{ "cells": [ { "cell_type": "markdown", "id": "british-monkey", "metadata": {}, "source": [ "# Stochastic Variational Inference (interpolation)\n", "\n", "Implementation of Stochastic Variational Inference (SVI) using [PyTorch](https://pytorch.org/), for the purpose of uncertainty quantification.\n", "\n", "We'll consider the \"Gramacy & Lee 2012\" function [[source](https://www.sfu.ca/~ssurjano/grlee12.html)]:\n", "\n", "$f(x) = \\frac{\\sin(10 \\pi x)}{2x} + (x-1)^4 \\quad \\forall x \\in \\mathbb{R}^+$\n", "\n", "We'll also add a little bit of normally distributed noise around the output for good measure:\n", "\n", "$y \\sim \\mathcal{N}(f(x), 0.1)$\n", "\n", "The goal is to train a model capable of estimating its own uncertainty on unseen situations. To do so we'll split the dataset between a training / validation set on the one hand, and a test set defined on unseen input values on the other hand.\n", "\n", "The function above is usually evaluated on the range $[0.5, 2.5]$. We'll train our model on the range $[0.5, 2.1]$ and then test it on the unseen set $[2.1, 2.5]$. \n", "\n", "The model is expected to perform less well on the unseen set, but hopefully the decrease in performance will be flagged by an increase in uncertainty. \n", "\n", "**UPDATE: inconclusive** - a single value isn't enough to predict the outcome.\n", "\n", "## Setting up the environment" ] }, { "cell_type": "code", "execution_count": 1, "id": "abroad-selection", "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import torch\n", "import torch.nn.functional as F\n", "from sklearn.metrics import r2_score\n", "from sklearn.model_selection import train_test_split\n", "\n", "cwd = os.getcwd()\n", "if cwd.endswith('notebook'):\n", " os.chdir('..')\n", " cwd = os.getcwd()" ] }, { "cell_type": "code", "execution_count": 2, "id": "normal-provision", "metadata": {}, "outputs": [], "source": [ "sns.set(palette='colorblind', font_scale=1.3)\n", "palette = sns.color_palette()" ] }, { "cell_type": "code", "execution_count": 3, "id": "proud-midnight", "metadata": {}, "outputs": [], "source": [ "seed = 444\n", "np.random.seed(seed);\n", "torch.manual_seed(seed);\n", "torch.set_default_dtype(torch.float64)" ] }, { "cell_type": "markdown", "id": "liberal-thanksgiving", "metadata": {}, "source": [ "## Generating & visualising data" ] }, { "cell_type": "code", "execution_count": 4, "id": "viral-intake", "metadata": {}, "outputs": [], "source": [ "def generate_samples(σ, min_x=0.5, max_x=2.5, n_samples=1000):\n", " x = np.linspace(min_x, max_x, n_samples)[:, np.newaxis]\n", " y = (np.sin(10*np.pi*x) / (2 * x)) + (x - 1) ** 4 \n", " dist = torch.distributions.Normal(torch.from_numpy(y), σ)\n", " y_sample = dist.sample().detach().numpy()\n", " return x, y, y_sample" ] }, { "cell_type": "code", "execution_count": 5, "id": "nearby-baseline", "metadata": {}, "outputs": [], "source": [ "σ = 0.1\n", "x, y, y_sample = generate_samples(σ)" ] }, { "cell_type": "code", "execution_count": 6, "id": "prompt-catch", "metadata": {}, "outputs": [], "source": [ "def plot_data(x, y, y_sample, std_err=None):\n", " f, ax = plt.subplots(1, 1, figsize=(12, 6))\n", " \n", " x_ = x.flatten()\n", " y_ = y.flatten()\n", " y_sample_ = y_sample.flatten()\n", " \n", " ax.plot(x_, y_, '-', color=palette[0], linewidth=3, label='Actual (no noise)')\n", " ax.scatter(x, y_sample_, color=palette[0], alpha=0.5, label='Actual (with noise)')\n", " \n", " if std_err is not None:\n", " ax.fill_between(x_, y_ - 2 * std_err, y_ + 2 * std_err, color=palette[0], alpha=0.2, label='2 standard error')\n", " \n", " ax.set_xlabel('input x')\n", " ax.set_ylabel('output y')\n", " ax.set_title(r'$f(x) \\sim \\frac{\\sin(10 \\pi x)}{2x} + (x-1)^4 + N(0, 0.1)$' + '\\n')\n", " ax.legend()\n", " return ax" ] }, { "cell_type": "code", "execution_count": 7, "id": "sweet-conviction", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_data(x, y, y_sample, std_err=σ);" ] }, { "cell_type": "markdown", "id": "abstract-journalist", "metadata": {}, "source": [ "## Split between different sets" ] }, { "cell_type": "code", "execution_count": 8, "id": "aquatic-hawaiian", "metadata": {}, "outputs": [], "source": [ "x, y, y_sample = generate_samples(σ, n_samples=10000)" ] }, { "cell_type": "code", "execution_count": 9, "id": "juvenile-counter", "metadata": {}, "outputs": [], "source": [ "def compute_train_test_split(x, y, test_size):\n", " x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test_size)\n", " return (\n", " torch.from_numpy(x_train),\n", " torch.from_numpy(x_test),\n", " torch.from_numpy(y_train),\n", " torch.from_numpy(y_test),\n", " )" ] }, { "cell_type": "code", "execution_count": 10, "id": "balanced-dominican", "metadata": {}, "outputs": [], "source": [ "def find_cutoff_index(x, x_thres=2.1):\n", " for i in range(len(x)):\n", " if x[i,0] > 2.1:\n", " return i\n", " return i\n", "\n", "cutoff_idx = find_cutoff_index(x)\n", "x_train, x_val, y_train, y_val = compute_train_test_split(x[:cutoff_idx], y[:cutoff_idx], test_size=0.2)\n", "\n", "x_test = torch.from_numpy(x[cutoff_idx:])\n", "y_test = torch.from_numpy(y_sample[cutoff_idx:])" ] }, { "cell_type": "code", "execution_count": 11, "id": "greater-birth", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(torch.Size([6400, 1]), torch.Size([1600, 1]), torch.Size([2000, 1]))" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_train.size(), x_val.size(), x_test.size()" ] }, { "cell_type": "code", "execution_count": 12, "id": "unique-rouge", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(torch.Size([6400, 1]), torch.Size([1600, 1]), torch.Size([2000, 1]))" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train.size(), y_val.size(), y_test.size()" ] }, { "cell_type": "code", "execution_count": 13, "id": "public-rugby", "metadata": {}, "outputs": [], "source": [ "class StandardScaler():\n", " \"\"\"\n", " Standardize data by removing the mean and scaling to unit variance.\n", " \"\"\"\n", " def __init__(self):\n", " self.mean = None\n", " self.scale = None\n", "\n", " def fit(self, sample):\n", " self.mean = sample.mean(0, keepdim=True)\n", " self.scale = sample.std(0, unbiased=False, keepdim=True)\n", " return self\n", "\n", " def __call__(self, sample):\n", " return self.transform(sample)\n", " \n", " def transform(self, sample):\n", " return (sample - self.mean) / self.scale\n", "\n", " def inverse_transform(self, sample):\n", " return sample * self.scale + self.mean" ] }, { "cell_type": "code", "execution_count": 14, "id": "bigger-exercise", "metadata": {}, "outputs": [], "source": [ "x_scaler = StandardScaler().fit(torch.from_numpy(x))\n", "y_scaler = StandardScaler().fit(torch.from_numpy(y_sample))" ] }, { "cell_type": "markdown", "id": "norman-henry", "metadata": {}, "source": [ "## Define model" ] }, { "cell_type": "code", "execution_count": 15, "id": "geological-island", "metadata": {}, "outputs": [], "source": [ "class VariationalModel(torch.nn.Module):\n", " def __init__(self, n_hidden_encoder=100, n_hidden_decoder=300, n_encoding=50):\n", " super().__init__()\n", " \n", " self.encoder_shared1 = torch.nn.Linear(1, n_hidden_encoder)\n", " self.encoder_shared2 = torch.nn.Linear(n_hidden_encoder, n_hidden_encoder)\n", " self.encoder_mean = torch.nn.Linear(n_hidden_encoder, n_encoding)\n", " self.encoder_std = torch.nn.Linear(n_hidden_encoder, n_encoding)\n", " self.encoder_dropout = torch.nn.Dropout()\n", " \n", " self.decoder_inner_mean = torch.nn.Linear(n_encoding, n_hidden_decoder)\n", " self.decoder_mean = torch.nn.Linear(n_hidden_decoder, 1)\n", " self.decoder_dropout = torch.nn.Dropout()\n", " self.s = torch.nn.Parameter(torch.randn(()))\n", "\n", " self.prior = torch.distributions.MultivariateNormal(\n", " torch.zeros((n_encoding,)),\n", " torch.Tensor([1.0] * n_encoding) * torch.eye(n_encoding),\n", " )\n", " \n", " def encoder(self, x):\n", " shared = self.encoder_shared1(x)\n", " shared = F.relu(shared)\n", " shared = self.encoder_dropout(shared)\n", " shared = self.encoder_shared2(shared)\n", " shared = F.relu(shared)\n", " shared = self.encoder_dropout(shared)\n", " \n", " mean = self.encoder_mean(shared)\n", " std = F.softplus(self.encoder_std(shared))\n", " \n", " return torch.distributions.MultivariateNormal(\n", " mean,\n", " torch.diag_embed(std),\n", " )\n", " \n", " def decoder(self, x_enc):\n", " decoder_mean = self.decoder_inner_mean(x_enc)\n", " decoder_mean = F.relu(decoder_mean)\n", " decoder_mean = self.decoder_dropout(decoder_mean)\n", " \n", " decoder_mean = self.decoder_mean(decoder_mean)\n", " decoder_std = F.softplus(self.s)\n", " \n", " return torch.distributions.Normal(decoder_mean, decoder_std)\n", " \n", " def forward(self, x, sample_shape=torch.Size([])):\n", " encoder_dist = self.encoder(x)\n", " x_enc = encoder_dist.rsample(sample_shape)\n", " y_hat = self.decoder(x_enc)\n", " kl_divergence = torch.distributions.kl.kl_divergence(encoder_dist, self.prior)\n", " return y_hat, kl_divergence" ] }, { "cell_type": "code", "execution_count": 16, "id": "secure-aurora", "metadata": {}, "outputs": [], "source": [ "def compute_loss(model, x, y):\n", " y_hat, kl_divergence = model(x)\n", " return torch.mean(-y_hat.log_prob(y) + kl_divergence)" ] }, { "cell_type": "code", "execution_count": 17, "id": "familiar-albuquerque", "metadata": {}, "outputs": [], "source": [ "def train_one_step(model, optimizer, x_batch, y_batch):\n", " model.train()\n", " optimizer.zero_grad()\n", " loss = compute_loss(model, x_batch, y_batch)\n", " loss.backward()\n", " optimizer.step()\n", " return loss\n", "\n", "def compute_rmse(model, x_test, y_test, x_scaler=None, y_scaler=None):\n", " if x_scaler is None:\n", " x_test_ = x_test\n", " else:\n", " x_test_ = x_scaler(x_test)\n", " \n", " model.eval()\n", " y_hat, _ = model(x_test_)\n", " pred = y_hat.mean\n", " \n", " if y_scaler is None:\n", " return torch.sqrt(torch.mean((pred - y_test)**2))\n", " else:\n", " pred_ = y_scaler.inverse_transform(pred)\n", " return torch.sqrt(torch.mean((pred_ - y_test)**2))" ] }, { "cell_type": "code", "execution_count": 18, "id": "altered-custody", "metadata": {}, "outputs": [], "source": [ "def train(model, optimizer, scheduler, x_train_, x_val_, y_train_, y_val_, n_epochs, batch_size, print_every=10):\n", " x_train = x_scaler(x_train_)\n", " x_val = x_scaler(x_val_)\n", " y_train = y_scaler(y_train_)\n", " x_val = y_scaler(y_val_)\n", " \n", " train_losses, val_losses = [], []\n", " for epoch in range(n_epochs):\n", " batch_indices = sample_batch_indices(x_train, y_train, batch_size)\n", " \n", " batch_losses_t, batch_losses_v, batch_rmse_v = [], [], []\n", " for batch_ix in batch_indices:\n", " b_train_loss = train_one_step(model, optimizer, x_train[batch_ix], y_train[batch_ix])\n", "\n", " model.eval()\n", " b_val_loss = compute_loss(model, x_val, y_val)\n", " b_val_rmse = compute_rmse(model, x_val, y_val)\n", "\n", " batch_losses_t.append(b_train_loss.detach().numpy())\n", " batch_losses_v.append(b_val_loss.detach().numpy())\n", " batch_rmse_v.append(b_val_rmse.detach().numpy())\n", " \n", " scheduler.step()\n", " \n", " train_loss = np.mean(batch_losses_t)\n", " val_loss = np.mean(batch_losses_v)\n", " val_rmse = np.mean(batch_rmse_v)\n", " \n", " train_losses.append(train_loss)\n", " val_losses.append(val_loss)\n", "\n", " if epoch == 0 or (epoch + 1) % print_every == 0:\n", " print(f'Epoch {epoch+1} | Validation loss = {val_loss:.4f} | Validation RMSE = {val_rmse:.4f}')\n", " \n", " _, ax = plt.subplots(1, 1, figsize=(12, 6))\n", " ax.plot(range(1, n_epochs + 1), train_losses, label='Train loss')\n", " ax.plot(range(1, n_epochs + 1), val_losses, label='Validation loss')\n", " ax.set_xlabel('Epoch')\n", " ax.set_ylabel('Loss')\n", " ax.set_title('Training Overview')\n", " ax.legend()\n", " \n", " return train_losses, val_losses\n", "\n", "\n", "def sample_batch_indices(x, y, batch_size, rs=None):\n", " if rs is None:\n", " rs = np.random.RandomState()\n", " \n", " train_ix = np.arange(len(x))\n", " rs.shuffle(train_ix)\n", " \n", " n_batches = int(np.ceil(len(x) / batch_size))\n", " \n", " batch_indices = []\n", " for i in range(n_batches):\n", " start = i + batch_size\n", " end = start + batch_size\n", " batch_indices.append(\n", " train_ix[start:end].tolist()\n", " )\n", "\n", " return batch_indices" ] }, { "cell_type": "markdown", "id": "independent-finder", "metadata": {}, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": 19, "id": "fixed-sheet", "metadata": {}, "outputs": [], "source": [ "learning_rate = 1e-4\n", "momentum = 0.9\n", "weight_decay = 5e-4\n", "n_epochs = 60\n", "batch_size = 128\n", "print_every = 1\n", "\n", "model = VariationalModel()\n", "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum, weight_decay=weight_decay)\n", "\n", "# Halve learning rate every X steps.\n", "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda epoch: 0.5 ** (epoch // 20))" ] }, { "cell_type": "code", "execution_count": 20, "id": "located-optics", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36,002 trainable parameters\n" ] } ], "source": [ "pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", "print(f'{pytorch_total_params:,} trainable parameters')" ] }, { "cell_type": "code", "execution_count": 21, "id": "sixth-workstation", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1 | Validation loss = 2.6720 | Validation RMSE = 0.5597\n", "Epoch 2 | Validation loss = 2.4929 | Validation RMSE = 0.6748\n", "Epoch 3 | Validation loss = 2.4598 | Validation RMSE = 0.7345\n", "Epoch 4 | Validation loss = 2.4480 | Validation RMSE = 0.7747\n", "Epoch 5 | Validation loss = 2.4290 | Validation RMSE = 0.7971\n", "Epoch 6 | Validation loss = 2.3848 | Validation RMSE = 0.7934\n", "Epoch 7 | Validation loss = 2.3553 | Validation RMSE = 0.8061\n", "Epoch 8 | Validation loss = 2.3165 | Validation RMSE = 0.8100\n", "Epoch 9 | Validation loss = 2.2458 | Validation RMSE = 0.7893\n", "Epoch 10 | Validation loss = 2.1759 | Validation RMSE = 0.7738\n", "Epoch 11 | Validation loss = 2.1453 | Validation RMSE = 0.7907\n", "Epoch 12 | Validation loss = 2.1036 | Validation RMSE = 0.8003\n", "Epoch 13 | Validation loss = 2.0459 | Validation RMSE = 0.7979\n", "Epoch 14 | Validation loss = 1.9917 | Validation RMSE = 0.7980\n", "Epoch 15 | Validation loss = 1.9468 | Validation RMSE = 0.8017\n", "Epoch 16 | Validation loss = 1.8918 | Validation RMSE = 0.7962\n", "Epoch 17 | Validation loss = 1.8842 | Validation RMSE = 0.8215\n", "Epoch 18 | Validation loss = 1.8188 | Validation RMSE = 0.8082\n", "Epoch 19 | Validation loss = 1.7292 | Validation RMSE = 0.7778\n", "Epoch 20 | Validation loss = 1.7559 | Validation RMSE = 0.8170\n", "Epoch 21 | Validation loss = 1.7582 | Validation RMSE = 0.8331\n", "Epoch 22 | Validation loss = 1.7072 | Validation RMSE = 0.8126\n", "Epoch 23 | Validation loss = 1.6486 | Validation RMSE = 0.7879\n", "Epoch 24 | Validation loss = 1.6234 | Validation RMSE = 0.7816\n", "Epoch 25 | Validation loss = 1.6176 | Validation RMSE = 0.7866\n", "Epoch 26 | Validation loss = 1.5959 | Validation RMSE = 0.7818\n", "Epoch 27 | Validation loss = 1.5817 | Validation RMSE = 0.7814\n", "Epoch 28 | Validation loss = 1.5712 | Validation RMSE = 0.7822\n", "Epoch 29 | Validation loss = 1.5466 | Validation RMSE = 0.7742\n", "Epoch 30 | Validation loss = 1.5348 | Validation RMSE = 0.7728\n", "Epoch 31 | Validation loss = 1.5657 | Validation RMSE = 0.7928\n", "Epoch 32 | Validation loss = 1.5594 | Validation RMSE = 0.7928\n", "Epoch 33 | Validation loss = 1.5273 | Validation RMSE = 0.7815\n", "Epoch 34 | Validation loss = 1.5621 | Validation RMSE = 0.8010\n", "Epoch 35 | Validation loss = 1.5709 | Validation RMSE = 0.8071\n", "Epoch 36 | Validation loss = 1.5114 | Validation RMSE = 0.7809\n", "Epoch 37 | Validation loss = 1.5209 | Validation RMSE = 0.7880\n", "Epoch 38 | Validation loss = 1.5119 | Validation RMSE = 0.7862\n", "Epoch 39 | Validation loss = 1.4690 | Validation RMSE = 0.7680\n", "Epoch 40 | Validation loss = 1.4909 | Validation RMSE = 0.7787\n", "Epoch 41 | Validation loss = 1.4571 | Validation RMSE = 0.7650\n", "Epoch 42 | Validation loss = 1.4309 | Validation RMSE = 0.7531\n", "Epoch 43 | Validation loss = 1.4515 | Validation RMSE = 0.7624\n", "Epoch 44 | Validation loss = 1.4751 | Validation RMSE = 0.7740\n", "Epoch 45 | Validation loss = 1.4813 | Validation RMSE = 0.7763\n", "Epoch 46 | Validation loss = 1.4994 | Validation RMSE = 0.7847\n", "Epoch 47 | Validation loss = 1.5018 | Validation RMSE = 0.7852\n", "Epoch 48 | Validation loss = 1.4984 | Validation RMSE = 0.7850\n", "Epoch 49 | Validation loss = 1.4690 | Validation RMSE = 0.7726\n", "Epoch 50 | Validation loss = 1.4399 | Validation RMSE = 0.7605\n", "Epoch 51 | Validation loss = 1.4420 | Validation RMSE = 0.7617\n", "Epoch 52 | Validation loss = 1.4510 | Validation RMSE = 0.7657\n", "Epoch 53 | Validation loss = 1.4350 | Validation RMSE = 0.7584\n", "Epoch 54 | Validation loss = 1.4443 | Validation RMSE = 0.7619\n", "Epoch 55 | Validation loss = 1.4590 | Validation RMSE = 0.7685\n", "Epoch 56 | Validation loss = 1.5116 | Validation RMSE = 0.7891\n", "Epoch 57 | Validation loss = 1.5589 | Validation RMSE = 0.8072\n", "Epoch 58 | Validation loss = 1.5658 | Validation RMSE = 0.8092\n", "Epoch 59 | Validation loss = 1.5495 | Validation RMSE = 0.8026\n", "Epoch 60 | Validation loss = 1.5372 | Validation RMSE = 0.7977\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train(\n", " model, \n", " optimizer, \n", " scheduler, \n", " x_train, \n", " x_val, \n", " y_train, \n", " y_val, \n", " n_epochs=n_epochs, \n", " batch_size=batch_size, \n", " print_every=print_every,\n", ");" ] }, { "cell_type": "code", "execution_count": 22, "id": "developing-martial", "metadata": {}, "outputs": [], "source": [ "def plot_results(x, y_sample, y_pred_sample):\n", " f, ax = plt.subplots(1, 1, figsize=(12, 6))\n", " ax.scatter(x.flatten(), y_sample.flatten(), color=palette[0], label='Actual samples')\n", " ax.scatter(x.flatten(), y_pred_sample.flatten(), color=palette[1], label='Predicted samples')\n", " ax.set_xlabel('input x')\n", " ax.set_ylabel('output y')\n", " ax.legend()\n", " return ax" ] }, { "cell_type": "code", "execution_count": 23, "id": "elect-vacation", "metadata": {}, "outputs": [], "source": [ "x_val_n = x_scaler(x_val)\n", "y_dist, _ = model(x_val_n)\n", "y_hat_s = y_dist.mean\n", "y_hat = y_scaler.inverse_transform(y_hat_s)" ] }, { "cell_type": "code", "execution_count": 24, "id": "minimal-flush", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Validation RMSE = 0.5091842581970948\n" ] } ], "source": [ "val_rmse = float(compute_rmse(model, x_val, y_val, x_scaler, y_scaler).detach().numpy())\n", "print(f'Validation RMSE = {val_rmse}')" ] }, { "cell_type": "code", "execution_count": 33, "id": "unique-demand", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Validation $R^2$ = -0.026665375456965235\n" ] } ], "source": [ "val_r2 = r2_score(\n", " y_val.detach().numpy().flatten(),\n", " y_hat.detach().numpy().flatten(),\n", ")\n", "print(f'Validation $R^2$ = {val_r2}')" ] }, { "cell_type": "code", "execution_count": 26, "id": "tribal-causing", "metadata": {}, "outputs": [ { "data": { "image/png": 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ABeSEEEIIISTIxEr+jc1i1xFvKSggJ4QQQgghIYu1GmdLQgE5IYQQQggJGqn88daAAnJCCCGEEBIUhSXlotVVxJa9b0koIG+h8vJGYuDAvo7/rriiP266aThWr16OxsZG1T7nxx9/wMCBfWEymRyf++GHBZJ/Zzab8cEH0q9j+euvExg4sC/++ON3r98jUORuE0IIIaS1mfbpftHnW0O6CkB1yFu0e++dgmuvHQ4AsFgs2L9/H+bOfRzR0dEYP/5uv3zmunVvIDraIPm6bdu24rXX1uHGG/P80g5CCCGEhL4GK3s6Z0a8PoAtCS7qIW/BYmJi0LZtMtq2TUa7dqnIyRmMa665Fjt2fOW3z0xMTIReHyX5Op4Xm09NCCGEkJZOKnd8Vk6XALUk+KiHvJXRarXQ6Ww/+7x5T8JqteKPP37HiRPH8dxzi3HxxRdh1aqV+PzzT2A2N+Dii7MxdeoMpKdnAADOnKnE88/Pww8/fI+UlHYYMeIGl/fPyxuJ224bhxtuyIPVasUbb7yKjz/+ADU11bjwwosxffoj+Pvvcsyf/xQAYODAvnjvvY+RltYeGze+jvfffw81NTXo0aMnpkx5CD162FblMhqNWLLkeXz99VeIiYnF2LF3iH7P4uIfsXz5C/j99yNISEjAyJE3YPz4u8FxHOrr67BixVLs2lWEs2erkJLSDqNG3Ybc3FsAAJMnT0T//gPwyy/F+OGH/6JDhw547LG52LPnW7zzzkZwnAa3334Hbr55tOP12dmX4Jdf/odffvkZ559/PqZOnYGsrN4e7eJ5XvR7irWbEEIIaUnekFiZs6XXHndGPeQBYD5SgLoPslC7MRl1H2TBfCTw+cQWiwV79/4XW7d+ioEDr3A8/sUXn+GWW0ZjyZJV6NnzH3jxxVXYu/e/mDfvebz00mto2zYZDzwwCSbTOQDA7NkPo6amBi+++CqmTHkIb7/9JvMzX3ttHQoK3sGDD87Eq69uRExMDB55ZDouuigLDzwwHQkJifjoo8/Rrl0qPvigAB999AEeeeQxvPLKBmRnX4IpU+7B6dOnAAALF87Hvn0leOGFVXjiiXl49923RL/rrFkzcemlA7Bx43uYMeNRvPnmenzzzdcAgOXLX0BZ2X48//wSvPVWIa67bgSWL38B5eUnHe/x+usv48orh+L11zchLi4OU6feiyNHDmP16leQm3szVq1ahoqKvx2v37jxdfTp0xevvbYRF12UhRkzpuLUqVMebRP7nlLtJoQQQloSq8hzgzolBKoZIYECcj8zHymAac808PXHAPDg64/BtGdaQILy5ctfwNVXD8LVVw/CVVcNwMyZUzF48BCMGjXW8ZrOnbvg6quvRY8ePcHzVrz33tuYOfNRXHxxNs4/vzNmzpwFq9WKHTu+wuHDh/C//xUjP38Ounbthssuu5yZi87zPD74oAB33jkRgwYNRseOnfDQQ/m47LLLYTY3IDY2FhqNBm3bJkOr1eLNN9fj3nunoF+/S9Gp03m4++5J6NLlAmze/CFqa2uxffsXeOCBh5CZeSGysrLxwAMPMb93XV0tqqvPom3btkhLa48BAwZi6dLV6NUrEwBw8cXZeOSROejZ8x9IT8/AuHF3wWKx4OjR3x3v0bdvP1x33Qh07NgJV199HerqajFjxiM477zzMXr0WFgsFvz551HH63v37oPbb78T5513ftPNRgK++OIzj7aJfU+pdhNCCCGtRcGo7GA3IaAoZcXPGornAhaj64MWIxqK50LX2b8TGm+//U4MHToMAKDT6dC2bTIiIlx/8g4d0h3/+/jxY2hoaMCUKfe4pEiYTCYcPfoHdLpIGAwGdOzYyfFcr17/EPzsqqoqVFWdcXk+Kakt7r9/qsdr6+vr8fff5Zg37wk8++xTjscbGhqQnp6OP//8AxaLBV279nA817MnO0iNj2+D3NybsWTJQrzxxqu47LKBuOaa65CcnAIAuO66Edi1qwiffroFR4/+gQMHSgEAFkvzvXp6ekfH/46KikKbNgmIiYkFAEeOfENDg+M1F1+c7fjfWq0W3bv3wB9/HFH0PaXaTQghhJCWiQJyP+Prjyt6XE0JCQnIyOgo+hq9vnkGs8ViAQAsX/4S4uPbuLwuNjYOP/30A6xW1wEm9wDfTqfTyW6n/XMff3wuLrigm8tzBoPBKTWkeSIo63PtHnzwYeTm3oydO7/G7t27MHXqvZg+PR833JCHefOexI8//oBrrx2O668ficzMWcjLGyH6vTQa8cEkrdb19VarFRqNVtH3lGo3IYQQ0lIUlpRDA+G0lcQorcCjLRulrPgZF52u6PFgSk/vCK02AmfOnEFGRkdkZHREWlp7vPTSChw6dAAXXNAVJpMJhw4ddPxNaalw/dDY2FgkJiahrKzU8Vh1dTVGjBiK338/4tIDHxcXh7Zt2+LUqQrH52ZkdMSbb67HTz/tRadO50Gn06Gk5FfH35SVseuWnj59CosWPYeUlFSMGTMOK1euxb/+dSO2b/8SZ89W4fPPP8ETTzyDiRPvw5AhV8NorAfgW+UXey87ADQ2NqKsrBRdu7oG3VLfU6zdhBBCSEtRWFKOaZ/tFwzGdRwwb2jrqD3ujHrI/Swy+zGY9kxzTVvRGhCZ/VjQ2sQSHR2N3Nz/YMmS5xEREYEOHdLx+uuvYO/eHzBt2sNITk5G//4D8OyzT2PmzFmoqanGq6+uZb7fzTePxmuvrUNqaho6dOiAtWtfRHJyO5x33vn4/ffDqKurxR9//I709AyMHn07XnllLRIT26JHj574+OMP8MUXnyEv71ZER8dg+PB/YcWKFxAXF4eICB2WL3+B+blt2iRg584dMJsbcNtt41FbW4Off/4Jl156OWJiYhEdHYOvv/4K7dql4uTJv7B8+WIAtsWKvPX111/hww8LccklffDOO2/BZDqHq6++1uN1Yt9TrN2EEEJISzFn+wE0WDw7wTQAlg3v1aqqq9hRQO5n9jzxhuK54OuPg4tOR2T2Y37PH/fW5Mm2HO9nnnkCRmM9evTohSVLViI5ORkA8OST87Bo0bOYPHkCEhISccsto7FixRLB9xo16jbU19dh/vynYDQa0bv3JXjuucXgOA6XXPJPdO58Ae64YzRWrlyL//xnFM6dO4eVK5egqqoK55/fGc8994Kjl/mBB6Zj5colyM9/EHq9HnfcMQGLFj0n+LkRERF4/vmlWL58Me68cwx0ukhceeUQTJgwCREREXjiiWewcuUSfPjh+2jXLhUjR/4bkZF6lJbuQ07OYK+229Chw7Bt21YsX/4CevbshaVLX0R8fLzH66S+J6vdhBBCSEtRaRReMdyK1lXq0BnH0wotOH26FlaBlaJOnvwDaWnnBaFFwRMRoUFjo1ghIuIsIkKDSZPuRmbmRbj33ikB//xw20dTUuJQUVET7GaEFdpmytD2Uoa2lzK0vZQR2l75W8uwXqT+eHn+YD+3Kng0Gg5t28YKPxfgthBCCCGEkFZKajGg1ooCckIIIYQQEhA0Bi+McsgJ8dHKleyJrYQQQgixKSwpF30+ydB6w1LqISeEEEIIIX43v+iw6PPPDOkm+nxLRgE5IYQQQgjxu+PVJtHnW2uFFYACcklUhIaEKto3CSGEhJNILcd8LiNez3yuNaCAXIRWGwGzuSHYzSBEkNncAK229ebbEUIICR+FJeUwCSwGZDcrp0sAWxN6KCAXERubgKqqCjQ0mKg3koQMnufR0GBCVVUFYmMTgt0cQgghRNLsbWWiz7fmdBWAqqyIMhhiAABnz56CxSK8qlRLo9FoYLVSUSK5grW9tNoIxMUlOvZRQgghJJSdOWdhPieSydJqUEAuwWCIaVVBD61CpgxtL0IIIcQ3Y7M6BLsJQUcpK4QQQgghxG/yt4qnqywY1j1ALQldFJATQgghhBC/WV98gvlca14MyBkF5IQQQgghJCha82JAziggJ4QQQgghQdHaq6vYUUBOCCGEEEICjoqrNKOAnBBCCCGE+IXYhM5x2VRdxY4CckIIIYQQ4hdiEzqpukozCsgJIYQQQojq7i/8X7CbEDYoICeEEEIIIapb+93RYDchbFBATgghhBBCVGfheeZzMTptAFsS+iggJ4QQQgghAbWQ8sddUEBOCCGEEEJUVVhSznwuUsNR/XE3FJATQgghhBBVzS86zHzObGWnsrRWFJATQgghhBBVHa82MZ9Lj9cHsCXhgQJyQgghhBCiKp1IhDkrp0vgGhImKCAnhBBCCCGqKSwpR4OV/Tzlj3uigJwQQgghhKhGLH+cCAvZgNxkMmH48OEoKipivubkyZOYOHEievfujaFDh+Ljjz8OYAsJIYQQQoi7YyL541ougA0JIyEZkBuNRkydOhUHDx4Ufd3999+PyMhIvPfee7j77rsxa9Ys/PjjjwFqJSGEEEIIcScWc4/N6hCwdoSTiGA3wN2vv/6K/Px86HQ60df997//RWlpKV577TXEx8eja9eu+Pnnn7FhwwZccsklAWotIYQQQgixKywph1hRwwW0IJCgkOsh3717N6644gq8/fbboq/76aef0KNHD8THxzse69u3L4qLi/3cQkIIIYQQIoTyx70Tcj3kEyZMkPW68vJytGvXzuWx5ORklJezV4YihBBCCCH+I5Y/nmQIubAzZITtljEajdDrXQvLR0ZGwmKxoLGxERER8r9a27axajcvrKWkxAW7CWGFtpcytL2Uo22mDG0vZWh7KUPbi+2tvcdEn19+w0W0/RjCNiCPiopCVVWVy2MNDQ3Q6XSKgnEAOH26FlZaxhWA7URTUVET7GaEDdpeytD2Uo62mTK0vZSh7aUMbS9xUz74H/O58dkdcHWnNq16+2k0HLMTOORyyOVKS0tDRUWFy2MVFRUeaSyEEEIIIcT/zpyzMJ+jyZziwjYgz87ORmlpKWprax2P7d27F7179w5iqwghhBBCCFEmrALyyspK1NXVAbBVVOnSpQtmzJiBsrIyvPPOO9iyZQvGjh0b5FYSQgghhBAiX1gF5Hl5eXj11VcBABqNBitXroTJZEJeXh5efvllPPvss8jOzg5uIwkhhBBCWiFWFRWqriItpLdQaWmpy7+/+uorl39nZGTgtddeC2STCCGEEEKIgGeGdMO0z/ajwdJcKCNSy+GZId2C2KrwENIBOSGEEEIICQ+5makAbIsDHa82oWOCAY8MPN/xOGGjgJwQQgghhPgkf2sZNvx8AhYe0HLAuOwOePW2Pq26zKESYZVDTgghhBBCQkv+1jKsL7YF4wBg4YH1xSdwfyG7LjlxRQE5IYQQQgjx2uvFJwQff2n3HwFuSfiigJwQQgghhHiNtdY5rYEuHwXkhBBCCCHEK4Ul5cFuQotAATkhhBBCCPHK/KLDzOdiI7UBbEl4o4CcEEIIIYR45Vi1ifncS7kXB7Al4Y0CckIIIYQQ4hUNJ/w4B2B0n4yAtiWcUUBOCCGEEEK8YmXM3KQJncpQQE4IIYQQQhSjCZ3qoYCcEEIIIYQoNntbGfO5xCia0KkEBeSEEEIIIUSxM+cszOfmDe0ewJaEPwrICSGEEEKIIlLpKrmZqQFqSctAATkhhBBCCFFkxuf7g92EFiUi2A0ghBBCCGlNctbtQWmlkfl8pIbD0ut7hnQvc30ju44K5Y8rRz3khBBCCCEB0m1JkWgwDgANVh73bdmHvE3FgWmUQvlb2ZM5Acof9wb1kBPVFJaUY/a2Mo9JHhyAcdkdsGAYHaCEEEJar7xNxahusMp+/c6jVchZtwdFE/r7sVXKvfHzCdHnQ7lnP1RRDzlRRWFJOe7fsk9wxjUPYH3xiZC90yeEEEL8LWvlLuw8WqX470orjZI90oHGWgwIAKIjGEt3ElEUkBNVzNxaJrkq186jVbSIACGEkFbnvEU7cLLO7PXfry8+gZx1e1RskfekruOLru0ZoJa0LBSQE5/lby1DnZldi9TZ/KLDfm4NIYQQEjqyVu6CSLlu2Uorjchaucv3N/LRzK3i1VUoXcU7FJATn+RvLcP6YvFcMmfHqk1+bA0hhBASOvK3lvnUM+7uZJ056OmfdWaqruIPFJATnygJxu1CLReOEEII8Qc510gdB6we0Qvl+YPRI8kg+fqdR6tC9jpK1VW8RwE5CbgNErOzCSGEkHAnpyebA3Ds4cGONI+iCf0xPruD5N950xkWCJSu4j0KyInXvL1Dt0jN/iSEEELCnJyKKqtG9PJ4bMGw7rJ6ykO1l5x4hwJy4jVferrpREIIIaSlklNRbHx2B2aPctGE/kiL0Yn+/friE0GpXJYRr1f0OJGHAnLiNbGe7vHZHTCoUwLz+WCdSAghhBB/m7xln+jzgzolSC6W9/Pky6HXitf0nvqJ+OeorbCkHHUNjR6PGyI0mJXTJaBtaWkoICd+sWBYdxSMyhZ9zYzPxUsnEUIIIeEmZ90eiK3FmRajk7w+2i25Trymt5kP3IhzYUk5pn2232MBwCRDBBZf24Pyx31EATnxitgJQM6EFACob6RkckIIIS1LaaWR+ZyOs/V8y5WbmSp5TQ3UBM+ZW/ejgTE0TsG47yggJ15h5Y9zgMswXJIhIkAtIoQQQkLbsuGekzilLBjWHTE68fre/l7Fs7CknFl/vNLomcJClKOAnHiFlT/u/vAzQ7qJvg/lkRNCCGkppK5p3vYkL5TINy+tNPr1ejpn+wG/vTexoYCcqMp9/kluZqro5M75RYf92yBCCCEkQPwVuOZmpiJSImKTWtLeF2K94LQ6pzooICeKieWPj83yzHUTm7xyrNqkRpMIIYSQoCosKRcNXH1N4Vx6vXi6S52Z90svudR70uqc6qCAnCj2BmMCiXv+uDNW5SaJik6EEEJIWJi9TbzaiVQKp5TczFTJBYMmf7JP9aBcbCQ7RsfRhE6VUEBOFCksKWeWcxKrmcLKOadVOwkhhIS7wpJyj3KAzsQWAVKiaEJ/0eetvPppM2Ij2QuHiZdlJPJRQE4UEbtTFuvtppW9CCGEtFRi18bEKK3kIkBKREeIDy1XGhsDVjCBesfVQwE5UeS4yJ2yUP643aycLjBEeO5unRPEh98IIYSQUCfWi6x2jvWia6V7padIrBQqV6AWHSIUkBOF0hk92jE6TrQHIDczFX07xHs8vvNoFfI2FavVPEIIISSgxHqjNZz6vchycskt8D2YLiwpF110iOaAqYsCcqLI0C5tBR//T2Z7yb/99s8qwcd3Hq2ieuSEEELCkli6itVP86SKJvQXLSkM+L6Cp1QZRbFRcaIcBeREkW2HTyt63JnYBE6qR04IISQciaVy+nOeVMGobMkgztsVPMVW5gRseexq5sUTCsiJQqw8ObETkp3Y8BbVIyeEEBKOWKmcHGzzp/zp9mzxXurSSqNXqSuTJXLQ5eSxE2UoICeyiaWVsE5Izmh4S1hhSTn6vLgbaQt2oM+Luyl9hxBCwohQ0QIOwDiVSh2KWTCsO6TWyVSaupK/tYxZ3hgAYnRaqq7iBxSQE9nEFj2Q0wtAw1uu8reWIXXBDty3ZR+OVZvAwzZScN+Wfei2pCjYzSOEECKhsKQc84sOw9hodYwCZ8TrsWpEr4Bd81aMEF/BE1CWuvK6RAC/kK7lfkEBOZFNbNEDNe6WW1PPcNbKXaK9FtUNVqQu2NGqtgkhhISTwpJyTP+81JFyaeEBQ4QGs3K6BLQHOTczVXKCp5LUFbF5qByo9ri/UEBOAkpsh2stEzvzNhXjZJ1Z1mvv26L+MsiEEEJ8Z+8Zd2ZstAblWlYwKlvyNXJSV6SC9nESOevEexSQE1nEgsIkQ4Ts9xGbgNJaJnbuPFql6PX3qbTAAyGEEPWwihnIKXLgD5Ea6cLgWSt3iT4nFbRT6qn/UEBOZJmz/QDzuWeGdJP9Pq39YPa2t5tWSyOEkNDCKmYgp8iBPyy9Xrryyck6M9IW7PB4/LxFOyRHbsdT77hfUUBOZKk0NjKfo3wy+cQmxorxdYEHQggh6hKqrmLPIQ8GObnkgC1HPP35HY5/n7doB0SmiAGwBeOtvUPN3yggJwHHqkcuY7Qt7IlNjJVCueSEEBJaoiKaL1xJhggsvrZHUDupCkZlywrKG3kgdcEOpC6QDsa1HI1uBwIF5EQWVp1TOTlr7pj1yPmWHXRKpZ1IbUmxtCFCCCGBY6+w4tzJYjSLVe8OnIJR2dCp2MFFa4gERsgF5GazGU8//TT69++P/v37Y9GiRbBahXfyDRs2oEePHi7/3XPPPQFucctXWFIO1g202SpWIEnYgmHdER3hebawouVWWsnfWiaadqLXcjiZP1j0PcTShgghhAROKFVYEbJsuHRtcjk4UO94oMgqj7F69WqMHDkSHTt29Hd78MILL2DXrl1Yu3Yt6urq8PDDDyM+Ph4TJ070eO2BAwdw00034aGHHnI8ptcHZzJFSyZ2glEejtvUNwr/ZUuttCIWjEdqOSy5zjYZZ3x2B9HX5m0qllXeihBCiP+EWoUVd/a0GV+rdK2SsegQUYesHvKCggJcc801uPXWW7Fx40ZUVlb6pTEmkwmbNm3CrFmzkJWVhQEDBmD69Ol44403wPOeAdyhQ4fQo0cPpKSkOP6Lj4/3S9taM7EgmZUPTuRbel1Px8lzwbDuokONSksmEkIIUVdhSTlzzlOwKqwIyc1MRXn+YAgMSMsyPrsDFW0IIFkB+VdffYW33noLF154IV588UXk5ORgwoQJ+Oijj1BfX69aY/bt2wej0Yi+ffs6Huvbty8qKipw7Ngxj9cfOnQInTt3Vu3ziTCxoJtyy3znfsJTa6iREEKIut7aewzTPy+FRWCQN5gVVsQcf3iwrImezlaP6EWpKgEmO4e8d+/emDNnDoqKirB27VpkZGRg2bJluPzyyzF9+nR88803PjemvLwcsbGxiImJcTyWkpLieM7Z6dOncebMGXz22WcYMmQIrr76aixevBgNDQ0+t4O4Ejrx2NEBK03pRFXqkSCEkNA067P9HrnjgK3jKtgVVsQUjMrG6hG9JEe102J0KM8fHLLfoyWTv8SiG57nwfM8GhsbcfLkSdx///1IT0/HwoULkZmZ6dV7Go1GjxzwyMhIAPAItA8dOgQAiIuLw6pVq/D7779j3rx5qK6uxlNPPeXV5xNhWk44KPclXSVGp0WdWXiqaGFJeYs6GYjVHo9Rcyo8IYQEQd6mYmY6XY8kA4om9A9sg/zoaJVR8HELH/qdKbmZqSHfxtZMdkD+ww8/4JNPPsEXX3yBM2fOoHfv3pg0aRKuu+46xMfH4+zZs5g4cSIeeughbN261avGREVFeQTe9n9HRUW5PN6vXz989913SExMBAD07GmbFDd9+nTMnj3bEcjL0bZtrFftbalSUuJc/s3qIbfwnq+Va03exbht00+Cz83ZfhCTBnf16n2DQWobiNUeX5OXLfj3nRIMzBP/k0VHsCr3YmWNDCHe7jNv7T2GsZt+EpxIHKkBXr2lN0b3yfCtcSHK223WWtH2Usbb7XXh81/ht7/rRF9TWmlE6oIdeHNUyz0+AUDLcbTfMdB2kUdWQJ6Tk4OKigqcf/75uO222zBy5EhkZLgeWG3atMGgQYOwceNGrxuTlpaGmpoaGI1GGAwGAEBFRQUAIDXV867OHozbde3aFY2NjaisrERaWprszz19uhZWL8r3tUQpKXGoqKhx/Fss3ULLweW1SlzdqQ3zuUqj2ev3DTT37aXU1Z3aCP79IwPPx/1b9gkGny/t/gMXtY0Oy54Ob7eXWA8cADRYgds2/YQ1u460uCo0vu5jrQ1tL2W83V7dlhShukF+3e3bNv2EbfvKwzrNMW9TMfM5C8/TfieAjkdXGg3H7ASWlUN+zTXX4N1338Vnn32Ge++91yMYtxs9ejS++OILrxvas2dPGAwG7N271/HYDz/8gHbt2iE9Pd3ltW+//TaGDBniUqP8t99+Q2xsLNq1a+d1G4grsZKHYrnlxDe5manMkpI8Wm69diE56/bIri6z82iV5AJMhBDf5G8tUxSM260vPhHWi7+JnYcyQqi6CglPsgLyOXPm4KKLLpJ8XVJSEuLivB+aiIqKQl5eHp5++mn8+OOP2L17NxYvXoxx48YBAKqqqlBTY7vTGjhwICorK/H000/j999/x1dffYXnn38eEyZMgEYTcusdhS2xmqp0ApJWWFLOXIEzMYq1/qmN2PZtqfXa3eVvLUNppXDqDku4X/QJCWVSi5xJud/HutihKhSrq5Dw4vWkTn+ZOXMmTCYT7r77buj1euTl5eGuu+4CAEyZMgXp6el47rnnkJGRgZdffhkLFy7EDTfcgPj4eIwaNYpW6lRZerxeMPjjQCcgKYUl5Zj22X7Bnm4dB8wbKj50OyunC3NRh9YyFfR1Ly/89u0Wjmk9hIQqqdQxOXiE5wJnUiNvdK4hvgq5gFyv12Pu3LmYO3eux3MbNmxw+XefPn3w9ttvB6pprdLQLm0Fe0MGdkrw+QSUGKUVnPAo1XMcLuZsP4AGgbweDWerNS61/XIzU5kBOQ/bBSKc8zGlFJaUe70SLADM3LqfLpKEqKSwpFy1hcl2Hq0Ku2paG35mdw5QsSyiBsrtIKI+Lv1b8PEjjAogSswbKrwq5b97hs9JWkylsVHwcauC8lhiaStiF4iWYLKPQ9t1ZprkQIhapsg4HuMjNSjPH4weSQbJ1z742X41mhUwYnOmaDE3ogZZAfl///tf1NUJlzaqrq7Gp59+qmqjSGgoLClnBpViueVy5WamYozASp9v/fIX5QA3EUsLasmTagtLyiE2ZUwLoDx/sKz3IYFjPlKAug+yULsxGXUfZMF8pCDYTSIqyN9aBnbxVpvx2R1w4MEcAEDRhP6SQbnJwotWLQklUueRcOrpJ6FLVkB+++23OxbicffTTz/hkUceUbVRJDSIVfJIV2lCp1APfIOFx5ztB1R5/3CXm5kKDWM4tCWPkkpVkVkxwtYjJbUc9IzPw6sXLpyZjxTAtGca+PpjAHjw9cdg2jONgvIWQGouR3QE55E+VzShP/PcZadWCoy/iS3uRohamDnkEydOxG+//QbAtirnxIkTERHh+fKzZ8+ia9fwWcSFyCdWyUOtCZ2sHnjW4+FCrEclyaBs6sbtWR0E8/hbch65VBUZe49UwahsZK3chZN1ZsHX1Te24GEECYUl5Zi5tUxwRVwOwLjsDqruOw3FcwGLWyqbxYiG4rnQdc5T7XNIYOVvLZOcy7Ho2p6Cj68c3guTP9kHsWU+wuEcJra4G1UbI2phRgYzZ87E1q1bwfM8Vq1ahaFDh3ostqPRaBAfH4/rr7/e7w0lgaflhNMiONAQnRSxHt5nhnRT9F4LhnVnlhl7o/hEyF/MlJIaHnbvFf958uVIXbCD+fpwuOCrTao0HQ9bechDlfWqVbvg648repyEB6m5KoNEJvjbH2dNTgfg2E/D9RilamNELcyAvFu3bujWzRY4cByH//znP4KrZZKWi5Wj3Hr7HOUT6+FV82ZG+dIcoW/qJ+KTx5QGkOuLT6BfRpuA3ESajxSgoXgu+Prj4KLTEZn9WMB7h5WUptt5tEq1EnRcdHpTuorn4+FC7EYmRqfFwmHdW11nhNRcFal9JzczFQ9+th8mkTdaH8YdC+G+P4TCOYvYyMohv+mmm2CxWHDixAnmf6TlYQ3FqTlEx0rfiNGFd+lDVu6ktiUnfqsgf2sZxIqjsLZfdIT4hg1EDmgo5FDnby1TnJe782gVctbt8fmzI7MfA7RuE/m0BtvjQSZnsmnWyl2iowp1Zgvu27IP3ZYU+bOpIUfsyJJ7LVhynXBKi7NQnYDdklf+VeOcRRO51SMrIL/qqqswZMgQ0f9IyzO0S1tFj9s1H6BtUftWO9RubMs8UJ8Z0g1azvOU39BoCdkTtJTCknJmzqS3lVFYF8WWFt9LDY+PFajKA7BzWO3EckDVIpZDHSjerqBYWmn0OfDQdc6Dvv9ScNEZADhw0RnQ918a9N42OUGH2DwEd9UNVqSJpEi1JHmbipkjolpOfrpGbmaq5PoSoTpx0pdVSVkCHcSyPs/Xc1YodEK0JLJml61bt87jsfr6evzwww/47LPPsHDhQtUbRnzn61DUtsOnFT1u/0zTnmnNBzlvC4TsByoAlzbkZqZi9rYyj4DJzNvysIM9HOjNNhS7sHgzumA+UoCv02ajvfY0/rK0xcKzY7DZaCsv1tLSh9xvWEYaijCzzcam756MLt2fAeA5tJ2bmYr7t+wL6vYIdg61rzewr6uQNqDrnBf0ANyd1GRTJcG4HQ9bEP/z5MvVa2iIERtt4QCskLG4mbN5Q7vjs+1rnI5n13OZtzfNaqVcePM+47OFOwikPsf5Gsm6NqpF7PN8PWfRRG51yQrIBw0aJPj4sGHD0LZtW7zyyiu47LLLVG0Y8Y0aBz0rD1qsBrngAWrXdKA2VuyB5eDrtmCd02KqfiiePDcRgHsA1hbmI/Og65ynep5b8/sdAzgtwFvARWe4vK+321DswqJ0ApC9DekRtjakR5zC/MSXAACbjTnggLBb8Y7FvYd2pKEI8xNfQrTGtr+lR1SIbv9x2c3VaNz3o4VnxwAYLPi5au1bwc6hniKRey+FB5Czbg+KJvQXfV245Zyyg45jyNtUrDgYtztZZw7ZY8/b38j57+5sbIvThuaA2Z3S7/2v6J3ISXwJBsfx7HouA5RPwFYruDV+PxOWA6/B3sXh/D5AMvPvvLmBDXQQK/Z5vp6zfAnow+08Egg+r9TZu3dv/PDDD2q0hajI16GowpJyZjoEqwa5+UiB4MHtjK8/BsuBVx095+AtuC12K55os9YRgKVHnIKG45EecQqmPdNg/H6mqsNirsNs8OjFV2s4T4jSi5hQG6I1Jsxss9HWZkjX7PYHfwy5utc6ntlmoyMYdxDZ/vaLo9B+ND/xJbzx0QrB7+HLvuW8HXhzHaCJdH1BgHKo8zYVS6ZDRXDSddulUlfCYYi6Zv8ml30T+kTGKzkkVHzs02fd7+Nqsv7g7W/k/nf242akwTNn3pt1KBqK5zqCcTvncxkgXe9c6D19PUebjxS4BOPevo/Q+wqdIwM9kib2eZr0a+CR+KjgnMUK3KUC+nA4jwSDTwH5uXPn8N5776FtW/GcYqIuOcGQrwf9/KLDgsP/HIR7eR09FV7gOGB07JfMAMxy8HVVA2M5vfiA99uQNaHzX9FFioNY1me11zanDamxaqoS/jqZuu9vzt/R5XUi2398dgfB/ShaY8Lgs4s8tr8vF3Tj9zNh+naSYzvAfAbgeUCfhEDmUBeWlEtO5IyP1OD4w4NRMCpbcgVFsTz+UMiTF2M+UoDT2+512TfRUMN4Ne8SDHrDPqqgpH3+zh329jeSuvl35k2pP9Zx20F7CgfS81CUdg9GGIoUpV6pEdzatovw3azVyyBZ7BzpbRDrLeb76hJgObwJrt+dg7bLKNnnLLkTud33e9N/Hwnp80iwyEpZGThwoMdjVqsV1dXVaGxsxNy5rXsjBpLcITpfh6JYQR4P4V5e0SDXTmtgvkbLWZkBmKM33f3h+mMwHykQPHmIDYdJnaztz3u7DYUmdI40FGFewkvg602OtosNrTqCRcaF4i9L802wWqumysW64Jt23wdAvTzIvyxtkR5xyuNxse2/YFh3VL8pvB8lamrB19cCcNr+jP1Rah9h9qrxZnDaaMSMCdxKs3Imw9mXNAdsKyiK1W0X62kPdp68EOdjHZzG83zBs1NSmOccAGkxOlzbLUVyUp99VEEqfUFpeoW3Q/re/kZybv4BIFLjXak/5vmUAzinHvlF30YgN3OWb++pILgV2y4WnsNIQ5Fg2o57/rjkftgUcEZmPwbTd1MAa0Pzc5pIxSNpcvePyOzHPM91WoOt58jsfv7jYT3+BQB58wLtnyfWjpr9mzz2e5bWvmaBrID8lltuASdQCSMmJgYDBw501Csn6hI64OTmn7EOQrkHfXq83iOHfKShCI8kvoXajacUB7n2/GzT7vsEA2weGpy0tEWHiAqBP9Yyg3KhC5rkhU+XYOvNZLbVdjIX3Ibgmob5hNlTfXi45jFbwSGCc6sazsgb9JgY66beqm/KibaNWEhVvVGLS969EN7i9eQkoTSJhWfHuOSQA5C1DzMDefdTmMXI3LekLuhiN0v2Y8H4/UyXuRLaruNg6Kf+BHipyXALe/6Cug+mupxHxmdfLBpo2gNM93MQ9ImAqdLj9cGqNc6aRC6X842ts7QYnWPC5u6jZ1BaKd7ZIKeOtpLcYV9yo70NUll/576Nll7fS/R9WITPp66iNSbcFfk6AHkBua/XOYD9vQEggrN65LnbOf/ecvdDx3WSd7+RFz6XsIJuJfsHK2g2fTtJvI0ySU3kPrPrMenOuibhtGaBP8gKyKdMmeLvdhA3zANOZo+enDtXMZ0TDC4BufsEO/cTAPsikIGYG39u7vFlnKg+MF2LnbXdPAKwc7weMd3G2IbWhL67wAVN8sLHyikBXE7mus55tgmoLj2hPCyHN8Gc0l9wW9pTfdy3l0YieHMmNtpQpUnDU6dvcamy8s6vJ/2+8I3UTYKDl5OT3hBIk3BM9Ep4C+21tptATfo1aCieC9O3k5j7tFAgz/MCATlg2x/dR260BmjSr0HdB1mCx47UXAkuOr1pktirLp9jOfAq6qoPIWbo+zK2iDxCw/sjDUV4POEVJGpsowFcrdPe23TcPtN/KXYf7cgMNF8vPoFnuv/Ps2eL09ny5J179/yQJy/W+yfZEykkIgZorHN5iOeB7cY+gi93rp5SNKG/RyUW1qRhb0bmZJ8DZB5b3gapQn/nfPNv96/onaj7QPl1xfmaZK0/DvC84DEpNmoh9p7eTg60dRRNZo6kOKft2H/zv/lkmI884/L5coJOLjq96Tro9lm82eO3FQu6xVI+hL67UNDM6lxROyi21Pwp74UKzyMtcVKorIAcAH7//Xe88cYb+PHHH1FdXY3ExET069cP48ePpxU8/YB1QlbSo2c/CO07runbSY4hM48D/7+PAOYzqAUAfRISK8YCaO4REJtg11ixB3y9QG9b0wEmGsw19Rzm/99wjDAU4RwfCQNv+5wz1ljMrboL6/o9DnNKf5i+vUdwW7lf0CQvfCZ277i+/1IAcARj4DQQmuxjT89Ayh2Oh81HCvBW9Gy0j2P0iAt+YKLHiYUd7HH4d9XLOGZ0/R2MjVYUFa3DtWXv+e3kZNr7qOxeDm+GHVl12zcbc3DdkHvQLTNVdq9Q2x63ouDIfoyO/RJaWGGBBuf4SMRy5zze3z5y41Jxx2IUqbgAl//tiWseCRLAl3/NTLOyc68AVNtUAUiTfg2sx79w+Y1nbk1x+duRhiIsSFwJvUYkSG06bosm/MxMXeHBOAfxZoB3iqL0SdD3eVbVfU3sdwagvEdcawC4SACuATnHAUMMe/HUWdeX6wVWn/p58uWOoNyz+o8t1cL4fY1Lx4H8TgvPc7eS4F0oMNH3X6ooWHFcAyxG217PA2escXi66k6XnuGFPX+Bac8Cr6uaOAeGpa/3EBzJ+svSFvMUVFtRpdQmx7EGvADY8tydj6s0rsIWxDd9vqxzXtP1UG7PNDM1cO+jzBFe+3vICVbVGF1wxvpMbVxHWGqOiv6te4UzOZ8VyNKRgSIrIN+zZw8mTJiAxMREDBo0CElJSTh9+jS2bNmCgoICbNiwAT17Sq/EReRjHuCMHj3WQSS145qPFHj2Dpgq8ULSMjxmfQVPV92FzcYckQl2x1x7Au000dD3XwJd5zxbpQOBYM7eew4At/88HzOiXHs1ozgzEptW8tR1zpN9Ry914RPrzQdkXvCb0jMqan9E7b53HSfI9KYjitUj7sF8TiC/zp704tn+48c8c/tHGoowO1Z+frpgMyR6JIXSFFjU7GEZaSjCtWVTUVvMuElpujly7jF/pjtw5u8djhuiCFih481o4CMQyTU6txSa9Gsc39P14iRScYF5Y8JB2+0O6DrnMW8eAbj0Yrlvd036Na6jQU4VgJyPM/tvfFXERGw25zi21aKkFbJuBOUEEOzXOG2bRnk3aUpITkqUc2PIaQHeKjk8L3ReE1pV0nykAN+mz4Wl7rjgzXa0xgTzgdc8j3unXkslARD7xpxH3QdZtv3k6Icex6V9v9D3X+o4t0qp2b/J5RrANf2fWI3rdo7Rcci1vgxepZJ9rzSMwwzNSpdzvr1HfvPJE/hX9E5kn1rODCrV6CG1Xf+E0yid8YDnTS5vhum7B0Vvttz3Q7nXMdFROLFzsS4BtZs6Atb65mYyrgdyRxfkbGexOCPx8rk49fl4sO54nOMAx3vtfbT5e+oSof/nc8pGwcMUx/OM5CUnN910E1JSUrBy5UrodDrH4w0NDbjvvvvQ0NCAN954w68N9afTp2thZXXRBYmth1Y4aGzu0ZM+EYm9T8yNPzOft6u36jHrzCTMbLNRsDeDidMidvTfAIDajclgdz9w4KLTUVtfjRhUezxbpUlDxqgSAIy0Ca3Bo5KFcI+8LVgy9Fso+j6iOdIBJRCUR8TAaq7HX5a22G7sgyGGvez8dHie6FgEb8o4HfSXrXTcUCnZJvoBa5j7Y0pKHCoqPKteCPXUyurtdac1AJoowR6kWqse0ZzJNWNJ0e9u/0Phfdn5e9e+1U7iIs/Z8rEbatyGr4VvxliONyYj5+Qaj15bKfZ9Q2xy5/863okY/izzeff3UkLsIi91vpDaPlxENCL7LXHZB1n7sH372Y3P7uCROw9dAmCpc03TEcBMi2pqt/2Gy32UgzWhU1aKGOvTmkZU5MxhMH7cm9mD6bx9OAAHM3KZ3y92jOf1Qex3LiwpZy4UNNJQhGedapYDcDnXy70WiJG7jU1WLSI5C/O31Xa7E5bfP/A857CuTc7BptzvJpsWgPB5x9vj1KMtmkhAGwOYq1zmtrHijPMnHsbRTyYJd9zZXuXYd8xHCjwnvLq9X/MNtnAdOKH9MJRoNBzato0VfE5WQJ6VlYWVK1cKLhBUVFSEqVOn4qeffvK9pUESigE586AUuFsUw7642XZc8YufzfHGZOYEO7GThn7AGtkBLutixvNARPc7HRcx6BNt+Q1OJwOhbeG+0IO9vc4nPaELhZztEQrEL/7NYsdI52PWvttVeAhUl4jYmw8q2yb6JMTmsSuMRJ3egtNFsz22u1Bg+N/245CkrZX3uTKwthkXndHUGyzxHTXRAG9ipIy5Xuw8csj9xMpz6Ha8AEVp98i/YXY6DvK3lnlM7rTloL+KRE2NrH1M6CIoNeIiFkyJ3gBq9IBV4KbDqSeybc48nGs7wqM99bunQss3py3ZOxvsC2yNcwrGfQmGRTGCNNbEPbU7CLTd7kRESn+ZKXK2Y4YHh78sbfGdpR9y9Z9BePTOs6PIY7RH4Pv3Wv4NKo3No1b23PwO2lPMY1WsI0kq6FQy/4BvStsBgCQtq3Qmg0Aql9xrutIOEPmUB6uy2iIaB3DoPM2Eiooa5nXG+TeT/XmMThelHZbBIBaQy0pZ6dWrF77//nvBgHz//v3o3Lmzby0kgsM02i6jPIclzWcUpSNIDX1KVRwBbMO6m4050EdweL59gUuurRglFzXWhZ/j4BrYmCptJ/UBL4l+f1vpJnbut3PeoXOOvexJYjI18hrbMDanII1FBlmBEqeV92as37/pcamLtoPWAH2fZ9kfc6QAdd8/CL7RNpzK1x+D6dtJMH17D75pn4IFVaNd8lXtkxLVwtpmcgITAC7DwB7vYa5zyQ039FuIuupD4Mu/9ra5stgrYCiZCIeI5rrBC4Z1dwnIlfa0A54pSu4jLnz9MZd8W2Zu7Lf3oKF4ri2QEyopCQgH42432md2PQZLzXiXi7Gucx5mfbYP+bGvIFFjC67O8TrHd3427V0YTp1E3Qfp4C31XgfjkjfKFqNtn9/7qG0ui1vvu3t6gdqdBJYD6z3y3MVGHZxLEt4U8SnjXW2pX+4pC6zFdkx7HnIETEVpaXj05M2OXnGpfc+eRuVNaUelFXk4DjDyemXHlv1vtdGCkygF9yvzGUc6lux8dC/ITSV0uWmRs99ZjBBLs7TT//M5yZQtWd/dYrSdwxiT8cM5t1xWQH733Xdj1qxZOH36NK6//nqkpKTg7Nmz+Prrr7FhwwbMmDED33zzjeP1QnXLCZvgMI35TFMgKnB2V5ArJXZxcwTWEv6ytIWOA666YiIio3vJD7QVXtTk9vo6f3/3CXBomgAntzSfx7ZXFIyLD5/be+C+aLgCv7VnDfX6kUo3FqzcV22XUR4jF6yJw0BTrmKje1Br237ttRV4IWkZLqndj6fOTlSh1fJTPxx5xr70igrcKMcMfV/5xU0B5yohrFKPAGzDy7y5eR83Vbq0NcOpxKng5G0xAjnQpv8+IlhFwvTfRyQDDr7+mMBiJSKceiKl5suYGnlEcQ2Oc0ySthbPJ60Cz/PQm5tz9X1xxhoLgJPoUeWbO1mEbobdzu+yb4hlsQrs4zKPE+YzTbWr5b6vpQ58vW2CrcH8F+YnvoRLIm2TsKXmPzgCvIhoj6o5zs8LdXBBwyk+vjtoT8ECjeLOFKF9XGq/N307CY0Ve1T+vZ0+o6nTAGDnjQuOLMt7d8+HOJ3LuUEqZ918pEB+h5ip0mkEXnlZ6FAlK2VFyYRNjuOwb98+nxoVaMFKWWHmk8kklY6gxtCrbdguFgmaOpgi02w5fV6214ZjnnAqLXEKhsltKTG+5lnyDWcET+xy/taWo7nBI/iwH1H2KgXbzVfg1y4PBD43XZcIThfjcsIC4DmszEqt4CLBGdpJpgrJzeeU09Nn5YGHKqdiF67CrnbjEGmp8vrrc9EZsNYdE9+f3HM3RSZjyv1M1pA5MzWI8T5y9hfJHPKIGKCxHqw0g5gbf0ZhSTnua1oC/kB6HjScyG+ka1qG3v49BIbmazey6+LHjjktb1haZO0Boe8AALUF3ZjnJh6AhdfIq3zkpXqrHgV1g5EXs0PZTY2g5vSCQKU/VVpiXUal5J6H5e6rYqy8eDVaAI5j1fTrEqB6P/t1ukTAfBaAer+17M6iJs77pbLUIw5cao7/RtY0kU15SE7XrKbtCkAkN9sLETGIveUoc96QM29jFXuKCsAu42gnJ30zEHzOIT9+XNkQSnp6eBV3D3RA7msgDsBl0iSLGrloSk9EAABO59lDZqdPagrsXAMTCxeF6afuwZKkZbLzVlmLlPhf88Uy6vQWnNo+DTCfcQTizu2395K/OPIf4icc+8XGy/3C43fidLYH3FaD8zgZKyUQgMnN55S7Px5vTMbo+vX47trj3gfITbnsrNJqPA9wUa69q2rl6wqd+OVWcwDkB5lAcw450Fx/XFHevT4JMJ3B8UbbhDrRydtaA7jkfuDLi8CamwFIB+Tq5mjbjkU1bqa8xfPACUuy9PZTwHn9Br/lszup0qShz9FVjn+Xpv9H3s1L00iZd72q8vAANPbc4ENv+TENTNmEaiZNJLQX3NY0eiieEiTcDHk3ox7sv4XQBFOpj4zOAG+uU/x3UvQD1qBDvzskA3KfYhVNJGC1gDWZ1YaTTHMNFLGAXCPnDT744ANEREQgPT3d4z8AeO211zweI8IcJ1hfA0kZB6wauWiyg3F76gunFQ/4Gmo8D3p9EqIvW4bNxhzU8XqZH8gHKRh3zYuL6zkKsTcfxNbs33DCkuyxveyLSug650Hff2lTaUXOFgjpEmEbMciAtssor4PxRl6DDbXDcLwxGVbe9n7QRnrOVLc2+BaMA450B+P3M1H3QRZqNyYzT6Tu+5/YCqfOOmhPYUfcCGYtb1kaamA+UoCvjH0EF8HjOKDBbHFJdfDnCIZp76MyL7KcawqISM18wHUVxc3GHBj5KIUNqwSacoRfSFqGQ+b2qLe6HoM8D9toS3K/pmBIpCQkYOuVF6JPAgC3Y4FF3onHfiy6fH6AnbA0VyLpoFWpwkPcBagt6Ga7yfBzMA5NJKzmWhxIz0Vp+n9wMD0XWhm9y1x0BvT9lwrP11FJvVWPmWem4fPuXwCA34JxLjoD+gEv4XhjMmvRTAVvpoPl8Can84nCN/Qq1VADaKJgOfAaOF0M9APWSP+J80fWHxMPxrWMY1qX6Diuhcg9Ln2KVawNEA/GAYAP6jlCLmYO+e+//w575/mqVavQtWtX9OjRw+N127Ztw7vvvos5c+b4r5UtiNwVvaRxkguM+CsXzYP9zpy1mqYzRkCo65yHf0c/jUjO88DyqpdeNW69G245szX7N6GuaDaurjsOTit84u2gtVWzkVvDVQmet026Ghu7FScsyZhe+QBWX/4P//YWWowyh9BtE4ft39l24Zbm+K19yYFvqhGcG8MuV6az2Er6qXdM2tjrRDuXt5N3o9Vcx9zxiMgxLLSKoi8BoYYDBkb9gm/OXYQBUSWORZXeqr0aWZlDkHU0n/m3zguSoNFzASZwWpcJv86Llpm+vRee6QXyghh7Xqy35zmeB8DJDf89NfARMHDncCDdNkdErfOUvycDN9MAVjOStA1N/5KX5uEoP+frSK8IngdmnbGlUGT9eAVMmgq/fI59MmBD8Vy0156GBRpE+JLuYlGeAumCufifWDUoqyOgdp5E7etnOgh9J04L/T+fAwDZi/YxP555nlNp1EJBW4KJmbLy4IMP4rPPPgMncYbheR7Dhw/H4sWL/dLAQAhkyorYcK5Scko8BWK4U0l5Q7H3OFw0m51eILgbagCt3n/fj9NB23WsYN1gr9OONJHQX7rCNf9aZioDAFtPRYQe/LlKwW1yjtcjKjJa9aFHnzTVNFc1P1EF9pKagcjPlUPb7U6POtHmIwVocKpMAzTP69hSf7mjFr29Nv3YuK1eB5fO7++8b5msWkRwVmhFcsub6wMLBdcAtDGIvdWz1rUqqSZiKXJ+1KBNAG+uUVYnvyXRCIzCqYjngQcrpyqu+qOYQCnN4HYCAZ6BqP3fGqiZG+8rewzCnB+jT0Lne096pKx4rE5ttQDn/vJzY7XQX7Y66GkrXuWQV1dXY//+/eB5HuPGjcPjjz+Orl27ur2xBvHx8ejWrZtk4B7KAhWQ2y4+MoISTgtwetEya3asRVgcSyHLCcw00bI+S7gBtlxdX0tzcdEZsNQdF5xQ5teTI6cDYBUOiJuCZ0BgIqSc0QCWpm3m3Yx29XoMAosDtNG+9x61ZAILbug653nUbp/6Zy4uidyP22K3ukyEkzUxzl+0MZIpUUK59f6ruRwA2pgWvz8HMzDledh6q/04ETe02c/1oX7O52zlOxurPa+jnA7Jw152WRcgUB2FgpwWvAsWnyd1fv/998jMzERMDCOPKMwFKiBXfPHRJwGNJvGTPqcDIuMA05nmYFFgSWUx9olWDcVzYa0/Bl7Jhb1pB1djQpz91OPxuJ8uClx0hq3msNi2EqxSIX2ClGqzfsCaoE1CC1tag63+bJDmDgSFLhHJQ5Y6Lmj21Q1fSFoWvODbS0JlykQ7KPRJ4LTR4Ruw+4UG0LUBzFUIRJBm4TnRkRHiZ95O8AxF9u+i5nfSJQJ8g7JKaU0L3gWLzwH5hx9+KPkhN9xwg9J2hYxABeShuAqke9rLI688h/t0LykqP2hfltmnXuNAc1QHCFa6QrB7PYL9+UQu56Xg+7y4G29Fj1elkkdAaaIBjvcojQloGB0OzVURgtqjFkrcUujUXsTMXfDTNuTSAQh8yhIJAb5cxxWueq4WnwNysTrkBoMBbdq0wY4dO7xuYLCFbA95QNhy0ux1tWtKFS4MYscqiRbSKCgNReETCASO/cY5dcEO6VrhoUifpGhkg0u9Aqg55JIm1lxGLvT5Yx/WdrszvDo9AsIxAzyorQDQNKJtdPt9mq4xukThlA7iO1/qzgchhcXngLyhwXPiRl1dHfbu3Ytnn30W8+bNw6WXXup7S4Mk5HLI/UosCPU1QA2tCSdERIgNhfK8bc/jwUEDngJyQRyON7aFgTunrNZ4sDXVOpd9buEiAY1WcKEp2XNwQmjf9r/Wfd5VY2EiFVoB/YCX0FixB5aDrztSM7Rdxzkmanu9CmaQJi23FlLFMdTmc0Au5oMPPsD69evx0Ucf+fI2QRXIKiuCB2UgDziFPVUkdHnfC2crr+eXdB0vJwgLLaoUOE03osEI5Lz4zAY+AhxvgU4TAr2CctiXLff1vNPqAm25Wu9In6O6j4opTd6cV7Xd7kRESn+Bdth+G1nzlVjomu1nzQv9BYLPCwOJSU5OxpEjR3x9m1bD0G8h9ANeciwQw0VnQH/ZSolFMlREB3aL4Uswbui3UHRBB/G30NpSClwWi9BA2+1OxI7607u35IITjFv4pgoB4ICIeFulk0DiLVBaBTuSa0REOKWsmM/YFgTzFQXjgrjo9OaF2Tz4fIkPYRz4+mNoKJ4LbZdRTQutqfCuXgTjhn4LGesZ2I5Tvv6Yl9deDV2zfcU8Npqejg6dxSxl9ZALBdxWqxUnT57ECy+8AAAoLCxUv3UBEsgecpbALf0sPrzJgwPXSntbWgt7r1JjxR4fesltgX1ESn/X8pr6JMBikjXrPRRyxUXboE+y1RIMpZruPmu9vaktFZd6BTTxFwgfy1ykrQpFCyNYkUufBJjrPGqK+5VTxY5QLNrgNT/XmA+spk4XxnmcVTraX8R6yJkrdTq77rrrBOuM8zyP5ORkLF++3LcWtnL2koOBYbVVNxAY3uMB7DZdhPO0J9BBe0pZsKRLFN7h1RquJrJJBbp8/bGmmz9fes94WA686pl+FWa/s+g+3mi0zeA/9GYLuTjZbqK8mRwZCjdPSslOgwrzeuL8398g4oLRsBxYD4/OFtWC8dDJU2fuiwrPPdpudzbne0u9N+s9zr/R8b8DtjJ2AHAplzVNqg617+NFh4I+Efo+z9pWL3VLDdZ2uzPoCwU5k12H3OMPOQ7R0dHo2bMntFrxIYFQF8we8mCU9NIPWMNcHfJ4YzJyTq7BSEMRliQtk39y0ifZhqVddvhwmmEeDj2HHLPUmYV3Da9DKngK0V46WRffFpa3bK+mJHdkxLYqaBziuRpEtMjsBy2A8P99Q2NioxQN9ANe9GkkWM0bQ203z1V6eR6wQkHt9abJxl6V5wyLG8FwuC5KaFoluHl10GOO87p9tDhUesgVT+qsr69HXV0d2rRpg8jIAOdb+kkwA/Lagm6B7VV0rKzZVvBpKw90O25LP/pv+3HKqjk4Vho8A48DmdMB2kjZBfytvO0dQiqw9AWns30ZL3ta7XmKQid9k1ULjuMQyTWq1FiiSICCdsXBggoqLbEw8lHhV/dckgbQJ6h+7nW+mraYc5damq49zPK/juNIOAgM1CiNlVdW7cm5Sof5SAFMex8Nu5FCcU4da0oX4VH0MVpAG+WX97evEmw+UuDZUx7g0oeqTOr8+uuvceONN6JPnz7IyclBVlYWbrrpJnz11VeqNbS1MR8pkDhwVe6S0kRC3+dZ2/9mTHSwOn3m01V3od6ql//+1gZwupimCapuJ1Te3LTipVxennk1kbYJhwEmflvL2b6/Nsa7iZQRMY7SWbrOedD3X+qYFHy8MRl1fDQF40HCRWcErAed44Cz1hhlx6Tvn4r22hAJxiUmZynDN5ViVJd9cjLHSZ0TJN/Jdh5zPl9wYd4B1rS9I7Mfa1oUyonWAP1lqxE75rRT0QOgkdfAytv+v9rBuFjxXy0nv/QqX3/c9YHGllYnvikYt57zXzAO2CbVe/v+EueGug+ybMH4fx/xrGjHm22PhwBZEd+OHTswadIktGnTBk899RRWrFiBJ554AnFxcZg8eTK+/vprf7ezRRLLG+eiM6Af8KJ6H6ZLhP7SFQBsOycriNA65QluNuZg1plJigas+Ppjnieo5mdlv89JSzJOWJIVfDIATgv9pSsQM/R9BKq6gJW3pflsqB2GSkucrZ62x9dsesB8xrueE7cbGV3nPMTc+DP0A16CVsMhUaNCBQviFVtPX+C6QhM0dZh1ZhKONyY7AhV/StTUBPDbSeCtqlWj4qLT/V5dwZcAUtvtDvCnvnc9X0imfbl9IKcTqBoUxF9Tb6uC4t6pwEVnONI+7M/H3PgzYsecxsNnpuAvS7LLdUktrC2h+HfTJTj+p3ClFaU47ytgKaVLhKx9wnzG/2m13k6e53TQdh0n+hK+/phtZJn1GSEycV9WyspNN92Ebt26YcGCBR7P5efn48iRI3j33Xf90sBACFbKitisbPvMX1Vmbmuiob90CQBI5rjZc8idPZ/yCnL1n8r8MPEZzfLeQof3Y2aj/uRujI7ZCo3sE6Stnqg/KtbYc2/ty1Zz0emIzH4Mnd6zBUZ2itN8ZH62+8IF4bycuGCFBCKL8/EZqeWw9LqeyM1MRe27XUPmoiJJYfqa48/UqjndlPfrW5UhP9IngdNG+5gT3lwFyZY3ezwEJh1qAV08YK5ynD/F0gTMRwpQ8+1UROJcwFroVVqMJhL6S1eod71uSqEApK/XPtMnAfp2QPV+/31GoETE+NSDb09r8TefU1YOHTqEkSNHCj43cuRIlJWVed+6Vsp8pMA2QU+QBqZvJ9kusmqELtZ6mPZMsw3LiBzc9VY9Fp4d4/F4fsVdtgVfZOFtVVXchyQVqIjug++OVeGm6B0KgnFbr5cjUFWL1gD9gDWIzH7MIxjXdc6D+31cokbtFRRttXZr3+2K2oJuqN2YjLoPsiR/y8DxYv9s6tnlnf6/pUXXS1aHlYfL8dk/vQ1yM1Nt/zBXBadR3uDNyi+cmkjHMefcu6roY3m49MZaj3+hrA2BYqpSIXDmYT3+hVNv8ynE3PiziiMMGV6kBlqabhp5W4/l7sm26yBDQ/HcgAXj9pFNVjDORWfYAjahnmtrg2O0W5VRF96MhuK5giMJatVadzBVygzGg3h+ljtaYD+nuKyPIVdoXH9ktSItLQ1//PGH4HNHjhxBXFycqo1q6RxBIzP31ArAXv9YYKhOa1A+pGUxivagcdEZWHRuMjYbczyeS4/X23rY5QbZpjNOJxLlkmr3YGrsm4jWKKknyzWd5O9TGKhKXNQtRpj2PATTt/c0XSSbLibfTkLtxrbY1eFejDQUKfg8pdzTXWyfL/Zb2i8ujnQGRbm3Sm+mlPcGcRwQwVlh5PWYUTkFXY8XYmblFPmfG+HNCZfNt1xfGy46A9pud/r+RmKfAWBJ0jKUpv8HT7RZi2//rHL6fDmBQBiPS2hjBFIaTinat09YkhFz48+O92Gn1gWbVZV8eaHvF5n9mMccBOE0OxH6JNuIXc0hHxsonrvL+n1E28rpvG4Ou2fcdm2p+yCLmXJob6tgfrwX7O/nfkOl/+dzqry/cl6mDHE6gJNVXVv4z6MzEJt3QFksYan3IuUnNEp6ygrIR44ciSVLluCTTz6B2WxLiDebzdiyZQuWLVuGESNG+LWRLY1PeWacFvr+S22TM1U7MDlEZj+G/gPuQrTO9UJgiNBgVk4Xt7t1QGzX4aLTHScSb4IADXi010oPHzW3xWlWvtgEO10i9APWuOYuDnhJukGCpalsn5em+RvzE19yBOVnrMG/OeU4W/DR4/h7iB1zGrGj/4Z+wBpZFytttzs8emW03e4UDxC8DB6iNSYsTlqGkYYifFSf49bzKXJqsvKq7ftqBOPQGhCZ/VjTxFsfe1pEtqV9smAEZ8XY2K2YE7/W8Zx0IGBLYXDd99covmlWvr1U6nlijQDInFBr5YHtxj4uj4XSCn0eeIvP+7jQ99N1zsOsM5Mc812A5v1KNisP85ECdW5oRDuJhH8f0bZGxrn2KKuSi+202ibretaUR26/TvrK/t3NRwpQ90GWY2QUgG1V0pCmgeMcc9lKJA97xevOOU36NeyqPEy84mtEwFZKlyDrbHnPPfdgwIABmD59OrKystC/f39kZWVhxowZuPTSS/Hggw/6u50tik8nMt4KXec8zwC56ULu3bAW7xhyi2oqNjzSUIRv2k/CL2k34dqya2A+UgBd5zzbhV8TCeYdZVNwYuftRe8vi3BZxuY31joNwcqMEsxVju9gy6c8bvvePva4RmtMmNlmIwBgbtWdaIT3vTRqcb+h0XXOAyJFbhY4raO0onuvjKHfQugvW80+wfEWW96r274oh5YDFiSuxEhDkctkVdH3sNZD22VUc/DuY28iK11GVloEp4W2yyhHr6u223jvGsFpbUPivLyeGo4DRsd+6fpgBOsCZAvGDf0WuvyujuPZh15F1ufZA371JqbzjkoJLp/EuJDyTddkOw0H5MXscPl72b2ZqlZ3kUmXaAvsvD03uZ2H7QpLyrHZmAMjr5cXhEfEeKYAmM/YRnj16qVPuAee5iMF3vU2myoRmf1Yc49yn2chp1NI/g0J41pjqXPsW7rOeT5W+mpOUzR9N8V1ZHbPNFh+/8CH9/YvLvUKxI6pcDnHxPUc1XRel9cp5Mxy6E3v0rfMZ2znQzmxEONYCQZFdcj379+PvXv3orq6GvHx8ejTpw969uzpz/YFRKAndSq/4xOgS4T+n885AoHmovfHbSdKj0V6xPHgcNHJ92FstGKkoQjzE19yTRlpmgjlKKwvhNNCf9lql4k6gnU/JTTCgFlV9+DJ+BeZaSv24FHJJBrmpDBOB8DqU/k6K8+h23HbCXlc0rd4Ivkdl3xzAM2/TwAWWrBP/ivPH+x4jL2tbJNhpYhPlnV9D6WTTo83JqPHuFIA8o4Pj9q/Pk5+8sgflbO/Oxqjs93smM7YbkDjLgBfXgSPbc1YIdfxNgoXd7FNjuUAjQGwupcUtY0ayVn4wlEOTGJSaAMfgVprlPikZYFzgKoTTt0XYmHUfGblA7tPkHZZLERoNUqtwbZa6+FNgZ2zoYmE9oLbvPhcTnTCZK/l36DS2IgD6XnQyKhnrx+wRuQY4ACNTsbaClzTf0I3mxpou433/J5NvzPQfN7keZmlCJ32EQDMNTfc+Vrn3HnfUnKN51KvcFoV08eFeDid57VWG2N7W1VKFkq0z23bA0BKShwqKmyVwIzfzwzsRGoZ59ywXhioJQp0QK5qhQx9ErSdbvA8obks0iPtpDUFl5+wpW8Upd0jvBCI5AIozUGZ64VOGf2ANZhTdjEqS9/GjDYb0V57ytHHwXMa6LqNd6oeIPP9pQIs+520l4GDe3Ua50DYmT8qwLjjeeDByqnYbMzB6hG9HBP/xC4Sck9MrPcQqgRTt+0m8OXySqJaeQ7xt9n2HXkXUM8bALn7A+vC28hroOV4aJwCGtvvNQmKLpICwYQ9SBINblS+URP6TeRwX9GOt1pwwpLsmFDqcbPuzu2i7N35jr09fKu24nnzyWyfPsmxboOcGxalmldNXQ/BYJV5vhXeNuybjeb9L+NdWylZ5jle4D1FOxEcN6OMcq6c1jbqo0sQWa1Z3vcpfb0n0iMqJNvs/rdyg+Naqx5xsSnNFWnMdQp/8+Z9S25Hkb1jSUk7mZo66dx/c13nPFsgfPD15oWXIqKbAnSF552IGNvvKRHkOv9u9oBc3cpgnHjnh0yBqqziTJWFgYh6HHlmQsMpmkgoyrs2VcJy4DXPnbxpkR79gDXSw35aAxacGe34J3MhEMkeZNuwsvH7mTDtmeb1yUXXOQ/bDp/Gx8Yc5Jxcg27HC9G16b8rqz9GREp/8ffXGqDtdqdrHnSXUeIBm7kKnM674WGh/FSPt28akvU+GFd2qM5ssxEjDUWYva25ApLYELBtouo9qN3YFrXvdmVWP2At6uE+5Gc+UtB0opSJ41C7MbmpspCMl7ulQjnmLIjNsG+aQ8Azji8NeAyu3uwy8c/2/xWe7C1GR5UEwRQRwd9A/Q4Bb1PjnOtAx47+G1l/f4ick2uw2ZjjWJvgeGMyezJg0/d3fj9tl1HNqR+c1tYr6LEdbL9L89wO4d/JkWrmxYVdKIWO9V6c1lZZSrR+sZfsQYstGGP89szzLe859O92DNqDH/d0B/tcl4Vnx8BklU7FsQd27BeYwWmj2dcZ3oLmAgUaCJ/HhL+/+7n6VJeZghNSWe22i8x+TNbRFcOZoEm/BrFjTjWlZirrLnfeTnInWduDccDHVFatwTFi7n7OcfRKO/YnHmisg7bbnS6LMMnSWCdZsIH1PdSp0Y6mije27xcz9H3l3yGEUUAeJLrOeYi9+aDnJMNLV9guRipUuuDrjwuWTnIPVvX9l2Inf6Xj76w+7BZ8/THhGwSZ7AfW8WrhHrhj1SbRA9v+fZzzZSOzH4Pl4AbRGwR7Trk37PmpIw1FGGkoQlHaPS65kK4XR2/JnwXOcUB6xCnMT3wJA7n/czzuOTGXwXyGWZJMalEPO1tAJi/I5HlA41JZSIJYzp+FtRosh9ibD0LXOQ+myDTBV/xlaSu433lzsmftS6I343LfW2bsrtakxXqza2C4uelGuevxQubNjfP3Nx8psI3g2QMC3gL+1Pcu8wDsQXjsmNOOQII5qc/bY1UgaK0t6MY8Ln0J/F3ex+33auAjYDpX43QDyqq1JxIwu01edD8GBdttMTrmugAAJyM/w5FyJ3I98rzOsF5ohrJqFpzLOejyQXfhwPlzHTeDVtEyhc37jq5zHqos0pPtOQ6wHHjN0aGkaBE3t31LTv67+/7t7fHKOgcDTcceI0XEcvD15o4M2ZNfbRtcrISm+/eo2b9Jovefa8ovlzNXg4Mm/RqXR+zfgb3vMeIZtUtIqsD7ejTEK4Ul5ZhfdBjHq01Ij9djVs4g5DKGlR05x5zGq/xm+4FhnwTqqvnOvLCkHNWm5lqkvq+KJiNi0CfZlhh2yxu0n9TS4/U4JhQcAbDWH2eOIQilXQgul+us6XO9TbEBbBM7H094BVGc2TGc71gdLEI8j81GIH/VmWS6kHCbliQtQ90HhY7tYv9PMi3EqRauO+H9ye3PZQZMyvI2xfNjAXuwJpRS03yRSPjnk6ja9QAMXPP+Za/Bn2jwPCV6kx4hdnHVdc6z7Wte9rrK3V5qTVRiHYsAUGWNRZLWc6VY5+/PCg6tx79gptSYjxTYUgbceXmsWqGBwT2N5rspovnPvtyku7wPZ0uH0oBHlTUWMVw99NaztidZ+4A9d52Vb2s6g5gxB2xv0ZRL7xh90yUy39c+2Xtmm42I5BrFG960re3bzLT7PsFzkPN1BlBzMZvmYgP2a2F2dDqmnh2DmW02MlNu6q16vFE/FjOa/p23qRgJVXdKp1o1fWZzaoc8Qul+9v/N3E+b6uo7i8x+TPF8K7G0NMk1OZy+o77PszB9ex8A6VFw+3VB8LwocNNb9/2D4BtZHSUAdAkiZaDdr4s8LIc3wZzS3+MawGqPtssoWA5ucN2unM5WQjLEUA95ABWWlGP656U4Vm0CD1tv7/TPS1FYUu7xWuehJ/1lqwWWQJbCyb4gzy86DItTN47iJeuV0hqg7/OsaE/rrJwugkE3D6Dcym6fac80z55difrr9s/1tYZsoqbW84RvMUr3tGgNtuocYp/tZRk0jmu+MbBvF9v/l47qfAlG1C4p5zxMKXYzICelRtc5D2XnzcXxxhRYeQ7HG5Mx68wkbDbmoOZco8fx6LEYjS6xqUep6X+7H5syZu0ruWER+7cY038fcVlMSmwRFjGzcroIPj7SUIQYTuBC6xZssHughR93BBLux60+yS31TH5KgcbtZreheK7EZESuKYde/iVSrHdYAx7djhfAyOuh10gEPU2lbQ39FjJ7Lp3L4pm+m+J6jhE539mrV4mXlRVezl6w0pLbvi45oqBLFHwPFvu5yzn15vm2a9CBkVbJ88CsM5Ow8Fhf5G0qRrclRdh5tMol1crKc81rNAi+ibJgnHVOaq4aJVBdROBAlqyE5U7iPCP5Wzj1SOs650E/YLWsXmP7cStntLSheK54MK412IaYhdrJaQF9gufjbilxLt9BoD22KmErXR+/bGVAJ3LKRZM6EbhJnX1e3C3Y05RkiMD/Rp4UnIxhZz5SANP3DymaKS13wkLagh0ufdqCVVaEiPbauk8WkV/1wS51wQ7Bx0cairC03VrRtBXnXgOx3mD3beRSrUbhyIQ3s/Sdt4etIg2rF6ppApjC3hv394i58WfZk4eEel+EJoqxhkpV6ykTmLkvRm4bey7biTPnPLdlRrwee++9TNbneFT5aJoI6O3kWBaeB85YYwFwgj3Ssijcjs6EthVzYqAuEbE3H3T8s/atdsL7LKdF7Oi/PR5mbhuhUbWm80qVNQ5Wnkeiplb8GGz6fRRP1BXTtNS5rnMes+32Sd/yqptw0A94ye3Gw+1vXCbwsVMBnP/uHK/HI5W2G0/Wbyc1EVjq2BKdzOgx2flY8zVE4QhgI69BBOc5oug+uZ5lpKEILyQtE04TlzuZVubxJDYRvnm05zhzdE+oDXKuo5ITS7UxgKVe+e/YtH/KOY+IvY9jcjbzWHSUchB8Tk5lsFAUVpM6zWYznn76afTv3x/9+/fHokWLYLUKD+XX1NRg+vTp6NOnDwYNGoTXXnstwK1VhpUXfTm+Qv3uqR4TcDx6tGTWKAak816d677ubD/JZbVJ94lbgruJ1iB6AvVchMQ1P1QOLePC+um5HNHFFzx6H1n5cQKPu6wCqGB7Q2sAp6A3DVoD9APWOE7ItRuT0VA8F9qu4wR7kDTp17jm4XrBvl1k9c5yOsGJmkITxeTkmlda4jwmkcntClAaRApNbBJSJRCMA+zj1JlgzyRgKzcq8XfNF2j5d28cBxj5KDxddafHxDbZGD1Lcswb2t3jMWYvq/siPqx9lvE4c/80VQrc4NmCkz4n1uOff72OByunim8fU6XtRrFpIRfZOC3s5zIu9Qo4T1DVdh3r2MeERmhMVi0MnAkH0vNglfOb6xPd5pwIHChNtcDFAzje5RxsD8YB2yR0jz4oGSM7UscWc2Ssqdffnu7m2E5O8wqU0HJWj9/ZnnYmx2ZjDt6sHSa4DVjnYI9rmszzEnPF0aYVn53Pp2LnBKXXUfFRSm3TgnfC53Hxv+Vln0fY80AyZM0VEXuuJQq5gPyFF17Arl27sHbtWixZsgQffvghXn75ZcHXzp49G8ePH8fGjRsxe/ZsLF++HJ988kmAWyxferzwhWJmm43Q8udcH3S7eCqaXCSQn+bMPbBqr61wWW0SaJ649UXv3xA7psJz8qnYBB5douAiJELtcF8MwmUTsNZg4JsWX5A5qUTf51nPtAJNpKOkGYvoQa+JbBrea94ekhOWnC7q9hsK9wDXcniT52S3/kthPf6F7N+fFejav4/0yYwTHNJj5QKzTs7OF+9+f61H/pnJjiHj443JslY15aIzPEaKxPYZJRKihCcROT/O+jxmykNT7r0Qz8m9tkriAGQNFXfQnsLMNhvxq2G41xOSvE1DspfOdMZavMtzshrrOJV3/Eqx1h9DUdo9GGkocnQmiN7sWYy2YXIlaYC81TFBnD/1vUsgaTm8yWVRGOcb0TPWOHCcbVRDw/GI4KzibdMabLMV5RzrFqPoRDh70BM75hQ+7/4FtjQF4yMNRciL2eHWO8y5LHDlLVbKmHtteub1rOkcKTXBTxOdgc/jH3U5n9jTzuR66uxEGC73vK4Z+i1kpj7E3PgzOk8zKepYkgpuPf/tFpQ3dd44l3GUc/5j/Ra2jii3GyC387icibxyzsWR2Y+Bi4j2aIPkBNim18it6tVShNSkTpPJhE2bNmHZsmXIyrItEzt9+nQsXrwYEyZMcJkVfvz4cXzxxRfYsmULunbtip49e+LgwYN4/fXXMXz48GB9BVGzcrrgvi37MNJQhJltNqK99jT+srRllhl0vnjKvpDKGDIXOhnaJwDOtGzEwrNjHCc2+4WYNZFPaBIFa7KE2OJFjgmQaJ4Qo+WEg/J/Rxeh7oOpEBzOFThYXSfYiKcxOGNO5tMn4XsMRnpdEdprgb+qz2HP//5CrtTCLk0Xdbu6D7JkT3ZTUi6RB2C06j0WdrJvF9FJiiLDsOyeHul9kwccZfPsJFOjGKXc7O0W2meUaLA0t8P5eFxeNxbAINHPE/vOysp+8Y7ASWoBHXv1nPTGzzyH/mUSCg7kpvi4W3h2jOACYoKT1QT2N95S71gBWPL1mkhmzjeH5qpCgG0/m2lhT/oDAJjOQD/gJdeUI5G1COzbTeym1Dnf2v6/je9eiEiz66iJ8yRPIxeHGH2EY1Gp5iF8mXiL8LZxG+GaX3TYcZac2WajwDHH2276nSb7e0PuuZZ5/DSdI22pDgxN+9jtnfOQt2kQdh6t8qqt47M7QNd5sOKJ6zX7N6GuaLas44U5OVm0/rftnOD+/krPf6zfgrV/Of8mUhN5HRMxJdqi65yHuPgonBbZXnL2GW/OT+EopALyffv2wWg0om/fvo7H+vbti4qKChw7dgwdO3Z0PF5cXIyEhAR07drV5bUvvvgizGYzdLrgL1/uLjczFZ9vX4N5Thex9IhTnsNmTdzrmsrJL4vNOyDZDtbJ0LlcHgD8pLta9H2UBLoeOcVCEx3dLmxCwfhIQxGeSXgJfL39gmLvURDPq5NTGUTO99OkX4PqAwX4J/8+uKajJz3iFK6tfhZ7DMPRT3taJLfdNRhSFOAqyLH8q2kRF+cgs8uAeS4BQ/P3as7jlMpLlFPBRIjQpGUAjuB8WcdC23fWJdh6Lp2CEzml3FjVYKTUmS0eNwXpEafwRNxqmI/0FP08seORtT2kfm/9P5+Tl3ff1AbnXjp5OemeE70FL/LfToLp23sk9wf772ffz7Qxwse/4+LuvriOPX0EnhdxwPW44811kitCRmtMmNlmIzYbc4RvFpy3RHS64DlBcO6D002G0ptSg/mk4OP2SZ4cgJNui4gpucly5OE631hoogFdFEzfTkJD8VxEZj+GY9XNAS4r1YivP4a6D7J8DnjknGulziXM48sp9QUACkZlI39rGdYXn1DUxvHZHbBgmGcalhT3qiFiQbHYglNiE/1ZefxS5z/WjbXQSKec8zizao59IqZZ3rk4ruconGs7gvl97Z/F2me8uXaHq5BKWSkvL0dsbCxiYpoX90hJSXE85/7adu3auTyWkpKCxsZGnDoVusn+MwR6JzQcJPP55AzRyB3qlXpdtMaEh9u8xayu4Exuvq7clBvnC1uGQIoPq3dHbLa7L5y/X2T2Y7Ac3oQYVHtMHIvWmJBeV8SuMS3Qc6goP05mMG7Po9zsWFSpAKPr1wsGSc6Lv8jJS/R2+HB+0WHmc5uNOY7tG3vzQcTmHWDuS7700LOGV4X2p2iNqXkkh/F5toVDBFIeBHLvHU8x82s1zXMIuozCicYUWHlbL6qchU8AeXWPAV5eGlJTz517bqnQ8ei8n4kNqes65wkvvCVSMcH5vOKRl87QQXsKRWm20aRZZyahDvGeLxLZZ6UqRyjNaWU9bk/34eF5wyr4W3I6ZjUfXee8puPmdFNFD74p4LOlwdV8O9UlHZGVagR4/ub+InUukZv6AgALhnVHef5gxEdKhzM9kgwozx/sVTAOMKqGMPZhsQWn2HO82NXRxM5HSub3KDmPs44HmIRH8tQoE9qahVQPudFohF7vetKPjLSdhBoaGrx+rRTWjFd/qGb0TnAAtHGdYKn5E9q4jki8fC7ieo5qfkHKHfh976PgGXfWXEQ02ubMQ1yKdF5uVM48nN52r2g5ovYRFbg47b+ISxnFfI0StTIPVG1cR6Q0fYfnhv8Dd75bjAanrnJ2785xx9/Z1ezfhDO7HmNvU4WOfjxP9KaivfY0OvS7A+h3h6zPFvodWL+jMa4TLDVHPT5TE9UWnC4Glpo/cU7fHk/9fQs2Gwc6no/WafHc8H94bBuvpNyBmvgoxdtUbJJkkkEnu23GuI6C28B5nxFSs3+TR69Ww/cPYlSbScz9yVp/HBEin9ehn21bVO54CNZztvfg9Eloe+US5vZgHndNN1t8/TFYj7yN589OcPQ+s6pheHxnl9/Gs822v+nksZ0kj0uLEY2/zEOHfnfgueH/wG2bfmK+NOr0FsHtHBcfhbieo5ifJXTsumP99u6cR/lmnZmEC6edUn4eSLEdw4LfUcExa3/9X1vvgc5pjpD75MM52w9i0mDbSG/N/k0w/jKvOT+ct0Ab1wmJl9sCPqnvIXSOisQ5x8gBwEg1cub0m3tD1vaWOpd4ca6pmj8cGU9txYkaz+u/XsvhlZuzMbqPdys62r+T2CJSco8tvv44kq9dL3Au4BB78USkMLa72Pmv8ReBaxPrd1S6bQWOh6O/zGMcjzyMH/f2eD9Vrj+tQEgF5FFRUR7BtP3fUVFRXr9WSqDKHgJAtTYVCVbPYcyz2jRk/Kv5YncOwLkK19zDyD7PCi9moU9CZJ9nca7tCI+/EdR2BCL7nRMdGuUAnPpyEmqqz6nS6ywr5UZrQMRFs1HR9B2u7tQGERzg/G3/srRllOtKd/wd4DlcaKk56vP3sdT8Kfr8X5a2iLe3oe0IGP7VPEwn9Hu6/g7Nw4xCv2PERbNhERg61F0y3/F9YgFcVVKOr7YfQKXRtuiHXgtU15xz2TY+kfO93IgtLPPMkK6y28baBs77jJC6otkeQTDfWI/ZSZvwl1F4fzppScb5Up/XdgSic12HYkW3h9vvLVRWk2+slw6eWN+56bdhpV0I/Y2c49JS8ycqKmpwdac2oq/7c/sjMAhs59NFs3Gu7QjRNAX3Y9f9mBD87UXY01cqKh73ap9lUnDM2l8fe9kyHC6a7Ugfc56jAwCVRjMqKmo8f7emtQciLprtGPKX+h6sc5TzjadzqhErz97+myul6Lwr9bt48bv9dN8A0efV+E5C3Pdh+2Os/f0cYz9C5zxmG8XOf6y8cObv6OMxIXY8uv/mKSlx6l1/fODtXBm1hU3Zw7S0NNTU1MBobP6RKyoqAACpqaker7U/Z/f3339Dp9MhMTH0lkS1W147RrBc0/JaeeWaPMawOZ2susfu7EPC+gFrYOEYNzA+lElzJzhMJlCpxP171De6ft+FZz23n9Bwm9KKIHKIpfooKbnlTG7Kj9RQujOjubnay5lzFubiU4EyK6cLDBGupxoOtjxOoeodLEq2gTOx/F6h/aneqseCqtFef57Ud5Aqq+kePDkvaCKnDUra7c0S3yMNRShKuwcH0vMc1U0AQN8gnC9t3/5yhspZQ+8APL6TttudTqVZPYkvfuM9uces8+sHN6X15Jxcw6wEYtr7qM/nLKkUGbvNxhyMrl+vWqUbO3+cd32hRlUmyXRLRrqH1P7uzX7EOq4DXR7QtS0CgvibC1GS0hNMIdVD3rNnTxgMBuzduxcDB9qG3X/44Qe0a9cO6emuO1Z2djZOnz6NI0eOoHPnzgCAvXv34sILL3SkroSiNyoHoNLQ6DLhbuHZMdhiHIB5EndwDcVz4bGsrsgS53J8XD8I289MwoI2SwUX1FArJ8zbSifuXHt3Tiuexe/L9xGq/mBbsCUOT1fdqajkljfkTG6ZX3QYxkbXQM/YaMWeb1/BtWXvBKV3wB50zy86jEvMXyI/8S2kak5BU58O8xFl7fBmgo9YT9XmY64TE917MP05oYjVLqHgyd6ecrcJgCxy2+0xwVeiatEN0UWYm+A6CdY+CVxs9MpBE9V8/AhUhBIL6ISClpz/G85M6zFFpkl9/YC5PbsDc+JhjE5rCwwY6YhKzlma9GtgOfAanH9DVmfBrJwuiIx+DA3uS5v7UFZOzfOurz2aalVlEmu7VCEBQN0KIazjWs4y9mqzt4W1+I89t/3ox/NgqfkzqL3SahcE8JeQCsijoqKQl5eHp59+Gs899xxMJhMWL16M8ePHAwCqqqqg1WoRFxeH9PR0XHnllcjPz8eTTz6JP//8E6+88grmz58f3C8hIT1ej83VOR7B27ikb2Has1r05OGPIHN+0WEcqx2EqbFvSl9MfeRNcBOp4dDglk602ZiDraYr8OfMK5h/521FEDH2th/ZOQdpmlMewVuSIfiHk1C+9khDEWZENVem8bVcoDdyM1Pxr+idaPh+nawKBWoSvViVeZZjtCssKVfUg69Gu87x3o20+ML5uJQKgp5JfRcGs+ck2JltNmLR2TFY4r6CbtN2Fhz2b/TsdfTmHCeU1lNv1SPxn0+KfOvAWjCsOw5V1guW6KszW1D13yfBGqeQe84yHymwLR7mFBxZeaCgbrDg/m3bt6XL0imh1nlXjWBarSCM/Z3EVzW1tzUQ51h/BP9yMdPemha4UqtMrS/8ETv5Q0ilrADAzJkz0b9/f9x999146KGHcOONN+Kuu+4CAEyZMgXz5s1zvPa5555DWloaRo0ahXnz5mHatGm47rrrgtV0WYSG7w0RGsxs85bkUJ8/hqXsAZzcVJBAa2Tk9rMet/PXggIf1w/CkL89h5+1HPDMkG4+vbcahBafEqxME4QhRSUVCtQkNtQrdhMlVh3GX+1yXk3RXYxOfLEUtdokNozOKuPXXnsaHxtz8L4hX3A7y01l8OYc557WY18kJpR6vgBbiT7WL8hK9wHkVdgChANQDQcMMez1eK1zxZy4nqMUpU6IUeu8q0bqi1pBWLgsTqM0BUYtzAWIhBa4ClIqS7is+Bn8Lj03er0ec+fOxdy5nj/ahg0bXP6dkJCA5cuXB6ppqrD3uM3eVoYz52x1kPMT3kKUuULw9c4nD38MS9kn3LnXFP6bT8Z5A54J+kWNtfal1KL2/uoxmF902KXqi12bqAi/9qbKZV98yplYZZpACmYvBaun6pkh3Ty2l51YdRi1uLdry4IdzNcu9LJUm5qk0mweKb0Itz/s2Wso97dXco4rLCmHBrZzgfsoRyJjFdZgYxUvZaX7QJco+5zF2sbuxz8HyCpp6w21zrtqnCvU6q13/07auI6IuGh20K+NoYL1m7MXIFKn3r0SwUjp8UbI9ZC3FnVmq2NRkvbaCvfFch2cTx7+mGQ2K6cLdE0fbq8p/I8TBfj5kq8l31fNZcxZtIwNw3rcmT96DFhB2pmmqibOArF93PmyxLm/hWIvRW5mKvSMnSk6AD3S7sTGfULhhi8y+zGPSeDOOcpCi3kB8n97uee4wpJyTPtsv+CNuU7DYd7Q4N+8KLHw7Bic4z1HKFmrHguRO6GTh3/3JTXOu2qcK9Ts2Xb+Tp3uOkjBuBuh31zstwr0pEp/xE7+EHI95K3BnO0H0GDhGYvcOGEsA6/2TsRpXNeo5zTS0a7ay5izjM0Sngw1NquDap+hBKuEn3uqSKC2jxD3peC3G/sgL2aH5BLn/haZre4EMrUIjXgAttzeQApmJRy5dJ3zEA0wy/ixzhxKeqjknOPs51B3GgCv3ZItWaIx1Gw25oCrBJY2rVrrTc+y0DYWmtAptMBTqFGjRzOYedWE8Rs6C/CkykDl8/uCesiDwF4jWqwsV6Du4IQubA0WXjJ/NlDlrRYM647x2R1cdtQYHYd+GcG54LLmALgPAQer/Jf5SAHmJ76E9IhT0HA80iNOIS9mBwrqBuN4YzIC3TtQWFKOPi/uRtqCHbj083SUXjAv5HopxHqlAxkk+ztnXS26znl4NW2zYBk/jhPeZmr3UFUKjEgBtvQVbxd/CYRBnRKYZSN38lf61LPsvI2dc+nd5yT4K11FTWrtL8HKqybNv6E2rhPzNWqmKwZjRFpt1EMeROwyYdKzt9VQWFLOvLBJ5c8GMh+4X0YbvPXLX44bhzozj2mf7QcQ+GF85xJ+x6tN6JhgwCMDz/doR7DypRuK5wouBT/EsBc5J9fILpunhsKSckz/vNRRhvFYtQm5RZ2xaNgXIZF+Yafl2KkWs7eVBaytrMWTgNDLiV4wrDs2/fIXTG4bzsrbbvKFtlk49FD526ZLD+LsrpcQxXmWjfz0XI7PlX10nfPwcf0g5rwIIDRSn+Sg/SX86TrnoUO/O/D72i6qVz1zFswRaTVRD3kQ2C+uwa5sItYjJ1Stw1kg84FZvfhzth9Q/bOkFJaUO4Lx9Hg95l/XU/ACF6x8abkTuwJBqCZ6vdkScj3BYulPZ84FLm1FbF5EKOZEuwfjdqybfDWxLlyhfkFrKJ7rCMbt7GUjLTww9RN2IC3XzK1lPr8HIWryd6WaUFuQyluhfv5qkf7d0xbAuZfrqtKkBXQIX6wXXGpYM5CloFgX+EBc+J0VlpRj6if7cKzaBB62Hs073ikWHKIPVqksqYldgUzBYPX4ivUEB8MCieolgdpmrF56IHx6Ne38vc28rb4UbFI3zGYeyPchoC4sKRed+0AXfBIM/p5UGS51xqXQ8RkE2w67Lo2d07Ss8r+rXg7o8AqrFzwxSisZAITLrGU1zd5WBrNb0GS28pi9zfMCGqztI1UFI1C902IBmZwKOYEWzHrkgG17sTZLpwTxpe1DkdAxoZa8TcXM50J9wqKcSiisFT3lkNpXb88OzmT4UNcS8o9DnT/z+UOxgpc3KCAPAlbPdCDqHjsTmqAYrdPKHh5vbRNmWOkLrMeDsX10nfMQfdkyj0VS7BO7ArWPiQUGYj3BwSK2qFMgttn8osOCk0s5APOv66novQIVXIjdxPgr1Sd/a5ngapd2oT5hUWjkTKgSire95GKjTzpOejSoNbLnH9tynPmAl+QjvguXxZukUEAeBKyeaam8bbXlZqZi8bU9kBGvBwdb79LavIvDbnicuNJ1zsPo+vWCVTACtY+JBQah2IuZm5nKDDADsc1Y24uHsqohgQwugrEy7YafxXuP/XHuUvMGxz5yZtS1F62E8oaXveRiF/Rlw3t59Z4tXUvJP27NWsqIPQXkQSC3dF4g5GamYu+9l+Fk/mDsvfeykCwZJlZhIhxqNweD0D7GARjaRXiRILWJnVhCtRfzmSHdgnJcivWGKk3vCWRwIbaoUoyfFlUK9OiKP25wdJ3zkHLzr/jHCc8bZjsrlJ/b8jYVi+bQU0eLsJaSf9zatYQRewrIgyA3MxUb+x3AN+0nOWrR3hCzM9jNClliKTShkBMdaiXpANs+dsuFaS6P8QA2/nwiIDcx4RgYCI0YLb62h9/bK9brqzQADXRwEa0TvoTwfOCnV4ql0HjLnzc4Uj3WSvLwpVJ5QnFUKlS0lPxjEv4oIA8C85ECXPjn42ivrXAs3jIrZhW271gb0B7f/K1l6PD8DqQu2IEOz+/waXa/P4kFRIGq2CEW+IdiSToA+Gi/575k5v074S7cuY8YBeLmQSzoVhpIBTq4qGLkitc38qqfT6TOjf5IofHnDY7UvnXmnEX29eB1iRSXUB2VCgUtJf+YhD8KyIOgoXgutPw5l8eiNSZMjX0zYD2++VvLsL74hCMYsPC22f33F/4vIJ+vFGvoPlAVO8Qm9oVqj6/SSahqCfc0IufVRfu8uNuv30fqvZUGUoEOLsTy618vVnc0RuzcyME/x6G/b3Ckzl9yrwdSAymheo4KBS0l/5iEPwrIg0CsFm2gqmCwhsnXfnc0IJ+vFKsXMVA5pazAIxxL0vmbWBCRZNAFsCXK2VcXda41f/+WfX4bPRLbVjpOeSAV6OBC7IaBh7opZWLnxnF+Kufn7xscsUWpAHkjgFkrd4k+H4opdaGmJeQfk/BHAXkQiNWiDVQVDHaAG4I16cAeug9UbiRrMuTwXu0C8vneYE2s89eEOzuxwGn5DRf69bN9JbS6KA/1e3vtxLZVo5eHYiCDC7HqNIC6KWXROuHu5OgIzm/l/Px9g7NgWHfJ41Gs7nrepmKcrDOL/n2optQRQlxRQB4ErMVbltXeFrBcP3YKSAiu2oLgVw0RyscGgE/2/R2Qz/dGJOM6z3pcLYmMAC3JEBGSVXycsQJktXt77cRuwANdBtVbgSh/mL+1DHXuq3LBdh5bdK2yOu1K+fsGZ6HEzcTOo1XMERqxiZyA7WaF0lUICQ8UkAeBffEW51q0i85NxpDBEwN28hzQMUHw8cEXJAXk85WyVw1xvl3gAbzz60m/5ywXlpQz867/rDIKPh4KWBPuWI+robCkHGeMjYLP/atH6I4m2IkFwf6YQDwrpwsiBe6OdVz4TMSTOmepcXyyUux4Pvzzo3MzU5mrtNoJTdqUs139fbNCCFEPBeRBYq9F+2XvEoyuX483KgdgftHhgE2IO8IIJA+cqg/I53tj2+HTHpOXjI1Wv0+EFXv/jiGcQ84KLhP8mFM6Z/sB5gSzbYdP++1z1RKMIDjWbcgiMUqLZcN7hX2gaTdn+wGf34OVYhf44or+IZUDzwPIWbfH8e/CknLcv2Wf6N9Eaqh3nJBwQgF5EAlNIJv+eWlAgnLW0Hwo9/iy2uzvibBiPaNKlzUPpFk5XSCUdnvWJL+cmlKVjN5xIDBL0PsqkAFMYUk5pnyy32WbaTkO84Z2D7tASiyPXGyfkENsXw1UlSV/WzCsOwZ1ShB9TWmlEakLdiBn3R7ct2WfZGWVpdeH7rmJEOKJAvIgEppAFogeX4DdexqOPb6snOVACOWc6NzMVGg0nhGLlQdmBqHmfLjkRAfKg5/t95hEbeH5oPw2vpLKI/flBlCsbr5UlZJwUjAqW/AG2l1ppXSnyeoRLWeEhZDWggLyIGL1vAZisRuhSZKGCE3I9/gK5dvWnGsM+9rX/mJijPXXmf2TRy4WT4RLTnQMIyqKFLi58VZhSXnAfxt/kgr+Zny+3+v3Fqub76/qKsEitXqnHIlRWgrGCQlDFJAHUTAXu2EtEx7qPb4xAkt1m3n/VMCwC/aiRP7ij5sYsWH0cAkSFg7rKXhj0WBVb/XJQC0AFkhih0O9tzUcWxnbOc63EwuVOSQkPFFAHkTBXOymsKQc84sO43i1CenxeszK6RIWAROrQog/85NZw+LhMFwudmn3R1AY7HrxasjNTGVOfFWrHrnYKFi43udJTUz05mZG7G/E8tbD2cJh3o9Sjs/uEBbncUKIJwrIgyhYwUswJ5P6ipWH7M/85H4ZbVx6rTSwXfjCYbhcLEjyVxk/oVSocElXsWPd+PmrHrkzf6066W9Sx8MbjNKFLPlby7BeoNwfAERquYDUPw+G3MxUrB6hPHUlXM5JhBBhFJAHEWtRG38vdhPMyaS+CnTAZ6+E4bwoCcdx6JfRxi+fpzaxC7Q/Um6+P3YWJkvzvhUdwWHxtT3Crtcu0PXInYVzUDVe5GbCqnDkT6j2tt3S63qG3T6lRG5mKsrzByM+Ut4lmoJxQsIfBeRBxKrL7O96zcEqH6gG+wJB9mBSywG3XJjmt4vzzK1lLaYShju1U6PsPZrOgVd9I4/vj51V94MCwJ89+uEwEuUtqaBQ7ncvLClvEfMRfHXgwRzRcohazlZRhYJxQsJfy0zCCxPBqrKSHq8X/IxwKEtXWFKOd3496QgmLXxzT5o/LkqsihfhVAkjg/F7q50axVpNccPPJ8IuYMjNTMV9Iguv5G8t8/o7iY1ERUeEawZ5s+gIjjmJc/a2MlnBdDiM1gVKwajsYDeBEBIA1EMeRKyUAX9fksM5z1co3YaHepPtWiKh35uD+qlRwZykHGhviKRTSBG74W4JS52LfQexEobOAlH6lRBCQgkF5EHEClR4+HdYm1XyMByGgVlpNYGYbBeu7Gk+zjd6PIB3fj2p2n7WEldTFDs5ertku9h2SjJEhMUxKMXX7+C8RLyQllpdhRDSulFAHkRiKQP+Di5zM1Ox997LcDJ/MPbee1nYBALBnGwXzrYdPu2Rk2tstGLO9gOqvL/Y/hoO5SGF3C5R7cSbmxmxuQctqWoIK2jWcNLbTWolypa0nQghxI4C8iASSxHx5wTLwpJy9HlxN9IW7ECfF3eHVaqH2DbzR09sS6irDbD3p0qjOquciu2v4ZY/brdgWHcIVyO38eZmRmzuQbjcFMvxzJBugqvqWnng/i37mPXFpfbFljKKQAgh7iggDyLbqmzCl/xEPw3LhnMNckA8aPFHrnLnBIPHY+GSb+9MbGRh9jbfK8awFtIJtxsXdytE6kFXGhsVvVe4HGNqyM1MxdLregreJPMA1jPmfEjd5FDvOCGkpaKAPMgiGV1wPO+fmXDhXIPcLlBL2edvLcPOo1Uej/ftEB92vXRiNxByJ9qxFJaU46zJ8z10nH/LBwaCmr+zWLAZpmn2onIzU0Vrj9/n1lNeWFIuepOTFqMLu+OOEELkotkxQcZaEZD1uK+CVWpRTYGq5sFamOQbgSA91EmV8fPFtE/3CwZeGg3X4gMoJeUPxYLNcF2dUwqrxKqdfSXOQ5X1gje/zn6efLmaTSOEkJBCPeRBFuil4APVu+xPgcrrZsX3LbCKn9cKS8rRwOgGNbWQeodiVT1YS7u7k0pXCdc8eylyRkjWF5+QDMYTGSlRhBDSUlBAHmSBrgneEmpFh3Md9WBiBTW+BDszt+73+m/DhVTespzc8Cmf+Gd0ItTlZqb6nI6j44B5Q1vmDQshhNhRQB5kga4J3hKqhti3mXMgadCpuyu3xAl484Z2h84tOvI12Kkzh9GdnJekjkWpiYj5W8tEb3hbel1tX9JxtBywbHivFp/6RAghLftKECZyM1MDdsGZldMF0z8vdZnYGa69y+eclueuNDZi+uelANSZiDfjc3bP7/gwzfe1b5f5RYdxvNqE9Hg9ZuV08du+1xKWgbfjwE5VspeOZG3HN34WT2tp6ZVDFgzrjt1Hz0jWFxeygoJxQkgrQT3kISJQtcHDeZVOZ/6uFlPfyO7SDOd8X/uCUKuayvndv2WfT/ub2AmkJSwDbyfVyyuWuiNWaQRoWfXHWYom9MegTgmK/mZQp4RWsW0IIQSggDwkBLI2eGFJecB6SP2JtRBNOFWLCRY197fLGUFWSwumFgzrLhpQepu6E66jLd4oGJUt+/vGR2pQMCrbvw0ihJAQQgF5CAhUbfDCknJM+2y/SyA27bP9YZkvLVaFhrUKoBKsZAtNC8jCUGt/Kywpx3d/Vnk8PqhTQosMpqS+U96mYlmP2WkQ3qMt3lgwrDtWj+gFvUhZpx5JBhx4MCeArSKEkOCjHPIQwOrtFVuO3Btzth9Ag9vssgYLjznbD4Rdb+asnC7Mutobfj7hU6BTWFIODSdceeb2rPDv0VRrdGH2tjIIdQz/+neNN80Ke+6l+7JW7sLJOjPz9StFVgFtyQI5Z4YQQsIF9ZCHANay46zHvcVamETpEuChQOyC7msJx/lFhwXfI0bHtYgeTdboAgdl1WVYK3z6uvJnKIvRiR+TWSt3AQBy1u0RDcaB1pE7TgghRB4KyEMAxwkP37IeJzasrePrVmP1INe3kBJ/s3K6CG4jHpCdthKOaU5qWChxQ3ayzoy8TcWSFUXCqcwoIYQQ/6OAPAScaUE914Hkr5U0A716aqDlZqYyt5HctBWxqiItua52bmaq5Kq2UqtOAvJWsCSEENJ6UEAeAtRKIZDCqgtNy1K7au0rgcqZFCtWVaSl19VeMdy33O+WVoGGEEKI7yggDwFqpBBIKSwph1BpbQ1a5rLUvtzItJRa7d7aILGQjVTA3tK3U25mqk/VdlpiBRpCCCG+abljy2EkNzOVWTFErUor84sOe1RYAYAEQ0TYBlDRERxzAZ/Z28p8+l6tuRKE1KTY9cXsgL213OGvHN6LecyKaU11xwkhhMjXWq6fIY81yUutvGVWYM/KXw8HYitB+lLpI1CrpgZTjySDX9739lYScOZmpipeeTItRtciqvQQQghRHwXkIcLfecstcaKiP3qw87eW4f4t+wKyamowFU3oL/p8zro9go9LbYfWFHAWjMqWHZTHR2rw8+TL/dsgQgghYSvkAvIvv/wS1157LbKysjB+/Hj8+eefzNfW1taiR48eHv9VVlYGsMXqyM1MxS0XpjkqOGg54JYL01QLOlvqREVWRQ9vKn0UlpTj9eITHhVI/LFqaigQK71XWmkUDL5b4nbwRcGobKwe0Qs6kZzyQZ0SaOVJQgghokIqh/y3337D9OnTMWvWLPTp0wfLly/HpEmT8PHHH0Or9awEcvDgQURFRWHbtm0ujycmJgaqyaopLCnHO7+edOTvWnjgnV9Pol9GG1WCcvt7zC86jOPVJqTH6zErp0vY50k/M6Qbpn223yM//l892il+r/lFh5nlANVeNTUUiK12CkBwBVexsohKUzhaitY834AQQog6Qiog37BhA6688krceuutAIAFCxbg8ssvx65du5CT49nDdPDgQZx//vlISUkJdFNVN7/oMIyNVpfH7D2zal3sW2LgkJuZiu+PnfWYaPjWL38pvpkRCzbDObWHRWwyMeBZB5+VxgIAei1H1UMIIYQQL4VUyspPP/2Efv36Of4dHR2NzMxMFBcXC77+0KFD6Ny5c4Ba51+sYFDuQi2t2celf3s81mDhMWf7AUXvI1bKLtxTe1ikerXtaStZK3eJrj655Dr2BFtCCCGEiAupgLy8vBzt2rmmGiQnJ+PkyZOCrz948CBOnz6NW2+9FQMHDsQ999yDw4fDM8eVtfqf1KqAhL2iqdKVTq0i5f5a2siCnVSv9ozP9yNn3R6crDOLvq6lbh9CCCEkEAKasmIymZjBdZs2bXDu3DlERka6PB4ZGYmGhgbBvzl06BDi4uIwe/Zs6PV6vPTSS7j99tvx6aefIj4+Xna72raNlf8l/IRV+9nCAykpcQFtS6A/z5+eLDqCVbkXS77u/sL/iT4vtk3CfXtpOPbNSH0jL9ozDgBajlO0DcJ9ewUDbTNlaHspQ9tLGdpeytD2kiegAfkvv/yCMWPGCD43efJkREVFwWx27YlraGhAXJzwj/nJJ5+A4zhERUUBAJYuXYorrrgCX3zxBfLy8mS36/TpWljFukcDICNez0xPufPNvQErJ5eSEoeKipqAfFYgrP3uDzyZI53WtGb3H8znEqO0zG3SErbX7VkdRBf7kTI2q73sbdAStleg0TZThraXMrS9lKHtpQxtL1caDcfsBA5oQN63b1+UlpYyn//kk0/w99+u+cAVFRXo2rWr4OsNBtfFTfR6PTIyMjzeIxyIVbx4o/hEq6rvrFSSIYKZniK16qSdVeS5eUNb9rZfMKw7Nv3yF0xyN5aTCA1H+yYhhBDio5DKIe/duzf27t3r+Hd9fT1+++039O7d2+O1Z8+exT//+U/s3LnT8VhdXR3++OMPdOkSfhPwxHJwxYJFYit9yKJGDn5ryI/2dlLm8utpMichhBDiq5AKyEePHo2tW7fizTffxIEDB/DII48gIyMDl112GQDg3LlzqKioAGDLOb/kkkvw7LPP4scff0RpaSmmT5+OlJQUDB06NJhfgwRYbmYqcyn4AR0TZL1HJKPESozYii8tSG5mKsYrXPZ+UKeEVnGzQgghhPhbSAXkF110ERYsWIDXXnsN//nPf1BTU4PVq1dDo7E189NPP8XAgQMdr3/++efRu3dvTJ48Gbfeeis0Gg1eeeUVRESEVHl12VjBX2sJCn1R1yg8jnCkSnxCImAr7dcoMIeAA7BwWOvpAV4wrLvsoHxQpwSqO04IIYSoJOQi1+HDh2P48OGCz91000246aabHP9u06YN5s2bF6im+d3CYT0x5ZN9LnnPWs73oLCwpLzFrdDpjrWSppwVNmdu3S+YFmSI4FrcdpJizwdnTfKM0WmxcFj3VrddCCGEEH8KqR7y1i43MxUrhvdCRrweHGyVV1YM7+VT8FNYUo7pn5fiWLUJPGwLDU3/vNSx4EtLwVpJk+Mg+l0LS8pRZxaezFjfGNzKO8GyYFh3rB7RC4lRWsdjSYYIrB7RC4cfGkTBOCGEEKKykOshb+3UXt5+ftFhGN3SOYyNVswvOtyiAqtZOV0w7bP9aHCrFGLlgWmf7QcgPDlzflF4LiTlb2rvh4QQQghhox7yFo5V25z1eLjKzUxFjE54d26w8MzAW2w7JBnofpUQQggh/kcRRwhSM+dbywnX4lajHGCoqTpnYT7nzQ2IWDlFQgghhBC1UA95iFE755u11osXa8CEPFYeOYvUNqWUDUIIIYQEAgXkIUYs51upwpJysDrCMxQGr+FgVo74glD5W8tc/j1n+wHma1viCAIhhBBCQhMF5CHGl/J97uYXHYZQRzgH6eA1HEn1aDuX8issKUelsZH52rFZyhbJIYQQQgjxFgXkIYaVdqE0HQNgB/E8Wm46hnOpPiH2XvLZ28qYr4nRcY563IQQQggh/kYBeYiZldMFhgjXn8UQofGqRzuBEZy2xHQVu3lDxQNpey/5GZEJoK1pdU5CCCGEBB8F5CEmNzMVi6/t4bI40OJreyju0bYteOO5/qSOa5npKna5mamIlNiruy0pknwPQgghhJBAoYA8BOVmpmJWThekx+txvNqE+UWHFVdZmV902GORHACIi4po8QHn0ut7iT5f3eB5o2JHtccJIYQQEmgUkIegwpJyTPtsv0vpw2mf7VcUlLPqbotNZGwpfLnhoNrjhBBCCAk0CshD0JztBzx6txssPGZuZU9EJK50XpQt5EDpKoQQQggJPArIQxCrF7vObPF6gaDWZtlw8bQVIeOyqdQhIYQQQgKPAvIwI7aYDWmWm5mK8QoDbCp1SAghhJBgoIA8BInV0pabA876YaMjWs8SlAuGdUePJIOs1yoN3gkhhBBC1EIBeQiSqqUtJX9rGVh1RBZd27pqbBdN6A+JtYLQI8lAveOEEEIICRoKyEOQrxMLnZeIV/u9w9EfMwYL9pRrOFvPeNGE/kFoFSGEEEKIDRVdJq0CBd2EEEIICVXUQx6iWHnkMd7U8yOEEEIIISGLAvIQNW9od2gFYu+GRt7r0ocUyhNCCCGEhB4KyENUbmYq4vWeveRmHphfdJj5d4Ul5dBywqE31dkmhBBCCAk9FJCHsDPnLIKPH6s2Mf9mftFhWHje4/EYnZYqiRBCCCGEhCAKyMMUK23lOCNYrzcLB/eEEEIIISS4KCAPU6y0lfR4vaLHCSGEEEJIcFFAHsKEJnXasdJWhnZp6zF50xChwaycLuo1jBBCCCGEqIYC8hA2Nos9CVMoVi8sKcdbv/wF9wzyWy5Ma5ULAhFCCCGEhAMKyEOY2CRMz2mbwJztB9Bg8Xzm49K/VWwVIYQQQghREwXkYcx9YmelsVHwdazHCSGEEEJI8FFAHsZmbysLdhMIIYQQQoiPKCAPcYlRnosD2TnXKRdbvVPsPQghhBBCSHBRQB7i5g0VX8zHHoiL9ZZLvQchhBBCCAkeCshDXG5mKmJ07B7uGZ/vR2FJOXNVT/t7EEIIIYSQ0EQBeRhYKFJtpb6RF+0dz6AFgQghhBBCQhoF5GFAqodbrHecFgQihBBCCAltFJC3cJSuQgghhBAS2iggb8GSDBHBbgIhhBBCCJFAAXmY8Ca4fmZINz+0hBBCCCGEqIkC8jDhTXBN6SqEEEIIIaGPAvIwkZuZikGdEmS/Xsv5ry2EEEIIIUQ9FJCHkYJR2bJfOzarg/8aQgghhBBCVEMBeZgZny0daKfF6LBApHY5IYQQQggJHRSQh5kFw7qLBuWDOiXg58mXB7BFhBBCCCHEF1QXLwwtGNadesAJIYQQQloI6iEnhBBCCCEkiCggJ4QQQgghJIgoICeEEEIIISSIKCAnhBBCCCEkiCggJ4QQQgghJIgoICeEEEIIISSIKCAnhBBCCCEkiCggJ4QQQgghJIgoICeEEEIIISSIaKVOABoNF+wmhBTaHsrQ9lKGtpdytM2Uoe2lDG0vZWh7KUPbq5nYtuB4nucD2BZCCCGEEEKIE0pZIYQQQgghJIgoICeEEEIIISSIKCAnhBBCCCEkiCggJ4QQQgghJIgoICeEEEIIISSIKCAnhBBCCCEkiCggJ4QQQgghJIgoICeEEEIIISSIKCAnhBBCCCEkiCggb2XMZjOefvpp9O/fH/3798eiRYtgtVoFX1tXV4c5c+bgn//8Jy699FLMnTsXDQ0NAW5xcCnZXuXl5bj//vvRt29f5OTkYPHixbBYLAFucWgwmUwYPnw4ioqKmK85efIkJk6ciN69e2Po0KH4+OOPA9jC0CJne/34448YNWqUY3utXbuWuS+2dHK2l53ZbMbIkSOxaNGiALQsNMnZXqdOncIDDzyA3r17Y+DAgVi5cmUAWxh65GyzQ4cOYdy4cejduzeGDBmC9evXB66BIeLkyZN44IEH0L9/f1x++eWYNWsWqqurma+lcz5bRLAbQALrhRdewK5du7B27VrU1dXh4YcfRnx8PCZOnOjx2kceeQRHjhzBq6++CqPRiBkzZqBNmzZ44IEHgtDy4FCyvaZOnYqkpCS88847KC8vx8yZMxEXFyf42pbMaDTiwQcfxMGDB0Vfd//996N9+/Z477338MMPP2DWrFnIyMjAJZdcEqCWhgY52+vkyZOYMGECbrnlFjz77LM4dOgQZs2aBb1ej3HjxgWwtcEnd/+yW7NmDcrKynDFFVf4uWWhSc72slqtmDRpEgwGA959910cPXoUDz/8MNq3b4/c3NwAtjY0yNlmDQ0NmDhxIi6//HI8/fTTKC0txcMPP4yUlBQMHz48gK0NHqvVivvvvx8JCQl4/fXX0dDQgCeffBKPPvooVq1a5fF6OueLo4C8FTGZTNi0aROWLVuGrKwsAMD06dOxePFiTJgwARzHOV576NAhfPHFF/joo4/Qs2dPAMB9992H7du3B6XtwaBke509exY//fQTCgoKcMEFF+CCCy7AyJEjsXv37lYVkP/666/Iz8+HTqcTfd1///tflJaW4rXXXkN8fDy6du2Kn3/+GRs2bGhVJ2e52+vzzz9Hu3bt8PDDDwMAzj//fIwfPx4fffRRqwrI5W4vu7KyMrz99tvo2rWrn1sWmuRur507d+LAgQPYvn07kpOT0a1bN4wdOxbFxcWtLiCXu80OHjyIY8eOYdq0aUhKSsJ5552HzZs3Y/fu3a0mIC8tLcWvv/6Kb775BikpKQCA2bNnY8yYMaitrUVsbKzjtXTOl0YpK63Ivn37YDQa0bdvX8djffv2RUVFBY4dO+by2t27d6NLly6OYBwAbr31Vqxbty5g7Q02JdsrKioK0dHRKCwsRENDA06ePImvv/4amZmZgW52UO3evRtXXHEF3n77bdHX/fTTT+jRowfi4+Mdj/Xt2xfFxcV+bmFokbu9hgwZgmeffdblMY7jUFNT48/mhRy52wsALBYLHn30UUyfPh1JSUkBaF3okbu9vvvuO/zzn/9EcnKy47Fp06Zh7ty5/m5iyJG7zRISEsBxHN59911YLBYcPHgQe/fubVXn/Pbt22PdunWOYBywnZd4nkdtba3La+mcL416yFuR8vJyxMbGIiYmxvGY/UAqLy9Hx44dHY8fPXoUnTp1wltvvYXXX38dZrMZ119/PaZOnSq7dyrcKdleer0eTz31FJ566inHCfqyyy7D1KlTA97uYJowYYKs15WXl6Ndu3YujyUnJ6O8vNwfzQpZcrdXx44dXfa3+vp6vPvuuxg4cKC/mhaS5G4vAHj11VfRpk0b3HjjjXj//ff92KrQJXd7HT16FOnp6Vi5ciXef/996HQ63HzzzbjzzjtdRgJbA7nbrEOHDpgxYwaWLVuG5cuXw2Kx4IYbbsCoUaP83MLQkZCQgJycHJfH1q9fj86dOyMtLc3lcTrnS6Me8lbEaDRCr9e7PBYZGQkAHpM16+rqsHfvXmzbtg0LFizAnDlzsHnzZixevDhg7Q02JdsLsKX59O/fH5s2bcLq1avxxx9/4Pnnnw9IW8MNa9taLBY0NjYGqVXhwWw2Y/r06aivr8f9998f7OaEpCNHjuDll1/GU089FeymhIW6ujp88skn+P3337F8+XJMnjwZL774It58881gNy1kNTY24vDhwxgxYgTeffddLFy4EP/3f//XKid22q1duxZffvklZs+e7fEcnfOlUQ95KxIVFeURSNr/HRUV5fK4VquFyWTC0qVLHUNM9fX1mD17Nh5++GFoNC3/Xk7J9vruu++wYcMGFBUVOfLm9Ho9Jk6ciHvuucdlKJjYtl9VVZXLYw0NDdDpdIiIoNMSy7lz5zBt2jR8//33eO2115CamhrsJoUcnucxe/Zs3HvvvS6jCoRNq9UiOjoazz33HCIiInDhhRfixIkT2LhxI8aOHRvs5oWkjz76CN9//z22bt0KrVaLCy+8EEajEQsXLsTYsWOh1WqD3cSAWrVqFZYvX47HH38cgwYN8niezvnSaCu0ImlpaaipqYHRaITBYAAAVFRUAIDHhb1du3ZISUlxyffq0qULzp07h8rKylYRYCrZXr/88gs6duzoMonlwgsvhMViwYkTJ1rF9lIiLS0Nv/zyi8tjFRUVHkOapFltbS3uuecelJWV4bXXXnNMNCauTpw4gb179+K3337DsmXLANhuZH766Sf83//9Hz755JMgtzD0tGvXDg0NDS6BUZcuXXDixIkgtiq0/fLLL+jevbtL4H3hhReipqYGVVVVaNu2bRBbF1jz58/HG2+8gSeffJKZskPnfGktv5uTOPTs2RMGgwF79+51PPbDDz+gXbt2SE9Pd3ntJZdcgvLycpw+fdrx2IEDBxAbG4uEhIRANTmolGyv1NRUHDt2DOfOnXM8Zi+ZlZGREZgGh5Hs7GyUlpa6TPzZu3cvevfuHcRWhS6LxYLJkyfj8OHD2LBhAwXjIlJTUx0Voj788EN8+OGHuPDCC3HTTTdh7dq1wW5eSLrkkktw8OBBlxHBAwcO0AiDiNTUVBw6dAg8zzses18jW9Mk4pUrV+LNN9/Ec889J5o/T+d8aRSQtyJRUVHIy8vD008/jR9//BG7d+/G4sWLHWXTqqqqHFUbLr30UvTq1QvTp09HaWkpdu/ejRdeeAG33nprqxleUrK9rrrqKsTGxmLmzJk4ePAgfvjhBzz++OO46aabWtXJWUxlZSXq6uoA2GbXd+nSBTNmzEBZWRneeecdbNmyhYbHnThvr7fffhvfffcdnnnmGbRt2xYVFRWoqKhAZWVlkFsZOuzbKyIiAuedd57Lf1FRUYiPj/e4kW7NnPev66+/HlFRUXj00Udx+PBhfPnll3j11VcxZsyYILcytDhvs3//+9/4+++/8dRTT+H333/H119/jcWLF+OOO+5oNRNhS0tLsWrVKtx11124/PLLHeeliooKWCwWOucrxZNW5dy5c/ycOXP43r1785deeim/aNEi3mq18jzP87fddhufn5/veO2pU6f4qVOn8tnZ2Xz//v35559/nm9oaAhW04NCyfY6cuQIP3HiRL5v3758Tk4OP3/+fP7cuXPBanrQde/enf/6668d/77yyiv55cuXO/79559/8uPHj+cvuugifujQofzmzZuD0cyQIba9brvtNr579+4e/w0YMCBYzQ06qf3L2W233cYvXLgwUE0LSVLb648//uDvuusu/qKLLuIHDRrEr1u3LhjNDClS2+x///sff9ttt/G9e/fmhwwZwr/44ot8Y2NjMJoaFMuXLxc8L3Xv3p0/ePAgnfMV4njeabyFEEIIIYQQElCUskIIIYQQQkgQUUBOCCGEEEJIEFFATgghhBBCSBBRQE4IIYQQQkgQUUBOCCGEEEJIEFFATgghxC+oiBchhMhDATkhhLRgPXr0wKZNmwL6mQ0NDXjqqafw7bffBvRzCSEkXFFATgghLdg777yDa665JqCf+ffff+Ott96CxWIJ6OcSQki4ah1roBNCSCuVnZ0d7CYQQgiRQD3khBDSgjmnrKxYsQKjRo3C+++/j6FDh+Kiiy7CmDFjcPDgQcfrx44di/nz5+OZZ57BJZdcgoEDB2L58uWwWq0AgGPHjqFHjx4oKipy+ZzLL78cK1aswLFjxzBkyBAAwIQJE/DII494tKm6uhoDBw7E3Xff7Xjs6NGjyMrKwuLFi1XfBoQQEuooICeEkFakrKwMr7zyCqZPn44lS5bgr7/+wqOPPurymvfffx+//vorFi9ejLFjx2LNmjVYtWqVrPdv164dVq5cCQB49NFHcd9993m8Jj4+HrNmzcLOnTuxbds2AMDjjz+O9PR0TJkyxcdvSAgh4YdSVgghpBWpra3F8uXLccEFFwAATp8+jccffxxnzpxBYmKi43Xr1q1DXFwcrrzySlRVVWH9+vWYNGmS5PtHRkaiV69eAIAuXbqgU6dOgq+7/vrr8fHHH2PBggU4ffo09uzZg7fffhuRkZEqfEtCCAkv1ENOCCGtSGxsrCMYB4C0tDQAgNFodDw2aNAgxMXFOf49ZMgQ1NbW4sCBA6q25fHHH8epU6fw5JNPYvz48cjKylL1/QkhJFxQQE4IIa1IVFSUy785jgMAR444AKSkpLi8JikpCQBw9uxZVdvSoUMHXHLJJbBarRg0aJCq700IIeGEAnJCCCEu3APvyspKALbAXCiAB4D6+nrFn/P111/jm2++Qffu3fHMM8+goaHByxYTQkh4o4CcEEKIi2+//dYlON62bRsSEhLQrVs3REdHAwBOnjzpeH7fvn0uAblWq5X8jLq6Ojz55JMYPnw4Vq9ejT///BNr165V8VsQQkj4oEmdhBBCXFRUVGDKlCkYM2YMfv31V2zYsAEPP/wwNBoNEhMTkZmZibVr1yI5ORmNjY1YtmwZ4uPjHX9vzz/fuXMnMjIy0KVLF4/PWLp0KaqqqpCfn4/U1FTcfffdWLNmDa677jqXHHdCCGkNqIecEEKIi6uvvhqpqal44IEHUFBQgEceeQTjxo1zPP/888+jQ4cOePDBB7F48WLcf//96Nq1q+P52NhY3HnnnXjnnXewYMECj/f/3//+hzfffBOTJ09GamoqAOCee+5BSkoKnnjiCfA87/8vSQghIYTj6cxHCCGkydixY5GcnIwlS5YEuymEENJqUA85IYQQQgghQUQBOSGEEEIIIUFEKSuEEEIIIYQEEfWQE0IIIYQQEkQUkBNCCCGEEBJEFJATQgghhBASRBSQE0IIIYQQEkQUkBNCCCGEEBJEFJATQgghhBASRP8PqJnvRcxMVK8AAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_results(\n", " x_val.detach().numpy(), \n", " y_val.detach().numpy(), \n", " y_hat.detach().numpy(),\n", ");" ] }, { "cell_type": "code", "execution_count": null, "id": "tamil-transition", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 5 }