diff --git "a/code/2021_spring/\350\224\241\346\227\255\346\230\200-\345\234\272\346\231\257/code.ipynb" "b/code/2021_spring/\350\224\241\346\227\255\346\230\200-\345\234\272\346\231\257/code.ipynb"
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index 0000000000000000000000000000000000000000..b26d7f2ae92f74cc184515694ce7dfff429258e4
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e211322c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "55101095",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['livingroom2', 'foyer', 'kitchen1', 'cafe1', 'laundrette', 'livingroom1', 'cafe2', 'kitchen2', 'market', 'pub']\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_dir = '../training'\n",
+    "test_dir = '../test'\n",
+    "import os\n",
+    "classes = os.listdir(data_dir)\n",
+    "print(classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a6ba9752",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchvision.datasets import ImageFolder\n",
+    "import torchvision.transforms as transforms\n",
+    "\n",
+    "transformations = transforms.Compose([transforms.Resize((256, 256)), transforms.ToTensor()])\n",
+    "\n",
+    "dataset = ImageFolder(data_dir, transform = transformations)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bfbb6bd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<torch._C.Generator at 0x11bd7d230>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random_seed = 42\n",
+    "torch.manual_seed(random_seed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4e3efd28",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "90"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "139e16ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import random_split\n",
+    "# train_ds, val_ds = random_split(dataset, [80, 10])\n",
+    "# len(train_ds), len(val_ds)\n",
+    "train_ds, val_ds = dataset, dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a6e69537",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def accuracy(outputs, labels):\n",
+    "    _, preds = torch.max(outputs, dim=1)\n",
+    "    return torch.tensor(torch.sum(preds == labels).item() / len(preds))\n",
+    "\n",
+    "class ImageClassificationBase(torch.nn.Module):\n",
+    "    def training_step(self, batch):\n",
+    "        images, labels = batch \n",
+    "        out = self(images)\n",
+    "        loss = torch.nn.functional.cross_entropy(out, labels)\n",
+    "        return loss\n",
+    "    \n",
+    "    def validation_step(self, batch):\n",
+    "        images, labels = batch \n",
+    "        out = self(images)\n",
+    "        loss = torch.nn.functional.cross_entropy(out, labels)\n",
+    "        acc = accuracy(out, labels)\n",
+    "        return {'val_loss': loss.detach(), 'val_acc': acc}\n",
+    "        \n",
+    "    def validation_epoch_end(self, outputs):\n",
+    "        batch_losses = [x['val_loss'] for x in outputs]\n",
+    "        epoch_loss = torch.stack(batch_losses).mean()\n",
+    "        batch_accs = [x['val_acc'] for x in outputs]\n",
+    "        epoch_acc = torch.stack(batch_accs).mean()\n",
+    "        return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}\n",
+    "    \n",
+    "    def epoch_end(self, epoch, result):\n",
+    "        print(\"Epoch {}: train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}\".format(\n",
+    "            epoch+1, result['train_loss'], result['val_loss'], result['val_acc']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "91ee3017",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torchvision.models as models\n",
+    "\n",
+    "class ResNet(ImageClassificationBase):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.network = models.resnet18(pretrained=True)\n",
+    "        num_ftrs = self.network.fc.in_features\n",
+    "        self.network.fc = torch.nn.Linear(num_ftrs, len(dataset.classes))\n",
+    "    \n",
+    "    def forward(self, xb):\n",
+    "        return torch.sigmoid(self.network(xb))\n",
+    "\n",
+    "model = ResNet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "caadaa6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_default_device():\n",
+    "    if torch.cuda.is_available():\n",
+    "        return torch.device('cuda')\n",
+    "    else:\n",
+    "        return torch.device('cpu')\n",
+    "    \n",
+    "def to_device(data, device):\n",
+    "    if isinstance(data, (list,tuple)):\n",
+    "        return [to_device(x, device) for x in data]\n",
+    "    return data.to(device, non_blocking=True)\n",
+    "\n",
+    "class DeviceDataLoader():\n",
+    "    def __init__(self, dl, device):\n",
+    "        self.dl = dl\n",
+    "        self.device = device\n",
+    "        \n",
+    "    def __iter__(self):\n",
+    "        for b in self.dl: \n",
+    "            yield to_device(b, self.device)\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.dl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0fd965b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "device(type='cpu')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "device = get_default_device()\n",
+    "device"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "65268cb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data.dataloader import DataLoader\n",
+    "batch = 25\n",
+    "train_dl = DataLoader(train_ds, batch, shuffle = True, num_workers = 4, pin_memory = True)\n",
+    "val_dl = DataLoader(val_ds, batch * 2, num_workers = 4, pin_memory = True)\n",
+    "train_dl = DeviceDataLoader(train_dl, device)\n",
+    "val_dl = DeviceDataLoader(val_dl, device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5674ffd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@torch.no_grad()\n",
+    "def evaluate(model, val_loader):\n",
+    "    model.eval()\n",
+    "    outputs = [model.validation_step(batch) for batch in val_loader]\n",
+    "    return model.validation_epoch_end(outputs)\n",
+    "\n",
+    "def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):\n",
+    "    history = []\n",
+    "    optimizer = opt_func(model.parameters(), lr)\n",
+    "    for epoch in range(epochs):\n",
+    "        model.train()\n",
+    "        train_losses = []\n",
+    "        for batch in train_loader:\n",
+    "            loss = model.training_step(batch)\n",
+    "            train_losses.append(loss)\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "            optimizer.zero_grad()\n",
+    "        result = evaluate(model, val_loader)\n",
+    "        result['train_loss'] = torch.stack(train_losses).mean().item()\n",
+    "        model.epoch_end(epoch, result)\n",
+    "        history.append(result)\n",
+    "    return history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "12fc6eaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = to_device(ResNet(), device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "88047db1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.9/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  ../c10/core/TensorImpl.h:1156.)\n",
+      "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: train_loss: 2.3112, val_loss: 2.2676, val_acc: 0.1275\n",
+      "Epoch 2: train_loss: 2.0688, val_loss: 2.1804, val_acc: 0.4050\n",
+      "Epoch 3: train_loss: 1.9301, val_loss: 2.0873, val_acc: 0.7350\n",
+      "Epoch 4: train_loss: 1.8459, val_loss: 1.9947, val_acc: 0.9575\n",
+      "Epoch 5: train_loss: 1.8036, val_loss: 1.9084, val_acc: 0.9900\n",
+      "Epoch 6: train_loss: 1.7598, val_loss: 1.8329, val_acc: 1.0000\n",
+      "Epoch 7: train_loss: 1.7270, val_loss: 1.7711, val_acc: 1.0000\n",
+      "Epoch 8: train_loss: 1.7056, val_loss: 1.7204, val_acc: 1.0000\n",
+      "Epoch 9: train_loss: 1.6797, val_loss: 1.6820, val_acc: 1.0000\n",
+      "Epoch 10: train_loss: 1.6623, val_loss: 1.6508, val_acc: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 10\n",
+    "opt_func = torch.optim.Adam\n",
+    "lr = 6e-5\n",
+    "\n",
+    "history = fit(num_epochs, lr, model, train_dl, val_dl, opt_func)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "dfdb23b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict_image(img, model):\n",
+    "    xb = to_device(img.unsqueeze(0), device)\n",
+    "    yb = model(xb)\n",
+    "    prob, preds = torch.max(yb, dim=1)\n",
+    "    return prob.item(), preds[0].item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e557d9d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted: 0.9877517223358154 livingroom2 Label: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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oDO9+413+g7/z36H9Z54f/PBHRKXIOZDTAkjKbZQm6sA+7Wg+dDx99IDXz+5oFEzLDAUa75iHiXHY07b+WBAIiJpr5XfY3IczvSBIZ/36cMIfQMnaWVDlrdNfCbColMJai3UWrVW9cdSbdiRShx4yBHPIligYBdYYvLa0RtM68N5ijfxOqxXd5oSvf/dbxGnLy8+e89nHz0BLGrvd3WGt5tHjc1LKTHmHtQazKLx1RBVAWcE8jOXVy5e8/7Rn3O6I80Qh4ayp8Ttxdn5G24zMw455lOvptCOpgFUG0zrSsqBKgDSj1RsoVylN4z3OW8IcyDnz/MXnfL8kUhgpxWGcxnrHg4cP2O9H7Ohou46mbTBOsrth2HJ5+Yy2bxmnmRAXnHP4tuXm6orXLy8ZdgNN41mCtBm9txirSXnBmIaUI7v9jt1+x3a75XRzgtKKlBLzPDOMGqMdYEg5E0KQjpYuJOrmPWR1xqASlJxwriGmyKEda61FKy0l7WpdM0GFSXKYWKVQOaMVUEuPP9si/4vWlyQwyBlqtNTNSr91sibIKfKLX3zE7d0N73/4AU+ePsJoTQpB6vsMWSkMunYnCuvzDb/9vd9hc7bm/a+9R86JKRRSKeSKYjjg8eOnfP2732a73BD+L4EQZUOhNFYrnK4otja0bcO3v/Mb/O7f/C6/9w+v2e1vSTESEljjCMvMuN9zslmjjK1txLdxxxoQinydj0hxfgtkVLXlmY+nf1FvZUNIGWCswXuP1vI71IGXcHgeJb0datqrazBRFKyugcFaem856R2r3uNsYUmJaXfHH/8X/zWXX3vCd//6N3n/219nDvDs5y+gRIzRnGzWPH7nCffbEb0baLxnnmcp74p8dkobMJbr2zt225+wmwYwGpWzfLa1/Hr64QeMux3bu9uKm2ga35CKtA/Xmw1hnEhLqLyVUoOmqsCbwWiL0Y6u6VAovv8H/wQfA9Zq2r5FqUJKguSfnT2m6zuM1czLhPOeq8vI3f0djx48IefE/f09vjFM48g0Sha7LEGwGQ1KJZw3eK9ZlqmWJ4llXJimmRgTqWSKVuSSGccRa6FrVsfgrbRGO+FAhDkQQ0BpyCWCypSSpGxSGqMFP9MUjAFnwaiCUgmthT+isqkt8cKBHlJyPuJw6QvsyC9JYAByPRnr7ZzJb52asL3bcX11zavXV3QrxzBM2JptS8ZcIb0ChczDBw+wKjFurzg561FWEcdALpAKJKTlZK1hXu45f7DBOEWMALq2+UBVroBWmq7v+OBbX+c3/sZv86c/+Mfc/PglKUhaF0Ng2O+JYaGkJBFfFbKS7uKRrIRwDcoxtRNUwCiNs46ua5mniXGafvX6KGqgeWszGCPdBdRbCYc6gogSJLSQdOq/ayVlhHeGtnGsu4aLk5bNxtE1lmVJbMeZ+/3M5z//BRdnlmI8Lz5/TS6Ktu3QWtE2DteYGnSgcQ6Fou9W6Oi4M4ZcP7+EJiqIFLSxGGskiKExuuFudw8x0K96QKGKYbPZoLQho3DGYIyAy1qb+rdCF0NSkiQr7VAkVt0JShvBMGpbq/FyXZdYmO/uWa/XnJ5usM6wG+9Z5sCDi3Omaebli2doDNv7Lfv9PWenp/TdmjArtBoq4CefpNaKXBIxy8lvtSeMC/v9SJhmlnkhFS3ZQQzk0sgpjwRyY60AtgXubu+IYcEZSy6JUhIxJihKQM6KCx27L1oCu6mHgVGFosBKZMG4ym3Jh+7e24XqX7y+FIFB3uybJp2qp+bhZlco0JasCmGOzPNICBGnhX2XVUFl5CIjSO3jx4/4/LOPCMtrFImmbch3U63HKj+iceQS+fTjn6F1pm0b5mmqJ3ut/5EbUimF8x6lEjeXnzNPW5ZlIUVNMYIeD7tdrXcFFNPqgB/kQ8SSLEAdWJSSKVljaZuWvl/x6PFDbq6u4PaGGCIhxfpD+s3NceAl1A7EG2ThrT+HlFTreuJopKOpcNbgrKXxjsYbnFVYA0YXwrCjsw20DcOi+MWffspcYF4M69UZ1hqUatBGsdtuWeKCUnKyG6v5znd/i7vre66ePSNMmoKQj/yqB6sw2uGVxvhOWJLekYZASsI7cdYDisZ7xnHGG0PnPV4bkqJuGuEtFgQQffjoEe9++HX+9Ic/ouvXRCIGgzpkXsbQtD2n3Yp5kjbnzc0NKQXQMC8z8xgYxxmrLKt+wzLPXL66xNuGHBUlK0IspHTIVOS655xq21WyzIIizJESC8YYYipYZ1ivO1ar9Rv2KtIG10VIVtM0SdfLLkKQQ4hcVltKKTXAK1rvSUuALHtGq8PBULBGYY3GKoXRSvg15YthC4f1pQgMwBt0rga2X2nrITe4sXJKGqXIEUGCSRLBjz8HxhqWZeKHf/p9rm4fYHVDv95QXt6gVP0wUDTekRV8/MlnnJ+v6VY9N9d7jLYSHA7pnpL63DjD5eVrdLhhHiIxabK2QozKhZLBWeFBFOqHkrP05t+8E6DyEIrCKMPF2ZnQY43GGkvjPe+/+x5LjLx+/ZphHA8RBaWEeitBoQKtNRjK90GpwoH7oCsLUikwRjBKYzSNczhr0EZRtLAsQ0iM+x2bE0/Xeu52A7/3j/6Ax+9/wHtf+wYpZsZxQmnY72b2+4HXl1fMMaG0wzSe04tzFBbXOJTWWN+wWq3wjaPvezwFpzTKtIDCWAVzIswJnSyWjDJKMAul2PQ9Z5sTruwrlrrxchFWKjVzODk748NvfoMlJlzTsEwZg5QWMcG0FJRueO/dD5iGkakyF9vGS7+/QI5glKEUuY7vPn2Pru3p+56TzQner9nuEp9/9gJFwXsn5UmMOOskCKPwtkEXhXEe71tKTLSNZ7PZ0LUNYcloZY9ZYsTgmhbXdtik0MMoQT0WSBll9RE7Qin6vmceJwFf4YhL5QTeOlzNdEtKlRUs91z+YgnDlywwqBoI3noDR+2CFpqyUuW4WeX7qj5KajBFIabI9//o+/zpj39Iv+o4Oz1nHBZJ4YucpJnCEmY+/vRT5jhRSuR+O5NKQZMryGcEb9BSv6MLN/fX6Gi4fHWDa1aoHLHOUUKSer+e6M5aIGHSTMgTKWdyMpTSkLMTNLkUur6l7Ro+/+yKVc7c3N1AyTy+OKfre6w1fPLJL4kpcYyeRw2IphzIKwpUfd3U4EDldRw2kFw7sEbhjK5nbiaVRC6KYZi5vduiTUMxnvvbLSlpnO/QRpNKJMfCEiK73YA1mlgkQzqUSv/F7/0jzk8v0EZ4GtpYulXP+cUZ+7trmCe8a6BYQdzlLMRgMElKpGIUXdcwOM3u/hZnNH3Xsd/eooyw/eRUVeQUefbpx9jWo4znbndPKoqwJLSVbCUbQ0iZ58+eY7Wh9S37cSCVgjGOZZxx1vHO46dM00zJ0PeOnCCXXEuaTNM1oDU5Zryz1GYlqrJJNWCKIsWE7x3WN8x5xLceYyUYCP/CCihdg5BxjrOHD5nud5Q7KfdUQTa/kuAg2ZF0a7QSYE26a0oCTcnoyrxVFGJIGFeJerXM+CsZGBQHUIk3WUM9JYE32UNNyd+QNmqsVoe0SR4/LzPjNHJ5ecunPBPmWFGoA32Zwjwv/OhHP+YXH/0cVCEsEaUP3LE3bcRSoGjNdrflo49/znjS8+rVNe994xts9/dYqykZlLGgFTFJL/z8tGOcR3bTTC6FJWZSgJQaSpLyo+9XLMvMMOzZ7veM48Cq76QuHgchL7UNaS8UZYxCGXUkaUlZwhFTODJADsmEqmXF4WslnRarauZFZkmZaU7M+4n9FNl9+oxY4G4fMcZLBlQKqWo5phhZiiIlCCmjyDjX0rUN1zfXXL16BTmAEnQ95cSjRw+5e/WK7VWQIBOlnaaVwhtHv1lzMy6kkMAJs1TljM2Fodb6l69f1YNTsIZcCqYUco5cXb4iFs36wQWuWVdeAejG4dqGcZqI08jJqsdaSy6ZGBNLiux2ezbdhg8+/DrPP3/OzdW14BchYb2rQOJyLBFzToJhAFZbvLW0jcdUspE1BtdI92GeJkrJWGPxxmG8xyph8s7TDK2Rg6T1bG+SiKy0xqIwFTQz1hBDwlqLQUpBY6SMQSka3woTNe1RWLS25ASmdtRkm/wV7EoUkBteVX3EoaxAvUUtfkuYdPypt+7+P/eEUga4SrstudKHlD7W9ihIKTEMi0R8o9+UEG8/a03fl2Xh008+RT99xPr8FOMd6T7JcytV6ceQUsE7x2bTgtbsp4w1CkwiO4VmQy4thYLRhTQvGDSXl5ds77Y8enjOMo6gFOvzM6z3lHGuMVCCQqlptbwl9Wde9xuSk1bqrT+H+lZTiibmTFoWclxIGs42Z7z/9W/wsx/9jM3ZKQ+fnnP2aE9zshbefxZOz5wyU4woIIaIVWBtrgg6xJIxWhSSvmnYbre8fvWKFKO0VksWkquSwNB2HW27Ynf1mhIlU8spk0KozM1C3/d0XccwLhSSbBitySXjrcNqTQyZrm2xzgvmpMC1LWcPznjw4IIyL6zbjqu7a9FahMAyBbqmAw0f/+IjxmFEo1iWhZIEu/LWQcVotJbMLMVMsUI7Fwr9gSwmnZam8UK715ZcuRfZJXwFLFGKHBM6195RgWkaASrtXZSSIUZ655jmCBSWEKRzpWQ/XFycc3ryiGG75dWzX8BbTGD9BrU77qFfd30pAoMs9SZDqCffm7ciTJY/K689boz6DOVwYh7aE8dnfvP8B4Dy8LQHDcCh9685SJKhvF2rW8v52QkUIe88fPKQWKS8iSFglMVYAYpiKkJqSTOojC6QU0E7wSqs9qA7clyYd3folOVGDontMOG0Ic6RJS7MpdCtVmgnzy3aDSMtzfQWSHrMIA43gT4GCYPc1NRrltGEUpjGGd9klC0Uo3jva+/RP/4Amo4UZpz1nFycE5VlN0XGMEuvXWmUscLxyBlHEZVqTKic0EW4Es5IS/V+GLi73zJOYxX+WJQupJBqiQPjsCdJS4jGe7wxeOco+VDqJNq+Z4l7SpYyTCtFjgJAGm0wWsA5paxgSVphrZMOlTMM+8g0z+z3O5ZlwfkGrRRd30GC68tLLs4f4Kzj7vaevm0x1qHR3N7ecXN1iSKhNcQYKdmhVZVip4RySlqMpYjsfFnIQfQK0zDQOEVygVIS7733HvM0MywDJWWWaSYFef8pCqenUJimkT6sjoeiomp3kCB/fn7O7/zO9/j0Fx+zvfmcEhdyrnLsUt6Aj1+wlvjSBAal3pxy6gimFY5dveN9/zZKKetQGhy+PjyfqMuyPMcRx6xkoMqSeXPCHtJx9eY5qPCCAm0sq/UalS3eeaxzxHlBF8k6rHOS8sVETJmQIvtxpuQASfruJSuUEQqv0po4L4RpQoVEmBesMoQcCEtgMhMhJ+Zpptuc0K1WctKqJNz8BUoqpChAnDGC5OtaOnAIckUfmZIAIWZKWXC+B+0wVmF0QOvC+uQU3ze8895Trq9eE5eAsZpHD5/iN4/4yU9+xOXtK+nlO4VvPPtpIiyBjXZsjCUoy5ADaQnHz8tUVWcpVKVqrtmSvMb9/Zbt3RYVI8qILDzHiNUaZQ3ZyCeSqshOMsuMUgLWaqNxzhJSpG17uv6Ez6xFacnmxmFgHPeUJTMqw/39Pf1qJSSheWEcRtbdGjzCFs1QcmGz3tC0DffbLfvdQE4J7x1hnpiWhb7xcoumSAyJroWDJk2rgtHgrWUeR1Q2qFJYlpknjx/x5NH7XL28Yne5RaMYtjus0sI10ECu3BOtiDFU4ZyUFU3j8c6y6lpuLi/5/NNfMGxvaFvHMgZSnHGuFVC+vLWBvsD6cgUGrd9KhaSlWN6Si/5Z8pYC/qxA5MA4PJqqCD2ON/VJOVYhhTfKAn3AOFBVXq0qkaXiutpwcnLKptsQpi3TPFeyTW1d1TiWktSJ0zwwLRONy4QlUKxBGUC/wUqMgtPNhvFuj8XQ+UaCUy7M48ySIu28AAXfNJX4FUmxsFqvSVEotykLAn0AwKT+lc2IUtV/QJFTYZoWzh6c8c7Xvskf/Jf/mM5rLjrL0mQ++tmnqPY1d7sBbUThp6xh9eCMBx9+i9d319xsr9lPE1hPsYYlLbQx0aJplGNgJmFYssYoK/x9VAXBpM3qjMZZIaipLJ+9914wAwtt1zJNkzA8S8FWopgCGt8A+WiWIveOAHnGWozRhCWQK+1aK7i7umYJMyftmpSki9D3HRREQ5MSzjlO+o2k8angtJWMJSVOVmtu/R0pipoTIMVIDJEcY+0SqXqASFdAKwF528YzR8NmvaJtGvbjyDRN/PLTT4VK7Tymlk7eN6RlAS00e+s9fd/RNA2piEzbQA34klmRE7fXL4nLhNWGhTeM2zd74S0Q/9dcX5rAcAQZS5FOQC0rVFG/GuyKOnovHEPAQVOAehM9fiV1ehMYDiBmPuKUqgYj4QgEbSlYFAlTRM9wcDdarzuePHRcvhykFnVe2mYli+TaKLS1NagFioZUKdpaGzIiiJmnhZIVOiVO+hXj9Q6tBFTKRUgvyxxEnDQtLMuCbTw5Z1zjCWHBty1GWSRuSTBVOZFzomQBnjKRnFIFUw1GN6QU+Ojjz/js+RU/+Of/HL1E3luvef/xGVcv7gk2U7zj8dOHnJ1f0PawD1vO8g7rFdp78jzz6P3HxBjZXRo8mk23om17tiESxkBGRGoKKSkEidSVfHTokGhSKfjGYZ1l2grnIxdR2Gqr0ZUd2XeNXNMS3wTgkjBGi4LReDHqsY6wZJx3GDMLgBcSHkvf9ozjhLMWq+UzbbwjxUxYJrw2lKzo2w6iZhknHjw4o1+tmULkF7/8nLAfQYnORGvpWnRtK+m9ViidKSrRtMKstAZab2mdO4LAt3e37G4nvLM4Z0hpoZRMSomUE84YihUl7GrT8ejJE+63Ba0LcRrIMVIqoaykzNVrKXFKSiIO8w254lfKVCyq/BXNGGQVVK3RjpiDOmxvqf0LCEnpUDoc6qgiCMEhFJTyVsaAZB9SOugKQZQjWGe14fx0g3KGV/uEsj2KhRDusW91QZTWWFdwTnO3TDilSSmjNSQiSgvRR1EoKRNzSyHgupaE/HzhQFsuTMPIzTgzTcuRv4BWLCGgSKQwM+5HhmFPs2lRxlCUxvnmWGoZY7DO0miLUYWcZuIiYpqiIJYiKkzbYYxwE/7L3/9DpiXRWMe56cB7lq3ls/0tL3fPKJ2i/7Rlsznl4dPHPHvxkq99/oLbuz277Z5U4PnlK85PT6QsSgVjFFY7rDWcbU64z4olRbKRbKVxnvc+/JD5fkvY7nDGkMzBP0HYgVlJYChA16/w1pHmRVSgStVWqznaoB3YhxSNUQanC9415BDxzqFMxOTCshvRzpBDxFZV6TyNKFUoJCESGc1q3dP6nicP3ufy+RXPnn3C7fU1N1fXTDGJf4MSwPNgM6it6DAOOhS5X6Vt7Jwl2Sgayypx7/oWbRQpBaxzaCPqYd94ximQcqaph0AGTk/W/NZf+2t89unCzfUnjMsk92MWYDkskwjQDtemgHMNISxkXY66Gr4gKfpLFBgyKkdUOaSHilz0kRN/IHmUAzBwDBxvZQO1jn17lbf+c4wVR6+Emi1o6ZurxjGOC8o1bKwiThk9B5x1oAy+aTlZe25fXxJjRhmJ0DmVI0qsjSaGwPausI8tGM/p+pzMgNIRg8Mo4WFMOfPsxStUVpU56GmNEapsLOyGgWme2A9bTjmVG00lubkoOK3wzgnIZgxeVFCcPFlzefmM7f4VtjE07RlWt2QUAcW0FIYp0q9WPDp5yMPNCa1veHZ1yTjtsE5zf7vl/uY197eX/GT6ER+98xNCBtyKfQH/8BTzQUUyVGasDM1+veLs0Skfh4/oz9bsw8R+PwCF97/2Ne4ur/j87scss7AOvZXXb1wjsvUs1OiuWzPc37MUSDGzzEFSaiUUYd1YYo4VX3AYJ9Zu2jhCnHDOo1WCVNhvtxQNretYrTZ459gvI9M8UiLkVEVpxtB3HU3bio9BiiyzbK549JwUbEcphXVGSHf6gP/LfWu9F+q30VhrCLNkfUYXbNui0KSUuNrek00kxYg5MByPtO9EjJF5njFasxvumaf5WLoqhWSDJUvmXBAQvDp3lZLeNO2OCNqvv740gSHnCDnSGGmjhZRR5Y2JdVaZXJJE2wNOcIAOlZwib5dRUl8dygfeCgrVAYk3IiNjxPxzPwei9SRjWDsrag3jaNoVm80F733wbd59r/Dy82dvBSB5DcYYci44q8S5J2vmreLZ1Z5vfP0hpSSs1Tw4XUmaHzOt9+z3AxpDUxpiDDjvcM7TtgVu74kpEWJkt79nZUFpQ6cbSFmQe2ukr64Ubdew6lZ853u/xcc/N/zghz+jmEzKAcjEvCGkhayEVLS2nvOmxxRFzhPaTTw+vyB6RSRwctLTNC3Pf/o5t5+LVL34gZ+9eM3JB0858z0uRmJR7P1EwoBWXF1fMY4DJYmWwTlLKYkf/+kPYU6QMqHSkRUGGwJeG9q2JUbou57H7z7lZYyUZSGFpbaAG4rSmE7YpiEJuaxbrTh78IDzd95H92teP78WMpw+ZCuO9dmGi/MLliXhrMNmQ9pHciioIia107hn3o/kObHetKQUMUjput/dU5Loa4w1LGMiAUprQox0zsm9oDRaW0BXGXpkGEbMdkevV9iuFzSrwDiP2E5wtZzTEWtRRwBZuDaffPQRSmnmMEJY5H2ZKtDLpTpHiauViO0EfzmaIP9rrC9FYFAIXXfdWzyF/RgZl0KIGWUdvmvRVrFUt7Vclcrp4E7zdkwocCQo1X8VoYpAMseHlUyJhWwK3lm89zy/2qJ9R7EO5xSqaek3a/r1CavVGZvTR3TdJIxDVBVYWZzz4l5dskRza/GuY54My2y5vFmY5kSYBtTX13zw2FJ0oV1t2GxO5AaKid1uoG0a1ps11npCjLRmzapfM0wjdmXJKdNoy5Pz93l8/oiUI1fXV5QcJT31nvH+jmE/MC8Zyh6vZhovm7bkhVISKmfWxtEqjdGZpVzx4J2ey3Hkk8+vCSVzdpH4zm+e8Tt//TcZP7shTpmb2y1qmGmKIe4XeuuODlD7eWKeAzefvRZxT1HHzyCGwP39PS4rWqVQ1tJ0Lat+hXEdIcqNvCwiXX5Xa0JYxKbOSgdis95gaqs6KZiWBa01jW9p2oaHjx+zT8JZaX2DIvPo8WNOn74jFOZiGGPAt+bYTh0r6zGnSElijrLd3tI1K0oKFOXIJJZlZJkn8YqwlsUa4WJozbwsNE1zBPvmEBnngDeROAfGacYMM/3JiXQbqj+ldR4QXkIMEYHKakdJi/VcCAufff5Lmv6EnAMlHRSe9Y8S4xzBqGzl6NTiW+lK8f7i60sRGEDReUffOdI0kkImzZl5ThRT6Ndr8WR0vqZb0iXISTTsKcqfGNOv9m6P6IQECVECWqZpYtX1rLpONsU8cH91hUOssZJSKGMwVRKrtCZnRSm69ocPGIbCGlstuOrzA0VpUBbnWzYbQwgFY0/Ae8Yx0njDet1TFsWjiwdY63j+4gXTNIn7dCwcBDmrfsVqdcrleMs0J5SKWKv57e/9Dr/923+dab/lP//P/t/s93uGKbFJnpACrlmjmwdcXW/ZuMBsrunXBpUmjFIUDI3zoDO5yfiN4fntHT/66IphAd1Yxly4v/+Y733rHT54+ID95R3NmPj606ck44nDjD/vMURiSigNbddhnfAwVLak2rLU2rHqN6gQMSFRqvtR27bs9xMFyfqcdeQQeP3yBaWIZ0FMIopqmpYwTYIvGPH7bLyUIHFe+NH3v485f0BBTk/yyLDfY+63OO/wruWDD96n3Th+8clIWgLjdiKnyDgM9G3LuBt5Nb1k2o2cbs7wzrCfZ0qRlrUt4q3YepF3v6HqAhV7iEH8Fp11KBtpXEPrGlSGeT9yur7AGsuq7xmmO+IsDuBaa1LNUA5CuWGKbHdbru+39H6F00Ki894KA1JrSq5ZqzYsYSFE6WBkZUhFod5OpX/N9SUJDNA4Ky0sQCtLWGZygJwWYoy0dFjrZVYBklLjrLAAs9Shsc4syHWmQCninJNiJi6R1apntdnw6vUrvvm1D3j48JTzsxN+9uOfsL2+xuChJFQF9Up1TEZVd6CS66bXFf3PwsdXVN9+SDlB0aSsGKtiT3uHUgaVMkYXnr5zwYdPv8Ef/8GPROXYeIzSnG5OALDWkYuwJ5dl4fZmy+XdHTFHLh61tL3n6defcvbknN1V5uTshPvdHcuc2Y8ju2EkoLkfCrudZnPWEuLCPO/QZBqjKUZhm5ZIJNqZq/2eH38+gHvEeavY5wXTnJJK4McfXVPeiTxsPN2qofErfnZzw8OLhzhj8VoMULNK0pL0tgbrhGlbYspobbEmk0IiKXFXPjk9wRrHMt3Rdmsx3IliVTZOY21fikN1u1mjjeE+JOY4A+l4Qi8hEEJkN81sVisSAmIqXbi/v6U0ns3ZGSEkrDfEFAnTjEFzcXrKdjewhJm7u1tKFGGUMlBIYn9fEkaBUZoF6ZgYa4kpE1Om8x5jDUrLY7xraLyn8R5bMmdnJ1w8fEhGyG8yI0LDIlYDJeZjUMkxULST+1crtHVoa1FRPCC9sizjKErZim2UQ5Z80MdoLWzZYzl9ROF/7fWlCQzOCHOtFLFjT1Ekp9or8f+XiR2kqDiQW94u85XRWKpakHIMDKYICUghDLX7e3Ef0mYhhi3Q0q88YefYjwvZLVBEB69KqYxBqotxldWmdGwtiTOS1HqllGrxDYYiaR8Bqz3zPBLn15yePuadd874+m98g3/8n/1XbLdbttW5uvH+SE5KOdO1DdM0cn15zXY30HQe9bhnWWbubi+5uVzz4tPP2I87IUPFwO12wDY3PLu84hefviKPC2oMnF00GLcc3YQzUKzCdoXZJn7x6Z7cPaLXng0BlR3JeZxuSNHw6dVA+47BtZrtfscwzxjrePDwISsP5Ilh2bOkTCKTSiYrg7OeEGYMhoQMnbHW0Xct/XrD7fU12ijatpF2W83QUo7knA+DUtBGSZuOmi1m6WjkIn6Ih0CgjahgZaNE0NCv1nR9z93NLa9ePifnhTAt4ivhLUPdODEG5mHBW8dmvRbZfJaOReMdq64lqQhKMCmKjBI4qG+p1HOl6kleCksMdH0jbdEiNJYUpTVZCmhlyLHgjJGDLUcaHKkUitJkpVDaCn3ciMYiLELnztXEpaRSvS6Q12adjDjgQBwsXzQufEkCg5IouswT4ziwLGIy4YzCWHOMjvlA6z3WYuVILJLn+fM8pgJY7+m6NdM4s9vvePDwFGMLMc3shy1t59k5jZ4PrSiOT3QAcKQt6IRNV29UkNNdG5kPoI4pZcKbwqMLR9YTc9nS9ZHT3rNZL1y+fslp/zn7/Q5rHXd3t6SU8eYN+xNV6LsWg0M1LXaMlCRZyziNfPLzXxC2I3e3W+7u70mpkLJimTPb+4mPPvmcu/3CxvXkkliSIYdCUtIyjWlhDPfQrHh1c8+cPO16Tes0fc4437LXHhUV/WpFSlteDBPffHDCeHMJCrw35LSQksJb5JpUkxDrLKbpSSgxs6mdpaZtccagjMav1qTbW97/ja/jtefzT58RxiLefvNCCoESE8Ya2q7j/vZOOj9aV72FRP+ipKTTOaMxeOMYEK1B42sJmgtt09aaf89BiGqU5vz8jPvtgLMt3dlaRE4UpnGU8qb1eOdo24YhSYvUaTGpaZoGU70hJZCUqpylmrUJJyeEAMbim1bMfMmEuBzLJTi4dimsc6BETZlTrgzGgrWONC4yDKkeTtZINyMjLdyDiU/OEfEmOTKEvtCW/HIEBiDEmXmOKF0wKuF0xpn6Ro9y7EqHLal2LN+KAkgH/NBvoF7MXN1zxpxYlkCImb5fsdo0GJU4PV/z5Dvf5g/Gkavxtno0HtqfQO1iAEeKquAJmlzkxjqQroTAIhRWozKnp4amc0xlxrqMtxGtZu7vBn75yS+JOTCHhSVGrBNiE/WEVEpaqNbAhMZbT0qFmBMxLLx+/hI1F7RrUMoDsZ4MYkE2TjNRG4prWfLC89d7Vhcd3rSEoogpMId7tnNmNxb6pmNz2koaPEWMNSTf0umOp6dnPH/9OTd3A9PDAg301vPNb37Aed8yD3dYZ3FF/AWMMWivee9bv8EvP3+BNwGnPb53xGVkGkfmuPD+b/8W8Ze/5Pb2Bqcs3luUk1OyVIS/kIg5Yxovdv/lzcg3Zy14i20aYQUWOb2tMhWL0njf0ncr0JBNPd2LEkNWVXCNo2k6rNnRNiv6dkUKkfv7G+bpFqUVecoMc2CcxOgnl8IcAn3X4n1zbEvmnAkhHjUsct9IZwE94Puexh6s4Xgrs5X3RClv6OOV9xJrd8FaQ+M9w34SsZ4WoZd3TdX5QMzl6Pr9K7NMDnvlC8SGL01gsNZivYbiSHOibwyNtxSjMbWOB47R8806kEo4mD5JrZaPPQmGKXJ7P5CCUGSVMShtcE7Trnveef8ppxen9DcLfXYEo4VMBOIRWTIxJ6mB9RukfarptCqlnuZSBpRUSGGsYpYtrcqgBFEmNZAd8yTy43memKaJVnVor46n4CFdta3l5fPXjEPGrdbkKtf97KNP+eRHn7B58IiTi0dY6+X1lVojV+T+ep7xjWdKBjVriquxh4L2mZvdhNZrvEqcbWAKRZhzJUpgior1SU8/dNxcN7y6G4gm0zjDt3/zm5wYzauXBa0j64sV17d7XvOKaZ4YpokQEqoovBN84ur1QEoZozTTMKJVYX97BwlOzx8SlNzcOiZKzGgj7E7ftri2EVekeSHGQCqJvpWuxjjMxCVDURgMJYLylq5dc3FxwRQW0rIlLomu7SkkcXAqmmlYWObCMg1cvb6vTM1ILBlvHJnCvMyEEFmCfPYpxWMHYZ4XQmx5/PAhjx+fYtvXTMMlFDG7necF7Rq8xDoJGAqc1Uwhi5YlVTC7CvFSSmjEkUmcrhqccxIoqq1frKIzEfkZVJRgecim9Vukq7+yGYMMaRFmmdIZ32icFeaeGJLUq6reChJwxBIOuYN6K4BwoE5rjTaWaR7RJRNyIBXPEhNTWAhFBoa23nCuO/Ztx5IXppjYFCUpeoJUREZ7IEnlVGXEKVfff6FVFwr73ZZutSKpSMiLBLdiiEWGkYY4YhxYb49CGW2RQRTVoag/WfPog6f8+JNfMs4Lep9JuSelzDBNXL28ZTUEvtFuUEBMgfvbGyCh4kJjMguO0q5AK+7GWepbqrTdeZJqK515waoBpQ1LzuzGHco72vaUadqT7wd88dzuF54+uWAOCt15YpjFrNZaTh8+YoriLrVer/ns45+z3y2crE5YbVZs7++IQcar6VL46T/5fVZtgyqFnITmvLm4IKeEjpkYJnmscZy/85TPPvkEbx0laqxpySXTWM/po0d03Zqf/ORPabuWJRi0kWG2WjuMceQpkFNhP+6IaWGeZ6DQtC1g2JxecHH+kO39PZeXryh5Plr6aaOY55lxnImlbjz1pjsVqm4Cpfj6Nz9knCKff3otdX6CEBKr6goWYxTMAIW1nlIGYjpgDjKLwzonJCcSznucd1LG1lJaZmgomqY9lrryOq1IzpVGV88Oc9gkX5IRdV98KQNa0mWlC0onORWwVRr9xg35QJXWSovj81v1pgxmEdelA8dB1Z9zzqJU5vOXz9HqnNaKkcfd9g5jNH1radcnPAuG55fXpDFyvlGEIizFOSeoaVyq9Zt3jjktRzAJLc5KKQRUKThXB4EAOSqSCWyHF+zHlojMLnDeVQ9BQAuzLpNoe8eDxxc8fOcUbu+xjdxACwaXFKUYtHLookkxkpKYzczzDm/gyUnLlGWIr256YoLr3cIUIkWJArTRHpZA1wu5ZjfNLCnQ9GKt3nmh3Z64ltBq7tRIiIEUF374Jz/gw0fnDMuMDrC8uuLq8lpEXSlTwkzrpZXr24blcoECVilhk1pLLbell58y3/4bf41Pf/oLGBcyk4ClBnY318R5YnPa4oo/Tqzeb+/oVmu8NcQ0k1UmJPF1VFpAwL7vCAnu9ZaEEKXW/QkhZqxxNP2Gp+9/yMNHT7h8+YIYM/vhisSMsqLc1NXXMZNRWgtvQQlI3LQd2ljutjuePf8lN9fPsEYMaIJ1WNPQth2lwDzNaCMzKku9Z0rOlVov4LbWFmszs1rQzuLbBmsdIHNEdPWXdI2H6sqFFop0URCroY7SAp4fRIlfZH1pAkNGoXWLtgbfBowqzMNCylom+hh7BJpKnTp1mFOJEvGKLqpOp5YLdPx+TuiSaZxFOdhPe4a5w2AZxoH9uEXpjCoLvS2UYWaOhZIMuzES9Miu3PHi/poP5u44Qi/nUrmTuaoqD5Olq2GrVdhGoxoZCiJIc2A3vSRnz362KNvhGldLH7EgzznTdJbThycEAu9+7R02D9ckorhOI+3Q/X7G+JF5DNzd3bNa9XKaasXq1NAVxzA7stkwKMPtHLifB4YQ8aqIQrFI6VBK5u5mYllEKlxUQsXAtN8yxExTPPQtJcP9OOJZ+MEf/xH5N77JarWmazpu77ZMw0yKifH+nhAixnXkEnn5/BkKao1PHYYDsabM0ubzDMNAipHOGmYFpUTG/cRPv/+H5BhZxoEcEsu8SKlHYXd7xTjsSASKKmhn0BYUCa0KztcsT8uouVW35vz0AcY0GOPBOB49+YDV5oSb6zvQYpDS9e2RhaitkXsuy3BZ790xTW1baaUrZbi6vGS/39L7rg7P9fRdjzOOJQeZpF47KTm9aasfSNWHKeoH6zoRmoHTBms8Tdczbu9lVog9dF8EX9PG1K8zsUQcRoKDPviX/PrrSxMYSiqULGBRMZC0J2dYBhjmhTiLVZii2r+nioIrK/3baoNTsjmOd5H4HtFFYXOh6RqKhdxo2q7FW401nlKk9952Dfv9lmXWUDTDEri8ucOOC03QXN/fs92L6auichmKnH4H/rquRBO/2qD8iqQ0YixcsL1CYwgEYrLc3O5xlWtBySIOylI6rc5OOX/8gFkVzh6c06xaYonc7Lc467lYPWIexAF5DjPzMtP1PamamPSdw+tATHvu5sDNotnFhZlCVrpKxRVLzJR1y6Iy19srijGEZWFZRgqFdbthsz5lmyPBOUqxzBM4m7m/2/Li1SWPn2iU9oKt5FQdv2W2QUEwj3EYhYpsDClKF8pUrCfnzJIiaXvP3Y9+Rlnim2uCnHrLNGOMWKWpqpw9kN1kyTAXpQzayDVEZ4Z55PXrlwxzojY1CVmhdEvTrgSYNJphv+f+bsv2fitaFSVZTinCSpXJ0w5maaEeqHPaSMBAyaE0DHv2w4TTnraVz1YbLe5WRtWSQABuIeRx1D/kdAC5patgrSFlW0uNAlY2dwwJXwleijdTsH3j5eeNpUS5dgcrw7+6WokKyGsFoTrZ2NbDkrndj8R4DWRO1ytSyux2A1ROukIL10FrKSkO1ldV4JRywgKdtYx5EYCn1l6t9zRNi3UCdr5+vcf4C2AhpsJYIo1r6YzDWA+IxbnSDqhWW8fxcBmjIMeIWa1I6pQpdmR9SskBq5OcaCiWaNlub1j7xDju5QRQUp9r67m9H3l5fQWNZk4RZTU6OwyavGR0a3jvww/E0VobjHf4vqPpe5Ra03R7VBroO7ifBqYhkbMmo4+0cWMcSjmudntWKwvWorVoFTabjhQiYS7c3O2wbc/CxBgXdJZBvvO4sKTMHBaGYcAbKxmbko2ptaIYS1EGtKlOxbWzUEDpQi7pyN0oKaHHWejUgLZSS6faBqxp4FFopJXYt0kL00hKr2UosfIGrJRkqSSarmVeZPBLTIWQEk0uLGFhGRbmGPFNJ6PjvMU4GW5rrJGukXfVsl9a5ymlyr3R4uZU6qbUpvqIKNAGbcWwdwozVjuMqVbzCE08pXScSF4A58Rdmyr1B0VYIpqZVbMScRcCTi9hwTsnz1ef92ARIAmZuDdQVGVH/vrrSxMYRHFWwBiyslgj/VjtZkLU7BcB+7oeclLcDZGQapsnJ4xSUC3Fi66ko6LIObKxmovTDbkkduNAv+6lNVTnLZSU2N3f8eL5C4y9EFCIsbacNAUDGJSyzEtAFS1OvzowDANd31GOsyMKu/sttt8wl4YxtJSiubq+pWsUZyeOzlvG/ch+uOVs1RB3BXLAVFGULoa7u4H2ekvyC0sMtH1P121YtxvyELi+vsGZlhBkE47LzOrsjMeP3sGYNSE9I84FbxQPNg1Baco2s2z3gCJXQ5miE+O8kHWk6eXwa9oGZwvQMk+Fl5e3tHTEsKBLEZcqI3hO129Yn5yhYj4OUM1ZXIrTocwzHG3wLOCbhhJk5JpzQuqKIQpRKUaKkvYfVONa7TDIjA1Rc2qsE85JSpF5mphjYdKZda2vU87EIkIt4xyrswuUbdnudnjvcY0jkYgpoDVcnJ9ivCfljqIW7nctsewwRjoBKNm0JiIahnmicU7KmXRQMlajIe0p2jKXLLGsJEKYwEhgS0ks7Q4lg9GqYgsy0Syng8pSDoocM8VUfE1D04ilXIyRxkuwURUHUbXcETf0A0CKRNAvsL40gSGmzHY/03WOvr9Aq0Voq7agjBiNFKhdCUPGUFStqbTMrVIIsaTEVM0+FCVnnILkLdMws8TIplic9jhn66h7hdOKPC3gE8kK4aQgUV8BZDDaMgxbUs5Yb9Ez7Hf32FrDZqWqLf1CTvU1KwvKY5yYrGhtmZfE3d2e/W6GR4bGt6R4OCmlC9J2ZxTVkrPMPRBgcaFxDbr33L3eso8LL19d8p3vfJdN63n+/CVtu+LsrAPVQulReaR1hQd9phTNcFN4qcThSA6ehOsacIY5jHhniCUTg8yQVLZFe0OME8bY46DeIp1Bbq5v0brw+PyB4ERGhuAoJQIz7T3GtPimFXp5hDRPkAX4k9alqSMBBD1XRR3nZsQkZjPLIpgCReZgpDrEldrO00aLx0PK5FRqzS+2+CkVjPWEsJXnd4pEFos2ZCBMngqt6gkx4LxhtVozzgvWt7R9YRi3hJSPA5fDslD6HqipPIWYEikJKzfGxDgMlJiIKeGMAwRozHX83MFeEERCLR4PsiWNsdUx2oCWoCSBJEtGxGHuiQjSjLUSmA+dFPWrw4i+6PpSBIYCTHPg089eYf09773/lJOVIyzywXJE+yWzkCEzoipTSBvwoJ7UQJYRUDL2TsE0BXb7oY56dzzYXPD44h26BtbrFau+ofWG1lixRh9HqIauAF3TYpXcgN55OdmNZdIwThN9CJWjLlZ0zjpyLJQkug9jWx486mmsJkwDu+2OYVmY48wwjNK60rZafWumOVC0J+e2Er7Eqm23GxiYYC68fnmD1Z7tbkdBuiOlbMVq/aTBqAbvNhRjsIxovUDJbNeWj9UBJJW0MyoIFeNpWo+yTjKKUvDG03Y987LgjK56lEJMkqkN+4GmdZyuT8VOrKb8CdE8qJwpurL2lWQAytpKHKukNar0OIn4SG7lzMHjQhW59jLmLWEQVaF3XqjdBQkg2pFTJsZMioWUMvMio97uLm+4en2JdZZEYFz2MntBa0zWxBJIRJlVOe9pGkdCzGDbfs3y8o4l1ZnRSsRfSmuKqm5Z5Q1pScqegsLiG81+0LRtD0YTU6x4ggzGpSIfpeRjSSFthlTnqIDW9kh8orJ+S3W2EjvEalpjRTpQSqnsSTmoxOHsr2i7MqNYomI/L+Tnr/ja04eYJRNDPt485bjh81utSfEWSNX5qfrncrgW1Bu11BSuJOj8hvff/zpKzVgbsUZGr2ujBbQJCzoCOWK15bvf+jZzBFepuc5bGQVmLEpb4gHoqX3m1cnJsc9NKSzLwnYcaL0nzhPb7Z4lijXZOO5Zd+K4rFCkohjnBeVahiHj+lYmWpdMYywxJm7vblhCpDvZsNpseH11hdMKazXD9o7dqmGzafHujJQ1qiS0EvR/u27Ff6G8GXkH1FO6YZ4zl9f3UCI5grPCthTUO9eTyUARWzatDDkVYhRNAUXVwcEHUx11xGCOSGFl6JUsHJPqSFnLhLqpDi68tW5WVdkqh6A6XlsQ/KGkQhY3Oay1YB0of2RRtm3H6ckJUxxQRpN1RplMLgGjHCEXwn5mmSdCmDBVd7CEyP3djpv7bQUdpZRxTYMy1cPSCs/AuxajE9ZuOdlsWJ+eME977nc7+tWaXCTYHijNUMlcSqGsqQeA4CUxxaN/aNNYsaMzco9SDOQgQ42tEb/SmjmVJDoVueZ1aheVOfwF1pcmMDhr8dqS58LN3cBpt+dhLxdaq8PAW/hVdpO0vURMk9ms1wz7vZi0ZvkAVJZI7H3DNO1JWfwejWnrKLt7pjExzIl9CGTT0aIw8xY1D2QaHp2fMywJo6upKQJAWWtpmlaUlwjYmRI0/ZopKfbjxH4acc1KaMy3tzjriQliEpA0IyagpmTpwsyJOUXWTUMqGlfEV6A1mq63LHHh5uWW9fqE9957n/0w0nUrlnFH4zTb+1tONis26xVa9WK9hqPoPVYrTldRmJpFrqmzFqUdKtWJRTkyziMUhc6GOe9kTJrSlJwF+S/C8jxM3kpRBGXW2NpGrgK3Y1aij8ChVgZlatpLqcxGAymhla39d47gZcwFqENs0eKrWRH8A3CntUY7z1IRftt4+pMzmu6MJ++9w8NHDzk7veDkdMX17Q3jNLHMgXkciGEmpMR+GBjHgf12y7DfEuIi4KU2DMNEigbXbshFiPfOOpq25fzsDFCcnJ9xdnrOcD8y3O94cHFGu1rxehmkXHWOmBWkdHS7PswUtVZ8Nko19ZWy5E1goCAu5MZWAp1wNEoRqbYx4rVxGK+glRITGSWB919nfWkCg64OuDlHQoTXV3f0aLT3WC2qtIN4SilpCaZD26pmEOv1ijAOpCXKVIWSOYy9SxUJb9qOJUa0Fd+CV1f3GGcZo6F79BDbbljpFd2m5+GmxRrPyboj7yeIQQavHKdZy4cScwIkfX12dU1IGtud4NKO7W5ivQlcPHrI3e0d87SwzAuvXj6XuQNK6nKjDCpBGCNJgWkcxsvsxbhPMCfMyjDHzOefv+J8c4rCkJLMHrDGEFJgjrPQY40hZ1BmhVEtihNICm+uOKSvqoqBjHNkAvO8xzpN6yzLLD6UxUhbDQWlTu/OacGqLC6CKZPq5jXWiuGIytXdW1D1XFS9kSUo66KgiBI2F4XKAMI9OLoZayGSGXOQFYslOxq0dSgjU6iKUmAMxYixqm08rRMNg8ozXmv2d3fcXd1wt93x8uVrbm9vGHZ7wjISUqjMVgEEvTM0TtP3LZuH52zOL+ibnpvbLc9eXHJ7txdWoRInp67vZVBujOx2O+Y5Vm5CYpxHpnmqXpGqDr6VuSCbkw231zdgDNZakb9HAWRLDdy6aiVSThzmo6WSyTHJIKUCKJndOU4jOaZKCBOlpgSfGjC+4Po3CgxKqY+BLeIyGUsp/65S6gL4PwFfBz4G/uNSys2v83wpCVCTi2G3D9w2I6dnXkZzBWonQVEOBum1Hku5oFJGJRl2YqnzLY24CxWk3vS+oWQYhgllG/b7Oz7+7Dk323usKvQPHrNer3F+xZN3NMuSSEnx6NEZS7ki7veU1IrPQE3dDpwJgaU9L16+4PnVnne/9k1OcsOyLFxdXaOMYbVacfX6iufPXnB3d0drCvfDxNlmTWctucAUE0nbilfAfj+gSiZZTY6i28jJEGP1QpxmotX0rVjP9/0KY12toOSG0LSU0lFyFH/DClxpZCNEnWg3ltl6YgqsT1YsiyMHoDhu7+4x1VtxGgMxBvrO4lyDtTLDUUa+yc0dcySkhKmouLTfDFZrrMp1g0hAzCWjrUUnc5x/oVA4J2mztAIF0HOmqX6QnfTzY6x6lYY5FYy39JuePGVev37B3evP+eUnP6Zb9RRguxsxtsNQhP6+anHtOdl4nrz7Lo8eP2J7e8Xt7SX9yYb3fvNbrNYnlJD5yY9/yrPnryvOVRiGAdN6trs90zBivWUYZlKQTCynRDp0cFKUtmiM+MbjjKFtWpZpZtgPx1O/OCsekYljpmVrizTlRGurH6hahN9hNH3f871/59/h5z/9CS+fPT8yaPPBBv0tCcEXWf82Mob/finl8q3//3vAPyyl/CdKqb9X//9//Rc9SeEgWBLGV0SxnwMbhEKrS4KSj3NvVSnHEQ2SN6gjqv/2kyoFZMU8LvW3KCYfmJfIvCRevr5jP82sW0ffyiDSGAdKkYCDMmiTcCYz7m+J07lQnKvizVpDTDJkJuaCti1GR3KCZQlCm02FV69es15vhNSSM13TQlmYpliFNHC/G3l1v2N1fsFqfcK8SDs0p4Rer2RkvfP8xrd+k7iMOGtrrcqRIt61HW3TIROxIrbW2KUoUgzigeg9ZY4oY0g5Qs6YpqXtOi6vRthP9H3HHCe6vmNTkiD9jWMJyxt7tRTFoFQ5whIxKkgZZ4yYi2Bp+p6m7zHOY7LCloK2iupbK602xGtBK+hUS9P6+vkJByWmSKo+ndo5kobdfs/t3Q273UDKsOTCUDJPS2LlV2AK7bol5UhYFi4ennNx8YCQDZt+DXmm9RrrW4Yl0zQtjfeY0zXLvOP27o7tj36GRnP98hX7YWCeIlpZUMJATbkO/ElC1gKNypmhJKZ5AmOFyVoSMUfxdUQIaMuyEGspZIyApm3ToK0jRDF1LeaNSlRazFJWOGdRUXCKuCxcPnvGcL+tHAdF1gcrw7fK7y8GMfyllBL/EfDfq1//74H/L39BYFCoKpcWk5ZcpPk454LxLRawo0yiVhlBqbPMAzwIWXTJIsZB3JRUzsQMyxyPk4VSBSLHIfHs+Wt2+xt+/tEzusZw1rf0vef5yxtad+hCGE7OH7BME0plTtc9fdfJa0wCilkj9NmUMrv9wDwt0hNXkELEGBHdLEtke3uHtZ6zk1P2+x1hhiVkhjkzLSOfv3jF1W6gjwXbyfDVaRyll11gP800zrNanxLmN4BrKQlVZLisdw5XU/plmoRjL5GTHGRGZ9e2zGGqfoWTZEZZpMU3Nzv0vaLvB5ESK4tvO2IUS7nTsw0xOFSeSCkRQqxmr3LvCbquBaS1HauTNa7vaNoVXnt0iDid0Sozz5OYo0wjyzLXuQgRdnLixRDYD3uWsEhpVDQxFwqBcdxLp8l4rG3RjSdqxYtPP+XB+UOevveEZV4zjzNd6zg5WYHSXN2ODEFk8X3b0vQt+9uJn/3sU37yk5/ywXvnNN5wd7vj5Sevaa1G54Wm6Y68AFVkKI6xFqWFyi2DZhSNsYT9iNLiWi34acJZe5T0lyxgozXSvUpZ8CWl3qT+kkVYMapR4Opw3RgSjbYkxK8iTjMvf/k58zLLftBFBgWhxS0rCuNTZnf9+uvfNDAU4P+pZPLs/7aU8veBJ6WU5/X7L4An/6IfVEr9XeDvvnmiNzgASBtoSbDkTNt1rKO4JatcMGg2Xc+yiHtwzkmcire7OkcwUlIW1WOKR/VezHJyxqhYZpjmzDgCIdGWSBF/EFqfOOk72kbz8tlLTk4/5/T8nPfeeZfTs06yjZCqVl42A1mymfW6IyvPkgIm26O4xRlDCJGSF9pOVHH7Ukhh4W4nwqTdktCuZT/M/OKjj7m4OGe92rBerRnGSUakNZ7TdV/JNcLsU0XQbAFidQ2UoEIm54A2MjujIH87ayoDUXQASmuu73YsMRGroGmYI2pJjPM93nqc1RgrkhWnEMCwCDZ0HMwm+KKwEo0GqxjDyP3dgJ92pCmRxgldEjHOLNPINOyZZhnYa41CKZmupZSBrMklUkyWLgoO5zu0SfjO0ds1JEvjevqTlqAyu/2esWk4PbsgqIbt7RZlFBe+IeTMzbhjt9vx+OKEi/aESKJdrenihtfPf0pYGnLRXDx8yIe/+TV+9if/jFZrfNOxD5O0yoG+78hG/Dl8I+BiyeBwNG1D1/css/hsaCUzNRedZSqY1hIItBF345ryS/avqhGNTOHSWvPd3/kd8hy5fP4asmRRUHDWEJdFpmmVgjOWJcXanpQhvzFIYDDo/78Ghr9TSvlcKfUY+H8ppX709jdLKaUGjT+3ahD5+wBaaUl8iphgAMI9pzDOM6vNir5vSMnVb2nWq47YuBocknQbFPTekjWQ8/HE7rt6akcY5kjMRk5ZCpu245sfPuXr7z6kaeV0XK1XPHnykIsHj/iDP/w+zlug4H2LUoaYhHCUS63jimAafddzdvYOzy/vGW63uDr4Tka0GYqRujjWVpVzjiUl7oeJWBJYT2OclFJxJiyR/tGKcZzYjwONd+yHkbjMvPPoRDoAOVOUQitwRtM0Dms0aRbHp5izTKjShRCD+FTKB0BOGdMa2rZhCQNJRZQSEE6UqNIvjzFSkiFPiZQXWm842zSoOs/C1FpY7PMiry+vuL+6IRehdxetySlAinht8UaupyaT8wIYlG5QTqN9wZEJS2GaMuuTEzanDUVZrl9tWVJi0zacnHREDNvbhfube07Oe5rWk7JjSYE5z0QStrHENLEsIyEX/vZ/+Lt89vnn7C5fEZc9c4y8983v8d/67/57/ME//Aeocsc0Dzz58Cm//Td/l9uXP6HVAzlb/N4yL/l4igszUWOUFqu3DGWRKWKnxmH7hiHsxLDWeUpJQkmnsIRZOi9K473HGCu4xaE0rNtgs9nw17/3Pf70+3+MtU6IUnmp+0SRchKr/iIjBVOMHLqTbxvAfFGY4d8oMJRSPq9/v1JK/QPgbwMvlVJPSynPlVJPgVe/znMduOIlZ4quaIA2dKsV77zzhCWGWq8VDg9IKTPNM3d3d7x+/YqUMg5Yr1paZ8kl0naaR48fCFKb4G4YeHk1kPKIUoG+MTy+OOXBgxOMNfjGs9qsePjogvc+fI9ffPoxc4qEuIia0lh82zKPe3m9SlI0Yy2nZz1LMDVqVyRejCHQSuOsIO3zEoghop3BFM8SYsUy5GcUYhnnfMN+P7LdblltVsSUGIaBZR45WVlON51Mfa4EF2ON2KQrcUluvJO6uZYzetEkpbFoqOq+nDPWwYOLDQDDNLEbByH45CT8BG3ISbZyUQ0hRRlxb9zRTQg0ISSmObIfFmJlAE5TxjeWtm0wGJy2DPuZnODkpGFzcoLC8+pyxxIzDx9s2JysSMGyvZ8YxnuU8/Tdir4/5/72Hk1Ae4vH8+3vfMjLZy+wNmC85jvf/TbPry+Z48SjRw947/E51y8/Q+WBkuBs3bI82GDnO1qd2YWB26uP+PBr71HSDX1r0cZx+ewX/P7dNYTX+JUjFyefX23zLvNM6zzOOWzlglRkskqohRujDizRIvyKhAyvyUnMWApSJnjVsN/vpDNRDmQ+sYT74T//Pvv7HShRUU7DiFESsLVRpBBYpoWm7+pkxmo4e+hllH/h2fyvXP/agUEptQJ0KWVbv/4fA/8b4P8G/C+A/6T+/X/9i5+M40UtR4dnVac8ZZy3uFYdjVarl4kMAm00TWuYph131zeYnGlyodeWUjJxSaRFpLVGWxotfP2cZ2Ic0WUhp8ASIq4UVMlEr1jmPcsyMC07phgxqhFT2SpwSQfaSAVM1+sVznZc34zstjuK8qgDIKUERzmk2U0jCH+MkZSBJBFdUHxhT0rLKXBzc03TyLj23X7PsgSSLszLcmTCySVUdRy8CJBiXDDWSn0e0lHJ2HW9iH+KtP9yiowxYCurc73u0Vawh9XKo0qDcx3DPrBExRgyJY+UrIglSb9fwTTNAOx3I9v9SN80NF3HFCzjONFow3qz5uL0AZqGV5c3aD1gO0tje37ryftshy2uicQ4cbI+4Ztf/xbPXnzMfryBEnj/vafMD065uX1FSoEUA48enzKON2hlmGNgGO8wtuCtZby7Yj+PqDTTtBvaxvPjH/xTQgr0BnrvycHwy5/+kPurZ/hcWJ09wQZH3I8M17/kpDGsvKeojtaNNM7L+PphZHVyirOGFJNkD8oQQ8JaQ9d0MqGsyDDheVow3kvgyOnYcTnoJayVdq/3jmWp/qFKAtDLFy/YrDaUypBMMeK9ZYGa0Qke44LDNA6NZHq6zjU9yLq/yPo3yRieAP+g9kgt8H8opfzflVK/D/yflVL/S+AT4D/+dZ5M2KGH9Fwygpwzy7zI5GNTHR2zROcD800bqU2drSlZCOiSKElIMcs0Mo/3LCHhXU/RjrxESBCWwDLPvH71ilWreXCy4e5qi83n7DdrXvmXfPzRx5w/fIi10sdPWViaZCEDyYcFy5S4n3e8urpjmgKubWoNXrXwb0Xtw8zMnCQt1Vocqkquj1MyNTlnAfKapmEcBsK8cFDF5JTfPKdSdUZhnVOoCjEuWJWq6k8eq6q5rrPiMlWUEsefXNju9pQ8SAu2tn6aaoteSpHhqwVMVmhlySWRQiaGFmPyMS1OWSZxr1cNzht+82vfZnt7Rw63xHlEkXn69CGnZz3b3QumUQDZBw893dSxhB27uxmV74nBsmoT3sq1fPXiI/qup28gZ8Pt3Z4f/+gPaVsvwiwUu+0rbNfT24ZlvyUMWzqvcDbTrxx5NxFUom8d/bqh61p0KYS40PUe70RUt2oc0ULXevq+Z7ujCr1Ev6CLrsarmrgsGCOcEq01KUi3JqbCPM2UIjqXmDLGN8cgXruzwleIYtpjrIZaAocQ8M7Rt23txokEXQbLyP3rvRNsuRz4PBzvLWOEdXqYyPVF1r92YCil/AL4G/+Cf78C/odf8NnQCpI6eOPXKUbCahGKqxG7s0Nv/rBBlIKkxZxCKwMlkRJgGlzX4EvEGTlRlZYugFYa5xqcbQkx8/LFa3yKnH3nW2xvbnFKcXp6wdDuuL+55/GTpwhAJI6+SmmMViyznFrznNjt9tzvFq7udoRUsFQiVNV4wCGlFJJKjIElitNQOdwhwgc6MgaVEhfiUjLzvNT0UJ5nCaGy8A7v7dCz1pWBGSkarK3v1TuULiwpY53lMHNAacOqbcmI54E2lqEOdbm5vhcOiRETEpQ7zkXVWkhBS1hwzkCO1Uq98ODxBY3ThDCiyp51G7G9ZRws1gbG8ZIUJja9otGWZdpyd/1Lmk1PZzI6WLRe2G9f4pyhax0lwcefPKO1j1hvelIxjJNsyr7vcEaxLAlLxqtMYxXKQiFx2p9wsl7hu4ZYFuZQ6PsG11g0jvW6ZxoyXecFokuLGBAbTdtKCaGNwThDRuTgrfZV2xHesF5jwhZDWGaGYc8cErlkxmHk/PSc/TjQomSmBArnLfv9nvv7W8ZppKiMWwwlQ+M6jJJBtfM0YVpNDgFSwjtLnJcjfqCNdDhKqexJDgIrwMq9Ub4Q9PhlYj5SSKqQ9cEJGmLMhJgqgntwDoa3m7LCx8+VEw5kRUQzYUjZkIrBakm9pgDDOBKjkG28bgADSZHmxBwzj548ZbPecHpyQd+u0Bla17DkQi6BcZ6IWTgC0zQxL5H9buTl61tu90HKh2pDF+KCMgqn5DJnMilLazBlGUha1IEcVYmC1UFYVc6/MeY4oUq9FRSXZTnajltra3fBoLCEkMTiK0bStMO6hZPTDd54kbM7T0QRgTnOuMayOVmxLalOktIMw8SyRLxvWUIipwXnNEYbsVdXovhrvBcSThH5cOMM1hn6zrO/H7l59glnJy3rs57WdxjniMsO5wrrlUevzwhLRHmLX1sMnrtxEtm10zjvsU4yhiePL1h1nr53hAy+MSiV8F7Ow7YR6z7pEgUZYKQVpiBDbrVGI+PhrdH1emtpNRqRcqf9nuXmHrVZ11RcOCvuaP0+Q4T1qgdkLqh3rnJrhG27WUlbe2EQTkkuOOcJ2zu0ERepmBJzmLm8vuT25lYCbGNYr3pWXY9RuVL6jTgVVq5HXBZsvQ2MFRt+nZWQ94jVgl4sSTKJrIR+l/6qaiWozbdyYDcWcfGdxolxmml7+4bBdRTQlPpzpfrmCdqflSKUTA6RMkfKIr3nNEam/UQMDpML3hhcSTjAa8WL55/z/rvvVQ7ADFtFjhlrLMM88NFHP+fytWIYBuYY2M8Lw35hGGZubu4Zk6Htu4oGJ+ZZ6MlWGwGO6msUQlSdmHX4wCrP/zCU1FqLsYZpnlmW5e3LJBhHepNFmNqiTCWLcYwWZmZeEvN+jzYzTWNrainGNCUL0BtjZLcbMFbo123bsdlsWJbI1fUtFIX3ALa2esVjIOdcB61UN6haNh1SVuss3hvi/UyJhrOuJ6pC8YZpnCgh0fdNFVMplhSOk5a2OWNKzezswZtBs1p1OKtxTkmpiJIAf8ByWk8ugtuUGGi8IwB3V9dsHpyjnZHRdSq9Abtrlqa1tAfDdmS8ucf7Ft87NELC8k2DUrNsvMrElA5tqUN8NFkV4jKRwoLWhX61gpzo214wpiwM0XG/5bPPX3Bzc8vNzRX3d/cizLOK+WzD1I2Mu4GT9ZriCpvVpmp/kpCkqhOWsxZTwWplDQXFHBZp0R80BJV4Uf6qBgbZ3IkUZchoIYubUN/VG0Mdg0Gp7ZdDj/PALU9Vs5CV2Js7Y4nZMt5kjEMmTS0ZbzRaFxqr2bQeEyOahC6J+/sb7u+2rE9PuOj80YIsxoXf+8e/R+sijx6spQWHYTdMzIuwKp21VZtPJVMlsWurjE0RxSQxbs2pjtKrWVCtHxV11Jk2UGvTw1yLUqXI0hZMDOMs7Ugt9mIhRLb7PetVh2s88zKQAKsV4zAw7HesTy9YdR2ubuLWN4Qlc3N7D4CzA/26QxtD2/Xs9zs2J2uU1oxDJORQuRviOxFjIDsnTMsYGYaBdtUAMkXceYctcP3ZS04en+NaSzKGaRFar3KaHCFFcEXarmE/4DdrobdX3ERmdojBqZwL0ga2RWEyaKer+/Wh3Epiu24rSQjhmtze3NKuLOuNWLsbEqQiw22KgSyjDm2M7O5GmuaUEiMoiFFG4dmcmedZ5ldWUhIgp48upBTEjcpKBtG1LZSCsZrrm2t+/NOf8vLVLWGJxLCwHwYBr50G9rx+ecXF+QXTtHC22XC6PiE6LXtCyflPbXwqJU26mCPeWcIgsz5VhgM1WKm3Bhn9mutLERgKYJzh9GzNnDWN7yjiVsW77z6h7aphSd34HMH4GhQO4+Hk1hCZcMqQAmZIhL1i8YWkFc54NidremdYXTzke9/7Lq4kYbh5i+t6+u6Md957j/XZmvVmRagzMVOMYA1GC5EkFcU4JVJWuKYhJ5lnobU+0qZ1paPmSlE+KBEPqrk3WQ9QFNpQFYMyryAd+trwJjACMcF+P5HCzP3dHTnPaGXY72eGvcMoTVqgq3MQp2GkAP06V8t24TdId0dOzJAjd9sdw1zngyrxhkCP1RFL/AK0ecPlB9FslKKO7D8AcsFpxepsTasUn/zwT/hG+x1OOkueRozKItsGsqaWVLK3xputGNI6hUtRbNa0KCsPZVguIsUucybsJpoTsZ8qSnwzQ31Nbd/SX5zR1JkU3rijYXKKMjHMKLk+be/YXJzxSFtS07LEgd32Hm08oczEEKolu+H+/g6/biS4lUJOgZwLT959h7Df4xthPqYiBrVLnHl99Zo/+dGPeHV1Ry6eaZxZFiF3oR3jtFS3Qs92WLi+/YyL01PW3QrvCzFN4u4dM7p2GkQtnFjCjHMCSuYYSSHhrcVIj7M6nP36e/JLERhAUtjvfPN93p/S0YrdasPp6VpOifymNVfn9LwhcZRcswcBWWJKLPOMygq7BDIK07d0vcfpQtt3IkBZtzx8fCrgpIJpXujXGzYnZ7Trjn7T0a5afvjDP2Y/7jk7OeNi80g4FCEyDjNLyIRUJ11XtyCUiGistcfNE2tgSUk8Dw+vV/4r+ax4vcjfB/HNcZU36aBkR4VcZFR6SplxmoRIYy3TtIdc6PwJ6kDZDgubk1O8b2i8CJ8OAXW17ulPenbDHms8MSaG/UQMAgQPaZaZir6VDkrOFGcqRbgO5IlBsh2o8mBFKhnbNXTO8/53vok96ZmmifurGzbnp0fg1GrNMC3EDGq1ridwYY4LaZtZaSM3uVJyOBR57SrDeLdnfP2aJ9/8AHuyFqwK4RGI8lJhOid+BdZw8egh87KTzkKd71AKAiTmiHt4glmt2N7v8cbT9B2+8QxbkUEbKd4pyIEcJrk21nqK0fSbtehElCbFhTAvzCHw7OPX/OBPfsSzl69RtmW/H9nvR5QBaxV3u0oL14amcaAK05y53Y189vo12ESYZmJKWESRmqtTWSoVt6pCQaMUU5TJZKZQ2Qx/BTMGBShj2Zye0p0qpjAigimDVoVYAhVaFDS9lKP5xEErUTuIpAJLiAzDiLMNINOMMIUlByHnRM8wDZj7wm66x2qZz7iUxJIzU1wY4gjbyM3tNa8ub8gpsnIOXc4gGnJM3NxKa7KgxWOgbnpbBUbKSSsp50xYggio4FeCQj0uOBBES6nMukO5hHqrlJDHHwKjNgZdhCbtnab1DX0j79kYTes8qhiWZZZrgyYmmaItIKl4WM4h0617rHM0ztM1ihISY7WVoyih+iorVOsSIVmUsnUGgoBgymiZtGyk9o1FQNDiNRfvPSFo4Rfow3CeSkopKTHe7ojesl6tOXn3Md1Jx5QWSIkQFlrVY4yuupdMTooUE8t2z3z9mmbdct41WK0IWqFd9XkwhyyifjbeyrAcJcY7WRWobT2rNcVAMYpixCbQtQ3W+Rr8Up1jkoQCHTNhmlFJs3QzWOkg7IcB7z373cA4Trx49Yp/+kd/zOXVNSEXulXPfpoZl0WMVmJhXhJKFS5v7litOk5WPSCy9NvdAK8yTilWtpUZmUnAa1VLKpmBKW1TlJQW6cCsAiJvZZ6/xvpSBAagzgSEUicWl+rZlVV605ZTqtphH8bEVeCxVLHK4YsqdjlZrWBccMYya/GOVEW6A8O4w/eIZ6CTG3s3DKTSMMbCLoykMKGN5fGjx2gy686gyghFbNiWOco0IOMpY6IsMyAOyqmOIQt1XFyIQfCHg/vQrwSGQ453wCPU8QOtcPeR2CXGRocGuKIkGTBiUmGaRzqr6RqF82IfZm1HqOY0w7ywignrGxnTpywpaZYlMoc9aIgu0TqLNdC3lu0w4ZRn0/acrdcoMrnMhBwpWVqjkryVyvOXSd+6yidQhawTysj0cN802DPNuN8dkfycEyUsHKaj9I/P8N4Rdnc0fUfK1UFbHcR2dYitMfi+haVlmieWecZ2LTrJ+HilFehqGlsSTguXQwO2KGElaoX2lqYTp3B1cLEmUbR4qCpj6nBlARxLFvu+OAfiHMS7MiwY3RBqGzHExLLMDNPAj3/+c15dXWOsJ48LS8jVXiALd0W9YTpOSwDAG0PXOEIITNPC2FiigqZvaBtPieLyLZ6XijCLktMYMbEpFHKJUM1f0l9VazcQVWSNyXIDqFKn6FANgN5IrY/4QnoTFQ8ws6r+jCcnJ4y3dziliKqQrMFUsG+Ji2j5jVjQFzSfff6KlG9Yn56J+UopKN2wvbvlZOWZphGyx6oNVluc9XT9KSEp9tOWt0eXhZyJSUCqA+YgdNUDWYG3/n6zSilVBlffaAXTUkxQuQdUDnxKghOkJKeyVtK6tDZhTOHZ55/z4de+y/p0Q5givmkriUZOdG1EGdh3nv20JyElXFgCYZkJsQh7D0frHI2x0t1wDVd3VzLvI2nA1/Ioc8hotKaSa2rQRhydxVtBNqWqnpzGaFabFW0nA4eMd/I664RxXd2irDGECtJ65+VxZ2vOH69RztG0HdlqTDpI5jXoKg8fJxrfiqF6AZ0KyshcDm0svnFSJijq/Sctcq0lM2saJ0NmxvFoz5ZixChN41ydMJ159fw5MUacF2zn+bNX/PKzZ2JNrzSoyBLCGxv4UurUNFEVC5U8EFKiDCK/VhnWfY/1hv000TkngHyR7pvWCuMsVOBbaymbUhKJ/7/O+tIEBi1Qd71OMnuvlAOOQAW/5EM7YAtCCc4Hqg5wqL8TsRKEZIhLpHgZILvkyMp7Ykjc32+Zxi1Wa86NZ1kSt3cTxbQULcNo9vuR2+t74uI4WyVUPoMciUskLZHSCec9hnCkbIPMswh1irV6a0DJX7yk8j4aeCJ27KUUwR/eAlxTkglbJWvBNJoGZ534FRJQRjGHkVW3xmpLY7z4HsBRFZpTwHrHunNo02KUwjsoseXqZisU7DijyIRlwuiWVBaMKSh1cHZSx9kQB2LWkXJSP8BScZ+8yiL20oZlmvB9i/GG04fneOuFSFT/6IqmWysW6dLyFam51ZIxRQPdxQltsyIbwz5MItlOiZi8SPYPJqu1x09O7O/v8JsTUT9qYW2q44Szek9W30RxU1JVQi2HkdWatmloWn80ZS1kbm5uuN/es9qsubq+5dNPP0drR0EzLwu5ZJZpesutiQpOvykxS9GkiDhWW0OY7lh1PevTFcnA6BfWzlFpE0fnaKM18zxX7Y6WaVe1I/7FmpVfosBQKnKf6/ShkjPlANxTW0JKvj6A+eowSCML+n8AXg8DPaSef0MfznVc/UGhbuuAFK1rOqctKQfmpWC8ePrv9xMplyovFju0EAQvyElaj6E6Eef8BvMQo5R0xAgqobGCQOoYQKjvj+Mr5ogpvLk29e6pldKB5ppzkhZvVpRsjtfw/0fdn4Tatm3petjXszHGzFa2k7PPOTeJUCSPkIL3ZAtssGz8wCUbgXBFWBVnws8FCVdcsOyKDEKgghMMAsEzFvIrWLJqFkZgsMEWToR5trKXxovk3nvynaxsJiPpSXOh9THm2ufeiHt2yIgd496z195zzTXXnGP00Xprf/vb/ysXxNGtWvbHR2XkFUvxLUYUiEQypUSKJB7e7XFBgUzvG7pmw2q9JsbEMEVMUjFZ57xK+ZuCkBAy0xTPE641QDirNnRFhGmcaL3Bt375pNYavIVvv/qGbrfCtesq0KpamotruCiegNMkapxGZQHagEinL+YhOf3vHKCENCamBpzVIbCm7RT3KBOC2tXbGHEyC6Gcrw2VgSuVKTTT2q3VTKekwmk60TWqdq1iN5EkWuLFlPijP/pj/tbf/gNev3lH8NplmEvdXLRdXZVtdejKmCpSpPdCmi0Ps2BL5v7+gT6NPLvaEUumn0S9PJhVqjW7TikpGFl5IPNn+96C+7XHRxMYNJJLTeP0pCBUVP8cDN7DV2X+WbOk8TrJoCKlpQhiLdkUslUbuoKCbuvNBVc3ax4ebznPwut8wuF4gtGx22j70ViHYKuIjGGcJnKq2UnK9cZINcOpmn3MfAtTmZvzp/x+1vBnZBEyZweqEoQpGKO6Ck4qLyJGmupsLKLdDILOTqQUkWJZtyuapiVYNZgteQIK1grbi4bpNILRuQ9sZIwTSEPMA20nuOIwbiJKJMcjoTVMccA3nSoVWwVPjYHg/OIIlnIiZcPQC6t2pxOW3izPVaMgZVNORKDga/RzTndAQRe8sQ4ftOVYUiL7rFlk1aOmlp7zzIatIjpGUsU+1OcxJgX8Vust4oKuHkvd8Y1WcVaFcU39r1TewjwsNcXM8Ljn8nJHKkV5Dr6QUbbr4/6Rb759ze39HTEXpgwxZZLkKhEws1hnPUupY9Q1cGSh70ctqBtD5z1jTJzuH7FW2HQB07QErKLtVoPLvPkomUqwXo2Rnt4rP/T4aAIDlS2YRVFidbfWoSXq4tC/Pf1LTdENS/RV12wtQ/qUGUOgGEtxDb5dgS0ULKv1Jc+e3fDl1z/He08sBawnC9S7vk6/1bo7ZcR4ilhyLsSsfficK2pa5lFXp8SAympMWVN3W2/o8yd4GhDOAKTUc6FfpKLNSeXDrNS0V382VyGalM47wziONMFim8A4DTSdhaqxUKRQjCOWVOXFEqHZ0TZbfFBcxlj1BDUYCJ6dujjQOo81SntOJamfA1Kp6mrA4ioAnIueG+c9Plg15/Haysw56Q1tChc3V8vj1UlNnZpNdWm2YGyljxvo1lvi0BOcJ3QdnKYafAVDxjiPyedz55zDiJoSCRDjxPF0IFhLs15RCJSi1ngsY//U+1Mq8qQB+HQadFLVaAboapmRsmYMtlFS2+P+wJt3b3nz7h2PxxOHPpKKYRgjqYLoqVQR2/re5yUwE5GMMcSswXNKCYMjhFY9SKy6i8luy/VmV+8bq7aOWcsukUJJpap7G0wx70HcP+T4eAIDWj9mo2m+OiWp+IqpSP7C3lrSBy07cq3RUhZSonLLha9v7yBlnTpMgosqWFJQjcDQbijGI84Ti6GgQzLz8IpIdZ/OmXEyOL8jhA5vMmWcyEWIMdE1Das2kfNYyUuzo2b1Ywyh7qJnvoWui+9lC4vq9VJwKlln1M6Gq+5Mpvb5557+fEzThMeRpUWA4+mgqX/ZYOwWEaP8g+BYbdbc3R4YhwnvBR909mDmXogouClFbXyCc8v8hoh6h15etNp1QUVPs1cuQC6hBg2L8w2rdatpuLX041EnYR10V1tcCOrXYKjtTj03M4tQCWsRweC82sFbZ8Gd5dLVGlPQbqnF2KzZz8JINHV6VUsvvGpSIr4CrLFOlNY1JgKlUJhA1vVzeKRmoRSha1pyysSk05aaUSbuHh55e/vA3eORu+OJlNSqIIsQa1dqWQeiYjUq/KsljKlRrOhtoBmBJJyLTDHDceLWHXC5sO1Wi/mO1PHuVReUXFcUhD8vsb+APAbQC5JLIZas7SGr2gLWnmv2JdsSsEs9JhWb0GhbUHGLmIRhUm0/azwyJuiPmh46x2k48XA88e2be9brjml64NvXbxExBBNUh1qUll1yUWNR63FWp+0wqQJT0K46roy6MB2OA2MutN5XKzGtm5UFOZuAnMHSp2dAnqBEC8075SWFxc8L9/xTMzCpZZfD+wbvAs47fDBYW3C20DQOCGTRneTHP/qch/1R6buSiWMmxYJ1tb06TVW2XFWiZnfvGehqGs+NC9rdMNW4xs41/vx56mBXTdPdrHjkVIqO4ClYSpzNWO3i26EhQdPjxnuMcbqLWweSQLTvb2vpIEgV89GsUeYMAACrZZR3rNcbDQ5GmZNOBG8dp/mk1owhpcgYD+x2O7y3NE3A+4ZSBnWuNlpmgpY9Bkt/Gnn79o77uwOPjz2nfsS6gHXzTIxZcCh9V2aJXfP39drXbEwquIhw6gc1DI6ZQz+wbX0tkawyHlODraQ6ajkx+3/O1+NDjo8mMEjddcY4YmrP2FrtG9sqPoKAKQZbzreV4jkLd5AKjlOMZRIh4BEsksDkgrNC6ALH4553727JCQ6HXpWKfNA2URKmcUBy1erre7y1jMOoXHmrLUnng361OuCz8g2v8xtu9yeVKXOzN0JFt0vFQxYWo3nvDMzp3vx1HsbKJQGB2ZfBeTMjmXWGQPEVa1210PMgQtMGHUQbew52jzGRYuA4Hrm+vGC33dZhorKg/UAFfbWMmUsDyWXRuFyvOtU6DI4meILTsWRrXd3lVfHbOo/3LTkJMQuhaelWnsaj/h9F5ecs6nVpZ5AZNE1PSY2Mt+uaCWgnouSCo1oNm3ktWKUg181hySJTQVIBcXhnWXUb+uG0TKqe7h5pnJ7HGfzTdVWWCVbnMjlXRSTR8qmkUhWzWm2DWs+b1+/48hffcHf7yP7YM8VMtwpnMLza8s1r9Qw51Yzhe9uFyFxyzNRnleobowZBnOpuOuvJJuEbhzF6TbLLWh7Lh4YEPT6awICoEMY0TUia21Qe61XtaDY5xVTyEHN7UzSNKoUUJ0qe021dGDGrivJms6GYjJB4/vwFx+Oev/O33vD48KiLP2f6U48VS7Y63JJz4vLignXr8Fbbj2oAUtuJIpSKzjsxtMFyuV0zFWGo72luwUltlS2alsuub56eApaWn9UFUWSeZMzLc2sloUFkRrProi514S5tT4S2C7igqLpvOmKeuLpo9Iaf++lGn32ma+tRsk6gqCJ0xFrDdrNhiiM5RRDR7C54ZT9WC/giQomaaTEWgnc0a9U/KA6wCZGJ+HAknyZWL3cYD5SniIuCqXFKT8DcWoIVzRZkbgUbR2hWpCjk0lOMJYurU6RCjllrTOpAnlElpa9//gWbELj8jZsKdGvQttay2W4WM9kUhRxVCs95TxoHvFWrQnXGEr766ltev77l1EctaaudQcm5bmxSs905+MEsfLys51oiL1Wz1G4LOociJZFyYYiqnHWxu6RtO94ce6wLHE49V7stkgsxpTOo+YHHxxMYqOhsUZ5727S62Ow5vYKafllbLc5mTTv9nvarHSaf5yfm17u8vCSXiXE64Z1l7I/c3d6xf9xjvadtWzB1dNgUVl3DzbNnbNctlB3H/kRoVdV3GdoqBVeRfgvkaUSyemGaoryDUFWCM7PD0K/uHEnFTZbMaEkrz2XUPEMAM/p8PnOKhUREGgUPnaFtG0LTsrvYse52lBLAOJq2xdrZJk4BTOcseaZAP2lxzUBiLipSg4G2acgpagAopXo+1LZq7crMwSrnTON1NzydJlxoQRzONCCO6dBzeH2Hu+hwbXMGmYs6VlujmYgS93SSVLKoT8gMFBY9c8Y4+v6kE68J7g8jGyNIZ7m9fWB90eHbhZKJsYbr6yudzuQM6s5l2brbEpoGxBMat2RzzgfGFBGRKtwTKGJ5/eYNx9PAFHWOxehIrRKQmCdJ6rWrDN6543YG1c+LY14LZd71K3kulcIUM6kIn//oc26un3P75o5chGEciV2jQ395fuEPzxk+msBg7exxqKepCJQpk2U2//T4Oviive6Z/64ZRNe1/OSnn4PxTNPI4XDk7u6RUdRmXUpR5BzL8eGRtmtYty1D6PEhsFp1iEDwDZ1vuLzY8pu/9SMab5j6kduHe6YU1T0JVSuSesWccTTGMOSBGGMdArOETtP6p3Fg3hxKqcWEWbrnvE/znv+r6aCpAXFeRU+ChzG29rBjLTsKkDFVG/JwOtC0avoCqmhckqa2n37ykmkYuLjc8vU3XxKz4jW6gemNaEo1SbVV8dpYTBZSTLjgNGikqENJcs46QgisuobGFbzVCUnEkpIB47Gm4eLFhu3FJWbtK2AIUnI1XQk426iCdAUlTSXx7B8OmGaj563qJk6niYf9iSQO37akbJhk0oamUdB5Bn8LCmy//OmPMFk4xLtaziYNgGKwXq9drh4lWdS9O1S/zFwKwzhhCOAc0xTpx5GYDFiHw9BVPYkp1u6BqMI46JuRJRZ9r28wP6WuFW0vawmpa0e/Hk5HSoF393eaFYt2NCja2j6vO3lvHf6646MJDDMQPUfJGBM5FjKZ4J2mgyarXJfxxCEyTZFUMrkOo/zGb/4EHwLD0PPu7R1jnEj7kWADvvFAS+cd665lvVtxOh0xkrWtYz0pebquIzhLKRN3t2/wThiGyOE0YCiUstFUGKNj8dbgsXTeU2ptn7OSV0JQvv6cYWgNXzOBZZ+oYWEuD+QMqM4Bggop1FGCs4t3YWHtUScPdd0XimS6VcsUDY/HB/xauOoc1lzgG8/d4z2n/Z5t60ljxKCDQc7rx5ol+RGlNReDzhWIxSrnqIJnNRUu+r1SMlJt6MQKPjicUxym6Vq8b3EukEbDJCf8bkO4dozTkSQRjNbhKSa0h2lVEUsWlgFMhbwfcBcr3VFzpqTEsS/E7Ci+oek6bClYAuILF9sdPgRSGmpJV8gYTNdAFsqk2aXkWGnglTkohly0xYg1mFyw3tKt1iqWM0S8L4RgefbiOX/wJ1+QjQrtbtpGGaXeYYgUcZRU9FzKuQuiiYqOAsxsRoOtsxxP1ko5a4impKplt3d3vJ7e8Hg6YkNg1TaUyuPQirKuow+8Hz+iwKD1mAC5XhzvAl3TVR9DUymewjfffcfD7UMdvCpIznRBEXLXeOI4sX84MIxDrc20/WYttE2gW7U0TSDHwCiQYmYokZSF4+mIKRlLwciAlMgYE2MS1m2DZOXXuyo/J0VbTM5YdRsytXxplNsPc3vql7mp34cf3//mOZgE7yqSP+MLgimFPA0Yn7Fdi0VNTm1l6iGw3uxYk9k/3nF794bV+prN+krFRA2chhN3d7c4gSSRy5sbjFeuQUoWkUDWVUyhELOa8gQRmqsNq8ly6HtMzvhSMFnVii2eknUxWiw+mApkqs6DRehWDSm1iytzWdqwetNY5yhW1Z1s9WLQIKAg8cXFJaP32KSf29RMIIQO1zVgVbimsVVjUyLeBozUQEpt8Rkopo5mVUBXyVBuwRqCdTTWsgqNAolVuUqkjrUYi/WeH/3kJ6z+1t+jn/aYYrjYbLlYB5IIKRamlPFWz0AqRUlIc9YHzCPgmknOgHt5klXoE2fS0lR5NjFGBc5TYrNeLXWmKoOzYG6/qoT9046PJzDU+llKwVgIwbFer/HeIWgdO9fV9/cPvHv7Du8anLN4ZynG8fab1xRUrmyaMjFmDC1pipwej1xebLm8uqJdN/T9AUvBJxVjvX94ZMgV9ZfE1bZjFQLTlHTRekPjLN5YfAGfayZQh7i8cyrLXo1XnP3eLX8uIuvtL08//PmfhlpPz1iC4XK3AyPYkjBFZco7B8EqtuAqXTclFbIxYsjFUqKCoyVm+ikSB8GuNYhpVgIxF4oRTIkYayllJOcTeSpstlfEKBg8QsbbATEjl13LdrMjpsz9wwNv377DlYlGvNLKUSZeEqFgsTaogU6aqpVeIrig0meuIRUlnRmxNdirKxm1gzOdBtr1Wr0a44RtGzbtmvEwImJIJdNYR9et6XMmLWdZuzVFhONxD9bjYq7Zl8VVRGMeirIV6CtK3kDQ620KuFgIop2RMUeyFJxvaLsVTdfhfeD5zXNubq64vb3DO8fFpmW3Dpz6xImonhQ1uludIMeUjMk1LFQatswU7aVFwtKCNFQQ2TosVuUFbGDVrdDt1VUWpCycl7l8/ZC04SMKDPq1FCE0yvMXKeq8lCJYQxs6GtfQVCkxpX06ggusQosrmSY4phQZbMSbzBg1+gY8jWtpuzWxDIgRxmlEUsGEhlwMMYN3ltYZnl9fcrFdUZLjYX9gnCLrtqGxDpkmSipVSEUFRk0FLhcNvjr1tYBynIsIJbbwnm7vuX1Z684a4q0x3Fxd4CRxOuwpOdH5wG/8+DO6VeBnX3ypnQFncVW8NE3K1nz3+pbh+ICfRm6eP+d6dYXDE40yNHMUxiQYU5A8z5CcQL5hGmCzailJhV63m4B1LXFMSOp5vN0zDD3bix3hxSVtWOGdR0zDflS1o2Mc6QdhvenIVkeVnfV8/fOvuNhusW3L7tmavh8pcWK7DpRiMbN7VYrImPiTv/cn/OgftlxeaumQijDkkVwzjSmLjlEbiyVTJg0ermYGBmhCi7OBw7t7mnVhfbGpV04ocUKGCePbiqM4yljAeLIU9u9u+eZnB2Je4b1njGop+GzV4ZqgXScxWOv55PlLvvnia4IT1q2j846epL6SpdSui7ZlnVWNBStoVmEgGxVulYo/zMHB1JzaVnxtitWKrmpbGAw+hEqwM4g1Z1WsDwwK8BEFhjkdCCHgg2MaJz0pRvBeR2NtnbBbrVZ0zYqSNa1atR2rpsWkid22Y0gB049V+WnS4amSdScF+nFkvWo43SrVOFnBhY7WWyyF3dqz2251bt9aTEqQdFcqU+Lh7TsyobpmlZqu1RHiGqFtVYqWcvZ/0CBRL3ZtD/IkO9B21pIrgIA3sO0CK+u4anSAywVH1/nKMRCK6C7YtIG2DUyjyry1psVLQzMlLnNgZRuScWAVVJxSJmXBWIgZcBZvBhzf4ddrpuMbcrnk2Ec2q0s22wbTbJjGka+//ILH+3timtjudlxdbtWt2RlWw8ibh0fIjmmEu/uBSSIQcUl4/e0jh01me32NX8PpJEiE7XqFMYHQ+MqgPEBO9MfIlMC6gAtBGee5uk0XRemH04lpjBTbqRKzoTpSF0oxtOsNbej47t0DcSxcfvJMSx1jGR+PHN/dsVq9xLaOOdmztQtQREhTxPp1TeP1CS6Eiv/Y2sZOXF9d8vzmClMiTdD34Yy6hc1rY84aGjHcdGvWTctxGHicRoaZYDWvjRl4xi5bh7WWnJO2cTstd9oKdA9jz2a1W0qrGb760OPjCQxoXRmaUNtSVM0ATYdy0b6zimuqaejYT1hr2HQdVnQoqJAY08hpjMSkaWTMI1KUsDINA9YaYsqMOVO8JTvwbYM1ntP+gODI2dAPE60VprEnRUFiYTydePv6DavdM03Hn7x/o+0D3bnc3GeoQeFJcHjvU9dooeDrHCD0D5GCN4ZgChtv6NZbfZ1qVnI4nYg5kymkEmm8xXvL6XiiC2tcMvgIW9vQobRsEzwmgQ0NUxFiVhXsMQvWB7bdmuE4QRfJsUNGV8u3N5SyoXWe7WbDJ69ecXt3R8rC3cMDBsNnr15iG8tu5/DdJSV7bm8PPB4OTKhc3CZ0rNY3JGvpJ8H7jrYt2DZg/ArXbNi0Rh2Zhj3vvv0CNlseDyNX1xnjPSYIHks6nJhioimOdDixf0y0zz7BNB2gN2COmWEStqlALkxkrMxsWZU+m/Y9x4c9V58914ywuqhjDGI9m+sr1jeFvvd19kDl6DGWoR9pm6Sq5FLYbTe8eHZD8BCcUp5Xjef6Yo0bEqUfVQoAUUWmNnDZtay9w/eWxxh5HFRvae5UaBe4MAsUOecgwn5/5GK9plvZOl0pKme/apGkpjXOBW1zfmB4+KgCg/MWmWRxzolpYhzUO8E6T/CBYLWnDNWApmSCMzBErNM2zjAkjkNiHHWcNZXqDek9OUdOxz02GIp3rD65IaXC/dtHhmliKpkpZQ79RH8aubpoFUUuMA0T1m5V96A6TZXaNtWWWQ0TS130fhhQ3sCT1suTz/4rUz1RwZOSIq4JBIPy/NvAgH5WsRYTHFOe8N4xjpG+nwh2pXMiUclg0Vim6ocxlciQI65puH75At8E2tUK5xtOw1BLoCNiDM5GLi5fcDhG7m4T3mhde319ye76ilIl0I7jwN3jO9oE67Vnu1mz3V3SNI67h55oA42PrEJg3bSUUn0Xy4RxmSKZPkWm04hYy8pYhjgxWotsNrx7PHLdb3Ci5B4pjRLOJDNly8p7VtsWaRzGK9/EFUhSJxuz6lKun1/imwzYKraScd7VlqbDWI+Ys/9HwWJXHeGiYzxWWnIqNVuwxKSgYkK1KdarFa8+ecGrVy95/e1XjI+3rFpP27Ssp0x590A69SSM0uat4EhsgsOwYnNxQXp7Sx6nSmyaU8yzgIxmRC2HQ4/5vEGMY5zUw+Tq2TXtdkUaMxa3tKjPY4g/7PiIAoPeZDFFjGhgSCmRk3YoQhCMVe19u+poVg374wmXNVOIWU+kFRiGTEqWYRxpG7/sDhhD03ruHvaQHN16y4vnn/D23T2x3DImZfblrJOH5Mh222i/RKolWNvw8tOXiF2REaxI1abWHX7mt5siiJ2zhDPWsGQQNXbMMeL7sUKqfHPw0Bg1SHFO+5XFKH3cWB36atu24gyOmDNTLEypkLwjbFaMSbkVvLtnvN3zeBq4u3/gYrfl+YsXXD2/4fL6GdPxln/wd265uvAEAtiI8QeKWLr1Jfu7nuMYOR48j4d97fmruG4qmbf3b/HHnlUDz57dkNPAJy9/SrdeM9IQXMfVxQWUzOPDLTlHxnhEjBqlDKfM4XSH7zqaQ2AajngxrDfbCmR6wBOzdidSrtyA0bC7XGG6ltGHxa4PZ5myIWXNOKcs2LbDt0KumpHWe7rrS7r+GfgVYgNiMkkgSVlie7PqiOXIWE1+5qyzbYJeW6ODVqv1it3ugk8+/YzT6cDx4R3eaom5Np51E9j3I4KhW7dst2taAxIFL6oHeZhG+nexjmc/vUNkAWB32w0xTXjvKUaFjF1wdKsrZse2DFVK0MBf1MAwsxSd87RdgwE6Uym5UdVudIosEZqOi6sdr1+/pRirhJg8su7WTFNhnDIpnesyEb2BHvePXD5ztCtPAVIa+Pbbt+xPI1kS1ioIJ6WQpoQ1qjbkrMNIWvgCvm10+KeKts5yctpVybjaulPcYW4VaeDQ0m9GlcwCPEjlKCzBI2ecFC5WHevO0wSP9YZsjLoYjRNilN9RkijYJJo6lyKMU0KsY/XsGcd37xhuH3jMXyNYrj99xe//F/5x+qHHBsez58/YH0+kqTDEFfv+hsYf8JIwxVAkgkT2j/dMhxEfGtZ5zdXVJTEn+mmkWOhP9+T4SOegjCPPX4Fkz/bqR4Bjs9mohkGKHPs9w34giZAlVqfswmEcuWlXEAvjKEwiqqFhoB/BW8+QHDHCkAxDtqQorHA6dm0tzrmFyDQl6EfL4ymysy3JBKUoHyNlSly2a5rdBZtnkeQavAnEMpHEkMQRkyF4T7e9wLdHYrmvOhEsG07KmeLUB1S6wmq7o2nX+LBeWurz+ukaiwMS0LQNq3WHizoTYkrGG+Fyu+HNg6pnaUehdjOkTtOKOql1QSXwNtvavatSeTMPZorqMTEzUj/k+GgCAyjwuPXb6iCcFldgb1xN7TKTgCFzdXnBbrtBhhFrCi44fBs4DXmhkKp1msqYZxH68UCh5eUnGx4PRx4PIzk3SIEQLGIV7fXWqJWb8ayaNaXt6e8fVSOiYh4pZhUMlVkOvpKSivb0fSoIOilajCxdFOoN/LR9NLdqMZopqDVZYdU4rndrusbjvSUbXVBjrthA26pqchLEKR8jSyIl7eacxpEkW6Xp3p9Yn5TCG7zns//yf5F9f+KLr77EWpUwl9Dim8/49//jt0gZ2HQ92zZwc7GDPDH2iTyqmOnlixsur24wzvL13Wsal9mFa453mePjI7dvH2lWF7y7HbgeDRevfgpOxU6P/cDruwN3d/uK8o+aDsfCOBWyHejajjgapr5nVkG2x0zMmce9YgBjsfTFYSO4CVojbJuAc14NaA3s+8xxykw2cZCJNqyIfc/dvmfVNNjkydbwLlYsx0ZOp8hxNHASxAkdnrEYpgKpEpGsSHU+rxoS1hGalv3jHuMCvooEF6y2NlEwsw16LVMWGu/wTahK0wVLRtLEpmnovOM0JQwFgwLYFuHm+obf/M3fwOXCaf/A29tbXTdG6V9F1IxoHEcOp36Rk3u/B/brj48mMBS0NedNZZTPUa7y9FXQyaht+3jCS8unN8/J/QlvHJvNDozlcOoXDYG57WcM7HaXfPrqgt0O1htDP2o/3diKXzQeSYWE4hLOWO0xi+Nyd827r2tgMGCs0UiMgaKCKbVS0feds/IjCiQnVUNGh5WMVKbbwlqpI9j1fRqjsupGCte7NZfbFY3X35sFJoTH45FUwDW62EzRc5WigrPqSZF5d//AGBPST9hiGHoVef3iy6/4gz/8Q158+hIMvHn9mourKxIZYzq+/DpyPBZuLoVPX8B21WCyytSvuzW2WRF8xzBGJjLHNBGHI5+83HL5/DmvBe4e71TTIBd+9vM/4lVY4dsNmIbX7w58+d0DsQifvHrGN3/yM4pYsCqEk/aZdSpIMgwnYb0KdG1LdB13hwPf3QtiAdMwRUFy4pD2fPoqsL1qEGOIImAC+7EwxonBrPDTxG//5DO8j/zi6y9ZbVvGsQVr+eZoKQ89z65XOLNmn0fevj5wOTS8fPmMkh1J1CZgHE+Mfc/uOtI0XQWQDc4F+nFiGkcwliyqwB1M0QEzoGk8q7YhnQZCnRqe3a0MQE5Y72gshHrNNbE0XF5c8tu//Tv89u/8DjJFDvf3HA8P9ENCjMWHhpSK6oTM/iULRvEXNDAAjONAFgPBk6MqF4lIRZZ1gKc/Hnl8/UDnOrqgdNZTGQlNUOHWuwNFdH5/lnfLWQht4GLTYsqBaUDJSAiZTDG28spV0JO6w3vjubs70BlIU6kjyrP83LmDkFOqQ07KdMw5EWaRkyIUK9qftlbRZY0AzHy3gupLAIqIp8jFquHZ5Y6uObMps7E8Hk+8fntH23UYp96KPjgFKUshx1izGcfu4oLLywtks8FOhdY02BCQVcOb+z3rq0tevHyJ9Z6SE0jBuYB3u1oCjRRTJ1QH5RS44HCmcHh7z/E0cigjd+meFyGTDoU49HjJXF2s2awanm2v+P/8nT/k7/3B3+U4ZD7/7Hf59t0Drx+ObC4vMN2GURzTVFRvwjpIhnhSRShcg+u2hPWGx9PE1+8S90dLQt3BJBucCYSYeWHX0Ky0q4Wl74XbPnIcM/dMhJXhU7vhxSfP+OoffE06HLk4BXxo6E+BcZg4SORqu6NPW949nHgz9Bz9QFc8Q9SW9zRGYn8iThOTm/C+VcVrqypYapWo+otDTLig9gjWg/eO3bpjHCcsBYuw6hrKqDMaOpBXWAVDI5FsLDZ4thcX/NZv/w6vPvscHzrazQUvX37K6fDA62++xDhH07Wkoi3vXIpmC7Ma7F9UXwljDTlGhiFirON47BmOver050KqtXQ6DQyHgckO9NVcw7eBbrtmygrImcpbUORZA8MwDAynB8SOtOjsvzMwxkgunpgKJalm4CJL7zwPD6rEQ34qVkt1n1Yj0VyVefAqBpJT0lLCFkwWVqgqVLaW5DzJ2RokzIIJlazlAymyCY5nF1tWXVBJNlR1epTC29sHTqdB/TTjiDVCaDxUAC+lgmC5vL7i6vqa1arFO8vj3ZHvvrmjP41ITBx+/hVRCs9vLmiaRickc2aaTvpeC/STp+8NfQ/5mLh7fUccJ7xXLn6z6igbCzslXJTpRBoOFFHLwa+//BkvXv2U/eOe4z7h/YZnN7+BWA9B8Zovv/waEafy51XQxZqg5jDWEJoVznUUGzhOA2MRYhH6UWXdrLWIc3TNCtusmYzX+ZfgOR6O3MfEWCxuglUIfHF74pAbRtvxuN9zTEdCmCg5I8WTD5n9uCdOE/3kCMWTv9vjpok81LImwzglpmmkadfaHSoqvuqtKl2llBahliKqB2qNCtesuxVdO+jPILRNoI+ZYsBawVph1zVMqwbXdvj1mh//5m/y2Y9+TBbYPz5ir29wuxWfXl8yJfWvCE2LYBiHHjF5Gbs2f5EDA8aQp8Ld6zvSmBgPJ2RSc04xDpwnx4TJhs4oCm+yliB9mWhWK5oQWK8Lp9NI5qzsZFHvAhcaghe6xjHFmY2WKwuwYI1bFHacUaHNVbfi8UHLiFyyZgpG6ceGqpdQ6whbFYxSypicdfJfknYqslS9f0XCk3MUZ0nFkEXZeq5ktl3DJzdXXOxWi1NSQYgID4cTb+/26hjVdnRty8n3tF1DnPIii9q0gZuba3zjEUkInq+//Za/8we/4BTBNoEQtCwjfaKksrbVtl2dKYkxc3d/Ih4HVlhKf2DoE+MY6dqAdxYfBZshj4ljKQxtQ4rCw/FE03UUHGN8zXgquM6q1kVM5DjijSGeTiCOtsq+GefIYuh8wFBwJkMpPDw+YoaRgmOz3RBzxNiqwi2ioq/W8frdntM4YiTjjOfd45E+WYwJxCkzTI/8R8e/y/X1FdZZUhHSMOBjUrITQmFkmKoORCpkA+Y04eKEiwkrqjGRkjCOE92qGrpIqTMmid1ui6CbThHL4+HIs8vdMgvinaMJQS3mcoFSKk4B1huKzey2HV3XIt6zuXnOZ599ig+e8TiA1XX6cHgkp4ab589pgmU8nTgdj/RTVMwMt5SsH0p+/EgCgwExlFg4vH3ETQre+TpAI8YyRtEyQ6ymm6VoykS1dCtCSROOzHplsTj6fiTZgu1aGic8POyBQuuDsttyVt0EYVFV0qEoRb9thk235SBvVDb8Sd2WUlRQR2RRtJ59CTLKXGvq65oi2KLtR1uUe4FzxAo6emdYNYFV6Li+3PD85hLv1YUxSmbKWkr1BfpUWAeDqWYszjtC8Ih4hpTJueDXQX0FJFfSlTIGs4BrO65unjEOR8DjnQ6GjUNUZSjxhKapuImQRFi1G9rQcDvquLk1TicQBVahJQoMw8C7fSJFOPWetqh/48o3fP7pP0QynoaW4/7A3ds3jI97nCi33zrPcRqxvgEXSMcjlkzbOHLK3O73TDZg240m2qWoTydS5wIMxgSGPjMMe6QU2qYjFsfl7lkNynDoe04HXQMvXz7n2c0VIhWkdlY1OqtrlZQCstFzLAYzOCT3xF4zUe87zS4LULOBkhPjOCiVXIRnz56xsokv/nCoknTKYxFRXwpE/TAlRfUgaQI+BMaSMEYI3mBXDS8+eaFzJdazFov1gbZ1GCtMsYdS2Gx3XF9e8fjwwP3DIx6HNQGqgtmHhoZfGxiMMf8a8E8Ar0Xk9+tjN8D/HvgN4GfAPyUid0ZD4v8K+K8BJ+C/LSL/3x8QFmoZJJAElw0+u9rG0wVoRYG2iCrgqPah1k8GkBSxRnjxbMvz589pQkN/GikibHY7rDHEeKQNiaYp7I8H5eLnhLGtSpeJdhlSzAz9gImiAFoqOKvq0KkUxOlkm45Xu7pzaZZhrSVR2OeJNVXpWnT239aLY7Oy75wI7arj2bNrri43dKE6PTmdcBzyxIQw5sQpThxOE1MuuFxIBnAW61TApQkt/WlijInnq06DJyp/7qyjq74KRcBV3v2sf9aPA8ch03VbvF+xXq9ZrVfkqINbXeholV+sU5yV8VWqXP5utaNxO4YCRQLNRgV2Ss4Mo8WUhPEO7zPvXn/H/v6O1A8E55FiuHz2HJcNqUTVPDATjXesrONhPPHVl19yIpBtC8boGLfzzEIw1loFkIOjehCqbof3ZNHNxVmlI6+CgzRx/+6dZkrOk53qUjprKVXmTk2ptCQtOGyM+AoaB99gasCcKdKzyEvXBS6uLvBN4MXLF1z+5FOmhwfSeIAqcGyNoWsb2qbVKU1jlc5enbjspNOzhcL18xtevPqEm+evKiMXnHe06xbfWLyBOEaGU0/rA5eX1/T9yN3dHSmOlKybqQ7K/3AA8odkDP868K8Af+PJY/888H8RkX/ZGPPP13//j4D/KvA79b//PPCv1q9/5jH39l0Vt3ACtlBT4xlZVe77JILH4xcugGCNcHV5wY9+8pIXry7ZbDd4oztW0zb44PDeseoC6zbgbOYXX33B69f/T8aj7uBxisQ8EeNINIFBwE4ZcsQbw6z3N4uzzIIsxhW9v4xU6XWr5U2ccKZFKMSiIhuqHE0tgzLewY8+ecbV9SXBWbyW2eqklSJjikzOUKwB69gfj0qqqboLUmtWDZJqy+ZcoFutFCgVqc01aBoFyMiRuzffYR2UOJLjVGXs9DM567m83PDq5TNOR8/p7Z5pGDAmqVFqFUK1FSPJuV4Yq+y6XAxJ9OYtUhjHSJ5OhKD+lTJFTMxctg27tuPq8poXLz7hYX8gYnChJZZM8JbNZsthHPnizS1f/eI7im+VE0CpcwS1L23UlHYeMDKiwDNVu8AZV+XoLapdoj9nnZryzhOItk7R6sqbRQAcDkcokU8uVjQVMLZG0/ShH9nsIj6oFke76lTXwlpC2+C9oW1bJPZKWjOiMgLe46x2DHzQjM5YSxZRFTJT2Oy2fPaT3+Di+pq2WyGi2FtMCRsNPqxo2pbNaodcqOQ+Rfi9fyTwJ3/0xxwOvQ5U/TmmJX5tYBCRf9cY8xvfe/ifBP5q/fv/Fvi/ooHhnwT+huhgwL9njLkyxnwqIt/8ut9jncF61Qw8U4lNpefq8s5YoqghjXMWZ3T0uela/rH/7F/h858+p497YoqQE8Z5ihkZY8GHFW23Zre9oOsCVzfX/OwX3/I3/8NfqOqR0VFvJBCCrTtipKRCqKCYjinorpJTNYGhzssbC5JpnMPXlqYNnphT9QYwOMAbTVmNg6urC24ud1gjWJS8UrLeULEUhpKIgorKOk/bdXSNqhQPfc8wrirgmJmmRIyZ9WZL13ZawqAkq1IyTevZrBu2riFLpm0du1XAGgFJeAfBJZxNGDMw9HdM/Yk0DRyOR6TViT2qclFBmHLCx8SGNSIaKKz1Kp5TdQTEKkHMuSrJbyzrxnG52fH5p5/yu7/9uwQfeNjvyTjCekM2wjQNiIErY/mtN3f87M09Q2K5DlI0m9T2r1BSqn18KCXpfIDVm93WdWRmZao6R8psTQezG2Adtz4n38aq32koiZ1/gVSl62JgGCYeDifWux0ra5VnM0488qgeJhROp4GYJlQWRm9RWwVzl1Z8/TtSEGsoxmBXa178+KesLq859iMP+9esui0YxzCe+Ob1npQTm/WaZzfP2O22WAN5ilAyNy+e88nhxNvbAx+OMPz5MYZPntzs3wKf1L9/Dnzx5Hlf1sd+bWAwFt1RjJBLqreRfqAikWJVVMNQ23K2YI36J7569Ywf//hTIkcO05FpnMhJ5cR8dSouxlDkkVIMq9Gzu9zwe7/3l/j7f/yWw0NW8NKDt3qRdzYw3u2R04DF6bRbzWpKdYYqddcqWQOFWGHdtVzuNkx9JJbMFJMqIaE40CzH1gbPixc3WKuIcRGQbBbANGLI1qrEmRh80/Lppy+5eXbD8XAkjqfqrAz9MHE4Hnl4eGTdvcThVAPSVAFaCldXW/7yP/qXCF79Fo0UNpsVvnGsTUcrQtc2NE3L470jmIntzY6v7r7jeBhwNOB1JyxJJfdiKRyOPaFp2HQNOKdkHBIwZ3waCL1rMbbh2KsjuHcDb+8faL7+CuOc6kVkQyzCaTpxOB04DSOxCN+8uVejGtFrKWIRpzMgi+7AvFqM0n9VeKUursoFOOtc+IWiTv05I+fvL+L+AmYWqskZSubY97S2Y4wTU87cP+4ZpqhSckkt+2b3qlwSU38g5RFMXprcutVpeVzq+5jfQ8HgupabV5/hLy757vaBmAQRiw8DITS0jdNuRxa+/OYtP//iO569uOFit8YUVdBedR0vPv2E3ZffYJw5/7IfePwnBh9FRIwxHxaOAGPMXwP+2pNXwli1vRdXastRg0I2iWw1NZxLhFIiYrUevH52yZR6HuMDKUelFzsV70xFfRzFOBUQERhTwvUnnr94xuc/esVXD18AaiWXJWO9Zb1uMaeeqQeKTrbV/0MRnPXV79LhfFjUpSzC5cUFt/GelCsOQQ0ghmV3sM7Rdi1Fzq5VUlR2fRgTvS2kYLAugBjiqHLhzhhFsWMCUUn647Fn6AdKzjRtW92havlVtDvTdYFPXt7Qtmordzwecd7Sdq3Kr3mHdx5nPVcXHRebpoJqhZwmrq6f8+n2BTElprGWH1L0NRpPmiamccA7MLZgiw54gSFYQymeh8eeb757raDkes2X37zhP/y7f6BB2zpS1nmGIY3krPqa3WqLb9f85POf8ObdPac4acnMU88EFkxASwhtp5olMgC1FKx/nRdv/WctS4VlAG6+zsZYnBHMpES4lAYmMzFNo4Kv3aoqQqnh0TRFYhq4qnTkaRwpomDi+Z3omtKZOlWpKihpywbPs88/Z/fsJVGMOpnXbPn+7oFpHNmuO9arNVmgadbs456vvv2Wh8eW59dXXG43JFGT493FhWacZvG8+kHHnzcwfDeXCMaYT4HX9fGvgB8/ed6P6mO/dIjIXwf+OoDVbZPNdsXmekU6DEzHyHqz4fLqmtW2wzQe8S2h3bDq1mqkIom7d++4uN5wf7in+IJ3TZXPqgY0FZGe0eAYJ/rTwNu3R7q2ZdV1mKIEpgI4q07Z2UfiOOliKbXrM2v7Z8FaD2Jp2xWX19eEtsFE7U2vuxXHpud0HHFWzXOnnBYGWxGVIM+1U2GsgaKtq2GMHA4DdttiQsvx1JOnSN8PtNsNkkWDQCWulJw5HPbkXFitWjabNdRd1BqzZA22+jFK0TLMe3XaAs4LR4SSE5aEY8LYwqoTrq49z27W3HzysjL9VOAk56zBOSeO+yNf/eILnb+wen8WVPvQtB7rOlarFZ+1F4oJeBXbiVIw3uODtjenrFiEDSqN17QrNusdIbR89c23/Ed/629zHHrEnodYz1e7WrItjl2cnyT1jyd4ETV4nl/j/Mf80trfEixBvUXqnu9qJ+PZs+d03arGFPU1bRoNFNYYnfMp8iSbQYeajOZy+h40GxZnefnpp1w8f4HYhhwL4xSJUVXJDodB53lK4XQ4cjgONKuOrmlwocGYxGH/wKZr2Gwv8a4htKGuu/90Sol/G/hvAf9y/fp/ePL4P2eM+TdR0PHhh+ALAIjw2Y9ecHXZcLo/crgfeHb9nMvrHVc3F/hWW1ndaoux1UfRwNgP3D/c8+bdG+I40nYtFlVTyotISlFgxyb6454YR1IcGA4nJGe1VK/OzpiJZgpM4imVRzFzx2b/iZTOnAHjPb5VgpBJCYOlbRo26w3DacKg5qqxlDrlpju4thgzIhmDRbIQY+bweOR0HNk0LdI54lTIk2IIbtLJPl27pmYM2lXYbtc8f35D16oPgjE6WGXM7ARuFrNZKcqpSEkXGVWwtlgdwlmvGl59ckm3CtxcfMaPP3vB9eUl690OG1YLEp+lEEtiShHXrvHNmhyjAmjOaIbnPE2zYr27pNte0KxXuCq4Yr3XgOs9xjWMY+R4OpGNeiLEKRKniWGc6Icj7WbF5nLLcezPpQOc7++K7VDPkt5/9RY3Zz8H3T31p78/W2SeBIbFDrCerzmoeB8wK7TT4ztt79YyoSCEVrsNTdfyOJu+GA3MZU5MmGXjq3q1sbTbHZfPXpIJ9GNSf4qigSRltUEIwdI0vpaLRbU4omZXm82axsH+8QGMcLGtk5YU1FPjhx8/pF35b6BA43NjzJfAv4AGhH/LGPPPAD8H/qn69H8HbVX+Idqu/O/8kDcxp21NE7j+6ae4n8JmdU0chVO/p9t1VY/QkImkOJFSrGYunm53gdzfkiawY1Ywzy4IBQhkGynOEZMKswbnmOKIQ7jYrCBbdR5K4HMhnkbIwmzJXub+yDwlWf+bxoH721uGNGH7nsZ5jAl0oaFrWhVFFd155jLCGDRjqINixjj1xxxHTqeenGAaIimMWOcxHladofGdGr9IVCDUebp2xcXVFS9e3LDbrLDOarekSgyLQLZSA1anu2q1hfehVJS+BrmaVfimYb3b0nWO7fZzLq52rLaXNN2WbBoqdQtxytPwueC6zPriRlF+p36gzukosnGKT5iqxJVqu9V6B9YRc+LUHzkde07HE/3YM4wTMU6kaSLHkRxHKDCN4/cw9hk8mHdh817QkCdPqetZswsj9brOC1APefLPWbLfoqY6Bui6Dlfd0dUQSYV6pb5Q13VsNhsFn2unaIyJrqny/3n2ray/eMY9nGN9cUUWx/E40k/Kl7DWkfX2wIeGtlXLPmsMaxomdX0mjhO9JOyqJafI69dveLw/8PDwuFgVfMjxQ7oS//Sf8q3/yq94rgD/7Ae+B70YAs4F1ptLrDO03Q7bFugcmEIWQ8rCcOwpBcZhxPtcU62IadZs2xVWMiZH0jTWMedKgiiqlYChjlEL3nt22xU//ewF/XMVMRn7A8fv7sgPfX2+ouDzfFqRSm5KkRIHjg8jP+/3XDrHqoDH4nzAuqAy9BimmHBG27AF8D7Qtg1FiuIaOZNj5Hg8Mo4T3ipBqWk72sYRrLbfXFCz2NPxSD8OXFw9Y911hHXAW7N0b8474qwyb8E4QtsCan5jrMN7U9usKmKrdvbgmsSLsMJabfF2TaDpOsR1Oo5cqqxa1lnDIrmavap+QCmQ8txszmRGHXOvIjjTOCJZKcA5KU40zWIqU9JgIJlUlBXoSJgcKUlZk87OWdxyC9fM6Any8BRMPNcIei2tnAPFkzU4t81nmf95XVI3BETYbtdM/QQixJygqIu21NIlhMBmvaFrW/rDkdVmiwkNSRJeOGMZ9ZdKvWl912BDw+PhRMLjjMcaS0aIo46lB6+Cw4ob1SlPCyVn4nBiOI1MQ0fwqkNp2ift7A88PhLmI4CpRrQG47xy4YuQRVPelDMpaYqZk1CKwWbVLMxicW5FLlHTZLEUsct8OkWJS9aYenItY5+wztK1lquLlkvXYoyQhg1fHkbu7g41YmlrKgPJVLKSCG3r+OTZJRkDMWKHDEUJRJREJmFtqC1zlRjRroQOzVikmtUGppwYxonD45EUE2EVePXyOVc/fkVsPMYFHo8DCUPTttwY5eY3TYPzHucqdmAEkawTeujAjg86Duw6vdGND9VY1tM0Db5pVE7eGpzX5TBOkTYl+tPIiHAqibhXUC1mZVfq9UhIydVgR3cu1Sco5KRajDErZ8Bbg3VOzZtFcKYu7qxln3IuDDlm0jCSymzqk1XBumRympA8YY3MuOFyj1UCcFWhPgPF8iRICpX3gEWMMJvKziS6ZVc1RlubNbQVKUxTREohOMMpZ1JWl+8hDmSJSFGLgrl9rA7pE916x2/+pd9jf/uah3evtb16HsHTgGQsvlOlsJIToVHru5zVezPFTBwGXBdAOpKeRNV+TCPH/SPOCc9vrula9e2wzhHCSsVsngajH3h8NIHBGMNhf+L+NCHiKlqrNX2RoqKfWXX0F4sGQQdFajbQNQ5Ppg1qie5ECUOiCihYayrbTi+qs45xHHl8uAfXYJzFFzWydVQvBLRWV2F2liGq65trfvzT36EJLfHU88Xf+yMevn0HmLpr1TQx66CMNaZOkVu6rl0wAqpb8zRMpFQt70TYbtZcbFbsKYxSeJwGMhZXCi54nDX044CJyqLbrFpcUB8BcqHESBMCm90a33S4RseLrfOa0VBFVGMmnkb6fiBLZooT05RIKTMMo3JCrOIJUnfPUjQwlFw0KOQ5n7KLCxVGeQ1iLd5pu9g6t0i1xxoYc1bST6m1eEkZyVmDuq0u2kWZgE6ExgrWaV2eRWXqEeUd2KWMMBTJVZC3MhLMrJ5lnnQsqrDJHAAwVcujzr2Y6nSpghUEV6cpholpGmqQ0Z08Jc05mqbBGENJWTtHBXzbYX2DVP8RBYFnGxltRbcr9clcrbdYF9BhV8WAHIV129B2nrYJeFezXmvgVPAXO25udnRtyzRNgKpeHY4jDw+Peo4+8PiIAoNlGEa+u3vNMCril1KmlFxbAtVHMelgzQzeqOGrwTnHJy+esblc4ayOEKt47LwjKSagfzd4pwtmGAbevn1HFO25u2yJt6clys67ybyliOjvNNbivSc0LbaAd4H6LP2vaPuVau92dqFSAVJywTUtKRXGcWQYJgwOZ3Q6tD8euX13y8FkRizDOFFcAyVisycEj3NO50SzIUiDNVQbeXWEPk6J6TAiLlHMiQxE0VZniYkYEynW4Z9c++xSMGW+xZSUM3dA9DRoWp1L3fXqnArVi1EnTfV6qqKYJVlLrGVGrp6evgrWGCA0AeOcBtHQEFxdlqbe7DlR4oQ1GSNJA3bdLEqZTX2V1zCzUzPV3NdYZGl9zyVBrl2YOSeo7WSZOwc1aIieBSew3a7Zbtc01tCEE1NU8K8JnlXXMU5KYpumaZmZMcYSc0QwVWJeMa/FWAdd2qFrWW82XD1/wWq9Jmdh7BNHmXAps2obmuAokojTkYi2y71XX5W27VRVqihA6WspcTge1Hph/mUfcHwkgUHqSU28u73jdIqqYlV3Z9Xgr05VlSxk6uOz/bpy4TOUEarijZRUU0H9LTlGaHWAylSL+lXXkWLi3UNPjNCIx4+1Hqy3+ZmWUs1jpfCw3/Pd2wOr1Q6ThNNpqJ+kGpqii01QtZ85ebQogNY0Op4bU+Hx/pGSsqL9ApKFL3/xJen118TO0+6uYLOlGAUq58W8uGIbwzgNKmdnVJ+SmOhPPcZ7lYNDsYaEUKLCh1o2z0FzKcoxtfRCCqamx8ac62EF6uuNljNkqYtdzV/0mjntSgRPE1YKPBo1bFm1OjlojAKlTduojPs4KhfEeUrWEgURrDhVxmaqg1dA9cZQBWTRGYu5XYm2/7SLoACeVJJSqqWQrfJs8x85abcJq4l+rp6hkjImF1pncMYQvM5XeKfXoWnU5yRnvR5pSpSc65o1jFnHsZvQgKHqTNolKOAN3WbF1dUlu+1GsbQ0cewH7u73THFi0ympaRwjp9NBg5D1tM2K9brFOsupPxFcwIeWpunYHw4cjgca76r50dLD+UHHRxIYdKmbyixsm3nHAkxVUzJP2Gi1R69jzJXdWL0dxvGEd1K57DoNSWUpTjEqwCbnnabtOqxzjH1kHIUkkY04vMxgldQ0dL6RlMtwOPb87BffEcIanw3bAs38caTumCyQV/2UugNO/cBDms6GsHHm5mv6mEToTz3jBCU3JNPQhFZvGqvq1jmORGdrUFSdQ2uqv6WxmFQ4Hg406zXFOopRckFB0cEkNfBWIGsOztRUXyRXhmdeglyp7V8dWlJqtCmiQ1ne61xKKTqc5BX/aILObjSNnp2UM6FptJzCMPQ9oDyLlDNlitq5sVYnXkVJJK71pKHH5KQy+8ZUU+46KGb0M1hTNwk7E51m60Bbs8BZYm/WftBgWvLcutUssczAYM6QtFTxzlUyk1SqNVpeVA5KsJrqz3M0KWlwC84osSy0jGnU8z3rh4aG7cUFq+qFeRxGXYvDRExqoOFdIXiBbChtQ8kqnhNjYpq08xQpRF/Y1anP+/sHgg+s6tzMhx4fSWBQ5Nh5x3a7JaYZ/NEbyjm/pEoLe010t9TvOQWz8okpj5RqoopZbFDJUpiKUIynCTrH0PcDw5SQIjhrCN5iUm1JGqM4B3Lm5Zvz+9U61jBNmZwKq+onWLvjUNNqmHcwWYxMjECKiRg17TRGB3moqazUNoi1Dir1NU8RSSNCIVkwPqgRj1UmqDqFK5kpVK3LoT9hQqDYTJaZ2DNnXVLBMi3XRBSsnDskgipQieiCd94RvIqthhC0lraanVg0MPlGJd2D9zRtq3Z9tcxru07P66StVmMr4NkEYpy0bHCaUcylmrWqwEVll+bxhBEFAWcXaKmlIkZp5d568E7LIWNUAr8U8Avyo1lILSVsFey1VTBHXb2X6hXEY4vK5+Wsjt5N2zJNPcFZWu/rBpSw4iFnHu7uWG/WjNNISnEJrM77miWl6hSlYPJmd4HgOB16TlMiVnvFtgkE51l1jjgOeGfZbTZgPMfTWJ2/J+KkprpNu2a93nA8HsmpcHl5xbs36vlh5D8d5uP/3w8xWv+3oVEcoMypeJ16s9p6macbqTATzswYHlM/EvOgct1WJbGyGKZS6aaPEzb0rLuAdQYpniEqCp9TxFQ6XRFLFt3dcwUgSw1I802rE3uWnPWkJ1E680wmElSwRXv1ulP6Oj1qAFcVrOvUTsUqNSio/LkCWofDgFtZ1tazWhvaRpmTJWVMcVpaWFe7JRoYctXNTDkTp4mI0dZarsCaMQuCbkAp1CIIDW3j8aummpU4DbpW++ZFBOO0o2GgcjAM1s4Bqn4Uo5mM9vidgpRFFa9cNYYV6zBGywZqHazBwJNKYiYYgcE43f2dDwTUkdrUMnIeZs+iYiumOoBp6aMF3FQKJpmq76nn29UdQxIVbygL38Q+4URoglXIYyTlSE6C9xYjBWcEbyFOfQUqC95oZipFyClxe/eOq4sNJDVHss4Qp6Lic96z3m5pmpUqQsUC4tGxjIh3hu1auQvTkMgp0XYNVxcXhDDQ9yNjVAl9FzrWu0tiztzd37NZK5g5nxedsPrh9+NHEhjO47FzSv0+WUXTW6gXt6aB1N6xst2EYezJ4xHvlPfPULg7nHhzvycV8O4XtKElOBW5eHZzyWefvOKTVz+iFM/xdOL4eCLjVZi07kgzSUpKRR8RlfASZSxKguKhGLP00cXolBwViMtSQUd7/lS2fvYZ8Mqol4EVQxsCxRkeH24Z7vdsNmv+S//4f4bdxmmLUDSQlKI2dVNUKa+U1Cgl5gwlMQwDxSrD0FuLM0qHds4TvMd7zQagEnasRWXvz96bimvYJ022qlolM/3YLnoEzqrcna3ljfeeqURKyfgQCNaR6rTpTGO2ZpZ7t2cGs5QacPS1Zj1FJ4qWGGu1y1E3kPlnjT1bwJvq6+F90FK0gsAzSr9kdxVffkqTrh9P/5ILpURSSYh4plS5M4DHMA4DCWF7ecOD9/TTSN/3HPYHxnEAUZq6tYpTmKpF6pxTrZAQKEWzojhmRJLKBLSW9SpgbMG0LclEckq44NluVnoNx0QSZVvGUnh4eMvQH8lZOPYDj/v9WXrwA46PJDDUo6ZvSytnXociyxCKFGqNpgvGzrWoEbrVmlx9IETUBr0fEsdDwljHkCduhwMlZ1abjsavefn7P+Kv/OV/jL4/8vbtd/zf/2//D17/4jsUS65dicWsY5aB13rWGb2RoVQh2O8d9tw/XyTia3dCd0RFvsvcsaj4SUFompZnnzzn637P8XhinCZ2Fzsut77KJVa/C6M28HM9q6Y9iWHK3O1PnMTg2jXW6eyBt3ah+p5xnCfvWeY22txfMUubsr7pyv6U5SacU2VgCdqa4s83tAYD57UEa1pVOHLLe7HafYIzDbmea1PxJSq+M6+Jc91c39cMjM43dP1c82c0Mz28lpi2/jf/+BwE5vdUBB2Bzlkvv1PX6yRZsy9jwFrGqJmE71o+/8lPeP3Na1LKjMNIjFFNeVImVIPdp+eu6Tourm/wTQfFk8ZESiMpJ9rgWHdByVwV12nblmEYGcdeuSneYpPBi4MCd+9u6U97yIX+NDCMkf3jY+3sfVhb4qMJDFJ351wJIvPimgkhyyKt2YP6Qzp8o8DS9mLLs09fYBGCbwEFs357mpiioW1WnE4D3379DYf9ns9/9Dm//Vu/yY9/9BmhseS84eb5BW/fviaPE3lQFac8lQX0lDloFe18e57KzZtlRFcXZiX8sKzk5VZ770Z8glss6asWL7QVrT9bmZs5u8VZ3RWd09cNTtmNM8gWk9C0HbdDwjSd9udFzjfZr7wIT2DSpb36/tud/32+gefaVW/6+XEFKhUIbZpA3yuY6TwE7xmmCVc9OBVLqLv4k583RrU6VUdTNTakLvIZSGTO+mUOtizkQoPRDM2wfG4MT3gMZvlwc/YwA9umYgCz3JuRhCNQklLbfdNgnWPKiZgL7WbFZ59/zn9g//0Fx3A+4H3QgF2ScmCkjlhby8X1NeuLC4ppkEktGWOKGFNoOxUYKiVqYKghzlcqv0k6KVyqGvR4GOlPR0olgOUEMRbGYazn7C+kfPySy6kk9xiRqqdnyrz0zjW+zKi0MZhhvjHg00+ecXmxZdVtcCZoW20W5RB4fNjThsJxv+H65gKRgZ9/8QfEqLx8Zw2bzYq//Fd+n2lIfPHzL/j26+8oUWqgqn6ZCM4ZjMlsVy3BBHwymFwqBiLVhLTucFoFLbve8t7lvFjLzJeo0vWZSh4SOS9s6qKuK79QdDbGGIxRgpGWPQVnDc7NeIIsv2tRPQLeu+XnSFF/j4JV74ePZY+u72F+T+b9H62PKz8CwlJSiMhCvVa3qHOWYYw5f94n70czBp0FCIsUOpV8dg5Ec9Zyfs/mHOTm92nqo+aMtTwtVQVV87b1ycVYitXMwTinhrcG2q7DOY91nmHqwasd4jdffUU/jCpOi6VpW9brNdM0UeJwzoSN4gvXz1+AC4xjYRiFfhgRUdXvpvFoe1wDrpLjRLtQ3ulrogB4zonDfs/QH2mCwQX9+eC1larX88PmJT6SwACggGMaJ4Z+pIhBquCoJvLqJpWz+gks7LTad56GPd4Wbq6v6ZoVVb4HTKRQCU8pk3OkaQqn0ztyecRYNUVJSTUgUspsL7awM7y9fYsPlmnWPlgWsrZVQ/C8ev6KxngeXt8xjdN8n2v5M381RolI81Y2t0LVlK4OV+kfc1kkVUnI1Y9h6/fOV1eW96Tr/lwamFmVyNTBnblsmIPT90/9kwfmVPxJU+hJKXQOJOcspv7umlrrbqiPKSQzG/oI05Rr58HSNA19f6o7oa1o/TyJWHdsqaSorFJ3575PTa9F/SNmTsUSUJb3roFZn6tdIWPMkvE51ELAALmWI36u6kS1NUrt5sSYGGMiiaMNnrZtmeo4/Opygw8Nt/cPUM2Ick446+lWa0oc2A8HXH1zRQpN07LeXqj2xginY2QYJ4xRwhdQs6hzWTXzTbzzjKMKyIrxS6a0WndqUNR4rPEggXe3D7+6zP01x0cSGOrJz5HD/p79cSQVsGJpvLK+qDP+IThWXUPwjiZ4mibQdi1tG2iaQBwGSkpYo8BVkum8w2MIwan35Jy6ylx7Kl/CO20pDf1EkkRoLdMxg/gnO6NVGTpn2GxXtAQezDsKWS+InXdpLUOczLuoLNWCGOqAjvklcR2pGpHG68J1pagrVmUZzmxMY3T6c/4MZ7lZ3ksGzLJr6i817908ZwCO5aFl69cSqJyzg+UHzXu/YClz5rRXBBWbtUpWVs+DSc+B1bmGksuM5ADzhGLNqDDnMfc8qdR9Ja7ppl/p0vONU8ockernqzdTFjUXrkFEpJKPDFgp2AJIQdLI2PccciZ0K1bbHaU6Y1EKOReGvidlQwieKU/c393yeHhk01+y3hw57Ef++B/8IeMw8Pz5S0JYE+PIeh2IaVLwWUX8ePbyJWIb+kE49YnD6USRTNcoDyPmCLmaGWPVm6JUXofRtqrEVBmfwmrV0rWeTdfig4q/pGQITajtXPjlHeFPPz6SwKAX2lnH9fUF603G2IC3nrb2651TVNnWlpM3M4ItGG8J3i3pofLcVZy1823dNZSoo+1EfT5L6plV9ckYvPdkJUJweb3l4bUi+fnJzmZmIMnoz+pQV2XN5bkVmc+fq6L2ZQZQTVnSQ8s5QIC2y4wx6px1uePly+ccTgdE8qKFqVVXremXTGIm/Mzn8/0JxDmrOKc08z+XgvwJPnXODqQCved2AUtm8t71m/GGChCehV8EdAOsvhXnlLZpG8bTsHSVcsmaGc0BqQ7O5Zwrqek85WiNRXQyGe2CzKKuZoFHzmdLr3vjlI3YdS3OWE77A/3hwDQNpDjqUJgIOY00XQONjqgvLNyiHI/gVyBCGnrSNEDeklPiu9ffcnd/CyVz+06Nc3KJ8PySFEdwARFDt97x/JNPidlUZ/Jcg32u2ckEVWI+J+o6YyGcUe+V4AP9EBnGEeuU5m+dXp08mw+VDzOamY+PJjAAdF3L559/ijFOvfdyUuu2OmBD1YWc25FmFs+wZ0BJY8WcburrWmvrzTqz+5Szr/dEpUyLZgIis2lLw2effcbtl99xfDsu6e2coltXd6faA1fRQOU92LkRqYUv851nqaPRdeGe98ozq1PHxLXVmbKqLYWmUbpuLUvmgDjv7hUBefJYxeUqbjN3FX75dn7/eG9TWWoKFvETntzU842vb/fpK9d3ZQ3GKLMTY3DOYr1TYFnO7MkiBSt6Brz3pDjhKwCYDYshC5IRZqp2DRAV4Jxb2DMzU9/b/Gn03DbB8+mLK/rTA32vOo2nw4E4DNVSruBM5ayknuP+nvbSYINdSFTewZhHDI6UJnI60TqhcwZTMmnoIU80zuJEfUJTjkjqcAYN7tbw6rPPaLsNh1EB7sPDgZwjXesp2TDkRPC2itjmOnuinzXlUl3QFGxOeWQaI6uVeoHkUmnfUcj5PEL+ocfHERjqVuW9x7mV/r0UJjUqqD4SwOJYNNfQ9Y8FkHzSBZDaMhMDsyov59l8O/e7rcV59RSwNeUuRolDnsLFbsdrebcElJnopFLl1AU+p3fVBbuUinnZhTijv9XUYDXvqvV9Pj0XRok7wzTw5t1bHvZ7Hcv1czpg6g2ioWbWnZMZyFyepb+nbvF1F13yh+W7TyLB06RgySKWduGTp35/mc3B6hw0tBvhvCelBALOOTBaq0uRurPV812p3s554jQtv8QYnb0wKlqh+p81Yyi2qDwdKn5ijAagObkx5xehFKHvj5C3rBtH/zgS+xMmTxhUVFivWdZrg/C4v2XtPetti8MTfCBYox4ZkhAilkxjobGi12TqWTeuMiJRzQ6JOAqhcZQxsb3Y8fmPf4r4hnzsmU4DlEzrDV1Qj5IiysykFJwBb7T75KwjWyGmWDt4uqnNPA1qGzSEQPGQpplt+Rc1MACgtGRrwzK59zRlzTNv31lsmW/isvDhLaYCW7oInfNY7Pmk1WMGyZY+tqk7jjn3mEt1gHAlk2NGNz1DrtoDUHfXusMv6LadWY9zDayqv0tNb84/915O/zSNNwbjNDuJaaLrWlbjyPXlhYJKT8/Y0mH41RfePDmH86+U86/8pef+qkcWxP+9b53BzIXPMA8qQS21KsW6zmLYKgRpqr2aqTMHzlpijCBz6/JMiZ5LE0MdmJuTL0FB2iJk4vLm5mAvdQPRbEK/Hvd7vvs68ePPntFYS6TKtFeW7fl8Si31FPQcx4nG1vFrgaaKwwpCsJX1KaoEXvLIdt3qjd44rO1IyeJsweKI1vDys88IzYrXD0dOxwHnLBfbNV1rKCUSDxFXafCq9C1ko2xL763OpBjHOCWG8URKem6Dd2w2HcaaKtNvqmjxjDt9AMDAxxIYDFBpvsM0EIuKYJaUCM4rRwCDsToz0bSNKho7h7G+DlGp3Jhx5syWq7iAzBBXvfvK8lX3XwQd061ko1L9GExJxJR0RRQhjZE0Rnyor2108Xhx6pSFVTn4hVJ7TrNnfEPq+5mPea0XlLFajLC9uuDV569wjWeXIrvdmlXTPhEh0bmIp6/xS0x4M+/kpmYlM9vvrCC9JNtyrmNleSVZ5lWesubOQCbnaCHU2ndG0qW2IK06OT+50UMITNOI0Cw8hWnS6Uj9OVXE9sEv5VUuWTcNHE4KWKl0aKWmz1nZfC4RWdSxWTgARnkp1S+ylDlKPunYcMZ5ulWLdSq/N4oQrNK/u6bFZEFiwgmqHZFTfa8FeSI33/pA11gkJ4oRNrsLtpfXvL674+GUmIaEMY7Ves1m7cnRkabMOCXAkrMoE9YK41T1RNqG0HSIUZYr6Oh3qI7nKSUmI0gSSrZnTKf8Unj/M4+PIzCgC/Kwf+TLr74kZeHq5hmvPnmJNVZBlQVPqGl/XWhKSJnn3+f/ZjBMzWn0eJoOm/Nu+z10XcGruSDJSF2EpqCTjzHhvTLS1qtGZwEmHTgqRSXixcw5x9nQZNmtzYxALMt4QQeU9yDY4Fjv1jSN58LCJy9uaEOga7wqR4pRnsdcJpgqk7IECwHjqnkJzKQrg7b47Ey+qhoKCkXU8CKVoj0jmQLvEcyMrRyHMyGpiOpnGGPwNtR5Aq0tvPfENCkpzTqC84wy1IBrlmwu51TLCUs0VVOzKm3mXOpE7BMOqrKnakfJM3uKmplNyTyerVmKtY5hOOqQmCjYJ6ZiQ7OquBhMla2zYqqdfAYry8yMGNESIUcKKj+Xc8b5QBMa7m/vCUFp3o1Xn44p9rjguXp2Q3YO2zh8TozDHkPCELVDkxMX2xWP+yOnfmJm4E9JCUrGGsZScDGSY1EBHAohGJrGUkpimiZc8ZANIpZUqsfqBx4fTWAAKDEx9gNFLBbLdrOtvWyN7KVoYT7jkbbOmS8ZAfMfT9Pg7xXeC/A3P+f9DVDev00XDCJnA7OikGSCMay7Du8dMU8LI3IOKXM54erMwTkkMYMLS7Yy1xFSHZxSyaQSaY2lCZb1psH7wKIK6mpdwlNc4Ukv34AVOQeP+mxHppWEr8zK2b5MjCEbpTIrJmtUJ7KOGQql0rs1/V/qoSWQzh0SLeNmdSfrFGBsbEOMiaapu7zzTHHSbNApg3WeQsTo94vkpQOhG4Ijp6gGxNTBufmaiSyLf8Zu5mEqLSc0MBQ5T20u130+hcagUl9Fz13OOtUqWTUpTFn4JVM/VVPjeTFV6rf19MNEKdou9LbBOf0F3XrN+uKShCVmYRwzWMuqU0Wmcexx6L+3646UEqchkbNUNSq1/5OYKNOE1DHv4KBpLN5V9Smj5Y2eGPtkO/wLmjGA1oghhCqZDTN4NKeHzF+/B6bMAeH75fbcNvv+MS+s+QflyePz4heqEtG8A1mDbxratiOnI2mKnA5Hvpq+pRl1y55rYdCywVqW7GCpX2VOcWcnLXRysCoeSUoEV+3lJBGcY9sZMmpXh/EscmV/6nE+V/OnswiNZFYyYvNAzupXWd15EOfryHdd6M4jLpDRATARS0FxhAXnFMMyr2z0dXSyknPnwVhc8IzjgNAgGDW86Q/KyjPK0ARNw43RLGOcUr2RVW1Zx7G15NPuZs0Mnyx4XTL1JjiDNro2FPBZSh7z5M8lyD4JtMZYfPCUbBczG+scGWEcB4xkcgGV2a5Ad53TsVWAZhgGBIfzsF7vEOM5jYnDKTFOkYClDYHgUReyktU/1FmaEBiGzJiVyKRlsiNJVrl4EWgCrQ+AkJJqmDrv3isTn26CH3J8VIFhnt3P8w4qKs8liycD58fN+et5+3gfYvlVYOw8Em2eLvD6+My3t/U5TwHJlAvBGLyx9P3IIR/IU2H/cMeallZmTkGe4xkiGXWELnWqUosb46BtPOv1ms1mxWqzpuvWTFPij7/4AmsEbwzeZ7pGMLkHLMaGX45+UDeE+th736qSbLWMCGSaMmHyCYkRqa1DKidEllJCR5xdaDEuqKiL8RTjyaL6jdkYig1VKUlvjnkAyVZXbVt1IhQMdnVWQll53npyminfSqISd870ECpDce4glRqgqp6WcwvIOa+JOZukro0Fq6w4Q5bCFCelXj89V8JZ/HXucqEAaRXIWlq/RTLGgjeuakVYZptC5x2rdasTzqkwxgkXWnwT6LoNpyFyyo5Trzt+06maWMkZHzySM6dhUHk2Z6pxkqhfZp0kjVOumZmlWzW0bUAkV4NlDbwlZyx2aSf/eY6PKDBIdSyeR1O1dDjLUn3v2TLffO+XAj8kNj6pKFgUhJ+CcUY5Bxr95+WVoFStxNNAGQWTLWUq4BIi2kXQ1EEo6nqDD8rIXK1a1ts1bdew3q3ZXWzYbjcqceYsRbTV6Vbw9e0txhScK3St5/7uHUN0XD3/VLUJZg7B8mm+f47MUr3MAdAY0bYZtV0ohVISs3P0WfuwIgrJUqJ6RMwWbBaD9x1iGtqLK0rbcdtPi6fofKNaa2tppdiAM47gG6aUVIjWGLpuxWH/iLeqj6CzEgnvHEsGIE+o2EvLVJZr9nQtzGeCmpEtr2JqTlixkDnQL2VK3U8N8+M8+U/LPf1taksPyrewCLuLFS60alFoLdYXLi62pJy1LBGDmEJoNxjfMiXox0Q/TLROOwlzaSpIVbQWxmmg8Q3dqiWi3RhjLDHW329h3TbsLlZ4Mmka6vnXFSxSMM5jZJ4H+QucMRi0RnPWkWxZPqgixk9SxT81Aj4JC790Hr4fMs4v8t5T55p5frxU3QUUsLu8WLFqPWndYmzA+jW79YaLtmXVOGaLc+MMTePpmpbdds161RGCJzQe50GlCrU8KSLkFLVnHeDlp5c8jHusKVgXESuIC6zCFYbuPeLOk3xnWeT6z1o+PO0mFMGWpNRqUe2LYnJF9Ocsp9bepWY2JVWpOKmLtyDliNiAHYXQBVyV1Dem1sLGKzPVskxIguIGeZzqYyq3NtfwIkXbltMEtZ15Hk9WpqpZSsmaDcz8kPlm+BXXcmlVUGcvYCFYaUZRtT2K4ggwg57zRiFQssLXJWNMwTjL9bPnXF5ccHF5TQgt/TCwP554PD6ySev6yzWlLwUur59hw0Y7KP1A03R4qxjMFCcoCd84rHG0s/IVhu1mhQkt/ZRUiKdEvHMEb1VsyJxbuwb0fCbNUlvntTSrExofenwkgWFOIetAhClV5Zf3toPznlGDRpXlMnYe+eU9MPG8Un45KHy/zJjpxcqH0pS6ZB36EVP4vd//LX7rd3/EamXIwysFyHCUDE0Iquozq1FDVa62+KDU65RUrCSVXP0SqnQbVOxdAUEXoFs3qmEphSkmTimwWnfEXA1WjSB1LmLeJfUzyVL7G1NVrOaWnAhFEkmSLnAL1ltK1rTXiNFWK2eGp4rB5vp7apdA1DMhjY9Iv0HKSkFPq98TVMLecB6lxsyEJ3WdMq7BGYNvAlN/Wm5e9QVNyyVXvoPgvVOCk6nYSxFsmQGcJ8FvucTzbAzkXH/GqtDMMPR1ihOWCEANfEWDQ6m41qxK3bQN682Wpm1ovGfdrmibAEY/Y+M6LlqPX3k2242WIkWzL+dWbC5fMmQtnYwLeGdZryzGwXRUzwwfqnxeCNAKwzACwqptwTju9nv11SgFI5bRZaYxY0ymqSzcIqJmQCkzoL4cY6wg6aJ2/sOOjyQwgBGregZlRpLNsiPMQzH6xDOZqP4g8LRVaZ4EkR/4u6GmjPraupxtVSdybDYrfvobn3B10+JMxjQrZvdqKQaFxJIKrZo6EWcsYi2x7oiZXJNRUZ8E6xHmNmsFJatsna+ajWkSxiB88fod613Ljz/9EdpylHoeqrLU8inmc2SX4KSZl9bQOjauAqNzKr2IlwgVeJzPx1n7QmXg56BTh7tTYhp6imvVkbu2kmfk31UNzJwzrmiQD1W/oL4MIQROjyrxn1JCsOSkDtosz6JawFXxWdF+iQateV7GKMfD6OcoOYMPbDcb9o973UBqSZpyrMQ2LUpKzuQarEsuZCuIU2WrzWrF85s1N8+fsbnYqWBtjEzDxDRFpmnUcqyS0pquxfuqTZqhFEfTXhALKsM2JEwpBG9og8NKVv/OJAz9iBmnOV0jpkyMiSKWKQqHxz2nvscaaLuGGL0Sp6QQg8Nbh/UBRKdUs69cjpnv/4HHRxMY5revjEGLe6JVMC/WJ/jjn3Hj/1Ck4elrme/9GzDVC8BaunUdZaVUT4tz8jqDoFBprDwRVmEuVt9HAuYx6Kd7nTUWsQrgNc6ThkjjAtMp0x8nwmqWwq+999oY1SzB/BnnRJYTN8fWWR1a9QHOY9vy3o+c63uhzGywOXOnZDWdLa2AqwxAp16NxhqcV77DFCearq2ovYOSkeWGV3JSikqbDsET6w43g5gLHVtYxID1hi4Uq5kOhkWwd1azzjkzjdP5DFuLccrENF6zh1wyWYQkYKyn61as1h02NKzWlzx78VJVxL0jT5GpqE7m3Db3PoDRIOKDSv+XmPTNFss0Fm7v97y7/ZbTkJmSYJxjd3VJKJqDee8pwNj3pDEpNyJqJ2KaEtOUKRmGKSI5YX0gp8hYkhrPVAzMeP0Mqq1Zy+GqoPUXGmMooDejNbiMsslEBUQRpRY/rakrmP4Dj18XLJ6OFc83bs1arKtej3rT1s6apnToLqu3j10EPua3+RQTmUPBTMP+/s0KNd22sAoNx2Mij4YcJ1wUOuextc5fUkPDsgjOx5PPsWwWc7o8187nQHJ29ZoFYeafncuTGvzqzmPmPysO8BTXkfr5Z9Up5xxTjsvZt3MtXKSWLfpYqsrSthKWctJyYraue/rZ5k8nqDU89ToZzFK6lFLw3vPw8EDTNrXFahFjmVKuYK9+Dtu0XG3XPH/+kovNhv645/7+kTxFHu/vCKGp4KUSxuZ2s3Me45xmYMZUGwOPM7NpkCVNJ958+y37hwdKKuSkQSif7mj5jLDaYIxyLOYsr8zrog5JjeOAwbDpGqzttFyYyVZS1bJnUBUqq5M6o2LOzMcPTBo+msAwb6fWzB4JZunWL6Kcy1GX2nv3u/5jqTrM029+eMQEdJTXe4aUlH2ny1tVmmW+IOfyZf6155vrXMZKVT9ZlnYl1IiV9xa/MYamCaQIUgZWred6teIyNLgZvUbxB8wsJDtf9/cj5RJKFTDQMnMu10otad5/Zi0vZuDyPHw2a1HOz1FcRAMmcwZU247z53DeIQgxRrq2oxipqtEZU4d7jNHso1iDc6HOTuSFUg1n3oGdgw5Vyai6iWFYLO5yNarR8qDULEPFeLKIqnWLzhJsdju63Y7nn33Kjz/7nP3tPT//2c/Y3z+oQIvzVcNS8MHjG2W6WhfYXOzwXYutrVhjHFJg6EckF3IU3r2+JZ16mlyQkpTfYSyn4yOvfzHx7LOf0GwvamBx5OKUNRoasKqs3bYFb4SL3RqDsD/0TJNiVWIFvJrZNO0KYz3xdFoYk1A1K2eSzV9EjAFYFpdqAS6J9/d2jV99LGFgjhlPwuQPmS57jzc1pyMGTK2LkxQFsmrpkEtZEHZ4et7P9c85KCitV0U1zjoSVIxAwapSabfqFmVdpmnBu8Kz3YbLVYulkGu5YmU5Y39qCWGtBthcCVUqViIVa9D3pZJmZ9VkOEu6La8s8415fvWnzn1QB5isw4qm8aFKijVNQ4wTXddhUOPcOAzvtw2NFkYGQ/Ce4+ksS28qwGcrhlF1SuZ3ocFIdIc1TluJ0zRW3kS9IqLuUyUrYq+4kGF7eYk0Dd+8fsdx3/P49h2PD3tAf783OisRp4lTmkhFswMfGpq7Ndura65vXtIFDwWmKXLan0jThC2WeOwxMaF68Ar4OmDrHfvjiW9+9jNe/uQnNOst3jpSVY/OlCWzC95jJKpMH5ZpmuhPPWKgaxp82xKaFd63WnqMEyG0i0oVT67lhwxSfTSBoVDAg/EGidXQfRmX1htqOX4pI+C9v8+Lban0vxdY3pu2fPLnk44YaiKi05pKU9b/jGNxLZqdi+aW0EyyeSJfoMBj1iCyYAJS6xFjURquZhS5GIq1NJs1mwvHON5y9/pEGTf454ITs6SOs3ybffL+rWF5H4Iy6cocWGdB0Fo7zKnnXPrM75UK4DrruLq6oQkNd+/ekNKkqWmd1dBsRWXjVBFJJea9Vx8JMIuy8akfyNXD09bHU4y1g6M041KzECMs04XeqiFuzvH8vpmJSwUdudbPkBFMrnMtZplUwQLeqpiNsaJ+l1hiTvRTZL3ZkqeJ19/d0j/eq3pWrbGkKNAZ6qi/NbqLS8qkceJ0ONF1A41fYW1hGkbu3t1zeHzEF0OeJnKcNCgYvcalXouVczwOR15/+Quef/4jus1Oz0FRc10pUkulQtsGUhoZ+4HTcKwYh8X7lrbbYX3gOEwcD0dSKnTNTMOvreg/x/HRBAZqCw0LxRSKebKjyHulO/D9Hb7+/QPTpSVAfO9nzqxaDTA55QXhLYs/TP1fvQDgWDSV5qlE0XFxKfO8h6bHsyaD0Z6i3sSigzoZmJhouwkTDcFtOY6Fw/2Bi7apOMBS6X+/Y7eAg4IGtGIAVK/gqf16WYoBoE58Sj2xbdvy6tVnfPrqM477A8fDofpIqnr37CMhxi64iamB2BqdSZAsOg1pA01QJeM5a2hDYBhGBQnVGIJUEhjFA4LzTGliBqJh9rR4Yh9f2YazO8dS9MgsZqPBtw2Wy42nP02UkojZUoolFXhzd8dOLMFv8LbD24bEtGAzqgWBBtVSpzStGvxY53HGQzFILohYVasqBiOWOA2kYSRXZ/X5PCswKTgROmfZ7x+4e+24eWVJWddOyplhHCAXvGrQcTw+Mo6TCr+Iq/6ZAcGxPw5MQ4+p585Zp6WLc+pKZn7FDfRrjo8nMMzHGTpXdHr5MMLMTnzvmOnQf+oxv8Cf8aTvB5QlXsjCTShZw4GtbdS57HmKbSxf5ckixVSlKdUFKMyycIDzUAeKqpAElIyzmSwR5zvaVcfrb16zk0yUCcFB9eVU5We7RMkZT5i7N7O7sxK0qhgKhlK0dhc4k4lQs5jdxZaXn37C1fU1D/s9t2/fMeW0nOZZ1u6ckZ3LJlfdwsxc24oyV9u25XA8Ukqu2IAHRqY4KVfBWqVnV03LGWeaPSeWkXoxv7QjFJnVsWXBGrRdWDA5sQ7C1k9MuVCSMJF1WtF5drstzhjevX5N6iOmRDwZY3P9bBlT5nLrHHCc12swZ20zU3Ie53fWVr3KxMyePE/NzMtNSXPeCLevX4NrWV8+Uzn4GBmnSFMNcoZ+oO/7SgxrkKwBeOhHTmPEVMJT6z3eWrq2IWftEoU6j/KBe+bHFxhmopIu2qzTifPHeoLAPy0BlkO+93fzZ5+KpcQwv/xqBTAiuMpofAqqqTK1wZQ5KDwJYFIT3rnltrCu0DkD8WTrwFtcEwhNy3A8koZelYzJXGwdqUCxgQkHXUd3fYlpPE0bKGIoKSMVZDOYauPmOG8OVQpOZl8M9WSY/Tvmc1VQotVut+XlixdcXF6QKXz73WseHvY68Yn+npm7P/MfqNnLHB8XvGORWavyekZr/xgTXeeXrGcaR6giLiFofW1qZ8F7V0HQen2spWs7xuNUN16pGhKztF7V0ayCPpIKJk9IOjKdDki8IMdIP2WG/pYXn73k6uY515cXXG0P3L654+Hda2U4zjqXFYeZsytdU8oxOQO0NRhJIsVIrq1SzTI1jX8iz1Hbq3V9iIq9lHHku6++5kfdlmKd4lFASsp8NWUi106Lsw6ZIqf+pJ2bpuXy+qKO5Ts2q05B3zoG3zS+trQ/bPT61wYGY8y/BvwTwGsR+f362P8U+O8Bb+rT/ici8u/U7/2PgX8GhZP/ByLyf/qgd6S/FUM94c7Ot56CdNRU+nuA4sIreO8xfa3zn7/6mHebOa7Kk5vbO428GGXmLVDOcgcKM416fhfzTeHc+X0KkLFE40k+4LuGy6sL+jgxDQPZWkIu5PhI04g+lhpwns3NNXjP1c01F1c7HZBJhcP+wOl0IsVU02pzln43VUsypTpZmFV2v1KiQ9PQdh2b9ZaLiwu2Ox1xv7u7593dLVNSwGumbS+1vZxHu+f+5nnaQG+m2Zg2V8NcHwIhBPphYN4znXPEKS6DUt47SkrqlLWUA3MEEzarjue7T/jZH+9rwONcQjKDK0p6sgWM6M1qGEnlgC0Bi6HbbNnfHYkpk7JSvi8v1zTWMB1uSdPZbk8zPJAkpCrlbismsvxaUWNggypal5zIMWpwqvL3s2PaLA2y4EC17d06z5v9gXdv37K+vtEb3likaDAvpShoKpZhGHm4e0SwNM2KrnMqHktD2zRYVxXG7Ty38v7d8EOPH5Ix/OvAvwL8je89/r8Ukf/Z0weMMf8w8N8A/hHgM+D/bIz5XfkBCIjuanU3QlPWM1HoXO8v+fL3wcdat9cNimWxnFfP+alPwUhTo/jy0/Vn5jJAZuUl1Mk56aKbAbtZc1EFgeYhHFN//RlvKGh3IFtouo7L6yu2mxXjwx2+a/AInRGO9yMxDViElLRluloFdhcXTFPk7u07fvqTn/DZq89IKXM8Htg/Hjgde8YYqwV7oggEW7B4CoZgG9rU0NhC13YqGbdeEXxDypnb21vevH3DOI6VQ2BVNck9AXLmSyC6/yzKTjVAIjr70IYWQE1gS8KLmuK6OlxlK+XaWUsqBee0dKAqKxkLripzbVYrxsMe74SL3YYmBOKUuLjcsV5veP36zXJNrQjeGnJFHpousF61OALr6467viDdltMh0vcT0zAtqb63KuyuKtU19V+yvlp6SSU4CYvMXKndKYvFlIKkRBpHNR2e11XFQ6yYRQJeg22p50OD4etvvuXz1QaxuuKMCClOCIVpjBweex4fT4xTZrPdVfMeqfMTDuuUA1ILuyd+Eh8WFOAHBAYR+XeNMb/xA1/vnwT+TREZgT8xxvwh8J8D/l+//kdlSTGXuQfgHA1qH365Ga2y83i/DTPDcvOjfzrra8Yy3icILbDBjBMkwTA7KitAaPNMRZYn9IrzYI91XjEFM09CZqi1tbfVI8NYcswKZIVGJzfTRLsytMEQB0gjDKcTGUMpE7/42S+I08Drb77mxfOXbDYb1us11to6wdmp8OlwJE+ZVchsujr7IA1Ej5REzIm7/Z7Xt7fEcaTvB/qh15TfuVojG1JR2AMpLN6hUkHVOUqgNTjOLzU2iM5FlKSAas7LDhanCdd2GFGrulPKy9CQNYYpRpqmwXvPqu3ofENxlnHYEzvHqlsRxz2Na1g1LaCEH2ctDQ4v6uQUGkPwSpBrwpYuWDYrYV8MPqw4nnqOxxM5ZZrgdayZOQPSUK77il5DZ+2iJq1ekFTZfpVoF0FLu2mCnKtsu67DWWJuBp1nQpL+kMFgCS5w7Ef2D3d0ux3eW4qoinQqmf40MI0TU4xgDO0qcH19CcYSfMD7SkFPCWppY50n5qxFxAfGhv8kGMM/Z4z5bwJ/E/gfisgd8Dnw7z15zpf1sV86jDF/Dfhr5wfOgWEuTmdoawH0jDnffAsWoWFgeeICHJ6/mvdQy1+GYd6DJuagMFN7TZ1dmFsmy7uqmQF6szhjtMa3SozBeYwLqiQkmdB2FO85TAnxnucvX7FZdbz5B3+fw3GgJWKLMhtN1TGzRig5au/68MB43EMR3n33mjfffqe/1zlF+43Fu1B7/WVZtDMJSpKakxSj7c5cqsoSsqS6bra9mmERcwZTNQkzOi5o6uBX/X4RRdmlFOX+I0twybkgfq48DDFGvNPneO9rmVOVMI3RtqZVIHTqRx5fv2HVWvI0cH87QlHG48PbW4b7B6QksIam6di1a/LpSJaItYUm6GdLU+D++JZpchRzSdut6fvEN19/S5oGXr18iUM1JFT5qdT27nyNK2V9Hl4zLN0yUAq2EaVDx3GstPnzgjxnw5xLMipvRbR9ba3Dknj97be8agLedwz9ib7v1UohKbeibRuM82x3Ky6utupj4gIuNIrTiKhnil7ZZVDvQ48/b2D4V4F/sX7yfxH4nwP/3Q95ARH568BfB7B2ISzo6awLr4gwA9H1h84B4r2ygSUonGPDOdM4v/L5qU//MQeXGSeY6b5SEfPglM+eSiFJVagGTO2ze69O0sZbQhNwIaDNQQXULBDaQDKWXgyTDVw+f4XzjrvjxNv7nptGG5XxVHCbpvb5nQaG/sT92zeYnJf0MNe0NqdMn5ISpLKcT5ZBOVpPgdUK4pmaFSxeHDpbXcsHW9tyNUigg0Zzdm0xi4omMzhXdIGHKlqi58bQuIaUE/PNY60O+MQUl9MfvK8ZosVQNMAW3SQeH+752R/9fX7vd39KQ8YVQ7CCI9O/fSSNCV5eIMFhGg3+iUKWjBPBGO0m5UmYphFvV1pueE/XdZR85Pb2He/e3rLtOoLOy2JqeVgRo/OKMbNL9kzAm9eJlhfjOGgqb0xVu67AaI0hZilbNbvMUshV/8JYlcR7eHzg+pPntK1j6HsdyTc6pWvwFCzWN4QQWK0aSnHEWEswHN7PzlNOX3fBgD7s+HMFBhH5bv67MeZ/Dfwf6z+/An785Kk/qo/98NdGFnHT2axkPrOLcMdcbixv4pdf58ltvkANzJGeJzfKEsGl0mEq0CZCQtNB33gyGbEtYjy2saxWa5qmrUhxdccKqjMwezhOKRMntUKfxoEocOozd2Pmb/7Hf58+R768PbC/H7h83oINfPvtI49tYNttaYKnmIlCYRhOeMeiS2Ari2qRwa9zHDITsexMV356TkwldGoLU0FUs4Bpc3twvlFh5t7XG7aWXTpSXEsn9D3NXhqlUhNFoGtapmlSh6kaRK01pBh1gAotu2aQzlhD07SKFxhTgbyRVRfYVUr4pm3Jw8jw3R6/n5BPdFCo5MIQI/v+RCkTWI+jx6SCF8FZlVc/7SOGwLrT9xaCZRom7u/ueH61U6n7ilFJXXPypKW7+GzU92vq80ou9MOgkpzO1XmWJ+dsXoc1y80ipKJfxTiyFPpxwPmAlFyDwoQx6jDlK3Eri2YCeS5NjCGmTEg6wGYLGFQ3Yr5G5sPjwp8vMBhjPhWRb+o//+vA36p//7eB/50x5n+Bgo+/A/y/f/ALL1u53vyl/nXZ/WsLaV7gMG+Q7w9BvfdSy7+1nuNJwJhpvVKFRAs8yRZ0F9xu1/xDv/1Tnn3ygu3FGm8VkGxWnUp5e4+1Tr0zvabjM9gkY6+tIlPIGSRlYix8/XrPz979gugLj/sHmjEirBkn2J8K/TCpL+eqpduo1oE1alyydJ2EZYEuOY9QuRClqhidgypwVkM2tSgydeTamlqazWDiXDBZjBFC0yqtVzLyhIH6NOAqskPVnWiwVm8o5zx937NerzBG9QZOpxOtnfv6mtqUetd47ykxUqZIF1p+/PmPyWPifn9iPB3YdB0742lFBVcb33Fry7JWihHVhayGNvNEJmLwjaGkE75d0baOUlRuztQ1MG8adgGjzSL4KnNxUERnFGBx06KWSNMUlcYtSkfPy25k6pzKPP0p5FJp9aKSc8ehpxjD9fVzQvAMY68B2am7tasWCUkcMdeLWRmeOWeGUVi3gc45BOWEZBRItZVYN6/zH3L8kHblvwH8VeC5MeZL4F8A/qox5h+ty+JnwH8fQET+tjHm3wL+DpCAf/aHdCTeO+ZqgaonIOddXt8Qy8I9L+BzV8HM9cSTR84/+h6a8N5QkJaAc7dB2XgxqzDnzc0NNniyFFbNis1qhbWWMSXSOLFarRCjbLpYVNik5ExKME6JKWZShpwL3nqExF0/8HDa09rC9coj8cTd3SPWrLHB4TZrRskkMruLDaZM9Mf9kgHMGc9Mfz5PWVasZgbHztex6kuY5Zy5CoTO359f2MAi1y8CPrRMZThPlALCbOc3U72VHl0qBhBCAEHl4sehTm3P5YT2+RUX0YwhPyFEOWv543/wh+zf3tK1DYfbdzQSKVPP9WbDM9Pixqg4x8OBsAvEta8xsBLQlg6XRSShrOoj1mzYbDqyqLO0t57jaV95KOdSta7n86KsZ3dpUy+Zq1Jhx2lcHLV089KbVkBxmRnMrgxaeTJQZ51he7HFby8QgRgzkmUZ5w6hwbtQVcA9xhtCaMm5EELt6FBZtlbTtTxzL+Yhsg8sJ35IV+Kf/hUP/2/+jOf/S8C/9EHvApgBAjPnoTU4nAdtZiLLuYR4v2MpS114DgeihfayA7CkCTNhZSGuoBcNUeJMlszpdKAfe7rQMcVELhNpmhiGgbZpCKEhFzjknmGYsI5FGVmkQC5KVMIi1ilzsUReXq551l5w9+hY2cyLtWPrM5N/zsVqxRR7KBlrhevLHY7C6VF1/UrNnOYAOGtBGGuqMaos7EzBvHd65vYblZS0aB3Mp9+cnzofMRWC81gXiCkzj/ImcWSZFaukti6VHzG7dSlbUL03c8nVHEYB07EOOoXgKNkwZa3NnXVY77h7c8uXf/xH+jp55OXlmh+/ekHsB7797jvavbpHp18MyGfX0DRkpwHHAhITpjFQHCUL/z/u/iTmtm3L74R+s1rFLr7vO9W99933XhS2I+wgHGFbMomADhIt6GQve4hEKbIDQkg0SLJFM1tIbiFZooElJEACCRopIYREgwZl4sy0w47Cr77Vqb5iF2utWdIYc669z33hePdaCTpmPZ13zv2KXaw955hj/Md//P8xKEpe2Ix7+l7k0mTEWwhRnZVMS46HbweGlrNesKgCdQxb7uuyzPLpNMIXClXMJSDUx8mlxQkNqqBNYXd7w02/4/XjiceHJ3IughUgE6TW1Ey0muQYY6tpbUGc/5qRs5QkpaQPgGGJef8ZB4b/X101EZB/rzlqvVre3xayYj35Gj7QBDvaL6xoZmnpXH2gVjZUEtKqi1hA0jP58FOM+LAQUyTajCsypDPHmXma0UrhXIexHbYTMEhpEbRtQa13FqWM4BBWoVxHvys8Uxu6cc8SHMV7OpWwBPJtzzxtWPxS+QSW3g1QEqeney493HYS1jT3CgBs6VZr7rTvF6g27K2M0DXLaDMpLU2WW5GzyLT7EElJ0XcDyyKgoU+JWWnASDBSsqGUEul3EUqRUediMl3XsSwzuhvEucta/OIr5tAGftphoHDG0LsOh649f0nZlVHi/OSDsCZzIcaF7uVOTGWj4Dm3255SJmJI+GRIsyIGhR0SxmWmaUFVFuESLm1aVTteSefLkmgLsjRBG1lThYt3pM6FFEI9wOr3KLUDxBqkpWVZJKDWxzfOsrvZEVSPPc2ScWSwnRSCtgrGKqPXjFZpLYrRKdPVYJ9yFavVUFKRAbcCTefhqq/+na6PJjC06zquXZR863qvQhmXDsLV7/zLAmINCA2LkKCRa1qm18e7PIRMEC5+JgR/qT1zISsBplISXsJ0OlOMxxhHUQWjYaiU1N51xHo6a21FAcoYeiNuTPt9R8yW9w8HYpjJKQjU6Qy93aCdg2JxpiP4qSWyqHY6S/5emW1q1SMACZqSZAmhRrwjjaT+tf6V70nqKYJEEkxzZQ9mlVffR+M6xt0N05JIi+fkZyY0g64eF2vAktPSGCOTgb28RmsN88xqMy8mPoYQI9aoimUomcpU8lq0BmcNKksNrlC1BMm4LJOXMktTMFxUm2JO9MNAmc8yEYoVG/vajhw7y+PZo61CZwU5i9OZaqXC1bJZD5gMuqlstp8pa8MixrhK4WslatQtOJaa4UkJ0SZtyyqEa3tbOR+yAMW1u1TDZcEVLt4mGl3EvxItYro+pLV8SqmQjWQJus6PqFqyqCuPzu9yfXSBAbiQjD74ovzVuOuqmpOsAeGDsqJt8W+hCi1nbkBbqyLWFlstXXLifD7x9PQIaKzp0GoUnnrT8EcRs0y/pZw5n08MnROyj1LkYSA7B0ajtSXmQEwyKixiH5ZI4TRPghwXpO40TjwljEMpi1aGlPy6RBVt8On6PV2nvXLzWnBomZP4eVaBWGUEG6hZQmtpNVXngmxSbetzGosbNww3mcfHM8tiwW0p1U9C1W5O+11rjEi01alSpcA6S/ChCrDUwarDAas7oPqJ1F59zJGUA4VUKz/x9dBKY3LGlIKtQGOmsBl6nv/ohzwskafTAZ8iuiSsKUDEKE0KIqz7rB+Jqefd+3eMvVCGtRVh1nVdVOxGLAyq0Em7/60Ma/e5FGLlLthvlWK58UlqWp9Kqn/U6jhmriTtm+pVc18zq6mFcFG00ShkurMow+ITWgVUkfZ1ylncquqLKEVMelC/vhd+0/XRBIZvb+CGoCqtLzqL9RTUV4NVuqG+V9UC6wen1g1CPVUuz1eDwdUG00o2aPAzx6cH4aAXzbt37yBlbvYboZ4imz0lIdKkIqfH4DqMMsScmc8Lcz7hc6IfR5zTpKJw/QjKkM0RHzz3j484axk6izWW3moGq3DaYk2P1u7KNLVUSTJ5B9fj4WVFZJH33YCtCi7qJg1fAcLCJTU25uKvWX9YNrMx1esRUlbY/gazGbjtC6ca5Citzq4j1+SqRpSIqRmhiJlt8OKjKPfvUgLpOkEpztMRrXQVhy1VHKVgjOZ2v2NQM4kDuqb2Tit++/PP+Rt//Mf84z//Cb/88lc8Pj2xc4nOZrTTpNmzxDPKKJ49e8Gz55/jp5nsjyt2oGn6ku2+SYkjgr95tcATsZUeo7R0fUom+JkcQv1U6kZPpeoCX4DxNlqfURQtXRVtqiJYfU7nLDEE+Xy1qSWIQsSJNUqJ/4BY1wWclnIsFwG3czEoDCUVWD8fxfeKCnxEgeH6KlwUknJDdBvgptRq8dZS3QsKeX1qtseqf1/VWK2kzaVOJiIocSoJjcL7mULm9uYGrTvOp4XFe96+PYlqjqsMw1Yzpqp0nBpCL4+Zanp/Pp8JywxK0w8brBvJodCPjsFaYk7Ms9jRmb4TuzjUOuFZ6mOnqiAk71Deb2u3yRFRZVvUh+++YQnUUqLhKBfQvdKmlVp1CxunoUXkGD3GjoybkTRFrCqkNUDXjbS+rqblGNa0Wr5/cWxSBaxzxFQt442Qc0KMGJVJIbDdbikhMAdRfOq6HqO8ZCXWysSnooqaBM5Pj0ynI8ZCUJHcCQNT97KxUslsN3tevPgxu+GWr7/4Be8eXxMe3qNifeVGSjWNcDJMLQVa+p7q4dKGuKKPLPMiprzVZpDa7lZQR2kq52Ndz4IfSNll1y5cUy+LoRArVbxQqtShzLyUatcnOFCW+2cMpQhZ8yKiU+cyfm0nfLfrowkMDSdQRq8LO6+qQy2S1wygfiqNfaCaAMbVofkBl7F1NmhtSVbsQMZfcx0Kqrx2SvWEsBjbMSTF3c0NwZ9Zpgl/OhNSIqSIywmtO7S1aGtrGVTBq5amZ+j7kUJhmRcOjxPL9kzfW7pxoLeiflRyYTlPxBn6zrHd3qA7u/IVRD35gok08LQtrPytwFjqe69Jb72nwkQQc1e1un+J07S5bHAtWIvSCq0KYZnotx2boec4LYhI7xWIZsxqJ4gSzQK/BAHt1g9ZShTlJJA7a1jmcEmdq7ntPM/4aWIcR9Q4wBFC8MxToEweh2bcDqtStT9MqBghe1QOLFNg6zrZRErTD0aym+QYhg05Boa+44ef/xYvX7zk4f4dh8d7lvOBOZ4F01DgVGHsOzabgX4YwVimJfD4dJK1lDM+eUISWnYba1daMItc73dT2V6H6moXwaCxWsRepFN0CdBVmgI595Wow9UbaZXBKAg5ElOgw0Gp6lRZAy3DUaT2Af3rGhhaRmBqaqQrGNZAk1InQXw173TO1ZP+ggSrCsg1BHmttbJMsaXcuOkSwWuRTRMSzLRNVANSjbwoJVZzbisb43gmq8J0npm8B2XpzEDfi1W9qZLdQTXFpgBFY6snYdJB3Injgj9HqCaopRSGvufVqxfs93uePXtB5zrOx0chqegmrVZQSQJly4QarHoBaOQGXDV35d+VodgWoojuCndAa3UJyvXeG2exziHu04F+GMk5UBCAklreSZpbn6qU6lupCMFju67KpF0CmqqPrxSCPRjRX7BK8/7+gWmaUV0nA07a4eeZr79+zYspcLNEvDZsh5EcM+d3jxwfHnEabMmMmw13+xtSuCcmUTDyUeHcyNPhzPn0ls5sAUVvej5/+TnlxWecz+95/f4r7o8PxBzY70ZuNxtc16G0IWVFKop+TMxB1mcIkRgFr7FXB5FCrVyKVfY+V37t1XRpK6lq2FgpEikXwQe0JsYCKmEqr6GxIU1jtrZOREFUpmvbXqnvq8JwuT6ewAAr0CL/bn3by7etc7z55j27/Y6hH7BWE/yFd399YK6gXK7pfk7EmGqft1SRlRYg8hpcrLE1tpRLbaaaOpL06bs+oqwmZOG2zUtmmibe30eGzjJ2HUMv1N4WJKSdBDnWUduKOq/6BjkTvMiPTfOI6zrevn8HpfDV118RY6ABfa2EaZGv8TwEqZMSRF/iAahmNlvvaT2ZtKnzEvWUkrr6kopmFLNPOJdxriMWw2YY0FZTojx/qUBnCyqNYK6U4Aree4axqnE1OnErDWuW4H3AKb12LE6nkxBzUmKJNSsIiYd3D2yL4YXusMrIPEnKPN4/8POf/Izd3Y4/+Bu/Rzf2UApPT4Zh1PjlyHmyPOt3aDMIlV05kq/amUUyntubZygT2d1sOJwPWAOpQFwCqEhB8XQ8U7QFpUgxEUOucpSlkrjq51HXztpZy+0eyOeektDCKbUbY2s2W+9TykKZJovCtLWlti5lwxcF4yiGudvNhsPTgWme6Z3FWrVS1Fs5cSmuv9v1cQUGrnkFqsVQKFJn3d3e8XQ4YJ1jmic0iHuybvXwBRZu1VXKmRSrqk6KFegp6EZmaoADskiMEwlvKTUaeUcer8DKgdfaVEuxnq7XRJ/onSaFhcPhwOHpCa0N47iR6K9Ahh3qBq2bu7QZg9ppMFpzf/+er775mlJkUvJ4fy998jYD0QIVXHVXGq1LTqZcMwHFRUWqSaEL0CiSaGun8Sr70PVeBh959+6e+eTphpEdlv0Lxe3dDcc3JwmmRa2dhpqetVeGMVqowsHLWHAFNEuRLknKqcrBU0eRQdeuTQyeU/ByEuZCSoVAIgCJyFISJCHz+KUQ/Mxnz37I55+/Aq05z4FXrz4hJs83r7/CdoWXr34L19/w2Q9+RImaN1+/lalTwMcIxTOdTjir0ElIS3PlKiijMdaSKSJwM+zIyfD+9YN8hqwfyEqUk7XYMtRLV6OtuaZknXIS1mJd8XIQKWKqTlIpopV4UVIMxnR0fU9WBXSVb9Oa5Xzm7AxdZyRDMyJ6W8rVINh3vD6iwFCBMt2kM1XN8GUxxxj56uuvcF23DhBN5zN939N3nfxuQ4Abep+rx0CSwJBzWpHwXPI6F9ESFaMLzhjmmD5oAzZqcS4NtKt4RxFasFKGYXDc7EdUycRl5nQU8ZRzmfAxkMgMvQijWNvL/EEJVTYtrwukIIj+0+mAtT1GXgTkli/omoo3RydVNSWvF2CpgGK9tbVL01J+mVhtcvZCp1UFSFQrt8rxSAViocTM4fHAIUaytbx89QPM+5lUN7ixZr1XIQS6blgnO7vOMU8zm+0WhaLrOmJtS4r7FpWwk2qHqdC7bgU9VWnaD5qUoehCilGcynL1s9TQWU1nC5vRCXioNWEYOJ4nitmBXnj28ke4bqAftzjdcTpOPJzPEBPGinL1ssxMjwceH59o3CtlHbozWFUYNlsSiufPX3B785Lp6Z9yeH9AV6UvAR2bMe6lzKsjaxdrRSouVA+uomupVcG2nDNhDvKZlwJOzIisVew2A8b1+BQJOa+bP0MV2ZWS3GBWsPRf48CwwouUVMi6cOnXK6bpzOFw5OWnn8jJqjTeB1QVqtBGfl6AmyLS6TkK6SWmtcvRhqNaJFcUTMvEkWCSY8Yg9uMyrVbEgUo+7fpaqyFKASpJSPRYNP04XGrzXEgzzOcTKZ6BGaMdxhSGwayYiipakG0UtuvoXYdxDpULvmYFTewE2qnOVY6opKZcCU31q2u7jLUcWoNw/W+lhR1X6tBOIy1tNxtevHjOfnfD4XTmMUTevX2H1h2bccSfpooNXAKyWMMtjEb8Pa11TPOZEALOCQtULN39mjlJZiFYi8wOXu6x7JU2uSl196pSXfEWY2TC9Ytf/IyiqjdHypwXz3kOnM6e4DM/+Yt/wd/+4z9AqYQx8MmrF+zHjuV8ZppOfPP11zwdH3l8esc8e9E+0FUzIhQ6Y9kNGx6PZ47nI5+++hxqLS9YAhVz0WtJJRlnWT+LVf4Okb5POVFMAltWnA0UISSWEDFKse07tIZx07G/2YoMXklCtouSSRqt6J2lc5qcF0pOKNVdAHiuu3a/+fpIAkNZo6tw8TO6UHXz6vdTWvdBq6l1VXCW36sfQpGgEFMkJXGYTknaOi0otGUn8lmCQHfGyESjLjy7vcVZsaaLGbIXxeHVQFdfgL624VpX4Pr1marauzFgegdZMU+esASeDk8cj7l2A4R0JIHJ0NmubiBqu6/qB9YMZR0/r/dOKSXcgyLCKyhVHewui0K20fUoTcvMLnVxCyFNhMQ5uSebjeAKOgSK7QjzzPb2jofjJPfjyivQGsEVhCZeW6DOsiwLnduu/z0v04p1GKPwi+g2qCQirjpnNAaQLopMT16x91Slb+dahqTA8fGRX335K47HiRQLWM3N8zuGccOwHShlYpoeSWkionC9JUWDtT03dx0+vOeb1zI8Z/uOpDTGdPiYmKYZpgTdmdP5zM1dIpcsA2I5onWBvM6vg64BQSGfGVdl31ULOmTouozKEGMm5osMYExR1lE3sN2NbDcjzmoKUSjRIRFCYBxGNn2HQ7GpGg1ibZdWrxHZ6hcvz990fSSBgbqxudRDSngGNP8DI20z9WFitgZBCSyV615584L0J6nx04WkciEDKQwwWk3fGZzWWAWbccPNdkfKEHJmms4VYZe+PDUN11rYd0KxppJ3WiKZr9qqBWUMtusxriPHzKa3RD8TvGeZPTnFWiKN3D27FWeiEqWv3vCNqgwkgJ9pDZh6Ay7Jg1EXvEBBVR66cCcbkWdt4V4BmLl2bDrncM5QSqSUAATGXuG2I0O/px8G3j4YkmokKimxrHUs3hN8WHGFrus4nU4ruKiq/ZwIwGQ5wVVAxcCmc9xuDb1R4vdYX3OpILCuHAs5ORRFaUKMvHv3DaU7ofOMih6dNEpnBhfo+4g2HqVPHI/v0SSMzpTi+eLrnzGdDuw2PU/H92QV6bcDm+0L/vmf/ZyUD8xL4PHkOc6RTz878Oxuy49+/Ds8Pj2xzDOlxPX+F63Xzx1V6r3PlwyiSABonhohF9Q4oFNmiYmYxLUMJcQzoxX94BjGQVzFUhT1rSLAbeccVmucUehs2G06XNfh5560JL6xdUr2alz+u1wfT2AAat5Ya/7aMajBwNjLCUkjjMBVTV1L8ZwlW4ipMh3bz17AoBZPjFJ01uBss4tpKWFVXTKGru/ZjgMxeqZ5xnsvMxQ5oYt86CsSXbUooQGWXFpWNXAUpVBGM4wjqu8olSGYU8T7RQaofBBEfwm1e2BW1SS1jvVylTXAVU+33slLPnD5Ga5crOXKtfNTkJFnAdQVw7CpXQrRDyykiqdkXKfpHdyOHaesqu5DQ9QlOKxzAqXpFhhCiGw3G1DibH2eZ6zKmBzZdQ5jDXfbgXLeYowixnT10oV41IR2dSPxFMkpihbtCK3BKFt1JiLaJEr2hFL49PYTUBljFSUH5vnM48NrTocH/Lnn66++4PHhnmINxfT8xU/esNk5dvsbcil4n3jz9sA0BZ4/+yXLccIvy5ppNX2EJj94KSdqmZUzKYrFfc4QUyLkjImBPHt8iFAyOQU0MPQdRovKFUAIQejgrseajlgUPnrBNUxdd6V6eXQOtKNzFsNl4PC7Xh9RYFir4LrJG8/gqj4r1z/dvtdAN/mdnAsppsvob+s81Idv52ajUVutsCpV/4IGe8q19qKRhSijuYbkLCEKFVqV5jgMes12rkk9oK42jpzKsvms0WRVh4UQTAEFh8NBWoIp10EaRa5qzRpWN245/Vm5GZeP/gPwgeYWcylBhITTuU6UmqMoERclKHlW4LqOnCW9v+gJFHISFeKcPPuxQyeIlDUbAynxUqpiNVZeU+ec+CRkcRwbO0NQCacKd7c3WBI5zGw6Cze3OGcJodR+fm31Ugg5klUV20UytkBhDpnOKGLUFU+C5ssZwoLPia9fv+HVK3h8ek8JhYf3bzk8vsFpRd/1dE4Cxv7mjm5jGW86/v5//u/y/Pkr/vwvfsGf/LN/wTIvHFPiV7/4FTvrKClxKSZ1XWOsa65UXCqXqthU25AicRdJKpFLYvGelAtGZ3IWXUzx/yyV1xDIOHrnsF0HaJr7VcwRq0WOf56q52f7nypoMup7yqJ8RIFBXW0g1qnAhiHolh43d+l6tZ8VKW5WIQxhI1/St/oUMm1WLml0wxlKod48V+cKKvpfVZ9Ulj+6JJqiozEisRVSkom2FCSA6VrnK5lu01pBkuxHay3AKjKtKSd47QwoTd+PKMtqH2+VlfbdOp58nS3InwZ1QQ0NjSfQvlPTcPmW3A9rDH0/YI1lmgoxJBSGmBWnZRImaOujl+tEVHQiFr9gjeXFbsfb47RiFKVcapoYo5ifKBmsiikS/Mxm6LBOc/PpCzoKKs2kOLPZ9uyGDSaKapQJqjpULbUMkXZhLIVUkMlQNHNM+IcD+31m9oKkxKJQxaLQ+BA4L4EpJX70W7/NeTpzfP9A9BNWZ0yB+XxgO1o+/fQFL370OY8h8ff/S3+T3/sbv0VYMp999oxp/gHv3r4nLJ6b/VYAPiqZq65fWVbqSv7ugp+VXAgxiadFSKBhsxsYxo4wK0qVr7e6Gu4YRQyJVO37+n6g70cBV2PVJqmaGDmJ8G6MotMg4wPVWwIx+fs+oeEjCgxtUV+BaiXSXAsVjRxENZKtX2wZA4IK50okyTV3Wj1LFI3/U4OL6A3oWmqofGGLUZoSlICeGiQoIKWtJmN1/e+cMXWjx/kg9WV1hSoxo0yHAXSKklKjJbPImcucf2kHMkobXGdJAZQPMsGodKXcVlu6irRcz8upmma32YqVrlwuSlVtMjBT8N5j9MLQNQ0F4Rd0ruM4TbRpy2aKm4u0Rxug6z1Yrbm72fI4TTIspWztiCjQEMOCMxptrKhSGUVJM51R6BgxJUOOUAK32x6jC36aOR7P8v701RRsybhO2K4+gS2SbS0lU7oOtGEJgVQUMWXmJVH1qld7PacHXjz7jKeHI1/88lfsto7T6Ug8z9hOsd0O3N7s0LrgnOav/e6PCX5CKcuPf+tTPv/hK96+uefnP/spN9uO6XysawYa4NOW2wqEN/4CldhUIvOyMPQd2ig2m55+cJwTqNou7vsepUzlNmQx7LHdBeQtrCA7sOIvfd/R9w5nhRuiKqAuNPerDPI7XB9RYKjNykqHNiqy62HXZ0JUTKnBeOWD+q0NBrUZh7Km8mvRIFdpifal8jaIkySlVJOTi0UqtUYvOa2P3STtdcNAyJCCdANKJpdI+WBxaJIWX0qTMyqBVgWdMySPsuaDrKapMxUM1nZMywOH80yZF2zdHAVT26QN1GoVV16JNi045ASZJBsj15n+6jidUiFn8UO0TtN1A6lcggdKaLnCH6h8EFXqTELGzwk9dGw6x6bT+CmSS5Vgz1oGoWIgBdmQPWC7jFMJFzIliAtU58SHc5mO5JRYFvjzv/gJ52lmWcKaBUo6nTlrxXsUc/2EvNV0u510PvwjKUdCiEynwMZ1pALHU+B8SpipcDos7MaRlOHt+3umpwfiNHP34pabrmMYelKK3O1vsN2Gp8cTBdCmUGLi5nnPZ/4Fy0mEYNrNasSlmhtImdM+1yKvPdayQ1vFbj8Q8kLBY22h7y0+O3pTyDGIknYR8pJzPdo4ClRg8tIJstZRShIcqshEqiR6Cae1iCqXD3bCd7o+osDAKiqhFQxd5sVtYtc94M0On0wt4Vbu3gVVr7vjssm+FRSuT9YWIEpGlbi2mFSh0k2pj9FmCmoAuFIKRtU2aQ6U7MklSnApCd1gkkJtU3mk+yFaDiUFdFYkr/BZ9Bayzmvah9bkLI7H5/PCPEVMymyHq7kENEUlVrptCwytps2ldmNkUaALhowxdj03CoV5mdFaszObqtDcMftQadwNk9CryEgxNfiUQvCRoQsY5bkZO07TEZ8TFF2DaUBHWfR70zEwk/2E0RmjO4auwxiFMSKBH30ghch0TvzkJz8TfooTH8dOOUqRMuKsIBvFAWGJ4gzWezbnidsbh9WZtEScK1gHc4gYt6Hr4Xg688/+5M/5g7/1t3jx6jNev/6CJT6yLJHBF06TZ4kerOZuMzKOUsufl5n7p/ccz0eMsWyfbdjt9tx/9cAS53Vt5RpQFZJhtmxtLa+UdMyGscMNmk47bGdrQlgYe0cJwkEwGowVQft2YF53kpSkfqJjkai2ABcMLVXditSyvu+5Fz+qwCCX4OnGRjp3JPj3KPMDnL1Zuwbi3iNXA3nqUN8lk7i6VjDo+llaVtECwPrsXIIDl3R9/f+1mC8INTO1eVfWLKOtA3UBPhWJFIS+Q1GEHJiVtNt01VTs+5HOdjKzHyObrqNnYD5NGAuUSA6XudGmq0ChcjTUld1eFffQrGpIfT/gul6mAZXi6fHANJ/le0NHCZ4cE/vNBtNmF+rbFZOaUhc8hCDGOCnN3O4G3t4fCDGhcoLscaZwezOyH3s6Es5B6TSb7QZVCil6qZ8TQm3OGecsvTNMp6kOnBlSjGQDBdGGKAomwNcbHf2CCkc+3zievXhJChO9M/QbQ+kVpuv5m7//dzg9Bf70z/6Uh8MjP/35L3h+u+fFy8+52T4Tzw4DPsM0TRSXWN6/IShDP94QVWGKC0uc6NRQOSeaVBrofMVZWFvFqmZ/F2l9AG0M3WDRVq2GO1Zr+q6jeDifTzJHYw3KGFLMEnCvMIPr1doIfSnWhakb+Bgr2ze3Bf/tbfFXXh9ZYGg7VJhwc5jIasF2WzqzwSkxEqEq4Ihh0zU/AS4j2utXrlo1ZX0ayRBa2lyufhKuBgguqXorY8h1WrPJjWdQbTS8XWV9VqWkXClNqFbye0iKaDQJRdKGnGfIhd12U0VGrQBcyfI+ZTb7Hmvg6y/e8OLZFmOytFCnSQKDNkIIQ0ExKGWJc+DmZoe1inHoGccNrht4Oh5BZY5PB5L3HA9PwF6k8NEMXUeJQQJexQyU0sIkzVCKJoSE0oplmdjeDHRGc3g6o4ri7rbns1d39FYR5zOdVWxuNqRiidGTvKekC7Asn7i0+ubpCCljqjBJVpBDBFODe5tGrJ9RrHb3sXR02xuGbsdmd2I8e0LOuPGGH/zgh2z/2h2H84nD8YhfPL/45ReUUvjk1Se8+Oy32Wx6TAeTP3HyR3yKvH30jGEholBmh+stRjniojgfPUtI66ctA1LQ/C1TXadkSeuVUaL/UU2Cp2VhYzrIia4UOmc5TjMpRUxXZenrY8cUq8hsxVzUBVdQNQtRMaNXtnA9LKsnyL/K9VEFhlYmqGLJyZJzQHcapRY2fWDbBQYlJ2+jbARYgZ/LaQ5t3nTd8Or6+/WZWueglSA1W1n70pTaN5dMpdlSrJwE8no61EqzKjVffp+rx9JwcUpO4JKpztAFlTRxOjEdLDd3d+hxJOfEPEViyWAMpnOEXBi2O3ZbByVxf/9eVINT4vHpUAErhXMDy3GhHzdYgzhkmY6SFbHITIg1Gh8yMUTOpzObYaCzjuU88ZQzrrMMfUdnLTFHzj5ikJmF4FNNYyPJL3RWs98MfPLJp2xHTZgfiSGx2/eMY88UPdMyU0KQrCNJgNQVYAwhsPgzD+/vodQMrJZ7OXpIAXTFMFS1BioFUxIo8KFwmgqb3Z7OOHp9Js2R2Se++NWX/J0/+ozf/72/yZ/8yZ9ynmfevn7Lmzdvcd1P+PTTT3n1yUvunj2j3/agK7ltUfioyVoT447oRV16Pi6cD2d0VDh0bXXrNWXPlLX8yhVXMFZjkkHlJB0nLcrhZJkvKTbJDEgVZxFatQgOF5q/ha5ZQ8WYSqYxYSsnipxqpqtk3V5rkXyf6yMKDG1zZQqRkg0xgtsatAqQHxg7j4tnnt5E3GZbnZjL+vsfbsb6tWu84VtUQRln/zYWUXe+umAZ8rvtI69chcqzUFc/o64fgnLVEqnfUKy8CKXq+FIRgCgWKDEyH8903cjm2XOYA7ExN0sT/VD19Clr27JliavWRC1Rcr5QbHKRzMrUaUZrNNYaQgXPUsxM80LpCq4UYgoMfLUv0QAAZT9JREFUeWC72YGS8XGDRhdVu0Meo0DFhD9PqAJ3d8/ox5HzfM/Yd/TOEOPC+8cDPgYxYimKTslUaoyRlCMxJkKI6Mrt2G46liXR9Q53MzJ+vuF0OvH23QORvFLFtYa+03T9yLPnN1jXs3jDvBTmRZGyZZlmfvbTn/D87gUpFrquI4XIbrfFdYaYIz7N/PKLX/Llm9dY59DWAZqcNHNI+Jyk2xUDp8OR6XRmNwz87mef0JnLKb5+/KVcBGFzAqNxXU9RmSWJw1TXm/X8iDGSS5Q2o9JoXQ1risZosaUrtTRpH3abG8lFrZ04kYmT9aWt0MlV5Xt8u5T+TddHEhjUuh+1gUIihkiMYK1G5cQ0vZfZ+wf4+Z/cs332ku2Pn1EG9+GpTss8roqDBsqtBcU6vykbXLdIfNlErVZvoEEDONevqVZD/looWmPU+uPXmUr7opbWZ8MIrNGgDCkkgs/YbkMsT1C1Gimsg0aSQstGy3WGJIbK9iylHiiX+f8PnKq1vG9jxL9BHlw0A32IpBjpOks39PhFgFNd3Yx23QDaQghYlXAUiInT+cCbrw8cwltcP3B72/Pi2Q6rlbR2rUEVi0FS5s5YVMl4L6WJsR3DsJF7kQ1/+Ie/x/nscc7inOWTV59S0PzZn/8537x9i/cBNGx3G/a7LeNo6TcdXefIGVLSUCzbcWAz7igp8fOf/xRjOqxTbPY92m7Zq5FMobM9qcASPCFkQhS7ufPxzPuHJ3xMONcBmel0FjfrTqFIqPUUr0dEXSslswoDOWtxfc/sJ3FMz2lVPIfK1i2i7QBg3NUMjhKdR3Xh5q4rLbdDT0lACDFjtJF7qqomAw1b+H4lxUcSGC6XDBQlnCsiCqICKSz4JZJLx+F84v7hiXMyuM/26MEJAFTBn5YhlOuioLBiAO3EF2+FFkAuH65MaEpgkBFZyTJUyxBagFinFlv5cZlfazmEQliOFBGtTVm+e8lgah1KWX1xYhVPTUWRk6IzDqOEb7FKiBU5iVQp0gIrSEpfwTlVrjIlrevaUKAv/HvJGMRns2lfUMRSPi8zPga6rmeMGecM2lSVJwoqBO52PYZCiZnT8cQyT7x9eyIqxeFp5MtfRTpr6buO7Thyt9+x3490zoj4iY9gDOMwoFCEsHA6nZjOJ/a7nnFw5JxY/MLD0xuePX/F7/2tv07/RU9MmXE7iI+HqjySlLBGlJXH/Z7zPDFuNhxPRza7LSlKhri/3XNeZowr9H0nRsBJBFl2eifkqAzJJ6bziZv9lphLpXTPBL9B5cLoekyj7dPS9roAcqVq14zNWEsqMEXJPpIq1GbDmgQYJeKwPgaGsQdF1ZBUoESartQ1pGo51cb2BIcQ4VxnqzI0V9OodaV+n+ujCwyS/sB+v2G/18T0hpQiIUbmYvFGk7YdZTOAcxV4rHXVWnfVm1BKrcHqQ7eTvP3nB/eq5YJ5rd0kONQPoJR1rLY+2VV6cAE7165Ec0uWHF+AohoU1uGvq/QzFUmnfYJXrmNeFkpW9PbSYow+rsFGVVHAlCsIe9W6/bDAuqS5q5u1lnl9W303S2oKVRcoJoRACOLjMAwDxjrx0DTC91fG8vB4T+8E7Xn54hm927OkRD8KiLgsC6d5xp/OJL8QwoZpHGRYTcsmPh1PLMvCMk2rW3ScZ+b2Z5nwKTJ8/SUvXn3G3d0NxllO85nz+UiOExpRmr7dfYZGc3uzRz0Ubu5uOZ0ObMZeFL/0gLEdz25vWYLHBy9zCyETUsLHhK59GOU0u9s7bp6/RBtDyYmHx7dEP4EYk5NnTzYVY9CXTCGnXKnK8tGHklnmmdkHlhhRRkbNJYmtmp1WugkxJskSa1epCRDJ56rWKrZ1I6Szk4kxYZ1awceiqjRcWVfB97o+usAgG0sGk3IRcZWUi3jyGYtXmle/9UP2z3/AzYsbno4PtSSAUuuthvx/GCNbkdEqgQoXts1dT3apSNrPKpGA+1Ym0uqEFhIKldew1g61A7G+gA/nPFYMoiAAW4U+Yk6kCJ0bOZxnlKpzFEVApRjTyoPPla6cYsJYVTsS7Q6q+jLqcYSqOprCEXG6qhRfUXfX+6MuAbMUYUiWAtbKjIO2Cm0D2lhCmHFG+qHaLmzHLTvlQGf23Y5ctkDzwRRNizjPLMHXzzaLG3bK1fpvYplmlulM8F6CnpJM6nB44jxNvPrkU3RvOZzO0vsfLJ0xaAq9q7yQECk5M53P7Pd7hr5fSxhdPT065wixY/GeaEst0zxPhxOH45l+GOjHHmUglUCIMykHtNWQ6lorRur8NhdehIC0igFloW4/nWbQhpAyIWd6W52vrmnqOYvOaaECmbqWkXpdx638yKVATvggXiXrc8VCtFWdXLURlwpWfnBc/ObrowsM8gY00+RZvKjhoizdYOiS43FamHUnAWGrJcXPwvNv6ZuqAzSsp2W5AgIFMcZQB0zUpTxYU8HWgSgUratcbwMNGmHlumi4uuFX2clayqhSB3q+9dNFEkFdQUmVM9b0GNtxfjhICw3fqhlSzNWIRpGzumQw6+PWzKQOWQmQpVGq1ZeZGGaW+SxCNqlFz7po2mu+YtaVItLsGeEtlKTQXmGq9V6qWYfSC8VNUNNfowW/UKWQjSZR8GcFUaThQ/JMcRGcIUSiD/I8qQrxtmBfMy2joKTA4eme/cvn7HY7rNYYHemsZjv2jEOP1ZYQIl3XYbVle7PFGWQeJifQomlhtIzBd9YRfGExnphFV/H5sz3jZiCkQEYcwrwPbMceXTKm78mz4X5+K+l6ucyxQMMXKjBY4DwvuK6v49YSsHUT+VkDeUbplvfLQaJL80GVdZRzpqSCkE9bx6EFdmmZ51TLSy0DVnL9/0VgAJTC+8A0ZfZbR9f3ou/oC1PMvJ0fWZZHxk7xYj9K+k+rqURstcmYX4JDvermVlCNP9scmmQqoqhQa/+1hizrYm0YRSNdq6v0+4O3cI0lsCYkf/nn0+rRAsb2+Cj96O04kpcIilX+zVm9qi2jRM9RuJBKTq1rObcaIHKMcjIuM5qCXzxDP9J3PcbYmm1cXm2TXGuLLudMabhn5YmEnPGzl3ajlgDUWZnryEpLcK7lVyYTiiD7KkOJkZgjcxIFLq1ESl2Vy/Rou2dq3Tjy6oIXoZt+3GBtx3bTsRktvRP6cE6ZJXs2Nxu6fosqSgIDMqlqjUVpW5M10cYECDESQmC/3bLZb8glMs+KUpJoJgwjYZpwxrHpbnlcDsLwBLLObQWtEn1NWzRkoKhKia5iNK2LUVmkco9lLVmr10xiZblWR6kQxQW7pIZ9yaLSq2x/ZUfWe55Xrs31Ifbdro8sMLQ3IK5NIYKxA0YPxBgw2oNKTNPCdE4ia1XGumQuEVREVusqq/oF14zIVXBzbe1R77Ns/IthLDQVaVUKiusRW1kITacvq5ZR1HdSN9U1jVq1sLJWHPX1NEXhrNF24DCJRfvYOc6LZD8xiyLV0HcUkgQkGfRYF5JuPoVI2VFK5vj4SEkLRis225Htfk8/brjZ37BMC49PB/FrWO+PpJ5KXb+Xmp7mKrRS/SdizsSUIQnKnqKE3FQQJ6Z60sUi6two0aykDgc1JmvOBWNEtbo5jdcE5vK51FIuhMj9/QE7we3NHdoMgOE8BYYe+nFkuBlERKcYxCO2+oRYV/0vpFWYUyHNgcPpTIiJm5tbeR85EMJSdREUKmVMLgyjMEIPj4+8v39PSrHaG2hKzQBSquphOa+2cUUbYoaQIloV8ZIopcrTNXeqJP6nxtSNLfqOTesyBJFvU/UQaWUoJVddEXGxgkSMIgTbAsNl5X/36yMLDDSwvw6eAIgdnNEZo6DvCp0xxEYUWu+RzD1Ul4Q6st1Gj8V5WWlaR1jovCXWHrE8UFlJIXBBBkVXAHJNyUvL/ConQcRiRSaD6vtApSdfMIj2xlQtQ+RDE6MVqqx7KZpieo7es9/2dL1hPikS0oVIRfAEGcO7bJYV8aztsZwSMQdyiOQYGYeezWbDdrdhu9sxTTPOWpJLOGc4T5cJ1la2tFn+9WBq4+xVU6lJQ4rjUXsJ8m6lzma1Elz1g5Sc3JWfg67Am7TryipnJ8bB9RW1z7emy0oLB8J0PbkUDseJw9PEtEwMm4HtHrbbDb3TWCMZlrOizEUWSzutDGjN7GceD0cScPvsOYXCskw03McoTfIBcmbTdxgMj/ePvHn9htPxjNWOlTRe30fOVUIwZ1IR4LFYKW9SkQBojZjMFC4anbmqlOta6pQkfp2m3quQEjbpdRaHmrP2zmBdL/ojCuZlJsaIxYl26npYfb/Q8NEEhrU0rzoIOUPIoIym6zoyCUVm6C3WFsZNt6bQ4uEQRfEoQ9f3JOfwwddNrtGmPk+TEtOsCkwVclg3QUu61PUrK8378gp0/FYobv/Mf8VncNFRSPXUbEAgtV2V6VFs9zuULtjOYK0wDHMSDQhRVC6rnuWyzMQgylIYje07etuhsuLFi+d0TjQljXEoJRqHunYljLUrnqIq+ek66azhrLbVymUDlCxCsFqv5Uuj4jbH5guIewn47UEvBRwrmBpjklNSa4ypgjb1dFy5aaUwGM3z2x3jZqQkRciIDGBRHJ5OHJ8mtFZYZ3CdZhx6ttsN29HSYdFYYsqcDgFcz/5mg9GaaTpTVEYZi87S+sXAMIiuw9P9kbdv3rNMswT2lUrfsktWcDAX8DmzxIwxHUuYiSnTWYfW1YKwQjy5tou11SjfyoI6SWkKBb0eVoWI1RrbWfquBqai8V6e1xi7LsRVrPh7chjgYwoMSlXBz9rg1YbFRwqCJDekH5rRqSUGT+8cz/Z7nFOc5pn7xxPn80yIpS7atqLqkVEE+DF1huF6YGr9gD8oOSoHvvWmy8Wf8rLsr09uxaqvcHlIGl169W6oU5RrCVTvgfeJ4qMQajTY3tF1TmYisuQ32Xt8jEznM6fDAWtE68BaS9f39NsR6za8f/cgPpuqoIzFuIGCZZ4m3r99TwgLj48HcmaVdWuvdU3H6mv9wDuhfiv6ICWSblOxFz6/1qqK2H471FxVXFeDPQ3LSFGtytOSwdT7W2RzlFTI0dPbzKYrOOsIBVAd4+ZG2oNRlJJ8iExV3+HN2we00WI2021wXc9+u2G37fBpJsxTlX6v7lAqkUtkHDfkFHl8OPD0dGJZIjkVss+UTvxTW6navFYlcGrOU+AcIiZ5vE8oo1dWaqndm5yE+ZqzHHNQCDFWvUy1igGDrNveWTZjj+0FhI4hrRqSi/eyP1JGD3YtX68/y+96/cbAoJT6MfCPgE/ro//DUso/UEo9B/5XwO8APwP+rVLKvZIj8R8A/3XgDPzbpZT/6De/lELrkwk3HPwCfil4F8Q5ufZnZeJUCCgaGLoOpTKboRd3KF2IR0G8jbJiX5+lHGhtRl1T1jap2MA3uYcthW4pQVkRxtK4EuuKrl+raYKcbCuceZWiS4AqVbCWmmBLxhKJKzW6UJbIu/cPDN2tSMn3HafDEVUy58OBUxLx2Fzr+LEfMU6jTBDlI2vr/EEUGTEyOXjuDxPv3r7n3Zu3xOhx1tIPjn5wXEGaEgxR63Ca0heTnPWqJ3kuCV2kHFOm+Wy0P9QgKb+7Tpu2YNg6vFmhSnXsqqK9pnpqXoLHhbCWfKAED7knl1ip3p7NWHB2IGWZfAwpEeNIioXFB6bFM8+J+fFAzo/c7Ed2G4exoKsittMyNl6KZtzsCNOJ+/u3nA8zJcm043SayLFgjQNX10ut+UulT/sE5zlwjpF4XgQzGi1FGU5ThLLQdw6jrOhVaitYRZZyVitV8RDIKuO0YjP0bHpTs0YBG3NOpCiixzEFTJ1ryTnRWRnp/rXU9jtc3yVjiMD/oJTyHyml9sD/Syn1fwT+beD/VEr5D5RS/x7w7wH/Q+C/Bvxe/fNfAP6n9e/ffJWrkzplwgJ+Bt/N+DATY66osqfEhA+eVCIxClg0R08siWHr6MaelAzz7JnPSwXTZKEp1QRZ/gplm/KXfKs0dltetR7Xn/3wLdTrqjdRrgLHFY7R6ueWNXSdw3aOx4cHOp355JNn9P2G4F+TQyQFj7OKcdzhrGM6nxmHkZQTJiM+CMqSi2Qfjw9HfFg4TwuH48T7t28Fd9hu6PoObTpWko28JHnr7b3WIl8rBcasr1UpKFqhabTqC8bQtCW1aXJnqmZc3+qvrvfuivTFhQtgrKk4Rb2blR6ec5IpzZRIZBYfiNETQ5Vv0aJaNGgoToLVJlt2YWDxijdvj6QijMnzdGSeF2KOdL2jN9IiH4cO7xT3b77BL2e0soTgmaYzyxywpquHzKU7ULIQm3KCOQROiycgilI5RRJRBi5jwYfMMBTGrqcfe7TtyT6IjoIRIproOw7kHjpn6Z3FGbEaStWFK0UBfl3n6HJezX+UUnS90MSvbu13vn5jYCilfAV8Vf99UEr9M+CHwL8J/Ffqj/3Pgf8zEhj+TeAfFTka/q9KqTul1A/q4/xVz7Seob3VDG5gv7FopQnhQIw1MNgecsZomWkPOfJ4eqKI24uMq1pNZzq0ciIdljR915NiIHhRXMpKr7nCukFrpqLqV67Hgmk8hpKhpHWDXILH5bEaF6BlB5dGYLl6t/V/RUm/G8SPcNPz6Q9e8PTwlnfv3jP2PfvtLSDU2ru7O6xrrdmCtk4YdhhKMcxLIpzOTFPk6elECl+ijca4Dms6nt89o5RMt9vSDdW8xE+UvMjrq2YpF9p2C23XLTQFJqPSVctWXfQO14ygSKeBVoKtOA0fZB+qZWdXt0kmToUeLgQptaLsuUjrT1SOxPty8Z7zdEaHIGtDy2lralmilaHvxOwmhgnbWXa7DfMcSEmRQ+F0PPD66QmLYjOODGNHXGRsPCyep8ejqEohqtfrsqCR8SQLirlwXjyTj+jOYZ0jaWlZ+iBErFwsS5D7Z3qLxa7ZjzKFYbBsq+NUSHL/Uk6EKGPWOUuJ56wlo1BZoVQQYRsnbl8+pIob6YaCf+fre2EMSqnfAf4e8H8DPr3a7F8jpQZI0Pjl1a/9qn7trwwMioI1im03cPvXfkc6ADnRuRMpP4iScda4riPFIC2oboMbHEkV4fJbh+kcRWuWKZD8ibFzfPbiGc4a5nnh6enE6XQmlFg3Y8ZWs9UElfAkG6PVZxIk2iu98KKbOAea2reHC1JWgUZN7Tg0bKLqSrbHKJqSTR2IUTireHa3Zb/RfPXVax4ejtztt+K4XJSoLWcvp0ZRxKI5HCbO08LT44nzWQaBUqruWQmGwaERsHHcjOjeYfa3JCut3lQyZvZrKSfvstnnqHVjX9yvyjqsdUEQ1LrBPxgF1peNv7YdS6EyFqqrufx+W7y63r+m79lGs0E2Hqp2rYqUUnnlCWQUkXClvtVwD2Msxnbk1JGTbCBtI0p7jE30WqO1Iy2dYDizaCP0TjOfF54eHwhezH1TiETRwatcCOlASNASIdrzLKrPGpl0tL2rr10x+8y8LJKx2ZGQFCUqQpI1MgyO7W7AOSPriIKPgWnOZKvXzoY11RQpRZk+rNmI1RofFhQLMUTZXUpzbS/wm67vHBiUUjvgfwP890spTx8qNZeirhvf3+3x/l3g3wU5Y3tnubnZ0hnN0DuGoWeZZ46nL/FBpv+UNuQEXS9Tfv3YUwwYJ46/ORfO08S0LPRuw6vnL7jZjBgjYhedtWzGDcfzxOHwSJjPTTOFqqaIiG2zzvs3esI1DtEorI26rag8BrhE5YZ5gljA5fStOqOi2nWm4qK8lPD+xHbs+eEPP+OrX31DChFdiUgpa0px+BC5fzjwzev3HB5PzGdPjgqlbK3vNVhFColsqoKdkpaYMwJsejIJQ1IWXcRbg7LmClzXU6UIv0BrXXvt0tJrP7J2aa7qr1Jl0is+J0SoQm3/Xn5O2qKFK0IJl6GklplV3MKoNUNpmEPrAtQfp5DXzy2SBBgVhhAlRaZlwSlDCBOFgFK5qlwptpsB1TlSTMwhcD4FHt+/JWfPbndLyT1PT/d0pknYy3vMiEZmKkJEmvxCzJkcEkVpbCMqpcQUF8ah53a7p++3ZAzeR+bF07mO3X4v0vFFxHhCLIQY6JzGOvGa0Eq6FzJeL8+vlTBMS6lrjpahqevb/Z2u7xQYlFIOCQr/i1LK/7Z++ZtWIiilfgC8rl//Avjx1a//qH7tg6uU8g+BfwhgtC4pRQqZYdziOosviaQ0ygxk7yjFkJLmcFzY7W4JKXH77Dnjdo82lvPk8fMZ6wyfvXrFbrPD6qZ5INZspsppDePA/mbD/du3HB8P+BAxToM2GBSmTq5dgMh2Y9vmVQ1rYi0UyiXVhos234fRsqyp53pKypflv5XY2p2nIznN3N4849NPnnN6Osn8xDzz+t0T79498PrtA8fTRIxJUudsBWg14kMh5iup6loWjBJlps04Mu4GSm+xyvA4J9CWVN+TsEjrjl93e3tPefXrUEpjalP9gw7MCrLK+885oZSp1N66wbW53NdVbLfei5qhrYlXESahplS143ofswB9pSH3RbgQqmZmmsYgrGUIUHKupj4B5RA2aPBQmj6XJpdMbzTPbvfcPx54/fUjSivubp/R9VtOh8A0z5hhbG+YwoV3E6ujlK9ZhdFGJONDDSAlYRR89tlnPH/2EmNszbCk7ewsWGM5n2diLBjTY9woCYEytdyVjpp0M4QLo9KlM6a0pu+l5G4fy3/mgaF2Gf5nwD8rpfxPrr71vwf+m8B/UP/+3119/b+rlPpfIqDj42/GF2DTyyl2nM+wCEU1RdFTLPTEYpl95v7hzJQGfIocTidO04wqAtR8+skrtpuxtpCEc9+Sp2ajJtLmkrbf3e7otREmpV9YYqZoRW9Bt8Vbomx6pURyDV2BNH0l1S6+ktct1VVdp6YOLetoi37VraxenSkrlNNSjxbJfKw17Hc7lvOMMR3v3p148+YXBJ8J6UKIUXVAyiiwumAtGCOl2eA0u13P7e2WcejpXIe1GucKvTWEWDgnS9aGXOJK1ILrf+TagtQrTqBUNeOtJ3kpGbS0+zK5NoCuwqJqTL+Wgcj/NVWstnrX9pq6fCNfEUN0I6E1IpGSQbtMlfgrki1JeGvW8tLxyEBun5G6tBdLTsSSIBth06rC2O8xz+94fHhgs+vo+g7v4f5wYvaRoWuq3I1ncDGTWUKoPA4l5VSSQybGKFI/Ch6fDozjlmd3zxmGgRA81liW5cx0WjA6o02H0qZmWoqQCjEVrFakGvzlPVMBdVH0jjmxHXrS4gXxEraWtBG+4/VdMob/MvDfAP5TpdQ/rl/795GA8L9WSv07wM+Bf6t+7z9EWpV/gbQr/1vf6ZVU6ud5OlZNAlst40dUuSX6B07ziTkkurGjBMXxeOTLL7/gr//Wb7PbjThVi88rHEDqZhlG0VXQYp5mUlhwxjC+uCOGzLvHRx5PR5YQpZ9uNU4h6s5KSoVcFKWYuq9zHVQJNMCx9f+b3NsqBUfbY5f6Yh3BVq00Uat4ivS5YZ4neuPY7Ea0MSxzJIWCMZ2YqAorSvQVtMapprUAzmnGwbLdOHZjxzg6+s5grcYYMIhf5M1gCFEzK12FTfnW6fJhziP8i3wBCNUFA1BNVKJOU7Zx8JwTSmtRfFJNOEayEKVyDahtCrC9r/rsra1JDfJV4j/XzbZiHK2OyGWlird5BZGkULWVKllHGzbKqZWEVa4vR2KJlJzo+w3D0EnA04bH44E37+7xMVZpv8tnW7K811hgiZlYh6VSxQ3s2nqVQPf+3T3HpyPPnz9nu91xODzhpyO3uy3PbjbcPr9D246QWngrLD7RWY1yMmFrTc2Kah1ljCGXim1oQ65ln2613Pe4vktX4v/y7aVydf1X/5KfL8B/5/u9DFh8ZA4JZYTgFJL0/M3QY9SelDtCmuVGh4WcI/McMCVjVKZTiXdv3rDZ7Xn2/HZ9wcJlL6QYWULER5F678cOoxQpBuboMU6xu92TQhIVpSh8d1Pl3ESzQFI5jEjZ55iESJUDpk5qXpZqq5mvF3tZM4gG2LUUWynpnFjjKGJRIz4K84nNeEPXG4wp9J1dAbiGtmstBCWjwBlF5wybrQwXjZ1l6Ayd03RWY52pG1lA0F2vOZwLM/IyVwBbyUlbfm1BNVWo9nlnYehRgb4qPa+rmKvKmZSknMmqib62D6eI7LmANGuXp8EN5RIfJGsoiZxFxyGECFmGjgxKjIAyNSBlmXmAtc5uXhxUvcmc1OpvqiqmQYaUAqHUGZAy1/ukWJbE69cPHE4LuhTEbrkN3laX6iz3wsdMKk1PoUqvNX3QUofUAO8Xvv76SylzDezHkeAn7h/uGYaeu2cjvbOEDDEEYsyEmOidMCcbdCBzESDkLEeqE5goU4WFiyjRfI/ro2A+FqBoi7JyEjpbMFnhQ+Th8YwqnqK2PH++Y0kTP/vVF8QswpnRWByZZ5ue57fPWZKYzQrnvqpNzwvRBzm1rEMbQ0yZw3xmWRZQWpiBaIyxDDdbYsi8f3/PaZmEUeg6tMrokiqopgjFM+mMyp6RtOo5SmpXxTYamHYJVfJ89YxsaseliAvT+XhmNLc421PwLGHGOcezuy2bTlE6TUyZIjIIdU5MwBRjhBk39B3bbc/YWzaDq1oE0pWwzkmf3GiU6UlJo7JIumUurEXpztbMpzS0pNBGgHVN+QtC7sqtLKj9Xklha5aGrhlGgyFqpkARiXuM2PWtCldXqCatfKBK1wmYN59nTscT/TCQQoBUB90aLlKzmRpB14xCuAaNiFQnPlflb2kxC5hZOD49VYErxf3jkYenmZAUFumIiF29BK2YZVAslUxINbusr1syp1Yh1dxSK3IMaJ158eIZn756gSnw+PjI4+Mj42bD7u4Zm6Gj1xazBHkMI2ZEnRVtSFXS2gnRSWFsEoGdrKEYCVJNmfx7XB9FYADpOBjbobQgsfMycz4t5FzYDJZhuOV2v+X5y4Evvvpa+tghEkygpESIgVcvd/iUmCYB5bwXlR5lLN0wrBz+8zxzmo6kUuSUtnYFisbNnqHf8M3rd5zmha4fGbcDRolpS2cKp9MZT8JHTek3FJNJIWGuacMrat7S6lTHiRvmkK7XPqVkXn/zmj/91Rte/PBzfu+v/Tb7rUzLhbBwd7dlu+mw9TRbbeeKpPdag3WaobMMvWUYHePQ03cdXZVxs8ZijMNYJew5LMtp4nw6Ms0LXV+Hnq6AVi6xrgaLWtNqeV519V7bJKoci6YOkUntK0Nt8vtKKZFl07XDQMMp4nrvLoDmpfSSb8nmjUl6+fO8cJ4m5rRQKPT9iOt6oVUbpBXNhYcaKwH2UlrKAJN0mdSKuaZU8MFjjCZkeHw8M82iUakqVpKRAbsUJSgIFVrW4lWRI+9HXXRGpTwSr43f/vGP+O3f+TGbXtqg283A23fv2e33bLZblNLV7NbS2Q7n3NqZMEZo5yEDqWImRSjlhTp+Xcf5/zJpgL/q+ngCAxrvE8tyYF5OWGO5vdnS9wPn05HTOaB2imEYhJCSjThB47i7e0nIhl/+8mfMIWK7ToapnGMzbilGQJmpkmDm4FFGpvSs6wGNtTAMG1CKb15/zeHpDEbkvTabAVOdmUtYMDHU1zxisiKrTA5nSlnWdPx6R6mqxNMW+TVDtXEftFFsNxt+/ss3/NkX/5hf/uIL/ov/xh/x2asbYkh0rrDbb9BZvCZL1atUWajdzmisU/TOMPYO2zuMdThrcKZ2K0ydVBX3GqZzYDpP6Gp7F1IiGYWpyseqnoitfLgwGyvwV9+rVkbA1lZ+1K5Ma5UprTFtkvLS+10R9ZYxmUZkKi2+lMt8SanNIWUkcCnFuBkYhoFxO3I4HylaMU9nlnmRAbFOskNrLNpIaROr8IlY9NVMp5LXrl4a3i8A2M4xnxaOp4VKCVhdoVJMBIqMWtcyIoR4CX4Nb1JUbgw0tXGjM7/3N36H3/vrv1stGWF7e8cPf/A5Dw9PHI4nDk9PdMNOMoKYAYM1XR1vl2nhXBIhwRKSOLCnjLWalLzwfa66St/n+jgCQwG/BEKI9H3PdtvRWdn4pSisVTwdigjEbnqev/iMWArP7+6YH09Mp0Q6vGZ/syH6xLJ49rs9veuIKXBaAsviBRXWCj1I+zMrQ8bSu5GcM49PJ7yf2W83DC97vnz7yBQjtmR248DNZkRFDwbO7x8wWLyPRAxaWTJeRGmpiyxTsfB2YrVzq9QJzCJst5wBi+scfTegSuQXv/iG0Vn+jb//t3n1bI+PiVzg+PTEdjvQbXqsMxg01sgsftcZOmslzTQGYwxWm7V00NYCGu8jx9OZ0+xBGXb7HSe/kMNZ5lHKBS8ROQtFrhk5WdqNsjdrD70qRKmKK0hJ0IhMdUNrBUWh641ZE4LcHDhlCKueqZKFGNDZVMAu1+5Eqi7jIoaiNPTOUbYbhs0GCpItxoRfAst8ZkFYo8ZoQhzWskG0GS9ZTuugliySdq7rUFoxzUeOp0XAS32FW4Uo3JpcqjePBFXJpNb+ywdBQWlFbw2//Vs/4u/88R/QOUP0nr7rGPsNYPjss0/Yn868fThwOp0pyuKXhNEGU5DgrVu2Iy1SHxIxlgq2JrETbGur6LU4+67XxxEYEIGLoR94/nxPTgveT6QUoMAwdjxXL1EMWDvw6tWn/PyXvwIMn376A3KJ9P2Z7eDoxlsmLwYm76f3hJLxWphv1vUkLQPPzvR0roesOB3PLH7BWM2L53fsxpHHwxFlDUsM7LVmyYmz9/z4k1eM+w2Psyd5SQ78nDGlCRQ0IlND0FhPUAEf85pyF1jn8ctahxo0lqIs9++f+Bd/9lOGP/x99ttOsp+SyDmicFjjcMbgrMM5Q9dZnLFYI2i0sxarxV8glcJynpnmhWkKhJQouiMaw9PxzDR7Nl1HJq44AnWmJFa0Uyldh3KoSsWgiq5EGyrwKFJy9e0i7/6iZtW6FwIK1g+/4hkJLmzS9rMCxEir1JR6P+WeCUBcI3AFdLXWmN4yDIayEWGXpY6k++CZp4ngZ5S1zOdEjBOlZIwR7Cd4j6lA4jgMxBSJIZNiroFLpNkVVLk+6aY0tehh6Nlv4eTP62FQ+y9oq9mMA5+8uuXv/b0/5vZ2SwqB3bjFGUMpmhhhWWQWY7+/5XBeWIKMpPu0kMJMjAHnOlzv6n0U9Wjx8ClYW6XlK3W/6Wp8n8jwcQQG+Uw4nY4oMjf7HePmhnk64cMMCsahxy/w9PTIm9evefvuLTkVdr/9u7z65JZRv0erCfJIwRGTyJn5klGuX8kuTlt661AIgOVr2rnfCfFnsJZSkhikaEXXO5TR+BA4pUS/29LvN5hffokuAZUEtEu6J5Ep8UyT510nMJqce3NYakhQTV8bup2yLDKNEnKXMrx+/Ybbux2/+zs/4ubmhrtnt2zGHtd3dH2Ps5IROHsJDG2jxQzLMjPNM8vi8b5qKmpDMUIdz9rhRk16eMK4AfJCyCIEYyR3r+KjCmXKBVPQen2PK9O2pv7ykdZZkfZu66Jsgi3tKldBo81BNNn+nEUyrdGaWwenbcIQ44r451woKZO0BBdTn8hYw8aNlNITYsTYQt9PdJ1mM/SS8QUZV04xEOZZQNI99ENPPCdc59j0TtyzKFjVWqaFxMVAQMbbNZ0TLdLG/NbG0nU9/TgINd9nDoeZ58+eoZ3BWQNZHk/a9kLX18YBnhQ81miKtoxjzzzLazUZvPecJ482FpSR1zCMIgFXBBguZISz/9235McRGIDtrmfoHI/vH1mmwItPXjBu7jD+yLKcCCHw8PTAPMuJ+cMf/ICXz19hrWWeT7ghoAws4YwPPcpahn6HyoUYJdWCzNZ0zAfPtEx01nC738l8e6cpOkPxpCjc9Kyk/+z9jFYQyTyej+z3exIanzNzShTjGO/2DCXw9O5LSpkxJaJLRJeEqeWFugIM2/bJRVppIWaeTjM+JRHnyAa0CLR8/c1bbm5veLbZgNWEFIkThFAYhh7nTBU6Eev0GGKVRo9VeUmeVKmagipNQlOUxbiR/XakfzqL9kNxxPkoikJIViBAZct4gLXlqCqxUHrpq6w90CzWrkuK1oVomcSvXQqR7G/4SyVJNaHjdWhNqTUjUdqK4GxB5gFoGUWiZL3e84KAnZ0zGCv6kLfbLd4VYnQiKZczvbaEs69zDKIN2Xcd26EXCn1WdLWUyqW2SBuIqgpay9SmsVpmJLqeruuqOK50w+I58Sf//Ofsdjc8u92RstxPIScVihEhmSXEqhOZGPqenDV932EM5Cyu5ENnyTlxniaMqe8jWmlbhkruVxew+rteH01gGPqOTz55zoN1vH/3yC9/dubZ81tubrdYPTBHj+068nxCoYgxMc0Tu37k6fDAxgnLq2iE+2AGck1zu06xnAPTaebp/kjJhZcvn3N7t8dacVoS0k2SBafUlSx3JoQgxBLj+OKXv2Sz3TEvnvPiWVLB9AP0PV2/5cYWDg/v8dMJnT2WQCauGq2sdOEL50Em/hKPjydmLwNApfWotbg/f/Xl17z4/b+OdpbT8UT0udKhDV1vsVZhrPQAmsaEtqY6Kgu4WDQkVWXP9YC2PUoJNdraDpSiG7bEZaYgOpAK8TuQe/OtnpcSXIE2gl5PqNY5WLsbWq2yVg1IvGQNTZiXD7ID+c1S9VJFHXl92ppRBB8/aJ4YY0HXPCWvaMWl/KhlhwQN8Y2ULI5Ve8JYi+6VqIbVr3WdZRgccrBLcLVao2nMU0nlixJjmr7vuLvR0A1EJSWHcD0kYBrjeDyc+Sf/7M/4u3/0n+Nmt0Uh2UfMEqCWeWbxkc4ahm4DUDkrimI1plisFb+LVy9f8PB0EDk8Jc5Xg+05LHFtEX/f66MIDA1yclbx/E5kwd++fcfrL7/g4X3H/vaWYbPHDRt8hPuHBx6ejvwQxSfPXmCdIkZPLoYEhARRZH3IiMtwWDyawtA7coyQIyonNFY2Us7SUy9SN+d1IRV0KXTGcbO75fD4nof7L0SRR8npY7UmKxmfvRtesNlueHz3jun0RFgmVF4wKqLbcM/VuwYoRRFCEn5CafekEnPQBB95eHhk8YF+2BCWRWi8MRFCIKWE6zWumKqPaSoLTkt7TrfZDoVxA8oM6G5AGUez5rPWsCwzo1XEhEyrKmqnQUk5dHXa53JRRqZtat1Kprb5a3dDX5r5FZZcORKXNSCP38YhGhAoyP5V5lHBQaUU87xwfHqiEKVkSQlrXHUlUys3ItdyR8a11YV3AZRK4a5NkEvwyJkYJfA4Zxg3HcPQEbyIr8qV1/eScgKVcUrGvI1V+CQZQHt/7b2rLJ/z69fv+OnPf8Hv/s5vY5VaxXdy1fswqmCtXgcKYworB0NVB3K5hZqbmzvmWfAHHwK6VNGbtqa+X1Pi4wgMIO2cFEWZaOjg5bMdxyMcp4mH+we2ec9mv8X1PcpqilYczydO5xMpO3yMdFiCshzOgTh7SoZURAx17AaGnaPvLaVkTqcTT4/vGYaBYTPgug4xiq0nVmGlvXad47NPP+OP//bf5Wc/+VN++vOfEJhZQq78AYutKr9JZbrNyCfD5/jpJfdv3nI8vIM0UUqQLkUFy2rDkVJkMk+Z5uJce+SlYK0T2a7Z89XX3+CMqy1HaVvGOhWQEuisanZvSNmINHrVenw6n8hF8/KTl+A2pNpXzykQgif6hRwCufQUpYlZVQJVqZ2V+jnJ3hdQSz44+Xr9fvlgs685Ud3leT29L2YrrIFFXW04uOIycHXqXZUZYfFM5xMxzTThyX4Y6bpephPVFdei/a7OYNq7knHk9nfFiilJskSTRbpNG8U4dvSDXTclFQ8hBckwc0FpIT0tXrQQYvXY0Oj1xommRERrjQ+Jn/70FzjX8fLFc1TJWKhcBbviKdYqVO+YZi/CLEXGqxMJlCKmhOt6+n4g5QgJYg6kEi5Z3r+ugcGHwOl0QuWMs4bdrqPrb9AHw/3jkTdvXjNOe1wn7D2tNafzifuHB/IPP6EojU+KOcE8Z5apzkIMHbvtjrEfhbmoI1qLBd6yBOZ5IoSFcTOKFdvVBxJrxN9stzx/8ZLPf/RbUCIxJ/Tbex6+ei2bWxkoQqM2RtF3HZvNllOZcEPg1vZYNTE9vcefD+tp26TWRQwUnLEoVUk+0t9CW4NTHdNy5pdffMXLu309/ZAZfN1mDCAjBqhLKBQSbhhx/YgbN/jjIjoNdJKFBE8MC9EvYtKaEjlJo34JEbJHKYczWgbRL6kM36oFrq6Kw7fKoR776td/hOtQ8pdfH369lRv1jUqASYWhFx+OeV6YponzNGOMlU5N74TA5iygVjDVNhHbdgCkcqnBa8bjlxnrh6oipegHi3OGaIRzUCjrmHXlTMk8SyxMITaE5jI/Ui4BteRIKUIFP5wmfv6LX7Hd7njx7I5OgdVtdlLMi2NYUEoxdI5zlJI5lzrSbsRoaZq9eHu4gaEreD+jEDMjXf6K2/wvuT6awJALGGepSTzTfGJaAujC7mYD5xm/LBWJdzTBjyUsnBfPODqeHiceDm8ZNq+4udkL0GQUPs7Vk8FiTUFF1tHUYeiJ3jMdT0yTyKQVrQh+waeCiRltBooyfPnNNzwezvioeDotPJ0X6DpMlJTS2I7BdehiePv6iek8s9vf8vz2c7RaOB/ec//ma+7fvcUvCyIrUs9HpXBOY7WcCCpT/SUdWikGPVJS5P7xxGBl58WUmZaI0uIKPege13VMp5kQE7djh7I9yg4U1VEIeO+JfuZ0OpGDx2pVe+g9h2USubRc8HMU7oNxlKyqSgWXI56rE/8qUFyn/u0HWnaxrs96en57rYouRSs16hcr1lJaoGlfrKdv1w+4oUObiWGzqTqPEe89y7JwKAfhcViLsYbZK1LwQGaexJI+xrgyE1OUdrNWmrAsDGYjat1OsARRpKpdkDrp6XMBo1FZczzNTDGTsbUdW0uDNjNR2jj/yg/n/uGRn//iVzy7e0Y3DpTkRWmMyomoosGus3RRtBtULSGkapOumTGGoTMMvUNTSH2k64xgaOX7xYaPJDDIIoklkaMnehm5DlWi23SOrdrweJwoRWO0JZU24hv45u07XN9zmGEJkdtOM4wWaxRFSa149hOTL+zGkbHrJatVia5zbPY7Fmu4f3rk4TyJPXySUeg5Ft7cP4L5gl988RXHxyfevn/P2+OJY4Jed7iScZuO7bBlejrx7puvyCXz6pMXPHu+p3PCetzdvWCz27F/9oLX33zN/f0DMfjqn1HorExHkkGp5rHYkXOi70YUhel4oO86IJDrotRaLOyn44khpurJYAhFUUIGl1YehZ/PHE4HtNEMTpy0jSroOrJcKsc/lsLD04lxeEEqAjw20s7K6qufHfVrcAkK63hCOyrVRUgPWCdP1/KhXP5qClDw7fb7ZaxdoYhZhuL6wWGsYdxuGJRgCKTCsswsi8zJhGVhmQI+WLL3xKi5vw8oFaoZDpAzyQcsmu1mwKdMWKQjZZxZ16pWjdMhE6kxJxKKJUZOS0KZDlRTta4EsCxvuly9qdLk+Iviyy+/pnc9f/SHf5POlbq+QRdhzeYSQRdcLx2LiChHS+YomVBKCYrGaUVQit45Omeuq7jvfH0cgaHA4hcOxyOKVA1BqttRisQQ8EvETzPjuMU5u7aHispMiyeEW7bbG7TKOGcxWpFKJGWPUgbXO3KIHI4nYp/YjRu6ZmkWJlDw7PZWfBpi4BAnZh9BJ9Kbe75++yA0U+8JMVNsh3Y9OStC8pxj5Bdv3hLPZ253Oz755BOG3YDSEZ+yMP4o4Hqef/IZz1+85P27t/zyV1/y5vU9IPoJWku/XlSFdE2DLbFkTK2HlXFoq0koMBHd9cJbCJ7j6VwxiI7j6czGQDgmlmXC1Dbb0Fmss5jaj7fVWk7VBdYo3Mo4hnFHCieST1ejzqWeQupbFYViBeT+qs97bUPUTKPRhi99hA9XsWp/XQWRmkb4sOA6sxr4FG1QRktLsrds0ygGPCEQU2Ce4DA9EUq1p0+BmGtbN4qHZqel9NhtdzwdpXXrjLQumySAuFlpQs7MsTBFz5wyRbsaOGqNUanrNeJduiSqAqrIjg258Od/8S9QZP74j/4A14/E4MmlVG0QeW5jLAUhPKVS0NqhrUFbh5/nFSdrXaEmqKu+FWJ/0/VxBAaEu54yGBS53kwxGxE677ycmeeJl3WsWFfUOgNKO1J0dOOAMQFrZKGkHEWgonL4XdfjjKPExOHwRGcdzlmRUe9lQCXnTAkae/b4eWEKE2er6zxBrptBWI45Frw/Y0wm6sDeKH78O5+z323FV0FFifRtZgAZkQVF1w18/vmPuLl9zpfPv+bnP/2Sd/cncjpTSlr1q5UWks7x6WlNB5swizaZojW6Mh9zMKQ6XKSAeT7j84xxHTEEbC+yb33n1lNf171trMwU0OrXnOmtEfVhOzKVQGiDX0gar/VlPIlWP7eOBFw2fv2+zFt8uDjLB3DlZfOXb+W+TetCbkBZKxrR0syX50biTKpkslJpyLZzWNWBVai3E50x3Nzd4L1GLYYcE8kInqBrZ0BbwziOPDzdo7SjAGe/iDKYkfW6xMg5RHwpKO1Y3akree16VrRlWO377a9cCm0s/5//2V9gnOUP/+D3MbYjBi+YqTIyrKWks9UMfZIgS2iliDGSoqUUmKeFTLO/44Iaf8frIwkM9TVn6ny+LOBMoVTTkNNhAmSTdH0nMtlKk0qmFMP79ydudoqu6xk3GwF4oqRfLY1eP56avsaU0FV6uyjFHDxh8fiUZOhIGd4/zOixY3AaZyRwKUSLwYeFzii2z0Y+f7bjbuMYOkMu1QOjZAqpcp2lXjQoVK4akUYxbEZ++KMf4kzP+OU7Hs6F96d3pGouoynVfESMbWVDaMS/Ul6L0Zq+6yhVD3GePDFmbm520lt3jpJyDQIZ13VSGqg6tq0rvuH0B5vLWEffj3U6ciGkRdh5RV4XqaCr/8PqvCy//eufL1T/zzVBaNQGCm0243L9ZdlDS8PlJeaVfAWXgCQfc16fL9fPQUogpPugZWbddo6YLTqJkOoqrFOCrBRV5MCwjnlJ2M5wXry8B22rGG8hFCWS9TWwpnLxNF3Rl5W/cnVvms7n1ZdCgj/985/S9yO/+zs/wmjpolGKzEJkL0HcWpaQhe0ZL63uGBMpigRf1oqYqwfKpfb7TtfHERgUldvfQxGa67x44a1rTUlyo8ftDhCCiWlU3VTYjFucMpxPC2Q5yYdxg+0sIS5470kpk0rCIrXXsNtgTUeMMnF5OB0E6KuGp13fc7eDn9wfSFkm2lRIqBzRJVOq7sP2ZsuLmx23W4tVser6ySaRskCCmVEg5CO9En9yypymM9O0MG43/P7ffEa/f8Gc/gk//fkXqCIaD0YbnHMEAklVxaR6AlsjvAVnLKoqCOdUKMmz22xQVqGsFZt5sV4WVl/dh1oVrDFoa3HdXD8OeZ22Unn9slCUxvW9MCx9uBoJuQSSliZTvtVFkJ9Yv3+96Vs7rXAl/vIvK0VUe55CUdKu7TpXg1lax6ehDmtRA9BK6y719NTC7WggaCmi0KVYuxMNGzHWshlH5uVA3/ekXAh100mQseTKKm1vV5Wy8j/avWnvjA/+WWgdJSkvNEoX5iXwp3/2L7DG8Fs//gFaKUJcxKg4ZYwV3ZBcBwNTvhCvcpYZkly4qDe1bOt7XB9HYABKLsznmcWfyAU6N7Df7Rn6Tii+y0zXd8znM8vpjJ8mMKDihtvdnk4Lgw9VeHx4JMQt2+2I0aBKRCtF5zSbWjJoRMRlWhamZRI358XTdT3G9RQNz2+3PH+Ct8Uy+QU/z5ADY2fZOItThbEz3Iw9WkVSTnVDFIwylWik6/CNtChVBdameWKazsSY2Gx2bLd7SlGcPfzB3/o93rx9SylRuhXWYa0jhiDkGSduyabSkY2SjEEpMSvxNhBMFBzBGZQ1GGOJcaEg7eDOuXqKlur4JI/hvbANBVGX1fR0OKENuH6sfXVL9pm0hMpCzDSNgjUylMvvtxpbrZvh11uVtdqWgNdO/wZQ1oKjOXe1jTZuNjjXEWJYs8L2vSaQIy/jIsAih29rH5a1Nd20H3MTfAEk+hnhRZiJobN0zuIrVTu3MkZf3ueFxvRhKUQ9DBqy0DCV6x2r6muVe37kn/7JP0dR+PzzT1EIYzIkL6SpSmBKMQsV3mhhZtY2e0oZlVJ11cqrn8d3vT6KwFCQsWs/e4ZxYNwMONfjrGyAnAJaFXKuxKUkJ7exBkMmek8IZ149f8Ew9jwdDrx9feY4DGy2G+6ePacfHJQAKbAEz3Sa8UuQmYDB4ZQhpczJR/KS6Potneu56TRfvzuwIN0NowsYLSBUmlCxyDBVTfGFwSl+m1o1peJ6SmZYloXz+USMgc1mw7PnNygMp/PE+/sHvn79AEnxu7/z27x/85UMD9UzvGRZeMZoKFUARgt42HXSjjTGoI0I0imFDBOtjyEj0UaLUIuCmjnUcd1V+EN8ElLJ+OA5nSeePb8BIjF7lBUrN6U0ozMsy1lqXi5K07U3sXYjoKo+lULV7v6gbGmtSk0NDErVuYq8Kl21ti5KnKJu9lsUuuqrfWsGo6l21wE2haqMw2paE0VHQf47k3OsLMFS7fKq6lTKGG3YjRs2vafvLScf5TUrRPuzuWWVXEFFLmpebYV/WBXJpS5B4TKi3oIWPD4d+U/+yT9DG8vnP3gl9yFJqRACkGt2qECrDAa6zhBSoInB5CTaGd8HX4CPJDAA7LYbPvv0M7QTco7UTr4aphacNRxORwqK2/2OH37+Ga6zvLiVMendiw377VCNXR2H45nztDCfPefOVyJI4XScxOpcaWzXY5wmIbPsRVtsJ4Ixj4czZ/9EPiU+6XombfC24JcJF2Zszrzad3yydZyf3uO2A7vdhq53Yl2eqlsVstm8X5inhRQywzDw7NkzAVXnwOPjO56eDhxOZ5YpcjxEfviDT4nLoQ7fyIJpfgxaGVAJpWSTK4q4eSvRA5CZCaqUnQizGC1aiULaE+LL2gnIVV5fG2JeKj1bKOHzvBBTxrmeXMAvUm4Y6+jo2W1HlEqc56WqFFetA2TDiPiqhIlciVK6iTrmyzRmXd2sY8JaVcIWFBKhFHyKjJ3Dastms8E6t2YBl3apZCS5XIGaa2aAeENW/cmcBGRtDldtR+aqhmS0AIkpJaxRbAZH31kgsnK3UTWgXWdCf9V1lSX8Zb9SsyUFoA2PT0f+4//0n9L1f48Xz++wNjFNJ1IqOCvGSsqIHkjvHH3f4+eFggDKqgn1XmM23+H6aAKDUiLsWUjEGskFXpZa6dWrF2znHbmIzNWrT19ijWbTdey3W6wxFS02jNuR/c0NT4czb9/e88XXX8FrxTj2jMOA7feYKpceyZI6A0pZQkqc58DxdCbmyOgso9Z4Y/AZ1M4wWs2zzcindyP70VCiZ5lOgj/sdyhbwTjEEWiaJ2KKDP3I3c0eY2Qu4e3bRw5PJ6Z5Zp5nqV2zjOw62/HDH/6I5XSSE6/NKRTRQFToupca+NbogLW8sLr+MeKGbTU6ykZLMVX0vA32yMlurcixSWcjkVLidDqRYsaHTNdbGo1bDHoKfllw1tFZRTeO0u6dJ7wPRAk3EshIUhrR0vyaclcg8bptKZqLilQUzgqGkqaZyQeGbpDAp1U9CbmqTMoF5KuH9JqVyDu9BJA1e2hlRBHUP2ayT5zPZ/a3O5G8V1C0Yhg6OueA+Yq+TXvkq8nQclnUv/ZTH17f7tLUH6uvVQLww9MT/+//+D/hj//wb7PbbuSzUxFroO9FrlBG7zus7TinCaWg6zqsNrV9enlZ3+X6KAKDApbpxPH4wDD0qIaVVBNTVaCzHUPfr+lqzpEUFkHuS8RqTdeJmalPYg2udWHYOKYSOJ/FoWq7g91NR+dU7YAYVMkEPzNPM8scSFm091zvCDETlkzKBbuxbDZbdoNj11vGXtN3FqcGYu84nw483b+vJ7mmKEMuim7oub29RWuDnwOPDw8cjweOx9OqXJVyWlFrZzR+9mzHHWkROqxZTyjZyCKnVqcRqfoArBU+F7+HWs+qOjegRFRE6crhvyqFJTu5AFUlF2JIoCyn04Lth2rwIqIqRhvmecYZhTGO7eYWNEzdwOPjgfuHJ85TwFrH7c0Gq62IoOaINU1jAUp9HS2QKaXxUXM8Jjb7kaFTzLmgo9DPVX1t60hWbd21gaYG/H04yVmnI3SdUyn5KotoB3UTvoXT6cw0ndludtJ6rtocu/2O8vUTDaLNNetSXJdKDX3l1zKCX4Md/tLdUH+2lljGdry7f+RP/vmf8td/93fY7zboHKts30BKhaIcznbkLIpO1tq1pP12EPsu10cRGAByThwPD/h5YLvb4jonPXwt5IxalQoNeJ4oOWGtou87rFHYWh+fzifOi6/y82A6zV23ZxwHTueZafbM/ondbsv+ZkOOkcPTgWU+A4phHHB2xFjZaloLM1AhvAGTz6jk6OxOUOCUCFkC03azYV4mzucjS8nkrNnub9hutsSUOR6eOB/PnE9nlmXG+yAnVB3xFhMUUR06TI/c3AxoU9PrGhhynSwU8xfqB081fi1VSl74+ZcgmtdMowniluvSuC5ArWVwyhhddRyyZGDjyHma2MUehXhpZLLwR3JCZSXovBTdQsM2Cw9PZ16/eSRn+KO//Xvs91vCfMSnwGYYpORqUauWRdKa1aipkB6PaGtRJldzWkNMSEeA9tqzqD2Xqu5c5eNbyr5mD637UES7ofEAWif7WsfWWiHQHZ4OdK6n70cpJyzc3d1g7GsomlSBUqUudgDfZwv+lQf4VSYh5ZXhzdt3qFL4vb/xu9ze7emNdOd8yMxz4unhwNPpwM3NDtdL+7TxZr7v9dEEhu12y36343w8c3zK3N7u2Ww2aK1JKbEsMzGIUo0zGjc4rBPdOx890zKTSiHmLJ6BpY771gW/GXv6vmfxmdMp8PRw5PA4AZmhN7x4+SnWKWmRlgwqVapswTmhYRujIEX8MnH/ENlud3TOYWr9m/2CNorb22dorXh4PDCdz2ilWaLnfJ6YzwuhtplKrWdLFpDrdD7ydJx4PBZ0t2fcaDoHmXTFFZANpOuJL3180PU0vgSJKpSiWMsypWTjxdiQ6lIFVrIAlAqs0ZUgVmoNnhiGgcPhgPcRMJTsZUpRybzAEgLaOlDCt9BW0W02oDtiFjfqbtzyyWc/4Pj4nnfvIlkZaTvXYabmc6EELAGV17ZsTrl6MSpx9m4djroxcyV1lWo2VNBrK5LWPai7P6WqAkUi51D1Hy+eDzkXumr+60PgcDjS92MFayPb7Yizpo71w3UvolXxV2GLvwREuPr3h4Qv9S8jIVUNzVwy7969Q+vM3/njP8IOA+Mwwnnm57/8GT/92S9BwR/98R+Kp0UNCh+Qzr7j9VEEhoIImu13N4x9z9Pjgft370k+VHfrSCHRdw7X9xRkMSyLZ/ae2S90XScS8cqia41cqFqPyuGsnHbHshBtIYVMTAHQODew2exxnSJ1M9lPwpwsCqeUSK4ZOYmVqeWKD7y7f8BWF6iNNWz7jq4TunLJBWscx8MTy+xFpXr2xBDXGXmFDNacp5n379/ydHjE9APj/o67l5+y2zhUmTlPAZSw2KiDPJpc2Y9GKnSj1lqyWce1MiOXZqArwSGlWJmLsuBUTbVFbVocjnJN1b1f2O02tZ28MGx09WYIFGtJpeBDpDOgrEUZQ28UbrNjs3tLUU8UkzBOmKm39jn9tud4fGKeJlSKgg8ZtXJTZIw8QxYANyPsRfFxEFGUnDI5RrIVQlHzdFBZgkrDF1Z5syKeFClThVqiZD71OUqSFm0uUIyiH0dMNJzniWmZGepEb+dknqXUDkQLC5mmWQGXYJAEC2odEl3+0o3fPheo2EcuqxgNtAZLWdfLl199gzaGP/6jvw3a8mc/+Sl/8qd/xjz7Smqj8mmo9/M3ktR/7fooAkOLuiA9+804cAie+/v39H3P3e0N3bBBa0Ugs4SF6TzhQyAjegi668QyrhSKllOvcwNGGYKXOYtl9viQ2AwDr55/QiqZp6cTi/e8/uY1+5std7sN3a4jZ8+0nKQtqXStJVv9Tm2lSgkUU2TJif1mFJdkv5Bi5HSaOBwOQjXWWogndU63FFiC5+H+gbcPD5QSuXn2jN3tHWrYi+aT0fSuZwkelnb6VLZhUeItoDWpqIpDVMDKmFpONI1FCSYqZoyxeO+rGIgED92IPsjjtZanUpqcZJS86yzzWWZVJDWXk1ZpsW83uaCNRVkjZjhiPCFya1pXI1thCI7jjm4YmaeJ+XTCL/PK3NOomjVUJSuqhH0LpEXGndcSIqvVOCanXDkZqkrRydqiFAlmOa8/W1KLArL5xD+01I6NkNKGYcfxeOZ8mnF7I/e9zpes7UhV125BTunaXbkc0a0NSXsT8t+0v1tbtsrtt8BQhXGuj3qlVOVMKH7+819wPosZ0jev31CKQVstg2UhiCZHyrUl+z3TBT6awCDtPT/NKKQN9/z5HTEm5mVm8QshR4qCpURm76uoqRK2onWUCsZZY+iMQynLcvLcH+7RKrMZN9zud1jrxG9AgVKa/Wh4PEw8PZ05vH8knGee396wv9li3cDpfMTHQBNBsEpufo6JwTmGXmS5pscnjocj1hiWMLN4zzLJ+GzSCa0tUTJdvA8cT0ceHh6Yg2fYjWw2dzIw5UQrcJk8201HN/T03nOact0YMm9hipb3UPEEoLYfRaeyGZuI8W11XUZq9YbCK1PBXaXWU0sbjbOazeBEvyBnKIlhcByezuS4qadgFlKYMWtrUFijcppmmgJ2qoY4ZlXBVsqgyGwGx3a4EVu4ZWZZzqQsU60pxzrIIU5W5TqAVbl9qN4QV+VATiKkIoCmbIicGz09r3hK61VcWpqsZUXbjF3fs0XhJ1GX7gfJnEwVXb10Uj5ojazdlVJP+esg8OFGZ/29b5cUawYh36yQSZ2/UDLo9v79vQQybdFW5jR8iCzLzOC2pJxEWepf4fpIAoOiJOG+O2OqOq6cjCEHTtNZRDGtxoyDACqq3Wg5hfp+YOg6ci6VCyB6CJuh5+ULAQCbK3MuYRVL6XrD2O243Y4cj2eOp4m3b99xnrbc3N7SdTdoszDPJ3L2uE5YiL2Tv8mFEmVTHQ4HeudYwoIPkeAjMYiVXjGamArH04mHhwcWP2M7x92zZ9iuTsy1roRVxCSnurEdwzBgXazvVchJuhg0NWNIjX4tDtxtmk6rhkE01ltlOdaho5YtNBq9EKIk87jZb0iLSPinuNA5LeSnkNHGkctMqr6PGSQ4a7UGhKLqhk0J5eSTSqGWAOupLZTjvtvR727Z7CCWQAgLujuSrcP2Hd5nSgloJaPFWSVKqSY3XM1otFZlgtJaoFxKivVa/3m1KZUEi1xKDZby7XHcoFDMpxNKWbxPWOtQKq3Pdznd64O3jby2TmvnyNSuUW6BRH5PwUqpFkHbi+qShJJa8tUHaNwQraXkUkowIaXljUnpXbOkFL8vtwn4SAJDe/M5Z7IWo1m/LPgkb1CktMQpqcQgNmvaopVmGDZsdjf0ndClH+8PBJ/pu57PP/1EXLCtquAbFBKKVDnkcupaFHZjGfo9203Hw9OJ4+GJ02nBdR3GFqwtbDYbOqPISAmTUiKFSPQSBA5PJ9LQE3MUk9PKpEspc54n3j08cZ7PGGu5ubvDDpakRaJrPbuMmMXkOvySUgIl6k4yDFSHn2oQUEZBap0KBZl1swveIJOrrW1pjRCmUo7Ix98IOvILcrprxnFgSp6SPTkFOmPRKJY5Mu7sOiuRawkxbGQDNfiNXFC5YLRoZh4e3vOF9yzTzDwvhMoZANl8m3HDuLtlu9+w2Y68+vSGl58WYlg4PN1zeHpAW7ElbHqNuWia0bBS1XC44QrreX25LnV2a0XUr67diiq4s7b4JIPabDb4eeHxcOZ09vS9w54XYvqQREUpK+B9PSii1s+jdkqqQU9ppc7V62vj4y0Dk8lgwVB0bpmdqDY1N61WorQAs/ilqnF1NIu673t9FIFBePKFw3QmxYW+c1grlvBKa0wHNmdsTIQUmZeZ3vVsdlvGfsN0nHh3fkuJke1mz8vnt4xDT+eMgGRZ0sgVxVeuDjxKLS7pY8ahcPuBcex5/3Dg4fHEw8OBUhT7m0FUpVVmmmdiEpGQuETCEgnTwnSeJfPRdTK0AnOPDwceT2d0J8K2rncoVQNCKVI3lroQG1OwSNstp0jwM8YARWYxrLGoujMl3U9yamX5HbUudkn324bIWXwVD4cDnTN0zuCMpKCq/q91CgTVthACMUTG0dJ1lmlaGLbbtc2XEozbHbv9fj3ZAEgJUyJbp+md4f1XX/N+bQ0KP8L7IHV1G/TRHf1mZLvv2e23fPLppzx7fsfds1eM4xYfNcv8HjcMuHFDVolYDLHU6UjEE4SS1pW1enyui62snpqltjFzEnJTaZRorS5/UChrGHd7no6PTNPE7X7D4/GMz1XchpZhXMTqG2YjqztJGRga2KvXjEJMdSUor/L4pZKqWgDjElSor1tV381rrkpKoux0Oh4JN3uZtExp/b3v05r4KAIDwJIjcwr4sBBK4mbYo01tXQEOzTjKMNDDwxOHpwP+HEnpHV3f8erlC25vdwzjgOwVSVlLjiv45px0DXKOtfUmkuNKFxTCRxAGnMeozMvnO+7ubnn95onpnHj//simK6TlzOI9sw/kmClRuAchZJSSGY6YAqfTmXfv3rMsgf3z5wz7DUUVYo5XdWNlMdIClal4RhSue1GEMGOMQyFDPap6BUgMKeScaorJWtMqVZmBTelaimhi8Ix9FUuFKiVbhCeiyrrQfMwyWkwhxMQGkfg/TidyHikgylNKc/fsBW4YZIIRcFrcq3e94Qd3W0qM5LjQJjag4EzBOOn4xCRGvTEGgp95eszEnPjTf/4XDJsNz57d8fLlc9FR1B2720948YNXlBzxYeE0v8XHhVCaj4S8V9E4yJfqX9WMqgbPhgOIu1eqatHSYSh1MKwSWejGLUXNxAzP7jbcH3rOS6jBoD5rSUK5XrGDykqtmglrObNOXqrKS2kzK/JrSq/vAjks6ueqRfI+rdnOJRA1MLTAGnC9D1LuXadN3/H6aAKDtYZxt6WLHcs88Xg8stmIqW3rAOhSRDzlNBHmiBk6Out4efeCT199grGZlL2QhnJBK4O1BtcP1f5dWHMhFHS1O2u+CClElmliXjwpZ3bjhu1uw9lH3r594uHpjA+eV3cO0pnFB3yMtAMqJdFgCClznmeeDo+cThMKePbiGf1+S6ymJrki7o2JKEeXkmlMJR++LIa25BIajbWKFEqlA1NxBCW4glZVoSlLl0ZdtBpKEdKS0WL0+4PPPhUVLK0o1c2l0Qe0kVZniKJ+nPKFIu2s4BOiC6GJJbHb3TJutqtykNYy8jwdT7hS2FS36JjLarzTADtjtFgGGi2msLk+X1GE2uabnya+PEx88fMvuX12w+3dLW/f3KMwvHj5gt3+liUY/h//z/87z57f8Fs/+gxne3IWRe6SUxV9yZenVm2CUsBNCVh2BShbu3f9ozUZQ0jy764XcJaSUUq2UKFcBHDX+YSrMuEDjKOsWcb1jEepmx3V8MxakjTAtJn5cAka9QFp/6lqJ8M519Kzf7X9+K/0W/9fuJSungiDxlon1ltnT84aoyN+XojLJAYy/cir5z+k7weWecYvZ969fc1m24m6k5GAYLQRYlItF5YYRIOvDg2VUpgWGaeO1bVJlJw6lNacjwdOSyAnz+l0JCTL6HocMz4mogD2lFTwi4CkfvH4ID6Y+2c3pJQpRpGqeEuqoOf1wmsdgRWEUs2mXT5kQd09Q+84h8hV5bEOycgkZwW6tHhDqtp2K7mshrHWOLrOYqxkFIJatG68nKLGyvRojJG1nYZ8XWuF94G+t4QQGcexumfX158y5+ORN199xXI+0Xw6danejy04AMpAHQqU2ZUsmpO5BpKEIlcDmZQzygdsKTy+u+eXv/gVm3HHi5cvoWS++vqew2nhD/7gj3jx7I4QPfN05KuvviCT2IyDlF9a0Y8Ok1QlzylisjVziXUg6zJ8JMmHYp4jPmSsE5MXZxQXhsCHR7KCFoHkC3VjN6CxXd+KFfXvymm41D4r9lNSLYyuUoBLZ6Osj+O9uLE3h+9/lesjCQzSfpHWmgYLJmkOxycOx3t0KQzO8vxuz2Yz0nc9Rol78dAb/Lzgw8LxaWa327Db7THWkRFRjSVUff0KsrWecfCBEANGK4a+RxtNjJHFz0zThJ/P+CgAWAgzqRiWsYARs5eUFTFk5mnh4fGRw/HI0HXsbm7oxo5Exk8nssroKt6pVEs7y5pWr3dBC1utJYW6SbhRCDlxc7MjLgeh/FZ00VRhmfW0qAGi9muQM751HUydtjQSXCrdPOeq+FPbeapyIE6nM9teyymeSy3HNNP5TOd2lNJO29YyLcynI2+/+pLT4wPi155R1eZdWsRXEGA9FdtEZa7ZTVFCzc5F6M8pZyJiDaOzWP7lxfPu6Q1vv3mLNpqSwMfCV988srv5hP3+GegNr9//BYXAX/vdF6gSKTHz4pUT8FVp3r1/x9t3J5JC5O90Jn2Sqh+F3MWU4HhcSKmw348YQuWLCLmqflyXz7FAG1/9NsD461fNFNYy41tZQPtX4Vv37erflA8CxPF05v37ez799MWV3uP3u35jYFBK/Rj4R8Cn9TX+w1LKP1BK/Y+B/zbwpv7ov19K+Q/r7/yPgH8HSMB/r5Tyf/jNzyM6iqfjkdN5IvgMGDbbm/9Pe2cTI9l11fHfufe9elXV1T2eGWeCYxvioLAIm2BFUaREWQLxxrALCxIJpLBIJJBgYcgmUlYgEiQkFCkRkQICIiRAWEhIJAiJDQk4wYnjRM6XjT3j+e6Pqq6q93kPi3Pfq5qeD/c4w3QNen+p1dX1eer2u+eez/9h4IVUlPF4xGg8BIWmNkYi7x07OxOy4ZCyzFkuF8xnc5LUhtHivJXMRvKKpqq7AhYNynAwsAtLA4uFdTkWZaRZr4rolliVpGigKBMkUaqyJi8r9venHB5a/h1gvD1hOB5Sa2PdkgrOaWwvXmW12+Adur6JzQdWsQDVaDhkOIRlmlBXBZOtMdXSvotLrDbA6MTsROr6Jur1hTU3QmIWJk0S25ymAUwZC2jdgArOS2etVEUJ2YimNvIW75UsSyhnJZPJNvNFTtNoDJhCmedcvfg6i4M9nNYWB/HGV4jSXaQKXeOTWQkh+s0aLefo5Uf+RO8hUYdLAGqTI3VoTMUFlEGaUi5Lnnvuv/nxy+d5/G2PMh6PmU1Ldk6NSAdjJNQ0UuF9Q1AbLnv56ozLV2e4xALV44FZLC422CGefGll9OIdZ8/uEKol/uI+SeKpamizITdYA2vpw1tc7Wu/17IKsjZkR1fPbYOkb7TB25xQWZRcOH+BrUhD8Cb0wrEshhr4XVX9pohsA98Qka/Ex/5EVf/4BuFE3gV8GPh54G3AV0Xk51T1DpUWynw+58rVq9RVTjYccurUNsPRFknqSARCmTOdzSjKnNFohCNhkKRMJmOybAjAMMuoi4L5fN7RWw2GI5LEU1ZWlOQTz3g0IssGMapurcVlUdgg2Kqirs2PrqvKXLQYxGuaQFnWNGXFbDpjd2/PSFNHI8bZFmVZIN6Ci60P2I4S01Zza/z3xpNh1ZlnfJJeTJkNhyPGWxNGWWDqTIklTnjooZ3O+kHjTEpp27zj+4mLUe5gE6KxWIqIizUgTee7dlaGa9vU7D6rnAxxA9NdqGniOXfuLZw58zBFedmGnogDrdm/foX5dA9vET6iusKr9SfYHO9osRl/v/EihBhPESJXZvuZFotJiFF7D21jW5Z5nKRG4qoY7yJCU+RcuvAq1y9eIkkGiAsMB+cIlTIaZJasDg3UNUJNXSt1g627fQDe+Uj+6wiSsMxzqloZDxO2tjLyeUk68HhH7GqVNfJa6U50DXTMbTe6EFER0Ma52qOBG1yt7oV6p73dBk7odIwq7E+nvPbqeba2x10c427whopBVS8CF+PtmYh8D3j0Di95GviyqhbAyyLyQ+C9wH/c/kMAcUwmE8ajU8bjh4tmd4MSIBUcKYu8YLHM2ZmcYvvsKdJBRlXZKHPVQOIc21tbFGXJwXTKwbUFPk2ZbG8z2dm26kJRyrokz0vyvKAsCqq6pGkqS101VgHXBIUg0FjD0rKouHLlkKo4oCxLstGQyc5pfJKABsqm6OLaGk+AbqOyRn2mckOk2GPcjaPhiO3xFviUolyAJOCCzaCsStJswPb2BIjpNR9dr9bXVY0bCyBYtsV7QiMxom9ninNHHA3VSKrkcFY2gfexKiFWSZqSgOEw4+Fzb40Wi1A3FU1dsXftCvvXr+IIiMRZCq51NQQXVidqy1cZugvePisRcCrdbMn1Cc1OHMFZUZM4445wjWVmmqDUKmRejH2qUaAy+nWU8y//D/lsytuf+GnOnTuLF8uatCxNGpWrZXu0Y7fCJVQV7B8uaaThzEMj0hQOqRiME9LUoXndUcEjq7bt+G6004klWhW28QMdw5R9u9U+iIHpdbSWwg0zOILdkJhZIYROqSfeo0G4fHUXrl2zSuG1OMRxcFcxBhF5O/ALwNeB9wOfEJGPAM9hVsUepjS+tvay89xCkYjIx4CPga3D6TNnOffWt9CUMzvpnHSDTuJeIs3ifL7I1Xh9d5fDwQCHnWReQILRdDWhYTwaQS0s8xImZh4WZRF/coqipC6byLJbWdqqkciTpwR10Cj5smI+W3DtYEaaVOxMUk6f3SEdWrVfQ0uSGGKBy2rbtRvDTpLQ5dBbdyaRhMx7BlnGeDyxCL84kILprKCulKYBbQJpIiQJxvPXNHEcL6wX9bRVdt6tinQUF8fZEecU2GUrrW/bXrjYCW53WRcntKlfG7qyvbNDmqXUCj5NqcqK6cGU3avX0KYikZiec4qGVZNR2z3ZXpqiim/7FIhRXHFYssjiKhrz+RBPZreyipxYM5NTm+DtVUgcDNQTkkjLhvmy2ijT6/u8OJ2y++hP8djjj5KNBkSVTFtijJo10nauingO84rZYs5wmLA1yQihpKwr0ixhmKUwq7qMQ0t4g9I1VN14NbBm2UUl0RVC3cJSaF/Z3m5LoiNJD23QWixo22ZQUKiDuXBFaVm2u9QLx1cMIjIB/g74HVWdisjngE/Hj/s08BngN477fqr6eeDzAN45deLMFXA2PUi1sc3jUwuwiVisII5+Dz4wm0+ZzyV2Zk7I0gStLf8vXtiaTEiTIfNLl5nNZqgoVV1SViVlXVoVXWMVinUMlGmIJrQqZVWxmC/Z3Z1yfW9OFRynz+xw+vQQpI3mtywfMeos0A56be06VasChNig4xzpYECWDUkksZMyTcAbJXleBg7nFbPFPsO0JqXCOTsJWq4EF9oOyHY9AWxMOt5iNnUdOop2UxJWWNMG+9oTxnWvB5DYlScM0ozEJ9HlEkbpkOF4myaay14c8/mSsMipiyWegPesJjZ3vAmhM5PX+KFx1m0FSeho6VWtEU60jbo4OnUgzvpNJHJOOI8EcDG8KhbIsLiOijXVIagDxBFc4PrVqzSh4rHHH0d8Zo1eMbYBxOyYVVFWNewfzAkhsDXOyIYDimVuMQjvSNOEVVRQVszQ3cl/NCNwJKAYWodzldm4aULXmt5YpTy1a1Lz3qo+Q2Sjqqs6uqkr3g6R9dceD8dSDCKSYkrhr1T17wFU9fLa418A/in+eQF4fO3lj8X7bgsF5oc5+TIwHm0xSLI4ErxBnKcJUGmzYupRi5APsgHaBMq85Nq1a5za2WGcZbgkwSWOKjTUGDPSYjHHwlrWDVk1lXX01Ro7JOMwFTVFsVgs2D3YZz6fo8EmWTnJGGQZiNi7SlvrF//Brg0dxChxMJZeF8tkvQhpmpANhqSDIYnPIJ6qTXDklbIolhzs58xmBYOxEckMpSLzSQzr23e3iozESo4jN2FLQAu2KSz4qV3gURuin3/jSdUaDq2V1ATLyWejsQ1yLXOa4EizLWtoqhVPbEnP51RSkUhF4iJXRLCGY5FYwae2OW92dQWvNpsxIF26suNaaDdL54Mbl2SQSGbrHWA0/m2Gv60naGdotA1YIlAjNAiH+1MOJns8dPZcZxW1nI8SB/iITynKhsW8JHGeycQ4GcqqQdru3Sy1tQxN5H/odES05Ex2iW7kehayszJuUJWdH7LKdnaKvbUOWhIe6AwUDcapAdF9a98jHlgrJ/bYOE5WQoA/B76nqp9du/+RGH8A+FXgO/H2s8Bfi8hnseDjO4H/vOOHKCwWObvXD5HTIx4+u0PilPl8xmJpNQNVLP9sv3RVVbHiT/BJQlVVxjI0HqMhsFwWlLXxIFRNTZnntjiiK4rtYMrGmHTNV82XC6b7UxtN5pXRZIs0GVEdVBRVvIha06z177BKOViZ9K2J78T84TQxss7hcID3KZAgwdmk5qJmWVao5DQVlIUSQmIXQCyYSdPMTu7QRDfBgySkceObskwiPb35sE0TqKoGn4AGpaoCVaWkKxu3cycUJTRQh0BRNFR1A4lHvacOGpt7HHlhOXLrMg24QWLOSmKXXhMsmxDc+sYOHafA+nTVNkhqUZn4HLVTdqWobKKUaEIQRyOegI8TuGK2J7QbMhZ1xc9t1Oo68W1zV/xc51jmOZO6iaxVcdBOe/IjqCZMD3KKXG3SdZJSFDYqsWmiL58kFqVpGptw1a1rrE3pvocp6zYe0PZkuKM1BhrnWqhEt8Fa6120DNrCq7oOsUGqidZCq1jogp52gDZWs6N3pxTgeBbD+4FfB14QkefjfX8A/JqIvDvK8wrwWwCq+qKI/C3wXSyj8fE7ZyTsy3ifUlewt3uIaGBnawDqGaQZ4hq0Lqnqmrq00XGhafBJwnCQkTjLzR/uT8m3cuqqYlnmRsQiQl03lKVNEHLeiEsaNb+ZrqahZm9vn73dAzQo48mE4c4WmgihXo8brzR82+gUY1jdxbx+cjjnGA4yhls2KzONZd5lGZhND9ndz1nkFbUqw9GEQToys1rElFxd4UPFeDREqWnqgEus0QcsZQk2geji65e4fOk6y3nObL5AvPLq+UuICMtlQVE0XLl2zYrARFZXS2Q+CipUtbI7nVHHEmFxgqsbpnsHXLh4ldqbAnAiVFVJVdYIAQk2Vo8QmaFigLWtG9E20rhuIbenbLQCbWOs4hyA+f0oHk+No/COxlnsx9cNLtQWu5HVf8jFWpFK29ItAI2j4MyVu3645LVLu1zfX9j0aJ+gTc3+QcELL3yf1358kf1DJS89Pm24cuV1hMYmZGtN3TTs75eRRVusPcMR+z5ix2VoXSl3w6HW0B5yEqnqJQYu24CrrALEbQ2Ik9hgFZclro1IrHpnpRwkBnBtP9bdY3cDeTMkDvcaInIVmAPXTlqWY+BhHgw54cGRtZfz3uNWsv6Mqr7lOC/eCMUAICLPqep7TlqON8KDIic8OLL2ct57/KSyvrlC6h49evy/Rq8YevTocRM2STF8/qQFOCYeFDnhwZG1l/Pe4yeSdWNiDD169NgcbJLF0KNHjw3BiSsGEfllEXlJRH4oIs+ctDxHISKviMgLIvK8iDwX7zsjIl8RkR/E36dPQK4visgVEfnO2n23lEsMfxrX+Nsi8uQGyPopEbkQ1/V5EXlq7bHfj7K+JCK/dB/lfFxE/k1EvisiL4rIb8f7N2pd7yDnvVvTblrPCfxgPX0/At4BDIBvAe86SZluIeMrwMNH7vsj4Jl4+xngD09Arg8CTwLfeSO5gKeAf8bqit4HfH0DZP0U8Hu3eO674nWQAU/E68PfJzkfAZ6Mt7eB70d5Nmpd7yDnPVvTk7YY3gv8UFV/rKol8GWsbXvT8TTwpXj7S8Cv3G8BVPXfgd0jd99OrqeBv1DD14CHROSR+yIot5X1dnia2Lavqi8Dbdv+/zlU9aKqfjPengEtxcBGresd5Lwd7npNT1oxPAq8tvb3LVu0TxgK/IuIfCO2igO8VVd9IpcwdqtNwO3k2tR1/kQ0wb+45o5thKxHKAY2dl2PyAn3aE1PWjE8CPiAqj4JfAj4uIh8cP1BNVtt41I7myrXGj4H/CzwbowI6DMnKs0ajlIMrD+2Set6Cznv2ZqetGK46xbt+w1VvRB/XwH+ATPBLrcmY/x95eQkvAG3k2vj1llVL6tqo6oB+AIr0/ZEZb0VxQAbuK63o0K4V2t60orhv4B3isgTIjLAuCKfPWGZOojIlhjPJSKyBfwi1l7+LPDR+LSPAv94MhLehNvJ9SzwkRhFfx9wsGYanwiO+OJH2/Y/LCKZiDzBcdr2751Mt6QYYMPW9XZy3tM1vR9R1DeIsD6FRVV/BHzypOU5Its7sGjut4AXW/mAs8C/Aj8AvgqcOQHZ/gYzFyvMZ/zN28mFRc3/LK7xC8B7NkDWv4yyfDteuI+sPf+TUdaXgA/dRzk/gLkJ3waejz9Pbdq63kHOe7amfeVjjx49bsJJuxI9evTYQPSKoUePHjehVww9evS4Cb1i6NGjx03oFUOPHj1uQq8YevTocRN6xdCjR4+b0CuGHj163IT/BVJTFE3Q7DV+AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "img, label = train_ds[0]\n",
+    "plt.imshow(img.permute(1, 2, 0))\n",
+    "prob, pred = predict_image(img, model)\n",
+    "print('Predicted:', prob, classes[pred], 'Label:', label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8ea2b097",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['shot135_109',\n",
+       " 'shot50_1469',\n",
+       " 'shot64_1566',\n",
+       " 'shot75_737',\n",
+       " 'shot188_953',\n",
+       " 'shot74_2080',\n",
+       " 'shot75_1783',\n",
+       " 'shot96_493',\n",
+       " 'shot10_1972',\n",
+       " 'shot176_938',\n",
+       " 'shot12_527',\n",
+       " 'shot98_873',\n",
+       " 'shot48_307',\n",
+       " 'shot12_715',\n",
+       " 'shot98_1088',\n",
+       " 'shot162_1550',\n",
+       " 'shot74_797',\n",
+       " 'shot164_1437',\n",
+       " 'shot207_1272',\n",
+       " 'shot176_336',\n",
+       " 'shot223_1822',\n",
+       " 'shot218_926',\n",
+       " 'shot231_972',\n",
+       " 'shot34_1110',\n",
+       " 'shot44_2141',\n",
+       " 'shot42_2026',\n",
+       " 'shot8_834',\n",
+       " 'shot139_784',\n",
+       " 'shot61_505',\n",
+       " 'shot162_1134',\n",
+       " 'shot96_16',\n",
+       " 'shot90_320',\n",
+       " 'shot156_891',\n",
+       " 'shot105_1006',\n",
+       " 'shot2_136',\n",
+       " 'shot57_299',\n",
+       " 'shot84_1541',\n",
+       " 'shot15_1006',\n",
+       " 'shot18_841',\n",
+       " 'shot203_566',\n",
+       " 'shot62_1859',\n",
+       " 'shot15_1234',\n",
+       " 'shot59_477',\n",
+       " 'shot48_1738',\n",
+       " 'shot47_911',\n",
+       " 'shot207_63',\n",
+       " 'shot42_1491',\n",
+       " 'shot33_2122',\n",
+       " 'shot207_1424',\n",
+       " 'shot164_1261',\n",
+       " 'shot79_388',\n",
+       " 'shot23_1602',\n",
+       " 'shot67_414',\n",
+       " 'shot98_1418',\n",
+       " 'shot38_544',\n",
+       " 'shot1_33',\n",
+       " 'shot87_919',\n",
+       " 'shot56_39',\n",
+       " 'shot224_328',\n",
+       " 'shot63_1325',\n",
+       " 'shot88_1549',\n",
+       " 'shot111_784',\n",
+       " 'shot84_582',\n",
+       " 'shot91_374',\n",
+       " 'shot12_1221',\n",
+       " 'shot45_1484',\n",
+       " 'shot152_789',\n",
+       " 'shot88_559',\n",
+       " 'shot2_794',\n",
+       " 'shot175_1414',\n",
+       " 'shot195_254',\n",
+       " 'shot136_768',\n",
+       " 'shot64_1592',\n",
+       " 'shot1_101',\n",
+       " 'shot54_8',\n",
+       " 'shot73_1610',\n",
+       " 'shot23_138',\n",
+       " 'shot101_1800',\n",
+       " 'shot46_1699',\n",
+       " 'shot93_125',\n",
+       " 'shot98_1840',\n",
+       " 'shot163_534',\n",
+       " 'shot74_763',\n",
+       " 'shot57_609',\n",
+       " 'shot205_221',\n",
+       " 'shot137_155',\n",
+       " 'shot11_1493',\n",
+       " 'shot115_1168',\n",
+       " 'shot119_276',\n",
+       " 'shot34_1779',\n",
+       " 'shot110_1676',\n",
+       " 'shot204_1262',\n",
+       " 'shot75_962',\n",
+       " 'shot98_2256',\n",
+       " 'shot120_196',\n",
+       " 'shot54_433',\n",
+       " 'shot51_343',\n",
+       " 'shot105_119',\n",
+       " 'shot67_1576',\n",
+       " 'shot40_1163',\n",
+       " 'shot2_1842',\n",
+       " 'shot56_1703',\n",
+       " 'shot56_2186',\n",
+       " 'shot35_6',\n",
+       " 'shot207_1843',\n",
+       " 'shot30_1585',\n",
+       " 'shot186_2131',\n",
+       " 'shot34_16',\n",
+       " 'shot110_985',\n",
+       " 'shot161_1918',\n",
+       " 'shot73_2862',\n",
+       " 'shot51_515',\n",
+       " 'shot75_708',\n",
+       " 'shot110_1212',\n",
+       " 'shot48_1363',\n",
+       " 'shot37_751',\n",
+       " 'shot120_506',\n",
+       " 'shot183_806',\n",
+       " 'shot19_1667',\n",
+       " 'shot21_732',\n",
+       " 'shot73_2298',\n",
+       " 'shot111_1152',\n",
+       " 'shot168_1632',\n",
+       " 'shot139_119',\n",
+       " 'shot93_1862',\n",
+       " 'shot130_2097',\n",
+       " 'shot210_1309',\n",
+       " 'shot88_1112',\n",
+       " 'shot141_1524',\n",
+       " 'shot126_2293',\n",
+       " 'shot111_1360',\n",
+       " 'shot48_1397',\n",
+       " 'shot26_213',\n",
+       " 'shot137_44',\n",
+       " 'shot9_53',\n",
+       " 'shot243_335',\n",
+       " 'shot147_140',\n",
+       " 'shot209_493',\n",
+       " 'shot119_1352',\n",
+       " 'shot51_1140',\n",
+       " 'shot185_789',\n",
+       " 'shot119_282',\n",
+       " 'shot88_1576',\n",
+       " 'shot96_1324',\n",
+       " 'shot63_1128',\n",
+       " 'shot148_2027',\n",
+       " 'shot52_522',\n",
+       " 'shot57_252',\n",
+       " 'shot224_317',\n",
+       " 'shot160_2085',\n",
+       " 'shot73_1079',\n",
+       " 'shot197_806',\n",
+       " 'shot98_1427',\n",
+       " 'shot93_1608',\n",
+       " 'shot114_1228',\n",
+       " 'shot75_1922',\n",
+       " 'shot204_888',\n",
+       " 'shot83_898',\n",
+       " 'shot199_886',\n",
+       " 'shot144_1030',\n",
+       " 'shot206_1307',\n",
+       " 'shot74_764',\n",
+       " 'shot88_308',\n",
+       " 'shot163_533',\n",
+       " 'shot49_1827',\n",
+       " 'shot15_491',\n",
+       " 'shot205_226',\n",
+       " 'shot29_233',\n",
+       " 'shot33_1102',\n",
+       " 'shot161_306',\n",
+       " 'shot114_497',\n",
+       " 'shot2_793',\n",
+       " 'shot175_1413',\n",
+       " 'shot112_572',\n",
+       " 'shot21_538',\n",
+       " 'shot1_106',\n",
+       " 'shot128_1923',\n",
+       " 'shot91_717',\n",
+       " 'shot83_296',\n",
+       " 'shot73_1617',\n",
+       " 'shot75_1770',\n",
+       " 'shot56_2378',\n",
+       " 'shot51_981',\n",
+       " 'shot120_368',\n",
+       " 'shot111_783',\n",
+       " 'shot84_585',\n",
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+       " 'shot2_1675',\n",
+       " 'shot5_1694',\n",
+       " 'shot86_577',\n",
+       " 'shot95_1356',\n",
+       " 'shot79_70',\n",
+       " 'shot202_134',\n",
+       " 'shot205_243',\n",
+       " 'shot34_1186',\n",
+       " 'shot1_351',\n",
+       " 'shot75_593',\n",
+       " 'shot63_1723',\n",
+       " 'shot65_415',\n",
+       " 'shot224_1069',\n",
+       " 'shot237_1573',\n",
+       " 'shot154_1938',\n",
+       " 'shot112_517',\n",
+       " 'shot73_644',\n",
+       " 'shot161_363',\n",
+       " 'shot153_921',\n",
+       " 'shot44_1938',\n",
+       " 'shot57_459',\n",
+       " 'shot53_987',\n",
+       " 'shot25_883',\n",
+       " 'shot185_1164',\n",
+       " 'shot25_1151',\n",
+       " 'shot2_1681',\n",
+       " 'shot118_429',\n",
+       " 'shot39_1498',\n",
+       " 'shot37_966',\n",
+       " 'shot21_139',\n",
+       " 'shot184_2203',\n",
+       " 'shot65_1012',\n",
+       " 'shot181_42',\n",
+       " 'shot2_392',\n",
+       " 'shot163_1216',\n",
+       " 'shot74_157',\n",
+       " 'shot73_1216',\n",
+       " 'shot37_1995',\n",
+       " 'shot181_838',\n",
+       " 'shot233_987',\n",
+       " 'shot26_840',\n",
+       " 'shot96_253',\n",
+       " 'shot178_318',\n",
+       " 'shot62_306',\n",
+       " 'shot15_1090',\n",
+       " 'shot186_373',\n",
+       " 'shot32_418',\n",
+       " 'shot205_1943',\n",
+       " 'shot57_1248',\n",
+       " 'shot18_685',\n",
+       " 'shot38_1370',\n",
+       " 'shot21_757',\n",
+       " 'shot37_734',\n",
+       " 'shot120_563',\n",
+       " 'shot66_1005',\n",
+       " 'shot67_1183',\n",
+       " 'shot74_739',\n",
+       " 'shot30_443',\n",
+       " 'shot93_1209',\n",
+       " 'shot56_2117',\n",
+       " 'shot191_1695',\n",
+       " 'shot79_48',\n",
+       " 'shot11_1254',\n",
+       " 'shot38_946',\n",
+       " 'shot43_839',\n",
+       " 'shot25_1169',\n",
+       " 'shot82_865',\n",
+       " 'shot75_799',\n",
+       " 'shot29_860',\n",
+       " 'shot57_461',\n",
+       " 'shot230_524',\n",
+       " 'shot147_125',\n",
+       " 'shot8_1948',\n",
+       " 'shot101_1402',\n",
+       " 'shot153_541',\n",
+       " 'shot114_892',\n",
+       " 'shot55_2138',\n",
+       " 'shot33_1709',\n",
+       " 'shot63_1971',\n",
+       " 'shot184_2009',\n",
+       " 'shot60_739',\n",
+       " 'shot1_69',\n",
+       " 'shot111_1553',\n",
+       " 'shot224_372',\n",
+       " 'shot188_797',\n",
+       " 'shot57_237',\n",
+       " 'shot32_420',\n",
+       " 'shot148_2042',\n",
+       " 'shot65_73',\n",
+       " 'shot85_578',\n",
+       " 'shot119_1105',\n",
+       " 'shot26_878',\n",
+       " 'shot60_1506',\n",
+       " 'shot178_320',\n",
+       " 'shot3_1169',\n",
+       " 'shot118_247',\n",
+       " 'shot59_1741',\n",
+       " 'shot96_891',\n",
+       " 'shot61_1646',\n",
+       " 'shot98_471',\n",
+       " 'shot67_1721',\n",
+       " 'shot211_881',\n",
+       " 'shot37_1959',\n",
+       " 'shot9_1006',\n",
+       " 'shot5_815',\n",
+       " 'shot8_1180',\n",
+       " 'shot34_703',\n",
+       " 'shot84_1729',\n",
+       " 'shot37_196',\n",
+       " 'shot1_1450',\n",
+       " 'shot2_950',\n",
+       " 'shot97_1001',\n",
+       " 'shot63_158',\n",
+       " 'shot114_268',\n",
+       " 'shot98_2233',\n",
+       " 'shot129_1666',\n",
+       " 'shot77_1670',\n",
+       " 'shot31_1230',\n",
+       " 'shot209_630',\n",
+       " 'shot68_690',\n",
+       " 'shot38_1384',\n",
+       " 'shot84_1943',\n",
+       " 'shot179_1446',\n",
+       " 'shot42_2418',\n",
+       " 'shot16_1073',\n",
+       " 'shot148_1859',\n",
+       " 'shot9_496',\n",
+       " 'shot33_1399',\n",
+       " 'shot21_591',\n",
+       " 'shot54_200',\n",
+       " 'shot1_1034',\n",
+       " 'shot119_2122',\n",
+       " 'shot233_980',\n",
+       " 'shot98_688',\n",
+       " 'shot26_847',\n",
+       " 'shot69_891',\n",
+       " 'shot96_254',\n",
+       " 'shot62_301',\n",
+       " 'shot15_1097',\n",
+       " 'shot161_532',\n",
+       " 'shot205_1944',\n",
+       " 'shot57_208',\n",
+       " 'shot57_806',\n",
+       " 'shot229_336',\n",
+       " 'shot43_1586',\n",
+       " 'shot86_27',\n",
+       " 'shot229_1570',\n",
+       " 'shot2_395',\n",
+       " 'shot237_1322',\n",
+       " 'shot153_1586',\n",
+       " 'shot206_287',\n",
+       " 'shot77_1489',\n",
+       " 'shot156_29',\n",
+       " 'shot74_150',\n",
+       " 'shot218_1361',\n",
+       " 'shot113_782',\n",
+       " 'shot53_980',\n",
+       " 'shot23_961',\n",
+       " 'shot25_1156',\n",
+       " 'shot25_884',\n",
+       " 'shot5_1667',\n",
+       " 'shot144_1052',\n",
+       " 'shot115_1790',\n",
+       " 'shot56_2128',\n",
+       " 'shot95_899',\n",
+       " 'shot205_244',\n",
+       " 'shot29_251',\n",
+       " 'shot156_458',\n",
+       " 'shot1_356',\n",
+       " 'shot63_1724',\n",
+       " 'shot161_156',\n",
+       " 'shot194_1372',\n",
+       " 'shot196_450',\n",
+       " 'shot64_1503',\n",
+       " 'shot146_718',\n",
+       " 'shot75_752',\n",
+       " 'shot161_1942',\n",
+       " 'shot2_705',\n",
+       " 'shot82_17',\n",
+       " 'shot143_854',\n",
+       " 'shot64_446',\n",
+       " 'shot5_1693',\n",
+       " 'shot67_1148',\n",
+       " 'shot29_1962',\n",
+       " 'shot189_1957',\n",
+       " 'shot22_1371',\n",
+       " 'shot62_791',\n",
+       " 'shot33_1194',\n",
+       " 'shot75_560',\n",
+       " 'shot237_1580',\n",
+       " 'shot155_1243',\n",
+       " 'shot105_1407',\n",
+       " 'shot233_974',\n",
+       " 'shot201_336',\n",
+       " 'shot2_1024',\n",
+       " 'shot184_787',\n",
+       " 'shot22_1715',\n",
+       " 'shot111_1598',\n",
+       " 'shot125_1497',\n",
+       " 'shot136_993',\n",
+       " 'shot149_35',\n",
+       " 'shot63_1186',\n",
+       " 'shot84_513',\n",
+       " 'shot15_1251',\n",
+       " 'shot84_1716',\n",
+       " 'shot2_361',\n",
+       " 'shot153_1572',\n",
+       " 'shot7_1508',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "folders = os.listdir(test_dir)\n",
+    "folders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "09f5815a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "82973"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_ds = ImageFolder(test_dir, transform = transformations)\n",
+    "len(test_ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0a19c5d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "now: 0\n",
+      "now: 2000\n",
+      "now: 4000\n",
+      "now: 6000\n",
+      "now: 8000\n",
+      "now: 10000\n",
+      "now: 12000\n",
+      "now: 14000\n",
+      "now: 16000\n",
+      "now: 18000\n",
+      "now: 20000\n",
+      "now: 22000\n",
+      "now: 24000\n",
+      "now: 26000\n",
+      "now: 28000\n",
+      "now: 30000\n",
+      "now: 32000\n",
+      "now: 34000\n",
+      "now: 36000\n",
+      "now: 38000\n",
+      "now: 40000\n",
+      "now: 42000\n",
+      "now: 44000\n",
+      "now: 46000\n",
+      "now: 48000\n",
+      "now: 50000\n",
+      "now: 52000\n",
+      "now: 54000\n",
+      "now: 56000\n",
+      "now: 58000\n",
+      "now: 60000\n",
+      "now: 62000\n",
+      "now: 64000\n",
+      "now: 66000\n",
+      "now: 68000\n",
+      "now: 70000\n",
+      "now: 72000\n",
+      "now: 74000\n",
+      "now: 76000\n",
+      "now: 78000\n",
+      "now: 80000\n",
+      "now: 82000\n"
+     ]
+    },
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/1r/c378dkrn6hdchgmf6rwfr_t00000gn/T/ipykernel_73818/71712544.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'now: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m         \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfolder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_ds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m         \u001b[0mprob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolders\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfolder\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m    229\u001b[0m             \u001b[0mtuple\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mwhere\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mclass_index\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;32mclass\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m         \"\"\"\n\u001b[0;32m--> 231\u001b[0;31m         \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m         \u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "ans = []\n",
+    "step = 2000\n",
+    "for i in range(0, len(test_ds), step):\n",
+    "    input('now: {}'.format(i))\n",
+    "    for j in range(i, i + step):\n",
+    "        img, folder = test_ds[j]\n",
+    "        prob, pred = predict_image(img, model)\n",
+    "        ans.append((folders[folder], pred + 1, prob))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "1882c762",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "82973"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(ans)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "21c97fae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table = []\n",
+    "for tag, pred, prob in ans:\n",
+    "    if(len(table) == 0 or tag != table[-1][0]):\n",
+    "        table.append((tag, pred, prob))\n",
+    "    else:\n",
+    "        if(prob > table[-1][2]):\n",
+    "            table[-1] = (tag, pred, prob)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1b1d04a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(table) == len(folders)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "02f39b43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table = [(tag, 0) if prob < 0.5 else (tag, pred) for tag, pred, prob in table]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "dbc867d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tags, preds = zip(*table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "59da99ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(('shot135_109',\n",
+       "  'shot50_1469',\n",
+       "  'shot64_1566',\n",
+       "  'shot75_737',\n",
+       "  'shot188_953',\n",
+       "  'shot74_2080',\n",
+       "  'shot75_1783',\n",
+       "  'shot96_493',\n",
+       "  'shot10_1972',\n",
+       "  'shot176_938',\n",
+       "  'shot12_527',\n",
+       "  'shot98_873',\n",
+       "  'shot48_307',\n",
+       "  'shot12_715',\n",
+       "  'shot98_1088',\n",
+       "  'shot162_1550',\n",
+       "  'shot74_797',\n",
+       "  'shot164_1437',\n",
+       "  'shot207_1272',\n",
+       "  'shot176_336',\n",
+       "  'shot223_1822',\n",
+       "  'shot218_926',\n",
+       "  'shot231_972',\n",
+       "  'shot34_1110',\n",
+       "  'shot44_2141',\n",
+       "  'shot42_2026',\n",
+       "  'shot8_834',\n",
+       "  'shot139_784',\n",
+       "  'shot61_505',\n",
+       "  'shot162_1134',\n",
+       "  'shot96_16',\n",
+       "  'shot90_320',\n",
+       "  'shot156_891',\n",
+       "  'shot105_1006',\n",
+       "  'shot2_136',\n",
+       "  'shot57_299',\n",
+       "  'shot84_1541',\n",
+       "  'shot15_1006',\n",
+       "  'shot18_841',\n",
+       "  'shot203_566',\n",
+       "  'shot62_1859',\n",
+       "  'shot15_1234',\n",
+       "  'shot59_477',\n",
+       "  'shot48_1738',\n",
+       "  'shot47_911',\n",
+       "  'shot207_63',\n",
+       "  'shot42_1491',\n",
+       "  'shot33_2122',\n",
+       "  'shot207_1424',\n",
+       "  'shot164_1261',\n",
+       "  'shot79_388',\n",
+       "  'shot23_1602',\n",
+       "  'shot67_414',\n",
+       "  'shot98_1418',\n",
+       "  'shot38_544',\n",
+       "  'shot1_33',\n",
+       "  'shot87_919',\n",
+       "  'shot56_39',\n",
+       "  'shot224_328',\n",
+       "  'shot63_1325',\n",
+       "  'shot88_1549',\n",
+       "  'shot111_784',\n",
+       "  'shot84_582',\n",
+       "  'shot91_374',\n",
+       "  'shot12_1221',\n",
+       "  'shot45_1484',\n",
+       "  'shot152_789',\n",
+       "  'shot88_559',\n",
+       "  'shot2_794',\n",
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+       "  3,\n",
+       "  5,\n",
+       "  5,\n",
+       "  ...))"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tags, preds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "ce4fc167",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'shot': ('shot135_109',\n",
+       "  'shot50_1469',\n",
+       "  'shot64_1566',\n",
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+       "  3,\n",
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+       "  1,\n",
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+       "  1,\n",
+       "  1,\n",
+       "  1,\n",
+       "  10,\n",
+       "  8,\n",
+       "  10,\n",
+       "  8,\n",
+       "  1,\n",
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+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  1,\n",
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+       "  8,\n",
+       "  5,\n",
+       "  1,\n",
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+       "  5,\n",
+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  8,\n",
+       "  5,\n",
+       "  8,\n",
+       "  1,\n",
+       "  8,\n",
+       "  1,\n",
+       "  1,\n",
+       "  1,\n",
+       "  8,\n",
+       "  7,\n",
+       "  3,\n",
+       "  8,\n",
+       "  3,\n",
+       "  8,\n",
+       "  8,\n",
+       "  3,\n",
+       "  5,\n",
+       "  5,\n",
+       "  ...)}"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mat = {'shot': tags, 'label': preds}\n",
+    "mat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "313800de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.io import savemat\n",
+    "savemat(\"answers.mat\", mat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "402702d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({7: 1297,\n",
+       "         10: 3686,\n",
+       "         8: 2899,\n",
+       "         1: 3380,\n",
+       "         5: 959,\n",
+       "         3: 6303,\n",
+       "         0: 407,\n",
+       "         2: 631,\n",
+       "         6: 829,\n",
+       "         9: 321,\n",
+       "         4: 108})"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from collections import Counter\n",
+    "Counter(preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c707ae70",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git "a/code/2021_spring/\350\224\241\346\227\255\346\230\200-\345\234\272\346\231\257/preds.mat" "b/code/2021_spring/\350\224\241\346\227\255\346\230\200-\345\234\272\346\231\257/preds.mat"
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