Transfer_Learning_Project_1_TF_with_pretrained_weights.ipynb

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    "colab": {
      "name": "Transfer Learning Project 1 - TF with pretrained weights.ipynb",
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      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/yanayhollander/Tensorflow-2.0-practise/blob/master/Transfer_Learning_Project_1_TF_with_pretrained_weights.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
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      "source": [
        "# STEP #0: PROBLEM STATEMENT  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ll4dJoqibSQL"
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      "source": [
        "- Transfer learning is a machine learning technique in which intelligence (i.e.: weights) from a base artificial neural network is being transferred to a new network as a starting point to perform a specific task. \n",
        "- Transfer learning is a key skill to acquire as a data scientist since it can dramatically reduce the computational time required if the network is being trained from scratch (i.e.: starting from purely random weights). \n",
        "- In this project, we will apply Transfer learning to repurpose trained weights from ResNet 50, which is a famous deep network, to perform classification tasks on a new datasets. \n",
        "- A pre-trained ResNet50 model that has been trained on ImageNet will be repurposed and used to classify new images of cats and dogs\n",
        "- The new model will consist of two part: \n",
        "    - (1) \"base\" pre-trained network  \n",
        "    - (2) \"new dense network classifier\"\n",
        "- The feature maps that has been previously trained will be augmented with a new classifier (new Dense layers) so we do not have to train the model from scratch \n",
        "- Fine tuning can be performed by unfreezing the top layers (base) and slowly training the entire network so an improved performance can be achieved. \n",
        "- ResNet50 is a deep residual learning architecture that is widely used for image classification\n",
        "- The ResNet50 is trained using ImageNet which is an open source repository of images\n",
        "- Citations: Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei. (* = equal contribution) ImageNet Large Scale Visual Recognition Challenge. arXiv:1409.0575, 2014.\n",
        "- Paper: https://arxiv.org/abs/1409.0575\n",
        "- Data Source: https://www.kaggle.com/tongpython/cat-and-dog"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cYDnXwPdbxLS"
      },
      "source": [
        "{0: 'tench, Tinca tinca',\n",
        " 1: 'goldfish, Carassius auratus',\n",
        " 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',\n",
        " 3: 'tiger shark, Galeocerdo cuvieri',\n",
        " 4: 'hammerhead, hammerhead shark',\n",
        " 5: 'electric ray, crampfish, numbfish, torpedo',\n",
        " 6: 'stingray',\n",
        " 7: 'cock',\n",
        " 8: 'hen',\n",
        " 9: 'ostrich, Struthio camelus',\n",
        " 10: 'brambling, Fringilla montifringilla',\n",
        " 11: 'goldfinch, Carduelis carduelis',\n",
        " 12: 'house finch, linnet, Carpodacus mexicanus',\n",
        " 13: 'junco, snowbird',\n",
        " 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea',\n",
        " 15: 'robin, American robin, Turdus migratorius',\n",
        " 16: 'bulbul',\n",
        " 17: 'jay',\n",
        " 18: 'magpie',\n",
        " 19: 'chickadee',\n",
        " 20: 'water ouzel, dipper',\n",
        " 21: 'kite',\n",
        " 22: 'bald eagle, American eagle, Haliaeetus leucocephalus',\n",
        " 23: 'vulture',\n",
        " 24: 'great grey owl, great gray owl, Strix nebulosa',\n",
        " 25: 'European fire salamander, Salamandra salamandra',\n",
        " 26: 'common newt, Triturus vulgaris',\n",
        " 27: 'eft',\n",
        " 28: 'spotted salamander, Ambystoma maculatum',\n",
        " 29: 'axolotl, mud puppy, Ambystoma mexicanum',\n",
        " 30: 'bullfrog, Rana catesbeiana',\n",
        " 31: 'tree frog, tree-frog',\n",
        " 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui',\n",
        " 33: 'loggerhead, loggerhead turtle, Caretta caretta',\n",
        " 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea',\n",
        " 35: 'mud turtle',\n",
        " 36: 'terrapin',\n",
        " 37: 'box turtle, box tortoise',\n",
        " 38: 'banded gecko',\n",
        " 39: 'common iguana, iguana, Iguana iguana',\n",
        " 40: 'American chameleon, anole, Anolis carolinensis',\n",
        " 41: 'whiptail, whiptail lizard',\n",
        " 42: 'agama',\n",
        " 43: 'frilled lizard, Chlamydosaurus kingi',\n",
        " 44: 'alligator lizard',\n",
        " 45: 'Gila monster, Heloderma suspectum',\n",
        " 46: 'green lizard, Lacerta viridis',\n",
        " 47: 'African chameleon, Chamaeleo chamaeleon',\n",
        " 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis',\n",
        " 49: 'African crocodile, Nile crocodile, Crocodylus niloticus',\n",
        " 50: 'American alligator, Alligator mississipiensis',\n",
        " 51: 'triceratops',\n",
        " 52: 'thunder snake, worm snake, Carphophis amoenus',\n",
        " 53: 'ringneck snake, ring-necked snake, ring snake',\n",
        " 54: 'hognose snake, puff adder, sand viper',\n",
        " 55: 'green snake, grass snake',\n",
        " 56: 'king snake, kingsnake',\n",
        " 57: 'garter snake, grass snake',\n",
        " 58: 'water snake',\n",
        " 59: 'vine snake',\n",
        " 60: 'night snake, Hypsiglena torquata',\n",
        " 61: 'boa constrictor, Constrictor constrictor',\n",
        " 62: 'rock python, rock snake, Python sebae',\n",
        " 63: 'Indian cobra, Naja naja',\n",
        " 64: 'green mamba',\n",
        " 65: 'sea snake',\n",
        " 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus',\n",
        " 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus',\n",
        " 68: 'sidewinder, horned rattlesnake, Crotalus cerastes',\n",
        " 69: 'trilobite',\n",
        " 70: 'harvestman, daddy longlegs, Phalangium opilio',\n",
        " 71: 'scorpion',\n",
        " 72: 'black and gold garden spider, Argiope aurantia',\n",
        " 73: 'barn spider, Araneus cavaticus',\n",
        " 74: 'garden spider, Aranea diademata',\n",
        " 75: 'black widow, Latrodectus mactans',\n",
        " 76: 'tarantula',\n",
        " 77: 'wolf spider, hunting spider',\n",
        " 78: 'tick',\n",
        " 79: 'centipede',\n",
        " 80: 'black grouse',\n",
        " 81: 'ptarmigan',\n",
        " 82: 'ruffed grouse, partridge, Bonasa umbellus',\n",
        " 83: 'prairie chicken, prairie grouse, prairie fowl',\n",
        " 84: 'peacock',\n",
        " 85: 'quail',\n",
        " 86: 'partridge',\n",
        " 87: 'African grey, African gray, Psittacus erithacus',\n",
        " 88: 'macaw',\n",
        " 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',\n",
        " 90: 'lorikeet',\n",
        " 91: 'coucal',\n",
        " 92: 'bee eater',\n",
        " 93: 'hornbill',\n",
        " 94: 'hummingbird',\n",
        " 95: 'jacamar',\n",
        " 96: 'toucan',\n",
        " 97: 'drake',\n",
        " 98: 'red-breasted merganser, Mergus serrator',\n",
        " 99: 'goose',\n",
        " 100: 'black swan, Cygnus atratus',\n",
        " 101: 'tusker',\n",
        " 102: 'echidna, spiny anteater, anteater',\n",
        " 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus',\n",
        " 104: 'wallaby, brush kangaroo',\n",
        " 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus',\n",
        " 106: 'wombat',\n",
        " 107: 'jellyfish',\n",
        " 108: 'sea anemone, anemone',\n",
        " 109: 'brain coral',\n",
        " 110: 'flatworm, platyhelminth',\n",
        " 111: 'nematode, nematode worm, roundworm',\n",
        " 112: 'conch',\n",
        " 113: 'snail',\n",
        " 114: 'slug',\n",
        " 115: 'sea slug, nudibranch',\n",
        " 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore',\n",
        " 117: 'chambered nautilus, pearly nautilus, nautilus',\n",
        " 118: 'Dungeness crab, Cancer magister',\n",
        " 119: 'rock crab, Cancer irroratus',\n",
        " 120: 'fiddler crab',\n",
        " 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica',\n",
        " 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus',\n",
        " 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish',\n",
        " 124: 'crayfish, crawfish, crawdad, crawdaddy',\n",
        " 125: 'hermit crab',\n",
        " 126: 'isopod',\n",
        " 127: 'white stork, Ciconia ciconia',\n",
        " 128: 'black stork, Ciconia nigra',\n",
        " 129: 'spoonbill',\n",
        " 130: 'flamingo',\n",
        " 131: 'little blue heron, Egretta caerulea',\n",
        " 132: 'American egret, great white heron, Egretta albus',\n",
        " 133: 'bittern',\n",
        " 134: 'crane',\n",
        " 135: 'limpkin, Aramus pictus',\n",
        " 136: 'European gallinule, Porphyrio porphyrio',\n",
        " 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana',\n",
        " 138: 'bustard',\n",
        " 139: 'ruddy turnstone, Arenaria interpres',\n",
        " 140: 'red-backed sandpiper, dunlin, Erolia alpina',\n",
        " 141: 'redshank, Tringa totanus',\n",
        " 142: 'dowitcher',\n",
        " 143: 'oystercatcher, oyster catcher',\n",
        " 144: 'pelican',\n",
        " 145: 'king penguin, Aptenodytes patagonica',\n",
        " 146: 'albatross, mollymawk',\n",
        " 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus',\n",
        " 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca',\n",
        " 149: 'dugong, Dugong dugon',\n",
        " 150: 'sea lion',\n",
        " 151: 'Chihuahua',\n",
        " 152: 'Japanese spaniel',\n",
        " 153: 'Maltese dog, Maltese terrier, Maltese',\n",
        " 154: 'Pekinese, Pekingese, Peke',\n",
        " 155: 'Shih-Tzu',\n",
        " 156: 'Blenheim spaniel',\n",
        " 157: 'papillon',\n",
        " 158: 'toy terrier',\n",
        " 159: 'Rhodesian ridgeback',\n",
        " 160: 'Afghan hound, Afghan',\n",
        " 161: 'basset, basset hound',\n",
        " 162: 'beagle',\n",
        " 163: 'bloodhound, sleuthhound',\n",
        " 164: 'bluetick',\n",
        " 165: 'black-and-tan coonhound',\n",
        " 166: 'Walker hound, Walker foxhound',\n",
        " 167: 'English foxhound',\n",
        " 168: 'redbone',\n",
        " 169: 'borzoi, Russian wolfhound',\n",
        " 170: 'Irish wolfhound',\n",
        " 171: 'Italian greyhound',\n",
        " 172: 'whippet',\n",
        " 173: 'Ibizan hound, Ibizan Podenco',\n",
        " 174: 'Norwegian elkhound, elkhound',\n",
        " 175: 'otterhound, otter hound',\n",
        " 176: 'Saluki, gazelle hound',\n",
        " 177: 'Scottish deerhound, deerhound',\n",
        " 178: 'Weimaraner',\n",
        " 179: 'Staffordshire bullterrier, Staffordshire bull terrier',\n",
        " 180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier',\n",
        " 181: 'Bedlington terrier',\n",
        " 182: 'Border terrier',\n",
        " 183: 'Kerry blue terrier',\n",
        " 184: 'Irish terrier',\n",
        " 185: 'Norfolk terrier',\n",
        " 186: 'Norwich terrier',\n",
        " 187: 'Yorkshire terrier',\n",
        " 188: 'wire-haired fox terrier',\n",
        " 189: 'Lakeland terrier',\n",
        " 190: 'Sealyham terrier, Sealyham',\n",
        " 191: 'Airedale, Airedale terrier',\n",
        " 192: 'cairn, cairn terrier',\n",
        " 193: 'Australian terrier',\n",
        " 194: 'Dandie Dinmont, Dandie Dinmont terrier',\n",
        " 195: 'Boston bull, Boston terrier',\n",
        " 196: 'miniature schnauzer',\n",
        " 197: 'giant schnauzer',\n",
        " 198: 'standard schnauzer',\n",
        " 199: 'Scotch terrier, Scottish terrier, Scottie',\n",
        " 200: 'Tibetan terrier, chrysanthemum dog',\n",
        " 201: 'silky terrier, Sydney silky',\n",
        " 202: 'soft-coated wheaten terrier',\n",
        " 203: 'West Highland white terrier',\n",
        " 204: 'Lhasa, Lhasa apso',\n",
        " 205: 'flat-coated retriever',\n",
        " 206: 'curly-coated retriever',\n",
        " 207: 'golden retriever',\n",
        " 208: 'Labrador retriever',\n",
        " 209: 'Chesapeake Bay retriever',\n",
        " 210: 'German short-haired pointer',\n",
        " 211: 'vizsla, Hungarian pointer',\n",
        " 212: 'English setter',\n",
        " 213: 'Irish setter, red setter',\n",
        " 214: 'Gordon setter',\n",
        " 215: 'Brittany spaniel',\n",
        " 216: 'clumber, clumber spaniel',\n",
        " 217: 'English springer, English springer spaniel',\n",
        " 218: 'Welsh springer spaniel',\n",
        " 219: 'cocker spaniel, English cocker spaniel, cocker',\n",
        " 220: 'Sussex spaniel',\n",
        " 221: 'Irish water spaniel',\n",
        " 222: 'kuvasz',\n",
        " 223: 'schipperke',\n",
        " 224: 'groenendael',\n",
        " 225: 'malinois',\n",
        " 226: 'briard',\n",
        " 227: 'kelpie',\n",
        " 228: 'komondor',\n",
        " 229: 'Old English sheepdog, bobtail',\n",
        " 230: 'Shetland sheepdog, Shetland sheep dog, Shetland',\n",
        " 231: 'collie',\n",
        " 232: 'Border collie',\n",
        " 233: 'Bouvier des Flandres, Bouviers des Flandres',\n",
        " 234: 'Rottweiler',\n",
        " 235: 'German shepherd, German shepherd dog, German police dog, alsatian',\n",
        " 236: 'Doberman, Doberman pinscher',\n",
        " 237: 'miniature pinscher',\n",
        " 238: 'Greater Swiss Mountain dog',\n",
        " 239: 'Bernese mountain dog',\n",
        " 240: 'Appenzeller',\n",
        " 241: 'EntleBucher',\n",
        " 242: 'boxer',\n",
        " 243: 'bull mastiff',\n",
        " 244: 'Tibetan mastiff',\n",
        " 245: 'French bulldog',\n",
        " 246: 'Great Dane',\n",
        " 247: 'Saint Bernard, St Bernard',\n",
        " 248: 'Eskimo dog, husky',\n",
        " 249: 'malamute, malemute, Alaskan malamute',\n",
        " 250: 'Siberian husky',\n",
        " 251: 'dalmatian, coach dog, carriage dog',\n",
        " 252: 'affenpinscher, monkey pinscher, monkey dog',\n",
        " 253: 'basenji',\n",
        " 254: 'pug, pug-dog',\n",
        " 255: 'Leonberg',\n",
        " 256: 'Newfoundland, Newfoundland dog',\n",
        " 257: 'Great Pyrenees',\n",
        " 258: 'Samoyed, Samoyede',\n",
        " 259: 'Pomeranian',\n",
        " 260: 'chow, chow chow',\n",
        " 261: 'keeshond',\n",
        " 262: 'Brabancon griffon',\n",
        " 263: 'Pembroke, Pembroke Welsh corgi',\n",
        " 264: 'Cardigan, Cardigan Welsh corgi',\n",
        " 265: 'toy poodle',\n",
        " 266: 'miniature poodle',\n",
        " 267: 'standard poodle',\n",
        " 268: 'Mexican hairless',\n",
        " 269: 'timber wolf, grey wolf, gray wolf, Canis lupus',\n",
        " 270: 'white wolf, Arctic wolf, Canis lupus tundrarum',\n",
        " 271: 'red wolf, maned wolf, Canis rufus, Canis niger',\n",
        " 272: 'coyote, prairie wolf, brush wolf, Canis latrans',\n",
        " 273: 'dingo, warrigal, warragal, Canis dingo',\n",
        " 274: 'dhole, Cuon alpinus',\n",
        " 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus',\n",
        " 276: 'hyena, hyaena',\n",
        " 277: 'red fox, Vulpes vulpes',\n",
        " 278: 'kit fox, Vulpes macrotis',\n",
        " 279: 'Arctic fox, white fox, Alopex lagopus',\n",
        " 280: 'grey fox, gray fox, Urocyon cinereoargenteus',\n",
        " 281: 'tabby, tabby cat',\n",
        " 282: 'tiger cat',\n",
        " 283: 'Persian cat',\n",
        " 284: 'Siamese cat, Siamese',\n",
        " 285: 'Egyptian cat',\n",
        " 286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',\n",
        " 287: 'lynx, catamount',\n",
        " 288: 'leopard, Panthera pardus',\n",
        " 289: 'snow leopard, ounce, Panthera uncia',\n",
        " 290: 'jaguar, panther, Panthera onca, Felis onca',\n",
        " 291: 'lion, king of beasts, Panthera leo',\n",
        " 292: 'tiger, Panthera tigris',\n",
        " 293: 'cheetah, chetah, Acinonyx jubatus',\n",
        " 294: 'brown bear, bruin, Ursus arctos',\n",
        " 295: 'American black bear, black bear, Ursus americanus, Euarctos americanus',\n",
        " 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus',\n",
        " 297: 'sloth bear, Melursus ursinus, Ursus ursinus',\n",
        " 298: 'mongoose',\n",
        " 299: 'meerkat, mierkat',\n",
        " 300: 'tiger beetle',\n",
        " 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle',\n",
        " 302: 'ground beetle, carabid beetle',\n",
        " 303: 'long-horned beetle, longicorn, longicorn beetle',\n",
        " 304: 'leaf beetle, chrysomelid',\n",
        " 305: 'dung beetle',\n",
        " 306: 'rhinoceros beetle',\n",
        " 307: 'weevil',\n",
        " 308: 'fly',\n",
        " 309: 'bee',\n",
        " 310: 'ant, emmet, pismire',\n",
        " 311: 'grasshopper, hopper',\n",
        " 312: 'cricket',\n",
        " 313: 'walking stick, walkingstick, stick insect',\n",
        " 314: 'cockroach, roach',\n",
        " 315: 'mantis, mantid',\n",
        " 316: 'cicada, cicala',\n",
        " 317: 'leafhopper',\n",
        " 318: 'lacewing, lacewing fly',\n",
        " 319: \"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk\",\n",
        " 320: 'damselfly',\n",
        " 321: 'admiral',\n",
        " 322: 'ringlet, ringlet butterfly',\n",
        " 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',\n",
        " 324: 'cabbage butterfly',\n",
        " 325: 'sulphur butterfly, sulfur butterfly',\n",
        " 326: 'lycaenid, lycaenid butterfly',\n",
        " 327: 'starfish, sea star',\n",
        " 328: 'sea urchin',\n",
        " 329: 'sea cucumber, holothurian',\n",
        " 330: 'wood rabbit, cottontail, cottontail rabbit',\n",
        " 331: 'hare',\n",
        " 332: 'Angora, Angora rabbit',\n",
        " 333: 'hamster',\n",
        " 334: 'porcupine, hedgehog',\n",
        " 335: 'fox squirrel, eastern fox squirrel, Sciurus niger',\n",
        " 336: 'marmot',\n",
        " 337: 'beaver',\n",
        " 338: 'guinea pig, Cavia cobaya',\n",
        " 339: 'sorrel',\n",
        " 340: 'zebra',\n",
        " 341: 'hog, pig, grunter, squealer, Sus scrofa',\n",
        " 342: 'wild boar, boar, Sus scrofa',\n",
        " 343: 'warthog',\n",
        " 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius',\n",
        " 345: 'ox',\n",
        " 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis',\n",
        " 347: 'bison',\n",
        " 348: 'ram, tup',\n",
        " 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis',\n",
        " 350: 'ibex, Capra ibex',\n",
        " 351: 'hartebeest',\n",
        " 352: 'impala, Aepyceros melampus',\n",
        " 353: 'gazelle',\n",
        " 354: 'Arabian camel, dromedary, Camelus dromedarius',\n",
        " 355: 'llama',\n",
        " 356: 'weasel',\n",
        " 357: 'mink',\n",
        " 358: 'polecat, fitch, foulmart, foumart, Mustela putorius',\n",
        " 359: 'black-footed ferret, ferret, Mustela nigripes',\n",
        " 360: 'otter',\n",
        " 361: 'skunk, polecat, wood pussy',\n",
        " 362: 'badger',\n",
        " 363: 'armadillo',\n",
        " 364: 'three-toed sloth, ai, Bradypus tridactylus',\n",
        " 365: 'orangutan, orang, orangutang, Pongo pygmaeus',\n",
        " 366: 'gorilla, Gorilla gorilla',\n",
        " 367: 'chimpanzee, chimp, Pan troglodytes',\n",
        " 368: 'gibbon, Hylobates lar',\n",
        " 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus',\n",
        " 370: 'guenon, guenon monkey',\n",
        " 371: 'patas, hussar monkey, Erythrocebus patas',\n",
        " 372: 'baboon',\n",
        " 373: 'macaque',\n",
        " 374: 'langur',\n",
        " 375: 'colobus, colobus monkey',\n",
        " 376: 'proboscis monkey, Nasalis larvatus',\n",
        " 377: 'marmoset',\n",
        " 378: 'capuchin, ringtail, Cebus capucinus',\n",
        " 379: 'howler monkey, howler',\n",
        " 380: 'titi, titi monkey',\n",
        " 381: 'spider monkey, Ateles geoffroyi',\n",
        " 382: 'squirrel monkey, Saimiri sciureus',\n",
        " 383: 'Madagascar cat, ring-tailed lemur, Lemur catta',\n",
        " 384: 'indri, indris, Indri indri, Indri brevicaudatus',\n",
        " 385: 'Indian elephant, Elephas maximus',\n",
        " 386: 'African elephant, Loxodonta africana',\n",
        " 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens',\n",
        " 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',\n",
        " 389: 'barracouta, snoek',\n",
        " 390: 'eel',\n",
        " 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch',\n",
        " 392: 'rock beauty, Holocanthus tricolor',\n",
        " 393: 'anemone fish',\n",
        " 394: 'sturgeon',\n",
        " 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus',\n",
        " 396: 'lionfish',\n",
        " 397: 'puffer, pufferfish, blowfish, globefish',\n",
        " 398: 'abacus',\n",
        " 399: 'abaya',\n",
        " 400: \"academic gown, academic robe, judge's robe\",\n",
        " 401: 'accordion, piano accordion, squeeze box',\n",
        " 402: 'acoustic guitar',\n",
        " 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier',\n",
        " 404: 'airliner',\n",
        " 405: 'airship, dirigible',\n",
        " 406: 'altar',\n",
        " 407: 'ambulance',\n",
        " 408: 'amphibian, amphibious vehicle',\n",
        " 409: 'analog clock',\n",
        " 410: 'apiary, bee house',\n",
        " 411: 'apron',\n",
        " 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin',\n",
        " 413: 'assault rifle, assault gun',\n",
        " 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack',\n",
        " 415: 'bakery, bakeshop, bakehouse',\n",
        " 416: 'balance beam, beam',\n",
        " 417: 'balloon',\n",
        " 418: 'ballpoint, ballpoint pen, ballpen, Biro',\n",
        " 419: 'Band Aid',\n",
        " 420: 'banjo',\n",
        " 421: 'bannister, banister, balustrade, balusters, handrail',\n",
        " 422: 'barbell',\n",
        " 423: 'barber chair',\n",
        " 424: 'barbershop',\n",
        " 425: 'barn',\n",
        " 426: 'barometer',\n",
        " 427: 'barrel, cask',\n",
        " 428: 'barrow, garden cart, lawn cart, wheelbarrow',\n",
        " 429: 'baseball',\n",
        " 430: 'basketball',\n",
        " 431: 'bassinet',\n",
        " 432: 'bassoon',\n",
        " 433: 'bathing cap, swimming cap',\n",
        " 434: 'bath towel',\n",
        " 435: 'bathtub, bathing tub, bath, tub',\n",
        " 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon',\n",
        " 437: 'beacon, lighthouse, beacon light, pharos',\n",
        " 438: 'beaker',\n",
        " 439: 'bearskin, busby, shako',\n",
        " 440: 'beer bottle',\n",
        " 441: 'beer glass',\n",
        " 442: 'bell cote, bell cot',\n",
        " 443: 'bib',\n",
        " 444: 'bicycle-built-for-two, tandem bicycle, tandem',\n",
        " 445: 'bikini, two-piece',\n",
        " 446: 'binder, ring-binder',\n",
        " 447: 'binoculars, field glasses, opera glasses',\n",
        " 448: 'birdhouse',\n",
        " 449: 'boathouse',\n",
        " 450: 'bobsled, bobsleigh, bob',\n",
        " 451: 'bolo tie, bolo, bola tie, bola',\n",
        " 452: 'bonnet, poke bonnet',\n",
        " 453: 'bookcase',\n",
        " 454: 'bookshop, bookstore, bookstall',\n",
        " 455: 'bottlecap',\n",
        " 456: 'bow',\n",
        " 457: 'bow tie, bow-tie, bowtie',\n",
        " 458: 'brass, memorial tablet, plaque',\n",
        " 459: 'brassiere, bra, bandeau',\n",
        " 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty',\n",
        " 461: 'breastplate, aegis, egis',\n",
        " 462: 'broom',\n",
        " 463: 'bucket, pail',\n",
        " 464: 'buckle',\n",
        " 465: 'bulletproof vest',\n",
        " 466: 'bullet train, bullet',\n",
        " 467: 'butcher shop, meat market',\n",
        " 468: 'cab, hack, taxi, taxicab',\n",
        " 469: 'caldron, cauldron',\n",
        " 470: 'candle, taper, wax light',\n",
        " 471: 'cannon',\n",
        " 472: 'canoe',\n",
        " 473: 'can opener, tin opener',\n",
        " 474: 'cardigan',\n",
        " 475: 'car mirror',\n",
        " 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig',\n",
        " 477: \"carpenter's kit, tool kit\",\n",
        " 478: 'carton',\n",
        " 479: 'car wheel',\n",
        " 480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM',\n",
        " 481: 'cassette',\n",
        " 482: 'cassette player',\n",
        " 483: 'castle',\n",
        " 484: 'catamaran',\n",
        " 485: 'CD player',\n",
        " 486: 'cello, violoncello',\n",
        " 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone',\n",
        " 488: 'chain',\n",
        " 489: 'chainlink fence',\n",
        " 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour',\n",
        " 491: 'chain saw, chainsaw',\n",
        " 492: 'chest',\n",
        " 493: 'chiffonier, commode',\n",
        " 494: 'chime, bell, gong',\n",
        " 495: 'china cabinet, china closet',\n",
        " 496: 'Christmas stocking',\n",
        " 497: 'church, church building',\n",
        " 498: 'cinema, movie theater, movie theatre, movie house, picture palace',\n",
        " 499: 'cleaver, meat cleaver, chopper',\n",
        " 500: 'cliff dwelling',\n",
        " 501: 'cloak',\n",
        " 502: 'clog, geta, patten, sabot',\n",
        " 503: 'cocktail shaker',\n",
        " 504: 'coffee mug',\n",
        " 505: 'coffeepot',\n",
        " 506: 'coil, spiral, volute, whorl, helix',\n",
        " 507: 'combination lock',\n",
        " 508: 'computer keyboard, keypad',\n",
        " 509: 'confectionery, confectionary, candy store',\n",
        " 510: 'container ship, containership, container vessel',\n",
        " 511: 'convertible',\n",
        " 512: 'corkscrew, bottle screw',\n",
        " 513: 'cornet, horn, trumpet, trump',\n",
        " 514: 'cowboy boot',\n",
        " 515: 'cowboy hat, ten-gallon hat',\n",
        " 516: 'cradle',\n",
        " 517: 'crane',\n",
        " 518: 'crash helmet',\n",
        " 519: 'crate',\n",
        " 520: 'crib, cot',\n",
        " 521: 'Crock Pot',\n",
        " 522: 'croquet ball',\n",
        " 523: 'crutch',\n",
        " 524: 'cuirass',\n",
        " 525: 'dam, dike, dyke',\n",
        " 526: 'desk',\n",
        " 527: 'desktop computer',\n",
        " 528: 'dial telephone, dial phone',\n",
        " 529: 'diaper, nappy, napkin',\n",
        " 530: 'digital clock',\n",
        " 531: 'digital watch',\n",
        " 532: 'dining table, board',\n",
        " 533: 'dishrag, dishcloth',\n",
        " 534: 'dishwasher, dish washer, dishwashing machine',\n",
        " 535: 'disk brake, disc brake',\n",
        " 536: 'dock, dockage, docking facility',\n",
        " 537: 'dogsled, dog sled, dog sleigh',\n",
        " 538: 'dome',\n",
        " 539: 'doormat, welcome mat',\n",
        " 540: 'drilling platform, offshore rig',\n",
        " 541: 'drum, membranophone, tympan',\n",
        " 542: 'drumstick',\n",
        " 543: 'dumbbell',\n",
        " 544: 'Dutch oven',\n",
        " 545: 'electric fan, blower',\n",
        " 546: 'electric guitar',\n",
        " 547: 'electric locomotive',\n",
        " 548: 'entertainment center',\n",
        " 549: 'envelope',\n",
        " 550: 'espresso maker',\n",
        " 551: 'face powder',\n",
        " 552: 'feather boa, boa',\n",
        " 553: 'file, file cabinet, filing cabinet',\n",
        " 554: 'fireboat',\n",
        " 555: 'fire engine, fire truck',\n",
        " 556: 'fire screen, fireguard',\n",
        " 557: 'flagpole, flagstaff',\n",
        " 558: 'flute, transverse flute',\n",
        " 559: 'folding chair',\n",
        " 560: 'football helmet',\n",
        " 561: 'forklift',\n",
        " 562: 'fountain',\n",
        " 563: 'fountain pen',\n",
        " 564: 'four-poster',\n",
        " 565: 'freight car',\n",
        " 566: 'French horn, horn',\n",
        " 567: 'frying pan, frypan, skillet',\n",
        " 568: 'fur coat',\n",
        " 569: 'garbage truck, dustcart',\n",
        " 570: 'gasmask, respirator, gas helmet',\n",
        " 571: 'gas pump, gasoline pump, petrol pump, island dispenser',\n",
        " 572: 'goblet',\n",
        " 573: 'go-kart',\n",
        " 574: 'golf ball',\n",
        " 575: 'golfcart, golf cart',\n",
        " 576: 'gondola',\n",
        " 577: 'gong, tam-tam',\n",
        " 578: 'gown',\n",
        " 579: 'grand piano, grand',\n",
        " 580: 'greenhouse, nursery, glasshouse',\n",
        " 581: 'grille, radiator grille',\n",
        " 582: 'grocery store, grocery, food market, market',\n",
        " 583: 'guillotine',\n",
        " 584: 'hair slide',\n",
        " 585: 'hair spray',\n",
        " 586: 'half track',\n",
        " 587: 'hammer',\n",
        " 588: 'hamper',\n",
        " 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier',\n",
        " 590: 'hand-held computer, hand-held microcomputer',\n",
        " 591: 'handkerchief, hankie, hanky, hankey',\n",
        " 592: 'hard disc, hard disk, fixed disk',\n",
        " 593: 'harmonica, mouth organ, harp, mouth harp',\n",
        " 594: 'harp',\n",
        " 595: 'harvester, reaper',\n",
        " 596: 'hatchet',\n",
        " 597: 'holster',\n",
        " 598: 'home theater, home theatre',\n",
        " 599: 'honeycomb',\n",
        " 600: 'hook, claw',\n",
        " 601: 'hoopskirt, crinoline',\n",
        " 602: 'horizontal bar, high bar',\n",
        " 603: 'horse cart, horse-cart',\n",
        " 604: 'hourglass',\n",
        " 605: 'iPod',\n",
        " 606: 'iron, smoothing iron',\n",
        " 607: \"jack-o'-lantern\",\n",
        " 608: 'jean, blue jean, denim',\n",
        " 609: 'jeep, landrover',\n",
        " 610: 'jersey, T-shirt, tee shirt',\n",
        " 611: 'jigsaw puzzle',\n",
        " 612: 'jinrikisha, ricksha, rickshaw',\n",
        " 613: 'joystick',\n",
        " 614: 'kimono',\n",
        " 615: 'knee pad',\n",
        " 616: 'knot',\n",
        " 617: 'lab coat, laboratory coat',\n",
        " 618: 'ladle',\n",
        " 619: 'lampshade, lamp shade',\n",
        " 620: 'laptop, laptop computer',\n",
        " 621: 'lawn mower, mower',\n",
        " 622: 'lens cap, lens cover',\n",
        " 623: 'letter opener, paper knife, paperknife',\n",
        " 624: 'library',\n",
        " 625: 'lifeboat',\n",
        " 626: 'lighter, light, igniter, ignitor',\n",
        " 627: 'limousine, limo',\n",
        " 628: 'liner, ocean liner',\n",
        " 629: 'lipstick, lip rouge',\n",
        " 630: 'Loafer',\n",
        " 631: 'lotion',\n",
        " 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system',\n",
        " 633: \"loupe, jeweler's loupe\",\n",
        " 634: 'lumbermill, sawmill',\n",
        " 635: 'magnetic compass',\n",
        " 636: 'mailbag, postbag',\n",
        " 637: 'mailbox, letter box',\n",
        " 638: 'maillot',\n",
        " 639: 'maillot, tank suit',\n",
        " 640: 'manhole cover',\n",
        " 641: 'maraca',\n",
        " 642: 'marimba, xylophone',\n",
        " 643: 'mask',\n",
        " 644: 'matchstick',\n",
        " 645: 'maypole',\n",
        " 646: 'maze, labyrinth',\n",
        " 647: 'measuring cup',\n",
        " 648: 'medicine chest, medicine cabinet',\n",
        " 649: 'megalith, megalithic structure',\n",
        " 650: 'microphone, mike',\n",
        " 651: 'microwave, microwave oven',\n",
        " 652: 'military uniform',\n",
        " 653: 'milk can',\n",
        " 654: 'minibus',\n",
        " 655: 'miniskirt, mini',\n",
        " 656: 'minivan',\n",
        " 657: 'missile',\n",
        " 658: 'mitten',\n",
        " 659: 'mixing bowl',\n",
        " 660: 'mobile home, manufactured home',\n",
        " 661: 'Model T',\n",
        " 662: 'modem',\n",
        " 663: 'monastery',\n",
        " 664: 'monitor',\n",
        " 665: 'moped',\n",
        " 666: 'mortar',\n",
        " 667: 'mortarboard',\n",
        " 668: 'mosque',\n",
        " 669: 'mosquito net',\n",
        " 670: 'motor scooter, scooter',\n",
        " 671: 'mountain bike, all-terrain bike, off-roader',\n",
        " 672: 'mountain tent',\n",
        " 673: 'mouse, computer mouse',\n",
        " 674: 'mousetrap',\n",
        " 675: 'moving van',\n",
        " 676: 'muzzle',\n",
        " 677: 'nail',\n",
        " 678: 'neck brace',\n",
        " 679: 'necklace',\n",
        " 680: 'nipple',\n",
        " 681: 'notebook, notebook computer',\n",
        " 682: 'obelisk',\n",
        " 683: 'oboe, hautboy, hautbois',\n",
        " 684: 'ocarina, sweet potato',\n",
        " 685: 'odometer, hodometer, mileometer, milometer',\n",
        " 686: 'oil filter',\n",
        " 687: 'organ, pipe organ',\n",
        " 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO',\n",
        " 689: 'overskirt',\n",
        " 690: 'oxcart',\n",
        " 691: 'oxygen mask',\n",
        " 692: 'packet',\n",
        " 693: 'paddle, boat paddle',\n",
        " 694: 'paddlewheel, paddle wheel',\n",
        " 695: 'padlock',\n",
        " 696: 'paintbrush',\n",
        " 697: \"pajama, pyjama, pj's, jammies\",\n",
        " 698: 'palace',\n",
        " 699: 'panpipe, pandean pipe, syrinx',\n",
        " 700: 'paper towel',\n",
        " 701: 'parachute, chute',\n",
        " 702: 'parallel bars, bars',\n",
        " 703: 'park bench',\n",
        " 704: 'parking meter',\n",
        " 705: 'passenger car, coach, carriage',\n",
        " 706: 'patio, terrace',\n",
        " 707: 'pay-phone, pay-station',\n",
        " 708: 'pedestal, plinth, footstall',\n",
        " 709: 'pencil box, pencil case',\n",
        " 710: 'pencil sharpener',\n",
        " 711: 'perfume, essence',\n",
        " 712: 'Petri dish',\n",
        " 713: 'photocopier',\n",
        " 714: 'pick, plectrum, plectron',\n",
        " 715: 'pickelhaube',\n",
        " 716: 'picket fence, paling',\n",
        " 717: 'pickup, pickup truck',\n",
        " 718: 'pier',\n",
        " 719: 'piggy bank, penny bank',\n",
        " 720: 'pill bottle',\n",
        " 721: 'pillow',\n",
        " 722: 'ping-pong ball',\n",
        " 723: 'pinwheel',\n",
        " 724: 'pirate, pirate ship',\n",
        " 725: 'pitcher, ewer',\n",
        " 726: \"plane, carpenter's plane, woodworking plane\",\n",
        " 727: 'planetarium',\n",
        " 728: 'plastic bag',\n",
        " 729: 'plate rack',\n",
        " 730: 'plow, plough',\n",
        " 731: \"plunger, plumber's helper\",\n",
        " 732: 'Polaroid camera, Polaroid Land camera',\n",
        " 733: 'pole',\n",
        " 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria',\n",
        " 735: 'poncho',\n",
        " 736: 'pool table, billiard table, snooker table',\n",
        " 737: 'pop bottle, soda bottle',\n",
        " 738: 'pot, flowerpot',\n",
        " 739: \"potter's wheel\",\n",
        " 740: 'power drill',\n",
        " 741: 'prayer rug, prayer mat',\n",
        " 742: 'printer',\n",
        " 743: 'prison, prison house',\n",
        " 744: 'projectile, missile',\n",
        " 745: 'projector',\n",
        " 746: 'puck, hockey puck',\n",
        " 747: 'punching bag, punch bag, punching ball, punchball',\n",
        " 748: 'purse',\n",
        " 749: 'quill, quill pen',\n",
        " 750: 'quilt, comforter, comfort, puff',\n",
        " 751: 'racer, race car, racing car',\n",
        " 752: 'racket, racquet',\n",
        " 753: 'radiator',\n",
        " 754: 'radio, wireless',\n",
        " 755: 'radio telescope, radio reflector',\n",
        " 756: 'rain barrel',\n",
        " 757: 'recreational vehicle, RV, R.V.',\n",
        " 758: 'reel',\n",
        " 759: 'reflex camera',\n",
        " 760: 'refrigerator, icebox',\n",
        " 761: 'remote control, remote',\n",
        " 762: 'restaurant, eating house, eating place, eatery',\n",
        " 763: 'revolver, six-gun, six-shooter',\n",
        " 764: 'rifle',\n",
        " 765: 'rocking chair, rocker',\n",
        " 766: 'rotisserie',\n",
        " 767: 'rubber eraser, rubber, pencil eraser',\n",
        " 768: 'rugby ball',\n",
        " 769: 'rule, ruler',\n",
        " 770: 'running shoe',\n",
        " 771: 'safe',\n",
        " 772: 'safety pin',\n",
        " 773: 'saltshaker, salt shaker',\n",
        " 774: 'sandal',\n",
        " 775: 'sarong',\n",
        " 776: 'sax, saxophone',\n",
        " 777: 'scabbard',\n",
        " 778: 'scale, weighing machine',\n",
        " 779: 'school bus',\n",
        " 780: 'schooner',\n",
        " 781: 'scoreboard',\n",
        " 782: 'screen, CRT screen',\n",
        " 783: 'screw',\n",
        " 784: 'screwdriver',\n",
        " 785: 'seat belt, seatbelt',\n",
        " 786: 'sewing machine',\n",
        " 787: 'shield, buckler',\n",
        " 788: 'shoe shop, shoe-shop, shoe store',\n",
        " 789: 'shoji',\n",
        " 790: 'shopping basket',\n",
        " 791: 'shopping cart',\n",
        " 792: 'shovel',\n",
        " 793: 'shower cap',\n",
        " 794: 'shower curtain',\n",
        " 795: 'ski',\n",
        " 796: 'ski mask',\n",
        " 797: 'sleeping bag',\n",
        " 798: 'slide rule, slipstick',\n",
        " 799: 'sliding door',\n",
        " 800: 'slot, one-armed bandit',\n",
        " 801: 'snorkel',\n",
        " 802: 'snowmobile',\n",
        " 803: 'snowplow, snowplough',\n",
        " 804: 'soap dispenser',\n",
        " 805: 'soccer ball',\n",
        " 806: 'sock',\n",
        " 807: 'solar dish, solar collector, solar furnace',\n",
        " 808: 'sombrero',\n",
        " 809: 'soup bowl',\n",
        " 810: 'space bar',\n",
        " 811: 'space heater',\n",
        " 812: 'space shuttle',\n",
        " 813: 'spatula',\n",
        " 814: 'speedboat',\n",
        " 815: \"spider web, spider's web\",\n",
        " 816: 'spindle',\n",
        " 817: 'sports car, sport car',\n",
        " 818: 'spotlight, spot',\n",
        " 819: 'stage',\n",
        " 820: 'steam locomotive',\n",
        " 821: 'steel arch bridge',\n",
        " 822: 'steel drum',\n",
        " 823: 'stethoscope',\n",
        " 824: 'stole',\n",
        " 825: 'stone wall',\n",
        " 826: 'stopwatch, stop watch',\n",
        " 827: 'stove',\n",
        " 828: 'strainer',\n",
        " 829: 'streetcar, tram, tramcar, trolley, trolley car',\n",
        " 830: 'stretcher',\n",
        " 831: 'studio couch, day bed',\n",
        " 832: 'stupa, tope',\n",
        " 833: 'submarine, pigboat, sub, U-boat',\n",
        " 834: 'suit, suit of clothes',\n",
        " 835: 'sundial',\n",
        " 836: 'sunglass',\n",
        " 837: 'sunglasses, dark glasses, shades',\n",
        " 838: 'sunscreen, sunblock, sun blocker',\n",
        " 839: 'suspension bridge',\n",
        " 840: 'swab, swob, mop',\n",
        " 841: 'sweatshirt',\n",
        " 842: 'swimming trunks, bathing trunks',\n",
        " 843: 'swing',\n",
        " 844: 'switch, electric switch, electrical switch',\n",
        " 845: 'syringe',\n",
        " 846: 'table lamp',\n",
        " 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle',\n",
        " 848: 'tape player',\n",
        " 849: 'teapot',\n",
        " 850: 'teddy, teddy bear',\n",
        " 851: 'television, television system',\n",
        " 852: 'tennis ball',\n",
        " 853: 'thatch, thatched roof',\n",
        " 854: 'theater curtain, theatre curtain',\n",
        " 855: 'thimble',\n",
        " 856: 'thresher, thrasher, threshing machine',\n",
        " 857: 'throne',\n",
        " 858: 'tile roof',\n",
        " 859: 'toaster',\n",
        " 860: 'tobacco shop, tobacconist shop, tobacconist',\n",
        " 861: 'toilet seat',\n",
        " 862: 'torch',\n",
        " 863: 'totem pole',\n",
        " 864: 'tow truck, tow car, wrecker',\n",
        " 865: 'toyshop',\n",
        " 866: 'tractor',\n",
        " 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi',\n",
        " 868: 'tray',\n",
        " 869: 'trench coat',\n",
        " 870: 'tricycle, trike, velocipede',\n",
        " 871: 'trimaran',\n",
        " 872: 'tripod',\n",
        " 873: 'triumphal arch',\n",
        " 874: 'trolleybus, trolley coach, trackless trolley',\n",
        " 875: 'trombone',\n",
        " 876: 'tub, vat',\n",
        " 877: 'turnstile',\n",
        " 878: 'typewriter keyboard',\n",
        " 879: 'umbrella',\n",
        " 880: 'unicycle, monocycle',\n",
        " 881: 'upright, upright piano',\n",
        " 882: 'vacuum, vacuum cleaner',\n",
        " 883: 'vase',\n",
        " 884: 'vault',\n",
        " 885: 'velvet',\n",
        " 886: 'vending machine',\n",
        " 887: 'vestment',\n",
        " 888: 'viaduct',\n",
        " 889: 'violin, fiddle',\n",
        " 890: 'volleyball',\n",
        " 891: 'waffle iron',\n",
        " 892: 'wall clock',\n",
        " 893: 'wallet, billfold, notecase, pocketbook',\n",
        " 894: 'wardrobe, closet, press',\n",
        " 895: 'warplane, military plane',\n",
        " 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin',\n",
        " 897: 'washer, automatic washer, washing machine',\n",
        " 898: 'water bottle',\n",
        " 899: 'water jug',\n",
        " 900: 'water tower',\n",
        " 901: 'whiskey jug',\n",
        " 902: 'whistle',\n",
        " 903: 'wig',\n",
        " 904: 'window screen',\n",
        " 905: 'window shade',\n",
        " 906: 'Windsor tie',\n",
        " 907: 'wine bottle',\n",
        " 908: 'wing',\n",
        " 909: 'wok',\n",
        " 910: 'wooden spoon',\n",
        " 911: 'wool, woolen, woollen',\n",
        " 912: 'worm fence, snake fence, snake-rail fence, Virginia fence',\n",
        " 913: 'wreck',\n",
        " 914: 'yawl',\n",
        " 915: 'yurt',\n",
        " 916: 'web site, website, internet site, site',\n",
        " 917: 'comic book',\n",
        " 918: 'crossword puzzle, crossword',\n",
        " 919: 'street sign',\n",
        " 920: 'traffic light, traffic signal, stoplight',\n",
        " 921: 'book jacket, dust cover, dust jacket, dust wrapper',\n",
        " 922: 'menu',\n",
        " 923: 'plate',\n",
        " 924: 'guacamole',\n",
        " 925: 'consomme',\n",
        " 926: 'hot pot, hotpot',\n",
        " 927: 'trifle',\n",
        " 928: 'ice cream, icecream',\n",
        " 929: 'ice lolly, lolly, lollipop, popsicle',\n",
        " 930: 'French loaf',\n",
        " 931: 'bagel, beigel',\n",
        " 932: 'pretzel',\n",
        " 933: 'cheeseburger',\n",
        " 934: 'hotdog, hot dog, red hot',\n",
        " 935: 'mashed potato',\n",
        " 936: 'head cabbage',\n",
        " 937: 'broccoli',\n",
        " 938: 'cauliflower',\n",
        " 939: 'zucchini, courgette',\n",
        " 940: 'spaghetti squash',\n",
        " 941: 'acorn squash',\n",
        " 942: 'butternut squash',\n",
        " 943: 'cucumber, cuke',\n",
        " 944: 'artichoke, globe artichoke',\n",
        " 945: 'bell pepper',\n",
        " 946: 'cardoon',\n",
        " 947: 'mushroom',\n",
        " 948: 'Granny Smith',\n",
        " 949: 'strawberry',\n",
        " 950: 'orange',\n",
        " 951: 'lemon',\n",
        " 952: 'fig',\n",
        " 953: 'pineapple, ananas',\n",
        " 954: 'banana',\n",
        " 955: 'jackfruit, jak, jack',\n",
        " 956: 'custard apple',\n",
        " 957: 'pomegranate',\n",
        " 958: 'hay',\n",
        " 959: 'carbonara',\n",
        " 960: 'chocolate sauce, chocolate syrup',\n",
        " 961: 'dough',\n",
        " 962: 'meat loaf, meatloaf',\n",
        " 963: 'pizza, pizza pie',\n",
        " 964: 'potpie',\n",
        " 965: 'burrito',\n",
        " 966: 'red wine',\n",
        " 967: 'espresso',\n",
        " 968: 'cup',\n",
        " 969: 'eggnog',\n",
        " 970: 'alp',\n",
        " 971: 'bubble',\n",
        " 972: 'cliff, drop, drop-off',\n",
        " 973: 'coral reef',\n",
        " 974: 'geyser',\n",
        " 975: 'lakeside, lakeshore',\n",
        " 976: 'promontory, headland, head, foreland',\n",
        " 977: 'sandbar, sand bar',\n",
        " 978: 'seashore, coast, seacoast, sea-coast',\n",
        " 979: 'valley, vale',\n",
        " 980: 'volcano',\n",
        " 981: 'ballplayer, baseball player',\n",
        " 982: 'groom, bridegroom',\n",
        " 983: 'scuba diver',\n",
        " 984: 'rapeseed',\n",
        " 985: 'daisy',\n",
        " 986: \"yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum\",\n",
        " 987: 'corn',\n",
        " 988: 'acorn',\n",
        " 989: 'hip, rose hip, rosehip',\n",
        " 990: 'buckeye, horse chestnut, conker',\n",
        " 991: 'coral fungus',\n",
        " 992: 'agaric',\n",
        " 993: 'gyromitra',\n",
        " 994: 'stinkhorn, carrion fungus',\n",
        " 995: 'earthstar',\n",
        " 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa',\n",
        " 997: 'bolete',\n",
        " 998: 'ear, spike, capitulum',\n",
        " 999: 'toilet tissue, toilet paper, bathroom tissue'}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fr6pOOh0hHaj"
      },
      "source": [
        "# STEP #1: IMPORT LIBRARIES"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PcsD7hiN_XXL"
      },
      "source": [
        "# !pip install tensorflow-gpu==2.0.0.alpha0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QqPjanlXkVzi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "bf7c6354-ac33-43d4-a2d5-5c8ed3cc5b67"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import random \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_je5sPfjk_r1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "87b13d41-29d4-4fad-b36a-c2258a3b5769"
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'2.2.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqOSkGp9R55q"
      },
      "source": [
        "# STEP #2: IMPORT MODEL WITH PRE-TRAINED WEIGHTS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bo7rP5ZIb7pv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "2f7446a7-13c5-42ac-9011-15045d66ab58"
      },
      "source": [
        "model = tf.keras.applications.ResNet50(weights = 'imagenet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5\n",
            "102973440/102967424 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jWb45OshSDsr"
      },
      "source": [
        "# STEP #3: EVALUATE THE PRE-TRAINED MODEL (JUST TO BE SURE :)!)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-cfqEXakjdjd"
      },
      "source": [
        "- Check this out: http://www.image-net.org/synset?wnid=n02835271\n",
        "- Search for Bicycle\n",
        "- https://lear.inrialpes.fr/people/gordo/ImageNetResults/sift1000_accuracy_withPrior/results_sift_0253.htm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IFhOG8Pjkue2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "b3926682-4741-4e44-c40a-cf6a22c294d5"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uU_Nd2zQjjuB"
      },
      "source": [
        "Sample_Image= tf.keras.preprocessing.image.load_img(r'/content/drive/My Drive/Colab Notebooks/TF 2.0 Advanced/Transfer Learning Data/African_Elephant.jpg', target_size = (224, 224))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JEun36Tjjr7"
      },
      "source": [
        "Sample_Image= tf.keras.preprocessing.image.load_img(r'/content/drive/My Drive/Colab Notebooks/TF 2.0 Advanced/Transfer Learning Data/bicycle.png', target_size = (224, 224))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Po32oDA3jjpt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "00851347-1ada-4d63-8b0c-4a4bed6bbdfc"
      },
      "source": [
        "plt.imshow(Sample_Image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fab33672518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ilz+1nNBWeYs+rjfi24WLDmCzfh20jbjwb4rrTarLbIp8DuZQHO465a4nd0njPh+A3wcuSABSChcuFePjD3fh8s3Y+98bJgV7dGG5+vm6B9vwUsRohcR6gNX3RQMfCiUQFEoEwUV8/69xfHqez7/wM5w88U+YmauB9kHEgEdQ8KiKEvu7dZKogIifZmujz2D6CrPVjzCz9AmW5jUirDIfVvjcFz7D6Sc+yuziIhiJEBHt9g7EAY1+yOlHT3DnFkg2KIUFHnniWWJliEyCtQVK1RoA1ujUh8xpdusTqEVQJQpeGc+fwivsYj1NacoSDXxM3KPZaHCnPUA02xwvHaFS9F39tLm74H2+3IeAy5d7QdT7+t05OJllWxysY/Kecw7jjyfh3ee5y7vIUC5jP/xeHoiwLrXbGVgzljcrJypkQmSEHfEG2V8HgXP9OUhCZv/lkAEAUoxguc3ozUzIsro58k0KiRQaJfN6IWMXDuFGUgVyL4LuoNuR/17ilJwbw/ExYmT109EVjrBUOU3mohn352A+FEpAqYAjU99GrbRMP16jUjVcv/k85con8b0SRkBsYmwSEfW22d+4hA1LVMoXMbNlPD9BqPMkQRXp7aGkR7ngMUwCKJRAKZdFFUtkYkkiSbE6Q6NjmS9plpeXCEshESIXflPoVAsLIyfBsABhiwReAU/OYgxYKRF2FiUFRU8TljyCYAGrPZr7WxQ8H4lhYuJlSDyVignER2oVRW7iHiY52eSwY3Jr1Gx2yCQmHn+Z+fA2S0ARKepybRnhjYXBaIQ1zroIgbASZRIXvbaCQLlQqbGQ5TE4+iETnJyfax1NN+phSpWLPNxP08Pd9aRIKD0/xiCxxC6SjDHWxYqkG6wMcQghYZR5OIlKskxG55+L3GClroUQ6bUIJCLt4qSyE7kblo9CcMBC58nBjAy86zZm6CDXleydQiDJcRTpASofKclqeBKVH22hJ0jDw8qHQgmA5OLDz7Fz/WvMHFUsLTzC1q0/YHlhkcWFGYxuYGUX24+Q/UtE2y+w2SryyLf/AGePP8z27S3azYSWvs6dvZc4s/gxlhePsXj8HMXqHFZ7aBsz2G8S0kMGPcL5BU4e+xYqlSmsFWhjAZ0OtJu4xuRgWy4+LYRKNbrAuSluLihRAg1FBVZaMLCweAIRK5cQFWRhw1RwDRM31SkXwSQtlEHMwxSAddMjlXoz8m3tKHMxPTBXxY4morBOYDLVZIVNBciiU4E0wrXvW4vRBit9PC+msXWTpVCxvt9ieuUsiAAjnEIYZadZcojDXUtGeo0JLpMKlJ3o55j/s2mSmAVrMKhUKA0xBpkqCA9FIgAS1IgwFCm0YPz5QBGpkOeJOYQ7vRTje5XJezZ+QoBTRfk76sbeywl/XhHo1O0zB4jBcV9IU33vdoEm2iJDODbHAeXWZtgEhAthi0lgdGj5UCgBa2Fl8RhffeNFFk+cp1KqsvBQh0C3ofcuUfMa5bBFq90C2WGAx9DE1Nffonh8Cu312d2+wvbt66zvv0NZnaIaw+LqPFOVKoGMiIc9lLrBnRtrVGceYmn1IsWwlgq/mOjLBJxM/46AoAXjgtjpAYcIWB6SA0FQwC8UEEKOp3kabDfGIJTIfXfQ3tuxv37ALbZph621HLzPk4k2+T6mdQCLQltIhHJhV1Klkk7UwEQoYsCSUMBK8JMufuMt4qt/TKc2y9qVNWz7Wa71yjz8+JMEfgFp/JFvbjFkmTEmHS4h3HhOkJJiLGwmXTAkM6E1A8fyS1BGYIChUARWYG3E9SvvsrJ6irBaRpi8Kb179h+Wo/B+izgg3PmaedLwXmnM72WRJ+q8j98nTEUeFQhnbIwZawApFfcqHwolAGBMgZWVE6zd6HPm6U8wJdaptNbY6K6xtnWFve4u79y6TG0+JI5LXHjku8EMuX3zHQqhT7FWxQxmWFl5BqM9/uAPX+SJpz7KagGSQZ3NO/sszTZJihUqsw9TDJdBeBj0WKCAPGx11tIJx4TuthmkO6jRZU6Dj4+QygOhMKO2BUZrpLBE0QC/ECClh7USKyZdhnGoSo6g/wiAjrJ2spVm2S9wmKkZcUwIrDRYGyOUJTCWuN+nVg6JtSQMC7RaQ7QqYGWABeJE4LWuoxpv0d56gUJvyKDf44mTR/j8l3+dK9uCIG7w0Dd/J9rLRhJsIkm0RXke2ARjx2hjLDD5CH76XmQvgTSKOB07rXw8Ilpb12mu3WY4aPPiC1/gxLHHmTm6wDOf+G76vQjP8zioGg/Lf7gHPXFoGQn/Aa7A2pS8PUShHFQAjogd25l7Kgwx6X7kXTqZ3sesaj4y4JClQEmBJ8wopcTz7h0e+NAogUQEzJ84yVd/83kWz29RFHU6u2vopacpHOkQa8vicUVQUFhT4OTJbyLu1qnfuU0oigQLp0j8AstHStjugHVt6UcROzsb6FjTHfaI4iqVGZDFKkJ6GOtyq+9Kipl4f48pMqqT+a45Xy8nhVZaEm0QnkWnd18Aw2EPJTWQYIzCKRCJFubAaeyEYXNwcjLV5WCfsol2d3GssZSSpNfFG+xQkQM63SZxt83U8RWGGkQMc7bE+naL9XqfhdXjzFfLJPVXkI0rrN/eQ3keM+UitXKZalHxkbMlXviDX6EQBvT7EUpJwjBESJ+ehuWTFyhVplK3xY5Q+kiosiXiqYlzGXCpsBlQylCwmvVrb3Dr+iXqWze5ceUSzcY+1XLIV65fYWg10pNceOo5pPIwJr8IaDyeB5HaoUk4E8Iu7tKpLhQ3vob83wlXhrvf36tkyC67V4ZD6qcKYIIPONB2oKDgCYqej7ZuqdL9lnB/OJSAtaAiirWY0yciNq++THj8OO3SNBVvmcXlafzq44RlRdLvMWjvEdoSpVqF6opPs9Oh5PtUlwMKcgPNPsdPWgZRk71Ggdm5I5w8IhH1PcywTyXEpYEa3OK3HMP7/uDapJoYp4NOLim1GJRO8KREKpX7zYJI6EdrBF7IsCcoFisIXC7DBOiwxqGAHNyczAPIJkcWlc5NFrI5IxDSYuIBUXOHIn1U+w57N1+lR5840lTLZbziNns7u6xv7zAzO8/udot2J0JtLkGlStHsUyxpGs0u586cIyhYrlx7h4X5BdbrDWbLkktf+Hd0Ok3AUimXeeKJZ6hvbLN37XWmVx/lqY9+M0HgkwyHKCnxA0W328X3fSqlgMAXDAeaYZw4EhDwTIvWzcusbd7h9a/8MW9ffhfj+UTxEE9Itnc7FEKfkvT41X/5s/ylHx3wxHPfTozC8wJA41K8reMJJ6xvHoOk3n0OCmb+/5gAzN7a0dr/EZzIfJ3RgqT0uHwYVIrJ7Asx2YMse9BlI+bdQxcylVLgCZEmIxmsMQiZEchuSXfoS8JAUVSCxGZrB7hn+VAoAQsUzbskvVscfSjgC791h9MXvotw+iTGFNGizFxVksiYoBhDD7rbt/Cmj1AMpyj7NYTZR5s22mwRmbdICn3C0jEK04IhuyQ9SRJH4C1BUMNYSTyMEdbgF9ShFgPuYSWyMkLjbjehkX/uzAvWGAKRUCwoYgHa6JFwKiUY9DbotiR+8RG8wKS6/+CafpGB/QnYN9LsNr/yLuvUxIUg0KhkiN/fonntS7SadygwpL23zZWtLRYXlpmbnWN9c496vc7GxiZ31tYYxoZHLl5kb+cGjZYPWMpVj0Apbm7cYW5ugWYz4rEnHqMeXaLbvEGprFlaOEI0HLC9sUbN73HhiOLdy6+h9R561dAFGo0mpVIJk2i2traZmpphcXER6cHG5j71VhevWOLc+fP4zevc/tLneOnty+z3+hTLJZqdDtEgplgpITwPm1iCWpGkvskXPvvLDPstnv3u/4IkFTFrrXPtMl8+HWRDGtHIUIcgjX7YCdUwQRymgj2ig0bTwyG9CZyR+TYWhBjne5gJeDfpjAohcDSR63umazwJgRR4SiJFxplIpHRp1QIDwuALgacAkSIuKybDvQfKh0IJCGGRg116eg5ROkfgv8j29i6LJ49ilecypqxAWg+R9LH7N9i9/RbMn6G6fJ6phdMUhE9zc51BPKCvh3TZQpvL+J0htbDKdm+e8uwxVFAisR56OGQ4jCiXKuPwzH9usbnJkRKM7qME69jgftQhSYaIwgxKBWM+MQ1fGd1DKZXGgSeXiYLIIZSxZRHi/slAecgb+IqocQdVv0q8e4vrrzxPq11nZWkBgcs7bzT3uXnzBpsbOzz22MNUwhJTtRqd4YBup4Vf8BBYOo0Wwz4ExSJbuzu0OkMuXVvjdiPhxHKNhbkag16fiu2xuDrPjOixf+cqi0fm+eQ3nac/jGi+/TxaBdy8s4YQHmhDo17HCwrcqM1Rrs1w+eZNdvfqFEpVrr+xQkX0uXXjKonyGUQxJb8wUrjD4RBhLVPTZer1PSyazcYaX/3CbxAJj2/6rh9CC5DGRRJG4btRSHUc2R9nKGbil5JFNlMkrt5EqPEQI2GtHnEdUgqUdLn9wjhux2CJcwliNnWFlJDpvMirEYsUTvB9T+BLi0qPlwKkUGm4c7z5SObiGgsmPYe+zyqlD4USkIDwTkG4iC8VZx7v8/qLr3H0+DHwpdOa1qKsRA/adLcus7d1GREJwvmTLjQmZ+i1p9jajNDFEC1nGES7NDZamMIipeocioBSsYKJB8SDmFKxhPIE1h4uRIff4DFCcH/z4C6f1GIQEur1XTr9BrNLJUK/PPLVjQaMQgrB5tY2K0dLBL4AVK6Ncb+kFLmw5L0TTvJ91Fqj4yZB4x2Gt19nY+02vV6TMAzRWlMsFFheXmZtbY2trQ0QUG/UqYQlHn/4ES7fuM7G1iZIQafVotPpEQZV/BB0HBNUBItHV7h0+S1Cs8BHn36EjVu3mKqUKBUFp596hJ29PbBQCEO2tnYZ9BMaEWzvNNwEFpJhbGi361DvUa62uLW+xrDfx+t12dvbxqBI4gEL8wGFsEy312M46GGtR6/Xw5OSOA4YDgcYo+n123S6Hr/77/8/PvWDP8JGu4/NNkSw2YrEvFIVI4Ug01socGHZ8ToHkd7hjG2XE2M+eR/G7YeeoRD4aQTH5fdHcYKJ3MYsQgp8KfE8NxaZy2itY/etsfgeFHzwlSDv2mfIwkVhdBqqFhNIxNg0e/DQoKQrf2IlIIQ4Bvy/wKIbNj5trf1ZIcT/BPxNYCc99H+0bm+B+zQmscFRijJAYpg7cYLw0k32b2+yeP4YyvgkIgGj6bSbbLd9yqc+TrMRUy7W8KzECp+Vkydp7B1nv19j6dgSpYJi782X2atbphcqBGpAv30DkRSoVGYpFosY6+BW3m8bQ79sUws7+t7ddAFCg/VTpy5ykM56OCE2WCHwJPgCPBUihI8VDvqpdHszv1hC2grVYBrpeZiMMXepIXflC5BTPOOwoOMLsm45wkqipKAQ76C2XmP3xmtsb62BUMzPzuF7Cj8IuLO+Qauxz/LSEtVqmTAM6bQanDtzmsvvvsl2vYFJNFJ5tNs9hokhmCojoy6FcshcrcbstMfKdEAoYMaH6ZNLDIc9er0u/b6kVK7Q7rS4s7ZGvdej3uhyY2OXxx57hjvrW7z6xpt4QYHEGDQGtrYZRkMKUuF7PhZNZCN8T9JuNzEpjzCIIiqVkH63D0h6nU5K8Fgwhv1Om2Hc47d/8R/zrX/xr9JUZfwkjbKkRsU6ljJd+uuccxfLtyl8T7emS82usAYl/TR/wCLSBV7Sk2mCv9tvwBcSgcHzFEXfojxGoVesRRiNVxzDeIlFqjQ3JV0zYY1xef9SpfA/Qx2GsWvohNvNy/H+blK47EEzmrsWe5+lxF/PluMJ8NPW2oeBjwJ/WwjxcPrb/2GtfTJ9vb+NRWSAsiCsolCpcPGx83z1pa+4G2JSPWYt8TCiZ6rMn3yGQmURHVuiKCJOImbnpwgK05TL5zi++hzV0kl0VGJ2/hwzs4v4XoXNzdts16/THXaJbSq8AkeujJarityLu9zskSKwwlHpQBb3dwm4EmsT4uEGvfYOUhZQvksXFkikFSRJgjUe6CIZIM2SfDI/1VrQxqKN+z5r3drMnZAjTZ+5Jta6zSo9YVgKh0yJFtvrt7h0+QobW1skccyg36PdbjOMYrQxBIFPOQzpdevIdQsAACAASURBVDoMBwMEhrm5aba2Nun3e6zducNgELG5vcPKyRNs7+6ytb3D0tIigbIszpRQWHwFR1eW8ARUK2U8P8ArFNFxRK/TYn9vl43NTfq9Dl964Ytcv3WLVrdHq9uj0+0RRTGJSVz0Q8BwOCBJYoRJUNISR0MaezuUikVKYYkoGkJqBaeqFcBidEISxakiNHzutz/Df/ilX3Cbv5jxeCbGog3j8Sa1ounLWIHOxje931lugBTgKfB9ge+B7wmKKRlXLipKpYBKKSAMXB2ZzSPjmGjfk5QKHqWCR9F3Qi6scfyUFHhS4ClBwVcUAw8l7849ODgfHSLNIi3OrTEp0uGAC3Ow/ImRgLV2A9hI37eFEG/jthr/k7YHpAwnkoWVVdTXXiPu9lCFqoNHWqOtQIVzeJVZ5pYipJR02m2mPNBRxMzcEqGqUgnnKUlYPvkExenT9IaSoZYUykvcvP0GQXmZnWad5eUVQq86YoLz6N+aNCNrtMur++smjESQpFo0g/CCLAVWCQNxn3KpiqzN46mALI/A4mG0x0xlmUFsiG2QixuP3ZH8fqU6FxbM8ttdhUyPZ4vrFZKE9p03WXvztzl7dNbxBxYGgwjPQq/TotPrE2nD0ZVFOp0OpWKRIAgolSrUG3WKfkB/0McPCg6iKg+kRCA4d/Ehev2IS1fe4slHHuXFL/4xTzz2JEJ5vPn2JZr1PRaWj7K1s8mR5VUSo1HKY6ZaJR5ahB8wsIr9Zp+wHOJ5Bee62ISC56MQxDphaGNsZJkuVykFIf1hzOljJ2i3uxSlx0AYksgyGAwpVwJUQxInHtZGCAFJEtMctNm99gZTAloipflyARgr3G7LQrjsSZmx6dmxgLIWhcb3JAUvwfckfuAjbUKW3ecriacU1hoSXA6/s75ygsxVKhNWjyRJDiQZjbMUM8WjdZbFem9FcBdxbeUIFYqUxMx2gj6s/Kk8fEQIcRJ4Cngh/ervCCFeF0L8nBBi5j+nLSssoFBhiYuPP8Kl174GJGAtWmuSRHPs5GnCaoVytUavOyAshUzXCuxv75DgMzO3gNGG2akaM0vHCcsVjDaUqz6z06sszD7K/PwS+/t1dna3x0JnzOh9xvJnZIvbcVgBKuWKYhK5zVBvY4lHWW5OGWjAJx7M4AerFMszSOHhCY1vBwgbEw0t3W7AfjshLFUdSYa6aywMB17W5RuY7JVOVQNY4+DioLXGnZc/ixruc/XaNfb26pjEkkQJe/UGnd6AQrHI7Owsw8EArTVxHKOUIgxDCkGBbq9HtVLFGEOSJOzsN/jUn/8e1m6u8fLLL/Gdn/w2/vx3fJzTJ1Z56vHH6DQaXLl2nXa3z9T0DJ1ej2JYIij6DKMh9VYb/AKFyhT1VpfdepN+HGOFpNfr0e/3GfTcK/B8PN8nimOGcYQXFOh1+yAlcZKgtWYYRUxNTdHr9ZBSsre379CVNWhtmJ6eJkk0BT+kvn+NX/qnP4u2Fj3B3jujo4RIkxotCMewS5HgSUPgCUqBpVaSzFY8ZqoelaKkIC2BEvgSAk+glEZIgzYJiU5IkhilPDzPS4VfoZTA8+RIbxtjJuYcgJTODckiEJOLs8ZK4OBCpKwtrXX6N912HEf+3i9P4OtWAkKICvBrwN+11raA/xs4AzyJQwr/2z3q/YQQ4iUhxEuN+i7ZpghSSTzpgV9gan6R1154laTfG5F0hWKBmdkahaJkeqZKpVKlUimjRMz6rQ2a7SFBqYCnLP3uLtKT7O9v4klNKSgwVZnm+OpxQr/K/MwC0SDC3iX8BwmhXKaYEFghMWJAs/cONzZ/iyhpI2yaKptui6axmEAgCyFIH6zFI0LqNr3ONhiBCKYI5xbxPD+N8zLiBMVhfsh49Ma/jd4KBB5+IPHosFqzTM3MsraxTa/Xx2jDfqOJ8nwq1SpBEBAnMXGS0Ov1uHTpbQaDAZ7nobWm0WggpWR+fp44jhGe4ujR4/T7fU6dPE7UbXL1nbe5cfUyR+bnmZ2e4l/8wmeZnp4lLFexBirVCrGOKVfKnD53nuub27z81jv0jWBnv8nN2xt0Or2RlQw8H4EgGgzRUUwgFUUvYL/ZYKBjUB6NZhMV+C7cCniehxCCvd1WuvxaUyqVuHXrFkoqQlWip1tsXntnfG8n5mG28g6UBOVZlGcIPAh9KBUl1bJHtawolwRFT7oQnGX8kBkhSIwhThISbTFaYq1CCH/CtTTGoI0eCSyQ8gLu5SmFJyS+UnhpBmL+NZFmDSPDdVCRjMLUOFfk/s96+DqjA0IIH6cA/o219tfTDmzlfv/nwG8eVtfmnjvw8GPPWM8fx2lJuVw7PcWxUye58cZVLnzLE4SBB6UQv1jAjxIGgx7KB6U6xL0OM/MzTBWnUEFEp73BcOcKly5t4JfPUpk9gbYGD0m5XMJaWF06hjYagWK8gaW6f24Abotzm1ja9RdQ8deIeyv4lY+jKbkjhMCzmgCD9hzjrDAM69uYpAvhIsViCeH5FJRGo9IYduzIxdG+5Y5fcPGA/ARw23sLQbpiDIQVKKGht0nr1d+ks7tNqVRid2+fbrfPYNAF69HpD6iEIbt7u3i+R8n3aDZ2OXr0KIk29AddqpWQerOJKhTYq+/z+JNP007gF3/x5zm+eIJHn36GuWPHkErzzqVLrF+/yaOPPcEP/8AnSKIBoRcSx0O29xrs1zs8/0fPc/Gxhxkajycff4yd+i79Xo96s4G1Acrz0cagpSJODEEKZWVqSXvDCJlousMm8WDAglVM1abQcczCwgI725uUi0V6/R5+4OMJQRRpgqJFC41KFOWSZnGuRLsZEWmdWnuJJwVK2NTvTvcHABQSTwmKvqBSEASeI/ESo13K9Sj5yKJRLjPPOt7BGkOh6NzFzB3QWmNFgrS+o6GUBq1RMkApm4YAFUZnm5FbpJ3IGUy5CsjClcYaXDKZSPMOxIhQVqMtaezomHuVPzESEK7Vfwm8ba3933PfL+cO+0vAG++vvbtfhULII08+zpe/9GVMv4uVQzzTJ/QSAhlTb+6xvrNHp7vNK6//Lq3BHY4ciUl6t5Hdd1i/dgVP1VhZOUGhUBgxwFloJQgCAj/IesDY8opDXrm+WkkcRazd3MYmEfXtV4j1YFQ3uw2eKIIRFBng928yp/r4wsOWFNbLFI1BpjdMkELBLACRtmZTX1WhUubXMdhSZKnhbrXfUEKxe4VhvM/8/Dw7jTabuw2QPqXqNJ3BkM2dPZqdLkEYUqqUKYYhx44dZ3p6mmEUsbm5webWJp7vs7fX5PnPv8D6xhbFsIZA8sjF4yT9Drdv32B7a4t4MKRaqdDqtllYmCYsl9jc2qRSq1Gbmmbp2Ekef/xhlpcWOHv+Iq+89jpxFLGytMDpkyeYqlbTLbEchK1Ua3h+gPKcMi4WixSCAIHA9zxWV5dpNht0Oi104vZmtECcxKkPnJF3EmM0FoOO4c7t6/zTn/mHJDqNu3sCXwl8KQikwFcO1geeIPAlhUBQ8CEIJFI6gYuThMTYFAk6FyzRhjixRLElio17YIyAxEKkNVGcEMWJI3cRDilYd9dciNSR0i7Ub0ZGUIgcmSeyKACpW6jdK40gZC+tDUa7R+HF1qZbkTui8355Al+PO/Ac8OPAdwghXktf3wv8jBDia0KI14FvB/7799PYCG7b8ctaQe3ILKfOnODzv/U7eMQkUZ+3Xn+FXm+ADYr0Om16zSts7LxEP1qj1/oyV976Z2zf/BzDgc/pcx9lfmGRTrOJtJng2BE8c0Oet/oWt77eTLwczNdgDRIoFCJmqufp984y7EXYJMpl9Fg8EpQZULRddi+/zebXbrC+NeTKtTa7V5qYgUtblgKU7aPEeEmtEAYpLYGMqZUHhF6TAgYlBFJkf7O+jvMW4madvbdeRskS3W6XVj/GqIBGp8PtrX2MLKD8kHZviNaWer1JN4oIwhDPDxhGMfPz85RKZXzfZ2e7zuWru/QTw+d+/w/5yz/2Y6xvX+fxh85TwLC9scnTTz9NIQyp1+vEcczW9hZ7zRavv/kOQRDy8z/3/1D0JclwQGIM0TBCGM3ZkyeYrdU4dfIEx48dpxAUMFg6va7jtaQkSRL6/T5WG5IoZtDr02zWKZWK+L5iOByMCC9rLUEQjHxi3/cJgoBhNEBrg68MVVNncaZIqaAoeh6BlI6NTxl+zwOlLEoalNL4vsWTzrZqi4soJBDHliSGWFsSK4iMJdESYyU2zeCLDPQTGCSWoYbYChIjiBNLoiGOxuJnjVMmWuv0NRZutybFmQltD6xQFRIrJBqBNgdeiUsQ0hqSBJL4Xsj264sOfIHDndb3FRI8pL1UMHMJU0JQqJQ4ceYk//FXPsMzz5xnr1Hn+c9/BW/qBDEGO6zTbV2j07tMq7NAu75E0t2nHa2wdPIJyrNzSN8SRUMXuxVgxHjzCheTZfzeZskakMVj7+5rhPIa1Kqz9JIi7f136TevU1tYdO1Ygy/6JPEO9dYA65cpHj9PMDPL6jI0dztsrW0xvzhPuaIwyQDheQihHJxLz1kKYSpso1TCTi9gqMM01zwdoBQaAnhmyKrapHz6KNubAb1hnxtffhVrDfVGEyOLlCsFiqFidnoKHfcRAnqDIfuNFiB559IligXFyvICQVBge3ebf/AP/h7Hzx1l+Nuf45HHz1EuaDZ39li/dYub12+wsbbOhQsXaLRaKKWItaYbDdFa8Wu/+iv8tf/6O9nd2aU1jIl2tzmxukJ9Z4MbV68xNXeUbrPP1t5eSqcKBsM+w6hP0fMIi0UGgwFKeiRxgjYJQVCmvrXDzOwsw0gTdbusrq6yvbE+yqKUMmPkXUw9SRI8L+byG6/yv/79n+an/v7/ghWgpHsKk8on4RiBEMq5CEIikGDcpi3auEy/7DFz2YYqiQGjXRueLzHGRXYSM34uo7QgdZYAblBS4o3QoMKaFEWkVJS1TjnYLPQrsj44iOiWvqS7IFmLtnIkN9amroQSo30H7lc+FBmDkIU5sscmBcAQcKTN7OoKqyfP8tlP/wK180f5iz/2w4RTc+xvb6GKMbKzQ3+4z+U7/wkzfJhBY57q4jcxdfws5dBDGMXi7CyBV3BMsABt0zAOafqYAFIfDpvmDRiFW7ahEbKI8bvIpISONugPruGVj7BYO0viP0poBL4akJCQdOD6zXfY2b9OdeEUi0urBIUiVkh8zzJ/bA6jI+7cvoPZUBxbnqXgC4QwKCsBz1mBfpHa9E2OyD5tPUPUC/DsHmHhCM1Bn6Iqsc8AryWoNW8y7L3C0sIsb+/v8erX3mDQGzCztMpQKNZ2WrTbfTw0vcRiogE2HrA4VyNJDNdv3GLl2AqV6gzbW/tM1QKe+7ZnubZ2lX/4M/+ET33qExSPePyfP/WLfN/3foyrVy8zvbjC2ROrbG5u0e0lVGuKZrvJkYUySk7xzc88ie61GVQSrl2+ijV3ePrio9RPnuH3v/g6azsbLB6Z4uHTR9naq9M1Il1paZBaj0JotVqJ/f09lFLofoxQPrvNJoVCESEEw2FMpA0FT6KEQZsBFkG/P2R19SjtdgcE9ESPo8UeJd9ZbiWU25TU6Jy/7aKhwoJJNFqBldJta68FiZYkmd8uJIkeP1zUGEcK6ixAhCDJNiYx4CExQmCcf+EmIemTrFPl5R5ak3INVpLglM9I72cZgNnmDDjDmfXdGIvbOkCAdMu43ysl/kOkBBRGm1QGtSNd0sdPlSpTPP2Jb+LX3/wKdn+TmblljF9mebVII9rn5rUy09Pfz2ztNN/86FH29vYgfIpaeASrBFZKZFgaPT0GLMqNEaQwi0wZWDckQgiMco9+cnHlPsIGSBrE+l0uvfsmF8/+AKGqU58S1JshoVb017dpNerUFsuUlh5jqIvEWhPg8sJdKNBNwJMnT2ONoNXap9XZY25uDjzlWAWR0FQ95H6ZZtxh4DeJfYNNlqkREwaG7vYaxwND0n6Jhy5W+OP/cIMbb7xCpzfAWMNeu08j3qQf9YnixC0hNglRrBEGet0BWxt3mKqUefzxx/F8+Opbl5irhMxOL7O9cYdnP/kpvuVbP8KVd9/kt37hl/nbP/k3uXntHZ5+6iMu+ShJ8HwPKRO2tnZ46aUX+fbv+BieJ7hz6zqztRr79Tqz8wusnLnIL/3qbzB/ZI7lBYk1J4itx+3tOr0YomSIJ10ilTAalbLm29tbWGtZWVnBM4ZytcrtjTWUci5DsVgkLARYo/E8nySKsVaglMfe3r4TMG2Qymf9xnV+4V98mh/56z+Z8i6O5Mv2KITUx3YETboLkNtGLdGWaORnO7fNWkhstr+RJdaZXy+yqQYja23SnX80WrodmDzlEEdGBibajpZAGyxJPicEUjc5++BYI2Ms2QpGpyAsvifTrcffW/Y+NEpAJ9pl7FmLtcnIJ3BZc4Zwdo5Hn/tzrL/zRbR1yTblYolCqYYqnOPR8xc5MlvEs9col6eRlSkKSoN0Wjso2TQl0z0Q1D2Q0/mTjlRJ9xbIMvJwGl5KD2s8BAlC9Kg3/4A42eDs8afwi9vsNt9ley2mv7NK0o2pb9dBJAQLx7FqikqlQtHzyCaFNbjHlUnXlySJqMzMkvQjbly9xcLKMpVqEbf89U3adR9ZTmjWI7xKBZ3s8bd+5Hv4b//u32DQbDF13HIkKBFvlfA8l4O/v99m0I8YojCJIRokGG1dVpqStNpt4mjAdDnk2PFH2d/ZB+Fz+uxJfv93P0ctLPD6V9/mv/yhH2K93uN/+Omf4vbV11mYLfPO2y9z58YmZ8+cQkpJu93m0tuXWDl+Bqk8PvU9f4H9vTqvvPkF/sqP/hCeV2C73aUShPzRH36ORPgMB0OqpSqlcpW1zV3OnjzBTrPN2sYGnlR4gWLY7+F5HnEcU61W0VpTr9dZWjjC2voas9MzJMb50N1ul4sPPcRrr70GBPh+QBwnaG1pNBpMTU0hpUJog4/Bt1H6oFoxSu0Yhw4FibFOAQnhEsZwFjYxzr8eH+nQQJovNOKZssVD+TLKO0mz94x2GSeJBYlJw304uA8gFCb3qCcx2lQ1v6WY52hQqzDplmLu2DR8aLMH796/fCiUgMUilU3JN4W1Edb6KVvq/LbpqTLf+i2P8UL3Kr5IQEJB9BkOWsTWZ3HBMuw8zxdfe42Z2Wc58dCQQbyHSXoEnsJajSgsAAECmz7sM92hVaY8vPVyGXsGM2igbUJYWCKxlsbeV9je/h2Or34HHlPE+ivs7t4m2lP09u8Qnl5i4aNPEUcGvxCSUMCTIETscrvTZyAK2cdIgTUlhJIINH6oOHn+JNtbO2xsrrG4eIRbb32Wqu1w4bEfRBrDcdXmS1/6R3zXowFPzDc5+cQp3nrnCvumw/XXn6fT7fPUs8/y7lvXufLiy3R6PQjSTUGl2z1YW9BxQqFYYq/Zpt1qcHR5hVdffYmbN96k0ehy/uwpEu0RyxKD3g6/9Ruf4bmPnOM//tvfY36lgPUr7Pf6XDh1ivr2OgsLR5hdmOPazdscWVzl+77vY0zXKpw/d5YvvvgKu/t7FEo15LBHRWmMrfLam1s8ckFw4dQCu/tNApsQFnzanR7GGDwpGAwGxHGM1gme56aqXwwIw5Bhf0BQKo6IwF677SJA0qPf7yKFh1I+09MFt8uQcclWrc4uSX+bo0eKrO9qkiTGksbasSglkVYSJQ7kj5JyhMQgSVJBllIicenciTZjnoZxdMvVT78j2yNg/MAXlfFOeeueLiI0JgFEuqOimUgLsaN3jB80kg8lMF6DkmUMfiAhwj/N4rrnoLe1acaWFGnihhOiTvMqZX+DUlWjGCIYYpMWs7WAU2cXUOI2b770FbbWY6wOMbEkTiReaZaBLLDR7DKMBykH4B7uIYVbwy9wW325wE2Sfq95/fXf4c7aC3hqD8sOr732PFfeuMHmHUMp3MXreRwtX6DdK5DM9rjdvUGihygMmiGFYICv4lEyD0IilSXWDS5ffY1evwtpPBhhSYRmaWmBo0dXabeGhJxD6x71/XkG/XfZufV/8fiMx1//Gz+MX4Ct7XXC0Ofco8/wyLmHOHPhAsMEVBASaUupVMSY7Mm0ApDpimdJkliOHjtBZWqabqfFmZMnOHVslZXlRW5vbLG+2+Yf/eNPUwhC+p06l999k7/64/8NR+ZqzC4scuGxx9ja2qLRaGCtpdlo8MQTT9Fud/i1X/9V6vt7vPzSKyjfY3Z2lu29Ojc29iiWy9ze28WWBEGxyPpmg/W9DhuNPu1OH2mh6AcYo0dhXaUUURS57EVjOHbsGH7gE0URpVKJ/f19tjY33f6CSAK/OAqNxXFMv9+n1+8zjCO6ww4vfPkL/ORP/CRRHBMbx/Jr6+6PQ56QaMeyG6vSz44UTJfvpGsQHBJJbLYWIV2XoI07PjGjulHiHls//t0Qp8e5B+QqtBVom2aGCouRFisMRmgMCUYkaWhSYoTE4H6z0qTPjvj/qXvzGEmy+87v817ceWdlHVlVXdX3PT33SQ4P8VqROihzrZVWXlhrWytYhnZhGYIt2ILBlbyAAK8tYy0ZWhleQ7uSQC13JegYkxJJkUMO5+zpnp6+u6uv6jozK+/MuCOe/4jMqm6SwxUsCRg/ICsrojOjsjPe+73f8f19v+Py9G6j2fcDnO2N94UngFC025dJKHPl6ld54tQivX6FY0c/jEJjFEWEvmKnfQcVROiaQsohUfMezbUGteM658/+Oal5nI/96A9hmxabrRGhO2J2roDlSCqFGQy9TLYgdGLASCETAVNYCmIx5P6d+7xx7j6mFuEkK8xXDZordQKjix+9jih3uXLtdXRvkUOLJv0oojK3j/PXLnH23fusHtf55Mc/ROAGGKlA6BJ0iRoLQGhayttvfZVrN8/y0Y9Ps2gfJBH6OHRURClYlsVc3SQo/B36WycYtG/Qv/cSdytdZBBTNRpYRhfZqzInZnnppT/isX37afR7mIbFyOtg5XIMNvpITQM9RRMaIs6SULFKUFo2mfxIYTk2U9Mz5GyNt6/eZ2F+lrwtSJICV65c5/a9e/z9v/tzGJaHbi7xa7/2z/jHP/8zrLz9NscPLXHs8GF2BgnBsAtJxI17G/zyf/tPCLs7nL/4LsVSBdVwkcVZdkYC2yoQBiE3bt2lVnSoV8ucmt3HtTvrtLu9bFdOJe4oQNd1fN/FcfJIqdFv9QltC8930UUOzxuQhB7F6WUcP6TTahMnsNdVJzGSlCSJiWWKYTjoOizP50jiiFioMV4w41wkNbPFJPbIRNLdShK7wJssxh8rSI2FYvY8gIfhvnuxezy+ziRfMPYfxF7cL8Z/S07ceCkQKssTybEk/APFM/bIabMryL0mSaIkzRorHyS4/R7jfWEEBILVey/TaEmOHD5K6OY4e/bfce7tlzh58uOcPPFBSkWdxM8TxwEyDSkWbdKyZKs1RCUG+6aPgvEhqtVDqDSivgDNrfs0dzYRWovZuZNIUc1yDmRfdqLGMX8SosuYb371TRaPHeJjH3uezvZ1SrnPcGf7IqeXS8hignX/MXrNK7zw6LOknVtcvdBiZdgh0E6zvlVgdsrizIkiMh6SM/J0my0KpTJ2qUScRYCQtPEGK9mEEwqVyt1mlUlsmYhxeroyQ0l5zN39Js35WUZzZZrvXOXK3ev0oyb7F06SDENSz8S3EhQJK7dX2Gq26Q9GGLqRJZfiKMMfqAkqLsOzT1zpbqcN7OP+vfucPLzE1uYW+VKVfreLUvuo1arsbG3x+p23+errF/ACOH7iGMP1debrdRqNBncaLkuLs1TzFX76p57m93/3d3nyzHEUFq+8epajj7+AfmONKI5pd/skSrE4M83h5XmmKjXOX73JxmYLxo01pmkSRekYOZj1F+RyeQaDAZVKmUKhRHOrg+0ILMui3+3hDkdYhkGUZJUlNc77mKZJNC7zpSrFMgz8Xptw1EXZBYRMMpBWIkjwABPQHkrq7bnTOpNuzWSXCWq8EFU6holNGIS+OzcADxiBB338yZkHFnV2zb2rpLtzJEsCjlEG2U8xYaDKAElZaD35e+oh8dTvHO8LI4BKaLev0+0Ipqd/iFJuiiceW+X82Xe5fOlr3L19jueeepG8scho+C69bpeRNyLpjOh2Ug4cPUBl6VE8bR8jf4SmWRiWTW1uH3EwzWhwm9EgRbciNGmNwUhgWAaBPyIY3eVLX/kCn/70L6AVC2hJF1u3sHLLpJVFLKNNv+nx/KP/mLR/h6R5g3uDY8SVkHJhk9q+eYzqx1muzuENr7Lq9piuPUHsp7hyhFnMkygXUyju33+X9ugap09/hmp5ZsyjJ3ZxRum4ZGSJiBmtT9L4KkMtx6yTcqN9nxcef4GNYI3N3hz3tjfYKUieOvw0Ha/PdqOJbudodlfpjTxMy0QIgRdCkkRZ+Uhkcm6gaLVa6JrgiSceZ/3+PRZnp7h7d4VHT50hiBKcSoVDR4/w0p9eoLnVpDozz52NryN10E2Dw4cPUyraOE6OS7cvcuHcOj/yo5/jl37hl/lH/+hHOHbsON985R1aQ5/2ufNEUcTI9XAcBzTJoUMHKOgxt65fYmFuH6lRYHVtc8wUnGJaOkIqfD9r147jmDQBPww5euwEhrzHTms963NQkkKxSJKm41p9ShLHpEoQpCmaoZEzTcbE/ty9eZk//L3/g7/7X/wiaVJEyARN2KQqJhEhkI5jdbHL85Cmaaa9wFjfT+wG/LwXpiRLL485AMXD0bdSkyRfdjxGJWTvGs8FIfboxSYbGIIsackDmJqxCYjTZIyaFLvG6rs9k4fH+8IIKBJGQ5MgiogMk0DYHDz8OGee/DitjRZx0KTXu44mj7C21URpJYauR3NtwKEjR+j1LVojk/n9OqGbZd5VkiJEDs12GGznqdbGhJNpSqZOK4lTl7C3xrXbf4yoXuPK9uscEicRUYf2oMNycZr5vODtS1+jAvaBWwAAIABJREFUpHkcn5njlTs3QGzjSZfHHvtBur0tbt68zHL9DLViha11RW3uEKZZYjTs0ttps7pxh9Onppid1bjaG5CIF+gP54hDnVRX488z/jIEFBydRTuh0r9B/UNPsHL5CkZSoDpdw8lb9G81eKp2BqMfcHl9g7naDRaWT/Jnb/0FufIsvWGMG4QombHVRGFEBsXZS1ipVBGnGQNf5Ae8+IEXuH75HY4dOUahVODuyjUOLB3jzt1VwsBjbnGBZk+xvHQUL2rwpS+9xIcfeYKlpX2cP3+eowf24fa7XDz3Gv/TP/1ZesM+zVaXGMUPfvZHOHdphfubTSzLIo4TtFRw/eotjs7mObi0wLX7DTa2+gihZSVCoVCkWLbO7OwMW5vNLKsuBFuNHTTTYDDoA1m7bbVUJkHhxRFxKACNNM3gwQjF0Pcw4ogkBSefp5jPoadtNNEjxUSJFDe4gi5TdPMkUZK1h2dVAzlWphIosYcRmPRtkI65/CYb+3cwRk8MxPdK0E12+l1vQGW7/KTAkE4IY3avlE2UNE3Hpevx5xqXEid5gImB2C2Kfx8j8D5JDCoqC4c5deoZ0tTl8sXfpVqEfvsyr3z5t7h2+yJvX/9DyjkLmxwJKXHURQUNtFihzAXmFg6QxhZRouHHijCFWKSgIoqVIrpRJFAhBjKDpcY+dqqxttEhEiUs8xiMFBdW3iVndeipKv4owpUBA/cOr7+5zo2Vq0TaNlE6ZKF2FztqUcwf4snjz7NP1wndAvn8UaRRQxhFSjOL1A/N0e4N2d68Qthb4eixM3zsIz/JwQOH0SyRuX9pBImLlCGxNEi7K5TO/TrtnRukQLfbBScDrJDqHDj4NPuPnubw1Bn0TsDVtVu89fXzCNtma+SxNRgg9RxR6BP4g0zjQGmgdKIoJFEeQkYITVCrTWOYcOHdt5CaTa5YZX19m9nSLKPODqWpOj/26U/xK//sV1FOlW998yuoeArLLPIvfuP/xHMDIn/I00+e4fFnnuC1t17jwltn+eqffw3DyjFyh/zZH/8Jly5fBqkwbRPbMUATHDp0kI985INs9YYMI0WpWsHzR9iOieEYlEoFirkizUYLXdezqgEpQiVIKVnaN0Xkhxw8eBApFEkcoaNwcjmKpVLWvitTYlIMw8YcC8CEfkiv26ffabN66wqp8Eg1hWHMYBizxKlBrDJ8viZ5wLWXCJFhTDQJmojQZIyuJZiawpApmsxo5cQeYzqwtwi/sz04O8du/X+ShxifyjzDcbIySRmXsPceWfJSkKZZqVClkl1s0ViWLTOn720EtM9//vN/U2v5//P4zd/8jc8XZnv44QpTZg5bP8+//8I/58KVGwjtMCoq8uJHfp7m5jqXLrzLCx95kn0VQTza4srqBvb0ArpdAE0niLNYexc6ioZplFEypbmzDvYQLXYgirlxa4WDJ46hG1Uq5SOkKsf1W1+ls32bfUtHGG29ht8XLOzbZjh6nOWTS2xuXmTo3mdpeRbfl9ilQ/T7edbXR9SXl5iencGyLcIwJo5i7JxJfW4WTXR4561zxGGOSuUgpXIFzTARKsN/R8IkRUPfeJPi2tc4+aP/gOmcyZuvvkp9rk6tVt1tGUWDilPm0rlLiIKN56eEnka+XCJNYqrVEkEkGHleVm2R2qSglO0yaFkHnaZTq5aJh13yjsHzzz7PYDAgjEKuXbuGZUiaPZfhzgaPP/0U/+YP/pT+aIBtOfzMf/ZT7Jup8Sd//O/J2RYAtUoV28rx6f/ox9m3/zCXrl7n6spdpuaX2Wy0UGMQTBJHaLrO8UP7Sd0epuUQxIJGuwdCous6hmGSs/O4I58gDJgQY5iGiW7otDptkjggjBS+P6Jol8ay8Tpe6OEHHmEUUsw5maakkECKpum7Jbpmq03X9Tn+yPPEiUu3/xZRvEbOMUCZZHX4vd1bksGANSnQpcQYd/5NMAW7XIUTsyGyfNf35iGc5BH2jMOuLOzYMXxYBFrsGYfJe8Y/Jh7GbsvwAyXKPQi14l//1q9tfv7zn//t7/ws74twABEwVwU/7TD0bjJfr1KZOcCTLz6CbTzH8QN5NrYTSraPYwT4/XUo2IyGLj3f5O7qNqcq+xEqI42YfDHZyPjXhJCsrb3FhVs3eGr5J/B7BtX5WXwFUzPHiMKEYW6LxlaL9b7L0tI2Rlvx7CcLXLj4QU48dpx7ndcJkhEb2z1OnqhQLdq02z73d2KWDhzBcuzdlqMwiZG6jmFa2JbFUE1z5NQz9Loed1fOsXjgOFZxZnzDs770Aj5h4yLPPH+S7vYq2ytXyTs283MzGEWbIHCRUsPJ5eg1h0zVFhkMLVqtuxh5myDwqJXy3L67Rhzq6JpJqmlE0SQrnbHTSs0ck1ekaAKOHz2MP+pw8+Z1DMtGSkm5XGYwHHLs6KMM1/rMzM0j1QV+4b/+Lzl1+nnMpENtqsDnPvc5ujtbvPjCc1y48C4fefEDvH35Jp/46Edp7PTYbg1prl5BaQa2baEbNlEUEkYxja0NFo/MMjs7zZU7mxkuIM7KbiqQqBjiOME0LdI0QdO0jKYchVPIMxr0mKvPs7V1l1SBFDpJGpPLOaSpwh8TpggBURSjaVm50TCynIhKFcJYpx//W4JRSr9zh87OCMP4OkeP/CTV6uMYmgZp1gosNY09BqAHaMMmCcTvnNbiYaYq2FuwDzUC7Z6DZJd+XOzmrtQu2/EDjTXjPMSD19hjIJpEFf/h8iC8T4xAFA8RiU+743JFdLEqH+fTP/URzp373xlu9KiWS/i9ZcpODnPqNb7xskuFQ5x49FmeO/UIZmGRBIFKEgyp8aDsmsIlTtoIkdBpv067cYNzvYM8/9yP4RQrpEoShlmc51gVfuyH/xs6wxCv8zqDQYe33rzB/W6J5ZM+dy6+xeXrZzl15AdxCs/Rbl5jo7vKwdPPYuoZrZSaFGtktiukSgeZYucPInI1GvE53O51hgMTzSlg6EWUEORVi0LrLMUFh5XtAUvlATs7DZbmF7j07gXOPP8kcRzRam1x9Mhx7m/u8Pq1u7x+7l1OHV+iVIFioUqlXGIwDGncaYzr1ikTbjzGZTOlIlI0Cvk85XKRcrFI4o/QjIy9t1gsUq/XWajXSPN5UrvItUvv8PjxQ/zET3yOp57+LD/3n3yMn/jsx/n1/+03+MynPsmtWze5ePkCwjB4/bXX6bdbSGkyd+AYjWu3EChCP0CQGWTTMLBNjdD3ubt2lVariVJg2/a49x5s2wCVEEa7UtEMBgOkrmHLHJrlkCuUaDQazJTKaEJh2DpeFGeAITmGapNx8EmZAcMUZAtaJdy6cot/+3urLMzPM2q5tLo7HH3kOIfTEH3MLSCE2DUAUqRj7oExk2+iHnL7Hxy7/TDf5f7vHU92atjL+mevYew5PZhHyODFu3mdcYMU8LBhUXvHD3aZvtd4XxiBNBVEcQIyxh+49NuSc+ffYhS0EKMZ3nrjHk+f+iC6DJk+OUCMOuhpTHmuRHVfAT+KGHkprpshuHTN2LWEugzZ2rxImpokkYftORw58hRmoUSUqgxBrCSIANICubykMOdQ6NzljW9vcPart3j0k6dRNBg27mI5ZYQoMHIFUTxifl8OyzDR1BhIMrbCGdc8pOPOsSS0GbouW4OUI/sXGQ2a2GEfTS+gScW05pJ0LnLs+Q+h4pCNjs/BAwcyrnopOP/GGwyHQ06fPs2tldvcuLvKK+ffQZBjujJNtRiTt6u8fvYcm9sDNJllyCey24KsRKZp2jhZJokCn0ohT6mYZ9S3GAx7dDsDkiSlWq0yGA5459wVXjxzmkKhgmXP8p//5N/jV//H/56gdYkvf+klpudmkYaG64/44Ic+QKE0xY9/9jP869/5fY6eeoILl66CZmIbRiYoKjWUFOi6hkpiPHeEpgnKxSLD2CdKEhzHISWh1W5iWxa6oaMpDcd2GPQHAAyHQ3TTptPr8cEPvkg46FOslLByDpevXse2DAzDJI0ipJBImY5bj1MMw0QJjSRJcaiyPHeAhUULZ3EBL44xC3VKxWV0qSEVCJl5DVJmu7+mZQtXpGkGA1aZRrpSAkSK0FJUEiGEMZaYe2Dhqz2jodQErj4ekwI/k519zyDsLfa/+rraCzX+f5AYNM0qM0v7qBcf4dChJ7l07ctsrNzENhL6bJPjwyxN1/nKly8QtH8E3S/jd2/x8ptfpNd6k7y+jWWGeElEqCRKaKQqJFYKIUOk7lOqFiiYB8kvvEC5Opsx0moJidBJNY1USjzWsIyEk7WI1751hdhyePbHfpicWSUJrjO3lOfYwU+xb/oUt6+scLcRc+nqCkHgZtZXCWQq0NDGnHJZGUkpBaZGrlql4uzHFyGm0SAJbhBHfebEDvbW2+w7/BhxqFi7t4Xu9rh14wbt9g7L+5fwh0MOLi1z5+ZN5ubnafWHDEPFwf11Zqp5Aldx7vxFNnY87nd9ttrdrESWjKmsJlJVKh1DYwF/gO53Wb27iq7bKGBnp0kUBQSBz8UrNzEErOw0kbFi+dgj/P0f/0/55X/6KyS+SyRyPP3UUwTeiP3Ly8zP1fHcEaNgxLPPPUUvCFFSYZsmUZJgWDkMQ0PXDJbnZzmyXKdYLoFISYRAt21022LkjhBKUC5XMUwbw7IAQb/XxzBNoigiDkI0XxEGAXa+RL/vsbKyyvXrdzB0SbVSJQwS0CSmYZImY+F2lRC4Q5IwIk0TtrcavPz1t9huzzE9/9P46QEur3yd1bVXUamLHPP57/L0CQOlbFIliZMWifCJE5M0HRJ7I3S9jVO8gWl+k0KuBfAwdyXwYM5gkgRQKjM0Ks36CjKDkmlUZvmBjPU4Y5POHhkLlpF9pvEFFYyvoUiTybX24Mvfa7w/PIEEms0RR5aOUJqFqlGlXikhGFCYXeSHPvoPaDSukuT288KZZ1i58W/Y2rqHO3K5ce0VZubWyU19ECFrWHKILjy2BvcJkhzVXJluN0fOtFg69GHaXRMrV6TXWeeApdPSjmGkKZEK6PbvIqOArf4ixSMOprGO316lVD9EwbxH3jpCqfg8ll3AH/ocPfEB0kQydD2cirWXyVGZ8s+kjIPKugZJBVOlfaQ0OVCP8Tr32O4I9LvrnJy1cF2BO+iytbWBberouoFSAtf1sSyH0WjE9PQsmxstRqOISr7AY6ePsrV2nSgVbHcCbt3fRhoWUkvHTVnZRJJSgq6jaRpF26ZiSw4uLEPsMhx5OHkLYQhqs7PU5+pEUUxtNuXq9ev0Ri7/8DMf5X/5l7/Bz/3MT/Njn/sELzz3Ia6uXEOXGnY+z9b2NnNzC3R7Q86dPYt0ZvnLV95kbrqKFysUBgmCnC4RaUBBeFgijyZMRGzhj9rEiSCMU6TMYMIZD4BO7HoImSUMnZyDYRqMRhn5SBKGdNpdlg4d4NatWyilyOeKdLsDpJT4XoBhkCUUTZter4cQgiiKUVKiaYqc7bMz+Etevzri6JEPMzX3Se7fO8ts8RCLC4+PORwmW3CEIkRIGETvYmpPk7OHBMFX8M3XCIIDRDsWZqFMPzQwrb3dGMQDeYKHEYUPdwZmzw8BfNQD5mPX9Z+UArPE516V8YFkoxjnFb4PfPivbQSEEHeBARn4P1ZKPS2EmAL+ADgA3AX+nlKq817XSFOXvt/G9X0WC9NMJ3dwwhVEXGF23xJR7yLvvnubFz/8LKPBt/DSbexSDStXpT8MiUSDgneXQtlA07cZ9Ha4v3GLoT/kWP1J5qeP4HZGpHGRfYsHUMJEN1MSsUVBlvHTInEg2F85yPlvvsRw7gja1BQ7jXtoqsui1aXbComTQywuHqRYKmGUc1j2DAXHyCburiCI2o3lNDm24uN6sxSKUqFEe1AmSgNsu4m7+VXmrBLdURlHrxAGI6rlImkS4fsahUKBlZUV5uuzLC0vMxgMefvVS3SGAWvr93n11W+zPFNgZxRx7e5qRoIeBQgUhmHt7g4igwqSs0yOL89RthQ5U5LL19lq7nB/YxOlpUjdQDMsdtpd8vkijpNn2Bty6+YN5mrT9Lo9Tp7cz52Ve+Qdh7yTQ8UeqVJcu3GTQ4dPEkZnGcUhlXKF+lSZULO4c2cNS7dZqM1i6Qpd+YShz8zMAmz1CeMUL8iouHSpYVsmtVqNwWCArukEQYBm6LuMukmSECgfxzQIvJCWSvD8ENM08b0I08xozG07j1IJxjhkMQxzvKNn3YJSWcigRsl8lsTX+OIf/CozpXnqswvESqKkPr6f42Y2LUUIRcgdtjp/xtJ0HbvcY7TVJ+kusd31KOUFUfIYxbL9HQnA3d94EBiSAYnEA2773oJVqIfsxUTAZK9KsGccHrYZ6gFj890JygfH31Q48AMqExp5enz8S8DXlFJHga+Nj99zuG6b7k4HRY35aUHfS1lpu7TFEqvdJbbdBu9cuIxtbLO28zWSWBAX5lG5Z/DiE/RGh+m6ebojjfWWYqPRpOhUyYsySXeTsh6zs3YbPfIp2gqh2dSmDrMzDCmk22ik5IyQo3MWiV4ld3SK2eonWd73DylPH2S0eZ2NTZODRz9Bdb6E6UiWFvZRyZnYuk7ZtLCkhikFlhAYQmFKha0LLF1haimmllFc5UyNonOIWCzT6kmmNMV65xquGFHO2wSeR7PRRApJs9nC8wL6/QGj0Yj1tXVylSr3mx3OXb2CbtvMzC8QYvDVb5/DSxISkaLpEqlZpGgIqWfU14aJTKFWsJgtCIgG9Ppd/DhhqjbDhQtX6A8C5urz9Pt9CoUCXhgRRQrbMihXiizU5wijBG/QoVIp88qrbxL4PpZhUKlUqS8uYeTylBePcvnGLeaqeQypQRxRK1gcnskzXytRzjuQRty+e48rKzfoeCNioe0SY0iZ7XT9fsYgrGkS3dCQhsZwOMQ0s4WsxuQZ/f6ARIFpOZnhQ8OyHECga+NEpGlkXXVSn8CmkFLD0BMWF0q4boNzl/4frHyBmX1HOPXUpyhWjxElYtzfn4G6fNfFi28Q9ItY0XGEvMxrr/06X3n59/C5wMLsCWIxhW7PZ6Ege0s+QYwfGQ1ZqrKi4OQ5swZynFCeMAfxoHNAyrjVbpyDmgjVTHAESZoxGsUpYwGVDB6f/m16Au8xPgt8dPz77wDfAP6793x1GpNsD6g8ZbO9ehOZn2cwahPEOo3NPnk5w6NPPMPq1hV2mn2E5qBKp5je9ylsEZEvVtAsC93Is7q6Ra8fMbs4T3X6GH5rlU67Q20ZWutvUCoohCaJjRqGvUSo71BQTXSjw/pal/qxoyiVYOtFKDpMF23eeO06h05+hKnpIoaw0bQUITLJKYHczb5LpY17uLPym5TjG5YyBoRaWTKqMI2n60TDt6GnyE/btAb3qaY2hUIe3/fp9kfYto3vhdRqlTGHvc21d6/jB4JYJUxX6pw8foaNzXV0ZwpNJGPqbIM4FUgrg9+KVAN0cqZgvpojX7CoVhZB6qzcvkO1WibnFIlDSaO5QxyFFAoFGq0+zVaHk4frYOroKO5sr9HdbtOrVrjf6BPEIcLzqJSn6Hoxw26LlU2XcilHuZRns5egpz7ztSKtxg47w5CjB5awTZtSMU+uUsE1JdrWCI2QZFzO9X0/00CwLFKVkIQxFhqu62KaJpppIKTG0PPJWTaFXJ4oHxAGAVEUjmnDNCxNkCYJo9FwLGyrSJMU3bAzlQeREowSHHcaQ58l1VzuN69yLHiO4dBF0yCnF0mJGY026fX+kEF8hX21J1jZfAljqkh3a53+qENz7dOk02cxikfRtTIobywNPp7mEyzBBIXIpAU5eSDc4AFHYK+PIEM7K5TQdkFF31El5KELjD3APQrb9x5/E56AAv5CCPG2EOJnx+fmVKZQBLBFplf48Md8QHfA8xW1qRoijMF5np5+FvRD7N9f4gee/wQXzr5DvuIy6Ht4wwKmfZzp+tMUyyG16X3MzS9RqpQxDQ0/GNHaaZKXNqeXlnF0nWF4lZZ3A7PcptN9mcb9L6Lp6xTkNHq6hem9iu3fZGX7Xeozh8lbZUypUxQaWlrgmad+mOWFOnlLz4QhTYFhZvVmTQepZXwIUs+eNV1gmBljrhyLTU4SS5qWHecwqJeKLFQX0UITghg3GLC1tTXe/TQ0qTEYDKlUqgR+yNr6Jl4YcXPlDmmSsSVvNXd46UtfQSmFadkIqSGkRt7JkRM6lpLoUlErmTx15jAnDi9RzhWZqkxRyOcJg5Bmc4eTJw4xPT3NvdV1KlPTtDo9oiRhenoaO1+k297ByRf4vS/8KT/4qU9w6coKv/iLP0+SpBw6eJC+20NIycL8ErHf46f+4x9lp9UmGmxxaHGafK5AffkocRLTbG6iVEoQhBimSbvdYjAcYBgmmqZnictxdcP3faIwzjAAiULTdHzf39Xx03SJH3j0ej0KhQITnsgoCknHrb7j+fbQ8yReTkKN1nZAHHR5/JFDHJ4/ynz5AAV9js3tC7heA6XphEnMO5e/yE7wh2zv3ODb7/w2Rs7nyuUbbDcTuu0ch0+WGHgD3r34DSDMdvxJQnDsj3+nRsB7LqrveN941Tx07nu/5r2W6N9udeBFpdSTwKfJ9Ag//NCf/07FhL3zv62Uelop9XSxnGd65kl2Wls0ti+BKynYFfq9dbzePW5ef5MoShj2oT/QaI0soniA1O6j6auMBrcZ9rYhSaiUKlj2iCR4B0NexCns0OmNGIxCpDmLY8as3riIHvs41jZaLGh3+3R6ZezCB9El2AZoqU/stRFSUCxMY0gdQ5foWtaVtRtzjT2CsRo5QhMITY6poRnjwLOblxFUZCWiXBxQQPLI0TNUzTqH54+Sahqzs3MkSYLv+2xv71Cr1dB1E4GOF0RMzda5t7aJZpj0e31e/ta3UVq2eBAZpZZpmuRMjdlygaJjkTPgQL3EgXoRW1OQQLfTJQxDjh8/TqVa23Ulc8UKt++ucefeBobp4Pk+rU4P3/Pp9focPXYGy7aw8wUGgxGu69PvjxCWSRSnnH/nAiLqc+vWHdY2tymaKaZIcb2AnYGPbpoYuiSKQgzLJg6zBGaq1C6zVJqk6Lq+yyEQhuFe++44HyCEGCv5gmEYWJbFYDB4aIGkacKueMcD9XhtXPMHUCImCJq0mmcplu5x/HiZWjUkSN5gbjaHLnNsNi/y7rU/YTgI2FidYtj38QY1Gls+g0YdN5YcPHiMSze/RGe0AirB0iskBA9VBZQak5EI+T16CB5e3A+fz8rYSon3XMvvZQgkKVJlj/caf+1wQCm1Pn5uCCH+CHgW2BZCzCulNsc6BI3vexHhUJ7dT2nqEo3Geerlj9F0HXpbOpu913AMGPYDWtsW7mgaY6oI0W28/gIyPYcbjEj0A+xf+jiLC4cplJ7AilfZ7nwFT4BhL2HEJfzYxHAFTk4Sj67gMSLt6XRGVeyZKfRQ0u228dw2epwQRB5LR5aRQpKkexMpVaDSiUKMhtAyctJJNldMWIvGY8IZJ6TaTQJZgyaNrRZDx6HmLJBDEDkxOT3H/Pw8N2/epF6vMxr5xIkgCGP6A5fCyCNOBSqKEalGfzQkTBSmZeIHPvlCgVKxwGzRhDRCmg6WbjNXNnBEnGkiaga9Tov+5gaWkycIY7YaLUq1Op3eECEFfpyysdXMeAnRKFWqvPzqq/zsf/WzfOUvvoZdyNNudbDtHNvbDRaOneLm7Vt87bVv8PipE7xx9S7CKjA/PUMYhozCgNubTabyGlXbxs7nsrh3DBBKUXi+jxAaURRijTsgHcfB83yiMUfiZLInSYLlmFiWiaHt6frFcUyShiQJOI6TtUxPAFMiazaSUiOKol0vzbEr1PIaUbhDzw3QHIM767fIlSKk7vDutT9ka2udwwdeYHOjTq/dwS5I+n0omz6+hBee/AG+8O9+i9nZWeqLPUbeFdDn0cZu/F7Xn/quJN57rCwm1YRJYi8LA/aQid/LkDx4ToiMbfjBdufvNf66CkR5QKpMkDQPfAr4FeBPgJ8Gfm38/Mff7zpSFHFqt6D8Bokr8ZIFNu+3KcgBg/sXOXB4jgP7a9y42adcXmB/fZHE3aC3HTDy1zDyHYpT+xAyxrHLiPQEg602Q9VlFORQYpO0I+lbFo5jc/qREu3enxO0coS9eUzzRVSsE2yusrZ9j1z5IIv1KoXqHIkySTU9EwSdoDYnMM70PdReH7rBY6UgLUObMZ7I8WgHzx2xtnafhcV9KC/A0hJ6gwFJGLO4sEASK0aui2kX8P2A+tIizUYLzZB4QYgmDAzTJg0jdKmh2Q5Fx2Hf/DQqdLm5cpv6/CKPnzrBbEniGDphmOL5AYZpEcY9bl6/jmHl8OMU4bq4Ycr09DSt7oiS4xARUpmawhsNubu+gXCHSGCqnCdJY0q5Io4V0GwPuHx5heeefoZyscDL//eXefzUcWZn6+z0+qxvt0mVJEVneqbOYn2au6trtDt9vDG817RsTNPB9yCKIqIootPpYFkWSiWoFLSxtp8UgtgPSOII2zToDYeYiEzGW0m8OERqMuuWjCLQsn6JlMzTkFIjSWJQGnFoErkWnWbEcHuEaeg4psatW3+JZfbp9W6zXH+RhfnT5HMa1y9qVGs6oX8bry8YqBF/9Ke/Q+AuUS4+Q730JJ3hJpXiUZRwdxcyTBCAWR/D99rWH97R9xCA2asnlYJxY9AewHC8jkQmaDp+myblbtn6b5NebA54RQhxAXgTeEkp9WWyxf9JIcRN4BPj4/f+EFJRrN0i7M9QLM6RLy8xP1sjJ0rge/TDAptbW9jFIbM1B91rI9IKhhzQ7N0liod4gwZB0MYQOraqYhgjwiAmCmz0SHCiqjOjV8mVZqmVHSzbxxtu0+maiPwU7kBh2nOIJObwkSXqB/ZRmS5ndWqZjneRNNvVAYTKsBzsiUFmrn5mKTLgR2YkJiKTmsjo00wR4fUa6KaOrdt0dloMQw8VBjSbTeIwYapL42p4AAAgAElEQVRSwXWHKBVz/cYKb59/m/r8LLdv3iKMhmRcMylpHKPiBBWn5G2b5fo03Z0tVu6to5lFDCmZylvUyhWE1AjjmM1Gg41Gk1GQ0A8VPT+lFySs7WTntlt9wlQjTGKErpEqRWNzk9YoIofGof2LmEJx/sJlyuUpZuqz2fu3m/TaHYLYQBeKnKUThgFbO00QDpoAw7BQaGw1+3QGEVa+Qr5YyjQQo5DRcDAmY9XI5/PZBJYJUgPLNhFSZNJkwxGR56PLrGLgxmGWRwEMoWPZDp4fEMdZ3wRKYUgDiSQZMw0lSQJSESYdGo1t2g0JUZFWI+LOvQ63bgxo7lwHpej2uty5d5tK7hRLS88yV30U3fAQVo999QPsW/wUyiwwv/w8VnmJ6eoHQPce2MkVQk0o7L5DYeg9x15tQTwQ12eJaDFpEsyS0gJ0qTAl2GMdRVOm2LrC1sHW39v1+Gt5Akqp28Bj3+N8C/j4X/U6mpag9B7tRo369H7qc/vJTy2w80aHtH6C2vHjbDciwEMLC0jLYOjn8cMWTm6BylTIsH+XVvMeBXsKKQWB9Nna3CKJDQ4eOMUh3eP2+XVylZN4nsAw5rFEA9Oc492Lazz77FFUEnP4kRnyZZ1Y6Wgic+WFAqWSvW4yOYnp9r7YB4+FFPiuB2lCoVgc8/pNKgUJideEoE8+V2Bqukp/kEFhTdNkdnaWYWeIpmeAmUfOPEb/7bdZ2LcIcUqv18uUdoSGIXU0QyMIutn7dQ1NF2xuN4mFSbVgcXj/AiL2CANJGIb4YcTQdUmFZBDEDEYRMRAlkBCRxgrP8wAIg5Dpapk4hk5vgG45fP2b36K+MMPIC5BkBq43GPHI489z8ex1lpZn2Gl1mJ+r4fsebhhSqlah2yfyY/wgoNVqE3kBW+0uYRrTHAVZk5PKePtsSyeK493QSQhJmsa7cXMcZ2g/pbSMfiwMUEKSz+cZRiEIQZomSCEIgmBsoAWGqSPijKAkTdNd8hIpBXGsEQ19Zqbz9LyIZi/Ezaek2oByPk+15uKHA9rteywvL9H0/gWFcotOWCRX2M/MvjM8kX+aqdoZCrk5pJ4Qh5nO5d4cee899/th/IUc04SMwwHIWqnFJPc/bmbSBBmxrTahQEvQEAj5/bQ13yewYV2PcQcz1ErP0tpQrOx8nXpZ49HHH2H+zMf5wPNPEXsWc3OnKJZ0NLvC/uNPUq4ex43q9EYOupEn8X267RajSOIFAj0SqF4PXYV0WztU7Bxzcwdo7ITcvddEhS7N5pBq2SAMfHLTFcrTs2hYSBmN3S25u9Yn6rG7Y3x+V0LtwaFSktCHNM5uglCTKIJksI1pGozCmBs3ryIEFAqF3V3LdV22t7d3r1uv19m/bwnf9XDTiBMnTmBa1u5inWjYD1yXG3fWSHAo2A7752ssTJcxDYPeYMR2s812o4lpOUQpbOx08AJFFCmSJDN2k7hb2+2Y06nPLiINi/nFBda2tnEsg3K5gpQJnW6L7mDAuVe+wo/80N+hM/Ip5vN85pMfp1atECcJuWKR5k4LQ9fRNY2c49Drj9jpDvCDiDBIieMUXdewLRvTNKlWq5hmlheYJAbjOMZxLOIkQqmUfr+feQFjafMwDHdlwDMF4BQhJZZlZdiDNN1NyhmGMb6fmQ5EPm+Tc4qQakxV80wXJZYMCP0O3sijUKhRzNVR8g6R+W2C+B61ygxhYjNwQ7Z3hjzx2GfI2WVUGpGEMcY4FPwPZecnc+i9R1bsk+PCohTpWI5OZU1TWqalqEuFNu4MzQzCOAmpZMZP+B7jfWEEkiSkcd9gfTWmOP0cIXeZy3vsNODg0cfQjBKhGDK1OEOSq7Ez2mTobrK4/zTl2cdJxRSlfBXHKpFoDppdp2IXqTgLlKfnaO70uL3Vwi4WKNh1bt/Ps7axzub1HsWSpDYzhZImdjWPZpoIzRzjusd0DOJBGObYnRsXPfYMwN6NTtOEYilPuWigkmD8nux9UtOIeltYeQMRKbqDHmHoZ5oAiSIOE+bm61y7vsLy8n6uXbtKc2eLdqdDs91kfb3J+voWjpXysQ99kEF/gCY0HMfG8wM6vRFInZxjsjhdoWBKhIQgCPB9lzgKUWlEpz9i4MakZNgGVJL1PowluYTImHG3mjuMwojlk6fpbe/QGrTRtayd+Wtf/Utct8fywn7S9oD/63d+hz/5s6/xxpVVKlWH/csHiCPFaDDC9T2MYgnSiO3WNv0kIpGS5X1zLC/WsS0bhEacxmiGRrFQwLQsbMtG1wxsy8kSjKPRONRKcRyHOI4ZDAZYhSI7vR627aBETBwLpKbjx/F411QkSYSmZUYiw9OnqFRlylQm9MM2m+0NpBEzPWMxMx2Tim1GrLLefJ3rt19ibf0yVy++zs0LM1y+volpDfH8kPX1tzBNH6ElqFCiGwlSGNk9H8+ZMY/wpG6068prQqAhMo1JMjES+aDXoDJGaklGlb/7EKBLgS4ydSNNZIImE1HWPS4DxfcpDrw/jEAU6UxXjmKYMbP7DqDcMip2+frLr7C5cQGljzD0mFJ5lrnlI8wfcEjiu2w03mV2tk7OyZNGAWkSUSkL8naAY1RQ5mnysy8Q6Dni8jwdv0Wn65KrPUKt/ASrtySJWkQ3cszUZxHCRNc00BRMQB6TtCwPuGzZ0a4rtjvE5IcgTcbtq2J8A1Ig1RAKTC1B003mpkqUSzWUUviuSxgGmIbJ1tYWpVKFwWBEHMfIFPLFAtXaNM89/RxSGjim4NbVK8RJjFKZeIsmJbZtYZgamoS8KQk9l97QpTccUSwWmZmeQgmB62ddblLTd+GnD2LakyTNevtR3FtfIxY6sefy9PPPEicpX/zin/FL/8Mvk3MsvOGINDH58te/ThBKXn3nGmffPkvOznPoyHGq5SoHDx5iNPKoFvNUp6pgmRi2iWkZGGbGeKSUIk4Thu6QVmuH5k6TMAzJ5wrjkCBFkzpSZojATFREZsrFhgGaRGoapm3gODl0wyAe/390TZKqhDiNdz0npTLewiSJSVSKH7kMvR6dfofOYEQQBoTRAKFJOo27bN19h837I1aubzPsl4mCQ6AMSoV5lmc/iKElmNJkbfMiSSxRMtybH3s7we5U2S1fqnFcT8YzuCtMPfn3XeT/OJfAOAkoZWZglBonACfLWe0+Twhs0+/jiLwvGoikVuDYsc/gpG+xevsdjhx4FJnkmNs/y/x8ne37PjPVEpGn0ehfxbF9OjtrXLn5DZbmnuLx0zUqlYO0eint7jls0UGFJmb1RaSlEcR3WGvqsLOKsBv0hh7P7j/E1eQ2temUxX2zGbw1jTN14HHf9qS+K2C8e0yOHq7j7h3s3eEwgoJdRJAwCjPAikLHVgGmZdDoXkWPigjlkqRFhNTo94cEYcTOzg6zs7NcuHCRYyePMWh1KVVncYcBCwt1XC+gP+ji7gxJ7Ty6aRKNE12GYSAASxeUCw5eEHB7c5tisUQtVyIIQ3a6m7T7wwx6mkbsLfwECSSxyrAJQkPTod/v02oMOXHiAB/4wCcYNlbxvAHlvEVRq9Lv9Thy6iSWY5EKjTDyefPNS2zeazI/N8vyoX0MBwO84ZAwrxhpOlvbHTQSNjZaBNImn3NQpERpgmmaoElK5TKh5zNyXcIwRNd1pK5lUmPjkmCSJExNTTHstpivzZCmEXEMpmXgB24WQScJtm0Tud7u4nnwGkIHw5FIWxJFJq6b8UDkrBqWGeANHcp2jkpJx/djNPM4pqHw3BYJAc888QPknWO88dYfUSoUqFYe55vf+k0++uI/ycLAyRxRZLgRJgnCyQYyISN9EAw0CSUmicDJfMvi/V2lIznZ9ydbvXx4fqYwISV5z/X3V1mkf9tD1y0Ep1icO0N3q8WjR0/yztuXOfxomYMH61w8fxPLDthuv03eDJGhpKAHPHWyzumDJmU7wimUsQpT+O6IXvcKwsxTmT6MpU8RuiGDaAXLWSVf6HBsrsfdSzc4efp5DG0Lx9HQtQz7r2taJl8uGKP25C7aby8huDceRG3tOg0KEBLNdJBa1gmYIogBWwQIEmYPlLHnfFyvSz6Xx/UDdMPcldQulUrMLSzQDzwWDizjuSHf+tZrRFHED3z0I/SGPlapQjyOew3L2qUQty2DoweXME2DZmdEuztgqlKikLPZbrS4vrJKo93DC4Is4ZYme0AWKTFNc9c7yHbKhE5jyIEDM/zP/+u/BM3i0TPHuHzpPGffPEvByWEaKScOLvPjn/1Rev0dUmWwMF9GAHMzNXKWgVQpq/fucP3mHQZuzKjvYmk2juWQpime7wMwGo1Ay9iWbNumWCyi63qmOeg4VKeqxEm8u2iCIGBxqoIpFWEU4nshURTgui6OnYmW6oa+22ozAW4lSSZ9JzToDRvkirC0sIwtp9BEnv4IVFJkqjrF/kOHULqGH7ewywkz0/NI0WXkD7h5+1ucu/iv0LUQz79DkgT03T8GFXxX7V5CttMrNSYt2QsRhHh4u85ex0RdLLsnD+45DyQTM3LTNPNgx+3GafIAvuD7eALvCyOA0nDdKq+9dZbUtUmjJi9/43X8II8gT6s1otPzWFt/DTPWCXodDiyYHJovcXDOobe5wq2bf8H66hcIRy/Tat/B9XMUrBIbq69gBQWWqssUzTl0L0c91EhtndqZETu9beI0wNCMTKNeE2hy/BAyMwIPJAMf6g3/rkdmdSePwcDDdT1QWcNHRIQ7aKBpgtX1lFu9lwgCG6RJqVKh0dqhWq3uQmYbzR2e+ciH+Fdf+H1ef/11/l/m3jvMrqu+9/6sXU9v03uRNEW9Itmy5W5sYzA2cQgQSIAQSIBALpBc4JLy3jxvEkICJMEJgdBCQrcNBtwrli1ZvVgz0nSNprfTz9lnl3X/2GdGglDe97257+OlZz+aOXP2njPn7LXWr3xLT08/DzxwP88//STReB1KOE7fxj5QBIp6eZGqTSXobG1gdPwiZwZGyGYyBLDRpO2j/GwPoRq4nosn3TX4yWpR0PckcNaovFJKRkcusXHjegrFCn/32S/T0d5OLpsmEg6zuLjI2OBx9mxex/mTL3H7HTeColAupZmfmSekQ2tjLbfdcTtbduxmfsni6msOcPXV+0gvLVPMZygUi9iOg67rvveg9CHDlmURi8VIJpOoqkrRKhOJRgmFQ75Vt+tSKpVQPBukX+lfNegMBEyi0SjBYADT8FM9/29kbQERQkHVVHTTJZ5Q2dDdRXttF4rnYzEcR2N2dILFkTSZ+RJ22WTzhrfSsS5JU0uY1976J4wM2gguEg3FeenoYZ48/AmW5mY4/vI3r/g9q3Jk/uFb30lEdeH1v79yTvz07F29x7zqAnbZwORK+BCspnWrNQ/Pqxqc/pJ84BWRDpQrGSZmvsTWPTfiKM2Uy+NUVhZYWFaoVLKEYmX6d96Id17hwuAFovEiKSS5NGSmxxiYOEuyNYyCQX1TiGRyJ6q+nlLBJoTgUnYF1c5DpYfaosvxi4eJdjZiexm6uzZhqCaIss+6g7WlV8pV02mJW32jJXKtUAgSRa5SiAHF96GXAiq2jSIcTM1ganoIpTJBW1OcmeljbEgmmJ48ylJoias31qIZCpn5RVobG4Awjmfz8CPf4N53/hF13RFu2rWVlg2b+d6DjxOMmqRC9ZyfnsPzKizMzBM0dL9qXDUUCYbCtLR3Mjhwjkg8TGtDEtM0KbsCI5ogEElSKZSQtoUnVRQhMQyfposnfBANshoyewQDYTp616FKwaaOOpKte4jV1FKxA8QSAcp2CTUYp6dV4JQmOTM5RmdLinzJZf/1+9EUjfpUhAcf+D4aLu3Nca7a3Irr1mFnVpjKOHB+CENTcV2XhoYG5ubmWdfRxcrKMlNTUwSDQcLhMLKskF5ZIWDoGJpJQnqUS0Vc28N1LJ90ZVWIBQK4boV8OoumKj4kOhymUCyDgHLF8mHKqsQIS1xVZ3wgTURMI00P1fDoTPYT1hKMLw+hqtDc1sbM9CzHT3yB7RtvYFv37bx86idklgosLYSIBIepTTjMF4ZJBNZRX9vjcwarxbnVYp2iiLUinpT+Y/6u7qGqfhfD8VbvucupgFB8d8HV71VltVLA2vMuOxRTjS+qdHZ5hYLRz4xXxCKgCoGXN0msV7CsMnOjeYK1BkNjh5i7WINjlRFqju623QwVT2GXHdKzY5wfmCHrZvH0CvXlZgKhICsrJooSZdNWnRePfIv5Sy+jaRWmFizufd3b6I9OoUe6URI9nLrwFBs37kBTNVb12q5cL4W4vOIKWPvQ1p4jlcvcANaAngggYAjfAcgq05gSpGdPMHIhQ9qaZ2y2QF9PPStTFTKFc1RyOoW5PJsPbOPE8+fIpLNs2XwVh079KY8+GabFvAurLHn1LbeStS3mFgtcuDRLxbLIZ3JEIpE1ei3A/Pw8x44d5+ULI7S0ttPcWE++UERqgqVcEct2UFUV1/V72KpyGWfvS5BpuK6FaZpUKhVsYTM9NUni2i42dLXwnSefYam/l42d7TQ1t3Hu5VOUbBunvERdfZKlYxf4+J//OS+8+DxCwOjFcS5dHMeQDvfecweaInj60YdJJWP0ddRj5cvUJ5PkigUKts3i0hK1yRrm5+dpamoilUqRy+WqkYlOJpMhHAwgFEmlYmGaGqpuYFkVAsEQkWgFw9CwbRWnCtIolsqoqvAJVlUugaqqWCUbRRU0tiaYHZMMHM+TamvhwP43Mnz+ElIa7L/+NZS9EdL5kzSqUVw7wPbt13P29FE62mJcOP0UolzHUz9+ifqGOrZueT29PQFC4X60ai1JVVS4IjVY7fGDXDPEXR2u9MVRf15bb42xqqz+9HINYdXJag3Qht/h+VUko1fEIlApeyxPeWTWn0bLx3jh4VNsvuZakvU7mBwfIVYjMfUAxwYfIh5voKt2P/n0ArW1EDRrCIUlNWoWy9EJxPqobezCVmdQAhmKcgazPEneqxBUXmRgIE103W0MjbxEpVSmXE6RDAo8T/GLLle8JYrmGz/4RfNV/L/P/6ZK6HClt4YHV6qCD0J6OE4RVSoYqk1NTT3TwyFGhx/F9qapuJKW6C4yky6L4Xm80hhhLcrXf/QN5KxDYyLJxckVWtfPUBe+i+vesYn7/uQrxGJxapvbOXniCOVSyafKmiaFQoF8Pk84HMYwDPL5PIeOHGXTxk10r+umsjzNyNAYZ0emsYwwnqf9VJlISl/dSVS7G6s2YMpaj13hda+9k2jUxSrlcVwXJRhBUuHQS8d870ItiPCiXBgdImAY3PePnyMcD5GIRVnf0Uzd0gr7d8a5ND6E7Uoq5TKLc2Xqd/SSsQqMqovomoYufLtz13LIZtPU19dTKOT9gqcQrFTdh0Fi23YVE+A7+FqWRTabRQiPcrmIpinYlkOlUvGRmkYAr0r9VoRvVqqoKo6lQ1klkXDAqCMSW0+5nOKqq3eRy2ZI1jZQtBLkz1VILw+wbdONmEYjm7bezktHHmDX7uvZu/cepuemiUWbSSQraKoD1CKEXe0M+MVAxJW7yJUdgMv3mVDVqov05VRiLRoQ/iGlb0Lq8xCqUcNa/dF/tid/On39ReMVURMoWVNEao8xeDJHR1JQWJ6lc8st9PYmWVzQ2bn/rYyMD5NfzrGxp4dUbYDRYRUzsJ662Ga2dPVjKB7JWCOCBEdfeIFjzz7OwvA5ltPTaHSyf+PrCXs2kcZ2FCVIbThMXbjC0OCPkSKPbkjf1y4AhikxTN8RWdOFL0+lUBUJESjCwXNcn/1Wzc80TaCpLqYhMQ0wNP+cdHqe6fllNu56K3ff9Xlu2fRxXr/1w2wI38xtW9/Ltg2v4/yFC4SiOiOZEdo3NNDYGGF0bIS2+lt48KlBvvnwv9Pe1kZHezt//X//C62tzSgCKiULVVWJxWKEQqEqms6hbNnEU7Xs3bOTsCGoSSWoq6sjHE9hS983x3GctcmOrBqboKCq2lptwbbttZt0eXGJYi7Ntq39RCIRnjz4EqVylkRtPWNjE6xkZkAzSKXqqIsH+PM//QRX795HWFUZG5uib+N2NFXQ1NjEbbffyt2vv4Oa+joW0ivkCkUWl3KULBvXdX0NQddFCIV4PEZnZye5XA7btjEMA8dxqmxJnVKpgOs6lCsV4skUgVAY27artQ0fFBQOh5GSNfTgasTkui5myKCnq5uwUoMa3oCMxhiaHkKPajQ1r2d+Zp7zA0eojfUSiybZf9Vb6OjcimIGiCTK9Pdu5pZb30E0VUc6M0s+N4muNVG2alDUbNX92u/zCzwfD8KVOT0Iz0/iRRWdKuRljwNdARX/HAUfIOQ7aHvIKlz9yrrUqsW6Uy0Sr/puyysluH9mvCIWgUi4gav2fJSV9CKhWD3Llk1+LktAxChWMkQjcXrXX0NDWz35GRtZNjFj65mYkwwNDfL8C4cZnJlnfr6ILFRQKbM8P4suSvTVpNi0rp6ou8T9X3+RgyeOk185TFNXD5qyjtmxQYSVIYCBqlQwhUpQVTCFIKhAQEhMQNX8D811JEXbwJIKNiAVD6FKUAS6arA8coyBg98mEQ3iFLNgSwpLy+RXcgTAp7/qKkYwjm5HGB6cIhpPks0L0gsmLx19GUNPcs2re3h+6CXueececoOTfOkb38WybA7cuI/3faSWXHEKJaJSsgoEAhqKYlBx3Gq7WLC4uMyZUydwKyU0RSNj2cxnMgSCJkbYRCoCXA8rX/QnjKEhVUG+XMITAiMUQjGDoKhIYVPJzZJsqEUKh49+7EPkcwUeevwROlta+dYPn6GYLpJZXmJkeIxd27Zx9IVHaWmop6GlmZ6eDfzHl79B0PRoqq9h/uIsJw9PkF6wmRqfx07PkaoNYYZDVBwfDWg5FkJTGDx/noHBs5TKBSzLIhIMEg4E0TWDcsVF0xUCmootJdligUK5RGt7p29rjsRzLQJBHVd6WI5N2SrjOTbSrWA5sLI4w4mXjxBs7SfVvJXOjqt57+/+X2zt24Oqq+y/9mq6unuJ1gfQ1PW0N/ZQSmt89zt/T8WKkisGsFyXSsWjt3cfTW19SEXBDITwCOL6UqVVbQEFpIqUii8UukpE43Ka+dP7dbVgKwSaUHwQ0eo/oVahwj6wSEOgCwVT1dBRCCgaBgq6AoYq0MQrPBKwKxZDlx5nx+ZfAw2MgE5HRx9GOIDteJQLU6RXFqiNdaDpNj/44WEUPco1176KcGiWcvkMpgkrxRk8tcI1r/odEsl6CiseyUATlRWLzOQSG3cJxicWEUaFC0MHmZwfpKUzRiCaxCaPIkOs9lsVRaxBUBFUlWoUpOehSRtN2OjC8d9czwHXxnMsunt7ufGmWwnqQZpb2+jr72P7jq00NdbjVkq4rs34pRkKpTKe69HQ0Mgdt72BfD7Njv59LOWnicYauPW2AyxOezz4wFe4qu9dfO6++3j6qYO84d7XcOtVXyCqd5DPVKhvacUMhlBVhXgigW4YlMtlkskkLS2tGJrO8kqa8yOTuIpBsWRRLpdwHB+cs9p7B6oRjYYZCPgQXT2AbgQIR+Os62xD100mxseZGh0hVZPktte8FT0guenW6/FQOPjCIcKRMB2trRw9coS/+8wX2bVzJ7oG7/mdN9HQ0MxTTz3PZ/7hX3nsmadZTi8TDIU4cN21tLY2kqpJEY9FAb8967cqfU5FIBDAdmwK+TzBYJCFhQWk462lQmsuy9VoaPUafthfIRgMEovHUTUViYbrBoiETNZt3kp7/04mpgbZ2L+J9sY95DMuQTOIpkBNTS2paJCRgZPs2tnP6MUTLFkPkEzVUi55dHb2+pNaeoQjMQwjgL+9r3aKLrfnLteT1oL76sSvaimslgZ+pq2/ivxbjdB88pCCqqhrjMorW9iqIlBVgaYpGNUjoL/CI4FKxWZ6psjRkw9gajWoygwj0z9AU12kJzlx6Ec4uXG6mlOUrRAb9zZjxlXUYBIRSaDGEgRj+1i//i7C8QjT2ZdobG5l655Gdl5zI31b38BCro7RSYXF3HGeO3iIlroOHDvB1LTLS8ePcvTkY+SWl3yPPqEglMshm6r4Et0KEDQ0UmGFmrBGMqyRjJnEIzrxsEo4CAVXMjY/x+L8BY6deILB80cZGR6mXMySSMYJh4LsP3Az8VQNgbAP5vnk3/41ba0NbNx0iTtufzXtnV0MD8xxVf9/I0Y79/3TR1lcXGDzpu20tiX5vbd9EOlcpDbURaVcZnklTTa7glW2yOVyRKIRGhsaOXPmLHNzC5QqHmVh4immz6UvlvFsF0XXcAU4rouuaeB41Xzb5/gXC34UUSwWCZi+fVdNIkFucZ708hKf/+rDnDxzkubmWp584TAt3etpbW3jxMlj9Pb3cMft14EHiWSCllZfiVhTHd76m68lVpugaBeQmkK+VGJlYZ7F2Vkf664q6LruE55KJXQtwPDQKDU1SaT0GBkZIRKJkF5ewXNcNEMnk86AJ7GtCisrK0Sj/mKyykTUdR0pJfl8gUgsTChmouomgboeoi1NFEtpPNeht6+TdR3rCKgmhqpgWxU625qxcitIL8P8TIaxC2Vmp3NoqoGuhXx4sFDWFp+f20ZmVR/wsjagKz3fMQt+ac4uuZzT/zyeys8+JqW7xlxVhIcQLkL84u7AK8KL8JN/8xd/ZtYo/MFbPklcdzn5wiOUQ7MkAjVMnJ/Bys3TWluDbtqky2VWSsMEAgbReD8YJus3bKQuuRdD9VjJZHnmmdMYepKWxp10rdtJwKinZ+s+6pv6WMnoXLW/nqWlEYYuniUa8RgYmGD3q9bzla9+nl279mPoYYRwgCvAQVXveT8i8PUBfCyBiyI8n8AhBIZuEEsEMFQHBYvGhgba2rqJRoIonsXU0CniNXUoAmZmptnQs4Gx0RzZ9CAZ5UmevX+WjT19nD49xPxChdf/WjcdDddx/8P3c/Wr9vCXn/okjz10iA9/6OOMzsoYfG0AACAASURBVH2f33nr+3jiiWcJJ2JEI1GfwVgqsDg9wfX799HR3oHjKgyMTuFJFxUPhSoEV9dRDA1V03CtCkKCqqtrObOiCExVoaOtkb29LaiGiukKZhaXiNW2ce7SBLUBycJilje+9bfYu3cvS3Nz7Nq+ja6+br57/2PkMmkuDA3QmEyya+8+tm3dzJbNm2hpbePU6aMkUwkuXBjj6IkzrO/pRyLIFQo4jt9auzg+Qank4wWisQiuK9eAQ+FAiGBIJ5fPEY/E14hPrudRLOT9oqH0remsiu9RuVo5t12X3m3Xc8Nt9+KS5sB11xOLdWBVLCKhGLpQWRUhGZ0YYXR0ENsR7Nt7J/OzgqbWGNlsmWg0iar579evsv1aEwYSV3xd5Rb8vPFTnalfgvj7qd9RBXmtmZpUHZKFgC/d99evXC/CmtoUneu6GBr/PN96+CKTAzliG5sJqw7plSmc4jJHlp8gZ15i/903sEFsQdd8TnhzvAUrN0FNW4XHvv8iC8sZbr/9Dvr6rwGhEzezFHJZtHiEcGM7vQtbuHRuiaXiLK6VZGokTf/WToYHjtPS6ZLLLRMO1F2GaVWHj+byS7NVc3MUIdARqLqO9HzsvqaquIrAdQ0KSxPgLFFf187ywhw/euA7RMuTYASpr6ujVCrx4AMPMjH3PNe1XMem1m9z6/unmLg0zImTp/n05z7HicPPUpQDfOxjf8wH3/0O+rZdx003ugwNvUhv+26++LlPEY1GsYF0OoOqCTasX8+eTd1MXhylNlXLhdFx6mpr2NHSwIXz55mcWcbzfNSaIkDXNYrFEkHNWOtZl8tlAgGdSinL7Tfcg+esEAk3kK3MsnPndorqJQJJSX//TpaWSzz53E9YmpthS3cXDz/0AB/+s4/xqc9+hheefpz2rhv42n1f5NCJf+LGa3axc9cOejdv5lvf/x6Z2TkGzp4ma1mMDY8QSqZ8T0fHJWCYNDU347ku0WiURCJGIl7L5OQkjuMQj8aYW0rjOA62baNpftfDk5JQMIhjV3zMvKoSDoXJFbIEgyGCZpwde27nmtf/BsEg1Nav8NzJh6gJ5Lhm72+BV0YoLgLfcLS+uZ9eGy4MPU1n9ybCtTOMX5rn1++5AVWYSHwexqrsmX+//OdJ+/N2e3HF47/s3F92DbgcffhRgYsZMAB8tmGVVfmLxv/nRUAI0YvvLbA6uoE/ARLAu4CF6uMfk1L++JddK52ZQ+jjxPQE8/M55vIL2C+FeM214BZz2F6RBT1PITzA0FCZpcmXqOvuYd9Wk+PHTtHa1EF9spf9N9Tx5BPfwrY0dDVMQ1IQlCqp9UGEK3jqxWHqGmo5OnwM08njraRp2tzO86dfYMPGOL3dEc4OPkBN7XtRXQVd0/FRXRIHBVVKkB6qAlFTRa0uDK70QJe4joft+gqyalCns2cjqqLhuiohw+TXXncTueUZAsDKyiIN9RHKuW40zaSxo5UTJ3/E1MUBpEzxe+95B3/6l+/mzW+7gcf+8RQnhhcppEyWJ+d5/e2/xcGjJ9jW3cvpgWk0WyWbzVCTiOIimZqboacxRiAU5G8/+yWErNC7fQeuB/myixowfJssKVA9BWm7uNLDUSGk+q1BNWhgmCaus8zI2ZeI9XXxxCNPsG/PHjKlHMMjAzQEYhCrIT/0LDs39vP3L55me18/U2mLVDhObn6MfftfxaM/+DETl2bo61/HyTNnqZRtthYUXj56nm8//G1MTxAMtNHf18DZ0TEK2SyGESRUY6JrGtFELaGIztTUFMVigXAkSDhci1UqspLOYQaiqKZW7dRITFPHNDXKpTxC0XEqDp6nENJNbNOhq6edm9/229g2oAsSse3c/KpWNENHaBU8oVKqJvBCqOjCoa21gTOnQ+Dq3LD/XoLBKPm87TMwUX20ImIVLHK5wnclYQjxU6G7jx+oukFVT/HtyP3vLnMMVkVFhP+7wG+LKpeRj1RxA6qQVWVqGx0dPBchPST/B2oCUsrz0vca2A7sAorAA9Uff3r1Z79qAQDQiBHTFLrbtjA6PER9wyb+2wffQSIcwDDLlEsuET3AuthO8udrCBtZEjUXGLj4LVo7w6xfv4mQ2UZPbxvtG5owQ4KG2iKmO8/506O88PAhpgZeJkyZ7NQcdbEyK5kR/vjjf0PPphvZu/kabrjmnYxdrGN2doovff2vkE7AF2kUPodbxe//K0jMKt3WD+NcJIKyBSXbw3Il5bKLXSxTkxQk4jqu5+LYGdLpEV48/gBnR8+wlJkmX1hBN1ze/O73sGP7VRx56TT79t1IKlVDU3MXr776XrIXg/Rv6uDdv/smAlqGaKKB33//H4CqEDANBk+dRLXLJJJ1rOvq5r3veBedDS3kCiXOnh/hzrvv5g//6IO0NtUycv4ciqquYfS1qlSXZVkoip+HK6pCsVgkm83h2Q5/+Ae/T00iQiqVZP/+/USjUVRV5cbrr2Npucif/tWn2bhpC1/+5oPs3bmF4YkpZLCGvKNzZmCAobEhkCXe+c63sbGvn67Odvbs3sHywizPP/0YrTU1lMsW0yuTbNvUzYbONpKpOmqq2oTBYBAhYGVliY6OLjTNx0ZcunRprRUope9RkMvlqjsilMsVGhp812dFE3jSJV9ysNMWK7kiUqqoqoZtCzwZJRxuwzRr/1Mu77oulqWiafW84Q3vQdeSqMQ5c2oMu+L81H28BgkWfrFOUxRMBCYCA4GhKeiqQFN88Q9d9YVA/N7/atXAbwX6jEIVgYpEwRO+hb0PYHPQNQhqkpDmEQsoxAIQD0AypBA1IaJJwprPI9E0HxX6i+fff824CRiRUk78qrzo5w3bKfBbb7qX2Z8IYhGNopdH8wL8+MFnmF9wWbHmyJztoqOrF1m6RGONSnZ6gpV0Etc5TH6xmdlanZpgDb9527sJxirMjEzwowcO0rv/WlpfdRMzc+dZSi9gWzpTF4tEzL18+Zt/wetu/xvOPH2M8L4eNnbF2dBVxz9/9e9QjEV0tcYHZUiJrmnVVRlMvdourBKFJJKy7eK4/oc0OnICXVZIBluouA5Nnc3Y6QVmx6dprukkFuvALk2joRNuUTny1JOUbZd3v+e3+fEPH6G5tZHfePNbeM/b342kzNPPv8SpwdPs2ryTu259J9J0uTQ5TTldwrNVNnXWc2RikbNnzzJ47hzdHV0UbcG6Des4P3iGs+eO4nlgGDozM8vVye6r8riuzx1YxQUYmsmqku/SwiLHDh3mhm1dlEplXMeipKloQuXi+DDCcynYMDw1R9em3axvrecr3/kRuqozs7CCUBTWdW8g7HoMnLtAPBLjTW+8l9npWfYfOIAZCqMoHvF4PWeGzlFTE0E3DGLxKLbtYpomsViMfD5HIGAyOzNHTW0M27bXVIfD4fBaKqBrvuCoW1IJmgIPF6tSwqsiOwNBjUKlQLGSRXoqvlqUL9EuUcBT4Yoi3Or/mumB4uB4NkVrAbNcoau7HtDWEKMa1Vr/FeAfIQSrRP5VY1j4WVyqXIscFK5AFAoJvp8U1Qeqhqj+Oaoq0NcWEA+kr6/o25tovqAIEk/6C4r8/6FF+BvAN674/n1CiNNCiC8JIZK/6mRVN7FKJc4OfxXHstCDOhNjswwOn6VYDvK5L/477/6jjfTtaWByaYqxwSARZytRI0V3x3b6N3WhKHNcHHqEpYmf8OKjT/GDH75I9759NHWvAzOFEmqEgEpLTy2d67eQL8+gu+tQK0Mc2L+ZxmSEm6/eR2N9G7fe9OvMz+UwdBVNXRVvdMBzCOgaogrK8KTEdl1K5YLfPhQqtifZtGUPe6++hb7tPfRt6UbTVGKRCP3rNqA7Op//3D/yta99nWAwzIWhs2zcuIm6uhSJGo18IY8ZUonEwnR0tFEol3jvBz7Bu97+Pr74j4f4yB9/hJ6+fk6fPs3Y+DBf//d/ZnzwDMWVBTRDp6G9lY7+XlL1zdz+6pu59frr2LN7H+t6+zhwww309/dXUXVKVbnHr5zrur6mzmvbNpqu09BQx7bNmwjqGoMD51AUQTQSYef2Hbz4wkE6GutwPIV0ycbVg+zetpmVXIndW/r56Ef/O7lcgTNnB/nBgw+RWfZ9ARaXlrg4OcXhYydY37cRpIKmmdx2y3UcPHiQ0ckplhbmKZeK2LZNoVBAUaFUKpHL5alUbEql0lo703F82bFgMIisMgPBZyKWSyWikQiBoAFSkMtYZBxJQ2Mbvj+j5otwKI5fQWe1wHcFkk/4hWA/DFdpa+mkkK8gPfXyc/BhwIrwIfCKWEVfepcRfsLz8fvSXQvv1w7hp5x+dFkFAykequZhGIKgqRAyBAFNolWL0uDhyCrHQPo8AVd6vpuRqLJWPZCeW2WK/uKagPhlrYn/J0MIYQDTwCYp5ZwQogFYxE9x/ifQJKV8x88573eB3wWoq2/Ydf/j72HuYIYHv/sT8p5HXW09+YUFQp01XH/PXRTnpzhy+AxbN3TQnKrjyWefZOe+BnYceD2h5I0kg1kKw4f43rdfpn77NfTv3YqhBwgqcbSAw/j5U0yeO8G0fJYb+l7NY4cfJ2c10kSKSjnPzXe8ja5NXTiuQOLg2jqhkMCVEsfzQ8qQqqJIiet5uAgqTpGRiWM8/8yT3P7aj6BoLmghDE8SjunUxQPoAqRTZGl0gLmxs5RLGRqamlhZSlPGYXH+AtmlIK991628+PA3WN++n6ceewFdE3z4z7/I8eefZWlliJPHXqSxfTMXXj7BE88epL2rhcNHTvP0c0/w7De+TEf3Ri5VdD73te9RciTdTTXs623iwtnTXFy2SFsepnCxrBILmRKeUFGFh1MuYNsSVTcJRyOYmr7WdxeuxQfeeQ8dtQGcikM0GmZ5eYmQYTK1UuSvP/99lj0X6dl86sO/zf33H+SW117NH/2Pz/J7v/1a7r7r1ViWZHR0iGBxASNaw/DELCHdQKoGpYrHiy8MkLHy7L2qkbFZi6HxPBW7gFWyiISj2LaNbgaRroNj26xbv46VlRXm5ubYvHkzx44fIRQKYGoqlmURCARRNZ1CPktjfT2ZkoMgh4pBMtnAr739/Tz04x/w9g9+8j/dy54rq+011VeFrkJ8VXl5t3SQKPhKQLZSJQVJgY52RVh/GfYjhIqfMlbx/dXrKEIFnJ+uFeCjAxVFVLEDHioKQvq8gFU2qrd6FeGiCh8ktDp8WbjLtQIHX7cRYNu6mmPyslXg2viviARuB45LKecApJRzUkpX+oycL+D7EPynIa8wH4nFBC+fPku66LDn6t3kMhkujQ6TTIXZ1FNLfvksrjNJf2cDqQi4gUkOHLiOqVGFS6NF6mMOtYEQJ07PYjZoJOp1pBtmcT5PNp8mW7DpaH8VwYhBxZpgevbzKGjcdefb2b4/wGJGJ5yqsDQ/jef6H4tuSBwXLFviuBLp+BbVrmtjSxtblhCGQ/eGeu666zexswOUpx5DeiYVoZHLVZibzzA3O8fMpTHGxwaQnkO5ZHFxYpJYMEkx5/Hy6QlS8RRf/Mv7SIU7eezRZ7n5lgN8/4HH+MwnP8p37/8qLx5+mr/77L/yD5/5NDu3buZfv/QFjh45xe4dr+LBf/sPaurbOHTsIJcujtOUCJDQHQwBbZ3reM3r72bz1k1UKhXmlzPMLWXwVIErXaxyGRC4VSitpmpYlQrgax5G4xGmJy8yPTFFNp2hXC6TyWRoaGxk187tKNIjEg5jOx7hRB1myOCxx5/kwL6d3HP3PdTVt6AHIgjPY/fuvcTitdg2vPDiC0xcGkHVbfbs3cGmzRtpbNtAJlfG1AyaGhqwXYeF5SUC4RC27Vf/m5ubWVxcXNv9V7UOKpXKGsTZcWx0XfcVoqTEsfMEjASRcJT08gJf+bd/QNcj6KpWReCJtSOg60QDAcKmwFQFBj4S7xcluKIqMO3bmfk5vc/Wq4b4yLUdWFTzfVGNBqTn+KkmHgjXn9CaRNX8ya+oPs15lcHqSokjvTV/wiqWEFB8v0HP57Q4rq8X6boerifxXOnzXLxfnKb/VywCb+KKVKBqNrI67gbO/qoL5DIZNGs9E/Mu97zpDXR0dINdQQ0H0NUi+ZVLaOGdTGdyZMUKM/YyxATveO/7uePVd6CV4Ynvv8Czh1/ArGmhp2cv8ViSppYEuhnEdvPMpp/HNAM0N1RQS/VMz4xjBNIoeg9ves9tnDubQzo1jI9fQlVMFBXKjsRyJJbrE4ZKloPQAtiVCI4bY3homod//G/o+gaa2tpo6W0BDxScqvGljmYEePapx1mYu0RrZwfbd++hob6BsZFLzKws86a3fIChC4Pc87p3klkwiEcb+cpXv0A2U2HLlnXkCzM8//xjfOgjH+FPPv4xxi9OcPz4cWpqUoyPjzM5eZGDR46wsDjLM088ytVb+zh36EWGB07zre/ez3KugFA1ljMrFKwKaEEMM0gkFCYSiaIHgpihEKquUSz5gp224xAIBlnf0UxPZxsNDU3+DquqJBIJylaRYjHPH37w/WgCsqUiH/ofn2TdxnWsrJRZt249D/3wIbL5IsWyzcrsPN984FFWcjahSIK29vXcfsdvMDI8x6NPPsXi/AwPP3qI6dkV5manyeeyJOJxTNPEtm0CoQDxmhRWxacKA2zfvp3FxUW2bt1KNBpdwzasciI8z6NUKqEqDpoaYiVtsZSzCdbovP8DH0dIiVot3q0etlXBLuUxhYfm9+7W6kA/O1Yhu6sHskrXFR4Sdy3EB9unEyt+AU4TAkNRqroVoGusmdYqOKwWB6Un8TxfBbriSSoe2FLgVE1GPfx70pMCW4ItqT4Hyq6k4glsT6lqSPrmOb9o/FeYj9wCvPuKhz8phNiOv1iN/8zPfv6L0CIcOTrAvb/2HkKxJJFELeGaZfRQHePTc5TMIs3Bw8RSLWhKkmhgkbpoklAwytlTAwy+PIRtz3LrzTfTt+MWhAEFJ03JHuPCwCF6+7YTMeoRNZdYWFogq6QoWMuspGfRvW4aG7YyeP7b6JGdOOkKhbxFNK7juBLH84tKtieouC7z6XliIYdS0cVzl4km0rhCoqn1aGYUo+Sv/J4ERwqeef4wTa2dPPn9r5NJr7Cxt4eBMy/T0ryBvXs6+Lcvf5/bbtjHQ488Qnpxlre8+TeJRj1ySwZHXzrBrbfeTDJm0Nnewde+9nUOnTzCP//T5zhw3V5m55e4+zfu5Mv3/T2ttR309jbw6U9/mnCslmuv2ceG7i5yK1nOnBn0fRGFRygQQtEUrHIR6UnKloOi+e0126pgBgMkEglUXePma67GFHkWczkCqj/Bkskk8VSS0+eGyFd8C3XpSsoyyNLKEqdPjyG0OG/99Vt54pFH6F7fR0MyzrMjF2nI5Vm3YR04FuMXZ3FcBRuPAzddz4vHTiKMHLIepqbH8VSdeDKBbduULIvllWX6N2wgk05jGAanT58mEAgQCvuLQqVSIRQKYds22WyWYMBvdQa1KJaVx1Md6npq+b3f/TYVWbqSD375PjQ0dOFh2S6WJ3wD0Wqo/rPSPNIXArhCC0CuiYVcDsergqCq762xigryqefVREBU1YCqr+UyrNjf2V3kmmT9lS9XrLYjudxSRPqpiaKIqqagrMqL/fKU/3/Xd6AA1PzMY2/9f3sdx3OpiAVePHKYnrZbwQhR195BTUsPRS9GS2ucVKBA6/qNJE2TzITD4R8e5oGvnmbL3n2YqWlynMZejnHsa0fo3X03iZYUIxefpSFWz4XBQ+xYfzuVdITp4R4itRapmibaG69lduEMmfwAW7fcydhEmp4N7Zw9c4Genk4cNegXXaQP8TIDAfRgiW/e/2FsW5DJLvCqa1VcdZ7lBY+IGQG1hCsNQOC4cNW11xPyliksTKFhgVDZuWMXzRu38rn7/wdvfvv/ZHbkOWoaU2zc3Mnhwy/Q0lxPOK7x3e98n9e87Vt8/L9/HNNsoa6hnptvv4sNG9bxV3/zZ2zbsY2nX/gRnpamb92dPH32DH/7t59kdPwi5waHmL80gWmGuXhxhlAkSLGQRVcFru2Rz+bXDFOF4rspJRIJNEPDtm3fA7CQww3YxOtqkHmfxWeaBiPDI0QjIS4MTeBID+l6LKyU+Mq/P8xHPvB+Tg8MUVffzHX7dvDgAz9AZBcIJ2p57uBPSCXj7N25lYXcEuGoSjwV5ZknH2dhMc9y2YdsF3J51HCElewc4XAYXTcxDYPZ+TmEJykUCoTDYRoaGkB4dLR3cH7gZX8SqioRM4j0HCzLAmkQTSnUJnv5/T+9j6KUWF4ehZRfvJOXdf1s6ZLJL6MrJno4iSeqmA95eQ76VX/8an415FeE4hf2VAUhWAPmqKq/4/tFQseX95C+YaoHKCo+aKu60awCnVZhBp70fKLX5SZDdZKxpjfgSl/KfnWiq6KqrHQFAMlXUnqFS44rClQqKvfcdgNUyjQ1BxGBOFOLs7S1r6e3uYU7r7mX5lCKYz85xfD4Ehuvu40tN63j0swhjh5/DkkvNa23U9fcjqrOYbtZTG0r7e03UpdqorHJJliXYSGdY3rGpbH9Viwljm5sQGptFO1ZQsoi+aJNR18Pp05PUipZlHFwpELJdknnSliVAK9/3d/iiBK9W9roaf1LBk+N8+LJY2SFgiVVHE9ge76efyZT5vFHH6cmHuPADTf4EtHYDB1/hiY9xYWzRxg7N8rc9Dinj52ipq6Wf/ny13jvn32GUFjjuv7ree9H3ocjLQaGBrnuqv38+AcPkYw28pt3v4UzIw+w8zW1TBRmKVo2AQWWVnKMjV9i187tjM5O8s633sbezetBaoQiQUJBjUDQBKEQDAQxFA3D0Ki4ZSquRckqMnZxgoBpo6thlpdmkcLHugvFxCrA2OQolZzF9PQUUnig5hEhhc/9y98zdWmQzf1dpJdm2bOjl5YN62mrj7Nt0yb6ujuJhKLEggFqE7VgGTx98DxL6RLCleiBEAFDw7MqOLZDJpP1TVWqrEfDMOjv7SNgmExNX6KQy1PIFoFqOa46iYJhAz1gooc0wtFG/uxTn0fm51Ach4qsxZIeiqkRjihEozqeY1HJ5qhklikX0uiK34LTqoeuCgxNJWRoBHUFwxBoKmgqKKrvA4DnIl0/51/rAggHcED6RUVbSmzPD+srDjiuP5FdzxdZtfGo4FGRHo6s6gQ6wt/RPZDeZf3ASrUzIFfNr6rFwFWotF/U9ta4Cr9ovCJgw5qu0t7RQ21dgsWZWbZsb2RqbgzpKPQ1pAirJRYn5jh0apHJdJCA7rK5bx9i4TxqZY72aAPhVC01TRotzfWUKlBCJ14TQWpRDLOLimXgOiaBcCPZ0jy7WrZQsW0amxtZyaQ5PzzKtbt2MD2XI1JbQ0dvB4uLy9S21OOJaglGBcfzsGQdd7/mez7Sy3Tp7C1jqHmW5ueJ1HWAcADNf/NduObaAyyPnWR6dIypiQl0IYnEE/R39fH048+zfXMbtc1h8ssmr9p3Nd//wY/5xEc+xCc+8RF+8vRTvPj0cV7zunu488672Lf3NXSvS5FMpPj6N77H5MthPv69k9xzp4JutjA6NkrJKmGX0jz3zHPU19TTWF9DW3MzDXUNPPuTl5ifW0YxDVRVVCXLJQHTADwUqaCYIaKhKJVKCRuDbDpDpNakJpFkdnaWxtoGZgsLnD51zK+kez6F2XIdhJCU81kmhgYJqw6qESQcjtLX28jccoaJ4QHqGxpp7+xgeHSc7nWdpFo6OPTCM1Qcl7hbwTAMgsEYQU8SCofJpleolC027txFJp1haGiIeDyObgapOB6aIuns7mZifJxgIFA1gK3uwIpByVaZGB1FpsdI9DXhSI/M/AIXhmbZsXU9AU1lpZKlNmbQ0N1DztbIlX0WoFCqzj/Sb+Wtaf5V0XxQbSdeEXLLKptPSlnVD1rFHfjOz7BqXruqa109T1xJH/R/l6zm/v7fIwB3LXJZTTuqL8K/rutVGYyrD4nqS/4/Dxb63xoSj0RNHCMQorNnO+niGI3d52mvbSTirfDSwTSFqEP31n28+sZfZ2rkGMfPPE9bax92sIhQsqRzA7S35FhemcCTgkjQJJ+NIeoK1NVuQFcURodzNNbtQp0+Q0M8RXZ5mqBSRyJUw64d+yhbyxRzReJ1tShBDV24qI6N0DRfGVYIP8GTLtKwqmouVcdZ2yAZiyKEh65D2fYTQlXREBIOHTrIpvVdZLNZmprqsV2Lk88fJh6qp66hlYsTg6hajIJTYjm7QMf6dTz4g2dpbmhHUyT/dN8XufH6/TQ2hHjPu9/Pk089Tq5UopBx2bmjldvvejP3/eNXaElGSNU1sv+aq3AsjwceeYqV2RlcVxCORDBUCIUC5MsWmmGg6j5q0HYdhCLxbId41ODe19xEKmETCSbIFBfp6elhfmGW7u5uzp+7gOtq5HIFQqEQlUIRBYGnKOiGwba+NkrZBXIVC0saXBobRxhhDh45w+4t3QTMAOcHBknEogxcGGZioYCuB8FQaWuIk3McSo6CrlYVnzWNmtooFy5cYOf2HczOzRKIhpFli6XFRSzDpD5S5ysH2RJhqJiBIOWShee4NHasp6ahmaKSJWDNobo2Iy+fYtcNtzE1O8+5My+xbfNm6pqayOdyuEYSUQXqiNUdFqrAMXeNrnsZGCfXeghC+HAdt5oSeJ7A8/xzV+GBUl7O41fPrNYh167nf11VDl7N79eesAo79n4K2KSsCodcQV1eRSLJtcXlP49XSDqgsLA0RThSi2KqjF0cJ1g7zHR6gPsfP037vj1su/Z2EvUB1EAZGQyRm5slZJZpqotSns0TsLLkly+SL1hMzs9zaekgkbCHLJdZmp8FkSMcCdHdtoHmaJBKYZqapER6AlNzEF6e4eETFHMTmKqCrgga61ModglDlajSzw1VwJASDRuNCoYiCGoe4YBCJFwhrC8Q1R10PBRZQeCQzizS1FSHQHLgugNs3NgPy06asQAAIABJREFUmsv83CyNDSkeeODHNDWu57P/8A/c/52HqK9tIhhOcujYCcamJti6s4/rDuwnX8hx/U0H6Ozu5Jpr9tPYVMu23XtYWs7w3E+eprWtmeeee55ysUxtQz1Hz5yls7uLxuZGMsUcp86+TDqTIZkIETRUHNfGCJgoatWFCFB1jWhIx0pPU7Es5ubnCQbCPmJN1QgEg9TV1hCLpyhVbMLhKJ50EZ6HIsEwTD7wvneRya6wsJzhxKmXKVllDCNIJmcRiyfw8IhG4wRNk1AgTCpZTzgcw644GLpW1QH0tRsi4TDJZJJKpYIZCDA4OIjruhSKBUxDJ2CaeNLf+bds3Y6iqChV41UUlUAkwF996i/QVQVbMXyJ9kQ9e177WlxTo7W9hTvveh27d21meHiYk2eHsCpu1d6r6vCj+CrUmirQdRVdV6p6hVSPVbhwdaMAfxf3PGzPpw5Lpep6VI0qVvt8a4pAXE5n5FoPcLV46OMP/EOw2hq8sm+hCK1aC1CgCjcWQqnGGr98mr8iIgHHrtCW2oCqhXEch/HZC8TrC5i47LznFsK1vaCmKOXz5LJzGEENMxzhyUc+S/+GHDG1G2dZZdIroZkmih0i415ED5xi6twUmZU8nbW3kitUWNfaQUFfZH7lBc5dfJJY083s3rCZs6efJ5VIsjj3JJq8BaEAQZNiMUdcUXGFTxzyhEBRJKrU8KSGqjg+G08VpGIJFuYvMjiSpal1A2gO0vNvolSihsLiIpdmJ4gHo5Syed74hjeCI3AkHD9+lNfeeStve8sb+eMP/QH/8q/P8MB/fJnx8XEaGxvJFU+Tnl5m774DxCJxUo0tbEp08o4/eITdm7sx8jmu2reboFBZuHiJzXfcyL4DN1OXTPDgN79OKGiwa+cmzEiYYgVU4XJpcYVyuYihBDCE747kIkmGI1w4fZb1rTdTVxslHI6gB0JMzcyTzhZoiAfYs+Vaiup/ENF1VKFieZKQEETicSq2izQjZBbnOXtumA1dTeTsaXLlAmYkxu5rrmNoco7J8wOYkQVGT50gmQwSCUJTVy/58QnSuQWfN+B5aLpBOBKhUraIRKMITUVIgVUq4No28XgCq+SSq4qomrpCqeShajrbt1/LwnyelelxFOnhKAFKJZeAFCiug5AKxaLNhYVJknWt1LZGUZQr7eWA6j4qpS8RDlU5b3zBGdf1ENKpRgiqTyLzy/L+RBR+MRCqeAEB4oqOwCpR+KcARavAo9XdfvW1rLX6VmHIa1jkKgAJoAp0qkKI/Sv+Yj2BV0QkoKkmiVgjoDF9aY7ahEVKraOSURkaOE5meRhVVfDUNEFdob2hjfbuLuJBh1Sok7beVxNo3oMIbyddNLm0OMdSWmV0/BSmGkbIBVSp4rgOR049yXRhnuWFUyjeEidPf56zg4cYnXyeQmGKuvomCoUijhfB9QTBcJRysYBE4ngq0pFI8rjKCkKpVD8DE9s2KZRClAu+GAdG1QBLShRNpbaxATNscOTYKZZWcuzY+Srm5zNMTM4zc2mW+mQDaiTOYjrH237n/WzqrLovzc7ymU9/GiEVTp44zRe/8E+cPnOcDes7KVds3nD3HeTzObo6m6lYDkIzGLs4yeylSQYGXyaWinDn617Njm1b2dDTi+sprKxk2b6pjxt29tLVlMI0FB9mWqXCqrpOc3v7/2LuvaMju687z8+LlXOhkIEG0Oic2YE5tURSpERFr6Ipy2kcNLP2yvI6yDatsWRbHnvG47Flax3lY1mJlEiRokg22QzdTbJzNxroRs4oVBUq5xf3j1dAN2lp5XO0nqMfzjtV9cOrBxyg7u/97r3fQK1ep96oU6uVKZUKDA0NARCKxEhl8qAZlPMF7rj1NkRJdHrkhklX3ybC7e3YkkRHe4KGYVNt6GzbMkSz2WTm6iiV1XmCfg+9PSHCIRvZrdDR28PLJ15lZn4O3dDxeDwAqKpTyPR4XKymVqjVytg4HgXFYtFRf7ItREnEHwxi6SYBj4dYOMFHP/YL5HJFnnjqG6ysZQlFw0iqjGWZ6IaO0ZIzs0ydarXi2L61/BfWLedNy4HomlyfW5cIZ0MzkBsOYYNW7pYEPJKIW7peSFQkAVkGWXayS0VpvW5hBxwmg41o24g2NyQMjrGtk6NY11sVguBQn0Wj9WiC4KR3gqAjiAay8oND/cdiJ+Bxh0kkBjAtm9dfexHVVcUs70L255icPIfkh0i0i1ioE8k2MQ2dUGSA9s5bWFpL05lo0tm7g1jPIMszx5keN6gbHurVWeRoje5EGK8/Rnd/B5WlM1RtE6+/j/7uw/R3Jsk3M0RCAyzNN3n7vW8nl1sjHgqAKSIrXrRiFsntwsBCMZvoWBiGC69sookSog2KS8cQLbzBIB3dKerGHKLQg23apNNpuuN+qmYRWxSp1JvMzS2huv3845e/zKHDh0h0djI/Psdjj3+b/r5N3HHLAWYmJxka2ETQ/xDHj79MoqOXvTsHAYOXX3yBnp5utmzdTnFtni3DgyzVXMwvvUa0LYZHVelqj3Ph4iWO7N5KoVDh1ZNvEE+0kU6ucKlR4t0P3s1tHX38yzeeoVAqg6XjUd1Mzcyy/4HbyRfyiIKBris0jRqKIuP1uZhdTvFnf/c3xINhqs06737nu7hydRSzXiWXySNKKvsO34poytjVJt5YnPG5Rbo6PFwbHUWxdbZsHSYY6WAhvUb/QBupooE36GeTZ4DphcUNhmCz2aSYK+H3+SgVC7jdLrSm5uDxbRu3291SRZap1Kp09fcxM36NeqWKgQ+3X8U2VT7+8Z9F8bdTK9fJrBXo7GxHbbkSuVWZgC9Cra6BqG7w8tfHdZjuekC2cnAcxqIiChvVeQEbVb4ecKJlOlh/W0Rraf87bcT1tp7tYAVopQOCE+jWDeAAhwvQyu4Fe2PX4NiXttI4cd08psUfsG0EwUSWnPMU+cdcXqyhW8S6OgGDpn2WQmE/4f5b2HbrrfQO7aJZMZi98l0yyTWWk1OIaph4tB8lsI3JOYNr43mmxqdp5At4BR9tnu1s7hvmyKG7EZQ5/J4uZNWLRQOPp0lHvB/bDmLIUW675xGWZ7JUm1m27LkJwdOFLEvYmo5pC2g2qIqEZZjYaNh2lYYWBd2F1riMZYk0jSKZ7AKNhsDY5DzPP/8E0xMvOjoDooikKsxNTnD+/CtILpFYIo7LLSEpEgObB9l9aBc5vUx/uI352TlefuY7rJaqfPtbjyFgcWXkMgcPHOKP/+hzDA/2MjoyRmY1y3PPv8JScolapY7X5Se1luPUyde5cnmM1VSS9kiYSCDEyZOnmV1KUapqVAp52qNBOuIRcqkkcaGEqJWp1mpYWDTqTbp7+/D4fIBFOBzE63Pj93tpNhuYpkGl3mRhZbXVW7f4v37912jz+qkXSuiGwfTYJMvjM3hUF/X8KtVSkWAgyG23HiEWa8cyBSTJxUo6y8xihkK5Qja3iojNkYOH6GxrQ9d0wKE7K4pMOpWiWq3gdvsRRQXLFMnn85TLZXK5HOnMKoqiUCiWkNyO2emOfTehmU5uHAp58XhEjEaOUn6lhehzitKmZbK0vMzS8up1Hb8bDrm1qXaJyput6GzHAESWJSRJ3OD/iy1VYSwTUcIB/goWLllClcQNFWGlRSd2XgsoIsiCjSKCWxZxSSIuWcAtC/hkAa8i4FNEPDJ4ZacW5ZEsfC1a8frhwsSFiU+RCLpl/KqI8uNuPiIIJpq+xMxciWx2lr07fwNvu4wshxkaaqNeyZBdvcLc0hNolLjn3ihtiS5sy8bnidHVvwvbzGFqDRZmVigkcyQCQWKxDmwri8eMYWOzODPHaiHJtk0R2ruGifTuYDFVobdPoK3jFqKJCJaoU8it4o93sY4CExQ3tXoVd1DF0Oapmz7cQo1c9goDW28hmZrn9MV/ZnBgJ9GOAQ4d2kuxOo0iWCBJdHZ2cWnqPIn2LjZt2oSNyOLSKlqzzv0P3M+J06fp39zGN77+HG+7/15S81NUBMiurnHhwkVkWea555+jr68LwbQ5+rajTE7N8tqZs1y6OkV3IsTjT7/E2EoOG4ne3k7CbQmKuSK5XJ5UOsv+gwfp7OogtThDb3cv+3cN4xEbuGWRLQM9LBdq6FoTWZUJeN1ozSY+jxewURUPhm4iywrNRhPJFUR0+xAEm2qthi2INDUHKh2PBihVKmRSi3j9IVDdXLwwQrYKQ70xFhYW2b1tiFyuQM2WaY9EGerrp6TB3Pwym3t7ESwbVVEpFosUi0UERaBWqdLb3U29XkN1uVtbYBGXx40gtQA1tk0gEKBWKZItlvjUr/8m5bqAbi+xtLxGrHMAQTI5cHAf9abjGi3LMo1GhUQijtcXpVpb9zFmo3C34fpjO8xRbHtD4HN9+7/uKOgAgVp1A9vB8a/f0/8tcEfY2NGspyDCRidgg37QKgi22oGt9p9trdcWHXiyAGBZLYRjSzFKFhEFE1NYb0l+//FjsRMQaVBrnODk6b8jNSsTiYaoNmRUa5i2WDuiJDK5nKFUHEGr5Dnzxj8yNX6GeiGLPyoztNvLTTf1EYtFqWoSuiCSzRssr6RZThZoCFW0agahUqFeaJDNJ0muvEpy4RSpuadxq2UU2c9ycol8doH08hiGXWnleza65ELTaih2BbRJCqUr6Ab09wzhNleI+1T8gTFefOmPuHL1WwTDIWK+AHYlhyzplNYyRENh7r3nYQZ7h5mZWebb3zlGpVBH9oYdtJ7bw9C+HUTDXtq7u9i7eZjb77wTj9tNs1EnFo8wOnaFeEcvSDC4bZiAz0OpUCLS2cMzJ8fIZ+tE22PE4gF8oQgen49quYg/4OH4iy+xtDBPIBCkp7eHcCyGLUrMZWq84+jdvOOWfWyKhwmqMNQZIxrwEI1GyOXyYKsYhoVp2HR2dpOrGFQ1G0l0LNVlSWFsegY1EOTI/h14I2FsvY6BSNbyEGnrw7AkypUlduxKkC+vkcrlyeYLlFeXySeTWJaCKarUqyWq1TKmZREKhYhGo2BL+HwBwpEolmVQqZWRVAXZ5cbl9WBi43I7ugKWoaOICjfdfA+FShlbqbI6f4yFhQKmbmGJHuqa4ASOLYBlEwr4CAaD1BvaRuTZ1g0Gsw5gFMM0NwhB0gYWAHTdwLTAxrE7cwREHaqPaTlbf8tebxVeb90JLVzA+gKwnh5ACxG4wT+4XmsQN7oETrnSaqlGGraAbgsOO1QUkBUJ27YwLNAsC/1/g6jIjzQMU8DldZMvGoi6m/HZSWy3iRDRaZZyzM8ssmXbOzH0DLLipZBPMnL2JOVCGm+7m+XUBDkD3GIOr0/GrUQpVMs0ygIe0aKcP0s+XSMUzhFVfGg2XBpLsc3OEHanCAQF/MFObK1CpZLGo7rQqxUUv5NvIkDA78VsLmKaLjKLl8nal7H7olwa+Wfeft8nGNr0EbyuVdo7+/CHuqgULSRdJ+ZSWLNt0qkUllnB7fJR1UqoLpWBwWG6+rfS3nkcTfdx55E9RP0hggE/fr+PxWSaq6tL6HqDTYNDXJtYxOs5g+Ty8MC738uOXbu4Np9mfnGRXK6CoevcecdBLp46zfT8AluGNnHu/EWCoQhLy1ncbpXd24fJ5dZ4eWmOPcN99HZ1spJaQRJ0Ng/1UKs2EPQac9Or3HzrLQRDXmrVGqpLxuv1U6s1+B//64vYlkypqqNbJkgCiktBkGT6+gcoV6oEQmEUfxBRdNMwKjRMg4ZZI7OWRVD8VBsWqsvFnr27SeYzNFJLNEwHZ2BaJqIobPgPiqqMLEnkcjk8HjeSYmIYOuFwGE3TEASBRqOBy+UYkIbiCX7zd36PeqOJILrw21107B0gn11BVPzYtoGuNVEVH9gmjVqNQqOBqASQFYeL4FTwuV6V5zoib0PSi+stvg3AzwaYaOMyG+PG9954jbeO9WlnrXDqDaZtt2TC7JazkI0tOD6DgrXev3B+hmTb2KbluGoJAoYpYvy47wTcLh8ecRvpJQlsBdlnk03NUtEKpLIlNMtmz9abiEe30d27n86O3USiLgwDooEhVKkf1TfE/Ow8YrNAb08PPYl2JMtL1CUi5C7TqEyhBNcIxDxs6o8TTRymvetm/MGt1Jpd+EIRBH0Fl2rT3z+E6vUitQRFRBE8qopoVPC6+3ErAtNzf8/Zi2PoYhhLrhLx7+XArk/SFf4AXnUvnT23sJpZY+zSZWanJpi4Ns783DyTMwvEYm0MDPbjVmxeeP5ZsukihiFg1uoce+572JhcGRvh8sgIY2PjnDt7jv03HWRhaYWXXjlBajXF0vQMd955N5m1HFarQNWViLFv7z4ajQa5Yp5KpUBvdzvlcolA0MPA0BCSLLO4OE+1WqNe01iYnyOZXOaFl06QKVVpanW8fh9LqQyq2002m6W7t4fBoSHm5ufJ5nKUKg0ELAfb3gLSeH0eNN0gmUrTaGps27EDw7BYmJ3njTOn8Xg8eF1hlhfX8HlCKKKAW3G47/FYjEjEMU1FdLoTpXKZ8fHxVuC7kESBeq2JYej4/V5cbnVD/iscDm8oDblcLjweH3XDxh/wQDVPNLEPUZ+nXq8SCYXA0EivLqPrTaqVMhfOvMHV0bHrbTzBaetKot0CLLXk5EQ23IPfZDd+Q51AWK/WC9e9Atat7d8sC349KK93G9bRgU6Qmy0moRP0YJiOhqXDJBRZpwiYLedhy7LRddORuDOhYYg0dRvNFDCsH/PCoMelMD6SRyvYBGJ+4vE2oqE4Tc3E51Uw9QaZ1Ailcoql5WlyuSlqtRKRaJzungMEfMMEw934wgGQgohiAtH20azU0EsmqtqF4uujahmEQt14PSY7dm+is7MX0dtFe8c2kskU9ZpKW3wbituLR/E6/3DBQrQVTERko4mNRE/vYXzeGG73FR568D5cLpmIfwhHIMrE0C08qkg4GqZcr+PxBegfGKSnaxOiIFIrltgyNMhKKoVk6Tz40MfJZhYQTR1vQMRApz0ax9YNYpEYw1uHcSsqoYAb3TKp1ho0aw1cHpmPf/RDqJZMOBxC9AbABWuVKul0iYWFLJLqo6OnmyO33kIg5MPl8rBly3aGBzexsJJidmaasM/Lru3buXxhnJGxKVZyJUq6g6eXJZFsqYimNVjLZhncPIziUTFNjZpmtKzbBQRbxTAanHrtHH5viKnpKVTRTUMT6N7UBU2NesFDf9cOZEtCskW8qptqo0E4FuKe2w7SmYgQj0Vpi8fRDYNGs4nb4yYYihKNtaG6nMJcKp2mVqsBjvx4pVxBluWW+lAZwTCRJA+GBZZWJpmps7RoE2/b3Kq0G+im4xFo6g1UWSESbcfr8SGLNqosODb1rUNaN/hoLQ5KSyNQas0JsGE7jtiiFlutWsKNXYb1YLXZsBh3agxOqmBaDrfAbN35TdvCsMzW8xaFuGV7b7N+njPvKAzZmDhSJYYtYomi00MQHemxHzR+LBYBAYvpq0vYdQN/WxRZjNHbuxfVa9JsZqjkU6SSq4RCIuXiCrVanqnJa9TrOpplspKeJF9cBkUk3r2FYNsQkfgwQ0NHsMQBLF8C2Z0gU6iRTcmspsMYmsba2gyiGkFRvEzNvYHb14Xb1UVNN0kXCzSbJpYpYFkO/NTQPVSbKr5QL32b3kVdW6ZaS1JvxijVnH8Ogo2JhiAF8IUjCKJOe2c3ijfI6LVJlhbnuXb5IrIkoNkizXqD0SvjeBQZ7Cp33nWU3r5BNnV3MzTQz84d27nn3nu5euUK+XyBwzcfZu++Azz2+Nc58eprbB8eIr2Qolqps1oosbC6iCHLxKOdNJoSfZs20xYLg2UyMDxE07RwebyIgkCpWqOjo5NEIkFfVxsxv4ue/k2cuXyV0akFllezKKJMfi1LvlDEHwgzOjaBaQvYlkmuVHb+f7aNZRgIokUhX6RRynPi1RO88foZyg0DW1XJpjOMXppFlb1cvnyZ+fkUyVQe0W3h8jbZt6OD7s4o/ZsGiEaiJBIJEu0JQuFwyyxVpFqpOFThQglN0ymViq02orYhkVapVDCNOpVikVJR442T53G7qly6epneLhvDyFGprTrZtGiiqln6e/vo6+93OP+io9i7DuO3WoQc2/F3u0EKzHENuu780yoQOrA/p43XwhRsHBvXsjeIPo6ZqAMY02wL3TYdOLooOKxEUbihddgSJFvnDrRqVpJIC4MgOEQnUUAVQRGsVtHU+X1/0PgxWQREMitJbEsjWygxMXOGULQP246ynJ4nEApTr9co50rEg14iPg+JqIhgGqhKgEDAj+JSsWwbRfURiCbwRhOYSoxA5y4ibUO4RB+C2M3lq5PE2t5OX8/9ePyd+LzbaWghtgzuI+oXWVs9h2Q3mVyYpVarYtsm9WaGcnEZS1CQ1RCC4mbT1nexZfunEMQOdM2DJTnmjzYiuqFQqZXIro0S9qu0t3fQ2z9EW6KDdCbLiRMXCYZjDA4NcWV0lGPPPYXPFeD8pddIpRdoVOtIqodiscDx4ydRZC/xRJxQKIAqSQxtGcIUDRoVneTyEtVajZXkKgsLCywtrxAI+kCAYDCE3+1lZWGeUi6D1TRItCeYnZ1lcm4JAZtiucqrJ1+nXCrz7ofvo1qrMjJ2jc6uLi5PrFDXZUItey0RgZGLl7AtR6l3XdnH2c42sWwLWRTwSg0M3WQxOc/evdspVjS27NjG0PBmenr72LVnD+Vak7nFFRLdUfwhKFfTCAKUqnVMy6Kjo4N4PE6j0cDUGxRzWcLhEG6PB9XlotFooOs65bKzEGmaRq1WwzAMCsUM33vqMWQBasUCS3Oj3HLzMPPzL2FUZcbGTpLNrKFINtl0jkuXLuJxq7REvTAtG8O0nK245dyx1/E664G4Dv+1Wnn6jc6/N+IMfpAj8I21BFOwMQW7pV+wDgh+szz5Oo1QwKn8S6KAKoEiGKiChUty1JDcrcMlgmybrd/P2oCFf7/x71oEWoKhaUEQrtwwFxUE4XlBECZbj5HWvCAIwv8UBGGqJTZ64IddXxQlbMtk6+4h5ufn6IrsZDl1kUZtje3bH6Zv8114Q91MTE6i1+tU8mk8og9FbuL1RIhF+5AlP7pmU6tr6JaNJ+BHdPlBMgn6o0iKn2hwD7FonLaO3YRiPRRrdSSXij84SG/HITyyhllfJTk9RSIWQXLVMMR5EHR0swZGGqtyFbQishymu/chkDuwJA1bdDsrvfORYGbmIuffeAXbkNDrJcrZFJYlUC7XCYQ8pFNpRi6P0NPbSWp1nngkwbZdm8jkRsDSee2NEYaHB7jzrls5d/YyhVKedzxwH5FQkCefeppYWw8TkxdZy2bo6OxCM0yEVhW6Vm9SbTZYXF5k4toEifZOdu3ew+L8CivLy9SaDZbSeYJBP4nOTlTVw2oqg2aLLCyvYIkKuXyRJ559mX968jhfP/Y6n/3Tf+A7L57gPR95pLVNFvH5fBuy5Zbl5KpN3aBRz3PgppupN/Ps3zXA0MAw737/w3T0bWLPwVvYsn0HkiRRLJTwBEMY+BgZm2M5meaF4y8zNTNHNpcFHCdhjCbYBj6fn0q5TK1We1PBrVQqATiLgK6jKCrjoxeIhlwc3N9Df1cbnfEwF0aOk8lNEg7uxLQlKhUN7A7inX0IkuqIeFi0KvrCxmsn8J0vhxDUOq/VFrTs67ZjG/BiQfi3H/TWeKuLMC034g3CUms3sV4rELGRBLslVS5syJY70uaOy/Q6F2G9FLkOb5awWxKpP/pO4B+BB94y9xvAC7ZtDwMvtF6Dozk43Dp+HvjiD7u41fqlRbfIpv4EF889z9LyG7T7dzCVfBZXxI1m25imh6mZedyBHmqWCUIb2fwipXISUZTRmia1WhVsBZcSQBR8GFqZbPYqpu1mcWmBO29+mECwk2xeIxTcBIIH2eVHlMuUsw0i8S0oSpC27jCGlWdu+XUEQSAQbMcjakiVefIrM+Syy8yuvkhNKyOIjjCK1SKINKngCXVw4MD76d60hZePP8/42AVsW2THzl28/f67sE2Lc+fOksul0JpVnn7qGaqNItlcjheOvYjiUunr72Xz5j60pkG9UcfQNLAsLl4c4cTJc8RiHvbt2UMgEMTC8XQcn5ihXNUJBYP0dPdgW+D1h0GQuHDpCrGOdu4+ehR3IIQkCvT1beKhd76T7p5eTp29wP6DR+gfGCKVTpPOFzg/tcSzp8d4/uJVnj8/zl/+63cQRHOjKCdJEgIgSSoen58v/MkfEk/E6O7dRCazRD6zQDadZmTkAvPJNCtrBbLFIjft38NN+/fh9UXwBTYxO59jbmGJ0avXHBsy3WB+fp5MJsPy0iK2ZRIIBAmHwyiKQrVWIxaLIcsyPp9vQ8jDtCy0homla1y6eJpSfpVU5nVWV+Y59co0Lo+H9u5+vKEILm+YnoFOOvoGqGlOm2+de2/R4vgbjjiJ1rJLb6kCtpR7Wp/f1h39rU5C66nCW4uC6+c4WAMR2b4OSFqvPwg3nCfcUHeQROF6qoGALTguBdfrCLYjUiaIWKKEIggogvD/2Qb8dy0Ctm2/AuTeMv1u4J9az/8JeM8N81+2nfE6EH6L7uC/GZVKHasG8d5tRDpNbKOD0bNjhAIBEpHdBLwy5VyFgKeBK5BjKTuGoblxeWySq2dRZAWPu8P5Qwhr2EKdmt6gUiuj2/MU8xb5QgYZL4orzmJyEV0rsLRwDVlqIEkijfosoicCUoKuwa3Uy1VUKYoibsWW3UhKG03fAaT2m/H4YmiNKZrFJYorp5C1UWrFS0yMv4iiQCGdwmpmGdzcT7ytja6ePmfrjI3LrbK6mCS1uMj7P/yTLM/McOTAIYZ2bmFtqcb2gSMkYhGOHNyOrln85f/6F9wuE6/Lzfz8LJFEJz0dMT7wvvegiF5uvv1mbr59H8Gwn4998L0c2LYLo6ZRLKaZX5xheu4alWqaZHKViM/L2MXLJOdmqRezjM8sMDG4MMksAAAgAElEQVQxzcili9S1Bn19PWSSSfK5PBo2tUaTtrYYutHA7fFgiSrPHHsJRXW6JqricsAwtoBAHVVSuG3fEOFoJyMXz1DI1hmfWaJYyvH40y9w+cpVTp8+xpPffZyG3qSvL8zrJ15hbWWZLZv3097Wi+LyUK9XqTbrVKpVBAQkWaVSqVMpF1FVhZ7ebiRJxO31UyyVESSRhq4jKQqGYbKar7K4ssgLzx7j0sgFBobfRns4QlvHbkrleWztPB1RnWK+wuzUFTQzjWHUnBTABOsG/r4oCgg0HAMZy7yhHShcF/NARBCcYJdE4U1Bv74wWJajErS+wKwXGp3ag9P7lwQBaQOAhKNfyLpk2bpuAICDdrQwsVoCJiLObkJqdWsMc90dqaU89B/kO9Bu23ay9XwVaG897wYWbzhvqTX3A0elXCQQbCAGG0RjtyO4Fjl62yM88c0vERQHKJdX2DbUjdBs0hXtYff2u2jqFbwRCa+vi0KxBrZCo1mjYeQxrDqyKhMMB8CyqDZUDEPnjjv2spa/xLWxN1BFm9n5F2k21pCoQHMBMRhC8vvwtUVRrDi2oRIPDyOYUQxLpi53kLND2D4/lrxKtbaAYaQoFSZILz3GuZE/59VT/xN0nSuXz/PXf/UnjF5+nXgsRqKtk7nZRXp7e/ngRz7O/GIGb6STns5ufKqHqZllvMEuBvv3sJbJcebMKVKpNP/1s7+O2y3T1dPH1q1bya6tcv7sFRbnJllLp/H4/MTiIcJBlXKpyC2HDzM01E97W5i+3jaOHN5LsVjG7/fz8LveQb1W57nvPYsiWMTicRYX5rAEgUg0yvjYVUJBP6apc+Cmm2hLJMikVqnXqmDbhENhbNvm8OGb2b51G6ZpIbVov6IAMgKL0+PYqORzeRTVi+KN02g0aRoWnpAfWzJJdHUiKSoXRy+zksxw5tRpXnrpRUyjydatW5AlCZfHzcDQEN29vSiq45Rcq1aJx2MEgwFEAXRNR5QkypUK5UqFpq5jGA5yUZFltg5vJdg1QGqtQXp5iQ9/9CeJRmJMjJ2nulrEJS8xMX6axaVRcvm5lgWY2Cq4iS1arkl2bZlGo4okyjTqzbfocwhv3gHYb64P/Bt/wRbp5zoq0b6hpmA5R+u7Gx0H+/qOYF01aKMAadvIAhtdGlrXcYqRJrqoYVrqdf7D9xn/vxQG7RuhTv/OIQjCzwuCcFYQhLPlchWLKo8/+TjB4A5isTiXJ07SmfAxP/91ro2ewePV0CydUlVnbrpITS9hu3zs2Xsb7W3t5PMzFHJ50LtpNhRMw3YUalwxvIEczXqBauMEmeJx4m0BTEthy5ZbqFQhvzZFwHcTF0fOc+y1JzBFF0a5SiDYgeozMdU5mkYdzVZp2hIj069jiQr+eJClSoOFlSrJpQvEgnlQXiLeUWb/wQeJxzfz+GNf5ur0y9x08xEs3WR5KUnb4CCGJ8ITT3yLMxcv4/W4eO3USWIdQ9RqGgvzixQKBU6eOMGlS2eZnp7mb/7+y4xMznL67Bk++JH3o0g2mqnxN3/1RXJrGQYHurk2PsG/fO0bJDp7WCtUyJfKSIqXgYHdFItZqrUC73rXw/z+5/6Qdzz4ELt37aVUqVJtNMnl8/g8bpaWlnCpEquLy6iqCwSBzs5OFEXBsiwqlQpLS2n27NqH1+vF5XKhqioIEqV8Gb3ZIBAKIckKguxidHKGcCRMNBpEUixkxc3CQgZZVTl0+DC33/o27r/nYT76ofeQiPmQBYuOjnawLCzDoFwpE2trwxcIoLpcLC4uYlkmsqJgmCZut9txIm4xIJ3uQBG/T2A1eZVE7yW2bu9gLl/H740SDe2mt+NWnn7ym5Sy82wb7iMR3sVaKodt15idvcQffP7XOfbCd9ENnVq9gSxLKLJIo1ZFlh1zz3VF4x8M+HmznRlvWQxMWp4A1+PhTY/wZovz9QKsZYOFI4Jqb5z/llSD64uGbbqdtnWj8gNj8UdBDKYEQei0bTvZ2u6nW/PLQO8N5/W05t40bNv+EvAlgEQ8Znt8BkMDvVjUKTYm2HfoEez6NC987wK2bJHtn+HQ3T9BzWhQyExhm/uJR29lcW6SgNdNcmUVv9dPrVGj3qjhdgcx9CbRtj6Wxq8yvXKFsp2kbPvYtncLhmwTix3GF7FoZGW+9epz9Lf5qDYPYllZsouXGBpyU147i1YDJRQH3YdX9jK86SZUIQqBJfzuJM3ki/RHBynUzzI5cwaXfYzhHb/KUrbGSjrN5asVnnz6WdqjbZw9u8Dk1RE+8JP/iW/8w5/xi//516hlpwlGLM6ducLK7DU+/elPkVlb5cRrZ7l06RLve9/7+MuvPMX4Uo5KPstq9nU++Qs/zQlFJJPKcNPBfRw/cYparcbk/Aq9A8M0TJGQN8zUfIrVlTQPPHALtUaZickpLNumq6efZ555Bn8kjm2bbB8eoL+/n4VsleMn32Di2jRlzaGjVmo1wuFoC7jjIZlM88x3v0e1WkVRFDRNw+8NoNca9Hd3sZbPsJbP0tAMjHoNj+y090RMBvsGwFKYnL7K1bEcvkA3ITnIjpsHiV/xEY11Ylg2K8kUuVQGfyiI0WzQ09NDvpAnEoxw+coosuJibm7OsU5TVWcHIAioLheSoBKPdWJqLizrvViSn7nZRWKdM3ztm1/jkY9+kJ/8mZ8ht7ZKvVakO+EnGAih2DLJhUX27r6JPXv2Yts6wUCIul3H53U6UNWq9qa7vCAILSNUYQMF+P0Kg47i0PX3/bDFY71mcOP7bbulHtwKfLP1JSMiWdcFTewb3qPYNqHoGo3KW7P56+NHWQSeBD4O/FHr8Ykb5j8pCMJXgSNA8Ya04fsOQRTYtGs3//DKP6FIHgY23Ulv+3aee+YYhh7A7S4yuL2fZLpCNBJBaAaw6zaDm3cwOTPC/PIqnmaKRMxDYvAIiuBF0wyWp8eZSl7k9t5tmFaOpJ1A0TwElTgT115mcXaawT17yCzXuO+h/Zx6+gr12WMUoxGMSpbTx0/wL195ij//q1d4+WyVzn0GgXoQl7SJhlLHpxu0efOoAwleOXaO/v5b8VbPYucFfGqQt91zB/JtvZw49RXmplcor4YJudw8d+IM+aLC8vQVlhbL/NzPvZ8//vPP8+lf/Tz33fcJLo9c4vKVSTwuF6VKla9+7au89/4HmJhNMSXYyKrA499+mvvvvo3Hn3yOVL7GHbfdycLSAptrTaYWVkl09+Byw/j8Il6XI2qZWs3z3AsnCAd8fOB9D6ObJsl0ji1DhzCaNeIhD03bxf/5y/+ZMxcvcOKN8+SzeeeOWKsjiiL1Ro3lnM7bbr+DK7NzhAIJsE2apsPIuzI3T093nJivjR179nNlcpamaZNaqzA5sUbMcxVsF9t3bsMdsEmlSzzxrSd57oyXfKFGR3SZgg61WgOfx4sgiKym0jQbDRJtMXLZAsFAiFQmg6HpxNs7WFhcIhyJo1eChNvrKEqQ7FqR4SEP54+f5szJFzm87xYSYTemMcWrx19gaibDz/78LzD7xhyhjjUGdx/iwulX6OlQefhdHyCT11oBaZKIR8iUSgR9jm+hJHjRTRMBAdNwIM6IOtgK2GpLhq6KYCutLbzD9pEQQDSwLQlrIz1o6QRwnVq83o5sZfQgrLcTbSxBdrACGA5aUGpgNdpQlBqCYmLpLnQaiIIbw7BRBahXXAx2/eBQ/3ctAoIg/CtwNxAXBGEJ+L1W8H9dEISfAeaB/6N1+neBB4EpHKfiT/zQH2DbnD73Gm7ZQyjWSaVQ5tuP/ymiPY1t2dxx28+jlcKoYoHZiRnOvvQKsZ4h2pY1ZGWAQLRCcnqSrvAtWEqVhlRBVbaSTK2wsjZK4MAR5q8F2DHYRkY0CArfIbtUYi5bIbaWR2se4sln13B5AyR2d/Pa4n+jON/DrYd9/OSH30MsFOPIgUmePuZFd89g2DIdxmXi9u8TTvTStfkB0hmbgrZCovcoW/Y+gN9bY3gwzujpV7n34G3s7Otn0/bdPPXN53n7w5/k2We+SnatSK0xysTkfo48dAc/9VMfIrmcZrCnnTNvvMKhQ7dx85HbGb82SSmb5bWXn6dv21a29G2ht6OdnTsPkM6V+OsvfpG3ve0utm7dAoqL1NpzDvXZklFcbo7cdoh77nuQRMCNxx/g1Zdf4rlnn6Z/cBjdhsujY/R0tGEAXp+fv/nrv6ZYr6K43JTKOUSXC1mW0XWNjo52FpeWyBbyeDweNE1DkWU0Q2Drtp28+MZVPvPo73Ds+bPce/9dXLo26YB9JFgrFTh5poDHo9DQc1wbv0w6WyMWa2P/rr1cujZFW1uQSiqHz+clHA5RLpfx+Hxohk6ukKdZayJIEn6/n/RqCmH9rmvazCyM4vFEyVWK/NIv/SKBgI+hgUFC7S6SyznKeo633fsT3HXLg0zMr1CpNfD5VXxCnYWrV9nc10e9WWT02jXaE8OIkoRtN5mdyRLoEDHqvSwnL+GSYhhmk3hbN4qitO76Mog6klLGMgHLC0LLfqwVwNgWht5ExA2SuBH01yVLr2fVjljqRrLAdW0BxzXKtlRssYmkB1mrfoGV1UsgbmfP9vfRrKjoZobXz3+TtoTG++/8LF9+6sEfbRGwbfvDP+BbR7/PuTbwy/+e664PAZG+rWF+56f+juRamG9++fOo7hUauX78kRS5xgRys51qPYml1Qj6o4Tbh5iYyeF3exBddSZnLmI33QSiInWlDduWWVm9RLxnBbffiyK5KWfGsUIDhEIBdu0vUZ58N2trCyjyCM3SbTQUGbPZTn/3r5ByaVSmdLpjp/iDv7qXd979CZq1EDV7B6q+QLOZQnAfYrVg4VudYcfQTnbefAdnLp0nVW4g1SHq7WD/wT2E7AKjQoWvfOlLDO0c5Oj9D+BTDM7Hj1MqGjzzzHNs2tLOyZdO8sEPfIg//uxv8ulP/Sp/9cV/olBtcustd7Fz+yCj49fo6W3nwumTXHN7+PZj3+GX/8tP83M/8wiyKpPPF+lu7+CuO27hzMlTLKxmae9IkMqs8enf+AxSs8zw5j4CwSA37dnG+LVJLlwY4f5f+SWuXDxH78AwL790jFtvvZXvvXCMI4cOUqiUyBXLeFwq0WiUQjFHb28vL71ymv7+LnRdd7bkksjVaxOUqg0MOciuA/v4zGd/j2Cgh2qjRv9AL/NzSdaSFR5+6HZ8fpX52SVMU0AybU6++iq66kWwNTTDorun26l6iyC7PHjcLav4CKwkk1SrVSLRMLNzMwTDUVxSiL/9u0d59Ld/DVF1sbi0wP3338/IhUtIiwbBYJzHv/k8d9x6L3/8B7/Dhz/0QQR7nq1bZHq6NQoVC6/bw7krF6g3TNriw8iyiKE38ARBMnrxRauE4y70mkIs4HVYhbbspCGqSD7XxOPxIch1JHzIShMbg2bDRFJsSqWiU8OQJXSjFeDrGISNsto64AiwrzMNafkiymIAw7QQBB1sL4Z1hkL+KrJt09WvML/6z8xNLwM2hw7uoFof58Lixzl63xHg298//n6YO8n/jtHZHrW/9PW/xROIk4js4MSx36RuNUinY3THtxJp30y+fJ5K+SLJaYHsWpODDz5CtrgZ27JQlTxq6VWKy5MM7D2A7T9MvmCTmf4isf4VHjrwUcyCi8VCDcPjY1P3MhOTMFncR1Wvk7s2StuO2xHMOA2PTqiZxGiM406P08hcJXQwiM8nUSjdj8u3nazRh+hawyi9hFX7V/q3/Qb9/gyZK6MU7Rx27AANqYeeQIBuqYaoZTh74jKWd5n3f/BjvPTct7GKFQZ6hlhdrDO4fRvnL5/lC3/xLXbv6OWPfvf/5syZs8QjITQbcrkCl0av4AtGeOaJZ5DdAkfvvRu9JqK6BGSXj76tw3zh83/Gps3dPPjwu4mFgjzz7Cucu3AKCxVFlRnsTLB3x27uPnofK3Pj1EoZ0rkKO7Zv48VjzzO5mGLb9m2U6wavvn6K/v4hbjp4hGee+R6S10M2u0Yw5KdQrhCLhKmVyrhcro3CFUAwFOTF40/xzb/8a0qGzpPfO4VhW3R0d5NJ5dna18d733Mbk+OXEXQ/R+97gK/867/w1Pe+ixiI0aiWET1eAj4vqiyjaRqhSBuiLCILAstzs8iqiiAKVKtFmpqBbtn8jz/9fzj89s0ouocH7jrK7bffia6bSIqLzQP9JBJdNJpFPvCBn+DcmScwjQbR8ACvnD3JVx97nPboEB/92CfwhHw0DZk9e++gXCrikU0E2U+zKaKqFYoNE6/bQ3J+kvbebRudC73RxDAa+PxeTFPH7TOp16s0ax68nhiiVKNQWANBoC3WR7Xe3AjwDbmPVrsQwFEZvi5djqA50OnyKh6PC1UOYQoZZpKfo17TWFowsOQmHeFBMmuLBCMWKCk0exa/GmHs/CL//beb/2GGpD/yEASZS1Onyay6mVt4ke8+/1V6+/Zw4OZ7cLvjnDt/BdHYgiQEWVlKIyoCikvGthuIiolhhMAawKSdquHDFj1MT18jtZBCqm1DFFzMTo+S0eqUawYzq00qxgoetY4U8uOO+dCa+7CUCm2Gm05L5a6DDQIdPXTcfIhO/51MLL+BT34bQuMqgcY8CTVPRN2J5P0LSoUK1YURPGEfmTWdWj2LS86gGCtcHjnB6fHnqQjT3P32hxgYbiOdvEatPsW3vvk9Tr72LLXyGsNDW4lH27l4dRKPP0yhWGfvvr08/q2vEQgq/OzP/Se6evoYGtzM4VtvZml5jsmJCZ568kWSy6ts3jyMbQm0tSUoV8osJZcIBn3s37MNVZLwulW6OjvRdYuRK1eZX1phLZ2iWSngdbv46Ec/yj1H387qagpN09m7bx+6YfPG66cZ6t+MYRioqiP0YZpmy97c2aI2m00MQ6PeKLOWXuPiG8dpC0SZm5zh9jtud9yMGk3cqotYOMjuPTs5cHA//T2bGN6+k+6BAfzBAMVyCUGUsS3nrlmrVbBtk4mpScbHx6nV6yQS7fj9fvKFArIss2/fPt73vvex68Agv/RrH+ZdHzjKzNQSq8sLiLbBhQvnaVYq5NJJqvkZvvBHv8P4xBJtXTdRs20EdScf+djnCIY7MW2d4f5eOuJt6KaO1+vC7XYhqhouD0SCPQR9vZRKJXbv2Y3U6hTYto3HFcHrF3nx1S+D2GR85jhr+TEajQYCApZtEG+L0tGRoNlsXIcEt1qG692Dt/IKNiTIRAvT0lhIvcalse9SLObJ5/LYjc009TBDQ/eTyReoN1I0rWUWFlbRywP43FvoDn2aLYebPzD+pEcfffQ/Psp/yPiTL/zJo5/+zFcYmZymrkWoLM0wO5tBdN+FZBZYS11CincwMzaHRzW47R1xaoaBN1hDlCs0dZt4TAQzx3IhgUUXcnGEXCZFJHyUfVu76Grvpdoos1CK4nMbyGKYRtlFzg6hLV7F59eQ3BHMeoMPPfKzXHnqMoVkgcXpi4TbDI7u+jDn62H6hSJe18uU8h2YgotYOEl/pAfbHcCupdi1ZZC01iBg9pAZW6CzYzN33voIY2MpzJqHV567QH93nGtLl/BunufTv/2vtEW6OHX2ZRQX5PM6w1v9rHGR7z73ZT73u1/i0NH7+Mj7P8STT5/iL/7bZ9BsF7/1h1/gyce+wid/5Rfx+FR+7/c/j0f00NefwNSbnD51ltOXr1Gp6fzUz/40DzzwDjo7eti9eyvHXjhGR2eMj//MJzh/aZQvf/mriFKT+ckFDEkimUqyVqxzZN8+FFXh7OhlJFkmEAggiTK6bqBpBm6vD0QBWZGdbbMvwm/9xq8y2NfB2twsX3vxDZZnp2jv6iKfLbC2nKdQWOTaxTOsrhQIR/wsLU7wwiuvsrC0CkgYgogsS5TzZQzdkTvzetxIgkAwEKBUbNBsFjBwUVfmEcs2v/ip/0Io1Me3nvwKnX1N/vQPHufJ7zxGvarT19eOPxRj8/Zt3HbvO9m88wiZ1BqhYIzBnYfA5WL/rl727zqAJWhE4z0IgkhNM/H5IizPTyNIIs1SHVMOoNkWXk+ExfnXcbnbqdQyeN0h5hdfR7dlNm+Lc2H8bxkZO8Nicpbtmw9z6doosm8Cn7KNpuYw/9axAuvAIwfxKLRUjNZTAIdqjVRnOf0yZy9/g3IliepfIRLp4Nr0s+TrU6TTJaKJZTYlDlPVJhw/Dl0lVypRM1bIlcYYaLufr/79xeSjjz76pbfG34/FIvDHn//co0MHtiBJnSwnZZbGXiS5ppDY+iCSmaOrzUTwRFgYv4wgGsS7toPVS7NuYouLiGYUr+xBUQSqVgJR9FFcPUe8s593vucwM5fG8cc62bIpwGCij7JkYMojuORl1or76O0fJiqsohEAK4a9lqZhvkrTlyFXiNOMv5Plyl72hh/G63s3xdXP0NOf5spoBZ/hZXxsjFjnPvxRmdETZ3jpeBJPNEbFgL233s/icpMXnn+Mcn4evyqzffBW9hwYYHrxKi+f/CqvPjuKrpV448U3+N3Pf4wTV/4ZefM06UKdp7/5Ks2cHzmgkGs2aOQWUT0RfuGRT/LOd95DIV/A7fZx4dw5enq6eNsDd6IqAdoTCa5cHactGuHUyVO8cvwlRi5eYnZqAp/HQyQSx6+4CHsD3HTzHVQrOQ7s2s2ZsQlqho2i+BBEi9Hxa/giIWzTuSs5Oa2Mbdsb7UGP24Wm1dnUN8Bf/MWf4THzPP7Y9/CEY2i1KoVCEcuwqDSa1PUyhlajd2CIzZt60bQmc7NzLMwtUCpV8Pv9eHw+wuEgoVAIWVYYGBwgGAyiaRorK9cIhTr4yCNH+dh7PsLIzBMMbtvJz3/qvbzw7Wkee+y/U8rq/MF//S1ef/UVitU6JjC4eTNtiS7Gr03TbDa5496jYGqsJleYnDiPy2Vx4uTzBH0dFEtVECVU1Yvi9jA5+jyJti24Im6azQy2IHBgxwCra8uUCwbeQJ22xG70yOOcv/gsDx19BEWO09mzlZGLM9xx18NQ3c3K6usE/TEQVK5DgpwMYB0qLGw0B5yComFUaRgTLCZfw7ZrBMJuDKuEXrUoFRdoVAwO7BvElpZYXqyRSqtUqpP/L3PvGSXZWR7qPnvvyjl0VXWO0z09PT05SDOaGeWAhCJYSGAbMNhgY4x9nI7taxu4B2ODDcbWOWSuMEEIBIqjMJImSZNDz3RP55yrunKune+PnqPjdY9Z3OXzR7tWraraVbvq1/uur97vfZ+Htsa9LMzFKdQmaWhL4fIu8rNvF9+9SeArX/3sZ8f1X9BSvwVJ6CC/eoZw2368DU1YtRpLkwOEY30EHSJOj4Ddb8MQ13u7rc4JVCOPHS+6LFM1JATJRMlcJVNcwtAsyJU4Q4sQ9YrUBz0szU2SyYYoVhygh/AGw/z8+18lVVrC1e6lVouRmj5FT1c/DfV3IdJIRsjTXbdMrtgE8uvk3QbeyIeRzDgXRgfw1u/C4cnTHmmmtTFCsLWdHJ2EbSEcgoKRT/PYh95PKjlJW1OMSH2U5144wc0330djaBsvHT7Kjr6dnLk8QrqwiFWL0uTvYHv37Tz7wmG8Xi9dGzZx86FbeOONI3S01bFr1w7qIlE2b9mOWi1Tkwv0b9uKRQoxPTXC9NwskUgdm7duRZZlDMOko7WJfXv3ki8WUGsyo0NXae/oJl/Mcf78OaqGSL5UXh8hzmWYX1rCEwhQKhTfmdmvyeuKcNM08XjclEtFBAScThcPv+dO5OQ8y2mdilxiZGSMbDGP2+XB0CRuue0m/sfXvsT+22+nt7eHhvpWDt18C5NT89isAnaHnXK1hiCYKIqMIFgwDYOaXCMWi7G5t51kusTC8gA//9EzdG4N8d++8jw7btDpiu3k9PHj3HXLzXzp775APl0iEA5TFwkwODjIzNwCW7ZsJdoQ478/8XXmhuMkkhrd/W3kmaehbgf1dX46urpwuf04HA7kWonU3AyxlnYKwluMjZ5ibn6Z+Pwi2TzkqsMMXplgTX0OzfkaYr6fatFLW0cbTS1efv6T77Jv7/tYXj3J2toYAV8jkmBDFNfHsd/p+pOkd4aIxOucIEEQsFoNro2/ysz8KQr5Iom1BeSyncGRVxHQ2dl/Hw7fGjNzS8zOFrn//sdRlQSpNROnvQmXK8jlkwY37/5tnvzGG/9hEnhX4MWsdg/33/E3OAWFWjFDRc6xbfshqlUr2Uye6akruGJbuXjsBQL1sL/xExSKHdiDE7gsDci1fmQ1jJJXkFwmVquJXKwBKezyNeq8CgU9yJlFD0vKEns2bmcopXFmVGZri4fJ7Bl+54/+inh6hqVCPQl7nk2+eXx6O2+PPU29K4ej7XlGEl/CWP5X+mIBhrJfRLW00Be1cs97vOQ9P2N2TSDWewi308lzr71JZ89WPEErxVyNWHuMl575OdNjlxg5N8ZNd3VRjVd5+sknSa86sbvbmVmd49p0jn03m9Qu9XL3xx/km9/7Nx545E5+8O0fs3PbDp58Nsk//f0XOH3yGMlUgmK2xpmzl6jk0+y9cQsvPv8yVwfW8HgruJ0ONEVhdm6e5eVVens20rt5M1cHBlhcXabutoPoWpkjr7zE4toqt95yC5VrI+xoaiTg8qAa0NTSwlIySdVaQVGUd3h4sizj8XjeSQoepxNNVRFMnanJaa6OTaOra+zYu5PXj59kbnEet+hHkWVESSCbWOT/+cnPODc0y/4b9oAkYrUI2Cw2ilWFXC6DIEiYRpWaXMU0DUKhEBPjy7g9PkaGVpgdmcIWsqL8aSvNof089ZN/4Nmnx7jr/u18/ZvPUBdq4Kb+Nm66+UYqZZmDH/oQV65eoKtzE7fddDsvPPs16toirC22sffQH6CUa3jtBfxhCxXFy8TkLMtz42zt24TV58Mj3k+x/l8I1NczezZNd187nRv7yWTKCJ4FzlzSiU8LDMz+OYfEe9EqVt773m5m45+jq+kT6OY40XoP1egUemAAACAASURBVDKgm2SyWeoiEVRYnwUwjPUeg+ulAtEAQ5dpbqrDGehgZamGnHNhqAH279lOOjfO8bNP0dG2DYtrP3fd08jTP/0OH7j3k7w48BP8IR8LyzM88uhN7N76v23kvXO8K5JAsVKmXHYgGjfR3bRIcqOPVGoMuz+Gv1miT96Ew2PB54jhsikoZhGrHTTZYGVFQjQsRNqrLKdnyS1XqG++E1kX2N13F6mMjC4XsYbC5CtOHNUZhuIFuhsDROxRxkZHuf+GTmoLhykWLJhiL/YUOCNBCoUMUnkWKeLGXXuZmjFL17ZGKmlotC2RteR56oV/pKv5Htq3b2Kt4mHwsk7MU6OjJYzNkaEhVk+AMo2bBE6ujvLxTz7K6dOX+NJXfkJvVzu7+t/DRGCavq17eOKfvsXHf+8Rtu7LsDv82/zoR9/ngx98iKHJZeKpNNOzk/TdeJAf//iHzE9PIQgS8cQaSyspJKuN3/jYJ2lt3c7q6je56567uXxliJomML+wiN0mct8Dd9MeC9FT72Nkeon51QRbdvSzMD+Pxd3EcrZArVJmMZ6makhY5RKSzUlLYx1Li6s4vD50rYbH4wEgl8ni9XpwOOwgaBRqVdx1UZKJNBs3buYXP7vITCpPraYiSSKymWVydpo33nibtdUF6qNh3ndfL2fOnWdxdoFLV8Zo7e4iFIrh9QYwTB1VVfG4PNhsNoaHhmkJdTAcP0tHv41Pfe591EoSNx5s5eTLMt/9xy8xN/4Wq8Uq//C3f0VTzwbeGBnl0x++h56GXYxfGeTk2csUCyUefv/D/F7n3/L8y9/n3JULzCT/gltvfJDLYzOcvzTALbc9yr4D+wgGPTRHQ0ytDGF39NLa9FGSlTPccOt+qhWNt8+9wWT8DR669YtExHvpu20rOOo5O3iESGOEfds+grKc5dLc00xlL9JsSjjMRykUS9SFgsjGetOPIEgIgrH+iIhu6Ndbgy2YSGg1kezyEpHIPkanV2jq3oSuZxGkNDarnUoty9RImWLJ5Jvf+SF7D2a5Niaw/aZF5lNhJPOXz/C9K/4OPPH1r342skGgI+Tl0tV/wV9XJp5TCTq3Ynek2NzRyrXL11hKZ2hr6iIYdWBxZ1DMKoZlIzItNHgDiJVJFGOaukYXftFFc0RgeclOS08N0W+SKXUiWAVWU24W5ubpatJJXz7PSrLEX//zG/zuY0GW1+qQva1o1TP81mOPcOD+9/PqkTfJKT4izQMsjY7SGrIxMpdgdSpBesFD58YEAfdBasJWDNFBKZ4mElZwusqcP/48z//iBwxePYvL5ufC2SscO3qcb3zrX2hq8vDTHx9hcHCRfG6J/Tf28K/ffpXJ2Rk6O7Zw2y138Nqrhzlx/BiqYCcQa+PSW69waeAqGBoBf4QTJwfp7WxBV2s89dRTnDxxjGQyx9jkInWRAIm1FLJcw+kNcOrMALqi0tvVyrXhYZLJJGgqPV091EebyRRKDI9PYLXaiEVCNMZiNDfUc/ToUTRBxO5w4nTaUBQFXddxOhzkcjmcTideV4Af/fD7BGw6yytpXj12inx6mdV8ZZ25J66TcQxNZ3JslFIxx2333I0v2kQ8HkdE57985hOUaxWmlxYRBNA0DbfbjVxbr6aHQiGujZ7n1x69jZ7Wel46fI5grEy+4CFdiFCan+Sts2d4/fRZYvU+dt96D08++V/56KO/ztFXXyXst1OrFPG6HFhsNno37mTf1hvxuLvRKjmMaoHGSCsHD9zO1ennOXvtRwT9LYzPPYPD28PE3BGqFQvHznwB1EnKmovFxFsEgyXGVp7h4rVv07LrPK8ffY1wg5WyMsXZgScoZUWGpseJ1KdZGzlAU2MrHp+bSlnBZnWCIK4PEl8fQFoHlly3DSEiGD50o8Dc3AynTyzQ1dFPOFCHxb5GQyyARXSzvDiLoi4gWcts3hbk/FvHae/OE1I+gz+4xFM/+DFnTiy+e2sC//rE1z772ON/RE+kkfHlF+jfvJF8xYLobEIQo2TyYwyde4Om7k7sooA3qCCJHqpVP/liFNGpEPBlWVkYYX5Nxufroq9NoFpQKEkamVongaYOSood07RiOlXKupWVpRQN7jl0Wyeb7/1t8rNv4fFpJE0nDY52XKqfH/zbkywUZwjXRZEEg4jDzratu9i27X629O5FUxLs6u1jMi+ybWMdnS06BT1EvniNdDHHwtgK+WQKi2BHV1R+5xO/TqkYJ5fMk87McOn8KIduOcjttx7klcNHKSt25hbLHD91nK//69P89698kb//8pPsPbAHt7+O33z/e3j9+DkOHbiRkeExHnnv7egG+H1ePvQbH+Ls2QvccMN2br3tZkrVGlableXlZSSrk3BdkInxYVYXF2hubCS5lqKrawMhn59ofSPnLl+mVFVQVA2Hw8GFs+ewSQKyUqWlvZtCsYhwnXkniiIupwtJElFUmaDbx8d/60Nk4vOMTqxQqtXQ5CwLycK6JgwBURCx2iy4HA52betn364dvHXiNHMLi2zu28jk5BSD10bRrm+fSaJIpVJBVRQEwcRqtXD7HfswNSuf+u3f4+fP/ZSbbuvhIx/8HNmMmz/5+Ed55dh3WVwQOHRXD2fP52mOlUkvmYRCDYxPjqDLNdLJFXo2NWGqMp/5/ffxwps/RCmWaIgGKJdNwhEvnb02VDNBOpXnwsUBvJ49GNUwfZt2otnP0d1wN0upZS5dPQrVFg7s+12qyiyJVJLHHvwS45e92G0qLbFDvHX+SR58z/coJy3s7H+IfGUcp70ZU7RRqRQRJK4nATANHVEQMczr0nJBQMSO0x4iFIrS07OJ5qYgqpInFu5GsmgMDZ0llYjTs6EfgRIt3TluuV1icrBGKQ9rCzY+/Wc7+OoXTr17k8DXv/n3n+28/WesXLNQMErUuYNYEInFgmiyFQtOnEIJVdEpljWs1hJ1MQHdEGlrzOBzSug1G07Jii/UiNdRh1acJFko421cA4eA3Z0ik3djd18mGw9js1kRfVW8LisRv4WavsCmbREM43lCwTocpsrc/GmcPjd3PPxBxFKG2bkCIXs9py9LeN0Sg7Nv8vZr1yhYe1ibPsfQ3HGuXbhM37bNbOwMUpNVHIRpa+1DrSgsLo7z7Is/pq+nnUKmyNJ8njvu3I9cyXL7bbcwt1SgWCmSLxX48Mf/gM2NdlZXkzR3halWNRLL8/zi5Vfxup309W7iU5/6fa4OXmVgcITP/OGnyWaTdHW1EYnFOHfpKp1dPSRSafbt20chV0BWZELhMDu2bmZlYYaAL0BHRw/NLc384sXD3HL3Hbx95hyaaKVmmtx24ABuu0g2n+cjH/8kI2OjaEoN3VzvDShdR3tZLBK1YoGP/eYjiJrK+YFx8rk1VKXK5NLa/+qKw0RRVFRVY0tvN031MZSajs/j5fhbpzg7MMxaJk9VVtA1DVVRqFaryHINWZbXacSmycCFMaKxGIefO8XCyjKzQ9Mc3NXKox/8OH/8mU8yN3+NbCHJ6OgJiqsmj37g9zFcQbJVO8nVZdw2AaOUZmr4Ant2HeIP/uizSOY6Q3ExvsDLbxxmcbCAXo7i9nahVnM4/Sqe0AhlZYqh4SR2X5nUqkBney91jTqnBv+Ken8XW1r/L2Q1z5mz3yA10crq8hSldIyB6R/T0Ogg4nsP88mLRKPtGFKBmjqDJHqQRBugv9NAZJrXxaWIiBYrFjGA11dPe0cfo2NjzE0nmJ9dJJefQxSzOO1tvH16mq4NdQyNnCZsfYSrl/I0t9/HRz7wBV44/We8/OPUuzcJfO7zf/1ZW6CBWJeftuhd6HoVu9OgagwRsI0QdRcI2JqIL69DKSPRNXQ9jCQ2kS8OYGgTFOJpioVreMID2JQi8dQA8ewoblsTpn0cm82LXN1OrbCAz9pAX7eHRK4HyTpJfYdCi2lhMrdERS6iZhZwR6sE6tzMrNWwUWR2dZRunxvRArrdy7lz57GaOZy2ANZyjbYdW0iXorRHt2AtrZJMLZFZmmDfrj08/oFfI5UYZ2vXRvbf0MDu7Qd56idHuePOG+lva6erZytP/expFqZWmEksI1qsDF4bYUODg4nxZSyOAPt27mN8cpD/8sd/jM1m4dLlq7z+5jFkXeDRxz7A73/6z9cLfotzxJNr9G3ZzonjR1laWmRmdoFSpUKpVKRcKtLUsYG927fS1drAqdMn0a024uk0A5eG6N7URzqbxW61c3XgEg8/cC9nz19ldnmJeGINm91JTZYxDAOHw43X5yWZSNIYbea2/bsJBgIMjJ1nYfYMptPN9FwaTPW6K299a1GwibR2NJCcn0MxqrQ1xzhz7gItkSB2dxDJakUwDerq6pAkiZqqoWkaoVCQyfEhRNHN4x99gAcevoNTb79CjSh1nQEe2n8vJ+ff5Lbd9xFoeRgjucINd97OleEUx84dZnr+Eu974FE2732A8aVLFGSJjb3dnH/rBKrZwJYbO7k2cAmj6uV9Ozeya89tzKbeoqZfZc8NjyNKqxw7NUVX8wfpiT7Oc4f/hgf3f4HGbRLXJl9g/K1tbN1Tz8TsVais0dd/L5ZIipxxHjkfZVv7fWRKV+jf8D6QrrC4sMrEysvEovWIugu7I0c6W8Jh92GaFsR1zhg6AjoCEg50VaSlsZdNm3azecshejbczsmXB9m8YRvYBE68WuXBez5FwYhz/70/prllJ6nqUTyRN/jRE+l3bxL42tf+4bM9GxuIRrNUhSRh8WEijQmkkgOhBqWMzFIiQ6mikkgsIQk5Wpr7kOjGorXitQvUqquIpgdZduNylXH5NRzWTbjsMUyhHk03UZHRtUWsUgMu2wrlUhaBAoXSMNFQGDnjA5LYI0skZ0OMDK1QKC6zcfdmqsk8jc5W9KqVZDlNOBjjjpvvoW1DJ5nCPB6Pgijl2dBTj9Mpo1Y8oEB9U4zujVuQKzUWZueYWjzBMz99iWLZwuTEEC8//yreQIhbDt3IL54/iizo2B0uEqk03/z2V7h0+RJvHj/NtUtj5CtxpufGCPjDnD9/hebmZgaGZqiPBiiXc9xx2wE0XeHBhx5gaTmOJIn4fX4kq41iuYzP78Pv87F37146W+o5duRVWts7SSTTxBoaqKuL8cJLb3DfffeyFo8TDgVYXVrk197/GG++dXJdWoqAw7k+w1+t1jAMnYDPhyCq/O3n/5KpiWnGRlYopBWOHR9EFUwU1bjOxl8fuQ2Ggtx0wx42d7ajGSaCYMMb8LJn5zam51dJpBLIiowkWbBY1oGmAb8fl8NBJpOgqbGDlvYovq6NTL/+BtHd/dTSCpXMMlnZ5NrpFbIsY1MksqkFXJKDu26/kZv29DG3PIkuShSzKeYWZgkHHCzOXSBW58Pn6cYXqMeTmaKBVSaKcywqVtLxHFa7hXIFlGqObdu6mU6+wd6N7yWzssK5wRlyCfjrP32Q8ZkRbFIjP/u317DErnJ17Cyt4Tu55cDNnDl/lOHBRWbnr1HfbjA5Ncr45BFcziiVSpHLVy/R0NCBINiRLOvYNpN1EpHwP4lDmAgIWCw2YJ2KfNedt7Lrhv28+uxzbN+1nZ88/UM++J4PgxDCahEJeTp45fkVTrx29j9MAu+KtuFqVUEUhxgbXKHB6aWQPsPa6CqZlSylUg1ZMIhs8LJ7XwvRYAS72YVWkbA5FvDVH8fpPU0sJNPa4SIcBU2VwQLucB5vAGzOFIXSFSRzjIg3iGKMkEqn8TtKqJZd2JzNTCfzxOw25GKJkVELEqMc2LuHoRNzTCwt4Io4OXb+PIlMilDQx+rKCJlkmeaOPkxXlWOv/4i1yXFc5hKF1ArZ1ApWwUfI10C1VOXs2VEa27qIr8WINW/k/R94lP/x9W8SiHRy5I0TSIZCviYTCIao1mr4/X4eef9v8RsfephPfPg+RGuZPXu3Uq1UOHXqNN3dHczOznPPnfs4d+4cXV2duLwObr/jTman1lheWGB8eJjlpUVS6SQWSaRULJJIJHjiiSd46fArbN7cz8jIGE1NjXjdLl586WUef/RhLpx6mw0tLRTKVe5574N89ctfZc/OXXR2duJwOd9plbVYLDjsdhDgxZefQldqIDqQDI1A0M3nv/CHWG0STpcTBMs7MwblYo1kssDm7Tvo7e3nyLHTnHz7HGuJOKpWRTdUdF2nVCpRKKzXFEqlEvPz89x68614fW6+/KV/5u+++jke+NhvcP7EGf7kE/83kQ47f/lHXyTckEJSZOoiDjKZJbq7fdSKq4yeeZlQ1M7xt19FkRNEIlHOD75JVZuhPlDGqi9x6uyrZBQX1h2fpuHGj/H4Ax/iE4//PRbDgz/YitVbxze++yc0+ILUypPUjDdZXHybZO0wL77+ChYtxKnTP+TGe0Ispifp6NyONyDw0ouvkVWXWSsP0LUlx8WJ71IoLHH3rYcQhATx/CkMSebq0MD6yKVwHQt2/fY/A37dYyiiqtq68ViwYgouLo1dZvemjURcGv/yr8/wvSd+hmboSJoNVUvzwQ994JfG37tiJfDlr/79Z7s3B2ht7iHqkJibdoFixVpfwBm0kq+UsFhEJkeHQA7hD0QwhAqyXgTDiaEAYgHNtoTH7sSBiGLWcDu8yOUQmSwI1iRedysWxYngmKFcdCNYbKimk4jTSnwlQSJzjnBdHYVCmaDDg1q1YhOCZNJurN4s4XAzw9NX8AZD7N3Xz67texi8vEo8McW+gwdprDvI4Lk3KBdrpEtxxkan6e7sx+V2MjM1g89tpVLI8ZHf/DCjgyOUCzkcbhurKwkm5qYZm04gWAVUVccQRJyGjTsPbuXs8dNMzSfYvHUDG3t3cPnSVQ7ctJ/unh5kWebAgQMoisyR146QTKbI5AoMDU/Q2tK87u8TBKpyDd0w8Ho8+Hw+tm/ehEOCck0m1lDPytISNUPi9TePcsPePVTlCslCmfpYlC2d9Zw8d5nJ6WkCodD/Un/JMgjgdrn48z/9fXLxNRYX17g8cB7VsoBhL/ChRz+Gz2dhZWWFSkVGkiS8Xi9rK8sklxeZGptCMa186nd/hw2tzVwenqJUqeByeQiFQusQUaBYKBAI+AkFfMzPr+AP+RDENQaXZ+jxbmTo0nlUe4LDhw/z4J0fZnD0OHarG1fMz+JSnvfedzeZ+CIDVycxVRcbmuxkKwLdm51saG/Fbq9DR8WUTAbXCtiC/bTVdTK9MkYqN4UhrTA4eYyl6iXagnuwmSPMly7zxuFR+vY0spxaoJKxYHeqpGtXqWg1glGJfCZOetXK629e5P4H7sEdXiGVyrKaSBKKqFTzAQSbTkdHO5lMiUCwDkm04/eF1/sxVBVV1zBNfT0JmOvYMLvdhqxUqSoKaDXWUmOY5TXkXAWXu5N7H9pLtixgt65LYSIRB//0d0+8e1cCTkeIXX3b2RDtYDUew9Xkx9tbwGJRUSoF1EKV1HIcn0vC47Fi2AqIniSKOIYo5SkUHMzOCKQTEnbBxO8pYBNsaJUSNnsRn7/Gtt5DxFMjFKpn6OuspyGUIp44wZ7uKRamh6gUl7FEtlPSTdoDbXicPvyOCo0dQZTFEYxEjMPPDlAXbke3Zbk2PsHFgQtMXruEoJkks2CzreJo1qjfI9DY0UFnXweFQpyf/+QnLK+M01AfRjI08mslbtqzg3Ixx8C1Me5+z910bd3Pzq0b0dV1SIWhQwWJz//jP7GQLbB9y42cPT1IS0sTXV1teLxeJiYm0XWTc+fOYne66OreTMeGDahmju27d+L0BZmYmaVWq2IYOoqskM1mkSSRpeWV9WV2wM/E+DjFUglVg57Nmzl55jTbduxAECR+9vNnqZTzVIpFNm3aRKVWw2q1YrFY8Lg96LqOKAiU0hlqhTLZdIHEsgOLO4pmOti5eQOf+J372bO74/peuEA6naRcLSNYrNy4dy8up5Xvf/c7/OVffJ6RsRkqFYV8Ps9qfJXV1RXsNitNTU3kczmsNgd2ux1VMbj7PY9xW+tWVvKLfOB9t+MNBfCGcnzrW09z18HHKBVrLE2scPPu+7hwZoB4ycvdB+8kZIeliTWWk+ep6VW2bHuYLfsfwhnrolIrszHsolw8RzZ3nIX5i5QNjdHhNC6nAzGp8L47b2Z4YQo5OErT1hB2j4X61j6GJs4RazdYiKeJtEDEt5UG/wYq8iI793ThcuSwWcIspuYpVyskklUUexpPncT5i2+RLV3l0sgznDj3JDOLQ1TlKgtLy4yOjTM/v0CpnEfRc1SqaUqlDBMzk1wZuIDFlNnSdAjRFsYh2Zk7+zx2zcE3v/KHGGIVXQ7iEbp+afy9K5KAZOqsFJaYXDUJtwbwuQbwWnwoOTeFUhmLaCUS7EMQbKi1BRSlQLS+DpvNxGLPIoppLIJKZ1M3bQ0b0C1BMoU1UrklcvklqtUcy6vT2MQC/mCQq4MX8fjcdLU1cWV4GUUqMJcZIT+fZCWpIUQdNLjdpGaTVMQku3f3Eo9fwVQVEtNZ7GYd+azC26dPkVYyhGIhZEscnQIuq87g5Bgza4NkzWvMrY2zY+cumjqaOXLmTdob/JhqjrmVRc5fHKCrvYXl1WlWZlf4yG8+Rqkko6sVbKIXzCrZtEY04OXO+/ZS39TGP33123R19zI+MU2soYkL569y51238+bbp+ns7eLkmQt43VG8Hjuj4yM0NzXT1tyGy+7G5XKt96sbKkgCgsVKfdhPcmWRcDDMjTccIJnKEG1s5qtPfAefy0U4VEc6V+L22w9gtVmQrtt/VVVF0yo47Q4skgVfpAWvx8mV4WFaWlLc/b7dNEe7eOrZb3Dm3Os0N7uI1ccQBCuiqCFgRdYMNItB/9ZtzMWzLNQ0XHVBFEOnVCtTkytYLCKGquFxObHaJGYWVinVqnhdAeZOjnBx8DT3HngIU6snlVhg+FqJDftqHH/7eR55fDPbenbQ3uZmx+56mptbqIl2evqbSFWSBHytuB0NZNMpvvLFL/PWq69TyJZ589KrVDILrOSHKChVVnIzOO0GqSUdtVrPzNw4fmc9znIEd3SNxpAFt/44MW8LE6PX2LG7mdmrRY6/fYSsepWAz8q9t+8j6rUzPTdOY4uPQlrDqFo5dWKY1468ydjsNaqqjlwp0xCREIQk0wtDDE9NkswpYLEgOtLkakfRjUWuXD4FaoGmOgcSOkvzA7S1bePQQx9j/523c+HNn9BQ345F0aiZBYZnE780/n5lx6AgCN8D3gusmabZf/3cl4H7AQWYBj5qmmZOEIR2YBQYv375WdM0P/mrfqNUWiMY2oXTE8LhLmGzq9hFL83hOqYWUwTcASLhRjAzRHxWcBVJJEaRLCaqImEIInafzlJyCkUuolk0FAFKVQWhmKa7r5uaskpjtIFoJEQ5n2PPtj1MzF5hcmYWTcoQ9vnBlidS58NVpzI4nCMaayESbaKUW2GjEaWhYQupWZkNwVZMWSJY18HAtauselOYgSKtO3bQmG/Gu3AGwRslnY2Tn57jq1/7HIVUjV37D6C01PHCy69xaXAcp03kTz/4Yd4+/Rbb9/spa4MEAgYVxYFAmtRahfrOGPfe9whf/953+cCjH6C+IUCxXGZuehrRgB27t6DIKts39jI8OIpNsrKwuMxaOoXV5mJ2bhmH04LV5iASja7z8zWFgNdFc3MTM3OL9PT0EA4HeeqFl1iNLxMJBbjv7ltpbG3hzPkLFNQqU+cGKMsKmiEiWcV3inyVSoWAz4dRKVKplgGTnt5ejh1/nvikTM1ixeaXOHDwRiqqzve/s4jL5cAQoFipUpM1du3cwfjECOnX30DNpXE7XLgcTjRNwyJZSKfTpFJrBEN+rA47WlanpiislnWKFScPPnI/Lz77FHYxhFBbIWL0sfuhzcRLbzM2ukZd+yx33/KHSMYkQ1eWKaur1Lc3sbF/B0pZ5LlXX8JV56EgxvEGFO688wZ62tuwuTUaBDcLSwqrc2kKahJFUfE4/bz3jlux+BW+9e1/5tTYMVTPaTy+OorqCrHiflKJVxHCJqlFDxu7YsyOJ7iUm6VcqbFsmvg8dagKNIYayNdmiAaaUQo1ggEBl3+RdO4ENms/dWFw2KyoepypuSs4rToOo4rLV8HmbMJh9RFPT7O4OITD7mJheZD2xnZ8kTba1SBf+G9/xO/84X9lfObiL42//z8rgSf538UjrwP9pmluBSaAv/h3702bprn9+v1XJgAAzYTBa2Pk8rMUsyp2m0A2t4BSNXCJfuqj9YxPXcRiVanVKiSWl3B7KwT9FmplO1ZraF1DjYCByNzyIgYOJClEJFZHLp9A10oszC+yOJsnubbGs8//gGRylUpeIODxYTeaqWkykqSSXJ1lZHGcWGM98aVrLMw/TWebha39vfzJnz5IY3snO3p7sGsKFT3NXXc/TG/PXqaTs5wbPo3iXGNq7G3mp5c4NzTD6lKFziaDiC/Jz198meb2KC5/BAMrxXKJ3/jobZw++zSiWENRDCySi6ZoA04LdPdu4efPPs/BfTcQ8DoYGRylWqpw6MAB7HYbpVKJcxcuINfKOKwWWpvqiUQjtDQ30dXZRn/fBgKBANVqlVQqiVwts3PzRorpBCsrS9QUFUO0ce78BUy9wpb+zXR3d5FYXuTlF1+kVCzQ0dPHDXv3YLNKaJryDgBTkiSsVivVqoyhypRLZWpKhULNJJEw8AZ1DOcMudwa2UwOQ1+jb9MGMAVESaK+qQ1FETh57ATpZAKn3UI+m1rXgZkmDqcDTVXx+/0Eg0G8Xh+KqpIvFFA0Fd2qoYsCL772JFPzo0yML7B922byZR13bBWrfhtb9zrJJ90MXrvAD5/6KS0bnLR2NBGLdfHgfQ9TrA1idVoQPQWwqbS3NdPQtJFENcuVwRFmp69y9PWnkdUqdYFtQIyZ2QSHX3iOtZkgQi7Czr67sWYTvH3mIkrVjyiHcLm82EwrpgLpRBylWqapx4ZdcpKKVymka9Q0jxJRzgAAIABJREFUFdFuYBEAHVLJJIl4mlS6iqplULVJbJZ5FPkMpcoJ0JNk4mmMao5Cbpa1pbPkFk5TXL7A1csncFkqiEKWTHmSeH6R7l4v9REPkmWK8Znv/eeTwH8kHjFN84hpmtr1l2dZJwr/pw/RCtlcinwijihUKOc9YFop1/I0NTTTv3UL7qBMqRQnGvbR39+DpimosoHH5yIQCOJ1tGGW6wgHm2lu6Mbv8eFw+DAFEQQVXbWgqjUURcVq8bGymKdUKlIo5zF1g0DYpFqIgGbBbfRhrXbx8tVTtErQJjkoylOEA2WUahzDUBifG+Ba6gibtjSzumgwfuUkc4vPoZozKKJOqKGLRx95jM984rc4eHAbHruD1Mw0tx7aSTQa5D13H2Rj72Y2bdzIwtwYxbUI02MJDMME0eDQoX2Ajf23HKJ9QyexcJDzlwZ5+IEHGRkcRjc0brvjDkavjSCIVsanpnA7HVRKeQzdIFcos7C4CJg4nXYsgoFTMvFZIeywEAmFqItE8bosTC/Hqa+PEQl6aWlu4sKFC3R2NONz2FDLFc6cuUh707ogNByKYF7fHRAEEbvFxl//zV8ABoWKgs/tZGptnESqTKFoxykJJFfSXLryFl1dffzBp38bi8WKYejMLSxgdXhoaWklVldH54YNeAIhNF1H0xQEDBwuB4ap43I7keUq6WQSURKRZRmn4CTg8aNLFQ7csgWXExxeBbExwfJkAqtlkoCnkVTcymq8Sv+BKLZghfPnr9DS5GFtbYw9e2/lkYfeTymnYAt6cdRHKCTKDF2YZGoixZ4dt7F75w56erahm1UK1SxD46OEY3UMXT5CU/QmDhx8PyFnFxabg7HhRQ4f/xE9fQ2ELM0Yhszy7BrDw5cZHB5idQZaYy3UCjqGJFPWilhtfrAKRJtcxJpsmEiYpkK5OIemxBE0CavRgFkLIRkeNFnH7wrh9ttxeSSaGoLcccshGiMxAm4PlbyGXAGtWuN9Dz1Iam2WG3be/Mvj7/8keK8fvwW88u9edwiCMCAIwglBEA7+sov+vXdAkQ36e/fhDs0TX5tkdVXC5QqSyqxQLJd44/jLKNoaIkWyhVXSuQwupwenx4cnohOIuPB7AwiawNziGAICdpsNWa0gWZyUcjJOuxdfyERWZeqj3bTHelhenKEmFzDENMWqg3AQHJKV1VScpiY3NSOEEVjBZnHQED6EV5eZWh5EU0z6Orqp6/EzNWHwb099D68ZRjAbWJ4VyWZS2OvTjKWfp7lN5qEH78LpayAW6uaRBx6lb9Nu/G4XSlVjenSEo0cu0N+/mXLex6699WzYsInBq1kMi52/+eJneebwK5y7OsAzr7xFtM6DqYLdKTF6bYa6YIiR4WGsDhe5dJqm+kZGhkYBicb6KNu3bmLb1j6iATcNPitR9/p/7On5FU6evkhHo5+SphEOe9i5dTOFbJaOrnZmZ8cJ+pxEgn5C4RhirYY3EMComZiGse7NE0U8djcfePzXUNUyo5MrxIJhslWTqrrK7GyRmQELlaKVayMLzKwuMXDlDIIoEvB5QIBXjr7J0Mg1OluaaG1rw+nyoakgmAKlQolyuUI+nyYeX6FUKpFNZ7BbrMiyjFwSEA2Fs5fP8bNnnsNZ3YCo+jh4ZxvVeDurszViDQ2sra0yszTF7ptjKNY4G7o7yKZTLCdmiRdWGZkcpLUxTE+4icpihvnVeSJROzs2b+HK4BD+RpVjb/+YxOo4bpuN+cUyNmsv3XXduOqLHD3xDImMRn3Qx8HbN9K1zYUgqlSzItHwBnb03IDTJSGYTnqb6vETg4qA0wKpRJZSRadslrC7rWiahKnYUOQqSFmK5ThldQmbrYTVYgfDRNZq+Dw+JMlBQdbRTEitxvnFL17AolqI1Dmx2qtUqstYHSa5TAGbJfBLA/j/aIpQEIS/AjTgR9dPrQKtpmmmBUHYBTwnCMJm0zQL/99r/713oLHVbf76I7/OS0eSyIqLtUwWh8OCL+rH5pWQFY2GSJhCeobMyhxOo5k6TxADJ5oik6vOINoC+PxhluKzkFtBFEErCeS0DE6vjXS2iOiQ8Pglirkl/FKUWKiF5USRTEqkog8h2pwsrkWIZ+axmS0USyqZapW+vs2sZGroljizmoIrH+D40TPYG0Xec8dWBscW6NxQz3wxyGDtNMV5kQtDo1RMgcbH7ubAth3I+Vni8YucPHmSyWuzOL0WHnzwDq5evIiuOxgfW2L7tr1oVgstHXsZHpqkpVDC6WtneTLL8lKSXTs2Mzc3x6GD+zhy5E101coHH72LgcEpQnVRIgELuVyBAzdsx1/nx+uyk1hdZCVbpbGpjaZogPpomOHxKeZWVpmcmac9aqVaKPDWuSHuOXgD/b09PPviYQ7t2UqxUKKrp5Ojb59D29hKpValVCmC1cBms6ELGoKgo2gykstHsVymWsvS3SdgW2lh4947eeOVF6iPemlt2YvdZ+fwL97EQKCtrY0NXV2cOXOekM+JKldRDZFwuI6qrGG3W8A00HWdSllHVRWsFgO3y4UkSfj9fkSLQSGncMsNu9EqTryGlTvu3cOJU8eZmH2FULuFySsqa7kF6jSVqZEaG/vbsGyw4VLr0FWYmrrMzGCNjs4GIk1+lpZSBOpknA4nTS0BXnnzdSyuCvfdt5tLl1YRrBGioXo6NwWJL53j6OnT3NjxXrSqhstaxS5JWCUXiSWd9vYo8USWpqYGLBY/Vl1iTZ/FsaojSCKL81kMARrqY1RrZbJKnnLOoOKADd0eMGvopoFN8pDL5kBzIKgCDpcXpaxRqWlouQqrwiqeoIOltVnGZ4fo27oVm1tkKbeIO2RDMdLkKgu/NI7/0ysBQRA+wnrB8EPXCcOYpimbppm+/vwS60XDnl/1XXJN58SZ7xEJNyGKbmw2K+VKmZohMzo7goKMKluQrA5cnhiRuiBKSaJasqPVIJOdo1yLUyjnUGsWakqWcjWBw2Jil9Y975oikEsr6IoJZhV/0I3LESFa1wgESSQHmVtcRdG9TM4uo8iwabOTVM3GUroRt0Uiu5hH8lUoVUYJNUmsTM0wMHqS5o4Qul/D6fDh8HlYmbOSWwyTmDJJrhrEM0MsJy7gDuR49ciPGbl2mbGREX7w1E955fWTeLz1KLITtztIOe/gu996gq/8y+c5dEeYaLgJVaqSyRbpamvlwsXztLVFCXqCuL0OfD6T/bv2kYxfZGhwBF1XaGttZG1+ErOSo5BOkEqsUa4qVBWBgmxS1gyqmobd7cVfF6O9PkqspRdREFien+GOW24ln8rS1d5GXdCHYCoMjUwwNTOL3W59R7ihmea6AsNQ0SsGVVUnmU8RCGW59cAWNvfcSG9/A0GfG49LRKsJOF0iNbWCKFmQJAEDgbVckWtTcxw7cYq5uVkUpbKuoi8WEYR1WInH7cXt9iJc5xlUq1VGRgfJ53WspsSmnj7y+WVyyRqaU2U1vUbQ20l9ZwtWu5/ZkTxTQxWmLnsppAV0TebVF18mPqvj97iQtSwnT11EN4J4vD5iTSI2dw23y8LS3ApBTzsdLVuZH54lvTzHzPQxqrofVa9hVpzINYlq1mBxokx/fzd2ZxkbG4kFWjh64m3GJuawiSJ5XWBmfg7BbmKYNpSKQC4pszC3hqZWqBRryIqCIOnYHRKx+iDNzWFETKrlKk67C7VSwOPQaG812LTRT6W6Sn1zgH037wabyrXRYcpqHl3SiKfTVGsCmvbLLcn/qZWAIAj3AH8G3GyaZuXfnY8AGdM0dUEQOlk3E8/8qu8zDJO6ei+ZZJVYgwubTUHTDPLZPFbJSTKRR/NZ8YWg3m3H7TBYXCihSyXcQTe64sBUSlQqFcp6DZdDoFqWCboCKKpOJpchYIuSzNeoOsFmt5AuLxEMNCNVQTVGqPNuRBedIBXQZBsdLR3AEqtZAY9YxB4uklFM4ktlNrQ24rCm8a7FMCSDqQkZozJFelrFKYfo8HpZUQtIdgXRqHHp/CXKsoHfZrDn5j7mxrNYDD+hSCvjwzMUchnKVZVUuYgoWCkXqzzxjW/hk2KsTA7h1gI0dwQYHZmgs7GZlcQ8Ha1dLGQWiLXA1fPj9G1poFwJMXR1msRakQ0tLdR0AYvTQyggkUiusby6isVuI+Bz4Xa7qFVUcAXYvd3PhckUVo+H1vZ2vIEQq7PD6EoVuWLHKkrkShUki5WwP0Q6n0bTNEzTQrUiY7E4kUWFjo19zEwPUa+1opb9rFWvYAmkWUnmUHSTRC6HLtSw2mF2bhFBN1FqMrlcEYdFRDcEdF3H6pCw22wIQK1WW6ftiCKapr9j51FVFaUmo3td6JYiR149jM+m8eRPv8O2R/3EWj2Ipo211SE29nbjdtmYXpqiUkpTH+vFIVjZ2bubnC6h6GVs3hyJtSSTU1ew2UUky2aWli+gmUncfjdvHjtGe8sGWholluYvYjceYnJ2gcY6J5PjUzijDlZO6LgGa4RCVXo3bOMX33+T2w7cyNYdW3EtTjA6P4smCjg8Dpyx9WSqFgxc3gIxd4CmphAWs0q1aiDLBgGvF10XWFtbRdctiIKDWlXHYlYpW1I0BRooV0A1ZFZWVnA73YgWKzNLK4RtVvzOAIKzilpeoFTVfmn8/cqVwHXxyBlgoyAIS9dlI08AXuB1QRCuCILwjesfPwQMCsL/y9x7B8d5n/e+n7ftu70Bi7bohQAIsIlFJCVSlbJcVCzLli1Zcc2JfRI7zffETlwU24lT7OTELT5REnfZki1LlqwuiqokxQ6woREdWGzv7a33D9C6uZlRzp3zxx29Mzuzs7uzO7M732ef31O+H+EM8EvgE7Ztvzn/6PIlyw7mY3EWktPkK3MEG8IYtoloOwl6wtRLNTxKA6rYgm5WWIsvYFsymcwi2ewKWA7KeQ3drKILVZLZOLqUp6Ctkiun0I06DlmmUq2SzWSplmvMLa5hyyZrsQSxBZOgbxMueQMONcdAdJhqPU2+GEd0NqIZNZLVNGabSDnlZC1TJJZI0tamYmkCoq0wNnGO+eUlGpxhom1BKvIiussgkVhgZirDYlJjZrlEc4+TlsFGmqPNeNxubn/fRqamzzHQ24FeLtLZ7WNkU4SZyZNsaOvkng9sRtRU3nf3B1icW0UJRJEUhetu2M9QfxN9GwO095cwix1093VQrgvkyzqW5GBhLcVqMkO1XkM3ahi2QSafZXl1lUKhRL5c5tz0Ii2NASYuXuDFQ6/R0Bji1VdfI9LWRDDQgFsVkW0HpWqJ1oYQ8uVMQJIkvLKTXLGMgELeEkFxMHZhilQCzh1bpFbKo6pOgk0+mpujhCMSfrdKg9fN5MQ042NjSCJIsoIlSNjYqE4FRZYvW+sImKa1vl8gihiagQ3ol1uHTtWF0+FlMbZKoVBk/MwEM8vT/OBbh+kZChBwN6DXs7hdoAQ89A5uYGRLB5Jh8vzzBylWq2QL6fUOSd2Jy1tnaCRMT28TgQYZj8/F4MZmtl+xg1Je4IVnXqZ/IEyoIUI8tYo7oNHZNkJTR5CN2wYIuUMsz6ZIpkq4pSA3XLeZ5546hEMR2TSyGae3ie7OVnq6u/E3uMECl6wSCIt0dzUTbWtBdcpYdplsNk4+U6VQqFCplimVk5QrGZbml0gsL5NKxsklanjcARS3RWJtlalzS4ydGaOzvQnVWUWo6wRcTbh8NUy7/Ob6+98J9E3AI//2Jq99GHj4f/ee//nSdJ2ZqQoBbyMLiSmu2HwluikjSU5MQ6CluZWQ10mxmsQgjUAHDtWPHy/VnEK+nMYphKnVTUyrhKWBLXkp6AUE04XqMTFsDVFSWFqdo6HmpFJz0djiZe2ZCVp6/MRjGURBIF9cpZYLc64yQTDSwMDOKsULfjxhB4ZUwut2I+oaHcEgzroN/nZUReLEeR/Jssl8fp56toqhVBAtF+lEkUpSoyJWcLiriMur7N9+L48//CQvPPcrPvflm3jx0Bz93c3Mz07zrrsPsBp/ha3bmimXpgg3XoGpJDh/cRGH6uYXv3kVn1LAG4py5PAU4QYRs5Zldr5OopSnoaGRcinLS0dPUa5VQZKpaTqCIGJY1uXKO2Ctz6G/+NJrhM0Sne1tZOenePXQQXz+Vuq2STAUpqanEEWbro521hJJVrPZN/h/klFDs3REZOKZLC6vSs02KdRyjJ8+Rz5RJNgRZGh4A8vzZwj52hCbdJyiQL2UxO91E2kIUtRMSpUqum1im+s79Lqpoek6Tsc68FSSRLxON8XVZWzbplatojpdaCWNRMxBwKFQdKiobj+NDQ70qkrZNHG4fYyfOkP3pi5qBS9q3cfy+Slsr5cMFrLXR2xlhh5nH0a9DUVxEg6GsZ1pnJ4eZNtBOpVh08gQp/JO+lt34FLaMLQixVoWr7OB/uEGYrFFtFoZXSnjkNxkV4tMTMwQCLs4fXqc7bu3c/3VW3j1yDhN4SDLS3HMuhvZllidLeMJpFCtIi6HSGOjj3qlxnRmGV+4YX0epl7GJSsgWHjcKqIq4vR5UDwK5w5eYsvIduolF1MrL1MqFlC9KtVahexqCiWgU6+Yb6q/t4S9mNvtwhRqzM6l2TG0E6QkTa0GqUQKl78NxeWgWoN8IYHbW8fltpAlA4rNlFICgpHBEkWwZWxDxK348Tka0cUKlaKFJAmoTplAIEC0pZG51Yuk88scPeVAt1K4g1W8bj/nxiepCSW29PcQi9coMU/dIeDp6UMT6hTmUnQO7UNLzqN6HAi1EKLHiyq62Du0n4XmDLnkHKVsFa+/kXKlRrFeYdNQFy3duynVz2HW16iUJilm4Kp9Q8zNrNuknTx5ktWVRe6S385Ax2bOnT1Ea7Cf188eBKr86tFfIksCNiLLeZ0f/OIxqsUqsSUnF05nKEkx2q128oUS+WKeclXDsC0EUcMwDQRRvGxlfRluKax73JdKJWyHjw09DdhdzcxNncPjdHBq8hwH9l5FV7CT4ZE0UwuL2KJIpVRBlNZrAjo1nE4Htm2TSqyRTiYpl8ooikWkpYUTxy+yOziKbikIpp98qkBdExgYagHcLMwsU68FqdbXC4D+QABd03E6VXTTeIPSozoUDMPAG/CjL+jrFGIb/D4VUVCoFRT+4BP38tKhJwi1t3Di/EmOPRtH9lyge2iAPdfVSafCdLSpHHvhJayaza53XEPVLjN3aRHZoWIpbmoFjUSyRDaboSal8Ep+Fs4lKdRPc/ONdzA7HePxp19m65VhVLfA2kQJj6eEVTeYujSFt8FDJV0mO1/B1VSkXhcRnTpLC1naUqt0uaL0t3egFwoYWQVds+gd7KW53YnbFaGxucb0zAy1+nodxLYMisUaomTgkEVsUQZRoKKJKFUVHCqqV2FkZBuW5OTaAzcz9+AhXO4AM7OrRLpk/IEQZsBHwXrzTOAtMTYsSmBYNaanV5CdEsnCLKacRrMrIEGxvsbcSpJySaYv+naaG4YoleYwawF8agRRd1KrVFEVFadj3WdeM6pY1PB4g1RKoBs16lWDoDtMINRAW3sTiwuX2Ni3FUXyIQdWaOoO4vQ4CDX4CQeitA/LrMW8XEgso7l0ZEWkuamMZq1QSlRpDAcJ+RT8QdgxEGV71IuV1QmoDdiiRCCk4FAE1nIJ2jr7ibYPsLAY4+LEYSSlhkyAo0cWGR3uxe/zs7SYxOE22b5lOxt6Opid8KPbK/T3ZTGEOi29NqoChiUQyxSpITCzlCRv16maNpfmFkml01R1g7phYNr25VXddfn/R8z1b8/Wtm1z9PQk506dprGti2uu3Yci2ATCEXQs6hWbge42soU8iupEVVREcf3sbosipXINU9cQDJ0f/Nu/Y5sCfpppb+nG4xFxqzILi4voNTeSWSTSEKKvr4kdOzro720hFAji9XqxLIt6XaNaq1IolahUapimhY2FbVo4ZAWH6rjszw9utxtFlJAcApaucP0t13Hz9TcxfuEMoS4Th+kmVVhlaT5NtNNBaklgONpFpKOJntE2mlrdaFqahYVJLFvB7XVTs3KsrKYQRQXF6qardRvlok5Lcwculw/RASulMfL1SywtLtIYbGNhLobDJVKt13jbrTcSDocJBlo4dvw8WG5amqP09EaINLYSnzdpCXZgUKFjIAiUKWqLuCMrIBZwewIUcxKRZhctbSoCIlpVx+dupKu9DxGZcqVGviyQTFq89NIRxs+PIaqQrxcxRJmu3l5W1pbJ1qexXBkqdgaNOo5A/s319/+PzP/rS9PLRNokerZKXHnNJjaO7OSlE8sMtW5FsQeIZ5YpG+OEWuKUa0vkclnisRpaVUeri5SKTizDh2CbGGaReHqFQqmCYPmRRBlVaEQvetHyNvOX5gg4g/gdLWzs7kGQW/AoIywvJNhx05VIoSrxwkUc/hqmJTM3meHseYHlFQelLi/xskBD1zBG3kDx2fhCZaqlEtmURqWmMdi+jZHOvazOrLGWzhIrLHBy7CwLiUUuZqdZulQmv1ClrSNEoSySzWbp626ip6+f0WgvJeEwGzZtxq51YAXWyDsNkrqELLppCvVR1y10TSfoC/L3X/86h8+eYzFfJle1ydYMCnWTdL5CWVs3o7ABw1wPBoZpYRo2miFS1mwqpkBdkDl7cZZQJMAzzz5HJBRC9bnwWSoXpy8xPXeesNdNY0OYwdFRRNtGVdX1wKLL+MMyNhqUTObmLuJxtGIJK1w8dxa3R2Byao5KOoNm69RkHdUZoWblUAMJdu8YoKXRi+JQcLnduH0enG4XiiIjSzJulwfLFojHM9S0OoVCEVl2rOO4anVK1Qq2bWFbNpZd4MzZcWzJSVO3m0hvndylBRZPTnHmVIZkbJInHz1MsK2BjJzixNkTSLYfrytIsbiE6MqwdVcnTS3NVE2BuYUcTz57kB1XD3PHuz6AbHnZckUfu67vxR3pxuXoomcoQueGRmIri7R2RGjrCuN1yCzHV9l+xdVYuo6u+dh+xTaiza0EwgpT8WN4I824Wgq0bXZjOEvE00tMzB9lMXGW7lEFd7CObSkEgkG6o90EnWEwBZwuk2KhQKFiEYjICLLFUmKFuexJ3L0TTEwdJxfzUKZKQ1cWl5yjXMoxP10h6Ot+U/29JYKAZRqsLBRoDG0ln3Tz8nNHKFfyeBstyvJ5Qq1+RrZciaz2sBifJ5uLI9hhivkiuVIRxSOBqFOrVhAFg2qtRLGaQVQEStUsplDAdpQwTINCtohT9JFPJ4kvLmFbAY4ePYFqjXJh7iVWV3VwmFSlRbxSAz0tURLzHmamLTKVBKZtIHlkGnpD6O486WKGfFFndjlHWRNpa3IzO3aBJk+AnpYoTtFHW9TF1PRLrK5mkBQFA4toRxebt20j2tZGohijUK/wh1/5BN3dYV594Wke/dlRPvTRD2NkPYy2t2LpMHU+jmmKiCJ4PV4OPvssklPFsGUqmkHNtNFtAc20qes2hmljWTbm5SBgCyKaBRXNombYVHWTSt3AEiWivT3EVlfR6zotzY10NkZYXFwjkUmzMD9PIhEnnkghCcJlhoGFKIUIBNoQxDInX/tnBOpAHH+LyfBoF3v37sbptIk2d5NM5ykZKTxNGVo7AywsLBCPxXDKNsV8lkwms54N6BpavQ62BbaNKEooqoO6VqNUzF/OaNbNNjRdQ9d0UqkUTz35a5ZWFlAkJ0eenuLMuQWGW/sQHQJu1csnfm8vgi9NpZyjXpdQHSFkW8blUsjmUxw+fJTDr45jCRr9Q20MjXSxuDzFs88/x8HnTnDw0NNcmDlOW2uEi2Nx9uzcTN9AH/uu28Ng/xa2bBnF41MRBZstm3dQLlXIZ3MU7CVOnr/AsTOvEfR5qVpZVFcAQfNRK1toNZlaOUh3fxvJVBFf0I1D9pNOaOTSRTBFbENAEus0RbwIlko6VwXZJtAYpnMkRbClRjKR4OSZ53H4bfYc8NLZ1kV/eAs1a5JyOcnacupN9feWCAJOVUXBwofB8to02WKcHQPbOD5+kU3bhqiX/bgaHCwml6maIAguFNmL1++nqhepWykcrhoOVUYWXaiOIJZYRqNIuFUk3FshIVygubORjUNb6GkcYvPIIFdu2YPXCqKKzUSHY6QWlunu0Lk4XaHuMTlxZJl00sut73URT6+gTjroMgfJziyxWM5yaWGRC2cXERGRBBg/keTFY5dYWFmlP9qOIy+xciZHd2sTDZKbFk8A0ysQbBNpaQpz+vQYWk1n7/7buXhylr/85s/4+8+/wE9/8BP+4m/v49GHfslISy+FuTVcDola3aBcqfHZz/4Za/EETz7z9BsGH6ZhYpg2mm4giBKiuA6zFEURRXYgCw5qGlR00G0BLvfbDcOgJlj87OHnyOeLJPNl+pqb0CsZ0vE0TZEuPH4vrZEIqWQaxam88d6mWCHS7OAj934Il2TSvylKza6SLVcxZPB5mrj1urtobHfiUEWinl0IlsDEhQTltBfDknBIoJo1XA6FaqWCaINDVdfRZoZOuVxCEA0sy6BYyiNKIpZlrqPKikUcDgeKonD86DmWZ5ewK3V6ogo3vXMr/Ve0MbpzE3JtmR/++0HcXSb33PkxusOjpOM5cqkc2WwRUQriliLMnbeZm6ozP7NEKb3Mzk0b8Tkj/PrxVzl6chZDEvEpLci6QKVWoVbX+PmDh4ml0izMTfHaK69w4LZ3YFQ9xNbW2LR1O82hDrZv3sbqbJXXXxzDLPs4eeIE5biX3aM3UCwUkRQPmZxJuWKSSyjElwxSKxVqBRBMlcZwGL/PTTlTwjIlfKEA5XqdQChKpehh8swartR2BjZ007PHT66QpVCZpVywmF6epatnkO6mrW+qv7eEqciXvvKl+3ZeN0xqvsJVN97M6ann6WnrIbtUZXF2hZmZizQE3ahmL7ZRxO8OYRtu6vUymlbG7/MiCw5sBOqajVP1IosO6vUqNRZZTK1x9nwGrxmi0R1Br9TxBwN0hgLItxUVAAAgAElEQVQcfvYMzQODJGqvImV62bN3AFd7ksceKTLYsZVXzpylp7nIVe27aLabmT9yktEtbUwcWyLSMsTM1DIuXcLlcvP6yWNkHBlCrT5CrjZETcJhq2TjJg1NG4j4/JBPMHloDZe/g6Mnx4hEQsRzp3nXjcvs2fk5Hn74ID29w7T3bSDa4uOOu/6Cz3/1W3z2T/+Y106coVzTKJVLrMRiIK3/UwqXgZbriGwHIKAoDtwuBzYW1bpJuW6iW2AC9mWbqt8uApmYVIo6TtVg7NwsV20eAJeF2+2jVqvS1dVGLJllKZ5Fq5UxLp/LTWkVcPLRLwiY+naCmy7gpRMZhUJaZPPIVk6eOM7d738/G4eH8aphivEa58+dxe9vxut2U0wVAIliRSOZSqNpdTTDwKGsuxYpiowiC3g8PtLpzLrHjm2tew4Gg1iWhWVZlMs2O0dHec/730FRdtHqG2W+cAZt3snkmUUml3LkCjUCwRCJ3CVEp85Q3w7iqRyyauGVvFSLJovzaXra+7ly+7UoYiMLCzNoZgFfQxdOh8Spw5cQpALleohsZR5fWEZSakxenKXB24En4OfYS+eZODeDI2BiGyVaWy30Wo2r9l1FrmbidNfJFStYNRtLrmFZAj5vmEhjiAunpzF1hUZ/JwFnhLbmdoYHNmKUVfxqgEymgi3aNDQ5cPovUcxVaA/fgKJnWFuN0jFa4OLMEeYWCgiCA9sFhw8vEfKWeebRtbcugciyLWYTs0SD7bz67AskYxpFl4eWSAhDkvjvn9rCubMrxJJzDA2JGKUc2axIoZDB63IxONCBqfuYurSAaVh4XQ60qptkbpmhPifZlSIb+luQEgJGrYq/3U86HSOoy0yOHefAAYNich8t/hCL4xrqYAGnMsX0mTk8SpXre2/DmhJISUtsvWKaui9Fr1tgbPoc8apCOF8jFz/J7e+K8Mrrfuyyi0JV5uKpJYKNYURRIzv2Kp/sGEYIaExJRfr6O3D5HGzevJHQiM2ZVx8H5Wl2XbmZ40eOM/Z3X+OO913H3AO/4Omnj/Llz30CwzQRJZvJqSkkRUZSQTCFNwi3oiAhKev/1KZhUipVMC0d3RYxWIdbypcDgGXZbxQHHZKAx+FDUQtky0XKlTydI12c+dmTqB4XPp+EJKzjt4E3so9szoleL9Hfcic/PHgezagw2NzOFXu38NRTr7CyNkW0O8yhJ54jWVlCcGroRYldo/vwtjqZGZ+hr3eAjf5GfvXk8+i1KsFwA6JDxrQMqjUDQRDIZTJ0d4YQkLCxUJ2OyyTk9QEYTdNYXQvycu44WWOOVxZW6ZcMaFdIJSuYsshHP3IdoQYPT514HI/PSWdHM8l8DFNO4PanKWdqGKaJJWQY7N/I+Jkkzzz/NHqtgMOjEmjWcNKAkU0zNLyBls4IFUFHESukkqtcefUBfvqdZ7jldg+zs+dZXihy4M6tvPvevRw+9gRlU2d8ZoyG5k3odYhVC0R9A+RTRWSnSiULtUIOrW5jaQZ+QaYx2ERHczdesQfT7cFyrxH0p6gaAkFPkHK8jkNupKm9DSvjRHQbbAhv4ZWFl+nv6MXrbqCjo5ds8hSvHZ58U/29JTKBf/jW39x327uuZnPXMF2NvfRENxJ2RcnFK3TuHGf+XAP1Qhyfr4bUqLG0ukDA6SPobAGjiZn5NJl0gS1bBmls8VAsuKiXRXqHm7B8WeyqTL2WI+yKYOcbcDYU8TTYdI/spKJPIDQ1cvyp10mlXHgcAWp5kc17OsmkZom2utnc2c7y6Rf42i9jXFi2aYu2QrGBRp8LqR5AKFrMTmlU0wbRFh+CVyBTXuLArmtwN/i5dGkC2WljFHLUdI0te3YxfT6Ot7UfryuEXl6hlgqQjq2yc+dWPA1N/N6nP8tX7/sGf/0P/0gqsczXvv7N9bYoIm53Iw/84hv86ucHEUQLSZRRlPVinSzK6771pgEIGBZouoX02+OBICLLCgisuwKJIorkANtiw9AmNo12MNjdztKqxkhfPxuHh/jBD3/FPbffyInZBAoaVVOhWivjQMHtVDlzNE19rYdLS2MsTpi4PRKGlSW9FkJwFilWSvQPNTAxHicRc+L1NNOzsYGIX8UpNOFrbGJgZBjTdJPKpNnzdhdbRtzk0x5+/0sNXPs2P6LDy6WLIggagm0jCiLVmg6igG2atDQ2cvHCGJu3RXnmxQWawgqHDibZ3d/D6LXbMcVx4vEUExdiuJ0+Zi6tsbYyieDI0tjQxdU7byXa1kwqk0RwVMml1piYnEUN2HT0+JFFCYclIsh5UsUF5nLnWU0nieVzFNZ0eqJBRnb1ExQN9l91M8+98gq3fWSY5548yuzUAlbRQU0XcMoetLSNVw2STWXZvmkLQZ+HZCqOrimMjPbS0tBDJNRGf/cgDocHURLXbdmcfo6/foF6TWfnFVtpjfRi1T2IaHRu6OHYyRcorMmYlIm2d5BeMukeOUtVK9LRuonnfj331rUXEwXw2Z14LD/p5TRaziS+uMZg10a6Wttp8e1Er9gEGgTylTKuiI+qmiKnrWKYOm6pFY8UpjkSxDIqGHoSw8ximiWyMZ3YfJbNWwa4ct8GlECSdLpKhjJPnv6fXHXt7YzNHCGdbsLrqKIIBi6fztkLU/j9HpKX8oxdXGBvr58Or84Vd/gRGmrEWi6xMDfN4SNTCHqBpqYw+25x4wxN4gvl6NkQxSmLhPxu2jv7iK9pzCzNce3N95CLF/DKOh4lwnU3XY1eaaVn5N08fXyOs1NJNFOgs62Vhx97mg9/8B7+6mtfxuVR8Lg8fPijH8eoFzlz8iSWtW72uQ73AEVep9jYgCApSLKKjYR1uU1oW+v9eMMw3pj6W6feCMiyxP5rrue222/DFCwkoFKu8a/3/yuhQADLMhloDWKIDgxNAyQcsoRpGExeXOLlFx8jOTlAvZrl4tQ4nc27aOta5dTYYWpWGpfHxuus09PrZXC4i2d+cYTlSzlyxSQrsVXOj5+jucXE722iua2bK26I8xffdHLt7ntwO/azYZsb1RnCMixq1RqqQ0W9DEX1eDwk0kv09LgorGUYHfLicjdzy92DiGqCQm2MG274MLpTZ/fOTezfPUKk1UP3QBu7927h2uu2ILpj2Ng4HCq1kotoy2Z277wRWfRglTzkVi127NzBf/v4fyPU1MLETJZyVWfyfIy+aA9uy0FqdZ56OckDDzzI3n39RJv6WV3I46CPzFoV1RYwsiJTFyaw9TLX7L2BdKLA7HSKkK8Xl+ymWtbQ7RqRcIhMNkUmkyS2tkCxlMGyTLxuH26Hj/hKhoAnQiTczuxUjNhaHL0apL/7SsLObSxNreGV2xjqGaKtIcjere97U/29JTKBb/zTF+/bsUdBqTsIOFpZi2VoCodZiU+yPBfGNqsoofPUDBdFvc7ZqUV0qYhe8dPgGsXn8iOZFuemTjC3soQ/0EBTWxRfcwyHywVaEAOLucw4HYMbyJoXuXTyEo3+QQ4efhqvV8FtuyjGcnRsaeL1uUO0jTax+HoVj9aOp0Fhy+3vImfEuGZghE39Fk8eUrHWynR21BgaHOCeT3+EmpVg5lKAi/OXMMsOirNZpiYm2bq1j8/8j3fi8KZ57NGTvOvm99K3YYRzZ0/yxAM/5cypk/zmqZd46KkfcdM7P8juPfv54J338MBDPyedzbCaiONwuND1OucnpnnfHe/ie/f/FMu2UVUHtgWiKOFyqqgOFVlW0DQd22Y9xbWty1AL3hC+bVsol48OhqEjiCLjZ8/y1BMvcuct+2lpCzE3tURTU4ienl6OnDiDVkqS1l2YWgXLNlFECVEEt8fDoZee5857JYK+eQItzYxs8SNbAqdOT9HbNUiwoZP9N3wEhxng0FOPEA4JbBzYSf/AACYyiWSG+fkVkpllVmIFHv2ph5w9xWr+Ip6gi+Sin+NHj2JWdG6/492MjY8jKw40rY7b6aRSqXHXndeyNLGIodRwiG0UpHMcfbJE+2CU5556EbegoNehqhTwBmWsmo+56QyTEzHOnFmiWlJpbx3g7Pg4AhbpXIzuPi8XTifIlTJcsb2bteV5xubPYVgW73rH25k+P0HA4aSoVbnr7j8kvuymramLtdQqpbyHi2cvIglhyvkCN+//EB5ZYWrxBKZQpKd9C11d7Zw4d4yqkSLS6KcxMEAhY1DJ2CiKG9XpJRAMEAyuU7dnJpfo6e5ny6YtyCiUChXa2nowNTcepYNrrt9NtSIjltwcOf0YRuI99HeNkszGefAnr7x1MwGn4qM12sHY7BPUKhrVskk8lsTpbsCWKpRKFm5/CIMc5YQL1aWxnKiRLpeoEieTWyKVjVGzK4TbvEQ3ZnBGx4j2BRHdOp0DAfZePULN1jg+8RyHD1/CIsQnf+erRPw+GmtBWh05EjkRp93N7bdeQ3KqyPvffS+6Z4bViwI/uP91/mjvh9DOF9HWtvGFe9/Hxu4KvaMtXHXj7aQWdR577Dyz1mmypXYG2rfg8XhwKCrHxo5x8sJzuH0ZbnrPAK39Ec6MXeTs+EliqTQra1m62kp8+y8/z7137uSLf/4xTp89z8LiEuVqDcsCExvNtChXKzz8+G8ABa/XgSzL+HxePB43Dof6RqFMkETsdZjNG2d/+H/ui6L4xuCNZdtouk4qk6ZUTuPzu1lcvMCOndvxet2cHT/Oar7KyPBGLL2Oaa9v7l1eHsXQTT79ya/hUbbz/e/OcMMdPRw9fpwvfuYJps5VyeYTKEqNuYVn2H2giz3Xb2MlmcXGwdz0CtMXLjA5MUFsLYsi+qiloDkQxWVFOfr6NEvJ11hYegZBd7Ghf4AXnj9I3dCpVquIoogoitQqAoWKRnNLF1MXs0SjIRpdLew40Mq+a/bzz//0AJ/69GcZHe3n0swcK5cSTE1OIhpBHHKYlZU4p04c5dFHHie1ZlAo5jDsBIoo0DOoEO4AyaFw9VXvRXB04Fd6eObJ39AU9qJbMsfOn+XEySPccfutvPzqswR9QV58/hDRaDuhRgcuv0zFziIYOv6gh47uTnwRmUQmTmdHhK5BCV2oYdR12gIdjI6M0tUVxaEIlAoVVMWHz9fIti1bCAf8OGUn9Uodt8uNxxXEKQdpj3YxNTEPSplitsLNN32UloE17v/2c5i+V99Uf2+JTOCrf/2F+959x41cv+mvqS3HcMqdJDiMLHdScSYQrCqVagrdUaVclqnXJPRcCb8zjGw3EnTLeAIe0laVxu48hrrI3PISsdkKZdGHqMUY6urmwtlZmiOd7Lvxd/n47/x3vvznn8JdL2Hh496PfIEnnnqJjmArxeU5rujbzdFTBgX7GKKSpmGmRKF4mHqLjx89OUa0scRaWOWmPZ0Ew1t4+pGf8vLRNRbLTqbGVlk+s4ACpFwq7733OgZDGvOJPHtG30vdVvjR/Q+wYXQX82tpbti7iXp+hWTRYO/eTv7Hl/4njzz7a6rFKi5VoWaxzgAURHRdJ9Dror4qYgVNvIoPp6KgVWuYCCiql0q5hsftxLItnE4ntr2+N2BiI0kiiqwgSustwt9mBpIg4nN7CKgqe3bvIahbXJyaZS2W4IYbr2FpYYnFbJlEOsFXf7LGrbd8iCM/LbNW9SBaNtOpQ7zt3l/x4x9UcboUjHIrm4avoRTrJxxKI7iKvP74Erraxt5d15COV/C4RLaMXEVXVweriQKaVaa1ZRCr1EbY5SaX0pg5E+fdt99BakXnhj23ceiVl6kZGqrThSisj0Bj2YQbg0ycPUYxl+bu993AH/zR1/nx975Hz84BLi1O861/u59Lq8ssL8VYzSyiF3SWzkWItEmUSdLW0saB/ddzz10fRaJMW7OP2w7cTd/GLiZTs6wsxuho7OKhZ39MW3MDs2tT+JVR0uk1QnKYq665jpXxKoEQLGfnOX/2BPuuehtbd3YxMXEWAZ3eEZtwpIvVtfOsxNPUc01s69tO94AXn99FPgGDrVsZ6hmlauRBqeGMuIgn1xBtA4/LB6aNT3Wjm1VCHjeVchlvKIAkiigOicXFGKFWFxuG+zDzrSRWJ9m55/fZvv8Z/vErb2Eg6d/9zVfv23nzPI8cepIjK0fo3hCivcfAGzKItPpoaq6SL5eolB1oSZkObyej/VfidQbImsdxhQzCLUlaezUuTMeoFmv0dHVz59s+xsz4MvlCnBfPnmGl4GF0dAMrJ09z6dlxWqMLiOoBZiamGd7UwsEnl6mrY9x0TxNpcZm1wnGyiTpvv3UT+EyEDo3FwnsIRk049xq722EmV2V69gxCzcGFyTW8bRIfed/Hee8Nb2dicpwFc5bBhjOk52ap2iIeWUSuRhg/tsjwlq3E1hJ8/ktf4brrr2JpKsYTTx5ndOMwT/x6/LIrr0DdEMA2kRRlfUomXKJaLxESVVTVjdPpxuP2IFg6ilHDKdsgKmiGhGmJWJa+XkkX1ldyBdaLgpZlIUkSsizjcbkA+OTvfoSFhXn0fIxItItapUq5mKejq5MLU/OoTje//7Gf8Xsf+CUH7n6Vuz/yBV545lVsZ4obrumio70RfyTOpz/+HQJ+N2uVb2GVC7x8KMWV1w7TGGnk7776JarVKkMDvRw++iIV3cQVMpmaPcnY+Wk8nWdI1ERWkk6CzhUa3BsJOQf5+td/xJ3vu5XpmRksy0a8XNdQJJl8UcPfD5u2DXDoyaNMzl/gxecn+fAf7+HE8bPs2ruJD7znPVyaW2L82Crbtg1x89uuZd+ed7N5Wy+rK6dJr2ZpaHKyoW8QfzBE/2ATxXyG1XQSxQizdXQY3SpRypZwO2X2bd/H/PwUp48tkE8V2LJhJxMLz+P0+8mnJCanZwmFggQcPqib+KN+2jvC5PMmYd8m2rplos29oNk4hCCNgXYsTadmVshqeQTJSSVdpMEXIp1Ksbq0jNvjwetxE/B5KRRzmLaNz+NjeXmFhaUlnC437c1duOQBLDlDMBwg7f0G2dUeHvj38bduEPja3993XzBapNE/jL+rQix9iqVEif4dHnKVPPO5FzHKQVpDG2hW21FlhYnF8ySrx3AFw4hON2FfFYEqy6kEOzcfYMvwNfz4wQfJZbIsrWQYGN3Azj4PG/qu5cWTz/HI4+fZ5L8VR/1FTp6N8dBDr+Fzq6yuZJGal3A2bCAcqTF7TmTkig62brmGpVU/Be0Swz0JSnPwzZkEJatIvuDGKTSQLU3zwT/qZ6R3K1Ovz5IuJ8iKAt2BOnt37CLatp2Rjlv4yfee5pZbrqNnsI/5+RSXZha47t13cfrQUV5+fZLnXniWzSP7mVuaWk/xRQe2qYMoYlk24W4/hksjVAjgCXhQZBmvz0c07KOjwcu+XdvxqxKVYgUbAc3U0Awdy7aRL4tGktdPgqqqvtFS9Pl87Nu7jY6OblSrSCDczLPPHGT/3qtJZZMgOpifXeQXjz7BtbcnuPXGP+HRQz9jx94st737Kj730TFOnkgwONTOptEhvvvdb9HYVmKov5G+3h3svv4uHnrwfzHSv43m1jBmXScSGWZybppDLx1Dr1t8/C86uHL/PraPtrG2PEmD2MbhQ+f4xCf/gBdeeprTp08hyQrVag3lt0VRy8YjBHG05snGl7hhzz3ccc8+fv3QE4ihAoroRfEWGWzbxcjWrcQWZ9mx/XrcqpuNG0c4Pv4jcqkSSxMlxs+d5fSZc5ydvEjZXGFxcpnBkQF84VV27xqlv3eIWr7K6WNnWZyYZu/+60BTSSfmiTZ1UbFyFLUabqmTiakJNu7eiOqW2LtnL2cnZyjUZtGrbnwBmbXsWVpafLT5+mgMRJFliWDQgy3rtPY0IasKiVyCUDBE2OPF1jQCAT9GvYqpawT8PrK5PCBRqdXp6+tlYGAAo5ogOpilrkfI1KaYurSIIHh49Gdv4SDwnX/77n1/8gefYjX2EAHVz5adV+PqrfHt779GtbrEtt0buXH0D3n8se/THd5J1RTJqcfp6o6gFzdhSkVCtpeV+QKmI4NguMjEswhWlmKlQCpXYW1xlan5Eqn8i4inm/j6Fz9L2ZjjlcUCv/zJDxk/GqOxMUd4MM7JcZOykeXU8QT3/+uf8dOHfkmrtJF49jzXbO9ispDjiekKt+4cYqYm4A972di0l2MvT/Da0Sy1kkxbpJHXLhxDanLRtWmYldg0mzfewcM/e4y4fpptV7RT13w8//xLbN66nc995k+4+sDtHDz0Iq+8/AjxTJzBwSGmpiapGRaGVgVRwragWjNI5SqEHUEcqoBRLXH7Ow5w17vexkB3J4Jl4ZV0cqUSuWoNzbQxLQtEkEQRSZT4rc+Maa5v6hmahoCAU03zv773EB++++1cvHiJD37wQ6TyWR584McMDAyQz2VIpdv5ync28Od/+V1y9RWmDvmZOp9gaVbkqmu7ueV91/DEM9/j0LMXEBst1IYWRnoHuf7qDxCKpJhfXKEpGCUcDLFn980EAyPse+cym/ctUjQNpMbjRKQqo1d4iTS5UQMmTz7/S1bWEkg40Q0Tj9eHZVrU63WC/gCKDUFFJCArvPT8MUrVDJajSs9mlYvjS2iin53DB/jhD7/PavIM+VyJ9JrF8y/9jKpe5upd99Lbthu3L4TLF+L83Hnm43GoCMj2+qJUV3SUhx9+mEggzPDAJvp7NrCWM1AVhXCjn6Y2L5HGHRw5eoK73/t+Gps8fOcnD5JIz9MSHWTntj24FRURgdYOJ+fH5nBLIp2tI8SXF9FMDdMwUWSRpdVZUoUV/BEPimEzceQkXttBR1OEajZBOpWmWilxaWaWhdU1LGP9t7w0O0XRXObXvz5FxcjTOlDE2zDF1Xv3842vPPd/VhgUBOHfBUFICIJw7j88dp8gCCuXDUXOCILwjv/w3OcEQZgRBGFSEIS3/X8JAvlcgp8+fD/nF0zc3X5eOfkok+fPM9jhIj0rkZruQDRlrt/7fnoGNVoaFDLTbdRn3kl3VxCPJ45DXSHaodIS7MUhtJLPZVGoEJZUAkEnwzuHuP22myjWRlhxpvi3XzzIDbuu46aNvfz8pz/h6PGD/ObxS+QrMn/7sz187E9v5dY792LYKSYnoOxLokXPcP8PniAqdHLlJo20N006sUxPs02Dr872XcN88tO/R0NTC01dTey7eR+GAr946DjPP6Vx/7cO8sjD52nZIfPAL3+CLAbYvLWd22+5iVqlym2/8yFefOHHfOjez/DVv/sO5y+eJZNNohs1bNt+o9XnFmUC3S6aNzZS13QCHifHXj3IAz/6ISdPn+NffvIoFUFlbGGJRLm6vo8vS29sD9r2Ot/ut0U1SZLeGADyhRQamwK4vR6u2rWbv/3GNzhy6hQdbVESa0s0hlR0cYKd0XEmntnB2vF92DU/lVKRxmiaQtZiZI+T0R0+rrnyZv7k9x9gLFmkLTrK44/fz8PPHsLfEOLe3/ld2to6+dcf/A1Hjz/AC49d5NkHFV56WuNfvxjguz+s8ONn1vB4r+WpZxJksg0I+mYcigtRFKlWq5iWiWmu37JCDtWKIEh+KoUKv3n4NRw+k/RKA4LtJJlb4dv/8k/USzk2bd6BaZXJFzLs2H4NuZSXb377GxTrk4xu7mBu4SyirBOKDHPdgXcy2HUjTRGJQ6/9kNZoM4/95iDDQwN09g3TO7iVpeVZLkyUOT99GkuQuXBqka/c93/xzJOPsGegj0almSZXLweffoGh3utpCDWwNAv9Xb20+neQylQIhULkMlVcqgu9pBNt7sSr+mmqqciZIl5FxO9wMXv2PAtTF0nEYkyePcfc7AylWp10OkOtVkFRRLpG3Xz0M5t49dT3yaZ0wsqNnH1NfXON/7bC+18Egf1ACfjRf4CP3AeUbNv++n967UbgZ8AuoA14Hthg2/abOxoA/kbV3ntzD8EGi7KcpSYWSczWKSWgf7+fv/rYZ+gJ7uGRp/4BXT2IXu5E1FtxKQpOv0VmaYmwv4muoevwdkj88vFHCLlkVhOrHD6eYFd/N4VsGNwJ1KYgq+k1btn8OwzLLfz9n36Ba9+zi6VUilzFouHKKZ590YN7qMTd79zFg/9+gvbwVu54+wHmzR/y8Heq/OG9NzBvLbH1Oo3jk3WWT/u4c8PtfOXbX+KDf/hB7tn/uxw7+iSvnT7GxHyW2XPLRNRG0pkE22+qcSFlsLklxJ9+4Od8+Yt/Rmu0nROnxkgX60RCKh09zTz3zBjL8Txf+Pyn+Jef/IpCLrdu7mmZRLY4KRoFErE6XtHJsL8ZX73Ezh1Xo+s2VT3P4ROzhNs7uLSwhKQ4KVfLlEp5BHHdC8C2bFwu9/qAkSyh1TUy6TwHdjVy09vupBSPsXvvZj73F//Mxz76Hp587DHefsttfOkvv4urI4JDdVEtV3C73WiadplupOOSgnSHojT2x7jh2q0U/BmaWgskZ+ssjq3RMbCbkNfH2aNFYstLXLvvVg6fOkHTriVGNt/Ij77zODcfGKGnuYu//tb3uWJvH9s33MLX//5vKRUNFEJUylVUVV2fEbn8+YrDptkfxNRmWY7V2NDdThmRzlGb6IYoPlcrpRWR6cWLGPIaeq1KZknAF2zlxnffQE9LF88+fATFqTC6vZe5lQVePPQCRrHE6MhmXO0CyZUcn7jrLn791DO4HT4CHYOcee1lnAEHpuRANOHipXm+9vm/4bHfPIgv4GZs5jj7eq5hsH8UR0jl0Yd+w/W3XEVCGMeYd/O2q9/D8so0smQiu1XKpTTZWIyuxmHqlRgri1Mk8yaSEAJLwB/y09bgYyWZ4dEnn6exv5/9+67FLcgkiykcuKDtacaOtXFq9je89yP9DDXcQrYwx0ff86uTtm3v+M/6+z/iDvwX123Azy8bjs4BM5cDwn/9GZhk5EV6enuZnstzy6ZPUY+LXH1rA/0dG4gvq/zVP/4dXpdJb+eHqaKgqXEaO3zUpRSrAkyUFjGdFZ5+/hjNrVF8Th8uNUCw1YMv6Jt/5cwAACAASURBVOTWt9+E0x/i2OsTXJxNkcnWGRjsQnUpZOo2R8cnEFw+Tj4+wNXX70AsQcit88k/7iIUKrEwn6K/43fZe5uLzbv24Dc2EF8TqRRUnK4q3/3HL+MPuVmYz4DmIODsYefAdXzlk3/F7931+7hxkhXLFFSR5TWD556P8fTLr7CSibG0ssj42DyPPfIrHnvxUY4eHkeyFPZdvY9vfPM7LK+u/L++rWK9jCWLtPW2EtnhRYpCS7iTM6dPsGV0E4rs4Kr9+wn4/PzfzL1nsGVnda77zLRyDnvtnGP37t05q7vVaqkVEZIIkkki2ARjY8Kxj+1jjrExNraBQ3A6gE0yAguBQFlCUrdC5xx2znmvnONM98du61J1zfUt31u39P1bs2atWrVmjTHH9413vE+j34NFsaCptTcqAVEUESXpDeGQLCuIosD7P/Au+jcNUTKKqIg8/rNf8uA73onLKeJ3rRuOfvPv/gg0AUwJm832xgGjYayf1NucAt/7xTfZvPEI//aDS5x4fo5nHpljZmQF3b5ErbpCV+QgzY1tZHJ5VlbP0ttj58oVgW/+08+Qal6+9LkTPPvzU9T5vdRFGrnl8N3IkgfTkNANHattfSbCbrdTLBaRZRmrzUU2W8CCBUVwszgfY9umm7nz6DsY6jnCP339OURB490P30FDfQtNgV6C7hDj4zP84AdPMj+5wrZdm8ibKZ546uecPf1LHnrHLXzxL7+EJMlk4jX0qkQtA4d23c3eAw9w8tXTBAJhMqkaaE1EGk32DO1nZW2SsYlhHnn0p7R1DVCqFNEqGayaSXdnHaJho7NlD/tv2Y3VK9LQEUSy+2ho66SxpYuGUBcOu0ytnCeeTtE11M/7PvxB+jYM4go2MLqSYi5TxBoIsBrPkU4XyBhV9h44gt8pYlYbefv97+SP/uABOhvv5Ovf+gqehvSvjb//NzqB3xEE4eqN7YL/xrUmYPFX7lm6ce3/sn6VO6CWDSSjzOLcKDd37+KvvvAlyh6FHX13c9eBXbT5G9mxxcNPjr3A48PPEm4eZHgiw4nRCc5MJtHFEk6LnRMnnyZbOIENWF0pMz1dYP/ut3HyVIkL16+xI9TPHZu2sSvShU1Z4fXzL/P+j3+W0ZkkVpuPj37ok8imSUBwE9ChGLVQqqjs2r+FHfvCrGZ/Rimvc9f9f0JLxzx14V1sHOxGElUG2trZ1bIZYbXEocHDvOdtn2D47Ajf/uevcmX45wiDWbKRMiNzPho6vRglgcce/Tu29mynu7OPr33jo0xM/4ys9g323q7jVDbzi6d/gdXppi4YXH9YooggioSb6zEkgXguiqnrpF0xKq0V3nLvbTTVeenpbMPttBNbiSObIrqqIosS5g10mCiKSDe2AoIgIJgC1WqF7Ts3YPeLtAy40cwChiqyujzG8NVLtHV1Mjk+wfMvv0hrSzMC68WdruvIskK1WqNYzFMsxfjrL97JkcNvZ25+nplrdrLzPbSED5JY6+X0yTjPv/II8ewaVquFC5dW+NEPTzD16goTxyXOnbpGuM7k3nvfi1vu51//96vcf89vkIlX0aoGuqZiscgoirTuc+hyrW93BJlAIERdfT2GlEOtVfnO977Hl7/8dc689iJvvXeQ5186xqP/9lMELLT1bCddrRJq8uOUnFw4dZWZhTFkZ5WhoUH27L2d09em+fQX/hhVqWJXHHS3dtHR0c5b77ufEyde4kPv+gAXz1/l+AvDiNIyJ19ZwGXX+MSnvkBJT+JvlLl/z16WEzOsCQmuXrzOrUfu5ujNd3P11DWSmVXiyQViK2VkRWJ68TLXRy6j1fJIjhrxfIm51TzRbIHh6Sm27NlLR2sPfR2bkUwZyaLw2x/+Hfo3dPDgHe8iECyy8Tdi+Npb+c5LH+TSxFXyMYMjdw7ywpOzvzaQ/6sDRP8IfJ71htXngS+zDiH5f7x+lTsQjtjN/e6tnLg4TNVawAhY0LMqf/cvj3DkyBDvvqeNmNmA7vOzNhrje6/+BDUnUpcoE4n4cXoEFGkVRbDQ6NlOcUlhJrZIRajw4pNPcNfh97BcOo1b62F4bYHJ2Sgh6vjuk0/z7vc8jCJ6KaeXufz6Za4NF8lEf4m1NYwRPstTz7RQ57lA/b4hWq3vJOP9OR/7ZJBCrpcrL/+MxUKKzkA3rrZ6UtMmN7/9Dj760O/yykvP88Tzj9B1qA0hoDD14gpCyWBkdA6rQ6A/0EL3lhDD16b4zQ88jGSUWZ0u8P3Hfsz++/J8/M8nqZZWUUQJQZeomCaapoMIayvLKE4LsmgScPiIWDxMXp9nf49JdGWGOp+daCJNwOtHNUXs+hrlaglBEP/PMwETpPXngG6YKIpMIrmCrq3bve/afJAZxxRbtrcwPZygYKhsbm3ne4/8iIWZWeyBAJq2rjrEBEGUUDUZt6sBxTbAF//hfdx221bytgxBb4h/eeQpDGuGSNjJzLIfh6tMe0c9Q4PbuDT1IgvJSaYnYvT3DOKy1fMXf/tlMlkBWXeSLq1RzFexWtedhCqVEpIkIUkKgihSLBapj/gpa0XKcpV77h3kyvEKh+/cSKTHxulTTzIwtAVvm0Rfx12EPB6yqUU+85FPsvOmLTz3wmv8w1d/hM2rUt/qwmsP097SQXxlBS0vkE7nMM0Fjt70fn70i2eZ/8dHePid9/DEU6/isDp521v72NDZxEBfN2MjF9h/IMyum7fSt7WFHzzxLZp6B/B7XdTtdzI6O45irXLT0E2cPH2RAzt78Ao1ZEHj0Z+9zB1H7sVqVBmeWiTYtZGP3vR2pqbGQFTIGRUW1uZIp0ts2buZci3HyIUL+NrKdG+WOXXpEYYXrhGbc/C2O79IRU3h9aR59BfXcWj/4bv4v54ETNN8A3EqCMK3gKdufFwGWn7l1uYb1/5vVyVd5cmxs9x56GEuTXyf+LhGoMlGyObn5KkZzo/9LbqRwV4LU1uxky6k6B1y4q6P0Le5l/TaFJWom9yKiqVZxO7N0OrQmTSdHOneSGZ2lNGpKdp2Bei0N5KQkyxn8jz8ofdy6tUXSUwWUVxeXn3lNZrrAiwW1nhon5ORV/wszE+QV+zou2wEPA4E005Tey+Xzj7P4GAj4Y4CkpKkHM8yPCXzr+/7b3gsFh54bxsbdjZRLhcprumUE2DJ2Qi5NHRZ58tf+iqPf/07fPHPPsCXv/1t6u0i2/c1kV2TOP5jmekznSTn/xjJ0DElG5g6IKLpNZyylYJaIuKVWZyZQVBCfOq9/51ceommBjd5w6BQmqVUWKZc0dErJg7BxCoKGKKILMvomo6h6zidThw2AdHwc3DfDkyjhfHxCZLWGE/+/FHaO3+LA/e+jZnLF/jnn/4El7cBi20NUVAQJR3B1DE0Dc2axy7bqSRCGCs5FL2VV06eIOKuR95m0Nlcjze0l3xuklR8injcw/6hIeyKTmvdAOeuL7I0H0OorIKQoVAy1jHkdg8uj4+auoCmqTgV643pQRHdNJEFAX8wSCqZo6khSCoTxRr0kVqdZmIeRmNpPv2Z/8bi4hxaReLWg5vRdIHHn7hOKJrgDz/1O4iGFW9Yo1BbZWXNYEXPcOzYa9x6dD9bhnah10wKxTmOv3wClzvMFz//JzQ19TG9lMFqqzG/FEV3u3n8Jy/xxb/+Df7os1+lfquNXzz7OoZFYHLpOoVojf0bb+LW/bdQTq5gc7vwWkR+8MN/4/bbbsdWU9jQu41nnvolB/buAsnB1//+Wxw6sJP3v+PtrC2mmLt6jT17d5DNlajVyuz87Y+RyBnUqmUyyfO4tbfSFoE/eN9n+fP/8TVS1VXe9VErroDM297h5uc/+v8wCQiC0GCa5uqNj/cD/945eAJ4RBCEr7B+MNgDnP3Pvk8zTPb1buPaxAj37P84cwvfoG+nnYnRNbweJ7KqIBY8FKI1Gjq86HmBqZUkrnKecjWGq9pJl7cOi2uFWHqaSj7LfLZGtppiwWohpAVo7Onk2vIsc1dSjF/Uec//uo+l65OMTy/T5W8mqmvMxeLEczE+9psPcOzUCO//436mZ2bZsGk3x4e/wC03H+SmO4NUxWvkTZP5uTyF8SZeG4vTVG1F0KL4vU662/1k8l7qIi7SiQVSJZM7Dx1lZWUWb3OWdLrE7//ex0nMJ7jz/tvIJFMMDg3Q3tLNA+9qxKns519+8BRTkzoG3RhSHEVRMDHWUd26SK1s0NIxwPjFGbp2NvP86UeIzyXZ/PAnqRYqNNfXMzm5jMdjoRorgalisSg3KgoNQzcQEMhkMmguJ5IpMHLpAsEmPzXJTWu4i+27DjA9keCfvvswbzl8N9/69r/wwP0PIknrNGKLVcbUdBTFglfqQQpYuOPtPj768Ad56bUf0tZ3Dz2hPk5dnaGqZ6irr7B940MM7Wjg+OlvYhYzPPKjFzl3eYqCVkOUg6xFdQSpisWqYLWJ6wajDgeGYeBx2JEMlWwhj8VqRxAEiuUy1XIZUaxgGg1Qa6OjZSNXrdewKjJOewMLkzG++vc/YPseF88fl5CMINdHJ9g1NMStt96B2x7k1KWL6EKFbCmLy2PFbKry4rFf0NzQQ2f7NtKaQslw4jasfOJ3P8nHP/YJfD4FUVKp6DFm5/xoeo4//MxjbN3Vw7/93RlCfitN9RuIldaYS13jvvv2U5VyjIxPkCqt8K53vgdBfIwXXz7GJz/1OzjdFjxuHxbFRqFa4tN/+HFMXWV8fhavO0BjoJGcVkAJKly7donXTuTYtOkwtWqRzvottO5qZqG6zAuv/IDBTXW8dqxMm+u/82e/fxWf2AS84z+Mv/8qd+BvBEG4JgjCVeAw8CkA0zSHgUeBEeA54OP/WWcAQLHKnH75IuPTIzzxxHcY2tRJLqrT3uxE0e00B/1YHRrtPW24IiHUiolRVmhwNNDq2UZ/RxMiK5SLCZIrJUKhjbRs7QCrRL+jgaqqIucsJCfdSGodoYhMxN9IpWTSvbGHtrYmRMFkJZmgppdBcOLxwPWzx2mv7sRuqIzO5bhyfYml6AwuywY84TaWkxliiya7ujp590MNuOzQ0uzB661jeGYOwR2hv+8m1pZTPPn4z7l87jJocwx0athcEtaIF9mp0NRQjywqfP5P/5Z//cYUQwO72bHbS7XUhqkkyBWz6yO/ioxhVunqqcfrFlAUk8/98btJxtLMrs2R1yBVXWJwexP9/f1EQnWYpgGYyLKE1WLF1A0EAyRxXSlos9mQRCsYRRanpsnmS1TFHLH4FepaQmzZsZWF+TKpZJZbj97B7t27yaayOJ3O9QSuaciyjKllWU7EKNpnufsPPsGl2cu8euwsU/OTWCWN3/3kp3B4UuRzV1mLJTBLHtw2J0fu2MFvf+JtHDqwkU1Dg3zhr/6GHbu3Y7NZ0DSN5eVlkskE995zF33dnbjsVgJuJw6rDb/Xh8/jXbclV2zUajUuXb7G0VveQrg1gqqWWVteYWFmno0D9eiai4WlRaZnrhGJOHG4bVy5OsZjP3uO+Eqa9pZmBgaGqK9vQxIt2G1eUqkC50+c4MrFC3idTtwuD6lkGlGB8ZEptm/ZS99AP1eunCOXUqlqUZamsmxs6iK9lKelLcTy8jL1ne2cOz/G0y8co3fTRnQDTp+4gmAIfOCD72Nubpbjx48xMzdFVauxedcWquRYis+SK2RR1QKyqFEtZXDaJZoa2hjcthmP305TWxCfr57V6CwzwwXKmQYO3nQzn//8B4jnRrlw8jhXzz/362P8P2sR/v+xXHbBfOB921hVNQL2JNmag4WRVUSbSjhcj0UusZiNsxoTaQzacAgyil3EKNewKjrZTI3+Ziv1YSda1k2XLULJvkAubbB1622ELDZOjaf50Y+PcfdtR7kevcot++5mdXoGPZDGF7dx+sowy6ksqXKBy6cvcOT2t5BOmty010/WfRVbv8LB3V1EwgaFfAKL3E7Q2Y7LXkf01R5efPxn+BusZEoFsrkoZU+Gji0NqNkyuaJAJV8iGk+z/7CT2WEbtx75CI+/9Cxf+/0/4rvffgQtncXuc7I4m+DVU+e477eqXHltB/OxMUplAb0GSALhBi9J1yKIIi2NYQLWAq3O3Tz3ymX+9H++j9yIRqUo0NEW4Kknj6O43EzOr1Au5UhmMxRr6jpGzFwHesiyjKGKvP2+vfzWe97JuclLeJoEpkcvcXTLA3zmk3/O+37rf/LMD/+eyXQWiyFwx1238aOnnsNisaBVa0iCgCIVSRWcOJvjePsGOdjfirkkUFHT6JpK1p3k6M31OLNBCiLUiq/gUrewefOHOH91hpeOn+ba6Bgr0QUOHT5EMpklnc5Sq9WQRYH6oJe1xRnue8vd2N0+Hn38KSpVA/GG7LlUKhEO+oknxvDVhWl2Ohk8uI2ZpbMkVkvcece9rK7keeLZn+J32mlpbiUWrRAM+xga6OaBt72Npfgsn/+rfySVUNm9u5+GhkZS8SLtzU288sozdDS342tsAVMl6Ahy8MDNjI2N8Yn/+Xm+/e0/oqOunfd94iPs6e/D7qxhr2tjMnENd83G4nKR3vAmNm3ew0BXE319PZw+dRmn24oha2zbvp3Ll8+CuT4Krng14sVVtGoVqSDTFApSKZjkiiZWr5X6jmYWlyfZ1LeBy1fOEPDVE3K34fY2MT3/Oq3BPtr6m3nx1LexVjdilxQO3f6x/7BF+KZQDH75S1/4XP0GPxOTI+TyRQrlMkZRo6+3g2wuybvuezeT16/SPeCkydXASiGB3S+ztlLC6oemARctbV4SFZ3Z1RS1UhnR40EIusknorw+dpZoKktXbxtmQSe3VqOvt4NEfA05UCM/V8I0NOYXo8gWC/29Tbz8yxcJdbVi67tM64CNgaEm5GIv506OMHHJyp17PkRiQmJi6jlGL42yZfMQB4/cxujwKFWtxK7bIwxsbKWw4mc1HqNSkQmF2lHsRTZs2sOWDfs5c/USzc4A589dRdcMYqk0n/zM+zj8liKPfFPm/vebXHw1RK4QW2fwYeIPufG3uAl63ZSKAkObulidzBAO2QlGIhx/8Rhtba3YbE5UXaRYNckVC5iGjqbrqDc6BPqvTBNaFSutbS4CHi+hRi8rc3FCdRtw6Ba++/3nuTb3KvccuJUXTp9hz/YdPPvs02C1r6sNTRNFlpHtOj6lnrC9iY+8fQuCCtnyEjY5yOBQF2bDIjv6H8JlWLly/RLVhErA3sHMTI7hyXGW41EaG1tRBAvLSzFK5QqZbBafz4cggFqt4LZZGBzo4/lfPo/N6aZUrCIAiiQjShoiMk3NTXgDVuokGSNoJ1YaprdnE+lojkvnTtLU3o4i2Bjo7iNS30+mkGL/vu0UinlK1Sper4eurnbGxsbQdY2NGwexeiPMTszT0tVNrprH5XSQWc0gyCrx1BreSJr4QoZyLoXf00VjnUQmO8f5iTmC9g52dbXhtYdoi/iIJotkMmVm5saJNHrp6OwgW0wRi0dxu524HE7KaoGqUGIlvoLLbUOUJfJagZV4juVkFdOlEdfHcfoF1qIzSNYyTlcTouBgZnESv8vGqZNnmM/+HIvUid1iEsvM8thPzr557cVESaK+oZPo6DixBTtNfRZCnU4mhhdZLQl86ZvfwVtnwV2u4/L4JLJTRpJlmtqDFNNp3JKOYVQolgvkgOvRNIfb6pAVg7yvwuC2bZy8fp1uglx44Qrvfei9xNaWGL1ymerlDL2bd2IXRHo3dxGPZXnxtYv4fG42Dx7l3NokS6d8iHcE2LenjdeXxmhxu/npEy+yub0Ni9tPz34/4ZKbn/3ke1y8cAFbsw11OsFINEpvey/evBtPgw/NXmFyUeDeA3cg5ap014kkVhYYGZ1nLZPg6PZOrp2LonnzPPfCszx6fD95PYbNK6BXFXTDjqrUOLzrALVCjni8xpXz11GzMn/+hQ9x7sIiDq+LvmADZ+fi6JLM8NgIVU3HoSg3pgcrGKaIcsNoVNd1ZJsFh0/BEc4jOQ2ccgqbzUrIHmbvnk3MzK7i8ljo7Rogn1pFsDoRJQWtsk4iMnWdat6KLmeQa3aWFzWSsRVyqQyCmCe25MbUArxW+iYnn1hi+4aj3HTvnbQ2VcjMDxIrVNjujzA+s4zsdCLfsA3zBfxUKxXKqopYK5BIFOibi3LowGaeev44guhAFGVMdKyiE0k2yRZLlKLLKO4gl1+9hOhQ2TLoZHo5x+DAXsZnxrh8fpp0IkekoY3V1DJf+8dv0tnagaqrDF+9hsXqxO62YVMMRMPg+vkz3HnH7QiCwMTUBGrORBDs6JKNZC1Gva+X/uZufHURrow/RXdbD6KhkxJnuOP2Q8TPn+fA4UNU1qpMDo8jVRQC/g5yKQO5w0FzQzfzs6MkcyUsFiuJchqry0mDo4/LZ8/iCMDGTX1IooNsbRmbzU02l6dQjlHfGCG1kMYZWmNpdYJEdBl/eB9NvT10DGwntraKmpO4/+7fBb7xH8bfm6IS+MqX//pzoZYqPc11bN90Jwf2DBLLTHFg3xCBxghltYhZLrC6GsMZtmJ1yuQyNYyKhfZWk+7OCkvLGomMHcUMItklJqPL+H0mRd1EwyQUDHPi6mlKsSJyW42x0Wl2bdpKPp7jvR+/j7m5FY6/PIIpC+RyORaXVkhbX6WrvhUt76ahdY2Rk3Y++xfvZWObF3vAzqmZJzhzKUZDWyuBgJVsMku2kMEZVmhpbuVd9z1AKNhCIpakq7mHtWgG2VrkwIEDTI5PszA9SsAWYi2W5fajG/nYBx/E7TX5iz97jVBTgN//2LO4fFYMNYwo1jBR6OiPsDAzTdgdpinSweunXyPQYGUuMUy1YFAtFLl9934m5qIMT65QVKvouordYUfVVUrVCqqmv+EvIMsysiLT2eWnv6+LZ156lnpXJ1V1DYfFz/TYDJ5whIvjs3hsVmZn1pCsEk6Xm0q1us4NNNcFR7DuP+h0y0TCAaYmxmltXqdIWyU3tx2+n8XFeS5eGCMezZNcdOOwBUFxMDu3yLYdu8hkc8zOzlC4IQKSBLCYIqVMgq3bNmFFIptbZmE5iig7gPWhKgQRSRbxuL2UsnHKmTSJqobH5uE3H/4ALz59jGI2TTy7QsBvwUUAh8vGb7z7gxiCgdMHhXKBbLKCw+6ip60TvVYjnohhWiy4XC4UZE68dgK324/D7SdfznHl2jVmJ2KkkwKXrv0Ym02hZ/AgQZuVxOoac2sV1hI17jq4jZdeOkNRLzM+dp1MLs8th2/FMCUkQUfXDCRZwe3zodYMaiWNbD6K4C/hqFMxpTLFcgLBWkKT03h9ASTJykosCnKNpuZWbC4bLruDyak5Nvbu5Nzxq6RSaW7aeQRRdPHFv/n7N6+pCALs2VfPXUcfZMtQC1//h28TcnXz+ivXGbl6CoulhM8dpq2lhVyuRimpYmahlCzjtjlBs6PVHET8drz2Erl0Dk1QyGZLzI+tIdYcnDk2hq4aBIMNxFNJrkzOYQgVdFNjdmaExrYODu7aQK1UwiKaBIIB3nqrBbtjjs/+pZWLJ6Ocv/IsX/mHv2BeP056aZqOul3kVytMnJmlnMvT2dOE092E37aBoOREi1eZvBbj3JmrHH/tJAGxnpt6u1FTMXIplbZgN/NLafp7+xD1Kkuzi7xy7Br1DY186St/j2AxUMthRNaJP1ptPaCNms7tR+7km//4v6lvsGDzFwk3NbFn7yGihSxlLc9AW4Ricg1R1xFFiYqmUtVUBNbHb3/VaETXDbIZjdWlKvGkhbnoHC3NBzDtMn6nlWvXR7g8OkkuscLvffq3URQLNquMruuoahWHw/6G/kDTNK4NT5JK5di98wClnIN0tsDqWoVHvnWW08eibNlwkHc/+DE2b97HsVdf5m//9itMTUzx3LPPcOnSeWRZxOv14HK5EEQRn99HV2cnTruD+lAd5fy64em/U4oBNEPHRKKQLWGqGnX1Afp6OuhqDfL9f36UyekF4kaGO+6/jdamEBbTJBXPcunSNcqVGt29Awxt3k5dXRuRcBMrsTihxggdfe0EvW7279jF6JVhmlpbUOxWGlubEGSZcH0r+w4cZvueXrZv2sSOoU0c3rKX1dkVNm3dCWqBTVs2US7mGRk7j8dh4w//4E9pbQ0jyTXa2xtRSypry0lEyUI4EgFDpFIt4fRZ8Ic81DdGsHkUgo0eGlp91NX7CQf8CLUqfrsbn+JgcmqMtUyc3r49dLZ3cfnyGSKherZt3oJpqFgcv77of1NUAl/4yz/93EDXfr73/X+iu7uNidR1Hrr7QU5cPoUi+wkGIySTVT78mw/zi2dP0twSpMHXsN4a6VQoFAzWYj5stjBWhwvVFCmikEnqlEs1plaWUaw15EQDn/jgpyjkc4iKhYZQI2fPX2HPTZupGAoBmwdREhCpkUqlyUb9rM1mGJ5d5f73+BkfN3nowXfi8Yd55blRhmeXGOj2ct+hIeZnr7K4IFKtefH7dToaPMQS01y4dp1YtESxWsRhK9PXG0cvtLK4qLK1v4eRqUmK6SJ7tg6yf+ch6pvbWVjKcXHkOmW1iKJogImm1RANkU27e6mr8zI5PE6umKNQrJHMC8SjDh5/+qV15kDRwGr1MT41j93tp1DOI1lkNFWlptYQBAFFlm/4CcjIsoXl1QmGtjTRsztCMlMj0tRCrhDHSFbo3rSJiYkZmuvcvHT2Moook8mmEGUFWDcx1Q0NTQVEE0HWqa8L8NC7H+DM2ddQBZHungE29wzid5pISonJ6SlqRRfJeIaG1giRSB06kC8U0Q0TXTcwdB2b1QIWkb6edhYWlvDZFK5cGcHp91JWBUwDBFFEkpV1M1XDpClk4X0Pv4NnXjqOp67Evr0Hkc0aIVsDO7fuIOwNMD86BoqVimyiGTrx1TSzkwuY8rpL044tu2iMtHLu5GWQYHDLIILNRtlUCQR9BLwukrkML7z8Mm3NDazpI0yMZdi6K+fVSAAAIABJREFUdTcNoXoMvUqxouLxuJAMuHTqIp3d27h8+TrJZIZDB26hlK8g6QKZVJrVtSiirOB2ebh87Sy4qjj9FuojrVhdCrpco1oxaavvYHEhzrHnn8Tlc+EMhShli0g2KxWjxOLkFF6xFRkbvT2byGVSuF0+RiYn+e53fvbmrQQkSWF24gpbbx5gcu40nR3gapWJtDRjcTmZWBzFXa8Rz1+juclKpVDBKjrYu7cZi73E0jwogoihZ7DbJXS7gE0SkQwv6biMaii4DR/JaIzJ6Wvookp9YDN14Q7CkQChiI9Ik41kTmXXji288z3vJuh2sWvwNrS8g6XTbmRzgAc+2kZLh4+pK0sspmps3qwwsFmiUF0l7N5MT283hlaiUs6hO2uEWtrZ3L+HW/Zs4e7927FbQatYSRc07nrLEVTVQjWT4SPvfw9BTwuLK6tMjU9Qq8UJduRAl9EFQHNQM8HtcNLS187I5DUOH9zNoaM7qPO4cVjCONG5Zc9OyqpIxWry6uvnUA2DSrWCaQqgm4iI6Po62UcQhRteg6DrReJrJio2AmEFwalQLK+RzGbo6O3GIc/R39ZItGDi8Xh4+zvvp1xWsSoWHHYXqm6iU0GSrQiiiN8fom9DL889/Th2m4VcIc3o9Ajnr1+me0MHre2dTM/PoAoldmzfyIHduwkGA6RTKQQE7HYniiJTq1aoqSq6rlPM5YjF0qQy82zf0k+tImBKChrrVGtTrSKYGopFJBgI4/b4CNp8yM5GMpUo24Zuw+W2kM8XmFlcIpErU6qlMOQUalnFpvjw2H1ML83S0N/IrXccpLe/h8amZlYXl7hy/hTJ2DLxaJJwOMDMzAWqpQpHDu0nGHCz5eAA3Yd6CDe4+P63vknRptA4sIl9O48i2hUawgFaezexb/sRirk8sk2hpakVu9XF7OwMiixgGiYr8zF0qUxNilETNVZjaUZmxshXyxiixtz0ChZZYtPWZlymF6s9RH17B30b2ml0Rwja/WzdOEQgWE+tWMLnD5KI5zl39dfbi70pkoAsWNDyIg/e9mEevOP3WJ2wcOqlY9zWfwtum4lasRHxhaiPdCNbdByCiCNo0NwbIRa3MB/TGVtdYGF5nvRMGlIGYVnBIpSob7LSFPTiawyzY08zplrjrUfupr9LJBDUuOfe26loFVaWZ1meGWV5NY7FlNiyeSPRzGm8rg4cTgff+F/X2bfxQWbmV6joJe556x723bQJ2W/F0dXM4ryKoVoAiVJZ4tTVJc6OTBFpqOfWPXsxcwpBSwRZa2H3zm2cOvYirx5/llQ2hWnC0uocM9NLXLgwyqnLT/PRD+/Eb/FhqjKakEWWZQq1Co8981M0t0xLdxsOwcH+7Yf47Kc/wlB3O76gjEkZpy1AR2sLPm8AxWLFNE0skowiyVitVmRZfmNbILDu9uz1Oeje0EpiKYXLtkg29yrl8gIXzp+DcpBbbr6Zgf5eSsUiX/zSV1HsTkqlEibrHoUYLkSphqFJaIkA5XyU6FqF9o42BEwEoYoUrKGEgywtFRjq34nHo7O0PMrFi+e4eu0qyVRq3RFZraFrGhaLBVEUcVntnHr1JKIi4/P5CXnc7Ni6GU3T3tjW6Dfkz4VSmbXlGJ/73BcIRprobz5MLG3Q2u9joTRD98Zullbn2bSzk4bmCL2RbrobGxGMAi2NJhvbFPRylnMXL3Lu4iVMROoa24glckxNzNEQrmdkZIyFxAqyImBSweYSkLDRGglQTJTxBt3MTIyyMDHG8PA0HrmGN9JLQ0MLBw7u485bDzN9/TrTkxNEE6tY3Qrd/f2IioNoNoHdKdMUbkDUwJRVrIIXqehEqlnJm2tU9VUkl4OKdZn2Og8XL0xy5fQoSslJfsHC/PQkm3o3IBsO2ls2UEyqDHQ2/tr4e1MkAbvNxqc//RCppSqzs1O01Nv43lfPc/LScU6dm8JQTCaGV5maWEYTdUo2jZnlWZ7++SsszelYXQ5kD4SCQeLjGcR5jbqig1ZLhBZLmLduuZOQHgJNwekQuXDuCZo77Zi+KkMHN1MoVVGsLlweB9lsmkf/9Yf0DrUzMTZPU7MPr1+hq9NGccEglZlHo8rwyi+5em2C8rKLSlxn5/6NXDk/TKlYxGIz8dapVNUCT/zkSc6fmaRcraEXY1w6meYn3zvN1PgKhWKexuY21FoFSZFQVZVkJofLJ9JW14Sux9DVCoJoA0RqqopoMchVsyytLLMwuQKqQDqRJ7YWZ3RsDMkAvSpRF/QgimBz2DENg3KphG6s67b+3W0Y1rUCiqIgiHDu2iXK5Qwem4jVUFDVCtHYCt//zpOMXL+OAuSzGULhCL6AH0Fep/FqhoFpWDGFKqKgoFYLSKJEpDHI6yfOk87mmJq7QHN7Ao/fYHCwi4XZOZJreVw2F5IILrebukgEQRColotgmtRqNWq1GtWKjmBKVNUqtUqVtfkZlmamqdVqb4BXTAR0w8BmtyNKTmSLg0y+xM379iNqJb77vUew+9yoKqytZtArOrWqSa2qIJrgcCrYg3W0dfezd8N2WuraUHUN0arg9NkolFNE48ts2bwJt8tJtpggnUqSTEUpVUr8+MdPcvniOKLNS9+WQarFIpdOn6WztQlFLrESyzM/H+XMpUsIpkJ8JYOmKjg8PlweJ06nh+nJJUZHF6iWoJisUi3kmFucoFo2qPe04LH48djrsNbsBJwB3D6B5Mo8fo8HyWVweeIChVqSakWgnCuSziyTSxWwO8to+dCvjb83RRKw2R1cHD/G0794mfG5U4Q922gOdjCfj3LrnV1Y0Vhdy/HSC8cQVBtmVcAhW2mPtFPJOshkMtjsEi11PfS3dtJm9VCLV3FoLjwFB/VCAz1SI8szyzQ2NVHXFCFQ10iimKCh3U9VL2HxWKkoBQRJJx5f4cnTTxBdq9DX3019XTttDT2QH2dDbx/33f0gvlYvybydbGGJKxfmKeUNAj4/pXKZYqFEeq6CHpXo62zA3+AgXC/gcwRZnjU4d/Ii2VSZUqnCnfc8QKGUxOkJsrg0g90tMzfl4Zl/O8vbH7oVf1jCMNbHda2ihN9poSXg5sTrr3Hzgf1U1Bzff+wxTl8fZ35plcZIkA3dmzHULIgq+XwWp8OJaFEwpXVxkKIo6IaBrhsIQLVcQZbtPPfSa9RkDaelEQthWtsHOXrvXdx191uxWS2kE3FC4RD33XM3krGOPpctCoZpYhgqggCKRaRz5zJWewvJbJRw2EdbX4hdhzfTEtnC6Nkoai3NTfu24rBZcdgV4rEV5ufnyOWLmKZJrVpB01QURcFut2PKCk6nTDGfQ9c0nIpAYnX5V7Y0692BmmZgmOvEZYfbz/zcCrlCHuI5ctEiG5v2MnJpAcnlJ6uZBJ0BFMXN2PQsis1JLp/BUqsRHZ5k6solVleWqAg1JMmG017H4OABfv74Y4yNXgE9S61UpVapkUmrbO/ZTmFJIZuvcPzCed7y1geoljX8LpVYeRWRCunsMoa9SlYr0N7XjWSzohpVPG4vF06fw6io1PtDBNxBGsL1lMopisUMyUyClZV15aOaM1BrVorpGlouwMJCGZ/FidsXprmzk84Nbpx+haXFWQRFwuZ143RFqFV/ffy9KZJApVLjyvUpPvP7Hyfs6eFjH/5jNm/z8oEP7uXo1nfitQo0d8gkUgmMlIpcELCYFiTFhcXqIBxwE7D4WFpLoznLWDwCkc5WrHaR/r5uEvEYi3MzbOzsoy7YzMxolvhCgvHRC7zy1IskokX8rTkS1SkMuYwr6KCjqwOXx4td9hMJNfE//uDj1IpFnvn5a4xcGSe34qBjsEJkQzO1cIzRpRNs3bYFqyIRqPOTSZvompVdezoJhg0MzWTr1g0E/A7QsmhqgXCdl3QiRTyRQq3WyORyXL56FZ8vyOqija6BRrbulbHaBDAERFFCruTZtWUjh27eD0qBI2/ZzcE7ugg1hrBYrOza2cO2wU00NdaRzWeplIo4bXYcTgeKVXmDPyjeeIOqmoamm2AI9Lf38MKxs0QTJobZSrZYxRW209DiJRgKUt/QhK7phAM+ZFnEIsvYLHZMU8OUChimjCFWqNtYZcv+MK1DEbbf1E1Lj5/uvk7OnVljw/ZWSlJpHYfuttHR3sTWrVvp7OzGZrcjSAJunxeb3bZOPhZFHE4rTrcXv9vL7PwqA4MbsSgWJEFA1XUESaKmaYiShGEaWJw+DAP27N5EOltALdlob2ti79BRrJKLDYM7WClkKJaLTI4ME4gEaWhtRrQprObK5BUPol0mnZhH10xamyI4PQK79mzB5nZjajZsig27w4rHHWFqfInccom2hl4qsSXOnD7L2UsX8PgjrEWXyWYszM7Ms5qYpmZUqUop4rU5RmYvUVZTNDaEcbklerobOXxoKxv62rA6ZUL1QQRdYml+mdHxCSx2By6vi3S1QK5aIVLfR1NDD163nwZHKw7Dg0UPMDl9nXg8RijcxuLcPE5nC53dvl8bf28KsZAgmAy2H2FxdgyL4cHtUlheWiA+WsfOwW0MDW7BFfDy+DPHCZoyfZvaiUZTTMzFUEsmalVAy2mkxDmcTU6sdi+mxQK1IpIEgm7Q3t3Djr278butuC0wPXye/bs3cfqZKZC8jA5P09rvJSzU0RBppFotcvORw3gDVsqqwsWzl4gXllCkEOeOJxC9VXwOF/GkiWSx0bzPDUtVBNZL9qY+By67neWYwOUzl0iuphEz4Ha72DwYxF9fx9Wr53jm6Z+yZdPN+F1xOjq6OD+yzMG9fs4ej/Lyn/wrg7trKEI7olBBFTXuOXqQht4OxieugqZz4dg4Fn8Bh63KkVu3s6WvjWJmGFNQSGVy+D11OGQrmiRRqZVv2JStB5eJiYCILCnIosDujVv58S+XmJmbo2N3F5NL1xnq6Gdh4TWeev4kktWHZLXwta9/jbrGFpx2L9VqCZtFQUOlphtU1AInX5C4bUeVUGeINnc9k2NL1KIWlhaG6e6tw+FpYHZ5DLGg4z+0HY/HSz43RjKZpFAqomkauqqh6zpWq5VMPEoo4KPFE2RhegrB7kASBVrrGpiLRtEFAUmRESQRXdWoVFVkrcaenZsJhVz4OtpxiUlypVXyhRQ3bdlOT4OTmelFRGzodoUzF85jEapY5Spzs6dxe0UiLQaCYLC8MkO2UqSncyeyYifo86MLVqZmJsiVNLo7BsjWBDbv8ZJbGsHtlagWc7R19jA8s8Sm7n2cXJmgr38DVocNX0QlNT1CY3cjVifEMtNEWh0UczXWUgs4A2UqQg2vJ0hbRCOTnGJuIUp9fSNSpoAogNUjklIXCdtakRSZQjKPqaksr5hEAi20RLpIZ6JYnRaiqVms8q9/378pKgFJFPHJLZw79zotPZ1cfP0kt9xyB2pcZmHhZZptrQw1bMVqNfHaw+wa2EFrcwM2a5CIvYXoTJHsmo7XqKOw4sGihMln4yiKjCCK1PQaoWAEQdfIFLP4Qw10dfdhV33U4msUEpMUFkNYfQ30dfYRaQowOzwMkkE0E2P/bbfw2A9/wDM/XSIXraKoEn6XRLkYwe70YFoFYgkNQTCQJFicTlKOCdRb2+htvY277vgNNvc5MUSZ1o1dqIKA11WH3eqhrdPLnv17ue22W+nq6MXr9TI5EUU1IBSJsDDcRaVSQpZNLFYRf8BHuZgiHs/zre8/zRPPnsYoucml0siGzuL4ErImkcwWkCxWbBYrtVKZfCKNVq4g3Pi/uaE9sFqtWKwWdN1gYTZByOZgz9B+qhkVtxkhnp6jmrFgk628/f47uOvIYTRDI53KUioaqHoBr9fPrm31vOvBAzgsJiHVTTq9BAWZ6YkolZjMxKU0b7nvXjzuMK3N7dx191FsNiuFYp54Yo1ytbBONVIUFEXB6XTicrmQZZlapUY8FkNTKyzFYqzFMwwN9RP0+BCAmqbe0CyoGKZBuVYmXygwfGWEueklAuEAkr3E5WunqKsP0toQZtuGjeiaidNjA0nE1EWoCbitFkqVEr6gk7pwhFR8ldGJM2SyeZ5/8jnCHhtNTS7y2SJT4zMsLs5Q0yvE8iuoRpwZbY5b3rKDQztvYmhTP4rVTTobJVxXR3OkDY0i0WSGStkgUKdQqOYZnx0nXymCvO796I8E8HgCmDWB1pYAvb0R6hu9qEaefDlBppJgKb6C1a4g2qBYKhBLR1laXmZ6cY6grx6LYkMTaggWlbpAmMn50782/t4USUDTdPr6OlEcNbq7u3jisUcpVXPMzMbwBe2Mn77G9dfH2Nu1gWqhwuzEChbdTmI1QSZWxGZ4CDm9tPgCaEWoVSoM9DWx68B2HH4PC4k1Tp69wJmz5zh99gpaxUcyU+DY81fQDYloao76kIeltRrJfIbl5CJH9x7mxKuv88sXnmHH/m0kF0sIpof5sRWWly+zulji3LUlFuermIZAYiWOLNvp6GhH0yyoqpVEchlNyyGbXoIuF8GGMA6ng0hnhHhhCtluwVlXI5E/h2oa5MpFtm3fyvRUAcEiUKtJGLpMjQSKxUq5XOLC+asce+Iqde4uugZC3Hp0B+NjS7S1D1Arp0ms2rA5PSyurIGsUK5U0Ax9XSBkGAgmCKaJIksoynohKAjrjELBdNDZ2IIDgbXCCk7NzdjyBdpaN3Dv/ffjcLtRJFAUO+1tTYCKwLrqcNeuIJl4GkX3YJQLzEUnsOtOFuZjjC5OsJC6xMjVGWyin2wiRyIRp1QuksnkSCZSbxidrv8e4Q0TVMMwkEWJdDKLWqsQiQRJpf8P5t4zSrLsqvf83Rve24zMjMxI701lVpbJ8lVdVV3VXe3UUrdMS8g2QoDMA/QYxCDU4gl4b+aBDEKAmCcJI6T23lRXl+/yPr03EZERGZHhfdwwdz6U0Ghm6IGBWbN0Pt21zzXrrnX3vvvsc87/l6RncJgl3yoACuHueaXSXb6CoNRisdrZsmsXr7/+IvPz02jKVgZ7+zh0+BDt7S0kYhEMFhsDm7tobW5gz/atNHs8CKKFxz/8JMOb7mNjQ6AkigiyimpFYOvObdTXNdLcUY/GoOGe/YdQqmDnviEO3tuNscaIp9XGSGcjK4F1TDYVW/q2ElnPYXWYyUsporEUoqhCjwWxbEAtGFEIJgS1nnAqTEWVx7sepFJSIZUhkQ0hqIp4Ws0gFrCa6nFa3FAQKeYlEpkksqxAkiAZzWJ3WDHZ9Fy9eo3gaoSF2QWsOg1KQfWe/vdLEQSUKgWFYhqzqZal6RlK5SKtXQPcHruJRmVnZGg3tfY6Pnrs89Q01nHq2mWikQw1NgfJVAJ1tYpda6G9oQW7Q42oK6M2WInkimxklazHJSSUKLR6cokYuY0wb750mlszQWaCRarqOrxLASrFHAk5j7u7j1A6jaSQQYbv/cmfEi/kMbusGG02UmkJW5OWTGiWTHkRj7kZh1OJwaSnUpQwa+zYLG4KxRIrK+eJb8RJZbTMLXuZmVnkyEP3kxPC7H9wF2U0/PjFH/OPP/g7Jicn8a4sYtQZKElZCtkigjqFQjAhlxXIlSrRSAk9bVhsOezmWvLZHJu2t9Pd58DdWMueo30E4usE1wNUi2VkZBwOOy6XA0EEhbJIFYlqRUaQIZVIUJIUGMwOgsEIetPdZ3U0uLA4XbS7j9HT10VOKvD6W+/w4mvHsZjNVMpl5GocoQyRjRgnz6zx1jsT5MtVEqKW1rZ9xNMadm/bQ8+QitGdO9FodbQ0dVPIwdTkLJKcw2p3UyyayGVlBATKUgm5enfI8s9/d4VcBKGETq3CabfgqHWxuLJ8d3ltsUS5VEar1aFUqiiVSihRYDDaefihR8glsnQ29rGl6wCVbIl0LEW2kGZ88g5OixGLsYxBV6bWbMeo06EUNSQTGaYXF4inMhjMasp5Ez0tLhrrQTZJTCxMYm7UUJYTfOijx9i8vYGuTjdapYLdw/totjQyOT1DjUmLWaUCWY2sqDA1P0komKBcLmOz1uCwNJOKS9Q4mlFpjMSzUdayU0wsXmV2aYFESsLrj4FKxOrSUxEryBWZ5ppGnNVBQmMCNy/eJh7PoNLb6WrtZvvoPuKpDUw1RlpbWqix1JHJZdHQ/N7+9/+jr79nKxQK3LxymeWVFcL+NN3dPQx2DiNLEs/+05vY7HWYjDrOnDhJV6eLlbVZ5KJMd0sbTjlCIZ7AbrWyZ+d91DV4UKolctkca4FlfAsJLHoBu81JOJaikM1yeWwMpclNLBXFv7TG9l2d9Hf3s3YrTkVRYd6/SimVZvRIHzMXF3n5+dfZfk8n2YwORaqArcnF3HU/tZp6moY6EHImBjbX4Sw0Ioji3QU6yRTbhvooZyporGs0t+5kIw3heJR33jhJcMNHH9Bcu49np8cYPaRlfmGORL7Knl3bEZB569S7lAFR0EEVqpUK66EInoHtJGJ5rMpeIqV5SpUyPd0dOIw2LA5YvrmO0WBAqykiU0WjFMkUJMpliVKpQhUVFblCpSyRzmRQaNWo9SrOn7nMB7o81Hfdg8shM7W4QS6b4eXnn2FmNYjd3USpmqDW4WTVu4bRoKJYqGIxGbhzI4xOZ6EKKA11bN+yk/NXruOuM9PW8yhjN+dxmEdwuxuZubNMdD2Hp9+B2+Ok1qdHF5RJFu7KlUmSRPlndQtRFLEbVBjVdUzNrOBwmUAU8Xq9OGx2otkMgixTLldRKESqAHKVdEEiGUuiFPSYTS6uXR+nIKUpymrmFm+QiIXptns4ffI0eVFJnTOJd9lPOBDg1u05nA0uBjftIpmNoKoaMVo0HD/xKqvJDId27QEz3IitYHKbUell1GE7FlUBTU6NqLWzbccAVbnAD3/6fTz9/ZTVEaRwjn3DBymKEbzrXlYXl3Ba6vCurKM3KaitqyWUnkGBingijt1lRqVRsLi0RIvYgsVqoSoIRFNZ3G436/4Uycg07u1thDI+shWR9fAS8bUQPYM9iEIOA0aCG2GGRna9p//9e7kDT/8Cc2BFEITbP7O3CIKQ/4W+v/63BAFRIVKp5lFpjYRCITb1b2bq1h16e/rJ5fJEciGujF0nm8rhUtUx2NTLkT1H8Ljq2btjOw8ePURniwelIKBTGNBprDhqXKi0Il0DjbQ1W7A7a1lZT+KPVAmXYdofZm0tSvemXvbvPcL7PvgxAnNp1EoN4fUAaOyMT60QK1So7WnHs02kfb8Hk7qdQwffj0vZRaVqYu6qn0JihqzXhFSSKQsiCkMRQZPF7jIj583kEhs0tLrxNHeyqX+Im5evcmjfw1gNHgxKF1/+jU+webSBjWCQoaHNaETwrnjR6fQ/x47rdRpEscqRQ4cZn7nK/fd+CI1CQlWsMHstwOJYhFOnrhOPABURk8GGKAoIyKTTSYpSHpvVhNPiQqc1oVCqUKoNGExGKpUyuUwCRVVBf9MuNhJrLIfiCPos46tnGD2wHYfdziMPPURrSwPxdBqUSqSqCklWEItHKGUzmA1qlAoRchJvPHeWXGURo9GB1xfHH7pNS72OteUxbt+4TjAYplrVszC/gChmUCm0ZDN3FZH/+Z2VP0Ott3hqiEULqNQKPO4G2tramJsLsryy+vPzKpUK4s/0EVLFLCi1vPH2CYqVCnqzhUMPPsDwrl1UVVDfVEv/1s0sBtZw1nu459gOBvbX0jvcQ6qQJZaJ09hm5Z5jHZitMm63GrmiJFdSYDNCq9tNba2TA/fuwu02EZxNYa4WMQoNRNfWUBvqOXHmZcaXp3G2tOFoV2NzW9E7VKQjafxzCULeLDWWRip5ESlbxDczSyYbx2VvoaGmj572AdLJDGrBiE6jQS7nqXHYWA5MsxwIML+6QFYZoH+kj0wuQWB1DdEpYdQacNU2o1BXkKolVpaWqJYrGDTvzR34twwHfgTc94sGWZY/JMvysCzLw8DzwAu/0L34z32yLH/u3xIEpFIJjaFCbWsDgz1d1DhrCMfX+bUvfgq7W8d6Lkxzl5ahkSZ88ysYlBYseguZZJ4L5y9DVcHuPftZCwRYWfQSWEmQ3KhgULlQqXQ0dHagN9sQjFr6Nm9FadWhqy3x+IceYGTnZnJSlW9/54cszcbJparMTgU4fvo6c2MxkgmReDGHz+ukoF2gY6iJcHARW10tOcGB2hIkHkhz/CeXefXlF5GKEhq9FlFh5OTxO7z8ygnimQIKvZ5EOk4um6PGWcO6L08yViSdyzDcto1ixkFzYyMz09M4rOa7W3RlGZVKhVqtpiTlkXJQyivYuXMIr28elSFOs7ue3aMjnHvnMj5/nL//4QnyhTxjE9NoRCUGtRqVKCJJRQw6FSZdCR0ZNCKoFFUqpRJyCcolGbPVhFFlwmMbIJddYGLiFoIadEY9e/bs5tyZsxw6tI+V5VXUKgVyVQCFgkKpxOd+7ePUucxs39KHkijD/Va0goNXnp/i0vUxvCsiK4sRgpEFYoV59CYNjlobaGNEyzPEcgEQ+Lksukp1dwwrCAImox6qWUa3DNDY4CYa3sBiVNPSWoMgQLVyt5agUCgwm80oRIFKVSAcjqBSKPF4mvB6vfi8PiLRGPmixMKSj0Q6jc5kIhIrMnFnld6ePvbs38uBg7vYsnkLDc522jxuFJUcsqRg79799A9upViBG7emSYXDLI8vIkVkHOYOFsNX8EXTWKw1UNFQU9eM0djArbFbzM/5iQtRBJVAuSzS0TKClFdQW9eEz+/D7nIQ2ljjyo2rJFMpTFYFsphEpaqgEfXYNG1kwzrktAWT2sXGhp9cOYDJpCFT9OOo1+Bu6GXZt0ylIjI3F6C3dzPdnV0U8wXOnz7z7w8C/0/cAeHuNrQPchc48u9vlQo1BgX1LhPRoI/r168QTYdZDS0Ry6T43K9/CYdpEwa9i2K+RH9vHwvzC4Q3Nti8bROoqkTiMVraO6hz11NfW0u1WGVpZpWcFGMpvUAkO42kTPHOqbdw1Bj44leexGDQcfP4lO8nAAAgAElEQVTOLNF0mldeuUSuUGZpPkRgtYirWUON0UCtzUUknKTDthn/hXbuf2Q3y9FpNEYzjz8yyqPHPo6kF2jp9xBd9xH0rZHL5ulo2UNX6zE6uwbwdO2lKlhRiiLZTI4PfeIT3LP3HpLxHKN776NSDjI2eYNMrEo2leXqxXfZvnUr6XT659N5ClFk3549TNzxYrcr2YjFUGhctG1tQ1VfQTZqOHzvESwmM82tTUQTKVSygEmtRSoWSSQT6HRqPv6p+/jMx4/idqiwmDOohCLpZBJZ1pDMF/FG11CYNlheivDwPU9SLTaC5GBybIL2tjZ+8pNXeOx999PSWIcsFVCqFMgKJVevjRNaT7I4tUwmYuLSyVss34EX33gWvatK11YbocIs6lqRw48c43Nf/Bz7DrdT1zCEq3GEhnY1TtddybJCofAzMdG7x7ZaF1u2DKFWyGTSadaDQfK5JI89/hAuVx2C8H8UEVOpFGWpiJQvMTI0jFlr5JUXXiYUWEevULNvx14KuSrrwRjd3d0kCl5862P455aJx5ZoamygscFKNOzjB3/1T6yvRGlo7MYXjjCxOofZoSIanaS/ZRNt7XU0NLXQ1F3PxfGbJHURZKeds6dP8djDn6XF04/FUqCSVSDlggTCS9hMai6deYdAyM/U3BieFheDWwap6FWYzHoa212ImgJXrl0kk9BgN/bgNLWhkAQ0QoaWGhud9U2MDt3Llk27GOgcwWRRUdbECK9NEU6FqIppkgmJmblxqtUqRoOBpqam93S//2hNYC8QkmV5/hdsrYIg3AJSwB/Isnz+X7uJwahjc8dWLvv9GKwGnHYDrc1teFcXUOgVnHrhLW5cHaNcCRCV4sgamUByA09TI1t2jDJx/TZWQWR8ep5QLI6jKhBP5EjmCqhzJbSVBhLVAJv2ZZi4FeQD9/wqa/MxxgOX8a556WgyoNRW6OpsQK+1I1dKaPMiTreFtr56fJEcZ8ZfIbuuwN34P1Mp5QlVzjEbqicxI2GlFZEIJosea72VXQe2c/8DD3Hp5E3qXAOUlGmuvHsSh72FpTU/ew2H6eodIJY+xZnjr7N3ay07R7fy8IPdPPfyy7z2+puMjY1TX+ukrFSxvuZDrVCzc+dhnn77f0HrXeIj936BjEbL0uw4OTHOQ4/dg0JdQSXHuXT2Blq9FZNeTzEjI1WqVAQl1UqOM8fPUqkoqZZLNNUIfPShT1IUzbz0+mni8QThZIBgrJ7OxiECY/NsxCbpcDhIZFO88fbb1NpsTI7N8KmPPcZf/vi/su6z4nRZmVsN3P2gxCJqRYU3Tt2ie2QFpaHIwvIS5sZ1ajRRVqcX0SgayIcEghtn0TtyeMMV1kMxwhtqREGLRqtFp9VQLEpotTr8wQAKuYLeokcsKwkH1xndtYOuzhrSmTxKpY5KNYcgKlGp1UgVGa1KxTunL2J06klGY7R4anjkoftJFRN4WmpZ9S+yvpZgy7Ze0sooPa1uYsE0q7MJzCY9ecMGOSFCKlGgTllLfbuKtdhV4olmEmEJTSpFowlG+o6Qz8VwD4ncPlvEaErzTuQqn7j/EwTWZkiKq2wedFCu2DAmaxH1KXY/2E6dXaTeVUfAP02xsI7NZGNqIUTaOkNDvgMx0oBGpyYfyiFlK+R1VcLrEYxGPbWuPAs3vNRUrQy113ErqSYWK+BsULJnxyh6wUSukCebWketaUasSCzNzr6n//1HZwc+wv85CwgCTbIsbwZ+m7vKw+Z/6cJfhI9E4ml+/Nxb9HVs4iO/+SRzc9P4V4KEojFmp1appCo46mtYi6bIKMpMhZY4fessl2+f58btcZzOegKBIEaznmxewhcMsxFLUudspMY4Qq12hIWJLDOXGvjK7/0X5iZmOXHyeVrbPOy7ZxMd3TWYzEm6OxrY1dWDJlvEodXw609+lOhqBqsuTTG1wtFPOFkcv4xF3obL2oc/usq500uYnWqEionO/g6MVi2JZJBgcIlYMkK+kEXKZmjx1DMwtAm1ysCf/ck3eOnZ5/mLb/0FDXV2lCodxVwFg1VPb08fkUiB4++cY2lpmXg8jkFvoFxNU9NYx5e/2cncVJabV5a5dO51ikkleqWKSPQCvrEFPnzvUXLZi6jVJeKJAoVqCVGvoSpCjbOBruY+tCorJp0LpVxDXV0P8wteorENrFYjrz93i3Onx5ldniFUnefg0a0I5TxOq533v+99PPTgvXz1q1+jCuRTtRz6lRiRpB9RqaRUqVBGJlktobLZ2LKrj0eP3Mfm/v0EFpREkikuXD3HytosvX1unLodKBK9OFQW2ht7MRpNpNIpcvkcmWyGcqWMQqEgks7gcjqJbMSplIvo9XqSqQzPvnIBfSWJSi6gVqkpSWWsVguCRkkkFuXixQvMTFxlamYCt6eByxcvcPP0RWKBENHVEFKmhLZixz9RYnk8i8lQj96hpnOgHYu1if7hIR79lYfo2tRJJOTnEw//Ooe2vZ8PHPsEmcgG8xNl1lYFymUdFqsGe30tmzs6OfLAAxg0Gs6+dYImXS9y1MbDhx5DURaobXfQONzC4KZN5AsS9hobQ8ODDG5qpbulmU3N22j1tNDa1YjZYaVqzNPWVYe7xkNdrYuqNktRHWN4pxuD2sbtiZMolWq667bQ7GhFKxQ4c/IcioqC86ev07N5gGQmR158by3Rf3cmIAiCEng/sOWfbbIsF4Hiz45vCIKwCHQB1/+v1/8ifMRdb5cxG7h66QqLy7ewGA2M3Z6ioaeOA3t30+UapqYU4PatO0QScYyxPJu3bKXRWUcg5KPWYkFUgKgCj8dDOBxBKpaYnJpGmprFZjUzuvcQ/o0ZfP4NPJ46goUKy7M+9u1+mIjXz8G9+1lcnuH4xNM8/uh+3n/kUULJAlpdgXvv+Th/8D/9d4ytPj74m91ohWVKGTMjW5rQJqNsGuonEg3x5gtnKKas5Oq0rKyssh4McOvqGewWPblskY98ooG9u3eh2T/K8ZPneOD+vTidVjQaFefPv8uVOxPcf98H+PrX/zOXbk5w4twlFKIKrVqkWNDz+onvkb8yR9BbwXpEj6JUJRmPsbG2wa98di+7my1oCr1cvK4kciOAUllBVMiIJQ1KlZKZuWVuXB0nkkhRV2PDbIBlvxefP0i+WCSbzVIuKLDZ7Dga1ZTFEqtjaww4t/Gxj+3A4W7m5s2LxGIhJu9cYyMUZut2J5Wkk9vnMyhFEYUgUpIkDIKBJvsuXr92ke6mCAMjCvaN7qXW5cG7XKDOYyUTaiXltZP0lpiZXSCbKVIsFn+2v0GJXIVCIU/bwBBdWgPZmRW2jGzmuWefobaxhXfOTTHS2chcME4kXcTjaSRfyKBWKVHqdaQyUbb31rKeVPPa62+hU1WxCFAxVunv6GQ9IrG45KVQLiGWksTCJkZ6d1HMZ9neew+mugqCPsnJa7cp5xSMnZmgq7eHaMzHRx59HxfOXmU9OoWgLdGk3kZzmxEpWKWltxmCRe6/5zCNnT14l5a4ffsOW7qHuHjyLcpWmWPDH0YuGpiemiVTWGdpeQFPWxvNnXu5cf0ykY1lnG41uvoY5WINAf8amaoXs82CtqonGgqxuhZDqqjYSK+xNpnm2IFHcTtcJONFFlOrdLZt4trZSbRGMy5T6v/7IAAcBmZkWfb/QmCoAWKyLFcEQWjjLndg6V+7kc1qJa8pMblwB9V8ma6GWloaPeRLMvFoguEHBjGlC3zvu2M0eBxs69iMwaTn5o2ryIosy6sVTAYXq/5V1tfCNDc0srIyRyyVQK82YbVVsTSDuakNWQjguwJaeYD6DhUmu0wpbEatsfPF39/Pn//379Pf00NdZw3+K2M89MFjTMWfoW7Ixp7h+/jrHx2nvSvD2mqU2JIWIRfl9oUJ1O1ZWrvbufq2F2dNK/U1bVyOXeXI0b0MDw0jyRUuXx6js6WDHzz7Cn29vXR0ejDqzKhUVR5//EOsRSM0NNaxtLLE9Ws30ekM6M0WouEgVTlLaqNCOWNgx5AZja5CPtmFYIxQ1RZ48d2TzF/cwqFWK2deCVDv1hCMVCjk8gz2t9Pd1UYmEaaazzI2PYnGoKfF3czISD/Tyyss+FRk8lmctTUsro1hbOjDN7nEsGeUugEnuqiVr339G6yue7GoSjz5sTr0nUqO7PkW3/3GF9EKxrvKQqUyiFrEippsssKnv/goKys3iITrmL8J1ayK4MZpnntTIuPLUgin6ejYSmMuz3psBpPJRLVapShJd5c0K9WsrK+yc3iE5hY3awEfe/bsZMkbphBO497fh7OplRMXpvB6vWiUYHPUoFCo6N3Ui54FNjYSiGo9rU0uTG4VCqUGm17NM6+9yetvF3n0iYf4wq//Bl7vGqVykTtTF7h+5zR2lxn/2hIedydSMk39aDfXb45jszrQik4kSU+Xs53CRpwV2cdI+3bSJQl1LMvEpVu0DXWRTObJZTZIrqVoHm5m+tUFLLV1+JYvkIqVMWjKLM4FcTWI9PU5iITK2DWNxEU/LXVb8a1tkA4m6ezsR2VpY/bOPOu+PJmCRCRUpFiJM9xxlNfOfp8t7TA6OMyRg/W8+9ZbiLKK1vYWkIoUROnfHwR+xh04ADgFQfADX5Nl+X8AH+b/XhDcB/yRIAgloAp8TpblfxVmms1mGRnpwW108Z0/+0s0soKSco2ukft57L5d/Je//i63x8cZOeSgGtdREdKE/GFQqlGqROpqetCqVNzxXcfoNFEWUzzxuQcJeDc4/up5zE02IoyTSyhJrEY4tu0BpqbiFLICixdu0e7ZRkkZwzvt49Mf/gzzY14UVi0Vk0gwXSATjtM7qmR24w5FTYCO6hNs29mMd8OLp3E7U/5XMIUM9HnuY+DJIUbvGeTquSgKbZWGBidWnZmUBP3dmzl79gxDwz143LW0NbYzNr6AVunkx//4T8SyEoVcmT986tts297PxbFxLA4LUrWKXFYzNT7H7z51gG2j+1id1NDkTrG4kgXRgdC2yszqZSzJ6+jqhnjiE8f4zrdfoqu7g5amVt548w0297Yx0t2CQVnl4swyl89fAqudUCKJLIjIgCjpaTI1kl7PgajjT/7077n3003Ez7s5/+5VnA1u7t2/lVC4zCP3PsjXv/THaNBRVYjks1mMBgNGtZXA+jJ/98z3eay7nWunQ4zsbiZtPItV2sfulsfQaoycX5inpWUzhXya+elF5Ir8M7yYArkiI1OiVJJo61QimuK0iHYmJ5bJ5wtkC0V6+vTMT90hlFMiine5hIVCnoKURa+1E49E2LK7n4W1GRbnV4lHNyhWa7l3116unT1HS08DrZ4uunvrmZq7SEtjD5WSgf4RN5FYmWLCxdLSTa5fPE1DkwtZIxJOVGnrq+PkpZPsPfwIl09cZu+h+4jnJgj5vXj0dUQ21GQrFXLJDd6+Oo534Q4dPVtpzOnp2dNPp62ffF5Df08rK6trFOUFOvS9BEKznDqzzFDfKL0N21idmUXvUFHT7iKWTJHye6lmZWrczejVWvpah+nu3UMuWeILv6FluH+QwFKArt4unI9/CrUuT2OnnjMnr9Dq+g8UBmVZ/sh72D/5L9ie5+6U4f+rZnfWsLyySNNWF0eOHEJZrXL0kcNcuBVkau4yVX2QA/dt4+a7Nzh6eJRkMk2Luw2LqxmffxZHg46hTQMoJyrki2WKmTLLvhWam5rYO7qTUCpAYtlPVSUT3IhzZeIO9XVuzr/wGhZjGamgxtOrpNGzFxkdvZu6OHXmLDVOkcGezRx/aRd5rY871+/wwP17mJsIUu9uwWqoQ9bmqG8yI1arjGxr57Xn3+IfP/s3mBwq2tp3EIsr8BGmf3Qn12+/wq5d2wgF/dgtZvy+NfxrQUYG29g8tIm3z5/HataxZbiXWpeTWDSNLAbQG4yUchKZhJ6//csrXLl6DZ2wmV2HHiQj3Gak6xjjG8dp7ehGFytgr8mh0hUoFKr4fTEmx6eoliX0Oj3NngaaG+tYSebRi0o2D3SzEY2hEEVk4a5OYI2tiTQBOnu7+MwXeri6fJZ0dAOhWiIZjXD99jLVfIrv/MN5OrsNxCNJGps6UKsUJONJFEYFKlFEhZ3mmi34ms6QiqSwq/qhokBDC3IhhLqsI58qY3VacLjqKcdidzMAQaBSrqDV6pCrAs1NHnRqE3/3o9cIrm9gNpq4MLbO/fdtZsvgIC+fvI5/NUBjY+Pd78luJRiI4XYZiaVCDAx0EIiOIUky73vkw6TXw1hsDVjLUaR8CqmYZWXFS6mSgWqFWnsPje4WYpEiasNRtm3fwU9f+TG+1AJmT4ayLs56skpJP86v/vZhXnpujMP7Oskmo9y6+i5lrQu9XounoZ72ZBXftA4RqLWbMDcdYuzibSSxwrmTp9i9c5j2xjqcZhezoXkGe7vp6+rDZrRQFQeZmL5FNptFp7URiieQ9SI9o31cedPPjZu3KWaMnLt8my1DvfxvP/hvfOt7P0RZynL+5EXMzSXOTv2U+3YfhbjxPf3vl0Jj8Pe/8rtPfeRj+5mbmGB2chqD1sDEzBiO2gpqPcQKCewmMxaxglrnQKsxIVdUaA0Qio0RSQTIF4t4fevcuHWDWCKDQgVjd24iVyTS2Ri5bBm7tZZYOkxtk4L7P/AgI0NDqCsW6toaqKrW6O3Zxp2bt7G6bezdvpON3BqXb58g4IfD9x5i7N0l9gx+mPNnrtLT0Ueto5ZQIkSmuEgqO4+CIMefjtHQscFDn1Kj1BlYmEjy9D+9xbNPP8fWrX146l0IUoktIyO89uqbuNwedKoqA52dSFIah9PGpuFRZKXM1PwKgkJJFQG5UkSpVjC0q4f/9Jtf4sVXX8S/GiMv5PHPhrHQgAYBp9jL82+/wPlzY2j1IolUGKoiKqWSepuZUjqKJKXxr8dYCEaps5rx+QIsr/rRa/UUC1kuX7mKLBaRDSo+/cTnUeqrhCci/Nbnn2RuYZ6EVEZtdtDUXWJhJoNa4SSXj7Nrxyiry6vYLBKIJZRKHX19rSyvBGk2bcKabycVzLCRTLIQXiKZLeBdWUAQRJK5IqGNyM/FTwvFHApBQaVaRtSEcOjdnHtrkkMHd9Pf10Nffy+L8wscP3kOhUYPSs1dpSRBQKlUoFJpsJgMHNg9wv6DD3Dk8MOcOnOWqZk5EvEcPn+M+tpaAv5linIZnz9MJJrFpHNx8+osc8vT+P1LqHUQTiRobxtgaXqBfbt2cefaBKWsltCGgny5yGBfC9ePX6EslUlHCggmLel0lIFNPbz9yju0tW8inctQLia5fHOSZDZNqSxy+dQE1VIcWSoyPR5j5+hRohsiuWyaTDJKMlmmnJOpMZuot5hxWE0MN/eiSGcZ3nGQuZUVxi/fpLm/m0pjiZ33DeKdXsBls7E6s4poLuBwD6IXZC6cvc7Lx8//8moMWqxmvN5VFhYXOHrfQZqaGunu7sVklMgnUpgkI5aqlv2H7kWl0GEwGkjm0ly5ehm7o5Z4ssr0pJ9Krsro5l00eZoJBTZorO0kk09gNAmM9G9lS88ARtGARWPj7dffwBdYZXTvYTIJiQ2/hMtST4OzBYvVyJvPv8D1d1dQFCwMDfVQilfxOAawaNwUs2lQZ7k9dg6xKNLX3YV3JcPZMz60lgyL01pe/UcFl09O4qpR0TfYzb2HD7F7dDsKoMZRh4AKfyDEwvI04UiAb37zz2lscOPxePjjb/wxr75xHJvNQaFQQJIKgAq9scSxj7oZHN7MY489SltDLZmkCUkUiIQEZscWqapl6urrEBUV0kkQqjZKpRKVShmz2Uh7WzNyuYpalkGQWV4JEg3H0Gm1FPJ5Mvk0kqRldmqF4GqJ8yfHWFtI4rCbWZpfxqCFspTHVVvD1EQWldKBJFUwGa2M3RnnwL4dqESZ3Xt2MT8/xam3XqVG38P49SCzc9NojSZMngQlo59gLMLBg/fzwccepaW5EYG7Q0NJktCoVWg0GiSpyIF9B3j1jVP4fElEZCplCb93GVmCTcN9PPHEEyiVSqLRKIIgUChIlCQJWRYx6jy8+NIL3Ll5jo3AGrPTXn7y/DtMLK4yMzXD8uo6F6/OYLN5cBr6mLgWJZ0sYjI5cLmacdZ4WFuOEgvmqKlzEV8v0VzXxeBgG/FQme/91bOcfmUZUVCx7/0fZCUSpijLONwNvPbOKVZWIjz64YcJBSJo1E7KuQr/8D9eRSvrOLB7P02ebipVN3OrRc6dnWfLpiMU8gKVqgaLyU1TUzsa0cLC/DzVvJ65iwuceuUcb7z6Fv6VVR58/F6C0WnQSWTlJGqFxHe/9y0e/dwHKCYrnD5xheefOUFeeO/C4C9FJvCDH/zNU129ZlzWWtobPawu+VhYXqS1pZlbV+5gVGuRM1rmolEsOiNlWcLvC+C0tzA/nyERlWhwNNPV1oRUkJmamkCrUmBS1yIUcljVGgphKBZF9u48TDlVprdtF9dunGHb3n4qGTsmjZ3lpUVqLW68G/OkV9f5/BN/xDvPXKO1rpO//NYb7N7fTlk9xoHtx7g+9Sa9fbXM3p5mMfwmTR31OM2jLE5HqPfY6ezYgaaiQy7JTM74oCRQKWQJh8KICi1yVSSdTSNqq+wZHabRWs+u3bvwev142od488Tb3Hv0IcYmJxGVChSygnK1gGswyKmLf8X62iy/8ehP0LeuozAX6WrdR8wfxe+fY3axikqoQVYmqVSLVCsyTZ5G+js8GBRlcukcVVTEpBKSpMRgNBJPJXn0kUeRxSqNjT001OuZXpjnA+/fwzM/PY4inUUsiUQSQYKhIhpRTbFswOUwoDWKKNGRTqfxeZfobRvgzLkbbB7ay+joCBfPvUu2mkdZr2U6MAVKF2a5n6Q/iVVd4sqFd7l45RpVUYlCqaJcLiNJBeSqgCDIlAU/Yze9VJNmtm1tp8Fdx/Mvv0kuU0Cqlrhw5QYoNTQ0NLC0tEQqmaZSLZFJpHFY1Dz02EH++I/+K1sGt/DxX/08VaWK2roaoqEAlto6OoeGaKhpYvrWOFI+iaCwo7eakXIlVr1+4rkYI3uGUdfHiUWiZMpJplYn+e0v/RrL3glKASO9Q0beOPEsVZ1ASTDy0qsvc/3GNb721e9grKnw9hsXWPJF2XdgK2a9iWTYS4PLhtlqJV9wshatkEjG6OjsJJHKEw7kUKpUXLlxnoIkMhv2Mrvk5/yl68gqK21dXbR7XIRyAZZWxtDEMkyN36KnawBRWeKVm8+wuWaA4f1NWFW1FFUVXnnxX84EfimCwHe/882nRvoV/NoXvsCff+MPeeh9h5gYP8f2nbtZXspTTK6TTaYIxINMLS1C2sCWoVHS2RIzi1cJRHzkiwWiySyJXBK1QSKWStLW2klP5wALS4vUtzfg21glnAxx36OHuXX1GvsHj+Iy1DI9PsGLb7xIYqNAPl/iY5/Zj2i+TjKX54O/+gX8/iJHHxigvtaJL+lndGAr9rYKxrKF5fW3WPbGiK0YKOarnLxyHaU1wtEHttPsvofn/ukdevub2X1gE3abg3g0hVqnx+auIZJMcuCe7ThVAkqhhNboIJtJkc6kqagsvPr2W2we7CKeLFKSJFw1LoylJj74vk+SiVopKwOUJIlyrEI5X8Zd6+bwlm28efk8lVIGKqBWqpCB7s42tve3UJUyZDIZjBY7/kiWYrXK/PwsolJBU1sb85PzzC9NspEt8LEnfods2sl6KohbAevpMv/5i1/i6ddeY6i3lXQ2jW9tnWxOolSWEBQKFAotqWSO5lY39z3wGKeuJNi54xCrSxskk0FMDlhfCmI2bjDQ3kIhWsHlthJLSwRDcZBlFKKIRmMAqiBX2LLTxUePfJzjJy8y3NdGX1cHOpWSCX8If6yA3mghk45SqVSx2mqwWi2AgKBUkC2kSUdCqOuMdLd18tLzL5HP5Whr66QkKChUJNZDGwRXN7h05Sqrq1GMFjO7dm9mNRDgxtgF7LoWpq/coqRcw1QnYjNp6GncxbXbZ2iw11FrM1OOrBJKS2ha57Cp+jjx8nH+5E+/w/Pnf4TD3MPVi5fQq5ScuXEBk8aAzWCipduD1Wjj9vQljHoNfd0DxOJxCvkS169fRcrm6R/owuZxYjQ42da3g/c/8iDGDiiLWuzGVtxdbQRiGe7dewhBWSW4ukRLeytVdZFiOk9ZZcTutNHe4eR73335lzcI/O6Xf+8pu+AksHobTZ2NwKQPe6uWpaVlFDjYuWM/clEimlFiszTjrKkjHEhx+do5HHUuVFoNVouZbDGO3lKlvsHFnn0HiMTWKckVjE4jgYSfhcAM23YOshZcoM3TjUKpIxyJcPnmJe45eAiTsYaAbxVVys3Tz92mZ0Tk0qUXuTr2GjuODbK4OodTaCHoS3D8mZPIgpuVuRhy2YpUXSRVLDCypwFJkeTWuRTKvJHP/acneOxDj+OdjpPOFDl67EHUejNlQaKYy/OTf/gRmnKFYiLORiyOUWfh+3/3NKFsGbXBgChlmRyfwmKzU62WefDY/Xz/L/6M++4/zO35aX766rPsGB3ko4c+zYHDm1i4eYM3T95GrVUgiuLPN+TUWvU0WDWYjAYsNjvFYp7phRUShTKf+cyTZHMFzp49i4BMMpFgdMcujnysTPeAi9W5MHvae/jRP7zAvnt2MzMzR1dLA5dvTVKUSqg1GnLZu4pAggxKrYFNm/v4+K8/yu2NH1LbKDC0tYHWthFu3nwNEnpclj7On3sHddnAYM8Qiysr+EPrlMplqnLlLlpMqeTTn/wQe3Y/iFXr5tzJSwz1N1AtFUhm83ijKRLZPKIoUK7kUSiVZLIpctkCAC6Xi0Iqy9rKAh/94EdYmp1kz+geausbCUXW0avUrAcClIoSOp2ZgweO0b3JQLro5Z0zbxHYWOPjn/osw1v34bG08t3/9U2GNtcSX3UTyZ+ksaYbjabK4mqcjUQMq8fF/FyIA3ue5MyVM2Q4xkwAACAASURBVEiaFN0d/eiqDn764xNkclGS4TRdze3oNDZm5+YoFQREvZHuTR684XVcNUYKuRiJXIRcNY/RKONyaWhwWWipbyScXGR+fpxKRESS8uR0G8R9fg5s34fdXItdV0+i4CMb97Jt330MNw8wfuMKl+ZO8c5rC7+8QeCv/vbrTz3wsAFX+yLOxmMYjRLxRJQPfORX0GGkqb6DsfHbFKpafGthXM0WMqk4gY15DGYtQ0MDtLe0YDRr0RlF4ukIm7aMUCpJnDl1hsWlRRw1NozGWmbmFmlt60ejtnPp+gSnTlwmmPBx8/oNvvU3P2Jq6hJvXf42D3+kDVVmmF172zn54jXmvFGGhjrwrfoQqlambr1LMDPJ+5/4EAZdMyM997FrczPLCzdwO3bwpd/8KgFvnheefxqvL0EsGUDUZlAoNDhrHEiFHCeOv43T6aS/1YNCrqA2Wsiks3z+q1/n9p0Z7A47Z985Tr3bQbZQRq6WSafS/NYffpFF3yQNHd1s39nL0QN7WVu7zdzSChsLPq6OLVPm7tr7SqWKqFDSUW+j0abH5nSRl0qEIzEC4Rg5WcnLr77BxkYElUqDy+nEarEwPj6B1pakvXEPZ146hcekpVxRUyjFSMUTCFKGlv4tRKIx1BoNSoUCm81295liBZWg4Mh9u9mI3eTAyA4unniJxtocv/aBp9i16SjlSpEDR3czss9EJOHHavOwHokTTyQBmUpVQq3Q0NxooqnTBWKCN147zbGDo9y6fYufPH+a+rY2oqkcolKF2Wggny8ilUuk01kUCgXlchkFIt//2z/HY3bw4itPUyxKpAsSFy9dYLivj76eHlBmuf+xI7R2WcgX43QO1NPW0cnu0UPkyn6uXrmB0RKj0amju6MXg1LJ1v6DZIJRNvU9RLjoZ3x+hq33NrMR3WBxKYDWVmDbaCuCBH//13/Jis9P29AI/R1b8W+EmJ6fI5XPkM1V2LRtAL2xgq3OQllI09xTR+uAC7NJZKC7j3x6nZmxcyws3uHy6Qmc+nZszfVU6lIU4iJ1kgNPayeLs0FWFwJYHRZkQUQsa9FrNbjdBoLVq7zyY+8vL5DUZFXh6W2jojAzcWeBnXu1JMIWvvC7X6VT30Kjq41kIc9KYB27000+X6G1rYVwJEClKLC+GiNnLKC3qFhd8hGILzCzvMhw3w4G+3pJJdJM3JhEqdSyebSf8xcv8bEPPsZqeBZJWeLLv/MV/tsffp18YoPhXV2EjcM8dPCbPPmrf8DulIrGQQlZ0iBnZPp3DRCdK/Pk534LU30WX9pLNJNCqJYopjzIUjMHj3YR9/sQ5DUe/5X3o7VLLC8UMaoMTM/coFopMLRpM909PdS4a1FU4tTXtvDk53+f3//Kl+lu6MHUUMdT3/gjnv37HyKqtJAvEovFuHUrR1/PU9ToSrz42kWSqmW+/XtP86kvPYix6MBitJGXkuhMlp8hyO+irm0WEy6HDalUZmp2jjdOnKW5s5+dI9sJR+KUpRIqpZpMMoVSoaDOUcvR+3uIxi/Q0xuguW0fzqkgNoOKwd4uHn3gCA9/9isIChUqQSCXy91VB9ZqKJNHI6rwLqS4eEaG1E22bN5DPDXJcugOvuUQVWWYWMRJojyPvcvGxcurrCwvU62KlMtlLBYDOqWWmek7nLx4id/98iepigWuX79Ge1srX/hiH8+9c4VsPo9CpUIqglZrJB3bAECWZZRKJa2dHbz+9muExleYnMnx4Y/tI1FQc/rsdcwGA5GNEA6nCn9snNtvLiMqgzz4gQGWruRpsPehVUls2hykd7CbyGqSWvMAtS1qtFUrq9EF0pFlOntaWQskkGSRzpaP43TE8a8pMYsmzq7d4vQdH5uGDjG5ch3v4gpVU4F7duyjpb0VQdITCGYIXp5BZ9By7OBBbPYakoUE/bv7WPfPo6hUsBtqyYugrQe10cD0rUlmQzfR5s3UNJgpXlYh5zWYrGbcLitaUUl/bR0XL79Fq7mZwJXke/rfL0Um8J2/+O5TTnU9XYcKtGu1fPubrxGdi9E9eJDamk2sh30Y1VYUWgOhWBShCsVEhrqaetTokZVKYpkk6cQGNW4nZrMJk9qGTmGmlK9y8+p1Suoqde1dPP/C2wxu9qCyxPm7n55i7OoUuaiCD3x0D6HwGs+c+RGR1CwrkQCV+tdpG97NpvYWrFYXelsDvpW3uTn5Ai21HTzzk5PsfuQBbE0hAoEorzz3MqJOJB5NceXUHGa7FZUjST6bwSQ40RbKOOxmFryrXD53g0yigMJa5MDwCL/zla9h0Ch4/LGPEI2G+OznPslXv/YtEoUINoeTQrGCyWJGVsCC9zqaMmQy11hc1jG6aYSdA1uZvX6LfEEgE10nWYRKFRBFupobuHfHZsxGJTfHbnNnchaFyvi/M/deYZIc55num5XlvTdd1d3V3ndPT4/BeAAzAAaGsHSiEcmVo/xqtZK4KweuKC2NSK1ISqIcD61IioDo4O3MAAOM7emeae+qu8tXl/c+9wLQeXjOWR7to73hfxPx/BGZcZPxPRkZGd9LqZzH3zNGuVwmHAmhUit47/s/wNbuDtlynrvek2FuScZ2qYqvZOLg/kOsBEJoNUq+9HdfxenswGi2kc7mUKlltKU27XYbo1HFH/3up/j7r/0TGdXzqI0NQqsJ+oaP4fFOs1F4E41Dz8tPL3PhwnnaYpPduRZ7oQLNZh25vIlCoabHa2X2RoAWeUJvztGuVTg40kPP4AjXr6xwefUqUlOHTqdAkCRMJjX5XB6NVv9/sxW6ez089OgD1PR5unpUDPS4WUzc5MSZU5yYnuGpF59mtxjC1a3j+KlHqOfMXH55hbZMjladxaDTcNvBU1R2XTxw9j7y6RyxlSW6HHZC6QgLOylyyjD5bJBoIcTGwiazi//MsSMT1HJ1ajIYP26hY38bh8bE0PQhtFUPe7U6LqsFvU7kxuwSnSYX9548RksmEIpFKGRTyBWQV5TRdLlpK+HZ5y6j8qSZW94iHxNQNtsMH/Vy/ORZ7E4bszdu0uhKojEaYLPMn3/xz3jvyXfga2kZstv4i++8+dO7HPjbv//M4+985DG+8o2nKUdTqNQu9N4OTtx5LwN9vbxy4TliQoRgOk0il0WlakOzRSqVoS3JSOaylCtVpLoMlWBhZ2uHarVKPJlkc3ubkckpKjIBf18n73rng6xvriMqizRrejrtPg4e2kdJ/21s3UoWN5YJBtKcPPYIkiHGm08l0BnSDPlOUCnskQ6oGRt+BL+vh9def52JY7cTCWholCvsroXRGPXcffo0fd4hLA4nw0Mj1ApKRElGOLBNS5tBoytx5OBJ0ukM2XiC8e5uapUm7VaTG/MLPPToo3zi05+l3BDQGVQY9HYqtRqS1EapEPnPH7+fU7dPEo01+KOP/T0VtlmMXaCSULJvZIQb165TaKlBLoIApWySWjbOcH8XHT4fokLN6moQd4eLsfF9BMNxHn30UfZicfRaA9u7OyiVSurtFAsbC3Q5D9Kr9fCn//3P2TczTTKZQafX4vR2sRuOUmvWkcvfogM3my3ymQzXrtxiaWOR0cNuxgcmme45hUxmBWWdtHSdRqNGakOGrTXBIw/cTS6dYn07gKCGVkvFH/+XX2Kgx8jkoU62lrPoZUaacg23Tc+QzMb5lydfYDvTRqPWYjBpaDfbSEKL6al9VOvNt5YCopxivo3B1uLs2Tv56me/hsOuZDNUx67TE11ME0/EeezD72Bre5fwdgyzpo7Z4MTl82IyuNGYVKwE12g0smgEC/FkhuefeZXf/YP/wfBhC6HgHmq5iZmx+0Bo49bvx+22YjSl6NB28sbVSzgdPhZXX6VPdgfrexfIN7JkQzX6PCbykQQuuwGPQ8tTz72Ewtxkd3eXUCiGoCkSjq8Sz+4gGupki1muXAmwG8wxNX6Mg3ccZeyAA2uXiXPXzyHK2hgNOkxqNUMjHUx2ehkYvpNqeg8tav77P7340ysCn/7044//5p+coN7OIK3rMKm1yDxavvv9c7x+4Ue4u3oIZSLo5U5uO3yCSrlNJJQmlaoQjIWoN5p0d3VRr6aJR7aoVtvU2qCxGDDauqmr4sRKK4hihcDOOvlcHlm9F7fTy7nzL5DIBCmV5USqm3zw3b8FUpqbge9QTk3TIe/i9B138KPzb6KUq8nFlBw/fjef+tTvI5cJyCxZVKocY53HuX51jkpV4rWXXqdVrjI08jYQUldFqdCRSWfYiEeYmLwDr6ePLv8gRr2FVHAHr8dH/8AAGoOB7WCY0ckDnHv9TXZ399CojLRpUSlX0GrVfPg3xpC1xtgJJEjvBRGqLSplGb7uPnrtDjLxBIvbcQSZApVCjr/DxWi/lw63lWK5QrPVRq6QsbuzTrvRpNqEJ5/4PvVylfWtNbQ6HblslnhQ4hd+9pc4/+o36TOOceLMXVy5ehOTxUksHgZRSSQRp9FuolQoSCT2aNQb2M0OJEmJy69h+riHvVgIt95Jvr6EzJxgZStAPBQntr5NdW8Lt26EgZEpHA4Xv/aLP8/H//CXid6aI7Bwi0a7xYGjp3jmuTfYSiSYGeulr68XtVLF9fVdTEYzolxCq9JQaTS49567uXZ9jnq9jiiKqDQqYns3GO8/yQG/h4XlDar6IvFCmBeemuWhd96HqBOoNNMYDZ10+/fTO9GN2aOgWKgg07ZoaFMEtxPsLqZY3tpCkll4/88/whe//G3MopdkIMr80lVUxhB93jtAHaCSN6GWfCytbuLv7mBvW8eNC3M8cOo9nBy6C4W5SCq0zV2njqDqLHJx9ipTd/jJZhoc3DeCXK0mmU5Co0EwESNWKZIrtDg4M8rMUQtHjg8xO/8EU55HCMfDTPUfJZOP0javIRa0/PCFl7i3/xD1TBidXU4xmuYzT5z/6RWBz37mLx/faT3N6PAMP3f/Y+hsLn74zEUaNSN2p4JQpEqlUMEg09KSqmTTLVqigqGxCZrtBltbO5QLVWw2FzKZBVFtoibUKTcb5IsxLKYhClkjRpOCN67OEQ8luHk1xdbWTfbv72H/zBgj3nsoN0p87hN/x713H2ctGOQjp/+KKxefpcfcS11X5I2Xb2I3Cbz49A8olkvEwkluO3YGlbJCYS/N4lKIVCaDvKVmbHgEnVZFMh0hmdnlpRdexWh0cvXaHnMb68wcnmbl5hrBZoAOrYVzL53HZLdRLJbYf/AAf/yJz6DWWt6CakggqpTUalU0Gi0H9h3nk3/8l3z4fQ9w7tw5nnvydRptEyUpyh133ENsI8jC2hZtJIRWHbNBR5fHSm93B6JCTjgUoVprolbKyCSTFCsV1Do9Gr0Rl8tBtVJBrdbgtZ/g+LE+4ptxDo8e5IfPnePpZ8+DCH29fVQbbULRJPXmW9Rkt9uDKMhRSir0BgvD+23sO+xhdKKfVKTC6uoeDSTMBg+1nIDZKqHVdZPfazBywE2ssEi+ssk3v/wM//T1Z7nz7Bky5STrxRfxdjrRuVsYJT21aoOtUAlnr458VqLerFDKFxE1Gvq7fVy8dPUtPJlMwOY0cfjoBJPTAySCK8Tzedr2Oq4eOz5HD+VqAbfPSbVV4LmXn+OV117ipRdf4QdPnWd6pg8RFUa9kVSowEDXJDa7ExQq8tUyD7/zvWxeW6IupHn3B96N023j1mIElb7IweNHuT63g8PrIlFZ5Rd//Z185AMf4a8+84/Ed6/wS3/6LpYXQ7z02svUdC0U2jqZjBafx4scAZNFicPgQS3q2avsUW1kcJjtqKv9pMMCLruP05Pv4uVXn0RSxlDJC8jEHIH1XcLhLVYWahwfHkVhSZGLFajUyvzlEz/Ny4EvfeHxfY+26DCc5St/82XCzQyXryyQi8kQ1RW8/imisTC1Whq9Q47d1I3WoaVWr2ExW9HpdRw+bsXVVyScyFGoy2grYGhsjHK2ic3Vonsowe5OmkKxjlqUo1aKTI75uXFjg2IhyEi/mutXlvCZp/itj/4cX/zmt7nN9iDlVgKfSsZmaIEDg+Ncu3iVhx74WRyebrZWN6lVBWLRAIMdx3n6xWfJ5jLU8xKJSJy1lZsUymXstmFO3n6QXLFJU9YgqVng1twlkuE9tsQFhFgbv6eXlkxCrlQRiSe4dG2R3r4Rvvb1L/Gd7z5JW5ChUMipVit8/A8/RqsgI7d4gO4uJfc8fJaIFCeX2GBndg27wcHV6/O0JQm1CJLUpllOoxTbDI+MYjFbSaez+HwudCoNHZ1elja2+a3f/a9EQ9tsbW/T6+/lzRsv08xp8DuGMYtN1BYXr164hlwpoRSVhKMp8uUagiinVi3zmU//Oc888yxiS4FSq8XVKefUXdPshne5+/RJjpxQ09DsUEi2aObNTO7vRTLlaLdD+Mcz9EwfR2noRWVwIjfriVZjHDw9idy3xwc+dD//8uR18ptlxiYGefnaRQS5nHxeQiaXUMqUtGQCN65cJlesoFQqUanUlCp1VlbWCew9hVTVUZalaKtsWI02ZAUd+VSS7dgKge0U/Z0z/Nov/Dz3nx1FY2jRPzjAVP9JsiEF6cQmOp2bm3MLyFUyNHoLiUSEPkc3977rUZ4592VGx3tZ28ySjCq5Fv0cm7tpwvlrVIpHyBZqzN08RzItcOK+M8jcW7x6fpPTD5zg1s0rmHCj0myyvh7EIPcgqsqU93IYDVY6hrrp6+5l7dYupcwS/T0uErEYu8tL3P6OnyGRjdM0buLUObEkOsnoVrFpZjh2uI83lp9l5fU4vrEhPv+NV356ReALf/3Jx329NshFcdp62ZpP0RRlRCMx4mXomjBw61YQb4eTdKpCJFlEp9Bz2/7DrC5uMTRtomm7zpvzq9QyPdBS0KJGJpFgfXGXeivE0nKUcq2Jy9xBb8cYMlOYh277VYb8fWhMeq5uzPErj/0xVfb4b5/7b/gPN9i8nOGOe88SK6dYnd9FZ/Kwnarygx8+g9tlxefupr/bh8Vuw2nRMze3gkapR0mZAzP7OHrqDB6Hlw67hUwhx+u3brAY3kWoadna3kLv0SGPujk0MIFBaaCz28Nr567yhb99ks99+k+YPjzDux5+/1uOtqIGq9XC9maI5354nosrz5LWXMPX5SQYXmN74xpOVQ82vwpKAdbms5jkAmOTfoamR8klslj1SnQ6A9H4HpuBAOvrGxQLeWx2BwdmjvDZT/8FfSMOanWBa5eu8753v4+r1y8RCIf5yAce4c/+8u8x2B10dvmxO928PneTtiQhCqBWKOnp9nPpzTeQaNFAYGb/DK7REiatmYZym3x+CZlkR6z20mpq0BsEvDodZkUXJusEb5y/SGwvgGBtI0p1urxGVGYVrWaTSfv72J5boi0K7O8f5/KNNZ740Q/4/Oe/gkoLyYIMrVBFVOgRFAb0WhWNVhOr1civ/dx/osNt4+QdD3Ph3Bb5cozL82tEVvOs31hGZ2yRSKfw+5xUFWGWAgkW13bJJuJolEquXHwOpWhgNx7DpDHy0Nl3MHdzjsmx/agsdkokmZqcQJCMtJoJsrtlNBYtFmOZdNjFXcdPsrb9JqKuSldnBy1Zm70bBSa73RQzMUolgd3dKAcm78Jj82I1K8nWcqyub5EuZlDIDKR2UiyshhgYuo2aLEmxViNWyGIxuDAaLIQ3RHZWciyuBwlWWmxX3iBhqPDPr72K7+gUybqR7/3zT/Efg5//6089/lsfewc/+t4zhLYqvHE+gt6mwd3hxdfjQ21UojfaMetVxDMZjhw6Qbe3l1Q0Tl0W4fUrNwjvFTHazFRzTRqVCvFwAqUow+wo0KiYUcu0lNJVIoEoGuTIRBnUFaxtXEOU+QjG53jpyTnMth4a2gTOQYl8SsbOWpofXH8Ok1rNzm4Im8NBuZxGqVIja8jwuZzo7DrkUptbt9bIF0qUcym6vT4mJ/cxOjiITCUSqeS5tTmHpKqwb98ot5/uRdn00oxkOTo1Rr3ZpkKOT/zJ3/HHf/ibvPjii4TjEQ5MTmF0GMmnMqhk4LDYaUsNpk/1MXVsirlr89i79AxPG7EbO5DJVYRDaXbCEZQKOXmpRXQvgV2h48jMNIMTY+yEIqytrdNstnA6PYhyBWaLlY2tINdvzOL0+FAqjPi8XvYSe5w+fSfk41y8NsftZ06zsr7F1lYAuUJJpVymVq0iE2W89MortFotnBYHmWyZ9fUVlBYZSpxk0ymkvIZ4apZaoZNm04Df6yOdaDJ34yaSToW1y4BeqyOwHcFhcSLKVXR02dmeb7J8Y40Ob4LbD96FUevg4twSTz/zCju7obfApDIRlVyGQq1CpzJSKpdoNGvIZG2i0SQDUz5CwTAWUYdGp2JjLUAuWsHnczM6OUijLWK3mlGoNFTKkE1nuOPYKcSmBo2oQykz4HC5cDp8vHbuIjvBCAarg4XFdXaDISxWNZVyC4WoIRsJY3d2MjblpLvfhd0l5/kLz2BzjPHqC8/T6e3mzbUFLN1mzr95hQHvQVKJNI1Km0Q4hk2tgXYVjc6KzmRDQkKjEGlJaob6p0hu7hKLltl/YoZgcI8bb96iXTAgqpTITTIMQ3sM9g/T2elHV1VRqEmICRXPPH/1p1cEPvmpTzyeiuZRqgSsxkH2jQ6TzFSxWF0szG7RaJepSykQFcjragSaLC4sU0i2MdmdGBwKNPoaJ89aUItyxLoLrUKP2WBBUGTRqg3097lpFdSUi22GuvoIhMusrd9ger8XheRj6qCNWFDiG1//OBOHS/zdl66htDdZvhjCZLJSkclRldRIlTYHjh5kaGiceChKNrlHWahz7MAUokzLxvbOW5PVZkVjNJIvF1gJrLOeSCCTKfC5baQCSQbdY0TjIU4cnEHdqpHYS/G17/4t9YqKUjHL7s4u5XqFdCTC7Nws02MdFEsFkpk8taaMSDSLv9uPzFRCbq9h9WiJ7yjIx+vYe5z4Z1yYvN2g6KO7o5selxFR1kLvcZMtVqkWywyPjCNXaomEdohHohw9dpRyK8nCzSB6tZpoPEy+kCUaDePWqXG43eybGkdv0BEL75LLllAoFIgKBUqVGq1Oj8lkQSYocHm7qLZDnHxXlavnAyR2RPzGw1SLTUTk6LVqktEVqgUtlBTEQwEEUcJpH0Atc1HNyOnzj+A0W7jwwkUqiktMT5zipSdf5djJB3nm5ZcxW91sbC+h19lpCUWMGiuFcpOfefhegtEY7bZEtdpAaLd49fxFvGMCt983zVPPXWXxSgaPZxRHpw9F20gmW0QtN0FLjcNhYrBziIU3b5HaTnDPfY+xuREnvBLAYLFgtzuplCssrC+zb3Iap9XJytosokzFfXffz+Wrl6kKEtt7AdoaMxuLWzx479288eZlJoYnabagWImQSQa599QZivUKoXCCoaEOosksuQr0mrvYS+QoFmrEw3GUMonlxDJtWRSvxofb5CG+k0FRL+M1DfL9c99kdGaAjZVt3nn4w+y1c2xffolC0Eyl0WKga5onnnjp33eKUBCETkEQXhUEYUkQhEVBEH7z7bxVEIQXBUFYf7u0vJ0XBEH4vCAIG4Ig3BQEYf+/NYbUlqg1BOZv1Ll4cYFXzl1BIdcS2gnR5/dz6sgpBnudVNoFGoUmUrNOppSg3EoTiNygUIph0/bw4rfKXHpORXJPxvSBcXr6epC1VbSKLjr9HXi9Pm47foxwNEHnoIdCtY7J4Ech2bj6+gq//Uc/y+zlGF//8lWMZT+FFRW3bsVJRPbQt+3kywoMTj/9/fv40j9+mVB0m9BeiGwhRyi8y+zcdVKpNPVGnUwxzY3NBS4vzrO+u41RZ6Qlg63dNVw+B9W2nEpBRiNe4+qlBS6+PsuRA7fzO7/7y9x1771MTYzidrj4sz/9BGODw1BrYTDaiadKIG+ibuvYnttFZWoTym7zxA9e47UbT7C0cpFgMIdK7Cdfb7O8fo3l1XlqzQZDwyMI1TqyRp1yqcjsjTni8QQCEieOH2VrY41SqcEHfuZ9mAxKurpcaPVKNBqR/+ub32doYIB2pYpeIXLy4BT5XBbl22jyfKGIXK7goYcfRqaU8bE/+BilepvFGwq6fdPceXqM8RkFgz23E9xepZJJ4O/wobFJFMUKGpWXTtMQ1EUiWxHMKiv9vgmGPWc50nMXpa0hXvz+PPc98D42d8KYTBpkyhRDEypkMiWNeptUOkk8tcuQX0W5WqbVEjCZ7MiEJlP9R0gHW+Rbe8wcGwZlnbo8wf6TfmaOjSNXyenyW5kcG6RVV3Lr5hbuTj96m4WvfeWvMemUOH0uZHKRWq3C0EA/J4/fRqOW59aNWVaXwxRy8Jd/9VcM94/gd1l47zv+A4M6OS6VlRd++AJ+wY+mYuWOE8fpdO2j2zNOaGsLsVWny9aN3zlMf/ckjg4fa7kIGocGpUmJymVFNOuw67tZuVlnIVik0XQw5DvEWP9RlnfWkVkUrAQ2uO/MPTz91BxD+n70liO8/PoiCrmCuv3/zGOwCfy2JEmzgiAYgOuCILwIfBh4WZKkTwqC8DHgY8DvAffylq3YAHAY+Ju3y58YtUaFDz1+gu9+aodm3sDAiJYXnnqV7a0UXZNZ9MZhGmEFH/rwSTSSk2rSwN4T36FeqaOQBEplkUSuwMxtneSGq4SjFdyDIsV0EUk1jkrw0mq22cvuIje1KWqLDA90UtzrYGBomsP/4Tb+6PdmWV99gx9eu8Jv/Pbv894n3odKcDLY30O9lMTn6WMrv8U9d9+NWiWjd8SPX63g/kfPkqoUubWwyvzyPEqlGbfNhs6hINQIoKzb0WiVSIUAFptEvdLg2vI8GX2dmhDFqh9F09OLTKlH0WixFdnmgXd/hOeeeILO0XG+8IW/wOtycvHaEpJaSW93F/F0HKHZIhSA2x6ZIR4OcHz6LNfffANPRx+9PVZ0Zie3Nldpq3JE4hkqfj/lUp7d9SiB7QBGtQybq5dCJoXD6eTqv4IjUgAAIABJREFU/AKtVp3hwXE+/AvvYWR4nFubr7Ee3CKTj3LPqRGWb90kGEsSz1SZ8Nvx9/eg1GhoZ3LUqg0KxTKf+9xnGRr10t/VjVLnIFsWOTnq5sa1JcSCgFZn5fiJ97O5tUQq32IvtYVjok1yvcm1pZc5MHOC+FYAaPPkt1e59/bHyOSz/P7v/AGC0GJuPoJCm8VltPHB/9TLjbUkX/m8QLUqkclncNmgS7dIpSanURERZGlEg5FYLIxW1YNR40VSznL7fUNMTvWRKC0xv/QGrh4nemON5flLtPUmHn3nWRYXrrEYjFKlRLHe4D3v/hk2Q2E2lhaxm02Y7G4CwS0mR/q4z7efckNOoVhkI7KNpipjQXGNVLxM//gBbDYPzayRm9tvWah5vZ1srgRRCUVqiTZH99+BQtmiVotw3z33kEgs8dLFK7QacroGhqnKagx0OlA3a5QaO0SzVYr5AUYO2AiVini7BpgaOsq52Td4fesFDNffh5i382u/8AHWViJcfOPKT5x//zvcgagkSbNv1wvAMuAFHgK++na3rwIPv11/CPia9FZcAsyCIHj+/8aQi3KWbi1xbf4lHv3QQW4uXefjn/g9hqfdmI1jFKoBdDYl3/+nAB6Xnq5eF3qdDYWpRFFeJ0saY0eZnj4/Ki24HW3UGoF77jnEoZkJLPY8e8kFVEYBq93MzMFxqvkGB46P8b3nvsZf/8On8Y8biWdr3Peuo/z5F/+CZMTAUH8f77j/DG63EbWqiEyZ5itf+yLtVoaJ/iHK2Rb//J0fkk5FkSQFBrMNjb5JIZchmc6htaiZuq0fs0NLRZEjV4nSNzCNq7uLmhTlcN8+0qUEMlFOt8eFQWdGpTDy7W99DWfnIM1GjaHhQd75nvfQajXQa9vcdeo21CoVGq2WZgu+/D+eZ7LnJOdffYmN5RwVIUOumqZpzeIf8BML5ynky4SiIbZ3d1lZWYW2gMNhxeYw4+v0otYpWVxfJ5zMMTzeRSS2xre/8yQvPH+RpryNqlNiaKgfnUbOxFgvHp+NqyuL1Go10qks7baEUqkEwOvzotOZabVb6BVqBixDrN5af+t0ZDrPpctXee7lp1gMrLCVuUZXv55CMcdu+ib79nsZG+hHo7SgM0qo9QratKi18sxev8aF8xfY3FhlemqaUydO89nHl1h+Q4vPKaBRQrffyt2nveicB2lU8sjaOTQazVvPMW0S6Sg3LqQ4Mnovx24fRqEDk03PxOgodx+/HbWoobfXh8uqo1Ep0OnpZqi/hzNn7uDMPccJRwNsrM6RjCdJJeIszl3BqNVxaP8hCrk2GxsLtCp6LEov3d5hYrspkkEZod0wlbycK/PLPHjfXVx85SnWN67RkicYHj2Ky9SHqFEQLST44fPf5Ut/9S2WbqVYWgwjqOUo5AVk5RIajUhVISNbbFISq4Tyy/zzxW9h9Ms5tO8Yqd09Nnf2KNe1hNNZkuUQF2afoKoMkylWfvL8+7dE4MdDEAQ/MA1cBlySJEXfbooBrrfrXiD4Y5eF3s5F+QlhNumwK0f56K9FUJkKPPjOd6B3N/mVj/eTiNuhIefBIyd549wN5mdnkZqdyDVlHjz7MOFkgKXVq0z6ezBJOrTFLvaND2DyKcmUykT3lhgc7GFpdQuvqZPN+QAWq5GiUOO9//UxNp07XLn1JB/66AO8emGWnHgT77Cd/YdH0Xg0RKMB5GVYWH+FaqNJs2xjceUC09MzPLN0E39PP/M3LrG320LrVVOsZKkEZPQ6u8gIBVbXbjHkGEdTKVKW1TDqxnD7Zume6MUft/PahVuM9KjJRtLorRZMMhO5WokrCzc5ODnAZmCT2ZVN/H09PPDOo2QiDfQaHeVqEUGuQSdzUNm24tCPcW7+OUrlLD2dcSalOpu7MVQ2Bfa6hb6eDhx2BwqNns5OP5JQIdNskU7EqeULeHq7OXD4JMnMLYqlNGfv28dzL6cp1ivUaLITitOSadDqJQrJCI8+cj8/emmOtiAjsZeiXq8hiiI6rQGf28f2VoAOh5FUNIlIC6NbRVPpZOhElVjhKgtXJaoBC0KhhcbQRS2fZ2W9hFa7h7XbQzi4zFD3FPNLs/RN+fjeN75HJBLgyKmH2I3FkGnkbEV2eOxDH+BTjz9Nh7kDfW+VeqGH3/jPX8ZqUSBvV1Hp9TSqdcqVEjIlvPgvN9ALx3H7B4mml3D5OjFp7WwuL9DOVejv6eTM8cPEU0kWb65j0qrZDoRYWfoWB8b3MzncT1ybplqtEQ3t4PB0cuXiG+zG0rzr/We4+MINhFaOWLFBEyedHR7Wlq+CpKWtq/A3f/MF3vngWfbCK+SrCdLRXUTRzuy1C3h8Fsb397N8a5FAIIDFacft6kGmqNDh8bC7U6Td1DDo89LW1GgZKsyu7NGl1bCbXCOaiFNuyBFkHp6+Oou3Q0u+JMPZSvLWCZL/QxEQBEHPW/6B/1GSpPy/2kABSJIkCYLwk0f5X9/vF4FfBLBaNBQSO7h0vVy7don3vef9XJj9HtVWAounRS1jIptbQmXIUs7k2Vy/zNi0mVI5jtcmYJ8eQV6x4bb2oZ+ZoFQOsHV5lcXwLR585GHaNSWdg0qCN9ewq40omjLSzSRbu+d5/8O/icp8nc2tq6zvrnLgmIOj3YMoc3WWli/SPeTCOd5N37CTlfkA2WUVixtzZIU0lkGBheXXEeUtctkqVo8BtCLlsIxKJUulVUMua6NxKqhqtAgFLYXyNdqNPA1BYK/Y4La7ZqhVMyjLFnKyAg6ZFhVK3vHwCWrpMrNz84zOHCO3MY/Skicb2qOQy1FuVFColdRzJf7xc18nWa9z9NAdbIcWKXurXD+/SzIVpFkoMTQ5w5GD4zQzJcwWK6XmWzsGGrcNmVGJXNLwkV84zfPPX8XbrSG6t8LLL19jfMyDrGQkXWvjdtpI5MqEY1GU+m5sFi/B4FPoTWYEQYbD4aBaraLVaOiw2fjRj56kp6cDnUrFZuAavX2HSO210Lu7Wb2+Tj4iYXM7qWeq9Put1Du7WdtZRmHKojV7KK2mKFoihFMxZkxjTPf3MOAyUCjm+NQnPsH+fWPcdWcvN65eolYrsFsIU0k6IXuFmmDA4alxYHqUxa0iCqUCpUJBqVSnVtrm+98tcuzoGC3RQCaUZHBMQ7FQoBZqIXTaiMRKLGwtILS1NGptVCoTdoeSbC6HvCoy1O/h8pUlRFFJsd5kN7KDJGh5841ZtGotPf2d/OCZ85w5e5ab85fJFet0dfoQTS3UUov1cAKtWU2neYbkZgGF+ipZWYVcao/904eRiZdx212IVQu6Nng6dOwGVlFJ/RwdO0YoGsQ+IlKoyxgvHCQa22a1EUVhFBlyWXFlBXLpNuMKI+GMB6Q4y+n1nzgX/7fsxQRBULwtAN+UJOlfuYPxf33Nf7tMvJ0PA50/drnv7dz/IyRJ+jtJkg5IknRAJkhsLyRJ7zXp6fSwuDVLW16lXFITC2Xp7exAL+tidWkdo1ZDvZVGa6ghKGNUW3kQq6RyexTyIutrS1x8+Qod+kmc8j6qGS1WNzz80O185Ncf5tCdw7z3507zK78zDi2RUGoVf58Nsarj4GgfBrWbcy+fJ5Je5P6zJxg9LGf4qB1BH+PIiQke++ApNDo9Y/s6UVrBN+DD5nIwNtZDOdMmtFHB2NlC79ChFHSk40ni6RBGuxlRmWbA34XXaiRyawmn34drTMGtyCoKjYyWCUb3T3DfY3cxf/MaNxdCSDqJXDHObjKITCfi9nhJ59OoNBpuP3kSZBJn7jzOyIiXD3zkNF1+O/ecPUVXRyfdln66XD24/TKylQ0a1Ailgly4dp4bqzfYy2ZAJeIf6uMHz/+Ann4PFpOd73z7GQ6dGGHM38vW4jLB5RCbW1uEgkHmbi5TbtS4sbDA6OAQSoUchUKk1W7RbrXJ54sEtsM0G4ssLS8zPaNmbMzD2uIqktSkmVEir/gZHRqhXmkhGvRIyhitVgaH1kp8M0B+b49OtxezSUNwfZdLz5/jxtUr9Pf2k9hchWoVeaPAmPN+AmtzGDQalFozynYvKpUFq81Bo15jcHScdDJJvpAnsZeg1WiiUMpAaFPIllFjRajI0FZF6pEyOkFHtaoikaiRy8hptrTIlQYGuofJ7OUIRoIEgrsUazVK5QpanQVaYLHb0Ou15PIlBLmM4G4CnUHNk09+i43NDdpNkTYiod00iXQJh6+LlcAyOqMeQW3BOjjKe97168yMHSedT1NvFynW4mi1LYx6aJflbCxkyafKxONhnL0uQskAsfQWifAeClkHRo2XQqJKMZqgmUtzW9cw+mybazdfRsg3caT6/v0i8DZv8B+BZUmSPvdjTT8EPvR2/UPAD34s/7Nv7xLcBuR+bNnwv4x6s04lU0MldnP+hecY9B0jmyrj6pAR394mtpQnG4thNXuQhCTe7k7MRg2VZoRkPkJbU6MsJFlZmafP62J04DCSqs6gbxSv0Yvf34Ne5kJtVSO6I8hdOyxHruDqMJIUlgmEI5w5/BguYYrV6wWkipvpO4xM3tFJ37COPnc/9XaW2flbmLxa+kYNKBR1tLoO6kIDZ6eVUqlKtaHnwPQAg0e86HxW+vsH6RvoJF2Is3JxjTHvCFatm2ZRRNwxspPe5YevPcFrywtkGxsYXC5SshKLoTfRadzYx9oYJptk6rvouyXWt3JoRQOnH7gTu93BxOAAlVaVgb4+/C4ny8sv4O3UoBMcDPQ4eejdJ3n0I4c5cMJJPJVAbupE77eRUgbx7NOzurPM6tY2iztrNLUV1sNzbAZvoTOq0cjt3LiwhFKpoMtjolrYo8tlRKfVUEhs0qqVmBjox6bXoFWKiKKATBRRadRYuizUK1mQVdgr1BAVE6QiXipJO5n4HsVqFJNJT2dPHxlFnFQRai0z2qqd5rab9dkCyaTETiBJJayhkDbTPXKAV64s4LSauP/Be3Hb9Lzw7Ffpcd6J3+/C7vSh120gV5SpFQt4bHYy+RoGvZ5qvY5Ko3rLskxSU6jUiCTKJFNB0sk0O0vLjLlGsVnsCKKC7a1tdgOzrG+uEE2EWbl5C73cxMzhSUYPTHDx+hwKg45sqUYqnCCZzGKymInGItxcXmRzawuD3kAumsdh66WUrnNjboNoMInR3sMLT18hn1ETDOyxE11h82YZqSzRapdp0qTDM4TXNYHRakCmaNGqtnD6jPTstyC351DZWhSyemIRCblBSVbKU20V8KrtOOxe9ioVBmYOs2toYhocYOKOe1B0a//9IgAcAz4I3PljyPH7gE8CdwmCsM5bIJJPvt3/Gd4CjmwAfw/8yr81gFyhYGS4jy5nF72+IXKZbVTyEsWIFpvKz/qtDSKJTRweGzlaaF0tWmUr4yNHmN5/mMGJ/XSOODF1trB2qhicsFKTQqj0DfSmBulSEJWhSbtWoKkKUJIlyRfKBIMLGDVdmNQD3Nq6gsfvpymtU2KJfbd7mN98E6PyCFq9FWXbwfDwBLWSkq6+KuvL29i0fkZ9U4QjSQYGJhnY14FokFEu61hc3yZXriJXKglsBTh/7jrz14O88sp15uZjFJExv/AqioITm07HzNH99Bv76OrUoLNaqYgWxo6YEG1RnENy3vXYzxPc2sPX18s9dx1gsEvLd//hHzg82ceTT3wTjaRFlfcgZfR87ztXiMVyePwm8rUYV6+ug9xGOBPm5voqolWD0dGNXC9QQk5L0ebM/YeZ2DdMsrnH5NQI6eUA1UYLT4eL97zjARxWM0a9irHhXkRJRCXXsLBwg5H+XjxOG4IgoVYrMRhVTB7TEIhKaJQ5vv6N19hNXafeLBFKrNGmhtFRZ3H1NRqtOQadPVTjdnRqCwpTEU+PlftP38bA+ASo1Eh6M4LTi6gyoDeaqKssODutiG2Bcq5Il9eN12nBZlKgFaocPzKJx+XH7Xbi8bjJ5ovYTHZaTYlGs02lUcRkMTE+OkY+k2b/9DEsHb1Mn7iT8anDCE2BQjqHSrBgM3totGqoDUoKpSwyuQJJkGGy6EgV0lQbFYqVIjq9kWQqQSqeotZskKvW2NyJ4PV0IUkyOnuGyGVLTPVMEA+l8Dg7keRqJKyoVHamx0+xvLTJXqSKVurEbRmizzWOXKamXG1TztVQizp2tqNs7kS5cv0y+UKdYrSMSa5GyDe4cukKXqudickR9o/3oTS0GNrn5ciZGcqKJsfuGvn3i4AkSa9LkiRIkjT5Y8jxZyRJSkmSdFqSpAFJks78K2Tk7V2BX5UkqU+SpAlJkv4/CLL/d6g0atLZOM16jUMHjjN3dY5jM/vo1g7hNo5gczkoNcrcuvEGlbQPu8mOx9eDwzSBXC6Rz2+DUEFmzLGVu0RdFcU5JGLtE5CZc1xZfoFIfJd4eJNu72HKWRP7Rg/gdXXRqjUwKFuEd7N4huvotB5q6g0S1Sw2q49sXCSbS5LdVdHV0U0yEqOUb7EyN89eZIXLlwLMze3Q1tTwj8hJJHMk0xUSoRyZTIpqWUar4sRi9fLG7AI3b4ZQyCwIooleyyjqph+voou5C1E25pd49jvneP3CJnVNgZ1lMxZjL/c+8AByUcJgaiDqe3jin77FySMj9HQYufehUzhtVswuC7Pz63i9Dgb8Q9jcVvIFAVVtkGZNhsxZQnKFETUlLJoOPMYx5KKFtd0dNHo1axsbhGI79IwMc/bsXSiULbp8HQz3dbMVXKHcaHDx0nUkuYYTpw8zuzqPtsOGyqCjXCoiCCDKZST3Upx7do7HfvYkg96D6K1mRNsupk4l5l4zgtjgvvt6GerzoWg3SUcCpOMxqvks4dIGFbWMYGyLeGaZzhEX7/7I+ylVUgRTQbROAzd3gly/dYmJ4Sl6u41cv3wBpVhjenwfOlmLR+5/lHe95z683i5CoSiVaoUPvu9DVMo1dHoTBpOC0fEeujs9jIxMEU0UsLg7CSXi7CXiVAtZ1IIanTBErWDFZR9HEvXshMOsbq+wvr5Kd0cfJrOBRDaCUq8mkS2yvb3L1NgMwVAUo8/BRjCE2+eFdotWC0YHBtEZzbRTZXp9vZh0TpLJJIJcSSKZYXkhRCGlopLVktxu0qo2sNlMSEKbYr5MLlOjWgKvaxSD6EdsqPHqeqhE8kw5+2g2m9iMFnKFGvumpkhnq2xv5Vld3uHS7BLJwk+52/Dnv/iZx/cfcVOqZWlrYbTvKHaLg9GeQQrlPMFUHJXGSGAxgkXnRalr0hAENOo22dI2jXoCg7KDWk3AoLaD1Ca8F6PdUGJxG9naTGFWuuh22lCIPQRjaTTONutX93Bo3Xzv+9+kVNBhsWrYCK3g6PQgKI0UUgLZSJxQfBdZWUNPt4NGUU4+0USvcKGVG1GZtcTSm8Ry2/g6vWSiSTJ7Cax6Fx0OO0JTJJ9qI4oyDAYjqb0YBp2RbDiFotpDTSOnU+9h/tZV3nhzge6ufch0MDTSyXOvvEJbLWM7lmQ79RTZHSvTxw5hFE0sb99ipxAlV23ido1y6NRpsrlVurq1HDg0jbNDQGjqMMrV1Ms1ejunGBgYRa+1oJO72dneYSe8g9mgQq1ucG3+GudeXcDutBLL5YnV2xh0EvlqkfVkitdvXqWW1bGTi2N0SwglMzKNmo5uK4KyyV133Emr3SC4FaHD1kuHe4C+ERumqW32VvMMjLs55t9PXqoTJ4lCrmLpcgClyoO700QqtUxRX0Sp1JBOi6TyGfaPjZDNFtjdjGB1mVlLBCikS7z3vjuRCiW2IiEyxQJnz95LshBla2ORD33wo0wc6iaXy/LNr3+DLq8HncGGu9vEw4/chUwUScSymHQazGYnyeRb35zm5+fZDicoVYpkskWMFi/ujk4GBge5cuMSl167yeRRJfm9GrV8k71ICLPFglKhJxaP43Q6aLYE5pdvUWvWSKdTlCo1REGGKKrJZNIIGpFwYB2n3YHFYqNZlxOKZ1lbiVIqZhCEKhqVlka1gtnYxupUUalVsZo96JxmPF4faoWRpkxJJpOgmisjSEoatQZul5vxgWHyxQZSS8Rk7SQaS5BLZ5ns7SGaSHPt4vJPL3dAENp0D2go1CtcnZ9Fa07x7DOvsbIdJp3R0pBpSMRKtFoqoskY0WScZGmLWiuPzmjGqPajU+mplII0pSJr6wlC6w1S8TxvvHIJp6KHUkHLdipJvHmB3dx5VPkBGpkm6xtBrN4+2tYCjbYBl7lFYdPD6tVtVAYBoa1FJ3QwMjpJcLlOIaZFKHu489T97JuaYKi7h4me+6mXnWT2QK82oWpakFdFzKpOpieP4uuy0ahI7O0U0KhMVHJ1bCYvcqOaaimB2+hiZLoLvVVgfGaKYHiThYUleg65KNZiGF0l5EqB6ZGzRDNf5aO//HOMjkwgtSy43R3kimXCOzvMTJ2gUdfi7e3GoetGWTMSDyTR1Z041d10WyZpph1cOb/LtfOriHmBqe4utNoK/X2H+PB7fh+/e4qBnknO3HcM0aXg/Np1GjY5U/cf4sGPnqVsynLx9VWUKiWp5Apbu0F2YgnS9RpivY7R20YQq2yuhPB3+ymGtzGZnLjsZUzGXcw6A5tr26hdBUanZjh64l76Rw+gNOlwdXTj6+hjdHqSTFrD5Ze3KeQKnL7rbqwWB7YOI3aHjZHJIzSUIt4+J/vGR9EaJERNka7RDgQVWCwtgoFNzpw8zMSBAa5uvMxt9/RjdKtoaxQIRhNavZon/+VfSO4lSKZLtAQlCr2RlkwJgopsLkWzWebWzXmSsRx9PQPEdiusBzcpVbJo1Fa0SgOBzU0EGZRrTb79xJP86q9+lEZdoK9/AI//f7b33lFyXPed76c6T+cwHWe6J+fBzAAY5AwQJJhEimJQJr2yJNvS2l6HXXv13j4+2/ssS7Ys2ZYty+lRsnJiAimRBEBkDDAYzAwm59DT3dM55+7aPwg/82nFY/vt8QN4hM85far6Vv3xuedX9T237kzXbSZXKOPf8COXScmls7ibm4hFIsjkFbK5LKqaEhq1gEppxmpuQVNTi06nJRbLc2N0mVyqQmAjSiySQCbIyWXizK+MoneATCmnqbENUZRz3757UGt1yAQpyVgGvUZHNitFp9ZilMlpdjW94/13R4SAKJYpFWLIKzoUMhNDw2coV4rMrl9gPTZEbX2CaqWMp9XBwH4L9Q1WssUIJQRi0Qqb/iRquYwWazPpRBqJTECnV7Cl5zA9jQfoam1CoReZjoziT8ygEo0Ukit4Wms4P3KK7i27ae/toLYpxfFtv0U1BmZpGz1tdnYPPIxFZ+LqzQvU2roBCQ3tbpqbB0Bq5pXvXWJv9352dB7DrHeTSoo02/vY3b8XtVbHxmYUlUbKw4/sxWYzEormiUXC2BvqaexqIbw+xezUeSKbWZIROZevXUMi0RP0rmOQqziwfweD/YOEFkvsPh5i5PwYFbI0N7tpa2pGrDrpbd1BMZHAZq2la8se5uaDzN/MQjlNqeAj4k0yeu4G//jnzzNybhqtbBNrbRWhlCYZSyGoVExOhrDWlglGYmgVamTFVTZi61S0ZdAkeeTxQdbzCxy6ZzflqITJ1XliySxLy6t09jhYSd9ELqr48Cc/Qk//Vp546gliK3mCp2ooqguUUjAZm8Qg17C3tRens0xFlqZQDLG2vM7CUoSCJEFVImfJO8P27YcoFwyYTQ76+vayvLzM0f0Pc8+RY6gsjrdeyppJ0FzXQjgTYs/+XaSLKVaDPrzrfgrJPEcP7sdSZ6K+SY3Pv87w8A18/hgqvYBOrWb79q3YHFY2QxHcrmbKhdJbK2iKZYKb69y4cY3llSVMeietbf2IgouqrAadUY2+3kQ8UyAYjWG1O8gXKphMRnwr66RSBSy1TgSJEkGQsrC0iCiIBNY2UMh1ZEtlDLVqpIKIgjIuq4GWZjc2u5VsPoNOo0KrV1DrrMVlb0YqUbIwt8Lc1ALVYplUOE02n0YUFOiNRpob2zFabGyGQkjFChqNEotBSzJZQKs1k60ayMfe+f67I140Wq0KlIp63G43kuIEepOUfDGCf0MkkUvj6kyhNNg4vud+loM3kAg62uq2MrcwTaunnZJfxYVX19BoiugcUmx2kU2fBEGeRasykUkW8NT3seALoExX2NVjodGxk7/8m7/E3SFFrdLT0qAl74tzZukVSiUVDksDyzeXEFMFBLmOZD7JbOYmQeEa6aCL4tAm186GuTE0xaGDN1FrY0S8OdRSLXqtmeW1DPHKMmqDFpvdTn29mS09NoqVHFpzlUjSS+LGBC59M1aTjsm1IBKkjI5do3N7O+6OboLhCEWXkotvzhJZryWRjyCmPCCWmfLfRFUvUiyvIWrTZPIrLK+WiEcT+AJRurt70NrLFJQJ5iPLxLNGvCs+JHYJA0f6SK/5ONB2hNfOjqGobrKlr4VQWE44N0Uwa8A7k+D67CS1VjPr8z6EHWZi2RWajAM8fOIgQV2SU88PIckG6S7XIM2VadpzjOB0kum5aWRSkWo+iN0hkCosEgxWqdSYMbrmsZm0XBmJcnNYTs6/Qr4Yx6xvIrOaI+7KUFDE0OplzId96DfzzCxN0dXTQXenjXVZmXIxy+ziAsqaOrIqFdt3DqIyV9gMpYiGNzhzepzx6/M0Nbtw2WuwWAfJlpJcuDxKIl1AUmphJDqE1uKkuamZQMBPjboGu9VKIZekUi5QKZVAKJErZLnnviMkcjlGN89R52hgObrBZipA2efAZrVjMZgxGW2UyllmFlepFsucP3OW9tY+2ltdLK6uk0zl0GjVJOIpLFYdggiFjIAgKKmIcQy6WmrkJYrFIKm0hGpJiV5qwuq2E4vGScTi+MUN4ptxbNpaLCoRY08dIxduoFOoKVdKVMUsjfUuTMZaQr4NstkcZo2L5dgyJozveP/dEXMCf/wnn332yPE9qBQKUpUNUpHCW7PN1UH2HTjGlSsnicTM7Nk5yPzcHKW8FKU4rCIQAAAgAElEQVTCide7TMjnpVatJ5XOMe/3otI0kglLsTn0JJNFRibGMRstuNwuEvlpRkdm6OneS6vNSr2rC3VTkNdPn6XF00gxAJemhtm+r5crk8+zMjlDKpdmMzvH0b2f5q9f/mui1Uk21zaJTVvoaGrixPHt1LpUGGx1DF24RimXo2dLJynS5IohookoMtR4anuQkkQlFdh5bAv1DXqMopq1xVXaPA42NtO0tHcTjAQoilGazU4iYhF5bYqu7iwyiY5wpEJncxMWYx2XLl+gztxIV38Ds4tjxAtRQqtJoqtekuE0OquB9fA6/vAG9kYHg/f303uoEVenk7mlBSpiGu/NAvXNrZy453F0ijT9W7ai0YlcunKe62NzyIyQTuSIzUupUaQJ5ATmRyZJBjN84A8HufDCHK2tW/nlT/wS2dkq9cdriQ4FmFydZ8M/y9ETh1meD721tkDvfbR3GalmQSypGFtbZHLYRyGYRKeWUpVIURs1NDldmFVtpKICh3bvQqEtMDW3xN693dTazGzmfZAWefnHL+HzxjF3ORkfu85KYJb3P/JJDEo5o1Mr5MOwbVcvm5EYVaUEURknHIjR1lwHQp6ML4GrsZmW5nYCXi/xVAy5XIrJpCGVyCCVKREFKWa7gURxlYpugbhsmU7TTkYXZhCUSgyiC5vFTC6dQV4jR5BDIJLErNexbWCAahnaOutJJPJUKwI2h4EtvVsQxBJqtZ7O1q1IVGmKxRjxcBSTxkguI6G1tYlCNkxLox2t2kQ4GSKRTSITQS6V0NfvxuFQUGszUyOzIxZzUBUwGFXEUmEsRheBDS9nrw4TWFrFW5xioGOA11+7eOf+lPhzf/x7zz7zvsNsriaxO50szoWxGu10drn408+dRufaJLmhRScHQ42GSllBJBnCVWclFihz/fI0oVgAt7uNcsKATiVQZx8AqYjGkWU+dI1CVobD1YbMfB2bvImN9CLRUhZFTQGN2kWDq4tXX1xF79lgcnQCc6ud6FyVE/dtxx+KkwwHSeUmyCYNPHz0E6xOZ+nubCIQnsfru05to5ZgNEckF8bTq0dli5EM5dCpBZx1Jqy1FsKhMIIqSY2ziqW2numFRWKhECqZkWAiyZF999Pb3obb42D3PU9w9cY15sZW2PStUBX0NDhs1Lq0vPHqq8jFdvq7ByhXQtS61JitEgw2B/FEnIbGLtTqPK3uATq7GzCadfgDMeb810gVkhhkapqanVRlMvLE0BtlNDW1cfbsJZLRDaZm/TiMTvbcU8vM1XUsCg8mu45XfziO1gzv/9hRpodi/OIHfpP7jr2XmcA3CIsTLM1mkKki7H94Dy1uNwa5klTCR2gpBkqwa6yUsnLm/WtMLC/hUNShyhqQaJRITBbkmjBN1iYWlmPI1SZ6W5sJZWNsRFbRWqUUCzKioTCpbAaV3szY/EWyyjAPHjqMN50jFy2Tzhd5+dxF1rMh/o//+Gv8/ff/nLbOQXLFIDu37kKjM7EyuczRHbsQEAiH/USjCSLxDCURqMpY84fx+SNUpRXkOikqYxKt2kSxnGNlbQ1qyjQ7ejCrHeiMKq7fuI7erEOtVlDMl2m2KoiEcuiNVjQKSGermMxa7j1xHE+TmdUlH3MbU6hkSk7c8yiZaBmzWk1zo5NiNYdCDl7vOvWuOnQ6E5lihIo8hyCVkoyn2EytIGoWKFfTREMy9IKZg7t7qFFIESQKVuYmKcdLaF0aGtrMFLU+Dmy5h+9+59U7d2KwWoGZ4SKLsxP88Nuvs3//cTJxNXEv9PbbuO/R/Rw70UQpEqGY0jF2c5KF2Rnmb6zRZG1DKJhw23vZvXsbCl2arDzBXGiYyYUFbI5WLA4j/sA6UV+CzaUMsdh1ZjcuMLP2JrX2WpaWRxg+M8ta5VWWxnLs6fwwR3Y9iMXQTDltprNxFzfOzqEveTi8+zAbKzM4W5NcHH2R8eUZOjq3YrbU0NRb5NgDPVisNtobHqC95QhajY0anRR/cpaNzQhZMYZKL8dXWOfoo3uwOOsRZCZam1roaa8nlZKRL1T50tf/M6d+cJ0nHnyS5qYOTLJ6wmsJNJoSvS2P0b21RCobIlNYR0BLjULDcmAJV2sTB+59DyPj09y8eI6ltRCTowEKpTgyoUKt1sTxPR9BWXVTVhbRWxxkiyG+/u2/Y2ljmlZHMy1OFx/8wMPk4ylWp4osry+wb+dBcrEEAz29XL48xne+cI4vf+lrLG/8LWFfApnVz+rSNcpyBe4mgaB3hTq1iVanC71RR6lYZGZ4mZHLU6zMbdBc20I5pSIukyBtDdPVA7t6t0HJSCIuEtrcZGFlHolESq6SIByLU+9oArVAThJArhX53T/6HFUxR09bE9H0JudGXyNZTrPtqJvf/r3f4Cennqdc8FAjNRJYW2BlcYF4MswTTzzK1sFOyCVZnp8lWygjyBUoVCr8wTDFioCyRsPI9VGqRZEHDj2NUWXAKu+lq6WdSgo6G3tZXl0lnc2y6vWiNcpRyfXce3Afe7d1UhKK2E06lmeXKRfL1NZqGL0xgkqTQ2eRYnCpSJfiXL4yxKH9O2ltcWIyySgmffhW58nly0RTBWLJGLFEjGwpj7wG3E0umvoGUGqcZLJyEvkIdqedmfE5lmcWKBeSqNUSGhptNHpqaels4lDjxwlGQu94/90RI4EvfOmzzz768T7OnPehVKgY3NNDtWBEq8tT3zZDdtMEFhF5WU6guIreVE97Wwdtdf0kgln6+trp32lEVjuHvDaH0e4kWZmh1qknGvKyt6eHLvsenA4VkrKb9dUMOruOxGaVrCzLxTPj2LSwvedpwktFFhaH6enq58zrZ7n3+HZmr0fIlXw0Og6xEVunf4+G4alJtuxup73LgUbeQEmRYilwCbOpiWzCzdpMAputisGoAlGFL7BJvphnx54B9JZaFDUSxs/O4l2oopJrmJoaY2FpFJlJSqEogDzGU4/uZXTmeR598lFsmg6yqQr9W21cfrOAXiew/8ARSto0hWwbuXwJkSLeZR96uYuerk6uDC9xeW4Yj9VIk3E3tdomisU0c5MbrMykUMgKtHqayBXDWI0NOAz1CGIOURXDafNw6ptnaGqzcOzYh1laG8VgcHD06EGcpiQuvZVw5Qw9rbtwNmgYunaTbS2DtDfXcfncaVqsHQg5OXJLHcZWF4tLG2RSMgLhNGFfji5HM9dHF1mKzVCtKFiZylNnbWNzPUUyK8OgrBLOLFDnaOb1cz+hvaETu76Zi+MjSPNFlueijI2Po0LgjYnrWKw2bnqv47JbKMs2sZpakWcCLPk3eM99DxBbkWGsVVAoFJErpIQ2/az71qn11LOwtonZambbti50Rik2q4VQaBOFRIpFZ6Aklmhq7uDq9XFkpSQ1oh6FoGFldY2GZif5Qga1UoJQVFIphpGIWsbn59g72MLq0ho1WiN2u4qmhjoWVkaJZiI4XA6k5QpGo4Jd2w6QTaXwen3MTkyTyZfYOrgNX2Sd0dGrZJNFWtq20OA24rGaiUYLFMNqVEUDg70tJP0lxm6cpbG+mdnFKEceuY8fn30Tc2MtiYiU/R27eO38y1wbmrlzHwe+8KU/fPbJp9+HxaFmbT4GqgDtjds5feY7NG7vo8YuYXEsQCyoYOchA9sHHiKaTBEML1KVRfFHJjE7pGisOvQWD0FvCN/yGlJpie7tddy8lkcsqlmeSTMzf5WdRzuYm4+gl3RwdfUkLns9A917UYQ05IU1wvEVHjzxJPPBb6MsOWnUdzOyNM1j73mEucBpzI4Uaksz6UKOekc9xYKSlfAk8VyEfF6GzlTD6uJZZEIBrdLI5kaZbFZOfYsdo6EWR70U72qOTFBC74E2NIIJiUzCA48f4frEdQ7vP04mZKRvoI98FrzLbkLZSdRqE/GoD7GqQSWvIFEWkWPBoNHz8ndfQq0pMzjQRzUEvukx2pvqaVco0ZgtbEaitDYP4vFswZdYIZ71o7ZEaXJuZzMYoBgrMthtY+bmAg2ebXiHJ2hwdOCu2YJVs8ae/Xtwm/t45MH3YJIa6Wrfj6tGQZOhm8Gte7E6jzDQIqUUklIjc3BwzwG+8f3vYmwyUNtQi0tuw9OkJF9KEAyLrPv9WJwCeo0ZmVLDk48/QSYiJxkPky0U2dd3jM38LI0uJ0aTBZPahlip4TvfOcmhjp1IBAPNB+p4+r5fwKnQUiOXcPShY9hLLYTzK0S9OWxykZbu7TQZPWjkcTajOWotVmbn5rE567B1uTA7NTjdJjR6gVQ6SsAfYHZ2keamBg7t2U0qEiMYWERRVVOpZNFo9NTIdAhFDWq9DpEyBoMBVUnBrl3d/OiF58gp5Tzz1NOMT4xx/z27qW9sJxJZYcfObpZXIsQLIQrJHKNvzqKsanHb6nA5PaQLVRZWNnA2NNDX18+6349CoaOzwcNg/1bCvigGox1JRUY4GMRlqSUZTWOyOjFojSQiOX7w6mtobQKJbJbVwBjXh9fZPdiCy9zNt7//wzs3BL7813/0rK1ZRUWaoSyLoZTV4bK6qLXVY63X8MbIq+zfX48u1cmar4JcpqaQEwhshpjeuECqtEqNyoRM0sD10XGiS3Hsyi3kimW62rfS3byLzY0FBGMUvdqO1qBl/uYk3rkSDz7+JN6pMMmEjwZXI1ffjPGhj3yY8MQ8x468j87u/cxGv8eOHcd5/fRZPv7Er3Lm9EUqhRJd9j56enaSyEWZWR9BV6PFN6dlbWmZijIAEoH+rj1o5R4W5wJsRiZ4/fQQk9PXaGm0I4o6xHKVtdkY/YdctHZYGb8WQCGqeOSJp/j8Z/+M/h09tHe1U9+hYW5Zw9rqOvHAEhsrYWQu8E7m8NSb6D/USH1DB/k1L7a0Al9qAbXOgqPRiUJrIRLKUd9gQG2WgFSOw6FiYXGFrT2P0lLvRNBn6O4aREBPna4Fh95OODpPXX0ToYifah4KoRCLM8OsxHKkC0FUBQn+mXUkCpHAWpSKdYzpl+ZJZKSMTy1hsbroadxKJZnH70/ibsuz7YQGu8PJlaEVapvqObLjCMd3HWH05iSvnXyBTCiMTKKipE4jlKyYVUm29Ozg/NAYre566gw55ldCbNlezw+/fYrljQ2O9B4hRp6//d4LmI1KHj78DLPr5yiFI2jsOlRyKf61MC0NbswuA72DZlQ4UWlrqRZruHTuPAGvn4HmPg7t34tOrkOOSLWUQ06BVDBFMhlhYHsLJY2WzVCIbDZBNBpHoZeTSKYpSlTUKi1IdVKMVRM5fYmwP4JCY8Rt0aI1G1iaCnDfffdy7uSbNDTYOHHiCNJqiu0D21Bq1KTTKVK5EtWCgMVqJp2I097cSnNDA77NZcrVDJWClJGrU2jVAtFYkky6SF9LM5fOT1LrcfPYB0+QCafI5cIoa2D/4aOseTeYmpnhwoXrd24I/N5nP/PsM795D3nNBbZu20057cWprae9WcebL19CZQhTW68mueKhuaudde8soewMlno7BdHO2s0y77v/KVrcAxjKDejpxOgQEVVV4r4c6yvzuNxGzEY9NXIlL578OlfOLPIfPvokP3n1Iod338PeA838+Z9+hYfu38Gqb5r3Pfrr/PF//yLD17/DiWMfI1q8Riy3Qi7vp0alprKhohS1Mjt7neXlRYpZB5LUdqrFFLZaDZViGYu2jt7Gh5gb26CcCWF0BejoaKTHvZ25uQW29XWQDJQoF/NUpQHGzmdY9aZZTb/BWmSedCyBa1DJc3/zd8zM+jHUx3n8PZ9iaHIId+1uOrYXyCYSLExncHdbOHPxLJlEkK7GZnbvfxiD2UmpVMZZ34nNWoesxo83eIVoOIlGWksulmD48hm6OvtZDweQoeTc+ZMYzBoyORlZWYhGTwdvvH6SX/yNjxMNJdELfrKmGrYclbLi32DL1q1MLa6RjUjIV7JIY2Y6+reysOCjXJAiVsoY61xU5RmGzqY4ey5IJpRgm7uFBoceiSIHcgnLq2M0OD1s7d/D1r4uTv7kRzidRqQlIw3Nu3C4rORyaQrhCmVZglQ8ycLyEjKpQLunm9dOX6GoTtPb0siZN3/E2uwc+3a2c2rYxy889CtkXBGWQrMozWHErIb5mVn8S37yoU129u3mmfd/hPYON5VSGjGbQCtU2NhYZuf2HlRijpGbYbzBNKPXr7Hgmycc3KCUKVGmwNVrP6Gpo5OOTicjEwskMnkkVgnTI9cppiW88fIFYlkZFaGMRalh954DdHf00tC8gXdJh06vZGphnDPnrrAeTtLX38urrzxPnd2ERi2i0giE4kH0Wj0Gcw0WixmxqsZqteF2uJFnpOSlBlT6EN7RGIvJOdaCy+zcfwyhIBCJBomlowxdmLxzQ+AvvvKFZw8/5mZ6epQ6035amlKMnyuh0FtxN9TgbDGwsOjHXNONWDNDT+ducuUMr526hAKBlm49YwvnyCZrMErcCBmQK4qUgnF29W4nFJ0nV4ohzTko5jXEEgkG72lDUiPH1p4m6hMxKDsRqk38w198E6vVTLEqJRYN8sH3fpyCMM4f//XfYWoOYVEPsLfvFzEX2rh6eQGjoRaxWmF1fZ3Ll05TqWRo7zTT1lJPxJdkKXCDknYWvclCQWJmZnGGiXEftnoJqmo7J+59P42eZjK5MEpVgc1gmY69bUhVfg4eOMzw2jWeecZBe+NT9PX3M7v8Ch2WQV549TmylU3CvgS1LgUmWysrc+Moq3LanHu4Oevl29/9IU9/+GnqmzuolIusexcZHZtGpaxhY3WdYjrLzi37uDE6jc3jwO9dxqbrZmZ5geXAWUS5mkSkwPGD9/HHX/0rfuXTn2Hotecw93qY8y2yo+swNrWVRCLExkqQGmkNDfVtnL1yllwmx5tnr9Dd20FFLyGRncdl6aGsmqejxYZL246t1omuIcpq+BKFUhTviu+tF56UJDTYBzGadfQM7OA//fZnkJAjnk/T6drG3OIaSs06B488gAIpO3YcZXVujjHvOSK+OcwNCvY0HqKrtZ9NX5VQeYTR0YuUjF5KKQ/lsAaVVI27w06snMDk1BKvrDG+eg6pEtQqI5NXxmnwNJGNp3GYVcTyWXKSLPcdOUKNSUF4fQ2d0oRGp6RGXUaqKGFvsBNPiVSLElKxMLu7dzE0NMLBgwOs+n0Yao3455d589xZFucWSGc07N65i2wsiH9jgUZPE91beql3GoiHNygWyiTiUSSyCqveJYKhOBqdkWKpQiIZo3t7JxeHL7O8vESXQw3VBuYK8wwPnWVb3zaqWQkqRYpsMYy9Wccr37+DRwJf/eoXn/3cf/t1vv7FcVKbcWQVN8vxSyRrFrBYjZw7f5OD2/cxPjWM1tDAlXOXsZpqcVo9WDQOatQa3PY+YqtldDVgqhFoqj2AzdhEdCPCyswGrzz/JnJFEa3FianWSCAxR412K5dunOP8lUv0D9QwufR13nP4v2HRa5DUbjB0Y5lU7iyf/ugF0nkz9z30y9h0dRjlu/B0KrHZjRTFIGIBHn30Aeo9tch1Avfe9yDp9AJSeZSR5evM+wNonWkOHtlHMJwmwGVq1BYGdjTjK/6YruY9LG5cQ10X4+DW9/IX//BNnjp0lLnMRVrdA7z+xteYvlyEgoq5iTTvf+9DiNobNKs/jlpt4eDBJ5ldynH61D+wr/chtDUCNSYrh4/cz49ffIk3L50lm0tiNGtRaeoQCg0o5WomJuaZXfThcbSRTJbRKdQc3f8gw0PL/PLHP8HUiI94PEnUq+Y/fupTKApOsmkTepOV/v4BTn75dcrhNNcnL7HpC2BVmYkVC6yshIlHYuzevZOdR3sI569w/cIGHquLjXiSQinBYPdW9JYWrl9Okw11kkgZ8PrDeGwmFOoadvY7CCSmyRlVlAQfNqWR4flFFibH2DLYSGS9g+dffo4HDp3gL/7hm3zyqSdIy7NoVGWiymYssQz79r2fTDVOxjiL/2YWs6yLiZFpBEmSSDTE61cu07ZfB55NajxVOpqPkVhTMDp+AbmyhMVgYX0jTFaQkU1Geeqj7yOZybE2M4G2JOfUqRE6uz2UclJ+4UMf5cL0OXbva+KlH7yAx+YgERRZj6To7a6nrt6A0+pg0buG02Skr7MPmSHKa2e+h6CogEpCRRamXFlnZWUclUFErddgc9gx6nQUiznMLhcylR6Xsw6b004kGsRoMyPTyBifDBJjg6o6hSSpobOhl939nQxdWEKUGHnt3DiLN3/20uR3RAj8we///rPl4hJiaZljOz+J1SPBn1nnzKl1hKQTpSpHva6Tnh02lvw3WJwIYauVUZFUCfhg+UaKbZ77aXfV4fdOImiGKFZnmMy+Sj7po8Fu5qGHDrJ9v4fxxSX0tWp6ezWIEoGTr5wDXYxC2IDb+BQf+Mh2vvnc1+jaaeTCzed55sTXMdYXeOix9zLhfYNDgw/yyunfJia7ytT4HEu+M7jMLSysDqN3rxLXnWIlMEne78au+gDdPXuwNwcZmbyBo6aLVvcWBJWa2YkcPt8iRw98iNGhi6yEzzI2s4ZJaGZ+cwJnuQ7jVgmG9DPUlO+jwVmmpVNEIa1H3Rzh9Rdv0t+5RPfAvVy7fgN//gZVmZePPfbrBL0lFr0zqJV6psbGEJUiwbAPndZDpermjdMn2bZPwq/+xi9TrWzBqq1nfmmdwKqXRDQD0ij53AaP3/dLWBxmXntpivc+/CEWF0/h2ari7774BrKaIOnVJGadnsatHST8SfzLAYQaPU889glSsSR1Hg9yQ5WKYpEHj36abDjHaiKAxSyS9/nw+tKIeROpdJ68chOjyYmyCKlilMxmBJPTzBvz38W7PsnC8DoxUYpKiKOu2U4wNM7gjkG8i2s4Wtq5f+9+Tl48x0MPHOaNcYFP3tfN5LQPXbeRb/7t/00kBy2yATK5CsuBq2iMOtpbm+lpO87ZiUusJ64QnvMRnIwRC6RxuGvp7W7GaLbTumUfsryCRKHCrkN7iSwtoEFDPAlybYH+7gP82ef/hp4j9czOvYagUDO4dStqvZuha3OIWYiGl3ny8fexGAzw1IPHGZ96HnuLBHu9CbXNRktfP5FkBI26hVAyjb1RjUJeRSKVYTWYyaTTpKtl6j31eNe93Bxfpq+1m/XVdRbmlwioF8gVyyijtWgNNq4Pj9HZaWVwsJ2lzSVszRbOnhy5c0Pg81/8w2dlrRJ2PNTDyPwLJMpBFLSREeexmBTY7TbmJ2dZWEnywXs+jdlhZzUwxY0bk6QLQfbsNRIvJRhfO4tap0aI9dLdvwdp1sAbb0wRKKiZ8Q2hdFro72lBbtawGlqmGG2k0bONVKTMh365l5Dfx9WRmzz1SRUXxkO89/jjvPr8LNcmLvKR3zQwv3aGYGKFZGYTuaSLuK+AbzXPPSf2UK3o6e17HDGxD2nUSaPNzGzxe6xueNnm+B202W5Gzk1SX9tNR/cWZBI5RTGBTlfm+R9/BZ1aRk/DfrKqAO5GHWv5lxjoHSSW/CGP3vNbGLQyYpl1IpI5/GMxBvcep2xowe8fQRRKjM+to9bBUmwIh3ELNXVVVoJDtO5yYBFqiYcjROIxdu/twG6RY1CZ8W9ukEvFSIaXaW0y8aFf+Chbu/qYCr1ArrLA6Ng5Xv3x8xw7eoJrbzzPjbGbeKdG0WpNdHraSJb8/Pcv/T2PnXgGmbKJBz+1k//9Lz5P11Et08NZXA2t6KVWqrUCM5cWkNmzhBlFqZLhMPeSK6rZjE6gspTwWK202ly0trSxEQ8SUsVR1xT52EMfZk/bvayGfVj0eiyeCFu7j6I3ikiqC/QO3ktffzsLo3JqnQqcdidH2yyEMzkK+RA3hibpNrbw6U99hWuL38HgttDm2UlPQydWj5MUVepsXRgFB74bFZxGB/c8dIxEVc7kWgB3dyP+2CyqmirJUAiDRoPO5sCfDNLYX8enf+23+e63vsLgQ1XqdI0c6H+Izr0mlKoQetcKDreWX/v1/8TZodPc/+BDqJVrkImSlawymtgkXZIwPDLBykoYrV2O1zuKzS1jITxFNlvCamgik6mSyxYQVHKkCguBzSVcVhUboSBUq0gUZczGDmLBFDZ7hT2Du2hpbuDsmWFKgppoMk5qI86lS1N3bgg8+0f/9dntjxrwrqVobNyFWmjE5z/Hg+/tYfRiHJfZRiyiRFpj4qtf/gGVqoWjR3ZCZRNJwQWyBjR1UjLFZRY3ZvHn80x4Z4lFKnR1P4ixZZ0PPPJZZI3D/NJnvkRyrYpSZaLNbeMf/+q7PH7iU6wGXyVPDke7hmyxhrlNH6d+fA1bYxdf/L3f4YXTSwiBB7B0VnGbHESTMxTLSlq2dLCWf51i0kqqPEc8MEKdRYXD6WZ4eJ0XX3qFbCzAAyf2YDGq0OmlXB0+C4oIzjoPWzt2MXXlJke2fxy7wcWXn/tTqmUDR/d8jAZbB9HVEJdeP0ffIRf/8JUXUCjcbN22G6ejjoWldS6cG0Iqenj4sRO4mzTMjN1EJt/g4lU/pMxEvYuoJToOHzrMRiDEuQtv0tXRSndXP0qtgen5cercOrbv6OJbX3seo82OvWTiy4F/ZMBxhMM77+fg/Q/S3Cpwev5lrs+eom/bfVyYvIlZL+XPv/o9Lpw+iWz3j+hq/AMUljH8q1eRe64x75tmZX0GwTTPwf178fTtoMPTQ2VTw8SNEApdDqtdQTKdxaG0oCpXWfSvsL66QWOTg+46NzcujzExOkXBlaOns5ftzgdRGBIsLw1h1feQqa7R199GpTSDtV7Gn/zhixw/0MLQjXmaXC3I4gKr/gVilRhjM29SzMtYnl5ioL0bg76OfCHKS29+Ab0xh1ySR1KqggDe9DR5ocB6YoqRoSGC63HuOXw/uUyappYunA0G0sV11sIzPPOJT3J1yIvRoEeLwOXR81w8HcRjVSOvseH3L7F9Zxunbl5lPjrJyOQ8ec00GsU2xKiCkjRDNBXEoKinXuQ6HkoAAAmESURBVLcDXyjK5PICdrOBnrpWtAo7CrkSTY0CigXUUjnqqoO1YAxEBdlUFoemm64tTWi0Mk6/fo5ELEiNTIZaLiORyqLWyjh9evTODYEvfPnzz37kVx/j3Pk3qFZAr6lhajjP6OQMRw8+zbk3RpGi5sbYdRRSGVsH3cjkNtRyDYN7nLx+9Qqvv3yNRk8LEkmBSHEFk0OOu1lLVruCodHM4toMJ19+Do1hO8cHdnPkSB+XL/2Qe48dx+pe57d+cZLmHXq2DOrRSA/xu7/9Q5ocKj78gYMokPOdl75No8dMKJUkNpfk6MEtpCt+Aolpinkr7z3+YWKJVcqFKdIxBXmpidBago8+8mm6m/S88v2LXLp0A6UOiuUyx48eZ3T8PFHfJB99+Jc4f36MiY1v0Op5iMn1l5HKAujMJkYuVblwdoz+nv3oZU08+ISLcqWJ73/j76kWcngGFEiqcPInX0GUW3HVqulq30dZiDM7820KPg1ShQ4kIo2NHlZWQ0xMLZCvJjCY37qwpBI5E5NT7NzXSUvXNiyORirOU9yzZy8f/vDv8uT7f5ezo3/FSnaFDz3+Ab711R/QeshNvcnBN/76WxRKcVL5DPW67fzlZ5+jmhzg+Z/cZOBQJ0azDavdiKQgIVRZIxdQ4r8SZO/OA3jqXcS8AUr5IrKqgEaiJC2USCUi9G/tIboeR6gaaW81sllOs74wg0GuoGmXmlJFZHVsk6o0iFFdCwUdAwNH8WwbpRRWoNTV4V1do6CE6+tn2bIbPnDsV3jPve9nV/8WJEhZ3ghSRsbwlRQOSw86sxS92kijZweiTsbcwipGdTtbe/o5fPgeFiZX+NHzLyKRy+gb2MbNG/PECiMseyN8/IMfYzN/HmW+lT1bnWgVg5RDah546iFUWhmCIMWFgT39gxzq3Y3C7Ka9fi83r5yjrMixs7+JTFKO1aJnZe0GQjmLSW5CVZGTDKWolCpIZQWqYplioYh/c4EamZpcpkqwEGZh3EskN4+AlPaGHuo8DpKZAoVclYFtO7HZVXzzWz97BaI7IgT+z//rf3vW3VUisFnE4cqQT5gxWOR4TDpWli4ycnqFY8d7ESUhsqk0xUIJUZ0mElxj9OYcxkYnW9u2s8ENtu0boLnJhlSWw+asx1Cf5dqNy/R3t5OMq3ns0BN87mt/gsppZWT6PClpkj1dv0Ze8SrLqfOUkwpeOvnn2KytaHRGmmQP8OBHnuAP/9sf8M0f/1d2bh1kb99OXvraDzEZt1LIOKgW5Xz9m9+h3dVOOqqltFzL97/5E2aXZjHqpFCtoLGaqcgKWOvV9PX3I5d5uXz5FU7s+CAL6wlWls7QYupidOVVPPV72LO3l109H+VT/+G/8Pk/+s+sz68wMfkmmlonL568SZ9Tg1Wj5o3lf+T+3c8wfu0qI2teZMUSWksFZbaJ+HoNNpWRU5cnuHbjMtl0jB179nDksW424qPkcwlkRTkmnYmSrMDkwhJWlZaELUf7WjNvrvyEEGsMX7+E02RAIur58Z+dxSK4WJbPsDp3ndPPj9BUd4BDg/+FL/z+52luqGd8+SRPPH2A/qM17Os6TK+1j+z1MkOv3CSS92NQK0hkk5g9coxlE4qSQGOvByEno6KR0d/l5ievncPjdNPcaUIrL7I6X0Nncz3B2VHsLVsoxpR0DbaREqNcHV7HrE0gSEqcPT+H22TE0dKP37/AxYXXMblrcVTrsertXL9xlW9876tMT87h3ianq38QszNIsjiF1b4Fq7mR+dVrFOUVurpqWZy6wvDSVQwdNt68/CbOTjemRiXLi17GL1/FWGsllFnGYLKh0NiwW1ys3YCCbZaW1lai2Q02gvO89PwpSmSZWPez6L3E/MQcC+PrDPT0M7C3j12OE7ia2ujr2k02mGfq0gSO5jbSUhG1RkpTZw/BUoDp1Au07fawGt9kI5BlddFLueBjdW0TV7cSaVnNwuwE3sgcFbUUg6nKwcNPMn/Txw9eOPkzQ0AQxX/Tm8L/XRAEIQRkgPDtdvlfoJZ3tz+8+/vwbveHf98+NIiiaP3pxjsiBAAEQRgWRXHwdnv8f+Xd7g/v/j682/3h9vThjvgV4V3ucpfbx90QuMtdfs65k0Lgf5qweJfxbveHd38f3u3+cBv6cMfMCdzlLne5PdxJI4G73OUut4HbHgKCIJwQBGFWEIQFQRB+53b7/GsRBGFFEISbt5ZlG77VZhYE4XVBEOZvbU232/PtCILw94IgBAVBmHhb2890vrWW5J/dqsu4IAjbbp/5/+P6s/yfFQRh46eWyPunY797y39WEIT7bo/1PyMIglsQhDOCIEwJgjApCMKv3Wq/vTUQRfG2fQApsAg0AwpgDOi+nU7/BvcVoPan2j4H/M6t/d8B/uh2e/6U30FgGzDxLzkDDwCvAgKwGxi6Q/2fBX7rZ5zbfet6UgJNt64z6W32dwLbbu3rgLlbnre1Brd7JLATWBBFcUkUxSLwbeCR2+z0v8IjwHO39p8DHr2NLv8ToiieA6I/1fxOzo8AXxPf4gpg/Kel6G8X7+D/TjwCfFsUxYIoisu8tUDuzn83uX8Foij6RVEcubWfAqaBOm5zDW53CNQB62/77r3V9m5ABF4TBOG6IAifuNVmF/95GfYAYL89av8m3sn53VSbT98aLv/92x7B7mh/QRAaga3AELe5Brc7BN7N7BdFcRtwP/ApQRAOvv2g+NZ47l31p5d3ozPwV0ALMAD4gT+5vTr/MoIgaIEfAL8uiuL/a7ng21GD2x0CG4D7bd/rb7Xd8YiiuHFrGwR+xFtDzc1/Gq7d2gZvn+G/mndyflfURhTFTVEUK6IoVoG/4Z+H/HekvyAIct4KgG+IovjDW823tQa3OwSuAW2CIDQJgqAA3g+8eJud/kUEQdAIgqD7p33gXmCCt9yfvnXa08ALt8fw38Q7Ob8IfPTWDPVuIPG2Iesdw089I7+Xt+oAb/m/XxAEpSAITUAbcPX/b7+3IwiCAPwdMC2K4hfeduj21uB2zpa+bQZ0jrdmbz9zu33+lc7NvDXzPAZM/pM3YAFOAfPAG4D5drv+lPe3eGvIXOKt58uPvZMzb81If/lWXW4Cg3eo/9dv+Y3fummcbzv/M7f8Z4H77wD//bw11B8HRm99HrjdNbj7H4N3ucvPObf7ceAud7nLbeZuCNzlLj/n3A2Bu9zl55y7IXCXu/ycczcE7nKXn3PuhsBd7vJzzt0QuMtdfs65GwJ3ucvPOf8Dd6sE8oOuHwYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1nxbnpgjpfn"
      },
      "source": [
        "Sample_Image = tf.keras.preprocessing.image.img_to_array(Sample_Image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X7EdBGZPjpdm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8a768cf7-70a3-4baa-bf78-b180d74ff4e0"
      },
      "source": [
        "nap.shape(Sample_Image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIQ80ilwjug0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b745ea01-552e-4034-f4b9-13f6c225b506"
      },
      "source": [
        "Sample_Image = np.expand_dims(Sample_Image, axis = 0)\n",
        "np.shape(Sample_Image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UuMGFPIpjyTx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "45e5193f-3efb-4f5b-a16b-1a6a957d3191"
      },
      "source": [
        "Sample_Image.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "255.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0iaD7r-_jyN6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3cea8cd1-ca36-4abe-9769-f288986222ee"
      },
      "source": [
        "Sample_Image.min()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "meG5R376j0Pf"
      },
      "source": [
        "Sample_Image = tf.keras.applications.resnet50.preprocess_input(Sample_Image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tB75P9fEj0M6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "024e18c7-f150-48dd-faf4-992a723d7679"
      },
      "source": [
        "Sample_Image.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "151.061"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnORgzknj0KT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e3d501fa-7186-411d-c96e-24306e804a24"
      },
      "source": [
        "Sample_Image.min()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-123.68"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTP6Hctuj4uA"
      },
      "source": [
        "predictions = model.predict(Sample_Image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tV5RHWiEj4rq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "76316be4-fa23-4982-8225-21dd0b46701c"
      },
      "source": [
        "print('predictions:', tf.keras.applications.resnet50.decode_predictions(predictions, top = 5)[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "predictions: [('n01871265', 'tusker', 0.65755934), ('n02113799', 'standard_poodle', 0.12074297), ('n02504458', 'African_elephant', 0.0957178), ('n02504013', 'Indian_elephant', 0.08039243), ('n02102973', 'Irish_water_spaniel', 0.022264002)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H38yzWMAn7hu"
      },
      "source": [
        "# STEP #4: APPLY TRANSFER LEARNING AND RETRAIN THE MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "daq3TE6BnFjp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "64d27d50-d2ba-49ca-c0a3-ac713115757d"
      },
      "source": [
        "base_model = tf.keras.applications.ResNet50(weights = 'imagenet', include_top = False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94773248/94765736 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69Ky2RWenFhr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0c2fb136-dc9a-4b24-fd12-299dbea5aba4"
      },
      "source": [
        "print(base_model.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"resnet50\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            [(None, None, None,  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, None, None, 3 0           input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1_conv (Conv2D)             (None, None, None, 6 9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1_bn (BatchNormalization)   (None, None, None, 6 256         conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1_relu (Activation)         (None, None, None, 6 0           conv1_bn[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, None, None, 6 0           conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, None, None, 6 0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, None, None, 6 4160        pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_bn (BatchNormali (None, None, None, 6 256         conv2_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_relu (Activation (None, None, None, 6 0           conv2_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_bn (BatchNormali (None, None, None, 6 256         conv2_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_relu (Activation (None, None, None, 6 0           conv2_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_conv (Conv2D)    (None, None, None, 2 16640       pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_bn (BatchNormali (None, None, None, 2 1024        conv2_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_add (Add)          (None, None, None, 2 0           conv2_block1_0_bn[0][0]          \n",
            "                                                                 conv2_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Activation)   (None, None, None, 2 0           conv2_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, None, None, 6 16448       conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_bn (BatchNormali (None, None, None, 6 256         conv2_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_relu (Activation (None, None, None, 6 0           conv2_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_bn (BatchNormali (None, None, None, 6 256         conv2_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_relu (Activation (None, None, None, 6 0           conv2_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_add (Add)          (None, None, None, 2 0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_out (Activation)   (None, None, None, 2 0           conv2_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, None, None, 6 16448       conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_bn (BatchNormali (None, None, None, 6 256         conv2_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_relu (Activation (None, None, None, 6 0           conv2_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, None, None, 6 256         conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, None, None, 6 0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_add (Add)          (None, None, None, 2 0           conv2_block2_out[0][0]           \n",
            "                                                                 conv2_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Activation)   (None, None, None, 2 0           conv2_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, None, None, 1 32896       conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_bn (BatchNormali (None, None, None, 1 512         conv3_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_relu (Activation (None, None, None, 1 0           conv3_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_bn (BatchNormali (None, None, None, 1 512         conv3_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_relu (Activation (None, None, None, 1 0           conv3_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_conv (Conv2D)    (None, None, None, 5 131584      conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_bn (BatchNormali (None, None, None, 5 2048        conv3_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_add (Add)          (None, None, None, 5 0           conv3_block1_0_bn[0][0]          \n",
            "                                                                 conv3_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Activation)   (None, None, None, 5 0           conv3_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_bn (BatchNormali (None, None, None, 1 512         conv3_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_relu (Activation (None, None, None, 1 0           conv3_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_bn (BatchNormali (None, None, None, 1 512         conv3_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_relu (Activation (None, None, None, 1 0           conv3_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_add (Add)          (None, None, None, 5 0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_out (Activation)   (None, None, None, 5 0           conv3_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_bn (BatchNormali (None, None, None, 1 512         conv3_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_relu (Activation (None, None, None, 1 0           conv3_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_bn (BatchNormali (None, None, None, 1 512         conv3_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_relu (Activation (None, None, None, 1 0           conv3_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_add (Add)          (None, None, None, 5 0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_out (Activation)   (None, None, None, 5 0           conv3_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_bn (BatchNormali (None, None, None, 1 512         conv3_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_relu (Activation (None, None, None, 1 0           conv3_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, None, None, 1 512         conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, None, None, 1 0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_add (Add)          (None, None, None, 5 0           conv3_block3_out[0][0]           \n",
            "                                                                 conv3_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Activation)   (None, None, None, 5 0           conv3_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, None, None, 2 131328      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_relu (Activation (None, None, None, 2 0           conv4_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_relu (Activation (None, None, None, 2 0           conv4_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_conv (Conv2D)    (None, None, None, 1 525312      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_bn (BatchNormali (None, None, None, 1 4096        conv4_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_add (Add)          (None, None, None, 1 0           conv4_block1_0_bn[0][0]          \n",
            "                                                                 conv4_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Activation)   (None, None, None, 1 0           conv4_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_relu (Activation (None, None, None, 2 0           conv4_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_relu (Activation (None, None, None, 2 0           conv4_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_add (Add)          (None, None, None, 1 0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_out (Activation)   (None, None, None, 1 0           conv4_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_relu (Activation (None, None, None, 2 0           conv4_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_relu (Activation (None, None, None, 2 0           conv4_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_add (Add)          (None, None, None, 1 0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_out (Activation)   (None, None, None, 1 0           conv4_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_relu (Activation (None, None, None, 2 0           conv4_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_relu (Activation (None, None, None, 2 0           conv4_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_add (Add)          (None, None, None, 1 0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_out (Activation)   (None, None, None, 1 0           conv4_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block5_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_relu (Activation (None, None, None, 2 0           conv4_block5_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block5_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_relu (Activation (None, None, None, 2 0           conv4_block5_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block5_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_add (Add)          (None, None, None, 1 0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_out (Activation)   (None, None, None, 1 0           conv4_block5_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block6_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_relu (Activation (None, None, None, 2 0           conv4_block6_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, None, None, 2 0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_add (Add)          (None, None, None, 1 0           conv4_block5_out[0][0]           \n",
            "                                                                 conv4_block6_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Activation)   (None, None, None, 1 0           conv4_block6_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, None, None, 5 524800      conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_relu (Activation (None, None, None, 5 0           conv5_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_relu (Activation (None, None, None, 5 0           conv5_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_conv (Conv2D)    (None, None, None, 2 2099200     conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_bn (BatchNormali (None, None, None, 2 8192        conv5_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_add (Add)          (None, None, None, 2 0           conv5_block1_0_bn[0][0]          \n",
            "                                                                 conv5_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Activation)   (None, None, None, 2 0           conv5_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, None, None, 5 1049088     conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_relu (Activation (None, None, None, 5 0           conv5_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_relu (Activation (None, None, None, 5 0           conv5_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_add (Add)          (None, None, None, 2 0           conv5_block1_out[0][0]           \n",
            "                                                                 conv5_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_out (Activation)   (None, None, None, 2 0           conv5_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_conv (Conv2D)    (None, None, None, 5 1049088     conv5_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_relu (Activation (None, None, None, 5 0           conv5_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_relu (Activation (None, None, None, 5 0           conv5_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_add (Add)          (None, None, None, 2 0           conv5_block2_out[0][0]           \n",
            "                                                                 conv5_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_out (Activation)   (None, None, None, 2 0           conv5_block3_add[0][0]           \n",
            "==================================================================================================\n",
            "Total params: 23,587,712\n",
            "Trainable params: 23,534,592\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2dwWVydWnFfk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "58c72239-6b96-4823-b001-244bd9211dde"
      },
      "source": [
        "for i, layer in enumerate(base_model.layers):\n",
        "    print(i, layer.name)\n",
        "\n",
        "# Top = False\n",
        "# Total params: 23,587,712\n",
        "# Layers = 174\n",
        "\n",
        "\n",
        "# Top = True\n",
        "# Total params: 25,636,712\n",
        "# Layers = 176\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 input_2\n",
            "1 conv1_pad\n",
            "2 conv1_conv\n",
            "3 conv1_bn\n",
            "4 conv1_relu\n",
            "5 pool1_pad\n",
            "6 pool1_pool\n",
            "7 conv2_block1_1_conv\n",
            "8 conv2_block1_1_bn\n",
            "9 conv2_block1_1_relu\n",
            "10 conv2_block1_2_conv\n",
            "11 conv2_block1_2_bn\n",
            "12 conv2_block1_2_relu\n",
            "13 conv2_block1_0_conv\n",
            "14 conv2_block1_3_conv\n",
            "15 conv2_block1_0_bn\n",
            "16 conv2_block1_3_bn\n",
            "17 conv2_block1_add\n",
            "18 conv2_block1_out\n",
            "19 conv2_block2_1_conv\n",
            "20 conv2_block2_1_bn\n",
            "21 conv2_block2_1_relu\n",
            "22 conv2_block2_2_conv\n",
            "23 conv2_block2_2_bn\n",
            "24 conv2_block2_2_relu\n",
            "25 conv2_block2_3_conv\n",
            "26 conv2_block2_3_bn\n",
            "27 conv2_block2_add\n",
            "28 conv2_block2_out\n",
            "29 conv2_block3_1_conv\n",
            "30 conv2_block3_1_bn\n",
            "31 conv2_block3_1_relu\n",
            "32 conv2_block3_2_conv\n",
            "33 conv2_block3_2_bn\n",
            "34 conv2_block3_2_relu\n",
            "35 conv2_block3_3_conv\n",
            "36 conv2_block3_3_bn\n",
            "37 conv2_block3_add\n",
            "38 conv2_block3_out\n",
            "39 conv3_block1_1_conv\n",
            "40 conv3_block1_1_bn\n",
            "41 conv3_block1_1_relu\n",
            "42 conv3_block1_2_conv\n",
            "43 conv3_block1_2_bn\n",
            "44 conv3_block1_2_relu\n",
            "45 conv3_block1_0_conv\n",
            "46 conv3_block1_3_conv\n",
            "47 conv3_block1_0_bn\n",
            "48 conv3_block1_3_bn\n",
            "49 conv3_block1_add\n",
            "50 conv3_block1_out\n",
            "51 conv3_block2_1_conv\n",
            "52 conv3_block2_1_bn\n",
            "53 conv3_block2_1_relu\n",
            "54 conv3_block2_2_conv\n",
            "55 conv3_block2_2_bn\n",
            "56 conv3_block2_2_relu\n",
            "57 conv3_block2_3_conv\n",
            "58 conv3_block2_3_bn\n",
            "59 conv3_block2_add\n",
            "60 conv3_block2_out\n",
            "61 conv3_block3_1_conv\n",
            "62 conv3_block3_1_bn\n",
            "63 conv3_block3_1_relu\n",
            "64 conv3_block3_2_conv\n",
            "65 conv3_block3_2_bn\n",
            "66 conv3_block3_2_relu\n",
            "67 conv3_block3_3_conv\n",
            "68 conv3_block3_3_bn\n",
            "69 conv3_block3_add\n",
            "70 conv3_block3_out\n",
            "71 conv3_block4_1_conv\n",
            "72 conv3_block4_1_bn\n",
            "73 conv3_block4_1_relu\n",
            "74 conv3_block4_2_conv\n",
            "75 conv3_block4_2_bn\n",
            "76 conv3_block4_2_relu\n",
            "77 conv3_block4_3_conv\n",
            "78 conv3_block4_3_bn\n",
            "79 conv3_block4_add\n",
            "80 conv3_block4_out\n",
            "81 conv4_block1_1_conv\n",
            "82 conv4_block1_1_bn\n",
            "83 conv4_block1_1_relu\n",
            "84 conv4_block1_2_conv\n",
            "85 conv4_block1_2_bn\n",
            "86 conv4_block1_2_relu\n",
            "87 conv4_block1_0_conv\n",
            "88 conv4_block1_3_conv\n",
            "89 conv4_block1_0_bn\n",
            "90 conv4_block1_3_bn\n",
            "91 conv4_block1_add\n",
            "92 conv4_block1_out\n",
            "93 conv4_block2_1_conv\n",
            "94 conv4_block2_1_bn\n",
            "95 conv4_block2_1_relu\n",
            "96 conv4_block2_2_conv\n",
            "97 conv4_block2_2_bn\n",
            "98 conv4_block2_2_relu\n",
            "99 conv4_block2_3_conv\n",
            "100 conv4_block2_3_bn\n",
            "101 conv4_block2_add\n",
            "102 conv4_block2_out\n",
            "103 conv4_block3_1_conv\n",
            "104 conv4_block3_1_bn\n",
            "105 conv4_block3_1_relu\n",
            "106 conv4_block3_2_conv\n",
            "107 conv4_block3_2_bn\n",
            "108 conv4_block3_2_relu\n",
            "109 conv4_block3_3_conv\n",
            "110 conv4_block3_3_bn\n",
            "111 conv4_block3_add\n",
            "112 conv4_block3_out\n",
            "113 conv4_block4_1_conv\n",
            "114 conv4_block4_1_bn\n",
            "115 conv4_block4_1_relu\n",
            "116 conv4_block4_2_conv\n",
            "117 conv4_block4_2_bn\n",
            "118 conv4_block4_2_relu\n",
            "119 conv4_block4_3_conv\n",
            "120 conv4_block4_3_bn\n",
            "121 conv4_block4_add\n",
            "122 conv4_block4_out\n",
            "123 conv4_block5_1_conv\n",
            "124 conv4_block5_1_bn\n",
            "125 conv4_block5_1_relu\n",
            "126 conv4_block5_2_conv\n",
            "127 conv4_block5_2_bn\n",
            "128 conv4_block5_2_relu\n",
            "129 conv4_block5_3_conv\n",
            "130 conv4_block5_3_bn\n",
            "131 conv4_block5_add\n",
            "132 conv4_block5_out\n",
            "133 conv4_block6_1_conv\n",
            "134 conv4_block6_1_bn\n",
            "135 conv4_block6_1_relu\n",
            "136 conv4_block6_2_conv\n",
            "137 conv4_block6_2_bn\n",
            "138 conv4_block6_2_relu\n",
            "139 conv4_block6_3_conv\n",
            "140 conv4_block6_3_bn\n",
            "141 conv4_block6_add\n",
            "142 conv4_block6_out\n",
            "143 conv5_block1_1_conv\n",
            "144 conv5_block1_1_bn\n",
            "145 conv5_block1_1_relu\n",
            "146 conv5_block1_2_conv\n",
            "147 conv5_block1_2_bn\n",
            "148 conv5_block1_2_relu\n",
            "149 conv5_block1_0_conv\n",
            "150 conv5_block1_3_conv\n",
            "151 conv5_block1_0_bn\n",
            "152 conv5_block1_3_bn\n",
            "153 conv5_block1_add\n",
            "154 conv5_block1_out\n",
            "155 conv5_block2_1_conv\n",
            "156 conv5_block2_1_bn\n",
            "157 conv5_block2_1_relu\n",
            "158 conv5_block2_2_conv\n",
            "159 conv5_block2_2_bn\n",
            "160 conv5_block2_2_relu\n",
            "161 conv5_block2_3_conv\n",
            "162 conv5_block2_3_bn\n",
            "163 conv5_block2_add\n",
            "164 conv5_block2_out\n",
            "165 conv5_block3_1_conv\n",
            "166 conv5_block3_1_bn\n",
            "167 conv5_block3_1_relu\n",
            "168 conv5_block3_2_conv\n",
            "169 conv5_block3_2_bn\n",
            "170 conv5_block3_2_relu\n",
            "171 conv5_block3_3_conv\n",
            "172 conv5_block3_3_bn\n",
            "173 conv5_block3_add\n",
            "174 conv5_block3_out\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9Ifd_L8nFdX"
      },
      "source": [
        "# base_model = tf.keras.applications.ResNet50(weights = 'imagenet', include_top = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXplkmJHpIZv"
      },
      "source": [
        "# print(base_model.summary())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "taC8Qkf5pK9h"
      },
      "source": [
        "# or i, layer in enumerate(base_model.layers):\n",
        "#    print(i, layer.name)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bd2UgUtKpMnf"
      },
      "source": [
        "x = base_model.output\n",
        "x = tf.keras.layers.GlobalAveragePooling2D()(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LCxMyCu_pXPM"
      },
      "source": [
        "x = tf.keras.layers.Dense(1024, activation = 'relu')(x)\n",
        "x = tf.keras.layers.Dense(1024, activation = 'relu')(x)\n",
        "x = tf.keras.layers.Dense(1024, activation = 'relu')(x)\n",
        "x = tf.keras.layers.Dense(512, activation = 'relu')(x)\n",
        "preds = tf.keras.layers.Dense(2, activation = 'softmax')(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1YNlmSRupg5W"
      },
      "source": [
        "model = tf.keras.models.Model(inputs = base_model.input, outputs = preds)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ivMg7Bu2priF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ba21d761-5885-468f-ba56-3b630fc770f2"
      },
      "source": [
        "print(model.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            [(None, None, None,  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, None, None, 3 0           input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1_conv (Conv2D)             (None, None, None, 6 9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1_bn (BatchNormalization)   (None, None, None, 6 256         conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1_relu (Activation)         (None, None, None, 6 0           conv1_bn[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, None, None, 6 0           conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, None, None, 6 0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, None, None, 6 4160        pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_bn (BatchNormali (None, None, None, 6 256         conv2_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_relu (Activation (None, None, None, 6 0           conv2_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_bn (BatchNormali (None, None, None, 6 256         conv2_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_relu (Activation (None, None, None, 6 0           conv2_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_conv (Conv2D)    (None, None, None, 2 16640       pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_bn (BatchNormali (None, None, None, 2 1024        conv2_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_add (Add)          (None, None, None, 2 0           conv2_block1_0_bn[0][0]          \n",
            "                                                                 conv2_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Activation)   (None, None, None, 2 0           conv2_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, None, None, 6 16448       conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_bn (BatchNormali (None, None, None, 6 256         conv2_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_relu (Activation (None, None, None, 6 0           conv2_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_bn (BatchNormali (None, None, None, 6 256         conv2_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_relu (Activation (None, None, None, 6 0           conv2_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_add (Add)          (None, None, None, 2 0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_out (Activation)   (None, None, None, 2 0           conv2_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, None, None, 6 16448       conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_bn (BatchNormali (None, None, None, 6 256         conv2_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_relu (Activation (None, None, None, 6 0           conv2_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, None, None, 6 256         conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, None, None, 6 0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_add (Add)          (None, None, None, 2 0           conv2_block2_out[0][0]           \n",
            "                                                                 conv2_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Activation)   (None, None, None, 2 0           conv2_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, None, None, 1 32896       conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_bn (BatchNormali (None, None, None, 1 512         conv3_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_relu (Activation (None, None, None, 1 0           conv3_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_bn (BatchNormali (None, None, None, 1 512         conv3_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_relu (Activation (None, None, None, 1 0           conv3_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_conv (Conv2D)    (None, None, None, 5 131584      conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_bn (BatchNormali (None, None, None, 5 2048        conv3_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_add (Add)          (None, None, None, 5 0           conv3_block1_0_bn[0][0]          \n",
            "                                                                 conv3_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Activation)   (None, None, None, 5 0           conv3_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_bn (BatchNormali (None, None, None, 1 512         conv3_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_relu (Activation (None, None, None, 1 0           conv3_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_bn (BatchNormali (None, None, None, 1 512         conv3_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_relu (Activation (None, None, None, 1 0           conv3_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_add (Add)          (None, None, None, 5 0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_out (Activation)   (None, None, None, 5 0           conv3_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_bn (BatchNormali (None, None, None, 1 512         conv3_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_relu (Activation (None, None, None, 1 0           conv3_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_bn (BatchNormali (None, None, None, 1 512         conv3_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_relu (Activation (None, None, None, 1 0           conv3_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_add (Add)          (None, None, None, 5 0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_out (Activation)   (None, None, None, 5 0           conv3_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_bn (BatchNormali (None, None, None, 1 512         conv3_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_relu (Activation (None, None, None, 1 0           conv3_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, None, None, 1 512         conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, None, None, 1 0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_add (Add)          (None, None, None, 5 0           conv3_block3_out[0][0]           \n",
            "                                                                 conv3_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Activation)   (None, None, None, 5 0           conv3_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, None, None, 2 131328      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_relu (Activation (None, None, None, 2 0           conv4_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_relu (Activation (None, None, None, 2 0           conv4_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_conv (Conv2D)    (None, None, None, 1 525312      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_bn (BatchNormali (None, None, None, 1 4096        conv4_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_add (Add)          (None, None, None, 1 0           conv4_block1_0_bn[0][0]          \n",
            "                                                                 conv4_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Activation)   (None, None, None, 1 0           conv4_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_relu (Activation (None, None, None, 2 0           conv4_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_relu (Activation (None, None, None, 2 0           conv4_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_add (Add)          (None, None, None, 1 0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_out (Activation)   (None, None, None, 1 0           conv4_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_relu (Activation (None, None, None, 2 0           conv4_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_relu (Activation (None, None, None, 2 0           conv4_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_add (Add)          (None, None, None, 1 0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_out (Activation)   (None, None, None, 1 0           conv4_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_relu (Activation (None, None, None, 2 0           conv4_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_relu (Activation (None, None, None, 2 0           conv4_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_add (Add)          (None, None, None, 1 0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_out (Activation)   (None, None, None, 1 0           conv4_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block5_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_relu (Activation (None, None, None, 2 0           conv4_block5_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block5_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_relu (Activation (None, None, None, 2 0           conv4_block5_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block5_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_add (Add)          (None, None, None, 1 0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_out (Activation)   (None, None, None, 1 0           conv4_block5_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block6_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_relu (Activation (None, None, None, 2 0           conv4_block6_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, None, None, 2 0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_add (Add)          (None, None, None, 1 0           conv4_block5_out[0][0]           \n",
            "                                                                 conv4_block6_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Activation)   (None, None, None, 1 0           conv4_block6_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, None, None, 5 524800      conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_relu (Activation (None, None, None, 5 0           conv5_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_relu (Activation (None, None, None, 5 0           conv5_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_conv (Conv2D)    (None, None, None, 2 2099200     conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_bn (BatchNormali (None, None, None, 2 8192        conv5_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_add (Add)          (None, None, None, 2 0           conv5_block1_0_bn[0][0]          \n",
            "                                                                 conv5_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Activation)   (None, None, None, 2 0           conv5_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, None, None, 5 1049088     conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_relu (Activation (None, None, None, 5 0           conv5_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_relu (Activation (None, None, None, 5 0           conv5_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_add (Add)          (None, None, None, 2 0           conv5_block1_out[0][0]           \n",
            "                                                                 conv5_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_out (Activation)   (None, None, None, 2 0           conv5_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_conv (Conv2D)    (None, None, None, 5 1049088     conv5_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_relu (Activation (None, None, None, 5 0           conv5_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_relu (Activation (None, None, None, 5 0           conv5_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_add (Add)          (None, None, None, 2 0           conv5_block2_out[0][0]           \n",
            "                                                                 conv5_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_out (Activation)   (None, None, None, 2 0           conv5_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d (Globa (None, 2048)         0           conv5_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 1024)         2098176     global_average_pooling2d[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 1024)         1049600     dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 1024)         1049600     dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 512)          524800      dense_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_4 (Dense)                 (None, 2)            1026        dense_3[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 28,310,914\n",
            "Trainable params: 28,257,794\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDeMGukkptR_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4fd72302-796b-4cc1-c4e2-d87909634e82"
      },
      "source": [
        "for i, layer in enumerate(model.layers):\n",
        "    print(i, layer.name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 input_2\n",
            "1 conv1_pad\n",
            "2 conv1_conv\n",
            "3 conv1_bn\n",
            "4 conv1_relu\n",
            "5 pool1_pad\n",
            "6 pool1_pool\n",
            "7 conv2_block1_1_conv\n",
            "8 conv2_block1_1_bn\n",
            "9 conv2_block1_1_relu\n",
            "10 conv2_block1_2_conv\n",
            "11 conv2_block1_2_bn\n",
            "12 conv2_block1_2_relu\n",
            "13 conv2_block1_0_conv\n",
            "14 conv2_block1_3_conv\n",
            "15 conv2_block1_0_bn\n",
            "16 conv2_block1_3_bn\n",
            "17 conv2_block1_add\n",
            "18 conv2_block1_out\n",
            "19 conv2_block2_1_conv\n",
            "20 conv2_block2_1_bn\n",
            "21 conv2_block2_1_relu\n",
            "22 conv2_block2_2_conv\n",
            "23 conv2_block2_2_bn\n",
            "24 conv2_block2_2_relu\n",
            "25 conv2_block2_3_conv\n",
            "26 conv2_block2_3_bn\n",
            "27 conv2_block2_add\n",
            "28 conv2_block2_out\n",
            "29 conv2_block3_1_conv\n",
            "30 conv2_block3_1_bn\n",
            "31 conv2_block3_1_relu\n",
            "32 conv2_block3_2_conv\n",
            "33 conv2_block3_2_bn\n",
            "34 conv2_block3_2_relu\n",
            "35 conv2_block3_3_conv\n",
            "36 conv2_block3_3_bn\n",
            "37 conv2_block3_add\n",
            "38 conv2_block3_out\n",
            "39 conv3_block1_1_conv\n",
            "40 conv3_block1_1_bn\n",
            "41 conv3_block1_1_relu\n",
            "42 conv3_block1_2_conv\n",
            "43 conv3_block1_2_bn\n",
            "44 conv3_block1_2_relu\n",
            "45 conv3_block1_0_conv\n",
            "46 conv3_block1_3_conv\n",
            "47 conv3_block1_0_bn\n",
            "48 conv3_block1_3_bn\n",
            "49 conv3_block1_add\n",
            "50 conv3_block1_out\n",
            "51 conv3_block2_1_conv\n",
            "52 conv3_block2_1_bn\n",
            "53 conv3_block2_1_relu\n",
            "54 conv3_block2_2_conv\n",
            "55 conv3_block2_2_bn\n",
            "56 conv3_block2_2_relu\n",
            "57 conv3_block2_3_conv\n",
            "58 conv3_block2_3_bn\n",
            "59 conv3_block2_add\n",
            "60 conv3_block2_out\n",
            "61 conv3_block3_1_conv\n",
            "62 conv3_block3_1_bn\n",
            "63 conv3_block3_1_relu\n",
            "64 conv3_block3_2_conv\n",
            "65 conv3_block3_2_bn\n",
            "66 conv3_block3_2_relu\n",
            "67 conv3_block3_3_conv\n",
            "68 conv3_block3_3_bn\n",
            "69 conv3_block3_add\n",
            "70 conv3_block3_out\n",
            "71 conv3_block4_1_conv\n",
            "72 conv3_block4_1_bn\n",
            "73 conv3_block4_1_relu\n",
            "74 conv3_block4_2_conv\n",
            "75 conv3_block4_2_bn\n",
            "76 conv3_block4_2_relu\n",
            "77 conv3_block4_3_conv\n",
            "78 conv3_block4_3_bn\n",
            "79 conv3_block4_add\n",
            "80 conv3_block4_out\n",
            "81 conv4_block1_1_conv\n",
            "82 conv4_block1_1_bn\n",
            "83 conv4_block1_1_relu\n",
            "84 conv4_block1_2_conv\n",
            "85 conv4_block1_2_bn\n",
            "86 conv4_block1_2_relu\n",
            "87 conv4_block1_0_conv\n",
            "88 conv4_block1_3_conv\n",
            "89 conv4_block1_0_bn\n",
            "90 conv4_block1_3_bn\n",
            "91 conv4_block1_add\n",
            "92 conv4_block1_out\n",
            "93 conv4_block2_1_conv\n",
            "94 conv4_block2_1_bn\n",
            "95 conv4_block2_1_relu\n",
            "96 conv4_block2_2_conv\n",
            "97 conv4_block2_2_bn\n",
            "98 conv4_block2_2_relu\n",
            "99 conv4_block2_3_conv\n",
            "100 conv4_block2_3_bn\n",
            "101 conv4_block2_add\n",
            "102 conv4_block2_out\n",
            "103 conv4_block3_1_conv\n",
            "104 conv4_block3_1_bn\n",
            "105 conv4_block3_1_relu\n",
            "106 conv4_block3_2_conv\n",
            "107 conv4_block3_2_bn\n",
            "108 conv4_block3_2_relu\n",
            "109 conv4_block3_3_conv\n",
            "110 conv4_block3_3_bn\n",
            "111 conv4_block3_add\n",
            "112 conv4_block3_out\n",
            "113 conv4_block4_1_conv\n",
            "114 conv4_block4_1_bn\n",
            "115 conv4_block4_1_relu\n",
            "116 conv4_block4_2_conv\n",
            "117 conv4_block4_2_bn\n",
            "118 conv4_block4_2_relu\n",
            "119 conv4_block4_3_conv\n",
            "120 conv4_block4_3_bn\n",
            "121 conv4_block4_add\n",
            "122 conv4_block4_out\n",
            "123 conv4_block5_1_conv\n",
            "124 conv4_block5_1_bn\n",
            "125 conv4_block5_1_relu\n",
            "126 conv4_block5_2_conv\n",
            "127 conv4_block5_2_bn\n",
            "128 conv4_block5_2_relu\n",
            "129 conv4_block5_3_conv\n",
            "130 conv4_block5_3_bn\n",
            "131 conv4_block5_add\n",
            "132 conv4_block5_out\n",
            "133 conv4_block6_1_conv\n",
            "134 conv4_block6_1_bn\n",
            "135 conv4_block6_1_relu\n",
            "136 conv4_block6_2_conv\n",
            "137 conv4_block6_2_bn\n",
            "138 conv4_block6_2_relu\n",
            "139 conv4_block6_3_conv\n",
            "140 conv4_block6_3_bn\n",
            "141 conv4_block6_add\n",
            "142 conv4_block6_out\n",
            "143 conv5_block1_1_conv\n",
            "144 conv5_block1_1_bn\n",
            "145 conv5_block1_1_relu\n",
            "146 conv5_block1_2_conv\n",
            "147 conv5_block1_2_bn\n",
            "148 conv5_block1_2_relu\n",
            "149 conv5_block1_0_conv\n",
            "150 conv5_block1_3_conv\n",
            "151 conv5_block1_0_bn\n",
            "152 conv5_block1_3_bn\n",
            "153 conv5_block1_add\n",
            "154 conv5_block1_out\n",
            "155 conv5_block2_1_conv\n",
            "156 conv5_block2_1_bn\n",
            "157 conv5_block2_1_relu\n",
            "158 conv5_block2_2_conv\n",
            "159 conv5_block2_2_bn\n",
            "160 conv5_block2_2_relu\n",
            "161 conv5_block2_3_conv\n",
            "162 conv5_block2_3_bn\n",
            "163 conv5_block2_add\n",
            "164 conv5_block2_out\n",
            "165 conv5_block3_1_conv\n",
            "166 conv5_block3_1_bn\n",
            "167 conv5_block3_1_relu\n",
            "168 conv5_block3_2_conv\n",
            "169 conv5_block3_2_bn\n",
            "170 conv5_block3_2_relu\n",
            "171 conv5_block3_3_conv\n",
            "172 conv5_block3_3_bn\n",
            "173 conv5_block3_add\n",
            "174 conv5_block3_out\n",
            "175 global_average_pooling2d\n",
            "176 dense\n",
            "177 dense_1\n",
            "178 dense_2\n",
            "179 dense_3\n",
            "180 dense_4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pw0D8fWrpvEL"
      },
      "source": [
        "for layer in model.layers[:175]:\n",
        "    layer.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAHdCEyzpvB0"
      },
      "source": [
        "for layer in model.layers[175:]:\n",
        "    layer.trainable = True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lwm5XlEDpu0_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "452b4bfe-2ced-4bb8-e271-2bbeb9296f90"
      },
      "source": [
        "train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(preprocessing_function= tf.keras.applications.resnet50.preprocess_input)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory('/content/drive/My Drive/Colab Notebooks/TF 2.0 Advanced/Transfer Learning Data/train/', \n",
        "                                                   target_size = (224, 224),\n",
        "                                                   color_mode = 'rgb',\n",
        "                                                   batch_size = 32,\n",
        "                                                   class_mode = 'categorical',\n",
        "                                                   shuffle = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 202 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VqFpbiPNqWJ8"
      },
      "source": [
        "model.compile(optimizer = 'Adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uL3VSVYPqXuV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "d3b97003-2f38-443c-bb5c-b8171e2d94e5"
      },
      "source": [
        "history = model.fit_generator(generator = train_generator, steps_per_epoch=train_generator.n//train_generator.batch_size, epochs = 5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-44-5937eafcefcc>:1: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "Epoch 1/5\n",
            "6/6 [==============================] - 27s 5s/step - loss: 2.2102 - accuracy: 0.5353\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 1s 196ms/step - loss: 0.1751 - accuracy: 0.9471\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 1s 206ms/step - loss: 0.0869 - accuracy: 0.9740\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 1s 194ms/step - loss: 0.1647 - accuracy: 0.9647\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 1s 191ms/step - loss: 0.0413 - accuracy: 0.9824\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0wR3uKW8g5po"
      },
      "source": [
        "# STEP #5: EVALUATE THE MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hW_BIEqpHXPk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "outputId": "e88a1e31-7f20-40c0-961e-96e872e9bd6b"
      },
      "source": [
        "acc = history.history['accuracy']\n",
        "loss = history.history['loss']\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(acc, label='Training Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Training Accuracy')\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(loss, label='Training Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training Loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UeyESvFZq4qD"
      },
      "source": [
        "Sample_Image= tf.keras.preprocessing.image.load_img(r'/content/drive/My Drive/Colab Notebooks/TF 2.0 Advanced/Transfer Learning Data/cat.282.jpg', target_size = (224, 224))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BZWRQ6Dr4QT"
      },
      "source": [
        "Sample_Image= tf.keras.preprocessing.image.load_img(r'/content/drive/My Drive/Colab Notebooks/TF 2.0 Advanced/Transfer Learning Data/dog.309.jpg', target_size = (224, 224))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dl7wHJuuqoOk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "661af089-a200-4069-c54f-1569664bfb3c"
      },
      "source": [
        "plt.imshow(Sample_Image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fab2f7c45f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFeqoExjqoME",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d417b8fe-d5c0-4859-b55f-e2a206474fa0"
      },
      "source": [
        "Sample_Image = tf.keras.preprocessing.image.img_to_array(Sample_Image)\n",
        "np.shape(Sample_Image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uUO29ZcqvBj"
      },
      "source": [
        "Sample_Image = np.expand_dims(Sample_Image, axis = 0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABspYt1yrumR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "eefd2fa3-a916-425d-d95b-a80ff965e297"
      },
      "source": [
        "Sample_Image = tf.keras.applications.resnet50.preprocess_input(Sample_Image)\n",
        "predictions = model.predict(Sample_Image)\n",
        "print('Predictions:', predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predictions: [[1.9383465e-04 9.9980623e-01]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J7pUBTXMw75-"
      },
      "source": [
        "# GREAT JOB!"
      ]
    }
  ]
}