diff --git a/exercises/01_penguin_classification.ipynb b/exercises/01_penguin_classification.ipynb index 4d8b931..95eb953 100644 --- a/exercises/01_penguin_classification.ipynb +++ b/exercises/01_penguin_classification.ipynb @@ -143,10 +143,11 @@ "outputs": [], "source": [ "from torchvision.transforms import Compose\n", + "\n", "# import some useful functions here, see https://pytorch.org/docs/stable/torch.html\n", "# where `tensor` and `eye` are used for constructing tensors,\n", "# and using a lower-precision float32 is advised for performance\n", - "from torch import tensor, eye, float32 \n", + "from torch import tensor, eye, float32\n", "\n", "# Apply the transforms we need to the PenguinDataset to get out input\n", "# targets as Tensors." @@ -396,7 +397,7 @@ "\n", "# Print the raw output from the net\n", "\n", - "# Transform the raw output back to human-readable format\n" + "# Transform the raw output back to human-readable format" ] } ], diff --git a/exercises/03_mnist_classification.ipynb b/exercises/03_mnist_classification.ipynb index 1e18ec8..3bcb28e 100644 --- a/exercises/03_mnist_classification.ipynb +++ b/exercises/03_mnist_classification.ipynb @@ -101,6 +101,7 @@ "from torchvision.transforms import ToTensor, Compose\n", "from torch import Tensor, tensor\n", "\n", + "\n", "def get_img_tfms(training: bool) -> Compose:\n", " \"\"\"Return a composition of image transforms.\n", "\n", @@ -141,7 +142,7 @@ " The target as a one-hot-encoded vector.\n", "\n", " \"\"\"\n", - " return #???\n", + " return # ???\n", "\n", "\n", "train_set = MNIST(\n", diff --git a/src/ml_workshop/_ellipse.py b/src/ml_workshop/_ellipse.py index d2fcb2f..b7e84ef 100755 --- a/src/ml_workshop/_ellipse.py +++ b/src/ml_workshop/_ellipse.py @@ -1,4 +1,5 @@ """Ellipse-drawing dataset.""" + from typing import Tuple, Optional, Any from numpy.random import default_rng diff --git a/worked-solutions/01_penguin_classification_solutions.ipynb b/worked-solutions/01_penguin_classification_solutions.ipynb index 4b204e6..fcab877 100644 --- a/worked-solutions/01_penguin_classification_solutions.ipynb +++ b/worked-solutions/01_penguin_classification_solutions.ipynb @@ -243,6 +243,7 @@ ], "source": [ "from torchvision.transforms import Compose\n", + "\n", "# import some useful functions here, see https://pytorch.org/docs/stable/torch.html\n", "# where `tensor` and `eye` are used for constructing tensors,\n", "# and using a lower-precision float32 is advised for performance\n", @@ -399,10 +400,12 @@ " (1): Linear(in_features=5, out_features=16, bias=True)\n", " (2): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (3): Dropout(p=0.1, inplace=False)\n", - " (4): Linear(in_features=16, out_features=16, bias=True)\n", - " (5): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (6): Dropout(p=0.1, inplace=False)\n", - " (7): Linear(in_features=16, out_features=3, bias=True)\n", + " (4): LeakyReLU(negative_slope=0.1)\n", + " (5): Linear(in_features=16, out_features=16, bias=True)\n", + " (6): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (7): Dropout(p=0.1, inplace=False)\n", + " (8): LeakyReLU(negative_slope=0.1)\n", + " (9): Linear(in_features=16, out_features=3, bias=True)\n", " )\n", ")\n" ] @@ -410,7 +413,7 @@ ], "source": [ "from torch.nn import Module\n", - "from torch.nn import BatchNorm1d, Linear, ReLU, Dropout, Sequential\n", + "from torch.nn import BatchNorm1d, Linear, LeakyReLU, Dropout, Sequential\n", "from torch import Tensor\n", "\n", "\n", @@ -440,9 +443,11 @@ " Linear(in_feats, 16),\n", " BatchNorm1d(16),\n", " Dropout(0.1),\n", + " LeakyReLU(0.1),\n", " Linear(16, 16),\n", " BatchNorm1d(16),\n", " Dropout(0.1),\n", + " LeakyReLU(0.1),\n", " Linear(16, out_feats),\n", " )\n", "\n", @@ -618,13 +623,13 @@ " Notes\n", " -----\n", " - The ``model.eval()`` is also very important:\n", - " - It turns off the dropout layers, which are likely to impair the \n", + " - It turns off the dropout layers, which are likely to impair the\n", " validation performance and render it unrealistically poor.\n", " - It tells the batchnorm layers to _not_ use the batch's statistics,\n", " and to instead use the stats it has built up from the training set.\n", " The model should not \"remember\" anything from the validation set.\n", " - We also protect this function with ``torch.no_grad()``, because having\n", - " gradients enable while validating is a pointless waste of \n", + " gradients enable while validating is a pointless waste of\n", " resources — they are only needed for training.\n", "\n", " \"\"\"\n", @@ -660,7 +665,7 @@ "\n", " Notes\n", " -----\n", - " - This function assumes the ``preds`` have had the softmax \n", + " - This function assumes the ``preds`` have had the softmax\n", " applied to them along dimension 1, and that the predicted\n", " class is therefore ``preds.argmax(dim=1)``.\n", "\n", @@ -694,49 +699,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 0 time: 0.146 seconds.\n", - "Epoch 1 time: 0.096 seconds.\n", - "Epoch 2 time: 0.093 seconds.\n", - "Epoch 3 time: 0.092 seconds.\n", - "Epoch 4 time: 0.093 seconds.\n", - "Epoch 5 time: 0.093 seconds.\n", - "Epoch 6 time: 0.092 seconds.\n", - "Epoch 7 time: 0.093 seconds.\n", - "Epoch 8 time: 0.093 seconds.\n", - "Epoch 9 time: 0.094 seconds.\n", - "Epoch 10 time: 0.097 seconds.\n", - "Epoch 11 time: 0.097 seconds.\n", - "Epoch 12 time: 0.094 seconds.\n", - "Epoch 13 time: 0.094 seconds.\n", - "Epoch 14 time: 0.093 seconds.\n", - "Epoch 15 time: 0.092 seconds.\n", - "Epoch 16 time: 0.096 seconds.\n", - "Epoch 17 time: 0.098 seconds.\n", - "Epoch 18 time: 0.096 seconds.\n", - "Epoch 19 time: 0.093 seconds.\n", + "Epoch 0-25 time: 1.934173 seconds\n", + "Epoch 25-50 time: 1.844448 seconds\n", + "Epoch 50-75 time: 1.831056 seconds\n", + "Epoch 75-100 time: 1.817979 seconds\n", + "Epoch 100-125 time: 1.822820 seconds\n", + "Epoch 125-150 time: 1.842434 seconds\n", + "Epoch 150-175 time: 1.967782 seconds\n", + "\n", "\n", + " loss_train accuracy_train loss_valid accuracy_valid\n", + "0 0.578070 0.496324 0.586362 0.484375\n", + "1 0.490388 0.742647 0.495531 0.750000\n", + "2 0.417000 0.819853 0.406423 0.781250\n", + "3 0.371912 0.841912 0.356070 0.828125\n", + "4 0.325209 0.871324 0.310226 0.890625\n", + ".. ... ... ... ...\n", + "195 0.019916 0.988971 0.026766 0.984375\n", + "196 0.021192 0.988971 0.023146 0.984375\n", + "197 0.022928 0.988971 0.024764 0.984375\n", + "198 0.023786 0.985294 0.026085 0.984375\n", + "199 0.023932 0.981618 0.031793 0.984375\n", "\n", - " loss_train accuracy_train loss_valid accuracy_valid\n", - "0 0.759151 0.279412 0.711391 0.375000\n", - "1 0.531332 0.584559 0.468364 0.609375\n", - "2 0.393045 0.779412 0.365031 0.796875\n", - "3 0.304282 0.915441 0.293924 0.890625\n", - "4 0.261206 0.937500 0.252864 0.937500\n", - "5 0.221856 0.937500 0.210456 0.937500\n", - "6 0.190321 0.963235 0.171965 0.968750\n", - "7 0.151137 0.966912 0.161003 0.968750\n", - "8 0.134465 0.974265 0.139327 0.968750\n", - "9 0.127652 0.963235 0.123636 0.968750\n", - "10 0.133551 0.959559 0.103402 0.968750\n", - "11 0.116820 0.985294 0.095507 0.968750\n", - "12 0.114006 0.970588 0.084369 0.984375\n", - "13 0.114615 0.955882 0.079413 0.984375\n", - "14 0.076280 0.985294 0.074852 0.984375\n", - "15 0.088352 0.981618 0.068665 0.984375\n", - "16 0.111566 0.955882 0.064788 0.984375\n", - "17 0.083331 0.966912 0.060488 0.984375\n", - "18 0.084988 0.977941 0.057387 0.984375\n", - "19 0.065010 0.985294 0.055872 0.984375\n" + "[200 rows x 4 columns]\n" ] } ], @@ -745,12 +730,21 @@ "\n", "from pandas import DataFrame\n", "\n", - "epochs = 20\n", + "epochs = 200\n", + "print_interval = 25\n", + "\n", "\n", "train_metrics, valid_metrics = [], []\n", "\n", + "\n", "for epoch in range(epochs):\n", - " start_time = perf_counter()\n", + "\n", + " if epoch % print_interval == 0:\n", + " if epoch != 0:\n", + " print(\n", + " f\"Epoch {max(epoch - print_interval, 0)}-{epoch} time: {perf_counter() - tic:.6f} seconds\"\n", + " )\n", + " tic = perf_counter()\n", "\n", " train_metrics.append(train_one_epoch(model, train_loader, optimiser, loss_func))\n", "\n", @@ -758,7 +752,6 @@ "\n", " stop_time = perf_counter()\n", "\n", - " print(f\"Epoch {epoch} time: {stop_time - start_time:.3f} seconds.\")\n", "\n", "print(\"\\n\")\n", "\n", @@ -786,7 +779,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -812,6 +805,7 @@ " linspace(1, epochs, epochs),\n", " metrics[key],\n", " \"-o\",\n", + " ms=1.5,\n", " label=split.capitalize(),\n", " )\n", " axis.set_ylabel(quant.capitalize(), fontsize=15)\n", @@ -850,8 +844,8 @@ " [3.3600e+01, 1.1300e+01, 2.0000e+03, 2.1100e+02, 1.0000e+00]])\n", "\n", "Raw output:\n", - "tensor([[0.0035, 0.0021, 0.9943],\n", - " [0.9547, 0.0013, 0.0440]])\n", + "tensor([[2.4082e-05, 4.3393e-06, 9.9997e-01],\n", + " [8.5355e-01, 6.9033e-06, 1.4644e-01]])\n", "\n", "Predicted species:\n", "['Gentoo', 'Adelie']\n", @@ -900,9 +894,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.9" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/worked-solutions/02_penguin_regression_solutions.ipynb b/worked-solutions/02_penguin_regression_solutions.ipynb index 3487313..451fab8 100644 --- a/worked-solutions/02_penguin_regression_solutions.ipynb +++ b/worked-solutions/02_penguin_regression_solutions.ipynb @@ -385,7 +385,7 @@ "FCNet(\n", " (0): InputBlock(\n", " (0): BatchNorm1d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Dropout(p=0.25, inplace=False)\n", + " (1): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): DenseBlock(\n", " (0): Linear(in_features=4, out_features=16, bias=True)\n", @@ -406,13 +406,7 @@ " (3): LeakyReLU(negative_slope=0.1)\n", " )\n", " (4): DenseBlock(\n", - " (0): Linear(in_features=4, out_features=2, bias=True)\n", - " (1): BatchNorm1d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (2): Dropout(p=0.1, inplace=False)\n", - " (3): LeakyReLU(negative_slope=0.1)\n", - " )\n", - " (5): DenseBlock(\n", - " (0): Linear(in_features=2, out_features=1, bias=True)\n", + " (0): Linear(in_features=4, out_features=1, bias=True)\n", " )\n", ")\n" ] @@ -424,9 +418,9 @@ "model = FCNet(\n", " in_feats=4,\n", " out_feats=1,\n", - " hidden_sizes=(16, 8, 4, 2),\n", + " hidden_sizes=(16, 8, 4),\n", " input_bnorm=True,\n", - " input_dropout=0.25,\n", + " input_dropout=0.1,\n", " hidden_dropout=0.1,\n", " hidden_bnorm=True,\n", ")\n", @@ -620,42 +614,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 0 \t 0.215 seconds \t Valid Loss = 1.7032.\n", - "Epoch 10 \t 0.172 seconds \t Valid Loss = 0.6962.\n", - "Epoch 20 \t 0.173 seconds \t Valid Loss = 0.1980.\n", - "Epoch 30 \t 0.171 seconds \t Valid Loss = 0.1256.\n", - "Epoch 40 \t 0.171 seconds \t Valid Loss = 0.0798.\n", - "Epoch 50 \t 0.172 seconds \t Valid Loss = 0.0716.\n", - "Epoch 60 \t 0.172 seconds \t Valid Loss = 0.0617.\n", - "Epoch 70 \t 0.172 seconds \t Valid Loss = 0.0506.\n", - "Epoch 80 \t 0.172 seconds \t Valid Loss = 0.0528.\n", - "Epoch 90 \t 0.171 seconds \t Valid Loss = 0.0443.\n", - "Epoch 100 \t 0.171 seconds \t Valid Loss = 0.0448.\n", - "Epoch 110 \t 0.171 seconds \t Valid Loss = 0.0396.\n", - "Epoch 120 \t 0.171 seconds \t Valid Loss = 0.0363.\n", - "Epoch 130 \t 0.171 seconds \t Valid Loss = 0.0381.\n", - "Epoch 140 \t 0.171 seconds \t Valid Loss = 0.0324.\n", - "Epoch 150 \t 0.171 seconds \t Valid Loss = 0.0272.\n", - "Epoch 160 \t 0.171 seconds \t Valid Loss = 0.0213.\n", - "Epoch 170 \t 0.171 seconds \t Valid Loss = 0.0210.\n", - "Epoch 180 \t 0.171 seconds \t Valid Loss = 0.0179.\n", - "Epoch 190 \t 0.173 seconds \t Valid Loss = 0.0185.\n", + "Epoch 0-25 time: 4.234758 seconds\n", + "Epoch 25-50 time: 4.243387 seconds\n", + "Epoch 50-75 time: 4.252240 seconds\n", + "Epoch 75-100 time: 4.297547 seconds\n", + "Epoch 100-125 time: 4.254026 seconds\n", + "Epoch 125-150 time: 4.280980 seconds\n", + "Epoch 150-175 time: 4.243602 seconds\n", + "Epoch 175-200 time: 4.236814 seconds\n", + "Epoch 200-225 time: 4.238001 seconds\n", + "Epoch 225-250 time: 4.244598 seconds\n", + "Epoch 250-275 time: 4.229454 seconds\n", "\n", "\n", " loss_train loss_valid\n", - "0 1.959217 1.703167\n", - "1 1.679813 2.034752\n", - "2 1.506878 1.823231\n", - "3 1.319360 1.565340\n", - "4 1.262722 1.461631\n", + "0 0.403945 0.258350\n", + "1 0.334612 0.286643\n", + "2 0.288723 0.248097\n", + "3 0.235608 0.204395\n", + "4 0.210375 0.182948\n", ".. ... ...\n", - "195 0.033906 0.016302\n", - "196 0.031433 0.015598\n", - "197 0.035795 0.016132\n", - "198 0.036828 0.016387\n", - "199 0.037099 0.017189\n", + "295 0.011671 0.008663\n", + "296 0.009973 0.008757\n", + "297 0.009214 0.008859\n", + "298 0.010670 0.008706\n", + "299 0.009724 0.008328\n", "\n", - "[200 rows x 2 columns]\n" + "[300 rows x 2 columns]\n" ] } ], @@ -663,24 +648,24 @@ "from time import perf_counter\n", "from pandas import DataFrame\n", "\n", - "epochs = 200\n", + "epochs = 300\n", + "print_interval = 25\n", "\n", "train_metrics, valid_metrics = [], []\n", "\n", "for epoch in range(epochs):\n", - " start_time = perf_counter()\n", + "\n", + " if epoch % print_interval == 0:\n", + " if epoch != 0:\n", + " print(\n", + " f\"Epoch {max(epoch - print_interval, 0)}-{epoch} time: {perf_counter() - tic:.6f} seconds\"\n", + " )\n", + " tic = perf_counter()\n", "\n", " train_metrics.append(train_one_epoch(model, train_loader, optimiser, loss_func))\n", "\n", " valid_metrics.append(validate_one_epoch(model, valid_loader, loss_func))\n", "\n", - " stop_time = perf_counter()\n", - "\n", - " if epoch % 10 == 0:\n", - " print(\n", - " f\"Epoch {epoch} \\t {stop_time - start_time:.3f} seconds \\t Valid Loss = {valid_metrics[-1]['loss']:.4f}.\"\n", - " )\n", - "\n", "print(\"\\n\")\n", "\n", "train_metrics = DataFrame(train_metrics)\n", @@ -708,7 +693,7 @@ "outputs": [ { "data": { - "image/png": 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37FANxV+V8yHKQnV3ysKARz8Hoic6p3NEREREbdy9996L4OBgnDlzBr/++qvBdvX19di4cSOAW8GILXz++ef4/fffER4ebrNzujIGFnbk5+MBAPi+bjCq/3ICiBim3hH3LDDvFIMKIiIiIjuSSqWYPHkygFsjEvr89NNPKCwsxO23345BgwbZ7Ppdu3ZFnz594O5u4oI+rRwDCzvq4CGFW8Nas6U1SiA0Vr3D3ZvTn4iIiKj1UymBnH3Aqc3q7y5Ym2vatGkAgI0bN0Kp1N8/zVSpadOmobS0FO+//z4SExMRGRkJT09PBAQEICkpCTt37jTr2sZyLA4cOICEhAT4+vrCz88PiYmJOHz4sHlPzsUwsLAjQRDg56OOUEur6gB5wzBY2TUn9oqIiIjIBrK2AStjgM/uA755Sv19ZYx6uwsZNGgQbr/9dhQUFOgNDCorK/Hdd99BEARMnToVhw4dwt/+9jecPXsWvXv3xoMPPojevXvjp59+QmJiIj755BOr+/T9999jzJgxSE1NRXR0NMaNG4fLly9j9OjRSEtLs/r8zsLAws7k3o0CCxkDCyIiImoDsrYBm6Y3v6cpy1Nvd7HgQpM3oW861LfffovKykqMHj0akZGR6N27N9LS0pCTk4OffvoJX331FQ4ePIhjx45BLpfjueeeQ0VFhcV9KS8vx5NPPon6+np88sknOHz4MDZs2IDffvsNzz33HP7zn/9YfG5nY2BhZ5o8C8XN2luBheKKE3tERERE7ZIoArWV1n9VlwH/ewmAqO8i6m8pC9TtrL2WqO8a5ps6dSoEQcDWrVtRWVmps08TbGimTEVFRWHYsGHNzjFgwADMnj0bZWVl+OWXXyzuy+bNm3H9+nWMHj0aM2fO1G7XLIvbpUsXi8/tbFxLy878Go9YhIepN5ZdU/9DEZouQUtERERkJ3VVwFthDriQqL7XWRZh/alevgZ4dLD6NF27dsXo0aOxZ88ebN26FVOnTgUAFBQUIDU1FV5eXvjjH/+oba9UKpGamoqDBw8iLy8PNTU1AIBz587pfLfEvn37AACPPfZYs33u7u545JFHsHLlSovP70wMLOxM3pBjobhZB/hGABAAZQ1QVQx0CHRu54iIiIjaiccffxx79uzB+vXrtYHFhg0boFQq8dBDD0EulwMArly5gvvuuw8nTpwweK7y8nKL+3Htmnr6WGRkpN793bp1s/jczsbAws60ORY36wA3D6BjEFBRAJRdZWBBREREjuPuox4BsFbuQeCLR1puN3UzEDncumu5+1h3fCOPPPII5syZg59//hmFhYUICgrSToNqXLvi6aefxokTJ/Dwww/jpZdeQu/eveHr6wuJRIL//Oc/+POf/wzRRlO02hqXzLHYu3cvJkyYgLCwMO18uJbs3r0bAwcOhKenJ2677TasW7fO7v00hZ+3OseitKpOvUHWMASpuOqkHhEREVG7JAjqaUXWfvW4u+F+xtCUbkGdV9rjbuuvZcNp43K5HBMnTkR9fT02bNiA06dP49ixYwgMDERSUhIA9QpRO3fuRHBwMDZu3IihQ4dCLpdDIlHfMl+4cMHqfoSGqosm5+bm6t1vaHtr4JKBRWVlJWJjY/Hhhx+a1D4nJwfjx4/HXXfdhYyMDMybNw9PP/00duzYYeeetsxPOxWqVr1BuzIUAwsiIiJqhSRSIGl5w4OmN/4Nj5OWuWTNLk2C9hdffKGtXTFp0iRtATuFQgGVSoXQ0FBIpbr9r6urw5YtW6zuw6hRowAAmzZtaravvr4e33zzjdXXcBaXDCzGjRuHv//973jwwQdNar969WpERUXh7bffxu233445c+bgkUcewbvvvmvnnrZMp44FwMCCiIiIWr/oicCjnwOyUN3tsjD19uiJzulXC5KSkhAYGIhff/0Vq1evBqA7DSooKAhyuRyZmZk4cOCAdrtSqcSCBQtw9uxZq/vwxz/+EQEBAdi9ezc+++wz7XZRFJGcnIxLly5ZfQ1nccnAwlxpaWlISEjQ2ZaYmGi0wEhNTQ3Kysp0vuxBp44FwCJ5RERE1DZETwTmZQIzvgceXqv+Pu+UywYVgHrVJc1qTEVFRejZsyfi4uK0+93c3PDSSy+hvr4ed955J+6991489thjuO2227B69WrMnj3b6j74+vpi7dq1kEqleOKJJzBs2DBMmTIFMTExWLFiBZ555hmrr+EsbSKwyM/PR3BwsM624OBglJWV4ebNm3qPWbp0KeRyufYrIsIGS6LpcauORZMRC+ZYEBERUWsnkQJRo4C+j6i/u+D0p6Yaj1BopkY19vLLL+Ozzz5Dv379cODAAfz888+IjY3FoUOHMHjwYJv04f7778cvv/yCu+66C5mZmfjhhx8QGhqKPXv2YPhwKxPenUgQXTytXRAEbNmyBQ888IDBNr169cLMmTOxaNEi7bYff/wR48ePR1VVFby9vZsdU1NTo12TGADKysoQEREBhUIBmUxms/5fLKrEmH/uRgcPKX57IwnITQM+TQI6RQFzM2x2HSIiIqLq6mrk5OQgKioKXl5ezu4OOYA5v/OysjLI5XKb3+9qtInlZkNCQlBQUKCzraCgADKZTG9QAQCenp7w9PS0e980ORaVtUrUKVVwl7FIHhERERG1PW1iKlR8fDxSU1N1tu3cuRPx8fFO6tEtvl7u2p/VRfJCoVMkj4iIiIioDXDJwKKiogIZGRnIyMgAoF5ONiMjQ5slv2jRIkyfPl3b/tlnn8WFCxfw0ksv4fTp0/j3v/+NTZs24bnnnnNG93VIJQJkXuqBodKqRkXyAK4MRURERERthksGFkePHsWAAQMwYMAAAMD8+fMxYMAALF68GACQl5ensxRXVFQUfvjhB+zcuROxsbF4++238fHHHyMxMdEp/W/qVgK3ppYFi+QRERERUdvikjkWY8aMMVoqXV9V7TFjxuD48eN27JXl/HzccamkSS2La8c5YkFEREREbYZLjli0Nc1qWbBIHhERERG1MQwsHEAzFar0JovkEREREVHbxMDCAfwaRiwUVZocCxbJIyIiIvtx8TJlZEOu9LtmYOEAmloW2hGLjiHq70VngJx9gErppJ4RERFRWyKRqG/tlEreW7QXmt+15nfvTM7vQTugk2ORtQ345kn1jsrrwGf3AStj1NuJiIiIrODu7g6pVIqbN286uyvkIDdv3oRUKoW7u3vLje2MgYUDaAKL24p3AZumAxW6VcJRlqfezuCCiIiIrCAIAnx8fKBQKDhq0Q4olUooFAr4+PhAEARnd8c1l5tta/x8PCCBClNK/g1A3zw4EYAApCwE+owHJFIH95CIiIjaiqCgIFy8eBG5ubnw9/eHp6enS9x0ku2IooiamhqUlJRApVIhKCjI2V0CwMDCIfx83DFUchqBqiIjrUT18rO5B4GoUQ7rGxEREbUtHh4e6NKlC4qKipCXl+fs7pAddejQASEhIfDw8HB2VwAwsHAIP293BKHUtMZNp0kRERERmcnHxwddu3ZFfX096uvrnd0dsgM3Nze4ubnWrbxr9aaNkvu4oxB+pjXuGGzXvhAREVH74Yo3n9R2MXnbAeTe7jii6oNroj9EGJrjKKjrW0QOd2jfiIiIiIhsgYGFA3i6SeHp7o4lddMbtjQNLhoeJy1j4jYRERERtUoMLBzEz8cdO1RDkXvPKkAWqrtTFgY8+jkQPdE5nSMiIiIishIDCwfR1LLIDU4A5mUCiW+pd8jCgXmnGFQQERERUavGwMJB/HzUgYXiZp16utNtCeodtRWc/kRERERErR4DCwfx81avL6yoqlVv6NBZ/b1aAdTXOKlXRERERES2wcDCQWTe6qXe0rKLkZZdDKWnHyBRj2Kg8rrzOkZEREREZANc2NgBUjLz8MNJdeXLHzPz8WNmPkLlXvjF0x9eNwuAikJA3sXJvSQiIiIishxHLOwsJTMPs9ano7JWqbM9X1GNs5U+6gccsSAiIiKiVo6BhR0pVSKWbM+CqGefCKBIlAMAVOX5Du0XEREREZGtMbCwoyM5JchTVBvcrwksrlzOdVSXiIiIiIjsgoGFHRWWGw4qAKAI6sCirqzAEd0hIiIiIrIbBhZ2FOTrZXS/ZsRCrix1QG+IiIiIiOyHgYUdDY3yR6jcC4KB/ZrAIgClDusTEREREZE9MLCwI6lEQPKEaABoFlwIuDUVSuCqUERERETUyjGwsLOkmFCsmjYQIXLdaVEhci/8Zfww9YPKQif0jIiIiIjIdhhYOEBSTCj2L7gbG56JQ0dPKQDgnT/GYmT/O9QNbt4AlHVO7CERERERkXUYWDiIVCIgvkcgxvQOAgAcyikBvDsBgjrQYJE8IiIiImrNGFg42IjbAgEAadnFgEQCdOis3lHB6VBERERE1HoxsHCw4T0CAADHL99AVW090LEhsOCIBRERERG1YgwsHKyrvw/C5F6oU4r4V+p5lEo6qXdwxIKIiIiIWjEGFg6247d83KhSJ2qv3pON1MsiAOBMdrYzu0VEREREZBUGFg6UkpmHWevTcbNOqd12vaFI3v6MLKRk5jmra0REREREVmFg4SBKlYgl27MgNtmuqb4dKCiwZHsWlKqmLYiIiIiIXB8DCwc5klOCPEV1s+3awAIK5CmqcSSnxNFdIyIiIiKymk0Ci/r6ehQVFaG+vt4Wp2uTCsubBxUAUIRbIxbG2hERERERuTI3Sw769ddf8cMPP2DPnj3IyMhAWVmZdp9MJkP//v1x5513Yvz48RgyZIjNOtuaBfl66d3eeCqUsXZERERERK7M5MBCqVTi448/xurVq3Hy5EmI4q1cAF9fX8hkMigUCigUCuzZswd79uzBm2++idjYWDz77LN48skn4eZmURzTJgyN8keo3Av5imqdPAtN8nYnVKCLzB1Do/yd00EiIiIiIiuYNBVq8+bNuP322zFr1ixcuHAB06ZNw9q1a3Hq1CnU1dVBoVDg8uXLKCsrQ21tLU6ePImPP/4YU6ZMwfnz5/Hss88iOjoa33zzjb2fj8uSSgQkT4gGAAiNtt+AL5SiAIkg4s2xwZBKBP0nICIiIiJyYYLYeOjBAIlEgtjYWCxcuBD3338/vLxMn65TXV2NLVu2YPny5Th16hSUSmXLBzlBWVkZ5HI5FAoFZDKZ3a6TkpmHJduzdBK5j3n9BQEoBf68DwjtZ7drExEREVH7Ze/7XZNGLLZs2YLjx49j0qRJZgUVAODl5YXJkycjIyMD3377rcnHffjhh+jWrRu8vLwQFxeHI0eOGG2/cuVK9O7dG97e3oiIiMBzzz2H6mrXS4ROignF/gV3Y/F9twMAQmRe8A8OV++sZPVtIiIiImqdTAos7r//fptczNTzbNy4EfPnz0dycjLS09MRGxuLxMREFBbqv/H+8ssvsXDhQiQnJ+P333/H2rVrsXHjRrz88ss26betSSUC7uwdBACoqKmH4NNZveP0j0DOPkDlmqM6RERERESGuGQdi3feeQfPPPMMZs6ciejoaKxevRo+Pj745JNP9LY/ePAgRowYgSlTpqBbt2649957MXny5BZHOZyps68nAGBE3UGIlw+rNx5dC3x2H7AyBsja5sTeERERERGZx6TAYu/evTh79qzZJ//555/xr3/9y6xjamtrcezYMSQkJGi3SSQSJCQkIC0tTe8xw4cPx7Fjx7SBxIULF/Djjz/iD3/4g8Hr1NTUoKysTOfLkXw93XCf+1Gscl8J1N/U3VmWB2yazuCCiIiIiFoNkwKLMWPGYPny5Xr3+fv7469//avefV988QWee+45szpUVFQEpVKJ4OBgne3BwcHIz8/Xe8yUKVPwxhtvYOTIkXB3d0ePHj0wZswYo1Ohli5dCrlcrv2KiIgwq5/WEkQVXpN+pv652d6GfPqUhZwWRUREREStgslToQwtHlVaWorKykqbdcgSu3fvxltvvYV///vfSE9Px7fffosffvgBb775psFjFi1apK27oVku16FyDyIYxTC8uqwIlF0Fcg86sldERERERBZxuYp1gYGBkEqlKCgo0NleUFCAkJAQvce89tprePzxx/H0008DAPr27YvKykr86U9/wiuvvAKJpHn85OnpCU9PT9s/AVNVFLTcxpx2RERERERO5HLJ2x4eHhg0aBBSU1O121QqFVJTUxEfH6/3mKqqqmbBg1QqBWB4pMXpOga33MacdkRERERETuRyIxYAMH/+fMyYMQODBw/G0KFDsXLlSlRWVmLmzJkAgOnTpyM8PBxLly4FAEyYMAHvvPMOBgwYgLi4OJw/fx6vvfYaJkyYoA0wXE7kcJR7BKFDTaGB6VACIAsDIoc7umdERERERGZzycBi0qRJuH79OhYvXoz8/Hz0798fKSkp2oTuS5cu6YxQvPrqqxAEAa+++iquXr2Kzp07Y8KECfi///s/Zz2FlkmkOBa9AKOPPw8RTRO4Gx4lLQMkLhoYERERERE14pKBBQDMmTMHc+bM0btv9+7dOo/d3NyQnJyM5ORkB/TMdqpvG49ZRy7j/7zWI1BVdGuHLEwdVERPdF7niIiIiIjMYHJgsX//fjz55JNm7du/f7/lPWsHAjt6YodqKE57jsSebp8Bv28H7ngIePhjjlQQERERUaticmBx/vx5nD9/3ux9gmBwPdV2T1N9u7BCCTFyJITftwPKWgYVRERERNTqmBRYfPrpp/buR7sU2FEdWNysU6Ja1g3eAFCS49Q+ERERERFZwqTAYsaMGfbuR7vUwdMNPh5SVNUqUezRBV0AoOQCIIoAR3qIiIiIqBVxuToW7Y1mOlS+pDMgSIH6m0B5npN7RURERERkHputCnX58mXs2LEDRUVFCA8Px7hx4xAYGGir07dZgR09kVtcheuVKsCvK3AjRz1qIQtzdteIiIiIiExmUmBx4sQJfPTRR4iLi9M7LWrlypVYsGAB6uvrtdt8fHywZs0aPPbYY7brbRvUuSHP4npFDeDfHbiRg+zTJ5FZGoUgXy8MjfKHVH8FPSIiIiIil2HSVKjNmzfjo48+QkBAQLN9qampmD9/Purq6hAeHo6HHnoI/fr1Q2VlJWbMmIHff//d5p1uSwJ9PQAAReU1yEUIAGDH/jTM/SoDk9ccwsjlu5CSyalRREREROTaTAos9u3bB19fXyQlJTXb99ZbbwEARowYgTNnzuDrr7/G8ePHkZycjLq6OnzwwQe27XEb07mjFwAg/dINrDut/nVECvna/fmKasxan87ggoiIiIhcmkmBxcWLFzFkyBC4uenOnCorK8PevXshCAKWLl0Kb29v7b5XXnkFISEh2LNnj2173MZokrd/vXgDF0X1iEWUUKDdLzZ8X7I9C0qV2PRwIiIiIiKXYFJgUVhYiODg4Gbbf/31VyiVSnTq1AkjRozQ2efm5ob+/fsjNzfXNj1towI7qqdC1dSrkCuqX2P1iMWtIEIEkKeoxpGcEif0kIiIiIioZSYFFoIgoLi4uNn2o0ePAgAGDx6st8K2v7+/TkI3NacZsQCAy2IQlKKADkINOkPRrG1hebUju0ZEREREZDKTAouoqCgcPXoUN2/e1Nm+Y8cOCILQbLRCw9BIB92iqb4NAHVww1VRvURv4zwLjSBfL4f1i4iIiIjIHCYFFklJSSguLsacOXNQU1MDANi4cSN2794NAHjggQeaHSOKItLT0xEREWGzzrZFjUcsBECbZ9FNUqCzPVSuXnqWiIiIiMgVmRRYvPDCC+jUqRPWrVsHmUyGwMBATJkyBQBw3333oW/fvs2O2blzJ0pKSjBq1Cjb9riN8XKXwtdLnRQvAto8i24NIxaaCWbJE6JZz4KIiIiIXJZJgUVISAhSU1Nxxx13oK6uDiUlJRBFEYmJiVi3bp3eY95++20AQGJios0621ZpiuQl3B6EXLEzAGC45DcMk2QhqKMbVk0biKSYUGd2kYiIiIjIKJMqbwNA//79cfLkSZw/fx7Xr19Hly5djE5zWrFiBURRRExMjE062pYF+nriQlElfHP+h7+4bQcADJScx1cef8dNt2B4S/4JYKJzO0lEREREZIQgiiKLI0Bdk0Mul0OhUEAmkzn02n9ZfwzKrG1Y5b4SAoDGC2yJAAQIwKOfA9EMLoiIiIjIMva+3zVpKhTZT0pmHvaeLUCy++cAdIMK4FaOBVIWAiqlQ/tGRERERGQqk6ZCff7551ZdZPr06VYd31alZOZh1vp0xEmyEOZhrPidCJRdBXIPAlFMhiciIiIi12NSYPHEE0/oLYDXElEUIQgCAws9lCoRS7ZnQQQQhFLTDqooaLkNEREREZETmJy8DQADBw7E4MGD7dWXduVITgnyFOpK2oXwM+2gjiw2SERERESuyaTA4vbbb8fvv/+O9PR0VFZWYurUqZg6dSq6detm5+61XYXl1dqfj6j64JrojxCUQF+pChGAIAsHIoc7roNERERERGYwKXn7t99+w7FjxzB37lwoFAq89tpr6NGjB0aNGoWPPvoIN27csHc/25wgXy/tzypIsKROPV1M1WSNLu3jpGWAROqg3hERERERmcfkVaEGDBiAd955B5cvX0ZKSgqmTZuGkydPYtasWQgNDcWDDz6Ib775BrW1tfbsb5sxNMofoXIv7apPO1RDMatuHvLhr9MuHwF4SfICvqsdhLTsYiibRh5ERERERC7AqjoW1dXV2LJlC9avX4+dO3dCqVRCJpPh8ccfx7/+9S9b9tPunFHHQrMqFKCe7gQAEqjwvHQTZrtvw1XvPhh141WoGsV/oXIvJE+IZiVuIiIiIjKLS9ex8PLywuTJk/HDDz8gNzcXiYmJUCgU+Oqrr2zVvzYtKSYUq6YNRIhcd1rUee9+AICSymqdoAIA8hXVmLU+HSmZeQ7tKxERERGRMWatCqXP4cOHsX79emzatAnXr1+HIAiIj4+3Rd/ahaSYUIyNDsGRnBIUllcjyNcLg92DgU/+jk5CRbP26krcwJLtWRgbHQKpvmxvIiIiIiIHsyiwOHfuHL744gt88cUXuHDhAkRRRL9+/fDCCy9gypQpCA8Pt3U/2zSpREB8jwDt4/SMyxgIwB/letuLAPIU1TiSU6JzHBERERGRs5gcWFy/fh0bNmzAF198gaNHj0IURURERODFF1/EtGnTEBMTY89+tit5dT4AAB+hBl6oQTU89bZrvGQtEREREZEzmRRYjBs3DqmpqVAqlZDL5XjqqacwdepU3HnnnfbuX7vk38kfNaIbPIV6dEIF8gwEFo2XrCUiIiIiciaTAosdO3ZAEAQMGDAA48ePh6enJw4cOIADBw6YdJGXX37Zqk62N0O7B6BEkKEzSuAvlCNP1J3uJAAIkXthaJS//hMQERERETmYyVOhRFFEeno6jh8/rrNNQxCaJxGLoghBEBhYmEkqEeAp7wwo1IEFGi0IrHmVkydEM3GbiIiIiFyGSYFFcnKyvftBTcj8gwHFGUT5VGNfo8WhOvt64o3772AdCyIiIiJyKQwsXJWPevrT6wkhGNd5GF7ecgo5RZWYffdtDCqIiIiIyOVYVSCP7KghsJDcVC8p++jgCABA6u+FzuwVEREREZFeDCxcVUNggapiAMDY6GAAwIHz17Hx10tIyy6GUiUaOpqIiIiIyKFMCiwWLFiAkpISqy5UVFSEl156yapztCtNAovzheWQSgQoVcCCb05h8ppDGLl8F1Iy85zYSSIiIiIiNZMCi5UrV6Jbt26YP3++zqpQpjh27Bjmzp2LqKgo/Otf/7Kok+1So8AiJTMPs9anNxuhyFdUY9b6dAYXREREROR0JiVv//bbb1i0aBFWrlyJ9957Dz179sSYMWMwZMgQ9O7dG506dYKvry/Ky8tRUlKCM2fO4Ndff8Xu3btx/vx5iKKIP/7xj1i6dKm9n0/b4aOuUSFWFWPJ9izom/QkQr387JLtWRgbHcLlZ4mIiIjIaQSxcTGKFqSnp2PVqlXYuHEjKioq9Nau0BBFER07dsTkyZPxl7/8BbGxsWZ17MMPP8SKFSuQn5+P2NhYvP/++xg6dKjB9qWlpXjllVfw7bffoqSkBJGRkVi5ciX+8Ic/mHS9srIyyOVyKBQKyGQys/pqF3kngI9Go9Y7GL1uvNti8w3PDEN8j4AW2xERERFR+2Tv+12TC+QBwMCBA7FmzRqsXLkSe/bswb59+3Dy5EkUFhZCoVBALpcjKCgIsbGxGDVqFEaPHo0OHTqY3amNGzdi/vz5WL16NeLi4rBy5UokJibizJkzCAoKata+trYWY8eORVBQEDZv3ozw8HDk5ubCz8/P7Gu7jIapUNKaEtwamzCssLza/n0iIiIiIjLArBELR4mLi8OQIUPwwQcfAABUKhUiIiLw17/+FQsXLmzWfvXq1VixYgVOnz4Nd3d3i67pciMWtVXAW+p6FXdUr0UlvI0254gFERERERlj7/tdl1tutra2FseOHUNCQoJ2m0QiQUJCAtLS0vQes23bNsTHx2P27NkIDg5GTEwM3nrrLSiVSkd12/Y8fAB3HwBAb1mtwfEKAUCo3AtDo/wd1jUiIiIioqZcLrAoKiqCUqlEcHCwzvbg4GDk5+frPebChQvYvHkzlEolfvzxR7z22mt4++238fe//93gdWpqalBWVqbz5XIapkO9MDIQgOHJUMkTopm4TURERERO5XKBhSVUKhWCgoLwn//8B4MGDcKkSZPwyiuvYPXq1QaPWbp0KeRyufYrIiLCgT02UcPKUMNDBayaNhAhci+d3e5S9fakmFBn9I6IiIiISMus5G1HCAwMhFQqRUFBgc72goIChISE6D0mNDQU7u7ukEql2m2333478vPzUVtbCw8Pj2bHLFq0CPPnz9c+Lisrc73golEti6TYUIyNDsGRnBJkX6/Aq1szUacUERvh59QuEhEREREBLjhi4eHhgUGDBiE1NVW7TaVSITU1FfHx8XqPGTFiBM6fPw+VSqXddvbsWYSGhuoNKgDA09MTMplM58vlNKm+LZUIiO8RgGnDIjEoshMAYM3eC/gu4yrSsoubFdAjIiIiInIUlwssAGD+/PlYs2YNPvvsM/z++++YNWsWKisrMXPmTADA9OnTsWjRIm37WbNmoaSkBHPnzsXZs2fxww8/4K233sLs2bOd9RRso0lg0VhXf3Vi9ycHLmLuVxmYvOYQRi7fxSrcREREROQULjcVCgAmTZqE69evY/HixcjPz0f//v2RkpKiTei+dOkSJJJbMVFERAR27NiB5557Dv369UN4eDjmzp2LBQsWOOsp2IaBwCIlMw9bjl9t1jxfUY1Z69OZd0FEREREDueSdSycweXqWADArx8DPzwP3D4BmLQeAKBUiRi5fBfyFPoL4gkAQuRe2L/gbq4URURERERaLlnHoqqqCpcuXUJlZaXO9hs3bmDhwoW477778Je//AXZ2dk26WS7pR2xKNFuOpJTYjCoANQ1uvMU1TiSU2KwDRERERGRrVk0FerNN9/EP/7xDxw5cgSDBg0CoK4LMWzYMJw/fx6aQZDNmzfjxIkTCA3ltByL6JkKVVhuOKhozNR2RERERES2YNGIxa5du9CjRw9tUAEA69evx7lz53DXXXdhx44d+Nvf/oaioiK8++67Nutsu6MnsAjy9TLQWJep7YiIiIiIbMGiwOLSpUvo2bOnzrZt27ZBEAR8+umnGDt2LFauXIlevXrhf//7n0062i41ngrVsJTu0Ch/hMq9DFbhFgCEyr0wNMrfIV0kIiIiIgIsDCxu3LgBPz8/7WNRFLF//37069dPp8hcbGwsLl++bHUn2y3vhuBAVAI1CgDqWhbJE6IBoFlwoXmcPCGaidtERERE5FAWBRYhISHIycnRPj527Bhu3LiBO++8U6edIPDm1ipuHoBnQ8Z+owTupJhQrJo2ECFy3elOIXIvLjVLRERERE5hUWDRv39/HDlyBFu3bkV5eTnefPNNCIKA++67T6fduXPnEBYWZpOOtls+DaMWTWpZJMWEYv+Cu7Hsob4AAG93Cfa+eBeDCiIiIiJyCosCi5deegkA8PDDD8PPzw/bt29HbGws7r77bm2bgoICnDhxQifBmyxgpPq2VCLgkUFd4O0uxc06FXJLqhzcOSIiIiIiNYsCi+HDh2PLli0YOXIk+vTpg2nTpmHbtm061bA3bNgAX19fJCUl2ayz7ZKRwAIA3KQS9A2XAwBOXC51UKeIiIiIiHRZVMcCACZMmIAJEyYY3D9v3jzMmzfP0tOThncn9ffsXwC/SCByOCCR6jSJjZDjyMUSZFwuxcODujihk0RERETU3lk0YkEOkrUNOP2D+ufMzcBn9wErY9TbG4mN8AMAnLhS6tj+ERERERE1sCiwKCgowN69e1FQUKCzPTs7G4899hhiYmLwhz/8AWlpaTbpZLuUtQ3YNB2ordDdXpan3t4ouIjt4gcA+D2vDNV1Sgd2koiIiIhIzaLAYtmyZbjrrrugUCi028rKyjBy5Eh8/fXXyMrKQkpKChISEnDu3DmbdbbdUCmBlAUARD07G7alLFS3A9ClkzcCOnigTini97wyh3WTiIiIiEjDosBi9+7diI6ORq9evbTb1q1bh4KCAkyePBlnzpzBO++8g5s3b+Ltt9+2WWfbjdyDQNk1Iw1EoOyquh3U9UL6dVEncK8/lIu07GIoVfqCEiIiIiIi+7AosLh69Sq6d++us+2HH36Am5sbVq5ciZ49e2LevHmIjY3Fnj17bNLRdqWioOU2jdqlZObh14s3AADfpF/F5DWHMHL5LqRk5tmrh0REREREOiwKLMrLy+Hj46N9rFQqkZaWhkGDBiEwMFC7vU+fPrhy5Yr1vWxvOgab3C4lMw+z1qejoqZeZ1e+ohqz1qczuCAiIiIih7AosAgLC8Pp06e1j/fv34+KigqMGTNGp119fT08PDys6mC7FDkckIUBEAw0EABZOJQR8ViyPctYJgaWbM/itCgiIiIisjuLAov4+HicPHkSK1euxKlTp/Dqq69CEIRmdS1+//13hIeH26Sj7YpECiQtb3jQNLhoeJy0DEdyFchTVBs8jQggT1GNIzkl9uglEREREZGWRYHFokWL4Onpieeffx79+/fHgQMHMGbMGAwfPlzb5uLFi8jKykJcXJzNOtuuRE8EHv0ckIXqbu/QWb09eiIKyw0HFY2Z2o6IiIiIyFIWVd6+4447sH//frz33nsoKirCoEGD8OKLL+q02bFjB2JjY/HAAw/Yop/tU/REoM949epPP70K5GUAQ55WbwcQ5Otl0mlMbUdEREREZClBFEVOwIe6DodcLodCoYBMJnN2d5o7+inw/Tygy1Dg6Z0AAKVKxMjlu5CvqNabZyEACJF7Yf+CuyGVGMrXICIiIqL2wN73uxZNhSIn6Hmv+vuVI+ogI2cfpFAheUI0AMNp3skTohlUEBEREZHdWTQVSqOgoACffPIJ9u3bh6tXrwIAwsPDMXr0aMycORPBwSYum0otu3oMkLgBqnr1yAUAyMKQlLQcq6YNwZLtWTqJ3J5uErz3WH8kxYTqPx8RERERkQ1ZPBXqm2++wZNPPomKigo0PYUgCPD19cXatWvx8MMP26Sj9ubSU6GytgGbpgN6JzwBGPYXKHuNwxFlHxy/UoZ/7DgDTzcBJ5IT4eUudWhXiYiIiMg1ueRUqKNHj2Ly5MmorKzEgw8+iC1btuD48ePIyMjA1q1b8dBDD6GiogJTpkzB0aNHbd3n9kWlBFIWwGBQAQCH/g3p5xMQv/1OzAr+DcEyT9TUi1xmloiIiIgcxqIRi4cffhhbt27F5s2b8eCDD+pts2XLFjz88MN46KGHsHnzZqs7am8uO2KRsw/47D4TG6tzKT6PeAOLz/XAuJgQJMWEIMjXC0Oj/JlrQURERNSO2ft+16LAIjg4GL169cK+ffuMths1ahTOnj2LgoICizvoKC4bWJzaDHzzlBkHCFB4dMaAsnegajQgFSr3QvKEaOZcEBEREbVTLjkVSqFQoGvXri2269q1KxQKhSWXII2O5ibAi5DXFmKo5LTO1nxFNWatT0dKZp7t+kZERERE1MCiwCIkJATHjx9vsV1GRgZCQkIsuQRpRA4HZGEwvKCsfkEo1XmsGZZasj0LShVLlxARERGRbVkUWCQmJuLMmTN4+eWXoVQqm+0XRRGvvvoqTp8+jaSkJKs72a5JpEDS8oYHpgcXhfBrtk0EkKeoZlI3EREREdmcRTkWV65cwYABA1BSUoKuXbvi0UcfRbdu3QAAubm5+Prrr3Hx4kUEBAQgPT0dXbp0sXW/bc5lcyw0srapV4cqu2a0mUoE8hGAkTXv6eRYNPbeY/1xf/9we/SSiIiIiFyUve93LSqQ16VLF+zatQtTp05FZmYmVqxYAUFQf5quiVP69u2LL774olUEFa1C9ESgz3gg9yBw5kfg0L+hHsG4FReKDY+X1D1uMKgAgCBfL7t3l4iIiIjaF4srb/ft2xcnT57E7t27sW/fPly7pv4kPSwsDKNGjcKYMWNs1UfSkEiBqFHqr67xzUcwfEPw8s3H8VNNf72HCwBC5OqlZ4mIiIiIbMniytum+OSTT3DlyhUsXrzYXpewGZefCqWPSqkewdgyCyi7DDy0BimSUZi1Ph1A85J6AoBV0wZyyVkiIiKidsgll5s11Zo1a7BkyRJ7XqJ904xgxDQUKbywB0kxoVg1bSBC5LrTnWRebgwqiIiIiMhu7BpYkIP0uEv9PXsXIIpIignF/gV3Y8Mzw/DHweocl04+7qipVyEtu5jLzRIRERGRzVmcY0EupGs8IPUEyq8BB98HwgZAGjkc8T0CcOVGFb4+egW5JTcx96sMAKzCTURERES2x8CiLTi389bPO19Tf5eF4fgdC/HSL4HNmmuqcHNqFBERERHZCqdCtXZZ24BN0wFljc5msSwP/dP+hnslR5odwircRERERGRrDCxaM5VSveRss/WfAAEiRBFIdv8vJFA1288q3ERERERkSwwsWrPcg0YrcUsEIEwoxhPSFL3BBQAUllfbq3dERERE1I6YFFhIpVKLvo4caT4NxxwffvghunXrBi8vL8TFxZl8vq+++gqCIOCBBx6w6vour6LApGaL3ddjv+ffkKhnWlRReQ2nQxERERGR1UwKLERRtPjLUhs3bsT8+fORnJyM9PR0xMbGIjExEYWFhUaPu3jxIl544QWMGjXK4mu3Gh2DTW4aghKscl/ZLLh484ffMXL5LqRk5tm6d0RERETUjpgUWKhUKou/lEqlRR1755138Mwzz2DmzJmIjo7G6tWr4ePjg08++cTgMUqlElOnTsWSJUvQvXt3i67bqkQOB2RhUNfUNk7S0ERfzoVmlShzggulSkRadjG+y7jK2hhERERE5Jo5FrW1tTh27BgSEhK02yQSCRISEpCWlmbwuDfeeANBQUF46qmnWrxGTU0NysrKdL5aHYkUSFre8MC04CJMKMZQyWmd7eauEpWSmYeRy3dh8ppDmPtVBiavOcRRDyIiIqJ2ziUDi6KiIiiVSgQH6071CQ4ORn5+vt5j9u/fj7Vr12LNmjUmXWPp0qWQy+Xar4iICKv77RTRE4FHPwdkptejCEJps22mrBKlVIl47+dzeHZ9OvIUuknflox6EBEREVHb4ZKBhbnKy8vx+OOPY82aNQgMbF4QTp9FixZBoVBovy5fvmznXtpR9ERgXiaQ+JZJzQvhZ3DftRtVeqc4pWTmYcSyVLz781m9x7E2BhEREVH75pKVtwMDAyGVSlFQoLvqUUFBAUJCQpq1z87OxsWLFzFhwgTtNpVKnUfg5uaGM2fOoEePHjrHeHp6wtPT0w69dxKJFIh7Fkj7ACjLg77aFioRyEcAjqj6GDzNC9+cROOc+1C5FybGhuI/e3P0nFFX41GP+B4BFj0NIiIiImqdXHLEwsPDA4MGDUJqaqp2m0qlQmpqKuLj45u179OnD06dOoWMjAzt18SJE3HXXXchIyOj9U5zMpeRnAsRAgQBeKPucaiM/NqbLuSVp6jGRyYEFY2xNgYRERFR++OSIxYAMH/+fMyYMQODBw/G0KFDsXLlSlRWVmLmzJkAgOnTpyM8PBxLly6Fl5cXYmJidI738/MDgGbb2zxNzkXKAp3ieYJvCI7HLMKOX0ybKmaNIF8vu1+DiIiIiFyLywYWkyZNwvXr17F48WLk5+ejf//+SElJ0SZ0X7p0CRKJSw64OF/0RKDPeHVl7m+eUhfSS1qOAXfcj1XheXh5yymUVNbZ/LICgBC5FwZFdkJadjEKy6sR5OuFoVH+kEpaXrWKiIiIiFovQbSmil0bUlZWBrlcDoVCAZlM5uzu2M6PLwFHPgIGzQQmrAQAbDl+Fc9tzLD5pQQAfxodhW0n8nRWjQqVeyF5QjSSYkxfuYqIiIiIbMve97v8yL+t63G3+nt2qjaBIkRm+6lKoXIv/Gl0FP6zN4dL0RIRERG1Qwws2rpuIwHBDSi9BBz6N5CzD0Mj5QiVe5lQUs90380egW0n8vQmeTdeira2XsWK3URERERtkMvmWJCNZO8CJBJACWDHywAAqSwM/x64EA/9EggB+hamvUUTfEyL64r/Hr6ksy9U7oWq2noobtZjx2/5zUYqGtMsRTtsaSpKKmt1zsFpUkREREStH0cs2rKsbcCm6YCyVnd7WR4GpM3Ft3cVIUSuOy2qaY51iNwLq6YNRGLDjX+IzAvvPdYfG54Zhv0L7kafEPX8vLMFFSZ1qXFQAXCaFBEREVFbwRGLtkqlVC85a3BykoABvy3H/hdP4kiuQruC06DITvh4Xzb+seMsIjp5Y/eLd0EqEfDJ/hwAQL8uctzfP1x7pm4BHXA4pwRVtfUWdVPdE/U0qbHRIVw9ioiIiKiV4ohFW5V7UKeORXMiUHYV0stpiO8RgPv7hyO+RwA83CS4f0AXAOqpS5ociLMF5QCAXsG+OmeJDPQBANQrVRbnbTSu2E1ERERErRMDi7aqosDidmFyL/h6uqFeJSKnqBJAo8AiRDewiAroAADILbmJ5AnRVnSYFbuJiIiIWjMGFm1Vx2CL2wmCoA0gTueXQRRFnGvIoegV3FGnbaQmsCiuRFJMKFZNGwgPqe7bSuZl2ow7VuwmIiIiar0YWLRVkcMBWRhgcHKSAMjC1e306N0QWJzJL0d+WTXKa+ohlQiICuyge5kA9VSoG1V1UFTVIfGOEHi6qa8Z5OsJAHh6dJTRaVIC1KtDDY3yN+cZEhEREZELYWDRVkmkQNLyhgdNb+kbHictU7fTo3dDLsXZgnKcyVdPg+oW4ANPN932HTzdtAHExeJKXCqpQnmNEh5SCWYM7wYA+O1qWYvTpJInRDNxm4iIiKgVY2DRlkVPBB79HJA1qRHRMUi9PXqiwUN7a6dClWunQfVukl+h0a1hOtTF4kpkXi0DAPQJ9cXgyE4AgBOXFUiKCcXbj8Y2O1bm5YZV0wayjgURERFRK8fAoq2LngjMywRmfA/II9Tb/vC20aACAPo0BBFXbtzE8cs3AAA9gwwEFg0rQ10sqsKpqwoAQEy4HDHhckgEIL+sGvmKasi83AEAwTJP3NdPHUgM6+7PoIKIiIioDWBg0R5IpEDUKCByhPrx9dMtHuLn44FgmXqK067ThQCaLzWr0TiBO7MhsOgbLkcHTzftMSeulCLtQjEA4O4+wZg5IgoA8OvFG1CpjNX+bplSJSItuxjfZVxFWnaxdolcIiIiInIcFshrT4LvUH8vyDSpea9gXxSU1aC6TtXwuKPedpqpUDnFldrlaWPC5ACA2C5+OJ1fjhOXS3EwWx1YxPcIQN9wObzcJbhRVYfz1ysMBi0tScnMw5LtWchT3FqqNlTuheQJ0RwJISIiInIgjli0J8ENCdSFWSY1bxxISCVAhL+P3naaqVCZVxUoraqDu1RArxD1sf27+gEAdp+5jt/z1PkX8d3VhfgGNeRgHG4YyTBG36hESmYeZq1P1wkqACBfUY1Z69ORkpln0vMkIiIiIutxxKI9CWoYsSg+D9RVA+6G60akZOZh87Gr2sdKFXDXP3frHQnQTIWqU6qnIPUO8dWuHhXbxQ8AkNUQVHTp5AX/Dh4AgLioABw4X4wfTuVB5u2OIF/1krNNV4fSNyoRIvNEdb0K+iY9iVCve7VkexbGRodwtSkiIiIiB+CIRXviGwJ4+wOiymiehWYkQHGzTme7oZGAjp5uCGgIFgCgc0dPbZ5DTlGFTtsrN6oxcvkunXMculCCuV9lYPKaQ832GRyVKKtBaZVu/xoTAeQpqnEkp8RgGyIiIiKyHQYW7Ykg3MqzMDAdSqkSsWR7lsGRAEA9EtA4QTolMw9l1bdu8n85cx0jl+/C0h+zMOfL483Ok6+oxrPr0/HOzrN692mCF2N9MVVheXXLjYiIiIjIagws2pughjyLgt/07j6SU9JsdKCxpiMBmhEFzTQojTxFNT7am2M0QDF0fkAdvBy6UGy0L6YI8jU83YuIiIiIbIeBRXujXRlKf2Bh6if8heXVNhlR0EcTvKRlt5zUbYyftztUosjlZ4mIiIgcgIFFe9PCVChTP+EP8vVqcXTDetYFBKU36zD148PN8jaIiIiIyPYYWLQ3nfuov1cUAEc/AXL2ASqldvfQKH+Eyr1gaB0lAeo6EUOj/O2evxDfPdBoX0zF5WeJiIiI7I+BRXuTvQsQ1EvB4vvngM/uA1bGAFnbAABSiYDkCeo8jKY39JrHyROiIZUIds1f8O/gjiFR/tq+WMNQ0jkRERER2Q4Di/YkaxuwaTogKnW3l+WptzcEF0kxoVg1bSBC5LqBQ4jcC6umDdTWsWhpdEPDUICib59GSWUd7lzxCwDgwykDrR614PKzRERERPbFAnnthUoJpCyA/ryFhpJyKQuBPuMBiRRJMaEYGx2CIzklKCyv1lu8TjO6MWt9OoQmZ9a0+tPoKGw7kadb3E7upR2JaFr4rjHNFKZFf+gDEYCXmwBPd7dm9TXMweVniYiIiOxDEEWRc0MAlJWVQS6XQ6FQQCaTObs7tpezTz3tqSUzvgeiRpl1an2VsUMbgoekmFAoVaLBAKW2XoVhS1NRUlmr99wCAF8vN5RV12N0r86YMjQCs9anA7AstXvDM8MQ3yPAgiNvMfZ8iIiIiFyVve93OWLRXlQU2LZdIy2NbkglgsGb+WO5NwwGFYA6eCirrgcADInspJ2m1TSQCZF5orpeBUVVnd6AQ4B6pGRolL/Zz6+xloIoIiIiovaKgUV70THYtu2aMBY8GGPO1KTB3dRBgaFAZmdWvtFpWZqkc0tpigE2DVw0U7Ya558QERERtTdM3m4vIocDsjAYTpcWAFm4up0DmbqylEQA+kf4aR9rApn7+4cjvkcApBLBYNK5zNvdpJt+pUpEWnYxvsu4irTsYp0VpIwVA+SqU0REREQcsWg/JFIgabl69SdDn+knLVO3cyDNylL5imqjORPdAjrAw63lOLjxaMaGI5ew7cQ1dO7ogZp6FdKyi3WmaDXOlbhYVIUNRy4hv0z/FKeWigE2XnXK2hwOIiIiotaIydsN2nzytkbWNvXqUGXXbm3z9gcmvAdET3RKlzRTjADjCdnm5jJs/PUSFnxzSu85AOMrUgG3xnZWTRuImnoV5n6V0eI159zVA8+N7c1kbiIiInI59r7fZWDRoN0EFoB66dncg0DaB8DZFCDmEeCRtU7tkr6k6KYa3+i3FFwYyodoOlZjCv8O7nhv0gA8/skRk9ozmZuIiIhcEQMLB2lXgYXGpUPAJ4mAewfgvndu5Vg4eDqUhlIl4lB2MWZ/mY5SA7UqNKs77V9wt8FRAaVKxMjlu4wGKebq5OMOETC46lTTPgKmBUBEREREjsLlZsl+yvMBQQLUVQJb/qze5hsKDJoJBPRQrxDlwEBDKhEgkQgGgwrAtFyGlvIhLHGjyvSifA3lBrFkexbGRodwWhQRERG1Cwws2qusbcDXT6DZxKDyPGD3W7cey8LUSd8Oyr8wdflZY+3sVV27cbG+ljCZm4iIiNobLjfbHqmU6gRuU7INyvLUK0llbbN7twDTl5811s7Uc5ircbG+EJmnSccYC3KMLW9LRERE1NpwxKI9yj2ouyqUUQ0Te1IWAn3G231aVEvLz5pSQdvUJWytMax7ALZmtPwaGgpyWMGbiIiI2hqOWLRHFQVmHiACZVfVAYmdSSWCdjnYppkJplbQNnYOW3l4YBeEyr2MlRtEqIEASLNiVdM8EE0F75TMPJP7wVEPIiIichUMLNqjjsGWHWd2QGIZQxW0Q+ReJq+0ZOgcxnTydkNHz5YH8aQCMDCyU4vBi74AyJYVvFMy8zBy+S5MXnMIc7/KwOQ1hzBy+S6zAhMiIiIiW+Fysw3a1XKzKiWwMkadP2HOZKEZ3wNRo+zWraYaV8YO8vXSqZptzjlaWsIWUNeqOLQoAbtOF5hVrA9oXmjPQyrB48O6IiE6pFmf07KLMXnNoRb7veGZYQaTvpUqER/sOo93fz7bbB+XuiUiIiJD7H2/69IjFh9++CG6desGLy8vxMXF4cgRwwXK1qxZg1GjRqFTp07o1KkTEhISjLZv1yRS9UpPAEyeLOQTCETE2a1L+kglAuJ7BOD+/uGI7xFg0bKtUomAET0DsezhvhCgf3qVAOCtB/vCw01i8kiHZtoSAOxfcDc2PDMM9/VT38jXKlVYe+Ci3hEEa1e9SsnMw4hlqXqDCkAdDIkAFn5zCvvOXseB80WcJkVEREQO4bKBxcaNGzF//nwkJycjPT0dsbGxSExMRGFhod72u3fvxuTJk/HLL78gLS0NERERuPfee3H16lUH97yViJ4IPPo5IDPxU+2qIuBfsQ5bHcrWzJlelRQTiv0L7sYXT8XBz9td7/kaT1sCAMXNWvxwsvkUpKZ5E9aseqXJzcgvq2nx+NKbdXj8kyOY+vFhTpMiIiIih3DZqVBxcXEYMmQIPvjgAwCASqVCREQE/vrXv2LhwoUtHq9UKtGpUyd88MEHmD59eovt29VUqMZUSnVSdkUBUJwNpK8zsmJUw+f9j37usLoWtmbO9CpTpy198VQcXth8wmBRvsbVwgFg5PJdLa561bSyuC2qiXOaFBERUfvWLitv19bW4tixY1i0aJF2m0QiQUJCAtLS0kw6R1VVFerq6uDvr39Z0pqaGtTU3Prkt6yszLpOt1YSqW7exMjngHduV49QNOPYpWftQTO9yhSmTltKu1Bk9Ia/abG85AnReLZhGpU+mtyNtOxibQCkEkWrq4mzIjgRERHZk0sGFkVFRVAqlQgO1l29KDg4GKdPnzbpHAsWLEBYWBgSEhL07l+6dCmWLFlidV/bnMuHDQQVGg1Lzx76NxDSD6i8rl5lKnK44UCj8ahIS21diOmF9ky7QdcEKkkxoZga1xVfHL6ks99dKmD6sEicya/A69t2Ib/sViBhaEqWuTRBzroDOXhiRJTVwYUtEuyJiIiobXDJwMJay5Ytw1dffYXdu3fDy0v/zeGiRYswf/587eOysjJEREQ4qouuy9QlZX96VfexbygwaCYQ0EM3eMjapq7y3Xh6lSxMnTzu4tOpTC3WF98jAB/8cr7F8xWV10CpEiGVCKhTqgAAD/QPAwBszbiGOqWItQcu6j3W2IpWlnjzh9/x8f4cqwryscgfERERNeaSyduBgYGQSqUoKNC9yS0oKEBISIjRY//5z39i2bJl+Omnn9CvXz+D7Tw9PSGTyXS+CJbXuCjPA3a/BXzzFPDZferlbH96Ddg0vXnORlmeeruLJ4KbWqxvWPcAo8XyNN784XdtAvXxS6UAgFC5N74zoYK3PVhSkE/DlkX+iIiIqG1wycDCw8MDgwYNQmpqqnabSqVCamoq4uPjDR73j3/8A2+++SZSUlIwePBgR3S17Ykcrh5RsLZmddk14OC/oL8aRMO2lIXqaVIuzJTVpMyp9K258T5XWAEA+Cb9ijmVRGzK3IJ8gHrq04FzRVj4zSmbFPkjIiKitsNlp0LNnz8fM2bMwODBgzF06FCsXLkSlZWVmDlzJgBg+vTpCA8Px9KlSwEAy5cvx+LFi/Hll1+iW7duyM/PBwB07NgRHTt2dNrzaHU0NS42TYf6NtleN4cNuRq5Bx1adM8SSTGhGBsdYjSXQBOANJ0a1FTjV7NzRw8Ulre8dGxLQuVemBgbiv/szWl2jZY0TSw3Rt/UJ2vPSURERG2HywYWkyZNwvXr17F48WLk5+ejf//+SElJ0SZ0X7p0CRLJrQGXVatWoba2Fo888ojOeZKTk/H66687suutn6bGRdPcCHsobx1TZkxZTUoTgKw7kIM3f/i9xXN28vHA9Ypaq/r12vjbtUnYA7p2MunGX5+WVsDSTH0yJ2gxdVUtIiIiahtcNrAAgDlz5mDOnDl69+3evVvn8cWLF+3fofYkeqJ6SdnDq4EdL9vvOimLADcvl0/kNpVUIiDQ19OktiFyL5xtmBJlqUBfT+3ISdORlcAOnsjKK8P//dhykGNsBazaehVe3pJp9tiV6atquS6uekVERGQ6lw4syMkkUiDuWSDtA3XCtT2mRVUVq6ddteKie02ZekN99+1BOFdYYXDVKUuu1XRkZViPAHxyIKfFla0GRXbS1s0I7OAJCEBRRQ0uFlVh3cEc3KgyfVUqzTmHRumvIdOUs2/eDV2fq14RERGZx2Urbztau628bYqsbQ05F4B9ci4EdcL4vFOtor5FSzRVslsKGEJknri/f5hFuRGGKnTro5nG1PQamqP+NDoK207kWV2Ar/E5Ta3u7aibd3ODB03OStPfCauXExFRa2bv+10GFg0YWLRAXz0KW0t8Sz1C0gaCC0M3840Zu7EPkXlicDd/fH+yeQ6KJTe3+m6gO/m449HBXfTeQFuqcVDQ0kiEobwNW9+8mxs8tEQT1O158S4cy73hsJEWZ4/sEBFR68fAwkEYWJigcQVtn0BAENSVt4uzgWOf2iYRu5UUzzNFSmYeXt/2G/LLDK/81NJNqi0/0dfcmH5y4AJ2ZhUitosMucU3bVZ8L6KTF3a/eLdJ/daM6hgaJTFnRMYYS5LOTeXfwQMllbeS7+05TYrTsoiIyBYYWDgIAwsrqZTA3n+qi+RZpeEmso3kXBw4X4SpHx9usd2GZ4YZXHXK1p9UrzuYg9e3ZVl8vCFuEgGZSxKx+0xhiyMRvl7uVr8uTTV9nQZFdsKdK36xyRQvczyX0BNz7u5p9WiC5vnszMrHJ3oqsnNaFhERmcve97tM3ibbkEiBMQuAoNubT5nyDQPqq4GbN9ByJoEIQFAXz+szvtVPiyqqMK1OhbGlWU1Z6tZUKZl5WGLDoCKggwf+/kAMFn57CoqbdTiTX44l27MMFs8TACz89hRM/TjjwPnrJgVS+j7R9+/gjpJK24zGmOPdn89hw5HLeH2i5aMJptQM0byeS7ZnYWx0iPY14pQpIiJyFgYWZFuaZWo1U6Y6BqureZ/+wYyie62neF5LTF0hyhFLsypVosGbfkv4d3BH2qJ74OEmwX8P5eJgdjF+PGU8CVwEUGrGClMf/JKNL49cwoP9w5EQHaL3JtnQdCdnBBUa+WXqCuvmjiYoVSI+2HUe7/581qT2TYsRuvqUKQY9RERtGwMLsj2JtHlAYEnRPVNzNhrnfmgCGRcZ6Rga5Y9QuVeLy72aujSrNY7klNh05ae3HuwLDzd1kcroUBkOZhcjK6/M6vM3VVJZh7UHLmLtgYvNbpJtHSzZWtPRBGNMyckxpLC82mCAla+wLMixNVsFPQxOiIhcFwMLchxzi+41Lp5nKHjQt1qVbygwaCYQ0MPpgYZUIiB5QjRmrU9vNlajuRVKnhDtkBsjW1XCDtFzMxgdpp6nWVhm33yGPEU1nl2fjqdGdMPdfYKRlVfm0BwKc6ZXNR1N0NB3Y7wzK9+qJPPADp54YfMJo1PQzAlybM3aoKdxvsnWjGsOS5onIiLzMLAgxzKn6F5VEbDpcaBXInDlmPqxhiwMiHkEOPh+83OU5+kmkfsEAP0mAb3/4JQgIykmFKumDWz2aa2+G3R7snS6VYjME5OHdkW3wA4GPyHWBBZXbtxEiMwL+XYOMDQjGJbS9H5831B8f8q0kTGJAOx6fgzGvbfPrGCmcZ6Ivk/tQ2SeqK5XWRRUaEa8IKDFKWj6ghxHMDaqZErQ01K+iauMyBAREVeF0uKqUA6mLbrn4LefE5ezdfYUDlMK9/l5u+P9yQMgkQgoqqgxuZ91ShXuSN6B2noV3rz/Drz23W+2fwI2pPmU+0JRJf6Rcsbk49Y/FYeKmjo821CjxJzrWVo3oyXPJfRE14AOeG5jRott59zVA8+N7e3QRO+07GJMXnOoxXaNVwBraUWspmy1PDERUVvHVaGobdLkXHw/D6gqdtx1y/LUAY0TlrO15epOll6/pWlZyx7ui1G9Opt9bnepBL2COiLzWhkOZDvw92kBmZc79r10F9ykEsxaf8ysYw9dKMbz9/ZCYEcPFFXUtnxAgzxFNT5qqLBua+/+fA7+HdxNavvBL9n4Jv0qkidEA4BDEr1NnYKnaWfKilhNOXNEhoiIbpE4uwPUjkVPBJKWOfiiovpr+9+AC3vUuRvtiGZaVohcd1pUiNzLqqkkKZl5yL5e2fBzPgBA6mIfHGu6U1Zdh+sNywCfvKIAoC52Z6i7AgC5t/rGPe1CMc4UlKOoohYeUgFz7uph306byJwVsPIb8lSeXZ/e7OZdM60oJdP49DClSkRadjG+y7iKtOxiKFX6x2GUKhFF5aYlowf5emlzMSzNm7E0j8jU50NERMZxxIKcy9dJc6Jv3gA+n9imKn2bKikmFGOjQ2w2BcZQYq7SjHsze04V0mh83hOXFfCQSnC19CYA4LXxt2P+phMGR3JeTOyNV7dm4vilG1ixQz11alTPQLyQ2Acx4XKzP2G3hCYXw5zlevUx9vpamvPQtKq6oURrfTTTmPpH+GHE8l1W/f4tySNy9SV6iYhaEwYW5FyRw9U39y0lctuLE6dGOZOtpmVZu9yrn7c7Ppw6EMO6B0AqETCgayeH3KSfvFIKT3f1gG33zh3w4MAu8PaQGkywF0V18rZKBFJ/LwQAHM0tRUpmnjZQe3fnWXzwy3m79Pe5hJ4Y3M3fpGrl1jI2rchQEKlZreu+fqE4evGG2cn7E2NDMWJ5qsW1RyxdttmU1aqaBuGDIjvhWO4NLndLRKQHAwtyLolUPWJgcvE8W2u43vfPAbeNBa4eVS9p6xMICAJQeV29ZG1EHHD5sEvWynAmS2tjNM7pGHFboHZ749EUUz/xtsTJKwp4uat/f7Fd/Jpdu+lSsH/5ovnNp+Jmnc5qRCNuC7R5YNH4k/PvMq6afFxMmAyZ16yrKdJ0WpEpQeT3J02sPdPAy12CET0CbJJ/Yu6yzS2tVgUA8zedgJd7ps57UBNgath7dMPZiz4QEZmDgQU5nyXF82ytqghYGgaIKv37BYnuvnY4hUofU+e0+3m7o/TmrU+jjS21qxlNie8RgFfGR+usDmRN6CkACGhIuj55pRSeDcX9+obLm11bw5Sbac20oZaKIZrrtfG344kRUdqbSHOm+dx7R4jVgYW/twfSsou1N7QqUbT5SFJ1nQqpp69bdQ6Zlxv+8Ug/s2/sTQmKq2qVqKrVzcNqmn5haLlbWwQEnKZFRK0NAwtyDZriebkHgTM/Aof+DYtHMHwCLFtpylBQoW9fO51C1ZSpN7sfThkIiUQw+yarcZAxNMrf6DSpxnkagP5cidcn3oHnN51AWXU99p1X10Xp10UOQ1q6+Ww6bUiz6pY1NNN6GgcVQMtV3BsfPyWuKzYcuWRVkDNj3RGdm+gOnq4zQuffwR09OnfErxdvIDbCT6cSu6k387aqtaIvL8UWAYGrV1K3N47UELVODCzIdUikQNQo9VfX+OYjGN4BgLIaqK00cAJBPZLwtwz1tKUzPwInN+kW1rOZhtuJlIXqgKidTotq6WZXc5M8rEeA1TcFTacqBXbwBAQ0q7ehL0+j8QjJx/suIOOyArX1KggA+oQYXsfb3KVSNatuvbzllEX5AsaqsRtbLrgxEcCE9/frBFmWaPrJfGWN81dQe2pENyQ0jA5duF6Bse/uxeGcEtysVWLP2UKTb+ZTMvPw5ve2q7WiCTAPZRfjaO4NvPvz2WZtGleN1zwHQ/8mTJmm9fKWU7izVxAyLpfa7ebbWTf3thypYYBC5FgskNeABfJckEqpHsFonNdw+oeGfAxA72fSTUcQVEogZx+w+Qn1SlD2MPVbwM2j3eZmaD5ZBfSPEjjjk1VDNxMpmXmYv+mEzvQWYzcslhR3A4DaehWGLU01mB8iAJD7uMPLTarzybkpN0+m1HnQvPZ/Gh2Fr49dsTgp2lUEdPDA/z0Yo/O6iKKIkct/wdXSm3igfyi2ZhjO73hqRDfc3ScYEIDU3wtMKrpnCZmXG8qq601qa+h3rVSJWHcgB2/+8HuL5xAEQLRTvoc1N/fW3MwbGqmx5O8Jp5IRNWfv+10GFg0YWLQiWduaj2bIwtU1MQxNS7JrpW8jnx83zc3wCQD6TQJ6/6FNBRmt4T9wS25YWqpWbqzisykBl6XL/ipVIg5lF2P2l+k6uSv6+rbr+TEYsXyXXZLgHcG/gzsOLUqAh1vzskuPrz2MfefsMSLpOM8l9MScu3sanEJlDkPvLUOje4ZYc3Ov7zmEyDwxeWhXdAvsYPT6mn9vxp5/05Xk7PEciNoyBhYOwsCildE3mtHSTXrWNsdX+jbGNxQYNBMI6GH5aEbT18GJIySuPOWgpRsWawMEc26ybBVwmTOaorhZq/c5OJp/B3eTR09aen1TMvPwrJX5LK4iROaF+/vbpo6LodGwxoy9B23xb6Wl52Do+qa+p+3xHFz57xeRLTGwcBAGFu1EfS3wzu12yruwkqHRDEPBg74cknYyQmIuS6c0aThrWogx32VcxdyvMlps995j/XF//3C9z8GcG31L+Xdwx4P9w5EQHYJBkZ1w54pfTEoqt+bGkUyjL9/D0n8r5vxODI2snCuoMHnJZmOBp7nPwZpRFqLWxt73u0zepvbFzQO4710DeRpOVlWsXg3r0L9vjWZUl7YcPDTWdHvjczZeIteFRjocwdwk7KasqVZuq2KETZm6Ipemnb7nYM6NviH+HTx0plm1dEPWUgK6KcnNltZPsQVnVNuxl7UHLmLtgYs6wZ+pq2U1/bdy6EKxyb8TzUpaC789hde3ZVm0QlfjJPa7+wTrTJUz59+7wdW3ymrw7s/ntI8bv0aN35uOGOmwxTU4IkOOwsCC2h9DdTOM3bA7+naiPA/Y/Zb+fcaWxTWm7Bqw6XGgVyJw5ZjpIx3Ggo7GAYoLJ66bexOuj70CBEuZuiJX42rU+p6DKStN6aM5/54X7zKrErVm5SxrpoiZeuNoLkPTiBrfVOaV3sT8r0/Y5frOUlJZpw0yOvm4m3RMYAdPbZ2Ti0VV+OSAeSuQiQBKq+oAWDdiVlJZh2FLU/FWQ3K/UiWiqLzGpGP9vT0wd1OGSe/7xq+RJnhW3KxrVsSzcWBtbm6LPrZautgeUzJtFaww6GlbOBWqAadCtUPGPrVvepOsrAfWP+DsHjuHoaDDyw9IX2e4qKGp07IsyZcx87jGSdgCVBgqOY0glKIQfjiq6oXBkrPo5VOJ5Cl3Q9ptRKsZrbHVilz6bjyaVphuzBYJsNbcTJgzF99UpibV2+ParY2PhxRe7lKXWxDgnj6dcfyyosV+aQJIAcCNKsetmGarWiYajRP/Af3/pnZm5dslid1WwUprWPijrWGOhYMwsCCjVEpgZYy6MF6bmQjhRMamehlLatcEE/ryS1pIhk/JzMPWL1djsfvnCBNKtNuVogCp0Oh3aslojbHAxlg7G0xJs9V/zE1vSgZFdsKx3BvYmZXf7FNZZ//H39JqXZYwJ2fG1tc2VVuahtWeNV7+WDOaofn3plnFSyWK+OuG4wZXfdMIkXnh9YnRAKA3T6S6XtUwMtScoUR8Y0G/UiXig13n9dZqaWmFPUcFPfbSVkZWGFg4CAMLapF2yVqA/707kKkjJIaO0wQI+9+F2DC9rPF/BWKTxzrMGa3RF5AYC4BMyZ8xMchR1tfj9OEduHnjKrw7haNPXCKkbrab6eqK/6EaGq1pzJQbcVNyOiy5NnArWAGA17f9hvwy06boNCVAXZNk24k8nRtHBhqW8fGQ6tSycQXGRgjtrXEivqFFHh7sHw6Ztwe+PJyLAiNTzfQFK4aS480Nepw59aotjawwsHAQBhZkEn01NIzlZhjN2yDHcfAtmD1+79YGOfqm+Jk6emKr4yzdZ2DkJiUzD29uO4WIihMIQimuQ4YAHw88M7Ajarw647lDPrhapv/GxdqbAnNXEjL2Sa8xjYsDNr0haov5HvbEQEy/dx+NRYjcGzuz8m1WPPKLp+IgkQhWn7PxeczJZ2k6AqTZd7GoChuOXDKYQ6UvMd/QczA0suKKH8Q0xsDCQRhYkMksuZHS98k1kSMYC3IsXX3M0uMs3dd4mlvjf2PF2RCPrYNQrn8US/QNxeXuk1DoHg5PeQgAoEaRrx7VGZIA6dUjVi0u0OwGIlIO6eU0o8FR02CocZ6P5vERVR+oIDFaHBBoPfkevl5uKDexIrk9z0H6NV3ZzRY6ekpRUWP9qJDc2x2KFqaD6WPpCJCxxHx9mi5iYe7U0cZ/Q2yR7G8KBhYOwsCC7K5xQFKcbd60HlNxhITINKZOOzN1ZKWlf9ONgjHx5EYIjQp1Ns3zKRJ9sVU5Av3unoyho/9gsC/K8KH464rVcKssxHWo/9/qjDKdYKWbRzmm3DME5cFDkXqmyKxPjyVNFjrQBDwmv8RQ33S9eG9vi0dW2koFeWrbTAnOmubW6Bs9aczSUdWWgpXKinIGFo7AwIIczlgisjHNbogCgX6Ptjy3n4hM4+DpjWbl+Zi4z9CiBEc84vB8mifCKzO108eA5gFJgnAMD7rtR4BQrj3FNdEfS+qmY4dqaLOgo/Goy3XIIAAIRBmmJQyBMmI4Jq/9VW8/G5+naV9+bQhkVk0biKToIBzZvR3rf/7VoiDHHB09pejo6W5RfQ0iW9OX7G+oloopwcoLd0XgkfjeDCzsjYEFOVVLoxn6ggdTVyM686O6QJ6xGcYc6SBqU4wFK6IggWBqQNKIZmrJLlV/DJBk6wQdxo4TZWFYVDUNGyv66yz3HCnkY4rbLoQ2WqWtsRLIUNbzQXQLD2/2N1EzqvOzarBOkNFSwAM0D6L0BUODRo7D6V9/xunzZ5GSC6RW3WZSIGPtKA+RKcydsqUhAFDVVOHSykcZWNgbAwtyKZbWdjBEX9K5qSMdlgYdDFaIWh2joydWHCcCSFU2D0hEUT27yxrXxE7YUH835KhqNsqihARSmB9ENf37VevZCZe6TECG93CsPO2HLlW3RnxknlIkdpMipD4PPS5vRjBuTXNrHAAZyqUBjI/cWBqc2OOc1PqJDCwcg4EFtXmW1l0wunRqGDDoieZJta0pcZ0BEFGrZnR0xtg+C4MaYyM+xq5nKJdGIXY0OnJzTfTHG3XTETlyEr7PuGJS4n+i5AiSm9Ts0T2nOhjLFUPNCnJMHQGy9JwMeuxPVVOFywws7I+BBZEJbFEl29hUL7NrVVh4nMaYl4GRz9l+tMZSDHKI2ixLgxxVw3FCr0SIV462mPifoeqBeyQZEKFeHcmU65ka5Bgb5bE0cDJ0XNNRHkuDHEfvMxZUOTrg0nft+ppqBhaOwMCCyIFMqUbd0giJOcc1vWGXhQNJy4Doicb7ZcpojUmF7owEQPaekkZEbZalU9eMntNIkGNpcGTpPnMCGVfaZyyoclTAZWgBhlcqJmHdO8sZWNgbAwsiF2SLEZKmS4RamrNiSjBkyvXsOSXN3LoSxtjjOBaTJCIz2CXIcfRxLhRwqUSgrEZEp+XlDCzsjYEFEbUalgY5LdVdMLT6mC2Os7Tydov1IQzk+bR0nCsFMq7UFyJyeZYGJABQWs3AwiEYWBBRu2OrESFrVy0z53oGKlqbfZyl086MVgg3I8ixxRQ4jgARkZnKakTIl7XTwOLDDz/EihUrkJ+fj9jYWLz//vsYOnSowfZff/01XnvtNVy8eBE9e/bE8uXL8Yc//MGkazGwICJqp0wd5Wlpn6lBjiVT4CwdAbI2WPEJBLoMAs7uAGvhELV+7Taw2LhxI6ZPn47Vq1cjLi4OK1euxNdff40zZ84gKCioWfuDBw9i9OjRWLp0Ke677z58+eWXWL58OdLT0xETE9Pi9RhYEBFRm2RpsNI4ADK1Fk7jc55Nabk4p75RnrMp5uURGcMRHyId7TawiIuLw5AhQ/DBBx8AAFQqFSIiIvDXv/4VCxcubNZ+0qRJqKysxPfff6/dNmzYMPTv3x+rV69u8XoMLIiIiIywZApcSwGJqSM3hvKI7J3zY+6Ijz0WUiCyobIaQL6srH0FFrW1tfDx8cHmzZvxwAMPaLfPmDEDpaWl+O6775od07VrV8yfPx/z5s3TbktOTsbWrVtx4sSJFq/JwIKIiMgO7J2T4+jzmzodztpio6aM+JgSrAz7C9Az0fSFDYyxxygPAy4HEhpGLOwXWLjZ/Iw2UFRUBKVSieDgYJ3twcHBOH36tN5j8vPz9bbPz8/X276mpgY1NTXaxwqFAoA6wCAiIiIbCogFAhp+rqhs3eevqtZ9DACBBq6t77jhscCwl4BLh4HKQqBDENA1rnkwFDgA8I8Ffl4MlDe6l/EOAO54EOh5L9BlMHDlKHDuJ+C3LcDNW8Xz4BsKJCwB+jTKNQ0EEAmg/5/U19d3XMcQoP9UwD9KfS1BUAdBHYJuXa+y0PA+S8+p7zhIoC4RqE9r2WcpO5zTNwRloxcAy56CvcYVXDKwcISlS5diyZIlzbZHREQ4oTdEREREpigH8G7DVwvtXpls4flft+A4R5+TzFcO4CkAQHFxMeRyuc2v4JKBRWBgIKRSKQoKCnS2FxQUICQkRO8xISEhZrVftGgR5s+fr31cWlqKyMhIXLp0yS4vdHtXVlaGiIgIXL58mVPNbIyvrX3x9bUfvrb2xdfXfvja2g9fW/tSKBTo2rUr/P397XJ+lwwsPDw8MGjQIKSmpmpzLFQqFVJTUzFnzhy9x8THxyM1NVUnx2Lnzp2Ij4/X297T0xOenp7Ntsvlcr6R7Ugmk/H1tRO+tvbF19d++NraF19f++Fraz98be1LIpHY5bwuGVgAwPz58zFjxgwMHjwYQ4cOxcqVK1FZWYmZM2cCAKZPn47w8HAsXboUADB37lzceeedePvttzF+/Hh89dVXOHr0KP7zn/8482kQEREREbULLhtYTJo0CdevX8fixYuRn5+P/v37IyUlRZugfenSJZ1oa/jw4fjyyy/x6quv4uWXX0bPnj2xdetWk2pYEBERERGRdVw2sACAOXPmGJz6tHv37mbb/vjHP+KPf/yjRdfy9PREcnKy3ulRZD2+vvbD19a++PraD19b++Lraz98be2Hr6192fv1dck6FkRERERE1LrYJ3ODiIiIiIjaFQYWRERERERkNQYWRERERERkNQYWDT788EN069YNXl5eiIuLw5EjR5zdpVZn6dKlGDJkCHx9fREUFIQHHngAZ86c0WkzZswYCIKg8/Xss886qcetx+uvv97sdevTp492f3V1NWbPno2AgAB07NgRDz/8cLOCkWRYt27dmr2+giBg9uzZAPi+NcfevXsxYcIEhIWFQRAEbN26VWe/KIpYvHgxQkND4e3tjYSEBJw7d06nTUlJCaZOnQqZTAY/Pz889dRTqKiocOCzcF3GXt+6ujosWLAAffv2RYcOHRAWFobp06fj2rVrOufQ935ftmyZg5+J62npvfvEE080e92SkpJ02vC9a1hLr6++v8GCIGDFihXaNnzv6mfK/Zcp9wmXLl3C+PHj4ePjg6CgILz44ouor683qy8MLABs3LgR8+fPR3JyMtLT0xEbG4vExEQUFhY6u2utyp49ezB79mwcOnQIO3fuRF1dHe69915UVlbqtHvmmWeQl5en/frHP/7hpB63LnfccYfO67Z//37tvueeew7bt2/H119/jT179uDatWt46KGHnNjb1uXXX3/VeW137twJADqrzPF9a5rKykrExsbiww8/1Lv/H//4B/71r39h9erVOHz4MDp06IDExERUV1dr20ydOhW//fYbdu7cie+//x579+7Fn/70J0c9BZdm7PWtqqpCeno6XnvtNaSnp+Pbb7/FmTNnMHHixGZt33jjDZ3381//+ldHdN+ltfTeBYCkpCSd123Dhg06+/neNayl17fx65qXl4dPPvkEgiDg4Ycf1mnH925zptx/tXSfoFQqMX78eNTW1uLgwYP47LPPsG7dOixevNi8zogkDh06VJw9e7b2sVKpFMPCwsSlS5c6sVetX2FhoQhA3LNnj3bbnXfeKc6dO9d5nWqlkpOTxdjYWL37SktLRXd3d/Hrr7/Wbvv9999FAGJaWpqDeti2zJ07V+zRo4eoUqlEUeT71lIAxC1btmgfq1QqMSQkRFyxYoV2W2lpqejp6Slu2LBBFEVRzMrKEgGIv/76q7bN//73P1EQBPHq1asO63tr0PT11efIkSMiADE3N1e7LTIyUnz33Xft27lWTt9rO2PGDPH+++83eAzfu6Yz5b17//33i3fffbfONr53TdP0/suU+4Qff/xRlEgkYn5+vrbNqlWrRJlMJtbU1Jh87XY/YlFbW4tjx44hISFBu00ikSAhIQFpaWlO7Fnrp1AoAAD+/v4627/44gsEBgYiJiYGixYtQlVVlTO61+qcO3cOYWFh6N69O6ZOnYpLly4BAI4dO4a6ujqd93CfPn3QtWtXvoctUFtbi/Xr1+PJJ5+EIAja7XzfWi8nJwf5+fk671W5XI64uDjtezUtLQ1+fn4YPHiwtk1CQgIkEgkOHz7s8D63dgqFAoIgwM/PT2f7smXLEBAQgAEDBmDFihVmT3dor3bv3o2goCD07t0bs2bNQnFxsXYf37u2U1BQgB9++AFPPfVUs31877as6f2XKfcJaWlp6Nu3r7YQNQAkJiairKwMv/32m8nXdukCeY5QVFQEpVKp80ICQHBwME6fPu2kXrV+KpUK8+bNw4gRI3Sqn0+ZMgWRkZEICwvDyZMnsWDBApw5cwbffvutE3vr+uLi4rBu3Tr07t0beXl5WLJkCUaNGoXMzEzk5+fDw8Oj2Y1DcHAw8vPzndPhVmzr1q0oLS3FE088od3G961taN6P+v7eavbl5+cjKChIZ7+bmxv8/f35fjZTdXU1FixYgMmTJ0Mmk2m3/+1vf8PAgQPh7++PgwcPYtGiRcjLy8M777zjxN66vqSkJDz00EOIiopCdnY2Xn75ZYwbNw5paWmQSqV879rQZ599Bl9f32ZTevnebZm++y9T7hPy8/P1/m3W7DNVuw8syD5mz56NzMxMnTwAADpzTfv27YvQ0FDcc889yM7ORo8ePRzdzVZj3Lhx2p/79euHuLg4REZGYtOmTfD29nZiz9qetWvXYty4cQgLC9Nu4/uWWpu6ujo8+uijEEURq1at0tk3f/587c/9+vWDh4cH/vznP2Pp0qWsdmzEY489pv25b9++6NevH3r06IHdu3fjnnvucWLP2p5PPvkEU6dOhZeXl852vndbZuj+y1Ha/VSowMBASKXSZpnxBQUFCAkJcVKvWrc5c+bg+++/xy+//IIuXboYbRsXFwcAOH/+vCO61mb4+fmhV69eOH/+PEJCQlBbW4vS0lKdNnwPmy83Nxc///wznn76aaPt+L61jOb9aOzvbUhISLOFM+rr61FSUsL3s4k0QUVubi527typM1qhT1xcHOrr63Hx4kXHdLCN6N69OwIDA7V/B/jetY19+/bhzJkzLf4dBvjebcrQ/Zcp9wkhISF6/zZr9pmq3QcWHh4eGDRoEFJTU7XbVCoVUlNTER8f78SetT6iKGLOnDnYsmULdu3ahaioqBaPycjIAACEhobauXdtS0VFBbKzsxEaGopBgwbB3d1d5z185swZXLp0ie9hM3366acICgrC+PHjjbbj+9YyUVFRCAkJ0XmvlpWV4fDhw9r3anx8PEpLS3Hs2DFtm127dkGlUmkDOjJME1ScO3cOP//8MwICAlo8JiMjAxKJpNk0HjLuypUrKC4u1v4d4HvXNtauXYtBgwYhNja2xbZ876q1dP9lyn1CfHw8Tp06pRMcaz6YiI6ONqsz7d5XX30lenp6iuvWrROzsrLEP/3pT6Kfn59OZjy1bNasWaJcLhd3794t5uXlab+qqqpEURTF8+fPi2+88YZ49OhRMScnR/zuu+/E7t27i6NHj3Zyz13f888/L+7evVvMyckRDxw4ICYkJIiBgYFiYWGhKIqi+Oyzz4pdu3YVd+3aJR49elSMj48X4+Pjndzr1kWpVIpdu3YVFyxYoLOd71vzlJeXi8ePHxePHz8uAhDfeecd8fjx49pViZYtWyb6+fmJ3333nXjy5Enx/vvvF6OiosSbN29qz5GUlCQOGDBAPHz4sLh//36xZ8+e4uTJk531lFyKsde3trZWnDhxotilSxcxIyND5++wZlWXgwcPiu+++66YkZEhZmdni+vXrxc7d+4sTp8+3cnPzPmMvbbl5eXiCy+8IKalpYk5OTnizz//LA4cOFDs2bOnWF1drT0H37uGtfS3QRRFUaFQiD4+PuKqVauaHc/3rmEt3X+JYsv3CfX19WJMTIx47733ihkZGWJKSorYuXNncdGiRWb1hYFFg/fff1/s2rWr6OHhIQ4dOlQ8dOiQs7vU6gDQ+/Xpp5+KoiiKly5dEkePHi36+/uLnp6e4m233Sa++OKLokKhcG7HW4FJkyaJoaGhooeHhxgeHi5OmjRJPH/+vHb/zZs3xb/85S9ip06dRB8fH/HBBx8U8/LynNjj1mfHjh0iAPHMmTM62/m+Nc8vv/yi9+/AjBkzRFFULzn72muvicHBwaKnp6d4zz33NHvNi4uLxcmTJ4sdO3YUZTKZOHPmTLG8vNwJz8b1GHt9c3JyDP4d/uWXX0RRFMVjx46JcXFxolwuF728vMTbb79dfOutt3RujtsrY69tVVWVeO+994qdO3cW3d3dxcjISPGZZ55p9gEk37uGtfS3QRRF8aOPPhK9vb3F0tLSZsfzvWtYS/dfomjafcLFixfFcePGid7e3mJgYKD4/PPPi3V1dWb1RWjoEBERERERkcXafY4FERERERFZj4EFERERERFZjYEFERERERFZjYEFERERERFZjYEFERERERFZjYEFERERERFZjYEFERERERFZjYEFERERERFZjYEFERFBEIQWv5544glnd7NFr7/+OgRBwLp165zdFSKidsfN2R0gIiLXMWPGDIP7Ro4c6cCeEBFRa8PAgoiItPhJPxERWYpToYiIiIiIyGoMLIiIyCKCIKBbt26ora1FcnIyevToAS8vL3Tv3h2LFy9GdXW13uOKi4vx4osvomfPnvDy8oK/vz+SkpLw008/GbxWcXExXnnlFfTt2xcdOnSATCZD37598dJLLyEvL0/vMadOncLEiRPRqVMndOjQAXfeeScOHjxok+dORETNMbAgIiKLiaKIhx9+GCtWrEB0dDTGjx+PkpISvPnmm7jvvvugVCp12l+9ehVDhw7FP//5T9TW1uKBBx7AgAED8PPPPyMxMRHvvvtus2v8/vvv6N+/P9566y0UFRUhMTERCQkJEEURK1aswOHDh5sdc/ToUQwbNgwXL15EYmIievbsib179+Kee+5BZmam3V4PIqJ2TSQionYPgGjufwmaY7p06SJmZ2drtxcWFooxMTEiAPHdd9/VOea+++4TAYhTpkwRa2pqtNv37dsn+vj4iFKpVDx+/Lh2e11dndi7d28RgDhv3jydY0RRFDMzM8Xz589rHycnJ2v79d577+m0nTdvnghAfPzxx816nkREZBqOWBARkZax5Wa3bt2q95jFixeje/fu2sedO3fGihUrAAAffPCBdvuFCxfw/fffo2PHjnj//ffh4eGh3Tdy5Eg8++yzUCqV+PDDD7Xbv/32W5w5cwZ33HEH/vnPf+ocAwB33HEHevTo0axPI0aMwN/+9jedba+++ioAYO/evSa+GkREZA6uCkVERFrGlpvt2rWr3u2PPfZYs21JSUno1KkTsrOzkZeXh9DQUOzfv1+7z9/fv9kxjz/+ON555x3s27dPu+3nn38GADz99NOQSqUmP49777232baAgAD4+/sbzMkgIiLrMLAgIiItc5eb7dSpE3x9ffXui4yMxI0bN3Dt2jWEhobi2rVrAIBu3brpba/ZfvXqVe22y5cvA4DeUQljunTpone7r68vSkpKzDoXERGZhlOhiIjIJQiCYLNzSST8742IyNH4l5eIiCx248YNlJeX69136dIlAEBYWJjO99zcXL3tL168CAAIDw/XbouIiAAAZGdn26S/RERkPwwsiIjIKps2bWq27aeffkJJSQm6d++O0NBQAOoEbQBISUlBaWlps2PWr18PABg1apR2W0JCAgBg7dq1UKlUtu46ERHZEAMLIiKyypIlS7SjDQBQVFSEF198EQAwe/Zs7fbu3btj/PjxKC8vx9y5c1FXV6fdl5aWhlWrVkEqleoc89BDD6FXr17IzMzESy+9pHMMAPz222+4cOGCnZ4ZERGZg8nbRESk9cQTTxjc17VrV7zxxhvNtvXr1w933HEH7rnnHri7u2PXrl0oLS3FXXfd1WzJ148++gijRo3C559/jj179iA+Ph7Xr1/H7t27oVQq8fbbb6N///7a9m5ubvjmm28wduxYvP322/jyyy8RHx8PURRx7tw5ZGZmYsuWLTrL3RIRkXMwsCAiIq3PPvvM4L7Y2NhmgYUgCNi8eTPeeOMNfPnll9oVoGbPno1XXnkFbm66/82Eh4fj119/xdKlS7F161Z8++238PHxwT333IPnn39e7zKxMTExOHHiBFasWIFt27bhxx9/hKenJ7p27YoFCxZg2LBhtnnyRERkFUEURdHZnSAiotZHEARERkbqTIMiIqL2izkWRERERERkNQYWRERERERkNQYWRERERERkNSZvExGRRZiiR0REjXHEgoiIiIiIrMbAgoiIiIiIrMbAgoiIiIiIrMbAgoiIiIiIrMbAgoiIiIiIrMbAgoiIiIiIrMbAgoiIiIiIrMbAgoiIiIiIrMbAgoiIiIiIrPb/elfIc3BdCLIAAAAASUVORK5CYII=", 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", 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" ] @@ -748,21 +733,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([60])\n" - ] - }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -771,24 +749,36 @@ ], "source": [ "from torch import concat\n", + "from numpy import diff\n", "\n", "model.eval()\n", "\n", - "pred_list = []\n", + "predictions = []\n", + "targets = []\n", "\n", "for batch, target in valid_loader:\n", " with no_grad():\n", - " pred_list.append(model(batch) / target)\n", + " predictions.append(model(batch))\n", + " targets.append(target)\n", "\n", - "all_preds = concat(pred_list).flatten()\n", - "print(all_preds.shape)\n", + "predictions = concat(predictions).flatten()\n", + "targets = concat(targets).flatten()\n", "\n", - "fig, axis = plt.subplots(1, 1)\n", + "ratios = predictions / targets\n", + "\n", + "fig, axis = plt.subplots(1, 1, figsize=(10, 5))\n", + "\n", + "axis.hist(\n", + " ratios,\n", + " bins=10,\n", + " label=rf\"Mean {ratios.mean():.3f}; Std dev. {predictions.std():.3f}\",\n", + ")\n", + "axis.set_xlabel(\"Prediction:target ratio\", fontsize=12)\n", + "axis.set_ylabel(\"Frequency\", fontsize=12)\n", "\n", - "axis.hist(all_preds, bins=10)\n", - "axis.set_xlabel(\"Prediction:target ratio\")\n", - "axis.set_ylabel(\"Frequency\")\n", + "axis.legend(fontsize=12)\n", "\n", + "axis.set_aspect(0.5 * diff(axis.get_xlim()) / diff(axis.get_ylim()))\n", "\n", "plt.show()" ] @@ -810,7 +800,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.1" } }, "nbformat": 4, diff --git a/worked-solutions/03_mnist_classification_solutions.ipynb b/worked-solutions/03_mnist_classification_solutions.ipynb index 6a4e342..68b4d90 100644 --- a/worked-solutions/03_mnist_classification_solutions.ipynb +++ b/worked-solutions/03_mnist_classification_solutions.ipynb @@ -829,7 +829,7 @@ " Notes\n", " -----\n", " - This function assumes the ``preds`` have had the softmax applied to them\n", - " along dimension 1, and that the predicted class is therefore \n", + " along dimension 1, and that the predicted class is therefore\n", " ``preds.argmax(dim=1)``.\n", "\n", " \"\"\"\n",