notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# frame extraction function\n",
    "def extract_frames(video_path, output_folder, frame_step=5):\n",
    "    if not os.path.exists(output_folder):\n",
    "        os.makedirs(output_folder)\n",
    "\n",
    "    # Open video file and initialize variables\n",
    "    cap = cv2.VideoCapture(video_path)\n",
    "    frame_count = 0\n",
    "    extracted_count = 0\n",
    "\n",
    "    # While video is opened\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        if not ret:\n",
    "            break\n",
    "\n",
    "        # Process every frame if frame_step is None or every frame_step interval\n",
    "        if frame_step is None or frame_count % frame_step == 0:\n",
    "            frame_filename = os.path.join(output_folder, f'frame_{extracted_count:04d}.png')\n",
    "            cv2.imwrite(frame_filename, frame)\n",
    "            extracted_count += 1\n",
    "\n",
    "        frame_count += 1\n",
    "\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()\n",
    "    return extracted_count\n",
    "\n",
    "def extract_frames_from_folders(root_folder, output_root_folder, frame_step=5):\n",
    "    if not os.path.exists(output_root_folder):\n",
    "        os.makedirs(output_root_folder)\n",
    "\n",
    "    for folder in os.listdir(root_folder):\n",
    "        folder_path = os.path.join(root_folder, folder)\n",
    "        if os.path.isdir(folder_path):\n",
    "            # Assuming each folder contains multiple videos, we'll process the first video in each folder\n",
    "            video_files = [f for f in os.listdir(folder_path) if f.endswith('.mp4') or f.endswith('.avi')]\n",
    "            if len(video_files) > 0:\n",
    "                video_path = os.path.join(folder_path, video_files[0])\n",
    "                output_folder = os.path.join(output_root_folder, folder)\n",
    "                extract_frames(video_path, output_folder, frame_step)\n",
    "                \n",
    "# compute optical flow and outputs images, iterating through subdirectories\n",
    "def compute_optical_flow(input_folder, output_folder):\n",
    "    if not os.path.exists(output_folder):\n",
    "        os.makedirs(output_folder)\n",
    "\n",
    "    # List all subdirectories (fall-01-cam0-rgb to fall-30-cam0-rgb)\n",
    "    subfolders = sorted([f.name for f in os.scandir(input_folder) if f.is_dir()])\n",
    "\n",
    "    for folder_name in subfolders:\n",
    "        input_subfolder = os.path.join(input_folder, folder_name)\n",
    "        output_subfolder = os.path.join(output_folder, folder_name)\n",
    "        if not os.path.exists(output_subfolder):\n",
    "            os.makedirs(output_subfolder)\n",
    "\n",
    "        frame_files = sorted([os.path.join(input_subfolder, f) for f in os.listdir(input_subfolder) if f.endswith('.png')])\n",
    "\n",
    "        for i in range(len(frame_files) - 1):\n",
    "            frame1 = cv2.imread(frame_files[i])\n",
    "            frame2 = cv2.imread(frame_files[i + 1])\n",
    "\n",
    "            gray1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)\n",
    "            gray2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "            flow = cv2.calcOpticalFlowFarneback(gray1, gray2, None, 0.5, 3, 15, 3, 5, 1.2, 0)\n",
    "\n",
    "            mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])\n",
    "            hsv = np.zeros_like(frame1)\n",
    "            hsv[..., 1] = 255\n",
    "            hsv[..., 0] = ang * 180 / np.pi / 2\n",
    "            hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)\n",
    "            flow_image = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)\n",
    "\n",
    "            flow_filename = os.path.join(output_subfolder, f'flow_{i:04d}.png')\n",
    "            cv2.imwrite(flow_filename, flow_image)\n",
    "\n",
    "        print(f'Computed optical flow for {folder_name} with {len(frame_files) - 1} frame pairs')\n",
    "\n",
    "# compute rank pooling and outputs images, iterating through subdirectories\n",
    "def rank_pooling(flow_folder, output_folder):\n",
    "    if not os.path.exists(output_folder):\n",
    "        os.makedirs(output_folder)\n",
    "\n",
    "    subfolders = sorted([f for f in os.listdir(flow_folder) if os.path.isdir(os.path.join(flow_folder, f))])\n",
    "\n",
    "    for subfolder in subfolders:\n",
    "        subfolder_path = os.path.join(flow_folder, subfolder)\n",
    "        output_subfolder = os.path.join(output_folder, subfolder)\n",
    "        if not os.path.exists(output_subfolder):\n",
    "            os.makedirs(output_subfolder)\n",
    "\n",
    "        flow_files = sorted([os.path.join(subfolder_path, f) for f in os.listdir(subfolder_path) if f.endswith('.png')])\n",
    "\n",
    "        accumulated_flow = None\n",
    "        for i, flow_file in enumerate(flow_files):\n",
    "            flow_img = cv2.imread(flow_file)\n",
    "            if accumulated_flow is None:\n",
    "                accumulated_flow = np.zeros_like(flow_img, dtype=np.float32)\n",
    "            accumulated_flow += (i + 1) * flow_img\n",
    "\n",
    "        rank_pooled_image = accumulated_flow / len(flow_files)\n",
    "        rank_pooled_image = np.clip(rank_pooled_image, 0, 255).astype(np.uint8)\n",
    "\n",
    "        rank_pooled_filename = os.path.join(output_subfolder, 'rank_pooled.png')\n",
    "        cv2.imwrite(rank_pooled_filename, rank_pooled_image)\n",
    "\n",
    "    return output_folder\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fall frame extraction \n",
    "raw_vid_dir = 'MULTICAM-COMPLETE/FALL'\n",
    "output_frames = 'MULTICAM-COMPLETE/FALL_FRAME'\n",
    "\n",
    "extract_frames_from_folders(raw_vid_dir, output_frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed optical flow for chute01 with 312 frame pairs\n",
      "Computed optical flow for chute02 with 166 frame pairs\n",
      "Computed optical flow for chute03 with 187 frame pairs\n",
      "Computed optical flow for chute04 with 209 frame pairs\n",
      "Computed optical flow for chute05 with 153 frame pairs\n",
      "Computed optical flow for chute06 with 251 frame pairs\n",
      "Computed optical flow for chute07 with 185 frame pairs\n",
      "Computed optical flow for chute08 with 142 frame pairs\n",
      "Computed optical flow for chute09 with 186 frame pairs\n",
      "Computed optical flow for chute10 with 188 frame pairs\n",
      "Computed optical flow for chute11 with 164 frame pairs\n",
      "Computed optical flow for chute12 with 187 frame pairs\n",
      "Computed optical flow for chute13 with 241 frame pairs\n",
      "Computed optical flow for chute14 with 299 frame pairs\n",
      "Computed optical flow for chute15 with 217 frame pairs\n",
      "Computed optical flow for chute16 with 246 frame pairs\n",
      "Computed optical flow for chute17 with 307 frame pairs\n",
      "Computed optical flow for chute18 with 200 frame pairs\n",
      "Computed optical flow for chute19 with 203 frame pairs\n",
      "Computed optical flow for chute20 with 201 frame pairs\n",
      "Computed optical flow for chute21 with 241 frame pairs\n",
      "Computed optical flow for chute22 with 221 frame pairs\n",
      "Computed optical flow for chute23 with 1076 frame pairs\n",
      "Computed optical flow for 23 frame pairs\n"
     ]
    }
   ],
   "source": [
    "multicam_of = 'MULTICAM-COMPLETE/FALL_OF'\n",
    "\n",
    "compute_optical_flow(output_frames, multicam_of)\n",
    "print(f'Computed optical flow for {len(os.listdir(output_frames))} frame pairs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed optical flow for cam1 with 735 frame pairs\n",
      "Computed optical flow for cam2 with 741 frame pairs\n",
      "Computed optical flow for cam3 with 735 frame pairs\n",
      "Computed optical flow for cam4 with 734 frame pairs\n",
      "Computed optical flow for cam5 with 741 frame pairs\n",
      "Computed optical flow for cam6 with 736 frame pairs\n",
      "Computed optical flow for cam7 with 741 frame pairs\n",
      "Computed optical flow for cam8 with 728 frame pairs\n",
      "Computed optical flow for chute24 with -1 frame pairs\n",
      "Computed optical flow for 9 frame pairs\n"
     ]
    }
   ],
   "source": [
    "raw_nf_dir = 'MULTICAM-COMPLETE/chute24'\n",
    "output_nf = 'MULTICAM-COMPLETE/NF_FRAME'\n",
    "nf_of = 'MULTICAM-COMPLETE/NF_OF'\n",
    "\n",
    "extract_frames_from_folders(raw_nf_dir, output_nf)\n",
    "compute_optical_flow(output_nf, nf_of )\n",
    "print(f'Computed optical flow for {len(os.listdir(output_nf))} frame pairs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed optical flow for fall-01-cam0-rgb with 159 frame pairs\n",
      "Computed optical flow for fall-02-cam0-rgb with 109 frame pairs\n",
      "Computed optical flow for fall-03-cam0-rgb with 214 frame pairs\n",
      "Computed optical flow for fall-04-cam0-rgb with 95 frame pairs\n",
      "Computed optical flow for fall-05-cam0-rgb with 150 frame pairs\n",
      "Computed optical flow for fall-06-cam0-rgb with 99 frame pairs\n",
      "Computed optical flow for fall-07-cam0-rgb with 155 frame pairs\n",
      "Computed optical flow for fall-08-cam0-rgb with 90 frame pairs\n",
      "Computed optical flow for fall-09-cam0-rgb with 184 frame pairs\n",
      "Computed optical flow for fall-10-cam0-rgb with 129 frame pairs\n",
      "Computed optical flow for fall-11-cam0-rgb with 129 frame pairs\n",
      "Computed optical flow for fall-12-cam0-rgb with 109 frame pairs\n",
      "Computed optical flow for fall-13-cam0-rgb with 84 frame pairs\n",
      "Computed optical flow for fall-14-cam0-rgb with 60 frame pairs\n",
      "Computed optical flow for fall-15-cam0-rgb with 70 frame pairs\n",
      "Computed optical flow for fall-16-cam0-rgb with 54 frame pairs\n",
      "Computed optical flow for fall-17-cam0-rgb with 94 frame pairs\n",
      "Computed optical flow for fall-18-cam0-rgb with 64 frame pairs\n",
      "Computed optical flow for fall-19-cam0-rgb with 99 frame pairs\n",
      "Computed optical flow for fall-20-cam0-rgb with 109 frame pairs\n",
      "Computed optical flow for fall-21-cam0-rgb with 54 frame pairs\n",
      "Computed optical flow for fall-22-cam0-rgb with 55 frame pairs\n",
      "Computed optical flow for fall-23-cam0-rgb with 74 frame pairs\n",
      "Computed optical flow for fall-24-cam0-rgb with 59 frame pairs\n",
      "Computed optical flow for fall-25-cam0-rgb with 84 frame pairs\n",
      "Computed optical flow for fall-26-cam0-rgb with 60 frame pairs\n",
      "Computed optical flow for fall-27-cam0-rgb with 91 frame pairs\n",
      "Computed optical flow for fall-28-cam0-rgb with 65 frame pairs\n",
      "Computed optical flow for fall-29-cam0-rgb with 98 frame pairs\n",
      "Computed optical flow for fall-30-cam0-rgb with 69 frame pairs\n",
      "Computed optical flow for 30 frame pairs\n"
     ]
    }
   ],
   "source": [
    "# computing optical flow for fall images\n",
    "input_raw_fall = 'URFD-BALANCED/URFD-FALL/ORI'\n",
    "fall_optical_flow = 'URFD-BALANCED/URFD-FALL/OF'\n",
    "\n",
    "compute_optical_flow(input_raw_fall, fall_optical_flow)\n",
    "print(f'Computed optical flow for {len(os.listdir(input_raw_fall))} frame pairs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed optical flow for adl-01-cam0-rgb with 149 frame pairs\n",
      "Computed optical flow for adl-02-cam0-rgb with 179 frame pairs\n",
      "Computed optical flow for adl-03-cam0-rgb with 179 frame pairs\n",
      "Computed optical flow for adl-04-cam0-rgb with 149 frame pairs\n",
      "Computed optical flow for adl-05-cam0-rgb with 179 frame pairs\n",
      "Computed optical flow for adl-06-cam0-rgb with 229 frame pairs\n",
      "Computed optical flow for adl-07-cam0-rgb with 179 frame pairs\n",
      "Computed optical flow for adl-08-cam0-rgb with 179 frame pairs\n",
      "Computed optical flow for adl-09-cam0-rgb with 149 frame pairs\n",
      "Computed optical flow for adl-10-cam0-rgb with 299 frame pairs\n",
      "Computed optical flow for adl-11-cam0-rgb with 299 frame pairs\n",
      "Computed optical flow for adl-12-cam0-rgb with 249 frame pairs\n",
      "Computed optical flow for adl-13-cam0-rgb with 264 frame pairs\n",
      "Computed optical flow for adl-14-cam0-rgb with 234 frame pairs\n",
      "Computed optical flow for adl-15-cam0-rgb with 274 frame pairs\n",
      "Computed optical flow for adl-16-cam0-rgb with 239 frame pairs\n",
      "Computed optical flow for adl-17-cam0-rgb with 229 frame pairs\n",
      "Computed optical flow for adl-18-cam0-rgb with 264 frame pairs\n",
      "Computed optical flow for adl-19-cam0-rgb with 249 frame pairs\n",
      "Computed optical flow for adl-20-cam0-rgb with 269 frame pairs\n",
      "Computed optical flow for adl-21-cam0-rgb with 279 frame pairs\n",
      "Computed optical flow for adl-22-cam0-rgb with 239 frame pairs\n",
      "Computed optical flow for adl-23-cam0-rgb with 219 frame pairs\n",
      "Computed optical flow for adl-24-cam0-rgb with 69 frame pairs\n",
      "Computed optical flow for adl-25-cam0-rgb with 109 frame pairs\n",
      "Computed optical flow for adl-26-cam0-rgb with 94 frame pairs\n",
      "Computed optical flow for adl-27-cam0-rgb with 99 frame pairs\n",
      "Computed optical flow for adl-28-cam0-rgb with 84 frame pairs\n",
      "Computed optical flow for adl-29-cam0-rgb with 124 frame pairs\n",
      "Computed optical flow for adl-30-cam0-rgb with 399 frame pairs\n",
      "Computed optical flow for adl-31-cam0-rgb with 249 frame pairs\n",
      "Computed optical flow for adl-32-cam0-rgb with 199 frame pairs\n",
      "Computed optical flow for adl-33-cam0-rgb with 199 frame pairs\n",
      "Computed optical flow for adl-34-cam0-rgb with 190 frame pairs\n",
      "Computed optical flow for adl-35-cam0-rgb with 279 frame pairs\n",
      "Computed optical flow for adl-37-cam0-rgb with 349 frame pairs\n",
      "Computed optical flow for adl-40-cam0-rgb with 329 frame pairs\n",
      "Computed optical flow for 37 frame pairs\n"
     ]
    }
   ],
   "source": [
    "# compute optical flow for daily lives (non-fall) images\n",
    "input_raw_adl = 'URFD-BALANCED/URFD-ADL/ORI'\n",
    "adl_optical_flow = 'URFD-BALANCED/URFD-ADL/OF'\n",
    "\n",
    "compute_optical_flow(input_raw_adl, adl_optical_flow)\n",
    "print(f'Computed optical flow for {len(os.listdir(input_raw_adl))} frame pairs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed optical flow for 37 frame pairs\n",
      "Computed optical flow for 30 frame pairs\n"
     ]
    }
   ],
   "source": [
    "# compute rank pooling for fall and non-fall images\n",
    "adl_rank_pool = 'URFD-BALANCED/RP_COMPLETE/RP_ADL'\n",
    "fall_rank_pool = 'URFD-BALANCED/RP_COMPLETE/RP_FALL'\n",
    "\n",
    "# rank pooling from URFD Dataset\n",
    "rank_pooling(adl_optical_flow, adl_rank_pool)\n",
    "print(f'Computed optical flow for {len(os.listdir(adl_optical_flow))} frame pairs')\n",
    "\n",
    "rank_pooling(fall_optical_flow, fall_rank_pool)\n",
    "print(f'Computed optical flow for {len(os.listdir(fall_optical_flow))} frame pairs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      23\n",
      "      25\n"
     ]
    }
   ],
   "source": [
    "print(f'''\n",
    "      {len(nf_of)}\n",
    "      {len(multicam_of)}''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed optical flow for 9 frame pairs\n",
      "Computed optical flow for 23 frame pairs\n"
     ]
    }
   ],
   "source": [
    "# rank pooling from MULTICAM Dataset\n",
    "rank_pooling(nf_of, adl_rank_pool)\n",
    "print(f'Computed optical flow for {len(os.listdir(nf_of))} frame pairs')\n",
    "\n",
    "rank_pooling(multicam_of, fall_rank_pool)\n",
    "print(f'Computed optical flow for {len(os.listdir(multicam_of))} frame pairs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      ['RP_ADL', 'RP_FALL']\n",
      "      non-fall = 46\n",
      "      fall = 53\n",
      "      \n"
     ]
    }
   ],
   "source": [
    "BASE_DIR = 'URFD-BALANCED/RP_COMPLETE'\n",
    "ADL_DIR = os.path.join(BASE_DIR, 'RP_ADL')\n",
    "FALL_DIR = os.path.join(BASE_DIR, 'RP_FALL')\n",
    "\n",
    "print(f'''\n",
    "      {os.listdir(BASE_DIR)}\n",
    "      non-fall = {len(os.listdir(ADL_DIR))}\n",
    "      fall = {len(os.listdir(FALL_DIR))}\n",
    "      ''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 80 images belonging to 2 classes.\n",
      "Found 19 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# image augmentation\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1.0/255,\n",
    "    rotation_range=30,\n",
    "    width_shift_range=0.3,\n",
    "    height_shift_range=0.3,\n",
    "    shear_range=0.3,\n",
    "    zoom_range=0.3,\n",
    "    horizontal_flip=True,\n",
    "    vertical_flip=False,\n",
    "    fill_mode='nearest',\n",
    "    validation_split=0.2\n",
    ")\n",
    "\n",
    "val_datagen = ImageDataGenerator(\n",
    "    rescale=1.0/255,\n",
    "    validation_split=0.2\n",
    ")\n",
    "\n",
    "img_h = 128\n",
    "img_w = int(img_h * 1.33)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    BASE_DIR,\n",
    "    target_size = (img_h,img_w),\n",
    "    batch_size = 8,\n",
    "    class_mode = 'categorical',\n",
    "    shuffle=False,\n",
    "    subset = 'training'\n",
    ")\n",
    "\n",
    "val_generator = val_datagen.flow_from_directory(\n",
    "    BASE_DIR,\n",
    "    target_size = (img_h,img_w),\n",
    "    batch_size = 4,\n",
    "    class_mode = 'categorical',\n",
    "    shuffle=False,\n",
    "    subset = 'validation'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# configure steps for train and val per epoch\n",
    "train_steps = round(80/8)\n",
    "val_steps = round(19/4)\n",
    "\n",
    "print(train_steps)\n",
    "print(val_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EEAe9OfrsGaeI9gBGdhGJGRVDfbnPBXNIClAJEjIxQsrgnOBcQswhJqBCyhlSQiwhKCaeJA4qocJwuUGSoqI0UdAcMUsYQhU9VTZQg1juDNkAMbQyoitzZhcSVTLUIIgiLpPrRPBlLiVB8MEjqUKzQ81wWUldRaJm7cFIOL3AVQsOZMVEesQMRVmKp88jLDnaFHFCOZco9xuxRHaQ1QBl7htWFWRf5nYup/LbpNx3/pO/+fef63j/gcZrwpObnv0uy27xDMyeb4f9gcfnzwGnDieujMMZzDL5maTcH378bsf7+ybFv3wCu61x0MCuE3Yd7ANrgQUlFwkllpwMz4WGLvgXffqYASlhF+sSRk3LTZ/5I5YX77C4uqAWoeof8eie0q8aFuN92lmDc7OPXm+GlIx2d8KXv/QlvnT/V7k8y7hmzOTKFfYPJjQhkuaRxWnk+ssvIWnO6fkj7j56l8W9e1y7d4vV4pz333iT86Zmbit+7qd/kV/+h79OHxfkkBFTtMlklwnrhAtCFEhmYA1JFPIlIgEFwjrQSWaZFa09OXeslmfPvwNvvwq3X4G+h+OHhSA/eQgXpxAWcLyAR/ehamA0hr09ODpCDvZxk1vo+FWqySXTayek/oRufsn6fMXlgzWXD9eszhf06xUx9ITO6Dvj4uGc93/7lNHsm4yuX2H/B/8c1194BRX9yNy1Seb87Bxnjiu7h1zdmeGeWvbeo7t0/oyDesmyu2S9bNC0R6x3iarscYp0l8h6jbWOCwuMK8dLR1doxfPb3/gSD9+7w7j2/OS/8tO8/unXSClx/OgB33nrDb78hS/x7d/6CvP7j2inC7ocuUwd55enXPYL6mVHcMKVOtOI58QCy4sFe6sR3oQqdIQHgTg/Ja8SvqroVpeERnhwdpf5m19G778J4zFXxoe8fO0mN164wv5kn0arZ/cFz3+dRXUYCTEhZiF5QBxRPLmOrJsKogcTsvpC8Eal6iMuKz4bmsHVmVoLISMpl+1JUGfD1Mh1xA3Bu60MzUaFoTiQjMYSuGIJ8QnJHo1DICtKy6ok0DGyK9eHoUj25GwklOwdKTnMwOWEIGRLGBGhRxJ49bi6YmHQmUcs4g1CNKIIYhHNWubPaoiuqRFCliE5K6gzUIi5JHCjhBKM67AdImxYHzHBx1KfowZGmYgglIB2M5kTA8nPkOADtV7iHhtiHytJ3edFWGfur3vun/TsTOdc22+4tT/lcDxmVjU0lceHCnde4R8kwu4at/8+1c379N07VJeH+Pl1+tUR6+UO83nDaNwwnrU00x0qN2U2eoHRlat073yT7vic86YnScfx6OvEEBG/5rrfZXo6xUbGaLbDi6+/RGUTfuOrX+PWfsuPvf4SV+prnNy74M2g0BsNDR6PNyWljFdPFVt24g3GYYSaPCHhNskQ5UmwuCHeGJ6bHvo7YBdQXQc9hBA5sZ6Taod3XOb+uyvcX/tlJtWK/Rf3ODu6wnvjzP76mNfrirpWrjFlxohm5zouNLB8RNvM6dPbpKajd1fpwpIYOhbeoe0Net9CHDPt5+xJ5LIJ3Nc1azJV6qmdw6qK4AwXP9ngxQYSaIOn4+pnXnz2Ux/6un1UJG4f8feHfdf3ge/anOdZyYes54Or+d73Uvue3/nBtz7GZfox4ECMHjh18O408Ha4YJ1XHMgZe7zFixgjOqArs9nFDFa7uH7EpK5pRi3eP6K9KrC6BHcKh+9CNQd2wNJQIQm8Dfwq8G3glCfscXrqAc/unM01ucnzGSUp6Yb3psBV4AUKIe4oucj3eUKY3we+AHy1rHK9D/WVUtAgLwl7ywNeuPICl5eXzGPP5WLB+vicajrDjSsEeyqrJt9NksuT6+KDh9Eox1aq8ti8Vso6PB79Fxr3zmZTPvvCPvsjT9QyboWQWTUV61FDxEEwkihaZkQomdqtEHqqWBJQPnu65IiaCZUQYkX2nozR9saod0iEyxi4f3rCxWKFZbhybZfd3Sk6rsAcOeVCMEQhEFBJqI8wkNLJ12RTyKmMpznTI7ghkSFeyJtx1CfoDLGavhI0GzjFx4RSEik5R9CMeENyiQuidwgZ0wYsFUIcIGdSEEQdtiHEEUQiOCtzBVMQSjwZIKOQBRtiDBFDiWTNJAWGOFViBIPaK2qB7KwULSQjr8sJk00/mbPDjNB3hL7j8uKEB++/yWgyYzzdY7Z3hfFsn2a8i9YN5iuy82TnQGtUR/RNpk9GipFRH4Z4LGICAUFqRb3hgpX4xLTEL7HCe8EDgZ6cHb046qwkExYkKlF8FiwIqTGyGl5SmcNopE7gzZdYJkHUQGUKeNQyhgeLJGf4ZJAgR4+UqxbUEXwAl6iC4gxwRvAlAUfwQCGoyUMhqSuMiNOMJIVciPdKMiKFAEgKKVflPESQmHBOMG/lXO0NcxGrGJLUJZFapYylhKhQacKykULGKWguRV4fC1WFeSWroimTQyzFl1WNipBUUYE8FMjkZASDUcp4B5KMFCIxZlydUTJOFPOe1NQkUUISqmxkK0nClTPWIlhM1DHQZkOdo6oaVmvDhxWh67FkBC/QjoZKT8OL0pmQLGN1Q5+M8bpjOqo5lpI0jylQVz3OMpoCfQwsU2QJ7CJMtcaro6sUaofGgMtjulHL3AnLGJjGREOFU6WLiU6FJGCVo3JCnQXpM6RMl41gxkQcjVNqV3HZrYnZSGo0ajTiUOeQxv2Rzi8KMG49XnUo7i33vicLCGIZtTKXkeFDGRmunFLsW4iIRIy53AddmT8hQxIdG4KkEkWWsEAeb4OJkIFqGKtNSrFj2RQrCf5hYbHHM6WyLGVOZSaIFroNkWHwNmS4Xhg243FM8niVw5bY5kV5hiYzBBV7Umj7Me7p99455ewHDji/vaAf7TPZnTK5XHB2HvChQtcdOpuSxp60DlQ54btUis+bGd3yEW0K+DpiOxOO75/QrTtG3YjprOFU1lRijHzDqXP0PjOSjKojixByTd+v8esVzWzJJK54mHruTae8fH5CHRLML+DqLXBTOF8gVxKH13Z589E5ebGmqXdYnT2EmdHlBeN2xfz8hP32Cs3qLvVkl0siNDMaN2IRHCF1OHdJcDt0zBnRUgFXqNnVxLw9YrW84DIELtanHLRLZv6Al3ZvU9mYnXCG+CkTRuxVn31yDNzOcKAyRFfi1E0R43o4Xh6ogR1K7Pm0d+bjQ2nDB06BR8A7wINhRfC8pPhHQVX5X/3P/j32ruxBhtivefjojKP9Q+q6ZpO23YTNm7rSj4JhXJze4cbBlLHMuHrrKjqFK+bB6oFsL7tjaTBeUMZSD9ZCJU++S59aK7Ki7LSaOa/SkHEYDrAFpBOY17C7J2izCa0DRgRqBFeu+4fAd4DXKAWww9YEEgsUz5IKRanoEepnfq+BpOF+o0+R9oWQ74ctfP6rMvPh0f9zIBlZE94pIYLzieAyFhuSj3gt83ERRTDMEjhDo6C+5DrUOTKZLA6Xc6li8ILlnmwjxNKQ9xKSGqZKNivkdla0M1jVaDemTo4qGj5mkITUmewU00gyw5kv9cpmuKSIebKVnFj0SqShto40FJaLF3yUIb+lZBXIAckKoqAOUcN5Q2OmcqlMIZ3HRNGc6K3cX9WXc9CFjNOE80VcEhPkKFj0SHL4XAqQhxJaTDNZIkEKWVsr1BQhSs4QrZR7eCmfCpIImksZRjKcDOSxKkJZ1/NCFXJWokuFaE+GhqHAVyDjUXrwAUsK5hCJSC7HTSSjSXFeiNmotAgEelVGLpGTIlYKXi0ZOeeB9CpzPMnl3HC5EGA5UwpzN+OhMcSJmxylA61Q5/BJy7GkfF7FcDkjKWE5o1aj0ZHEyCnhcirjqbrCkTilq42kCS8lR+qSon0DyQguk13Ja1YDGS9eiclBFDylcKAzj+WEmEdsRONXeAN1gvOOFASix5kSkxCsRtwwx0zD3Sr7ItbMRkxQJyFlR5drgiuFE8knWteVa+FfMpS4S57NMz0n0fvdK5dPbl1bfCL4qJSnCKhoiY+fzu1KCYGa1jOZ7WAG8/NLUgofsaY/2vi+SfHfef+CvdGIvYlndwT7I9irYMcLBwJ7wGogyI8pgdqYEpBUwxdtrtlPOlH4dEK+EEZgEeJFYHWSmNSKS4pJD8fHyKKj242MJDDrPL73dLEi14Jz9pEbaGb0K7hz1tEdLfnar/wOX3/nTXIQDvYP2D+acXR4lXQ8xy6WnD865rXbr+KmQs+SugqcX6x4dPdbnL31DtZ66tqzv3PI4rJjvloTHfh1pHaKpYpkSk6Brg+oFsJShiRcjBBTYJIzSRKXaY22FZNmAlSsuo9xwssQCDYt3HwRrt+E9Rou54Ugf3AHLs8h9HC5gsszuPM+jCewu4tsFOR71/D+Ov4oMj44Z/eFR4TlOcuTNYvjwOLRguXZgm6xJKVIzp7FPHP3bIXk+/zs1ZeYNR8dlKoITRugWmDBOH9wyeIyMtsrZ8bD+Xt09SU6ylQTpa4aqjBhuYokt6ZLj1hd9gQBaYVVf4n3Ndd3b7HqFkSNLFYraivBZVKlriqu3bjF1es3+eEf/jFOf+GY73zzd/jiP/2HvPngDpcXKy56w2aR8a5nHBtyqjhZR7p2hBsptlRcWuM7YeWE3Ah1gmauaBjjxrCWyGLUQR5xMF/T37/DW3fv825/wNGLL/DpZsbMtYjfI2nNuSjXn7t6qUZyhKHKNDclUMzmCM4TXYtYxqeSFnCWSXhUrSTA6QuZXVaBKSgRcVBZSQgnDE2QXMScoJViOaK9Yilj6hGpyVaVBLQpSkbUo3lQBlkqkwUD86WqFI0l8ZpdUXqaw3IFJAgOX/UomaRKdJ5cBUyNaEqWAE6JQcACGSW6jDfDZaHNDkmGtRGqUgBgWTHnQCISC+EtrgTtogbiMXO4NKjZNokf7LEK/OnnIrcvz09T4TKQbzJ8PJtiSCES5KOvie8HTilBc4L5ReTiMvLegxVHew23DsdcnUzYrVtaX1Elh1s73EkmziL9/ilu74x6714hxy+OqJfXCd0Oy4cT6uOK6e6InYPrnL5lrC5W+MUZk+OGVTPHXw2Mx47dQ2U0GjObX+WWfpbJ3uvkWcs737nLxfyYT92+wo/++Ovs6DX+yX/9O9w7fkTtGsQcFa6Uv4pSaU3rd3m1vV2mxZvE1kZJqpCbokJR8Yg+xXQ5A7eGZlZOXH0A6QIb3eNBOuNitSJXQtaGeVjzMK04nkRcHclNT9/DhZ/xLRreco57u9c5ah3777/Pqk/Mpy33+8y594xrYxozYWmcTxpWO2PWq4xetOysHXt7NVf3IhaVOhpx3CEcs6wTjkBY9s99vD8Uv8vwYE8lADeQD/xDkCfV/t9zhZ/8qP/s3x81aH/Ydjx5sQSOw2sfsvnfexfJhyzzB8KAfzfugu3DwsM9l3joz/GvrfiU99xuV1wjMSMinGK2ou93wH2KajSDBrT2zCpF6hk0x8j6fajuQHOfkkV8FfKsJLS+DPw6Jfjb5Ak3JPfmITwpVBkSbjiKQgSeODhssm6eEkBOeaLaYXjOlGzX6fD5DnizxHzdZ4DbQvvZClpjyg0+9dIhN//4Z/m1X//n/OPf+Oe88e577H36ZQ73ZhzFyM64oTFDtIXDq+D8h1YBy9OZweH+saLciluenFmJUsTl4fd4OnzvUozvhcmo5bUbh7x2OAYVsgopJdaqrNua4CsYVK1i4C2TgdZ1qPa4aNRZcObIqSqpy9roUzUUwYHGzCgoLgkhJU4Wc45PLwghszMZcbC/Qz1pEPHkUDJlEhx9jqAJ1QwuQc5kdSRxWCpJCG8lVSoqSI6oGxJrDGNrKmN58opZIZldzriNWjgHsmVcNZxeyeh9PQzAjqyKs+HkzJkUQMyRxhVuQ3hYQlzGRIpKHEG1FO4lk3KvMCkKAxG8REwy2YPhEFFICXLCqcNbwiSVwjfA1mBUZPFYBu8++QlpzonF/IzF/IzTh3eompbZ7hHT3UPGswOq0RTXjDHvBnLck8QIfY1LPbhIS0K0xFgyONpIB00yXPCkqAjCyjJRjVoddSq/qc+lsDJbSXt7lwg4LJYCFMvlqJoVhVPIxcVHY0IqRRxIKERXdpA0FTIdN8SDQq4hUoEWNVSTjURPb5lBLIMhpQAiDTGWFtmKmqCikANJKsyn4l4QikrdEBIDC5ANnwqPbuJJqSSEnYLiy33fCZICWuyKcJVhUXEpon1GnOErQdPHT7ZJ5ZG6JjvBpUyOkWyZXNWI05JYlsEERwXDiIUzwA+JkRwLOdnWNpDiHlUl1UUhFDI4M2I2VI21Ztai5JDxIdDm/JgUtwwudPR9R06Z6D25bREvoBmfHUuDkBP4mugcsu4Z05IpRQwZoSLgckJTIMVAN5DiBwgTqRlpxaUKVjskBpwJVlUsm5pFH9hPkQbF+RoJS3qFiGCVUlVQiaKxKPu7XBFyKbhoVKm8L8UL0UhNxjRTi0PVFaeYP8oQGLUer0Ivucw9His+S2woVpLlwyQHBoLC7Ak9JRmSJVIyGl/GVqGMm3kzL3omLtvEncO3yECUDMuaWSFZNoGnbbbpqc8P57tRiprMCnmdHi/B42SvDWPiZvg1KcFKIcRLsczj9z6YxdrEvUN883Hu6CcP5pw+WnK+XLIYd+zPxky8p15F+kVGJx3VZEzaGZHPApYyvku4cUDrGXHuSV3EjXvcuOVS4GyxYNzP8LmiaZTYJRp1aNOwjivGLtPiiUDODaugzLqAhhVTXfEwrHlYT7isGw7CEpZrsIQdXkXOvoGs1uwfzUhOSBdLmsMpXNTlPuM6pFqQZEkQR5PAx4BWcKk142bE5WLEOi6Z+Qt6d5WVnTHmRbwE9qjZo+NBfUBu7uNiZuKnzDhgJI6bO7eQ+gZpccHDfJ/InEp2NjOSAQMDrhNYa+G2NyR4y5M54jNh14Y5DxTy+/3h+YwSbI7BZiX+TdOyvk8Qf+pP/Ax/+ud+BhmSpl+5+3X+D/+n/zM/8qM/xp/+hV/g8596jda5Qk4yENgbPEOM8Fiq/cJLL3Gwe0B8GBmNGthVnA7p3+GaVoNpAtkUD6xBXihvN+viyPc4Dn9cKNCU4jVGxOHrKqBKhjuF3VmpMd8UqIsoTgJIhzADc7AWbDa8nwVUEVbUwCEj4AYlGhSaTHGlmmx+awDeRWgpJXpTysSh/Ov7TnB/JD65DqNqmZRLjlmSQMqoRiw2ZHGYJiRaiZslk0xxLhUCOJfXSAmcK+6DWdFUXHZEEynn4nxjhlohM6MaOQk4pQ6ChEQdIlXfY1Sk7LHBhQMTZCjcUytFYn6414ZBZGKuIlku69RIUikBRpThOijb6aMQnSDOQxXpHXhJiCmSc8nbkRE8ay9cphZvqZDmgEbDEXHeBuEJ5CCluCU5fJKh7EoJDCIbLWS4ScYr1CI406fIcMFJcRpIEukHMlzM8FZiJu/K3SNLLAYS8fljAOcgZDcUzRouleLS7B3i41BgppgGxFXltw2FZjk7TAW1jGZ9PNxVkujEk1zJqZIVcxnLxWXLsqJa8tSkiFVl/qQZUjZyKkWuiGC5wsTjs8MlK+cXhgZfcraWQUpRJymBKDoUeErykByRTJbiQkUqBWZWGbm24ipAcWaySoi+kOvWQ6CGBCXDW+4gYgl1kYCSUyZVEHOHw4E5LHskCFqXORqxlMrVWYmaiZVjZY5sWuZiLiGpqM7JVmISK/noaLAMRsaDL0WglhLyLyEpXkIQe5Jr+SQhICJY3pKnf2AYck1PZ/wfR8tPxZw68A9mPH7Ak9BcVXHe0/eRZJsE3RYfxPcdM3zl0XvsVGP2mgkHozF7k4a9sWd3bOw3sF8JEwczgSAlbrkA5sOXTHhCkG9Eep9U+rjEfkZFKEFWdpy9lzk5hqhjXth3TMzg9BLuRzQ68tKT9iu0C0yrMZPqGrZ7gPrp9/yu9WXgy7/zdb7+lX/Gt+9/i+OLC8SM8c6IUevZGTlkoiQ/pj0YMzucYa7BkZC8xNo5k6OWul7xWuW57OG1V36Ie3cfYD7S9UYfMtEHpM9kV4hfJNOYB1eRLRP7RJeLNWbleto6EljTZpj6CmdKkE/wBq+uEN6jMVy5Bj/4w3B2AiePCkF+dlzUZJdruDiGOzXUbbFZPziAK0fIdIaf3MDtB9r9M/ZfOqZfnheb9Yc98wdrlqcLusslj9ZC1YyYL1fU0tDU1Ycmqr1W3H7pJm+/+1WujHdwk5qvfvk9fuwnPoO6zPr0LpVd0rcj2Nljf+cmlY1p10tOj+9x1p/TLQPMJtQimHa0leP21U/x8q3P8G/9m/8j/su/+Xe5dfUaIaw5W/dcHbW4ZoSIMJ5MaEcjLpcLPvXZH+cnf26Xr/7ab/PPv/kul6PAzu4e+mhKiCsu0xynI9r9MfPzc+b9Ca4TpB5RaU2dYDdPYNHSXZ4SqZgejpjoiPHlJbsXpRKzmnfM33iPtyYtY3fCAxszrz5FvL7Lvza59lyHN7iMqieKECUj3pUkZsxkqYZgzZGtBMBF0Gzk5IrdKiVJ7EzRrKQk4ATVRNIE0aNSqkZdLNX8Whd7H3OG5YBERSkuCOIy5jOWPfSGSkabgBAwi/SxJO/FBcTK9iVxJLVBFVQCPRcNNNPWEJwQoiO6QHHJE1LySITgwCslQUYeqkszIg6NiloJ/kJWvAOVgEmZMDkDSaUaVTL4WKpCTQoJXgjwkpQpiooSuPOMJfp3k+AbAtxsUGhImQiUIEWx/Px30M/deIl7izNOF5f0IaBZWK8y76xWvP9wxf7OBTcORtzYm3DYTJhUFVV0uK7Gnxu+TYSDOX5nTrV7l375LtXlEdXFdUJ3SHe6w3pe8cb8lDfzeyzyfXZnxtGLnuWVNdaumd5quLZ7i+vNZ9k5+hxIQ78+5Z23vsStwz0+//kf5XOv/iind9Y8XJ7Qx8jR+ADD0OzoYk8Wo25qPj3+4/yx9oefELRP+RxH6bnrvs3YGQf8ACT3ZJkwFCXYzTKLSR24Dhufcpbu0zKm7RzN6JjMCu+XrPs1h4/uszep6bjKxTzzhRC4215SX7vKz+SKn2lu0E8mfPHahPvVitWbD9CzjqnM0fkCzRO63WOqyxpdQT0b0ezOSOOOq2cVo75mteOYj5Ss98hrIfSf3AQdSpHXxwmLHp998uHk+e+67mcWkI94/btX/F38uz1FJz7PD/qopNKHfPdHfvhD3ttsk5SZxeOFnk2rPvv/p1fzseyZakoiqjMmowsO3BnjnRmf4jpXUZysEO4Ba3qMhRjZOqa1UQNVtoHQ61G/gOm7wGlRSjKF+QvQj4pwZk0J+O5SsmJP2OEn1+HGnnLzY/WpZ4bnTfXk5jORQrT3T322pQSWlM0hDb/1BZA5jEfF0CSPSpJDcNTjEdHgm198m7/x1/8Gi37JdHfKtWszXpjVfPala3zm1k1e/vQr7H7+R9GDlxBz1DmXxOHj42CDzaNgVflnK+XnryipODWYZF9cfeTZn/q7o6dUAPze8ZOfeoXXrxzQhkgmkjBWCKtxRdfW5CzkbJgUsjaZULse79aQi2pXTZDkWSUlNpGgSkzVQERnJr1QR8WicdGvuf/ohOU60HjP1at7jGcjtPZYpCRpUkWMRW3hNCE+slHvRVeRM2BlbLScyaJ4K1aASCHES21chKWAeqLaYJtexlmHR3Ii5YR6w9xgm64VyQ9chVbF+WUz1sZCXFJ7tDjokc1wMhDiT6nEFSPHYrdYbNO1JBGlqGuyNxIl3ii26RERpVbAYikOVIHeyMFhWpGzlHPTh+99UD8mYuyJsWe1uODh3bdox1PGkx1m+1eZzA5oJ7toO4WmIeeIkcBFosUifJQSz0jOuL6ouY0IUpdjgy/HNRqSjUoyvRZrzOQMn4uSxWchNzVmpVAxO4dGwaKhLqJmmAOXpBwHIA2KFhHFZU+qShLYSaKrhdAXxZhmIWUr7RCSFnciL0X5k4vamSoT8RBL/VvtIwZ0UoozohnZGWRfipCdQAIfFQ0OFSFkT6w8URKVlWSrxlKcXOeixFJKCwB14JNSykbLeSfDvejjwFTQpiY7RXLAQiSnRPKOyjsQxYkQN0UABKKBpoyXop5PMRFiZmKGs1SSnKLkpnqsFLehIMA5WKuxBnozmg0pTkVVNahWEHus67GUiTWEpqGuPWmZcFKTUbqUsaoowqTrmMoObmiLVdKaAZczPgXCQIovbCBBqJi5igciWK3IPKCxFAis2prFak0VI6NseF8hZkXtL0KqBF8ptSgaIpYyvRldzFgt1KrUviruWAFSW5LANQ4nio4qkpMnnsJ/xCAIbevL/VmKtXhWHUKegXjGUCtUsQ2nd2KjFH8ypwk5kTO4dlNwVkbEZ3SYBmiZW+ZNRCSPOe9StLKJ/waWQMwev4491qcX8nqjFC8sCBva3Db3NWOYi8njMG5TH7XZ7kG6CZYHEkOG7y3fZGxCu+G6+RgxW7dac3JvwdnZgoudBUc71xg3Ne16zfoy4XalFLfsTrBqXlyyQsDFGq0SNLuEy0dU456qGbNsGx7N59zqj3Cxpq0runWgkYqqalnJkuitFKY5hVixDBU7/ZrYrZg2S9puycN2xulown63RFYBQgcHR3D3G3C5prkyZW9/yuXpnJ2jGW3aoV+uaKtMqC6o2itcxo6ptNT9msqPuHTC9WaMu2zp4iWzvERtzVrOSNzCY0xpeEmgbne4enWPV/1d4JAVMyampUiidTjZ54rbo5QsfRAZWIA0sF//Lv0de+CEIut5D7gzvCaU3jhHEGewmkA/gTwFJsXN6BPCbGeXf+d//r9g/6VrgLBcrvjP/87f4h/8f/8ef+u/+M/5z/7W3+Av/+Jf5t/4xX+VV199lca5wg314BuGE9ge5wrQch1X7YTd6xNsL3H/O29yslqye+VF2tEBLU84ZvGUFkWbXffU/pEE5q10o0RB90CK382MMgU4pUSwhzPBXofki+r3CVwhw1kCa7JMCEfgroC2m8tn489QD2pvYTIUygQr0/dNEWpZ38Hwze8Dr1Ii8U8qv/xJZakprEUykks49cRouCYSnIdYk/wKnzNmQ4tEBLMMvqjFxQmWMuJcmbsPavGUDFEwDWSrB7W44AbyTxA0O6pemC4iIyIxday8ELziyFTSE9UgKTlpUfv7xNoUMgQqOnEkBcugalgVSeZwSXBS2uAQFLMK04jXjFRGlkI2u5yLY2OG7GS4hffghXWqCbKg6TLOMs5nRIvuIfUMZLgvMZ2V8yNSVJFZY1H7iuHVaKT8dstFRJYRnCiOEqetNZHESoxj5dbnnA7jXCwdFZInBC1z0I9xuJ2DnJTkEikXxyONgrnNmOqHffABtXgqbR9FCinutKjFvWaCGJ1zjF0sJLcp+bvU4pDElbZoToqNvTkkeZz5UsQZHULCiw2vxVKwLK60OFKDYGiIpTDZOdSGeaIvBWaSFC1+5aCJhJFrIbqSt0wiGFVxErUeVErMqQGzQpiLM6KWNiguC6lxxJzJdVGdEzIpZbrcs16vaaueBo9LxS2ys0hHxtTw4iHV9FT47Ki0x1el0CSblhghJ5qUqbxuonOSKNEqkE9YePIvCr9PIaqYDKzq95XR2+L3A8OQ/rvVPKTMxnrpwz7OctXTxRPMlPRHdE7z/eD7JsV3X91nfnzJ+eIh91ae2cWI/XbC/njE3rhmd+zYHxf1+F4FExWmUgrI5pTHOYUYP+CpgsNPAMWds8fzCGFKXE+5d9c4WQo0cOvIwS5wPobdG4wXY26dR2Q3IvUj2C0DtM5GPLbW+rBYyODh2X3effsL3L3zPhcXl+UkdErdjBiPRtTmmE4rOCw9GB/ZBX3jCFrRXzTE5ZKUEvuTm7x+5Ta7k5u88kM/yD+u/xmjqaM/qyB5XOogCDkKcQKNZmpLJPPFSiUZ2Sp6YGVLkhoyHqFpRB+qUnkVP0ZAt5kMfhCb15yDwytlcvTSq9Ct4eH9wWb9AfQd9BfwcF76kr81gukM9vaQwwM4OEDaV2ln0MwWTK8ec/T6cbFYPw70JxP0lRe5DB2T3tPUT58xmwu69Hx55fardD/xGa7u3+b4rOGt71wQQo/0kfVbx9S9sc4tfjQDuySuLslpTbi85OHFii5l9qg4FE/jPa6vuXb0EiDcvv4Zfv7Prnj56mt0Jw85e/tdpteOmL78OjJE+33f85tf+TJf/+bX+VcOf5x29xpj/5C8itTOMaomLNRIzZI0A1+Pcc2alBrmeYUPa1xqkF6xFlpvnMwrrAtcXQszl4h2wr3LNePqCrd6o77sefc+5PYW91Nk3p7SrM/hh56PFO+0BjWSh2iUHkTJUw3WVClHRIvdJDmjZiAdLiltUppUzhnfwyBFK8qTTZm9GWpaFGC5WFIqgqViwVM5j4tKHytCbaAJI6HmEGdU0pFdB5JISdEkxR4zleBSYyK50usXzbgUioWmJB6Lir2QXVGNac5op4TsyZbpLNKYoVXAO8P3xcA8mi991ZNgqSFpjeSSbCSX5EqWhItlO5VS1elITynBDYYqTCOz+etpElwoifdsOtiiy5OJrQy2rYM1aaZYkWb3/EUvr+zf5NbkkONuzoPlOQ/mZ6z7npwzBOH4JHB6HvlOu+DGUcv13aIen1YNbfJU0aMLj28TcRbpDu5THR1T79/Bzw8ZrW7ShzPO85oYW3T2Arb3Pnp4yaSacFIldl5tuLHXMuU1VBvsIvLmr/0mb3/nLi/tfY7XJz8JFzt8+xv/nHfeeYuxtuwczAhuzfJ8jfSlp9W0ucJPzv4SO1X73bdtBerIXM55YHMm+RqtHRTSIzOU1+0MTJqAjMCEhxcLvt7O6WxFZTBeH7PrInOBVbzCxI3ZjZnlZUvVrbnT12At+t6S41XDm7OW0YtH2NWW0ajCL98gPPgmJ/NECmNmfkL/3hzF0U4asAVhIgQLTFqHOE+oV8x2L5ikQDdXYr//3Mf7w/DxCks/sKc/YMX+zCLPLFoGV3l81n/3euWZ9zdE8SY7Ko9flcECzESHBKc+Xk5sk7gETVr62A02biaGZUWye5z40TQQ+9GKlVcWJLhSXSx1SaqbL608kpXvTCA54ZLDmSEpIWvDoyVxP/SZrVPPSFpqX3/wF5Vnefq3f+Qe/j3B9jvEhPGiw5+eM2kM377Irk6ppHh9GnsYe3RSs0qZVj1uWixOL4cK/KlFGpsjsoQ+wckuLK/B6eGT4K6nZMVGwMpALiFUkJsyWYYnCban51ebdq1PW5ZufrQf3n+X8h1nwIsUIQhPLd/xhESvwAU4jrATYfKUgufBnYd84de+wp3vvM869zy6c5e3vpb5zVr55cMp+zsTZrOGvdtXufmZz3Hz5Zf5qcmYW9mYXH+B+sXbpMku4mo0OQQtVsYUJVqJBNZlI+ae3EM+KEnBjSvn7348nz8q/vTRHiOXwRIuOKpe8bXHosfWxUElihQCw4oqoHFrhEAdwWfBZ0efPNElgpeiEneeDNS90YZim75IPQ9OT7m4XJGTcXhlh93dGW5Ugflim549FoRIKH2sB9sUy1YIcfSxbbrLmY6haCxvbNOlkBUulx6muSLWgpiBKj4lFH1imy72Adv0wYJSGxDD22Cbnp7YpqfWDa8LwmCbLiXGMaG4RoWBoMnl5Cy26aBabNOzQpE3g4SSQau02KaXtivFWvtp23RBUF8SSP+iYJZZLS5YLS44O76Hrxqmuwcc3HyFo1sv4CygOYIFzPpyUoujM18sLLNSZ1cu1ezIUn5bkFI8Qgx0qiRnBB0sclBidGjxDCpqbFdkeTkLuRaqHHC+L5WFwWEmj89TjSVe6we7Se9LkbBlwXwiJUeVQK1YZ0oqSqQ0eBlvrD7NivK8NYUml4LN7DBf2JFEIdcDw707l4o5M0cWRZPhZUXKDaU2oiH6iDcFEv1wrkVTREqvT81CqAYrfwWx4Xs/BsQy0tQkN1wHIZBTJtdVSZSrlBidDFrGuVhyv/hspVA6ZUIolKOzVFr3qJLqiuwdMSmWi9W41kLvEiut6JMxCpEmZzyC8zXO1RA6rO+wEEl1TagqmsqDJBylh12fIlZLcWlarxknoaYqCi8THD2VFAtOe4oUT6LUpsy0Ll6krUfO17gY0caxamoW6ZKcI6OYCymO0UtplBQd1JVSiaAhk2IqJiMpkxEaVWp1eKfEWBLlppna1VTRIU31mMj4owhRoancJuwq19Og8t7MPMq18kTRjUFWwUyHdgsARh8TZPCVf0z4ILChmm1D5j2h0ss2sAkdNuS3PSHPN0z6UMD05HY6fN5sswHD9+Vn1lveL1u/cYl4PErLhjTPwzxNHi/7+DvZkOj2pFBTn79wNefI8b1Lzk6WXNxY0o0T450Jk8WKi3mkCp5+sUJ3dkjtA0KXqGLEh4w2EW126ZcPaVNPVSfyuOHs9JLVuqPpW8aThgvXU+XMSGvOnBIryOuEOIXkWaeKFNZIt6bJS8ZxxVkO3B+NeeHC06QA8zmydwTNLpwvkSuJa9cO+NoX32AvGS07nC4e0O5AtAVNvWLVLdjxE+r1OdVozNIiqZoxrlqWwRMtUOcFpkVBWbHDLo7P2YzXTajpGIe7ZL9HyCAEzAsRh3mh0tIjuODp69WAVA5m+3RizyiMV0cJLt+l9Ac/p5Q4tmDTogRPU+imsBqX10ZTmLRQeZ7p1fdxIcIf/4mf4C/9a/+9Qog+MN658yanZ8fcuHWTN86/zW//yq/y9X/2G/yj/+L/w7/77/xV/sJf+vNMdmZILWUgm1MKQI7KtbLpQ24pkbvM4vycO8v3WOuYbt3xwugDUedwPiMGrpCPlxjLthSr7SdBV2skAa4pvc6HXTAy4x4RFUcU4WJcCmSOWKI8IzMn05KGjs42HuLjp3eltVwihOG1hrLMA4V5W6jvmkL0kfbAzUBukcyRrLQFKeuUxz/r8d3l6X98cN7x+whzpZAup4z3GekFUkZcxEJFxpV8VS7texQrKQqXsSGOyiQ0J5IWpwAZ1OLmBJHiflMNTj0EhzNPpRXjsGaUIqOsNCZUOdHHhCOiakTVoRWhG2yti3X6Wmq6Som5QlQR61HNJY/mKS3eguFSRmJpKWjSF0GLj2ixchziDCNHV3Jvwzw8+4w5j0kiRKUh4OpMNsiBUryaPJK0EN0ICS010i4RKSpsVWM8KMMll2IAs0J0l/rwRKeZqBk2rR4FvB9ac4oRJRKjJ0SHxWJB7z/OeZEFp0ZKjuRK8YJ+QC2eURwO0x7xvuQ0ZTjST6nFXVaSDqlVyfTmilo8W8nJOisEcs7kXBTdWRTpBXMecUIbHD6XIlVJ4FJCpDh4istI9ggZ6kyWjFmicSXXG7NDzLE2QymOn2CIL/Mny3lw3CiuWjmV+aaLjuw8QXvUinCorjOjHAmu9P92KZM1Y6qEofWOqhBjydskM/ARC0vuXzwiLTs+f/WoXPtA7TIoODJ9TnQYyVEI+gxIxGc/OGsIFhzOEmMi2fvSn56K7Eq+d4snsCHu2uJffphB7OPAWw3zxC2+C9/3HeD6qze4+mJiebHg7OE5x6cLHpyfM7ls2avHhSCfjNgbV+xOhL2m9B+fOTjUUsu3ogg+PlCA+LGwiW9KquUcyytkHWh3hGu7Fa3WjKalf7GlhO7XyPQq7iRQ3V9h9QMYd+TZMa7dYWP6vplvwZONzZb5xjvf5Ntvf4s7d+5gIbMz3SV7x5X9F5k0+9RVw/5eze7t6zy47Dhv16wmFdV4RqIlH/dc3HvEMihVHKNL4a1vn2MHM0Z1hU1nhD5RmyJE1iLEVlDn6btA7KFfG+LqocLJiJLxtTKaTKniCG8tdci06WMoUfIcpKE0y9xMFD+CJK+b8pjuwCuvw+ISTh/Bg3uFIJ+fQT+HR/PSh/ztpqjODw7g6BDZ28NNX0EnQx/y6wt2+j3W1YzVxSm1dx9QzW0u5mFQrGpefel1mvoajxZz6mqOJejXax69eQZ2wFz2sLkjPXxIWi4YVyNW2bPMNasmU0tg7zJSNxXNzi6TnV1EhMo1HOwc0oxa3nzz2zx48B7ri/d4dW+Xnf0bAMwXC95+912SL/ZwX3n/LS7jmrAMxPUF1WyMhXPG4456nCFHqsZhqWa+7plfRBoytRuh+w2NBmRt2KLDHr5F3pvS25rOhOUiUl3MWcSefi6k3HDZN7jbkXbyvV0OvheiL5XuWYfEQedKP+66BJFOwZngnSGxWN9kJ6Uveho8PEr2D8OTpSQno4ViklQnUKEVozMp1c3iIGtJ8KVIMsVrLgrFTQLdFKs7zNZkl0rSXbXYgyRDcwmoky/kvSKoeXwqyZdiFRTI0UB0CP5L8GfJYWFCljWJvhDZDsxSaU8gkHNRj0koA0luHCGCukSVS9JEJWHVkCCJjopULI2eUYI/MaOWjaJh6Au+sUV/ogRXMMFSIcETkMXImkGLAr38zuc+3NRNjTYVt9oR18b7zHcW3FuccX9xxvliURoQJWG5ML65uOSdZsnh3gU3D8bc2JmyV49oK1+s1VcOf2rESaQ/OKXaO0Nu3uPEHXG+mJBPrzJqb+N2Ml1+B7ujXNl/hfpigtU30HYXS7B4+4yv/tPfITzc42D8I+w9+GOczu/zzffe4Sys2D+cUfuK5RL6y74UL/iK282P86dHP0Et8qTJ2saKWcAx4nb/Gb4tX2DBt2nlRwr57YzHzZZkkB+qYXbO/fXbvOvm9CNjliva6FkkI0xH7NZX2NmZkWKRyOquIitoqsCtBxfU7QFfkDVdt2Z6qowewI3LQP/ybY5XE7pKuOJgHN+mC2csLhbFnlLWVLZLpxVJLnC6ZLauWaWrXIQl67x8/gP+YbDN+Tg8hiKBkuArk1lycX6QBCpumJwqLjrUHLV6xk1DVTVUuGILh0Oi4AdbTRNX+gmbkM0V+1pKrynNglhLrhWVpvRexeOjx6VQDomCSlGBVVKssZxB3VeFENEaCRFljKNHXV1s36LDRSsJnLpYw1Uy2I1ZTZVajMHWTMAqIUuPxlGxE7NEyA7nhSoIyerS4CBnsisTWKXHW0lhVKEQStkVsiajuCi05phGT/VY7swzebtnJh0fU72/wYWc45MS7q6RO5HWtYxf2IGxI4+FODF63admQiLjqiXrusPrg6GFxT6yBlvul5hqNxfOdzGGB9fgQVsCup6SV2wofb/JkO5BNwc9gngENiohxMbbt+eJZaU+9aiGxwg4pBDtO5Tc5XL4zKYPz2YnrSjOlgsQD7qGoxugrz/ZFzln3nnzHe7ffY8uJXLMxL4nWEKCMI+JB48uMenpvvhV+v6/IiXj6v4Onzk65DM3b/Lap16juXKTo1df5fYrL3Ptxi0Orl2lGo2HeDYzdMNGd6FO0H+kMPSjjvDzJ9THQ6FXxqPiseRJTqmXI/JaWY4cuS5JtUDPmAVqAc2JKpdrOqeaHiM2mWA1WariVBQSk770mIspcbJY8OjkjD4kZpMRh4c7NJMa0adt0z1djpgmvBaikGwlLhD/jG16oBAwmuPAKWghzZ1hFqF3ZO+KLSRSlCQmOPSJbXpd4pYqGZ0vBX4MysbHtumWyQHElDzyOMsIUj6vz9qmi5QEVspggzwxDbbpTlIpbnPlGhdR5Cnb9EoyRsIUnAi2NCx7TDe26ZA1FZvuPwCkFEkp0q0X9Kszbk6M0ahCfY1qxgKgRblvGgpBCkg2RFxR7ntHNk9IRSEeRbgwT48QTZkEJWtRygdy0duJIC6V82hw9vHooAgP9D4RzBOjoyp1lOiw74u1psOiw6dYnISy4HMhtlwGkTDEhQ5zQ79F80hIOE0DDZJLMYUrSs+YM+od4sr4UKwrFRfK+lMSmiTUkum7Fb72rOrS1zz70vcwu2JDqrn0ySSVggfRiiyZSHhcOPHxkNGmJrriWCAxkGMqiiBfFO0bhbhoSfbGXBKBfpOIjokYSs9QnzPqi1I8ek9sKtJlSURuEvOmiZUYvQkuBJoUac1wWqGuIoc50ndY15OmI0Lt0dqDBBQr/cBTjymkuiKENW3M1JUnUAraYoo0EnEpYinQp8giBnrvqJIydRWIkmo3JGQTIp7VqOFSjD72jEKmqisUI1omqBAcjCqlVsVH6EMim9GlYpdZqRtIcU8fE4mSnHUKFYq01UBy/tFMHtW1o3XFtJynleLDbAYDZ2XfCDxWdKdNYSI2WJgXUtyyFFL8ce1iIbg3NXFlDlmKHcsIWYj2wlsNyVkZfLWGnuRFtbohqp/o08sXPBlnB2pw+C3Dqh8zYuXvDSlu8sSq/VkPpMcdxp/82Kd+vJl9LJK08nDy4JzzkzUXyyXLacfu3ozpvUf4y4hfg647/JUJadqQL5dYzPguouOAupaYW3IXyeMOP2m5OL7gYrnkWjehnTY4P6fqM62ryFXNOvWMnFAnI4th1rAIK3a6HgkrdtySs37NnfGMH3KeJkW4XMCVa9hkH+bnSOjZPZiglaM/W1DvTnDzlnRlSXBr2mpOz4JcX6G5uIfPkDXQ+RHTZgI2ovKRI19RySs4xoiV2Hl0Ae0x6PIQcX8MeXEPlwxW34RbO/Ryi7kTrrDJBm1MxTdliIEScD6gSLorihr8EcXa6A7FesiB1cAu5BeLGnw9LhbpMoV6BPstVJugLlDUyRcUivbjo53u8O//e3+VyWxSXrgKt6Yv8r/+D/4Dvvjf+fP8X/+j/wt/9+/8El3f8yv/7T/hC1/+Ej//t3+ev/gX/yK/+Od/ngMZQz1F9osnTozlPFYnWDLieWK9XLLciQTv+Nxkj52nvn/Dh5dJdKQE9ZmVLLgjPVPbYZY8ddNCHylKCA+Pr9aOfe4wtTHCHhnPVAzlBGFnWDYC50OsvF9ybBiZNNgkD1/fQZONdkKZNwxH9JqU6YFnKI7ZFCyqIxs8PL/D8TqxOzri5u708ZCbh9U+puXz8BisTDf27cMaf1+QrTgMWc6YKy5UMRneRYJWaKpImnA5Y9mVNm+mJR/mHJoEdUJOucRClHuy5kza5D0sYLlFoi+iFgSvgl87JCd0XVohYRmvQq4d5ExAi+OSVGQVogR68cShlWF2qRS3Rh06/SWqHKhjLHbULmPqsDojVpTsQaUIvDBcCOW3J8NJGb+TKZYgS8nB9OroKwcRJJY4TpPDDQWpafCCME0kKb2gRY1mM17n0rYqDS1yqqENRq+lbzhWyFwn4HxRjyOZRCJmR4gNKYKLUKmgFdjHSLJZcEgdB7W4I7lY+nN/QC2ecYXm9wGLpU2QSEaSkbWo8OUptXjlSm/xzjnGaaMWz0V9n2o0V0gsTk3EGg2KSqTOAS89PnqCeawprpM+pMF+vjhmyYbfxqMxodEjQxsjHcbnMtMcilZcRVBfxtAUBlF/VeZZQwG2QyBVKKnMAxJUmshWYREY5gHZeXxwpXWABxfXJQ/rIhU9o7HjW+/PuTXe5cakxiwPxVBCI5msSrbE0sp5oU7oSYRkWC7npboS4/qY8XGNrx2aI33asElbPMYnlI/a4v+/ICJDi6M/mnOb74XvmxRfiOBrz+hwh/H+jNxH5qdzLo4vuHN6zoOLCybzht1mzMF4wt6oKf3Hx8J+Y+xVMPOFHP8kiys7SlxTU+HsKpoekDjB7dfs+F2mOzXqAusHx8S+Q3LEQs/FGRxOKuzIoZyTJNDYFGUHkVFZ+TPeXtD1Pe89ust8ccb64pLGtfzpP/nf53I9Z+/KjJdeeR36cyaNMNJDxs0F4mA28ox2x1yerllPJ8zvPeLR6TnOdby/ekDtprx2+3P81I/+GL/2m1/nUtfkVYerMu04EutSH7foMyGFxwOlpYRYD9IjeHxSam80dUBXwnj9MU74L/46HOwV4rqZQT3lsfn9R1mEbV6fzmAyhZu3S8/xi3O4914hxBdz6FdwsYTzY3jn7bLsbKcoyA8PkJ0jJu2EKkUWcYXRYPa0ffoHv1+Yjl/CUMbjwAsvzGhGntPTnm+fgx8d0cWG5fEZcueSfrnmcgq2drhJzfRgTDuK9Ms1TT3m5qc+hfe+WBSZ0tiY9978Nl/66he4uLhgtx5x+MIZO3vXMYGH4QHTozG3927w8u3r/PLZXdapJJs19az7+6yXj2i8crDao28DXbVGUmQUPb02dKlHKyX0c2QnMBl5msUek35NjqeE1ND3DtyKM/GsRj0zUerTCzTW1NEz3rv+/MdbIooQskD2eAs47SErVTKwjMsOHw0JCZIjp6KsKEWDxYYQbOhgUCrsfTZ6Kb3oHEDIuCoQ1JFzQlTI64qYBG8OJ6XnqFmmzobPJaDNg7ozOSnOXZpL4JxL8CvJ6F0c6voztUUUh7hUVKKSMXEkSaiFQvb1DonucY+m6IqqJ4oQrCi+IWMxo1rhcxqCTyWLIxOppANROgFxZaqHRFLKj9MsjxM/GxKcYi20sVQWkRJAps378pgEzzqkamSYs5nBQMZ/nGjlKw/e4oXZEZNmROVb9pua3XbG7dkVHnUX3Jufcby4KNbqBnFt3Lnf8eC4443JnBtHI27sTDgcjZlWDXVSfKhwc0+/Kxy/7Dj/dIdNL7nSJW7E25BewtHQ7fe4i13e++017CQm1xaEk8i3v/lFLh/1vOpe44Xxdewk8nB1j3e/8yY6qhjNdqg7cLGB/SmdrQm98MPNn+GWm5b987RF86aWJwsT2eMH8g9R+ar4tG2gZ+DugV8DLxebhNEC54WxdFj29AFyvsKknlI1a+orQqpWzNZGFTsuz5fI3hUmdcOuTtlrd5lfqTlbfof6/TNunTh0vMNbt3eZT3uqFLiqjml3xMXsGt9Y3qWpGlQq8tox7wJhZ40cOZLMyHMHk0iYP3z+A/4hmDjHFWup1zVqmWgNq3ZEFB36egnVKjKJGZzDq2BaE13NpC+V684y6wNPqitqGnwHidL7yTTjM1g1TOyzYCS8NeRsOBI+G4mKHo/lClc0XhAqlIYqDTbO0ePpCrmAIppxQR8riTSVynZRINYImSoW2zC1SBOMNkVqM6IJvVf6nHFRERWiF+i1zIhZIVnw6lh1CdWAU5CUS29MHwnZQ1ScZpyPVEmph/thTIIkJauAGlVOVCGX++hTp+eH4ZMKje4vhHa5Yn15jM0jk9E+cj9AdLRT4DrY0Qh8yxRjWk+Yc4co90k0zJjQpBHy7hpJE9h9HR42MJ/AuSsk9YpClDueJLNEIb4EOod8RonO2sG+dFh2c40qT+zSN8KdBrgNfG54vjW8/y2e9CvXYR2bTNcj4D4wBnkJqgnPRLYxRL71lW9wen5cYggrBVeaBRMlZ2NNT8xrYuoJuSeQead7yJ3jM371zfcZ/9ZvU6ljPJvwJ378c/yVv/Kv8xOHf4YxI6qn2P0iljPUb4zQ5SOOaeaJp/yYJ03Yn08t7kNDkOJCEkXpvGB4fM60YvgV9J0RrKbNRhs9XsegFd5HnHess5KaSBBXbNOrohod9xTb9GSchzX3j09YdIHKOa5c2WUyHaN1hSXFshXb9GQfsE0v42F0TSHdBtt0PmibrkX1WxzM00fapivFGjDlVNQpCj4nonrykLfOrvku2/Q82KbjixrZrCTpTOFp23QnG9v0waVFHeZcSVBJJPliC/nYNj0lRJRKKYlJ97RtumJalX6SUmzTo0Fu/uDTDZIzB6nnKEO0TJfd0DfQgZTEaROLopg8xGeVELyQYjlmXjKhMpY5szTF4pjGBIuCeaOj9NIs8VBxolAVLCo+O4I1NJZRhTSoihDIVSkulFxUMGoRiaVffOgVdUYVSzpd1RB1pWe9WXktlTgp55qqiYUSyx614uKz6VGagoADh0JMCEKVyvaTBWeGz5E6Q3CKSI8BORdL9KjFQt85V1xGNOOrhBSf7zIGZVeS/h8HlpG6gsoXC/XBPj0DVtWoFLIcyZhUJbbOZdsqy+XcS5kUEimD13JcnSg4R6p9sRu1wd3ABDSzUugwbOhH2pihrsK5mrA2fFhh654EhLqCpgG3KMlQUbqYCwlf1/TrJW3I1FXFXIpyKOVM4xM+RyQGQggsU6CvakamzFxNq45clWtTY0KlIjUNl94VUjwmKteirlhv9uqJKmjtqLxQ98Ky35DimWiZShyNKt47Vmsh2dD7U6Eyh9YeaRzWff8eqiIlIa9aiv/kYyiH/6BRNY7maVLchp7iG+G0FbeBjWH5Zj6TB0pZoDh5mNHHWHrQD0rxQnYXd6FnleIFT9PVj1XpRlHzD+Q5FNeTDVG9Sd8++TfFqcs2ynR7Unw89OC0jZX7hkA3ecKn2pP525PtKoXfT7v9yEBOMbiYPC9GI+VivuTk/oKz8wUXO0t2pwdMRi2jrqe7iPhdxYdIt78Dj1bEmPFdwKWEOiXohLg+w096fDtm3dScXC446vZwoWZS1/RdoNaGUT1isVozqx3Nqsw3NTYsgmevX5G7NdNmSd2vOGl3OBmNmV10aBdhtUCOrsAb78J8SbW/z87+jNXJBbvX9qiOJ8RuhasiuZojfskq94zclGkItH7ETGcc7b9CpMNJwMsVYFrG4GUpcJZHhSAjthijEv6PM4ymGJ6GUtCqj494pii/K4o95JgSIG6qN79DIcOXZRmbQX4J4rRYoq+nkMbQTGEyBj+owSVSmmxfAA8pce3G6ujjk+LOe/7SL/5l/tRP/dSTeWsSZuMdpuMZf+7P3uSdd77Bf/V3f+nxZ+bzOX/9r/91/uE/+Af801/4O/zln/hJPv/pH2Xv8z/MygtvvXGX0VqYXZmyc30PrTv2xrtceXSL/9d/8/d49PnML/zA5xk3Q8ueTbsBc4NgAUQSR9KxS81KPOta8MeUtkITKEF7BjwqNft2COGC1EemVUVTrUGHYgQDZEkp02qoi89SKSDc9GoPwMOABKFqO9BMmuw8rqGtKE6j2KZIhuJGJZB7o3uv48Hdt8gvddzc+fTjnOSm7nYDc2ALkHOQoyc1Lr+fZYqW7LFaPOVM5a2oxXNRi9PX5KbDqUEyxEoslK04C23U4mapEOFa5piaBFFDtEa1QbIvDo6SkTA4oyGFxpQSi6lF3FAJ3zstSmYdnHPME/ClDYY5aFK5z0qA4PExUxFIKWFOh2JSIVdDf2nJrLNCKL3QfYBKM0ggIGRp8VauKVVXLOMxVt7R9i1+XQoDfC6zp1yoVEwzWRJBQDRTD2S4mjzVN1yppNyTg5TcoTEQ0QLODa2LKPOTYEKIDTmAJmgYyHBnhRCX9L0P6vdAjorzJUeQh9xlylb6pX9Xb3GHSUB8hQWPfJdaPOGyK85UVnqL91aR1KFBERvidnNgHmcRbxlvEZ9KTIgVBXZpERNwOeGy0osjR8XUE3VooxQUzTVmgV7yk97xTkEqkjjWqqXYVwSzhMNKz3kXKUVGARGj0tKqx5kWtygzEr7MAwWCczAUK+ATVtrQ01ux369dQFJGW+XqzQP6PvD2xZwrO8W1y/pipS4ClTOs6nDOCOYwHyGVIilXQ+6F5BKqhs+ONirB5ceuC/aJehj/IYIM//suO8gt/mWDYUPBFdsakA/B902KB4b6SxGcE/yoZnd0yN61fbrFivnJJeePLjg9f8i95Rk79Yj9ZsL+eMzeuGF3ouyNjBsjuFoJj+c1H4anL7zHywx30Mdzl/LGGXCGcIgDjsC3sLum3e0RGpZOqYDF6B5dPCOva0JOLKLS+gP8ZIoPGZMl/fIRVf0qvi6TtLxMiFd0VMKpi8tLaBr2d3aZ1/epqxk/9Nqf5O77b6G7kWuHh+yOD5mZ5+T8gtaNoBLaqoWLBf39R8RFsTUNMbPoLjk9u0ubHZ/+zC1+5qd+iq/+9ltcrFYsstKIMXUlGZRDqXPsiTDKCGt0nXB9T1XDWD3WrUnVmk4cExmVXh/Pi3dP4f0TGL8LO2M42IX9Q5jtgx+D1DzTnOmDB1OkWKy7EbQjuHq9WKqfncLxA3h4t6jJQwfzoQ/5vfehbrGXXoHP/QgX6yWn5w+oMMbt0yrojXXZ5jwRoFSq3TqaoVfGeF9x5/4jFtKUZG1M9OfGeFlRhR4WAemhHiVspqyckNaOJhlXrxzhXLk0lvMV9x8+4mvf+gpvvf8ejx4suDI75NUHD7nxmU8T1ViGU37u5z7Hi3t/jPX8DD+OjDqoZUxKji5VePUQl/zJz36e6y/8MN/65le5/+tvcnFyxv0RPBhdgmXCKjKvI5ocE71BNT5H99b0FzXeR7RNJDXaySG7vuIg7LE7OSJPA9PDo+c+3MV2VIoQzxKOHk8kh6r0l9WEJXCdQ6NhRKQScinNJFH6u+ATqgmPKwk5KepvKImIZCBRkEaKPZQZORfVtIoW7kQykgfiOuXSv8hXpfejGkoslbQu4bTYUAkOcUXlpcnw1hUFDKlM8lwhy2NSJBu9GSmHod936YuHlSrcoEZMxdjIU4I8zYbXHkUJviFHB7qpSAexUkGbfCoTlFTIa7Oi9n7Sj+6JPXTpC65lWilDpb7LQy/ycla7Tb++XCaBRSxeFG0fZwr3S9/8CkfTGa8eXuPlg2sctjv4tmHSVIzbMdfH+5ytL7m7OOXh5TmL9bpMjKJwdh45vZjz9njJ1b2aW4cTro4nzOqWpq6Zr6Y8vLiOvxyz9+IddvOCydt3aXgRd7uBz5wzv3NJ99V92tUI44y3v/Q+X/zKt3HScvUFZXz4gHMu+OoXf4U33vo2bmdEVU+oskPaTDNr0asV3c5rHL7/r1A9AhsEWY8VpTy1i3KmkQZh8oRUM8pMSC/B+uLXJWNy3sfciKapyN6zEyfM+mvUNqarHlE3HZNHmcnpiH7VEnLg6GqL250wWe5TuyOmV2qaSc34uOPot+b4uz21nbC/3/MoXHLVVUi7x8Ol0I8FqyK1m0J9SVif0k1WuJmD1TFxpYQUqMefXMAuKqW1Q6iHczMzdrm4jkgpCBqvPbvriokFgvM0GbQyeg/j1CPZcFYzrYRl48niwDJ1sEJw+MEVNyieXAoOnOF9guzJoViBJU1YEoRETSbHoRLZQrF9lYaVGQ0gLg6JWUFTKMQW4MTRW09KDnXghv5dzqAmoetMayWBU6UK7T21+FI9LLGQ6xIQEVyVINeEtTImQ6yIQzU7qmil1LHc38QrHsOZEbzhgdoKsdJTenT5JHij9CH+PvBJxKnytYbGCXHRc1l1TG9fx7dKvA+r+7CL0O4CfpBuS88OiQsuEZY4ruCqCsaX8DDDgyvwZlUcJtdSGN+NzEQpvb9XwEKgr8EOwKYlk20UtccmibW5RmHT++ZJIvqA0tNw/NRyI0oC7ownCo/NZy+G792IUBqKyvypDNh6ueT9t9+kW69wUoqOklHs9XOxQk+SIQ6uz2z0ZiUVF1Pi/OKSnDPj1ZrlsXEktxk1u8iHqrszxnpYR8sH0nFlfxM/sCN6SqL2+cbwaGCpQjWRKiU6ARWyOCLVoKpV2hTZX/XUqbgNqShB62Lz7zOm0Dtf7Jgptumj4JAEqxh4eHrG+XxBjsbVqzP29gbbdBw5PmWbbj3iMqpPXF/SxjY9Fdt0NaPnWdv0x/1gXYaQkVwRKkFyIRw2tunF5CMUgqKyYi2dhVD7QgpqhUl+1ja9LyR2bkq7gzKypnKTKk0Zy71EDTZ9xIdHqja26YksxTbdGByMQgDLpQWMxads0yF3gNQluYigPhdbP/es2PAPCvuTMQdemRqIZZbmWFOKBLOnkFd5KFBw6UmMlg0kFG+EHInqiD5DcEhsWWngMsMs9/S+Islw6UZXkqg5o6lUtkQMrCKSyKK4wYYxl1O4FFdSXEdEi5Wmt1wUg8lhzhMt4ayoRfFAEoIMLkSxxGElOZyKBbo54pB4VMlIyjjVwc7ZI0mw6ME8FWucQC2JkHskV6gv2yXZkWMqSVTZtL4xshuIUS2Evov6WPH23MilOE1qXxKlMWIxkXMm+0KCo64obsQQ1eKKYY42lX6bmiGHob+lDX3QVUEdqa6IVvqKN0PxJZpYqdGJEJNRx0A7kOJVVbNeKS6s6btQ9IVOSG1bhPFiePV0JvQp0VQVIWX2QmYkFYhDLBJzptGIyxlNgRgjyxTpVdlBGEnFSCsuquLkoyng0oi+rrn0Sp96pjHRGjhfEfpIUCFqKcirKqg6gZAwM3pj2B5How7vXLEWjJDUqNWoUdQ5ZFRhF913HQoRwbuKytePH7VvaPyIsZswchPGfsrIjT/eMf8DRN04Wj9opoWnyO5BTW2gljeC7mGZcq2JFbeNp5XimOC8Y9McJ8tGjT3EPENSyGzTk7wU0BQCfrBP3yj3SoXwoGwdSiM3DlyPe4QPzD0MRPyTFj+PteiP8xbG47ncY0L8KaX4QKDbUHQ9LDKszNhQ4x+nCOLq9TGXlwsevn/O2ekRFy8sCON9xrMJk/srLi4iPla4vkd3plj7iNQlfE74PhGqgLYzusUJ9W6PqxN5NuLkZE7sA77PTCYNZ9LhxWh9y4UKvTdGklHnsKSEVBH6NdqtaPKK0WCh/nA85cWLMzRkuJzD3lWQEXaxxh0aB0c7vPftu+x2gcbvML84oZkY67ygGXuqfJWZvcCOC4gaQXZxsodjBewjfIpiDySFv/YGnyqHJUYZXC1K3EZ1C0Fx6Ma1d4gjBfSQJ3GVUQLCxXCO7AAXYDdgPSmK8DQBnZW81qyBuhrmjamwppxT1OWnZR00lID0xrC9Hx/XX7zBv/VX/g0O9vfLudYbzEH2wcSYLyNf+K23uf3KbfpFYLFcMp/PyZZYLZf8zje+hVTw37zzDX6hWTMazXjw3indInF1cURz8YDRyDjcm/G13/mn/PL/4z/kjR/8e7z87/9v+dzrfxynCucr6gy2v7lnWVGtckCNUFFajKjvoZ2DjSEG8BNMhICi3Qz3psOdtsRbinthQr0hVBYZZATj2UCO3wGOEFqEcfm+OsCVdbEVrxVkXFy/hlU8eZLH94tSVSN4X3Hr5stcLuYkV6Jwsaeu/w2Gj+YJuLoczo87NH9fyEVQ4QQsZXKVcDL0Fq8jUT2SapJ2RS0+9BYvavFiMaSp9BYnJUS15ECtpjaPBI/GCh8yjeaha4AvBW7i6Z2ycolRKjkxP+TTulpIrqZXT85DbGaxFD05Q1MuljNVURo3IkgSJBY3Q7KSRIiaCK4UAJoqFbHEjJR7pWUPzpXWRq7cQyVn6qxIMYbnUjy+6qi6cj8OyDN9w00yXqEZXJ1yhjD09yiW+UaSYpWeKXk9Z5Se5lrGHdNIMCVETwzFBa8ywRU+uexrzZjk0nv8OSFW4l1thoLP5EgaS74xGS58uFqcWNq2fbdavLj6pCDU2SG44oRkjjpkvFEsyjG8OBIZzcOYZw6sIkshvYXS4i2blbkWw/guBh1YqEmuGooYjJCK+1D2FWa+xNJW+s+HXNpiqkSQjOJRl0qBhCWkK4IrUJJ5zGIZV4ORK8W84T2kpEhwRHFkyYhFsgacTyWf6o0Kz4s39rh//5TzmGkaD65ColClki+tBaairMyI2UrBAEbAqFw5v7Pl0i7LSkswcQkXS0H5Fh8CKQ6mlvOWGP+XHYP77eM8+BbP4PsmxUc80bWk4W8HOKf4nQmHszGHtw5ZX644f3TByfGck+WCeuHZrTb9x8fE6xV7B0rN05OKEpFkNnOdiMWHSDUdgqWM2SOMOWJHYLuYOiLgRbhGaTWpAsiUWiZcwUgirJiztkvOJ2csOKWPE3aaGZMDT7XfcJkr4kWHy0ZLZPfwyS65PD1jfLRD2VoYNZ4f+9wPwWrB+aNzZvVNmsqxdHOuHV3h6s1r3NhvqXCM7p2xyJcs8op8sebBvVPivKd1LbPJjNWq4/jihPfv3eHKecNbv/llfvAzP07jhdVqzVoK+ZW6gK8zUcqA59SXAYM1+IALmbZtqCol9qUi+yStuVxH1quP0R9jDWDFJml+AffmUL8PkwaO9uBgHyZ70O6U3kI2+Jp+ZKUDxWL96nU4ugqf+kFYLUq/8Qd34fwUVpfQnZPuvkd88TZvHr/Lgzvvc7Rz9dmgdjhv7t+7T7oX2d3bY3J7Uvqm1H5IQMK79x9B5Vn0Z6TTC3TZUblE3UDXFYWPs47gEzGP6KzBOuHo4BaqyunpGV/8yhe489Z7XPTnXK6FaFMenvd8/WvfoP6TP85k5CE27OyMmU12efjWd6hdzXS0wknPcq6sFku0dezs3ebHf+RnmbYHvHJ0Hf3pn+f8vRPe+Oab/ObdbzBPgV1a+nTKyXJJc3CT5e4OjTygbQwdXTJWx/S4J5ytSc0uM3edl9Y7tC/u4Hee3z5dU8Y0MwqpBMOpEFglIDZcm5CcgBafHJkMlaIhE6wow4viqiSYswhZivra6TB/sQwYOSskJWHFrlMSqoJLsUxkfC6fd6W3kOWi2pE6oclK9b8MCdAhf561xmclpUxlCbQkKrOLYEUVJNGhKRKTZ005ZX1IOAmMM7Sm9GKYcyVpqqV6sYnFJp482OpZwJcccQkuRdAmk3NZp4gQU/VYDb7poWdZHxcApA0J7tMzJLgO2SHJhUwqLdKLklw3CohhQfs4ATvGg8sLHl7O+fKdt7l9eIVX9q9xa3ZIU9W01YSr7Yij8R7z3QXHqzl3Lk64WC/pQ4AsrBaJt5Yr3r2/4nDvgusHI168dR03apB6hFWfpnv/kIv8DvH0PtNpx4xIM15ioWH/9Ij27ICzbsXbb7/LxeI+N3+wYvyZlrN0whtvv88/+dZv8d6DexzEPSajPZp2RuVbZHwbf+sW7P0pRvenhF5Ky7hNTLshvV3ZV2v9Dg0rJH+2+OK7YaEcy2DmtUwcqgaq99AWvPeM9yK7V/9/7P1ZsGxpdt+H/dY37J3jmc+da67q6m40GkMDDRIkBZPgBIkMSrBokpYlhig7bL/4yRF+sMOv9osiJIUf6AiH7TeHIyhRkjmYg0iZpGmQGNjdBLobPdVcdzr3jDnt/Q3LD2vnubcK1WDjFsEJ+CKysu45eTJzT99e3/pPnukTZX21YFkv2T1z3FjcAKZsmODvHJFvKGu/ZvcyIZvK+emCc79irg6dCwebNXenexzszsiPPuCt7pREx4MaWY56DvcibTOmX0WkepJfGgv3fMWsc+RUcKN/nlY3pmKSAFHNeaGrlZw7qGZxOe0dbdfiApaxiUM8rNWY7UF10Ah4AoMtegCnhRGeTTXwyJWKaABN1Gw2yCp1OK/Nymxa+qGPKXg8WSyzt7hKUSHWiEYdmpiKuEoJDlc8hYIvHdEr2bWUGnEVy5bTQqlCU+06CtmhDrooplQd1GFgDfHWDc2AUq4BjoIx8a2ZUinV4Wq2RUJRs6rOUD3EIcfVb0k+WAyKOiEX94Mh3tc9oOfv1LywniGsWPoF4TNCuLuH0BATaKeWirICYoFwAnxIYMWYnvfZZ0yhaZbIiw4YwUNvJ0oVA6e3wPV6eJ8T7OcXGL6LAO0AnvGUnLJVh29fUnkKltfh/R4Pr1lgvcgOc7/cGT5ze53r8Nlu+D4N8Apw/NF9cf/d+9x/7yENwqRxrMRR186AcWTonVv0RsXhJdKMR8x25jTBk/rC5fKSLmdUPXdnr3DjxguE+EkkFQUqwgKbhNqP/a4fNrLBdC/bCWts+/k5RyuO7KvZozvzUty6jrgqUDxIYsyKSBmy3AYrvABOChHF9w2ViG88s67SbMyGr9fKZbfh9OySPhVm0xFHh7uMpi3ijeCCOiQH+mdt0+NT2/T8Mdv0ogYiuFqubdMLDsJgm94NtunXcSGfZJtupJhQleQbtlnW6gJe0wBePGObPgkMBumoGmh/bZuudp07fcY2vUL1z9qmF2oYbNOd2aZLrXjnCWLWBde26WtFS0RdoFpPE/WZglCjEsq//BXp0U7LzFV8VTJK9piFuRMjR9RijrHqzCFDzDixqqDRQO0alEymVFOOVIHeVy6j0gBr78gukLQlhx7qkNeopvYXV4zwWMG7hFQHpUGimnuPiilNsqCazTocs3QU9fjiqCFRasBHI2a47JFtjeSKqZa1UAmoBqhCbkBCxRehUTcQISAUgSrErKgUGhQXlUlx9CniN1Bb0CioN2KWiFjmZVVTQFUBMTVXCg1tUYr84IrjTxquFIv+aSLVO0KfqCmZWjw0eGdEAC8G7gfnSJrJCBQlNAZSlpzJRYlRTS0kHryjtNEy3IswHgiszlU2rrIWRy6VmBKjak4NMbYonpA2dF2Plkr2jjIa47zVmLYSc/SlQBPpa0XWG2a70Y4hHaVWRtITtBBKImdTiq+HG8eYyMQHzoJDo0dywmWlNg2L6FmvenZKZlzA+0jVjt4JBaFEIUYhVjc4XBV6DfTVmretdzQh4MRA8ToSRjEyH0dmTgj7a1g0jOKYUZgwjhNGccIoTBjJeHiMGMmEVkYEAk5tXn1KXv3Xb4gId+c77NYpYQNVBA0NrjAcVWc2q7qtSQdgWCwOyoByO98qlVQq3gebM+0DbA2pdgesw1pum/e7Bd2ftU+3T3mq9lap19EzAs/kgj/zjQaVpAx/u0XvhWr3vi1gNywntwux7WcKT8uT7XdhOL7Dj7hWoKsO7/d84+5L+7z37pKTB5ecnay4WC1ZtR3z3SnTx6c0y0xaVdxkQ9zfpZu3lKslWgqxy3TjhG9n9FeOmhKhTbhpy8XJBefLJTd2pjSTSBNtPTFygcu2pas9yQkNwYgttWWTlky6jlDWzPyKs7ThwWjMomnZy2tYruEAdP8GcvY9uN1xdGuPt77zAfVszc7eMWWz5uzyjJomvHTzVXbD60aK8KbebK5XtMdwTR4c1rgFK5XGC7KbchGEsQyvEoEh8/gju3uTrQk49k+biQLw8tMzQju42IfNGJopTCYQ44C2V6zxdQbyGCtoO6yO28Fsi+YYIN4M7/np5nSA2DT8D/7IH+Znf+Znhm+pPPzeByy+d8FLf/AzXC4v+O477/Lv/s/+PH/yP/kz5NOEiuPb3/g6f+v//dc5PT/n5OQR/+Qrv8qf+FN/hB/78Z9k+eQ+aXnJB6sL3vngjN3FjDgJ9KHhtS+f83/5v/+vaWdzbu8lXH2Lq/U+vq88OX3APB5S44gnNSN+zFymHAzXbUuF3feBBKWFy5smKmkqD4BbURgfVHCPme6Pkb6H9hAWDlKBncd2DESArwFvDg837MulrcVlYjWrB0oCvyUqJOzgLsDtDa+Ra7Kt3w88Plvyl//P/zd+38/8OF/80R/hlVdfpZnNQdx1H9EBLvBb6IB/+uHVUQoW7VJMLR6CIkmsDgoZTQ21SXhbwHKdLa7gvKnFBbOIdtWhtLQlDm5VZXBg02tHLEKiqLMosepIUgneWb3sTF29kchKw7CkSgQGANkJMSVCtR6bVqgOs8yOI6QUiiQISvKBLA2lBBOPaKaqmNujmptQHersIpWE4GsgaKUWJQaL4NHg6UvAJzU+9QCGV1G8U9rrOkvI1WZoLw6PkqWwcYUsaiIWNWGzD27gTVQSSkqRnK3eCyqD+NncppBiivTtuv9T3L8FqFkgOLx/qhb3ValZTTUdn6rFFVujSQxoZxnuDqVkTxWP04DXYNnt1RNjBVfwooQMTgPqHE5NwBSq/T3C4KA5fEYR1Fv/lXot16FUKN7jWk+JguRMrt7y6r3xIhKBGhyShKZYTF71FeeEkAMaK1Iz9I6ogvQO1Ugvaqp+QKIJiGqpeK3UqhZbKSAFVLKt6ZwjB1Aczaow7TOtWK+XgyNOfWCcK0mFNjqmqRJUcclIBO1QUCQGrhSZIN5qfmEga2e0eJJGcvCotr/JEf2dORyCk2CRDVKopaBan5K6fxcj/9ds6HDMTAz1u+Oj4wcuCW5irbw1ViZmnibPJKwJFGKg3Z9za3fG0b2e1cWSi5NLHp5e8vjiktlyxEh2uddMuTltAWNsqfNsa+A5hbRZsr78kNF0RDs9RKSh6CM29RTfX9JsXiTP9ljFhjFWnn4kanIAoTzKmgsu5dt07pR16FmMdnnpzqtMfaDZn1Bq4HQzolwsEO0o8zWh2aXmRCprJOxe74MmtnSPn/DDP/Q5Hjz6gFG9iRsVmr3A8a0xR0eBUdsii8rBjUP2JgdsNmve/t7X+ODt96hhxI3ZjDZ4JqPAh+eJk+U565UyeWeGbmacPHoMSfBibLtaOqR4XPXUDM57vDO7W6HDe0cTIkGE6mFTHLU6ulzI9VOc8F/+SXjwCB48hOXSLIp6D+sNnH4IzQOYj2F3CoeHsHcAzRxci6nIh/FJILlz9oh7sLMHL78Bi0sDxh9+SHf/Id3FOecP3yV1ibYZf/TvN6BB+eVf/sd85e/9Eq/deoE/+Rf+LJPdua1pxfK9Lk8eMpsmcrpidXFB0zpGe4Hx+ABdj7g8uWKhict1D32k2UTa+S6z/X0ur674L//KX+XXvvU1bh3dYP/GPvOjGwRfGYdE2BtzSWGmDRMmTIMB9++9d8Ll5ZKSrAxJZUPfdYyD8MKdVxj7Ge9++7t09YrX3/xhbn/2BW599gV+bPNlzpcrqkaeLNe8d3LC8lHhwclj5DLRjq5oGuEgRuIyk64ukGbM5N6IyZuH+BdbU4o852gxW2GfBcmWq1eCNT99FXzXDLan44EpmHDZUQs4pxQVeoFIxUWBquTBQgdnivCm6rUVklfLYxStiE94bZCacJKNfRqU4k2hXoZmQ2DIJRbIzroYXqvZ4lRwJeOqYTdm52eTvzizXpfsEAkowQDuobEypWOe1RQz4nAjpTaOkGwlELOp01yK+Ghq0BAUl7E80a09n1OcerQIiFV521zwrRK8elM88X1AcNFBCV7dsOgfmjRuSCgXAwGK95Zx+pzDoU9xpdTzax++z/ceP+J4Nuczx3e4t3vEXjvDjVv2msjOaM7t6QFP1pfcX53xZHHFpu/t/pqERyc9JxeZTbvPnZ0VrJesHz/mhePIS21LeeOCi8N3KeOeXebMZ69y486I06+d8uCD9/nVh9/Aj1bsvDAn3tuwOF/w3W+/xztvP6AqbDYrNldX5Dgluh0iX8KdvInIAa4T1hVmjq279mDlPGxsLUTvcf4WbHPLwOamCugCJqewU+DwiDL9LucPFzSzKf2x58oVel2wCZGyKDRXnsODOyx2D9D5I57MniAzT3d6ykILe3GH9UnkyaPHrBYXLNbKg1g5GI8sUS23bLrIDpFZmwnNkpGv+GXPpuyg7ZQgkXvpFp9pb3B0Z8avrr/Fo+6bz328f+PxlwFYqEOL05HVEYsj1kJfPVI8lIBqoaqBDCRj9hZXcE1lUwJVIk005nIIBZVirjI9SDVlXwnZ7IerRR0Uh+X4qoPqiX2hOCE7xVNxvofBYldV7bgWW5hLBKkeZzkCCErxQKOM6Ckbsx8jFLKH2lfGFVw1lvnGFfowNFBrBl/RUIh+WCQObhKhKglni0Icki0awmWPS+bqQBlI2GKkl6LgqzV7S6mId0NWo+DLs+XWPwPyFvg0q432csWmf0Lz8hntC1PGMUFVutAj4xVlNALfUOhpuEBYARs6eh5xxojERPdxaYx4B5+/AnHw9swcKMP22sEKt4SJcTJPCzHhusk8EMWf2bZnxlbl02MgeI8pwPd4KsS5xEDyXQwY317jE6xTeoD1Vj+L9SyHz6i1cvrwCd2yJ4ijaQJJIrnqYOmuqBak5OHrem4czfmhn/gMr7zxCqrC2cMnfPWr3+St999nFMbca19itJkiKxksJJ/dkG0DfxtS6Hh6HIeOP+Pvs0Oe//6dFDRaY6l33ggbUShOCX1GNdFoYqQJoqIFJFcQYVQ9fcEsqVUYZaFJ4HOmydlsotUxyYG9yQ6NG3FwPGM2n+LarW16RUqkZFNqOKkfsU0vvhmU16b+o1aKWD64MwmE6e8cpkrewMdt0+Mn2qZbxEqRQAnD8fQNfJJtevyobboMtuliH2oEO1E0PWObLgbMe1GzTfdmm84ztumIZRCKJqo3i0JJSumf2qY7ESRks01vBgVL+fQN9U87jme7jL2nUqhOqMGTtaHTQF9hrAXNBVftHPNaqThysDl3C6Z3DHN1xZqKWsi+0GehC56Eo1Rw6qlOSeaVjq9GtKltQHzBYeebG5SgVvs5U85gtucFb4DW4MgRpAPfkYjUQfjiySRRy6uksQzSYk3BHDyEim8KTgo1m0JLtDdXES1UF3BUo66IkTMbVXSRuDo9h1HFzxuayQRtIwFPUbOxEElo8BRvJDEpliEa86cDSF0p1qwcQHEAUqJWA8WDd4M1vSNhFnmKqWVUIaJGtM6FlAqTUcBrsXPTeWoTKMGTCuYKVK2eSr6yAXJRxjnR1kJUU0mjphTXvkNTpjSe0rb4IINqCBBHXwu0Y7IP5K5jqmOLHjB4EqmJQMWXRJ8TXcksa0adY4wpxdU5dBSQRTaX/FFk1UQWiw03S2ZcFR8iqDW/swglCCEIDZ6QC6lAxtlaJ7TM45y7TJjKPnsj5dWDQ3blLo/9HgclE8rbjLSnIRKINDREIoEAVdGqpp4ZnqsmqvZUhmdNv8kR/Vd3+CZyYzRlWqKt0ZYN63ZEW0Z0Ys5nrvb42tCUem2CrN6jYjW2pccoXa3mluDNzetZgFkHnBo1wuPwv0PDVa9fZ3fR68J9ALeukewtVM51i38AyGWwOr8Wjw+34619+nV/YluvGHub7b1arhuIHwXcP1LiDCi+Dqr25x2vfO5lfu0rDzg733D2aMXFxYqrOyv2d46YxEC7yWyWFTdPpuzdmaAPV9Si+D7jc8GFCs0OaXlBGHXEtmXVRE4XC26lQ0JpGLWevq80LuJjy2bTkYO5wtgEG1mlwLTvyd2aeVzTdGse78y4bMfs9mtk3Zmz1v4hnL4Fq8Rk74jXdl5m5+0Ro9d+iBuvfJ6T0SWjyQvsupcINENryPJ/WYuVQi5+tPQRKEFZXUK9zNTbG65mEx6olXk7w5/BR7wKYWLrejoxd/N9rE599s0lwvQl2IlD+VVBFpgK/IKnBegIKy5vYoD4COs0DoHX1yzQNfB7nv+gA7fu3uEv/Af/ETFE61s9ueD/8H/6P1L6zP/mR/937ExGxKmgixWLjSKbnourBW+8+XleefVlHjw4ZXO54OrDb/BzP/cnme4cMhnNKUXou4CWnsoVWTL9rHB354C7N34GkR1UP6BfP2L95BFeptTxCQ+7K7o85XHNrPuWO8ef4WCw1raF2MvAxpT8hyNQwckle0wovkVvTXA3wfeX8OghjGfwcAzTHvbeBwnAC1ixfvuZPTFY3jusP1gBWTFIObFfrIDHdki7XVsXHDw9Edarjv/HX/xv+dt/92/y1/7KX+Xmi0f87B/8/fzsH/+3+cmf/mmatrVrBz65N/nbOLZky+rNg6qWCrHixFNKxcdMlojUSHXdoPJ112pxnDnFuOrNJj2P8CEwkoTLeSD8KLk4ykB49Sj4gjpBCyTnUVdpvbIOwrK1bGqouLwlhpi1tUvgkidWs5vOQ8Oq5oZahUpEG7NZR7bk90wNDq0mQFEKrliPQZ3V2DLEqOErrhcjyVTFS6Y4zyZA00DtM0UU55SRCFGdgeHKkM8uQ6J9YeMq2dl92FcDw4N3Q4+vkKnkEkjZo9kIwWELhnuu1eEVq2eNjCCfiuQkOEQrNXmcM7X4NlvcFQPuTS1uTgCqARFDV6Q2aGlA40AOsF5ntGoJhkhLUQe5WPQPhZocoXh7bzHnJRWLXxVvDLQsivPZhIUZqnqqV3pRsmsQLxQp+FSt54Hd+6oLlOIMeHeeWjMuZloN1l8tSg2CZKhaiWS8lCELXsiScUEBb2IfJ8TB9ZNsfZJaPQ7rOYs6oq8giXnKjAAJjhqFaRtYRWWVB6YGJjYaVZgUNReD4PFVkVJNCCMGoPuiyGApKdVcqDQoKUKN/ae6xv9NGiImOvXqQT1OjdagwZNKTzZa48D++Jf8ZX93/NaGVp425n6X1fDs+IFB8SOsT7VN5lkN/94C5Ftw3AHBCX7cMhs17NzYI28SV6eXXDy64NH977KY3eHm7C5VHVe5kKOjFTFbN6nkVaIN98B1VFW8jPFyRFtbUt1QfYd3T9uOsF0WAVR6HgMjPHPO9IonLND1JbVGXrz7EvvHNwiDTca+v8f87h+m7Czpr57ghoZaf3GfB2fvMd3bIwQr2i/Or5Ds+PIXvsRLt1/kG//0Q6azKXd2X+Gll4+ZtNbUY+5NbeYE8crJ2QXrPtGtFkzaETtty62DI956/CEdhVwK55uODy8fse43UDzR2/KuSKVzY7tFZ8V7hw8NNBkXPBI8qlO4yrQxs1GlS57SeK5p1M8zXnkJXn7R6POnZ/D+fQPIV0v7WQesVnCygndPYORhf8ds1vcOYLz7TLin/80LUO9hd98A8rsvMUoJXzMv5gVNM2N/vvfR1zvY9GtOHn3IyaP7pMsltz6o/NREGUUDxbvNhvX5I9p6wSQvyH0BjUjTsnM4Z392yFvvj3hwWtnkK6LLTCcNh3tHHB0dcbE456vf+zUePLrPfHeXm+MpL7w54/R7KyRdskpPyN/4RfLhq0zdnPlkTs6Fb793n4cPKzPn6HyH0NOMIIQp926/hg+V5fJDNqsFml5HmxEiwnQ8YTKypd7dox0+99JNrqricmHz4HOcnrzN2egt9EGlu/qA0Pccv7DDvd97hL/R2mLzUxRwsRaCN7tzqdusIj+QTJQy2IALHu8MUnVVhyacHeJqwZ52q8zWoBC3LcoqSjWb1yH30AtIrUSfoTeqr4i5IviilnfjlRqN6Vh1UJQFoJpyXIpQxONSQZJQ4wCqDwBbrZ7qKlIavK/kajdyL0rMieCUUe4ZiVJrIGYD7aQRRAKjXGmwrPUqploKdVCRilhGkx/APxeoxVGKI5VKoVBdNTvVaxB8aK18HAQf7NefBcEVzKpVhOqEEjw5RmPlhmikg+ccbx4d8mS15mLTkWrBO8gl8f7FKR+cn7I7nvDy0Q1e2bvJzdkeTROZNpHxaMKt6QGXu0serS64f3XKYrOhlMJEJviLEZfvXuAXC+LZJeXWAf2NFYc3HtO1K9bLnv4y8NI8cPSZMd3lkve/+uucrk+4HVvi9Iyy94AP3rvga994j652xHZgLTeOMttnpn+YnfVPcbkKtEu48nAaPyYQ3WJRBXCeIC9Z8bxllV/XA0NXLnWw9y30Zx9zdrLhq+dr3EHD/o0pD65OObl4m91+zk46ZsYhad2yGfdQCuFBTx/WeN7myWhJiU9IVy8S31fcWlikFQ+PNjyulSDfpnDK5GyKbHY5bjyzGzdowxXdagWSKCPPnTtzsl+y0o7Oj9jdm1FXN577eH98OOcJEvDVI64MGWZ+YOlbRpXSA6aE9gSbB7LHlUJsjGleGHLMSGj19BjZJPeeWLM1172y8UCt+FDxwVqYpXprEFRjNlfJqAxZZoOip0gw9nKTcdv+chWKV6QKRRKuGBtbKYhaBvh1pe6yKUsEXA0UKeTg2Hh7faQYQUQyPpkNmFQ1ezhRQshk7ww4cRCz2SOrgssGOopGUywOYGD1llkWsuUcm/1vIYbMRwtQ/Wi/7uO/+zTj8wFZNMxu3qSPLave43uhjAVevSCNHOfRswobbkjPjBHCip4NjiWXXDLp1+w9aQhXMzjysNdCPoazPei8FX0zTAwtPGVNRp5aCsFTgsqWkr7tOT9LYFGexhPuYL2yuzy1UX+I1Rzb9zvHrNwvsebnEQZQ7/CRqna9WPHBO+9yuVrgG0/jx/Ri7BkXAtVl+vWGblXQBD46fvonf5z/4Z/7ed783I/ifeSdt97jb934m/yNv/938Rv44TdeYedyhr4L8hIfZWUCViVvb1TbYx6f2QnnGMJ/g4+h6s89KpW+OIp3FHEWAeKUQMYjBIFxL4yrqRa0DvbUoZKrUpxYM0scUhxNKbSaEVGSz5gXsWNvZ5fbN6ZMphE3quRqYKdoQ8meRI+4gvOZa9t0Fyj4a9v0oAaiOgFXCzKoASry1DY9m236NqfV10+wTQ8GiogKKTy1Ta9i1tDAkKE82KaPPmqbbkpihzxrm16gfMw2HcwasEo12/OtbXo20D96P9im16e26WtAGlNmIPhQKVitoA5iypTyL1Cm9AljFCM7cUKHAfyII7lKJ45OWzJKQ4cksyWMooQS6L1QxYANrxnF0xdT5+OVVpcEMtH85skSB2JTbxEz3uEohCL4aOpzLVu7e8EVZ7E8gx16GHKJVZSqjbkHOEVSg6+K1oIm2/HBmQ9A8Yk8gHNINtt+Z5nIfYtZ9YsjZEdwHk1CyYEmgGohVIFBf6UqaGqRXFlurlhcPiHX3tatswnNTktsWpoQaccRN6loaJDW42qh3SS8r0j59KA4TpBRSw0y1C2JWiq19TDkNTvnjAzgzPK/DCqvIIqrDnIl93Zf8los5UocNUZKCKTOGupSjOCaXGEj0Bdl2ifaXAhtQ4gtIgHJGek3aJ/Io5Y8GjFqG4or5jglnn5wgqlNQ95smBJwEoa7nIOaiJhSXHOiL5lFKag4RhrZ8dG2a+Tx5x0+K+ID/bjliiWUxKQojW9oqkcLRDdiNg3Md4+Z5D0moxHv7d9hfus2r44cx7vn+EPP62lCd9ZxvAfz5ojlu2P6q8DF5TmTjWennKI5UWqm1o5Ul3SaISQ0JPAJbRKEhLTV7FJbJQ7P/7oN54QY7F6pqcX5nkhgKkaYXjvYOIhJ2OnbQaHvKZ0irqVhigdm9LQpUnNhx8/ppp4QPF6cpVVsgWzdzvODAlvlmjS8vaduycM6EAxlIM2IynU5sc0X16H00y05GrOi1WvgW67Bbr0G15/5wOEzt5bt9optBNYWJLfvbG8nT8F59/zH+7UvvMbB4Vd5/OiCJ/evODtbcXlrRZpWpjszpidnXF1m4qEnrTb4vV1K+4SUlHHOhFTpm4y0u/TLM0b7Pb5VNpOW08WGzWZD7Bom45Yrt6TRyiSMuHKX5EapXUFCgBpZ5Qbte+pmzXi6ZJJXLLTwYDLl9uKCWAqsVsjsEG78HpjdhHTM3UMP30rwi7vEG2Omtxn2/7Y4Gvb1GnhL4bbC4Zb2IFsaG6td5SttpXv8kNnmgKPZmKUKCzE4dcIz5db1eSJce6nvDMjY8KrtUtATIPYIT0BOgCdYYakY8H2Tp2rwrWqwYCj7Fdb5XGMfNB1e/3xDnNA0DT//8/8uX/rRH0NESOvEP/wbv8DezRk/9XM/wtWkY290wCzMee/yIVcXKzYnF/RdZnX+BJHCbtPyxusv86X/0Z9g5+jQ9nXTcnjnNVy/T9MEZjemPF6e8Z0P/wk6OoZRBTpEJjTtihHvcf/9FcFDGc/odg64eXPEqs20XKJ6OPQmtjXs5Ol+F2jZpRmotZaPPEeaGdy9PRQ8wGoM+ipPs5eOP34Uue7obueE2gwe52647rYs2Suruw+fvoXWyi/+nV/iq7/yi3SbDZv1mpPLc777wUP+y7/6tzg62uWP/pE/ys/89O/hR37si0z3j4zcKE+7x5/GneufOaSaWrxiavFcybUQg6dkwXlzYNPcUJoe5yoUNxCJDCAPIeLTBF8diBDrBrBYm1CV3isxD7ETwaOl4osxjouzeih5Ya2B4pXei5FOJdLUDb5WxCUkgcuYq5QaOF0kmNuZh1oCa2zd7/pMEFvr4yr0gewKLq0YVWWa7LrLsVpedxFcsoxvqjeCgKsIBs6us2eiSusLcVAYUy3yqgwObI3Y/aN3lhuOKm7IpfbBXoNYDy5VR0qtgeHVBHyueQYMF8tCt2WoweFVTBGf3PP32K5jQopAcfgwqMXdoBwf1OLOByQki0bsxkgRYgdZPeKFphZzRfJDFCSRKsWEQAmoVos75yjV6kwnDnER8z7rKa5SG7EluXdU7yilJbcOqpJ8sbp8cBpyUhkNrkelVkod2boQA/S9+sFJL147YVankL0dC18p4sEVarCosDyQ1NqccAOpuFbMOD9HqmmhzNGrekQGq3lRauugLwQt1FKZimejns5HI10Wc0UqAiVUgjpzSKiOiBGwSw3mdlwKwYHH1hZOErEKJSkdn65G/zdlOCCKH+JJBmcgVTwRh9J6x7p2Q+SVTc2fRoP5u+Nf0HBijy0xKwta/vVbo/x2jh+4K/NZrP94hpWGEz4KkK95aq/eY3WKFzE216Rlb3zEzaNdPvNkzMFsjIgxsXYbbyAVWGaKeML+4WBLZasbEQd6iximhLAB3TV2KQyLESukjdWV8WQ2CEvteXjyPlenjyndknZ5yN3dWwR5tkj3xHibsA/t3osgDZSO5f37fPWffpP1ecO9z77Mod9HLj0vvvgq3nuO929wdruyzMILh69z72iEd8Z8fzagZpk2LLoFNWVKlxg3kVs3bxNzw4WDX37r66yurmgnDZu8ovQbSJAqZrcXAqNxixDN/htH8J6u8RTnacQxkZZWPbWvxCikUUMNEdd8ipNdhgK4aeDWTXusN3B5ZeD4g4fw+GSYPR10Ba5OLYd8+gHMR2axfnAI013wE1ORb286nwSSi4D3eG/T8Gdf/+KAWX3stQ0srxbcf+89VldXdHnOL/3C13lp/kO8/IIVzU+ePObk5AOenD6mv6qsekihEkaRaWi5c3OPnQJ5c0JpWiaTEbNuxN50xuHBHt99/3t03YYYWmaTGZuLNf1lz0YT1VeW6x791a/wUD7g9kuvcOfuDS4vL3n84BG+P6DXhhLPmU96RtOGuDNi93gP3wSO7znOTmak0tN8ZPNtOwVbNrRebGH30l1uv3iH7s0fYf34ioff+R6Li/u88tNfZnRr+qkU4tcjCb435ZgxP1sjUVdrQvRgRY8KtSm4IsRSKEMOaEHRaIojnwXvLFfYiYMygGNYPni1PrEptsJg4eEyphMSnBgj0gkUX4amqjXu0GTNCbdNuKuIM3ufUoI1PLyiVUgqZtFawpBHZ8V3CZW4gUYyxfd4LQN71ZSdMXkcAZ8rXixlUhWIxXIixeYkHVRuTipZlFIipnMt1Jipam0U1GwErYljTFk32Ctul2HqsKavDHOZE0oI5AEIzy7YA28EhSqfKtrltb0dbs7GnG96Hi/XnK/XrHM2QxURLtYrvvLe23z74X3u7u7z2vFt7swOmTYjXBhz2Lbsjefcmx3yaH3Bw9UFEsa4OCZnZf0ksdfNOH38Ih8cX4CMaV+McKKsLzvqT81ZjU+5nN/nvYu38V1gqntsHi84PT3he995xPsPzqyZ7mA0GrE/u8dt+eO09SdYTgKbfUEaWE6hTEA3oB5rmBnh2YYT87beruevwciH8NJ/B0encLKEiwX8+ohfd/COV47CGL+5hKsnlP6c5cKzm2+wCQc8TAuerM9Y5zWdnrN4ckqze0Ha6Vi7cxx7TN0NPGtO44Y8U8ZySuUReX9Dv75ken/B/mLGy3LMoX+FCzbM3BPGespoOeZt7TnPG/qlcir3qW7x/Af8Y8NiRJUalN5hau5qzOg6NJaks/wlr2JZxdlTWyhaCKKDUNghxogx54QwNC2rKSZBSd4QUC9mWakeYzpXMVWlZEqoOA95qx7HUfBUCRSX8dXhssMj9FoHAGyw48qVWjy4YCS6WgZ1qeUJOxWzjZWAirDxjhyCWbxtkzJlcI6gsfkqYFbBI2zxWsXs65yJy7NYdmuUiktC1cEJQirVV7PrLc6sfEs1lfonBfjox56vx6eb0/UlR9zsUK7ewJ064mSEU6iNQ3b3CTLD4+ipLPBMiHgm7DPmh0gUPD47JC+AS8iNWSA+7OBkbgSTsTzFeo+xiMAzrBDcbsKz7qrbbayf8O/tWjRifa8JBrjf5SngfIkpxR1msf4OxsycDe/lsEbZdZNMuXx8xbe//j6PLy/pndKpOYJMR2PayZjqCysEUsX5yNHePn/si/8Of+CVn2N/79imkb1j3nnxMV+/9xYvHdzgzc+9gfTOCuH3sP7e0fDd5dkv8iwLYLtDPKYyWvC8+eGfNKq3bPDeK06rOaZ0pozVXpl0hTaDOE8udn6qgyxmn15FyAhJPQ1qmYY+GCnTexYqlCbQ+khoJoiv+IrFEaiQamTlvUWSgKkS1ECt4iOa7USwS98AWFfLAG6Ye4yZeBToHdUPtulqtumhbm3Ts913o6ICsVSSa1BfEQyYdc/apmfQ6tBxsOxyFbNdd9VymT9mm14LH7VNd0Jw5n5xbZsuzuwuS8E5RxwCTa9t0zdq0RfOUyrEj9mm+1IH8P1fLii+Mxoxds7sFDGHm1wC6whgUiEtanVID60KRaz5VVWo2RS4vVh+c6kB9R4nPY0WGtWBrNCDD0MD3dQtDdVICaoEV6lS0WJZi1qM1JADBsi7QI6FUgTVBmoY5u1Mbiyqx6UGBgt3traLMmRfVgPAcVCdRfzUgfSkw/nuqoFaVRucZlIN+CHz2lmZidZAV3pT92QlpcQqXbI4T5SScKLMdqfMbswYH8wJZYSIJ+QOKSN8/nR2+a4UU+3GYA1vBM1mn15UIUQYQHElo+IRJ2Q1Eleog515UVIylwLLFVe8CDkGSvTklSNXiwUQPNkbKN6JQi40JRNVCL4hhJace1walOIOUvDQtiALI5w5T18ha8XHSN4smVZP6yNrsXu9aCJIxdeClESfTSleQ0OsjplviS6gbYS0xvdK200J/jYSxhxMXmY/7eCWS96/nJEnnpvTEZ9rX2c2e4G+jjnageZGwI89t5fC4c4L+B2lNqDHivdDJ/wAygx2j5RxvGD15C2q2yCjghsVpC341pRXzoM4c7IRp1aAlmBZ9yXA6vmv8Z03XiUtl6SrBTX1Zs3/jDL9t2s4YBQD09hA8tQaqTEYEBPUOG9aGBVl0lei83ZuJ4cPESWSQmXSm9pLZcSt6U1WrWeMWcyXWrDmvkXRBIHkDPwuMGDTWzX5Fo7W67LBPQNEb3/j1eZn2OLWOqjQbX7fvi9VBhKQgSACgyp12AHXWefDOqEacfaZhPFnSpvhd8XuaZ9GKX7jzhEvvHrMW9+95MnDSy5ON1yuV6xGHdPdGdPHp/hFJvQtbrPB7+9Spg11tUFzIfQZP0640JJXDSVlfNPjpyMuzxcs1hsOuimT2Qjnl8RcGbnIeYj0pVBcJVTDI6s2rJJn2vW4tGHHb1jmxMnuAUUScVJgZxfKK8je3CzIV4KsBSbDTvA8rcuux/C7BvSoorsPETYIx2wJgj2XLNw5tyc3aF68C6XhGLjrrLTccjB/w67e/sAB7Tbg0fqEFcuURXo8bwHv8jQb/AgrLLe+k2AdzVNMPT7kkSP2Or1jf6djSOGjCTm/hfHqj36RF44P+A//vf8xO34HXcHV/Q3j2SH/yX/8FyjzKzZ5Sa4JlmZlTK44n6luw2a1ISKcLRYc7QRk/Co+3rJdIUI7nXDz82M0K+tuyV/6b/4So5nwe3/kP0BkbfdSJmzKh3zz4fuscmUGsF6yp7vc23kN2VGQyrL/kM55Zm6XSDP0OhkyZjE3KZGPUjy3SgDUauQdQetA7B6uXbku/gdy+rNHtRNYxafAdwXOHcwmEBts0h1ID6osF1f8wj/6O5wuPkQ1D1Emgc1V4r2r+3z3e9/lF37hF7lx44g/9Pt/kj/wc3+Mn/yZP8Td/UNmo0rT7qMSftuAccUswct1TrRSq0XoOfXUUnExX0e8VJdwtYJ6gnN4aXEaKM4T+orGaiIW/EAsqsQeXPHk6uy8dI5aFcmQfBjW1hmfGNxGMVVxybT0eBSfK5KFTGNxGAIlFKwr11BcIKMEWeBUcRqgCFWhxkKfNpyf3iednvPK7jGhneJ8oikWo1O8qbcppjhmiJXJOliiS+QKI9W3YtNvscxAotg5k6SSfLV7QzGAPXirfQSlukSqjpwjJQmuGMXRBSPpW8yj5YbngZ7lsPk7Ack5Ns6x+hRIn2gdnCSrZYsHsxkvxcQAFuMTCZ2n9p5GEu0mMCqVUaok7ShttUhGr0N9rSSKbU8x4iJO6If7YBSoYQCBXUEkD7hIoDpPDcXm9xrIXsjOG6ip3nhhDnPsrMncK6XgnVpkTXF4Zw5CdtkLtQZc9eAGEDwYG97J2ohvki0/3ikhBXPYqxZB5xWqi/TBE1wgJLv3FG/zdWP6DIp4OnV45xB6XO9ofSWKo0tCqS1kIzfXUPDiiFIIOjgaq7mnJV+hmm+UQ6iDaWf0Hu8Lo+KsBvzdYbidmpreiEPgxeMx1zR1QisK2lPFzqnM7wLj/8qOazDcQeNMTOu8YTC5UPsKacgP+x2eGf9bsk/fB25h7bzz4bHEepLbpMQ11qfcAuR5+J0XYTfA3fmI2WTIPhZjK2y5WFvsUz4yMQ2VtXjQHYSJ2Xgo9NJRakcpJ+zGQEIIzIHKhsjD9Tlv/crbrL9+nxBbXv7cK0za2cfw2AGYEjUgUEETnN5fcPloyd//8B+z+kf/gNcOXuEP/tiXuPHqMd1JoUYlh563PrzPC3dfoZHpbyimFEVGLdPJlFsH++A8927c5OjoNlenK27vvsrP/+yfZiHv0i/Oefsr75PSgpQquQquVcQ1xD4jWaBUswPLZjtdMqQsBBHaaUOX1zSxhfGEjGfkP7Ia+fRjPIJRCzeO4Aufg8XSLNbf/xAuLkw57oCuh4sE9y+heRd2JnC4C3v7MN+HdrBZ355+30dFLuI++VcC6/WGx2dPuFhdQTrD/9P/nl+en3Hz6A8yHo94fPmQZT2jV2XpHdWN0Ox40kG5VNqTK64ursjrFVITEuas1+ZyMJtNKbVwtHeM9MokjqjrDRcXT1j2GfWRGB163tG3S8rkPZbrz3N+fsHq7Ao/OqaWFlfPQaGZj5kezjm6dYhI5Pj26+Cf0Pc/uE5MRBjNZrTTKXsv3RzAwqEBMGT7Oj6BQPCDvj8KotSQydqgYjZ4ri2kHOm14kmmPOvULNWLszzJAaAKpZrdERXVxprMw7+lerwbrBp7sbwfMnnEUNgp1XtUrbnhDEUmqqJqxUxQIcUGoUB1BjrbWsCU5N5AfRWBQWFOMUtPs95MhNDTlEqrlZFUViSLKhCHE0XF01bPphpr0RrpnsYXUjArJzdkphcsHz2qEqOjy0KuZi8Ua7br1Wrvp2rwLXNduC7MC1CdM0v0GEghkn0guUDBDSp9h5YBS9p2hj5FBeLxHMbAXmy5OR1z0fU8WW04Wa5Zpp5UC+BYp45vPX7Atx8/4ObuHi/tH/PawS32RjOa0QjfNszGM27ffIHzG8c0YQ+97Lk4+5B8PmZ+52UO46vs318zerKh+EtOj7/Fg4vK2xdv8Z1vv4Wkymdu3+HV3dfpPrjkvfMlv/Q33+XirCc4ITrYm93h0P0h7q5+ikVsefyaUO5AeA9SC9+LsJfgjsPykp+KD57KBLbToe+hfQif/4fw534BXlvD18foX21YfTfx67NKdzHh8SJwsr8hd+AQJn5E6wPZnXI+/weU3Q0u3eKyT5wuW8anh4zawHo+ZV0i++6cmT/BzZfsTgOy7pBVQPKMvl6xt3vFnXzA+HQO9QVqm5hOA7f3DhntvcZ4swunLRcnD3hw+YDqL577eH9kCOAsz6pi+b7VwVZ16X2hI1CGHOBIJRWHRh0sr1pK7hACrkbUB6o2qPb4HIzINLDhXbV7VYzW1VCgCvhqCywpSvUGtBQsc8saIJFQFZEKDmKugwrImMwieYgliAZCVzFGdlScV4IzqzqSI1VPGcDq6gJZvalHvUOrIzslUqnOFswwZL8hFBVKjQTzMMM1laRKSFAD5CqIyzYPuUr2nuASJQnqBGogDDTBbbz2Jx6Pf85rsccP3+bkg1PWJz3iGvbu3OHWyzeZ7DXALljvnxE7ZCZ0dIyRwRx2yO0LG+jehdMTWK8hruFJgLK2JihYcVexotBEC08V4lsMuPIUHHfDIz/z82ev0Q4D1veHv50Pv5tgjbUbWOF5hmHLASs6z4ffz55+vi7h5K1zknfcfPEOZ09OKWdnBFUm7Zi9vSlxrJy3DU0ckTXzpde/yO/57L/F/sERvvG4jbKz3mO6nDJ1kT/57/xJXvy513B1mMm3BIAes+oUeJoNvj2weo2LK0Kvx3iJeAIiW5n8dsc834mwkEjvncUcqAEAVQRPZSKJkfSmVMBK6d6bN3LxjtSYuryUSMiVUO3vE0LnGtbeU30ghkoQh5eA6yG7IfsNIWhh4oVGGlQCKiNTrGPW9KakNsArDd/LiRHatipe9Rk2CtKQvNmm44WQ6jO26dlIb97IL1vbdOGpbbrfwidFqclBCBAH23QUJwaMCtsgu6e26WVQpagYMO8+yTYdNXWsQOMcov1T2/SslG6wTR8sHj9umx5KItX2X7R7528Yu9MJE1oD8KqQvdj+9ok8KIWpSnKexhVzAnOVFKKdZ9IbGUorog04COqYa2bH9aCeC+fpgs3XDrM6jIPtYwqQvLGMAormQdep1pysUhFVkhcDBLQSfIVSEAbFUrTmYypDVrg4xJltpFdD2Kqv5GDEKHVKIBjRSR3em5uC8wPgr2Yj6molDHEasnFMRbmoGc0VXz2qgVgzfS7U2pO6FVkz682K0/MTxMNoZ8YL9+6yO5oQLYjjUx0vV4yIIU1EgzNSR87UlA0jjY0pxMTb/UhMUVuKknG4ogRv9XbuC1V1AMWrqbBjoDTmnZIKxDoob11h5ZVNFkiFNhdaFCeBGFtyf4H0a+h6SlVSdOhoBO4SJ0oQRy5QtFLbhnRxxqhPTCcNS5wps0piFBOhVpqSqV0m9Q7fzDjUhjca5XHxnC4yFx9u2PvwdV64s8fhrvLDLxduHOziHwXkquNgGvhgmrjqLlmt9pm+N6NZOV66KcgRvLuCdAnOJknq0bAcPYNuVXl8uKZ5qeOzcsb89Y53ZhdcfNhb4VIF1JndfmmRzRjJo+ExJpQpTZ595Pl5x8s//dNWLynkvqNfLumXS9LwnNdrymZD2XTXz9drgk9xqjknTNrAOFomqdRIyYDzuM5AQK+2hgNFO/A1Up0pxkKboARcHvI7HYgGYgg4AiErMZkFemwdVSPRCw1CLoI6GKuAC5RqxI2giisB50wVCbZmVX3adRHMgt3yve27yQCCO7fNIucpIe/69mzvs1Wqq1hj8BoCrwyguOOZG/nw5wOYp+Y68mnsdg+Odnjlc/f4xf/v21ycXnH2aMn55ZKr22vm811moxHjTSZdFvxOMWXowRw96yhFCV3Cl4xrJvSMKZsFddQRJrusYuB0sWB/bwefI9M2klKh8Q1tO2HZnTNpHM1GSaJIHbFMSw7IzGeZ20d7vLjzedzoAD2+ieh3IO5A2THC5FpgrvCTCl/I8IGH4j667hpI7IqQNBOOOojvY7KaFcqbVDyXtHzAXV4lsCvGp9rWRWN+gApJgI/IDcCxZsQTrFCdAJ/Dui/bWq0M3+Mh1tVcD280wxife6A7xtzoxbZ3Mbz0C7/1Yw3wn/4//xKvhB1evnHE+apS+sLCr/mRP/rD7LSBR2dvs149pDZXdKNAX5T15ooRHW3q2Gw6ShDGbaTdyyQugPtYkWzbL4O45Wt/7+v85//ZX+QLP/waP/Taj/HZN+8SxpFTt+Hi4pQrjGS/WGeqXNI/eI/28pijz98gtoHzBx9w9OIbfPvyOzz+9bf5oXuvs1NGXLz3IY06Zl/8Au3uzvfZ0iuQFeihXdMbcGuFZoWEAn5m58qWR8pweY75KOFg69XrIluyw7Pj4duP+Na3vkMYB+gE31ktUVMlFYsZUQePzi74L//G3+Uv/+3/jju3b/G//d//T/lTf+Lfo2Hv+Q7kDzqcQK32nSrgjFxZtOKDpxbBhWJRY6mlNBknDqcNTfKE6KBUeio1WEK0Fkc/gFWUipNEisAwj0WxgKyuCSRvQLTQ0Eomew+10tRMqIUwENXxBphHl0EdfYignhwC6y1xQSpaI22XqK4nIpSaOX98xqP7jzh59IDgKuPsuHOrwTXWT1QEydFITUBWCH5LhLd9pCWQ2sKGSMh2FQYRHEqSTO9M2X2dG+5lAEyVKpmkQuobSjYr74jgPWj4KBheTAaDwwQTCSU7x8Z5VlpZrJcsV6vnPtzqFFGLC9HsoXc0ClqCZXCL5b+7XAi5IarQdEKThZFkglekFpJaFGX2SvGCpDLwtmx/4qq5CgC+2Jqj80KOWN9QA9U7I5CSqcUT1LOlpahY7yVoRnKm1R5qQas5fBaNiCto2JBFcM6Ta2Mxmi5Rcgb1CBnn1hCLkXckg1ekBhym8PYYiaZmUO9JxVk/xjlCVJyac1/bJHMOHAiHVZRaAkkVpwmXlWlWNnVGoSVrTymO4BIxVGZDHA8eks/kgRGnODqJiBTaBFEGK38tBCq1/sslKv+rMBzWH3ZqNZpTiw/0EmkFOxfE46XBnJnLdbmZ6qcqPX93/HYMUydC8DCK0Daot6gZH6zHqkUpm0LtMtQCuSC1/I5Ukf/AM8A2UWeMlZL7WAl5hfUjL7Aycqse/w3546rMvef2dIcwWJL84OuHAUjaCHpW6fYzD8uGNU/QZsVcHrEjjsiLODyJlpU2nNx/lye//i56psz25uzOD34T0FCunyQnLpYrXAmkmjhfPObtzYLlvQMoa371e4/Yf+GYxXxBl08odYeu3yOMR7/h/acS2T3YY9JOOd4/Zv/oBhuF+2/f5737p/zEZ7/Ezc9+mb/+d/5r/j9v/QK5X9vUrRHvHMFXRBNl7ei7QmiEQE9eFWpX8clTYqXQUsMY3wRGjdnH7Y3Hn7Cdn3Jst8972N2BnTm89jIsV2az/sF9uP8A1r01tXvMcv3xEkYPYdrA3g4cHcLOvlmsh61HqnxfgPzpMNVDV1aUkVIaZRyU9cUDvvIrHXdvH/GTv/dLXK1OyTHBtIWSGEeovedyLZy8d0Xz5IomedoU7MK/6rkoZxz91CEAP/T652l/doevf/OfsKlLPnz4kNMHj0ESfjymyzOKa2hvFNrYIhpYXnV0HcR5iy+RtLCCdjba4fbxHkeHR4hEPPe4cXzLFuK/5d0vbHPWei1on4k+8PB8wXRnxG7zfLRlAUrj6WVwSsXA6BIiXh2VinQeasJ3lVECmkodZLl1yM+2RntCKzgCogXxgmqB4pFa8CpoB7kJUCpazeamBsBlXMasV6VSfSSjtINFknqPaKSox1c3WPBYdpKPSlaPZr1WrKqzRglSib7SSKXmQpBkeUIUqndDwzITaqUgtAKhDgSrXBFfTWGm1agBqngHSa2RUqpHcWQRYrYiISYDwQXrvTPkglfZMiKd2VXGwQ5dvD0Ga6I6qMF1i4RXhVopqSf3HbnfPNexBnjv6opZjMzbhqlrmIwjB23L7fmEJ6sNT1Ybzjcb+lLwYqSDBxdnPLq64JsP3uelw2NePbjFzek+TRMJN24zPr5H4yO1WdBNE93lmg/Lr7BTTtl9eNtAkt2OC9Zc9ivOTh5z+vCMo/kxL+4dE2bKKgsPvlf58HsXltmMIr5ld/pFdtovs160nItwNQU6iBXWU8Pq3sVuaAeYOo9sTNNrm2aHobXlPhz+Zfiz34PPKyqJejPT7Y74Knuc6RFkofqGnJfE/UA+vkW3GSPrjtQ/wM1PGU8rJx8uGK8OuZVvIutEW4Qy9VyEEU+uzui6FfgN5TwxlkibRoTe0YQNflR5Wx2+cXTrh4Rug5MnLE/m3JwJ6bwwemvNeN3xxaPPceDvPvfx/shQIxyF0GIWBZkwWFdFlKSF4BPRmCvmSiCV6pUaeqoKrpvSOUfvPCoRn/1gxd7TVZsXKgXvA7iCasYRB7syQaMB8SV5ikYIicGnwJqNyQ2NEgNGenU4F+y6cNYkFRxZI0JGQqY60D6gNWGKRAZXhkInZvlbg5K9SZKy9wiNFYGYcr5oIPiEOk+qkdrb/JEceK/WwK/hmslXpBks5myJaQnKzrJ2FbIUFME7LPdLtt1YntEd/fMf3/zWt+iX0G0qm1RolxfMjydM50/VyRHYoUVpgck1YUeG5iVpYbl9FwUeXMK0mhpxd9j+bYb4kqfi560KHJ42nZ99PDv0medts3oJvI/1KANWYDK89z5wB3gV61P2PG2kjbFeYMIU5cm+24xdfuKHfw93XnuZD957jw+/911WF0+ITphMR7iYGU2uaJqW0vV8/jN32fviBP+qt8X4srL4xhMuu0e8+vqL/MQf+DJx1j7dFuEp4C/X//nYGJr0V9CfCiezfWb7U3b8dgM3PFU17X/yAf1njCSeJEp2CUKwrLlSGdVEUzY4l9HiLa8ZoVGh+oIEITmzLveu0CiglR4opWWjnuLEwENjclkcigqBYErzqsN1HQhiBDCKZddFcSSZkgXU9VTXE3xBUmcktGvbdIVckRzIcVDyOSEUy8vzKsN1PdimY6B5Fy0mSCVShWvbdGql9IKIo4w9YasqVFN4PrVNN8Dk2jZ9uDmXaI0y7/Iw94FubWAH2/Twcdt0hboBxBxjqIKPlSqF4pwBsnmwTd/+wb/EMR+1SHR0TsniWLWBHBxeKrEkavbgHapGCMpBCE7pndALBoxqxqnQ+kqOhTZ5pmuhbTzFKyk4shdCHRqQXiENziJAeabkd1WAYG5CUvC9Zf/lmi02RxqKDupczVYXJftj57Mpc53NYq5GcyPBIc4yc7W3ec2Z7RBSArGkgaAz5CSWOtgEFqpGQhWCFNBALz2Szf3MyFnCyAtarGZTMqlU+q6Qa2LT93xm74BDGaHiKc/vxAlggHgtSNNAMAtMV6sx/VXRGA3cd87672JZ9lXtXjSuFR+M7FBzIWfFN1uluEecN1BcHLkKjZps1Etl45ROBE2FJidG1RTgITboSglpQ+oSWis5OCNPO5PiBHF0g821jMaUEIl9YncceNwLeaOsnWN80PBDu7dhcourq8BxmrHX7nMkwvjWHkt/zK9fFo7XE+4uZ4zGjsNjuLVjjhL0cDQfM78N9RaM6pSrf1IpF4UjJ8QCdxJMrirtQkmPoH9/w+UtmL4yZlI82UEardmRhxzIGh8OGfX3WD0KhDzFD4+Qp8Q6JpYxoY6JdUKoo4Fo47YFv83DzzmaLb4tQNOibQsHh9eYbq2FnDI5p+vntFySFgvS1YK0uCItltRkx0VLNWVn+c3JtOID49GYqQQCwxyppgrLtJSieF/xklBXcEHQ6vDFrgXF4UNBSkUFuuDRRgjeAHdVb2SMUmmkUEsgNpAxm9YRiqOgg42qxkpbIrUPiHa4RmCtODx1a7vuMHY0w5prAMWfih7KtQDGrNMN1Vax12qVZ7phZosuxqY2Tptsf4pZvF/vrGF9OQDnn4bcGBvPC6/d4+h4zNXVkscfXHJ+vuLy1pKb0z0mszGTJ+csLjMhBXxKyM4M2lNyX2lzwadiUUOjHbrFJc084ZtKPxtzerXkpT7hO2U6brhkQRQYxZaVw5w5nDCJY27GlxCv7M8+5JVXGprJITN2eayOLswZy8sI+1YP7g/b7SvIExh36M4NWDRIGfbrYAepdKxJfFgvOQqFPXawom48HDtoGTEDxnY12S5Vhb5DfUAlPAXat3XjoAqwEky2B+z6c0UyTwMgp8NDeOp9ecVTBH8C3AKdQ20hRwPB11h9usRqzMvh7Z4TFP9jr79OUOirEhyULEzjDm0UNGXauMNsAj7ucveFfZ6UK5ZXHzBV4ZXjmyxvCV2plM7z0ssvcDB/FdFTqMmAYxFUlfNvr/gv/ou/yNvf+S4fvvsO73znfX7/H/iDHN67xYufu8nn3twjjCb0m0tySWhTcUfC5f4lk/GcWQJfVrT9Oa+EO/ztX/ivKa9/wM3DfTYXPU4bbm9e4nhnTiMflwYVbHFwAWIOGnK5QB6LkUjvHljTfqgZGQ7dSgbHCv/MJRUYyuPfeJ31m55/8Fd+kUcfPGE6m5L6QmoiqUt0/QZfYTKf89LLL1K8cP/xE548eUgzFz7/5svMxq8g8pEMpH/uQ50DLfgKtSo1OBzZwMJQcdmZ3bkvVAKSpoh4fIkE7fE5Uys0TijB1rdeCrU4aolkV/Gu4GulOkcWxzqM6MSjLtLU3shBCnkA1fe6DKrULNaDGxa2Wq3G7xpHGvrLRQanp2JzXhlcH6UvnC0XPPrwPU4ePqHrE1Uzhczbjz7k3v4ONyc7oGLAu2QjuwdA1JyHqGRfcT2mlhdHXyNVC22xGrpzhSJqOdDVRI/eu4EAWEgoKQdydpBNROMd1gt0gBR0ANS3UZCCrVOKGBi+BhbdhsVqyXqzJn8KUEjF4bNHXIMvINnuVR6rL12x2MLqDLB3fUtURTQjWmm02MXgCjglOKGqZYEXCdQi+GpiJltTFEqo4KznUpyn14gQTNWdHSV4fEiQE021e7sX0AxhlQn0jEMGV6iiZA0WSYVHpRCwvkx1Si4V9RClEmrBaSLWTNLOiBVDnawq1GRum1K89VqjRRlZE8hEVgyZJ35w/UI9qhaJlx1svN2k4rDG01pAN9C3BHHoQJ5ytRBcJWC1vFNHH6ErjuLMNaDD07qB/KaD67sojvyprvF/E4ZR82UggAt+IHdHCrjBLl8qjkCkJWlCMfJj8ZaS9rvjX6ExrLEleqpvrDYYInDx2dbTgJt4iqgZ9xZbvuWuULL+jhKP/8CguC3x7BExkHyCmQ7dwADy8+FxxVM79c32/1XZ36wYxfa57J5VK333iCeP36brW95eCTrecHg70IymCBVhgjV3R+QK5dEH+EUlzg7Zu3mTGzd/MFBB25aNK/ioaE7sjUa8sDtnd9ayDiu+9v4vcvFOZO/WIc24Y3UwY5PvMr1mnW73mTCKI9545YssFyvG7ZjZdEa9WtEBSQt3bhxzsDvnhTufReJfR7QQnUfV4ftKWPaEUPFJScmaeb6vNKlScoOvphQvxSwus3S0DbR9wzT+NoDiHx8ipsTZAuQvvwh9D49ODCA/OTHLdQp0CssKT9bw9gMDrPfncLhvAPl0D9wYJA5o1icNawn0dUFoCq727E0g+8AHHzziV7/2dX70Sz/Mw/uPcSUQp57gYBaUso6cnoA+yCzp8Tt7jGdTalmQrlb46YjPfPZ1RITGRfbjAS/v3+PD8+/wfr+GviN1G6iB0awh1Ya1VnZ2DhiPJywue0rxzHXFfphxxpyj8ZhXZ29w99aEnfn+sMvEwCKtoJZ09VuWDalyUi948tbbHLRHfOudd7n72Zvs3nrjuQ7jOjqyj/RDB0Cc5RdqrZZdU6s1IFLGp4ovwfJEy+AWKHKtopZqnrrqi+VzV0d1GVfVVGGiVA1oMVqZ+kp1tiAVMTUSziyMBEfw5RokcWJ5o16suU81G1Q7K0zho96s2k3FZPtZnaO6CH0ly9BcVyGKZ+2EHodzpg4QzbjqTeWllYTHqcPs5DMahtwczGo6IeQCabBYliFL1eyDtkC4GAjuPbkZlOA+kAmmBEeMWDCASlsgXEtBc6akjrRZsVle0S0vWV2e0q+Xz3WsAU5Xa658z9mmY9YE5k3DNDaM28he03J7PuV803Gy2nCyXLHJ+bo5cdmt+er77/CNBx9wZ2+f12+9xM7LL3EeK23qmB007I1eZvneQ06XF/zS43/Aud/n3v6L6HTNen3G6tElm/NLZCmM4wydB9J4xbvvvsMvfeV7XC46nFhG6bSZM21fxDEzda7CZAG1hzC2Zs48wFThwkwHOLT1x1PgzTHYICpaF9T9B8hLBeem1KJ87YMFX133vLezwyWvIE1ixpI4H+OOAuuXdyjnMx6/e0p4ckpee6YpUs52uSMv0MmcpevRsmKiG+7ymM6f4/2aEHaZ1Smt3+dsNmI0WzAbNcTZgkcfrNGJMt/zbJ485OzyEXp6n2l9wPmm58EH7/Kn7vx+/sgLP4+ejb7v8fxnjmemF+8dIUZGzRhXzeq3BEcvZktLhdYrQRLeW45jyc7cDKr9W6h4o7vjUkVLY4vrEJFQqSOlCGgOpqr1flBvBnCOTVNwVUgjD7XF10oOFcnQVANFqjegqjol+wAEA+XU1ItSPLn1hOQJKpRqy7/eNWaPC0O0ggEDSqE6syyr2II1acaHno2YfVoSi2JwrqApkLMpPYsGvBcj7IRiLmGCNTnikGWuAm5QBDrLq81SqaFaI4D0iS0X67F+vOr8dJD5h4+fMPJ7qEau8ppTKSwmgQPVp43GIQtsC4V/9GQRiDPQO3DqjEwyH8NrES6LMVAST1XfW4V4euatnmlWXivG9ZkHn/DcDK/NWDG5/TpbIY8APwX8MYyJeYXZF72BZfzE4WeX1oO7u3+Tg2aPB+eP+F444J3dYx6dPkLTBZOmkFNHcC115ZkcKj/5x15h5/U5MgiN3NQxeaMQv7XmzZ3X2Tva/40A/w+Ce4jtm/6JsFp5JpMxTApPpTDbavn5Rmqgc46Ns0ZZQ2Geoc12ohZsYauhsvEyLGADG+cgK7EUmlIIKmzw5ChstKWvDaVzjLD7JaFQYweYdWFVyzGuIkhNxjB3BU+PS9EiDkRpXDUFtguotmRGIErWivqCSof2ZlleBtt067PbdUnNVC1mmz44RyTfUL0Z4Kr/qG26Dll7dRQZ9BTf1zbdf6JtuiO4anPG1jbd+Y/ZpsNHbNPXiuaAukCtBgJpyBQ1Mo6vFcnWaBL37EXwL34E59lvJ3gpbMRzPoGrtsFJi9RMyImm9jZftkIaCpPiYR1h7TySW3PXKJUokTbBqG7oXOWCEV4qaXDVqbS4mpHBbcfnQi6O6ioxOyNiqscVj1dn+8fVaxKUEwUHtThEB6tLB6Ef5tZg0RvSe2owS1DLSgTNg6rMCU6DZd5rxDkjX9WhDvWZgfBRITtTwNdCQSjq6QtGwHSYQ5AEUhVTWUkgScFFyzROpSeVyq6MCMkbseJTAKQAku3cY+RxbUv1K3w2pU7VSgkNzrvB9WjAg7xDUyUpTIsSxKJ8ai6kXJlEo6UYKO4obaTgSUMdRbXjtXaVjThyrviUGNWK896cydThU0ffdebK1Izxe0f4+SmUOU0/pbLgpfkt4v4ue4c32T+AjnNSOqAbTxlPb3G4/yavHr/K3jxxvzxAL5XpAriCuGi46w7J361MXWBnH05HC5r9BXqr0C86WieE+YTuYMLu3ox9GmSSeSQnnH8L7p2Pmd6PNGcnvN0tCHpFWV2QT/fYC5+jmc/ZuyOM0w7dB4l1D/PN57j13Z9g774RLTwRpxFRT9E6PApFK50Wkq5JuiHRkdQeX+LHnut472w2tm7YXkPDQ6+fHTE2FnE2DD06sgY4A79HIW029KsVabUcnlemMl+tyRt7LpvO1jTBEUYte5MpTdyuqyrqPBIrTns7L7QQNdN4U3cVArW2ZHGoFnwtVC1sJLCMHnzAD+ce1dF7QRtzXZBayakFl4kUpDpw3mKtVPHZo0VRl/EqxISBONLY2lRG5NihVWkxYKkUWzeKCLU2kBXx5hYhqgNebqpyeWYq3tZicv0QW6c7u+lv1XW2SMNUNmzXmMMOf94hcPfVe9x+YZf33ltycv+Ss5MVF92S9Tgx3p0xfXJBXGbCWnHrDXFnRj9rKIs11ELsCt24x4/m9KdCTT2+ych0xPnpJVerNQf9lGbcEKPQ5MJOO6W5+yJ3wy4vXM55YX2Dgxtf4KwRVot3CfkBoksmkigaSVdT6nSK35J8rkW7DthDyWykx48dsQlWN3QFxuZOE2k5agMzabEsJZ7BOc2C+wUGDXdhcCC6QpenlLSHuD3cbSPve6w05QIGbufT+vKMa7chZY7mBomXiJyAfIjNkAVTgx8CO6CD8r0fPnchT0HwBVaDbrA6daJw1AHPty6Lwx4bOxny8sLwIaCMad0EaRzZB3ZHLV/+7GeZ7G7YXTVM8oz3339I7SO39m7y4t17eImw2oOSzDUJWJ2v+U//8/+Mv/L3/zJaK92m52tf/VUe3H/Cm29+nhe+esTjL77Kvc9OYJ7Z353Qr075ysPH/P7XvsismTJuIdzbQeOC5cklf/7P/AUYX/Luw0ck1rhuyePNFQ+vLng9tuyMnhUHOeAeMAZdIesL/NrD/gyOjiGO7MDHYb9m27fBP3UQVbU1/9YB9uNrKFXl5NFjfvlb/5hVd4mmxGQyoWn2uTi/IOdE27b8uX/75/n3/8yf5cnyAf/VX/sr/OW/9t/y+376Nb7w+R/5bQfEwdwA/eDZ7Ko5h7lrtXjBB0fOHq+BQEOsIMkjFJJzaHKIq1Sy3cOruTaBuSEW8Wyqs3t+bazv5DwiHq1KxeO0I6paRFInkBpKNDDSFW9RpYODkoqSXaF3Vh9JdnixerkfbMw3ecXq4SPee+dtzi8uKaXSNA3tKFKKsO4z37v/mOPJFB8KTXV4rEeHzxgcXnHeQDdRBzVRc6D3DGR2zEWqqmWCX4PhZsOeUVLxpOShYFFLztrIJooxlKdswXC16NYiShahd4GVCMvUc7VasF6vSbmYzXl9fiajS2OavhLEhDOiFq/mvRLqQNLHiLceh3O2FnJaCKXgnBlT+GKkRhlZ31UVc67CEYyvSvF+UF6rRTsWh2ZPocU7W/eFImjy4E0o4FJPITN2DpGCpK3ZmVC9t2tYR/jSUpxle0sV+mJkMvUyREklcBt8myi+4GrGD7FFpEApQvXOiM8I5vE+ZH7HQKgBceYcito5S2/XCxE0NyacKgVVj0epCp0Oriah4GuDc0v2NDMvFUFRVwgloNUjwdOHZnCqyowytKrEaqIfrY7kfmcqYz8+rAZyiPqBpqZ4V/ESrIWjRqAHIRDxEsja47Dz53cx8X+1hlCJQXA+De6fgoYRqLnh+cYhbcVnRVxANorzFVeE4ITUKV33L3sr/sWN35JXxMf7gA1Wy7QYz3PrbrnAatRzrJ7cAE6Vl65WuAnI/KPWRj/YUK7W51zKCU8ePWG5FjbTht2bb7DHK0Nzd47iCXhukXEHL3N+6xGLLMhoxu7u/g/0SQWYz8bsTlrWpePOy29ytx0xPzhgUTfUVhl7R9soe/MJDLYsn6RCF3HMJnOm45lZXiOMQ0MjDfs7O8z2x4gT9uYHxCnEKOQkRBVEPVoKJfXI2jMOc0YxMpG13ZBKYBRHqBthJpZCKZW03EDK7N7dfY79/CnGdvvbFl64C3dvw6aDxQI+eGAK8ssLrAMlg836E8shHwfYnaKHt+DuG0j8pIWGsqWT3jp4gS998cv4i8yNvXu8fxm5WixoPGw2a775lXc5u29s1NZDnDlCGxkXR5QRs+TY29khHEbyYgObDWOUF+++yOnJBScPL0l9z/6tltOuwRXwsaVsErP5IW/+1Jf51vsPGAW4dfMOXhynHzxhlAroGnWVcdxht9mjlRmTyQ3Lz3lmW1TPrXk/P3i6Avh+49nFtgi9Zi42jzhbf8i733ubTSocbZ7nurJRQjEWXe8gBAOpBSuOnUOqw0titFFiNdA4dg0rB3VgfoYtCCnRGMGSjb0tHleNzSlDvrMbWHquFGq1fBfLJwLwlGSNlCpmN9XqAMo7h89YJThcdyrZFAVgjYlBJRrVbghZhFqgOqV3SuuFXDxNgUKh90ZPjsXUnE69KZOq4EpkKKGN6aggtYAEalE8mYog1Q9MeKFIxEkmeSXHp7ngyQWyeMqQC/6sElwHhR5VqVsQvFuT1ivWi3M2iwvWi3PSZk0tliP5acZlt6INgU2OrJPnskuMY8dOE5nGhh3fMptGjsYjLuZTTlcbTlZrLrvOCBIOSi28e3pCcZG7b79NfHnNcTNjEubUZod872VUFmwu3ubDcsZVs8TnXdLVOenkima1YOwcpTnn8bJycXLG3/vFr/Htt98xSzHncN6xM5swbzKN9sZWbmB6G9LUppfm3Fwjm8Yc/HYUy7f2PAXrilnnqnvEInyFd758zuvzC8ZMeVxm/OOvj/jeE+VyrvTa0nRzdkdrDmYz5HBMZkRZPeH86iGb8JhahPa84XZumU4d7WhNCIIuW15ct2hcc+JOWTlPwxESdzlvppzsV6azwnpnh+leYLY3ZvqtNf1lTz+bUpoJ5fKS08UjvvH1b+IuMp/76R9lfeo4eXLJ5zh4ruN9+/f9hDE8nSOWyM6mZTdD7Qf2rwheMpqF6sV8kDKALURQTy1bIoiaH4sKxZsxbKmYgqN6jLNdSHhCVtQ7xBc0i537gy9DksaulZrQFJCsuALFF3zs0d4hBXKwaBT1atdIiQgWeVBV8L6S+oBL1iUTMIWKQnbFAlhUqKL0EvHiQCNKQRBcEXIwRWyOUIn4EqzCpyICJQeKmMo0iBCrZRR6MeeLrCAu44eoBz8ACgYIm228VkGv9UYfH59QOzzXkbaxXC/opDKfTBm3yqPLJ3z9W1/n8I0fYuojfjOg1ztjiOGjtYuaapcV4KbAIbxX4OIc0hXMFnBnDq9jReAD4FsYUL4FwBW2LuxbBci1Vfr2o7av205l29vXNupx+zfb9yqYinyC9SFvYSKj1zA3zH2soj227yEbIe55QhpTOGAxS1wqaDPF9U9w6YzV5ZJxLIzajjuvOV758hHtbPbMd1RmhxuOjhtuvfZ5QgjPf2D2YPwmHFcYNXWwTh9h1fOzEvvf+khi97fgTKkd+0roBvs+NTU1wf4d1RoyyXmKQtRCk3tTJnoDCbI2ZHUoDT5HI5mFQvI2J/uysmtaC6WzxZNlb6tZUBfBlQrVU6WSveU3OwJOzAI3OEwRUqLZrftMCZlQIEvF52T346rUamoJvOJLoeKfsU1vjZzCU9v0kpx1VhsDNxX5ZNt0h4Hx38c2vXqlbG3TFSSbn2N81jZdBtv03vIQyxCTItFs00s0gMXnTK5xcPv5zdWav92j8Z798Rg/sMi74MlEtEZz+AGyc4QqtCRGWvFV6aOpQ3qJRIVVaaz2qzAuDieZq6BcloAj4piYYrTprMFSTLlTvalIPWaBDpa3rir4zpFCg4aEqt07nDPwW2ugJod3dq76mKgKgWrNQsVmdVV88pAF9RbVUWUAjCvGhk8VSR6Gv6cqYYDCfAVfEmEAuwQl1YIfLEhV7B7mcx0USY79nT3ivGG1XtCddgQcTbDrLPmEtp9OhSKl4GqhoEjbUL0gtUKyerAGj/cedUaAKaIDubjY/ako0fwOKLmQ86AS14KTiBNnSnHnyMVdZ1c7r2y80osjq6PtYa4NI9cy3j1id0ep08Lh7ozbu1/iXjxif37BZR+4eruy291i7/Sce7yMvHIT2XuPOPlHvCIL7uabiPs8IXwJnw7x3xK4/ICX7mxIk0ewzqxGtzjpPd2Z0j7u2PGV3RnonQfs3TllcrDBHz4E2aByzCkj3ufzrJixf+sC+QPfRuZrzk9afJ7T7D9hlt/mvHkHLYLoK4TYgt9n2U1xdcbJO8eUM+FldQQ3Iqc1KSeSXpFqJtNTfaL6p88aMs3E004j02mknQaabVTcc4xxWl+T11QsZqI+A4iXwf3m2WeVwf6boXEt0LYtOmrhwGpHRSmlkkumlELOhZITabHELVfEVeFw5GmDo/aVqtFUXVSiL6SBdNJoocmQxSPe5v0aHNlV2mRrseLUCMZ1hFcBV8kCTc24WnDOtidnD84AIBWL3QpqFp5aFHGVLMVsT6uQxOFjpuYRUgTJrYFmVGKqJFdJ6qndCMXj1hNG0aN9JmmyNakD6aKpFkWHte5wrcF17rjts2fyxHX7/7bg1S0W/uk4LyQR5jsTXnnzBb7+1YcsrlacP15zcbFicXPFfGeXSfA068xmVXGbnnAQ6OcT9PGaWiu+z0Y4akHDjLRaEtqOOJqxHCzUb5ZjXNjh+KhlVneY3HmdVTNmR1dM3v2AnfWa0WuO/XbMZPMGLoyBd5hqzxs6pRlBGZzb3MoZOHy93yJoJPslXiCVxKaswQkTHROIRBH2ZHpd+6njGU2/1c7TbYzOElMqjR3+5jHyaIScAjfMSCoM/AbtwC0YcpSxtcsFVlqNgY0gpy3cPoJ2W1DugR5AbaBE2DhrWK7sc7fkymuuYqiwW+DFBHtnsHcC8Qnwh57reHvtrVcy3HOMXr8CbuFax6RpmTAZKkOhcQ1v3P4RvDasHq0YX0Hjxuy9egNcpBRwwa6N7mqJVsc3fvnX+Zv/8K+xXF9+5LOXy0s26ysePXD8g6slh78a2d8dsz8f8e477/Mr33ibf/S5v8t/9D/50/zEj/84k/kxvT9nsudYXCy5fHzJfHrMbJzo3rmgOW+4edhyfnHJ4vyCg90dxpMJTxHvW0CFcBNuKrTNtfBFt8d6pVAz0kJ7Z7v+Gsr/jf3TRa5NJZ8ORQ5HfPnn/zjT4wN+/Zd/ldUys9NMeTJ+RDOO/Mibb/K/+A//l7z6e9+kX/VcPljytV//Nf7nf/5/RYyvfPwNf1uGOovWoxa8essWjw5XClrAucjITakp0WAEzlqFGgU0WL64szX4YMVIzOZ8Zm6HUKWlE5AcCWr1mHM9giNWc16KUvC9w2UDSKQKrmzBY0FDxmVvtW3JjAfQPruGGhyFSu4T64tTLh49Znl+wXq5QoutlZsmcnBwxHRnAlnpr1Ysup59PKKOpkLXKDRbMqqB11ZrgaSK8z21Cr13A0iseAEf3GC9XimSSdWTUkSzEWi9YMRZz+BeNSjDUROaDWB4EnPXWjnHKicW6xWL1XIAw4VaAlKF+Ckm9ZghFsVjylqVgvPZLOH7gPNQYzFHy6rmTNe4YTmsjLTgs+BqixPrx16B5WNjTpWdj/Qei6vE5mVby2dAbc1cK06zOS8WT+yEtiihVPA9WiLVBaQdXls9UsIAfoNjICl7oTpHlYBIJWbMKYqExN480asiZHwWqI05kwU1hbpURIWaoUiluGDioSIESeAtt9xVEKc0VSipsVgnDWSXTJQ0rNu8U5qa0bDA5TG4zKRmmoItKtRTiVRxRO9ogpGVm8EqHYw4KOJxxWIEy+8kSez3GyomThMGV5yBmOKCkcmdA9QEJ1Q8wXp4Q79LnE1PvzPHEOUghv2JDzSxRRC8OKbjHdBEiA1N9Nw/vc/iavHb/I3smJjgcCDN1OEAZ4ERSDBXXxjW1iiiphB30ZTj9XeIicIPDIrrMw27p+qip28SeGqvPsMsbDfYJH6GZfXda3dwzTPo38eAvt/08xXOc8dSV5yur1i7SicTVAONHLItqbds07ELvPjaj9KkPb71/ndYtpGm/cFAQ+c9L775eZybcLhe8kNf+nH2JhOaImy++126daaZOG7v3eCVVw8ZHYyZtr85W1Tkml/MaBb53CuvUl9MNLt2CHb25hzszzh3JxQJtC5A68mx0m9AfMuomTCZjBhFR82F+TgwDhMKE8R7WlGcS9RuTc0bbt05+k2/02/7cA4mY8siPz6CH/kCnJ4OKvIP4fwclmtoAmwyLC4hBeqt15EwWJh94nkhvPetD3jjhR/mxp86pt95jff/9j8mXJxx6+5tNl3im7/2DmWVGfnMeDcgeIrL7Dee0XiHtvGM9hvy3LM7mtGvHTt1zunJGf/X/9d/xVe++uvcuDHhz//pP87O4S0O54/xzYRufMFrr73Cl37yx1iOv8msu+K1V15Ea+Xx228jJVNnI5ZuQ5gHVv0pbz1yvDj+zMe2YU3iQ2I9tIbzsBq/3tpSbP+5Z4qyXCE4UCUCd6LjolnQ1SccH9/jcP78StIa3bX9uWC53kUVCRnnKyEVQgJfLYOx8YLiidqQasUXTGXqrAlfPAQUJ4msHqo3qydns7NownsD4Yo4mpCJWqhZrEjJVqhLhOxNoSpD0zsP6IpSLbPYQfUZKZ6qVvgUhdZZM4XS4IbsSxVTfHkxa3WyJ9WA95WYTVMWiqPxptyW6qkoUTKqASmK+kLBU0UINVyzC12EJJHkIylEuomQXSAPmeBmiW77+9lc8JoTmjOpW9OvF2wWl6yvztkszuk3K1K/+XQKhE8YH1xcMG0a5m1LGyJNDqxS4KrrGceOWYzstA0T3zAdNxwO1upnm55HixUXmw2bnGljw+FkF3e2xK/e5WoSOXUOf7BHunsXefEmo3hAfXTB6rLn8uIhSCL6lugrofGMDh3L7oL/39e+wa/86jfJGaJr0CLEpiW2jizvkPgm4/GX2H1lxPkfFy53YPk+7P5tWAc48QaKtwzrbcdTa+Og4B7xXf27/PfhF9ibJ75AofCQ9x83vP/1PVydMa/HrFKhugvOyxmczZn6CasPN6zfv898eUU57LiY3iKHe6xWu6y4Yq9d4Gcttzcvc3xxk814wXJa4cixU25w2TiuDlas9lekwxXNUeayr7zoRtzyiWW/ZOUid/b3WTaFxcmC04sT/uMv/VFG99Z893u/wINyyuf495/reB+8+BqDCIbYBWYXE0brCzbyhNEA/EqwZrrXAMVAg1ogZhnsbitRIHkxmzNRsvTmZlBau7/5nqwCWnDiEV/BC0kFvDerMK/U4tCI2RYTkAi+mMrVGmRKkEoWHd5HqE5w2VEHpjdiKEj2isdbPp1zaLZcWakCWqlieFqoYizJjKmQXLUFdg0QKiJKLUoV+2JeK04KRRzihOIagnRItQV2rIkSoLqB9a9mIRpqgGTwt/MVh81zvrCl4TIcCn6zZsynuuJ7x6htuLlzkz51nF+Cfrih7F2RvCAXHdqv6UaFujvhajzj8OCQxgc2V1fow1NG5wk5EeRiBY8v4f0MyzvwuV0DxF/MoAleaWDXwT8RY0RGrtfk16D4dlOfBcK3BZHDCsiWp/2zF3gaIHnJUyH1BfAB1rQE+AywhxWc2/fu7X0U4F1I9+FqOed0VQnrNZ+fHXM4uUfXv8fb/j6bVWW9s+DHvnSHo4PX+GgeZaWdZm7e2+f1N974TeJ3foDhwc+UXTJWGfvhs7Y75vlHEYcGs50LCUYl0WgiDo4mIpUiHpeHXRRNIR6ASKFJwzZrppGhU1UDtZ8Q1FNcB86ad9pDdgFXIiF7a7aoKT5KNUW4q40pfgEGm0UdlL5OBPVGnHHAyDm0FooIkkfUQZWLJJxW0N4crkOPkgfb9DAw1MJgmz6cZFUpyTLQ6igYSQWbj8TXwS73qW26ZFOPmBBaqFvbdCmoFMr3sU0PalaMIsNtZgPQoBaARwimgSnOyDwxl+v6amub7n/7+7Dfd8Tg2RvNzLXUOXoipdgFJAi9NzJgxiwvc+1pBmO9mltrhJRCqp6IELUSJdGh9C5Ss4Pa4PIIcclsHSlEp3ZPqA6KgXmIKUIjZqXpfEVDpbqKkIhSzUJxUOb6ahmJQiF7RZJDq8PKuYriqeLxHnwWkjcw0TshZHM9ElVyNUDOeU8t2RwOarC0FTH7RzeoSYsvlE3FO/tZiOYJLIMlew2Zl27cJOxOWHRriiq66RDx4Cu1EWr76Q64poLLA0TSNpTtCZQStVRK09D6ob52DqUizqPSGzkDIWjFq6fPhZSMrBW0DEsNs0+nsczR43mLHO9QfMNlDhykXSbdhLh+ic+EL1APA9/Zf8zh/oK8eIS7OODueofPjXaY+Bknh5/l8cU/pZycMJtM0T7QhIAc3QMe0HBAO4D2qkuUA64ersn+gvXtc2Y3FyzlIY/en5HrHrKAV+eV3dcfcfjKArndQZjQs2VArun4kAcI35IjApW7vMP4+JLpv9Vyfp5Yvf1tXtxLHMuaad9TSySlM/L4OxzemzLfmbC+eJF33rnHh5ee5ko4Kh2Pxt+GmAljIYxgMnHEcaQZR+J4RDOJxFEwFVR45vEppvWu5OGK0+ueixcx+vsQowUyxEMNoSfiKB8Byj+qMt8qzb04YmieqnwFdH+f8aWjvWrYXTympivctZeig77BRbPJ9NqbDakIWgMO6J1SRAdTWuiczS1avRGR6pBRKWbd6nAUxdZVykBg8RB6pHiL7xIQ6Q1U8MN6uBer5zaeVBUpmWZQoGl2RDKxJjp1SB+sFPGC7wS3af//7P3Xs2dZlt+Hfdbe+5ifvf7e9Jlluqrd9LTHTGPgQYEMDEFACICAghJD1IMUFCOkB/0FeqH0oAiGghRDohyoIEQxRhI8MBoAA/Q0MbZ9mS5f6e31P3fO2XsvPaxzM7O6C4PqrJ6e0Qg74lZm3by/3/2dfcxee31dr5pJZB/IuSSIkgqlS52RZVCLrUuFWdkKZD2LdzsrWHoYt69hFOyh8TH2aQ2OceV5/jPPsbb+ffb35xzcn5uF+t6C3fE6o/GA4dEps5NIsenpVg1uY4pWB3SdUneREDNdmZBySrc4odpoqIfrVOc2uVwOee7KZ/DnXuYoRMZdx1oxZiZH3F80NK3QXnWcDuYcUDAeemAf5X1oP02ZYFHDQmYMF3Oqm9u4S4F2ooyfIg4MV4HczHn/+g1OumO2h1uUu+fxm+sfaPVpp8RWWQwdVTaKoABxDq0DtpXFiRJTwVQLOFXqww49CrAuaAGHDo724MIMyvf6W24dOMBqyRPgXWCrP1/sQN59ku+46OvWMxB8CSx7oH8E7HWwNYf1Ixjcheo+yAJoe9Los44FypAZgZkKNYEpF3DAyXKBamZUeubtkrocUYWKNWfuj9X5CdO9MZ3CzCmzZWQqgTIqtw7nXL93g3qwznhc8df/2l/i9P+yzw/efuPxbxYcKSW6kLl4aQe3ajl4sCAeRO7dW3Hv/gn373+LO7cf8uXPf56v/vzn+MLXPsHe7lUOHt7m+NEhG5M1UzeXcO7iBtOqZjqs+Yd/65f4p//8n/JHf+FP8Gf/3J9nPBk//p2c9WDPLoIG+AHoPdDPRNzkCGINbgxPUYgHJaT3sf3E8zwGxlWt3G9HA678sc/hdyvOfXaP7r0V+X7N+3feYXtV8x/9L/4Uz//xF3ClI7hAm1r+6l/5a3zyk38a4dnFJD/OUNerxcXWcJfFHM2kJqSakEoKVVSVghafzdb5jJAuRcb14G9yYpbPXnDBMtOFQMwBiYFKFZcTRKh9tD1ychTZose8mBNkESySJaqJqiRHXOfR7AhkvGvMzYlE4zKNBOYnMx7eucfp8QGxi0irpiJX6x1uTKZcvXiR6ajGp8yD+pDjJrJRWg/OE6hyJrahJ6X3US4hoC7T+Z6QL55FabV2hcdKcwPDo/rHueE+WW64C9ZbsNxwI1fF/ho6yw1vgc55ls6xyInT+SnzxZymtUzqnL2RBLIwKAZMh5NnPt+j1sqS7NTiI7Xvf3q1Zodmyqy0eFIKxFjgypIoBdDgYmuEeslAQReF6AuSD6ZgTz20G40EkOWJI584IyD4bHsoWWYySoURLkKhvZ25IKJWE3cJIZgLiwvmUpDBeSx+y6lZtosp9kUjgYYudDgfcS4jyfagImoGcl5JZe6d+pRcFrZXzAlRjy8E1zYE7XDZ+sv4SBJPEiNlqqtAQbLF0UQViijUCjkIWVakqiPlyCIrQy2QZMisC0pySt0mJEYWvoDgCPkss90y7FtAOqH+1zpnI01iQhILqRGCeHOdSFDgcBKIridHUpBcS3QWaeWzQRV/2EddFrgwYDzYwDsIGgjlkLIqCQq+dPjJGiFUOPXkDjyBrEtKd0rOHV2ZWDYrUvt7hzhnhE4DuIrsKqC0+0kiLmQQE1Kc2d+73hUig0U9qHEGfw8/4h+o8ZFB8SM6BmYe1uu8fhQc93ywvzkAJli/EicU0/pJIxS1WfbWLOcpVu6HNYozkEohu45F2yBjsxsZxoEVW0+9TlXRnEmzxObkMp96YY1Z0VEVHxUUD2xcvMxk5xxt21LVNaEI5JxZuobB2OMTuNZT+gkX1s4zCB/NqjzFxPxkRnPccf7KJtI3NopUWnbMsMKNC7wGUuHIKI0XiqogF4luALU6imDWaVpl0IjrHGUaoHXNvMgUueXaJ699pM/04eMxL/pjvMfZW8iTt9regq1NePE5k3k+eGg26w8eolHJYUTrHC53lK546k0U27m0HB3DL//a32ZxsuQzn/s8f/TnX+L6wT53dMHVa1dZrRqWTUcoWpZ5hTY16aFD247pasGknDAra2YhEbsWT2BaDPnZi5/hrTff4B/+8t/m3r17PH9+m/q//xc59/xLHDw4YfnuW4zHI17Y3WB7UtPxgI3dQFkKcRU5vnebVVixjJkizdgMAa8VxyfKeGP9g7OrynHq2GoDcbUPg0DQMaIBSLA8gHpqdvJnI5xd54nuoGHZwGdf+Bleuhgo/Bqj0Qd/x48zVANCYWzGBJKV7CIhJVKyoldXAZccQ3UMNLHyyQCi6NBSjUmWlZRiD2SZPZEmZ5l0GtDObEzVJxzGSlQL3EE6wWePktBszEIfHYp18sVZDpKyJLmqB6V9/1nVCqiUoeoe20dKdBQpkgorTNWZWtRph6REUAi9waqVcxmHx4TsgtPOmi2A5EShilfL44gSKCWCKClbwdhqSUNN46DVTO5BCD1TjmbL9UtdS2yWdKsFzfz0sRp8tTglda0p434PWYvzRcuy6TherhhVJeOqZFiUFD6wioF523HcNAx7cHwQCrbKwFpRsjusOWpaDhYrysEa66M1sjTIqiEqtCjNvQfEt15n+MKE4VrNctExTwVrGxPqvT1EEvPlCUfLlioVeBe4e3rMo+WSgQs4Am0wAkThPbl5wIn/Lc5NrzH95EVu7MHhNiz2gH24+Y4tJS82GIBW8ESZeoY9+UNurd7i1TLxP//sz4OsiHqf79zKnObEIE2YtkOGbcNycELbJGaPWio/YLAqKB5uMQkVi3lNwwAXGmI4ZsmSTg7JXcHaZMRBDNzNc+6PWmRvzEZVMz59wM7wbepRphllvK9Z7DtOr3fcP50zmJ7w/HMVP/P5TXyxxt/9pZtc2i3Z/NSSN2ffYL9LtONnrzSL5snaV0ZHIJBDybEWiG/wYgptEUf/BEJE8c6UYuJ6K3MBSRmPNSbLVlhpDzQ4yJJwYs121zNAfGt5urG05q3Z0lpuniI03kPubTPRXjHuET2zxgLnHURPdN4Un1Gt5+wS2hm8Vqga8Ow62wjniM/GjiiISBZyl8kEymhAoogihUJyONeBs8ZASKAummN7Suat21uxaQRU8dLhcLQIWTyl2nF3WajVNrtET8aTcTRSfshy+nuDjOnSIYVjo16nk4ZxN2O6gPDQUXUOyefRrPhX9vl7r/49/sX6Pb70lc9z4cXn6GYrRvcPeF5K9qgIWx45bGCwDl95Hi6U1rRYnIB/CNUGXNuCc97U4mePrQrrQsATEPzs6zEpiA9iwhXmZLmBsSvPFDtnDcw51uw8we7pElP28NT71v33DyAewaKxrPdRGrMxvcZzk4q1asXxSjl2B7A4JQR44fmfp/LPPfVmxiJX2eDFlz7B+ubms4Pi/cvsjuhwnPYHCE90Uh9jSEYyaMpUTcMgLQmut8cXI21kteu99Z6V82ap1yllY7nBZw1/l2xjmzq7XhMZEbuGJeR+42v50MnbvafiqXqLwOQKIkrEagBNAjHjgq3z2QE+knM2tZ6abWKREipm8yfJA4UB12qAbeoUZEH0ncW7kEm+fMo2XdFOIXlyXRg7WgXVbNaEP2ybjilVcp8lnr2x4EOvSn5smy4fYpuu0WzTnaArRaN/bJvuHWjoLBKl6G3TU6bT6rFtuiVIfUxp4ccYo6KmpiQkZaDC6uCUU5lT1WuU1RAfCpIEvIMTFdoKmq7FJyVnA16TBqKLVCmaekiVFQVNAajHpRKn1ryNkmmDJ2pJaKIBe5pJlYPUmVAzWr8wlQlCsrXCe0im9C9SosqZNiSy86Y2N5aVndfUqxR8BhV05ShESOKNVJEUn52tF873jj+Qz/az6u3aEMPsC1U0mbLaom1aSlE0BAocXiJtUhY5M6rGbA2H5LKg9IFmZ4/F7AApIVWtuQh9wCnqxx/aWs2KKq4qif31KrEjR5sTQjAg33kMtAl2PBlUHLXzVFQonjLVDN024+k11sMeB7MOXd1lty740i589qtTimu7HB+t89rXj8iniegGBHkeebjBcK1mM+4gD1Y8ePeAImYG0/vo+AjNBZPZMYP6Bu4XAvfubfDWvufygXJxbYIfbSGhsnWVXUQGqEvI+ZaTZsyNA8+5wU2CDGjrq6RiytW5Y2swpllbspjsU/mGIAVJF+S4S9ARLQsWwLiYsiEb7NDRyQb71QaL3UhXfp/Nk1NG+QXm9z0pLti51OD3blOPLyCSaHmParJNHI1hHcaXS15Y7iCSkcLhCkEK91gVg+//FEVjJMcW7TryqkNjR3hh79nOtysM0LWHG2dZ2ILa84inl1TrgjixzHvgMXCuPFGXW5TBUwpz9wRAV6BYFkgKdGXFqc4ZYoTj1qvFTkVPHS3+QaNly3qvtsfDCCNJldZHkhPLPu1KVI1E7FQIXsjFwnJ1NRtoIGcNanPck2yEqugVr4UdndheMIcMRHKyvZ/LFmGRs1kRq8+oBLyz57p6UCcUMVNkSJ2gUhJdxuUWJxlWgTpbVE72iveeQQo9KVvQXJpyPdt5N3KZA9c34K3LaOryZxzt6RIdl5y7vMel57a4eWPGwztHHO3vcrJasKxbhmtTxocn7J9GQlfhFkv8dEI3LEgLI6iGNhLG4NfPUQfP+ec2+cza57jUea5UJVV1lRjHHB5COY04FxkcCeO3r8H2i7TViiVzpgJThohuQP48yJClCZvx1FTiqSpH8soBDXMqRghjwBUFbhbZO5yxEVrWhqYEfczMRSEJegvu3hHmn4BrS2zfdglONuB6hI0yszZseHSw5PZ1z/bxIRfXh/jRLlUwqc6pKt8DHpXwss+sqUdGAlewuvIUZATsAcUCTh9BOm8veGj/zry/kbzCOMLFDtZOYP0B1Psgj8AtwJXgRyDPAbsgzw6aIUOUOQsdckBJfSgMA5TjzNLf4+DgLpOiIsR1cpUpJgHvPbmfpsLbHqxAOB26vuwVro2nbJ17iRsPH7I2GPDX/uJ/l3/4j/7BB0Dx5WrJrXt3+MLV83z1019jWg15963XeO/uu7itip0XthEN6GDCu8fH8Ob3Wbu6yc7eZ6E+ZLw1Yu3CGs57NmXExlpFZs7he/u8/t3v8Y3/9uss5y0bw3VeePkK9XTCcLTGaDT90WlIIGNg20OtwEOQiOoU8CR6R58BpOaxgBwATdDMhN3JkPU1KD8zZLx5BT8JzL7rOTk+5MXPX+WzX/0qripRTcz1DXRywL/zp/4CZXFGSv29HxGh6IUumjOFlqRcE7Sk7mOCvGSrl1BzSHLZxByh6B0pDLQVb3vqpa9o1Z7pNkmm1FWnQEOFKXlDFFIueiKirQ2ubCkESk10PrNSEyIkLZDWSIRVWJD9nARUXeTOQcfNm/c5nR3TtQ2eQKBAXKDo9/0bgxHb5YCyF+8wGDFbrEg4hglUOquzOnDBm0scQld4nG8JqkaezwmhIBee7BJZjcDadRUp2nVTquA/AIYbiJyMEvgYDO9QorPc8EXOzJZzZos5q6axCMgUIAtehdJVTKcTRsMpPlS/6zn93YYK+OwQF/s4kwLf0bvvZDIFMVrP0eCPFm0tlnJeFkZc1o7OKx0FCYdLwUhhLoPv8AoqQgDbT2WHqO036GxdCkVCCpN9exI+RaTN/V60eMzt8lnN3StVSKoRMsF3OFo0CGc2/YLiVEETBR1OzBGrc85s+bMnFdn2QybZx9MRNUB0FB0U4shl6t02s5mL9qQqdSazVMkmxFCISa1ecI5VKAjaUXYwzVjMUie0yZGyowvRHN0ceFVKovWBXWO3SLR+dBKl9dkihRK4IuHlXyvFbQYsesapRZiSQm+hLlR9r8CRyU766KWaVV7ZOfKZlP7w51B/+nN/zmpmbzFklTOSSswRt1qRygaREikrcJ7kOlwzI7uG3K4ILrI3DDysaubt76FaXM9InaEXpPS1as5QYARyZ85oiG3CpXMEZ2zPKDx2lPvDfk7hxwDFH+o+JTUDaoZSUOF4GrL84WTKM4D8zF6dfiEGnjREmwxFa+xf9Sa1/NAsaaXRTBhMaFoBXzGuR4TiAlt+kw8rak4PHnLnrVvU1QXWL2xybqN6koH0EYaIUFQlxQfU5UpsG0ZFTRsrQi4Yj9apy8FHa5ZmmB2d8N67N3hwp6WOI3Y/OwAH3bIlzGBcD0ljy8Nazb3ZPFcBUJYuAUuISwappZABTSjJAergqXFQCKsQGI7XuHz12kc/4A/7sI+7zE8f208IJC/7LLTpBF54DuYLODgkOk/sMi5BWRc/9MJTYMW9h3Pund7j6OSQ3eMLjNoVv/jlz/JwY5Mrz13hd771bbJrkEmkchUu1pYTdaLQtSzcEfNyCfNEms3wThhQcH635sHJI4pVw4jEp8/tce35F9k8t8vwF77MUOecvPc+myly5523uPm977D+mQt0XeTB7Xs8eHCDZhBppUaXLYwcdV3jfGBz4+nNk83peDWEo8xRccJK7rO32qRMO7C2QgYt+MkH5+zsvOiK1u/TxMRueJnxuv8hesozjGzKbyH1FqErkkbiomN1sGJx6ii6wHA0ph7Y9Wi53RlfevAtSR2qvb0qyYRcmBWl4olOEelwasrO3Ders+ReMeotDyn2n8RFa+qIZV2YBZLlfXrNxOTpnFCljJdEdGYx6FWtUaXG9vbmnU7nBJ8cnWQaPLWaZTtJaXr2v48FCcFrpuszVJx0ZDXFVHZmQYt3ZDKdSzi1jJukYqrwVECpaO7IOaMpkrvOLAqXc1bzE7NDPz2kWc7pmiU5dh/v/P2YI6aIZEeKmbaNnC5XDMqCUVUxrkoqXxg43kVO25ZBCIzLkmnZW6sPS86Px1y8cJmVG3E9JlKKeF3h5h3JdQQiG+0DduuS5ek6x7OKxSSirsO5QOs9p23HbBEZuRGU6yQvnHQtq5wYSmJjMGVzepHgzlHoGheKksEM3j+BvAVdqcyuKFQL9kYDswI/cyJ2Yk0OBYoE7pS2imz98Quk3XMogUdc4P30Jq0c0BSnrJrIMDnq0YRaLpCaTPNoSaGZYSppk5IXwmZc4MOcQQrUg3VORzucuAYdHLMZBhw3Dymnx6w25hycHlEf36eUW6xXJTSeVVORb61R3V8jSKDeWbAzbhm4Q9p2zsG9Q164dIHLn6w5PrrO4kEkHW888/meLM/QSaWKnqINqHpaVzCXyMApUT2t0QahiKRozXQEA5RVzCpdEpWPiEuMOpiTbGOuxvAOeDRFy6mKjiTaF9g9kISSKNAuGdNdPC4GQmePOS9KipU5VUiiYAVdAa0QnWUZu9AhziHR6PQh2uasxNlzQgSXsVwcWVG2lkVVamvse4TgIto6Qs448eSoUChOjDWt3qIXCvUUaWXPKzGFodLnuzkhFolgOAxRzIrX4if653IscTGZTecPDf1dHtsfZ3+2We8RZEBeCIvTFbNmznBynmHqkH1g2eCKMe7BOt9/9X3eXv0G7fW3mf7c17iy9xLnBYoyMaxb1s+XsOpgvYZzgkyBtxfw1nUY34G1Idz7ItyY2gQoVgCak2Z/MBiIcPb3s7JKMRJL1X8lYN9eyxKrUMfYPX0Xy+RZ9N+7AlzDmJdPc/islwD7kGZQTmDSWWSGZ0xaNJwuH3H94Abfe+Mt3njnXc4/d5mLV76EuOkHSxyFkAdsrr+I/9BIl48+zjgAZk89xHzgn/7XswP48YcghNZTpI5BVLIPrFyiTKaTcA6UjiwFKfRuMJ1SRMFjUQLBRYTMgszBckXWSBUy3kNyyez+XMLLipKO5AXpCiOw+IRiqmjtlb9OOjovZsmdE36lZnwj2Yh04nEpQSFINOcGEUFdpNBMylCl/NgGOOCRPCRLomgdnVeSLjFwXCBHYjQnGErBq6lERHJvm+5MriP9luNp23Rcn6PX26a7J7bpCFYTYbbpTjvLMhdBkpIbASmNdIDgQjIb78J0Tj5GUvqgbbog/Ub192fsDMf4rGY7q55bN9/h3uKQ9ckmW1vnGK/vUI7NgarzwWJmRAlqz+BOO4LPlCniaFDnaHNFdoEyrZgHR3I1TgIEzCY5dmbD1wU650h9o84VyVxJOovViaU9v1UFOiEHIREIoevDN4zZnlOGLqA50Dis0SiCl4TkjHclEsUUT2d0R+mIrrBmDs5AdPXkbDWppkAroBIpu46MR1xGk30e6bW73ntGwbIZNQS2RgNGPpByNtN4X1COKtzA7Dszju7jluhda01LMhQBikB2DomRnPp4oaKi9itCvc7QO9bSEFgRcmZ9u2S8M2Dqt/DzdUbNLhvdBdYHzxF8zdtv3efo0SmXpxf5xDnY+kRgNTQb08HkHA/KhqOkXN4O7JwfsLaeLBNydh6Xtrjevc5bh/8APz5hr7vIYDaiWh/R7Djub0ZeOT7mZDXG3fUMR0PGe5tUg31Ex2jeIj1wLO/NuO8e8qibgZtxsa2YtHNWa8ramuBW8OD+mJNFwflPDdkaKy5MkHABKKjn99g9OWVre41LRcVULrIgcl1KbsqKw/E5Hul5mniO66cTiqO7nLu8pBrug3sL2GE4+BKTHUe9BeWm4oae6n6NLlZGpMiZnCKpXRFj03+tyNpCEZEqIWVEhgnKxIAvPdP5HsWWjPSkI/oscWf3hnuKbEsfG4HteUTNCaFvYz8WLziR3sr1CVhuiHrvhoA3N5FemXgSClxY4ZwQU0lyDt8v6D4a2dm2TUpyViPi1NaRNpOjw/XAtMv0LmcGKLdeEBfQLAZCq+ID5L7oyThyf4CqYvbCKdB4wCfE2X5OSqVQZ3auKZF9hmCKzJQtVzQVmOIuKyn0e0cckq0WhYDrwGGEmCCZMrXUqTEVo3fMsKxfcWp1YtcTg3KNqq1b0jjLdnrG0e4f00122drd4OqL5/j2b97keP/UcsVnc2bbS0bTMaOypFpGutOEH0eCQLc+Ro4PmRSJrWlB+syX2dj7DDvVIRfdDV6SdSomKDXkEbqCtSQUySMRwuEWawTipnBQVEy5y0j/BZ5zkF+G04v4quDMDCFS4gclcs1AyosMaOmV3oKp+oeOQRmp96aE3UswmgArVO+CVHCyA0eOHD31HPwY6OyadgIUUKCMS4dsjNCRY/rc0MgOBRDtOmBxzGS5oKoquosLtNxFqKyeXGCZjuf71+Cg66BRK7CP+snfSbA5h61jqB9Adcf82KUBqcFvgbwAcsbY7BVXH6PvYv3phCdzTSGMoDDpJ+PihMFmYlhMkHgJpSRlITPj1skRq2bB5c3AqBxCV1ClNaQOHIswAGJYUudTZAG/9vXf5Jvf/84HfndKkUf37/OD3/kel6qLfOr5l1jGGbq1ZDSAc3tbxOOC3ed2+OO/eIErkwnXJlMWtKydPw+rB7hhSUUgh4b97iaLZsayWfDi517mv3f5P+BnP/s1PnXpIouDG/wX/4//LatT5TOf+hJ/8s/9Iju75+yeqQS+BNqZs45jA2gRvQ0M0KUnZeMhFOefmu6zVnKE4fuC31HCmmPnaB2/P+To4Qnd4euU+RF/5t/880zWPtO/KJE54pOfe4kXX37p4zk//ZgjZ4vwUinxUhG8p0Sok7nfJHHgE0EzyTuSZosFDBmv2YDv4Fi5whTVBJJgyl7F3I4y+NwSQsS7Bh+zOULlQEgAGa8FWmZErM+qUYyULg0qQyTbPSwkCvEMs7XlC5TZKtOdrNDG+nuokeHFWa2cMyxPG6pOKbKjSoKTEl8ryyYyCBWF6+9bdcSelCpaWCxNiD1W0J+uTuhKT9MFcqPE1tnzSoVgqW4Ghrv0I2C4AFGUKI7GBRYo82bF6Xxm6syopOzRZOtTcAXT4YTJaEooB+ZY+TFQIJ+zWddngexJ/Wbfk5BAv18yMmZ2SszeatHC0Q7FcsI7IUTFaWfOAqKmQlUjd2VNBFEIVpsWCiEaq7NLtp5qtr2Z14R3rYmLsiJq8SaJjDjBeSEnb3FEPuMKIbpEJ1D6hOZIiZKj9VLo4zFdKjCKWDIADovi7KxUxvXxSmckD/HmeKAxUPhkpDoVSEZQzdkiSbJY7JGzHD/rqTglVy1Nl6iWyignXCqIqSB1wsJ3zHJggMWCmdzVW3ySU8oYaXwgmoLDrt+QTNWrivz/i0f07zIUiJrpsPaLYsrvMmdCMBv92K/R6oRaS3y2SMMoLT4YObQ9m8s/rEBquUEl0VxvsxE4oUNzR9KWZRNxrgFtWaxOYTGnbRtEI1cnNRtFyQrPoCqYz36PEOfge6ytsl4G9D0I20tIUkKbcbXHOddvB4TsexdeEaITnMeIsOkP68l8Mj4yKH56fJNQDViUI07dkJIhAykYYAD5WczL0+Dc00zmDx2+Zy+kuX1Va8Dah/5o6Rxb9SaLjYts7X2C6WCD9fHFD1V/K8o8nTArTpnLDYblgHFZfewCSDG2vaSa5WlicZLYLNcpPhTI/5AxV+a3lixOOmbzJe+/85CNSxcoNgOTNGWSpuzrASvnkUooToRqVaJ1TZY5OZ3SdtagS21HyA05Dci+AW+NBik6tO1Ym+6wtbHzMY727JielnM9PX6CxaQIjEcwGlpOvT6FAT8eC+ABcJmj5U1WWVnOlYc37nP35g1e+vRnOf+FTZx33LzxA8qiYbAzYdg6Rqclqa25VyQaH5A60mikPF0RBgWuqpBuxaNb77D35U/x81/5ArP5nP/x/+w/ZGNvDxFhfW+PP/YX/iLtyYwwP+Vbr3yb7/zqd/jGL/868XiDCs9vvv5tqs11JpNdylxy6BzjKeztbFKEDwL8QsmgO4d6ZRA7Ck14PTE2vK/ADRD5sNszgyyoRsJuuY3zPwFAHGjSiq5taA8Ome3vk+MKJw7XOtqTFjqP15qBCFeqEQ5bJH0Q2uweNzFN2aX2MTGlVnaWJS5iBW9wauCHi0RxfVHaZ2x7szxPwTInYwGdWkZHxveKdiHQmoInlzShwwWQJPisePVoTrjkSM6TvaDR1KDZO6JLJBLaOSKKeDihRrxn7DKbGtHc0pS28IfokGT531nNVkTElDmpc3hnB6upI8iKrA7VmjhfsJgd0C4XLE8PH4PhXbMkp4T+PgavqLa2KRGzWUpJ6brMfNVyVIRePV4xKAqaGFj4wKztOAwrxmXBpCw5v7PN7lpNG0oYDTkohXh6QFg0tPueUVeyHUrc7AS6jsXJCafHCwZ33uPchR26MKRpPUsXUC34tz/9Ff4HL3+F/9erv8U333sTbVZsbkzZLPcYhUt8/uU/wef/zBb5qrBYM0c8Omj/yfucPvxVjuJ5Zul5ttwWuJFlp+gSYkf08GD3be7NHuGurPGqu85DdrinlzlhRqwWZALtqsTlROUEWffovMFVB6yaIxabj3jUtbTTdcYaWasqitOCR48Cy3ZFMy6Zr42hOKFaHHLhsGVSDJkdRB7dnDNbVNTdLuPOU+V9qnDMaFAzOhXGi3XO3bvI4lbmd27/NnpvSqgCt+6fcOETkel+y/z42Qv2ibSP/+5DJNUdqR3QibJUuw8KzEYqU1Bky8qMISNZKTTbhsQFtFdM00KpGWEBbkzylsPlW4wNL9kyeV0EdeRo1lvBWRarZAPZs5r6IWL5laWYYr3LGdEC32VKFVxUVtqhhT1jHA0hA60pxH1IVsihJEpTmPdSK5fMytxKus6AwpjISUDN/lOyQ7NShowGY51LmxHvTJGeU49pBbMHE4/izEpMUm/NbBu81oHk1K+i1tQwK+ePVlR+XMLy3t463Rzu3brLm4+us7i4y+DF56157lpkYSHdR8V9wrjl+fEFynKNiSu5uHme6Qry8ZKbD6/zenOX6eYuF/2U8W+9hy8uI68eIvffRSaH6HoBD8/B7TES/RPSo00V0YEPnMXb2r9VmNr8LHtnwpO4gzm21M/770+A8/3PHfevfxn4JGazvsaPim4XkJage1CsK/4YumHm/v1HvPneO7z/3m/z6tu/yWu33+N4OefqS19gUq/9CCBOqzjnoPxoTkA/MvSpL+uj93TSs8ifn8xwfd593awotDESC2LArCpJPclZ/mCUiI9KFfsGiksGWkRH9InXDx7x7tGKKxeGlIMCzY5SM6SO3Ha4OpFLa7YEn8iFNwV0SnRakdVbsycW+Kh0Plk9V2bLvU3mCBMcZCp8G5EASR1E6TdmjlIF1JG8ARPO8HPKrKYqzwFiQUtJG4ToAJ9IpRI0GqVT44faprt0ZpsuoEIOHkQIElFJ5GBtHgO+DQh7YptuCmMH5CUoBVk8moUimMNOEtfbpkc0uV5VaDeFlx6I/X2iXYv37G1OCUNz2WiicJxXLOYzlrNTHj26zfp0nfMXn2O6e5libDnp6oXWBVKucDHRuZahi3RZ6USIvS3h7jLz/ukhN1c3ubD9HKGuSdrZ8bfWGFOJiGY0elZUpuQM4KQh4fDJE3Jjyu5cmmokdOY85DMiEZ/obUMLUCWgJJ/oXIJUYB4dQApEiTjMJhIfcX2+Sibjk6I50GZPUrN6dppxClnMgjxJZhRKGhyd7/Be8CGQXY3LgbXBgKrzKB0ikSI71gZTfPKmgAmepB95u/3hI0ZCzhRJyUVFGk0J3YjSDxgU1zjfDnmOMRtbkMvLhNke9ekQ/JKZvsti/JD1zSUXp+vIe+sUjy6wu3GN6nTAahVJp0sOi8jli8J41dD91pxVvYsv19jYvsid3Ru8m494tPmQz0rJhXnm8nBAvCAcX1ygzV0W8ZCWY+YRyt0XKEefJM4GDDRyPrdsFgukieQtRasEdGg6Jj0SHl533Ohq7vmCZn6Zk6PLnC+vMbhwnuNR4MYK6qFw/7RmubzC2jEEF1iblJRuBOqoBlNecgl1ngAIjpqSIco6wryYsLFp3lDukpAXFd17mTAfIpczOtmmqtbYvdAxk2Nw0LIgdjdpjg9JriFLB0UiDIVQC74SqlrwpRFyRErkbP8mz0582cxn+do8Jt0m5PGfWXisAj+zRc+9j586sZrKHoI8rTJHzVWk71k/Bs29OIuhceB9JhKYq6fKBZEBnU8QFJcco9znckZFsezarihIKKEHmz2ZlBMpW1xz1kwIPXlDXK+IrChcJIkBhLkEukCKns4ZmcrRQXBnn5Lsjegsanu9XPbxGVWGpGTxpNwDJYUBHpGCTkxRSKKP5VAQ36vQBAnJ6sJloIqJyjtyyMQUUAmUXilUzPI7Z9SDzxFthaiZmBKtf/Z7vDnep223GFWBqy9fZWv7FW7dXPHw1imHhwtOtufsjKaMRgOGxyc0J5nRxYJ6MOX85y6z88LbfFaXjMvnuDf6NLlbY6cU1vUYp8qJljhfMxRPMRXWFAMG3gnIXRhuCt0oc8oph23JhWKDbdnAuw1kXICDsq+zCngCXvGkjFMySiTS0hYz6s+PcdUGuAYoaPQRnlcJsYJune6kZrT9KcY+WExTv8UZK3xCYeA9TjxrNUh9hoQq2nTkhye4AUzSgq3FfXbXB2wUxwj3QT5tLnuVQuGe6DuaGuLz6IaY0HtrAfF9GD0E11sQ+dJs0d1lTF6+hkmZzwLL+65mhsdMgGcagYZ1VoipvMuzj1kw4NO0HNHOItfvHPL8i+conYAMmbgli0dv0xSZ4aAg3l3RNpuUVz/DeDwgaSQcN7ywe41lueL/+Y//AYdHhz/y23POXL91k7/5t/8m65M1rn12i099bYedacH+jUfEruDf/MW/wr/7lT9HJQlHpNED3pt9l+9+T3j/lXt8+atXuPTVCQtNlKPEhZdeZPfqFyne+B4np+/zxu0TpgW88eZtfuX/80/x8n/n+f/0P+Pnf+6P8uf/8l/lxZdeYnd3l1vXb/Dw3gGXr11i+9wuXvaQBwG5BZUAP9NP/w8NH4ED0JswD4HZrOG1777BD77/fQ7uvspn/sSX+PQX/9TZFQsECneBz3/pc1bX/xRHET3ihwQKgihlUnxKiLc9cHJqe9XQEzrVQHKcIJ2SpKITRy7MnSkTyG0fC6gCLlLSUfYujCJG/BB15F6lZm8XyTkQYwAVEkorHiQbsCkgfR9ORam0pOwiqsK5umJ7POVBk4jqyRpR7Sw6TDwisFpFulnD+nREkkSRA9Pg6WJL0kzpI74QihhYyBlRIEFO5MahhRC0IyWI6oidw3VjfF4ROovvkEItcVIy6hJm8G7AsMNUwAaGe5bArGuYL+fMFwtiUlKyWA+XoSAwGoxYm6xRFEPEmVOkpMzHCUf2rcN7Bz6TMKJmoQkXoM1nKmroghJdQXSQc0C9Y+kqIweXFcm3RtzvzCnTK9b3SBntErlurdLVktCZ2wpiTgIJtVhJMXGIrfWZCo/GCu8sPY4uk5ziQ0ch9nuiOJKD7JVOzXJdYoP4YFGQcnYOQMjkTO/yaj3bqI6iz4lHHaUoK3UkZznxmiI5JKR0+KjgPNopzlmPIjtHcpmk5kDj1XqmOUeapHRZiZ0RATyK90r0RtpNAqoKlVJganDFUeaOKErnrRkREnTJ2+dMnpR+eiSZP8gjk0l0KMVjbM/U+wJkovhe92DORD4LSMHKJSrNuFAyyxA1ks1U4A/daGd3aOOCmOYEEifzJcvVMTkpy1Vjz20sDiOmRMrJ3BldQckl6qIiZmFSDzgMM1L3ExbGOQdlbfHFIla8BeupGGAiSJkexwgUlGgFSRskJbIzFXnuXbCKnGhX6Q/luXx6fOQKfn5nQTFc4YcnhEFFUY1YhBGlDKmoGUjJAE+Fe4rMJ/2fPzSk/09lmxzaCHIWEjniSQFzNjKFKOvVlNHVn0NE8L219g8D3Qo0qixo2Lp6jqCOUC7xMvlxDvdDhyAMyjGTeoNusKQsI+VEPhLYrqowz8wfzhACsWt4b/9V8vfv8YnPPk8xrNjY2+Yw3TR2WxuQNlO2HR0BLRqKPKcUY2evohCSsfeQlrY8JJczYy0ft2xf3WVzbetjHG2Dyb3ONP/wBCC32fhJA+Vn8/hh06l0qG4hrHHl/FVe3PoZXr3/Om3rcSGYEjE4VJX9B7cZli11WVCulGGCRgPN0LOsSoZ1QTFf2VGNJzAYI3mf9w7uw90JX/6Fn+P81Zf59BeM4a+qiBOKqqLYqdCtddKbrzJbzLlz8za/8RvfYL5Y8cqtd1k/3WBrumIw2qStNyga4bnLa/jy6WtPQAI6nBLbzKDZsmb5yEGYgG7xWGr3I8MBA4oiUxSTD52rZxnvf+s1urbh9OSYrm0ZhppBNbSQiRV4hFIyizbjxbLWyNLbk0LqrOlb9HalpmhPuGA5vS5nEGuOqzOGYVRHDp6YrNHtnEUUeByaFC28Mfp7sa94IYqphGL0JMEYtGrZ3U6jqX+wOz2iiPbxld5UEj4rQRyRgGhH5zKrPGAhU1IWkBOmasxKMKZpJba+hF4N67IgnRArU3u5rHiD95DcEXLHajXn3ruvcfTwLrFdEdvm9xUE/+GR+02TaEbVkcWRkydlIUZl1XacrhoGZcGkqhiWBa0vWMbAouuYd5F6Y8XYzWEaKMaeMsByCVI5xtsTRquS2m8we+Q5PDolni5Rn6hKxd9dUoWOcnOKrm+wNxd+ZlAwOj/ma2Mozm9yuH+fjXLMsTumngrX/q1tyq858PBJUb6tcPPNu8Rf+6/p7v8LXqt2+aWww5fSNufTHuOwTlscEcoJtz+7y9f/3Tc4fzuzdWnBK/6UQORmvsTJwQDx22gsKLVFfUMsPW0n+C5b/vSgI0tNdhO6cUUuA2UaMD9ZMlskVGCQVtSjjG4NOV1klrOWSxKpF+vEXNOVHVV3kY3lOrgbzCct07CB956wKuhufILQrPHe22/RhGNagXvHkUvTglJL1F165vP9tHVrloi4TOgyiCeJI6C0CJ2YnVkqBSFSZLEGq7fVPGNRAZIdziWcz3Qn+6yCZ1RuIKEzQDwas9CFTCzM4UFaU+0hmRQSoRFcNBA8OWfAi1M61Z44E0xNEW0jh2aK7CwmoTPlj8+mYk1dAPWIZHLPzPa9U4QkU0BpdiB9RRczrm/jCUom4bKjSsnUWH1OsnQOddGaqWqNAu8EUj8fSQhNIPts1ludfb5cGqtac4GLEJM1rP+lBKYPlYw/+8P98vY6b85e4xtvfJt3j2a8fOkvEYZjtHJQrVBx0M24//47DJ6ruDw5T7G9weUXL/D8+QHl0hPnwv6NIe/GBeeuefyLG6xXa9Sd8PbRLb7/9X/CBRZ8cvMcl/I1QvsJ++Vn5UJv2nDkjNm+np86qoQtcRMs//GF/nUrTDl+pjZv++95rCm2B7rCQPIJyHlMCfSB/pZCBXlb8XW2fOfFKZ4lj47e5B9/5x/x29/5TY7377PqGoq65JJ/rg+xfPIWnG3myhJ8ePaz0QKtwuTpiNmf7OZbcmYQOyqXwQsxg3SB5ADXA8ReiCHiVakkUXjF4yiiGpCtjkNWvH9wxO7eBYYTBb8gqq3z3vcNNVXIQiCYJa93iCSciEWZRLPmq0rFRWfZhl6JQei80KVAyAFvdzUhZkopcX1jjCTkPic6e6VzQvSO4COZTJRI6IysljyUooRo2XFZjZwX45AskFxHzB1moC/QN3xyPLNNN1Wh+jNyWyJ7SI9t0xWJEZEftU2nObNNLx7bpuMjSR257HP6HtumA4+rClNBCr8/qgTnA5uDCXilk8hpTrjCE8qCdrkiNS0P9h8xW8zZOjpg/cJVJmsTKl8QvYHJThNtFmbRUfXAb6oStQg+Zg7393n/1rsMC2FnetksnSNmPSKJToVSE5Iy2gm5CHTB4fLQ5t5nKvU4yfiUyRLJLvT5jBn1FrGRfGGNZAeSzP4xud7jlz5NsnGkTqBSogvkBLheQdvZ58hJjHSJ4uhwmB1nzoJmI4NVYYQvMp22IIlAS3YVPnuKojC1K9CSGRSJrSIwUKwxmR0pf7z957ndTdxgCmGdOD5Pe+USo0Gilgv4bofzacj5+ojpRmbW7jA4njIaFMwnpxy0C5arGZeblucuC+1kyQ+++5tIfoRrJszzAbvPr9iSORujmuFJjb/pWes+x3R4hfaqY/75JYs68F6a4+88wF+fcEknHBf3uOvfZ/7gHS6eXOTc6mcZdCNysYtuvMz6xhqf0cjOwxvM09u44oRmqkz1AegCdVc43Tji7U/AwUtjisU11k6OOJ2veHeySb5Q8GisxC1hHagbx+j2BJYNy+mEUgpKrAGFeMLAnrJnSup5zpxKy0RaJuJYZx/0iE++uCKujYjXF8xuX8Y9/ALjFzeQS45BuMUNXqcSZaorwl5muDXAFwHfN85Fnt4f+6fW8KeYUMozP+plBU6cPVeeVhj0iHbuucVZDTi3QAln9yK9TTpPAHPtVeYGpkPsSTln/w2i1MOlRcSsHFEDrVbmgBEUXGbpHYXP1GcKRIGUFZ/Emt0IKTlKVYvf0QQJutr35OjOwBZv+eGhSKSYepKK7eWSOHJlJy8jRFeStUCgJz1GovME1EhUWUjZkVTR0KJiyjoVT25dXwsa8dqybzxmI2TXh3e5/5ZF+EgsCG3ChUDWQFME1Htca4qZLEr2ilPbbabo+qx3JaRnb2wuFwe0q47xpOLyi1fYPTfizu0l+/dOOT5ccNItaAeZ6dp5rh2uc6HZwp+/RHvpHKULjMlc0pbh8XNMbwRmjyJxdEI7KVkMx3Rb62xNPC7axdIVIKdCOFJYU3huRevmPMw1t9JFUthjQyq8VI89qx9fytpf2x3glexXNMyAAwoKDvOMfVHOD45Z4wHCVZQRsIscXoQbj9D2iLiAoj1HGG6jvR5GnDw2CHr65tEzEplCTpn7R6fc+a1v8ebiAcMXlly8+BLCJ8zukwOcnIOyfxZkLH0vCHkhJBas1m8yWruPjzd6tuImuCsgm1jo41kOj3v8e63BCBwpPALuAf/Gs93gWR1NFNoEsySMhvQAk+CpqMtdHs1nnIwaxBswBJ7N0RaZc6ze+T43uwPmK5DxnOfKz1L2RWtcNNy4cZN/9s++zj/8p//od6HiKsvVKatmRvPaCSodgxC4f/2EravbfH7zMwzkSt9naqnkIuuTQ95c/Gf8nV96hzv7X+GvvfCLbGy3nHcbrOG56Y7R9Ux70pAWM/7pN7/Db//GqxwfrRCF3zl8jW+98gb/5//yb/LlL3+R/+G/92+zFEeXA9kt2D73R2Hu4ZV+vi/woc9QRWEA8iWhe5R46xsP+Ge/+d/yq1//+9y9/T7luOCv/9n/iFA+VQ9IYry2zZPoop/eqBnjW09FpkwJF6wP6CTThDNHJCW4TGhN5LEIiURN533/zC5MMZ4STh0FJusOOSK5xecIYpEUyUd88FYbq9ImqHDgIila3KHmZCTdnOktfUA6W0HUKC7L5C1mUJStKvP81pjT0zlNK32kmlIWFSE4ckqUvmTZdNB1OAl4gRJl5Qs6TRSl7YSdRooMq6B4iYTkaPpaMovgBJwG2mwxjDlAqNQEKGJ9hNznhtOD4VmgE2hdYCnCIkZOl3Pmizldl0jZ9bEb4NUxqoZMR1MG9QTxPTk+2V6f3NB1z25rXCbFqSOS0dKmN2ZTXq6CELNHcOTKHK1IkPu+potiCn6J1m/EUaWI963VmTHYel8oaCA0kDuMdOwAEnjrIDsRxCtJsfNBQkNEvSd6IWcrUpyYEKBzQohA9Dgx0VAK2cRKrujfOqHZ1KTRA0VrET1amI2/swiUpEKHEqSjkGzxKv05xmVz3cx9ulrvDIoolC1FcrgsdBTWfyocLda3CQSakKhUqaPhEuKUCscyCouqoxDw4tFsAisnDvGJUjqyD7bPwJw+O8kUAiH//rl3/UEatrQ3KBWIkY4jHknWXa+d+Xrgch8ToFSuILoIqlQukylZiRI19Y4Wv99H9ZMd77z962jsQDMp9xGqTxHdRUxVL2COSGL3meaOR4sjdicVpRN2qwFHwwGHxz9hUDwUSHDW6yu8kdJ7p10KwfkSCREJicrVTL2jLCviasByNmOxXBIT5r3pLCM9JKVLf3BwjN+L8ZF36f/8G9/iwrk9zu9tM5q0VJMFfnBIqGuKasTMG0BeS8WAggGegOt5lf+SotE7SB3EOVRt/3E+bMKfNCuLf0XGhwJL7WjLks3hJtvldgYrxQABAABJREFUCCcV8tR7fJwheMZrUzTClYvbFLX/aJverHSnR2haMa4qki44ah7x6N27DHdqXrh4jT/xF/8sO9+qeOPOTd59cJ9Ud+Qu0nmzohwEpfKBVXa0IogvqboaL47kM7Fb4tvMsHO8sH2O8XD07AfavGEArZuA1FhH+l8FkOtT//8THjoi5yHeebaKS3x2++dZbiTKTaUaDh8D6vP5nOWyoRoKQ1pKTaTGcaqJRXFCWwZqamgaNAgqY8pU4lLJSbvi+2++jfgj5r92nV/5+q/x+S9f4aVrL3Nx9yLr6xvGLnWOzWsvMFhfp7h/j3t3b3D79m1OFwsWcUXTJba7lvWNiC8yO5fXCcUPA9yCBiGXQtFViJ9CfQLuzJLrXzaHDhjjZNSD0cnU4u7jzfn9W7dALA+jyAFNSkoJJ4U1tp03ULqDrvFUAbza5hUVs8rzBoB7AS/Rsj2dGRolZ8A60qEp4rRESvvMBeClwOUSlYjDkbUhhd5eMwv0zR7OICwNuGx/d6oEEp6Ic6YKVVXLs1OH66xpmsUhLpGipbomJyx9yVE7RLUmhxWdF+YuM8AyI32BFYYxI6lvrmhfaAK4aKA/mcKbVapIx9H+Afu33qJtf7q26B91dF22uXKWT6QIoglwpirMjpQ62jYyWzZUZWBcVYzKkkFREHzNaeN48+4p0iwouxVxFQk6YJ5GFL7EVZnD1YrVcWR5aIoL51oGrkKi0p4esH9wh1XxNp2O2BhfZFBMuDE/Zmtrna31Afn0mOhm7H2hZuPTATwksf3yW2nF4gdfp7v/y6TVKSs95tfcDX6jFSZ5wHaYIMWE4vLXOPz0HdpRYvE5YeA63gWmOqddvEI8isgKgm9pigOqCtywZG01JDQlDxaHdJWDYkTV0/ldO+HkZExcJgIzxipcqJYk9w4H5ZAbwwX5tGVyuKR2BcXVzHjN2LjDYghuj0V4QDPOjCtH+/4J796/y/3ZGxzO4WQA6jPDcsJm3qNrRtz5MKr8Rxz5aXqfQFVkKCIhOrKUJNdByDj1+F4EUWYhOFPhpOARVzzO4nU5UEhCfWI+P2RewXBLiBSggVoVYr8iSDLnB+ds791nSToRJLW0RNuYi+lNJIvZbwalTsmAt77D7cUYvVqayjGrQ4OSSrWMsgzOBWK2zbJIRoJHoxjQkQXVgEuQ+memuITThKj5sUkSXJd6AEiMxazemNdFIuWAU7Wfy6Z8zThcSAaqlIAv6EJC2kRGUHX4MxX1T2HUDyre+Pbb/M53v89RTtQb3+PV+iLDa5fYLkvK2NE8epcHzXtsXdui2hDWL+xy7dw214pN6m7E4dEjbj96k9P1hivjdZpVw0k4plsTvtF8k//8zq/w8MEdPrn9Iv/Tcy/zF4ZfYFpMrN7rj3OpyltJWWb4OScMz5jYZ4rxgPUfH/Yf/OwSD5i7+BkgcAai70Cf2oF7gDlZXuWJHfvZKDrC9ICYM2lZc3pyn+vv3+L7r/8Wr775WzzYv0GKighsDXZ43v+sgRpnv0ex61eM0OGepabRvnHXYhaDNMD9/mDq/mtA37roX/Rs63hImRD7aI9cktUsqFeq1i7yphLxGQoxtakiNMGRBNvs5Mj7x6dcOLfL7s46rXcIS8iBzjuksPxWAbRxZusbQHKL4lnEEUruldmOToVQJFPvESBnAyK9p00VmizjtpDCFLm+xUkk9A0dzaYqFJfwkig0IwIpeHw28qsTY18beCR4WrS1jV8SodNAk2q6AKmKfX5cR6fZeHtADmab7iSSndUqiLnTnNmml/5p23RrnOWV8CO26ajZ9YoSYiSl0NsV2w3hROyZZQbBz3SuP+6Y1kOGUpFjoPWOeZ3Y3N7Ck7l//wEpJnLOzOZz5ou3uXXzPYbjCTt75xjvnGNSb1LU5hTQEugKI0ZKFjyZLmUOFzMma1OGoxGFCq4x41KXz445WPxEhFw0BthR0OHJOZktuKuI4izrUJfW9IyKSoRkzgAqJdk7srZEySR1uOgRbyrvhGVwE/qGYd8gVIEUM6KBKGKq0qi905Fl1EcVQhYQc6/qCkEz1DmY+kcFL0baOgMzsjpwCe+FSip8ynivLDQS2o/XcNu8dA6fhDxfci41fGrtC0hXEN0Gt/eFOM+c7Nac1Lc5cK/D+Zrdc9s0yxmrW6dc8Re4PJ4yOZeYvXDMavQ95ve+iw+BsB3p/JgL4ZMMZucIG2vIysP1PfRozPgiOHcJVwTqouK0fo/3D9YZ3d3j/vcekUPmy/oyl4bXWNu/hF+WtBHSToH/DGTnOb435KYbcH47sO5r7p8+YtZGlrHlsLrH6dZFBm7Ayzpkkoe8dv8GN07uEJstjkYb1JXnCOGFQri6FhndfMB84jhYX2cAlCvgIKIF3ElLvh1X5KN9dvOSdmPJ3nnYCWtUUiNcoB4IXK7IfpsH188TDs4xeNMhD5XV5YzfUaqQqCio6gnCwJq2Z+BYUkOmMz2ZRw0k7M7+BO0UeenZzrdL+fEaYnWUrQ0q/fUmYkQd53q7WQNVVHJvt95f52cqczUS0hno8DRgnjH71EKUULWolGgrZC1R1zuCOPDB7E1dtmeaeqEMiaSeqIGojkpXFFnAZwqXSCEaQJ1LsusQHxE88SwX3dmzJDux+Cq1BT+LIKEHycm4FJBoTdnGRVwJRRHxXcCLWb3iKgoSVX9MXQ+wksQUleKNGBUdXW9UGCTjc2sEYTnLN0/WAHaWl+6xfWCmo00OQkGhRi7tNBD6LPScn73PFJnRHB0TJ3usbYx5/lNXePP1Q1azhnTicScDOneVa8MvcG2rplsWLB82PPQ3WboFxWIHPVhH5heZnFSMjyBub3GkJTUjtnc9RSdwG073lYMrytpEGDyXmcVbyLgkCYzdlKuDgjmQP4x1nxVOE/rbGe5n8hd+wOmVWxzqHum0ZK1W7hNxemxOQsUGks/DyRC5Ac3JJxnO79Au3yT/zAZyccQ77SHFaWRtMGCTSQ8A90C4QkqJpmlYzOa8+8orvHP9Fr/zrW/z9W/8KkcnM/6dv/Cn+YUX/ypu5wKdUSlwNJjIpj+GJUZ+GEJedvhpRmQAxZeATSx4fMAHGCiKkRoXwAFwR+GhwnGEpoO1DmN1/vjDJSXegeCFzd0P/5l64Lk6HjwmUyrK0gmu3ia+PeZ4/wH7KTG4nMirU2IBxycPOVgu+Bv/5d/g//g3/k+czv7VwJ6qcrw/55Vv3qSqKuMIDIe89q1XePnipxkO17DE8pJz5Z/kjz/3Bn+HN/m1v/stTg9W/JW//KfZ+9xL5I1II/e5eGXCdP0cv/5Pvsf/4b/4b3jn3fcNMFBTqENksWp55Qdv8skv/3HqtSWlHDAZX8Z5Z0mJ6zwhXiz5kRpfieBbZFohw1NuFf+MX/76L3HrvXfRpHzla3+Mn/3MZ3+ooi6g9xD5aY+iKymkI/iVrRnRg7NoIHMfEovjkoCPnqye2Dm6ws6+82oxECoEVTR1OEnUuUFE6bLDOYuIyT73kYRATqh3aDbb40RPtFLIzkB1cbkHTExvreogZhrxZFdSphWSOyrg+eGEB+stD05niCZSUsqypi4HlEGofGDeJFZtZFIVfX1uiugmCqEtCT2xMRBNrSxqKmmxaCe8RWIoRmqPEolBKSVDyr0yvHcpwZEFWoEojoVzLFJktlwwWyxou46UhJwCkiHgqIsBa6Mpg8EE5wq73nO/nmtL1854tDjgwer4mc+3DqLxob3SFEIqhJRqWiktxUkhaCY4671l59GyxUeHNB3OJ+tr5kQlkdJlQpdAMq6w/VtOHunEQMfCZiUL5FxggUHmsCnJSNBSiFmHEwldB9kTveKDQ9SjjVB0CcT6H+IdnWKCggTeq/VznJJ6kFucIhIQcfgsaDaAGm+1tWgkayAm6xuZ+1ef5Z2tdMJ7RDHStsMiKbPiCQiJ5C0uyy7aQFcKc/EUKVF25o/jfCbnRIiZpoCZc7hcklVwEhlKZtTXRjnZPrIQNVVstr7uv44UfzKURMuKQgvb3+aECxVIxIvv89dDT2jI+KAUUrPKLeIclVojZSmrxzGW+Q+Ryni1WAKcVedPxlntAgRf4L0naYLO3CZVlcP5MfNujY3RCF9NWB+3nMzmpPQTmiCxaDARe5Zq74okzoMXpBBCSASE6bhmb23I5nhIUQm5Ubr9Gft393m0OsF3iU4heYVSSMkIOH9Yx0cGxX/9l7/O+t42565c4MKlC1w8t8P21hrDSUcYzvGDgqIesSyHzN2IkgEDqRgQqPG9BcNThcgZhpoDNBWEJdYY/DB17EcvYASonWdzvM1aGOOlsEX/J1AEiQgb29usZpHKw/bFzR/j1RnkkNItufPgiEW7YKENk7U9ts9fYrQ25St/+k/yyS9+jus33ucbv/3rvPrKd7l19wHkjE9CQaAMBYvOEQeZEBx0Ad+aslWokdWMKgrPXb708ezi79yF+j4MB1BNLJhTxsAIpORHAfKzrw/7nR937g2A91KCQlw5PnXts7xw6Tz+csfuzpMdxeH+KUeHjkBhgIUX5tJy3DYkZgxXJUWXWC2XaCkwW9GIUCYY+DGLUIArWcwPuffma3zz7b9DJUPWwzleeu4lvva1r/Gzn/9ZFs7hhzWT9TWqQcHR4TFZlY3JlD/3b/13uH/7LpPacXXjHDtrux9q1eQClBsC0ymiA2CIMiDKST+72x963RoZSTg9Pubu+w9Y29hhY7eiHFQIz9Z4i5rwPuDVWR5P9kgUnIS+UMgEBMmZpKbyDv1aIGSzLccbKOwSrkhECbiomHAnkyUhtIgUZHE4F1EcGnuVjmQk+z7P0+YrKEQcPjqjezshe1ACyau5o2lnylE1MFzBGu7BYezXPj5G1Aqr1LM1xTGnInohKXjJOIlWlNMhlOTsaCQxEOz9gSIrjQc9s1hFofNMc6RW02fcbhZPmO1/AEfurAnlnMP5Xmklvb6kB8dz9uTeWj3GzKrpOCkC47pmMtijWQqr3CBtQBYPcW1NmddpBkNibQX6YK6smkzTrZjFyFpdUGiJd56YPd08ctwdc7S6z7w64lq8TK4DqWspY8DlKdV0g5c//RLjnkp/AvwAQXRJcfc3OGxPKJwirCA2nLYd97LnehywNnyJ0QuJblkQXp3yR9pHDM/POBof0RWbTB+tGDQNMSRwK4KLeFcQmlM2Sk+xOWBfI4vQ4sQzrDLttKZbnmOxusAgwrQ6Zs0l6hSJ+YTZozmyDETXcFxWLGXEYjTmeNJy2j1CxkIdTujafVqOGQ08jSTePr3Ow1lCpMbFDklLVvcrDl8ZMztxHMz2n/l8F+7JtejULNElNJDM4ssFMTCrA7y5CDgU76JpqVUMaPCK02i5T1moRAlOKIaW0Z1wZFF86xAcbTbLV4mQHJYfrgnprAGQJaJOcNrhi8psL5ORYITO7G2zIo3gvCOVQI6UK2uaagaXPdl7UIfkDhpBkpDKhIgQnKBdfpwBLtgmzjJiFa+Z5DwxWcM0+2xMYi+IGPDukiLiyKkiO08uxdjmXbTPqgKds7l1YqpHUYoEuGxEIdJjZ48PPtX1Q7758Z4dsyZz796M927e5mR1SAgFk+WKB9c/xc7GNqNVQxkfcjQOTEfrrMIC5wNFt8IXB/jTDj065G58j/rqkHPbU4YSaE5OWc1aVkf7TNYGLHSDvFXy/eHrfNpd52f4JCH6Xu2rvJX3+Xv5EV2xQVtu8Tnn2HW2dggY8B0wULwR+3sNnIPHvl0d1iTLwDrkISwFhg6eSgV4Mm0ReKTwSGmOM26ZOHg/cO/mkreuv8/dg3tkojVIcTy39RzPfeoSYaNng3j7nRoVaSMy+hhgVgfzRcNgLeA5Bd7iCQNggClWNuzAmGKo/48/qtZcgwrNkANJElGS5ay6flPmfM++18eAyUqtSaLBQObhaJ0dX1K3sCgSsVAWA2s+5cfEEcFJzzxGjCSSLS88mjwb54x0FlVATVWXKYkes6jLT0zZVGu63L+362hD6pt0CXPsdUjKSPQkPBqtiSuiSAqgGJgp4FNNIqNqrIuAx+uK3Bm4QnIoNQRPVCPn5KiEMqJoX1t4RBz0tuneOQLJLB7FgG1dKY9t0xPmRuCSWWV7JSTLTbdMaquNndh7Z+3Z1z/9viyIsD4cIl7M4hCPVnB+w7M5KXFOuXPngeV1Y/OcUuT0+JB2MWPw4A57l5/j3IVdiuzJkiCCEExMKJmTmGk0cvXKFdbGm0Y+yI6MEGJH4SJeWhLeyAwkGjrIhT3Lg4doG3GzTfeUsaKK/TWXW1zOZC9EhEwinFkgi6fQ2NvlB7wKnWLPI29qlIjlJos9lElZqbIjidnIgcfpCqcOocCrOQbgIqVaNqFFJRdITpRiLiHeW22cfMJrIKhd48klXA7kj5nHNm+O2JCKtWLIxYlyLkCIgcWs4XC14uHgNuFnNwjbJ3TdGyzkhL2tS1w+WWe8vQbv1fipR32mqxasXb7GMs1x2rBz5RFrJ4+o7t5HVxt0z6/BlQnuWoU8yoRW2Lg/5NJz59hwm6wGQ9LkIu2jimJ/wieH5zm/t0NRnIOpR31LaBbkaUXeG6Cbjo3LI2b155mVnrJM3L71HfaPIrPVjPLcMS9Mz3PVZXYB7xp23CkP2xMevHPEw+5FLuyN2SRxZbngUtcgqxVVVOarTHt6Qrj7AB61LAdrvP/wkO++f8Da4gHlzpxLf+oCe7LJlPOWtx1bOGpRKVjeu0x3PKWohdkt8N8X8uU9Rl/pCLv7MCxg1VgNEXuxcVT76rIFLna5/17uo5oy2pmdt39GUFxcoE/+7suBvv2m2gt189m3yU8B5xZVJaa+FovyfoLzWee39/Ii9SpzC0jzhFxa09iv0MLAgpyFmHvrzDbhSeQMSbKBNCK02ZtFutjCHn0mqOBzx4oT6Gq8BrxWZEl4HwkZI5w5b4o4rygt0pVUubdv7d0VvFjcjoo3s58UUN9Y298nnIuIeKJztOJYJY+PJTlEumAq8ZQVT8KJ3fOaLYbBJTELSZdRzfhOyQ4aPDkkcFj2rDdyZEhC9mZtT/JUjWEpRRH5OGYQbphpDg/oLl1gMhjy5a98iXuvLjg4Lrg8+RSTxS5d62lIjDcn+FtC9Y2Ccv0qoY5UbYWsKoslXBOakcK5ARsvV3CoxKNM1l7VvqW4KjGuAr52jPUCqlAuTtgOwrwUHmh+nCmaoSdxRfL7D8g/OCH91gidDZlxwv7+KcfHiRD3kNE220PHaHyf8afWcHwGdApLWB0kjjSzs648ylDtTZnIHebvvcf+e48o9y7wuS/9POtFQQKOFkfs37/Oo1t3+PVvvsrvfOO3+cHrr3Ln/l1UoIkdKcJr37nBwxuHbO1coFaHUqG5tg9/VmrsYPFHGcJoQnAvYotXwQecHs6A2COsjrwP3FVYdqArmCZ4WeCig7Vnz5BHM1ul6534nir/z8iUKONBYEQ8q5RImjlQx2Bjm/rK19h5/iI7XSTvlSzlLrT7/Mrf/1WIynMvrHHp4i77B48+2sdR6GJCpcM7x+3rD/lf/S//U+6/eY9//3/077N5fgo4VCuWN0e4VWAxb/iNX/kWb//We3z189/iZ7+8x7lP7bB7eYeb92/zv/tP/q+89c7bpA9b+0T5q3/9r/KpT32OEEI/6f1M7GDOAYv+R39I32Pzs4/QknXEycn7/KNf+bvcvfs2SKaqS/7qX/srrO2uPyEynTWdHwdW/3RHoRYfmHHkILgITpO53GLJyapCUscyWI81iZC81YtoNiK2VyR0T9S3nZI1GBFEEogpiTvR3sREQSNeCutZidU+RUyEZFnl3kMkkWKBcwnpHNE5HEqHYyElZTRl9qBOXNkd00omxpYuKqUPTMc1o6GFqbZt5LTLjHqyVi7OzpsQO0/prM9k/2yLVOcCMZjLSXBGYNKUyd7yjVfBU9KZgj2DwwRpHVg/z3kWOTNbzjmdz2jalpQgJQ/ZLJ5LX7E2mjAcTgnBIlWdYmu3RjTOOVgecH95zKxbfbhG7yOOWREs59yXtM73sh0hJyMNG68sQ0wWGUfAqeCT4qORZjy5FwopGpTspc8Wj0iCOtna3Dqx/igJ78zFLzoQHD4K5ICIiZ7U25ofewTYR0GdkSYcnhyEgFD0JNOg9rMpWSSUw0BP56ym1+gtRqR36fJiWefeNzgNoJ3txDTiUjbyWOnoelBcnfExQs44TYTsEA1GiogWSyVeCbHDa9/3kZKIZxmEqsrUKSFicUmFF6LW7EtpjlDB9qCTrHgJBGnxzpw7oxQU2VHEiFOLevuDMgwz0F7J/9MfJiOxyD8PFOLwXvHO0AlnNmuAI2BElUozUV3/jFUqrJ+20sZqVuXjJBL8wRriEPJjPObsnnDOmXtTNht5HwIaW3If8JAVYhu5NzthbTCAIIzriqIuSfPlT+iz2XNVBDRHKMOT+t+byMOFzHhYs3d5jbWipCztnIYyMUxDOIos20zj5hAa669gNXuLYS05Zyt+/xCNj1zCz/aPmR0cc/et67y1OWXj3A7nrlzg4sULXDq/x9r6kOG0JQyOCYOKUA+YhzGlG7Ita0x/RMoDtmPzMN4GrXpM9eMDqB5H4WqClI8Z/M/2To8/5eOxt7fD1mhC01xisD75yO8lSShmA8YMmC1vQ6X40ZDNK5eoh0MQwXnP2tYOP7O5zad/5gs8eHifV95+na+/8k1u3n6b0we3aKW0pv9qic8FRVmAdhTiKFxFExcoicsvPPdMx/x4rEaw6uBkAf4UamfZBIMhFFMIU5Ah1sE+syfq6YcfUJB/YBae6aMoHcg+sMtqlfjWq++gK3jh2nkuXl3/gHXqwaMj5gcrhpMNcAOaOsLWjLppqA+EtVkmpAVdM6fTgnh6RKwbjtyCdjlg31UMy45Qt2yNlcPjxI13bvDNm6/xtw7+NmjixRef5+UvfxGZnXBue5PjgxkP7+2jCj//87/Af/wf/69Zzua88dYrZBepdnZ7ZeSPrrjmiHVmw2cyOUck0X2oOs2alIk7t2/x+utv8vaNd9k9t86Xv3SNaxd/Fmu0//hDEIoQCKFAVoLPBmoF7Rn2uQfFciRmpYqWpyupt7dEcD6aGsz1D0s6MorPZs2KaxDOcrwTiGXdOLFng4rZruIxa/bOrJ7AIgOcmgo9OctcEjI+Cr63Tc04JNIrscCpZXmKN3VrIqJJQDJN9hRloqWwRncypRheCK2pkJIKSCAl6M4Uq1mQqGZdJGKblgSVKKVmCjLJZdYHJUUIdN0fYM+YrKbKStZIc056gFwQsQKb7EjiycmRsiPGTFmUNEXkNC0p54EiebqkEFe0q32Sn9FOGloy7aImNZmVZpLvGA+mrKdNNLUUFewMCopmwvzwlLEfcmF7yuRqxfHJMY/uLTlYKZONTUaT59Dg2Bf4x8A3UdLxjNV33iUVRhAKzhS+wSXmGpm1S2K8h0wX+DRkfHvM3ulVdt445C98L/HPP+fo3j9lo1IWm8LpQUdLJq5aIitOxvuEXJOSI7QFtEvaEAmjLbq8pHXvM6kfoPGY+bigmda4bo1x3VBvKJPNIUdxg/lsHSXQDsakAh6ku6yHyPLegrqb0BwOuP3OEQ9kiY9TVnmBc5lRmRk3uzx6Y8ri6IiYTp/5VI/b9vHTxGojR+cypdeeNAYlGQmdZSyK5bOmdJa/2vUbOY+Ptll2fXRBUVVMCkegQ0RJUiMouYpITiQKhGxAkVgMQfKF1VLZGdPYJ7yujP0sHufN9ic4JeRkAJQ6I9m4zkqFwlTa4AgacVi8gjiHiwXaOXzIqFMK16t4xPJEtQ1G1AlKE4ToCrPnDYl24MiFI8fCssSjEILgQ0nqmdj0n7kgMsgWzZCzkF0wwEQzLnpiCgx0iQ8ZSWfPgh/hln7I+Hh10OrBkuv3bnH77k1WzZLt3Ue8f/AW7Wbi/LmXcBrYqByjekpqFswaR1mumPljYvLE5n325TWOtx5w5eoXmI43WHMjjpZHPLx7iwvrNT/78ovcuP8QV3reDq/y/9Zf4m7+Kufyecr5iPdO7vOrzbf5J/lNumLE94sdvlhe40+Wn+aFcpepK5mMPdW8Qo7CE0A6QR9ubxXqArgObED+BMTzIPO+wbnBBzMdE+htyO8FVocDFoua1Ynj5jsr3rtxwjvXr9Msl9ZgEiG7zFe/+kUu/rG1D4iKKCB1M1a33mD43AVcce7ZzkmGahCQMvU09F1M9vIQ67r21ruP41L+vR//dwC1dmgSlgSci4CRTeiJbCHb/ZSjJ6swx7MImYVTy89WCLlg6ktqzQw7pSaxwDa4WaDFk3OBeCVms0X3QfHJkbLZIZYSLe4kFnRakMpg9nqdkQBFhZAdURJZzOHGJVOQo0LXVRAM5EjB4wuhjKaWaNU24JKVqJmQWyQka8aII6vQ9U1H50BcIkhLTB6Hx7cGEtHHLnTZo9KrLJqCTIlEhSI9rmUQsRxVjb3zmECj5M6jLphtuhd7bqqQK7NN9zHSaf0B23TBkRSk17hk/9O36hMRdiZD6tIgjpkLdBKofGYoY9qtTR4+2KdJBpPZsKdVzMpyueLo/n0ubtSUg4GpJhUKPzeQWRxNNGXAhb11gkAkmCpTM0lMua04pC3x2jIOvUuYLMi+tB2cE1wUUkionJoyRWu6OARKRBqL1vEtmiyGQ6MiJHIoSFGJrSdkb/nILiOuJyVFI0Wq8zhJvRWkR1yJl7NNvulsnBo44cmU2exLnesMVBcla6IKMCg8kM0AQzxDb9ekZUBDcMkIZx9jnBwJg83M7ktDdq5UtPFVHhYDVt/eJq48xdpNyu075O0lE5RBd8yGX2dvNSY/POXRyZLlqmbxSsFiOiLKJynikuHuCje5Q3fccewvMt+4wPH2JU42JrDTcuGtfUZ3B1x7NOLC+YpiUKFrI/QXKtIl2HwFBudeQL46hjUPi4S8ehd/7xF+7yJs1NRrcD4UjJzn1VHBkWbyaJf46Jj28AhxD9jZ2mZvlJGD20hdsDbKyMWK05uHlA/e5mKjfFrmXLp3jD/ZoGkucvv7gZM0Z2/+NuHhD6ilRNqLlJvKixsTpsMB9en7TG7BZOMCbryGxA69cRseOJhepWqHnK88YQcjaR3C6u4ax99Ujj7VUVyMrN0/pJyBT4ImA8I1KZrO/rSvnDDnGOP0kJOw9ozne1kqqNVmpmISRHmsaLLKx+5NOUPHAc1qgLn+7ipzLxB6RQlizmVtkVAvVJrx0kLnaDWQvBFMyMmiojQiWFZtFy2CJ0km5EBQxakgkkhdx53jR+zsTAgM8FHMyccpLitV/zm77FBvJCyXIbQ9+TlBgcenaDEGWfCYBXtqMz6AdxHxZr2q0Wq5FIQyeTQHOhxerGleiKMpIiD46C2qKyRyKsAHCjpcirSCxWJQ0BrcRBDBJ6vtNCa00J5MkClVjHldPHuD0IUp6Wid3aOX2V2fcu5nPoP7N87zz3/jd+jiLl18nsHuOUJRI49ApkDtmaQJJJAGGAgaYRng1W0Y7WZeqOw83X2krI9g4wLUIkywWlUEqp78nqo1TsTTrhI7h0uUATcdNJM5F4dQaWBZA1dnSHfA/XeFcGvE2uoK6eg6R7zF/ZemfOozA8Z3AnIyRTcqxIOu7XNy4ds8SCt0bYuySITTu8TdwKQ+4LCccXj3TV5/e4IcR7559x5vv/5Nbr/6LfYPHnDjvX3u33xA0yyR4BiMx5R1Tbtq+f5b3+Ob3/0HfPJLvfV5V5H2BWnAXYJZMAvn4MB56y3oQrALqEfWFlhJdhcDwU87WHQwibDTwXkPF0uYDszlUhwfp18pzuGnQAUf0hoiaWOBlFJgTM85Hsc6Ja8fzyj1JsNLO2wMdzmuMkNZ8cZ3XuPG2/dYzRYUVcWXP/Mlvvv91z7S58kp0a1aUrY5chp5593r/G/+k/89x7fv8x/+T/4DRpfP8/pbv8F//X/7uzx4uKQsS0TgeN7wjV//Nr/5WwFfOtY3R9y+d5vvv/4G6V9it3r+/AZ//hc/i/cPUDaQxyp9oAAtfhQM/8D8sQvacue97/Bf/ef/Df/iH/02bbNExPHpF77EF7/wx3DFUy3uGVDLh+uufgpDyoR0mQ6Pc5EqCCla/IWIWv3plZXWT3KtnVlIG5hk7KaQ5wTJBNfhJZENtcQ7xSfQHoH2OZDKDh8EkuK0QXwkIogUFivWedQJhVrkRatAzGQtcAmQbOCHijkteiN17lZwOBzwaJ5J2YI7gneMi0D2gpaBebviVALDECi0o8pibiJeyBLwaj0Gc22D4DqLRiyEhCOqt96E78h4lqEiZsdQIrW0RJTonIHhqsxWC2aLOcvV6gNguMtQ+orxaMTaZB0XKrPSVswqPUdyWnK6POTu4pDTbmnz3a/jzzoWxdm+ztYJnwDU+qPJegZlEpwmi3rTBulKXDLHklwa8CT0+xYUKTyuzYSYyHiSV7KzTPGM4QzOAS6Rc4Emh8sRlzxa2KNOkzOCv4A4O/85WdwkTgkSiMn1jp+QevcurTLaR1iJi2QtjFDdq7e9h+yUSEa8UNORtUOSxVjaMRa2Z/AJ8Q4nFm2FTxRkpO0juFxBks6IZxJQEoU6JAdzSHVWb68CzOqEtC2VWgLaafYsuhLRNTrfUDBHVWlR2mzkP+ehEKH13oBwH/HJ8fspFffOUxYlIkpdjhiPJ8ybU/b3D/i4Iokfd1hXwsDvSKLwBVI4XDA+e5GVQj2+d9dJUlgWfY5kqYmS8S49JoWrRto+PvUPi8hYxWprJx4XHLiAD55CCmJMNM2Crl2RUyCl1Ku1zc2JrOyfzFkOMlsjz05Vcbce0CxWPxExnUMJgCcTSeRs0UniAE049RTOsbFdMxgMGHuHaItkGKaMZs/GZI2chZPoOVwdsWqV7M3RRCnJ5hxvxHUyqDkF55zJ/z/MfPjxeK0KqYsc3z/g5OERd9+5wdub6+xeOc+5S+e5fOE8u9sbrG0MKUZLwvCEqh6yPawR9y+xfo0Cqxp8xceiZT01AsJIQn9wz160Pt0WsrcSQuHxa0Oq3krtI6uxgyOeX+fOm/c5XRZsX1tn7+oaa1deRMsPzo2IEELgwvmLnD93gT/yha9y9+A+33n9m/zgwS3efu899m/fo2gSrlXiIlNGIceO6Aum2+vsXXz2/FkAHl6AagWDpfnSzVr7CkuoDmBQwGBiKnI36avXGrPAfZr3+sMg+dPz9dHmLnNKq/dAAncfNPzqb/9jTo6OufzWBf7M7Gu8/LkXKGorBA/m94h+iRTn6cohIgZIFHNl1FnWRREyp5iNi0tLyA2tr5h3DacnSuuEqYsMhsZS61aRZrmg6Ro0Rl597TVeff01ihAYjie0XcfsdI54xxe/+CWqsuDm7duEUNANhrxx/y3Ob5+jCjUpdziCWUT9yDQYuyozIvGjtbvmTMqZe7du87f+9t/nzoP7jNZrimmiK7c/5BUffdR1ybntHapQcbx/QnsccWrxB0687dqCLZBntpJZA4UoOLO+yRpxaio0XK+ESpYNqiIIBTl0qOmpIBeEaCpMFWOqijiCWnMxZ2+sMtE+J+cMoFe8T5ia2xkI3mVycMaOStZgSWK0Agk9WN+Z7YtD6CpbnYVojS7XGOsrFUTJFNnUZ0kVSY7UWUPJ4xDpCEDShDghS59BnjxuBdkLU1dRhnBGdP6DPdQaeikLKT0Bxs+ABctzNkBCXWCyMWA8TuCXxFSSKdBWSHFJS0NqPaM293FuU1ZZWGpiMiiZVAPLeYyCFpnJqKAclnRVzaasM/DrjPyKfZdYuBVcCWz9qZo7L55yFDZ4A89vAffIdK/+GvHBLcralFtFbxmMeuqu5dHpIQ8OHnHvv7rFhZ/7I3zpK1+l3dzhNG7zSAf4d8bs7e5wITW8vn+deXubhkOcT4zGkW7thGYWkXKDutwhliWsFnT3DsmhYeQ7BtsNi3TKUT1hMlB2/AkXLzi6nQGHy5q3TyraNGZ6WlDeKtiuEuN6QNetOD4a8lx+ie3ZiIP4Gtq9SjdQrpbXGG1usfXcjK3ROVazAVrNKUfPziJ0zZNSVJwghW1Kxbke9Em4ZArJhEdiJviEZCH3Sp6kQnYVyUPsEoUzVmioSooAw2w2uE2f0diHCpBc6JnuHaI9CIWgyeMlE1wiSWv3voDLlfExMKA9OGgKkOwooyOFjrZQgos4l0mtZUQWSdHkoTO7y5z8Y+s2dbbZCjmjscDZPoykmSiKKy3vuBFjnyenIMkUQuJQdWhuyU4gJFAsQ9IZ61tSJqhn5RWXrLByyZHVgh00YTvMxyfhh27BD/nexxknD464cXCHxXKBF2F1OmNxumCl0Iln4B21Kym7xEnjyG6EnA4p6wJPR350yvGjh9wbDVkdL2hfeZcXBy+yk3e5UExY/8SEq5MNvv7t7/HarVus0oo3+AF33E0KHyA43s/3eKe9w8M8h9ZzkIR3/Zhv+pd53n2CnTBmrY385S/+Ja4OriKHWH54xhqUHgO950AF+mkl/pzFtpcrMeD8Ek8s1hVoIF2H5d1ImEA5EfaPTrizuMlrj17j3qPbpNyCmBpsMKy48OWCfHVuXebHYiGlax/QFj9gWFYoex/59JztaUSBEkLZr59sgq5DvgxyAvIQ8+Q8BJlhYPmzjSas0FyRk7foZvWEBEWwXNbkPB5P4ROqkUUOzKmJOFJKBE0UqqxJolKPqCMly1OVZFaH2Tk6L/a8jwHxkZBMepV9RmI+mzxIxiXXDD50BE34riCpI4qt0y47fMoUORIlEwtPzn3+sgYi+fHPRifEQinIuNQ3+bxDpbAcu2jrtDojA0TX4Uh0ImRnlvG+ABqhIyA5UETQIJSxM/DSAVnIreCLYE6KEqHq0GAKcZchNwJSkHslsPNmm557V84QIzGZQ9XTtumqDlVL/c3eWfjhT3l475jWFaUIp86ZJbgXKgpcbsz5QiGEgDhHzgb8ivTqfIT14YAt7yjpmDvfN9Ez4h0ks0Vdmw6pzkiEWAyH9Oq3mJxZhAoMnFBky+XV7MmpowktHQnfp85LbnHBkfKSLGWfnzywrEJnJITotbdfBt96XBJzO3KWr+nU4XKf5S4GMCr0VslCksz/l7o//fUtO+87sc+z1tp7/8Yz3/nWvbcmFgdRIinJkixbst2KHU9AuwM0YCRpJGgg+RMCNJKggbzK+ziJ00mnX6TjdIJOALftTrvt2N1qWaZkUxQpFckqssY73zP/pr33Gp68ePa591axSJO3KEpahYNz6tzzm/aw1rOe7yRaBvV6IuPpa8zyTs1nORYxYNBb06C4TM4FaRwlJHPVEbOorhEkZ1xxJO/NKCx/um78qPWU6Tb3LlXk0YobClzLxPf2aE8j6ydr2gcKU8hasXs4Ybo4p7+ndO+MiXKHtb/J4WpMuxkzmTfszCJ+N/C9OpMvj7h8fZfTMuFRXZEL3IkwWif2xyvyTPFna6SaIWEKB4LfhuraBE4SBI96gbmH1/fQ0KIk6DP0Sjk5Q8KIWTPnFFiXCVO/xdZsQ+k73n/yLVYPW2b6mLIz4/TSFeKkpnvzMeXuId2lFnerwuWr6Puv484vUZVAN1+ymCb2+js062u4NGbn9il3vjhjO+6Qvlmj3zlH3QS55FDXkR4+Rso+PjdwOdi6s8GM0B5C0wuX9j16s+PEP2TMGjn0xN4ZOJsEjZ4SHRoDJVp+dWk9ufWUzlE2nhI92//DFzvf6WljScHZfXShrBMMIGcAymUAzZ1ekB6tMTb00p/R8AYL4wuVueqgMhdwjQGEpjLPSJXQYpbqIhC1pmiNkAhF8OqQ6MjFI94h2eFdNlcdBfWO2GXaRUt1o6XO2foDongFl22PWJyzWIneQSkoieRNIy9qc66qt3o1Z4u1AnoUF4tZ6npFQiY7qAbp2YWFsFAoRaj8kDsuFeKGiC114AOdmn1wTYeXZNnh3lPEEYcM8sYqOag8TgVSRAUaZ65BmqqP1ng/7v19+JeZbd9mt7lD7cbUc8cv/OqYt955EzeN9JVjun3A1mwLaYFLSnwF3L4iox53WJCTGl14lm3kZHPEXpjh/Yz2kjC9ImwHnhKDtuJHS6dW4HvB826E2f2er7x7DgrNwQjfO+pgYB0nV3C724z+vOPywYrqn9X44yfsj1vOthseH4x4dLoiPUxUR0eMfj7R7CiSD9ndu0eUhrD9Mgdjh5YNS/eQm3dOmV16wpNHE37vd/4x//A//T2+9/b7nKyeUFwkixK7HpLi1VOHMZ+59SqvvnYbUeV7H7zD+8dfJ+dfJ3iP9juUtiEMVdtIlUVKuM6xPXFQCawFfQRkRd7HgPCzBK6DcQ93HNzysFPBbALV85YLP4HhBJkoTPJzqLgM7g+CTyMIw70bPwA/BrfLWM7Y2nyDD07uM7nyOe7sXmXXGci56ROhmbI+W1OHil/79b/MF6/c5v/6X/w/+db779DnH07SLzlROkW8HTnJ8Kg74n//f/tP+e2vfpWXXr7NH7z/Xb799vdo+8J4OmU230LGnsqbPfV6k3j0ncc8Pnr8Q+PjLl/aZ29vhyVvUvg8c8bP9C0y1ArI03P4dChIEjTC+f2Wf/qf/y7/xT/4b1gtViAwqxt+7Vd/ndkdExuhkNpE/G5k9PoIqX7c85eAE2DOEPb3Yz7eRsiFnDLizH0mq6LOcqU7EXppKCpk8ZYNPzj1kSFowGfBSQdVPexBMy6KkaCkUCWosrn/WOOsEKOR9LyoxaOV4TV7E7U4tQUjieAwxXjsHVkdVSqIUcjpi9IXz1gdXoUJwtYocLwJTEeBUT1iPJrgfU3tCrhEjMqmRKaVZWJXamrTToVUiine1eNywXtTxiogxYi2vWRKyIg3sLMfCSFUZCdon0lqS/ay71iuV0MGbjFFc3G2xycwG0/Zmm1T1xMjGzCA4VrQ3LLpzniwOuKsX5OKCUGKbfHxn4K4qmKZ2RfOUB4d7IwLTU6EWGgyOE30KmgRfDT8IlfmsiVq7lQZjyvZ4oPwdt6cEZ6r5NFg9asgSAlQlCYWejG7dUKhNN5ytMVTvKKWSYexHxTJNZIF33SWY96butQRQS1SSV024mh2ZDBXAfFkFLIneJBssVdOBKfWQy/FWR1d1PqNSQdhE6aAz5DwjMi4BNEV1FuvyA2fTZ25xyStbE50kVgii8rU85KV5D1LrYmS8XlDlhkqG6o6IVHokzkOVlkHQ8JEcp6swZzWfqqguFBVFU1T4yUw39qjqae4FEGi3aejOYvVkr7tforvyqJOBz0x3kHwfogMtYjSytd4raiTkpyRKZwLJotMhUBPdBURoSkJdQ0SOjqypQ796cVMn42h0aPDWmVxYEqHklMyq/SilHyRI6mA2OMGwcKCxG7dME4VB9MtTs7PyT8BIZ2q4jVTOVN/92ouQWBEU4I5OTWIER0cVBsIndFrnBNkpMy1Br9F6GG5WNBtetYuk4rFXTgfEMm4yqKe+mRRN7HvKT8pK/if8nhhsycthW65oVtuOLr7kHdnb/Hm5T0u3bzKzVs3uHHtKpcOdtjbE6pb/iNq3o8ME5TaRSKfXjlhyy80rv4+wFpV6bBoGvcjgNnPl78Xl2mAIb/5xyysnLBwkbfP73F0fszmUcfPf+lzvDTb5oclf4sI27MttqZzPvPSq6y7Dd+9+y6/97Xf4Z13vsPR+/dYrDo0V2xiITVjrnz2DZrx6Ic8648ytmE9hUUCH2Hc2ldpIbWwSuCOoDqESQ2TEdRbpiKXGcgYO9Ift1n/JBX5Dz+WCz3nHne5olscHp3Q9Ufcu/tdHr33FnlxSpr+TX7u86+RS+bBydvkZsmZHOCTp2kL4/OG5XFPio48DtQ+UzsFXdCyJNU1i3pOEYcfZXJR1r3SroVlDIRmxnSWWa07cso2F6rSdZE2ngz/b/X3a6+8SmzX0K3p2466hg+ffEB+I0GAx4/uMW0O2N7/QS4DMixH339Uzo5PeO/t73Lvw0d8+OEHLNcL5rvX2ZpP2Jpf40WLdYAbV69x++Z1xlLxpJ7y3tl9nFaWd+MsozMM1MGsxgiMJIqIWeSIIsmZhaRXCn64xxxZHEoa8oojWRWi4CgU9cZQGqLUvTP1pylGCxSPc2ZJnnxExBow6i2bSpySS0C1Rok4KVAN15w68EpJPUE8lfUzh0aI9S+CizRDodY7T8yZlXPsiRk4l5xxOCpxxoTCiimJBQ2eXmvLhk/W6KE4fFImOqWuPoW12h/HGPxtSjEVvHg3qMexJhmFUVOzvTNBRhiTM8AqriidktqOxjWM/Zjd8TajaWFxvGK17khSsz+e4keFQz3k0foUQmTce3yY0Iy3mOzM6edLDlcLTu4tSOvCtb+YuPPLj9DZP+cf8dd5h0t0QDw7pf8Hf598GqlH3uiLMUNSepc4zz2LzZo+Js6/+zYn736Pk3/yX3P087/AL3/py5x94Zc4397j0hde49p0xOYrv8TDP/xDdr/6W8y7NWk7sZlEYv2EyfSUrnLoskLONsg6MwktdRNxcyEuBa0X1NOOz8yX7OwEHvot7i7mJAX2YPPeKdVJT6WeXDds2Gb7rOb13Z/llTcuU/kp9z/4Jq/uXedv3fmf4K7do/3cP+OID3nrmwseH63Q6ce9on/0Ycp/Gx5FMFDJF1OIuGJqGClmEy7OkbqAzwGqhAYhi13/Mkx2OXuiU0I1oXaFeYYohUghB9AUIJvCppeAbxR6Y+yKtISqwkdzJXChUHJBUCQMCiF1ph6VCHVAegNp0GBZZlpwopbHlTxCZ1t4J2hyVGKsdMuKLCQnNGqKiErAqxtU7z29K2Q/ATUwvZSI88Z+1DIwLCVbNjgBT8L3ph404lBGnQ5AjjH8Q7YCURk2C/4iSPv7zs4Ln9cfNE7O79FvVpSc8SGw3mzYdIllrDk974EK9YGwWRN8ZjQPhOkUpjNaYHH4hLeOFmzu3CKnmvS4ox57bu/ssR8gzw649bMvc3Nzld/O/xVffXKPB8sNT1iR4orzfsGDfM5pXtHHDs2JrhRaF1iE93jH/xbjWPN6fJl/54t/C17DlBxPgI6Plgp3QG9D/kXQVxJj5y3T9Aw+LsPTCKWGspdwVwqztICT+zzMX+e7j/81m3hOkYvsULh0bcr81SMYfx2RXSzgvDICaP+IomPSaodq2/3op0mBQ8gtlG0hzIdG9JBZT7cDeQfkJZP9+QjuzL5ecAn3pTbWvnNEV9H7TKCnyyCVo2Kwn8OxQeicI5eKnAbAjo6mWlOLR12hLxXr4il9hast264kcw1Jwdkx1oss8ILzHSrWtKGYEiOUiKPgi2W+qUKuK1QFilntSvHEIuB7nEZC7cleQB0hGgGliG3SqwHxiU7IoSIRhmx6c4NBzE2m0kitPdknXAWpOGNrJ1Moq1OEhFTKwPyiSpmmWGZ4KEIdFTfYRZZU6IOQvCOJUiSgVGgWQoDizTa9BCXkbFbdmJIYjJQD/plKXDCbwfan7yLTNA1b1dhsdBGgtqxeHEnA+yXXX3uVuqqZjWriesXJckFVzUh9Iq079mdTtsQRojl0bKJlEwdNpCQ413Dr+i2KjshOjPiMQqqpcketjs5ZfmWVC1rcEN1i1sSTWHH39B71JSE0FcnX5GwEBfUtUiqMoOcoaQZZWSvUrsXXmVCg7oOp+l1EvbXWnXqSQHGFlIPZQKdMEsv1dJrw2aI9fAm4Siga0QwimZgDohESJMl0sSfnRO1qs4gXNZtnVZwa7lJUzDq2eMKnbARdaq4ym78O1TZLjdznEZICsSrk0ZjV6Yzud47ZYs7L+9e5ll5l+uCYHKf0b7zEzpWbzMdjylw5ZpfdLc9+gLGHu154uAtPCjw5K+wdKa/FwhvvnHH17luwU1MFhVU26eXlOzYfNgI3A4dXAscBaiLTfo34yJF3dOvH9KePcUvHww9PWB453PZN8mzOXp94eb5NThva046zo45HKryzv8NROmV7e4utNnH9vYJ7M3P1ekMfZnxwOKF5WDiYCdd3HGEWSO2EtlxF0j5H3ZInD+7jd8645k6ZcIqyxJ8ckVPH2c4HHO0dsZfn7LSF2EF7pMy/C2Ej1jS6A+2XheMtYb2puV29RHq4Zn2vo3Se3Ac0OkpvwHcZAqqdr/ChxoeKEGr85MWJEA7hwtJZh6aaXULDhTSsR/J0sziA5sNjLoBzU5c///NFb0Of22cqhUJlM5TVbUWIksne0zsh90rRCtFAkd7UbNnhBHqXSeKpk9ltKhWiPak4xuOxuRD5aGQgKfhsoIUL5u6QyeZKliye4CmfKA15pHiLu9FMKZ6QhHFQUomodzS5EBMEb5E7kYs1XoY53+abohmv/mn2t1Ozu3fiyM7RJ08gkgKkylHCCNUKCZFWC3V/QSY1glYlEBkIVKUifYp7/Neb/z6jWzVuOhCRxTHf87z6C3d4sHGwtcYVkEMHT6DMelbzc6Y3hbW7z6iKNP7zyDXP/iTwF/YuUzX2OcfhY0nKHtxQa1xUpGeYSLoNmZe3I5OwwM9GXNn2pPdbwsNCenXOMYVyt+PG3i6zZkI56NmsEq6bsN3tMLu7oj07RI/g3GU231twZbqLHO5C9TK8UtGVLY6ysp4cU/SMm41wrfllru7ucPLwTe4++n/z5OgRfWwN36sqnHjqARB56dpV/t2//rf4jT//G8Tc8U//xT/i9Oh7eD8HDpCpUE2wxp23zz7HGbfbygs2Z3Cyht0WJm0PsyW8LnC1hstzBskl368GvyhOL75esNemw83IKc8AVxttNAPBnJUclKl7CU6ENDlF5YxJ1XBn5ybbYc/qF+ytfukX/gwPxwH93W8g7Zpzzrj8i5/hf/vF/zX/9Ku/xX/yn/8/ODo5+eGOp2XIPn3uV4ernv/661/D/f7vmUjBWgX0XcvZ8TGT+ZT59pzJuKFddJyentG2RvK86MM+r4ATEb7wpc9w69UpG5myoGHK823ijJT3CbIPsvOx4wYsYP3dzD/+z36Xv/fP/hH3HtyjOFM13775On/h3/lL1BOLW9SkrA83NC/VL2CkOLwYX8fungPgSz/ukwwfSQfigIdSk8jWn/KeqObcU0ogFJAUQAVXLParCWCsy4BWg21xqtFk8TNZC7lApd4cOshISAQRSnSUbFbsjoB6IAlSHCMKfXR0tfXuQrGcaBeEkqshHxpyEDqEUb/Bo0Rx7I0mrLc91aiikimhWMSi10DjHW3e8Pj8jC1/ieCNbO5EqVImaUGdUCsE9YRY6LI3Z6Bhr+1DoQSP1oU+KH2wGPaMo3UNebliuVqy2qyJydaFi9zwCs+0mbI126JpZogLRhLLw4VbWtrunEfrIw7bpandi1CygdjBO+bjMfvzF3PeBAYSZqDSjkozIgkkUFIglIS20HqHd5Yf79Qhok/vk5AHVbnqIBIQcBjZlBqJ5rai2ZtTni+mANdMcoLUw3ruagodQoswQiUgxKG0sFxhy91W0IRLdm5cnW1vWGdyGfLLs9XKInb9KBYX5RRUEyWbE2A11BBZhehqm3CL9We1MoI1OQ3iDCFHtRraK8kXsqpdu2KEDyd2DbbSm2AsDsRWqcia6CpHJUqvhUwG7/ClpdYRKVfktiMXc8axPrM5SqKFPtZUQCGx+SMGa51zVMHjq4bZeIvZbIdqNMHVY6QWKIouFrSbDb0mtMB4OjZy0U8JSb4wRHcycPy8ERFdcTQ4ED/EAUIO5gTbOCWoEKWC0NI5oUTbP1cEvNR27MuaEgp95JNbYX+KhlTuaQlguFAxcuRFaP3FKDoUWxcuZIJzHlFh1bd0uaFyjmk1YjKesIjnn/69AYgY2RSLjsCbu52EBqlAtWfVbdjTCQK4MiaHTMoeXyXKODMKgZSE0CgT37AerZlNM6enPRvASTN8nojGhK88uSo4HH3XUj5WR/xpGJ8iAenZ0KJszpdsFksOP3zAe3/wNvs3LnP11nW+8MZn+YU7f+YHP7jB+pDwE+sN/yD1tgJnCnvPBBwXe8lPHM9DuT0mXtrBFInWjfPDwiJPWSM/TDm+PFvx+PSY49NHrGNNWK7YgYGB88nv9+L3F+zw2WjKl177GT730uucLU5559vf5ptf/T2+890HvLdYkFPk+kuvEsKn9Aja3oKUISWIETZjA8J9gqozBfloA6W3Cv5sCfUSRg/NYn00hzAHN8Ns1isG/dxzn+7jCvLvPwqKstQlR+kJN6uKngVVHZn4QpBA3K749uEhL59dZTypOHnwAFlF2rQxtgwLikTa3rEko1qTekd0mehXlDohHqQXKhUatyGXwvH5mq5TqsmY0VZmvlaOz8/IXRyKFvvSMpwnEYILfP1ffY3P3rxJXG0YTWuUxKWdfUKoUC10mzXzeo3q1HIrP2H8oJvy+OiYN7/xBzw6fAT0TJrArau3+LVf+FUO6qu8aEwAwI2Dy+yNx4QcCKMtHvojtHhCHXBVZU6iwRoHnZr1qbqCZI/kbNbVKk/JLZZRY1Z4ThQvQg7giylENZoVswQ1JXoZbJF1eLzoAGpnZFCEupAoqTYF6vDaSdUs/TwQlJyEQcBlRSICqcYN9s+9WHbTRXbwVs603hjR0U0s78lbEWqqd49KRoPDFVNMqwbQitSP6V0gVXYtx14NGCtCE5RJ9cfk0/UjDCdixfYPGqpoyqZ8GuzVg3eMpzN8asjL2jI4R0qbWzabiMSCH1WEMMLFMbHvOV6tWG8iu+OacQnklDhlydHyjBRbvKuYzAs7LjBPjlPW6AYqHLWPfOGzZ3x5esZ92eYRyhprZOU/+H32vvHXyPEW53wNSQtrkJXCpo2crFZ0OZOdGHMtRt69e4//+MED/umbv89rf/bb7Hz5z3Pr1j7s3aAZTfj8wXW+8NLPINtTPpgJDx+9SXzyVbpJywcuoUvFLQqjVaLeKowqYbkq1Kml7gK19sSqEM8zq3cqqkcVo/kCvdJTrnX4bkN3XtGtd2nKPj9XvcLrcpXsH9NPvwuu5TOXblNfPmGx/SGbnQecLe/ynfeEo5OxzccvOBp5BsiKDHbD3pwXstOBPlgo4iEPdl+YbXrSgpBIajBKlTwihd47FsFTRhOk9DSpt5xJMZWeihjbPQtaQRJThisFH8pwPxlmEmtvWcS9GvBQGpz0XORn4nt7Xq9orsjBmr6qBS8Rr6bQljIQYToj0pQhhKLUGS0QcWbT5QrJeYIqdRFSp0gwxrrikWJumE4K6h3OtSgWoZC14HtFsrHoq5LIzg2uu+YvIwgqmdpnY4vLoNr6YXP0T7B6/Jd3f4eHJw+GuI1Cu15R4gaPY2syZVvcYL065salXerLO0x29qhSz6O73+bDB+/w9VXLyaRmvjem2kxhvM14LciuECZjwPPqlZeoVjd4tFxwuHxMnzpW7ZLz5Tl5s4bYoX1HTpFczL0jhY5NWDIKgd+49Re4fOcS8ppYnLZgoPg3+Ej0dqyUk6owS55qLCAgo+FvgafJLRmqCvztMXK9YXPvCQ833+H9o69xdPYhZchUUzKisD2bMB1HquoPsebmPlBR0ojN/RHu8Iu47gB5Q6xWfVqQcVECPCN2huErQ3kMp0/g5BrcnEKdxSzfT7Ee2xyYCdQKqQG5DHrZYsVfYIxQsk/0lbAJUJwQqXACjSqjLjNKjuLtjghSLOIkNZAFrXtTr9QZVyBmZR08KUPIHq1k0O0MJDj1ZPWkkvCup0hFcWpZsT5aNnP2CNky8HxBa2v2axGEmlozorU5V6iHUg3kOWuMhYvIhWTxJ+qt6k5e8M5Z7nQ1ZHhlywStigd1VovQo1nxbgB8VQFTa6uCK9l60h6C2Ma/ZCPVVikz0kjIGc1KJ44+CH1dsZFmyPFTtAdU0CbhcsalQir1AFR9v226oGTvcWvFy0+P/X8xxnXNblVZjamZqFavdARK3RCu7XNdGpwLhBypdzLXSmSVLZ+1bBI7umKMJ5RClWs8mRQVh2PTmZOAH01o3RiXjKaswXIRRRyu1IydKeKCE9ricLmAtvji8TlzqaoYqRKzpy3ZsheprXGZEtk7JFTk7CgoVVJEPBoGe85xHB4DeIdkxfmEp9BVDo+B10QsYxqzOC8X1weCeiNEmgmdGjEES4brNNKnlhQjiAE1otbEcxjZyrxOLMO4OIvr+DTjc+5L+NNXeHLX82i3ZRL2CacwomGyJ8zKmDRv2Lv0Ojtbt9k+92w3h5xf3eHdV3aYj4SrKNfFAPsxUBfl/EyZj2x6c+8teP1swcF0wp3JnLrqYNTB4h4SOmim6ANHOoZ+f5u1OB6MR/xB0/FWOcYffpf9Dx8zO3KsHvSU+phRI/Rbezycz+jamqk8YnJyTlqccGt0ymjkmfSvMr87Z6EdD057zh/9DnP9kJ26YatXlm2GDyZ8KBOWoozGD/mZKwuuXL7ES9NAf7jP+qzjPD/ieOsek/Fjtu8HxC+J88Do+j6ysyH7wknqOC8TZg8cuvBUKBIz7nKBvRpmEC8p78yPeaf7DrP3EyfvvMb4/hU2d5eoBkJVEZqKaru2702FG3vc1OGmgps6ZCbI+MX3ZGGwBIfBPlfFQJ7nfobh5+Fl9JkGnIvfPFWZy1PY/Kmy/KnKHCGIgfAuW343xVFJIgVD0a1xZo3yUoaoGoFcZWu0+4LvI5qMdFxyoDDm8rRmHh3FOTqnaAkkdfhoqiLxBQ2FwPA6vQ426EIuBecieMjFoVSmCnPRVGkiRpLximuSORFFcJqRUaZkx7j4QWFeKFWPpEBWU2iaUt3qap9tHXC+GPk7CNEpFEUkINLTV1YVOy1ImRjm6oUGc0CL/pP39D/KmLzbwEsAfhCIREpYMn59zrYfcWfnJtf8Fv6JQK+4WWR61RP8iBGX2cQWr46qEfwlwQ8ouGJlycVuVIG1mAHP+fBvW9h88IsqHAMH8xHcuA6zEbLrCHOhLZ7zbTierLh6dk54nOGJozs44WznmGnzMmfrmukHb7G1F+mnDeV6x2J5l817FXXXcvJz5zzeqyjliHqT2N9r2LgJSwmM+Dmc7HL7zoRXXr/Gw/tHlFIRc0KKEdgC5sAylzE/95kv8DO/+rPkHNmEd/nH//SYJ4/HXL4cnsVG1xe3glAF//QgqAe9Psg2atASEJ1D7Xn24OfHRaH3PLHV8alaqNKBNsDuc6+ZKfRQ1Tgx4poCdBV6nNnEhIwil29/gSATxFe4i86P9FRuxC+/+gbzdcsH3/k2j8+P6ZeJ6ZWX+Gt/+99l97Wb/Ef/u7/Do0eHdCmRf0ygpQxOEx8dynqxZL1c4YOnpPwRALypRzRVRc6JVWt9OeeENz5zm8NWmY8cl/3mo3xQXSOc8nHWqw5zRJlkPnj0If/Jb/5feOfDbxsgobYG/9W/8je587OvPnWDWD9cUEpPvT/70Z09PzJmwJeBe5iz04sNVWz/mI3ck6oBHAFEPU8506kaXD/MzcoVB51CLgacpWDhLhm0eLwKPmWrjwMkKVZ3MyQAJTcQ3QWcuWmoN0tj9SYUEVGKS+QUjAuSdLDJLuTgydnTKbQIvkCRwFSVy/Oa1lX4XFNJj3PFFOgZ2lXHw8cnzHzNK5d2yR5CyUiBpI7UO+oqm9KwBFwMxFJRJFN8DxLIOHopZByKJ8tAyI+JxekpbdeSs1BKQLIQcDRhxM5sm/FohvNm7++G7FvVSO4XPF4fc9ie0+YeiqD5wkVI2BqP2J2O2Z/VNOHFhUY+9jTSU6tS5UwEsotGoFcl+ws3xmHNUFsLbT3y1EXxsdAJmIl6jWii9xl1QpNtb1GckJ0Oa7+jy4JoMLWvFCQIqVTUSXA4Oi8UV+N9QWIcrPXF6lup6LPVxARTqUs2mZY6wUlgyBDBZwsZys76OD4FuwDBhFPicLHC+QDFnJNU7GkdoGIbOhFzH3BSUFfQoIircGRCThZDU5nIycuw/mseVPdivVugE1Pf1yrEUiMu46pjRGuKBgjKxiWcFGauELzisic5cxQDh/8J5yNfuPs47xlP50zm20zqEfVom5pM9iB1jS8NxYHPa1INZZ2JJVNXNVvjMZtFR9//hPKmf4T3bIFjNX6I17nw7GicR5KYUM1nxAtVtr5cRUFUjFAYwZdidvzqQRtUhdrVRFqyQv5T7qPuB/FdjtajABPrMfQ1P7JO6kDAd4bqqUIqmbN2Sa8NIxrmk4qtyYTVcml55OJw3lw/0o+pujad3/A4Z70R5z2+GuOrCa5JNBOHawqlLmR1xEmibAIuZcQpaZQprhncn6CuGqZ1oM/CZJxYThK6Epat0vc2+1QBaqkGAmaiSyYq+Ak4wv/Uxk8EFH86FFIXWXQnLA5PuPed9/BHLctf+g22D/Z+cD/4pxStVzDmy9Nti13HP9Iwuy77qgaYXLUm4a1hlzaIrxD5CB/36dCinC9OWaZzNCQmUnHJhx/68r0qfclMB8sX4ClrtWkaLtWXufSrl/n5X/qz3L/3iK+9+Q6/+7Xf5407r30q2xcA3KBKrCpoBnv3GKHv7Xs7BxI0nSnIm42pyC8Acn9m9ORJA6MZ1NvgZ1hwZ8P3A+Q/aBQ8Ky67HaZc4vXbFe/efpPrPkDYYfTZz+C9EFOiTo7D9x4RUmLCEh8izTThtjz5eExaOtZZyF3GW7gUzq+Zzyo4WtGvPXuuBtexLokcG0ZdTWi2cFs1Z7NzjjctXd89a0iAAbAoOUb+zt/9u/yTf/Jf8eXPfpZf/6t/hVuv3uFzr7xE5WtUI+J7UjoF3eeTgqQueu0fH6pKH9es04bSeH7uK5/j+tVrvHzr81y7/MqnJpTMmpqRGmgdfMVsOqFrC/VoRF1XtCk+VVj1pSOKQ6pMwKMyBpyBx4PtHb4YzkbBqalMravqTQnuvK0T3pM0gJoK3BWHDOxUJSGSbcGpywXUhGRjLoZilk/OJ1MtFCsE81BggVmpo94aO6pWUGGsmOwLO32iVmNXrn1+mtUXioBTvEsED8klqszQMB2CGUoBUVKpKU5QV8iS8FgjfrsZ/cDz+cc9Lu/ssNhsaGP/AzO/AC6s1TVDcR7vhFXfkxSCC7jW0aVCXzbUY0HGmWpaWJbE+smCh0ct4j2juSdVmVgKj85aVotE1/ZUdUVHJJUn+GbNwdaELSaMZMzl13t+8fYR19w575BomVCAToVe93nUnHBD/zY31y9zX/7v9JopseWkXXK2WVFUyRfMW2XYiFimfb77PovjR/zmm/+K3/3MF/nKb/z3ePm857R2LK4cEC9d5soSrrbbfDsd8kGTieU+s6YCLXhZI1IRO7Mgn20y+aHn/njCcjLi8HQHzrap+xFt6NltTrlaR3KlnIbCqN7lK5cvMafh/DsHPHx0xs7+PvXlUx6O/zVHZ6cc/bfCk9DwZJVZbBJV/yncP+Kzq1C9IuZQDji8bizzT82alpDN8sorZNt0RykUB2gw1mHp6b3SXuyDoqNXU086NUBZpNh9ngPeRXICp57ihY4RVUnUTmwdLQUvGQ2FqLZ5D6pQLB84l54SgimMtNC4RAJTIlZKSAUtZqWsqmiA7MzVIynGjHdCTtYk9iogpoyP6gjZM+oCUQviW5zriBSz9YobcmlRB7kEyD3z4JlUNaMM3hljOosp7kFxRMtScuasobEZCuKL8/AJE7Y+97tPOZ///a//c+4fPUGLkjXTrjas2zUjVaZ+zOxgi9lEONiCl65dofK7NNWIzd23eOv9b/D2B+/x6PJN/MsvMZnusrWzx16YMnqCdRJHCquMxG8z6e6zSSs2fWaVOh6fnrJZLU1JmSI5maJStZCwed1UxspX/vwvMfnixGzSL2K1BeuFVdhFGiFnKA+hHQnhplBflFnfwxTmgmUzZuBc8C8LOsos1ue89c5bfPvd79C2K0MAih3rum54bfoFrspfwZXrg43cOTm/x5MHJ9x78xqv1bfMYtdhIpFBSP60dLlwVhSMMQkXNxa9g24bHgvM1rD7PvD7wP3hGO4Bl8Rw+AOsNHpBUFzFEbxSqsi4KMgQW6DKqFdTcGSzrKtCJmjh1BUygRKE4DIJsyx3TjmrAm1wuCxkl8lizTQJGS09op4glVk3ZgFfGZvcYbaBXo00RxiaPYKI4iQSNEKqybkiqaClMiVHcWRf7NhIMYvdorbOaiJkpRGP76FUCfUG6mi2JjfOJqPU2b40DJEJRZ3lLqIU7/D0Qzawwzk1sp1m6pJpilJnaHLGYXUGxVtuONZ0G/tEykLlTAmsfUW9rshVQn0AZ+oGFRlsx/0z23QnEAWfe6h/+h5yk2rCvApkkimAYsZlIwhlF3G14tWsBp0mYu65yDT0xTEZTdjKQO7M+lIrJsWjLlK0sMqZRV6y5S4jcYIvlkdf3AatEy46PGY1riWRtdB6wHl82RC8w6ljd2rZ9et+jCbBlZ46RDbak2NN9j1JE54p4gfVewpkJ3hXEOcptHR1QUqGvkKCM22sWq6bvaxHstlA6mD7qN7cSnAFHUAwBqCt+EKfM20p9MlqwBrL88yitF7RIPgolKxkV1AN9CWZff+nGK88+QwPXc3uncDV6YRJG6AeU8YVE3r2926z9dnbnM5vcffJiA++vuFWucyD18e8OYKJt5tlpQktjgN1zD48pfvuGdf297jVCM39e+hYKQcT2BTykw3rI0FvZKYHmfC4Jv5+4u0H3+H9q2MW27sc77zMer6GSw8op4ccfS+y7Gpy29NIIZwGll0h+V24OmVzeUa18XSH93ncbJgyQVcd49Mxupkx64Vr9WV28yMmW2/T9QG3s8X9cpvHYZtutotsRy7vnnLlzgFup2J0VRnJO3B6yvWRh2abIoH39wLfaE/43PaMG01P7s7ZcYXLl/bo3z6H02OCu0wYefisR25b070z11pm7T775xHfbzO/s8/WS4KfOGQysAqm2HeLuh+AB55+qeWmvND5TpKfgtYMoDZipB3bWAx7rQs1+fCzlbvDz/YP8FRlrh8lZwwGKCKY/f0AOo5dN2RBmolmjtY0FgTn3KACs+dKYNmwQKWFoA0bNfJPngQuA1udkUlEIXoL0DLkHFQiUpRMQYK3uGbMKSx7cC7hteBTZREX3iLakprLh5OElgoXHepau7d9xkUDlSgRKbZHRwpNH8kawVWgQnEZJeEVGklIyESM1Nkr5jShNt+LisWBqVIGgrXVf9akjz9qQ+kThjzClBdPd43mdnH5asPV+hqXuU5DAzeA6wJ+SjX4DNaM2eyDLIA/xBpVN7Ca6Bln4qnAo2Cly3cw7uHngc8m2DqCralnPS2c3IzsVxOLxWgaE0UJ7PkNoTxG3o/oWmn2hIM9IdyeUd3zVDu3WPaP+Xr6gMXojJ1HZ7DYZna55vRay2l9l44l+9dfZsfdZEd7amosJD1w/fodvvKFr/C1/+49SgDNBSeeEEYU7ckx4UpF6Dy+qQlVxed//nXe+vZ3ePsP3uHyX3rlhx/ooQSfjGFidgR8tB91MTLP1OAXzcLnvz7liAEOsee6evGUGSdnjGQGjLmQtksN3PbM66vYH1+0Ap8/sxM8cHWyx/6XfoX7t2/yweEHLD48YVw8Y634tV/7Gd771pd5+91T7t59zPHhY1brNX1KP5wk/6MM1e+zfpWhBgrOm2uVc6Sc8c7zX/693+bJey1/89/7c/zaL80/mv6nU5Av8nyLWlGStpznBYvDJ/z9r/5/+M6936aLa5wUSlF25nv8/G/8ApPd6dNHLdOHTCYzrND+cYdgb2x/+HpxIqNVlOZWkCs10D4LUjLBFXKyWjYh9LkawNIWL466qJHSRJFoAFROZoFeVPBiLkjZWeazcwIUktp87iUDbujJGZAqVaJzAlFJXggqFKdUfSAlhVoH1xtH8omsjlWZMo+tza8JJs6cl7yarXe5+Hy90C16Upt47/CQy7tjdvwIKRVtFnoRkigV9vxk+wxeHZPBqr2IEnymcwEkW19w43FtwrcRoSZGI+C6IozCiK3pnNlkawDDLXpj2ICT04rj9TGPNqesU/cRMByEcVOzN5twMKsZV1bbmjr/xcY4Q+V660cM63ZRIanH4RCf7Z5BiRJQHKmy2lUk0WSg97jW1gMJQqk8QkKyEJ3loiNxIJA1toYNr+OcueOhii9Wv3trVpOKqUclh4H972yP5QqlsbiRHhMlhSHmqgSzYBaxrZaXQtKCw5OlkF3CUSAZBigyuIFFwz+yU6osOBKaE7kyooMbYl9KKETxVFqoUrKyRpTSBKtD2kIlxsRzZtwJUQlmdYgLmZF0VFmZ+USWQtLAUmpWJYBL+FCZ25dmfDKSdvBjlrVF/7j2hU/3x4YwHk/ZGU/xvmI626aeztBqRB2E1Dt8XuMkkXKiKRvaVJFLTyo9xSl7vmZniGddNB193/FMSPhHOcTIy1juepAholTs1SunOG8EhuCDkSmdubVJLASFsQu4gBFRcBaXKErjC1kTWZKZB/wptlHPXUK84Ri44Zh5E9EVzda0uhhq7h5G1DIcIZfMYrXh3knLaK9mXgt3tnY4X6xIClu7W4yaKaSe45NDFovlj/f+csYXjyaB4PGhoZk2hGZC7TJ7+xO2Dno0OJYRRpqpa8X3GR2cJEvVMyKSnSN3kFNDpcJWFZjNamJT0axaTpfKchPxJeMbb5EKWoNTkib6T2Oh9FMeP1lQ/GMjth1hk0ntD6CEKEZVrXkBW5sff1gm8FBG6vBDGVAwnts5fMIIWElkhsimgmm7De/7MQeLE3ZOPsDtX4e9q5/4PEULMWcaL+A9V3a22dmeXzzbJ477mzXffnyfW6Mxd7b3mDSjp+/VNrL2yKqquH3nJjdeusaf/fLn+YPf+jb/6r95kyt39rl95+qPe5hsvDGBrsBphPPeasHgoZ7aTJbLM4D8LAIJQjT1+GQD2sGit6/qEOpDGDcwmUE9A7cFMgVpgArVwDC3fGQY731F7Rs8Yw52Gv7tv/E/YnN8ThvGfNed03/4gOl0zPnpCafHGyiZWs/RmOmip+9GSFez7Rw59QQpaOXI9Ra1bpjlSNYVyY0oC5iOOna2KhwBOstX3JlPuXKwi3YtR2cnpJh4Xvtn64Llmrz3wYc8eHif3/nDb/B3/6P/mFuXXx82BxXz/TELPWP7B7gD/LDr4fqNy/z5X/8FHh8f85k7n2X/2g3EhRdkv350BC9QLEO4CrC7PWbhI+PRGDdkrpWieO+IYvbpXhyNCN4ZDNQ7gaYjSIICPiuqPcjI1J85k2Rg/JeA9wZaZS1UWE6jH1BpRyZpZexfZ81Flz2lmHrTB7WGjgYCjuIUcqIQgIS4hIrlSCrQF08lyWx+HOAcmxyY5YQDGjJT7VAHtSohewNVayFVmZDUgEHF2KzqKFU2a3EtxroVs7vpBxXmVtOYKvdPIE3qV954g/tHxzw8PeVsvWLdd8SPMbw/PqQY+/eIE5ppw7gaEYrQ5Uwpmelki62dMX5c6DYrjs5O6aTnoN6ncTsstbBarlksWtaLREwQJTLulC54Yp5TNjM22WxZr788QeeXWTLmjF+mY8YGU6i4W572Z36f9/nX7N77kE57So6su44nixWxZAMnFBjsUVWUyWjC9avX2T24hVA4vv8Bv/kP/zH/v//D/5G/8Mu/we6VA+J736W5fJ3q5BHV5pTu4IDNeJ/wyjX2Jw84PnyfRCTkBpc9fT5lwwp1NXlTOCk1fe/wM2GUatyR4GJF0/eUJuHcCbdv1Gx9qYYNHL51yEl7xOQGnDZ38SmzebBD9/g2q3qb/d2OOq8Zn7wgYgZknln5O1EsnmDI7C4CrSdUSgmZXNlmMkdBKUguSHYWG5ATWc3iLXq1vHEVRBWfHMWb9bqKFYTFx6HHp4Q+UGHWXlqgeKVVwayV1ZTiJeNFKBKJzmx8zJJo2IxJRitzhojiEAz0SmL2yoVEEUfx1pB2XlF1uKy4KJAFV6nZdQ6Pr4s3xu5ySep7NnlBrFb0kll1K05ODlnFnqqyjLcSE5+5eZ3bN27gndJ5T3KWnywFwsCcdg5KpfgSDYgPz83tH8uukotO3aefygH4g/e/Qx6KclVlvV5zdO9DHn77D9mSMbM33qA62KJykJ+siacdZyvl/lvf4tvfOOTD9Zzy2Z/lxrWf59VLc27WDS+fVrhDrIt6BiwVjpboek0fFrS553y1YXG+JsWeLkXa1JNy5HmqaB5yQy/NLvP51z4HVzCRtmJ9yIfDa4ztYe0aOgSvntETCJcx0GqEAdE6/P0pJuoQ24S358d88w//O77+1jc5PDnBkp9to64os7DFl67+Wa4cfAV0ZASgsqE7ann3629ydtRx23XI2cg6yadYo/kqQ706qGZmWGEYhnqgA5dg/wZs7xs4Xo2Ay1gGeo3VvaeYX2nHM0D8f/5i57u4QhKz/0Od2YwmR4UBvSMKVW2kjZjMzjyHRF1vwBdc8gT15NywrGraUFlTwtk8oZpxBKIaK7wMgLGI6WhdseaLy6bqNuWCqUqqApo8MbiheWm2dV4VPyjBDW6JlMrqABx4p5QioGbRq2SKKwRniutCoBoiTVKpcFQQGwoe9a3N+wwGgT2WnVsKwsjUEZXQUahyHvIMC55oqht1Q9PJ3HHaEOjF6gxRu8dFBYpZppkNb0IlEL0nBoamDJQmG0lQHCoO3yUk9MNF/NMb4hxbozEqQlaTJZ0d3aWTOeO9Kzhv2aaaBI2F7KLlieeCuA2qIxBHEKyZWMwSMwxyo4xwsjrj/sP7TGdX2R3toirkUHDZUXwg+0Qh4jSRSSzbSAwNVaWoF3oPVUo0nZ0rEngNxlehJzghOEdsez5499toaLh24xZVM8e5AOoQjYgo2ZllY8kJLYHQBRQDvYuzOrIqA5glAXEFL4qjRyQDQvEBlwOW+FPs+s0ZzRlFqb0QotV7JWQD3SOWMe88QcLQXEqkH+AM9aOO9Y2eJ6+2XL425fOjCaGZ8fC04sNFj9/01Lcvo2HM7EHN55bQn42QVwTdgz0n9EX53skK4gmv7Y2oFhuefO2Qg+M52wHqcoQslsj4CpzVlIcJDq+ybOc8OBxzrYnMlvv07gp6fclZeYQeLmkeHpJ0Rbz0gDo+YHy6z6jMSSUwnTZsRwirli4d4a70dLMVY7dLky5x794t3Kojnyg3NdOmxNFRy9qN8TJndnmLnXjA4tYVjt/4Ascvv8zW1HP5yRMuHUbqS2NkPoF5gLILDz0sO1hk3KimTMZ8OHccNYmXjh9TnR0z3p5w7XJh9xeW6Ltr+g9fhcUIf7eGq9fIY8/JCWw+XDJjyVVRdl/pqXYEqTxaAWrXQMnW4NWcKWctebUiLVdPv5d15Oq//9df6HyX0dA3seLV5hsVpAw/ixF9n/YzGEAod0H8FS5UKd+nMr8YOtTGClEFVYtS6IriNONcb1FK6shBCb7gk9XWkgu9G2xfsxENVWy/tPbQeU9pAuNe8X0xxMdVpmRMGQgGIg/1KDjInuwTNIUkRrDy0Vtsjo8Qsj0+BlzvCN4cgcwISciuIjgokiyWpxTLZnVCIFrcF4p4yCWZ9lcDlEgoigsFX5QimaAd4zLEd6gQVMlFyVobEK/g1BE0Wp4kpj5/4XECvI+pxccCWtCTEaVTyhVhHuamsPPPiBIqyobMGs8kCHIZc6L5Q+BbCm8A10Enzwj8AkwQJhhd4zbwM1iMNu+CNILfV/JOTztK1FT4IkycUKuyTHOOH8/YTmdUr1xH3Izm0iHsdEzGO5TtGywOZ7SckA4ecp4bjnY8t16fs723zatDJvy2L4zSjPRuZLR7FQ5Mpd00U770c7/CzYN/wcPDQ3rfILVnZz4lth3dZkOoHGePHqKaEQns732JX/lMx8O3z2h/fsVo57lQwueZ6QMvdTBFwKjz7rmyO/PJanADp58exIvnKrx4F/Wxh+8OznZbwBREKoSD4TXcs/3A4Iz1g/cHQ+05/HvVjLh19TVuXLnN5vYx3SohMuL+6qv8jb/+b/F7X1vyvW8vOH74Hovj73L3+AMenS1oo8Xb/aQ6FqpK27X0fU/RC3IBOBdYZ+Hrdx/z8mqLX/SvD3zY5/dB7tkHsidjs37A9773Xd77xof85r/6lzxZnCApU6szzUUTaXbK8LAIecPB/lXcdPYpP8nF+3hxYrq4CvVisJYTnLO5haKUZECfQ8lZ6X1BnKMqFZoyqoEyXJtBrW6xDXFCcagK6pSQHfiK7Ho6AbLiw0DsKZ4y9K58yabIDg6XPD4qeDWgCgGnZBUIBZ8zuQT6osRKGWchFEMlmwRJHBlFXR7IjpkuPQNCVu2Gh6dLpvsNXsw5zTmHFqHPHo85EIiAF6s5Q/GkruA00whIUFqUkjq0s1q2qRtq6XASmM9mzKfb+DCyPqxiVuklUfKa880pD9cnLFNLyYoWNwByQlMFdqcT9mYN8ybgCVCMUKxl/cLn22UjxJZgezOP7YUgmUumBiPTeyM22Nyk0Gd8zlSZocYOlpGeC0GKuTd6xXujIfQeknOk3lO0p3YWbVQqI535NBDknIMwEKNTRl2xvmkJ9rreSKFFHVIrmhNlyFsRN1yzCSiZUhlBrfSOatgDqE/4EJFhfU4umKueS7gLKpYpC4zMlhStEr0bIvQ0Q3IkMWvppIoTs+V22UimSAIE55R0QQpIlg3Su0QVMqNxxygLsZjlf+43tK4hSIUjUakjqaKdOZ65oKj3LKqKevTibozeB+rKonO2Z5fZ3rnKGM/ICbEeYvpCAyVRqVC8Q1OmZOjihuXyhBzPWbVrNMOVgyvU6tlQGFcV5+KGOfSPegzOQkM948TcF504gjgqD17NkU3IoMHc5ZxFMzo8ziulVKBKQmlQJARIBXxtbrMo5UdIhLxAWfTpQv4no5+uqmi22AXrbxUjB4gz+41ny93wgIs1vKClkGNPTsKT8wW3d7e5rFNqn3k43eHMd0zmW4TscD5wZXePHHvW7Y8eoZmKOVR4VXN5rWtqHwghoTVMt8bMJp5OhFIHMztsszmVeEXUoi9VMkqEyiObggQTKMbc4POIrWZCqR35sCN2EU0R8VC5gLpiJByJn55091Maf6RdGRHh6v4lZjvbn/wHEdsIDKTIi/H8ofsJ9YgB2wBMUDIQxBZO1skyiOsf/kp++DJMWiAXOuD+uuX8rXf4/Dgym/9gmpFzjs9/8bPs7W9z93vvsTsW6uloKN4Kzz7pwPpW5eT4kEe//3WOgyfffpVXPvM6Wo/w1tb7vu1XcNaw+sOvfZ0nq0KzNeJ/+b/5n/2YR2kYlTOPvZm3E7LOcJ4MIL9gM4/HMJ2axXrfQ+xhObG/q6LZ7o3WoC3EDlYR3BNojmASzGa92YZwmU0+YL2E3YnZWV0ch6LKqsDMXze1ivfMZtuMertg9rcmbCZbjMcNb37jHmmzYhYizWhM9IXT8x6JQo2nnkB2StLMooFqd5cmZaR0RK1oKZz052RVRts1VI6VVEhOTEbC9Uu7+D6TcuFsuSTHiGV/XWxWzNbTA1XlmcwnHFy6NDTrYHW6oXL77M53kKFZ8YlxVc8+/tMhIsx3rjLd2udVOrybDBvwn9Ad0iecFyrX4x3Mpg1FasZ+THHCWDNkmI0aQt3Ya5eM97YoKJ7iklnHSjBLJGcsW1FjY8eh+yIXygW1IqvGbFakZPxFgyYkcN5skeiHXJ2MzxUlRCpxFK2was0CSkQdEsCRKFqMoZgjWgkuOHJWy1aOnhRgoWO64AgJptozLYlSKVUOph7whT7UJCeEotSS0ORwleWaSvE0ZCo6qBwq3jYSTqmcMJs0/2ab8j+m8Ze+8gs8Pjvjg0cPeO/BAx6cnHCyWrLYbIw1/gk2Qjlllmdr2rYnrCrm4zF1Hdi0PaLK7vacETOqBCebFWfLc5x6xs01SrdLcecslk/oF2d0qyXqPb4taAm0ueb8qGNKT2oqkibih2teXxxQ777CA/n806hhaHnp8jfh3/4qy4OWe/+vnrJUUkwcnZ/TxYgakooC3huoUwRCVTPbvow2l6i8sBV2eOn6goaK69Mtpk54cu9DHt57G1xP6hbkJxP2XnqNz9/+AreaPRZ3dng8gf7+E8rjh5xvlHWCeq5sNhN62aZcmjIuK5qTiD8eMT4VcJcIO8L2fs/L821cB2s55m33z/kw3ePaeeDsqECKHL93ykQcL21v8eqsJk4b3Nbkhc93fG4vLx6qoGZvpOCKNX1SSUjySK1mg66KZEEqyCrkJKCJ6AMeRxscMY+JWVHtUNdRpCHXdpZEoaKHkGyjU4zUEuJA6GqEXAQNESnZLLy04JMxf0soFG+WtqZKsvwrsjGWvSsWw5AvNpvFCErFWwaVpWVRScEVKNnsMLM6gjFjqKVQq5FBjtuWfrkh5o6Olo7Iqm/ZLHr61NJ7yKqUUlhvNoSYkaaQKaBj+0yiZrdeFEn6NEba1ZEQnl3Bz8YPmhs+3ZyRP2a3lLVw/949YvvPaU82VIcdo5dfYjPJ3C0blk965Kxmc7Ti6Gyb892e6eQ2t+Qyr40ClxTGDwT91qBGGgu6COjiz3A3nXKYH9Bqx2q1JqZCTIW274mx/wggfvHRSs7s7EzYjE8pVcYPayQw1Beg+0oewb1jUIRJUqoeXC887ejeAq5jQPo5T4u0kiNHH9zld377Tb71vbssN2tzK3nqGgGXtq5w49U7VNs1BKumtB2xeL/m7EFHo4nmUEzRfYYVXAWj4l/cipUOTMnn1mHb81MdQBVgbPJ4ZIxZxO9gS1bEAPHF8PyLFz7diLNc51KGLGU8Pkd8Vmu6IcQhn00drKNQRxiHSJMGa12XWWtgjSM5s8gtKoRcmROKYs2kYCSYSa8IheQLTpWUdbDqdQZW2y1LiZ7sHOqT0RIGhaHzPU1JpNDgs1kDaoFcrPFXnOJKQKuE89XQ/OsNKJds1pBOaEpm1CZcsTTZjEOD2XnlCwKpc2hlttibOCJXwez1SovmmkajEYDEIaNELoorjt4FOi9krelLIMRo790ZaIoXvHZ26WZ7rapXy0YM1v/pEGJdU4IjtcXUFAQ0v7h0eDqeEGNPKvkT1+pPGlUV2NuuEbEGZBTh8OiY2aUKmg1FPD45XLTKUosOzgGeoBkvHSH01EmpooFr4jM+mKpTJXK8WbA8O+PeO99i8vIOvtnFp4T4ihIypapNMZYzReFotUIaYXcyRvzQQOw9m1zoVXGl4AsgQgkOm8hbfFLyZs3Z4i4H88xs/wANNVEnoB4JNSWYpapXJZKIyTbpquC9ghazekVQehxKjRAYCK9OIXpTlTg1QFwL2WdU7DOMXEVCqLJHQkFLQjA7Z9sSKD5bMyl9ylJ9/y/uEHcKGkypHtoRH9xreScumDfnnBxXVDmyM93mYCGcb6DOjko9k6LsRIinBX98xjwsqE4qmukldtwB1bRCRhGuTyE0uCjIpQbGwtXzGetvFvwm0NcjDicN9w8yq0WkOT7HNT1xx+Mu73A1ZvazJ76bOV/vMi6ZqvboyQn9eUufhUVXU6rAfn+bJh4wqx39ncQ7zbs8efI2crhiKyei9px74R7Kg5NTTt/+JrItjHfusDMOzEZT0pMj0vKMWjJ+Z0LXR5bLYyZxw3g2Yvus5XI85FubJzw4us9BKdz2gUvhBH9T6ULg/N5DQrvLJO1BEc50l5PSQFuD26fTwqpfIqf36btzcruiLCNlkSnnGV3ad+kqXBzj4wQXLzFKt3GlgX//xc63FEGcDg4z5eny+LQtOoDkBtYN/YwyfFdBCs/yxYc/ewb/XTQZh4a5OiM++oJPpmop2N5FQ0aqgawcC65Egia0FNQ5Yu/R2tSIRQqFQpKKznsI1khzqQy9dwUVy9F1imRQGiQ6KjbkQRGnkvDe+vUp2b3oQkGaHkkJlyoqrQjJolDcAC5JqSBYJFpymBW7U4oPlCSYdDwaeDM0+12JSJNxnQ4WrgPwLNmyRouZevgCJSka1GpUgVwqRC2Htw8C/lPUbb8AXFasU2x2NP7xdcLpddYz4d7WOZdkn90M0gqMjAzXyopHzLklmNj6C0Dq4V8JjCr0UuKsazmsEuIKB3jmzBA8r2bYAI3H6prbAh3UmwpdB76+9ZDt6ZxXN9s0M08IUI6mdPdv4OpLiO7CgwBuhF7uWGdlE4SjS8re9pjz8S3O3GNWnXJ+acZl/yvs6QMajpFUEb/bs/jaLs1nR/g9nja1vvgzX+KVq6+zWfekYkSK7ck24+2KPnV4n7l3/A75/BR/6RLiRrz+mV+me/dN1icnjHYmfKQm+9jcq2L68EMgIOyD9XLIPLN5kI8+8LnIgqHDPZhAvODEvifoq9DfVeIjqG9A1cjgNvmx8WO+xEXcopOaMD9gPrc3u6V/jpf2X+GA+7zmEscH90npHd45+QN+7+23eHB4xOlmxXLT0sefTB6oqg4kvOd/V1hr5MrnX+HVX/qL/J5zXCLxGjAi8HFFpKrSHZ3y8L271JsRn3vjZ/mf/nsTLl/a4x/+g3/I2eKMOgReeuMOl25cHx7kkX6Cj37gNPwku8kvMFw11Aem1MZBCRnfC25TEK1Q75CqxSdT/fpkTjuZbK5poRCyo1AhVSL5IY6sN2ty75SsBUVxrnq230EMBFdnJKJKccnqvIs5L3lFi+23nTiqPJBvvCJazPJdHKsqsLfpwFnbXJNwXjsKwRTFFHzIFlfjCn3J3Ds+5ObOjMYHnAgBTw+0BWpxECqcFIIqnToU26f7CI1EgjeC7zpBKY6SK+oK9rcqGj+mribm/KkXueEFyS2L9oRH6xOOu+UAYhkYXxSqENiejjmYjdgeB4IEKNWw3HZs0oZNenGP50wkiceVQHGF4jzRg2KulsUzELHLsLcp0BZ8zHgtSILKOdS7QSiQbS32jlqd9RZ9JtaOKIOqv3hcNuDYF8BlkgginuLM9UIw+/GiRgZOrhhZqlj8T0805bUovjgcQmmS1cDBk/tEKZHsK6iCXZtNRrPNpVKZNtilQklGdnZB0aIUZ44CziviDAj3YqCqqlpdAWYtj1LLQGxQhwSHjLxZp+eMy55chBgsYiXhKG5K7wXXC7FAxlORuVR6tBZiGIjSydET0ODovZAppByeq7J+/HH11quMqxGu2WXGxCKagiKbjO8SOUCgM0JubmlTR5vO6frE5nzJsltRUiamjPcVx/Nt+lrpc0c9hnFbsVqnf/Mb+ZRDn9LEHIiRVhzW0/AEpBSyeIuUKRhJXhwkm0saX+MoaE60GIkzkNAAlBqlp1VPKYks8MO2r/IczTAbM/7ZNH5B3vpja68b8VREzDEvZXIB5x2iF0TPp2xVI5XUHsSRYzQHXBViTJyteq7NHHUduLmzR9uegzPcMASlkZrZZPJjgeJaLAbPeai9MnWBoMH2uqmmWjucm+FqHVx/wVWFPHa4tTkgSYBYJpQUyX1BJz1plZA2k6M5ZUuVmYcx9XSPcxwbWovgqMyRwnmHvyC1/CkYf6Sg+LQZce3aNepx88l/ULDa/2MkvhUmornMc5FAw/g0ZY1gOYXtYJcUcEgVPnIUPgmT/P7XNGtI6kDoIuKVcOkAdreGKuL7HyEiNKOGWy/f5uatm6ZYCRfF3wKoQMeYyZlNA/uTKTuzCe8/vsfvvdMxeekKu/WIDnPbrD/2GqqwOTnl8PR9zn0gnn6Ki1A+9smnAaYeLjcQCywTnCRYF3AexiP7KvrMZn0dYTXYrI9as1kfbWDdw6YHfw7VKex58myfk16YeGVcP1NRd90av/Js717/yNvpJ4FREKZVzXTbNkAnDw8ZSWDSjBA3psRIWK7NHroscU5wFSxrpduqjF2aZvRVsYa57zkbdWh2zEtkezyiqhtW64rpJHHQbFFS5lwy+UlNuzgn97bIDUs9ITjqUc3uwQ5f+sUvM9+ag0JsW978l1/nwQcL9l+6xWe+OOfg2vT7xUKJiwv1+0+JCN7Xz878T3COkSL4LGZnTGbqPX3lGTdjEopzNVUVmDjHiBFZrXArOvQpGVQ0CBlPKFZga8CaxRJwJdF7W0iyZCQHy5fxg1WpV7RySAx4DWZ7LFCkQuLQjHB5UKEKZtyjQ9ZxBnU4BvV54wlRMfNPe19OPBkhl5qYGpKLrOpMNVgEjSRbHnHJrMXR+UCvFShMgi1GuSi9KxQv+JzxYnl3PUJuanKM1F1mq+rYGlcGUPwJdA7Z8S1be3Nu7uzwudsv8+HjR7z/8AF3Dw85PD/nbLOm7azpfoFpCUqMnVnDOEfyQsyJ1XpN3GyofGDsZ7jK8e6DD1gcnnJp/ypxt2OpZ7TLxzw+fcj55py2WxKaqdla+8zibMXJ8pTzRyds7+4QRp5l7fnGapd7u1/kHfYHd+WMnr3Dt/5P/1+2Hz8knnoWXaDEjvPzE1brDVrUptCnahpr4BctNJMJk3qHmM2KrG7g86/fYT7ZYlQNIJ6sqE9WUAWSKKIL0v0P8NU2O1dv8oXrt/hm7bibv8f8tFCtM21pcOcdbq7kcaI0kWrUgt+wWCiyfcCTyQ5eElvdJdL35vzWv3yTt9v/lrfP/oDTUNhdNtSnV5hPryI3P6TbOaIeX2P79Dq+TDmfvv3C57tUz5oSzikUZ9mPysAEzjiE3oHmQKDFYzllF8RH7xgsLTOdmv1ZjAVNNdEX2rqjy45+UObLsCEXIs4nnBoQpSo4Z5t9T6EkA7MrFUL2ZmUrxbKJnDHoE6a2004ozkG2fO+SAkSz9/fe8slxiosJ0WYo5tVINBaki2bbVGqGKlukis0hplgXdfgcTLWeoKImkXEOIoW+tLRdj/PQVJYUbAV+bSzQ4b+cLcrBMtMKKaan1+MnVjJP5/OfTPPGuYt11ADKmCIPH98jp99CN0tOHr3KXlXRs+akRDTW+L4ixUw+LFz5rhAOYLkWtoHld2D/fdATkCkQK6T7PBt5n+XiN1ltOs4XK7rYElNHjP0PdJ4QJ2idaEct7iK37aIHeQtTj08zMfWoq6m8p4rYfT0ZFoaLPUfA1FCXgEdAVOKTlnfefoevvfVtHp4+IMXN00OsqgYeNI5mb4Q0zzdPC/Swe2PM7mbE6Li2PmmFAeENHy0SL3ZoBVOTJ+B04AFkkDWmYk/2/1zwJh1GBB1jIPlNPtU6UbIDL5TBqrumo0rgiyLRUcSTGnCl0CloKcxEaLRQDXNl9I6u9iRn96QvFUGwrEExJwTEGXFNxCzU6M0S04sdUzDbLfXWgFJFK6hChovsNudtk+QU6jy4G5p6zEnBFyMbihNyZTZtDnlKIPUiZBnjBCQnc6HRCKVB1DLU/UXsR3GU0NMFISU78MWB14wUc5FIYgTL5Ao9DWN1BBQXhC6bkrCouWdoaDCAqsf7aGtL8cPmGNSpKTqiM9WFj3gg5GJRDdmjuabkmvIpmrRXPvMa3WJJd76gazf0XU8cIgp+EAevriv2RyOb7xHalJlszzjY20a9IxYoCYJm24eoDoQyj0RTe3gnqAuD9Z1ZwmeneFfIKI8XS0rKPDl8QM6/zSu3f4757MCs+1PB14UgSpBCnxJHJ8dotWKyt0MlDqKHATDPJeAk4YI1RnJzUWwWQnLs7e2yfWmL7d1dxkloNwtOTt+nXW4YT7bYubJNMxmhubHNeU6U1GPRHIGgHsmFiOLUyK0RA8CMoOUJak1BqRTRxCZ1JLe2618LjsBIjRRCFhoHyReb88URGe4XX2yB+hRjOoPPVc2gNFPueeXDy4F0bYaIZ1JgftMj1YIP73Wsmpp8/oTybUjzEU3ZZnrS446PaC5tEXcPcD+/w+PHjoNJRXPrkvXBVsD7asHDCu4YXj67YkTeXcdprxwx5onfhnpNfWVFfmMO2SPHW8yoOTtzbB2+xLwdMdm0nLZ36edC9/qrrG9s0S0zl45n7DTCbkqM9YSpfIfR/rus34fLm9vM5VWOH465H59wlp7QvdsT0inz9pz9ak7M8O1373F0fMK4W3Fpa5vTvuVIhdWHj+E73+Lw9AnfePQBby4OGW95/gc/9+f4yi9/hv39D/HufTbXPe5Vi0mq3X3EB2YyY74Fr/3sEet+xfndMUfvrui/V9E8FHQxx2tNcDVOGryrcVIjwSOVIGNn3yuHfArL/NI2A4FILT9zAMif/gxc+KvLsAe1Kv05Hppiip6PqM2HNsXFd8ARkBIMKBZBkydoIdXOplZn7ltooio9wUWScziSNdxFEE1AYSOOTa6IGvDqcNpCECKOjGVOiou4opBN8W3qGoFgIAvOGzitglbgXaFIwolSO/C1OY1QAqIZ7xPZOQQ3RCiapa7VtmoOQ95q0hyEXDzBmSoTn/AkqrogF4AMfqgTPB5T54kWsrN1IAzAQlCzazc3iWK9kRcdbwC3FEbnwBZIzehazfX6Jt8Z/yHf0m9wyuv8otyEzuofN3KMmXMLYYo51BDP0L2H8Mo2xC3iu47HB5H3DiqkaZnnlrm2aLcmPgk8iIWrV/fZns+oL9m2yRXH7ukep2eBJ9UD6lPh5X4bfyAEV2juPMAdHqGP7sDhDWQ1Rx+OOdp33I+Rx5ePYbJGBUZT2I7fxi9bjt3PcH4auIkyGQnVhxOm9y9Rbc/RzxSLUl/D9nSXn/3cl/neve9x3m5wlUMbz8HV64zrii6d8cHJe8TNEyo9QESobzZc+/ItSm1Navm47eDFGH6dueA3XtTJDqg/Wocrz75kuN/ItlB2HXQt7F55sfM9ga4oywj31ZJ09hjSY17sGT9hmEPhxahlh2p7i5//q6/w2V/pefjWh8TVbe48ukLzzS0OHx9yfHrEvftPuH+yYHHW0q5b27v8BEfXd3z44Xu83sOl0Rivm6GkH/KTPt4YU2V9vubee47PfOVnuHJni9d//iv85b/5N/hf/Af/Af/Z3/t7fPDh+/zt//Hf5urV24NwT5A6fNSW/Y9xhKpHkxL94LShnpA8UizHWwcHzlEsFG3BxcF1TZEypDIlGdwKHS46aqLFT2BZz+ojeI/5NRX8UJfnYvNTUMGFTPJmnS7F0VQWK+N8AS24aoMrDSkHRByaTZWLF5woGxH6nBklU/BOyHRqrkhBrXb3XpjORpxvzggoZ5slj0+X3NjdRpziQ0bEsdKCD54dJwTMYYpQSD6gWakK+F5J3voS4oGqQbXBhRlVY6pnpyDZ3r+Wjq475+HqiON+RcoWF6PZ1kbvPDuTEfuzMTuTQOUCUiqkOEQ7urRhnSJdURPyvOBom4Ab5HTZm1W7aLaaylmfIBfrxxQptj6VTEgZX4QKocnRYpqkIgrkaDbnKpHO8ZQwF9Sszr2aiCENDRGPBw2oTxB6WhVqHJUKLquJDYA+CxI8BLM3l+jM1ryYVXYBclIc2dzDckXuHepNve7UHFa0mL4446nJBN+YOyiCF3MZI2P7bLHYJlEFb5/X0RoBW4LFW1lznewVXCShBM0G2gfb96oqJTVEGtZ5TMkF1TWJnjo4pprYwggaTm0yF6nog5J8oZdCKglSQ6lefLLYunqHSiu6OCbHjA8rsihr3+Gkp0uFuFgQtWe5WrDeLIklE2Me3vOzjVvOmSdH5zi3x8QLUlXsTae0fSSnP1pg3PiFDi9KEMF7s7hXUZLPSPb4Rqm9eas5l81BKASUgA7uqJIt0s2lAhJoPKxqiKlhRKGUlhQS5RN4JwOV/bl1sAyAuAwRyEbuSRgG8ccCtw4W84aYDHWf5iGi4qKZNbwzVcjF4sqCe/ZvqvSx53B1xmpri9o1RnjpIKWMCyY63PQti9WPmSmvWCTHBOqRpx5Si7M3wl9eOJyMqTqbG6QqOAkk8ZTiqHolVRYXmnWFuIRkT+OFdYS4OKVlTTVqkLqmakZM6z1GtMRNS9He5hIc3nly0T+RAsGPjz9SUHw+mXLl2rVn18fzY6gtuWCoPjeOsajIBgN/geeg4o8f1B990Rq4LwR6NpqYl8nTJ1aeMerk+Qf8oBEaJjhemXj6l25QX56ZDZImnlZhn2CRLSL48PxhT5hcSOxTbh6SK48Pl3hpd5+dX/2LPDh6zJPFIWUcmCHsPD0WHx3t8pwP3nqTVPVI1X86W+3EU8eo5968XTHew8jDQW0W68tBRb4sA3pRwagxgPzCYr0bmyrfR6h7GG+e5ZB3I2ZzYXd7eI3neAXHTx5TjhPN1vQjH7qM3PC/Q9tfldXZESKemMeQA5qF0EXadkHxSi6elAsJodQ1/dkaiR4dCRpgPoIOT1l5YirMJNCEno20jOdb1kRulV0fcOM9+sNjWJyyypE+Jeog7B3sMt+es7W3xxd+5ueZTozxsVqec/fh+xwdn9JNlebhiPnBLcbho7dg6sA3P9pVrUUpZ4pOwDfyqc53qIac3YTlxXhPHSrqOlDjoa5AKmrVpwWoG9SkrvQ4bxanOVe4oYEibsgLUiVnkGJ5GxS11yqCOsuV9IgBYUXxSdDgEC1o8OQhl9SayGZFDN5aLMUaLZKDZYsnK8hIzlRBwVGKo9NgDSpxaBkhcQR1eWq93NeJcRJQs00vkok5kLxZ+UiKeMxCLxVBXCEFRwme6BtKqtFQoF6bTZ+HrZEpxf8kjrCV0E3E9Y7xZMSlO7d57fpNHpwc8d6D+3zw8BGPzs44Xa/oc2YynhKqGu+hqRrGW2NGzZhNG6EomyIkCg9ODombDY8e3CPlhAs19db71AKHx0/o2w0pWfN+5JS69hRNpLZlHdesl+dc7s944+Yt9m7M2Uxf5qvyZ/iAKV5hrE+Iv/n3Ofo//wu+9/AUaUaMxg0xtayW58hzcRgiampCBNWCiHD78ktMRgYgrGNHNXKMZlOyq1i7jsZFcrtgKruEcU3oWvy4sFk85Ld/75R7n/0cB8sz2mmFyz2T4NCdHS7lHSaLI0ZlxUbPOe1aOl+RFbqJctqfI03BrwK/5JRvHf0O335yl8XZQ+aT65ymB3xrc8jNPc8XP79FvdpwT5dcqq/wkv5ZlscdnR6+8Pn2z9l1OxWgsg1UCUQso0wkU7wfIgQGtYRTSnLmlmDbbGNvDllVKYHkQJ+VOCgGc6kpHrwqpSguZNRlCGZL6If7KrmKSiJNFiJq64UTkjNwInlHuWhuiUeJaA1kgd7js0edhyYSk6ehkHPAFW9B4s4jg12blaweV2yjJ/S4gRXu8FCEoA4vDi/erJkpNBLoxJF9YDQbo145Oc/E2NOJkpOBN1JDFFO2ekwxZZtsB1LIpRCKUfx++IwgP7EKX0Rs/lE1sAxzXjk8eczvvvkveOu9N9kZz6lGFdVsjKsbJArr5YJp3Obl8Bhtj1i/tMPJuGFnNfToAwMAKrRdZtONOXqy4ejkjNPlCSn2FM0/tAAWJ7jpmKs3bg9g5zNGre4Ou5xySrV8xO74Muf1PmdRGFdm7PP0IA7f1SlpXejvJlxxLFanvP3Wh3zw+AGrbmHqAayxfSFWfrx4wMPyJmX0Bk72AUcup5TROVu7E/a7y/jdyjqVUwy8fgWY89EXf36sMdX3CriPkcIeWg+AGR+Nl33eXuuiOH3B4bKnV7PxJwn1oMa16AihVEIsnihCT2QmysRZXlhyig7OR9EloEaCreuaB5s0zSRn67eKNcdK6I1IIoN9LYHkGnNoEWx99oo6R/aQ9OJ+tnkGHOoVF81ay1Pb/F0PxLfocNnAV/FGbpFoNr7iwKWCc6bS85JAElpqxBWQITojRIRM3Qd8MZvvupyjzhNDjXpPkZ5SOiQ6NsHRmbUDRT0uW8afR6mDuVmoBjQpyfBxPJYZaLeuDtCP2TNKbZl3puIEr4WMp0SMXf+CY/fObXLf0S9XdOdL2vNzuuWKbrWijz0pZ/LHAtvGdc2kHtnciqdTZfdgD0JlpATvcN72ISoGPKskU3k501N7MdA8y5Cjm00tWhBO+szZagVYRMLRyX2m22MmWyOcVmYJWXpcMU3eps08OT5hPJmQ+54go6HJYSRGr6YMdy6aORdKCRBcxuM5uLZPqWqqxqFtIfQt58cLHj16RO0esHt/wng8J4ymTHYPGFdjZADWXBvQxmyhXTZlRNFieclG6kdyhRJITvHZrsleIn1JqAihFCpfkQZ+TkmFHiEEIw2WXEADCQdSkT8lOXK5POPS6MAs7YBmkpm/2jMuS/a7nvl6zJXKs6yWtJeeMMme9ekDzr57zEIzmR2S20C8S/vWLqvZCWe7V2hnNV9uLvEZdpjjcA1ILcj3MILPu4rrTaUU7wvX90bcbmast25w8lLFKjwkVTPcqOHBakEXWtazbfaPDpid1lThkK1mzDhvOOwzqZnAticFpf/uCacf3uPq9BHXtx7wqBzzOI7ZW29zkG+wWX4Z7n+bt86+Sps3pG/cZb1Q3qvGfLhY88GTD/jgySmrTYvkRLtYcHK+4u6TJ2y6ljb1bPqWNkeuXb5KvPzXmMuv4t37FE45kzWPWXG13aXqbiGrbbb9Iwjvo6Mn1F7Q0Rd5FG6x63fZmg6Ady1QYVm/lRiS5azRq5Kffi8UAtsvdL5HztTcF8uEFii2fbIt8gUgPszBF+C4fddBZQ7mjWfjAt/7qMocQvK4HCjOUYogQfDJ7HSj80YaVICIFiWKIwdMGZ17U5QFI0f3DGtRXyObhhw6Ikr0Qu/sPXsHoUDWiHqFIc8WUSTURFfA94NuN1DUVKsulyGWy5ZPg3+GmJ+LnwqoeEoluHpwAMkFxZM10Dlv9UeOiDM1mniHF3OL6Ak4baj6hpIs8kIrI81IsYZx9gmHWQarN5Wfin5f/NuPNa4AIwF2sGaS4Lc8B/UNkj9HyxNiaYldxgeHdBAb4V2RpyUKsUA6Bf8eZT6nf3KT9u1r5NUO442yvTdirIV+/T5hfMjB9hR/uqJJDrec0D5x5NvCxEEzF65szznM9zg8/i43/OeY1DNmVxONPkD0W7A6hDRGV9vQVnQbOO2ETXeNdL5mVj9ku0y4NmuZzx/jwgPOZcnqbId0GtB4wtZ+pHSP6L4TGK8uI0+E6suBz975DDvTOeNdx/6VHaZ7l9ibHjCvHLHMWZ/e591vfofPXXkZX4/NmeZgm2U+Yb7aUM+ewd1Phzz7NgJGz//7RS12ccNd7JkEK+DyBr1wPFwmOFmiJwvcv/WCoDjQTKG8BLsRVrXQCVzjJw2Mf3SIOOrpiGo6YufGF6B/ndfOfoZbv/AFHt97i3e/+XW+8eAup37G4f01733zOzy5/5h23ZHSEEfyY72esL23w3y+xeT6S2yHiuXhA44Pjzn7na9y7f5Drt6+gvc1FeH7PrkO/aPNZM4v/sYv0kxrisCKzPZsxhuf/Sz/q//wP6RfrZDaGvBkiB+cIqkhvDz+/uvgj2FIMTBLCJbTK4pPmSJCkEwpflDbWoarl0IWU4VrEDQXvKtQJ/jO5qw8qLgvjA00gzjrdbhooHUOOoCBRmT1YlnTLoM6oXfe6pdg6nBPMY8lb+5TkoysUJzDERG/YVN7KjVXnFAKo+jZOFBndtq1CpNxg1QeUqEUeO/kmN3JlMnEE5zQOCX7QbBQhOQ9xbeIzzgvFCfkYo5PnfO0PhClgVTjNFAcZOlNeR4zaE/slzxeHXHYndPmiGR5apUuTpiPRuzNxuzPahrvEa2Q7BFNxLxkHTtaVWIR+uJInyL0eN3UOGqqdBG9IMAGNFqsYwXORWu4poEoLODZMMax5T21Kn3OrKqKKGJ26j24KiHe6suqz7anEo/LRpgLDkrJaBCCM+FWkcpqX7V13WHqcvFqMLZTioB4I5cJCpsLxwkjvbkenOpgrC2UBOI9ErKJGtTAXResR5skWgSRGoFZEaRURDzZdXhfCNHyyStJaD2owrSya25QiQnOHJu0M+GSGmmteCVKTZYJKY3oxSE+oWIRYR0GuAcqGi+UZECdVzUyNwx7DG+qZffi9unBi0WAIkijpFLo2lNSWdGuWs5PT+jaFakU4keA7U+amwqbbknbjdmZVATvCJMRp+3ox86W/nGHBZJlBOMpOlE7F+pQZ64DXhRRYeQKxcO48lQqrNX2iDl68BXijTyuarLBiWQyFZoz2UfaUkjpo0TuZ3C4AcdWBfunWhKHYRWiDidGpEnCELX20wNdRa1nqfqcJlx1OHb+mf7l6bgAyJ1ZriNP8a7TdsNhXnIQlFGl1Fq4d/cuqgkfPN2mJ6cf79q0u8ZTB089CYQx5Bq0jOjJnPeZeRSCNCRXEVPClUhUrG/jHZUTNCq1U0QC3lXE2uHDhvN+zWp1yqge4bcn6BTwMK9ntKEhpxOSD2R0wHjKEAHyJ3v8kYLi29MZ127c+ORmn2L2kQMx8GlzE8PK5zxTQivWY6yAMdlWfgL8G5pKqVjD7AIsvGCD1jqj6lsukF/dmH15qdzQ8B0Mzd2FOuX7h4inAi41gXztGiJCLgu6skJkTu38R/qfP3h4GMybQKB2wMY2cuLZGo3Zun6b1/Ula0Y+N118fGwWZzw6vcf4xgjJie7T+PUtsasjDG/xAiB//sVVzHer8bBXQVbYZDhNcNrag6oAzcC+zfmZzfrpzAgEdYRre4CwW8P52QY/DtS1ZWWvH92jOupxb3jUX7B/xTYNz328xXLBB6ePWeWa1dKhmtiajagmHVXdUlWF09axaQtx3VOHXQLToUk+JbYrdp3De8/huCFSo8mYc6OZZzSbsFGhDcruzoxp7ZH6EvXpE05IbDTSVJErO3tsbW/TV2Nu7N9h1JhvcVLleHHIabdm5HtKbTblzw8F2gBjzw+/dgpm4xgjb/7ON6mqKXd+6Rbj6eSFgfGmBJzTgfXuqepEjeDdhZ2XM5BHxJhD2dM7pRoVRi6CF1KpEM04IikUOgdNrwZCFVs2LKfHFAup6qyxoabeQjxFPWWo8N2wiDg1K/ML3EBzIKldbxrUlKcxkMOwlBdHTp5YCRlr8uRUoSSc9EhdCH3G5YqsjkQ/sBkNwHeSKOrJ4kne7F9KsvepUsg1tMHTe2vcRvW4XFFKJuWAU4f2jnEeUbtqcKb4kzXizgS/XajaSFmucZ1wUFXsXrnE7YMDjl9d896D+9x9/JhORhxMXkFqR18tqKaRauJIKJvjyMHeAVmFartGi6M9PmM2GlsRO5tRHCxjT8wbcu6pQ8V0MmcymtHUDSoteVTR5y1Kzlzd3+Lg8ojRtYb3/Od5lzsocKUo/IsPuPd3/gmb1YJFv6BszglrP7AIdSjgYVQ3EAJ92xH7jqTKrYPr/NyNz1JGkHKHjw1KxXLZc9ImqiZwsGMeIlIgr1oCkSsVnIvnvZPMerXi9578DqUUJpMRHxbPcnHOqM/sd5EvlimTB4nFXqA9GLFMsHXo2D5zpH7DeFqT4hEPuse0Yc31ap++1DiXmd/qePmvvc/4WubhH9bUTw5ozl/DHx0w6jtG+tkXPt+ufba5czhCwMhBriBZKOLJIpZapkLSwIiEaKJ3FUWN4CKiSJVwEUJSYz4DmYpeGyYFFvz/ufuzHsuyLL8T+609nHPuaLObjxHhHmNmZFZmZWUNZFY1i2yRLDRAEoTUggABEiToqT+A9BUkQG8C9ERIgBpCgwDRIFpqit3N6i4Wa2BmVVYOlUNMGZPPbrPd4Ux776WHfczdY8jMKI+KotQbMDdzs2vXzr1nWmv9p0gUByiF5kahl/y3iijZ7jcOABYmZ4nbnPcYekv0BkQH5nhui0wEjTarw6PNlmAmg2wmV8r0EUQdJgx0Kc2s5GhyM2nV4lNmUfdJKE0kpOw2YWO29zIqWbGIIxBx6vE4Rlszrt7YxxSJewcT2uWSDiHFgQggLivMOwtOwUfEphyfFbJlJ/0Fr5S/EWprGhwThI/2CjEllssFq+WC+0N9ZK2lKEscQtN37Gzv8+fLf83+gxOu+q9wZTSmGZVEf5lbxewx2H7mTvjByff42aMPOD45oq5Xn3nbLm9f4erlCtU3QUYIE1QmJLHk+m6EFnOiCFSa4z2eBsSfXgrr85b7DxeEZc/D+qd8/70fc1wfs2zXmEH9aSCTsRQ6Wg7jD1F7FXgVdIzxxzgZUaUdKrOPPG8yKD7jyedP2wYDlKA7PKlpI3BENgKqhw6pGn7fyCeB8M9xTGjvsNbSexiFgERHj8tJbmZoRUyk1Z55Uio3NG1DjFBtPMfe5fMKwRowNhBtzvWOJJIUaCpIfQYvehGcidkWy1ispGw/ZzPgkcHwiKaIMQmniahgycSzKIHkhns8FrSDmAcdKSWsBxOFQhM+Ah05x06Gxt0IiiNFxfQmX59szLF1koc3qIfockqoKiYEPBETEi4U4AVjsspFHaBCUEMvPjfgPo9p+0KpxeIS0OchptESZwOqHUYt0jOcF55EzEPNCKjNWZIu4cjHcE/Eps/RIA5gr5/OGe/1hLqhX6/plkuaswXN+YK2runaln6wgNzd3EDdiEaEKEqvJeV4yNXrAilA8Ep0DqRDolJEQYJgsZQoptOscpGcHhfU4GMmCj44X9M+NexJSTk5PeS56yt8sUUSoQ+CBoNIxbKt6bse6xv6vsaNRllA4hLEmImHaB7eGcGkEX2b1SYuOsrSEuKI1Fmib3GypHCWpIm6j/TnS8pVg5NDiqMHvPDcdcoqk+tcnhKSfImIxQxAiJKPZS9CTIE+dIg1qO/pYkObeqLk+1NE8cZiE6jk7PmYFBeHxlocIWUbPY1ZrfV51n/xX/1z/tH/5B8xdyVNU/Nuf8DZRuDW9DleThskLbl3dALmlPLsmHT/EXHdMCkcVxYVN4JyNDvjxxtHvG+EZR3o9Iy0eZU/0J4fr2p+3W3w2rLCnqZMbp41hFcOONlZslyCu3OF+SPHvO/YCgfMnlthQ8HZ7TX3XYfaBWHaEV6a0I7eZXXcccms2duYsn11g9tljWuO8ceBdBA4e/uA0ftvk8yaaiJcHt1Exxs0xZjRfJPtncsUj7a5Ea5j5j13Dh7wL777/+Du6Qecrdec12swBpxj3a1ZLhY07RolUvqCwhekaOg75fRkxQcPzgi2Bm1YyyVuh0ecjQ7Z2tyEVQnvOvT6JOd5z1rskWDOe8y4oPyyxy4SehayC1ZKefCcUs4Ub5XYK7GD2CupU2IfufyMoLjrBrNz0adc0vUxAJLtzWUgu7kh3kKfHMcXwJ7oY6KQXCjNJf9MLp6PlPsntZQS8drk66IVeiPZLtOANLmfwjiiSbQ6MEhMVnAXJDQMWoPgkBSJNiE2IdYRUomEQDIRmxTjA2pCBvoHZXcGxDO9yEWyioUMhBcpYiJDn6dkKm4mDGjGAxADThN1MmjBQOAUVAWbFKxDpUd9C2oJ0eJiREpLJBKCJXSSHUhiidisTgriwYPtEsnkwafrlSgDUUoSz262S64LGoHaZ2LdFChBQsF+eQuzFv70X/2A070Je1cvE3yCseNGkev2ANiRQmlgsg+7M85/tsP9v3QUx4mb+ydsrU4ZlZvQdsjYsbd5k93pGbLqYdnysBvx/n3Y2FFeMMqWNdzq93jX3iFN1igTDCXIq6TLQrD3iG/+Mdbsoldv0G5u0jQVK1NgPnyO51djbk7uMH1+jfEbaDxjxj7xbJfjJrE6qol6goxH6Aczyg7MWJHvCC+sXuY/+b1/irkW2fIrVufnnDWe7cmI+tzyxoN7fO/bP+HF3/hdqp2KeKKE847JnhI+XFPsj2BrqLOWPHHneUqolRvHi9JrmBPmQOKsBg8dNA26auG0Q08b4ukx8fyItlnT9S17//GvP/MuTyIknzUHj4ZD4MYzP9tfbT0uYYuC0e4lvrazS3r1m3Qvfp23Htzm3/sRHxwfsfvt73D23gOWB8ccPzzh+M4dVsslq64jfoZmZmNji7/9d7/FV3/jN/naP/6f8RtXrmAW57A6Z7W4x/5+T2mGme7PWSbAjo4pJ0+U3+bkER8+Omb30hVmm9sU0zFdc0YfwWvAywKOEuyMYItP7xv+BleSDCBdxFoUriVisSlA6rGSr6tWLmplBRsGFbHD2Rz/Z4JiTCRYn/tkARM7RBIqQsBgQ3Yq6tWBZiKTDMCEGkX6TPSMmqO1jECWY2cCfG4dBUvC2qz265IhmYAxOYaslYRI3m5JQ+QODkzEE5mNS9yopF/2qAiH63MetCc8P9nEqMdFGIuld0JICRfz+6PRYlKPEcvKlrRSsXTQS4mmCofHh2yDriSCj5j1OSfLQx7UpzSxzzegaIiaSRnjUcnOdMT2pGDsHaIOSRZJkRAXrENLk9JHwPA29jT9Z7cr/sTq89w7mTwaNzRYDFJY+hjRBOKEECLS9MQ6Qq0UPrHhI0UEjUKREi2ZXCzke1xKAkExYhHbkZwO80tLihYbQXwg2j7bU4sgajE2K9U1JILYnAdvHMZHkJDr4JgBc5MyMNaFC2WuIyXFSsRfxFMlsjAhZvv7NFxuNQpiDY4Ooz29WqKBOIDWyUY0phyUYAqESK+J1FeoyT1UVJNjXS6IqjhI+XVGFQLZ4a/TMdoVOQPZOCQq0RREaYgS8An6IJhWSKZgHAze9ijgNEcFRitEF9D07EpxCTV9bBCxrOsV50f3WK/PCDHQ1i0pRvRTRZ2Pb0ZPLSXGjmW9op9vUFiPd4ntSclyXecs6y9oJRJR+hx5ZXJtUTAcd0YR4zDZHpbOGLCeYJSRM8yCoUOzqwERHxMh5nx46012VaLHB0+VKmo1NFJ/ZD71RCfwFG4nJsc8ZGRumJ0Ihix+s8YOLhoXrNEvfsCmT7moPtlQk7fL2OxyFB7TUJ88SPWJ+6oxGOPogrJY9+yPlKm1XJlX3D1ItPWzX39EhMJaSi0o0wgfPKWFtXTUbUez7NgsHFtuSjBKJOQ+I41YFsK6M0yj4mMPdSAR6KOlZ0QSQx07OiJtc4KjZiRzxFt6CUzHjtR6QmwJfY7JTCqE+OTdcEVBUVSQIhojXeg/Efv4H2J9YaC4iHBpZ4/Nna1Pf8AFKF7xkWIlDd/e4omr+sWbaBiOdZUMsFrzqbj4xePfX5yzPDnn8nzG7uZGvnnLwD5xxXB8KumDe6AjmE+R7YpulTOJq3nJp1dST77nhTxEV0EZ0ekSsR2FjH95DTacGTpAoCIgbhf72Fd+uCwIWPklELvCZHOX7RdusTfuONOGwjxbMw7kYW5Bluv74cM+9fHx990MrJeZgZmHK2UGyM8DLHpYK4iFyRh0nHPI+x5sBD/JkQuqtKllfXzGpd1dnMuM7DQSmtDx0x/9EGMdzz93i9lGtny8UOMfHh7z7gcPOQkNdB1pne3Wqs0GhyeqxyWDb1eEJUz8lHK0haWlP2+ITUuyNd4LfuRZ4zgMHWm5xk+UuhPOBUIxYcsq1go+FjnvKnWU05adUWTbT5j6LZqiYHNj/vh4m21MuPGNXVZv3cNNE5UX/Mf3qYIv+VQiRhryf/tFYHW44oMP34JZwxtnf04vsHrrN/jVr/wtXPFsp7QRB9Jny1IMrvc4EUzKysmgCSsRm1y2OB6YhSWZ6ZpcVoApNqvyUh6eBpPV1cZkS2GrDAOFQQlu8o02maxBRSPBG2jB2UBKHsi5cDFKHniT2axY6F0eDmEMfRiTjCGaPivV7MDMTT5bvadEKhVJHYlsBysmYmyLDVmtKjFRpjAUYwA5b68zjnWA4Bwr6+mcJ/VCvk2bnFUukWDJdn4p89smZcl5+1e0PfkbWIsjoRhZfGXxo4TtAiw7pO6Yq2M2G7E/fYXXbr1Is7dLe7BNexxoygP81gHFKGd0H06W2DrRxoKRjPGa6O0cNta4wmDsmJWxrI3l8t4NwuKAmBJVVTB2FbZQghlhTAla0q3XVEZ59KgnvuFZ/YObnJIzWsI7HyD/539G8/CYyfavoeU9Fmcf0NV1JjMMgKNAtvoyefoRU1Z53Ni9wa7b5P75AVoo2mcFSAI6a2jbiF0qUxsJeoy2CbWG0yUYKjbKxOLBA2ajit54+sWSNi7R2lC3DXdJ9J3BpylndUGwu1Sjks10zvOdwPkR3bhlUgjHZx3S9miKPGw/ZDZu+Z3fSnz9JTi+/4jbb13iSvVr3Lz6KqYWDn/WsfhseOOnrvAUMUMwkEqCyY2OM0pMufkGAylnNWW7sZwv3CdyUSoJJ9lOfa6GjkBPQzSWTmGcvYIxaiEqwWTr4ZQ6sBCiw0rMrMPeEJwjGlDjM46YhIBQdF22kzOCmIANHqNFLnijItbSOZAArs+uITYlUszWl94MVmq9Rc3AjE4R7Q1OBcUP+ZaZVOOMIFS5MRTFOIfrEo4KZzquza9wZbKD+hq7Z/kw3MElg3UJtQGrDk2RSiL9xQBiUNMnY+ijofB6AYk/KZN/UZHwOYc4WWHxyeZAeHKPUc2WRjEluv7JSPfhwT3OVv+SyfzbXHr3Fa7sXefS5g1uTb/B3+pvcYk5VSz4bv8n/OuD/4Z7R/eo2/VH/o4fXFBSSp9QrVoRruxNWbV3mVAQ0s8oBCwvgGzR6h69lggz3Khno2ixjCg+xX0HBjCgjaxbx/2jc+6sDjg+W+BstrVt1i0xxAssACOWna1dXnhpG+OOyN5EFsMG0/5FqqWhrDeQy5IVXFfIgPjH3XOe3gbJ7s/d5oB9C3RzJS5h9OMe3o05rmdiYCwwklxjXdRTn0NmFm0geMVhwShqe1pjOAGaVKHJYVJiloRRjJkBbgCUpI6lt4QsMkcCeZJj/OPNyjBvnwGImIe5anK+m1XBqMkWs2SWcYwDe3voX6Om/NbJYLWuiRCBWoa8PCWIkMSBCYQCVHp810Oy+VokkOyFAjK7TmTloKBkQp2ki5yxIVuXLCn0NpLEkaLBWsG4rN52scfHrPBNmol+SSyWjiD5dVmreaCDobGQbL5WWI0U0VKIQ2J24zAWks1W8KqaexaThyES3YBR5exb+Rz5s5GM/ogtMLbA+RF+Ome01THdb+hW68f26s1iQWhatrYvUVcVMSa6JKx8oDSWFCzY7H7jrJKKbKuofc6vLJLiklDaTFTVGAky1GwpD1Froxy39Sea2qauafoWN83gQ5DBmSQKISgpRvq2R/tIbzXbOiLgsyuB2JSBNTypKzI5ygm97zDicQOAYXpDn5S6bR5fXBNKn2LO72w7Rr0y8Zk04UToI8Q49CA2R+sUQxtjsYixqO3yeV10NHVgTY7NIA4EEGdYk/AJohn+lhjIFWaOCSGDfjnk8dnXP/u//jNWBy23Jhsc3ztgYQ1Xf/c1bv3m19gyFacnHXcPjkjVfbrTB0yBEynwNex2lmqtFK2nD3POZpYwrdANoV6ecdIe07Xv8Xwx4rl3dhmvHPLihPVeQ2dr3tIVR6cV5dYR239+SLlRcWm7Y/HoiO3mEjtFxMl9+s0VV2QDKVvk5h3a3YaufZ5Gn4Nqxh4nyPpNrq0+5NIiUdQzdkvD5fHrmAPHwYMJh5ciq+ohW9V7+JsNl7ZvcPVnX0FqYf7he9z/7jnv3zkgpI46NkQDYj2Rnr5t0BQRgZG17MzmYC2H52fUXcPt4x9R8w5VW7CSy2iwzHbfYxZqwgcnsAiY9ZqzsMXy/Arn73tWp5uM7IjoIbaR9cE5oYnENhG7J59DrOl1RdAlQVf0uiSZmsv8b59pf69SpsBn7pQOtpH5OvMYLH/8WR+Lv/PHYKPOoDRPlqSDCFYvgL+hHhHFFC6r8kwkahoGi4LtDFLme5mNPV6z/SwoKRVEn6+j4jKoLX1iajpCstTDYLCTRGcS0SYSHhMNMQZUwuAAAV5z1mkvuccjJTQ41AhG2xzhY3skRKJzJBFMzLW7jQlSJjBbTRiJOZ9WsoOFqJJSJlt7SWgK2UFEHUYiyUJMFU30eUAn+X4SLUgR8ZqI5NggFYcpDCoeMYI3Nju2OUNUi+rnGOpNyS6hPwT9UwhfAfd1MNuClSmzwvDW+/8Nb3//p/zOr/x93PYWt3ZvsVX4x8CukuO8xFzCTAvK18Ycnwo3beDSxhs4+yYiHnEd3PNgRsj2ixBa9AFs3oZOlYOvwawNTHYtW8kyCXDeNpTjSGcd53INsZucbv4Uff4ROzZit+5Sjx6yMiUPUsnWe4n5e3Nm+y8jvYEPTpB1i1nMEHWMVQkbCd3bY+MrI8zCoPcUeoF7wvUv7fEbl/4e8XKgf/QBZ++8gT2v0brn4dE5P/vgEQ/uNvzab33Il/72Ju6RsBcdzfoRh+8nrskmbsPk+mpBJhpcA+bDSRBbMCdDMbzLwA5BuwWs6uyAeLpGj8+Ip2d063O6ekGbWprUcZ4azlPN3jPubiUf+xE4J2e7v8anCyI+MXv/a17ZltZiqznuld/kKze/wY6BN7qa02/8bfzyHe6//2Pe/8F7fPDH73Hv3Q+5c3zMybJhtV4TQvd4nikizKZT6rohxIAVYVR4dl97ielLL/DQllyaz3lBI/WiwsgDkC2E2SdenaoStKE9Pqe/0yPFNuW2I1Lz3p3v81/9F7/Pc89d5zd+93e4eeMGI7sEs41g4dol2DefeMMegy4Db4ikhJQfZi2Q+sFlzHz6MO5Z32OjuDhkfgfB94KxHdFmYExdhAGks8nlwzGlnAUtkWAESRajPeqzeEOBZA2aulyTSxauqKYMwjvBDqrZKBAwqESsCYM7jsFLjhTzkrJ7kZrBEltBA7hAkVpi9EMG8AC4O8tocIobGcVgaVO+F5GUSjzjomTJkqRKFwJ3jk/Y2dhgavPcXEMm3qpxRBwJi5oM7HfOsXSWPhl640iaQV/VRLLZpUgkEWLHYf2IR8ujTI5KwjAupfIFm5MRu7OSaeGwQ2641USMNetQU8dIpxdguNLFQBM6mr6l/xzXc2ficM/NjK1gdIgn6emlIaw7+mVDu2jxjUKdQAW/OWdr4vP80gg2CdPU0kVPD6haaKs8w5IAvUW9Qb2hSzaLjCQz01JQoiPPYxQIDrU96iIhenpjUUn4EFGnEBQfhGCV5HK/ElRxIeFtR7KGECULKVJEXY7KIimNM3hjsp2NgJGsOA+qgMEmhxAJts+OYWiulzWnWBMqUgOpiNnRzwaSzfMeg6BSEpMh9RA0EIpEGGKNNBq0V6y2iE3U4Zy6PuSaFFyiIDihdoZehBgFH/LcIxlDjCWdr4jGDo5nz7bODu4S+pam7lktz3MtqjKopH9+7S9yoYRP8NTjVJV1XbMKU5yvSKZlNC3wZ0WeeX6BKxuT9yQMxihOszNsNIpxCdESLKgRnHiUnjI5vFjUlqQoeNfTJmFlaryNlJJJOGMBnCGmgrFNtMFSf6qCWJ8AzSK5VlXJrnH6JFJNTe7dRARnsq26JnIP+0W+SZ948jzscN7nqNbYf+JBmmK2WjeCYHPs1XjC5nwDV5Qsux7nS+bliMmkommaT/k7n22JCJX1FMWIYjTNM82iwJOoQp5Vd02Dlp6y7LLqHk8riV4M1sGqNahxUFpc4yj77EZVFQUxKt7nuWfXrmEB5WybJAbxnsnYktKMajohtJEudRyeHbGuV2xduUQ122SiBdK1pNBzsjjh0dHRs73Yv8b1hYHi1hieu3adcvwp1jUKjzMYP4bb9sPHxfwRsrn4MXBdASxEm7OsJ0896GMraeK94we88eO32AjCr776Mi/dusmoHFBHM/yiJOTyBF1aQtfAWnh4dEoXW14YXcMVP585JB/7ypmCTbNNQn+JE+bTtJhAbCMhJYpRhTHZYvXCLv4xyPMZljiLLSfMihnqSr7++m9/xt/8lDVkO7EaNqB86uNpBfnHAfKLjXUGZgIzNzAdEpwFOO1yrqYxUFUwN+RQy7wq7zg+boibCXEd3fWGtJqwqNf8f/7lf8mjoyO2Ztv8/f/kH/Gbv/Mt7JDTtzhdcv5oSb0OSGMoJeHKnuSgbaaoOorCEIo1wghTl9kaz1vabo3TVc4+ix4vk2x7HBW/dHQkDqdn4C3z+Zy5G1G1HU1cslxDE1y2j5k5nLVcKjzVdJvrVy8BuVdwZsRXX/ld9q/dY39nh5nfQ/zH96xmdwSVj+x0TcrbP36b04MTjj54yNHDA+6ev8v2rU0CCyJKqzXWP3tmobghKyYZNBZAJKY+Wxmp4FOGHN2QnagmYYwQNCtzUUVT/3jo69JgW+QM6iKSskWKCYONryXnOKoiPis0Y/QEEbA9xue5jGjAWnlsp9lrfl68ELSkiWR7dSwUFok9+TsBH3tiKoahUZ8zJVWGjzVn9RLTHfHidIZLnmBzohUKxkSsbXM+aipYOqUTxfiC4LNrgYY8aAkhUvdLwuqcZCJjZ6gUAomNquL++TPvli9srQ4jdSkUI5M/qgK34/EhIHUgrpaMO0u5sYM8d4Xj/SkP7iyYs8FoFMB0VDJjlI6xvmdZG1JyjArFOtgSiyksi9bj05jCC35jjFuNiU2D+MRkoiQSyTuK8YRQj1g/TDTNGU2AemFYnq+IO4osz6j/+X9O9863YfcSk3SV2fQa45nj6NEHNIsVKWSgJCUlLtY47+n7QBzyevq+YzsUqLvEne6IqIJ2fc60HxW0K6VoWlLZUseevgmI8RTlCGcCOkqEdU0fGlw1Yys4phvbnGvHYb0mWnjQtxTFiD6tSYcnFNNtykL4rRAZe/jzrcDl5/aZ2qv8SfMDDuwp/WLFtIh85bVdrDgO6i3K3R3m5VWK2YhgYRGPOYxvA7/6TPvbmqfvVoIapXMGmxKhj5QkemuQBL3z2BiQYBEB3zrWDqLNrPASKEzCRLiUlGNXE/EUGvP1QlxujCyZxWlzTnfsBUmalXmDyjwmMzQOGUuwRtEhPiGJZjJTyLbnTkF1GFpqNtfCDOxeA8ZaNAzKVgVPT6d2mLoOuZVRBkb8hbtAQGzOIHTJ4kwupgsZYX2JKvS2YNOWzKIB8XSuZGM8wtsWtYOSFMXYSGoF5xQblNbmG2QZIt7mqaU+ZgZ/dMnH//dxgulf43pa3PHxYdvF/0MILM8PWZ4f8ujuj/mxGIpixNbGTf7F7BabOzfZLkpu3/1D3r73fdbNRwFxay1lWeXjTpSm7Wjb7iM/P1+cUS8mPGhL2rUyn8D2aIobJ7waqA3q55Rlh4uSwaifs4RAVZwz9h0hBrZu3uIff/l/zm88/B0eHR9z950PeP/tn3H77m0ODg7Zv3SN/+k//U/56te+iYjPgwYiGjYoL5VU1RSu2yzbmTKA1vzCYqwBPgCWApfJY9baQbwcKU4OsMcr6ApYFnBSZi+r0kHhMptkxC/+A79grceZvFWEQLIdtUmcGVjFkkgFqcIGQ21PWbjELHY4zVnhtTUEa7IlWcx2YDYlNBgoILqEJAcaMqEgAkYzMCrZPSKqYEhEjVk552RQgWfmfj8oEiXl0jEVgo2KSYaYstIkSFamiFcwYRhIWbTJwxMRRWy2vHWYrGYxIC4gWRKIqEGiEAe5YpIExhCNJ5FrFkMGTXJhmuhxpJiHacYr1vW4lCgl1zktFUEMJIPRRHKZdNdJQu1gcSkWrxHvAk6z60TMyeSIZktwQR67VsQU85DvGZeWwgXKlW25TbY9tB5bjhlP54x3AqHNCvK4rhlvbtMU0KuQBoA+ikWJ2W5QExIdPgasiRTOYJqcGReTJVhLIZJzAImEGBGfAbiFt0g1oqoq1uv143y8FJVFvWC0tZUBfBcxISIKTV/Thxy909UdkwAMVpriJA81nMkA2NqjPVkpO7gIqQZSVFwqkZg4Wi05P18+/ts62LCTwHuPDYay05wzaixItiTtY8AYQxSHjYZoCxLD/cHn+1IS6KMSUo8qhF4ZyWA16rMSor8gPEVPjIHkE8k4DJEoin5Ox6C0aFg8OCS8vEtvxvg0YdJukk7OCd2C9uSQs5PvsZisqdMe435CXG8Q20MauySOV1QI2+k67zSWs80ZozBBbze45R1e+VrPc+KJi7c4q2uSmxE3rlPKZWgMR4sef37Ottviy69cZTXe5oMfjZHDMUZOuL7XUNct17oVbHjity6xFKF+/1WW785pO8/4ZMWOHHHD3kaLRHn9VTZem7AGPnwj8KOHK07q+9j4U+arwNh8nd1v7WF+u0QXsP8Hu8z+qMzKfLU5nkQzcUFUsfnqQlE4vvzyK3zty9/AuxEfHN7m7Q/f5aQ/YnFUoHGDB82CxazETkf8iCMu3T/kchuZP+dxvEz82XWO/rLibNXzwran98r6cMW9996kSUcEtyS4BdEtCeMVpuyxI8WO0uOPavTsPZkdBqQ6AMdBB4BbL4ByeXxbssPQULiwu2QAvOGJ0vwJcJ7IPdXF1wZPJ4INjpSyAlQl4UlYNUgSLPlYRgWRnOmt4uiMUtiEjdnZYxYCpa95VFj6zg5EQEdQg5WIMTniStTlXs9YNAUSiQLokiCdRVIilSETj8XgyCB5ggEcz6RkTB6CJhRN2bguR4KAFUMRlQ6lE5eJ2GWXncqsGxwjsuNFigYRy2DuQUqZPOUDFLHHDH+j9TYTrFTorCGZhEaHqsF+Hq34RWm4C6tNOFnCZqvMTZ4Cjctb3Hxxj//8//7H7L/ydcYhsR2vsIkfgNSAEomMsDiEwMgq+88Pevoqkfw51tyHRwkevgTbp+g26L0j5PsVo2aXWwTerZecmSX1CyXlC2t27Cl3zyesyo5iA96OgaWJtG7Ec5NrjEfXOCoecGoXbDFm24x4wYzY8I7zWc2YNfIXa8LDCvO8w+0HNqdLqr0RB/MRPyNxq2opXlMoKuSmpVhZXpzucfvDQ5b3hMrsUrVHHDxc8P7b9/jww9us6p7//v/2h7x06Uv4Fzzl5pJUP2L5YUk3b3DOP5k3LnkKcY5g3wQegM5JqeP8bE1/MmV0UFOdHBDPj+jWC9rQUseWtTYstOGkX3LSLzmqTzmtz3jWKdvFCLQmY/YzPjEOfbyU7ISF5HHZRa3+7BXEz18igvMFN4C9kUdGcxy3WNz8Gj+99oe8wQYf7m3xzuqEu8uWg4dLDu/fY3l2Smh7XnzpRf7J7/1DVsuOP/7Ot3njjR/zw3//fTZffo1X/u4/YDkt2CJHZLnpnIIRORTy4685z5RWy2NYLAhdwJ1eQuc5iuFeV3JYTTl+4z2Wp4F/+I+/ysuv7iJSs1puYdRRzcrhNX30uZPmWrR/pDz6Adzve9q4ZHPvlPrw+1y/ssH2jZtU25fAj/iFzcdnXC1ZQWjUYjvJFtNG0JBBYIIBn0k42JiBQGvB2lxbG5drWM0K2WgU6/P1T9RgQ7a7EelQd0HaiXn2qpLnndKjZDBQBsIQGnFGQRPRlIiLWALEDICaITpjwC9xagf3qSfOZ5kgKRTO0Az3n+Qt8/mIgzMlxoQV5WBxxu3TU56fbzNyDiHhNTtaidHHwHjnoXGR4JTUGxTBRSFJwLoOoccMMYcaesQWEG3OZgYK59iYjNmdlswqhxvA8MyVr1n1NXUMtJrz2fuk9DHQhJ4mtHQpxzp+Gqn8s66oAbWCCT1929OvO/q+J7Yr4npFXwf6piW0AacOEz0GpUjKFbvLqHL5fm0sZRQmbWDts3OLDYptBDWGzngieY6CMZmYm4cbXNDwNaTsEKgm53WbNNivG4xGCAEbcwAJnUMEep+dB9BEMkoUxZpAkgwoW/X4qPQukAoZyGUx54oHQ5QIxaCWDQlJCbzibHbkxEfEDCBCn7NhjMlOgNFGMj8j592nmLApkNST+fW5j7MRYmgwGnJ9ozWnjx7x7s/epa9bRjdeZm97B0ZC8IY+CS0uE9kYCNVRsmArJuRz2Kff/eBd0kVu8mdSPVw8JIO+RgcXxceoj9J3HWfnNfNqhCJYUzIdjTlp2ywa+QLWxe0yIpnaK/n8NmIhCuITzuTjyUhBgaVSw9iNKMkkbfWJqjIslxXoEWuaHD+aDCmWYNaIEToX6eKINtaPrbUzEfTifTBEciRZGizbUwJJijFP5mn2iaE/YvKxYKISNJvB/40svQDxs6PHY0emp/68iFCNxkzGFdONObPZNqoJlwIaek5CJGrH2Biubm5yfrak7/+qfaPkiBQ3oigmjMsRs8Lg3QQTLRNrGY2y61FRViiJog50ZSQkJVYuC4ecZe0VDYZiMcvX3ChIDEQXGc9LVssaKx66RN8FXBfovKP3Yxht4ouOtG4oqsjcjbFGeORgPJnl3i1llxJrhGlRceLcx6IF/ubXFwiKW65duYqvik9/wMXrfmoLlCeg+Piph3YwNIFDoxLJPoq/oCJch57T1Tl1e05a9bx/f5PR9g43Lu1mtdGFpboaZLSF+oRbdywCnIWOxfEhNzrFXr+CTAaq9Kesx4D/Uz+2n3YhvPBLCG2GzK0bTpiG45/d4a33PmD3lZvceuklvLWY4S0SPt2t81M2hL5V6EtiP6PSyHPj3V/2Wz9/bfNEtt+QK/cLD/uCj6rIPw6QX2zsRSVqyJ3qJQt7BTQJVgHOc/YauIHxLtlSMl3km/TopmU0nvDgvUccPjzi6NEBTbXiO3/6He6var760ku8/PJNzk5alstAipFq5hntVWhlaFphhccXPUV/Qtcv6XWTZr2gSA19YSg3PaNqjGhFlyBIRYgTpLNU3tEWNdgGceCKkonZZtQpK9sy2ZoxdnucpjOKUQ9OmRQjptMZl/YzKN7VPX/0b/6Mt975gNmlLa793i0ms8kndqokICQoPlqAd33Lw+P3+fGPvsfR3YdogDb2xINALCLT6SYhWNq2o6rKZ9rdrsgZ9NJYJASsGkIfSCEgZcK5vJtjCoNVaS63okmoGmI09Dbrpq0aJBqs5OG0mpSzjyLDfk0k8RkwiQETBUg5v9TlYa8MqpoIiLg8eBdHJxaVPARtJQMBMUh22x0UEI6A0Q6reXsiw8DXCikqydX07YIP3/gpZex4/cuvI05IOFSFQLZJ7USJRog2A27G5MiGgIOY1ZX14pTz44esloecn55weXub168/NwxIYFb8nOvff+DVtwnXC3WdaAuDHxnKUXZJ8BOHmyZkneiCYbM2bGug3qywo5tMqxc4PrhNt1gxNTNemASWCZqmJU2U8d6Uaerp6xW1BIo2Ow1Icoiv2ECYjwL+mtI3SruMiCTiCNLE48tA/FJF/NYGbN0lPjB0/+rP6f7rf4UzNZdfOSW1P8CdVJhHibC1jYbI6nSZ8URVkkZCTChKiNmzZd0cwGjJ1bDBmk3YjIR4jjEB6xNNdDkyoFJsEjoxaIw0bYeMPTIt0M6jbkqoAyPbM/EbHGlDZ3LhZmJN2TokeaJtSN0501Vid7TNmVvxJ28c4m+fs0MBJhBVceOC6Vg4X5e8eQp30hi2Z0yrDdZ34OjNhrsPPuTA3H3m/W3Nk5ulIENTbLMqw3QUGujwCIKJEZN6XExYMZhoMUWidYaEIUouM0WUsSpLbVEXmdo+D3KlxFiXlUMpoRpQ9UQElzq0SOATBv+YoW/IkQzJmAzWqM2WS2pxA9FG1SJOsRqJMWBDVrWrGujz9dMHwaVcRofkMjPbCE6zakk0ocnlmAfJQ2WJlmA6MPlJcq64AXUkxkQbGTuhSAEbhEny7JVTimQwoaNHiMZldqrNzOmLEbVqAhmIPd0XmlbzV1qf1iY8VlB87OeqCdVE0yy43/yQ+w9/iPzMUhQjQt8S0ycHweZiSG9yCEZKyuC4DmTQ/f7dBe+8eY/VssF5z8xXvD7dYH/vOZwzuBXofkeqV4Qzjy8dsmFgLp+s/bpA1y9p0znqlL0rFdsvvsqXyq+jozFXzZTm5IyHDx7xve98n4O7D/iPfvt32dl4nub8lD6uMS6wOFrTPrhLyZjNy7uMJlPEyS8owPTxvxE4UjgAHopwHWUb2Bz3mNEpbLRZgZ1MJmZ0DjqfP45L0AI+ZTj4WVay4EKHTT3RQJdcjvyQ3NyaNEJ1TYfj3PU4kxhpVsOtjYGYSW9is101g3o7JSEEl4HI7LA32JalPJhxoCYM+VoWsZBMT/ICqQAfyCeGDMcSBEnQPblfiyhGEhbNpJlGEWdysxTARiEOtr/Gxbx9KatzTSQrnZOSXBpgpGyrlkQIJg8NLS3G5lwzVRlAnnwtDNnUgqiK9A4Jw1DCZtDemYgxgYQluZRVjKJYkzApXxOTsfTG0anDAk6zClJMAiv4Ltc4SR2WkHO25NmbdQnZxUQvWHwxg0CiGeQS8Ujhsb7ETaZI6NEk9ASCicQis5A0yKC40azCkUjRC6EQrLgcHWOAsdAN+WCd5rGJiiVZpZWCdgy71WVc4fjJj9+gHpQMoe+5++472BQZbW0xnhc4LOum4fj0MB8PMbBqFuxEEGtIhcUawYQs93ED6aCUnkDO6lMVIFJqj9FIaC337j/IzPph5dlUJhhX1lNEjw0eVzii93hDvn7ViSZ2iHM4O0I9tGop1BGTG2rRSExKSvlzUMUXJVoqxvQEhYaEGJeHyyIZWJdM4hKbCJ/z8v/c7jVSWmLKku2rl/Bbc8rrjvvnf8ly2fBgorRXNijiNSanzyPHNVc3hOiEwhSU1QmbpqZpWyaV4fxqQZAtzLs9xXsNN2djrk09KS55Jx5wfnzE/tUxV+w+Lx7fIt4f0R0Fqr4lvd2jOz3j9Rythb6P+BQ5PE+8785ppobR1suo30DbGXogTI8b5ocnPHe2wnZQbGxz9SvPsb1f8IMPDW+frDiSI5y+wwv6HptpF3vokK6EHUE6KMcFL12+zhsfvklIMffvYgaA2GIQOmBaTXnlhVd5/eZXcKMRuze2KOeW5nxN6B1Ts8esXpAmYMsxZqfEzfZxfWB9u2btZpzf2yIcWiY+0R6fcdsfMi1+yukrf46MWsqRZTqxlGNLOXb40QauGuFHI9xohBuNMe7Z+jEAE0y+hmTtXQa2yRFXFyPDx4A58uT+nS5uVmaYH3xSZa7DtUeHwV2ruUaSAGAxKWDJ0TVODUImGdkYsEkRdXQux1bgwCSDxB5rIzalbI0eGyprKE2kip7WKEZivo4rGRBKgiPSJRBHJk8N4J/1EeuUzmUAP6lFo2ZgA5OthSHfp4aBvRnqDRlIWkaEJDmqwltHr4JVB6YnGaVFsK0h2KyAsSmhg5OJAElyjmbRZvJPJDtpYDIhoFdQsbjkhrrp2e1X884Dns/32W0HflNR7REpgBFffukbbI3fw7Qj5qNtCluS5eVrNCU6M6KWTeYAqadwhheuwGljScWL+KJEVt/PCuirv47uvUhUx4pANb2Lr865snvM1ranP3WsvMEUSr89g3bEiZwz0przztGKxbQbFI2jSAVuJUjY49L0Bjf6kt1TIVaRZqPgeNLSXDqkbyfs7Z5T7Ubq9j4P1hs8qq8wORfipsCmy+jb5bwPy7Wh+/6S7k7PC69fpXQeTRXLo5bFuqPrG77z/h/wj/w/5Pmd50E6vN+lnC5o7ANGTPKsb0+g0jxYfEyIK4DnQOdo6jhaW35w74Cd9x/wan2PLrUsUsNZqjkNS066c07qcw4XJxwvz1kuWtarZ7c3DeRDfUEW/tyCTz16lIwZ3V/nhIerPpsMJTKP8q9Py/xkXYzuLjLXBcOm2+ebt/5jdn9jkz/d/EvWqwOKxvD8KZzevsP9d9/HNIlv/da32DObXNoQrv+9/wX/fP3/5NHZEQ9//C73fvBdXvjNb1HZihKDmJ2Lv/jR16yRXpf0Xc84jtDpCo1HLC69geUF7p1bTrdf5ObvGSa3j9lH2Nq9hphLLFeP+KM/+LdIf4Pf/Pu/xuZ0PDznxTUvZ9/KMmEODeu1cvvuMaE/4E/+h/+OD+78kI2p4yuv3ODrX32F66+9ynT/Gma6nUG8XxLb+fOWTXYogxPWSs5ZTYJLbsi8zo52HQ7bB3wqwGXCeLKSr00q+ZpUghoIxoDJ8TyRARhPoC4MymCyspJEvqyFwRUINEbEWogGO4DekUDAIuR4UKeKqsmgofZocKRB8dyLpRnDtOvxmiN0QnJE5zG2x5nAvPKMyoLQBSLZNeytO3do93te3tun8tmlrrWWzpQEPNEakomZ+IYheYuPmcBO7HBtg4t9JoMqKJbSjyl9SaMd8/GInWnF5thTGAvJI0kQ7Wj6mjr2tEnpkqFPEFIcwPAuz1U1O5uFXknh2YHH0NQsl0vWxwcsTk6JXUdMQmkLKi1IUbNlcIhEYo5ctUIdWhYhMOsrCp8pa2ikTNkFL9lEl3pMMCQxGBVCD8ZKFgnYPPdKarPdPj3GZWBch8ckSUPMVcKEfDGR1GFsdg60UXCd0FOiheQoWQDpMcN8hsLk/HoGG/cANmZyhdWBsNvlKJPsaJ1lftkJJhORrQlYieAMvdiBKJzt+aVIkLrHNQwKts+Cw85ETEqYpHjTYZ2imoh1oDKR7Y0RR7Hj7vKI6XTM9rjIs2DJsy8tSwiBODgxGG2QXpGfg/N8pv39qcfKp/R4Mvzz2Cl4qMl0+P0hZ5rhe+fnK86qMZfmMwoSW+PIYrWkb9tn3tZftnL8TkSjISWLuLxZokKipyoshRpSqphbh3EzpDIk7YgCDqESgxvPSEEx/QFiDcGMKF3CnEeiRKx1FC7hO0+rwwn9lBTUaHrK+Whw/UFylLAaRLNrgYjJ+y5ZZCBtiAzUzy9aMf7UEjRHKQ3k048MvQARw7Qs2JyO8ZMxSXv6qGjX58hjBT9yTJyyPR4xGY057T+jqm7ANUUMxnq8c5TOUZSOooxMJorpLY0rGNssPrIUxCbm+NvQIdZTNhBNAt/m+Mtgic4SnBJcwPSRLibm8wnt+hyNLpNkhew6ooAYmr6n7haEfo2xQmE841FFsSrpY49xhuAdLhVAj7WecVVytvyiQPGPTzY/fX1hU9rZeMK1q9fw5c8BhZ7OE39qteRCcfTU9zqe5Itf3Oj5BXnfAE3d0Z4sSKklRnh08IjVuwWjjQ2uPgbqNU+RMOAMZu5wfWAURkz6OenkBK3mSFH+kpDnz7iWR7B8SIgGrebYyYjQnPP+u+/w7e/9JVepuXzzBWbW0gwv9cK1/LMsxTHZ2uayJObTMZuzn8c7/Qzr0fDHS7Iif5O8z2qygvxiAwuyV2jBJ23WL2js8OR4zL52MDI5Sj0NTJ6Ua03nYWPbYaxwGFb0OuKF6jp/+Na3WZysODtZsXIBuf+AH9x7SP/bZ9x88Tpvv3+XBTVaNLiRYi9tct4Z2tSQDGhXY7slpm/xpmGskalCkhJTTRjpZRZ1Yq3ndOMpLz//OhuzTc6P3udRdw9bOLAt1bSksnPiKitEdqfbbO99k4N4SmvfhBg57g2pmDCZ5wCAD969y//w3/8+9x7cw4+nrM8P+V/9b/5TqlH1sR04TJ3l46B4TTI1tkx0uiAmg0hB1wfaVlksav7w/HsUZ7v87X/wtWfa3WZgYNngMcEyTobleUMfA7ubJb7K+0TJKlFHn9WZpGxRKX4YVOSsnB5Dkf2fMkil2cgv2IRRgw95yKwxZ7FFDGosarLeRpIhkUHw1oxyIYcl4DKbMBlIOesmsxkDaQDgVS4o9wYh4swS2wtiCsRk1Xgg4Y2wXLWsQo8vS1oDItlCR1VoJOf0kbJiKYiSIqS+oA9rjg7e5+Hd91kcH5KG7MhuMqcJPWMsiGVaVL/wff8PtQ5OWsYjR1WYbCXUJLqlYCtDMRLKkcWOprCxh6WkomQ2qTjY3mA8NkyCZcMmpHlEzz12x4azCAsbsRuWxbIg1oauXYGtmUaHtAUUc0wZGG0smGwm+hXUCyW1Sm8NZmfKeLOi+nuRu79ZU9rvcvjv/iXn/5c/oj1ZMtkoGFeBa1/zPPxuzaMHEZssXky2adY4MKBlYI6mx45Ebz084o9O3ufvXX2ZwoDZqjCnNV4FZyzl1LKOnr4QrDRU4pA2q+niaERVVowKT90mzs+OaC9vsqoXxHaJaRokKbaYIdUYqxG6wKpdcNwG7qnlJ3HJPZRyfUoqa8Z2G0kFWhY8v73F2VvK3YMFJ6vE1umEpt3krFPSVs18siI+/Dz5MhcFuyJisltCBDRb4gabc3ujM9l+UrPFT9EragOV5IFlcBbtJOcKx0gExiS8dpQKSZUi9hlUG8glPSHfB4IlaAbKLWYY3lokZFW5pOyx4pIgEik8mDYNFsSaz20TkWTwqkTpM4u4yA1i1xSULmViVUiEZBHTIxFM6lASovP8Lhgy89GkTHTAYjMtOxfc1jAyRVZ9x0jlBEkZdaoERoXPltR5wpyJPSEPmHs/OL0ET2M0N6R1/1gINOyRj+wfffzPf/j1WTZDNdK2y5/785gibddhk0M1EUL4RG6UMZb3791jsjHDpkRsI+2spT45p7o0hpnSPDrg7PB9dOHYuvwlKrfxCTmNJiUuLO3RiA075upuy1n7Abc/bJDJiM3dq9hre1ybzrh+4zqbly7x5pvvoGPL8cGa48UJlp6REdpFx50PPuTh6RGXF9f5+uZvMd3YeGzV9ZE35/EuzEW8Sz3aJPS8YQ3cLg226InHD6jP32EiJaNygmiJqM3q8GQHkDwD088Kivs+4lIEMbR4ljnNLVt1pR4rS1K1Bqt4UYiWGqitJZnslCBhgJUk586LWEQthSpiu0x0sIaoOUtXDGhQrMukOLX5nmcwAw4e0S6fE8bl4RomN01ugLH6FLKNugTEgokGjRYJLkeuaLbTNS6QXKCVDJJLsGgSYi8UgxuB6VuCdyTvM2iaIpJPUNxwE0iaMDEzlN1gjZ4D/7LduiFB8hnINJGkAZMSIg6Xuvz/oUlPCMEKyQqF1VzLqmSLP8lqZpMMNkR6Cbm+UYNPBkeP/Ryg+PzwgFAU9GVF8J7kXc60H+oVIoN6MoOGrjR0mnMao1MoLqyTI76JmEbRmPNNEwk1FtMKjDIxEKOkiWJ8nzO8xWDalMmEKWZ1WWmZbm48JhxDHkSfn5/xs7feYDKdMJ5NKIxhuag5HKzSQggsTk5J+2DHFmcdhIjXOOQAG5wD9T2+y65DREsYiAmYnnV3zmJx/njwfbGi5qHiuCypvKcIBS7BWgyRQKtKo4k65DrVSk2dEokyH3uFYBLEPpBCD2KytWAKODchGIMT6FwgpQ4jBicZNJSoJOnyvdDYT3GG+qutX/368+xfs3zrG3Mu7X2F0+mS94pzVvfWHJw2HOxvIddf5UW7xwvHFacfLLgUeoqNSxh/Qr2weO1Y3TviVeeZzyasjKXeHXG9e47p8YR4t6XB82h2wJv1kpcPlhQuMnk4Zt5tcCZrFpxwtpgxtZtcmSVWlx9x0j/kXnvIAyzTrU3OtiEd1py1KyY/69m97xkH2DcHvM5l5vZFrNlntHODpT/jUfeAh1aQnSlF2McsWorjG8zC88hekf2Ffx/MHcurl27xbyd/REgpo0Ux9y9GTXaCUJjYkn27yU1/hVBayvGKg2XBuRWKyjB5zfFCt0kqnqcXyyNV1s5z+PBN6sP7BL9H0Rpe3gU77zip3sftP+T5X51yq/0qEhVxI8RV4Kr8WRzo0PwOnz+P++7SZbW0yRgs5rFC/KK7yce6PHUdUdHHQPdj0FyzO4TCp6jMs9OX6fK1XTRhLDifLyAJICneJKzG7KoUBXqLjYmxUUw0WU1oGpBIwiJBqfpIVUYqVRKeMFBUsn17xJoM5rfJkCQDFyYlxGkmopkcaxGGe0EkIcmDRpJxJCtARxIgWVISrOYIgygGxZNiS1DBmkQyIfehF7mGaXgnbcJJzhdX8jaoySAWqhgTcAOgZGykY0xnMlCUhOH5Oly0xF8Wc/cLVyajiYPJK8P/EZ5ApcLNG1/l1Rf/kK9u3uSF564xl3IoQyYEFRbk+VkHlCYD9WMD5WlBv7xCeHkP105h3MH+l1BfcRgii2sFN67MMT0U5TZ+dIPFacP99X3WXc+j4FG34utzT9AlsS85ObnEvJ+wbwxb40M2flKz+XCTiS2YbglpEjjbXRO3Fzyszrn7QmCjvc9e3bM8Fw6KFSfrnlvHNxgHQ/Xrlo8UyZJPqeeq65TGM9p6n91ZxUE/x1cFSRSM4ScPfsJP3vwDbnz172BkB8dLbPdnLA5+wmyzxFXXkVKeUpcoWTYu5GJyinUjiquG+/Oee5MN2j/7PovVAUf1GSfrU46XZ5ycr1guG0IjxBZSb0jh2UkQF3qTM/II7dLPOyoUFh2cKnibf2eIm//C19P9iojBV7u8/Ju/w6Xf/B1+ZbXk9t2ekx+9zfmNtzm9+TXSWcWr+9eJ8YxA5FRW7L/0IpvpeS5fvgYffsD2y1fZ3LlMigmx0xydNvyhC/VjExo+XB8wLve5vDFC3X3Wxbv8u3d/Rn34PGfrK/idMa/dnPDc68/hQ8/W7BZQ8PDDFR9+0LD35TWdOQMKojYs148Yl1dwbsxUQKcG+RLcvCGsv9vxp9/5U3703r+jqwPny5K7jxb8wXfe5MVrf8KXXr7O177xOnsv3GLy/Jef6b10fSKZ7HomEknkWtEEh9Mu67xS7sOtOKIlP/6xrfKQ8l2aHL2jMQOUvaFTsH0DYvEiuYZzOSIOJAOdKbs4pSGb3BoFFSKeKFnQ85hcFCCKx4jDRCHZAD7Ts+LFgWGhxlBmfhJis0OjGZ7HIUwKx3Qyo667wfkx0fQt7x08ZDIdc8lPMBqJpaGpUrbadi0SMtHK+Qihw8VA1zTIEqxPGMkW7kYhqaUqS/Z39yGs2BpZCmsRLZBoEO3pwpp16DIYrpLV4fEiN7yljT1BEzEpsU/EkEgxE2Cfdb39g+9SrzvatiV0LY4hMtOOs8DK2EHbOtzJJWJsQTCGo65my2XiiJoAJgs1fK901lC4THgjxoH05uhjnrFG6cAN/Y7Ljiw5Tz6zjJNXil6QkPdlJgxAMiVojsizReYxG42IeNAsCNJks5jAZRv+Phli7xBVohisOkoNqAzxlDHPWpwzBJtQl++bmgSNI1rJNUbqixyhYgI4SH12MBSJuAAuAiZBETAkypQQzVbxgUFUpSCFsLU9p5xOGW8dc3J0zEF9xnQ2xorHaSQkQUIixYhzBoPikmKdo0xf8BBmAEoFm+m6mhXy6WnwVJ9+cCaNrPuOGHqMVmyaGafViuO+/8LU4lHz3C5JykRyESqxVIArHRQeH0uqNKKYVKSiIBEzUTqZ7NQY8nm4Na6IaZMzEVIHzoGEnmLR4yTXe95FuhBQDY9f+pMyQHL8lGRmpwxOuklTBoGTIRkFzcKLhMnzPbWZcEFCU8wK/i96aZ5HacrRkyL5PLnAvjQlVk0DtmC7shgnWCK9BrzmyIEQQL1h01TszGacLRaf6Gs/vgRy72Ntzil3hqL0uEmBmZTEakZqPX2ElhbtA14MhUR8UmxX0AYD04hIB2GEjQWNuYhBihkmLSLTFBh1EUZj6tGMbplV/6oKqceoB+3QOMFHBxSIKp6KcjpifL4m1D1+q8JVJaVY2nWPEnK8xWdcImbgHAx1sipFOUFTj7UlrhxDjHg7wlYVUtin0wl+7vrCQPG9zW02drfzgOTTGsN++OtP/UzJmGv11IYpucAfPU0giblY/nlLVBmtIrvvrjluAtt+yvPjyyxPoT6rSaXHypN0z8SFskkYecPWpML1G7THpxg6ymd5l1QZ6H8D8F6Dr2EyxlpPSgWpqTl/dMi9e/c5a1Zc1gShJxYlLU8w5s+6xhPLy7eusX0+ZWs0yVbxz7oukRkKFypxyFX4mFyRx+HnNXlgcdG7XSjIC57YrBs+xWY973gNifNVw0Jadjc3qEZTqlHOFjo7D6Q4Zb61S7tYU7qC8WiMupI6JtarJSEuWa2W/OjOT1lXC8qywRYOkyb0RmiKNW7dY6PB+k2q6RhnHZskSnp6J5AmSNhkpWcs5me8/OINfvPV57GlZ2P7Ndb1kvffe5/D1R26bsEsWY5HM3C7GDNhMvJIdJwWjlY9Z3Xg2q0L63Tl8PAhi+4UMxLm84Ll+uRx3vVHlomfAMQBjEvsXPXsnE85WoxZHTRMyxKZFYTzlvW6prJLHhw/eubdrTEPtq0aLIpLELqeVV/jzjylmVEWJSoOrJA04iTmIcLAjw/J5pw4I+AsnSQKydbVGIOmOLBOQSWS1GKMHZh6BjFDPl2yaLL03tJ4Q10IknIGcCk1RpVePUoBQQc2ZAdE1OQhho+KRENBQIgYgcZ4NGVGozjYuXyFplmzbiPjiaWxQrIWE8jguTFZ+Ro7ojGkkDg5fMjpyZLF6SHn54f0XUtKCWMs0+mYoigJaugMJJcoRgWflaH0N7kOTk4plp7paMR45KkqQznk8sS10FQGNj2+LNFx4qxY8iAeUIRAvyy5LHO29meYtAGnQjOyLH1gtTzC2ppQC9p6/HhEWXSU55ZQ5/e/nnrWY2HdNFjfYHegbiyuGLGxWTCbRM6ioQzC5ukpi794F9s3OCOk2vLeH/Y8+qEhtEq76uhWa0Lb5QJTh3zFT3FBWq6X/Iv/9v/Nt7cv8fyLL7H/pa8wnXviQumOzzDjGXY8wSbD5jyysoHa9/QliC8Ia+HgwX3uffAuZQHP3XwuK6Sd4EZT+lrpUsWJWAo3gtBx+uBD/vlPfsTvi3D9V1+kd1PGRtnd2ACxlKFga3WVV+V1THPA6ck9qvWI2Yc32J9fZeubwJVjNrTj5DvPTrCQdME+zVOnUhVMIppc8IpakISEzAzGZ6DX2IiaSGWEmIS2S5gmg0fGRPpoc+OiudlHLc4NTF8Ng+VnkRukIds7u/7mKIYSgZjobSaoWQRcBtGjFiTT58gGFwl43NDQqw7WyE7Q2COphAS9STgviBqK1NNrRJId1GQeTWm492fFEQm8ah7kYnDWDdZ6FlsK41TQDVZeKPS57cSYAqOCqsXFRO8CRh3BBrQy9K1BkmZGtGbFVdHrU8DwRUHzsaWf+OL/L1dKSpd6JPSAfKKoFwN3Ht7mp4/e5CtXv8nYjUi98CAtWMXEC/WYamtO0Y8YjUrs5SnlFZ9rj48tXcHyx4bloxnXrs3YKRveWZ1wEGtUl5hwgMZLYLZyth3C7s3n0ZHlqKlxJnFpc49x6Vn1t+mMwnQDmsTx4RlFuUE1KDMIoFFJVjFWBoWt0ocVDw7e4+5bByyPO/pVg+/XnNoTDh68ByenXJrN2d/YZne+xc5si63JJoUdYWyJ9UW2k33GZTCZ+JKylX/noLeZ/GILyzSdIppAEj4JaoXGOOrSEF12Z7AhK/xiAmcSyQhhAJxFM0AQBqvaSK6zRZTYK2pjJje2hr7Kal9Si01gs4XDBROCJJYYEtLnQavzKYMhwYCCsREZtlEFQkxgTXaRaC0pjwYxKecwRlwe9EUeXxeEbA0oKcelRLGIGezfNaADcJ8Uopi8PVaBmLPEncv1iGRlQpQ+59eJDm5G5FzwlLOwTUo56xxDDnYwoHkAGYwlGIO6XE/E3uNxFDw7ycksAoUNeFeTCkcsPH1REoqC5B160VulQUFuE2ojgUhwAesNNhiSjSQ6jFFSrLIyxylKSzAerMl2zEZJ45St6p1BiaQqZwdqD34piBSEJtDUzSe2t24a6qbBnpyAMpAInwyBDg8PuX3nfa7ceBE/8ai1+IsYHslM6JQcEMCknA0vNhMU+siqXdHHT7npD2+DNzarnQpFvSHamEGylAcSkWzvmfrIkoRYi+gMG/NJ38SG2EeSGFLMqhDj8gACNJ8LLkeLGLKzRtScMS9WsltD/HzM6qm9iu0FN3uA3auxReJ4WdHEgGxVjPcmzN2U66lgPxkuzTOBSCaKri1H3YJV29NubLCrJTwKNOMpy6Jle15x8qhG1kpfntO2PWbVc7LouTNJHB/dpfYLmv0V1fyM8cmcF5ZTNmeG1eiINXc57M6IW1PmL4zwU+HBX/wl63vCrJ6wl+aUI0d93nN3eoW9V17GVY72VLnzgdIfJK42kb26Qx5usb2e4rsZcVXA24reB/lRHhpd37nF5nxG0wWwShE9flrgjCUss0Xp1saE7cuGr//dq+jmgrdPe95bKItHgbPjh6RUYcoVhg7TO8ayRXl1zkhewZ1XRLXMrqwY+ZLV8RkNK2CE8y9iq3UmZlwAeMOwLrPvejBNJjebkL/HK8+0v0+3tzBJMSkrnW2MmDh8TimrZJIOn58Cy4drlpGLzkOfqMwff81jlXkUk0GY4fywJHzNoDzRfB12iok99EoyCe+yEmhNdoByBJzN1upKbrAKMYz7hCPPcKjA2lx0JReG+I0yE5UyHxEjQsQhaug1q9JJmucISiZCiUHp8SpEucgbFYiWaEzOvUxKkkAwHukThjRkdlrMQGAWTOYulNnJxJLViZkYaUnJUqbASCIj6agwrGIkLc/prRIxSFlgC0sfDZrKPFB85nUx4AJpgDJHmuXhSZ6ElRv3+dVvbpPMO0yv7GEmFwoE6KzhTfLo5ZYMoKnmkYLdUrojwwePCsLeVfZePWDLZwPkLbH0ozlrPqDCgkxY6pj3qwlLW3BrtGYrnnI/HCK9perGXE+OydaIO/eV9eltqvFdtscPuPbQYO5vkL6l1L9WY8r7OJ8YyQz299hef8D05IRufk7Y7mnPl4wOr7FppsS7I8Is4ucWK0U+tbww+fWCnekej6p3kaJmWZwhPiAugyarZsk7P3qbv/N7/5TxfAcUfNPx4Nsd9mTNtW+Rj5/HpCRLHlodAEpgl14tKjAeQ9pf8Jf1e9x+5y0Wy5p6FejqhPaG1Duy8ZjgnKGaPHvNFoataPn5Vwglj0rPOggetgxU8onR6d/QGoih1YQN4GvVhC9vKMfXtvjLk5vUDzv42RGzrmPn8g2O1wsW937Kq7/+KxS7M379xZt8/fXX2J/uMRLPsv0Jq7jN3saLGA2kuEJqWB9mkcW3//R73DlZ8tq1q+yMzrlXv8mPHtyne3RAfTJltFUy+8Y+V37lOs9dfx2Ho120fO8HP6TfWPDcV5TZyINAp+/xztG/4t23S75083e4dfk1qnIMVig2hV/5O9eYX/lbjJp7/PmbP+Lw5JwULH0s+dHPet6+fcQf/tkbvPjcJf6z/+P/6ZnePZscpWaiavQW6Tza2zyzsmWuxWxEjc3XKlUsEAcXxJSfJAOpiWwlbQVLIEVBsm1QzjCO5CxYGxGraMwq9WAigg59i8m1rcsK9HhBIIccX0OZZ3Rk+2yT8rUkR0RbAkofE40xTDXhENQGSlUWxpAndJ7KlYh1EHL0jGpk2aw5a9ZsbJS4qaeblDnHeugDVPPp6ppE0UOK2bUyCARsdhQRg5YOkxzqCqbzkmoFLijE7MjUhzXr0NCkRJ+e5Ia3saPpO5rQETQSVYkhq8M1xmyvrTrM7p9tnddrUMGKyQ5CIZEGMA8S6rL81gzAngzEEyuORR84D5GJs1SSo4TERGzKgHbjElHIDnRiKFyetUYCXbiIZrLEZFHbIxJQLyABF5XksiuXiXn+kSUC+bN6JdlEisO9f4isdAlEDYkcsZUQUshkOYyQYqAPZriRW4xTjA3ElMl6yVpCclnslBwpGVIdUdNjAqRkwUWyXniwWJcc1aJDlJUZ+m3bpUx19tne36gnSlbJ44SZs9i4Q2oCy3rBeTtno5zhiDgVjMm9sGDw0VAkZUTA6afM5T/vEjIONgCjMmSt59z7C7uDn3OcDThV03asukRlIikp86rivPFfrFpcszLbasKqyzWWG4RvNmK0wpZz4sRTkGvRYHJfiXqCKF0ROd8yLLsJIaxBIq4xjEZTlm2DixmcdcbhpKDX8Lis1ot/s6XcMIfIZM4spczqcSMJlewOabFYY/CugmHWV3cNPXk28tcLjMtj8F4uZlyqpJgBeDHZwSqJyTEKufGlaWrAMt+sGVUjxHmsM7nci4ITwVtLIbAzGnOnLGmbT/bUF8sYwRhw4rIzozM47ylLjxsVFOMKxoZl35PqyHodIQa8EYIv8ZRUVqmwLHvJsRTqaKJhVSZMiDjtibYgFIqveyo6RAzrwrNOSoqKt+VwnBtYK0Vl0NEW41EmeRlVFstzQuhZnC+ZqWU2m3C0OGHd1MTY0iTD9vZ1QmzxtqKLLWUxoSzGtLGjGm9hVHCmoDAlklKOAY0NVaywVUWyBuMKsJlyZJMQqwIkUZhfPkP/wkDx/e0dNna2P71yu7DlHn/y2yuyMBnyr+aE4CdK8WE+8ktpkuOy5JXpPoufvkdVQ9q8z/b+PuE1IWi2lcxPZWgJj1OYDIaN0QSxJce37xNPl8jViLf2IwqIX7pU4fgA5rtQGJAzKCsodxAUO5zaMz+m2vgR6rPaRAZC64xPsU1/mhjAx3+Y7VMmo5LJ6OdxTv8Kqxo+ZnwUAD/hCQBeMdhR8URBvib7QTnyPqp4gu5f2KxfKMgFYhs5byOnLjBWGA02vykpR+ctUkCKUJYjLu1d4dLeDXRnzrF2QMfGeILBcri4y2gjIV1Pcx5Yxh5TQaIm1S2Fm+I3dqAOmLM1fV9TN2ukXlMlobcVYd7gpmNu3rzK2+98j4MP3uebr3yVf/K//t9R/s7vcnx6zP27P+POuw/43v1DHtQ1xyeJ2fpdxt0pRblgLYagY5574RoAMUbWaYHdMBhrmW1MeO76HsZ88lhKURD3SfP9cbnFq8//CrtbN/jKV3+L7/7JDzg4vY/fFuzI0N9dM59M2b+284nn/KzLKsQANvVYybbD3imp61isT5iNHNVgBZ5Mvvk5wOY4FDyRXnLGTUwGZzTn9SaTMzeJ5ADKDlQQk9lbRrJVk0iefQQdoVrQS6KzmQVoQyLZzOBP1FkdakIeYuoAUKji1aIxMdVI1QUCQgqaC3mbLYE0ZItmi2Nvd4+zo0ecNy0VDjtsQwq5UFQX6EToU0OzaqiPj3nnzXdYrlafeP/Go4r9nUtszHdQ8QQXMRZGZbYx6cPnyJr7AlbSRNO1NF2LXzrG5YjxuBjU41n9Jl2H1Kcczx3LfUNcrBkf36NajVk3DpM6JuOe+daMsHOZ2sJxWDJ5dMTcHzObg6YVW7NddFryiDXnbo3ZKDlPHk6njPdX2K8uWByXTE4uMYubnFVf5cFixuJHE+IHb9H88Nt58Gcc0SRMHzl/uKbrWs4X5yzPl4SQba9+2Wq7jnce3OGdB3eovvcdbr72JapRxemHd3nlK1/myrWbXNrbywzdSUtfRWwvdGfnfHj7Xd55401OHtzj177xVbyZUpaXKKoRk9ARy4azXli7CowSwpI0Knl4esSpTfxaucvMTQj9jHg2ptosca5gJFu0neNOW7C6tMuNyYSdh1dw5w51S3R2j/XZIdXs2W/XsXtys81jNoNxIVu2hWybGyTRmx6vlmDz+WdMwpgeJ0LVl0SXWe5ilT4aohVagSJkW7mkEMtiIK8Z4kXhapVkerBKVJcbG+mREElWcuPfGXyIROdytlMYFEHKQJRXzDBUCAgmCQX5XlFIRJwMloRxsKfLrE2RBOLz+Z96FI8xLmdEGkMwoBIoxDLxJSF6fIo4mwvOKnmc7VFnMWQbTh8NqVO8z6rXPglIQguDpEChhmgNFkFiVo4mG/gI2P3xC7185Kf/o1i5B/m0V6UcPXzE7/+X/zWnx2d8+dd/jb3pBt+99wHTekxzt+blra8yvbXFxu7rcM3BtEA+9qapgh4LzXsGZYKZWaaXJ3z54evcvl9wmt6mbb/Hm6cP2Nn8MuPZPi5Fnru0C97TnZzk/TraoCeR0ohLe9fYKCzdbMyl/W2qUf6bUaFt4Pwkcnh+jik7drcVCS1HRwe89f5POfywZdo49taGuj3jTn2b7334U1bNEmezddasLNkcjbi0scHlzW32N7a4vLXLrJpxmV9/xjc6c4LAUBjDLLmshk3Khi7Zit3j3NnOCr0taZwliWaLMpPBkj4JeCX4QXFIl4/rLuc8p5Dv3UkysSGRE0+ye4sQnNBjkNDnEu/icWrpxIORnA3XG2g9RnqcV4Tc4BgEazP4DgURfQwMSFSsZoKPyeg2ohYVm68pBIpIHtJIyiIPLRCbAXB6csNnBBMDIoaoBUSGjPIMznjJjwsYWp8jG4IoBIc46AhISLiUh4peE8bJgNskUuroTVbOJ1XU5tpPyfVF73KWXZeezYYTIKY+q2B6g216rO0pXE3yhlAWhAEgj97lYRgd0fWAJRmHiwnKrNyUAEkkDxpCBngphFgo1uYBm9psr2+iEH1CkskW8tqTep9z+aJSL9bD0O/nbHf89J/1IXDvwbuMZnM2ykuYQqldwir4JYhXGkq8WKJpSX12NOmskNTRtj3h52SROWOwPpFMh1qHczmPLhkhGaGXPltOas4UVjoa6UmmzhaAWrOOHW1K9Kq0nRJCom1bjB1hjMFgKJLBFwMXmohKT1QgZvWPhs8HY4wnuzjraHQPW1yn6AOshT7BZOrZLYW9rmfruIOHljQynARwDyP++JQjlHbzeRZuRn9nRX96gJaB8cFDrnYdVzpPjAlTnnB9uiT0K84OVrTiOZFjortHNzvmXBdMzQmT+hWKZkQxPWE+qfEW3Kzh2vgAv27Zbmfszva5NL/ErWSRSeJdrvGz0SaFPcCePSCeNLyzbhB5lxdHHc+nknHaghCQ6TkP2prJO2M2q10kGkIQxu0+G+UWh+6cQrOF/2xcsTGdIZsODQFbBerijOa5d7h8fc5zR47rP55y8Naat+7+Gc+terbnNyFWpHe3Gb81Z5RKjLvEdH+Ho/Y+XXWH2E+4e3jIB/EOk0rpwojxnGFg22fwW3qe5MFdrGG0p89+jq/MKCteJI//csuc4y2MpsdAuR1Ac5MSJiYkpidfq2aF+TBMNHrBRx+eL0vEiN6QHFgi9NnVJRmhd4YgdhhS53zmRCBIwphsa+psjyfHalnN+a+1FI9JQ8nkYWomJw9RF4lBHdXjHNiYrWyj5hitPqXcLkpWhhfdcI+yEaLPsVxEjA6D+yTEmN8fiYPCW4RikDWK2OwEERQRSzQRiXFQGYERO/SZ+e8XQQnBEG12O3Ih13cmKYcPj1nWK/oUUecoRkV2DukS5nNk0H6kIHTkGyeRJ16Bh5zUd9nfexniKY3+GyZcR3gdmOAkj1tO+FhpmYAzQd+HdqSsro9Zy1s4vc+c38K5kgXCI+4zocFyhXtscc+MGFeBxtWE8UOaM8vp4SXqpeXH/XsULwsHo2Ou85eUkxP6UYufuOz61F6mitu0LvCOOeYRp0xt4EZ1BetOkDTl2CjH4ZgP409I/QusfmKIZ0fs/fZV5qPrA8MDzGvK5gtrTDCsHtXsXGrZ3B5jraOPPTElfvidH3F+fM5otgsFFLuw/MMFB3LO1W/qx0Q0aXiDpyhbNEn4y7MVp7ZDHv4Z7e1/zf1773Ln3QWxFVLMznQqSlE4Lu1ssLuxyQs7V7i+sffMe7shj8qmZH+gT7s7KLDsMnheFnn/Tj7lcX/T6wK4KKywtz3mue3n6V6EF3/lFt2DQz788EOOtGayUfKrzz3PC7/xa3ztyk1mpuLilU4ntwhBOCZiwwlar9k0c8ZbnuUqcvudN/i3v//H/Os68vyr15ntTFgmQ3N0hq5PKQ9H0JxQyRFfvnwVpePNH/2Un/zs33Jtf85L/ZcY6VYmareXqI82OTj7kP6nf8rBT9/j9dd+hdnNWxTiMNZw60tf4bn/w0v83s/e440ffI8//fd/zLt3bnO0WNDFiqareHS85j97xvcsppzZrEaIMZNDxXiiRiK5X0xeMaKEKGg75EMPTHIjiSFzKDvgaBZwWCGDra4gSkeG0QbnKQtWA0EiyQacgglDHKFRfJ8B12QShUqOA4qGqIK6HpM0u4eg2UHtQkw2KDaTJBrJwK0M9u42QoGA9Rhr2BiVPPSGZdNTx0CXIhhLY0paPyUaT2tsrs2jZjfI0CIhMTJk8lOSPBe0LUEg+QojJXjBmew0p9GDH2O6mhRXLPqGOsWsDI8XueE9dchgeK8hK8NjIg5W6ZqyiEclzwz1s0gLf86ywKiqsOMSUqJbremaFlIixpjnnwasNYhzeOOobImzBaqRs65mu3B4Oyi6JR8/np5IzrMMOIIUJDosPUFi7rmDR43DxZ5kYrY6JxJ93u8XRIssPpCBuBzz/EbzzMYN/WNKgtGeNICUlvwY0w89g+kxIvgh4jJiCGQXFSeeJIqagtSXBM0OX0h2NkskTLKoj6Q+ZEFCn7JToG2ptMMnR1KlT5mk7RTUgtEub4cdYgYSA0CeY07K0rO/vc3B2YLzds28tVQjjySLCYboEr6PVAm8FaoLNf1f91KGc4cMij8WCXyWWiETM5qu5bxrGY0sjANl5xg1JaHrPiE4MCJ/LeCvqqKpR5JDjcmzO4G+y2RvUwiVsTlnmgIfDSsLySRsLyx9T28jLRHG4JMnFYZKE6NeWFclJ/SYLlBYTxci2fcgDqrvi9cwOH8OHBUhEyF0mP/rQEpGDJPJjMn2NpVaqpBFqfdPDzmvW0wU+qAX7+rnXuIyJqiawXdRIYVIIDtMaFKwfnjMcF5q7pP70HFyfES9LjG2wPuSyWTCaOIZp0ShATXCxrhiPKo+FRQXBGtz5IIYxRow1mRQ2AvReSgKfKlUQFsEur6haxs0QF33LIqCYrqFN4bdVCIxYpAsZjSKTX2O2TCGSWeYN46Zyc6ZTTLY+RxpPFMreDFEN0XtiNBBKRU2jrE2ESip21O0j1y58jqbdYcfbxCkY1ZcY8t5YmqwYsEYxBXYFLJLXRoiskSxwWAKg4ktMRpIeV4cTIdrDGUSmqrK5B+JpKKC2JI0O1P2n4H08oWA4s5YLu9eYr61+ekPiOSj8mP26YmMre489e2Ll+DJfagZCNq/0FNcBJlX7F27zK1/M2Hx3jFsGZzdxot5ytFbHhcQF8PWfOOx4A3zL73E6qymjgmfUy2G3xu2X9Mw9fuUC6mA+nL4WQCtgcykkKck0+Vkg1d/9evc7hdMr42JPo/W3MX2fPx5P7IRn/bS/5ou6p68Q4bGBEeuxgMZIG/JDIbAEwv17eF3AnlHXgDllicK8pInCnIHZuSoyhE7UjIxF1z3DNo9vHeH2W7PumypO6UYj/hHv/pbTP7O1/lvv/fvWD/c4hvf/C2WqzVeYVyMMF2k7zxrFhQijEy2mXFzC6MdgijL+h6LsKTvwLUtG3GNuVwT9wLTuef1117i//UXP+Lo+Jjj0xMeHR+xZSxb4ym7X/kNXnu159frmncP7vCzN9/n7Mcf0N5bIbUhFbBVWW5evvJ4h21szfjKr3yFNkaev3KJ61euZMuop5dCWnrs5id3hYhQuKtc2b7G/oZy6Xde5v2Tv2BpHrGx4bn99jGp3eGlW8898+6OzmRL5KFwSqKUhaBrIWpPnWqSGWHIRU1KDmuzAjtKzFwHyeqDzmbbarUmMwqNQYa8lTx0VKJ4kitz0WUsKokQLL16xPqsbkp5sN4bQ0oh/98kxMSs0pSW5WrN3MwZuVFWQdiEV8VqzlgyBCRJLqQl28ualBAEXzjm4wmLrmefiI0BlYhIzgVNsUfXkdXylNDVLM9W1M0nmXkiwt7OFteuXqas5qycoUgNRYpUhaEq/f/PgeJPrz4GztYLlo2l9AWz+ZRyaxdvJ8zbgo3GUbZjkolYEXwAHxak5owTD4vZJsFDvbvBxubzxPBDZq1hU3osJUlg2RWs6GgmJaYSNu9NqFYz+vWC81hBNaGPO6y2XmAxuc7RcSKsI+3bf8H6aJEtMl1mnPahJUSlXi5ZLpdY57DO0jQN6a9gJ9Ss1/z0L76LsXmo3afAer3CyOvsbuzTmwLrldi3HNz5gJ/82Xc5PjpgvjHn+ctXma0uEZqKSMXET7KKsEpMNNDQ0I08TbIUlefazpzRaEpJRdvD+aolBUNVQZy33C6OeUfWTM9rTD9hNio4a8+p33mX4H/A7cP3WBxuP/M+frpwVoTOG6JXJEVyFnrEuoQxiVayDaUKg/0Y+JTVgo0RxAXos61uShA1q7SDGoIRego0pcx6xBGlzwVNEUAtqQdNWZkOinWKjTbfK0224kwtuBCz1a8krMsK0oSSTMgW69ZgQswAmVWiyWBbyqHkJOvIulnBBEVSdqvWKFlwqJYoAUsuQC2OioIUyfZgdCA9XiwaA2gE2+MsjBNDJnm2cPNJ6J1CbzNYphGj8XE2mhaJ3qX/0YHez7IuhHUpRe5/cJd/+y/+FT/5g2/ze7/7H7F/9Sp9ZzmVOtdxjQUmeZoPn6x7EphHStFFzNYBcQxmYwNP5MpiTn+0xY9/9H1+8v536fS/44X9l/mVF17h5osvs7G9y9h5zuOEwwMhamAUS7b3PE4TdjKnHI1A8gDzGIgT6IyiDtbRctxGnDhGG1u8fO0VJr7H3UtMVTlZH/Lu8fusjuqcIW0Tbd1wbFtum3P8wwO8NYwLx85kyvZ0yv/+n/wvn+k9rdXgooNkSEYoBAqjGYgIygpDUItJBryl9gWJrKiLJlt5WyzJKclHkiY0ppybjRBEcoZfskRJRA/JZVCxGywbidlS3AGms6gGkg+ZLCPZHcn1BtFRVloiEDODWxyZqKINJiQchmiyHTWalSNJC3pRDAmbsr2iHa4lBrApDxzxQ7M8NKBJNFsEp4FlPmSFC2BiwkWflRRDE200ZykGHwlJ6XtHMgWhBDvUxUIgSSYTStCcaxaFJEI0dshYj3ij+NQjMRNuMwHHYlO2Z3/W1ZsMvBoNGfQKFuktpo0UdY13DVpYoneEssRVmTREjDgRnCjB5YYzqFD2Bdoqtk0YE9A5qM/XfDFKdNkSTvqcyWe8xUTFtEJRG3xvkORYnn+SMPiL1kUkgQLr1ZI7779B06yYTR1u4pEA9dmC/d19tDR0DMSKTL0g2UiIJttz/rx7vxGcN1gbUdvn/HcMvVECAZUuR3pogRqL2DykQ3pahHVccVivCa0iyZLoc+ZpyPeEpEIShyfHa/QSBucGJaXshCRDnMHnWV3RUsY58U+/jHnnFtN55Mreir59C/EfMg6w3faY+6fUh1c5ns45dIo5O6c5OObIj4jtBssHcPzwHYK+w3y34IV5weV2jntUMrdnmFkgXTM0yXEntvjrY25uTjh+/z5HjxZIuWL6+gPkUeT87oq4vo9Z3mWzVwqds6GRkfVU13aY7zxH/b6Szg7YvmaYjOFRPOTN5l3gLm5acNx7pLvHST9m82REuQ74ySGHN495ty2o7llePPs6xdhiRj3VSpkV26jewfl87o5myv6NMXujbfTMcu/4EZ1ajNnHypfYty/xzf0X6Pd/jBmtSLoP+mXEOFywuFZyzzsDnYNvx8xulEgTOW4tZXPOxtVjzBQw2wPba7BJTwWaRtBb6OyTz61BG4N5/dn2d1wusa7IKjuRIY82x7tIznrI7hQygMFkVY/RDJobza4WWV0eHn9tkg4Kc3LkjwpGIhcDmSjZ0lyMDDFVblBWhZw9GwxqMgEyic/RCgFs8gTTEZ0SYsIaIAkaoauE4LNiW+3gLKSKdimDQ1YpTc5M7QfSTZIc5VAYze6Akq1dMUpMAlFxInmIHxVPh2KRlJ1AXDYLQW1+HS6aDNyYgIk2z5NUia3L943UY4Ws2ovZZYVo0eCG/Z3QGGi7QFdHutijEqjrnhBb+npF6J9dKa5Ng5RDcKCHJ8MXBVY0+iEPTi3BjjEEFsv7TKYB5BroBBQWCiuBfqiTNEFaZ02GbWDfCiUlQV9glCKI0A8jrFan3NVTKm4DLbPiBYQTHskjvHzAfvvbnI2mrHcSJha0zYeU5ZL5dJaBne0G+Ub4/zL3Zz+WZPmdJ/b5ncXM7uLX99gjcs+snSyyu8hms9XobWYEaRYJGEiAAAnQgx417wL0pGcBetGfMcJoBtIs0HT3TA/JJotkVbGWzMqsXGP33e9qZmf56eGYR0ZlZbEqI4tkH8DDPa5fv4uZ3XN+5/fdyNPHpEWD/f4Me33KYlu4mCVeb48Yf3AMp4amf5VXzU2u780xXztms7KcTqc8XYOezJjcAXelDiNB84Baz5jc2WH3n32Fh59c8i//4o/p44ac4IMn9/nokx9x/aVXSwzf68qdP/Sczj8m5W9iuFIIXR3T65SGVU1jDNvhPe7/6If4RUt6vI2srqNhAySasedge4c7B9e5vX3InfEe1/yUXa3Yyi/u/nFJqSvfuDrdn70egD7DZQepgn3zaUrhF+7yPSMWv8gf//JRRDzCG8NT2C3HeHKN8Z0dtlLHsjtj31XsTQ+o7RWpNaN0qExpKk/AMnZ7mK0ZFou2Qnj4iJuzCffu7fPxgwsuTxL9csPp4ojYZ6Z5UkjNG+XNO9+hdq+zWF3y/U/+As2e8cXr2H9zA75j4RqkxzP8J19nO3qcWHTe88f/w39F+/Blbrz2JreXNbdlh+bGLje++lWuvfk6f+9f/Avuv/0OP/7un/FnP/o+j45P2IQX38FlI2i8qjtNid/CEcWUY5LjkANdognFCZp86WEhOM3EZOiRYovtwMRCghIEY3uyGWqS1EAWnA5uPo6yzplY9t2SSckU1wOveLGDhXYBzaMoNkVAiKKD4WmZ00MGhrhFbEK1ojNCkwcSklWqBL0tTh6+8UhTs5xnsoHJdIeDg3vs3X6NzVgZSQ9RyJqpQ0a7JV0MRPFonbBqEfW4VJNrQxpZ1DdItqAUdzYpMVS9s4S0pO1aemXIDVdCiiU3PHT0uTgDpfSpVbqmXwTDcyoW2y86dqcz9nduMB3PqCTS9y0P7z9lfjovewiTcbaibipqV5WWuBWcgCTLJkUuQsAahxODHfYxLicET8QT1JBcKjnug3ufRICMMd1gaT3kKiuQLWoH4nAqa3O2hfBg81Ugi322dyp79aLazqIFtE6+uLNowlpbXKQ0YaXwMAK+5MEnj8gQw5JdyWofLPutFvJH0szGZMQEnOnxOJwRPCUmyWPwFAdAleItVa7D8pwkUHFD/8ZgYnGLyUiJU/GwNRVWmzVnm5Zb3lAZYWQECRU2CcYFsk2IvcpV+RsYqkPM59Abf6Zw/OyQn79dIaeSIb9ctVzfqrA+44xlZzyi23R0fQcI3ntmkzHkzKrd0IX4K223/7oRNBE0EUPGNIpzZQ9VqSD9kDMvgZwUfLGC11wA/5gM3gy9tuAwrmekMEqeCRlfgfNjnuauEHvcCtcNCnrScBQ+JRKYwZ1l8DrCGjfUvkpKRSm6u3+N8c4eKRtsyjhf3Bq3RhM27TmBjB3wh6xfvhdnpNTfKSkpF1AaU/qMV9B7ugJhNf+cGUAIPReXl8hlIeMYY1hu77CzNcEa4d7WFmNbUWHYHc24vFyQ86dzkRNw1iADKG4oynStBuKS86Tao770tpMYNDq0s0M/JZH7QBd78J7oHK0RRmIwLuAErO2wvcE5i/aBIMK6Vuq15+k6c05gVTm2Dl9j1FqwieiugL3SW+4MuFSRZIGOBG9GqAQmW3slbkwjiiemDusSoqb0VUVLv7cfbrMW0UISzZrQ2JKzRcUgSUF6MIY4nBOyFAe8LhAtZO2xvQ57qL9+/I2A4uOm4datWxj/OQVjwd3K9888ezfc3Dx31yso6dldEz+nNP7cMcw36WKNvejx5z1elVkz5XBSlWL7ubuOMD/Xrih7T6HamtBJsbd59jsdTP/6BPM5iJJHW5ix/wwgLbC9Q9RIuzlm5I+xLpYd9zOL7MK3vP3qm/wj0zK7fsjY+udA+88Zf+0vf8Pj6jgDVy4fz1TiE8q5iBTge0WhvVrKCZwBu3yqMP+sDftAeZWZ4eBTbmMZqmjOLD46YVpfY2EXXC477M41FrcOuHO4wz/5ve+wmZ9w4+Yt/vLPf4jrHSPdQTuHs8JaA9p5pvUINzVEbZnPz2nTjLYZQ1hQ1RZjExu7RkZrdnZHvPXaa/z923+fj+484HurJZvU8kf/5l8zUuH1a7e4+cabNHt77G/PONj+On6ZORHL5Lde57s/eY/HZx8w9pnJqBAgsionx3PWF4lXX3uFf/x7f8B4cmWp/fPDTuWXZNAJVywQY2DnYIe9cEAdKvYn+9z6LUNVHzBtXhw0iyolw9MkssmFIe89woZsEuvUFgUmmVxmf6IOVkUiZCeUCUvxEkhq0GixpiyQEkcYF4iU/OLe2FI0B7A507pS+CYpyyICopZsM9APDKaEyWXBtMZiNhs4OaeaTKi2AsGBGxr9pZTLBKQ0y6+y9pyUos4IToXdnV1WiyXZBOrUUaehQ2vBDWDgyaql6zqWy/Zzi4zJqOGVWzfZ2d0ma8XGwjgETFSssTR1xWK1fuFz87c1Uk6suw0hOLZchdlY9GjFQe/p7Q5hfIeL+QXVesV+CEy8EqLh/NETlishbk2xZxdsi7K709C3SrV7g3W2PP54gUwrdsdC1a64gafiBg82kFdnuFGgH2XmpiUsT7D3n7KYf8L5J3/Mar4q6yyQScRYCsxmPObmzm3eeO1lIPHuxx/x3ocf0LZf7FjnlMlkHjx8UGxcP37A669+DWsrFv0lF+cnPPrkI+aXlxhj+K3XfpvXRl+lbj0r35NGhnW9T20UCYlxZ6itZd6taI/O0QCvXruLdfusessybpi6jBWD8xX1zNNuWc6Pj5hdrNBxJL/8Q97/8Iz+9BGTd5/SzCbceOnFNQKhz8+mHDVCHiVkyFPySaG/YghGNt7i+kRFRl2ZG5Ci+haTsVV4tulIyaHWQF+sqJIUy5qkhVlqO8GVnRykRBhshU2SwapyaG4aQ1JDsAWosokh07h8HsVmNBtyHsA7KSrCjMGLIMFi6bFq0eSKJFPzsBEpuU1XC4y9IqeZVMAwIhlPhaXNFq+5UO+dH2LCQ9nEqlJFpRumiJJdWVRKkgHJqLjCqJaSD2fIJImDsuvTJbSciKuff3FO+dtY3v8uxnjSUFlL1/VsT8fs7W7RrgPtfEN3vuZ3fvu32AuvMZIDRjemcEHpHu4J3OL5eM3yLYPmRH3wmHT4J6y3HzP1L5Gaa6TZlPWx8PhJw9F9OF+dcfnw+1x+8JCHP3qPr177Ji+9+W1G9S5VVo7pOVu0xJsdB7cOGU0mGFsuGgeMpJQ8B2OHHe8Ss+JF8ELZ6B7e4ZXXhPyg5/KDY1ZvzwmPQvEgyoac8rClyyQDUSKtExYmcDTfIOb4hY+rZgeahygRhdyTTVEFrLxlZSo6EcgV5BEmWyoTqW1Z03LLs/xC6QVvA0qxkO1yIkWIA0HlytkOTJGpEAkGsjPYWGyyStZXmTeyGcDylKlCaaYkLdl0KhXZRJIJaAKXHdb0xCGD22Ulh2LVZ7XkBYspIBDZkLWAH45cbK1FMMFiTSJlKRmJuRBkxBhEi417urLgVgNSFBvlpBRAvCKURk/MdHUq7jiMiSGj1pIqixWlShY1iUqVmCgNAjvYGktCXMblAlCRlSo5si3nXr9Eg7U2lkixg0tkRCMyAOQmWCSaEnNhI3UVqMbN0MwsCk/NAl1R0NloqBY9ujG4nNAKut5Rp2L7XeZusFJy531QEI/NNVZbxn3CZSFEy+V8/sXeyHM9IFXl7PyY84sT6rpmPB7jKNZzW7vX8FZQ02DYkIiDTaZhvdxwPr/4pU+RcrH8x0EeADIxQsxK1oCaYp9cbKdNIVNZi8mZzgQW0nHeLWk3gQpbLnmTCHEbKOvYp3sXSrfEFtJHCq64nZhMqL7crL5YdTRzz/kiwVfB1o67yzH5Qc9q8gFmK1K7C6p6l3YiLKcTnhy0fJIfEC472ieHjGKNv1iz0SNe/ppy4yWItuXBhwqbEXcnHbu3J5jrDRKW9GvHu+k9ajPlQlZUJ2NeeXWbl25fcHj3h+S7x5y809K+E/FPrzG2N4nzO5zd6/ig9vSjBVvbUyZNz+TOEd4fcXm0YrMzI+16NrkhrPaonlrsuiEtdzhvam7e2+Zkt2KxsYy0wU/WjHYTZxfvcvn2Mddu3uJN31FVIxIb6lnk+o2GO9sz2seG5WrN5qnSnUyQ2zdx0zu8+Z3X0fPX+cnpH3H5k0MObjVQC7IB3gJeVjT29HxEx8QUoh0AAQAASURBVBHV1ojm8ibN2T4+O3Zv3cDXX0fTPXhyH1aKrgysBV0btAXtILegLeSu1NRbLwiKf/AX/yOuqqiaEb4ZUzVjqtGYqplgfQXGFqaosTDE4KSrosIMDWT38ypzoTTEn1eX+wiVFrIwGhEMsc5odiQthGnJYHuDTz1gSh/EgKUjZYeLihot5KnkyTAQrUDr8jvJptwHgfzpXG5yITyhffkAaUGz1RQic5KMNWawpMzFOFPLXlmzDLEUgDOYmInqQMCbOLQtLHZI70iVJV813QK4VMCgZAtqWOL+in1zIfiXBpvJhWQVlUJytA5ywmrZX6IQBb6M3W74C4f/mqJblLXcbSjFzzbg6c52efjuzzg5PqZdKOfLmv1/8W18tVOm0AC3WjgcQ7RKGxPnKzhZG1wjXP99wd6AsIKar2IVunEhH3h1dAini8CB32G7zti8wdqbjHVJ1Z4yX7/DUm+yc2OHPXuN+wthebRhwzXOJ5nRqGP3TU87+YT5D37CzvkxM/cW31rfYO9dZX9hkUdLWAXS8SG37DXM37tGF8bM1bBbHdKla0xiIZx+OixwiI9LjN3GzV7i9vWvsdXssFxckCTy8MkJ7737fb7zD/+XhSw7m3LnD++RH71Hrj8E7lBsDxk+Ca4ghUAfIn04QzmnW79J07/FS3fvUcu/5ZqvubdzjZvVNocyYlt9Af8ChQicXxw0m1OMFn+Zv5/qpyrxkS+l71RecG/wPCj+Gx6fdqeGYQz1qOGe1qTJ7JnL58//RYPlObPQoScqmsgXhvHpPt9+8x9x0S/xOw9YnmTOjy7YnM7RaEnbFTpOvPyVA1577XVgxrsP/4T3z/+Kw5d3uD46ZDVKeJvxzlDfavjOwe/z6vyrnCwuOfr+ByyeHnOW3ue7//ZPePyn7/DGaId//p1/yte+9btsf+UNpjuHfPUfH/LWH/wD/ukn93nvr/6KP//TP37h42Q0AAYfS0QRomQtDjw5CxEt7hVRnhHScwQxgvURsGS12KGeMTkVu+cEWRxiSi8t2bLLMGGIQYKSS+4ySIKkhHqYsDQhtpCjcjKoSSQbUFFiHAyK1RUQ1ARyKC4kWEPKlmQszif6bPFD7QyKi5kNFMUwhnEzYnt7n63rO+zdukNV70K2REl0YvCpo+6USejJ9AWc1sAmKF5cSZZ0BuMbxAvWleOW+uIMYkQgBUKKBONpc09IEHKii4FN6OhzIGqxaI99Jkcl52KVrsLglqUlfzgmcszkL5Ex/dq1u+yMd5hWIyQH4mjMKHt+2j2kbwPeVkwmI6ZNQ2UNWSN9jERycesUy1KVsVY0lB6VNVrc+yRijCuleZYS6+Y8tUZc1eKH6JFoM7kWVIc6uATX4zotboYFTsOpQaItZIxYLNOzK6pfGWLfjCkbYB1qgsggqoiKiqEzNSoVqlUhqGaL9orYUHooJg/SwiECQDNd1+KqCbV4TFdRkxCXcC4hFDeXbIuLr6CQUunpSGnCqJpy/WqJ3OywaDDE3pMUojHkKrGaX7DeLNl1jr2xZ5ocSUBtwmohQCSBnP6GzJN1EKYIw+b5l11Xv3i7ZiURWa3XnM0b9vY81iZmtWc+qgkxMBqN2ZltU3mIMeKbiuVizXLz4n3njNJpImgRzOUaXJYiYLHQi9BVFpsyXQ/eKDGDSUWgIhLRHKhDg1Nh1NccBCGkjHc917XiumnoTKQ3HjE9ksyzo2DgCiIbuIGCNYat0Yi6qqmcp65HHJ+fkXJP7WtSFzDZYozSkzFti1OlcWNy2oAt1v9y5Ur0wkcHckoYtc+IC7/Iyb6S2iq/ABukcs1dbYBTzhyfnnJycY4Yi711m5d2dhg5z83plKejEavVEqAI05zBuyuXXUG1EEFMlkHU52moQIUu5yIKSgoywqghVgEzAdNuSLrBRsdaDXsyQUyg95mcK6xxrMSRK8F3LdmNOPFbBLdk1XXQBZJfg7dlz3yVc1T0/vTak0zEmABOyaGFXEQXiCIhEk2HWlAJZO2BCSmV/pE6xdASouIl09oipEi5x4vDGgNiUVV6r8UJRfqCEahBdFWECH1PiIGov1qc+DcyA0ybMTdv3y7Zwp9XwV3Jvz/z7C2fiogLn7BkUXlK0ZWvAPWrD8pfN/pMfzQnzjeoGLx4msNtRuOfB+p/qSL7amjGXYVw5YzGSPf4mM35ijqssCaz2brL7PXDgbE9jOGH+fKSJ8ePuTXr2NmuwRX1WnkjDSKC9w2vv/YNBu1HAR/4HNz/b7NTvuJT2/OrFwOfxltdfZ6vbNKvLPGfV5GX2rf4Q20Nf3elIL8ABpfdz1O3r+ZrzlPHS27KarkgpA6/O2F0Z5+Rcby+fxf27iIIH378mIsWtkZTfOPZ2Dld2xOChXqM95b+9BF1UMJM2dqpyHZEckuiGDStONhV/pN/8j9ntr3N97//U16efpX6DUe+vkBtzcn5GZsH7/FgM0dsxa07d4je89//63/F97/757z15kv81h/8Q37yp+/BzgTflK3A+cUZP/jxX/H4o1Mef3RGPt/hH/8H32Br+zNbBQH5dQLkBdRlvLHEjWW9FG5e36N2e4i8+I6n1khKZcKxsdj+1s6iNpIl0+UOzZHaCoFMkiGHiGJ95wyIiyXTIymdlnwXgw5gtymPj0UrRbPBplIkJMmFuS8FoEtkYk70QYutsUoB0lhhqsGG0ica57i1s1NoU3XJFJdcJmWjpmQMIlhRAgafILpEHuxUkxomsxk5lfc20rJv6CnWqI1CnRTtEo8en7BsV4iVskHIpUjMqmhWxgbGMbMRIVhDwFAZS+MrRu5XZD38uzREcNd22FQGLhb03Yq0qtHzjstVj3l6Tt3NGe9ZwmybuDslPz1jdrHELwIX44a8zCw3G2xlifszOvXIkXBv5ZmddwQdM5aWdmzJ0y3M7h6Gp2h+yOXqI+YXhsu332Hx+IjNeoFGCyYjAyGiRDE5qlHN126+yeH2Nlkj/i3P05OjLwyKPz/aruWTj97n0cP7GCPEGMtmYqhqtqfbvHnn64yqQ9bak6sNcSdyWffsV1MmcwsrT3QzUh/oe+XG4S0ODw9pA3Rq6H3NeeqQviUuPKcXn8D2nGu2o6kjy9U5D+R9nlye0q07dtaHjF6eYT6nvfDrjs104C4OqhDvEsmV6z13hb1aXByE3kAlkCXj1IBCn3wRRWmGIX9MTVEt5QEEswkqCy73xCHPOFZC1WtZ860psX5aVJYplcgCsSASEK1JBvKgtNNkSVlxJGxfFqFgS+6x8VoUQgbsYCFnMsgwJ4kOtnIMDH2TSSLIwESWDGqL7ZHDgkaCFkt1o4Jki8Y8NBoUxGLykJ2VlWgcymDrZoudtKaiqOqyhQCCI/oeN6hDve0G+tdz4/PW9N/AOm+t+TQX7e94lMwjy+5syv/2P/on/Pv/8Pc4P51ztF7w6q17uNk2nywXzGqhH61pmyX1ZJtwL+Ee20KuC8AJpYs24VmRmDaZYFry7QvStVOYHhNt4rxdcxGu84kI/e5d/PaCXb1kbBLrXpkvAnHqmE628HcbZAb5wTEff/QzPvjgKa+/9Dpf/Qf/4FltYlVLVp4UNbAFvBnKIyn2rFSWxiu8UdHcGDHbjYzSB7w0Tjxeznm0mLPqezYh0sVYcpX7/Iw5/jz58ouOUW+KCoREikpbF9vFnGFtEhunZDNGwhY+2sKaNcLGKtYavAtELcoxr4JJjpQhmEAm0puEUJW8bxKirtiFJSWbRMluGjINjSloRy6tDOkKcJ2zEIdswioLaFHriRaVMVL6dBlIWTHBkIxBydg85ISLkkyikpILl8QQB2awiEW9JUrEUcBfyQCCU4sQISmmL/NGAV+KhXwWU9T1ZCoFPyiANwJ1SKyqYiGoCp0pMTe9FUIofcYs5Tgq4DTjUsRIoiZijUFVCG7ISJeEpPxs7/EiY5yVLIOCH0sUS1ItjSpNiAaMGkyyuNYypih81A5sexkAb4EqZ5q2bFRdBtdZzluHqsUkyC6hEsvrlWI9aA3gM1YN0Rb7utPFGfPlxRd6H591WpfB4r/tWtq2LVsPa3nn3R+ze+06s/1DmtoyqjKRSL/sePDwPhfPgeJmmGWvMssLgb+weYNYIp6ctVhCpmLZqbGw7mxlcVpjoiVkJTnBNGPqSU8foesTMXdYI5jKsJFQGn3ZgS1gmzUFhOwrgL6srU7IzZeb2Edxm714yJFdchJXHLQT9j8Stuf7pOtv4XYmWLvGjMY0S6GJG6SpmN67jd1ZcrSsSA8TdXPJ/o6w84ZhdGvBH+slq91X2Frucv9ceKPZw/s1y8kOM+1Z/fRnXC7HsJgx8sLIdYx1QyNnmP3E5t6I2XFFfXwLaW+xacc8JfDB2ZqqH6Hzbc6qBs7nJD3FRQgHb1K7Q07uC3ExY+fkFr4LrJwwfsWjv3OPavF13FFiHD3uJc/0tYbFD5QuLXjzpZe4c/sGVR5xvl5zHi+o5RK7WtBgGTnHctGyXG9Qs0SaMW5fuPXydX72P73M5b8CPdBC3DHA71D2qTLH84CZrul0wbk6LnxiNLIEhfnTSyYnC+L3PHkTSak0zlNOBAKRQKAnEoj0JOn4Jt98ofN9cfS0/CCCEVNIPcZgjMHXNVUzKiD5aEQ9GuOqBuvrZ9+tr5ErsHzIsSgq80HJMajMnTG4ztKklkkWrN2QZFiPxBK9Ygn4UNQ6zuQBtFCyWtRltFI6E9mkmqiOoIYZqdRFJhElEagoeMrQmNRCxFQp831wDpJiU0KzLYQnsVgSxhYwPqkQXQG6VbQo47RYsZtIUYI4ARtJ0pMTJPHUUohYJIhWEGNxptSSKhlrIvkqY9cIEjJWS56pk1hInpgCgiaP0Y5qaIqUesDgxeFHL74HP/6LyHgpxN057d67jG9/jPGWrfAG1r/GeuNYLjrO5hf0lw0H8WU+/pHnlTdG6BYsBV59quQJMFKivWA1Dry3NeOMMb8r8LUM7RPh8WPLUUik66dsb3XYZWT3JHFDJlx7+S7kW8w+EILZZev2CPPJCevHT+l3PmIz/RrT3QmvjreI7pCnP8s81gV7bz1hVAfmix3y9R7z+ikyW1A/vIH+uYHxHv0rv818mVmHmkOBJhls2qOplKArrnvPnq8/5RbIVbPvOpdLw1Y/xV9MmOWbTEYNSgG1VqvIv/yv/pI/+Ifv8cpbb2KMYzR5k5dftXj3mFJAfoNP4Vtlnc/46KzivccXbEa32PnmbzH76phveodc/hbzH3+F6V+9zVayVKmQOHJKdCFy3G74aHHGh8sz/o8veL4D8AqfX+5fCYIueohVyRIfyZdo2F716v4We4hO5Je83s+GIA1FQFYIgt11jH/L8Ub/VfK1c+5+9BF/9j+8y6OPH7BZrxHj8Vt7HFwzfPv3pozGlyxWc/7sL34E7YKX//6Mm9uGeThBZtuM622mGGRsONzZZ1/3mU0m5B9CevgOxz98yJ+//T5/ocp/+/aPeOXwkP/gK7/Nt7/9D3j5t/8ekzuH7L30Ct+5d49v//N/9sLHw0gaajQhWXA+YnNf6hST8TlgQwbjCL7UEm5wtxGTScmCZiwRiSUiTLSoxK56aYgl+AIKqUBKgg9g1dCLFIITYHJEJA/5xqlEEuVUemUuFMt2Y4oqM0OfDMZUiOkQI7ggiE0kW8iFyUKwCQkQYyp2wcGQncNqZH97xuzVa5idCbia0GXy2mIwg79HpI49lowxjiZHNn1C1dE6LSp52+N8g2BJKWKS4mOmoB4Z7UtfwVRjwnLDKmxoQ0eXAlFzsUoflOE5Xe27GNa5oh7NYQDDk34h18HPG2NXMbIGLy0Mvc1r05ruzjVOLzb4LExHIybjGiNC30eMdFTeUPkRvhrhnKW1BhsGwqYmXFSmJLIkNioYKkYhkVKJksx6BRoXRXa2Oth4FkIE+SrqbnADBFwSspY4lBylkNikWOdjCpHMmgy2ED1FLBFLsmBs6ZPk4CBYUi7uXpDINmCG2DjNhbgrDpqs0K2JbcBmhx87xCukUhv0JMAhmnBS7PhNhNoVojMxF6dCbwvpOjqSASSi64qoQlDY9MJ8ueLs+Ji+7Rhnw8GNmpEVTKZQNrT0lmLnSOZvgDX0jPn7BSffqz9TQJQQN5ytloy2dxhbR3aW3cmEddtSjT3GZNpNHGo0w2w0ZtVuvpRaPOZINokcPRIEjCH7sk/LbEi2wmpdLOuTIVphZBIhB1L0iFq2lwYvNaoQqhIt1qvBGMtsNKLplqxdxltKEtGzt1/Ij0rpZ1X1Fltbu9RVIvUbrAhRIs14wrovvQA0QqY4VMSelAPONoxGe3ThaXGEKLZDRbzyJY6NZkpUzFVX77mHEhGaqsY7R58DXd+hn8ufe/6PCpkJTZysL3hrfxevFTuVsrc1Y71eISI4YxFX3JoMBjWFmG0pvZvy3hTpIiotGytMkqXCFcKnh9RbjC+RdTFEohc2k8RTL8Toy1zphFo9pi3OIRWKyxFMxE6EBo/NhZAqboTPFjEVWTyh6uhNwPUbcoROE05bVDdkDJv+ojiCknE95AjRRFIEdZcYB7YP5BSLI5MxZR+tZoh3KgJMK6XnEjUUd0FNQKLvADJmiBoxZsBrwt8RKD6bTrlx69bnsxKvqr2an5sjlAKK2+de1BUo3vDptSvDhfir5pe06OlO56R1AGNxTY27McONzDM17q+DrTd1RUo9/XJJXs1Jfc/ZJ3POlxv2qjWucdR7sWRdPPd4CnQo59rSmhWLTtnKI5yAqi8sVSn3FBGs8bCy5HGxvlpSzJ1e3JzpS46PKF5NE0qzYMynx/2KGvo8QG6G/18B5IlP1eHnw99c2azPKNfA6POfWiOcfrzi5Kxle7zF5cOndOsVjMdcm25RmaGdKCWz+6htaadTZrViuhbTFSAk6IrLVYs3lqo3jEPLVlyR5j2LNjPvLYwrjBOabPnJn/8VD44+xAfPy/uvYetEHRqm1rN16w0qY2h1w2VYs3j4AT9+96e8+/a7rFaX9N2SaSPopmN67xpNUziwF5en9H2kGdfUEyWO56i58pz/NcazzeGnk6dqwlUjDKuSB2N3+SUS8197SCys5xQMgkfE4HxZdBDoU0cbAmPXk1UIVtDAYKFSinJHacJLMlSuNInIBpN6YlgThwxxDCQrpGyBQLaZKpVYAWs6Yg4cPX3CYjHnxv4dZtvXCa4wf2pjUYlF7YOnqQxhsNXWXHI6E4JKxJgKk0yxEhwuVs0GwRbxqCm2l0kVr5nKWCR1BfDA4dQwTTByI8azPaZ7e1gSm9Ulo7piMhlzfHZJ7AJWFRsjziYMlmAMaKASR1P9kgv938FhpyPM7owQBb/ZEK3j3EO/OiYcbRi3c6qDhst9S3sb8t6MnUY4PJ4zvjzhbLnhdJ4wuaHSivnTp4gXrmXH3mLDTqustefYw0U4wdiK6+4tuo1h9uaY9e6ajxZPCW1P11eIjogCMfQlqcKCcWUT1zjDVm1pjGMj0K16cveL2StfdCjQ/xIbRNWIrRKhAXVCW48JK0W0Y0HP9PIAYxsW455Fv+Ficcad2QzrlE27oW1bkoxIueLElCyzaQWb9oKQAtkH8tRyfnnOskucJ8cFDfts4Vcvvkl7fs2DUvAkA6YvrhAxNSQyIUvJGHYOaQOiEUvC2kRHhYmlOagY1iK04umiZUcCIuA0FYW3BKxIAY5y2fyqlpgEi5DV4kioJLIogWI/a3KgN54gCQaQCZcKU5wEVomAeMGE4rucLKTgSoF2RbgTi0oBQ3opn3uXh7w2iYV8Z7rSUEUKuE8sYLraQuixkY6WNmyo/JDTYwFRnBY1qBHBmiJExxTAyUoBSTJCpRYkkkhk9+l5+PV5wi829vZ2CZsNbQjkXICfrINF3G/oOX7d4ayjqiq+9fob/Gf/4X/I3Ze26fvE2arD3vgK+6+9xXvHD/jxD7/LZThjsj9mLpZr1R68WZX6wwCXoMdaiqOb5baz+RmfLD4hyQmb88Dhtse6KVuzG2z6CZNmi5f299maWZqTC5qLFaZq2HYHHIxvUi8bWIMeZC7Chxy370MLpw9OyOcbuKZglbDZ8PHHD3h7tWRve8yNa9eZuhE3suBGVVEHS/nCgpkYJs2M37I3+dr1Md2tyFlccdwtOerm3J9fcLRacbJacb5piTl9KQLDWvpBhQ7GFeIWAjWJvTbSrwKncUE9EXw9gkH5oSYSEVSLFXDpDxX2qUpC1RCHpr/ViFhbDrwMbi5JsS6XTdmgWtasxcJXBE3FFSLGohJMUmy/dLDvZViL1Qz2gFoyaC0ZsRYjg+0cWhS9BowpigFswIgl2bJjyLG8VoaahJgp2G1xhDAw5CBaUMGnTA4gQ/FaSHnCNEesGqwt9sMXCeZuhakqojgsxU7XKwWkCpCDB18a59Fkks0UR3lHnXWwH05EIyQFdZYvw1Mved55cMEZbNtFihOPmGcAOZqobMSYjE+GTjJowuHxRrAR6qgQexwOoWJdeboRuJAwYjDJYKWAzKIlZ8yWPBrEFFeNdWj52cOPafsvuf4OaopBCFLUnSlx8uQxJ08e04zHjGdbHNw8YDodcfzRQx4/PSalMvELgrOFjNGnUOZjY8hVTe9G5FwRKoNIwBUEjYzB2YxhNOQgOxJCzAHTeCY3J9hrW2xfRlZnPcvLU+pKsCPL0hVFjkWYSlXyFgc164AA4gP0hJ+r4V/o0PSJqAvu96fUx3N+b+ub+Cc71JM9qscRffOAtG2QsWLGcySd4uOYr+O47lr6vY+J4ZJ1dcn8YMPFrOUDd84SQXcMedpyjLLDIbNmQVoq1x6tmCws7WrD07Tkskq8vbzEHreMsiIckHLNuNqwPc40I8Opf8LTpw9JS0e/N+MpNW58m9sfPWKVjpjVG6a7hi61XHwcMU+Vikg/m7PeFaqvOX5yeMDyw4r8fseZn9Cc12wfH7D+Wc+0dWxSTRP3mKYDJgHi/CPy6oTUCNqv6NvE5bLj7PEZsf8YV43Az9FbT7l+3bC1nKK7lLVlRbGFCoCb4NPLeJcgGRa9cuOlDfdGDcv1Iz5eJHYWTwntmNglknREOpLpwHZg++F7B+Yq3/U//VLnHR2sQp9TpfbthtXlxc/dzViHrypc5XG+eqYyr0aTATyfUDVjjPOIcUVlbh2qhUisqSivUEflAkG1OIAI2DT01LNQSyQb6MQRnUG9kEOix3JmakJqSLHByJKxTQhKxBdAJ1NcNGwiKSXaSg0ZCyEBAYOSXaaKhYyWPaRsKUkagkRK7I2WDYFGqORT9Vm0ZS3NGHCmBOj4jBop0TbZkaySTEadIKns30rWry/9fJtQccXRKFuCQE6m5OE6jzUVmNKANgZsNjhfc/36i7u1jW8J/t4pR4vvcvrkIXXsmO5vMdqfY+VDprtCsx0wl4Zqq2a0GvH+/+d9pt+ace33d7kva3LXsb9wmFGNv7fNq94QEN5W6APgYLwN7Trz6GJNfnjOvaVn9/w65lxw0znm5hieWMbv1fCWgWobDv45zXLOWqZcnLfs78y4bg027rL7MSz7mjg74ShbnhwtuXUP3F5N7yMfb0fu37QkV2PuNqw2mdliw/atRNNZ7MUYCzyun7K9N4ZYw5nCgTwTZYhY6sk2nbX4FRAappNrKG8jUpRY/9Mf/Tn/z//H/53/y//t/8r+4R3EjKhMBaxQ7YA3EEalCNeWrB0PFqec5o47118mTba4ZoQDgMbhn95jlj+Evmfebzhr13xwecr3Tx/ys/kJ91cXnLXrFwbFx3x+lrjyqUp8ATS+tM3Gn3PfX3u84B9ercF/E1DRp2N49GCKWGYfqDPSbtjeGnF7fMArNy95fPiQo9OnYAXral46fJVXX/kq8xz5s+/9T3z0p4+4fnPCG3cPmVHx7p/8kB/9Sc9bf/AV7t65zWQ0K0RJgRsvXWdnfxvzbzr0v/7vcAm6nDlZrDlZfcJf3v+Eu3/6P/Dbd+7xT7/9h/zub/8eu9/4GtX+7IXf5aRT1nawMx88drMRHFqInanUEQGK04go1ipOEiYZUgZHxGhP7wTUEMRBBSaWCEOsxaZU3C0EUEuhmMcCAufi4EEq2Gh0hQiUxWB8Qm2pkTXa8jFRS4gjWnWMUot3RR1qjMHkiHaWoIEu92QSPQmLKZnPWUg5kxWaaUM1bYhAnxJOlCyBuOyJucVopvNQe2UkGbFCtxZSynSuRWuPsR4voRBtE7jU41KiC7ZkS6dU+pFWCaLM2xWRRMpKjJkUMhrzQObS4qYygOEa0wCW65Ar/uWuaCjAnKRCPq5V8aI03mB3xjgVQjRM3ZixLT2EaC1MG4x1GOsxpjiuBBGiydQhDniAw0YYayTWWpxUtBAluqSIWIzYosdWi00BfC6RK1kx0WDEEr0Uq2oHQTKVt5Ayxpb9HKmcB61KXKZqSVgPzqJaF8V9thhNqI0Yq2Sfyakb3IADLgc09WguexUZ4qkqDTTGMJ7UtCSiWYNcgXuFHC3W06uyblc0zjLGFIIcUjLRjcVmX7Lm1dOFTO4ztErUQNslzs4fcXz0IavVkozwiTvn9niX8WxEZTImCnkA0xMlzes3NgyFHH6FmGYdyALD/3/V0Od+0LImrNo1od1hPCpRYrtjx/lqRQyhuBqn4nqWxeK8ZVRVrLtfjPz8dUcg02ogyQhrBOfskLsuRO2JumHLOYxYWnVoFjqKbbjPguQGpyDSYuoS65NcoqMQM/xoRLNp0PWywEdS4ryKxblgEazAdDZmurtDv25ZrFdoVEQCvnbUzYRVUILtEVtcIK0anFZE77DOs2WE5coTczecEylOk1/C6QWU9EsuGCOWyjmsc8Ss0A8uSJ8dV0CWDqQJKeDvZdvT5sies4iMuLmzx8nlOaHvkSt1tFiSLUKmzGDopnYg3mdsDtAbqssi9DO1LXFJTqgrJWfPaOZIPpOrCjNuWOVIWufSRzUZ7RM2jKito5YANhCsx+VE0xhM76krX6I7MKhTujZCXLNZP2W1OqMPPalvEWLpjcTi3iFSYU0CMl4K4TSERNLB1TQrsSvuHpoVYwxVXVPZUqOXiDJDnzpiLC4sTT3CUgRZYooIIOXSUzAo+vnMhJ8bv3FQXES4ff0mWzuzX+ADAgPiQAFbnxtXGOpoeFFXWGtPKRyLooQrgfWvLPLyoqU7XaB9wrgRbndKddgU9fqvOxRk2XNx/xGniyXWLslxxWT3DbZv36YKT+m7OX6rZj68pdGnf8oJ0G7t8cp4Qh0Txm9/+tDrhPgAzoN1iHryJmMnGYthjPwNF6K/YvSUE9ACp5STMh2+Ggr4fSVlfx65z8PXVY74FkUl2A+PtaAUvoN9+ueNddvyzoeP2J1ssz3d4nuPvsvq9DHNaou4WpHjLtaVJ52vVpz0F4wOHU3d4TpltSwOAOt2Th8yNjaMvGU8hiov0OOWvMnk0NFkz8HhFg7LD/7iLziZH7G7vc10a4dKDO5kSd7PHO7s00y3sKuK0EVaeirvGdc1s1s3ef2rb3H80X2qM8t+c0BTF3VwU025e+0A9gUz2+bmS4c0za+v+FSUvMn0ocU2Fl9VGOuYHR6SRh5nC5v+cz9rX2BoNgQtDdxid0rpfUthE8ZsWLQdW1WHd1XJbDDCsGyRc8JEg9gILhf1UALNibDJtLEjWBBboUkJGtioJa03hH6JN5ZmVjGuLdInss5pN5cYbiDSUeo1xfelwdnmouD1DpyLbKyAeFRgk4RNH9h2BXQkG8QJvfNob9DsMVQkzfSxZdm1aPQ4P8K5okCTmPAKnXVsj7d49cZ1jPGYuGa76chi6VPFwSuB5XzJaryFtyOyGexjk0U6jwVGTT1sxP/uVZt/7TAGf7iD1g0y7yFGZLJDrj2h7Vk/fooj0qeWlVYs928Q77yMk0cQT9myF9S5ZaxLXCX0zSluo+ysDPvG0+SKGIRjOeVsMmZZWXZzZu9Rg+oNqhuJg9sw9o7tHc9PfcXJ90FOFOlMKfik+GgU5Yzys/n73Ew3qOspx0dH+NGIqSb6tiOE+EKww8/9jZTNpWpRxswXC/5/f/Lf8O//fou58xJn2tC0hnQx5wcf/RlP3/mYhjGdj8z7C3aqEf+zb/0jdm3FZXdMH1bIWKhkhDQeM/Vs+RqVDfPVGQ80cZki49WMdXScTKcYP6M9W1F3Ry98aoP5lCEnIvhcYXOx27I5PmMpZig5stHgJGNIYDLRZlICo2CyIRjhQmuWcYxNY85ZsGsTyfT06gjWFbtdlGQoLM5UI5rRXDZowYG3SrSK6UClPEElpQ2TJJYtkB9ywkWwsfAyTVa0VsiJ2JW1wOcCSFpVTEqgRcVutEQpiBhIxaLOqcElT7Yl91u02IqpUbJGVHI5GjGxDi2jXIMWq02sRynqWKFsFo1Q7JzFFXsyl4oqMzJYcAs5yi/M0/KZC+7qt6Jfbj7/e7/3KudPlsxXa9brjs2qo28jfQyElIZivrCy/yaB8iug2DvPb997gz0zgsuORddxtgzc+8oBuR6TBZpJzcEkc+OeYzaeMmr8cwRILfWEKGkeiEeXXOiay/mSdm/O0WaNxgnVucO3W1QaeLrYsAiGnDvG2xPu+Jpruw7TO+q+4tDvwUkmhhWLxRPe//GPOXu6Yq86xOoUPU7orIPdns38KSfvf8B7P/4+8/4Yd+C49do1vnHzHnfvfYPD+hZWa5yrMM5Am+HJEf5yhY8wEs+O2eUVt0eqYD3rWUvPQluO2gUPl5c8WX1B6+nnxsIWtYYYQxaLV6GShCOxXl/y7pOnHB2fUY1H3L59m8MbN6mbKSYWW/OMxaki1pEAxJBMpu1L48iasvkyCQwlGzBpJuIwvSmOCulT1mREcOIRIsYozgZCFmy6Ykcq1uSyAU4Zk+Mgti2fbZOv1CwMEQjDZyfmkg9uPBISSRw+2ZJvaxUrxcZMteQgGg1FiY5FugJ0CEq6svk1BskWkwWvYGNX8sjUo6psXODpcs7HZ6dkn7GNo9ndYnxYXIlsVaPZF0VFLCCrUtxoJNVkF4meApBKVcj/uYD/X6bVnGmGrOpUMvY0YbNQDQB5HADyJIbaOsQpgQJweVNIMqgyyuB7SxKhlkRvDIsxdKMIjeIj2LUh9XnYaGfEdBASEh1iBsegClwjGGNIXyKH8QoQ+GWjXa9p12sujo4x1pQ8yM/IzQUKUDLMaiEnVmpYWoeTmlyCMvBDtruJbVHXk8kWnDqkz0TjcJXBVkIlFeIqqvEO27u3aKrMSNsC7kmkT0LUmqSRGiXZQtjKlCatkeKW8GVGPTnj9AJGtubJasXjRc1MvsrpPpzNAt3lGXvVjMPJmFoii3BE/V5m6yIzbu+z0z5F2jFP5sdsNkvS3pS28rzRvISXKffPjmjnATO9yXhRcfJ2pvkp3LaXrJpLFvst8z0w04rL5Q7vfhjo2oBMenK23K4WjPNT6kpINFycCWddS/BPON+ZslgbZGXY3rHspAesJPLSssbt3qPbhgfnC07cgktxjNOGrWrG7jSylKd8NO+5+eAae3s3seyw/iBwedHg5JApHTvVMTle0PbK0/NL7j8+Zt61vP+jR3z7n91kq/oA7PvwygWrD/YI7zS8dG0H+4qDo1wySVcVjEd065fYqPJULdokDsfHjPxNptPAdoY8XfL46AGbi0tEIpgSKyXJI9FhokfaBolb2Pi3R4jNKdJtIt3muRvlU7KWmNJEqprnVObNmN39e1zf26LOPVUxICWnjPhArASJBpsFr4o3EXE9WQylKWDJQUsTTYVsc5lX85S57bCiTLF0HpJNeM0YW+x+M6U+0GzKmqOmuFHY4vIjGsrrV0OWAlRoELIpynGlIucaVNC+5PBGKbauyRhEMjZZrCSyN4WwljJWWlCD1R7rlN7VJXYkGDQbiFKyUDOlkZ0M2SjqIiFlnBe08Uhfk61DUyIlGDc1d7e3f/HE/Jpj59/L6CRz7WKXrcUO88VT7LIlbxmoe6bjzKsvz9CV5/yJYbOa4+Y1P/mj96nmX+HGmx2L8U/Y7TvM1h2keQMjymtquLMSbAeyB/0UuldaJu0jtj5u2T87YGIadO8GZ+Mton0Pz5pNvovImEktmN0Z2x/NeDkm3l09YiuPcbaCJEwXYCcNF+4lun6OVAtO+zXuZI9VM+F0ecZXfn8Lg+MH7wSWS9g/+xhnPG52j9G8xixg7+v7rA4sP80r3tCGUbKI/7QObnzN0ZNHhHnDarMuTlHiyBSga7GY81//N/9ffu9fvMp//L/+3wwOUB3LMKXvJ0ztJU3jKOtuxUR3OdyxtHsVX5tOWABjFFLmwbrlo/UKuTjh/oP3+cn5E965OOK0XdHlSMwR1TBYoL7YeIlf3oDtFS6eyxKv+fzc8b+N8ZsSlz+/rH/uY1YUJZCBpEIfLaOtGc3tloOTQ/bPDqgfOxaLJfPVnJdnX2E6/xYnlx/zl//2L2nbju38BrMn36bavc7jo5/wyU+f0OVLHr2yz63xa7x+93XquzVWYDyp+Nrte/yn3/w2X5/t8PaTx7xzdMyi7+j6xIdnF3xyPue/f++n3P7v/nP++Rvf4He/8Tv8o//s//RC779OwsYU4NCYCsjFLtZYsCW6IaehHiVhzVDHJENQwaRcXOazL701fLFHG0jrpWa+iuop8Rckx1WCmCGg0RBEyB68FjVxNor4El+EGmJfowI9jpRGSDZEXZOkRXPGJU+vrgCKIZOzYY1Qk0guU+fStyzzuOJRTLRoq4jNeBOwMbK52LBYbQii9M2IdV3T2IQDTFZ8nTnvwEWgVhyFuKZ9Au2GqBNDMoLJZY4u60TCNY5soG+Lc1aOeQC7r3LDB3A8JnJKg5W6filV7WeH2XRIbvDY0i43kFSZCuw2nsu1wRuLEY8RSz24dmFLfrcxkWwFIaMayTmT0JITbxQnkSpXROJAJC6RQskYglFyNtjB0U5CAGOwg4NXxBSgvMrPYk8km9L7dEUxntSRYwFYRS3ROHq1gx1LcfcygyJOgyOjGO0LqToHfByUpZuizFVvClEtJ3pb7Ja9KU5VWXpybSCVSM0EkCKxExaLyIW2zMYNO1OLNYKqK7nWQCZAq2hv6EPp61+ulpyc3me+OKfv+mFPUdaIHz99zKQacW1UUZsSb1d2rfY32AeRZ+Jw4TlR5/Db4iX1xdeOGBOLbs2t0RgvpV+3NZlx0W4IqlTGFvcUBrc1sb/qIf/aoSitdgXoToYifRticaKioSfaRLBK7wIheWxb4RL0KjRZSbbHuViiGG2krzrWyZDaGdbWTCe7+OWKSMtAvycRC8lfS/2Hq+lToutbNCdUChQsKngTGDWeuA74rSmmqkvPwMQyZ2QL7RJNxXUg2gKaW9N8eSfFz3OTKBtQ+piIfV+EAldi0s8+3eAAhSqaEsRC7g6bjqN5y+2dGSMV9nzFaDwhxb7suSn9GCj9EVVbXDatQb1BNNHRQxas80iuWEugBZSE29tCJjU2zvExk3Mk5B5XWVIsCnDXW+pQF7c440gmUrUD1pSnJGuw4w6TExWBiGHTzZkvzjk5PuZyfVEi+awh9S1KwDtPHzNt16GyxnlBVKmrCqwnG4u3FVkhdj3GCyPjSDGAZnxtcc5AskUxD6hRvHNghKaqsAoxgVqLSo8R8L7Co7TmV3/mfuOguBHhzo2b1OPmF395hXRfKcWfG1fO2wfP3RaHu1dXE8rV3/+KV62qxIsN3fmC3CXMlsftTKl2v6D9rICoZf2o5WdPzun9humW42t3xuzf3MPKGMIppp5Slgm9mgcRYA9hJg1TN0Lc8+1wpe86fEqFlTWxiAqbyYIAzNhhxG+uGH2hMaccZz98z3ya7+kooPYWBSS/slgvB+HTXl/pu5fbPYUxcMV0CAyMyXLXZ4uFKvP2gieX7/HKwTXiouejDz7g7PwpsXvAf/tf/Fe88u2/xx/8/jcZNxPmqwUXq3PGeDwtLgvNpib2iaBlEu4k0VcVvVGqtCLHDTkGzCrgzDZp1tAcTnj6/k9ZhjVbfpvp3hQjmfF4wu7+Pts7Y6LLmLUhrnuenD2mDYmd6XVe2XuL333r7/Nv/8f/F1vR8dLB/jPb1bt37vAf/Sf/gtA5NsYxGk/w7gssVAqLj8/5ydt/yiUnvPKVr/HKK1+lbrY43CoNl8+zn/+iw6ZS7JSlqNjuWFGME/qcISdWcU1iTIVHjBaFlwoSdTiPuWSS26EpmhVNShc7NhoLCy9F1uuOdSr2mN1qxabbUPmGO3KdLZmB8+zs79H1YEcWsX3JgpSSB5n7iuggSV80qxHwBrUKWLo2slmtmTQ1lSsMXBVBqRBjSaLYUCzULy+WPHj4hFdGjmrP48lUqvQ2Qy6sy+lkSvQjlIxxFclkumSIYpBqi9mNCakX1tnhEYRIiagrwNq4cpjBZvrf5WFGFe5ghz4a/HqF2go/aWiNIVzOMRoYiwEiYW05+eAYbx6iUWlm1wm3tmmPz+nNhqXvcG5DdWYRGs57S6TF1y1uBnsjTzM5oCIybTMHYUT4eMr786dcjg2VHzOl5yRQCmAjRC0bGGMiYsBXjt4GHmyeEC8SmxTYPdxhEsasF0uWiyXdpiek/OLHfmDHXhUzWZWfffIxR2f/Odfv3GL71ssc7L3C8f2f8sMf/hHt6lNWprWOb37rO3zz9rdoFwtiWsG4o94y1E3FImU+evIh92VM3TTYsadLlrmx1FIsq5Jr4GhDW/fMbr14A+YqKkmlgEsVxeIqX20miWTjUeOQ4FANJHG4XBQ0oooEg3FFO6DZFyulJBAsSxzOQ01FNrZkUGppFqtT+igD4dVhUi6Zq6JEMRiNRFNUiCoJk01hOwOS0pCpZBAFSYUhmHsFjaUZjZKlIruESVqQH1GS8WgUNEHMGY8thJXCj0ayYJ0OG6tM8lIY9UMMRU6FqUgKZQOYHKrFTt0MTEUrseR5AWHIm81SspyNlhTPrIJXhw+/7Pw9Z6r+G5oi/tH/4i0efbjk7OmC1WLD6mzF4mTF5eWKedfTpURMmdgncgwlX2mwW89DjvtvAipXLXlJzgh3D25jYwMhkSuL228Y7R3QquK29vnWb/8DDhw0I4c325QQYIByXWADbHV0uuJ4/jHHq8zB9m22rHDaeWKbWR8/5qP2EcvFCUfLmpNQlGGzypbGhxmDJky1pp+cs94Y+vkFH3/0V/zsyX0u5hF74Ni1Y5ZPDXWCuNPxtD/m4fkpra5REr2uWMuSD+0pR48fs3tyg3pxk4O9l7n+yjVGbUZO59iqQWxEUip5aClhgG1xbIvnupnySrVPPFT66/GvP5h/3XHOGSSTjCk2eCokUVY58MOzU04uLphMp+zv7TNtPBI6jBesVoXBT4kEQUoWOSaVjanNtMkRgmIQnHF4KxhJ5FisuwwJK5RGizXPNrMx9JiomL64MeQMxinOl0ZK0IQhY1wki8Elg1FFsyvWWFqUFiqFGU0fsbnkyyqGlAVnwcailuhNpvivuaE5OAjakyVpPTTEDEjJAEtKsb2mNO8m0lLbjmwSLaXWeLja8PD4jM1ySSAQyMiTY6qPayazhmo2wY1qRvWU0WSKqw3WGEQd2eVBSV2TomIs4AwSPyca6QsO4zJXmeoZGSzs02CdrlSZZwB5NdSHRsDaTM6WbC2mFWzOeAVnPHlgmIcGsCUPudKSp+uDYm2hO6hCthGnFq+J0AcajXzl1dusF0uOzs6/xDv79UbO+XPtLZUy3zw/c6kWG9GkHjGe3I1Q0yCyRrXHpKJgcFJY6xlFbLHsc9Yhfcnwi0bQLJiqxnlDEkf0a6QCCY5+Y7nMMMsW6wJZLDrYnZpkqfsvh4p/8zs7/PBPzxjLNtYKfThFDk74RDf8QJ5QJ8vLCGv19OEUPVmy/HgX2yvZXtDzkD4nlgf7ZDNjc3/BxaXSzCZUWyNMN2a6EzENXDxc8PGHkdnRBF+3+O0Kd7tjfCexv6ts3u154jrEC+Pas5Ri99l0lnokhKnQ5Dn3qsQ6HTF/mrlYLaHzVExZSSRut8xeg+lNw7qbcPqwJoYtPlm1HGjH9dmC/VcSx3nNWb1G31hyMPkDdi/uov9l4rtnP+MsHpF2N5jdBf1yzuNHF7z7+JjHx0/pNfDeR3/GelOxtWeAU7CPWY8fc2wz4dY9qltb6K1IsHMqbgIzvHOcAOcZGhUiB8AYa77K9DKi60Do3+V4/hNMrDGxwYYRrp/iw2T4PsX1E0x88cib38h4nvQ2kFVi37Gef0rA6g+f8uY3v8bUeYxGxDgSjmxSIUylgfpsIlYS6gbypGbUBnyyxXY0K04tURNGWzKRlQi1WGKwQEa9kkik5MgZkimgRukj5kGpnoiqOAFjE8EWWzqXAzZaYh0he6KUusuYRKoSYgLkoWluPJLLnrYyBRBX0xaQIbtivWvKftWkCsklN92kNCTDWZwWpxKjQLIkL/Smo4BQhqpp8N7Rx0jAMht5JubFz7dMBMyYafo2+dESXa9Y96ccydvcvfsNjHmV1+++zEt7yumjc/783/wF423LmEzzumP2lREbbtGeLpnuHIBsEA3U7FI1ilYlcqOTDc49opn2VHd3sbGCJwHtnrIeP+HEX2DfCFxu7ULX8PKqQj/eUD9NVJVStQ9ZLpT92T1EFFMnLjTz+KFn6+s7XH9pw9n9NY/vT+le2+bwIHJv+zH50Zyjiw2nJ4Is1hydWNzLI27b24wqYWdj6RFiU5O3S3CiDmmyUNZoWd/n+Ok5j/vHZF0SSfQpEFJPzIH+9IIffPcdvvKdP0PchL4fs1q2pJzYnz7ilWuRLd+BTknLiFldstre4eNVYCcuOZsf8+Rhzw9+uuDRB0/48O0/5537PyHkUCw7cyKlNNgrgzcv3kLd/SW3q8IyFP3IVZb4RP5u+oHybN/8mUbd38iTURw7KKSY2dTRjzJ5WmPcHe5uCb/bdXzvu98n9ysun5zx/p8+5Kh/n5NHRzg7YSTXefB2w2X1kO+9+wF2JdQfLjlbX3C6rRyGmgM7Je9OWR094OS73+fV7evc/uYev/vqgvunR/zk0QPefvyY+5dz1jGyWHe83R7x7tG/ZP97/5Y/eUFQ3ATBeaUzbiB55+IoJobsEhT4soDfTrCpgJ198vgEVnNRtEqFxEjGFDB8aPRLpoCmNlNJqcGzaCGEmzL3J/HFrS2WWqkQOIFUHOIQJTuly66AI1rISeW1lD7BKAhBbIlRNCVSSHLG+Iiq4kIhAYoojQjRCFkjJhimucKnWMhGxtL6CpzBj6YkV7HKHT71GC3CJrFpqN+K6xO5p0uZmIUGAUlYU45bFCFFRbKjMTWVqVh2LTml58DwQv3PKZUc4Ct1+N9Ab248gmgD3urQCy12x8kL45EnhA5RJRuH8waTzZCXXcA8nzPoBpGMJ5e9gwI6kLfSQETwSochi0FdLkSywY0rWkqsT7aIUUwqLlniClgUgiGahJGyLqstcUhkJYslpRK/otmVvdQgWFAR1BgkZGK/Yrk6I/UbZs02k2ar2DxLUb9njTRkJFkCxXklJ2VtrxxGMxINCSWIDK/RkPrEctFxcbpk061YbY2Qgx12t5pCRtPiJJiHfzT0ECIhRebLIy7nZ4Q+/Ny51Zh4enHC+7MtXHPIrpcSp2UNWS3hmZL7y4wBDFFK7/m53yg6RESV3vkXbfhozsyXa06bjq3aYSQxazyXXUvJfyrW2VaENgc24cVV4lcjaKDXQDAVEaiNxaeMEY/TCjEejQWQFxfITSKtCylHdEDxhrhGHZStlVdWORGzMtaanfE2pxcXxbY/yTMyAcP1Lr3iRpbkPDEVkogZstmNhYPJlKfHC+ox1I1BjEPMhG5xzsXFEavNEnEVu1vXyT5TVVOEBh0cfX9jYzj1SSNtSIWwrSBGPpfsbQQq69Cc6Z+L6Uwx8ujynFemE8a+psGxPZ6yWs5xvmE22WVSe/qw4XS1Ac2oqwoZXzLZ5BLtRA8KzjnMdIJpSiwHdcLommAU1yQiliAWkZrKWKpRIkZDJzU2lLi7lGtyV3pUahOJeuj1FJfhdj1nGTpOL484uzyliwFX1Rh1Q3yDEGMixFTmJCDGTDHBCtS+qOsdhqgZ8Y66aXCuou9aYg6IL/Oa5lAs5K3B98X+Ra2QUhycQErtYq0hWuj7ni4F+l9jD/4bB8Wdtdy9dYd68ksY0kXl/nPqYuVTMfHz4uGOUhA+m6auBAi/6lUHpTu6JFysC8DnKtzBjGrvi21YBLC1Q5odLvSSk8uO9uEl7zz6K1796l1uv36Nr928TmX9L9SKgjDm0ynvs7+PYkiLTDWOuEmRIqZ2xXmeMx1PsfKc1+rfxbjgU7X3FTD+/PdAqdoNBeyeDF81n2YXXZ24523W86e/V1eYc4afB3ZXmyNGzSlv3v4K/WKOLJe06xVH8w2bH/yAJ1m5fXePr7zyBpvVknQ6p+6bojjsN9R4RjYxqWFOZBk3xLYmNRUGwVSOqvKMXIetDedd4O72bW7cPeb04hG3r9/k3vW7rLoN4/GU2WTCnYMdPn76iNPTx7TzJbra0IeSvXN+8ZSffO8d/uHv/yHd0b/i1p3rz96LiGFv596z/+tn3uuvHFnZdMc8PX6f948+4uzymO3JFjdf+gru1woh//WGyYWg0KpllMHYsrAaa4vtuYl0cU3QjrGpcEhphETAJGqGpjSRGCl5jMNEH02HpkDWxMX5nIv5mk4zXeoJMZFyZqyWkThQjzGG6aSBmxW+ElIdUAeaSjZFlo5kEpIHsFuFkCDbkjszPz1j3S64vTMrVs4mgVhyrImpqMdCDMzPnvDgwdssLucsli2ys4WXRBLFGTARvAiNmeBzIGn/zJqvRCtnpJfS+I+WKDWaBOd6XApYE3Cq7Pga5x199/mW3P+ujOpwl9yMMPNSXMpkm9R44iYQ1wsmClu1kDwsfcVaLdeWgUnbc94o99sxpyijfoVdnHLzYA+pDbpMnOmKpVOuucQNZ1GrtK2lt56223AeVogxrI6ucbnybJYbrq8aZvmUD6uWCw/at+Q2kHOPs4aqqrBjQ6TYrdTGY0c1dUjUkwnNbJt+sWK1WNN2PSH2xHTF1PkC4zN3z5q5WMy5fGeBffc9xFpSTL9gxTMdT/jD33+dr/3BHuPwGk8Xhzzc/BjJPW7k+KsPjvj+j37C5WLFuJlw8849Du/c5uDmq/iNoENBaavM9OAQN9l/4XO7qA+5WoVEBe0c0lVEaWlswuoAEknJ2zY5I8FiVekHO7QsWiDoaIpLRBa8FAy6N5klgs+2ZIY6JeGwqWTB2xhLEZNLbm1CUMuQIyQU1+lCXoi52ImpFO5uSo4ktgBoNg52yqkAFEnBgZGABFcsNJMdcoYVK+BjYU0rmWiFymYkJARXLDCzLWxDMuoTKiXTUFMiZYbCNmNMImYwUtSv2RSbOZySnGJcYR0bmwsbvy/NU9QRw4Y+fT4ZSod/f25Z+JJWu9/4h7/P+PpjLj48YXUWIHZsFnPmF+esTlcsFj0ny5bL9YbNpiP2kZQgJkVTJqVETGmwv9NnLPu/bogIdVOVjX9MJY4jJWKMrNuWv3r6EQ/TP+LO9DrjnRHj/X20GRci5fYhY36+tCtbxgztmrx8gkwcUu3imob1U+FioezMIhCw8xV5cckyLfnowX0++uSSi7ljYWq8cRxMJ6ybbc7HU3Zy5tZkwkU8ZpUjR6f3effyXY5WJ6ytQdOcw5AJ3ZjN44qLJzWndcVxt2TpOrJGJnXEi4dVxXy+5qMffEA6OmM2+YTxbo0PEXnwmD0sN8ZTDpoxUzH4nPE5lXz6lAp4GzMeaOTFy/HGlngJJ0AWgikc/o9Pj3l6csHh3gEv3X2Fid0tma5VX5TGwzpmQ4clEDSTcrE9Lzl9BiUiLtLnii4ZTBDE12ANTqCRiPOpZPPZhCSIfSTniCQtsqcMYEmxLq4ylWKNw9hY4k1yAimgeNJisYcalITJPUENQo3kModgBsCZYvlI9kPmXi5W30NmmEkgwRTaSnYQzQCMFCcJk4tKx2vCa1GpZOMRhfPc83C+YL0pmYCSBaNCDkI/j8T1gnx0TtaiFhYjjHa22Ll1ja2tKfWoYeJrjCkkHUkl4gU1eInU7ZcgOSmITYNDRZmEs1ZlkzooyI3mEoUhSrKGpAX4SeKRnHFAhcGZoupJ0YA3CEMkjpbHyLlGHUW5YnRwzCjXrqTEYmMw0eCNcnvn4G8FFP/rRvwci7R1v6DNgYkdIVEwTlCtBstSxZri7JG1xThDNA7NhjpVmLWlr5ToM/0A1uUMse7BKMkZGiBrInTCGlc+C9kUVbrNWO0x8cvt577zh9/h4Yf/msll5ub+Sxy++jKzVzLN5qf47ad8/fYu150FVsyjYdpuYaYjHo0mPD2wtFjmR4Lc+G2axrE6+zGjOGf2tMHOHZP9bSq3YuKE0J/CvRWnrmZ80nNjkbkxh/HTMaxgLS2b2QX2TDBxi81qwv2V0I5qJjcqnuxfcJbPcKslN5o73NyZcnL/kPNHHru0mPEDZC+TbyTEVmyO9iA6muxwjy7Yjh8wqz7BuMhWs8PkcJdq/2WkfhnbTLn+rQ2zjwInl4/I0xUhPmbjjliszzg6PieEACZzdnxO2NwE/R3gEfXibdzJ90hc0puGyeaAeJlYa8BP15jtGcbCVGGyhEkDOE+Yb2OWYN4DOYPtHx3QHH8F2ze4MMLGunwOtdRFQ+g2Ki9OdPrbGjZb9mLP2BXnjhgHK/FYE41BTY+NPUZDafCpFFISmZwskks8TNCKLKkoWaoeQ8kSLbO0Q3IciFMlvxQ1ZBHEgtiIzwUYChYkZYzJRFFiglrKnJRsid5JGbyhqHXFEG1RMLksRBUqDRgVojiSCqL5GRGpROR0JIGcHTlryeT1xW2EMKLNlipbRmowEp5ZDyMZvGAlURvLZFyjqSbViWnlaL6IE+FnhiYDixnn/yry4JOO4+kKd0cIXeLGtYc0oztU5jZ+BuPZAefhIaef/JA3f+cVxjdGJAM2vMbqVJk+6NG0gsklNHNwD9iwhxVhi6e8ESP9+jXa6ib2dqSzG1bn5yz6FtlcINsd09trqvXLjLtd3IOO/pPE6nCPh33D0Y8d5q3MgSyo9y+oRNjOHv3pDmZ7F7c+42O3IexesncQMYvHVCeP+cprnqf3RmzCdfx9WLb3uRhnmgZctNxK16mcx3RzuvVT6pt3iptiOUI0uxXnDwPbb7TcfG1EeC+y7FfknBGEl/ZvQLrGkw8tXQUXqzl2usX05i3Y3sfWDTfPP2HWR5JxzM/f4f0f/IwPzCVN85SmmxA+mHD6vqXPQq4zKXd0bUvO4MTRuBHbW2OuzXbY2/oldocveg0AQeGihVDDnvmSWeJfdgTgqcKeFP/2v+kxiCsEIWHR3DCj587OiN1vHPCd177KN37ne/zoL37CX/70+3z/p3/F/bOf0dbCP/nOP+Ole99g7UZ8//THtLMzfMqswgS/UqT/MT84f4/DJwdsbm/zzk9+xIM/eYe74z3u7hxyOJlx7d4OX7/9MifLSz54+oi3Hz/mx08ec7ZpWYfA0WL14u+tjoyJ9IyJAqbkQWBShFjAxrIRLqTUSCgE0UFVaqTsdVW6wfs84fJgM6wUwpLNmFSUSTkbgsk4n7C2gN3EHqu2qP9Q1JviAKWG7AwxW5JxJClilzIM1mUqE6gVHAmXUwH3LEWdHSOd0ZJDrorPg5W6zXh1dJKp+oizWtq/WoQBjW/I3iJ2UKdrZhUTM8mMcUzUso7FMcTaAviOcmadKHsVH/HB4MXgKOpYJWIlM64d52h5n4NVuuZEvrJKz8WW968b5kv04LcbS9SAStHWXumaNUFNzagxrGJCTEc2DcZq2YuEhDMUxbh6xBQWrXSA2iJgygarBiOZXBWwtEOgEqwTOkqfDtdjB8KzG0BzzWU/Fvtim59xqIG+gejBSkZyIcYbkwcQXiBUqEbERKwmYm7p23NOnz7m4vICay1bt+/hUwUN2JxLNF4FfSe4WEi3KSnZBHLUklojxY4/bAzRQ+4spldin5gve+bLluV6wapvGbuGkWuoKkP0JTaRBNF7QtsSNdGnlnW7IITwuT2LFBPn7ZpzSSQ/YpJKlFesFdVfnTn8q4f+kp+hODL+qmvqs2D58/dX2rbjNKypmhrjM5UaKlE2XUdwQB/pBdZt96Vcu65GUmWVWzqaQpAWKfvBRmi0wVmlcoWwU8VcnAQ001vBilJlg24qyB2xNvRZ6awlVpGUAjFBM2uoLkZsQl8qxkFMqQKRiOZAUxmcTGizIYceJ4amGrM9nRJTT9Se9ekJuZ3Q1DusVqcQMgdbh0xne0yaLbK1BF/2dT5B/I2srMWFSRn6HANYr1cZYPBL55liC16s5E3Oz2LAAI7mcx4fttwbWZwabm5N2cRDJls7eGOwRKaj4qJ5sVqRMAOpNJc4DW+pJyPGeyNmBzWmKRbuedMVQl/liLYmiSfFEZJrrHrqFkzIZJOIYRApxYKzJK9IMiSdkYZYIa9rVrSs2iXzxQXni3P63JGlxBhI7AsUqEoIkayK+LIHUGUgUJUYIrEQYiTEhB+NcX6ww3cVRorro3OWxtUY39BrHMgTmS5GYojUlcd6S4yJmgY/MqyWS9brnvh5yv7PjN94rbU92eLw+nWc/yWA3ZBr9Nl54SqJ7nl9eeGKPjcSv17IdoTu6IK07MBaXF3hbm5hmy9uJZH7zDI5LrRm4Q1tXQHC8UVLPFsx2p3x+kSeCd/luX/LT58/AY63G3SSC7AGZSFPsPzZY+Krd7Fb05JLpwxNqi/80r/ciJTj/TyJwQ9fFeUcXn1lSi7bVSD81vDV8KlyXPj5HHIKa2qliQphNNh8KMrx2SlmXLNze8bpTz9kzxhkaLDP1ws+fvAR/+P/+7scv3KG7rXsXtsnrDx5HpGRR7YSNrd4q/S9YbMIhNCRRtBrxE8rZDZiNIqAYXYw5ptfe4P/+J/9Y/70p3/Cvqt4afceJzFgrKWOAdtn9qqGTVXT64ZtP2VDBLPmcvOUnz1c8PXf/accvHWNg1vXnh3GzwLgX/g0ZqUaGWa7O+zbG9STMb6Z/pI7X8nyv/goDqVFSWVUkJQwV41mShZ4JLGRnh2TEXVgIEugV0FyRhL0onR6ZWUESKKyCRcynWQuYibkSEiDoiczKBQHxSeWkmnu2doGsYUFqlpsQXrNJXdUMjIANtZ6TO+QytPHnovzM9p+Q+wDO7YoqYxL2NSTk8No5mxxn/c/+QFn56eIGOZdIqdB6TYcxmQcWTNGVxhxaFZcCCSXMdmWItNkTC6gbLRCDkq3XLLTyFBcGmbW4Zz/dxoUN6MauzcjZottF6hY7LghGENcLpHYMTGGqoaNd1z6mmq2y7YKu23LU+04fudjeHrOdlAqmdKbEQf7E3auL9GTc45N4tKPue4N8/UZ58ZS7d1iWe+yOX2E6ztMypjesisj3hLhtZszfrxzwA+nDd35HP/0CJ8jyxrW4wVrWRLOAqjSTYQqZcIm4/seZ8ao32Y8WdN1a/p2w2K+ZNO1pF9jcfxVQ1WJgwL088bdG9f49u+9zL1vXGOa97nRbXHjomFxfsYHHzzmg/cvOTm9ZLFYcKZnvP/eT5lsb3Prpde5e/MezXTGZOcmB7evUe+M6bsXL9j1ysZTAYQQarK44virHSMT0cGqS8QWoCCVvEcxZeOdbUSNwxoDEssmXQJqC2NbTNkEqVFM9gg9BqHDkS1UaVAq+pJ5ZrXkQdoA6hK9hZw+zepWGezCkpJNJPui6CuS07I25lxcGMRRGgvqSm5VLnltOaay9GQp8xXDYxhbQnhyyVorcU+KWEE0INnjBtZh1KJi9xKR6OiTIBLAlFVfJJNsOVZoAfudUJjrOujAtbjCPGPA/oohw3l60fHNN/4F1fYnPG7us7rIMMn0+RR3fp/Rx0esztd8crLg/sWKk9WaVQq0HXTLxHoVCCGQ+kCI6Zndek4JHRSaRcD98wz7qq7Yv7bH1FTEqHQhslivmC/mdLHnT977Hv/Fn7/M78y/xUtvvsm1m9tcirABDkR+rrQrTGpls1hw/sF7dMun7Ny4y/bta9hRjdRjFvGI+8cnVDnD2ZzV8UNO2ic8OH7C0bxjuTGskyVulAWOS7/D/cbx6sGMV279Y67fexOWY05i5NHpH7Fkjh01hHiK2jl+kvAGnFpwjpmbcKeZlXKobahOxqTNlK5XQgvOZS7bMz74yUN++sHbPHj4gKlv2BttcW/3Oq/uXeel3Wu8tL3PjquYOl/m1AFcNF9iAztKjt5kqljWS9HMk/Ulx5dL7t68xY1rB0yardKE02LlnRRyyGXTk4sFWtcpySpEcDlTDbbmbc4YtaABnBuU2x51Qp8dhIhPASSQrZIiaISSF5UgF6t1yZ5sysdPcsksFwp5xWpATCK7kulcrgEwSZAk2NwPURY6OEZkVIQYXSHHhTIvWAq4Ixkynt5kvBTFblEBepKR4mQhGauZSY7Ueah/gDYYjlYtl8sFJYqvuGVYhvlGIMdI1kSvfXExIbPatJwfn+K9o25GjEcjdvcPuHHtgLqpcVLy0n2WcjxfcOhKycUJEfEgLiNa7OTIgqofiEeZVNvibDGcA1VLnTPexpL7CJhoMVqcQcZtIfqQDRog1ZkkFstVEzdjYsl2T8FAjsSkZRl0nul0xnq9/Fwl99/FyDlzcX5KXe1htqdMq8RIIi5GNAqtCGoK2Q1V1KRBbTNYn6dIs1aCdnSpR5xg7AiTBqeEIIiC8UpU6KUnuAzZljUhGYREJV9OhTLj69zUR0i2/Pa3/z0mvzOF5j6z+ATyY87tii1uEvOI1O6BuUOe9MxHiao5JFvDwnWcL+aEIDQu8ZX9XV4+uM3FoxGb9yyLtGFvdMnBdMM3vrLm9KBj9E6kOu84CJ7rl69xvNwhjNfo9vcJJ5/QbSwTd5vjesxRFprUsQprwqwQEPX2DpPFddz8TW4eGfYuLujiERevd4RlzfGy58lJ5NR7fHbsLaYctA12UnOZzuguV9SbA7rbv400Y6gN42/X3P7hFqt33qN3Txi7c7pFUfGm1A/76sTRJ2uOf7TFndFtWN2Ej9/gYH2Luf6Y4+8+JskWW2dT6p2byD0Db4GMYXYObx3B6aRjeX1FezFGP2pYfU/Ye6zMzsdMlk25XlDUBLJdkeyKbJckN3y3S/b4j38j1/Hf1Nhyjkl2VBlwSshK64symmSxEnBJEHVYSZBdyZcUQ8yKy8VpbOGkkG9MxuUNE21prOCzQ0wgV5CdQzUVpzHJpc7TEo2kEksm7rB+mWzwiZKbKAmNxSGELBiTwBX3EjWZkB0WwSRT1gsplusmZ1I2JWsxN7gsZBMxPhU1fPJUalCbioLQlwgQsxFMkoE0mclqUGOpmhn7oxoTe7wmRtaRsyO5wFSLavFFxyYYFn/cc/6TDmuhsfe4OM+MZ0tWyw2NBx6CXAi8Kdy9+yarzR8x2r8o654YZh42e0JaZPThkrw+wd86gsMzmsk5ohOgZ9Rep/npIVP1rGrHh2PL4u51nvzoGnv3LXU7p8lH7NYG6TtcmmEnHl+vmebrpCd7nGzWOPOEg3uP2RuvmM232Dz8Jm0a0U73kZdmXOyOuWBOZ7dwL91kZ7rDVrXNO48v+Is//i9Znj7ia68eMD2oMdO3WIYp1eUE+25muf2Qwxszat0rfRSxzA7v0X87cvpkzq37M2796DqrgwO++a1v8c/37/G7ssX5S3dYnjtWlWWx2OC9MNaGo8tLLs+eMnN7bF+7hrFPGV0sqT56TB8XuFstedIgE4ep5tiVZ1KN8TT4qmJnPOVga8rhzhZb04bxVk31BaLwfp1xlSW+NEUlXvMls8S/1IsBlgPT8jcgoPz130N5Ms+YA/ca2xq4PVuzcGsezZSv/rP/gJt7X+G7//Jf80f/8r/hxx+/zUuvvMnX3/o9vvb7b/LuxQMu//SIYJ/Q2Y69nZdoDibULvF4vmJ5PuX0MvPn/+Z9FidnfFyfs3f2kOvjGa/u3uDmdI9729e4tb3PN+69yh+en/LTxw/58aOHfHh+8cLvP5FAMz5v2FDh1SN5MDvOBhVDjkXdZpKWaABTbM8jWgjoGsrcZlwhW2qp7UpIuOEqdDPlAmhan4sDRyoObaIDYTRDMra4IfZKzo7eNYhmMpnsBRMym3bOxfEjDrdrDqoZXixKpiITjJY40pwJAtE4gsn0XqlCRKziTKJRZSWFUKgoVTSogHWOsbOoZLIkMoYQYrEDF0Fx1JJZ5kwfoTKZ2tpi5x4yGwL9QLRSX6zCs1FSSlgbqRpLVTv6NpA1k0N6pgz/VbWpAN466vrFP9/WeqJkeiMkBCcZqwZEMSi1M5xtOjZdT/KJugZrLGIsCOX+zhQwCkNfCaZ3mDxEQEgCI7ik+CIcJw7CheIGlUvurhZRkAG0hZgMiar0dLIjOkuuMjl2mDogNpODRaKSI9iUiP2a1F+Q7QrxSiUWyQFXJfav71KPRuQYERGCdEXprcWpygDYTCAW7ocWFy5I5GyJxmPdlcgq0Wkh1McQaMOGnALeW4yRolgOGTWZWCVSLJEDIbV0uaPVnpCLm9XnAZHj6YRbt29x/fp10mTMmXNcmliI014x8uVB5DJ+SR2gAIXc8WxC/IW7fvaG5/6vguZE1y7JU4M3FdZatuuG+fKEsOiHmvQ3NxQlhI513xOrKSY7xCve2mK3nSPWOZKuSEYgeGwyVDlgnCWQUCLESJMN2TaohxCVpWR6yv22p2MW8/mnmJle/SOs247dbBiPGkZUqBps3eBVaLsVZycLGh0ToqGSGzRaUW1VVHYCVaTuO/oqFKfRUGNMh80dJv0mhIUCxg4xFUOPVkxxhhNB0yB4UchZC8HHmULKSYk29KU3+ZkYsBQDDy8W3B5vsW09ViacbWWkssWSve+oq5qdnSnzfk5OHZJrpuMtdq+PqXcq6rrCmCJUim0CIi4nshGCVgQafO/wa09WR1Il9mCwSDTUGMgWleLZ09UeWoNp8xCnMed0ccpyfUbXLgk5knIgG8V5h7Ulxzwr5Bwx3hXwWxii8BTnHK6qqJqabIBWkbxBYyKGHrG2RFQq5bxn8FWFisdaRSqLdsrYZHJlEG/J1mD6MOCNCkaoKsuk+tXMvt84KH64s8futcEE/fOqoM8BxRVYUwTHV6mEV3jsSEuDWKEAtb/qFSton+ieXpDmLeocrm6obm/xIvEKcRU57wIhK9FW6PaEG7cO+Ke/tcvebk1TmeeA+1+v7JOh4aLmCimmrHEefA48Ob3PnrvLsvE01Oz8mo303+jY4lOb8/JZKv+/AsiLzOQXFeSJIvE/GX5/pSAf86nsf3gzETjqArcq/6mK3EA0Y66/8irN3ozKlmKnrqdMd2vcVkPILe+9/QPu/+X3+F/9H/4j/s//u/89Hz14wscffsyHR9/n5Mm7mIs1fu4Yr6Bd9eRgqY0nuhG+rlFvWLYdjYF//A/e4v/P3H81S5Zl+Z3Yb+29j3B9ZdyQqStLdVVrgOgGBoQgALOhcYYcI23wQj7wO/CB34CfgE8zZiSN4oU2pHEGI2CNwXQDBUOLanRVV2VV6gwdV7s6aovFh+2RoiqrKzsyexrbLCwiPDyu+/FzfJ+11l/9vb/9W0zcjO2lMq47bpSHCInz9pzQBYbrDZPRhFl5xDCd4P0z6tRybVa8+/gD1HvKUclkv2Q0/gpz5JxwcOeIv3P7f8HZeknTbxktDoiacj7MZ9aLg+Jqs/pHjaCasirKGCpnwGcLo6SRIQpRTc4kem6/pJEQIXhDbyzeKDZlcEyMYh2YMpG8gpUdE0yz4mDHnAsxsgmeioTZFY7O2QxsRckiDM22zFJEkEAMCZ9stgcphN4Ykka6pmXbbvCdR8oSIRddLgYSEe+3fPTgx1xeXeRj18Sm7UkhZx+blG3zMnE3g+poQmJANOSNm4hRR577C1poztNjy/XqnANGzEY1kmDkctZsZo58+SUmnxtjv1xezCc/UCgOFzCdoNuA6XvSaA6jkhASw3rNWGH+XCVe1bTVmIPJPje6niQ9K1vQnK85Wa6YWosujgidY7FRTqo9rnVFbSvieMJKPef9hv7AouOSrjyg1BHxYmBvrNjpiFnTcnjV8aFG+toxffWEe4sFt+s5d62jekW4uvsOl9PEB9/33P9z4XoLfTSkKpK6rFDV6FEmpMEzdD2b9YbV9Ybldk0/dPgQvlJbe2ct0+kERfjOd17hO799i3FpaJ5s6R473HCHqrnF+UdXXG1Ljm7coWs/YLm8JAbPcH7O1fk5P7F/RF1X1OMZJ2+8wauvvcK9vTvAf/pC7yttPq2Ql2yn7hqMbrAxgKQc/2ESKgYZss6nSAYzhJ1jSMr2CQk0WQQLRhHTYVGMUazpSUWBmIRJitXcQIdUICiiEVvGnTuMYD2kWBBMbnSSsVgJBFU0mWyPbjTXnZogQY9SqJLEESkQH7AmETEfey4VmhuPoAUiARsTFrd7D4AovQ3UohjNNnUm5Wye3Lp7AhEvPcl6EpaUJtmKLuX3XqYM5osN2GhJJqtuNVisCZRG6CSr10VKkno+7aH0i666r+JqvFO/wuRozvLbr/NsPeJRMVAsLvlm8wH3nj4lrJY8Oj3nR9eXfLjt8CZxtRp48nDFw/srlmcNbdvQDB198rsCPg8WYhJCyiB59BkYtCLsTedM3YhKhNLCuCqorSX5QK89p1fn/Ns//xMqaxhNR1RHN6jKCsYlDwQWKHs8dwsSfAi89eAJ7374GJtW3AsFX3M3KfYtjbvPf/17/4KuUQ5Lh21a+u05jSxpYsPgbWbCD0LoLa3WXHVrYrS8fOsGR3ffZHr3mP6h0NuKy74h6cBIhJk0FHZLuLFG9sfMpOXGxZaxWzB7/VepLlt0CJwS+XAQVsOKOGpx1SWb1cDF6oKzqzOu2xXX3YpHm3N+dPYRVnKm2/F4we3FEXcXR7xxfIfb0wW3JwuORxPuvOD53lOPV/LwC9iGLY+eXlJPFhwcnFBUE8Ai1mC9Q6LiGFAhO6MUitECKynbk2ERb4Ce5H12XrCJZAJWswIwREMYsvKBlIi+R0m7DNvdsMvkCAE1hiDg1FOg2GgwpWRVt9NsBRnycNCaPPgTIxhyLmzc7SU22QxGBhCxpOK5CiSrCIvd0MWF3EmoiaQqYWJEUok3Zc5X/Dj+xTOKnjIlNFowOTv7LDU83W7QJFjrKAWCWGz2L8wkPtUMvquhEMGUmbUdgWEItO2S9WpLES0H1R7TwWEK0ELx1uDNi0+ay+eDzQF0yAC57EiqxqaPBUciFqN2pxKPOM0DU9RQGsAEeieUcaciMR47WMouq4WCycQXa5TgM6Avkkgu5w0GFKTPiWKFY3HvNt85vsnF02c8fXqfTbP59yI2pu067j/4EdvVOd986VtYVyEp24IWwZDEElDEJEpJGBtxxlKYHA2QVNHO02+21PMezD7OGqqk9GFn524lZ4DatOtjIsFbTCwxydF+yW7b/8sZxZMjQjlgKJCxAakYs49uL3i0ES4HpRwMX/OKWz9ieHbBtunpyhFRztn6jvbehK0KG3/JabA00zl7QTj6U8Ozi2tOF0vk7zfUNxrGc08RPd2TDU2zIF1O8eVdYrjgxskEc9vg1y2XpxsaPeQiKMWHV9gLGN98iZGZcBj3UR8Ra1gsDrnRRNo4J1494lF3yfLsFHNVMqpuMV8Ii94z7xKFjIhJaN0aMxiSDzzvl01luPm1fTZB2JjA9tRyvazQYFEbSDYgKMurnvv/zYZfuTKY3tKnPUp5iUl8hj7oMd5QvinISwUyCPwZmbh2CjpRrm895On8h/jpEXL8beLxPkeS2OcJi/oniD0n2hXJrkmmI5l293v+Benfa1DciLA3qkkU2SmIiC+VvlICJaqWQhzR9AwiJO+wNkCA4EqiWASfSYqWHahdMGl7DnYGiysxUIHuohuMyfnkRGUoHCEJRTIkcbl8KyJSAp3k7HLJ+6zb4QDYTJbWJFgxFEERF9CY88mzgSiIlBiNGYzCYEPOMxVrEFeiKtk5Tlx2/LExKyajYFzOdrW7jFSDgEItJYXJTik2DRQpqxM9uVYt9cUnNdd9z+TIcPN3C6pbRwxhwsMuUVQdRIeuDpBzgStgBtP6AGtHDFxRExEMdYxs2p7LoscMDRO3z/a9a+ruEPv1O4iETARd30SejIk/hnMrrL9bcrm34Cx8k1WXqJ/+BFfe52vHY3TzKvt+xqJ2WE18fTKncDAanqD2HcKtAjOpKGxBMVsymx6RqjeYn5S8WQr3OKAaA+Ndtr0qRbNhefmYFC+Jsxpeu8Ozp/tcPrLMO8/YXPDk5IKpeZuK3wCqbD5oDlB9QrOExf4R/+Cf/iP2v/Utvv3dv83fiiOO/uQZ7bbl3djzcDNg1pFxm4j3n/H+6nv4sKT62m9Svvkmi5uRG8Uht+QOTx6dMW22jO5U9M6yuHPMXlOSXrvH14/2OTt/zGxeU9YVri4xpsCII31VGMpuDQpXA4Qyq8T/2rLElTzva8k535+ThvlXsz4lKBJwUuLKAsoxo3Kgefcdpk74+q9+jfrsnMtnP+WqfUZp4GJ9yml3yg/f/T4fvfcOq/PHzOsZq9mADmvWW2XU1WwvLX/0gz/m7Z9+yHRcUY8sq1HH07LlnatzDkY1L8+Oubc44ubsgMNbr/D1m/f4O99Yc//s2Qsf2To6pgRGyeNt7q8zQMrOySzXhUmVOgnqBIpA6RWRHTE9FUAm6pETc7N1ucm9uzEZKFVf5NntjuhkooFUoCYRxaAuq3+Tt2hweGtJSTASUIF+27C6/4jHHz2k6Xrag0Nuv/K1DBaLZKDdgHGgzpLUkpKSrKe1WelZOw9OGQfYAG1UGi8YkyMtrICzmbwWQ8MwDLgErSQmRbZHr42hCIaVz2pxp4LRglKERgVCYpBEUENRCNZb1AvegTGWuqpYbdc5JuwLguHOWqqyZDIZM528uD1CTYEh0cWcAU6ymbSQLDYJNYYyCQ8vLrFJGVUFs3HFfDLBVDUGpRSfXdqsJQZDSMKgGWDH+F09nkWFRizGlsQdGGdlZ9WdBIngo82k/lDkHsAMJPG4YHO0UIwkCfix4P0ATY+/3rC9uGC1XBIQ5osFx3s3kLHDuJJEwJmS8bxGYoAIfhhyhJ6pMR6iWCJQqlJoAFvkfpJM0M7XrsNYQ0gRJKEFqM/EN6kcRShAhK73rIbAyBY579wnBg997Gn6jtVyyeryis1687nnxFrL8e1bjGZTkia0SETrkLESTfqYVPKVredo6OcB3yK/fNjzs6Lxnciw6QaaLcy0wtaOyThSbQqG/svbpX/eGlKkHRKRSKeWaXLMKVEEnyLR9jiNuU5PYKRgZAJDEjqEkgqNjv55/xggOCWYRPQGlxLTWY0bWTRmxfXHinoBPwQ2F0vKGyPq0ZzQgzEjNCimDRwevoGRTH4siwI3NKgpITb4ITCEBi0FbK5hSELrIvaruH+LYMwu4jPsZnxiEJNrXbHPrdPzDE3EUhQZ8wgxxxd+fJ18aqkmLtfXxHiTSeEYjR1H28SZs1gbsiW6cZTqGY0WSF0yXUyZH4yopnk2G71C3xHNThlfWXxQdGvYbFp8t6WipGAMzmGEHYVBCKWlshNqlD4oXiLDSjDNQDQtXjv6Zk27OSOEDjXxY7V8YSqsyQpYYwyuKHNURIoYZ2HYIbw2Ia6kqCoQg/qIxrQ79wMgO2Gp4GydxVY2oiJE7TOepLvaHYvRnemKGlw5xhlIscOknfud/eUkp68cFD9a7LN3dPD52Fz20cro96fWc1D807efuHv680P4onniKLs88Q2pC0g5wdUVxa0pxv3lGxa/DSz7gQynOQprOdobcedwRFn+rC7889/Px+vTRIDdoCjnaAVS6NleXHL51kOu5QmT18+Zvv4yM7dgNJ1S7wAwEflksP6ZF/6KYfNABsafF8cDn6jHnzMWevIm/Rwgf64k/7SC3JNzyC35vM/JJ7rK73heVlRG8rkKBpkKv/X1X8OrZ1TUNPs3ufmtX+MbhyfcHCJVXfDw6hx9ckHfr7nxym2O9/c53t/nu19/nevtr/Pg9B3e/9EPOf/RY2q9QC8eUbsaGY0xsylD3RGHFb3AZLzgH//d/4hZOWN4CnVRQxlIoxK/2XD5+CnHE0c5ukHvxkwnjsUc7n+kbK/O6U87zpbXJB948uQp/8t/9J9mYFX1C9ukP+d1/ZyrgCrELeo2lOUht6cv4VFWQ08blhy4OeYzTA+fP9gXWZLt6GIR0ZhZh1EUtSarj8gbWucjfRScy8x+JRJiYIiJAYfVCkwEVaLsBrRWdxafBmvNzg4Dov/knu9DZL1u2J8lnDE7RaUAhhiFlFLOld3h/oVkK2SVgugcwRgSSvCRvhsyYENWrXvjaJPZqUsjfbdls1l+5vC3Q7/LBjaY6Eik3VQ5/+ZIBPWZoYXNx0UAUxBTiRiwtsHVgVFd0vQBSoNGGDso7ZdobSWD4NZarHMURUFZjSjLr6ZTlcJSnOzjk8VtVyRT4MYjBmPw1xtM7JkYoSyhKxxLV+Bm+yyw7A0bNg42Q8JtGmaAlBXBVpiuIW0bmjAmLksW8xO0b1nFDj+e0JQN23hOp1NcJZTjkpkTFjpnMhjiYsa6v2AthuJyzXgoSeK4mBhms4bfuNVx8A3h+uWCt04i3/+x4adnhk2r+NIRYgnEbMXdJ1Ifmc/3mE22HAwr2nbL1eU1q237lYHjX3v9NX77b36XzdbzyusnVCVs2lPOnxVc/6iG6xOG2RPO4obX7rzC7Xsv8/Ldl/jBn/0Rjx89xIfMNIwxst02bLcNV5dn3P/Rn3F4eAz8n17ofZ19+Fa2gNRcjN84vsvBzFDJQEnCEklmp4YxMYM7NmK9w4jDpvzdESf4bAGRG58CnAQmKTKxHpWIkzKD6z7tMswNzuV2Q8UQMcSoWWFHZhNrkmwlLZptMpNDSFjN1uSSciMPCsYRUUwEkzwaUiaJGN0ps3OUQjSZfWytJdpcUViyJTPPmzKTsFqQHKQBSDv70xhRjbsCNxAk25MZTfRa0OMoJGJNJhCpCpULtK5ATc5/M9FCkR0zkrOELvzyJuhLKsSfL0fBYX3EwS1hcWAom57ZeM6dg33GtzcQW6ZDx632mlVzTWGUEHp+/PZT/s0fPuCdt55w/+FjLi+X2NgTNGWlQUj4neZWYyIOkRA8FmVUVwguK8rJ2W5OHPW4pLCWyXhMJYapWibdiPnZhHrs0MPIA7PiJ8PAXjnnm1XFTJTzqzOeNR1pPEOvIs+eDMz8U8qTFW89+bc8/PBtrs4bfrLtMamnLhPjvZJy7IhtIHRK2AiiFWWpGBshOmo9Im4O6JeJdWj46PpdtsOAsxNstEwPZ8wPauxIkUnDhDXTqKT9CcXBAbxUkYJjvN6y3S7pJ4ny5Wuai0f07zyjcw3rbosP2QpLZEeE3FnA3l/3PNic80ePhJGrmJUj9uoJd/eO+T/zT1/ofJfiqDThJbINkfefnvPw9JLFkWWysNQ4QqqxyWaWvulBhcJ4Ctfn+ISd9ZfDZeWKE9ohEXyAssCJwXpFDWhosNGzGVq8CThXZccIVVzY8R/VgqtpTU1KoCZgDZQGKpcz84SECfk7r2owomh0JHUkl4HwHFkgQIHaQMYnHGhWGxi7S4KOlpAcFqUkYXwiGugk1wdYh3qLJCElIaaQv8PPCQWSHSMaTTxqGpZdS7LZPaKIJZURjEDwSu97PAmrMB7XHJ/sU02nJBNZty2np89YrleUpePl4z0mY5f3tmQzAINkct8LLnHZetAlIakQE6QedIC4I65KIVROc/aaTZmwoJJt5yM5agIhaaIvMhkymZ3Lj0acNUQbMEPOiUyarR2lckRjCUHyAG5cYB0kV6ClpTQVN+/dYbw/5fzZE87Oz/DD8IUiGL7qJcbslP35OJtuSb95htEFkhJ9jFiynXzUgNiUXU+sy9agmln24EkxEPzAsHUUumUWKsaFUpG4IjG4MverNmeixgSIJZgyZy3HL3fsm7MrtpMrWlqCDqiOEDniqPldjn5wyGVdspwec6etOdoq1ekTFqsr9pfntC7ACVR3C6pZSzrvYQsyMzw7P+Pwow3H96fY6FnOI4+7SPXRGuk3xM2EIdQsWkHbFV11H28f85KeUY0Hlt2YR8nSTT2FnrPQU+Yx0T4JTMdr3HnHEEuWSWmLX2E6G9h3B/Q85dHFCr+xdNtLpuVNTo5HTI8bzDuXFKdPMXVB++or7N29yV51I5NhWpCHwsLOWdw7wLRbwtWCImxI8oxgPSKBhLL1a87aDzHz7BbjfOL6QcfVsoOJ5piyBchC4ClwH2hBplB8XZm9LjwbXbMyMJqtOHxlj3ix4q1/8S94+cGfMvGbrILZWYXLjiwjqpigxH8PCCF/0bLGsF+PcDaTbdGsavOpwmOxkkmSXjKxb2QDkSorHBVUs4uHQyk8OBUg4TQQbUmvSlOWeBySFKuKwaHRoBqJEVyySChJNoAJORLHkBWAwi4f0CAu7ADqTIZ2EbDZpcxKyvEHhZKCQNDsFlQ4bBwwJuKMA5MH7z4UO2I2eBWcycCB8aAmEnaELEURp6j2YPLQ0KqgEYzdDTEkZiA+ui91vufTivGvZptdKWCkFdPwjex+5CxoCb8CecjeY5pzTLrF0E+hyoOx0liqtsI7Qz05YnlpWW4rXuYAGyvUnaLdmtX9muLPI6MLy51COQTaUeBfVp42TpH5IZPFGTfKDfNpS3HrJmHpGJIhHcH0ODHvDeutcDktaK/m3JzfZHZjAaOa29aB22W+7zr5GCNt07BaLfnTf/N7mPGGb337u3z7u9+lKu/iljPi2YhmGNA3a1a+JXY/hfGb5AGTIhIpwpzy8ohp921u3F1STz3rzVNOj1+nOjKEriFebCkRfu3wgOnxmM3Q8ywMXG47nj57wA+6p9x+Bk2/4d50n3s3xtzZE/Zu3+SjhWV1XPDm5CXm5TFv/eQ9/uif/X9zD0JWe3Wtp2sbuvarc39LfDZLvOSvL0scyBx+Rx4p/Y/0Jp5Dlp9AU7n/gg2il0w3f0aYGW7s/wfwO7/Bcp7Yv3vCxXuP+Bf/7P/FH3zvn3P//CGb5oqpnXDr9i1G/g7x1LNdX3PeJbqzj/jjH3yPddNQr8eMyzHzyZjxuKAZK8vtlqfrhp9cPuF4POXVvRvcnR1xPFlw/MreCx9bGyosMAsdI9fTJiVFBzbvQ6IJS8Ilm/cuMbkXRbPLWMoER02KE4OPYHe9OijJ7BSJKWEkEso8X04x97MU2RkuGiXhELWkzhKxO0GdMIhwfX7OxbOHXJ2d0fYeHxMPLq9442DFnckCcWYHh1l6EwkxAx8W0BQJJuEtVDZlByOUuS/wJuCDoVfFYUhBCKIMIdBtGoL3aFmhWNoApc07R20t10NP1w4MFRTWICYbvQUnRB/xPkIwufc3iV4TPgEuu7fF8BfXnkK+F5ZFyXhcM52M2Z/NOZr+IlfOX75EAjY5igidy7b0RczOKjvcjHlZgE9s24a+tbTNlm7ouTk/oKoqgliCWJzPbzK57ICCDiABdYZkE4WVnYuWp9VEkT8Isv+0BXXEYChIlNISJRLYCRwk4KkZ+pbV6Snb9oKh7xi6jrZp8D4whATWgBuxN1HKlOcvlgKfUq4DnIGY6KOQGkWSz+MM57BFjlkx5OtcJc9gsqLd4kR2goUiG1OoxVjLYNoMdhqHitIOgeXmGi8JS8XQtfRbz7ZrWW3XrFcrhv7zbdMBtusNV2dXzEYLTFTikDBlgNqgzsHPqHX/ytbHKuhftn5eTq6q+BBZNQOHY8FiqGrHbFLRNl3u17/ilTTS9j1DH9GRB2uR0pKKHIeYvOK1wqVAXQ4ohjbEHIET8iyvGhJDUaBFRMxAgTLzEEtPWxSMC4OtS9i0fJyVLFAWBaUd0w2BEARtDcbXpLJGEKp5SdQOwkDEMWjAmS6L1hwYSTkyK6Qc3ePyDNSkKj/2osvs+ngjGJfzsFPoSCHHAKXdrNLsIDuTwBhIKTIM5Bi452s3l9395eOHGz/wbLvh5XpKMpYjW3E2NOiozHsaSjGtuPP6HezYUjjQPkLjseWufvYQEvQh0l8G2ssWPwwMraJRGNRRlyXRZRe4OATUKmlUUFYTanUohsLaLJawiaQDIXi09KgToiopKTEEkkJVlLiqIhKJYaAoLNZahhBI7e4NJUFcgbUOZw1BXHZ2MrtoDnKkpjECu/uLsYJiiUYwNsdhDD5iUya4BiJpAGsEqQucBVNY+t7RtT1N+8uFiV8pKF66gts3bzJdzD//Cc+R7oLPFFmBzHQ6+tTDnnxplHxyQQn8cvv0BP58jb/eoilbmLjjOeVB9Zcu7DQq3TZw3XmiKlGE2jqO5iXWfNEfp9Dlm7d1nyIHKWhMOSMmDTx7/IynTcWjFp48vo+srlk8fcbh4W3s669z7/AA8R41JrMqdHeXNMVfjbV6meBaMjpqyUD2lHz+OjJI/pzkEPgsQF7wWYD8+Z+fA+Ql8DVwVjgy+b/1IaLdQD2qGFU1NRUoLPZvcvNVz/433kTLmv1Ryb/8t9/jrfWKEst0/kmmU1WWnJQn3Ni7wW987Xfo/1HL4/fv89b3f8j773zIxXZDMIbV8Izl+ppSLa+9+ivcO34DTsGvlbKumd1wTKYzyo+2TKxhf2+P6tU7rDdbJm7EYuKYTV5mtppxkR5gf6KkLjA/mnJ864CubyjqL541FTUnzvz8BaX0/hnDxdtMj3+LWFSc9ddMyjFBNwiLzz47u+S80BIZcCUwgEWIokRRAroDsxSvgXXb0NUd08IQ1dNqyOcueSIVloKKnENsTMyDYL+zUElgRTIjKO6ybTTf5IeQuF5tuXUYGJUJcYYUc+5P2rH/bbJEAogiUuBMwtjMvkzG7dRqLTEGok+E5PDG4a3ijUUKg7WJNq3oh882s13vWfWRuqgwavBZKICNUJYxZyRJzOpWNRRxB8obdkzMBNYjhTJbjNCtxwwOS2bMjv8SALbI7kZrLc45iqKkKCqqakQ5mlCOxpTTGW781YR8FUd76HgMbUSGHuoZOioJPhI2GyoSM+fQEpqqZFuOOZgecNQPuNRzXTuay4bDtsNZi46mxJiYpZ55NWK77Gm6MVoNqEl09YRwcIdkr3EmYL3j2dYycRtKWTI0HuKCYXFAGkpGac0o5GvoYqF8MOq52a448h2Fgf07yt/7j4Vf/ZuWjx5H3noWeXqeOLsPq5Vh6CzDIKhUzL3nTl2j/Yyub3kyXvB0ueF6s2bTbOl8/8K2r2VZ8u1vfoujk33GMeK95d/8Vw956eUjnnxY4p8csXcx5a3Ru1xVz9h6j7qSxeGU3/3d3+aj+0d88OEDnj05I8aIMVmlaI0jDYp+CRLoe+/+6cd/FhG0veT1r73CaLSr1IzgkiGooDvjDjWZsJNUGbCQBmKCagfCCAkjBYVNLDQwTjFHJ4RcaFmjCAaN2XYzJAiuyEM/IiZlxomWGZTHZNeHhKGOuzGzzUV/IIM/YhUlEY3Z7XcBVyTSzppObLGzzspqUbuLchjqjlITLoegEdTkQSo2WzWrZGeIpDtrcIgaiSazwBIpgzqS8CYQk9uBUZKHpNZgUZxGvFiEfLuzSgagkuK8+1RU+Kc3+1/05xdfgiDGoQK1KMdU3BlXOJnBzpzajmBvMbDHNTBAGjhYXHN38YD33njA9//8p/zxn77Fk/MzvAR02H3GJkfSYKDrB7ptj/GRQiqc2h3pNTuBJBOwY0NVOG7uHfOtw2/w7enLHKpDl5cYO+Gwrmibgc3b7/D0rMDcusnRccHQX1GKcOfWSzA6olgnylHJ6PCCxVr57rdv8uijNY/eOWVoekiGOIBHGdqeYZMIjcUVdQbuqppRteB4fpchRB6dPeVieMx76x+zHFr251Pmx3OO7k2ZvVKSRltsSrhCYT7DjSrYvwVmjDWWA4Tf9B13zZInXLHc3uL45IxRo1x+9JTKONqhpx2GDJCrIvpZoNzHgY3f8rS55KeXD1/4fNud/WJD4r3rJT959JSm7ejbDhrF3HqT+bTGqNldgwaxEN0uSiApmKzQMxoZomfbe65jgxaGalJgFFyfsDFbzQ6hQ9sNiYFUF2jhsOoy6UU0ZyjHrC5n5zwjI0eUmq1YipQoU8RqwEiRmcSSFQ8uCakfkJQzd6LNQFO0Fi0zm9hq/rnZDtJiUoEdcoGdUiZm6K62EpXshiOSawW1GKOMfI5ngKw2DwaWomghTGYjhr7HDwPGGsZ1TYGlb32Oi9FIXZa8fucWRyd7uDrnkzepZzqyvP1B4O5izsvHi6zilgwk+fwFzUSjF1y9C7kRNJm0WCg723SIKavIkwczyaBwlHzOrUIUn5ntSQlJSTvykRVDqnZ9SsrZeyYGikHBG6o84SK6AmM9xliGSmAypfSJvs92i5IS1lj29o/Y2z+kuv8+Z2enaJ9jjEKMWaWhyl8VZijGUNdjDm/dwlSGqhBUPNpny/4hbCj0ea4mxJQVDqIBVYeJO2tQkzPakrNYZymLCieWKgjO5jyzVHgGHxEfmbiSWoQ+kIfdKL0kks2DyS+zlq/+GGZP2Zx3dDeWzGQB6piaOxxceQ5uTTibjFlIha4iF8staa2MbKQ4amnujdnOB+Zry71lyXJbcDSZcjSqmaSO2bFggyWczBiKNZPrCnO6YKm3WHvHOl4SFz29O2O/3cB7ljRMif0B2o8ZzwPF/pp9u6Hcq0mpojEb3uE9amfo717g6yVDV/Fy1zCshfay5iIKI2MY9YJcGvzYMhXPWNd05SFM32C//BWKD25lYvUSGKBoj7FP7tE/hXhR4+QUU7xL0ICXrFbbxjXno3dIf2vAjWvMpqH5Z3/O/fs/pl5b5r9yj/nhCUwKuCXQCbwrcAT2DWE226fmVUY+UPglX5s0TArDuy+tMOcbpFsh5O9RG5Uu7n4PShMSax954yu4nv+qlhFD7Wo0gfT5u2BKiyXmNBqrWA/sXIE0KXihk5LGVERVSgUTAxZHnRQrDeIiF4zoCiHYCckIRfAU1pMSkIRodhvNTjVeijA8t1QPefifxGKjICns+II5XsftNo4kZLeQ5EgxEW22KXaFIn1AxObBZFTUBYK4z8S5ach7QJQ8WFMjFHHAmIQxPc5EqFN2VpGYh3TBIcGA5vzzhCcScs7plyjfJoWDmMESdtnk1sw+GZaqgmtAHoI+w1QF05MDnr075ujrCab5/rZ4xbISS7ctaK48B02NezJCZgkdVeifFsx+sMD8xECXqMpANRimr6/4xsuP+XO/z03b8pJEbo3XGHMJB5blg5o+3cV+TaimBhtvs5f2GEmgvLvAmFz3fnogZYHrq0t++vZP+dEP/4x//s/+a/7V73+PoAP/9//Lf8bf/Sf/cXY8W8LNOWwDDJc9m6fXLC5LjJvB61Py1GwFnDM/qDm5PcNd7/Oj7gfsF5EDVjx9/FP+f7//Pd776Xto57h9uOB/+h+8yne+fpfgL3nNCps/6RiuL7j4yYi9b+6xd/Ml7n1rj73XLeV0gTl6CcoN5/sLXLkADItvGqo/POHi/fs0255m29O2+VfvvxpQXIEh5SzxUMGR+fcgS7wF9vkfFZX//JcSwEFZc/Stl8FbZLzH4WsV3xlV3Nyb0rz3Ef/iX/8h/+Xv/x7Pri65eesOi5sLfNvRnK/QMtEMcH16xlvv/CmPr58hQBcaNkPBdVNTF2Nm9ZjZZMRoXLAeec7WF3xwfcmirnhpdsjd+SG/+oLHZlPESsCkxKRXhtISrcluiB/X5AFRIexU0GoVUYskhyRDLzkuAhMwIpgUc71VZjAh7j6vJNkhw6hBUiY1KZZBKlJ0OVNWU1bSpUTSRBe3PHvwHs8ePsn5xDERY8Yvtv3AT06fsn+3oC7HqAglkeAiITlMEozLzkHBZmA6WoiawAjiEkVytEWiUw+awaLtkAhdIA05VlFbSxJlWTlqq9hSqYOjGBybocdUkX0xiC2pqPEqpL4ltgmtBoKxJIWtD4ShI/mc//kXAeJWhLIoGI1GTKdj9mYzjqczjiYTxu7F7dNTzEpR5wwm2jyHsEDQPBfQwKyCm/Mp7zcbQlS0N8SrLZVayr0TRgiFSVhxJKvZHl1Cjo5Eds50A4UoIhbnEy7tyG1WMlHZZTW+2EAkUfQ+1+kh5zdHu6ZZbXnn/Q9oup4Qw44Oxie/MhuI1WrLRX2NK/aoigpbQmEMaQAJRSaUJY9RS6+gKaBDIElkrS1ovr5jYTA2C3wKYzAl+ZpwFocFn3DkPbHAElMixkifAudxjVlv0JidX/q+o+lzjx3iXwwIp5R4ev8BL89PqIrxTnWa8P2AMUJwX/GOuxNx/cJ/+wtXFpR8ciZ2/y1BGBJbN9BFgTZiRNgvpiyL9q8GFEfptGPddSziAkyOytGkSBKiLcELwUUoA9YMsEzEriaZSBszud2qJRQ1FJ4aRxkDYmDiLJuyYjob0257NNTUozFmUjNfnFCoRVLC2ZKYEkYadOhIxhKK3IsTEibmuMPoQLxixKDWUtSOPmWFcSFKEIPVQPgS7q0i5uPvRowBj0FMkcHK3dwPsyPGqhJTJs1qirv96GfnfrIT7j0XvgrBRz66vOL1+SEzZ9GghM2G1G+Z79UUR0fE2lB0HqNCiCm/jyQUTSAOidXplm0X8K3H4khRCYPHDxGNmkmwZUlRF1hKvB8YzEDsE0bOscbizJjF4T7TwyNcKcTBsb68Ig6BtusI/YAxhiFERJRyVFBXYwKRrocYAjFmEiyiUDpsyviO7uYWQp5FjSc1cRdzYZH8cebkNmw5ypbp4jJhtd3C0OFTIhU57jemQDc02L5Ay4KggfV2S9O0n8ls/0XrK90BRlXF7dt3kM9DjJ9fJM9B8U+tnvz06lNPDbvfP36DcfekT9lv/9xS0KSE8zV+2ZCAyhW4mwvc2Hxh5e7Hq0ss20AbPEEMilAVjsNZwRd2PozACvxYMdPMfkhAioHYtFSzGTGW9G7ERhPp8A7Ljx7glktiEHQbeddaSqPsmWwFvrd3COmSwBqZ36aye189MH5+AXs1TKocXLgRuARq+cQS3X/q13OyfOSzNuvPM8g/rSC37KypMwCrClI42mVLGQusy2xpRZke7/G1gwnGWUQMTdsyqSfcevkWY79g2K6BW5997ztlyGg64bXvfINXvvUmm9WG66trHn7wkLfef5cPn3zI8skl/+Hf+sf5/wRwk8Sd4z3GiwJXOA725nRHJ5zcu8l4MmVhA86uOJhN2ZuMuHznnDdfeo367x/wz/7b/w/NtuHhB89YmBPmt74Yw1BVaeOWqf2c54tQ1PvEgz1wjj5s4OoDRos9mARE1uQpUWaZpG6LnbyYUlyTIZpsWhElZw+lIPgu5NwhK8SoNE1DO+925y1R+ICEPHxwTlGJO2Z/zmcxGGzMzXpPynnlmpC0U9Jryt8JTWy7liFskWKEuoKgkFJWIUTAJEOICULC2Wy5bJLgxZHUQhT6YSBpyjep4BkKIZnMtg0OiND2kRQ/C3wOKbEOkYOQI7RIEFQIO0tmSdn+ZySGQS2pyJmmRczsR3UR8WAwuHKKxHY3aE2k6JiULoORn1ecCztrjzx4dUXxCQhejahGE4rJFDeZ4CZjTFVA6ZDiy9unS1XgjhZEHKbZoBjsqCY4S7jeokPDBMOogq6wXBcVdrbPXBw3uhXLIrGODrvaMgNMWeFthWtbjmqDRGHVJrajCjUBlYTDkVrH/GCfoh9YPXnKsKmx4xVuf0k/fEQ73MKVB4xrT7/LIdnIlhCuaWzkwyh0Dw94cDzw3buee3XDwSvC6B7cVUMbhGaZGB4HNm8F3jt3PA2W+WXF1y3ISjmrF3zfLXhn2vCkXXLVXLO83rBtB/qhIwT/l8oef+Wl19g7vsm66SgKpagc7z1u2PpTuo3Q6Bmnds1jeZcueLZdT6JHrKU0hjuvvsnevZf44R/+KY8/us/J8QG3bp8wNIm+S/yde7/xpc837Egow8BMEiNNeAOdKYjBZbvKRM5B9dnqUJ2CHXJ+LTlrPEnev80OSBBNXElJVxQ0jNGY8uBSDSKWkPKGnyQPOFPM37NkstVy1MyO0rizPUaIYkkWIOe8BoHC5IFpzhjUzIA22fJcVHDPi4MExAyKKQFbeAo1qFdiFFQCiZzrU0rYFa47BbQtSJpo+4QnEaLBkxtS4xJWXR4OJ8WqwdmIWiUkS5LckHp1eEcG7GxiK8JcKn7RjfpnsfKvCisSgbEVqlH+vEUyyPj8FQRH9kZMYJTJofKN3/gmr7x5xetf+5C9vd/nD/70T1g210iIzPYrJjOo5xU4OD9tuHx2zXDVk7aCocDHRBgGonhiUgoKFqMZf+eV3+Iff/sf8vU7d0lFy2Cf0BcVM3vCq6MD9g9e42KIlPtT5vWWsAnclIqiOCEcWIaqZT4e6JaGm+aQ33xjzHRYYk4tG63weLrOs256ujbQdQn1lrrwVKWnrJQ7Bwvujm8RVx3Pmoc86N/hoycfse16ptOayWLEjVduUdQjnpw95mobORpN2TucUAQP4x4m0zyQQhiXFTfSiCIEREuu6oLJ4lUmr69Ytg2rruG63XLd5F+rtmE79MQYSSkRo+ZYji+pCPJJ6ZLhvc2WHz58SNN2QFZnPTt/yHJ9wdH+CXf2X2U+HlNU2b4w53MncCmTzJLgO+Gq7ThfXdPHyGJ/kTNVU1ajWKv5nm8G9ipLtCOCURI1gsnfO2LO+0sJTQNWBWcdsSrpIhhvGaSkMw4rSpEUcRErEYNiE0QqKGJmWKvJ9u27uBajZBcIiUgyQCLFhEhWImLL7PRgE0YjEhImSPZ70h6rwigpVerybmMMg4F1AcE69soDFgtD37Zstg1on2NQxNKUHlNaBi04mk956WBG5QTRyBDBYxhXY04WB3z79i1qW5KixYujx2UiHZDSi9+/ty5gEVzKZKZPA+R2pyBX8n4ZrcEr1ClX1dHm3MLOA3XChkihFjQRQ0Hc1d9GwfrsDhHRnNsec12IVSh0R1pwRAlY02dSUcrPUSKutuwdLzB1QWx7us02q1B8T9f3BB8ySPaV7XgAwqgesX9yzP6NE9wIbK2MJgbdDsjS07cQ+kiIQ7ZbM8/z2D1DMhgxFFrnWA5JBCNoWVMvHA6D0yLbSGPoNdFZYaIGqx4TDGUP3uT7R0HY2Vp+OWcfdytg8XTnLdtn9zl4bY0r1ozqLTUd5+/NOD0dWItjezmjHgoO02/jyh8Ty7dIsWX/qqO+KNhrap71ARnGVN8oOTickV6v2N7vKcZCuuooYsXI30EObnBdnHG9uWAYLTmennBv9TXkcoZ1S+o05WZ9F5kl/HGinDVchxGXS0dolMUMZrWHYsVQvcujZ3uMmkNu+Vu8fqukOSzoZq/QX91g+yTiPzxjzyemckAfDokPp4QPR0QsxXcEfj2fY/vvCswfHLI631JVc/aPeupNjitRFBXY+o6H7Qcs3QOOF68ymja8/g+WvHe25PzZE9o3fwTfub+bSRwyzEeo3KC4MUbGWc0RGo9fNiybDZe3EqZt2NuseGfb8eHjDeshsfGRxicaH9n6SOMjnU8ETfzvvvT1/Fe3jEBdGGISvMDaVizdmEFzeeVSjq/xYhEXKaNgTcI76Ivs4tWamOtkFWoCyUQGoHFCZ3IPV/SWUEUCJVEygEIssCnk3qlkp5is8CkhKeSoB5uyYltyLRltwmiJ8QksJBfyPi/sHL7ASMxxHUUmdRZ4imh3g/60Eybs4nYMWGK2FY42OxtpdkqxSShVMJqJVoPNtR+SKEh5yClCPwT80OAmI+QF3AifLw3C8B4UI7BHwHj3szbAQ+AGsN8Db4GcEcMJw1Xi+//qPofLNzj8jQXFzOINvIXQ2ite4SHjTjBPD9GbNTwC870DeNsgS0B60Ev4t2APHvLq32iY21vsyz5FcwR2DjJHa2X8xoR7BbgKnOwEGSw+Y/yoqvi+o+m2vP/uO/z3/9V/yR/8/r/m7QePefLkCW3TEnZOQv/D7/07/t7f/0+QkcAIymmkXl7zsL9PuLjPy6lnVLwJLxe74nUKTHFFj7n7fUzT8Pfmr3D88gHOzvnRD97mwm9YpwjjnmbkeLB6zOjHgcObY27ftvzw3w7c/+ADTsI3OLl5m3uv/ipFUXDqH7JfjhgXFmJiuX7ARXlGLDaEJw2nT6958P4zur6nG3qGEOlDYPiKAIjnWeJrA+OdSnzEX5NKXMly9YLcGvy1guI7QJwpwhSZ3Ni9RUeycHw4Y/z+gvP6hG+//Jt8dHbF9OEjCjeiGzp+cvbnjFcPKMc1YzPj9PQZHz17QDt0GLFYE3ESGGxPF7dsh4qrpmZUjJmPx0zGFaOJY1X1nG8f8/blM/5XL3hsVTEwMT21JDQJ2xTwzpLczuo8+x9hTMJpdjRLwWWchFxzORJehBhyxrjahJqAd4Hsbyg5AzbmPj4kgxhHKhxBxsQhq+wsiSRpJzLZ4OOa7fUFl+dnnwDiCZ7zNhV4eH3F+/MFY2cxdQnikCE7Mw2FpVAlioMoRBLeJFxKWJvAREoEq5bBC70NJIFBA56EN5GgkRQG1FpaPC0w9zVlssyqgiF29DHH/RSmwBmogrCKDUPbIsHhCkObEtu+pW9afNMR/Oc7MhkRSueo65rJZMxiNuVwOuPGdMq0qHCyU1q/4CqRDOaaHKuTgqAaEDGYZHDRUGvk5qzk6ZWw9QERh08D59tr7KjgqJpgKSlUMdlWhT4B4qgl924ulYQU8IXF2Ky27nF4As6EbLIfI9FYkrGMY8fE+HwfDuC8ZWtrPlRDO/gsiHg+Xtkdy/Ox5Wbb8PjpKT4kbh8dUe0MOAuEYAIhCapFdl2Mnuh7+hjxXonRE1ODIWGcw5VjRiNBd5Gc4hylWGwypNTStStC57NKPigxDAwh2/aTlBAiKURCCoSPgcZfvtabNWen57xx51VKyfFvXRvpY4GOv2L7dPRLtDbyM4MhzWMaZ5iP5xzN9wmqLFOWo01cxd54RNv3pPgV53oAQxpYhYaum2D7knIETsEjeJMdOa2L+JC/55KEJgWMKkYTIkUWgibFiUNKA1VNOViakHjcCxRjpicFpY6pyxmm2vWyhaXwnqBtng0mC1F3edUxRzkkg0tKKmN2JjIGZwpUHckVmNDih55tGmCIhK7Fh+aFP48cA0s+Ld4ThCzSi5nIIAKlyeCt9wNJQ75GjWQ2J/rZ/UV1h3vln54fSjxeX/Fes6TvV5y3G+zIcXL3GFMrg3hScIjE7IIE0Aa2bcfm8SnrZZPxmKLAuorK5D0t+ZSBaoSIUpiKejJBjEW25HlO9BBzlJ1zCTcy2FHASSC0A0O/ZLtaoTFiywKDUtsSo4otwLo8IxWFFDNRPYUBI4pxBqPZ4dAWWWDjUiJZQzUqEDWkNhNmgyYCASvKpKwp3JiEIUrCh4EQBmLoSD5BFNq+o+sHYkxYEcRkWkmM8Qu5wX6loPi4qrlz9y5if8GNJJO2PqP2VnZ5GHyClSsZKK92jyclD7mfg+J/wdIu0p+t8Kv85bG23IHif8kBlCp0kesu0AdPlGzNMp9UTEf2i4PQOzC/LuTjY7tUzZavTcAUnlUbedwoy9RSLUYU9Yjt8pyoiel0xLrbsI4DR+MJzVVg+/ga686ZH3ZYDl7UMPsvXn/yOPs+zys4GsONKdytIDhoTC6aIYPk83yMdOQT91w9/jyHvOUTgLzg5+yYEsrGeKpZsbMJyxdx6AYYEsWkzteUZHV9Wg3cWdzjt77zbQ5uv/SZt/382rnQnE86FrDOsjhYMN+f89JrL/E3/u7f5NnZOU8ePOWlN16mGwbSJPLTt97h+JU9FmX+mbfvHLLYG1HUBTWGSX1AtCOMTVhakoNFtWAhgvNCu255+733+dpLX/vCH3NKCe30eZDqzyzByAHj8a8DjlpWjI5fxhRHIA25W94Ch0C1s7F9sRWwFDHnFgkJ65QQfbYJ9TGr8GKiN4GNbpG2yJam3lPvimyVDgqLWosJgpKzQqJIzjEygZgkW3rI8+zRnRqVRB88m75nD8XYsGMR5RzWRMpWxzEhAxSlAWNwmgs9TZYYE13XErwnqXJ1fcXe8T5SFlgDPnhWqzXL1fLnChVbWlpr6NLOGsrmjTyJELA4zRnqgkEStAnEJMRkUJwUkGQR2REUXKAPCoVHB8O0LnHWfmKpa0y2nHIWV5QURUlV1hkEH08pxxOK6QwzrrGjGqkcOJMLwvyCJPnyhY/bm2HmU0KXKLoOLcfIuCIkZVitKTUxLx2pUNqqYlPUzGcHHA8eqx1LV7Bddux1PZUxaD0hJWWuPfujinaTWKqhrwwQwChGS0YDzJeGVSwY4oSeltTB5rpirzyhW9eI3zKeZob1tk/4y1Mq9YxGB+xXt9m/6HD/7pr7zVO2R5b5VPC2ARKj0ZRXRuBuXlC81vM/WTnO1h32geXwrYB5kujODvhb+9/kD8MF/831Y+5vD2jnLdtuy6ZZsVwtadqGrvO56P4LPseyKHnzla9x68YtNrrhXnXGy/s9V5vIs/M2M2dHhqU0mEFgqKlqQzI5czkMilewByf81v/8H/Luv/5DDlEm0z36A0vpZvyDv/lPvvT5/vi8G6GIDtcpGkFdTWsKNilnS07NkLOgXMCWkUoSg7VEUxI1UpCVDFEFK5GVOC6dIZgS9WNKoLEWKki9QcRQmETURG8dsXKUO5FFUskKz+yogxhLXwZUBFJFsAFTRAYCThIOgw0OlMwuz95vWBECCSu6U4pndwo1CYkQkmYbWxs/joUo1YB41Gi2ZMcRgkFJediaQiboSYQiZBW5UZx4lGJn/aJ4UXyRG1JVS1tEQqkMNoPkqpYqNLuS92euJNFPPfLVgUPPf5LdCR6fP5ZvvZ8uYHabimSnETsqmZRTXu8W6O+cUN37NR4++4jt+UP2jxpuv5SYHirXy5YPfrqhPdqDXtiuPMu15/z8mvXS0zaB5IWqqHn5+CX+6d/+T/jub/0ubmRJ23Pa8TV2bBFRGBJ7/ZS5KRE1mCrA7ZrueqBrV6SmYLTtoW+53grT/WNeO/GEcwO3lctyzGrVcN5u2bSXdGtPF3ZNeIpgLfWkZH7sUOdoLnu08qyba7bdOYMM2L2S6R3P4u6MqCWPPlzz7Af3qQe4tXfM7GTC/PUOubnAlFOOxofMJiVDgFlfMWkt+5fX2K1ytLjNZh7YpsDG92xCz3roWfctV82G62bL5XbDst3SDD1N3398b3iRtS0TT7Ytb50+4HJ5/Zl/SynStBvWYminMybmZr6PWcViMJDtxoMSPPRDx+nFOU/PzjEuNy1jctaeCdkmy3iPoFTzGi0NqgK9y6Bpigw71r5RhyaX1ZQ6sNqeY/uGxfwAI6Ocha02OzfUllDnTEMnkSQRm6C2ERPMTjmTGwEVxaAYzQCYmISpO6y3uGixMiA+53bZmG2wRdKuUY2UGpknpbQ7sDfpjqWZAU8B1Fnq8YxJOSLGQCFCZECtECSxLzX3Fgtm4sDnQb93Axo8aVC+dXKb4/E45xybXPd4ky3CLIpxL/5djz7ntXsjmCJhNTtWuCjYZLDGYI1Q2EhSQ0gu74U1uY5Igd4mxrvBolPFS0EUQ0gGNYpExQZLioYkHWa3kbgiZJagFYyJ2FCQxOBwaPQkItGWWGsZkYh1hXOGNJ4Q6ilD6unaju12Q9O2DIMHHwkx121f1sBQJLu21NMppnJYhKoUpHKUsUBoQPKwVBUCKRNDJEduoOBVqVJCYyAWhhAtUbNbjsGCNTkLTyLe7xrTneuVSQrRgU/Y0FMVCuVAb75cvfb4vcD9nzoeXPT8yfF/QfntC04OS5LZw8+POb1/g9WTljYtce2Iw/6AZTymvTGjtxOG7opF7Ni3G6o9y14oiG7F/TZwdfAKOptx3i6pOqibPYZ2nw9Xa9q3/5htt6XVnuJywt17h/zmK7/B0+LHnOkP8f0lZVtTdzfYrGqWccZ1NJw3kdosuLMdc7MqaCvHMKrYPJ1jD77JaP+AVf02tnjIZLJmtoHZ1iFPE8YbqvGMkT3i4EOhPr3Cbm5mYvZvkUHUIzB+zPVy4KW7ytFkRPHMICYT2JVETIGHT59wcfEnHB0fYW3klbtv8Nu/rjz9wZg4XoH9gB1CiDm5h/6t3yHG2xiEZXvO6QdL7j/a0pees7cecvzoCf3pmp98uOan75wxxERMaXdnfz7Sev7d/muBtb7wssZwWNaIWHonrAvHylSoOJwoPgbURKrB41LAWkVjhBTxO/e+wYEag4uR4LJiTUXxLhEoQEaoJgI+93QSsSkPvoxmBbd3hsY6JGa2ZRHKbMubIsNz2/SYlTlSJNIOQFJyTrn1QoiCS4GIQa1DCHkglnQ3UjbZ3UerTN5GcZJQ3SktHBCVUBoMEQaDdPl+E4HkFDWaAfEqR3YMnWe12hK1wY5KzJfYvAQgQngL7GvA18gF2xb43u7vv7MA++tst2/x/T9+m7f+RLHDXf71v/wTTn66z6/8k5eoX95nZhwUQlk2FK5luGrY9i8xbW5SbC0yAMUSLd6FYoWc1fAHdxhPTzD7FbObh2CPQeZgpoh2XF6tWc9W7LsJBzarwvthYNO0lIXlRz/8Ic8e38fawPnlU/74e3/G7/0P/4Z33v8gzzl+5nj/u3/+e/wf/vf/R8bfGiMFmJsNB5t3ue0/Yuke0/aB0+42xw8bRvfGO8JBVlQczG5zvf+veeWNlynLGaQp9+4d8Y3vvsLo5XvIJLK3DdQlPFn3JGeZzaCi53K9Zdh7htEJQzxh6m9ycS3cuLhi77Th8fKUH8YfYfcHKpmgP6m5etLz7HpJ13f0IdAMHdu+o/sKlOLKLku8h1jBzPw1ZonDJyrxQ37pvPUXrucz279IxPSXWs9Zo59MiQtVispQFicYv+BX3nyF+St3+ejBQz56+AE/fv/P+eDRY1SesjieMy0rHj99xmp7BWIRTZgo+J1ww5mIMwN9aGj8mmVXUa9GTKsJs/GYyaRgM3rxL7hLA3VQSpttpSceesl2wllIkh2NkoVUddgUiNERrc01UcykbxvAFIKYQDRhF+WnpLiLiqH8mPIcU0lKJWIKNGaSOy73/Nv1NZKuqCY5amhiKvZnM1arNt/P9BMwFKD1np+enrEYjTkuClwSvHMM4kgURCyxn+HKNd612BhzFJPkIXh2w8xOQ2KUQTMhMaYBlSxASL5DYnY5u3aOsqxQJ1RSMtaKbdeydgMzO6NKYNTRNx3rzTVhNMWZgi4kmqbfWbIPpPTZvkqAwjnqqmIyGTOfTjiczzmeTFlUNYVYUMFExX0JO+20i+cw4hEDMVo8UIrBoIhNqElMjeX4cMr6/BJPdoCN3pNWZ0gBNyaC1YCNQjCGWCZCVEJ0mAA5XkCwIVEpRBNRl0UCQ8i9j3W6o5ckki2p1OZ5S4JCAoUzvH54g2ebzY7g9vnHFFJiud3sSCWR23qEdSXBCMGG7AwQHJKyrX7QRBsz+TX6iKSIM2TxgoUhKMkEyhgpUiKZgSFGms013aajC4EYPRozDjOkrDhF485pSn/RW/2FK6bI+08/5Gh/n4Oxw1lFbUVSB/2XFxr95dZOIfx5c6Hc2ZJnM4oxMKon7M/2mYzG9CTWqSOoMpWShbXsTycsu47N5sUV0L9oRZSVb1kOW7bBsGjnqBG87SklEsQhKZ/bIQSKqsQmD0EQcXnPtZFUmRxDlhwpVaQYOW+vOG8DmgJmXFMUC+zgkNChIthoMVUi9QlBsNaSQv45JoLUDmMi0Sm+yPuhKyKh7xjahiZ6Bp8J2TEOJB8JIbywKyjwWUxSFNGskn4ufhMAzSQGTeTN1ACSzyefEeYJYi3GuFwrxede2dmx8n63onIlfaUUzjBER23zd16aQHCWvulZnl5z/fSaZrMlpZSdoMoqO7gmxacAIUfAFCbX+MYIrnRozFF5fehRBozmuns8dlSTEmM8fnlJ17Ws11u22zYLIyuHKUyegRjB2oKyyPKkFAJGBWOLjHlYcGKIQ0CsxVaj7DxZGEpTES1o7Ikh7Jw5I55MenFUJJSkAUPGKFIM9NuGrs3vJQExJuLOFj8hGCtYKxgjpC8QYfaVguIHiz1u3LyZbed+dimfRB7LZx/u+AQzhXytDWTH7o/js+Pu3f6SAittB/rzFdoFxBa4yYjiZIqt/vKVmbaR69bTx0A0Wem5mFaMq+cb2RdYSaAA2R2cPj8u6xhMyYMff8S7j65Jdw9IlVBXIxwV7WWb8zG8p3clL9+5i7eOlAxn10sGveBbx3uM5IvbdP+l1o1DWPXwpIfTDt65gEUFB5MMki8qGJWZ2nqx+zzGfJJD/hwQ93ziEPCcFHHnsy81aODDq8fcHQpKe0gxGRFj5Hu/9z0Wkynf/K3vUE3zhVNYx62T2wxtYjG+jSs/SwlQ4Bz4MPVMuoY3ygmVdRTyiVNAUTju3r7J7VsnXG42/A//6nuMNOJu9hR7BSo9hpqisOztzT7DQHPFKL9KWWLiCfOYuD7/V4wnJUd37zIe73Gwv/jcj1Q1A81GTLYa22VFT8a/WFWe33N+TWf3M8KB7D7o5372gmrk0dk5ry1Ofump/dzXsZLtTlV2cbtDVnvFSDP0RBF8ilAYNk2Dq6cUxlBqbo7F5EFFcIq6hJU8MExWM+tncIjGrMTcDXAzIzG7J4jmDTl0A76LFKXFJEG9A7sD3U22lE49JC0xhWQ7VbHYJDRtz/X5GT56FOXxs8eMJjX1dIrvOlbLC04vLlitm8+c06ODA77+7W9TzsecB6H2UIvHabbpGIygZqekD4KQB+RdKSRncENWFaC6yz4viHbENZG5zRY4i8UB9WiE+GGXC15Slc+V4GOK8ZRipwS34xEUNivBXQYAVLL6LklWTSSjuyypF1/iLOWNfYKU2GabSUTjEd5ZwqoF3zIWQ12BLy1LV2Kme+zZkoPtmq1LrKNFVxsWqpiyxLsRpu84KsFiWW8HNuMJmIiKYq1BtaYslEHBrzoq79hLFYWrWXY1jwpPWTnmq5Z61eOqRDFVumrEsHRMlwUvz8fMO0P3wZrl1Ywn+3s0rkDX17yyuOTuywm9HakNHDQFVoQTMyDfqOFV4M+E+q197k5fYo89wtmc758pV+tLLrYXLJsrpn5D27ZsTq9YLtc5r973xPTZ4fZzgsNmdc72/JKDxYTfXrT86tETzqZTvje6w9m5pVkOFOoZjxccVjXWL9looo82V3YmW0D6tuPeN7+B7SJF3yOF8tLRy/z67/zmlzrfn16lUYoUKExJHIRoEt4YRBSXDFEMsRCCYQf85kIsJsOQEiNVChOpTYvBMKjBl4YULMaPSDYwlD2qWYusFjA9RiMmWkKZ6CuLyXJqxGhWo6IUGhHpSClni0cc3hQEzfblpgdVIXmDqZWo2QoJ4whFoCJkZpQqpJx9Y5Iy2ERJVnWLZGA/pWxLpniMyXboUZSghkFDVjtmnwrUZsBDXMTabBUXnQGxO+WwApGYhFAoXSX0Ng87qh6GMheZn1w4nz0nX6VW8vl6Ppd6TjpO5Frrsy/983/TAM6OOT66ze+8fsSl/DbtxROOi2sOp+cs5oF3P1yyv71ksnDYckRbK0+Hhp/+6B0+fOdD3n/vAeoDtav4+p3XeP2NN3A3KpgXmO4WEzcBbcBvSMMWedLjniqcJXhzjN4bw70RbQf+9Am122K3loODGezvs9/X7L12wb1yy4NHZzy8/4z+w/c4a9fE3hJUSdaDK3NDVhfMTkrqQ8UsDW5Vs3m0QTaBcgzFxCNF5PJZx7pd8uH9LY+fXVGiXA0dizimbs6o3oeCY55Vr1Hu3WJ8ZDh2AfpI+qiB0xUnyXHHVgxO6VyiJ7HVwCYGtnFgEz3roWPVt6y79mM1+Yuu83FFm5R6MmI8rmmb9jOsWCM2W5MbwZYJVwpDYcEkXBR0KBH1hCFwsdrw5Nkpm82G0XhM37RU1uT9KaWsYhKHsUXO6jYG4xRrK6SF4JtMgNkRSTX67N7iE1cXl6w2W6qi4MbxCSe3X0KYkWJB6h3qDMYEBhszyChKkIAvFAaDapntwmMGuINRjCSeb1NShJwZNmRgTNTsiG2CtWAJuAB1ApsSBoskxQsMZHLOVAO9WHwMeDFYLAbJCkQMBmHiCg7rMVNTIRqIauiDEMSRes8xI26XYyoVkuahlQK1yaC6pETyL65KiInstkPOeP8YIN9lqLko1GJzHrpGgpZElzBW8n6ngikMtksU0WE10lhLY/OwWE22OgtiUVJ+bjWgTgmVw6RIlIT1ivQGSQmqDBZLLwQBsYFClLoc41zAWvDRExjR1z2j8Zht3xE6Txg6hq4lhUDvfc4eSy829AI+HggYlGQMqgUEzc4kNu/lKZGbZJOJHjgB51AcfaZpYoecO+3JvZcFTKkgCetANdDH7LAgSRGT4zmyQMSiJuR86xhIfDkA5b/7779H38F+dcQP/+VTLtOYv/G3e954Be6NhStaxvs16oVFeYA536O9tmgUqBeMDpT9+YRv3Byzf6BcL895smx4551rHs0CoVaqvuT4YkPpLafNNU8ffYiECygtThWz3PBu+DMWN8dMv15wetkR3nuInF4ztBuGy0CsE6O9Dbcqx34qeFULbp1MuZ4munGBnA6k7YYH9YSPwpr19RNuPXzKy9uSo809rtKIdZjxqHtKt7qk2465vFgTlwn3oUE+EDgBeQr74z3Gx/vUNw2rPhCJ2CIPy5Jmh4lH713wJ3/yPV567Talc/itZxq/xihUrK/eZwjvUNtngOLkGp3VwAOgYO6W3K47rjdwdfmAq4tT1kMihoqlOJr0ifrgORCR/5r7tL+SaLOvcFW2ZMSItHOW6LUi+AIxjmASJEsjkcGVBM2kYUPO6TLEj4e2agNOs3jAl7m/2/cDl13LabjiqDpERi5nzKrHqcnjf9NBCFn9Ey2hqjCabYWdzwpwGwsgS9dztAFZeSg9Gi1FzPcKJe76ZYPXHZlahcpEoksEyaAEpBxnI4qaApsUo5ojWJPkAaZNiIsEl5AoqFGSFZJRYgBJwhCU1aYlhCFbIIvyZcy7NEaKSpC7Ar3ABbnNPwL+MeRAYEHtbWI854MHf8RqqHjt0HJ0OOKDd35E8YdX/Prx3+ZXJoGVfcLkuMLdGbHpRvh/N0MfmRw/UCS0fore+CkaHWZ9AG9f04Qn9P/rbzEt7mbSuwgaAhc/+jF/8OM/56q23Hzj63z75CbPnp6yarPS6v/5f/3P+Td/8G/49nd/m//ZP/xd9hf3+NXXSx48WPLw2TM2683PHe97D97n9//bP+AfvfQPsSuLdBV7scK+6elHh7hhH2kdT/VDboYNY/dNnisw5uYOJ+WcoV3jfaTSc8RccOuNBfb2axyaCvPDnxDOLjhzynyaOJkdcnPvNu/yHhQNafGAK1HeeSQ8+VHHtJ9yZEdUhxPi+BBpLglnnniRHZUuthvOlxc0Q7ezAw27e/uXW8pOJS45S7zirzFL/LlKvIQXVts8bzT+Khqa3RKgFOGwGLP/3Ve5cSO7BH1r9CZvv3/GH/3ojzkdznnn8XtcPbtks9ogJrG5WGdwzgliLGosSQxREyEKJuVYLRcDzvT0oWHr11y3NaPVmGn94pF1zoMvTQZdU6TE40yLTyMg99xGDSZFvMskxqi5p7Qq2CQkyWFigZRJRSm7qYlGQjTE0mDVIBSYmDDeksjxOcn6TAztIhen9zk9/YgbNxeU4z2sLXbZ6hFjrlHN2bA/ewovtmt+dHrKr7mawjiGNND4SF1NMJSQeuqhYLA9w1BgSiVlwyaSMbmnR4gSiUNg6BIh9oj1aAr4GAmDEmwBocC5yKQos9tRE7leLpFlQzwqmJQjurZhfb1ktdmCdcxGBZV1DNYRSoeYRGkN3idi8IgIdVkxnoyYTScczGYcT6fs12NK47JDUtIcm0Xa9fQvtoxkgqeLhlpSFkBEg48dzkJhcj9SGuXWwZhnzTWrxpP9Uw1tO/D06oK9AsbFCDEO6zyxJOd4N5aKHC8nScCQhTKa6/9UGCTujkIAza4ryeaIl8oknM33nWSEO/M5B+MJTzfrX3JkMPjAo9NzyrJiPBqjUhBSVgGrRjTmiMwQMzCVoqJYIKs7s2QqMuiASYYUIInmLPi+p2uyuCkMAZ+UPgViiNmOXbPd/5dZy+2SP3r3z3j59gmv3DxGapcjcf2XpeTu1nOc+4s8cSes+Pm1qyiNoSgsB/M59WROUmUd2h0xMPdIjSrjucOWwqQb0bTtz7mhfhWriQNXQ8tVrBj7LZMyUXghJQNFpI+OooBoDcY4CoTYK0XMJO2uVIxRVpJdGl0vO+t/Q2UCYnMvj4IUjqIxRAkkr8QYKZMyBMHqQGULQgBrQdNAx8A2tPhtj0gJMeC369011KNR0JRytFrKZMkvg4l/ynARPlas7x5QJcXIQI+wEw3s3NhwJltsPh8j72wCzW7WgMl/d85Q1jX7x4fMRxP6psM5g1pD7ywSlWEdaFYN2/WGdt2w3TSEEDA2R0JYcfl+EHNfMoSeFMBqdqZ1Lsc7BOsJvcf3LX7oCBoysaAs6aKh1Aq8p0+K79vcX1tHSB3JCzoMOZbP2UzIL0fZ4S8W2MJ8PKdwWEQd4nJ8hhGb4cEYUCmRJNldMCoqWc3uQxalZfFYjleK1uD7nnazoe0ziG6ETNz/1FdK2dmzGxAjeZ/8JesrA8UFuHNyi8l8inxeOadkkHT22YcjGRQf88mw9jkofrBjqonunvhL4j0UJW0GuosVqQuIm+AWE8qjF6juIrRNZDV4guYBnxFhb1IxLr/gMOv5+/6YTZL/OBaB0jK5MeHxE8vpVcNYDL0JDP2avYNjLp894+rqEa25pC8esZh/gDs4ohpPsUXPxdOG9953vPR6x9wliqrGumrXhH8F5fT/9gSeeXi3g486OOvgtIen51AbmDg4nMDxBI7HIA46l2OfPFDtQPIFGaDoyEB54udU0V3o+PD0XdywoLAF++OaYQisug7f9vjoqXYVejWq+M6vf4Pz8y3l/ujnLPENcBulV8+Pn3zEowjHezf41vEtpvLZK9OIYAE/tKwuz/nVu9/kqLq784755Hk/+xqZQelw8TYHc8N0f8Trr7/GN3/jt5mOF0wnn0NUUOh85KMHP6YSw8GN20zH+3kj/EJe/J9HxHjOnFZCWHP/vUe89sa3v8DP+pyfbgc0FHgE2QXGiwV1ieXQ5jmfCKUp6AZPcAF1VVackXMaVXKWaLbNSFl9qZIH5tFinpsba2bFWcnFst8dV0qRvh8Ig8cOJeIMRiyS2Cm4IhEl+pAzS60j1IoGIYbI06fvcnFx/jHgvd5u+MlPfsKoqnaq94EhxJ+rP2b7e8z2piRn2BRC5wwjo1RBSeoINh87xlCgWLVEFCuKI7N3VciMTbFEsQQDvQiDGFKRqKZjDg6P8SFR1hPKyYRyZ4luRhWmrqDM2c66Y56lnW1gztbLKgWV50QzwX0JaycAtzeF+ZQ0JIquRYsaGWUGll9tKDQysw4poStL1mXNdHbAwRCoY8+jsWO76pm3LaUxMJoSkzLxLQeLmn6buMYylPDcnlm0RgoHDoaux3shBKWwhrErWPvAipqi8uAaFpOCG7Mp81GJ+A0XacnBHjy6Pme9Tuy7julQcL1xrFkwoSBcBE4/WPHRSQ0TZZ/EwSqhahnvCceTgiiBcJwYLTxDteBkf8JrVeBsO2cxTDi7XuB0zTb0LI4POLnesj+/Sa+GD9/9EZvra1bbDYUR3nz9ZcrxmMmiQNlyszii8nfpTj03bkV+Y1Hw9hy61LPZjmi6AHGgtgEpC1xp8b2liAVDCESTKKsKd3CA6Qaq2PPymzcpbPulzvenV2F2iqodmUMxpFAiRcp2xMkwiEVdvqaDKi2OQQrExayONoIVwUVlI0rUDA4YIOyIcSaEnSWlRYJC6ikKuwOfS6JzkDKDGFOAUXzwiDZYdRROiczRnQuGNdna3GBQI9nBwrrdHq0gER9BYiIExWuPjx21RpxYogyZSb8rEqMIcQgYq0SbnSGMiWTD4KyMIgpeFdWE2AyiOAPZzhMkWsSzY2dHbE0GlZKQnENCSTBKdJ+YI33e+uRf5GcfeKHV7X7NyPWU8MXnXGIFNy05GZWc7CtXqoSTA+Y24HiGaMPtWwP1+TWHC4+bFgwL4cNhS2FmjKsp7XbLk0dnWGdYnEwYbrWEk2vseIpJI0iHYPfADNhxDzcTXN9HLwZ4eI+OEctXKhhHipuaz82iZH9e0q8TtBMWexOmLjKxezjGPNmsOd+u2XpP27ZI4RBbolKSKmFyQ6gPLhEMq8cbzi4j295RVWCDodsKj4Yl7Znn7OGabeoJ44GrMkDhkE5Jy5Zu3XAZGs5GW/Zen3J71lCvtmzee4uLJx8yEcteWTMrSuZFxX5RcGAqcCNiAQ2RZhTZalaTr0PP9kuojlYjix1NeHP/65ycHvOD7/+Atus+/ndnLQ5D1y7Zrh3OzFBb4Z7nOlvAw6rteXq1pGm2aNxZbPmBOIwxTnCVobIOK44ghuQk8/RS/s7YMpfpEgqiWiRlizsdQESp64qiLJnPJ8z3x9hijcYtyRia4EnLAleOqUYTjIVgekQTMWbwXZLD+pIUwaeIGAXnM1ElKSZmUl9KQrACmh1cjFgcEaeesRqmATA282bUMWhWCUcVkIiNmmsak187qWRCUJ8I3rOoS+YjSzQ5/qXUDNKTDFWqmRY2k3xTzjA3USmigyB4mwHTLxzB9DnLJ7Ozit8RdZ4D5ALRSt5/C0OfDWYQEslm9awZMiikDswgmJize5djZTuJWPpMkEwFwYIpAzKPtKOQr5cIyeYcN+MrIKuswRKrhC00Z+8WHlVPMYwwIbO8xyODRptdB0YjRn5AfaKLnr4dkJDwvmO7WdEPnpgi3vucQ/45asPn6znBNMasIOm6htA1SJihzmK82dEZYEgKURlU8SaRdrHGmsjxHU4zIKiejQ9En6fDGkucc4yKEU4EkQCa89GtD3mf0RHJRDw5Qy4rU3duZ/rllOJX7RWFTNgr9qlVOf1zx/d7x43/8HVul1Muwvt4LbiaHqDmJarygAMjVPaK9883aJcovWO8uMt4SAxXDyiuG/x2n/W2IpWG4tqh14JM4OB4wbPrgm1f4N0WkYDagNmD8YHn4KDg7FzxyeGbQFoFRtsCWyZgyfj2mJmdEJoAdo/DcopJJcPdip/UB/STGUP4VaZvjZm//wFN+xDaQywVLYZqkZC9C/p6xgfdmJO24c3TOfb/DXIPeAYzO2JvPqHZnnO6Pufp6hoffVaVxlzHPHu64v/xn/8r7u5/k19//W8yXLyMf9SxXW7Ri8DWd1SlJ7PXL4EfkU+4Y6+q+PqdMWfvwfn7Pf1Fvm5U2mwDaizee6wxVM5ROkvpHIWzTIqSveKvxMftK1vTskYtlJJwQlbY2cS4KglS4cSR5Dlh0hGwmCL3JUEqDJ4oEaeJuOuDlLx3LNYDD+8/5Z3TnzL67rc5rG6i4nbxGjnORkzKeeZJkCr3hVEz2C7GYGIifkzMHDAmYGJJpCCkEi9ZLaI2kSRgjWbnGbLluwsFURrEKskqLoCoQdVgxGeHoaA5jqPMhCJcwmrew3xG34hi6TVnhidJOcs0KY0mUmGwNuf2Fl/QwvXzVv/uAJeW6jChqxJ5ZuA7wAlwmzw/6QBnGc2mHN+xPD7b8GT5kHuv3sS4gYvHT9lcPONgMiXwPv3+d7C/fcz4dk3xx4HSAIegdQLbcX7YonHC/PKMLvyEbTSsqm+yEMm1oipp8Jw115zKhjgozXv/jj99JxIPDti7fcJYSt548y4fvn/M4Te/yebogD4lbhzf4c03foU//LM//lxQ/HJzwf/tv/jP+K1v/xpH6xvwtEBGrzL65pbJnR7GFtm3dO0TuqZgVGWHRhEo3YRyVvPh07dpYsm9SWTdRWgruusll31F884Zzx59xLq65uD2Ef5on8WrjmKaAZwiOOryjKoKnK/gybJjbQ3H45fomgq3ntJ3S5QBLdZEGbhaXZI01zpZBvDllrLLEu8h1jA3eXT215Il/nxG25GzxL+Mk/Dns2+/wrWb5QnYeQYG6aE8cnyzvEPthNPHD/jJu29z8fgpq9Or7Pz1nMA0CEjKGd7WYKwliUXUEDQSdg6F1iRc9AympfUbVt2LZ0yX1hCS7OY6oGqoPflKUqEXMCllwooIuiMvkgwZXRRMUKQUvDeEwuCMRVLAGIjOMvgKJOe1VjIgZgAKko4gDphgqAO0UaiLAmNKbCzQNCZ6g2GMdSWqzefidClFHl6ccjCdMK3nRGNBSsR1TIxiTEAwVFHZOkePQIIqZaPeKiXaBG2XaIaedjMgleB2wFGMOZYgtEDcYnqLWRSoUYYmcHV+RdcP9F3P4d4NZEdKcYXLueDllLJ2TCcFbVcjIe/xSYVt09F3PePxiIPZnOPZlMPRhNq67JeVFJuyRb0lIekXYJVfcFkSLhhSYQi6i5QSoAhYyda+hSgxKNNRxc39Pa7b06z9kwzarTYtj+ol9QnUdkTCEhDUCMYFYnjuiRMICjoMO4GDJfV5hqpqsX7nBEcPmncuYyPWeNTl6JPxWLh7tM/pdvuFQOem7Xh2fs3eQjDGZ/KlulzspsyKUZMdVYiZKaMiDFHZNFeUpUNtznxuXU1lKlCDEIhDJAyeqAOeSJcCQUMGNL8Kto2CaLbx9l5xbY8YT8GXizj6yy557uD3HPj6uaU5XnE0woxLgqQs2ggxE2Geg7sm0Q2WemJZzEourgtS7L/y9xs0ctm1XHUjjstANcTseBoLrNmSKksfLCUBqXosjrURTPRMOkcfC9qQCEXOxy6KgA8RZ5SqdrjBkfyQiZd25xqWfHZrHCKu0BzpmCIb9bT9gMGThsigMSudU0S1J2nM6uUUEfLMMBnBGUsIESQriF94xc9uELnF2/XCCtmpymOiy+5Ju70w/7+dzDwjw8jHs36lLmtu3jhicXgA0wJXRWg8MXpsErSy9OuW8w8v2JxviLGnsJnkZYyhdCW2MBQ21+ziyYpyIx/3uom4yzYXQgLtBRsTRZEQa4l9QrBY51Cf6LsB2d27es2ioajDTigIFUUWbIqQ1DC0A66wuF3s8ZAilgg7J6iydhlfMvk+nAJsh+xGZyJUrkIqYbPd0Gw2dF2DpoRwndX4ViiMzWQMdPe6mdz6PD75Y0L9bsZljGSi5y9ZX13tJcLdm7cYzX6BcjnyucB2IGOm+596LBuIfKIc/xgU/2XvNoG/3NJfrkl9xNQFbm9KefQCG11StpvAZhhysweMioKDWUHxRT+1X/C+5VP/fO8b9zCzA97+wUfE6y1BoRzvMZ4foBdP8d1AajzS9MTJitnhiJP9A+azivZ6y5O3H3BuPXv7I45uvkw5Wnw17PSbBZw4+GYNncKjAd7v4cMOLrtcxV+v4YNrmDrYG8PRJOeQH1cwWFgZuOITgPy5ldLPXCJXyzXLRxsuJ8Jdkzerje85uHXMNCbGs0/YmcYaDo722T/cg10T/nlQ8cyOmO2NuX74lE2CcHzC51X5ySmMle1FQPuKYZ2oZ+YvLOZ9CDx4+ICnF0tu7B3wH/1v/hHL1W9x4+YtVlft52bXK0rXd9im53J1wbPukpsnb3JvcQv7FXwLnZtx+86LqcQhK42iVQbykDAYpSkD9bSGa0tdFIynI6YHM0aLCalJuJCyIiiBYhALEhWNQiiyLbLEfGNLKOoEt5sEp510UT5FqVNVtr4n+IGKMt8AjMFozgWxCKltaLqGVgylLTCNI8TA1eljnj7+EO/9x8ekqrRDTzv8xcXBEDzRkNlUs0hnIoOxuA5MijtA2uI6R4wBYwypUJI4MIZkFZMyUSJg0TKBDQQ1rBWsCKkcc/jy1wjO5ey5qshq8J0lelaC7zLZP6UGV3lOJMv0AxPBeMXEneXnCy5xluJ4P9ucrlpIihmNCaUjbjpiv2GOYVrD4AzLsoLJHnNXc2O9ZesCq1Siyy2LqNhRSShGmL7nqBasGq4az6auSWan8DUGGOGKrLLt2wGfSnzsGY1LfBhojRClRqNijl7itRuHHFrh3XffZdsFFpWhHAeul4G4KSiGOYtF4ua4I5aP8KZmpftcnBti69HJPudxoGwKtp2Dg4Kvnyjd2TUpdJTr+6z0Bp0c001a6pFhGuZUNwpKM+J60zDUBYtbJxTmiJjg5u27rLqedrWC5TNuLErqQqhmI+raUBoYtgveeXTEtDunvndFKit47QbEl7n+wQek5Rpbxh3ZQrHjEvGGqXMMLmeaDjomba5YTBwvc4f44KtjfoqBaMFrvu5LLEY8/3/m/ivWtnTL84R+4zNzzmW3Py68uS7drexMVWZTlVRXVVcV3QiEFy11q4GmkZB4QgjBGw+8gOC5hYTEA6LFAwKkUlepAZVrVZpKczNv5jXhzfHb7+Wm+czg4Vv7nBNxI+LGPefezBqhox3brT3nmm584+8SAiSyU1K0+CzIVoEjGDQbkqmJJCwDORkiiskGPwT6dEkbWkZ+ilTFAscOiuS+5PlGwdqtOlUzqgNiUiFRkhDNIMWlwqlgc4cVi8YMYkh9JKeMt3UBxGyxSM2xRUzGGkXbAB3YqPSxQ3RJdJlEU8CM1JGTxVBjtSo9alJSkwmaSzZaCsV2U8Co5bIb2FNlgkVNLhnHqVh2sm3GTLKYOmBdRNSUHMsgEIsrxrpwjbaN2jOY9/b/ddu8Fet2vnZG1heVUh5ha8pTr6XEA1Z8zTnVttfMDYAgCiMsFRaRPWDO7cmU/aNIte5wuwN5smRnteHN79zm4pVf4rdf/zZ/8u673F1c4G84HugDrDXsmqZY2ocMfVt8+Ks9eHMBMoMLQ187NqbCyggnG2gccjDB6ZxqPEXo2Vy0XF6uWGwCnRO6fWV6c8RhOCD6AT3fcJUuabsVy3VF6EdUtcNON/i8YNkcczZcka3DJGEa95DzHeJQE84NYbXBaKLNHYMO6KZMzut1R16suRLDZTOiFU+fWurVKScPf8hmc4kTS9U6vFjGxjNzFTNfs+NqplXFyHmOnOe2GxOcMrgpffMC9/MhFnpWgJNHZ7T902eeNRZvCkB9dnrGermkHjeYmcfXI7wdY2pD6luOH51ydnZOHMqwKefi8BGtUHtDU1UY8YTtcw+TibYMnaqcyCmTMQT1SFRMjGXhaQRXGSbzKXY0prJVIUwrGE3EbsOj8xPuP3qM5GJvONvdY364y2jc4H1N7afUwSDRkVMgp22cSdUgLoNN+FAsD1UKIz2LojkDxfLXZ6GOuaihU1HnBBydUdLWeSWpLYNLk4lDpu0G2rZl6Fq6uGE2GXMwmdOY0teYbAgoIUPMQuMclWQwJS7EUnIcVSNioFJDMpbhRZZftixwC9i6XVuz5d9uB2XeZaiFnB1oQsSSc8JEB0apgjLKA9YlOq0ZashbS33dRgLlUabzgp1nFFPuwyaS1CERjAp5m/sumrE2EHYCVjM6lPdDksFkX0DErXWeWIdgML0nhkJWqOodrAjadzTjEUMc0JToho6+bQldT4yRmDJha/F4fYfcmc14+5WX6LqBx+fnnF5esF5esjvbxxhPcBnpXSE4rJXcwhAyg2ZSSJikJS7EFzW9ESFWA20sqvNEJA+esR/TV55Zk6lQQgRtlaSgzjIYJRvLplJCCb5HUyFraQxffjy/RpmxoU09g54zaSwXfaC9nBP6b3N4p+Kl3Xf46HhD3DkkjGr8PFFPdrHZ09DSbjyXj0Ycc4PwsOfiuONRCnS372AnStVfsbxcM8jA0dHAzdc3HHjFLx3Z1Wgfieseb3sWdwfG7OFO5/j1BV5g/O3b7L70DezBGHfbMjo8oF/1zD88YfL2m5idCbtieLydBI4AThOPf/xjHnzyDhWW36xmvOpu0PQ9vhdc9mxubnh42/OjNzoOPplw410L7whUYF8XZiLcvTrj9GJBt86kWI5HlkTKgT4FfvAX9/in//Bd3vi3/w7VGFp7xoV7xPqe4e6nN4lvClNb08iIZJU2DrTdivN1zcnpgIm7/MbRL3G5WLJ+uKIaRVZH+7xu5kjbs+Nr9lzNnmvYcw27vmZqK7z5K4G3vnbNmroID7Iw7pQHDz7k3vl9RpWnHs2YzQ6YHRwxne+i3jBgCoHaWJIKToTOQQw1nRYSdFRhpJkVmdO4Zmdnn8loXqzSUyarxZHLgL63qBhqTdTDQCuQO4tunaWyFQzlnqO5xxgPpiflKZmKFB3tVndmkpCGhPGZ5CzGRIJJZNsQfF8I2sZQJYGkOAU0kowh4zBRMS6TTFH1aaiwZsCQiVQEcSTdgvgmsdI1rSu2jJVzZCP0+fl7tnTZkC46DC3VYQVjhd1nujULOJAebDPhl779Ku+9e5fVceTjyzN017HzjSNG+/uA0gTPSiasp2PGb6+oJ0r8saLfexfZvIdpjgntkvPXGtaXjqUazK0J40nDKg98eH7MD3745+R3/4zD2zu89PpN5KpnunWDudvfRy96UuP5ze++zJ78TeLtQ7yxmNOOXXvEN26+/aWWlSkn/umf/wv+s3/4n/Pv/cq/xzIsWB2uaPKUg26CZodzMHKmkNV4qsoWcx/2HnKSHyDuDqF5mcUH53zwew959/gBy+MV7YO74HrGNypO/vQmr/t9qgNF95SLxwOX9yteff0Gr9QV7++e8cHZKX1qWT5YMLH7mM24kIb9kqw9s2lNZKAY75fh74uAZlAG1Mu+GC5OXJl5TvgrVImvKBsx+ik/+1V13cZ+nZ24fv9edIcbYFRmJZMjx7fnL/Nfu/p7dCfH/OPFmnc+eJeuf1bVXxRXGkFj6fWuAXIxxX0wqmBSLIIOY4q9ujw/cdXU0GVDI1psZLOhSjUbZ1Etr1/gm7Km7HMhsmoSsi1gi93eY53ZuqEY0METfMOgjrxVHUYfkSHjCYhJeE34JCQqMJmD3R2kiRhbEfsxOfsSVWjHhSDwFed1TIkfP7zHrYOb7ExuYCuBYY21makP4AKVKTFpQT1iFZGenBSrkSYo55sVm65FEggNqEdUUBlQoG0j/SaxoGOIHSPfkIfAZDJnVGUaJ/gammZOM57RpZbKVTSuwToHpmY+z4ikYgCUhNDD1WrBvK45mkwYuaoQJTOfBcO3BMkgSnwB548IiIUkkSTFaScYCGLZYApojpTnX868OpnyoL5i2ZVzzCEYNZxfrtmpRxzMPZW32JBRdUVtvwXbkwZ6VYwMxEFpk9suCiI2Adah1XXufKbfnn9GTJk7JIPimY8mzEYjFpuWnxZkpKoslkucq2iqcYlaUi3PKatk2cbhXQNlcSCnyKYttvZ9unZvFdQFdBRw4tCcCTmSNZN1IMRICB0hRV5YKbCtuna8fuuQo2lN7Fd0naDWMXsRkPR56onqc1tPx+Hbz3WrhyvOoVm3saNkJAdy2DoiVaY4XfSGkfdMxjWLYSixYM9Z16Sj63nVdfVp4HxoWdmBsYwIKYGrsMMIjENtAUStCfQGYl36tJgiYIlJ0N6QRYlSlcxwNThqRhZM7qh0IK4TQ7fYkhChI5E2HakrauFAEaqoKHXweGvw4lBjGASkL8IUspSIRgowWt7W4hhm7YtY+3yJuv/Jl7div5gKSHKNSEd95s0VvKuoasfuwQ67N/bwoxl+ZMjiEC0Edz9JmDWcP7xgvelQzQxdT4yZ0XjEaFxm5imbQrapSqxcDIUEnjUjWmJhxZVzLqciENRt3LGtK9x0l5E1jIeOMERyiFi09Mlb2/WUB/phO2OwxQreisP5ESrF8lzJCA61FiXiTXFHRrW481lBJRM1kwMQi4OEpsQQekZVhXeOYegZupYY0pP310jB37Mp81krpmAm1yJq2OZul9/I6BMnjevj/1X1c1uxVc5z59ZtmvGXdFORJ9GV16UUALynYKbXNfA5p/RrtfVPO38TDMdXpEWLGoN1Hnc4w+/+7Ow+jZn1JrIeAklKo9JUnr2JfZKF/FNrO4v7vGX8kxLB1Z7pbMZ6E+n6Ed5ajLP4ZgdVy7BYk6cb9OyC6e19bh9OMXsH7E332JhT3vnoQx6ul6z7G5jZhpuj+Rcr9Z+nRKAqwwfmI/hWA6tZUZB/OsAHLTzoYNXDxQruLmBsYaeBoyncGMNuBThYmcJAnVBA8mc2cdOvGSVL7afs7OwjIrjG88obLzFPRdF4/Ybr9XZ9xQEQhCNx/NbeGyzqQ/oQGW9VIp+vST1h5+Zt1suediWc31/x0ts7X+lK0IaWP7v35wwpMw2Bw6Nf5c5LNzn5+H1u7B/xdEOfbBAA86qmOXyFD1cdF4tLfpDeY785YOYqCmxebhg/exVmzKtvvf0cv1sqqGHjPBujZGcQm9Dg2Lt5wNuTA0bTXcYHihklJEX0QSKvEgkK+18po4ntcDRuH/TiBPqtfY/oE8aUUuzmlKfqSUVZtz2bvqOJE3zjC+jmDYojx45Vt+Hu8X2a8YzaWqLCEJSz0we0m81z7Xu/6WEQdGtNn/1AtoaE2d4gE8nk7QO/jJ7dluGVJIErFkkhGoIV4nigspkhWkLvsJrJtVKNDzDWbXPBt6oISajkJ2rwJ5NtKQNnk0ESmFhAcIkZE1MBxl/A6sfOxpjdGXEA327IvsGPawaE4WqJy4mZc+CVrq5YuJrxbI/9mGlSx8PGstoEJm3LyAjUY5Iaxqlnf+oZOuUqCv3MgoQCUkgD1mOsshkCIQohZMQWxXGrkdY4jCjWwK35LrOjN7F9ZDAPqXJgxIyLsw1nixP22ikp7zGsJsx3NtzaWbHoejZhRmdHuMFztHObjV+yeLikW+yx1xxx/+KCOJlyGZfsVRUy3+HRaYM64eXRQNMFGneAm9xienmB42Oq6Dh5pITQ46xnNJ4wn87ZuMyyu2A1wEw6boxOOFkPmDAldj1X2SOp4/KVG6xf/2vETSZfTEF2adcLQpep94otYzkxauqqxmA5ef8dVmePuPOdN6jSFW138dzH+yeOv4B1GdFIT0MHqC92wlbLwim7DKEQMazPqDHl36BEydQ5Y1NhJTunHA2Zdx484qPjY97+5W+yP94vhEgsIx0YNGAMmMFSO8XaoeQ5tuX52q6vENMx25mQ1eCI2GBwMpCHhj4NfPTgB2zWC15/7WXqvRk5JxYnVyxOzjAJvFRISNiozEYT5pMJVVUsSlSGwkbXQMqQGGizI6mjch6XMkkTIRn6GAgacVGQmLnqNiz7jmYyLtusis2JHB1SJYi2KA/UY3SgToUtHVJAeiUNgXvHy2IBBk+eD9fA92eA8ue0DP581RSzSaX0V8Wk72vMn7b3n+yhteADyGYLqE+LihyxOOtw+x4mYzgCOzliZ2jZSQMvA9/47i/xG23Hj04ecfrxX7BJmfWmYlJ7amNgaGFzAmQYzWCc4ZUbsJNxZp+qaagqh5c5yj5h3JHTFcGe00nP4/aSe5tIv3EsueJxvWBZJ+q9EUdpxpBG9Isr+sWK07bnzt6M5aOO4/4CN0Q+ufqIRTrH24pZUzGVOW5R4QfHsMqkNlEZT4iOdbekmhS7JzNEQpqx9AO53uCSZ9FdMTz+hIcPH5DaHm/t9p+jMg4fHK5zVGIZWc9oC5Tv+qIm36lqZu75UyRnm6LAPb645P69e08WbEYMlXFUzhWmLErf91x2S7rT0pSX9ZmBnOmH8BkyRkqZMMSixK0bxDSIdRhjsXZAtp4KagUloUFIWtR61pVFH+qJTsnO0ownODPCqCLZQ1KMDvQDbFYtXduRtmSvq+USe+8u1lr8eMRkNGV3fIM7s1uMvCuEIis02dJ0xQ4+Z0vMoWT5OYraxkQM4LMySoKTvF00ZZIaWix9iohNeCkWg0mFdVLOLzcsLpcMQyDmjHOJw6M96iaWGBgDgxTnmr5SQjaMMVQqZLUln08EaxzZBkwUtEi3Cfb5lcNqSlZZUTAXVXLazlfMNQFdHZqLbX7UDFmKhViKSCrWiyYZsIbeeaJkMGUoaKzAKBMmwuCKnaQ3SnIWiUJOFSY50nb4Zm2PtR1Dk0tm5Tbr18SSV4wpRDmpDGoVxOKjYGuD9RGvhrYqShPnLNXIEDQhMbIJPaFdMrQDqQsMcWDdD4S+ZBRngVs3Drh5dITLlhu7+/zBD/+cdrOmDQumeBgUtZHUJUIboFeGnMk5EuKWSKCRth/QQagmHnImiyCpOP+E0ONMRUoJl01xI+ghp4z6YukWGy0fDaivQCvSEMgSXmhABdCrZW0S7+d7HGnDeLfijbdfZbJzC51uWL9Uc/Ygc3aa6fcf0U+WNN7g5ZAuHrA5jlStcnXWwahl2Uc2cUGOPV3s6K3l5JVD/PQ2YdyyuPcDTq4C3dBQzcbs+Tn9pqM7gT/65BPGLwfe/vt/hzdvnPCt9Qbzm78JR7c/s1aLdo1OWqxtEONYiXBXi9rjZS351H+4uOR8ccKoaVgcPmJ/9xWqT3ZhIrRVQ5yDuiuq+jHpcgz3pugKuKHI4cC8uWL0ccfOsmLuZkSrbLoWjZF2GAg5YXPNw/Y+f7j6JxzsjDgbwWpfWC084Z+9xK0HO7w+D7zy8oh48xXuXu3y3geXfProEns+8J3zir92Juw8HqNyQD6Y0c0W6OSMuo942c4FEERMWYPrNSHnX9MSYaepsRqLtasqagZiGjg7XyFccOIeUN+fMB7PaMZjmtGE6d643MerOc6UgVsPZOPIYnGSSUYZJBOIvP7qa4zHRyRKvycJiILZOidkq0UdGwtQ3SfFxlxI0t2K6Nb42QgRA1oIzM4OaIIkjmxtWSsh+FRiM5Itlp7GZLKHXFkGtYX4nMszKYcKYxPetNscVk/2ikjGBIfkYllsKffTsq0CElGXWAVIlRDxTLwn2Kr08c9Z7gJMqrEVsOFpw/bkeFEGY/fAvHSD3fk32JmsyfvCuV/wxjeO+PZfe5V6NKZfX1FV32Dq55yL4YGdMn5JODhKDOsF648/oGmEqbXk5UPu3XqNdrrHzX/jDrOm5fTuPf7Rv/iXvPeD97DHC/7B3/0dDo5mpCtl182YuW9wpb+LMxek3uG6yI35La42Lav1Kf4UqlHEP06Yr3A0e7w84f/2L/9Tfue7f4Mf3niHYc/wxmvfYLp7m/jRhjFjJrcyeWR4kkEIQM2Or7GjG1Sbt3l8r+LsnQvuff9TTh60LFeAZqppwp5n7v+Zcvu24F5K7L4y4v7JivP1MUO/i2w8tR9TY3ELZd09QHYumdWv4dp9lus1MSt1PcZXI7pNuyWv8kJgrlKyxC+HkiU+NdDIX2GWeORplvjz7te1EMh/jde4BuFrfqoL6FeW8IwFVpktOgy//vov8dJv/wdMNi3/l8UFH92/+yUkhqI81CwQM2JSAcetKfbq26F6zIIV80Uv8DW3M6NWWIgwSRaXhcooVYx0UpO0xLqIFVIq/fxQmwJq56IO7oVio+4FK4mIY7CepK6IcyWDGixK8KX9tkmoYwfJ0Bshp4RIoqonqBpSKNGFkss2zUYzzuUC/Qq18KbteHDymC4mRvWYtuvYr8a8eWuOrw29NVQIfbCssiPZjEkBFzLjpFSihApc8mAsbNcUKgnnLCEkUh8R54hJkcYznY1oZnsQAtaDnUyonEdqGDGGrFSmKPyLF3bG+OLmIgnqccVsNmWeEg6DaLFKd5qxFGK7ZohbMDxRHO6eu7LHbFWq2RWiacpKl21xS1FH0DL7cCIY6zja2WWxeVzWVALGKCkp988XpAR7c4OvHJFY9HYmUemApJ4+gbUZm8CZWOKUokVVSSYV4r4TUlKSWtqqKMhFy2thlJGtGTdjVl1f4ot+yiRiCD2Xl+dMRoFRPcZXVYkjskW9HIMytD3tZkPftyUreAvs5mwRsTijdDETYo+1FWhZS2ZNxJQIcdgCqj+fEsBbV2LGcrH3L6oEYf2iLKfr+tovs3Vq285cfvL3FM2Bvm+JcYStLcSEMYokU5btQFULxivOlTXebFSxWTpCfj4i7k8IDYUn2xlJnHc9531gd5TBWsZ2QLIh9YnRKJERBoXWDKzF45Pd4hslI9xTIbHekiQzU1Wir7DGkr2n1ZbLoeOivSy50JKJGtEEjbUYcTR1We+rQs0Ih5LMQKXlvB0UNOQCnCNYBLIiRjBSwn/4GiDpz15FrYw1iJFCJM/pMyoYQfDOMZ5MuPnqbSb7c6rG4qdKTAnFFeLpsmd1fsb6+JLV5ZLQdxhrcEYwUizWrVEq50gC3mohpmSIKgw5gSSSJIw1WFeAdtWM2YKsZrtp3ld42zCuLclVbGxLqocy6zGWSCG8uGTwOqDGlfujK+IuMQrGkHLGOIutHUbL89Mai6ub8owNGU0dJismZ/KmZbXp2KSBmAMxFhxgZzrDOIvzjhDjk2d3icUq++lQjFzjg0pWsF+gFtdcrrMvjPb+XP3cQPGD+Q5Ht25i3Jc8REpUxk+Iddvtl697GuUpKL4VK5Vm7etsaVS6xxfERQfW4aoa//IcU8sXqne/qvKm5Im3seRkAYzqiv2p+3rXkcJWIvul254p5JHVsqfrIilZrBFMdkz3XmXnxjnh9CF7Q0V+cIK+/jrL8xZftdg2snn0mIu7dzmOLTIdsdNuOFL9xVznUG4gcwszA2/W8DencJ7ggw4+7uHhVkV+v4cH6wKo7zawNy455IcNHFRQf3Y0f2PngDdeucGu20FMWRLsViN2bzXFOvtzpXy9nr2xFc10/yt/JnUb1o9PmLp9RjLjxis3CpD7FbVcnaFpga0bsh24Ol6yf9uxd+cA62flXNvemDDlwowhgCbqnRnjkYPTjmrSEX3a7s+AEviJfIGfoeoXyDtq7YjBCIPLpHGFTTUSlEltaHZrorf4eg0u4XNPHFnyZoPkkmeOZBQLmjDGlgaTTK8N4Ogll6GIKTZFQrGvr3yxTB5iJKmybjecXV4wO5jijScLJDMQFdp+zePNipPzc8zpGYjQxzJsfJGMmaHtqAfQ8dYOzxmMUUyVEGOKehUBzag1pFzUseLKsTMJUlWAwuQyyZUohEwkWsHZMqRXrUhSzI6KClyfsJ2EAk+YTFHYJd2C4AkTc/k3ZFwoA1ybQH4Kk/Orqrp9QHI19rKFpNjJmFQ54roj9xvmIoxriN6w8DVpPGenmnC0WtObyIKKcLXgpZiwVUWspkgY2HeJ2jacLQPLpiE/yRKXJ1niAYpKPLkSjTDyxBTYCCTjkBTYbWpenu0yrkasHn1KTgkfDZfrQBw8GuGYFUtrObaRvdhTBctVnjBOh/S9ElYXBAfWzgHDyE3Yt3c4GyxtnbA7h8xcYjQYsjGwO2ISi9H0InquhjlYy8GtExq5JFnL1YnlrM1ISIgOxbbRVdisjJsaMYEYTzlenbERwyJ5No9q+rhhr7nP4XjO+OUxenCD5b2GzcXAyGbUK+sh4oiYUFG5GY3CaljyzV8O5L2/4NPNQ36Nv/fcx/zZ8lYQq3QJTp1w2dSIqcg545JDfb8lNCqiBpsEgyF5aF1mlhWXlMEowVokG6oshHZgGDpqMdhYHBaigRQUu114EGGSDKmCzhTmpNNMLSW9t4nQRgeq+BTJJhKSUieDjYGUWkYTqFxCu4Hl2SMePTjGqaMynpFrcBgaA9NpQ2USAUNvt6zqVED/pJmYIxI8TU4Ihi5bUkqshpZ+2CBJ6eKaTdqwCh0HuSJ5g8lCdInayja3y5KyEnLEbYSpgIjS5YQVhVgWeOnJCuwL6vqxY3hCFHrRmvFMrjhf77kJ5fG1daymAvyUYqsv10CeQeoAtx0lbL0A5TTT8hc10tTKK/s32Du6wYf7U9QLfnq7SCZSQs8eIMfnsHsEr+5AVcFORuYBS8VMnqVGNgy25cKeUuuSlbvkZD9x5nYZhjEx7uGvOsbrJfXIUI1q5qMxh1czHl+ecLlecHmx4Q//9GMeHFzhguEv3vuAk6sF49Gcg51X2B/PkU2xWt6sAsO67GcP2GqMY0ZvPeTIRiDaJdUA3bGn7R5x+s6HHD84oRJDbS3eGZy1eGtw1uCdwxtLtQXKnTi8cXgKUD61Fd95zuPcDMJi0/PuBz+mH3oEKdlOxtA4gxMQ1eLMlBNdKvl9+lOGDSlEzk9OIVtqneAmHqgRB05iYf4OFsQgKZHjgNcEThAnJF+hfcRkiFYxNmN9h4YSiyJJudxc8sGju5xfnD0BxAHyduETcqa7CiyvFnTjS17bVfxkglCTkjBSi69HhWgXC1u6LJwGTBKIlionJjnSpLIw1m3XlWwqxmpiipLaQkjKehh4fHXF6eWy2GlokbXvzcfMR1UBS30mk4hWCTWIWGYxUUctPeBgELUMVSE9iVMGb7fzF8G/QPzJ+eklvq6pa4/3DlttAfJtTpoqiPWkaJEqYaRkz5FsGVolLRbxAjkVN5BmsGwM5CahLjPUEBtFbCInQbHk5PEbwSaLUqO1IgTUDWSbyA5sjltSgsWm8my4dtsyXkEryOCsIq5kXwo1xmZyNtRO6O2MnCzENaM8Is6mdG2PbvPD1n1Ht+mIMTDEwMH+XiHdSWRUjTnY2WOTejRlJCU8FdkEuhBIQyL0gS705XxTwVYGkyH0kbYd8IMhzUaMqxGQCQFySvS5JeWGrIYcHR2GIWcsHsSQrUUqQxKKKwkFMDfUpPxiy+3JbkXleio15NbwrYNv8zu/9Q+YHO2wzj/i7NU57lFgdvKATTjFfktgd8bl8oKLs4zRyDRW3J7uMtu7wTTO2LWBN24e8OCNV7g7HnEXy54x9A/u8en3H5IftHjx6FJRP0JXib4daNPAop7gb7/O7KW3+M6qhems2GRankzRshVWNrB8/33WLvPRes3vnZ2x+uAD6k8/ZXP8kINx5Duv/Qpv3HyTV49eoxJPWmf8rOVymgiNY3Nyl+X5ilZ2SPMJNgjh5cjiu++zNN+ne+RJQ0XlpuAMq9CRc8Y3FXPrefP2S3z7my9x65WGs3bF426ga2p0WLC6MFy859g9GnE4uk1fvU7uj9gb7nHRK5vYM71M1CctblUhexPIDp+Eri99SUyJPkX66485sQwDF6Hnb7/QUf/FlRjD7miC2uJqsFKYH8y57SKPHxzTrjuGEBjCJcvFJQDGGKbTKdP5lNn+Pndu3qAaV9hsEWlxKWMtEC1dymCF3dkINQGrlphsASO0L/MdMVRS7Mt9ApcGkhp8NkhSfJKSERuEDkvOQqMRNR1Ra3KGvl+xXp0x8g07zQ5qbKF6JcCWe4+JlLW4GMKg27+TyNFSWYt1kUBEgpZrOBiSlGdD2maTF2Z3jxLZxEgb+uIyZIo8JYrd/sxzHo99qOqA0wewfwvujH9ydhSAINA7JqPXeePlH5JYY0dLDm8fcf7+Q77/r+4y5Iq/8bf+OrPDEQPwoVhOcuTd+w+4lzs+fu0mk1lNnh5xKx9z46CnY8MH732PN8MpQTNJWvwNy/7oJbQb8+C9C8w6UuURm/qMZHZ42K1JayWfJSadIYmArGgazwWf0vkFBzu7PDp7+IX7rJr5V+/8Lv/7f/q/5d/5X/93qec3yAe7PL675O7/93vs39nnjX/zLezr+1z3gqrKEMfcf3jE3ftTwoWjPvMM54dYdhCzpNM10cCIjA+Oi5Mzrs72mB907N9y1LOWy7jk4/vKrHmLndEuM7fDEAdi6OnqQKNnOG8I0rG+iiA1VT1C2xaVLSr+AqSXDCyHohIfX2eJP/ervWApJUu85sWzxL8ubnwNwv8idroW5A3h8NYb/G37G/zx3T/EpJ6zxYp115fe9yd+aaseTzyxwX1ir24c2RiSvgAong2YxEXwGFXGFjCRJsHSO1CHQbZ9sdtiZLa41FBAXnUQrCFjGPqGFAqQrKYAvOXjhsp0OBmQXJU+lJo4dESTSNISNGGC2TrVpa2KMjKYxGQ8ZlQ3bLqnIhMjBmsdMcYnCuKu73h0/GALcGa62YQ7r3yrOFM5weaMGM+QakJvaaJS5QIITZsRxjlctmT1mG0PHl1FCOUZqhmqeoKzI2qpSN5SS8AZBVdmiE3e3nKzL1FOZLyAqkXEgVMSFrUZFcsoNEjXY0PG6jY3fAuGJ5TwBAw3IJbOvoDSdhuxVtywSua9S67kxlO2KxSuKoOUJcZ8Mse7C7rQ4qwpgpmcOe+uGEj0zrJva1KGkEvUm9ceZwfwQtoSkY0OpQ3zZgv4Fr5EnwohNXuLGMNKBDckRCKDLWI0Zz2oLb3rM1fJE1X39jqhvCzt0NGFnqqqGE/HmJEjtgPDeiD0AznnAtSVVym/p0pKEaFEYqqC9myjRIv6tDjn6Qs55j277d57JpMZu/tH7O3tY0YVWXvo+m3ElkG/DL/6BdXTXftCRHxLek7EoaNdLZmZGSYZbLUVV6UCjE6amlnV4GxZe00rz2VVfcY59YtKro8n5knPLuSCIV2LI/Tplgnl2K03PafLJUfjGfviyEZJ26ixmGp8UkQ92ULdJHqtCJOAyT2VF4w35JVHrZCMxzFjVzLedhg30KnBWiX2hotVREk4D66qaIzDmZrGl74sRodVi5oS9YZqcfggkTQVdx+2UJwU/CGncm7ri9zPf0rJ9R+9fiuN4IxnPJmwf/sGOwd7mDrhpjXOlH1NWmLO2ss13eMNy0dnLJaLspZieymnRNg6VDprSFFZrtZ46/BeMFKBZnLe3iurhCAYW5wmCkYrBbzG4GwRMnixOB2Q5EpEbeiQquCoOYOGSFONcTuGsQ6s24EYIsZXWFuRoyIyYAyFgBAy2WyJ+MZhssVmQ8wJbRumIeN04Ly/5NFmSa+5zBFVGWIghMjIeaqqou+Hz8yGNG+BbqMlBlkM8fqesY1uEcOTxDLd/o75Gpf3zw0U35/tsH909PQMeLaUp8C2fPbLLYUQe30NbpX8NNsbOfD1rNMVtEsMJ1ekRYc6h6sq/O0Z5qeAnF9UcZ247Ld54pTh4nxSMWl+hovopyjcr0Hx5dW6ZMeQn+YM2or57m323A4vGcNuUPbntxi6MXcfB1CLWUUkCiFYVtliqqqwJn4elfjySbrI1iJF4LaB2x5+c1IA8vsDfNjBxy0senjcw6NzGF2gE4/+2gy+eYSoe0JU2J/sUL3xS2hQxJU/aq7V4F+wO/K5j19UTx4x27/x5Gc/+2zm4uSYP/lnf4DzUya/nHnL3PmpTf107PnGG6/w8Qfn/H/+f/+UP5t+n//q3/qbHLz1OmGzZjapIQxYU2NGZYVx/OhTVg8/pqKiUWHfz5DZDpVz223qsASQ5wfFf1bix7PVVsKwzdVNPpesX7HkUJOl2TL3CnCrUlhKWSGY0tTZrQW4K5Lx7cPWkVNFK56gkWwCQy4gtlIaIOerohbrC8MoxsjJxQWzywlx7Ih9YNgsWVz1nJ4+ZrlcMPT9s93El5Psvmb1fYdook6GPCh5EExV7L6VjFolUTIwspRW0UiE4tiKJgimqBNw5ToWFcyoECNUEpq15OM802wIJRfcpAIOmJiQWIa4JmRsUEzQAoSnoq4Rzdvxcvn8ecvszhmi4NsWdRV23NAhhOUamwNTY7EVdJXnyleM5gfsxcxs6Hk4tqzawGS9KYrP0YSkUA8tB7sVsVOustDVhpKznjFSg/EYB5sQCUOxHsWAM4Z1iHTWPlGy3ZnscjDfR7qBk/PHSBdKtlAMpDwmSqYbGRZNSx1XbDaWfGFpq5qdWPKx+y7THZ9w1EzYd2MYZWQ3Y+cHVMYwRvHdkvVVZAjnyDgQ3ED1yht4O2dzGbF5DbNDbB0Z5xFjN2JyHjlbRJILyP4uyR3SrCOH1pD7U9QH+lHFMnrWsUGHGh5ekfyfM3v7NQ5e/wax3uF875TFMrK6+4i8PiMPK6g9XhMxW+rJmG++doujnTG7twKXH3/xYOl5S0PJyh6kqCXLYD+UZ6kdM+TEIJmVD1hROnWkbc6szxG7tTDqMFTWMBghmczuwS6j0R4xg1AWhddse8lgZcvKjhFri1WxU6VxBuurrcJKMbkuSkdr0VGAHo5u3GCmc0azKTlXWHXsjGY8NqfbrMpcgKGthY+1Gc0ea1LJXctb9ZYW8IiUCFoAEQ2elkSvGy7iirbdoBoZ8kCnkV7gXAUzCN6BMbmoCbLFonS+AOWaE6SaTMlYz9kiScHmZ0DlX2yV2VS5PzxtP776+aCf+8SxjQmy5V6FK0MaZQIsQVqwDcKG8tAcFWIU9jPP7knVcOf2m1zR0lvoNGKGARMCdryD7N0Et+0CBZDqJ0gBKhnLEqMrnCbG4vB7mekhBJuJyTF6uINPu+ioZr434603PJHIvYsH3H38AWebjo8eXvDJo2Payyse333EoInbd+6g8irj2T57o12WFy2biwUJYeigz4Y6K2ufqWvBa1XspoZAZTryes2yPebegwc8uDrFGUdtHSPraayjsdusWWvwzuLsU7DcW/dUTf51OvYvqR8/vMv902POlqdPFgnWlMxBQyHmZVH6nGjjQEyZr8sjSyFyeVZIJweHSjVtsJUjqVJFRaKiUbEMOCkqACuFmS0i5NqziYnsI1JFrE3gFNtWXHVLfvzJh1wsF+SfMmD2xvKto5u8JDNGg5BcYjCKup7BZcgeTSOyB0NGXEbF4KLig1KXWRrRFlA6Z+it0krGb4fchcicOF2tOV8uiup8600+dp7bO/OSQahslZUJTEaDJYvisuIlIcnANqLCOIOVopwUnzG53IfkixifX7P6zYahbWmtxVUVVV1T1RXeW6wrxz2bCs3FfthSLOrIStQSouClx2RBssMp1GshOlAnDB6Sj2C1WM3hyAmajcV1lpxdIRO4Hi8d2Q70FjIGiYXo6LJDkwc1hTxJcenIWIzkQjZONSYVKzXr81ZZ46jUIijJTbCxkCqs7+nbnipHvEwIbVdyB2OgrmucgBHHgFI3FdZ6fDPCiCtDGBWIJZtwoGPIPVmFxjusM1gDI+ORIEQtKvRERgclZUCEuF37JBSjiSEbUlYaNYhA0OuBkoW4HWRjwGbsc6MNpcajEat2TT8MNElh6Ln3wV3WD+9z2vSsbv4OR7+0pP7BB4xTw+zKcbzsCM2E6tUZe2cr9N4DfvzgQ7TdAw/r1/YYvvU2D0YTeoU7KN9UxRzc4OTGy3QPj4GBOipBhVA5Ojtj3WTSjTmXD+/y3sUFty9W2CSc1YZ2oiy6NY9PH3D/o7t8/Gcf8vF7n3K2uOJqsWCxuKLrhzKI8Zb/1f/4f8h/9Pf/p0zvHBBDIJydsOkrZMczHTvmYYeDtiNcndDefkS49RK2Nri3DbPvNExPB87Tgkszoj6Y890b3+XbzYj57IBb9ZSbFx2TzcBsc8jVJ4Gl7xmkw40Nu0Pi0Biq+YiNyfz4/RXdg7tYWTLpE7eSob95i9S1fKoLbrkVI7siXm04i5EPz89Ytgsuupbz7b+zbsN517IaeoYY/7UFxY31zKoR2To6UTa1cGN2kxs7B9yYzfnhux+yWn3WfSvnTNt2ZVCqwlvTXXacIcWMMpQ1SRaMZlYxcevODeqmwUbQrdrREcvax1nYOksKmV4tg2MLZmeyT/gIlTPEnBEMki02CjRFOdQkod+seO8H/4pbN28wf/ttrFX6PrFZ9ixXPXZqGY1GxMkIX42ps8flDUjAa3mep6SgQhApIIaUgb24iGaLEcE4xaiQUIahJ3QdOF/6PSlwQX6B9q76DQCDDjOY+AKqPDvsuFbhDoATrA288fYaO2/Y3d/j5Z05Z39xxbufvsM6V9x8cIPvHv5Kee4NgeHsjD/7/o94sNmQv7XL1UzIuxaVOxztL5DlwOM/OGH9Xk+aKVdyRZc71v2Ixxcdj/pH0Ece+Afs35oznu4wYcbEj7nUU+JwSS8t0SurruckLTGHFd/57rf50cfvPHm+O+eom4ahG4gxsOiW/N//8f+T6a+/zG/8l/8WtTng/L0HhHhFYxuwgtuCR9futqvzDffeE2KsuVxHXp3cZH25ojdKrmMB/nLJm/cpsYjC/XMYzmuqUcW8tpydbbh/95JvfPeS/aMxLz8e8cn9zHnXszGRyewxMlrRpgUraUk+Mq4bSEqOEU1Fzfi8NWS46ktE0cwULPqvNEv8OsfyRUaI17PanzaKUoobQvPlP/v56cbPNN0qyxFM0/Br//Zv8b9sTvkn/+KP+dMffsAHdx/x6OyCxaalHcIzgN3n/vqz9uq2ZLKKff43x6mSU02bah6jeJOZ5J5aKPcx64mati4aYDURYySLItmixpB8Jqonmoj4SA6lv5Ot+ls0kRanmCYw8TWd+hKHmA0pWozZECzE3lNpyZbN1lBeRUlpwIgwHY2egOLXgKLxHkWJ8Rni6vaankxGvPzmbWSvrHvVCp0p6noi5GwYtCaa4jA3dh7b1DgtRKvcFTdIsZBMJpoSOWStI0mNz9vZmilCEq8l8sZkQb1S5QJymUyxCvdKyq4ov7fzO6+W3ieqIEyHhEGLq7FCMErBmQo5trfQobyIPrmvcyE5DMVR0hiwzmD6YvEcLFyDzDkbEpCTIeVU1h3ZPiUX5kTXJXRtwO1S2eK+aXJCJQEZ67XY6oeigidFklSlDbWKN7HY4hvBuzKDbSsgOdJQsu03qjSVwUix6b8GTUUKMaQom3V733t63agWB7JhGJ58fl1PRp4iqD7jC6p8BnQHirPWC4h9Pl/GWIw1TCZTbt68wc6Nl/DjCc4lnPSkYBlcg3UV4qsXc4J40fqyca4qmiL9umXkPU01RnH4qgCPVT1mZBw+FVGRamAkNZOqpm27rRDjc7WdP0tRYBTtsmwFWipFyEEhaui212MLRKJCIHGyXnMrrdmfeqIqxAZrhGQLWTgkYbBSermpon1CegVbHFXVC00uYrjWeIwNZd2aLY0VblYNctuhFxvaLuGkOAtYamprmVUFY4gCeYiEbBicRUMkx4DkhJHiOCQIRiyiFBqGSFFMf0m0y8/lYKYCxhsj+Kpi52iX3Vu3GO1M8L6QV1Td1gYeYj/QXbb06w3rZcewahn6SOU81hpCzvRDWzLAsxa3T2sLiYUyj/De421F37ZojkwqjxpHTsqQIIthUo2LY+BWiGhHFieGNARC6tEYCDGQYsIYV+Iit+eK2rwlJAl1MDip8FVDUEPS4vRgKdslWIxYyB4fDbHNpJQRTYg25FT674ltaMyKIeYnmJ1qZgiBuq6ofIVzjpSeRpfkLdFIxSAoVgxpe6xVty3MM8BQIUB8vQ7i59KDiQhHe/vs3zj64s7lGuke8xOg+JqiZrr+cuSp4zg8w7b4GqB4PF/TX6zIQ8JUDW4yoroz/dlBcVXiOnLRFcvEaMvA8nBe4+wTnPWn11co3K93KyVYXKxQLKLhicq7HwZczkxczVwde/WUvV9+lWgr3ksGYxIHvM1vvv0Gt4fAuVMmk92frXH8qvp/tUUN/pLAXL6yeQWgMXDHFOXYr49hneHuAB/1cLeDxy2cD8Qffwy/qvhX7jz5VWcsO9P519qsn3X/yg2RpwdtiLAOsFuDERzK5cUF4teYaUDqn/4XxvU+e/UdfnDyiE27IDWeWe75/g9+yO/98Z/xyv4+b9+c8Pp3foWDl18B4N1PP2B49CGbx0uOZM6N8R6znRvk/QVpFHFuRVmRfA61/0uqvs5EU9hIhTkZUVOj3mGHsllWwGrEpIiYQLCZQUru91beWOzUcwYxhH5MyCMGhKRCFkuXit0llNcskSnPOjkobdtz95NHnF4tC9tw09KHSBeKpc9P1Isg4pSM+BgD42SYtlIUVn0ZWmuVSKY8BKwCkjB6zS415N5gY2YQQ7aA2eZrZosEio0sJReEXPLFn6rB01M1eMiYoNig2FjykCUX1t6zILjk8jAXtaVjec7KVY25GCBGZD4j1Z7UBeJmxRxhVgvBC4uqqMTn9YSb65bODluV+JKjmLC1J1VjJAT2XKaxDZeXkYX3JFfU8GIEdISrhGRgaHtC9oQYqEaOmAKdQDAekwbmVcXL0x2moxmrTz5htV6QQ6DLgSxVefhhGO8fMho2NFs1+sUqobMpddfSrHNRjGxKduGm6fA7A+d39viwnnH6UWS6yuz0LQfmBl0Tudr8kEuzYbp/SDcZIcuE+pdYNLu08QE6y+xnw940M+46HrULtDE0q57DqueGaWk7y2WOpHHNSj2DArYM2JaPWu6nBzT9HvHWHuv1wNoYLl+5DWeOJiu19KSQSE3Pzi3HbzS/ycHqgPzJFX/8/b/gv/X8p/lnKwFJcNmCN6TcAIWpJy4Rs+CjJyhEY7BSchZzBuOUtfHEpCQtVsOWSGcKy/72jT0m2NKEafldv80kTwj4jATIul0QbsFko4q0SqL8K9pEIXpFfUJcYu6nzGROjjUkMDahKttkFtla9Ra2RSYXUMjWRNOXrFybMTmDKBIcJfsm4owh5AJ6xJRQk8lOULXkXDNqJrimomscNBXGWlDLGqhJeDJtXdO6KXYI5FRy4pJ4JGWMrBnZhiyRLcRclAjI1ilCnnzOM58/d6kSVYtVNBSQ5vOPlmc+12e+dI3piP3sz1/PZBOWil0ySqbHclpyP7mDMmYBeMzTSByBA98wxnOlgbOuY3HVczi5yejWFEbNFwziPr/vmYqGW7wG0hPGHW+NWu64zMJ4Up7SG2Vn09BuLJP6FuPDW0xne7x8eo83H77MyeWCTy8e8+jRXe59/DGPzh/TDwFjhb4fqOspO7s3uAwPWeiKZT5m3a3RKERbhhOTUYeziXApSLYMlz3BtpyeP+bk+JgwbAhiaMVwKRaRLfBtHLVxNNbTOEtj3VZNvlWSb1Xlz1tny3Mu11cYsTTGYE3JbSqPHSVrIOVMFxIx/exDhxACj44/5eTsAaPxiPF4wmTaUDUVEqHddBxOdtjZmZGdBVfu1V5GGBUkbRBiySq3BmsNfR/46OFDzhdXX0sN8NJ8l9d39qkri7XFwUKNslFLTwaJZNciucJKRInYJIxTyXF0qogrz05JW8cSWxS0dutsM4TAycUVx2eXDKFEJxlRvBhuzebsjirUBwyFkRwUUIMjYWwh+2SzzcuqlFwBNhGTJVKVvHJrEC1X0ouUqpJiJMVIv9lgrMX5Cl/XzGYTwtSCqTDa4yWTco2RHhA8mSSBYASisB551rNAmgaK4h60VmwWNHtM67HBYNsBk4rS3NQdtumIW7XHMBRHEcShydENDj9UWE3gUrknCmAzKVrIhmqbC48pkTSapeT5mkKqcMnSY1CbMFTYcXHksCZRVxOGoSfniBiPy3lrdZxwTYVvKvxkhDNVaRG1RAUYGbAm4Kst4GUUYxVnLXt+yhAimz4gW8JqCD2DCs57FIvZ5oT3SFnIG8U5pdLI0CZSLupTdYLb9r5J7Qv1qAA3xhV7sxFNNcanzPH6Hu/988ecO0/eOeCtl36FA+uYDXukZFh/suHROnBwo2K6WbB4fI8fP35Ad7lkcj5iOhlzHs55eLBHauaMz64YL8/5cHnK8vgDLj/9ADcsGWxkMGPWzR7kCa32pNqx9+gjxg/v8dH7H/EPf/B7nF2dMtqd8e1v/TKz/R269YqP3r3L7//x93n84PQLe/dhSKw2UL1eITcMngpuTmHxiOTPscMhr25m7Lt9Tm475NdGVN82T3LxKvMa08tfQ7rvQZozvXXEW7/dcNnX5NERL995i8OPVpg//TGrzrB+lJGbc2wzwaLUszUSF6yNYS0Vq9MWrk7Q9IhBTojVhtFkn/M6084voOuYL2pMmnHveOCH93/E+w/fpw99Gbw8+a+QhFVe8KD/AqupPM4bkhU21tFVpgA0apnduMX9e49Yr1qss2jOZIW6KkMwEMamYeQMu72CRqJu3bKkDKxgYD6ZkBmhySPJoaNCDLSZ4jaRMuISQRKbGLF1A94QNSE2kzeZThwhOawRJBViZfQJyUUNOcLzjW99m539OU1TY+MAyzXff+cdLi8vsd7ijCOmRAZuHx7y3W+8Sd04apOoTGIhFdiIEVeckTLUOWFIYAUZmqLCrCGI0K1LProQMDZhfSZXiobnf4ZLDRoycTzdqt21WEyaZ5qjCbDPFlDc4/aNv8OOvkMfAov3P2B3vE+VlXvrY/7J9/8V71TK6fmC0K6o4hnnl452PkaqhBNF48DJ3PLe4j7p48ds1kuufAs7nlT3bC56+gcPeHn3dQajJN0wcp51dvR9ok9TTDbUvpDU1A10momprFxv7Fq+9UtvUf2jiq7v8NYyn80YzWasFlcsrgKqsLha8J/8b/6PHB39Xxly5LXdI/7n/8F/xMF4nz/+vd/n1/7Gf4mJ28UcAQYav8vc3mR8+SHvfP9H/MnDf8Ry8QCXA7mxpCrRbVrspqOVjtUgXBxPmH9zn8ntHeZ7ysO7dzlbWb51NOLWW3P2yCwe3OWT9zfEVaSygusvWA0bwtrTbwpIGvuu5F9eh1g+Zy0+nyUuf9lTn2dqSdmI5gVe41ol/nV2IlHiFA+/+uevU4Sf11JegNntt/jt/+Z/zG/+W/8VHnz4Dt/7oz/mD/74B/zZjz/m4wePOb1csNi0hJS/FCDXlNAkEJ8fsLtEOM81nWsYgkO4YoNnT6DOmdYBuBJvY0s0IRZMsmSxJAuSBiR3GElEMWCLHbUx4INSZbCuwZOoUiakgSgVqplIIa8SBZMNOW8zZbMSNRMo0TTDkBj5EdbYzwBqXxYFMhmN+eavfovdO7tFmZgH+ixE7JaIWOywg1EqEdQbnKuL8MlFHI7gDSlbRA2VJHovQCBZcCZAgsqXOF6Hx6qWiDcxeFcMarZyFaIxhMqWtffWvtyoK7nZYohk0KH070ZL1I0aBEs0wkYycTt1S/H5e/SULClZti02ThUPjG3HYB0me5xabCoOACm4Qk7J0MeBLoEaUxyzyNicMW2LrSr2x57GWpItuckuOpwIarU4TmJwWUAj5IqoUtYpoiWHPiiahUEd675i6HpCt6HrAusQikL9iTaVYkGt24EtzyoWP1tfuI57Zn7w8wS8v+jvFPDeYMXha8/B4U32dw6Z7EzxW4BXYos1Jc88GbAjh4waknOE/JerFH9SP+05ohBCZLnYMFQD3jv6EKm9Z09GXMWeC+05mI8YOcVaz15VsRx7Nuv02cO1HS1dvwdFyFCyuY2j5C/n7dVkFJNKJnwZDOn2FFA2/cDpesneeM5BZehGgSBDEYAkg9FMoKIfDMm2JR7UN1gniMnFxWA1QrTFNAFXRdrkkFzRGMWZin1nsDcOON9syDHhbIP3NdOcsJoJMeKNJRuLpEQ0wrCCwXeYPpQoNFHEWKw12xmhoBlyiIXQ+AsoYyyj8Zj5zj57R3vM90foyKDOE51D+wHJmdxlupOWuOrZXC7Imw7vamxd4UcCpiPHEvdoxDKb7yLW0G3WW6e0jPOKvY5zBXTo2LQbhEzlPN6MSaIMqUeTEKMWN6etgDFHuzXxFlKCaATXjLFkUozYnHHOga9JGknrvsRUUsj+WUtfTi5kmqIc97hoIBi8hUFrsvbknPA5o3ZgsJ5xyDRmzLzyrGIkPRlACn0caGJdCP+VZ3gm2q88twopy4lgTHHvTNvvFdRcMCJFvMBWWf41bj8/F1D8Ok+8Hjdf3OBknubMPFOB0vRcK8V1+3mmWHaqFmtiga0l21dshEI4XxGuNsVX3lW4ozl+5r+Wj/xnN0zZbCLLIZBQshT7y/1Zxc/krhH5yqycrDD0idViU9QEkrfEMKEPAy4rlRhsgvGNXeykIlWOzQBdNhzeOGI2Fd4WQyuZ5vNZ29cn2PPU/+Nd2BnDazvw+gzequAVA4fy1Ab/i15bpJxVOxbmDXyngcUUTiN82OGuImp+ssV9EZXzk/qJob9CUMQqWMsTj5blCUxvQVWDq5hNpwwa2L+5g7U//ZKwuSKvSgbbbDrmtVdeYv7KK1z98M8YZMPdTzrsZo9XXulQVbqh5+6DB4SzS1IU1mOL95nDT46ZzizyqoU4gJ2gHFP8E3YR5tvj+YtfKsVrcFsLK9VQrOPUW/LQYoaBRltGLmJiorMDm2YgDZ6Iw+Ztw5UtMYA4g4YRWRsgISYRVQmxLHMK2xBSDsSYnlGJFSv0dtMShv7JzS99hYrsRd11VJXzyzOm9RFJisWHJJDeoN6A9UXN4BRretQW8CqoYKNiBqHKIDETrSG74nSQo0FyRqPHKMU6KsWiBo8JuwXCry3RTd4C39tmVJStOrw8cETLv6c+Ks+/z3EQfLtGXY0bNfQiDIvVViVusJXSViVLvJnts5uFaeg5qQ2rPlGv27KIr0YkcVRxyeHMEXvlalC6naI6Vsk4U6FSY62yCYkhQAw8UYm3oWdjTMm8TYlb4xmHsz3sEDm7OCF1PYNGAhkVR5IB8Q04Q71OjCdzBgOjkNmlxrVL/GSE5Yj5TsV4ryHIJYu9NcudS45jzYVVLq9WhFxTjxyz+pDZ7oRNeJ9NPMMslrw8uc1wcIDkjKx3mDYdQ7th1fasPMitOTONjO6dM45XJFeR3YzNAgYPm3qK6aaFATlbsjcbY3o4ff8Tqg8e0V4tWBjP6ju/xEt/7bv8Wvsa4dMHfPLwjAvtMM4xubHLrVdf5+rimO+/809e7ER/tnJpdH0WfB6Ii2MGTSRvcQmoG8RMEFNslUI2WAkIDkNgS2gnUqyBPYr6suC1zhNdxAaPisNIjzWG7ApojhiSVaIYkjUYG4hecEORg0SBbDLJBUKWJ0x3J7moi4CUS2OrKizagZwVu33+ZQpxJKKF5WgpVmQqWCME41AS1uRrbgCI4AzYlHHGsHv7gD0/gmwYYo2tlXpeGvBuZIjOojETNg51kSCOYArrczA1ORampFqPzZCdZXfXYLM+eUZfA+BPQPLr+/yTZ+Hz3/fzpZKrWNiFdmutHJV0GTGNwUy2jcz2OlRVBgu9gTGytdv+ydfdJlDTU9bvAWHOLmYbghOAbUo4dygztuKmK4zKOINja5nujWi846lP11fvbkn23EdkH0Spq0SlCzIdN9iQEBbA2Dq6iWdoJsSbU3I94ajaZ5+BfT1hB8MNAb+6og6BB6dn9P3A3dP7vPf4E9oUeHD+mOOzDzm9PCENijplMq1pJiOGWSSpJa1rejw6DHSrSx4/POHy4oqcM8Zu72XGIGIYkjBgWBtTMsmNwxpLY54qya/V5M9bd24coMZweXZRhumUs6osIrQA4ikSX4gZXUDY5WLBcrEow4frvkrAvlThJ1OsUVyscdaTTaZPPepa7LXqVjNBEmfna47PTr4WIF65itsHh8zGltplrIHsEp3UBDyaQ2l3TSq5uBrJKVAP5V7gciHoxGxJFJCys4oaqFQxObHuex4u1xxfnNPFDhGLUKzF9sYjjnZGNF7LPSNvIW0pNrtOi7rZihC3ZLbkMpptUcsoGNFiwYmBKqE/5wFMTokhtQx9Ry1CHO1iqkBUQ2carDfFmUYiUittZ1mjqM2sdwfipAcvZKvY5HC9kqPBxAqNNSakwswWMC6gdV/2cQt4m2ggWGyukOxR2Q70BCpThrgiJc/NYciiZBOLtbuFSoUoDkPJE89GCBiS2yo1E0T1SM4YExGvGGvIfaTPytpusChXdEjdUPsRY1dUBWIiYehQkxES3mZctVWPZ4t3jto5vK/IGdy6I2RD1IEQE33IZeAmWq4vKWD3wEC2RbFQ+OgD3TqjtafJDkdZf2YD9gVUZgBvfGMfDjzT3SmT0cDHf37FJ394zmlbBqKvvG747suvsKpH/IuHC9apZd1usA8fsb48JVyeswxrxKXSm8UWHl5h/vNPeGV8k6NYcXl+ysdXHzCkCxabx8ymFaOqJmYwfp/DyYz5quV4c8xMBqyp2J063nrrLWbn+4xmB0zr16jCBCstt+fK0c49To8vyMMX667effchi8ctRzd3ABAfcbsRkQp5sAdXhnkUYpWxfo1UsajKLiNaGcZTRxhtSDqnMtB1SiaxDiuudgz6q7dQWrrHxZHNDQ39ZWRi16ztBfdWd3HrKYdHN3HNHtUwoJ1yOZxzsb6kHs7Zm0wIlbAZGz7ZDKR0ziZ3LM2KznQMdiggOPrkY9Hf/Otb47rGOkOUSLSOQWtcH1CNeMlM5jPuTGbs7s6Y1pbUZZL1VPUOKQojDYxFqDOF5KNabGC3IEuXOjYbYbep8KYBDFl6BrFIpRATlowRoVfhbLVh7GtGIwvGFTWMMfSqRKNYVbyN4CCKQX0gWqE2wu3DW2AaJCokaLyn8hUpZ1KfGXhqI5o3K+4QmWgAEa6cQ2xRGtqcMBohJzT1aPaogSCZZMvQLSYYguBFSBqLe9d24CYvdEsXyI4ga67oqHTFlIaKoye9oqLkHRjW0Awj5OJthg8TnfsY4zIhttypdsnDhE++9wH/5//k/0RYLvmtv/tb/MZ/53eY7tacDzXJUXJ+hwjvvcenn37MqB/o20icKjO7w667hT/sOD6+y9VywdHrtzk+UzapJ7YZS0TsAvolnp48i0zGIxgyZ4+v8FjCRcfZvWOuF6spKyEkpOtJWT8DQrZty727n3K4t8Obv/VvcKLH3P+jE0ZVw403X+Lb395BiWzaR6TRMfs3dkmbX+Yf/fi/4J17f0LyA7cOd/G5xo8jed0R246WQnY4fTTl9dMpTdqjYcHl+YLzbsnND47wboxfWLrNOY9OjrFjU2i1Q6S9WtP3SkyZy+UFKYWfyzD9sofUwMTASP6KVOJQ1gAdJUv8eeuaMfuzZImP+VIXzesf63nB90UFokXsBH/0LV47eINXf/W3+Z2//wGfvvPn/Be/+yf8yfff452P7vPw7Jzz5Yq2LwTSn+xKX4wE0UkiSCSahDceUk10LR2JsVWc6YjiEEkgxSI9YgjWFgV0jhhNWA3FkccExPRUUmFzyd01knGjQnYPZEJWei2KP5yQYumXkiZyytt1FbQa6HQgxUBKifQMeKnXc7sYyZ8770eTCS9/8y2mN/fBWAaJJPH4WAMJl2Mh5Uok2wLOZWMxQJ0NSSzBWqx1Ze4lxdHImUCwWhbnORUyYyj3V5u3CkQrYDLZsCV9l9nBQFF7iyTUlCRjFzMiuWTcWsfaOLwM21lBUaV3AgNaSKE50/fxM6r4n7WM3SYvZUFyWQtGQLzBK1iJiGS8LUCUVolmAnsHc84eXdLFAU3gK0/djJjv7LKzP2UyGwOOYcg4CThJZT4ZCpipYgth0IKm4mopsl0VWoXkyNkTckVAaWNLnwNRE0GLWv163fhs5Z8xn/oa03l6yfwCCIJSrP2NMXjvqeqapqqobEMzHnP48m2aekKKkLQvUU0BsiSssdTii+NVX0jL9hfdsH1ekPC57z3Rd33B93NSuran7wqukLNinWWzGYqrDJnGG5pRiXKy3jGpPX03kJ45j42AxWCyAQeZjGzXaYggrkRqoYXcraa4PWouDqiUbzHExPnVwNluJEzHrKY9MoJm3VKtlNoKIfWYjSd4h9qK6IWgiTokspYIgUYdTdsSUsA5h6kbqpRgSIxzBVJBVZGaiOCK/XpyxCEWUZkapC724CYozldUccRgIiplWK6ai7uXKdc22/Nbfx640/X7agVrhfG04ubRDZqDl3AtGt84AAEAAElEQVQ7Y6wZsC6iNtMnyFGJqw2b4yvWyxXtYgmxvLdiLUkM0iXUbWcNKTMMA048VT2irh1oJqYI2yjIIpIrMUQpFldf5z3G2nK/EagQ6AJBYDACmqByJVPcaFGFU443VYU1UKVYHNOqGmsNPgkiNZWtYBtrqdGiVrAZTCj7KLUp2e9RMSZjjZKNhW0kkSGSbMUQhYqauR9xanpazU/mnyknQojUVUVVVVjTfmZ+lbf4iJqClZhtnvm1Wvz6mrou5eul3fxc+rCmqnjllVe/OE/8ulm6RrqfqX778VlTuevbrqewv8iUHfuqeYKWm384WTJcrMtiynrcrV389DlWKX3iso20YSiLL2BcVxzMPF97rnHtAvIVSnEF1stA1wU0Z8w2XzhlpR96xklpxGCTMr69h1jDCqHfsvvqxiIOPILDluH+s/V1bOe/rM5auNjAvVP4Qwf7Uziawxsz+NYIXvWF5Tnly4/Ntc36noNdi7xVQ579fADwr1MKshF0VJJLrDFIVcPtW+DLyXh44zb//v/gP+ST+x8xP9z/Wi87hMhffP9HXB2fc7S7yze+/TZmb4e9/SOMr4m2Y9N1TJoRiHC5uCANHZtkMLM59auvMKs807QgHL9DOmxoZlNEIsQNw+ISN97FNr/Gi61Uvn7ZpKQkmBSpm4xLSk6BSz1nGFpS2+ONMPMO57UQGpMgOZYmzKSSm50Fnz05WHzOuBQwxhBV2Gxahq5FtTyoSh5IYBjCk7yI0kRlclKwUnK3tyC6vqgk/EtKUT568IC98Yzaz8iaSiOAwlAG/kRbbD+8ARxqLLUkdkJXuBedI/fCwkO2lmQAHKKxgOaSyH2ibSM2FCW4SUUN+xQEvwbCzTMgeAHEr7tL2SpoEUqD+5xl1h2EiEx3yU1F7AJps2aiMK2E5GFd1bTNmFvjHY7WLVEGFqaiu7ripRCwzpKaKRojuyYw8WOuzhKLqirDctGS4aEjfA3JCP26J0bLkCK+duScaEUJxiE5MPGOV2c7zMZzukePuVhdwhDoiSRxZZCuipvPSJs1I1+TnaEPAT/aZaKJyeiAvLNLqwN5qrSjATNqkb05iQvoNuiwixmP0JHlql7ip3u8NnqV3d2Ox+mHtPaCcTXh1nSCOT1lWD9GfUU/XZHw9FGorcLjR+R0HzMyLIaGdduwWLbsRbixW3EiY9bdgtnEU0+EoQK5WDG6PGOSi60smyW//sYe/+3DG8yav8P3jlv+33/wezz+w3/JhxcfY+4vWa43fP/d9178ZN9WxKCxwqvQtB3vv/djVusFYhTrDJPdfUbNQVlMS8Z6S4NSjxume2PG1KhmHIrJW4eTbJjOpoynM4odU0VGCFIjgJMe7wovtZfIxToymlbgHCZm+lhsK2MCYwfEDwUESa5YfOWy0DW2ML1NrpBguLq4KIs4I6VJ0nJNdykyiBZbQjVkJ2StSkePFjBnUDAeNeV+BBFxnmY2oalrojos8/J93zFITzQWYzMub89vUXocSQRJilqD4lHZqiwlI8lS6QhL/qws+5n6eT4V3ZXAzLOV0G97qEyKl8hyCk1TVkctEKC/Uu7FxOqW4c6O4fCJgv16W69TmEFF6NlmjVN/xhbYUZx/lpSXHijipnr7ux44rDyebUTK167PsfDUI6kogq3ZoGaFqw3jyR7D3sDHOwOn+5ecEJlOIvtdw/h8zDd6y52jMbOUqTWyf7jDKsH54pJ//vv/BM+I9arj0flDNm1PXTVMdidIN8H3kVXIeFOT6wJ6Dkm4bJecXV3Q9UU9dP2+GfMUHDe2ZOZlU0DBKIZeDFdSsuv8C9qnu/GY2zNwfWbTt0VBSyFgJE0M+UUB8evhxjOLkpw/Y3n+wcfv8en9T5jPd5jPdphNpozmBq0UMQkXhD7CZj2wvLjk7PKSGL+eQWFdNew1DZWti4OLFGAkuYxqxIRUwFwLKYUSzRECQ8wsNRPV4qOjEot6JVRKzKm4sjiljYHjbsPx4pzN0G5j7SKqinee23s7VJUjS0ZSIcp2QApK1ganBjFgbBmcJqdbRno5vtFA0C0BJBSiXPhFTcG12ImpZnKfUTeC2hJzIqNYW6JSerfCmb6okkYKlSVWDtEBq4LvLBorYvKkVALsrFEGjWjTg49kW2wqXZDCDM81qC+DwG3rknSrDJWIla1N5ZZ1Iz0YF4tqUcugJpG2RGZT4mp8sb03tkLxhFxsjJVErYFoBjT2rFNLzIl1GPBSMWp8Ycq7ohbqNZRBqFFQA85txQ8eX9U0Tc3UNYgVnLes1rDsEjkLOQZINU4FiyNFu7W57LfKf4q1qghDbiFFfKhwRsBVOJsR+dmGip+vw9sVq6bBHtYMGOJsQUhr8ipiY8/607/gzx99SHu55pI1Q8pYdYR+gKtHTFTR2hD9DGzG1BUvNY7u4WMm8z32/JS88kw2hUw2rxxTXwhNTjzezrFuhDGZru1pOePO/pyDQ4v1E0Z1DXLAsMgQrxhnz6S/gZVJGWJ/iRnp9z78Y/7V7/0z/p1b/z2kSWheMammrPodQv4ObarZ3SyYXRxz8sN7HL1xi73mNcTX4K+oqg73So+cP2ZnX8FGxlOYpiuu3vkeH62U9tGKtMjUfc3R3QNe6d7Amz3Wh4+4TInRZo1tT5jdadl9+TamnzN/sM/lOtLGjnqVcG2D9kKolbVs6BlgY9HzAo5aKbEFTizWFIeQ2j6vxvEXXCLM6obKGCQIxigYi0bD0jakasOdb7+N8SNcNCQZcBpL1MHgsQhep4zDGlIi26KEq3O5B7Yo5xdn3Lvc8I1XGw52XgZvkGhJTWawSiMBYxI2ZrSL3H94j2nf88rLbyCVIaQGcleIBprL+rJKZHGkviGLUknAqYL1xfUhQNbEJgaGL8nTdNbQWMG4BN6iriT3DupADSkPpNBju5Y6J8Q09N7iNZOTIfaJ2IUnFo1RlbCJZDO8UCQGF0A0dAfCj/kxe/oxb8l3qJ7IaRUlsfIGOxfWx0sef/SA9vEGOVTaTYvHcOfO6xx8coBRS3fvP2WVN5w9Oqf7ZIGYphxDbZjIErP8lPW9JeiM0cvTgrZvjpnZgb04xdYND+IHPDr9kFe+/V306IBN17EKicYEnFzgxpk2WnI9Znprlzok1pcddYo0U8ePP3iHfmurmzWz3qxYbdY/AbCNmxF/+6//Ov+L/8l/yBv/4N/l/b7n8k+/hzMz5rcOMFPIOnDvwV/Q6jHGzqGe8Oo3j1iaO3gDdWPJ6jDaQgNdtMRNz2l7yeXFGuk9h5ObPH78mI/feZfzds3l5SUPf/nXyavEn3//z7h3fJ/92ztMzQwrFVeLU84WF/RhoO1aUnoRU+WntbIlS7yBp65Kf9mlwIKyEddN+vO8xrVK/OtUpCwMvmK09XmB1HOvja4XHVEKwCo10tzk4BtH7L/1G/za3/wHnN9/n+/94R/xJ9/7AX/wp+/y8YNjHp1dcrXZMIRiMfvzqIkIyZScW5WWSj1VXjOWjA+GJiiLkZZ+UR3RCsEYbFAIIBIxVpFc1t1VgkaVLIFsBJsElxPqEoPxpBBK1KEp72RSCKG46oUYiwtq3pSc8CQoBrEZcUJKZe52XZ+/VqEIGvb29tg/2keMR2NCrSFnT1KLpExOCV8Va/ecMtmZIoYxirdaFowOEIuVVIjbxpCNJYnDelc4tykjRmlS6atFLKEqjnbJlGtepcSl2WvVazIF9M7FcU6MQkpEtQxVjR2KT1xnlF622ck5MwyREF5c0RxyIZejGbVxO0os6z9DAi9EsaVPFxjI6Axe8vukCk6Wa7z17Bzs0RxOGe3VOKs4BBMEsxmo1kUQEMRChCqX1B4jINngvJDqVIB38aRs6YcR5IaMARuQKhAk0EVLq4lN7H4uOd6/AAj8SRkjOO+onKduaqpmRNOMqZuKuqmobIW3nsYmKhtpk0fFY02P1YwiWDxGJxgp1vUYg/1Fxdtd8/6vmwYtJ9zn7eO/8j3TayeLp4SFFJXVevmEjHfiaybNPo6MtzUz51l7zyZeo23yRBUsImTZzsyk4E+oLeC4kRJ5JVuCiUkFUZRyDpefSaxCT5szoQbbONRnqCM+bPCdw2qDxKIaXjQDOQlmC+ZjWowv+aNZOnLqGZmaJQ1YJQVHCB6TLV4FqrJeUy3rvF5AciFjWqPklEgiWGOoTUW2NYMJSJKiEo6RaA1JI3kbt/Ci52jdWOb7NeNxxf7+hL39CVY8MdWsnaP3ihhXnmWrNYvjNbGLLM+P2axbxBZhgRiBvCWIdgNDLPQc74tzkddiE67DQCATY8YYj/GZyjfl/p1CcUIWoZlMcVJIMilv+UPicSPB+K3C2Hh8PSKrYHTAeEtdG9SYrWhIsJUnxUQYWZx6qlhvnzO5zBNSLvPaWNbvVhUyZJ+JYqkcpCRYF4nGIyaQ1GByBJPpjaXKnqkbM3JLuuHaQn0r1AmFpOWtx1ee1PVPAG/NlHx4o1gpoqgo8tRCXWGrNX4yE/46t7Wfy1hmVNXcuXMH476kKxq2f+mZb1+zAOEpVn79tWsHH4Gnudw/peHSNtKfLkjrvuQIugp3c46b/IzsfFW0z1y1kT6GwtoAZpN6myf+NduzZ7f7c7/y7G1tvRoYQkJzxrpyQIeY0BTwCpWUm9j41g5iDBsgZGhUmGzP7euXd5//IwuF/edsJ5Pd2t5rCT/aXMLDS3ivgn/ZwO05vL4Dr4/gTQu35Snd9Yv+5HZSLC8w+P1ZS0RgClEj73/wPucXJ/zS62+yc+POE2Deec+bb7/NG2+9Rdb8tQD7LBH8gK0sOlgOd/exAr/ynV/m3sWSiw8/YG8+p97bKQo5Z/nmzVd4WF2y++bLHN55ifnlks2n9zluz9He88b8Np7AcrXg5Lzl0MyYN/4vTWngNkW1I7FjFBxVNAw6oDkxtBtySFB7UAsZnGbIaZuZXewnxVhyVjQJzmSc9hgttj+LxSmXVw8J/YZtwgfJlOfs5zmJWbcD62yeWJ0U5ssvbv9jjAyacNmQBkFdaWBijsTtAwkpCwcjDrUJdRnbgwzFzlNVyAvDYIVBoNpaelgyPgk6JGjzFtzOT5yIroHwYo1utg0TT4FwSeXysUoymWSUaHKxo3nOsm2LGo8bN/SmZIlL6pkaQ1XDpvJcuIpm55DdqOz1PWeNYTkkmuWGMYI0YxIWN6w5mHpyEK6C0k4dRWqvGPEgFdYpm5gJfSakkiPtbUUXWlpjEWshdtwc73Jjvo9NmfunD6HvCVqOjZoRkYC4muygaRN2NKLXAkruO4vfdLjRhIWdsugWDJcDRxuD0xGbRzB7w2LtDqmqGMae1c6G1eiSauHozi5o9jr62rJwNYfWcDOtGI8GHkfHQia40Zhbs4A7iUzCgMwbRgc7HE3g4SPPD9+NuOgZD5FR2zJtHImeuo6sg2OFJ1eePeOQruXmfs3k6lO63w9c/Z2/TZhN2XvliDdHE+zZPdYPP+X98wve/fOPOD+/+nmd7miy+FySYnKoCrEjRdJQ2Nyr1QPQh1xfdM57RnVDM655U++wt3uTlMtzq9KMs4asqdhX1lM012R1YChZxuIRk6hywKllGQLHZ2fsKczqQ0QzxZDWMJiEN0rC4DWXoW2lJbbAglBY9U4yG430XQdiy/X3jOg4xEhOWwBGbWkMTSZnSt5ZLAOCentPE0kIirHbxSxsLXhKgzeIo6KHmHCqGGO3KsFiDJ+kWEzlVPbbqiFrRDBYDDaXe+HTg/DZ/5Ev+frzlNgMlX2i3CgLgIw/DEg+Q+1tEINMBQL4XhidC/fPWlZeCOMR822+Vq9QJbCxDPGc0cKFk89sdXm7gDlPW5+sTw1zZPvz9fV+aYIcQbY32ycv9UVPvesMs23Tk4HOloDoSQWuwo8F/+oNwviKsVmws9/zyPasFjU1DXvNEa/6PTYmM5UbpHXDaH6fdV0WTGfLEz55eJdPP/yUTdsxGs04mFrEJ9JpYBkzQwaxAa81jRWGoCzXLVfrjqDy9J4uWgY5CYS0HeQUIkBRkpsnHwVhEEN4gTyzan+PZnLA5MYdlos1lycPWK8v6UPPZkgMv7DsrM9WCANnZyecnZ3gnMNXHt84pjtjvFhWVxtW647QD+Rnvay2LPXPSQu23xNuTMfsj6dkLO3WAhC1aAArGbUGnJJcJAeDZrDZ41PA5URKsSjUgaiBPlucEbwY2g4eXq04Xl0y9B3W5K3lfOlUbu7OOBiN8NagBgYrBCu0AVQT3pjilGC2ihgtTG3FEBWiFltGNdc2j8U1xqRfTH6dNQbnq3J96jYLTLQQhyWRoyV6T66LUkdRXBTiUNQCNnsIFbJxJCk9n9WA1VAU2uOeWCXwpVXRzmCCUAeBqCQCyRSgOOdi55gpzHTJgsuRZC3ZZKQWciqkVZFCuzFpm2RoMtYaojMlKiMX+qARh82FJOlFcZWgNcRgSHHAaxkAjGYVtgikCCGRVLfkKUNWX0B4a6isx1UjRr6mripElVlVQU50wZb7luYnGW7XNputBnrN6HYQreqKRXgG7XO5nhuDJ6Ehv1AeKcAk1pz96IqPf/cxx+uWdsdzY77Pm6bi/qfH/O/+8f+BxeqKb3/zW/y13/gt6tEhtZ9zOPYISn9xjo+JFK+48sBOZLYzZ6eyPHr0PivZRX1PPQt0Q8bYipHNVE2JEEg5sRp6LtaJRdhBGmGZHdO6xU2E3X5E7h2LGJA2IRhcblhvBuJX2JA+OD3hP/vH/5zffuN3cKnl8upj9nbfYn9+i/PwKkMvdP0Z3cnHrPvEjQcb5m9EzMwDlnF6nfneXc5uPmJ2q2K8v8Pe7h7xbMSf/cWHXHz8MeenV/g4ZYcd1lag+iY7kxtMuu+ysS1tPkGN4KfC9C1Pf97SPw5cDS1tGGgnNaNqTB8ztspMjMWlKbaa8WbyxMtjKuuonadyjmobueb+KjMqv6IEYVaNqKQ4OpioOIlEqQhb4pC3rqh5TcLkgKRQcsExKBYjEUy5TlVAcsa6kvEYYmLRdiwuzvnB+vc5vPESr7zyLebTKX4ozhCIYMtL0LVrzs9Pi5PGSy8h2hSlTN4uErdOY15ysREOAAZVh2jCaMn4MhiyZk4WS1br1Rfue+NNifcwgmrJsLUx4sUyUPIXVTd0Ekk5YEJPCA0pFgBlswnEvi0NkVViSGwEdOJfjPB/F7glZNnhjCN8WhHTBBl99shhYRDlMl7wXvcBkyayKz3LiyW5A+M9bWW58+Yv88qdb/LR8Y8wa+Xx735Mh+FqfUUK0O021DmQvLL7777B+JuH7PjM5keB5YcfEy5WfPjuFfc//oTz2nF08/v8yq/+WzDf4e7jMyxXLC9X2EPHbOcmw86E7s1DDm/t89ffeov0+BG/+6d/xA/f++gzIMsXub5V9Yh//z/+H/E/++//13n79bcZJvt8+2BEGu1xJhWznTEqQoiG1QquuoH5/oLpS4ZXv3mLdhU5Mi+hzvC4PeZgesxL+5nj4wWPLy7o2wvanHhwsiSspwyMuPH6a9jLS4boOT3vqUc1zBommwmz+RFHR3cYKQxdyzIu6dYRUzlsTqSwzZF/gcOt1WezxP+yZj1PN4Cnlk97/OVniX/F37vetPoFN+vJ9vjti+bt2iHaAkjsvMbR/CX+3lu/zr/5d+/x33j3+/zhH32f3//jv+BHH9zj3uNTTq+WrLuekNILjaCqWDM2mU4HBkr0T52VXbZZ2F5obU2OpedSO6D0qCmuOY6I5IBLGcmugBTZkV1HZyCbmmQSGCEHIUVfIsmkiKjUKtluGEKmSxBjxxDbAlRpjXG2xP6Ygbbvv9Qu/bp8VTMejVGFnH1x/wmFCGpIWDUYO4BsezAcGUs2BjVCrpVsMsbGclvbWq7//6n7r2fLsjy/D/v8ltl7H3td5k1XWaarfc8AY5ogQAiiGCQlUaGQIvTAB/4DitA/pSc+SYhQMPggBSCRwmAG4GB6ZjDd0666y6fP647bZpmfHta+NzOry2ZWNaFfR1VWnzz33H22WetnvsY4g3Fll8m2lImSy75S24yYTPaKtUrKQscElyjmbJrwuahNinHkqKUeH1n0WYvEeG8tURxpiKXVlpUQIsOQXll58jJCNqiU6yhOsWqpNRETJMkEtXSu2NOJZAZx5GQwk5rbh3MOQoUwxdQGbTqMG4oFVMyYIFixWNcQw0BnocrFC9qIAYp3LwIGSxKLc8AANreElBgo8vJWlBQy69OzMqzbrF6szf5njiIAKMUf3Bi8c1TeM5k0TCYT6mkZilfeU3sPlUetUGmFt8UeqakGbALnLDlZojeoccTUYHxCbS6A6m9qFR4/9pJYIOO/XgAffOF998lO+fiaFil8VLnYrFhPphz4poAy/YSmjnR9IOeMWDCMPWYjGDVk84xw5pIUwDCZlGUkpkmZ27hU6jBArNBMJxxeO0BqMCkUZdWc6Z2ldgaTFV8VMkAdIylEYjSwX2wZqzxQu0jXlgG1k5p+8ChCpCLEjO4yURLZ5jKwT5neJFQKkS4pWO2pWoOvBB8UDSX/JOnVem+g9Bhz6UujGQO8ipbTn/7TO9S+pnFNIYgp5GyKhLsaYkhoN7Bb98TVmna1K8qDNqMx4SuPMbaoT9uE4nDeMnSKBiWnSE6Kr4oCgqls4RjHQA4RKxYnDquGOKqLiLE47xAx5MsBc1SSK3lhNZnSVBayG0dypvQkYiKm0iMsqr6JFCIkoY8Rk2rqxYLkJvg+oWFLcEKyAzlLqXOyQSRhE8RU7HgrI2gsPTBjPFksKrGQBUwg2pqUIl4m7PmadYiEUXkQEYY0EGKkaRp8VdGPxJPLp+GSLW5FR+uE0apXC8kQEcQU7/hPay19WnwtQ/EbR9c5Oj7+dJlypdC/6999uRsPwD/32kAhH1+dly+ZbOVtoH9yQd4FxHncYoq/OXsJP3FIu8RFF+hTofiDsJzWzJuvkJ59wTA/UxpGm1VfUGk5j6xKZYgBiZEKwWelnk1ors1RgbUWSZZFhvlzV+9Tv+UrXd3nVvEkkMyI8IywXsGTFfz8HiymcLSEuwv47rzIrN+Q4nv1+9aD+uRJEMBCt+34u5/8O04fvM+xUfau3+CThqkiJcn4MhFij5t4lvtHHB0ccHDndahqJgj/i3/0Y3Y/+jZ7xmH3FgDsHRzzp//0P2NoO6b7ewRNJHfCew9+y5OQmdSRb1MBtzCTfTb+hP3m2xTO3e+nVKp3EHwgpjIkMVp8L3MWJJlCSvYesSMLYkhF6lgNSQ1plDbKXrC5JLRiEpZE3K74+OOfM8RAZYrMkQrAMzRq2ZKfRcojGsqWJkdpaH4ziVpTVfyD777F0dKTc4vJE2KqUUJhcedMwlFZsBXEaUs0mSBC1Xvmuww24lxmErTMebRC3ICSqUxCcpGLd5rRpKMv+CUjvCC6LpngUGwUSsNJSTZfDcH1cuh36T/8kiHDALM9clORQiJs1swUlrUQHWyqmr6ZczTZ47jrSdJzYTzdas1xCHjnyPUMTYmlDCzqKZuzxIVxhAqQguwVnWBqQzYw7HpCcgwx4iuH5kgnSm88Jg9MvePufMFyvk/74BEXm3NyH+g1jCzxIvVj5zO072isJzvLEBPi58xyxGdIzZyLmImhZR0iu/OW3EAzOaIZ7lDPD5mnp5y0G64tO27ljtfTI/bjkvdOlA/9AZ2pkdPAjVvn3NybEa6/xtPN6wxBWLifs19/xEJ7ljcaDvYOuXFtwfJ9w8PH99n00HrLrr3AVC23D5ZI47joDEMvTDZC3VpSagnrnmllePpuz7+c/RXzH89pbr5Jraf88M6Sj8IxDx9tuH8By4NrX8ftDlDUHUrOQMYwmU4YdMpuuysNH4Xnn8gUI4PpsdFQZ+FoCKgUFqBmwZhEjA5JkIcKmKPqcDoUNqUTgrdYAj4W7eGzk6fEnKn3r1FbKdJGlR0HIjrK6gqDFTTnSyU1MIrxiTwJhKF/YeGQ8bhVIadMiAWdrmJAA+JTYW6P8lOX/uNIJtlQkkvjcJnCYLIJT0tyFUEy3mSaQZkMkV4UTZMr6TrJPUENOUdshkEcViaoN5iQsLHF6jOWyTOk6jiy1k++9vLRVwbjGe1elE03sHpyjuQdzWHN3CW8mDJjtmCvwZ2F5SBP2NZFailQUrcqg7SQdzBMwM/AmM/emSbjP4lnnoBXxeBz3x7W0J2CXoOqKp0TqT7jgz/xouEZetIKwhTsFJ0H+qphN7R0k2J14bZL5ovb3L5R4Z5kgra4iePazQnDcELcy1R7yuPzjzD//qc8eXhKHxLOOpxrMKkmXnS0/UDaGdQaZnXFXtOjrbA+P2MXOtJoqyEjiOvSe6tsdZcM8nFYTnn2jDEvDMpfNlQMYsFVnsnxIbNrR6S0ZnNxzv179zk/OyWE+DVgo798xBiJMdLuYHW6/uw3CsVnXOzoaxVfYKDXleP64ZLkHFu1pXAXpa88mjKqYCnFfEqFVWIjNEGYJINVh2qi2FgL26xscy5lhSqnmw2Pzk6IfQ9cgs2UqEVdyFQeV1GkxaWU0mFkq1NZagMigWSKt6PJhih2BAwK0ViiKqpFNcKaDpIUJPM3EKVxVIbiqoWZkoXSPBHIeQQrVUqwRW4xDxndOTRX5GQBR68FYW+0sOmtGcAWyUcrmaSOpJmmNVS9wyZQSRhVnDpSqMhqMa6w+CXWaIo4gewyTktuI2LQZHBRirS9s1gbyZIImvA5EbUBm4sfJgYjPZIEZ0eApfM4qTBNhanHpo8t7P2clKFLpJAZciarGb3fxwas9Uy8wzmLSiIlcM5QTw0NE+qhou13BawcCux+qCxdZ+iTUgtl/ScRwsAQS/PVKeP5y6SkxFdr77N40jD5oKKJG7p2Q17eZpg5XM7sHcF5t+bR6pxviTL0isjAfqOkvqe3wiYFpO8wJDyCDUqd56S25e8+/Dl2MmfWOI6XFdW0IYnDNAZXTxmY8CQrph04v9gRQ41sKp4SOH59yRs3b5KuGx6819LmntQMpEHYrtdsN+efy64cYuDf/vov+Zf/6v/Gd+c3MMlz8zv/iF/1Pe93G3ZVIO+egN2xZ2+Qns5JrynegtCQhmP2pq/j3JrdJjNjysIdMnTKXCcc+An9NDE1FfuN59r+hDoHmqrC1m/gd+/yYPWY7smadZ/odzOGiXIO+PmMOYfIfEGqrtFUNQunVMsjTk+2nH/0mBtY3EcVxZVURk9P+Z2a5j+kEIFFVdFYIWtiSJl2aInsYxY1kiCplNQoJLxmjCQ6k8keSMXz2jqFPKDGEtVis5LVsOt71usWVaXtW+7dfxexie9/64+wMgHbM2gBMnqbyZR8tF+v6bYdEzPDuYxFEWdI3oIJRR4yQzYRNRYZisKZGAHpyxWIyqMnT+mH/lO/ezOxiA0Y40kmoFjUWjRFjESsGZtqY5MwysAuCtsEUQfOtjvOdzu8KXBIFEINKUfcK+zhvA1MYU+E/yi+RtPdZj6vyz5EHu08DFOB+zHwKwlsvn/E/vCYk1+8x72PW7ZPFZ335PqCP3zzh/zge9/htw9/hjcZaaFdb1k9fYyGyO5Dj0ji+rePOLo948Zbt+kfnPLhLx/w0f/n5zx994Jt9DTVgmHWcLr7kG33HkfuFreahp4pT3aBv/gffso//c/+aw7+5A/4TVjxN3/xV/Bv/g1/92d/xk9+8pMv9OWtfMOdu2/w7Vtvk3fCR7+8x+Frlutvv47zc5Zq6PuBjxtPL47sr9MozNiw7ldMzJJ/ePDPOIzf41E6pat+zeHxTbpmQ3t0xja9yfXKcPP6lEm1RzwrzKO5+y7nq0DuAndrR7035/CNG2zuPWWyd8Di9hQrG+pbU/ZObrM6XxN3p6xPzrj/7lM0JqZ1/bnf7fNi5v8D8BLf8Ope4on/4LzENWU43SB7U8pGIc/6sZePaJLCIBeP+CMWdw/53ms/4Hv/+L/g//jwXX72N3/NX//1T/nzv/oFv/3oIQ+ennK+3b7kEZVcoFagCljjsRogG2LMbMTw2FTEUCGDBzLZWFwGdRkRpdaIIZCwZJtJastsSy1DHPB+KNYSCYaOkSFNYVKLIWnPYDK9y6MXdbiyDosoRiM5KuuuZbv5dEDRC5GF2CeGi5YqT0jGFbCUyVSmyJKLmyIm4LNgJRCSFEU2tePg1RYgkRGyOEydEKdjz7HkTKpCwGHU0EvCScRkJRUd6AIuzQZ1prxGxPWG6AVrMlUekJSLQpS3ZUiTLJkJng0xBIYhlX7A1xhOSi/QYUdFksQuGwYDURx9sgxe6BrF4QoQAsFqxiRDYx2aqhFsK5AEqxEfBdNBEx2iU4JYsokEEwnJFHCp0SKRncDEiOSGnCGbVFib1YBkh00DputYP3jAg48/RsdzIGa0XCNjjC111u8xmxEpw1vvLN5bKl/j6gmuqaidLySNWcN0WlF7A9Yh4rC+6NcpZZCrPiN+wOfCoE0+EhJgij2G08IkNmpxQQgvv5x/3rcZGyA6zrC/gfOopcMyDJHT9ZblsUOdoNkycRWtc7TDUBQETOHJY105NMkF6DgelhkXSmVc2ounL2rsaD/lODxcsn94QN1UxCzYXQDr6QWqmJDsMabCakbqgKkn7JE5D1oQjSrEVGGCJ+RE0ClVijhr6Dz0nSf3HZp2DMZCsIjtWYdIa5VcC14Vnx05OTRlso8YjTgdoB9IQyiKF6pFqcoUCzMhY43DGPNKz/z+9BrZKAFDlyuGYNjuDLtNZrO6ILQfU+WWWCQwqWyDWkMYBlKOiLOF3R61cEK8IUYQa1lMZ4Q4EMIAksEWBjeUWtLWDmsdmoSUBzRFKjvD+4ZsM5oSzoF1NeocKUbcqEZorCelos6EG/stKdP2LTkFRAwpR7p+x9DnInjtG7g9o1lO8M7hB4tNiWQtIoFsGsxY41ZSZjfqE0l9YfFncCaRjEVyJIst+41tCCJMRgn12hbAzuV+nXNhixfAS0XnLMNzNlyay76aXemzWSm2bFqo4Yw8nGfv/xJr2Ncytrx9fIPZcv7pQ5rLSffeiy9Hirzmpfq2UB7AANQ6DsUvn8ovSNgUJW8HuqcX5DYi1Qy3nFJd+wyP88//MHbb4iceKcghZwz784pp/SU/7Go14XOH4sMAm9W2JAlS/A5QpQ8RSaVx4ZJSH8yo9icMImxGpM5MwH/e4QhlpvrK8cIdVVBEeRyQhwz9Ds628MFj+ImHoznc3Ye35vCWhzctLKSAIi6BAp/IdFVBtXgBFB/Or6dE0PHwd33Pk6dPaGzCziy6eQT1AVTN5/6uq81LXryzm2bC3TuvM2t2vP7GXVzVgAgOuDmbotPJCwhiEaGZzmgmRSTLiWO3f8iT2SEPH9zjrU7olkdk3mBXbVlOlyzdjVeDIX/V8IJaigxHBi+JiCDOMDMTkoCpLIM1EDJZLBINJCFYS5bCYE4GrC1JbaWRDmHIPcaNckpSvCdGBeNx+xWMjHYJYyhKyKXBqrwo0/p1x53DI16bWdCekCLeCDtxDFL8iIzPSGQcUoNWgahCTIZhlGthnK/VPhMRWgXSgMfiyUVyWS0uGnLUZ5OiS0l0yYUycckCH//Uy4J5HILL1cgsv1Ihna3HThuCNYSzDZJ6ZsbgK9hVjgtXUS0O2MewN3SsPGyGjFvvClSjqgimxvcbjie2sMR3iXZvAlJ8pLxxKDXeKl3KDH0mRIeieGcZQsfOCGIMkntuTBfcWB7ik/Lo9DGhbYk50o8s8SQRMRUy8bizDW62IFCYrHt1jdmscJMpG6nYxA02ZWKIRJvo/QJm+wxpn/DeOfHRA261a94yke/OIzO1uIWjtx4322MaoG42nDdnvG+3DBcV988tqpYsjj1RZlXETjw9U1qzYNtEbrxRoX3DOgixUhYNLGeRHBTvAjcPE8u9mkYWdGvDKm3YDJGha3n/7z/G1D/l9vqUNx6+g61a1m/s8etqisqMN46vv9qNfhki4DPBBawIIo7rx9dY9A2rs3MePTl5kdkllMIIwZmKWip8HalCAbUkrbCdZUjK+dkpGpQby32qykB0ZW/LDq87qpRpouBTYNvviBfKjU2Lm00LA9AkDBl1o+RPFJILKFVBBQZKAY3HY1At7E2NRTJNtaDEkcIu3HWRhctl4CgWKxk7yvPm0X885wQSEeOQmIvvaEg0KoQhsx42qDe4maGymYMejE0MRnGDoY5CJZ6+U2IeEBOwXqjwWAZMymjKvL99QojDC1I+WfXqv1X1almAL4dq/Kw4CQID7NclfWp7ZXeS6fGgntfqQrA2KCZR1vRdS6U9zbTCmAbEFfcaI8hEoIJklWfkt7ICPT/El8vXFSyXTrvP3vvCoqU1uCPYeggCM/2cLtgnEqkR8PbsM8uGIuKZ1Dc4qhsuuM+RbrnJgjt2irOOD/3AY61oD69j6xnXJTI7jMS9DneyT8yOQQMnT+6hvVClBpcd6+2O9eqC/qw06bv5lK7q0CFycvqUvt2Ve85IWauvcoZRWnlEtMJzf44DcgAT0ytt9y7bqwLX5VA8mt2Ea8cT5vMlH370PuvVOUMYiiRkSGMD5MtJSn2joUAqjESQT9nrDb1pOHdNGYQYoc4WTR4nLcYEjB2KUkS0kBQXM3XKGC0wcXGQVEmjz6Jm6FTZdjs+WD/iYneOxFwkkMUyYrIR40kGkhiMZqRwyCErPhcmQqXl0jMOypOxI2Aoo1ZAIkYrDAZNBRGuLmHM8I2cTmsMzvnLJwItlPFy3AKKLcmKjJ5qxhBijROD6x2aDGkEL9qcwCW8H8Z9VMnikPE8++TxoYZgiVIG3jL6Qia1mFQalzZnSIpJQlJXyAgY1BXZPqOFCZ4w5JywpkiueyNIMGS1Y/mgIAOMzmQqQrCm3BcNuORJMitebCgpKcMuEtu+DGbHRUooQEQBTOOo58UIokgEBrI1eISJdUzqCRu/JsSe2PeIZnKAEBWswXmPwyIaSL1CUpJC8gCJTg1kxfJq8un3H55xf/WUM78m0jKsT1BV+oOa+XHF5F83XPNH3HrjDcT4UjvmTKoyvU/kmcVtMtN6ynKmLBZTnAzce/yUJ092TOaGnTFcPHS4xnNUVdTHDe3BFHNwSD3fJ2x74oM1u7ZjaCzp+oz6+99nf7HgojulmzzETQCpyEnJg+KqLwIbKx/c+4C/fu9vOf6n/4inH5/x879+n8evXcfeuotbbgnLBxzVT1jUN8BdI+TSJxAR0Ecs4inzE8t6GzlfbDlO13BNzc3pW8gtYXmzhUnFYuo4ONrH9mck3WB3FvuwqBHlbaZtIyn3LN+4yfJojjt5zNHskHav5qNui+41TA+OqSZLNu1AY2HhJmSxJHKxqigVC5FM/A+IcfV8iAjzabFXyQqDRp5ePGVykKmrA4zxSKI01Sl7VlZG5mGiUnAUD1cBspbqxJqMycK6T2y77ur35ZR5+vQhm1trJm6OZo+rEprK0KMffR77dke/WbGYH2KyxUmDSgsqiPoio56KzKnJEdSCZAZbmtxKptfIbtd+JiIhWUOIBnGWJJFsgVT80jVJUTvLxQrHiBKNp6dYpfREBh+IE0OIGZMKMy9bSzeqFb10zECGjN8qd6MjOeFchW3a8ZpNaJqgaUPoA/2gLPcPqU42nDy0PLwXWa0TWxWq4QyT1zzZzfnDP/ouJ/4/5/s/+CHzZp+LX75DOjX0MWFyQiRz+vCMB3/zK/rtmid/+T5/98//jl/+9S/o+oH5YsHRdUPlGtZPW2K/xs2mLO2Wn//mHX7+0/+Jv/qLf81PP3gf/nnFh+/8itWjR/QXqy/d/HfWMJ9Pef/eU86+lTn+wV2m1T7yMLLddeQF5Ie/YOV3bOuGmwvDwl3jjAnddsVb8w3TazNmYYoNhqfDffbSPodWYT7w1mSGPUgcfWuKLGe0J5GoG5Y3JpxtLPXpBdfWHzNxwuHhgtX+a2wnwu07llt7A3G4yROdctonjKw4++h9fv3//DnDRxv2F8uXvtwLgal8rq32NxsDhY795VwDPz0Khv/Ls8TXFD+lz/nSeTy0l5FNV4Ue5be//A36y3e5ce2Y/ds3cDcOYFIjzjzrq42A4CtlvkEQU8P0FstvHfNP7v4hf/yfPuL/8O7P+Nu/+Tv+4i//jr//9Ydf8YiehfWCN8rEKmtT/GJDyqxVODNCkITEAUNdlKZyxtpQBjliCHjqEMimAMfJiUQB2/bDULS0opS+dbKYZPGmx1oDVumDEKNHyCNL0SES6PqenCL9kFitTum69kv12/rYc35xTsiR3aIoazWThsZ5rBPIhlRPqLA4O8AIxkzZlOGMlGFvDh5yRig2HmIiKV5eHEtyBrIv9bIVRB1ZIhYDJpNzj5ii4CBS2PzR2GLvKPkZk1Fj8WMXQ+9KrhcHJfVfjx3CJyM4KeQZBG8qcAObLOxESNmBF5JRlEj0ueTd2eMkFpJWEiIFaC8p4tqBelBm6rBGcCbRa1HorGPx4B0o+7VeEkgiiAo6WghpIzgTsXVPkwTpC8tufzqlv34NMzE00ynqKpKCmzjSDtrHZwx9R9ftGEL/tQMIChvcYGxhutZNTeUdE+upncW7CTQ1lavxzmMrh68dvnJUDozNqK0wqQxp1TvUCEIEsSSvhfBgM8YVtYCgiskRMyQciqjDDd8Ek09fzEX0SwznviqicsyFFWXV7wjsYWKFR2h8YjKpGWJZSxBTiARjj8KoRUxGs5CxBZRhQbUMJ3WUny6zGctyccDR/h4uK6nPpNrhg2JaxRthbqEm05li/Vs5i7MGTULNhLwNZAcSijWXiEfSAMkRclGJTXGUwDYO77uSawahSYlMURtNzoIxVIMSU2LoBlJWhn60QIh5tIEo19yIK99fwfqqgKD15eXyz8OEoXes1z3rTUu3WTFsz4mhQ1METXhraeoKa+yo0lb+QTKNOKyx46Cz9E9TKj2GsemK9RX1ZMKkrog50HcR4yx102BEiH0gDoKzNX4ypfK2EJicxZCpjCWhhDRgEeIwgAwMfWToW0y0pCyEvme33TLEWMCXImgcGGLC2CIxsduuqOdLhqrGdx6bA0Y92bSoUayBmMssxyQt0uquvJ4TWJNK/14MWXIhWppAbx1NVCZmysKv2Y2e6Jeb/5WEeuXx3hdyx/hsZAWTR0tVSi4uUqiWOYM1hfklZpTQ/xLX9Wsait9kuvyMCeyl6uVzzc7LmXHHi7Yylwv65UHJ8wnX50WGcFL8xHNImKnH7c2ojidf8IO/G5qVdhNZ92OSIVBXnsN52Yi+VGS+0M87K4QBNutdGaiZMhzLWiQoXc4lmchKvZhS7U3YAduxSF3aTzKvfl/x3G/MZpQiongvtz1cdPD+U/hJBftzuLUHb8/R705hleF1g/zwxQa3aubs9JzNbs3scMHR7OAzh9WfLLQ++b7Lv5XnXvATz1vfu8O+HnN0uKS9/wtqe4i9dgf2r5d3f+JzMnAWE+n8lOuTBmaLq8+uqwnffvuHhLuRqnrxIsvYEP/0U/fs9cpVaBD6lWH9UcO7B4anpiXtdvwwTHFSvxIT+CuHSRgpvjuZARVDwlI1FVYcgwI2EwaBXO5V4ywpK70UTxxTiAXEJFhTmCrBJpzzHPnb9F1k6Hq0j+RhIHT91WD803gVOZcN8JvkW4gIVVWxyaVKywlmoiVJsCAY/DhAs5qwGomqSJLSTLKCmoRNgtVCZgpG6UUhGCoDSsImTxbBUGRMZSxmss0lQTOZNJ5HxsayXJ2Vy4XwEkA0onBf5f6op9DUxJAImw0NysILuVLaqmZbTTiaH3CtDxgdWDnH7mTL8dAXGdnJkpwSc+1ZTqZszxIr5wmuDFqKt2qDrQzZQr8bCNEwxIz1BlGlI9OLL5u3NdydL1nO9gnn55yuTqEvXuJR7BUr2S33ie2Oha/IztHHgPgl0xSoMGg94yJlcmzRnDEaaZ3FVg3zxRLZJrYffIy7eMK3jq9xa7dlvuuYzGvuuTWyL+xVlrZ7Sugs6zTDnq1Jjz6gFiHYhFQtRyK8HW+hG093uOFs27CZ3eCDwx/QHj7Gn/yK2WHi9p09MBvi+QUpdBzMPHevKyIT+os55mzNNjxmzQ65uGDx97/i6bu/4no6553G8/70iEcHt8l7FdXbb73i3f4sJkZIdSRYS8qGa/Nr2DCln8/ZblrO1+vRE9ngnefm8W0me/vUxlI7hyfjbEZDeX7rbMg502133Htwn+6w48bxW0yWcyqfi6xQLMzOKANiBGcN/WZN317QVJPCXjcKzmMkjWtDQZ6r6QtijqoMjjMEEbIpHkhZxwG9lMbt5cKy7Qe2s0R0Bp8d5ApNDtEtThui7IjJ0NuCxI4qCAETKhotSOvzoSP3EacWU1U0zpMko6lA+QomO2EsmNFSQjB4CUjsQS0hJE62HW0YPnM509/5j5ePkQBxNUp2TU2zOKbZLx5AjXOlOFfFbxL+3gYuVmAH+vOAvb5PvX+Edf5qAK22MHIvVT6ey1tR4KkOGDIL6iIXD5AUseVnEmUVe2YK0oBvkPn4QZ+X5/3Oxs6nJD/jGFosTvfoJfIdBm4fXGOint4q2ifs0lI5j/0w0rTKYmLpreGgmhNuHSL2bbrNAZIq2AiPPzxl/d4F2y7QrVsQYdj17JwlDD0X5+ekbrhiPIhIQfUaUxhsZmSyIc/+d3mLjojWjL4SCqIWh5KxJpaBnRiyGCqTSZXh1ut3ORyOafuOi/Up/WpH2g3kmAgpF3/k9M3utZ8bWvbFTytRrbXkqmLjBWvAZsdgJvg0hdRgqi3ZCSIJExMmZSZaPKYMJb+MRCLKkAVNRZQgzwV/WHNj/wbN+YT10wtW5xekVIY5zhpuHyxpvCcTQYWghaEiGRorWFf+vxGlMqawm3NGTSYbUxQyRDEpomqL51kqLPLhGzrV3nmMNeMAeCwURqUduaRL2VFGThhVagwiBoYygEaK91vxnEuEqGQsYVSrmSTQbHGDK6xhLYyCPEr5JZHCcHTlXrdByTGRxnU2jccmQ0BNuSYpW5It4JCs4JJiR5YTGdRqASvIpdhf8ZCLRsqxi0FMIpuqVMQ5EoYdbd8jQbGhMPWTKhlHjhC90NQeYx1JFE2g0ZR/NGHFMZ0smM07Nqs1XRzY9T0+Cm3XE3PE13XxjYupNJBGX+NUZ4yJpDQW6unVBqS/OLvgfnvCk9WK2Fjyes2hCOfdlmoSWc7mhGTwS0+uEposKXuMr/A5YCYT4hDJUsAEsap51O/41b37XJye0fcZb5vSEHeWlWtYt4bb1R1ev3nM3nxCrk9pzY5HF/fYrIVdP+e3v26ID/ZZnz+hbXa4fc/QKtlUHN+9y97+PnLvw88dkq125/zkg5/x+o+u8cGDC4JWNN2G1y+Eyd0Fs+N95sdnuNk9zm88ZU/3mVOhGhB5wPX5KbO8oQ0Gu7XIxZycGmywNDrlcG5pD5eIH+iHlqZdEapAOwxMp5m6rhjCgMlCJYapESqn7Fiw62ZMFnB72tMPHyCriG4vOJQd0xuZ0Pfc/3BNGAJZdby/ik3LqzSP501DSqnkNqrknEcQ3auHs4ZlY8uaiUEkMl9OmB1OyTYSjcNGQ5Qiuyu5DMmdCojDJUVsKqoTsQYjBVBtyxBgYwzxE4OVdtdxtj7D7u3haoGm2Nd0Q+LJxQlDjGhKnJ89Yv/aTWqpEDVk8ZioWMm4UX8miF7ClkhWMRoK+CZ5nCSGz2CJA3QRpPconlxlrC1MxJxLs85UCU0R24OqoKKQCxxKamE2X+LFEdtM7AqwcDKryd7Su5f3kFdVWAlyJkX15TV4V+Cx9dCdsL3/kOH8MXUd2bvxOm9XB1ycOv7W7OO/90OYPUYePeb00QOObuyz4l2u7+/xx3/yH3P8Bz9ieuuQi+8sOVuvefjTD4sioVXOTtfs/tv/idrC7skZYTtgxZNix+piRQiR3XyD5MQbt29j2sivfv5z/l//73/Bhw8+ZrVZc+/+xy98l8ve1Je5V/uh5/TJI37565/ys7fe5ujt7zA8PMN2p2hziHw8sPnNKTv5Je4Htzj40x8yqQ8gddw6epNwcJ9qP+Emlv2uZnhYI90+R2ffYn+1w6eAHg+c5jPS2cCFKHpQcePWMVWc4ZoH7D1akduWuV1ibuwzsw23Nj0/mJ7SL1ccTZfI/A6m2dK+cYcfH7zJ+q+ecmux99LXu6IoKv3+e3iURHxNIau8rJf45ed82UZkpNRwn4MjUF7VS1x5vNry5//q33Lx4T2OFjPuHh3x7Zu3uX73NtPja8i1BdT+imwAPBuQZ0rxhEXsksn1JXevvcXdf/jP+C//9x/wzt//9Csf0WWIiVTAPMNgSj5ZA/iEtRFrLSlBJ5aw21HJFjfLJV80lpgcnalwZKqsaA5FtcZndhc93cWapqrxriKmCnUFhlfbhKNYAGZD8d/Ogd2mpZ40hLjj4uKCdrf5VO/wz4qcE+v1GdvNitMnpyxmC27cOsYeHTKYKc4VS8aoFkxTQKO2kMwGDLWWoVTMFrFlYGUyRXLcCFZlBDqBujwCjgpo1iUhSVF7cxJABJ8ziYRaKV7DKZM1YcUiKnTqCgjZFk/1Al63MMirIdA/Iy68J2ARq1gXESdoFDRURYWFSI4l905aSHE1CbQoJgUbiWFN1w3kITEbLGo81pf920rEW4OTUL53FlotKnyTCGNDAnJCXYevHcaXvmE9FE7bWhydrzi81XDwgwOYemLvCKbGmuJdngalXSzpzs+5ODvn9OyUvv/s/fXLhFBqYmsE5yzGV1jvqZqGZjahmcyYuPrKgtJbB3VRE7Pe4aTBO4dxeSQUZdS4UivmQjLKBlQSGYfasrJkwBsIPhUb1GRQF4uPclDs1+Al//Inpah6CUImfUVG+aUUuxJS4mKbudZMmPli27UbepxtyZowhgLW9qW3TYijAlpRL9XLAZbNFAf7YiM28xXzgwmToyVgSDFjU2SIjl2l+JSZRgghsMmZFJWZcXhTABvaFxvBSE3qBds5kq+IOUJ0DMaQZSD3Cesg25rKwOCLBS7Z0CSLs0rrlH4C2uViczxAjEqfArtdoIuRqCW/tCIj4TQjxmBMUbsV63gFB1J+8dP7DN2W1G/R2H3qe9J4HlQjmYBI8UUXI8SUSakoMYiBqqqoZ56kiZx7dusenME0bux5FiC3RSFBVoNGi4ij9hVNXY8AKMGmjEEZNJKiYEVQIkMX6DqlS4HUtqShgIj6GAixgLaNcRhXlXkQJS/NKdLvtsS+wzae5D2mt5iYEW/IElBTIVnIkvAZ+phJdrS2i4AmrKEoDuRcJNQJJDshRsFLw9I3nPY9/Sh9jigxRUKIeO+pqoqu60nP1dKqjPYgGSO29CEuvcWLGMmVM9+XiVceis+aCbdu3aKefgade1Qn/STR5xKkOHvutcAnrMe/rCxPgv7RBXHVotZiXYW7ucQvXuLrDZnNLrIZBtLIJGu853DhLklAXxyXU//ms489A32f2W52aC5DRFBiVmIcqDLUl37iN/YwtaNX2OaSuy0/R770KvoEzTepYf7cEaiMMuuUZluIsD6Fh2fw9w72JuRbU8x/cxN0vFfGkxlz4qI7Y9VuMFqh4yJ89dGXm4NmMomTiw/RVHFt/1Zp2n9KlIZ9+bllPeXHP/4nzOslk8aj+/vl3AxPITbgFuO7LxlcUjANBszJb6ALpLe/S6qPCnMFi/cO490L3+OrRNe2PHn0mKf3VgwbOPYdt66f8/RsxXz/9Vf2H/yqIRnEKNjSnM+qYKBSiAhZy5BcNZKzheRR20KVyVrU9WVsHqhkkjOkujA3jbPsLQ4ZYqTfjH6D2y3rswu2XTeyoT/rHH79yaoITOophwfXWewfsX+wYJsjlsJMT4YihSvFKzhaLd7BoR6l0HPZaMVjBSQpXivEjkwgVeyYwBevZBkTtIz1WnzKR2/wNDamBcbfd/mdL4fgMtachblVkp9LCaqXL6fdbMLgDfFkA7FljqWpofeOM1/jlofsq+Fav+WiUlZB8astcwWpG4LxuLbl2sRCElZ9ZjupwaTyPY0DGlyl9FkZ2khIFVF75r5hCC07Y8B5NG45Xiy4uTigxvDw8QNiV1jixUu8JhIR49HaUa13uGZGQOnVMvce3+1wdUNrGzbdDpMiIabSuPNT6qpmb7ZH9+5D2tUpd5dLZs2U6brnTnOMZsOHlGb4LPQ4as4erolP5mi9YG9+Bs0vaNmBzBlmh+xfTLmzvcGqOuPd44aP9XVOb94Eeczspx9yiy131jC4GScTJQyZ7QPPA8lM9QnVdErNKdXaYjrD0nv8sOVss+FvU8Nm5niQPU9TJk8sbjb7wuv6ZUKQMixRRxZLNpbKeIz0YE1BJZolb775BkeLBd4qqZoAHomCrwYyHQlTkswsxJEplZPQti3v3/85j1cf8q1vf49b+68jxpFE2FaGkAzZN3jn2W03bM9PODi8gWUEeuSMxCIDlhOlmNTStYjGYzRBzqgRYl8URjKjqoIpA9jCvhY2Q8siFX/yOnu0byB5RAMiBb2eKFOYrEpEIKdR2q0wn5yryTiIBmpHJ46UQUNArySAR7SyppHVNBb1ozSpqiApIDHw6Wvaq7gbfcqnOahHecaIEFowE4tWlqlqKVQoeVgnCX/vhH08dlpj1hNy1ZSOneOFhoGKjHvrJ45WR6Bj2jC3vviaKYXu/+LbgGde415Aqi/45i+xBVRiWLKkkYg4hyyhVuFb+xNuOOXkcUtte4I/Iw0tYR2Y6YrjA2G+t2Rpaqr6kNNHmd9OPGe7R5y2ns3ZmtD3dO0WESXGSBhGn6NxHb9kijPej2VYJ8+G5PLcf3MJcpJXKtCYFRaExAKw9GQkF3+/4nnVUDcLFppY1gu2/px+UqQD+5QYQmQYBkIK5JTIKZdC42sawrxK9H3Pw0cfEVWYTvapK0+dFdP3JAdqKoxVjO2wNmFykbMGiChkLf59RukETG2QqkJmjrpS3MGc5WvXiFtod4bt2YbN2WNmHm4fzJl6y7oa6JIZsTaCs6XREgQwEWOUPjuUCdGkoiqjSsCTahiMIrErw9hsR3Pxb6YdXlV1UcZQEDMqaQDjA1mY84xDcQRN5lljO0MWU5jdKDE7fB/IxjHUqQhvJ0jBIWkcriZTbEukVJ/Fy9Ag1pRbffQDVysYA8mPjSwtcpsplfOQfVm3ite6oCRMkJKTSovLCt5RjO2KzyZS7AcklHUb43HWQ45gLEPui7rIJUlcCshHXEayghqsepKtUQGfEmoGpE7YAOINTqccXL/NdLLDJGUbOqTtODk/xzmDrReY6IpvWdZiJWSgtOMENQGnShtejZH0wW7FeSoWNLoThl1HW0eyVULvqSdLZtlCvUSm19BQE+uGWTWna8FXnsmxZz6DtFGOlt/i480TQqx4+uQp1cWavf0jXN1g1LANEWcdtxX2K8th6Fh1T1lfPGS1OWcHdO2aP/8fT/hrYzlYzLj9+pwmTsnqmOA4PGg42N/HWUeIAUFo6oaqqSArm+2alAvT4yd//w5v3HiT2f4tqKaEXc/TX/yGxZND5LUG90cLpm9F/NG7eP8dYAok1OzI1a48o7XB1sLH3UPihWE7ZB7uNpyuN7SPL/DdhvnG8vqs4vUfHSL7Qj4ta95u1eO8J8Ud4WTF9nHP6Wlg8Gvm255m0bE7i6SYaaZQT1saG9mFLesQ2G778l1yLl7clxYtLxnf+tEPiX2xiInDQBgGYj8QYyCmRErp6s9iLzU+5V9izS4NtIqIJTtQKubNlGgbSAYXI1kCydniDWmViowdHGosPhjc6PWY1JCyJWJo1dDWCWOXvP3db/Pw3gM262KdoVl5952fsTl9yN7RIa4xoJn1+Zb7H98npTKQuf/gIZUseO21t3GTCbgyvBc1EAoDRRpLFCHZ0sSVZBDNiJrClPmcoXi/TfS5JmchUdNrQxgVIhhB4ULCqRT/W8lkjeScMHWF9xW2qokzRw6eWirmtcHaxOBfvt/SXrzLJNyBm3VR5nHlDoeGqrqD3T/nvbjjSX9O98HHaDzn/i9+w+lRZHboae2O+f6Eqp1y0CjHe0pew2yYcv/JY5zsOFmfkKaRFPsCQnOpNOG3PV2MqFjEPRtna1Z2my3dtuX89An3HrzPEAJd17Jrd1dyu5+Mr3LXp5x4+PARq+2/4uGj3/Cv//Jf8Ae3vs8fHn6Xf/IP/9cc7h2y2vtTHl042skxh+7b3KJi30aw0Nw9YNB75HaNTALL1YxduoaLC94MA9Ztyccz6mnPg4/XmLjl4nBgEKjiTd7sPHN/izSbcd5McfsTsi5oHsHcPGLmf0PVbLg5P+Rg/x9grwn5rTO2x7+l2b38Hl7L799hECgX57IBe8Rnqll+qc9JlCLjy5yGHaWm+Jzfp7yal/gA/OqXv+Hsw3tozjy+WPNkteHnH9/j+J0lb9+6yWvHNzm8fYPq5jXMvIHKvTgcf0a1hQHEOKiuMblzyD+4+YOXOKoSvbXkoCxJ2JwZTJGbNV6YmMRARiVycfIhH/zmPd54+zqz+g7ZOdQZUIuKQ1O4wjsaW8BCtbWcrs/ZZUfd7OFnIFIUeRwGE5UUpUiva6YPLdt2w65ds1pt2LXbsQf11ebD3nnmzZQhl35pUVPsid4Xiy2FSl0BhCZKYmSBpASTEEqtqEGKVeBY65uy1CMerCQyBpvLQCmYAlYSHImEsYpLoKFIkEsOxSsbW+SKXS4s+WyQaEqedqkmV0Mx9n41FZ9Pi3VqSn/QZQY1OAQxqYBEUQZbctlKwCZLnTIuJDLQEWhDR2x7Qi+QLL119M4xjGS5IGXEaE1RN0mqJElEhaiCT7Fo1lgwJpFsjdFUsClOidEQUeaLlmQd28aQq4zThIsDOTgYSj++WizI2w5vRnbrV4iiAFCAq0YEay3OWVxVU08a6qaicnVRt2oqvKtxTYX3Nd6Ay+OwyxjEeYxq+c42I1ZJyaNYHKX/IlLU7NQagqEoA2aPyQI+kCWUfjeAJBgVXnIdoP5m1LvKeSj//qzszI7fURUkf1W5+mfvTTFwtllxUE+YMAFTsdWEl5ZEhzEVMpngao8TLc/iTskykrWMKUxd6wq5wRkO9pfs70+wdQFmxJyJWnoNahOxF9LFBS09bq9mWU2pcs8qO0SFKtck0xCwGFOKsd406OAwWWnTjixCyh2TScZZi3bKYBxR98v5qHvUQJKMcS0mdIQoRBNIQB8H2rBlM+wI4VmfzVzWiKaoA1lryVpsg/IXWLt8XuxOvlg1RCmYc+/8FdHNAMZaRDODZASlkszcV0yahp7EkA2mz6WGT0oe1SPqWUNUZTcMBZQwgmmiteR6nDMAdVDI0MdU5MRzJoVADD0hBIYQixpoLpSeAjQv92bWMgdBLNYkYs5kMYSupW93uGZKqDxNP0qoe0s2CR2FEjMGK7koA2smZoO3kLNgR8CtkUASM6oaKr0xeK1Yugm13TCEWK6eCFkzQww0uaZyHuccKT17Ti8VrPLoLW6NIeXSF1YtPKvRIfP34yl+MF9w/cboJ/7JxOgS9vcpQ/GWsh9dqu5c5mlXOQnjz1o+P+EaE7P+8TlpHIo77/G3lpjqc1i7nxF5lzjvAn1OpFEndFJ7Dmbuyw/EM5/LcC9+4rBZtYSQxySgDCOTKjEMzLQMxZ0YJrf2QIR1hqhl1j77MhnjSQt3vhYN9S8Z4wm6pIUlUw64SzCs4HUH131JjlcJnZpS9VlhcniAmx+yP5m9cM1Ulb7tWa3vM/WCny4ZYub04VPM4FgcHFA1hVWtI9nKFEANmYjrDDZbrh/cLYQYQOwUqRPkDaQOmKEqRCm4KCh5/tIIen1O/2RFpzUPn6x4uzvB7B1jjvZZjY2IQ/vVBbA26w2PHjzm6ckZeZtYPLa8ubzN68ubNEfzb6xh+lmhqpiUigSkluahywaXMsmM/iIDxEzxSJSMVpmYlSEa2qzMUsJbwyCQnYIUfyBrLBMPajziBZdS8QTqAv0QGXg16dgvEwJgitT5fDbnzo27HN18DSaFBdYOa6pocNkwWArLJ0tBDRtHtG60uTAwVGRjaJKn6SKNmAIo8AXh53LG2eJhQy7Sq9koLkL0kVbjOECR0b/l0klYRx7XuHXqiAjSgtVTHaVsxuuVeQVU46Qq0qLrNXVWFpUhe9hVNW01YTHb52gI2Nxz4Sy78579rqcyFq1n5Kwscs/+pKFbZy6wDLUBIhhFtEGq0oAvXuKFLeu8Bc30kulNhWiksobXZnP2FweEixWnqxO0DwwaicioWjBgp3M0BSZiUOcIOZHthAUZnzK6mLHKQoy7stnnQFuB9RNm+wvcLrG6OKXSyOF8QT20XDOWuTqeAN5O8VGhrTE6ZZUNs41lxpbKDOThEXa6Ym95h5xqnqQTlqdneNmnWVeofERdLdGDCVLfYDj5e3ZDh1QzhiZQV7AYZpx8ZHmattz60cD8D3YcnkzpflsjQek1sK33yGGCebyhee/XTK0w3LhJOL7xys/BZRQEYCZmRxCHJEtlPVrV3Hztdd44PsCZKT4pFaGAqZPBkjHZkixl6CsCkrFEjChplMFMmlivL3hw/wOu7x+DGQf6ziEiBNORcyaGxNPTRxzffYOpHJTE1QAScUkZBsXTkSuHZEdyDkMGNUSJnJw9IYa+PD+iWOuwFBS9ouziwFYD5EKHcEmRbMFaCtewDL7Dc9LrKZXCKqMka6mrSWF+WgsmkWzxsQxZML0S60xflefd1g6rhiyCS5ZsfJFVdQGVgDBcXYDLwfKztW985SqVevlFUT1UY5LaZYibAtbvBliMJoaXaUruA2wC9toNTCX4ax6uu+LRAqVLtR1g4cCagrpFC8NZtcirA9fwZLOkKg7PL+xhQkmHLCU1uADanLlpzGd+y0t/+PKFRqnn8f/K5Yn6tJ9DuA4c0ABa7tEGTCVMEaYChwczXr/hicMeu5NTHj/4kLMYGfYcbrnk9oFlYvd43LTIkwUPrh9yvrko3uEx0vc9OYbSXLk66ePh6TNneIQyABSeqci8wCYvxRojo/xlQ2c9USHHjGaHl9Hqoxekm4CnWCWQmdia/XpJii1du2HV72i7ntRHYuqK5USIpJQJKRFiLIVRTr8XqXURwThHjrE07HPm0YNHPHn0hKP9a3zvje8zmU2pal+UpVLEhIjEAiptouJSYZik0YMpiZS8JDlq6/DG4xQ6HCEXzz8zbZjbmsnsJjdvvYZxkUZ3oD19FemCkDvBB2iMxYrF2bJHJTHgMilnvApWlZ0ImUiMQqggTWFwiTAyo234Zlrita/KUDxrafyVxX7806JWMaSrNSinZ+vOpV9LecLH3CRDMKXgNuLI0RGHCk1F4aD4iwsZQzYOxGIko7E0YMUUG5pstTSsXJF1VA1E0xTJYiO4WNDtluKXGbPFaUZNwPuIpRTI6bLANbbItGVwEjDRFNwSPcGC4jDNHFZF3i2L4pwl40ixx6RQQABpBF9hi9KIHVcpobB8akujC+pqDmFgiC0nqxNOT54ymzYMN24QnCPnIsnYpYwTS4OCL0XbEJS2e7XGa8grkgaidohmptGyP1uwmM4YhgkVjzh6bQ7HC9ojIU3g0Dfs65zJqWV6sODQL/DuMf2g7F8/QJ8katOwa3vaXYtzFUvjkGwJRllvT3j00Xt8XClPSDz8+GM+Oj1h1w0klxFTs+kTwTpuLG9T2X3C4x4l0Uvk8cUDunZ71bKz1nJ87Zh6PiEMA323LesKsFqt+bO//lu+94eGG7ffYNJn4tBhuobmrGLx8E3cm4Y994QDp8A5Oc14728jf/tvB9brDVJXbJbKk/o+XTin3Vxw/qTjfBuhNyxMTWomnGUYssE1NclvqZ2ieUDSgNlmVpvHnKwipxnMJNFfbJn4ORN/lyxL8rBjFc8IecfTzQVPd1u2qwHVTM6F1Z1y/h229FeJ/R++Ubw8QySHRB4iOkTSEIl9T+gHYteXQfkwEEIghsL4iCEQx6bU80zzy+swqycYU4/rH7SVRaVGhgIJNxSVICUTrdJnS4Ubm16OXBWRx2AzyRqieoYI0SuhUazzfGf/LYauuxqKQwE4fXT/Hh/dv1cYYmKIMRYQ9hhd3/Pbj37BQODOG9+msoLknmSUbjPgu56DWzeIAmorrCkKKTY7hpy4//Tx1ecZU9hXebwu5TlSLpxQuYZoLAOeRMRpLmx4GWUsTckdJAtowhDJNKW5QFlPslqytWQMSewVEP9l4t0PTvjB9+5gai6TH77TRpoh0TRC3D/iPEX+zX/3/+W9f/FnbB+d8iRH/rf/m/8l//X/6UdsZ9ewmyMu9u4wO5zy+vde59HPHvPOL37GX/zF/8iT9mPOz05Y/+Yxbuc4OjzGmylJMzYrakruu1mvabsXmU9ZM93Q8eDhgxHU9/UU7CLCnVt3ePPtNznfXXDy6DHvvvtb9pa3+P6bFTLv4EDppjXrj6/jZ9/ht0zIFwNvLR4iJoG+hp9+C+odlY8Mu0Tn9ouE7tGcdH1gdxMaO+PQdfxi+IBuMaFqDviTyTX+ZL/BhnNMfcQ9bfjAKOsgXPuOwdVHzPu7LDZrbu3dLJ61qmg/YbqwoC8PdLoUSPqfJTYUKvbLeuheDsS/DGkJSi+3A65/9vsv28XKy3mJq8LTbuA3//av0OfWXVWlHQIfPDnhw6enHMze59bBPt+9dYubr91hfnwdc7wP0+pZ3SLjd7NcNhMp0oEvD0xPo0WeD4JHiPWlNLEgsWKiiWmnhC38dhhoh0yWChlV0Zy9fPT86NSU8D6Sk1I5R8aw2/V0/QVzjcz2DvDeYrFkbJHYDRvW3Yb1xZY4DKQUGMIIILqqSb94HGfEcG1vn7du3GZvPmU9wPmupXIN1tZ4KCxePNIZFMVZBVOVHNMUJqwRh5VEMBFJDZIUdYneujEFS2S1GJ8hBkyGWjOqMGSLYgswXSmSvCkjqYAcomHMF0dQ/ejhbqB49Koh+4ypK/QbGIpHM0EJkDNeBqS3MKrPWc1oigyhJ+YC4q6NUquSkyISiCjR1lRNIS+ItwSUVkfyoELOpY7xMZIIJNEyCM6j0lFQbBbEFhXa3CjWFwtAJZLF01eejMPGXOTUo4XeY4InqaFKinaJoevZbNYMofQwvPfszZdM6oqL9Zr1bvscu7n0Wp1xmHGQ5YzDW4urHPWsoppMmE/nuKrCmQI0xnistRivRVXLClUGn01hlloZ93Yp/sEoVmK5liYXQG6y5GxRkaLElhUvBlWH4Edlzw7JgIuIU6JkEFcWoG8oymF/xpMllB4yhUDxpQbiz6+jz79dlc1uy8WsY1JNcFiWzZwudGyGjPdznLGlV+fAO4tU0OsIFlCL0ZL/zZsFi+M95lODSiJmg4mFZe2IJAtDt6U939FvVyST2O8PqQ4nRQYfQ0olc8pNAXyYUNQiTNUT20gUJVWenpZoHLqImCGgtobcYC1EaegVQo4MRHJM6KBoyqQhklLLbghsupYQY8n1chqx3iPiW8oa6GxRMQohkj9Vm+7rC9WiWlBParAW1eJ7TU6kmJlUgqsralszcTVZDaKR3Ac0K954vFZUTrBzRzaG3bYl9t0IMPFYU2wEypan5BiJYaBvO9q2I8aiFILkYjN5NXG9hMprIWWM7OqiKpdKfW0MkstanVKg266YLvcJzlG7ChvD6B8fSJJxIuRscZIxiQLcFVeG4oPgbcKYAqzNuahbiIkE68ghUZkpe75iFxLxsmchwhAGYoxUdU1VeYYhPAcCLuBNNaUHfAmCSOPfMbLOr77fF8SrD8UXe1y7efPTEx2l0L8/BUm4owx3L2e7RRSrAAmvHvBLFOIXRF739E9WxHUP1uOqGv/aHtZ/daxh2kZOu0DIiSQWZwwHi5rJF7GZno/PAAJcHS9lKL46246SMAljykI4hIDGSKVQiWDFMr21B8CqgD+YKtRfJhl9vPs9D8U/GWWgV25Kh1kewdaWJns2cKr06xNO3YZq2eD2J9RXk+vyCSlGPnjvAz589B57TtFqjzZXCJa2fcqNbmDvxnXUWB486TiaTbi2qBCX2ekJcz/HppLIZhLCBsMcEQdm79k1yrl4Q8vzR2+Q/du0MmE23+daOCPcXyF+jj/aL4nXFXPuq0UXI843TPwUg6FqDjCPOyaLA+TGy0uwvWxkq6Ug6QLRmcIeSAYTEslGcg4FDWaEQXosPdlmonWoKSzYpKYgIEdQgjFC8CNUR0GMIYkr6M9UFSTrMwjM1x5GBCtgrcFYh60qlvuHfPv1t6nqZkw8AtEYUlVDThgr9HUNNeSoSDZXPsaGhBqDyRmfDRLBq46+VOX8ZQtGCppHxRBc2VwuCZM5m+f6Cflq0zaY4v2DeXEIPgI9CvsjX7FgMfpKQILgLOF8C6FlJlJY4pXlwleY+T77rubadsXGJdaxQi82HKhiKkfwU2y347gRJBtW24HtdIaaUa7GWpSGyitDVvo+EqInMjDxFUPq2SHFBzR3XJ9NubU8oDaeJ08f0rZbcgz0GsimIUkEHLKcImen+MmcOA77FrMJtt3iq8ISvxhabCpDHTURraZUTc2yWTLcP2NYn3F9NmNuDYt+4Lr1JIFTb1kbS9+3tM0ej+NAMzvgmu64meD9c8Paz2iHDeJO2Jt67t8cuDjrcN2E1buJfB1u2nc4sa8RD2rOux63dAzSsb5QvnVwxMIv6KYD275ndsNy641rkA0XdsfJeWTXNvjZj2gHQc9/hp6dMs8D8elTwgfvAf/nl7/oL4QpQ24DxjskVgx2SaprFjf3sJUv1q0MxBxJxQYHMbHMk4MQgiV4xTuljpE+w/C8/JrC2dkpq3DBcjGhhytU4jb07DY7VOH07IJ33/klr137Hvv7B+Ay2Sc6HTi/OGNRW2ZH18negjVkZxDt6TZnPHx8j5TT6Kc0wksUtJjWEkLgfLOCSYOaTCVKZQecEUJZ5EhRC+tIM5GBpGWnGETJ3pN8sT0QUwb+Ql8Kg2jIyRSxKW8LeEg8JleoaEFv5og6hSZxdLSP9n1J0OAK/HXlPy1XY/KrvszLhmsKE0WBkEATSAPSQVM9cz3JCjxcU0mFWIHKIUtfwGoygts2GZ4qVIJWIE5hlwhPN8SZxV+bl6IWQeTzu2xK2f57IBlDzyUb6lmf7XkcpLa5WLKYjrxoyX6KcwteNCWUq59/9oqMqeOzgZ+OPyIKdipMpzWqFbM9S3XQMd05do1ncmi5szBIMNjO8OaTGQ92t0gzxS8rHn/8iIf3H9FuwxdvXePaXf5Tr75YGZBzlaxfDcdfMoIfRkiyIglistjoMepJ4gp73CRiduBrald8rpvFknmCLgz0uy3tsCHEiM2CM5YgmRA7ttst23VL6HpCDFfglzwajr3qDi5iaCZTlssZi+WCybUbWFcxDGssmRxa1qcXHDQNe82WhU9YtZhc1Fr6LKOHXi65HGXNSlkIwNoYBjV4sRjvqamZ9IFFjGxdZGUNvcnkXOS3pAa1nmQnJKOIK/pF2nnS4Bl6R5sjE++oUUiJSC5swSw4zTgyRT9DIBqi8WQbkWzI1MVi5GsOESmMC2EciJsi/5tyQXJjyXZsvlyuMTo+I9mM6Ozi06VSJDeFjIoiVhG1kD0xlcF3VBlluhNkIaZigaFiEJdpUka8MCSAcs1UITvBJHCSQQQdMj7YKy8xK6VGUgu4XBRAjGL7VIr4pCQviDP4TkniywaVI9FUXKKTa29guqDvtqScqGcVNZndYOm7HRqUbt3SuClUFaYJaGcR9YhLeC05vhiLUYuheEFWez3+7Iy269kNPdNJoNeB7dDSa0aMK5DFXpGQCUFpu1fruN09MGwaZVhXhC4Test1ucWdxU2MCL/ee4efnT3g6fqCG9+/jjleoG5Cf5FggLATLtrE0rVoLVycfUjoOvYXDcvZgmG3Ie52BFdhJ3sYUzGI8t4HH/H43oNieRMDYZuJg5DrhPWmMNGco5nOmU732VycknMiOOXDD9/ngw/eK37uFCBeH3ukt6QUyfpsCc2q/Paj93h48gTnPVPv+N/9+J/yn//DP6aRCT44jmY38d7gZA08Zd1n3vn1R9z/8CNuuZZv//BPeXLjOu91Gz58/4z1+SM6m8jR0K0jwTQ0dIROuTtVbt1aMvOe5p2E7jLOKG7uSdOM9wPpoke7lp3t6CsY5iuGCoY2s7uANirnp8rZ+Zbzs02RSxz/0efY2y8TwdeIB2kKg8eVFagUBCmXZzplSEoOpZGW+kAayp9Xw/KrP3tijMQUuXZ0RD+f0BpHMkrrLDGaUs+MEq6XO6wxFm97JFmwMBhHqjJSQ6IiiR3tK0KxCYgZMwiSHF372cyrGFP5XVdRBtiqiRAT73/4Dg8efURdF39RMY5ht2N2cMjt5RScoXIZV3mkjsSUePTgIb/+4LdXn1j5iqYqfppt210BJU9ipJl6Oq0xocaZNDYst2QtbbFgwOUCumkpwB9ZBZy/BBkkhpxQnZHFY/G4V/CofP/DB0x0QTUknpw9ZvPxU955/0P+9tH7nE88Tx/c49/9xb9mt7pAh6H4aS6WvH3ru7z+3/xfaL5zBMmiZ4LsG2zj2AtrfvL/+JAP/uxv+ODkQ0IYyH0Ezaz7C157420ELeoDuzXr1Yr1ekeIcbwicEkfvbqTx0HDq2763jn+q//iv+Sf/eP/BHXCahP5u7/7Wyqr3DxsuPH9JdW3zonHEUlPuH1jxf71jDdwxFCIFukatBbjLXqtZiKZg9mcj9oKc2E5PDfcSY6btWN7mpjVd/jWTYO/UTGbfJt/IPvs0YIeABOu9ZaNwPfmcDAvDdI42Sct9xD/HOQhWtTeQKYvfxL+/9pLHD6X+PNCKEWqfc4XssRfxUu8U+UX//7nrB89/Zz3KaebLWebLe88eMjRO+/w5vF13rhxi+u3btDcOcYuZ9D4Z8PxS6YWXAr4vVQkLYPYliKq6RJgEykZAhWQEBM5nM84OjgsQ44R6C3FPLVgEESIzmItBFeIKZO64fDgkJhOiENgu15ReUvDDFoZ+0WJtNuyXl3Q7TYFxJfSC/7h+UvuV65yvHb3FsfzJRbPpGmwviHXxU4CEqKFvUulmGyINPgsqMv0okhwCAmxgDOkoERR8EU9yJCQSAFSUtiKisEJQERzZshmZC4XOW4RgZQYxJf5ZsgwslWJHvUDRDuOhQwZQRoDnYf09Q7GjVNMUiSAhIwh4zJMtQDwEqn0HHIijZBxIeOkpzHQSU2ezBC07LFiSDrQZw9RqTXiJBeLIwsmSWHYGktrYUiKS4JRoerBGMiVRYaa6KeoywRNxdddBRvBt4KJFWqKPWRISu4C29Was9UJ225LNWn4wc3Xub63x6R2VAKPLta8d+9jnlycY63D+4q6mlDVM+pJTW0ttfNYZzHOMJnU2KlFrEfUY9TgjEG8HclAihfKQNuUewmrSMpXfeMoBkYmvBJHBc8C5lWXsUaJ6iALRnMZlA2ZNIDRCd4ors6EFBCfsBG+cdT353z8pcJP0vSljkOEUhcpaHrx/SEOnKzPOTqeYazgm8iBmxHWHXgtfdpY8khvwVUVEoq9QkqgXmiWU/buHOInDRK25e8lEMWgRKo0sN20rFZrtusVMURMYwn9QDs46rpi2mcGcYgttlp1cgXkYzpSpWQ3EOIMF2tSBHUlN0lUJFuheGJODKYlawAZGOJA6AQzZIZUwLwx9PShL3UoUpQChCLPP6ru5hBJxpV1woLNWgCP31AIo9qsq/DVBOPcaJMs5JFsIAjGecQKQSIZWwDi4plXDlNX1HX5MikYhIyNidr6khNLjWYYuh2b9ZqcAsMwkEMBgcYUgEzlPNVIUpPMyJwvPtujwjjl00vknErNa0brL83klOh2mzKQn9Uk73DRYlMme4uYQLY1NkMSoRKljZnsEhmLGYlHzuQRPJ9GCPwAtiJEoaZhzzec2J6Y09UenHJmCAHvPXVV09mO8BzLPytIVtRpsXIzxc7zki0OpR3yZfbvVxqKGxGuHxyxf/QZ2dVl53XKC1lOpjDFn0dKXpZHly3VgljhSzHF4/mO4XxbijHvcYsp/voE+arfLitxlzjvQ5FAsx5vLIeLCmflyw+hLju7n364BbGVlNX5lowBDVeDsj4M2JypxBQ/8fmE+nBG1nEoLmUo/oXz/qzwZPclD/ibDkEqA3tV+QJzSse+jYT3VnTtUx6db/iYU97+T/6A7//J9xBvUZTtest7733Ak5NztmI5fP2QjRGW3mJGZs7HZ0949PiCd3/5Ht+5+zp//Oa3oepJ1QVmYaGalUbbmK5lIsJzkkk5E4eE1Ab7/EUWQc0R0709vAgHkxquXYPFHBFh/+rbfbVQVbrY0sxqJr7hcDLh+MYR8quncOrh9mGpDH6PkcWQk5CyEGOEKmMoyLE4BFLXkXoh1BXeR5wGBi2N/jwyhrIIEUOvNQMGn4SYY/HWU4Nmh+jocROFmGKRMOHZYOhVUpIyZCobUeUsjffUzuEaj6+n2OmUg/mS5aQiSiZELcypPPp2+Aa8oK54hGIyRgVSpJaEs5kkFIm57HCmDHaCgCGSNBTAixRGxWBHNrc1WCm+42a0RBAEtHDCFTNKohfU8CUD8+p/MgILpBQIRZRDPxt1+CUipExYb/Bk5s5BpfRVxcrXLJeHHA6RKvU8bjybi479rsNbizYLUsosUs/+sqLfZlZYhgqgIKGFGnGlOd73gRCEEDPGFpDCVhOddYgoTpTXZnMO5gfk9ZqnF08xXaCVSBjh2lEH7GKf1HcsjAPnSjFnG2aa8UlhNmWdIYQWSYrkyFAJxk+YzudUQbg4PcHEnmvXDqnjwFGGRWU487AyNRcayd6zEWEH3Jrts7cLTNuW9WDYZk+cz2mnmZvXV9RGeRAt6XTH/GKOSWccTE+p3Zrt7IJh35KahiQVXUiITNlvZuTjCXkVOP/wnJxqHnwEw5nDrgamXKfpb6Mr4UH3C3ISLB7TZXx7/gpPx7MQEcQJlS2sGZMjWRwx1wVFKxFJsSQUsXjCqC1I7cKgBoJHYmH9l3oz02smy4tZRxgC9z7+iKgBqSeQlNV6w6P7H9P1hY2Ss3Lv44/Yrba89eYPmMzmtPmcfrfj8f37LPYOuTGdlkKITDSRrC0n9x6wvlg9+2Va/GcypviTqZByYndxSkyByXRBs7fE12XIrUMqPpgIKUQGlOB6jLFEgYQhyZhgJ1NkMqxikh39fZRotDSNsSQ8wXjUjFvLKL8pWahVWM7m2KpkOJd7xovDcH7nz5cNVxVp8gSEjpInjRJ3dnw9aTn35smWql6Uxo8fJeUuWdlZodUi9+0FUwkMSv/RwPkH5/D9PQ6vffHxXK5UA4WkMhdh/tx3LZJxpVe3HF9TlBB72qenVBQJZjNVmPfFE4saeS7RUkpeGfls28I1JfW4yjMBszdhb3ab6hw6IjqbUHmPdT2To575Xcsb1RHcnDKf73N/ckCF4f79j1lvtl+tjh7f+0zi9nJdL+DIl43owbYgsYHs8VmwgyOFuoABXQdaihxrB1AlJIdxileDTAz1vGFmr2OioiN7L1MYiu2wY2g3dNuObjcUlHUYSLFjCANDLEOJrM94Jl/lvDjn2F8uOLhxwP7RDapqSqoBmeBMQKTjzpt7uMFQaSaajIvgh0wnFSRBQ8QmxcRETEpSIUmiN1qacMaCMzg12FDOtXpF+kzSll1aIX5DPZ3ipCq+Y6rkaBBxRabLFEWVWAVShqiGwUZEDTbUDCOL0DPgVbG5eEJaSVRdQnaOpJnsOhrz9Uv1eecw1mG0sLMvZfwZWQFlMn5ZXF0C70bFD5WixsDlMykUU64RhY0UsFGGS6k1RMjRYF3J56xRgiue6U4y0UBURS2Y7DCMdhUyYLS8T0kYk0neghMkK5IEq5moxSajAjQWyUuTynGZLKQRxGC01EsxGkI2iMlYKTmsXc6oqoq23WDHQXolDRnF1CXXiqE0Z01krAEceIMkj9WMWFsGkcbh0pT5QtDbA5uLNdt2B2Lo+p71al086yoPkpAQ0ZAZMHTDp/vOfdn4EX9C71oumh2bvkf2Gr7z9o9484dvkZvATz76e/77v/pzHpyd8L3Vj3nrj/+I3aFwerHBnXjkzJFkx37VsKgN1RCovOeNG7f49re+w9Onjxk2G84vzti3DU01KZd/SGyGQKZ44JGUIQZ27YZ9VwFTFM9mkzA3PIvjI2zj6Psd93/ygNV6e7UYpJw5PT3FmHOy5hcaGwA5Zzbbcg7d3j7qIh+v30HryLUw4YAfklNmYRsg0a4esTt9QNufMJOa/ccndPUSiZn+NLE+HejbRNooqYWUC5rcTxMre8qtO5l6MqM69OO9b8iNUh1l5keWybs7WK/wXkkysN617NoZs36B5JqQauLWMURl3bVkHWUvn8vfXzbasSlgKNYahvLYGhmBaa7UASJaJEVVy1BbFbnM3UaWuY5M8zSUYfliOmXXWKKtivWMiUgVyKH8EpMKQ7wAyQqzo3XFkibZQDKJPFGi2HJATnFZqUJCOkF68NHQt1/+npfx+z3PZEmxZzvsuBjxJAqcr1c8engfX1VUVYVravzM4yycvv/4Kq8sNxRoHhki5cMZ+sC6D+P67tC2IqnB2YStBqKWGgJjisemycX/MSbiZqCyhrQ19CnT9plhMmV+eMhkukdlXn6wcu2o4f/6f/9v+ev/4V/x+PEjHpw95nS9ISMM4cXPFRGqyjCTit35BR/+9ik/eP01pDZwzFVtWF+f8vrrNzBDol9vR2Wb8nfbizUf/fY3WMNoA1Nk+Z8PhXIvffJgv4bZgbOOvaMlySW6bcDieOv1N/Fuy51rFcf1x9TLNdX0CMuOzeI1FlLzmoLfmyL5DpwqrCIcWMQIXix3J1PuNzBdwLeOa64/rnFPYD/C8W24/dqPSVODl4olAeFjkOuoGvas8IdK2aW0sAeLc9PI6rWFtdtOlKoyTL4mxvzvLTJfn5f4l2WJJ76Sl/jLHZbyeL3h3b/5KTl9cR6twBATD84ueHB2wc8/usf15YLv3r7N7du32LtxA3e8D8vJmEONR/QKSIbe1PS1xZpARWYqESfhyjopeMsWR7WccLTZ53y3JbQBNytKFDICFYMWhTOvYAaDJPDecrTco2tbzto10Srb3ZZhaLFqycmQkrIbtoS+I8ZIGgczeVS7+mp1TLEGMeLweMQIB7OatcZCOLKmWFo4Q3aZFEYwJEPZv+zI6FOhtBEcaksPzKWMz2VP0zyOEXwiW0i2MFVNLOpanohNgApBPRGL2EzSRBoc4IpPs8kUyzhBRUvuaA2aLeoipvJo+/UOxSUOaGrJXYtLSuOLpZpYBZNwJpETWK2KhZWJ1JSBUXZlMDaoYtSQTQG4khx9HhiS0mXwGskmEE1RtotW0F5H66txUJSVoAY/CKmy5MlY0+S69BljsTxUgaiOnIsce1bIMTIMOzbtKSEN1E3D0cEt7hzeYuLzlTbxfH+Pb+VAXdVEW1HXM3wzxbiKSeVpDDijhQnpary12CqDESoteXbO1Sifn8jFrJOCG/ZEDFjFBsUMxWc6a7Ey1RE04USpbCFIxGjooyV5U3rYucJKQkyPi4Kx5Z4xO4uxrniQSwD/zTKHP+duIV3JgH7ZnyiS9KVMS5/4OWXTbWlDx81qn0GEauLZxC1dCEQdAX+jz7YTIZqKbCNN7djba5heXyKzCs2GZC0mgSZXlMPajs16y/piQ9u25JBRY6jsBHFLMhOM9SRRtgJiDV4T0ZRnIISEWoNznowhKDhfIaZikg3iIkEtKomQBpLrig92DAy5I2SIfUfbbYh9S07FllTRItM/Wnc5Y7C+9OtiGhhij5oiWS4x0dSfYfv80lexADuRKzoGORaZdud9ARBbA+KxvgBiKikD+51GMJ6JK3YA1hayoo7gdmcLw3ziLT1KypDjjn4Y2HUt3TBcEYSccZjRzkwzI4nBXu1jIqXmTzKCbMvddFXX6+VA2RisEUIqdU0KPe12jZ/MCFWF63whoXkP0qGmxooS1eAlFeWPnIliqWxpQxibkBGckLLgM2AjvSkWElM7ZeY2tH26uqUVpQ+BJiW88zjviPG5v88UNbhLCXUpBM5ilVHk1fWSgPIF8UpD8cp5Xr97l6qpfzeLKdPHkul8gjw0UPKky1n5ZUJ0+VYt86jyF1+UhCjEky3D2YacFDvxuBt7+KX7ytLpDJn1LrIdinRJEmFqLdeWNV/JKiryuUPNDLRtYrtuR2+UXFBcGbqhx6VMLQYbldnNA0xlaYG2WBUyl9Lw/txoI1x8/ZIwLx3zCvZ9ueMcpbHWQ78ZuLm4xsF5TTX0XJvNCmMNIEG769ntBtqLlvmi5tbdO3z71nWqMHD++BHrdsPZ2Yon769Y/eIdfvvgY85++u85WFZ8+0/e5mB+64qNW+61Bc9gnyPaWQStPG5cHF4IESpbOGdmMoM3pi/+9UucCkXJtuX4qMLsLXn7zjEVbfFOrOpX6Y2/dKTkxuTI0xsYUrkng1FiTAx5YFBFtPjtBk0MQQqDVCGIknF0pvi4ODUMwRUfOJRgLBFHTj20kNYdm82WFENhYY6Dga/CrLj0qjGmTKGcr7DO48QwndUsJ1NqX1E3Fb6uMd4ysZ7GKJtsYfRQ8a4wI7K3JC3y7qN6ZkEnZsGkDGZkpErx4SMCYgk+IVmIWoZpRa6vJPrihDyiOa2AxzKoJ12xwJ9jg3PJBv/sIfgVH1ZLs+elr/emI/c75hhmdSnIzn2NLA7ZMxU3thtWVWKVLHKxYZkV09RE32C6nmtV+S6nu8C6aVBbjrt4mTb4qjQPhnYgpoqQeyZNTYgDrTFk65HUcTSdcHt5wMQ3nHzwId1uS0yRPkdUKqLEUsxMa+x2TVU1JCN0ASaThmroqKqKoZpy0fXY2BNSwppI8A2Nr9lf7hPundFenHB9OmVReRbbC274wuY6sZYLZ+liSz9bsgmRhXUs256DruNpTAgWPyjbYcn7HybWFy135muarmOZHdeyRZOy3T5hb/GA/dkjeuPZ7qZELHtVi5E1D9uWbVwi1ZLu3PNk3fJogIPJjLnp2W5OkdPf0G0z6/gxRiJqCqtev6KX0+eFHb2NJSqSBwYCyS2wRrCS0Tw2Y43BkIpHjMt4KZ44aiwmUzzJETo3MFihbhq896OnD6Dw4IMPuXj0CPEezUocAv0nPB9VldOLEza/+EvEGlJMaCqSzU9OTnn//Xcv3/lcIpSv1ouytmdSDoDj0tIalC5GNudPMe0pB9VrTO2cpIFEGXLXZHKGOCodRGAlAyaWtb6PmV4HnMkYzQhuzE8Kej2bRFZDVEZhFC0ySDnjpSDkrApz5EXQ1TcYVSF10SvEHdgZDAkWTcmLA2Wb0e2AOw/4g6ZMyyfmRarGAOwyzE1pwivkXulPejZDxEwnHCEv7IPPr+CX+V0GVsAj4Ay4RamPPc+G2fcoOeFl76wHorf42wd4sVjvoTG0pjBwqxe6cgWB6577/YlnRA/GY9jxidRMBLEWY5bMjhwzjlDbIHhUNtRHS978RwfsbyvuPk48OHqPpzdv8/P9OT/5O8svfv0bQigAkpf2j1VeiVEIMD1vkL6G6ElGya6MFqzpUKsFRJAyakd7iwSVGoxCL2Dx4+uuqPX4AFZJZAav+KZC95ZoSvR9Rx4iMQ7ErqPbbun7gb5PxByIORGGyBDCKI37IlCmqiqW8zkpJtq+px96ck503Y4cD7HUiBgkxQJa8AYrBo8rXuF+3DNzZPCWGJSUEjkVObWYi0SYcUrvS1Gucbw/hCJDWWiBtKZm5ZUuQs47iFuG3Y5q4mjShFkq4LVUQ5ctgYz15gp1rCYxeEPvBTohZ1/UIbPHxkidB0xWZAhIn/FkrAwwJEL4+kGrlXMYV0yAVEZZfrn0pzOFwS3DmEsASMnFRX4nnxgx7SUfuWR0p3EorkXsSZSicCMUycIOVA0GU1jbCMmCWo83EY2ZmG1hGFtFUhoLZYO6SJV7kvMk65GhZO52cKRLgCKlqUm2mGgJahiy4iQhWfEIKXuS7UEyafQ3c/WCmfNkdogJGKcs/LwAhEyEIRFjR4UiVcaoElRIKuV3IYgktBJ8csylwV87ZracE7qe84tTzp6eknNmNl+Q00BuLT2RbKDPyrbbvNK1veG+x2RvQtcq7XTgzj96g+U/3MfuGdr+MTffWLK9aHn04X3u/+w3/Fn1z9lbTnnru2/zJz/+r7j93T9kXVnuhYbjrfIPJre47gzT+QMWxze4SJmhC2xOTujDRyznOyazA/CepJGYBmIeCGFgu16z2a7pUqSeFArg2dOnvP/b31DPK9q259G9+1ycPmGI8YU94VJ+85NRTyYc3bjNzTfe4Af/5Mf8r966y6FmTk4/omeL9on23pLZ2RnxWk9wFU/e9aTkYKJ8NAz86i//hod/8TM+Sj0X/Tk2NkhqCKvAMKQCxugtw1pYbbbE6y3mOvifZfK/S6zkgsZnDvZnVFY4vMictQO7KAzqqOaBELdcBIMPDpMNJhiMGEIeii/2FTvh1db06qJHjSEbQa0QTbEZwFw2ROGy9XapaH05WDZoIQWMLHNDYZpfssxdLhZGmhNqI7mOhYmuRSEvdqCai/WHifQ5YUyRq802IaFYy2RxSA1GU1HWCpZ6Z7CDI2bI6ctTK5Wy31xlbnppafPsPTK+Hvqe0Pe8sIJ+Cqq6j8MI1spXQ59hGLj/6AEy3aNyU6aasNJR5YBLA1EzGcHlDCYTU0nshggyyt7mXgghE9tM7iJ9ZZnUQvMKcMb/6B//mP/uX/73/Pk7f0+72RL74XfzAin17vXja/yTP/qPefPWtzjcu84mfkBOb2PtEjYjAnJ/QCbC6z/6Aa/deI1f3Pvl75yg3faL16SvYf793OEXWV1vLYtpQ3d2Sic9zdTgnGX/6JiLp4/ZdRXn73bcffMYpq8TnaF3h5zjuIsgYsE8R8bxUDI8YZqF2xt4Yw6HM8HcLnU0TxRzS6iXk6u0sYAq58AK5BDcpSq4wbRFMUZyQq1FzDN571DB4+8qdwRmrwxh/T2F8owlfo3PZW1/4ed8FS/xDSWx/wKW+KWX+MvEAPzqF7/h9IOPX+rnL3YtF7uW9x4/Ze/X7/DG9SPeOL7J7Vu3mL52A7c/h1nzSrYB2Riyk6ISpD2DM0xaQ28SQ870WMQkjCg3Dxac3+/o2hZf+WJZFoU4MvckK9FSGNhJEWvxvmFWzzl1a6ImuqGoWZAHNpu+DBEkEYZA1xX7spcpWQTBZiFsd4R9w1QtKUupD8Wi1hPxWFsA6i4VdneSUi/H9Cz3zCaXOj4XZUWTSr2sEkePWoFcLNvUjmDHLOSqSORilRgdSR1JDJrLMFVdyUlJZTAirgC3JDrURiSNYHf+f9T9WbMkWXbvh/3WHtxjOGPOWZVV3YVuNBoN8gK4E0HyQqQk6kEmUTSZSTKTmT6EvoJMpo8g6knPMok0PUoP10yDiaYrkrrEHYALoNHd6Kmqcj55hohw9733WnpYHudkVWd1VWUWGtDuzqzMkxEeHj7ttdd/8qizsIx4buI313yN2+eYFrBCNCG2jMgCrUpIvv6qakgJ7CpsxFguKoK7UYkFYmwUAW0VpkKtEzYWWim+bo2JFL2etWaUZlgLtFopTdGSSBawBI1G1IFW5xx39YgtNY+9UxMsT7QAoUViFUwnamwcH5zQkSmjslqswJbUaSLG6sB26MnHd+kWazZ9RlOC2MEsjkoCMUNIgoVMDEZvvo6SGDymUqGZ94IjRtAOwa3RCQZxFmrMtLlkk4sOgivwU/B8ZTV3YmC+TgVnu1bB556uIhSkdpgmYg1YDCgJq9/kbPdVxwxGzvbXX3W4u/Ts5vWG3nnVysvNwL1Do9fISjNlecLH+tLvpSjkGMl5RUhCazsOTo45uXVE6oVmlalMWBJajsRm1KFydb5ld3HJbrNjnEavEy2SWdLJAYt4QrKeUlfs+kw3NWJRNEHoJqJk+jFSpkYul9QyIGHBwhKtJIQtNU/EVCktYHlErCBFMSaqbihNqVpQrddRDKIQidjs6hpEyKkjpc6nKzXKVJgGB02xRv8O4sOb+MMZCP/MubFra/ZxbAzTjrxYuvpaDSWSA6QMXe6xcSIOioSOQCRYo1Lc4aopPZEpMLu+NcZxYjMVkgSCtVne/9pnm8424gFFPc6pKTEFTHwtHURoAZhFjbL/mc0YgzaPvA0Baa4Wb7UybC9Zl1NKl+lzJk4jsbpblGrx9zTBpJEalNqoIdFFQSvEUAkhYyFSgz+voFDigtoanSw5zgteTYWiexawUFqh1MoyJbquZxonmu67vMwGRkYMTrhywogD5TOnH/kKNc87geI5Zd5/731i/oJm/b6C/NynTPOP+8/9LOK4YNt3UeFLQXGrSnlxRTnferZKzKS7R+SDt/hqk3Kxqwy1XOeJrxaZ43VyEOGrjMbNd37DW/ZEoO2mMs65AY512KxYKCzV6MRtmpf3jwnJQfHRIBusZ8LHr92jXYGrv8GAjK87DjMcz6B4ACawQbFWCLqkU+M77z+k//6j68KzGsjqmB/84Hu8WK+oJ0eM92/x0bKDPjJddfzoh3/FT3/xCy6fPmMcLrm6XFIuX5LbHawdI/ss23nctNANKJyrklTIs1+jsHjj6/0vfnN+pWnr9Qnqc0CIAKdHKz64f8Lfu/sd7nzrQ5f2bZegS27Cz197075X8FXZuV9zaILYjElcR99QRCPJhEalmjHYSNBCsOhZ4Du3ucOMO8sVrQ+UOVdYK/N7M9UaEiOm5vY1pbLbXjAUV2UeLJaspHF+uWE7jl+4jxIcBBcgWEBCpO96UhcIfaLrlqRuQRcC68Oeo8UCQeiy59qEEMgz2zaIW5qH5A8baZEqDjwGcVvCKIrMjKMoAtUnXklCjZ5BH0NwAKwpGmVWVwW0CJa8kRQw1DJhfoC7veLc8LluOuONasFV6jLnb15De95oUxXPsWuC6tvb7U4Xl2RtHOYIHey6jqvcszo45VZpLOrI81Vks5k42A0sJEC/oqmwagO3jjLjzjhvwriIQMGkEWQFMSMRpnGi1MBUlZA8y2lnjV3w7L0kxnurNaeHp9hmy8vz59g4Uaz61CGRxkhYrDFrLA3ImaJKDR13o5A2FY6PudTI1DaIGbEVxmzEvGR5tKabhItXLwll4Pbt+/R14pbBUYhcJLhImUtr1JjYxkiplXuSOdpuyGXiKerZT9Fo3QFDUZ6eGWHM/MG9Rzz63vd5cnnFT19teDIqt6ef8UcfCNJ6/tWfwzgmHh2+x0EoPNl8zNXQWPKI1g64Ki+gF4pVwrrQL14y2nNeVmXaXfmiNSqSGlG+IVB8VsxQ1RexbeD5y2esHnyLbhHdlngKbpfJ6NdnhaTNwUkLbveVq+d9ibMQ2yLw0Xe/zfrkgL/64Y/ccYJZ/bXd4dDnrx+lTI62tJsmJjYr1H7N8KJIMRHsWsZg/rwWIaZEt46UaGxbIYbioE50MKgJVJnzIUNk2xpZJ4+PsEYJhZQC0iWG5NnqIkbQAq1zVrVCmNrscKOEZMicU4n5cbhWXr624/bad+ANf36b0c/1wdSg7SCdQJugP3DgvpkXwrzY0KnbT0oMsJTP1msjPhGvZkWYgW6U6WqkHXUsD7s39ok+HzeoOBj+E+DMIGPc1pncJ25xfTXv8/69AciL5E2hJrBzElFHmOHvzx4twWYBjLyRTxlwx8h88xbmNzqBMq2B1dxbFwoLEofc7Qp3OuF4dQE2sTpJ0O84f/WMy7NzhnFimCbGUqitUptSW5vzmb7OWXv70V/1rowOjRhlnitmdUYVMCUUc/ZxcJBKg1s60tzmLzWPQiEXJDoxLEQhdBmVjqCNrimpW6EWaG1Epy3ro0OmYWBSt+dttTFtK+MwMg6ebztOA0UbJ0fH3L1/m9snRwgwDIXzix0//+QXbK82XL4842B9QheO5maaES0QpXOHi1CoVqiWKTbnvQahhYmegsnETpQiCQuJXWioKKGDEKHLGYJQWmAniSEnyBMLFnTLDqkOpi6TcCSNvribQt0IY4NqmdwH+hCQ1LBsFE20miBEahRKc3VZ1zLb0BP7RG6FQKErSmgj2iYuvwIg8XVHTokQ4g0oLoDabJXuILlIu25QuzONXBtDmAhhzuDCIJg6qSKo3/7myoubG9vfq+a2ZSZ7UG520zD8+EvFiFTcJlNZIF0hY7QYaLERG5TZZksVROf9b14fqTlY3VIA86TMTo3KAkWZgkKLRKugDREnIdBkdjBJCCuEiZi8GdFjTm5qwgSEov48iOKNNqlEC642jYZJwzojCYScWXRH1FxgUp5Nn2JRMFF2Y6FpY5Fw22dgfMdMcb33iu5bn3B8otSnRxy+/x7xxK0AkzYODoLbZ5pRRrfN3l1ueHV2SRdu8f7vfZdbH91C+wXdU+PWk0C5eMnL5884324ZFV4NA2MtDBcTm8srFv1TDlZrCDBNhWkaaM0tJ1WV6elTzJ5cN+R++pNEl7Nn0NVKM5stDb94CLBYLPj9f/8f8U/+4/8xdAuuQqM+vMdBhrISzkrl+eKUZ/oeD+td9C9+wk92z/iv/vVPQQfev/sBP/vkKf/s+S8Yz0e2ZYIus+o6AspQRgYrREtkesay5OwXic3lwGJlLO4vKavAVduy6hvHCItyxGJ1wCRXXGwmamgcZ1cOTzowaSJ3grSRPia/zz5Xp7wL1+nw8sIB8RBm8p07zzhQHtAQX/uzoLNlJAFsv07isypzmdc8QTYQKiqGxQbR3bu0mf95EWEymCIqDU2KJUACFs2J62akWoklegN/CgSrRBM6qWy347Vt/lcfNwfMFMrXyWR/w7E2M+rncp9VlSeffMr5qyvuntzn9x59j7yEPjRibLRBKVrBzNWOTZ0A2jw2SyVAgJg9uqMLQsoZaYFc3n5NFuOS//CP/yH/+f/5/0bdFdCJy4tzXjx/hRnkLvHhowf8we9/n9/+3veoeht2gdPVLS7Ptjx5/C95+MHvMV0OjNszDvr3kHrEYbzD977zHf704z9l1SV+/umn1/bov4kRgBQjOUe6vudwecD66IBbJ0f0t5d09zPfWh4xvqi8fLnhcHGH/tEf8ur0PX74s1MePb3L0Ycd33sQmHi98SWw9v8Q9rV1ZCXGD1bCIsxWzj3wUOFQ4XBunl1H2gSEh8D5vAafY6QRQsbtGc8LcgIceTZmBDYIf54jhcbv/caO5Dcw9lnib5vQtwfEv+plXvDm7t7+6Qs2+XqW+Ndtb5nBs93Ij//L/+bdHrjMTiZXG15ebfjLTx5z58c/4jsPHvDhew+59eAB+d4p4e7xW27cm3o1Rszy7MaUGVpkaL4O1eTryMWq587BIUNpdEVI1Z1qnJST0OYk9RKUVhXLE/0aWlSmUtFaKUkINdJMmUrDVJ3kWnyN8vWHE1oWecHxck2KC1ChZKUZFBNi12Op88xYelprSGjQeY0VTGmWPFMbJag7/riArqEhzAnoTmlW3GY3IFSz+d6M2GS00JgseIkYGoEJq+4+JNEISdEWvS9gs1qcCNSZuMqcNx7Q3Ahdh+2+vEfxVUem+prMAkqlaCDFQgyJ0AJZlWCFwXOe2EhlXYyDaCQVJDgAPOoOsYIORtmN2NQgKNYapErZk9fUSVqlmovrqnGnO4DUMQaIrSCD9xBTmCC7EldjQqofYzSRQ8LEr8VIx2IVyItMzgkdPfASmehkzgYOSgpKiUtiCiw7ILuDnivWAzEkUpaZkR9IYWTP5lNAC5joPvHJybuhgUXvs0QH/kKItMUMlKkiYSIiToQAj7OrM71OGtJ6olXv6aZZMWqgZJI6wVbMCYMTQih/OwSnr/fY8g6FmTlZgrn0+Rwp0FQ5251RyiGnYU0jcSRrnodzj5zpPCYV64ghcnz7lG7d+XmXdv2Yt6liZWI7NC6ev2RzNTDttlhz4DlIAjqiLFmEnpOUWcsCG5fUJSSJDHUgmhCkI6jRWSS3K+iUIfsaKaigYeTKRrR4j66FyKATbWjorjK1HW2a/PowJUoixh6nFCUXp2Hu8iqefe5uzEYkMYnHRagYqpW2uXzrcxb362zmjt5M4nzNA/A6Iq9UX3ObuaOaREVMiLlHUkZKg87d6JzwuaNqpbRKGSdiEixHtw+fRT9RnEQSJBCj25g38z3aO1YJM2ZlLhaIRF/f2h7MF18DmFdC1/nre1DckivvRWZiqVJ2W8qwI3Urak50UyQ0JeSISnVQXHFClKgTdaxRzd111YQYmsf6SaVJIFn1aCYRVtZzlJd0YUPZk5RE0DkHftHb7OSYaNPNmtoUt4OfcZQogs5kEVWPj/gqxcU7geLr5ZJH7z8ifpGMeuJXqpw9I9DgGn7c/2zBDRYonw+X/IJhu8rw5Iy2GSEmUr8gPTwmr78mgGCGjcqrXWVXJtoMQByue46WX2Ohs1//f8Fb9qD45qowjhVTI2ZfvI2lYrWQgc4gh8j6wQmSAhtgVFgaHH7ZWTODq79joPhxdjQ/4lfdBnQ7QQrEKoxloDs8hhSuFf4pCXdOeu6e/Dbtd7/DX1bl45xYAIuLHS8uJs5fbjh7/JSzT39Jv4wcSs/xw/d49Af/Hqv733KWYHz9IvIT4722yrh7wlWLHB3cI8fuKxXk7z5tCrEK08uB5cPI8tYxEhdwAhR58+K+AWcgp7z9gubXDA2NkAU00KxRW0cStztv1RmIU/PclRxgqoXLK89p7PpE0QMmCf4gbp6NUaI/pGpUb6SWhrSBabtlGLYgwq3TU44PVkw2UYOxezpef/29FYnM+RepS+QUiQGfBGLP+mhN10VClwlhSRcyfTCWq0yXOlowutyBJEcBUGon0CDW6ErFKG5XREJa9ZsvNJCCRW+lBgQtCSnOMAteOYBAUAe+oiQsQ2lC6NyuQ4sQUpiVmYGW3Kq/ecf5V0Bw2JsH2rWlumqYQXDP/vkMova253vYcCSw6qBk4bLraasjjvsD7l5csEuVS+so5+e8r0rsOmq3RqaJO52RJPHyauJquUKD2y/FGDBb0mWjGIy7iVITVQv9IlO1sBNoIRF04mS54NHhCet+zcXHP+Zyc4lNhcEKLXS00DAT4tEBdnVOn/cq8cZ6tSYPA13umPKSV2Ug1EIrioRKywu6fsHR+pj2/JLh/CXHi56jrme9u+B+TETgRQ5cxMym7ZgODrkohT4GTofCne2GF9rYWmPUkWmZ2ZIwRmDJKt2Gj/6I89//ezwfCq/+8s/Il5G79QXvfWvJg9u3oav85E8Gvrf+A7519xH/5U/+X5xd/gwkkxaVdW9Yv0F1Rx0GVivFbiubiw1lWQnNF7YMiRq/IRazgVlzdreAlolRK8eLQgjQtCdHo0khlkLnVy4ZyNWIJTGJeD4sMIowdIFpaayO1txOmZ/85KfXoPjXGbLfvzfJfr78a/l7xQjWCCGRDlYcrpYc3F6S1pWuJayBTUrECS3NoFqlYm7RZsY0VAYpCBNBK2aV0nXEmFjE6OxLU1QFqYk2KsKOWDKSAyFX0ihU3JK3aeNstn+63l/Zf1f77Hfgsz97m9HhfYYyc4w0Qed1N425CdUMeXxJvzrwObKPsJLr5iDV3Do94mC5gFWjXlSGYYCHa9ZZfqXU2dc4+3lScGv03fxzxWuZ8XxknSNykFlw4xw0AVnxWISAL4CDdysl8Bk1+BeNN83RwudU4ld4w3SuUfdP3/17OwRk7zMJ97oFi4/+Xa7uv+TB3VOmVy9YRnh+dsHl1Y7tMLIbRnaDg+RTKYy1UEp1opjq26vJv2REczBDk6GSoCRa8xpWs5O3RCq52gzuCRobFlxFYM0XUipK0kqqQhFBLSExEWkz8xj6YGARC72/v1tQV5OrxKcJq4KtC9IqIRhjreyuNpSqnJwcsViviBLdKvNgwWpxxG4aubg6Z7LKZrwk1p4Ulv6dqkFIc960L6qlORO7BqWmSmjKokGZZoJLUKYQmVLGghJbIyNMFCarmCVKXVJMUAsEjD5CCiuSCHmcVZPqWYwDsFVfOGotLASW6vXSthSQSkkBkSVJAjeQVKQWdUXlYkmN1TP76sTwDfdfBCGljjjnzEkIWJgXi2JuBSyz4nm+zU1n548ZPFMCyfSaee1ZjnZtk+jkzHCt1vA8crtmmu9t1pnrFLsmkgaqBuIkMws+sAuRESEFhebkoxAyobj1Xet7Ui20MJGsojVSRbCa6EwxiXNUhTderSZszPRz9pZ/r+h54xroqd7Ek4zl5NdJV+lrILRIQK9zz5r4V+7jOGf2uZV7U79fEtEtdmOjdEtkfcrV3TvspoEYgtuLz0z+ZUxUEYbNu9mn/8VPf8p3F5mTw5HN9ozdjwLHO8PWC/rTiU6PWXeZs8+9b9gO/Ot//V9z9H+/zdGnH9Jiz6uPz7n45Qt+8vhT/uQXP+F8GinTyO7q/BrgVlO2w4btsPnCfdLPgZZOCvp68/5qseR73/2I3/3ue9w9cseI8/MNPz4+YLj3HuHhKd37j3j06H36bkE4f8H5xz/kz3/6lN1mSdkuGS+F3XRBf7yizz3Lq0ptiUCmSGFix2QDpoHYDJkCz3/W8/H/94STuyvGTcTSjuFqZLyITAeR48VtTq3nl5OwufoFJYzYFOjiCmkBC5cUnUit0EW/57XpXId8tkH2NmO1myMexFVv+/9qwEFwkZs/B3eB0BDnP0c0yrXSvMZZZS4QxchzXMKe9CINovpaSCZBihCGQCiABeqhEUfxqKw5SiLXgGkga/VnTQqEUXw/dOBnT3/BWL6Y5Py3NYIEUkyUMjDuXhDlMevUk5IDVkolzhb0VYU2gVS3UE9EJ+sKVAmkVWSZM6tl55a87V1aakv+6B/+Af+T/9G/xw+++xF/+If/Nv+fP/lv+F//r/63XG7hv/M/+GP+e//+3weD7Vh48stKVnh+8TE//vmS7viCew8zTx5/yovHyrevjjh+cMzF6pw/+o/+If/T/9kfs3u+4X/zv/tP+S/+1b/4pg7nrwwBogRCiiy6jnWXOFiuOFkesTo44vDWisXDB9y9dUJ3b01cC607585Bz7dvfcDy0R/S/eD7TIcrhj+bCOfGehFYz2TH1zFxlvMvZqB7GpHco8nFh9nwPOYscNvPzaU43yOOHkXYhwDxFEk3ZEzA68K1wc4VjwhUUwog4tnQ/48W+L2/tXDwrzlGvLi+xbs1sZxf9uXD8Bp7T1z4NS/bq8TfZrcGU/7sX/wZl0+/OEv8bcZ2nPj5+JJfvDhj/aMf8+Gd23xw5w5/+D//j99qe50WWgqMkqkxUUPHZWho8WsrlnYdWZgJ3Fr3/HxzhcUrlhIJk9GaESlISAzDRKX4cztMhAHOXlww7RpDGYGRJHEGBpiJaoVavz45L4ZAn3sWqzUnR6ecrI9ZrFa0tOAye+9QbEHKnh8cVGm5Zxcgi9C1SgrFyVoDoIJl0BZn8uOs6G1GDOrWuuZrPlMjBJyk2JSqgaYJRamjILGiscyZ4ur5tbUR8hz91sRjwKJAFKTt1eLBc3VxYDcsEowRvoTE95WHdLNC2zPjJQQkJHKMpOYipNSa27eHxlALl02ISUgpE6PSmZKbMuhMMqyNVhsxKbVWpEWCCmVShlJo24JUJUtCQnY3GKn+PKZio69KWASC6EyeL/MaMIHMlIRoSPLaToKQU2bBHCdiSomVsTNigBa8Xi5BofV07NXZEZIDgXkW/jTpsBRBorvCNINmSDQkOllBo4L5A7xacTvoqRG1oWQ0CzWYu4eZ+DotBITgDofNe7ax8x4NJlhkduVMvloJQukCNruRiSZCaNC9Pant3ca+a/IlY797c9lt+37CF7TLNsPAJxeXHJ8e0fdGqsqyy1whIImhjAgDJ6tDFgsQBlo1xipYDDRtWCnoduLybMPF1SV1mhC83xtDJs2M83VcctDfYp0O6ELntVMpDKVCNXJ2F8oalTrbKas1dgq17diGK1KAkrz/vh0KOu2YqjHWRtls2I0jbY5wDBEsKNYcJ7AUCCokUULw7HKaojQMdRB8JuqqVUyV0d4eI9O9wOWaYOfnwXHwGbuY/6dDZdrukH7hGMP8mnZlUDbUYcfUKlNVpnGgVXcJkiDXQrwsPRaEHBMpBlIIjNWFQClFYq1OLNgTJmbSgEhATa97UCG4K4H3c+b1vf//WkGuZk4mUn+GhihoVRfu1ont1QWLgyOmvKALmaSV2jISdt5vEaFooBMlVENVqeIufVqFFCoh9lgLqLgAykJlipFlVfqw4qjLDLu255KBCGOZWJae3GW6zh1J94QSNSe9KE5yiRKoctPLYE/2/5LxTqD4g9t3Ob59irxJKmQ4S/Bz9gSKN0f71z7cuCETYnNh1PhCtfXrn6FDY3x+gW4LkhLpYEl3b8nXFtUZTJvKxVCYtNFmgPRw2XOw+IoPyn3HF75Q4a5Aa8bmYkepCurqHQym1qBVOvO8mG7leeJNhCt/KZ06tvyl4+Ww9xD52x8ZOFpcN9b3SnG92kEOzs6yhtw7+Izl/Z7tATASuYzRXQbMyClz/mLDk18+4eVPn9BCY3n3DouHH/DBD36X+9/+gP6wo+qEm2e87gnrI7BktbpLVWH1Wt7AtSXvV7S6/bqvB2PaXvH0xQuGq8g/+OC3WR/Pvrb99UuukQUDj4M44O0tr75kyOQNXwTqZIgoMRgSvahoze3iKI2xurXPMiaaCCH1NBFai57nEourmGMihErArXnC/PBqVkCERe5ZrQ4hJYIllquBlC5o6jbcKSRSSoQgECPLRaZfRFKKbpXeL1is1/QYMSY0LOnMyCHQ94GE54N7foWrKiwLEkekBWKnbtEZOywKoVZvxopSs2I9SDDiohBHgSkSTDASoSVUXRUazehMKShSMiUEL/xqBDOkQpLGKJ4nFGbF0T4P0E/3PDGZ52youiWVqVyDfd66tv38gOwP6FuMROUgJEJnTF3PWexYH9/htBRWdeTxMnK1mTjY7uhEYLGimbCsI7eOM2Uwzltg7ANI8Sa09BAyIRnbUikFSgWCZ7tclcI2RFf7t8aj1ZrbR7dhs+XJ2VNknBilUQwgUm0kLNeoVVYmSM5UVVroOBBIpRIOj7myyFAu3eK+FUoG6ZYs1iuWZK5e/gIdtty9f5e+FU6bcrzouIxwETpeSaPFyDZFxmniUcwcby5IpfAMYygDRZRdv5qtvxrrmDm69R4v3/82f517eLrlpPak2hPSbf765XOuSmUz7dhN57x49YrT4Xd5+QtBKsjRwPEHS26tTvjpy8Krl5dUETZD4NnTym5Scnbmd02KtOY5Wt/EEJyQ4XcIEoWj4wMWMdGCFz9mDeJeLeNzVLA0N12hRqfeWRB2uTLl+RmogSjxNWjx6w01PLv1aw4vAt0mVPAsnG615M6DBxwvM3QNTZPbeIlnD1uNJPFMqloD2MQU1RnLxRjjSB+MaSxeMEYwSySrs9uX0YIREYIlmk6+UMWdZcQMLW7Pq6Fw1QamOl4vXl4rpV/7z2v39Dvgpwl3lilbCJ3fh6sMMbxmnb4thBc78urYbYUWe+v0edKpwKAuO9/XHKNRzwtTVOTOij58ts5809Ju3/Qa500mX5MTjm/mZBUHxddAMmAyZNP8cw8ciCJ+Fmy/GW9xrZkvxhlxO25cKarckDXltd8NyCFxe3GX2/0d3usf8sH/8JQX//AXPHn6hF88fsLHT57xyyfPePnqiqvNjsvNju1uZDcryYfRgfLSKrW2azX5O4pbAKhitGxodEaupjRbeimgtMlIs6UY6hlsLQRqcFekloTUCrEJFoRBoqspohEopKJ+JEIiWfHIgE6oJQOBrvXEAjnMIPaiEUKjDwNCpdw9oFVIJZFmNUwx0JYpVlgeHbA8PiB3meVqSRSZF909Vl3hglRXNc53OVa9oVIrsmtsq7u7RFNqVS7Hik2BtHAbrTEYdaxUg1YNK1doyISU6FIg5YBnECa64vmDNYyMopw3pSrkLpA7sGCIRphcvSyz1W6WEUmJGBKEJVNIVImzI0dgEmERMt1yzd34zXbTJXiEjCu657y5MF/nmDeNgtd216PN7PA9QWdehLv9w3w370Fxjf6Atj2MDqI3KnPh9d9mWpOAobOwQ7AiaIik1pg0YEm8wUeiWqJD3RnABAmu7tAQPXtRQZN/mKphVJrkWTEgTOpNA7ITi1UqGiKiOt/dDdOMLiKmjWaB2gCpnj3XItCIAbQYqDg5LBdEIqqJ1iWPFJkbMSG4BaSuEnfv3ma33dGKcTXs2O22hBapKVKtUad3Awh/+vIFP1j9O/T2kvNXG37x6WOufrLg9u8+IHSH7B7fZtG9OSPv7Nlz/ul/9p+TukwKkVArtVSmOW7Amzve6JFZSfk3Q9/51SExkrueWgbG7TMgc9xDtcbjdMDxwS2mB4+IByt+psYiCC+2W55sG4MldPKm/cmtU97PF3TbDlsKm6uBSy7ZMbJrBd1VttvK5XZiqAPypLH6f97l9DhCVa5ebdm82PE8ddxdL+nDAVZ7OlkyVmWqO2QQQl7Qp0K1Hbs2kVPyNQbmmYVeyF9HIr31mGMn9kZSrx2xeV0o7C3jTATdA+Yibosq8pqKPFwrziUFYucgQaKhNcPUY6GR4kQqsDxXYlNCg6CRc5KDjJpoFpmyIs0I1bPMrVcMV+YQlCEIGoUUo1vK/x0YKUZuPXyPo9u3WR4uWByJ17wkdtk4aJWtBIpFgkbGVqmqbFGfM1qlhOhuFQRCcNvS5WLFcrFASnMV+VsPY70+5X/5v/gPCQcrDu98nwePfps/+ed/ydPDh/zuH/8Tzp59yubJL9mdnTNeVPKYkZR5ed548fSA8ydr6vaEaSh88ueV5396yX/x9F+RPzpkebrmqD/m733v+/yzP/1XnjP5DQ7Psowscs/R0RHLW8ecHCw4XERS67nVLbl7vObwvROOP/wdVvEIO2m8f7zgB3ce8P4//m2WR/fQ5Qkvk0dWHPx2ZFlA1n585LMTzWssWsM2l7TLiXj7DrGLDA1SdScN75u4Y8oVxguEAfiOQTd605l95ObrBVkf4AFuMWrK480rnuXEsj9kgXC5MTj6O9Jn+3VDuVGJ/2or7OttZ6+4+rJtvJ4l/gWv3beJ7S13ywyeXGz46Z/8a/RvyP3AzLgaRv7NLz/hx4+f8oe8HShullCLUAPBgveYQnUiklRCG+lqI0ajC42Tg8iPn1/x7MlTAgnUndRWq0OOT257bFwwWimUq0s2Vxfshh3DOFJb8blHnBwpuGLOQbSvPieFOT5y1S9ZHx9yeHrEqj9iuT4ixdl+KRoSGi363IIpZbauFQlodSGEqBMooylVhNhmwDrN+5TrrBY3UENlQiVCDnM0jjLFyGBCECMUkCLYDGRqghSVpsnthLP3L61FbJYKSlSoyQUvAqJe85oGtJvV4sM3oxa30KEhIX3FUqBWQQrU6DnCCT9OsfrE3QvstNBp4FAKIpBbom/CxTRRp4lmE1UqrQnVnI6aYvK+BELsIirGIgpd6pCQqKki0dAQCdHdv/oUWESYwgwoBqOZEEJEqys5PVLSy/7WDOtv9rUFc5eC6OuOEBOdZkJ0++oSk1vll0AWw+IcV5cAUwepq8drWmtEqc47Eo9YQpr3WoNfR2l2t1ItMCVC53bntVVfWwQn+aGCmLhDVE1YnUG+6j2GELxfa8GoAi2n2ekTbEYKfvPjzV2NLxoidi2o+Mwm3rhp5fHFJe8fNm4Fr/eSCXU7MA2V5eGS47sLYrcgDk64qMFdA6wZUzOmqmzPXnH+6hW11OvI0iCJ1PXkuGAROlb5gOXS3VoJ4vfgoJRaCDGgNJa47bmlRpPAdtrRFCwJJSrZnLQ86s7jVkdF6+hxh7PzXlNlskqwQExuj+Skn0ZOPbPKdM5ZN2820Ch1otZyDRq/6/C1Ev6MnVXXwizis/006SprUaPuJrqY3D1hqNQ2sp0mxmmitHodF7S/GnwJ7hbo1gxrzXGB6GvklCJTa668jpEYIiF4FKSfevNn87xfpoY2JYQI4kr1PejuT2DHgAJyDWO6hXqYVfHu7qGtMm6vaGUiLDpazsRxJGgkpECVhu2V/yJkjLEqGtXd6+bjH4PRQgKdPD6DiZrW1FpILDjKPWfjRDPdF2ZUrUy1krpMlzuGMHzG9cTV4jrPqXFWwu/nPr48jpt3BMXfu3ufg5PjN//jvtr5bAQzDc91POCmrqrzz3ubwXzmH755ff+ZoZcjw7NzdFOQ5QHpYEV3f/Xlb3zD/g6bysU40YCGMzJODzuW3dco1Sq/VuGuQClweb6ZG1c6uzkZUylIra7IU6NfLelurajAZfOb5HC2Tfiy78LTHYR3Or3f3FgEOF76teB9JxjBLrfOZh8rgiIPD79wEwN+3TTcHvUkJdbbzPCsMl1E4tGKbvWAcHiLKfQOmk5CWPZzNtobNiqwljUtuG3//iVNIYwNWX2141cxJmuseD3H/guoW/O/LfoOFpFdMkrgMwo19u/czUK1/WJt+ZV2561GVw0iRBWCmhfVQYjm1jjNDB1haoVYjWVeELql50YsemqCJkJniYRRoiJRya2CZZBICCAdhGx0XYc0L4ybRZoay9yz7DpaCISU6VIi95GYhSSJZb9ktczElCEviH1w5bg2YlwhoaPThEQhpkoQo8a9DfTM2FI8Q28RIDYkCaEZIWRaEmdJdtA6Y+oiORViNxJCh42NVgK4xhTpKg04Kx33p5FccNVFHzwLJZiDEAadVZolb+6Kug2n4cC3CdqiK8FVXluT+1QVmHtfYm4BJQ50vEvDbW2Rg14oWTjvemx9zGFecX+zZYiVCzL14oL7tZG6jtatoUycxMoyHfDirHDRdQ6OihGigC1JPVQRxt1IaYnSCv0yU9vEToSWOmgjJ33P+0cnrBdrzn/6UzabS7RURi2oJNrM8IrrFTpsWHQ9GgNDqaR+xbK4ZWXp1rwqhVB9coxSKbkjp47j4xP0+SXbV885WWSOF0vW23MepEQAXiThPCW2baSs11y1Rh8CJ1Ph1vaKC62c05jqQFlEdjGjZaQLxq3lAYv3P2J7/4RhqKyevOLUKqzv8OmTC/7lL0f6WLlTLtldXbI5/xd8PFSe7T6l3LlA08jTpz2LbiKdN/JG2KbC2bjj8fnArjQ6EosQyNGvq5a+mUW/mCCW8QjZQMpLjg4WYA4+YMUXpgksBaiROAiJzBCEq4XQFp5jpqG5DVk08k6IQ6Wr5V2i1956qJlbvQMEYbXsOeg6lqEDRkaNWAqoGpqcz2yox/GEgtbKdhpRMzo6WCTICa2jA+KqrHTl79KGmKLiatiewM6Eqg3RirYEmohi1DCCuYX0MA6fu2tvWJqf++k7geLgQu+2g3To9mTLoxusWzHsYkcukdT3jlS/bp2+R7InhTuzw4uB7ozxYqD2wvKgo39DD9j4LHer4nP3Dp/HF8ChCF28mfPO539XnEPXN7zDn+V6oxOu/tlPg79yib1+vH4tmdLcnn+48jetj0F+fTLJ53+ea+KDez/g0f3vU+tAKVum6YrzV8/49Mljnjx9ws8+/YRPn73kyfMznr+84OJqx24YZ0X5a2ryUphq+dKIgF83Suxos9t0nNm+xDmeh0aIisWJiUCqRmTOltJKjIlGR4vOII4lgroqIMRGLw76NpSpU4rotVqOmJxsFp04k1rGTChiTp6RJdkK3ahIqtCrE8maEqxx1SasNE5PbiE5IjmRo7AgYXPuVLRGKtUtBK16dIk1BzsLhKboZIzmXQ9tytmw5eVmh9JIKZC7jn6xIlvvc7wVqIZKIyWIWaipg9SxEiFKIAaPk7m0xkbcmtwidKpkP6pMItTqzcagimSFWjEJRB3oCIR+QUs9wTqCwqIVooHmt03QfPMIIuTcX+PZIsGTeMzmFXaEuM93MpCAtTCvN+emEvjzzfZLY3+myt4dQF1x7hJSubZMv2bDBEFmS0uMWSk+L/N1v30nZmiJOPnNlSxe0WQmEUiBrhWM4I0tE7J4HACx+bPehSmE6qTNJo3YNWrnJMxmwTOfQyNZQxEnbxKQ2tw+sgWCFAKNZomQxQlDLUP2xkrM4upwBdHozU6qR+VEt2jvBW63Nbu4opaKtcbFlTIOhSJQtb0zOPi4PuUsv+C9fk0bR1492RGOCodXjXhRuWLkW9/+AMnG+fkVz1+8uFZymxnDdgfbb84e9Jsa4zTy6dNnpL8Q7j64xaMPH7BMK57/7GPCj87Qbkl4/B2e/9u/zavdwJOf/Cnds5/TqqKs0NzB6Y6j+2t+r32XPKyI4wEXz17w5PITdrVwtNtyPlzw5Okzzs8uKLvK9GQi58bm8BKdGh//+BOev3jJq1dXHKzucby9opRznu9e8vLinDoMtEUiLTeUuGVoE8M0kFKmbCdqmzwvfU8oeMfGW1R34mFPnL0m0Pq2ZQYDYc81fx2pugHL97f2/s+ESO4zmgIhedSTjgGLwiJUVlbotwYSyTVyniNDL0gaCNqIU4bQmMZAN0dB5VrcFrMqNo4Eqfzut+8QdeLPfvzzdzoO38SIMXLn3j0+/Oh7rO4ewgFYP5FFmXaKtkDcVYoFSpjoLFJqY4exM8H5to1dGshR0BJoE3TZWB4HuhCQYAjvQnxRQpi4+63v8PzJKy4++QWvLo/4g3/nP+GvWuH8bOByo9RtQ4fCMFUylUVpbC97pleZmArd8oDlYsEnTyaeDP81m6PHLLtD/s3Tx9yuK/7+7/0xp//0n/L8/MU7H1cRocsdi65nuV6zWh1xfHyPW/fvcnKYuBcX3FufshsLIex4+EHP6qNHPLz7Axb1iPVS+e7xQ07bPcLt27BwEviBeNr3cm+LqOIsuuwuLzq6isqyeeQkO8b2Q85Kz8l0i1e/rBxZRh7NzR0U3Hyde8ARSza9sP6iInv/Y3GLXRuNEoTdkPkFidR7D/P06/QG/zbHhBffd3l7YYXx9bLEL/hKWeITb58lPgI//Isfcfbzj99yC1/z894BeN91wqiJFswBAzOSeo52lEqOI7kYfYAFAWLkXrfil+dPGMfpejsXl+e8PHvKvfc/ZGyN4dx7DLVMcxn2Wp1hYHv3kq8xBMg5seiXHK5WHKzWrE+OyKulCxW6DtUIWZCYsNbN8WWuRJQU5kzvSIyeD64tEEpzW3ALhBq99iw625hXxALFGkEaZBBT6pxZ3lgyivfLRCNGIYhSS/RehDUXwTS3TQ+1uYV6cAWx7NXiCQdMYyNcq8V9PcQiwRTgmyAMdYmQIM6Ka8QJkkF6mnh2dw572/LA0DI7FeoYnGAg0GlgDZynQDElqqufK0rFMItYU/q0IIaemATJjUX0etmfX4GYHchcLJTcOSnOUiDj9vVFMxJ6TJOft85YiHrU2tSoIVL7OViiuQpcQnSgmYKRQAKhOginOBCIKskCYhlSgCbEOOLWpyAWkdAQa+QWqK0jJKWZr11zg7JQigZC7dyVsgVvblzvy5yXztznDw7kmwkyZ6UF8bWtloBFqFZwargwRXH3Snjnmu3tx2c/9wuJqW/oFc3veON2wNhOW54NL1gd3qZOAbGOapXj+8esjlYQogsnMOJO6AajMmFV2V5seXF2wXDpPn++rnNSTybRxUS/6FmzYtmtWC07pjjOx74ybYShGLFv1BwI7YpuqSiZqVTGXWDSSl5FJM+COhJqPTVOqHjEQZsMQkKyeZ/djNYaIbtCnBY9Lz67m6Pg30GLC3tC9fgFta//LPyiEWNErv/n6+99XJFIwPCeZFWlmVK2F1QdUVOmwfdLTalar0Hoz5xF8+9ps7ZBZ4eIkppnicdAFM8Aj7haPLQ69zXt2s0yzNFLprPbUfJ7RpqrxSXoHHPg63SZfzcMtYaY93hCEOpsT16ngd3mktQvKV0iTpnYGi1lJEwuNhBzHFVmULxrVAvk+fEaoschWHDcI6hBaIwhks04TGuWacs4zedstoGfysSidXRdR0yJ2m7mRjV/zjv53x0Sm+q1+OSr6AjfGjUNIjy8e4/18RcAmfva4TW197403QH3X3tp4bO2RaLzi79s7xpMzy48T9wgpEw6WdPdXX5tyqGpsbuqXEyeaqcCq5y4deBK1a8kAnZCyhcWjYaLNsoEVxcbICBhzl01ZwFmM3oiSY3+aE13smRrcKl+fA7Dr2/e+gcZvPg7BIovBY6WN1lGDZiMOhXPKSkFRAmnX4z67phFXfPmalHGlyNlzOj6A24/POLg1kOK9Hz68Zb73VMuVDn94D6Hd1bk9Kvn8Pph5n8B/BxtGoyvGrdFiYvsjT1eO+avIRZmsJsGnpyf8+jkDsucfRGlM0NH5Lpf+PrHL8OCozv32IYjXnU9S15jys43blUXa37VLIR3GVkMFYimNClUCZhkJCghGiKVVka0VvoQCL3n9IaQSLkjSyJ24kw8OrdhFiMEIzRnc2Vxdl5IQupnTlLGWUuixJhYHx1S54yMPnX0B5mYA1kSfehY5EjMHWGxhD4iEogKEpLbfOyyK+RkoM7WnS07ezBW5zplhJBdqa05IsmgRDJCVZntM2eATYwpJNqqEU2dqFANDTvaQqk10pYdpzshN5yhFNySMGiYM1cCaXRG5WSGaWAq0W3R9yD49TX1BSC4+IO9mdeTs4bkrc/3YRRiB7suc5l6Foe3OG3GQRl5shCuhspys2UpAv2SSqQvG+6ddEyD8WoyhtOESJkzQ3sIHTEam1Ip041KPAZhVyq7udAPKA9Xa+4c3kKGiRdnT9FxoFilYJhkmkxIWmAJFltF1plqxiSJ+zkSLjbI4TEbywz1DFEj1EJJiuQli4M1S81szz7Fhi2379yi18qpKidd5irAecpciFFF2ObMMIzcz5nD7SXrMvEzjGkamYIyrFZMkxJCZRUiJycPsG89Ytcl4sfnLMqWKJXz2ji7El7u1og1YnJl4ZlccBX/ghAq8SxSzwt9htjvHORLO1K6YnuxxeqWJJFiiUkTURMLCeTwDSrFBcAXkaSA5EgTmR0IIsGUFA2N/uKWA2pGWWV2h64MSqGioZLV1UJ5K3Q1MNVracZvdHgZGFAUJJNiJEj0YtoMGQUW4uBO9aK0SaMlo9WGVMWmRqkNi0LXN4Z5YScoWToIigb1LGPxlo7FHcISM88dDlT6VKnao1FJVphK4XJTuRpmBv/ri5vrP9tNMfyOx0KBaZaEW3IL95zm6Dabi8dPL+mk8++Ro0+s+9NmwDC/cOHPUatGu6xMw4jeX7BcvNkR4HVQfM+N3CfKK94nW3KTAmLzv+X5fb3h6HcLs9pEwM0oSMtr7O7XPP1eP4Dy5hemAKue6fGWtKzEw0wOr/VCv2Cz+9WLVEAz0pTOIl1Yslrd5nj9iEePGs0mtptXXF6ecXHxnKfPnvLzTx/zyZNn/OLxU16+uuTiasvF1awmH0aGaXrTp36lcdV1CI3c/A4w82ZLVKNTxU2yIy01B7TBF5/NIwQIwVUfIoipC4NltsRW9eYFkcQ8N6LutIBized0Cc3Jfc3dF4JUIhFrXhMplRCrL4amipmQMhwuItIyIfduj5+cxWzmOYiS1RnMzRnOozS6JFAEDebZ1Z1n/VaFsQnaMgcKwzgwtYpNFVQp2lAmmhRo6ovLrqNYIhocpMAiZM+gCxNbLWzahKVEk8C6JYJEShKGmChRqEsoLaKTLyXFQCagNSRUgkzIFJAqtAatjMToxK1vcsQQSSnNdEy3TvehDpBZ9IJSXluCq7hLhDmjWmZWss3LYvZ1UAAr4RrYBq6BcItznrgj8eyt0wW/hvYv9WfmfG5RDM8LFDNCE5J4s7RJghCYSCRVzPxaUeYGQVASnlVnqF9bNWHdiAaYbO96tIAwYAlkSlQziMU/DyceVwNJyb97SLQ0+XWPQEq05mB6DJ4rHMEJH9FICahGSEImkfoAFql5ZF1XLDYd025LqQX9BpptIhObZ+c80RXPz2Ac3Tbz05+d8+LlX3O5KPx3/5P/PjnBj//iJ/wf/w//GWdnZ+/8uX/To5TC4ydPuLp4RdsVvv/9b3Pv4QMO2iFH23NuH65YbLac/Vd/wubygrCqHB9mDuSI80XCbi3YnuxoB9CXFYtyj2O7zb2T23z38reYpspfX77gp+NTZPUz+jsvMRIxdITDzHkC3bhSidZ49ewFP/6rHzJd7Jh2lb/+xc94cvaMbtHT5cS2wm43cjltsFLcLn8s7KYR1T3p5Bs4Lmm+k21utM0NN7ipF25+5wY8v74xeaPKPIrQm1AXnrupzZUozRIpCL0qYolgmWaRXV/dfSkYIQ/0rbEsgWmO4bDopBUq5KGxHQuqDlo+OL3DT5bPqa3RWkX/llTjMUYWhwd0i0xUQSalpuyWmjlQo9A0wlQoETzH1qjNmGqltEJrlYZgEhE1j7RCYVRCqYhCtXdz/xhLoG5P4Aq2zwYuyhXjdoNsYaiFNEFXV8Q4IouKjRMHKfHerSNu3TriKp+xe9BzZh27dWVRlP/2g0dsQ+Tp2YLfOvqIf/xvPeA//T/9798aFA+zKupofcjp7VscHJ6wyGvWhyfE9ZLl/du89+3vcKes+GjX8f377xNy41w23PnH73Pnw/e4pcf8yU9ewVQ5Dg+IcfbYFq8oD/BSayty3TbSndGuKkECuxeBVwHW78NJ9xIIdEe3OFqtGUd4/MOXLPKa1b1DLyhlwme1RqSxlJ7eAkHx4pg4q5he68EEvP68MqbHAyV1lFtLclF2CAuBW/mLq9C/M8OAS5yN+pvKEp/m1+8zkb5gk++SJa4GT642niX+/wdjjPjzVhvjZseOS05j5iRHOhvd8jVCb5FldSvrD9cn/EW//AworqoMw8AnP//pnCvcMPvmXB9CiCy6nqPjI9aHtzhar1x80kfiOhOXETR6hmzwel40E2cVoAav/bUGJARUjNwqGXcXKcmJl2jGWkYzjCoEi6Q2901NKc3t1GvoKRZ8DTI6iOqqX8/dloq7MsZGi7iIZ68W32eLT64WlxggKlIT5unDs1o8eM3cNSR32PhuUTcA2hlBGiLm9t4WoQVUC4NNJFNW6qSeUTLREowjQ4C0EJazmMekscaY+o6ijdoKtVVUm4PRLSNmLBbB4yM7SFHmHiwejSWRFCo5RnKIJIGibnOeLIIFqgVUI5NAmno0Cy0oVZzkbzn7s7T6elJimMHmSBsDEkHnaMogjRCcPGwSCeFG3SzmfU3BSM0IFqB1bpdvQiqBFM2FCqnO9TdomBCLBLxXQ/TPjGZIc4CbYGgS2qxyS1rdvl6yOzRQqRZoNfu12YLHKbVIDQ1Jv8H6ZI8P/AoBY27Q/coD0W7Ix78yPo8w3IymhU8vzzg8XrLbFp5uL6giLIqRx4osG22IxGkg1glLxvZi5OrlJS/Orxhei1sIEokxEWP2qCs6QoskoAuBdayYRHaTUYuLTC1Gb2HXAbKv40qoNAtOxG+G6kDMS0rsqDt3JpC2o9WJcZz7CqIzjUFJASdtxoTheds2KaqNHJInJcSATj0U78lVRjYziPxNjDyncIe9O9B8eqoprRV0pnnvqQ21QC0FCZ5xHWXGFq8h6JuzeP1nY79SplUnxdRW/V5EyDFSisehpRhIe8twu7FQD7NzpyJz5KMRYsCY+yoSULdKuI5Bk1ldzd5GneSuaNquYziG7RWroxNKynQxEav3aELwec4fQAFESQa1KrUzJ5VOkGNFQkbEnSc8s6wyxUwrSh+WHOWOy6k4B0YAkWunxT5Guq6jlHIdS7jHUpoZWWy2iPdIvWsL9S8Zb42aHq7WPHj4Hql/Q5W1r3ZmccLrY8CL3eVrL92D4tcv/RK19fUbFaan59TzLRYCKXbk945JXzdPHGBULneVzTTRxO00+5y5fZi/uuptXzh+gVB9f6EPu8Z2s8Msen4KbgdYysRCoRchIqweniDRs4t2c0PmKHyF4vGqwGXFZcZ/B8YywZGzU8j4+d0URqsspMfqxoPS85vPm+HXTWW2WDWjjJWL8ytKgxoSB4cn5MUxm42w7g9JF3Dn23eJi+xOOtcKuNduijcwHRpw1YzOJuTVFXbnnjfQxXUzst/GTmEovGLkql5yKEK8NFhBy42yLSyOFtfbLPNX33/DxdF9vvt7gbMa0S4zzf+WbN7F0d83u+P9TWPi/rBLismIUihE2pwZLMGbmaoVbQViT4yQcyZ3RshCbInQCTHPTWoTojSsiqsJoudZThKIfUBsQc4dKWbqqHjLPZLXB3S4xc8idSz6BTkHV3vFiMQFOS6Jq4yGnjhbP8YQkGZIdkZQDJEWhHFmIiVrnj1CRMmITF44JUNaI+lIqxEkeU5PE6SNaPDs4Uqj9hCkEAeIOPu1RkFSZepgMUIXKjtxVlW07M8SNaokiiUU/3uZfJG+Z9+FPQg+A+G2typxTM5dS+emgUWB4Da3bzsOeqFmuOoWjIs195dH3N1sKTJxKR3D+Tkf1EZKkbY4gFI5jZVFXPPqrHDZ97ToDekQAVuSstDEmIaJ0iKlVXLvNvM7MYokghYOusyjgyPWy0N2H3/M+eYcGwsjhRYSKm6FnU6OaFeXLHNHi4GxFPrFKXka6WKidmtetQJ1pDUlSKXkTOoWHB4ewcWO3fkL1kk4Xa1ZDpfcC4ke4dNOuIgdV22kLFdc1kaMgdPSuLu54rxVLlHGNlD6xFYy2EQU49bykOV732J4cEoZG4sXF3Q2oKFxfv6KzTgxaaWXTOsPSUfCZntBDFfcWt3l4NWCOj7F4sCraSR3G05OO0Ix/nJ75c4H0tAwF/CauLJEqN8MyUmAkIWhd8uibfTMJfC8KI8a39umRyzBlJTpIFH7hqwawdStJEWwVllMEGskVief/G20isycxai4JeyuFGptNObCUPwZ4YEOrjQ1wzOHxCAJS8ksWgeSyEExrRStSDOSghLnDJ4KrdCoTOyI0uHpRQWtjdwE4kSJ/kAfW+BqN3G++6LFtv3av37d0cyt0yVCbdB93jq9KeHlQL8+dfZ8H71pNueOep64wjK4fTpAhfKqMEwj8fZdVp9rCL6+XtvPWfu5e+/0AnCM13/7q3lvrb6Yf5baDDqH1ya/HewxxH004po3FbB2Q05M8Jrf5s3Y2ymte+RhJixuiip5fTuffZNv98n8wScLOMwwNVfTDxUpFZkaoTSi9HTLA07Wj7BH8DttoNQdZbri1dkznjx7wtNnj/n5J5/yybOXfPrsJc9env/Kt/mqQ0tELVC0EYMRoxEMOmlMErCoiCaqCambAcvJGfxR98dNUYtOIsOczF/FVR02Eya0YpqIMzsZa7P6FlI1pEYsKtkE00QKk2OkKVIl41Z4hZQLJq7ihULXGiFWxDJJvVasNi+saIRUQQxJgUV0OzyJkJhoFIam1MGbTjEE1ilgXc9iyozq2VjVlHHaMA67GVyN9NJ5fWJKypk+9k7QNGNThVdFISQOLJJwRaBFoUmi0jNFo0ZDUkT6JWkyaAVQlAniBNZQBsZx5NNnr9iNI+vccdB9s6B4lzMhOLNDJM5kTrtpmEqAMIN2gluny1wGz02l2ZjeWdX7RX1QB4kNdxDYI23q1msz1j3j5XJTI8/oQtiD7Po61uCAWhBvjvk7ofmJccKFJnIbmVB3t1DDooJ2NFU6K4Q65wWmCiLk6PE8scw7ZQWzBS24Kj5pI8z7HxnB3GnITBFrIIEonm9YzQjJ0OAekloqSRrBjEQgNmf4htmiLpAIkqnSUU05nXaUSbna1K+0GP+ycftkze3Db3P+y2PGC4+aed4+5ersU3748SfIg8z9B/fpUmAalfV6/XcWFBcRjo+OeXj3HqvVARqUTz/5mJ/+9SfYZJSnyvu3v80H3/k27394l04L4eXPeH+sfMvW2HbB+WpFWRp1YfQnay7CiouXA1cEigWO2x3uLHrCshHyAVx2tD5z3BfSYk3OHc12yHjFYt14uDzh+cPnbLcjsu7ZSGEXRiYpBIxFyqwWmZgC02bi8vyK3XaLoDRrM6npm2m4gTezjdn+zw+aW76yD5DwX7Mz9PU9uv87cKMuf42cEoJCV5zAi5P7tPOInjy6elGb292qzFa44NmvwUmHFjokG9IC1DkSyC1AmARsRtDyrVv8o//WH3N5ueP85TPOnj9lu9lQp+mGADhb+n5zR+4Nx1IbbRqJVNJstSpDoEnEsnidbSC6o9UBKZGk3mSdpsb5ZmAYJiR71NfeIr/sKuNWaYtIy7iLxlvvZKTPD9D+Ga92v+Tiky277hDLiW4JxxeRw3pCt7zFxGNu9xt0eMXJ4oCj01PWh4dcbCJlndidTthR4bcObvPtQ+PJL59hizXD+cCf/Pyf0y++Ojq6B8FzjvQ5cXRyytHpCY/uf8j92x9BWDMJhMPE4e2e04/u8ff/3h/x3faQh2eBW98+4tPFRB4Kv/XbtzgIQhqMHxwecY663WkU+Jzq+srgh8AthQ+3jbOfPmV3ccWd++/Tr5bc+hTyDrfnvjsRuo9YhkR+MfFbYaDLnlLtUStLnBzm11oh8BJYT3DYO2AzNCVfDKTn58jqCB6sXpsnhbiprG/3LMfCZXPC8MFvnvv79ca+AC/8ZrPEN3iR/2s+b25zvXWW+GTKn//LP//Gs8T/pkYrgd32kpdnjzk/e05tYMfHfHj/FsveCBqRApFEbp59fTuteHhwzNnFBZ+fW2p5exLtm4aEQEqJg/Waw4MTbt+7zWK1po8dNIh9gi75jJMSgUTUgNBAjC46yDzpHC1WEsGEmAOBDmzEAi6SCUqIO0pQLCy8BpygJaMTQSy5AMSEWAxJQmnek1Bc3aghushA1KMbW0CDulpczZXpqoR4oxa3Zh6nEAU0upujzbCUBjQ2Yp9hmuAdiQaRgDQlV3fOqq0QR2MqFas7Ym6U2CNksEhujYyxGz22JC4EiUqIybO/E2hqWJuwuld+NvqWsIUiIZJzYokQstBlCJ0xFmPXlBpgTC4aai3QiDPNObpaugkeLm6U6FW6kKghQfA5IJnNbkkJK8nPczZirmiCZkLCRU6BCUxosQPcMl7mVUXV5kkMwS3lVYI/mjX4miMK4oHkRFMnS6uBNGonYEoQhQaCk6o1OLGxiefNmzTGILTmblNoAMveUlB3hxHtXKEqQtWEhd8UKC4uPpFAs/or15rt10/7Jsoecf214wteYPBqc8kvL88ZRqMwkYjUzcgiBWJKaJk8r146ht3AixeXnJ9fUOrN8QgSyKln2fXEFAipowuZqO44FFKgkakl0HYju6lSbAMRprSiq4ZuAzUEQlbG6lE1Xe7oCIgmJovUNmK7HW03MQ0D41QJObnbnRq1KK157IQ2jzQVxSOEtM0RlwltGcXoKQSJ9KGjDyPbb4iYmSRdK8HVvIfwOgj+pqGYG9eI269HPOZO7Vf3yUHx2VJ9xhG0KUGVao1AJKZArJ67nUIgRV/Pqu3X/r4/QdwJQ83V4l1MrhZXr+8k+Pb36/Mwg+hgs4V6nK9XRa2hGpi2V5RxIK4XtC6TSyQ09QiNUCAsCM1jnJIZtSqalSbRH78KKTSPmFC3049WsNhTKixYcJyXPA076t5CXaC2ylQLfedq8WEYUL1xcNmrxTWai/CCZ6p/1ajCt+6yH68PuPfwgds8vKmiqddH9zNjhyti95i3mxrdAIYKN4zEL6mUrCjj03PqxQ4LkZQy6b0jQneTD/1Vh24rZ0OhzCdHRFgvO07W8aupxPeAuPFr88RV4eLV1gsiVUIGzKim1DJ6lriEGRQ/BhEudMbaDVZfRTX8cjezLv4OVOwBOFx67tKeJDEAVxM7LRyFFWOdyA8PHXx+w2jcgOI93jw/Px+5vLqgVIMgpL6DINRRWKw6UoocPFg6q/HzY+fNL1fAffafKjBl4e6dSNsOvPj5xxw8uMtitbg+tdPVRD8o4/k5n9glTz/9hI+OTsnfeQCdYU1I+0Wn+PW9PxTg93bsFjzI73HPAjZbbTjPEr9QNgYVNiew8rLpjeP1e/yd1jtBZlYRiAkDgSkLZrOiGUXEbY36KCxSZtH15Bho0iBFYk6krMQG1oRqHTUWV/aEggYlq4OPsQ902ZvCMjaMhkRjadkf6BFyiuTY0cWevveiPIYjiAnmvBlrHULFJn+a7FlPZuZ5eOJNfbHqOTkEjIiSKHFCtEEwpGsQMlIXznwrAaVQQ8RyRS3RotvxpLoH0wwlOlMyQWwTkUawBUKkBfXmizqhopkXuTHOmUjwWSU4DgarMucRymw/6kC4ueDet6Wubn3bkXpj03WcpY7l8R1Om3E0jbzsA5dDZXnlKnFZrGhEunLJncNMm+C8wO4ogvhiKEoG6YjJ2FZlGpXSEkqhSz3bacM2BEKMUAfeW9/i3tFt4lh49uIJNkwUGpMpJj2VgnQLNBgLNcKiYzRlksytGMnDjrg64Eoym3JBaA2tlZoUugP65YLDvGb77KeUq3M+vHOL3hqntXGr79lEeBUyr4JRVdh1md00cTclji8uWU4TP8EYykihMizWjA2EyjJETk/uIx9+wHaZCZ9csNhdkZjYtMKrl6/YaAET1otApwMqxrYZDCNpvGAqlXScWR4ltwQ7H9Er5ZNt4MUu0EyJ5nZQnQTP76XQ3kGFElO8fj7knAn9kk3ODuNGQSU6g1GM2BRJc5b5nA2pyRe7ZCFGJdrINC2wkulLdHv3Tmmjcbl7RW3l1+3O39hwtaKhrbK52nDZXxDWkb7vKCkSQ8F0R2kTNgniwWRIdALAkky2eREfEts6MKkD7Z0aZoUqbotWFlAGAXHajCFUGmihEyPkTNRIK0IbKqVOtJl1a5/d6W98TAZ1B3ENpcHR6gbrboC92hHPJ+K9zpsES/msdHsCtgq3gjcQDGwwpouR2gvdycKbF5+bdJTPlnzGjXX6hBecB3y2EbblZv23tFm4s68B9/VOA5utVPbl7xvvBsW7qVuwAO1ECN0Nuexm+L6nRfyMm9HNXn++MuXmPG2BQ3GmXU6wnplsap69MjZkbDBUKA0pjaArsi2wxSmH773Po0duEbfbvmKzecXlxUuevXj6pm/0lYap0nDbsGbJVdAoLUZyLH5QLaCaoPo9HKIrp4IJUpQRiLkR8AgMQkCkoSRidbZ3aA3EmcmhBY+egZlxD6QKwS2qnQwWCBm0uJuEiFERao4kEnnqoHVIqLTgz5vGaw2SILO1tkBuSKrXYmSRgjQlts4z58SbOpIg5YanJnSMVtmOE5txoDUHPdpM+JGFEGLPMnccdZnF/Ky9RLgohV1t9KljKYkYPU92Uo9P0DxAAml5tiUXtDOkBC9l84qmPU0nhnLJy7MrLq92gHBRJy7Hb7ap2XXdNSjuyv+5uYI3CQg48DtbI5leV6SgXmu4vZjMP1UsmNdHeLwLdsNqZ16nXtunvwaKmzlQbHOdY+Zlji/65sam8NrSe/7EufbD3D5X1JyM2rwZJ1GpMVAF1AS1SKeRvnNQvE1gqUOlkK04O17xfGMzEuKqchRthcVMQCvizVKd5TWmMpPDwtxYUCRn1DIa27WNY7SFN9JmO/9MJbVMyGs4fuiNS53Ybd5deXT/6A7rdsikh5xtGk/1jHG8gF6Jd5aETjl/8py23TJV5fDwGPjlO3/u38RIKfH7f/hv8fu/8zueyd0ij59e8Sc//Jd0dw6p6yVtnbBe2U0Dyz4SwzEnR0s+XL3P9mVjejXSh6fwsGALoXseiB8fcSDvEftTzrcD59MFayZ0ccmiK9zpV/SrQr86cLerczi4zOQewncf8vR7E6/OtuwEZFnZlR2H7x2ye3zBSeq5d7CiVOOTZU9LSmFk2k3Uqb5B8fOOx0jhxppkppzNBBOd4XAlULmZ3CTM97tj3T6vGte2juD3bFIHLdQiItWb2CESk2DVQQtQQoSlRdq4wGKA2YqxEQhzwy6gaDKCRLayxixTmVzZs1J6zcR4wOHxAe9/+BHT+Y5PH/8ILRUthVoKxZSxmIPl2q7XQvuMxbc6fjHSpUybwYOzZy+4unVOXh4RNHj0h0Sk+rxto6INrARqi3PEg3F5ueX84hWtekxOFG/2eLZnoErBZsRQvoo/4xcNqz7fTs/4V3/65/xf/y//jL//D/5dVn/wIcupsnpwwO2dsbsc2T47gO1Ajj237hxz+v6SoTvjW5rp8gnxVkTjinWeeFGUdPxdLp895urTX/LjP/8hzzcvvCae3uxi4RmVwmLRc7Bes+pX3Lt9wm99+AEPf+d3OPzetzgbeg5eHHG3POTO8SHhkXL0KPPBd0957/QOxyz8MurgVBas1NsuBSWulAWR53vQer6G93vSDF6BR7YVqC2wPFzDnUS9s0BeQHduxKcgZQ3/6BLuBeTcyFdGfpQc4LmqFEnsTFgeRIIYW+BHCBX4rQVI7TAznp6NLB9fce/xYzg1uLWEhSCdEBaRtiustsayD+SxMa4Si3dpuvwmhnGTJf626DN8tif7ZdtoeOl89MWvNd49S/zx5Ya//uf/8m8sS/ybHj/64Q959vQXqFUkRKJkLuOCzalyGBwcQed4TIvo7PD13q3b/NWzTynlb2ZNHQRyyqzWS5aLA44Ojzk+PWJ9dIT0mVAgG6QuUKSn4qTiFFwQEVWowR2kEJAmJPUa3tSIFgm5UqeIiRLFSGZoVL/RU/VoxXlGKwRKjE6mLBCaEVNDo3jElEZ/VpjArBZ366sIqr4mCZ6/S1UkGhJdrGNqN2rxMWPdgNXo+xoCZgHrFRkyNr1LFAZI8ZqwNQVTUoVCpTWl1USrPV2K7jLkFSnREmIjRQdKXdCJ+6flEFh3sG2FWubjXR0E79eZ5apnlTpyTt5TjEKNwWNBkxFz8z6lLBibkINglmjZqETPJ08Jy94/TTOZVYrRpzmP18zr4z4gLSDWSOIOKUSvSMSEQKZFXyNqE1xMAsGEqjCZECW7Gjx6RJU0J94KFY06q9YDKagvB6oT7Vqo7nAWIoRCDJ4JXqO7B0iMXrtPCZXo12rd2yYbRYyomTwLG2x2hRJRgkbaNyQ8+fJxQ1J0Ve6bXjI/Fb+J/OvWuLzasOxXc6+iUpgoLKijMrWCTrAbdpw9f872ckt7rZ4NhLmeSvRdR+zcibGPHTl0dHGJpshOxWNmrDJxSbMJVLGSqLqglIZUJ9RLraQckJVQVpCigiqhVupUKYMyDBODNjoRYhBQw7TSWvO1WXUifRcydG6z3qr3NqBz9zip9BbI0rGMHUPb8U1U6rs6XavBv+ow5rXwvDZX2ztj3Pz7Z17LDIwHv7e0VkJKFK3k4JbxKQhTc2JHTJHYAlVneH5Wert7roMHTZ1csG/e7UmPLXgPa74jrtflNtuPM1uoa3Pko5aRYXNJvzqk5EyOmaSVqhkJ4+wY4kT3LA1pDrBXIimBVoizWtyzvwPBGhYqU4j0qizDioN8wThO6L7HIDCVQq2VLidyTh5ZNh83VdyZAnO0R2QWOttXSsV46yfAyfqQew8fvLmaMbxzuffDfG3s8Nps39Bs3CiA99J32VuQf8loFwPTi0vatkDMpL4nv3dEfFPQ5a8bZui2cTZUJm1UiUQRbh32dG+w3f7iHeILFe77NqsqXJ5vvfljnr8CxjgVpDU6c8JsipHVwxMwuDSvHZbGGzM8P/9deDE4K+oLQObf6Ei4dfphuLnaimGbyUHYBnUaWN6//4VK8cJNnvjejWlztmNzdUVRo+8SYQbFMaEPsDroCTn8KjlCBBbxprP+udEB70dIMbMdVvzs2ROOdju+94Pf5sLcarmdXXF6eUU7XtK1jr5VymZLjRNJnEXlql/FzcTnj2Z/afiFEiT9iguBCZ6Btap0k+dGhM89dl/XYRp+TwWcLPC2a54dSmeB3nzhWMQYA5j2THXjDyWtLPvM6mDNQb9iET0zIxRB+0gOgWjeKJDkBf4+67GfbcUnNc+xkUAXvdEa0pxZKUofEmqeU7ToV/SpJ8YFadGwRSTU7CCxBkJNcxEWvQFjkaBuA6/JbZssGiGoN+TNbY+1JC/WqdTkBcOQlNgaDI3aAqk2YsnEDCUZpQkRnxjKojr7sEW0RpJEb1OZYZaIbX4oz8w1A5oloprnw3eVNE0OgM8PalNudB9hVu4Gb1QrRjOjqVFrpdVKLYX2DkzhmoSrrmdaHnB3ccjdYURl4jxmxstL7pVCSgnt11itHFI46NZcvGxcpEzNfr48a3RJ7AWNMG4GSstMpdAtMtoKg0CJGdGJdU48OjjkcH3E9vETzq/OsakwWnGGp7jGLq5X6DSwTB2aIlOthLRm1SY6hNavOa8N6o6qRqSwi4kYeo5OTuB8y/b8BesUOFmtWI5b7sVIJ8InSbhImY0Wat9zZT5x3qrG6eaKUSsvMaayo3SBberQsZDFOOlXrB98yPTebcqk9M/O6W3AovHq+Ut2RZl0oo+JVWzE2ji/nGjjRDcK28tLLnUkxkSfj1nnJU0CT8sZP3lxzq4JIomKuYW5+bWbJBDl7Uu5Ow8fzpiFkGKirY646DIhdNTcmHqQOPn8q5E2d6hyAEueAyZlcvJLg1iFxUbRASxGNAUkTJgWnp29YPobWsD/uiEiHK6XrLoF1mDcjlxevmIRerpuQWdKrspYlamNtGREMXJT6CIhRDrpWUpAyLTm1x1NPMOrjuxaYi0Hvv5uEY2uThXB7aLTyE4LWf0pHRWorsA3a+hXJLLYOy6ESgEdIByADdBnV3JWm4kDL7b0eUVMcbZODzdNL4XraMyVq6hNjXbVGK9G9LRjuc7evPn8fvNZPuPr1ukTcIKD4nu7RMV7dml+b2+4SvyGsuoL8gASfebci8Df+OkFeKVwZgwFHmtidQ9O4s1nXs/Dwpw5dsNpjNd/G/Ck8zXe3TMkCjzACYf6+Q3h83aMLss/nIHyqlDUFeWDK8llqjA2omS6fsnR8iEP7hu/9Z23f54r0dWuGgiaCTKhQBn93rSUHPxQn08gePxBrv7crT7/tJq8ucXoSvCYkOqW7Ne96zlWRPOIaCIQiFI9LmW2/MOMGt2knRAgu9JamtJsJpM1oQBROj8RWt0uzHSeCxPSYIyVFNOc2RVQR+dRjVQxYhASgdY54zklZms3pUkllYxYYKqFbIFR3XKyt0RngRQSqy6TU2QKvsubWrmYJkyNLiqd7zHVAoxKnUBKJPZCyBUsIq1HqqAaURNsVtWYTMiVkeM5q8WCqRRXi7Rvlg3T5xkU13nVLfjNEwBz4lJgJjTgOV57+NvjxNxkbd+ukdl5Q4KCeVa8Mx9mCNt8baTm9ul7MPu6ySO+Hb/R5/zxmeFiRJC92vX14ReZg3kONZl5wwpcKeQ55omGYikwBKFaIlSQKkhz8qHS3OUgGBq3mGVs6gnVICtqRguVZg6aZxTUm4ciSrLq5MTgJM8gionQTBiZY3GAGIVkEQkDSRpKIC4SGtfUeIexTnw6PEbbu7ViXpD5f7/8GVebkbODM+x45Og4c/90waclcn51wfjyJWs6Hj66y7e/+xF//ud/9k6f+c0PP7ernLl1cESXVsQCISW+9dF9NlwhfeDw5BHp3kPszpqn44aruGD73m3a0UO+f+8POf10ov70z3mqv+TFsmdcrpEFHJ8ec/vqLpPA8/KS3eUnbJIRyyXb1SVjt0Os0OlAPodwBvfCPY4OlyweZtbHAy+GJU9fjcg4shyN43SbmO5wJAuO14nzOCHHh3Qna27dOeT8xXMuzzacn2/YbofruVtkjiZ4yzF2c3PK+JVfEQPbuyzMd+zscGeyX0vsVeZ+T1lwQstyVkWp5Ou5NJliVYkqJBE0FgcJIvRVqLqjELGYZjDCSNbQZqBC6Xsww7qE9pkaFqTqDa4k4qCzdQiJ7lbkQfeR5yvVAW0jkwbKWNldDpTRlSFNjWoVq267XpvHaejc0nTysH4G1H39ifrw7l1+8OhDhlL5+OULfvHkYx4/+YTV0W2WB0uqBhpGCJOTspsrcEwF9aAQNMDioGOlKzaX3kxt1RXkMSohRdQKUaoDG/XtUdKml0TrWI73KOeJf/Pjn/CP/qN/wp37S57/8gXh/CVhfUy801OmntAyfXcAJx3hNHB8Wzg5WcIiktoRrzaVT3/yhFur73L3/il6PHC43PKIE/6DX/4DPnn/ff7iL3/Ez58+4ejgEATu3L/PB9/6Ns/OL+gFjt9b0aVI0jXfufse/+i7v8P7v/8dVj/4Hp+mA/SXid/7ec/tVUd6IMTvRJaLG1HKfi45oHAQKsqCKpEzAs+ovEe4afXNwAXmiTlPTHg2g7nnwN3jQ5ZtQROjnQhn78NyA/2Bko5XCAEZ1Ukd7z2g1C1yKJQs/PAMjns47IQD4COBX4oD70cGuQqrFLgMgdN+Qb64QDZ3YOHN9tQniowsN8pqHelHZVwZq7fuuPyGxoTX8nd5++bQvnn5VTzODc8SP+ALxUH7l71tlrgZDBh/9ec/4vyTx2+xhb+d8dc/+TcASBBiiCyTEHNmWwN16uiDzs4c0ER98RiVI1tz784drnZbogTOXr165/Wh74j3mZeLBYdHh5wcHbPKR6xWS5brQ2LuaP2MiFefy7IUXxvj0QMxqJNtA8xev0QiMbUZyCzQGlUyLRiR5s9IdQAaEaJWmiUsGROBVgJaAyEpSSLNlFwrMducx26oBcRcAR5CI6jRymytHpWYcCK1+pojRAeizRzglQAkgRZcLa4Cya3ANRpxMavF34GxLueK9C7QEecko7WhrWIEWg0MLRCjk0dTdIV3U6FaJVJI4kC3JicJyCCUUallVk/3Pau85CAsiF03xxMGP0bJqJKxrPQakRSompAcaKF6RFbsKCGgLUKESKNv1dfXamhLbknuCiNfc6nHGgWUaJVIgGaYqNMaxNd9xQSLRhK31McCKkKM8/kPzXuIFommaADNc8RA9WglNUGmDo2Kzu5n4NElZgkNhqhRgEki0hIRo0SQaXb8rB0Snewco1ClkqxDRDEqhM7jF8X8OPyGhjV3LvmiK0xmxrG77nzFIXIzj37mw2CRPBZRFM+4tkZtiobC1eXI9OqKq80lU/GIP3ltkzEJXUyk5FE5nSRi6kn9kpw6ggZSFIzCpMKoW0YdMSpd7kgh+JqsVXbTxDQVrM/0cYV0QuwNs4hJxmLD1Bhbo6m7Utam1OrXm4j3a8WELIE+JpIEJukZRSlioI0uNpBEDZHcnLS5iB05jIzfAIG1fZ3z8tpQvFXkxE/1Y7O3K+eGX34Dis/tMBG0GdYarUV/FuJZ4lNzAniKgSiBIOpOG+wt1AMiLm5UVZoqKbl6XEyuLcb982dyiwSaudLXtKEyO9G1ee1cK7vNBYendyg5ozETdCS0hKZxtlD3/qjRyAZTVVqnN84QpnM0QgIt83OhUNISHSs5LDnOPedTYdpf0yKUVii1ssyJrusYh+kzJA5TZpt4QyTMYkn7SvPmW4HiMQTeu/+Qo9PjLz7rBfhc3Phe8XvADVa+t5bumRkU+yvhK9in17Mt49klWpTYZ9LpIfnW4usvRBuMm8qrcXKleMrkEBwU/zqZQV9i++4LKuPi1Wa2N2gu5jYYy0RUVwamZixPD8lHCxoOihtfERRv5nni5nbVf+slexI46f0E76+2Ccow0eeETUZrI/Hoi3PgC37dCG69CnB5tmMYJooqy74j9B3NjC5kVklYrjskfcEGRb6QdBFwRcvPNiNXFwFbHmKbT6A8pNBzNU3cvX9I7IW0XvJ+WvCtB38EFhiTZ0TMZnPXTK89eHCzN1888V63HJdCWCh++bmVjf9L+pXX98AZ70YE1lZIllDzVWgdYeg8h2c37dhsN4QI66NjFocHpLBAZHIEoyVyjHirxpDYCCqk6CwhyUKQiog/wJ0IK4QQyCHSVkYXjGoZ65RmPTF1pC65a0JIaArk5BmuEgLFlKgF0UDM1S2SzRulbW4Ia24OlqfkmbS4RR4hEIfs/frVgAa3zWkhABWZAlqFTiMyOpCUg9Eis3JKaUlI28CyCuux0E2GVmfX5woVZ2ASZJ5kvBWswSef0tQb5/O+WnDW6yzGQsFz+KpSa6WWCa2VVovnFr7jomjoMue5pzs85YTA6bjjZQ9Xk5Eurpzk3S8ooSMPV9w/SNQJzgdle7QEqShKDhmTnpSMXVWm0SiVucgPTGVgF1xhIjpyf3XEvcNb5AaPnz2m7nZUrUzWsLCiyoRIB8tMd7Yhrg+ZzBgscqvviFeXxMWaS+m4qpeIKpRKCQ3pjujXS9ZxyfDyU9r2ktvHhywxTmrhdtczCJylxEUQxqrsFgu2w8hplzk8v+J0HPgrU6Y6MoXGsFwyVSNQWUrg1tFd5NvfYrvqkadXLIYNSSY2deT81QXbNmIGqywsrFCnyvZyRCYlTRmrAy0ZZWpsX+04aztKl3lZJp6PhZh6ojaCNlRuFDnRGsnenhH/8Pt/D5C5USuwyFzGROwatpqg8+eLSqVNS0IJpN0MQBzIbEdlxNKQAkyJbhSmqmjMVAIhBFoHp3ePuT/e4cmz59/M4v0rjr7v+P5vfYvT1Yppqvzox59QmzKasgqBpAEmiKbOUIyVHAKLMOfqaIIUWORMsiVTM2JcYdI4v6hulZpgvTyEHNhOjWloaEy0mJ0MpNCmiSl15OCNhNYC08ySvinc3nxcvqmjNU0QkmNYfXbce2+drlNDPj6nWxx5rdSHG6vDPaFxn9fSzYBvhXpRGduI3TpmFT8719y05m9+voeWBxwUN7wkXHEjzi54zy7jvZdeuSGshfmFI9j8hv0s+EbjacMl8hPYZFxp4DL5dvZW6/L51887vAUe46D9IUKg4wrFeMYhRuQAZJ5702vv329DuWbe3iDv4qySzA3r0+xaTe6/ZqC8tRlQfrvRTBBrSFt6jp02BwcFNC0wE6QUoo7X7PypZMSEJMGbCxaJOKMfS2jg2n2+Boh1AlxN3ARMHdgmBcSMrBUxxeZ5TZNSUYIYUQOpTmCRTrPbWe9vhTgzoWHOwQ64fbuD/Dk7xGMhUalOwtOE6MJdNKTSYiNFpe8FMWdKI4pGJ8B1dKyXx1A7ajVavaRYY6wTx1HIq46WXH1sCrtdoU1KjsEtuaOrMhuVXXVlYGeJYJGgFSZX6YtESoBG8vqCgFRlNRqn61PK+pihFbZlYvgG7S9DCMSUZ46Gz7cmNjer8NiY6MeEvR3zHhTfezILBJ1Jjfs8sWAzqO4kR0GwPTlLBaIvQGUGykCu7wlzZPv6s24s1RtKviFYzIt3s32GIkQTt4mbO/JiGUK9BvywANK7OUMJcwZdI9cI0ajLhImSJdCZuXsR0JISw+jfJbltclQQa9QAVQMhOMkhEDwmxEBj8LzgOttzSkRTosmEtkRrUGKgb4WcjCBKF+BoccQ9LVy8OGMc340o9tPVHcoPPuJgeMmtTwphOiIdBOpxz+HLxxzUAb295PbihHC44Nbx6jPNlt/UcILYTAqLnnMfQkCAFDJdjBz2Cxa75sQQ8efOwSLw8OFDdmqsTt6nnDzg1a3AoYxc5FPOP/o2L777be5L5ndvXXJHlny33WVzvOPjb61J3zrGPgg8+bNP2b18CodPOTlQVG5xNhxwlSZ0/QKWgV0qsDPClMnHytGyslgXLvPI7qjx/ronPYXL7Uhuxyy6QNfD8m7jMEWODm5z+/AeZ/cesXlxxni+4/mrF/zk45+wHTfEFMk5k9LbK4/a3slBXPk9d4SvaSOvZ4x/FjTfN48/pzKHOVKhp0RvqqkERgKdGlkLUBmiQPSogRYTtUuEMNGFBhVaS7ObQiLYiOWAJSUjlJmQI56ZQBhxe81209bJSQmHC19Y1R6pRtFEO5qoh0opg8clJAiqMCltakzT5Lqc2DCrjGOhDQO1GcWag+it0Epj2XX81qP/X3vv2SRJdp1pPucKdw+Rkaqqq1qioQgMOTNcztjY2s4X/oD9AfuTZ5ZDEDTOUoAgJFtUl0oZEe5+xdkPxyOzutECXQ2CJMwfs+5SmRGRES7uPee87/sWJydHKMrR5g2yZkK3IJWeReqIrlpEThG7LoYdvgjJKQRTxZXoWMQlVRy52LBWrYVSkq3lxKJ4TGnjyd+gKf7XP/ob/usf/Xfc7k2++8M/4T/85+/zzre+w/tvfY/LT3Y8G5+wcyOPv/0e3/ven7P/+AVt/wFnZ47jc+Wdd39At3jEz28SP/r7X7P/5COaF9f4t87pNseUOvLigyf8+H/8LSELb5095MH/+Tbtw4c8fvddlqs1oaksN0fsUuW6ZrpzT7r4FbtnwsPld3nzj7/Df/yPC47OH/BDt2LPxxz3mfg4wMP2U+57yl0kNxsdcQy2twlrVpi98Pow4n9Yd/Z2nLcL4V2UMK3NPgI2dWBxcYFvT9BVR3gLUtnzdPOSjfes6ZBzh1ZhPBZS3FCmNdm33wAcXKutQY+nx70GdgGOExx3gadNZFwsic9ewFUPZ9HcYKLDtR76zLo0LEpmV+D3Jix8HSqWJb7g958lvvjiLzl81MrrZYkr8PHlDf/07yRL/FMISBSONsd8963vszo+IdXIloTQ2/CxqxCUiEfVsY6BP/rBd6ENyL7yP//yL9ntdl/9XF/2Mryj6zpWyyOzSz8+ZtMuiGFBu+yQtjEn7eCR7MBZ0xNfCZqg2qApk0V5cGbJ7UWw6eVEqDY8KKIUGWywttj6sjgxa90APZGSPPgBpx6fbf2ZAO+VXCAURwiFHIQ6ViwzB2usO8uvrsVDOajFzQ2rqsPnirTmfqdJbF/hHHhFxojGAc0eV6EegklaQWJEv8EafUAhKcEJVTIUR6liDig1Q61UVymuxbuCSiaqEJ3Sl8reZYKMdNPeqJbRItvygHeeLi5YdWuaxRHOm/q5aYr1RYvirNhJ0UDxNoyhMVCnOExXzCnRBlltD6relPRjdsRJ9JLIOKeU0jBWG64rjRCrTS/5jLk4iaN6yAIqFltpXtGV6pQsSs0F54LtE3NGULzL+DYgOAZVtAY0OKjZhmJdQXxGKySEycQFTS2uVHIoaHBoCTBESkyMwfaxsZqvDjUgruCrDVZkKlE9QrLGIDYQ7L+BG+PXp05l3M9eAa2RJ24aLv5t+7ci07SH3tWHRYS2XXD64DGnZytuLi7IVQgEpELyid1Fz+XLa4btFsXO5RActVqN2nmhCQEvAREsXLV6gnQmBMMhyw4JEYYB6EEGfBR8qLSriHOK6602MNbCjmnQvSs0NVJ3Fj9dvVIQi8kSi69yOIsXzplcM0Egtg1RIq0PRBFcEXJV679UoagVkkSC1Q1Ksb2gNCx8ZKzD76zW9nVRpq2wMOV1WyP7iyzUecVC3ea2zYEiUwgEc951QlHrzYbg8cUs1A9NcVU111xxqBZKqYRgRTxR7t63Q+PYIRwCFA5NcVwA8ThXpses5L5n2G3xxx2pDbQpEEqhhkCVEfVLfIWCXU/GrGahjrnpaVW8q2TnEUkUcTaM7iuDcyw1chSXdGHPmLI108Qa3kNKtG1D07T4sKeM9ydKVe6crGTKFpcpa/2reK0lXfCed956m9g2n3c+Tx0dfqNyeSjKvponnqcvbw+bu8r97ulLUFXK5Z7hYmsTzsuIf3BEswlf2zqdVLndF/YpUTBrsiYEzo4amt/2GnlYPH5JTKAC+31hv+3RalPbbqoVDeOIL5VGPL5UFg+P8U1gD/ST4+H6txF/Dxmuk01ofIPM4d8ZjYfjCFHu5WC9MvR7miai1wVpBDnuPvdzU2yd3WOHxDFQUmV/u2U/JMZa2bQBCYFclCiRRRCa7kuOg694WxT4xZMdf/E3P+e4v+G/fm9DSoWzznPSRJoYkcdnqCoL2ul2Crc5006q01dnfX5zNOFLXoAetBQeZJjU5gHlEtvGnXOvlzfc9L58o0/bW6p3FkdJt4z7HYvFklqUfr9Hx0LTdsRujW8WRBcJAioFakOMEYI1lryMFGc/hw9mN+cc4JSuCLeTfbnXQIgNsY2IZAqRlAtOHeIaWm+pN95Pi6To0CGjIoRib5YVSAsu2K8iVslXNaWTkwq9XXzV2TSos8FGu/gMEaLllTpRiAmPEEqkThlPTYGxqXiF0pj9h7qKa0aa0pol8d4m6CgH94eIqKciOJ+tqxDKlIMidtPAmuE6zQLWquRcyMma3zmNlJzNTvB3bM24b1p2TceD1SkP+hEYufGR7cUtj1MieEft1mgpbCSxajp2l4XrEO5U4s4DdPhgTZVxO5KKI+VKiCbx3FMZXINQ6ILnndWazeqE9OKCl7eXMIz0JLJ4VJRaK+FsQ9ntWIRI9Z6xZHw8oiuJiFDbFdelUnNv77cmhuDwsWO9OcJvE/3lC1oqZ6s17bjnoXiWCB82cOMbrutI7Tpua6U64Twr59tbdiVzhTKknhTFVOJjwUvluF2zfvQO45sPzPHgxQ1N3aNSuL65Ybsf6OtIEM8qWgP5ZjeSh5EwOnSsZu3vBfWRqoUhCS9vnvDRhz9nu9/SHq9YHC3pYsDXgpSMYpN1+Rss49oHD+8uECIK7S24PVlBOkdxFnvANMhBcbjR4WpGgmU8+cFZVqtWvNqEuKPi+oHSOFpMnvzW2YKlf5vLy6spZ/NfhrsBooll17JYLGlCQyMtm+NTtsMtbYRAtslqb0rCqEJfBeeVprNspFI9WQN0tkDsFAItLkMdE9f7LVfXtywXtzjveH5xxdjvOYlHNOsVY/Hsk7DrC9GZSio7YUQYSrFM3G+YU/bbkm9BGmuOH01yDFNZK9on4m2hOV5MeeJT4/Zwv8wKfTWV+LRC1F5JlyMpKOFsQevuv/zA1BL71J/7V/6LmOa64T4qpOdedN0yWae/Er2jNqeEru9/Bvic2cPDgdBbIaJkuF0IrrNI9E99/aFHYEYuKHZnvQB2CicIZwSGuiE4s+UWxD67MkWvOHfXSLy7v+urjy/3b8jhiQ+ets6Z7fp62rzmeq8of02EjJRoDjUkSp2ssFvbyJCKvfYKOEdONqkrNaBe0Skj02pFBVc9rpp9dhWPRGsOixaLVJFiP4oXXJ2Uws7bZk3MbakODRKLWfGq2SsGdcRkaoAUIuIma2pXcZqtkCNC9Q5PtfunmEq9uoJUW6NoUKRMjSMRxFkKlsvVmjFq99ZYHdlHRDNtV1FZ2L1WC7vtjt1+zzBW9kUorUMkM+SBXUn2cTrQaAUdX5VtVXYVolrxzAVHTFYI9JopsVCqQ/xAi8eNFfaZsB8RhI7AykVOugVl8bvblkfvcT5MrkLuPtaqWhMXtU2hqbPtiNFqE+KH4c3D4Vqn/yPVCqEyFR/r4Vi3SXKpdmgdssgOrbqpbT014KazW8UKYeYbZ413mYygdTpd6vQ6xApaTs2c7n5AXO/yle8Hb6bzrJj1es0OfLHBRt9Qo5KzuSfgQYJt+A8KW1uhCaKWPxfUTdnnDapCDiOmZpkyKXWK48kgIZs6ZipYFBqq2ACZRKGpiSqwWSw4PzllN3wzC/X8ziP82QkP6FjLc5xzbGviwxfPGYdrls6cAK6f3fLxT37Nj//qx/+iDXEr6tgn4SbrvkXX0i06ovcErTRNpFl0eO/R6mibDV0MLIi07ohTOWJ9EritlcsyEpanrGODnL2LXx0RjyJvP95Qzt+knj7Ar1uOty9pXCJ2nkfLU87OAjenZ2z9KRcrpd/9gvOja/7TsrLhiI+3Jww9hCZw2lwjoeXi5QnioPE9++1z9n7NsXuL92TNsr7ktjoajqmLUxKJQT/BrW5ol5nOK5v1imZ5xmp4QI4PWJ56buvA5mTDxe0ntDGy6jqWzetoIQ1bK9yfn4fOlNVRX+mGM63pHHfn9l3j/FPNcvv3KFZYLj6jNFNx3ixma7Hvj5P9oC8F1CxNtQqS7BrvplM7YE0c7xNVTd0nxeOSKbHECdFCx4gkXBiRRqm1QUZAGqRxNChoIHdCLkuUSpEyxS7YoFIqlu3ufTHnnn1Fh54ipijPVUilkHd7FghH69XUO/EE3/DGo0eURvBNoLhAnO4RQRy+eqoGfFF806DRQUmIWkOmVcdivyeVTOoLqRRUC+ocA4oSSNXxTSLF/+JH/x//5b/9OXIi/PDhn/B/XfwZbuVoxh1HJXNRKqn2LBrlv333If67x1xvM6tmQ3RbFotHXA4L/uf/+hX/+Lf/zOqBIjXw7Of/wNnuOdJ/hFzcMOymKJ+qxOOWN997mzY0PH3+CScnLSEuePz4bY5PNnzrTc+D/Rtcpcdc6Nu8d7zm5OQFziWieFZnb8CfgkS7vhYULaaFQIQ9NvRYklpuZHnGfnXFUh7SEuwgUm8bcsEGbgNwUzlHWB4VLqXwIfDk5kO+9ckN/v0HplptITzz7GPLla90nRJSsXXq8YKucdwIvACOvK09r7Gl7UphJVNTXCzdL4qwWARubhtWwcGLG3j3aJrYhLCIjNuB1dCwDPAyV5L/zHTovxUUe+MPWeLfxDTy66jE93xplvhh6d1jzoav87JGrfzkb//x302W+KuIE1arJd96/1ssN+c2VJOUG2dKOi9T7mlxjHhTB0ZHs1hQgqMJgTcfvc3PfvHTr//cYk0TFyLNcsFqs+Ho6Ih123DUtfh2jYstY+dx3uMRSlZisjpajVNwzmiDphoLgtXj7N7jQApjqIRdMPecomgw4YILCs72C0WUJJGxRPqpFuezmM23F1wSW98HNXVvEmKBEJRxUgerE3MTcv5OLV6LR0qmRmuoM0U5StXPUYtPe7FqA6R3avHiTG3eRsivP8iY62gN+DDdm6f1ro4gBYJURHpEMl69DTYHIRKsltT3DPs9bdvQNY4sBfGB4805selYdSua0NCGaS2r1nzyKog4Co6igsey1r1YhvWgDl+WVBW8FIorFAeueLwoXiohDPbe+UmYVd2UvX2ILvJma69+crOc1uu52L3TO0QdJVbGoDjLg5xisRRSsX2JtwZozYIUU6Bn5yhe0DAN91UHOZLVhpbL1mKsqgNctHpxlckpFtzo8KGSveKLIC4jxSMuoDoScCTJeG0sa/kVtbgc1lW/Vz7vOfXzfy+f8893X/bq1wnRB47Wx5w9fIicLyEkutRRS8IFhzjH1bMrrl9cM0xRAW0T6Fp7X4YxkauJ10II+KmKos6c0EpWShCcFls/hkiVzCI4tKyptSKrJc4HSm91TCRTSo/Ugq+RZTnEqgkqe2RoGKuzeABNFndQmRy8MqqCCw0uRGvSuzD1ICx73jlnedU4Ui1EZxnW2TtiqXgJdD6yLYn0O66pfx0UO1zrXePWIVN163CPfPVrRXWy+oeaK75WUsm0wRz0gnP02YYOQvD4JOQqHCzUK5ZfLiJTyp8pxt3dftrZYFHVuyH1g4K8ThbsqhWdLNRLtr15ySP97pZuvSGHQOMjriSkBsQni4ienH1Fqg2llEJ2gRCgjI7oMs51qHM2aFoBnxmcp8uThXq45jblewt1IOVEzoXYRGKMpJTv1fZqdYnqKkEE79z0fnw1r9UUjz7y3jvvWo7z55GmF/6ZR/+8IcJDaqPAp6divuqVjVOe+NWOqtCEhvDGMfH4NX6kVLnamXqjTDLzuzzx37axfGjm/4YcyTj8aPttZt+PaK14b839VCs5jyyq0orgq7J8fIJrAztsER/VFvZ8/sPf0xcL/XXLg5DjX5d1hNPmPpozA3sl7fe0m5Y89IRVY/lNn8NhnX1Qk3XA/jaxu75lP1jhsl10IEKpSuc8yzYQ1q87Gmv9+x8+POHvNw/4p5/8kicfX/CrXxf+/L//CQ/f2tw5Echd8xtQZRUc4bOfzutsmEYmldzhWM4IV9jYbwbeAeIrJ7i81sTtq/holkjFV7Yvey6ePyXJiIstReHBw8csVxuWyxUhCrb9FUIN1Ojw075WneAkkj1UPymm1GQCLkGjC1wcqYPgYwuNt8dzHhEP4gnqqNLQOZ0U5VC8UCmT+rwQHaBhUiNhhXefUTp0yqRtshVtsxTrrqSCujBlOgm+BFy2PPdSocRkfYvGFuhpjNTRbvSKWHMm7KZmYIFmpLg9NXpuS6QZpyGXKV8pAersdqQOqJXcCtkHSuMZx0xO2SzR06QGL9YQ/13kyHwZL2NDc/yAExXOh57LBq7HSnOzZQ13KvGw3/JgYRPBV31iu+pALPPPO4/SERtlX5Whz+TSUBhYxY4h7dk5h/iA5h2P10e8eXxOrMpHzz6i9HuSlkkl3pHJSGzQ1tNcJ+JixYgyqOOoaYi7W2K7YOtbrvsdISfGVJBQqM2arms5Xmzof/4x/fUF7202LB2cjIkHbcPg4MJHLr2QirJvW7YpcxI8x9dbNsOeD1B2ZWQk0bcdfRUg0YpwvnmA/9a3uT7u4MWO7uaaqAODFC5evGRbE6qwah1tNWX/9rpHh0IcI5qHyW1AqM5Rs9KXxO3tBbeXV+QxkW57ds0VzWbJYrNisWgJKL5kpL5+hnxtDn7Ydlw5l8FnRMw+TEqdOhMev4PmAhalsvIjeRvYeYdUj6pQs9jwmBSyFqIGmlQ56jNPbkwBsvRLHjx4g10/kFLPvt9Ti7VbDl0NB3gxZWCesiNNmSqTDdikchPuFO4ibppktEK8qmUDqlZWywW+iYhYzmbTNhS3oG0ibUi2eAwCTUF3Qh6BaPbJzSJSmQq+3kMxK1Faz4KGB+GMOG7o/cjghWHoubx8SX+zZd1taNsl+1q5vbkl73tyB32ukEdq7cip2AWyfvl5rb/xm9dDBwhnwACL9tMDiDzdEpPDBW9Tdiv5tPq5xxTX536yL1fqrlo0wsKxWUXi5wycVT5d5Mp8Ok98iSm2X3U0uX3lexaHb3q1CDdJw2VSvX9BKs/9E/aKZusv9yvBL6wJf2iK373qev+HMr3OW+Clwu1umgvYd6yWpxyv1pSq1P6Gm3/8B/LTK9qzM9rTU8LpCdK2uKaFGKwd8dnJgMOvX6Um/xJ1zVch1WEm4iO1qhUJoocY0VyRmnGaES+mFMgCGqhSqdKTU0BTpPGK5tas2D2Epsc7UwznGvDFsqlVTSEStdhGqzLFlkB1Hu9BiuU3I5mEZULrON2qI0QxJYc6qGIajYqbGvMKuSC+UJiGECY1oHcFSVP7tYEqAc2OMtkJ+6pEKab4q5EoDmk8oo7YBNpN5OThKfurW/p+D7Fwvb0iDJH9sGOfdoh6Tlcrzo5WuNbsA3POjKmgPrMbPFIjTXA2Te8qoxdytny8GCtVEmnYsb/dmXOO8zQSiOIJ+KlZ9LshhoD3EQdUcRy6VoeEMBUHbrw/caahDZmuoa+Obtrl2ZxBpkBiNHmkWvda3aHBZmbrd8f8ZGlnU+mT0/r0aDLdXsRGIOzvZLJ/nu4Hahd6wAYMp1Q0a8DbQY5OSh77s0zi86kRX6dG/JQLPIUngzaMTvCScQmGKkSUUEZ7vCj3rggMdvzmAgJJbXixBk/1w5R96Ow+yTSs6BK5egTb56RkA5lEIAXWzYrvPH6D7X7/jT7j8++9R9y9YDM8Yf30F3xwk/hgMCv69dIKU9tauRgS27HhZH3G248f8+zF148zOTRXD5+rmwoLItA4T9dGQteAb8xOMbaszjecPHiDk3BEqMouXVHo8X5hhecqdIuOhV/TlchyqdyMn1CKpyeyX53Qvv2QpT6kze9zFh/wxj5weivoGyuOVoWT/IT3P/k5q5+NXPzkCcM68WgbaNMtH6+PuH74Hu/92bf4/vAr3tNf0l1sOflY2D17wlG35a33v0NoHvPTnzS8vOroz4RmkVkcrzh+8zGh7tHnf0+42PFW85DV+Tvcjlf8Q/kx23GJvxo4P4+U4xP2ywU69Pgu825zTsc533nzLXbbX9H5SOcirbz+PrSVPXYmTrbowF3Ekgq88uurtxn76F5pmgt3WdcCLIMjaCVRkZymiASo3pOduRKhYYosAO+Ukhuo3gZ/s7lN1CaT2oq2isaKFFAJuGGJ9JOow5sFe21GU4Q5oWgDqUVCxTslO49DcUXQqLgakQy1CDnaPlMqtGSLppJEplIWiqQFKWdr2HtPEUctI0HBhxWhmPtWcYXF+oziHKEJuAB+VCR7VJUYKoUG5zORTPUtiYAkT1N29HlHEwIBD2EkB08aMyln+u2edBzoXEvW18+h/dGP/l9evPx/OD89BXoef+sh+90V/UdLTliSFycsHp7w/bNTllyS9hd0z/+Z4/d+SG1O+euffcSP/3bHxT8LaSu81Gf0LiPdQ65215yGxLvvv83/gfKjv/wxt9kR1isunn/Exi/INTFGAUaWJx1/8sY7/OCdljb8EdkvuSmeVkDc4+nYmxwMuvuVlVOFlwPsIuVh4MOFXQbP4woGwbWVBZhqFHe/VLoZYBXg1JsNzXNwl4Krjg+Pb7mVkaF1jOdH+AhDVoYXl9SfPGHbncO7C3jHQ07E7Z5m6OBI7uy5e6wp3mFrvWNsuRWALVAChAxHy8hVbKhtwF/ewL7YAGMDrrEh0XZXWZ0Fun2hb//VK2yfj2IL2pbX8yg/8HVU4gUr1G340mLUF5SHfytU4eObHT//i7/6d5MlfqCJkcePTnn/2+/QLDaMdYRiLoc7LyykcIxDktDXgDrHKMKLVhg6hVIJrvLe2+/w5JOP2O62v/Vze+9Yti2rdkVYLWnWHcvlgnazYB1bNjQk35EWJkQozhrHMU/3FydoalA34ryivuJ0coMsgquFrIkqHl8h5RYoVFdtoESDNVxcIGMRFGMJaLEmS0bRHIhuQLwN5geFXKameBFCkSmywtTiWk0lruLBVYvJyc7qpn6k+kOjx+NzRpqK+IrmKVt8UjJLMrU4B7W4OFsXtiDD69+/A5Xqsz2HBmuQa8bFStFiLkUilFrp/JImCIlKKELub3j+9CnDONKuFpw+OGWz3tB2C1arBc41OBxBKsqACiQntqsQUDWL9iCAV1zwlKJUzEFMK4iFh1DJaLEtllNzshSnU31msrbP1lizNX4luIKGiiRTlCaBHOzRolRKwo6bYAr0WjwyNUdUBY2CoxAKpNKSJBBFTbEugmhPURhdxZXWnGlE0KT44vCpkrtK8XZMUSzirzYV7T0xVUpUihdcUVRsoFqcNffFK0Ur7jNqcfcNIgpfG5n+96kYGP16NeC7zZu9/ja2PHzwiMXDY5pNR/E2cRaPGpZ1xbDb8+LZS/p9f/c0ToSmjcRlZ83MUgmTpNn2XdH2PlXMIUUrPldiNKdFjQ5Zt8TdgkYHXFtI0dy3dExkCiMDWgY6WdB2yjJU1FfEO3oKabulpvHuV0tVVfBirqS54rVBpbE6Bn4qWyZCqXg1twKXHbkWGpcR11DV1OJBHK1rWfjhX70pftj31lqneDg3KdwN4b5Bbop8UzxrrWgp1OLI3qzRQ3C4UmwIxln+e6r1LkO71or3U345jqqVUiouBpBiQ+gHNfX9S7OYNQ6PUez8cd6ixKoNrvbbG/LY4xctJUZCtmhb1zqyZNRFi+0TIVSllEoJJvRyk4DRu0qVgMhIFY8jUUNDKpmGjtOm48UwMKhdg3CQqlmoN02kbRuGYSC/Ek9ZlVfy2ME7i4z7Kl6rKb5ZrXjj0SP8F2RAk/gNu57D4KLy6TXayCSuPpz/X5LL/anHGyvDsyvKdgQfLE/8jTV+8TVnD1WpQ+VmX9jndN8U7xqOFu6rXsY9h8/iC55e7anY7wtDn6aJF/uXVArkfGchGkJg8XCDeJu07SssKqy/RIV++Fm4HWFboLWFzL866wBHjR1pHkigg0LJuNyRU49fx3s522dQbBNzyCMNwO02M9zu6FPCe0ezbO2GqbAIjmUMhOXrb1akCutVw9mbj/i7eIp6z20+skru4Qb2GZwIzWeKnIpQSuHqdsfJ0Qrvfstj02Mnvhyk9YfFf4+9Ewss3NRMqOxm9c0+6yz22pMqN9tbnl9dkR08euMRx5tTlusVjgZxgguZqglxGZcF58QWfV6sCayWkYMTshnS44tQQ0XF07at2Zlrg0RFXYPGiKtC66cLcRAcwfJlAlQSMlpx3NdqE4YKaLLCUK2IQHEj4huQiu+rFS4bT652QVSZVG3V4Q8qROK9Wskr2TucJLT11LEzK5EakCLkviP4bIoIdZSYEa/ssoNUaUrEi9I7geIpbrK2bYVQM6kqVZX9vmd3u7VG+EEJ/nu0udw3S06Wx5yNCa8j18Gzv+g56Qei92i7QktlUUeOFwv668q1eNLhYu0qoitca0WvcduTi2dMmRDNU3WQyuga0EzrhbdWR2xWx6Tray5vLmBMjJrJVhKjMOIXR9Q0svAeDYFUC9UvWGsmVkUXK66LULMNQwVN7JzD+ZbVZoPfFa4vX9JQOF2t6dLIuQhLcTwPcOMjN5rJMbITK7yfVdjstmjOvKAyjj3Jw75tKUMhSOUotKwfvE1+5yFDgfj0ipYeXOX6+ppdnxhqIohnGZVQCrvdSOoTYRRktA1J9TZpqZh4sy89N1cX5HEqWqtSh0T/7Irhcst21bE4PWKxbGniV90Avpj8SkNdUMKUAWURtPZ6SRHNwnpfOBkyvoz4RnkRHLmzbB/NHqmOXMFVZzlXMm2iS6Ay4FAG5/n2H/8ptVSGfsvudsdQRm6ePiGnvU0tq8M7a4oNJVGk4qtZATvv8eJNjSOKOI8P0wRrTlM8g5iSlUAthfVqgVOHFiuytguPjy1Hm0DTOESzbbiL4FUge9BIQ8tCOvpG8G6ycNtP1uex4hphFU9Q8XRRUck025602XM5Zvb7HRfXF/T9jhcXzwhRWBRP2pr1p/M2HZlSouZ7+/TPPdt/R5cAzbYkEazvfZ8nDvJ8S9ssba0SnUmpD2uFiqmtHZMVpn1jvs6M+wF5f8XiSwqBh/Kocu/wcrBOX3NfiLx7qunPDmhskNhw2H2wgE5d8Cmh+otjQtL0pAnLGV4KSy9f3ESfXuyhKT5iE/yuwC7By8ueopVHK+H6k0948tOf8Kv/9Rfkq2tOjo44Xq85Oj5hdXzC+vSUeHaGPz7GH2+Q5fJerQu/Ofqr3DUmP/XGveZtXEqLc3WaqJ4UwM2U/1oKosneAy+UbLbeVUFCplSoJdiyoyo6VIo3s6FEZPAViZWggVTAiRKS4LKjOk+NI0RrzKOmQGYUVCa3Cye4aWOSRc1unUCuSvAFLwXqZKUHVsSoQnE2CONypWalxgLVIcViWmrMCJ6QFLLDiUfJjKK0KpYBP03fSADRgneJhbTUsCbEI7qSqbknjT1PX3zCs6dPKTVzfHLM+dExLnQ4l3FaGBRwiVqF6gtBE168zcNKRaoSk8Ul5CqkrFy/3JOHEe/Fss+9EILFxkT53RXUgw82zAPc54nr1Iie1PRSOFgp6+H4m05WFbGGxtQk91rNQcAdJr1lmqiZlObVbDErerd2sufmFZt1vVsvm/ramiYFez47PcxxBKdWLHPYhDqgHOzODk3wyUJOD1cY7hrkiil97PvvX5BTPylo1Cz/BYo0oMnU3+KgBpwUW1ei5FpRzTgLypzW4VY4qE6IwWy/fa44vBX4pJBThezxWalRSVJxbWZTA6enp4ybz+SJfU3K/kPShz/ho+dXhNuBj3LDC2lp2JPZ0nohB8fgr9nEgf/7v7zPB++u+R9/90v++u9/dvc4r15iDr/3zt3dd4MPFvkkDh8DzaKjWzQsYoNHaVzgaLGmW6/wsmDjGjbHRzTffYv49rtwU9hfXtLf2KJZtQAABwFJREFUPGEcPkZ6T84NiqM5OqNbnxP6ntPNLcv3Krltaeqa8wcbvv39tzjJf4znj3A7z+Wz5zz/4ANuxl/ztuz4/tnAqn/KODQ888LzorjdwPCxoMcB5xa8896ah90NbncLGlltC+/ujjiOG76V3oZhRXAPeHp8xO1xZH+2IBXl9qRjqJfo6iVL/4TVtmV1tsCfrPn2DzzPr684WUQevLVh17Q0Nz0v9k85l1t+OGyIH625dMdc+gap9e44fu3Pu/i77aZjakBaCNs0RGK82ixXHFUPzXI74A++CIdzMin47AilEnyhYu4f2QW8FNoMkgtSlTFEBjVlkA9Crs6GMyvQQdpYU1yKmu0t4MeMk4iqUKJSm0TtlFohVUXSNISjnhIUJttGGm+T0+Ko2RNcRSSQ/JSHLpPjVh3NsjUWilaCU9CCBEfwAZUVTSkUdfRiwz2jKj57fPD4ILQu44MNhZVs7hF+KsDW6hDnCc7ROYWwxB2ZGmfcDpOLjVBrJufE9nbLPo20i4bb/Pqf+N/89f/m2QdPWG33PHn6AbuPhVi3VLnh+2895j//4D1EMv1wwz/8zU+5vH5JqwN/eizk8ojdL9/lJDzi8abwjy9+wrNnP6ceHXP0H/4T4ahy9vgBf/Lth6z/7Ps8ev+7/Kx4Lj/5kN0vfsqCyspvOF2+T+tX+Ox4++SIRbdGRHAI51PdXiXaEdVfQfk1xGPwZ+Dta+la+ADcSlktKrckxrqn69b0LGnkMHIzXX1EzQaxukklJLCA8stCWnnOTzYEhJtW+av+JeN1oSSlfHDFm0Ph/PQIWbZ2yAcxB7PrBGctjbf6Ym9nDUvgqUBSW4u2ArcCg4eQhWXjedkG+mbBqr+Fix6O1vcW6o0Q94Wla+j2me1UsP83xyGX6ME3eIzDIOdvq7i4wrzpv+QUOOwLXlfE0QP/9Hf/PlXi337vPd79ziO0CZAUNxZELGhPqycFoagjZEeZIjN6qZTOhmZ1ypTYLI54dPYmP9/901c+pwjEEFktV6yPjjlZb4grc2eMbcS3Kxbi8RJAHa4ooxdGZ8OlgWpNimKqOymCt2BAlIAixDqSvCNQrQFbHCGMOC0Q3OQYBEUCSb05JdZiTkOlgDY4b/GFOTsaX6jOLJG1QG10Uid6Qkl3zoSWvOOmxodDXLHXWRxSHCWCDxWqt6Gpg1q8BGssqUNctfVyteEpVwFveecaKtK+flNcteLE1pFSlFJsUys1k8kUsdqF9wEflOjsPl2SZzcWbnc7HEpoTojdhrA4opEWcVYHClnNglhNzKRxMgGv1Zz+cDTOk8nUQfFi1ujFqTW9nYJWojPnLK/ToJNag9kJaIlIjaiYy2G5G6IFcIzRjpPqpjptDjZsUAuiiu+xpmRtkaBUMq5CIKDiKWqxSSUUagWRaAMY4ixnPNkQnkdBPaUeBgDNfr9IteEHJseyYDnzPjl8LJ9Si/OqWlxNLW7Z4vdq8a8SLfyL8dmn1S+oEH32r37jIqo0TcvDR2/w8OyUsjQRmYiH0qHphtvra56/uCSnbK5Zcj+EXKpS0rRHnFqiDmfDzlLw2J5csb1T8eCigyZSKAQaxAueEWlapNj35uCQ4YbOFWS5IjYNjc/UnExcVRrGPLLf7uj7HWghRG9usq0NLu4HRXO2mmHjKWK1S5mGdpyr+KIWzSBQ1ZG14KQi4snOEatOavGGbc6U33O01P2nxL2FOpNluViz+vDvn/1aqk7iOqHmgg+BpIWAx3tHcEIqJjTy3gaBCvfD5Top8g+586VOQw/OimAH4ZHWewt120VMj1ELqgGcmxr49nV57Bn2W+JiTY6BMASCFooGxCWqa3CiFHVEKYxZ0aZQ1OO87Qt8yCRn145aJ1c5V0niaDWw9AuW4ZZxrJ96b8Y00pWGGCyq6tWm+OE9q86ufYem/1ch+vsOHJuZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmfk98U0GmWdmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZv5NMzfFZ2ZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmb+YJmb4jMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzf7DMTfGZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmT9Y5qb4zMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMwfLHNTfGZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmbmD5a5KT4zMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMz8wfL3BSfmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmfmDZW6Kz8zMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMz8wfL/AxR5JEc7bA1SAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2000x2000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_images(images_arr):\n",
    "    fig, axes = plt.subplots(1, len(images_arr), figsize=(20,20))\n",
    "    axes = axes.flatten()\n",
    "    for img, ax in zip(images_arr, axes):\n",
    "        ax.imshow(img)\n",
    "        ax.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Get a batch of images and labels\n",
    "sample_images, _ = next(train_generator)\n",
    "\n",
    "# Plot the images\n",
    "plot_images(sample_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\vian8\\anaconda3\\envs\\fall_detect\\lib\\site-packages\\keras\\src\\backend.py:1398: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Users\\vian8\\anaconda3\\envs\\fall_detect\\lib\\site-packages\\keras\\src\\layers\\normalization\\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
      "\n",
      "Model: \"efficientnetv2-s\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 128, 170, 3)]        0         []                            \n",
      "                                                                                                  \n",
      " rescaling (Rescaling)       (None, 128, 170, 3)          0         ['input_1[0][0]']             \n",
      "                                                                                                  \n",
      " stem_conv (Conv2D)          (None, 64, 85, 24)           648       ['rescaling[0][0]']           \n",
      "                                                                                                  \n",
      " stem_bn (BatchNormalizatio  (None, 64, 85, 24)           96        ['stem_conv[0][0]']           \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " stem_activation (Activatio  (None, 64, 85, 24)           0         ['stem_bn[0][0]']             \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block1a_project_conv (Conv  (None, 64, 85, 24)           5184      ['stem_activation[0][0]']     \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block1a_project_bn (BatchN  (None, 64, 85, 24)           96        ['block1a_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block1a_project_activation  (None, 64, 85, 24)           0         ['block1a_project_bn[0][0]']  \n",
      "  (Activation)                                                                                    \n",
      "                                                                                                  \n",
      " block1a_add (Add)           (None, 64, 85, 24)           0         ['block1a_project_activation[0\n",
      "                                                                    ][0]',                        \n",
      "                                                                     'stem_activation[0][0]']     \n",
      "                                                                                                  \n",
      " block1b_project_conv (Conv  (None, 64, 85, 24)           5184      ['block1a_add[0][0]']         \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block1b_project_bn (BatchN  (None, 64, 85, 24)           96        ['block1b_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block1b_project_activation  (None, 64, 85, 24)           0         ['block1b_project_bn[0][0]']  \n",
      "  (Activation)                                                                                    \n",
      "                                                                                                  \n",
      " block1b_drop (Dropout)      (None, 64, 85, 24)           0         ['block1b_project_activation[0\n",
      "                                                                    ][0]']                        \n",
      "                                                                                                  \n",
      " block1b_add (Add)           (None, 64, 85, 24)           0         ['block1b_drop[0][0]',        \n",
      "                                                                     'block1a_add[0][0]']         \n",
      "                                                                                                  \n",
      " block2a_expand_conv (Conv2  (None, 32, 43, 96)           20736     ['block1b_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block2a_expand_bn (BatchNo  (None, 32, 43, 96)           384       ['block2a_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block2a_expand_activation   (None, 32, 43, 96)           0         ['block2a_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block2a_project_conv (Conv  (None, 32, 43, 48)           4608      ['block2a_expand_activation[0]\n",
      " 2D)                                                                [0]']                         \n",
      "                                                                                                  \n",
      " block2a_project_bn (BatchN  (None, 32, 43, 48)           192       ['block2a_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block2b_expand_conv (Conv2  (None, 32, 43, 192)          82944     ['block2a_project_bn[0][0]']  \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block2b_expand_bn (BatchNo  (None, 32, 43, 192)          768       ['block2b_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block2b_expand_activation   (None, 32, 43, 192)          0         ['block2b_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block2b_project_conv (Conv  (None, 32, 43, 48)           9216      ['block2b_expand_activation[0]\n",
      " 2D)                                                                [0]']                         \n",
      "                                                                                                  \n",
      " block2b_project_bn (BatchN  (None, 32, 43, 48)           192       ['block2b_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block2b_drop (Dropout)      (None, 32, 43, 48)           0         ['block2b_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block2b_add (Add)           (None, 32, 43, 48)           0         ['block2b_drop[0][0]',        \n",
      "                                                                     'block2a_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block2c_expand_conv (Conv2  (None, 32, 43, 192)          82944     ['block2b_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block2c_expand_bn (BatchNo  (None, 32, 43, 192)          768       ['block2c_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block2c_expand_activation   (None, 32, 43, 192)          0         ['block2c_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block2c_project_conv (Conv  (None, 32, 43, 48)           9216      ['block2c_expand_activation[0]\n",
      " 2D)                                                                [0]']                         \n",
      "                                                                                                  \n",
      " block2c_project_bn (BatchN  (None, 32, 43, 48)           192       ['block2c_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block2c_drop (Dropout)      (None, 32, 43, 48)           0         ['block2c_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block2c_add (Add)           (None, 32, 43, 48)           0         ['block2c_drop[0][0]',        \n",
      "                                                                     'block2b_add[0][0]']         \n",
      "                                                                                                  \n",
      " block2d_expand_conv (Conv2  (None, 32, 43, 192)          82944     ['block2c_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block2d_expand_bn (BatchNo  (None, 32, 43, 192)          768       ['block2d_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block2d_expand_activation   (None, 32, 43, 192)          0         ['block2d_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block2d_project_conv (Conv  (None, 32, 43, 48)           9216      ['block2d_expand_activation[0]\n",
      " 2D)                                                                [0]']                         \n",
      "                                                                                                  \n",
      " block2d_project_bn (BatchN  (None, 32, 43, 48)           192       ['block2d_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block2d_drop (Dropout)      (None, 32, 43, 48)           0         ['block2d_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block2d_add (Add)           (None, 32, 43, 48)           0         ['block2d_drop[0][0]',        \n",
      "                                                                     'block2c_add[0][0]']         \n",
      "                                                                                                  \n",
      " block3a_expand_conv (Conv2  (None, 16, 22, 192)          82944     ['block2d_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block3a_expand_bn (BatchNo  (None, 16, 22, 192)          768       ['block3a_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block3a_expand_activation   (None, 16, 22, 192)          0         ['block3a_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block3a_project_conv (Conv  (None, 16, 22, 64)           12288     ['block3a_expand_activation[0]\n",
      " 2D)                                                                [0]']                         \n",
      "                                                                                                  \n",
      " block3a_project_bn (BatchN  (None, 16, 22, 64)           256       ['block3a_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block3b_expand_conv (Conv2  (None, 16, 22, 256)          147456    ['block3a_project_bn[0][0]']  \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block3b_expand_bn (BatchNo  (None, 16, 22, 256)          1024      ['block3b_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block3b_expand_activation   (None, 16, 22, 256)          0         ['block3b_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block3b_project_conv (Conv  (None, 16, 22, 64)           16384     ['block3b_expand_activation[0]\n",
      " 2D)                                                                [0]']                         \n",
      "                                                                                                  \n",
      " block3b_project_bn (BatchN  (None, 16, 22, 64)           256       ['block3b_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block3b_drop (Dropout)      (None, 16, 22, 64)           0         ['block3b_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block3b_add (Add)           (None, 16, 22, 64)           0         ['block3b_drop[0][0]',        \n",
      "                                                                     'block3a_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block3c_expand_conv (Conv2  (None, 16, 22, 256)          147456    ['block3b_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block3c_expand_bn (BatchNo  (None, 16, 22, 256)          1024      ['block3c_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block3c_expand_activation   (None, 16, 22, 256)          0         ['block3c_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block3c_project_conv (Conv  (None, 16, 22, 64)           16384     ['block3c_expand_activation[0]\n",
      " 2D)                                                                [0]']                         \n",
      "                                                                                                  \n",
      " block3c_project_bn (BatchN  (None, 16, 22, 64)           256       ['block3c_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block3c_drop (Dropout)      (None, 16, 22, 64)           0         ['block3c_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block3c_add (Add)           (None, 16, 22, 64)           0         ['block3c_drop[0][0]',        \n",
      "                                                                     'block3b_add[0][0]']         \n",
      "                                                                                                  \n",
      " block3d_expand_conv (Conv2  (None, 16, 22, 256)          147456    ['block3c_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block3d_expand_bn (BatchNo  (None, 16, 22, 256)          1024      ['block3d_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block3d_expand_activation   (None, 16, 22, 256)          0         ['block3d_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block3d_project_conv (Conv  (None, 16, 22, 64)           16384     ['block3d_expand_activation[0]\n",
      " 2D)                                                                [0]']                         \n",
      "                                                                                                  \n",
      " block3d_project_bn (BatchN  (None, 16, 22, 64)           256       ['block3d_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block3d_drop (Dropout)      (None, 16, 22, 64)           0         ['block3d_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block3d_add (Add)           (None, 16, 22, 64)           0         ['block3d_drop[0][0]',        \n",
      "                                                                     'block3c_add[0][0]']         \n",
      "                                                                                                  \n",
      " block4a_expand_conv (Conv2  (None, 16, 22, 256)          16384     ['block3d_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block4a_expand_bn (BatchNo  (None, 16, 22, 256)          1024      ['block4a_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block4a_expand_activation   (None, 16, 22, 256)          0         ['block4a_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block4a_dwconv2 (Depthwise  (None, 8, 11, 256)           2304      ['block4a_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block4a_bn (BatchNormaliza  (None, 8, 11, 256)           1024      ['block4a_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block4a_activation (Activa  (None, 8, 11, 256)           0         ['block4a_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block4a_se_squeeze (Global  (None, 256)                  0         ['block4a_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block4a_se_reshape (Reshap  (None, 1, 1, 256)            0         ['block4a_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block4a_se_reduce (Conv2D)  (None, 1, 1, 16)             4112      ['block4a_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block4a_se_expand (Conv2D)  (None, 1, 1, 256)            4352      ['block4a_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block4a_se_excite (Multipl  (None, 8, 11, 256)           0         ['block4a_activation[0][0]',  \n",
      " y)                                                                  'block4a_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block4a_project_conv (Conv  (None, 8, 11, 128)           32768     ['block4a_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block4a_project_bn (BatchN  (None, 8, 11, 128)           512       ['block4a_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block4b_expand_conv (Conv2  (None, 8, 11, 512)           65536     ['block4a_project_bn[0][0]']  \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block4b_expand_bn (BatchNo  (None, 8, 11, 512)           2048      ['block4b_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block4b_expand_activation   (None, 8, 11, 512)           0         ['block4b_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block4b_dwconv2 (Depthwise  (None, 8, 11, 512)           4608      ['block4b_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block4b_bn (BatchNormaliza  (None, 8, 11, 512)           2048      ['block4b_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block4b_activation (Activa  (None, 8, 11, 512)           0         ['block4b_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block4b_se_squeeze (Global  (None, 512)                  0         ['block4b_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block4b_se_reshape (Reshap  (None, 1, 1, 512)            0         ['block4b_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block4b_se_reduce (Conv2D)  (None, 1, 1, 32)             16416     ['block4b_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block4b_se_expand (Conv2D)  (None, 1, 1, 512)            16896     ['block4b_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block4b_se_excite (Multipl  (None, 8, 11, 512)           0         ['block4b_activation[0][0]',  \n",
      " y)                                                                  'block4b_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block4b_project_conv (Conv  (None, 8, 11, 128)           65536     ['block4b_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block4b_project_bn (BatchN  (None, 8, 11, 128)           512       ['block4b_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block4b_drop (Dropout)      (None, 8, 11, 128)           0         ['block4b_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block4b_add (Add)           (None, 8, 11, 128)           0         ['block4b_drop[0][0]',        \n",
      "                                                                     'block4a_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block4c_expand_conv (Conv2  (None, 8, 11, 512)           65536     ['block4b_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block4c_expand_bn (BatchNo  (None, 8, 11, 512)           2048      ['block4c_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block4c_expand_activation   (None, 8, 11, 512)           0         ['block4c_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block4c_dwconv2 (Depthwise  (None, 8, 11, 512)           4608      ['block4c_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block4c_bn (BatchNormaliza  (None, 8, 11, 512)           2048      ['block4c_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block4c_activation (Activa  (None, 8, 11, 512)           0         ['block4c_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block4c_se_squeeze (Global  (None, 512)                  0         ['block4c_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block4c_se_reshape (Reshap  (None, 1, 1, 512)            0         ['block4c_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block4c_se_reduce (Conv2D)  (None, 1, 1, 32)             16416     ['block4c_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block4c_se_expand (Conv2D)  (None, 1, 1, 512)            16896     ['block4c_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block4c_se_excite (Multipl  (None, 8, 11, 512)           0         ['block4c_activation[0][0]',  \n",
      " y)                                                                  'block4c_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block4c_project_conv (Conv  (None, 8, 11, 128)           65536     ['block4c_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block4c_project_bn (BatchN  (None, 8, 11, 128)           512       ['block4c_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block4c_drop (Dropout)      (None, 8, 11, 128)           0         ['block4c_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block4c_add (Add)           (None, 8, 11, 128)           0         ['block4c_drop[0][0]',        \n",
      "                                                                     'block4b_add[0][0]']         \n",
      "                                                                                                  \n",
      " block4d_expand_conv (Conv2  (None, 8, 11, 512)           65536     ['block4c_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block4d_expand_bn (BatchNo  (None, 8, 11, 512)           2048      ['block4d_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block4d_expand_activation   (None, 8, 11, 512)           0         ['block4d_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block4d_dwconv2 (Depthwise  (None, 8, 11, 512)           4608      ['block4d_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block4d_bn (BatchNormaliza  (None, 8, 11, 512)           2048      ['block4d_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block4d_activation (Activa  (None, 8, 11, 512)           0         ['block4d_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block4d_se_squeeze (Global  (None, 512)                  0         ['block4d_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block4d_se_reshape (Reshap  (None, 1, 1, 512)            0         ['block4d_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block4d_se_reduce (Conv2D)  (None, 1, 1, 32)             16416     ['block4d_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block4d_se_expand (Conv2D)  (None, 1, 1, 512)            16896     ['block4d_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block4d_se_excite (Multipl  (None, 8, 11, 512)           0         ['block4d_activation[0][0]',  \n",
      " y)                                                                  'block4d_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block4d_project_conv (Conv  (None, 8, 11, 128)           65536     ['block4d_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block4d_project_bn (BatchN  (None, 8, 11, 128)           512       ['block4d_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block4d_drop (Dropout)      (None, 8, 11, 128)           0         ['block4d_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block4d_add (Add)           (None, 8, 11, 128)           0         ['block4d_drop[0][0]',        \n",
      "                                                                     'block4c_add[0][0]']         \n",
      "                                                                                                  \n",
      " block4e_expand_conv (Conv2  (None, 8, 11, 512)           65536     ['block4d_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block4e_expand_bn (BatchNo  (None, 8, 11, 512)           2048      ['block4e_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block4e_expand_activation   (None, 8, 11, 512)           0         ['block4e_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block4e_dwconv2 (Depthwise  (None, 8, 11, 512)           4608      ['block4e_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block4e_bn (BatchNormaliza  (None, 8, 11, 512)           2048      ['block4e_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block4e_activation (Activa  (None, 8, 11, 512)           0         ['block4e_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block4e_se_squeeze (Global  (None, 512)                  0         ['block4e_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block4e_se_reshape (Reshap  (None, 1, 1, 512)            0         ['block4e_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block4e_se_reduce (Conv2D)  (None, 1, 1, 32)             16416     ['block4e_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block4e_se_expand (Conv2D)  (None, 1, 1, 512)            16896     ['block4e_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block4e_se_excite (Multipl  (None, 8, 11, 512)           0         ['block4e_activation[0][0]',  \n",
      " y)                                                                  'block4e_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block4e_project_conv (Conv  (None, 8, 11, 128)           65536     ['block4e_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block4e_project_bn (BatchN  (None, 8, 11, 128)           512       ['block4e_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block4e_drop (Dropout)      (None, 8, 11, 128)           0         ['block4e_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block4e_add (Add)           (None, 8, 11, 128)           0         ['block4e_drop[0][0]',        \n",
      "                                                                     'block4d_add[0][0]']         \n",
      "                                                                                                  \n",
      " block4f_expand_conv (Conv2  (None, 8, 11, 512)           65536     ['block4e_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block4f_expand_bn (BatchNo  (None, 8, 11, 512)           2048      ['block4f_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block4f_expand_activation   (None, 8, 11, 512)           0         ['block4f_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block4f_dwconv2 (Depthwise  (None, 8, 11, 512)           4608      ['block4f_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block4f_bn (BatchNormaliza  (None, 8, 11, 512)           2048      ['block4f_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block4f_activation (Activa  (None, 8, 11, 512)           0         ['block4f_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block4f_se_squeeze (Global  (None, 512)                  0         ['block4f_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block4f_se_reshape (Reshap  (None, 1, 1, 512)            0         ['block4f_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block4f_se_reduce (Conv2D)  (None, 1, 1, 32)             16416     ['block4f_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block4f_se_expand (Conv2D)  (None, 1, 1, 512)            16896     ['block4f_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block4f_se_excite (Multipl  (None, 8, 11, 512)           0         ['block4f_activation[0][0]',  \n",
      " y)                                                                  'block4f_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block4f_project_conv (Conv  (None, 8, 11, 128)           65536     ['block4f_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block4f_project_bn (BatchN  (None, 8, 11, 128)           512       ['block4f_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block4f_drop (Dropout)      (None, 8, 11, 128)           0         ['block4f_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block4f_add (Add)           (None, 8, 11, 128)           0         ['block4f_drop[0][0]',        \n",
      "                                                                     'block4e_add[0][0]']         \n",
      "                                                                                                  \n",
      " block5a_expand_conv (Conv2  (None, 8, 11, 768)           98304     ['block4f_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block5a_expand_bn (BatchNo  (None, 8, 11, 768)           3072      ['block5a_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block5a_expand_activation   (None, 8, 11, 768)           0         ['block5a_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block5a_dwconv2 (Depthwise  (None, 8, 11, 768)           6912      ['block5a_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block5a_bn (BatchNormaliza  (None, 8, 11, 768)           3072      ['block5a_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5a_activation (Activa  (None, 8, 11, 768)           0         ['block5a_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5a_se_squeeze (Global  (None, 768)                  0         ['block5a_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block5a_se_reshape (Reshap  (None, 1, 1, 768)            0         ['block5a_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block5a_se_reduce (Conv2D)  (None, 1, 1, 32)             24608     ['block5a_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block5a_se_expand (Conv2D)  (None, 1, 1, 768)            25344     ['block5a_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block5a_se_excite (Multipl  (None, 8, 11, 768)           0         ['block5a_activation[0][0]',  \n",
      " y)                                                                  'block5a_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block5a_project_conv (Conv  (None, 8, 11, 160)           122880    ['block5a_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block5a_project_bn (BatchN  (None, 8, 11, 160)           640       ['block5a_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block5b_expand_conv (Conv2  (None, 8, 11, 960)           153600    ['block5a_project_bn[0][0]']  \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block5b_expand_bn (BatchNo  (None, 8, 11, 960)           3840      ['block5b_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block5b_expand_activation   (None, 8, 11, 960)           0         ['block5b_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block5b_dwconv2 (Depthwise  (None, 8, 11, 960)           8640      ['block5b_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block5b_bn (BatchNormaliza  (None, 8, 11, 960)           3840      ['block5b_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5b_activation (Activa  (None, 8, 11, 960)           0         ['block5b_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5b_se_squeeze (Global  (None, 960)                  0         ['block5b_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block5b_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block5b_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block5b_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block5b_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block5b_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block5b_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block5b_se_excite (Multipl  (None, 8, 11, 960)           0         ['block5b_activation[0][0]',  \n",
      " y)                                                                  'block5b_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block5b_project_conv (Conv  (None, 8, 11, 160)           153600    ['block5b_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block5b_project_bn (BatchN  (None, 8, 11, 160)           640       ['block5b_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block5b_drop (Dropout)      (None, 8, 11, 160)           0         ['block5b_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block5b_add (Add)           (None, 8, 11, 160)           0         ['block5b_drop[0][0]',        \n",
      "                                                                     'block5a_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block5c_expand_conv (Conv2  (None, 8, 11, 960)           153600    ['block5b_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block5c_expand_bn (BatchNo  (None, 8, 11, 960)           3840      ['block5c_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block5c_expand_activation   (None, 8, 11, 960)           0         ['block5c_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block5c_dwconv2 (Depthwise  (None, 8, 11, 960)           8640      ['block5c_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block5c_bn (BatchNormaliza  (None, 8, 11, 960)           3840      ['block5c_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5c_activation (Activa  (None, 8, 11, 960)           0         ['block5c_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5c_se_squeeze (Global  (None, 960)                  0         ['block5c_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block5c_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block5c_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block5c_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block5c_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block5c_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block5c_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block5c_se_excite (Multipl  (None, 8, 11, 960)           0         ['block5c_activation[0][0]',  \n",
      " y)                                                                  'block5c_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block5c_project_conv (Conv  (None, 8, 11, 160)           153600    ['block5c_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block5c_project_bn (BatchN  (None, 8, 11, 160)           640       ['block5c_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block5c_drop (Dropout)      (None, 8, 11, 160)           0         ['block5c_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block5c_add (Add)           (None, 8, 11, 160)           0         ['block5c_drop[0][0]',        \n",
      "                                                                     'block5b_add[0][0]']         \n",
      "                                                                                                  \n",
      " block5d_expand_conv (Conv2  (None, 8, 11, 960)           153600    ['block5c_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block5d_expand_bn (BatchNo  (None, 8, 11, 960)           3840      ['block5d_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block5d_expand_activation   (None, 8, 11, 960)           0         ['block5d_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block5d_dwconv2 (Depthwise  (None, 8, 11, 960)           8640      ['block5d_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block5d_bn (BatchNormaliza  (None, 8, 11, 960)           3840      ['block5d_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5d_activation (Activa  (None, 8, 11, 960)           0         ['block5d_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5d_se_squeeze (Global  (None, 960)                  0         ['block5d_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block5d_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block5d_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block5d_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block5d_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block5d_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block5d_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block5d_se_excite (Multipl  (None, 8, 11, 960)           0         ['block5d_activation[0][0]',  \n",
      " y)                                                                  'block5d_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block5d_project_conv (Conv  (None, 8, 11, 160)           153600    ['block5d_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block5d_project_bn (BatchN  (None, 8, 11, 160)           640       ['block5d_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block5d_drop (Dropout)      (None, 8, 11, 160)           0         ['block5d_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block5d_add (Add)           (None, 8, 11, 160)           0         ['block5d_drop[0][0]',        \n",
      "                                                                     'block5c_add[0][0]']         \n",
      "                                                                                                  \n",
      " block5e_expand_conv (Conv2  (None, 8, 11, 960)           153600    ['block5d_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block5e_expand_bn (BatchNo  (None, 8, 11, 960)           3840      ['block5e_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block5e_expand_activation   (None, 8, 11, 960)           0         ['block5e_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block5e_dwconv2 (Depthwise  (None, 8, 11, 960)           8640      ['block5e_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block5e_bn (BatchNormaliza  (None, 8, 11, 960)           3840      ['block5e_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5e_activation (Activa  (None, 8, 11, 960)           0         ['block5e_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5e_se_squeeze (Global  (None, 960)                  0         ['block5e_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block5e_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block5e_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block5e_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block5e_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block5e_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block5e_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block5e_se_excite (Multipl  (None, 8, 11, 960)           0         ['block5e_activation[0][0]',  \n",
      " y)                                                                  'block5e_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block5e_project_conv (Conv  (None, 8, 11, 160)           153600    ['block5e_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block5e_project_bn (BatchN  (None, 8, 11, 160)           640       ['block5e_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block5e_drop (Dropout)      (None, 8, 11, 160)           0         ['block5e_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block5e_add (Add)           (None, 8, 11, 160)           0         ['block5e_drop[0][0]',        \n",
      "                                                                     'block5d_add[0][0]']         \n",
      "                                                                                                  \n",
      " block5f_expand_conv (Conv2  (None, 8, 11, 960)           153600    ['block5e_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block5f_expand_bn (BatchNo  (None, 8, 11, 960)           3840      ['block5f_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block5f_expand_activation   (None, 8, 11, 960)           0         ['block5f_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block5f_dwconv2 (Depthwise  (None, 8, 11, 960)           8640      ['block5f_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block5f_bn (BatchNormaliza  (None, 8, 11, 960)           3840      ['block5f_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5f_activation (Activa  (None, 8, 11, 960)           0         ['block5f_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5f_se_squeeze (Global  (None, 960)                  0         ['block5f_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block5f_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block5f_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block5f_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block5f_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block5f_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block5f_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block5f_se_excite (Multipl  (None, 8, 11, 960)           0         ['block5f_activation[0][0]',  \n",
      " y)                                                                  'block5f_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block5f_project_conv (Conv  (None, 8, 11, 160)           153600    ['block5f_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block5f_project_bn (BatchN  (None, 8, 11, 160)           640       ['block5f_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block5f_drop (Dropout)      (None, 8, 11, 160)           0         ['block5f_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block5f_add (Add)           (None, 8, 11, 160)           0         ['block5f_drop[0][0]',        \n",
      "                                                                     'block5e_add[0][0]']         \n",
      "                                                                                                  \n",
      " block5g_expand_conv (Conv2  (None, 8, 11, 960)           153600    ['block5f_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block5g_expand_bn (BatchNo  (None, 8, 11, 960)           3840      ['block5g_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block5g_expand_activation   (None, 8, 11, 960)           0         ['block5g_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block5g_dwconv2 (Depthwise  (None, 8, 11, 960)           8640      ['block5g_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block5g_bn (BatchNormaliza  (None, 8, 11, 960)           3840      ['block5g_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5g_activation (Activa  (None, 8, 11, 960)           0         ['block5g_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5g_se_squeeze (Global  (None, 960)                  0         ['block5g_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block5g_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block5g_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block5g_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block5g_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block5g_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block5g_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block5g_se_excite (Multipl  (None, 8, 11, 960)           0         ['block5g_activation[0][0]',  \n",
      " y)                                                                  'block5g_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block5g_project_conv (Conv  (None, 8, 11, 160)           153600    ['block5g_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block5g_project_bn (BatchN  (None, 8, 11, 160)           640       ['block5g_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block5g_drop (Dropout)      (None, 8, 11, 160)           0         ['block5g_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block5g_add (Add)           (None, 8, 11, 160)           0         ['block5g_drop[0][0]',        \n",
      "                                                                     'block5f_add[0][0]']         \n",
      "                                                                                                  \n",
      " block5h_expand_conv (Conv2  (None, 8, 11, 960)           153600    ['block5g_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block5h_expand_bn (BatchNo  (None, 8, 11, 960)           3840      ['block5h_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block5h_expand_activation   (None, 8, 11, 960)           0         ['block5h_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block5h_dwconv2 (Depthwise  (None, 8, 11, 960)           8640      ['block5h_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block5h_bn (BatchNormaliza  (None, 8, 11, 960)           3840      ['block5h_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5h_activation (Activa  (None, 8, 11, 960)           0         ['block5h_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5h_se_squeeze (Global  (None, 960)                  0         ['block5h_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block5h_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block5h_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block5h_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block5h_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block5h_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block5h_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block5h_se_excite (Multipl  (None, 8, 11, 960)           0         ['block5h_activation[0][0]',  \n",
      " y)                                                                  'block5h_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block5h_project_conv (Conv  (None, 8, 11, 160)           153600    ['block5h_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block5h_project_bn (BatchN  (None, 8, 11, 160)           640       ['block5h_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block5h_drop (Dropout)      (None, 8, 11, 160)           0         ['block5h_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block5h_add (Add)           (None, 8, 11, 160)           0         ['block5h_drop[0][0]',        \n",
      "                                                                     'block5g_add[0][0]']         \n",
      "                                                                                                  \n",
      " block5i_expand_conv (Conv2  (None, 8, 11, 960)           153600    ['block5h_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block5i_expand_bn (BatchNo  (None, 8, 11, 960)           3840      ['block5i_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block5i_expand_activation   (None, 8, 11, 960)           0         ['block5i_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block5i_dwconv2 (Depthwise  (None, 8, 11, 960)           8640      ['block5i_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block5i_bn (BatchNormaliza  (None, 8, 11, 960)           3840      ['block5i_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5i_activation (Activa  (None, 8, 11, 960)           0         ['block5i_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block5i_se_squeeze (Global  (None, 960)                  0         ['block5i_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block5i_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block5i_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block5i_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block5i_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block5i_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block5i_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block5i_se_excite (Multipl  (None, 8, 11, 960)           0         ['block5i_activation[0][0]',  \n",
      " y)                                                                  'block5i_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block5i_project_conv (Conv  (None, 8, 11, 160)           153600    ['block5i_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block5i_project_bn (BatchN  (None, 8, 11, 160)           640       ['block5i_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block5i_drop (Dropout)      (None, 8, 11, 160)           0         ['block5i_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block5i_add (Add)           (None, 8, 11, 160)           0         ['block5i_drop[0][0]',        \n",
      "                                                                     'block5h_add[0][0]']         \n",
      "                                                                                                  \n",
      " block6a_expand_conv (Conv2  (None, 8, 11, 960)           153600    ['block5i_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6a_expand_bn (BatchNo  (None, 8, 11, 960)           3840      ['block6a_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6a_expand_activation   (None, 8, 11, 960)           0         ['block6a_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6a_dwconv2 (Depthwise  (None, 4, 6, 960)            8640      ['block6a_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6a_bn (BatchNormaliza  (None, 4, 6, 960)            3840      ['block6a_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6a_activation (Activa  (None, 4, 6, 960)            0         ['block6a_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6a_se_squeeze (Global  (None, 960)                  0         ['block6a_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6a_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block6a_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6a_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block6a_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6a_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block6a_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6a_se_excite (Multipl  (None, 4, 6, 960)            0         ['block6a_activation[0][0]',  \n",
      " y)                                                                  'block6a_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6a_project_conv (Conv  (None, 4, 6, 256)            245760    ['block6a_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6a_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6a_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6b_expand_conv (Conv2  (None, 4, 6, 1536)           393216    ['block6a_project_bn[0][0]']  \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6b_expand_bn (BatchNo  (None, 4, 6, 1536)           6144      ['block6b_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6b_expand_activation   (None, 4, 6, 1536)           0         ['block6b_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6b_dwconv2 (Depthwise  (None, 4, 6, 1536)           13824     ['block6b_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6b_bn (BatchNormaliza  (None, 4, 6, 1536)           6144      ['block6b_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6b_activation (Activa  (None, 4, 6, 1536)           0         ['block6b_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6b_se_squeeze (Global  (None, 1536)                 0         ['block6b_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6b_se_reshape (Reshap  (None, 1, 1, 1536)           0         ['block6b_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6b_se_reduce (Conv2D)  (None, 1, 1, 64)             98368     ['block6b_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6b_se_expand (Conv2D)  (None, 1, 1, 1536)           99840     ['block6b_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6b_se_excite (Multipl  (None, 4, 6, 1536)           0         ['block6b_activation[0][0]',  \n",
      " y)                                                                  'block6b_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6b_project_conv (Conv  (None, 4, 6, 256)            393216    ['block6b_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6b_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6b_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6b_drop (Dropout)      (None, 4, 6, 256)            0         ['block6b_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6b_add (Add)           (None, 4, 6, 256)            0         ['block6b_drop[0][0]',        \n",
      "                                                                     'block6a_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6c_expand_conv (Conv2  (None, 4, 6, 1536)           393216    ['block6b_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6c_expand_bn (BatchNo  (None, 4, 6, 1536)           6144      ['block6c_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6c_expand_activation   (None, 4, 6, 1536)           0         ['block6c_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6c_dwconv2 (Depthwise  (None, 4, 6, 1536)           13824     ['block6c_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6c_bn (BatchNormaliza  (None, 4, 6, 1536)           6144      ['block6c_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6c_activation (Activa  (None, 4, 6, 1536)           0         ['block6c_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6c_se_squeeze (Global  (None, 1536)                 0         ['block6c_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6c_se_reshape (Reshap  (None, 1, 1, 1536)           0         ['block6c_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6c_se_reduce (Conv2D)  (None, 1, 1, 64)             98368     ['block6c_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6c_se_expand (Conv2D)  (None, 1, 1, 1536)           99840     ['block6c_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6c_se_excite (Multipl  (None, 4, 6, 1536)           0         ['block6c_activation[0][0]',  \n",
      " y)                                                                  'block6c_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6c_project_conv (Conv  (None, 4, 6, 256)            393216    ['block6c_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6c_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6c_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6c_drop (Dropout)      (None, 4, 6, 256)            0         ['block6c_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6c_add (Add)           (None, 4, 6, 256)            0         ['block6c_drop[0][0]',        \n",
      "                                                                     'block6b_add[0][0]']         \n",
      "                                                                                                  \n",
      " block6d_expand_conv (Conv2  (None, 4, 6, 1536)           393216    ['block6c_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6d_expand_bn (BatchNo  (None, 4, 6, 1536)           6144      ['block6d_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6d_expand_activation   (None, 4, 6, 1536)           0         ['block6d_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6d_dwconv2 (Depthwise  (None, 4, 6, 1536)           13824     ['block6d_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6d_bn (BatchNormaliza  (None, 4, 6, 1536)           6144      ['block6d_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6d_activation (Activa  (None, 4, 6, 1536)           0         ['block6d_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6d_se_squeeze (Global  (None, 1536)                 0         ['block6d_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6d_se_reshape (Reshap  (None, 1, 1, 1536)           0         ['block6d_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6d_se_reduce (Conv2D)  (None, 1, 1, 64)             98368     ['block6d_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6d_se_expand (Conv2D)  (None, 1, 1, 1536)           99840     ['block6d_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6d_se_excite (Multipl  (None, 4, 6, 1536)           0         ['block6d_activation[0][0]',  \n",
      " y)                                                                  'block6d_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6d_project_conv (Conv  (None, 4, 6, 256)            393216    ['block6d_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6d_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6d_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6d_drop (Dropout)      (None, 4, 6, 256)            0         ['block6d_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6d_add (Add)           (None, 4, 6, 256)            0         ['block6d_drop[0][0]',        \n",
      "                                                                     'block6c_add[0][0]']         \n",
      "                                                                                                  \n",
      " block6e_expand_conv (Conv2  (None, 4, 6, 1536)           393216    ['block6d_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6e_expand_bn (BatchNo  (None, 4, 6, 1536)           6144      ['block6e_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6e_expand_activation   (None, 4, 6, 1536)           0         ['block6e_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6e_dwconv2 (Depthwise  (None, 4, 6, 1536)           13824     ['block6e_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6e_bn (BatchNormaliza  (None, 4, 6, 1536)           6144      ['block6e_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6e_activation (Activa  (None, 4, 6, 1536)           0         ['block6e_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6e_se_squeeze (Global  (None, 1536)                 0         ['block6e_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6e_se_reshape (Reshap  (None, 1, 1, 1536)           0         ['block6e_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6e_se_reduce (Conv2D)  (None, 1, 1, 64)             98368     ['block6e_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6e_se_expand (Conv2D)  (None, 1, 1, 1536)           99840     ['block6e_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6e_se_excite (Multipl  (None, 4, 6, 1536)           0         ['block6e_activation[0][0]',  \n",
      " y)                                                                  'block6e_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6e_project_conv (Conv  (None, 4, 6, 256)            393216    ['block6e_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6e_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6e_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6e_drop (Dropout)      (None, 4, 6, 256)            0         ['block6e_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6e_add (Add)           (None, 4, 6, 256)            0         ['block6e_drop[0][0]',        \n",
      "                                                                     'block6d_add[0][0]']         \n",
      "                                                                                                  \n",
      " block6f_expand_conv (Conv2  (None, 4, 6, 1536)           393216    ['block6e_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6f_expand_bn (BatchNo  (None, 4, 6, 1536)           6144      ['block6f_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6f_expand_activation   (None, 4, 6, 1536)           0         ['block6f_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6f_dwconv2 (Depthwise  (None, 4, 6, 1536)           13824     ['block6f_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6f_bn (BatchNormaliza  (None, 4, 6, 1536)           6144      ['block6f_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6f_activation (Activa  (None, 4, 6, 1536)           0         ['block6f_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6f_se_squeeze (Global  (None, 1536)                 0         ['block6f_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6f_se_reshape (Reshap  (None, 1, 1, 1536)           0         ['block6f_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6f_se_reduce (Conv2D)  (None, 1, 1, 64)             98368     ['block6f_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6f_se_expand (Conv2D)  (None, 1, 1, 1536)           99840     ['block6f_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6f_se_excite (Multipl  (None, 4, 6, 1536)           0         ['block6f_activation[0][0]',  \n",
      " y)                                                                  'block6f_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6f_project_conv (Conv  (None, 4, 6, 256)            393216    ['block6f_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6f_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6f_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6f_drop (Dropout)      (None, 4, 6, 256)            0         ['block6f_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6f_add (Add)           (None, 4, 6, 256)            0         ['block6f_drop[0][0]',        \n",
      "                                                                     'block6e_add[0][0]']         \n",
      "                                                                                                  \n",
      " block6g_expand_conv (Conv2  (None, 4, 6, 1536)           393216    ['block6f_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6g_expand_bn (BatchNo  (None, 4, 6, 1536)           6144      ['block6g_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6g_expand_activation   (None, 4, 6, 1536)           0         ['block6g_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6g_dwconv2 (Depthwise  (None, 4, 6, 1536)           13824     ['block6g_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6g_bn (BatchNormaliza  (None, 4, 6, 1536)           6144      ['block6g_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6g_activation (Activa  (None, 4, 6, 1536)           0         ['block6g_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6g_se_squeeze (Global  (None, 1536)                 0         ['block6g_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6g_se_reshape (Reshap  (None, 1, 1, 1536)           0         ['block6g_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6g_se_reduce (Conv2D)  (None, 1, 1, 64)             98368     ['block6g_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6g_se_expand (Conv2D)  (None, 1, 1, 1536)           99840     ['block6g_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6g_se_excite (Multipl  (None, 4, 6, 1536)           0         ['block6g_activation[0][0]',  \n",
      " y)                                                                  'block6g_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6g_project_conv (Conv  (None, 4, 6, 256)            393216    ['block6g_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6g_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6g_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6g_drop (Dropout)      (None, 4, 6, 256)            0         ['block6g_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6g_add (Add)           (None, 4, 6, 256)            0         ['block6g_drop[0][0]',        \n",
      "                                                                     'block6f_add[0][0]']         \n",
      "                                                                                                  \n",
      " block6h_expand_conv (Conv2  (None, 4, 6, 1536)           393216    ['block6g_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6h_expand_bn (BatchNo  (None, 4, 6, 1536)           6144      ['block6h_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6h_expand_activation   (None, 4, 6, 1536)           0         ['block6h_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6h_dwconv2 (Depthwise  (None, 4, 6, 1536)           13824     ['block6h_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6h_bn (BatchNormaliza  (None, 4, 6, 1536)           6144      ['block6h_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6h_activation (Activa  (None, 4, 6, 1536)           0         ['block6h_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6h_se_squeeze (Global  (None, 1536)                 0         ['block6h_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6h_se_reshape (Reshap  (None, 1, 1, 1536)           0         ['block6h_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6h_se_reduce (Conv2D)  (None, 1, 1, 64)             98368     ['block6h_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6h_se_expand (Conv2D)  (None, 1, 1, 1536)           99840     ['block6h_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6h_se_excite (Multipl  (None, 4, 6, 1536)           0         ['block6h_activation[0][0]',  \n",
      " y)                                                                  'block6h_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6h_project_conv (Conv  (None, 4, 6, 256)            393216    ['block6h_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6h_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6h_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6h_drop (Dropout)      (None, 4, 6, 256)            0         ['block6h_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6h_add (Add)           (None, 4, 6, 256)            0         ['block6h_drop[0][0]',        \n",
      "                                                                     'block6g_add[0][0]']         \n",
      "                                                                                                  \n",
      " block6i_expand_conv (Conv2  (None, 4, 6, 1536)           393216    ['block6h_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6i_expand_bn (BatchNo  (None, 4, 6, 1536)           6144      ['block6i_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6i_expand_activation   (None, 4, 6, 1536)           0         ['block6i_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6i_dwconv2 (Depthwise  (None, 4, 6, 1536)           13824     ['block6i_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6i_bn (BatchNormaliza  (None, 4, 6, 1536)           6144      ['block6i_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6i_activation (Activa  (None, 4, 6, 1536)           0         ['block6i_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6i_se_squeeze (Global  (None, 1536)                 0         ['block6i_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6i_se_reshape (Reshap  (None, 1, 1, 1536)           0         ['block6i_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6i_se_reduce (Conv2D)  (None, 1, 1, 64)             98368     ['block6i_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6i_se_expand (Conv2D)  (None, 1, 1, 1536)           99840     ['block6i_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6i_se_excite (Multipl  (None, 4, 6, 1536)           0         ['block6i_activation[0][0]',  \n",
      " y)                                                                  'block6i_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6i_project_conv (Conv  (None, 4, 6, 256)            393216    ['block6i_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6i_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6i_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6i_drop (Dropout)      (None, 4, 6, 256)            0         ['block6i_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6i_add (Add)           (None, 4, 6, 256)            0         ['block6i_drop[0][0]',        \n",
      "                                                                     'block6h_add[0][0]']         \n",
      "                                                                                                  \n",
      " block6j_expand_conv (Conv2  (None, 4, 6, 1536)           393216    ['block6i_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6j_expand_bn (BatchNo  (None, 4, 6, 1536)           6144      ['block6j_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6j_expand_activation   (None, 4, 6, 1536)           0         ['block6j_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6j_dwconv2 (Depthwise  (None, 4, 6, 1536)           13824     ['block6j_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6j_bn (BatchNormaliza  (None, 4, 6, 1536)           6144      ['block6j_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6j_activation (Activa  (None, 4, 6, 1536)           0         ['block6j_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6j_se_squeeze (Global  (None, 1536)                 0         ['block6j_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6j_se_reshape (Reshap  (None, 1, 1, 1536)           0         ['block6j_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6j_se_reduce (Conv2D)  (None, 1, 1, 64)             98368     ['block6j_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6j_se_expand (Conv2D)  (None, 1, 1, 1536)           99840     ['block6j_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6j_se_excite (Multipl  (None, 4, 6, 1536)           0         ['block6j_activation[0][0]',  \n",
      " y)                                                                  'block6j_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6j_project_conv (Conv  (None, 4, 6, 256)            393216    ['block6j_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6j_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6j_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6j_drop (Dropout)      (None, 4, 6, 256)            0         ['block6j_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6j_add (Add)           (None, 4, 6, 256)            0         ['block6j_drop[0][0]',        \n",
      "                                                                     'block6i_add[0][0]']         \n",
      "                                                                                                  \n",
      " block6k_expand_conv (Conv2  (None, 4, 6, 1536)           393216    ['block6j_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6k_expand_bn (BatchNo  (None, 4, 6, 1536)           6144      ['block6k_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6k_expand_activation   (None, 4, 6, 1536)           0         ['block6k_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6k_dwconv2 (Depthwise  (None, 4, 6, 1536)           13824     ['block6k_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6k_bn (BatchNormaliza  (None, 4, 6, 1536)           6144      ['block6k_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6k_activation (Activa  (None, 4, 6, 1536)           0         ['block6k_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6k_se_squeeze (Global  (None, 1536)                 0         ['block6k_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6k_se_reshape (Reshap  (None, 1, 1, 1536)           0         ['block6k_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6k_se_reduce (Conv2D)  (None, 1, 1, 64)             98368     ['block6k_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6k_se_expand (Conv2D)  (None, 1, 1, 1536)           99840     ['block6k_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6k_se_excite (Multipl  (None, 4, 6, 1536)           0         ['block6k_activation[0][0]',  \n",
      " y)                                                                  'block6k_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6k_project_conv (Conv  (None, 4, 6, 256)            393216    ['block6k_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6k_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6k_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6k_drop (Dropout)      (None, 4, 6, 256)            0         ['block6k_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6k_add (Add)           (None, 4, 6, 256)            0         ['block6k_drop[0][0]',        \n",
      "                                                                     'block6j_add[0][0]']         \n",
      "                                                                                                  \n",
      " block6l_expand_conv (Conv2  (None, 4, 6, 1536)           393216    ['block6k_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6l_expand_bn (BatchNo  (None, 4, 6, 1536)           6144      ['block6l_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6l_expand_activation   (None, 4, 6, 1536)           0         ['block6l_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6l_dwconv2 (Depthwise  (None, 4, 6, 1536)           13824     ['block6l_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6l_bn (BatchNormaliza  (None, 4, 6, 1536)           6144      ['block6l_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6l_activation (Activa  (None, 4, 6, 1536)           0         ['block6l_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6l_se_squeeze (Global  (None, 1536)                 0         ['block6l_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6l_se_reshape (Reshap  (None, 1, 1, 1536)           0         ['block6l_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6l_se_reduce (Conv2D)  (None, 1, 1, 64)             98368     ['block6l_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6l_se_expand (Conv2D)  (None, 1, 1, 1536)           99840     ['block6l_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6l_se_excite (Multipl  (None, 4, 6, 1536)           0         ['block6l_activation[0][0]',  \n",
      " y)                                                                  'block6l_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6l_project_conv (Conv  (None, 4, 6, 256)            393216    ['block6l_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6l_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6l_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6l_drop (Dropout)      (None, 4, 6, 256)            0         ['block6l_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6l_add (Add)           (None, 4, 6, 256)            0         ['block6l_drop[0][0]',        \n",
      "                                                                     'block6k_add[0][0]']         \n",
      "                                                                                                  \n",
      " block6m_expand_conv (Conv2  (None, 4, 6, 1536)           393216    ['block6l_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6m_expand_bn (BatchNo  (None, 4, 6, 1536)           6144      ['block6m_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6m_expand_activation   (None, 4, 6, 1536)           0         ['block6m_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6m_dwconv2 (Depthwise  (None, 4, 6, 1536)           13824     ['block6m_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6m_bn (BatchNormaliza  (None, 4, 6, 1536)           6144      ['block6m_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6m_activation (Activa  (None, 4, 6, 1536)           0         ['block6m_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6m_se_squeeze (Global  (None, 1536)                 0         ['block6m_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6m_se_reshape (Reshap  (None, 1, 1, 1536)           0         ['block6m_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6m_se_reduce (Conv2D)  (None, 1, 1, 64)             98368     ['block6m_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6m_se_expand (Conv2D)  (None, 1, 1, 1536)           99840     ['block6m_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6m_se_excite (Multipl  (None, 4, 6, 1536)           0         ['block6m_activation[0][0]',  \n",
      " y)                                                                  'block6m_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6m_project_conv (Conv  (None, 4, 6, 256)            393216    ['block6m_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6m_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6m_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6m_drop (Dropout)      (None, 4, 6, 256)            0         ['block6m_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6m_add (Add)           (None, 4, 6, 256)            0         ['block6m_drop[0][0]',        \n",
      "                                                                     'block6l_add[0][0]']         \n",
      "                                                                                                  \n",
      " block6n_expand_conv (Conv2  (None, 4, 6, 1536)           393216    ['block6m_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6n_expand_bn (BatchNo  (None, 4, 6, 1536)           6144      ['block6n_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6n_expand_activation   (None, 4, 6, 1536)           0         ['block6n_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6n_dwconv2 (Depthwise  (None, 4, 6, 1536)           13824     ['block6n_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6n_bn (BatchNormaliza  (None, 4, 6, 1536)           6144      ['block6n_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6n_activation (Activa  (None, 4, 6, 1536)           0         ['block6n_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6n_se_squeeze (Global  (None, 1536)                 0         ['block6n_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6n_se_reshape (Reshap  (None, 1, 1, 1536)           0         ['block6n_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6n_se_reduce (Conv2D)  (None, 1, 1, 64)             98368     ['block6n_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6n_se_expand (Conv2D)  (None, 1, 1, 1536)           99840     ['block6n_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6n_se_excite (Multipl  (None, 4, 6, 1536)           0         ['block6n_activation[0][0]',  \n",
      " y)                                                                  'block6n_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6n_project_conv (Conv  (None, 4, 6, 256)            393216    ['block6n_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6n_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6n_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6n_drop (Dropout)      (None, 4, 6, 256)            0         ['block6n_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6n_add (Add)           (None, 4, 6, 256)            0         ['block6n_drop[0][0]',        \n",
      "                                                                     'block6m_add[0][0]']         \n",
      "                                                                                                  \n",
      " block6o_expand_conv (Conv2  (None, 4, 6, 1536)           393216    ['block6n_add[0][0]']         \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6o_expand_bn (BatchNo  (None, 4, 6, 1536)           6144      ['block6o_expand_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block6o_expand_activation   (None, 4, 6, 1536)           0         ['block6o_expand_bn[0][0]']   \n",
      " (Activation)                                                                                     \n",
      "                                                                                                  \n",
      " block6o_dwconv2 (Depthwise  (None, 4, 6, 1536)           13824     ['block6o_expand_activation[0]\n",
      " Conv2D)                                                            [0]']                         \n",
      "                                                                                                  \n",
      " block6o_bn (BatchNormaliza  (None, 4, 6, 1536)           6144      ['block6o_dwconv2[0][0]']     \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6o_activation (Activa  (None, 4, 6, 1536)           0         ['block6o_bn[0][0]']          \n",
      " tion)                                                                                            \n",
      "                                                                                                  \n",
      " block6o_se_squeeze (Global  (None, 1536)                 0         ['block6o_activation[0][0]']  \n",
      " AveragePooling2D)                                                                                \n",
      "                                                                                                  \n",
      " block6o_se_reshape (Reshap  (None, 1, 1, 1536)           0         ['block6o_se_squeeze[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " block6o_se_reduce (Conv2D)  (None, 1, 1, 64)             98368     ['block6o_se_reshape[0][0]']  \n",
      "                                                                                                  \n",
      " block6o_se_expand (Conv2D)  (None, 1, 1, 1536)           99840     ['block6o_se_reduce[0][0]']   \n",
      "                                                                                                  \n",
      " block6o_se_excite (Multipl  (None, 4, 6, 1536)           0         ['block6o_activation[0][0]',  \n",
      " y)                                                                  'block6o_se_expand[0][0]']   \n",
      "                                                                                                  \n",
      " block6o_project_conv (Conv  (None, 4, 6, 256)            393216    ['block6o_se_excite[0][0]']   \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6o_project_bn (BatchN  (None, 4, 6, 256)            1024      ['block6o_project_conv[0][0]']\n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " block6o_drop (Dropout)      (None, 4, 6, 256)            0         ['block6o_project_bn[0][0]']  \n",
      "                                                                                                  \n",
      " block6o_add (Add)           (None, 4, 6, 256)            0         ['block6o_drop[0][0]',        \n",
      "                                                                     'block6n_add[0][0]']         \n",
      "                                                                                                  \n",
      " top_conv (Conv2D)           (None, 4, 6, 1280)           327680    ['block6o_add[0][0]']         \n",
      "                                                                                                  \n",
      " top_bn (BatchNormalization  (None, 4, 6, 1280)           5120      ['top_conv[0][0]']            \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " top_activation (Activation  (None, 4, 6, 1280)           0         ['top_bn[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 20331360 (77.56 MB)\n",
      "Trainable params: 0 (0.00 Byte)\n",
      "Non-trainable params: 20331360 (77.56 MB)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# EfficientNetV2S Model\n",
    "from tensorflow.keras.applications import EfficientNetV2S\n",
    "from tensorflow.keras.layers import Input\n",
    "\n",
    "# load EfficientNetV2S Model\n",
    "base_model = EfficientNetV2S(weights='imagenet', include_top=False, input_tensor=Input(shape=(img_h, img_w, 3)))\n",
    "\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "# last_output = base_model.output\n",
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " efficientnetv2-s (Function  (None, 4, 6, 1280)        20331360  \n",
      " al)                                                             \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 4, 6, 64)          737344    \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPoolin  (None, 2, 3, 64)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 2, 3, 128)         73856     \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPoolin  (None, 1, 1, 128)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " global_average_pooling2d_2  (None, 128)               0         \n",
      "  (GlobalAveragePooling2D)                                       \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 1024)              132096    \n",
      "                                                                 \n",
      " batch_normalization_2 (Bat  (None, 1024)              4096      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 1024)              0         \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 1024)              0         \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 512)               524800    \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 2)                 1026      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 21804578 (83.18 MB)\n",
      "Trainable params: 1471170 (5.61 MB)\n",
      "Non-trainable params: 20333408 (77.57 MB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# model's architecture\n",
    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout, Conv2D, MaxPooling2D, BatchNormalization, Flatten\n",
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "    base_model,\n",
    "    Conv2D(64, (3, 3), activation = 'relu', padding = 'same'),\n",
    "    MaxPooling2D((2, 2)),\n",
    "    Conv2D(128, (3, 3), activation = 'relu', padding = 'same'),\n",
    "    MaxPooling2D((2,2)),\n",
    "    GlobalAveragePooling2D(),\n",
    "    Dense(1024, activation = 'relu'),\n",
    "    BatchNormalization(),\n",
    "    Dropout(0.2),\n",
    "    Flatten(),\n",
    "    Dense(512, activation = 'relu'),\n",
    "    Dense(2, activation='softmax')\n",
    "])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# implementing callbacks\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
    "\n",
    "# Define the checkpoint path with .keras extension\n",
    "checkpoint_path = \"model_checkpoint.keras\"\n",
    "\n",
    "# ModelCheckpoint callback\n",
    "checkpoint = ModelCheckpoint(\n",
    "    filepath=checkpoint_path,\n",
    "    monitor='val_accuracy',\n",
    "    save_best_only=True,\n",
    "    mode='max',\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "# ReduceLROnPlateau callback\n",
    "lr_reduction = ReduceLROnPlateau(\n",
    "    monitor='val_accuracy',\n",
    "    patience=3,\n",
    "    verbose=1,\n",
    "    factor=0.1,\n",
    "    min_lr=0.000001\n",
    ")\n",
    "\n",
    "# Custom callback\n",
    "class myCallback(tf.keras.callbacks.Callback):\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        acc = logs.get('accuracy')\n",
    "        val_acc = logs.get('val_accuracy')\n",
    "\n",
    "        if acc > 0.97 and val_acc > 0.92:\n",
    "            print(\"\\nReached desired accuracy so cancelling training!\")\n",
    "            self.model.stop_training = True\n",
    "\n",
    "early_stop = myCallback()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling the model\n",
    "model.compile(\n",
    "    loss ='categorical_crossentropy',\n",
    "    optimizer = tf.optimizers.Adam(learning_rate = 0.001),\n",
    "    metrics = ['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 1.4583 - accuracy: 0.4750\n",
      "Epoch 1: val_accuracy improved from -inf to 0.47368, saving model to model_checkpoint.keras\n",
      "10/10 [==============================] - 61s 2s/step - loss: 1.4583 - accuracy: 0.4750 - val_loss: 6.3553 - val_accuracy: 0.4737 - lr: 0.0010\n",
      "Epoch 2/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 2.0827 - accuracy: 0.5500\n",
      "Epoch 2: val_accuracy improved from 0.47368 to 0.52632, saving model to model_checkpoint.keras\n",
      "10/10 [==============================] - 8s 810ms/step - loss: 2.0827 - accuracy: 0.5500 - val_loss: 29.9097 - val_accuracy: 0.5263 - lr: 0.0010\n",
      "Epoch 3/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 1.4999 - accuracy: 0.4000\n",
      "Epoch 3: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 384ms/step - loss: 1.4999 - accuracy: 0.4000 - val_loss: 3.5379 - val_accuracy: 0.4737 - lr: 0.0010\n",
      "Epoch 4/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 1.0704 - accuracy: 0.5250\n",
      "Epoch 4: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 394ms/step - loss: 1.0704 - accuracy: 0.5250 - val_loss: 2.9875 - val_accuracy: 0.5263 - lr: 0.0010\n",
      "Epoch 5/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 1.8578 - accuracy: 0.4375\n",
      "Epoch 5: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "\n",
      "Epoch 5: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 395ms/step - loss: 1.8578 - accuracy: 0.4375 - val_loss: 20.9329 - val_accuracy: 0.5263 - lr: 0.0010\n",
      "Epoch 6/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.9759 - accuracy: 0.6000\n",
      "Epoch 6: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 393ms/step - loss: 0.9759 - accuracy: 0.6000 - val_loss: 16.6380 - val_accuracy: 0.5263 - lr: 1.0000e-04\n",
      "Epoch 7/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.7779 - accuracy: 0.6250\n",
      "Epoch 7: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 396ms/step - loss: 0.7779 - accuracy: 0.6250 - val_loss: 5.1056 - val_accuracy: 0.5263 - lr: 1.0000e-04\n",
      "Epoch 8/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6050 - accuracy: 0.7250\n",
      "Epoch 8: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "\n",
      "Epoch 8: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 402ms/step - loss: 0.6050 - accuracy: 0.7250 - val_loss: 2.1890 - val_accuracy: 0.5263 - lr: 1.0000e-04\n",
      "Epoch 9/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6718 - accuracy: 0.6000\n",
      "Epoch 9: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 393ms/step - loss: 0.6718 - accuracy: 0.6000 - val_loss: 2.2720 - val_accuracy: 0.5263 - lr: 1.0000e-05\n",
      "Epoch 10/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.7377 - accuracy: 0.6375\n",
      "Epoch 10: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 391ms/step - loss: 0.7377 - accuracy: 0.6375 - val_loss: 1.9635 - val_accuracy: 0.5263 - lr: 1.0000e-05\n",
      "Epoch 11/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6244 - accuracy: 0.6000\n",
      "Epoch 11: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
      "\n",
      "Epoch 11: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 387ms/step - loss: 0.6244 - accuracy: 0.6000 - val_loss: 1.9529 - val_accuracy: 0.5263 - lr: 1.0000e-05\n",
      "Epoch 12/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6757 - accuracy: 0.5500\n",
      "Epoch 12: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 391ms/step - loss: 0.6757 - accuracy: 0.5500 - val_loss: 1.8087 - val_accuracy: 0.5263 - lr: 1.0000e-06\n",
      "Epoch 13/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.7100 - accuracy: 0.6375\n",
      "Epoch 13: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 380ms/step - loss: 0.7100 - accuracy: 0.6375 - val_loss: 1.6903 - val_accuracy: 0.5263 - lr: 1.0000e-06\n",
      "Epoch 14/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6240 - accuracy: 0.6875\n",
      "Epoch 14: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
      "\n",
      "Epoch 14: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 386ms/step - loss: 0.6240 - accuracy: 0.6875 - val_loss: 1.5588 - val_accuracy: 0.5263 - lr: 1.0000e-06\n",
      "Epoch 15/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6571 - accuracy: 0.6625\n",
      "Epoch 15: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 386ms/step - loss: 0.6571 - accuracy: 0.6625 - val_loss: 1.4704 - val_accuracy: 0.5263 - lr: 1.0000e-06\n",
      "Epoch 16/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6721 - accuracy: 0.5875\n",
      "Epoch 16: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 382ms/step - loss: 0.6721 - accuracy: 0.5875 - val_loss: 1.4320 - val_accuracy: 0.4737 - lr: 1.0000e-06\n",
      "Epoch 17/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6698 - accuracy: 0.6000\n",
      "Epoch 17: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 385ms/step - loss: 0.6698 - accuracy: 0.6000 - val_loss: 1.3867 - val_accuracy: 0.4737 - lr: 1.0000e-06\n",
      "Epoch 18/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.7520 - accuracy: 0.5375\n",
      "Epoch 18: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 385ms/step - loss: 0.7520 - accuracy: 0.5375 - val_loss: 1.3008 - val_accuracy: 0.4737 - lr: 1.0000e-06\n",
      "Epoch 19/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6687 - accuracy: 0.5750\n",
      "Epoch 19: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 392ms/step - loss: 0.6687 - accuracy: 0.5750 - val_loss: 1.2493 - val_accuracy: 0.4737 - lr: 1.0000e-06\n",
      "Epoch 20/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6779 - accuracy: 0.6375\n",
      "Epoch 20: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 390ms/step - loss: 0.6779 - accuracy: 0.6375 - val_loss: 1.2047 - val_accuracy: 0.4737 - lr: 1.0000e-06\n",
      "Epoch 21/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6775 - accuracy: 0.6375\n",
      "Epoch 21: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 390ms/step - loss: 0.6775 - accuracy: 0.6375 - val_loss: 1.1453 - val_accuracy: 0.4737 - lr: 1.0000e-06\n",
      "Epoch 22/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6908 - accuracy: 0.6250\n",
      "Epoch 22: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 387ms/step - loss: 0.6908 - accuracy: 0.6250 - val_loss: 1.1019 - val_accuracy: 0.4737 - lr: 1.0000e-06\n",
      "Epoch 23/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.7454 - accuracy: 0.5375\n",
      "Epoch 23: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 397ms/step - loss: 0.7454 - accuracy: 0.5375 - val_loss: 1.0634 - val_accuracy: 0.4737 - lr: 1.0000e-06\n",
      "Epoch 24/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.7496 - accuracy: 0.6125\n",
      "Epoch 24: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 399ms/step - loss: 0.7496 - accuracy: 0.6125 - val_loss: 1.0357 - val_accuracy: 0.4737 - lr: 1.0000e-06\n",
      "Epoch 25/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.7390 - accuracy: 0.5625\n",
      "Epoch 25: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 402ms/step - loss: 0.7390 - accuracy: 0.5625 - val_loss: 1.0036 - val_accuracy: 0.4737 - lr: 1.0000e-06\n",
      "Epoch 26/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6621 - accuracy: 0.6000\n",
      "Epoch 26: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 421ms/step - loss: 0.6621 - accuracy: 0.6000 - val_loss: 0.9707 - val_accuracy: 0.4737 - lr: 1.0000e-06\n",
      "Epoch 27/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6609 - accuracy: 0.6125\n",
      "Epoch 27: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 400ms/step - loss: 0.6609 - accuracy: 0.6125 - val_loss: 0.9411 - val_accuracy: 0.4737 - lr: 1.0000e-06\n",
      "Epoch 28/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6039 - accuracy: 0.7375\n",
      "Epoch 28: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 390ms/step - loss: 0.6039 - accuracy: 0.7375 - val_loss: 0.9253 - val_accuracy: 0.4737 - lr: 1.0000e-06\n",
      "Epoch 29/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6039 - accuracy: 0.6750\n",
      "Epoch 29: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 386ms/step - loss: 0.6039 - accuracy: 0.6750 - val_loss: 0.9119 - val_accuracy: 0.5263 - lr: 1.0000e-06\n",
      "Epoch 30/30\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.6362 - accuracy: 0.6625\n",
      "Epoch 30: val_accuracy did not improve from 0.52632\n",
      "10/10 [==============================] - 4s 388ms/step - loss: 0.6362 - accuracy: 0.6625 - val_loss: 0.8925 - val_accuracy: 0.4737 - lr: 1.0000e-06\n"
     ]
    }
   ],
   "source": [
    "# training the model\n",
    "hist = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch = train_steps,\n",
    "    epochs = 30,\n",
    "    validation_data = val_generator,\n",
    "    validation_steps = val_steps,\n",
    "    verbose = 1,\n",
    "    callbacks = [lr_reduction, early_stop, checkpoint]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting accuracy and loss\n",
    "accuracy = hist.history['accuracy']\n",
    "validation_accuracy = hist.history['val_accuracy']\n",
    "loss = hist.history['loss']\n",
    "validation_loss = hist.history['val_loss']\n",
    "\n",
    "fig, ax = plt.subplots(nrows = 3, figsize = (15, 10))\n",
    "\n",
    "ax[0].plot(accuracy, 'r', label='Accuracy')\n",
    "ax[0].plot(loss, 'b', label='Loss')\n",
    "ax[0].set_title('Accuracy and Loss')\n",
    "ax[0].set_xlabel('Epoch')\n",
    "ax[0].set_ylabel('Value')\n",
    "ax[0].legend(loc = 0)\n",
    "\n",
    "ax[1].plot(accuracy, 'r', label='Accuracy')\n",
    "ax[1].plot(validation_accuracy, 'b', label='Validation Accuracy')\n",
    "ax[1].set_title('Acc and Val Acc')\n",
    "ax[1].set_xlabel('Epoch')\n",
    "ax[1].set_ylabel('Value')\n",
    "ax[1].legend(loc = 0)\n",
    "\n",
    "ax[2].plot(loss, 'r', label='Loss')\n",
    "ax[2].plot(validation_loss, 'b', label='Val Loss')\n",
    "ax[2].set_title('Loss and Val Loss')\n",
    "ax[2].set_xlabel('Epoch')\n",
    "ax[2].set_ylabel('Value')\n",
    "ax[2].legend(loc = 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 12s 211ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      RP_ADL       0.00      0.00      0.00         9\n",
      "     RP_FALL       0.53      1.00      0.69        10\n",
      "\n",
      "    accuracy                           0.53        19\n",
      "   macro avg       0.26      0.50      0.34        19\n",
      "weighted avg       0.28      0.53      0.36        19\n",
      "\n",
      "Sensitivity (Recall) per class:\n",
      "RP_ADL: 0.00\n",
      "RP_FALL: 1.00\n",
      "\n",
      "Specificity per class:\n",
      "RP_ADL: 1.00\n",
      "RP_FALL: 0.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "# Load the model\n",
    "loaded_model = tf.keras.models.load_model('model_checkpoint.keras')\n",
    "\n",
    "# Calculate the number of steps to cover the entire validation set\n",
    "steps = np.ceil(val_generator.samples / val_generator.batch_size)\n",
    "\n",
    "# Generate predictions\n",
    "predictions = loaded_model.predict(val_generator, verbose=1)\n",
    "\n",
    "# Getting the true labels from the generator\n",
    "y_true = val_generator.classes\n",
    "y_pred = np.argmax(predictions, axis=1)\n",
    "\n",
    "# Generating the classification report with zero_division=0\n",
    "report = classification_report(y_true, y_pred, target_names=val_generator.class_indices.keys(), zero_division=0)\n",
    "print(report)\n",
    "\n",
    "# Generating the confusion matrix\n",
    "conf_matrix = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "# Compute sensitivity (recall) and specificity\n",
    "def compute_sensitivity_specificity(conf_matrix):\n",
    "    sensitivity = {}\n",
    "    specificity = {}\n",
    "    \n",
    "    for i, class_label in enumerate(val_generator.class_indices.keys()):\n",
    "        TP = conf_matrix[i, i]\n",
    "        FN = conf_matrix[i, :].sum() - TP\n",
    "        FP = conf_matrix[:, i].sum() - TP\n",
    "        TN = conf_matrix.sum() - (TP + FN + FP)\n",
    "        \n",
    "        sensitivity[class_label] = TP / (TP + FN) if (TP + FN) != 0 else 0\n",
    "        specificity[class_label] = TN / (TN + FP) if (TN + FP) != 0 else 0\n",
    "    \n",
    "    return sensitivity, specificity\n",
    "\n",
    "sensitivity, specificity = compute_sensitivity_specificity(conf_matrix)\n",
    "\n",
    "print(\"Sensitivity (Recall) per class:\")\n",
    "for class_label, value in sensitivity.items():\n",
    "    print(f\"{class_label}: {value:.2f}\")\n",
    "\n",
    "print(\"\\nSpecificity per class:\")\n",
    "for class_label, value in specificity.items():\n",
    "    print(f\"{class_label}: {value:.2f}\")\n",
    "\n",
    "# Plotting the confusion matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=val_generator.class_indices.keys(), yticklabels=val_generator.class_indices.keys())\n",
    "plt.ylabel('True Label')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final Prediction from Video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 9s 9s/step\n",
      "1/1 [==============================] - 0s 269ms/step\n"
     ]
    }
   ],
   "source": [
    "# predict the fall\n",
    "def predict_fall(rank_pooled_image):\n",
    "    rank_pooled_image = cv2.resize(rank_pooled_image, (img_w, img_h))  \n",
    "    rank_pooled_image = rank_pooled_image / 255.0  \n",
    "    prediction = loaded_model.predict(np.expand_dims(rank_pooled_image, axis=0))\n",
    "    probability = prediction[0][1]\n",
    "    return probability > 0.5, probability\n",
    "\n",
    "# function to overlay text\n",
    "def overlay_text_on_frame(frame, text, position=(50, 50)):\n",
    "    font = cv2.FONT_HERSHEY_SIMPLEX\n",
    "    font_scale = 1\n",
    "    color = (0, 0, 255) \n",
    "    thickness = 2\n",
    "    frame = cv2.putText(frame, text, position, font, font_scale, color, thickness, cv2.LINE_AA)\n",
    "    return frame\n",
    "\n",
    "# create video from overlayed sequences of images\n",
    "def create_video(frames, output_path, fps=25):\n",
    "    height, width, layers = frames[0].shape\n",
    "    size = (width, height)\n",
    "    out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, size)\n",
    "\n",
    "    for frame in frames:\n",
    "        out.write(frame)\n",
    "    out.release()\n",
    "\n",
    "# final prediction pipeline\n",
    "def process_sequences(frames_folder, rp_folder, output_video_path, sequence_length=16):\n",
    "    subfolders = sorted([f for f in os.listdir(frames_folder) if os.path.isdir(os.path.join(frames_folder, f))])\n",
    "\n",
    "    for subfolder in subfolders:\n",
    "        subfolder_path = os.path.join(frames_folder, subfolder)\n",
    "        frame_files = sorted([os.path.join(subfolder_path, f) for f in os.listdir(subfolder_path) if f.endswith('.png')])\n",
    "        \n",
    "        rp_image_path = os.path.join(rp_folder, subfolder, 'rank_pooled.png')\n",
    "        if not os.path.exists(rp_image_path):\n",
    "            print(f\"Rank pooled image not found for {subfolder}\")\n",
    "            continue\n",
    "\n",
    "        rank_pooled_image = cv2.imread(rp_image_path)\n",
    "        is_fall, probability = predict_fall(rank_pooled_image)\n",
    "\n",
    "        frames = [cv2.imread(frame_file) for frame_file in frame_files]\n",
    "        sequences = [frames[i:i+sequence_length] for i in range(0, len(frames), sequence_length)]\n",
    "\n",
    "        processed_frames = []\n",
    "        for seq in sequences:\n",
    "            if len(seq) < sequence_length:\n",
    "                break\n",
    "\n",
    "            label = f\"Fall ({probability:.2f})\" if is_fall else f\"No Fall ({probability:.2f})\"\n",
    "            seq = [overlay_text_on_frame(frame, label) for frame in seq]\n",
    "            processed_frames.extend(seq)\n",
    "\n",
    "        create_video(processed_frames, output_video_path)\n",
    "\n",
    "NF_vid = 'Final_Testing/INPUT/NONFALL'\n",
    "NF_Frames = 'Final_Testing/INPUT/NF_FRAME'\n",
    "\n",
    "fall_vid = 'Final_Testing/INPUT/FALL'\n",
    "fall_Frames = 'Final_Testing/INPUT/FALL_FRAME'\n",
    "\n",
    "NF_test_of = 'Final_Testing/INPUT/NF_OF'\n",
    "fall_test_of = 'Final_Testing/INPUT/FALL_OF'\n",
    "\n",
    "NF_test_RP = 'Final_Testing/INPUT/NF_RP'\n",
    "fall_test_RP = 'Final_Testing/INPUT/FALL_RP'\n",
    "\n",
    "# extract_frames_from_folders(NF_vid, NF_Frames)\n",
    "# extract_frames_from_folders(fall_vid, fall_Frames)\n",
    "\n",
    "# compute_optical_flow(NF_Frames, NF_test_of)\n",
    "# compute_optical_flow(fall_Frames, fall_test_of)\n",
    "\n",
    "# rank_pooling(NF_test_of, NF_test_RP)\n",
    "# rank_pooling(fall_test_of, fall_test_RP)\n",
    "\n",
    "process_sequences(NF_Frames, NF_test_RP, 'Final_Testing/OUTPUT/no_fall.mp4')\n",
    "process_sequences(fall_Frames, fall_test_RP, 'FINAL_TESTING/OUTPUT/fall.mp4')"
   ]
  }
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