{
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    "# Semantic Segmentation and the Dataset\n",
    ":label:`sec_semantic_segmentation`\n",
    "\n",
    "When discussing object detection tasks\n",
    "in :numref:`sec_bbox`--:numref:`sec_rcnn`,\n",
    "rectangular bounding boxes\n",
    "are used to label and predict objects in images.\n",
    "This section will discuss the problem of *semantic segmentation*,\n",
    "which focuses on how to divide an image into regions belonging to different semantic classes.\n",
    "Different from object detection,\n",
    "semantic segmentation\n",
    "recognizes and understands\n",
    "what are in images in pixel level:\n",
    "its labeling and prediction of semantic regions are\n",
    "in pixel level.\n",
    ":numref:`fig_segmentation` shows the labels\n",
    "of the dog, cat, and background of the image in semantic segmentation.\n",
    "Compared with in object detection,\n",
    "the pixel-level borders labeled\n",
    "in semantic segmentation are obviously more fine-grained.\n",
    "\n",
    "\n",
    "![Labels of the dog, cat, and background of the image in semantic segmentation.](../img/segmentation.svg)\n",
    ":label:`fig_segmentation`\n",
    "\n",
    "\n",
    "## Image Segmentation and Instance Segmentation\n",
    "\n",
    "There are also two important tasks\n",
    "in the field of computer vision that are similar to semantic segmentation,\n",
    "namely image segmentation and instance segmentation.\n",
    "We will briefly\n",
    "distinguish them from semantic segmentation as follows.\n",
    "\n",
    "* *Image segmentation* divides an image into several constituent regions. The methods for this type of problem usually make use of the correlation between pixels in the image. It does not need label information about image pixels during training, and it cannot guarantee that the segmented regions will have the semantics that we hope to obtain during prediction. Taking the image in :numref:`fig_segmentation` as input, image segmentation may divide the dog into two regions: one covers the mouth and eyes which are mainly black, and the other covers the rest of the body which is mainly yellow.\n",
    "* *Instance segmentation* is also called *simultaneous detection and segmentation*. It studies how to recognize the pixel-level regions of each object instance in an image. Different from semantic segmentation, instance segmentation needs to distinguish not only semantics, but also different object instances. For example, if there are two dogs in the image, instance segmentation needs to distinguish which of the two dogs a pixel belongs to.\n",
    "\n",
    "\n",
    "\n",
    "## The Pascal VOC2012 Semantic Segmentation Dataset\n",
    "\n",
    "[**On of the most important semantic segmentation dataset\n",
    "is [Pascal VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/).**]\n",
    "In the following,\n",
    "we will take a look at this dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "origin_pos": 2,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import torch\n",
    "import torchvision\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 3
   },
   "source": [
    "The tar file of the dataset is about 2 GB,\n",
    "so it may take a while to download the file.\n",
    "The extracted dataset is located at `../data/VOCdevkit/VOC2012`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "origin_pos": 4,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "d2l.DATA_HUB['voc2012'] = (d2l.DATA_URL + 'VOCtrainval_11-May-2012.tar',\n",
    "                           '4e443f8a2eca6b1dac8a6c57641b67dd40621a49')\n",
    "\n",
    "voc_dir = d2l.download_extract('voc2012', 'VOCdevkit/VOC2012')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 5
   },
   "source": [
    "After entering the path `../data/VOCdevkit/VOC2012`,\n",
    "we can see the different components of the dataset.\n",
    "The `ImageSets/Segmentation` path contains text files\n",
    "that specify training and test samples,\n",
    "while the `JPEGImages` and `SegmentationClass` paths\n",
    "store the input image and label for each example, respectively.\n",
    "The label here is also in the image format,\n",
    "with the same size\n",
    "as its labeled input image.\n",
    "Besides,\n",
    "pixels with the same color in any label image belong to the same semantic class.\n",
    "The following defines the `read_voc_images` function to [**read all the input images and labels into the memory**].\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "origin_pos": 7,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "def read_voc_images(voc_dir, is_train=True):\n",
    "    \"\"\"Read all VOC feature and label images.\"\"\"\n",
    "    txt_fname = os.path.join(voc_dir, 'ImageSets', 'Segmentation',\n",
    "                             'train.txt' if is_train else 'val.txt')\n",
    "    mode = torchvision.io.image.ImageReadMode.RGB\n",
    "    with open(txt_fname, 'r') as f:\n",
    "        images = f.read().split()\n",
    "    features, labels = [], []\n",
    "    for i, fname in enumerate(images):\n",
    "        features.append(torchvision.io.read_image(os.path.join(\n",
    "            voc_dir, 'JPEGImages', f'{fname}.jpg')))\n",
    "        labels.append(torchvision.io.read_image(os.path.join(\n",
    "            voc_dir, 'SegmentationClass' ,f'{fname}.png'), mode))\n",
    "    return features, labels\n",
    "\n",
    "train_features, train_labels = read_voc_images(voc_dir, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 8
   },
   "source": [
    "We [**draw the first five input images and their labels**].\n",
    "In the label images, white and black represent borders and  background, respectively, while the other colors correspond to different classes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "origin_pos": 10,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
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IsEyQOt07lRwRjxAxZyCkRCVgx4eUVZAKZw1KxSi4MTbl5LrkvYe1EQcEypsPyOuG3Tu3uPL2qyitYmDmBVLGA0OpjICPa8RHglEIAZ0XbF19kdXzl3jpnc/xH/2Xf/tU8+q9p9SC8z3PvcmEZt7SSkFPw7DocdhYpNBkWiEwBDujMQsW0wOMkownNaocYkSOLDOqLEfkqwSZ462hV+bIok+wDVpKculRYR+pG9BztIrOiURRzxWLhUAqRd4LFMLFfJkXqCDBS3zwCCeRjaSnc3KvMK1BG0/rPHlesbF1jn5vhS+883Oc2bpCVVToIkcgqCrD2TNn2ds/SNBwhD2aumG+mDA+fErVG8Y1lxyy0w4hQIoMKQJCw9OHT/ju7/xOQl1OQPSJLBN92HguBCHjWg1ieRaEE8+Jz0/IglQxFus4SkLGLJeMaMzl56/w+isvE/Ds7uzy//71X6etW5wziVgWHVAfN3KKStPvE7Hm2Hgdw5tCiiXkGEgGTMTrQSRjLANIwZnr13jj9beiA8jJbPuPH58ZtH8JRggRGrOuY2dFSFFKYp5ESLTS2GAJKYEa02Q+wjE+8ouEPCZUeAdCBiQyLQaJCuAkCG/ItGY46HNYDZjPJjjpkFJFj0mk2E5EyMz6EGFOEaFGRIpIRHroEnKL151lGWWvz2C0wmC0Tn+4wqC/QllVZDqLhIm0Ln0IhLQZXGJieZcILd7jvEXr/NRz6+Yztu7dB2+YzFs+d3GNyYMDtoSlePQxD977CLt3G7HYZ4BDXbqKOvcacv0K49bjb3yA1zrdDJZEFUSM0pQUOB+wISwjqo6ZF+gg0zhRgYCSKn5e5xPEE3Og3kWvuxs6wbrWxYi1o86IXo/J5bMMbj/kxt/+rynFAc/98p+lWt2M90dqQnA4Z5BKJNZbDkictYiONStgtLXJ6traqeZVCMFsXnNhOGR/0lAoQ5CakXK0doF0LqVSawb9IXvGRLhVC2SpyHRB3hug5Ai8o1dkmKAYCU8jJHWApp5Qiin9bAJyghYznHfMpwa3CMwOa7y1qBKGqxm9YUGGxPnASrlGmRU45zELw3y6oJIZQWiECxjjmS4a1ta2GA7O8sL1F3nu2suMhutYH5BSI2R8LSkEmc74/Dtv8/6NDwjB0zRthD59jHQe3r/H6to5lM6x1vxUaV+EQGlBUAEpM5QKEBwEj8eDTw6hjysysogSFiM8+AhbS58MVfCJQZ3IRzjiBdpPZNJ8ym0hBBJJs2iXuXjnWhazKU3dgnPJmEU2qgsh2siOpNZhDGGZIY9pEUJywuMVSBFi2kRAEAG5vA6VUEWPDJGw4gMYYl7vGMP4o+OZDFp/dS2snb+4pOUu6cjLn7sfu2lKDBqOiQCC4wcu77k4Tkme+PMnhvjR78WP/9tpxqflGMOPfn/8v+W3HTQXgO37dzna23vmy/Hes1jMl9GKkAJCNGJxzUiUznDexXla0qMjUyvISGgOwcUcmw8g08JPBsfLmFMT/vggLsse585dpG0bTLuDdB4vJFK66PF5idIS50TyGiVSxY0jCATRUZ4jdCiVQmtNWVUU/SFlNSAvSrKUP1NKobJId5ZSRkrxkryS8ojBH89t+vmnidDW+30GL77K3tMdVta3yGcHfO7wG0zvvId0U0QlKIo+4twXaAaXuLW/YOfDbe7c+Q6j86vUT3ZQvRwlRJp7wHmCip6kc37p2Xrn44EtFEIJRDhB1iAghcI6F/MWOpF6RED4SCfpoMvopUZoWSiFExGmjOx2gas028OSs4sxB3/wN9j5/u9x6Rf/PFe/9Kvk/QGgUFLjg0vesU2vLfE+Xkee98iyMq61U4wYzUty71gpYBFajDcEJ+jlEq81VsT7PF1YnKnp9XqQVRSFpmwlDoWWFlTGqDU8N5/x0vSQYXDc761wc1hy1zxle3+fo8MpQjl07vEtrG+VrJyTlGUFSmJqy2yywLSKagjWOObtgsl0zCgrkTjmdYsxLcY4nMt54dpLvPLK61x77lWqqo/OiuhwEZ0H5z2+aVBaopTmc+98nv/2b/531K3BOse8NrTWMl80lGWPo/0deqN1hFRkeXbqNSsEMU8tFVK1ROZWSExEkWBrEF2JR4IdQ/CI5I+qIBJBKCyNzKcaHLEsmIgRVAeJe0+iMy3tpgwd9f5E1Lek+HfnYnylsPx/90CSa92dGst3XJ5rIjl+AoEMnRG0mOBpiIzJn2DPns2grZ2/wP/2P//rZEqhtEKng0kpmQ7J7l+FkmL5u/j7CIdImQ7GFFrKbvPKjgHXHZ5dWBohs5Pfd5PfOcN/xNjBH7Vy4RP/fOLnmIuKXsDyRqXJ6/wB3+XbfUiHS4oiQsyZuBD49/7ELz7LdJ64aMF81mCtxVpHkWUEQGuFdZZAhlIZQrQx7yIEXqTwO4QUrbm0IiMzSqAQwaVcT5wl18EJUkZ4UkJZFVy4eBVBYPfpk0RMiJGDdIJMKpxNBJMT9ylOUkgs85j/0VlGXpaUZY+i6pGX1dKYLckgHNPWSVj/Mv4Q0ZAvb0NI5N2fghVy1NZ8495tzv723+Lly2v46TZ6tstaIVErVwjrz3HgS37v2z/gm9/8hwzLAaVQzPce88bodeaqwObxPjgEwnuc81hjYFmX0xmb+LMPAR0xFKRWaCXAe6wPKKHwREjYueSYEKIPkubHW58eEw1mIEYJznusdQTj8MIzaVvyPKc022z/5n/G02/9Dpe++he4+NbPocoCGWLEF50ih0AuS8FidC8+Ucv0LMN7j7Exqr64onjsNa51hLyPtFMqVTKxC6wzCJlT6kDwDWVW0DQ1XkgqrciCIJ+MufzgAUf7T/m6NJzNC3peMOz1EUXOZDLDh4b+qmR1vYfOMsaHMw5vLQguMm+rgWZjs896b8BaNsAYy3gyZjqdM/FzFgtDrjJ65Yi3Xn2d56+/xIWLV5E6p6gG5LqI0C0CgccaG6HF4JA+IJTizJktLl28wM3b96ibloPxhEGvpCpz8kwyn0/pr26Rl+Wp12tcEAIlY9ClFGgJAk8QcZ10p/qy3jEZLtGV8AgZc+HxYYnEEZZOYvf79MclekDntAEIlyBEn4yNJXiL9za+9vI8TGmCDjFKJms5llGaj4ZOiE8aVRGWez7ioB3AEaO6kP7eBCjorv9fUIQmACXTVzJGMUkZPX6potHSKbEo02OlDPH7kwYsfbZYG9IlMVn+TRJOfH9s1bvrWBoz8Udt18nHdR9/iRMfz/Py3w6XlSe9WalQSx8iMZdEwKfr8aEr9kyeR/jj0N1PH0opptMJtrW01lIWJfhYG9YaRwgyOg8dq4jjg/D43+hJBVKUI0KcXB/nPiCS9xaWnyquYUFZ5py/eBWtc/aePqapFzjnsMEhpej2QppHsSxijSQ/F8kfWay/ycseRdmnzHvkeYnO8lQgLk8YsuMb1105yzkVnV1OUduPucHPMAaF5MubNeeaj7CPMg4Wlrq8wODS63z71g7/5G/8N9y6+TE7B4e0TaRsb62tU2aKO0dHNNZxZXOLc0UfJ2y69/G1vWnRibATqfoeJSVFlqEzzTI6S7RqJePaCakQ2MqACw6NWBa8O+fRStFaH50ZG2vQYl5Bx8MtVxip+fa9Qz6ctPwbnzvDmb7EHX7Mw//2P2T7D/8eV375L3Pm1S8gswwpM6TQn0jGS6k+FZn45xnOeebzOZurI1YrxcxpDg8PaZsprZlQ6pbMgOpvMW0btFJ4B5kW5NLhybD1Edgp9354m8PxlDp4Ns5kuGEP2eQ0vYJqNGRFOLaPFuxu10wODYORZrCiuLjeZzSo6OUVtrGoIOnbnN0nhxzUEySC27cP6Fd9zm5u8cu/8Es8//xL5FmBLkp6g1GsB4RUChUZpwoBSmHaNp5rQuKsQQjJC88/z83bd1Ayo20a6nrBYjZnOOgTXI0Snkzrn0gt/+OGICB1KocRErprjF5sdHZIMF8qc1lu0BPRVFefGl+TlHoIySB6lhtweY7E74PoIsFUEQLJ6MkTj+KYmLZk1nZ5M7d8vdAZOCE5jhbjX4WIz+vO1y4yC0SyyzGOJ5dEqu6/TxvPZtBEZ6BAyZC+ogGLBit6kkqE9LuAlD4lzUl/+6QxW34v4mGm0r+RQxYN2TLG6M62H4nMfjRK48Tvu/FHjJnobkoXIUdvxScWmoxhzvFCSI+JjPMQsWrSYwmoT1QWPdvQWrG3f0hd11hjY9idID4hDCDTXMplsrm7ZLpSkBPQn5AS4aOHLxJNPH4+tYQlfUiEBqIjkec5Z89eJMsUu9uPmc3GeKdQMlHpESmylggZIxIpFUJEmLHIS/IyJysKsqIkz3O0ziPMqFRiVHWqDnKZV/rRGybDyXvVARSnH71c8fy5jDtHNXceBiZiFV8Iig/+Ib/1z97l0f6EHoZzm2ep2watZCS8SMl0voiep3NkWUaVaZAK7+M9yaSm6JXoVCAqUjTZIQyiU2MIJIp+gmp9ZI8iEyxkj4urQ4KOcDE34ayN9yqAlBrvPXWzoBEBJwO//9FTvnb7gL/0hYt89aVVhjmY7R9w5/9zg8fX3+HiL/9Vtp5/A6FUjO4RkXADCHF84D3r8N6zPx2zaTYI0jPMHHvOMT7aRWAYLw4gK/BWYYSmV1UUBZRZwIia4HYI6ojZOHBv/wAdAmdfeoutL/4Mv/CFzzEarWIFfHjzQ6a//3s83Nllda3H+WsleRUYFhWr5ZBcZATnCVgQmqNpzf7RnHnj2Fwf8ZXPP8frr77J1tZ5Nje3loeyzo6jKJUcDKVkzDs5jxKQa4UPDmNbrHUID++8+Ta/9Tu/S/CBpnUsmpjj1SqLxKZ2ii96mPb0LEeS0xiIQYKSqRhNBIJ3SBHLXJAdq9nTGahPQIBLrz2kKL2L6iLSFBHu8MnDsXMxhVxGdtFx98cQ54kXF8soqoM3/XJfHxv1Y9Qr2bCYO+681pRiWQYGgpTO6JxgC0mLKDi3ZEb+uPHMyQklIiVZddFZMkZayPT7ZNSWkdlxRCZFjNy6anSVojUhTkRjy4gsRQMnDt0OdvxRA/ZpBq2b8AiKHUvcLKMN0jLoPG4h8FInDDo6RBAhvQgrpRvmHc7YZZiens1PCoV/0shzzXQyoV7UiUHklpGUkslwCVLOKX4aCMscVHR+IvQgpVwaK3xUE3HORVjUmxh5WQhe4FNhNiHeB6Ulq+tn4obaEUyOJkjlkocoI5QhuohaLXM8WZaj82jMirKgyHOkViliT0QSme6dOPYGI15+4l51zgXQ4elieadON6x1/LV/+oQPD1quecX1n3uHr339azx++IAszzl77hxFaDE2btxIAggMS83+fMy8aZk0w2i0swyRZTircDYedDQSsjweQCLEw8cn+EbE2jqBSOSetBYT1Ngl1gUxSe5DNOhSKLyw2NZhjAXh0VolaryEOrBvar57dETRHzKtF/w/f/cW//C9Hn/5Zy7wledHjDC0d77Gx//Z97j/4s9y7Vf+CutXXojetwQ7P2B861u0RzunmtcQAtPJnL29fc5ubDDol2RaMJkvCMEidB+djdBFRa+vkH6fEPZonUXmsFKW5Nkat48aFrri2gtv8G/9r/7XvP7y8zze3eGf3bjB0ycP2X38gPW1VX7257+Aktvkuae2Da6xbM/2GOYVmpz5fB7LF7xGUfHitZd45603WFndYGV1Ha0y6rYlLwp6/T5K6eiMSkmeZQghcaaJedB0YBrT0tQ1Ukm01rjgOXvuPBtrK+zvj3HOMp3O2N8/IMszzm6uYOsjbLmCsfaPmcFPH4LEjhUOiTyWnApdrqmT5Amp9DApd6Q1FhmG7nhD+RO7LEGF3fuQqs6OI4UOdowKINFeigToHEdXxy93jPSExOCNm9hzErPqJOHiW6RoTXQGUZwgl4n097hOOwkzkQhnojuHP2U8M+QY4cT0hV8aJSlYGjclOshQnDBk6Xd03mv8vRQnH9dVhodPRGZLo5Y+UGeYTkZo3b/xyDj5+/iTtQats08+IiQIiGPPRSY8GFhK4nShukjGoWm6SEoew8LJAzrNyLRGSUvTNjE/40zcZIDSKtGEYw2U90sXJ33G6LnFSEDEzZjqupwPBKHJ8h65kOQp/+d8wKf8h3UGay3OWqy1GOOoeiusrp1jMZ8zn9dMJ3N0Fh0WHxzWt8f1TjKgZIbWUGSGQmcUuSfP/bI4tMuX/egIiQTRGTffAQ9pGiUh1qX8FEMJGBU5Z15+AfP4CQeHY/AOXRSIAO1iQhPAuADW47AUZREZltbTtC37R1OMtegsIws+5p1UIJBhjUErjdAZCrGUHwvOIXw8jKL7EfNm8bCUy8Mh0UlwLm7UDtJsrUsFwHFPOA9KC1TaGfOmxQRBpSSbwx5ra30K3/L/+M2P+K3Lq/xPvnyRNy9U9FzL+MPf4Qc3vsHgrV/m8pd+Gff0hxx8+Id854N77Oyfrg4NoGkbZouaxrb0vGZjdYXbO6sgQec5MtcovQvuKUUvKqgUOqOUBQqNbWDnqWW0dYkz117hg5sf8/33v8d4fESmFGfXVymvXsPXe4R2D+UL5nVNqAPtPDCe1fieo8oLZnWLliVvvfY5Lp2/ynBtHakUmcwxxhCQ9Ht98rI8ji1kLCFpmgUQRQWMsZH45KPGZ9sayrQePFCUJc9ff46Do3cJPtAaR20sh+Mjnu7tc+nCOYStyYveT7FqRXLmI/wiVcrNpr2+xOP50S9S7uyYwNTpYYoOljoxOqhvmRL/hPMYloFfJBdxTHI6acS6nFiXo+tgxZOHcjgmkkVkKRbJSiIGHwlmLNMN3bm8RDy66wSqskeWfTrr+RkjtIDAJ+gwfRHlQqP4Z7zIDnJRISYqlYiMlRitRSp54s4sc2MyJIgxTX40fMmQJOPWxVudX39CI2Fp5Lo5/KTvT2T3LcmjHZkh/utD9z7xeR2TxofufbobFpDBkSuJFhnWOVprYiguTprSZxtKazY3R7TNHO8dxjiKE3m0aDwiO7A1prsVxx+WkDRXO08nLs3WOoYr66ysbkXCQSfDtPSETqy6NAc+dEXNYakB6WwsdDXWYIzFGkPb1rStoTV1zCUsJiwmj4ApWgoyGSWQkL2kHZfukDhJ/vlk/HX8c+fbdff99EMAQ2Hoj4bsbz/hzre/HyFWqXE+alsG5zHO4pzHE9hcGWJMiyAWUwuVSiVSLtC7yOiUIdKI27bFE/Nn3h8bJbAopXDeL/dEEB5JZDi64AgpOjbOolAxTxvAtiZBKx4vZYTFkCADHo8LIHVMpkuVYaTjT790kbcuz/jt97f5P//GR3z1xQ3+wlsrXBoqyvqAyXf/Lt/4+m8xGEm+dueIv/GDp8xqc+q5bZqWo+mUwVFJryy4uLXBrZ1DxqZG5jOKfEpVWkqdo1QGAeq2wckaEaBZKMZjw+bmeZ7uPMKHQy5fvMJXfubLrK8O2Xn6iJvvfwM5vQPeIPOKjAqUYLiq6eU9nu4dIXuCt177Ei8+/wqj0SrOJcfNOoKyrKysoPMswm4I8jyjK4nwNkZkIaE4IXhaY9EyFtD3+1EoV0oRZcuU5tVXXuVb330XHwLGeY5mC9ZWhvSqAdPpHGd3WDl//adatFJGR1orgRIZ8aRMUU8QUcuVyHKmi36WjrpPpMLAJwzf8ng6GeWk53cbcbkhZTobfTSSnogm+K4izC+N2CfGEldMLxSW1nRpZOUyMddBMpFRfZKfFNWPTrDnhUf+kTf7o+OZIUcRbLresLTMsXapq1MKqBDpnScJHZJYO9UZKhFA4NJfEvQYxFK7axmFBZY4b9QwPI7EllFyuhPLg7G7yd3zgieYNlrM7klwwrPwOOuSSkM8yH2C74RKTLEQItPNuiV7UziPCDF53xjzE7HdnzSkFKysD6kXC5w1eBdVsQNEmrYxQLlk0R3f1tB9jHiwLQ1FglNDqsYnyoeK4NPcixj5JLFjmXJtJwsepVAJHkiv2e2y5Z6J0ar3jun4iO9+52vUixFHBzP6wVOUMsp4yZg/kylhetJ4nYQcl/cz3WdIDNOlq3e6IQSUYcbOD3/Ag/t36ZcVCI0PMSK13mONSzBvhPT6Zc5R24AAJQW9siQQaGyLD1FzUAQRFRGsizC7FHgRI2ORYi+HR1iLENGzrxcNsbuBQQhF8AGb3jcEsMFGsojzNHW9JMkUWY4PbQKHPK2JOb34d4mQklbA/VnDl6+e4bXXXuGf3JnzW9/5gH9842P+3Our/NnXVtkYOAbOMa4zXhkV/Py5ghsfndIJEwLnHOPJlEGvx8qiJuztcnZD4ZodyiKQC4mkwHlH29ZIIenJjJyS1hqcz3B2xvbOAza3zvNLP/enee2VNxgOKp48uc8Pvv6bVH5GkZc4lbE/OSJYFwW3g8U1iuefe4kvvPN5Lly+TrNoaJqaIi8RePKiSsLR0YFQSkYnIBG4orpG/NnZFp30FzMVCT29fh+dHJJA4g5ozetvvs3g7/wG0+kC7zzzec1s3vA0qYec2dIEf3pHoUMsukhHKht38AmmYrRHEbkJIbJvIUY/SypbImBEwXJg+byw3BtLY9a9c3fAJop/LIVN50gg5b1SqiJ10jj5xSfSCOIYVkwcg3iuHXd7OMbeUjF2F5EFT6LcLQOeE9b2U8ezGbQQwNkEcXXVGtHKWuMJShOUjAxBJXEpjFdao7VaJskkkSAgA+kQTQZPnLDKyZpDF0CnOgzRfaQ0WeEYCow312NsLEBWWIy1iBCoG0ufCryj9SIVAzpsiHJDnugsqMR0gohjSx9D/uADwRhcU+OIMOHh0TjVSWUcHY6Po6dnHQG2zp5hb3uSaNosc0gREnWAWuajjltbiOMwP41ofGRyfCRdse7e3i47T3fp9yrKoiQrcrJMk+mcbMlEjJ9ZCRXFRyV0gqsR0oyJ57jXQvIkI+nEGYdUGc5JjHURRtUaVJS66u7pyf/oHJnu2rvlHVL+MshEFjrdtHZjMZ+x/2jMoKgIQrFYLGjbFutsIt7IWIqiFL0ip9SaQ2+jIyUDuVZYE4kxIot0Ymcsbd3QOhvVQ6iiA7HMiYklGScA09kC71wi08T8s9ZZNIDpniopsC7Kik3nC4yz5ErRqIZM55giKjNEJmQ8dF1HrhGSW/sNb2zVPP9cn790+QIXtlb4ze/e5r/43m1+68Yh/+N31vnqi0M2Cs16v88rF87y979791RzOpAaCThrmS9qjHUMVY+rK5s0h09o2hrnHVJASYFXnlwoelmfxlpMaMicQMto1BGKr3/zPR4/2eNnv/Q2L1y9zD/RngcPHpNrwfhwgQwZXjiECly5fJkv/dLPcf7CNXrVINZxGkt/MML7mMus+j2cdTR1gxSSvMhxCSqXUmCaJkHA0TgLGe9Dr9fHOUfbNMgsHpFVUVH1+0itOXvuAufOnuPD8S2Cd1jrqU2ks0+nM6oyZ6VZ/BQrNqZkdNAooZEix6MhNFEYmHjLO4QpPiWmA6K4d3cmdMZNxv0MJyI2CEtN2HDivE2GRngIqQA75eri+8RYNr5/NETdCIgUTHSfoovAPMdoUPdeYgk3RvKaOvE8kQxjoBMwkKl0oTs5Pm08k0GTIlBKG6mtwZMJlkoWmVZkOrIgvbcEFw/X1nmUU3gX69OCD/TKHJVlOO+RSLJcE6vg4wfpOA5LCvfSMKfJXRbTpJvqLN4Z5q2lsSFW+otYNtC2ltY55rM5Kx4yrXA+0BqzTAp3zBqd/AgpJTpTaKkio8jZxPhxUfyUgHctxhim8wWCwMHBeLlZnnWEAFtbW+w8OqBpmugxeh8NQQhIEaNHpSNzbrmGujxaorbLZa4q3fY0h1II7t17wN/87/8ueZEfC5GGyLAsipKiKCjKnKoo6Fc9ev0evV6Pqt+jV8Xve1UVSR9FQVlWycBG4oUQguAc/eGQqifJ8xyZZQSZmI76BNNxiSt3WH9nkk8mi+N6C8tNdvpxfyZAZzgpmC9qrG0j8UdqMh2V9K1pqVtDr9RY56JWpXXJI5XMm5oqJUsdYIyhbQ3j2ZyqyMlVhg2xuLppG3b2nvL06R7jyZjGmDjXStEbDPDWkRU5RZ6zMhwx6PfRWtPv9SLl20UyyKxpaKSkLHKMizlPFzxta5ktakSWL8VivVAcWvhod87qzh6b5+CXXqh4cetl/uD2Rf7Rdz7kP/qnj/j7Nw75tz+/zheek4yKnCo/3dyWneq/0lgbWwQ5LwiNpx8GZDo6PWVWUKiSRb2gyisWtmF3eoAUnpxAv1TsTSIqMZ9NsM06ayvrrI1GNDU83Z5wfmuDc1ubKJ0T8Fx/7jpvvfkWm2cuMJ3OOTw8JM80RVkmZRWPtymNIBU610hkym/G1WRM3FNdoXss/NcYY2POLUShaYhLsm5qFm1DkecMh0PeeP11bnx4MypYWIdpavYODijzgC4vc/7y86dfsEKgRJSykxKE1PEcSvBMdCgF+I4s0jm4nbnoYrRYxtORAsLSEp6AKZf0+PTCS1RGE4LEe7mMyrqoKip3JASsu+QTxixGl7A8yJcf60SwIjhhFLtecmJ5PXH/SyQZdHXLf6w5e0aDFkKkDHsfm9w5HenLWkrwGuE16Fi9FTX7JFqLiAMrgZYQhEAFhzcxVR6EwNquWV+CzYgwY3d2xw/X5YeOK8gDKRQN0RspVIwgvfeYFIWJEJUYQiJztE0knQTvscf3OkJooSv+S4ZACnKtkHicNbQ2boCQosLZbMG8MRR5Bkr9sZP9acN7z/rKGlIRi059LOrUOks6fAJrXHQYlE4tWtJ1dx5Oorl2Xk03ujnzeHSmo7gucSG5AM4EjGuZLUyCGxIkkF7GJ+iiI+8oKSirkn/33/lfsr62ChB7teFojUEXQ6QWyKwgy3OCiCKrXcF9F/ElOfnkFC5jbrptKYmb5icpa//zjBDgoPYQdIQWrSGTCiUERjqMMwgnMMFjrGFU5MytpbF22c7FOBe1FWWLUwobPE3TMJvXzBbRoamLkrppePTkCXfu38U6A0EwnowRAUarK6ytjFhZWSUvS/b292nahno+Q8lI5lnM50mM1yGVwpqW1keou8xz8PFAXjQtjbWUWYFpZ7Teo0VklN46Mpy5u8u9nQkXzq1w/syI/9HrfV7bfIN//ME5/tkHH/Pv/4Mdfunlln/7yxdOXWwykZZqBAf7DQHPZDahKHIyOaRSfR6NH1DpjEpVUb5JWMazKa1p6cuc8WzKsFQ8f2HI+KMDNJEmX9c1s+mMw3HFkyeHDIcrvPH6G5zZvMDm1pkon5blWOvZ392FEPDO4GRqDZMXCBUPSJFYwd55HPGMsdahlYpIjnNkyZBBbKSZ5Zq2jUQpraMhJAR0nsX75BxN2/LKy6+g1N9JsmSeIBSj9Q2++Eu/yuVrLyH16ZVCIGkbhi7tkPqG+Vh2EzdNpFAtFWSI7MJuD4kT6aAlokKXKoJjun4nHSGI1W5p/8vutylhEXz84kSpRwiJhPIjUGYynB29vwtAYkwilvt9edwmXkV0bLvPHo1xPPt1RPViKPEvLkLzPrCYRyzctRKT4CStNM4qgs4IKkKLLkE4goBTEi0VTSo8NTYpICTjobr+ToilFU+3BSEFpm4o84zgPU/HM5wLiNagy5yVQYnuYKsQITPro8aaS56/DzEZTZJf8ohYdLjEnRMRolN4RxBmE9CaWSJnHD8mRk/BO+rW0NYN1lgWi2ZJInnW4ZyjKCp6vZymrpNiiKFIwqhKqWQ0yvh9F47TLZ4EDZLw8NSsr1O0J4TYaiRE0kKXN/AdzTewXDBSJemmdBf0UuGjk+SKxkrrLOaBgsO5FiEkVa9P3TbUtWfv0HHu7GZUTRBdri55g7Gafgm1dU5MF4wJRNw74vTF6t3wAbJCE+qQonuwzjBtohEOSDKpMNaigEG/x87hUXJcoju1aA3T6QxnLVmWYYxjOp8zns9iGUq/z+Pdp/zwxgfMJhN8iOt8fXWN0WiENy1l1WNtbZXNrbN89NFH3Lj5ESDo9Sr6vaj277sNHaeBLM9YGa7Q6w+xlcf72MfLmjZ2H5YCYy2tzBFKYlA8bjyHxjMQhnfff8ztR4e8dv0Mz68Fzn5uwGvn3+Lv/3CHrz9+wjf/m5scTk5HL1eF5vJzZziabDNfLDgYj6mqPoNeTk5BbnsYWzP2Ux7On+KNRQkILVRVQRkKJuOG2WTBIA8cHe5S9HrM5gu+/s3vcObXfpGNzS362QZXLj1Hb7hOUZY455iM9/DOk+cKpSJZSusclWcEQVL4SJJiUqR2PnHNZZnGu6jVqIMny/PYziepXwgiRb/X71Ev6mX9niDWgOZ5wd7+EbfuP8F4QZlnvPbaq3z5y1/m82+/Q28wom5aZHP6HBqwhPNBo1Rn3AzRoMBxQXIXtiWlECDhVizp+D4VLHdF1h26CEt0qvuDSAYGoSK7PLjIdOx24nJDdsYqdYhI7y2XhuyTXPRO0qpDkULHbkcsay/FCRUiH1xsvprOiVwJNODCT8ZrnsmgHR1N+M3f/F1WV1Yoi4L1zS2KsoDgKTLJma0tsjwjinmq1NxQkKmkCWgNt28/oigLpBA82RvTWEdRZknhPda1nYyMpQgoRGTaOcd8UbM126UvMvbXz/KVd15i0MtjHk6K2ImXpIvnu9xmyv2luqwQokCrSwWtUQTXY+uG+XSCzUr2d/fYOLOJmtfUTYsLMJ3NOTw8YlHPaeuGpjXMJhMCknm9YDw+HQXaGkvwkrW1VabjGtO2n4AvowRSC0SYL44lqH3i++PHn1zY8duOEhtxdh9id9lAQPpAkOkZSdQ4iFTf1oEEnTESYil5Fn+UOOeP3zNEnb9Hj46Qss/GVk5ZDY9zcZ1iSHqtpdcWWDIaO9JoXLjyp0IcWw8PDqY4EQtpsyKnbRtsMHjn6WUZudaM+oPkjXqcbehliqAq5jayOsfTKdY5tNY0bctsEXMkvUGfWbPgxgfvc/nyeYa957n/4AlaSVZXRhR5QdXrERB41/LgwQNu3b1LU8eI2LSG8dEE7zt6dqeUHnfuI/mIlZURZ89e4MzmJkJEuNOmw6k1Fsoc6w3eOBbBcX+a8dKKQgXLeG/Mb+5MeOHKOq9cWuVL5xZc6a3y4e6Qf/SdG3z8YPdU8+q8Z1KPKUoFTjCf1ywWNbN5xvpKj55b44f3PkDpI4SXmMaiUWihad2Upo0Qkzee+cQgisCTRw+QWQUh8Lk3X+WX/8Sf5OHH77OoDS4cMZ8eURUFuc6QuUBphRA66R7KGH1lkbiU6yxSyglYa5dF7oEQkY8QVVncoibLNFpnEeJSCmsdxhiKooirX0qCENx98JRv/OAbvPvDmwQz5dd+7df48hc/z/lz59BKU5S9JEwsT+3cQrfjYl2k1lGiLgSTiBexPkyIAL6TiQuRfXicsqKr11rqN4bOVTrOu3UOG3RBU6olS9A63qNDjEpjOthHpidh+XWyJizKVZ0wsohP5vqWkeJxBLI8xWQXaR53GCEIZPDo4JgujrB+Rmv9spzlx41nMmht0/DxjVsoqQDPaG2Tst+jWcwRwXPh0mVefOUFhqPYx0gmqEkrRaYkOMP3P7xFCJJMweRwjPWxe3KXCJQi5rEGgzXqeoFrFhR5hnEWEmV60cy4NFpj/+CQvfEMkeWIYNES6qMx+wf7DAZDqqqXIpWAbRqMVgmCi52DbUrSN63h++/f4OGTHZ7u7bM6GnDp0kXarGAxnSAFDFdXuf94l/l4j9FwSJnn0TD2+iwaAyo7tdCrc5amMayvrXG0/4CmbZdq811tHsESQiLOCLnMHy69rQBLhhLHOPUx+ygu7tgINC1sOrpNkp0igCM2kJQC4QVB+ghnnmBmRJGLY2Fhmwg53TUb49g/mLOytqAox7GVTNlbQg2QmFMpWaykXNplkTZBVGD50eKLZx8HjWfFZwRvohPjYg3eqOhRVBVSKGpjaVsDIlLwL53ZpMhyFnXD4/3DCJ0C1lmElPTKkqoq0UJSaMndvUesDnKuZpK1+YTNzU0eCZjVC1yIKiFV2aMJcPvuHSbjSdJxDIhU7iKkINh05IikGhI8Bmh295nO5kgpGI1GBB8odcZB8IjWUBQlVRabirbWsT13XOuZWFaTSXIt+M6He3zr7hG/8voZzq9nrJWWy8VFPnpwusLq+azl3W89JFcVVTXAGMPh0SErgxLrAoNiiG82ONo7xFlLwGKaRSSJiVgk7nw6uIQgk5YQGkwLT55s8/0fvM9Xv/gm0+0HlEWG1oKq14+sz7xAax2JTCoSn2Rq6RNrBFk6XjbJhgU6eb7YOFeRdGZ17EKupIztf4Qiz7N4Xc6Bknz/wwd8/XsfcPfBE166fom/8ud+hReunKPfryITOp3RHbrQNW79qUboMkYRoYkkPBcZ5sikmp+Egrt8VBeJCY7zaqKLgLo82wn294lr7BCepZZu+m+ke3jjkJyI7j7BPD5mQHYo2Scd0JQOSdcgl3s9lQ91jrCIKFGM4iKKhJAoASso8qwizzVSu594zj5bDs0HvHGgJR7N9oP79IZDtFIoLblz6xbjg3021/uRhRUCZa+XIDpDmRXUtWcyb+KkCZm05mJuR6Sk4Fqv4sxwxMePH+J1hpAK50Cg8NJzUIwY6orGOW7uHPHYCOrJFDnZp20WPHz4iLppuHDuLOsbm3hnwQWkznBE7zIvSobDPqa13L59n69/5zssFovYTt5t8eLLLzCbzSDE1gmBuOj7/R6j0TAmRdUCLxSWBu35xKH/TPNKYD6fMRytgHwQKdtJiDbP4y2SMiQvPh1+rgvqTyqgxI0bj0oPQXLSUfT4hFVz4vdi+RVCbCFBAFznJQWiCCR0qgIxLXfCOLnoaTmXxJpdlKrZPzjg0tVXEGqARxE8sdYuGHplRQjRSHjvGQ6Hy6hsSQIKx1d32mFS/UzbmljYm+fIMnbRFkhmzZxmUeN9YKUsKMuSV154nkFZsr13wJOjKcYaXrp2jc2NddZXVynyDA/MZ3O+/a2v8cPvf0BZ5GhZMtI5f9pJrgwH/LW7N5iblo21VfKs5PDokCePd7Amln6sra9SFQW1aWmbBpNq/rz3BNdF1wLhHbPpnA8//IgvfuELrKwOebKzg0cwyiWZznjuymUypTiazZkf7eGlQAvJzEt25Qr15bM82j3g//77D/ilawN+8eVNnn+uz7B3utY83kHTapwWlFXMo05mUx7vPKVXlQhvuXB+ixv3FFK2BN+QC7Mk2QVitCuVphCW61cucO78eX7/uzdp6gUffnSLP/ev/QovvPkO091HqCxbdj0PIeqfBg9a5wiponCv9bSmBRHzvChBrjK8j3224mEpKbMcKSWLusZZQ6azpTGyxlKUJdu7Y35w8wHv3bzDZDrji288z1/41S9z8fw5hIC2XsQcv3fkebGs3YxoikvyaD/FEF35h0BJzRJGFJGmH2R0epaUYHGcPogjFuyHBAd2J0SX61qiIF0+awk9iiWuY0PgyfyAzWIFmyLAhGEuX+PHBqJdTrzjPXSqJp0CCJ06SIoEhaBDYqLSSNf3BpzS7JgmqjgtI79Pd3Ofmbav8Pi2JjhYX1lF5Zq2nuO8wrsF24f7PHp/SrAtRVkRtESXFUppLp4/y6OZAalYHQ65eu0Fzp47G7Xuyl702s0CXVQcPrxDXk/IlaD0Ktpu2zKu4dLmJudWeti9Xd7//gdo7ZhN5qh2QiYk1lryXo+dJ0+5e/8RR5MJa1pRSEm1tsbCQu0sZzY3aNqWvb1Dnty7jzMtZ69cYXf/gL/7D36b119/nfW1NTRRYZ0Qc3PGx2ymC7Gbc4TfT3/sSqlYLBpW1/oUZUbTNDibINE8Bzxaxs2mVPQAj/N/EDGGY88r4SRLsdWuqWQsiyclc9OCCwkz5wReEVcWUasSlkSZTh5UiCXFGaLeoGkNTRt7ujWtYfPsNd5654v0+yvE/JBlfLTPwe4TRisVK6MoDFtQsL+3R3Kvl1DpJ/lMp5/bmBcV9PtDhFQYZ6jbFu0VWmqccbRJxkxkPS5fvMTnvvAVdJYhP/6Y5rvvIgRM5wtWh5bZfM5k4pgsanb39/nm+x9jjKFflgxGK3ip+Dt7h/x7fsAbIucfHewxm8zY2tpi+8kOpo1R3vkLZzl/4RzT8Rg7c2hd0jaW8WQSJbE6jze5/wGYz2e89/77vPryK7TWIYAzwwwTAm++9TmKLOPho4fcvGk4kp6JGrHjSw7rhnxuyPpDMnGV39+b8+7v3edfezWqaZxqSIXur6GzXiRk4CND0EbFm0Jrrm6tcG/XMp1MYxSVdfnbqDhU5Tkr/YoLmwPObK2xtrLCz7wW+MMf3OXh48fce/CYl66/yt3ZmCJXSJmBEKkuTCA0uJCEHYIgKEmhy+V9b1qDc5Ysy3EhXp8zhqrXp6qKGImpqI+ppMS5wJO9Md9877u8e+M2Ugh+/vOv8Parz7GxOorr2DusdVjno36rSBJmxHPZ+4T8nJLxvFy3IZU9IVL0GQ8aD8mQkUqWujYx6pjFGXHIE+f+sdGCYyMUDZJf5qmOof54DngczpPk+GL+povG/Ak5rR9n1DrHtMudQUKaluFsLEVaEkQ6Y+Xj9zIkfajU+UOksgO/NKo/fjyjQbP45glBRthgPj2MxYZKoXQZE7OZQPaHEfdVAq/Eso6gNQcoLFrk9LOK3M94dPMHbKyvcffuA4Zlj4PZPkYGcA7hWkZlQVmMuLpxnVxmrJzd5MxWj1Ffc/7eA/7xH3yDg90j8JD7moPZnPVzWwyHGq0UA1lEBWwFZVHgdEaeQSFy6kSR994zGPQQIeb2prM5UitM66gXDTJYhon1FunaMZFu29juxXqXepk902wuh5QS0xqCh8Ggx3waqeXWGQRV9J6kwCQtOiUVDrtcr8e6GmnBLJmELIksXXnCJwoZ08ZY2rAIvqS6lKhYsVw6S6MYUu4i+Y9B0LYNs9mEuqkZrW7x2ts/y9ra2fh472nrBfs7j5jNx3hnefrkKfs7eyiteO76i6yMRmRapdrPBI0kCPWnyUUASKUoqx6tabHGRONrDV5I5qktfGwFD8J7Lpw5x3PPv8iZs+cR1ZDsN/4e4+mM/fERWkr6sx4uBPYPD7l19zaL+Yxh1UMrzY3bt+j3euzu7vF/m435Ny5e5Jt7O+xbExVffJR/OnNmi9deeRlnLLZuENMps3lNUzfLth8n2yix9Jxh/2Cf3d1t+lXOoKoY1WP2nOWVN95iZTRk5eZHHBwe8dHU0QZNfzSkkJOoatM2EcnJK9rhc/zX7z9lb3rKSEJIdLWByiocjsy0BG+Yzmt2dg+4fvkcPS04MypoLVizQCpJrjVVFrh26RybK33WRgMyBWfObJHpjK2zF7j95IjpYsHTnR1ef/Vl1s+cp1mMIUDTtgQfGPb7uHDcCDbI2O0AAq2Jj9FSU5YFCMFkPEPrWDYRnGM+X7CxtoINgrpt+eDOE37vG+9x/9Eu1y5t8j//87/E2sqAqiyjoXIu5a8FTROL7q0zFEVG09QIwHp3LCLwUw6ZnNMoCK6JDOZ4rzqjFXNgqXu679Tzu+gmHBu1cKyreKyK1EU7J26pSHmy0Bk4kfgGHNP3Sa+doMbjiOmEkUnn1fHPXaT2R1Gl5emVrjFxRmI6IsQ8olApspYZQajYgeBTxrMZNBEn2oeAT3URUbA2qiY0wSGFRskcnRfoIJGpyFEKwaQN6NT2e/9wm/3DJ7Ew+Z6M6uNCoVWsU4rQliOThjf9i/CNXRaTBTurT7C/8BLN5ZLbdx6zs73Hux9+zOUr53BrIIqcsZ8y3Z9GVl2QOO8IUiDanLz0FHmPPCsISFywzJoWlWlW1jY5OJxijYlq9Trjwd1HOL9g49wFrHdR4LSpAUFjDMbGwkr304iRpiRyYxyroxFHh9sY0+KDXbY1RwSca/C+TPCsWnaMPWHOYHkQypTnihGaVkm8NNF4SRsiCGJngSW27hLoIKNXFGIB/XECOPZI40R+7mgyYWECL772M1y9+iJKx35u3lkOn+6x+/QxztRAQCrF2XNb9KqK737nXV548Rr9qkKpgJAaUnfajjnl3E+m6f5xw/vAYtHQ2AZCzJ95AjbEHnad6kKndjOpaz6+fYv9g0OePHxAr+qxs3vA3t4BZzc2qYqC3cN97t+/z5PHj9laX+dn3nyLsj/g1u1b3Htwl16/YkcEfreZsdLrcTAZEwunFaPRkC994XMMej3a1uCCY39yFOcm5RNddyglQwYnawxh58kjLp/d5MzFqwx2D9mdG+b1gqoqEVpT9Com+/uMVkdsbm5S5nlq7RIbU4KIjTE3ztCK0zVPFVKjixVGwx4bqwMOHiwwi0OatmVR1yzqllY6Xjg34LARNK2iX+Xk0vKzn3+N569cRIRApqPDm+lsWabz/JWLfPO9OxyNJ0znc8rhJvPpEQIo8oIAGBfznZ0STpZlydnT9AcDRIpsYlNTx5myQgrY2z9gfSWmDBat47sfPuAPvvku49mcF6+e59/9y3+CsxsrDPs9msbgA6mfXySLhGAReIz19Pu9xJqNEVqmM0KI5RNK/3RGrTv2fZJci6iLSnB/2qedU4lMCvwQdRP9iRcJy29jnurHg/jdQ+UJCmTUg3WRNBcMAZeiuhPRWZe3616ngxI74xSOI774eNIZ1dH0u/WUripFdct8nMrwQaPICcQu4ic7u//oeKbVXJQDzl18g9lkjBQhKU1kuOVhekJtS0QVCK2jBNJwdYO8Xy2jAq0yYpTg06LzGGMgHAseCwEvbl3gyXeeIscNs7rGzo84eg/m4iI/vHGb3voaL77yMlWvYrQyQCqFUt2Exg+eV2XydFWCBkRiOEqEKDhQORJQ3uCtxTRN7G/lDNYZ6raOizYEFos6itEiWTSGum5p2ja2ZD+l9FVdL3jy+DGjUZ/BYEAIj3DWUebFcmFHL8ext7vD+vowIYLJ0BCWxdVLuC4xCr2Lfba0jt5lpOonkWORQvylUkBSB0F2yjcsddiSAUOQlDFCMjqC9a2LvPDa5ymrQXyODyxmEx4/uMt0ehAdirS4VWqZcvv2HZQWHBweMJ9NKbtmoJlGqyzeRxk197ou2acZAnDBYVNhfCCkvEJSViCyuAKRDfd075B7Dx6x/eQpj59us3dwgFKSL7/zOlsb67z3/g1+8OGHjIYjrly8yObGBk/399hSipXRCtevv8D+3gHDQZ8FYPs9eiZGaFIJLl44z7A/IIRAkeVcuXCJ4OCjpmUaZkjrUSEsI7XkWuBFoGuMO69bsrLP/fsPsEWJlZ4bH37EaDDg6PCQRV1TL2p0NmVnm3RwCIoypy8qJrMp0+mMuq1PjSoIoRiN+nzu1esMBxW3VcODm+9hTMNsvmD/4IDRoOLMxoBXrhQMVla5eHaTMlP0slhjVxY5ZVGgs5zgPXkZy0Heeu1lvvneXR49fsze3i5ba6tkWY5pa9okCi6VpD/oJ8q+wibHJzKaAzpTqDyq4Pjgmc9mKCnJi5K98Zyvv3uLD+88xLvAz7x+jXdefZ6Ntagy4lMT17woU56sQwoEbdPgCPT7/ZRvK+j3K4xplnWjxrTMJs2p1yxAUDKW2iCT9mGi6fsutpGpRjSBLEuYMdXFdY7aCbixi5JOGqRlDq2LmkR0sEMX2YWAc4LgRDKWcc8cy29150JXRtAJVXT5vO59Y+2fSAe7xyOXZ5ZIaFDKp6XfBzIUkno252/9vb/N0dE+Unh29z6dyPRMBk3hmO8+om4F1XBIUGUsLJWxO7LoPHcRVRiCkpFEIgLh8BA1m0dmUZZRlAUqy8nziqwsyYsiqXtE6aw8z9DeolrPu64hKwITbxDS84o0DI7GrKgcl40oX7y8fE0tk1hU6A7meFhFogLYtiUEh63b1EbeM5tOybIMnRWEMMXWNXlZJDmn6J05H3Aejo7GtE2DkJq2MRjjaNuoGnJaLceyLAneMxlPWF07y5Url7hy+Qpnz29weDDBtNHoK+W4dfsjrLnKmTPrkDwmYKnUvHTqk6fkUqlClmUxWvOxDCxGXLFeLepWkiJwtVzMXWJ56dGJTvcxutJexAjq8tUXyYpeMlaBJw/u8PD+LZxpOC7PjBbSWcuNH96lbmpeevk5YtSml3BmVPy3dAoDMS/xU+QjkkPqnEtF9sftQTrnSqbDwxNojGF/b59eWTCbzvDesTHqM58v+K0ffo0QAsaBkgrjo5bh+sYat+7c5ebHt/HesbmxwcbmBsE5rl27xrmmYTqbEkJgbXUlGtlUtF2VFdeuXqZuFtx9GAlBIRk0F/zS2ZbwiYPNBEFwlie+jxeOne0nTI961HVN0xiCM7RtS90Y8kzjA4zHE/I81joORyvkoROaffaRackvf/ENrl48h7WOyWTM9v1bNM08IhdOoHXFZFbz8oVzrGyeoyiy1FlCggg0jaG1liwvqPoDZKpHXR31EEKwmC+o53PE+nqMCLOMKsujjqWPyENrWha1Sw5rJEpYa8iLnL6EMs/JdM4szPnhzYf8g699h+m85sz6kF/7ube5fvks/bI8URsZlvO8jH+kiLCf9EglEmoQyJSkWSyoiQQRZx1N2zAaDsnz0xdWd1B/XPUBoTr2oUgNPTtUMSz7B0bZKZYdnrt2mCIp9p80QJ8kr51kMMfHeI7JJ87HcinrzLGRPJGfC/xR43gSXuxKJ2LTxhORV/fY5WVEo9hFiiLpUwohaYzh9vvf4x/+7d+gns7YefzkU+fu2Wj7bYvUBUePH3H09ClrFy4w3d+nHI2iEnnq2SQAKWw6ZOMBuKjb+Jh02CJjO5LjanCxPIS7ZOJG2eOXNl/m5+c5rrYYN8DqgHhas7v3IT+4+QFtr8TIwOrWJjYkSDP1jepqngKpQFgdF+qFQNLWiwxDlMJJHZ0DpWm9x3rHpSuXaetJxOsD0bDkBUJoNBovTVRZT4WbpxkhwNr6OkIorAtcPH+W0TDHtDO8b5EqYzo+4Nbtj5GiT15kiaEUc5ORxSQSzh4pt103hDa1Ts/ybOk9dXCeWNL8ZdIUjInoblMvWU/hmEbewRIdTG9MG2ERbRBoEIrJ0WHsTSXiOo5lB3Hxt2bMysoRL555nizrRyHY9H6xEF8m7D5uU9d1EP8pRvAhiZ0SIQtPjNZS7ixGm9FUGGs4mkwxpmU6n9HUNZc3Nrj38BFCwHxRs7oyonWOwWDE3v4+ddMwnU+xSZ3i3PlznN06w97eHrP5grNntth+dzv22nKRcdlBVPtHR2SZ4tq1q0ilePj4EXVdQ4jFvq11S9JBt2+E80xnU/KsYN7UlL0++0cTrAvLKMU4h5vPWNQto9UR1lqU1AgEOo/Sc1tr6xGKPsVYHfW5eHaLRROh3Befu8rs4DHvfW8fbyz7hweM+iU+VzTzCZIthIxsQKWi99RLuqJCxLSA8FAUBVmRR36A8+RZRlFW+OEI08zwzjGbzci1wvjY3VulNk7OWqqqwrSSPMvBw9P9Me/efMjXv/s+3jS88fIVXnv+MuurA6qiSJ8mogDeB4oip2kiq9kRz5MIgwXwqQQgQcMoUFmEydumwTpHr6xSGuL0LEdv3JK8JYJHyhKhNBJLUKkzgIuIkxCO4FNXChmW+SrhYwQXF0RssMxy3yaJjs6YLQ3RcScOuZQXjO2rnLNp+aXzQJDU65LTm3qciSRndww7HqN3aSKjmRUn6uK6x8iOBZnILRJkFjtcDPoDbNOymMw/QUj50fFMBs20lkf3HkXPXkoOnz5FaR3FP5WNBkPG4kQtPUhFkPFDKxUbTB6TkT+J63aLJMs0rYtFl/vTOYd1n1JJ7GJGEyxzazh67zY7TJmsaNp2zuHjbYIUqKpESYVUMm5eoeKNRGJVp/6e7lMKp0UQzGcLjJmjq4qQiitjPywbhZWzPLWYiK06srxCSE2QBiNEFFk2p48iugO1aRrqWU2VCR48uM3B3j6LuqGuZ9jW8fqb71AN1tG6t2zXA+I4AZsWYyzh6FiO0SDkiapsU2SwlM8SHqmyVDMIS1VsqVKfus5TS15e8ppidONSQj7RgyUQfGx308EdQkBqyR7rUEp8KMnEDlUxx4QLSJEn7+y4vU30BJNn/GMw/2ea305UOcSCcS+j5BeJLUdqG6OkIssKdMpROh9QSrO+vsb+0YydvYf0+n2sczx68JCXXrxO3dQ8fPwYheDatStcvnQZZyyT6ZRFvWC2mLN384BH29t4Ys7SBUeR54gAs/mMxXzO1tYZrly6DAT2Dw4iiaVuCdTES43zLwGdaYxzDIclk709xm2DbS/SLGLdjiDQKwqaICl7FZnOqPIS1zpciCzLLNc0bf3j0in/XCPqeAYGVUaR54TgeP21V7l18wPaxZy6jR59nhc0dY2p54zW1sl1RpbFOi+pFKZtY5+5XEenSkoePTmgrWtGwwFHRxPm8xnGGBbzGTrVntkAWkboXKX8sHeRiSd1zkcPdvjO+3e5v73P2Y0Rv/j553nx6gX6VYlSeunkhrTWCQGtwCRIUwhSA1xo6oYi11hrCEvd2lgb2x2uXc80iPv4lL7tchwrzgt6/YpXP/d5cDWQpbZa8QSVAkituToDFQW/ZUpD+NRkRMRIj3jfZNpvMTBVCBmQQqGEjgGBil3sdRFzlmfOnefP/Zt/MZVgpdyaCwn1SUpKweG7A1ZEo2lTd/Tgj2uMfThmM5Pg9C7sjBGihRRjnr9+Ha0V3pqluv9PGs9IChEgNDJTIGPRNFoRpI5eeooAcJ7WeYSIVt1LiZOGTuooVrqrWKAtojfZpXS6KCqEQOM9H+1+zPX+Fot6yp6tOZwfMQ4Nj/Oacepl1DrD4d4+ea9CSJlaRujUaTbKXXVfcR7j5PngESHW0PSHQ5pFjUk1YGWvoijLZQdbn1rKNCbCjEJHjT9nYyNIk3Jspxmdw2St5+GDR9y+uU+vX/Hyy6+xsjJiujAUeQ9rLd5aarugrCKkIdJCTq9Eh4bHpZI6CTgXxYEVeOsQ+Jg/Smwm4w3C22WOBhWjgGVzus7jSpCmSlp4oaMMJ3KF911NyXFur8P5ffoSQqLECoezHkVpGPQeYsWLy9foNmaHpR/nBk83QgCbWgEt5XlwdPwsn5haEWaR5JmiLAqklNRNQ1HkUUVEZjx6usPO9jbDlVUQMJvX7O3t06S8qneOXlHwwzu3qXp95vMZ3nt2d/dp6hYtJfPZlNm0otqIiMK8adjZ20Vrzbnz5zmzeQYQtG3NbDrHHFmCF1jskpSjMs1ivkBvnKHIMibzGbPFgiLLsfiUM82xHkzT8HQ6YXW0gvcW4yxZXqKlwtrYQeCUM0uvUpRlHlV+jGVzY5Vz585y78496tbw4MlTXur16fVyHt6/y9bZC0vnREpJrjTlsMR5T2sMK8Mhe+MZv/3736Y/KKnKkqODfcYHa/QyiVaxfhBEJI+ldWm9w1jPo91Dbtx5n9sPnpJlmldfvMKf+oW32Rj2MKalX/Xi/Rbx/YWMEm/WWjKtsc5GBEJKlFZY6+OeC4HWeXRRIonSd7PFgl5ZUrc1WuslgckFT1GWpxZZgLhHXdJtDMDGxgZ/6X/2v4hM3JTrjjkzF/ui+UDoDIx3kKS82nnLbD6N0nQSBlWf4CUimLT6FSL4FBEJRIiEvU6ZP0rlgZeBqurx0quvxj1JWLagEiH2Aol5fJkEi2VqeyM4OtyjtpDlBWurQ4TUCKXje6tOqFykwIaYnuqiRiEIQtIu5lFYY7lr/wVFaJ0FDS4xBnxIHlFACUWm8lhc6znuf5Uiha5zcUCCSIWtqb+WUm4JK3XMOS1jEfXH2YK9ez9kMZmxkAFfKGa9jHmmoh6byiiGK2RlidIqhq9KLVUEVNKbXB7WUoEjNloU0TNbO3uGxXzGeO8A27a8/rnPc/3lV5FSs5hOIjU/QT+L6Yw8z1FZjjWW1jhM3dI27TKR/6xjOm+YNzDsKbQa8MprL7CxscJ0OuHGRzfQasSFy1fJ8kiqscbjXaej1lXwJ/hWnFCfEIEQ4twWRcm1a1e4d+8BTdPgE5sw3tS4oJ2MHaKFC1EhhNR0bwkHJ4/qRIlCjFw7jD56lC741MlWLJdJhGwjxaFtHD5M6ffP0Ku2wR9iTXYcocm4kE/06T31CIkxGEsIHMFGSZ9lLJqgTZ0+n7EOGxyYWHPWL4tkmgPXr1zlGztPkd5zcWONt69fZW/vgN3WkJU5q8MhQy2oFzWZzvHWMp5OWNQ1mVbkZUHTGg4mU0YrqyghKbOMtm158OQJg1FU3t9wNrU80bSmRbQ1YRFw3iKEJ5MRbcgyzWDQZ1bPadqW2hhUgt4a29Ik4q1rW/YOD9FKkRc51iy4d/+I+Wx26nmVAvqFjpqSSbhWIXj77be5f+8+Migmi5rxdEqmFYOexruGldWzybHoHCUo8owsi3qa3/3BTcqqYGNtyGIx4dFDw/Vrl5FFFh0NJXHeJiUZmM5qbtx9zNe/9yFHkxnPXznLv/7Vz/PaC1cpqjKKitctuixxwScER+Fc7IcnU4mL97HYPWV7MTa2/tFa0StKPJ66rtFZFmHfPEvdOhTGGvK8IC9zRBKaPnVyMi3J4BNRwgXMfE4IHQIUUiG1IoQ2dhGwjqdP7oHM0dWAB3dvc//GTbLMMz44wprYqLfXr3j9c+9w6eIVev0h08kUgWBltML6xhZVOYgqJzJC/0pJdu5+xNXza0wmLdP5HOscZZHT60XlH5f6IrbG8N4H7zOeLzDWsru9w/s/+GG0D1nGyuYG6+c2aBrDfLKI0oiZYrA2ZGVlg/WNs+ADj588RCroDdaoql7slCACQks2zp/DeYHYOfjUuXs2pRDiZhFLY5US/mlBzI2h653lU65KpAmKLC+9JAAcU2BJebZocHSy2IGA8pK6J3l0vmIiJ3jrGa1qFjoWRGqdo3SOzHSEMZRatp1RoivKi9ZfJcgxIECFGGYnaEsCV1+4SD2fc7j7lOsvvUyel7RNS9M0NNMpZX+EWdTs3r3DfO8pOs8JqYjb2dg7KZySvOB8wLk+SlsEkqe7R2gVePjwIT/8wQ95480vRfVvlQFJEsoLhOoC95SkXUIRGjiOiL2LDLK/9Jf+IqZt2d/b48GjJ9y/94DtnR0OD6e0rTlmPhK1HknNQLt56oxmZ9BCghe9N5GhSpzz4EIyZiImqBMs4VK0+2Rnl42NARLY3m2R3GGyqFldXWV1dZWqKmIRLV3N3E/h7XqfIsoIlwYRcyPdOkFG+LRTIjDWslhEj3beLCiznLptyXTGxmrJz/7sz/J0b58H925xOJ5y5colVKEZViVXz51jYRwehwmO/mDIvLEMV1SEGEWEYXtlhfeBo9mYR9tPUjH6lO3dHaoLEXZ0IZDlOYPhCFlnmPYAYyMUpbKM8+cuUGQ5nXqoaaNTpbVKDMeCSgucDyxsLByXStFaQzOvUUpQVNVPRbix3kCICvZSxdTCpfPnWF1b5WBvH+c9B+Mxg15JnsHtjz9g6+wFeoMV5vNUVoNcOlyLtuX2/W2qQtMrVni6t4tre+SZTg1UYx1VawP3n+zz7ge3+fj+E/plxpsvXeHNF66yubFGnmlUFh0KnIs5uzT3IjlkUsbckNKKpmmxROKUTFJY1vlo5HygxaK0Ii+KuK5TN4zWtpRlRZVVKK1THRxLNOC0oxWSqbH0pKEsA1JmHIwbjA0UVZ9hf0RrPY2poyKKaTma7LFoW7a3n7B7cMh3v/F1tDccHY6X+bAr186Tfe5N7tz5iJdffY3rL11jbXUDa1qO9o+oFxOuXL3Oyvo6Uiqm4wkPPn6Pnb19VvpDrl29jNKaw4MjDvYOOH/hPGfPnWVldRjVUbAczWfUi4ZmPubjD9/H2ahR+srbbzEYFTzdfcrR3iGPbz1A5pLn3nyezc3zbKyukWUF/f4IlWlGoxWMabl35yZXr15n68J1fu2vnGN2dMCv/wf/8afO3TMXoZy8T877JYuGJEUTo4NAVxga+6JJDAGkQOs86a8lGoOKOS/Xtqg8R2odGYlx9yO8xSuJWBlRH+yjiJGZ0Apd5KysrUXF7UyTK40LITJyvENpjQwh3dCATFI7sdYpHgyRcCMxLuCsJy8rpkcTDp/eQpcF4/GU/UePePBgh/l4wmw8ZXJwiLMmGnYAH9BFFnNHpxiroxFvf/4d7t3+LpmSmDaWMOzt7ZPpMib5jaUsgBS1OOc+0a/JJxbhMV1WLNlyxlqktUyn8RDZ3DrHhYuX+NkvfxGCYLZYsL+/x872U55s7/D06T6HB0fMFjXWmdjGBbFUIgihK+RMMIuwuPkcrRt6veEyMosRfUhU6GgIm3qB0p7hcMCNG7fQ2nP10oQn24K9gxkrozkroyFlFWWOtJI/1aEbvd2w1MSM1xUXsVYqKsCo6PS6EKhNi5yOKfKcpm5ZKWKrEk80+JlSrAwG7A9W+N5HH/LSCy/w8nPP4V1gZi3ff+899vbHqKxkc32DquoxXyzYWFnhvQ/exyxqtjbXsdby+PEjnu7ukeWRLDWejFm0NYtFFLpeWV1jc2ODx4+fsH9wgHGOEBRZXnBucwuX+vQZE1VajDcIF6jbGlH0kFJQN3UMpwCcjaSLgUJlWSxS/ikOXmuiscgytWQXZ7ri5Zde4vd+73fJMs1svmA6m5FphZZznjy4zZUXXqPfK2laS6ZUbLYLPN7e5eGTJ2xtrvPcxXOMVoZ8dPMmf+tv/nf8mV/7U+weHPCdH37Mw50Dqlxz/swaf/XP/CwXz2yglaTqVWilCcTiZ+8dOhHVfAhkSqcIKqSehzrJy2VL5uCiackyjQiBsiq7ypaIPjgXITUhKIoSWUYyiFcK35qlkyTlsZLPqebVeULQOJ9x+949Hj54iMXx0qtvcfH6K7z33W9TSMmDJ3c5e+FirP2t1inEnLWRpBSSd4cDnj54ENd3yqlbG9BFSb+Ah/cfcOfmx2xsrPEX/s2/wtWLV3n08D6/8d//f6lry7Urz/Po8QMWs32++uWvUFZl7D6AxBrDxuY6Nz/+kN39Xc6fOUdZlkzGc3SmObe1yvbqBnHHxWi1rmecObvFa6+/yd7uPrfO3qBpFkztgsY5pvWCvso5d/YseZGTZSW37tzg8qWL3L55gwvXrpMpqPpFbBz8KeOZpa+Cj7mVGNkk9pqNLeyRKrGxSASMEw3oUrM35yMEJoUkK1MCXghUHr0ok9TNvbPk3vL8fEoQgu8Zjw41zg/QIiZ080zTK0vcss4qwhAygE0HKEBV5BA8bYjq5DKJo3YEEGsMWuZk1QCZl+zsPKU52KFXaJpQ4aVmNp1Rrq1zoaw42tlG5FFjMuYPWpTOOLj54akWcFUVIDRlfw3aI0IQ1I1jOBwyHTcxx9C2Jw72rjVOWOaF4q+7fNNx8jTgmU4OUVrT61UxyvYGYwNOxo4IVdXj8pU+V65dTa02wFrDfN4wm0zYPThge+cp+/uHHBwcMhr2lzk47z3WWyaH+xyM93jr7S/RXVEIAp+6HSTxbrTWXLt6hf29CbP5gqLqM114NtamTJsBrWk4OHKUdYVOBAJrT3/oCikQwUcZNgBno/hvSJ2SiQSZLNNoEctMQh6id06IBfhBkJdlLDnxMSe4trrOk+1HOGtYGcW6sodPHnN4dMTa6jrBB27ducvm2hprKyPuP7zPeDpldTTEtYabtz/m0eMdkJJcKKw1zMZTnu48ZXNji141ZDTqR7kuY7m//YjFoiEA58+fj+w6PJnKIMSo04coFE2AItfMW0fwhrZexL1b9dC9HlVZoUQs0Viy2Z5xSCkpypJMSZyzSAIq1Q++9dYbfOOb38C0Lc2JJqhFVrL96B6Xrr1APljF2FnMbSdI+9a9x9x78JCyUBweFQz6PVZX1/jwxvv89b/3u8wXDS9cO89f/rWvcOX8Bsa05EVGkRVx3+vorDZNnWpg9Yn8TILonUfq2GiYEI4ZeCktUemMuq7pVVV0EiFC1T5E7UkZRSAilB7iulCa4F3ck94vae+nHVmhObN5jg9vfI/bd+5x4fJFbn70EQ/ufMyDB/e5c/tDPvfmO1x77gWe7jxhfW0zli/M5ti2QeeaarSCaW+fQEoCAcXWuUv8F3/t16knE4oio6oK6tmc6XxBvZimIveCj27+kNFgiNYVdx8+wRM7pEshcd4wmc2pbeCb3/kmprVUVZ+NzQ0G/QGTgzGLWRPfGwFC8uKLr/Frv/inaUxDeEHxCz/3VebzQ379v/p1ynKAViX1Yo53LbotyPIWKwUXzl3n4cMn7I8POLO6xdXLl2L/zE8ZzxyhiS4vpiSmXpAVZeqBJo+BISWjgogP3XqKNbsi0ToTqcBbi0sJ2o4H4ENA5nmsDK/njNspC1HQ60t0OSIUA2SWoZWm1+tT9HosmjoW+wZwxqLzHFyEOQUCL3WEXFKCs6M22zYaC6U0jaux1sSkaBDo3hDnY6RYjYbMxxPGT5/imhrdK+kNV2nqGmdMgnMiDHqaEUL0dtfXz3K0M0Flirq2rK6O2NmeJBpwm5oTRmjW+ohfpwwagiXrNc5ld7+Q3H9wj/v3H3Dp0mUGwxWyPI95Imw8UKRGeoWUaX6ERAhFv99nOBhy9sIF3nhDIQVLI+qcx9iYfHbWgjPsPLqLe/1z0Dk6dNpvYRktCiTTac32zh5rG2sY49h+usYL1w4QEnTRx1tH0ywwtmFRi2UN4WmGEFF5ozWG4eoazc6T2HYjRAeMEKLuXlqoLtYlxO64Ke+rdWpom7xdrTWz2QQBFFlBmeccTKbcvHWLprF8/vMvopRkMpnyZGeb/f19qn6fjc0tpBCM53OOxhM2tzY5s7WFM5b9o0MODve59eA+m1tnWF1dITjHw4cPWVldoVBRym04GrKxspbo6jllVVHmJYrItnXBRiWUumY4XCXPMvp9g2lq8qKKhrOuI5NXytjv6lTzCv2qoMgjQ9U5h06CBmflFs899zw33n8f52EyXTCqKga9kul0xuHuNoNBbK3TMfJqa3jvg1tYZ7l7/wFVFiFBhOT1N17njZee5+LZdXq9iiIvcM6iZQEykhqE0tHIOBvzeUov6eAhxPIHmR1DzB0N0ZgWqfN4ftgI0xcpP0YiOuVZJGDFjuGk2tTYNgmSwYtQQKK2f1IU/FlHCIHdo0M+vHkLmeVMmoLxeMbF8xeprSfPS7Yfb2ONwLQhFtLXhtFwyG//zj/lYPcpR0f7eCAv8oQkBXb39/mv/l+/zvhwj8nhmOA9155/nkVruHL1Gmtr60ip2dnZZufJAw4O93jrzc9TKE1dNwwHfaTMUWQ45xjPFnzz2x/gXIv3UXPRB8diMWd7+zGtbaNMVfBsP3rED374Hiur6wz7Q4p+Sa56ZMBifsTj7TtcvfwS77zxBgdHe2zv7aI8fPu7X8fbFin6mHrGk8ftTxRaeMYTOC7YEDxVlbN1zbH/uMbbfmTSycRc8YHgWpy1yDxH5nFShY50WWvaWLvlbKxvUIosz1Prh4RzOwfW8FKv5n0q8tzSirMoXUSPSiuyqkcQiqKoiMIZUb9NZxrqBqUVWZbHHAkBZCCDRP+MsJkkJEpoLDNwKfI0KBoSvV3EQvK8qnA+qk74ENX7CbF2pp7PT63hZoylUJLBaINm+jh2124X9FdLVJZqvZzDmBatKrpWMM51dSFd8fMxfZ+UnBZ4qjLnoxsf8f5736csB1y5+hzXn3+ewXAY66KSRpyUAqXz1D0hFsD61CfNmEhF1qkTbwjRjDofiRRrZ8/zRlVFIeRUa0Lo1ssx2aeuax7ce8Tm1hZKChZtza0Huyzmnjdee8zMXKSqKrI8p1cVtKY90QPu2YdSmtY6kIZLz19lb2cn5qGSOowlQrd5npFlOZmUOGMxIhCcx/gQmb1CIlRq15EJev0Bzz93ncFgyHQ2o8wURV4isPRTburBg4coJWIPp/GYQa+iaVt29/cp8owzG+tkStMvSnpVj+FgiDWG2XzOw8cPEVKysbrOk50dGmMp8oyLFy5GEQApccCg1yfLMjKdoZTGesB7ZvMZs9ZSFSU+BAbDFZqmXkJrHkGhFLk85ckb7T4+RDJFUWQEF4UIVkYD3nn7LW68/wHWWmYLy7zu0Zg+YWq59/EHbGyeo+gPErHAsX805ebdewx7Ff1exaWLF1gdrdCalpdevMrz1y6Sq8iwiw1OIwFKSYXQWWTRmhYhBFmWpVx/R5QSVEWOsQ6VugGH1NAzL0oQibGd+v95T+rGoJdGDSnRucY6h2ktWQYhRfsekl6tRmt9AvY/5Zo1FiUc585f4tGTB7TzfV649hzWBcoq59Ll6xRZQdYveOn1l+npjO9/5zv8sz/8XYKb4+YT2tmUz331V1jZPBeJd9LjnUWKjMvCYI0nBBfZ3a3mwf1dPr55n8nhLs7EEqu6adje+S1ksDgbYimPzBAhstd39g55tP0YrQsGq+toXRKsRZRw/Y3Pcf2Vd3A+xDZKOvAP/unXyHXO1uYGWgkyYWnbFjefM+wPeeHaVSSSrfUtVlc2uLC5z/ZuzPE3tsXagFeCsig/de5OFVL4AM5NCPk2xVrJfLuM6sghJlI76EvmZTRmKmLSvmmS9BKgojq0znSECkTqJO08Qcf8XC/POZoLqmrOtljDC01wFplayuS9PlJrbGtS80W1TOpGzDiyobSO/dY6Fl70rlwycgLlJahYHG3bJtFhYzTiXEjUdLFUrddCYK1H5xmWqEJSDUfHObVnHJPJNMJZKqM/3GB/536aS0mvV9A2LcZY2rahLMpEXLU4F0sffHDLupcIKcbiXWM9WTFkfSNn89yEJzvbPHj0mP2DJ3x447vkeZ/z569w+fI1Ns9uUpZxoUS1DrM8HITs5GzkCRHU4y7fbdtSVP1P5KeWQxxL7pBIF6PVVZy3HOwf8YP3PmBlfZ1qvElrDnl0/wfMmox6sSBTBetraz9VnkdlGcXKOs3kkPrgkGow4Gh/Pxpkpen3Bpy5cJHhaAMz3kZnWaKCe2pjGBIPK080jpIAzrG+tkazyJnMpmR5wXwx563XXudgfERR5CghefutN9nb32U6X5ArRZVKQYxpKbIsilwLmDcN1sdcTlmWDPp98jyyH+ezKXXTkBcFa2vrnD97LtLEiWs8LyrKKrZoatoWoQQm1ddZ02IbQ1aVmPksdR+XZIVmNp+zmE/RnC7XI6WkLPKURxexHCRRr733PHf1Mr1+j8VsivOBummYzBZsrY1YLBbcu3ODqy+9w93HO7z70QPe/+gO+4dH5FLw6gvXKMucvCi4cO4MVy+cpyxiE+D5fEavKmNDzoQWubQmtY6RlVuq4UdjG/uJSaSKAsKZztCZxoUo2hcJUS72S9MaIWKdlUztTlQWJa06NKJpFijdQxfxXHE25es7hm7HzTrlkMbSF4qf/4Wv8MGHN5iMJ0igMYGnu/djB/TekJ6oeHD7Y373d3+TnZ1tCh0Ns/dQ9IY0ruXG977BYNBH5xWTw6cEEw2byjKcNalmWJLlVWxW7FOLn9RoEy+o+n2aZoF3MbIVqVTHE0GOLOvRtAtynS+d6eCh6g0QWlP1qtjIdnpAW3j0eAHBcbR7h/lkwVZ/lY2VTQ529xgfHSB1EcXtvaDIcwajErGIaaxe1ecndUl8RpZjwJqY/LRZw2ynxVpBsC3Ox1yY0DpGWVIh8wKZmnfaekGwbWTBybgIhFJI4ZDaIbOCYthDBIe3LV5qtCr57fcNF66OsKpAUUdYQwSk0OhMxoMXUnlArK3pSCfeeaRKUJGILCzTthFbTzUswcP+7i4Iz9mr19FFjrOW6eFRWpQCZ2zExEPANA0q01GFYz5H5xmgqWfTUwsUG9PwtT/8Nr/8Kz/HoydP2H/8hI2tVeqmZTDosbMzpW0bWlPE2jkRPVWfVC6W/0+4vjGG1ilktU6R5Yw2MtbOXuTl13+G+XzK4d4Od2/f4P69O9y7+zF/+DVBVfZZW9/gzNnznDt3kfWNTUajAWXVi00QU2+0LMuJbcVlLJD0nqauY1SXGhEuuZdp/khsVwUUZcl8MaNtDO+++z5IzaDfJ8tK9o80Vy567j8doKVmPpuws7vDoq5PNa/pMrhw5UWe3PmQB3fvL1VShitrXL5ynYtXrzGZ18wm00TpDwQZoVFPjEh1ppekh0jVlqyMRhw4y872YzKtWF1ZRUnF1avXIstOSlazAevDAcZZatNSN21sBxJAK01eZXhiXVOmdaxlC7FMwRqDkIKy6iGzjKKqOLN5ll5Vptx1lCsrCs1oMGC8mJHlOcol45IR86F5zPvgPUWvRwgRoupXFe3CUJw2+BXRwAsRozJvHTrXqCS0vba6wtVrz/HBe99HK01tU9GtUHgUdz++wfdvPcVmA15+7jIP7t0nl4LnLp1ldViRKYXAUfZyqiID53DOUVZVhPalxDgH1oMMKJnFHmHdoZuuI8tyuk7VsZlsRmssWkYloKgVqvDeYXwgJ0LKUQ5OsVgsyBKL2HuPVpqV1VVCCEvFk05h3ycovoMdTzuEEhzsPWW8GLM6GHLu3AWqPKqa7I6vxDMuMlU4PDrAqQGteYRtWqqyj8gqVi6txq7qwTM5PMT5/Yi4mCgF6EONdwFpPY2zKLWIsGGIdabWeXSW4Qg0bZT6i6olAWcgKwucD7T1AtNMGK2N2N/doexXeEeSLZsx2lhjenBIUZXUiwaV5ezdvxNz+v0CLzz91U1Gq+uM1jcpCkUUH/YIND6ssyECQjokecxr55/ew+/ZIjTvcaZBSEk9kcx2RxAEVVkTioqQ6ji0VAitCHhsvcCblk62OASHQBEi4I8JEus9bRNDXJ0VBBHI8oLpZMbYSQo3QroMlMQFifAa7RRKVzi7wKfq+FSTHtmTiZLtrENqloWvse9RLCiNC9DRW11FScF0fITKEl1cCJq6jjp/3sdNoGNebjEZI5RkMFpFBMF0fEjRq07dA6nXy/jaN/+A7af32H3yQ168fp3hqGKxiB6Jc/vUTU1pqkTfj3JR3kVPsrNmNnhaa7FkyCSsKhFMJjOUUPT7K/QHa6yuneeFV95hMj1gd/sxhwe7jPefMjkac//ex3z0wbsYa2KtU1FRVX1GwxErK6usrq3RHw4pq4rx0ZRvf+sbvP72F3nhxVcj+0jEJHDomvrFux4PaevY29sj05IPbnzMYtFQ9gSHR4cY41ksMr7yxTH1TFE3gnrRIHT20zi7IGBrfYNh7y3u3Pghg16PjbNXWF3f4Pz5C/RHK4zn9xmNRuzuPUiFoY5CZ1R5QaGzZYJfkMSVfaDMNOura+gsp2lb+v0evbKizLMYtYjYf9dbR57llFmGLYpIQnJ+Wb9jTNLKsw4pHE3bRuOT5xRVj2DjoasyTaZ1Ut2HEKKRksDayioLY6LhFYKaQNs2SC1i8XJR4NtYmN2vehRFydHRIVu9jL1TnrsRZIn1pT7lpGSixEfBYMnPfeVLfHjjA5yN5JDWxPyeDzAcDHnh3DrPv/kVPr73lO98/z0EnvW1VUYrQwaDHvjYM80Hj/FEyE1rjA8EGzuQx+7VGQEfBccBRKAoS7qyD2NsjLiUBqJ8ldSKAh21Tn0g13lqOimXqkDeWopM43w8RK21S4GGTi0jz7NjmasQ0RzbWrLs9FqOosgIymOsYdHUTCdHaJ2T9Sp0VtEbjPDG0TRzelXFO2+/RakEOw8fMjs6wFHhjI7CFlmGMzY1641Cy3lZUM8n5GUPU8/RWb7Meassp20iWcu0NSorwLd4L8iKjGbRkuUZxhiqfp/gHN44FosFzjqMcbi2IRcVgli3a11AWYsxLdWgYnK0oBgMQUje/NzP8eJrb5IXFc4GWuEQGDxRjF2IQFH0kDpPguySn9R78pkhxy7vFIIAWUU9Rp3jnUtUfY9rGzApvwOY+QJb1xTDISLLUMojhE7kENBZTqZByzbKEwWPNw7b1hRVjpKgpU1yOZIgPUpkqI5VGTwuVu8CYF2bIjURVRyyDGdMWow6id9GHT8lBbUzeBch0/lk+v9r773D9LzqO+/POXd9+jxTNKMuS7LcLeNewBgTEghgWCC0DcmmbJLNpm5Jsrk2+27IJiFvdt9lN9mQzYaEECAbqiE0Y3ADg3uTZVtWsXqZ/vS7nvP+ce5nZGHL1owkjzS+P9c1F1gzz8yZe+7n/p3zK98vUWjUQoRlEQVdhGVh2w5hp0OaJHiFAralCFuTlIeWUfA9ZsbHF9xeblsW551T4bGtWxgb9BFSkcQJQRBTKRewLDOsG5djgjCgXCrOXac0MbWMVCmiJEVLH2EViMLI7DKFNG8uLUjiGIT5eULDQG2YwfooApVJW5kBzLDTpDEzxeTEOFOT40xOTLBv3w62bQswOovSpNWkZKY5xdqN5xLFIVIaJ+c5TTYh5rYYcRQyMTGOYwnGD48ThOGcSWiammYMrzLMivWX0es+yKNbY6SUrFm92tQ5FohjWZAq1q1ew4qxlTQaTaampki1SX8eOXSIVqNJr9Mm7nVJ0ypKS5xUMVCuUPR905qeOSVrpebawAueh+95Wat2pmqCQkkLR2QqJFZ/QBxM+UZjOaahw2QREvouFUliXAnS1DTTSGl8B5lrrEqNZrQ2MmPCMvdsueCxrFahEwQo1zZKE2mE1ha9KCYIuni+j9WJKfkFqqUyca9DXTewTmK3YNs2KlVGai07QVmW0T4sOj5rV6+gVKowMz2ODmOiOAaMbJRng6NDpFZ86Rt3GkFhx9SfKuUq5WIJKSSD9ZqxPbJsPPeoGae2LNN1OKdgI+b+DsbPy2gZdrodbGnjeu5cd7ZKUySCJE2zBjcxVy5Is6CllGnAchzPdINaci6omc5iM7dmpKYs0xgmJb5tox33pNLktusyuGwM2/OJVYIWFrbj4RU8wGSegl6AcIx01caNF7BibCVJGNFsNPn2Hd9manycYKZhxCa0wrI8ol6IUygQRwGO55MkCmEbVROVpU3TNMLxfeLYZKKMD5yL1jFRaOQNtZQIZREGYRb8bZIwRUrHlCakQxhEpn4dmrnlXtv44XVbXZPSjyMQBUq1QUYHh3Fcz6jAZJq4UvRT4Vb2/vPMM9vut8Ad59rN92JLrTG2K6ZTLA66hK1ZCgNDppVYZTJWUoA0aZRYguVYRj1Za9JYAZFR9LDNa4QwitdKWKg4Jo1DVJri12pg26bz0coeDZZldmD9TqQk8/DSmeyTTs1DPk6yo3/mg5UkJDI2wSmMTFdjJpIc9NrESWqcioUw5ppa4TguSRTR6/aQjoVXKtGXyxGWR2e2SdBqmM7fk0gzLBsuc8kFa2k1Wriuh9YQRTFJ7FMoGMHUOErMAHexAJgBzyQRSGlU4oVVQAsHlcRIy2i5aS1IYtOFJaXAdd253WzcM12crmsbzUrLAxdK5RrDy9dynmUj0HQ7LSaO7OfwoX0c2LeHQ4cOmxqUFNi+qU2GUYRjy0xH08LzjHakpkgcJzRnmti2xeThw0xOGIdqy7LxPJ+RZSOMZU7Rmy68gaLb5vDUTgaGz6FQKM+dShaCytrzp2cb6NRYHg0MDKDRtHs9M4slJYNDIzy5+1narQa+51Ms+CwbGsZxHRxpm4Ym1U/tmiKJJcXRkZHM3iVVIFKjRielaUnXWpssrTKdnnbmJm40Nx00giiOMT1RAsdyUUJjCUnfhBwgScwuWimNLY3fndIJynFJ0pTpZgNpSXrdHlZlWdaJGuMKI3WVpimtZoNisYjneqwSMQ+exLWdSycrNZf2R0CiEg5OtrnvsWdIscwJAGPOOT09BUmXwurljJ37Gr71/S088dRTDA1UWDE2wrKhOgXPRQCDQ0NYlsBxHSzbzHlamc4qmMAvHAtLZkrzFsRxjLQcM5IhBKVyGZWk2ca3b3RrdvmWZYQWLJkZ10qzQRBSmrEg1zF/0zTJ/FlEVqNSJImir4cvrSygZSMQqU6yssbCKPk+l65ZT6IV2w7tR/o+rutjuebZ1G9UU4nGtW0sx2OgWidOErY/dw8z09OknSZp1MMv1wnaWWnLzjQaBwbQaYpINUrH2aEi86HUNliOGXPqBUZAQhkxB9vOattukSQJzYEE40ZveyaY6cTMNtqOCVBBp4flu0bIQkIcBLilElG3g1cs8szTWzlvwyYGHAeQmSSZNAcejCGu6xURlkKoTPfxVNXQzBxajEiy7p44QakEu+CDTlFRiO35WL6HsK259nG/XOmXo4wQqDS1lzSO0KnGssyRUgthPmeDVDaOZzoYhW0hLGtuXsToHqZMHtpvCsECfL+I53l0mm3SNKZUqaBUShIlpCrB9Xzi2FiyK61xPM8oNPR6RvnfcrC1JgpDHNeh4tfpdTokYQ/b8/BLJYJ2G5XEVIZG8FyfXtAjjUK8Ypn2xORJmXxKabNitEqrSKZXZ2wwur2QSrlAq9UkjkxaNkn6O+GUMNRIRyLsEkkqEKRobdqohZRz7ec6ezhGYWx+f9vCL7jZn9WoeIRBB9u2jSICmhgzs+f4JUZXrWPdxk2gUnq9NkkSMT0xSavVQkuPOAozwVrTbecVCqxcu55adZAnHvoBaRLSabU5cPAgcZwQJwmO41IuV1k2toK1qzbQ2/0Y437IoSMJF54fM9N+nlnhAhFCMjRUJ0wVloCwF2I5Fu1WKxO11QxUKkhpsWz1enZtfRSVTlEqF1kzthzPdRC2hYVEWmYzZ2bvTDOE65rApJSaG+A3nnL90nXm04U2WqVzEmKZwn9W97GESdcnWfCTCFKtjeVNJj4rJQhtHvDSktgSVGJmL5MoYe+e/aRaU6wNsX7VAN2gh6sL1Ot10jAGzzT3HD5yGF9qVtdP7tpqpYwMnjDv526seHL7Hh57aie+Y7FhzTKuvuJS7rjjDoSAbq9HXCkwsGwZV934Zg5O9fj8l76Gb0tsUkQaYkkzO1cuFamUS+YELM1wvWVZJvBnQdhxHGzbKPUncWb0abvmb5TEZl1aZ5mcLGApU1fTWS1ap6ZWZuyLTD3Ntu25Tsl+rbC/idHanBCF65HEMbEyIsxRnNLO7IaGhwZPahMmhcDSimf27iGyBTXPCBKL1GSNbMtGOIJIRaRCUC1X0Knm4KFDPP3k0+g0BsejUPXRqaRUHcCylBGA1hYqikxA0wqljMoTfUslABJzUEgVSkVYlsXAwDCNRhNUStQLUCmZ2DeZRmxyVIYvk+NCK5yCZzZgrocWCstJkVITRSm9RhMVR+zZu5vh11yO5ZhUtbAsk5lAYzlGSBsBpApJ+pIzfvPcRigsYpQ2Mj626yCyOQdhCWzXw/I8pGNmQrQwMz5apebYXPCRmTJ2koRGRcSSZtpeg+V56Liv6m52P8Ky0ElCHEVIxzHDm7ZNEie0mrNIy8ayLLqtJkkU0ms0gEzaSkoKlQp+qUKSpIS9LsVCEUtKktA0h7gDAwSdLt12i0KxSMUvkiQRYdDFcW08v0bY6xKETXOa8Qv0ZmaY7XXQaFy/iApCOo3phQ9TarN7l5ZFrTYw92BUGrrdkPqgiyImCCNsNyYKY1xXkaqIKHEolsr0ejEqVdhOv9nFDEGqbFLfspwsHZMlAS3L6NVlqgnSEvheAaQg7IVIKUh1SqFQNJ1xTjEr6mtK1TK2rfG9KkncI0oUs82ukTZDmgeCX2V0bBW7n91GuzlLFHbZt3cPSWL840xXmoe0bJJYMzExweSRIzywZRvFcpXzLh6gXjlEK9g4NzO00IubxhEDlQqFQgGdQi8IsDL/i0a7S6dttPJWr17L8NAwzWaTUsFjcKBkTl+6b3GUjZVIgdDS2JYo4wyhsxQXWmJnAgL95oS+7JbMUr1CKZQ0zSBSG8X4VCtsaRpnyEYiLGECMmQiBpZt0u1S4HsOni3pBYIg7DFQG2BoxTpKlSpuoYDrFanUBoiCkDDsEQYhtuPgup7pANQBdSd9YVfqCWMCdpimPLfvCM/s3MfhiVnKZZ+brr6Q9avHEEJy38MpYRzj2ZJKtcp1r7uJq6+5Dun4fOYvP4YipexZVMsehaKHX/B57rld1KoVCoVV5jmQjfJEcUySpRwdx0ZLQZSmJqgClm0TK4XjZDJVWWoqTRNs20WpxHSTYs+lFftfZ2epOddxQFtolWSt+ObvJ4XRf+zXyQQQBN1sJs3MrlnVMsWif9Jt+1prHt25A+V41AaGsuCo58yQtTQjJRJFoVBGaYWKIw4e3MvsxEHCVg/SxNxr/RlfFGizVi3tzFHbyNSlSYhSR2fnzCNCGUeRbKavUq3Q7fXQugBCYDuZ9Y5Is7lV46hiWR5IG0sKcGwcmc0kC50F0ASloYgp9wgp2b59G9dcdT2FomfkDC0JOAg7xZZkTT39rvOXfhbMr8tRa+xiGeW46MwhVmSdbZbtIF1z7EzCMNuVgkpiI2vlOKRJhBDGCkBIo4iv0hSV9FAqxROmwJwkUfagsBCelxVzI9I4mpshsyyj6JyomCQMIU2Jwh5R0KOvYWh2wQlBr2t0HAV0mrPmwZLlvkU2g2JJ080Y9AKjKZm53fY6HcKeOaUVi2W6zSZxFCAdh3KlSmd2lqDbQthywW37ZjATE1zmis6A0ERhik4Fvl+gG4a4pSq9OEYQkyQJUSSo2gVq9SJpktLr9ubqWb7jmJs9VZlrtMhuPGtOd86oKZA1KMSUKxUc2yaKwuwUGsylWD3Po1guESUp3SDBLVSxPZ+Zg4doN3tGV1NIpG0xODzC7OQ0UxNHCHshO3bsptXqGC8wbQY+/YJpXGm3GtgSwkSTigID1WX04mGGnD1E6clZcdi2w2yzQ6vVoV6t4vge7VaXVCtc22V4cJAkUQRRQBCEgMR1C/iuGZh2XRfbcYwjciZrZBTbjftcnNVuLcu8P4zpaWruLTC1S51kD8ZsfjFzFDBv6KP+VcYJvN8+ZVLJUpt0GtKYitpC41qSSrmAyhqEEiUJFaxcvY4oG+GwHJuBgTrT01P0eh20NrYmtuMyODjIBr9HxWu+RPLmpVFKcd/j23l4607SNOW8dct5/1tvoFL2CaOUp3cd5J4fPMaO53ZywYWX8Kabb+Tiiy7CksaW5La7vs/O3XvwLFMv0TqlWiojRUqtVubRRx9j44ZzkZ5Hmips25QZrDhTis/eLxqJ47mm7p1mAsNzgsNqTnQ4DAMKBTOUjciUjLCPmXEUIqvRWXpO4QZl/u5hFNKcnSIKAwrFIrVanUq5jLRtLDurUScmG+RY1kltwrphAK5NdaCKFCqzcNJobTopdRKZoOK5iCwbmlg2F118Kd7POmx/ahtBu00cxkRxZBpokhidpsQqNZk1rdCJGbsxsnTZCUubTl6VZIa8yjgQtDstCsUCvu/R12btD5+b/zZ/kzSJUCogzkpAXd0PRaa+nG3VQGqkJShX6ixbsQwlTBejyOxreF5tEoTxB0WZktepSjlatkW5LGgFKUI6SNsxPmaWkeGJe12z7NTYnJDVHBCCNEnQIvMpszPViTRGJBqZ1a2ioIdGmPmIbEjbiOsauxmT1+2ikpYpYjrmhlRCm9b6LPUzl8/XmjgMTNeZNI0dps5ndsRxGholiay2R3bxNKBik5qzLHPSSwJFo9sx/ku2g+O6BN0O5jQo8Qr+gk9oIpuXE9mH1lnruLAJ3AqtXkh9bCUzaY1ExgSxwhWg4oRuq81zzVkKxQpDo8solYqUpSYME4KwAwh8r4hlSeIkzZybxdzOs984YjvOXJdYq92mVCrQ6/ZwPVPPcx0XaZmdbBiEmWSZxC0OUK0nCNlkcuIwSamKFuZ0eOTgXiYnDvPY448yMTlhus0ShWWbJotyqYjjOqRpZFQFLIuCX2GwPoBtlWlFAxQLEwu6pn201kZnrt2m2WpDp5OZcVukSUIUR5TKJZLEItDaKNY7DkGc0A1DqqWicVaXCiXJfBFNg4gUYClNKo2rgdYCK1PDUamGvtK/MNYnZG/yJEnnzG37Oos6VXMPYikFKVmg0xJ0auyaAN+1KBdcBqs+SayZbAYUC5Kde/cz0wpxHQ/LMTU9AJ2YYOoVfCwhqZTLBN0OfjqNshLT3LUAJmdb7Ni9nzddfzGrlg3juRazzTZ33beVJ7btYbbZ4oL1y3nvO36BtatX4vsFOt0OSdDjSGOSL335m0TdFvWhKgXfoVIuobTCscCRNk8eOsDuPbt4zeZLzQlZmmeI6arMmrotCzdTjfD9grmutpkVFGiEJbNBZ7L2/UzFIzvt2I6LcaTInCAyE5Fu17gXAKazNKsN1oeGs2dd35YpRiiT8sWSeJZ/7MN+ofcsZv5KJTGQouea30zGy+rbrqQqMzwx2YJapcpll17BxRdeik7N74NSZgOWZuLcz7N7EspIvBmj28z+Kms6QjNnDZVqATrptyyAVihU1uav5jZqJkNhrKm01qTatN/rOWd0jRZZl3smpO5IB69YoFQ2OpzmWj8/aCkUcs6g92Xi2fwCmkpTmkfGUe4g2EZ6yfU8MzGvlekciiNUEpsCuBBzD/n+hVQIrNRcSGMpbtJtaZKlaGTmupxpO6ZJQpopZdDXScuK0SqOstpc9nO0eeJoYR4QSmtsoSFNzU4kVdjCKPGnaZz9EbNOPJ3OFXXNVB+gU+IwMjYzkP3xzc3Qa5lZOtPQIknC0ATxBSC0hiRBZydcpRUogfLLKH+IdnCQZfUCaVSiK1Jc3aWQddzZUtGanubxB76NSh02nHcZGy64iNpgnVLRCBv3goheEGFL25R4MreDvq+ZlXVx+r5F0O1Q8H2T97Zsosh0h4ZhSG2gRrvZplQ0D5/pqUnAFMWHRoZZtWKAH3z3ftxCBddyeOyRB9nz3LMcOHgQpRLS1DThOI6buRV7ICyEltSqdTyvSC9oUS0V0JZNko5S9J7pZ44WhFIprVYLhCDSGpLMP0pHtHoB3aDHQLUK0p5rAzeiviH3PfE4N11xJUN1d27gWguz3zRvbo3AqE/ECcbFWGmkkiiZWfjQtxPJWvmlILFNE9NcBSs72VmWCURaCKxM2NW2nayzz5jTetkpbrqdoJRRIZmYnmTvxDRueYgkTUhUgtPt0rRnSTKvQUSmz9luodOYAa+HsDzTALAAhgYqvP8tr0ULmG10uPuB7Ty+7Tks2+HKSzZx9WUXMjw4YLIhaOI4RKUxSMnn/unbTM1MM1DyqdeKFIoFapUClZKL77iUShWuvfYGVq9Zk50ctBFuyBoifL+AlJI4iU1npTT1FrM5SudmX/sNHEKYlLvpyDvWpZnMdcPNNmhpqnBcl0q24Usy9+n+prrTMdkk1zV1Zq1TZJpCetSBQggjjrxQhMC8VxIzAiMyYQeNZVKF2Sbf6JFLNGaTmrWtIKUZvNcSE3B1grAUpGaMXug0a3JRaGU6Mk1gymrWKktRYoKrCSBZUMr0GXV22uqfmPpxRmvjp6a1lV3rzDeRbGOGRsksoOrM+9IiC9LGLJi5+dpMAtgcMeacPZ6nXvsC5t0U0umkELURVoDluKSZO6u0nOe1z9pgkwmGusYEVJmpfBPs+/MimcyV1qgoMtYyWQ0o7nZN8bL/C2Vfa45fHC1iiqw4rTITR7IzVvYm1ro/OKmPNqJAVqPr90MbC3MhFI6tSBNh3AFSnclmmeHR7C88VzAVSpMoDWk0t4aFoIQwXlfKMrUYU4BEO2XSJKHRTXEbIUlREceatlWiqjqmC9O2qA8Ns3LNSp585FHuvG07d3/nm2zYeDGbr76W1evXUioXqUiLKIrodQOCoIeb6eE5jlEaR0Cv18X1XIqlgklBRhE6jNFaUSyXCKOIMAgJeyGe7yGzoWgpJXt2bse1FYeOTFKuRLRaTSq1In6xgBYYU1Rh47gWXtHHslz2HzhEqVplaHCIRqNJHE8BmnKxQLmsKNdW4BemsBeufAVAlCSkSWLat32fTrttVCEcGxlbtHs94jTBdzwcr0AYBag4oBkq7vnB97l882VUK1XKpaKRt7IFTiZxlCqNQOFn11EpZQwPtc4CnKlTCSFMR69Wpl08O4GhIe47LYt++7jAduzMDSjrJMx2/lJKoigiChO6vYAj4xNsffZZCkPLsR2HoJcSxglBHJPMzBgnCtuIaAsEs+02g0WLkYqFSgLkAqWvpBQ8/uxeHnvyWQ5ONhgZHuA9b73ZqHwUfOOsEAboMMxOQZCEEfc/8hRPPL4Vz3WQwpjN2pZEpWZ21LZcCn6RtetWUq8OmCY0IbAsieU4mSmpJo5CUq3wPWPqKzCpTKX6dUGRuVH0nT/MQ7FfYui31c+ZDms91yktZf91Escxp7wkMfWzSrWSnRJNI1WapoRRnJUw+uK9Cs/zFny/mv1tghaOkQSWCUijCqSVQmXPNa0E/ZyjEVrou0kIjBFnmj0fHbROzWZJaxC20cBUMnu26kyw2zw7BSZQZmaIWeo880AU5uuFkigESmR+hdoCUrSwTTAU/VyXyDQ1BSiBlqZjEpE1EyGRMlNiUtZc0JOZ6agWGimytGiqMq3O41+7+Z3QlCDRFsQJxGZQDmHah00HIoA8+vAXEi2CuRSgtMzsmbBspO2jMekuc8I1OV0RB2QlA2SikK5vdsRpnBloquy4nan2ZxcMR0KC0duzhKkNWRYJEHW7pkElUZBZXhyTmhQwNCBYPZhS9SFSARNN2DchCTqmiQUh6KvLa20eDubUZ95ASqVzXT7zRdmW8XpSCiGU0ciTEmWbTqtQ+DR6Cu2aG68dwFQqGBQgpMbGY826TfQ6bbZt3cbU5BQPPnAHWx+9j/qyVVx69fVcevll1Idr+IM+RpE8MqmVMMIv+GYQVwoKxQJJqlEp2LZLsVwliWOiMKTbMw7OYRASxwm2YzMzM02aJDy3/Vka7R71kUFcq8Bso8GOHdtpt0POveBy9u/dSXN2Fts2J5lms0WxUqVUrBBHxjx1cHiY6ckpHnt0C6Mjw1i793LdDZdgyYXbxwghKLgu2nGIkoioFeEVCug4Rlvguj4F38PzPYQWlMslnnriIRrTR7AFTLbaPLb1Sc5Zt456tU6p4OO5Dp7v4Tg2rmPa7h3bMY1SlkOKytr1szrlnGq0cTkn0z8kVSRK4/Ud2zNFC/NeU0RpgkoVcaKMjU/mJxcnKXGS0Gy1eOSJJ0gLFZY5PmEU4WVzceVyFfr1bCFwMoX9QrnEmjLUKqC7Rlx6IUxMNfjSt37A5Ret551vfh1D9ZrZZWtFFPQyn8AYKUy3pkoTtu08yB1334vjSKpWgXJBUvAcHKkpFV1KlTKFUplOEFGuDrDr4FN0g2mkNidbrU2qNkqN1F29PkatPGTSrJbLyNDyuXGivn2VlHZ2H5jv4TgOSJHN+pmTjtZ9/dG+Ma75fL/WqbXKmkay2r3OzIEzQXT0UcFi0FhCnJT+KFqZ9ndpkSCN7ZUQKJWYk5TITmD9k4zqB6F+WlAbFR9NdvLScz0N5i7sd+D2N0vZ65CgHEChZRYU52ZJzQlLY5y05zZk/SUL0Nr8zsbEmSw1KY+agAgAywQzLPrfWfevfXYSNBmLbBRDmr+PkdkzXfWn7ISmVDrZnZ7aM5/XnC0cAJ44+W+zdiEv6neyKa0Ryrg9S7tAL46NWkF9Gd0gwI4itHTQScg0HrZqU3LMw8ItlFizbqOxjU+fIZxooFWP6cO7+Prnn+W2L1c57+LNXHvja9l47kb8okeh5KFSCMOINDGNKVNTM3h+gTgKqdXKxm8rSeh0eriOSxjG+JkIscQMae94+hn27j/A8rXnEdireXKywGRQoTXwOoKS5pCCS6/q0t07SdAbJ5rdTqU2Qcm3TLu6hE6nyezsDFqYGuzOXbsYHhvjoQefJgx7C/6DaG3mnzzXxnddiqUyjuvSnJ0lCXoUC55JR3Z6OK5HFPaYnR43DtOWR7lUYmZmlvH7H2DjORtYuXIlXsHH7djGckZaCGljSXAsM/gvEGgpkZjdvkCTpBrbskjSdM6eLEky5QxlGkk4KpOJRhsFkUx7T811zmm6vQ6TE5Ns37sPb3AZY8tWAIo0THBwKRWK2MrUrKM0xvFMEE4iI/QcygITtsOQHZPEC9ss+J7Lr/2LtzE2OIBC0Om0zfCy6ho1HmVm/uLE+ObtfG4/t93+XSwpKBULSJ1S9G0KhSKVko/QCQVboJKIffsOM7bqHJ7c+SBh8zC1ah3HdeklPTQpYRwQJQprjxk895wCUaq44uIbuejcq03NMksRC8y1VYnxMovjKJsBNH+/fuu+mssCkUm4HS2V9IOcEMKkH7MGCJFt5qUlCIIenueTZOMFSbzwER6tNe3mLEE8heP7xh/MLmC5IKWDtByEcJFYz0sM6uwUZWWBzryfRZZvPlrSE6Zso625pLdCoIVlUpvSpDaNEZzM1nP09KUxKkv9bBy6bxmmj57I+j9JGNueVGD6KUS/NUSak2WWUJz7vbN6GyLryCRBKwstTHlI6WSuWeV4zLfLcWQ+X59z4igtso4qUy9I7aKRfrIkQhklFBWFIBN0muKXS0w1FL5WKB2B8in4ZVauXE8YaeJ0J1GriScFtpCEcYMtD9zB/ffexdjKtdx4881ccc1V1IeGKJXLmYJKTLvTMV2hCGZnGtSqAyRRknV6GdmbXq9HoVik1+3SmGlw+Mg4cmgzD3WvYnxmmDAxOy5lKeqliBXlAMezWLuuwraJC+nUbiJJpiF6jEG244mEVCuENLYxCk0QRHTaHRQWYXgSunjCdFSWikWKpRJDQ0M0Gi0GB2pEaYXG7Aw6TsyAtaXZ8fijNJrNTPbL1EE0itB2eOjJLTy+9UlGhodZMTrK8rExSpUynudntRxpivR9vc3sodgP/LZtk/Tb/JXOhH3NqU1lGoBAVlAHtEYKI37daDRptJpMzzbYPzFBaWQF52y+BtvxcKQk6HVwrIg4ibMsghH7jhUUPR/HK+D7BfMw8Ms82Elx0wKBXNg2rlouMFyvE2VZhUq5ghD6qJKGNuk4jeDZHfu4/Y67ieMQ3y9QKRVRKMoFh3LRxZVQLLjYtk25NoB1eIqHHrqf8rBNz7WZ6s5C1wwu+14BlVhYWrJ8cJRKqcx4a5LJxgG+++Q32HV4OysGVzNQG2K0vopSoYrveSRZZsUvVtBaE0ZGvd0YDluZX1o/c2M6UZPEjByZzkdzenZs15wQ9NHZw1Sl+EXjQt4PhmEcL/ienZ2d4W/+9uMkUYBbdLHtMo4rcR0Xy/WwXBvHLuLbHrYrsGwP13KNPJYtELaLLc08qOPYWNJobM51dVsCiTTzdGT3qZCZR56xjurfvzJrmzexyljEICwE/f8vzNylFiBMGUcjM+MCDVk9TWWlHURsMmrZdUZgUqeYE2fmJ4/QKSkaKYyvmkojVBoQZxJux2Ph4+w5pxaTwEYpAYUq0i2iwybIAkkYkgZdLHxiZdIbYRgTRym9tGeGxBNTHygUiixfsYZeGHLkuT24ac+cNmzwbSgmKa2J5/iHv/3ffOULn+Oqa67j+htfx9oNGyiUigzV61AfMKc2pWg128aa3rJBpCRxTKVSpd1ugbDothsckefzbHodQVIwuyyhUKnAJuVdl7dZX2sSxgFporg+sNgxUeLbTw1x0LmJtl7PBudRinqaIIpMWs0FSzbZtWM7w8uXk/WRLeyyKk21UkYrCIOA6clJHNdFOJL68BCdboc4DVAaDuzdTbc5S7FYIAgj0qyLTCJwCj6eVyCKQsZbTfZPTCKe2EKp6DM8OEjJ86nVBygXTODwi4W5gV0JxJaFldV3tDIdZklq0idxkpCkZucZR1Gmst9jpjFLu9VmujFLN06wvCL1sRWs2HgRo8tXUiqWKJZKhFFoTnSJotGcJYp7RKlN0fEZGRymUCoapQ2VkqqUar1Gc3aWZijoLdA8VWRjCDpr7lLC1D6i2Ege2bbNTKPND+5/jOd278R1PKqVEqViiShKaHXa9IIQ24JCtYjteriuS9ALGRwc5MD4EZJWg3a3Sd8mxrFcWuEEjusxWl+LWxpBuAVi0URIh4pfw7UFR2b3cmBmF/sO7WTt6CbWrthAoVjGcf1Mod8o4/SdIxqNphF6dv3MUFaR9ptRskNEFIVZc46Rh+rXRIMofd7MoXkwm2arhXc5Br2AiX37CcMAt1TA8YvYnsa2fYRVwrJTIzsoNdgSoaW517RCaseMIMmscCNMck/orLNc90tE5kQltchKQ4AAqa2s3phJrgmJEEb/UgjzfSzLlHksobM0a5aaFOZnIOXcOIrV/14SExDRmZkqgJrTmIxVDEogdUqqI8BcV51ItIpI0giVhoRJwmxz5rjXLg9oZwxZ55zjkRaGUVGK43hYrke720IHASXHTN0LyzE2N1FEV7WoZmKhtm26hkqlEqNjK+jMziBnAlxLEglQFvgpVD0YKHl0wjZ33f5P3H3Xt7jowgu49obXcf6lr2F4dDmeX0RKKBXLhGFIs9UwUkpRQmgFCCmZPDxBVL2Y58QYoTYDyKh+sV3ikrCsFOL5Dq5ng04p+iGO6HDn44KQGjN6DbsYZJN7H2l7G2G3w8zMLN2gR7k6wNT4ybXtK62IghAhLbq9Lt1ej1qlRrlUQqUzJGFEGPRIwpD27AydbpvVK9ew/9BhXL/E4LKVrFi+Ast2CHpdJiaO0JyZwfM8HNc0KQTdHt0oYveOvcRhBxXHuNJGSHCEMA9j2yZW6dyQbJokpErPCROrzPLFFI8dvLKZ8fMKFWr1MQYkuK5nOkyVpt1o0mq2KLge5UqRcqmMENDptSEVdHsdVGTmlfxykYLvMT09RZIkjB85Qq/Xw7KcTHdzAXerJjuVZu7fidHetG2LIAjZ+vQutjy5nXanZYKW7zFQq1DMPN+KRYckiYxdTtk3os6YLsLzL7yEtgi476m7SJKI0YEBHNvBsSLqlTFWjqwz6h2JYPv40zSDKdYuW4dvl1hZX41SkuVjaxio1EELytUanldA65S035YOmdSVplatmLqYtMwJm/5s1tH/7TeOpEmEQOBl+qJeoe8PqOc+zCzcwltzo17AjseeJon7AuEuti1Mgx1Zu4LtIC3jX2jZNtKxcSwzQiUdI4ItbRcpHGxHGRcU7WQSdykiG162szqhkBIlbKRUSPpBzclOa5juQ6SZFZMRGqffr4REofr/Icz5TAid9UIIk3LU2ZiKUQJGiqxjXKYI5Fy5RRKTaIXQKhNeTyBJiVVIGodEqSKKo+NeuzygnQGY7JIRbE7dGkmqCNsN/HIVHQcEnRZpL6ZQKpuAoVLiKCAOY2JhOvg0Yeb7ZppKSqUateFRZhuTSBSOpdCyb3cProKS6zJU8mn1Yp7esoUnt2xh9epVXLp5MxdcciXrzruQWn0A3y9QKJbQOqHb7RlriEaIX6nyxS1DdJKjzQxzuzU0SSoIIjMjFEUK2wKdKGZn20RhgvYUAsl0WOHh2U1Uxu+HcBrLtlk2OmYsK/r1pQWilCbo9YhS49NXcD0U5iQTRD5xkpAKSaRSCvURZpsNdu3bz/LV5zA8tpo46NFuzmBZRvXdtmxq9TqO7SClhe3YuGMOrlcw3ZRxSBynDI4sQ0UhzzzyA7q9tnGEwAzL2tJhfGoSlGKgVMT3CjS7LeIwQgtNtTbEyjUb8csVLNsxotOpJo1DEzzSlIF63Xy9Sul2ellKOqZSqhDHEY5jVDcKxeJRsQBpgWXc0S3LNsLLC9UfzWaVwGSSWr0mh8YP8cy2XRzeN0MYxkghqVbMadV3PQYHa9iWxK74CDmERlEul/BcmZllSuxuj263SzeZwStYrC6vpOR6xqpGWpQKBSYbh7EsgWf7RFEHz/GolQfp9TrsObKTyeY0Dz77Pa6+5HVs3nQtjmNSjpZlY1syS4WSjfqYU4LSzM2O2bbRvXQcc4rzXH9u1qofqOZknoSYU+F3HHMKbjZbFIulBd+zSZIQdgMQAtuzsAREQWJMOrUmTU2gMy4D/T+Cpj+EDCYzIYWFU/KR0iLqtI/pFO/X7UW/1bY/xYARX5irkcmsaz1r4EP2U43mVCelNLJcQoAwM39kXaXmQ87VJrM5lmy5/a7S/kBAmqVxdTbilWa1wMjMdKaZY7jShJ3j20nlAe2MwMyeaWEhvFrmyGtMNhFGg7CbxnOnsCjoYNsWURAQOakZRdCQeo65SbKTQLVWZ9ZxkVHX1ATS2OhQCInnWKTCxtJQ9m1Gaj7tIGbX3v3sfG4f99xzN+vPOYdzN13EhZddzTmbNuEXCpTLVQqFMt3WJG1rI4eaZlf7vPcV/RmVFOglKdKWFG0jmowUtHoJWO7Rziuhib1VqPr11IIfYEnBxvNrjAxK9u0LeOLRhQc0gGani+s6FIslgl4PFYSkaUK72TTi07Fxdh5bNkapXMG1HZaNjNHstGm1mgQJrB4dMhY+hQJhEBEEHXQ2HxYGIUGny+iq1QS9gILvUhkY5Mje51AkeK6beQAqCtVhqvVREsuj3ZwmQdHqtKmUS+hShW6rTafVYNezTzAyuoax1etYs3oNluMwOzVNHIZ0ux2iXohf9KnV6owfPkhjtkGhUGTZ2HI6rTazs5O0220qxTJOwaNQrmbiBQGkCc1uzzh5L/iO1QRxjz37d7Dr0DPs2ztFZwKk8qhWa9TrdYJOg2rJoVhyqVVr2K5NEIZmnz7XLQjDg0O0Ol3CsItl2bS7bSZbB4kImAqnCFWRWqHKTLvNRHMKx3IJVY+Uo/OvzfYEQkMnCUl0at5PWwJ0krJ+zSUMDozg+yVsJ2t0UJowjUBnxq1ZzUxkM6qmFqaNZJywjOh0NjbRV4zpf/QxQdAhTRWNxuxJ3LEmNLm2jW0bI2STyhfZCIjEywxQRdZar/sah8qkR1vNFn6xwM3veCfDy1bzyY/+MVopSsVSFnyg7zsGZiwkSYzpsedZ9LuTVKpQiXHGULYNKkEnaeY1KTMRbuN3qLO1o4xCVJKkOJ5rfo7WIDV9TVOddTMK+g1xpnnI7Ckyk+hsQFsrUKmZ+VMqEy04DuJkcr05pwYhxASwJLtHTxFrF9qQlF/bl2VB1za/ridEfm1PD8e9rnlAy8nJyclZEiy8fSwnJycnJ+cMIg9oOTk5OTlLgjyg5eTk5OQsCfKAlpOTk5OzJMgDWk5OTk7OkiAPaDk5OTk5S4I8oOXk5OTkLAnygJaTk5OTsyTIA1pOTk5OzpIgD2g5OTk5OUuCPKDl5OTk5CwJ8oCWk5OTk7MkyANaTk5OTs6SIA9oOTk5OTlLgjyg5eTk5OQsCfKAlpOTk5OzJMgDWk5OTk7OkiAPaDk5OTk5S4I8oOXk5OTkLAnygJaTk5OTsyTIA1pOTk5OzpIgD2g5OTk5OUuCPKDl5OTk5CwJ8oCWk5OTk7MkyANaTk5OTs6SIA9oOTk5OTlLgjyg5eTk5OQsCfKAlpOTk5OzJMgDWk5OTk7OkiAPaDk5OTk5S4I8oOXk5OTkLAnygJaTk5OTsyTIA1pOTk5OzpLAXuwF5IAQQi/2Gs50tNZiIa87Fdd25egQ5ckZdKpO9ludMmTBo1mrcPDw5El/r4Vc2/yePSEmtdYj831Rfm1fnuPds3lAy8l5CaSU/P4vvJ9z/7+/QXd6i72cOQrveBO/vnv/KQloOaeNPYu9gFcbeUDLyXkJpBT4SYoO48VeyjHoQ+MsG64v9jJyTiOOY3Ptta+hUPTn9TqtNBMTUyRJOu+fGccJs7MN9PPOiGEY4Xnuy77W81xqtTKwoGTKCyiXi1RrFdDwxBNPc/gENm95QMvJOQtJD42z/vxrF3sZOaeR5cuX8YYP/CLfebI9r9d5jmRTxTomKJ0o1YJFwVH0X+w5ktdfVOYH27q0eslLv1hIJjuaND01GdNUwXgzZnTA4y2r7+JvP/6PL/uaPKDl5JyFpJMzrK7XEIIFPbhyznykFOyfDLn3qdlFW0OtaPOmq1fzqe8dYN9EsChrOGe0wE2VE+tfzANaTs5ZiGp3WeV7OI5DFJ1Z6dDTjRCCQsGnXq+yZs1yNm5cw8aNa3jyyR187nPfXOzl5SwieUDLyTkbiRPKvWDJBDQpBStWjDI6OgyAUordu/fTbvcYGKgwNjbM+vWr2LhxJatXD7BsmUulYlOt1tG6wKFDAQ8//PQi/xY5i828AlreTvryLLS9PCdnXmhNMU0plwt0Ot3FXs1JsWnTOn7u597G+vWabnc7WqfYdgnbfgvdrqJU6pGmE8RxAymPUKl4WNYgnY7P9u0NOp2DSCl44IHHF/tXyVlk8hNaTs5Zimx3sayz+y1cLPr8m3/zboLgC2zdugc4umcWwqJQGMR1r2DFiisZGjoPxykzOztJqzVFuSxYvXoFvR7s2DGBZVmL94vknBGc3e+GnJzTiBBgWRaebfH8B+0Zg21zRq5rHrzznW/E95/g8OHdL/ic1ilCCKrVVUxP7+DgwYeo1TYRhmX27evy5JN72bJlBzt27GF2toVSZ87ge87ikAe0nJwfYni4zu/80gcopgrbtljdCwkWMNNzWhECf/kIv/iBt9PsBfz957/J+PjUYq9qXjiOzY03nsuBA3913K/pdMZ55JG/plJZwcjIB/mTP7mTLVuepdlso/P2zpwfIg9oSxwpJZ7nUioVGB6uU6mUj/n89PTsMU0FURTR7R5tz9VaE4YRSmm01q+KXfAlF2/idc/sIvjanQAEZ5Dk1RxaE/63j3Oz4+BedgG9W97IX/z1Zxd7VfNi+fIR6vUOhw+/dA2wUlnB0ND7+b3f+wf27DnwCq0u52wkD2hLGN/3+Hf/4VdxBlbRVS5TXcl4M5mbW7ItwWVlyfPTVgMFgVBHA5xjCWzVJU1TbGJ67Rbt2WlazSZTk9NMT83QbneZnp6l1erQbM5vCPRM5NLzziH97kPoXrjYS3lJ1HQDgOCu+3nr//OrfGagwuxsa5FXdeJcdtn5dDrbXvJrBgc3Uancwn/+z3kwy3l58oC2hPE8h6C4mj/+8gwLPWQITEu1AFxH4NgDVIvDDJQcKqstRi922FiQVH1YPxDyJ7/3YcbHp0/lr/GKYlmSC8dGSA4cXuylnDA6jBi+71HeeNO1fOHW2xd7OSfMypXDNBr3H/fzo6ObSdOb+O3f/puz+p46GcQZ0DMdJZo4OQOzFC9CHtCWOFqz4GAG5uyWKnOCS0INoaLRSV5UNeDn3jhCoVBY+A87A6hWK6xXCtXqLPZS5kV4z0O857d/gW9867vHpIzPdI5XB1u16jqmpq7kIx/5BDMzjVd4VWcG3W6PenGxVwGdIKETnGE15OOQ+6Hl5DyPtWtWUD5wBNQpaDgQAlEpIetVrGVDOBvW4Gxci7NhDXKojqxXEb53SrbharbJ+i3beMPrrzn5db9CTE21KBSGXvRztl3iy1++91UbzMCIAqv0ZfQTc44hP6Hl5DyPyy7ZBFu3L/wbWBJrdBi5+VImRy8i3biJ2RlNr6NpdftzUhrd7TG03KUkuhQaRyhM7qPUOIg8sI/00Dg6mH/9Lvz63fz0b/8Cd93zAJ0zyOrmeOzbd4RKZTVHjjz2gs8pFeddjDnzJg9oOTkZQghes24l8TfuWdDr7TUrKPz8B9lpn8/tX2iw69ttVDLx0uLBAqSo4fh1hldexYVXF7hgfcAKtR/x6CME9zx4wsFNzTZZu20Xl22+gHu//8iCfodXkq1bt9Pt3ky5vJx2+9BiLydnCXDGBjTLsrBtizCMFnspOa8SisUC6x0b1WjO74WOTendb2bvxT/ObV8O2fnoAdLkBE8X2mQ3w67iwPYeB7b3+I4tGF61kouvu4DN//Imqt/6HOLZZ18oq29J7OXLSPYfbWCJ73mQH3/vW/j+Dx494084zWabP/3TL/AHf/B+du78K+L42LqlbefKHznz44ysoTmOzW/+8gf5H7//6wznJoY5rxBr1ixn6PAkzGeIWgjK//ydPLP5ffyfP57i2QcbJx7MjoNKNOO7e9zxD0f4s//t8Cn1QXo/8nZwnWO/UEq8qy495p+SPQe52vep12sntYZXiu3bd/PXf30fGze+GyGOBrBud5zNmzcu4spyzhjmUWI+owKaEALLsviJd/0om6cbDHa6J+SUmpNzKrhg0znIp3fO6zX2Oas4cs0tfOaPdhN0Tn0nWBJpdm7X/N99N5C++33gHBvUvEs2HRvo0pTy7v2sXLnslK/ldHHHHfdz110Ba9e+Ye7fZmef4+qrV1KplBZxZTmp0oTx4rbs18sOQffEBLjnnXLs+xANDdUJgpDZ2Sbdbm/eRejBwRrXXrOZwVoFKSWjQwOMVEqULIuRJGHnX/4Dw7/200xMHDt/Ui4XWb58GZ1Ol4mJaeI47wLKOTVcsmE1yee+ceIvcGzcW97MVz8xSa91etua9zzT46vLr+R9H4wIPvm5ufSjvXYl9rKhY9KOYt8hVq8cY8uWZ0/rmk4VSin+5m9uZeXKn2V0dJIjRx4jSQK0foIf/dEb+MIXvrXYS3zVEsZmTGcx8WzB7OyJdbvOK6AtWzbEJz7xJ4yODuP7LnGc0On0eOSRrfzmb/4hyQmmagoFnw//5s9QfuAJWk9uR2tN1GoTNdvMJopOucjqH7uRR6dmiOOEjRvX8mNvvI52p8frLzoX/7l9yMEBpstF7t26nSROaLW73Hn3A/R6Z88MTs6Zg5SSNaUi6dSJt4kX3/oGnqzewLaH9pzGlR3l8XsaXPl7b+Kcix4jetIEK1kt42xYc0xAU4cmWHnVJa/Imk4VYRjxp3/6aT7ykX9JrTZDo7GH/fvv5V3v+mXuuusBpqZmF3uJOWcB8wpovV7An//53wMwPd2YG+AMgoB0HtO7jmMzUPCJ4oRlr72Cw3fdz+o3Xs/00zupvu0N7A9C/uGRp7jjy7dTq1X4nX/1AdIvfIuo1eHIV+8knG2CgIFN53DL9Zcz+exuaueuY9UH3sZf/c3n5/Mr5eQAJt1tJwk6PgGzTAH+9Zez5+r38vmP7Eelr0zzhUo137m1yS9/4F1E//FPAAjiGLlxLdz9wNGvC0Ks+RQezhAajRYf/eiX+P3fv4lG4++I4y5R9BA33nglX/rStxd7eTlnAfMKaK1Wh69//e55/xAhBKOjQ9j20R/3V1/5Dhecvx6U4h2//Ys8ceAwex2bL3/sMxw8OI7WGs9z+Z1f+Uniz36DIw88cWyXl4bZZ3eTBiHdw5MAWK+58GXXYVlGrFcIQbHo47ouaZpy5MjUkhPeTRJF0dFIcWrmhI+HJQWbz6lwwZjgqwuYnzqbkNUyxQ++gwMX/Aif+eNDdJuvbDpm95YWj712lE1DA6hGi9nZJl7p7FZneT779h0iDAeQ0kaphImJJ7n66vdy663fOeO7NnMWn1ekbf+iizbyx//6Q7S2Pov+obSkcGyCvQdZlaZUx0ZY+c9vIUlSWnFMkKas2X+YZ+9/cSfaNW+6gc3/4Rfp7NyLPTzIHZ+8lR990w0M1CrUf6iYPDxQZaDoU7Ysqq4DcYLv2IgkwRsd5j9//PM8+NCW03YNFoMwjEjCLlII1Gl6GKwfK/AvXl9navuD/I8Pf5UjRyZPy89ZdCyJffUV7L707dz/VI1tf7ePqPfKb4BUCt+/O+H88zehHnjE1JBrlVd8HaeLXi/kwIEQ160QBDP0etOce65NrVZhdnae4xRLgLPvnL24vCIBbf/+I9zzzE7OGxxAPc+qBAFFKenONrHaXYqHJ6j4HjpJEJaFtC323PbdF/2e0rZp7z/M1j/7e2zXRfou7xmoGDkhrQmmZtH66AOn6HuIoQE6StFKFc0gREcxE802wcEJntu9/3RfhlccrRU2Ea5jkYSnp2lh44oij97+BW790rfO6h20bVsUHefF/TJdB/cNN3DH4Hu44+NTqGTmFV/f85k6EqOuugC+dz9DRyaxRgZZ3BWdOrTWHDkyy+rVfvbfCqV2c9FF53LvvQ8v8upeWeI4peQoLHlyeqyvJl6RgDY72+S//s+/w/mhlmMwFieOY7Nq1Ri1qvHqqlRKDFTLvOWGK7DufHE1bq9eZflrr8ApF0k6PeQl57Hz4DjjrTaHp2ZJf6jdv7X3EPd9/pu0Wh2SJMlqfvqlVRzOctJU0Wu3seTpm0nqhSm61TmrgxmYum4pCCF5YQqx+OYb2XLR+7nzvx5EneSM2amg20hoj22k6NjE338U95Y3LvaSTikjIxWS5GjX9NTUVq677oZXXUCLopg47GG8LhbvvlP67PFFf8WUQrTmGCPJPv1/e7EuptF6jTH54ofu4vIRDm06h1X1Gu0v3ka6diX/8b99PFcW+SF0ElLyLVq9s0MtezE5blBOU6rLCli2II0X/60dR4qmM0y5WiZ8/Bm8K8+ujsaXolQqsmpVhX37js4dtVoHWLOmjJRyydW5zwammhFBdHZc9zNqsPqHCdMU/0UUD9xamaH3v42P/sWnOXx4glU/cj2WEGf9KeF00JidxbXP6D/zGU/3m/ew7tEv8u5fXYnlLH5VQwhwyx7CtlGzTdpfvG2xl3TKuPbazTjOLtL06MZUqRTIN2SLgZSCVJnGsuN9nAmebX3OWC1HgDu/9zA3vvfHCf7yH+genkBISXn1clb/zLv52Le+x65d+/jvn/giF190LtPTsy96Any1E3Q71MsOe1/Ev+yUcQbd0AtFSok83jszVXQ+fSuXfQD2v/UmvveVafQiblhtR1J1eqh2F7Qm2bVv8RZzCikUPN7//hvYt+/ji72UMwRN2RO88bLBOU/CPrYlWFa1ebFkoBCS2x6d4fDMsR3HK4Y8fur1I0ThiT0LbEtQkCFjxQa/9ebicdOOluPTjo6+d4QQ7GtIPvvdI3P/dt6qEu+8qkq8gGf0cNXma1tOzIn9jA5oTz21gz/5ynf4rV/5Sbb/wf/CKRe54MO/zu/+97/lsceeBowW3Pbtuxd3oWcwjdkm3tjpO6F1Q8XK4Rf3tDqbqFRKlIKQ48apVNH7h1t589tDBv75j/DNz7WIg8WJauW6TWHmAHH3zLeImQ+vfe0VFIvbOXBgqbS4nBxJkvLJv/wrRkeHX/C5EHhocuZFN/FXXnkJl2/4Mb7+0LEBzbUlrZ0/4DOfPNFZXU2vF77sjLHvu8eMZNVqZd7/6//hmK9ZXnd55o7Pc889D/zwy19+FVrTbLZP6GvP6IB22WUXcNGFG9H1KpbnEjZadPYfZt++3GriRGk1W4xsemEzzqmi2U1w3LNfb1MIAS83Q5cq4lu/xrVX7GXgX/wU//h3vRe07gsBG15TZc/WNnF46gOelPDmn12J89iniJdYiv266y7k4MF/esG/CyFYEmmABbBt23Ns2/bcvF7j+y5XXvjWF/1crxecctPU+IfECLrdHnbaxcrSlX3a7c5pN2xd8NbdcWzOPXctpdLp8QgXAn76A7fwG++4hQvWbeDCn/sJLviZ9/BMu0ujcWLHzxyYmWly/iqfevmM3rucVcQPb+HcI9+lMvjCjYLtSj7wb5Zz4y01Bpd7L1lfEBJGVvt4xRN/G9ZGPa7YHBPc+xBYx3md1vhnmai3ZVksW+YThi984BWLw8zMqLwh5ASZmWlSf5HHcl+s4nSjlCKdj2PFKWTBT7mfeM9b+LX3/TMe27Ob3/jtPyE4xQoRWkOqFUmjg/Br3B5GbHlmF09uffaENSNz4Omnd7Duzn/kt97xPv70K0eYacdLelThFUEIJoprac+8sMXfK1rIb36dm3Y9wc0/+QYeiy7gm38/SWv62F1sue7wrl8c4fzOwxyaLfBPDy1n77M91Mvc2usvreDu3k5Q9BGeS3p4EtJjX6QabZaVT89G83RRLPrUapqpqRd2KY+MbObrX19aogenGtu2Wbt2BdffcCXnXbqZ5wKBYwuc5216klRTqQ2e9rVoDY6lj5mf64aKn3rfO7n6hqvRCsYPHeKRh59g796DzM42UadIymhBAW1sbISfe+dbCJ/czyUXrGblylF27tx7ShbURwiBJ22Cww0i3+KrX7uTPXsOntKf8WogimK+8pVvE0YRv37TjejyCrYe0jyys83+yWDRrSHOFKSUWWrrBNAahHzRlOLIag/3wHPQamBdeyU7PjZDbcSlNRMfU7+vj7qsD5+m/f0nqNgWH3rj5TR+7Xru+9oUD982+aLjAZYteM31BdqfupVk70uk3ZV6oRnoGc6aNctxnOYxYggAQlg4ziYeffTvFmllZzYDA1Xe9vY3cuHlVzIjRvj+s12+cmebbniEf3fLCmRzN2m24ZFC8OTDT5z2NSVJQtQY52feeD4lVyNUQsmXpGnI+vMu4FDHI/Uv5JI1N/HPhmHf49/lU5/8PEFw8iNXCwpoSilQGp0orMkur7/xqlMe0ACEFObjVZo/P1VorfnmN+7mW7fdQ70+wMUXn8stV1/O2OvO544dFrc9MrXg7x0litqys8NM8qUYGhrAabROuDm83j1AbWSUmcPPexMKuOT8BHXPNtIjk8j//lFe/7qf4Il6EWkJDu/qEmWNJAe2d/jTQ0PAj4MGdxd45T3H3akKCTe9f4wLew/T3Lr95H7ZMwzbtvjQh36Mw4e/9oLPlcujbN9+6us+S4W33/ImGstv5iO3z9DsHt3wX3t+jc5z9/NnH/34oowzfezPPs7Q0AC9XkC3G6C1ptPp4fse69at4Kd/5Vf4o6+2afUSPnTz63nr22b4wufnYd10HBYU0KanG4w3myyXgmi6xRUXn8/fno45sOzbhVLP228t54UopZmamuHuux/g7rsf4JxzVvGWX/jdk/qerV6K7Z794rhSCvQ80ubO/d/jumtfw7dvk0SBwnEll91Y5cr2N0kPTwAQfu8h6g9v4Y3r1/CWN9/M7PAG9s5U2LVDsffpLpMHAsKuQqWaDsDEC+9xIaA+5nHj+8Z4/fnjtH7rk+YEtoS46qpLOeecJk8//UIbnpGRzXzjG0/kM6bHQWnFeCOi2T2a/nZtwdsu9fjzD3950a5bo9F60V6HTqfL1q07uOPLX+Qd13+Q//vdcVbV4EtP7zglP3dBAS1NU77/xFZ+8prr0XHKI9977LRcOK01dsVn5+z0Cbdt5pw4ExMzrCnO8vM/upxHdnbYfrBDu5eekMyNJWHFkM8tVw3SfPbVV99IDxzm2son2HTdxXSG1zGyqU75iW8Sff32Y75O90LirduJt27HcR02DQ1w0YY16M2r6Fy/jHZxlBkGmGwXmBjXNKfjuUnV6pDDxVcWOG9FE/v7X6X1u99BLbH3gee5fOhDb2DPnk/yYjNVjrOKLVsefOUXdpZw53fu5Vd/+2pGqiNMNCOiWLN80GPvE/dyONtYnYnccccP+J3XXs9/evc5bLnnazz11KnJOiwooGmt+dRn/om3XXs1ji/5p6/deUoW82I4AyUeu+/xfGj6NNBud/iD3/0vbNy4hiuvvox3v+lies4YTx5IeXx3mwOTAVHywoHOyzdUedtlBcLx7dx562d58MHTn5c/3WitEQV/Xq9Rz2xn4JntDAgBAqKXK2xHMemhCdJDE8DD2MCAlNQLPucuG8RetwqxrIy9YhSkRDebhJ9+nGDvQfR8JN3Oogz9j/3Ya6nVdjE+/kKXBikdut3Cq1Jl/0Q5eHCcj/ynP+LqazYzaDvU6lW8KY/P33pmq8dEUcyf/uFHKRZ9pqcbp6zku+AuxyiKiFVKyXEpFLxTs5rnobXmka3bqAyUufe+R0/5988xdDpdHn/8GR5//Bksy2JkpM5FF2/iXVdcxtBrz+VQr8hz4zGp0ji25Kp1FkeeuZ///Ydfn/OtWwrs3LmPbe/6Mc67+VpUpiuaHDiCmjyBIV99EuqtSqE7XZLnuiTPnYTjg20hHAfhubjv/XF2nMi6F5nVq5fzwQ9exo4dH3vRzztOkfHxkCjK9Vlfikajxe3f+t5iL2Pe9HoBvd6pVTAS83kgCSHmvlhKyR/9/q+z+byN/Myv/T8cPnzqfbCEEEgp57p0zga01vPeHz//up5JFAo+q1aNMTxcB8wmY+/egxw8OP6Kr2Uh1xXmd22Hh+vc+NorcCyLKy6/iNffdT+92+9dyI99xSm89y08fen5tIKQbzz0JLfdfu8Jv28W4561bYsPf/gXKZe/zfT0sy/6NfX6BrZtu4K/+IvPnsyPWkwe1lpfOd8XnanPgzOJ492zCz6hKaX4w//3ryiXS6clmIF5gJ5NwWyp0esFryppscnJGb5467cBsC3Ja8/gGsQPk/g+/+UTX2THjhc2VpyJXHPNZtavb/LMM8evnVQqq9i+fen5FOacPk5K5K/V6nDo0Cu/W8/JOd0EUYLI/PnOFs6W9G+5XOSnfuoN7NnzDV4qV+v7Y+zde/iVW1jOWU/uK5KT8yLs3ncQce45i72ME0MIkhXLTrlaz+lASsnP/dw/w7bvp9d7qflHAdSZmFj4jGTOq488oOXkvAjPPLOLfeefg7Xs9EsFnSz26uU85rlndJt2n9e+9nKuv95h//7vv+TX2bZHq+XRanVeoZXlLAXygJaT80OMjY1w7TWbuX98iuof/lvk0MBiL+m42OtW0vyl9/OXn/36y9p8LDYrVizjl37pJnbu/MILJK5+mFJplO3bZ4jjF+pl5uQcj1yCPSfneaxdu4K//pUPUb3rftT9j9O7/3GEZS32sl6I6+BesIF97/1xfuf/fJbnTqbl/xVgYKDCv//372dy8itE0cu7ZdRq67j99t2nf2E5S4r5BrRJ4Oxoo1oc1i72AnJOjmuv3kzl9nvpnYlzPa6Ds3419g1XcGTjWu49PMHf/69PLcoYxXyo1Sr83u/9NJ53J+PjL96i/8O4bpWpqVOvD5uztJlXQNNaj5yuheTkLDZvuOka/tVF5xL+t4/P74W2bSxcTnWXoSWxBgdwNq1DX3Ex42Mj3Dc+xbfvfZin/u9Xzwp90w0b1vAbv/FuLOs7jI+fuERarzfO6OiZX7/MObPIU445OZgh/rdffznWX3yaNGtEsFaOIssldKeLTlJUO2tQSFJ05tLrnLOa7s//BLoXUJqaxT40gdp7kPTIFGq2iU5SdC84sWAnBHJoAOfcdYhLz2Nm9XKeCSMe2r6Hh277Lnv3HjrlygqnC89zeec7b+bd776AAwe+yNTU/BI7Ydhg1aqzpMs054whD2g5OUC9XuOSok+ayV4Jz8X92ffwlV5At9WhVi4yYJu3y2CxgJcFtCnb5o//16dpNFoMDtZYuXKUNeesZu11r2FFtcy1G9YQ/NuPoKZnX3YNolSg/lv/kofrNT76559ix6e/QqfTO2vmy8C05Z9//np+/uffysjITrZt+wvSdP7jBIODF7Jly6lRYM959ZAHtJwcIAxDJm2LtZdsQk3NEP/SB/nY0zv59D9+7QWddpZ11AzUqNmYjr1ms83u3Qfoi2Vde+1lXNnqoJ7v5SUE2Ba8SPeebneZ/v0/4/wPvp13/ugN/M+PH6Td7p6W3/dUY9s2F198Lu97381s3Bixf/+t7Ny5sHK7bReIovU88sjJ+2PlvLqYl5Zjzukh1257eV4JLccNG9bw5puvY8OKZXz9uw/x7Tt+sJAfCRh/tY/+x3/Na/76s6TPa9oovufNpG+6Ae/+x+nd8yA6ikkOT8Dz3SSEwLtmM9tueSP/+g//4rTXyk6FluMv//IHuOWWdezffxfN5r6TOlUOD1/AnXeW+cQnbl3w9zhDyLUcTxPHu2fzgHYGIISYIO8efSnWLrQhKb+2L8uCrm1+XU+I/NqeHo57XfOAlpOTk5OzJMiVQnJycnJylgR5QMvJycnJWRLkAS0nJycnZ0mQB7ScnJycnCVBHtBycnJycpYEeUDLycnJyVkS5AEtJycnJ2dJkAe0nJycnJwlQR7QcnJycnKWBP8/VTrejIik4dgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 540x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 5\n",
    "imgs = train_features[0:n] + train_labels[0:n]\n",
    "imgs = [img.permute(1,2,0) for img in imgs]\n",
    "d2l.show_images(imgs, 2, n);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 11
   },
   "source": [
    "Next, we [**enumerate\n",
    "the RGB color values and class names**]\n",
    "for all the labels in this dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "origin_pos": 12,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "VOC_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],\n",
    "                [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],\n",
    "                [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],\n",
    "                [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],\n",
    "                [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],\n",
    "                [0, 64, 128]]\n",
    "\n",
    "#@save\n",
    "VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat',\n",
    "               'bottle', 'bus', 'car', 'cat', 'chair', 'cow',\n",
    "               'diningtable', 'dog', 'horse', 'motorbike', 'person',\n",
    "               'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 13
   },
   "source": [
    "With the two constants defined above,\n",
    "we can conveniently\n",
    "[**find the class index for each pixel in a label**].\n",
    "We define the `voc_colormap2label` function\n",
    "to build the mapping from the above RGB color values\n",
    "to class indices,\n",
    "and the `voc_label_indices` function\n",
    "to map any RGB values to their class indices in this Pascal VOC2012 dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "origin_pos": 15,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "def voc_colormap2label():\n",
    "    \"\"\"Build the mapping from RGB to class indices for VOC labels.\"\"\"\n",
    "    colormap2label = torch.zeros(256 ** 3, dtype=torch.long)\n",
    "    for i, colormap in enumerate(VOC_COLORMAP):\n",
    "        colormap2label[\n",
    "            (colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i\n",
    "    return colormap2label\n",
    "\n",
    "#@save\n",
    "def voc_label_indices(colormap, colormap2label):\n",
    "    \"\"\"Map any RGB values in VOC labels to their class indices.\"\"\"\n",
    "    colormap = colormap.permute(1, 2, 0).numpy().astype('int32')\n",
    "    idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256\n",
    "           + colormap[:, :, 2])\n",
    "    return colormap2label[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 16
   },
   "source": [
    "[**For example**], in the first example image,\n",
    "the class index for the front part of the airplane is 1,\n",
    "while the background index is 0.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "origin_pos": 17,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 0, 1, 1]]),\n",
       " 'aeroplane')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = voc_label_indices(train_labels[0], voc_colormap2label())\n",
    "y[105:115, 130:140], VOC_CLASSES[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 18
   },
   "source": [
    "### Data Preprocessing\n",
    "\n",
    "In previous experiments\n",
    "such as in :numref:`sec_alexnet`--:numref:`sec_googlenet`,\n",
    "images are rescaled\n",
    "to fit the model's required input shape.\n",
    "However, in semantic segmentation,\n",
    "doing so\n",
    "requires rescaling the predicted pixel classes\n",
    "back to the original shape of the input image.\n",
    "Such rescaling may be inaccurate,\n",
    "especially for segmented regions with different classes. To avoid this issue,\n",
    "we crop the image to a *fixed* shape instead of rescaling. Specifically, [**using random cropping from image augmentation, we crop the same area of\n",
    "the input image and the label**].\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "origin_pos": 20,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "def voc_rand_crop(feature, label, height, width):\n",
    "    \"\"\"Randomly crop both feature and label images.\"\"\"\n",
    "    rect = torchvision.transforms.RandomCrop.get_params(\n",
    "        feature, (height, width))\n",
    "    feature = torchvision.transforms.functional.crop(feature, *rect)\n",
    "    label = torchvision.transforms.functional.crop(label, *rect)\n",
    "    return feature, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "origin_pos": 22,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgs = []\n",
    "for _ in range(n):\n",
    "    imgs += voc_rand_crop(train_features[0], train_labels[0], 200, 300)\n",
    "\n",
    "imgs = [img.permute(1, 2, 0) for img in imgs]\n",
    "d2l.show_images(imgs[::2] + imgs[1::2], 2, n);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 23
   },
   "source": [
    "### [**Custom Semantic Segmentation Dataset Class**]\n",
    "\n",
    "We define a custom semantic segmentation dataset class `VOCSegDataset` by inheriting the `Dataset` class provided by high-level APIs.\n",
    "By implementing the `__getitem__` function,\n",
    "we can arbitrarily access the input image indexed as `idx` in the dataset and the class index of each pixel in this image.\n",
    "Since some images in the dataset\n",
    "have a smaller size\n",
    "than the output size of random cropping,\n",
    "these examples are filtered out\n",
    "by a custom `filter` function.\n",
    "In addition, we also\n",
    "define the `normalize_image` function to\n",
    "standardize the values of the three RGB channels of input images.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "origin_pos": 25,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "class VOCSegDataset(torch.utils.data.Dataset):\n",
    "    \"\"\"A customized dataset to load the VOC dataset.\"\"\"\n",
    "\n",
    "    def __init__(self, is_train, crop_size, voc_dir):\n",
    "        self.transform = torchvision.transforms.Normalize(\n",
    "            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "        self.crop_size = crop_size\n",
    "        features, labels = read_voc_images(voc_dir, is_train=is_train)\n",
    "        self.features = [self.normalize_image(feature)\n",
    "                         for feature in self.filter(features)]\n",
    "        self.labels = self.filter(labels)\n",
    "        self.colormap2label = voc_colormap2label()\n",
    "        print('read ' + str(len(self.features)) + ' examples')\n",
    "\n",
    "    def normalize_image(self, img):\n",
    "        return self.transform(img.float() / 255)\n",
    "\n",
    "    def filter(self, imgs):\n",
    "        return [img for img in imgs if (\n",
    "            img.shape[1] >= self.crop_size[0] and\n",
    "            img.shape[2] >= self.crop_size[1])]\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        feature, label = voc_rand_crop(self.features[idx], self.labels[idx],\n",
    "                                       *self.crop_size)\n",
    "        return (feature, voc_label_indices(label, self.colormap2label))\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 26
   },
   "source": [
    "### [**Reading the Dataset**]\n",
    "\n",
    "We use the custom `VOCSegDatase`t class to\n",
    "create instances of the training set and test set, respectively.\n",
    "Suppose that\n",
    "we specify that the output shape of randomly cropped images is $320\\times 480$.\n",
    "Below we can view the number of examples\n",
    "that are retained in the training set and test set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "origin_pos": 27,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1114 examples\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1078 examples\n"
     ]
    }
   ],
   "source": [
    "crop_size = (320, 480)\n",
    "voc_train = VOCSegDataset(True, crop_size, voc_dir)\n",
    "voc_test = VOCSegDataset(False, crop_size, voc_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 28
   },
   "source": [
    "Setting the batch size to 64,\n",
    "we define the data iterator for the training set.\n",
    "Let us print the shape of the first minibatch.\n",
    "Different from in image classification or object detection, labels here are three-dimensional tensors.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "origin_pos": 30,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 3, 320, 480])\n",
      "torch.Size([64, 320, 480])\n"
     ]
    }
   ],
   "source": [
    "batch_size = 64\n",
    "train_iter = torch.utils.data.DataLoader(voc_train, batch_size, shuffle=True,\n",
    "                                    drop_last=True,\n",
    "                                    num_workers=d2l.get_dataloader_workers())\n",
    "for X, Y in train_iter:\n",
    "    print(X.shape)\n",
    "    print(Y.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 31
   },
   "source": [
    "### [**Putting All Things Together**]\n",
    "\n",
    "Finally, we define the following `load_data_voc` function\n",
    "to download and read the Pascal VOC2012 semantic segmentation dataset.\n",
    "It returns data iterators for both the training and test datasets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "origin_pos": 33,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "def load_data_voc(batch_size, crop_size):\n",
    "    \"\"\"Load the VOC semantic segmentation dataset.\"\"\"\n",
    "    voc_dir = d2l.download_extract('voc2012', os.path.join(\n",
    "        'VOCdevkit', 'VOC2012'))\n",
    "    num_workers = d2l.get_dataloader_workers()\n",
    "    train_iter = torch.utils.data.DataLoader(\n",
    "        VOCSegDataset(True, crop_size, voc_dir), batch_size,\n",
    "        shuffle=True, drop_last=True, num_workers=num_workers)\n",
    "    test_iter = torch.utils.data.DataLoader(\n",
    "        VOCSegDataset(False, crop_size, voc_dir), batch_size,\n",
    "        drop_last=True, num_workers=num_workers)\n",
    "    return train_iter, test_iter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 34
   },
   "source": [
    "## Summary\n",
    "\n",
    "* Semantic segmentation recognizes and understands what are in an image in pixel level by dividing the image into regions belonging to different semantic classes.\n",
    "* On of the most important semantic segmentation dataset is Pascal VOC2012.\n",
    "* In semantic segmentation, since the input image and  label correspond one-to-one on the pixel, the input image is randomly cropped to a fixed shape rather than rescaled.\n",
    "\n",
    "\n",
    "## Exercises\n",
    "\n",
    "1. How can semantic segmentation be applied in autonomous vehicles and medical image diagnostics? Can you think of other applications?\n",
    "1. Recall the descriptions of data augmentation in :numref:`sec_image_augmentation`. Which of the image augmentation methods used in image classification would be infeasible to be applied in semantic segmentation?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 36,
    "tab": [
     "pytorch"
    ]
   },
   "source": [
    "[Discussions](https://discuss.d2l.ai/t/1480)\n"
   ]
  }
 ],
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