train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e9baa05",
   "metadata": {},
   "outputs": [],
   "source": [
    "from src import *\n",
    "\n",
    "from tqdm import tqdm \n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "from torch import optim\n",
    "\n",
    "from scipy.signal import find_peaks\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "15f46e61",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:1\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "888ff8f6",
   "metadata": {},
   "source": [
    "### Model Implementation PULSEGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d530edba",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Generator, self).__init__()\n",
    "\n",
    "        # encoding layers\n",
    "        self.conv1 = nn.Conv1d(1, 16, 3, stride=1)\n",
    "        self.conv2 = nn.Conv1d(16, 32, 3, stride=1)\n",
    "        self.conv3 = nn.Conv1d(32, 64, 3, stride=1)\n",
    "        self.conv4 = nn.Conv1d(64, 96, 3, stride=1)\n",
    "        self.conv5 = nn.Conv1d(96, 128, 3, stride=1)\n",
    "        self.conv6 = nn.Conv1d(128, 156, 3, stride=1)\n",
    "        \n",
    "\n",
    "        # decoding layers\n",
    "        self.deconv6 = nn.ConvTranspose1d(156, 128, kernel_size=3, stride=1)\n",
    "        self.deconv5 = nn.ConvTranspose1d(128, 96, kernel_size=3, stride=1)\n",
    "        self.deconv4 = nn.ConvTranspose1d(96, 64, kernel_size=3, stride=1)\n",
    "        self.deconv3 = nn.ConvTranspose1d(64, 32, kernel_size=3, stride=1)\n",
    "        self.deconv2 = nn.ConvTranspose1d(32, 16, kernel_size=3, stride=1)\n",
    "        self.deconv1 = nn.ConvTranspose1d(16, 1, kernel_size=3, stride=1)\n",
    "        \n",
    "        # activations\n",
    "        self.activ1 = nn.PReLU() #not inplace, I want to copy\n",
    "        self.activ2 = nn.Tanh()\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        # encoder\n",
    "        x = self.conv1(x)\n",
    "        res1 = self.activ1(x)\n",
    "        \n",
    "        x = self.conv2(res1)\n",
    "        res2 = self.activ1(x)\n",
    "        \n",
    "        x = self.conv3(res2)\n",
    "        res3 = self.activ1(x)\n",
    "        \n",
    "        x = self.conv4(res3)\n",
    "        res4 = self.activ1(x)\n",
    "        \n",
    "        x = self.conv5(res4)\n",
    "        res5 = self.activ1(x)\n",
    "        \n",
    "        x = self.conv6(res5)\n",
    "        x = self.activ1(x)   \n",
    "        \n",
    "        \n",
    "        \n",
    "        # decoder\n",
    "        x = self.deconv6(x)\n",
    "        x = self.activ1(x)\n",
    "        x += res5\n",
    "        \n",
    "        x = self.deconv5(x)\n",
    "        x = self.activ1(x)\n",
    "        x += res4\n",
    "        \n",
    "        x = self.deconv4(x)\n",
    "        x = self.activ1(x)\n",
    "        x += res3\n",
    "        \n",
    "        x = self.deconv3(x)\n",
    "        x = self.activ1(x)\n",
    "        x += res2\n",
    "        \n",
    "        x = self.deconv2(x)\n",
    "        x = self.activ1(x)\n",
    "        x += res1\n",
    "        \n",
    "        x = self.deconv1(x)\n",
    "        x = self.activ2(x)\n",
    "        \n",
    "        \n",
    "        return x\n",
    "        \n",
    "        \n",
    "\n",
    "\n",
    "class Discriminator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Discriminator, self).__init__()\n",
    "        \n",
    "        # encoding layers\n",
    "        self.encode = nn.Sequential(\n",
    "            nn.Conv1d(2, 8, 3, stride=1),\n",
    "            nn.BatchNorm1d(8),\n",
    "            nn.LeakyReLU(0.1),\n",
    "            nn.Conv1d(8, 12, 3, stride=1),\n",
    "            nn.BatchNorm1d(12),\n",
    "            nn.LeakyReLU(0.1),            \n",
    "            nn.Conv1d(12, 16, 3, stride=1),\n",
    "            nn.BatchNorm1d(16),\n",
    "            nn.LeakyReLU(0.1),            \n",
    "            nn.Conv1d(16, 20, 3, stride=1),\n",
    "            nn.BatchNorm1d(20),\n",
    "            nn.LeakyReLU(0.1),\n",
    "            nn.Conv1d(20, 24, 3, stride=1),\n",
    "            nn.BatchNorm1d(24),\n",
    "            nn.LeakyReLU(0.1),            \n",
    "           \n",
    "        )\n",
    "        \n",
    "        self.embed = nn.Sequential(\n",
    "            nn.Conv1d(24, 6, 3, stride=1),\n",
    "            nn.LeakyReLU(0.1), \n",
    "        )\n",
    "        \n",
    "        self.head = nn.Sequential(\n",
    "            nn.Linear(696,128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128,24),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(24,1),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "        \n",
    "    def forward(self, X, Xc):\n",
    "        # concatenate both ground truth or predicted + original signal (used as conditional)\n",
    "        b, c, l = X.shape\n",
    "        \n",
    "        Xi = torch.cat([X,Xc],axis=1)\n",
    "        \n",
    "        Xi = self.encode(Xi)\n",
    "        Xi = self.embed(Xi)\n",
    "        \n",
    "        Xi = Xi.reshape(b,-1)\n",
    "        proba = self.head(Xi)\n",
    "        \n",
    "        return proba\n",
    "        \n",
    "        \n",
    "class PulseGan(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(PulseGan,self).__init__()\n",
    "        \n",
    "        self.G = Generator()\n",
    "        self.D = Discriminator()\n",
    "        \n",
    "    \n",
    "    def forward(self, X, Xc):\n",
    "        Xrppg = self.G(X)\n",
    "        \n",
    "        # Input of original rppg and Ground Truth\n",
    "        proba_real = self.D(X,Xc)\n",
    "                \n",
    "        # Input of original rppg and generator output\n",
    "        proba_fake = self.D(X,Xrppg)\n",
    "            \n",
    "        return X,Xc,Xrppg,proba_fake, proba_real\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0d844c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Gloss(nn.Module):  \n",
    "    def __init__(self):\n",
    "        super(Gloss,self).__init__()\n",
    "        return\n",
    "    \n",
    "    def forward(self, rppg_gt, rppg_pred, proba_pred, lbd=.1, beta=.2):\n",
    "        # accumulating loss for all sample\n",
    "        loss = 0        \n",
    "        b, c, l = rppg_gt.shape\n",
    "\n",
    "        for i in range(b):\n",
    "            d = proba_pred[i]\n",
    "            Xc = rppg_gt[i]\n",
    "            Xg = rppg_pred[i]\n",
    "            \n",
    "            # loss += 1/2 * (d - 1)**2\n",
    "            \n",
    "            # loss += lbd * torch.sum(torch.abs(Xc-Xg))\n",
    "\n",
    "            #loss += beta * torch.sum(self.fft_loss(Xc,Xg))\n",
    "\n",
    "            loss += beta * self.peak_loss(Xc,Xg)\n",
    "\n",
    "        return loss\n",
    "    \n",
    "    \n",
    "    def fft_loss(self,Xc,Xg):\n",
    "        return torch.abs(torch.fft.fft(Xc) - torch.fft.fft(Xg))\n",
    "        \n",
    "    def peak_loss(self,Xc,Xg):\n",
    "        idx_device = Xc.device.index\n",
    "        d = torch.device(\"cuda:{}\".format(idx_device))\n",
    "        \n",
    "        rppgC = Xc.cpu().flatten()\n",
    "        rppgG = Xg.cpu().flatten().detach()\n",
    "        peaksC, _ = find_peaks(rppgC, height=0.5) #arbitrary height simply because normalized rppg\n",
    "        peaksG, _ = find_peaks(rppgG, height=0.5) \n",
    "        \n",
    "        dist = self.min_dist(peaksC, peaksG)\n",
    "        return dist\n",
    "\n",
    "    def min_dist(self,I1,I2):\n",
    "        # NOTE: NOT COMMUTATIVE\n",
    "        # I1 should be ground truth\n",
    "        ret = 0 \n",
    "        if len(I2) == 0:\n",
    "            I2 = np.zeros_like(I1)\n",
    "        for val in I1:\n",
    "            diff = np.min(np.abs(I2-val))\n",
    "            ret += (diff) # penalize way more large differences \n",
    "        return ret\n",
    "        \n",
    "        \n",
    "class Dloss(nn.Module):  \n",
    "    def __init__(self):\n",
    "        super(Dloss,self).__init__()\n",
    "        return\n",
    "    \n",
    "    def forward(self, proba_fake, proba_real):      \n",
    "        # accumulating loss for all sample        \n",
    "        b = proba_fake.shape[0]\n",
    "        loss = 0\n",
    "        \n",
    "        for i in range(b):\n",
    "            df = proba_fake[i]\n",
    "            dr = proba_real[i]\n",
    "            \n",
    "            loss += 1/2 * df ** 2 + 1/2 * (dr-1)**2\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1995ec90",
   "metadata": {},
   "source": [
    "### DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b3057eec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # TO AUGMENT SPECIFIC MOTION DATA\n",
    "# eg = pd.read_csv('../experiments/csvs/phys_pred2.csv')\n",
    "\n",
    "# intervals = {\n",
    "#     'exp41':[700, 829], \n",
    "#     'exp46':[1620, 1800], \n",
    "#     'exp49':[1700, 1900]\n",
    "# }\n",
    "\n",
    "\n",
    "# ret = {\n",
    "#     'id': [],\n",
    "#     'pred': [],\n",
    "#     'gt': []\n",
    "# }\n",
    "\n",
    "# for exp in intervals.keys():\n",
    "#     a,b = intervals[exp]\n",
    "#     roi = eg[eg.id==exp].reset_index(drop=True)\n",
    "#     roi = roi.iloc[a:b]\n",
    "    \n",
    "#     preds = list(roi['pred'])\n",
    "#     gts = list(roi['gt'])\n",
    "    \n",
    "    \n",
    "#     for i in range(100):\n",
    "#         ret['id'] += [exp+'_'+str(i)] * len(roi)\n",
    "#         ret['pred'] += preds\n",
    "#         ret['gt'] += gts\n",
    "        \n",
    "# # new = pd.concat([eg,pd.DataFrame.from_dict(ret)])\n",
    "# new = pd.DataFrame.from_dict(ret)\n",
    "# new.to_csv('motionpulse_gan.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d179f163",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_dataset = PulseDataset('../experiments/csvs/phys_pred2.csv')\n",
    "# trainloader = torch.utils.data.DataLoader(train_dataset, batch_size=1, shuffle=True)\n",
    "\n",
    "# train, test = torch.utils.data.random_split(trainloader.dataset, [len(trainloader.dataset)//2, \n",
    "#                                                                  len(trainloader.dataset)//2])\n",
    "\n",
    "# trainloader = torch.utils.data.DataLoader(train, batch_size=3, shuffle=False)\n",
    "# testloader = torch.utils.data.DataLoader(test, batch_size=3, shuffle=False)\n",
    "\n",
    "# dataloaders = {\n",
    "#     'train': trainloader,\n",
    "#     'test': testloader\n",
    "# }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ac5f5a3",
   "metadata": {},
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a91428ca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  0%|                                                                                          | 0/50 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/49\n",
      "----------\n",
      "tensor(0.0234, device='cuda:1')\n",
      "tensor(-0.0743, device='cuda:1')\n",
      "tensor(0.1206, device='cuda:1')\n",
      "tensor(-0.0021, device='cuda:1')\n",
      "tensor(-0.1285, device='cuda:1')\n",
      "tensor(0.0246, device='cuda:1')\n",
      "tensor(0.0382, device='cuda:1')\n",
      "tensor(-0.0018, device='cuda:1')\n",
      "tensor(-0.0075, device='cuda:1')\n",
      "tensor(-0.0245, device='cuda:1')\n",
      "tensor(0.0204, device='cuda:1')\n",
      "tensor(-0.1357, device='cuda:1')\n",
      "tensor(0.1360, device='cuda:1')\n",
      "tensor(0.0730, device='cuda:1')\n",
      "tensor(-0.0198, device='cuda:1')\n",
      "tensor(-0.1040, device='cuda:1')\n",
      "tensor(-0.0134, device='cuda:1')\n",
      "tensor(0.0175, device='cuda:1')\n",
      "tensor(0.0108, device='cuda:1')\n",
      "tensor(-0.0965, device='cuda:1')\n",
      "tensor(-0.1338, device='cuda:1')\n",
      "tensor(0.0200, device='cuda:1')\n",
      "tensor(0.0848, device='cuda:1')\n",
      "tensor(-0.0622, device='cuda:1')\n",
      "tensor(0.0418, device='cuda:1')\n",
      "tensor(-0.1093, device='cuda:1')\n",
      "tensor(-0.0962, device='cuda:1')\n",
      "tensor(-0.0813, device='cuda:1')\n",
      "tensor(0.0491, device='cuda:1')\n",
      "tensor(0.0067, device='cuda:1')\n",
      "tensor(0.0328, device='cuda:1')\n",
      "tensor(-0.0428, device='cuda:1')\n",
      "tensor(0.0088, device='cuda:1')\n",
      "tensor(0.0259, device='cuda:1')\n",
      "tensor(0.0136, device='cuda:1')\n",
      "tensor(0.0074, device='cuda:1')\n",
      "tensor(0.0354, device='cuda:1')\n",
      "tensor(0.0062, device='cuda:1')\n",
      "tensor(0.0257, device='cuda:1')\n",
      "tensor(-0.1017, device='cuda:1')\n",
      "tensor(0.1740, device='cuda:1')\n",
      "tensor(0.1071, device='cuda:1')\n",
      "tensor(0.0378, device='cuda:1')\n",
      "tensor(-0.2264, device='cuda:1')\n",
      "tensor(-0.0130, device='cuda:1')\n",
      "tensor(-0.0042, device='cuda:1')\n",
      "tensor(-0.0311, device='cuda:1')\n",
      "tensor(0.0183, device='cuda:1')\n",
      "tensor(0.1103, device='cuda:1')\n",
      "tensor(0.0898, device='cuda:1')\n",
      "tensor(0.0271, device='cuda:1')\n",
      "tensor(0.0695, device='cuda:1')\n",
      "tensor(-0.1701, device='cuda:1')\n",
      "tensor(-0.0974, device='cuda:1')\n",
      "tensor(0.0893, device='cuda:1')\n",
      "tensor(-0.0764, device='cuda:1')\n",
      "tensor(-0.0080, device='cuda:1')\n",
      "tensor(-0.0192, device='cuda:1')\n",
      "tensor(0.1005, device='cuda:1')\n",
      "tensor(0.0511, device='cuda:1')\n",
      "tensor(-0.1247, device='cuda:1')\n",
      "tensor(-2.6003e-06, device='cuda:1')\n",
      "tensor(0.0542, device='cuda:1')\n",
      "tensor(0.0854, device='cuda:1')\n",
      "tensor(0.1142, device='cuda:1')\n",
      "tensor(-0.1003, device='cuda:1')\n",
      "tensor(0.0157, device='cuda:1')\n",
      "tensor(-0.0284, device='cuda:1')\n",
      "tensor(0.0932, device='cuda:1')\n",
      "tensor(0.0014, device='cuda:1')\n",
      "tensor(0.0446, device='cuda:1')\n",
      "tensor(-0.0111, device='cuda:1')\n",
      "tensor(0.0552, device='cuda:1')\n",
      "tensor(-0.1451, device='cuda:1')\n",
      "tensor(0.0141, device='cuda:1')\n",
      "tensor(-0.0522, device='cuda:1')\n",
      "tensor(-0.0438, device='cuda:1')\n",
      "tensor(-0.2118, device='cuda:1')\n",
      "tensor(0.0863, device='cuda:1')\n",
      "tensor(-0.0207, device='cuda:1')\n",
      "tensor(-0.0482, device='cuda:1')\n",
      "tensor(-0.0817, device='cuda:1')\n",
      "tensor(-0.0443, device='cuda:1')\n",
      "tensor(-0.1473, device='cuda:1')\n",
      "tensor(-0.0237, device='cuda:1')\n",
      "tensor(0.0896, device='cuda:1')\n",
      "tensor(0.1545, device='cuda:1')\n",
      "tensor(0.1527, device='cuda:1')\n",
      "tensor(0.0985, device='cuda:1')\n",
      "tensor(0.2924, device='cuda:1')\n",
      "train Loss: 4.3812 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  2%|█▋                                                                                | 1/50 [00:02<01:54,  2.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 4.3989 \n",
      "Epoch 1/49\n",
      "----------\n",
      "tensor(-0.0466, device='cuda:1')\n",
      "tensor(0.0706, device='cuda:1')\n",
      "tensor(-0.0634, device='cuda:1')\n",
      "tensor(0.0787, device='cuda:1')\n",
      "tensor(0.0988, device='cuda:1')\n",
      "tensor(-0.1149, device='cuda:1')\n",
      "tensor(-0.1962, device='cuda:1')\n",
      "tensor(-0.0200, device='cuda:1')\n",
      "tensor(-0.0168, device='cuda:1')\n",
      "tensor(-0.1828, device='cuda:1')\n",
      "tensor(-0.0935, device='cuda:1')\n",
      "tensor(0.0397, device='cuda:1')\n",
      "tensor(-0.1368, device='cuda:1')\n",
      "tensor(0.2060, device='cuda:1')\n",
      "tensor(-0.0857, device='cuda:1')\n",
      "tensor(-0.2954, device='cuda:1')\n",
      "tensor(-0.0978, device='cuda:1')\n",
      "tensor(0.1871, device='cuda:1')\n",
      "tensor(-0.3209, device='cuda:1')\n",
      "tensor(0.3294, device='cuda:1')\n",
      "tensor(0.1604, device='cuda:1')\n",
      "tensor(-0.0780, device='cuda:1')\n",
      "tensor(0.1306, device='cuda:1')\n",
      "tensor(0.0174, device='cuda:1')\n",
      "tensor(-0.0617, device='cuda:1')\n",
      "tensor(0.2473, device='cuda:1')\n",
      "tensor(0.0747, device='cuda:1')\n",
      "tensor(0.1072, device='cuda:1')\n",
      "tensor(0.2234, device='cuda:1')\n",
      "tensor(-0.0426, device='cuda:1')\n",
      "tensor(0.0030, device='cuda:1')\n",
      "tensor(-0.1952, device='cuda:1')\n",
      "tensor(0.0560, device='cuda:1')\n",
      "tensor(0.2956, device='cuda:1')\n",
      "tensor(-0.0517, device='cuda:1')\n",
      "tensor(0.1840, device='cuda:1')\n",
      "tensor(0.1116, device='cuda:1')\n",
      "tensor(-0.0444, device='cuda:1')\n",
      "tensor(-0.1981, device='cuda:1')\n",
      "tensor(0.1247, device='cuda:1')\n",
      "tensor(0.2024, device='cuda:1')\n",
      "tensor(-0.0146, device='cuda:1')\n",
      "tensor(0.0853, device='cuda:1')\n",
      "tensor(-0.5585, device='cuda:1')\n",
      "tensor(0.4518, device='cuda:1')\n",
      "tensor(0.3298, device='cuda:1')\n",
      "tensor(0.1341, device='cuda:1')\n",
      "tensor(-0.0039, device='cuda:1')\n",
      "tensor(0.2079, device='cuda:1')\n",
      "tensor(-0.0561, device='cuda:1')\n",
      "tensor(0.0712, device='cuda:1')\n",
      "tensor(0.0156, device='cuda:1')\n",
      "tensor(-1.7867, device='cuda:1')\n",
      "tensor(-0.1428, device='cuda:1')\n",
      "tensor(0.3574, device='cuda:1')\n",
      "tensor(-0.6353, device='cuda:1')\n",
      "tensor(0.2975, device='cuda:1')\n",
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      "tensor(0.7603, device='cuda:1')\n",
      "tensor(-0.4337, device='cuda:1')\n",
      "tensor(1.0581, device='cuda:1')\n",
      "train Loss: 4.3063 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  4%|███▎                                                                              | 2/50 [00:04<01:50,  2.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 4.8428 \n",
      "Epoch 2/49\n",
      "----------\n",
      "tensor(-0.1518, device='cuda:1')\n",
      "tensor(-0.4037, device='cuda:1')\n",
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      "tensor(0.3713, device='cuda:1')\n",
      "tensor(0.1584, device='cuda:1')\n",
      "tensor(0.1127, device='cuda:1')\n",
      "train Loss: 4.6357 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  6%|████▉                                                                             | 3/50 [00:06<01:48,  2.30s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 8.0453 \n",
      "Epoch 3/49\n",
      "----------\n",
      "tensor(0.1251, device='cuda:1')\n",
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      "train Loss: 29.4669 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  8%|██████▌                                                                           | 4/50 [00:09<01:45,  2.30s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 64.8366 \n",
      "Epoch 4/49\n",
      "----------\n",
      "tensor(0.0088, device='cuda:1')\n",
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      "tensor(-0.0007, device='cuda:1')\n",
      "tensor(0.0088, device='cuda:1')\n",
      "tensor(0.0034, device='cuda:1')\n",
      "train Loss: 60.2398 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 10%|████████▏                                                                         | 5/50 [00:11<01:44,  2.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 80.2666 \n",
      "Epoch 5/49\n",
      "----------\n",
      "tensor(0.0062, device='cuda:1')\n",
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      "tensor(-0.0110, device='cuda:1')\n",
      "tensor(0.0179, device='cuda:1')\n",
      "tensor(0.0045, device='cuda:1')\n",
      "tensor(0.0057, device='cuda:1')\n",
      "tensor(-0.0249, device='cuda:1')\n",
      "tensor(-0.0072, device='cuda:1')\n",
      "tensor(-0.0002, device='cuda:1')\n",
      "tensor(-0.0040, device='cuda:1')\n",
      "tensor(-0.0022, device='cuda:1')\n",
      "tensor(-0.0074, device='cuda:1')\n",
      "tensor(0.0053, device='cuda:1')\n",
      "tensor(0.0034, device='cuda:1')\n",
      "tensor(0.0034, device='cuda:1')\n",
      "tensor(0.0026, device='cuda:1')\n",
      "tensor(0.0033, device='cuda:1')\n",
      "tensor(0.0007, device='cuda:1')\n",
      "tensor(0.0052, device='cuda:1')\n",
      "tensor(0.0018, device='cuda:1')\n",
      "train Loss: 78.0855 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|████████▏                                                                         | 5/50 [00:13<02:02,  2.72s/it]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1966757/4048788386.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;31m# track history if only in train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_grad_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mphase\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'train'\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---> 42\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mXc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mXrppg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproba_fake\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproba_real\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtargets\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     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion_G\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mXrppg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproba_fake\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcriterion_D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproba_fake\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproba_real\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/baseline/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1052\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\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/tmp/ipykernel_1966757/3788774083.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, X, Xc)\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mXc\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--> 143\u001b[0;31m         \u001b[0mXrppg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\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    144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m         \u001b[0;31m# Input of original rppg and Ground Truth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/baseline/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1052\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\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/tmp/ipykernel_1966757/3788774083.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0mres4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactiv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv5\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres4\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     43\u001b[0m         \u001b[0mres5\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactiv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/baseline/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1052\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\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~/miniconda3/envs/baseline/lib/python3.9/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 298\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\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    299\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/baseline/lib/python3.9/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    292\u001b[0m                             \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m                             _single(0), self.dilation, self.groups)\n\u001b[0;32m--> 294\u001b[0;31m         return F.conv1d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m    295\u001b[0m                         self.padding, self.dilation, self.groups)\n\u001b[1;32m    296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model = PulseGan()\n",
    "model = model.to(device)\n",
    "\n",
    "# PATH = './checkpoints/PulseGan2_49.pt'\n",
    "# model.load_state_dict(torch.load(PATH))\n",
    "\n",
    "criterion_G = Gloss() \n",
    "criterion_D = Dloss()\n",
    "\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.0001)\n",
    "\n",
    "\n",
    "val_loss_history = []\n",
    "train_loss_history = []\n",
    "\n",
    "num_epochs = 50\n",
    "\n",
    "for epoch in tqdm(range(num_epochs)):\n",
    "    print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "    print('-' * 10)\n",
    "\n",
    "    # Each epoch has a training and validation phase\n",
    "    phases = ['train', 'test']\n",
    "    for phase in phases:\n",
    "        running_loss = 0.0\n",
    "        if phase == 'train':\n",
    "            model.train()  # Set model to training mode -> activate droput layers and batch norm\n",
    "        else:\n",
    "            model.eval()  # Set model to evaluate mode\n",
    "\n",
    "        # Iterate over data.\n",
    "        for inputs, targets in dataloaders[phase]:\n",
    "            inputs = inputs.to(device)\n",
    "            targets = targets.to(device)\n",
    "            \n",
    "            # zero the parameter gradients\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            # forward\n",
    "            # track history if only in train\n",
    "            with torch.set_grad_enabled(phase == 'train'):\n",
    "                X,Xc,Xrppg, proba_fake, proba_real = model(inputs,targets)\n",
    "                \n",
    "                loss = criterion_G(Xc, Xrppg, proba_fake) + criterion_D(proba_fake, proba_real)\n",
    "                # backward + optimize only if in training phase\n",
    "                if phase == 'train':\n",
    "                    loss.backward()\n",
    "                    print(torch.sum(model.G.conv1.weight.grad))\n",
    "                    optimizer.step()\n",
    "                    \n",
    "            # statistics\n",
    "            running_loss += loss.item()\n",
    "\n",
    "        epoch_loss = running_loss / len(dataloaders[phase])\n",
    "        print('{} Loss: {:.4f} '.format(phase, epoch_loss))\n",
    "\n",
    "        if phase == 'test':\n",
    "            val_loss_history.append(epoch_loss)\n",
    "        else:\n",
    "            train_loss_history.append(epoch_loss)\n",
    "\n",
    "    torch.save(model.state_dict(), f'checkpoints/PulseGan3_{epoch}.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dd7119a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb5d5439820>]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_loss_history[10:])\n",
    "plt.plot(val_loss_history[10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a33f2ed",
   "metadata": {},
   "source": [
    "### Examples of usage and results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ac2a4c21",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Xc = next(iter(testloader))\n",
    "\n",
    "inputs = X.to(device)\n",
    "targets = Xc.to(device)\n",
    "\n",
    "X,Xc,Xrppg, _, _ = model(inputs,targets)\n",
    "\n",
    "original = X.cpu().numpy()\n",
    "ground_truth = Xc.cpu().numpy()\n",
    "corrected = Xrppg.cpu().detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "cc80d5f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Illustration of PULSE GAN correction')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B/iDk5EKd+bpj9jQDA4PrHptdnaRtzy5Je+TSPi86Ocerb13iQ09uzWwdh8V1l6Q9vdHlb/7Sh9gbzO4wS+zfvTDmk2d3ZraOGw39IKLqWCw1KgDs9ExAY/CFgfc+coVPnN2e6RqSfe0jT5kkrSwEUUwYS2quzVzdZa8fznpJBgYGBofCxr4yQDq3PRu5424/4PxOnxeuzfGlty1f131p112S9pGntrjviU0+e253Zmv4XEbrev91TKNebxj4EfWKzULDBWCn5894RQYG+eBf/tHneNufPzrTNTxyOUnStpBydg3fNxKSvtqaazFfdw2TZmBgcN0jcak9N6PEKCk4vnCtzctvXgDgwfOzyxkOg+suSVvvJBn6bLWuK60qLz41x/3Xub3n9YR+EFFzbObrKkkzAY3BFwo29r2ZVvo6Xsi57T63rjTZ7gU8dsWYIpWBQaCGV9dd2yRpBgYGXxBIe9JmFKcnfdUvPNGmUXEACKLrs/B43SVpyXysWWXoAA9d3OPuE21ec9synzi7Y1wGS8IgiKlXbOYbJkkz+MKBH8bsDUIu7PRnxmA9qlm0v6VNeT781PWr4b+ekJwdVZOkGRgYfAEgjGK2tcppVmTKQxf3ma+7rM3VsC2h1hXHM1nLYXEdJmnJxZ+N1jWMYh673OHuE3O89vYV/CjmY8/MtpfkRkE/iKi5Nu2qg20J05Nm8AWB5EAbBDFb3dlIeBN5yBvvPs7aXI2PmCStFCRJWsKk7Q0C4hnOFjIoD0KIm4QQfymEeEgI8aAQ4vtnvSYDg8Niq+cjJczXXS7tDQij8pOj8zt9ziw3EELg6CRtljPbDoPrLknbnLHc8cmNLn4U88K1Ni8+NQ/A40YaVAoGQUTdtRBCMFdz2OmbnjSD6x+JOgBmZ1n8yKV9GhWb04t1vuTWJT72tEnSykA/7UlTSZqUsO8Z85AbBCHwz6SUdwNfCvxvQogXzXhNBgaHwqYmUl56ep4ollze967xL/LHwI9SmaNtkrRykQQ0s9K6PqRNQ+4+MUfFUR9feJ1e/OsNfW0cArDQqLBrnNAMvgCQHGoAF2aYpL3geBvLEqy0KuwPzHerDGR70uZ0r62ZlXZjQEp5UUr5cf3/+8BDwKnZrsrA4HBIzrMvOr0AwLkZDLTuB8NY0bFNklYqErnjxd0+wQxo1Icv7ePagttXWymNauQp5aAfRNRd9cWbq7vG3dHgCwJZieMsFAJSSh65vM9dx9sAOJYgMu6OpaA/5u4Iptf2RoQQ4gzwcuDDM16KgcGhkBApX3TTAjAbdUg2VrRF0pN2fZ5ph07SytRVJw2JJ+ZrxBIu7Q6KeqsDcXlvwLF2jYpjZRoSr8+Lf70h6UkDWDBN9gbPAddD30dyqNmW4MJO+XvaTi9gq+tz5/EWAJYlzJ5WEgZjckcwTNqNBiFEC/h94J9IKfemPP49QogHhBAPrK+vl79AA4PPA8l59tLTqh1oFqq3vj+MFUuRO249CZ/+b4W8dB5MWmm66q2uakhMadQZXPxBlkZNL/716RpzvWHgD6sjCw2TpBk8Jxz5vo/Nro9rC84sNzi/U740pKN7oBb0kHjHEtetNOR6QzZJm6sZJu1GgxDCRSVovyWlfMe050gpf1FK+Uop5StXV1fLXaCBweeJjY46z461q6y0qjOL02uuSm8cq+C2JCnhHX8f3vE9EOW/dx86SStTV51IHV+mh9PNwuGxn0kUDJNWLrI64/m6a9wdDa6J66HvY7PjsdSscHqxMRMmLSu5A7AtiyiWZqB1CUjdHc1okRsOQggB/ArwkJTybbNej4FBHtjseCw3qwghOLVYn4nccZCVO9rFtSWFUczH/vTtcO4jgIRe/nOTc+1Ju5quOg/KPqVRT80jxGyYtH4mQxdCYInrtyHxekNWZ7xg7KoNPk8c1b6Pra7PcrPKyYX6TIxDsjbwMFQImK9W8UiMQ2qO6Um7AfE64DuArxJCfFL/+YZZL8rA4DDY7Post5Qq4+R8jYu75Z5pUspR45ACyZQ//PhZ5u77SaTQqVR3I/f3yC1Ju5auOg/KPknS1uZrrM3VZtSQGKdaV1BUaqFM2jP3w2+/GcIb2yRDSskg89nPNyrKrtq40Bk8B1xtf5p1z8dGRx1qpxfrbHZ9+n5U6vsn7zeu4b9eh39eT+hnmLRmxca2hEnSbhBIKT8opRRSypdKKV+m//zxrNdlYHAYbHQ8VlpVIGlLKTdG86OYWE6eZ0W0JZ39xJ9zp3WeZ259s/pBN//4IZck7bnoqvNAkqSttKucWqjPRO7oZdgcUDdAYUxaHMG7fgAe/RPYfbaY97hO4IW64uwO5Y6AmZVmcE1ca3+adc/HZtdjuVnh5EINgAslVx4HY9+twhutoxB+9evg/p8v5vWvI6Q9aY6NEIJ5Y4hkYHB1xDE89Efwvp9R/UAGRwqbnSGTNld32esHpUrnB/5wrAkU5+7ohRHPnlNx+dNzr1A/PIpMWpm66s2OT8WxaFcdTi/WeXZrRtaelSyTJgij/G/A/UHA7/ziT8GVz6kfFHDxryck1f66lpouJEma6UszuAquh74PdahVOTlfB8p3wxoyaUmjdcFJ2qd+G87eD88eKdXpTNAPIiqOhaU/c5OkGRgcDK/f4crbXgP/9S3wlz8Bm4/PekkGGUgpWc8waXM1Fz+K0yJ7GegHo8oQyxKIAtqSPvTkFpWoA8BZTqgf9o5gkkaJuur1jsdqSzUkHp+rpcxamcgah4BqSiyCRv2LT5/lKy/+EoPKkvpBATTq9YSsLAgwTfYGzxVHuu+j70f0/IjlVoVTizpJK1nG7YWjPWmWKC5J+6/3P8be//wJ9ZcCmqyvN3hBTM0ZHsNzdZc9I+E2MJiKP33v+znWeZhza1+tfrBzdrYLMhhBxwvxw5gVzaTNos92GCsO91WngLEy737oMou2Mvp6MlwBYR9NuWOZuuqNMRrVC+NULlIWsrO6oLjBrw9/+iOsiW0+ftNb1Q9MkgYwMicNYMckaQZXwVHv+9jsagl3s8pqW1Ufs8Oty0DKUieN1nYx8hApJRff85+Y8y9D+8QNrw4AXfTLKDMMk2ZgMB2DIOJdDzwKwGMLb1A/3D03wxUZjCNxYF9uqrNsFrMfx42wQEn48zSZk1Ly7oeu8KJFiLC42LOgsXw0k7QysbE/pFFnNfhzMCZ3LKInbRBEPHr2PABPWbeoH97gAc1Q7jjGpPVMTxoAm0/AO78ffuZOuPzgrFdj8ByxmRxqrQpVx6bqWKUH6dm+KBj2pOXtnPrEeocXDT7Jk/Ik8s6/Uog05HrDIBwt+s3rHg4DA4NRvP1DzxD3dwF4xjoNwrrhe/WPGpK9K4nP52bIpBVp8Lc3CDm/0+dMK6Qnmmx0A2iuQveIW/AXDeUaMzsaNYhigkiOZOiOZeXek3b/E5tUwn0AzgdNqM7f8EzaYFzuaOyqUwyCiJ2f+0qij78dulfg2Y/MekkGzxEJk7bUHG20LhN9bQNftGXxux+6Qps+G7KNX12C3pYyAbiB0fejNDkGmK87Zk9LICU8/m749TfBu/7ZrFdjMGP82n1P87JjKmS9GDSgfRJ2TJJ2lOBHaj+v6v7mlEwZlMik+ZNMWt6jsrqekqQ3ZRfPabHZ8aC5cmMzaXEs2er6w4bEGQTpB9GoeTNpf/a5y6w4Sut6cVAp7OJfT+iPffZVx6bu2sY4BPjt+59gQe7yl6vfAZZjqovXERImLasQKPNAg+G+VtW9UUX1pL3n4Su0RI992aBjL4CMYLCT63tcbxiEMbWMMmOupuSOZpA4fPYvfgPe/q3w1PuUm5/BDY0rex53zqvvxaWBCws3GbnjEYOnC35VXXiaqznAEWDSbCvXkTI9XyVptbhL4LTUOd5cObLGIaVgbxAQxjKtOM8iQ08v/pjcMe+K8/seuZJWjJ7tVTSNOuMk7Ze+Cj74szN7+/FZTgBzdeeGn5PW80N+632fBWA9asLcSdNMfR1hszuUO4I61PZKniszCCJqroXQyVkRPWm7/YAHntlm2fHYp86umFMP3OAy7oEfjRiHzNddoljSLXlW3lFDFEs+9tH7AOh+yfdC5/INPyv0RoYXRvhRTBtlqnSu78L8adgt76z73IU9Uzy5BhITqqTgN2xLKu9MGzeZg/zJlI6n49GoQ1yZY98LCevLR9OCvyz09KHVqqrMfBZyt/H5C1AMk7be8ThRVQfS2a6lmbQZBjNSwsVPwWd/f2ZLmPbFa1YdOt6NnaT9xv3P4Pd2AFj3KzB/s5GAXEfY7HjUXItGRe1rczNi0moje5o6FvLc1+59fIMolizZAzqyzhY6SZtlX9r5j8Fv/w0IBjNbwiAc7XFOFCL7Jd8DRw1//JmLBL1d9mWd3cYtgIT9C7NelsGM0NVBcUv0CITLlV4M8zfB3gU1T7ZgfPb8Lt/w7z/Anz54ufD3um7x//1vLDzzZwBUdJI2G8XbZJzu5BynJ3LHSthBVtsA9JxF8PYgzNd1/rpJ0pI5C+Na190S5W7jkjtIrD3zo1G9MCKIJC16+FaDjV5EPGsmLehDHMKlzxTSGPlc4E354rWrDvs3eJL2/kfXeblmXa/4FVi42cgdryN0vIhW1U3/PlebRU/a2FiRAuSO57Z7AFSjLvs0WI/UwTbT4tPTH4RH/yecm10P53hPWlMXIbs38L4Wx5L/8J7HmLcGdKiz5RxXDxhp20yw2fH47Q+fnSmLlHwf6rKHZ7fY6viKSYtD2L9U+Ps/ckl5BLzv0Ru77eRASAmf/C3WnvlDYMikubZFo2KXeqZNkzvmrXhL7kc36GDX5wHYtRb0g/meaddRkpbQqONa11nQqMOPLW8mLakYNWWXoNImltB3F9VMoRIqRuP42DPb3P/QU8MfPP3+0tcA0794rZpzQwczoOaSnKypDfCCV1E6/f2LEN3YlfjrBX4YpwcaKAlv2XOyBkE8caABuRafOl5EFR8R++zLBhfDpnpglkyapwIvnv7gzJYwzqS1qur/EznNjYgHL+zx6OUOL16x6Mg6V8SKesAkaTPBH37qAj/yPz7DZ8/vzWwNiWKmEXcJnRZdP8JvnVIPllCUfGqjCyhFgMEU+B1AMrf7MDBk0mDYZ1sWBmlrzOictFzjdN2TZgf7OI0FALZTCX++ifz1kaRtPE68p6olFVst2bEtWtVynbCm9UXlPSQvrRjFSusKsO8sAlK5oZWM//cvHuXn/uRjwx88+b7S1wDTWcxW1aFzg/ekdQZhOlDxilchaJ8CGcPe+RmvzOC5wI/GkjTNpJVZtZ42+xHyNV7seSHHK0oGErhNzvs6SZsRM395bwADHXQ+9YGZrAGSBHl4/ZsVw6Tt9LWZjuvRoc5FltQDJkmbCa7sq+/tfU/MLkFJkrRq1COqKBZ+p7KmHixB3p8kaWe3ejy71Sv8/a476L201XuWJv2UTIHyzbCmFfStnON0VUSTWP4eldYCAOuxVofkXHi8PpK0//YdrN37o8BQ7giKTftCc3dMNqNa1IWqStJ2UHTqLCSP6/sevb1t9Re3qZy2ZoAkQc4GtLPuSTsKTcQdL2TRVs3U+zTYr55QD5i+tOsCXhCNVh3rLmEs04OmDCTGIQlsO38mretHrFa08UN1nss9CZX2TJi0s5s9XvOT7+byht5Pzz8A/mwCr4E/miAncscbudd2aG/dY1/WudK31aBYk6TNBBtpkjabggoMvw+VqAO6B2jDPqYeLIlJu2mpDhzApkU37vcVSFUJAsld4tnROL3osSJSqj8a/SDCtQWuPcak5Tgqq+eFNBkgZEy9vQjApbAYCf/1kaR1LtPY+AzASIZedpP9NPMKx7IKaUisRh2shkrONmQxNOpzwUbHo4UOYO58I2w9ORP3wCSQtHSVH1RP2qyCmfM7fV78Y3/KJ85uz+T9E3S8kDmhkzTZYDPt3zBJ2pHG5QfhP78e19+ZYNKgXDesQQk9aV0vZMVRwZ5Vn2Or60OzGDesa+HpzS6xhG5SfIp8OPfR0tcBk8OsE2OsG1khkEg9nbCDZzfY7un+I5OkzQQbHfW9/ejTW/jhbOYaZo0aLN0DtO67UF8s/KyTUvLURpevfuFxjrWr3DuerHbW4aduhk/9bqHrONLwhlLYF1nPpIo30ExaUeeZlPDvXgoP/Gr6o3EjLFBmWHkr3to6Lq40FmhUbM77Lf3gjSZ3lBIGe9S655ijOxLQzNfL1br2pwzJy7shsZM2JO7jaq3rpbCYi38tRHo2XXIz8sJvVP+dwbDkcXMDUD1pHS+cCaP16OV9un7E//xs8U3LByGKJT0/oq2TtA51LrGsHjQ2/EcbFz4Blz7DLb0Hx5g0FaSXXXyaJnfMc1/r+SHLrpLlOvUFNVemUcxcmWshGSAe9Xfh2D0gbHi6fMljGMUEkRyVcOte66TnoWx86MlN/uzB2e1pMAzI7aBD6LTUmIp5MxNrVtjo+FRsi54f8alzOzNZQ1K0sP19nLoqWm91PXVfFKwaubzn0Q8ibl9t8ro7Vrjv8Q3i7N64dx6CLvzJPy/FxOSa+ODPwpPvLfc9B8Mk7W5xdqzPusA4PfRUrPPYnw2XMiVWdCxBnGOc2PEijmn5PrU5VlpVzvcdsNwbkEkLBxCrC3y3ODsS0KgMfcbujnYxxiFOsI/bXKRiW5wLkiSt3IBms+sRS9IkgOMvVv/dv1jqOkAlyBNJWlXNFEosV8tEIgG5d4Y6/SSQa9Entqv4uGwOBLTWjNzxqEPLQ076T46qA1ImrVzL4vHCEzAaiBwSHS9kSfdOVlsLKlFqrsykJ21jX8kuhbev5gqefNlMzEMGmpXISk1bM5Y7/tx7HueH3/GZmUq5k9/d8veJKi22uz7MnTJ9tjPCRsfjDS9YRQi47/HZSB7TeyLoUGkuAKhCz/xNhTNpST/amZUmLz09z2bXV+xugkAXsQe78Mf/e6FruSZCH97zE/DRXy73fb1dALrOIi+yz6YzN0H3WRdVdAx1bHruo6nkse9PY9LyJVN6fsiqmyRp8yy3Kmx2Az3T+EZL0gZZGvXpcpm0j/06PHP/cClThllbogjjEInt7yNqcyy3Kpzr10BYpTNp6zoRSZm0+dPgNmBvBklaEI187jB0Qtv3yncy3OioTfrBC3sqiJgBkupiMx72L252feXwWOKQT4PnAb2v3eQ/OdGTBiUzaX400kNgF8KkRSzoJK3eXmCr6yMbyzNh0jY0k+aEXdXfcvwe2Hyi9HVMU2ZUHQvbEjMzDtnoeGx1fR670pnJ+4M6A11LgtdBVrQ0dv60klQNdme2rhsRUko2Oz63H2tyz8m5mZmHdHRcJLx9Ko0FbEuo+6J9vPC4KEnSbl1ppqOf9rNyZF89zu1fDQ/94WwLpFtPqLEE64+W+7666PhM64u4i7MjTuTzdZf9QZj7PGFgOOOytwnbTwPTVVfK3TFPt+JMkladZ6VVVbLg5soNKHfMal3FM1QzH37hSdp7fgI+8G/Tv04zDini4tfwEXEAVUWjbvQCJQ0qOUlLEpEVZ0CMgEoL2mszYdKmUdipNGgGdtVJAisl3P/kbKqLw9kxXURtDscSbHY8NSvNMGlHG3pfuyV8akS/n4wWKbMnzQujqUxa3j1p85aqejbnloglDCpLqupYMmuTMGlN2cVzWop57q6X3vyfnCfZM00IQbNiz2RPg+Gef/8MTSK6XshKJUIgEbVMkgZG8lgy9vohfhSz2qpyz4n5NGEpG10vZMkNEDJC1OdYbFTUfdFYhv52oeOJntroUHEsTs7XadeukqTd8dXqv7NsNbjykPrv1hPljuHRRccnGl9EQ3iwNRzblBQeC+mzDTKGT+eVC3k/iCcK+pYlCHM0Dul6IUuOZvFqc6y0KmrvbOYv4T/6SZq++JFwJhoS5+ouPT8iiAqSuwV9RaPqJKwfRDjWqGuMXcDFT5mr2ry++F4hNOq1kCQit89FdGQdKQS0T85Edz0uyQLSIcCzaLLf6HicWqjTrNgzm52yn3ECFbU5Fpv64Jq/SUmD8vRQN8gXuvJ4U3yepj08TOeLZtKe+gC87UUj4zzG5SGOpfa3vGXc81o2PTev3LA69rySsmcKcWUg6Ulr0Wc3rqlqPLL0Iti0oh/o0SIzYNLiWKo+H1Rv2qzQ8SKOVVWy6NTn2O75SJOkzQTr2jRkpVVlqVVR12IGUljVA6T3xGqb5WZFqUYay2rkTIEM61MbPc4sN7AsQVsX0faz+3OSpK3epf47y3t0Xc0pIw6VyVtZ8PYAwZPVu0fXwfBMK4RQCQfD/z/3AJAU9EdTm9znpHkRi5Z+7+ocS80KW11Pq0PyHZV19JM0rXXdmLuHO8U5qtbw8EoDmqLYtKAHgx1VlQD6/mSikHtPmh+xomVBKkmrqspvATTqtZC4Ot3cjNijweU9TzNpF0pdB2gKe6w60pyh3HF932Ntvsarb1uemTVxkpxWou7owTV3SjnWzXJQsMHVoRMTm5jT4ZD1bBfdk7b+sErgtfmPlJJBOL0nLVcZtx8ql1i3wVJbzUjbFclokXLv042Ox5lFl7rw2QyqikkD6JRbfEp6acf7J5pVZyZyx51+QCxVQPPhp7Zy7Un8fND1Qla0lMhtzBNEkk5NXyOTpJWKjUySttysEEQyLQ6WiY4XcqwyHhTrJA2U3K0gPLXR4dYVtWclSdreNCZt5QXqv7N0Vr7yEFhqjaw/Ut77evtQnWND6JmGmVg1VYcUUXhM5I4INUqF6aor2xJEORYXun6YyvepzdGuucQSwuriDZikaSbt3NwXUxERtZ3H04cKzdCjAKSm0LU9cz+IRqQpoKw985YFrWY2o+VWlc2uh2yuzqQnre7arLgD9mWdJ9c7Okm7VLpEaaTaLyVEAe0ZM2mrrSqvunWJpza67PbKTxSTQM4N9/W9UlFyx2bxB5fBIeHtQ00lKTeHQ2lIxbGou/ZoEJAnksrjhY8DEESSKJajc9JyljtKqVxIW/TTPQ1gi2T4Z7n36WbH5zWnKwBc8irQ1rMF9y+Xuo5pRlQwu/mPSUD+ujtW2Or6PHplv/Q1gAqAlnWSVtUmEVssqODTjBYpFWmS1q6w1FTfma1O+T3YKnFP5izOsdRKkjSdFBS4h5zb7nPTYgMYGjuNMGmBTtKaq7Of57f+MJx5vf7/EpO0wR7U5tiMJ/f0QuP0RO544qVw8dMQ+lONQ/Jn0kLmRU85A7uN1PDJqywqYilHqenRT9J0xfnp1ssAcNcfTB8q5eJDmqQNgoh6ZQqNmmeGnqkiJnLHIJL41cXSg5mNjsdqu0qLHvs0eGK9o9zQwoFiGEvEyOyLd/0z+JW/QquigslZ2FWvdzxW2hXW5mrA0IygTCQVTcdXAf9Ss6oPrhX1BJOkHV14+7D2UgbS5eRgVJYyV3eKS/qTyqPW7w9CbYY0lUnLRy7rhTFRLGlIzfi2VLC3HifzH8tj0hIjhJsb6nc733e03JHSe21TI6oxaU67Ntsk7ZteqpLWWfWldbyQZT1Tr9FeAGCzH0HzmJpJZVAaEhfjlVaVRZ2kbc7AKKszCFl2hszFclMXJAtm0qSUeGFMQ6t4hnLHcSZNgFOb7Ty/0FMGSKdeoVoeNspk0vag2qYnHXqiPnI95opUvCVFxzNfBpEHlz871TjEtqxc25I6XkRb9KA2B2Iog+27Wh2SI5t2HSRpqpp3vnY7ACJzkM4VmqRlta6aSZtiA593T1rHC1nJbEYLDbUxDtxF9UUIy0sG1vc9VloVKmGHnmjy1EZPMWlQusNjP6sz3nwMLnyc5SfeAZTPpPlhzE4vYLVVG1YXZ3RwgZodQ3VuVKcPMxkUbPAcMdiD+gKPyZtYG4w6C5ZiWXz+4yAlA38ySUvmpOU1VyY1uIm7UFNN/0LAhWS0SImy3L2BMkI4XlX76DMdWwX/AJ3ZMGnpZ7/xGEhJszIbuWNiGvKymxZYalZ49PJsHB67Xpj2ezTmVP/idmoSka+UyOAAfO4PYf0RNjo+loDFRoVlfdbNws2444Us2ombXpulZoW9QUhQVfdHUUlaoGO7xIE3YUxGk7SeMlUTopSRAAdi83Gl/lq9W/XHlSp33IPqHF4Qs2/Nl8ik6fMs6QfsXFYF/UqxTFrPD2nJXuqqndwXXXtBPyG/+/HoJ2la7rjNPD2qI3N15vXg10KZtPmb4PKD4HevYu2Zr9Z1ydY3XnUuvfg9Z0H9rER2JGHSxGAP326x0/Mz0qBZJGn6s9f3RPu+n6KGV7pGPjEeyEpANmckARHE4HfSnrT9QYhX8MFlkAO8fWS1zUPxTRzrjyVp9QKTtORQ62/B9tNpX9TUnrScik+JU2EtVoeabQkWGxXOe3X1hJw1/FdDwhataunUE3sW0nZVAlCyIdIgm6RtPQk/90r4yC/pnrTy3R039WezrPuPZjVapOtFzOskLTGZUQqB8tUkNyz+4Hvhnd/PRsdjqVlNv7Mwm4Jk1w9ZzMRFacJYsGTa16Z0iVmcYys5+qhxSAcqSg6ZMmmzmDOYmHUceyGs3KWKPmWZh2m5ox/FdKy5kQJxKUlaS6shvP2pJnNqTlo+n0UcK/l+Q6qiI2TmW1paHXJDJWneHlTaDCLBNvMjVdfhTKECgvQsjSpjOP9xNaur4CF5HS/KNCTOD+l1u/wme8WkVcHbw3db6ks2qyTNz1RHvH1YvBWxf5HvcN5dOpOWWHivtKqpdGsmTJoXsuj4CCTUlE4fYFsmB5epOh9ZePtEbotLLFIPtkcO07maU5wFf3KoAZz/2CSbQ/49aYkcuRp20kOtXXPYDhwlESox8E6KKUtarbARaIlw+0TpTJqXGodYw3649/80i44/E7njZsfHtgQLdVc5xfZmk6R1vJA5Pa5hbkGpAlKTCLOnFY84Vn01Z++nsflZVtuqhzQ562Yld5xL3fTazOuEcTdwC91DAj1wPjvLsl1zRpm0oAcVZSzC/E0qaSu5HQSAKw+rebrLd8LqC5Rqoqx5qVru6IURHWdh5Ho0Kja2JYopPCbKkOYqAHKwd4DcMT8yJTnPGnEXqiouT8ZB7YgkScsvTj/6SVqSoYcxu2J6hl6I1jVh0k59sfrv1hO6J23axc+vWjHSkFhpDul1a0E9oSRpUBDFbPcCVlsVGOwRuW31JUvkjiUmaXGsdOHpF8/bg9u+ApZu4xXOk+VJg3pb0N0YVuPb1YzcsfyetI4XslYZNlMn1cXNgVQ0vHF3PJqIY/DUd2pbtrGIRw71Qpm0cKDcDJ0aXPjE0AY+02ubyB3z6rXt+YnBjWJ8IbGZj0oPvJPvblII26ehik+t46UzaSPGIckYgu46X7b9+3S9sHSrc8WaVLAswVJjNkyalFKNodHjGprtBSqOpRLG+pJh0sqAP5S5vm7z91nRyVmj4lBzLbZnkLx3vJC5ZDRRtT2M/byw0D0kYdImkrSso7TfBTdJ0mY4KmLzcVi4BdyaYtKgvKHW2t3RC2L6Y0maEKK4mcZJW1JLSdajwf6EERbk6x3RS1oE4iGTlpjY7XJDMmm76uKHkda6DgPPqmNTc62CL35Co3YO7knL2TVmTvRUMCPEMENPadRyApqk4rzWBOKAqNJWn7Nbh9pCqQFNYm4wInestmH+Jk6LjfLkjn/4ffBr38j6ngogVltVqo5Nq+qw1S3f3bHjhRyrDnX6iWveZiepOpuA5kgi6AKS0G2xNYX1nKu5BY4V6avvztpLR5k0pzgmraOle07QSSuPqYNho9zAO5H0zekkoCPrqiqeuNaWiBG5o+69ZuUuXn3x7dixhxeWO+dwo+Mr5QSw2KzMJBj3wpgwljTpg9tE2A5LjYpyFGwsq2JGgYOLDRgmabUFvsx7L7fWhiZqS41K6dL+IIrxwpiW6KveL8tObd13+0Ghe4ifMGl2NklzJ41DskwazCZJ87tpEYwFvY6yxiVl5I49d2FC8dWs2vSKkHAnZEp9CYRF2FfFrmmKtygn+X6iclDKkFEmbUsmfdY3knGIvvheGLNnzY/0pIFi0wpxQgtGaVS8/VJ60jqeniekL367mlz8cp3QkkHWx6tDpiaVYM2dLNU4pO8n1X5bGadEnqpgLNzECdbLkzvuX4T1h6mffS9AJqBxZ8OkDUJWnMQJdI4FXV3c6QcmSTvK0AG577Sm9lTM1R32BgUxKeFAVVpX74KtJ4eJQqW4nrSeF2IRY4fZymOSpJV7n653fISAhlSH+z71oUKge6XUAfAjUtOESfviv00t3OcmcaU8hcCeCuQ2Ol7Kmiw1XbZ7Qemz0pLfuSn7acCZJoyNpcIHFxuQ7k/yFd9JhZAvCT+WPpQMtC4TyT3RyBg1jPQ5NVYK20O8A+SOE3PSsj1pMJskLfLAUTEJ9WQ0QQlF/SQmq7bxgpiBu6BkiP4wuW9WnGJcuJO2JLcO1TZxX+0N44o3x86PTBmOPuqk92Mys3cvEFrFdEMxado1Joy11nVjpCmzMBo10bpWmqp64+1PdY2xLSu3i59IPZrZhsQkQ48bSm9ckoQtkQUdq6j/itrc8HNur5UqdxyEmaGvScW5Og8Lt7Ast/G93lX+dY7Q7333M2+nVXXSjWCpWZ2NTt8LWUqStOr82MG1bNwdjyq08U3gZJm0TJJWc4l0c3LuCPrg1NVB4nWGcsdCe9IiVXiCNPBOBzaXnKRtdjwWGxVsfx9pOQyoqOJTaw3isNS1DIKYim2pzzvZ147dDcApsVlOX9qVh+Ftd8PD72Kz66WS6cVGhSiWo4xBCUgMU+qyl94rE661pi+tWOh7sX/85URScCoesjGLjUrpZ13yPWjEw3siPet6xRYkg2iSSZuruWPGIRkmrbkKdmU2Do+hD7ZO0ioNJWkvww1Vn2dU5/GjGK+SJIjD+KNRsQs8z2rKWbM6RzxQ9+44mWKJ/MiUjjZsc4L9NE6vOjYV21L7Zc7M7tFP0tKetEg5HIaD4YR3VG9DIRl6Ind0G2pj8Pamyh3zZNK8MCaWow2JdVc1Xe57EuqLpTNpK9r21mks0A8iRf+3T5QqDUqZNNceVlFrc6m0oNYrKWH09sFyuWP/I3xJY2gysNyszMw4ZDkz9X5kHklzxQQzRxU6CPLsZsbkZdKyuJhG60FadSToMtB756gFvzoW8tLwKwn30JkNVPGp44Wl9xmlbJG3j6y0Ad3QnsxK65Qo4w6iYe+Etw8IWHkBACfFRjlJ2t559d8P/Sc2M3LHtNe2ZNYk+Z1r8VC6Nd/QhdiUHTAKgUKhWd0d2pyTq6wGwyRNnXXlqkaG98TQeGjENK6xPKGwygv+czIO6apCPoBlwdypGTJpleHf60vQ2y7+fRMVQG0OL4jwK5Pu0mlRLm8kSRpAtY3Ua5kWp+dFpvS8iDrasC257iRnWv5Fg6OfpGWYtGk29MVdfF35dWv64k+XOyauMXlIk9LNKOqmckchhG6yDzWtX06SlhzOc5b6HJyGWo8KaLQTWkm9AYNpsqBqO9VdtwYlJmkv+WuEOLzJ+kD646UZJmkLqS1xm5qb6dFsLE2wzgZHBPoe9qwGW1PljkmyXdCh5tah2tJ/naw8FuHu2ELfpxnL4lTuONiBqBzGZrPjs9ysqu+yXsteP1BMGgxdFkvAIOsWrBvvaZ9ACpuTYrMcG/6EwXv6A5wKnkn7WhdnNP8xKbhWou4Ia7KX7GlgZqUVDX1PdGnwtFxjrjd0CFxqVtkuuf86ie+qmXvCtS0aFXuoGvF2Icp/XQcah4wzaW5j+PdZDbTOMmmgvi9lfFfSmEzF6UFNf08ziXNhTFrYH372lRbSU/2Ukz1pVm7S7a4fUkXvi0mCiD7TBvmrQ45+kqZNIrwgZpBm6KM0aiGHWaJ1dVTVWQ72iOUUrWs6+PXwbzncjIYBBKiLvz8IFTtSUMVoHEliVAkVa1ltqc9e2fCvqaGJJbF6wyTNylDrQyZtwS+h+h1HqqF68QznrBPcyvn0oUSOU7YbWzdjVZ3V6u/0fJXQh4NhscHg6CBJ0uwmfapEdnVkUx8OTC2ISXNqaQUwHiSN1sOjIP+etIh5a1hMAPU7DoKYqK739H4JFV+UffhKW40VEdX20Bp6Bq61/axbsLevPhvbIWiscVJslNOTlnHy+w77z4c9aY3ZDC5OCpWVcBiQKyOdEJncK0YhUCx0oNsXDZ6SazQ7T6fFvqWmS8cL8cLyzFsS46FsDxBkWl2S5L2A+yJh0twx45BBEKdSSDXMujn8R/OnYfc8pWOCSVss57uiz5Co0iKMJWFtCpNWVE9aoHusAapthC4wjCdpefakdbyQCvp3cYZJ8Ujh8YYxDsmYRHhhxMCdzNCbhckdEyZNNyQOpl9829YBTQ4N59MOKMhUbkrs3xgEMa4tsH0dxOkkbbSiWU5glWyUVSfTk1abg7mTxFgsh1dKWIQOZqptnojXOBkNN+GlZgU/jOkWUSm6CvYHWkamxzVA9uBK+jeMNOjIQd/DPasFCMLq0sim3tCBezEa/ozcEabuazpHy03u2PFCVtxkxtHQ3RHAc8sdvL7Z0X1X3j6iOjecSdeakdwxcdXUc4YAovYpTrJVjtxRB+S7N30132p/gNW6uvizkjsmiamTCcjn6o7udSn3XrlhkexPQjFpTtiF7jqgmDQol2FNjMGUO+wwLirjrDuISUvXFUeKzckmaa1jyoSobBVL6I0yafXFkpg0db8Ejt6/aivq51kypTB3R91jDVBtYwVaGTJ1VFZ+RceK0AXUbJKWyGBvKCZtvCGxOsmkNStOccEMqKrzNbSukI80KGEErdibpFE9zaSVJHf0Qh1A6GtQn1sANJNWcvV7xGEpQ61ju3QqqxyLr+TqsDl9EdrxqtLi8fA4S96FVO6ZBjQlWhOntsRJg71Q9+HEwWXMQ44e9L00EEqmEdUWx4Z/qiCgMHmI3tMApLeveq4zQYgQQvfa5uN02PNDlu2hCykMXWu7zrx+UjmBdz+IVBLs7aV9nHuDQFVjawslyx1jqtmeNP3ZyPlT5TFp+l48d/yraYkBJ6T6/RO5Y9lMWvI72/7+pElEVAXLNXLHopHKHWs8LTXDvPUkkDnrSrwv0ntiLBmaKyNJO8CCH1SRNC3mZ5O05ipEfvkupJE/yqQ1lsph0nRM5ju6P6s2pwrHZTBpoZbvA1Tb2LqYPr0nLZ/zbIRJs4ef94hjcdAbcbc8DI52kjbSkBgTVBMmbTRDL6wnzampRtDqXEqjZoe+gnKNAXKhUtXvIbGjsSStlu1J2yqlFywNILw9QNCe00zaIJxBkqZ+36qTkTvqnr1e/SSnxXoxG8DIItT1D90WT8k1HOmnuvNlLRHaLLGhemhV3ZsiAdEJPRhp0FFEEgQJ9R2P66OVt8TOt1eYPGTIpOHtU3NshN7HElg5Nlp3vYglJ2HShu6OAF17Qf28hMA7iiVBJBUjr2X0IyYAjeVSEwA/jIfJsTdMSqyFmzghNukMSgiE/X2wq1xwbwZgJVAKgWZFuZWVbxwSARLhD5PWudqYSYRh0oqFtwdOjX5s8bTUDPPmE8BskrSEURZjcdGwV7H4JK06hUnbGwRDE7tsT1pTDVYuvUA6waQtqRitaEZvkMj31WdQdZ0Jd+lGRcvb8y6mJ+cZQHVOjXlhurtjLMmlL63rhcw7OgafGqfr+zGns+RoJ2lJJUI3JFJpqcx1jEnzwpgwyidLTpH0boDSul4lQwdyGZTX8UJckos/zNBHGhKRpSRHXhjpYGYXqm3m6urLP0smrTrCpKmAZtA8ySmxUfystGS2ld3KVBeTg6t8CUgSWNYz4xpAVReLPrgMDonBHlRaeJHaO+SYZW8i1ShEPpswabonTfidkX60BE6Owz+7fsicreUh+kBNRovsWZPGKUUhDbhcK02KRgaH1xegv1P4OhJ4YTSUUWWSNHfpJioiIu6UIOP29qHa4pw4AUC7q6zDhRAsNt2ZMGl1PISMhz1pWbfTstiBGxm+khUOgphzchUp7MxZN6skTU4kael3t4QkbbQnLekZDodJWsblj5aerdst4fubReSPyO/UXMFoGDMVhUQZYqnPoOpYWvVVQuExzLo7tnDDLoJ44kxL4/QcEtauHzFX0a8z1pPWzSZpOd2PRztJyzBpfhhTrTiKTRpzjQHoBTkHNEFvWB2ptrH8fUBO6UlTH2FeTNo015h2zWHfy7AjJVRovIRJGyh3zRF791nLHZ062Go9Qes0a2zR7Q8KXsSwWvRkrAKapLqYzBYqc35MwhxW41EJyGQztZE7Hjno/qPkvh63oW9quWM/7wMtCtUssAyTJvz9icITaA1/ThXYnhfRsEcrjy19aO+iCwwlJGkjjLx2U5yrucNRB7UF5TRZEvwoVoUwSJk9AHtBsVrufgnmA14HKi12aLMn67h7T6cPLTYqbM3AyW85nf04JndMbPhNklYsdMFgEESEOETzN8+USet6IW1nsnhdhnFIMKUnbS6VO2aYtMoUJq2MIksWoTcivyttoLW3C04dT6q9rOJYE4x3YRL+YFTuCNBkMDnP2M6zLSlk3tW/R+bzHulJgxslSdN9QNW2qjraFjSXR5m0RDaTt+Rx3DUGSQOv0J60Ua3rMENv19wMk0YpgXfak6YDypprU3UslaRVtea4dCbNTufmJYjmb8IRMYPtggOaZLaV1eIKC4R2fbYSEM2kudKfkIB0vJDQndSFGxwR6CAoqdJaYzb0yR6Tu2ttqB0WMz1pdtCdKDxBvvMfO144TNL0odaq6qJP6IDbLCXwTvaRuoiUIVW1zVzdGY46qC+WtqeBqtKnvS6JBT/A/CkAKt0SnCZ9ZdAxiGLOsobYeip9aKlZYXsGc9KOVfR7JsYhCevaD8uzFb+RkUnSAOTS7SmTtlB3sUTJqhEvZCllLkbPuq4fEQgXKu3SjUNGmbQx4xBIzVZKQRxDHEwyaVD89yVxYE+LYPZEkpYwafnH6ZNJWov+0JBJI1/viGzRIEOmVJWqz0+9M24IuaNiL0J3jljqCuiY1nWYpBWRoQ+ZNFAXf5prDOTj7tj1IqokrjGjcsd+EBEm8ydKCLwHgaaMg35aJUobdYXQ0qCS3R2THrlMD5alZ6VFW2en/tvcoJO0vt0ABN3WLenB1aioBLbsgwtQvXFjBxfAnheZ/o2jCn0Pp0laSzPk+vtkWYK6a+cvDUnMkNx6Ks+xg87UJM3Oc/inH1K3QpWg6d63kUO7pPvUC9Tn3RK6obs2P8qklS531GqFOFIDcZM+wfnTADT6JSRpWu7oBTHnxfHUIAJ0kjYDueOKq99T36OTckezpxUKXTBIihpi+TbYegqkxLIEi41KuaoRL2SxquOrTBIyX0+S9+Lui+nDrDNMWpD0pGWStPoSIMpl0iJ9PaYyaQXHadr0aCROm9KTBgUwaWNtSQBzVh/XHu2xtq38FG8dL6TtJPfj8PNOcpGena8Z1tFO0hKJmXaNURd/1OGwmdpVF6l11Y5kolcok9bzQ1rTGhKTi+/oDL0MuWPSkxYOdeDz9TFpUMnGIRV72EuSwF5USVrhwyNTW2JVtem3b02ZNCGEmpVWortjalUd+2MHV0Ya1Fwx7o5HEfoeToIguzkpj2hWCxj+mWXSHOWU54bdqT1ptiVyHP4Z6SQtow7QTNq+F5YWeA/0PtKUw5ltc3WXnh8pWVNtQfXglmSdnTJpyViRZF+rLdCjTmtQwjiA9F6MuGCdhJ1nUkZ3qVmZiXFIKiXSFfLUOCTpP+ptlW9vfiPBUz2zgyBCCLCXb1OMqz5LUplhSeh60VSjhvlG8YYy3lR3x2swaXZinFEikxZpifAsmLSx86xq6560/nZqcpe0JRXDpCVkiorTF21/wggr3zg9Gsbp9mhPGsC+UKN1bowkTTNpvq2+AFXHnhjonGToxTBpkzRq1ZnOpOUld1xIaP0xrSvArlVe/0bKpIWD9Is/V3OGm3OJ0qCkAp66O2bkjrV5JS2QRX8mSZIm1T0RzJ+B7achUp/HUqvCVonujonc0Y4nHa+AoTWx6d84ehiTO9pt3Wg+puHPX7+fYdKEgGoLJ+xNqAMAHMvK0d0xpCaisarj7Ji0uhzaZo8EXPUF3Wi/X/haQCdpzpQkTQi2nFXm/ZKStEqLQRBzyTmhehb3VMFrsVFhtx/kb8p1FXS9kLkkSdPnTsWxqLv2sCdNRuXbm99I8Dqp3LHqWIhEvqe/o+26O3RELQGDIKLtTiZpSfI+POvy30PSnrRMkubaFjXXUgUmf7iXjKB1rNwkLUyYtDF3Ryg+BoiUq6Q3zqRlTO6K847oj7QlASw6k/4EVo6KNy+MaFiTw6zTs8SX8L/+Grz42w79XnDUkzRvD9wmXqw+YNWQuKJsg0MVEBfmGjMtSRP9Ka4x6u+5NSRWpmtdATqhpYbBlsCOqA16CpM2g/4NTwczQogJuWNzfoVYihKaY3Uwoy9PuHCbChZ2lMxyqVmdiS2xFXkHM2mNJWMcchShCw1eGOFYYsikZSqejUoBo0WyTBpAtU016kzo90FNHsljT4tjSc+PqIlgJIBwdKDTKTNJ00xazUok5fVRE4Dagvp5SeYhXqiNQ8aTNGDbOcZiWIJcyu8ouWMYseGoXrjsTCwpKZc18bP9HsP7Je0dzNne2mAKMsxIzbUzRmHqM1cD4Mu7JwZBRNOevCfKKEj6YYxjiTTIT9Cuudo4RLl+TyRpzdWS5Y4Jk5aVOy4AovjvShSA7WZmymXvmR0gIwXMk0yJQt2Hp+N0LY9esCeTtIRJy2NUmhfGShkCY+6O6n7seCHc882wetfh34yjnqQNdtMZaZBYe45Kg1ImrQTXmBZ9quPujmmGno+15zSta8KkdVJpUBlyx0kmbUTmUF8sLZjxw1hR6DDBpDXrVXZoYg+K1l3vjTVT36Z+rgOahRlIQGD67BhIDq6VUoJfg88TuucjZVKmuEE1Kjb9IqqOMKw8VtpU4t6EExao4lMu0hD9O1RFOBpAoEeLpElaecYhVYYH7NC1VjNpUFpf2oFMGtCtrLAQl1AE87RxSBCzURlN0tKB1iVKHjteSCs9A8dmYiU9aWAUAkUiYxxSc+whI6OLsnNJglLWcsKYlp18Z6+mGimmJy3bj5agXXOUzHLaMGvQTFqJSZomLUaYNMtW82QLZ9ICsCtD4xDXmnAAT+WOeZIpSdFxLE6ftyYVTXl6R3hBrJQhMCp3TOP0fL8bRztJe+V3wTf+21F3v8aoDX1hrjFhf5ih64vfFv2RoYaQr9xxEES0ncnNKNG6dgZhaX1G05i0uWxPWqlMWqS++DDqgoaSHuwwh+OVo7tOAmd7blQCMlfXm3ZJ6AcRri0Q4dWYtOURXbjBEUAcKyVAta0t2K1M4DkqdyxEvw8j+1otntzTQFvw55Gk6d+hwiiTBmPzH729oWSnIKRBRGrOVBs6B2aZtBL2NSklfjSepA33Nb+yQFsWLLtMDEt0/1GvsqLuDe3wuNRIXGvLLD6Fw4A8I/mfq7nDPQ1MklYUQl87n7aGLQ9jAffIAPgScC0mLTUOCbrDPS4n+FE8MiMtgWLSMj1pyZ6aoHkMOmX2pCWjm0YLYaW4oUb+CJNWdSbvmWSsTC/PMy0r34ehcYg4mEnL40zzQq0MgYk5aUDu342jnaSd+mJ44Tde/eIXasE/nGQOSU/a9CF5eTBpXhjTsCYz9FTr6oWlsSMHMWl7/UAZCtQXFdNZQgKQyoLiSAe4cyOP74k5qv5OwYvQEhDN6jrN0WAhGawpS2poV1VOa9TdCEbn2TWWQcalOtYZXAP+kDXxAh2ku/UJG/pGpQjjkORQGw7/rMveRJ8tqH0tF8da/TtUGLOHRlUeu4k6AAoPJgaJIiPjoDt1/mMJCgEve6Yl80AzTFpUXaCGP+x5KQKJVCvpP6q4sHgmTdIWtDFDuTOxsgH56L62N8hcI6MQKAbpPTGnzpis3FHvT4pFKlHumO0BsrMS2LGCZGaNeSGIpjNpczVnOCfNbSp9eBbNFZU0Jklc0ZjGpEE5cwXjEGx3dJ7tOJOWkCl5nmlT5Pug3B3HkafizQtjqlOStHZW8ZYjjnaSppG6+2WrzsnFd5OetALljlrrOm8NJlxjhkxaHg2J8QENiVrrOgj1nLgyjEMiJe3MMmk1l1hCxw8zAU3xDdwTsqDaaJLWseeoBTvFLiKRgCT3YmsJhJUGlu2aSxjLNBAsGl4Y0XBQSVgmmEnm2aXujmACmqOE1A2sNTrMeGKuTBHGIWNMWqVFXU5n0iyRD5OWFM9cGU4kac2KowtP+Q7/PAjJOeKSVJ1ro/buJcodk/lL1QOYtEjvr1G3wM/ESwLy1rAot3hL2mc7n/1sSoCUkp4fZs7ArEmE6UkrBZmCwSCMVQxQbYPljMgdB0GcmmoUjUEQ07Amg+Kaa1NJZrcWtId42VmGGaQqAL87Osg6fULJs9JSJm08SVssh0mzXLxE2u7YE0laxbZwLJGvd0QwJne0XTxRpT2VScvPO0IlaZNs/4jiLUdcJ0nawUyaY1tUHStfrSuMWvA7FQJRYf4qDYlhlMPFD6IDGhIzWteGljsWyNhIKdWh7UwyaTBWdS5BGqTGAWQrzqNJWtdZoBHuFLyI0QGftYozMoZgrp6RTZWAQRBPuKAlmK+77PaCqTI6gxkjM8/GC6NhlbaxOHKd6pUC5qRNMGltmrI3lBJn4Nj5JGnJ3u1If6LK206ZtJKSNF1AqTAM+NrZQcklGoeMzF+awqQlfUD9vQKDvCQ51HLHdAhtRtYG+ct3DkIQSWJJRko0DIDSfujaPAjb7GlFIdMfmao1hBgJ9su+LwZBRF1MJu6QuS8K2kP8MJ5axKonSge/O9mPBkruCOVJHlMmbYrcseg5aZEPdmV08HdNzwrTe4kQQpth5Vh4HE/SgL6o02JSfZDk2YeN08MoJorlUL6fIW0aFRsh8lf1XRdJ2nBI3qTTEOiqc66uMYGicN1hhWRgN5mfkqHn2ZPmH+Aak1z8tCctDoYHewEYSYqj0Z400PKCUpO0eKzi3B55vO8s0Ir2ip2do3vhEqYslYFk5I5Aaa5XgyBibsrsGFAH107fH/ZvGofHowM9sgG7MpyTBToI2kmf1sz7QIPMoab2NVlp0WAwVe6Y1zDrhL2yZTDRL9HMGodACUya+u66MrkGVVoVByF0caXS1IzBTqHryK5lZF/Tig0AoT8Tb6/A7+6ItC0eNvzrPX3YY1HSnjalZzDBXF2ZVcQSHXgaJq0QZM5YL5E7wsh9MSIRLmNJwfS4CDJJWkGqET+c3pOWGjsFvdFB1glaeqxKWeYh0+akgSr2FB2jRVrumDX4S0xLMu+t1CF5GofoeDyzT/Ro0GSa3FFdw/iQMeLQfGpSvi+EoJWoQ3LEdZGkjQwzdutKrpO5+I2KnS+TNu6CBgysBm0xefEdOz+t6yCIqKU06vAGEELQqjrDoY1QqHlIciOOSy9Ttqgflpqk+UlPmp6bNy539KsLuGTscItAxt3RtoTauDNNuWlFvsTq4twUq2oovrpocAikSZqjig8JizU2HL5RcegHUW4DpYGJQy2utGkyoDqZo2HnJHdMZ8HFk0xaq1oyk6bPESceBjSWJWhXtaW4EHqg9U6h64BxJm0fKu2RvhZL97wG+wUmaSmDl8gdbSX5DLoQeji2RaNil8aYpEynCJWU3HLSxxKpfdcPdZ/NF8aeJoT4VSHEFSHEZ2e9FiBTMGgPjUNgJNhvp2Mrir8volgZ7NTFZOIOWdfPcnvSGhWHvh+pmOOqTFpJSVo4VGiMoLGkR1YV2FeaGIdEMZYYqsvGzeVUnJ4nk6YZswyT1qU+NUnLyzsiLfQRTn7WqD7rG1vu6GarzpkMPW8ntKk06vQkLcnQoxxYHC+Mh0naWODdTqvOxfcZJdriRmp7qz6H+RkyaaOyoPmRx8NKwc3kUmbkjjH1tLo4rOjOldy/MQhiWlOcQEE1/O/2ywt+DT4PxEMmzRtn0jLJQWJZnKsN/9i+FjgNLCFpiemWxXnKHa3Yn2rBvz/IGIcUzI4kLLgjRwO+uexw3vpCaYUn0DOFdAEoC7etPpOgU0ZPmmZNRnpJdoDEya88dQBAJZHGZqREU11rvzDwa8DXzXoRKbJyx3CMSeuNymDLOOtSR9YDmLS5mqNlsAuAyJ9JOyBJq7uKSZN+b3pPWjNh0kpSsRzIpE0qz3JHHAzPs2SebfLe40xake6OQIc6DXmwcchhvSPSfVv6E3EXZMbK5IjrI0nL0qgwsmGAsuHPtck+dY0ZXvyeaNCaxqQlFz+PnrSrJGlpht4snklLgpn62FpGBr/OoiftACYtTOa4FJWMBD1l0JEeXJMStdLljmFEa4otMWgntH6gmOBKC4o0HzD4/JAwaZayLE7nLibJgS72NBLX2tzlISKtAIaOqgBPrTzm3JN2EJPmhTEBtiq8FJykpcPD49Em+7laZrRIbaEkuWPSZG+lBaAsKi1VjIuLTNLSQbwtBsmYkwm7dbc8Ji1RzEyREo2oOBpfOEyalPL9wNHRbk70pE2RO2bjgIKRthdkzH6ySFUjtqP20ALkjtOMQ5IimvQ6IzLlFE5Fyf3KkjumTNpYklaGeVgUgOXgJX2tCYpm0qbE6fvUqcvJnrS8vCOG5lOT8n3QcXrOSZpz7adcG0KIXwW+CbgipXxxHq+ZhZc6YU1uGKAy9KKZtC4NluTliafma+0ZDa09p80U8kJorKkfFMmk6Rtx3GVrpGF4rDG0SPiJLOwA45B02GZRQd7YwTV05BvKHedKb6aOadav0UydrPELJKD5gsCY3HGESYu1W1i1RTNh0nKVh2jHWl3pDGwVXByk4Q9zGK+RVB5FNMmkZcenLJRwn3qB7m0NB0pKZ6nPeK6unQNBFwCLr35PyB3HkrRGo0lXVpFFJq56X4vcFkEkx5i08mdiJQG5CoBG97S0CJYMtD73QClruuEwctZl5I6NJcYNZcqQ9qfsKgFdIfjvj/wu73ji/yOIAtqVNr68hZ3wLuCrChlo7Ycx9cZkmFxPkjS/g3Tq/NET7+TxncfZHmxTc2rcPn87b2quUi/N3TFh0ir80ZN/xAOXHqATdHip1eKNts2JIhm9jHGI63rcf+F+Pr3+aTatPb7S3+RVcYRt2TQrDpf3J30dnjemtCV1ZI16PDn2wMrJO2JohDU59xMy6pAckUuShqLsfw74jZxebwSJ/G7ECW3j8fTxRsVmfX9SsvO8MS1JE3VOT3WNyefiR7EkiKRqSBS2qgxl0KqNN8gWz6TVxGjFeWRYn+2o6ndZckfbylR+RzXgIu3fWMctZAFDi2xvXKfvdyD0S5c7ekFEs3lwM3XHCwmjGKexbIxDjhIy7o5+GA7VAdlBytVWWqnN1TxkbKaeZyuZTn2aPETkN/gTwIqmMGmZuTILBQRYk2uJJ8aKgGKLzm7qvb2+AJuPT3+BHHGtJK1dc9imjRgUuL/qfS25D0YGF2vpbbvmstsrZ05ayqTJyYR+1LRKJ/RSjkgiv1AhhPge4HsAbr755mLfzNsHBFSawzlpMNKrWKZxSJKkVQn4jpNrPPbxn+WVx1/JWnON9f46D2y9B3ni3Zzb+3pOF5GkRXIqk5a2PPg9nnTgRz74I7iWy2J1kX7YZz/Y5z+1LX6i9yyvz3VFB0C7O/ZkzL+4919Qt+u0K23+tHuB/3D6BP9982HO3Pbl+b+vlHpOWoUPdX8a7+Sn+Z4/V+dGDYvfacV82Xu+j5//mp+nUXXobeRbdJTAP/zwv+RNL/hrfMNt38BeXKMaX0XxdljjkKSQpPeobtDl7Z97O9/xou+g4TZo1xwu7uaYiJKT3LFoyn7ECQum96QVOX8B6Mg6jSk06pBJy0nryuQ8IdA9aYkDmVMv2DhEfZHqY0yaY1vUXXsoc6iXlKQF2jgksZkdq7LaOnH1i2qyz1hk97MHV2NYda46Fq4tSrUlHvYM1vAjn17QI5ZxZr5RecPPDZ4j4ozcMYpH9zRIg+NGRSUw/SBnDX9mT7tqkmZZ+SRpQcKkeRPf2+FokbCQKvjEWhLZdOiNNH23sxKVkoxDJtwdx5K0ZtVhW7awi07SLJeBdIdryRYLmA2T5kzp9xgZ/9JYVt+jIo2ijhCklL8opXyllPKVq6urxb6Z34FqG0nGVRlGGNaRYm3BSO6JivQ56zq8+a4381++7r/wk1/2k/zyX/ll/vZN/xGAf/XhnyqISYumWvAn+zORx7quE/zCG3+Bd//1d3Pf37yPX/u6X6MmHP5TVFZPmiqkfHTrQcI45G1f+Tb+9K/9Kf/jjb9KjODXzr+noPdV51lfwEb8KSr+i/mFN/4C9377vXzg1Lfwxm6PT61/CtCOxTnL9/ctwb2XP8q//ui/puN32I3rVKNJJi0vxVvqVhwrtv+Pnvgjfu6TP8dvfu43gcz8vBxxffSkTU3StjL9G3a+Fvxa6xpaFWKp3ntf1qnHvQmbdyc3GjWh9X1wqjyy9Qhv/ZO38gPv/QH+x2P/g1bVHgYSBQc0Qx34pKPSSEAzZhteFPxIyx1DDyyH//LQb/KG330DX/ybX8zf//O/z7a9Qygtwk5RSdqYTj/bTA3Q30IIoXpbSupJ6wcRDStgw7b4mSffwZf97pfx6t9+Na94+yv4y83/gHA3h032N3CSduTc01K5o7IsTtUBY4OUi2HS+qNMmkiStMlDzcnJOCSZncMUuWMrI3csw1Z9kJU7jgxKdjOFpwV1DQ5ZdLsWvAkmbVTC3ao6bMs2jldgkqYD8nT2ozspd5yrOaU61gI48aSUaCh3zBrN3Lj7WmHQJjaprMuJ+Xcf/3f8zad/j74Q0NvCthK36RKYtHSEh48nBEu1pZHHT8+dwlv/Gj5w4b38pStLMw5J9mcReWyh1rhcX04ff8XxV/Cm2gk+a0XslFD0SQrY917+GDW7xsuPvRyAO9a+mP+l0+UPdx9mo19AfKSTw4tSvX8rfAWvPfla5ipz1Jqr3ON57Pl7dIMujUrOo7KCAZu2ug5bgy3+y2d/jb24poo8Y26W6TDrQ/ek6R5rqSSe7z//fgB+/XO/zp6/R6vq0vF7/PGTf8wjW48c6r0SlJakCSG+RwjxgBDigfX1z0+n609zjYn81IKzWcm5WU8zaW/5xE/xL+//lwDsyToO4ZDN0cgvQx8OWn2kUuPv/dnf4+m9p3lw40F+9L4fZUc8MMzQm8ulMGlpf5xT5SMXP8LbHngb8eqv80jvz+gG3QlGs7D1BJGSHOjg6t4L9+JaLn/9rr/OZzY+wy8++Y/5QHWeqKgm+4kkzSKIAz7ibRDDiMNjeQFNTEOE/PjyEr/17J/xFTd9Bf/0Ff+Ub77jm/nszvto3vrvubC3pZK0G9s45Nc4Su5pmSRtKpOmv09JpTbXuTJjTFrfUrLh2hR5iJqTdvhExQtiBDFiSuDdzFbkS2LSaq6tE8bhWpJ+XymlZpJkoXMoYZi8Dpm0UfOBqmOxS4tqsFPcIrwOVFujSVp1TtnfjxiHlCThTvs9vEl345qaZzc6WuTo+G08Xwghfge4H7hLCHFOCPF3Z7ogbx8qLX76oz9N/eZf4Pcu/VN++TO/zGd65znrOiPJezlMmro3A832N9xRJ8X5ukuw9XqO1U/yu+H6UAabE9SctElJrepJk1iRP0zSassjz3ld+1ZiIfjQhftzW8+B0MnS/Zc/wivXXkk12Wstm7f6FiExb//c2/N/X60MuRApiV9NrAwfqy9yUseSFzsXaVYVkybzuj5Bj01d+FtrrvEbn/t1duzhY1nkzaQ5sc/AdvnIxY/wqrVXse/v85Mf/kk+3v95uOX/5oc+8EP8wRN/cKj3SlBaknYYyt4LI6pZXXB9iW3L4sGLHwHUYe+FMWGUU/UzGCCBxzrP8vuP/T6f2/wcu7GuvCYBu0aaoR/24qda14AfmHep2BV+8+t/kz/+1j/mhUsv5FP936Qb9NX7NFZK6UlLhopGdoXvfc/38vaH3k7kPsPj8a/zDe/4BtZrrWKtXTXSeVJaprTn7XH38t388Kt+mHd9y7twrQrvajWL+0z0Nf945yxXrD+j73yat7zrLfzdz/4cH6zXxqRBxQc0UkoGYUTdCtm2bb5k+SX86zf8a77rxd/Fj73mx/j+l/wkwvb4xPonVEIfdIcS3hsMR849LdOTNsKkJTIzXXVt6uFlubvWZhikgaUStoPkIXmMaPPCiLqVuJCOMmmJAUHXixQ7UvB9msq3wsFoklZziKUed5AwmgVXv5M+ayXjHkzI+4QQdOw5asFugYtQ89mS5KjmWmpWW2ZmX1ufrYkcv0gkAbkdT8odrYS9SXrS4AsiSZNSfruU8oSU0pVSnpZS/spMFxT0CSp1fv+x38Vyd2g6C3z3S74bgEu2XXrynsRFkWZq6hknP0hksDb3LHwpH/c38SM/VxlsEMkDmTRHJ2ebMsARDu3KqGT5xfN3MhdFfPDZ9+a2ngMRepx3Kzy99wyvO/m6kYduqa3wFdYc73zinfm/b5QkaZowsTKxfX2RE6FK5C90L9CoqH3Wy2svCQdsatOQ737JdzOIBlys9dLHskjmGecVp1uxz0etkEE04Dtf/J288ZY38kdP/hHnvAcIdr+I//iVv8QPvvIHD/VeCa4buWM6Iw2gvsjPLi3wlvf9Ez566aMp9dzLa6ZQ0GPPEgSxusHe9sDb2I10gDFWYc09Q8fnvAVvuv1N3Dx3M7Zl8yOv/hG60SaVlfcoxrC5Uig7kjJp2vb2aW+Tftjnx1/749wT/zQnez/AjrfDb8jdwg/KKJaEsRwJZna8Heb1rLTF2iL3LL2SBxp2cZV4naT9m4d+nd36O3hM/AfOdc4BcM5xMg6P5cgd/ShGSqiJgJ4laLijRiqvOP7FyNjmc1uf/IKqOn9BQO8pWM6IlGaDiMddN3XzS9zD8h3+Ocqk9VD/X4mnWxbnwaT5YUzb0a9jV3lw40F+66Hf4sGNB6lX1O/e8cphR0Z6W8eYNEApFcbmhBWFhEmr2AIin9B2+U+f+k/86L0/yk986Cc4t3+Onj1PNepADi6b0xexPyJ3nOaePHT0Lc9ufdpMPciMSkjulRIKhDccQo9ztkNMjLf+Rv7e7f+GN7/wzQBcyp51WUfUApHcm75O0qYxaQA317+IgYz4VK2aaxygLPjtiZ83KrbyDwC2pM9ibRFLjIbTdnOV1/YH3Hfx/vzYo4MQ+dzXVHHAa0++dmyxK7w8FFzpX2HXy7noo5O0i2EPpEXDWRw+NoVJA/JzYg/6bFTUPvGi5Rep17Zl+lgWlsjHOyI79/P9dKk7db5k7Uv4sdf8GL/wNb/AP77jt/AufRsvmH/ZxP3wfJHLqxRN2fthPDJ/QdYWuLdeI5IxP/i+HySydgDy07uGQ63ry4+9nA9f+jDPVnbTx7JIetLinOSOsfSIBDQzgffLj72cl85/NZXl9/PJyw8WbgaRNutqJu2h7gUAXrj0QuZqLlH/Nr72zNfyX73z7Pq7xQURjLmg6eBq19tNkzSAVx9/LZuu5GxQVJK2hwSe3DuL2/sSXlH5Yd75ze/EsRyuOHZG7lhO/8bQfTOgJ6yJJO1Yq0U0uInH9z6VGX5uHB4PwmGk2J839KEWCodIFx8+eeWTfNMfv5lvOX2Cr3rqt3jg0gM0E7ljnjLusM9Z2+LH7/9x+mGfvnQJpE0lnG6IlNfsx5Y2uPmot85b/+db+amP/BRvfteb+YXP/gyQkTtC4aNFFCM/ZK66QZft6FGs6nmVAIwxmkXBD0f32EfjHj//yZ/nvc++lz94/A/45j/4Zj42t4uFLC5h1DLLVDkxMv9xyJhAWSYRiRPo0GSmH/b54yf/mPvO38d8Mv/R9KQVh9DjWe2JEfsr1Fyb5doyjrC55IwxaV55PWm+VHFXwxlN0hKnyRXnbmwsPlSr5Z+kTRtmXXHS7+5W5I/0o6VoLPO6/oB1b5tHtx/NbU1TEXp8rFblWP0Yt87fOvpYc5k7Bkot8fhOzs61WhlyIdzHjhepORl/7foiK1GEI6yUSYMc1SFBn02ngi1sbpu/Tb12Mjv2oDj9kMnycKSMxweiPV699mqqdpX56jyvPfVaFhqq8NnJ8buRl7tjoZT9iMsQ8ETc5Yrj8Nbjr2MQDnjfxq8COQ5+DfppkvY37/6bAGw6WuYYjF58286XSfNi3YDpjvYofPPN/xAZNvnpj/0YfmOhUGlQcli6Un0BH+6cpWJVODN/hnZVyRy++yXfTV+G/Fa7BYPiJDkjQ1/DAYFTpRf2mK8Mk7QvO/0GAD5iTQabuSAYcNlx6YZd4sFNnKq9lOX6Msfqx7jsVjLSoLIkILqHJGHSxoZpztVdot6tXBo8Ti8ZV2ACmgNRqnuaPtR81P6yEz3JP/iLf8BKfYV/seezHXvcd+G+1OI5V7ljMOADlsfvPfp7/PZDv40XxXSp4YaT8iDVk5aPBX/LiXncdfnfzr2T063TvONN7+Db7vw2/uDJ38eqndNyx+KTtKFxiGr6fu+z7+W1v/NafvHxH6B523/gBz74nXza00l6wUxaaoYl1Jm1q7pb+dmv/Fne+S3v5MUrL+b++cdUGFgUY+Qp45CO3wck/Xib73vP9/ETVX+CSct7QOvU5SRV6kgV4z555ZN81X/7Kn7oAz/E9//l91Ov7yv2pjYPCKMOKAKRxzNCfe9lsEzNtbAtm2ON41xy3PQzb9fKYdISeZmnB9AfxKR5foV75s7woXott/tCSqnUDtN60twMkxYPJgxNAGgu86V9FS9+7PLHclnTgYg89iyblcbK0LshXccqd3R2AHh8O+8kTcsdg32saGnUCbO+iAWsOS0udi+msz9zi9PDPlu2zWJtkYbboGY36Ns6/hqLje2ch1l3Y5/z0uOVa68cebxdgPPp9SF3DKKRasZ9e08A8Ja5u3ndqddxaaD+nhuTFvTZ0Ena7fO3YwmLbnLxw9GLP3R3PCSNqjcjX0sMmxPsyBKDS9/G2c6T/EpX/b5FmYckh6Ur1e/88N7T3Ll4J67lppbMdy7eyRvm7uQd7Wahh6WfOnuqhv9dLYPJMmm3Lp5iyatxf1Xm2jScIvJ4sqYOh2Cwmro7Hmsc44pbnYEEZNgz2BOChjuqha+5NpZ3O5KYT3r6HjEBzdGAljsGUm3mn97/AypWhV/5K7/CX6fNceFyqXsJyxLUXTtn45Aem/r8/pXP/gq73i4d6jjhQT1pObg7hjFNJ+Leeo1+HPDzX/Pz3Ll4Jz/4yh9kqbZEY+1d7A/8Epm0oWz6/gv3U7WrfN89/4rBpf+FLW+TH3vwl5FQuJQuZdJ0IWxX97fMV+ZZa67x1he9lVCEfKJWLe6763d42rb4wY98I807/hX/50feynuffS/vp5cmqe1aefMfk+Kg0DP13vXku4hkxM98+c8Qy5jt6h+qdVjahdIUnvJH6HNWxNTtJjJqpGfdWnONi5VaxjiknIJkck8M9PdknElrVmxsS7DbD/jS46/gwWqF/b1zubx3oAP6g3rSKtpYbTPsT0/SGsscjyJqwknbIwpD6ONb9tAwZGQdK6x1t2i6zfyZtMQ4xN+FaHE0SdOqhJN2g4udizSqmR7kPBAM2LSt1LCl7c7jOUmcXlBPmt63t6Q6l8cZ1FYBRa3rI0kbY9Lu2/g0t/oBJ6KIM3Nn2BxcAML8Ppiwz4arbvbV+ioL1UU8Wzf8jzFpQ61rPhe/r7XX40laq+oQdV7ISxdfz29tfoKeEIVJ2IZWyB4SeGj3cV649EK1jppDz48Io5hXL72Iy47DxvaThawDxqyqwwF7mk7PJmkN1+ZYd5VPVCt09y/kv4jQ46mKuh8GvZV0mPXx5nEuZ+WONZd+EBHkZWBzAIa2xB6eZdEYc4YDaHM7AouPdZ5WPyjQDfQo48i5p2kmzYtV8HNx8AivOvEqjjePQ32BNWlxsXsRQLth5TvMektIqnaVfX+fD67/Pn1ZxY4mh286uTFpMU0roqsNltaaawC0Ki2+9+Xfi6g/xaPdD5bTk5Yahyim5pHtR3jB4gt4/ckvJ9h+DV9/8rt4fP8ZPpAxAyoKfhhjCXB0IWxPqus8p634X3XiVQhpcW+9ViCTts9nREBMTDw4zYuXX87Xnvla1mVAPNGTVkLxKVTFWKGvz6Pbj/LCpRfydWe+jre86C2scy/boT5rGkumJ60IRB5nRcBq7TQg0rNurbk2JndUxdqie60G+vwf6O/JeJImhGC+7qok7fQbiITggc18pq2kfaNTkrSqY6Xu11tR78AkTQCn3Tbn9gtO0iIPT4jpSVpzBQHc3r65ELljAKwH+8hgYdQ7wqlApcUJXC50L6RMWm6Fx6DHhjVMlNruIn4apx/ApOUUp29rFnWpOnrdR/qbc8J1kaRle9K8yONjVz7Ba70A+lvcOn8rMTFWZSvHi99n063gWA5z1TkWqosE6cUfldSlTFpONGpfb0bjSdqcPixfs/It7EZ93tlqFmYe4oUxFdvCijwu2TZ7/j53L90NDCurHS/knuV7APhcTpvi9LVk5Y4eO/o+yCZpliVoBCtEQnB5+4n8FxF6PFVxabltoqBFzckwaQJkfygBgeIDmiSJjg/Q6QMs1No0uUUdWMK6YavOR849TctDfGkhnD32wiu8dOWl6rH6IieiOE3SGhWHfs5yx01UYesrb/pKHtx9DwNc7NibeKptWbn1pDWciK4lqFuVkWbqb73jW3GCm/ns4NfZRqIkbEUyafociTykXeHR7Ud5weIL0u/t7Y3Xs9Zc478sLBSepKnB2nY60mVPV2bnKipJa7pNVsUZ7q3Xi0lcpQS/y9PSQ2DRP/cWfvxL/w1ffOyLCZFs+/sQR+l8sjKSNC+IqelinLSr6fUB5d5WFXN0539DmR/c4PMfC0Poczb2WaqcAIZmMmvNNS4LSZyqRlzCWCpH1AKRnHV9zdiMyx1hOMvvJSdejS0ln917Opf3TtluezJMFkIw78T0hKAfB9N70iotsKucFtVSmLSrJWkAdzTWeGIn5/goCrnk2Egkkb8w+VnVFzkRw3pvnYqjzpPcmLTIZ1PEKZPWchYIbH2WTfSk5eXCHgGSba18WKwtjjyeDnq/8Zi0odzxU1c+xSAa8Nq4Cv3ttGHQql7Jr+oc9NlwXJZry1jCYr6ySGDriz528fPO0JOK0XhPWkKjLtt3cc/8Hbx9rk3cLcbkYBBEqeX9Q1UlL3zhsmLSsonI3cdfhpCSB3eKa4odGWQeDti1JpM0AFuoL8tmERWryOdJx+aWuTOo6qJaw/HGcfpCspdIQLQ+vmiHx0TuGCZJ2pSDa77uUonO8Mj2I8j6EhR0rxh8ntBJ2iC2sWvPAvDSVZ2k1RY44ftc7l0miiMaFTs/JyyAsM+mVAHFrfO30ot2GeBiTWHSbAuivOSOVkRXWDTHAgjbsln1voNQ9vjpj79N2d8XmaQFUVrsuWQL9v197lq8Kz1Y+z78rbv/Fg/UKnx272xh64CMIUGkk7Q4oGpXqWWs509UXsYj1QobRexpcQhInop6zLvHAYeaY3GscQyAK44Fg91S3R2zc+zOi5BO0OGupbsAaFfavHH5n4Ozxfe95x/Try1Ar/gZnTcagsjjghyw4KokLSt3DAVslV6QVAXjNEmbUpBMmLSaW+dMLHh0kM9ZlyRp7hQmDaDtSrZ0UjKVSRMCGsucljbn9s8VyzpejUnT5mF3VJbY9rbZ7Oe4x0Y+Fx11L4TewiTrWF/gZBQhkfRj9b55kSky8tkkZqWufr+GM0+UxOnjTJrIT+7YdIbXfTxJS3t4bzQmLSt3PN85D8Dt7hz0d1InG6uynp8TWtBnw7bS6si8u0TkTL/4QgjlhJbT/IWerqiOB95JINH1I/723W/h6YrLfeufONR7HriWpOIc9nm4VscSVlrRnMtszo2509wahHxu/9lC1pGsBYbujru6IpI1DgGwLfVF3exczH8R4YAnbbipdQZgKHdsHAfgircDkFadi+7fSAd8cjCTNl93ib01ukGXi+0Vk6QdFcQBIPBigVU/iy0c7l5WLDX1RU74fcI4ZHOwSaNi52ccEkcQ+WzFPku1JZZqS8SE7FguIpyWpFm5GYfUrZCeJWiOzTgCWHJvYTn8Ov7oyT/iwVaxfUbDeYsDHtG9v3ct3ZUO1e4MQr75jm8G4P7B+cLWkawlkXAD7MZ+yqIlON38UgDu3Xkk/wVoBu/pcI8F5xQAVddOk7R1PROrVabcMYipOkJdHz1g/a7Fu9LH71p4KYMLf51PXPk43xY/xf2BkTvmjfPSJwbmnCRJ03LHhpIpX/KVSVjaq1h4QVIVjA+Ki0AVR3f1Ou4SNR6O8hlEH0QHM2kALSdODeamJmmgkrQwoh/22RoUeL+GHp7gqkza7XouZq6Sx8jnvE7S/KlJ2iInfF2IClUMkheZ0okCfGQap9ftOWKnr3qKx8mUHHvS2k7E9gHJeXqW3HhM2nBO2o4OiBdri9DbouE2OFY/jlXJkUmLfLYskWboLXcBaU8fkgf5OKElsr7eAUxaYsm9Nwh54+1vohnH/MX25w71ngeuJYjU5hx6PF6pcnP75nSI5NCSOYBKi3v8gAcHVwpZh1pLxjgkHLCrmctxJs1xVXCx1c8/GdkN+mxacLJxi1pLwqQ1VZJ2OdwHKUurLiYSkyC+epLm99T6HmvMwf6lQtdk8BwRKWdBP4yx62c53bxzeLDWF1jrKxfZi92LNCpOfhLucIAENuMBy7XltAK4bU9P0pwcCk+g5Y626klrTEnSWlWHaverqDt1fqfhFpakSSnxwlhJlUOfR/R3587FO6k4FlXHouOFzFfnOS0dHgrzCfQOgh9mnCaBvXgwsaedbt3BXBTzsW4x6oAYOBvs0bZPAowxaTb0d3Bti7prl2YS0XQAGfNItI9AcMfCHenjc3WXcP+L+H9e/R+xhM33zNs8kbdb3Q2Os0Kdty1bJWVZJg3goh5anBRrix45k7CrPRnhIHAtd+I56WgG4C53gUtEucwDGykQT0HbidnSSVoiuZtAc5nTvtprCpU8Rr5K0pxpSZpyLL4T9dnlmqTFIRcdB4Eg9OYmZ8rVFznZU2fajn8ZyG+szIY2k0kSpZo1DyJmzxITZIqT4zzjlhOzbdm0rAoVe3Seo2tb1FzrxkvSfN0jBSpJcy2Xem04y+XW+VuxqjkyaXHIhhh+8ZrOAtg+AzF58SEJaPIZktfVVszjPWmWJWhVHTqDENep8NpA8IHBxUIo9GGD/YBd2xqpFrSyFqNC8CIqrMcDrvSKSdSS5l1VAffZFRJb2BNJrFM5gSUlW/38DTKeitQms1ZXSVo9I3cEuCJUf0d5cserD/gEFdB099X9+1ilAp3iEmmDzwNRCLZLL/Cx6+e4vf2i4WP1RdaS4Z/di/kyacGAvhAMZMRSfYnFqk7SHHvCDAnUfhPF8tD7ix/G1K2ArpjOpLWqDj3P5U23v4k/sQaFfH8hI5vWTNqjUYfTrdPpPqvmPqnz4267yUNysk8v1/VEo0zaXuRNMGntmstyKNgIJt03D43Q46Jj48mIpnUCxxI4Wj0iEFyxnQmTiKLhhTFtV12nR4Jdbpm7ZWRvS5QKZ1ov5fdv/jZ+9vI6tzdPFL6uGwlnLfV9rwuVrCdJ2gn9OV8ihCgYLdYWiEEQU3MtejKiLpxJe3mGckeAFzbUOh/ZOjz77GdbLaag6URsWVeRO4Ji0nThrVDzkNDDQ05n0uqLgGBl0GW+Os9j24/l976RzwXHZqUyDziTCW1zlTXtnbA+uIwQ+TFpm5rQSJi0qlBFri3bPrAtKQ8X9pYTsWVbLE45z0CdaTdmkuYMk7TF6iKiuZweIrct3IpVWc9tuGIcemwJOdS62urib1vWgUzaYQ39kiCiR4SAlLnKQl189Tu+wWpxRfo8vPXw4d54CgaB7g0IPbqWPXJQpmyRXsc9trJ//9xmcaweaMlBOGBXSOar8xObtagvsRDHbGqmNU88GakgabV6MzA8uFbrqkJ12bFH+jeKljsm7OLgACdQUAdXZ+Cy1jzBY1YMncvFjCcw+PwQB2C7PL33JMIKeMHCPcPHagucCNXmfqlziWbVyXWmzGam6psEFbu2PTFWBLLDPw/3tl4YUxcRPcuiOYXxbVYdul7Im+96Mz6Sd8Q7h3vDq6wDoGqrPrBHw7203wnUvpb0EdxdXeacJdnzi2PTvCBOx4oA7Ia91NkxQavq0IwstqcYuxwakcfTrgq066ylgahruSxV57liTzr5FY1BEKWDzx/xt0euD8B8IymChVSbx/ia3qBwg5cbClHIWcemJVxEpM71mr4v5qvz1ISjHB4He8zXy2HSBkFEzbHpEdOwnKnPmdNMmpSSF+j2l4c3Hjz0eycFYvcAuWPTjlImbal+cJJ2sqNkjuNJmhdGfNW/fS9/8bnLh14rkY+HnGB2ADWyorGE6G3y8mMv5/4L9+dX3I8CNmybFe1yOJmkHaPS32a1vsLF7gUarp0bmbKpzTuSON1FFe63rcnCo52XC3sU07JDtm2LRWcy7gJSMiUvXB9JWjRM0rYH2yzUFqC+pKQxUnLr/K0I22NrkE8Vdif2iAQZratK0jbc6oS7I+TEpOlkpEOsNskpFaNWbZihv76mJCrvO/e+Q73vNAzCKGXSukKMJAGpu6O+Ce+qrWBJeHDz8JviNCTBVSK/3JXxRMUZoFJvsRTFbPn7ua/hog6S2u7qcC2Aa7ssOy0u2w4MdlImrfBm6mTw+QG2xAALOqA5076Nx+KeMigY7BS6LoPngMgHy+XZzlMA3DZ35/Cx+iJtKWnZdS52L1Kv2Pm5O4YemxkdfSJ33HfEVCZtaIh0+H2tZoV0LUFjSjGhVXPY90LuWLyDV1VW+b2qRB7yPaeuQ39n6nZETwieCfdH+p2y1c+7G2pvfWQz/wJYAn+cSQv7E/tas+pQjxy2ZAFFn9DnKZ2kufFaWngCOFZfZd2x0/2iXXNLm5PWcmL2heB8sDdyfWDIpO32A3jZ34Qf3YT504Wv64ZBpNjVk24bL4xTdhVU7/1aZY5Ltj1y1u32/EKXNAgi6o6kJ6AxReoIqiAZxpKeH7EyfwsrYcQj658+9HsHV7HghyRJs2g5jekMFkBjhfpgl9X6yoTc8dLugCfXu3z6/OGlmXE4wEdSs2vTn9Bchd4GX3H6K7jQvcBjOzmxaVHAQAiqlvr9JwZ/t1TMdLK+yoXOBRpVJz8mTSdpieLNRe2fm44zUXi0LIEl8nB3jGnaMVuWzdKYmitBNk7PA9dHkhbGqdY1YdJoHVdV6f52ah6y7uVjYLGhg/KURrX0xXdrBwQ0h2+yV1bVku5VNqNWdVjRXGmd4CVBzAfOfeBQ7zt1LUGcMmk9wViSNlpBa9SXuSUWucgLpq4lO8w6HLBLONG7AdCqucxHsDVlMO9hMZAhdQQJqTES0FQXFJPW36FVcRCiPLnjINYDPg9wdwQ41byNp4IdAjCSx6OAKAS7Qk8Xe+ZrmUHk9QUA1qoLXOxepFmx87UrTpi0+nImSWOqOiAdLXLIfc2PYmoiVO6OlclDrVVx8MMYP4z52vkXct5xeGoj/5EeCftcFwHPOg4SuG3htuE6qk4q3XphW8maH7ryydzXkcAPI8Xq6Z603aAzkaS1qg5uVGFHFDB3MfJ42nVo2zVk2BrZ01abx2fCpHmhqlInDN/tC7ePPD5XzygVbFcxBAb5IfQYWIKGVdEyw9HP93h1iUuOKkguNhRjs9ktOkmLmbMVE9+wprBEDM+63X4ArePc5fs8sn14x+mrWfADNOyITdtmaUo8MnySYphON45PMGlX9lWcuZ3DZ+hr1+CpTBooh8fuBm84/QYA3n/u/Yd+TwAiH18IXJ2kTpM7ApyuLHJu/xzNip1bn/WGiLGAheoCALZU++fWgXF6DgZ/YUTTVsYhi+4kWQA3KpMWxriOChp2vB0VpLeUZpruemrDvxPko/nd1MHvSk3RqFWSi1+dKg2yrfxcY3qWReuAzaidzdBbq3xZt8NnNj6Tr6Uq40zaKFNTdSxcWwwP7cYytwUhT+c0m2Qc6UZpSYgDduUBSVrVoRnabE6xEz8s+jKihpVa3ydz0gCON1ZVQDPYwbIE7apTggREDz7XtsTTpLHJwbVaPUMoY55xXSV5NJgtIh9sh77eR9rVTIJdV4nTCafFpe4lNSctiIhzMPAgCkbkjnWnjkWFji0PlHBDPpXHqtDujm574vHEPbDrhbxu5YsA+OAz7z7Ue05dh2bSGlZIR/eRZJOiViYRWZk7zWoY8nAByeJwPUOnyQDoRZPGIc2qjRPW6AkYTLlGh0Lo87Trcmv9GH4Uj/TdrDaOc8Vx0vlsczW3POMQK2T/AHvr+ZJ6fm9YhB4DYVG1KwzCKFWMJDheXx0xlJmvu2wVnaSFES0nomcJGgewVdOStCe65wmiw90n/jWMQxqWkjsuVRenPq6epAr9p6tLE0zalT2VpG3lwEZ6epTHwUzaMnQ3WG2scs/yPbz32fce+j0BiAM8IXDEQUmaitNPOU0u9S5Rr4jcCo9bQrIoKti6WCNidZZuu5UD4vQ8krSYhu2zbdssVg9K0twba06alFIdIolxyGBHbd46Q6dzhdX6KkI67Ef5OPtt6CQtYdJc3ZC46bhTM3QnFyZNST06lpiYJ5Qg2zdB8xhf3u0ikXzw/AcP9d4Ta9FVNBkO6CFHmDQhhGqyTw7txhK3Dno8u/8sQZz/4ZkOsxbqtfdif8J+H1SQVYsctmT+h0ZfRtSFnTJY2cPreGMtZdIAFpuV4g+uIMKxxFVtiZODa8FRfXSPV1zYN0nazBEHYLlp0N2uZiSAtQUATli11DgEyGdgbBxMzPSxZZuuI1XiGI++R25JWhhTEQF9y6JZmUzSspbFp5bu4FY/4N6LHzrUe05DUthIxgHA6PemnW32ri9ytx/wUA7V+IOQmmFFHntTkkZQhScR6cCjn7N9d+TxlOtwprGmrO/dbOHpOFu2RZDKHcvqSYup2xEdLfUf77WtuzaOJVKTCIOcoWdt1eyKsr53Rpm0pcYKO5aanwew3Kqw2SmeSWs7IX1hHZikjchgW8e4yw8IZcSTu08e6r0TFc9BPWl1K2LTtg52doTU/v600+Zy9zJ+NPy8ruyrMyAPJs3TMeuBTFrreKqk+fLTX86n1z+dz0iAKMCzBLYmFibcHbXc8TQusYyp1HZzY9I2BSxnktIwspFRna1C4/QYxxoQCsGSZvDGociU/PaoI5+kBZH6UCuORSxjdv1dRW8mTFrnMkIIXJbpx/n0pG2iK6q6IVHGLjJ22ZqidYWcMnTtGjNt6GuCEdeY1jHu9gNWq4u596UNtO1tPxwgxeRhOXJoN5a5NfAJ45Dz+/nPFkrljlp/vBN7BzJplahCF5l71XkgI2rCSYPlrAzkePs0u7bNQFuHLzcrbHaLdYZLpCg9GVC5ii0xKFMAW9g8WjFM2pFAFIBdYRD1kdKi4WYO1YRJw1Yutq76juViHhKFbNo2c04D11b3hi3bdC2dnI19Z/KwLE4KbDKZ51edTNLa2bkyzVVe1+/zwM6jKdOYF9LeVhHS1UlRM9P4PdJHUF/ihZ7Pk90Lua8jQWqGFXrs2UNzhiyaVQcZqjVudfOd/yiDAeu2zVp9RducZ5k0FVhtaKfNrMy+SHhhTNMeMp3jDr5CCGUSUQKrd0Mi9HV/UUUXakfDw4XGKgPLotdTxfAyzjovUPIyxaRNZ4lGGNbmMe701f1xWKv5pCftIHfHuhWybdksHuTsCEMmzaohkVzoXEgfSuSOeRR1Pc0a1pwDmLT2CfB2we/y5Td9ORLJ+57NIW6MFJNm6540d7wnLWHS9Dliudu59aTtCliwhrGyF8aIuKWKkYXF6RHCVr4HBzGoN5zc0c80b+77+8QyVklacyh3BKiJJTzyqTZuElFFpIeEF8bIsKUG2B1gwZ9Hht6yI7qWoHnAZtSqusPDsrmKAN6w+CLuv3D/oan9kbUESv7S1YHbtCQtG9DcqjfFp3afym0NCVLJAT4B0I2DqUnaSquCEyrZ3/YgX8evPpKasFNzl2rm8FpuKZOBrd5l/fdq8dVFHVT1ZERDTO/LSA6uric41TrFObcKHTMrbeaIArAdBtEA4grVbOXRqUBtIbXhD/R+lot5SKzkjksZyaEVt+hZ+ns8Vnm0cmDSksQoFnofmWL4kzBpXS+E5gqv7w/wZcgDlx543u87fS26wCJ8elOYmkSlIKWExhIv8H1iZCF7mlpPMubEY/cAJq1ZdYi0y952zgUwL+ghhaDhNFVAnmFN0llp2im3XXPpB1EatBYFL4ioi2CYpE3pYVQzsYpPGG9IJEyaUxs6PGewpG34t7v6rGuWcNYFEQ1Lmf1Mm7MIY3JHt8YZu4GDOHSSdi25Y02z8vUDDCQA1QsGnJRqzxlJ0rTccTsPuaO8Rk9aW4+q2L/E3Ut3c3P7Zt755DsP/b5JT5p9kNyx2gK3wSkdI+Js5ebu6AmoZhw/vTDCilvKhX0qkyYObYTlhzHSUknauBw7QVLwy8tB8+gnaZnmzST4Vu6OiyDslMJt2iuEIr8kbTnjsOgFMTJqsWUd7IR2+DlpEW1HDX2dZlUNw4sfxzKVe35Z8zSdoMPHr3z8UO+fRbJBd+LpFu/talbuuMyZQCdpe/kHNF6opH12PJQFTUvSVts1rEht4puDnHv0iKlb7rAnLXN4LWpJ7HZPVZ1XWpUSmqmVFKVPTEMcbEsMqrq41lzjYrVmjEOOAvQway/qI+NK2mubornKSV997wZS3VO5aPijgC3LYjmbCMQtepa+Vw9g0vJI0iKSJG2KcUg60iOExgqvHAyoCTt/CXfKyIf09D6SlTu2qsohzgtjqC9yJlCBxNm9s7muI8EIk5YkaWM9Ds2KTRDqfuhOvkzaIOgAUHMbqgc5U3hKk7RABSOJYUee1eGpawpVQN7V915zisX1XM0xcseioI1DanY1VdNksdhUc0G3NZO21CpB2h/GNO2AnmVN7b2GsSQNcFvHOSOqPH7IQef+NdwdXS3jduzp8Rqg5I7C4pTe0y90s0xaIncMDhfQRyEe6t8f2JM2p5O0vQsIIXjT7W/io5c+yvnOIYs/mkmzDkrSAJorHO/vYwubyN7IZ/ZnHOELqGTiHy+IsWWbLYupTJqV06isSCjTr0WttBtHq+oQRDI9cw6L6ydJc5QECFDujpalJI9dFXjOOatIay+XvqjeWPDrhREybLJlyQNp1DDKoSHR0i5o7vTNKJEGdf0wlXu+xp7Htdz83HoYVnl7Wj89nqS1RuSOS7SlZNWdK6TqnLB6hB67iSxoSk/asXaVOFRB4FbeRipIapab9qTVs0marqZseapAsKR70nIxezgAXjrgMz7QCbTm2lQdi90kSbMtI3c8CohDsFz8aADSnXQOa65ycqAC6E6sgqFcNPyxkjsuZb47MmzSF7463ieGf6p1HSZJ89Mk7eB5fq0sk+ZUqFbneaW7xL0X7n3e7zsNKQtOkCYBWUOkNFkchFBf5BY9r64wQ6TEgj/y2HPUd3h8X3NsiwglpUrYi7ww0AOy624jnUWVIJn/eCVUwUgyziOPiv9BiGJJEEnqQskd63Y1NQTIwsgdC0ToKbmjU0uHSGexqGeBJb1My80KWz3/0BKyq2Gg2dWeEFN7r0Gx4COuyq3j3BFxaJt5/xo9aQL1fbDFAYkRKAfS5iqr/T1sYY8waeta7uhH8eEkgJGHJ9QanwuTBvBXb/+rALzziUOyafFokjZVGto8htNb50TzBIHYyOc8iwICIaiMMGkxDm22RHwgk5YHmRJYau9cqk/vRWxlJfw54DpK0jJMWtKw11yFjgpkFtxjICRXeodnC3wpccXwoxloJm2bAy6+LYgPSW16umm6a4mpwQwMA4mOF6o5ccKm0dvhS9a+JLckTUqpncdsuvH0JK09lqQB3FpdLEbuGOmm9nCQVpwXpjRsrrarhLrqvLl/YeLx5w0p6QuoW8rxyrbEyKadmDBs68G3y80qUSwLrfaqoMqiJ+SBSRqoCuNuL+BE8wTrIibcN3LHmSPywXbx4wEyrqQGHSmaK6x2t3Ash71A7WW5VB4jZRyyrM1JAOKwSSgi+kJMyLjz6ElLJIaBHro+bZ5feqBlZNyvlzWe2XuGZ/fyGami1qKZNB3wucJJe/NgrDfOdqlV2pywaoUlaV5izBB67DoqwBln0gBiZxlHSrb6+fRbJ+j7KtCouc2JgHyxtoiD4EqszrqlplpfkaxJKke1ArqWmj01DcngYoMCkMod6xOJO8BSdfysqyAl7BSYvHthTMUepNLcabAsQSvrqtw6xp2ex/nO+XTUyfPBteakoYtPljwgMUrQOobT3WCtuTbGpHnp9+5Q5iGhh6ePkQPntaVJmnr/k62TvGrtVfzhE394KBYvCj1CIbBQn8HUhLZ1DLobnGqfoi838ulJS6z/M/GPF0a4zLFNTBROXnc7p7YkX6izclH37o5j4kw7JI5+khapC+raImXSFpJAI8OkLVYVs3Rh//CyEB9JNdPr44URdjzHFhFy6sXPx92xZveJhaB1lUnmoC++ZSkqvXuFV594NU/vPZ2LW89wLplFV7OS4xWsEUtm3Rh7q93kqd2n8ptkn6wn0C5omd6NaXLHmmtjWbrS180xSYt8+sKibuvZMWMb9jBJU+zHciuZH1NcQ3U/iGi60BWC+gHjGkAnaZpJi4H1Xj7upwaHQBToJM1DyMrk0PrWMezuBieaJ9j2VVKdR+UxCPvs2TZLlYXhUkL1vVaN1sX1pPlML/bAqLsjAI0VXq8P8TzZtGROWoVAS8pHq98TB2t9gVtElWd2n8ltDVkMh1l77Dnqvcd70gCc+gJLUZR7n+1An2NVt4kXjjr5WcJiya6xRQhxzHKz+JlYqZRcBOwLQeuAQuVczWXX9KQVgsDvE2WTtDG5YxJ37fjaOKGlEoKi7osgioliia2D4sYB9wQoBUu6jtZx7ugqB8ondp543u/vZVptpiEW6owXXCtJOw6dy5xsnUyZND+M2er6vOC46jk9VAEk8lMm7UC5Y7UNbjNl0gC+4dZv4Nn9Zw9VXPf0uSFQydJ0ueMqdK5wunWaXnwFP4wP398ah3hTmLSKaCKB7oFM2uGTNE/0aMQxtQPm442QKTng6CdpofpQq441KncEZR6i+2xW60ov/czu4RusA+So1jWMsWkTItkLJ4NvO49J5mGMbanNaFrvBgwHSaczGJrHoLPOy1ZfBsCnrnzqUGuAYTBTc2262uJ9vDegnW2MrM6B5XArFfb8vXxsXbPrSfolQo8dbbIwreIM4NZWqMcxm3lKg8IBA6F1+lMOrqbbxEWwGamgZ0UfXBsFNlQPgoi2o2dPHVQ5Y5ikndAN3xfDTjo812BGiJW7YxAPsJhy7Zqr0N/iVPMkW546UPPoSdv0tG12fehEFvra3t2yD2TS8pA7Xi1Jm5CGNFe4ubvD6dZp7j2fY5KmmRpXBvSFoGGPSsqHckddfKovcUsseGbvmdwLT7GW9iXFpz3boek2cazJ/tJqe4mlKGbL38l1DQN9veuV9nRpm9NQduveHks6SSuSSRukclTlvjnu7Jhgru4YuWNB8EJVaKw6DT2WYfSeaLktHGBLJ/grSfJe0FmX3BMC9X6NA+IigBPzNS7s6D2sdYw7++p3OYx5yLWGWScybnFNJk3Z359onkiTtI2O+rd3JUnaYdjIDJN2oNxRCNWXtjcsYN88p8bzXOk/f/WZHyWJqqPf/wAmrbfBqeYJ+vEuCO/w6pAoIEBQyaghvCCmYqkzrTdlXu5hmTQpJX4Y06PPYhTDAZ91+4aTO2Yo521vG9dyhw2krVXl7iglxxprADybg9TNExI3o4f3ghgXtUFsT7n4jmXl0pM2TNImraphmKQNpUGKSXvR8otwLIdPrn/yUGsA1bwNULVVbx5M6UmrOsQSRVsLoRwe9e+ft+QxHbQaHjxPKIHTWFQBTZ7SoNCnbyWzY+KJJE0IwaJw2dabVcqkFZqkKSfQnrAOtCUGlaTtZJM0x06ZZ4MZIQrAcgjkYPrhrufqnKotcaWvVAG9HOakdUMlb2tn3B0DP8ukjRaf7FTu+Pwrnkkl2tNJ2rSeEtsS1F1b9aSBcq3tbfK6U6/jw5c+PDJX6DBIZx5Jj65l0Rjr+50ogDWWOBOE7Af7uReekjOt6uqeNNs+cE9rtBdZjCO2tdFHXhhE6qypVVrTWRO3xbZtw2C3lCRtOCJBuTseVKicr7v4YZwG8Ab5YeCrZKjmNvRYhsmzbkm4bCcy2IJVIwm7CppJO+A7AnByoZ5J0o5zKgypWZVD9aX5UYxjiVRVMI4QXSyQBxdKk/XQucyp5kmu9K4QREFqv3/XmnZvPSST5us1HsikgZI8Zpi0ZL7b5iF6+D0dDwt5NSbtGMiYU7rn1qpsH14dEgfKOGRM7ljVxbduNHlPOpZFdIg4PdmjemLAUhSBc8CorPE4/ZA4+klaWs2w2fV2WawuDiVCreOqx2Oww2K9SRw2uJCDC5bPmGtMGFGx9MWPJ79Muc1fuEaS1qqqGzI7K43uOjWnxouWXsSn1vNj0hp2xmVrLElLG8lTecExbvXU2vN2ePSCRBY0UL0zTA/2ACrtJZajiK0cpUFSM2l1uz7hgpZgya6zrWfrDQOa4uSOgzCiZUf0LUHjoLkowLG5Gpd2+6w1VQHjkmMb85BZQ8sdQ+lhH8SkASedNtveFoggF8tiXydhFX2/SCnxfLWnbdn2hCGSLXKQO+pA2ksY+av02nYySRq9TV5/4rX0wz4fu/yx5/3+WSRBvSsDNXNprOepneytqdxxkTOeClrz7ksbkVGFA3Yta6qEG2Bxbo65cMhe5IVklmS10kp7kEfetzLHjq0GF9dcm2bFLrjwpK5PRRu7tA44A5PBxaYvLX8kjp9Vt66l/ZPGLYtWlW1t975ccK9ick/IRO44Zc5igtMLdS7vDZSMbvFWLOD2+nEe237+SVqQOLAe9Lj+HIifA5MWh5yozCORXOpd4sqe+v69cE27tx4ySRvo/fpAJg10kjYkMZbrh0/S0iKavAqTlhQeLXX2CHfr0OoQGaqetEqm3cMLY2pXSdJiZ5On43cQy+dXeEz27S4+C/HBTNoNbxyykGl8T2elddZpVhxkOM/lw0rdpCRgfP5ChkaVAYzJXxz78PMXvDBG6HlCrQMqRhOSnMQ4RUpeuvpSPrvx2UO7WyZMWtMK6AgLARPWt2vz6u+X9EZD+wTH99epO/XcmTTlNKmMQwaWwBH21OHNAI25ZZaiiE1/N7f39/0OsRDUnRrelGZqUNKgbQEEA5Ya6otbpNzRC9TQ194U2VYWpxfrbPcCkBXmnCYXHQf2TZI2U+hh1pH0DpY7Aif1kM68hn8mshRXf5f9SM1+BPT8xzF3RzsHuaNmjPoEWPLgKm+r6tBJDu3mKsiYVy3ciSWs3JI0L4yxBNixrx10R5maiT6C+hK36L6WZ/by7UvzM32/hD571sHqgNV2lVZssTUl6DgMBroCbutzbdyVbbG2pOYNDdRnsNisFOrumE3SFJN2QJKWjBYxksfc4YUJk5awq5Ph4YLTYEvGICWLulhbVPKeSJSTER6NKa7OCU4t1oklXNodwMoLALjDqh9O7hhdI0nTCoE4nj4GJ4V24j4lVGxwoXMhZdJuP9bEtsThvluhh6+TtAOHWQO01xSTpuPXucocjuUcamSRlyZpV2HS9O9/k1Sxk+VuHZpJCyMPKcRIUqqSNFUI7MrJz9OrfJLz/OHzHquS3I8DAloSpSKbgta4KuOQOPpJmjYOqeietMXslO+WdlfpXqFRtYmDBa70D+lgF4cqQx/rSavqw6wrpjXZS3oc7iD3wjgd+tqoHdCQWM3YRIO6+cM++B1eduxleJHHI1uPHG4daQO36nlqWJPmBifm1UZwcTdJ0taw9i9zZu5M/nLHzNDXvu4NOwithRVWo4jL2n0qDww81SRdc+r0Dzi4FitzSjI22MWxLRYbbqHGIWrAp89gimwri9OL6rHz231ONNe45DiwU4wRgsFzRKzkjhEejjg4STutD7RafZd+DsYhScUzYdL8MAZZwcFVPWlh/j1pyV4yIKSJmDRJ0WhVHTpp4UlVdxtehzsX7uQzG5953u8/shZd7BGRp/a1yiir16yqz3uYpC1ysruDYzkFMGlaUq6LT7tCHMikrbSq1EKHHhFejonaIG34V/fgxEys+jJ7tk3QG9qtl2Ec4kqfjmXRPiBpHc7EMuYheWOgCzWu0ySM5cQ9AbDkttixBfjdws+65J6IEnfYA3rRQckdAc7v9NUeUl/kzjBmo7/xvE13/DA+sB8NwNcF8Sg62GEZUEwacFKq10qSNCHU93uxUWGre4iiw3Nl0uZOKuWZ/k4LIViqLR1O7qiVZTJh0g6SOwKLXp+a3cSqbB6aSQu0a2clEw96QURdKyR6SIjG9ghL3d/Pdz9PzrM+Ia2rpE4TqoxD4ugnaWPDrEcOs5RJu6yYtGCBLe+QTEHk402x9kxpVGvSrrprPcj55k8eilr3wih1C2plE9EMJmhU/eVn/xJftPpFAHzyyief9xpgyKTVtRVyc4p7YJqkJRrw9gnoXuHM3C0FMGlRKndUBh4HV4oWFpZYCyN2Y+9Q1rtZ9PVA17qWgNQrUw6u6oLu39gBYLlVLVwa5Nq6mfoqjlen9MF1bqfPifZpLrkV2Hi0sHUZPAdEPtJyifCwr5KkndTFqWptJxcmLYgSuaO6J5R0Q9Cw26rAMM6k5WLBrw81GdIQk9+bBM2qPTy0m0nhbZ2XrL6Ez2x85nnLU0bWEiQGRAN6YpKpqTo2FccaGS1iI7m5eSp3h8esOoTIZw95VSatEqk9OE+Hx37S8K/7IseLTwt6oPWuVqao+Y/FFZ6SxNWWPl1x8BiaOV2lNnLH/JEwaUIXpKcXJOdVUUefdclc0CKQsKuhTIrXCwc+Nznrzm/rmGT5ztTh8fmyaX4YHzgjDYa9tnF4jSStrdoNjgc+lrC40L3A+r5S3bi2xVLTPbQFf8KkHWjBn1kHGQf05dpyLkyajK9mHKL2dNFbZ61xCquyeWgmzU+TtFEmLZGxdy1rovAoNQnyfJURyXk2kFdP0mquhW0JOl4+e9SRT9JS/b4jVE9aLcukZeSOmknrR106/iGarKNAMWljrjG17MUfS9JCoTaDR7efXwCcDPJMaP3mAZuRbQkaFXuYoS/dpv67+QRrzTXWmmt8euPTz2sNCVImjVAdllPo83bNpVV1hkza3AmQMbfWVrnQuZBWafNAMlibSDFHV6Pzj83VWQpUMHixe/jeRIC+nzBpk0NfEyzVl+laFp6e2bfcrBSbpIUxjngOSVqGSTveXFNyx43DDfg0OCSiEM+2AYkzbQhqbR4sl5VBF9dysavbufSkBQmT5maTNKjZLX2gjSVp+tA/zFD2RAXRI6LJwUlaq+pmHGuHSdpLV17Kvr+fC5OV7iOhr5i0Ke6B7aozMVrkTON4/nLH7PylcEBXxAcmJavtKnak7pPDBFPjGKQyJRXkVMf2tUXdx7rTS5K0KlsFmyEBRLKPFOJAd8fFRvHjAG5UpOyqnSRpU6T9tUX2bYugp+7F5Wa1MGl/ck8kcxbrB7DNMMakAay8gDu21JzF552kJaZlByDptQ2ulaTpONXtrnOscYwLnQus73ustlVCtdioHM7dUTNprnCwxFVC+vZJ9d9sklZfPqRxSGKeogaKT8z9BKgtgOVqG/6bFZN2yMJjYv3vZpm0ME7joa4QE4VHqT0fnjeTFkZAhC/kVYuOQgju/aGv4nu/8s7n9T7jOPJJWqDdWGwLdv3d0UHGeqAz3Ss0Kg4yVI8dKkCPQwIB1REmLaaubei7lpgIaJIM/fle/KSyGgptVV2bzqSBYtPSau/yHeq/mh25ff72QwcUSfWqJnzFpB3AXJ2Yryn9N6SDEm91WkhkrkGNn+1J0zNcDsKxuSqtQB3i5zuHH8UAMNAJvxr6Oul4BbCoq87b+j2XWxU2Cqo6J7NjrDRJO9iW+Fi7hmMJzu/0OdE8wZ6QdDcNkzZT6GIDgDuNSRMCmqtYvQ1Otk5iudu5DLP2tSzF1cWmxNSjajem7mmOnQOTpoOsHjHNqxxqreqouyMA3U1esvISAD6zfnjJ4yAdHj3Q7o6T5kMrrSrrulckUSnc4s5zdv8sUZyfm2DyuVQdCxl6DJAHFp9WWlWsSI9KyJFJG8RJP42W1Y5b8LdUQLetA7jllpI75j2OIEHCpHlxYp41PWldG1dxGOSGNElL2NWpBUllArG9dw5Q90XRTJqfyB0PMAwDlVCutCpDh8eVOzi2f4W22+Lx7efPpF2tJ20QP8ckrdICtwGdK5xsnkzljsfm1L281KzkwqRV7efG6OXJpPnaPCWOHCq2NV3Sntj/757jpvZNCHeb/cHhCvm+ZslGmbSIpns1Jk0nabtPP8/3jMHSajdx9T7EtfnaVNXV88GRT9KSBCaQXWIZjyZplpUOylNyRyVh2TiEBXviGpOVOw6CiLpdRyBUT9oYkxZzuIufHFABHraU1A5omoaxoY2NJfX7byp25Kb2TTy79+yhDtLUgh89h+uApGhtvsbF3UTuqL78t+qBhnk6PHrhcOirStIOZtJWWzXqoVrvxRxcPgEGmlavu62ps2NgWHXe6qp+yOVmtfCDy0r6F68yO8a2BCcWapzb7qc2/JcGm9DfKWRtBs8BcUAf3T9gHSBNaa5AZ51TrVPE9lY+SZqueCZMWsLm1J0mvSlz0mydSOYxzLqHpHGA2Q+MuTvWFwEB3XVuW7iNltvKpS8tYdKCoE9wgJzu+HyNy6kZkvpOn7FqBHHAhe7hR7skyDJpfjhAcrCpylKzApFaa56jAAaxT1UO55COB+QLep7ejn7PpWYFL4xzuRenIe1f1NK27KiILNJgfNckaXkj7XlM2NVpxiHNpCCpztelZoXNTkE9aeEwSRPy4EJGglML9REmTQB3Nk4eikm7WpLW10ya70/vtU0hhGLTMgOtt7p+Omfu0KY8kYqNqlNaU0aQJGl7o0za1mDreceMXtKXFztX/axYvRuuPMSt87cgRMyl3uHis0AbHyU9aYkareHUsRG6LWk0EYw5vNxR2FrtdpXzLG9cB0madlSJ1ZyfiQbr9nHYv0ijaiMj7VZ2iIpjGPaJp7nGuDZNuzq16hyJw9Kow3lCDSkR1sGXZbVdTQchArB8Zyphu2XuFvaDfba95//7p1VeoVzQxq2qE5yYr2WMQ1TV9eYgQCBy7UvzwkjLlAYMLPuqTNpc3cGKWjiS3IKqvrYlrh8wOwZgSf/+23oG2XKrwk4vUHbAOSORgMjnMDsG9MG13eNESydpjgObz9/xyuAQkBLikL6VJGkHBB1NNf/xZOskobVJNw/jkDgxDlGHWiprduoqSZsid3TmPsGntu5//u+ZzpWJaU4Z1JygWc0kaZatpIbddSxhcc/KPXx6/XASbhju4T3ddzNtX1ubqw4daxMmTX+F81YHgOrfGOgg56AA1LYEVVTCdLl7SFOsDPpxQI2M7HXcOESrOba1U27Rs9KSgLwfq7PtIPknJDOx8pPUGygMdJImYxWAjktgARZ1sW9Ly2CXW1V2+gFhgWfdQAbUJVeX8qHk/dmeNIA7nCaP7Tz2vJKQILp6T1pfRlRiQT94Dq/dWoPOZdaaa1zpXWG35zGv3TGXGhW2e8Hzl5ZrYuGq/Wig5no1lmFvqDJari4SxiF7z9NszdNsYhw7V5WGcuxu2HiU2+dPA3C5d+55vV8CXxcVR4ywgKpr07Cr2uBvjEzRcfp6f/15tUR5YYzQTFpTmCQtRVJ19GL1obbHWaaVF8D6I7i2hYMeDHiIJCVIaFRrlEatOjYNuza1Jy1CHfzP7D3zvJrck4DJJ6B1jX++3KyMJmkrd6Zyx2SC/PO1GIXhYVmR2jjkAInBifk66x1PfTmaKyBs6t1NTrZO5pykJXJH75o9aUIIAneOY5FyUMoDfc2k1Srtg2fHzN0EwNYgkQapzfJQEoYDkM6OSZO0g1lXgNOLjVTuCHDecYx5yKyg2ay+UIexax1QcGiuQneDU61TRKJD1+8e+q2DhEnT+1oSnDfc5tTCk20JKit/yXsu/u7zfs9EIdAFGlep8rarDn4YpwctbSWNAXjpykt5bPsx+uHhmJOk2NPVSdq0JGBtrsb6vqcCzto8ODXOeOo7nGeSlro7unYaGF8twLKrCyxGERf3DxfYjKwhDqkh0v1knDVJFCvJEO3lZrG9YMk6kiStdRWFwMn5zOBig9wwSJk0VVCZZhyyNKeC7J1EBtusICVq1Eve60nnLPo0uAZbhbovzu/0VUK2eAaEzR0R7P//2fvvMMuy87wP/a2dTs6VQ+fumekZzACDDAaQELNJgTIpyqJ8aUk2aUqykiU9vr6mTcmmbMtXlK6CaVqWLYmkLZqUKJMUmEWKoEBkYAAMJnb3dO6uHE7aed0/1t7nnKo6YZ9QQ4T6ngdPD6qr6uw+Z++11vu97/e+bp3N1ubYrz/K3bEtA0ypJWOX8wtQ32Ahu4AvfRr+AeVMl0kLQtkdYxm3YiZtFEgDWH0HvPqr4Dvw0i9R+42/DkyelebGIM3XhwJaFq5D6HEpiqrasqc7nx3P/ew65qoRnX5MWiDaHSnvnfr467nKMo7Wp2EumjOuL32QFjuq+OqgcgKkLTwFB/fAPiBr5AExlSwkRugp4yiTljI1ckZGDSQeZ9KiA3Pbb0+0GPTmLwzuH6qaO+4cOHcVWjvQ2mW9oMDCvfq9sa+hcy0xk4ZLUxvssrVcSiMlbNZt1f3OK0bzQunCxLLPQdfTcXfUtBOZbcfLtqqseLOTJ9nR4TCTKgzMjqlk1RxNzODGB5rTGKjuOl5FsxtDHK9AMWmbdYeyNYelWdw7c3j8g6uINWmNcuLKKyZtPa+e53o4PYMSzw7ECoF4zckZ2UjCfXImTWg2W/bks50q+xFagr4usXHlItfazlxa7TLs3gTg6drT+NLn5v7Nia8D1DqSMjVa8fPcJ7pisZQmlLDVcJREqbBEtblLwSzMtPHUy6TFg/fD1jWRKbPkzxak2dIng9ZhK443nyzdIi8F+xGojZm002g8QZc1aUX5RqOZtPapzcd9tVZspx5GlvJ95687DUl1xpqLGpKdWc4ZVge4Sz8RSFutZHD8UDUSDAuqF7nSUgzRJJLHUTNpbRliSS2ZU2F+ERoKpAEI84ByzKTl1J8Tm4fETJqRAKS9+4eguQlf+BfwW3+dmque70nn0uxI8umPkjsuPAVAbf8hhBZ77pQgrSN3jEFazKRp5I0srT4zaSFtTF/dv5OcUZ2embTsGUjrVkcy4yuXvRNWxQtPqz83XyZnWZgUppI7xgOJZs+8iONFckcjEzFpR+3dA9oQZWDcOrg19mvGN5ibYDGaK6Roe0H3QBMFN7L9Omv5NTShTdX1jZk0Ey8asO/f0VyO3JSOODzWH3GxeJHbh7dnYpvtBSFuEJKz9J6ctOG6dCezyLrv8LA+G+OQmEkzhmTHFK0ihpTsRnb93a7zaWxckQsacXZMeej3r1YySAkbhw7rhXXuZotnDo9/UBU56rWjw2VqUBB5bh78NutpNaRvy/EbP8crnkmLZ23dDpOWpSU4saHpmgJpDX9vYrfcOOOwqQmyQ7q8hfSxgOLaFdi7A4HXUQfcnxKg2JEaohl1YHNGfyYN6Boi5ZcQzU3OF8/PmEnrzqTZESgZyqRlSiz7Po9nKXeUAWm0rlFUv/kjYbIXsSvVU2bSHD9A1wSt6BkZ5O4IsFJO03QDDs+y0mZaseNn2GHSTu51pXQVISV7jpLBxkYunVnOGVZnDET6ZJKAtOhM0mFZa1e5sqtmnyYBac4IkGYTYqEncyrML4K9z1JKSZeFcdgD0tSzP2i2b+TYRODgaILUiLMRAJc+oKSgv/LXYPcmteh3T8ykyShw3NeHso7MXQOhIbZeQQvmOfCnm0lzj0fKdMyYdLJmpi+T5mOj+2sIxETrea/cMT9oVOEU6ksepDmBopzr0QH4BJO2eF39ufkSuZSOQX4qkOZ4irGLBxKllB2pTM6M7KpPfPgtcJWcbDKEHtH6+KRHfCRx56ojeexxeDR1k+XcMnfrk8sd45td+i01YD9ATncy0HoZ6o+5WLpI22/P5EDRirKTcikjkXEIQGnxPMu+z7a9M5PwVzvq2AhNvQ/9DjNCCCpS60iD5grHPqMZVgyi4+yYzIDg87jWevJj1gvr3LPOmLQ/sIrCNWO548CGQ+RwuB41ihy2pn7pWJZiRg5g8QEob+XxBbju8VzBEKFHUr8JpCHxa1i6r9aRIQeIE1K62mWQAezdYTW/CkynDoCISTM0WtHz3M8p7sSBs7AI9cecL50OSEvpYuRMGkAqX2XZD3jY3pwZe2TLgLTQu9fST8atWexH7Ep3Ju30govThkY9er2hcsf4MH5mHjLTckIPQ4LnK0DUb6/TNZ2SFOxFM0wnzgEzrLYboAloSZ/MkAiPuFaOZ6UtP0tl+zXm0tWJMmy9YLjcsYXEQk/IpCkGbUGq91YzDilHcRLz0Zlusw8b+fKjQ57+0V/n928OMcPzbRwhSI04G6kX1uDd/yl4TVh5nlo0Tz8pk+ZEIM0P9OFMmpmG6mXYfAlLLtAMp8sz7uZ+9pE7GrkTM2lu4CLxEGGBlfzKxOf0jnFIkvd6RvUlD9I8X2IZWmew8QSTVloHqwAbL5G1DDSZn0ru6EVSxnjA3g8loYw+fCue3ziWkybb4M2TM3MTmYfEwMgjHAnSavlYShc90OXzoFsdh8dzhXPcO5z8QGNH4dFxiPNIkNYJtF6Cw4c8UX0CgFd2X5n4GuKKDRNyKT1aiEaENQLPPvUkq9EDOwug2Ml801TnPdOnuwhQEQa70QEw7sifxsYVd77j7JjsgODzuOKstPv7bdaL69zHQ+7e6sxHndWbWFGnWvn5MdgEJzKtKLT3SWkFAn17qrwyUCy9kBIjsg6ON7VCnCvjHwVpbthd4yadcXX8gLwVyXKHbGrxmtbJ4YobTzs3yJpZ5jJz04O02DgkGGxMceK5jYb9zxfP86j5aGb5j50hdy3AiaSvwxQCmUKVZd+nFTidZuW0ZcuQtNCHM2lGhl3U3+dTymL7NGfS0qZOM5JP9WM641o5zpic1UzKPjan2G/+GmBFs7jn7gPKyEwT8PgUAHPbC8iYOrYMyAyJ8IhrLc4Fje+La98GMuSKUZxM7jjK3VFILIxOM3lolZUioNo6QENTcseMapgtFiOQ1oeN/MiNbVw/5H/81VcGN2h8F0doHcAysp7743D1W+Hb/xbluafR5TRMWogOeIEYPpMGSvK4+TJpFrDlFn44ORPuRIoIswPSurEmOTNH6xiTVo/ybmWQ5kLxwuTn9Ng4JAlrOaP6kgdpbqBAQ92tYwjjpHZfiOjDf4l8ykAE+amMQ2Kta+q41tXQyVkFWn3mNzzZIow//Em1roCD6m4Oq/kOkxZtlrqhQq0jCdu54rmpmbSUodGMWKHcAPfAE4HWhWWw97mWP4cmNF7efXnia4grlnR2mDSGz24AzC1fYNlXPzeLrLRW4KBJSYB63dQgkKal2IudyVIGxbTRlU3NsNqRtMLFxZAS0xr+fiyXMggB9yMmrS0DtkUIj6Z3zDurMStiTdoytr8f8NnFEuatVykby2jWLm1vOutzL/SxEJ0cm7gxVEgpxqIZHL1X3bAL2qZxrc2aUedxkLSTrtFORx7cA9JARYvcb0wnd4zVEK2Iqenn7ljNWVi61nV4LCyCc8jFrFJJTLOuHr2WSO6IRzueTxwyT5Ir11iK1rSZRYsQkhZGj3FIHybNzLOvAWGAEIJqzjq1QOs4IqEhPTJo6NrgfXClrPbmM5A227KlTwrRnVMcsNddsarckHGYsMZ8IXU6TJoXkLF0WglBWiljkrP0LkhbeRsUVrjSbnBz/+bYIxhDjUMCn7YQWJjJjEPmlNukvnODvFGNmDQF0ipZC0MTfZm0F+7tIwR8/v4Bv/bigKazb+No2shRkE6l8vAnfg7W34W2eJ1KELAzgZcCKCYthT5yfg9Q5iG7tyiJClIEE/k3xNV1K47kjrHhnaGRtfInctIa0XlWBqmOw+a4dcTdcUgTadb1pQ/Sogel7tYpWIX+YXmL12Hji2RNDRlk2bf3p3i9aPHpWFV33a9yVpFGHyatA9JKEyL0jnFIOBKknZA7gloANl8GKVkvrHPoHk78HsQ2882o85AdYkzRL9A6097nUukSL+28NNHr91as9c5ZBp7fxhfDZUHxdaxGB5rPPhx/PvB42YFDRkrcaOZw0Ma1mCrzAF/ZrKPA0WltXKAWqUwC6ZNlaCwW0h25I8BdMwWvfmjm13ZWIyp2dyQ6BA0CLqU1pQ7YeoVqahnNnN6G35U+vaPOMVAoWAqstI6taXbQdZScnEkLSetx6PrgINoTRjvZqspLi0DaWn5tJkxaytBoxiCtz/UIIVgoptg4tqad19WGPCtDJKcnizIJk1aorrAczE4dAIrNzWhGjwV/H0Mkq8i+poGjVCzV3OkGFysmLRgZFDuXS2HqggdnNvwzLUf6pMXwOUWAK/l1tnSNg8jKfamU6TY2Zli2q+6JNiGZBLlUQghWyj02/ELAE9/Ota03sAN77LnWocAjcGkLQUqYydbmwooKtN65QVavIoyuu6OmCeYLqYEg7VuuL3JlIc+P/+Zr/dk038E5FhuVuBaeohYE7EyovnJkQEpouEE43II/ei2QrKHer2nGkjpyx0gR0TuTlrMKyuCvh0yJQVoYpKllauw5ewTheI1P1w8Rmk1aSnQzgUnLjOrLA6QZGofOIcXUgEyohafB3mdZPyDwcuw7+2N/AHE5Xix3VAcou5dGtYonLPidwCHEJwxSrOZX2Wxtjv3aXSYtJD0kTwi6swHb9Z7N8uL7Ye8NuP9JzhfPA5N3fe14diOy/c4fz6XrqX6B1tQf8VT1KV7emS2TFkuNRnaL0kXmRBoh4ffemN4gox06pCU9Lmj9H5mrhXNs6xr7u+pgeeS9mWHFG6gbZcckqdVKhgf7Lc4VlOTi3vKT8MoZSHvTKwJprUjSle3jMAiow8X8E7D5MvPpFYS5z0F7ukOQGwaYPfdLLLkrpZWcuXVsfrMduelq6JOHf3ohKUP93syAvEVQjY+cpR91ra1dOcKkbTQ3cIPJAYJyd9Q77/0g98ClYvpkVppUjZlZzaW13QAhIC1c7BikDWk+Vas1Kp5adx41Z8OktZGkNVPZSgv6MgblVJm2ptGux5lY1inKHdXn0yAkP+JArmmC5TMb/pmXLQPS6J2554FMWk2NNNy4/xEAlovp02PSTJ121FBIUmqv67kvnvwOrrTVWvb6/njnAS+QgyV8gYOtCSzNouUGo2dFNU2taduvkRIVNPOQQrr7b1roA9K2Gw7399q8/XyF73l+jRubjf4mJYGDI4bHEw2shesKpE3Y/HGExBL6yLgCAJbeAsBbPAUIp1O8RUyaGSnegq4ZU84q0tI0ZI/BX2x+FQYp5jJzhDIc+/UdP0DTXfKhhCRxBzOqL32QFumCD71DCuaATKjIPORCcBvfyyKRHEQhnOPW8STzmElTH34eXwjcHrezWOsaBmnm0nMEMmDf2R/rNeMugI0kPSIkzzI0ShnzKJP23B9XuT4f+4nOQXxSkNZh0uI8oSED3GuVDPfjrlVRDaBy+JCnak+x1d5iqzWd4UEM0rKW3gVpCRYiq7jCcmDyYv3X+ZUb/3aqa7ADLwJpIzauOeUy+vr93weUJOc05Y4OHlk52vEKokDr/TbL+WV0oXO3dhG2XoHts1DrN7UiuWMzDJChQdoccvBYeBK2XmE5t4oQkjsHU4Z/ygCrxyEtZnPK6VjuePSAYAfqua5Z5yYGJ24QYukxSBsuy63lU0fdUGtXYEfZ7q8V1pDIiSWPUsrI3VGjGfpoDG72LB5RB6jGU7a9z0JmYWY2/C03IGcZiMDF1kbEMQDn5/IEYQldzg6k2QJSwsSOGMZ+CpVKugbAQSQbr+WsU3Gsha4ctYEknyAodqV8Ok2wr+aKWRHb6zam+9WVxbcDcGPzBUA1JDdOUe7YFpLMkAiP3lotHwPvF76Oy0I9Wzf2xtvvhjJpfsSkaSmCUHbyfIfW3FXYfh09VCBN07rP3HwhfWIm7XP39gF4bq1MJZJGHrT7zJLHTFrC9+hI1a5QCyU7Y55Z43KkJCX0kcHf6rUuw5Vv4gP7vwVMx6S50V4aM2luL5mSKqpzeh+Q9t/4/we1yJlx3Dk8xw/RdYd8GKpg8DepvvRBWix3dOpDmDQF0q7Zn8d21GFg0hsgtvZMRV3u+OFLGXqn+9rwulKg+MOXQbqzqW23hzjx9Cl1YJLYMJJJA5jLHwu0TuXh+f8IXvolVgOJQExsHhIzaQ1vtEzpXDXHTtOlbnvKwERosP0a12vq85h2Li2WEeRTBnYkUxp12AOguMzf9fNIv8B/8ZE/z0cefGTia7BDjwyM1OlfXXk3ADe21KzXUjHDdsPtHIZnVe0Y0IdeIltiUN3FR/s2GgbLuWXupyMW4Uzy+OZWzKSFPjK0MPUhn9/8U9Dc4pypmOxpAuoBvBMgTWWYFaxcdE1HN387VGvcgnVlYvm04wWY2tH1dFDV8tZRJq16GeoPwW12ZLqT2vB7gURG5k8tGZBF7y+bR7ECjw9t1RnPR+qAxgZXK1fH7sQPqpbrk41iRRyhtuBh65plaITZRaq+4EF9RvmPAjK62ZEZ9qtyVkVA7EVzcIvFNBuHzqnkk6mYG42GkOQSyLZUVtqZ3HGW5Ug1p+h4wUDgDrC08g5yYciNKLtwqZSm7vjqHDDDsr2AlKmpWfSEAGSlnGGv5XUdF40U2evfzaofcGOMEQwp5XDjkMChLTQyUXMlkXnI3DXYv4vm50CzafWAiIVi6kTW3Ofu7aMJeMtaiWJkMnI4EKQla2CfKMOibBU5CCZ7llwkljCSzaQBfON/xYq3D0wJ0jpM2lGQZhka2dgMy+2aLMWGS+8IbzL38q8C44M021NMWvYMpB0txw8xI3fHE/b7cWWrcP2DvHPz50k5agOZ1OEx/vDNaF6kN3g01/nwu0xarHX9c+JXqQTq7ZwEoSMCpIBMgg3qRKA1wLt+CIDUZ/4ZS7mlKWyz1abdipm0IaGiF2oKwN3ZaYGVhbkn4NHneLL6JMDUc2nNaOHLpnTsGDwnoZkLK1xv7fKdc/8DMsjw86/864mvoR16UejrcJ3+wuJbKQQhN/bVHFzsfrlxMNvOczyTZuOTEcke39VyBj+UbNZtlZVmb8Pyc/DyL8/02s5qRAUxk+aBtIbLQxbUM3Q1VJvntMYZHiFWz/0Sz2h11jR5dPNvxXESKWXiMdmsbYihxyBt+KB1LZc6KqWrXVZ/7t5irbAGTG7D7/TIt1oEZIc0wpZKaWwvVBlc2SpoJtQf8UT1CW7s38CbgStq0wmUGVLgdOSOo9a13Nw6i37AazvTzeYBhDLEEYK0ZnWMovpVNa9m8vYaSgq1WEzj+iH7rdk7w7a9gKwhaArIJ1jjV8tqDspPwmCcVaJqI0lp+lDgDipc/bIvudFUDYPlU8pKa3shaTNECtEBQ6Oq4/C418Omvfs/5arjcGPjs4lfu9uc7/9sBF4bV+vmtiaaS6tdAST5KN9vo9W1oV8oqPUvPm8CvHD/gGuLBbKWQSkzmEmTno0Dk82kAfnsPC3C8bNtw0A5bmsJjUMAVp9no/z16FKy15zchj+OlLEiGf0RJi3e03rIlPi/C6Fk7sV/BcC2PR6ZYnshum4rJu1M7tgtLwhJ9RiHDKxv/BGMwOaP8QlgGiYtkjuax0Baz4ff6rGrjkHa14nXmf/sTwPjf/htNwChDihpbfSHP1dInczgKq/DU98Fn/6nnMuvTsWkpU2t4/Y2zMXmfE393Z2d6P1Yfg4efY6cmeNC8cLUc2mdmTTLSJQn1KnCEtQf8Vf+0HWEfZXfvvPv2JhQGtOWkS3xCJ2+MFNcDTVutNXCs1yO7bxnK8lpu77S6YdBouwY6LHh32tzrnhOHXaf+V64/8kzl8c3s+Iw65hJG7apzSuQdr61iQxNHremlTuGmD3LvQqa7qoDmsfskOM1rmZcAiaTT7t+iBGta5khjDwodcCRMNdO/uPr1NI1MkZmYiat1565RUh2iJxuMQ60PrTVbGBhCeobPFl9Ej/0uXUwvRlRL5OWFKTNLZ3jnO/wqDG9cYgTzVSnjRS2P/hAPldT9+DjyDBl6TQzsbyAghnS0LRE9tbLpQxBKPuaLZwVvLj9Il/Y+sJYP+MglQQ2OgMMq6t6jhv+IVLKU4ucsd0Ay1DrUiahc2EnK61X8rj0Fq5kl7jt7OF5ya7RC1Szf5DaoR1FQmWic+I4Do+1aEau12FwoaD+ffG5TkrJ5+7t87ZzZYChIM0PHKRIMK8/oHIRYx77ECSuwMMVgpRmjowr6K1ba99NOQjZ3p1cPu6FHrqU6IZ6X9zg5Dm9F6TFY0l6YFCLCJhxFW+2p3LSzpi0Y+X6IaYhOHQPT2ak9db8Nd5Y/cP8AGomaGKQ5sc06kmQFkv/mr3WnhGrVghDll76RWCCD98PMKLFKJWESctZbPULSn7PnwX7gHOuPdVMWsrQaQzJE4rrfMSk3d6JHobl56D+COobyjxkarmjGmrPmPqYcscVCH0W9AZ/8m3fjNT3+eP/9Jc7bNg4FWe0xHLHQTlpAFfMAq8HDaSUne7irF2vOrbEBGQT2BLDyUDrQ/eQg6c/CGYOPv6TM72+sxpSsQV/4EI4gkkrrkKqSKF+g9CtstGeTubmyhBLHJ1J6+06tggh7HZS234TKQVFXUkNHzbGf33HD9A7csfBs62g5I67TbebBzd3DVIleOVDCCGUDf+0IE3XaCLJDllju0AkWuPzi9B4PNv8R0fNpMWzJGnNHCgti0svLbPmu9hyl4YzHTCxHXVgSespxZoMysOqPUlKSm5FqowYwM6aMQF1AMoZAU2hUUiwxn/7M0v89l95PwuFN++w9OVUP/axH+MfvvAPx/oZR0gymjkUuMd1JbPAPiE79g7LJfV5zRqktb0A01RrZjbhoXi1H0gDrlz+NnwBtz//04l+T6+Cql/Fz1DWGAOkRY2nlehsepxJg26g9e2dFgdtj+fWykAXpPWTO9odH4UJmbSUeo3GuLb0oadCtDUjmXFIVE7pMpUwYHeK+VondLH6GGFZ+uBzekoK6hTIPvcnyISSneZ4/17bC0DY5KU8A2m95QYhphHghd5wJg24f+U/YCHKqtp1JpQ7hnGSeWTt2YPQ89FBo9Hz4ccIPR+GpNNVslJMgNBDUqYCaekENOpcPkXd9k+CjvV3wcrznHv0MvvOPgfO+OYpcRetEbkamvrgrnMuZTBfSHGnF6QBPP4816rXeNR81Hl/Jqmm45M1dTRN0I6ZtCTdosg6m8OH/LFnvgmAe/YLfPbu/tjX0I4cr9o9UQyD6kp2mbqQbLQ2WCrFgasz3rjcUDFpMhzL8QrUxrWUU3M2G9KBt34/fOHnoTF5XslZjVGdmTQPGZrDO4+Rw2Nq/3WkX6LuTa7fB3AJsXpAveOpzqepmRgIFf7ZE9bc9BsQphDSoGAVJgo7VXNvkUJgiAERQDWXwg8lh/Fci5lW9+dLvwj1jals+ON1MqP7tIQgN2S+5QQrEDFp5wvnSevpmYC0luuTTSkmra0JUknmbeJoESH5r3/7f5/q9e3IVCujp4eyJrpucFHq3LSVAdTSKTWeQH1GWc2loQnyQzL14qrkLC7N5zESHgy/2mo5tzy2yYwNpDRzKHCP63LxIgA3d19lIQpjnrVRVtsLSEURHomas6hGgq6Jo3JH4MpT3wPA61/4vxL9nm5zvv/70O7kyEZNLieB3NHKQXGNy7bab48waccCrV+4p9b7t0ZMWjE9mElz/DjbdzLgkMtUAWjWx8yVDSKQJsZj0kTlPOVAst+e7IwOSu5o0kVpvWRKfE5vHstJy0qNuszA+fdSC3y2xzSSsf0AKRxyYQgTAuJJ6kt+hVPZBOrNHsqkAUH1GiaQE+YUcsfYNSYKyfNOzqT12lXHcsdMAK23/zBzvsv24XgdX9sLsMz4IJFM7giczKwRAt7zZ1k/UJKYSQ41HSYt9MgnYGou1LJduWNkscqjz3G1rKj9m9Fw8STVcn01uwHYkRwrkdwxdpqsP2KtsMZKbg0j9zpffDgBaJUqYNwZ4e4IcLWi/s03Nj5HPmVQSBs8nrHcUc0LaCo7ZkSeUFxZy6Cas7i/12Yuo6QN2+1tePcPKwnep/6PmV7jWQ2oOCctdEfPpAHMP4m5/QoEGZr+ZG61cXkyxOpZ7p1oUxVCkNUsmkI7AtIabgMZpgikpJausWOPD9LcXpA2YiZtLn8sKw3gnf+JYh8/8886gdZjz03QXcMzwqWlaWSHrLHLpTSGJri3G61ppXXYv4MOXKtc49W9V8d+/ePVjNwdlXW2SNSYo7DEtzZbLHjr/Nbm/8Lf/Nj/OPHrtx21Z6WNTGe9H1SXjSI3o8y8hUIKIWZ/GAcl+Te0OlIIatbg2JezSlbL+WUeNx8nN3mRUjl+6lYiueOTC8+iS8m/u/VrpE2dWs46HbmjFoO04XLpuHRNsFRMn4hnuFi5goHGawc34fGLI39P76G/X8XSwFwqAgRJmDQgnLvK5eARlsix0exl0tS5JmbSPnfvgKylc3VBEROFtIEQ/Zk0J5gWpKkzQaMxJrMVuFE+mzkWk1Yp5LACk32/MfqbB5QX+keZtD5yx1Zgc+Ac4AUeDa9BTkKDDMHau6kF4djZcLYXEsQgbRKTlgnrywKkaYa6CUeBtGyhzGNZoSy1icOcO0nmsWtM0A377Ghde0Fa7O4YpnHW3sdcELA9Zuip7YUd7XU6Qceob6B1XNc/yHmrDEzmCBcv0PXQJ59g5ul8LdcFaekiVC/Bo89xpaKo/df2Xhv7GuJqdAbsfRyhnshkM2ldJg3ga1ffh5F7g8/fH/+gGYe+doxDhhxoriy8FYDXH38SUAe+05CAZKw4O2a0VXVcsQ3/EZA2dwWufit88h+DfzbbceoVzaTZgYsMrdGdxye+HdHe5WJ4QDuYnJEG5cJl9jRdejfVnG7RPM6keU3SoUa5cYNapjYxkxYKFyEl1ggVRC3Xp/E0dwUufSN86p+wnl/BCZyxVQrqOqIGmPBoaoLskDXE0DVWKxnuxCBt6RnwWrD7Bk9Wn+SV3VemdjdsOUdn0hKBtPwSOSn5ifN/BG//bfzfr/7zzt4zbtmR01naSEcqjsH34eXcMo80aDp1TF2jlkvNXO6oIhJC0PYBqA7J5jyrZLWcW6btt5OraaIDd1q3IlfF4Xt/Zf5p/lCzxb+88+u0vJay4T8Fab8povnJEe6wvXUiKw0lBbxavsxL6UwiiX987hs4kxbNO+UjhUAriXEI4FSu8YS4T9GoHmHS5vIWQnRB2gv39nlmtYQe2fRrmqCQMvozaVHzb1KQls8tANBsjhmZFM2kWZpJKAcD2uNVyVoQZDmUk2cuujKg91/r9DH4OwwcvvsXv5uf/PxP0nAb5ENJQ2bwM3PM6emxG49tz0aKQOWkGWdMWqdcP4SISRsld6zlLG6GK5R9f3K5Y8fa86hrjKX3WPCHLofuIW2/Td2rk5EabTK0556hFgq2x9T22l5A1lQPZxIQUut0nfscrA2Ltbf+SQDuPv70WNcRX0vK0KkTUEzA1JyvZnl8aHfyu2LzkJXcClkjy439ybO4Wo5PLqWDb9OOnOkSyR3ziyoOoK46Q29ffDtoDp/bHN9Cu02odPpeiCYGL9oA5YVnWPV8/t3jjwOwVMrMfibNDUibAkcktyWGCKTttZjPzAM9c5Pv+TPQ3IIX/+VMr/Os+lTEBtuhA6E5Olfmie+Apbfw9fIGnmxN5SyorJKPgbRoU83qKdqaBl6PjNurc1Hu8X2v/ufUUuWJ3HIdPwDhkZYSYQ4/QMRr2s7xNe1dPwj1h6ztq4bLZOqAqNEmXFpCIzdCTneumu0yabE64PHneaL6BHW3PnVWWdONmk8xSEtqhgQ8kW3zZOEDSEL+n1d+b6LXt90YpGVHOvldKimXzTcefwqApVJq5muaF0iCUBKyD0A1mpE5q8lrOacalUnvVenZnYaB7YcjZ9KYu8Z/eFinHrT55Zu/PPOGpBeE+KHEiM5+mRFMfG+tlTMn5I4AT82/hZczOeQXfh5aw9ezXrfAfmVHIK2YUcRBopk0YG/l68kIl0VhHAFpRtQA2arbOH7ASw8Pedt6+cjPFjMmh/ZJMOhEIzqpCeekcvlFABrj5tpGwN4Uau0euZ9FVc1ZuH6RQyEJ/MmAmisDzJ5ImbjpKITozKR9Pmyw3d7m448+Tt2rkw9D6ijDobncEtuBDWM03Nq++syz8szd8Ui5QYhMKHes5CxuyWVqXntyuWPoY0iJFt3wR4xDIsq9FXr86V/70/zYx36Mhtsgh9K6BsJkLjPHdo/7Y5JSGvAo9HWECxp05ybu7vR/nfQ7f5BFP+DunQ+PdR0QWXObGg0C8glAwPk5tXjejQ81y8/B/h3E7d/jSuUKr+9Nni3UcHyylnHEBS3RgUY3oLACL/9r2L/LSl7JHx/UHyfueIGyqrYFpIXROcwMHfCvXOCP1ut8on6bV3dfZaWUnv1MWo80dphs63jF3cWMkSFjZLog7dI3qEyuj/3EWAvWWU1QMZMWJmTShIBv+C+5GConsQN3csmjC5ha9+DlBb1MWpqmOMakuU0KYUDJ3aC2/2BsJk1Kie2FSDxSUo7c1DqNp+MS7mvfBqV11l/9TWBKkEbEpI1YY89Vs931bP5J0AzYeLETLTKtIVLX3dHG0QSpJGtaqqCMfuqP+bsf/CBIg7/94Q91weQYZUdSrbSZiVw+hzBpCwqk3nikGn5LxfSpzB4B+Kj7vJquzvT3fymXEOLbhBCvCiFuCCH+37P6vTFIe9hMZvjjeU1kBNIcLyA9am3Kz/PW5Xdx3YefeflnKJX2eGO7TiPJbFaCiu8JTah7LTPCeKi3VgbEMzxVfYp96fFYenD/U0N/R698rv/1xSBNEQfNhP/ux5V3UJcZ1tzGEZAGSk68cejwyqM6bhDy3DGQVsqYp8OkFVYBaI47Ixb6CqRF58RxmLRmUCUUgsOtydZSV/pY8hhIi5uO0Tn9o0KdqV/eeZk9e49iGNCUKo6oVr7EvibwNpO/vh0oHKKYtDO5Y6dcPwSRjEkrZ0xuyhXmfJe9CeQ5oJLMLSkhOtC4kVTGMjR0TSeDxj08Xt17lY8/+rjSuoZK6+qHkrnyBepC4rSSy3JsPyClx0zaaFp/uZTmfC3L7742oPORq3EuXeNu/d7IjlFvhaHE9UPShk4dST7BcOSFHofHW1sNwqe+G4pr8M++i6uPX+XG488gX/jZxNfQWy03IJ8ywLfHA2kA/96Pw+ED+MmvZelD/wUAprHNy48OE7++E8laM7qVyPGKVJ7vFRUyCH7qpZ9iqZRmu+EcyT6ZtpQUJbYlHgOklTPYXshO01WNhBikCaHYtMdfgDu/P7PrPKs+FW2mTuiAtJJ1Hp/4DpxQHVqnCf/0hjFpRoampkGPPXXDa1CMmL/a/c9Q9+qd5yFJxcAoiJi0UW5YlewAJk3T4R1/muXbH0MX2kQgLZYqm9KmrXUHywfVuWqW/ZanDkRGSuU/Pv4CV8pXEIipGk+uH+IFMpJxuxF7kWBN68QBPOJircIztecIrNf5W782vpFJbHqQNnIjmbS1pXdghZJbu2oWTwVazxakxfO+bgTSapnaTH//l2oJIXTgfwa+HbgO/HEhxPVZ/O7lKOPucTNZZIPtqPc+padH3hNxiee+nx/c2eL24W1+4+CvoF/47/hTH/rP+Oxm8jyygdfTYaYiJs1KzqStVjKE8qTBzVO1pwB4KWXB1vDnpldB1a/akSlFaUwmbd8V/G74LOcaD9m2t/F7ok8Wiik26zYv3NsH4K1JQVo4HUjLRfdKY9z9JXBxBRhRpElSkJaxdBqhUgbsbY4XExGXGwZH3IrdIOi8vqEZpIXBZrS/uqHLvfo9ioFHgwy3t5vUoubT7hu/nfg1nVA1xPJh+OUndzytbhCojkYYDY+OAmmGrrFhrVMJAvacvYlmBxRIO/r6QI80yOL3TXVzbLQ2uLF/g4JUWtcglMzNPw3Azq3fSfyathdiJgx9BRBC8I1PLPD7N3cG2sqfm3+Ge4YGDz6T+Do6AY6mRkNAIcHh4XxVXe+PfeglPvDjv8vP3tDhP/skfOOPcFVk2Rch25/4icTX0FtNp9txtjWBgXKjS1RPfBv80L+Fi19PzW0hpGTFvM2LD5KDtHghTsfD1AkWodIz38MHD+v86hu/ime9DMLlCw+mM304ck09tsSZMbo5qz0hn3OZuaPMyDPfAwi4PZl86qwSVugTAL70kKE1lMHolBDUhbLB33f2J35pV4DVI1/udePKGdkTM2kNr0E59Hmx9A3UXPUc7I7RaY3NOgI80qEc6YZl6hrlrMlOo4/85fkfwNQtlkRqIhv+DmAM1LOfH+E0GUeLHJE8Pn6RrJllJb/Crf3Js9JiJr+zrgmNdAL1BBCBNHXo/sbz74PUQz73cIL3I85Js3KRtG3wfWhULnDB87gZRbosFdPstbyJ4kwGVcya2GEdQ0oKXz1yx3cBN6SUt6SULvCzwAdn8Yt//XN1DGHxKKEZRCyBTRnDHT+P1PU/zDd5gg+V3suPvvdHyQXXefXw0/yDz/6DaS4d6N4TynMSMubws19vdeJvjjG+T1SeQBc6L+crI0GaN3ImLTqTpktYhpYcpLU8fjN4O0t2nVCGR2ZsFwop7uy0+Jefuc98IdX5d8RVypj9jUPCOL5pQpAWAeDGmG7g0ndwNA0jkjumxnBatc1zAOzvTGbE5MkAq4/cMa547vgtxUudrxUCF0fP8tMfu8PcnALsOw8+mfg13VCtm9kvtzDr0+wGgXrzA9QDMUruCLCfuUAxDPFl2Dlkj/V6oU/vcaLX3REgb+TY6TGPeOPgDQphSIMMr280mFt5OwDb9z6a+DXbboAZ5QmNckGL6xufXMDxQz56sz9jeG7uaXZ1nfrjzyW+jo45hq5AWj4BSCtlTao5iwd7bQopg1998RFYWXj/X+Pqt/1tAF7fv3Fk3iVpNV0/YtIc2kKQHhIH0Ldql+GP/Qzm/+sXmQsCKulNXhwDMNnRoTWjpRJ3F3nu+/mBg0PSCH7qjR8h/8Rf5y9++E/xc6/+3HjXPqDaboCpR5lxCayq41rrseGfy8yx1e5hYVN5qJyHMaj/s5qgIuYEUGHWCTc1W1Odzr365FlpSu7YA9J6NrWsmVUznz3rZdNtUJAB99PXqOWUXHicQes4/N2P5Y4J5iVqOeukYy1Abg6e/vdZax1yf0xTJugyNX5kvpIfsY+sVxVouttrHlJ/CM0dLpcvc+Ng8jnb2AVO5aSp+yGV0F6cwhJEYdbvXn43AI+cL1K3x5tVjOdpMmauM4M8sAyLy5jcdNRnvxgdHDcPZ2c0FB/IW7JBJQjQxjCJ+DKvVaCXGr4ffW3q+lcvPEQElcQzaY7bdfxMpBoBJcF96rs499Kv8L1f/C1+IrOEt/8sL26/NJELa2/F94TsMGnJ5Y6DctvSRpqLpYu8lCskZ9IGyR0jR8VMqkjO0hOPUey1PH4nfCsLUVh2r+TxufUyddvnxQcHfMczSydGK4rp/kyaOyWTZmgGGQktbzwjIjdSXhiR3NE0hmc9HnlNS+0pe3uTNbwcQswe+NKrDAHIRWqJb8lfYiGrjFEKYcjF1SV+6XMPMYUyJ9oeo+Hmdpi0L7+ctFPrBvlBSCghoEXGyAzN7IrLy6+QDdUCc+gmZ03iUlrXnv8fhIgew4hsxOY9k13tPBSl0EdaBf7Z79+mVlRd7+3Nzyd+Tbsn9DXpYvTui1Uyps7vvNrfpGS9psJX749xHV2HHBdb08gntL39h9//Nv7Vn/0a/vi7z/GxWzudrKPY4fGGocHDFxJfR1xNJ1AzabFV9RhGGUcqW2URA9M84MWH4zNpmU53McHGNX+N9aW38Rt1k//lD/0EV6zvZN8+4G9/6scJwum7z203wIjnF5Me7oCVno3riNyxc91Pjdy4zmrKCjzakVsXckROWk/5adUN3B8z16W3XAHWcZAWqwPMnGLSok3XD33agU0uDGmJLLWFSB0wxmB53Nzy8ZXcMYF0upZP9TdDAnjXD7Hu2tw7GH9Tj9c1J2bSUuOCtMg8ZOMLXC5f5vbB7SMypXEqzlPK9MykJZlDBpRrbf0xSMnTtadJaVn03C1eeTye82c7mplOW3mcEUwawCWzzAPp0HAbnXnoWZqH2NG90gwbVIPwTT0A/QFXv1PtCfmPEOKHhBCfEkJ8amsr2TP49EoRxy4mDqG3vR6QlrQhCfDu/xQKi3D7I7zj5j/kB73P0PabE2caxhUbkYVS3WfpESqq3loawKQBXK9d52UthK1Xh85gOyNAWhwgnU4VyFoGTSfZ3n7QcqmLPIuLKlN2s0eO+ifefZ6b//13cOt/+Pf4Gx985sTPlrJ9QFoYYBMpoKZgd7JoNMZspDuRkYYWMWmDpKH9qhJls+1NoIwAcGWIJXpA2rGctly0xj8XCJ6bV+91PpQ8f/Ucrh/ykdfUPrNTf5hoFt8PQsJoPjIn39w1ahYg7dS6QbH8zqc5UuoYVyWfxpNlgInCnN0wOMKk9brGAOTSFQDeaZa5XlOEYd73uLC6xCdu77KzH9njjxEM6HghmpZc7ggqr+trrtT47Vc2+8o64+7B1hgHu5hJ00L1vhUSHh7ed3mO59bLfPP1RbxA8uFoVq6arlJNlXndMuF+cloZlPGAYtJiq2otWejrgFpMlWlqbV7fqCeW6sRMWlpP4fhBMgkIwFu/n/zmy3ztb/0t/klwl8z2O2j7Le4c3pn08jvV9gIsEWXHjNFxLmdNUobGo4hJq7vHZowWnoSdGzCh29JZJajA62HSzKFOoUd+LHcNgP39ye6fUIb4QhyROzo9IC1n5mlqGg3ngJ9+6ac7AfSFUNIWGWqr7wJgZ3uMIeuISfMISIOaqRpRc3lrMEhbezvr2UX2ApuGMx4oiQ9cbpTLkx8hpyumTSpZswvSFmOHxy9wuXQZL/QmPoR2mLSUHjGrWvLDVWFJxQHY+xiawcXSJTRzm5fGaDxBd10z9BxBKEcGF7+3oIKLP3TrQ10p2QxBWnwgr4dNakHw1QTS7gPrPf9/DTiBqqSU/0hK+Q4p5Tvm5+cT/eJnVkr4bon7CeWOTiTfSxmZxNJ+AFbfDn/+0/CXvwDf+0/4Jl81jf/3T/4eL9zb5+/85mtjmXXF1TGTkQ6ZMByLXS2mDbKW3tdt8qnqU2yHDptBGw4GA4TO6MegnDTfRpMSyyyQtXTaXnImrZg2WXzmjwKw8eATR/4+ttzvV6WMieOHR88vvoMbra3TgLS8ZtIMxnum3aiJbQj1ukmbjgDzWQXS9sfwbugtj2O5n94xuaOZx5CS681Dnp17FlCzZItzc7znUpXf/LwCmDvS7UQ1DSvbDxHRGT2XQL4/y5oFSDu1blBMOXuylUjqCFDNWhxGg/aTMGme9I9oXZ1jNGo+ynB5qyc7CL3gu1w7t0LG1PnFTx8ggB2/Ae1kg5i2FyBwMaXEGGMx+sCTi9zfa/eVPMZW61uNR4ld++LDjJD7ABTGcFQCeP5chUrW5Lde6oY0Xihd4m46PzZIa3sBUkI2Ng7RxFhGGcdrMb/CtibJh4e8tpHskBd3nDNGJrK+T9gpeuZ74eq3gG9Tvvc7/GP5fwPwmcfJWc1+FdsS62J8W2IhBCvlDI8O7U5W2pG5tIXryiJ+d/Lw8bMaUaGHE3f/wuRMmqhcIReG7I0bNhqVF8lhrB4lgtvj7pi1CnhC8Aubn+B/+uT/xO/e/12ALpN24RsA2NkcHQIbV3yQ8KRPOuE2c66a4+5ua6BT2tqlbwHg/mu/lPg6eq/FDiKQli6PvpZarjuTlqupUOtP/G9caag1/dbuZPmPMZOmXGuVIVLiw1XM6P3sfwgHD1jN1TCs+gQgLeqYC7XXjFrXnp17C9cdh3/+8v/JQlFd68YMHR7jz+cwbFF9k4Ni/4Drk8BVIcRFIYQF/AfAeDf3gHpmtYT0yuw7O51YoWFlR3JHPZLQj8pJ6/+i/z5PXPxGTCn5uc9/lO/+nz/C3/83r/MPfnt8BYDdAWk2GTmem54QKtD68eFJZuhyWUVK3DENxaYNKM+PZ9IGyR1dMlIijBTZVHIm7dGBzVIxTeWZ78OUks17yc26ihm1fh/2ypuDrvP1pBb8ADnNohGO16CNZ1s7TNoYIG0ul8cMNXaD9kSu0q6UQ5m0C6ULvEdkSe3d5q1Rfm0tCCBV5D2XatzacsnpabZ1PZGCqO0GEIG0/JdhmPWpdYNikObKMZi0nMW2qw6hhxMEWjvHBxKDoxbFsX3zc/Vdnp3vIvR0rswH37rCr3xhi7KRUx/+ZjL5mO0FoLlqwH6MD/+PvG2VtUqGH/2lL3YGXeOKD+JbeHCQrOsbL4wyjAfsk0sMQHWBPvDkIr/9ymbnelbzqzy0rJGWt8crXvRyvTNpUyxCi5Wr1HWN6/priY082k5P6GtSnT6oUO8/8fPwn/wW4oc/zMXMHOlQ8o8+/mE+8cYuf+lnPzuWy2TneqLPZxKQBl377M690TuXNq/sxc/m0k6xAhcn7sBJg1RCeUixUCAfwN64OTZReZGM0eox3fF6jUMySh3w0QMFPD726GOAWtfaIkN64Tq5ULKzlxzAxxI2l4BU3z7eyfq6q3N4gRw4Z7t+/XsAuPfSeJl+cfMpzrnJJ7B4P1fNcmenxafv7PLbr2zAH/lJEBoXf/EvAXDjl/8M7I/PpvXOpEnPVnmHSWXLl74B/sg/ggefgr97nYUv/mvSxjYvjbmW2L5NJgxx44H/EQoBsfY833/Y4ObhG7yy/9mBLMWkFe87B2GbahC8qUP5f5AlpfSB/wz4deBl4OeklF+cxe++PJ9DD9R9vtHcGPHdYEdMmq6p803ive5YpZae4ZrrUSs/5i9901X+8HMr/OPfu8XNrfHmneL1w5MOmQlmgJZK/aMilnLKVXBD12GI/ftIC/4gui5NG2sm7dFBm+VyGpHKs6Cl2dhLPq9fTCslxBHzEN/Bidi3RC6xAypvZGhKH8Lks4RO1MQWQr1uUmUIqKw0MzDZF8AE53SX8Ihb8XEy5a+/96/zDyrvht1bvHXhrfzjZ/4cX9O2IVXg+nIRKSFvVtjVtUQgzfaCDpOWSThjPauaBUg7tW5Q/KC4YTMxk1bLWRx46tBxOEHn2T0G0o7TqM/NP8f7jSq13Tu8beFtmJrBUhBAqsA7L1RpewFFq8rWiEUgLimlYo1wyYwZkpexdP76dz3N65sNfvw3XlMW+KHqSli6RWlssBi5oEm16ScFxr31/ifmObR9Xo3mJFYLq2xIF6/+EA6SS0Djbnqu44KW0Kp6QC0sKkD9ZPb1xA6PR0NfEzpeHa/qJYrv/tNcdx0et17m+/7Xj/L/vPCQv/rznyMIx+sgxbbEIhqmzo7JdC6XjoK0I3Npc9dUAPhXIEg7TffZsSrwcQwFlKQ0Ew9aV7IWqcBkb8KcNDc6gPU6o/Yah+Si++HTLdVb+9jDLkhryAwIQU1LsZPQzhu6B2+HIDGT9o4LFTKmzodf7w9G16MZ13HNQxxf2TM3vRikVUb+zLlqhnt7Lf7oT36UH/6Zz9BaeQ/82Y+R/a6/z6qe5abORJEVHXfHlI7v2wTjMGkAz/0x+MHfgW/+b1nI1LC1gFc3d05kQg2rdmCTlhJHqkPOKLkjF7+Bb5NZyuj8nU//HVbWvsiHb70+kXty3+vxAhAutvQVSPvqkTsipfwVKeU1KeVlKeXfnNXvNf7FD/AXTGVelsQ8pHPg1mJ2dcKj4cJTXHccpHWfv/iHrvJff+d10qbO3/jll8b6NbEE1pXRuWhM5mIQSFvMquDmjWxp6OG8a8E/AKSFLnFrJWvpYzFpsbHJQmGVTSHh1V9J9LOliEk7aPcAQt9Rpk+MEU/Up3JmjoYQYwEmJ5JNi0jumMitOKpKzkL30+zpGjTGbz66fSJlel9fCIFRuwKtHWjv8+7MMjpAqsBTywpLGKLErplOBNIcP0DobTIYajf7cpI7nmY3KH5Q7KAxFpNWjzpI9QlAmiePal3dIDxC/X//U9/PPzz3h6GxwZyW4kNf9/f4jkYTUgWeXlUfvq5V2TOsRAdeL5CEEkLhJnZB661vur7IN19f5Cd/9yYf+PHf5S/8bDejZD67oMDiZrIF0okd2cJ4dqM01rUAPLGoPqe4c7aaX0UCjwxjLMljMzrMxEyarYlEGXKDarGs5irOZR7wxYfJDruxBCRrZqPA8cm6iyxe57rjYmQf819++zX+v9/7LF98eMjPfGy8GaNpHK8AlstpHh/aVFIqh+iI3NFMQ+ViosbCl1OdtvvsWBW42Ho0FxaaAw8Ax6uSNdGCNAeBPZk0JDqAWT0znUeNQ1T3vE2IJrSOi2M+lHxm08cLQmqpMjt+K3HuYgzSXELSItlzkzJ03nu51plpPV55K09Fs7jn7ncy55KUE83YNKL3IZ+dG/kzTyypbutb18u4sYuumYbnf4BLS89z00rBg08nvoa4OgoBy+iaD4x7uFq8Dl/zF1lYeisAgi1ubTcT/7gdOKSkxJbq0DeKSUM3SD3zvfznO7vcr99jI/VP2Sj/CN/889/BK7vTmw21vQBhqLVWGYd81cgdT6/cJm+Xqgn3IIF5SCyB1WKQNulet3Cdpx2Xut/iXv0e84UU//HXXuTDr22x18+5dUDFe50TuhMxaculNBt150QjNGtmKVgFNnLVoc3rUe6OduiRjn511jJ6IgMGl+0F7DZdVqK5zoXKFTZNE+4kcwKPQdpxJq0tBClhoInJj/P5aC55nFzdDkgjmkkbwzikmrUgyCqQ1uxvfjesPCSWNhikqReJ7Pf33oB4jjlVYK2SoZA2CLwc+1Z6qOw1LtsL0awdFrVIvfRlJnc8tW5QzKS1gzrlhNkp1ZzJbjCHkJLDCT585RrTm78QnDxMVZWumd1bLGspDIBUgcvzeSxdw/dy7FmpRCAtHrAPcROFvvarn/gTz/OzP/Qe/qP3nudff/4R/+ZlJW+Yyy2ybSXrFECPxCC2qp4gr+bCXBZNwM3NLkgDuG8M14Afr97DjDIOEWMZZRyvpaySOaS0R7zyqH5CHtqvOqGvZg7bCyfT6QMsPM3TjouHzzc+K/net6/xdVfn+Nu//ioHreSHzS5Ii+x/E7LLcS2VVJaf9HMIxEmHx4WnErOuX0Z1au6zY1fo4XYcFo2hQ+K9VclZhEFeSUOa4w9aexGTFs+khaHED2VPmHXXIOgD6x/o/HdOhtxr6PzGFzeo5ZfZ0XV49EKi1+yYdRCSSgjSAL7+6hy3d1rc2ekPOlZSNR7qGuy+kfh3Or56dht+C13KRE5x3/mWZX7jL389//yH3kPW0vntV7p7yZXyVW6bBv7D8UFaL5PmdIyJJtvwF+aVA9x56/Wx5tLsQEnr25EL8lAL/rie/T7+yOEBv3f1h/gn3/LP8be+k432A377bvIw2IHX44UIXX3etVBC3Mg4q8mrtMYTzjZSCl7ZHt0MjO9FxHRyRyoXuR4RPS/tqObwey+ppuBn7yUPS+4w8dIdeyYNuntdPyOixewiG6nMUIdHt5OTNohJ88hEiqtcSh84R9tbMbO3XI6YtNwim7qBfJQs/LvYYdKOzqS1NUFmSmYnlyrR0IRinhKWGzWZZMSkjWPBX8mZ+EGBfU2H5iRMGkeZtGMzaUAXpO3eAieS26YKCCF4arlIqx0xeZuvjGx+2l6AZm2zLiKQNm4c1BQ1E5B2WqW6GQFO2KI4wjY5rmouxQ5l8qHk0E7eFYjLIcDsHUj0R334MUIvYuoa15bytNppdjWRWOsKKk9oko4RqIXkPZdq/Ff/3nWuLuT50V/6Im03YD4zz6aZjNGDLpPmRgP2hQSyoOOVMnTOVbPc3FKbbgzSHuarsH838e/pMmlduWPiPKE+tZBTbpeHskEYuLy+MVoj347kUWkzh+ON4e54vPILXI+G9F/aeQkhBH/+A1epOz4ffyP5oti1JW5jSIlpJbTujmo5ss/erPtU0pU+NvxPqnvan10G0pdAnZr77NgV+B0mzRSpEzk4g6qStbD9Evu6phw4x6zYhcuKNvLj8xa5aLZRl5Lvu/o9nZ8rhCGlcoWf/thtasVz7OgabCUzzFDrmsSWyZk0gK+/puaVB7Fp87lFBRa3kzd8HC8gZWg0fJu8lIned00TXFsskDJ0vvbKHP/21a2OvO9S+RKugPtbL43thho3n7KmTjs6GE868L+w8k4AVq27Y82lOaE6+MZMWqJ1beVtULuK9lv/De/4hf+Yv5dqgDvPi9vjydj6ld3LpHEG0GZSpXVK7g74BV7dHr3vxnEzUkwpd9QNLpcvIYBbUVzGs2tlDE3wqdvJQVq819mhN9EMUBwV0W92cjG3yIaQ4NYHmruNljv6ZKLjc8Y0EoVZPzxQ73HskLqYXaQtJPWNlyAYDfJK/YxD4nn9KZyvQUnAm5qGHKMJ2GXS1L8nqTIE1Eya65cmkjtKKXEEmD1uxcfDrAGlDIKj5/RIfXR9uch+w2I39JDOgYo2GVJ1x0aYu5wXGTWSlHDvnkV9yYM0oasbu2Qlk95VsxbbskQxDCcyDnGRpEiI0HdughNtjinVnX16ucTuoUVd+njNLWgOP4TbbhQzIL0oT2hyPb5laPy3H3yG+3tt/s+P32E+O882AXL79UQyqZhJc4Lksxv96spCnhsRk7aYXcQQBg9yJRjDQrwzkxbLHYVG2hjPKKO3UnqKip5h09BZEru8mEDyaHdAWn4845DjJQQX5p4iJwVf3FZK4GfXSli6xqfujLFx9TpejWkyA0ruCHRs+I8Yh4ACaTJQ9/VXTo10n53EeXaiClycSKJhiuSbaiVnchjM0dQ0vDHY6LjcaDDdjECac+wAEoO0a67H21I1jGjzy4WS7373NT52axehL3Cg63gJ5bB21GALBaS15Afvi3M5LtSy/Pyn73fma3urVjwfOXKNAdIiKUwjcMhPMEb1gScXeLDf5rWosbNeUD5ZD7QwsZQ8rpbnkzI0DF3rRGBMOkuyEGUtLWY2x2LS2qFHWobYQTSTlmRdEwK+9W8q85LCEt+y81O809njs4+/MMmlH70eN0CPQZo4A2kzqdIaAKaX50FjdBZVfOAOUYfYifc6wFp4mqVAdmIqMpbO0ytFPj3mXmdoIgJpgDbe9SwPyUpbyi6xEUZmHQNs+N0gxNQF2gC1Q1t2QVoupYxDRs1oPtq3j1xbPB+3iZeo6dSZSWsdBWm2mM75GiCXqeILgTuG+syN1q8gljuOMZNWzVq0gwotTcMZcywpzqgclPvZKSsLhRWlunAO1XnJUHvg9eUirpvBJ1SzeCP2tfuNewghOa+l33Q59pc8SCMGaQnnoyo5kwYZiqHk0BsvTweimbRjA4knEHoqD/nF6MPval0Brq8oGhVgL4F5SCx37IS+Tjk0/d7LNZ5bL/MvP/OA+cw8PpL9oJ0oDiBm0tpBk0wYYow58xTX5fk8b2w3CUKJruks5ZZ4YKXHY9KOgDRlwZ+2JgdpAAvpKhuGwWVzjxcTODy2vRaGlGhGBi+QZKbYuLSFp7noedyOTA/Sps5b1kp86nZytrdrS+xEw9Tj6vSPBlofmUkDqJxXfyZ0A/0yqZHus5M4z05UoYcT6fbNMeQp5YzFoa/mqPYnsH7vMGlaKvr/R5m0jmOt45A+eMjVylV0BBkzx/e98zxCwL1ttSHubieTwzpeAJo6TKREcmmIEIK/+E1X+fz9A37hsyeNhubyS+zqGsEYslzHD0gZOo3QJS/H74B+wxOKhY8ljx11gKGPPZfWcgK1pgF2GIG0CeWO+VSBLALT2OWlR4eJjTzs0CUtwY4U34kP5Ne+Fb7vp+AHfhH5XX+f97ubNIJdPvTF1/j7/+Z1/odffXkiMxHbCzAtJcmtjNG8OKshVVJLXo0Me+5od8dWoHK/AqZk0gAWnmLNdY4Y/Lz9fJXP3d9PNGYACqRlTP0IGBqnuoHWJ50TF7OL7HhNPBi41/U99/WUTUBGxBEmBqHsNr8G1aMOkxbJHaMs2w1Dh4cvDP1ZUIqprKUflTv6Nm1NIzMlcMhl1b7XGAMw2ZHiJoyZtDFAWjlrEQbqPLc3RqYwgBtFBfS6FfclU0ARKjGTlurK3K+vFJHx6+u6+p4h9aCpCIaLwuoAvTervqRBmhOECF0t3klBWj5lYOk6WalzGM1ijFMuEnOItWenqpdVplQfkNb98Ee75XUG7KVHOpxNkvn3PL/Ky48OcWx1HVu6niywL2LS2mGbQhjChDNglxfyuEHYyRlaLazyQJNw+CARrQ+9M2l6z0zaePK+47WYW2ZD13m+1OgwfcPK9ttkQonHGLKggS9+nXXX4d5hd5bmHecrvPjgMHG4dtvt2hJnJ2DSKlGg9eNDm/nMPButY5t3tLGPA6a/DOrU3GfHrsDDETGTlvw5twwNS1Pr394YDotxuVGX3IzWlvigFB9C5jPzXCle4JubLdh7g3csvYNFYSGsPHP5FOerWXYP1caU1IZfWRarw0RmTP3+B59b5W3nyvytX3uF146Fz89l5giFYG9nHJCmnFkboUtuwgPf1YV8p6Eyn5nHEAYPMwV4+JmxflfT9cla6h6wo/yqaVzZFvQsTeocNltsHCaTKduhTxqB4w0P7B1W4vkf4MlILvkXfuFD/J3ffI3/9Xdv8ZsvjQYEx6vtBRhmkywama8iZ8dTrYhJO6dZOHK3k5U4qJp+m6yUtGMzmUmNQwAWrrPm+9yvd/eRt5+vYHth4ugZ2wtIWxFIm8AQo5q1sHSNR31C1xdzi0gkW4Y+kEnzghBzyHPRlgGZWHGQUu/VqLm0hwc2laxJJnr+Y5C2aWUTz/oW0+axnDR3audr6Kqmmq0JmDQZG4ck/5wsQyMt1AjT/pjeEXHun3XCrbjPPVu92BekXVnII0JFQuwaJhwOB6ePmupevoj5pkeEfEmDNG8CuaMQgkrOJI3JYTh+loszwtqzU7VLfbWuTy4VkL4CR7vGaPaoN09oWrljXN/57AqGJvj8XfW7tw1dAaQRFTNpzaBNPpSTg7R59V7EDo9r+TUehLYKS64n69Q0e0JfPa+FL6ZzdwRYKK6zaehcTe9zZ2c0gLf9NhkZYndA2vQb1+PWVmfDfMeFKm4QJs5ti00H3NCZSKcvhGC5lObRgc254jk2W5u0ehsZ+UVlLfsVBNJO03127Ao8HE2tJdaYz3nOVOvffmsC45AIpHVm0o4xaWkjzb/64C/yLl/A7hv8+bf9eX4mda2zqT25VOTRjjqQ7Hn1RA5gthd2QFpqzHkJTRP86Hc9zU7D4Vv+7od559/8LV55rA53nfiIg9uJM30cL1RMmvQojDEf11tXF5U6AEDXdBZzizzM1+DBeCCt5QTKDAlwIofKqaJFMvNs6RpXxENeepTQtTZUAeOximOidU0InoqCgZ+73OBf/PB7ubqQ58c+9HLiplNcbS9AM5tK6njm7DibKq4AgktCgJA8HtHcaQc22R4zmen2uqdY93y23cPO/vL8+TJA4rm0thszaQGZCZ5ZTRMsllJ95Y4dBsvKDtzrRjFpbRmSieR2scJm1Fza4x77/SPXUV5OxKSBkjyeZNJE8qzFAZWLzq+N9hjujhGj1QFpYzZ78lYZgF07+Vw+DAFpg5i0xob6X48yLG3qLOYidUquOvJcumnfI/SKFKX/pkeEfEmDNDcIEdp4TBqoQXszTHMok7E2veUBqWPWnkM//PpDsAoQHb4KaZPlgvrw9/LVkQxWx2oW1d2Mf880Vc1ZfOOTC/y7V9TNvKUP7hj1VgwYFUib3Ar5SgTSYrZqJb/Cjt+iLURiANB0laumZWg4kVxr2m7RfG6JPU1jRdvi4UG7A0oHVctrqzwh1CFzYlti6GxcASGPIknB28+r7lXSjavLuk6WHQOKFXi03+ZC8QIAdw575gQ1DYqrX2lyx1Nznx27ArcL0saUTMTutnt28rmOuNwOSFP3S9+gVk2DygXYu03GyDDvOUrWDTy5XODBnvrePU1LNA/m+AEpU71OegLnsbeul/mNv/z1/N0/9hy6Jvgbv/QSUsouSJMeHI5e00BJylOmRkP65Cacebo0l+fubqvDQq7kV3hkWrD9GoTJQUnT9clGnXc7ataMlZN2rOaL62zpOk+L24nn0mIJWbzeT6oQKM5fZ80PubiyzzsuVPnR73qau7st/unv3x7r9ziRu2MV/U2XEn3FlpGC/CJPCHWPvb47fB68FbhkZXePmYRd7VRpjbVItvqgoZrDy6UMq+UMn76bEKR5AWkTfGSHsRq3lorp/sYhcVZaYW643HEYk9ZzXbF8eRRIe7jfZqXc3bMt3aKSqrCZrcDjLyRaR06CNMWkTSt3zEeZq81xQFpwDKSNwaRBd0/bH9M7oit3VO+7lHK43BHg0efhmPngcl65ju5lKyNJjG3nPqE7jy69M5DWW8o4ZHyQVstbiDDNIeFYGvlQhvgCrJ5FYajcEeDR547QqAAXKmoR2MuUR4K0rtVs8tDXJPWHn1th50A9uFuGmYxJ81ToayNwppI7lrImc/nUkaw0iGY4koI0x+/ICOwo32gaWRBALV1DCoElN5AS7u2e1Kv3lh20SYddkDYyT2hYpQqsp9WiEA9UV3MWl+ZziefSYkBvh85ExiGgNstHBzYXShcAuH14++g3lNdh/ysLpH3JVOhjC4HAwBrTZrwayVEOJgi0jq2STf3YTFo/N6zY2t5pHGHSQl9JjfcTOiuqyArVJEtPCEKuLBT4I29b46988zU+emuHX//iY2oZ9QyNYx6imDSNpgwpaONJL+O6OJfDDyV3Iwn3cm6ZB6ETqQOSS1BbbpdJs2XEpE0jdyxfYtPQeSa3m9jhMXbc7B7IJ40WeYqnHJuXt18E4GuvzvH28xV+9cXxJLltL0DoNgUpzpi0WVZpjad8pfR5ceP20G9tBQ4ZhJolZUomTQjW4uD5ereR8sRSgTe2kuX5tb0Qy1Lrx8QgrZTpH2id6wm0HtC8dgYd+gEv8PAFZCKFQCxfjh2pB9WjY0xafC2blgV+G7ZfH/4PAgppg0avrNK3aQuNjHlyXv/Vx/W+5kv9KhfN+zfc5D4OHZAWGGgCjDFBWi2j9rQ9b/ToSW95kQIhzv2Mm459GwsxSHMOTpzT14qq4bebzg2VO0op2XXvEzrzaKH7pgZZw5cJSBOIxGHWoJi0IMjiCYEzxlxaTKOava4xwQC5Y/zhb7x04sNfL9VAauymsiPBUWxV7RKMZVU9qp5eKYK0sLQM25lCopk0x49CX2MXtDEdlXrr8nyuw6TFIO2BYYzBpPlko8NMbFU9LUiLu/CerzTQg7KY4mr7SlY4E7kjsD6nMpRjkAbw9nMVXri3n+jn45k0O/TITmgys1xKs3Fos5ZbRyA6RiadKp/7imPSvmQqcHE1DU2aY0tD5uINzZ9gzjYa8Lai58fx+zBpoPT7e7eVE6xTVwoBlBMWQQaBxp5hJQz/DLDMCARMaQ/9x991jieXCvz3v/IKFasKRCBtO5mJSmwcUickN4aJSW9dmleHmPiQuZJfYcuPzQeSMXqgmk/xoc4Jp5c7LuaX8YRgNbPLy4+SHbBsAlJC69wHE8/azj/JU47LveZDDl0FEN95ocpLDw/Gkjy23QCEE61pZyBtZlVa46K9jZQaN3aH77vN0COLmJpdjWt96W0A3DvozmCvVTLc20u2ftluQMqMZlonbKysljM8OmjjHzMrKZgFMkaGx6nMwIakN0Tu2A5iZY+6rvic0nIG3/Mt1+eg7XUcluNayC6wGTFDSdaRbMo4+jq+yklLH5vX/+LDA771//dh/u9PJdvLYyatMQZgskMXXUr8QBuYJzesFrJVkLBH0M0xS1BORxmi3v+hcQnVi93/PnZOP1epIEOTXTM99Hy8Y+/gyhbSm0cLnDN3x95yAzWTVrCKY6WpV3MWbU9tqocHyedrYkvkkwOJQ0Ba6J1E6JUcYZBl2zCV1nXI7ITjhSACQphIez2ozlWzWLpGSpTZsjIJ5Y7KZr4uXQpT3hqX5rszHGsFNcR8P18bCNKklLz08LATPtl0fPKxC9qM5I5xF/7Q3QHkyLk0O3BIS4kdyx2nBGnzK+8iFYbc2+t2zC7N59lpuke7YwOq7SkJaDv0yEwoR10upfFDSd0WrORXeKPHyASA0jkl4/XGn+c8qxEVeBGTZmHp47kM1vJZzFBnX3rgDWeAj5cXHAVpx2fSOlW5CF4TGpsqQyiSO65VMuQsE0vk2c9VkoE0P8QwIiZtyk3N0DV++P2Xubvb4qWHDjkzx46ZgoNkrmCOH2IaAZ6AwoSs3qU59V7c2o4k3LkVJJLHhj5WU6Pldt0d2zJiLaZ4f+YzypXNMne4vdMcuY4EYYAbSbViIDWxjHv+Sa546iB950DJ6d52rowXyETuuXHZfoAUNrlQvuld6q/oKq2RbT5CeiXuj3DQa0mPHF12ddq9rrT6LvJhyP3NbkTDeiVL3faPWsgPqLYXYMVM/IQg7UItixfIE5JHIYQKtNZ1aG723esGyueAdrT+ZiK33Ljp0hrCpD08Zr8f10J2gY1YHdEYzUDnLP0oYxeonLTMsXiiX/mCYob+5aeTNZDiGJaml4zphDjKA7xAjt10BFir5jBDU+V/jmP97x+NlBm4n4E6m0cZucfP6avlDDLIsYmm9ju7vxIhbmQbwSLCd8/cHXsrZtKKVrIg67jm8ynqrvpADo8zBUMqNnRIab1yx2DAhx/Z8MOJD3+lnEb6OTYlELhDU9xtPwARuXzNMCPG0DUuzGUhKLFlGAmNQ0I1uxH6FKa8ltVymr2Wh+0FVNNVLM3icbY4MCvtk7f3+I6//3u848d+i2s/8qv8+hc3KKRjkDYbJi0GaTsEnEu1RzJpduiSCUNaYTyTNt3joq0+z5rvc2+76/i5XlXyh9gJc+j1RIHabRllx4wpmVOvpzpud3dbXChe6MOkRQ6PCe6XsxqzQg9HgJiASatkLQzfUnbBzfGy3GK5oxUNl7vBgM5j3HXce+OIG5amCZ5YKiCDHHupXCIGy/YCTCM68E3ZXAH45uuLpE2NX3zhAXOZObZTWTUPnKAcP0TXFVDNTXgtpaxJLWdxq4dJA3g4hjoA1EEua+kgJU4E0qaZSYvNBzy5h5Tw+sZwNq2TzaYZ2J5qQA7KghpZhSVWNfV+xrNHz59TjO9n7+4n/jVtNyAU9lRz0GfVp0rrCL9NKiyxZQ83RmiFPlmhY/sBuiYmYkZ6S6y8jXXP597+jc7X1irRXpeATWt7AWYE0rITAvfzNQU8bvfZ5xdzi2wQgZ0+e90w45A4+DsTXVd8Tjm0B4O04/b7cS1kF9h1D3AhkWw6ax1l0qTXVu6Ox+KJfu3Fx2gCPnVnL9HZIgZpjcBOlKkL0A5dMgxRm42o89Usup9Rc85jBFq7kZqkEynTb8a6t2JC5cQ5PROd06P3c4B5SLy2pZiHwDlzd+wtJ3J3HGceDeBcLUvTVz9zmHC4HAa7xgzU7MdzaScQehYZ5NgKBy8CcbXdrlX1pB2jQXV5Po9j59gSoaJzRzx8theQMiQOIfkpQdpStBg9PrDRhMZSbolHVmrggeZzkeTvr33rE/zJ913gP//ma/zIdyp5YCs6ZGaN6Sz4a9FM2I6u8Xy5zu0RTFo7dMn02BLH1rkT18pb1cbVI3dcr6h/U5KFtO0GZCydduhPzLpeiDauN7abXChd4M7hnaNzm1+ZNvxfGhV4OADSGPsQVMlZaEGafU0bG6R1NPxJmDSI8h8bR9ywnozCP/cMUzFHvjv0NR0/RJ8RkwZqOP+bry/xK194RDVdY9u0Ekm4Qc3aigik5acAjJfmc12QlotAWrY4ptwxYtKi+USYciYtAmkHwT4gubc3as42angJM5KBTnEEEIK1ylUA7keByfOFFOvVDJ9JaBAB0PZ8AmyyYfCmD+V/RVdkw78g8jSC4UxFi4BsBNynbUYCULnAWii432PpHjcI7ycBaW6Arsdyx8nuiQtz6vX67fOL2UU2Yul4n73O9cOBa3QXpKnntpJTYG2/NXhNjFU7MVDtvQ6ArVxZKVhGVC6lmLR4z3a8NlIIMmZ3rb6xWefmVpMf/DoFTn7xhdEN17SeRkfQIkys1GiHPhkpRjphDqoLc1lkkB278ehF15eKGK04SmTgNXRA2tHc3+VyGhnk2In2x0Hn9K22ura0qIDvnBmH9JYX5aSVxwRp52s5DsNI2paAQo6rO5OmDuV+EBLKIQi9Fn/4R5m+lejD3426lsMOE7YXgjjJ4M2irizkabYybIcu0rdHWmc7fohpRYeZKQFjTOvHUoPl3DKPNDEwK+2Vx3XmCyn+3Dde4f/zHU/xF/7QVd66XgagGX0uuT7DseNU1syS1dNs6zpPZw46JgCDStH5klagPpdpJSBkKqzpWR543eDZeOMadbiC2PFK4BBODNJWKxl0TXBnp8mF4gVafovN3myUmEk7m0ubfQWKSUOaY29qlayJDHKRNGQ8G/44z8aMjIC8QZ3H8jkQGmy9ckLG/dRSAc/NsBM7kI1YV20vwIybT1PaQ8f1h59bYa/lIf0C27oYmW3TuZaeKJf8FFmLl+by3Iok3Eu5JQSCR9ly4mclCCVtL1BMmm9jC4El9LGk/McrnrPdJaRMY2Szp6NK0K3I3GW6NS27cJ1qEB4xiHjbeoXP3N1LHq7t24AkF5yBtJlWnJWmp/DFQeez71dtGUYgLZh+nwMF4DM1HgQtQqnWmxig3E+w1zl+gGFEIG3Ce2KxkCZlaNztx6RlF9n2Dgmgb5PFGyZ3jEFa1FwppAxMXbDTHAzSXn1cp5AyWC2fZNIANvNziZm03uDstq/+bZkek7dfi4x7/tTXXORdF6r8Py88HPksCiHI6RYNoUFCt0VbKkdy1x+eKTeozlVz+EFB5QmPJXeMlCFjM2nHzuklJXfcj2YMB+0nm61NDLIq5uAMpB0td0Im7UIty4GvtPqHY4TzHZ9Jm5RGXSymIchzGEYb5hAmzfYDzHgxmnLA/nhdWcgTeEVs6dMQYqRltZIoqfegMOW1LEUg7fFhRPPnl3nUcUM7+TC8unHIk0v9zWFa0WBtdsowa1Bs2o6uczm1z73d1omh4t6yw8iqOh6wn8aCP6r1wjptQrbb6qBdyZrkLD0Zk+YFpCIzhuyEIM3UNdYrGW7vtPo7PBZX1UH9zOFxeLlNePBpaI9hiR8xaXICuWM1a+EFRfa1CeSOsWWxHskdBw1aGxYU15QdNBxZ1y4v5KPGU3TQGwGQHC/AjNir1AyeW4D3X5unlDHZPUixIwMldxyRleYHIUEoQUTNpynY+IvzObYbDoe2h6mbzGfneTDEfOB4xe6sOcsA38URgtSUigVLtyjoaXZ0nafz9dES7uiAk9EtnEg+PVXNP8Wa5/GgxyDi+XNlNg6dvvbn/aodqLUvF7z59tZf0RWpIq5Ga82dAYyvlJImkoxQwH0mIA1YK17EE7AZnTtKGZNCykisGtGjJk9Gn6zJo2mC87VsXyZtPjtPIEP2tP4zpQNdvel5hqLmkxCCStZibwhIe+XxIU8sFRDiqLS4EweQLSVm0qCbI3v8WgB+/YsbvO1cmaVSmu94yxI3Nhs8TPAs5vUMTU1Ae3/k90LEpKEpQDsBkzaXtwhkWe1pY8kdo5k045hb8cBzeqQQOXZOz1g6KVGkEUZr5gAyZbO1iUlZNbQC90zu2FvxTNq4IK2ctRBmBNLayYPyOjNpxwcSB9Ko/eWOpq6R00s4somvGSOYtOnyhIbV5fk80lfXtmXoIwftbS9AN6eXBUF/Jq3jhnYMtAah5PWNBk8sDgdp0zJpAHPZBXYMizWxjR/KzkBvv2pLX1lVd0Jfp39cOg6PW8q2WgjBejWbSAJiewHp1HS2xKCY5tvbzU5W2pG5NN2EwvKZ3HFUbb0C/9sH4O7Hkv9M4OIgkaExfqZM1sLxi6rr2EjeeAKlENCkxDBHyB0Bqhfg8efVf/fIHeMh63rYJoSR82C2F2Jo0fzTjECaZWg8f67MfiNNXXrYMoAR4d5xx1kK9Xzlrfywbx9al+bU+tMreXyka+qQl4A1akUHq2wqYtI0MbFzXW/VUmV2dY235Oqj1QEdZ7qUyo+bVtq2+DSrvs/9g+6s8fNR/mNSyaMTgbRs4J/NpM2yslUws1xTE098YaP/PLgTOIQCcvE9MYN9DmBt8VkA7t37CKD2utVKZiSTJqWMYhliJm3ye+JcNde3cbGQUQzWVnGxL5M23DgkYq96Gj7VnDWQSZNS8srjOk8unzzfdJg0K5uYSQMlmwZoR5LNGKS5fshLjw5532WlJFuNxil2IkO2YZUzstQ1LXHjUQWNi5GZcoNKCEHerLCna8gEADWubu6nAksD3YrjWnhK/ZlfOPFXRatEgIOdrQ2WO7a2MMKyOv/59plxSG85vo/Q7LFBGsD5qpKBHI4RlBd/+KZ2DKSNyaQBlKNco/3iykiQZlrxgP3pgbRtXR9pBuH4IVqUS1eZEqRlLYNSxuzklCznlpFINgz9xGJ0e6eJ44c8MYBJa0bgedqZNFDmIduWxVyk0b+z27/z7Ic+HpK0MJRFNEwtDQJYX303APcfdA/3a5XsyMw2UM5wsePVNIe7i3M57uy0mM/MkzEyfbLSzmz4R1a6rP5M2HUEjoK0cZm0nGLSWpqGO8aGBmomzZISIjm1M0whULnYZep61rWlUhoZZAkJqWti5IHC9gP0iL1KzeC5jevSfJ7dQ7U57+haojUNQKBcGfPmFCBtPnJ4jPIfl/PLPJQeuI1EEqGm28OkBQ62EKRmANKqmTl2dJ2r6YPR2Y8dE6YMzixYk6W3sOb7PHZ2CSIpbLyO39wc7RQXhhJPRlJU783PIPqKLiGgfJ5LvnKtu7nb/xzSig76WT2F7QZkZsSkra+9D4D7jz/V/Vo1O9I4xI3GTISmQE9mivXjQi3LnZ3Wibywuaw6H24V52HvJHgdNmfVcg6j6+qyV9XcYCbt4YFN3fZ5cumkAV7RKpLW0+pc1NgY2ezJHctka0fxKrE50729FkEoO2601WhebpgUM66ClaehjSF3JCCDHo1hTHbPzGWrBEJQbybf07wYpB2bsU4NanwuPg0/+Dtw6QMn/qqaVpEue6WlgcYhm+1NRFhSSirfPbPg762m3wAhKVnjg7QLtQKZkE5+S5KKaVQr2ihGIvS5a3Dh62D93Sf+aj6rOhm7hfnhckcvxIrljjNwQeutjKUzn1WM4pZhJcts09UBpDoD1mq5lO4yafllAB4Zxgla/7XHypGs3yIG0JIBgtkYENQyNXY0jYKt3otB5iGx9DWrmdPnCfXUyoVvQEjZYdJAOTze3W2N1I233QDTnF4ae76WpeH47DZ9nqw+yWc3P3v0G0pngdYjK8otS7qhAZHcUSJDc2zjkHI0kwawP8acLSi5oylRLCm9m1qfjfVIrkwX0KQMnbyh1uF9MzPStMP2AjTNIRWGaObs1rVL8zlcR70PqvE0QnYZseChVGtMfkyn4N6KZ2oe7qt9YiW3wkbQVHMtA56XX/jMff7qz3+Of/x7t/j5KLNIzaQpkDaLxlwtt8SurnPO2OXhQbvz+far7kxaGtufwfxRpsyqVcZHstFS63rK0FksppKpA/wAETGuOf/NzyD6iq/Kec63VUPy/mH/g3ArypLNGmnqtt9xK5y2llbfhS4l93a6brDrlSz399pD9zo7ygMlBmlTMPHn53I4fshG/ahipsOk5aqwe+vEzw2T8LU9tZZkelj5Ss5idwAQeiUKme83ziGEUFlpGoqlsYdHV8TxHa0OSIvm46KZtJjlvxjlOtYikLbbSADSUqUxmTQ1Gz/NPbOUV2B5b4x9tJOTZhydSRvKAK8+D9rJv1/MRTO9uf5MWihDtlvbiKCozn+BcxZm3VvPnlOHikmYtAu1LJlA4zAYLGc7Xm60WMUffnw4HygJMdPwJ/81nDsJ0pbz8YdfGcmkGR2r6tlrXS9XlQvZdq4yUu7o+CFSi0Ha5B3nuJZK6SNMGsAj0zrRhX/lcR0h4Opi/9dsSp8M0w3Yx1XL1DggQB7cxdS7B67j1e5ks1k0HR9DExPpro+XlZtnSQruHXblhOuVLG0vGNntsr0AQ4+YtCkWitjh8c5Ok/etvI+Xdl5iz+5ZmCsX1PziCAe/r+pKR2vS2ExaSBjqYzNpaVMnZyiAsTfGnC2AG8ZM2rHwz0FMWlzHBq07Xcf83MCuY1yOH6IJl5SUM9XwX5rLI4MedUAC2SVAKCMmzerP1ieptKlTzpo8PlRr2lJuCV+G7Oj6QIfH//l3bvCvPvuAH/vQy/zEv71JytC4MJfrgrQZzCFXMzV2DJNFdpASHgxY0+Aok6bmj6Zf01bLSvbfYYJtkwAAlMlJREFUax6yFh3GR5XthRCDtDA8m0mbdZXPUzi8jwxSbAyYZY2zsbJ6mkPbo5Cejcu0qVssYXK/2X1G1yoZWm4wENBAd3YzoIkuJfkpGsYXapHD4/bRhkFsuLOZzqs1xD3698MkfLYbm3V0wWMtZ7E7wN3xlagJfW2AUmghu8CmjBwGR6gkujNpkdwxOMqkvRHlOMbS7Nh5cm+I82RchXQ1Amn7I78XoE1IWujUp7hn1ooRo+kkz1WMI2XMEzPW4zecVuPXTxX7Nvz27D186RP6ReV66ttnxiG99fxF9WZMAtLOVbOkQp2DIPlBM2bS4twadxRIG/b6JfXhbxi5ofb3CqT50evMvot4bW4eGZpsZvIjJWy2FxJqDUwpyRmzZdKWcksAPOozIPvq4zoXarmBXd0WIbkZOV923NCCFk8U/dEgTTOp2z7FjHli6HfSWjcK3HO6oKjj8DhqnsQL0I0472nyheLCXJwf0+JrVr4GieSjDz/a/YbaZZAh7N2e+DW+4kvTFYgZ0fnslJQQuNgyJAzGZ9IAFvIKJO3bY5iVAG7gYSFPMGlmv0DtXibt2PzWYi4CabnqSAbL9gIQKgx+lhr+y/O5joR7xxg+7wtdJs0PG6TCEHOKmTSApWKaxwfqGYzXtEGB1o4fcHunxZ95/2U+/SPfxKd/5Jt48W98K9cWC+A7tDSN7CzUAeka+xoUPNX8GjaX1plJM7NR5Mr00ra1BTV79GDv9c7XVssZ7u8nM0OKmbSsPANpM6/KeYRbxwjz7Dr95zfjvS5nZGfKpAGspyrc9xudhl/Xhn8wgI9Bmi/rFMMQ0eNcOG7FDcm7x8YaTN2kkqqwbUTg4hibNgyktT0FhNK9TFrWYr/l9TUie+VxndVyhuIAILOQXWAjmsscJSOPZ9JiJs0O1bMTSy/f2G5SzVmUs2rNLaYNDG2482Rc+XRZgbSErFYbSSY6H016z1yqKkZz1xme79hbsVvxyEiZBHW+rF7/gZZW883Hgs1j+/3AK5AzozP8GUjr1kGEridi0uZymIHBgRwcMHi8OjNp+lEadZIP/3JNbeAPhAl+eyCFbHthhx2ZlVV1b61WMki/wIaRGilhc/wAn0OqoURY08+RLBUzbDcclTWnp6ilazxKZU4sRK9u1AeahgC0ZEhWzKa7181K03k2tzcQpHVd0FJRd3GGG1d+lfta2DGA6ARaj+g8q2Fq9T2lCR2vQB2gdE1we7vJ9dp1SqkSH3n4ke431K6oP3dvTvwaXxWVLieXO4YBIHFlQDABkwawWlDS5T03edcRlCGS2TOT5gUhmlCB9yeq0l/uCN2u436mOJRJk1JGDJarQNoMmbT5QoqcXgIE25lyApdJtYb7YZOclEr9MEUtFtNsHB5tPD22Mn2Ndt7YbhKEkquLeWr5FLV8qgvOA4eWEGRnsObHDKfjqPVkGEizI7VI2lQysFkwaUvr71Oyto0XOl9bq2R4tG8rZ80hpXJCI7OqszDr2VflAgAlkaHu9Y/gabnqgJw1c9RtbyCYmKTWiuvcN3TYeln9/wSB1vEMuBvWKQXTAfflUhpTF33HGuayc2yKKFLkOEgLhuSkuU0MKTF7nt1aPspKa3snvv+VR4c81cc0JK7F7CJb7iESRjNpx41DgnhuT13Lza1mh0WDyHlyyLxcbxWsIg1NIEdENcXVRpIRxlQg7cl5pbDq2OAnKK8D0iImLQii/z/+WvbEvIqpuC2jhuUxyWMcT+S7BXJ6dK+cuTt26yA6jEwyk3a+lsUMLOoiTJyg7kYheScQ+gQ06uW5BaTUuB9GH/4AOUzb6wltnOLgPaiWSmlCv8CGEIrWHyJhc7wQjzrVIJjJZhk7PMaHmuXc8omZNNVtbg6UAiAlLSHJzijoO2bSdnSNJ1O7A90du3LHlGLSZrlx1Z5kV9dp3lPmIUkDrdtugNTU95SnGKa2DI3VcobbO010Tee9y+/low8/2p0TiA1xdm5M/BpfFZUpJZc7RpupLQNkaGL1Y7FG1MVKBJK8xkjr+d5ypa/kjjGTNsS5jHQRonna44ZI58oKJG4bKQXSBqyrsUw8IJI7zpBJE0Jwab6IIfNsp7KJjUOcsEUhDGFKULRUTHfljtkIpOWrfZm01zZUx/1avwaU79DSxMzMkAD27S0yxvB1xHHVNWXMnHKLnQGTZqy8jSU/4MFed71Yq2TxQ9lZ+weV7QU9ckd5Zhwy6yqfB2BOmNjhft9vaUXN8LSRo+HMlklbm3uaPV2ncf8TQDImLTbFcIJDilMCd0PXWKtkudsHpC1kFtiOx2F6GpJSSrxADslJa5IJ5ZHrqkTM1XEZp+0F3NpuDpy3B1jMLeKGHvuaNppJi+SOXSYtdsDsMmkX546qoGpDnCd7q2gVCYWg1R4N0rzQwxeQ0kzcIJz4fPTEvFpDD0iueHN8B0NKdCOhwd+QulyrIP0sd2IGdPeNI38fgzTXzpOPyJQzJq2npmHS5vMp9DCtwl+HhDj2Vqx17Vp7To7Q1yt5pJ/nnh99sPv97W9tL0DqDSwpZ5IDdryWS2mkX2RT+krCNuBQ4/gBbhDiyUOqvg8zuJal4zb8+WUeCXlkIXq4byMlnK8OeL3ApSkE2Rlt3vGBZlvXuahv8/jQ7itR6DJpaQ7bM2bSlt8OwL17vw+oYeBqzho6aB+EEscPCYWSbRSnlKNemMtxO7Imft/K+9hqb/HaXjTgna0qY4ydMyZtaI3DpAUuAeARqjDrCdaUK3MqU2dfYyzDEjf0lXFIz0za0PnKmE07Jg08V6kgQ4MNYYDXGij1jNmrEO/EYWYWdWkuR+gX2DatBLNxag13wpaaeZqWSSul2W44+EFIKVUirad5nMpC/WQH/PWNOromuDTf51mN5Y4zWGdjJm1Xg2fLTt8DaVxttyvVUmHWMzgC5BdYkxoPehzaVhMGF9uR3FEgyMjZ3ytf9VVRIG0FQaAdEPZp7jSj59jQs4SS2e51C88BcP+BAmn5lEHW0tmqD7aEP2gp4OGEDUozmFNcKKTYrJ88A85l5ti0dyA3f2Sv6xhRDJQ7tskck+Z2DDqOgaFf+MwDglDy7kvVwdcX2fBvpLLJmbSIbWxHIC1tpKnbHlt1p2MaEteoDLe4CtG8bj1BbFV8PrJQ1zPpPVNI5dCloC5k4hn4ht9UrHvSqKwhNV9IIYMSD2KMcIxR3WopuaNt57pM2hlI69ZhZHVamGDYWwhBVs9zqGnIdjJ5UDuSgsQDodPMpBXTJlpY4lHcqRlw4HX8kEDsM+8HiBm6oMW1VFJyx90w2iwH5F817Kh7JQ+oBtMfZqA3Ky0KtM4t80g6yNY2BGpxiYFJvKmfKN+mpYmpZrB6qyN3TBdYlo8JQslmnw3DDrohkTPX6VevAXBv6/Odr62WM0Mz2+q2er+k1iIfdjOvJq21Svf13rP8HgBe2Hyh+w21K2dyx1GVKY/BpHm48UyjHD8nDeBCrYgRGGMHWrvhUSZNBbUOYVCqF1WTRjv6PXFW2uNYwjYAIMW5giGzNw4BZYXvOjm2NRK4TKo1vO43qATTM2mLxRRSwlbDQQjBUm6Jx30cawFe26hzvpbtP/fl27SFIDsDF9248bSjazybr3NnqNyxgZASy8zi+LOZSQNYjWePooplbQ9GzKXFM2lpLYWAs5m0WVeqAJkq69JDaB53D06OXbQiB2xdqHPPrOWOAPe3X+p8bS6fYntIbtdBJBls+TFIm26vmy+k2O7jbriQXWCnvUNYvXSEQRl16Lf9dtRQ6N6rlT4gzfYC/v6/eZ3nz5X52itzA6+vMyefr41k0tKmhhDdzMV2GI/KpHljWzVdY/v9uKr5wc6TvRWfsw8TyOljpZGBulcmPR8JIciTZk/XaNWTySzrXkuBNK27n8FkZIoQgoyosu0fqKbksTPPZnuTarqKF2hktajBcSZ37NaBe0DBLGBMaBpRy1TwhWBvhCQmrkP3ECElOeMoSJvkwwfIalUO/H3VqRlw4LW9gEDssxD4p7JBLRRSSL+ALR3aQgwEafUIpLWDQ6phMPVhBrpMWq/Doy0DRetH81gPok7rankQSHNoCY3sjOIJ0kaavJlnO1uiFg3a95tLi7Nj0mZm5jr99YLauO71hEjP5a1EG1dIk9IM2In5fIrdposXhCzmFjGEweNWzwZRvXzGpI2qMZk0JwJpMjQxJzEjqmbRArWhjQPSPOljSVRuEmpdG9p4evufgvf/Fye+vFzOIIMsW0GkDhgA0mImzZPezOWOoGz4pV9gM/SjjLLBMSsxk1YPmlRmwKQtFY+uaYu5RTY02ffzeH2jwbWF/g3GwLdpa9pMQFqXSdN5Ir03VO5oey3SUiLM7Gxy0qJaK55jR5O0o3mWeD2/Pyq3zQtBd8ho0d53BtJmX5XzrEfGFK9snjwLtdwYXKtzz6zcHQHWCmrm517jAUSZXrURe10819UIGlPPpIECaf2Yu7nMHIEM2C2vHzmfDTVWQgGU4wqBfkzaz3zsDo8Pbf7atz451HQsfn53MidN1Y6XEIKcZXSZNOljIDA1s2O/f5y5r2YHO0/2Vj5STtSd0bFVMZNmREzaNOejqpljT9f51Kv91WbHq+G3KIQSdPXao5jPUVUy57HDHdWcPHbm2WptMZdWMv9sNJZ0FmbdU4YwOg/5JHVpTtnPf+LGayO+U1XDa5APJVo8kzaFcQhAOVXDkfvRgfdkFgeoTqLLHvN+cCoI3dQ1coZaBLYNYzhIEy6edNRM2hSOSnEV0ib5lNEnK03vLEYP9ttoogvoTlQ0u5GboQxmLjPHlpUm37rfuYbj1bWqznJo+zPduApWgbKW5l7Q6hgfjOou7kcSEI8GpTCYycYFalPRhKYcpnoDJWtXlDTWHd4J/6qu9HgzaU6HSVPPxbi1XMoggiwHmpbcVRJwZUDvtjJ0Jg3gwtfA1/6lE19eLKQgyLEbO+YOMO2ImTQfT3WcZ82kzeUJ/QK7oa2G7YewaTFgPAiaVILpm0+LxaNztkvZJR6HrgKLTpdJsr1o1nZArIgdHYyzU0QCxJU381iaxY6us65t0XD8DvN+4nW9Jhkp8bQUbhB2wnGnrdW56wA8uPt7gIormC+kRsodYyYtFzfhzkDa7Kt8nvXIEbZfoHXLraNJiY96NmapGilaRUp6hvs68PAFQO11O0Nyu1RDMqDht6K9brq9fy6fouH4HUOSuGKZ4XZxQTWcImt9L1BKgUFqg3ZgK7ljzwhGuc9M2k9/7A7vuVTlvZdrQ6+v02RJZUcyaaByFpsRk2bLgIxQ13lru4kQyo/hyO/PDXae7K1ilCHZ8BpDvw96mDSp/t3TnI8WsmX2NI1PvfrG6G8GGn77CJM2jdwRYD6zSKg1aVUunmTSWptU04rpzGix3PEszLpTf/Wdf5Wf+66fm/jnn1xeBeCzd/oDpOPVcJtquFw/RqNO/OEvILUmduXCQBMG2wuww30WZmTW0a9qqSgLorg0GKQ5HsJQD2clCGcC0mBAVlqPPOjBXpulYnqwJbnv0NS0zmDsLGo1v8oDDYz6fXSCvjLDeBFKRcPUxczsNi6A9fwy90wT7n0cgLmC2rjCAW5ocXfRDRuUZnCvxCAt7jAuZBc6YbQA1CLzkL1kC+dXZWXKyrnVHwyuOxV4PUyaMdFBSNcEllZQTNoQ9uh4eTLEpNvJdf1gYJd4WBm6hiUKHIaxXXR/cGRHFtqnxaRdnMshgzweAXVNqEy/AeX4IQgPW3ozkXEfVwcs5ZbYClp4AM1uft2trSahhKsDXGtbcTbVDECaEIJqpspOKstiqK4hbowdr7bfJh1K2nI6mdLxWlt5FwAPHn6i87XVcmZoZhuAHbk7dmaOz2bSZl+V8yxHDbg7BydBQMtrkJWSVhgfuGe7160Vz3HfMOCuinkZ1ZA8bHsUsgqEFMNwajOZeK87/pqdrLRs5HkQzSONUlC1A+cEk2YZGoW00QFpjh9wd7fFuy4OB2jQ02QxUyOZNFAz7B0mjYAMCqTd3m6yWs6ckDDHzpN7rf6Nm7g6cke/PdKYKj4faVPOpAFU0xX2dY1Xbj8YCSQB6n77yDnd9UMMTaBpk0UkrRXVufRudgH27nRGcUBZ8JdTMUiLvn4md5xdzRXUm39z4/5IK2CAQ7+hPvxIXjnNTBrAelEN+r+RmoPG4yOdVoisqoM2HnYE0k6HRl3MKbp2Kz83lEkTurq+2gwB43IpzaPDPiAt6hjd32+zVhk8PC+9Ni0hOhLUWdR6YV2xWDLgicxhX7mjHTl96kK9D7Nk0gDWKte4b5pHNi4/lB1Z4/Hq6PSDw1MBaYu5xaMgraoCas8cHgdXy8opJicJmxa42D1M2qT3U8aoqJm0BJKUuBST1t3AhjmXjaq8WcYO68pYZhCT1pE7+qTDcOabWsbSyWhlIAq0HuCcC+qwJHQFiCozeG6qWQtTFzw+7GalSWDL0DsSboDXN6MA20EgLXZZnAFIA9WN30llqLjqMxkcLdImJSXtMD5czWZdW116HoD7Oy93vrZWyQw1Q1LXo9wd83Go95t8APqqqPJ55qP8p0c992hcLa9JNgypB7O9J+JaL13kXjoDd5Wb8Vw0IzXoTLbfcinkFNgpBjOYScure+r47HmHSbOiBnAM0kZYurcCJ5pJO3peq+a6s1/399pI2Q3THlZxk2VXj9b1EeqVrKV3Z9JkSCY6rz4+tFnpMzYSO0+OCrTuGIcIwB2eW9aOzkdCTg/sK+kqe5qO5hzy618cDVIbgR0xad1z+qT7GcDlilLrvaZlQQadM7IXeuy0dyiZCmhnROzueCZ3nFmVInmdGx7wmbujA2Ab8UDicSZtwhvgUlXJLV8V0UZ8zDnG8UOEoQ5bpyV3BFiLwOJWpjgcpBnqMKM6zrMBRSr8VT3Q5VSZjJ4+waQNNA0B2m4dKcRMwrXjOlc8Rz102Nc0ns3t9wdp0SFKitlLQADWS+d5pOt4d1Q+2VzU7RrUYTyIFtimdzgTx6t44+qAtOwiG82Nrg1/LQZpZ3Np/eqFzRf4xtf/dz6TSiWbSwvcjnGIlObkbliZ2thMmisDLNFdw0a6Ow6pcqpCKNo4heXBxiERk+bgR2HWs1/XKqnILMMwh+Y/2l7YWdfKMzAh0DTBQqFPVpp+1Dzk5mYDTXDCDjuuVtSJzk7gXNyvaukau7pBtqXYzccDmDTbt0lLSSNUe1x+RutaNVMjg+D+YfezWKtkebDfHqgOgG5OWj7ac8/kjqdQlfMUQokmdbbbJ2cnW16LbCg7IG3mqpHCOg81cO99DMKQuXyKUA4GDQdtj1xG/d2sjENgCJMWMzDRXtdVUPVnZuwgyn88dl3VnNX5N8UOq8elh4Oqmq6yK6LnpDFc8qiYtEjuiOyAtO260/m39lY8LzdIYvrzn7rHD/7UpyiY6pza0LSRjcd2DOKCmJGfHNiXs3PUdY0LBY8/9399hr/xy1/snkP6VD2wo5m0yN1xlHx/RD21oDwCXg0iBjI6p2+2NpFIiqa6T1IdkHYmd5xZlQoKJFlGi7/0sy/wb1892UXqrbofyx2PWntO6oAVf/iv+NHPH2MlbC9Ai0CaYtJOZ4NaL80jpc6GmRqYlVa3vQ6TVgmDE0G2k9ZyOcNm3cELQuWGll/uBFr7QcjjQ3uwaQjQcvYBZjJgH9e5wjkA7poGT6V3+kpy2l6dVBjiCvWZzNI4BNTGFQp4sPMq2Idd0DQIpLU9IKThNabOjoEeJq3RBWl2YHMYOX2RKkB+8czhcUBdq1xDCMEvFHIJmTQPOz4MhMZEM2kA87k52prGYWM78c94MsTiGEibcFObjw42W6XFgeAolhi2paeA0SlkXy1k1XVs5+f7ZpTF1XJ9tE7zaTaztovF1JGZNIDHx5i0+/ttlkuZwd14P5Y7zmadraar7AiJUb+PJkIeDgFpGdnLmszmQC6EYNXI88Dd78iFVisZvKC/e25c8UxaIc7BPJM7zr7KFxBAPkxz4J60V2/5bbIy5NCf3gSiX12tXCUA3ggasP1aR343sCHZ9kin1N/NoiE5d6whGZelW5RTZbbcA2UCFc22dmfSBsgdQ49MH4VANWt1gFAcb3OumuzcUk1X2Qmj6+sT59FbOUun5QYQhrQFpKNnZ7PudM4RvRU7Tw4CxR+9tcNvvrTBxqFHWjOpaxq0h5MaMUgLI4nspPsZQDWrSIQfeG+J/+Cd6/yTj9weSKqEMqQZukfJFG/ypiPAc0sqpuL1CPjGYP3WvgJr8yl1XuyAtDc5y/ErG6RFXc4nVnXSpsaf/Cef5IV7+wO/v+G3FUI/PpA44YHmqSjN/DUnTrU/euBVXd6ISTtFkLZSziD9PA8QUVbaSXlQw+4eZipBeCIjadJaLqWVZXW0QC7nlnlkWtDY4PGhTRDKoUxaK5J1zeowA7Ae2QLftVJc0Lf7Mmltt0laSloyBmmz7S5eKV8B4Iapw71PMNfp9vVfSPdbHpmUT0hIeQaOV2lTp5A2jsgdgaOSx4vvh8zgfJev5sqaWb598T38Zi5LY0TnEzjq7jiF3HG1oMDJo8PhDafeasuAdI+7mBOMsOAfUrGE+2FmXnUc+3Q8bS9AGGoTn/dPZ9Z2paCuYztbHip3rNs+mZR6vitSOxErMEktlXoCrWMmzTCOgLSH+21WyoP/3R0mbUaKhVqmxm7oQuDwRM7uqBeOlx04pENJI2ocztRuPbfMfUODrVcBWInm9x4OuBaIZfYOudjB+U2WEn1VVHkdEFQxaAb7J/66GbTJhpIDT8fUxcTjHYPqavkqAK+ZJtz9/Q5o2q4P2OvaHikrAmkzkDsOA4Xz2Xk225tQXOmAtK4RxQDjkNA7YcEPR5m0OzstcpbeUciMvMZ0jd0gek5G7CfZlKGMQwLl2J3RLFquT8PxhzNpA2z4D6NRio/e3KFgZKnr2kh1iB3N1AahMsHSJ5wHAyjn1RracHb4y9+s4ok+f7+/MVbTayIhGkuKzulTMmmVbA6CHPed+hEb/lsHCqTVLHWOTxO7O57NpM2sTMMiF0pCzeZf/PD7AHUjDqrDzkBibO0ZoGti4huwmi6DNHjU3oPCygmHR3WYiZg0//RA2lIpjfRKPAiiRaqP5LHu+Bhmk4xmkpVyZkzaiUDr3DKPdAH1x6Pt94GmF7ugzQ6kreXX0ITG3XyFFbnBoa0WuN6y/RYZKWlFsqBiZrbdxcvlywgEr6dScOcjPRtX/+7iftujmJudBASOWhMvRt2sIw6P3/O/wbf8d1O/zldq/fsXv4O2pvFrj39/9Df3ujtOwaQ9Ma8k3A8OkjFpoQxpEJCne+CYRu4YS7jf0HLgNY8Ak7h6QdpcEHQ6nrOstWIVGepspbJD5Y4NxycVgzRtNuvrYjHNRrSe5a08eTPP43T+iNzx4b7NcmlI8ylyj83OaNa2mq7iE3KoCZ7N7w80DrFDJdWq+7M1DgFYq1zlgWEgH30O6DIYuyOc/ITmkIuPImdM2uzLSEFxhQUp8dnvgJC4Wr5DVkr2PZ1C2hxqFz9JnS+dx9AMXs+V4e7HOvfFTrP/XnfY9jBMdf/OgkkzdY1K1uxrw7+eX+fO4R0F0urHQFqfg7+Ukrb0ThiHgAJpO00XKSV3d1ucq+USv5fVTJVd91DNOCdl0nybtiZIa1YH8C70AWmx8+SgQOt43v2jt3YomCpbeKTcMQJpvm9NvYZUouzafXuXxWKa+UKKLwwAaY1oDOXIOX3KmTSAlKiy52xC9VJH7nhz/ya1dA1DqLOn1ZE7noG0mVZZCg78FpWcxblqli882O/7faEMaQbOCWvPaWhUIQQWZXadbTXjc5xJ85XcMSUscqdgVR3XUjFN6M5zL5Y69ANptodptajq0cFiRgPty32y0nYIcBobHZnh2jAmLaLVc9ZsZjdAyRyWc8vcTeepef0H7dteU3WcZywLiitjZDhXPMdrxTm4+1HKGRNdE0MlIB2d/gyYNFBzafHGFTMCR5i0sxpazyy9kyuuy7/a+vTob+5xd8wYqYkbP1fm1LD7o+boGVtQLlwSKBwBacHE3fKn5pVj7i0ZPQ/H5mwBbD9Ei5k0qXfy2WZZS6U0MiiwoRsqKiLw+36fWtfa6AgKM1pfl4ppmm7Qsblfyi3x2Ep3AGsYSh4dtPsO8cfVCiKQNkMmDWBH13kitTfYOCSapzmImLRpZErHa3XuOi1NY//hp9Q15eMO/mC5416rBSLoAWlnM2mnUuXzrIYuwqhz91iOXjt0yIYhu64+830OwNRMLpUu8XqhAnc/2mGX+oEmKZV5lmao+7cww4Zkv731SuUKdw/v4uQXu0xaZBzSzwHXDV0kqLAC/eh7Vc1ZuH5Iy1XxG+eryZ/tWrqGG3o0dXM0k2ZFTJqvzKgyeoqtht35dx6v486TxysGaR+7uUPeKtDQxGi5YwTSXG/y+eq4KukKAHuOAmbPrpb4woP+IC0exzgeZj3NOR2gYMzRCnfUOT2SO948uMml8iUFiIEUZ+6Op1JFobMf0chvWSsNpFFbXosQecTydRYIPW9UaQW7yOqlEzNpbTdAmHUqegSITqmLuFRKEziL1IMDDgyz78Hq0PbRzSbV2GVrVjNpRXVQeRRJXuKstMf2Ng93VVdk2GGmGVtVz+h64lovrHNP1yi0Vbjn7e3mkb+3/TYZ2QVps9bpg5pres004cGn0QKHWm5wyOdByyOTjnX6s5GQzRdSnZm0WqaGJrQzkDZGiUyZ72w0+by9wa69O/ybe5i0nDX4fh9VlZTa0A79JocDsrB6qx43OUQPSAvCiSz4Aa4vriCl4A0vkjn2iWhweuWOYvaHPoCFYhrpF9iQUjlyDTjYNBy1rpWFgTal/X5csTognktbzC1GM2nq2dluOHiBZHWY3DFSNcyKSevOxhmc17d5dGD3Hb63I6nWvqv2tZkGFxfV7Mb9x58BoJYbLuEG2LOj+zM+ipy5O55OVc5z0WkidJvPPTzapG0FLjkp2XO0UwFpoObSXtdC2L9LydvE1EXf+6LlBniBRGgtCpqlDN5nIIGdy/cPtL5avkogA97I5FWTJfBw/cEzabGrYUaclEKuR6Ds8/cPuL/b5vxc8me7E2hdWBjNpKUUkyb9Nm2hkTFSbEZus/1AGijJ4zCQZukaDw9sTL2YbCbNa6FJSds3pj4bdUCapwDYM6slbmw1OllwvRVnuOVD2bXgD8KpJbpz6UUCbZ+gdB4O7iEDn1v7t7hUutRhIHN6nJN2BtJmWmXN5CBUb/JzayXu77XZ6XMQ7n74XRrVmQFIq6bnCLQD2vnz0No54sq23/YQxiFzRgRATkmPn7UMMihwdKt6DrZfP/E9jciCv4qhOhQzuhGLGYOMqZ/MStPgYOsBc/kUaXPwnEicJ5RLlWdyPXGdK5zjrnQw7R2y2Nw6AdLUgH08TD0rF7Teulq5yr2gRSv04OFnovyYwQtpKtUrAZn+XumVO5qayVx67qjc8ayGl25yOWKUHtQfDP/eHpCWnwKkldNlADzd5XdfPenUdrw68hCte/9O03wqpFOIMM8D1wah92fSIhm3BlTE7JsbEKkD/Dxb0do+SPLYsH3Qm1TQpw6yjisOtH58ELHQ2SU2RNhh0mLTjmHNp3YUCD4rJm2toOYm7uXKrLBFyw04tE8ectrSJx1K9j2DlKFNvb/11mpesawP9m6C1yZj6eQsfWhw8WEUS5OTUdPgjEk7nSqf5211JZH++KOjzH8rMsLYdbRTaUaCAkMbfpMDTSDufZxaLtX3HBazOqFoUozXjhmMOigm7eR9eLWi5uVeNwQgof4YNxgcvRTng2U4eWZ5/7V5MqbOP/rwTdwg5HxC0xBQTBrAbq6aiEnzQ4nn2tiaIKNnOs3WQSCtMgKkfe1VNevcdtPUNR3aw5uObV/N7Df86YF9KXK43YvOem9ZLSElvPTopINxvJ8Vj1jwBxOb+8W1UlhC6G0eGHMQ+mxtfZGG1+By+TLbDRchIKufyR1PpUpamgOp3ty3rJYB+lKpMY1aOCZ3nBahL+cW0YxDNgzV6WT/TufvdhoumnHIohmDtNPT48cONTdLC53B7t6q2x5Sa1JFV85+MyohxMCsNHf33lDTEFAMJ0A2OpzOqs4Vz3EQOhxoGm/J7XNr62iGXRz6eugrkDkwbHuKula5hkRy0zThzu8zN0CSAbDfdjFjnX4QzkSOOl9I0XB82hGdfyIr7axG1rKuNuKHzf7Bzp0KvE5OWj41OViINzTX8Pg3LyfIlImbTz1gaZqcNACLMnvenjIk6APSnEjuWBMW+iltaIsRk7YbRNKtAQ6PdcdHag0qaFMHWce1VDzKpC3lltiVHk5zE6TsSA2Hyh1DF12Cpc2mMbeQXcDSLO5nS8z56pDXz4bfDn3SEg7c2edhxSDtvi7g4QsA1PKpoXLHuhM14UCxaKcgjT0roHKepx0HERq8sveFzpellLSkRxaNQyc8VSYN4EamCHc/Ri3fXzUSgzSfJiVhKIA2A7Of+QFM2rniOTUvF0by4MOHQ41DOiBNO/ns5FIG33x9kd+JmmdJMtLiqkYGXbuZYt853yOvY6nrajWbtIUgbaTZqjtoosteH69aNC93vBw/wPZCnj9XZi6f4qBpUNd1aA0HabbfJhNK6p6Yeh0xNZMC2hHFG/Q3D6l7innPo3XWilko3p6sXQDgk6767G8++iQAl0uX2Wk6VLIWetwQPJM7zrZKRoYD1Bv/zGoRoO9QYozQe2lUZ0rXGIDz5SWE7vCaGx2q97ogbbtuI4xDVqyo43KK1p4XyqsIaXIzlVESpeCoVOrAqeOLQ+YkM5M6xrVUSncODIvZRQSCR4aB3njAcnH4wanlnw5IWy8oh8d7hsHbiwe8cYxJaweOmt3w9JnnxsR1raKcjF6rneto9Qcah7Q89EinXwzDmQDpjllJjw3/GZM2Xq1Es5IPG6NAWjcnrZienD0xNZOCMLH1gH/z8iafvjN8M+2AtB4woGZtJz/4KAn3HlQuDmTSdLPBnDBObU2by1sQFGiGDQIYCNIatk9Ag0oIzCjGo8OkHXN43BAh2PsJQZpHFjEzkwZNaKwV1rhnpSjY6l487qrohz4eISmhU7e9mR/Is2aWaqrCfcOAeyq4uJa3hjJp8f2ZC08nT++soiqfxwRqwRyPnVc6X7YDmxDIdu6J02HS4r3u9YVL0V7Xn9mKQZojG5TQZtYwniukaHvBCQldPC93w9tXXzh8gBcxaaZx8tnsgrT+z84H37rS+e9z44C0WO6YykJ9tLsjQL25TyAEGTPHVt2hlh8861zJWn2NQ+L3u5QxuTyfw7Yt6kIgm4MN9kC9DxkZcuDNRiJbERZ7EQhaLKZZKKR4sQ+ZEsv38z1AaVp3R4BvuqiMBT8cnX9ubb8EwKXyJXYarnLI9KOz2SkYYQ2rr3yQZhY4FMoYpJA2uTSf4/NDPvzeDKppjUMArtZUd/Ezrejh6WHSHtZ3EZrPUix5OUUm7UKtQOgscEsEEPonDlf72seQBPwhmZmZaUhcvSDN1E3mM3M8MnQy7ccsFIdvzM3YBe0U5I6gstKup3e4tXVM7hi4ZKRkL3K8Oo1aza+SMTK8Vl6Ee59gIWew3XBPzJLYXqCyp/Q2Oc3ChJlsXrE0YrPHhv+MSRuvCukKRanxoDFa7mgLAVJQTE13GC3rKQ41SSWt8Ud/8qP8H//u5FxYXB25Yw+TNm3nsZqq4YsDwuol2D352rYXopmHzKOf2sHb0DVyegWJZC8/N1ju6Ph41Gea/ZixdIpp42SgdZSV9mC/TS76nkHVkj6ZGW+/a4U17omQVPMhgpBH+0eZNCeag8toOnXbPxXW5HzpArezBbj3CUB19gepA7wgxI6659nw9NyNzwqoqCyoJ0WZtriLHe2rsVIlJwwOT+meANUALJgFXs9XYeOLrGW8vnLH/ZYCDXZQpySZGUibH5CVBioO50ashKg/6mHSBssd032YNICvuzpPKWNi6mKou+vxiueydkwLWtsnmui9lbNikKaAVNrKszUgIy2uctbsALLeiu33ixmTuUIK2zHxBDgjZqzbvk1aSg7d6Zk0UHvanuwC6Leslvj8/f0T39eV7x9vOk63ll6pLYGzzBfakWnI4RuUUiVq6ZoCafkIpBnpN53t/4oHaWWrQCgE9chS9Lm1ct8Pv0OjHgNp08odL5YVGPh8/YECPz1M2p26kh0uxNbQp7hJna9l8Z0FbrgRQO2RPEopcbK/R0W/xNOeP3MmbbmUZiPKRANYzq/w0ExR8zf7Wsb2VtNvY0mJOYMQ2t46VzxHSk/x+WyBC/o2O02Xg1Z3EWuHLulQsucOP2xNU5rQ1EC1oYFzyDXu4gbhiVmSrk6/QTlenNLFqV//+Ma1mF2k4TU6C+FZJahMmZUQHjUfDf++wMPRBMgZDFobWQ40jV/5M2/lHeer/OPfO8lmxRWva7mI0ZJSTt15XMwtgN5gL72i8nSOSWNsL0DodeXseIrqgNi6eau41JdJC0JJy3VxZYOK74M1GyYNjjaeek07aGxEGWmZoSxZK/TJitluv+uFde6HbQhclsT+iay0zgFTmKfCpEF04DV15L2Pg5TM5fvLrEAdEIWm1p58H0vzs5phFZZBM3mHMEEEfDKKSYiVKhnNpOH4p9aQFELwRPUJXhYuyJBn5at9G5IxaGj6dSXrnyGTBv2z0q5WrvKotUHdzB6VOw6bSRsgU7YMjf/wPed47+W5sRx8Tc2klCqxG4ONIZLHXEqpIBpNNWOYSZVUkPWQs1Q5a9H2AmwvOPL1XiZtPp+i2Vaff709nEmzfZtsKGkHs3EErRpZ9gk7uZtXFvPc3W2duD/qXh0LQapnrZiF3FEIQYHrbPuvYReWudne5FLpEkIItpuOkpH69h9II+krHqSVIgbmoKECT59cKrBx6JzoKsRMWqFX7ugHU3/4z849iyGL3Gp/VHWzIibtn774T/mM++PoYYm3m1Fg8CkeaM7XcoTOApvuLg0hYLsL0j7x+FMIa5OnC98OTmNmQdZxLZUy+KHsLJAq0NpkWeywUBghdwwdsuFJl7Jpy9It3rX0Ln4vm2ExUPKCW9tdcKJc0NQw9WltXABPVJ7gFWeHELjc/jxwciPp1emrYWoxE+lWDJDjoeOYERg5X3VW3UqXWAmCRHJHRwiQ01sWl80Ce7pOXjb5pusLPDyw+3alobfzqD7rYUPxSWu9uIgQktcoqy8cY9NanofUGko6fYoH74WMkhbdy5X7Blo3HB+hq0NoxXNnuq4tFtNH3B0BHuuKSXu4bw+VOgK0CMj2cYibptbya7RCj11N4y25g46BSVwxe5KOD+Sp2a9rl8uXOZA+284e7NyglleGBWGfNfygB6TlAv9U97+v+tJ0KK/ztZGk7MN31cxNzKRlhHrvT6shCfDWhbfycuM+bc3gqvvSkIakpOnVKQb+m8KkxWHbN8vKhj9eI4eCtCFzSX/tW5/kp/70u8a+xmq6ym40mjPMPCQXyR3brX11LakyW3VnaMO7FOW8Hh479x4BaYUUbVf9jvqIMOt2oJg0D2Mm90zZzCuAGt2PC4U0XiDZax293obbUJmfPWBpFiAN4Hz2OaTw+WeVKp/1D3nH4jsAukxaaxei2cE3s77iQVo5yo85qKtOd2yfvFU/uoF1DjM9w8uz+PB1TWc99R5a+udplFZh7w5vHLzBj3/6x8n4T/Oc+O+Y911lVjLDTu/xulDLEjoqY+mNykrH4XGnvcPf+/TfQwZpni2/H9zG7Jm04tFA67XCGo80mBPbQ7s/ENsDz/RyOvV1a1/HXS1k31UHvFjyKKWkHVlV7zinkx0T13Pzz1H3GtysnmPl8LPAyUDrWALiyWZXp69N/+hWcxZCdDeuK+UrALy299rUv/urptJlVhybB40HfS3POxXlpIWhMTXoL1tF9nUN7EOeWVEzcV98eNIJC1TzSZOQ7YkVgf4ZQEnrUlWZ/3wxiA7Vx2z4G94eCMl8yOmqAyLL9zdSGSV3PPb+94K0qmvP1BBpqZjuzKRljAxlq9Sx4Y+ZtGHVkgHZGccTxHO2902D69l9HuwdZdJiuWNat6jb/uk41kYH3tdNCyIXvyCUfaVWB20PIpCWDbwzJu20q3yec84mgTPP7z/8MEEY8Es3fwmA/3977x0nZ3Xd/7/v9Loz26u0q7LqHSFQQRISwvROANsJDokbcSGJgwvBlguxHUhiE8fwg69j4optMM12YnozxlggUK+ol9X2ndnpM8/vj/tM2d3ZVZtnZqW979drX7NTnzPzzNx7zz3nfE6DkL9To9QdAebXzCehJdlUM5GGsNwkHry51BOOYbbESGpJfIl4ASNpem+2YSJpADvcPug7LEsLAGueOTbrpBX+u1rhqKArLU4xggy/SxcO6Y/2AGB3+OgIjhxJSztpPcM4aX6XjWqPHS0p31cgHoTUwKhbLuFkFKfupBViE7vcVkaPyYymZ7zVZEoxhq7TvZgGjBWFqEkDmFs1H00z8T1zgJoU/M3svyGeTNEbjstIWqgD3FWnfZyT5ax30sp0J62nX+5MpCM36b4SaQKxAHZM2HI99GTqtKU9Ac6tWgWmBM/arNCzj8d3PIZFWKDjemo9FdCvn3wDc10b/E6Iy0jJDl8dtG/nnbZ3uO7p69jWvZXI0auocLn1SFrha9KATPrN9IrpJASEHb3Hd9JSMZwY87ksb1oOwB+TnZhN2UhaV6SLJBplyRQdEZmvbRQLahYAsL5qPL7uzcDQvkI9IXk9nAzgQ4D99FMdQdb1VLptmQ2LCb4J2M12tnZuLcjrjwmcfhpjEcKJML3R/D0YgZyaNOtpNxD2O/z0mGSK7EzdSdt0OP+x++P9uAExyEk7nRz+KVWyznZbWN/1HVTfGojL9MeqZMrQ6EhjmY9UzM9ukwbx/kyfsowdkTjCIn/T5bFQwdMd2wPRTAp3nbueo1Ybid7DdPbHRuyRBhBCw1Xg9gQZGX6LhSn2bvZ2Dm0rAjJVy6iatMnlcqNntyur4gf5G1r3RRIIs7TJnVQ1aYZT3oyj/yDxrgvYH9rKp1/8ND/e8mNuwstEZF280RuSAOv9tVT0bgG0zEZHmt5wnDKXXicVjxRsrqt027GYRF7F03p3PV6bly02Sybd0WYxYcqTrph10go/rlU4KujU009HjKTpNWmHonK88zmaSaS046Q7yrFm8GZJegPY57RS5bWhpXQnzSQgMvx8FknGcKZSRLEURjjEUUHMJAj3yzTPjJM2aJ3eF+/DixgwVhSimTXA5OoqkmG58XdXRydukz0jtiIjaZ3gqjzt45wsZ72T5tdTUXpCUhY1LVRxbFC0IhAP4BHmgR56gU7+kqZzSMV9PBPvIhYP8dSuJ1k5biVdfQ4qPfainHyr2USDpxEHNdwvenk+uI9PvfApymxlfOv8H5DoWyB3RAyIpKV3lQ/pheyzq2YDcNgeo+Y4Akj9qThug5y0Rk8jk2wVvOawMM8fzSg8/vHIHwFYmNDoNVCWGOTCqspZxdt2K7bAAfwEhuwepQfWUCKAL6UVNCJQ483W1lhMFlr9rWzr2nacZykyOMupT8iUnUP9I4iHJGPETGa01OmnO5Y7KgmbTERCnfhcVsZVONl8KH8kLRgP4k2RSeGOJ9ONWk9986neIyPy+/q7wVM7QAwJoCsq6xmqDV541/ocpGI17Erozsig/o+y96O8z59IFDzdMaVlU5Pr3HUctdmJdMrauOOJBoTRcA2jEHeqZCTwXT7G6w2t0+01ILvAtJvthtUfVTgqqHBUsEsXQ8oqyOZXlhOWAGU2P5YS1XuMKconIEKdTNQWMt58Ga8deo0GTwP/EHcS1yNpRqb2++w+Jvsn845FwxbpoJZuth4JDHhMbziB2yUdFV8sXLC5zmwS1JY58jppQghmV81moxaFwBGi8fiw6eBdkS5MGngL1IQ+l0pHJZ2xXkCMHEnTa9L2xzuwaBpOs9ycGal0xO+UTmVPKH8krcxhkb/VpBy3AiaTXJcOQygVy6Y7FmAT25/uE6eXDdSUpTPeBq7Tg7EgHk0M6HlZCO0IgAlVLmIdF3Kz61wuDPVD38HMuFXlsUG/ctIMweeR0aM+vRAy7aG3DdrBCcQCeA3KdZ1Y7SXeO591sYN8tL6GnlgflzVfSyKlSWnP/o6inPzmCg+VwdvRTGb+vsqL02zjoTUPUW6Vuwdeu1l30gobSSt3yejBgS45+Na56/DiYKPdRmVqZBWhUCqOO0/jyEKxvGou6xwO5pUdy6Q7vnHoDcqFlekpC7FEytAUECEEC2oWsF6PPpzvOMD2o4MnrjiQJBjvw5dMFvT8NPgdmTRUgGmV09jatXXk1D0FIAUyEnY/jbqTNmJdWjJOxGQCrQDpji7pJPXou46zGnzDRtICsQBuTctp/Dl8vcWJUuWUKR/toQ7wj4ee/QOPGdedtHjc0EhaXZmDVKyafdEOWcnROdBJC0QTCIv8TVekkgWNpGUbWmfr0o6aTCR7pKNef7xImtBwFbjfjsPioMZVwwGnl9qkXOTt68pG0yJJaWu6N5tR9UeT/ZPZZbNCx3aq9c8/nwx/bziOydpNnavekHlHMYgqmdZ3vq8Lui/jznPv5LsXfhdXIkos46QZtyEJsi5tQ7SdJLDUfZDNg5S2e0IxhHsLZmFmbqCnoN+Jep9jSFuKNLOrZrMr0UdIS2CNdA2bQbWjewfjUwOFKwpFk7eJvlgfHZ7KE4qk7U/10pLU6OmXc/WJRNLSWTlpesNxPHYLFrOJaq89J5JmGrFXWjgZw5nSiGsFiqS5q6V9wXTGW/5gSjAWxKtl23UUQggrTUulm2T/VBbYL5Y39OzPZABUulUkzTDKPPWYNI2OsFTC8dhlc+K8Jx/TEA+9ECe/qdxJsusizrEtZ73dTqPNT4tnHqD/sEKdRcl1bal0c6jdwwNzPsPSUJgHpv8t9Z56AhE95G1JgJYquHCIEILmSlcm/UYIQZVWzya7HXNgZOlyWbth3Nd09cTLSAhB0PFH3u/oJ5ZI8IfDf+B8S3kmqmpkMTXAgtoFHIl2c8RsZpXv0JAmjr3hOFbPbpJakpnxwjpp9T5npq8TyFTUQCygxEOOw/r93Uy7+//Y3G2m4YSctBhRYUJLWU/7+1Sezg4Iy+yAWY0+9nWG8tb9yEhaKhNJiyVlZOW0mlmbbVjx0B3tBN+4AfL3mqbRn+wGoCoRMzQ6Mq7CRSpaTSwV5ZjdDR27BtwfjCQwOw7iNDnwF1ApDrINrXN7pQVEilhI1j7XHq//owBXgRpZ59LkaeKg1UJZVH4X93aEMvel0x0tQtp2umm3wzHZP5ldiQApoLZXNk7Om+4YjiOsPTR46iEaKPi8oxhEpXTSFrg72NUW4papH2JqxVSIR4jqwiFGO2kLahYQSITZZbOzwnOIjYcGz3UxwtZ3OK92IZWpAs91fueADclc5lTPIYXGFrsNZ7ht2MjM9q7tTEkao0SazjDaWFY5Yq80p1WPpIl+pmiWTLRpxJq0YdIde8PxTL1apduOlnRhwsRhi3nESFpES+DUUoWrSXPLOufukNx4dNstuG3mIVlFgXhggAJ7NJFC02RblNOlwm3D67CwLSLbIdC9L7O5VGlLQDKqnDQjMDsrmBqLsyEod3uFENSW2YemO8YCQ3JdQ/FkpkjzdLCaTYzzl1GR+CC/OnSUB2oupCsofyyZgkSX8U5ac6WLQCRBY/VSHmxrZ0pMfgEDusJSmUn/QRQ43TF97P2d2QWDLdnCHquFYJ5muLlIFTQD8+QnrGFJOMprYiOxVIiX97xLV6SLZSYvKZPupBlYkwbZurR3qsYz17yHHW2BAVK5PaE4zvINeKweloWjBZHfT1Pvd9AXSWSafE6vmA6g6tKOQ4VbL0RPuilLaXjM9uM6aRFhkjVpp7kQ8nn0CU3feJrZIL8PW/KIhwRjQTypZGbzKVqAmjQAj7WcqNZDzNsklRVT8nUD0QSauQ+HyYs1ETO2Js3vRIvLHdj3K8YNiaR1hHqxlG1gVfUi2VuwkJE038CMjLQyak+8E0FqxAWTpmmEhBhRIe5UGecdxwES2IKHMZEaUJeWTnc0ZRbkxoxrk8snE0pGOWK14z22DiHypzv2hGKYrD00eRtkLbSKpBlLeQsIM1MsR4kmUuxNz8eJCBFNfheMTHcEGUkDeLuqiVmmPexuDxKKZRUeu+LvExXHuKRhqbyhwJG0I72RvFkis6pmAbDRbsMdPZrXSeuP93MweJCp8SRYCj+uTa+cjlmY2ehwQe/wm9cmk6DSm6LDlGCKcLD5cB9Ws8hsHOXDY7NgEkPTHfvC8cz6xmYx4Xc6qRBNrHfYIZw/kpZIJYhrSRyaRqxQNWleqdTbpW88gkx5zLtOz0mjT69b0tHF00EIQUulmw0BLwgT9OzPpLNXCT27SQmHGIC9jHMiUTZE2ojrDQJrvA6ODU53jAfwaAN3SELRJK4CnHyAlio3W7tSTLX6mNDfk+kdU+nUCzSLke5YKRcpe6JeuXjq3guQkcH1Cv0zKbBwSPrYB7pDJHR522B0MpoQvHlsHZ975XO8evDVvM/r15IFr90YgMnMZygnQBx71Ys8s/tZAJZgJ2EuTgpIa3krbqubt8sqGB/ZQSKlsS0n5bEz1A+uTVzUfBH2Ai9mGvTamSN6GkhreStmYWZrl3LSRiJda9MWl7UJDday46c7CoFWgHTHcree7qire2XEQw4NTXkMxoN4cia1dLrj6ebwN7lbMDsPcMxcA6l4Jj1nd+dRzM59lFkr5c6jgZE0m8VEtV1XePRWDqlJW9/5IsIU54aa8/UnFG7zqWqQEEG6V1q7GeotQbwjRKmisX5SQuAyQCFueuV02lMRDpo0prr62dsxNN3RpEfSjBrX0iqxu2tbMR38M+UuW94WEZ2hXoQpJh3caEA5aUZjsUF5Mw0JqWa89Yi+qRMP05eUDZjTaXFG0eRpYpJvEr912mkK7yCl5dgB9Jn/jMDMqso58oYCCYeAdNJiiRRdefr2VTgqaHI3sNFupyx6JG+mwc5uOb5MiUUNiaQ5LU6mlE9hg0UbkkI+mEVT5W95onDzzHuHWTm1ZsRokskk8DmHNrSWkbTsOFDlsePSWtlotxMN5q+LS0fkXSkNj8uJpwBr5LrKabhSKd7pzW7aV3vttOcIhyRSCcKJsD6fyc8/pNfcFiKYAnKdvrMjAmWN0L2Hzv4YFpPAm9LnVhVJMwCTiXM0GxEtyeZOqZ5XnSeSFowFZY80a7aRdSyZwl2gkz+hys3ejn40f7M8+fqkVW3We3O5jT/5LZVyQbm/Jwr+5kx/o6DupLnR094MiKS1VLqIJ7VMukFnbzMAn297hd/v/T13vnonB/oGNqQNxUME0SgTxvbPmVkxlYtjAlvlq7zc9nOmVUyjKpEkhjxupdvYgnaLycKShiU8l+rDHDpMBX0D0kAORt5BM0W4dMKlEO0r+MQFcFgXdXFYHEzwTVDiIcfBradNH4rKz6/B7ORgcGivrgzpPmkFEA7x670fe3Q1yWqvHb/LOkTND/RxLZnITGqFqEkDWNG0CpO1j1ej+jjas59tXdv41CsfxmTr4NJxH4J4xHBZ9fH+Gkyaiz02uxQwSWTH9Q19z5KM1DNfr+ErpJNmMglqvHba9EVEvR7dPGo2M80VHLmRdUSmg7oM+GyW6hGIPzgdLPD1ZcSQILu4Ehi7+ZR20rb66+DQ29S4THlr0toj0rGvd9VKdU7lpBlPZSve/r1YTIJtR3XnKBHhWAgmVXuwFkAobSSEEFzXeh0bUkH2J7upppuNenp/R3+QpGs94xzzpUAWGDLXDZfyOLt6Lhvsdvyxo9itQ9d96dY0UyNRwzIEZlfNZnMyQCraC7ocfT5a6uW5M/VaORaIcs28xuO+tt9lyyvB78vJFKr22ElEWokLweYch+kffvku//DLd4FsRN6haZw/pSGvCubJYrO5WRVN8VxoX04wxT4g3bE/LscybzKejaTpUVh3gVK3ZzeWcbA7TLRqJhxeT2cwSqXHhkinfhYh420wZ7+TBsy3SQfo7ba3Af3k5xMOSWZzXdOqWK4CnfxZDT7C8SQ9vmlw5D06AhGEAL+mD5RFOPnjKlyYBOw6FpSpD3okLRCJYzEJ7Cl9QjegNmB8hYzi7esMkUxpdPZZqE8IkqS467y7MAsz//jKPxJLZifzVw6+QlLAEqu/4PYMoGoK9xw5SFXgI4xPfZCvLfkaxMMEkjJFYEqt8YuHG1pvoCcV5QW3i8XO/Ww82JO573DydSyUsah6gWz2WFDhkIGRNJApjxvbNw44F4qhVHltHA6ZwWRlsnCwt3dvZoIZjJaI0mECLek+7Vogn11Gznri2ebrNV77ECUsTdMIxAO4U6lsJC2Z7pN2ekP/1VNWo6UsPN+vT+Q9B3jwvQdJaAlCez/FJY0XQiIMzvLTOs7xaKnwoMWq2SP0etquPWiaxv9s/h864+9jCp6HOa3+WODNp1pftqF1jasGizCz12plsmOEVgxAKCbHfFdO/XOhaC5rptFZwx+cDmY4ewc47mknTdOMjaR5bV4m+SbxnlmDRJgF9oN5a9K6o7L+pN4mv89jyUkTQtwohNgshEgJIRYW7cBVrZi63mdylYttaWXFeJgj/YIZ9YVziEbiyklXYhUWHvd6uNC1h426Mu2//en/w2Tt45qJN8nNSCh4/TUM76TNqZ5Dm8VMKHUgb6bB9q7teG1e6qIhwzafZlfPJpiKs8dqGTGaFhEH8SQ12rvMeOwWVk+vOe5r+5zWvMIhuU5alddOOCD7Lb7Tn900f3VHO0+/e5ju/li2DYGmsXxq3Um9v5G4VHgJaAneOPwGIDcfc4MpgZj8vnoSCbDKc9kfLWwkbWGLbFa9zzENOncR6evUS5LSTtoZ1sy6ZAPNSVLpqWdCysw7x94BZFF3fyyZyWeNJ+NEkhG8qWxaUFD30D32wpz8c/WTv9U8RaY3du6i3GXDEtHzfosQRnVYzbTWeGVaVMUE6aRpWqZnjogZs5gBaKmSUby9nf109cdIafCxVDPf74lz87Sb+drSr7G1ays/3/bzzHN+v/f3VKdgvtXgz6ayFUcqycVlk+g8sojpldMhEaY3bqGlyl2QotTjcX7D+TS6G3jM6+Fi777MxPX8nteI2jYwt+xSLIl0pLNwk2ltmQMhspE0gCsmXkF3tJsndz1ZsOOcjVS67XSG4uCqYKpmIaEleL83f43lsWSYsAlEvAZHnl3ak8FisuDVBN2JbI1ntdc+pFFrNBklkUrIDAF9URFPFiaSVuctxxydyqboJlJAZ+c2XjnwCnPKLiIVraMynSFg8Lg2vtJFLFzFrmgXCSDevpU7XrqD+9bdR435HNzRJRAzZvOp1pttaG01WWn1TWSL3UaztWfE54Ui8n6XATLeQgiWNi7jLaeDZusx2vqimZqftv6juFIpEpqxNWkga4/eCx8hBcw07csbSetLyPqTeoteKziGnDRgE3AdkD/P3ygqJ0MiwvnVYZlSn4yDlqQjamJafXE+/3JHOavHr+IZr5tFng1sPtzL/r79/O+BnxLvm8tNs1fL9FcoeE0aZPu1DmZp41LsCL5a3cYR2/+jLzawxndH9w6m+FsRWtIwJ21OlUzz3GC3j+ik7ejZwZR4kn7NxQdm1p3QnOJ35U939LuyUcFqj53OPhsTUoJ3YrLmuTMYpSMYI5HS+N2mIxknzZISLJ9yfOfwRFnsbMCnCX6353cZW+KuP3Lb//0tFzx6Ad9865sAeBPZNPpQZp1euGCK3WLirfhEACp6N+k90uRncSbWpJVmoDlZvHUsiMVZ37aeZCpJjdeOxbOZv3/571nz2Bq+8NoXAPAkE5kC+5DuwBWqJm1chZPaMjsvBVsAqOh6T5f1LO7Jn9XoY+OhXpl2Ge2DcDeBSFxO2FF9YWVATVqt14HdYmJfZ38mhD2zahlLuo9A3xFWj1/NkoYlPLzxYQKxAP3xfl4/9Dprohomq7EpU2lp4nPc7Rzti8gGhvEInTET04u0u2gSJm6YeiNvOR24TG+zo62PnnA/9/zpG6SiVXxk5m2GTFw2i4kqj31AJG1xw2LmVs/l4Y0PDxsZUsj8/fZAFJwVTNEni3RKzGD2JaWj4KQwO4/lwkxPMutY13gdQ3vK6JE2T24krUDCIQDVYhFhrZsNvhp+0/4OCS3BePtKACpEcZy0cRUuEsGpdMX7+FZlOV/d9iNePPAin1v4OVq1T+F1uKS8OxRUOARkQ+ujOUIEM6vnsNVmo8F0nLYiepTAZS28kwawZNxyQiYTPcgNg7TC45tH3mRhJEpYM17Jb271XPriQfa6y5mS2k17IDpEsKE/2YHAQkW6xcoYUnfUNG2rpmnbi37gzFzXyaGeML0BOadEsTKtrjhzHcAt0z9Ev8nEVyu3ccD7Ba5+8mrQLDQk/kK2vDFgrqvyyDrSw8NE0ib4JvBb/zI+2Bem1/Q2H/7dh9nZvRNN00hpKXZ072Cqf5J8sAHCIQAtvha8Vg8vuZz0DhJCSvPUrqfY0rmFGbEISauHD543/oReW0bSsvN5NJEkEk8NiqTZ6I8lmY+Ld5NB+b7b5PhpMQmeevdQpmzIjplyd+E+B6u3jjXRFC8deIkfbf4Rz3Xdg6P+1xztP8b8mvkZ3QJvIpZxkrORtMKMZTaLiblNfn7T2QAIxoU2y/rzUKdsY1PADfIT5bRm6pINNCeLp5Zzgr0E4gHu+dM9/PbIv+Ec92O2d29havlUntv3HADeRG6uqzz57gJF0oQQLGyp4HeH3WD3UR/UPfT+4ua6zmny0RGM0e2QDRDp3kMwmpA7EbHCD4xpTCbB+AoXeztDmRC2aJwv7zzyLgB3LLiD3mgvD214iOf2PUc0GeUD4SgYoII2gEpZQ9FqkvLZW4/0kYqH6I6Zi5YCAnDN5GvwCCv/UB7CNekbrHl8FR3Rw0TbrmFhcw1ECp8CAtDgG9grTQjB7XNv52j/UZ7Y9URBj3U2Ue21SdU6VwXjoyFsJtuwTtrelHSC3eb6ghzbL2z0pLLRiWo93TF3IRyMDXXSogWqSQOY7jsfUja+UO7i5+G9zKmegxatxW0zY4/JuivDI2kVLhKBOVxY9xf8oszLU8FdfGLuJ7h15q30x5LSEck4aYV1AsZVuAhGE5mFz7SKGfSZTSCGl8+GnHTHAjuNac6rOw+LBltSugx/Zz+Hg4fZF9jP4nCE/pQVs0lkpLyNYG7NXADerWqmJb6bQDSRiTqmidGJy1SJKZPBUfwF0JhDl+GfatWFfg7LaGYEW9EiaSDbzjw18cN8rrMbe3A8831Xw9GPcu64FvkAA5w00wgNrdPUVrby+a52zhGfpivSxXVPX8eax9Zw029uIpQIMcWr22dQJM0kTFw64VJecrtYufuHrPjFCpY/upxLH7+U65++nquevIp//sM/M79qLh/t7uG2VbM5p/nEUsr9g4RDMo2sB9WkAcw2lxMQGlc8cQX3vHMHzvH/j6YZ/8Mm1vKVN76CP2ZlZuL0a9EG4Knllq4Oymxl3LvuXvaFNhA5ejVfnvff3L/qfv5txb8xu3ImE+PxIZG0Qq3TARa2lLPuSJxU1VQmx7ZLJed0j7QRao2NYkzUpOGtY1UwyKqGZTy16ynWdbxItH01d0z7Id9b/T0eXPMgMypnMD2aLXQvdCQN4Nzmcg71xYjUzmNidCuVHl1+H2F47Uaa2U0y/39rRM+t7drD4Z6IdBjTkTQD0h1BKjzu7wxldvw9zQuk1Onh9YBUJrtswmU8svkR7v7D3dQ4a5gXDhsuPoCjDLz11OuqV1uO9JGIholoNqYXceKqclbx5OJ/4Z87uqgPV1LDBbQkP0Fr2QLpRKcnrgJK8MPQXmmQjaal88MVQ6ny2Onqj6I5y7GEupnknzSsk7ZHi2JPgc9WGKel3Oygh2ybhmqPnWgiRSCalbNOR9K8eSJpp6vuCDC5qprQ/r/BJCwcIsE1k6+hqz9KhcdWtBz+8RUyGjXX9UE+qHn4cMrF7XNvB2RrEfm7CcpC/wLvfqePva9LRqoaXXIBfIz8qmhpwvpmmMtizDjrsXk41+Ln/0QARJw9Hf28eeRNAM4PR+hPWvDYLSOKm5wuLWUt+Ow+3nN5qAzuwEySTYey6WPJlEbS1I3XUm3Ignw0IIR4XgixKc/f1Sf5Oh8TQqwTQqxrb28//hNGwlMD9jIaE1Li/X3dSbPandR4DZ5nB9E87Vpu7QvwkegkNm9eRk9PA/PH++WdBn0nGvyOIXPdAPwyKjVD8/P4lY/zpfO+xIKaBdS4ari4+WKW6+1yjGwt8s/n380vwk5uNVWxpnkNF7dczNyauTR6GplQNoE7FtzBw8u+RUUqdVIbGz6Xjb5InKQuytKnO2mDa9IAzjM38ZlgnGkV0+iLBrGYE9T57GhJN5GjV/CpQ+OpKnQ00VPLlGiY5y//Ja/c9AoPrXiKePdi2vVU6YtbLuZnq75PVY52RH+ssJE0kE5aIqVxwDWD2eyk0m2VwZQSiIYAHPedCSGeh7w5OndpmvbUiR5ICPEx4GMA48efWHi2YHhqcGsa353zKSIXTuJoXy8r/3UdnQG5oFnSsIQlDUvg3dqMumMwWthcV4BzJ8gFy3brNGYlX6XBkZCLGacfzMbKvKeZUV+G2ST4c4+PpUCk/X22HnXy2dWt2R1nqzE7vC2VLl7f1Z6Rha6qKIeqqXD43cxj1i5Zy7LGZRwOHmZO1RxMWy/PnBNDqZyMo2c3VR47Ww73ocXDRLAWLd0xTe3Ei7gpZmKC1c+t2y/CbBJcPc8v78xMXAV20vwOXt3ZjqZpmYWbEILvrfpeRqRCMZQqj52UBlGrH0e4i6kVa3jt4Gt5H7tPi1GbtFBmL0xU2G9xsZ2OzPV0X672QFSmC5EttHbntBaJ6E7a6dbFgex9mAw387B3BRv2PMaaSdfw9GvvUOG2Q6g4tbblLiseu4UD3RHW+hfAruczu53BaIIGv0PWpBmQStecVsvtCjFvnB+PqQmzBnvF0H51uYT0cdZlYHrf31Yv4m+OPktD/VtsP9rMPtObVNsrmBTfz1vJwvQ2GgmTMDG3ei7vtm/BlIwyyXSEzYd7WTNDNmIPRhIIaw/ltkk549rZle6oadpFBXqdh4CHABYuXDi0ydfJIARUTsbVu5NJ1Vfz1Lr3uQGo9JdgnK9qhbJGrnRs49/3LwFg/nh9szraJ0tPzIWtm6zzOdmQI8o1BJ9cm9Zox6h113LLtFu4Zdot2ft1RWwjN46FEMwom8CM3kNw/j/nf1Dnbnl5Ek6s32lF06RQnN9ly2QADFZ3BIiZK/hoxzFY8W/8xf/3JjWaxqNXLuHJ9Ydw2y2s2vYVzPt2n9obHA6vHBsItlFRMx1RKceDAUrsafXedCQtWvhI2jnj5Tr9wd3lfNMaZHlVEN7vLIloCJxAJE3TtIs0TZuV5++EHTT9dR7SNG2hpmkLq6urT93iU8Gj+5iBozgsDprLa7BZTANrODQNEjmRtAL3XwCYVleGx27hP7b6MAuNSyqOQn9xGlmnkeIhHt45GgVPLV0Hd6BpsGhChdxxtnnAZEyAtbnSRSSe4vsv72ZmQ5lcKDbMl5E0PU3LaXFy5aQr+fjcj7O4ahZoSXD4DbFnAFVToGMHK1rL+c3GI5CIoFmcIzaINASzBSYsZ27sHcLxBMFoggW5ExcYkO7oJBRLZvrlpfE7/Ibutp/pVHrkTmK/uQxCXUzxt9IZ6aQj3DHksXtFkuqE5bQbWafxWz30mICUHKfSTtqxnL4y+SJp2QyBwjhpAAFzA5cGerGEu+kMxqhy50TSDM4QEEIwrsLFga4QlDfLfm1xuVMeTEfSYkFDnLRx5fL9H9AjaT39GhVRBztNUd479i4Pb3iYlJYa8ryQLiXtMjBytKhpGQvDEeJlL7H1aCdvHnmT8ytnIYBAwmx402KAedXzeD/aQa/JxMqyIwMiaZ39YYSljypn7VkbSRu1jF+MOPAW/3LFZLp65TmpKfcX3w4hYOJKmvvWYTel8NottNbov1OD+uY1jNDQGshE0qoTx/Lfn1Y8NqgmbYAdPfsy66IhnMJvJt0DL+2c9eaJpKXnkW7NDVoSLdLDjmMBWnWF62vmN7JmRi3mVLzgDjQe3UkLHM3YazObBsjwoyvUprUj+qMJhACHpXDrdJ/LyuKJlfRXzwNgVmq7zHgrgWgIjJl0x6yHDnJilz1uck/+QA+90P0XAMwmwWWz64jUylqsc0w75GKmyCd/TpOPjQd70MonEO/YjdUsmD+uXDoBBu7uLp5UxZRaD//0gan86hOL5Y0N86H/GPTlaQSc7hNSjFTQ5iUQ7eOueWGcFhPWVAyvx1saJ2XSKuzBQ6yo6AHI5pwb5KTV+9P9Y0ZIA1EMId3QOiC8kIozxSuli9NNT9PEkjEOiRTlUUvmOadLvbOGiMnEgWObACnBDwxQeBxYkzYwPaQQ9UgtlTLivj+pR8u699HVH8vm8Dt8hZ/I8zC+wilTDv2y9yI9Ujpa1trqIgQGRGmcNjPVXjv7O6WT1hGM4Y2Us9Fm5ePPfZz719/PT7b8ZMjz0v3tXFYDo/SVk7m9p5eICHLE/3m6o92cXz4NgK6YCb/T+POyqH4RAC96y1jsPMiWw9nWBHt7jiCEJpuApzM4xpCTJoS4VghxEFgM/FYI8fuiHbz1IkhGOU9s5rYJMuJd3ji5aIcfaMsaTJEe7pjawyWz6rI9twxy0upGaGgNgLuaqGalIjFMynLGSTB489Y/Xs73uhLsEE7hN5N2xtK90jr0uaIyR/yj2mOnxmvnvU45P3S1H6UnFGdq7aDxM2lAr7h0MCUoHWQhRKbWOkPm889qR7is5oL0asvlZx89j/s/8yEZQNn5XLYmrQScrgR/6QaakyEnkpamZlAPhsEeeiiaFg4pbFrIv94wl0c/exnUz4Wdz+qRtOKe/NlNfrpDcUKuJlzBg8xu9EmZ+VjQ0JSTyTUenv37FfzdhZOzOcQNA8VDBhDWxQecfsNsyjBpFQgTFYde5t+XRDEJDVPVJOOPm4/WiwH4h/G7OW9CRSZiYVi6Y7p/TI4Mv+L4pB2ubk3+Zqbo9Wab2rfwxV9vyERYDgYOkhLgi9pYOrkwv/WVtbLjyXN7fgsMTHdMM1DdMVtr67IVZlLzu6xUeeys65eZEVrbZumkeWwy3bFI49r4Chf7u0Ik9VQlevaRSmnSSXNY9HRHY1K408cG+dmbIrWETCbKLE4W1y/mO+98Z0hj+Dd6ttIai+FyGJhiVjGRcyNRPm09h3jPQi5quppVFbMA2NOTonXwossA5lTNoaWshSf9FUzR9nC4N5JZHG/r3AXAuLKG7LhmgKrwaEXTtCc0TWvSNM2uaVqtpmkfKNrBm5eC1QU7n+NqXuWYcyLnnndB0Q4/gMkXgdnGJ+u2ce+Nc7O3G+SkHa+htSYEh7QqKuJH8r9AQnfujBYz86fHsmFk+DO/mRP/HacjaekI2v6uEGaTyHwmIMVVrlvQxBtHZATvwCFZuzikV2zSiEiaLucfzFmnlw0Opgx0kkOxRMF6GecihACTGaZcIp20cE/JatJOV92xdAPNyWBzyYVtMLs7UudzDFSbGhRJS9ekGaaANe0KOPCWDGkX2UlbNrkKi0nw/DE3lcl2FjennQBj0oJGpG6WFA85smHofRknrQiRNFcFjDsPdj7LqvBzxM1O5q35sPHHzYd/HNTPZW7/H/jFxxdno3nRACAKvuA83sSlyE86f78jJc9HeSrFZP9kHtjwfR7b9TMe+YOUQN/TJ+sY/HEHyyYXZqBvrJzG7EiU3x96HZC7pFazGOik6but7pSWTXeMJwuWwi2E4JxmP78/4gKHn/iBPxNLpvTWIsXbeZxaV0YskWK/pn+23XszmRBeA9MdYaiTlojMY2V/iAcn3MS3l38bv93Pl//w5Ux6VUe4g/WBfVzUH86cE0OwuaGskZswE227ipWVn8SjZ051x8xMrTPeIRJCcM3ka3jHlCAS3YGJFJv1aNrTe3+GlnBzaevinPqj4tRlj3ksdpiwAjY9huXwn6lZ9hH8boOdjuGwe2HCctj+u4GpfQY5aemeYIP7haWJJzUOalX4YseLpBntpKWzAo7jpJ2McIhTvvd0Q+v9XWEa/U4sg9qx3HBOE50p+dkfOiyzElqHOGmxwkfS7F65eRDMpprWD1KeHpLxFk3iNrKP7bTLINoLaGdmJO2MwlM7IJJWV+Yc0OOGdKPgHA/daTVjLnAYNcO0ywEN4qGipztOqHLz92um8L9t5ZiExspyvcg/Fix+yonVCb4m6MpThJoO9RejJg1kBOvIe4hNj2GdfR0TGmuLc9x8pJ34QM5kEemTg3KBUzDL9YmrJzxMCsgZihDiRiHEZiFESgixsNCvX+a0YDUL2uL6Jke4i4cvfphm5zwctb/l8UPfIhyPsLd3LwAt1rIBjUNPC08NH+gPsaX/AAf6DsjUEI+d93t38D+b/4f/XP+fHA0dxWmySnWoAZG0wi2GF4wvZ19XmFjdfDj0DiCbfBfTSZvVKBcqG3occoe7Zx8Bvb4yE0kzKENgfIWLw71hYokUHcEo/c45/GdngIk9hyh3lPPZBZ9la9dWXtz/IgAvHXgJDY3V/SHjF3qVk/D278NiErJxsb7AjGBjWhGcNIArJ12JCcFvHIKZYi+bD/fx56N/5lB0A97oxdR5fXJzcAylOo4KWtfITVBhgjk3ldaWqZdC1/vQntPNKRowpCVDOuVvOCctmkhyUKuiLJKn/AJkmh8UJ90RoHtf/vtPofRhcCTtQFcoo1Cby+QaD/X1DQC8tH4788b5qfIMmreS8cKPX0LIaNqI6/Q8kbQCzmdDmLgye67dykkzFm/dgEhag98hxRLCulhC2kO3Zms3CqkYM4SaGVDeIv8vQRj1EysmYWuYCcBMiwxpl2yyrJgEevrLAIoZSYNMmiHxEMz7YHGOORxpJ37H/2ZvM2h30WE1YTObhp24zmA2AdcBrxrx4kIIKt12jsT0iS7URZWzijnWvyfSdhlJ53tc+viVPPjeg1QmUjT7C/g99tSwJiQjOE+//zSheAiqHuOP0bu4b919PLzhYX6989d4TfpEmpvDX8Cdx3S95AHnNKyd23EQLXq646RqDzaLiU2HA3Jx071voDqvgRkC4ytcaBoc6gnTHohS4XVD7Qw48h4Al0+8nJayFr737vdIaSle2P8C42x+psTjRen/aOraxaRqDztynLQo1qHpSwZR46phWd0iflzmJdn8K57Y95/c8+Y9kCjjvMor5YMMqhlUjEDrGnk5aZVcG5WSqZfJy+2/y94W7TNkrju+k5bikFaNM94NsdDQB2QiOQYLhzjLwVkBHcO0IT4F4ZBMTVoo66SNy+OkAaxaMB2A+VVJfvw3i4bW5idjxtQbewau0+t9joGiZomBTnJ/1OB1us0NEy+U/6tImsEMiqSl63AOp8USBnvo0UTB69EGIISMlkBJTr7ZJPjqrVeSMttxdesDQSxQ/HRHkM2kO98fqmSUEQ7xF8eO2plQ1iRTDcYvKc4xhyPtxG/7bfa2aF/Be6SBdDbKnNZM35SzBU3TtmqaNswsVxiqvDYORvWdNn1TYVd7kGbLZcSP/CWmZAXzyi/mnrYeGioLeO5sHhqEnQXWCh5870EW/3wxAdvreCJrePHGF/nBB35Aub2cSr3GNnfnsZDj2qxGH1az4J3kRISWZKbYm013LNLmitVsYnqdV6oHljdDz77MIizTzNooJy1Hhr89GJW1gfVzZfq2pmExWbh93u3s6tnF373wd/zpyJ9Y7ZmAAON34ysnQ7ib+dUpGUmLyznO7/UWRd0xzV1Lv8ZVSRvC2sehxB852n+M8LEPcE6zXoNi0OaTYgT842HN12HV3aW2BMoaZG36jv/L3mbQd+JEnLSD6bTp3gNDH1As4RAhZCnI0U35748GQZhlJtIJYjWbcNvM9ITiBKMJOvtjjKvI//zrFs8gJczcPNOTf6xIGCAcAjKSluuk6aJmmQbk6c/fWqRIGsB0fTOprNHY4wzD2HHS0pE03RGoy9Th6E5afGCucTCaNP7kz7wOkH1LSkG514mpZhoc2yJviBorHDIslZNk3m//IOnycLccCKz5d3sKjhBw3UNw/Q8Ma0NwUrZMvRzefzm7o2fQ7iLIVIj0DpvixKny2NkX0iMiem+wnW1B5o3zs7JxNW3b/5o3/7ycRbEI1b4Cnjs9NeQ7tha+uuSr3DrjVs533kW8/VKqXdWcW3cuT17zJN+p16PD6Zq0AkfSHFYzMxt8PNstJ7B5pt1U2ZMyfbyIm08zGnxsPtyL5m+G7n1sPChrn6bUenUnzTjhEJBOWkcwKsVk6ubIVG29nuQDLR/ghik3cDBwkDJbGVe6J8onG70bXyGFjxZ6ujjUEyYSljWKTTXF7ffT4Gngy40X8/Thw8R3fIG5qftJ9J7D/HF++YBYcEyJhowaln4GGuaV2grJpFVw6G3pnGmaYU6ay2bGYhLDO2nxJIfSTlpPPictLRxi8G8XoHYWHNuaabMygHT0+SRLH/wuGx3BaEbUKl+6I4DZbMLkLMcc6cr/Qsm4MZ+Bd2gkDfKt04uU8QYw9xb46Iuyr18JGDtOmqdWprHpxfQNGdnxQR567o6zkQWJAE3nwD/tkpelomYmtG2RPzqDJfiHRV9MDKlLi/TIerRiyuC3LIVx5xbveCMxaZVMK9j/R3ndwB1nn9N6RqY7CiGeF0JsyvN39Um8xseEEOuEEOva29tP6vhVHjtt/UlZPxHuojcU51ggSmuNhw+f34zXYWXN9BqsJDAVelHuqaW8v4vrWq/jHxb+A9PLF9DZHyORlL25KhwVNAh9FzSTIVBYJw1kyuOrR0y0m6qZb95NhdDloYvopM1qLKMvkqDX3gCRHja/f4AGn4MGj1n+hgzafKr22LFbTPziz/vpCcWpLbND/Tx551EphmQSJr6y+Cs8c+0zvHLTK0zBAgiwGuM4ZtA3/2baZSH+zsOyd11zXQmask5YgTkZ5SLvPn6/uQ27xZQVLzFw80lxhjBhOaQSsO+Pci2WShjynRBCjDjXyUia3se3J089WLEiaSCdtERY1usN5hRr9mY3+nhnf/dxnTRAjt+h4Zw0o9IdayDSm+l1WZdWnh6yTs/2/TQ8mGIyQWPp1uhjy0mDjBBDjdeB2SSysuOZXFe9SV4saYi05xBK1CAvQ+0MKXm64Zfyhzd+cfFtqNSdtM5BTlq4u3j1aKOR5sVgssKeV+R15aQNQdO0izRNm5Xn76mTeI2HNE1bqGnawurq6pM6fpXHTmcwhuYsh1AXO4/JWoHWWg/LWqt480ur+ffrZe2nIZLFOUpY1V47msbAHkDpcU3f9eyPJXAXeFJbML6caCLFu6mJXOg9hCPeI+8oppPWIOXs9+g929r2b+eclopsPyGDNp9MJkFLpZtNh/pYMaWamxeNl2OqMGfq0oYQ6ZVpy0ZH68ubweJkSmoXU2u9PL9BLjon1pdgzmlZCsLMB6ul0qlMk9Xfv0p3VIw7T45Re14xvLn58Zy0Y5STEpb86Y6ZZtZFUMOs1eeNoxuH3neKGxtLJldysDvMG7vlhs3ITlpFVhdgMEaoO0JOr7T0Ot2OELlO2qCatFhS1h2fxYwdJy3d0Dog+1+YTYJarz2nJi2t7pj10D1Gh1FHAzUz5OWL3wB3dbaguJj4x4PJMlQ8JNxTvHq00YjNDU3nwvu5TpoxDXDPVCet1NSV2YklU8Rtfgh3sfOYdApaa3Im0KRBKTKe2oFOmt4SYEj/R4sjE40Ox5K4CjyuXTSjhrsum86iCz6Au39/to6iiE7a1DovZpNgU78fAHfoEAubyw130gDuu3Euv/jY+fzPbYtkuqPVCdVT87cVATmuFUOx1myFCRdg3v0i91w7C1eyl6QmaG0oQQG8wweN57Agvh6AeelURyhdmr1i9GB1Skdtz6tSxRgMm+tGqr+OJVKkMBF1N+SXv4/1Z+01muppcrOnbfPQ+05xY2PxRPnbf2L9Ibx2S6ZGLy+uSllbnA+j0h3L9dYDevTQajZR47VzdIh2RDp9P1HwzJDRxthx0tIpdR07MjfV+Rx5Imlp1ZgihFFHA+ndmsBhKcVrRAj7eJitUqxjcLrjWI+kAUxcIXfkQ126BL+Bu4tnWU2aEOJaIcRBYDHwWyHE7wt9jNXT5ebPkbhLRtLagjitZhr9OZO4UU6auwbCXZk6iXwNrUlEB+z69htQaG23mPno8on4Zusqbe/9XF4W0UlzWM201nh47ogcv5vEMak8mV5UGVSTBjC7ycd5Ewe917o5I0TSeoq3+TRpNXTtZmFZDze43uUtMYuJ1cYsfo/LlItxtr/H965s4G+WTcjeriJpCpC9245uhC1PyusG/UZGjqTJ+q+4pzF/TVo0IDeUi5HuaHVA1RRoyyMecortkibXeKjy2OkNxxlX4Rqq2piLnh2SF6MiaemgwbGtmZvqfM68kbRYIkU8qRkr8DcKGDtOmq8J7L6sSAZQ73dmG1oPUo3pjxncJG+04KmVUq8gCyRLReWkoemO6Zq0sczElYAGv7lDRnu99YYcxue0EogmSKa04z/4DEHTtCc0TWvSNM2uaVqtpmkfKPQxxlW4WNRSwa6AFS0s0x0n13gw5fZXTOoLAiPSHQH6ZR1dWgypLT2mQTaSBiRTGpF4yridx+ppcrNl72vyepFVaz98fjOvHkzSh4cpljbZCyyqR9KK7QSk08jTCrW5FCuSBjD5Inn5yr9SGTvEzEs+is1Soml/yiUAXOHcSEN6EyMRk72nlJOmmLAc0ODFr0PLBdJpM4ARnbS4rOdNlI0bJpKmO0fFqpOvnTl8JO0UsgOEECyZJMflEVMdIRtJG6y6DcY5ae4qmdGVu04vy2lonQjL6KLZSigmZflVJO1sQQiomS5FMnQafA4O94Rlo7xBqjGhWKI4NWmlRghoWgiNC6Xka6monCxD3LkDQrhHRdIaz5GD8ZanYPIaOPdvDDlMOu3hbJPhLwbXLWjkQMRBPNDJuwd6aK0ZNHkame4Iw+fvgy6VnE0NAQpek5ZBiMxCHETRU5U/uGg8SydXsS3VyDz7USxmU1HSHfNSoSs4du8Zel+kV6b/FYPKSTKd/L2fg8VJ2bxri3PcfNTOki1OduQEtDPnRzlpY57GBVLmfPIa+NCvMhvmheZ4NWkAqbImucmSiA56QKC439W6WbI2bnBt2GlEnxennbTK4zlpFZCKZ3+juRglHAJynT4gkubIkeCPDqhHAwPns1HC2HHSQO5uHtuccQTqfU6iiRTdofiAXNdoIkk8qZ31BYkZrv9/8OHHS2tDxUSpvqnXDJJMyOLYsVyTBnIgPO/jsOBWuPlnhuXCH69/jGJ4LptTT8DkxZYI4DLDx1dMGvgAw500WZdmNZuk2uSQSJp00sL6pFbomrQBTNWdNKcfTMXd4TSZBN++fg57TeNoTu2X43zGCTBYSXEwaSctnzJbMdMdhchG06ZdZkifxZOyZeolsPvF7KZoNF1/pJy0MY/ZCp9+W3fQjKv58uk1aak8WSPpdEfNN17e0Htw0AOKnJpbq2+cD46mnUZ9+rLJVZiETH0ckXQmRL66NKMiaSBTHo9tg5R0mBv8DoLRBIFIfMB8ForqkbSzXDtibDlpNTPkLmbfYWBQD4YcdcdQVF/MnOVh1AwOX+mdoXSvuHTNYET2ORrzkTSA1V+Gq+43tK+S3yWdtB7lpJ00ZQ4rE8aNA+Dxv56elRZPY3i6Y1Y8pC43NQTy7jwaOq41L5VRqyKnOqZpKndxzZrVOBJ9MsKYSXcsciStvEVeduWJpBUz3RGy0c1SprOnmXKJ3Izb+7q8brCSn+IMw+o0PJXQ57SS0iCoZxbkko6kmcrleD4k5THdn6xY5HPS4hG5+eQ6tVYa4ypc/N8dy7l2/nGaM6fLYAbXpaWSoKWMU7ismQ7xfuiVn31ahv9ob2RA+r6KpJ2NZIoSZcpjvZ4Xf6RHP/nCDGYL/UanBSmGUjNdXqbD3Onw/livSSsSKpJ2elx+nhTgabJHht5pmHCI3i4gp/lnbZljUCQtKxzSn955NHJcs9hh3odg/PnGHeM42Opzis9Lle5oc0s56cFOWjwsa7CKuSnWejF87JXSKPcOpuUC2eZm1/PyesZJU+qOiuKQmevyCGVF43Lhb65okTcMdtJOUbDjlPHWyQ2vXBn+UIe8dJ9cu5hcptR6sy0whiMTSRvkpGXmM6PSHQeKh2SDKZEB81kmknaWB1PGlpNWO9BJaxgQSYvk1KPpHvpYSXccDaQFTNIFo5EeeakiaUVBOWmnh0jvaobzqGFlImkFdtKsDhkFz5Hhr/cNH0kLx4u083jZv8LV/2XsMUaiWt/wad8O/fqCphSRmoqJQ9Md0xkCxdx8EgIa5hXveCNhdcC4RTmRtHSks4RpmIoxRdkIc106kmb1N4IwDe2VdoqCHaeMELp4SI7Coy4UdTpO2gkx3Jw2qPdmwameJi/16GFdWf51elB30s72dfrYctKc5eBtyIiHVHnsWEyCw+kwqnXgyT/bc11HFZnBSHfS0pG0UqdhjhGUk3aaDJcaAsbuPHpqB0TS6nwOesPxTP1Zbg5/OpLmPMt3HvHUyLG+fStsfQaaFhWnr9FgKiYOFQ5Jqz0WSzhkNNJygVx0hrpUTZqi6IwkkhXTnTS73S5FTIakOxY5kgZQO1tGlVL6mN5/+pG0EyIzpw2qSTNq0zGNowx84zKRtNoyR1YQK55dp4eKkb4/ChhbThpkxUOQhea1ZY4hua7pmjSV7lhkambog1Equ5hRkbSiUKbUHU+PESNpBqU7gnTSAkczV9O7jtnWItE8GQJn96SGEDKatu13cqyf8xelsaOiRQohpXu1QU6GgL8EBo0SWpYBGux7I5vuWOx0VMWYZaQNyWgihdkkpDKsb9zQXmml6OlXO1OuT9MtijKRtCpjj+v0A6L46Y4wQOHRZpGCWENr0sZGJO3sfnf5qJkuu9rrua0NfinDT2XOjrNB/Rfi8TgHDx4kEslTt6KAxhuhfDVs3QypZvjAL6EtKnfEzxAcDgdNTU1YrSVoCn4aOKxmHFYTPaFYqU05MxkxkmbgzmPFBNj228zVdK+0o70RJlS580bSxsTmU8002P+GbDw7s0Sy8xkZ/r1yoQU5kbQxvPnUeI6sS9v7muxfCiqSpigaPtdITloSW7pWy9cIB97K3plKFb8mDbKtkdo2QfWU4qU7mszSURsSSTNw0zFNwwJZt9p3BMrqs2n8qSjYZOuAsSLwNwZm60FMXAlv/Cds/1+YeQ31PifvHugBX0ROHGT7CRVagv/gwYN4vV5aWlpG7vQ+Von1S3XH8vGyaWHACvUzZG74GYCmaXR2dnLw4EEmTJhQanNOmpH6xyiOg90rHYIRI2kGOO7V0+CdH0GwHTzVWSetLyzvz1OTdrZPakC2Lm3yRcbvOA9HRoZ/T9ZJy6jW+kti0qjAYoPx50kpfocfrC4VSVMUjeNF0uxWfb3hrZeRcE2T0fl4P6AV/7taPU2K2rVtglnXSSfN4ixOWxFX5dA5zeh0R4DZN8Ar34KNv4Sln6WuzMHezn5wRjJZK9lgytntxpwZq99CMvFC+eN792eALLQ/2htBy1GNCaY99AKnBUUiESorK5WDNhz6YpJEGFIJ6ZydIQ4agBCCysrKMzZSqpy000AImZo7Yk2aAZNausi6fRuQk+7Yqxd3D4ikpZ20s3tSA6B+jryce3PpbCjXN2pyxUPS6Y5juSYNZMpjxw449DZc/T0wnTnjvOLMxm0zYzaJ/E5aPIXdon8Xyxrl2J2OJJWqXYTFDlVT4KguHtLfIaNoxVhHOitGSHc00EmraoWmc+Hdn4Om0eB3DlV3jCVxWE2YTWf3enrsjYwms5y4dz0PgaPU+xzEkini0VBOTZpxaUHKQRsBk1n+8ONhWSRrOvMWk2fy+VVO2mnirCiuuiNkW1foTprbbsHrsGRl+AfUpCUQAhzWMTDsjzsPPvoizLimdDY4/fI7keukKeEQybQroawJrn8YZl1famsUYwghBGUOy7DpjnaLvjlf1iAv+w7pd6aVSEuQmls3K9srrb+9eNkBrso8TprB6o5p5t4sS12OvEedz0EgkiCVW5MWTYyJ1P0xMFvnYd6HQEvChl9meqUlouGMaky6SZ7TOgbSgk6CdevW8ZnPfMbYg1gcstlpPCxD/DmsXLmSdevWjfj0E3mMIj8+p42ePL1jFCeIqwJC3UNvNzLd0VsPdl+2vyDphtbpdMeBkTS3zXJGbyScMELI2qdSv9eKidC1O3s90iPTpYwsuj8TqJkG/7BZOWiKkjDchmQ0kRtJSztpR/Q7S9h4vXYm9B2Umzz97cbXo6Vx5dl4zGw6GjyGzbxOOoLvPZrplZaKhQcIYZ3toiEwVp20qlZoXAibf02D3s08kXvyowncNjOmszyMerIsXLiQ+++/39iD2FxyUZsIGzYYJpNJQ173TMfntCp1x9Nh2EiagekhQsgFrx5JAykecrQvKmspkrk1aYmxUY82mqieCu07stfDPcXtkaZQKIYwnJMWy61JGxxJi5VQibRqqrzs2JFNdywGrgp5PE3L3paOKBpdE+eqgOalsPf1TBo/gyJpY2E+G5tOGsCEC+DoJur075kWCw9Qd3SdhR763r17mTZtGn/7t3/LrFmz+NCHPsTzzz/P0qVLaW1t5a23pJLRW2+9xZIlS5g/fz5Llixh+/btALz88stcccUVAKxdu5bbbruNlStXMnHixGGdN4/Hw+c//3nOOeccLrroIt56663Mc55++mlA1ur99V//NbNnz2b+8st4aeMRqJtD2FbBzTffzJw5c7jpppsIh8OZ13322WdZvHgxCxYs4MYbbyQYDI743ltaWvja177GsmXL+NWvfsXKlSu54447WLJkCbNmzcq897Vr1/KXf/mXrFq1itbWVh5++GEAUqkUt99+OzNnzuSKK67gsssu47HHHjuNszH6UOmOp4lruJo0gwutq6fJSJo+kdaVOTjaG842Hc2JpI2FSW1UUT0NgkezfR8jvWNbNEShGAWUOa30RRJDbpeRNH2MdNfImvjAKIikVetOWvv24qY7ehvkRl/uvJbpYVth/PEb5kP7VurdesBkUE3aWIiknf3vcDjq50EqTmVwFzazCS2RVXfsiyQKruw4mK8+s5kth/sK+pozGsr4ypUzR3zMrl27+NWvfsVDDz3Eueeey89+9jNef/11nn76af7lX/6FJ598kmnTpvHqq69isVh4/vnn+dKXvsTjjz8+5LW2bdvGSy+9RCAQYOrUqXzyk58cIj3f39/PypUr+fa3v821117LP//zP/Pcc8+xZcsWbr31Vq666ir+67/+C4CNGzeybds2Lr74Ynbs2MEDDzyAy+Viw4YNbNiwgQULFgDQ0dHBN77xDZ5//nncbjff/va3+fd//3e+/OUvj/jeHQ4Hr7/+OgAPPvgg/f39vPHGG7z66qvcdtttbNokC3M3bNjAm2++SX9/P/Pnz+fyyy/njTfeYO/evWzcuJFjx44xffp0brvtthM7MWcIPqeV/liSeDKF1Tx2929OGWeFnMDSamBpjO4rUzMd3vkfOXl7aqjzOWgPREnEwnKAz6lJGxOiIaOJtLDLsW3QvFimO471ejSFosT4nFYOdoeH3D5Agt9sAU8d9B3W70zXpJUgklbeAmY7HPyznE+KFUlLt8joPQDuSvl/OlvEVQwnbR6kEtRFdwMallQ2M6StL0JzZREULkvM2J2xG+YDYDq6njrfJEzRrIe+t6Of8RWuUlpnGBMmTGD27NkAzJw5k9WrVyOEYPbs2ezduxeA3t5ebr31Vnbu3IkQgng8f3Tl8ssvx263Y7fbqampoa2tjaampgGPsdlsXHLJJQDMnj0bu92O1WodcLzXX3+dT3/60wBMmzaN5uZmduzYwauvvpqpgZszZw5z5kjFtjfffJMtW7awdOlSAGKxGIsXLz7ue7/pppsGXL/lllsAWL58OX19ffT09ABw9dVX43Q6cTqdXHjhhbz11lu8/vrr3HjjjZhMJurq6rjwwguPe7wzDX9O/5gqj73E1pyBuCrkrmM8NDAVxGg1rPQu67GtGSctpUFnbx+1MGjnUUXSikpNWn1zq3TSwj1ywaVQKErGSDVpbnfOsrisPkc4JB1JKyuChYMwmWWZzl65yVw0J80/Tl72HpQOE2QjacVI266fC4CtbQP17npIAhY7iWSKvZ39rJ5ea7wNJWbsOmn+8XLn+/C71PtmYjkqPfRUSmN3e5DFEysNPfzxIl5GYbdnF98mkylz3WQykUjI8P/dd9/NhRdeyBNPPMHevXtZuXLlcV/LbDZnnp+L1WrNCBUMdzwtN995EPlEDjRNY82aNfz85z8f6a0Owe0euOsy+LXT1/PdPpKNZwvp/jE9IeWknRK5Da0HOGkGF1qne4K1b4eJKzL5+x3dvQOctP5YEr9zjAtWFBvfOFnDckyvGYz0qHRHhaLEVLht9IRiJFPaAAn3ARL8IOvS0jWlpaxJAynDv/nX8v9ipTv6cpy0NOFuKVZlLoL74G+WzuCRdxlf1gDdgMXBvq4Q8aTG5Jqzv7/i2M1pEkJG0w6/S73PgUWLgcXOoZ4wkXhqTJz84ejt7aWxsRGARx55xPDjLV++nJ/+9KcA7Nixg/379zN16tQBt2/atIkNGzYAcP755/OHP/yBXbt2ARAKhdixY0f+Fx+BX/ziF4CM5Pl8Pnw+mYb01FNPEYlE6Ozs5OWXX+bcc89l2bJlPP7446RSKdra2nj55ZdP922POqq9cjHfHoiW2JIzlHT6xxA1rJhUKjUZFMXy1oGnFva+BpBpaN3Zqy8qcgSRVE1akRFCFw/R1TcjvUo4RKEoMTVlerZBcOBcN0CCH2SvtEy6YwBM1symV9FJZ0xAEYVDKuX80Xsge1uoq3gbTULICN7hd2n06OfF6mDXMZl6OhbW6WPXSQN58tu3Ms4rsJEgZXGw85hc2LTWnv0nfzjuvPNOvvjFL7J06dKiKCHefvvtJJNJZs+ezU033cQjjzyC3W7nk5/8JMFgkDlz5vCv//qvLFq0CIDq6moeeeQRbrnlFubMmcP555/Ptm3bjnOUoZSXl7NkyRI+8YlP8IMf/CBz+6JFi7j88ss5//zzufvuu2loaOD666+nqamJWbNm8fGPf5zzzjsv49SdLdTqEZhjgTOzGXfJyY2k5ZKMGttTRgiYeS3s+D1EejORtK6+tJOWTXdUNWkloHq6jKQl4xALqpo0haLE1Oobkm19g520QZE0b72MoEX6ZE2a3VO6th6lcNKEkHVpgyNpzvLiHB+kfsSxrTR79IwUS9ZJm1StatLObhrmQyrBbHYCEEpash56dQkUfAympaUlI44BA6NkufctXrx4QGTq61//OiB7kKVTH9euXTvgtXNfN5dc1cXBz0nf53A48kbsnE4njz76aN7XXbVqFX/+85+H3D5chCtd/5bL9ddfzze/+c0ht0+ZMoWHHnpowG0mk4n77rsPj8dDZ2cnixYtytT2nS3UlsmJ62ivctJOifTEla+vjNGNP+f8BfzpQdjyNBXzP4zNbKK3b1AkLZZQNWmloHoqvPsT6Nojr6t0R4WipKQ3JNv6Iswmu2kSzZXgBxlJA6nwGA2URtkxTVWOk+YythxnAEOctK7iiIakaZgHqTgzNdlvMoqV3ceC1JU58DrO/vR95aQBE0MbAeiNm9nZHaTaa8fnOvtPvuLkuOKKK+jp6SEWi3H33XdTV1dXapMKisduwWUzD9ldVJwgruEiaTHjG382LIDKybDhF4gFf0mtz05v8Ki8L6cmzanSHYtPjV4zeOBNeanSHRWKkpJx0gZljcRyJfhBCoeATHmMBcFWQietcpJsCWAvA4vBm365+Jpg5/PZ6+Hu4oof1c8DYEpQbsp3x0zsag+OiVRHGOtOWlkjlLfQvFfWJh3q19h5LEjrGDn5Y5nhIm6Do30n8pyzBSEEdWWOIROX4gRJpzum1a/SJGPGR9KEgNl/AS9/E3oPUl/mJJCOYlscJJIpYokUbpXuWHzSMvwv/Yu8LGaqkEKhGEKVx4YQ+dIdk9gGC4eAdNKifaWNpFnsUDERKHK6pW+c7PWY7lEW7i5Oj7Q05S0wfjHj9j8DwPrDYXYfC3LjwnHFs6GEjO2aNCHg0nux9ssd51feD7D72Njx0BWKwdSU2WlT6Y6nhsUmlb8GR9LiYbA6jD/+jKsBDd5/mVqfg/7+ft0uO6G4rC1VwiElwNcE4xfL+paln4WWpaW2SKEY01jMJqo8do71Zee6ZEojntSG1qSB7qQFS9MjLZfpV8KkVcU9ZrpXWt9hSCVlG5FibjQJAVf9J0Lf6Hx8Qwf9sSSTxsg6XW2rTrkY5t4C7/2cvT1JAqmEiqQpxix1ZQ7e3t99/Acq8uOplfULuRSr0LpqinQSj2yg3ncOx0L9YAYsDkJR6aS57WrILzpCwG3/V2orFApFDrVldtpynLRYIgUwMN3R6gTfeGjbKGvSypuLbeZALlpb/GNmGlof1EWPtOJnA1S1wsrPwwtf41BEbnhOrh4b63Q1YwNc8k2SZge73psD/YwZD12hGExtmYO2viiapuXtUac4Dr7GbPPTNOGe4hRam0xQOwuOvEftVAedyZjupNnpDsmG2l6HGvIVCoWi1uvgSE7WSDQhN7IGRNJANqHf/ZKsBytVj7RSktsrLZ3+WUzhkDRL/55k8wX0/zwI3eExk/E2ttMd0zjLMV/1Ha6+YD42s4lpdSXoKK9QjAJqyxzEEil6QvFSm3JmUtYEvYOdtCJKFtfPhaMbqfPasIusZPGeDpn62FJ59ksWKxQKxfGoKXMMaDcTTUfSrIOWxeMXQ/8xWZdlH4Nrw7TCZe/BbL11KepqTSbM4xfx+Uunc/GMWqo8RRRPKSHKScvhE8sn8eLnVlDhHhsnvxSsXbuW++67b8jtTz75JFu2bDnp19u7dy8/+9nPMtcfeeQRPvWpT52WjWOZ4VSvFCeIr0lO5skcJ7eoTtociPfTLI5gR0bPsNjZrbcWmTgG+sooFArF8agts9MRjBFPSucsGs+T7gjQvCT7f6lr0kqB1QHuGtnQOuOklSCSpnP5nHoe+quFYybTRzlpOZhMgqZyV6nNKDmJRKLoxxzJSRvJnsFOmuL0UL3SThNfI2ipbF1aKgmR3uJG0oCG8E7sZCNpu9uDNPqdqpm1QqFQkN2QbA9IhcdYcph0x6op2b5kpVR3LCW+JujZlxXFUgq1RUM5aWOMr3/960ybNo01a9Zwyy23ZKJaK1eu5Etf+hIrVqzgu9/9Li+88ALz589n9uzZ3HbbbUSjciBraWmho6MDgHXr1g1obn3bbbexcuVKJk6cyP3335855j333MPUqVO56KKL2L59+xCb3njjDZ5++mn+6Z/+iXnz5rF79+4h9nzkIx/hscceyzzH45E7Wl/4whd47bXXmDdvHv/xH/8BwOHDh7nkkktobW3lzjvvLPyHeBaTnriOqV5pp0ZZushaT3mM9FLUQuvqaWC24evZnE13NNvZ3d6vomgKhUKhk96QTIuHRPRImm2wkyaETHmEsVmTBlA7A45uko2soTQ1aWMUta1aKv73C3B0Y2Ffs242XPqtYe9et24djz/+OOvXryeRSLBgwQLOOeeczP09PT288sorRCIRWltbeeGFF5gyZQp/9Vd/xQMPPMAdd9wx4uG3bdvGSy+9RCAQYOrUqXzyk59kw4YNPProo8MeE2DJkiVcddVVXHHFFdxwww1D7AH4yEc+kveY3/rWt7jvvvv4zW9+A8h0x3fffZf169djt9uZOnUqn/70pxk3bmz01DhdatKRtD4VSTslfHr+flo8JJ0eUqwGxmYr1EzH3LaRcls5Cc2KWQjebx87fWUUCoXieNR49dR+fUMyU5M22EkDmfK47TdjN5JWPw/W/wTaNsnrDl9JzRlLqEjaGOL111/n6quvxul04vV6ufLKKwfcf9NNNwGwfft2JkyYwJQpUwC49dZbefXVV4/7+pdffjl2u52qqipqampoa2vjtdde49prr8XlclFWVsZVV111wvam7TlZVq9ejc/nw+FwMGPGDPbt23dKrzMWsVvMVLhtA6SJFSdBbpE1SGVHKG56SP1cOLKBcluKGFba+qKyr4yKpCkUCgWQkzWi11+HYzLd0WHN00ty0mowWaB8QtHsG1XUz5OXu1+WDppJ9dssFiqSVipGiHgZhaZpI97vdruP+ziLxUIqJXecIpGBC3m73Z7532w2Z2rJTrXAM23P4ONqmkYsFhv2ecPZoTgxarz2zO6i4iRxlIHdl+OklUANa9z58M6PWGJ5i4hmZeexAACTxkhfGYVCoTgelW4bZpPIbEimnbUar33og2umwef3jU3hEIC6WSDM0Hdw7DqqJUJF0sYQy5Yt45lnniESiRAMBvntb3+b93HTpk1j79697Nq1C4Af//jHrFixApA1aW+//TYAjz/++HGPuXz5cp544gnC4TCBQIBnnnkm7+O8Xi+BQGDY18k97lNPPUU8Hj+h5ylOnjqfQ0XSTofcXmmRHnlZTCdtzl9A7WyqE0cIaxYe+cNeQPV/VCgUijQmkxiwIZlO8U9H2IYwVh00kE29q6fJ/5VoSFFRTtoY4txzz+Wqq65i7ty5XHfddSxcuBCfb2huscPh4Ic//CE33ngjs2fPxmQy8YlPfAKAr3zlK3z2s5/lggsuwGw+fsh7wYIF3HTTTcybN4/rr7+eCy64IO/jbr75Zu69917mz5/P7t27h9z/0Y9+lFdeeYVFixbxpz/9KRNlmzNnDhaLhblz52aEQxSnR63XoWrSTgdfU2kjaWYrXPldNAQJYeOFbcfw2C35d4gVCoVijFJTlt2QPNYXxWu34LarBLO8NMyTl0o0pKiob+MY43Of+xxr164lFAqxfPly/vEf/xGAl19+ecDjVq9ezfr164c8/4ILLmDHjh1Dbl+7du2A65s2bcr8f9ddd3HXXXeNaNfSpUsHSPAPtqe2tpY333wzc/2b3/wmAFarlRdeeGHAY3NFRtKCIooTp7HcSXsgSiSezJ+frxiZskY4JKO+WSfNX1wbms5BrL6bjm27YTdMqnaPmb4yCoVCcSI0lTvZcrgPkG1nan3DRNEUsi7t3Z+qSFqRUZG0McbHPvYx5s2bx4IFC7j++utZsGBBqU1SjDKayp0AHOoJl9iSMxRfI4Q6IRaSTprNK6NbxeaCf2T8zf+B1SxUqqNCoVAMoqncyaHuMKmURlsgkpHlV+RB78GpnLTioiJpYwzV+FlxPMZVyIbuB7pCSmziVEj3Sus7LJ20Ek5q1V47//PXizLnVKFQKBSSpnIXsWSKY4Eobb0Rzp9UWWqTRi91s8Hqkun8iqKhnDSFQjGAceW6k9atImmnRHoS6zuoO2n+kpqzZHJVSY+vUCgUo5FxetbI/q4QxwJR6oYTDVGAzQW3/xE8taW2ZEyh0h0VCsUAarx2bBYTB7tCpTblzMQ/Xl52vV/ySJpCoRh9CCHuFUJsE0JsEEI8IYTwl9qmsUiTviH53oEeEilteGVHhaS8RSo9KorGaTlpaqBRKM4+TCZBk9/JgW7lpJ0S/vGyV9rRjaMikqZQKEYdzwGzNE2bA+wAvlhie8Yk6frrdfu6gBHk9xWKEnG6kTQ10CgUZyFNFS4OdKl0x1NCCKifA0feg3CPiqQpFIoBaJr2rKZpCf3qm4Aq9CkBDquZaq+dt/dJFV4lHKIYbZyWk6YGGsWJ0tPTw/e///2Tft7atWu57777DLBIMRLjylUk7bSonwttm1W6o0KhOB63Af9baiPGKuPKnXQEYwDUKQl+xSijkDVpIw40QoiPCSHWCSHWtbe3F/CwimKQSCRGvH48TtVJOxFO1hbF8RlX4aInFCcQiZfalDOTujmQiEAqrpw0hWIMIoR4XgixKc/f1TmPuQtIAD8d4XXU2slA0nVpQkC1R0XSFKOL46o7CiGeB+ry3HWXpmlP6Y857kCjadpDwEMACxcu1E7JWsVp86Mf/Yj77rsPIQRz5szhxz/+Mfv27eO2226jvb2d6upqfvjDHzJ+/Hg+8pGPUFFRwfr161mwYAGdnZ0Drt9+++383d/9He3t7bhcLh5++GGmTZtGW1sbn/jEJ3j//fcBeOCBB7j//vvZvXs38+bNY82aNdx7773ce++9/PKXvyQajXLttdfy1a9+FYB77rmHH/3oR4wbN47q6mrOOeecIe9jsG1er5fdu3dz6NAhDhw4wJ133slHP/pRXn75Zb785S9TWVnJ9u3bWb58Od///vcxmUz84Ac/4Nvf/jYNDQ20trZit9v53ve+V9TzMVrJKDx2hZnRUIIeX2c69XOy/ysnTaEYc2iadtFI9wshbgWuAFZrmjbsmkitnYwlXZdW5bFjMSstPcXo4rhOWqEGGsVAvv3Wt9nWta2grzmtYhqfX/T5Ye/fvHkz99xzD3/4wx+oqqqiq0sWy37qU5/ir/7qr7j11lv57//+bz7zmc/w5JNPArBjxw6ef/55zGYzH/nIRwZcX716NQ8++CCtra386U9/4vbbb+fFF1/kM5/5DCtWrOCJJ54gmUwSDAb51re+xaZNm3j33XcBePbZZ9m5cydvvfUWmqZx1VVX8eqrr+J2u3n00UdZv349iUSCBQsW5HXSBtu2du1aNmzYwJtvvkl/fz/z58/n8ssvB+Ctt95iy5YtNDc3c8kll/DrX/+aJUuW8PWvf5133nkHr9fLqlWrmDt3buFOxhnOuAo5cR3oDjGjoazE1pyBVLaCxQmJsHLSFArFAIQQlwCfB1ZomqbyyktIuoekkt9XjEZOq0+aGmjOLF588UVuuOEGqqpk36SKigoA/vjHP/LrX/8agL/8y7/kzjvvzDznxhtvxGw2D7keDAZ54403uPHGGzP3RaPRzHF+9KMfAWA2m/H5fHR3dw+w5dlnn+XZZ59l/vz5AASDQXbu3EkgEODaa6/F5ZID51VXXTXs+xls29VXX43T6cTpdHLhhRfy1ltv4ff7WbRoERMnTgTglltu4fXXX8disbBixYrMZ3DjjTeyY8eOE/4sz3aykTT1sz4lzBaonQmH1iknTaFQDOZ7gB14TggB8KamaZ8orUljk3QkTYmGKEYjp9vMWg00p8hIES+j0DQN/TyNSO5j3G73gPvS11OpFH6/PxMZOxVbvvjFL/Lxj398wO3f+c53TsjGfLYNfl76er7bVdB3ZPwuKx67hYOqofWpUz9XOWkKhWIImqZNLrUNCkl6Q1LJ7ytGI6er7jhZ07RxmqbN0/+UgzaKWb16Nb/85S/p7OwEyKQ7LlmyhEcffRSAn/70pyxbtuy4r1VWVsaECRP41a9+BUin67333ssc54EHHgAgmUzS19eH1+slEAhknv+BD3yA//7v/yYYDAJw6NAhjh07xvLly3niiScIh8MEAgGeeeaZE35/Tz31FJFIhM7OTl5++WXOPfdcQKY77tmzh1QqxS9+8QuWLVvGokWLeOWVV+ju7iaRSPD444+f8HHGAkIImsqdKpJ2OjQvAbMdvPWltkShUCgUeWjwO/E5rUyp9ZbaFIViCKcbSVOcQcycOZO77rqLFStWYDabmT9/Po888gj3338/t912G/fee29GOORE+OlPf8onP/lJvvGNbxCPx7n55puZO3cu3/3ud/nYxz7GD37wA8xmMw888ACLFy9m6dKlzJo1i0svvZR7772XrVu3snjxYgA8Hg8/+clPWLBgATfddBPz5s2jubmZCy644ITf36JFi7j88svZv38/d999Nw0NDezYsYPFixfzhS98gY0bN7J8+XKuvfZaTCYTX/rSlzjvvPNoaGhgxowZ+Hy+U/pcz1a+c/M8yhxKNOSUmXU9TFgBropSW6JQKBSKPNgsJl7+3Eq8DrUcVow+RCnSvhYuXKitW7eu6MctNVu3bmX69OmlNuOsZO3atXg8Hj73uc8NuP3ll1/mvvvu4ze/+c2Q5wSDQTweD4lEgmuvvZbbbruNa6+99rRtGavnWQjxtqZpC0ttx+kwVscmheJsR41PCoViNDLS2KT0RhVjlrVr1zJv3jxmzZrFhAkTuOaaa0ptkqKACCHuFUJsE0JsEEI8IYTwl9omhUKhUCgUihNBxXcVZwVr167Ne/vKlStZuXJl3vvuu+8+4wxSjAaeA76oaVpCCPFt4ItINVqFQqFQKBSKUY2KpCkUirMSTdOe1TQtoV99E2gqpT0KhUKhUCgUJ4py0oqMkn4/u1Hnd9RyG/C/+e4QQnxMCLFOCLGuvb29yGYpFAqFQqFQDEU5aUXE4XDQ2dmpFvJnKZqm0dnZicOh+q0UCyHE80KITXn+rs55zF1AAvhpvtfQNO0hTdMWapq2sLq6ulimKxQKhUKhUAyLqkkrIk1NTRw8eBC1W3/24nA4aGpSWXXFQtO0i0a6XwhxK3AFsFpTuyMKhUKhUCjOEJSTVkSsVisTJkwotRkKxZhACHEJUihkhaZpqiu3QqFQKBSKMwaV7qhQKM5Wvgd4geeEEO8KIR4stUEKhUKhUCgUJ4KKpCkUirMSTdMml9oGhUKhUCgUilNBRdIUCoVCoVAoFAqFYhQhSlFLL4RoB/ad4MOrgA4DzTkZRpMtMLrsUbbkZzTZAsba06xp2hktj3iSYxOMrvOrbMnPaLIFRpc9Y8mWsTY+jaVzezKMJltgdNmjbMlPycamkjhpJ4MQYp2maQtLbQeMLltgdNmjbMnPaLIFRp89Zzqj6fNUtuRnNNkCo8seZcvZy2j6PJUtwzOa7FG25KeUtqh0R4VCoVAoFAqFQqEYRSgnTaFQKBQKhUKhUChGEWeCk/ZQqQ3IYTTZAqPLHmVLfkaTLTD67DnTGU2fp7IlP6PJFhhd9ihbzl5G0+epbBme0WSPsiU/JbNl1NekKRQKhUKhUCgUCsVY4kyIpCkUCoVCoVAoFArFmGFUO2lCiEuEENuFELuEEF8o8rHHCSFeEkJsFUJsFkJ8Vr+9QgjxnBBip35ZXkSbzEKI9UKI35TSFiGEXwjxmBBim/75LC6hLX+vn59NQoifCyEcxbRFCPHfQohjQohNObcNe3whxBf17/N2IcQHimDLvfp52iCEeEII4S+GLWc7amwaYtOoGJv0Y6vxidE1No1gjxqfCkwpxyb9+Gp8Gt4ONTZljz9qxqfRPDaNWidNCGEG/gu4FJgB3CKEmFFEExLAP2qaNh04H/g7/fhfAF7QNK0VeEG/Xiw+C2zNuV4qW74L/J+madOAubpNRbdFCNEIfAZYqGnaLMAM3FxkWx4BLhl0W97j69+fm4GZ+nO+r3/PjbTlOWCWpmlzgB3AF4tky1mLGpvyMlrGJlDjU5pHGD1j03D2qPGpgIyCsQnU+DQSamzK8gijZ3zKZ8voGJs0TRuVf8Bi4Pc5178IfLGE9jwFrAG2A/X6bfXA9iIdvwn5pV0F/Ea/rei2AGXAHvR6xpzbS2FLI3AAqAAswG+Ai4ttC9ACbDreZzH4Owz8HlhspC2D7rsW+GmxbDlb/9TYNOT4o2Js0o+lxqeBNoyasSmfPYPuU+PT6X++o2ps0m1Q45OmxqZh7Bg149NoHZtGbSSN7JcozUH9tqIjhGgB5gN/Amo1TTsCoF/WFMmM7wB3Aqmc20phy0SgHfihnj7w/4QQ7lLYomnaIeA+YD9wBOjVNO3ZUtgyiOGOX+rv9G3A/44SW85kRs1np8amIajxaWRG69gEanwqBKPqc1Pj0wDU2HR8Ruv4VLKxaTQ7aSLPbUWXohRCeIDHgTs0Tesr9vF1G64Ajmma9nYpjj8IC7AAeEDTtPlAP8VNW8ig5ytfDUwAGgC3EOLDpbDlBCnZd1oIcRcyDeWnpbblLGBUfHZqbMqLGp9OjZJ+p9X4VDBGzeemxqchqLHp1Bmza6fR7KQdBMblXG8CDhfTACGEFTnI/FTTtF/rN7cJIer1++uBY0UwZSlwlRBiL/AosEoI8ZMS2XIQOKhp2p/0648hB55S2HIRsEfTtHZN0+LAr4ElJbIll+GOX5LvtBDiVuAK4EOaHp8vlS1nCSX/7NTYNCxqfBqZUTU26Xao8alwjIrPTY1PeVFj0/EZVePTaBibRrOT9megVQgxQQhhQxbqPV2sgwshBPADYKumaf+ec9fTwK36/7ci860NRdO0L2qa1qRpWgvyc3hR07QPl8iWo8ABIcRU/abVwJZS2IIM1Z8vhHDp52s1shC3FLbkMtzxnwZuFkLYhRATgFbgLSMNEUJcAnweuErTtNAgG4tqy1mEGpt0RtPYpNujxqeRGTVjE6jxyQBKOjaBGp9GsEWNTcdn1IxPo2ZsMqrYrRB/wGVIVZXdwF1FPvYyZAhzA/Cu/ncZUIksQt2pX1YU2a6VZItfS2ILMA9Yp382TwLlJbTlq8A2YBPwY8BeTFuAnyNzuuPIHZa/Gen4wF3693k7cGkRbNmFzJ9Of4cfLIYtZ/ufGpvy2lXysUk/thqftNE1No1gjxqfCv85l2xs0o+vxqfhbVBjU/b4o2Z8Gs1jk9APqFAoFAqFQqFQKBSKUcBoTndUKBQKhUKhUCgUijGHctIUCoVCoVAoFAqFYhShnDSFQqFQKBQKhUKhGEUoJ02hUCgUCoVCoVAoRhHKSVMoFAqFQqFQKBSKUYRy0hQKhUKhUCgUCoViFKGcNIVCoVAoFAqFQqEYRSgnTaFQKBQKhUKhUChGEf8/uxoWpPgdFnYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,3,figsize=(15,5))\n",
    "ax[0].plot(original[0,0],label='main model rppg')\n",
    "ax[0].plot(ground_truth[0,0],label='ground truth')\n",
    "ax[0].plot(corrected[0,0],label='corrected rppg')\n",
    "\n",
    "ax[1].plot(original[1,0],label='main model rppg')\n",
    "ax[1].plot(ground_truth[1,0],label='ground truth')\n",
    "ax[1].plot(corrected[1,0],label='corrected rppg')\n",
    "\n",
    "ax[2].plot(original[2,0],label='main model rppg')\n",
    "ax[2].plot(ground_truth[2,0],label='ground truth')\n",
    "ax[2].plot(corrected[2,0],label='corrected rppg')\n",
    "\n",
    "\n",
    "ax[0].legend()\n",
    "fig.suptitle('Illustration of PULSE GAN correction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4d24e129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eg = pd.read_csv('exp46.csv')\n",
    "eg['pred'] = normalize(eg['pred'])\n",
    "eg['gt'] = normalize(eg['gt'])\n",
    "eg.iloc[1500:].plot(figsize=(15,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "615aa0d2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = PulseGan()\n",
    "model = model.to(device)\n",
    "\n",
    "model.load_state_dict(torch.load('./checkpoints/PulseGan3_49.pt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "47d65bfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUTS = []\n",
    "TARGETS = []\n",
    "for i in range(14):\n",
    "    preds = np.array(eg['pred'].iloc[i*128:(i+1)*128])\n",
    "    gts = np.array(eg['gt'].iloc[i*128:(i+1)*128])\n",
    "    \n",
    "    preds = torch.from_numpy(preds).type(torch.FloatTensor)\n",
    "    gts = torch.from_numpy(gts).type(torch.FloatTensor)\n",
    "    INPUTS.append(preds)\n",
    "    TARGETS.append(gts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1df8737c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate over data.\n",
    "\n",
    "originals = []\n",
    "ground_truths = []\n",
    "correcteds = []\n",
    "\n",
    "for inputs, targets in zip(INPUTS, TARGETS):\n",
    "    \n",
    "    if inputs.shape[-1] < 128: break\n",
    "        \n",
    "    inputs = inputs.reshape(1,1,-1)\n",
    "    targets = targets.reshape(1,1,-1)\n",
    "    inputs = inputs.to(device)\n",
    "    targets = targets.to(device)\n",
    "\n",
    "    # forward\n",
    "    # track history if only in train\n",
    "    with torch.no_grad():\n",
    "        X,Xc,Xrppg, proba_fake, proba_real = model(inputs,targets)\n",
    "        \n",
    "        \n",
    "    original = inputs.cpu().numpy()\n",
    "    ground_truth = targets.cpu().numpy()\n",
    "    corrected = Xrppg.cpu().detach().numpy()\n",
    "    \n",
    "    originals.append(original)\n",
    "    ground_truths.append(ground_truth)\n",
    "    correcteds.append(corrected)\n",
    "\n",
    "originals = np.concatenate(originals).flatten()\n",
    "correcteds = np.concatenate(correcteds).flatten()\n",
    "ground_truths = np.concatenate(ground_truths).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "afc60630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'PULSE GAN correction on ROI motion with L1-match')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,figsize=(15,5))\n",
    "\n",
    "N = len(originals)\n",
    "\n",
    "\n",
    "ax.plot(np.array(range(N))/29,originals,label='main model rppg',color='grey')\n",
    "ax.plot(np.array(range(N))/29,ground_truths,label='ground truth',color='red')\n",
    "ax.plot(np.array(range(N))/29,correcteds,label='corrected rppg',color='black')\n",
    "\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xlim(55,62)\n",
    "\n",
    "fig.suptitle('PULSE GAN correction on ROI motion with L1-match')"
   ]
  }
 ],
 "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",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}