9refvttrh5cpt9JUdLPxhLlackOCKOANM1wKiXpSQhqkBaJt+Qoy9rTTKVAW0eea8qiYbnyMn2cY6MMSipWK+3JFElEGIRUIkc3luVyzWq1bBOEfsTDgpVSDEdjTo5OWG9KdvcOqMqK2WxBXWuMsRwePmK0E/Bn/qU/Qyh2yPOaa888w617X2N3bxdtGlbrNcZY9vb2qcucXm/A9s6YTiek3+9x9eq1C4fA8XhAWXg+9bVr18jznOVySZZlFEWBaEn98MTw5mmFWBRFNE3Dq69+nN5gjDGO+XxBGHqrSXDEUch8lvM3/j9/nd2dXYbdPlvjMaPRiEGv17IOBEEYIqSkaRq08Rak5xz0pmmom4rBaERTK6zZsF6F7O8PeP/2ezjX9x4YSraRRzW9To9iU/lOPFD+fbHWGzVZEM6xKQpeuPkCTbVitVoStHaZ0+kZURAg2230YDCg0a7F4RvquqYoc3q9Hus8b2lbEql88fU4td9eBqFCNxbn7MVNqZSiaVpfCBmQZR16vR7T6fRilnB+DIdDqianrEpMC7NICVkWEUWCutJYI3w3Vnljrzff/MFFTNp43OexqHnhheeIkoCmqbn34B794RhjarRumOULfuM3vsIv/Pwv8sy151itVty+/S6z6YQ3Xv8+3W6Hqt5Q1xuCEAajjEeP5zRNhRCK1XrB4WHAYrGi3x0wHo0Z9Pq8994t0kRwOplQFjVNY7nxwnPIAN74/hsMRwP29g8wJgdb8/rrr/HJT4b0eyOqsqTf77PeFB8JVfx+VI/+YuYjB6Dtxe7TWcwHPUW8r8h5d689bHb+MU+S2j+UhXJOAXT+7NV5Is1ThlIXPyt+x6bCgL/6V/9zDh8+pNPtMhiOQAacnJ7yk3/gJ2i0wQBRkngTp8mE8fY2ycP7OHz02De++U1eeeWVC1hlPp+zni2YTifkZU6aZSAlaZYxGo186IbRVHVNGsdUVeV3FtJ3+0EikYFGRQptPSuk0Ya6tggZM50uWCw3aO0ZQGVZYq2/H7pdwe7OFrbWXuS2zDk+mnPpoMfkbEaaxYxHOxRFTVne+8hfz49E4ZZSUa4ckeyxPd7zqTHjhLdvvUsUJGyKksaCtgF/++/9A774Yz9LkmS8f/d95oslX//qV/n0J3+Cfm/E/t4+RhesMKS9iNH2Fv1Bj6TX4/DsmJ/7xMe5dOky21u7vPXWLfI8Z7VaeyxaiDbOTF6YnAdBRL8/xhhBFFV0uy12peeUZcnZ6YxPfPKLjMbbVKUvbmEYYqwhSRJv6rTcsJBzisWaB3fvEwYB+3t7WONx7zRN6HR7DAZD+r0+SZIRSkkQBsgobIULnk86Gjikg14acnR4l2KxwTQOoxQEGePtAzarJdpJVJgSKe/9YUtH6CSBipACskxAvaQbK4yKWC01xgqUisirGiEtSJidPCJQMVoboigmy7r0Bz47zzmH08ZjlTIgkhKFpcrXFzi1Nh6nc9bvIJwDpQKCIGJ3d49NkXP77h3SOMHgC4FxDYaasllx7eoeDx5MEEIQRTHGGNarkp3dMbZjkEIyGIwwBlbL3GPb6ZDt7TH3H9wm64Y8OnrMcDii199hMFjTyTrEcYjOQOI1BG+/8z2eeeY6m3zFer0hibvkqw0hCiEtgbDoOmezrOhkHcLO8AJeq6uK9bSgqRpm0xmr5YowCPju99/m+RdvsL29jZCK27feRSpJkqbYRqBLw8rOEc7yEz/xCvfvv8vdu6/zUz/1s4TdbbpxSFnrtgAI/LDjHFoTLQXWM4cc55uTp5gieD67O/+UkG1mpmqJhQKFRqEv3A2RCo2ksRZnHMJJVBAh21Is0Qhn/BDa+h0Y1sfuWefLtm4aQumQri3fEgxtco30PHVtLTIMiYKQsqywxhIq36wI53FyJSQqSFBpQvhsyuHhI6aLOXGc0O2kfO0rX+HylSto7ZXCcRyxWS64c+sWs+kc5yQgSdMOg8GITqeHlJI07XLk4PmbL6JC75aZF4WnBeuaKO5AEIKSoKyfFxWWta4ReUMsHeXZit4goWyWrDYTlusJk9kZQZCSbyrKQoMI2N0dE0bqQmFZVRUPH56yO95DZSG7ez2CUDCZnJLXDdN5wWZdI2VAmqYfWTN/JAq3s46yqLn7/h0Oj+6TdAVVXTKZTlAiYrlee4ZIHHncy1lW6xWL5RJjBWEQMjubsZhs/DaDBm1rDg72GAwHbIqcyXRGp5Pxne+8wcHeJQb9IZu1TyP/0pd+krOzU+7fv89sNkMpRRzHWAtRlCBFQBJnSDn3+XMqQAg/4Lxx4zmU9MyMoDV/F1KgWi6uA+LIY+POOnTjIQhjDPPZnMbUBEoSJwndbo8s9Vv0y5cutZ14K6YRCoMgTbs0VZe3fnDYvnvnvgqGggqhJEVVtPxphSsaet2Mpq58sRWOWAXgDM5qb21a1KzXNf3+gHnrZ2K0oTENgtAPXNtuyPu8NJRVQZLESOnfCyGgKAxGW7RuaPR5kon0kI94gotq7YfPi+USrRuUlNRt7Nk5H9nPEhzL5YzxeIAxog1kMEgJVVkgFag4QEhNGmXMZjOS1EMTaRbx0ssv8ODxXWrtB1LGOYaDAU1T4/B+M0pIlqs50+kpN25cZzwesr29xaNHj1mvVtx84QZKaRq7RgUN9x/cpZt2yNcFjV6BEvS6XawDK8BJ/+9yveLKtWus1wVl+diLuaKIPM89nl9WrJZLlCq4e/cRYOj1x2SdPrfe/y7Py5uk0R6RComCGCGVh2La98Z3rAqHaQvUh0ASTrRdtbuo53526JBCEasOdZF7C2Hnw5aN9T5BgbBYoUF4ZzucQAWS8zgyIVp3SG2QPEFdAqVQkUQh2azW1FVNr98jTGLqpkGoduhhK95+5232dneJo7hVK1v/BgrfnJxDLioI2OQ5ZVUhas8vH40HzHGMhgOss3TKkqQNVrh+/Rmm8xlS+lDwNE09xOEcVVV7PQI+KDhSMQCh1vR6vrCrMPREAwRSeXjv3r37HC+XWAnKVcQhJJkkSiCIHXEcMhpvc3I8RRsIo5RAxQjhKcZae0fSovBZk48fT9jaSoiimDCMGG8NES5mqSrCQIMTPNSrj6yZPxKF21rrt+LaDxRsXgOOfr9PHHWI4oB1vg1yzWAwQAV+qNjp9tmcnZDGmceFFcRJQtWUbPI1m3xNt+jTNAYhA4RUTCYztse7dLPeRbhsr+cl6ev1htVqTRT5sIUsS+n3B2RZp93GK/r9gR8QrlZ0Oh32dvcAQdM0BEFwUbzhyY10Tj904PPp2ovDtDL+urZUdU1Z1mRZRbc38NLoFkMEQSADsA4XREgVst7krQTdY4vWWIzUCBF6Ka2DKEzpZV2s1UglEMIiBN44K/T8W2H8YFHrhk4nQqnyIunaaIcgQIoQK30HbWzdUiQLwvB82ywvpOuehROC8Dz8DwbkPomQMsayWi59BxonbPINgQpQ6rzIC+I4ZLlaMh7tkCQ+Xd4njfgBlEVD3bDJFf2ezyk1BrQJMSZmZ3vM0alXj1Z1hVQBnU5GUeQY46mF3SwhzSJqXXL7/XdZbxYMBl0ePdZYZwgDRRQrImdQYUiWZsRRzGo1Z7Fa46SP6aqNBu09d2QoqLSm2+uyWuUYU5AkmvFo4GcbRlOWOed+5Ov1lPfvvMWVK8/g2GW+OGG1mnD96qfZ3j6g2xsQBMrf/MIPhZ+wQJ4aXPJDiLVrERHhnnTmbfuthCBSAZU2rUmbAOezE5WAQEpc4GPD6rJAqahNbfECIaV8N1tWFY2xF9+5rj1bAgtN3WCto2wMkdIej8Yv/ptNwSbPWa03iK4Pav7h44nQB/IiJwiDC2tmITOccaRZeqF2Bo+X+0CCjEAp4ihi0B+QJom/32xD3Wo3yqL0Vq9tMlPU3pPn8I5t/22ahuVyxXQypRGGUNQc7A0pyxqhFEEcogKJ1OrJfSN8cnxda4oyRyk4t4SQUrHON+SpJk4MQeSpsqHys4IojjxRQZvf8p6cHz8yhVs3mus3bjDe7vGNb/0ag2GfT3z8VXa2LzNfTLFiwa33X8PhCMOI0XCACl/hzr0f4ISgN+hz+dI1+oMer73+G8zfnvGt177Jiy++yjPPPMelS5cp8g2X9/fZ2dkBI+h0Mqqq5utf/ybPPHONnZ1dHj06JI4TZrMlw+GY7e1ttra2Wa83xFHM5cuXmM4mLBYL9vd3kS39DXwn6cU3rmWqeH9f0YaX+vgjC8IP6xCeU37BinWOOEkvunqpgnMFNFIESGUvVHXd3oDZ7AjnFL6rFd5DPA1RgR99djoJzz73LK+/9h0QASqUSAVhLOkNUupmSdiefxT7brfbzVgu1yAUQRCz2TjSVLZe1Z5FEMWqHb6scbYEAqTwSTyBCkmSCGMNi8Wc2ni7XT/c0tSVbrt0QVNVRHFMlERs8g3WetGNwy8wo+GIw6MFp6cTtrZ2SZOU4+MTgtD//HXjF+eqrBAoEIbVekatNwShIYg1nY7funqGkKd1zhcT8nxFJ20Y9IbcvPkcxjj+3t//b1AqbDu0gG4v4+jkiE43JIwszhVcunQZIUIm0xXWgQoDpssFZdNQG40FVBASJDEPHhx7VWyQIZxgsViRdiKM1ZTlmqraUJUlOztjkjTm8PAOd++/R7fX4ytf+W/56S8VfPzjn+WSvEoYpeAUzvoQa4Roqaa+2235J5xHGJ9vcUTL1ZZYsA5jJVL5QbJxJYac0kqUxC/uUl6wnyT+d7ZZL0mSDmnmLWuFFDihsE5QNYbNZuOvUSlYLJbMZjO0duzs7DIebzFb55jFir39XcqyYj5fMJvPODi4jGmMbxQM7aLi2mHfk4Dh8xnT1tbWBVV3Mjml1+uhtSaOY/97nc/J85y6rtvgCgiVZH93l27mo8KUgKYq/S7BGIpN7r1NHMyqKXEUE6uWwuqs9x4xjk6nS69uqF1DryN58eY1inKBpfQB02XB8dG0zVMtqcqaOGoQMrsIIJEKglCBE4y3YhANZQUqdywWK5Kox3xeEwUJSRz/6NMBG93g0BweHXN8/IDBsMvlK/scHh2y2TRsNitOj08pypyyKvxFJUNMI7BGUVaaJO0gVMhyvSGMI/Yv7SNEgDaWe3cf8oPX3+QnvvzjF2EEkYqYTqecT87TVn5aVd6CdGtrm6pqODubUpYVYRhx8+bLPPfcDY6Pj5jN5qRpwmg0xrXGOefYOHBxwRVVhRWSTVGRJQkqAGRAVWvmyzW9XoY478YRbPLchxVIeWH5KcOIsmjI4owsSXC25NGjI/Kqpq4lSdIlTTs4p1mvNxcdVr5ZcevWbZJOh02+QdsKsJSNZrfbxa4laZKyXtZsNjU/+7Nf4O/+vV/1g6jWZySOIrRuiGJFpxuSpAGOhvXGoIIQqxXWeouApqgRYsmq3eFJJcniEBWEhEGEbjRLt/KPbTRChlitaUpLgERYvAm+8M9dzuYMB2POTuc8eviYMIzIsowwEgQqoqo9Xj4cDiirgm43JYolURSQZMpL2Xd26V4fsFysefz4iDCM6HX7JHHsO5/1iq997atcu3aNq1cvMZlOmE6P6PVjlAgQChabBUFl2d4ZUBvD48dHCBmwtbuLwfLwwQNGoxGbzcZbelYNddOwt3eF9XKF0Q3Ogmsart94huVq6tk2QK/jsw3PTn0sFlKQqwqpCppmzttvf5tvfOOfcng44d//9//3TKZrnG2QMkDSBja095HvFNsiLrjozBUgnSNJM6bzgkApnIw4LeaIToaUjk4n8xBOXZHnG0xe0Cw2xFIx3tpitfFaAak8F3q1WnN8dARAWZR+B2R9GG8URRgDk9lthLzLsD9gsVzw5pvvtanmDQ5HWezirGFrPEbJgCT2DCuhBEp44yZrLdJ5WLJpNEo1F7mTw/6wvWe9bfNoNOK9997jueeeo6oKVss5UkrGowH9XpcwVDjbZ2s84s03C4zWHD5+xGw2a5OnKg4ODvjcZz+LFJ5LHQYJUZww6PfJraF2mlE/wjlvORuEnvpXFg3dbp+ToyWCoBX+CXa2dymqBVqX1I1XQr/y8it4zMCyWMxYLOa88vKr3Lv7mN3dDnXdoE1NkkQfWTN/JAo3Dooix1pNFIdEqsPW1ojJqbc1VCog63b4wnOfJ889VUjJkDTt8a/82X+Nr3/tG5xOpqgopZMlPHz8CCsMm/WGne2rfPqTX+AP/dwf4f/6n/4y42GP3Z1dnr3xHOtNya/8f/8Bi/mSutJeFSn8W9LUmueee57nnnuBfm/I8dEJUWSZzRY0jeHSwWW2t7c4fHzEzt4BUexjhs4pbeeKsk6nw8GlKyzmC/rdHpu2qmW9Pi++9DKHR4/QxlzscTd5Sa8/IIoSVBAhlMJYSxgmVHWNCwVCxazzkp2dS2i7wBpHVTYEgeLkeIIKHHGUIkXMdLZGKos2FVEiCWJFEEEYC7Y6W2zma6Qy7OymfPd736HX7zKfrcnXJVVtSILIm+iomiiJ6PZjVquKg4NLPHpw5gNPhcRYh4dMuOBuW2OxokHXNYVZeQaNlIRJimkM2voOT0hJGHjL07xY+11VEACO2hik8rTI86DnwaDnmT8owiDx03wU8/mC7e0x3V6K0Zp1WXF0tGR7a5coSkjiLpPTJf1B5lO8rUOKCqUCNpt3PO0yConiiKqqqHVNf7SFEBGNzjmdTrDGsNrkDPpjcJLp2RnCSZIwJXelL2LNktFozP2799vUci9UUYHgnbdvkySRNyKqK7I0Zrw9ZLPaEKcxgZJMz07Y399jOnnAfHHGclHw4MEpv/zL/2f+5T/z50g7I8Q5le6pe8i5J1CJc1wMFM93PKuiYl46fvOffp033n6Azva4df+xZ29EEUJJTFXg6oJnLw95+XKf3VFCkoT0eil17lkuUkiUVCDFRYivkP67pWkXYw2dboq1luVyyXe+820+9clP4ozl6OgIozXXbzzLdLqk181YLTeEKmDQ63q+v+BCZeu1Fo7NZtNaKDQopSjKgoO9gwt/oXP4EuDWe++Rbzb0uh026zX/l7/0y6RZRpal9Lo9RqMR+/uXkFJy84WbdDpeQZnECY8fP6bX7XLkXOvn72meWZZxYzRExgFnx/f4xtff4OBgi62dAUEkUCrn0eEheW5oGoGSIVmWcXh4jHEbokjQ6aYMBn3OJmeEKFarZXt9CN5+8w7dXo/NpvA2HBi0OQ8S+63Hj0Thts4SJzHWadYbyel0Qp7PGQ4O6HW2vYrJwt17d7m0/xzegN2Rpl1eunmdb3zjNVQQ+ST4littrSVNk5a2p5kvljSN5f79Rxw+d8TB/gFxHJEkCVevXmN//1JrLKVa1ojl5OSMQX/MlcvXL/jbTaOJIt9pG2MZj8cXWHUcx5Rl6d0D22TqsqrY2tpmsViwtb1NlmXUrfjnlU9+itOzE9brDSoIGA6GbDYl/cEQ0TrsBcpvbZ1zJGFIFEJVRSgV+e68pWo5B2EYIQixRlBXftuptcOZGqEsSZYQRFDUG7TVmMaxXq/QtaPbHbYrfHHBy7XGEaQObSvSyItQFouCzbokjhzOKZwVGGybQuLd/ZzzkU9SeHzQs0Fs64PuS03TaKyTCOFjq6y1NFYjhd+qBy31zAhPq8T5nZGx1i/kWRdjS7Qu0doRKEGSZK3IyOIw5HmJkl2KomGz1n5xCwO0XiKk57oLLHWdk2UJw2GfMAyw1tA03n62rmtUYFuqZUPTGC5fvkrTeLva/d19To6P6WYZm9UaLb33RLnxOHoYBt6oDLBWs5hvcANFHCbEWYc0CtC1II27RFGIVBBHKcvlCmcfI0XM1vYBf+pP/QvcvXtKECiUaPHfp7Us7b8e+xbenfHcGwbPwdZEvHP3IbcP5xwuLU3hWDUDSp354GcrMCYlDDTzUrK2GX1irHHI2mG0BWNRKILA/96DIADpI8Zsa13g8KI6AWCNx8iBd997F914H53/7s5dPv+TP/UUyyjwA9BzxTH4obcUrdNnwK1b77Fer6jripOTY569/ixCCHZ3dz1zRwj29nZ5+623eeetNzl89KhNYzL80i/9q63nPu21mjAYDEnTpDXJ8qxp4aAuK5z1WaVBSEtfVRilCMKQ7e1tTHWTnd0RQeTQtqSbNcAJVV0jCABBU2uGoz6r9QohfHpOHEecnp7RS/skSQdnS1arNXWtieIuZVH7hPinePAfdvxIFG7wAzxjvLpvs86paxj2D1CBIhQRWafDZFngsBjnfZPjKOb9O/fZ3tkmioIWE5It/9SSZSlBqDxDggbdGMqiYrMpKIonNq5pmrX+3H4LqnXdbtO8ci8MfZhv3Xib2SBQRFGMc34g5zFpv/mpqorNJqff60GLO+/s7HD3zh26vS5RGJLnm1Z0lHqXOLyoYHt3l/Wde2xvb+MVYIZAQNOYduruHeGCMOLy1Wvcv//OhfLSU+0kg8GY5XJGtzMiijJOzo7pdHpYURInIUEMTobopvbF0zqCQLU2sLLFmPOLG8o6TRhK+oMO1lY+Wq7W4DSBirAKrBFIqZCSlk3SyolaHxIvJvDwhy+sfqFDBN4uti07xvqf85wnXNcaglZxKr3N7Gg04uT0kLKocEJf8JwNjjj2OGZV1iB9RqWMFHVtvY9NrX13H/oUItkWtKqqkdIPPF3d+PDYxnhqYtO0cVt+4FtVjS8w4om/dByG1EWF1YYA6QUVedEmHSUX3jZN4/zPR+CNsJxDpRG6aUiSjs8gdIYkTlnnazqZwdKQpgkvvPACVekHc+dTyCce9Of/u5Bxtfi3aGl6XoLuEKzLmsIIGhnSOMdgNMDNVzghPIMpSAikxVDjgpRGhpTlBhV76CywfngpnMAJSdXoD3jRCLztr9UNtAyhbqeDs5qqLFqSi6fYKaUwLcMIpD8HcQ7/nDNiAMTF4D/LUrIsZbNZe1MurSnLiqbRxHFEt9dje2ebe3dicF5AdvX5qzz//PM+HKFuvF6isW2wgrxoKpz1JlnnUv44jlHSQGgJAz9Y9R5DCcPhrg/8oMFZgzUKJUKUrABBGCm6nY5vAowjk35wuV5v2GwKdOk83FfWFEWN1o71uiQvavJNg7G+cfqo4/eb8j4E/h/Ax9qr5l8H3gH+OnAduAv8y8652W/3OrJlWgQqIgoTgiBmezwkCPz2LYlDtvd2WOSjluNbEkQgCfn7/+Af8uM/8SlWy4K6KUjTHhJFVRqGw4Qw8BPabrdDUzYeQpDeL0DKEOscq/WK1WrTUvwiqjpHCsloNGZ7a4c4SaC1eLW2ldZKQTftsVgsOLh8jTCMqOsKaxybdU632yMOIjpZl/F4iyROCGSATJ9EOZ2cnhBGIXGS0B8MuHLlCnfv3GU8HrFcLakbjRCQb3LGo7HvHqwljGM+9ZnPcvvOO4RRjBQxkgCpJDvbezgsVy5fI8t6zBdztnfGaLsiTCwqdMSxpKk0ZdUQRglREKGUpG4qkjS+UL95q9mSrUG3xXAXbd6kQApJFEbgHFqcm+J7G1JnwQmHtaKl+mmUksRB6GPZNkWbiuJwT5lMeZGC707rpqYoSsIkRgovvOl2Qq5du8bde++z2ayJk4A48Wk3zhqSRPrQZ7RXcVpJXVvC0Fv0xrHvaIeDPmHkE+rLoqIKPNd9vV4DfgFRMgDjsMZS65ra1MhA0DSG05NTxsMxkVKsV0tCGTCdTKlLLwLqJCmb5YooiUliHzhdNzVaa0bDEQjHerViXVf0e6m3wFUJ2jQeFosSBCVZ1iOMOqRpj6oyXLv2PCqIcMiWuX1+eJzkCUziF4DzTttaSxApMIZBr0un0yWMaoQs2drpI82Eum4QCFQY+OGjLpGBRDvBdLEmzkJMpekIecF+cAJ/30iJPB+GCkE/ST3mrX1jk2Ud1mvP1jKNJo4SXnjxeYz2i2LdNNTG4FqlsAfq/es75ymCUgqevXH9It/x8dERoYxZLlckcUSnk4GAoir48S/9BKapsE1DkqT8wi/8YaQMqLVFOzDtIrHeFGRZpyUKOGQQtKHi7VwmikBXpHFMVDWUTU1V1GA1VanJNwVhJDBo5vMSIUK63QxrLVknYX9vzOOj+1grUSqmaQzz+QKtLbPVGTjpOetO4pDM5160U2vQDf9cWSV/CfgV59yfFkJEQAb8b4B/7Jz7D4UQ/y7w7+JTcX7bwxoYDnfodHqkWUSvn/L6a2+hwh47l7YZbO8gHnYgVKBqnCqJoz5/+Of/BN//wW8QRTGdNCVNEz776R/nv/irf4VPvPoptoZjTF1hG8t6VRCGIfnGUNfQ729TlhWz2YymMt7BK0zQcdBOuh1VrX0MGF6dlXV8dx7VMVVV0gl9MXdOEIYJSkXs7gYoGVx0+KvZmv29S5ydTdjb2yXdGvPuu2/TH/YZjsfUWpN1O4y2RohAcPfhHS/BDyJM27lYq9ndHiOEYbO2PP/8S0Rxl0FvgNECISJeuvkS9x++xS/84Z/h6OiU27fvEacpZ2czrj+3w3Cc0OicR4/uo0TKuLfHdD6hrCq6IkQXS+bTgjBQhKFktVzT6cZIITk6OqEoCvLcoJuGOILVZoEgQskIa2sQDiFbcxwr2kIZo3VNEAYkaUySpNTad5/GOpywfghrPf1q01rnOudQYdj6jTekqWC5XPHrv/7raNN4KEZFOBewWKzp9TrM5wuMbReJNCIvajodiXUWKR1RohiNeqQJpGmIJMFkKf1uxMnZMYECoSJiGZLEHTbT+9jG+AXeaqaTKVcuXSJAkEaRx+XDMavVCm0NqqWl9ft9pvM5m+Ua6RRb4222hju8d+tdyiInjiVBoIkiyfZuj+VyzXK1QLZUN2MkzkTkueBzr36Ka9dusloLPvaxz1BW5qK7lsJeCMdQbZJT+ycOfJaoiEKcCMHURK5mt5+wvzXgaF5xNJ1wbec5nh8+SyglRVHx8HjC+Mp13v7Ba0TCEikgkFTAOq9RUULYmiLdnxyxs7vjKX7G4KwljhMW6zWuaei0Xuf/8B/9Y65fucx3Xn+NV196mZ2dbW596xv8j//s/wShBAaHAYTyJnLSeZXsOacikpLlckkUKpqqYr1cUWwKevtjTk/OGPYHbG/tcHh0SG1Kjs6OCZKY/UuXKYqC+XrDcHvP70eCgCQOmE0WZHGMCmOk9Jzu5SaHQEIgUVFIGKfovKYpcqTV5MsFJ7M5RsNivmxDQgzGaYgMBsflqwd0uiEIQ1EuuXx1m7KsCYOYqvLBKoN+l2HftQPRpu2sQ+azNePxDtvbHUDy1uTdj6yXv5+U9wHwZeBfA3DO1UAthPgXgZ9uH/af45NxftvCrYKQ0WiHMIjJEXQ7Iy5f3qHX2aOsDU2tCWWPTjyGxmHrAKdDgjhl2O/wysufYLFYEkcxkohApnQH20CMUglZ0kVKSZZlFwbv1jrCKKIoa5bLDXWj6QcRWdZlU0yw1jGbL5hO5m1S+bkPQysocT67sixrlssVvZ7w+HVds1gsGI/HF/zSGzduMJlO6HQ6KBVSFgW6sVy+dJmjw2MG/RGXL11mNBwjUBw+PuLKlSvEUYYDqqrEWusd0EKJbiyvv/5dmsZSN4ZuNmB764Bnn32B7/3gGzx6fMhysSFLe9x88VN8+ztfI5Axw/4OVZ1ztzrk0tWrTKcLrl15gbzMee/WuwQqpJMOKJwljiQmkxhTM5stuXRphzTJiKMNde3xYmscUloQ3nscZwlC4SljAk9/UgFaNxhjWK02TKcL6toSqAQp202+eEoODW1CUN0KKFKiyL+G1jVRHBLKEK0byqIijBxRlFJVBqUkQvo5h66hm/T9EK/xw0sVCMpVgbKW5WROmmb0B2M6nR7dIschqesGYxqfmykV1vg8wsGwwwvPP0OapsxOJrx/6w6bzYbheMDx8fGFym2z2XByckKapkQqoChKjo6PCIO4FScZENazQoTj/dsPCIKQs8mUy1ev8eyNm1y9do2vf/2bfOrTn2O5KHj/9l1+7LNfRtcNpvGUUCm9AMYHGTcfoI6pC2WlbQfEHkDRThBkPeb5I85WBQc3Xuab332PGIhk4GckYco33ngLpR1R1EVgOT2ZUNUF6+kxoxs3SHs9Iil5Ydjz1LtWDv+0K6SKUtI4RleebfGDN9+mk/W48/4dDh895ODaNSazGf3RFhYwzp9n23OjZIB0noZqG4uwAozEY3PeGTMIJPv723SzGEnDYJiyeHh6kbgjg4jGFHR7A0qtSZIUgWC5zjk9O2E8HKEC2gbDayY88eE8pcdS1RW2qXAh5HnOZDIhTbqe7XQ2IU4iwjilcTmdwTZNk3N2NsMJQxAGrM82HB2u6fc7OOfVvbs7AUWeY0zr6eIETd2gZMLkbEHZcXTS7gfsDn74+P103DeAU+CvCCE+CXwb+IvAnnPuXNZ3BOz9Ti+kpGR6OiVNu3S7Ka+89IpnQLDBEhPGMUHiCKVmuTil1x0ThR3CIMVUDuEUEoXTAl05kqhLEvg/UeADasMgotPpUVc1urEYbb2YxgLnpHkpGQ6HzOaPsaahyEvKsiKOk3bw2d5wUnnr1hYHPncMPHcTPMfjvDWsRQhFXTd0si7dThc/Jc/ZbHJ2d/cpioIoSgjDGBBEUUrTeOw1TTOyLGu54aYVDATMpksO9i8zny8YDMdce+Y6Dx8/ZjTe5v79h8Rxh0uXnuHjH/8Ek+kxBwdbhEqggYO96xwcPMP9e9/kxZsH9I3m8NEZf+gP/Tz/8B/+Y65dvU5VGh48eMw6X3Lz5rPMZmfkhc+E7GQ9JieP6fZ6XnBgPK4dRSFVXbQiCV9clIqQsqau6wvhTRxlaN36DNF6XLgnrotCPPFM96+h2uKv0drzheu6wuEuePRKSsZbLSWvqjAIrBKIxlPsFP6+3xQF3SShm3bZFCXT2T12drfo9vosliucdTgn2RQFVluqsibrJigpmJ4eMxyOePzwMUb7pJ7Vas1zz73gg5e1vvhT1w3CepxctDz7LOsQhQlVvSEIJHHLcsiyDj/2uZ8gjBLyouLb3/we61XNN7/+HQ4uXWN7fIVAKQQOJSSupYl6sch5UtPTLn20zYnGCXkRAVabupW6OzZVQ/54TlUnWF0jcUjRYIWhMBGpLmi0pd/t8bGbL6FNQefKLlmgCEJJpEKaoiZNkpY84LF0qRRCBVjjA0OyrMOXv/xlokDxd/7W32S1rCjrhsePH/O9773O53/8D2AxlEXRioT8n7Cdh+jG0tQlm9WS1WLOfWf8zyohSgX3bt+izDftcDrh4eNDnnvhJpPJkvnCw41Hp6fsScXJ2dTH3kkvLPrGN75KVRaEUcj29pgvfPHzxEkG1qICSZql1OdKyjTw+ZVKESiF1oY0TQhjSRgJQhGTppJaa6o6p6iK9vfgFxeBYrMpMRqWiwJrPHPNWB8YLYUP21Yqoqk183r1z43HHQCfAf4t59zXhRB/CQ+LXBzOOSc+wqFdCPHngT8PPqhWOMA6hPOqrjovvdeBlEgUwjnSpEtZrHHGByZUsqIpIYtTwpG3eu12Bjhnefnmx7i8f41ur48UiihK6Pf6LOyCptGUZc3ubkoc++BPISVJnNLr9QmCiLKsscYP73y0WIQ22mPxQUgUeRWatfYD0/3zjv6JFWc7ZHP+ZlNBSBR6KWxdazqd3gWDoShKwjZ5wzsDeiwZ3MUiYLTF+cg8Ll26Rlm9TxDGaAPvvnebvf1djo5rhAxRKkIIRVHUBEHGcjFnNltS5g7dKKrS0cnG3iNbZuzvX6UuDWnapd/zNLvV7RUvvvgS3/3u61gLSRLT7fY5ejxlOBzjrCPPN6w367bbOjcn8p13GPgZ1cUAyNFSCP1Q1gqLc+aCWfJ0wpBn+LQCEnUuzfAxZdZqolZGbq0hiWM/dNKm9Qx3hIFDOS/hllIRqMgP6FYb+v2uH+haPE/fBu0w1tI0hrKskBrWdcFgkBHJmBrHZrmgrhuSJCNOoCg3Xg0nJFIoBH7IVWuDdN6OVEpPbQzDiDTtgHBY26C1p1B6q12HCgRN45jPN7zyyid49PCQzSpnejZhvVqwvb2PEB7COydonzvrtffUk8KNnxsZ590lz7NYszQgCQVYwypvPDNIJDjjC68TDtrCaExNICzjfhetJVksUc4gsVjbIDAooVDSO3Br46/NMAqx7fdTAezubdNJEpTycxCLoyjWPH50n9XyFQajIUo6sBqtK9JQ0jQbmromz3OOjx7xnde+SlOXYA1RHHHjuRvcu/MmD++9T5mvUTiiMGYyXdJPU2TYoZOlbejHKZv1hk1RkpelT4dqKtI4JGo9RLT2PthNUxEqRRgGXuOgAsAz1Pp9bxKXJl3efvs9wlCx2x8zHHdpXEheLynykro2CBcQRmFb6BOqyiKE9iSHuiFUbbSh9SnzYeQdN8MwwCHQjf4hGexvLb6/1+Mh8NA59/X24/8KX7iPhRAHzrlDIcQBcPJhT3bO/WXgLwNcunzVxVGIwHo58tmSpsmJwh61aWjMCkvNZr3BaE1tKwKxIWQFLmY47hIGEWnSJUu7FEXJZz79WYbDPtZYHxwA7OzsEobeu8ALZhTXrj2Drmu6nR69Xp/xyMtli7wkDL3HQaBC33UbTRhERFFMEAStQkw/ZXHpt/rnBej847puUMqHBtDS9rx82EemgQ8hWK83xLH/WMq2a1eSuq49HUmbNh1ek2U9treHzOZLpIpYrjc8fHTExz75JRrb+MRp4xVZk7MF4oWQ5bxicrpGBZKzkyVGK7CJ905pAqrCUhSezuc9rQNu3brFM1dvcO/uPaLIG9uPhiPee+ceOzv7RFHEarnk1u1bGAOBjNrdgSMIvFfEZrPGGsB5ml9V1aRJikcxHefZhUEbbnz+3nk+r8VY2yYX+czM4ahPo0vCKCYMQ2/sFSnm87n3dHf4GLvQooLQd+VhRJykBDLl7PSYOI2JYx+qUBY5m3zDYDRuaYQNVVUTiYg6L9F1QygE3SRmOp2TJglR3MEhsM5w+PiIbrfrFcDa0DRejKKEodPp4JygrgxxHJFlPRxQ5CuqsqYsc4yxvPfeewxH28RJh8FgzGc/8zkGvdvcun2bo8NHTKcnXLl6DSHCdlGX7TXnfsuCd37DS+UhQYv1BU9aRj1FN7YErkEKjSUgiEKMNohWmGttjVI1mA22DgCDso3nMyde+ZnnOU2t6WYdZBiiWtqe1YYoELiW1metv8+0qdjb20ZKi7G+W02yBNsU9NIdhv2UUBqKOscJxWx+wnwxYz6b8PZbb/Lt137Tz7DiiMGgz41nd3hw5x75cuYXEwGbtSY0kuXxY3auPMvWsM96uWSzWjI5PaXWmnWeMzmbEij4wz//s3S6WUvzbLzisSrodbueVdIygqz1DJP+oM9Oo0nTLt/+9rfodBOieEy3F1M2msmiYD73hnVx6mFXa/DUUeNZPnEctWrPJ+Ia5yDNEkJtWljQPlmcP+L4PRdu59yREOKBEOKmc+4d4GeBN9s/vwT8h+2/f+t3ei0pBMuFVy9J6dg/GFFUc9LIMF83rPICbSs6vYY0UXSzjCzOSKOY8WCfojqhqQqqomI+nfvBYqM5Ozn1pupaUxQVH//4JxiNRiSJjx+r64a/8Bf+TTbrNVGQEYYx3W7GdP6IQX/Mwf51nnnmWaQM2draIYoC4jjyW6Qo8L7cpRfuxHF8/r4AXDjH+ZsKP6lut6zgTZyMcXSyLoGa4XCeRYNisy7I0q6fOuPtXqVUlHXFplrTNCXXrt5g/2Cbw5MTkqTHoL/F88+/yJUrz7BaL9AaxqNtRqMtXnzhZZ5//mWkjNjfu8rHP/Ex/tpf+38TBl3ev/2YpmloakmRG6pSI0VAp9MjCBIGg7EvfjIkCGKyrMfe3iUWswVb4x2ee+4Fqqrk9HRGHChoRRIIR7fb5cUXX+TrX/8aRVGiZEi322N6Nsdah7beDwTpLnI9z/Ha8zAFbeqLAZxU4Izj7t077O/vssnXJKniiz/+43zrm98GjGchSgBBUZWoMPESaiFwUlLZms6gT9rpoGRAo0tOTk4ZjPrtzaV8oWkMaRYzGAxRSpKvV2zWUwSSQW9IpSWbvKAoSs8EqPUF19sYn3QfRaq9wgVhGNPrDojjlMePH2GMD2vO8xUvv3ydRsNytaasDK+88km+9rVv8MrNl5BC0Jgaawru33+Xq1dfIMt6WCuodYP9sK6sveGLqiZJU/rdFNts6MUpaxsSmhJZTuiGhiTtYq0gSuO229OcnR0SVqeEOoXKsFkuOTx8zHg0RPe8h/3x8RH3793hSz/5JfqDPlm3R7fbaROWSoqyJE79/RRFXiH77/y7/zbf+953mU7O+Nirr3D1YJ+z6Zzx9hbj8Ygsibg3fcTt99/n4fF7HJ0+YDI5YbVc8fJL25TFhjSVDAeKh/deZzFZkEbQjUMCBHox5/Les8wXEx69r4kG21ze2yUIQ659/BXKpvE5qcslN28+z3I5Z7mcU9cVdV3xm195TJYlDAYDUhl4WBJ/bYZB6HdSVU2awB/7xT+EczWPjx7y1tvfo9YVTimMVtS1I88rlosKYxxp6uMRg1ASaEGSpnSzjOq0ANGQpjHbOz12d3c5OTnh7GyKdWW7Q/nw4/fLKvm3gP+iZZS8D/xP8fOFvyGE+DeAe8C//Du9iE97kQyHfaTyRkZJHBMnAZmFIA6I0gH37n+Xpp5T5hWh6rAzeoarl0uMWzObLqgrg5S+m22amv6gSxQnLSVvG4/DRljrhSHGGB49ekwnTWnKDVW9YLWec+XKNf7kn/wCplFUlUZbw3AwotGld9OrqlamJuh2OhRF3nbFfnu0tbXlzz/2dDDTwC3pPQrCIGoFEvZCnHIRk4YgCkOKorhQXnoaW3Sx+EqliGVCt9unKGqOj8+4crVLp9vj4aMjgjAmSTpoDVHUJoQYqBuHVAmmdkwmaz7zmS+wXKwYjnfZbHKUCnjxhVd48cWXee6557HW8fbbXjBxejLl8PExSgn2dvfp9YaMtra5cvkZH0AwnaIry+7ufkv7Srly5TJf+tJPMhwOufv+XYw2jEYjPvXpT9LrjvhH/+ifsH+wR2MqHh0+8pmRh8dEaYIKlBeSwIWTorMWYfyWNs1SppMpKEs9L/jVX/0Vv8hJRSC9VFpr7VWnOKwxNOs1RVVwsL/L6WLOpshJ2pgxISRV2XB6ckqcZBgDm/WG1Txnd9TFjlLCIKTf61DVDqdClPUCibpufDjD/iVWqxVlUXqFq6u4dHAVZx2LxZrFfAEu4MGDR1y6vEcYOrTJca7gdHKM0RIhYxrt+NrXfpOPfeyTTCcnnBwfcnp2wttvf58/9+f+VXAleW5wLkCpyItfnvL1+MChJIv1iun8jIiKX/sHf5+f/WN/hmECl/sWZ2c05Slnx2e8+MlP8uJLN+n3U7bGH2dvu4ur1oQS+p0ezjiiMCFpqX5lXRAo62mkrW+91xK0rKLAJ8AY4w3OsjhiMZ8zGHYIlAU0WaSo1gtef/9dprMJy/kEawrmZ0eU5hiVaKI4YD6/Q7dziUYvkWXIRm1YL0s6QZ/NdErS77C1vc2lF66wXlT0I8nSVJSrOXNn/S61LkjSlMl0xptvvcN8dsLHP/GJdpHXVFXDdHJCXXcwTcVWb0AnDP2U3XnvFK01RVkgxIK0YynKJVEM2ztDpvM567wm33gr6EAJdFOjTUMTGObzU5wzZJ2YLIsoihU7u8Mnw2VX8ejR+yyXS6SU7Oz22JyVH1kzf1+F2zn3OvBjH/Kln/3dvZLwtCDnBxqiDhl+fxcpFH1La6AveLH6MazzQ0CJJJjGhA8SAhK29dAPGoVo8+LsEymukLjzRBjRciOdRLmI1CoPoAKRlQzNFo4hZ/fXF3isdeeRUU9UiiBQNvM3i/DWPlpojLRUoiZXuY+gArQ2XF9dAyEI7gcETvFT5mfovJMh7wZcKa95/Psk4OWbLzObzuimHfqdHo2uyZKEQIIzDbTDGRUExEmMswGBCImjCKtrOskAXQXUTY3pWlwQQhxhA0XpDLV0RL2MxaOCbNijbnxQcbfbJ89rhoNtut0h4NjfP6DX6/OJj3+Kt958myAMuHb1eeK4w6c+9Rl2dndobMNkPmFTbkApyqamE3UI4hSLpCxqOtmALF4Rhx1CkVHlDV/43Of9MLHYMOj1WW82dJMOo/GYPM85Oztrt6+QpZ12MNkQyRhpFKYxWOfl8Gnqdzu9rE9RFJRNhbAgpaGu1n6HFIZIJVgslxRVQ7fTwbgQq0NG40vUukTrCimV52JnCZf2b9BNInAFs0VOHAtqU1MUU4zzQctXrl7i9GTKZDJvLTsdceQ52XWl6WRdRsOEOOoiRUAUK5JEIaXxXGwZcDR5RJJ2CIOYQW/ESy88x+N7b/N+vgZjyOKE/a0dXvv1X+fZj32WZ26+Sm/Qx2qHrjR1UxGFSetAKdoEJQhVgowCGqFoSsfOpWc5Pjrk2d0O//qf+DLjvX3u3LrDb/7ar/NjN8e8+soeSImMQuJYQdj1Q0spaUyDDKDWvpiEgcQ2NcZ6Myqcd38MpMQILy6SUuKkVxGaqiGNUjqJ11MYDVUDcRgTS0kiHSZ2TOczrFz791JrYh3Q6Q8w2pLGPZqy4XixRGvoXY7BpCy1pplMSMSK/f5lHJYsAJcGjAYDDotjmmKOLieYsmB3mNCLBHo9QSrYG/X45MvXyPPr3Ln7HnHoSNQKVzWIuMRohUQitIOmQWQNq1nNYr1CBAarDE46impFGAqiUPndc5KRdmIaXWGtRhtDFGXEcZ/5eo7VBd1uxtaojxSW5aLkyt4OcZogpODeWx+KMgM/QspJ8J23KATprIdcxIhWPHp+BAw/+IQG8MNbFB9tyPL08WRn6aXA6ilZqe+hWgXk+qN9As73ouePffq1z9mnhg+S51NaU3Rvv8IWW7ABNk++5irHuLNNsBtgjWW9WuGwXv4tJUkSPYUFe/8EJf1QLVCSzWblaW8qgsor0sIwwjoIosgPA61FBh522dreZjlZYoxu49k8PSlQEUJAHCd0Or2WkpcSRTFxmJGvKzqdPp1uDyEFcRJz/dkbPPPMMywWC3r9Pts7O37orA26MeAkoYqJ44w4irixs0OtK1Sg2N87YDqboWtNt9MlUCHWeLfF+/fvs3N1D+cMVVUSJzF1XVCWZav0U8Rx5JWJpcYZUMIHBSghPUxiDHWLAxd5jhCCvKiIAp9c7x3gatI0bpkMfogUxTFCCtbLDcas2dsbkKQp89WUMO0Qhoqm9CKS1SpvrWhj0iymqvw5gsRoR1nW4ATr9ZJN3sVRkxdLhDKgLEiDUAatcyaTQ3S1wZoC5UC6AGkalrMZZb7CaO8n7sVOhrooMY1PJI+StG0wfFjBOcacpB1OpjOeefZZ9gcDVBjiggh144D33+yzM8gYd2Ma59D4QGvhZa046+1tjWnaYaiXbgrO7b7dU5pNv+X2jpbCD5QduFa+3kk75OsNJ8cn2KpicnxIvppRFgvKYo6zFVrnPqi6HQwa7UiSjKYy6DZoU+uGvK5pnBdxOaNZ5wXFXJPIjCpMCYRiWwpvr1ou6fcTIhVi64BBR7GYPSQIJcVGcnbyHlJaTo4f+3kKEqUlDuuNw4KApq45OzllsVlSbirSXgTO0DQV2hiSNKI76lIVnqpqjSWQlrKpsdoSBhFJ3KEuG4RTmMZSbkqkdWRZjLSCOIjIopgg8urejzp+JAq39yVouZelInvQ+x/6lP4HOYQVJHdS4pseLqnriizzoiJrbctQ8EOpMPQS+NFoeGElOx6NAG+HGkcJvW6/Ha4GZKnP0XMtC6YqK7qdLtPjKdZaBsMB57mCUiq8dzMM+mM2mxKtaReOkPVqgyQkDGKEEvR6A26+9ArPPPMMb7/9NsPhyEMq3T7ruWdhSKVIs4zBYMhwMGDvYJ/jkyPKsiRJMvo9w/Rs2i42McPhGGsMx4fHXLp0GWstRbGhP+izWs0oi5KgtWlVSrBeb5jPFygZIgIF1N6nXYDV9kKhZ61hPBpTbCpspJBpxHI5J04U3W7mRVOl95IpipwGw2q9RImKIBgRpwmOScuVhvVm0/pOG+LYx2d1shjnWmm1XtHUpg1/Faw3a/I8x+GFXUhNd5C2fuaWui55cP8u24Ntup0UqzXWNcyWU5LMkaYxy9UMA3TiLmcnZ5yeThiNx/QGQ84DlrVxLFY+zSlNIgIJk+mUXr/HYDSgqiqmyxmdLLlwzgOLkn73qKTz0nRjvK2qaOX9cYwQnmsftMPRcy9wgS+gnkPuGzEcqEChpKKxmm6nw3IRc/joEevpMYvJCdgKqwuqcoGMNLquCJIIEQjquiRfFcRXMqypkUqgFMhAsNxsUOG5xsLQmIr1vCALu+i4SxqlVLpC24ZyPiOLB947xpXYZsnZ/JgoDthsVhwdPyYMBUI6hBMoF5LILtujA7T2w/GqLDk9PUUsFFVRc6P3DEZbiqbC4IMYBt0eC7ekzAuqokRhKRuveYiCgEjFlEXBoDvAao0xDU2pibo9XJgw6o/o97uknYQ0/ucgwPlnelwYv5zfdE9/6Xy62kp6z1np5wGq7VL/hAbl/36SOn3+8NY4qeVGnrMWLl6yNTb2X/fJF+dPP4+A+sijvagvsvd++FxovReeei0PsYCz7cdPjZCLsqSwufde6HYoWzXhhRlT44vCfD7nM5/9NHVVURQ5/7P/+V9AqYBXX/0YYRgQJylJ1mFne4fRcESaJBSbDVjHZr1hNBrySD1AC5/iIoTnpYdh6HnX2nKwf4X5bE1dWZKtDqPRNlWl6fVG1LXFR5Uout1Byz+3pEmHTtZjudzw+OEhUZwx3o45uHyVa8/coN/vowLF5cvXKMuKW7duc3BwwGb9Fs754hCFikW+4PkXb3JwcIV8syEIQnZ3dtjd2WmHPT5UOMtS3nrrLXZ2KhbzBavVkqqu2N3d5Ww29fxx57B1RaM1zgpv3O+gLCqKomJ394D5fInWnjoXhTHT6RmYiiwWPuVbSh822+lS1g3aNAQq8pYGSYVoPSoAqrqklyWt/433olku12RZRpr4pPgtOcBSMl1MOD07ZTQYMuoP6XQyz8EGcIbNJmc6vUcQ9vhTv/RLfOu7b2BdwMdf+TR/+7/529y5e49/6U//aYzR3Fut6A9GFGXF3QeP2N7e4srly0zOTnj22RvUTcXJ6TFNU5MkMdgKFUq0qambkqzbJQ0DHI6m0tRVyXw24/7D+9y4cZ1up4cAGq1J06gVu6h2sXdUdUkUxVRlQ54XWGvZ2t7yFrMCet0enTRlPjtjd3BAujegzpeUeYUC/n/U/VmQZdl634f91lp73mfMOWuu7q7q6fadZ8wERZAURUph2ZYoU1SYoB4YIdGy6AiPIT/4wbIVtiNISwzRGhgyQ5AIiABJkAII8oIUAVwAdx567q45q3I68573WssPa59TWd33gqQhO1orIqM782SeOtP+1rf+339orUFaw2qxoNYtdVOzmOXUtSFJhlTFklWek/QHzLMz9g/HlMWCfLVguzfklc+/xPtv3EWFAUEckVclZ2fHTM/f5/33cuLQZ5COeO/u94j7kWtGJESBYLmcE4Ses2yoPUpj6SdbRFGPoiwoyhJjLXEQgZFYbanahrKqkQGEYcjk/ITID9ndHrD0FDvb+xyfTimrBqElTd5w6eAqVw4OqQpHbwwDj/FowNHje/zBn/xp9nd3iCKPr/y97//QkvORKNxrN7+6rhH1s45Y51fOWe0tkIGhNKc8fnwHgcfO8JAbl16izsFLNQ/uH9E2hvFwi9FwwBvf/w5tW9G2Bs8PuHHzOXq9Pr/8y3+bJEm4ceNGZ+YEZ2dneJ7H+dk5X/n1rxD4AX/8j/9x6rZhucooypqzswlN4+KuBLLjPivyYs7hpX0+/alP8ZnPfAYhJffu3sXiqH6e8lgtc8LOt2IynVAUBdtb22xtb/H9732P9K0+6Xlv85yvXLnM0fkRQTfovHv3DlVVEccxq9WK2WzGpUuXKIqCt95+nYODA27cuMH5+TlbW9usVhl5XiKEYGt7m0t7+zy4e4+d8Ra7W1t4UvI/+hP/Ip4v+cIXPuvc+DwfrQ2vvfYaadqnLCdIERBFCZPzBbqFXjpka7zLu+/cwfcipwa0zWYzGY1GjlaGQHkKYy1V0zBbLNje3mY03qI3GJD0ekynU7AW34/p9Ubcu/eInd190rRHkqR4nsdwuCSKIra3dumlA3r9gSs22M7Nb4zv+0ynU8Kgx43rtxiPt2iahvv37/HWW2/y3PVbDIdDlssld+68z3Q6pVhVjEdjhPCpGs1wMEJKj6qqWS6XWAPb23tUVc31KwfYtsBSY63E9xPy7JzT6YIwTLh2+SpvvvkOO9t7ZPmS8/Mz8mKBHwh6iY/FuRlGUUySpAgpmUzm9OqItBcwXy3Z2tlFhYoyz1nMV/QjQV5kbmgsHTSgjeD2red4/fXv4gmJ8hQP77+PFIaf/TN/miTt8+DRIx4+fMgf/WP/At/61nf51MdfJu2lgCEOdpic3kOYmocPH/Dw0X36ScLrr7/NbJFzdPSQd99/l53dbcp8SZIkLnhAumCJNFRU2ZLJyRPnRzLosz26hrEWrQ1tVXWBB5ZHx8dkWYbveQRByNGDBQcHjjratjWr5ZQnRw+4djhAmILF4oTVckLbFOwd7BAuY1oknjIM0iEfe/ljtI1hNi1YLCqqWrA6mRP0Y/b2r9PqnOnkBF3UvHf/IWeLOY1sCCvIC8PZ5JxXX32F1eKY5XxCXuYk6Yj5ImOxcFYN1oKUKY8fT1wj1UpiZbhxzWe5XJKkQ+LY5ZPG/RFlkdFqSxQmDLdGDHdSTs+PiNMIadzsZX9vC08FPHf9BarakJcVeVEwPV8yP1mQpgmDXg+RKp48PsVqn7/9S7/Cj3z5C/zMH/qDSPERh0oEbFgUF6O/wAk+bC/FyAYleoy3tqgKd/S2uGNYkgacnZ8ymy3JdwoGgx5FVXBy8piqakjTPlevX2e5WpCkCWmaEoQBfuC7mLIy4a233uTJkyc89/wNlsslb779Ordu3ebw0gGPT84x5+c0VqNbR+3zfN8NG+KIew/vs3e4z6v6NYQRyNDxkfEkwpMIXzozJd99j5LotcFSF556cXmd2+Ba5r62iF1L9pVS9Ho9PN+jriuKImM6nfDee3e5fesl8txhq2EYkecFJycnhJFLQhcC+v0eZ/aUJI06xRpdEAP0ej3OzyYIIbl9+0WWyyWDYY8//Ed+hjiKqJuKl195iaoq2dndpihztHUeFbu7e7zwwm32Dy7h+xGrVcbx6RlCeST9Pr3hkDBOaC0ssxyrDXXVuQxqUDKgbQx5VnYUrJBLh1dI05Q4aonjlFbXhEGA8iSBn6A8DyULrl11Ce33F48AQeAngMetF16kbmr6vSGXDi+zWCy7k4WiqjWLZcbp2ROuXbtOGAYURdlJ7lvmsxXZqkTYGikappMlq+yEotQMB7sIIbl/7wH9/oD5fEZR5jRt00E4gqap3UlNOkn4waXLCKl4fPTI+b4UhROj5IZ8OnfUM6HQxRJpJFhFXTvvlf5oTFE1/Mrf/VWuXLnJ1vYenhcSRz7f+fY3EVJRlBWN1nz3298iDH1m54/xGBNGAW9891scP7xLvTqlblxYwuy0YjRIKKoGbVrqtnY2BKamFw9oWtMN5huSJKSqljx4cJckSdje6tPqmvv37nF8ckIQuDzYsiwZDYdEkfNWr5oVV65cxg9gvjx1ugVRcT45pta3eHjvPUKvQQhNrSveu3uPZVZQljWe7zMaRWAVb735Ltvbh9QNnE2W+H7I4eUDvv/ddwhjiEKPclVSG0MQ9dFGgfLoj7bI8pKHT84piiVFXtOUhr5tkSpxoRHGCfCKVUbdCHzPQ2qLVC2rZUaS9GjaBiEkcdJDyYAwbJnMzkkGMbLR3L/3iP7QZzhyYiJhBMJ4DHoHTCYN436foZUUVcl4OGA+WzAa9Ak8Z4Vc5iuOHh1hdcOqaKi0oNEf8eiyNQqx9ju4uIxd74YSgWKV5TSVxqN05uo2JmgtynOS3yzLNnFGB/sHtNoQBBFh6Gh3W1tbG1l6EATdYKtxuYu6IfYikjTmyZPH+IFHrz9imTcuL8/UWAzWtp3RuxsYBmFI3dSbTnjN395YrkrpLEM71zMhxUZY4jBa9YHnbDbm8NZafN/fCH3WkJLDGgW9fornK+q6Qusuz7HV+MHT1Jkiz4mioBOnGIzWZNkKKS3T6TlYGAw00+mM4XDsnPEQQNHh5w4+MqalaSuqqsYYzWRyTlVXNE3DoD9kNp3zsdc+zqA/wPcDfM/n+RdukaZuw93fP0Brg0JQNy1t5UJ8y9JJ+8vSRYwp5ZGmvc7Gs0evg3HqumKxnJEkCcvlgtUqx++SRqR0vte+F3byb8NwMAIDvvQJIjcfqMpmkykqMMRxzJUrVxzPuj9ka2sbT/nMF0viaMDHX32ZqphTlwtGw5SHR49dx1fWVFXpvNINHb20wfc9kjSmaV3OoAoCJM6qNs8LvvD5L1IVBeeTc2djG/aZTWf4kcIPPDwkunPqq8rGeWhISZZXqMWKl1/6OFIEmNZlbubZitHIZZQWRcYqy6lrF4eGLjk/eUS2WrCcntCWS7J5hTYtunF+2f1hxGxVUlYN8/mMXhIgTcHk7BgviGlbw3G2IooiytJ5R5u25vGjBzw8eoTveZydnWCtZXtri+dfeIGz01MQmvlsymq14PT0EU3TUFUVw+GQyWRC1eQss5yknyKtc4sMRcz0+Jy8cM/fkz5KKLJljjEwmcwpK0MQODO41axkscgZbcX0kxgZwexkwSAabugDUZSyyEqKckLbFmDBUwm1FdC09EZjtNYslkuEH2JbQ1lrIukRho40YIzGmLYTgQWYTvw0HA5RkYdQgsB3J0GpBE1TYrXBVwkn52ckyQFCBnjSZ5ik+KFHz0jCJCEOAwQu/Py1j3/KmaElPb79/beYzz/iYcFYS2salFQbnHi9tG5o2wpfumzFMq/BCExgqOuGQMU0dcug36cqms5rQtDrDRDCwQ+eH7pjW6dWaluNNhap3NQ4LzK0aRFA1VlzNk3F0aNHBOEEIwKkF1A3jZM1C41oJYHnI0XAoN9HKdlxmJ2ysq7qzWRdKbX5XnUKS9222HXhlh/A9S8oB4EuHbxACNdtr/2/pRD0ewPC0MdiSZJ4I3uW61xL6+7PMVMEpgs5cLxxyHP3Ye71XNeopIsHq6qGoijY3d0hzzPa1tDv90iShMlkQhiGVHVNo92GEgURs+WM7dEYqRRt0yCkpJem3L794iZuajqbEYYhbdPSNM5P27FXHDySZTlxHDMYDNzG1OszHLrBaVkWNG3jLh7j8Om2MZ2nuOeSy8MI3/doW81ouE3bWqIo3JxgPBUQRyll0aCkYNDvkfQiFsspSdKn1+sRxwlpmrM1anju5i2y5YQ8mzEcpAwG+zx6MsGenuGrkCRSTKZzRsOA1cpDeoZ+L2KxajpvGYUSHkZDtsp5/vnnefPN1x0kI9yJsZw0JOmIQHkIY53TX2cmZSzoWtMUFdKPuH7tKmcnU5arnFb5biga+HjG+XtY0+IpiALJarHi+PF9jo+f4CsF1vmMt22N0Ro/iMAahDUUeUZVFEQ+hKrlrCqJkwGttsymM6I4IssyrLHotsb3FSfn59y8cZOqdIEedZWwPR4xn0x4ePSQyeSU+WLGcrVgMjlHScVoPHZzBlpmizk74xCjG7RWWBUgfAUSojDG9323mQuJVD7LPMcanzBOEVaRrUpsKxDGR9oA4UFRnBN7hqo2yNASRSnzRUZeVtS6wfN9hsOEBoHRLaOOSVR1YiZdFBgr8IKItD+gNcbRS6uCRrshdFEVCBr80PmKNJULKjHaUuYVWtcYYyiB6fmcF29fp2kAbYl8j6KqENCdpjw8T1A2LePdHTCWxarm6PRtVln+Q0vmR6JwWyxZOcf3Q9SHHlIJZoWpY4SIGUb7BIHDW8MowRN9sBm7O9tIIVguCvwgIUm3ODp6HyFgNBzTthVh7PPW2+8SRSlBlLKjW/I6Z75akOclZVFRFzVpkrA93qYoC1bLOY2GWoOVHtIL8JRH4CkUNU1VMxgO8D1XLKq66XIcFxitEdbiK49VuUCJMYHnUwlJXVYI4wrqBzcr5wu+dlqDMIyp67NN0TbGUlUNnhcSBI4t4imf4XCANjVNWyNqhef5JEm86ditNRtva8dMibpEakmS9IiihKCT5DZNxTq5vSiq7mTgOYez5cql2rTaWYm2LSfFEVvjLd5/5x36wwFeEHByfsbpyQmvvPIK6wBlax38FYYRUsB4PGZ7a5u2bfj4xz/OkydPNglCxhj6/eHGdREEg/6IqqoY9Mcb0yqA4XDMZDIny2YEgcNle72h45CnLvFba00cuY0gjnsgJFGS0LQN08mc/mB7I8oZjwbYvubx0QnWtFjjM501fPlLP8Hk73+F569vk8QRSmju3H3Azs4eD4/uM1+dYyjx/Zrp6THCJPieOzkFSUCaJDRNjTYNUgkmkwm9uEcgY0zthEZBmLAspuzubHN6dspiscD3PXRd8v3vfouqqsmLGkvA88+/yHQ6I4pCBv2Efi8CnTFfHPP2299gPjunbTS60cRxyjIv0e06ZKHmrDphfr5kPlshlCSJBPnqjLZp0VohlTvdNaalKAoeHx1xcHDA3v4ehpbT0ydgW0aDIVujIecnJ7x0+zZ//+/9Xc7OT/EDj0ZXlMUSLwhYrKybO3kwX5wRSkGcWCemalv8XsDQ0/SiPqtVzuOTE65cuUFW16gwAuGjW0u2XLC/tYUlwrSCbFnTNAWNtUxWS/JSI0On5qwqjZA9ylIjNMR9RSAsfuixWK3opT0ODi/x/vvvUzeafq9Hf7hF2B8yyTPiOGBeFpzPFixWM07OFsR+SlXXFFVG1eZIX3P75WuUy5zRqIdUcHJ2Tlu7k64wkqYumWVLJ0oyDUIEICRGaMq25Nf+0VdYTFZsbR2ys3+l8+b5wesjUbgRsL3bo6kNZlU/c9NgEBGNGrK5ZZDscvDqFq1Y0bQtTeljtEIKySpbUpQusbkoKurWMF8tWS0WnJ2cI6Slblt2dvdAKKxw7mzjXo/HxyfIBw/RpiEME8qiYHm+wljnvqaNARVQo2iXC6xuUUIwHvXJZg3zaciDB/d45533uHHzOYe9K9nB14agc6+ryhIpBElnAbqm9n1I8dbdZjrVoOeFm2JdVa6TA4EQkjCIsdYVsCAIuHv3fQ72rxD4Ph3AShR2gyblUlvazuZUCs8VUCk20IyUzs7S4uxHR6MRZ2fnJEmK7wedQVe5iWkr8hzdNqRhjC8ltmlRwtEVPd/j85//vONcdxzn9XNarZZMz84pchfa2jQN7777Dr1ez2HasTMAOz09x/Mce8T3fUAyGIwcq8RzzoxlWSKlZG9vz51EpKQsS7Isp2k0AkXbWvK8ZDweY63k0uFVEILWaLI8ZzTcZWdnZ9P5t21D0Dnyrd8ngNffvMPzz71IU1fUVUVdlPSTLbABt2+/hvSh1gVnk8d84XOf5t233uXo6AnZqqDX6/P1b36P0XifvGo4PT+mtQZfRUTJ01PFfDlnnlVcvdFHn81prc94OGS6WPL622/Sti37+5d55ZXXuHf3gRso9yLKMuN8csqbb32TXj/i5PRhBz8JdAuH4ZDpbImUPlEQgHE0v93dHVoraHRD2ksJA0O2ykGEnRR/yeN37mxoqPNlwffeeBPlN3jSIIVkOq2YnJ3wu7/zVR7ef0BeFFRNhfJhZ3+LIPZp2pr5sqSqWxrbOj8eAbquyJqSSVaCVVSrgtOTc5L+gBu3n+fevSPqVrPsHnvghxRVRdOk3ewnRuJzdvqY69ef5/79e0RxzHAwIMtWLJcrwqTPeHiA9CVSST7x8U9z9dKhm6EZS11VfO4zPwXW8lu/8Zu8++Yb3H37LlevHjgGzO4enhcwTntMj6fE22PaYgm2QXmWwSim1pI03eLkfEZVFgReQJk3rBZzhr0RSc/DWoluBcqG+GGfIE7AE9x58BaP33pMnRuuXr7Fjas3+Ecf9cJtdMvvfvU3Odi/xI3k9jO33b9/l1V+zsHuLeqmolgsEF7p7Cm1RZiIqi2RPoSxR9sIDJqd3TGaayxmU0zjcO4oHnDpyhWKssAL3WTeD2Leee89Ts7PqNsaazRVWaFx2Lq1FmE12AZJS+R5GAH9NObf/fN/jv/g//LvU5gGoQIWswnf/uY3uf3iiyjpRDRlUSO6AOIwDLvcymbjYXzR3W296rra2HS2bdvlZjr2xvriWePf1kKrNdI6qMQVMdFxbZ2bYBBEtLpCKZd0AxLfD8nzEqkEQeAhhMHSEic+VVkRhoo4DrA0QIPvW+omZ7VaOa6rcDYF5+cr6rLm6uElN0foHreQboNaP15g85idGZPD46UQhEmy8SpZ2+MKIVit3IWapilFUXB+ft7xq4vNRiOE6NwUO/JlB031+/1NDuHaiCqKIvr9/mZOoK2h0Zo4SVwm6PZ252FTd5RAv1Nstg5uuuA3HcUJSUfbG+/uUdUtURIjPUnTVmztHgAJg9Ehyh+hpM+lwyuU5YpaK0SQ0B/vsswWPDk65f27TxwNMgwwhAy3DzidlKggZWsnIUoSytrjfFazt7eLHw44OZ1gjGU2P+f0vMD3IYwVZa159723CPwIbOBi2yrDP/zK7zKfF4yG21w63Of6lYGjs7U5VVWTFTl37txhb3fAZD4jTrYoK83R8SleELC3t0eeZWRlwerhirY95wuf+wKLRYYnFUmcEMUKK1uED77vgbTM87lLOBcuM7QxGm0lvXibbHWMUBotfDzps1jkjPsHxIOGnfJ5nrv7ZV5pW6wC03fUYIHABhYPhYksspbIVtCGLd65xyeDbh408Qi+GvKnd362U1B3DVIN0ddDvG8rdPczZWOa7v9fK3+Ul6IvYPY1nlGYvkHVChqBFZYfud66mVdiMFvOKM1lrEpkKZySMrHUlPxi/Z8SBR7WFC5KTxqUb2jKEGEMkfLxAo9sseJHvvBpfBExHu1yuBWxNR780Jr5kSjc1hrGwx5J5GPNs4rFfq+H6JfUTUWSKCwSFTh4QWuPutAU5QptaqxoQSriJCCMAuxpS1bMKfOK3mDEfpiwylbM5mcU9YLD+Q5x7wplkTvcz7ZUTeXsLa10ai+cfNfoGk9KAg+C0GeQBnz/m18jkJYwDjHCo8oz/uFXvsLbb7/NtatXuHL5EttbO13Ht7aEdS/52oBeCIH9QMft1I1P06t933fUQs/bFKJ1cQNoaqekGwyccKlT5F8oZrLzPVGd454LQhVCuLw8Y7G0NE1JFDm3PG0ahLCUVYaldf7aSlKWgrrO8ANJk1esRRt+4Iqz89QWCJwNbV3Vrhh1nfZaaOUel8Drntt64Nq2LVEUda6Jzm9lfSp5Cre4LnvtildVFWmabjpt5wETdGIiufHKXm8aG/tTIUA6Bn0cRigh0a1L+BE8+zidZ0zIcDjs5PMuWadua/wogLp2DBfl4QchgY5pdUucjPCDgZsd9EYgffqNxgsitnb2CEKft3rvEsc9kjghjAKUsjx4eA+0pqlrsmzFbDLFiojeIMEL+qzylun0iMVswu7BFtoULLIcLxO8/PJt7j14gDaKprYURYNpPapaMhweoLyILGuZLzNWRUYy2KZunTe08gKEVERxhAoMvrAMtyJn9B9ofGMxlaapS7TVvPv+uwR+yM7WNn7gc3ZyjtYtxrYuHMFq6sp19toY6qalbjTWSvrDlGrukeULiramFr77nbrhcvMxdqtbRG0nxutsbD+01n3POodaf+BnJQy90Yf/ruq+eHr36xWSEMoL97P+71rv4X/g5xfX+ncEtLblx6I/xpz7CNWilEUq08XqRQRRj1Veszw5o98bcengEGkkw35K7FfU1Ucc4xZCsDXqEpfLZ5UufuARRD4Yl4e3XOWgc4QSCCzKj8iWc+o2dwNGKVA+SM+ibY2mAtl0OlyFoUVT0RpLXi6pqobD/UNWsynn+RLRZfXRXbhrRZiymsiTxL4gjhSxD++++V2S0COJQ1SYuC5qNObk+JhLhwfUTUtRlsRhQtu2m47Z8zzKstwUFvNBpMQ+VZIaY7pgAn/DPlEdXru+L6cKrDaduTXWfUnjPHKsxRqzSfnQusX34wtdu9tA2sYxLkznyaI8RVnmVFUBwqKUwPclQeDheRIweEqiQtGxPurNwNEaqKv2A4KnZ7+U8hDSbpwUe70edV1vFKKOCaSeYdisn/9FK9P1/a9ju9YbxPo1Kstys3GuC7eDoLzNJuhJiWlb2qahrZruuZcb0yQhhAs4ho2a0JQWO5OgBEo7f3ErLVI5OlmrDb6J8axABR5i18P3Y9J0SBjFKF8wHg+pSsHuzgFRFOL5LkFddoHOAPkq43F0hDbWOfHFsfNBzzKk8gjC2Bn45w3LrEKqCGMDjPVp2oaqskjpIWRAFDtnwaoxrPKcqqlJpcIgMIDyA2faoJz3hraaKAErWhq9RNsaZIsKLL6MmC0W9HsDWmNpjWWZ5TSdvz3r98gKZ8wmBE1r0S1IoaibHKUM1jau8VLOwmC1XNKrDxjaw/8eq8z//5cnPF4MPsXX5TFeqFDKAA1RGFLpiGXZMp/nzBdztkeR8zfRhtAXeKqmaf5/ZDL139dSSmEEWOGYHhfXfDkjS2Yc7B6gPMnDo/tU7RShIIq2eO7GNot8hjalYxUEIZXOsW2ACgVbu318pdgejSjqkt2DMf0tUL5FepY8z/mRH/1RssWU46P7RH4XYLCmxAlngOVLyVYvIgl9lADbFsR+yCiNUYHH9v4ut1/9BH/kj//L/NIv/x2ef/455rMZR48ecfnSVYwxVFW16SzXiTBOOv1s9Ot6WLk+ll/EWNfd3/rnURRt+N5xHLNcLjd/59R3jgLY6ho/cJ1i07qOCNvRCz1HozPGuRdq7ZJ+kqTHarViPp938I6haVp2dnZdtyoEQeg79zTTUhYNTae41NrSNGbT+V4s2OukIN/3oYMgPM9jNBqR53k3HG0Iw5DVqtjcR1mWG/qkS8Vxp7N18b24Ma43OmvtBkpZv94bSmUQEHb3vZgvaCqXFq6bBmEtk+XceYBfuO/1xmAaS3gWM35/hxazUb4aDKZr5S562YgB2EsGKSEOQ6xw74USAePhDqGfIKxANy6g+PDgCk1d0+/3CUNndzufzbEYBoOBCyFG4HmCyewBs9kJZdNQ1nPeeOsBlj5CRkhVIT3w/QjheUzmE3zPOdTVtaHX7xMnPeRsRastrTFkRcFsPmWRzbAdZVIIwSLLHRNCKsIgIlR9Qj9AIMnyBkxG1VhaLUn7MaqtqJsKFUhOTs6Jkx7WSIT18LyQo6M7HI4VYSQRYYT2YsrccvxoQeM1mw7bYmmoNqckRw1eU4Vt57HjKKvra+QiK0tK2Z1KN1LozmZDuktcKjzfhXYbnoqzEWKjqhbWdgNd41JzPM+dzC4+HuNgE2UV6sJ7L70YLxKOq14W+OE22bzizp0jqlIThQHTRcVskRFJyAI3v5I/YPa1Xh+Jwt00Le/dvU+aphx6ihe4sbkty3NOzk5pmpRXX94jKzJakSOtoM0XzOan+L5EaGfwZKjJ8xVCJNR1CdQoT7Eq5+wkOwxHEQOZ4oXWYYrKoxeEjHt9RmmMqUsQLgdTa7XBopPAZ6sfo2zjWAbWEmLopyG1towHfa4c7nN6eozRmqNHjxFAr99nPB7z5MmTZ55z27ZUleuSa9XSXjClWv+bSqkNHg5P03XWHbdSriOeTqfM5pNNMXNZjS79xpiWOJFOJqz6hJEPwjnUnZyck6Yu9dsYSRimtK3gyeMzfN9na7yLFB5RlDIabXFycsLbb79Lmrrvj58cEwQeo8GQpqmo6/VjFQjRZRhegEbWzw3oCqvEU4rhcIjnec+wSVwiUMFkMkEIcSGDMti8Fmu/ljUurZRyWY/d77gTQLMRdkkpSdPUBS/4PkYbslWG7/sEQYA1Ft9zPsxSSqqm7Hy15TOPXwiB/90Y+fCf/vKxC8Pq5+Zkf2BK1PM733CPNExIg4TIDzpIyyKlxbMW6ydIJKbSoC39xOHzVVkxWcwcJ94H5ftIlXL58BYv3R4RBSO+/Lkxk9lDTk6f8PjxEx4+fMStF2/w1ptvESUB/YHHcnVGbzSiahqWWUbTasbjLd5669s0OscKiTaQ5SuatiGOY2ftICyNbtnfGnP37gP6aR8p+kgUTaXw/RGj/ogsm1PlZ2R5zlZvmyhxVsRNY+hFAz71yU+TT+4xmbTUZUmFpsgt21uXCcuYiz5tX/F/gUW1JI5TF3aSJkwWCxbzFScnp8xmM+edg6LX75MmMYGvEKLl+vVDfuVX/g5lUYOVNI1xNNHtHWwQsX31Bq999if4l/7Mn+WsblnVBqMChB9R1o5Lr6qGoZLkp8f8v//yf8hPfuLjfPO3f5t+ErO/u0PsS04eP4K25dXsNlerS0+vdRFyfjqjzEuaquHeg5bHRcFi0RL5PZJwhFFLtnYuUS9n/PZXv833vvsdpvPih36ePhKFW0hJfzh2vtTNs1BJnKQMx2P6aY/z83OuX79OqXtUdU5TSSwNw/GQpnEub1ZLEC4zbjDsY5GEoURXrlhp4VFbZ06/nJxw8/ASum0dBXC0Rb6cYFqLwMN2zAypBIPYx7MujsroFucg62KqtBYURcl8sWKrqun3eg67jWOSOKasHAd7bQfbNausswg/qJw02hCop12itQa/U0lKqTpPcUO/3ydNY6bTcyzuOJ9lmaOOGU3bVETxEBrL4ydHRPFV+v2IPK+Ik23CyMNoQVXW1HXD9vYOUioGgxFJZwh1cnLCaDSmKEpAsLu7z+HhIUEQsrOzixCGMAgJohBt5EZstPbQ1kZvMGVrzMZfez3IdDxWF6pcluUGx3fDS9fBVFVJVZXUdYUQruj3+wNn2yvWRdzrOPRBB2u0pGnKbDb7kLDLGMNyuXRReUBRFKRp+vRx2i5DVBuM0qwlYms6o+d5KGNRH3zj/ilW8o9Hm04QIZiJlfNMoQABTa9kfvuMKPSptWWdx2m0RkhJWRQOPlIexkqKKidJFVLhhsxWU6maxfQEP9ZOzVhVfO/736M/aDGixgiPOB3zyouf5dHxCcV8hTYSbSQPH59Q5BVCeaDcxuh7mqYpqApFmqT4no+nFEVu+KN/5F/kyaPHVGVN4Cd87GPP89Xf+irnpwt8D2I/YTGd40cR5ydTpPAdDbWBN75/h9BmnE0z0uGAj3/s0zx4/xHSQHiSbJw/sfDo0Ql//H/8J9g/OODx8WN+8Rd/kV5/wOnJKUEQcungEE8G1HXD+fmUtqzZ3h5z7cZVfuqnf5xf//V/QFGUSAlhIPGDgOs3XuDW577I1Y99ku2bL/L63XMyz6O2EuVbwlCACjh+eEqqBAth6EmfP/tv/S/5pb/2nzO1luPTU955fIRvDV73Pl32r3L1QmX9+nffYtXmYCRohdaKMgLfS6laeHI2oV484Me++AqtFdRGYWQfKT/iykklFePBCGMsYR4+c5vWlqY1aNuS9GKyiUI3irYSLvC3F4OImFUVtq2RwqKrklhtYwvDqmpYCI1tS0b9EbapKfOWvGqwSiF8zenxOUVduHirIOlMdyTaOvvKQElS3yf0JEYLhCdREqqmQhsfCImjEf3+DlmREyYe9TqNW/m0lB2ZX0Db0toW4bmGwpMKF+H7dLVaQ9ctGqOxLg5gc1x3OLVLH1+t3AUfBB5VVTjvX9tgWk1ZNVRV7HLuqtqpPQVO3lxXaN0QBrETIFQ1vi+p68JhjrqmKDKybMlgMKBpKpqmcpuVbbHWpb1EUUwQJpS1oagbbBfw60lBHPiEylkDaG3AWBSS0AuQ1ol2POWgKcfkcIWzadxpxPHGu/R2F0rq7D5xbnDrI7IxhiiKaVv3eq1hJZe16W0MutZd/do2wAjjZhhC0BqDbmqkVCglUdKpN9eF052CRAf7fLhkr64e8d6dN6iqkt39PV544RZ50+DdHaIydwIQCET97F9uju/df2Xj0XvDpe74a2+zC3MIdQushxOLWLog6bYLwK6pyjlK5m4IPS/xA4+6tiyXJYPRLtr6+H6PIBywrCV549HgYbwUa2oWq5a8kHiewiqBFRpjaqQIKFYGXyiUVRgPKlOxXMypypK20uDDzvYeYdin1RWXLh+wszPi6OgB2jRsbe/y/nv3ODub0O/1KasGXyYE0S7D4Zi93pDH52/yuGg5bBuG6xdJwEsvv0hTlxw/fsj05JR+5GisH//Yq5hGUxbOHnVZFnz6xVtcvXGNsJfw4OgB7995SGs8kF0sIJIoCBzEee0mwe4VHswaJrMlJhCESUijW9rWcu3689z5/hsc7G5RpxFlKMkCeO61zzBbVWRZQVvVpEnEn/nX/3UW8yX9bxm4//Q9LkzEsm66tHpIej0sLgErCDSjfsyrn/0Jfvdr3+CVWzfZ3j/gYFVRNhempx9Yv6/CLYT4d4Cf7T5138Ul4BwCPwds45Lf/5S1tv6hd9It3Ro3WPyQ/FtgTDciFAIpfJQI8ZVGCcch1UZgrXJviXD8aaNBWA+07xK/jaFuG9pWIkxEICSB76HqFaGtSJVhECpqHWE11O26nAoCgVPAKYXoYrSMMNRNg5U+QRARpwPipEdVVXi+oNXuomx1S1stUV5E2wWyattghcMTvS4s9JnnrFuXO2daEIaqcmZDrki5XdgYh6u2TYPnKeIkpqpLlyIkXfEztnsGQtHvDRwc0HkZN42TbIdhtCmMQeBhrSuOvq8QwnY5ibGDNTxFEDiIwxhN09SEYYSQCt045oCFzidbEnieCzs2LRKB7YKPfaVQHd1uDf/otRzfWOq6oixL4jihqpwQaD0QXKe+G9Nx7LufO0aKGx6uaZZNU+P7YSdZdq9fXTtXvHVBt9YircVYZ3crpOy+BH437F07rbuTUlfIP3gt7FVMjx6zrBcEkSG4/jyzWYY66jnf9X/KpVqFmqQ/9HaxbajHJU3QYjpFbF27nFCDQeuaVjTOhrRu8GpL3Vj6vS08GRF5fXyZYhqP6WSF8mKCsEdROrHX1mjAdn/A+fkpZVujbY2SFiUCiD10K2gaRxoAmE6nNE2LCz02zOdLppM5w2GM1gKLx6XL17l7910C31kNay3Y3tp23iV+j3lRMDmb8+DdO0R1y5NHTygGJURPn/eVy4ccHz1kNZ9TrDLGacJ5XqDrBqs1om0JPA9bVyS+RJqWNPD57Cc+xSzPkHgIFMIKrLEIKdm5fBmZ9phXmkeTGbPzFUIaxjt9dNuSZwUyb9GLJXOjaZKQKPYJI8l4vIPwAox1OLjA0V/zVYEQ689Mt2SIESVGaMcoweJJD4FG0OIpQ68f81v/8B1uXDnEIvB8z9Epf8j6/7pwCyEuA/828Iq1thBC/NfAvwL8UeD/bq39OSHEXwb+DPAf/V73pbVhuVgSJzEfxA085Qqj8kLyrCQMYpTS6Nh5UOdFg0UiVIAnnNQ7CJxxTpikqMjHSGi0oCbFyJA0TYiVJvVqwsUpu0mMHUb4w4iVZ5lnpTOOF47yJa0rRlaAlAqjXTiAkSBCQX80oD/soXyFrgpkx74QWOq6ZLGcEqodrF0nn9SbRJfK1C5I98LzbnWD0gJr3TF9NpvhEs/dLr2GUIxp0cZBFdCjrhtu3ry5EZCsh3DWWg4OD0iTxMV4deyIuq4x2oByxW4tanEDMReqvLW1xWAw2AT5AsRx3LFk1kG1BiHphoyuwHueQnnytdU0zwABAABJREFUAi8bpHTJ7+A4vq6L7AauSrBaFnieR1WVFEXRSZ5L6rrefGntAnirqnqGIrimV64x9HWXDbYr8AYwNE1FkkTd60nXtdvNc1vf5/r7i+yVNUNmHWh8cSVpj/5wSN22NMaikdQ1hBdCIa21ZHqx8fIWnb81pXzG1vf3WuHbPeoXW8yOm7No027mHVIo6BoXK9yAvakNppXcvPYcVVEwiob4OqBa1NQ2Z//SJUa7ezR5Qa0kr9y+zeW9Xb761d/kbH5O1RYoafD9lOFwn5OTU4QwTjbe1FRNiwpCpFXU2vLW2+8wPT9jPLrO5HxGkedcuXrAu+/e5fjJOYPBmIP9S2xtb7NcnjJK+7x//32OHt/l8Ruv8wdf+TTT02Pq4NnC3U8Svvftuxzduw/acOvll1hO5zx58Ig0jNkaDvGHI0yrOXr0iPfff5/bL7/E/+7/8L/n13/zHzPqpYjWaSHaukELydXbt8nahumTR5zOW6ZnK9AtSu4T+h56seRr3/wet158hdnxOUurieOQ/b0dykRT15VLMRKCrCz5G3/rb7GczvlC/RK3ubJ57EpKPCmQgcKXiqpcEg36rgm0hmI1563X32A+nTGbTJmeT5hPp7/nZ+L3C5V4QCzcFpMAj4E/APzJ7va/Cvwf+ScUbqUUzz33AspTBPPeM7cNh0O8bfBVipQOW4s8haVBtxpEghUBYeyhPI2nFG2tUHgcHBzieWAFlCak1CGx5xNUC9J2ztU0JH/0Hu15zWcPR3x8f8Dr77zD9967R2mgaF1XjLRga1rd+WG3LVVV44UhAstLr7zI1ZvX8AJFHMSIRqNNQ+A7P5Gyyoj72zRtieeDlBaEpm1LmqbGa6JnEnzKKu+4nq5DnM8nAGxt7ZAkqaNSdX7QWtdUddnh6AO2t7c29pqep6iqkuFwSFEURLFHGLqTw3A45OxsQhRFKOV1nWj0DH3PWrdpjMdjqqraFMc1s6LfdwZQQeBTlgvOziccHh4wGg2R0m08fqDIiyXL5RIpJePxmMDz8APVHRc1ra6wrWU2d48nLzLKKkesHCNkbQq2Zoq44v7sMXLNd197la/ZJJ7nDKfWw2BwmHjbtrStg1vW97vOIb3I3764Eaz/dh0ccHGVTUUQJ+T1Y9791rd5Mlvw8oufJNY7m3fWYvkvjv4ih5d3O1y/z4/9yB9A/er2Mw3aRfaDeyB86CJeMyqUFKDUBRZFF61nrdM0hB5ow/T4nKu7+8wfPcBUDZ6vGG9tY6uKVAg+/fJLSKWQnmQ5m3H16nWkJ5nOTlEB/E/+p3+Kz332i+Rlw2Q65dHRA771zW8QRwnHR6dkWUnTapqq5i/+P/8if/Wv/ud87rOf4vKVS/yFv/DvMBoNmM8eU1cPuHz5Kn/kj75AXbqszu2jh2TzCTuJ4urBPr3AQZE8ffrs7V9le+uAbJqTLZfcu/OQxWLOIO3hK0VRlvhqxQsvvEBZlajARwvLv/9/+78C8KNf+jwSKPKCk9NTzrKGr3/tG4xeKFlYn/fuH2N1BEh2d4akfg9PGJaTE+68p/BVQJ47P25fBLz8pY8RxSF+GKJNQ17k3L1/n92dbZRVz3DEfWlJAomyFl9BKARNnQEaoUAqj1ClXLtymcD38aQk8QMW/PD1+0l5fySE+A9waE4B/CoOGplZa9eg7UPg8j/pvnSr+da3vkNZlmzpPX6cpxPZhw8fcTx/xMHuNZ67uUtRLbn7/vucnB7RNC1f/uIfYDAY8fVvfIf57JgkiQi8Pi++8BLff+91jKkJfI/T8xWvvvJ5+ltbnN55m9npHT7+udvs+IJZvcJbNexsj3nlZ36MP6H+Od65+5jf+p1vcuf+IyoDQimEJ8mKhtYKVJzSaEuxWnK6OCOan5JYzSyf873vfxNByHPXb7G9vcXRo/ucyBXbW/vMF+e0uuDK1UucHE+Zn59ybfYCOxxsnrMQmjt37uF5isPDQ+bzKSDo9XqEYYB1eVFo3fHWlej8R1yHPx5vMTmfkWUlaerSTlyMlgsDWBfh+XxGFCaA4Pz8nNFotKH+CSE2waVnZ2ebFPs1frzmpVeV4zoLIUgSZ/DkHovY4NGrlXNs9Dy3QRweHj7DqzbGUJYlvV5vk/TT7/eJoojVMt9AIWvWTJZlGyWkEII8dyZHq9Vqs/EopUjTtEvOyTvIxc0IsmzV0QodRLTmaK8FOmv+t3svng4s139vjPkQVPLXfu6/oiSnaVuKuqa895Asl/xksE0ie919wSu3XiIvFmAskfJdYswHlkHzxvbf49uvf5et8S43+6/yUvX5p9eeACFB2U6YhDv+286HRknp5Oaypi1zBgn8m//GH4a2RTYnvP3OQ9qm4WD3Jr/1O7/N4wcPODw4YGdvjzCJee/ePdK0R1tWbA2HbO+N+fmf/wW++e07qCBkuDVg72DMK699Aj9MCfqPWEyX2EYzCEN++W//Muenp/z8z/8CTVPR6zla6XK5Ymu8S9tqfuVXfoX9/Zjf+Z3fZpEvCMOItqnZGvTxpO5Y5d3zBX7zt7/F6byiFCHeIOAP/PRP8lf/s/8Xtz55yyk685yv/tZv8eY7b3Pz5k2Gh3sM4pS0H+M1mn/0q79KkxcIo2mrilUj8PeuEcmAYLzFYWDwPMXpPOPt734D6TmosFaa08lDpHLOgb4KWBULjk+P+finPkn0uc9QZSte/9bXWZ48YTQeEk7CZwr3ld0hs8IgrSXwJPfvvM8//yf+edI0ZW9nh+1hn6//zm/xyZee42B/l8VsRpWX/NIv/+0fWjN/P1DJGPgTwE1gBvx14A//M/z9vwn8mwBRFDOfL6iqipCe6927tfabLuuSsnKWlXVddiZIljxfEvg9qiyjWK2gbWhkgzA1cSApy5o6W6IXU/zlEWlQMOhZfJFizx8zP31CtsiorMY8eYC6+y6t9Ll+80Uu93zqXkStIs6zjEVZoIVEK0ljoNaGn/yZnyZI+tSmhioniHyG4z60PoGvwGqk5/Q/yhNEkU9rNNZq4iSgzPwPcdfBEoTexi0xTZ3l6RpnVZ6gzRu2t7dYLidsb48Joz2KcsX1G1dpW8vB4R7WSPJc0zQtr732GkJqrHVwitaa1177OHXl2BtrN744jonjeFOotre3Nxfeuht1aS6LDdWv1S2T6YQojJCKjobpcOK2rQhD54C2toZt24Y8z56BOdY8bc9TG1ilbWvattl4kVz0dlkrJteP56mRll1/vmjbBotFSDpFonLPwzRYnB2r1halnl4G646+aZoNE+Ui02TDODHmmXdsOBrR5prGgvAEedXw5PiIaq9iHTdqLZw+PqOsM1566SW+9KUv8fFPfZqv/4O7H0hYsiyyx8Q9w41b+9xID+H1i7e3WNtgtEAKNy9AOAYK1lkTeQisrpHUmHLF137zV7l2eZ9PfuImq9WSb3//Hm+++QbCGKoi47333ubOvfdIh0Oee/ll8mXB40eP8DxLf5B0r6+htzVge++Ay9cu0Y9C3nr3HvOs4nS6pMoKBr7i5O47SGHp91Lq1qNpM6J40DGfDMa29IYjbr/yAltqm6wuOT1+wMm7r/P1r/8OaZpsbBLWq1xNSROfVqdUTcWqygjiiCenJ4RJzPUb1/m3vvi/4C/9pb/I5778JcZX9vHigNBYtlHc+42vcuXyDS5tbUOtOT5Z8rCumT0+Ynl+jEUx1x697QNmy5YCAcpHSmibEqUKLl++xuVL12gaS78f8eD+GdcuX+HGpVskccTlcQ+bL0h+u4HF08/HT//oF9BeA8a444P5cVSkCHyf0FfYtuTVF2/h4ZqfUb/vHEx/wKa+Xr8fqOQPAnestacAQoj/BvgRYCSE8Lqu+wrw6Af9sbX2Pwb+Y4DhaMsmcUoYhKS2x4XNlrSXdsbsPsa0hKFPv5+i7QiBIAwD0C29KIbBgCj0wHhEnqCfhCijMEJy+flLvLgfsSVykh6EQYxXLbCeIt7eptA1pWlopQVd0y4mXEpjxO4Wx6uGpg2pW4kXRJggRIYJ27v73Lx9i/PpnKqpMXVOL3UUPVsrB5VIZzgVeh5h6IOIaTVOhZhElHGE94GBrBDWZeMp6ehLkbOU1Vp3BQ88FfDkyWOatkVIg86d45wx2uUuKomxYI1BKEFZlvg+WFrKqiCO0g76eIoTR1FEnj+V2a6TbdYCmLquN2IZJybyOt6xK5KEzpPYWIdpr4eY66Gh57nn1DRV97M17NJ2XOuOkeJJnL+LII6TDSNk/RUEwTPKU9VBBWsOOHSii6ZBKjaPcX0y0bqlaWoEavP815300/fgaad9cTPoPrsf+jzXTe0G0caiEdTasKwXtBcuPiEE16/e5PjkiGE6Zmdrn6uXrvMN7j0zoLZYmnpJL/G4cnmXw2iX6YXCLZX7ciC9w/Dd4zSuOHYR2MLAIE1JQ8nZkzeIbu4TJj16g5gg9PHDzqO8KlhmC6qmwV/O8YcDimXJ+ekpw0GCrzy2RlsUZY2dzSFQWGWIPcm7dx5yfDrhfDKjXGUUSjCfTQkDD4QhCBSvvvoyb73zJuPxkGvXbnJ4eJm4F3N49YChGWGUYGd3wH4iefjVr27mHhdXbEr8gc9otOWu2Z4HSnI2OUd4iiCJuHbzBl/+8R/j+gvPEQ1TEJawbBnlLZ+7+gJX0j67UYpeFeyEkv2e4K3sjPvzOUXb4nkx6faIRVNhtUIoi/TAMxX7O3u8/NwVbt26TVm06KbiypVLJGlKlhfMJlOGoWLgKZR0+aHrtTdMsFEHYQnn0b9YTZC0mKalri2h76EQWG06K2i5mbP8oPX7Kdz3gS8KIRIcVPLTwNeArwD/Mo5Z8qeBX/on3ZEUgl4vBQGDtg/zp7cNBgPMdkHgeQhr6PdiimqA9JxkOY1DlJUMBwPiCKJAYWpL31cE/R6VaglFxCdvP8f1Xow+P8MrLV7g0cqIURSTDraoaalMg5aWujEcPzzm6vaQrXSAfXCCFhYpE3TaR/QGJNt7fPqLX6auFtjZjLqpkQj69FwRkE5x6QZyruuL4gAhG6qO1B/6AZ7/YVtXKV1X7VKrdFeYLFo3zOY5RV5y48ZzvPnW61y5col8lbFczdjd3WY2m7K3e0CWlS6nLx7heYq333mPvf0xQaB4/OSIWy+8yJ07d9jd2cMimE6mjEZj8jxHa73BfWfzOTs7O1RV1RVCx7ooygKpko0nijO2EhcK6Xow6NSpUkqSJCYIfJqm7pRusD5FrKEVV4zdYE1KZyeg2xbVFW2s7WAbp2Rb0yONdvONtXrN62AVT7jTzHrI6Pl+x0RxWZBS+F14g/MQN9pgpOmUcI6L/pR2/dQG4YPr9OyUUtRY6aEttNpg24vP0zVbP/WTP813v/sder0eVWHIFu4kdXEJwPdawiRgZ7vP2O8xvUAZdc2AcL70xnasm9YxjoRFCA8pQBnBME7Z2x7i6wlhkHByOsULYq7dfI60f4nT0zNa3WCsdsEMZc63v/NtQuGj2obQ89BVixcmPDo5IT86xrvrkfZDhK5ZZi1FBXlWUmc5jdBURUldW1pdsbO7xZe+/EXu3nuXoBfx6qsv88Kt26zyjN7Ap5looiThyugqz+0OuP+1r7nP0gde3+1IEg8jBvvbpDtblC0MxyOWiyVn52doa4jThD/8z/9R50yoa6QxxBrs6YxP7V4iWhZ4pwvMqiRbFmxdP6Q0GXm9ZNVWECUo2+DR4hlAa6RuiQJ47bkbvHjtkEtbKUpG/MPf/R1e+fhrZHnF/ffv8tbrb/D26w2fe+0218oewwultc5m1LXGCNlRcysQDdpapHB5tasiJ/QChAVtNca69KEftn4/GPdvCyF+HvgG0ALfxHXQvwz8nBDi/9T97D/5J93XKlvxrW9/A88TXIlu8Mnxlze3nZ5OuDu9Q0jAp15KIFEcPznl0ZOHSKBZGV548YtMlg2L2ZxBIAjrnPHBHi8eXmInOiS2K5pyjt9O8Hwc2OiFCD/GaoMRDdK0RL4iHg4QQnJlZxfPSkQQ89qXfP6zv/Zz7G7vYcY7LGTEXCjOawONJisq6rIgMBZZ1uhMU1Qlzaim0R6PH53TSzU3bt5kviiZTufseQFhAPNsStpsPfN6qCBAZ4aqrvFLD086qlsQehjrBp+IhtG4R5z4BGGPKPKIwpibN3bI8wLfVyRxSL+/TbbKO2GL61wHg8HGAwXhCmAQBjSNJs8r0jQiCD3AEqcxUS8lq0ussIz7PVCCoqnZSXeQUnR+JhV7u/v0emPqqulSchzkY60g60zhR6MRnuexWM7xlAslXi6Xm+75aTfrLt3DvUsI3+Hra1pgUxUIazqIxOv8MLTzlBHroGZncCSkD8LRKKUQJEnauRJKrHY01KZ0JlW1BbsOFjYG31ddge/kx9oF+FrdwgegkqKuIBFESUCsJOfnUzxleaZxFILPfuKL9Pu7lFVNUVT8R//hf8rz5uVnho9CCK7f2CIME/a3YuLq2Qu4KWvqonTKXgNR7GOqGqUsvi9QnvOBN9WKk+M5TR3z8stf5P/xl/4y7z+Y8y/9y3+KP/zHPsNf+U/+ClmVgZKoICQOfZI0IUgi+n7IpeEu0/MZf+O//JvIZITX30KEMcL38CKfw70RWVZQZHUX1QVH0zNMmyOlYThI6Q3G+H7EH/jJn+K/+8e/wd/5u38H9Wu/xu7ePn/4D/0EnpVU0xV1FhBJydvTkms3XyKtB1yUN+xff57B/pC434PAw5Q1f/rP/Bne/u4bzM8nDHo9fuKLPw4NgEJJn0jCrhL0bMPXvvJreHnJwXiX567fZq4NJ53XfjK+xOhyj6+fzAkLQZQOsZVgudJIa7h8sM2PfPITPD56wM/97V9kOp2T1S3/4G/+DT73xR/lE5/4DNd+5o/w83/rl3ict2y35ikHHTezqFSDlpJQKEZ+QinKZ05ZUnVeNxa38f7QaunW74tVYq3994B/7wM/fh/4/A/49R+6kjjhc5/7DEJaBvU2nD29bTAcstfbR1Suk5ovFiAko9EWSRRgTM1s9RDbnJOaBZfjHn/oRz/LrdEQszpHLip0WzKbnNDfSgmDgCiI8AMfg6XRFUY3RKEPnkedLfFUCNoilJvwRgr+lT/+R5ho+Na9RywmMywh0wd3CCOP+WxGkZVEkWZ725BlBct8SW+R0JqaLCsIgoS2rSnLgtVqRRzHjEbjzgDp2SORFYJWa+c53mqk54rEmlanlIMmnGMfWCu6YZqgbQ1SKnSHDWs9w1P+xoyqKPWGWrdexjiWTBA429ko8qkrS1mXtNpSVk9T5i2wyjLnrW0trdad17fqjreGunbxZqoLSp5MZqRpTBTFG7+Ppmm6dB6zsXT1fZ+qqjZiE2OgLEtWqxWj0YjhcLjJ4BRCUHUBtWs5+3rQub59Y+rW4dJrD5Q4jkG4QGNfuc1iLZlffzk8Wz8Dj6zhE7GWvl5YaTogM0uqogAp8T0PU+TPdNxY+O73vgdSMZ3OmM5nXLl6zY3wLywpBbdeuMGP/vRPsZhlPPz6GTDe3B5FCV4qaTWbjExHMRSbfwcsVij64xFB7PPXfu6X+Pb373J47Tm+/9abvHv/EU1T8+/+r/5dmram7U4Hni/RusXWDU/uPubyVcsnv/gT/Bc/9wuoXo/J+ZTaakQU8Pje69jaIo3EC1PCtE+/1+dP/qs/y9H995iePSaNfZI45tKlK1y5fJnnb6Vsbe+Q9nvoRuMp4ZKkENR1yxd+/Cf5G7/wC4yuvMJo5HgNAujv7tAbDwjiEKskNjBoo7h96xbxqyFJHLvote0tqroibgVJ1ZKdnvL9v/cVDra2Obi9i5WKrx3d5Vv3HmHShDyNMDt7jMZDtscRc1sxXy5oS4NsFVvbA/LzU776D/4eo2Gf29cu8V5bMnn7IWXe8A//5i/ym3//K4x2L+H3erzz9l0ur65zid2nb2gLvgVhLNo2ZJVG+B3evTaBu9hd//BGe7M+EspJR81qEYoNx3i9PE/h+xLdtARRQG+8Rb9cgbT0Ip++79EPW27t99i70uPV/S3G5RRxvqAtM7KsoC5bgjBgPs9JYkNOhdAWpSVtXTJfTVBBQJQkxP0eKlAYbfE8ibE1utVEAsaBx2vXrtCLZ7x5dIY5e4K/twutoa5bEDVNA8pzBLCqcl4XSZICdmOKtPbWuOhDcnFp3VKWBXmeIYSkvzdgOp3S7/dpmobVakVVVS5wNg+Rm2FdSRQlFEXRxax5nchFbfBfo13YsSugbXfEdlJdB5F0QcXWoNsWP4goioK2bTec8HXBdC5+YsPEcAOlTpAgnK9Ilq0wnex9zdTYbBxr8YJQ3YBQdkIcCAKfJIkQRjzDKFl7lqw/Jx8cWl5cbgNznXjg+wgpMVqDtc6Ay2osirXd7Tr5B9zA07lNru0JXMyY1m1nOPRB0ZSlbZxbDkJjhcAPQidA6pbFcvToiP54hOcrkiRiOjlnn2cFN9ZaJmdT7r33Ort7l9jeGT5T29tW02rTOSeyUYsK4aLqXFAGIGMmi5K8qvnCl3+c3/nOm2Rlzr1Hd4jiHsNenyjwwTZ40tk7OHsFn8ZKWs/jbDpFZxV/7i/8Bd669xCrFJVuWWYLitkDPBmwmJdMZxnLrAQv4Od+4RcpZ0+IlOHa5T0ePrjPajHluZs3aY1B+SHDwRCazuJAuuiwcpWR9ntcu3mDxE8uvG7wq7/2D/jyT/0Iuwd7aGtpjHU+OkmIHwSIQBHIiLxcEQUBYVZSPTjl5O13uDTcpz4/4817D5lVBcdVwXlb0awK0nCXoG4opnNs1mBj6YgDgWKQRgwjwcO37/K9/AzaCt1UxMMRIpszChKyPKOpGugP+fSXvsSV555j57fm8ObTvEhpFLKskUJjlKUSCs9YLlboH1QHftDP1usjUbiNNe5obRtkGTxzW5YtmdRn6KJBXzXoTrVW1zXTbEYtYG/U4/powCUfDqTALwtC4WMxLKqS88kCoRRJLyYvakLluS+pHN5kBb708KUHLTTadZ9to6mqhsZoRODhRzFbng/jPgGSu6czImVJPUXluQLg+SFxmlJUGWEQkMQRo9FoYyO6drdbOwWuBSAXl4MZTGe25ArUulCtuz7HxDCdOnBdiJ2T22w2c+wCJbuOVHZqQjec8zzFOpQX6HI67aYLd1JzBwusB5Prjnu96aw3obZtNkNLl3DzlHUhpaQo8o2HyJqlsb4/hxk7LNwJi+iwbYnnudfGl4qkC1q4OERcsw7WLoNrw671EkJAV2DX5Vzi+M+6aV3eqKXzUln/0fpicvJ6NyR9iqOLVnfc7w+rXTFuAGqNpW1apKcc///CXiKAXi9le2eLpnE4ZhFFfHgJpPJ49503CaMI33vWUP/pKcC4cNq2RQi/ey3B9yLKqmQ02sKTBmFrqsYQJQm3Xn6JwdY2SW/A7mgHzwOvFRsFoEOBLEIqgiQha455+913ibb2ODk5IxkOEZ7EU3C4v81wuMO9+8cYPIQXULfGBW03FUIZdNswGgwQpiaMIxptQUh8T+GpGMHT04xQikW2oqhrjPdsB/r4+MT5hSsfo1sUlrapkcplVLa2RaNp65rE82lWGc18gafB1BYpfObLjOPVjAUWmcS8cOMKy7wgq0v8uiQRirlugBZhW9ANZWPxbU02PcW2NVhnP6HaiiSOUaGkbAz17JxhHPPk0WOipXlmKxYqhLZAYPCURyW7Af6F9/Li+r0K9np9JAq3QDhj+pZnj5bgJt7lHFu5tJKyqTG4QV0+nVCUOcG1a+xEAf2mpF2dkYiWoqnJrWaxKjidzlmscoZbI0JP0Y9jRmnCKI3RxpD2BsRxjOf5LshUgPE6d7m2xWDw0IRS4YmW3SBkcLBDPZ/jhZLHvqANPWQYEUcxadqnqjLSJKHXSzcClrWlaxAEF7jJ8oOnbuq6E4p0xv4XU2Euugb6vkfbtHidUVPbmg0tTsqntDZr1+54a6hFbWxTnx7/ZddZu2O37jaOdbFdDx0v2qlKKSkr1wWHYUgURWRZdcEgynGs18/b89SG9ve0aD+VvPu+JPBDlKfwOlGQ8tXGn3stsgE2QQvrLn4dp3YxUUgJgZCqY144BoYwbuArut+zwmIxWMQz74XFdCHOtpO5O0jJbZhPfcY3y2ikcIXaYMEatGmfvQiFYHdvh+FoSJYX5GVOr++G8jz7ayRJjyx/RLZaElclFzmyaxvRpm0Ah99b6YKFpVAuystTJL2UwBOYtuTs9DFbW2O+9KUvcHj1Cmnapx/3Wczm7mSprYPtpXTWrX5A3EvQ1vDOe+9wPlshvJB0NMCPQoJAcW0/ohenVNkK29aEnqSuK4LQIwlTVJPjKcmlS4fEkWtWrJRYobBAFEbUVdVphRRhHFHrlulyTu03EF58TQTz2ZzhcEQYRwSeR1tVoCTaagfZtS2i1ciqwbYN0mqUp2jKmr6fMOwNqJXLQciSiBdu3+bNt99kmZUo05B4PrQV1tbopqKsKjSaQeyj6wYZegjpU5UZPgZP10gvQFjLcj5hfnbCneNzRtNd9i9AW6tWo6TAE46IUGGwrVNccgGC+2dZH4nCHYQB165dw/ME4bIH7zy9bWdnBzO6iS5bVOhjpWQwHBF6Fp36cHrKvvWojp7wZHXOypSovS1+8xvfYlK5gYCxMJ0uOf72u1w9HNMLFaM04sXnrrM9HDIaDGiqmtV8gTWGy1ev8+DJMa21JL2Une09WgyyrtB1SZXnGKN4+WCHPJLctTnGl0SjPr04YOL7pGkP6XloY+j1emxvb7O2F+33+xuq3dbWFtE0ggthF64oOJxbd/7UdV1vCt7a23tra4vJ9KxLkXFc+CzLXCENXBFTQUBV1fR6PZarKVVVkaQxq9WKXm/QGTMZ4i7Fu6pcAlDTNGSrjMFwjDRmI4dfF/O1SKapK7TW9HouoUe3+cZFT3Udv6OFBXieT5ZlnJ+fE8cRZVU5/wgUvucRhcnGcjWKIuLYYHWzec7AZiNp23Yj/Z9Op6xWK5Ikecbrpt/vo1tNzdO/D+Pgqe+JEniBf8FC4Kkysq5riqJxeG83rGyauvPlgLR9lmPbtCVlu8ILAtI0oqpr6qb6UCMiBbzzztt4vo8f+JRlRmo/6E0i8FTEz/yhP8YyKzi6P+di4TbGya3zMgfWsweNwKI8SW8Q8uKVm3zj619jNZ8xHvT47Kc+jWkrtocDlNXU+ZJF7ZJ+pLPSwlOSwA+wxuCFPr6S+J4k9CV1kTEchSzPTmjaGmg5fjenqg2CEBd861FW7jnPyxWeaSi3eo45YS1h4GOlQFsXqGA6eG0jbvI9PvGZT/O73/zGM26OAKPBmF/4r3+Bm88/x4/92I9x9cplAl9R2bYLvBaEUhKhYLbixt4eVgb85je+wRcvPU9YtXzytdcIt/oc5XP+zne+yffffJ1VuUILRVYvKZUFo2mLFbYqCExLb9gjDiDqjanqgqwoCTwfG1hWixmNBmRIko75x3//V/hX/9yfZ/+bNbz+lBr3tbt3+MTtS+z1I/Ji2c2l7EaB+4MK9w/qxC+uj0ThLoqCRw8fkuVL/HnClfDVzW3T2YR7s/cpVgWDdI/t3oDlasXi/BSxOGNXVwyyGXl2hpIl8ThkFlQc24qH8wWeH5DGEbloufHSdU6PTkDGjMKAZZVzZXyFZVWQBBH9ICTLcoq65vDKZR4+ecLvfuu7fPObb/Lqy8/xmddu8/WvfQ0hJB//xKd44cUXqVIf8coNvnP3CW8/ep+oP6bIVty9f4/xeMClS5dIkoQ333yTOI6JIuexslgssNaSpr0PiQ02qj/dUpWV27y6odsaGpjNpty4cZ2T0yfUdUXtB0ynU1566VWKoiCJE/r9PkXuFIpxkgAN2gREcch8ttzMFpq2xRhJFCmXQtR9kJRyMEWj9UaYs/YtOT4+dqrEvGC5XDIcjpnP513nGwCKuq65cuUKQlr6/V7H04ZPf/rTfPs73yIOA3w/BBzbJUlSzs7ONik6QRBsYJi1XeuarhhF0cbHZC0YWnfma1FOGEXkWbY5KVxUQ7pC3FJ1Mv61knO9pHT2wH6guotMEoTehQCMZ+f+WldoXSJbTdsaWt2SpPGHfs/zJVm+JMsLZ2TlfTiPy/M8/ti/8Cf4lV/5bzh6coo+3mXE0zSYuq5ptUtS2d7Z5u7d97h16wWC0GM6Pee997/L3/m7f50vfelz/KE/9GWev3aTIsvppz/BfDlzXH8l8aUhz0p06xJynCGYg6cwhlEvpudLVmcnDAeaBydHfPYzn+Xll1/i2o3LbO2mWO2BkdRVS1U26Lbht37jH/Gbv/3bVE3lYC8pKIscz/dBKoSUeJINfLjJUg0dP/9//b/933D2tyvy955ujvffv4e0cHL/Mf/gV36Nl199iY996hVkHNLqFtMY4iBhLOG3fv0rHH7hi3z65vMMP/15Zm/dpz8YcOfO+9z75jGvnz7gYV2zKFbsXt9n3pZ88507mEu3SbYPSP0xovDwqozI9xC2pshLgsDn+tVrPH/7Rf7b//ZXUL6PFYZGN6yWE/rpgDQU+OrZopv1IlahZKQEvlIYNMZzUJ37+mfrtuEjUrjjOObw4DLT2XmHUTy9zfd8QhlSi5I8WxIuVzRly+JsweTu+3zxx7/A1776j7h5dYfd3T7CF+R1RdSL2bIe2jrhxdAfsbu3g8QwiEMG4yFhmnD/6IgwSmnLGmGgnwxZ1me89d67nEymaAOvvHqbz37qY7zzvW/x8VdfZby1A37E+ycTvvmdr3Hl+ee40usTXB9yWmYYbTg/neJ7PtYIjG4J/YDJ2TkA88WC4ydPuHn9OnVZOs+VC8tXPsOoj68kRZZTVQ1Xr17beFAbYzk7O0dri9HWUdo6K9TFYgFY6qZmuVqQrRp2d3Zp2sL9fnc0a3VDmkYsFhqlfHppTJ7n5PlT6mBZli4NvKpJo5gojEA7b++rl68grCAKu0FT9wGs64qqbPA85yro+z5FkTk+tHDY+uuvv05ZVvgqwvfB94MNpi+6hHiXKu+RLWeOUy4lyvPo93tUlSvWTtKuqSonhihyN5Rt29axavICsBsPEudL4lHXjet0lNyoVgWOzeF7HsrzEAKKUm+Gr1jnKLeeT3yQe/9v/On/Ge8+eof5fIaxhvPJhH/7z/95il+LaLrJorWWb3/nezz/wvNUTc3J2QnT6YT0gt0BOAjkv/yvfoH37r1HVpT0i4jRhdvzPENuWw4PDrBWs1rMeOfN7wKapi3ZP9jhz/7P/yTjrR0C5XN2fkZV1sznC7SxCCUwtiXLK4dtG4uyFrpNRypFXRT0+wPGwwGBJ7h6ZZ/z83Me3n+PJ4/ugLDkVYaSCil9lJAoIfGUpK4KxsOUON1je2vMvXt3iaMIC2hjNycyiXjqOSMEVV1zfHLMe+++x3hyjYin2L4UiuF4ANaymM555933+PjnP4VorMOxjaUoCnqLln/xcz9G+fAB3379DfaSIfcfvE9zeIX781Purc441yUny4xLN67xYHbGUbaiUpK2WtKvU3a3t6lsxeT8CT4NvSRAKY/+YMhoOKLIc4qsdNCaUgirafMMU2f82t/6G3w+v83NC6ySm7dexNg508WCxDR4ykMLsZlv/Q+2cHtKEUURYZhgfPVM4Q6DkEE8IBCKMFB4SAIbkHopOuxxdvQYhKZqW6qqxZMBVdkQGo9EQtE2mNbQS2OaMqOfRqSdradUAY2uOX50RF22JFGKF4149/V3ePj4IQjY393jxRvXCYRgd2ubvZ09jPB579Ep33l8ym5/zP037hGPt+hfusqkqUiiHkoFCKsQnYhKCUlVlC7kuGmoy4qmqrGdYOTiqktnDesrj8bzCYKQNO2hlNe5+EXdABFAopS3GRqCC9J14blu03A4rSQIQiwK3wtI0wRLF4vW3aa1CzLdhB5YwFjy1crJ74MIZSVVXtJP+jRVg9UWT/kO6ggC2sZZxgoh0G3Do4cPuXr1MmEQYnRLnmUsFwu2t7cIgoQwdF/GuHCFvb3d7pgoqKqSOI6wHTQgdUPbuKFplq1Q0kEcjrnSutvsUyWhi2Fb88MdhbJp3RDRebx4G+pfaxvn4qZc4K8xGt/zOyXqU/9tYw1KSOQHJCKO3jkANGVVsL3VxzQVGHf6WC8/ilksV5v7TXoftnC1Fua5RSUjxoOAfnbpGWtY5Snmi3Om56fcuHoZXeYMdgfs7W0zHvVI05jtfp+2hUVRdp8zS6XXBlSAgHZNG8TBFs5F0SI7N8W6rkmShNc+/iqHh5e5efMaa2th0yly/e71csIkgackrW4Jw5ggjOj1UrSxNNp2DpsSid2QDC4+aWstcRS7TeADauL+aMjlKwfoVrNaLdk9PGSeFWxHCYH00QZ0WXK5N2b4YI7IC2arOe+cn3FmK4ZpwHQJT+qKh1XG1LTshDGrpU+lAsIgpNUVn/nYbY4ePWK6nGDairIwBJ4gikOiKCZJE4o8Y5wOWZY5RrjnJbShWS24/87rvJzugP+0cO8OdrBFjWxLPOPYbNq2cEGd+4PW71XOPxKFe80qqesWoT9gNG9d0EKapgSeIvACTGAZxENkPODe++/z0n6f1lrKoibAFf7Aeni6gdp5X8f+gOV8xnjQd/JSIWkbTWssx6fnCJzR+jyveOPtOwhl2N/bYX9nm51+jzzLOLx6lVWhOZ5Mef/olIfzFfuDMavzx0gCBnuawEIvTtndOaCXRAjr6GxxFCGFwFMecRiRxDF1VeNtVIZPV1mWRJGPbl3Kt9fFaa29oT1PObOpjrrleX4XsRU6poEUXRjAmrGhL2BmAiEcpgyOtieE2jBGXISX88UWSLCWfJWRJCnCCrAC01p85Yp02zh6XRx1kWHWDebW50DdtgwHA3zfoyhbBJDEMePRCISHpwLnA47CGk0cRWhjKIqCsswJPZdmY4yGBud3vrGYdd7iq9WKosg3OLz73FiiKKRuNEEYdFmbzUaKr5QbgErpIRDUQiJFRw1EYA0EftDRA7mAgxt+EPVwvpgjcIwdURmGwx4nTx4SVTeRFzgGYS/hrffeo21qgtBH+PIZsUb3DiHDIcM4JO31iac7cPfp7VrXTM6POX18xM3Lu1y7tM+Na1e4eeMaB4d75Ksl88WKVdPQ6KddnRVrGwQ3RBVSbTD4tdsgQqCN67rLqiJJEz7zmU8ThiGDwWATFyeEoN/vbxS2a0roml+/jkBfK1YbbVCoTs4ukeJZF0q6vxj0+9Dv0wbBU9G4gJ39PbZ2d8BYhuMRl25cY7qYI4uSURyT+D5SC7atR/boGKErGtHyzbtvU8UxsWc49izHVnNUlah0wExrKukRDcZsb2+RNQ07/Zj3FxPyxRRrWuraoE3S5bAqfN9jMsnYHozJq4rWOOgMazBFwWpySq2Kp0nwgGjYyN2F6IaUVm9e84024Jm61+EoP2R9JAp3Xdc8efKExXJBuEyfmSbPZjOerI6JA580HjH2PNLUowoCsiwjn06Ir2+DMFRNRe0pkjgiSkLMao5uaxAKZQXVskAHEV7ijnXz2ZxVvsIaS3/Qx/N97ty7x9n5lBs3r3L9+g32toes5sdUfkAYH/L3f+Mfc3I8ZfvggB/7/Gf4tV/4eX7mJ36c3UuHLJRPTEwZxrz2yqs0VY6vBHt7e1hrNx7TaZpu8K0ojD40iGkbQ1Nr8rxgOptRFEuUJ1gspsRx5I6w4wFaN11n7QrJ9evXybKc/f19F0jc1E49KBzreDqd0TQVh5f2mUzmbG/vsVpWTubdxXmtg2HXBSrwI6wRLhlIBWjt8N7hYMxiOevMcFxElu3k6Fo7SCJNU37iJ36Cui6dV4rnc3h4yKVLlzg/P0NJaI2hrgpc7NoKIeTmCBmGAefzjF6vtykOSqlNEvzalfAiDXDNvlmzYNZr/TPf9ze4uVQK2alJwzB8OiTrsG53v7bzTm83Q+I13n5xFUWB8OTGgnY0HLE13qLxvYvRiZwup/z6V/87JpMJnu+hhOLPPXf7mfuzQFlrkn6KJyOwz3afx8cPmXsnHOyPuP38dV5+5WeYzmbuPVyVnJ0vsKpjmXTD1nXyzzMZoMK13mtLWGsdJW/9etVdWPHLL7+8mQOsX9N1Qb7oW752jFzPbBwnX2zet4tF6of5vvww98XrN69TlBlpEnPlymX6wx73zx/xG/ff54XDS3z+xm1e8PvMvvMWxfSMBQXvLaZ87fFDRi++yBsP3yKraiZKM/MkL928xul0TmEa9vev8InPfBarPH7+r/911wl3Yi3wNiww3SU5rZZLgiB0dshlRlHm2LZxqVhtjTXPQp93790lDOb0bQkS9nrbpF7AcrXcbHYfzGX9J8EnH4nC3Ut77O0fsLW9RVL04cHT26RS+GFIlEQUZclsOiVUEbppQBv2d3Y5O3tCLwoxceI8KqwFJfB9ReT76NYyP52ShjH7OwdgNIvZnMD3Wc2XhHFCnPZA+kxXx+SN5c69h+zubLE3iBgMQ5bxkP/zX/mv2N+9wo1brzFKQ37p5/5L/uy/9q8RBz6rtmWR5cyaFjW4TFsbsLIrNC113VJVDUr5rBPkpVRoLT+oniZNB6SDkL29PZ577nnqumJ/f4/5fN51Nq4DDQKfyWRCmvYIgojHj4957rnnODk5YzweE8cJ0+mU69eeY7VadF25z6A/pt8fIWXAaulEPru7u+R5vlEnaq3ppT3CMKIoKsbjMb1enzwv6aU98rwkTXs899xzGNNs1JCuKwtoW81qteKdd95hNBqwWMxRSjAaDzk6OqLXcypW33OFLowiSCIXYeY5FWiWLRFCdQZZjqveti3L5ZLt7e2NNasxhuFw2PHGi82gcX1RrIvLmpYYRU5UZExF0Mnp1138ukABm0JdFLkTNdWOX7+1tfWhwhJHPZbNFCkUvd4AY+Dq4SFHoSS78Nujw12ivQHXD7fZ3drh3rvvf+h6EEKwfXCZydljdFOQlNHaYBCAL3/5c+x87Mscbu9SZSUPHz0AITFCYqoGEbrINU88LQBBl+Zz0bjIoHF9bgehXGDfrDnzH2T0XOyu12tdsJ8165KbhvHi0Hd932tq53qzvGjHK6VEC7HZ8ATw6sc+huk08BKQpuXVF2+xfXOfgQaxLFm+dZ/+yYp79+9wt5jxXjHjUdvwtffeZtk0eNJt1kIq7p2eslwuefHVV0ApfvFv/k1Wy4w4jIijkBpBUzv748ViwdbWiPF4zHA4ZDKdENInSWKW+ZKmqpHagK7xvPBDcvVvfu/7vPDCiHQrpRWW08USEYiNv/36tLtea4fKD75uF9dHonBrY3j9+6+T9kKu95575jaLwAtDtna28WzIcDgkUjGqyMj7A4bSIE1NU2sWusQ0kkuHA9pVjkAgrAVtCcKAfr/H1tYWxmgWiwWL+Yz+eIyQHgjJMsu4//gYqwSf/OTHef755xmM+mTNkr/3G79NieHR+QmT+YxxHPDP/XM/xd0Hd7h/7yHLqobhmJ1P/ghNa1isVoSeJI0jBIqd7V2CQ5fkba1ld2ePpmnopQMC/1nR0VtvvsNzr9zA2IYHD+7x6quvcn5+RFkWpGmPwWDAbHbK/v4VkrjPoD9g0B8ShQm+F9I2BinUpihp06A8t1m0rcMvy6Khrhr6/cHm4rx58yZra9f1BVg3JZ/61CepqpLz81O0tmxtjXnw4B5JGmFMjTbtRmAzmy2pK5dFub9/wHA4pNdLEcKSZSuePHlClmVcunRItloCliSJaBonu/Z8hznXVUnbNsxmEy5durQpwEqpjW+3C0+W9Pt9ptMpSeKcBNfe4WVZdapI57e9Nrtad45BEBL4Tua/SX6/IJsHuiAG17F+UORzcYVhgvYbB90sVoy3XNByXY+hi1IQQrCzu83Va9cY9PsMez2+/Y2v80GsRCnFJz/5We68/V0e3H/AbDIjvmBrP+z3SFNF1dQUdYVUHq3tbF0RtNZZJSi5DqYWm691MbBYMBsq8dOrTTz1i7n4dxetbS+mAq3XurN/+qWfOemvT0IX10WV9PrvnmaPfoBp5UlYB2BoTSQFZd2QJD2SFvTZOW9853tcUTGDq4d8cv81eqsp//iX/wY10AoXEKwE1FXFbLVEKMFb77+LAWcCpTXCQlu3tN0mJ6VjMq1WS27dfoGdnR3apiXwLGmvx74nCELF8cNHNHVJWWboUfnMYz+8fB3rNeStZJhGeEqg22zzXlz06Flvii5v9gMd3YX1kSjcdEbwWZ6xMPNnbmlaTZYXTKYTPBPS7xdEaUQUhAz6fbZ8QT2Zu47WGOoKRtsG5UVoYzturocfhCjP45333icIfJf0URZEvs/+3i55o1nmBRpL0k/Y2t1FKo/jsxkPHt7n++8+wgQJ81VGNA44vLzHZDHjyYNHzOcLBju77F25TGFctuXj01OkaanrbW6Nt3jrrbeYTCZcv36DXq/HO++8za1btwmCqOvAny5P+R2WDFo7OXVZNNS1JokFAsVykdM2phsMKUDg+wHWutSgJElJ0x7Xr1/v4ASf8XiMk3PHXDq8Akh6vT5rHHydsn7xgooCj6qa4CmPttWUVUVZ1PT7PVbZEmNaPN/R7YqioMhzlPI2WOg6td1BIG7gNRg4dkwcJWCtkwwD+SpnMBjS6pYyd2ybjUdK1wmvMVUHhXTOgMZs0nvWXeGazljX7Ua4tPZrWd+uW00rXBd3sfvbfI9GSrWBAay1GwXnB/HHJIlZzV3+ohCSMIj4zne/zd7i08TsbH5PYjFNTbFa4VmDbj/sDgjQ76XuxBh61MGzn48wDIlCD6Oh0haJwdgutYcWYwHt5jcX03yesapl3Y0//bfdvEM+03WvC8r6/9dF+4PeLR+0wF1f1xf/9iKWu35f149t/bOLm+bF1dq2a7XBaENpLE1jEDV4wgfhcVLm7F/e4Y1H9+j5hnNlKaSlwXbwVqeRkNDoBmEVbd6AdDbCzruofcb3pa5rxuMxxrT0+316vR5ZnuMnfZSvyLOMbLlESdB1hfDVM2pZgPv3n6CZku32Gb34AsII8sWCOI5QXWOwSVb6pxTjfCQKtyssIVW7oqqfjaQK/ADfD2ha7RRs0OFoHtvjLbyiQCuf2mjqylHDpvPcpb9bBV3QsMbSWsvR8THKU0RJjMCQJil+EGGqFVXTQEcJ83yfLCs4P5vw+ntPOJ5k7NwY4ytBvx/hBXA+nzIvCgrdMk4i0tGISdspO7sPQVGWVFXN+fmEt99+uyuU8MYbb3L58hXCMP5QJ+L7Dj+TSjLoDdEalAyAFoGHlAHGOBOmMHSKT2vZhCGMRmOCINwMkNaqzSBwasOm1oRh7DxHtKO9rLnlw+GQ5XKJMYYwDBBEPDk+Yn/vAGOhLDMWiyWHh3tM52dYawhCV0yzLHPfdxJ3WOPE7sO4tqkcDFxwsbAC3bToxuHKddVitMW2juJojN3g2xePlE83hKe+SmuP8ItdojtJVJuCu+7EgyDYUA/Xn7819r2+eNbqUSHYCHbWMMEPMpnyPKdYrTtXRN/3eXDnPQblK8/AHMUqw7YtfhgSKIknf/BFWuYZSsH29oimCLB3nt7mAiBAG2itcIZo1jkiCixYEMZghN0M+C4GUGwK90be32GsF1XmF4rHD8NcLxbcDxfuHy7lvvh7H7yPC3f+9O+AVrdIX4IEjWG6WOCFIWnHi87rhqk0PKgzHi7ntEeGc89SKdACfCncABGD8GXHJnr23zMCqtadvNYP/+IJrKkbVit3beRFCbpmuVxQ5DmelBjdIPwP8o3gyeNTajvF1w2HWzMO0gGt1pvN859VfAMfkcKtpEQqD2lc53ZxmrO9vY3YuYanDLHXd7tf4ahaO4eXePCN1xn6EaVwxjt13XL05IzhresEyqcUkrJt0XmG8DyCJGEynXEyW3BwaZfR7h7z5Yq8dNJbYwxaGPKyYJmXPDmZcpJpKi0Z9PvcONynWi55443Xuf78NfauXObh/Qcsi4LT2Tl5uE3qK67fvImtS3ws0+mUra1tRqMRSZK6oUbTslgsGQy2+GB/YYyj2EVJiNEG3Qp6vaFLa5EBvhcyHIxR0mc0GjkjJCnY3d2lLEuSJKFtW2azmeuejSBOE0eHq11xzLKM/qDHZHICwjIYDFmtlmxvb1EUBUpJhsM+1rYURYYfdI55aNJejOwcCp8yLLrCPxhtpPzghDcb/+0LBTRJEubVHGssUjoJtJTKsVSQhEFM3dRs7e5sPsjrLn4ttllbAwwGA/I836TYrztvh1kDWJSUzwh1At93vHJtyAsny18X6PXF5CmPSjebY3Mcx2RZxvb2dsfYefY9Wx9x10wUl3bwdFlrOTk6JlQe169cYX9/m1+P3cb27H0ZHt6/TxT5HOxfp/Z93v/dp9LaLMthIRHKR0vlzPetRdIVcOMk+2sWw1pTrzvqqV2LPkTn4yKefYxrOuUHi/36dvinKy7rneAiHOPu2/13HXy9gW9+j/s21nmj67YlyzPevXeX3b1DIumzKHKyszMm0vLu977BC7du8dbZA753+ojKl1gJbVOjBEhPIiOPJEopl6VzhxSCtmPfNLqhauoN+8jzPE5OToiigHfffZe6qYjjmOnJjIaWuq2Qnocybnitm6aj+F54FTT4aZ9VZfjem++Svvwq+4Mh1rbP2Bn/s/C5PxKFGwQP7j8my6Yw8LnoiGgqjd96HGxtoYSP0NZNdAUUuWGWNUT9gKopWWYtq7yg9T1kEhB5gqWtKNuCIBwwL0saPHQYU7Satx4+5s2HZ4TWRRx5piFRhk9+8ZO0UvD2nUe8/+CYo1VDUxiO3nnI0et32N/d4fZLHyPqxfzOV3+HJisYD3bZi3uoJMEGktrzqAuBNJZ0a8ST4wmen2DxqGpDntfM5hmHhx7Wqmcu3Y99/GOoVKKkIDqIKFYuaDfe20ZKQVPlXL18iG4qpsspZefWty46a5Wj73nON9pCIAVlWVAWGcqTGJ0R+DFR6DDgKPDIVwswLdlyRhhG+GqX5WJF6Mco4dNqsK3C93yaSjvsNYkYDfskcUjo+wS+j+8ppLC0TUkYSPJsztnpMW3bsr297YY5SHq9HoUqKIqCLMsQShD1IoIgYCzHm8g0eEobaxqXXr+3t8fZySlZlqGEZHJ2zt7enlMVNi1Wu87/YG93Mzxbf9YwGqMF6xOxwGJN6wyEOn+RteBRSdmZRzncPY5CsB/GeDENvhJ4EipTY3SJbcwHERW++tvf5PHZfQgMxivY3v0gGdAVsCfHE26/sMV4NKKYKS56Iri6ZrCtgdZijUXT9TsX4Q9jHEe7exAOWnKfNKcfunCs74r5mhHhXVD2XXz9Lxby37t4rzv59b8tusbIbQxOqv+UofLB5/8MhAOkYYKWLb/1ta/xla98hZ3dXd5Tb3Dy6IhbN57j0s4ev/PwLq/depHvnJ1yVNSYMMXWNbI1T6mqBmQJZVMgpWLQH6C1ZjabIdoK3/OoyiVBGJMkKVm22rzXy9WS4+Njzs/PKWv3WRTSPb7aSPBTJD6CZ/F5GUukr/BDHz9NEL0QKxqwbkN9Kj77ACXw93h1PxKF2wKf/exnqeuMYTuEydPbDvcPWe35nJ0eoRvL1m5EPx5gtKEqCkZbI5TJ6CUJWIn0fFb5iuPjY1LlfE2aPOTodIKQJcIL0NYCHsa0NNqpfQb9AZHXY76a8dVvv87ZtCBfVTStIfAl29spL9y8wSsv3sYYzbvvv8v9b9+nyhsOd3foDQYss4K3Hp3x/P4LZIslShv6vT6j4YjXXnuNF2/fRnRdzM/+7M/SNg1h4HUf7ot0oMb5ZLSNy12sK7CW45Nj4jhmZ2eH5XLJwcEhutUY7YrzBvuVzgjKGutUiR21aY37ekpycnLCeDzueOQubWc8Gj398FiD2QwD+xtjI0fz65GmKWEY4ncYttZ6w3CJIld88zwjSaJnBoppmm4K8tq3ZXd3dzM0XLMNAB48eMDt27cxxnDv3j3Ozs44ODhgMpm47razeAV4/vnnKcvymc4wSZy/x0UKG/CUwmYtCAlSULf/H+r+M9iyLLvvA397H3vPde8+n96V76p21dUNtAHQjQZAYABCBEFQhCRaBcUJKqgYjUQT80GfqNDETFDiDEOUODMUyaEfkSAgAjRAA2i47q72XdVlMit9vszn37XH773nwz7nunxZ1SBIRXNXZL33jt3Hrb32f/3XfxUUqgQpkEIySWI7m3EkQdgkbKRVPMEKawVKLXyeRVGQphnCkXS7K8Rxyva5S4Q7jQUdmhfe9yLHrx7SHwzY37d6IMtfqOd5/Cd/8o/x2jd/mzxXpOkivUxrUKpWplrcuZpgoKsyZjbgWBnraRCsZpGY6e7zA5GghgjMYwPPvGc4T/E7HS45nYo8j7vPH3fxHIv7qFKRqpRGo8Hm5iY7Dx/iS4fVXo/7j3Z4481vo7Kcr914C20UsVHkzAZYAbgVM8lCaExnpnUhaQFTSKwsCyYTPc1tWFtbJU1Sbt++jeNI8jypKjaVUz19W3Xp8UH9bK/J+pkeZTLm4P4dNr/3w6jB0DoKc9s+7nn/HnjcQoi/Cfw4sG+MebFatgr8I+AyNjXgZ4wxJ8I+ib8K/Bj2df3jxpivvdc5pLDZe0EQEPnRguG2tLKYg/1jGmETDBaXLUugIv1jSNOYPM0QwuJYkySm0whoBw102GIYG3b3R7S6IdK1vG6tc4QwFGVBoUsc4xBrycHxiKyUpCUIBd3Q5cVnn0EazZtvvkGapowmFQ7suWxubqIdycPjIy499RLNRpu1nsDkOZ4jq/qG1uvIsrSiZWl2Hz1ic2uLVtnFYybveevWO/S2OwRhgNY5WRbTarWQ0iAdg+tJLC5tNaWtgS9pNBoMR0Nkq41rXIQ0GG/GLa4pckka0+nYdOI8t0lAUgjGo5HF94WssgjltOK7KpUNPiYJW1tnSFP7EUVN690DfN/3fR9HR8f4vlcpENpZQLvdnvK8azihhi3mhaIuXLjA9evXF6r0DAaD6YBT66T0er0qDd6xqehTPFtXXFs13acOatbr6iILtdHQFXyTZlYsS1TVboqiYDga4TkWUnHcYHYObWwAcK492n2E2/CIaDGeDHE8n0d7x6zlxXzta6TrcO2pp7l4eZMrFzdZb7URX170tFSh+dL/+BqHh9bil+PFcxkjMVaHDIypvOfKkAsBQuLgYCrMsb7eGqOvDeMS3dj2rxoIVe0FT+F8i3dIuWh4381wvxs3+TTuco0l1xIDy/1a7a7iOA7Hx8f4roeqqielaUKcZxg0eW4hs8LUtU/FdCCqz2HPY6WA6/dlOSho31UbuI6iBlevXmU47HP33m3L2Z/rew332PfrcY7/yY036N/XSAy+I/B1QYqhVI9nTS8Y/Xdxub8Tj/tvAX8N+Dtzy/4i8DljzH8nhPiL1d9/AfhR4Onq38eAv179fM+mlKZUOZleDE4WRclknHB02KfXk5SlRrvaehRak+cpMrSRdSkNrnDwhMtoMuZss4ExgixVSOmzsbGFMoqsKCgKjRRu9UBLjBRoR5IZSaIESjg4vkPTddheaSCVYRIPOTo6oihLgtA+IFNoVtfWmKAZjYZc3NyybA8jKLQm1wWT8QjXdRmNhtVM1qalHx0d0u22aKjWgvc2HvXpbIRI4U1LcPmBh3REhREqXNehKHIcKXCkAEfSjCKOjw7BVFQ5BKEfTINznmd5rBTQarVs/5VGI6z0qdI4leCT7/p4jkuaplOOtOt600SVWtzJyuFar3t7e7vyPuxHEAQBRVHQbrenH2Qt0mSx4KLql8fe3h5nztgBwXGcqSBXkiRTI1wnG9UMEUdKCxPMZYbW60+bztcfWK0saGfOViNZGwNVKjxC2ACz1gipEY7VSbGKgg7SdaaysHXzjls0VyJLjUyGrEy6JEMfxyzSBxtHIZfkJc4U66yMVigrNvV8M8pw49fvPfFbMdX9xWhbFFjWgUXbd8ex1R+MmCXTzF9/7U2fFoxcSIyZQvTVlL42NNUYMc8wOc3bnmenzD+Dd4NYpsyVheuFk/4Jfm4DyGEYMhqOMMbYEoFliZESBRRY7F5JQDFNqKqdhrpfQkiybDbIL9+HeupSyxzX8ZMkSSmKHAim1zcfCzgtq3ay94BcJDQbIZ3tLVQyqlQn9cJ9/93g3O9puI0xvyGEuLy0+CeBH6h+/9vAr2MN908Cf8fYs39RCLEihDhjjHn0HudASkEyTnCyxYCO1pBlBf3+CNfxSZKUKGwhsKI0WZrgRgGtltXvmBSKvFT0hwPUxhqTccrDnQOSUvLhl1/mrXduMxjHtp5j6JGXqS2fFPi4fogSYxy/SZZmrHa7XFrrcWU14rXX3saLIAxdml6EdBxOBn3yUUar00a4AlPkJKVmcjhgPBmT5zFGaFpFSaMRsvdol26nQ6vdpnQcXClxhHiMsB/6Hq4UOAKUtB9b4HsYo8mzjDRNcFyHOJnQCJs0Ah+DobfSYeeBwXUEw+EYpRRrvVUmk3ianhwGAY4jpnoTjpQ4WHbHSqcLWtOOmoRhiOtYamAYhrieR2+1R7vdmZYP63Q6lfdvX7zDw0Pa7Raj0cSee22NkxMrrNXv9yv51xaNRoOdnZ2pFkaz2SSOY46Pj6fMGCklnU5n6vHPG5S6Co6UEm30lPtbqwDW9L26rFntZc9LwbqeNdal1ri+j8Yg63R5wPE8wqhhK/sELn7hkY0yfN8jCOvg66w9FX94ARIhZk7PzzaBYP12z/5xB/YYA+13+zQea25TIjwbfBRoO3BXA1X9HDzXxQg7mM8zF+YNmBBmWjmp3q++vzZwaamLVHfECpTNDI312B/HZaczmcqzrbc/zaDZ7czpxmvJgN2/f59RMgTgpRdf5POf/zwYW2gcKcGVSKSVa3AlTqXzXjsCMIPIgErXZ3HGUGflaq2RjsQRDqpK+tnf3+Po6JAsTSsKn6jS4K04mTH25zQWPN9USjMSrHcjNlaaTEZ9tLAc8fpeTTNG5+/Fu70H77Lu3drWnDHeBbaq38+xkPfIg2rZuxpux3FYXV2j020QJU1btbJqxgjAJpO02ytgBGVRYLKMLE3Js4Q8y2mEVk/gaDQkzmNCRzOOE6Jmm/MXzvCVr3+bf/kv/xWELc6c2eJMa4t3br+D4wpW2m0aQYAx9mHlaYqrNVe2Nnnu/DZnWw7bq23u7u3ygZdf5mAw4XO/+UUc6dIMNWmWcJxpdgdDkrff4cLWi7SCJjv9I05Gh1xthGSpDXg5jsB1BGHgsbW5TitqP5byfuf2PQg058+fo9Vsk2cK1w3xvUb1UjvEVfFd3/UpilovQXGwv8faas/qLKQpWZZOE0zK0nK1B8MTer0eu492WWl3aLdaBEHAlStXyPOcj370o6Rpyv0HD7hy+QrHJ8dI6dBud6r7bD3ZF154jv39XfYP9qYa4/3+AK0NjUbEysoKRWFLrN2+fRtjDNeuXZvCF5ubm9MBZW1tjTiOZzMBY6aYd22k62U1ZCErTL+Wuq29tTRNmUysKlNdLKKu2lNTFQX17GUxU7I+XhRFFmoQJWHDQ8iIokzZ239Ep9uqPtb3CtD97tuid1otm/4Prv7JHpmcIFA0w4AyTattZwk2ssb0HcvWqfvZ6XSmZe+U0ojKpZ6PC8y8ZDvT06YOsArEdIZqoZZyCR8/zVucD2guNrHAqJwfnIUQqPmZEvDcc8/hBLJSm0yYxDFf+8pXSeeKjmgMjTDkhRdeYDIec3hwyGg0mnnxp/Rv/vnVSVj1OR3XJXSsSmatUy+qWM88e7K+P9N7sHQal4zVlS5PPXWB5559AemIKru4XID05j3u0wavxWP+Hpsxxog6dPy7aEKIPw38aYBut0ccT3iwcxtv4HHBf3a6XRQ1WW2us7a2RavZobe6ijQQygZr589x8EbBaDBCNMCRHp1um3G/wOiUk+EAPwjprrRotUPUMEOrjMP9R5z0JZ4nKUpFt9WiEQSMJwXpeMzWSo8z6xuc67Roo2k6AZeevsb2xbP86m99iVt3H5CVOcJIvu+lD7C3v0949RKf/fEfZ5BGtNmkLBPiXhcnMDQaYVVAN2E8rsuHedy6dZPVlXWCpbsXNSKKXDEaxmgtmUxSpPDorawTxxOSOCPwG+w8fMjG+jp5lk+zApMkodPuEE8mxFrT6XRIEvtyu67DxsYGTz/zFP3+CU9dvYYuSrLUGvesqiJzfHREURREjQYvfuD93Ll3d6qaZyvbWDW8r3/964Shz/b2NleuXOHXf+03uHr1Gllmswz39/cZj0dsbm7y3HPPLRjI7e3tqXdcf1RRFE09nvoFnhd1mjcAWmuMdCxcUnnhrmuhnRpDr7H3uXd1lv0oBF4Q4FWBqGarRVYVhbBhPUNvtcO9e3fI85RGI+IDH3of589d4J/+3M/xS+/8As1Bj5/c/tnf7av/ru1A3ec34n/EaJTwX/yX/yWNqMnNm7f5lV/5NY6OjvhzR3+WtfUVPFdSFlYNUuvaq50FLBW2rFxduK2eEc2zax4PJM6MRh3Sm7/vtedqDaHFu2sjvmwU55/h6QOcqZyyWZtnriwYRqAsCrIqRiGl5OMf/ziPdne5e/cuUlge/nAw5Mz6Jh//yEd59Stf4cb1G9NU/++k1ZCfnaV5tnoSkslkghVdE1MvucaLTB1WqAfZ+VF2elzJ4eEBW9ubdFY7SMcnnoymBrtmlNTX/6RBZuGY39EVPd72aghECHEG2K+W7wAX5rY7Xy17rBlj/gbwNwDOnbtk1le3aIQN3IG74LP7rs/25llWOiuUqqTV6uBLga9SGA5JBZyMMwLPlr7yhEMriuifxIxzzZoWdH2fc1urjMcPEY4AYZMXcMBx4MyZcyRxyeTogDzXvO/yZZ6+sIUeDzHFiDQtMEWHRw92iJMJwjG4RuIUhuc++EFee/CAvYMJO/ldSq/DU+ebuK6k2WjZJJpOm+EI8rLAK0uUVqhccePGdZ566ln8sk0wp6zViBpQTXEbjZBmswlo4nhi+cqeT7fTwOjSwjy+g+NYkaZLly4gJTSbUaXjUbK1tVl9JJI8L9jfOyBseGR5ii4sZOK5VjFRCIGRAuFa5bi79+6TZwUrm6uMRiMOD4+IomYVdZ8AhjhOuX9/B8/zSCsPsIYSoihiXngoSRJc16XRaExfUJvuXuK6NgPUTjltev58qyGQMLTFHmp8X2CDqKHvk6cphdYgBf3jEzY2z1AbCSFmNSS11qBKpABXWkU7ieG4f8x4MuHCxYv01nqsbfTQqiSNE44PD3jn7Te4+ebrJPEJQVtwe/srHB0ccO3aU5Sq5PDgkHv375MkCZ/8nu9hMDgmSSy0dOnSFb7xzdetKFerRavTsp6vcKfMmiw9ZvNki00cjsZHNMlpdEM++skPE4YhXl3MobRyDk4VfDPGYt02eGgqnN6+66LCvk0d0RRgCzW/u3GwsxKJMdoew1T7aTPdvzba89763Df+rsevt5n/N4NLFreTQuJ4M753FEX88A9/lizNmEwmjEcTBoMB79x4h3/xr/41J/0+tqh0Se28zo5r3webaWymlMWiKDh37hxCSOqhy/NcJpMxtTE2WqCNhUgMtZ8tqvcWMOoxT7nU4DdChHAY9oc0mk1k9dzqaxOyjltY+GdWKfX09m9quH8B+GPAf1f9/Pm55f+5EOIfYoOSg/fCt+u20unR7XSQobNguIUQ+J5P0AhI0sksacBoSl1iPIfxxJCVIIVBlxpPuhjpEmeKSZyy0izY2uhx49bDitcqMFpYMr4UjIZjRpOC4SSlUIZ4OMTRK4SBwQ88wobPw91H3Lp9hzSzaeFSCBpCEnQ6tNa3OR4kDE5ihsWYtZU1eu0V8ixnPIoJWyHSkbQqdkWz3aYsCl5+5RW0UVa/eO5ebG5ukGKLAIDh5OSQdusinufQakV02h3a7Rbr66uAodlq4gcBWisuXrxog5DSfqC2tqQLRuDIuhKMQesCx5VIIyiURpUKtwoAOr5PrkrSouD119/g6tUrxHFCvz9gMBhMWSqdTsd+HHlJmqREUXNqmK1GuEDrGRWvNtR1Eo6lUlkIR0hb3V5Kx+KnRldenZwahXkho6kXjhXkR1sNcUydRViJSxlLtHUcD89TVdKQrblpygJHFbiuw97uDmtra6x026z0OmxsrHLz1jsYrawsreOQxlYFrn90gFQlnZ7PylMu/pk2vXMeSgu8sy2Ci9torTnzVJPWUJHnAUorjsZ32X6pBUISNSMaUUSWFRgjaXTtu+gWbcL4JWwFdwetFI1GyOXLl2g2mwhhZW5LYa/bkXOZetoaDynELHg69QbNnJE6PRC24HHP/dNV8pQxplKSNCwb7SfBEKcFKOfOuHDueWXGx44lawXD2ezi/LmzuK7L3bv3GY8n9Ho9kiRjd3e/SkqT1Ak/M692ZsQREt9zcT1bszWJE5TSaG0Dh/W9831ver21JIBFR+aMq6nKkPE4U0Rpg85tHVbXdSmydDpoWCKQQBiqgPHMs383GO47oQP+A2wgcl0I8QD4b7AG+x8LIf4UcBf4mWrzX8JSAd/Bhmr+xHsdv25hoyr0mi5edlkUFHFKYUoUBcJklFrgFjEqnoAjybQhKzQOJUqVCClwXY+0KBkMR3RDn42tVTu9LDXKaAwSUcnh37x1m0I5JLk9963rNzjThstn1+itdgmCiG/8zle5++AhyvWsgJMj2ex2GaQxnY0t1sKC8V6fneP7HI8OiRoh/UGfnZ1HFDLjzNkzFtPttAkCq4L3Uz/9B/md3/rtKe2obmfPnmV/sFt5DAU3b77DhQvn6XRsYG9jY4Mss4qBSmtajRZra1YJsMaKqVgTNkg3oRG2cByPIPBptRvs7j2g2+lQZiXDvCSNYzqtNu1GC+MKRGG1uN9++22uXbvG4eEhw+HQ1qKcTNje3iYIgkplTxOGDUAwGAwAqoCgnH6MtTJdzQix9MSKrWAUrrSMAW3UNINRCFs82MqDzqaQdSASYxkYABhBnhUYbTMwZaUXYrAGznU88C2bx3El6SChKDIC32V1tcdkNODs2W3OnT/Hyuoqymj+6T/7x+zv7vHUtatcu3KZZhgSeR6dKKLTCLl09gzve/YZ2q0Ig8FxJGEjpNVq04pCjg6P0MommgzHY/7Zz/08P/oTP4HvByAkpTLESTZVIazvW12QuajqXdbXOy+rCtZTe8wzlXU24izItcAUqdqTjO0yzjo/lbeBTT23zeMZj6f9PguIPm6I6v7N/4NFGzDzauf7b2mZUjscHR1x+/Ztmx8wmUDtMVf3bnnAN5WrLAR4vk+z2SSKIpJGwsnJiS1gTK1r70ypqbXToavEpqnPXHVLGJvYtHyZ2gjGwzFpmlmBuJMjjOvNBqF6lqFnGuXvFTv5Tlglf+QJq37wlG0N8Gff65jLTWtFkg7tC5c5BHMli05ODjnhkEIV5CYh9CKa0icwBWp8RH84JCgKhuMRTqtDq9XEmBKG9kGlecE4jnlq5SpntrvcfTiiKG1h2ETnNALIlUJpF0e69DptWoEky0oMkqQwPDg84u2dPQrh2HmPsRXaP/Hxj3AYj3DaazRXInraYUPnBA2PtMwYJWOSPCbwPVxpdRbSOKbIMsIg4Od/7p/SCELaZm3pPmqb+eh5NKOI973vfTx8+JAoiuh2u/R6PW7dusXNmze5cuUye3t7JEnC5cuXp5od165dw/cD7ty+z+rqGmVhve/j4yPu3O2zfWadra1NDnb2KZKMPE4JVlbZuXcPPwoRvovRhm63Ow3+1Yk0dcr6yckJTlW9yPMsLzYMwwV6VA1N1Aa7/ogtE4SKvmaNUV3Fp651WRaqKqIw89oWKFi6Zk1oXC+gVBrHDXC8wH4MWY7jiqogskIbRVFmGNPk4aMH5FnKubNn+PDLL/MjP/r7KJTi1u1b3H7nHdIs48HtO/i+SzIacLS/h2q32R2N+ON/9D8hDPyKIinpHx5y7doV1jd6lCpnf3+XvZ0B29tXGI0njMYTsjznsz/ywxRFSZoWVM4VyiyyHR4LUFVNVYV1pwbRzNT67Dc0w0Xr+3maQfzOvscZ5LFs3BdnQDPGyPzzea8g4Gnr5hXyljHu05r1/hWuC5tbW2wfHPKbv/VblErZak7aSh+LCoB2PW9qGMNGSNhoMRgMGI/HpFWMZ6XX45lnn+Xk5JgsS1HKSgh/8EMf4vr16+RzDKs0K6dQiajgKImw0JWUzMP3jmOruyutyXJbCMRgcwjkHHQ3zw76d4Vx/1ttpSp4tHvXlqPqB7yPj07XRc0GceBytLdP2HJ4sHObq9sXWe+t4IaKeJLhSUFmSnJVYKRgbW2Dk/GIYhyTpBmHwzFHgz7Xrl1m9+AtsrxEumCkS2EKHG2rYztCUuSZrdO49jyeF3Hv/j6//eprlA2PwmgiJL1GxMXtLa5ducz/9r/9It1rKWvnnmJtfYNJmdGMGoSBix86CAdGgwEXL1zgwf37uJ7VF2l0V3j+2Wc5Otx7jFqGMTjCIfACGo0Ge/u79Ho94jjm3r17nJyc0Ol0+Jmf+RkePHhQYcYRg8GAsrRsgcPDE0sN8zwePnzI1uZZPM9lMCzY29vlhfc9wz//xV9ks71GO4jYWt+gHUZMxmOrfaGh02rx+37kRzg6PmZzcxPf9xmPx1MDW9PyauZGDZMEQTA1MnmVJFEHJj3Po9FoTGVSrSckyLIc13VwHd/GIEr7WdSKgPMJNtZgukgkWhmK3Aaf2p024/HYwj6hTyfwKQpbG/D45Jjr19/inXdu8IlPfA+f+cz3s7m5QakKXv3Kl/j7f/8f8PRTT3HlymW2t7dZabdQWYrrR/SPDtlaW+HHf+ynydIY3/PI4hgHWF/tcfnKJ/j6N77K7ZuHOI4NRrUjn36/j99oEjQkpQHP9RkMx5RVog/MOOUzj9bOOIIgsDPDOWMGMyMvK+M2b92mrJIlfedlqt/yMet187+fZjiWVQO1Nqca3fnjzfdj8biPJ+fMs0qWOXWigv6m1wAUf+b/SOG6tIzhFa35sFLzwPOs1cvm1gkrdDBdJup4wDwDxmBnUtLhlRoueuyuPN5c4ZKK2czoz+r/g8WupUS6zgIsqr/2ddQv/+rC/V2+b6ee4zvox7/zJqAKVimIF3ncvu/T6XTJyozdw3vcu3eX1UaHtWYTnWscJwSpUcYQZyn65JijeGSn5lrZWntFyeHxscUv2w3yfEJcKLQAIzSe6yCE1auQBl54/gXCRoNvv3mL3d0Dm4iiChwPSDTr7S7vf+Zp0mRCZ32V9a1NOisrIH0avsfW2ipaw/tfeh8f/chHaLUbvPXWm1y+dIHDoyMG/WM67SZSQrcT4S/JdvquR7PbwHEdysIG42xyizV+QgjG47GFLvKSwWCEVkeEYUjUiPC8AJ2llKUNPL711lusdNcqiMKh0+mSpinnzp4lwKNMCybDMUFoBwon9HECH+n7yIr5UUfma/Gm4XA41WYej8cMBoOpCFOt0lcUxVQlsP47jmMmkwkbGxsVnqgrj94nCILKY9NTT3ptbZ2ytOeuDXetmy2kQKPIigQ/8BmM+pXiYckks3Udn3rqKv1+n62tNV588SdptZt0Oi3yPOeNN77Nq6++yqBKPz462KPdbBB6HkJKVtttnnn6KhcvnufsmW2O93cREvxul/Pnz+A5DoP+Md/61tfJ0gSEQQqXMAjpdNp8+8YOZpxgsCXDRGEzLg2iynw0pxpYoMoIFtOixMuUumX7tGyEn8gWmVu+nJy0bKwf43Yzl3QDCwPOaZ758rr54ONpFnbB6J+Gv1f7i5UVih/6NIQhVHEOBx4Ta/u32X4vx/bfxczKF16AqEnxczZMOM+Df7f2XWG4wUZvi6IgnyyKkNuKMQ5ZmmPAeqthhCM9pAwQMgCZUhSKVNuiqKrIEI5TZQVaKGT/6Ii1XpetjTXS1BAfD9Gl5e4roUGoKiAsuXLpInuPHnB4eEw6ifGkxA9BGc2z1y7x/stXObu2yhs338EPG/RWV3F8n9EwIfJ8Nla6nPSHRFGDTreHUvms/mORk6cJWZqQTCZsrHcpw2Ahe67TahN2A4qyZDAcTDMPlVJVfcWEKIosnc4YRsMRk0nM+vo6YRhWQRQHgaYsykrO1GLejUaDCxcuMBwO6bSbiNKmWEvPVg4qtQ1UCpEjjCYM7fStTnKZxwvt83Gm1Wnmva55LDYIAtI0RQhhp5qV5rfVx1bT7LTZVJsKn1dVtZ9ZKjswwysdOw2WLpwMjigLRafTIfLDqdhUr9el1bIMm1Y7Igh8xuMRo9GQg4N9Dg722d3dxZUOWZYyGY0Y9k9wpMOnP/VJzp3dYnNznagZkWcpqys9/MDj6OiAsszAKMbDMWEjAqzK3GCYkGbaxlMwGJvfiGEuXV7UmOii8Zo3kDYo+C6eMUxx1mUo4kn4dd3ebfvTYI3HpvDmccN+mtc+D2/NL7fneXI/55caAD3Hs/A9zPnzj+3372MTnTbi7JlTIZJ3g0u+Kwy3pXa5ZFlOPF5MeZdCUhaak5M+jU6TjXUrcuR5AY7WSOmhBRRa4WLwpIOQnh0IjP1IcqU5PDkhTVPObG8yGhccnIwwWqENFKUVVxdG4AiHlU6HmzcmBGGAL4A8J1oNwZG8//mrPH/2ArKE17/9BvnVp2k2W5RGkkwmtBohoeuCLkkmY6vhOxkRhSFZkuA6kkYYkqcpGE2rGRF7HsncNTcaDRzHUplA0Ov1pkHBR48ecXx0zIc+/OFKq8MauKIoyPOcOE4W8GQhci5duoTj2Ah7I2zQXWlz89bbrHTbKKFxfJfWStcOclphCmNZN9rF9y0XuhapqgOMjUZjyp2Ooogoitjf359WwqmNeRzH077MY+SDwcAG6bD6LVKKqVjPLAFHTcWoasM9H6Sz0qUKITXjsQ2cbm6tsba+ZoN7WY7vu7ieZDIecfPmDnEck6QJjiNI0phGMyKOY5qNBo0wxHMd0BrP8/ne7/04jdAnbAQgDOPxiE67SaEK7j24w2Q85Mz2BkZIi2tqQZYXTMYJWT7Aj5rUDA5VGTAbS52F2+adztOCgvX9WIYkMGCmvO3Fb2m+LRuCebz7SV7du3nSp7V5I73sYT+WDTi3zfJs493OOYU1eNygJYeHJIdHNmvSGBu0tRsCAulUuLzS0+zPejYjKpqJrmYBtmiCdVSspIS0KfUL13ZKkHXu967fo+HOFGr24odobNyq2+3aSjtbmwuyCfNOUf33u7XvCsMtpYPvN+i2ewQrBZzM1mmNrcaeK85sX+CX/8XniGREL2wTaG0VuXKFLwVCOkjfpdPpkhe5NX6lTYEvypzrN2/wsQ9/ko31lJ29E4o8ITMFSHAMBI5DOwx5cP8un/6BH2A8GjPpn2CSMcG5Lh/4wHNc/52vcOfG22ysbbN7cMzI2+GDShOGAVEY0G03eXD3AXlZcDR4xGgyodFosLW1xdraGk89dY2iKLhx4x0+8IEPsPPwDnkqYU6KaP/ggEEyIAgCzpzbZnVtjUePHqFKTZpmjEZjpJDcvn0H1/XY3NxgdbWHMVTeucVOi8K1/Yoijo76Fjt3IE0rj911yLIC1/fotjrsPnpEo92kMArpuQSNCM9xAVtNvYY2ms3IRuQ9uy5NE9I0mc4ElFYUcTH1xutsvXrWUGcw5rmFVKKKty6lSyOKMNWHA/Yj9byAKKqpjILj46PKK8tJswnj8YhLl8/x9vXrZEVCEHqc3zjDeBRz78E9fukXf5E7d27jeS5bW1t86EMfxHUdsswm+zQaDZIk4UMfeD9ntrbs89rYZG/vAefObLGx0aPVbjIchnzhi7/D6mqPlXab3kobjX03D09OcGSI6wZE7R5erklVZhULEUjHnRY1MMCSIOQCxgs1Rexxw11vW/0yNdvv5iXP//4kjPu0Ppy276yD05WVbtC8YbMl0E5rT/LEZ4a71lGZedg2ACitJreZsS/qduMf/xO++T/+zzbDMbVl85S2FY7A6rXneT6drdYZskEY4HoWCqwFp372Z3+WLMu4f/8e9+7dodVqsb+/T5bl0+xbSzMU1LwaMZeoJA38xKWf4eWNj0/797e/8ueJ1YAPffAD/NQf+AOMhkPCv/hfwVKC2HxFoH8vDHepClCCfFgSH02YF+/QSuG7Lptrq7z+rW/zkY+8wkrYplAJKjtBMMZkBaV0EKFP0GrSP+mztbqGaLaZZDlpqSiFYJA6HB6csN5p89Izl/n1b3yT3HVpBk2M0riew2pvhS+/+iq/81uvsrG5ybmLZ7lw+QyTnT1+8/WfY3WlS+H7fPH2dQ51gRNrvv7Fr3Lm2jXaGxvsHR/z3NWnuLNzm0tPXWHj7DZFnPLWW2+zurHJcJyQJBmNaIWj4zHbZ5/luB0zZjbTCFttbjy8jeM4dNdXSR/uUhQlnufTanaI2wlpmrO6uk6WxxbGsJMGJvFwgdHh+z6j8YBJ3LeVZ0KHNIvxPJ/JJMORLmGjgeO7TPKUc5cvMhgMbPBxEtNPjwhCmzxjMAgJ2pSUSqNNie8FSAmDwZB2p4XneZSlqFLsc4QViZ4W6V1ZWcHzXA4PDylVavFs4eIHDRAKbUoc18PD5/h4QLfVIx6NSbMEUHS7TT7y8ge4fPki9+/d4s6tdxh48H2f+Dg/9NkfQkjJJE55tLvHt17/Nj/3T/4xaZpU3prEdQOStMT3JVla4ErB93/qE7z/hedRRY5AE0WSla7Li+/7HpLxgMPDHe7dm+C6PlcuXqLRaFAqjTIKJTSjcoJ0NEpDkReW0lgxRnD8ysAK5kNuFdnhMS90IVNxrkKP/RhqrnC1D995yv1pcMtpuPZpRr3uUz3jgSreYIyd9VRCaDNNaRv6E4Kpal7dFqVLZ6wYi4nXA5NV/FxutTREuVTaTDoufhAhpIeQqVWDVGaa7t8fWLhxbX0dYywl9PjwhNFwQhQ1bHxLac6fPcsv/eI/52d/9j/k09//ScbjMX/lv//vabU6NCLDYDgkHQxAz1QSVXUdUkhcx7H3ZOmZCKORQlIUisFwjB+ELProZjqLnP92f0887v89muvYBBGNeaxqSP2gh6MhX//a1yhf0nSefoFG1KZUYxw/RBkboVfKoEpjC3pO6WaSEoMyLqM0Z/fgiAvbZ1hf77HWiVBZTpbGeEKSGIfdoyFPPXuNb79+m6jdYnNzg/XOCl/75tcZ7x9x5QefIRWGm9dvMi4NXeFw9fIVOhvbjJWm1VrBCJcw6pIkJceHA85vn8X3GmxtneXB/R2Oj/qsrq5y+/Zdjr76NTYfXWJlrnqE7/uEYTitML22tgpYuGJtbZVeb4WisNXHtbEvvuPIhY+iFleqA4h1ua66kkuapjSbTVs6TCnixEIsu7u7U1hCKQVSTKEPC19Yb6XGrMHgOJYLfXx8bDWsK+/QdVy0Nvh+o0rcEEwmSQUDKZQSuK5P6DdQSuMEDkaXlNrSNdvtBmHo8uJLL3P9+lvcuXuLiytbvPrq7zAYHtFtt7h06QrD4Yh/9S9/mdW1DZAW91dK4wgHaSR5klsPLoTdR/dxHc3HP/G9PPfMRYwqKPIURyrOnjlDr9el3WriOw7v3LyB1iVRGNHp9ugPhrSaEW4Qko7GjOOYvCzRRiCEB6JSHDTY0mti0at8N6bAsmGdT4Ou18+r8S3vc9rf8215YJg/7vLxTvu5PKhYiGFRHW++GIAxNZ/v3esoLvfZJp646KXlrucSpxOr1Od5zAOqWulp4L7mwRtju1dDdDWUWN/bjY11lC6ntD/Pdzk43CcMQ37hF36BX/zn/7xyPko+9X0/wPrGJkHYQErHkrUrqmE9oPX7ffZ2d/n1z3/+sWuVUoCCo6MjXnvtNV555ZXlu/AYhfO92neF4a4J7UEjRDcFTGbrjDYobVNUHSnZ39tjt71Kw5F0wibG8assSBetIY4TQs+1OHN1bKWtyl6mDcejEa1Gg3arxZULZzi+cQuDQxR6nNva5kMvvUgndMkyW4vx4YP79HcfcnzY58K5sxyNJxxMxhxPElSVANEIGghhjVkj6nD//iNk4CA8h/Eo4e3+TcoS9vcOOTkeMB7HuK5PWWqyLF/wSADSNGVraxOt1ZRpYeUnrQF2HIfxeGxxZ/TUs6m9m1q6tKaahWE4PUe9PEkSi1NXpZZqPva8LoiUEsfz8D2/wqmZJrjoqtST6wCmpu35SGE5qwJJSWk/Hg3VhBKtqQKOEHghYRjQjBrW43AFStlyaBjFwcEOjggxJidOxqystGhEIY2oweHRAY92dhj2hzx8+IhHu3t8z/d+L05Vf1M6EqlBFwoHQavd4uy5ba5cvUin22S116QRCtAufmeFdrNZaZQbRuMBulTkZYEjHQptMw5dPwDpMhrHTOKUNFMobQOS0rEettbVTyOp9bCXoYbT2Brz6+v3dn7q/KQA4O+Gn32a132at798/OXzCiEWIJoFY14f28zoe6f1cXlwqK95qpe+5HPPv9fFZLJwrBqSW9TWBoWiLGx27vQ6K6w8UYpSFQS+VXpM06T6RjSHhwfkWY5SmiAMuH3rFjsPH+H5Aa5XDdBlgXQcgiAgCAJbtanX49Of/jQX+hdgjmMhhHWoRqMR9+7d44Mf/CAB8/eTaWLP/LN5t/ZdY7g1Bum5OL5eMNwImwXZarc5e2abLCs56fdZ7XToNFcRbgA4dpTWkKYZE1fSWFmpdCyql1MISiT9yYTg6AiM4tz2Bt++fQehoB01uHB+i5c//BIHj3bZWO+yc3+Hw4c7mKxgvbfCxWtXubf7iFt7ewyzEu1I0Ja+5whJqRTSdXnz7etcvnaF1kqbLM24ef0mFy5c5NHDfeIqrfb4+JgwbNBd6eAP/IVrzrKEldWuZV2o0gZRdR24s0bJqQMuRi3wdo2xNRXTNJ1qZtdFFOoBIE3T6fqiEnWyxYStcl49JbYGsK7YLpGiRAiF0TM5TmNAKUOW5YRBRF2OysIAdgqNVsjKE3KkrCqvS3zXJfBDfM9HCoHSBb5niwAXRcJweES7tc7+wS691S5nz11kZaXLYHDEvXv3eHj/IQ/u7XDv3n3CRoNXXnkFgahS6AW6KAl9n9WVc2xtb3Dp0nne9+IzSMfgeXZwkA6sdFp02m2kFAyHQwbDIcYYwkYTIR1UqVBliREuaV4yHI7J88J62riUZYHQoIV1MLQRU62L077B74Q5sByYfNJ+jxnM6Wfz+LL5GdmTGCCP4ehL55sa6bl1y4FF2yd77csG+rR+z1/zafKvBjBa43ouo9GIk4fZgmSu48gpRXS+D0qVUx2cWT9sv/K0oCgzwrXAspxKyZn1LeJ4QpZZZ8ip8hW++a1vkaYZmiq5BkCVOJXmTrfTYXNri0uXLvHSSy8Rvb0Kc3LqtTBVlmVVrsWixAWIJQjpvdt3heGuVeImcUya5AvrgqBBb9VBOJq0HJInOYEbgXSZpAVhp0chfVRpJVMdx1LMkjgmDHwibYjLFC1AOx79SYzQfTxH0FppcWaty72dPk3Px+QFX/rCFzk5OuL+7dvEwxipNaudJh//+Mdwowb9u/c4HCWMMoV2rZZ2p9nE73QZKIXje7z+5pu0V3u4jYCiKBmNJvR6q7ZAb7tNEPoc7O/R63XxglX8vXDhml3XIc8zlKr0RBxw62QNab0/xxU4rsAqxs9eeqXUtOCAlJJms8nh4eFCVmPNBqn3qTMba49lxheXxHFCFFncUlU6FXGcopSu1PdEJRBVJ8mYaXq7EII8T6tBIZhqPihd4PkOWpWURUaeQhyPSNIJTz99jTSNOT6acO3KJT75qR8kiprVYGUNxM///D/jy69+haIokDjWC9IaVRSoMifLUrRSpHHCs09f5mMfe4XNzXXSLCZNxmids7G5xub6Br221fx+6+23CcMQx7MwVbPZRLo+aZohHYVKc06OB3ZQQIKoNZkB6VpjLayjWfl1syQZHjeidZunVy57wbWXaZ/F6VPodwsonna+eQO7DJvMb/OdzApOO25dmEDOJaA8CUtfXlbDDlLORJ7A3j3HdREu7O7u8ubhwYLh1mZWWSmr6q/WFBTrzTpWBoLZABKGIZ6SlGWB4/hsb2/xyU9+nF/7tV8lTRNKab3/IAwYDMezLMkqk6fWbs+yjP2DAw6qf1prno3fzwZn56/MFiepHCPXW6xJWUOQTxqkT2vfFYbbc12kkKyuryFcF67P1vX7J4zdIZOkz4N7d7hw9iKNoIEfhDhhk3OXnuHmOzeRWiEleI4gDD1a7RaBI1HaEMUpSmhKBH4zIi8VeydDcB2+73u/h1e/8FUmkwk3XnubLCv41Cdf5if/0z+O7wiyNGU8yZBBg//33/8H5I5LJj2SYkIQREhV8L/+w/8vG089zfmX3k+4ssKLH3yJoBGgytLK1T64TxyPaTYbeJ4DImJ1tUW/f4LrOSxDn9ooHFFPNeuXzYrfKMWU2mdF3C2OPI+JHhwcTMt11Ukw85H8OimmLEt8d5aVOI+z2V/qOpZ1xXSBlLWAlEJKK4NZa21sbm5WmLrlkdsX1aHf7xOEbUCz92iXNEs4d+4cvfUenXaLVlWB/ud//udxHHjllVf4oc/+EA8fPSKM2mRZzs2bN7l37x55npOl0Gh0UOUAVWY2uITi7t130GVBWdgCDX/wp36S8ahPqQrG40O0Krly6RJr66t4nsPx/j5v3P4WQRjS664ShCG64mJPxim5ijnqDy2uLwSuG4AGhJW3NRhbOUkpSj1T3rP1Hm1pMMHj3uZ8ezdMs35e88b7tP3ezajXbYFKyAxaW/bAl9kr83DJk85ft9oBU0qhjVlgW7xXm2eZaG2r1k+JK0BZlDSbVmmy1sOpm633mU5nmzYGk5Ak8ZS5NI+B21J1htVeDyHs93ZwsMff+lv/C0JAu9NBOg5pbFlBvu/jGEBYhUmtNYJZvKeezY7HY772ta/R622z0ZwZblVaz38ap3gCDXP+Hv97wSpJkpRJPEF4Ps7SaCRdl6ARErY2+DP/2X/GL/3iL3H33h263TW6vfcRRG1A2jqHKPI8IXdhNBxS+h66yPEooCwppQO4GA3CKPYOT3j4YIcf/tTHSbOS/nDCYDTmta99m/3bt1jrRqxvrNPe2Oa3v/YaIgzRWqBMYdXnCk1vowWhj+cJlMnZP97j/KWzdNsdXM+j0Qz5/u//JMbYOtx5UQAa1xMEoUtePC4DaQMqbvVh6erjrQMYBq3LiopnOdzznlpdWqz24hqNBsPhcIod1lh2o9EgjmNbLaUq/xUEwcK0EjHTH64/qjpQWZYlrVaLOko/b/jrFHXXc4mTEYYCrS2WP4kHpGnM1Wvfy2vf+AaH+7tEUcjHPvo9fPITn8D3Am7fvMM3v/E6GxtbpEVBnhcUeUGelUwmKU8//TxvvnEDDbiBiypyknjC/bu3+J6PvsL21iZpOmHYP0CIkrXVVZpR01ag933eeesGWit816Hd7OC4HmGjSZxkFr9OUrQ2lBJwbAFqYyArSpS2Fd/t/B2oSH5CmpobZtFZY0CLqdjW/D2cb7Vhm8c3541dPRuaN6jL+8+/N/PHnDe4blV6bTGA+LixXj7uaens02/zFI2S+lzTQsPmtCK4jxv+et2p9wjwAn+KJS8XHpmXQqh1dTzPIQz9aVDS8rILCwu6Hn/4D/9hLl28yO7eLgcHexweHvD2228ynowoigxHeqytrfP0M89w452bCOkg6m8SWOutcHJyQr/fn+YqeJ7HmTNnaLrNBXp9u9PGUQKn8rqtxPHpMNH8fXo3r/u7wnBb+c/CvuhLpe1B4zgSP/R44403abea5GuaRrMJgYf2GkgvwJUljlYooVDGIS5tMA9TIjwHWZQEwiPThlIpcqNQouT+g4d89IXn2ex1aHqC0cEOJpmgOz5KOEwKxfjwiN3DQwrpkuGQFYpCg9KCJI/xHM3kaBfnfsD21WdwWg0cx3rJjitY6bWRjqRQWeUp2+v0Ax/XE2jPXQjEOHMBQowtXmtHeRupt3C3QCtbeqwubhAGAWmSEjZCtFIopYkaTU6O+4ShrWhdFgrlaest4yCwiSxa2cCa1e2efch5mROGbiWHKqtal1ZjxAZYFVpLjHEIw4g4GVcSrQXZJLYSABs9eqttTk6OGfb3KIqS8aDPhbNnWF/pUKqCPM94tPuQvf0DHu3ukSQZH375FVy3CdioqCoLVJHhoNDZBJ3GtFeaXH3mGs899wyeI7ly+SKtdpN4MqYReUSNFr3VbhVXOLKZocJQGo3KFYVSeJ4iLTVZXpLlVSALoJL/tYapNhCzijBAVTTYJnpZOdlatc8m3ZhqPZzuUT3Jm35cdOl0r7c+7unLLH3TLrIdlE492QeMnqtXacGAels9vcaal22PNR2TKs9RCFlds0Cb2vCaajcLGtUcbzuymel1LxvuZchovimlSZOcNC0qHZtZm8eOa6NujI0L1Rm6dXWb2nHZ2t7EVA9MCAfX9Tl/9hJvv/0WUjg40keVgocPdilzBdIgpK7k/2A4GJBnls4qpJ295mXK5pkNojiC8Xz/UjKV4pQOSew/kV20aLD/PYBKrFeBTYSRiyOR5fnaqgdf/9o32N5aszfRBeG5uI0WqxsbiPE+KinRukTjkSuNRON7Dl6jgZwUeEaSaUWBoRQaqTT9fp88T1htBfRaHuutgKvnt+htbdBc6ZKUiruPdknzglIYElOS5gWF0jgaJumEplToIzBCcuXiFYQfUugCsEVl/dBDSjGt1Sgdy9CQ0k7bcukspLzXpaemo68RldG2H5adgtrpmR94CCylL/ADJuMJvudb4SVjldK0BtfxKCpNYFVqjLaFmB3pVLQpy/7QyiAdWeG4lWaGoBpwnCoCX1YFAKoyTljt6KIsLTav7azC9yWh75KkGWWe4AjF2mobrQW6LFlfW2cc+Nx/cJ8333qbt2/c4MHOQ/qDIdJ1Wd3a5NzZpywn2GhUkZLEIwqhuXB2HWO69FZXePbZp/nwyx8iSSa02xF+4NFqNwh9F0eYSuVtzOHxEYVSNFsdjIKsVGRFia/BJHml2Cdgel0CVVoxJaNtoomuIBJr4SS1qqwQtf7IvPjT497TaZj0suFdFmY6bdvT6Hzzx9Ba25EEa35tcNsKetWzBSNqo840c7AWW5oac4F9McTs/NrUSTc1e7fqCzY5xhi7X603UzHO7Xrz+DU86b7MN8fxuXf/AbuP9lFq6Z5oUyWczdgjQlTnNjbmMi8n7DgO+4dWUXNwMiDPCxzHZXvzDNffvG6/LSVIkpz+4CF+YCsmGUkVdTekcWL15LWFhQpVIHJNu9siUP6C4daqxBhVOUVV/065xtngfHpQe759VxhuYwx+6OP5EW4ZLq3TKFWS5YYsK7h15zYngxGbZy5y9ZkXWFntsfbCc+xeTzlKrOiS09CgwTjgBwGuF3B0OMIUqgpyVdmAaYETagajY5qhYKXd4jOf/iT9UUxqBKmWvH37Lu/cvoOStkJ6MopJkqzSRFHkRU6kXIo0Jj45JhsN8d0AJVwc36rYea5fTVMNURQShiFxHE8hkFoes27zEIlWmtIYGo3GNODnuraQQZ7nlQ4J03X1/hZvXDQcs3+2WOpkMsERckFWdXocaQ1XrSdisTwHIewIq3VJWRSAwPccpPQ4PHiEMSVFmdJuN7ly9SkGB0e8/vo3cD3BhQvn+LEf/TG0lmgtODo85stf/Tq/8rlfYTSeIF0H1w9wXBfHkdy8dZPt7QtkSYxRJbrIGPSPCF3Bf/D7f4zV9R5CMq18EjY8XHeDKApZWenSikLu373F7t4BeV7gej4gyLOcSZWU43sBCmkFyarqLvU03ybRLKZtT38XovJmZ/UC6/s8//M7efeXMeb5tuxh11BWXcILWJADrbeb7lZh7phaU3u2vMbfrVmtFk8dp7nrEIv9nAVUZ+ZjGQ+3+1kjZKEiWzJGqcezJpfvgxBzI0rV2W67wz/9+X/CjRs36D51bWHfMAxYXV2dYuz2eNbzN0bzcGeHjc3NaVk7gF/+lV+mKAuKrGBrY5Pnn3meRtCwiTJlSVmU5IZpWrqd8VpHTGuNKyV5qSlVWcV1rKAdzAa5ujWjBq6UBEHI6uoqWhVzhZgrpphcLM9nddV5YvtOCin8TeDHgX1jzIvVsv8b8BNADtwE/oQxpl+t+0vAnwIU8OeMMf/qvc5RlqVN6JAeUi8SZRxH4nsOYdjgx37sxxhP+nzztddB+khjaErJ/Xt3uXLuLGc6ATffucGwMPgNHyEVg9GYLD+x4j7aymnqskTrggCbDpuplEk6JnAFzSgkakaEXsg/+mf/gq+/8TY0baaT8DzywooHuY5E64JRP0OUOY1WC8cPuf7Gt/jEj/x+xgoyBUVeEgZWgMjzbLEAVcEYRZHj+/VLNmtFkeN4FV7X8EjjZKrXUd3jGZRRHU8Im61Ya5rkeU6W5ezs7EzpgHXkfT7oleUZboXdzQs91QYi8BtkeU6RK6TQKGeWsn7jnbdQqqTR8DlzZpuf+oN/AKVzfv3zv8aNG9cp8phnrj3Pn/sv/k+MxsPqegMmk5gvfuFLDIYTdnYPGY4zhPTwwwYGQ6FKykQRNhr4TsHhYI/ByRHNKOQzn/4krmPld0fj/pTF8vTT17h67TJKlYzHIx7u7HL71jtIDKtra2xuruG6Pg93d4mTDK3AdQOkCMmzEsfx7YdS2Qwr8jRTY1yGOIS05aXq6mqn0fe+k3aaJz6/bF5//El492lQiTXIYsEo1Md5r8Fiuv/ScR/D38UM4663qfuntJqjRBqgqkY0B5U86V7Yvs8tx/Dlr34FIWxOQp4tMs/Gkwn7+/tTeWGbvJYRx2MmkwkXL16cet5JkhDHMXEyQrhglCaJxwxHA77/Uz+AG3rkqc0KFZ7H1tYZHDdAuj5FachzO8BrMgJhKlbUhCwecfHiOY6PDjmTjOnOJdRlecm4nNBqtbl27dpj117f5tn9fu9B/zvxuP8W8NeAvzO37JeBv2SMKYUQ/1fgLwF/QQjxAvAfAu8DzgK/IoR4xhijeJemp+JHK3giYt6MDQYnFEcxW9tnuHHrBkqVdDurNKM2JsutR5SmdMIOkYnYCwImRYaRDpMkpSxSkLC2vsrh8QRdBeM0GtcI2s0IjKLIU/p9xag/5PLTz/O5z/8GB8cHOIHLOC8RXoCvFJ4nCJSDRljqmKNwpUTlOcmwjxP4BEIz0QawXOjhYDytLSiFRFSZUllWUBQlUuv5LH/r7dVKeHOeyTzrw/d9JpMJNYWq/sDDcDZjqT+AmvpXB6ZqA+37vvWajRW2qgM/vm9nCFmW0+40iaIWx+aYweCENI2JooirV5/hB37g43z1y1/iwYN7vPDCNf7+3/1fuHjxPE9ducgHXnqeJI7p9c6gDCRpwcOHj7h//z6TiYWMwijCDyyTI/B8JuMJru/iVEG+wdEBd2+/xcsvf5Co8RJojSbHIMnyCZub22xtbNFqtW0QdjTg4cNHDAeWh72+vkmeJGgFg/4YraHV7DIcTyxkIBwKZUC45Mpm4JnK29baWAyU2mtcTjKpoQb7dy2kNb3vS97nsoE+zcA/ialRY78LJdtOwbeXDa0jnQquWNxmoRjFKQZ6ftlyUHH57+X95hlJFnrS1ewRaoN0GnPiMW990XLz2rde47Bv6XataFHjw3GcKdWudlzSNEFKOH/+PL7vT6s3eZ7HuXNn+dn/6Gd466036PVWOT454evf+Cb/4pf/FU8/94wtUTgYooXBDWzgOssNwrHB8CJTCDdAqwLXb+A5Lr7jsL11lhvXb7EdPcWZ6Mq0f1q65IXNldje3jzVk368eIXhlNs8bd9JBZzfEEJcXlr2r+f+/CLw09XvPwn8Q2NMBtwWQrwDfBT4wnucgyzLKPICqSuebNV8z8W4kiSJuXdvh/39PYzSXLpwiYvb52kHPtcubhFKTWIU7WbIUVJQKo0xEoNEq4LReFzBGwXKlAitaXgeP/T9n8Y1E/LJBJUX4HocnQy5cXeHo1FMgcQ4Euk4lFrjSIfQB6S0dQylY4MwZYkWoNKYh/fvQXsNEbYxQhDHScWjDuZeWllBKDli6Qk5jmMLEjsOjutidPHY1LIsyyVDYqZR83nvT2tDGAbTUb7madcGPgjCSnKWKU0wCIIpFHPSPybwPSaTEZ1Ok82tHvfu3iZqebzx5je5fecGo+GQ3Yc7bG/b6ejR0TH9kz737z/gtW//r3zklY8hhUuSpMSJQmlJqQpc159CYVL6+K4EU+IKyUq3wysfeZntrS7rvQ5aadI0ByNptdpcvHCBVrtN4NkA6a3b79iiCkVZHdcKavlNq61dlIqyzImTHEf6mMqwqAq3BreCS7AB4MroWKz09ECgqSJ/p3m89bbzhnoZRln25OeXL1Py6t/naZ+nsTLs/lAhzgv9Ou1Yy0HC5ePWfZkvX/ZeMwohBPV/jxufKYB+Sr+ffNzjoyMUijAMabVaC+ucKlZUQzhZluF5LlHUIIoi0jSdQpObm5s899zzfO5zv8pg0LfsLcel01ml1eoxGE1I0tzCl0pztH/A6rpN8HMd1xpp16fUGUpAnsakcUwZx9y9s0MUtvG9Rbj3M5/5Qb72+pdYWVlhc2OTOJ6wOPQsD3z1e/Dke/xvA+P+k8A/qn4/hzXkdXtQLXvXJqXFpvIiRxYF/hyzRAqBFDZKHAQhRaGYDEcM2wNUnuGGHtvb6+jRAakqcAHXcciUVSgTUmIUVfVx3waQjMFzHNrNJuvdVcZHMQYXz/dwvYDb93Y4HEwYF5pCSnAcpOtWhV01TqXfIR2JgwBlU8+lBJ1n7Ny7Q++iT+CEGFdMIYoasrD/qgclq/n5XHNdFxyNkBIpZqnsdauN7/JUt+aqzqhZjyuOzfNO6+o1ssJOrbFzph9nEASIsuTBzj16qyv0Vtu4rmD/cIejo0ccHx3hepLV3gpFntFoNLj/4AH37z9gPBqzt7fHb/7Ol+h01+i0V6dZlUqB0gohlcVejUblGRtrK6yt92hFIau9Li9/4H0Yk+G7DiWGKAzpdDusrHRZW1tHlQVxEhNPYvb39wnDCEx9jQ5lkeO51nArBRhBnlcFiYVAY2U+bWCuRBuzYLhrjPS0AGC1dgH/XZ4Cn6ZTchoksnDMpWXLz/hJ+ywa1LlgIYuDwmke87sd+7Q2PY95fKCabVQH0et/dvvTzvCdXGOapTiBdZaW6YC2ur2cq1Vq4ZIoihBCTOmA9fvt+z537txHa8VkPKHV6XLtqWd49vn38blf/VVKDQKJKTST/hDfDZFVjMvmLljHwxhDHsdkldMXTwq67TWkXGTGvfDiizhN8KQgCHziyZjldhpb6DGwfK79ngy3EOL/ApTA3/s32PdPA38aoN1q2xFT6ccK5+ZFTl7ktFe6PPX000jH5fjgkFYUMRr0SYVmtd0kzQcMtCJPE2volcG+Pdb4mYr2I7XBlQ7N0GN7Y4vrb7yFSAZsbKzRWVkhLQzffP3LJEpTOh5KSnAESEGaZZRFbqulCx9hrEqYDRYb0ApdZOw+uIfb3US7ETKkSsc1Fc/UTuk81yWOE1zXX9DlBTvwFMbi4FJaNodldcxezlpNbaovXD3s2vDaZVaxrM6UrKuz19ollhfrTaPyWWazHCeTCa5rtbOjVoNf//y/5gd/8NM0Io/j430Ojx7x1tuv8f6X3s/lixcR2gZM3377LW7eusNbb73F4cEBbgXnYKwMZllqiqIkz2PCUKLKHCkMrdBHm5LnnrrCxz/xMTbWVnEkGF0yihVSQKfdpt1qs3XGVlCP44Tjo0OGg75lx1Qp+lmWIxBVADhFhCFUcgRag8QhSTKEY2uAGmnva1kUFv4w815iZXhP85qr/y/DCKd5jU8y7MtGdHoO6gCgnnr7Qghcx6lYdk8wsHZje6z6eMwyC92q8EZ9rnmo5EnBQswTjH0VDKi9++kdq+mAc9dUX7dWcLrpnm07Pf+pcIJZmFEu91cpRZZl9r2NoikNsJ6J1mJQd+/e5YXnXmQ8ien3+yAEjhPwkY9+L1/48tfo5MYa4zS1hce1Jk9H1EU+lCotZdDzEUoTSAfCFttb5xHSxbD4nL0g4LOf/SwqT9m5d5dGuBjHm2fs1A4Xs0SBU9u/seEWQvxxbNDyB83sqe4AF+Y2O18te6wZY/4G8DcAVldXTRJPaHdXaInWAjXOGCuAniUF169f59at2zx95SpXzl+gHTUBReA6hCtt0qTHwfERO/0DytJFIQCFBFw/QAuXcjKBUtFut/nUKx/k5NZ1XnrqMkGzxdEk5itvvM03bu5gGgEEgeU7GTAK8iyz0zLHRQJlXuCGbhUUscVoy7Kkf3DIxqBPs7VC0GgQdNq0KzYD2Oek8gLXWMjELAdjjEAKWwW9no1Yg1KJ5yhDkVeiTg5AXU7KUrWMBsf1sQHLEul4JElCGNjqMxjDmc0tXM9lMBiQZBMkGokhSycICVEjpNPyOTnZY3fnDl/4rc9z9doVrl67wn/9X/15dncf0Wp1OD7qc+udO3zlK1/h9Tdep9tdIU9TayjLAjcIWVldRZeGJEmsfK4woBT7+w+RKuNT3/syH/voK6yt9fBcQCvK3CZOrPfWOHP2HLYuZcbew0c83HlAEIYIoMxsTcsLly7zcGcXpQxCWK3t4+NjWF2zxaGNVaF0PAen9HBcB42iqGZ6s4rlzFkNO92vU9en3iuLXuayR3taIHAe4ljwMGvDN4eXG60ptZU2daTEkXL63OyswHL6neq9mQrxMc0BgoqaJwBHWD1wMFOuOcbYRKIaNzdz2Go1eBhdY/62Eo8Rs8rmQsCUbjg1PPa89Ttut7ZJY3Yg1gv3qU6Pr+/FgqjWkr3wfB9cQxD4ldTCrFlI0GZU2gpLztRgG6NJKhquVoo0SRiORvypP/1n6HZ7tKMWX3r1y/ztv/v3+Bt/8+/w47//D9EMAlSSoNOE7bNb9Na6FFqT5jlxmjKOY3CsnEWWZuSpDd4fHx/TaXXZuLsCB7P+XX/7HbzGU3RaDYJGSP/oYAEqEcx4+3bmWzNieGL7NzLcQojfB/x54PuNMfHcql8A/r4Q4q9gg5NPA6++1/GyLMNxHZRWTOIx4VyV90bYxG1LGlGL1d4aN8sbvP6tbzA8PODjH3mFNa9JmkwQWcr66iqf+b5P8D2l4TBz+Mbbt3m485DhyRGpMQyKmEBo2r5LzxGUg2MuXTzD2uY6e6Mxbz18yBduXCePHCQCVxtcCTgOeZbhCmnxaG3pVZ5jX/CsSLBcV4HQAs8t2Xn7dTyj6LUCHBEhHJc0LXGli+c4SAzjeEzodygXyEHguz6ZSTEKEIKyULRaLZRWYASuw2zKVqY4rsXCR6MJvhcgZEWKNxLP96znpgXNVhPPdUnjCfv7j2g2Qqv3IRRe4LK22sbzNHme0O8/Ync34WMf/jB//f/xP9gEE8fqZq+srdEIuvyVv/r/5ODwCG0MBwf74AbEhS3YbHwX7Tr4WpJnMaooKNMYoTWT8Yg7N2/y2c9+H+fOfwhjCnt9qkQYSV4UCCF4/vkXMK5HWRacHBzSPz6iyBNaUYNsMsRxPBwkvuezc/cewvUIw4CyVAxGQ1bX1hBegJQKU5bkWuGUYISmKBS6hhKMOzVa882y2eXUWFluhFnYat5jXcaLa2N9Gl5td7b/s4HSOesnBEIYXCxMKNGgbTUkgcEBhDDUQ0jdIyHqf3MJRGD1o6t4hztX4GAe8pDzMEZ1KyTVrMTMoDVT9dEAuvIMp8ZXzhc/kAhdVZapYJPAt1x/VVZaAEue6bs1pRVGGcbHx4zv3VtYV2uU1IyWNE0JfM/qY6uycv5m6oKHBwd4rZBGO0IieeWjH+PMxSv8hb/wl/iP/6M/wTdf/So333qdk+NdOitteus9rt+8yShOaTTbPP/iBzhJC0sFzHIoS3zHYW93h4+8/CG6Zch8YRRVatIsw3cFRcUZX2wGK2mh7HMVj8Ony+07oQP+A+AHgHUhxAPgv8GySALgl6uH/0VjzJ8xxnxbCPGPgTewEMqffS9GSd3yosKglvS4j46OSLwRV1tt1tbWeO7550jHYyI/YDweUbZ8kmzCyd4D2lHI5uYG3/z6t3g4jDkYpoyTnMJocm3wXImrFJfObPLcubMkozFnn/sg33znBvcOjrizf8QkVxgcRFXrUgoJ0nI7hRT2bwFlVeBBmLoiufUqXBekVjhFxuT4kKOHD1lvraCl9fzrr0s4AiWqF3JpaM2yDCdyFryzugCvELP0ZRcX6Xg2e1EVhEGE51lhpChq4Hk+w9GQILBG3UslQbvF9vYGmxsbYBR3794BDNtntrl/7x6Hx/tcvnyR8xe2cV2XTtSi2WrjByFZXnDSH/DWjXcoioK93T0ODo5A2OxNVzo4WDF5KQQNL2SUjEiTIWWWoYqcVtTg3NOXeOapi/RWWihVAIo8zxiPx1y8eJEwDK3Bcxz2Dg/on5wghWXMBL5HGNgZhCOEVRZ0PQbDIY1my4pAOYZWu1NV0qnoj9V9rL28mfmtvGljobX5z2Ueo12ARuYCePMQyDyDpDbmtQc+r+DI9D1YDAZOe1R9uI6YQR5UWywiEzPvdnasepVZ/LlwnEUPF7D610se3vIMoT7XzMDPLa++DSFqhb4aStELx53OBoyZxjzm792T4gl5lmNcOxCGjcXgX1kZ7jp2U5YWbhRCTIXT6mN6nmcLigQBnW6Hjt+iyKx3/kf/6B9h5+ab3Hv7NVyV8coHX+T9H3yJcxfP8dqbb/LbX/gSN27ewckzZBrjFCWiKK02txS4pqDVDPCGizbswvlzRI2wYogZC9MttXm4ajlIeVr7Tlglf+SUxf+fd9n+LwN/+b2OO9+CShjmNBwwL/Kp2LnjSBwp6PVWCF0Pra0KmNYlR8fHpIlPq92kEQYcXL/BpDAUuCjpooVBCoh8l621HpfOncFFcTgc8cbdHR4cHnM4mpDkBnCtrjQOCIk2Nk1eYzNeoa5AQlWVREx9MwfwpERoxaR/wuHuI4KNc/Q2N8hLq0tiVeRqw72ICUKlPWwWo/t1hYx5JT97v6tzS4kQDp4XMB7HdnspAUUYNGg2N2g3IzqtFt1umzObm+zsPKgq5gCs02o3UKbLykqH9fU1Go2INCkYTxKKwZg4iTk5GXD/wQM79SxLVGETixy3EqR2DJ5jE2ikASkKPNfQDBugAxqBT3clottqkSYTiiInCDx6vR6O49ButxHCZpnu7e1xeHzCeDKm1YwIWy2E61KoEiE9Sm3QZWlnH45rYSWjUNoahcJoVFlOn9e8TkftPeql5Ysv8xyePLd62YueXz7/szYky+urP2aBO2qIxjoAtbZJve5JH/A8Y2X5+MvYe73+3Vgh78UWWTp7hR3ZmUk92Mwh7NW1LA4Wpw0Gy+uhnlvMWlmWGOxzXXbufD+g2WySJEn1LBcH0flj15mT9+7d5+zGGYwqcYRhtdvkBz71Pfzmr/wqV8+usbbS5dmnr/Ghj75Mb71H23NJj45hnNBQJc00ochzTKGs7DKaFc9lo9OkcbQYnOytdJHCUOTZE9+12vYtanL/OwpO/ttqjUZEo9GoPi7F/GMJwwAdBhRlTlnmnJwc0+t0CT1L33IciXQdkiwjSSesrK7w4Zc/wI17d1HHA1v/UFotEKEK1ldW2VxZYb3XoxH6/PpXvsidgxP2+2OGk5QsNwShZ42hsPh2octpUMQIWQGJYhoocqTlHjvVS+y7LlmpSSYx+uSEzmDAhUuXmKR5ldhhR13j2EFBmMUUWCmdaQDysQi6mLFDXNcly3Mc18HzXJIkwxbdLWy5LhSYkk474srVi7SiCEcIiiInTcY83LnLcHCEcARHx02eeuoplDpfDRAORVESZwVHhyc8eviQOIltcFRZryrwAzzXtTg6VgXNkRA2owprlEShYqXbYK3XwxG2b0qnpBkYNJ7n0Gw2uXjxIqPRCLCzrN3dXSaTCbpi35SlsoFHIYjTGM/xyfKMMktwHa/idpcUVf+0VkjHq6afizDFPKQhmAn4LzejZ3gsQmCklRpYznKr3415nnVtIOe97uVnWD/H+r2v+yMq73mKrZvZfk8cCOb7/YTBaBlff5LxXD5O/ftjx4Np7UxRDTyiShzUpzBI5mceBh7LGH7s4HNNa+u5a60olwgMzWaTXq83FTubXePsuddOj5SSLE354m9/gReuPUtmBgSuS2elhbexQqch+finPsaZlVXaUcT5lR5lltEqSp5Z30BevcrBYR8dx+RpiimtZkuhFZ2VFtvNCOF5FHP9i0KfweSIeDJElQWue/pzm70LdTzkybfnu8JwG6Npt9soI0mTDI8ZT7PdbuOvWOGldrvFBz70Id745je4/c7bdBoNnj+7Aa5Pc2WV/b1dvvXmDTqra/zgp7+Pz//GF7hz0CcpFarIcXTJRz75AtfOnCVJJnzpa6/yxTffpH32EgUxaWEIG5HFidG4vkOhSyZxQlnpNVi/W+AKSamVxcI9Oa0YLb2AUabAi2i0eqxtn+fpp5+mKEuk46CxQRohHaIwrDzuxRYEAaWTT186z/OmxUzrRIMsy+h2uyRZRhbbGvFCwP7+LqUquHzuIs2owde+9iU+9rEP0Ip8BoMjHu3scPvmO+zt7fKRlz/EJz75ccJGgzzPqgrqirW1DdIs4+7de7z2xq0KIyzRepbokyYJQeBa3C7LcVwPSYmDxJVw+dJZPvuDn+HMVpdB/4QsSyiUld4Fg99ocn77EmHQoK6q/fDhQ4bDIY8ePaLf73Pt2jWQgvFkwmA4oD/ok2YZrXaXZuTguAGuUKRZhpAucZpUcqsCISV5XsxBx48HxWpUuzaINUZqm5jiApXKxjRofprCXp1uPS/DuoxpL3t/Ulo6ae1s18sd6VTQg71X8x513XchxAKDaP589d91Ru18f05TATzNiM8POqftZzHw2e91oli9zDJSzGPGfwoHnMLnXhxUFk/neR5JmVjju2TRxpMxR0dHUzps/ey0tkWAaynjeebJa9/6FpPhgKOTAfF4iHDAawZsbayxt3OP4Z27rPghk50HxEnMm2+9Rb8/QqQlrSSn6brkUiJ9F9/z8RsB9wcnyNEE8g7MUZrbrSa/8cXPsbv7kMuXL3Lx4oWF/hvzOFRSL39S+64w3EVZcnx0RNReoek1F9ZpbSu050VKEDV49ctf4Pz5Mzxz7TLJYEBW5vTLnH5SchwXZMmE33j161w6v8WFi9uUrseNnRO6UURIgVPaFFVVlgzyGBN4PHy4j9aSZtCgLDU4Chxblac0JaUqEdXUv26mqq7jea59W6WDcV1yxyPobHL70REn+/fZTA0f+dEfozQGX2srw6oNKsvxEYgwpHAWRaaCwBbgnZdYzfOcs2fPTsuWeZ7HyckJqszxfVs1Zm9/lw996P381m/9BlHTobfS5eHOXf5///Dvsbu7w5ntLa5eucJzz17jxeefpigLjo+PKUrF4eERX/zCl/jGN77F7/vRH+P8uQsUpUIZSZKm+J5LXhTkaYLnOZRZSrMR0G4FmDIhyyas9Vr86I/+MGfOnrF1AZMhgyPNZDIEDEIKvCDgox99hTRNcLAl0CaTCV/+8pdpNpv4vs/ly5enlXjyMmd1bZW8VGR5gdKGSZwyyUtkFSjWGhtEFo4dHM2MQ21x11kSyQK7gxlUMu+F14FKYeaNldUmkUse6Xxb9sTrZfPe6jwNb/7otSLkdN0pHtc8lPAkhbnlAeU7badt/6RjTPswvy1M4wjzy047nr2OZe75IpSzALkLePGlF3nj+huMx2PKpVJ/ZaXHPTuPpf5hxIKxhtl9G+884NXf/m16UYtGGNLoNBjHKb1uDy9swmjCZDjirVvv8JGPvkw/HiH9A0bDmLDZJk36RE2XZBiTTsaMxkP68Yivf+PrnCtcVrg07c/g5MQyoMqSR48ecfnKFZanFAtMo3e593X7rjDcruOSZTlGTpB5sMAqGU8mJOMJq2trKKXY3d3l4b17RJ7ParvNS1cvE0UrtHpruP0hx4MhozjG9X021lcxboth5vJw7xHnLmzRjiKGwz6HJ8eMs5TO6irjB32EAc/x8Fwf4YFwICszkjynVArPda33ZYDKaNdwiSMdcFwKJINxxuHBHUq3ReaGFF6IkoI0zXF9WwJMFQqhFUZpijxHqcXHkKYJJjAIKfFclyiKSJKE8Xg8NUDGGFzXoT8c0e12aHc6TOIGYejxoQ+/n8Ggz737N5GO4cLFc7z00gs0owZRI7DFG4DXvvEaB8dDjo4HHOwfMpkkJElBEuckcUapDIXjIR2XrChI4pg0nhAEHlk8IU1jWs2QC2ee5rnnn6W70iWKQoLAr5weB5QmCiI63RatTptWq0lRFIzHY3bu7VAWikYY0YgiNjY2ODk5mV6nMQYvsLxyZaBUhiwvsSQ3iRGVRK60kJOpuMUGQ2mMpcudYlzn2/zUtPa4p9PtZZzRIib2GNWx5yUJZuFOu6uU4jGjPTPK1XG1mb5LQogq6ar2ktU0Vfw0+dd3a787vPr07R8LTM4ZFbG0T71sfls5h/PMBx7r35V+PA7wpL68/6WXuHn3HU5OHs8irvnd832xx7Fw4pkzZ3j55Zc5e/Ys3W6XMAzxGz5rzRXW2iv4gY9ygNDD5AVeqWifd2l6DoPjQzafvsrGtSsc7B2y9+iAItPk5QiTZZRxhkrtOxm7krf3H+Ls+MxXM3ZdhyCwjKcwbBA1m6SLUfCF73re+35S+64w3AbL05SOw7KbcbR/yKg84tKFK7i+x9bmOSajIXmacjJO0NIjLjK6q6t0h0P29vcZjmJu3nlIr9uiUC7dTofBcECr3eEkjhFKWb1uJUiHCWhbY7ERNeiurnFwtI8RUOYlRVbakVsbXAGOsPR6pw7AGA+NT1oahnnBSaoYKHAdjZEemdLc2zmgt7oKqkoeEBrXh0wV5CUIEyx6X26AccoqEQOSOKMRRhRlgTEF2miSNObM2W2EzGiEPqFn6DRDHu3cIwgCmmGI01un0+py9txZokZEmsTsHpxweHhAPJ5w89YtRsOU0ShhNBohHIlwPOvdFkUFObgYrSjzjDSZMBwcY1SOKgvOndlkpdNitbfCmbPbVXpxMk2iihoNVtody07ptqdsgEeP9hiPRxwdH+N5Ps1WCyM0cZogXZulagus2nhAUfVFI9B18E6Iiu5Wec56Su6rjIgG5ALnugaMa1M07xXP/z3fLN48CxTaGrjWqAsEujbCj3mblrZn5o121VfLpV4eUCx+PjOI8zjtouF712+pWv9eH369zWkxlPnjPCmQVg9s89vPQzYIG6y3A5ItRabra668aVndUzEDrapxcgm2Ac6d28JzJarM0GpRZMpKgZbVFEBgKsVGpaoZbqWXMhz2SZIJrufieh7R1YDu5cu02i3GaUJcpJyMRhzv7eEYTSv0KLOMIgxZ6fY4Oumzc7DPaDBmHJ9AWUCu0KW2uRWBz/Wde1xL1mnP9e7gYJ/ReDT1+h88eMDqnD6R4HHD/Xtmlfzv0YzRBI0QxwuQ+aJHNBqMOFF9VGZotVtcuXSNvMgZ9k843t+nRLB/fMh6u0O308XzQwbDEw6P3iEMfXrrm3S6q0RRRIHh3uEhURDgSQ+UQ3I8IvR9pO/RaDborvbYO95Hl5oy1+jCIB0HoRSOFHgSJFXVGuFhcElLl5O8YD8uGSKQUZPCaBxKxknMN791g09/+vvJ8rGVdJQaP3ARxlCONY5ZzLXygwalm6HKwtbPHCdEUQMlSlR1v8bxiKh1mSjooIoMVSS0GwEPdh6w2ltjY2OT1pkuCkjynEd7R+zt7fJg5wG3bt3m8OCg4kEHNhDrhJSmtNhwWZIWuU3pV4IsjoknQ8osxqFkkgxxBTz79AucP3eOKIpsTT9T+cLCZlJ2Oh02t9dxXVvirCgKRqMRu7t75HkGUhBG1vspBgX90YCVbo8wbCCELT81iScUSoGwmaJCSvudVizsGuqYGQ2A2f00LAUd57w+++fM+13Grg3GQi1ylrJda05XB6v+g0qMe3a8mm2xfI7a4NV9qeziPMxSB9VmxvvxSut1H5/8TT0Z+ni36fiC8V3qe32PFgNoi4PJ8r11sLCiMYZSW2fEboOViKh0wut/9l7Orr1uraZPEDgIFKo8xXALe2wquq42GqMMeZYzGg158OA+/X6f0WiA0iVawU//9B+it95j1awxHA45PDpkZ2eHt954k8mgjy8ljWbEymtvsLq2xmg4YvfRLieHx5wMDzBK4SBBG/K8RHkewzxlZeMy57uz3p30TxgMBgwGAxzH4fpbb/NRtSwstzizea8Z03eF4XYcB6M0mcow2WKXLl6+zEqvRZzHdEWb/b0HhK7k/Poq3/Pi07Sk5uDRPoPdA8q8oNPpcnQyJCkVpXEYH5ygHx4ihaZ/csDaSoeG7+EYQ5EWnD+3jRdFxHnJ8WjCV778RfzQo8gzjC7xJDgopLAet+v5aAS5soYbGbA/TBkpQ+6EOJ7HJFM4GHxXMuwP+a3f+S1+9Ed/GNeDyVhTFimFFHQ6m+TFCXpp+p7EE5zIgFFIoayw0727NNtN1tZW8HyXR3v3mEwGPLx7E1XktFttLly4xCc//ilc1+LDo8mEB7t7vP7GW1y//jbD4RCldFU+yVbE0VWWlhAgpEeeJ2RFwSRJMLrEAU6ODnn08AFPP32VT3z/Jwl8lyydkCQTm7Leilhb6+H7Pvfv36fX67GysmIV87ASsTdv3mR/fx9jDFtbW0gp6fV6KKU5PDwmilr0ej2ksN52nuc83NnDDxyoPWI0ZV30gTkveO4bn3mozBlTZ/oxPCld+jRjVuOjixStmcQqmKrAgplqz1ijZClpM4bIbCJZY+RSCJAzv7tmq1h4xRo0K3kwY6rUlNDlIOR8f+u2jK0/6bub3/fUIOQp90nKutDEDLOv79X0fMKZznYMVMljU3qM9bLnZiOwNEtYGlNO+iesra7Q63Ufe4a2rJ9E6bLuKKai6dbXs7GxgRBQFFbuVUrJ5z//eb7whS9MCyxIKTk6Oqq+kwIpQWlDkeW27qR0bPDdcchFYQ23dHCExGhohj2kO3tf6/bhlz/MOw+uMxqNZvVf59ZbCYcYz/OWkrie/Oy+Kwy3ELbArzYO7pLhXt9eo3O+QSNq0h8d8uL7riHzlEBoIj2m67r0ywRdlHjSoxc1uV0YvKhLLj3KokArRRS4pGnMsD8kcx2kMaRxThjGuKFLd3WNlXbE7v4xjoCsLBHYzEljqumM61IgUMKl9H2UDHk4TEiMJJceBQ5ZLvDcCKkNaINC4LqanZ0deqs9tJJIArqdDVRZEIUNMtddCE5GjQC3JXCkIQw8nr12hY985H24nsvD3Udcf+c621trXLhwlu/9yIfJkpRJHLO6ugZIeiur7O3tczKMieOcN9+8wWQSo5VNiC5yW1HI93zK6bTXfgye5zMejVBlzmQ0IBsec+ncOf7Ef/wzhKHPeNTH0QpTJPQ6LcajIf3+CSvdFVZ6K1w8f87W1BNQFjlv37jOeDwmiiK2t7en7ItaBtVxbF1MrTRZmlOWGfEkJk0tayYvUkpVpXpDlRVqqEN71nBQlW+rjdkijW7Zk55nWsx768ttVgZrMZlm9t6CEFWJPGH7VX+SUjoW3KkCcXUoT1R4dY321CbI5mTU9Ssto0Rr8diAchomPP8dzbNclpfP34d5b3n+2E/yxh/DuqWceoyn3UPL7pnZX2Oq5Do5Z8zzYna8pWaWfk+TEf/pn/pj/Pqv/xa/9OpXF7ZtRhFrqz3iJGYSpxWVV0wHzrIsef311wmCwN4XAVmacXx8jJQS3/cJw5DJZEKr1cIPQ0o8jAMqyXAxBJ6HKkvyMkPjkghltVC0QlU2Ju4b3MB/7F78t3/5v+U4PgRsEtmnP/MZcm9m5xzHYWVlZVrF54l5BXPtu8JwG6052N2n1V2h6XcX1s1UA126rYjCUXiBpOVK1hohQZkRSIiTMY1Gk/XeCuc3O+yNU0Z5aYOHnos2im6zySdf+RDrvS6e42CU4f7DB+wfH3IyGjJMChqRa8Vj3KzCaiWu5+F4LoU2KMcnFy6xEhz0h+Rei0xKFC5GuviOFcuqRSOM0ChtdQyuXbvGUA44OT5mb/eAwPdROkVryfyjiBo+G2dWaYQemJKyjPncr/4GnW6Xre1tXnrfC+Rljuf7FKXCDxp4QYM4ycnSnK9+9TVUqSoc0eHk+KQylrZwrapejMIUli0j7SDjBy6q1Ow9esCL73uBz37fj+LpFIkidEGonCjwcFyJcJqUpWZtdYV2q2Nf+MAn8H1OTk44OekTJwlR0wZWLVNGTj0Lx3FIkwxVSeU2my2GwzFGS8sScT2yrIAqaCgRlQyr1SKhDn4BxqjKm17Sd6hwcLvNIp4922RWTWY5wDWfsr5MvbMBSpDTuoXWS56HGoQQU08OZoWXhZDW2RRLzBFjTX9tcGovvm7zOOh8/5eDhPPfzvz2yx52TS+sj/NuMw87yDoL69zq76IopjMTx3Eq4ygsK0kppOMQBAGZKiqtlZl3P8/2WegHS8ZbKZI4ZmOtx4c++P6FZ9hut9hYX+P+gwSJgyPdir1ly+spVdDv93n22WeIopC79wqMTqf9KIpiWki5VhFUGIpSWZ0YIRBFiWu0Bd5UietITGnJwUaAkZK0LPAwC0qeAP1Bn0Fc66i4FHn+2IyiDowvz/Ke1L4rDDdAEHgINEWeMZ/QGvgeIgyRU0F5B+n5CNdBOx5Kazqb5zg6GpAPR7bS8tYax/EDXFl5OtIgtabXbnN2fZW1lQ7CaLI8I1ntYCQMJilpNsELI3JtUEKipAM4aOHj4JIZTZJrUqNIjcMEF40Dno8jPAwOWlX2AivEL6StQL+xsYF0JK1WhCoyBoM+zz3/FL/zW58jHG8QMVMMG48HNGOXyahg0D/i6WuXOH92m6DRoN1u0owadL2upedliizPyNKco6MTsizn6KSPQOD5PkI6lGVeJT3YlGRbMNa6pFLW+gj2Hm2s9bhy8SJPX71Er93EMw4GZY071htMspTts2fQShN4PlEjIooi+v0+4+GQ0WhElibTANzq6ipBEEw/8Ha7XT1zW80kSTKSxHpKM/W6umisVQypzCE1rkytuWHqQr5QY8zTTe2iheXzwk91my/dNj9tn1HTZv9mBqY+9ryBtTDHlI8tZDVYLnpPtR6FEYvlqWoOS10r0VTPaJmNcVqbN7zLOHP9cz45aPl4p3nYj/f79L/r+7ecrONWA6KYh23qc89h5HWbZ87opXO9c/06Ow/vMRiMUOEiZTgIfNqtCN/zEU2fuCotWD+LWqPk0qVLdDotbt2+WQ3Yjxe/yPMcZTSKqthKqYmki1cV5DbC6tUII8iVpqwgHyElutBWy2jJKAvEgjpnUZbMgCSmzokxxuZwVJ73u7XvGsPdaTdRRlBOFjvsux6O7yOEgy4V2ghKHBItEIUmEj5p2GNgAnQyxJ3E9FZW8AOP0DHkxlh8TWm213t0GwEBijxPKOIxgSdoRA2bZYfEcT3SJKMUjs1sxKUkQBeGQsOkKEmNIhcO2m+ghIvnBkjpYbQgL8vqRVUVA0IgpUsjiijLHM9zWOm1cF3Flavn+Mqr8jEoazjs0xg4FEXK8eE+T125wPtfeqnSTFHkRYmUHsk4YZJYwzceT9jfP7Ql4ISDQFAUqho8DI5b6UYwp5uhDAJF4EqajQbdTofL587ywZdeotvpoNIxOQXCAcdxcX0Pz3MpjGF1fRMJ6MJifXmWMeifkCSpxTqlIAobFMZMDXVdhbuuk2lxaEGWWR5u1GijdVkZrtoQLuKeFteW1BXD62PYj6X2YC3APB/gg9PZI7Ux00uGZPnnvBG3y+sOzYzDzKO2ffCks+A1T6/lCbPgmkkw85IfN/rz2XXv1h7v72K6+bLBmt9v+R7Nb/cYBXBuQKhL3tXLHdcmGNXFhRf6h4WN6j7NH8N1XbQQM/jQwIP799GiZDJJkdtnl67VZi96joMrXdLU6uPbqYvVzTFGsL6+TrvTIs9toYRa7Gl+ECuKgurVs5ykoqQVRgRCI4zEGIcSjZI+cVEQlwWFMOBIlNE49WxqrnmeRyACq7UTBJRFsRAynw9gz2Zo796+Kwy3MVagKPQbuOXiaJrHKWoUE0RNpO+Rq5JUlUyMYiQVa50e//K1+4S06XVbjD0ok4JGd83qVGQpWTxBZZoXn3mK9W4TlU7I8wmuKDEqZ+/wkLQscAOfEklSGoQbIqVEGYck9xiOxyihwfMoHZdCOAg/wncCJI5lOmiD77poUVKWBVpo0IbxaMIXv/AFPvtDP4ByBVHD55Of+hjj8RF/+A//NN/KH3D/CyfTa3alIAp9ti6dZeXD70cYS2uKohCtCtI45vCgz1tvX6dQ1nO19SxLHj3aZ21tHc+1GtelzghCb4pj26kY+EFkU9SFotdZ4YVnn+NHfvAHKdMJeZah0zEOELYjTvonCMelG0Wsb27Q7a7Yj6q0U8xh/4RHjx7RaDQIggClKi60Vjh+QJIk0ylos9mcZrjt7x1QFLZARhhE1XJrmGvmgZU1EAjHGkGtzDTaZz3vumDE4wE7Y+pg5aK0at0eE36aa6dF9uspfP1lWgLDYur6/L51xuU0SDI9nJmySRbto5n7aSVda5jFwgl2+q+nA9LjEMm8wZ6HIQwGo+YLZdTYP4/dtycNVsv3bx4SWB5MRFUEW2tVj6kL98eREqP0Y/fOGGO90vl7L+DMmW06vRa3bt3j3nC08FyGgz57uw/JsoyyzGg1WyRpVtHvxDQgf3BwwGg0wBhDltmarPYe2etfXV21UJ7vgxToUuGqgpb0aLkST1Zwl2OQTovjyZijeMyozCmRuNJK8C5ndkpHQmkT63q9HpPJBN/MPG5jzDTwXEN3fqXf9KT2XWG4HelQZAV5YfBTl3mJllYrQreVrcpduqys9nA9D42mVDnG9dkZOwz3x8h8QkhOaHIC19DptmlFHVaiFbxewva1K0zSIcKVEDYY7E04HsecDEco6SIaAYNEszMqyVQJ0kFLj0IapBdQGKtLIv0Az/XJtUYXlj8qpYPr2RtdlLoKPkkbZFIF+7s7PP/0Jc5srpNnMcOTfRzP43d+5zcYPQqYl4G8+tR5Sifj7r3bvDkaU+QK1wtwPMuxniQZCMfCA0aTJglpmqK0pUyeDPpWTL4ZkReaRrNFPB7iuhJHQFmkxP1d/tBP/RSXL52nGTUwWjMeDhDGGgcElAZG4wnnL15mfWMdg7Gp+8BoOOTtt94EVeBKSZIkbGxsobXC80Jcx0O6kn4cc3C4T6fTpdVsIaTkwf2HVaFZq4mS5wbXtZBHWRbTAKPjOAjp2kzIokpTFzYxBazR06ZOM6+9U1MNTtaIuK4zpSMCC5oidZtnUywEH1nMlKzxWWfqYYOo9VDnDLOg4ptTebPCKufVXqdWqgq2GpwqAHoq48WUCFx7nVN9Dj03eJxe+Lf+PS/SBXxb6QJEBV8IWeHzttOqhoasaOyCZ15j1/Mwk6m+haqj9twV2yTP8wqmtOsEVlN/lu5u+1kHxudx9jzP2dvboxVfJJhLxOv3x7xz9w4n/SHumcWiWhsbm1y6fJVvvvZthCMIGhKlBUVpGT5B4JPnOZ/73K/y0ksv8ef/67/I//w//U8c7B9gB0iB43qcPXee11//Nmlm40dBEOC1Wtwd9vG0JvQ9GmGA57vsH99Feh5KCEQQ0ggbuCE2WUwtzlZGoxGDZMDKygobGxv0B0NW5mMRxpDFMb7v0+v1KKpM0CcFzeG7xHCDoNXuWClVsySSLsH1HEJ80qwkS2K09m2VbaPITIJoBJRhaItIyxBlckpZkmQlB3GfhoTL2z2KICJNBjbiHDTwVzdIRg+Ic412QgoNO4fHpHgQhhjhoIQAaawWtXQBm9pRljYxR4hZUEu6As91KbVAaDHFQaXrcP/BXY4O9hkc7vJo5z7HJ0eEUZOz57YpG4J5H+L+/Qf0kyN2dh5w++Zdkjjj/R/8EJvbZ/CChtXGrqqMG6NxLDBKWRY0Qp+iyMjSmLJIKU1OmSd4rmB7Y5Wrly/yvuefY7XTIp6MiXwPoTV5ZlOGfd8nL0uiqMHa2hrNVhMpJYEfkOUZ8XjCzv0HZHmO7zh0eytEjZDxeFwFVaxhUqWBim4bhhGqNJycjMjyAmMEpbJeY20o83w+oLPIplBKzWyjnPcM7U8tDMaUCOlYZ5w5vLRia9SlyNxpNL+mClIjHrZYMDX0ISp0Xcx5RiCkM/OEK7jEesLVtQgxb8PtOQBhwMEabymsyiVCVNo3M7lZmM0EhGHKGXdkzQGfu24bGluAIUzl4NsgoawCppah5jg1tdDi7LOLN5XnXcMhNW4984JVpWe94J3Pectmeh6J49oiIPbcM4qg71pvX1f/lgPDWmsajQZvv/02brqxYLhfTn8/patQqwrO9JhvW8kzbBQ/zYcv/8T0/umqAESNc9d9Dp0Q5/Nt/sj2f45an3vnhC3A/Zn3ZdWf9ayJaUEKIWZSBOUZPXsQ1XMH6xi03Fm/AV75yMtce99VwrDB7u4uqlyEg+t3RQirivnMM89w7do1/t7f+wc8qX1XGG471ZVVTbfFhAn7sQo8zyXLFa50q0Cbpihy4jwjbAeIoUOJIJeunYZKH13miFKRZzkPj/t0z5yj0QrYuX2T+3fvcnLU52gQk0ufwvFJc0WiDMr10DjWEEiJ49SInG2mnqZjPT4pneqjEGijp5S3+sUBwfHxMQ/uP2B7Y5Vut0ur3aJQmjjOKAoP5lCvL33pq0zKAYPBkMODI5K05KnMDkQ4Gum4tmq8dKrsMPvhpMmEZhiSjgcUeY42JYXO+LEf+TTJZEIUBqz3emytrhKFvs38qmCY2utpNpusra3RaDRotVo0mxGTyYS9/b0p5CGEHaBsMDknFVbhschtDUlD9bEbzcl4iOO45HlR9dVqi8w4tsvZgDOmxQLboL73C1i0QEqDi83MsyqL1nILOQtS1hoj1sucPTt7CDH1mEVVJWZql2C6/TwTYoobz3o8xbqXg6M1FFG/F8LUqe2LH3ttGKim0E6daDQPv5g5q401IgYx3W627Qxnr2chUA8I89P407z12X7LQczHA7u6Gijq/U3FPy9xHG8BuzZGLbItKihs/hzGWEG1tbU1goMA4tn9jcwKdWhDu535jHI8E+CZHq2limBPbGNY9TbBO2XdchXffwvN9wOSJEVrCwO1Wq2lWIG9D3XVnjt37tgAf5Y+8ZjfFYYbrFhT0IiQRUS8tK5+qWv8y3Ecq8WsNXmW0VvtcHjkkmcGJSzfOjMGLT2kU4IumWhDc2ObsBlyZ+cRj/pj7j3coygFxo9Ic8MwKymEg3YcLBFQ2GmUFChdkfod+/FrYT8YIQzSEVUQasZX1VKjVG18DHmSkqY5fhDSjLp4vstJf8it23cp0iaCGbb/5ps3yEWC0po8Lyk1TJKUotQE1TdTFCXC0WRphlGlxb4nIyhSxv1DwsCn2fApjeR7P/IBW1tP2eAJqiAeFwgjKuqioNFo4LoOnU7HCs1XFd/jOGYymfDwocUQgyBgdXXVQjOlIC8s5dLzGxh05VHbPiptiCcJUbNJWShs3U2XWWLMoorecuCrDi5ObaFZwl2r5U5lxGeIxez/1qaKadHl2qMUcs6AS3ssKSWYGSQiTAWVCMCIyluujwk1Fi2FqFLbZxoeNRRSn2PeqE77Nu0vla4K02uSME2XP3XKPLP7C8a59nrr5acxUuYNtfX0LaxXDyjzHuqy4X6MMmnKeaS9gq5KjHEX+vBYANhUd6GG66ezCcHW5ib0PdAKs1SU4N+npkzJncktBoxw+w7dbpcoiuh0OqfGT8qypCxLHjx4wP379xeEs5bbd1IB529ia0vuG2NeXFr3fwb+78CGMeZQ2N78VeDHsOPlHzfGfO07uch2u83q+jYyDLnF/vxJLFl/roJz7RG4joPveVw9f479+/dJ+gOroSwkSZpbkSbh4rWaXHzqEsqPSOIYr7POyvnLHOeGo6Mh/bjgeDSmnxTk1j2zUISwL6LQtVclqmxAW47Iep4VtxUbNJLClg2T0p0a7ry0ePm5i5forKyQxCOOBwP29vfZebhHKz5Lc85w57kixRq6UoHj+9y7v8PG5hmiFpR5jsotUX84HlIUOUYVUGYkw4x00ue5qy/y3PPPIBxNHg+gTPE9HwdBnqRWL1krpGeLAq+vr9NqNbEayYY8T0nTlFu3biOl5OTkBNd18X1by3IymaC1otVq0mhEZEVJqRQng4Gt+4gNTvmhj0BWH7PAkS6eK23Vnrn06XnDUhsOrTXSsYkwRtdyrHpm7Sr4ozbKU8NUe5hTL7H2rqusRqTNdqs8TllTC7WawibW266918qjtmPcVK/E7q+r/c1C0NDOCSujXsMZlTY4aKoyjrPBpkrVnhrpypjV17TMDa5nc/XspmasWJ12WW2/WFZtmTGilJ4absex2Yf2Wc0CkXXArE4MmTJeKrilyPMK7qhnpBrHnTF+5gdaz/Om5zdGLZRRs1+YoUhS2s0W6oOaopuTf3XmRk+HL3dpIJMG42qkEOQVja4ODFuVQGaD6Jy9FPPHrE4QhOHsXRJiUUTMzN6rUpXYIt7V4F0jBZWjoJRiGA/4u3f+X2yurXLtuWtcuHCu0vJZLgq2yG2f3SOe2L4Tj/tvAX8N+DvzC4UQF4AfBu7NLf5RbJ3Jp4GPAX+9+vkeTXB4dMTDvSM4CWixOV2jtcYVgjBskBeGyWSM9FwQhlIVSCF46annuf/WLfL+BKM1SZoSBm2SJMF1JOuba/zkH/gP2Nnd58u//ZsUaUKrvcqHP36WX/v136bT8jgu94nHJ5QGUAWeI5GOfShK2aIBaa4QZVkFyEwF7Uwn8UzhlDk6G8JG0KXf4M6duySTAQf7u3zxi1/gC6++SqPR5TOrP8Fz0dr0mnOlyfQsYKPKgqPDA5LJkKxha0gGrktWpDR8GB4fcXx0yIUzm3z605+i12lTFilpEpMVGZ7vgoYkTqcfX+B4dHpdNrY2iKKGHQhdW1nn4OCAwWBAnuesrKyglOL/396fBtuWXPed2C8z93CmO7751VwFFAogChMBiBM4SN2UCCkalNV2UOFuM7rVTYestt3hUHSo3R/YX2S3HSE57HDbEXSIYalbLQbVralt2abIpkSJIEAQQ2FkVaFQ45uHO51pD5npDytz79zn3VeAQAJVAF5G3Xr3nrPPPjtz51658r/+67/Onj0r11bXHBwccObMGZGV9eC0ZjKe8I1vvILKcowRp1p5j1IZbevQOsM7qOtGxikY3UiBOo2WJsE/wlhLAGlAawu7ruhZd54mvbE1Ae6K52zbJkDT8qCrUEQ2jnV/L8Ni7YSLP+B4t61gtJpgJEX3WyOOY7TGJkA5oPBaS/3Dgc6J6Q2H7mESFX5v2uEDvulBO057sn0n8rXJcrHWDjTH0wISkTcvC4CMV4xZRG95sCsKEFSep0FLh1QqlIpRUVI3fm4zGaipLForCmPwoXhI3Vpc0+C8hsst+uKyW2AdLdZb7P7QE20eO0D9G1fYv3CR//rv/hp5UZBnGSfHJ3zpa19mtVrRNi15kWOMxjvPqMgkHmWt7N69Y11Z/vp/8b/nkcefYufMObLRlGvXr0JrsU1FvVyBc5w7e4Z/8Tv/nGeeeYa7B4esq5r3/NB7sV5RjEqKvOALX/gc/+Xf+D9TjqfcOTyiauoQf3EMKK7hPkUI8jQ202ntWyld9jtKqcdPeev/hBQM/sfJa58A/o6Xu/MppdSuUuqS9/7am35JWPpOu9w8y9C6YV2tqeua7e1drBKtscIUrOc1J+ua85cf4Wi+5PXXXkUrRdNWKGU5s7/NO556jEcfvszf/Vu/yu0rV3ni8UeZTia8+uprPPmOp3j+lWvoLKOclGhleMfTz/Af/Af/Pv/t3//7fOrTn8GYEVVdkxcjYS+YTCabA4cdLt0eUAajFd6IOp3FMSlL/tt/8A8wymLbSjjNeTEItvXj4aHjoSpyo5mUGcrW0KzwbcvhasHR0RGz2YgPv/dpHnv832S9WmLbivmSrlIN5NjWdBOnLEfs7e/x6GMPs1zMMUaDkt3CG1euBSEceeBGY+GeRm5uDJqdnJxw5swZdvZ2OTw64vDqNSaTGWVZUtc2KLL1PNm4LY47YjHWeTC4kvyzibXK1txgskRbW/f4becJB660D5mhrbUoIMuzDjPu5nIYz6gH4r2DUDvUORcKy4rhzIzBNlKFyDtHGxKr5FpUV0ZP+qACd71PAzdatMGJ+G04TlLje3RAefEMdcCWvRfaXjyfSzzpTXpfKDsjY6aTByk6Ed1YZ4PP9rrkYTcTcepgtGVstdAw1dBw97AJgBND6CMlUxQdhVURs/+GJQlTPDuLPOqwAPvws1rW5HlJpg1aQ9XUgEMrBziUsgxr4DjatuLuwW1eeukF/vL/4i/z4osvcOXqK+zsjliuDrlw6SzGGKpKapvOtqbcvXNAlktiDDqjqlfcvHmV93/gWV548av8f//Zv+CZdz3NzWvXmJQFTz/1FBfOneV/+M3f4OjkmM989rM8+vjjXHroIW7cvs3e/hm2d3YpipzxdAtVjGjbGq8UB4eHnCwW7G9vsV4tTrF0fEsGO7Zvt8r7J4Ar3vvnNr7sIeD15O83wmtvargViiwvsN5GslPX0sniPVIBXGvZ9irFZLJFi2Kyu8Nkdwd1o8B4x3uefoYnHn2Yc2cmTIqW3/yN3+D1117nfChx9PUXv87x/Ih3vfe9oGBVid5HNprwnne/i/Nnz/DxP/OznD97jt/79Oe5fecutmkwIxF0tW2LUhptdNiaC8k/OJI9yzbgj1VVcfPWLXIDxojHMR5POyO3Mb5gVAhSQZ4ptLc06wUnBxZvW5566gkm73wCaNnd3aEwYA1UrRTIjUbCOUfTWra3d5ltTRhPS0bjAjSUk5Ljg0NWiyVN02LbVpIDjGxPZYutsLZlPB5TFCXjsXjn6/U6bAs1yisW8wWZySSxQUumow9GTkqASSBNK9VpTkvihA2BO8kwjWNHoJEZDSo7JYqkQk5l2NHE6kDUseh0FKbqDb/qjHbKT7636EHs+3q9Zjwey+JGHzQVeCUuMH2lGdV9hyx88brSbXCnkeJ9YD5E3Dgad49GyqShFcobYuBzgDF3ePS9C78Y4RBPSJ6hmEEYfxctlpBhSIAI0R3eHb3naNhd2KVEFyt6030qflxkFCBl/7qpreIfyeJMvxPYZLNItSWBlsrMUDUWlENzii6KszT1iqptaNuGF158gdu3b9K0Dav1GmM0Z87sU9cV3lsm45KmrRiNclrncN5KHsmo5MUX/5Cf+qmfpMwzRoVhOsp56cXnGWUZ+9szHnvkYT74wQ/y5a99jcYK5DWfLzieLzhz9hxNU3dwqrOO3GQ4HC+//Cp379ymzDPO7O3yHms745vuOjer4Nyv/WsbbqXUBPjfIjDJt92UUr8E/BLA3u4evtNOHgYjrGvxtu0w7bZuJZVbaayzFEWJRbG3s83ezg6jcsTWaMT5/T0undliMjEcHx3x+7//GXZ2dxlNpxzPjzk+OsC6hjsHd6mdxeKwrsU4y4c+8D5GecmHPvAhzu5fQOsxX/rKV3j5lVdl8oVECGEhRKMSsPcIl0SaFQ6FpWkb1pVFjaQwgXWgTIGyazbvUV7kGCtBPIOnMIpRkTEZ5exsjcmN5tL5Pfb39lg3a6xzrFYLiQFoJdtUrdHakBcZ+9vbTCYjprMp40mJNor5yQkey3q5YDWfU1UN5WhEZnTnCUW8W7bessvRSjObzbh16wZg8M6jVUbbNNgA3Ea8z3qb7EQE79VG3GZhOjscDhWw4Dx6qOEjWoknqU0k5XnSOoVa9QuBD159xB07JogLcoLdGPseOw6LaoRK0vJjMkfFCGkjfHyPBFwVvSJd5+EGQ9qV1grYdhcEDYuXDsC5t7abI3JCOu8cgget6Nkx0DFR4muycITP9KuKnH8juBiPTxcppUwSnIxGO+52xBFxcQxxxFT9SCUcMk/oElm6cY47HhXqctqYyi/3yuFQXgyvC5rZgpVrEanyHnwILCtZyp1PCZr93PJOWCu7u9t85ctfom5aVuuaddUw3RIteI+jbHOMUaxXK4rRCLeucE54/1lmeO2VV1icnHDh3D4f/dD7ePjyQ7zw8GXqdUVRjihGIx7d2+d4VfHSSy/jvST4XL9+jaIoeObd72JUFqIkChilO72gWzduUK3XPPzQZZ6x9y8ecWoweqN9Ox73U8ATQPS2HwY+p5T6KHAFSAuqPRxeu6d5738F+BWAhx962K9qi/c5RheD49q2QTVrdFYwKnLqCnJyQNNWK2xrmU5hZ3ePk709Xh1PefTSoyxu3OFzr3yJVb1g3rSs1hU/+bGf5qtfeY7bx4fU6zVFbvjCl/8QO5phRgWjyQiF5sd/9MdYLStms22effYcly8/yj//5/+c/+bXf535fIG1DZJUbnG2AJXR5UGF4JM2QhXEt2jf4rWj9eCUxulA57NSEV0NgjQwm86oV6LHnWvFztaUC+fO8Pgjl3n88UfY2Z6yOD6kWh/RYGitCwkUwWPxLV55TK7Y2Zrw/ve/h1u3blHXNU3tKPKcgzsHnBwfslUWjLMc7SHPDJqSTGfy0OroqSnW1Yp1tcZ70EqL9+IMbQu+VRSm7CdcmJOZMeHZ7c+TZUlxXAVRclVrHUh9w2DW5iTOzL2eiHceowy2dRhlAmIQuMVdEV4xIr7zjsWAxy06IIUkEMhA9MSncmwn7u1xrsEYI8JjASbIcjH4ed4LBUm/bcdT7pIyOn0S8fS89mCQxcf1i4ho3RTdgiOLkQ/Xojs8HISr3aXWK1nQHGIoXWK0u11BSMBpG6FmaqVxAf6QBU+opiLz47HeorRonEdVwxj7gaGYlQ3ZkN633fcKDNUHo+Pr1ksANf28UkHDxYQoMD7osRsURoK+fmi2jJYCIL51PPbIw3zhuS9StQqvMxqruXz+Eq0V6G4ymSLccoNtFdqAbRraRgK4N67d4NqVK3zoQx/iZ37iR8jyAmM+wdVr1xlPtljUlqPlIZceeozf/b3PsL27x2Q65cb167zy8ks8+eQjZGqHQjkmRvU7nCzDWcXJyYpXX7s2qDUbqYCbbJ6N0OnwObjvO/dp3vsvQR89VEq9Anw4sEr+CfAfKaV+DQlKHn1TfFvOSpYp1nVFW1smSbFgrx3eNDhjWa1bdDbClwVZllMWGet1xbKxjErDbGeHR554jFzlYDMWByvaesnOeMwH3/Usz3/xOa6+8TomM8xmOxKYqB13b5+wXNdoVQjHejblq1/9Cjdv3mK5XGJyw5/6Nz/GZHvEP/pH/y9e+oastHXlMGUJWNgwvvKEiNCU917S4j1YoTpTmKwzLmwYp/O7I4EijOfxxx7n3/13/x20hqZaoXFU1ZrWuRAY7MfQB072zs4OFy5cYHtri6Zec+P6DabTKevVimtXrnLz5k0ee+wxrl+9hj9zhp2tLfKiRGc5ZSbu32K5ZLFcMZ7OOHPmAqvViqOjY+Ync1BSLHg0mop+dqY65gIkMp8+eMuq9xDTggHxtdTbiBMdRHWuKCIGG73gVBCKYCzk+CwzRJxXjEHY4rt7vZv4E73seN1KqS7d2HsbEr18Z3xGo1F3bU3TdOJB8bzpd6Sl0FJNbekjAfJTgwe3e3g9oah078E3dT34Hg8SAA5GXMf3lEIbg/WgnA/zTBZLo024Lk3dWrQCE3YKKaulaSqgl8rteuYSudZwHfGenUYdTO/npuFWmA5D3wxOx6B/3PVFzri8N7idODTrxnHj5m1u3D7gzLmL3D2ac3yyFBjWiM71bLqNVgJbXrr0CFfeuN4lFkWoom1bfvmXfxmTZUymE0BTtwStIfBoLly4xOOPPyEFim3Lar3k4x//OT74wQ/gfMvRwQE3blzj6Pgue3t7LJdrmuWatm0pRxl+A6GHfp4OA/T3HNa1b4UO+PeAnwbOKqXeAH7Ze/+37nP4P0WogF9H6ID/3jc7f2yjUY4ymqbc8Kh0hjOlbJOMobUa6oZpXrC9t8v61h2cM3hlKKcTpjtb/OFzX+XcSGHyEqNaVFPz+ksvsliuuXDuAquqZb5c41XGfFmxWnqsE6rbdDrjt37rN8lyw3t+6B3s7u4I/UvBn/2zf5qvfu1r3Lx9i8Wikty7+MR0zdNv9mWL6fBSccd7XO1wymJGGu9FDGcTp9zKPf/+L/yPuHz5MtZ5XFOhMqHUNbahqi0KH6AReZCMMezt73HxwgXhuVvLarlgNhUK5Cd/93cBEbyZTqes12vyPOf45JimaZhMZ8znd9na3qVqG7Q27O7vs721zwsvvEDbCv1J64xqXVMUE9kmRoOqe0EobWRQYsAq/emMzn1mZeSP9wGtdGsfH3Ax5HE7HR/6xWI1gAbEYPYLRtTpSHW247mjPnhs0YinxigG+aqq6tgYdV13AvibHGe4F56Ii1uWZ2RZzjrQ7FK2hyxGVgSLUAHd8WRKJ3MrmXFWjFpU1OuYIt5jlCwQEQpo2hblBZ7KVEh1dx7neigqyzNc23TBT4FB7L1V5Y0ZGOnYjDEd5S2VK9007DaxX1EOtqf8Bk64t7jgmXsifXRo+Kz3tA5MXvL+D/0In/nMZzBZw/aWXMerr7zKRz78QXZ3t1E4lssF1bpBay2VsZqm61NVVeR5TmstJydzzp8/z/mds2htcGgRubMtL774Ilprbt+5zVe+8mW0hqOjA2yzDnEAz2hScufwNqOyFOfGtqzrJZPpNIHu+vkWsf5+IbnnVnftW2GV/MVv8v7jye8e+Cvf7JybTSlFXVes1jWuHl6S8xnOj0AZUGGC+pzV2rOuTqgbxUg5ptMxVtXoTLNuKtjepXUlxjVo5yXrspyyXFsqq2hVQd04lmuHbSUFezwe8fjjT/LUO95BVS2ZTqch4gzj8ZTFesVHPvJhTk6W/Pa/+Fdk+UT0ofEolaTAesKWV7a2QkECozN5EK0D1zIpTaBNDcfjx3/0o1y8tEOewbq2NM0aUYrzITtTsFyNYzKZMJ1OmUwmlGXJqCzC9l/E4t+4c5u2EdrkZDIhM4bFcslyseDixUu0dU1T18wXS2bbO4wmE9ZHR6IP4hVvvHGVurYddu+Q4s629WjjA+4J3kejEQN50VgOixHEhzjVhk7nwaZuhVIuGEYzeB8IASzBRWVhiYEuCYoWRd4ZgR67TQswDHcD8aGJx6YwQ/+dw4rwsR8xQJm+t9m3CAnooOkRcxOi0bq32o7roKR0/FJd7E3GxnCxid8ejZ0KBRvCJJUPdeft7pO1AQayyULhwzhGyAdhzSRj0sMlQ/GpdIczWMTyrFsEN+9tB3f5Pqgb31bZcO6sVitu37nNrZsH3LxzyGpdy3zJNSZz2HZMXbes1xV5pjEmp6rmlGU5GG8dgsnOOWjqTrf+2Wef5fDomNt3Djg8mssiaPLQ1zYsahm3b99GKSca3pkmMyJTrDMjtmSdiwrmdDrA6b3vJQ/ifYixh/u1t0nmpEIrE7De4aQviylqPKK1nvVigdE5xuSS4l2v0SgWiyO8XuEyx/bODKsUR6sa1cDEjMjHBW6xxOuSqvHUFqpWibZ26wNtTjEqx1w4f5H9vbOs1ydSrRxFXTWsV0cs1g2z2RZ7+/sUeR4wOo0PRWvDXCNG4F23vZdcXR1KDOM9tC2jrRHbO+eY2nFfCgV46KHL6KKW6j3OBu/Ehm11qPBeFIwmYyZlyWw2YxYWGY/n6EA42E3TsFwsKPKcyWjMdDzBe8+JPaEYjSnyQoJmXpHlhKCpA6WxzrNarVkcLwMft2eKREZIhCUkuBW00kNqYYz9nRYpj//2tDQ18EbjMfJ5n/zI3733Jkbaub5IQeTPxyCawC290tpmVuEmVJM+PKe9lxrHeN2pAU0/k3qn8e94DcJ2ESORJUJT6Xc7JwHhTpNGm8EuRCmFdY6sk1gIAdow+JEVAiHQSzDmvj9W0QdhAaz1nXetQkBQLl7Gs8PItcZuwBvpopH2f3Psu3FmuJNKPXffAzLBMXAdI2hz06GVItMavOP6tavCplH9AjoajVmt12gFRSH9Wq36pPl0Ec6yrFtMURJ4zPOcsizZ2dkhL8Y0jWUxX8iCpUTvv+uDyTBGkxc50+mYrZ2d7vPT2Ta7e2fY3tmWIH3STtuV/LFi3N+JplCUxZjWaZp8eLHj8Yx8plkuF7j2hPE4F7qZ9xSZQQPXbl3h+q0le5fOsH/2HE5rrt45YpI79NaI0UiCmqva0npF1TjWtWVdW+qgsJdlmul0yrlzF2gbhzEF1kJVNRwdz7ly5SrjrR1u3ryLtZ7z5y/w+pVrqNx0WXAQJyvBoETOLWidyeIUtq+zcc75M7s888w72H59D273fW6ch1aEkZTRGA1VVYPWeKXQmaYcj9jf38fXFRqwbSOp+dZy88Y1Vqs1xmRszWaURRnYH/KgGzS729scHB7ivGc8mbC9vcOtO3eYr1YQovrLVQXOY/KYCRegCSOlt3TivcWMvaG3OfRmNhX42rbtPNW03l58iIuiQOk2GBWBoLRRASJxEugLAdSiKGiaJiTqCM2taRoWi2VXUzD1LNPFI0IA8Vp7z3hohN+sbVZL3zTasaWeaRyX07xp54Rx4dueKliOCrQ21E0tC6iWFP1Ma6wKmLcC7UULu2nbwOqQDN8gSyZj50F5jQnFPrJMYi61jxnAdHQ9pbUo+UnsUwKuWcayErmDzX7Gvg1kXtUQ85eMRjWAX1I4S+pHRo6/yEbI2DisHYo0TSdj9P4ui/kSZS14oTb6sGvK8pzlYo2tG7JMFqDlYgmq56enzoX8iPpjVVW88sorlKMxW1u7XLi4RVXVfOOll4NXbFlXDVUllFlwZHkmtuTsWba2t1guV1RVxfb2No8++ihGaynj1o3NcD6m8/O+8+1N3/0uNWsdR4cLFnWFX5hBafvbt2/SjpYY7SgLQ7U+YLVekeea/f0dJmXJUaH4cx//s1SZ57c/9fvM65p8NKPVjlvzFTcOjmjbhqaxqCwwJjxYk4FpKIwo6s22ZvzJP/knUUpz7eotXn75Gxwdz8nzMXXd0PqbaJNx8fxlPvrRgmv//X9PZRu86reu3Q6UGGyIuK9Be4OrK86cO8v/5N/+OT7wviepqhOu/lbGUWK45+sKSpFPzRTQrGhtS6YLzpw9x/mLF0FJllZ1cszR3btcu3aMUqorwzQej5hMpkwnUz79e5/iocsPdQ/P/t4ex8fHAFw4f5HxdIpT8Mh0xh987vMonQmlTymm44KmqkNSjvRMaVlMxFDrwBbJ7vFQUwnV6EELXuo6w9x7rvKw9BBK73FJADEaVBse/KGWc11XZFneeaQx+ePMmfEALonJKwrVQRQR40xhD/kOHxao4YIUmSJp4DGFBFLPqZsXJEFFLxQ77yXmkHqbg+zCYJh7tUCPa1tMJJk2LePRSIxeovsi2aQiP6sSY9i2bQhWRjqeNOV9oCeKZkqZ54zGBdb29Q9NWZCZLBQhcFJn0Un2cArdpItS/DtWc0kXt7aVoHxZjrqyYdroDs/3HbVQoJfxeCzVaWyUDeibUZ5qOecrX/wC40IqUTVe0djAcvEirbpUko2rtaKpGzx0Eg4xMJkKxFVVRdNYnn/+ec6dO49XN2laz3g85l1PP0NVVbz2+muczE8AzxNPPEHT1uE+K1arhqo6pKpEonW1rLhz+xAFPGptR3x2TooFRx2m2N72UEkxGjPZPU/WtKhhpA/ta2hOWDcLsqzgfc8+y2OPPcZ0OqGq1hwdHvOl577GldsHFJMJhZnQNkussbQRG81n6AJohKDvnfCHrW9QmeHo+IQ8Mxwcn/BPf+OfcenSBdq2Yb2uaNBUbYP1lsl4grMW30rJrj/xwx/mX/7ep2mdRelM9Im9AS80NDEhKtAAHS3gVMvaVRzPj1gu5xiVuOuh+bbFa0miUWXBdGuPc+fOUYxGFEVBlmUsVytca7l65Sar+QJtFFtbE7KsxnvLfHHCcnFMM9tiXBjOndkBY1hXNfPFHJ1lbO3usWgdt27c5uj4mGq9JguysR7Zgq5rSTRCmcCnVYET5mmdJ8/EsMTqNqnBkgksuxmInpgEbAXmiIYtQhw9Pt61sF211nYPrQ3azpEd4TuFRtNhhWJc47kl/mDC9rRtbWB0DDMQ62bdfT7P844Xvul1by5QERtN4ZPYNnUp0vdwkjF5+9YtJpNpwmbxsotzYhy9U+g8R+uMpg3MEiAzoq0+hH0Q3W2XYxthdLThWmXXERcNqauojEgC+GDGTaZpbU3VBkxbS+DZWWidR5mCQLenVK3sfpyMr/Je9F5cWDSQMl/eiuSuDvffuyBFbBTraiHMnKCv0rSBFocClaOVlC9s1jXKW4yz8h1JUwrOnd3lEz//cd64epNPfuoPqFctThlaLJkyWOdDHUqBj3QoUu29p6ob6rpmFHRKXGD85Llo3lerE668sWI63WJnd5fZaMor33ieyXjK008+wbvf9U6ef/FFbt66TVHmNGEhMlpTVWvKrEArWXStX/O+Z39oCJUohD4c7ks65+7X3haGu20bjk8O0SanHFSclIrn2/s7ZNk2ZTni5OguX39hSV4IBevWrTtU7YrX3niV1ilefeU18qygcaGgASINrz1YLxFoHzw36xzaSZ037xxV07Bcrjg4PCLLxRNrnMUpCSZV1RrlRdTeGMWjjzwMn/wkWXjY2roF73C2RSuPNmLjjDbdjcwyw2q15LOf/yw//P53DLC82JT3ZFoLre/iBax3lGMpdhANxMnxMdeuX6darAS/nkxxzjEajVitRJTdZxkoOHv2LMfzE5wHneVs7+1idEZtHUeLY1brKnhFIjIUKzxqpcmMoRM0UghUEzNjQrQfhnUbUyy3D6z1hi1uDY0Z8lada4PhizK5miZoF/dBJAbnhiGdLMWcI9VrM906/YwKBl1eHz4wIjA2DKzFBSN66dHrTo/pgpsJRTJ+b6+7HZJcnGc6mfYG3UvRAQWYsBAqpQLV0WNMXCTodilpCn9cdKxtB/fC+yHlUGnVpdTHOIoOC3I5KrG2GeD2WitWy3UY/8AK8S4xNnJcpo3wvsN3aB80yKHXhInjG/jnUVlTdm6yGLfO4W2Qww0fiNoym8HJqOKZa89sOmY6HbGqF7St9Ktumk7/HGSs2tayXC472CYVsfO+Z3o1TU2eF+At85MjFvMT7ty8hc6Ekvza66+ijaG10NoWu4rQlse2otcit1sHx8dL0Dx93pUSuYiq7R0NvgcwbonMVjS2wi4bSva79yTvv08TXy6XrNeyNQbwOHb3ply99hqLZc3h4QJvpZKHc8JNdcQU2l5RLW1Zloe07gkPP/IIy6UUBbDhM04Ej7FNjQpJErZtwEkKbhApxUcfW2siBxaCjHCeo72YxaatuX7junhATXvPlmh3d4fJTs7W1hZbW1vi0StZvVfLJXXdMD+Zi5aGEiaHCsyKqgqeaUA0q7qmHI2omgaUqOJpLaVKT+Zz1lVL2/b6GyriGSrGvXtjG7MF5SHVIRA2ZGjcG2Ac6lykr6eBvvT1CHXcD2rYDACmkfhNhkV6TfGcKdSRbuujkd+EOtL+pFS3FKp5Mw8p7ecAS0VjcT1kdEpf0zbEjOVfuScRJ1ZhYdG0bV+oII7NYCGBoH6ZLIJKnjEx/E23+Ml0iMyOnjniIsc7udZBgDXZPaX3RrIoLRYp55XOkQ7WwoNysntFBUaZBGs361cq5dE4Mq3Y2poyGY/I55U4XS4KXpl7HvwI0Wwuqs5ZqRZldLcoSzxG2FptY0GvyfNCMkuVYjQSIxXT9KPdNVrRNC1Oyw6pLAuKQHroJ0hPP90cw/u1t4Xh9t4yHiuOTxbM5wv2EsMt1UakmkYMZEUcarVaMRqXXLy4z52vvcByKdzkplqj8y2UyhABJYuzLd5ZVHL3+gfbkGWGvb1dHn/icV584XmRShVAVvQ0WkvT1gJjhFV6tVgwKTPWTYsFMg3ea5TOcb4JK6d8X57n+Naivaz4TS3JG6t1pK/17cKFC2yfGcsDCRR5wXKxoF5XLBZL5os5TdOwt7vLcn4i42NbmqalaarOg7StxVlLuTUhQwU8MmO1rrDOcXR0jM5KjDI4Ik1NDfRiIsNAG91VfpGSYkqYBq7HoeOYpuMbsdvU0G4azdT7jcd0GWeGwXlTQ7FpWHsxKt1d08CQJIY8NaTxgU353alBTo2pMYb1et3h45ue9WaQaTP5ZzD3Etrk5mKRjk+8pjSImR6XjvFpfe244wmU5fGy+wz9jtcZ+5Ri7qIqmAceuwvQ1UYyTdLvTWO+ed3RKfJKd9+dLmrOhcCzcngVxNZ0WESck+LfaRNcCaM1W9MRo6KgyHPqFmrX9uf24oB570Pewb0L5NDJ6L3eNJiqlKIJ0GDvnEgmsLVyvUoJu6dtWzSGzIgWyvb2lhQhSe123A3ROxRhlpw6d+BtYriN0Zw/v0Vr19R+NXivbetQCcKzs7PD1atXOTk54ebNm7z2+mvs7O3wwQ9/kJ/56R/HUfDaG3f43U99nnI8llJoCkACJKJb0UogJggbOefIioyzZ8+wtbXFH37tD7G2wbo2TNqWer0E51ivlyGNWgSZ6tWSp558iCvXbnEyX1PbVtKMbUOXyoZHeRFlUh7KTAIis1lJVVWhIMFQRGk2nVLXIsGa5znaGL74heeEVjQakWUZtmnJtETZUYKxgyfLCkDkS1GanZ1ttra2JSFnVbFcrZkvj0VzQxs00XPWGJ2FCjOBvqUUykj2Y/TGVcLl9T7qgPRBxRRCSB9+GHrap2UzylwwXYAVJEEkncybBir9bMxghCFMkwo9xe/epKalx0SK3v2w7M2+RG92M0iXBi5TrLvvz5DJEher6JikjkpcMFerVTdO8cc51xVjThetCAHEv2PykIuYtBpmbWqtOxppWeYb51ABThvuUtLFJV5zTCBJDX+apOS9xwBZWXbH35PBqj0xgc06qc3ZhKpPJh+WurHW4uuaFkVTW3QWdxmug4Csc6hgvL33XZ5Fes/jvZAKNVDXa2xrQ46A5GLE/jjlqWswmWgPtW2Ns6BUJhBRqO15crzgoUsP89STj7O/u4XWlqqqN3bZ4gShOHVcT2tvC8OttWI5P2a1OJZoc9JG4zGmcFy7dpWXX36ZF154gd3dXR555BE++tGPyETNRAdkVBY8/NBFdrZmtF6hjayETduEFbNXdXNOqD6j4EHNZjPOnBHN6aqqqZs1q9UKb1vyzLNaLCgyzfHJIavVHNtWlEWB8TWjzGNLKcQ7X0rtHJTqOLBRpKfIcrSGyWTEY48/TJ5nPPnUk7RfqVnSU5zqumZlFxweHnJ8fMz85ISyLDFaU6/XrKxlNpsxLkuuLebkeUae996i0RlFnlEUOXv7e7zy6iu0jVQBkG2nAePJTNGpx2ptguJh5PbKg5wVKngHAYtXQrGKQEo0YNZa6iCqH68jGp60pR7kadvB9XrdGbAI0cTjN43q5jnjA3aasdx8CDpFv3Ce1NhsepApzGCtpSxL2ralaZoBtr/5PZuGffO7AWzrux1GnzGnBueMn0kNdWpsIj57GhSV52KAq6oaYN4RDovc5XRBTSmKEFk9BI87CHH5Bt8OF4a4Y0l3EILPy+tVVUkgsijIFVRN231vOm6yYDSB/SOBotnWLug566qiaYaLvvWiSzIaT/jGC69Qt47WOeqmxjnZNcPQcEccenNXY60NC1movB52KeL4xSC6wiL4vG89ylnQGodGKXGYVHCERqMRV69d5fq1K+ztznjssYd54slHN5zpKJ5mJS6GIia03a+9LQx3xLSKPMcWQ+/z5Zde5taVl7h15yY/9mM/xk/91E91VDKlNOt1hco8rVW89up1vvr8y8xPTshHinVVh+CfQBZKidQiicaxMQbvWlarFcfHRxwfz/DedloUtqlo1w1tXbE6WXPnzk3wDefO7fOxn/hxdDbm7sEJn/3cl/nkpz4nOtwgEplKdSWtsjwn1gZUCkZlyWOPPY5yDVl2CInhfumll6AQjzPPc0ajEUqpjroEdKm6u7s7WNdQ1Sts65lMtlgt1xiTA4qDg0PqqiUrcqzzol+hFQYTvC4J7iilKPJcrj3ivUY0maO626bxE4paL4GasjpiS+GH9GEe3H3fBxPjgxypWRHaSuGBeGzqiaffmWKWp3nncaHZvJZUcwNkZ9RubImjQStL8foi1e20zMkIo8RrStO/wzDTxxD6hSJNREkx4s008njNm8emEE/0oNP34+8mz/DJbiEuelrrLnCntQ47mUiXk+vWqsfWY0thhs37MsDXncOrfnzT+xJbU7e8+uqrfOWrX6VuLSYvRfhKaaaPP8FHf7GfPyYrWFSO3/uDT3P9ziHzddOxobQOuHjrI8UI8KE8nbtnTomRrjuHIcuyfjcmV9odLzsXISWJrrok5CWHYa2lyAsUjvW64tVXX+XmzWtcattEB7UHxb2LfPnvCcOtaOpWVN42igVPZxPMuQvsndlhMhlx5sw5qqqiqhsWy5UMxuuvcOPmHQ6OFtw9WspNaBuskyUhFouNIUO6zD4jhXLbiA/XrNcrmnZN09SyYjcVqllTV2t2drb4wPvey3iUU5Y5Z/b3mM522d7Z46VvvEZdV1QNqGwiw66CkTR5dHDCpNZMZ1uMxhPq5TGbWNbrb7zBbK9kOpkwGo0Yj0ZkJqMsZbHyAdOzbcN4XNK0Wip8jHKUlkrRxmS0VhT7tDEonaGUx3gCrAKEMUmx1tTb00aCQqhYmFZaH8QMEzZKk6ok/hOCXihhTrj4zJAEDhka7RQLT4Nq8Xq62aKGad9pOy3dOn7mtKBPhBdiBZLUa43vpxBE/D2FVuLnTvt8bINFpvtu+r+Vwit6mVor6eYSSKbDZuPxAgGEhcS7LuYgf0sEPqTbgPddYYeYxo/WaJOBFThQIQFua9vgIJhgvCSDU7BbFYL8PihS2sH9SfsP/W4lvj8IwEGA5xIWUljolBcm1vb2Do88+hhN67h994jD4znOQ14PPe7Xr1zlpc9/kdeuXGVZByE6pBxbFCDrb4eKtptY1R6f6gtFiKUf41hmrz9PutsjSPbK+IiyIuAUXoW+GxNozkI9rOrqnnmrVI6nFePvo7N5//b2MNwelos1q8UKVw097vPnz7H93rPkmeHO3bt4PMtVJUp1iyXz+YLPfv45Xnr5NRyGrZ0zaKOk8gRGKrI71d25+PhHRkTVtNjW0Ybq1G2z5vj4DuClfp1rGCnH1rTk4csXeP+z72W2NWE+P8G2Dc62lEVGXmR4JRM6yxQE3WDlNUbn1NYFcX+PdZ6qamisY72qsO1QNGd+Mme0ZboARzkaidejwgbPC/avw/Y0zzVa53gnHmBV1yjVBH55js4KfKK1HFXp4nik0MggPVmBsw0608EwyL3KdEh5bqM0q2B6MRQr3yFUNUdfNADoFh6tJAkKf2/SRryW6GVuJrikyS+bxhPuhStOa5tb9NMCjOm50uBalmVdDUYJBHOP0fJeVPu8CxXcw9jaOBbyn4yZd0HITNHXOyVAUgJ/eifGRSiKXoqemUyyAyEk4IQ+4YP8dw/XOaXwQb7Ve5n/Niym1gVJAe9oGkse5A9iOTNrhVqbZ5lgvlZ+UsOcLm5xvNJAZfTO0/vnnYMwPh4658B7T57lXL70EJcefoQ7dw7QX3+VurlG3TjyfEgZfu3KVT735a+S5QVeGQrTBwhV50wEGyDeE50n4REOexKolHmR7JpsgKGUAu86pyUuAOBFfyhSDl08lw3PTXhPa7QxgTPfzzHnPfPFmvGswLsW5ZM6qPdpbwvD3dQtxwdzbt64jr2leZj3de9pDVmmMLlmOpty4+ZNXn/9Gleu3eDK1eu8/MprwjzQOQrDyXxJ3Tga26JNjlKmA/5bZzEqJAcoFVTHIC9LrG1Zr+a09Zjjw5vMtmesqwUjo3n44Uv8zE//tAQYlWe5WMi9sp6Dw7tMt3bZ3p5x/uIF9GFN3WQYlaMC/9tHo60hLzLmiyW/+du/zf/8P/xF7ty4wWqlSG/FhcuXOHN+yqQckWlNOcqp6pqqqiSiriRgmGlFYzVFMUIbz6uvvY7CM5rIxHYucNdtfDSi8XRdxl7/2lCASQI4jrquGI3LLtlFaYVRUkBBST0SWRy1og1b8qjzUFuLsxvCQkEHxTlEuc73C0bqfccApcmGxjoa7IgvpwGvNEsT+pT6TTpgakCALmstXbh6NkXPlkkxaxAZ2WjE08VDFtZWCjgDxmQCXYXFynrVGWKflKhDKSwiXOS1ZCCoUHiitVHDQyhmGI1THi8SHcG7VkESQVT2om2Q7X9L61JM1+KrOmTrFcFL9+KFK9MFMft+Q2sdbVhMUyZONNIx/pDGIiLVEXpKYBzruq5ZzEXsKWqvZ1rjlccqh84MVdPyyU9+kjeu3KQoJ1jraW/eGtiPrBxTbu12c6FuapSV5KZMKxpFqLIUdmkuEHhVMKBedpbSz8Cldl50e0zCfgrG26PwVu6V1iL8pnS3OUWC922gNEq+kFZCwXVkmGIYXF2vK37/s8/xYz/+EaxtMQokZPU2D04uVwtaV/HRH/kwu/Y8N/9x/971Gzd57l++wBefe47luuWJJ57i6HjO1Ws3eOONa4wnY9ZVLR6n8rSuRemM3GSgpEiu8p4sMxhToLwVip93eCspvpMiQ7U1y8MD7qia9fERs1HOz/7kT/HIw5eplyecnJxsPJwyqEZpmrrm6affySeKGb/6d/4+o0mJbeMDJQUFrBdqW+taNI6t6Yznn38R3RxSN9ukt+LyQ+cZTcG3lmq94tqNNzhz9owY/izDZJk84M6xrtawqEN5sIwsSFLGSiuuW+x73HQQLNKa3GT36HXEBzZm86VGLzVw1on/EI0oDOGNTQGllH2h8AL5hMDmZmBQd2n292Lkm98Vj0lx8tjSBIvJZIK1lsViwWKxYL1eUxQFDz300OB70oVkkxVTVRVlWaK1MGDSRJ94fPTQdPTinKPjNkY4BUJRAzXAodMx3uQZp1h62qTvLhRjDh5ft0Mbct3TRaiu6y4bN15/Xdcd9hwXwshmid5zWQoMFx2AiLsvl0vatu0McXq/RyE9P15HlmXs7Oz0jkISwK2amvVyQWs9f+ZP/yzr2vLyy6/xxpWrrGfjQd+zLGdcShbkerVGOaRepZdYhkYzLsc0bROSmxQ6E4PtQsDROYWnFUcvwhQ+TfwSMKVHXN4cg+7sRGDIWOdAe4xWrJarDZPssU6KdO/vzyjKnFPqhQz7/OZvf3fazs42Tz31GFmmWRzPIVEr8Sj29s7xoz/2U3z+C1/kD1/8OlXVsF7XZEUOypDnJQ5Na6FuQjq7cmRGtl/oUFbJtWBbYto13pEpz8h47PoEN4ZHLz3Bs3/6p8nynKIc0azWIcrrB55GfBDyTKO8Y3s25eL585ycnLC9P5NgJDHQVolMqoVMR5Emze27h5yZiphO2lQoFlw3a+aLE2ZbE+q2kiysLMe2luOTE6qqRqmR4JEosmyEbR1SUkx422Whqeqm8x7jwxkDvIXJUPRBtgHzIAR0q3UjHr7JyDItGGcTeKs+JCCpIZ9Y+m1DHcp+8nf31UuxuphpupkgEo18DCRuniP1wOPx8f3ToJJNKGg2mzGZTAZ0xXRrn2K2m9cUaYe9rKy8n+o6O+ekf0rAfw9SXispvea8ow76z91rCQ4clepgyK9OtUc6Gp2HtnXd9XnvUWYoBxvPGccp9i/SUjcX2s25HpkjcWzi8XHhjfcpz3OKohiwXVL4Kz0n9Ikv8f2ILctuQDEeFbTtiosXz3Lh4jnyS5cHT8wz/+O/wON/6mfCGLuhgfUQtWkijETyvo9QR3d4GngYnIT0g3JG1b2W/k53aHxtGGdx3lPMpqRHgpASrl0zXLxwjssXzm+GvgbtbWG4x5MJH/jQh1gsl9x9YcGC3ov6xsuvcuW1F3DWc/P2LY5O5lLY1sntaNqWvCzITYZvPbVrwesOcwXxqp2LGgl9NLnIDa5Z8fDDl3n6iUd56OJZ9rYm7M4moI1ojNgWsAODkHov01HBZDplunMW8im7uzsBOA7BPCfJBFFxzYXghlKGum7JdvJ7vKe2bVGNJCloI6nFpSmpW4tbNzjfUNUORw5eEUoAor3vg68EXFQNjWVsqdeV9iv1LsXQScBFfjcdDNCdk3spe/13+VONNkRGSs/uiQYgHd/7BRRTrDtdaFJvNz0mvbbUq08Xsngd6bk2DUzq0UeYadMgDQy/75ONXBg3F7fmMnhDDz3pX/paek+ih3svru+66+nGWEle7+YYxu+MY5EuXJuGNR2zlE8e/03HLX4upQVu6q7HRWeT35/yz7vzaNFRwTtGZY4xM0l5355ylJxzfGaf8Zl9vmebFzaJLPwjiBDa/ZGSb6kCzq8Cfw646b1/b/L6/xIpmmCB/7f3/j8Jr/+nwF8Kr/+vvPf/v2/2HXme88hjj3F4cERz4xbXuNO998Yb1/j8nS9RNw2TyQSd5TRtMAhaYZ0lQweqkOuwPqV1yADsecg+MSSikWCwON79zDv4Ez/8Pi6c3ePk8C7ONogIUqyC6JLP9Z5eURRMJ2N2treY7WzTqpILF89z62Apnmhc7XW4OSqUyPUSPFpXNUU+IxYbiG21WtN68diyLJcgo8lp1i11bbEWWqfJijLUrgsUNzY9CheZj/fgxLFFfvsmvCDHi1ATRO+TwDDwnWBTcCiB073duFXeXBy01mFiDrH19DxvZrTTnxRGiS01xtEbjP09bcGKx6YGftOQx3/7xeDeIOdw0YkevoKgbuijdoX33YK4ubiljkE04GmiSnpcd13EsRwueqlqIDA4X6Q6ptTKaLxTCmTcnaVxgHgt6WKbwmXp/Urvafq5TXpjvMa2leSyGKx0raSga5Ph8dhmRXb1DdpLD31TyOLt3ubXb3Lzua9QFAV107C1tc1kMkGpxPs/pX0rHvf/E/i/An8nvqCU+hngE8D7vfeVUup8eP09wC8APwRcBn5TKfW0997ec9akWeu4c+eIxXxJXW2UJbIKazVal2T5GGWkfJBXbaBLiSTSuhLuZl1ZMAqlJNvKaN3xJY3xrJdziiwjzwusrZlMSj7ykQ+wtztltZ6TFxrbuuDRi05D/J74IGVZxmQy4fz58xRYRpOJsCSs5fLFi9w5foV10CqWyuaBThcMX4OlauDW7btk77x4DwXy+GhO2UCZF5RlyWQyZV211JWjbRUeg7UeZWWxUtqHbC1F0xXh1QHbVh2jJH1AOraGcx3m6r3vkjziA9i29p6HL/Wi+i3nvcYkQkUpvp16krGEFoi3MRqNsNbRtk2vNuicFI9NDGKKh6ZeYpTFjP2Lx0RO8mQyuQc6gHsNdjTaqScdj+thktiXXstis0yX6mILQ0/fu6hwaAMFb4jLx39jRmP/LNiBce3jDmG0Vb+YZFkmRfPc8JxpIDH+HQO98b5HsaWiKMjzvPOgYyCy1/Rwg3OCOGFxRxbra25CaFprRqNezjVdgOMCUhZCGKjqSrRJmsCuAfTRHbb/6T/i4H/6lyAz98AdfetmZxiD/p30GZA+vFmmYvq6us/rp71/n5Yc8tI//U3+4G/+33jo0gVefuWQ3d1dtra26Gkrp7dvpXTZ7yilHt94+S8D/4X3vgrH3AyvfwL4tfD6y0qprwMfBX7vzb5jsVjy63//HwKKyWKHGZe697TJyctxt91s1lXAiDN5aIy8XozGlJOMmdc4nTOebKNMgXYO2pp6tWS1WjGbbWHbRvSbjeYv/IU/j8rAa4cpNfOjJRpF3ThQofhsGMDJZML+/j67u7tMp1PqumaSKVHuMyP2nOFHfvRP8PmvfT142oFSF4j6rhVcXSFR/he+/iLvefwsdnUB6AMu+/vn2NoVHeyDw0NYB+U+VWByjfOG2bhkXdUo04L2+FDWTYdLDgxemZxqSJ/rxjZs5yOaojfElzpPEAbKhGVZBtpW0Jwg0Jc6SC8aeoBh4C310tJWliVVYDnE7xctmmJAjUqhiBQGiedOIQClJJCYGqFOAyVhN3Tp9Ruc8VRND4aslN7I9YUgoheZZneuVitEZzwXA26liDFE9g3UdSO8Zq27Cuxi4F3ndcn98EEiIWDCHdTTUleSjp5leWeUXFLIImLPmwFPY0yX2JXufOIimC4cKUQW+1zXdXdOa2033vF+xPs9Ho87CmXTiIzqeDymqqrBghlFrqrVCuccRSFVneqmprHClFFa0zZLtv/2rzAeT1ivliyWC7k34XmN341SotnjpNakc46iHHHr9l1+45/9D7zr6XeT5wWvvvI6t27fpamtYORGgVYdM0lFHDvOh9TQRwhcBR5+96NDdfeAjilBBkRGVwo3j8sxZ8+eYX9/j2vXS778pS+ztzfj4oUzf2SP+7T2NPAxpdRfB9bAX/XefwZ4CPhUctwb4bU3bXXdYtQUvGejuAVeWaxu0Bqp7BEYbF4blC7A5IxG20y39zHlGKcyHIprV6+h1BrvLa2tsXWNshblWgrt0dpRr4/5hb/wb3Hz9T+kWayo65r5fE1W5ORlKRVEnGdclOzv74smdpjkTSO1Gl2Rc+PWVW7cusvVm4fouiKnpTUFtQPbOnKtUV5hlMJb2SI3eE4WSx5+/HHuvJazSjInnYXDoxPRDzYlHt0ZSEGgHHUj0rHxJY8S+Vgvs0QeHC0MlMRTu2fr78QzNMbgFbTeQShSoBAZ3B5mkQo+FgR/D96k9w6dZV0UXkV6m5cAZEzwUR6hRhLpcpI4YrTBWpEK8F6hVB646aIn4WwfYDKZIc/y8EBJYDcNYvY7hbbzFItC6nDGtO/YIlyQtnQxaNt2kK0aPdH4Xb22eJoe3lfzOTg4psgLjFHUTcPh4QnjsUi4msygjKGqG7yP/F7wrjeUzkd6mTAfQLGu2oAOKNo26O14hSXAItqIjKrRQtuMME4wGlopKc4h4brOi47XHHc+Ka1yE45KoZwUVoqfyfP81NhGSrVUSlE3dWeIXdvStA15WdDWLXlZ4q0PrB2HUQXa9EyVHGhtQ+3mKO+ZZIXokRDEtJS4Ls6KwJwBCmNo8fimZmdS8uf/3M9SFiOWiyWFaih1w/b2HuuqZllbFlXL4eEhptQ0TdsZUmstJstD9mUYX08Y4x4mk+Qi0EogTC0znKOjAy5cOMtoVMr42xUvvfQ873//+0JdWI/GotUXTrGW0r5dw50B+8CPAB8Bfl0p9eS/zgmUUr8E/BLAbLZNFNJzbrjKaCVauyLJDgTvS6FQJkMXI/bPXcTqjMYpqsYyXyxx2pCbAMBqJ9zttiFzGu1qJsWIdzzzBFujgtFDD9G2DcvVijt372ARrYPpbMZkNGZSjjp4oK9CDetqzcHBNY6Pj6malvGo4JFH9oW9QobRgcvtRVHQx5T3sJ2N3tgmjruuKnAyYR0KZXJixRd5amV7HAOQOsGvNWbwEAGD7X70hKLX6FQIEOlgPJzrJqSi18iIn/Uq0MwGeOyweG73sAZjDQimS48Ra6VRudTglNdEsrcoio4zLunVUT+lx2wjz5sQP9j0vuN1xGtJoZBk/p2KkW9u6aP3Gs+dYtzxtVR7JB4TNXecFz6x1oatrW1iRqLzPtQUlRhFp02huv/FYeugAO9jVl/YAXjQOiaLqHv8sxTiiTuMfhxkl5R61XHHEHcuqeFO+xzHLcpCRE857jI2xyO+lp7LOSeZnBFyCnPQ2uigSM6AjvBTMraqiz1JFaC4i8qLAhvgmTQXMl6r0RpFRmsts+mU5XKJdy1Zprlw/hy72ztkWSGJfauK40VFaUSY6+TkhCZ+jw6Sr152T/F7TDLPVNAyibCP91CORuxsb3PZnme5nlMUmtl0zGxrmyJUHcpyTW50x/u+X/t2DfcbwD/wMgN+XwmT/SxwBXgkOe7h8No9zXv/K8CvAJw/f9FLh13HNIgt2Ilu8spEJSR9GPJyxO7586ispLawqGpaD41zgEV5qbEIHuU0BQ5lHbvjMR959r3cvXGN2TRjNpth8oybt2+ys7dH62R7Ny5HjPKi2wJHL62qKhaLBTdvvkHT1IzGU7Z2tsnHM+GKe4cyAUII7IGwWZB+IWJS66rC2lNYJcZ1ky9SnOLnZPwi7SlmKQ4fss2gWXwQUyOXPrTdZ8IWHeiy/Qbe1/AeJvdpOMm8lwVFJcek15NqXaSLQ3ps9CyHAT/VY6Oae4xP7/n2ePMm3JGea/h99wZCU6OejmUfzDstSBnujO6LLCulGE3GtI2V5I62pm5bSbAhSZWnj/YO8XJh98jfUbJBdfh5d3yE6BguTptQkvdD7v3mwpaOR7qYnTY+m+OZBnY3x6x7/ZR7kMJWvhtDJZ6sl12eUlqkK5wjMwqbaLUYrfFhUXCJ4U5hGK01ylqyzHTjaYxma2vGmf0S50TYbrKumM0aZqOcxXLJfD7panhqrSGUcatrKertA5Ggw/sVmDwDNJPJGK2UQK1n9inyktt3bqC1ZjKZsL2zw9Zsi6PjQ7xv8N7Amxht+PYN9z8Cfgb4baXU00CBlLv9J8B/o5T6m0hw8p3A73+zk8nkCZU4/OYDLK/jJQintZYMMEDpjHK8xXh7h8uPPk4xmrKsGu7cvsMXPv9ZTg7v0jZrsDXKOvLGYds1I1r2J2Pe/eSjvPSVr1DMch574nEcnldefoWfePQRpltbkr0XvIrRaERVVdR1zcHBAdevXw/81xPyoqAoPd5Zlotj8kxRt67Tm4j3QDBlDU4TK/AsVyvaDVnXuD9WWpNpQ9OcLmWaBnPia3ESp4GflCGQesYpSyH1LFMjH4Nx6UOYPsDpljjFT7tFQg2DV5vGMjVMk8mkYzlEoZ8sG2qSpGnwWW4SdstwbKKXlzImUi9w8/vjDiS+Ho8/jeM9NPLx+yVxI1YLkjG36LhTsw3OK5wNmaFtG+qIinpf3MlFQ6rDNrurJpMsdJ0BpF/E0hY96825cZohj7+n3OzYx/h9MTkqHbt0fqTXPazb2B+b8t2VUlKZnntlDKKCYHePgDbuAoKRNdrQVhVZLjx3SYbqYxbOOax3WOcGKo7pWNR1kxRx6MvhKZVRFjl5nrG7o3no4j6xOLXo0GvqqsJaz/FizuHhEfMTkb/IMsmO9QiDjEwKKxRFKWqdZcmoLMmLkrPn3hEYWi1ta1mvTihygbLwAvG8men+VuiAfw/4aeCsUuoN4JeBXwV+VSn1ZaAGfjF4319RSv068FWgBf6K/yaMktgkmaHBuQ06EQpDKE6be2rb0FqHKUZMZztMpnu8cuUGL7x6jWI0Zrq9Q9PU/PjHfoyvfPE5rr72MvPDY3LvsVXNJINHLp7jA8++i3e/8zE8LS9deZXVcsmFixf5+Z//edZBBKbXR864c+cOb7zxBlVVsVwuOT4+Js9z3vOe94LyFEVGOSpY1Y53PvUoV26uOFq0LOuWppXgRtu0GK3QRtO2FdV6TZ6PaEwWhkua9RI0FC5Ub2Q2PWmZ9N196iZwTKaI3ml8ACNDIOXvbnpZQId33o+q1sMBPeYb/06NvqfXsE6LDsQFIs3Oi4vjarXqziuSpD0cEYNfErQqcK73PGNfU4Mb+54uTv18G/K5Y/LMpgeZXm9MiEm3/s457t69y6VLlwLeT2e4dIgbWOdkznobjLFDGU2uQxq8c4MknDiWqH5BigHP1HB3afDenyp1m3LOU9Gutm1RQJ6Z/u/kszE2kAp/neZxx0UxXZjrumZ7e7sLUsZrj31MPVLlVVfNJp2vwogRY9q0LdZZaivZmBYvBXm1ChnC/Y4rvZb4tzGG7e1tiqJgvV53RrwoCg4PD8jzgjw3GCOaLPjgLHiH0eL1Ox0Ne4u3nsJoVFFQFjvsb8+656hpGpnv3uNEKYv5fH5PgpVr19gm7DyBMpf53wXmHZwSvx+0b4VV8hfv89a/c5/j/zrw17/ZeQefcY75/BjnHKNq+J5WojegFWRGtvKz7R2KyRmKyT4XH3qUy0+9k1t3D7h7cMjJYkmW5XzyX/4uy+NDaFq2xyPsekVeGFaLQ579wMf42Z/7Gd648SpPPfU4Tz/9dIevHR0fs64rskICYIuTOYd37pJlGQcHByyXS7a2tnjPe97DfD5nta6ZjIW6tFhUqCznmaef4sq1z9OsK7TK0EbjavEABPMJW09guVrRtMWgz00jUAkETDrxhsIYJzKcamBk04Bb9EKiAUr5zDHav+mJR5x3E7tN7u/g+M5IJ9hveo5eqOje5Jo0C2+1WjGfzweep6SW9yyTuJDGBSrlHsfvjAtTXdf3FC+O17bZnzSoGV/zXjIK79dH6YdCq4zZdBuF6SEmDG3b0NhGdlhKFuu8KKjWobKSEvqmTYxeZHJoHQsa1P0cCF55vG9ta0Hdy/5I7+fmbik6G/E7NrnhqdGL8yfOq8geST3rdB7G74zZqKkXHRf0dHFUOgTckx1PvH91XQeRK+lDZAOlu55yNGJxctLNZXlumm6sokBXuihrLXx0ISGcDPqtlCYzirYNOw0U3ovDVXexCDrGlo6QVKgPmWc5WRGCsgmkMhlPIBj12IfxeCxFGWxMbNKMijHOB/qm9x2D5X7tbZE56RENXGstZsOIFYHLbNs1e3vbPPvB93PhoSe5eafi059/ke3tPfK85OKFS+zvn5P07qZmajyLwy3u3rrK4a0TXCsMhB//2I/x7h96hu39LfbP5KzbNZPpnojwNA0YWf3uHh4INLKuqFdrTk5OGI/H7O7udlrM0+mUqm5RehwCeTV5kfOup5/iX/7uF3Ftg9caQgVnCSZKSn6eSwGH45MTsmYCJHCJ2pABDcYm1YuOhkSpYbWUFM9LPaXTvPX0AQMGr8XjTqsEk0IFm4Y99cy897S2vef91BCmFLuUCRK/OxqqFLuOn02/P54zhQ7ieWMALV100utIF7F0vFMNkvT11AsFhTFZ2E7LzbPWhQpD8ToFq+3qnkIX7zgNk9+kJW7exwGm7Xs9kHQxG3Dtw+ejVogs6KaXhT1lHqRjsbnYpfMghcfSc8XxS/uT7rzS64/nj/cvzsOwncJ78byj/g5KIKQ0yzL+xO+wtu0MbtRVSSGxqAUu9y3S/ZIScFpE4dq2JcvLbsx1uKbWelpn8SGRz8fPACbPUc7J3PeEotuZZLJ6CcTTJZ4pYiatqDUn+P+poy7tbWG4QXQbrGuxG3xAhWd7NubypUe4dPkc733f+/BmysnqNqPJlNFkBkpRFIId5XnD4cFapFK9l7rlSqOMwaiWj33sR3nnu54gH+dsTUfMj49pbYtzlta2rJZrFqsVBwcHUv7LSy5mWUrUd3dnG6U11bqiKEqO53OUktewmtzkPPLwZTIDrq3xRm6SAgm0uBh8Eq3g6zdus7fcxjAsu4WSxBNNTJmOxVIj9heMb/As8FJ02fthnUMd6GGdsQgshc2Eg9Tgpg9UugjE9zbx3mhkTzuXiw+WvNF5m5E9ERcd531XeTwyTX+23/kAABncSURBVIQ/pXCtMF0idOCT77DOdRKoXVwvPsh6uItIDXGskB7phILBKuKQnEadjL/3fwdoJMux1uOSB1CCqhIAU6HCd9vaLmC2aRBTYzn87vA9zqNNXOwStkhgAnkv0Fp0DoyRPAe5DpmBeUINVfSp+qe1Qd/DPdSdMREKp/exBFjsdxhLO1z8ofeK47yxVoxeNwbx/EoF3XA6bjQuio2FMQpxkzgeJnCjbRyHZEGK35fCaEopsjyjquswl3SYMxod5CpQXrKcCTIDtn+GlFIY5Wmc5EjEBc8GeEzhUVayus2GkxF3z84GnfzOPCu5f1oot/dbLGN7WxhugXIdzrewAYmPCsVj587yb/ypn+bs+bOQjfhXv/8lXnrjgHMXLmGKEuc9y9WCqmqYz+dcef11Xvv6C9DWaNdSjkZg4dzONh/64We5dH6Xan2EU4rJ1jZ3b9yVrXldc/XKFVrnqNZrsjzrqj0//NDDHJ/M0dqDEqU/rcDaClEIyVCUFNmY2WSKVi3eVaAyPEakelFgMhQiQu91yddfeYOnF+fZZ7vrs9EaG+QllQkLj1dDT8+Lel6kh2mtsI14KbGYq3OeLO8Fn8QrU4ke9zCgl3p6qSe9GdwbGOYkALqZ6ee9R2UyOVOVvLjYSDeC8QWyosBbF4oaa1Fzw9M6qewdublK+WCEWnwrD7DzItdrsgwN4aExwYiJgY56HmLAe82PpqmDERMmixgc1+1Upa/S36Zpu4owznmsl5J0TdP2mtpKGE9i8AL1UWnayFtWvXBWOrb3LBBOjLTHhxhPLrzmkPSTmYwilzETD9WHBdySZ3mIrYghjEFToRJKJXOth4HWaOy6+06/wCqi5nfoYzBIbSzf1g2WxKvMhhhVnCvRA2+tRZmhzjpa8g7kepPgsfL4vAjyDtEbFdhUKanko7VmXVfoLAuEgD74GuMdMZBqrUVnmqqpKYoSk2WIvy01WMUhsLShxqXcy37umhgYD4tB/J62WpNlpqPTZlmGN56mFs8/SsvKQqvCgpMwn7TcU+tCcYuN3Wza3haGG+9YLg7RyqOZDN76+X/r42w9s+b27VtowGQl167f4eatIx5/5il0XnD7zjXuHh5xMl+xWq6p10vOnj1HvZhTLU+oVitsveZ/8x//VTSWarWgyHMWizmubrh56xaL+Zzlcsl8Pmc8HrO3t8doNML5lrpe0DZr9ne3WC5WQhnKS8Dzznc+yWrVYFtPZkZS4aI9wtmKPFOoPKOtAg+Y6DEqnNegCl557RoXizn7SUzWByYBSuEVVLUEeqJhjOpvbahcAkPKU6ritsk4iE0FI+ddnwJdFAVVVQV+uerw0Ig9xu+BXkAoxZ/T64hGqPscYYueF90xUdvZ4SnHQRw/64Ojdu3E+ER5V+87GqGTixBD4WE0GrNer7v+VFWFzXMMPV47Go0H6fHxod6EinrqmukeeK3vhVOsg8bazkB24wHh4RUtbvGwhlKhm+ybgXDXAL7oWUCigT6MSdQhCDgajQZerdY68eb6dtoiDD2sEVX9RH4gyMgSFlifSJgFep5Jap3Krtkynsy6hWm4+9PdGJajMgQg+yCvrQXTz5QaGm7fM37ieMX5o4OnHXd2JrCQysB4ibGKOI/jdZV5STESfnWk8kmSjep2b/EzsRBzvNY8z1mtVgmsJ4tiWRQsl8vu2dAhs7QoiwAHh8xR20juSrI7HmDoSVD6fu1tYbitbTi5c43caCZmI1BXr2jbNaNpQd1anv/y87QuY2v7LHXjWa5WFIVib3dGkeccac28cayP71It1rimIqNlNs75wPvfg7JLmtWcO4dHXL9+lePjYw5v32E6nbK1tcXe3h5KSYq00NMUbV1y584B00szXCtbs53ZDNCs1ivwijwvUF5z5+Yt9i6c4fz5i9y8W3E0X0mGZ2ha94EtpeDw8IBqp0oz3mVCeqmUYxuHCQ9rxHxjoCrFqoEOy4sYcIp7pzjtvXg1XcQ/fk/qKcXjUoOSYq4pbj7woJIW34tBw01MNQbkIvbYhpiE88M6kKmeRts2OCd9PDg4YHt7e4Dxxm1zGlCLfTktMLs5pvEBhF7To9fiUHilA77dV+lJMV6533pgfDZjFPF6YnZnmiwVxyb+bBr79H7WwejFlHGB1WT3FjH8GLhNxzzFldNxSEu5xbjB5k6sgx2CAWuahpOTk1MZLvH6u+C4yroAXrrQK6U6o6j1cLzSc6axjk2cG3raaAzQbsYQ4ng0jeDcIP+WxTj0V6Dn+B2px962LePxuAv2x2ufTCad3nt0jIBuzkdHKO740gIc8Zo3aaH3a28Lw+3alp/5iR9hVBY0NzP8Z/v3qmrFcjXHKYMud/jcc3/I4VqTT8+iTcZ6tWJd36VpPKtVQ71aY5QnUxpTjmj9ir39Xf7Df+8vcnJwm+XxbebHh6xWC1rbcvv2Hfa2txkFTmdKcVqtVuBammpJrjNs3VJmBXleoDHUVcPh0RGLqkGTk2cS/Dk4OCIvCsbjCctqTdX2XodQ2BTC+VWs1xV2NgzgyVaq97yKvOhu6L0PdR/Eih5C+v5pXve9D1+/jU0NSjRCqb5I+qCkhV5Thkd67hQDj/1JDWj6nWVZdl6JGMoa55uOHZNm5snD5cHLDmY8nmJMhgv4cp6XuLZFKxO8co93cUevcUF6QOR2pZxa3TShUIVJ9Mkli1NgD6lkU1eNpI+bnvKYGtW4uLp2yJrY5DfHexaNUOx3ijunO5vN3U6Ep1K6WWxN03TYdLzf0UjHMY+vpfcsGugYRPWAMoay7Av7dlivEYxd6LMCw2zv7KC0FBchWZjShUJgAS0yFMliH/t1cnIiEJPuxyXVsInnTEWvBkHNjbGDnl8ucQHdxYoyY4CYWSo7nG6noPoFPhrodIFIFxTvPfP5fDCu0AuDpdckc3con3vawvhmKPfbwnCPyoJLF84yKguWVpEWJrJOqE9Oa1ztuX20QpW7jPJSpFPrFcuTJU1jRfK0bmnrGttUKNtQZJ79nRnvePIRbl+/wtHBLdarBd45xpMJ49GY2XQ6uOlxq962DbZtUF6xs70bhOqhWtdUVUtTt6zWFU0r0qhtDVmu0K1ld2ef2eyEw+MGpSUvVrwgMTYq4LtZwOc2m1KCC+uNhy/FIWU7HLJCN4zx5rHx9fQ9CTz1i0Q6CdOWerypN5oa5gjTbHrcq9Wq2wnEa05bytpIPajOe03ojtB7UtIvCSgpLVQuhQRixZqCFION/Yn97rfsPgZqlesWAeekJJ0Mm+xGxMj7QOESnDvLNcoMYwOb45a+d7973IkhbYzJZuJQvD+RK7x5ns376r3vquvEljKG4g5iNBrdc0yEH1Cy+6vqWmIFoRRZZKMYraX2ZLznyX0SyMgPgqCd0Q03YnM3Efscn7/N/mzOxxQ7T+dfNOKn7Ry7Y3MjGjlB+tiGIGtMxImGO45VutOMi8nmfUh3cnGuxutMx17eG+7M0hyB5IT3zJnu+Pu+811s0+kU71ua1mPMEOP2KKlu02puHRyj8inFZBtTljS2QjlHvaypqobWemxrWc6PaZcnuHrFud2cs3tTbt+4wvGt6yxODlBKMNEsyzl//jyjPOu2mnmeMx4LbrFaWVCaclQw297h6PCYpmlZrlYsF0vxCkpDnpU4p3GtFAse6RHnz19g6427wG2BYj2dxCaKLmA0nU4l4yppUaNBqEf3etMD+pa/NyNw06Bubq3v0ZKI/55itOP3xfPGFo3paRM7NTar1arDS6OhOs0rig9uqnWSZRna+AG+ntaRNNqglOlwYInMi/dsG1lM3cBv6Y14NNpK6W4RECaGKDf2x0cGTp9k0TQOpR3aeNBDGuNgXMOuJI5VSptLdy7x+NTzTO9bPCbdEcbxOG1B6DB6M1wU0wU3juXm/eoMTWDDNNZyfHLMcrFkb2+vO6+zDlUUktCSiRHsrluLzohyegCldF6rvje2EA2u1jrI7/aOR5yzqbe9Cfml/WiaptO86bJsg2GMsE8xKtDO4pzUnpVq8ArbRvhDDHe8h+kiu+moxOuKMaJ4D1yISW3O8Tj+KXwT+eaned6ntbeF4ZZBgsa6bosam209baNofMYffO6rjKZ7ZOMZKjN41bJcneBqj2s8TVWxWi1Zzo/IfM3x4U2eeeydPPvud3L1tVegXpEbxXQ6ZTrbopxs4fHs7cyoq6rLXmpDpuOoHAWj1HJwdMJoPMFToaqG2kogZlpmgYYVuJpGPPLxaExmcprGdnrSWcK19XhsK9WsNx8+o/qUZmMUWcD9Uk+k25rTbzXTiRrfT4OLaS3B8XicRLhPD2Cm2CT0kEhMS9/0ClPjGyfk/v5+5ymmk3wT24yyqOn3mEwDtguYxevp9KHzHKMzhsJkFuekjBfeY0Lh3LQPWZaKmfnOC4/QQ4zoy5jmg+sWo1CGiiUtWaHvMSTOO1zrupT0CGvE79/0cOOCkCZHpcYsyzLKsuzw41gHNN7fdEFMk02iyFHa0lqQWkuSU4Rbuh1CuJdKK0bjERdGF7vU8qqqhFmRZzg8eSnGOwYKi2DMV6sVRmny0O8I58R+LZdLUQD0fjCX+uDwvaXaNudHCh9GzBroij547wcOWcT/Iwy6bmqKvCTPC4zxYdfehPNorGsHSU/xp5tpttdRj/cxzUOwrtdqHzzfxuBcf8/ighIXhm4RehOc++1huBFRKCl2Opxoiy+cYfGcrIY/7H5CDr6b4kCR8Bn+HnkYhXfOekyt0b+laSQ0TgMsFdzuqE6gAp3Pd8NxL2Yox4GnAH8Gz75cX/dOegzkfoePuUf58cc+/qZ9Vwr6Ug/SWmtxjQMtVLpltaboqrJLMyYTupjvI/fpqr6Jt8ZU8RT3axrZAsfjYLiljw/IpncTJ1uKrUZPJw1MRS8nDcpExkLTNB2EEo1NmvWplJIFLNSsjAGzpm4ZjUaU5ajzmtPajGlwUgGGYbJPqruR9nFzq+59z7AAuuvsFq88RxlNG8a0KIrAHw74fNvSNG03nrHYbho32DS6IMYgOi9pAd/VanXPLica7hQSS5klTd0MoKa4EMTriF5fXAiUVkyKKXVgYZhcUvKjUYnjmy6gnUBYeAbbpmUxX1AEjnK8rjR4Gj+bXksch/V6HRLc/AApiN54PD4GT/M873Z0TSN04BSaSyvKdzseetmA5VJ2zkUhEqtaBcOLFxnpML4RT2+aprvGOM9lDEvWqxVxV2edFG+eTCas1+vQbylMUtcNPjm3974PKCf39m0PlTjvWVV1gBM2jGaXsAImGrg3Q+3VKb+f4lD6e35/863Jvcedfrzv3u01Vv51m3BRbaCQKbK8ACV4ovdSpbooMkkP0AHlDoYFhl5d2/ap58ZolBIeunUiiyqGrn8YN2GMFH6J2HI0xDCkB5Zln2GWQjKbu6hIr4reyv237j2bw1oHXlOWY/K8JIo7ee+6LeZwYRPD7Zq2W8jiMYOkkwRqauo6FEW+vzpbt5g5SfxRmQanqG0jNDL6wF2hRx0M1Sc8aaqqxhhHUShGowKlNFkWHlpvUfguGSWFouKuIMVae8zUDq4R6DBmmQd9mTIxCmBMryMjySuAc9TOslwtWd5ZYbRma2tLFts6CXh6RFDD2kBTpMO4p+Nxl0QS4cE4D7p7bHTH0EkTvGIAUQphi/cscyR6uHT3vSzHg/m3qfeSzsPUIYiUzCIvyLI8UCylBqxtI5wp35nCVOn8sg5sG0q8aU1rPShDXTfC41cCw1nru5wKSfjq4y7xmtLktXjNp0GWaXtbGG55QMMgjS3mqQPsS7t8K8b0+6oZz/iZFSoXbrIXEBZQ/YOFbKGcD6mxyf2ND3fvIfd8WOhzJMSbCRM78KPTSTnY9rthoQB1ikGJRreqKmazWXdcum2MLTXW6TnicSl7wHvb6X+YwO5QyvTcaGc7455i4/Fc1lpconGRXn8YhkHaeZ9Zae653k18NYbZlBHedt2xUBKZWTtkgqR91dps9DVyOMICY/SgPymzZhMjT+/5cMfU4/+ph90tzj6JbeAD80aiAnlRMLL9Iu5c1NkR1pNTSrJ6BayT950DYzBaMHA2oLH4rzamy2pNd0GRMheNdR8TEFqfVMsZBrQ3oYv1et1RK1Ojm3q33dw0UggklKeRc5o083hY7Dr14jOVhV1UpPYKATvKHESHWeZhjP/cS/1L4x8pV/17AuOWCRHwrG0wH1pBfSFgdYbVuuL6jZs4MpQR/ElrSR8lyDfaVrBJ2zZ413L54gWKPBNjFY0cUdM3sBGIqmt5gFwEa48lpiAGR313jX3rcJb+1cTQguJkvmC+WPSJN36TiqeZTsdMxiPyPEMXlvH7590KHYXUrbu3IGsf6BkKO8XjVFj143fGyRQulMhE2TRom8YvNVgp5ph60c450V05Ph4IQJ3WYv9TBkkaiItGT7x7yfAT71nqh3QPWHKN8XzpQxYXE7/B+IgGMN4u74Z0vZTlEetJbo5DNzYofAiW1nU9xCeVCjskPxiPCB0VRT4wwr2IUjLuqn8uorFJIad4zenYDhZulS7WdLuluMNJE3TEi/YdvXEStOgjhBTvUWdMnZOkhOSeppBHuljGFsfehOzGOnic0RMHib1E7zni/rFf6fyMyVHpPU/PE8cwjn3E3uPxRV7glDCEfGd/FFqZLjjpk616Cj8KVNZTdFMcPj6bcaHcnDfx+9OcgXS80t3Um7W3heH23mNbR5GXbO9sc/nyRS7+1EWapmYy3ebLX/s6//Xf+Ies2CEfXUJnJaNRyfZ0hG8rFvMFB3euc3B0naOD6zRHN/jf/dv/CQ9fOktuFK3tie7z+bwL9mitWa/WnD17DmsbWlujjeCRx0dLIMOiWNu2z3wjGhEV/5RBDh6HbNdyWpXx6X/1KX7nxU/RoMhMjrVxK2hwTibPj73jfXzw/e/mzMWzGN+yPLaslmspHqk0npYy672QbrxiEkFZdK+lHsXQUEfsNzUGwQuRLHBgqHGSthQjjW0QzEKYQefPn+9kM6FPPIit28L7e5ktKfYet8bpbsEGTfYs64taxJ2D9xKEKpNgVzRMcfseDXm8jrQvaWJTz5CJF92Pbaq2Z52jDTBNxN+jB72ZlRnPv1qtukClBK2iE0HYYnu0hjyPcIgEvOO5Ojw59CFl8qQLd48F9+p/qcGOYz8aj/HVOsgIhAo5SJZr6gSki2kPwzAwqmmwtA06QTEYHzWxY8ygqWuyMJbj8ZjpdNrNv7quWS6XnUdqre3qu0acOXrnfaJOr9x49uzZLh8gJuDcs9Pobq3qdh3ey/yIVEfpd9ZhzylGrrXpArrRkYqxgriYpPBMhBDTRTDF69u2ZTabcXR01I3lJkV0s6lvhqV8N5pS6hYS57v9Vl/LW9DO8oPX7x/EPsODfv+gtT9qvx/z3p877Y23heEGUEr9gff+w2/1dXy32w9iv38Q+wwP+v1WX8d3u30n+/1tcB4etAftQXvQHrS3sj0w3A/ag/agPWjfY+3tZLh/5a2+gLeo/SD2+wexz/Cg3z9o7TvW77cNxv2gPWgP2oP2oH1r7e3kcT9oD9qD9qA9aN9Ce8sNt1LqzyilnldKfV0p9dfe6uv5Tjal1CtKqS8ppb6glPqD8Nq+UuqfKaVeDP/uvdXX+UdtSqlfVUrdVEp9OXnt1H4qaf+XcP+/qJT60Ft35X+0dp9+/+dKqSvhnn9BKfXx5L3/NPT7eaXUn35rrvqP1pRSjyilflsp9VWl1FeUUv/r8Pr39f1+k35/d+53mrjx3f4BDPAS8CRQAM8B73krr+k73N9XgLMbr/0fgb8Wfv9rwP/hrb7OP4Z+/iTwIeDL36yfwMeB/w+S0fQjwKff6uv/Y+73fw781VOOfU+Y7yXwRHgOzFvdh2+jz5eAD4Xft4AXQt++r+/3m/T7u3K/32qP+6PA17333/De18CvAZ94i6/pu90+Afzt8PvfBn7+rbuUP57mvf8d4O7Gy/fr5yeAv+OlfQrYVUpd+q5c6B9zu0+/79c+Afya977y3r8MfB15Hr6nmvf+mvf+c+H3E+BrwEN8n9/vN+n3/dof6/1+qw33Q8Dryd9v8Oad/15vHvgNpdRnlVK/FF674L2/Fn6/Dlx4ay7tO97u188fhDnwHwVY4FcTKOz7rt9KqceBDwKf5gfofm/0G74L9/utNtw/aO0nvPcfAn4O+CtKqZ9M3/Syp/q+p/n8oPQztP878BTwAeAa8Dfe0qv5DjWl1Az474D/2Ht/nL73/Xy/T+n3d+V+v9WG+wrwSPL3w+G178vmvb8S/r0J/ENkq3QjbhXDvzffuiv8jrb79fP7eg547294762XMkP/D/rt8fdNv5VSOWK8/q73/h+El7/v7/dp/f5u3e+32nB/BninUuoJpVQB/ALwT97ia/qONKXUVCm1FX8Hfhb4MtLfXwyH/SLwj9+aK/yOt/v1858A/7PANvgR4CjZYn/Ptw389s8j9xyk37+glCqVUk8A7wR+/7t9fX/UpkQu8G8BX/Pe/83kre/r+32/fn/X7vfbIDr7cSQi+xLwn73V1/Md7OeTSFT5OeArsa/AGeC3gBeB3wT23+pr/WPo699DtokNguX9pfv1E2EX/Jfh/n8J+PBbff1/zP3+r0K/vhge3kvJ8f9Z6PfzwM+91df/bfb5JxAY5IvAF8LPx7/f7/eb9Pu7cr8fZE4+aA/ag/agfY+1txoqedAetAftQXvQ/jXbA8P9oD1oD9qD9j3WHhjuB+1Be9AetO+x9sBwP2gP2oP2oH2PtQeG+0F70B60B+17rD0w3A/ag/agPWjfY+2B4X7QHrQH7UH7HmsPDPeD9qA9aA/a91j7/wPxPl4ek5Nd2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loading image\n",
    "img = cv2.imread(\"test.jpg\")\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "frame = cv2.resize(img, None, fx=0.4, fy=0.4)\n",
    "\n",
    "height, width, channels = frame.shape\n",
    "\n",
    "# Detecting objects\n",
    "blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\n",
    "\n",
    "net.setInput(blob)\n",
    "outs = net.forward(output_layers)\n",
    "\n",
    "# Showing boxes on the images whose confidence of predicting an object is higher than a particular threshold\n",
    "class_ids = []\n",
    "confidences = []\n",
    "boxes = []\n",
    "for out in outs:\n",
    "    for detection in out:\n",
    "        scores = detection[5:]\n",
    "        class_id = np.argmax(scores)\n",
    "        confidence = scores[class_id]\n",
    "        if confidence > 0.2:\n",
    "            # Object detected\n",
    "            center_x = int(detection[0] * width)\n",
    "            center_y = int(detection[1] * height)\n",
    "            w = int(detection[2] * width)\n",
    "            h = int(detection[3] * height)\n",
    "\n",
    "            # Rectangle coordinates\n",
    "            x = int(center_x - w / 2)\n",
    "            y = int(center_y - h / 2)\n",
    "\n",
    "            boxes.append([x, y, w, h])\n",
    "            confidences.append(float(confidence))\n",
    "            class_ids.append(class_id)\n",
    "\n",
    "indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.8, 0.3)\n",
    "\n",
    "# Building the boxes on top of the image\n",
    "for i in range(len(boxes)):\n",
    "    if i in indexes:\n",
    "        x, y, w, h = boxes[i]\n",
    "        label = str(classes[class_ids[i]])\n",
    "        confidence = confidences[i]\n",
    "        color = colors[class_ids[i]]\n",
    "        cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)\n",
    "\n",
    "print(\"Detected object in Image\")\n",
    "plt.imshow(frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compiling the model using DeepC Compiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-14T15:14:13.351699Z",
     "iopub.status.busy": "2020-10-14T15:14:13.351441Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading [keras model] from 'model.h5'\n",
      "Saved 'model.onnx'\n",
      "reading onnx model from file  model.onnx\n",
      "Model info:\n",
      "  ir_vesion :  3 \n",
      "  doc       : \n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): graph-node conv_1's attribute auto_pad has no meaningful data.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): graph-node conv_5's attribute auto_pad has no meaningful data.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): graph-node conv_12's attribute auto_pad has no meaningful data.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): graph-node conv_37's attribute auto_pad has no meaningful data.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): graph-node conv_62's attribute auto_pad has no meaningful data.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): scales is not a valid graph-node attribute.\n",
      "             operator Upsample will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): scales is not a valid graph-node attribute.\n",
      "             operator Upsample will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): spatial is not a valid graph-node attribute.\n",
      "             operator BatchNormalization will be added without this attribute.\n",
      "WARN (ONNX): terminal (input/output) input_1's shape is less than 1.\n",
      "             changing it to 1.\n",
      "WARN (ONNX): terminal (input/output) input_1's shape is less than 1.\n",
      "             changing it to 1.\n",
      "WARN (ONNX): terminal (input/output) input_1's shape is less than 1.\n",
      "             changing it to 1.\n",
      "WARN (ONNX): terminal (input/output) conv_81's shape is less than 1.\n",
      "             changing it to 1.\n",
      "WARN (ONNX): terminal (input/output) conv_81's shape is less than 1.\n",
      "             changing it to 1.\n",
      "WARN (ONNX): terminal (input/output) conv_81's shape is less than 1.\n",
      "             changing it to 1.\n",
      "WARN (GRAPH): found operator node with the same name (conv_81) as io node.\n",
      "WARN (ONNX): terminal (input/output) conv_93's shape is less than 1.\n",
      "             changing it to 1.\n",
      "WARN (ONNX): terminal (input/output) conv_93's shape is less than 1.\n",
      "             changing it to 1.\n",
      "WARN (ONNX): terminal (input/output) conv_93's shape is less than 1.\n",
      "             changing it to 1.\n",
      "WARN (GRAPH): found operator node with the same name (conv_93) as io node.\n",
      "WARN (ONNX): terminal (input/output) conv_105's shape is less than 1.\n",
      "             changing it to 1.\n",
      "WARN (ONNX): terminal (input/output) conv_105's shape is less than 1.\n",
      "             changing it to 1.\n",
      "WARN (ONNX): terminal (input/output) conv_105's shape is less than 1.\n",
      "             changing it to 1.\n",
      "WARN (GRAPH): found operator node with the same name (conv_105) as io node.\n",
      "running DNNC graph sanity check ... passed.\n",
      "Writing C++ file  model_deepC/model.cpp\n",
      "INFO (ONNX): model files are ready in dir model_deepC\n",
      "g++ -std=c++11 -O3 -I. -I/opt/tljh/user/lib/python3.7/site-packages/deepC-0.13-py3.7-linux-x86_64.egg/deepC/include -isystem /opt/tljh/user/lib/python3.7/site-packages/deepC-0.13-py3.7-linux-x86_64.egg/deepC/packages/eigen-eigen-323c052e1731 model_deepC/model.cpp -o model_deepC/model.exe\n"
     ]
    }
   ],
   "source": [
    "!deepCC model.h5"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}