Trying to get the CNNTransformer to work, but it is hard.

author: aktersnurra <gustaf.rydholm@gmail.com> 2020-11-08 12:41:04 +0100
committer: aktersnurra <gustaf.rydholm@gmail.com> 2020-11-08 12:41:04 +0100
commit: beeaef529e7c893a3475fe27edc880e283373725 (patch)
tree: 59eb72562bf7a5a9470c2586e6280600ad94f1ae /src
parent: 4d7713746eb936832e84852e90292936b933e87d (diff)
43 files changed, 1877 insertions, 1385 deletions
diff --git a/src/notebooks/00-testing-stuff-out.ipynb b/src/notebooks/00-testing-stuff-out.ipynb
index 3b74c84..62e549c 100644
--- a/src/notebooks/00-testing-stuff-out.ipynb
+++ b/src/notebooks/00-testing-stuff-out.ipynb
@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -14,6 +23,7 @@
     "import numpy as np\n",
     "from PIL import Image\n",
     "import torch\n",
+    "from torch import nn\n",
     "from importlib.util import find_spec\n",
     "if find_spec(\"text_recognizer\") is None:\n",
     "    import sys\n",
@@ -22,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,185 +41,431 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "IdentityBlock(32, 64)"
+    "from text_recognizer.networks import WideResidualNetwork"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "ResidualBlock(32, 64)"
+    "wr = WideResidualNetwork(\n",
+    "            in_channels= 1,\n",
+    "            num_classes= 80,\n",
+    "            in_planes=32,\n",
+    "            depth=10,\n",
+    "            num_layers=4,\n",
+    "            width_factor=1,\n",
+    "            dropout_rate= 0.2,\n",
+    "            activation= \"SELU\",\n",
+    "            use_decoder= True,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "dummy = torch.ones((1, 32, 224, 224))\n",
-    "\n",
-    "block = BasicBlock(32, 64)\n",
-    "block(dummy).shape\n",
-    "print(block)"
+    "from torchsummary import summary"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
-    "dummy = torch.ones((1, 32, 10, 10))\n",
-    "\n",
-    "block = BottleNeckBlock(32, 64)\n",
-    "block(dummy).shape\n",
-    "print(block)"
+    "        backbone = nn.Sequential(\n",
+    "            *list(wr.children())[:][:-1]\n",
+    "        )\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sequential(\n",
+       "  (0): SELU(inplace=True)\n",
+       "  (1): Sequential(\n",
+       "    (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "    (1): Sequential(\n",
+       "      (0): WideBlock(\n",
+       "        (activation): SELU(inplace=True)\n",
+       "        (blocks): Sequential(\n",
+       "          (0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "          (1): SELU(inplace=True)\n",
+       "          (2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "          (3): Dropout(p=0.2, inplace=False)\n",
+       "          (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "          (5): SELU(inplace=True)\n",
+       "          (6): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (2): Sequential(\n",
+       "      (0): WideBlock(\n",
+       "        (activation): SELU(inplace=True)\n",
+       "        (blocks): Sequential(\n",
+       "          (0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "          (1): SELU(inplace=True)\n",
+       "          (2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "          (3): Dropout(p=0.2, inplace=False)\n",
+       "          (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "          (5): SELU(inplace=True)\n",
+       "          (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "        )\n",
+       "        (shortcut): Sequential(\n",
+       "          (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (3): Sequential(\n",
+       "      (0): WideBlock(\n",
+       "        (activation): SELU(inplace=True)\n",
+       "        (blocks): Sequential(\n",
+       "          (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "          (1): SELU(inplace=True)\n",
+       "          (2): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "          (3): Dropout(p=0.2, inplace=False)\n",
+       "          (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "          (5): SELU(inplace=True)\n",
+       "          (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "        )\n",
+       "        (shortcut): Sequential(\n",
+       "          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (4): Sequential(\n",
+       "      (0): WideBlock(\n",
+       "        (activation): SELU(inplace=True)\n",
+       "        (blocks): Sequential(\n",
+       "          (0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "          (1): SELU(inplace=True)\n",
+       "          (2): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "          (3): Dropout(p=0.2, inplace=False)\n",
+       "          (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "          (5): SELU(inplace=True)\n",
+       "          (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "        )\n",
+       "        (shortcut): Sequential(\n",
+       "          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "dummy = torch.ones((1, 64, 48, 48))\n",
-    "\n",
-    "layer = ResidualLayer(64, 128, block=BasicBlock, num_blocks=3)\n",
-    "layer(dummy).shape"
+    "backbone"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==========================================================================================\n",
+      "Layer (type:depth-idx)                   Output Shape              Param #\n",
+      "==========================================================================================\n",
+      "├─Sequential: 1-1                        [-1, 256, 4, 119]         --\n",
+      "|    └─Conv2d: 2-1                       [-1, 32, 28, 952]         288\n",
+      "|    └─Sequential: 2-2                   [-1, 32, 28, 952]         --\n",
+      "|    |    └─WideBlock: 3-1               [-1, 32, 28, 952]         18,560\n",
+      "|    └─Sequential: 2-3                   [-1, 64, 14, 476]         --\n",
+      "|    |    └─WideBlock: 3-2               [-1, 64, 14, 476]         57,536\n",
+      "|    └─Sequential: 2-4                   [-1, 128, 7, 238]         --\n",
+      "|    |    └─WideBlock: 3-3               [-1, 128, 7, 238]         229,760\n",
+      "|    └─Sequential: 2-5                   [-1, 256, 4, 119]         --\n",
+      "|    |    └─WideBlock: 3-4               [-1, 256, 4, 119]         918,272\n",
+      "├─Sequential: 1-2                        [-1, 80]                  --\n",
+      "|    └─BatchNorm2d: 2-6                  [-1, 256, 4, 119]         512\n",
+      "├─SELU: 1-3                              [-1, 256, 4, 119]         --\n",
+      "├─Sequential: 1                          []                        --\n",
+      "|    └─SELU: 2-7                         [-1, 256, 4, 119]         --\n",
+      "|    └─Reduce: 2-8                       [-1, 256]                 --\n",
+      "|    └─Linear: 2-9                       [-1, 80]                  20,560\n",
+      "==========================================================================================\n",
+      "Total params: 1,245,488\n",
+      "Trainable params: 1,245,488\n",
+      "Non-trainable params: 0\n",
+      "Total mult-adds (M): 12.61\n",
+      "==========================================================================================\n",
+      "Input size (MB): 0.10\n",
+      "Forward/backward pass size (MB): 7.44\n",
+      "Params size (MB): 4.75\n",
+      "Estimated Total Size (MB): 12.29\n",
+      "==========================================================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "==========================================================================================\n",
+       "Layer (type:depth-idx)                   Output Shape              Param #\n",
+       "==========================================================================================\n",
+       "├─Sequential: 1-1                        [-1, 256, 4, 119]         --\n",
+       "|    └─Conv2d: 2-1                       [-1, 32, 28, 952]         288\n",
+       "|    └─Sequential: 2-2                   [-1, 32, 28, 952]         --\n",
+       "|    |    └─WideBlock: 3-1               [-1, 32, 28, 952]         18,560\n",
+       "|    └─Sequential: 2-3                   [-1, 64, 14, 476]         --\n",
+       "|    |    └─WideBlock: 3-2               [-1, 64, 14, 476]         57,536\n",
+       "|    └─Sequential: 2-4                   [-1, 128, 7, 238]         --\n",
+       "|    |    └─WideBlock: 3-3               [-1, 128, 7, 238]         229,760\n",
+       "|    └─Sequential: 2-5                   [-1, 256, 4, 119]         --\n",
+       "|    |    └─WideBlock: 3-4               [-1, 256, 4, 119]         918,272\n",
+       "├─Sequential: 1-2                        [-1, 80]                  --\n",
+       "|    └─BatchNorm2d: 2-6                  [-1, 256, 4, 119]         512\n",
+       "├─SELU: 1-3                              [-1, 256, 4, 119]         --\n",
+       "├─Sequential: 1                          []                        --\n",
+       "|    └─SELU: 2-7                         [-1, 256, 4, 119]         --\n",
+       "|    └─Reduce: 2-8                       [-1, 256]                 --\n",
+       "|    └─Linear: 2-9                       [-1, 80]                  20,560\n",
+       "==========================================================================================\n",
+       "Total params: 1,245,488\n",
+       "Trainable params: 1,245,488\n",
+       "Non-trainable params: 0\n",
+       "Total mult-adds (M): 12.61\n",
+       "==========================================================================================\n",
+       "Input size (MB): 0.10\n",
+       "Forward/backward pass size (MB): 7.44\n",
+       "Params size (MB): 4.75\n",
+       "Estimated Total Size (MB): 12.29\n",
+       "=========================================================================================="
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "blocks_sizes=[64, 128, 256, 512]\n",
-    "list(zip(blocks_sizes, blocks_sizes[1:]))"
+    "summary(wr, (1, 28, 952), device=\"cpu\", depth=3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
-    "e = Encoder(depths=[2, 1], block_sizes= [96, 128])"
+    "from torch import nn"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 70,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "96"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "Encoder(**{\"depths\": [2, 1], \"block_sizes\": [96, 128]})"
+    "32 + 64"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 106,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "336"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "from text_recognizer.networks import WideResidualNetwork"
+    "3 * 112"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
-    "wr = WideResidualNetwork(\n",
-    "            in_channels= 1,\n",
-    "            num_classes= 80,\n",
-    "            depth= 16,\n",
-    "            num_layers= 4,\n",
-    "            width_factor= 2,\n",
-    "            dropout_rate= 0.2,\n",
-    "            activation= \"SELU\",\n",
-    "            use_decoder= False,\n",
-    ")"
+    "col_embed = nn.Parameter(torch.rand(1000, 256 // 2))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from torchsummary import summary"
+    "W, H = 196, 4"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([4, 196, 128])"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "col_embed[:W].unsqueeze(0).repeat(H, 1, 1).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([4, 196, 128])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "summary(wr, (1, 28, 14), device=\"cpu\", depth=10)"
+    "col_embed[:H].unsqueeze(1).repeat(1, W, 1).shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 4, 196, 256])"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "np.inf"
+    "           torch.cat(\n",
+    "                [\n",
+    "                    col_embed[:W].unsqueeze(0).repeat(H, 1, 1),\n",
+    "                    col_embed[:H].unsqueeze(1).repeat(1, W, 1),\n",
+    "                ],\n",
+    "                dim=-1,\n",
+    "            ).unsqueeze(0).shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "784"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "4 * 196"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from text_recognizer.networks.transformer.positional_encoding import PositionalEncoding"
+    "target = torch.tensor([1,1,12,1,1,1,1,1,9,9,9,9,9,9])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 1080x360 with 1 Axes>"
+       "8"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "plt.figure(figsize=(15, 5))\n",
-    "pe = PositionalEncoding(20, 0)\n",
-    "y = pe.forward(torch.zeros(1, 100, 20))\n",
-    "plt.plot(np.arange(100), y[0, :, 4:8].data.numpy())\n",
-    "plt.legend([\"dim %d\"%p for p in [4,5,6,7]])\n",
-    "None"
+    "torch.nonzero(target == 9, as_tuple=False)[0].item()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 1,  1, 12,  1,  1,  1,  1,  1,  9])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "from text_recognizer.networks.densenet import DenseNet,_DenseLayer,_DenseBlock"
+    "target[:9]"
    ]
   },
   {
@@ -217,64 +473,163 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "dl = _DenseLayer(64, 4, 4, 0)"
+    "np.inf"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "db = _DenseBlock(2, 64, 32, 4, 0)"
+    "from text_recognizer.networks.transformer.positional_encoding import PositionalEncoding"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Xb/Leb+e5kVXAU+1Deb1n7QpbxU6WZS6kXmBb5Da2R2/nRu4NrDRWNPdpTlu/trSp2obqrtUr1YhVSTILMzmUcIh98fvYH7+ftII0LCQLWvm2ondob7oEdKmYe/n0OvjjPaWwSvVuMGiRUkjF2HKS4denIXynUgCh3w9gZx6DL2uPxfLOr+fxdbVl3pjm1PRxMvo1i7R6Ptl6icUHo2ge5MbMEU2p4mL+S8UEocxSrsH6iUodg2bjoMcnYF0O2zNyU2H1cIg9qgxWtZpmFoNV0am5vPLzGY5FpdOtjg+fDKiPt5Px7yV6vcxHWy6x8EAkjzeqyteDGxm0BZas11Nw9ixZf/xB9h87KY6JAUnCrllTnLt3x7FzZ6wCKtYKpbISSZ6pK8iEFUOU6lED5xm0oIE+P5+c/fvJ2rqVnN1/IRcUYOnjg3OvXjj36Y1t/foP/Y/ll1NxvPLzWRr4ubBkfBgu9uVbeSm7oJhPtl5m1dEYRrcK4sMn6lWYf/CyLHM57TLboraxPWo78TnxWGosaVu1LT2Ce9A5oHPFTFKMQC/ruZBygT9j/+T3yN+Jz4nHxsKGDv4d6BPSh3b+7cq90IxRFObAhslKcaewp6DHp+VbPU6vV5LLXf8Bp6owaAEEhJXf9R9SsU7Px1uURKt9DU9+GN4EV/vy3cu88UwCb6w/i721Bd8Pb0KbaqZXsU0Qys2JJbDtDbC0VQaK6vQt3+sX58OGp+DSRgibAj0/NdzyUCM4eD2FSUuOY6GR+ODxegwo56WTsizz454IPt92mXbVPflxdLMyt70qvHaNjA2/kLVlC9qkJLCywqFVK5y6d8OpSxeTrGppKkSSZw4Kc2DlEKUCZ/+50HCIwS+hz80le/dfSsK3bx8UF2MVEIBLv364DhqIlW/J+0RWHInmnV/P0yrEg3ljm6vWz06WZT77/TJz90Ywrk0w7z9e16wTvfSCdDZe38j6a+uJzIzEUrKkZdWW9AzuSeeAzrjYmHf1J7XJssyZ5DNsjdzK9qjtpBWk4WTlRLegbjxe7XGa+zQ3z78/mfGwaijcvAA9P1OqXqol7jisG6/E1P9Ho9zDyio9t4jpK09y8HoqE9uF8Gav2liqtGTy2s1snl5+gsiUXF7pUYupHauZ599BQSiLwz/CttchtBM8+SM4q7RfVa+HP95VBqxq9VGWilqbXvuTkzHpjJp/BH83O5ZMCDPaFpnSWHs8ljc3nKNeVWcWjmvx0O2udFlZZG3dSsb6DRScOweWljh27Ihzzx44duyIhXM5rEapAESSZy6KcmHlUIjaD0/OhsYjjHYpXWYm2Tt3krVlC7mHDoMk4dipE25Dh+DQrh2Sxb9HsebtjeDjrZfoUtub2SObql4OXJaVZQML9kcysV0I7/SpY1YPSbIsc/zmcX6++jM7o3dSrC+msVdjnqj+BN0Cu1WefWTlTKvXciTxCFsjt7Izeid52jxCXEIYUnMI/ar3w9naTH6wJJyCVcOVAaJBC5XKc2rLz4A1o5TBqhFrlKWjJuLqzWwmLjnGzaxCPunfgEHN/NUOiZxCLa+vP8uWs4lM7VSN13vWVjskQSg/Z9cqqxDqPK4UPzGF2bMjc+H318GvKQxfA46mU7X5UmIWQ+cews3Bmp+ntMbbWf2l3rsu3WT6ypP4utixdEJYiX1BZb2e3EOHyNzwC9k7dyIXFmJTsyYuA/rj8vjjZtMP2pSIJM+cFOUp68Mj9kC/76HpGONfMi6OjLU/k7F+PbrUVKyqVsV1yBBcBw7A0ssLWZb5buc1Zuy6Rp8Gvnw7tLFB12CXhSzL/GfTRRYfjGJKh1De6FXb5BO9tII0NoZvZN21dURnReNk7US/av0YWGMgNdxqqB1epVKgLWBH9A7WXF7D2ZSz2FrY0iukF0NrDaWeZz21w7u/63/C6pFg76EkUz4mFGtBFizqDWkRMG6z8rCkssTMfJ6YeQAZ+Gl0M5qYUNEmWZZ565fzrDoawyf9GzCiZaDaIQmC8V3doTzrBLaGketMq43B5S2wbiI4esOo9eCp/s/l68k5DJ17CCsLDT8/3Rp/N9OZZTwRncaExcexttSwZHzYPasT6zIzyVi3jvQVKylOSEDj7IxL3z64DBiIbT3zXomlNpHkmZvifGU0PHwn9P0Wmk8ol8vKRUVk//kn6WvWkHfoMFha4tS1C5uqd+DzeFsGN/Pns4ENsTCxQieyLPPebxdYdjiaaZ2q8WqPWiZ5w7iSdoUlF5bwe9TvaPVamno3ZVDNQXQP6o6tpQn9gKukLqVeYs2VNWyN3Eq+Np96HvUYWmsovUJ6mdb/n+SrML8ruAbCqA2lawhc3rJvwILuyqDVxB1KpTyV5BZqGfzjIWLS8lg/tQ21qhi/wMrD0ur0TF56nL3XUpg/pjmda3urHZIgGE/MYVj6JHjVhLGby6dI1MOKO66srJI08NRf4GL8llT3DSU9j8E/HqJYp2ftlNaEmmCvzWs3sxmz8Cg5BVrmj21Oy1BlRq4wIpL05cvI+OVX5Px87Fu0wG34MBy7dkVjUwH2xJsAkeSZo+ICWDsGrm2H3l9B2ORyvXxRVBTpa9eSuOpnbPJzSAmqRcNXnsG5a5cyV+Y0Br1e5p3fzrPySAzPdanOS4/VUjskQElADyUeYsmFJRxMOIidpR0DagxgcM3BVHNV78FXuL/somw2Xd/E2itruZ55HXdbd0bWGcnQWkPV3xuZnw7zukJhFkzeDa4B6sbzICnhsPAxpR/oxD9USUb1epkpy0+w69JNFoxtYdLJU26hliFzDxGZksvaKa2p7yf24QoV0M0LsKgXOHjB+G0mtRzyX5Iuwfxu4FkTxv+uymxjUlYBg+ceIj23iNVPtTZoD09DS8jIZ/SCIyRnFfBra2usfllLzp49SFZWOPfti/uY0djWqaN2mBWOSPLMlbYQfh4PV7ZAn6+VPlTlaN7eCL7aeIYPbSJpdngrxfHxWIeE4DFxAs79+qGxLt+KdCXR62Xe3HCONcdjebFbTZ7vpt4Si2J9MdujtrPkwhIup13G086TkXVGMrjmYPUTBaFUbu+ZXHR+Efvi92Fvac/gmoMZXXc0Pg4qzJ7ptLBiIEQdUJZBBrYq/xgeVtxxWPK40jR9/FawKd9ZtE9/v8TcPRG8/3hdxrcNKddrP4qbWQUMmH2QIp2eX6a1MaklWYJQZulRsKCHMjs2cbuyGsHUXdoMa0ZCoxFKrYRyXCWUnlvE0J8OEZeez/JJLU2+N7BcXEzkqvWEz5pLQOYNNO7uuI8YgduwoaI6phGJJM+caYtg7Whl6eb4bRDQolwueywqjWE/HaZ7HR/mjGoKOh1Z27eTumABhRcvYenlhfvYMbgOHYqFk+ksf9LrZV5bf5Z1J+JUebDLK85j3dV1LLu0jBu5Nwh1CWVcvXH0Ce2DtYVpJcVC6V1Ju8LC8wvZFrUNjaShX7V+jKs3jhCXcvz79fsbcGQO9JsJTUeX33XL6uoOWDUMQtrDiJ/Bsnz+Haw9Fstr688yqlUg/33i4VvFqOXqzWwGzjlIFWdb1k1tg4td+baoEQSjyEmCBY8pqxEmbANvM5rR2f0p7PkMen1RbhWMswuKGTn/CJdvZLN4fAuTbrMiFxWR8dtvpM79ieK4OIqDq/GDe3Ocevfm65EtzObea65Ekmfu8jNgbnuQgaf3Gr3ZcEpOIX2+34etlQWbnm2Hs+3/HzJkWSb34EHSFiwg9+AhNI6OuI8fh/vYsVg4msY6cZ1e5unlJ/jrShLrp7Ypl0bthbpC1l5Zy/xz80krSKOZTzPG1xtPe//2aCTTW94qPJrY7FiWXFjCr+G/UqQroltQN55u9DQ13Woa98Inl8LGZ6HlVOj1mXGvZQynVsBv06DBYOj/Exh5yfeh66mMXnCE1tU8WDiuBVYqtUl4VAfDUxi76CjNg9xZMiHMZApdCcIjKciExX0g9TqM2Vhug9UGo9crdRKuboMxv0JIB6NeTqvTM3rBUY5FpfHTmGZ0qW2C+665ldxt+IXUn36iOCEB2/r18Zw+DcdOnZj5Zzhf/3GVDx6vyzgzWEVhzkSSVxHEnVD2t9ToAcNWGG3JgE4vM3bhUY5GpfHLtDbUq3r/pYX5Fy6QMmcOOTt3YeHqisfkSbiNGIHGTr2+Lbdl5BXRe8Y+rC01bH6uvdH6+RXritlwbQM/nfuJpLwkwqqEMb3xdJr6qF9RUDCe1PxUVlxawerLq8kpzqFXSC+mN55OoLMRlh9FH1KWPAa3U6rQlWejc0Pa9zXs+hBaPwM9PjbaZSJTcuk/+wAeDtZsmNbWbGfCNpyM46W1ZxjQxI+vhzQSo+GCedLrlWXmkXuVlgQ1TKetykMpyFL25+UmK4VY3IKMdqkfdl3j6z+u8uWghgxubnr7rvVFRWSuX0/KT/PQJiZi26ghXtOn49C+/Z37lF4v89Sy4/x1JZlVT7WiRbC7ylFXXCLJqygOzoQdbytNj1tNNcolvvnjKt/vusbnAxswtEXpHljzz50necYMcvfvx8LLE88pT+M6ZLDqe/aORqYx7KdDPNHYj2+HNjbouYv1xWy6vom5Z+aSkJtAE+8mPNP4GcJ8wwx6HcG0ZRZmsuj8IlZcWkGxvpj+NfozpeEUqjhUMcwFMmLgp85g6wKTdxl9Ft+oZBl+fw2O/qT0xKr3pMEvkZlXTP/ZB0jPK+LX6W0J8nAw+DXK0+2HPVMqJiUID+V2s/M+30CLiWpHUzYp4TCvC7gFwoQdRmmWfiomnUE/HqJvQ19mDGti8POXhazVkrFhAymz56C9cQO7xo3xnD4dh3Zt7zkIlVVQzBMzD5BTqGXzs+3wMYG+fhWRSPIqCllWmh+H71TKkhu4/9Teq8mMXXSUAU38+Wpww4ceOc47fpzk72aQd/w4llV98Zo+HZcnnkCyVG/m4budV/lu5zW+HtyIgQZofqyX9WyN3Mrs07OJzY6lvkd9nmnyDG2qthEj7ZVYcl4y887N4+erP6NBw7Daw5jYYCLutmUYvSzKVYoUZETDpF1KuXFzpytW9uWkR8G0wwatuFms0zNu0VGORqaxYlIrwkLMf+RYlmVeW3eWn0/EsXJSS9pUN919OYLwL0mX4aeOENJR6edZEX5GXvsDVgyG+gNg4AKD/p5yCrX0+X4fWp3M1ufbm8wqBFmWyfnrL5K+/pqi8OvYNWqE53PP4tCm5OeeKzeyeXLWAepWdWbV5FZi6bkRiCSvIslLgx/bK0u2puxVRvgNICEjnz7f78PH2ZZfprXFztrikc4jyzK5Bw6SPGMGBefOYR0Sgs8br+PYsaNB4nxYOr3M8HmHOR+fyeZn25Wpv8yppFN8cfQLzqeep5ZbLaY3nk6ngE4iuRPuiM+JZ87pOWyK2ISthS1j641lXL1x2Fs95IivLCstVC5vhhFroUZ34wSshuSryh7j0E4wfLXBHpJur0Iw1ICOqcgv0tH7+30UafVsf7GD0ZaeC4JBaYtgQTfIjIOph0yzn+ej2vcN7PoPdP8Q2j5vsNO+tu4M607Esfqp1iYzSJV/7jxJX35J3tGjWAcF4fXySzh17/5Qzz2bziTw7KpTjG0dxH+eqG/EaCunByV5IqU2N/buMGghZMQqhRgMkKQX6/Q8s/IkRVo9s0Y2feQED0CSJBzbtSV47Rr8Z/4Aej2xU54mZvJTFF6/XuZYH5aFRmLGsMZYW2p4bvUpCrW6hz5HfE48r+x5hTG/jyEpL4mP233M2sfX0jmws0jwhL/xc/Tjo3Yf8Uu/X2jr15Y5Z+bQ79d+bInYwkMNqJ1YDJc2Qrf/VKwED5QZya7vK0UMTi03yCkvJmQxe3c4A5r4VagED8DO2oKvBjckITOfT7ZeUjscQSidPZ9D4hl4fEbFSvAA2r0I9frDzg/g+p8GOeXWc4msPR7HtE7VTSLBK4qLJ/7lV4gaPJjCa9fwefcdQjdvwvmxxx76uefxRlWZ1C6EJYei2XAyzkgRC/ciZvLM1f7vYOf7BmmU/t/NF1mwP5KZI5rQt2FVw8R3i1xURNrKlaTMmo0+Lw+34cPxemY6Fq6uBr1OSXZcuMFTy04wsV0I7/atW6rP5BTlMP/cfJZdXIZG0jC+/vhHm5URKq2TN0/y2dHPuJR2icZejXkj7A3qedZ78IeyEmBWS/BtBGM3VYwlTv+k18PSfpBwGqYeKFMRg2KdnidnHeBmVgF/vNgRN4eK2arkk62X+GlvBEsnhNGhpgk3kBaEmCOwqOet3nKz1I7GOIpylf15hdkw/UiZeoAmZubT87t9BHvYs25qG1WrAeuys0mZ8yPpy5aBRoP7uHF4TJ5U5urpWp2ekfOPcDo2g1+nt6WOr+k2dTc3YiavImrzHFTvDtvfUkbLHtG284ks2B/JuDbBBk/wACRrazzGjaPa9m24DhlM+sqVhPfoSdqy5cjFxQa/3v08Vq8KY1sHsWB/JLsvJz3wWJ1ex7qr6+jzSx8WnF9Aj+AebOq/iWmNp4kET3goTX2asqrPKj5s8yEx2TEM3zKcdw+8S0p+yr0/IMuw5RXQFSkj4BUxwQOlhcITtx7+fp2mJH2P6Ke9EVxIyOK/T9SvsAkewEvda1LNy4E31p8lq6D87p2C8FAKc+CXp8DFH3p+qnY0xmPtoPQszUqAPz965NPo9TIvrTlDsU7Pd8OaqJbgybJM5qZNXO/dm7RFi3Du25dq27fh/eILBmmPZWmhYeaIpjjZWvL6+rPo9OY3wWSORJJnrjQa6D8X7D3h53FKed+HlJ5bxJsbztHI34W3ehu3Mamluzu+779PyK+/YFevLjc//piIJ/uTe+iQUa97tzd716F2FSde+fkMSVkF9zzmfMp5hm8Zzn8O/Ycg5yBW9VnFJ+0/MVy1RKHSsdBY0L9Gf7b038K4euPYHLGZvr/0ZeH5hRTpiv5+8MXf4MoW6PQmeFRTJ+Dy4hakPARG74cjPz7SKcKTspmx8xq9G1ShVwNfAwdoWmytLPhqcCNuZBXwyRaxbFMwUdvfgvRo5fnEtoLP1gS0UFZSHZkLcY+2umzevggORaTy/uN1CfFUpxpwwdWrxIweQ8Krr2HlU4XgtWuo+uknWFUx7HOPl5MN7/aty9m4TJYfjjbouYV7E0meOXPwgEELlEp1jzCS9MX2y2QVaPl8UMNyq3hkW7MmAQsW4D97NrK2mJjxE4h/7TW0qanGv7aVBTNHNCGvSMeLa0+jv2skKbsom48Pf8yILSNIyU/hyw5fsqTnEup7ik3CgmE4WjvyUvOX+PWJX2nh04JvT3zLwI0DOXbjmHJAXhpsfVVZptn6GXWDLS9NRkHNnkoRg+SrD/VRnV6pPGlvY8F/+lWOf6dNAt2Y0rEaq4/F8teVB69IEIRyd+V3OLlEKUYS1EbtaMpHl3fByRc2PqdUD34I5+Mz+WrHFXrWq8IQFfrh6XJyufn5F0T2H0DhtWtU+c9/CF6zGrsGDYx2zX6NqtK+hidfbr/Cjcx7D7YLhiOSPHMX1AaaT4Rj8yDxbKk/djImnVVHY5nQNpjaVcp3tE2SJJy6dCZ040Y8p00j6/dtXO/dh/Sff0Yuw7Kt0qju7cQH/epyIDyVVcdikGWZbZHb6PdrP9ZeXcvw2sPZ+ORGeob0FEVVBKMIcg7ih64/MKfbHIr1xUzYPoH3DrxH5rY3IC9VWQJkrg3PH5YkwePfg5W9ssTrIR6SFh+M4mRMBh88Xg8vJxsjBmlaXuhWg5o+jryx/hyZ+WLZpmAicpKVYnA+DaDzW2pHU35snaHP15B0AQ7+UOqP5RfpeH71KdwdrPl0QINyfd6QZZnMLVuI6N2btMWLcR0wgNBtv+M2dAiSxaMX3isNSZL46Mn6FOv0fLj5glGvJYgkr2Lo8jbYucPWV0q1t0Wr0/P2L+ep4mzLC93U672lsbHB67lnCf31F2xr1ODGu+8RPXoMheHhRr3ukOYBtAxx58tdB5i4/Sle3fsq3vberOy9kjdbvomjddnXnwtCSdr5teOXJ35hQv0JbAz/jX4Z+9nc5EnkKsYbRTVJTj7Q9xtIOKWUJi+F6NRcvtx+mS61vXmiseH3EpsyG0tl2WZyTiH/3XxR7XAEQdlLvOl5KMiEAT+BZeUZdAGgdm+o00+pKJpauiriX+24wvXkXL4e3Lhc9xIXxcYSM2ECCS+/gqWXF8GrV+H73w+xdHMrtxiCPBx4rmsNtp67wa5LN8vtupWRQZI8SZJ6SpJ0RZKkcEmS3rjH+99KknT61tdVSZIy7npPd9d7Gw0RT6Vj5wbd/wOxR+Ds6hIPX3oomkuJWbz/eF0cTKDnkk21agQuW4rvxx9TFB5ORP8BJH37HfoC40zla/VaGtY/htb3S07dPMObYW+ysvfKkqseCoKB2Vna8WKDp1iTpcdPtuTN9GM8vfNpYrNi1Q6tfNXrDw0Gw94vlGTvAfR6mdfXn8VKo+Hj/vUr5Yx7Q39XpnWqxroTceIhSVDf2TXKXuKu74NP6apXVzi9vwQLG9j8Qomtra7dzGbxwSiGhwXSroZnuYQn63SkLVlCRL8nKDh7Dp/33iV47RrsGjUql+v/0+T2odTwduS93y6QV6RVJYbKoMxJniRJFsAsoBdQFxguSdLf/pXLsvyiLMuNZVluDPwAbLjr7fzb78my3K+s8VRajUaAfxjseBfyM+572M2sAr754yoda3rRs77pFBORJAnXgQMI/X0rLn36kDp3LhH9niD36FGDXudi6kWGbhnK6vB5BNg0Jyv8RRq59MFCY9wlCoJwX7s/oVZqDMu6zOTNsDc5k3yG/hv7M//cfIr1lWg5Xu8vwcELfnsG9PfvZ7nyaAyHI9J4u08dfF3syjFA0/JslxrUruLEmxvOkZFXVPIHBMEYCnPgj/fArxm0mqZ2NOpxqgLdP4DIvXB65X0Pk2WZDzdfxN7aglceK5+VVIXXrxM9chQ3P/0M+7AWhG7ehPuIEUZfmvkg1pYaPu7fgPiMfGbsvKZaHBWdIWbywoBwWZYjZFkuAlYDTzzg+OHAKgNcV7ibRgN9voL8NNj98X0P++/mixTp9Hz4RD2THAG3dHen6mefErh4MQAxY8Zy4+NP0Ofnl+m8xbpiZp2excgtI0kvSOeHLj+w+snZuFh78sHGCw/XqFoQDCX+BByeDc3GYRHSgRF1RvDbE7/Rzq8dM07OYNTWUYSnG3f5ssmwc1Oqbd48D6eW3fOQ+Ix8Pt16ibbVPRjaovwLFZgSa0sNXw1uRFpuER+LapuCWvZ/Czk3oefnynNIZdZ0HAS2hh1vK3sU72HnpST2XUvhxW418XA07rJWubiYlB/nEvlkf4oiI6n6xecE/PgjVr6mUYk4LMSdYS0CmL8/kosJD18hXiiZIf5F+gF3ry2Ku/Xav0iSFASEAH/e9bKtJEnHJUk6LEnSkwaIp/LybQQtJsGx+fcswrLvWjKbzyYyvVN1gjzUKdVbWg6tWhL66y+4jRpF+rJlRDz5JHknTz7SuS6nXWb4luH8eOZHeoX04tcnfqVTQCdc7K14rUctjkWl89vpBAP/DgShBLpipSKbow90//DOyz4OPnzX+Tu+6fQNiTmJDNk8hEXnF6F7wOxWhVH3SQhopVQL/kdbGFmWeWvDOfQyfDagoUkOUpW3+n4uTGwXwrqTcVxIyFQ7HKGyyYhRio00GKy0E6jsNBqlv2lRLmx/819vF2p1fLTlItW8HBjdOsiooRRcvEjkkKEkf/cdjl27ErplMy79+pncffONXrVxtbPirV/Oid55RlDewy7DgHWyLN/9tBJ0q1P7COA7SZLu2RxKkqSnbiWDx5OT7z1CIgCd712EpVCr473fLhDsYc+UjqEqBlh6Gnt7qrzzNoFLloBWpyw3+PyLUu/VK9YVM/v0bIZvHk5qQSrfd/6eT9p/gouNy51jhjQPoJG/C59svUROoVgXLpSjA98ps1Z9vgZbl3+93T2oO7888Qsd/DvwzYlvGLdtHNFZFby3kCRBz08gN1mZIbjLn5eT2HM1mVd61CLA3V6lAE3PtM7VcbWz4uMtl8SKBKF8/fE+SBro9oHakZgOr1rQ/mU49zNc2/m3txYdiCI6NY/3Hq9ntKbncnExyd//QOTgIWhTkvH74Xv8v/sWS8/y2fv3sFztrXmnbx1Ox2aw8miM2uFUOIb4WxYP3L1uxv/Wa/cyjH8s1ZRlOf7W9wjgL6DJvT4oy/JPsiw3l2W5uZeXV1ljrrjsXJVZgdgjcOb/f9Rz90QQmZLLh0/Ux9bKvPafObQMI+S333AdOoS0RYuI7D+A/DNnHviZK2lXGL5lOHPOzKFHSA9+feJXOgd2/tdxGo3EB/3qkZRdyA+7xLpwoZxkJcDer5WKbLX73PcwDzsPvu30LZ+2/5TrmdcZtHEQKy6tQC8bt9WIqvyaQcOhcGiW0lQZpSLwZ79fJsTTgTFGHgE3Ny52VjzftQYHr6eyW/TOE8pLzGG4sAHaPgcu/mpHY1ravQieNWHLi8qsHpCUVcAPu67RrY43HWsa5xm2MDKSqBEjSZk9G5e+fai2eTPO3bsb5VqG9GRjP9pW9+CL3y+TlCV65xmSIZK8Y0ANSZJCJEmyRknk/lUlU5Kk2oAbcOiu19wkSbK59WtPoC0gakKXVaPhENBS2Qydn050ai4zd4fTp6EvHYx0czE2C0cHfD/4gIAF89EXFBA1fARJX3+DXPT3ggN6Wc/i84sZtmUYKfkpzOg8g8/af/a32bt/ahLoxuBm/iw8EMn15Bxj/1YEAf76FPRaeOy/JR4qSRJ9Q/vyS79faF6lOZ8d/YzJOyYTn3O/sbQKoOt7ygzBrv8AsO5EHNeScnitRy2jjYCbs5GtggjxdOCTrZfR6irwAIBgGvR6+P11cKqqND4X/s7SRlm2mREDh2YD8MX2KxTp9Lzdx/DVR2VZJn31GiIHDKQoJga/776j6uefY+Fy/+ceU6L0zmtAoU7Ph6ItjEGV+aelLMta4BlgO3AJWCvL8gVJkj6UJOnuapnDgNXy39eT1AGOS5J0BtgNfCbLsvg/XFYaDfRWirDIf37MBxsvYKWReNcIN5fy5ti2LaEbf8Ol/5OkzptH1IiRFEVFAXAz9yZP/fEUX5/4mo7+Hfn1iV/pEtilVOd9rWdtbC0tRBEWwfiSr8Cp5cr+WbfgUn/Mx8GH2V1n80HrDzifcp6BGweyOWKz8eJUk4s/tHkWzq+nIOIQ3/xxlaaBriZVEdiUWFloeKNXbcKTclh9rJK13xDK39nVkHhaWaZpbdr7+1UT1AZq94UDMzh3LYJ1J+KY0C6EEE/D/nlpU1KImzqNGx98gH2TJoRu/A3nnj0Meo3yEOLpwPRO1dl8NpGjkWlqh1NhSOb4QNu8eXP5+PHjaodh+ra+inx0Pn0LP2JAn95MbBeidkQGlbV9B4nvvYdcXEzatAG85rCVIn0xr7d4nQE1Bjz0BuOF+yP5cPNF5o5uRo964mFSMJLVIyFiDzx/GhwebZ9EfE48b+57k1NJp+hXrR9vtXwLB6sK9rBVmAM/NOOG5Enr5Df4+em2NA92VzsqkyXLMkN/OkxEcg67X+mEk62V2iEJFdGtf5e4+MHEnaKi5oMkX0Ge3YqNtk/w3+JR7H6lo0H/XWb/+SeJ77yLPicH71dewW3USCQz/v+RX6Sj45e7CXS35+enW5tckRhTJUnSiVu1Tf7FfP82CCXSdXqLDMmJL+yWMLZVoNrhGJxzj8fwXbeSmwEOuH21nOd+07G64wIG1hz4SDeH0a2DqOnjyH83X6SguBJUMhTKX8wRuLxZWeL0iAkegJ+jHwt7LOTpRk+zOWIzQzYN4ULKBQMGagJsHMlu+yZVss/zduAlkeCVQJIk3ulTh5ScIn7cc13tcISK6sB3kHMDen4mErySeNUiOuBJeuZt4oOOzgZL8PR5eSS++x5x06Zj6eNDyPp1uI8ZbdYJHoCdtQXPda3B8eh0sb/YQMz7b4TwQL9dzuWToqHU01/B8tpWtcMxuEuplxh5/HmeezKdq4Ob0+BcDvoxL5B38tQjnc/KQsMH/eoRl57P3D0RBo5WqPRkGXa+r7RMaF32psGWGkumN57Owh4LKdIXMWrrKBadX1ShirJ8dbMp5/XBjM1bBMVl65VZGTT0d+XJxlWZvy+ShAzx5yUY2O2WCfUHQUCY2tGYvNxCLc8m9kCSJPqkLDLIOQuvXSNyyBAy1q3DY/IkQtasxqZGDYOc2xQMbRFAkIc9X26/il60VCgzkeRVUMU6Pd/tvMZl7z7IHjVg9yd/a6lgzmRZZumFpYzYOoK84jx+6jmfJ/67jOAVy0GjIXrUKJJnzULWPnxLhDbVPOnTwJc5e8JJzi40QvRCpXXld4g5BJ3eMOg+lmY+zVj3+Do6B3bmmxPf8PQfT5OSn2Kw86slIjmHFUfjOFLjJaxyEpRqm0KJXulRCxn4avsVtUMRKpqdHwASdP+P2pGYhdl/hXMu24m0euOQzq6GpEuPfC5ZlslYt47IwUPQZWQSuGA+3i+/jGRtbcCI1WdloeGl7jW5lJjFprOif3FZiSSvglp7PJaYtDxe6lEXqdMbkHRRKXds5rKKsnhh9wt8efxL2vu1Z32/9bT0bQmAXePGhPz6C859+pDyw0xixk+gOOnhp/xffqwmRVo9c8WSJ8FQdFqlUqRHdWgy2uCnd7Fx4euOX/Ne6/c4lXSKgRsHsj9+v8GvU56+3H4Fa0sN/Z4cphQw2P8tZN9UOyyT5+9mz8R2IWw4Fc/5eNEgXTCQmCNwfr1omVBKsWl5zNsXSf8mflTp8xZYO8KuDx/pXLqcXBJee53Ed97FrkljQn/ZgEObNgaO2HQ83rAqtas48c0fVykW1YLLRCR5FVBBsY4fdoXTLMiNTrW8oN4A8K4Lf32mPGyaqQupFxiyaQh74/byWovXmNF5Bq62rn87xsLREb8vv8D300/JP3eOyAEDyT1y9KGuE+rlSP8m/iw7HC16tgiGcWYVJF9WWgNYGKcghiRJDK45mNV9V+Np58nUnVP54dQP6PTmt7/0RHQ6v5+/wZQO1fByslF6f2oLYfdHaodmFqZ2qoa7gzUfbbkoqgULZafXw7Y3wMlXtEwopZl/hgPwWs9aYO+u/Lld2ar0F3wIBZcvEzVoEFlbtuD53LMEzp+PZQXvFa3RSLzaoxbRqXmsPS6qBZeFSPIqoOWHo7mRVcArj9VSCpBoNND5LUi9BufWqh3eQ5NlmTWX1zB662i0ei2Lei5idN3RDyyu4tr/SYLXrsHCyYmY8eNJmfsT8kMsV32ua3W0epnZf4nZPKGMivOV5dJ+zZTm50ZWzbUaK3qvoH/1/vx09iem7JxCan6q0a9rKLIs8+nWS3g52TCp/a2KwB7VIOwpOLkMbpxTN0Az4GxrxYvdanA4Io1dl0QBA6GMLm+ChJPKIJVomVCimNQ81p+MY0RYIL4udsqLraYq+7F3fqDszy6B0vtuNVFDhqLPyyNw8SK8pk1DsrAwbvAmokttb5oFuTFj5zXyi8xvoNJUiCSvgskt1DLnr+u0q+5J62oe/3+jdl/wbXRrNq9YvQAfUm5xLq/ve52PjnxES9+W/Pz4zzT2blyqz9rWrEnwzz/j3LMHyd9+S+zUqegyMkr12SAPBwY19Wfl0RgSM0UBA6EMjsyF7ARlNqqcSkLbWtryYdsP+bDNh5xOOs2QzUM4nXS6XK5dVjsu3uR4dDovdquJg43l/9/o+CrYuigJs1CiYWGBhHo58Mnvl8SSJ+HR6fWw5wtlqXnDoWpHYxZm7Q5Ho5F4umO1/79o7QAdX1f2ZV/d/sDP63NzSXj5ZW588B/sw8II+fUXHMIqV6EbSZJ4rUctkrILWXIoSu1wzJZI8iqYRQciSc0t4pUetf7+hiRB53cgI1ppxGwGrqZfZdjmYWyP2s7zTZ9nVtdZuNm6PdQ5LBwdqPr11/i8+w65Bw8ROWAg+WfPluqzz3Spjl4vM3u3mM0THlFeGuz/Bmo8BsHtyv3y/Wv0Z3nv5dhY2DB+23iWXlhq0sv3inV6Pv/9MtW8HBjS/B/7fuzcoPV0ZclT4hl1AjQjVhYa3upVh4jkXNadiFM7HMFcXdkKN89Dh1dBUzlmkcri7lm8Ki62f3+z6RhwD1X2Z99nGX1hZCSRQ4eStW07Xi++SMBPc7F0r5ztY1qGetCxphdz/rpOZr75TE6YEpHkVSCZecXM3RtBtzo+NA5w/fcBNbqDfwvY+5Wyv8WEbY7YzMgtI8kpzmH+Y/OZ1GASGunR/rpKkoT7yJEEr1wBQNTIUaQtX1Hiw26Auz1DWgSw+lgM8aIcufAo9n8LBVnQ9X3VQqjtXps1fdfQMaAjXx7/kpf3vExOUY5q8TzImmOxRKTk8kavOlha3OPfe9hTYOMMe78s/+DMUNc63jQKcGX2X+FiNk94eLIMez5XEpP6g9SOxizM3H0NjUZiaqdq/37Twgq6vKMUwjv387/ezt61i6jBQ9ClphG4YD6eU54y+953ZfVqj1pk5hczb69oa/UoKvffngrmp33XySnU8vJjNe99gCRB57chKw5OLCnf4EpJq9fy+dHPeXPfm9TzrMfPj/9MiyotDHJuuwYNCNmwHsc2bbj50UckvvEG+oIHF1aZ3rk6EtKdTdSCUGoZscpSzUbDoUp9VUNxsnbi207f8krzV/gz5k+GbRnGtfRrqsb0T4VaHTP/DKdFsBvd6njf+yA7V2j5NFzaBDcvlmt85kiSJJ7tXJ3YtHw2nhblyIWHdHUb3DgL7V8BC8uSj6/klFm8eEaEBeLjbHvvg+r2V7bO/PnxncF2WacjacYM4qY/g3VwMCHr1+HQunU5Rm666vu50LehLwsPRIq2Vo9AJHkVREpOIYsORNG3YVXq+Drf/8DQThDUDvZ9BUV55RZfaaQVpPHUH0+x/NJyRtYZybzH5uFp52nQa1i4uuI/Zzaezz1L5m8biR45iuKE+z/8+LnaMSwsgJ+PxxKbZlp/XoKJ2/slIEPnN9WOBFAe+MfWG8uCHgvILc5l5NaR7IrepXZYd2w4Gc+NrAKe7VLjgUWVaDVVKUcuZvNKpWsdb+r4OjPrr3B0ormwUFqyrOzhdwuGhkPUjsYszNx9DYv7zeLdptFAtw8gMwaOL0SXkUHslKdJnfMjLoMGErRiOVZVq5ZbzObg5cdqUajVM2u3GGx/WCLJqyBm775OoVbPi91qPPhASYIub0POTTi+oHyCK4WLqRcZtnkYZ5LO8HG7j3kj7A2sNEYqNa/R4DVtGv6zZ1MUHU3koMHkHr1/m4Vpnaqj0Uj88KdpzXwIJiwrAU6vVHriuQaqHc3fNPNpxpq+a6jhWoMX/nqBWadnoZfVXcqn1emZ89d1Gvq70L5GCQM79u7Kss0Lv0CyaPhdEkmSeLZLdSKSc9l6LlHtcARzcW0HJJ6G9i8bre1LRVKqWbzbqnWBkI4U/PI1kQMHkXfkCFU+/A9VP/oIjY1N+QRsRkI8lT3aK45Ei8H2hySSvAogISOf5UeiGdjUj1Avx5I/ENRGucns/xYKs40fYAk2Xd/EmN/HICOztPdS+lUzfpl5AKcunZU2Cy4uxEyYeN99elVcbBkRFsj6k/FEpeSWS2yCmTs4E2S90jjYBHnbe7Ow50KerP4kP575ked3P6/qPr3NZxOJSctTlkeXpgJp6+lgZQf7vjZ+cBVAz3pVqO7tyMw/w9GL2TyhJLf34rkGKsvNhRKVahbvLpn6TkRttkTOyyRo+TLchojZ0gd5rquywmPGLjHY/jBEklcB/PDnNWRZ5rmuJczi3a3zO5CXquwZUsnt/Xdv7X+LBp4NWN1nNfU86pVrDDahoQSvXYNju3bKPr2330Ff+O9139M6VcNSI/G9mM0TSpKXBicWQYNBylInE2VjYcOHbT7kzbA32Re3j5FbRxKVGVXucej1MrP/CqemjyPd6/iU7kMOntBiolK8IFVUvy2JRiMxvXM1rtzMZuelm2qHI5i68F0Qf0LM4pXSw8ziyTodSV9/TcLn87CrakvI43nY1a9bTpGaL18XO0a2DOSXU/HEpYvZvNISSZ6Zi03LY+1xpVyvv5t96T/o3wxq9oKD30N+htHiu5+Mggym/DGF5ZeWM6rOKH567Cc87DxK/qARWDg54T97Fp7TppG5YQPRo8dQfPPvD0LezraMbhXEr6fiuZ5smpUJBRNx5EcozoN2L6odSYkkSWJEnRHMe2we6QXpjNgygn1x+8o1hj8u3eTqzRymd1aWRZdam+fAwlrM5pXS4w2rEuRhzw9/hpt0Gw1BZbIMez4DlwBoNELtaMxCaWfxdDk5xE1/htR583EdNpTAGZ9gWRQH59aVU6TmbXL7UCRg/r5ItUMxGyLJM3Pz90WgkWBqp+oP/+HOb0FBJhyebfjAHiAiM4IRW0dwOuk0H7f7mNfDXjfa/rvSkjQavJ57Fv+ZP1AUHk7koEH/6qc3pWM1bCwt+F4sFxDupzBbSfJq9wXvOmpHU2otqrRgdd/V+Dn5MX3XdOafm18uiYAsy8zaHU6guz19Gvg+3IcdvaHZeDizGtKjjBJfRWJpoWFap2qci89kz9VktcMRTFXEbog7pgxSWVqrHY3JK+0sXlFsLNHDh5Ozbx8+772L7wcfINXpBT71la0zetHipCRVXe14sokfq4/FkJZbpHY4ZkEkeWYsLbeINcdjebKx37+bbpaGb0PlYfTIj1BYPrNTB+MPMmrLKHKLc1nQY0G57b8rLadu3QhesxqNjS3Ro8eQtXXrnfe8nGwY0yaIjWcSuHZT/b2Mggk6vkgZOGn3ktqRPLSqjlVZ2mspPYN7MuPkDN7e/zZFOuP+IN13LYWzcZlM7VTt3n3xStL2edBYwr5vDB9cBdS/iT9+rnZiNk+4N1mGvz4HZz9oMkrtaMzCzN3XsNRITHvALF7ukaNEDR5CcVIygfPn4T7i1gypJCnJdMoVpem8UKKnO4ZSUKxn8cEotUMxCyLJM2NLDkZRUKxnSsfQRz9JuxeVh9KTSw0X2H2svLSSabum4evoy6o+q2js3djo13wUNjVqELx2Dbb16hH/0sskz5p154FoSodq2FlZMFOU8hX+qbgADs2EkI7KcmgzZGdpx+cdPmd64+lsitjE5B2TSS9IN9r1Zu4Op4qzLQOa+j3aCZx9oekYpZJpRqxhg6uArC01PN0xlBPR6RyKSFU7HMHURO6B2MO3ZvFElceS3JnFaxmI931m8dLXrCVm4kQs3N0JWbvm3/3v6j6p7N3e/42SZAsPVN3bie51fVh6KIrcQq3a4Zg8keSZqbwiLUsPRdGtjg/VvZ0e/UT+zSGwjbJkU1dsuADvotVr+ejwR3x69FPa+7Vnaa+lVHU07T4wlu7uBC5ehMsTT5Dyw0wSXnkVfUEB7g7WDA8LZPPZROIz8tUOUzAlZ1YqrUnam98s3t0kSeLpRk/zZYcvuZB6gRFbRhCREWHw6xyLSuNoZBpPdQjFxtLi0U/U7gXl+4HvDBFWhTe4eQBeTjbM/FMMVAn/sOcLcPJVWr8IJbo9ize1479n8WStlhv//Ygb77+PQ5vWBK9ZjXVQ0L9PYmGp7C+OPwFR5bsf2lxN7VSNjLxiVh8TA3slEUmemfr5eBzpecU8XZZZvNvaPg+ZsXDh17Kf6x+yirKYunMqa66sYXy98XzX+TscrBwMfh1j0Fhb4/vZp3i99BJZW7YQPXYs2uRkJrQLAWDhfrH5V7hFp4X934FfM2UmrwLoGdKThT0Wkq/NZ9TWURxMOGjQ88/aHX5n0KRMXPyhyUhlNUJWgmGCq8BsrSyY0iGUg9dTORGdpnY4gqmI3AfRB5RZPKtH2P5RySRk5LPhZDzDw/49i6fLySF26jTSV6zAfcIEAubMwcLpAYPxjUeCo49Ydl5KTQPdCAtxZ/6+CIq0Yi/jg4gkzwxpdXrm7YugWZAbzYPdy37CGo+BZy04MMOgywVismIYuWUkx28e58M2H/JS85ew0JRhxF4FkiTh+dRk/L6fQeGVq0QOGYrHzRj6NvRl9dEYMvONM/spmJkLGyAjWik5Xpo+b2aioVdDVvZZSRXHKkzbOY01l9cY5Lzn4zP560oyE9uFYGdtgHtCu5eUvoQHZpT9XJXAiJaBuDtY84OYzRNu2/uFkmg0HaN2JGZh8cEoZGBS+5C/vV6cmEj0iJHkHjxIlf9+iM9rryJZlHCPs7KFVtOUojcJp4wXdAUytVM1EjML2HhGDOw9iEjyzNCWc4nEpefz9D2WCDwSjQbaPAs3zyk3GQM4lXSKkVtHklGYwbzu8+hfo79BzqsW58ceI2j5ctDpiBo+gsmWCeQW6Vh1NEbt0AS16fVKdTSv2kpbkgqmqmNVlvVaRlu/tnx05CM+P/o5Or2uTOectTscJ1tLRre+x/KlR+EWBI2GwYnFkJNkmHNWYPbWlkxsF8JfV5I5F5epdjiC2hLPQOReaD0drOzUjsbkZRcUs+pIDL3qV/lb66r8CxeIGjKU4oQEAn6ai9vgwaU/afMJYOMiZvNKqVNNL2pXceLHPdfR68VexvsxSJInSVJPSZKuSJIULknSG/d4f5wkScmSJJ2+9TXprvfGSpJ07dbXWEPEU5HJsszcPRFU83Kga21vw5244RBwrAIHvi/zqbZHbWfS9km42LiwovcKmldpboAA1WdXvx7BP6/FJjgYi/de49mcMyw6ECmWC1R2V7dB0kVlNklTMcfNHKwc+L7z94yuO5rll5bz3O7nyCt+tIa04UnZbLtwg7Gtg3G2NWDrlHYvgbYQjs033DkrsDGtg3C2teSHP0VLmErv0CywdoSm4hGsNNYciyW7UMvk9v/fLpP9526iR48BK0uCV63EsW3bhzuprTOETYJLmyBF/JssiSQpfQnDk3LYeelmyR+opMr8RCJJkgUwC+gF1AWGS5JU9x6HrpFlufGtr/m3PusOvA+0BMKA9yVJcitrTBXZvmspXEzMYkqHag/XOLgkljbQ6mllJi/xzCOdQpZlFp9fzCt7XqGeZz2W9VpGoHMZ99uYGCsfH4KWLcWxfXt671xGn0Pr2XgqTu2wBLXIstKM2zUQ6g9UOxqjstBY8FqL13i31bvsj9/P+O3jSclPeejzzP7rOraWFnf2thqMRzWo1UtJ8opFUaSSONlaMa5tCDsu3iQ8SbSEqbSyEuD8eqXYip2r2tGYPK1Oz6IDUYSFuNMowBWAtGXLiXvmGWxCQwlZswabGjUe7eQtpyrPYqKIVKn0aeBLgLsdc/ZcFy1h7sMQw85hQLgsyxGyLBcBq4EnSvnZHsAfsiynybKcDvwB9DRATBXW3L3X8XG24YkmRqhO2Wy8Mpp38IeH/qhWr+XjIx/z9YmveSzoMeY9Ng8324qZr2scHPCfNRPXoUMYfO0vCj94B11hodphCWqI2gfxx5XiRRaWakdTLobUGsIPXX4gMjOSUVtHEZFZ+sqbsWl5/HY64c6eMINrNQ3yUuGsYfYOVnRjWgdhbalh0YEotUMR1HJkrrKftdXTakdiFraev0F8Rj6T24ci63Tc+PgTbn78MY6dOxO0dAmWXl6PfnJHL2VP5Jk1kBlvuKArKEsLDU+1D+VUTAZHI0URqXsxRJLnB9xdxzTu1mv/NFCSpLOSJK2TJCngIT8rAOfiMjkQnsqEtiFlKzl+P3au0GwcnN8AGaXfa5ZXnMcLu1+4U0Hzy45fYmNRsXvsSJaWVPngA24Om0jT68c4P2IsuowMtcMSytu+r8HBGxpXrsbBHfw7sKjHIvK1+YzeOpqTN0+W6nMLD0SikfjbMieDCm4HVRrCodmi51QpeDra8GTjqqw/GUd6rnEb3wsmqDAHTiyCOo8rvdqEB5Jlmfn7Igj1dKBzkBNxzz5H+rJluI8di//3M9DY25d8kpK0fkZJug/NLPu5KoHBzQPwcLBmzp7raodiksprA8kmIFiW5YYos3VLHvYEkiQ9JUnScUmSjicnJxs8QHPw497rONlYMrylEZdAtpqqVAc8NLtUh6fkpzB++3j2xe/j7ZZv81Lzl9BIFXNf0j9JkkSbd15iTvuxaC5fIGrESIrixNLNSiPhFET8datYQeUrOV7Psx7Ley/H3dadyTsmsy1q2wOPzy4o5ufjcfRtWJUqLkb685Ik5f9HyhUI32Wca1QwE9qFUFCsZ6UoIlX5nF4JBZlKYiGU6GhkGmfjMpncyJ24CRPI2b0bn3fewefNN0quoFlabkHQYLBSRCpPzE6VxNbKgvFtg/nrSjKXErPUDsfkGOJpPB4IuOu//W+9docsy6myLN9ezzYfaFbaz951jp9kWW4uy3Jzr7JMh5up6NRcfj+XyMhWQYYtVvBPLv5Qf5DSc6qEG0xERgQjt4wkMjOS7zt/z7Daw4wXl4myttRQZ+Rg3mo9mcKkZKKGDSf/3Hm1wxLKw+E5yvLm5uPVjkQ1AU4BLOu1jHqe9Xh1z6ssubDkvnsj1h6PI6dQy4S2Bt6L90/1BihFpMRIeKnUruJMu+qeLD0UJYpIVSZ6HRyeBf4tICBM7WjMwrx9kdTUZ9H8q9cpuHwZv+9n4D5qpOEv1O4FKM5TltIKJRrdKhgHawt+FLN5/2KIJO8YUEOSpBBJkqyBYcDGuw+QJMn3rv/sB1y69evtwGOSJLndKrjy2K3XhH+Yvy8SS42GCW2DjX+xNs9CcS4cX3DfQ04nnWbMtjEU6gpZ1GMRHQMqRgPoRzGiZSARVWuydux7aKytiR4zhpw9e9QOSzCm7BvKsubGI8HWRe1oVOVq68q8x+bRPag7Xx3/is+OfvavFgs6vczig5G0CHajgb+R/7wsraHlU0oRqZsXjXutCmJiuxBuZhWy9Vyi2qEI5eXKVkiPErN4pRSRnEPk4ZN8/tcP6DPSCVy0EOfu3Y1zMe86UKOH8gymFfv9S+Jib8XIVkFsOpNATOqjVX2uqMqc5MmyrAWeQUnOLgFrZVm+IEnSh5Ik9bt12HOSJF2QJOkM8Bww7tZn04D/oiSKx4APb70m3CUlp5C1x2MZ0NQPb+dyWBZWpT5U6wpHfoLign+9vSd2D5N3TMbF2oVlvZVR/MrMxc6KYWGBLEvUYDNvMdYhwcROm07Gr7+qHZpgLMcXgl4LLaeoHYlJsLGw4auOXzG67mhWXl7JK3teoVD3/4eTnZduEpuWb/xZvNuajQdLO2WmQihRx5pehHo5sGB/pKhSV1kcmqVUBa7dV+1IzMKWxb/xxb7Z2NvZELxiOfZNmxr3gq2ehtxkZTBRKNHEdiFYaCQWHohUOxSTYpDNU7Isb5VluaYsy9VkWf741mvvybK88dav35RluZ4sy41kWe4sy/Lluz67UJbl6re+Fhkinopm6cEoinR6JncwUrGCe2n7POQmwdnVf3v5l2u/8Pzu5wl1DWVpr6UEOAXc5wSVy+1y8EsuZRO0dCn2YS1IfONNUhcsVDkyweCKC5Qkr2YPpWy/AIBG0vBai9d4tfmr7IzZydSdU8kuUkrzL9wfiZ+rHd3r+pRPMPbu0HgEnF0rmqOXgkYjMaFtCOfiMzkena52OIKxxZ2AmENKyf5KUhW4LOJ/3kCnhZ9Q4OlD6NrV2FSvbvyLhnYGz1pwZI4oIlUKPs629G1YlXUn4sguKFY7HJNROSpkmLGCYh3LDkfTrY4P1bwcy+/CIR3At5HSTkGvR5Zl5p2dx3sH3yOsShgLeyzEw86j/OIxcX6udvRt6MuqozHkWNgQMHcuTr16kvTll9z8/AtkvdjrUmGcX6+MsLYUJcfvZUy9MXzS7hNO3TzFhO0T2B8RwZHINMa2CcLSohx/5LSaBroiOHb/ZefC/w1s6o+rvRUL9omR8Arv0EywcYamo9WOxKTJskzqggVkvfs2FzxC8F64GCufchqokiRlpUjiGYg9Uj7XNHNj2wSTU6hl/QlRAO82keSZuI1nEkjPK2Z8eezFu5skQZvnIDUc3eXNfHr0U74/9T29Q3ozq+ssHKwcyjceMzC5fSi5RTpWHY1BY22N39df4zZyJGmLFpHwxhvIxWJ0yezJsjKy6lUHQjupHY3Jerza4/zQ9Qeis6J5af9k7O3TGdrciFWB78WzOtTsKZqjl5KdtQUjwgLZcfEGsWliX0uFlREDF3+DZmPBxkntaEyWrNeT9NlnJH35FYeDmrJz7JvUCPUt+YOG1GiYsuf78Jzyva6ZahzgSuMAV5YeikavF7OfIJI8kybLMksORlHLx4nWoSrMmtV9kiJnf1479jGrLq9iTN0xfNr+U6wsjFjd04zV93OhbXUPFh2IpEirR9Jo8HnnbbxeeIGsjZuInToNfW6u2mEKZRF9EG6cU0ZYJUntaExaO792fNVuDnnaHOyD5xCff638g2g9HfJSlGWbQonGtA5GI0miOXpFdrtiY5jYT3w/cnExCW+8QdqSpSQ/9iQfNh7GhM61yj8QawelOfqlTZApZqdKY3zbYCJSctl7rXK2WvsnkeSZsBPR6VxIyGJsm2AkFR4oc3QFTPWryg7yeLnWaF5t8Wql6YH3qCa3D+VmViFbziUASi89z6en4PvRf8k9eJDocePRponaQmbryBywc4OGQ9WOxCycvOZEXtTTONvYM2H7BI4klvOyo+D2UKUBHBbN0UujiostfRr6svZ4rNjXUhEVZCntker1B1exn/5e9AUFxD37HFkbN+H53HN8GNSDun6utK6m0vaUFpMBWVmRIJSoV31fvJxsWHwwSu1QTIJ4Yjdhiw5G4WxryZNNqpb7tdML0pm0YxInCpP4JCWTcSk3yj0Gc9Shhhehng4sPRT9t9ddBw3Cf+YPFF69SvSIkRQnJKgUofDI0qPh8hZoOhas7dWOxuQVanUsPxxNp9D6rOq7HF8HX6bunMr2qHLskiNJ0Go6JF+G66I5emlMbBdCTqGWNcdi1Q5FMLRTy6AwS5nhFv5Fl5VFzKRJ5OzZQ5UP3ud8l4GEJ+cyuX2oKgPtgNIcvVZvpTm6WHZeImtLDaNaBvHXlWQiknPUDkd1IskzUTcyC9h2/gZDWwRgb12+1a9u5N5g7LaxhGeE813nGTwe2gfOrIaCzHKNwxxpNBKjWwdxKiaDs3EZf3vPqUsXAhcuQJuaStTIURRGiAIHZuXYPECCsMlqR2IWNp1JJCWniAltQ/Bx8GFxz8XU96zPq3teZe2Vclw+WX/greboop1CaTT0d6VFsBuLD0ahE/taKg6dFg7/CIFtwM/I5f/NkDY5megxY8k/cxa/b77GbdgwFuyPpIqzMrutqlZTIT9dLDsvpREtA7GykP412F4ZiSTPRK04Eo1elhndKrhcrxuVGcWY38eQlJfEnG5z6BTQSWksXJwLp1eWayzmamAzf+ytLe55g7Fv1oygpUuQi4qIHjWK/AsXVIhQeGhFucoyp7r9wMVf7WhMnizLLNwfSU0fR9pWV5Y5udi4MLf7XNr5teO/h//LwvPl1F7E0hrCJsH1P0Vz9FKa2C6EuPR8/rgoVnBUGJc3QWaMmMW7h6K4OKJGjqIoOpqAOXNw7tWL68k57A9PYVSrQKzKsyrwvQS1BZ8Gyn5Ksey8RF5ONvRtWJWfxbJzkeSZokKtUqGxa21vAj3Kb1nY5bTLjN02lgJtAQt7LKRFlRbKG1WbgH8LODoPRCuAEjnbWjGgqR8bzySQllv0r/dt69QhaPkyJFsbYsaOI+/4cRWiFB7KmVXKTHbLqWpHYhaORKZxMTGL8W1D/rbMyc7SjhmdZ9AruBffnviW7058Vz7Nt5tNuNUcfbbxr1UBdK9bhQB3OxbsF6sNKowjP4FbMNTqpXYkJqXgylWih49Al5lJ0KKFOLZrC8CyQ9FYWUgMbVHOVYHv5XY7haQLELVP7WjMwrg2weQW6VhXydspiCTPBG05qyxzGtsmuNyuefLmScZvG4+1hTWLey2mrkfdvx8QNgXSrkPEn+UWkzkb0zqYIq2etcfvva/FJiSE4BUrsPT2JmaisgdAMFF6vTKCWrUJBISpHY1ZWLg/Ejd7K/o38fvXe1YWVnza/lMG1xzMgvML+OjwR+hlIw8eOXgo5cjP/Qx5ovBRSSw0EuPahHAsKv1fy84FM3TzAsQchOYTQWOhdjQmI+/UKaJHjwZJInj5MuwaNwYg91avtd4NlCIeJqHBYLD3+H91VOGBGgW40iTQlSUHoyp1OwWR5JmgJQejqOblQLvqnuVyvb1xe5nyxxQ87TxZ2nMpoS6h/z6o7hPg4K2MBgolqunjRKtQd5Ydir7vvhYrX1+Cli/Dplo1Yqc/Q+bmLeUcpVAq1/+ElKvKLJ5om1CimNQ8/rh0kxEtA7G1uvcDpYXGgndbvcuE+hNYe3Utb+57k2K9kZfVtJgE2gI4vcK416kghjT3x9HGUszmVQTH5oOlLTQZpXYkJiP30CFiJk7Cws2VoJUrsalR4857v56OJ7tQy5jWQSpG+A9WttBsnFL8Kz1K7WjMwrg2wUSl5rGnErdTEEmeiTkVk86ZuMxya5uwLXIbz//5PCEuISzuuRhfx/tsMLa0Vm4w13ZAWoTR46oIxrYOJj4jnz8vJ933GEt3dwKXLMa+cWMSXn2V9NWryzFCoVSOzAFHH6XsuFCiJYeisJCkEvcTS5LEi81e5Pmmz7M1cisv7n6RAm2B8QKrUh8CW8OxBWLZeSk42VoxqJk/W88lkpJTqHY4wqMqyIQza5QCRPbuakdjErL/3E3slKex9vMjePlyrP3/v+JAlmWWHYqmrq8zTQPdVIzyHlpMAkmjbJ0RStSrvi/eTjYsrsR9P0WSZ2KWHIzC0caSAU2NX9xhw7UNvLb3NRp5N2JBjwV42JXQB6b5BGWpx7EFRo+tIuhe1wdfF1uWHop64HEWTk4EzJ+HY4cO3PjgP6T8JG7gJiP5KoTvVJY5WVqrHY3JyynUsvZYLH0a+lLFxbZUn5nUYBLvtnqXvXF7mbpzKjlFRix73WISpEcqs7NCiUa1CqJYJ9932blgBs6sUQqntZikdiQmIXPzFuKefRabWrUIWrYUSy+vv71/LCqdyzeyGdM6SL22CffjXFVZVXVyGRSK9gAlsbbUMKpVEHuuJnO9krZTEEmeCUnKLmDLuUQGNVOWyRjTiksreP/g+7Txa8OcbnNwsnYq+UPOvlDncaXXTlGuUeOrCCwtNIxsGci+aykl3mA0trb4z/wB5z59SP7mG5K+K6eCFMKDHZ0LFtbQfLzakZiFX0/dXuYU/FCfG1JrCJ+1/4zTSaeZtGMSGQUZRomPOv3AwUs0Fi6l6t6OtA71YMXhGNFOwRzJt5poV20q2iYA6WvXkvDqq9g3aULgooVYuLr+65ilh5T+xE80/vd+YpPQaioUZsJZseqnNIaHBWJtoWFpJW2OLpI8E7LqSCzFOtno68Dnn5vPZ0c/o2tgV77v/D12lnal/3DYFGX5h+jXUirDbt1glpWiX4tkZUXVLz7HZdBAUn+cS9Jnn4lET00FWXB6FdQfBI7eakdj8mRZZvnh28ucXB/6871De/Nd5++4ln6NCTsmkJKfYvggLa2VZvZXtynN7YUSjW4dRHxGPn9duf+yc8FERe2DlCuityeQumgxN957H4f27QiY9xMWjo7/OiYpS+lPPLh5AHbWJlqgxr+FUgTsyFyx7LwUlHYKvqw7EUdWJWynIJI8E1Gk1bPiSDQda3oR6vXvm48hyLLM9ye/Z8bJGfQJ7cNXHb/C2uIhl6AFtlL6tRydJ/q1lIKnow29G1Rh/Yk4cgq1JR4vWVjg++GHuI0eTdqSpdx4/wNkcSNXx9lby5zCxDKn0jgZoyxzGtXq0Zc5dQzoyMyuM4nLjmP8tvHcyDVCn7Zm45QCOicWG/7cFVD3uj54O9mw/LBIis3O0Xlg51ap9xPLskzyDzNJ+vxznHr0IGDmTDR29x7YXnU0Fq1eZnQrEyq48k+SpBQBS7kKEbvVjsYsjL3dTuF45WunIJI8E7Htwg2SsgsZZ6S2CbIs8/mxz5l3bh6Dag7ik3afYKl5hCWhkqQ0R0+6ANEHDR9oBTSmTTDZhVp+ORVfquMljQaft97E46mnyFi7loQ33kDWlpwgCgYky3B8Ifg2UpY6CSVafjgGRxtLnmhctUznaV21NT92+5Hk/GTGbRtHXLaBfzC7BkDNXkpze60oKFISKwsNw8IC+etqMjGpeWqHI5RWVoJSibHJaLB6iNU6FYgsyyR9/gUps2bh0r8/fl9/hWR974HtYt3/B9qDPR3KOdKHVO9JsPdUfkYJJWoU4ErTQFeWHqp87RREkmcilhyMItjDno41vUo++CHp9Do+OPQBKy6tYHTd0bzX6j00Uhn+19cfBLauyn4loURNAlxp4OfC0oNRpV5+KUkS3i+9iNcLL5C1cRPxL72MXPTvxuqCkcQchqSLSsEVU9t8b4LScovYcjaRAU39cDDAfuKmPk2Z/9h8souyGbttLJGZBi7j32Ii5KXAxY2GPW8FNTwsAI0kseKomM0zGycWg6xXCqZVQrJez40PPyRt8WLcRo7E9+OPkCzvf2/aceEmSdmFptU24X4sbZR2GFd+V5J5oURjK2k7BZHkmYDz8ZmciE5ndOtgNBrDPlAW64t5c/+bbLi2gacbPc2rzV8te8Uoa3toOhoubYbM0s1OVWaSJDGmdRDXknI4FJH6UJ/1fHoKPm++QfaOHcQ++yz6AiOWmBf+7/gCsHGGBoPUjsQs/Hw8liKdnlEGXOZU37M+C3ssRKvXMm7bOK6mXzXYuQntDO7V4JioZFsavi52dK/jw8/H4ygo1qkdjlASXbGS5NXoDu4hakdT7mSdjsR33iVj1Wo8Jk3E5523kTQPftxdeigKfzc7OtUyk/3XzcaBrFNWJAgl6lXfFw8Ha1YeiVE7lHIlkjwTsPRQFHZWFgxubti2CcW6Yl7d8yq/R/7Oi81eZHrj6YYrCdxikjJKKJYLlMrjjariZm9VqgIs/+Q+dixVPvwPuXv3ETvlafS5orKpUeWmwMXfoNEwsDbxZTsmQK+XWXk0hrBgd2r6lKJK70Oo5V6LRT0XYSlZMmH7BC6kXDDMiTUaZTYv9ggknjXMOSu4Ua2CSMst4vfziWqHIpTk0ibIuQktKl/BFVmrJeH1N8jcsAHPadPwevnlEp97rtzI5khkGqNaBWFh4IF2o3EPgWpd4cQS0IntHCWxttQwpEUAuy7dJDEzX+1wyo1I8lSWmV/MpjOJPNmkKs62VgY7b6GukBf+eoFdMbt4M+xNJtQ38JINt2Co2VMZLRT7Wkpka2XBkBYB7Lh4k4SMh7/BuA0ZQtUvviDv+HFiJj+FLqdy9nwpF6eWg66o0i5zelj7wlOITs1jZKtAo5w/1CWUxb0W42jlyKQdkziddNowJ248AiztlFlboURtqnkQ6unwSANVQjk7Nh9cg6B6V7UjKVdyURHxL71M1ubNeL34Il7PPVuqge1lh6OUJKB5QDlEaUAtJkJ2glItWCjR8BaByMDqo5Wn76dI8lT266l48ot1jAgz3DKnfG0+z/35HHvj9vJe6/cYUWeEwc79N2GTlX0tlzYZ5/wVzKiWQehl+ZGXC7g83he/b74h/+xZYiZORJeVZeAIBfR6OLEIgtqCdx21ozELyw9H4+FgTc/6VYx2jQCnABb3XIyHnQdP/fEUx28cL/tJ7dygwUClHUxBZtnPV8FpNBIjWwVxMiaDCwniz8tk3bwI0QeUBEBjom0AjEBfWEjcc8+TvWMHPm++geeUp0r1ueyCYn45Gc/jDavi7vCQ1cbVVqMHOFUVK6pKKdDDng41vFh9LAatrnJULRdJnorkWw/8Df1daODvYpBz5hXn8cyuZziUcIgP23zI4JqDDXLeewrtrMzoHV9kvGtUIAHu9nSt7cOqozEUah9tX4tzj8fw/34GBRcvETNuPLqMDMMGWdlF/AnpUWIWr5TiM/LZdekmQ1oEYGNp3AfKKg5VWNRjEb4OvkzdOZXDiYfLftIWk6A4D86IxsKlMaipP7ZWGpYfrlz7WszKsflgYaNU1awk9Pn5xE2bTs5ff1Hl/fdwHzu21J/dcDKe3CKdeRRc+ScLS2g2Fq7vgjQDF6eqoEa2DORmViF/Xq4cfT8NkuRJktRTkqQrkiSFS5L0xj3ef0mSpIuSJJ2VJGmXJElBd72nkyTp9K2vSlXq7ER0OlduZjMizDDLnHKKcpi6cyrHbx7nk/af0L+GkXvjaDRKY+Ho/ZBswKIIFdioVoGk5hax48LNRz6HU5cuBMz8gcLwcKLHjkOblmbACCu5YwuV0tR1Hlc7ErOw+mgMMhjsHlYSL3svFvZYSIBzAM/seoYD8QfKdsKqTcCvmfJgLPp+lsjF3op+jary66n4StlY2OQVZCn9PesPBHt3taMpF/rcXGKnPE3uwYP4fvwRbsOHl/qzsiyz7HA0jfxdaBTgarwgjanpGJAslBUoQom61PamirMtKypJAZYyJ3mSJFkAs4BeQF1guCRJdf9x2CmguSzLDYF1wBd3vZcvy3LjW1/9yhqPOVl5JAYnG0seb1S2vlIAWUVZTPljCmeTz/JFhy/oG9rXABGWQpNRoLEUjYVLqUMNL/zd7Mpc4cmxY0f858ymKDqa6DFj0CZXrrLARpEZB1d/VyrHWtqoHY3JK9bpWX0sls61vAlwty+363rYebDgsQWEuITw7J/Psid2T9lO2GKy0lg4cq9hAqzgRrcKJr9Yxy8nRWVlk3N2DRTlQNgktSMpF7qcHGImP0XeiRNU/eILXAcOfKjPH4pIJTwph9Gtg40TYHlwrgq1eil7yUV9hBJZWmgYFhbA3muVo++nIWbywoBwWZYjZFkuAlYDT9x9gCzLu2VZvv2neRgwbBlJM5SeW8Tmc4k82aTsfaUyCzOZvGMyF9Mu8nWnr+kR3MNAUZaCozfU7gtnVkJx5alY9Kg0GonhYYEcikglIrlsxVMc27YlYO5cihMSiR4zluKbjz47KKCUopZlpTS1UKIdF26SnF3IKCMVXHkQN1s35j82n5puNe8UmHpk9for+/OOzTdcgBVYA38XGvm7sOxwdKn7fgrlQJaVv8O3Z6crOF12NrETJ5F/9ix+X3+Ny+MPP7C94kgMLnZW9G3oa4QIy1GLiZCXKuojlNLQFgFIwKpjFX82zxBJnh9wd6mauFuv3c9E4Pe7/ttWkqTjkiQdliTpSQPEYxbWn4yjSKtnRMuyPSClFaQxcftEwtPDmdF5Bl0CuxgowofQfDzkpytl54USDW7uj6VGYvWxsld4cmgZRuC8n9AmJRE9egzFCaIx6iPRFSulqKt3U/aZCiVafjgaP1c7OtZUp6+Ui40L8x6bR12Purzy1ytsj9r+aCeyslX2L13eIhoLl9KoVkGEJ+VwOEIsFTcZ0Qch+bKyz7SC02VlETNxEvkXL+L/3bc493z4ge2UnEJ2XLjBwKb+2FqZeYGakE7gFgLHRKXg0vB1saNrHR/WHoulSFuxC7CUa+EVSZJGAc2BL+96OUiW5ebACOA7SZKq3eezT91KBo8nm/nSNFlW+ko1DXSljq/zI58nNT+VidsnEpUVxQ9dfqCDfwcDRvkQgjsojYVFAZZS8XaypVsdH9adiHvkAix3s2/WjMAF89GlpxM9egxFcWIZ1UO7shVyboiCK6UUnpTDoYhURrQMVLWvlJO1E3O7zaWhV0Ne2/saWyK2PNqJmo9X+n6eWGLYACuoxxtVxcXOiuVHRDsFk3FiEdi4QL0BakdiVLqMDGLGT6Dg0iX8Z8zAqVu3RzrP+hNxFOtkRrQ0s7YJ96LRKPewmIOQdEntaMzCyJZKfYTtF26oHYpRGSLJiwfu/lfif+u1v5EkqRvwNtBPluU7C4dlWY6/9T0C+Atocq+LyLL8kyzLzWVZbu7l5WWAsNVzOCKNiORcRrZ89GpOKfkpTNw+kbjsOGZ2nUkbvzYGjPAhaTTKErfYw+IGU0ojWgaSllvEtvOGucHYNW5M4MKFyh6FMWMoioszyHkrjeMLwdkfapbjUmcztuJINFYWEkNbqP+A5GjtyJxuc2jm04w3973JpuuPsGTJPRSqdYFTy0Rj4VKwtbJgcDN/tp+/QVJWgdrhCHlpykqaRsPAuvz2x5Y3bXo60RMmUHj1Kv4/fI9Tl86PdB69XmbV0RjCgt2p7u1k4ChV0ngUWFiLdgqldLs+wooKPlBliCTvGFBDkqQQSZKsgWHA36pkSpLUBJiLkuAl3fW6myRJNrd+7Qm0BS4aICaTtuJINC52VvR5xHXgtxO8hNwEZnWdRSvfVgaO8BE0HnnrBiNm80qjXXVPAt3ty1yA5W52DeoTuHAButxcoseMoSi28jT8LJPU6xDxlzJQUYn6Sj2q/CId60/E0bO+L56OplGgxt7KnlldZ9HStyVv73+b38IfYel48/GQFQ/hOw0fYAU0slUQWr3MGgMsOxfK6Mwq0BUp5fQrKG1aGjHjxlMUfh3/2bNw6tTpkc91OCKVqNQ8hleEWbzbHDyg7pNKO5iiXLWjMXkajcSIloEcjkgjPKls9RFMWZmTPFmWtcAzwHbgErBWluULkiR9KEnS7WqZXwKOwM//aJVQBzguSdIZYDfwmSzLFTrJS8kpZPuFGwxo6vdI68CT85IZv208ibmJzOo6izDfMCNE+QgcPKBOv1s3mIpfsaisNBqJYWEBHIk07A3Grl49ghYtRM7NU5ZuxlT8jcVldnyhUiG2aeXpK1UWm84kkFWgZVQZ9xMbmp2lHT90+YFWvq1498C7/HLtl4c7Qc2e4OgjKgWXUoinA22re7D6WCx6vSjAohpZVv7O+oeBTz21ozEKbWoqMWPHURQVhf+c2Ti2b1+m8604qhRc6VXfzAuu/FPzCVCYBefXqx2JWRjcLAArC8mgg+2mxiB78mRZ3irLck1ZlqvJsvzxrdfek2V5461fd5Nl2eefrRJkWT4oy3IDWZYb3fpe4XeN/nxcWQc+8hEekG7m3mTC9gkk5SUxp9scWlRpYYQIy6D5eCjMhAsP+XBVSQ1uFoClRmLVUcPeYGzr1iVwyWLkggIl0YuKMuj5K5TifDi9Amr3AacqakdjFlYciaaGtyNhIabXh8vW0pbvu3xP66qtef/g+2y4tqH0H7awUlrCXNsOmWJfa2kMaxFIfEY++8JT1A6l8oo5pLQAqaBVgbUpKUSPHUtRbCwBc3/EsW3bMp2vQhVc+afAVuBdVxRgKSUvJxt61KvCuhOxFBSXvT6CKSrXwiuV3Z114CEPvw78Ru6NOwnej91/pJmPCZZIDmoLnjVFU85Sun2DWX8yzuA3GNvatZVEr6iI6DFjKYyMNOj5K4wLvyqVYZtPVDsSs3AhIZMzcZmMaBmIJKlXcOVBbid6bfza8P7B91l3dV3pP9x0jFKA5dRy4wVYgTxWzwd3B2tWVeCRcJN3YvGtgiv91Y7E4LTJyUSPHUdxfAIBP83FoVXZt6asq0gFV/5JkpTZvMTTEH9S7WjMwsiWQWQVaNl8NlHtUIxCJHnlaH94CjFpeQ89i3c7wUstSGVu97k08b5nbRr1SRI0Gw9xx+DGObWjMQsjWgaSkVdssAIsd7OtVUtJ9LRaYsaMpTBCJHr/cmKRUhk2RKXKtGZm9dFYbCw19G/yoC456rOxsGFG5xm082vHfw79h5+v/ly6D7oFKwVYTi4FfcUc2TUkG0sLBjXzZ+elmyRliwIs5S4vTRmoajikwhVcuZPgJSYS+NNcHMLKvjVFr5dZXdEKrvxTwyFgZQ/HxWxeabQKdSfUy6HCFmARSV45WnkkBncHa3rWL/2ysMScRMZvG096QTpzu8+lsXdj4wVoCI2GgYWNKMBSSq1DPQjyMGwBlrvZ1qxJ0JLFyHo90WPHUHj9ulGuY5aSLkHsEWWZk4nOSpmSvCItv56Kp3cDX1ztrdUOp0S3E70O/h348NCHrL2ytnQfbDYOsuIgvAwN1iuRYS0C0Opl1p0QFX3L3ZnVoCtUtkpUIMVJSUqCd+MGgT/Nxb6FYbamHLpVcKWs/YlNmq0LNBgM59ZDfoba0Zg8SZIY2TKIUzEZXEzIUjscgxNJXjm5mVXAH5duMriZPzaWpVsHnpiTyITtE8gozOCn7j/RyKuRkaM0AHt3ZdnI2bVQWHErFhmKRiMxPCyQo1FpXLuZbZRr2NSoQdCSxSBD9NhxFEZEGOU6ZufEEtBYQeMRakdiFracTSS7UMvwMPN5QLK2sObbTt/S0b8j/z38X1ZfXl3yh2r1BgdvUYCllEK9HGkV6s7qo6IAS7m6U3ClRYUquFKclETMuPH/T/CaNzfYuVcejcHV3uqhBtrNUrNxoM2Hc6VcwVDJDWzqh42lpkLO5okkr5ysPRaLTi+X+gHp9hLN2wleA68GRo7QgJqPh6JsUeGplAY181cqPBm4AMvdbKpXVxI9UPboVfZEr7gAzq5WCq44eKodjVlYdTSGal4OtAh2UzuUh2JtYc03nb6hU0AnPj7yMWsur3nwByysoMlIuLoNshLKJ0gzNzwskJi0PA5eT1U7lMoj5jCkXKlQBVeKk5KIuXsGz4AJXoUuuPJPVZtAlQZwcokyGCA8kKu9NX0bVuW30wnkFlasPqkiySsHulsFV9pV9yTY06HE42/k3mD8tvHmmeABBLQErzqiAEspeTreKsBywvAFWO5mU63a/xO9sZU80bu0SSm4UoEekIzpyo1sTsZkMDzMdAuuPIi1hTXfdPyGTv6d+OjIRyUv3Ww6BmQdnFpRPgGauR71quBqb8WqY6IAS7k5sRhsnCtMwZU7Cd7NmwTO+8mgCR78v+DK8LAKWHDlnyQJmo5VaiMknFI7GrMwPCyAnEItWypYARaR5JWDvVeTScgsKNU6cLOewbtNkpTZvIRT4gZTSiNaBpJVYPwbjE21agQtXnRr6WYlTvROLAbXIAjpqHYkZmHV0RisLTQMaOqvdiiPzMrCiq87fX1n6eYDEz33UAjtJAqwlJKtlQUDm/qz48INUnIK1Q6n4stLU1oVNRwC1iUPHJu64qQkYsaMRXs7wWtm2OrhlaLgyj81HAKWdspsnlCiZkFuVPd2rHADVSLJKwerjsbg4WBNtzo+DzzudoKXXpBuvgnebQ2HKjcYUYClVFqHehDq6WDUJZu32VSvriR6evlWolfJqm6mhEP0fmW2RiNugSUpKNbxy6l4etSvgruD6RdceZDbSzdLleg1GweZMXB9d7nFZ86GhwVQrJNZLwqwGN/ZNUrBlQqwEuFOgpeURIAREjyoJAVX/snWBeoPgHPrRH2EUpAkiWEtAjgVk8HlGxWnAIt4wjGypKwCdl1OYlAzf6wt7//HfXeCN7f7XPNO8ADsXJUbzPn14gZTCpKkFGA5EZ3OlRvGKcBytzt79PQyMZUt0Tu5BCQLpfG1UKLfzyeSmV/M8BYVY5nT7USvg38H/nv4v/dvr1CrD9h7imXnpVTd24kWwW6sOhqDLPYBGc/tgit+zZV9V2ZMm5ysLNE0YoIHlajgyj81HQtFOaI+QikNaOqPtYWG1Udj1Q7FYESSZ2Q/n4hDp5cZ+oAHpH8meA29GpZjhEYkbjAPZWAz5Qazqhxm8+D/iZ6s11eeRE9bBKdXQq1e4FTJfuA/olVHYgn2sKdVqIfaoRjM7aqbt9sr3DPRs7RWCrBc+R2yDd/HsiIaHhZIVGoehyJEARajiT0CyZfNfhZPm5KitEkw0hLN2ypVwZV/CggDr9piyWYpuTtY06N+FTacNG59hPIkkjwj0utl1hyLpWWIO6Fejvc85mbuTSZun1jxEjwQN5iHpMYN5vbSzTuJXmQFT/SubIG8FGUAQihReFIOR6PSGNoiEI3G/AquPMjtRK+9X3s+PPQh666u+/dBTcfeKsCyvPwDNEO9G/jibGtZoUbCTc6JxWDtpKyUMVN3ErzbVTSNlOBBJSu48k+3C7DEn4Ab59WOxiwMbxFAVoGW389XjAIsIskzokMRqcSk5d23bUJSXhITd0wktSCVH7v/WLESPBA3mEegxg3GpkaNuxK9cRRFRZXbtcvdiSXg7A/Vu6odiVlYfTQGS43EoGbmW3DlQawtrPm287e082vHfw79h/VX/7HqwKMahHRQBqr0enWCNCO2VhYMaOrPtvM3SMstUjuciic/3ewLrmhTU4keN47ihAQCfpxj8Cqad7tTcCWkEhVc+adGw8DCRgy2l1KrUA+CPOxZVUEGqkSSZ0SrjsbgYnfvdeDJeclM3D6R5Lxkfuz2o3k0On8UjYaBhbVSpU4okVo3GJsaNQhctBC5uJjoseMoiqlYFaYASIuEiN3QdDRoKtmynUdQqNWx/mQc3ev64OVko3Y4RmNjYcN3nb+jrV9b/nPoP/xy7Ze/H9BsHGTEKH93hBINDwukSKdnw0lRgMXgzqwBbYHZLtXUpqURM24cxXHxBPz4Iw5hYUa93uFbBVcq5SzebfbuULef8nenKE/taEyeRiMxtEUARyPTuJ5s/vUkRJJnJGm5Rey4cJP+Tfz+tQ48JT+FiTsmcjPvJj92/5HG3o3VCbI82LtDnX5K4+nifLWjMXl332AiyvkGY1uzJoGLFyMXFiqJXmzFGMm649QykDSi4Eopbb9wk/S84vuuRKhIbCxsmNF5Bq2rtub9g+/zW/hv/3+zdl+w91CWyQklqlXFiaaBrqwUBVgM607BlWbga36rfpQEbzxFsXEE/DgHh5bGTfAAVh+LxdnWkl71fY1+LZPWdCwUZsLF30o+VmBQM38sNRJrjpn/M5BI8oxkw8k4inT6fz0gpeSnMHH7RG7k3mBOtzk08W6iUoTlqOkYKMiEixvVjsQsDGrqj4VKNxjbWjUJXLwIOS+P6LFjKYqLL/cYjEJXrDS2rt4dXCrm0kNDW300Bn83O9pV91Q7lHJxO9Fr6duSdw+8y6brm5Q3LG2g8Qi4shWyb6obpJkYHhZIRHIux6LS1Q6l4og7BsmXlJ+nZkabnk7M+AkURUcTMGc2Dq1aGf2a6blFbDt/gwGVseDKPwW3A/dqYslmKXk72dK1jjfrT8RRpDXvZfoiyTMCWZZZfSyWJoGu1Kry/3XgqfmpTN4xmcTcRGZ1nUUzH+NtNjYpwe3BLUTcYErJ29mWrrW9WafSDca2dm0CFy9Cn5tHzJgxFMdXgETv6nbIuQHNRMGV0ohMyeXg9VSGtQiocAVXHsTW0pbvu3xPWJUw3jnwDlsitihvNB0Hei2cWalqfOaib8OqONlallul4Erh5BKwcoD6A9WO5KHcSfCiopQEr3XrcrnuhlPxFOn0D6xsXmlIkvKzL+YQJF9ROxqzMCwskNTcIv64aN4DeyLJM4IT0emEJ+UwvMX/Z/HSCtKYtGMScdlxzOo6ixZVWqgYYTnTaJTRx+gDSiNqoUTDb91gdl1S5wZjW6cOgQsXoMvJIXrMWIoTElSJw2BOLgHHKlCjh9qRmIXVx2Kw0EgMbl75HpDsLO34oesPNPNpxlv73+L3yN/BszoEtVX2FosliCWys7agfxM/tpxLJCNPFGAps4IsOL8BGgwEG/MpIKLLyCBm4kSKIiLwnzULhzZtyuW6siyz5lgMjQJcqePrXC7XNHmNRoDGStRHKKUONbzwc7Vj9THzHqgSSZ4RrDoai6ONJX0aKuvA0wvSmbRjErHZsczsOrNyJXi3NR6pNKAWs3ml0qGmF74utqxScU24Xb16BC5YgC4rSyl3nWimJYUzYiF8p7IXz8JS7WhMXpFWz/oTcXSp7Y2Ps63a4ajCztKOmV1m0sS7CW/ue5NtUduUgaq0CIjar3Z4ZmFYi0CKtHp+OVUBVgKo7fw6KM5TZpTNhC4ri5iJkyi6Fo7/zB9wbNe23K59MiaDqzdzGC5m8f7P0Qtq91b6xGoL1Y7G5CmDnP7su5ZCbJr5FqwRSZ6BZeYXs+VcAo83qoqDjSUZBRlM3jGZmKwYfujyAy19W6odojqcfJQG1KdXKg2phQe6PYuy71qyqjcYuwb1CVwwH116+p3GtWbn1HKQ9UpVTaFEOy/dJCWnqHJXpAPsreyZ3XU2jbwa8cbeN9jh6Ag2LmKgqpTqVnWmob8Lq4/GigIsZXVyKXjXA7+makdSKrrsbGImTqLg6lX8vp+BY4cO5Xr91UdjcLC24PFGVcv1uiav6VjIT4NLm9SOxCwMaR6ARsKsC7CIJM/ANp6Op6BYz/CwADILM3nqj6eIzIzk+87f07pq+axFN1lNxyqNqK9sVTsSszCkuVIg5Ofj6t5g7Bo2VBK91FRixoyl+GaSqvE8FP2tRtbVuoBbsNrRmIXVx2LxdbGlY01vtUNRnb2VPbO7zaahV0NeP/Auu2p1VApI5aWpHZpZGNYikCs3szkdm6F2KOYr8SwknFL2VEmmvz9Wl5ND7KTJFFy+jP+M73Dq3Llcr59dUMzms4l3BtqFu4R2BtdAMVBVSlVd7ehY04u1x2PR6syzAItI8gxIlmVWHY2lrq8zQV4ST/3xFOEZ4XzX+Tva+JXPWnSTVr2r0oharAkvFX83e9rX8GLt8Th0enVHwu0aNSJg3jy0yclKn6MkM0n0wndBVpwywCCUKDYtj33XkhncPACLSlRw5UEcrByY3XU29Tzr8UrOOf600cDZtWqHZRYeb+SLnZUFqytIY2FVnFyqNLNuMFjtSEqky8kldvJT5F+4gN83X+PUpUu5x7DxTAL5xTqGVYLWLw/tdn2EyL3K0nOhRMPCAknKLuTPy2byzPMPIskzoHPxmVxMzGJAM3em7pzK1fSrfNvpW9r7t1c7NNOgsVD2RV3/E9Kj1Y7GLAxvEcCNrAL2XFX/BmPftAkB836i+OZNYsZPQJuSonZIJTu5BOw9oVZvtSMxC7dnjW/PIgsKR2tH5nSbQx2Purzs7cWeMwtEAZZScLK14vFGvmw6m0BOoVbtcMxPUZ4yoFD3CaXnrAnT5+YSO2UK+WfP4vf11zh3765KHKuPxlK7ihON/F1Uub7JazxKqY9wQszmlUaX2t54Odmw2kyXbBokyZMkqackSVckSQqXJOmNe7xvI0nSmlvvH5EkKfiu99689foVSZLMuvTdqqOx2FoXsTPjYy6lXuKbjt/QMaCj2mGZltuNqE8tVzcOM9G1jg+ejtasMpGRcPtmzQic+yPFCQnEjB+PNjVV7ZDuL/sGXPkdGg8HS2u1ozF5Or3M2uNxtK/hhb+bvdrhmBwnayd+7P4jtex8eNEmn31nFqkdklkY2iKQvCIdm86YeYVeNVz8TWlibeKtX/R5ecROeZr806fx++pLnHs8pkoc5+MzORefybAWAUhmsLRVFc6+ULOHUh9BV6x2NCbPykLD4Gb+/HUlicTMfLXDeWhlTvIkSbIAZgG9gLrAcEmS6v7jsIlAuizL1YFvgc9vfbYuMAyoB/QEZt86n9nJLdSy8cx1vGos40raJb7q+BWdA8t3LbpZcA1Qlm2eWg46MbJbEmtLDQOb+vPn5SSSsgrUDgcA+xYtCJgzh6LYOGVGL91EGx6fXgmyTizVLKU9V5O4kVUgKtI9gLO1M3N7L6F6sY4XznzHgfgDaodk8poGulLTx9FsR8JVdXKp0sQ6qPwqUz4sfX4+sVOnkXfyJFU//xznXr1Ui2XNsVhsLDX0byJWIjxQ07GQmwRXt6kdiVkY2iIAvQxrj8WpHcpDM8RMXhgQLstyhCzLRcBq4Il/HPMEcHtueB3QVVKGWZ4AVsuyXCjLciQQfut8ZueX0xHoqywgSx/B5x0+p2tQV7VDMl1Nx0J2glLWXijR0BYB6PQyP58wnRuMQ6uWBMyZTVF0tGkmenq98oAU1BY8a6gdjVlYfTQWT0drutbxUTsUk+bi5MdPnu0JKSrm+d3PcSjhkNohmTRJkhjWIpAzsRlcSsxSOxzzkXwVYg4qe6hMdFZKX1BA7LRp5B09StXPPsWlbx/VYskv0vHr6Xh6N/DFxd5KtTjMQvVu4OQrlmyWUpCHAy93r0nb6h5qh/LQDJHk+QF3D9HF3XrtnsfIsqwFMgGPUn7W5BVoC/j+wptY2kXzWftPeSxYnaUKZqNWL3DwFgVYSinUy5GWIe6sORaLXuUCLHdzaN0a/1mzKIqIIGbiRHSZmWqH9H/R+yE9UnlAEkqUlFXArstJDGzqj7Wl2KpdEtfmk5mXeINAS2ee+/M5jiQeUTskk9a/iR/WFhpWHzXvxsLl6uQS0FhC4xFqR3JP+sJC4qY/Q97hI/h++gku/fqpGs/Wc4lkF2gZKlYilMzCUtk6E74TMk1n8NiUPdu1Bs2DTXtf7L2YzU9zSZKekiTpuCRJx5OTk9UO5280WOJrH8wTfq/QK1S9pQpmw8JK+cF1dZuyb0oo0fCwQGLS8jgUYVp74BzbtcV/5g8UXQsnZsJEdFkmMlJ/YgnYuigFC4QS/XxCqeAqHpBKyb85bh61mJ+lx9/Jn2d2PcOxG8fUjspkuTlY07N+FX45FU9BsU7tcEyftgjOrFIGRB1Nr5WJvqiIuGefJffAAXw/+gjXJ59UOyRWH4shxNOBliHm9yCuiia3+saK+ggVmiGSvHjg7icD/1uv3fMYSZIsARcgtZSfBUCW5Z9kWW4uy3JzLy8vA4RtONaWlvw27Gs+6i5mDUqt6Rhlv5S4wZRKz/pVcLa1NMl9LY4dOuD3w/cUXL1KzKTJ6LKz1Q0oLw0ubYSGQ8HKTt1YzIBeL7P2eCwtQ9wJ9XJUOxzzIEnQbCzuCaeZ1/hlqjpWZfqu6Zy4eULtyEzWsBYBZBVo+f18otqhmL4rWyAvFZqOUzuSf9EXFRH/7HPk7t1Hlf9+iOvAAWqHRHhSNsei0hkqCq6UnlsQhHaCk8uUfrJChWSIJO8YUEOSpBBJkqxRCqls/McxG4Hb1Q8GAX/Ksizfen3YreqbIUAN4KgBYhJMnUc1CG6vLNnUm2eTyfJka2XBgKb+bD9/g7TcIrXD+RenTp3wn/EdBZcuETtpMrqcHPWCObsGdEViqWYpHY5IJTo1j2FhYhbvoTQcChbWeF74jQU9FuBj78O0ndM4lXRK7chMUqtQD4I87EXPvNI4uRRcAqCaaRVvk4uKiH/hRXL27KHKBx/gNtg0evetORaLpUZiYFNRcOWhNBur9JG9vlvtSAQjKXOSd2uP3TPAduASsFaW5QuSJH0oSdLtRdoLAA9JksKBl4A3bn32ArAWuAhsA6bLsiyGFCqLpmMhIxoi96gdiVkYFhZAkU7PhpOmuYbeqUsX/L75mvwLF4id/BS6nNzyD0KWlaWaVZtClQblf30ztPpYLM62lvSq76t2KObF3h3q9IOza/C0dGBBjwV42XsxdedUziSfUTs6k6PRSAxpHsCRyDQiklUcBDJ16dHKQ3eTUUpvWRMhFxcT//LL5Pz5Jz7vvoPbsKFqhwRAoVbH+pPxdK/rg5eTjdrhmJdafZQ+sicXqx2Jyfsz5k/SCtLUDuOhGWRPnizLW2VZrinLcjVZlj++9dp7sixvvPXrAlmWB8uyXF2W5TBZliPu+uzHtz5XS5bl3w0Rj2Am6jwOdm7KBnOhRLWrONM4wJXVx2KRTbQRs3P37vh9/TX5Z88SO2UK+txyTvTijkHyJZPvK2Uq0nOL2Hb+BgOa+mNrZToPlGaj2VgoyISLG/G292bBYwtwt3Xn6T+e5lzyObWjMzmDm/ljoZFYY4LLzk3GqWXK99s9ZU2ArNUS/8qrZP+xE5+33sJ95Ei1Q7rjj4s3ScstEvuJH4WltdJH9srvkJOkdjQma0vEFl7860Vmn56tdigPzWwKrwgVkJUtNBwGlzZDbora0ZiF4WEBhCflcCLaxFoW3MW5x2P4ffUl+adPE/v0VPR5eeV38ZNLwMoB6g8sv2uasQ2n4inS6cUD0qMKagduIXcGqnwcfFjYYyGuNq5M+WMKF1IuqBygafF2tqVrbW/Wn4yjSCuW6f+LTgunVigl7l1MY+mhrNUS/+qrZG/fjvfrr+M+ZrTaIf3NmmOx+Lna0b6GadVqMBtNxoBeC6dXqB2JSfo98nfe2v8WzXya8XLzl9UO56GJJE9QV7OxoC9WKokJJerbsCoO1hasNPFS5M69elH188/JO3GC2KnT0OfnG/+iBVlwfgPUHwA2Tsa/npmTZZk1x2JoFOBKHV9ntcMxTxqNsvcz+gCkhANQxaEKC3ssxNnGmcl/TOZi6kWVgzQtw8ICSMkpYtelm2qHYnrCdyo9ZE1kJYKs1ZLw+htk/74N71dfxWP8OLVD+puY1Dz2XUthSPMALDSi4Moj8aoJgW2UfaAmukJILdujtvPmvjdp4t2EmV1mYmdpfoXcRJInqMu7DgS0VPZRiRtMiRxsLOnX2I+t5xLJzC9WO5wHcunbh6qffUre0aPETZ+OvqDAuBc8vx6K86DZOONep4I4GZPB1Zs5DBOzeGXTeARIFn9bdu7r6MuCHgtwtHLkqT+e4kraFRUDNC0da3pTxdmWVWLJ5r+dXKL0kK3ZU+1IkHU6Et58i6wtW/B6+SU8Jk5QO6R/WXM8Bo0EQ1qYxqyn2Wo2FtIiIGq/2pGYjD+i/+D1va/TyKsRs7vOxt7KXu2QHolI8gT1NR0Lqdcg5pDakZiF4WEBFBTr+e30PbuNmBSXfv3w/fQTcg8dJm76M+gLC413sZNLwLsu+DUz3jUqkDXHYrC3tuDxRlXVDsW8OVVR+pmdXqn0N7vFz9GPBT0WYGdpx6Qdk0Sid4uFRmJIc3/2XUsmLr0cl3KbuqwEuLodmoxUesmqSNbpSHzrbbI2bcLrhRfwnDxZ1XjupVinZ+3xODrX8sbXxfxmWExKnX5g4yLqI9yyK3oXr+15jQaeDZjdzXwTPBBJnmAK6j0JNs5wYrHakZiFBn4u1PV1ZtVR0y3AcjfXJ5/E96OPyD14kLhnnjVOopd4FhJOKQMGok9SibILitl0JpF+jariaGOpdjjmr+lYyEuBK1v/9nKAUwALH1uItYU1k3dM5lr6NZUCNC2Dmyuzx2uPm2alYFWcWqH0jlW59Yus15P4zrtk/vYbXs8/h+fTU1SN537+vJxEcnYhw8IC1Q7F/FnbQ8MhcHGj0me2Etsds5tX9rxCXc+6zOk2BwcrB7VDKhOR5Anqs3aABoPh4m+Qb7oFRUyFJEkMbxnIpcQszsZlqh1OqbgOHIDvfz8kd98+4p57Dn2RgXv9nVwKFjbKDyqhRBvPJJBfrBMFVwyleldw9r/nQFWAcwALeyzESmPFpB2TCE8PL//4TEyAuz3ta3ix9lgsWp0owIJer9zDQjqCe6hqYch6PYnvvUfmL7/g+cwzeE6dqlosJVl9NAYfZxs61xIFVwyi2VjQFcLZtWpHopo9sXt4ac9L1PGow4/dfsTR2lHtkMpMJHmCaWg2FrQFlfoG8zCeaFwVOysLVh8z7QIsd3MdNIgqH/6H/7V31+FRXF0Ah3+zcXcjCsGdBIdCS3Gn0OJeoFBapAYVSt29hRaXFisUd3dLcAhOnISQkBBPNjvfHxP6QbFANpndzX2fJw/J7uzMWVgmc+bee07m7j3EvabHRC8vS/ncVO+q9C4THmvp0RiqejtQ199Z7VBMg8ZMGYG5uhNSrt33dKBjILPbzcZMMmP4luFcSb2iQpCGpV9DfxJu57D7YpLaoajv6g5Ii1Z1PbGs05Ew9SPSlq/AfcxoPMa+qlosjxOXms2ui0m8VN8fczNxGasX3rWgXD1lyqYRzBDSt72xe5mwawJVXKrwe5vfcbA0jeJt4n+HYBh86oBPXVGApYgcrS3oVNuHNSfiycjVqh1Okbm89BLeU6eSsXs3cePGI+sj0Tu3GnLTlClzwmOdjU/jVGwavRv4I4mprfpTbwBIGmVE5gGCnIKY3W42GknD8M3DuZp29YHblRXPV/PC3d6KxQZeKbhUhM8DWzeo2kmVw8uyTMInn5C6bBluo0bh/tprqsRRVMsKi/a8VF/MRNCrkMFw4xzEhqkdSanaF7eP8TvHU9G5In+0+QNHS9OpNi2SPMFwhA6GG2chLlztSIxC34b+ZOYVsPZkvNqhPBGXPr3x/nAKGTt3EjthYvETvWMLlClOQc31E6CJW3wkGitzDS/UExXp9MrJFyq1VfpNFTy48m15p/LMbjcbgOGbh3Mt7f5Rv7LCwkzDi/X92HH+BtfTSqHFiqFKT1SaUdftB+ZWpX54WZZJ+PhjUhcvwW3Ey3iMH2fQN38KdDLLwmJ4ppIH/q7GWxDDINXqpfSZLUMFWPbH7WfcjnEEOwczs+1MnKyc1A5Jr0SSJxiOmr3AwlYUYCmikAAXKnnas8QI74S79O2L1wfvk7F9O3FvvIGc/5TtIJIuQPQBZaqcAV+YGIrMXC2rjsfTqbYPTrbqVvAzSaFDICMRLm566CYVnCowu91sdLKO4ZuHE5kWWWrhGZo+DfzRybDsaBkuwHLiL6UZtQozEWRZJvGTT5QE7+XheEycaNAJHsDuize4npZDX7GeWP+sHJQ+s2f+gdx0taMpcQfiDvD6jtep4FzBJBM8EEmeYEisHcvUCaa4JEmiT8MATsamcS7+ttrhPDHX/v3xeu890rduI+6NN58u0QufDxoLqDtA/wGaoHWnlOm9/URFupJRsQ04lHvsjapg52Bmt51NgVzA8M3DibodVTrxGZhANzuaV3Rn6dFoCnRlcJr+nYIrgc3BvVKpHlpJ8D7l1qLFuA4fhscbbxh8ggew+EgM7vZWtK7upXYopilkMORnwum/1Y6kRB2IP8DrOwsTvDammeCBSPIEQxMypPAEs1ztSIzCC/V8sTTXGFUBlru5DhyA1+RJpG/ZQtybbz1ZopefAycXQbXOYC8qrBXFoiMxVPK0JzTQRe1QTJOZOYQMhMvb4dajE7eKLhWZ1XYW+bp8hm0eRvRt4/w/XFx9GwYQn5bDnktlsABL5B64da3UC67Iskzip59xa9EiXIcNw/PNN40iwUu8ncOO8zfoFeqHhSi4UjL86oNnDZOeUXUw/iCv73idIMcgZraZibO1s9ohlRjxv0QwLH71lYbWZWhOeHG42FnSoaY3K4/HkZ1XoHY4T8V18GA8J71D+ubNT5bo3Wm5oWJFOmNyNj6NkzGp9GsUYBQXdEarXuGo8vE/H7tpJZdKzGo3i/yCfIZuHlomE7021b1ws7Nk8eGy994Jnwc2LlCtS6kdUpZlEj/7nFt//YXrkCF4vmUcCR7A32ExFOhk+oipmiVHkqD+ULh+EuKOqR2N3h26fojXdrxGoGMgM9uadoIHIskTDI0kKdMF4o8rDa6Fx+rTIID0HC0bTl9XO5Sn5jZkyJMneuHzCguutCjx+EzBnYIrPer5qh2KaXMOgIqt4fhCKHh85dvKLpWZ2XZmmU30LM019Krvx/bzN0i8naN2OKUn8yZErIM6fcHCulQOKcsyiZ9/wa0//1Rurr3zttEkeDqdzJKjMTQNdiPI3bgbVBu82i8V1keYq3YkenX4+mFe2/4aAY4BzGo7Cxdr05/RIpI8wfDUfklpbC1G84qkcQVXyrvbGX0p8idK9G6cLyy4Mhg04jT2OFl5hQVXavngbGupdjimL3QIpF+HS1uKtHkV1yplOtHr0yCAAp3M32ExaodSek4sAl1+qRVckWWZxC++4NbChbgOHoTnpHeMJsED2Hf5JrG3sukj1hOXPGsnpT7C6RWQY3zr/R/kyPUjjN0+Fj8HvzKT4IFI8gRDZOsK1bspDa7zstSOxuBJkkTvBv6ERd3iUqJxF6wpcqJ37E7Blf6lG6CRWnfyOhm5Wvo2EhdIpaJyO7D3fqIbVf9N9MpSMZby7nY0DXZj8ZEYdGWhAIssK58N/8bgWbUUDidz48svubVgIS6DBuI5aZJRJXgAS45G42JrQbsaouBKqQgdajIFWA5fP8yr21/9N8FztXZVO6RSI5I8wTCFDoHc23D2H7UjMQo9Q/ww10gsOWr8d8Ifm+jlZyt3wUXBlSJbdCSaSp721BcFV0qHmQXU66+M5KUVvT1AFdcq/67RG7Z5WJlK9Po2DCAuNZu9l2+qHUrJi9oPyZeV3rAl7M4IXsr8BbgMGojX5MlGl+Alpeey5WwiPUP8sDI3UzucssE3FLxqKVM2ZeO98XLo+qF/E7zZ7WbjZuOmdkilSiR5gmEKbAruVSBsjtqRGAUPByva1vDin2Ox5OQbZwGWuz0y0Tu3BnJSlTuNwmOdi7/NiZhU+jYUBVdKVcggkHVFKsByt8oulctkote2hheuZaUAS/h8sHKC6t1L9DD/FllZsBDXwYONMsEDWHEsFq1OFlM1S5MkQf0hkHAa4o2zAMvB+IOM3T6WAMcAZrebXaZG8O4QSZ5gmO5UeIoLV6o8CY/Vt2EAt7Ly2XQmQe1Q9OKhiV74XKXgSnlRcKUoFh+JxtJcwwshouBKqXIJguBWcGwh6J7sxktZTPSszM3oFerHtohEbphyAZasFKUycO2XwNK2xA7zb5uEP/9Uqmga2Rq8O2RZZsmRaBoGuVLR017tcMqWWoUFWMKMrwDLgbgD/1bRLGtTNO8mkjzBcNXpA+bWRnmCUUOzYHeC3Gz567DpXBDek+hNfAM57jREH1Sm8xrhBUtpUwquxImCK2oJHQK3Y5W+eU/onkRv0zAi0yL1Hp6h6dPAH61O5u/wok9xNTqnlkJBbolO1VQanX+itEkYNsyoqmj+18GryUQmZ9GnoWibUOqsHaFmTzizAnLS1I6myPbH7ee1Ha8R5BhUphM8EEmeYMhsXJQTzOm/Ide4C4qUBo1Gol+jAI5G3uJCgun8fbkNGYLXu5NJ37qV2HGvI8ui4EpRrTt1nfRcLX3FNCd1VOkIdp5P3Vi4sktlZrebjVbWMmzzMK6mXdVvfAamgoc9jSu4suRotGkWYJFl5bPgGwretUrmEDodCR9/zK1Fi3F7ebhR9cF7kL8OR+NkY0HHWj5qh1I21R8K+VlKITwjsC9uH6/veJ3yTuXLVBXNhxFJnmDY6g+DvAyjOcGorVeoP5ZmGhaZ0GgegOugQXhNfpuMMwnEnqiEzsJR7ZCMwqLD0VT0tKdBUNn+RacaMwuo2w8uboLb8U+1i0oulZjddjYFcgHDNg3jSuoVPQdpWPo2DCAmJZv9V0ywAEvMYUg6X2JtE2SdjoSPPiZ18RLcRozA4403jDrBu5Gew+YzCfQK9cPaQhRcUUW5EPCurdycMPACLPvi9jFuxzgqOFdgVttZJt/ovCiKleRJkuQqSdJWSZIuFf5535WEJEl1JUk6KEnSWUmSTkmS1Puu5+ZJknRNkqQThV91ixOPYILu3PEMM+4KT6XF1c6SjrW8+edYHJm5j2/EbExc69rgHZpKxoVUYl97DV1urtohGTRRcMVAhAwCueCJC7DcraJLRea2m4skSQzbPIxLty7pMUDD0q6GNy62Fkbf9/OBjs4GK0eo1Uvvu5Z1OhKmfkTq0qW4jRyJx8QJRv///u8wpeBKf9H6RT136iMknlFqJBioPbF7eH3H6wQ7BzOzzUyR4BUq7kjeJGC7LMuVgO2FP/9XFjBIluUaQHvgR0mSnO96/i1ZlusWfp0oZjyCqZEkZTQv8TTEhqkdjVEY0DiQ9Fwta08+3ciBwQqbi0sjH7w/mkrm7j3EjhWJ3qMsOVpYcKWeKLiiKrdgqPCsUlHxCQuw3K2CcwXmtJuDmWTG8M3DuZByQX8xGhBrCzN6hvix5WwiSekm9P878yacWwV1+oKlnV53LRcUcP2DD0hdtgy3V0bhMWG80Sd4BTqZRYejaVbRjQoeouCKqmr2Ags7g62PsCN6B+N2jqOSSyVmthUJ3t2Km+R1A+50e50PdP/vBrIsX5Rl+VLh9/HADUA0txKKrtaLYGmvVFUUHis00IUqXg78ZUqlyG9EQMwhCB2CS+/e+Hz6CZn79hE7egy6HBOuxPeUsvMKWHksjo41vXGxEwVXVFd/uFKA5eLmYu2mvFN55rafi4WZBcO3DOd8ynk9BWhY+jQMKCzAYvx9P/91fCEU5Ck3LfVILijg+rvvkrbiH9xffRWPceOMPsED2HXhBnGp2QxoFKh2KIJ14ejzmRWQnap2NPfYGrWVN3a9QTXXasxsOxMnKye1QzIoxU3yvGRZvl74fQLg9aiNJUlqCFgCdy8q+KxwGucPkiRZFTMewRRZOSjlps+sgOxbakdj8CRJon/jAE7HpXEqNlXtcPQjfB6YWf5bcMW5Vy98PvuMzIMHiRk9Gl12trrxGZi1p+JJz9XST1wgGYYqHcHBB47OKvauAh0DmdduHjbmNgzfPJxzyef0EKBhqeipFGBZdDiaAlMowKLTKaMggc3Bs6reditrtcS//Q5pq9fgMe51PF4baxIJHsCfh6LwdLCidfVHXlYKpaX+UNBmK4XwDMSma5t4a/db1HCvwR9t/sDRUqzV/6/HJnmSJG2TJOnMA7663b2dLMsy8NCzsSRJPsBCYKgsy7rChycDVYEGgCvwziNeP1KSpDBJksKSkpIe/84E0xI6FLQ5cHKJ2pEYhe71fLGxMOPPQyZQgCU/G04uhmpdwM7t34edX+iBzxefk3XoMDGjXkGXmalikIZl0eFogj3sRMEVQ2FmrrRTuLIdUopfIdPf0Z+57eZib2HPy1te5szNM8WP0cAMbBxE7K1sdl24oXYoxXdlO6RGQQP9jeLJ+fnEvfEmt9evx/PNN3AfPVpv+1ZbTEoWuy4m0aeBPxZmoj6gQShXD3zqGkx9hHVX1/HO3neo41GHP9r8gYOlg9ohGaTH/u+RZbm1LMs1H/C1GkgsTN7uJHEPPBtLkuQIrAfek2X50F37vi4rcoG5QMNHxDFDluX6sizX9/AQsz3LHJ/a4FsfwuYYxAnG0DlaW9C9XjnWnIwnLTtf7XCK5+wqpUdP6ND7nnLu3p1yX39NVng40SNGUpCRUfrxGZgzcWmciEmlf6NAk7mrbxJCBoNkppzD9MDPwY857efgaOnIiC0jOHHjhF72ayja1vDC08GKhaZwo+roLKWVRtUuetmdnJdH7IQJpG/ejOekd3B7+WW97NdQLD4SjYQybVcwIKFD4MZZiD2qahirL6/m3b3vUt+rPtNbT8fOQr9rXE1JcW+RrAHu1AIeDKz+7waSJFkCK4EFsiwv/89zdxJECWU9n+ndjhT0p/4wuHkRovarHYlR6NcwkJx8HSuPGXlj4bA54FYRgpo/8GmnLp3x/e47sk+dInrYcArSjKdpa0lYcDASGwszeob6qR2KcDdHH6jWWamyma+f6cW+9r7Maz8PV2tXRm4dydEEdS++9MnCTEPfhgHsvphEVLIRj9KnRitrMUMGgXnx18fqcnOJfe11MrZtx+v993EbMqT4MRqQPK2OZWExPF/Ni3LONmqHI9ytVi+lPoKKBVhWXFzBB/s/oLFPY359/ldsLWxVi8UYFDfJ+xJoI0nSJaB14c9IklRfkqQ7iw9eAloAQx7QKuEvSZJOA6cBd+DTYsYjmLIaPcDaSW93wk1dLT8n6vg58dfhaGRjHf2MPwGxR6DBy0ql1YdwbN8Ov59/IjcigqihQ9HeKptrN1Oz8lh9Ip7u9XxxsrFQOxzhv+oPV9YVn12lt11623kzr/08fOx8GLNtDAfjD+pt32rr2zAAjSSxyJiLSIXPU85doUOKvStdTg6xr44lY/duvKdOxXVA/2Lv09BsOpvAzYw80TbBEFk5KIXwzv6jSn2EpeeXMvXgVJr6NuWX53/BxlzcBHicYiV5siwny7L8vCzLlQqndaYUPh4my/LLhd//KcuyxV1tEv5tlSDLcitZlmsVTv8cIMuymGslPJylLdTpB+fWQIZYl1kU/RsHculGBkcjjTTpOToTLGyVsuOP4dCqFX7TfiPvylWiBw9Be9MEmyk/xvLwWHK1OgY2FgVXDFL5FuBWCcJm63W3HrYezGk3B39Hf8ZuH8ue2D163b9avJ2saVvdi6VhMeTkP337CdVo8+DYAqjUDpz9i7UrXVYWMaNHk7l/Pz6ffYpLn96Pf5ER+vNQFAGutrSoJJblGKQGw5X6CMf/KtXDLji7gE8Pf0pLv5b8/NzPWJmJOo1FIVa0Csal/lDQ5cOJ0j3BGKsutcvhYG1unAVYslLg9HKlsqqNc5FeYv/MM/j/Pp286GiiBg0mP9EEijYUkU4ns/BQFPUDXaheTlQZM0iSpFwkxR5VRqn1yM3GjTlt5xDsHMy4nePYHr1dr/tXy8DGgaRm5bP+1PXHb2xoItZAZpIyE6EYCjIyiB4xkqzDR/D54nOce/bUU4CG5WJiOkeupdCvUQAajVhPbJC8a0FAE+UGrE73+O31YMapGXwT9g1tAtvww7M/YGkm2gIVlUjyBOPiUUUpQx0+t9ROMMbMxlJpLLzxzHVuZhhZY+ETfyl3DBuMeKKX2TVpQsDMGWgTEogaNJD860Z4cfgU9lxKIio5i4FNxCieQavTF8xt9D6aB+Bs7cysdrOo7lqdN3a9waZrm/R+jNLWJNiNCh52xlmAJWwOuARBcKun3kVBairRQ4eRffIkvt9/h3P37noLz9AsOhyNpZmGF8V6YsPWcATcioTL20r0MLIs8/Oxn/nl+C90rtCZr1t8jYWZWIbwJESSJxif+kOVE8zVnWpHYhT6Nwogv0BmebgRFWDR6ZSKdAFNwLvmE7/ctkED/GfPoiA5hagBA8mLMaGmyg/x56Eo3O0t6VDTR+1QhEexcVYKGJxeXiKNhR0tHZnRdgZ1POrwzt53WHtlrd6PUZokSWJg40BOxKRyOtaIiirdiFCKhIUOBc3TXWppk5OJGjyE3PPn8fv5Zxzbt9dzkIYjK0/LivBYOtbyxs1eTMUzaFW7gL2XMppXQmRZ5puwb5h5eiY9K/Xks+afYa4xL7HjmSqR5AnGp1oXsHUXBViKqJKXA43KK42FdcbSWPjyNiWRb/hko3h3s61Xj4C5c9FlZBDVfwC5V67oLz4DE5OSxfbzN+jTIABLc3FaN3gNXob8rBLr+2lnYcf01tNp4NWA9/a9x/KLyx//IgP2Qoif8fX9PDobzCyh3oCnenl+4g2iBg4iLyoKv9+n49DqOT0HaFjWnIgnPVdLf7Ge2PCZWyo3Ly5thWT9/17VyTo+PfQpC88tpH+1/nzY5EM0kvi99jTE35pgfMytlF+cFzZAqumP0OhD/8aBRKdksfeykRQjOTpTuVNYzL5SNrVqErBgAbJOR9SAgeREROgpQMPy12Glr1Q/UZHOOJSrC76hypTNEqp8a2thy6/P/0pz3+Z8dPAj5p+dXyLHKQ1ONkrfz9Un40jLMoK+n7kZSgJfowfYuT/xy/Pj4ogaOBBtQgIBM2dg36xZCQRpOGRZ5s/DUVTxcqB+oIva4QhFEToENPrr+3lHga6AD/Z/wLKLyxhWcxjvNHhH9HstBpHkCcbpzkL2o7MevZ0AQLsaXrjZWRrHnfCUa8odwtAheukrZV2lMoELFyBZWxM1eAjZJ04Ue5+GJCe/gGVhMbSpLvpKGZUGLyt9PyP3ltghrM2t+em5n2gT2IZvw75l+onpRttOZUBjpe/ncmPo+3n6b8hLV1pmPKG8yEgiBwykIDWVgLlzsG3QoAQCNCwnY9M4E3ebAY0DxAW9sXD0UWZVHV8IeVl62WW+Lp9Jeyex5soaxtQdw/iQ8eLzUEwiyROMk7M/VO0Mx+brrbGwKbMyN6NvwwC2RSQSk6KfE3KJCZsNkkYvfaXusCpfnqA/F2Lm4kzUsOFkHjqst32rbcPp66Rk5jGoSZDaoQhPokYPsHFRpvWVIAszC75u8TVdg7sy7eQ0vgv7zigTvRrlnAgJcObPQ1GGPe1clpVzmFdN8G/4RC/NvXyZyIEDkXNyCJw/D5s6dUooSMPy16EobC3N6F7PV+1QhCfRcCTkpCk3NYoptyCXiTsnsilyExNDJzK6zmiR4OmBSPIE49VolNKQUw8nmLJgQONANJLEgoORaofycHlZcGyhcofQsZxed23h60vgwoVY+pYjZtQoMnbv1uv+1bLgYBQVPOxoGuymdijCk7Cwgbr94fw6SE8o0UOZa8z5pNkn9KnSh/nn5vPJoU/QycZXnXhgk0Cu3czkwJVktUN5uNijkHAa6g9TWmYUUfbpM0QNGAhA4MIFWFerVlIRGpTUrDzWnoqnW11fHKxF5USjEtBEuZlxZGaxpp1n5mcyZtsYdsXu4r1G7zG05lA9Blm2iSRPMF6BzZQTzOE/SmxdiynxdrKmQ01vlhyNITNXq3Y4D3ZmBeSkFqvgyqNYeHoSsGABVsHBxIx9jdubNpfIcUrL6dg0TsSkMrBxoLjraYzqDwOdVmmYXcI0koZ3G73L8JrD+fvi37y77120OgM9DzxEh5o+uNpZsvBQpNqhPNzR2WBpr/T3LKLMI0eIHjIEjZ0dQX/9hVXFiiUYoGFZfCSGnHwdg0TrF+MjScq088TTEPN0s2PSctMYuWUk4YnhfN78c/pU7aPnIMs2keQJxkuSlNG8xDMQdUDtaIzC0GblSc/R8s/xOLVDuZ8sKwVXPKopCXwJMXdxIWD+PGxq1SJu4kRSV64qsWOVtIWHIrG1NKOn6CtlnNyClR5qYXOhoOQTLkmSGB86ntfrvc76q+t5Y9cb5BXklfhx9cXawoyX6vuz9Vwi19MMcJp+RhKcXQl1+oCVQ5Fekr5rFzEjRmLu7U3gor+wDCg7xZO0BToWHoykabAb1Xwc1Q5HeBq1XwIrJzgy44lfejP7JkM3DyUiJYLvn/2eLsHFK7Qm3E8keYJxq/Wisq7l8O9qR2IUQgKcqe3nxLz91wxvXU5sGFw/CQ1ffqJpTk/DzMGBgFkzsWvciOuTJ5OyoORHUvQtNSuP1Sfi6V7PF0cxzcl4NXgZ0uPhwvpSO+SI2iOY1HASO2J2MHb7WLLyDXyd7l36NwpABhYfjlY7lPuFzYGCXGg4qkibp61bT+zY17CqWJHAPxdi4eVVwgEals1nE4lPy2Fos/JqhyI8LUs7pdr5udVPNO08LiOOQRsHEZsey7TW02gV0KoEgyy7RJInGDcLGwgZrKxrEe0UHkuSJIY2C+JKUiZ7LxlYO4WjM8HSAWr3LpXDaWxt8fv9dxzatCHx8y9I+vlnw0t8H+HvsFhytToGir5Sxq1ye3AOgEPTS/Ww/av15+OmH3M44TAjto4gLdc4Go37u9ryXBVPFh2JIU9rQOsKtblKteeKbcCj8mM3v7VkKfFvvaX085w/D3OXstc6YM7+awS42tKqqqfaoQjF0WC4Mu08vGhtWq6mXWXwxsGk5qYyo80MGvs0LuEAyy6R5AnGT7RTeCIda/ngbm/FvAORaofyf3emOdXtV+RpTvqgsbTE94fvcer5AjenTSfxk0+RdQZ04fgQOp3SV6pBkIuY5mTsNGbQ6BWIPghxx0r10D0q9eC7lt8RkRzBkE1DuJF1o1SP/7QGNgnkZkYuG05fVzuU/zvzD2TegMajH7tp8qxZJEydin2LFvjPnIGZvX0pBGhYTsWmEh51i8FNgzDTiPXERs0tGCq2LhzJfnQfy4jkCIZuGkq+Lp+57eZS17Nu6cRYRokkTzB+zv5QtZNop1BEVuZm9G8UwI7zN7h2M1PtcBTH5kNB3v8T9lIkmZvj8+mnuA4bxq1Fi4h/+x3kfMNuuLznUhJRyVkMFG0TTEO9gcoodimP5gG0DmzNtNbTiM+IZ9DGQUTfNsBpkP/RspIHwR52zNp31TBG32UZDk0D9yrKGsuHbiZz44cfufHtdzh27Ijfr7+gsbYuxUANx9z9kdhbmfNSfbGe2CQ0GAEZCcqsqocISwhj+ObhWJpZMr/9fKq4VinFAMsmkeQJpqHRK6KdwhPo3zgACzOJ+YYwmlegVQpPlG9ZpGlOJUGSJDzfehOPiRO5vW4dsWNfQ5dtuDcM5u6PxMPBivY1vNUORdAHa0dlXcvZf+B2fKkfvrFPY2a3m01mfiaDNg7iQsqFUo/hSWg0EsOal+dM3G2OXEtROxyl8FfCKWUU7yHrieWCAhI+nEryH3/g3Ls35b75GsmibK6lvXE7h3Wn4ukV6ifaJpiKSm3AOVBpp/AAO6J3MGrrKNxs3FjQfgFBTkGlG18ZJZI8wTSIdgpPxNPBms61y7E8PJb0HJVHrS5uhNuxJdY2oagkScJ95Ai8p04lY88eol8eQcHt26rG9CAXE9PZfTGJwU0CsTQXp3CT0WgU6ApUm3Ze070m89vPx1xjztBNQzmWWLpTR5/UC/X8cLa1YPa+a2qHoozi2bg8dD2xLjeXuPETSF22DLeRI/Ge+iGSmVkpB2k4/jwUhVYnM6RpkNqhCPqiMVNm4kTth8Sz9zy18tJKJuyaQGWXyizosAAfex+Vgix7xBWCYBokCRqOFO0UnsCQpkFk5GpZHh6rbiAHflUKT1TuoG4chVz69Mb3u2/JPnWKqEGD0d40rAI1s/dew9pCQ/9GouCKSXEtr0w7D5sDeepUu6zgXIGFHRbiZuPGqK2j2BO7R5U4isLG0owBjQLZGpFIpJrTzlOuwfn1EDoULG3ve7ogI4OYESNJ37oVr3cn4zlxQpnuaZmTX8Bfh6N5vqonQe52aocj6FO9AWBufU87hTln5jDlwBQaeTdidrvZuFiXvQJDahJJnmA6RDuFJ1LH35mQAGfmH4hEp1Np9DPmKMQcgsavgpm5OjE8gGPHjvhPm0ZeVBSR/fqTF20Y65SS0nNZeTyOXqF+uNhZqh2OoG+NxyjTzk8tVS0EH3sf5neYT3mn8ozbMY71V0uvtcOTGtQkEHONpG4RqSMz/z+K8R/amzeJGjSIrGPHKPfNN7gOGqRCgIZl7cl4kjPzRNsEU2TrqvTNO7kEXcYNvgv7jh/Cf6B9UHt+e/43bC3uvwkilCyR5Ammw9JWtFN4QkOalScyOYtdF1WqqnfwF7B2Uu4AGhj7Z5oTOHcOutu3iezbj+wzZx//ohK28FAU+Todw8QFkmkKbAo+dZQCLCpOO3e1dmVOuznU86rHpL2TmH+2aKXRS5unozVd6pRjWVgMadkqTDvPuQ3HFkD17uDke89TeTExyg2ia5H4T5+GU5fOpR+fgZFlmbn7I6ni5UDTYDe1wxFKQpOx5Gtz+GDTCOadnUffqn35qsVXWJiJtZdqEEmeYFoaDFf+DJutbhxGokNNb7wcrZi7P7L0D55yDSLWKtOcrAyzhLhN3boELlqExsqK6EGDyNi/X7VYcvIL+PNQFM9X9aKCh2H+fQnFJEnKaN7NC3B5u6qh2FvaM731dNoGtuXbsG/55ug36GTDay8yvHl5svIKWHJEhdH2E4sgL135N7tLTkQEkX37oUtLI3DeXOyfeab0YzNAh6+lcO76bYY0CyrTU1ZNWbZLABMqVGdN5lVerTWKyQ0no5FEqqEW8TcvmBbnAGVdS/g80U6hCCzMNAxsHMjeSze5fCO9dA9+aDpIZkrBCQNmVaE8gYsXY+HvT8yoV0hbu1aVOP45FkdKZh4jnhGjeCatxgtg760U81CZlZkV37T8hn5V+7Hg3AIm7Z1EXkGe2mHdo0Y5J5pUcGP+gUjyC0oxCdUVKEsD/BqCX+i/D2ceOULUwEFIFhYELvoLmzp1Si8mAzd3/zVcbC3oUc/38RsLRic1J5WRW0ayR87k/ZspvKKzFcm8ykSSJ5ieO+0UTi1TOxKj0LdhAJbmmtJd15J9C47/CbV6gWO50jvuU7Lw8iTwz4XYhoQQ/9bbJM+ZW6rH1+lkZu27Si1fJxqWdy3VYwulzNwSGr4MV7bDjfNqR4NG0jCp4STGh4xn47WNjNk2hoy8DLXDusfw5uWJT8th45mE0jvoxc1w6xo0+f8o3u2NG4kZ/jLmXl4ELfoLq+Dg0ovHwMWkZLH1XCJ9GwZgbVF2K4uaqtj0WAZuHMi55HN82/IbejtUVoqq6QrUDq1MK1aSJ0mSqyRJWyVJulT45wPL5kiSVCBJ0onCrzV3PV5ekqTDkiRdliRpqSRJopKAUHyBzcC7Fhz8FXSGN73I0LjZW9GtTjlWhMeV3rqWsLmQnwlNxpbO8fTAzMEB/1kzcWjfnhtff03il18hl9Lna+eFG1xNyuTlZ8qLO6NlQegwpUrd4dJvjv4gkiQxvNZwPmv+GeGJ4QzZNISkrCS1w/pXq6qelHe3Y/beUmyOfmgaOPpB1S7Iskzy3HnETZiIde3aBP31JxY+okz83eYfiESSJAY2EVWBTc3Z5LMM2DCAlJwUZrSdQdugdtD0NUi5Ahc2qh1emVbckbxJwHZZlisB2wt/fpBsWZbrFn51vevxr4AfZFmuCNwChhczHkFQ1rU0Gw83L8KFDWpHYxSGNAsiO7+ARYdLYV2LNk/pZ1jhOfCuWfLH0yONpSW+332LS//+pMybR/zb7yDnlfz0tVl7r+HjZE3HWuLCsUywc1N6rp1cApnJakfzr67BXfnl+V+ITo9m4MaBXEszgB51FDZHbxbEydg0wqNulfwBE05D5F5oNBIZicQvvuDGV1/h0K4dAXNmY+bsXPIxGJHMXC1Lw2LoUNMbHycbtcMR9Ghf3D6GbhqKpZklCzssJNSrcOpyta7K8pkDv6gbYBlX3CSvG3Cn7NZ8oHtRXygpt6NbAcuf5vWC8EjVu4NzIOz7QTRHL4Ia5ZxoXtGdOfuvkZNfwtMrziyHjARoajyjeHeTzMzwev89PCZO5Pa6dUSPGElBWlqJHe9MXBoHryYztFkQFmZihn2Z0Xg0aHMgfI7akdyjuW9z5rabS7Y2m0EbB3Hixgm1QwKgZ6gfTjal1Bz90O9gYYuuem/iJkzk1oKFuA4ejO8P36Oxsir54xuZFcdiSc/RMqy5WE9sSlZeWsnY7WMJdAzkz45/UsG5wv+fNDNXWiPFHIKYI+oFWcYV94rBS5bl64XfJwBeD9nOWpKkMEmSDkmS1L3wMTcgVZZlbeHPsYBYjSvoh5m5Ml0gLgyi1KuIaEzGPBtMUnouK46VYHN0WVbm6XtWh+DnS+44JUySJNxHjqDc11+RdeyYUio9Nq5EjjV73zXsLM3o3SCgRPYvGCjPahDcCo7MUka/DUgN9xos7LAQB0sHhm8ezubIzWqHhK2lOf0aBbD5bAIxKSXYTD4jCU4vQ1upF9FjJipNzidPwmvyJCSNuAnzX9oCHXP2XSvsyyoaYZsCWZaZfnI6Uw5MoaF3Q+a2m4unref9G9YboLRIEqN5qnnsGUmSpG2SJJ15wFe3u7eTlYnwDxsyCZRluT7QD/hRkqQnXo0sSdLIwkQxLCnJcNYCCAas3gCwdYd9P6odiVFoEuxGHT8n/th9FW1JVam7sgNunFXW4pnA2jKnrl0JmDULbVISkX36kH36jF73fz0tm7Un4+ndIAAnG9FnqMxp/Koy6n12pdqR3CfAMYA/O/5JdbfqvLn7TeacmVN66+EeYnCTIDSSVLItYcJmk5dWQNTMCHLOnsX3hx9wHTy45I5n5DacSSAyOYvRLUURGlOg1Wn56OBHTDsxja7BXfmt9W/YWz6kpY+VPdQfrrRKSrlauoEKQBGSPFmWW8uyXPMBX6uBREmSfAAK/3xgR2VZluMK/7wK7ALqAcmAsyRJ5oWb+QEPvRUuy/IMWZbry7Jc38PD4wneolBmWdhA41fg8lZI0O/FtymSJInRz1YkOiWr5KrUHfhFKQ9fq1fJ7F8Fdo0aErRY6aUXNWgQ6Tt26G3f8w9EoZNlhjYL0ts+BSMS3ArcK8Oh3wxy2rmrtSuz2s2iXVA7fgj/gU8OfYJWp338C0uIt5M1nWv7sCwshts5JVBEKjeD7PV/ELnTF+3tDALmzsGxfTv9H8dEyLLMtJ2XqehpT9vqD5voJRiLjLwMxm4fy4pLKxhZeySfNvsUC81jbj42GgVmFnBQ/ZYwZVFx5xasAe7cwhoMrP7vBpIkuUiSZFX4vTvQDDhXOPK3E+j1qNcLQrE0eBks7WH/j2pHYhTaVvci2MOOabuu6P+ufMIZuLoTGo0Ec9Nat2IVHEzQksVYBQcT++pYUhb+Wex9ZuZqWXQ4ig41ffB3tdVDlILR0Wigyatw/aQyCm6ArMys+LrF1wyrOYy/L/7N2B1jyczPVC2e4c0rkJGrZdnRGL3v+/aM94naYInGwZmgxYuwDQ19/IvKsB3nb3A+IZ0xzwaj0Rj/zI2yLD4jnoEbB3L4+mE+bPIhr9V7rWiVnh28ofZLSsukrJSSD1S4R3GTvC+BNpIkXQJaF/6MJEn1JUmaVbhNNSBMkqSTKEndl7Isnyt87h1goiRJl1HW6M0uZjyCcC8bFwgdAmf+gVtRakdj8DQaiVdaBhNx/Ta7L+p5WvTB38DCDkKH6ne/BsLcw4PABfOxb9WKxM8+I/GLL5ELnr6Izd9hMdzO0TJcND8v2+r0BUdf2POt2pE8lEbSMCF0Ah82+ZBD8YcYvHEwCZml2LPuLrX8lF6Sc/ZdI0+rn2nnsixz8/fpxP22GWsvG4KWr8SqQoXHv7AMk2WZX3dexs/Fhi51DL8XqvBwp5JO0Xd9XxIzE5neZjq9Kj/hTJwmY0GbDUdnPX5bQa+KleTJspwsy/LzsixXKpzWmVL4eJgsyy8Xfn9AluVasizXKfxz9l2vvyrLckNZlivKsvyiLMu5xXs7gvAATV4FSaP0zRMeq1tdX3ycrJm264r+dnr7Opz+u3CdpOk289bY2uL380+4DBxIyvz5xI4bhy7ryYtAFOhk5uyPJDTQRRQrKOvMraDZOIg+AJGGXUSqV+Ve/Pb8b8RmxNJ/fX/Op6jTzH3Ms8HEp+Xwjx6KSMl5eVx//32SfvwZx4AsAqZ9i7m7ux6iNG2HrqZwPDqVUS0qiKrARmxz5GaGbR6GjbkNf3b8k8Y+jZ98J57VoFJbpXVSfo7+gxQeSvzPE0yfYzml59SxhZB5U+1oDJ6luYYRz1TgyLUUwqP0NL3iyB8gFyhl4U2cZGaG93vv4vXuZDJ27CSy/wDyr19//Avvsv70daJTsnhZlBwXAEIGgZ0H7PlG7Ugeq5lvM+a3n48kSQzeOJhdMbtKPYaWlT2o7efEb7suk1+MIlIFqalEvzyCtBX/4B4iUe7FSmiqGG9V4NI0bddl3O2teLG+v9qhCE9BlmVmnZ7Fm7vfpJprNRZ1WnRvi4Qn1fQ1yLoJp5boL0jhsSS1q2E9jfr168thYWH3PJafn09sbCw5OWXvLoG1tTV+fn5YWIjqew+VdAF+awQt3oJW76kdjcHLytPS7MsdhAa6MGtwg+LtLOc2/FgTKjwLLy3QS3zGImP3buImvoFkY4P/b79iU6fOY1+j08m0+3EPkgSbxrUQa1kExf6fYOsUeHk7+NVXO5rHSsxM5PWdrxORHMH40PEMrTG0aGt49GTbuUReXhDGty/WoVeo3xO/Pi8ykphRr5AfH4/PiI44pUyHfsugsii08jinYlPp+ut+JnWoyiuiqqbRyS/I56ODH7H6ymo6lO/AJ80+wcqsmOvoZRlmtIS8LHj1iLLeWNALSZLCCzsY3Mf8QQ8ao9jYWBwcHAgKCirVXyRqk2WZ5ORkYmNjKV9e3PV/KI8qULUTHJmhTH2yekjJXwFQek4NaVqeH7Zd5EJCOlW8HZ5+Z0f+gJw0aDZeb/EZC/uWLQlaspiY0WOIGjgIn88+w6lL50e+ZsOZ61y6kcEvfeuJBE/4v/rDYN8Pytq8foZ/N9zLzot57efxwf4P+CH8B66kXmFKkynFv1gsouereVLdx5FpOy/To54vZk/wfynz8BFiX38dSaMhYM5sbA+9At61lClnwmNN23kFR2tz+jcSvT2NTWpOKhN3T+RowlFG1xnN6Dqj9XNNLUnQ9HVYMRwublSux4QSZzKpdE5ODm5ubmUqwQOl7L2bm1uZHMF8Ys3GQ04qHJuvdiRGYXDTQGwtzfh9dzHW5uXcVpqfV24PviH6C86IWFWqRNDfy7CpXZv4t97ixo8/IusePIVMp5P5efslKnra07GWTylHKhg0KwdoPEa5QLp+Su1oisTG3IZvWnzDq3VfZc2VNQzfPJyb2aUzZV6SJF5rVZGrNzNZdyq+yK+7tWwZ0S+/jLmbG0HLlmJrEwPJl+GZN0yit2dJu5SYzqazCQxpGoSDtZhdZEwu3rpIn/V9OHHjBJ83/5wxdcfo95q6endwKQ+7vzLIljCmyGSSPKDMJXh3lNX3/cT8G0Bgc6XKozZP7WgMnrOtJf0aBrDmZDwxKU9ePARQFlrnpMKzk/Qam7Exd3EhYM5snHr1JPn3P4gbN/6BBVk2nU3gYmIGr7Wq+EQjD0IZ0XAkWDnC3u/UjqTIJEnilTqv8F3L77iQcoG+6/sSkRxRKsduV8Obyl72/LrjMjrdoy8q5fx8Ej7+mIQpH2LXsCFBSxZj6ecHe78Ht0pQrWupxGzspu++go2FGUOaiZlFxmR79HYGbBhAXkEe89rPo0twF/0fxMwcWr6ttIS5sEH/+xfuY1JJniGZOnUq336rlLyeMmUK27ZtK9b+CgoKqFevHp07P3qql/AYzcfD7Til0qPwWMOfKY9Ggpl7rz75i3PSlIqmlTtAuXr6D87ISJaW+HzyCV6TJ5G+fTuRA+4tyHJnFC/Yw47OtUXJceEBbJyh4Qg4t1pZZ2xE2ga1ZX6H+ciyzOBNg9kWVbzfiUWh0UiMbVWJSzcy2HT24S0dtCkpRA8dxq1Fi3EdNgz/P37HzNERLm6GxNPwzETQmJV4vMYuJiWL1Sfi6dcoAFc7S7XDEYpAJ+uYfnI643eOp6JzRZZ0XkJtj9old8BaL4FrMOz8Ah4yo0XQH5HklYKPP/6Y1q1bF2sfP/30E9WqVdNTRGVYxdbgVVMpYiBOMI/l42TDC/X8WHo0hpsZT9jhRIzi3UeSJFwHD8b/9+nkR0VzrdeLZB09CsCWcwmcT0jntVaVxCie8HCNx4CFjTLCZGSqu1VncafFVHKuxIRdE5h+cjo6uWTPw51q+VDBw45fdlzmQYXmciIiuNarF9mnTlHu66/wevstJHNzZTrZ3m/BOQBqvViiMZqKGXuuopFgxDOih6AxyMrP4s3dbzLtxDS6VOjC3PZz8bT1LNmDmplDy3eUmyfn15XssQSR5OnTZ599RuXKlWnevDkXLvz/LuuQIUNYvnw5AEFBQUyePJm6detSv359jh07Rrt27QgODub3339/4H5jY2NZv349L7/8cqm8D5MmScravJsXIGKN2tEYhZEtK5BXoGPu/mtFf9GdUbwqHaFc3RKLzVjZt2hB0LKlmDk6EjV0GMnzF/DTtktUcLcTjYOFR7NzV4qwnP4bUp5ihF1lHrYezGk/h84VOjPtxDTG7xxPel56iR3PTCPx6rMVibh+m20RN+557vbGjUT27QcFOgL/+gunrndNyby2B2KPKoW6zMTasse5kZ7D0rAYeoX64e1krXY4wmPEZcQxcONAtkdv5836b/JZ889KrSgStXopU6B3idG8kmYy1TXv9tHas5yLv63XfVYv58iHXWo89Pnw8HCWLFnCiRMn0Gq1hISEEBoa+sBtAwICOHHiBBMmTGDIkCHs37+fnJwcatasySuvvHLf9uPHj+frr78mPb3kfhGWKTVfUPpN7fwMqnZW7iwJDxXsYU+Hmt4sOBjFyBbBONkU4YLncGFFzZbvlHyARsoqOJigZUuJf2cSN774go7+ofh98pEYxRMer+lrcGQm7PsRuv6sdjRPzMrMis+bf04Ntxp8G/Yt/db348fnfiTYuWTK7XerW46ftl/i5+2XaF3NE3Q6kn76meQZM7CpVw+/n3/C3MPj3hft/RbsvaHugBKJydTM3ncNbYGOUS1EywRDdzThKG/segOtrGXa89No5tusdAPQmCkzfFYMh4jVUKNH6R6/DBEjeXqyd+9eevToga2tLY6OjnTt+vBF2neeq1WrFo0aNcLBwQEPDw+srKxITU29Z9t169bh6en50IRReAoaM2j1Pty8KBpzFtGrz1UkPUfLjD1FqLSZnVo4itdJjOI9hpmDA76//Mymhl1pFXOMOl+9RX5cnNphCYbOwVtpkH5iEaTGqB3NU5EkiQHVBzCz7Uxu592m3/p+bI3aWiLHMjfT8OpzwZyOS2P3savEjnmV5BkzcH6xFwHz592f4MUcUUbymr4GFmJU6nHSsvL561A0nWqXI8jdTu1whIeQZZn5Z+czYssInK2dWdRxUekneHfU6AHuVWDXl6ArUCeGMsAkhzAeNeJmCKyslCFxjUbz7/d3ftZqtfdsu3//ftasWcOGDRvIycnh9u3bDBgwgD///LNUYzY51bpAuRDlBFPrRTAvpWkKRqpGOSe61inHnH2RDG4ShKfjIy587oziPStG8Ypi+4Wb/FSuBdXebEj5P77iWs9e+P74A3aNG6sdmmDImo2D8Llw4Gfo+I3a0Ty1Bt4NWNp5KW/seoOJuyYyrOYwXq/3OmZ6LnTSo54fK5ftxGL0EDKyU/Ga8gEuffs+uDr1nm/BxhXqD9VrDKZq9v5rZORqGfOsGMUzVBl5GUw5MIWtUVtpHdCaT5p9gr2liv2C74zmLR8KZ1cqUzgFvRMjeXrSokULVq1aRXZ2Nunp6axdu1Yv+/3iiy+IjY0lMjKSJUuW0KpVK5Hg6YMkwfNTIC0GwuaoHY1ReKNtZfILdPy849LDN8pOVVpUVO0MPnVKLTZjJcsyP22/SKCbLa2H9qD838swc3cjethwkufMfWChCEEAwNkf6vSF8PmQnqh2NMXibefN3PZz6VW5F3POzGH0ttGk5qTq9RhZq/7hg43focvLJ+3zX3Dt1+/BCd71k3Bps1LgxlKMSj3OjfQcZu29SqfaPlTzcVQ7HOEBLt+6TN/1fdkRvYM367/J989+r26Cd0f17uBZXembJ0bzSoRI8vQkJCSE3r17U6dOHTp06ECDBg3UDkl4nArPQtAzyl3b3Ay1ozF4gW529G0YwJIjMUTezHzwRod/h1yxFq+odpy/wZm427z6XEXMzTRYBgURtGQpDq1bc+Prr4kbN54CsRZXeJjmE0CXr4zmGTlLM0s+bPIhU5tMJSwxjN7renM2+Wyx96vLySH+vfe4/v4H2NUP5eOuk/g+/hEzN7Z+CDYuSqsK4bF+2naJPK2Ot9pWUTsU4QE2XN1Avw39SM9LZ2bbmQyuMdhweitrNMpo3s2LcGaF2tGYJMkY7xTXr19fDgsLu+exiIiIMt1ioKy//6cWcxRmt4bn3oeWb6kdjcG7kZ5Dy6930bq6F7/0/U/vu+xU+LE2lH8G+vylSnzGRJZluv22n1tZeex441kszDT3PJcyZy43vv8ei3Ll8P3xB2xqGPY0dEElq8YolTbHhoFLoNrR6MXppNNM2DWBlJwU3mrwFn2q9HmqC9O86Ghix40nNyICt9Gv4DF2LPMORfPR2nMsGdmYxhXc7n3B5e3w5wvQ7gtoMkZP78Z0XU3KoM0Pe+jfKICPu9VUOxzhLvkF+Xwb9i2Lzi8ixDOEb1p+U/LtEZ6GTgd/PAP52fDqEVEI7ylIkhQuy3L9Bz0nRvKEss2/gVIg5MDPkJWidjQGz9PBmuHNy7P2ZDxn4tLuffLQdDGK9wR2XUjiVGwaY5+reE+CB0pRCrfhwwhcuBA5P5+oPn1JWbRITN8U7vfceyBpYMcnakeiN7U8avF3l79p7NOYzw9/zhu733jiNgvpO3ZwrWcv8uPj8ft9Op7jxiGZmdG3YQCeDlZ8ten8vf+fdAWwdQo4B0KD4Xp+R6bpm80XsDbX8PrzldQORbhLQmYCQzcPZdH5RQysPpBZ7WYZZoIHhaN5kyHlinKzStArkeQJQqv3ITcd9v2gdiRGYWTLCjjbWvDN5v/3giQ7VUnyqnYGn9qqxWYsdDqZH7ddxM/FhhdC/B66nW1IPcqv/AfbJo1J/PgT4t94g4IMMbVYuIuTLzQZq1wgxYWrHY3euFi78OvzvzIhdAI7onfw0tqXijR9U87P58a33xI75lUs/f0pv2I5Ds8+++/z1hZmvNm2CsejU1l36vr/X3hqKSSegdYfikJcRXAs+hYbzyQwokUF3O3F35eh2B2zmxfXvsjFWxf5puU3vN3gbSw0Bt7nsWon8K6trM0r0D5+e6HIRJInCF7VofZLcGQG3L7++O3LOEdrC8Y8G8zui0kcvJKsPHhomjKK9+wkdYMzEqtPxnEyNo3xrSvfN4r3X+YuLvj//jseEydye/MWInv2IiciopQiFYxC8/Fg5wFbPgATGu3VSBqG1RzG3PZzydflM3DDQBafX/zQEe28mBgi+w8gedZsnHv3JnDxIiz97r+J0jPUj2o+jny58Tw5+QXKVLEdnyoVl2u8UNJvy+jJssyXG87jbm/FiGcqqB2OAOQV5PHVka8Yu2Ms3nbeLOu8jPZB7dUOq2gkCZ57F25dE22t9EwkeYIAynQBnRb2fK12JEZhUJMgfJyslSlPabFw4Beo3g28a6kdmsHLytPy1cYL1PZz4oV6vkV6jaTR4D5yBIHz56HLziaydx9uLVkqpm8KCisH5RwWtR8ubFA7Gr2r51nvsdM309at51r3HuRdu4bvjz/g89FUNFYPHmEy00i836kacanZzN0fqcxCuB0HbT9VLjiFR9px/gZHIlMY17oSdlZiDZXaom5HMWDDAP6M+JP+1frzV8e/CHIKUjusJ1O5PZSrp4zmafPUjsZkiCRPEABcy0PoEDi2AFKuqh2NwbO2MGN860qciEnl+vJ3lPUsbT5WOyyj8PvuqyTczmFK5+poNE92QWlbv74yfbNBAxKmTiXu9dfR3rpVQpEKRiVkMLhXVtaVFeSrHY3e3Zm+OTF04r/TN08mnUSXmUn8u+8R/+abWFWuTIVVK3Fs//gRjGYV3WldzZNFO4+h2/s9VOkIQSo1hjYiBTqZrzadp7y7HX0a+KsdTpm39spaXlr7EvGZ8fz83M9MajgJSzNLtcN6cpKkLJ1JjYbD09WOxmSIJE8Q7mjxFmgsYOcXakdiFHqG+NHVJZpyMevQNXkNXILUDsngxaVm88fuK3SpU476Qa5PtQ9zNzf8Z87A8+23Sd+1m2vdupN54ICeIxWMjpk5tPkEki9D+Dy1oykRGknD0JpDmdt+LjpZx5R5AznepQ1pK1fiNvoVAhcuwMK3aKPjAJM7VmO4bjlyXha0nlpygZuQFcdiuZiYwVvtqjx2qrlQcrLys3hv33u8u+9dqrpWZXmX5TwX8JzaYRVPxdbKiN7uryE9Qe1oTIL4H1pCpk6dyrfffgvAlClT2LZt21PvKygoiFq1alG3bl3q139glVRBHxy8odEopYBBYvH7M5k6c0nmE6sFxMuurLR/Se1wjMKXG88DMKlD1WLtR9JocBs2lPJLl6Cxtyd62HASv/oaXZ6Y5lKmVW6n9P7c9QXkpD1+eyNV16Muc9Nf5LP5BeTcvsXCVyqSO/QFJPMnmzoYrElkoNlWlhY8y0VduRKK1nTk5Bfww9aL1PF3pkNNb7XDKbPO3DxD73W9WXtlLa/UeYXZ7WbjbWci/x7tPoeCPKVfpVBsIskrBR9//DGtW7cu1j527tzJiRMn+G9/QEHPmo0DK0fYLqYePtbxP3FKPccix5f5bmesUsBAeKjwqBTWnoxnVIsK+Drb6GWf1tWrU37Fclz69SVl7lwiX+pN7uXLetm3YIQkSVlXlpVsstWC8xMTiRkxkrSvvsPpmZZkzPqYXR436bW2FysvrXyydarbP0ZjYcVMs958tl4UM3qcufsjuZ6Ww+QOVQ2noXYZkq/LZ9qJaQzYMIAsbRaz283m1bqvYq4xoXWRbsHQ9DWlAEv0YbWjMXrFSvIkSXKVJGmrJEmXCv90ecA2z0mSdOKurxxJkroXPjdPkqRrdz1XtzjxqO2zzz6jcuXKNG/enAsX/l9efsiQISxfvhxQRuUmT57876jcsWPHaNeuHcHBwfz+++9qhS7cYesKz0yAi5vgwia1ozFc2alKIhzQhKbdRhKflsPCg1FqR2WwdDqZj9aew9vRmleeDdbrvjU2NnhPmYLf9Glob9zgWs9eoqdeWVauLtTuAwenQWqM2tHojSzLpK1Zw9UuXckKD8drygf4TfuNdnVfZEXXFdRwr8GUA1OYuGsiqTmpj99hzFE4twqp2Tj6P9+A3ReT2HXhRom/D2OVmpXHtF2XaVXV8/4m8kKJu5p6lQEbBjD95HQ6lO/Aym4raeDdQO2wSsYzb4BDOdjwprLeX3hqxU3/JwHbZVn+UpKkSYU/39MJWZblnUBdUJJC4DKw5a5N3pJleXkx47jXxkmQcFqvu8S7FnT48qFPh4eHs2TJEk6cOIFWqyUkJITQ0NAHbhsQEMCJEyeYMGECQ4YMYf/+/eTk5FCzZk1eeeWV+7aXJIm2bdsiSRKjRo1i5MiRentbwgM0fhVOLoENb0H5Z8DSTu2IDM/ur5XRgg7/0NTHgxaVPfh5+yW61i2Hl6O12tEZnH+Ox3EqNo0fetfB1rJk7ro6PPccNqtXEf/ueyR+/AkZO3fh88nHWHibyDQeoehavQ/nVikN0l+YoXY0xaZNSSHhw6mkb92KTb16lPvyCywDA/993sfeh5ltZjL/3Hx+Of4LJ9ec5NNmn9LUt+mDdyjLsPUDsPeCJmMZZGbLn4ei+HxDBM0rumMu1prd57edl8nI1fJ2+ypqh1Km6GQdiyIW8eOxH7Ext+G7lt/RNqit2mGVLEs7aPsJrBgOx+ZD/WFqR2S0insm6wbML/x+PtD9Mdv3AjbKspxVzOManL1799KjRw9sbW1xdHSka9euD932znO1atWiUaNGODg44OHhgZWVFampqfdtv2/fPo4dO8bGjRv57bff2LNnT0m9DQHA3BI6/wBp0Uo5X+FeSRfgyB8QMgh86gDwUdca5Bbo+GitWMv4X5m5Wr7edJ46/s50q1P0ohBPw9zDA/8Zf+D1/vtkhYVxtXMXUpcvF6N6ZY2zPzQeozT4jj+udjTFkr59O1e7dCVj1y4833yDwD8X3pPg3WGmMWNYzWEs6rgIB0sHRm0bxdQDU+9rtQDA+fUQfVBpO2Flj6W5hkkdqnExMYOlYaYz+qkvkTczmX8gip4hflT1dlQ7nDLjesZ1RmwZwVdHv6KxT2NWdltp+gneHTV7QmBz2P4JZKWoHY3RKu4tZS9Zlu90j04AvB6zfR/g+/889pkkSVOA7cAkWZZzixnTI0fcDIFVYe8ejUbz7/d3ftZqtfdt71tYLczT05MePXpw5MgRWrRoUTrBllWBTaHeADj4G9TuDV411I7IMMgybJoMFnbw/JR/Hy7vbse45yvxzeYLbD2XSJvqjzsVlB3Td13hRnouvw8MfeKWCU9DkiRcB/THvsUzXH//A66//wG3N25SRvXKieISZUbzCUpLmC0fwOC1Rtf/reD2bRI/+5y01auxqlaNcnPmYF2l8mNfV82tGks7L2XayWnMPzufvXF7+bDJh7TwK/ydqc2FbR+CexWoN/Df17Wr4UXD8q58v+UiXeuUw8HaoqTemlGRZZlJ/5zCylzDm23FKF5pkGWZ1VdW89WRr9DJOj5q+hE9KvYoW+sgJQk6fAV/PAM7P4dO36odkVF67EieJEnbJEk684CvbndvJyu3ih96u1iSJB+gFrD5rocnA1WBBoAr/5nq+Z/Xj5QkKUySpLCkpKTHhV3qWrRowapVq8jOziY9PZ21a9fqZb+ZmZmkp6f/+/2WLVuoWbOmXvYtPEabT8DaCdaOB51O7WgMw8VNcGU7PDsJ7NzveWrEMxWo4uXAlNVnyMi9/2ZFWRSTksWMvVfpXrccIQH3LVkuUZYBAQTMm4vXlA/IOn6cq126cmvpMjGqV1ZYOyr/TyP3QsQataN5Iuk7dnK1azfS1q3Dfcxoyi9dUqQE7w5rc2smhk7kr45/4WjpyKvbX+Xdve+SlpumTDVPvgztP1faThSSJIkPOlUnOTOPabuulMTbMkpLjsZw6GoK73aqhreTmIpf0mLSYxi1dRQf7P+Ayi6VWdF1BS9UeqFsJXh3eNeEBi9D2Gz9L8EqIx6b5Mmy3FqW5ZoP+FoNJBYmb3eSuEetWn4JWCnL8r9dWmVZvi4rcoG5QMNHxDFDluX6sizX9/DwKOr7KzUhISH07t2bOnXq0KFDBxo00M+C2MTERJo3b06dOnVo2LAhnTp1on0RGr0KemDrqlSqiz2izAsv67S5yiieexVoOOK+py3NNXz+Qi0Sbufw3ZYLD9hB2fPlxvNoJHi7ffFaJjwtSaPBtV8/KqxZjXWtWiR8+CExw4eTFxunSjxCKQsdokypXv8GZCarHc1j5d+4Qez4CcSOGYOZgwNBixfh8frrSJZP19y5pntNlnZeyqjao9h4bSPd/unE9mPToW5/pSfXf9Tyc+KFEF9m77tG5M3M4r4do5eQlsPn6yNoXMFVND4vYVqdlnln5vHC6hc4dfMU7zV6j7nt5+Ln4Kd2aOp6djJYO8OGt5WZRMITkYpzV1eSpG+A5LsKr7jKsvz2Q7Y9BEwuLMRy5zEfWZavS8otih+AHFmWJz3uuPXr15f/20ogIiKCatWqPfV7MXZl/f2XGFmG+V0g4RSMDQN7T7UjUs++H2DbVBjwD1R8/qGbfbDqDH8ejmLlmGbU9XcutfAMzeGryfSecYjxrSsxvnXRRyFKiizLpC77mxtfKetMPcaPw6VfvyfuLSYYmcSz8EdLqN4Ves1RO5oHknU6Uv9ezo1vv0XOzcV9zBjchg196uTuQc7fOM0H6wdwXqOjnf9zTG7yIW4291eJTLydQ5vvd1PZy4Glo5pgVgpTrA2RLMuMXBjOnotJbB7fgiB3UYCspEQkR/DhgQ+JSIngWb9nea/xe6bT904fwubCuvHQczbU6qV2NAZHkqRwWZYf2ES7uIVXvgTaSJJ0CWhd+DOSJNWXJGnWXQEEAf7A7v+8/i9Jkk4DpwF34NNixiMI+iVJ0Ol7yMuCze+pHY160hNgz7dQpeMjEzyAt9pXwdPBisn/nCa/oGxOc83K0zLpn9P4OtswqoV+WyY8LUmScOn9EhXWrsGmfiiJn3/BtRdfIvvECbVDE0qSVw1o+Q6cWQHnDG/aZu7Vq0QNGkTChx9iXa0a5Vevwv2VUXpN8ACqntvAoqhIXvNrx/a4vXRZ1YWl55dS8J8S7V6O1nzUrQZhUbeYtfeqXmMwJhtOJ7D1XCIT21QWCV4JydZm83349/Rd35cbWTf4tuW3/NzqZ5Hg/dedIm9bPoDcDLWjMSrFSvJkWU6WZfl5WZYrFU7rTCl8PEyW5Zfv2i5SlmVfWZZ1/3l9K1mWaxVO/xwgy7L41xMMj0dlpYjB6WVwZefjtzc1sgxrx0FBvjJ99TEcrS34qGtNIq7fZva+a6UQoOH5bH0EkcmZfPNibWwszdQO5x4Wvr74//EHvj/9REFKCpF9+3F9yocUPKCyr2Aimo9XLpLWTYDMm2pHA4AuL4+k337jWrfu5F66jM9nnxIwfx5W5cvr/2CJZ2HPN1jUepGRz3/Liq4rqO5anU8Pf0r/Df05e/PeqsDd6/rSroYX3225yMXEB1TnNHG3MvP4cM0Zavk6Mbx5Cfx7CByIP0DPNT2Ze2Yu3Sp2Y3X31bQLalc21949jsYMOnwD6fGwVxRgeRKiGYwgFMUzb4BrBWVtS36O2tGUrqOzlIIrbT4Ct6KNSrWv6U3b6l78uO0i0ckm1zHlkXacT+Svw9G83Lw8TYPdH/8CFUiShGO7tlRYvx7XQYNIXbGCKx07kbpylSjMYorMLKD7dMhJUxoMqyxjzx6udevOzV9+xaFNG4LXr8O5Z8+SucAt0MKqMWDjDB2+BqCCUwVmtp3JV898RWJWIn3X9+XTQ58qhVlQ/n981qMWDtbmTFx2oszNSPh0fQSpWfl81bO26BmoZ7HpsYzfOZ5RW0chITG77Ww+avoRTlZOaodm2AIaQd0BsP8niD6kdjRGQ/zvFYSisLBWpm2mXIF9/+0CYsJuRMCW95UiBY1eeaKXftStBuYaDe+tOl1mEofkjFzeXn6aqt4OvNnO8MuNm9nb4TV5EuVXLMcyIIDrkycTPXAQuZcuqR2aoG9eNeDZd+DsSji7SpUQ8iIjiXllNDEjR4FOh/+MP/D9/jvM3UvwZsiBn+H6Cej4rVJMq5AkSXSs0JE13dfQv1p//r74N11XdWX15dXIsoy7vRWf9ajFmbjb/LrjcsnFZ2D2XExixbFYXmkZTPVyoieevmRrs/ntxG90X92dA/EHGBcyjpXdVtLQ56H1BoX/av8FOAfAipch+5ba0RgFkeQJQlEFPwe1XlQKkNwsAxfB+TnKydTSXhkFeMK77D5ONrzVrgp7L91k9Yn4EgrScCj9pE5zOzufH/vUxcrcsKZpPop11aoELvoL748/IufSJa72eIGETz5Fe0v8IjUpzSaAT93CapulN22zICOTG99+y5UuXck6cgTPt96kwto12Jd0v9ekC7DrC6jeDWp0f+AmDpYOvNPwHZZ2Xoq/gz/v73+fIZuGcDb5LO1revNCPV9+3XmZU7GpJRurAcjM1TL5n9NU8LBjbKuKaodjEmRZZkvkFrqt6sbvJ3+nVUAr1nRfw8u1XsbSTL/rTk2etSP0nAPp15UlJGXk5nFxiCRPEJ5Eu8/Bwgb+GWH60za3fwSJZ6D7tKeuKjqgcSB1/Z35eN05bmXm6TlAw7IsLIat5xJ5q10Vqnob3x1wSaPB5aWXCN64AedePbm1eDFX2rYjefYcdHmm/W9XZpiZKzdscm8riV4Jk3U6Uleu4kqH9iTPmo1Tp05U2LQRt+HD9V5Y5T66Alj9qnKTquPj1/FUda3Kgg4L+KjpR1xLu0afdX2YvHcyo553xcPeionLTpKTX/DY/Rizb7dcID4tm6971sbawnhuUhmqy7cuM2LLCN7Y/QYOlg7MbTeXr1t8LQqrFIdfKLR6H86thmML1I7G4Ikkr4RMnTqVb79VfrFMmTKFbdu2PfW+UlNT6dWrF1WrVqVatWocPHhQX2EKT8reU7lIij8Om95RO5qSc2kbHJoGDUZA5XZPvRszjcSXPWtxOzufN/8+iU5nmnfeopIz+WjtOZpUcDP6QgXmrq74TJ1KhdWrsKlXlxvffMPVjp24vWlzmZl2a9K8qivVNs+tUqZulpCsY8eJ7NuX65MnY+FTjqClSyj35RdYeJZSG5pD0yH2qLIOr4g3qTSShhcqvcD6F9YzvOZwtkRuof+mF2jW8AiXb9406f6f4VG3mHcgkoGNA6kf5Pr4FwgPdSPrBh8d/Ihea3sRkRLBe43eY2nnpdT3fmCVe+FJNR0HFZ6Fje8oo/XCQ4kkrxR8/PHHtG59f+PVoho3bhzt27fn/PnznDx5UvTDU1vVTtB8IoTPg2ML1Y5G/zJvwqrR4FEN2n5S7N1V9XZkSpfqbD9/gx+3m940V22BjglLT2CmkfjupTpoTKSvllWlSgTMmIH/rFlobGyIGz+eqP4DyD51Su3QhOJqNh7K1VNG8zKS9LrrnAsXiHllNFH9+pEfH4/PF18QtGQxNnXq6PU4j5R8BXZ8orR8eYq+Wg6WDowPHc+6HutoE9iGLXGLcKv6PfPOLOLAlRslELC60gpvwvk4WvN2+6pqh2O0bufd5sfwH+n0TydWXV7FS1VeYl2PdfSp2gdzjehHqjcaDfT4AyxtYfkw059VVQwiydOjzz77jMqVK9O8eXMuXPj/3YUhQ4awfPlyAIKCgpg8eTJ169alfv36HDt2jHbt2hEcHMzvv/9+3z7T0tLYs2cPw4cPB8DS0hJnZ+dSeT/CI7R6X7mTtP4NZVTPVMiyMsUpJw16zlKmpurBwMaBvBjqx8/bL7H5bIJe9mkoft99hWPRqXzavSblnPXz92VI7Js3o/yqlXh//BF50dFEvtSb2AkTyL1yRe3QhKf177TNdFg/US9rW/JiYoh7622ude9BVng4HhMmUHHzZpx7dEfSlOKlRl6mcuFnZqUUyypGxU4fex++eOYLlnRaQg33Slh7r2b0rn5svLLVZEa1tQU6xi46RuytLH7sUw97K5GMPKkcbQ5zz8ylw4oOzD4z+991d+82ehcXaxe1wzNNDt7KOSzxDGydonY0Bssk/zd/deQrzqec1+s+q7pW5Z2GD5+eFx4ezpIlSzhx4gRarZaQkBBCQ0MfuG1AQAAnTpxgwoQJDBkyhP3795OTk0PNmjV55ZV7Kxheu3YNDw8Phg4dysmTJwkNDeWnn37Czk40J1WVxgx6zoY/WsLSQTBq9z2V24zWnXYJ7b8E75p6260kSXzSvSYXb2QwcekJVr3ajEpeDnrbv1pOxaby47ZLdKlTjm51fdUOp8RIZma4vPQSjh07kTx7FinzF5C+aTOOnTvjPmZ0yfQ2E0qWZzV47j3Y9qFSTOqZiU+1G21SEjenT+fWsr+RzMxwe3k4bsOHY6bGzUidDlaOgusnoe9icPTRy25ruNdgfoe5zAxfx0/HfuDtfROZe64aY+qOoaVfS6PubfbZhgj2XrrJVz1r0bC8CfwOK0VanZa1V9by24nfSMxKpJlvM8aHjKeqqxgNLRWV20Gj0XB4ulIYr0oHtSMyOGIkT0/27t1Ljx49sLW1xdHRka5duz502zvP1apVi0aNGuHg4ICHhwdWVlak/qchsVar5dixY4wePZrjx49jZ2fHl19+WZJvRSgqO3fovQAyEpQqlDojX5R/p11C8PPQcJTed29tYcbvA0KwsTRj5MJw0rLz9X6M0pSVp2X80hN4OFjxaTf9JcSGzMzeDs9x46i4bStuw4eRvm0bVzt1Jv6dSeRFRakdnvCkmo1TKgZv/whOL3+il2pv3eLG9z9wuW07bi1dhnOvngRv2YLnG2+ok+CB8j4i1ioFsvR8wSdJEiPrd6G/389kx/ci/vYtXtvxGn3W92F3zG6jHNlbfCSaufsjGd68PL0bBKgdjtHI1+Wz9spaXljzAlMOTMHT1pM57ebwe+vfRYJX2tp8BN61lF6Yt6+rHY3BMcmRvEeNuBkCKysrADQazb/f3/lZq9Xes62fnx9+fn40atQIgF69eokkz5D4hkLHb5Ryvru+hFbvqR3R08nLvLddQglNr/JxsmFa/1D6zTzEhKUnmDWovlGuYcvT6njlz2NE3sxk4fBGONlaqB1SqTJ3dcXzzTdxHTKE5FmzubV4MWnr1uHUvRvuo0dj6eendohCUUgSdPsN0uKUdbiOvhDY5JEvyb9+neS5c0n9ezlydjaOHTvi8fprWAYFlU7MD3NsAez/EeoPg8ajS+ww77SvQXRyLptO12NI22QO31rG2B1jqe5WnTF1xtDCr4VRjOwduprMB6vO0KKyB5M7iMSkKHILcll9eTVzzswhLiOOyi6V+eHZH3g+4Hmj+Dc3SeZWSluFGS2VqueDViszrQRAjOTpTYsWLVi1ahXZ2dmkp6ezdu1avezX29sbf3//f9f4bd++nerVq+tl34KehAyGegNgz9dwYaPa0Ty5/BxY3BdunFMSPAevEj1cw/KufNilOjvO3+CHbRdL9FgloUAnM3HZCfZcTOLzHrVoVrEEGzkbOHN3d7wmvUPw1i249O/H7bXruNK+A/HvTCLngqh6ZhTMraDPX+AcCEv6ws0HN/7OvXqN+HffU0bu/lqEY9u2VFi3Ft/vv1M/wbu2B9ZNgOBWSjXNErzgNtNI/NinLg2C3Fm03Zt3a8/l46Yfk5abxtgdY+m7vi/bo7ZTYMAzO2JSshj9ZzgBbrb80rce5mbiUvBRsvKzmH92Ph1WdOCTQ5/gZu3GL61+YXmX5bQObC0SPLV5VIYOX0HkXmX6uRGOqpcUkxzJU0NISAi9e/emTp06eHp60qBBA73t+5dffqF///7k5eVRoUIF5s6dq7d9C3ogSUofpoTT8M8oGLkT3ILVjqpotHnw92C4tltJ8Cq3LZXDDmgcyOm4NH7ZcZka5RxpX1M/a2dKmizLTFl9hnWnrjOpQ1X6NBRTnAAsPD3xfvdd3IYPJ3nWbFJXrCBt9WrsmjbFdehQ7Jo3ExdChszWFfr/DbNaw1+94OVtynR0IPvsWZJnzCR9yxYkS0tcXnoJt2FDsfA1kDWoNy/B0oHgVhFenAdmJT+qbm1hxqxBDej1+wHG/HmSv0c/z9oenVl3ZR1/nPqD8bvG42fvx4DqA+hRsQe2FrYlHlNRpefkM3z+UXQyzB7cACebsjUL4Umk5aax+Pxi/or4i9TcVBp6N+TzZz6nkXcjcT4zNPUGQvwJOPALmNsY76wqPZOMcR55/fr15bCwsHsei4iIKNOtBcr6+zcIt6KUKQMO5WD4FrCyVzuiR9MVKFXozq2CTt9Bg5dL9fA5+QX0nnGIS4nprHq1GZWNoBDLt5sv8OvOy4xqWYHJHcT/t4cpSEvj1tJl3Fq4EG1SElaVKuE6dCiOnTuhKekm2MLTizkK8zsje9YkI+ANbi1bTuaBg2js7XHp1w/XwYMwd3NTO8r/y0qBma2UKqEjtoNLUKkePi41m57TDiAj88+YZvg626DVadkRvYOF5xZyIukEDhYO9Krci37V+qneBLtAJzNyQRi7LiYxf2hDmlcqu7MQHuVCygUWn1/M+qvrySnIoYVfC0bUGkFdz7pqhyY8ik4Ha1+H4wuVolIt31Y7olIhSVK4LMsPbMIokjwTUdbfv8G4vA3+elHpQdXvb7AzoAuiu+l0sGYsnPgL2nwCzV5XJYyEtBw6/7IPeyszlo5qgpejtSpxFMWsvVf5dH0EfRr488ULtcSd3CKQ8/JIW7+BlLlzyb14ETMPd1z798e5Z0/MPTzUDk/4D+3Nm6RO+4RbqzaizTLD3Nsbl379cOnbBzMHA7sJo82FBd0hLhyGrAP/hqqEcT7hNi9OP4iXkzXLX2mCs+3/b2KcTDrJwnML2Rq1FQmJtoFtGVRjEDXd1SnU9OXG8/y++wofd6vBoCZBqsRgqPJ1+eyI3sGiiEUcu3EMKzMrOlXoRL+q/ajiWkXt8ISi0ulg9Rg4uRhaT4XmE9SOqMSJJK8MKOvv36CcXw9/DwWXQBi4EpwMrAiFLMOGt+DoTGg5CZ6brGo44VEpDJp9BCcbC+YNa2iQI3rLw2N58++TdKjpza/9QjAzwmIxapJlmcz9B0iZO5fM/fvBzAz7557F5cUXsWveHMlMLJRXiyzLZB8/zq1Fi7m9eTPk52NbzQ8Xt1M49ByO1OFztUO8nywrhWJOLlZa2TxFw3N9OnDlJkPmHKWOvxMLhzfC2uLez3NcRhyLIhbxz6V/yMjPoJprNXpU6kHH8h1xsnIqlRjnH4jkwzVn6d8ogE+71xQ3qQrdzL7J8ovL+fvC39zIvoGvvS99qvShR6UepfZvI+iZrgD+GQlnliuVdpu8qnZEJUokeWVAWX//Bidyn1LMxMpBSfQ8DOROoCwrC5P3/wRNX1NG8Qzgl/2ZuDSGzjtKbn4BMwbVp3EFwxkB3XI2gdF/HaNJBTdmD6mPlblISIoj9+o1UlcsJ23lKgpSUjD39sb5hR44vdATSz8DWedVBmiTk7m9YSOpK1aQe/48Gnt7nHr0wKVvH6Xv4Z0bQW0/hSZjDeI8ASgjeOsnwvE/4dl34VnDqKa99mQ8ry0+Tvsa3vzW/8E3gjLyMlhzZQ0rL6/kfMp5LDWWtA5sTY9KPWjo3RCNpP8CKNoCHZ+uj2DegUhaV/Nk+oBQLMp4oZW8gjz2xO5h7ZW17Inbg1anpWm5pvSr2o/mvs0xE9UZjV+BFpYPhYg1Ss2EhiPUjqjEiCSvDCjr798gXT8Ff/YEnRb6Lwe/ULUjgt3fwM5PlTLjnb43nAs3lIpvQ+YeISYlm+9eqkOXOuXUDomDV5IZPPcI1XwcWfRyI+ysRK0qfZHz8kjfuYvU5cvJ3LcPALumTXHq0QOH555FY2enboAmSJeVRfr2HaStXUPm/gNQUIBV1aq49OmDU5fO9/6dF2hh+RCl71zd/sq6XQsb1WIHlD5YSwdAXBi0eBuee9egzmGz913jk3Xn6FTbh6961sb+EeeLc8nn+OfSP2y4uoH0/HR87X3pXrE73YK74WOvn0JUt3PyGbvoOHsuJjG8eXne7VitzM5CkGWZk0knWXtlLZsiN3E77zZu1m50rNCRFyu/SHmn8mqHKOhbQT4sGwwX1kPnH6H+ULUjKhEiySsDyvr7N1gpV2FhD8hIgt4LoeLz6sSh08G+72DHp1CnL3SbVmK98IojNSuPkQvCORKZwnsdq/HyM+VVmVYkyzILDkbx+YYIAlxtWTaqCS52omBIScmPiyP1n5Wk/vMP2uvXkayssG/xDA7t2mP/7LOY2YuE72nJWi2ZBw+StnYt6du2I2dlYe7jg1Pnzjh26Yx15coPf7GuAHZ/Dbu/VBoOv7QQXFW6GI45oiR4uRnQ43eo3lWdOB7j991X+HrTeQLd7Pi1Xz1qlHv0lL8cbQ7bo7ez8tJKDiccBqC2R23aBLShdWBr/Byebrp/dHIWw+cf5drNTD7pXpO+ZbQScPTtaNZfXc/aq2uJSY/B2syaVgGt6BLchcY+jTHXiBt3Jk2bq1TfvbQZuv4KIQPVjkjvRJJXBpT192/Q0hPgz16QdF65OCnt9SMpV2H1WIjaDzV7Qo8ZYGa4v9hy8gt4Y9lJ1p++zpCmQXzQuXqp3n2+cTuHt5afYvfFJJ6r4sE3L9bB3d6q1I5flskFBWQfO8btTZtJ37IFbVISkpUVds80x7F9B5HwFVFBRiaZB/aTsWs3Gbt2UZCSgsbREcd27XDq2gWb0FCkJ7nJc3Gz0mgYCV6YWWqtVv4VPh/WvwFOvtBnMXgZdq/Yw1eTeX3JcW5l5fNB5+oMaBRQpJtVMekxbLq2ia1RW4lIiQCgmms12ga1pXVAa4Kcgop0/KORKYxaGE6BTmb6gBCaBpedKpoFugJO3zzNrphd7IrZxZW0K0hINPRuSOfgzrQJbIOdhTiHlCn5OUoP0Cs7lCriz38I1o5qR6U3IslTwdSpU7G3t+fNN99kypQptGjRgtatWz/xfi5cuEDv3r3//fnq1at8/PHHjB8//p7tDO39C/+Rnaqs0Ys+CO0+g0avQEnP+9fplDU126aCxhzaf6FMuzKg6U0Po9PJfL4hgln7rtGuhhc/9al3XzGDkrDpTAKT/zlFdn4B73Uq+sWZoH+yTvf/hG/zZiXhs7TEtmFD7Jo1w65pU6wqVxL/PoXyYmLI2LmLjF27yDx6FPLz0Tg6Yt+8OQ4d2mPfsmXx2lekXFPuiCeegZbvKF8lPRtAmwebJ8PRWUqj856zlZ5+RiA5I5eJy06y+2ISnWr78MULtXC0LnpPupj0GLZHbWdr9FZOJZ0CoKJzRZ7zf47GPo2p41kHK7P7bz6tCI9l8j+n8XOxYfaQBpR3N/2EJis/i4PxB9kVu4s9sXtIyUnBXDIn1CuUlv4taRPYRvX2FYLK8rNh+8dwaDo4llOWq1Rpr3ZUeiGSPBXcneTpS0FBAb6+vhw+fJjAwMB7njO09y88QH42LB+uzA/3rA7PT4HK7Usm6Uq5Vjh6tw8qtoYuPyt3wY3M7H3X+HT9Oap4OTChTWXaVvcqkYv6zFwtH689x9KwGGr6OvJj73pU9DTwPodliKzTkX38OLc3byZz337yrl4FwNzDA7umTZSkr0mTMtWWQZucTNaxY2SHhZOxbx95V64AYFmhAvbPPov9sy2xrVcPyUKPza7zspQRtZOLoGIbeGFGySVdGUmwbBBEH4Bm45S770ZWEEOnk/ljz1W+3XIBPxcbfu0bQi2/J6/YmJCZwPbo7WyN2sqJGycokAuwMrMixDOExuUa09inMUEOlfh5+xV+332FpsFuTO8fipOtaTY6z9HmcPrmacITwwlPDOdY4jHydHk4WDjQ3K85z/k/RzPfZjhams5ojaAnsWGw5jW4cU6Z2dT+K7A37t8bIskrJZ999hnz58/H09MTf39/QkNDefPNNxkyZAidO3emV69eBAUF0bdvXzZu3Ii5uTkzZsxg8uTJXL58mbfeeotXXnnlofvfsmULH330Efv377/vOUN4/0IR6HRwbiXs+AxSroB/I+XiJaiZ/vYfNhu2Fl4QtfsM6g00itG7h9l2LpFP1p8jKjmLaj6OvN6qIu1qeKPR0xTOY9G3mLD0BNEpWYx5Nphxz1fG0tzw1isK/5d//TqZBw6SuX8/mQcPUnDrFgBWlSphU7cu1rVqYlO7NlYVKyKZG+7U5KKSZZn8qCiywo+RdSyc7PBj5EVGAiBZWmITGoLDc89h37Illv+5AVgCwUD4XNjwNjj6KOevqp3BQk89LtMT4dh8ODJTaXLe7VfVWyQUV1hkCq8tPk5yRh6TO1ZlYONAzJ+ywmVmfibhieEcjD/IoeuHuJx6WXmiwJb8zArU8ajD+Geeo5ZHDewtTeNGVUZeBieSTvyb1J25eYZ8XT4SEpVdKtPAuwHP+T9HPa96WGhMM7EV9EibB/t/hD3fgKWd0mahTl+jvU4qsSRPkqQXgalANaChLMthD9muPfATYAbMkmX5y8LHywNLADcgHBgoy3Le4477uCQv4fPPyY04/5Tv6sGsqlXF+913H/p8eHg4Q4YM4fDhw2i1WkJCQnjllVcemOS98847jB49mgkTJrB9+3b2799PTk4ONWvWJDEx8aHHGDZsGCEhIYwdO/a+50SSZ2QK8pUS4Lu/gvTryl3x56eAT+2n2582F6IPKSetyL3K1Kauvxhej76npC3QseZkPL/uuMzVm5lU8XLgtecr0rGmz1Mle9l5Bey9lMTms4msOhGHt6M1P/SuS8PyxjEVTPg/WacjJyKCzAMHyDp8hOzTp9GlpQEgWVtjXb06NrVqYl2rNtbVq2Hp54dUnGmLJUyXm0velSvkXrpE7qVL5Fy6RM7ZcxTcvAmAxskJ25AQbENDsAkJxbpmjeJNw3xasWHKOr2Uq2DtDHX6KDeUvJ+i0bcsQ9QBZVpmxBqlInFwK2j90dOfEw3Mrcw83vz7JNvP38Dd3pLOtcvRtW456vk7P9XshCtJGczae41/Tp6jwOoS/uXikK0vkZz7/2uIIMcgarjXoIZbDaq7Vaeqa1WDXo8myzLxmfFcTLnIxVv//4pOj0Yn6zCXzKnuVp1Qr1BCvUKp61lX9LITnl7SBVjzOsQcggrPQYevwL2y0SV7j0ryinuL8wzwAvDHIw5uBvwGtAFigaOSJK2RZfkc8BXwgyzLSyRJ+h0YDkwvZkyq2Lt3Lz169MDW1haArl0fXvnrznO1atUiIyMDBwcHHBwcsLKyIjU1FWdn5/tek5eXx5o1a/jiiy9KJH6hlJlZKOV86/SBIzNg7/fwxzPK9IG6/cClPDj5g/lDLt5kWSnkcmWH8hW5H7TZYOmgTM0MGWR0J6pHMTfT8EKIH93q+rLuVDw/b7/E2EXHqeR5ideer0TLyh44Wps/8mLpZkYuOyJusOVcIvsuJ5GTr8PB2pw+Dfx5p0PVJ1ovIxgOSaPBpkYNbGrUgBEjlFGvmBiyT50m5/Rpsk+f5tbSZcjzFygv0GiwKFcOy6AgLAMDla/yyvfmXl5orEq2yI4sy+gyMtAmJJCfkIg2MYH8+OvkXr5M7qVL5EVFKSPyABYWWFWogF3TJtiGhGIbGoJlcPCTFU0pKX71YWw4XNsNxxdC2Bw4/DuUC1Eq2NXs9fjiBrnpcHIJHJ0NSRFg7aSsV64/DNyCS+d9lBIXO0tmDa7P1nOJrD4Rz+Ij0cw7EEmAqy3d6pajW91yVPR0eOQ+ZFnm8LUUZu65yvbzN7A019AzpBrDm3f6d3p5Sk4K55LPcfbmWc4mn+VowlHWX13/7z7cbdwJcAjA38Effwd/AhwDlJ8d/UtlemN+QT6JWYlcz7xOQmYC1zOvE58Rz5XUK1xKvURmfua/2/o7+FPJuRIdyncgxCuE2u61sbWwLfEYhTLCowoM3ajMfto2FX5rCI5+ysyqwGYQ1BxcKxj1tVSxkjxZliOAx92FaghclmX5auG2S4BukiRFAK2AfoXbzUcZFSx2kveoETdDYFV4EaHRaP79/s7PWq32ga/ZuHEjISEheHl5lUqMQimxsFHWm4QMhgO/wKFpcGaF8pykAYdy4BIILkHgHAh27hAXriR26deV7dwrQ+hg5U5UUHOwMo0pOg9ippHoVteXzrXLsf70dX7ZfonXFx8HwNJcg4e9FR4O///ydLDCTJLYfTGJ8OhbyDL4OtvQp0EAbap70bC8a5lvDGxqJEnCMiAAy4AAnDp3ApQ2AndGxvIiI8mLjCIvKoq048fRZWbe83qNrS1mbm6Yubpg7uKKmasr5q4umLm4IFlYgrkZkrk5krkFkoU5kpkZmJuDDLrsLHRZWchZWeiystFlZf37VZCSrCR1CQnosrL+GzSWAQFYVa6EY4f2WFWujFWlSlgGBOh3TZ2+aTQQ/JzylZUCp5bBsQWwbgJseldpGWNurYzMyQVKSwZdgfKzTqucy/IywKeOUt68Zk+wNN2LeEmSaFvDm7Y1vEnPyWfz2URWn4jjt52X+WXHZar7ONI02I1crY7MXC2ZeVoycwvIyNWSmaslLTufG+m5uNpZMu75SgxsEnhf5V9Xa1ea+zanuW/zfx9LykriXPI5Lt66SEx6DNHp0RyMP8jq7NX3vNbG3AZnK+d7vpysnHCxdsHJygkLjQVmkhkaSYOZpvBPyQwzyQwZmaz8LDLzM8nIz7jv+5ScFK5nXudm9k1k7p1B5mLlQgXnCnSp0IXKrpWp7FKZis4VDXrUUTARGo3SKL1qJ4hYp9QxuLIDTi1Vnrf3hsCmSuIX3EpJ+oxIaSxW8AVi7vo5FmiEMkUzVZZl7V2PP7QyhCRJI4GRAAEBhtfvpUWLFgwZMoTJkyej1WpZu3Yto0aN0tv+Fy9eTN++ffW2P8HA2DjD8x9A07GQeA5So+BWJNyKUr6/O6mzcYEKzyonnArPgbO/ioGrw0wj0bVOOTrX8mH3xSSuJGWQlJ6rfGXkEpOSxbGoWyRnKrO/a5RzZNzzlWhT3YvqPo6iImMZI5mbY12tGtb/mdIuyzIFyclK4hcVhTYpCW1KCgUptyhISSE/MZGciAgKUlKQ8/Of/Li2tmhsbdHY2GDu6opVpUrYP9Mccy9vLLy9MPf2xtzTCwtPD4OeQloktq7Q+BVoNArij8GxhcooH5KyPlhjDpJZ4fdmyvfVuymjdr6hRn23/Gk4WFvQK9SPXqF+JKXnsu5UPKtOxLPwUBS2lmbYWZljZ2mOnZUZDtbmeDtaY2dlTmigCy+E+D5RtWEPWw9a2rakpX/Lex7P1mYTmx5LdHo0MbdjSMpOIjU39d+vuIw4UnNTuZ13+4nfn5WZFXYWdtia22JvaY+zlTPNfZvjY+eDt5033nbe+Nj54GXnhY25zRPvXxD0yrEcNBqpfMky3LykJHyR+5X2U2f/UW7It/lY7UifyGOTPEmStgEPqj37nizLqx/weImQZXkGMAOUNXmlddyiCgkJoXfv3tSpUwdPT08aNGigt31nZmaydetW/vjjobNiBVNh41JYhOUBhVjycyDzBjj6Gl2VuZKi0Ug8V9WT56p6PvD5/AId2fkFYiqm8ECSJGHu7o65uzu29R+4pAFQkkE5Kws5Px9Zqy38KgDt/39Gkv5N6DS2tkjW1oYxrbK0SZKStPmGqh2J0fBwsGJos/IMbVa6jeZtzG2o5FKJSi6VHrmdVqclPS+dfF0+OllHgVyATlf4Z+HPAHYWdkpiZ2ErCqAIxkuSwKOy8lV/mJL0pVwFM+O7EaeX6pqSJO0C3nxQ4RVJkpoAU2VZblf48+TCp74EkgBvWZa1/93uUQy1uqaayvr7FwRBEARBEISy5FGFV0rjNuNRoJIkSeUlSbIE+gBrZCW73AncqY08GCi1kUFBEARBEARBEARTVKwkT5KkHpIkxQJNgPWSJG0ufLycJEkbAArX3I0FNgMRwDJZls8W7uIdYKIkSZdR1ujNLk48giAIgiAIgiAIZV1xq2uuBFY+4PF4oONdP28ANjxgu6so1TcFQRAEQRAEQRAEPTCpVeH6WF9ojMrq+xYEQRAEQRAE4X4mk+RZW1uTnJxc5hIeWZZJTk7G2tpa7VAEQRAEQRAEQTAApdEnr1T4+fkRGxtLUlKS2qGUOmtra/z8/NQOQxAEQRAEQRAEA2AySZ6FhQXly5dujxlBEARBEARBEARDYzLTNQVBEARBEARBEASR5AmCIAiCIAiCIJgUkeQJgiAIgiAIgiCYEMkYq1FKkpQERKkdxwO4AzfVDkIweeJzJpQ08RkTSoP4nAmlQXzOhJKm5mcsUJZljwc9YZRJnqGSJClMluX6aschmDbxORNKmviMCaVBfM6E0iA+Z0JJM9TPmJiuKQiCIAiCIAiCYEJEkicIgiAIgiAIgmBCRJKnXzPUDkAoE8TnTChp4jMmlAbxORNKg/icCSXNID9jYk2eIAiCIAiCIAiCCREjeYIgCIIgCIIgCCZEJHl6IElSe0mSLkiSdFmSpElqxyOYBkmS/CVJ2ilJ0jlJks5KkjSu8HFXSZK2SpJ0qfBPF7VjFYybJElmkiQdlyRpXeHP5SVJOlx4TlsqSZKl2jEKxk2SJGdJkpZLknRekqQISZKaiHOZoG+SJE0o/H15RpKkxZIkWYvzmVBckiTNkSTphiRJZ+567IHnL0nxc+Hn7ZQkSSFqxS2SvGKSJMkM+A3oAFQH+kqSVF3dqAQToQXekGW5OtAYeLXwszUJ2C7LciVge+HPglAc44CIu37+CvhBluWKwC1guCpRCabkJ2CTLMtVgToonzdxLhP0RpIkX+B1oL4syzUBM6AP4nwmFN88oP1/HnvY+asDUKnwayQwvZRivI9I8oqvIXBZluWrsiznAUuAbirHJJgAWZavy7J8rPD7dJSLIl+Uz9f8ws3mA91VCVAwCZIk+QGdgFmFP0tAK2B54SbiMyYUiyRJTkALYDaALMt5siynIs5lgv6ZAzaSJJkDtsB1xPlMKCZZlvcAKf95+GHnr27AAllxCHCWJMmnVAL9D5HkFZ8vEHPXz7GFjwmC3kiSFATUAw4DXrIsXy98KgHwUisuwST8CLwN6Ap/dgNSZVnWFv4szmlCcZUHkoC5hdOCZ0mSZIc4lwl6JMtyHPAtEI2S3KUB4YjzmVAyHnb+Mpi8QCR5gmDgJEmyB1YA42VZvn33c7JSHleUyBWeiiRJnYEbsiyHqx2LYNLMgRBguizL9YBM/jM1U5zLhOIqXBPVDeWmQjnAjvun2AmC3hnq+UskecUXB/jf9bNf4WOCUGySJFmgJHh/ybL8T+HDiXeG/gv/vKFWfILRawZ0lSQpEmWqeSuUtVPOhdOdQJzThOKLBWJlWT5c+PNylKRPnMsEfWoNXJNlOUmW5XzgH5RznDifCSXhYecvg8kLRJJXfEeBSoXVmyxRFvmuUTkmwQQUro2aDUTIsvz9XU+tAQYXfj8YWF3asQmmQZblybIs+8myHIRy7tohy3J/YCfQq3Az8RkTikWW5QQgRpKkKoUPPQ+cQ5zLBP2KBhpLkmRb+PvzzudMnM+EkvCw89caYFBhlc3GQNpd0zpLlWiGrgeSJHVEWddiBsyRZfkzdSMSTIEkSc2BvcBp/r9e6l2UdXnLgAAgCnhJluX/LggWhCciSdKzwJuyLHeWJKkCysieK3AcGCDLcq6K4QlGTpKkuijFfSyBq8BQlBvN4lwm6I0kSR8BvVGqUx8HXkZZDyXOZ8JTkyRpMfAs4A4kAh8Cq3jA+avwBsOvKFOFs4ChsiyHqRC2SPIEQRAEQRAEQRBMiZiuKQiCIAiCIAiCYEJEkicIgiAIgiAIgmBCRJInCIIgCIIgCIJgQkSSJwiCIAiCIAiCYEJEkicIgiAIgiAIgmBCRJInCIIgCIIgCIJgQkSSJwiCIAiCIAiCYEJEkicIgiAIgiAIgmBC/gdb24j97yACmwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "x = torch.randn(2, 64, 28, 28)"
+    "plt.figure(figsize=(15, 5))\n",
+    "pe = PositionalEncoding(20, 0)\n",
+    "y = pe.forward(torch.zeros(1, 100, 20))\n",
+    "plt.plot(np.arange(100), y[0, :, 4:8].data.numpy())\n",
+    "plt.legend([\"dim %d\"%p for p in [4,5,6,7]])\n",
+    "None"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
-    "dl(x).shape"
+    "from text_recognizer.networks.densenet import DenseNet,_DenseLayer,_DenseBlock"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [],
    "source": [
-    "db(x).shape"
+    "dnet = DenseNet(12, (6, 8, 10, 6), 1, 24, 80, 4, 0, False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
-     "ename": "IndentationError",
-     "evalue": "unexpected indent (<ipython-input-18-9316fb6caa59>, line 2)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-18-9316fb6caa59>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    num_init_features=24, bn_size=4, drop_rate=0, avgpool_size=8,\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==========================================================================================\n",
+      "Layer (type:depth-idx)                   Output Shape              Param #\n",
+      "==========================================================================================\n",
+      "├─Sequential: 1-1                        [-1, 168, 3, 119]         --\n",
+      "|    └─Conv2d: 2-1                       [-1, 24, 28, 952]         216\n",
+      "|    └─BatchNorm2d: 2-2                  [-1, 24, 28, 952]         48\n",
+      "|    └─ReLU: 2-3                         [-1, 24, 28, 952]         --\n",
+      "|    └─_DenseBlock: 2-4                  [-1, 96, 28, 952]         --\n",
+      "|    └─_Transition: 2-5                  [-1, 48, 14, 476]         --\n",
+      "|    |    └─Sequential: 3-1              [-1, 48, 14, 476]         4,800\n",
+      "|    └─_DenseBlock: 2-6                  [-1, 144, 14, 476]        --\n",
+      "|    └─_Transition: 2-7                  [-1, 72, 7, 238]          --\n",
+      "|    |    └─Sequential: 3-2              [-1, 72, 7, 238]          10,656\n",
+      "|    └─_DenseBlock: 2-8                  [-1, 192, 7, 238]         --\n",
+      "|    └─_Transition: 2-9                  [-1, 96, 3, 119]          --\n",
+      "|    |    └─Sequential: 3-3              [-1, 96, 3, 119]          18,816\n",
+      "|    └─_DenseBlock: 2-10                 [-1, 168, 3, 119]         --\n",
+      "|    └─ReLU: 2-11                        [-1, 168, 3, 119]         --\n",
+      "==========================================================================================\n",
+      "Total params: 34,536\n",
+      "Trainable params: 34,536\n",
+      "Non-trainable params: 0\n",
+      "Total mult-adds (M): 229.41\n",
+      "==========================================================================================\n",
+      "Input size (MB): 0.10\n",
+      "Forward/backward pass size (MB): 53.69\n",
+      "Params size (MB): 0.13\n",
+      "Estimated Total Size (MB): 53.92\n",
+      "==========================================================================================\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "==========================================================================================\n",
+       "Layer (type:depth-idx)                   Output Shape              Param #\n",
+       "==========================================================================================\n",
+       "├─Sequential: 1-1                        [-1, 168, 3, 119]         --\n",
+       "|    └─Conv2d: 2-1                       [-1, 24, 28, 952]         216\n",
+       "|    └─BatchNorm2d: 2-2                  [-1, 24, 28, 952]         48\n",
+       "|    └─ReLU: 2-3                         [-1, 24, 28, 952]         --\n",
+       "|    └─_DenseBlock: 2-4                  [-1, 96, 28, 952]         --\n",
+       "|    └─_Transition: 2-5                  [-1, 48, 14, 476]         --\n",
+       "|    |    └─Sequential: 3-1              [-1, 48, 14, 476]         4,800\n",
+       "|    └─_DenseBlock: 2-6                  [-1, 144, 14, 476]        --\n",
+       "|    └─_Transition: 2-7                  [-1, 72, 7, 238]          --\n",
+       "|    |    └─Sequential: 3-2              [-1, 72, 7, 238]          10,656\n",
+       "|    └─_DenseBlock: 2-8                  [-1, 192, 7, 238]         --\n",
+       "|    └─_Transition: 2-9                  [-1, 96, 3, 119]          --\n",
+       "|    |    └─Sequential: 3-3              [-1, 96, 3, 119]          18,816\n",
+       "|    └─_DenseBlock: 2-10                 [-1, 168, 3, 119]         --\n",
+       "|    └─ReLU: 2-11                        [-1, 168, 3, 119]         --\n",
+       "==========================================================================================\n",
+       "Total params: 34,536\n",
+       "Trainable params: 34,536\n",
+       "Non-trainable params: 0\n",
+       "Total mult-adds (M): 229.41\n",
+       "==========================================================================================\n",
+       "Input size (MB): 0.10\n",
+       "Forward/backward pass size (MB): 53.69\n",
+       "Params size (MB): 0.13\n",
+       "Estimated Total Size (MB): 53.92\n",
+       "=========================================================================================="
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "growth_rate=4, block_config=(6, 6, 6), compression=0.5,\n",
-    "                 num_init_features=24, bn_size=4, drop_rate=0, avgpool_size=8,\n",
-    "                 num_classes=10"
+    "summary(dnet, (1, 28, 952), device=\"cpu\", depth=3)"
    ]
   },
   {
@@ -283,12 +638,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dnet = DenseNet(8, (6, 6, 6), 1, 24, 80, 4, 0, True)"
+    "from text_recognizer.networks import WideResidualNetwork"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w = WideResidualNetwork(\n",
+    "        in_channels = 1,\n",
+    "        in_planes = 32,\n",
+    "        num_classes = 80,\n",
+    "        depth = 10,\n",
+    "        width_factor = 1,\n",
+    "        dropout_rate = 0.0,\n",
+    "        num_layers = 5,\n",
+    "        activation = \"relu\",\n",
+    "        use_decoder = False,)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -298,29 +671,23 @@
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
-      "├─Sequential: 1-1                        [-1, 80]                  --\n",
-      "|    └─Conv2d: 2-1                       [-1, 24, 28, 28]          216\n",
-      "|    └─BatchNorm2d: 2-2                  [-1, 24, 28, 28]          48\n",
-      "|    └─ReLU: 2-3                         [-1, 24, 28, 28]          --\n",
-      "|    └─_DenseBlock: 2-4                  [-1, 72, 28, 28]          23,184\n",
-      "|    └─_Transition: 2-5                  [-1, 36, 14, 14]          2,736\n",
-      "|    └─_DenseBlock: 2-6                  [-1, 84, 14, 14]          25,632\n",
-      "|    └─_Transition: 2-7                  [-1, 42, 7, 7]            3,696\n",
-      "|    └─_DenseBlock: 2-8                  [-1, 90, 7, 7]            26,856\n",
-      "|    └─ReLU: 2-9                         [-1, 90, 7, 7]            --\n",
-      "|    └─AdaptiveAvgPool2d: 2-10           [-1, 90, 1, 1]            --\n",
-      "|    └─Rearrange: 2-11                   [-1, 90]                  --\n",
-      "|    └─Linear: 2-12                      [-1, 80]                  7,280\n",
+      "├─Sequential: 1-1                        [-1, 512, 2, 60]          --\n",
+      "|    └─Conv2d: 2-1                       [-1, 32, 28, 952]         288\n",
+      "|    └─Sequential: 2-2                   [-1, 32, 28, 952]         18,560\n",
+      "|    └─Sequential: 2-3                   [-1, 64, 14, 476]         57,536\n",
+      "|    └─Sequential: 2-4                   [-1, 128, 7, 238]         229,760\n",
+      "|    └─Sequential: 2-5                   [-1, 256, 4, 119]         918,272\n",
+      "|    └─Sequential: 2-6                   [-1, 512, 2, 60]          3,671,552\n",
       "==========================================================================================\n",
-      "Total params: 89,648\n",
-      "Trainable params: 89,648\n",
+      "Total params: 4,895,968\n",
+      "Trainable params: 4,895,968\n",
       "Non-trainable params: 0\n",
-      "Total mult-adds (M): 0.35\n",
+      "Total mult-adds (M): 22.36\n",
       "==========================================================================================\n",
-      "Input size (MB): 0.00\n",
-      "Forward/backward pass size (MB): 0.29\n",
-      "Params size (MB): 0.34\n",
-      "Estimated Total Size (MB): 0.63\n",
+      "Input size (MB): 0.10\n",
+      "Forward/backward pass size (MB): 6.51\n",
+      "Params size (MB): 18.68\n",
+      "Estimated Total Size (MB): 25.29\n",
       "==========================================================================================\n"
      ]
     },
@@ -330,828 +697,291 @@
        "==========================================================================================\n",
        "Layer (type:depth-idx)                   Output Shape              Param #\n",
        "==========================================================================================\n",
-       "├─Sequential: 1-1                        [-1, 80]                  --\n",
-       "|    └─Conv2d: 2-1                       [-1, 24, 28, 28]          216\n",
-       "|    └─BatchNorm2d: 2-2                  [-1, 24, 28, 28]          48\n",
-       "|    └─ReLU: 2-3                         [-1, 24, 28, 28]          --\n",
-       "|    └─_DenseBlock: 2-4                  [-1, 72, 28, 28]          23,184\n",
-       "|    └─_Transition: 2-5                  [-1, 36, 14, 14]          2,736\n",
-       "|    └─_DenseBlock: 2-6                  [-1, 84, 14, 14]          25,632\n",
-       "|    └─_Transition: 2-7                  [-1, 42, 7, 7]            3,696\n",
-       "|    └─_DenseBlock: 2-8                  [-1, 90, 7, 7]            26,856\n",
-       "|    └─ReLU: 2-9                         [-1, 90, 7, 7]            --\n",
-       "|    └─AdaptiveAvgPool2d: 2-10           [-1, 90, 1, 1]            --\n",
-       "|    └─Rearrange: 2-11                   [-1, 90]                  --\n",
-       "|    └─Linear: 2-12                      [-1, 80]                  7,280\n",
+       "├─Sequential: 1-1                        [-1, 512, 2, 60]          --\n",
+       "|    └─Conv2d: 2-1                       [-1, 32, 28, 952]         288\n",
+       "|    └─Sequential: 2-2                   [-1, 32, 28, 952]         18,560\n",
+       "|    └─Sequential: 2-3                   [-1, 64, 14, 476]         57,536\n",
+       "|    └─Sequential: 2-4                   [-1, 128, 7, 238]         229,760\n",
+       "|    └─Sequential: 2-5                   [-1, 256, 4, 119]         918,272\n",
+       "|    └─Sequential: 2-6                   [-1, 512, 2, 60]          3,671,552\n",
        "==========================================================================================\n",
-       "Total params: 89,648\n",
-       "Trainable params: 89,648\n",
+       "Total params: 4,895,968\n",
+       "Trainable params: 4,895,968\n",
        "Non-trainable params: 0\n",
-       "Total mult-adds (M): 0.35\n",
+       "Total mult-adds (M): 22.36\n",
        "==========================================================================================\n",
-       "Input size (MB): 0.00\n",
-       "Forward/backward pass size (MB): 0.29\n",
-       "Params size (MB): 0.34\n",
-       "Estimated Total Size (MB): 0.63\n",
+       "Input size (MB): 0.10\n",
+       "Forward/backward pass size (MB): 6.51\n",
+       "Params size (MB): 18.68\n",
+       "Estimated Total Size (MB): 25.29\n",
        "=========================================================================================="
       ]
      },
-     "execution_count": 30,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "summary(dnet, (1, 28, 28), device=\"cpu\", depth=2)"
+    "summary(w, (1, 28, 952), device=\"cpu\", depth=2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz= 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask = torch.triu(torch.ones(sz, sz), 1)\n",
+    "mask = mask.masked_fill(mask==1, float('-inf'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "h = torch.rand(1, 256, 10, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Sequential(\n",
-       "  (0): Conv2d(1, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "  (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "  (2): ReLU(inplace=True)\n",
-       "  (3): _DenseBlock(\n",
-       "    (dense_block): ModuleList(\n",
-       "      (0): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(24, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (1): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (2): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(40, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (3): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(48, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (4): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(56, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (5): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (4): _Transition(\n",
-       "    (transition): Sequential(\n",
-       "      (0): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (1): ReLU(inplace=True)\n",
-       "      (2): Conv2d(72, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (3): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
-       "    )\n",
-       "  )\n",
-       "  (5): _DenseBlock(\n",
-       "    (dense_block): ModuleList(\n",
-       "      (0): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(36, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (1): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(44, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(44, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (2): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(52, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (3): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(60, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(60, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (4): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(68, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(68, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (5): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(76, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(76, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (6): _Transition(\n",
-       "    (transition): Sequential(\n",
-       "      (0): BatchNorm2d(84, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (1): ReLU(inplace=True)\n",
-       "      (2): Conv2d(84, 42, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (3): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
-       "    )\n",
-       "  )\n",
-       "  (7): _DenseBlock(\n",
-       "    (dense_block): ModuleList(\n",
-       "      (0): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(42, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(42, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (1): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(50, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (2): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(58, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (3): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(66, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(66, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (4): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(74, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(74, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (5): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(82, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(82, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (8): ReLU(inplace=True)\n",
-       "  (9): AdaptiveAvgPool2d(output_size=(1, 1))\n",
-       ")"
+       "torch.Size([100, 1, 256])"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "list(dnet.children())[0][:-2]"
+    "h.flatten(2).permute(2, 0, 1).shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Sequential(\n",
-       "  (0): Conv2d(1, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "  (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "  (2): ReLU(inplace=True)\n",
-       "  (3): _DenseBlock(\n",
-       "    (dense_block): ModuleList(\n",
-       "      (0): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(24, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (1): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (2): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(40, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (3): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(48, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (4): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(56, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (5): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (4): _Transition(\n",
-       "    (transition): Sequential(\n",
-       "      (0): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (1): ReLU(inplace=True)\n",
-       "      (2): Conv2d(72, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (3): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
-       "    )\n",
-       "  )\n",
-       "  (5): _DenseBlock(\n",
-       "    (dense_block): ModuleList(\n",
-       "      (0): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(36, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (1): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(44, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(44, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (2): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(52, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (3): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(60, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(60, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (4): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(68, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(68, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (5): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(76, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(76, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (6): _Transition(\n",
-       "    (transition): Sequential(\n",
-       "      (0): BatchNorm2d(84, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (1): ReLU(inplace=True)\n",
-       "      (2): Conv2d(84, 42, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (3): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
-       "    )\n",
-       "  )\n",
-       "  (7): _DenseBlock(\n",
-       "    (dense_block): ModuleList(\n",
-       "      (0): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(42, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(42, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (1): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(50, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (2): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(58, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (3): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(66, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(66, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (4): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(74, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(74, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (5): _DenseLayer(\n",
-       "        (dense_layer): Sequential(\n",
-       "          (0): BatchNorm2d(82, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(82, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "          (4): ReLU(inplace=True)\n",
-       "          (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (8): ReLU(inplace=True)\n",
-       "  (9): AdaptiveAvgPool2d(output_size=(1, 1))\n",
-       ")"
+       "torch.Size([100, 1, 256])"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "torch.nn.Sequential(*list(dnet.children())[0][:-2])"
+    "h.flatten(2).permute(2, 0, 1).shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "DenseNet(\n",
-       "  (densenet): Sequential(\n",
-       "    (0): Conv2d(1, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "    (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "    (2): ReLU(inplace=True)\n",
-       "    (3): _DenseBlock(\n",
-       "      (dense_block): ModuleList(\n",
-       "        (0): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(24, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (1): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (2): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(40, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (3): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(48, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (4): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(56, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (5): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (4): _Transition(\n",
-       "      (transition): Sequential(\n",
-       "        (0): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "        (1): ReLU(inplace=True)\n",
-       "        (2): Conv2d(72, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "        (3): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
-       "      )\n",
-       "    )\n",
-       "    (5): _DenseBlock(\n",
-       "      (dense_block): ModuleList(\n",
-       "        (0): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(36, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (1): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(44, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(44, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (2): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(52, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (3): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(60, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(60, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (4): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(68, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(68, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (5): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(76, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(76, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (6): _Transition(\n",
-       "      (transition): Sequential(\n",
-       "        (0): BatchNorm2d(84, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "        (1): ReLU(inplace=True)\n",
-       "        (2): Conv2d(84, 42, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "        (3): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
-       "      )\n",
-       "    )\n",
-       "    (7): _DenseBlock(\n",
-       "      (dense_block): ModuleList(\n",
-       "        (0): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(42, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(42, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (1): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(50, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (2): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(58, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (3): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(66, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(66, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (4): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(74, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(74, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (5): _DenseLayer(\n",
-       "          (dense_layer): Sequential(\n",
-       "            (0): BatchNorm2d(82, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(82, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "            (4): ReLU(inplace=True)\n",
-       "            (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (8): ReLU(inplace=True)\n",
-       "    (9): AdaptiveAvgPool2d(output_size=(1, 1))\n",
-       "  )\n",
-       ")"
+       "tensor([[0., -inf, -inf, -inf, -inf],\n",
+       "        [0., 0., -inf, -inf, -inf],\n",
+       "        [0., 0., 0., -inf, -inf],\n",
+       "        [0., 0., 0., 0., -inf],\n",
+       "        [0., 0., 0., 0., 0.]])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dnet.eval()"
+    "mask\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([1, 80])"
+       "15.0"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dnet(torch.randn(1, 28,28)).shape"
+    "120 / 8"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "img = torch.randn(28, 28)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([1, 1, 28, 28])"
+       "120"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "img[(None,)*2].shape"
+    "2 * 60"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import yaml"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "path = \"../training/experiments/cnn_transformer.yml\""
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "with open(path, \"r\") as f:\n",
+    "    f = yaml.safe_load(f)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'experiment_group': 'Transformer Experiments',\n",
+       " 'experiments': [{'train_args': {'transformer_model': True,\n",
+       "    'batch_size': 16,\n",
+       "    'max_epochs': 128,\n",
+       "    'input_shape': [[1, 28, 952], [92]]},\n",
+       "   'dataset': {'type': 'EmnistLinesDataset',\n",
+       "    'args': {'subsample_fraction': None,\n",
+       "     'transform': [{'type': 'ToPILImage', 'args': None},\n",
+       "      {'type': 'Resize', 'args': {'size': [28, 952]}},\n",
+       "      {'type': 'ToTensor', 'args': None}],\n",
+       "     'max_length': 97,\n",
+       "     'min_overlap': 0.0,\n",
+       "     'max_overlap': 0.33,\n",
+       "     'num_samples': 1,\n",
+       "     'seed': 4711,\n",
+       "     'init_token': '<sos>',\n",
+       "     'pad_token': '_',\n",
+       "     'eos_token': '<eos>',\n",
+       "     'target_transform': [{'type': 'AddTokens',\n",
+       "       'args': {'init_token': '<sos>',\n",
+       "        'eos_token': '<eos>',\n",
+       "        'pad_token': '_'}}]},\n",
+       "    'train_args': {'num_workers': 8,\n",
+       "     'train_fraction': 0.85,\n",
+       "     'batch_size': 16}},\n",
+       "   'model': 'VisionTransformerModel',\n",
+       "   'metrics': ['accuracy'],\n",
+       "   'network': {'type': 'CNNTransformer',\n",
+       "    'args': {'backbone': 'DenseNet',\n",
+       "     'backbone_args': {'growth_rate': 8,\n",
+       "      'block_config': [4, 6, 8, 6],\n",
+       "      'in_channels': 1,\n",
+       "      'base_channels': 24,\n",
+       "      'num_classes': 256,\n",
+       "      'bn_size': 4,\n",
+       "      'dropout_rate': 0.1,\n",
+       "      'classifier': False,\n",
+       "      'activation': 'elu'},\n",
+       "     'num_encoder_layers': 3,\n",
+       "     'num_decoder_layers': 3,\n",
+       "     'hidden_dim': 256,\n",
+       "     'vocab_size': 82,\n",
+       "     'num_heads': 8,\n",
+       "     'max_len': 99,\n",
+       "     'expansion_dim': 512,\n",
+       "     'mlp_dim': 256,\n",
+       "     'spatial_dim': 357,\n",
+       "     'dropout_rate': 0.1,\n",
+       "     'trg_pad_index': 79,\n",
+       "     'activation': 'gelu'}},\n",
+       "   'criterion': {'type': 'CrossEntropyLoss', 'args': {'ignore_index': 79}},\n",
+       "   'optimizer': {'type': 'AdamW',\n",
+       "    'args': {'lr': 0.0003,\n",
+       "     'betas': [0.9, 0.999],\n",
+       "     'eps': 1e-08,\n",
+       "     'weight_decay': 3e-06,\n",
+       "     'amsgrad': False}},\n",
+       "   'lr_scheduler': {'type': 'OneCycleLR',\n",
+       "    'args': {'max_lr': 0.0007,\n",
+       "     'epochs': 128,\n",
+       "     'anneal_strategy': 'cos',\n",
+       "     'pct_start': 0.475,\n",
+       "     'cycle_momentum': True,\n",
+       "     'base_momentum': 0.85,\n",
+       "     'max_momentum': 0.9,\n",
+       "     'div_factor': 10,\n",
+       "     'final_div_factor': 10000,\n",
+       "     'interval': 'step'}},\n",
+       "   'callbacks': ['Checkpoint',\n",
+       "    'ProgressBar',\n",
+       "    'WandbCallback',\n",
+       "    'WandbImageLogger'],\n",
+       "   'callback_args': {'Checkpoint': {'monitor': 'val_loss', 'mode': 'min'},\n",
+       "    'ProgressBar': {'epochs': 128},\n",
+       "    'WandbCallback': {'log_batch_frequency': 10},\n",
+       "    'WandbImageLogger': {'num_examples': 6}},\n",
+       "   'test_metric': 'test_accuracy'}]}"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/src/notebooks/01-look-at-emnist.ipynb b/src/notebooks/01-look-at-emnist.ipynb
index d727cb4..0ef77b6 100644
--- a/src/notebooks/01-look-at-emnist.ipynb
+++ b/src/notebooks/01-look-at-emnist.ipynb
@@ -49,169 +49,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import random_split, DataLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d1, d2 = random_split(dataset, [len(dataset)-10, 10])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "55898"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(d1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dl = DataLoader(d1, batch_size=16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3494"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(dl)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-       "           0,   0,   0,   0,   4,   4,   4,   4,   4,   2,   0,   0,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-       "           0,   2,   4,   9,  32,  37,  37,  37,  32,  20,   1,   0,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-       "           3,  65, 109, 140, 204, 215, 217, 217, 201, 154,  22,   0,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   3,\n",
-       "          12, 122, 190, 222, 245, 249, 250, 250, 242, 206,  46,   0,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   1,   8,  79,\n",
-       "         127, 222, 247, 253, 235, 228, 249, 254, 254, 245, 114,   4,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   1,  35,  91, 219,\n",
-       "         244, 252, 247, 207, 100,  84, 223, 251, 254, 250, 127,   4,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   9,  91, 163, 246,\n",
-       "         252, 244, 220, 127,  39,  48, 218, 250, 255, 250, 127,   4,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   0,   0,   5,  20,  95, 219, 246, 246,\n",
-       "         221, 127,  79,  10,   5,  37, 217, 250, 254, 249, 125,   4,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   0,   0,  20,  67, 175, 246, 252, 219,\n",
-       "         164,  47,  22,   1,   5,  39, 218, 250, 254, 245, 114,   4,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   1,   9,  95, 175, 250, 246, 219,  91,\n",
-       "          35,   1,   0,   0,  22,  84, 234, 252, 250, 220,  50,   0,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   9,  35, 164, 221, 252, 219, 163,  35,\n",
-       "           9,   0,   0,   0,  46, 127, 246, 254, 245, 204,  34,   0,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   7,  91, 163, 246, 252, 219,  91,  35,   1,\n",
-       "           0,   0,   0,  10, 128, 209, 254, 254, 220, 139,   9,   0,   0,   0],\n",
-       "        [  0,   0,   0,   0,   3,  22, 159, 219, 252, 247, 164,  35,   9,   0,\n",
-       "           0,   0,   1,  36, 175, 233, 254, 254, 204, 115,   4,   0,   0,   0],\n",
-       "        [  0,   0,   0,   1,  36,  95, 232, 251, 232, 195,  47,   1,   0,   0,\n",
-       "           0,   9,  35, 163, 246, 253, 249, 232, 122,  45,   0,   0,   0,   0],\n",
-       "        [  0,   0,   0,   7,  91, 164, 247, 251, 187, 127,  20,   0,   0,   0,\n",
-       "           1,  35,  91, 219, 253, 254, 234, 187,  67,  20,   0,   0,   0,   0],\n",
-       "        [  0,   0,   0,  46, 207, 244, 247, 220,  80,  24,   1,   3,   8,  34,\n",
-       "          52, 164, 219, 253, 249, 234, 155,  79,   4,   0,   0,   0,   0,   0],\n",
-       "        [  0,   0,   2,  81, 232, 251, 235, 179,  39,  12,   5,  22,  46, 115,\n",
-       "         139, 221, 246, 254, 234, 188,  79,  32,   0,   0,   0,   0,   0,   0],\n",
-       "        [  0,   0,   3, 112, 244, 254, 236, 193, 130, 127, 129, 173, 209, 245,\n",
-       "         250, 254, 253, 232, 154,  79,   4,   0,   0,   0,   0,   0,   0,   0],\n",
-       "        [  0,   0,   0,  46, 206, 242, 249, 248, 249, 250, 250, 250, 250, 250,\n",
-       "         250, 243, 219,  95,  22,   7,   0,   0,   0,   0,   0,   0,   0,   0],\n",
-       "        [  0,   0,   0,  22, 154, 201, 217, 222, 245, 249, 249, 233, 222, 217,\n",
-       "         217, 202, 158,  36,   3,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
-       "        [  0,   0,   0,   1,  20,  32,  39,  51, 114, 125, 125,  82,  51,  37,\n",
-       "          37,  32,  20,   1,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
-       "        [  0,   0,   0,   0,   2,   4,   5,   9,  32,  37,  37,  21,   9,   4,\n",
-       "           4,   4,   2,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
-       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]],\n",
-       "       dtype=torch.uint8)"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d1.dataset.data[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Tensor"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(dataset.data)"
-   ]
-  },
-  {
-   "cell_type": "code",
    "execution_count": 44,
    "metadata": {},
    "outputs": [
@@ -288,27 +125,6 @@
    "source": [
     "display_images(dataset, 9)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/src/notebooks/02b-emnist-lines-dataset.ipynb b/src/notebooks/02b-emnist-lines-dataset.ipynb
index 84d853b..fb856c5 100644
--- a/src/notebooks/02b-emnist-lines-dataset.ipynb
+++ b/src/notebooks/02b-emnist-lines-dataset.ipynb
@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -22,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,23 +40,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "emnist_lines = EmnistLinesDataset(train=False)"
+    "emnist_lines = EmnistLinesDataset(train=False,\n",
+    "                                  max_length = 97,\n",
+    "                                  min_overlap = 0.0,\n",
+    "                                  max_overlap = 0.33,\n",
+    "                                  num_samples = 10_000,)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-09-10 20:11:30.358 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_load_data:134 - EmnistLinesDataset loading data from HDF5...\n"
+      "2020-10-25 18:35:53.133 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_load_data:147 - EmnistLinesDataset loading data from HDF5...\n"
      ]
     }
    ],
@@ -57,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {
     "scrolled": false
    },
@@ -76,123 +89,105 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "office in Arkansas after the______\n",
-      "in________________________________\n",
-      "by a oneshot technique____________\n",
-      "office Incumbent__________________\n",
-      "of the revolutionary______________\n",
-      "they______________________________\n",
-      "the scene but_____________________\n",
-      "Knox Ky___________________________\n",
-      "workers wife refused to have______\n"
+      "One of the reporters called to him_______________________________________________________________\n",
+      "and reproduce what_______________________________________________________________________________\n",
+      "plasma arylesterase against inactivation by urea_________________________________________________\n",
+      "bar while the sleekheaded bartender absently polished a glass Looking the setup over_____________\n",
+      "many areas of need in this amazing_______________________________________________________________\n",
+      "mulch over the seedlings now we must use a heavy one_____________________________________________\n",
+      "it later turned out that masked__________________________________________________________________\n",
+      "the Homeric poems appear oral compositions Yet they are written at some stage in_________________\n",
+      "through our growth studies we have_______________________________________________________________\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABG0AAAAtCAYAAAAUeZa4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXY0lEQVR4nO3deXSV1b3G8e/vnJORhEwMYdAgRAQjMwgKiIDSCy2IogWsMkhFkNKIAqWKFeRCl4rDZakLiqhQxQGprEtbL6JiABGQIHgjg4YKhCEiECAjMcm+f5yT0ySEIdiW9Pp81spKzt773e9+3/esBedZe+9jzjlERERERERERKR28VzqAYiIiIiIiIiIyJkU2oiIiIiIiIiI1EIKbUREREREREREaiGFNiIiIiIiIiIitZBCGxERERERERGRWkihjYiIiIiIiIhILaTQRkRqBTObYWavXepxXAgzu9HMDlzqcVTHzF41s/+81OMQEREREZEfTqGNiIgA/17BmYiIiIjIj4FCGxH5t2Nmvtrcn4iIiIiIyD+CQhsRuWhmNs3M9phZrpntMLNbK9SNMrP1ZjbXzHLM7Bsz61+h/gozSwscuxqod47z3GhmB8zsN2aWDbxiZp4K5z9mZm+bWXygfTMzc2Y21swOmdlhM5tcob8ZZvaOmb1mZqeAUWbW2Mz+28yOm1mmmd1boX1EYNlRjpntALpUGZ8zs+QKrystUTKzW8xsm5mdCoz3PwLlMWa2KDC+g2b2n2bmreb6w82s0MzqBV4/YmYlZlY38HqWmT1X4ZA4M/tL4N5uMrMWFfr6LzPLCowl3cx6Bsr/A3gYGGpmeWa2/WzPQ0RERERE/jUU2ojID7EH6AnEADOB18ysUYX6rsBu/IHMk8AiM7NA3VIgPVA3Cxh5nnMlAvFAEjAWmAgMBnoBjYEc4IUqx/QGrgT6Ab8xs5sq1N0CvAPEAq8DbwIHAn3dDswxsz6Bto8BLQI/P7mAsQaZ2bXAEmBK4Fw3AHsD1a8CJUAy0CEwzl9W7cM5VwR8FrhWAr/3Ad0rvE6rcMgw/M8jDsgEZleo+wxoj/9eLgWWmVm4c+5/gDnAW865KOdcuwu9RhERERER+edQaCMiF805t8w5d8g5V+acewv4Gri2QpN9zrmFzrlSYDHQCGhoZpfjn63yqHPutHNuLbDyPKcrAx4LtC8ExgGPOOcOOOdOAzOA26ssdZrpnMt3zv0v8AowvELdp865Fc65MvzBUXfgN865IufcNuAlYESg7c+B2c654865LGBeDW7TGOBl59zqwH066JzbZWYNgQHAA4ExHgGexR+4VCcN6BW4vraBMfQys3D893JthbbvOuc2O+dK8AdS7csrnHOvOeeOOedKnHNPA2HAVTW4HhERERER+RdRaCMiF83MRgSW/ZwwsxPANVRe5pRd/odzriDwZxSBmTHOufwKbfed53TfBWaclEsC3q1w7p1AKdCwQpusKv03PktdY+C4cy63SvsmFeqr9nWhLsM/I6mqJCAEOFzhGhYADc7STxpwI9AR+F9gNf4ZNt2ATOfcsQptsyv8XYD/ngNgZpPNbKeZnQycM4ZzLE0TEREREZFLR6GNiFwUM0sCFgK/AhKcc7FABmDnOi7gMP59V+pUKLv8PMe4Kq+zgP7OudgKP+HOuYMV2lxWpf9DZ+nvEBBvZtFV2pf3dbiavioqACIrvE6sMs4WnCkLOA3UqzD+us65lGraAmzAPyPmViDNObcjMI4BVF4adVaB/Wum4p85FBd4Zif5+zOreo9FREREROQSUmgjIherDv4P+d8BmNlo/DNtzss5tw/YAsw0s1Az6wEMrOH55wOzA+ERZlbfzG6p0uZRM4s0sxRgNPDWWcaThT8U+X1g09+2+Jc1lX/99dvAb80szsya4t9Pp6JtwJ1m5g1s6NurQt0iYLSZ9Q1sntzEzFo55w4D7wNPm1ndQF0LM+tFNQIzldKBCfw9pNmAf5nYBYU2QDT+PXS+A3xm9jugboX6b4FmZqZ/G0REREREagH9x1xELkpgpsfTwKf4P+y3AT6pQRd34t+o+Dj+jX6X1HAI/wX8N/C+meUCGwP9VZSGfyPeD4G5zrn3z9HfcKAZ/lk37+LfP+eDQN1M/EuivsEftPyxyrGp+EOnE8AvgBXlFc65zfgDo2fxz2pJw780Cvx75oQCO/BvpPwO/n1/ziYN/5KqzRVeR1N5P5tzWQX8D/BV4HqKqLzsa1ng9zEz23qBfYqIiIiIyD+JOafZ8CLy/4uZNcMfsIQENuMVERERERH5t6OZNiIiIiIiIiIitZDv/E1EROTHyMzyzlIVARSqXOUqV7nKVa7yH1Te3zm3rppyEZEgLY8SEREREREREamFtDxKRERERERERKQWUmgjIiIiIiIiIlIL1WhPGzNzVV7j8XgoKyvjfMusfD4fkZGRFBUVERsbS0REBCdPnuTkyZPnPfZS83q9REZGUlZWRmFhIWVlZZdsHBEREZSUlFBcXHxJxmFmREZGEhMTg3OO/Px8cnNzz/sMw8PDCQ0N5fTp05w+ffqfOkav1wtAaWlppXGbWY3vmdfrvaD3t9frpXnz5mRnZ5Obm1vzQYuIiIiIiMiP2VHnXP2qhRc108bj8XDNNdfw1FNPsX79ep566imaNWt21vZmRmpqKq+//jpvvPEGK1euZOHChWzdupWrrrrqnOe68cYbiYmJuZhhniElJYVhw4bh8VS+bI/Hg8fjwcwASExMZPjw4URHR5OcnMxbb73F9u3bSUtL45lnniEkJOS85yoPeurUqYPPVzkbCw0N5a677qJFixZnHFc+lqrCw8OZOnUqu3fvZuvWrcycOZNWrVqd0XdVUVFR9O3bt9q67t2788ADD1TqIy4ujtDQ0GrbR0REMG7cOD777DM2bNjA0qVLee2112jevHmldmZG79696datG+Hh4dx9991s27aNtLQ01q1bR48ePc455h/qwQcfZN68ecHwxswYNGgQL774IlFRUec93sxo2bIls2bNYv369bzwwgu0bNky+P442zFTp05lzpw552wnIiIiIiIiUo191RXWOLQxM7p06cKSJUsYOnQo+/bto1mzZjz88MNnDVd8Ph+dOnXiuuuuIzExkQkTJvCHP/yBOnXqULdu3XOer2HDhgwYMKCmw6x23AMHDmTMmDF4vV7MjLi4OLp168aMGTN45plnuPnmmwFITk7md7/7HUOGDOHpp58mISGBhx56iCVLltCxY0ciIyPPeh6Px0OLFi245557ePnll3nllVcYOXJkpaCnXr16PPzww7Rs2RLwhzhJSUmMHDmSuXPn8uijjxIXF1ep38svv5ybbrqJiIgImjZtyuTJk5k/f/55Q6+ysjIGDBhAo0aNzqjr168f119/fTAkat68OUuXLuWOO+6oNnhISEggNTWVgoIChg0bxi233MKYMWPYt6/ye8vr9TJixAjuv/9+xo8fz9SpU1m5ciUTJkygqKiI1q1bn3PM53P99dfTrl27autCQkJISUlhzZo1wZk2zjmOHTtGXFzceUMugDZt2rBo0SLGjBnDoUOHiIuLY8aMGdSrV++Mth6Ph+7du9O5c2e+/PJLrrvuOuLj44N1IiIiIiIiIherxl/5nZSUxKxZs8jOzubBBx9k48aN9OrVi8cff5yYmBhOnjx5xjExMTGkpKTw/fffM2vWLNLT02ncuPEZ7cyM+vXr07p1a6KjoyksLCQ/P59Ro0bx5z//+QctO/F6vSQnJ1NQUEB0dDQ9e/Zk3LhxlJSUcOLECcLCwoIzMzIyMti/fz8vvPAC27dvZ9y4ceTn5zNgwADS0tLIz8+v9hzh4eH079+fSZMmERMTw/79+2nWrBmtWrXi/fffJysrC/DP5ImOjqaoqIjk5GTGjh1Lhw4dOHXqFAUFBfh8vkrLcbxeL8OGDSM2NpaJEyeSn5/Pbbfdxs0330znzp3ZsWNHsH1YWBgtWrQgKSkJ5xx5eXmUlZVx8803s2TJkmCfPp+PK664gszMTEpKSggJCeGXv/wlV155JQMHDmT58uUUFRVVuj6Px4NzjuXLl7N58+azLjUqKSnhT3/6E4sXL2bgwIE8/fTTzJ8/n+7du3P69Gm2bt160c8xNDSUKVOm8MYbb7B9+/bg/SlfwlSvXj2aNGnCxx9/HDzGzIiJiak2dKkqMTGRmTNnUlJSwj333MPatWtp06YNL774IgkJCXz33XeV2tepU4c5c+ZwzTXXkJ+fT1xcHIsWLWL37t3k5OQwd+5cSkpKLvp6RURERERE5MerxqFNt27daNiwIb/4xS/IyMgAzr6kp1xiYiL16tVjwYIFfPjhh9XuD2Jm3H777QwdOpSDBw9y4sSJ4EyJkJAQpk2bxsKFC9m/f/9F7eUSGRnJddddx7Zt23j++edJSUnh2WefZfny5eTl5VXa7yQ/P5+cnBzCwsLIy8tj9OjR9O7dm02bNvHcc89V+yG8Tp06jBs3jmnTprF161Yef/xxioqKGDVqFF27dg0GQgAdO3bE4/Fwww03MGTIELZv384DDzzA7t27K80OKRcSEsJVV11Feno6K1euJDc3l6ysLLp160bjxo0xM5xz1K9fn+nTpxMfH09WVhbFxcWUlJTg8Xi48847ycnJ4aOPPiI/Px+fz0dsbCwffPABZWVldOnShV69evHwww+TmppK8+bN2bFjR6VrbNy4MXXq1CE3N/e8z+Dw4cPBexoXF8fChQtp3rw506dPZ9u2bTV+fuWioqK4/PLL+fbbbwGIjo7miSeeYP78+WRkZDBy5Eh27txJTk5O8Bgzo2nTpsHQKSwsjISEBOrWrcuxY8c4evRo8H63a9eOli1bcu+99/Lpp5/inMPr9Z71PZ6Xl8eECRPo0KEDeXl5PP744zRp0oRvvvmG7OzsWr9fk4iIiIiIiNReNd2ImMGDB/PBBx+wa9cuwP+hefDgwTRt2pQOHTqQnZ1NcXFxpeOSk5MpLi5m/fr1fP/998HysrKyYEjRqFEjJk6cyPTp0/nkk0+C5WZGw4YNGTFiBIsXL2bevHksX768xhfaoEED4uPjuemmm9i6dSv33XdfpdkiFT9cx8XF0apVK5xz9OjRgzZt2vDOO+/w5JNPcuzYsTP69ng89OnThylTphAfH09SUhJz5swhMTERn8/HmjVrOHr0aLBtcnIysbGxjBgxgpdeeon58+dXChmqSkxMJCEhgRUrVnDq1CnAHxYcPXqUvXv3Bsd+5513EhkZSWpqKsePHw8eHxoaSseOHZk8eTI///nPmTRpEqdOnaK4uJiEhATi4+NJTU1lw4YNrFq1iiFDhtCnTx927tx5xn0JDw+v9vqjoqIoLCwMPt82bdoEl5GNHTuW3bt3M3v2bFatWlVpg+Caio6OxufzkZWVhdfr5dZbb+Xuu+/mvffe4/Tp09xxxx1Mnjy50jl8Ph+NGjXC4/GQkpJCvXr1CA8PJyEhgWuvvZbp06dz8OBBvF4vgwcPZtOmTaSnp+OcIyIigkGDBtG0aVM6derEnj17Km2k7JwjIyODjIyM4FKp8uWCRUVFCm1ERERERETkotV4pk10dDRHjx4lKiqKhIQERo0aRYsWLdi4cSNTp07l6quvZuHChcGQwufz0aNHD7Zt20Z6enqwnwYNGnDq1KngjIni4mIKCwspKSmp9IHbOUd2djbPPfccmzdvvqhNXj0eD7feeitRUVHMnz+fp556iiNHjpy1fXx8PPXr12fZsmW8/fbbHDp0iC+//JK4uDh+9rOf0aZNGzIyMvjLX/5CWVkZbdu2ZdasWZSWlrJlyxbKyso4ePAgL7/8Mp9//jlfffUVeXl5ADRp0oT+/fuzb98+Jk6cyNq1aysFWVWZGe3btycsLIw9e/bg8XiIjo6mXbt2fPjhh6SlpQWDgfJAp2poVlxczMaNG5k4cSI33ngjZsb3339PRkYGd999Nz169CAyMpKZM2dy8uRJ3n77bcaOHcsbb7xxRkhVHtCUfxtTbGwsgwYNok+fPsyZM4ddu3ZhZiQnJ3Py5Ekee+wx9u7dS0ZGBsePH+fqq6+mS5cuxMbG8tZbb7F///4aPcvo6GjCwsLw+Xzce++93HPPPaSnpzN58mQKCwtZsWIF69atq3T/WrduzU9/+lMSExPp3bs3K1asYPfu3cTHx9OnTx/69+/PokWL8Hg8xMTEcODAASIjI2nUqBHDhg2ja9eufPzxx0yYMIErr7yS+fPnc+jQoTPGVlZWxs6dO+nevTthYWEUFhbW6NpEREREREREKqpRaOOc4+uvv+b+++/ntttuIyEhgfz8fO677z6++OILmjVrhs/nqzQToVGjRnTs2JG0tLRK+934fD48Hk9w2dDRo0d56aWXmD17Nk8++SRr1qwJhhkejwefz8eGDRvOCCQuRHx8PMOHD2fVqlXMmTPnnLNaAL799lu+/vprUlJSaNWqFe3bt+dXv/oV7dq1IzY2lr179+L1evnrX/+Kx+OhUaNGfPHFFyxdupTPP/8c5xynT58mLy/vjFklP/nJT2jYsCHjx4/no48+uqCvku7UqRNJSUn07t2bmJgYBg0aRLt27Zg2bRqHDx8Otn333Xfp06cPv//973niiSfIzs6mrKwMM8Pn83Hy5EnefPPN4DlfffVVIiIiyMvL49VXXw3uubN69WqGDx/OoEGDWLx4cXA2Um5uLqWlpbRt25akpCR69uzJsGHDyM3NZcGCBezduxfwv0+2bNnC+PHj6dKlC5GRkQwYMIBmzZrRvn17jh8/zoEDB1i9enWNQ5u8vDyio6N588038Xq9jB8/ntDQUF5//XU2bdrE888/X2n5Wnh4OL/97W9p0KABCxYsYO7cucH356lTp9izZ09wRlBpaSm7du1i3Lhx9OnTh9jYWEpKSkhNTWX9+vUkJSURGhpKQUHBWcf3t7/9jYSEBGJiYjhx4kSNrk1ERERERESkIqvJ8g0zc8nJyfz617+mbdu27N69m8WLF7N58+azbrY6fPhw5syZw1133cUnn3wSLO/bty8zZsxg9OjRZGZmAv4gp2vXrvTt25fS0lJKS0uJiYmhqKiI9PR0tmzZQnZ2do0vMjIykqFDh/Lpp58Gl3Wdi8fjITU1lUceeYSIiAiKi4vZt28fa9euZc2aNWzatInc3NzgxsghISGEhIRQVFR03r1e2rdvT0pKSrUb/VbH5/MxZcoUJk2ahHOOzMxMtm/fzrJly9iwYUOlgAz837bVr18/WrduzbFjx4iJiSEkJIT9+/eTnp7O559/Xmlmj9frxTl3xrhvuOEGRo8ezezZs4PPJy4ujnnz5tGvXz9ycnKIjY1l9erVTJ8+nf3791cKoC677DL++Mc/0qVLF8rKysjLy2PLli2sXbuW1atXc/DgQXJycmq8SW/dunVZtmwZkZGRTJo0ifT0dMyMjh07snfv3uAMr4rtp0yZwtatW3nvvfcq3fPQ0FB69uzJvn37gtd42WWXMXHiRLp27UpmZiZLly5l3bp1FxwWdu3alXfeeYchQ4awefPmGl2biIiIiIiI/GilO+c6Vy2scWgD/g+75cs/zvehu0ePHnTu3JmFCxdW+tal8j1FDh8+fEZg4PV6iYyMJCoqiujoaPLz88nOzv5Be6HUVHh4OJ06dSIhIYHc3Fx27drFkSNH/qVjKNegQQPat29PSEgIBw4cYO/evefdDDgiIoKwsDDi4+PxeDwcOXIkuHzqQpR/k1dBQUFwaZeZccUVVzB06FDq1q1LZmYmq1at4sCBA9Ue36RJE9q1a4eZkZWVRWZmJgUFBT94n5cmTZoAcPDgwQtqHxISQmlpabX3q3wT56rtw8PDL+j9XVVycjLPPvssDz30EF999VWNjhUREREREZEfrX9caFNT1X0wln9fISEhmFlwNpT8XfnXi586deqivuVMREREREREfpT+IaHNd8C+f+SoRERERERERER+5JKcc/WrFtYotBERERERERERkX8Nz6UegIiIiIiIiIiInEmhjYiIiIiIiIhILaTQRkRERERERESkFlJoIyIiIiIiIiJSCym0ERERERERERGphRTaiIiIiIiIiIjUQgptRERERERERERqIYU2IiIiIiIiIiK1kEIbEREREREREZFa6P8A+2i77OpGXmAAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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tWkVWVhZms5mZmRkaGho4ceLEkvp5pKWlUVJSgsPhYHJykvb29o81O9RHB4omk4mMjAx27NjB1q1bqaqq0stUQqEQp06dYmpqioGBAfr7+/WfHQ4Hq1atYt++fXrgw+/33/ZZpOx2OykpKeTl5bFhwwa++tWvkp+fj8lkwmAwMDk5SV9fH2fOnOHixYsUFhaSlZVFXFwcBoNBD8rU19dz6NAhhoaGlrX+5ORkiouL2bZtG7t27SI/P18PdnV1deH1eklLSyMuLg673U4kEtGPy1L6OcWaAWuahtFo1EvFtm3bxsaNG8nIyKC4uJhwOMyFCxcYHx/H5XLR1dXF1NQUfX19xMfHU1JSwsMPP8yDDz5INBrVt8FutxMXF0deXh6lpaXk5OSQnp7OG2+8QUdHBxaLhfLych588EHWrVtHSUkJ1dXVBAIB2tvb6ejooKOjg5mZGTZu3MiDDz5ISUkJiYmJFBYWUl1dTV1dHbOzs1fsi1IKs9lMZmYme/fuZceOHeTm5pKZmUlKSgqBQIBjx47pgcXx8XHcbjdTU1N69tJXv/pV4uLimJ2d1fs0rRRGo5H169djNpvp6+tjcHBwyTPvBYNBPB4PRqMRh8NBfHz8kj6nlOILX/gCycnJdHd3U1tbe0fKrOx2O7m5uaxbtw6r1YqmaZw/f566ujo6OjpW1HkRQgghhBBCrHz3dODHaDSSl5dHTk6OXnIUG0D39fUtaYCUkZFBeXk5FouFxsZG2trabinwEx8fT2JiIk6nE4Dp6WlCoRBpaWns3LmTJ598krVr15KZmYmmaUxOTnL69Gnee+89XC4Xg4ODjIyMEAgE8Pv9+l/5169fT3JyMqOjo7S1teHz+Za9bctRUFDA1q1b2bhxI5s2bWLz5s3Mzs7S0NDAyMgIXq+Xc+fOcfz4cbq7u8nPzyczM5P4+Hi9EXJaWhrHjx+nvb0dr9e75HXbbDY2bdrE+vXr2bFjh16apGkara2tNDU1kZSUpD8XjUaZnJxkcnLyhufabreTkJBAYmIiNpuNyclJgsEgycnJbNmyhV27dlFTU0NhYaE+k1pTUxPHjh2jr68Pl8tFT08Pfr8fn8+HxWKhr6+PrKwsVq1ahcfj4fz58/h8Pn3Avn79elavXk1WVhYpKSkMDw8DkJeXx0MPPcSOHTsoLCxEKUVPT4++f83NzXR3dxMKhfD5fDidTtLT00lLSyMtLU2/hgCcTider5dAIIBSSr/WXnjhBVatWoXdbicYDDI2NkZzczPvvvsuk5OTdHV14fF4CAaDhMNh/Zj8yq/8Ch6Ph87OTi5fvryieskYjUZKS0sxGo0MDAwsq8mxpmmEQiG9EflS7yGDwcBjjz3GqlWrqKur49y5c7f9mBgMBhwOB7m5uWRlZaGUIhKJcPnyZbq7uxkfH79nmlMLIYQQQgghVoZ7OvATFxfH6tWrSU1NxWQyEYlE8Hq9DA0NLakExGQykZOTQ2lpKZFIhIMHD9Lf37/sv6gbjUYqKyuprKykrKwMQM9Oqaqq4sUXX6SgoECfAnx0dJS6ujr279/P6dOn8fv9+iA2Nq17bIp5i8XC2NgYjY2NHDt2bFmBlOVSSrFq1So+//nPs3HjRn3a8mAwyPHjx2lqasLj8XD27FkmJiZQStHV1UVHR4c+C5TJZNLLcZbbcyczM5MXXniBzZs3U1BQgNPpZG5ujvHxcd5//33a29t5/PHH9XKzUChEV1cXly5dIhQKXXNAbDQaKSkpoby8nLKyMlJSUmhtbcXj8VBeXs5zzz2nl9D5/X5cLhe1tbW8/fbbNDU1MTExoWdZxc6LyWQiKSmJuLg4ZmZmaGpq4sMPP8Tr9bJlyxaeeuoptm/fTn5+PhaLBU3TePDBB0lLS9P77TidTjweD4ODg7z22mscPXpUL1mLRCIopTh58iROp5Oqqip9VrFYdlJiYiKVlZX09vYyOjqKwWCgsrKSF198kfz8fD3g093dzcmTJ/nlL3/JyZMnCYfDeima0WjEbDZjNptJS0sD5q/b+vp6Wlpa8Pv9n8h19XHFspnS09OZm5tjcHBwWX2u0tPTSU5Oxuv1MjAwsOSm1bF1VldXMzMzQ25urh7Au10sFgtZWVl66WAsmD0yMoLH45EZvYQQQgghhBDLdlcDP7HSm1tppmwwGCgtLeXXfu3XKCoqQimF2+2mvb2dEydOLGmAFB8fT3FxMRUVFYRCIT788MNbmoEqKSmJl156iW3btlFQUKAPTm02G2lpaSQmJjI+Pk5bWxtNTU2cOnWK2traK7IWTCYT8fHxpKenk5qaSnV1NV/5ylfIycmhubmZxsZG2tvbb+t07rGmxXa7naGhIfx+P4WFhVitVp566imqq6u5cOECg4ODBAIBMjIyKCgoYHR0lOHhYbxeL+Fw+JaOodFoZOPGjezcuZO8vDysVivRaJSZmRkOHTpEY2Mj+fn5rF69muLiYmw2G1NTU5w5c4a2trbrrtPpdPKFL3yBRx55hLKyMuLj4+nq6sJqter9k7xeL+3t7Zw6dYqGhgbee++9K/pDxRpzZ2ZmkpaWRkVFBZ/73Oeorq7G7XZz6tQpWltbCYVCnDt3jszMTJKTk8nIyMBqtWIwGNizZw9PPvkkcXFxhMNhOjs7+fnPf865c+f0oNHic6tpGr29vRw9epQnnniCgoIC4uLiKC0t5Rvf+AZZWVlkZ2czNDREIBDQG4cnJiYyODhIQ0MDjY2NnD59mpaWFqampvTlx8rxUlJSSEtLo6qqin/1r/4V8fHxtLW1cfbs2SVPlX4nGI1GsrKy2LVrl34fuVyuJX1WKcWXv/xlHnroIf08LTXwk5KSQmpqKvHx8WRnZ7N161aamppu+rvFYDDoJXjLFSsL/exnP4vT6cTn89Hd3c2pU6cYGhpaMedECCGEEEIIce+4a4Efu91OZmYmRUVFXL58mb6+viUPlGINhJ977jmqqqqw2WxcuHCBY8eO8f7779PZ2bmk5cTHx5OcnIzD4SAQCDA9Pb3swZpSCpvNRl5enj4DVKzkKZZNEQgEOHv2LMeOHePMmTN0d3fj8XhQSmEymfReM+Xl5WzevJns7GxKS0spLS3l8uXLnDt3js7OzjvS1DUajTI0NMQvfvELenp6+OIXv0hNTQ3l5eVUVVXxwAMPkJKSwtjYGNXV1WRnZzM5OakHtYaGhqirq2Nubm5Zg1Sj0UhRURGJiYl68+jYjF1HjhwhKSmJzZs3k5eXh91uZ3Z2lpGREb1s6XrrslqtZGdnk5mZSUJCAna7nbKyMiwWC2azmWAwyMWLFzl27BgnTpygo6NDb2gcOzexxtw7d+4kNzeXoqIi1qxZg9/v5+zZs1y4cEE/N+Pj4zQ2NlJWVsbmzZtJSEjQZ2nSNI3Z2VmGhoY4duwYb775pl4+d63tn5ubw+fz6Vk6RqORuLg48vPzcTgc2Gw2cnJygPnsFKPRiN/v58SJE7z//vu0tbXR39+P1+vFYDBgMBgwmUwUFhayfv16ysvL9XswLy+Puro6Wlpa9GDSShGbdc3hcHDo0CF++ctf0tfXt6TPappGbW0tO3fupLCwkNWrV3P06FFGRkZu+tloNEo0Gl3W74TU1FRWr17N+Pg4Q0NDy2qObbFY2LRpEw8//DAFBQVEIhGOHDnCgQMH9F5gQgghhBBCCLFcdyXwo5Siurqa5557jqeffppTp07xve99D5fLxdTUFKFQ6Iaft1qtlJeXs3fvXhwOB7Ozs3R2dlJXV0dTU9OSe/TEMo4ikQjT09NMT08ve180TcPtdvPKK69w6dIldu3aRVVVFQ6HQ//L/+JZnGKDu1g2SSxwVFZWxurVq8nNzdXLq06dOsUrr7xCfX09IyMjSypf+zii0Sjvvfcep0+fZnp6Go/HQ3NzM1u2bKGiokL/t2/fPgwGA0op/u7v/o7169fz/PPP88ILLzA6Osrrr79Oa2srjY2Nev+dGw2eY6U8WVlZWK1WlFJMT0/T09NDQ0MDR48e5atf/So1NTVkZGRgMBhwu928++67HD169IZlZZOTkxw4cIDh4WEefPBBtm3bRkJCwhXnxul0kpuby4YNG1i9evUV5yZWerNp0yaKi4v1GdguXLjAO++8wzvvvENvb69+zYXDYSYnJ/UMm1jjZYDR0VFOnDjBoUOH+OCDD+jp6dFL5K4lMTGR8vJy0tLSsFgsBAIBOjs7OXDgALm5uTz77LOkpKRgMpn0Rs9Wq5WUlBSqqqrIyckhGo3qxyYWMK2pqaG0tFQPRgUCAd577z2++93vcvHixVsq07ud4uLieOKJJ8jJyWFubo6kpCQee+wxioqKCAQCHD58mJGRkeve9/X19TQ0NFBTU4PdbsdkuvmvPaUU69evJz09HaPRyNDQEMePH7/hcTGZTHzuc5/ja1/7Gn19ffr1OTExwcTExE3XWVBQwM6dO/Um1tPT05w4cYL6+nomJycl20cIIYQQQghxS+5a4CcWDCkuLiY9PR2n00lnZye1tbVcunSJcDhMJBJhamrqisGxxWKhoKCAF154gbKyMqLRKC0tLRw7doympiYmJyeXVGphMBiorq7W/7I+NjamB36UUsTFxZGVlUVRURFZWVkEg0Hq6+sZHR29qgeQpmnMzc3pGT6xKcxjAzWz2czq1aspKCjQZxrTNI1IJILH40HTNH1a9PPnz9Pe3s7Q0BD19fW0t7fjdrs/1kxjyzExMaH3EolEIvT39+PxeKirqyM5OVkvXXM6nSQlJdHU1KRPb56enq6XDU1MTNDX10dXVxenT5/m2LFjuFyua/ZPMpvNpKamsmHDBqxWK3Nzc3R0dPDee+9x4sQJNE3TpyS32Wxomobf72doaAifz3fDwXgsa8NoNOpZPrHnYX6wXlRURHJyMjU1Nfq5mZubY2Zmhkgkgtls1sug2traGB4eprm5Wc+oWRyoNJvN5OXl6ZlJsaCPx+PhyJEjvPHGG9TX1zM2NnbTXlIJCQmUlpbqgZ/R0VGam5sZHx+npKTkioBP7Fo3m816oC5230SjUf1ag/nyt4mJCVpbW+nt7WV4eJhjx45x4cIFfD7figowKKVISEhg06ZN2Gw2HnjgAbKyskhNTSUzM5NQKMTDDz/MK6+8Qm1t7TWDxj6fj/HxccLhsJ75dDOxUtK4uDiUUoTD4ZsGhmOBtZycHAoLC8nNzeXBBx+ko6ODN998U79WvV4voVDoiuvW6XSyd+9etm7dSlpaGtPT09TV1XHkyBGGh4eZm5vDYDCsqHMjhBBCCCGEuDfclcCPpmmMj4/rs29VVlby6KOPsmrVKgoLCxkaGtJn4Glra7uiyazNZqOoqIjdu3eTmJhIOBxmeHiYUChERkYG6enpAIyNjellNLOzs1cFgZxOJ6tWrSInJwefz8fFixcJhUIopSgtLWXVqlWsW7eO6upqMjIyCAaDZGdn8+abbzI8PHxFIMZisVBSUkJRURFJSUlomobP52N2dhaLxYLNZiMhIQGr1UooFGJ2dpZoNEogEGBwcFAfbHu9Xjo7Ozl79qzemDcWfLhTIpHIFeubnZ3F7XbjdrsZGhrCbDZjMpn0acsDgQDvv/8+w8PD5ObmsmbNGrZv305BQQEVFRWsWbOGkpISUlNTOXHiBBcuXLhi+QaDAbPZTGJiItnZ2RiNRkKhEAMDA7S0tNDZ2Yndbic7O5v4+Hg9Q8vr9TI6OnrTzBSLxUJ+fj7FxcVkZGSglMLv9xMOhzGbzdhsNux2OxaLRc8em5ubIxgM6hlokUiE2dlZOjo6aGxsxOVy0dfXx8TExBXXgclkoqKigt27d7Np0yYcDgdKKWZnZzl58iRvv/223qtlKX1i8vLyKCkp0cvFwuEwMzMz5OTkUFRUhNVqZXZ2Vi8FM5vN2O12kpOTsdlshMNhPYDn9/vp7e0lFAoxPDzM0NAQFy9e5NKlS4yNjdHV1YXP51txM0bZ7Xby8vLYvHmzHqRLSUlhcnKSy5cvo2kaO3fuxGKx0NnZyeDg4FWZcbOzs/j9fkwmE2lpaWRlZd20VCw5OZny8nLsdjuAHkC7kWg0SkdHB11dXaxbt47Vq1eTl5fHmjVryMzMxO/3EwwG6e3tveraSU1N5fHHH6ekpASr1ao3/o6Pj2ft2rUopZiZmWF8fJyJiYnrlgcKIYQQQgghxEfdtcDP8PAwJ0+epKCgQB/QOZ1O8vLyCIVC+mC1paXlin4ji2faiWXWJCYmsm7dOkpKSoiLi8NoNNLZ2UlLSwvnzp3D7XZfNRgsLS3VSzlGR0dpbGzEbDZTUFDAM888w7Zt2ygpKcHpdGIymfQptUdHRzl69Cijo6N6GU9scFZRUYHD4cDtdnPp0iUmJiZISkoiPT0dg8GA1+tlcnJS/4u/z+ejubkZr9er7+/g4CA9PT0EAgG8Xu+KKrlZHBSK9bQxGo0cO3aMs2fP6rNWAWRnZ5OcnExOTg5JSUlkZmaSnZ2N3W6ntbUVv9+vzy4VHx9Pbm4uTqcTpRTBYJDR0VH6+vqYmZkhPz+fxMREzGYz4XCYqakpBgYG6O/vv2mgIj4+nurqaqqqqkhLS8Pv93PhwgXGxsZISkoiNTUVs9lMKBTC7Xbj8XgIh8P4fD46Ojr0bKvZ2Vn6+/vp7u4mEAjg9/uvuKYMBgM5OTk8/vjjPPXUU6xdu1bPTpqenubgwYMcO3ZsSUGf2PKKi4spKSnRZ3eKBXLWrl1LSUkJBoOBS5cuMTIyQjAY1Kd+h/leQ7GyyVAohNfrpampST/usR40o6Oj+v6stKCP0WgkIyODDRs2UF5ejlIKr9dLR0cHZ86coaOjA5vNxne+8x327t3LT3/6UzweD5OTk1csJ9ZbKXatJSQk3HC9NpuNLVu2sHbtWj1rK5b1Fcuwupa5uTnOnj3LwYMHAaisrCQtLY2MjAyKior0gOLly5cZHx+/4vqJZTWlpKTovZhSU1N57LHHSEhIwGw243K56O7upq2tjXPnzq2YWdeEEEIIIYQQK9tda+7s8/n0abXdbjcvvvgiGRkZ2O12EhIS9Gmzi4qKAPSeMrF/sXIuk8nEunXrKC8v1wfUfr+fkpISCgoKmJiYYGZm5qrAz6OPPspDDz1EYmIiZ8+epa6ujoKCAl566SW+8pWvEBcXx8WLF/nggw+Ynp7mK1/5Cps3b+bZZ59ldHSUyclJvazE4XBQVVVFdnY24XCYM2fO8OMf/5jOzk6ys7MpLy/HaDTicrkYHBzE5XLpQZ1YUCpWrrPcZrJ329zcHFNTU3owpqWlhbq6Oh5++GF27txJTU0NhYWFPPTQQ2zatImamhr+6I/+iAsXLuD1erFYLKSnp/PAAw+QmpoKQCAQYGxsjKGhIcLhMIWFhXrPpNHRUdra2jhx4gRtbW0ANxyMOxwOysrKKCgowGw209nZycsvv8z58+fJzs7WZwjzer309vYyMjLC9PS0vl+hUEg/Jzc6N1arlccee4xf//Vfp7Kykvj4eGA+C+TSpUt8+OGHV2WK3YjRaKS4uJiioiJsNhuhUEjvHVRVVUVGRgZut5sDBw5QW1vL5OQkWVlZlJaWAnD58mVGRkaYmpoiGAzqmVuxfkuL/61UdrudyspK9uzZo/dW+vnPf85bb72l9yJKT0/n/PnzPPXUU+zcuZPOzk79/MUopTAajYTDYVwu1w2nZI9lBX3jG9+gpqZGP48pKSmsXr2aoaGhqwJLi42MjPC///f/Znh4mCeeeIItW7aQmpqqZ5WZTCby8/P1oPHi32ex32nRaBSn08n27dvZuHEjRqORQCCA2+2msrKSnJwcLl++LIEfIYQQQgghxJLc1encg8EgXV1dvPzyy7S1temlUllZWZSUlOjNdJVSJCcnEx8fj9VqxWg0omkak5OT9Pb2cvr0acbGxvD7/Xi9Xvr7++np6WFqagqXy3XVDEUGg4Hy8nJ9mvWuri4ikQh/8Ad/wJ49e9A0jePHj/Ozn/2MAwcO6Nv69a9/nY0bN7J27Vq6u7sZGBi4YpmaptHT08Px48f54IMPmJiYoLm5mUOHDgHcE4Ptj2tubo729nYuXbrE2bNn2bt3r17GFytnmZmZ4eWXX6apqemKHjqx/juAPhiOZVH09fVRVFTEyZMnOXjwIMePH2dmZgan0wnMB/sWBzUWi50bl8tFXV0d7733Hi6Xi3Pnzl3R7+VWg24Gg4HMzEy+9rWvUV1dfUV5kNfr5e2336azs3NZTcetViuJiYn6dPATExP09PTQ0dGhb2tzczOHDx+mvr4en8+nBxFi677XS4FiM5itWbOGYDBIY2MjL7/8MqOjo/q+TU9P8/rrr7Nt2zY+85nPUFdXpwe8Fi8nJycHp9NJSkoKGRkZXLhw4ar1xRqtl5SU8Pjjj1/RBHrDhg382Z/9Gbt27eK3f/u3b1h+OTQ0xKuvvkpTUxNr1qzRs9lWrVpFRUUFNptNzyJKSkrC4XDo2YuxfkQ9PT2cOnWK6elpvSR0aGiI8fFxPVguhBBCCCGEEEtxVwM/MX6/n4MHD2I0GvXBV0JCgp7lUVhYyG/+5m+yfv16MjIysFqtzMzM8Hd/93f85Cc/0aefjv2VPzaV+PUG8RaLhTVr1uB0OnG5XOTm5vLHf/zHPPPMMwwMDPDSSy/R3NzM9PQ0kUgEpRR//ud/jtVq5ctf/jJf+tKXCIfD/P3f//0Vy4319vF4PIRCIX39K6lc606ZnZ3l9OnTXLx4kbfeeovHHnuM3/qt3yInJ4c9e/bg8XiwWCy0trZe8bnYFPdJSUnk5eVhs9mIRCL09vbidDqZnJzUm+wWFBRQWFgIQH9/P9PT08zMzODz+a65TYFAgKmpKb3BNty+cxMrL/J4PLS2thIOh5ccVIqVOJWWlpKYmIjRaGRqakpvZh1bfqxscHHG2P0kOzubkpISlFK89tpr/NVf/RUul+uKgFYoFOL48eP09fWRnJysZ9csDvz4/X4GBgbQNI3c3FxKS0s5cuTIVevLyspix44dfOlLX9KDPi6XC4vFQnx8PPn5+TzzzDO89tprHD9+nGAweN1tD4VCtLe3c/HiRT2bJykpCafTidFo1MtTY8FCk8lEMBjkl7/8Jfv37+fDDz9kbGxMP7eLp5a/386zEEIIIYQQ4vZaEYEfmB+Axwbh4XAYr9er97p4+OGHqaioICMjA5PJxMTEBIcPH+af/umf6Ozs1JslL1WsCbTFYtGb/prNZqampnj99ddpa2vTp+OG+UF2MBjU+9rk5uaSk5NzVYmRwWAgNzeXyspKMjMz8Xq9n+ARuvdEo1Gmp6dpaWmhp6eHo0eP8s1vfpM9e/bw/PPPs27dOg4dOsT+/fv1zxiNRlJSUnj22WfZsmULPp+Pvr4+ferxjIwMduzYwfbt2ykvLyczMxOYnyq9v7+fhoYGDh8+zNDQ0BXlfUop0tLS9IbescyZT2o/XS4XP/nJT9A0jcLCQj3DqKWlhVOnTi0r2ychIYEnnniChx56iKSkJObm5ujv76ejo0MPehmNRqqqqiguLqa7u5uxsbFPbH9WArPZzK5du3j66aeJRCJ873vf4/z581dl2phMJsrLy/H5fGRmZpKWlobT6WRkZOSK4N6lS5fwer2Ulpayfft2fvSjH+m/b6xWK+Xl5ezdu5cXX3yRsrIyQqEQH3zwAX/zN39DdnY2jzzyCJs2baKoqIi/+Iu/4MSJE7z++utcvnyZ2dlZzGaz3sT8wIEDesP2xb+XXC4Xbrcbg8HA2rVrKS0tpby8nPj4eGZnZzlx4gRvvPEGR44cYXBw8IrZDIUQQgghhBDiVq2YwM9isem05+bm9OyHWIPTSCTC+Pg4dXV19PT0XHP65hsxGo1s3bpVz6SITdc8PDzMX/7lX3LixAkmJiauGUiKDRRjvToWB35iPTtiwYWamhq8Xi9+vx+/3/+pzPqB+aBIKBQiHA7T3NzMj3/8Y7Zt20ZGRgb5+fnk5OQAXJHJYDabycnJIS0tjbm5OSorK7FYLMTFxWGz2fTG3rHZqwCKi4tZtWoV5eXlVFVVceLECTo7O/XyL5jv91NRUcGOHTv0zJ9AIHDFe25VIBDgvffew+12k5eXh8FgYHx8nL6+Pr0R+FLE9mvDhg0kJSVhMpm4dOkSp06doqWlBaPRqF+b+fn5bNmyRQ9yxJo03+slXjDf7LioqIi8vDxcLhcjIyNX3esmk4msrCy++MUvUl5ers+oNTExccXxjkajtLe3MzY2RllZGcnJyTgcDj3r7NFHH2X37t3s2LGDsrIyLBYLdXV1/PjHP6ahoQGHw8Ho6Cijo6O8+OKLVFRUkJSURE5Ojt6kWdM0kpKSMJvNvP/++9fMOov9TouVNqalpekNo30+H2fPnqW9vR2Xy3VHZ/ITQgghhBBC3N9WZOAnxmw2U1paSnV1NXFxcczNzTEyMsKZM2c4derULWXUGI1GfcpkpRTRaJSJiQk+/PBDXn31Vdxu95IGzot7w4TDYUZHR0lPTycpKUnPHoiVkvX29jI8PHzNQXks6BBbXjgcXnJ2yL0kNitVa2sr09PTpKWlYTKZMBqN+nTpsUGxUgqLxYLFYgHmZ22D+WwYh8OhLzPWzwbmGwE7nU59pi6bzYbJZOLs2bO4XC4mJyfJyMggOzubPXv2YLFYcLlcDAwMMDIywszMzFWD7di5ifUbCofD1w00RqNRfZpup9OJwWDA5/Ph8/luWBL0UbG+L7GG4JFIRJ+hrre3l8zMTEZHR0lLS8PhcLB161YASkpKcLvd9PT06FOaLw5+aJqm75/JZCISiRAOh1dsgCEhIUEPfMWmL4+JBVjLy8vZvn07O3bsYHp6mv3793P27NkryrxihoeHaW9vp6Kigvz8fH71V3+V1tZWCgsL2bt3Lxs3biQ9PZ3x8XFOnz7NwYMHqa2tZWJigqmpKfx+Px6PB4PBoGdibdy4UZ8dbWpqip6eHpRSNzymBoOB7OxsKioqKC4uxmg0EgqFaG5u5uTJkwwMDCzrehFCCCGEEEKIm1mxgR+DwUBCQgKf+cxn2LZtGxaLhampKc6dO8dbb71FS0vLsgetsYBCZmbmFQ1/T548yc9+9jNcLteyt1PTNGZmZjh37hzx8fF6mdeTTz7J+vXrGRwcpLm5mVOnTjEyMnJVUGd6ehpN07BarQCMjY0xMjKy7O1YqYxGIyaTST/uxcXFV2RKxaa4Hh0dxe12k5mZeUVT3cUNi2OPr5U9E+vrFJslrLKykq6uLo4fP057ezvp6emYzWays7N59NFHqaysZHh4mAsXLtDc3HzNWZJ8Ph+RSASTyYTVamVkZIShoaHr7mskEmFycvKGsz4t5Xg5HA6ysrKYmZlhcnKSc+fO0dXVxeTkJFarlbNnz5KYmEhlZSVr164lPz+fRx55hKGhIc6ePcvRo0eZnp6+IsgYjUb1UsX4+Himp6f1Kd9XotTUVBwOBz6fj0uXLunHNJalt2nTJj73uc/x5JNPYjQaeeutt/jHf/xHxsfHrxm49fl8nD59msrKSnbs2MG3v/1tjh07xubNmykqKsJisTA4OMiRI0f4b//tv9HS0nJFqeDIyIjeCP7pp5+mvLycHTt20NnZSX19PYODg9TW1mKxWPB4PNfdL5vNxoYNG3j44YdZtWoVs7OzjIyM8POf/5yGhoarehgJIYQQQgghxMe1YgM/sUH6F7/4RfLy8vB4PFy8eJEjR47w7rvv3nBwdT1Go5G8vDwefvhhrFYroVCI2tpavv/973P8+PFb3lafz0d9fb1ewlFeXo7D4cBut+tZJpmZmXqz1phoNMrg4CCRSISEhATm5uZobGxkbGzsvhj8GQwG0tPTycjIICcnh6effpodO3ZQUFCAyWRidnYWn8/H4OAgH3zwAdu3b2ffvn16Wd/igM+NzM3NMTk5ic/nIxQK4ff7CQaDRCIR/H4/Z86cwWq16tk0cXFxFBUVkZmZSW5uLnl5efT29l6RaRGNRhkbGyMQCGCz2XA4HNTX1zM6Onrby/ai0SjhcJiGhgYaGxv5xS9+QXd3N+FwmKmpKU6cOEF8fDwZGRkkJiaSmppKYmIiOTk55ObmkpSUhNfr1a+hWK+ZWIPj9PR0enp6aGpqwuPxrLg+MiaTiVWrVulT1re0tDA3N4fBYCArK4vi4mKef/55nn32WSKRCPv37+ev//qvb5qt19bWxpEjR0hISOCRRx7h85//PGazmbm5Ob3U6pVXXuH8+fNX3KcxkUiEgYEBfvCDH5CcnExbWxuXLl2ivr5ez1i7meTkZB577DEefvhhkpOTGRsb4/Tp07z22muMjIys2AwsIYQQQgghxL3rpoEfpVQ+8GMgE9CA72ua9l2l1B8B/wcQS5P5tqZpb39SGxab7jgxMRGlFKOjo7S0tHDhwoVbCvrAfKbDiy++SHV1NQaDgYaGBg4cOMCHH35400CLUkqfdSw2UI4FJsLhMG+//TYnT56koaGBrVu3UlZWRnFxMenp6VRUVFBRUXHVjDzRaJTR0VG9EezQ0BDT09PU1dXd0v6tNNnZ2fzrf/2v2bdvH8nJyWRnZ2MwGIhGowwPD3P06FFqa2vx+/0MDw9z6NAhysrKKCwsJDk5mfj4+BsGJWIlYl6vl8bGRnp7e5mamsLr9TIxMcGFCxfw+/0cPnxYz4TZvXs31dXVlJWVkZ6eTlFRESUlJdecLWl8fJzp6WlmZ2cJBAJ4PB6OHTt2W49ZNBolGAzS19fHqVOnOHr0KF1dXfh8PjRNw+PxsH//fhoaGmhtbWXt2rUUFRWRk5NDSkoKq1atoqqqCuCqay02K5jBYKC2tpaRkREuXry44gI/TqeTBx54gPT0dKanp3G73cTFxVFWVsYPf/hDKioqCAQCnDx5kh/+8IccOHBgSUGX9vZ2urq6+OCDD/j93/99CgoKqKqq4uDBg9TX19PQ0EBzc/NNlxOJRHC5XPzgBz9Y9r7FgsM2m41QKKQ3JB8fH5egjxBCCCGEEOK2WErGTwT4pqZpZ5RSCUCjUuq9hdf+UtO0/3r7Nm8+myMQCFBbW8v7779PW1vbLWVcxPrDfOYzn8FmszE4OMihQ4c4d+7ckoI+TqeTyspKrFYr0WhUbzQc6z0SCoUYGhrizTff5IMPPiAhIUGfkruyslJvBr1YNBrl4sWLDA0N4fF49EHu/ZDtEx8fT0VFBX19fbz66qvYbDY96HP48GEGBgZwuVzMzMzo5/jNN9+ks7OTyspKdu3aRU1NDfHx8TidTpxOJzCfXeXxeOjv7+fMmTOMjIwwMDDAqVOnmJiYIBQKEYlEiEajRCIRfTA9NjbGsWPHaGpqIiEhgezsbIqLiyktLdUbfC8W69kzODiI2+3G6/Xekaa7wWCQtrY2fu/3fg+Px4PH4yEYDF5xTcQCQz/4wQ9wOBwkJyeTkZFBQUEBFRUVV/Sfgn/u79PR0UFvby9+v5+JiYnrNjG/2xISEvQ+XC6Xi2AwyO/8zu+wb98+ysvLGRsb4+233+aNN97g5MmTy/p9EIlE6Orq4g//8A9JSUlh165dvP/++/T391+zIfPtEMvo8ng8HDx4kMOHD9+Xfb2EEEIIIYQQK8NNAz+apg0Dwws/zyil2oDc271hkUiEiYkJ3nnnHbKzszlw4ACtra231IcnxmAw4HA4mJyc5G//9m/Zv38/ly9fvunnlFLY7XZyc3MxGo1MT08zPT19VaPf2MxKwWCQqakpRkZG6Ozs5OjRo9ctW1pclhQLVtwPAoGAPkuRyWS6Ihjhdrv1xsKLAw8+n48LFy7Q19dHa2srr732Gna7ndLSUsrKylBKMTQ0xODgIP39/Vy6dIlgMEgwGNSbM0ej0WtmsGiaRigUYnZ2lunpaVwuF52dndjtdoxG43X3IfaZubm5O3JuotEofr+f7u5uotHodaf0jvXs8fl8uN1uent7aWlp4ciRI9dcrqZpeqPp2OxSK3WmObfbTUNDA4WFhWzbto2ioiK9zOpP//RPaWxspLu7m9HR0av6Mi1FOBxmaGiIsbExhoeH8Xg8hMPhOxIEm5mZ0YNVPp+PX/ziF1y6dGlFBuCEEEIIIYQQ94dl9fhRShUBG4AGYDvwb5RS/xI4zXxW0K13tf2Iubk53G43r732GomJiTQ3NzMxMXHLfxnXNI1gMMiJEycIh8O888479PT0LGngGBs0X7x4kby8PC5fvszly5evGQiIlQxFo1FmZ2f17IobLft+FI1Gr9s4+Hr7rGmaHgibmZmht7cXs9nM5cuXaWtrQynF+Pg44+PjTE5OMjU1dd1Az422KxZg8/v9N+0jdDfOT2wGtKW8LxbACYVCeL1e3G73Dd9/L/D7/Zw8eZINGzaQkpLCzMwMjY2NnDhxgqamJgYGBj72tPWxbLA7PYPWzMwMR48epbOzk9nZWTo7O+9YppEQQgghhBDi00ktdTColHIAHwJ/omna60qpTGCc+b4//xnI1jTta9f43G8Av7HwcNNyN9BsNmM2m68qd7kVdrudRx55hMnJSVpaWggEAkseDJvNZp5++mm2bt1KX18fDQ0NnDlz5mNtj7i+2GxeBoOBuLg44uPjgfkyp1AotKKnIhcfX1ZWFk899RRVVVUMDAxw8uRJOjo6mJmZueezY2Iz3SmlZOp2IYQQQgghxCelUdO0zdd6YUmBH6WUGXgLeFfTtL+4xutFwFuapq25yXLujZSD64gFImJZPfdKBoUQ96JYeaDca0IIIYQQQghxU9cN/Biu9eRiar4W5gdA2+Kgj1Iqe9HbPgec/7hbudLFSmuWW14khFi+WFme3GtCCCGEEEIIcetumvGjlNoBHAVagFiNxbeBF4H1zJd69QC/udAI+kbLcgE+5kvEhBArVxpynwqx0sl9KsS9Qe5VIVY+uU/F/aBQ07T0a72w5B4/nxSl1OnrpR8JIVYGuU+FWPnkPhXi3iD3qhArn9yn4n5301IvIYQQQgghhBBCCHFvksCPEEIIIYQQQgghxH3qbgR+vn8X1imEWB65T4VY+eQ+FeLeIPeqECuf3KfivnbHe/wIIYQQQgghhBBCiDtDSr2EEEIIIYQQQggh7lN3LPCjlHpKKdWhlOpSSn3rTq1XCHElpVS+UuqwUuqCUqpVKfU7C8+nKKXeU0p1Lvw3eeF5pZT6/xbu3XNKqY13dw+E+HRRShmVUk1KqbcWHhcrpRoW7sl/UkpZFp63LjzuWni96K5uuBCfEkqpJKXUq0qpdqVUm1LqQflOFWLlUUr97sL/+55XSv2jUsom36ni0+KOBH6UUkbge8AeYBXwolJq1Z1YtxDiKhHgm5qmrQK2Af/nwv34LeCXmqaVA79ceAzz9235wr/fAP76zm+yEJ9qvwO0LXr8X4C/1DStDJgEXlp4/iVgcuH5v1x4nxDi9vsucFDTtCrgAebvV/lOFWIFUUrlAr8NbNY0bQ1gBP4F8p0qPiXuVMZPDdClaVq3pmlh4BXguTu0biHEIpqmDWuadmbh5xnm/wc1l/l78n8uvO1/Ap9d+Pk54MfavHogSSmVfWe3WohPJ6VUHvA08PcLjxWwG3h14S0fvVdj9/CrwGML7xdC3CZKKSfwMPADAE3TwpqmTSHfqUKsRCbArpQyAXHAMPKdKj4l7lTgJxfoX/R4YOE5IcRdtJC2ugFoADI1TRteeGkEyFz4We5fIe6evwJ+H4guPE4FpjRNiyw8Xnw/6vfqwuuehfcLIW6fYsAF/I+Fksy/V0rFI9+pQqwomqYNAv8V6GM+4OMBGpHvVPEpIc2dhfiUUko5gNeAf6tp2vTi17T56f5kyj8h7iKl1D5gTNO0xru9LUKI6zIBG4G/1jRtA+Djn8u6APlOFWIlWOiz9RzzwdocIB546q5ulBB30J0K/AwC+Yse5y08J4S4C5RSZuaDPj/RNO31hadHY+nmC/8dW3he7l8h7o7twLNKqR7mS6R3M99LJGkhTR2uvB/1e3XhdSfgvpMbLMSn0AAwoGlaw8LjV5kPBMl3qhAry+PAZU3TXJqmzQKvM/89K9+p4lPhTgV+TgHlC13TLcw30jpwh9YthFhkoT75B0Cbpml/seilA8CXF37+MrB/0fP/cmEmkm2AZ1H6uhDiNtE07d9rmpanaVoR89+bH2ia9iXgMPDCwts+eq/G7uEXFt4vWQZC3Eaapo0A/UqpyoWnHgMuIN+pQqw0fcA2pVTcwv8Lx+5V+U4VnwrqTl2/Sqm9zPcqMAI/1DTtT+7IioUQV1BK7QCOAi38c9+QbzPf5+enQAHQC/yqpmkTC1+O/535dFg/8FVN007f8Q0X4lNMKbUL+Heapu1TSpUwnwGUAjQBv6ZpWkgpZQP+F/N9uyaAf6FpWvdd2mQhPjWUUuuZb8BuAbqBrzL/x1X5ThViBVFK/SfgC8zPcNsEfJ35Xj7ynSrue3cs8COEEEIIIYQQQggh7ixp7iyEEEIIIYQQQghxn5LAjxBCCCGEEEIIIcR9SgI/QgghhBBCCCGEEPcpCfwIIYQQQgghhBBC3Kck8COEEEIIIYQQQghxn5LAjxBCCCGEEEIIIcR9SgI/QgghhBBCCCGEEPcpCfwIIYQQQgghhBBC3Kf+fzXYpigdKFmoAAAAAElFTkSuQmCC\n",
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RURF+/PFHJCUlQa/Xw9bWFhMnTkRqamqVcXfr1g1ffPEFNBoNgoODcePGDfj7++M3v/kNoqOjkZycDACIj4/H+fPn0atXL8ydOxd6vR5Xr15Fbm4uPDw84Ofnh4cPH2L69Om1PkhXF/fevXuRmpoKnU4He3t7TJw4ESkpKZXitre3R1BQEE6fPo3Fixebp9G0a9cO7733Hu7fv487d+4AMCbnfvrpJ7z88ssIDQ2Fra0trl27hsePH8PDwwO9evVCTk4Opk+fXi4R1q5dO0yePBnOzs7o0aMHPv/8c9y6dQtfffUVfH19zUkbrVaL2bNnY/Lkydi5cydWrlyJZs2aYc6cOfD29sakSZOqvMeaNGmC7t2747XXXkPXrl1rfPCtq2bNmuG3v/0tdu3ahdjYWPNxz507h2HDhsHZ2RnJyckgIvj5+aFnz55YvHgxoqOjARiTFqNHj4aPjw82b95cbspMSkoKoqKiYG1tjV69eiE2NhZLly5FYWEhDAZDvaa9pKWlITIyEjqdDq+88gri4uIQFhaGwsJClJSUVLp+er0eAwcOxIULF1BQUAA7OzsUFRUhNze3XL3ShMj169ehUqng5uZWbZIWMH4fDx06hKysLFhZWaFJkyaYOnUqrl69Wu/v/IABAxAcHIz4+HhMmDABgYGBaNq0KV577TX06dMHt27dgpubG65evQqVSoXJkycjNTUVRUVF8PLyqjZuOzs7dOnSBfHx8diyZYv53q5LAs3T0xOPHj2qse/UajVCQkIwe/Zs7Nu3D2FhYdDr9Zg2bRoCAgIwffr0KqdW2draomvXrhgwYAC8vb1ha2tb94slhBBCCCGE+M9Xn+lRrq6uvHHjxnJTZqorRMQzZszgDz/8sMGmAZUt48eP5127dlU7dcDf358TExM5MDCQr169yqtWrapTu0TEU6ZM4eXLl7NCoaiyjp2dHUdHR3NhYSFv376d16xZw+fOneMbN25weHg429jYmOsqFApevXo15+Xl8RdffMGenp6s1WpZrVazRqNhKysrbtKkSbXHqmshIg4ODuaVK1dW29bAgQP5zp07PHPmTPM2FxcX/v3vf8/5+fm8fPly87QQIuIVK1ZwXl4er1+/nr28vGqN28XFhXfu3MkPHjzgTz/9lJs2bWqeNhQfH88dO3Y01x06dCjfv3+fU1JS+OOPP+aoqCi+ceMGx8XF8auvvlrpHIiIX3vtNf7zn//M48aNY1dXV/bz8+MdO3bUa/qNUqnkkJAQ3r59e6XpKiqVij/77DMeNmwYA2C9Xs9ff/01JyYmcqtWrRgwTtEJCAjghIQETk1NLXeOZYuDgwNHRkayj49Pg38X7O3tOSIigrt27VpjvY4dO/KDBw/46NGjvHPnTo6JieEff/yRe/ToUa6ej48PJyYm8ptvvsmnTp3iv/71r3We1jh69Ghet25dg02D3LNnD6elpbG3tzcD4NDQUF64cCFv2bKFW7ZsyUFBQezv728+v8TERB41ahSfOHGCt2zZUm0cSqWS/f39ecOGDXzo0CE+cOAAz507l11cXGr9fRUeHs4jRoxgIuIePXrw0KFDK9Xp27cv37lzh+/evcuffPIJf/vtt3z9+nWOj4/nwMDAKu/vXr168fr163nSpEns5ubGPj4+HBUVJdOjpEiRIkWKFClSpEj57ywNPz2KiNCyZUt06dIFV65cqfF/pb29vREYGIj333//uSyimZCQgJCQEDg4OJSb/gMYF/0cMmQIDh48CBsbGzg7O1c5iqWqUTqdOnXC4MGDERoaWu35eXp6wtvbG4mJiXB1dYWrqyuOHj2KyMhInD59utxCsDY2NujcuTPOnz+Pjz76CLdv3y7XVmFhIZ48efKsl8GsQ4cOGDp0KObOnVtl3CqVCiNHjgQz49KlS1AoFOjcuTPCw8MREBCAO3fuYPv27eZREaVxX7x4EeHh4ZWmxpSNm4jwyiuvYNmyZWjVqhWCgoKwd+9e5Ofno1+/fli1ahW+/PJLJCYmmmMJCAiAwWBARkYG+vTpg5ycHKxatQpHjhyp8t7y8PDAxIkTsWLFCvz0009gZhAR8vPzoVKp6rwgbEU+Pj548803ERYWhpycnHKftWzZEi+99BJ27twJwDgdqX///jh16hQePnwIa2trvPPOO5g/fz6cnJywYcMGXLt2rdIxlEolxo8fj9TUVCQkJDxTnNVRKpUICgpCeno6Ll68WG09IkLv3r2h1+vRqVMnHD9+HF988QXGjh2LyZMn4+zZszAYDFAoFAgMDMSFCxeQm5sLd3d3nDhxotL3pKrvTtu2bTF27Fh8/PHHDTKizt3dHd27d8eKFSvM51ZQUIDp06fjrbfewu3bt5GdnQ2lUgmFQoFBgwbh8uXLePjwIdq1a4f4+Phq4y4dHXTmzBl4eHhgxIgReOONN9CtWzccPHgQe/bsQXJycpULTTs5OeHu3bsYMmQIQkJC8N1335Wro1Ao0K9fP6jVaty6dQt+fn7Iy8vD559/joMHDyIhIaHS/e3i4oKpU6di3bp1OHr0KJgZTk5OyMnJgVqtbpARZUIIIYQQQojGr94LgGg0Gtjb29dYx9bWFkuWLEFUVJT5Qb2hGQwGODg4wMbGplLSxtHREYMGDcLWrVsxYcIEWFlZoWXLluY3ymg0GnTo0AGOjo7Yu3eveT+9Xm+e9nL58uUqj6tQKDBq1CgkJSVhzJgxyM7OBjMjLy8PxcXFleq3b98ePXr0wM6dOyvF2VB0Op15/Y7qHtwNBgMOHz4Mf39/hIaGol+/fujTpw86deoEtVqNY8eOmdf0AIxJkp49eyIiIsK8Pk9Nx//ggw9gaWmJ0NBQREVFQaFQ4I033sDChQvx3Xff4csvvzQnVtzc3DBgwAB89NFH2LhxI5gZxcXFyMvLqzbB17VrV/z00084d+6cuU5+fj60Wi20Wi3y8vKe5dIhICAAJ0+eRGxsbKVzGjduHE6dOoXz588DADIyMnD8+HF0794dK1euBDNj6NChcHBwwKNHj/DDDz9Uuc7Jq6++iuHDh+Pdd9995uRSdXr37o2RI0fW2rZSqUTXrl1RXFyMZcuWYc2aNSgoKIBarca0adOgVqthMBhga2uLESNGIDY2Fu+++y5sbW3RokULqNVqFBQUQKVSoV27dvDy8kJERIS5fUtLS8yfPx/x8fHl1i16VkSEwYMHY9++ffjTn/5kTnK0bt0a3377LU6ePAkAaN68Oa5fvw69Xo8RI0YgPj4eb7/9Nuzs7NCiRQtoNBpzYs/d3R0vvPACIiIiwMxQKpV4/PixeQpjkyZN4Ovri5dffhlr167F119/jb///e/lEjd6vd68TpGVlRVmzpyJK1eulIvd2dkZQ4YMwZo1a/DZZ5+hpKQEBoMBT548qfb+fvHFF5GUlGReTwkwJkaVSiV0Ol2lhKIQQgghhBDiv1O9kjYqlQpqtbrGOjY2Nli0aBFu3bqFr7/++rm9qvbx48d48uQJFApFpc9KX9c9fvx4pKSkYPny5Rg9ejTmz5+PO3fuoFu3bkhLS8PatWvL7bNgwQKkp6djw4YN1cZd+tBaUFAArVZr3m5vbw+lUokePXpAr9dj165dKCgoQHFxMQwGQ42L6taHtbU1fve73yErK6vcw21FzIwdO3bA1dUVQUFB6NSpEzIzM7F582aMHDkSly9fLve/+aVxFxUV1dqHubm5mDVrFnJycpCZmQkiwtixYzFz5kxs27YNf/zjH8slVZo2bQp7e3sYDAZYWlri0aNH0Gq10Ov1sLOzQ9++fXHy5EmcO3fOvE9cXBzGjh2Lfv364fbt23jhhRfQtWtXFBUV1bogbE2Kiopw8eJFGAwGEBHUajVcXV3NSZClS5eaH5gzMjKwdOlSfPrpp/jVr36FkpISHDt2DPb29mjdujVu3LhRqf2uXbtiwYIFWLRoEW7evPnMcVbFx8cHCxYswOLFi6s8dlmWlpbw8fFBYmIiNm3aZL5m1tbW5vV1Sus5OTlh2LBhyMjIwJIlSxAUFITFixfj5s2b8Pb2xqNHj7BmzRpz21qtFu+//z7UajVWr17dIKNsmBlJSUmIiIgwj1wrHSWzdetW8zFKXw+v1Wrh5OSEN954A3fu3MGSJUswfvx4LFmyBNevX0fnzp2Rm5uLNWvWgJnh6OiIOXPm4Nq1a4iOjkZaWhoyMzMRFRWF6OhoDB48GCEhIdi/f3+5UXotWrQAMyM7OxtEVOVrzR0cHODo6IjCwkJYWVnh559/hoWFhfmV9v7+/rh06VK55Na5c+cQEhKCQYMG4cqVK/Dy8kK3bt2gUqkaZCSeEEIIIYQQ4j9DvZI2tra2UKvVlRYuLaVUKvH++++jf//+GDJkyHN92MjMzMT9+/fh5ORUaVRMRkYGDhw4gLZt22LhwoU4ffo04uPj4evrC3d3d+zatQvHjh0r9/akadOmYdCgQRg2bFi15wcYkxmHDx/GrFmzsHnzZty8eRMlJSVo0qQJNBoN2rZti5s3byIqKgoFBQW4du0aNm/eXG6B24aiUCgwZcoUDBkyBMOHD68xbsA4MmX16tXYunUrrKyskJ2dDVdXV/j6+iI2NrbcSKGbN29i8+bNOHPmTK1xl5SUlBulo1arodPpEBoaiqNHj1ZKWN2+fRuXLl3C7NmzMXz4cCQnJ6Np06bmh1p3d3eEhoaWS9okJydjw4YNCAkJQXp6Os6dO4eIiAhMmTKlysRdXV26dAnTpk2DUqmEXq+Ht7c3rK2t8fnnnyM+Pr5S7JcvX8aYMWPQrFkzlJSUICMjAwsWLEBWVhZSU1PL1W3RooV5usuRI0caNIHZvHlzrFu3DidOnEBMTEytbRcXFyMyMhKnTp0yv72NiNC2bVsUFRWZ+/jBgwfYvXs3Xn75ZSxbtgx79uzB2bNn4efnB09PT+zduxcHDhwoNzVu3LhxeO+99zBy5MhyUwPra8eOHeXuPWbGsmXLyv1e+f77780LCO/evRu9e/dGeHg4oqOjcfbsWfTu3RteXl44cOAA9u/fb/6OlCZU5syZg+HDh2PPnj1ISkrCvXv30KxZMwwePBiJiYnlEpmlCx3v3bsXf/jDH7B06VK8/vrr+P7778td/4yMDJw5cwYhISEYMGAAkpKSYG9vDxsbG1hZWcHDwwNLly7FyZMnzftlZmZizZo1CA4Oxv3793HhwgVERUXBzc0NSqWywa6pEEIIIYQQonGjp3lwNC3IaabT6dC5c2fcuHEDd+/erVTf2toa27Ztw6ZNm7Bjx476R1sDhUKByZMnIyEhAYcOHar0uYODA1QqFe7du2d+MCp9uK+YhLCyssLWrVuxfft2bNu2rdYHYK1WC19fX/j5+cHR0REODg5ISkpCcXExsrOzcerUKZw+fdp8HI1GA2Zu8KkxlpaW2LJlC7777jts3rz5mZICOp0O7du3x6VLlyqtm1GfuBUKRY3JnhYtWmDQoEFwdXVFy5YtUVBQgIyMDJSUlODq1as4dOhQla+GV6lU5jVJHB0d8dlnn2H69Om4d+/eU8cIABYWFggODoafnx9yc3Oxe/duHDt2DBkZGXW+nu7u7lAqlbh69Wq57f7+/vjggw8wYcKEWqeYPa2+ffti3rx5mDBhQqV1kqpTcR0anU6HtWvXIjExEcuXLzdvt7GxgbW1Ne7evWvuQyICEVXqU7Vaja+++gpxcXFYt25dgycmn4aNjQ10Op35PgKqjxswfo/bt2+Pl156CR4eHmjVqhWKi4uhUqlw5swZbNy4sdyb0RQKBcLCwnD58mV888036NmzJ+bPn4/Ro0dXSpg2a9YMgYGBcHd3h4uLC0pKSpCWlgZmxo0bNxATE1PlPVH2/razs8OaNWswf/78al+3LoQQQgghhPiPdZaZu1fcWK+kTW0UCgVcXV1x+/btKtd3aWhlH3DqozTutLS0p05QKBQKKBSKer+++VkQEVq3bv1McTcWRASlUvlM/Vh2Udj6XHuFQmGOoSHvW71eD51OZ36NekPS6/XQ6/X1WidJoVDA3t4eT548eeY1gYgIrq6uyMjIqNc0tV9a6T1QmtgqLi6u8p6yt7dHfn4+8vLyoFKp4OTkhPT09Brvv2f9PVV6f2dmZv6iyTAhhBBCCCHEc9EgSZt7AJIbMiohhBBCCCGEEEKI/3GtmblZxY1PlbQRQgghhBBCCCGEEP8ez75iqxBCCCGEEEIIIYR4biRpI4QQQgghhBBCCNEISdJGCCGEEEIIIYQQohGSpI0QQgghhBBCCCFEIyRJGyGEEEIIIYQQQohGSJI2QgghhBBCCCGEEI2QJG2EEEIIIYQQQgghGiFJ2gghhBBCCCGEEEI0QpK0EUIIIYQQQgghhGiE/j+ACIbCjJMBhwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -207,12 +202,14 @@
     "    sentence = convert_y_label_to_string(target.numpy()) \n",
     "    print(sentence)\n",
     "    plt.title(sentence)\n",
-    "    plt.imshow(data.squeeze(0), cmap='gray')\n"
+    "    plt.imshow(data.squeeze(0), cmap='gray')\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -221,12 +218,12 @@
      "text": [
       "EMNIST Lines Dataset\n",
       "Max length: 34\n",
-      "Min overlap: 0\n",
+      "Min overlap: 0.0\n",
       "Max overlap: 0.33\n",
       "Num classes: 80\n",
       "Input shape: (28, 952)\n",
-      "Data: (10000, 28, 952)\n",
-      "Tagets: (10000, 34)\n",
+      "Data: (1000, 28, 952)\n",
+      "Tagets: (1000, 34)\n",
       "\n"
      ]
     }
@@ -237,115 +234,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([58, 50, 53, 46, 40, 53, 54, 62, 58, 44, 41, 40, 62, 53, 40, 41, 56, 54,\n",
-       "        40, 39, 62, 55, 50, 62, 43, 36, 57, 40, 79, 79, 79, 79, 79, 79],\n",
-       "       dtype=torch.uint8)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.networks import LineRecurrentNetwork"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "crnn = LineRecurrentNetwork(encoder=\"ResidualNetworkEncoder\",\n",
-    "                            \n",
-    "          encoder_args={\n",
-    "            \"in_channels\": 1,\n",
-    "            \"num_classes\": 80,\n",
-    "            \"depths\": [2, 2],\n",
-    "            \"block_sizes\": [64, 128],\n",
-    "            \"activation\": \"leaky_relu\",\n",
-    "            \"stn\": False,})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = crnn(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "targets = target.unsqueeze(0).type(torch.long)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "input_lengths = torch.full(\n",
-    "    size=(output.shape[1],), fill_value=output.shape[0], dtype=torch.long,\n",
-    ")\n",
-    "target_lengths = torch.full(\n",
-    "    size=(output.shape[1],), fill_value=targets.shape[1], dtype=torch.long,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ctc = torch.nn.CTCLoss(blank=79)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(6.9917, grad_fn=<MeanBackward0>)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ctc(output, targets, input_lengths, target_lengths)"
-   ]
-  },
-  {
-   "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
@@ -368,7 +256,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
diff --git a/src/notebooks/02c-image-patches.ipynb b/src/notebooks/02c-image-patches.ipynb
index 947611d..9e8d4b2 100644
--- a/src/notebooks/02c-image-patches.ipynb
+++ b/src/notebooks/02c-image-patches.ipynb
@@ -438,7 +438,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
diff --git a/src/notebooks/03a-line-prediction.ipynb b/src/notebooks/03a-line-prediction.ipynb
index 336614f..539fb6e 100644
--- a/src/notebooks/03a-line-prediction.ipynb
+++ b/src/notebooks/03a-line-prediction.ipynb
@@ -398,7 +398,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
diff --git a/src/notebooks/04a-look-at-iam-lines.ipynb b/src/notebooks/04a-look-at-iam-lines.ipynb
index eb0ec33..8132f44 100644
--- a/src/notebooks/04a-look-at-iam-lines.ipynb
+++ b/src/notebooks/04a-look-at-iam-lines.ipynb
@@ -2,18 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -33,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -52,21 +43,21 @@
       "IAM Lines Dataset\n",
       "Number classes: 80\n",
       "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z', 36: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: '_'}\n",
-      "Data: (7101, 28, 952)\n",
-      "Targets: (7101, 97)\n",
+      "Data: (1861, 28, 952)\n",
+      "Targets: (1861, 97)\n",
       "\n"
      ]
     }
    ],
    "source": [
-    "dataset = IamLinesDataset(train=True)\n",
+    "dataset = IamLinesDataset(train=False, pad_token=\"_\")\n",
     "dataset.load_or_generate_data()\n",
     "print(dataset)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -75,7 +66,7 @@
        "(28, 952)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -86,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -95,7 +86,7 @@
        "(97, 80)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -106,16 +97,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'A MOVE to stop Mr. Gaitskell from________________________________________________________________'"
+       "'He rose from his breakfast-nook bench____________________________________________________________'"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -129,28 +120,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Griffiths resolution. Mr. Foot's line will_______________________________________________________\n",
-      "be that as Labour M Ps opposed the_______________________________________________________________\n",
-      "Government Bill which brought life peers_________________________________________________________\n",
-      "into existence, they should not now put__________________________________________________________\n",
-      "forward nominees. He believes that the___________________________________________________________\n",
-      "House of Lords should be abolished and___________________________________________________________\n",
-      "that Labour should not take any steps____________________________________________________________\n",
-      "which would appear to \"prop up\" an out-__________________________________________________________\n",
-      "Since 1958, 13 Labour life Peers and_____________________________________________________________\n",
-      "Peeresses have been created. Most Labour_________________________________________________________\n"
+      "drawing-in great breaths that____________________________________________________________________\n",
+      "became great sighs of ecstacy.___________________________________________________________________\n",
+      "\"They have come!\" he said reverently, gripping his_______________________________________________\n",
+      "hands together between his knees and leaning_____________________________________________________\n",
+      "forward. \"Isn't it a glorious thing! Long awaited________________________________________________\n",
+      "transcendent event, the exalted desire of all____________________________________________________\n",
+      "mankind through all ages! The Kingdom of the_____________________________________________________\n",
+      "Mind is at hand!\" He turned beaming eyes_________________________________________________________\n",
+      "upward and shook his head slowly from____________________________________________________________\n",
+      "A superfluous precaution for there was no other car______________________________________________\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -160,7 +151,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -170,7 +161,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -180,7 +171,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -190,7 +181,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -200,7 +191,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -210,7 +201,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -220,7 +211,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -230,7 +221,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -240,7 +231,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -263,17 +254,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data, target = dataset[10]\n",
+    "data, target = dataset[12]\n",
     "sentence = convert_y_label_to_string(target) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -282,7 +273,7 @@
        "torch.Size([97])"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -293,16 +284,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "h, w, s = 28, 6, 6"
+    "h, w, s = 28, 8, 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -321,21 +312,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Griffiths resolution. Mr. Foot's line will_______________________________________________________\n"
+      "\"They have come!\" he said reverently, gripping his_______________________________________________\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 1440x1440 with 50 Axes>"
+       "<Figure size 2880x2880 with 50 Axes>"
       ]
      },
      "metadata": {},
@@ -346,7 +337,7 @@
     "# remove batch size\n",
     "n = 50\n",
     "patches = patches.squeeze(0)\n",
-    "fig = plt.figure(figsize=(20, 20))\n",
+    "fig = plt.figure(figsize=(40, 40))\n",
     "print(sentence)\n",
     "for i in range(n):\n",
     "    ax = fig.add_subplot(1, n, i + 1)\n",
@@ -357,16 +348,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([158, 1, 28, 6])"
+       "torch.Size([473, 1, 28, 8])"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -377,22 +368,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f8797c56cd0>"
+       "<matplotlib.image.AxesImage at 0x7f95e295e5d0>"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
diff --git a/src/notebooks/Untitled.ipynb b/src/notebooks/Untitled.ipynb
index 76c4d28..f114ed9 100644
--- a/src/notebooks/Untitled.ipynb
+++ b/src/notebooks/Untitled.ipynb
@@ -2,18 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -35,50 +26,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def convert_y_label_to_string(y, dataset=dataset):\n",
+    "def convert_y_label_to_string(y, dataset):\n",
     "    return ''.join([dataset.mapper(int(i)) for i in y])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from text_recognizer.models import VisionTransformerModel\n",
+    "from text_recognizer.models import VisionTransformerModel, TransformerEncoderModel\n",
     "from text_recognizer.datasets import IamLinesDataset\n",
     "from text_recognizer.datasets.transforms import Compose, AddTokens"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "target_transform = Compose([torch.tensor, AddTokens(init_token=\"<sos>\", eos_token=\"<eos>\")])\n",
-    "dataset = IamLinesDataset(train=True, init_token=\"<sos>\", pad_token=\"_\", eos_token=\"<eos>\", target_transform=target_transform)\n",
+    "target_transform = Compose([torch.tensor, AddTokens(init_token=\"<sos>\", pad_token=\"_\", eos_token=\"<eos>\")])\n",
+    "dataset = IamLinesDataset(train=False, init_token=\"<sos>\", pad_token=\"_\", eos_token=\"<eos>\", target_transform=target_transform)\n",
     "dataset.load_or_generate_data()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "config_path = \"../training/experiments/VisionTransformerModel_IamLinesDataset_VisionTransformer/1021_083538/config.yml\"\n",
+    "config_path = \"../training/experiments/VisionTransformerModel_IamLinesDataset_CNNTransformer/1102_221553/config.yml\"\n",
     "with open(config_path, \"r\") as f:\n",
     "    experiment_config = yaml.safe_load(f)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,14 +83,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-10-21 23:27:40.719 | DEBUG    | text_recognizer.models.base:load_weights:454 - Loading network with pretrained weights.\n"
+      "2020-11-03 07:32:07.256 | DEBUG    | text_recognizer.models.base:load_weights:457 - Loading network with pretrained weights.\n"
      ]
     }
    ],
@@ -109,25 +100,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-10-21 23:29:55.892 | DEBUG    | text_recognizer.models.base:load_from_checkpoint:402 - Loading checkpoint...\n"
+      "2020-11-03 07:32:10.285 | DEBUG    | text_recognizer.models.base:load_from_checkpoint:404 - Loading checkpoint...\n"
      ]
     }
    ],
    "source": [
-    "checkpoint_path = \"../training/experiments/VisionTransformerModel_IamLinesDataset_VisionTransformer/1021_083538/model/last.pt\"\n",
+    "checkpoint_path = \"../training/experiments/VisionTransformerModel_IamLinesDataset_CNNTransformer/1102_221553/model/last.pt\"\n",
     "model.load_from_checkpoint(checkpoint_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,17 +127,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data, target = dataset[18]\n",
+    "data, target = dataset[0]\n",
     "sentence = convert_y_label_to_string(target, dataset) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([98])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -155,13 +166,13 @@
        "([], [])"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -180,16 +191,259 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "    def make_len_mask(inp):\n",
+    "        return (inp == 79).transpose(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([98, 1])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_len_mask(target.unsqueeze(0)).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('to stel mire of a thar chishirchit<eos>', 0.20226626098155975)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict_on_image(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-10-31 16:35:40.255 | DEBUG    | text_recognizer.models.base:load_weights:457 - Loading network with pretrained weights.\n",
+      "2020-10-31 16:35:40.837 | DEBUG    | text_recognizer.models.base:load_from_checkpoint:404 - Loading checkpoint...\n"
+     ]
+    }
+   ],
+   "source": [
+    "target_transform = Compose([torch.tensor, AddTokens(pad_token=\"_\", eos_token=\"<eos>\")])\n",
+    "dataset = IamLinesDataset(train=False, pad_token=\"_\", eos_token=\"<eos>\", target_transform=target_transform)\n",
+    "dataset.load_or_generate_data()\n",
+    "\n",
+    "\n",
+    "config_path = \"../training/experiments/TransformerEncoderModel_IamLinesDataset_CNNTransformerEncoder/1031_150630/config.yml\"\n",
+    "with open(config_path, \"r\") as f:\n",
+    "    experiment_config = yaml.safe_load(f)\n",
+    "\n",
+    "\n",
+    "dataset_args = experiment_config.get(\"dataset\", {})\n",
+    "datasets_module = importlib.import_module(\"text_recognizer.datasets\")\n",
+    "dataset_ = getattr(datasets_module, dataset_args[\"type\"])\n",
+    "\n",
+    "network_module = importlib.import_module(\"text_recognizer.networks\")\n",
+    "network_fn_ = getattr(network_module, experiment_config[\"network\"][\"type\"])\n",
+    "\n",
+    "\n",
+    "checkpoint_path = \"../training/experiments/TransformerEncoderModel_IamLinesDataset_CNNTransformerEncoder/1031_150630/model/last.pt\"\n",
+    "\n",
+    "\n",
+    "model = TransformerEncoderModel(network_fn=network_fn_, dataset=dataset_, dataset_args=dataset_args)\n",
+    "model.load_from_checkpoint(checkpoint_path)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
    "execution_count": 92,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===============================================================================================\n",
+      "Layer (type:depth-idx)                        Output Shape              Param #\n",
+      "===============================================================================================\n",
+      "├─WideResidualNetwork: 1-1                    [-1, 256, 2, 60]          --\n",
+      "|    └─Sequential: 2-1                        [-1, 256, 2, 60]          --\n",
+      "|    |    └─Conv2d: 3-1                       [-1, 8, 28, 952]          72\n",
+      "|    |    └─Sequential: 3-2                   [-1, 16, 28, 952]         --\n",
+      "|    |    |    └─WideBlock: 4-1               [-1, 16, 28, 952]         3,632\n",
+      "|    |    └─Sequential: 3-3                   [-1, 32, 14, 476]         --\n",
+      "|    |    |    └─WideBlock: 4-2               [-1, 32, 14, 476]         14,432\n",
+      "|    |    └─Sequential: 3-4                   [-1, 64, 7, 238]          --\n",
+      "|    |    |    └─WideBlock: 4-3               [-1, 64, 7, 238]          57,536\n",
+      "|    |    └─Sequential: 3-5                   [-1, 128, 4, 119]         --\n",
+      "|    |    |    └─WideBlock: 4-4               [-1, 128, 4, 119]         229,760\n",
+      "|    |    └─Sequential: 3-6                   [-1, 256, 2, 60]          --\n",
+      "|    |    |    └─WideBlock: 4-5               [-1, 256, 2, 60]          918,272\n",
+      "├─Conv2d: 1-2                                 [-1, 97, 2, 60]           24,929\n",
+      "├─Linear: 1-3                                 [-1, 97, 96]              11,616\n",
+      "├─PositionalEncoding: 1-4                     [-1, 97, 96]              --\n",
+      "|    └─Dropout: 2-2                           [-1, 97, 96]              --\n",
+      "├─TransformerEncoder: 1-5                     [-1, 2, 96]               --\n",
+      "|    └─ModuleList: 2                          []                        --\n",
+      "|    |    └─TransformerEncoderLayer: 3-7      [-1, 2, 96]               --\n",
+      "|    |    |    └─MultiheadAttention: 4-6      [-1, 2, 96]               37,248\n",
+      "|    |    |    └─Dropout: 4-7                 [-1, 2, 96]               --\n",
+      "|    |    |    └─LayerNorm: 4-8               [-1, 2, 96]               192\n",
+      "|    |    |    └─Linear: 4-9                  [-1, 2, 2048]             198,656\n",
+      "|    |    |    └─Dropout: 4-10                [-1, 2, 2048]             --\n",
+      "|    |    |    └─Linear: 4-11                 [-1, 2, 96]               196,704\n",
+      "|    |    |    └─Dropout: 4-12                [-1, 2, 96]               --\n",
+      "|    |    |    └─LayerNorm: 4-13              [-1, 2, 96]               192\n",
+      "|    |    └─TransformerEncoderLayer: 3-8      [-1, 2, 96]               --\n",
+      "|    |    |    └─MultiheadAttention: 4-14     [-1, 2, 96]               37,248\n",
+      "|    |    |    └─Dropout: 4-15                [-1, 2, 96]               --\n",
+      "|    |    |    └─LayerNorm: 4-16              [-1, 2, 96]               192\n",
+      "|    |    |    └─Linear: 4-17                 [-1, 2, 2048]             198,656\n",
+      "|    |    |    └─Dropout: 4-18                [-1, 2, 2048]             --\n",
+      "|    |    |    └─Linear: 4-19                 [-1, 2, 96]               196,704\n",
+      "|    |    |    └─Dropout: 4-20                [-1, 2, 96]               --\n",
+      "|    |    |    └─LayerNorm: 4-21              [-1, 2, 96]               192\n",
+      "|    |    └─TransformerEncoderLayer: 3-9      [-1, 2, 96]               --\n",
+      "|    |    |    └─MultiheadAttention: 4-22     [-1, 2, 96]               37,248\n",
+      "|    |    |    └─Dropout: 4-23                [-1, 2, 96]               --\n",
+      "|    |    |    └─LayerNorm: 4-24              [-1, 2, 96]               192\n",
+      "|    |    |    └─Linear: 4-25                 [-1, 2, 2048]             198,656\n",
+      "|    |    |    └─Dropout: 4-26                [-1, 2, 2048]             --\n",
+      "|    |    |    └─Linear: 4-27                 [-1, 2, 96]               196,704\n",
+      "|    |    |    └─Dropout: 4-28                [-1, 2, 96]               --\n",
+      "|    |    |    └─LayerNorm: 4-29              [-1, 2, 96]               192\n",
+      "|    |    └─TransformerEncoderLayer: 3-10     [-1, 2, 96]               --\n",
+      "|    |    |    └─MultiheadAttention: 4-30     [-1, 2, 96]               37,248\n",
+      "|    |    |    └─Dropout: 4-31                [-1, 2, 96]               --\n",
+      "|    |    |    └─LayerNorm: 4-32              [-1, 2, 96]               192\n",
+      "|    |    |    └─Linear: 4-33                 [-1, 2, 2048]             198,656\n",
+      "|    |    |    └─Dropout: 4-34                [-1, 2, 2048]             --\n",
+      "|    |    |    └─Linear: 4-35                 [-1, 2, 96]               196,704\n",
+      "|    |    |    └─Dropout: 4-36                [-1, 2, 96]               --\n",
+      "|    |    |    └─LayerNorm: 4-37              [-1, 2, 96]               192\n",
+      "|    |    └─TransformerEncoderLayer: 3-11     [-1, 2, 96]               --\n",
+      "|    |    |    └─MultiheadAttention: 4-38     [-1, 2, 96]               37,248\n",
+      "|    |    |    └─Dropout: 4-39                [-1, 2, 96]               --\n",
+      "|    |    |    └─LayerNorm: 4-40              [-1, 2, 96]               192\n",
+      "|    |    |    └─Linear: 4-41                 [-1, 2, 2048]             198,656\n",
+      "|    |    |    └─Dropout: 4-42                [-1, 2, 2048]             --\n",
+      "|    |    |    └─Linear: 4-43                 [-1, 2, 96]               196,704\n",
+      "|    |    |    └─Dropout: 4-44                [-1, 2, 96]               --\n",
+      "|    |    |    └─LayerNorm: 4-45              [-1, 2, 96]               192\n",
+      "|    |    └─TransformerEncoderLayer: 3-12     [-1, 2, 96]               --\n",
+      "|    |    |    └─MultiheadAttention: 4-46     [-1, 2, 96]               37,248\n",
+      "|    |    |    └─Dropout: 4-47                [-1, 2, 96]               --\n",
+      "|    |    |    └─LayerNorm: 4-48              [-1, 2, 96]               192\n",
+      "|    |    |    └─Linear: 4-49                 [-1, 2, 2048]             198,656\n",
+      "|    |    |    └─Dropout: 4-50                [-1, 2, 2048]             --\n",
+      "|    |    |    └─Linear: 4-51                 [-1, 2, 96]               196,704\n",
+      "|    |    |    └─Dropout: 4-52                [-1, 2, 96]               --\n",
+      "|    |    |    └─LayerNorm: 4-53              [-1, 2, 96]               192\n",
+      "|    └─LayerNorm: 2-3                         [-1, 2, 96]               192\n",
+      "├─Linear: 1-6                                 [-1, 97, 81]              7,857\n",
+      "===============================================================================================\n",
+      "Total params: 3,866,250\n",
+      "Trainable params: 3,866,250\n",
+      "Non-trainable params: 0\n",
+      "Total mult-adds (M): 18.78\n",
+      "===============================================================================================\n",
+      "Input size (MB): 0.10\n",
+      "Forward/backward pass size (MB): 2.06\n",
+      "Params size (MB): 14.75\n",
+      "Estimated Total Size (MB): 16.91\n",
+      "===============================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary(experiment_config[\"train_args\"][\"input_shape\"], 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, target = dataset[110]\n",
+    "sentence = convert_y_label_to_string(target, dataset) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([], [])"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 20))\n",
+    "plt.title(sentence)\n",
+    "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n",
+    "plt.xticks([])\n",
+    "plt.yticks([])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "('Since 1958, 13 Labour life Peers and<eos>', 0.9999997615814209)"
+       "('Boyis cheed iitrincy- tarisaing one', 0.3990435302257538)"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -280,6 +534,335 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t=[12,1,1,1,1,1,4,4,4,4,4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t[t!=79]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.arange(10)\n",
+    "value = 5\n",
+    "x = x[x!=value]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([0, 1, 2, 3, 4, 6, 7, 8, 9])"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = torch.rand(98)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(1.7656e-43)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.cumprod(dim=0)[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred_tokens = torch.Tensor([1,2,21,31, 89, 89])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred_tokens = torch.stack([pred_tokens, pred_tokens])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[ 1.,  2., 21., 31., 89., 89.],\n",
+       "        [ 1.,  2., 21., 31., 89., 89.]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eos_token_index = torch.nonzero(\n",
+    "            pred_tokens == 89, as_tuple=False,\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "if eos_token_index.nelement():\n",
+    "    print(eos_token_index[0][0].item())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0, 4],\n",
+       "        [0, 5],\n",
+       "        [1, 4],\n",
+       "        [1, 5]])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eos_token_index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eos_token_index.nelement()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.models import accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred = torch.Tensor([1,2,21,31, 80, 80]).unsqueeze(0)\n",
+    "target = torch.Tensor([1,2,1,31, 80, 80]).unsqueeze(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred = torch.stack([pred, pred])\n",
+    "target = torch.stack([target, target])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = torch.tensor([0, 1, 2, 3])\n",
+    "pred = torch.tensor([0, 2, 1, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy(pred, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "acc = (target.argmax(-1) == pred.argmax(-1)).float()\n",
+    "\n",
+    "# return float(100 * acc.sum() / len(acc))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[1.],\n",
+       "        [1.]])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "acc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_acc = (pred == target).sum().item()/target.shape[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.3333333333333335"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_acc"
+   ]
+  },
+  {
+   "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
diff --git a/src/tasks/create_emnist_lines_datasets.sh b/src/tasks/create_emnist_lines_datasets.sh
index 98d4f8b..0940549 100755
--- a/src/tasks/create_emnist_lines_datasets.sh
+++ b/src/tasks/create_emnist_lines_datasets.sh
@@ -1,2 +1,4 @@
 #!/bin/bash
-poetry run create-emnist-lines-datasets
+command="python text_recognizer/datasets/emnist_lines_dataset.py --max_length 34 --min_overlap 0.0 --max_overlap 0.33 --num_train 100000 --num_test 10000"
+echo $command
+eval $command
diff --git a/src/tasks/train.sh b/src/tasks/train.sh
index 1fbc8d7..71a68be 100755
--- a/src/tasks/train.sh
+++ b/src/tasks/train.sh
@@ -62,6 +62,6 @@ then
     train_command="${train_command} -$verbose"
 fi
 
-
-echo $train_command $notrain $test
-eval $train_command $notrain $test
+train_command="${train_command} $test $notrain"
+echo $train_command
+eval $train_command
diff --git a/src/text_recognizer/character_predictor.py b/src/text_recognizer/character_predictor.py
index df37e68..ad71289 100644
--- a/src/text_recognizer/character_predictor.py
+++ b/src/text_recognizer/character_predictor.py
@@ -4,6 +4,7 @@ from typing import Dict, Tuple, Type, Union
 import numpy as np
 from torch import nn
 
+from text_recognizer import datasets, networks
 from text_recognizer.models import CharacterModel
 from text_recognizer.util import read_image
 
@@ -11,9 +12,11 @@ from text_recognizer.util import read_image
 class CharacterPredictor:
     """Recognizes the character in handwritten character images."""
 
-    def __init__(self, network_fn: Type[nn.Module]) -> None:
+    def __init__(self, network_fn: str, dataset: str) -> None:
         """Intializes the CharacterModel and load the pretrained weights."""
-        self.model = CharacterModel(network_fn=network_fn)
+        network_fn = getattr(networks, network_fn)
+        dataset = getattr(datasets, dataset)
+        self.model = CharacterModel(network_fn=network_fn, dataset=dataset)
         self.model.eval()
         self.model.use_swa_model()
 
diff --git a/src/text_recognizer/datasets/emnist_dataset.py b/src/text_recognizer/datasets/emnist_dataset.py
index a8901d6..9884fdf 100644
--- a/src/text_recognizer/datasets/emnist_dataset.py
+++ b/src/text_recognizer/datasets/emnist_dataset.py
@@ -22,6 +22,7 @@ class EmnistDataset(Dataset):
 
     def __init__(
         self,
+        pad_token: str = None,
         train: bool = False,
         sample_to_balance: bool = False,
         subsample_fraction: float = None,
@@ -32,6 +33,7 @@ class EmnistDataset(Dataset):
         """Loads the dataset and the mappings.
 
         Args:
+            pad_token (str): The pad token symbol. Defaults to _.
             train (bool): If True, loads the training set, otherwise the validation set is loaded. Defaults to False.
             sample_to_balance (bool): Resamples the dataset to make it balanced. Defaults to False.
             subsample_fraction (float): Description of parameter `subsample_fraction`. Defaults to None.
@@ -45,6 +47,7 @@ class EmnistDataset(Dataset):
             subsample_fraction=subsample_fraction,
             transform=transform,
             target_transform=target_transform,
+            pad_token=pad_token,
         )
 
         self.sample_to_balance = sample_to_balance
@@ -53,6 +56,8 @@ class EmnistDataset(Dataset):
         if transform is None:
             self.transform = Compose([Transpose(), ToTensor()])
 
+        self.target_transform = None
+
         self.seed = seed
 
     def __getitem__(self, index: Union[int, Tensor]) -> Tuple[Tensor, Tensor]:
diff --git a/src/text_recognizer/datasets/emnist_lines_dataset.py b/src/text_recognizer/datasets/emnist_lines_dataset.py
index 6091da8..6871492 100644
--- a/src/text_recognizer/datasets/emnist_lines_dataset.py
+++ b/src/text_recognizer/datasets/emnist_lines_dataset.py
@@ -4,6 +4,7 @@ from collections import defaultdict
 from pathlib import Path
 from typing import Callable, Dict, List, Optional, Tuple, Union
 
+import click
 import h5py
 from loguru import logger
 import numpy as np
@@ -58,13 +59,15 @@ class EmnistLinesDataset(Dataset):
             eos_token (Optional[str]): String representing the end of sequence token. Defaults to None.
 
         """
+        self.pad_token = "_" if pad_token is None else pad_token
+
         super().__init__(
             train=train,
             transform=transform,
             target_transform=target_transform,
             subsample_fraction=subsample_fraction,
             init_token=init_token,
-            pad_token=pad_token,
+            pad_token=self.pad_token,
             eos_token=eos_token,
         )
 
@@ -127,11 +130,7 @@ class EmnistLinesDataset(Dataset):
     @property
     def data_filename(self) -> Path:
         """Path to the h5 file."""
-        filename = f"ml_{self.max_length}_o{self.min_overlap}_{self.max_overlap}_n{self.num_samples}.pt"
-        if self.train:
-            filename = "train_" + filename
-        else:
-            filename = "test_" + filename
+        filename = "train.pt" if self.train else "test.pt"
         return DATA_DIRNAME / filename
 
     def load_or_generate_data(self) -> None:
@@ -147,8 +146,8 @@ class EmnistLinesDataset(Dataset):
         """Loads the dataset from the h5 file."""
         logger.debug("EmnistLinesDataset loading data from HDF5...")
         with h5py.File(self.data_filename, "r") as f:
-            self._data = f["data"][:]
-            self._targets = f["targets"][:]
+            self._data = f["data"][()]
+            self._targets = f["targets"][()]
 
     def _generate_data(self) -> str:
         """Generates a dataset with the Brown corpus and Emnist characters."""
@@ -157,7 +156,9 @@ class EmnistLinesDataset(Dataset):
         sentence_generator = SentenceGenerator(self.max_length)
 
         # Load emnist dataset.
-        emnist = EmnistDataset(train=self.train, sample_to_balance=True)
+        emnist = EmnistDataset(
+            train=self.train, sample_to_balance=True, pad_token=self.pad_token
+        )
         emnist.load_or_generate_data()
 
         samples_by_character = get_samples_by_character(
@@ -308,6 +309,18 @@ def convert_strings_to_categorical_labels(
     return np.array([[mapping[c] for c in label] for label in labels])
 
 
+@click.command()
+@click.option(
+    "--max_length", type=int, default=34, help="Number of characters in a sentence."
+)
+@click.option(
+    "--min_overlap", type=float, default=0.0, help="Min overlap between characters."
+)
+@click.option(
+    "--max_overlap", type=float, default=0.33, help="Max overlap between characters."
+)
+@click.option("--num_train", type=int, default=10_000, help="Number of train examples.")
+@click.option("--num_test", type=int, default=1_000, help="Number of test examples.")
 def create_datasets(
     max_length: int = 34,
     min_overlap: float = 0,
@@ -326,3 +339,7 @@ def create_datasets(
             num_samples=num,
         )
         emnist_lines.load_or_generate_data()
+
+
+if __name__ == "__main__":
+    create_datasets()
diff --git a/src/text_recognizer/datasets/transforms.py b/src/text_recognizer/datasets/transforms.py
index c058972..8deac7f 100644
--- a/src/text_recognizer/datasets/transforms.py
+++ b/src/text_recognizer/datasets/transforms.py
@@ -3,7 +3,7 @@ import numpy as np
 from PIL import Image
 import torch
 from torch import Tensor
-from torchvision.transforms import Compose, ToTensor
+from torchvision.transforms import Compose, Resize, ToPILImage, ToTensor
 
 from text_recognizer.datasets.util import EmnistMapper
 
@@ -19,28 +19,35 @@ class Transpose:
 class AddTokens:
     """Adds start of sequence and end of sequence tokens to target tensor."""
 
-    def __init__(self, init_token: str, pad_token: str, eos_token: str,) -> None:
+    def __init__(self, pad_token: str, eos_token: str, init_token: str = None) -> None:
         self.init_token = init_token
         self.pad_token = pad_token
         self.eos_token = eos_token
-        self.emnist_mapper = EmnistMapper(
-            init_token=self.init_token,
-            pad_token=self.pad_token,
-            eos_token=self.eos_token,
-        )
+        if self.init_token is not None:
+            self.emnist_mapper = EmnistMapper(
+                init_token=self.init_token,
+                pad_token=self.pad_token,
+                eos_token=self.eos_token,
+            )
+        else:
+            self.emnist_mapper = EmnistMapper(
+                pad_token=self.pad_token, eos_token=self.eos_token,
+            )
         self.pad_value = self.emnist_mapper(self.pad_token)
-        self.sos_value = self.emnist_mapper(self.init_token)
         self.eos_value = self.emnist_mapper(self.eos_token)
 
     def __call__(self, target: Tensor) -> Tensor:
         """Adds a sos token to the begining and a eos token to the end of a target sequence."""
         dtype, device = target.dtype, target.device
-        sos = torch.tensor([self.sos_value], dtype=dtype, device=device)
 
         # Find the where padding starts.
         pad_index = torch.nonzero(target == self.pad_value, as_tuple=False)[0].item()
 
         target[pad_index] = self.eos_value
 
-        target = torch.cat([sos, target], dim=0)
+        if self.init_token is not None:
+            self.sos_value = self.emnist_mapper(self.init_token)
+            sos = torch.tensor([self.sos_value], dtype=dtype, device=device)
+            target = torch.cat([sos, target], dim=0)
+
         return target
diff --git a/src/text_recognizer/line_predictor.py b/src/text_recognizer/line_predictor.py
new file mode 100644
index 0000000..981e2c9
--- /dev/null
+++ b/src/text_recognizer/line_predictor.py
@@ -0,0 +1,28 @@
+"""LinePredictor class."""
+import importlib
+from typing import Tuple, Union
+
+import numpy as np
+from torch import nn
+
+from text_recognizer import datasets, networks
+from text_recognizer.models import VisionTransformerModel
+from text_recognizer.util import read_image
+
+
+class LinePredictor:
+    """Given an image of a line of handwritten text, recognizes the text content."""
+
+    def __init__(self, dataset: str, network_fn: str) -> None:
+        network_fn = getattr(networks, network_fn)
+        dataset = getattr(datasets, dataset)
+        self.model = VisionTransformerModel(network_fn=network_fn, dataset=dataset)
+        self.model.eval()
+
+    def predict(self, image_or_filename: Union[np.ndarray, str]) -> Tuple[str, float]:
+        """Predict on a single images contianing a handwritten character."""
+        if isinstance(image_or_filename, str):
+            image = read_image(image_or_filename, grayscale=True)
+        else:
+            image = image_or_filename
+        return self.model.predict_on_image(image)
diff --git a/src/text_recognizer/models/__init__.py b/src/text_recognizer/models/__init__.py
index 0855079..28aa52e 100644
--- a/src/text_recognizer/models/__init__.py
+++ b/src/text_recognizer/models/__init__.py
@@ -1,16 +1,19 @@
 """Model modules."""
 from .base import Model
 from .character_model import CharacterModel
-from .line_ctc_model import LineCTCModel
+from .crnn_model import CRNNModel
 from .metrics import accuracy, cer, wer
+from .transformer_encoder_model import TransformerEncoderModel
 from .vision_transformer_model import VisionTransformerModel
 
 __all__ = [
     "Model",
     "cer",
     "CharacterModel",
+    "CRNNModel",
     "CNNTransfromerModel",
-    "LineCTCModel",
     "accuracy",
+    "TransformerEncoderModel",
+    "VisionTransformerModel",
     "wer",
 ]
diff --git a/src/text_recognizer/models/base.py b/src/text_recognizer/models/base.py
index cbef787..cc44c92 100644
--- a/src/text_recognizer/models/base.py
+++ b/src/text_recognizer/models/base.py
@@ -141,11 +141,12 @@ class Model(ABC):
             "transform" in self.dataset_args["args"]
             and self.dataset_args["args"]["transform"] is not None
         ):
-            transform_ = [
-                getattr(transforms_module, t["type"])()
-                for t in self.dataset_args["args"]["transform"]
-            ]
+            transform_ = []
+            for t in self.dataset_args["args"]["transform"]:
+                args = t["args"] or {}
+                transform_.append(getattr(transforms_module, t["type"])(**args))
             self.dataset_args["args"]["transform"] = Compose(transform_)
+
         if (
             "target_transform" in self.dataset_args["args"]
             and self.dataset_args["args"]["target_transform"] is not None
diff --git a/src/text_recognizer/models/character_model.py b/src/text_recognizer/models/character_model.py
index 3cf6695..f9944f3 100644
--- a/src/text_recognizer/models/character_model.py
+++ b/src/text_recognizer/models/character_model.py
@@ -47,8 +47,9 @@ class CharacterModel(Model):
             swa_args,
             device,
         )
+        self.pad_token = dataset_args["args"]["pad_token"]
         if self._mapper is None:
-            self._mapper = EmnistMapper()
+            self._mapper = EmnistMapper(pad_token=self.pad_token,)
         self.tensor_transform = ToTensor()
         self.softmax = nn.Softmax(dim=0)
 
diff --git a/src/text_recognizer/models/line_ctc_model.py b/src/text_recognizer/models/crnn_model.py
index cdc2d8b..1e01a83 100644
--- a/src/text_recognizer/models/line_ctc_model.py
+++ b/src/text_recognizer/models/crnn_model.py
@@ -1,4 +1,4 @@
-"""Defines the LineCTCModel class."""
+"""Defines the CRNNModel class."""
 from typing import Callable, Dict, Optional, Tuple, Type, Union
 
 import numpy as np
@@ -13,7 +13,7 @@ from text_recognizer.models.base import Model
 from text_recognizer.networks import greedy_decoder
 
 
-class LineCTCModel(Model):
+class CRNNModel(Model):
     """Model for predicting a sequence of characters from an image of a text line."""
 
     def __init__(
@@ -47,8 +47,10 @@ class LineCTCModel(Model):
             swa_args,
             device,
         )
+
+        self.pad_token = dataset_args["args"]["pad_token"]
         if self._mapper is None:
-            self._mapper = EmnistMapper()
+            self._mapper = EmnistMapper(pad_token=self.pad_token,)
         self.tensor_transform = ToTensor()
 
     def criterion(self, output: Tensor, targets: Tensor) -> Tensor:
@@ -112,6 +114,6 @@ class LineCTCModel(Model):
         log_probs, _ = log_probs.max(dim=2)
 
         predicted_characters = "".join(raw_pred[0])
-        confidence_of_prediction = torch.exp(-log_probs.sum()).item()
+        confidence_of_prediction = log_probs.cumprod(dim=0)[-1].item()
 
         return predicted_characters, confidence_of_prediction
diff --git a/src/text_recognizer/models/metrics.py b/src/text_recognizer/models/metrics.py
index 6a26216..42c3c6e 100644
--- a/src/text_recognizer/models/metrics.py
+++ b/src/text_recognizer/models/metrics.py
@@ -17,7 +17,10 @@ def accuracy(outputs: Tensor, labels: Tensor) -> float:
         float: The accuracy for the batch.
 
     """
-    _, predicted = torch.max(outputs.data, dim=1)
+    # eos_index = torch.nonzero(labels == eos, as_tuple=False)
+    # eos_index = eos_index[0].item() if eos_index.nelement() else -1
+
+    _, predicted = torch.max(outputs, dim=-1)
     acc = (predicted == labels).sum().float() / labels.shape[0]
     acc = acc.item()
     return acc
diff --git a/src/text_recognizer/models/transformer_encoder_model.py b/src/text_recognizer/models/transformer_encoder_model.py
new file mode 100644
index 0000000..e35e298
--- /dev/null
+++ b/src/text_recognizer/models/transformer_encoder_model.py
@@ -0,0 +1,111 @@
+"""Defines the CNN-Transformer class."""
+from typing import Callable, Dict, List, Optional, Tuple, Type, Union
+
+import numpy as np
+import torch
+from torch import nn
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchvision.transforms import ToTensor
+
+from text_recognizer.datasets import EmnistMapper
+from text_recognizer.models.base import Model
+
+
+class TransformerEncoderModel(Model):
+    """A class for only using the encoder part in the sequence modelling."""
+
+    def __init__(
+        self,
+        network_fn: Type[nn.Module],
+        dataset: Type[Dataset],
+        network_args: Optional[Dict] = None,
+        dataset_args: Optional[Dict] = None,
+        metrics: Optional[Dict] = None,
+        criterion: Optional[Callable] = None,
+        criterion_args: Optional[Dict] = None,
+        optimizer: Optional[Callable] = None,
+        optimizer_args: Optional[Dict] = None,
+        lr_scheduler: Optional[Callable] = None,
+        lr_scheduler_args: Optional[Dict] = None,
+        swa_args: Optional[Dict] = None,
+        device: Optional[str] = None,
+    ) -> None:
+        super().__init__(
+            network_fn,
+            dataset,
+            network_args,
+            dataset_args,
+            metrics,
+            criterion,
+            criterion_args,
+            optimizer,
+            optimizer_args,
+            lr_scheduler,
+            lr_scheduler_args,
+            swa_args,
+            device,
+        )
+        # self.init_token = dataset_args["args"]["init_token"]
+        self.pad_token = dataset_args["args"]["pad_token"]
+        self.eos_token = dataset_args["args"]["eos_token"]
+        if network_args is not None:
+            self.max_len = network_args["max_len"]
+        else:
+            self.max_len = 128
+
+        if self._mapper is None:
+            self._mapper = EmnistMapper(
+                # init_token=self.init_token,
+                pad_token=self.pad_token,
+                eos_token=self.eos_token,
+            )
+        self.tensor_transform = ToTensor()
+
+        self.softmax = nn.Softmax(dim=2)
+
+    @torch.no_grad()
+    def _generate_sentence(self, image: Tensor) -> Tuple[List, float]:
+        logits = self.network(image)
+        # Convert logits to probabilities.
+        probs = self.softmax(logits).squeeze(0)
+
+        confidence, pred_tokens = probs.max(1)
+        pred_tokens = pred_tokens
+
+        eos_index = torch.nonzero(
+            pred_tokens == self._mapper(self.eos_token), as_tuple=False,
+        )
+
+        eos_index = eos_index[0].item() if eos_index.nelement() else -1
+
+        predicted_characters = "".join(
+            [self.mapper(x) for x in pred_tokens[:eos_index].tolist()]
+        )
+
+        confidence = np.min(confidence.tolist())
+
+        return predicted_characters, confidence
+
+    @torch.no_grad()
+    def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]:
+        """Predict on a single input."""
+        self.eval()
+
+        if image.dtype == np.uint8:
+            # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1].
+            image = self.tensor_transform(image)
+
+        # Rescale image between 0 and 1.
+        if image.dtype == torch.uint8:
+            # If the image is an unscaled tensor.
+            image = image.type("torch.FloatTensor") / 255
+
+        # Put the image tensor on the device the model weights are on.
+        image = image.to(self.device)
+
+        (predicted_characters, confidence_of_prediction,) = self._generate_sentence(
+            image
+        )
+
+        return predicted_characters, confidence_of_prediction
diff --git a/src/text_recognizer/models/vision_transformer_model.py b/src/text_recognizer/models/vision_transformer_model.py
index 20bd4ca..3d36437 100644
--- a/src/text_recognizer/models/vision_transformer_model.py
+++ b/src/text_recognizer/models/vision_transformer_model.py
@@ -53,7 +53,7 @@ class VisionTransformerModel(Model):
         if network_args is not None:
             self.max_len = network_args["max_len"]
         else:
-            self.max_len = 128
+            self.max_len = 120
 
         if self._mapper is None:
             self._mapper = EmnistMapper(
@@ -73,10 +73,10 @@ class VisionTransformerModel(Model):
         confidence_of_predictions = []
         trg_indices = [self.mapper(self.init_token)]
 
-        for _ in range(self.max_len):
+        for _ in range(self.max_len - 1):
             trg = torch.tensor(trg_indices, device=self.device)[None, :].long()
-            trg, trg_mask = self.network.preprocess_target(trg)
-            logits = self.network.decoder(trg=trg, memory=memory, trg_mask=trg_mask)
+            trg = self.network.preprocess_target(trg)
+            logits = self.network.decoder(trg=trg, memory=memory, trg_mask=None)
 
             # Convert logits to probabilities.
             probs = self.softmax(logits)
@@ -112,6 +112,8 @@ class VisionTransformerModel(Model):
         # Put the image tensor on the device the model weights are on.
         image = image.to(self.device)
 
-        predicted_characters, confidence_of_prediction = self._generate_sentence(image)
+        (predicted_characters, confidence_of_prediction,) = self._generate_sentence(
+            image
+        )
 
         return predicted_characters, confidence_of_prediction
diff --git a/src/text_recognizer/networks/__init__.py b/src/text_recognizer/networks/__init__.py
index 8b87797..6d88768 100644
--- a/src/text_recognizer/networks/__init__.py
+++ b/src/text_recognizer/networks/__init__.py
@@ -1,5 +1,6 @@
 """Network modules."""
 from .cnn_transformer import CNNTransformer
+from .cnn_transformer_encoder import CNNTransformerEncoder
 from .crnn import ConvolutionalRecurrentNetwork
 from .ctc import greedy_decoder
 from .densenet import DenseNet
@@ -15,6 +16,7 @@ from .wide_resnet import WideResidualNetwork
 
 __all__ = [
     "CNNTransformer",
+    "CNNTransformerEncoder",
     "ConvolutionalRecurrentNetwork",
     "DenseNet",
     "EmbeddingLoss",
diff --git a/src/text_recognizer/networks/cnn_transformer.py b/src/text_recognizer/networks/cnn_transformer.py
index 8666f11..3da2c9f 100644
--- a/src/text_recognizer/networks/cnn_transformer.py
+++ b/src/text_recognizer/networks/cnn_transformer.py
@@ -1,8 +1,7 @@
 """A DETR style transfomers but for text recognition."""
-from typing import Dict, Optional, Tuple, Type
+from typing import Dict, Optional, Tuple
 
-from einops.layers.torch import Rearrange
-from loguru import logger
+from einops import rearrange
 import torch
 from torch import nn
 from torch import Tensor
@@ -21,23 +20,32 @@ class CNNTransformer(nn.Module):
         hidden_dim: int,
         vocab_size: int,
         num_heads: int,
-        max_len: int,
+        adaptive_pool_dim: Tuple,
         expansion_dim: int,
         dropout_rate: float,
         trg_pad_index: int,
         backbone: str,
+        out_channels: int,
+        max_len: int,
         backbone_args: Optional[Dict] = None,
         activation: str = "gelu",
     ) -> None:
         super().__init__()
         self.trg_pad_index = trg_pad_index
-        self.backbone_args = backbone_args
+
         self.backbone = configure_backbone(backbone, backbone_args)
         self.character_embedding = nn.Embedding(vocab_size, hidden_dim)
-        self.position_encoding = PositionalEncoding(hidden_dim, dropout_rate, max_len)
-        self.collapse_spatial_dim = nn.Sequential(
-            Rearrange("b t h w -> b t (h w)"), nn.AdaptiveAvgPool2d((None, hidden_dim))
+
+        # self.conv = nn.Conv2d(out_channels, max_len, kernel_size=1)
+
+        self.position_encoding = PositionalEncoding(hidden_dim, dropout_rate)
+        self.row_embed = nn.Parameter(torch.rand(max_len, max_len // 2))
+        self.col_embed = nn.Parameter(torch.rand(max_len, max_len // 2))
+
+        self.adaptive_pool = (
+            nn.AdaptiveAvgPool2d((adaptive_pool_dim)) if adaptive_pool_dim else None
         )
+
         self.transformer = Transformer(
             num_encoder_layers,
             num_decoder_layers,
@@ -47,7 +55,8 @@ class CNNTransformer(nn.Module):
             dropout_rate,
             activation,
         )
-        self.head = nn.Linear(hidden_dim, vocab_size)
+
+        self.head = nn.Sequential(nn.Linear(hidden_dim, vocab_size),)
 
     def _create_trg_mask(self, trg: Tensor) -> Tensor:
         # Move this outside the transformer.
@@ -83,8 +92,22 @@ class CNNTransformer(nn.Module):
         if len(src.shape) < 4:
             src = src[(None,) * (4 - len(src.shape))]
         src = self.backbone(src)
-        src = self.collapse_spatial_dim(src)
-        src = self.position_encoding(src)
+        # src = self.conv(src)
+        if self.adaptive_pool is not None:
+            src = self.adaptive_pool(src)
+        H, W = src.shape[-2:]
+        src = rearrange(src, "b t h w -> b t (h w)")
+
+        # construct positional encodings
+        pos = torch.cat(
+            [
+                self.col_embed[:W].unsqueeze(0).repeat(H, 1, 1),
+                self.row_embed[:H].unsqueeze(1).repeat(1, W, 1),
+            ],
+            dim=-1,
+        ).unsqueeze(0)
+        pos = rearrange(pos, "b h w l -> b l (h w)")
+        src = pos + 0.1 * src
         return src
 
     def preprocess_target(self, trg: Tensor) -> Tuple[Tensor, Tensor]:
@@ -97,15 +120,16 @@ class CNNTransformer(nn.Module):
             Tuple[Tensor, Tensor]: Encoded target tensor and target mask.
 
         """
-        trg_mask = self._create_trg_mask(trg)
         trg = self.character_embedding(trg.long())
         trg = self.position_encoding(trg)
-        return trg, trg_mask
+        return trg
 
-    def forward(self, x: Tensor, trg: Tensor) -> Tensor:
+    def forward(self, x: Tensor, trg: Optional[Tensor] = None) -> Tensor:
         """Forward pass with CNN transfomer."""
-        src = self.preprocess_input(x)
-        trg, trg_mask = self.preprocess_target(trg)
-        out = self.transformer(src, trg, trg_mask=trg_mask)
+        h = self.preprocess_input(x)
+        trg_mask = self._create_trg_mask(trg)
+        trg = self.preprocess_target(trg)
+        out = self.transformer(h, trg, trg_mask=trg_mask)
+
         logits = self.head(out)
         return logits
diff --git a/src/text_recognizer/networks/cnn_transformer_encoder.py b/src/text_recognizer/networks/cnn_transformer_encoder.py
new file mode 100644
index 0000000..93626bf
--- /dev/null
+++ b/src/text_recognizer/networks/cnn_transformer_encoder.py
@@ -0,0 +1,73 @@
+"""Network with a CNN backend and a transformer encoder head."""
+from typing import Dict
+
+from einops import rearrange
+import torch
+from torch import nn
+from torch import Tensor
+
+from text_recognizer.networks.transformer import PositionalEncoding
+from text_recognizer.networks.util import configure_backbone
+
+
+class CNNTransformerEncoder(nn.Module):
+    """A CNN backbone with Transformer Encoder frontend for sequence prediction."""
+
+    def __init__(
+        self,
+        backbone: str,
+        backbone_args: Dict,
+        mlp_dim: int,
+        d_model: int,
+        nhead: int = 8,
+        dropout_rate: float = 0.1,
+        activation: str = "relu",
+        num_layers: int = 6,
+        num_classes: int = 80,
+        num_channels: int = 256,
+        max_len: int = 97,
+    ) -> None:
+        super().__init__()
+        self.d_model = d_model
+        self.nhead = nhead
+        self.dropout_rate = dropout_rate
+        self.activation = activation
+        self.num_layers = num_layers
+
+        self.backbone = configure_backbone(backbone, backbone_args)
+        self.position_encoding = PositionalEncoding(d_model, dropout_rate)
+        self.encoder = self._configure_encoder()
+
+        self.conv = nn.Conv2d(num_channels, max_len, kernel_size=1)
+
+        self.mlp = nn.Linear(mlp_dim, d_model)
+
+        self.head = nn.Linear(d_model, num_classes)
+
+    def _configure_encoder(self) -> nn.TransformerEncoder:
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=self.d_model,
+            nhead=self.nhead,
+            dropout=self.dropout_rate,
+            activation=self.activation,
+        )
+        norm = nn.LayerNorm(self.d_model)
+        return nn.TransformerEncoder(
+            encoder_layer=encoder_layer, num_layers=self.num_layers, norm=norm
+        )
+
+    def forward(self, x: Tensor, targets: Tensor = None) -> Tensor:
+        """Forward pass through the network."""
+        if len(x.shape) < 4:
+            x = x[(None,) * (4 - len(x.shape))]
+
+        x = self.conv(self.backbone(x))
+        x = rearrange(x, "b c h w -> b c (h w)")
+        x = self.mlp(x)
+        x = self.position_encoding(x)
+        x = rearrange(x, "b c h-> c b h")
+        x = self.encoder(x)
+        x = rearrange(x, "c b h-> b c h")
+        logits = self.head(x)
+
+        return logits
diff --git a/src/text_recognizer/networks/crnn.py b/src/text_recognizer/networks/crnn.py
index 3e605e2..9747429 100644
--- a/src/text_recognizer/networks/crnn.py
+++ b/src/text_recognizer/networks/crnn.py
@@ -1,12 +1,9 @@
 """LSTM with CTC for handwritten text recognition within a line."""
-import importlib
-from pathlib import Path
-from typing import Callable, Dict, List, Optional, Tuple, Type, Union
+from typing import Dict, Tuple
 
 from einops import rearrange, reduce
-from einops.layers.torch import Rearrange, Reduce
+from einops.layers.torch import Rearrange
 from loguru import logger
-import torch
 from torch import nn
 from torch import Tensor
 
@@ -28,16 +25,21 @@ class ConvolutionalRecurrentNetwork(nn.Module):
         patch_size: Tuple[int, int] = (28, 28),
         stride: Tuple[int, int] = (1, 14),
         recurrent_cell: str = "lstm",
+        avg_pool: bool = False,
+        use_sliding_window: bool = True,
     ) -> None:
         super().__init__()
         self.backbone_args = backbone_args or {}
         self.patch_size = patch_size
         self.stride = stride
-        self.sliding_window = self._configure_sliding_window()
+        self.sliding_window = (
+            self._configure_sliding_window() if use_sliding_window else None
+        )
         self.input_size = input_size
         self.hidden_size = hidden_size
         self.backbone = configure_backbone(backbone, backbone_args)
         self.bidirectional = bidirectional
+        self.avg_pool = avg_pool
 
         if recurrent_cell.upper() in ["LSTM", "GRU"]:
             recurrent_cell = getattr(nn, recurrent_cell)
@@ -76,15 +78,27 @@ class ConvolutionalRecurrentNetwork(nn.Module):
         """Converts images to sequence of patches, feeds them to a CNN, then predictions are made with an LSTM."""
         if len(x.shape) < 4:
             x = x[(None,) * (4 - len(x.shape))]
-        x = self.sliding_window(x)
 
-        # Rearrange from a sequence of patches for feedforward network.
-        b, t = x.shape[:2]
-        x = rearrange(x, "b t c h w -> (b t) c h w", b=b, t=t)
-        x = self.backbone(x)
+        if self.sliding_window is not None:
+            # Create image patches with a sliding window kernel.
+            x = self.sliding_window(x)
+
+            # Rearrange from a sequence of patches for feedforward network.
+            b, t = x.shape[:2]
+            x = rearrange(x, "b t c h w -> (b t) c h w", b=b, t=t)
 
-        # Avgerage pooling.
-        x = reduce(x, "(b t) c h w -> t b c", "mean", b=b, t=t)
+            x = self.backbone(x)
+
+            # Avgerage pooling.
+            if self.avg_pool:
+                x = reduce(x, "(b t) c h w -> t b c", "mean", b=b, t=t)
+            else:
+                x = rearrange(x, "(b t) h -> t b h", b=b, t=t)
+        else:
+            # Encode the entire image with a CNN, and use the channels as temporal dimension.
+            b = x.shape[0]
+            x = self.backbone(x)
+            x = rearrange(x, "b c h w -> c b (h w)", b=b)
 
         # Sequence predictions.
         x, _ = self.rnn(x)
diff --git a/src/text_recognizer/networks/ctc.py b/src/text_recognizer/networks/ctc.py
index 2493d5c..af9b700 100644
--- a/src/text_recognizer/networks/ctc.py
+++ b/src/text_recognizer/networks/ctc.py
@@ -33,7 +33,7 @@ def greedy_decoder(
     """
 
     if character_mapper is None:
-        character_mapper = EmnistMapper()
+        character_mapper = EmnistMapper(pad_token="_")  # noqa: S106
 
     predictions = rearrange(torch.argmax(predictions, dim=2), "t b -> b t")
     decoded_predictions = []
diff --git a/src/text_recognizer/networks/densenet.py b/src/text_recognizer/networks/densenet.py
index d2aad60..7dc58d9 100644
--- a/src/text_recognizer/networks/densenet.py
+++ b/src/text_recognizer/networks/densenet.py
@@ -72,7 +72,7 @@ class _DenseBlock(nn.Module):
     ) -> None:
         super().__init__()
         self.dense_block = self._build_dense_blocks(
-            num_layers, in_channels, bn_size, growth_rate, dropout_rate, activation
+            num_layers, in_channels, bn_size, growth_rate, dropout_rate, activation,
         )
 
     def _build_dense_blocks(
@@ -219,7 +219,7 @@ class DenseNet(nn.Module):
 
     def forward(self, x: Tensor) -> Tensor:
         """Forward pass of Densenet."""
-        # If batch dimenstion is missing, it needs to be added.
+        # If batch dimenstion is missing, it will be added.
         if len(x.shape) < 4:
             x = x[(None,) * (4 - len(x.shape))]
         return self.densenet(x)
diff --git a/src/text_recognizer/networks/loss.py b/src/text_recognizer/networks/loss.py
index ff843cf..cf9fa0d 100644
--- a/src/text_recognizer/networks/loss.py
+++ b/src/text_recognizer/networks/loss.py
@@ -1,10 +1,12 @@
 """Implementations of custom loss functions."""
 from pytorch_metric_learning import distances, losses, miners, reducers
+import torch
 from torch import nn
 from torch import Tensor
+from torch.autograd import Variable
+import torch.nn.functional as F
 
-
-__all__ = ["EmbeddingLoss"]
+__all__ = ["EmbeddingLoss", "LabelSmoothingCrossEntropy"]
 
 
 class EmbeddingLoss:
@@ -32,3 +34,36 @@ class EmbeddingLoss:
         hard_pairs = self.miner(embeddings, labels)
         loss = self.loss_fn(embeddings, labels, hard_pairs)
         return loss
+
+
+class LabelSmoothingCrossEntropy(nn.Module):
+    """Label smoothing loss function."""
+
+    def __init__(
+        self,
+        classes: int,
+        smoothing: float = 0.0,
+        ignore_index: int = None,
+        dim: int = -1,
+    ) -> None:
+        super().__init__()
+        self.confidence = 1.0 - smoothing
+        self.smoothing = smoothing
+        self.ignore_index = ignore_index
+        self.cls = classes
+        self.dim = dim
+
+    def forward(self, pred: Tensor, target: Tensor) -> Tensor:
+        """Calculates the loss."""
+        pred = pred.log_softmax(dim=self.dim)
+        with torch.no_grad():
+            # true_dist = pred.data.clone()
+            true_dist = torch.zeros_like(pred)
+            true_dist.fill_(self.smoothing / (self.cls - 1))
+            true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
+            if self.ignore_index is not None:
+                true_dist[:, self.ignore_index] = 0
+                mask = torch.nonzero(target == self.ignore_index, as_tuple=False)
+                if mask.dim() > 0:
+                    true_dist.index_fill_(0, mask.squeeze(), 0.0)
+        return torch.mean(torch.sum(-true_dist * pred, dim=self.dim))
diff --git a/src/text_recognizer/networks/transformer/positional_encoding.py b/src/text_recognizer/networks/transformer/positional_encoding.py
index a47141b..1ba5537 100644
--- a/src/text_recognizer/networks/transformer/positional_encoding.py
+++ b/src/text_recognizer/networks/transformer/positional_encoding.py
@@ -13,6 +13,7 @@ class PositionalEncoding(nn.Module):
     ) -> None:
         super().__init__()
         self.dropout = nn.Dropout(p=dropout_rate)
+        self.max_len = max_len
 
         pe = torch.zeros(max_len, hidden_dim)
         position = torch.arange(0, max_len).unsqueeze(1)
diff --git a/src/text_recognizer/networks/transformer/sparse_transformer.py b/src/text_recognizer/networks/transformer/sparse_transformer.py
deleted file mode 100644
index 8c391c8..0000000
--- a/src/text_recognizer/networks/transformer/sparse_transformer.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Encoder and Decoder modules using spares activations."""
diff --git a/src/text_recognizer/networks/transformer/transformer.py b/src/text_recognizer/networks/transformer/transformer.py
index 1c9c7dd..c6e943e 100644
--- a/src/text_recognizer/networks/transformer/transformer.py
+++ b/src/text_recognizer/networks/transformer/transformer.py
@@ -230,6 +230,7 @@ class Transformer(nn.Module):
     ) -> Tensor:
         """Forward pass through the transformer."""
         if src.shape[0] != trg.shape[0]:
+            print(trg.shape)
             raise RuntimeError("The batch size of the src and trg must be the same.")
         if src.shape[2] != trg.shape[2]:
             raise RuntimeError(
diff --git a/src/text_recognizer/networks/util.py b/src/text_recognizer/networks/util.py
index 0d08506..b31e640 100644
--- a/src/text_recognizer/networks/util.py
+++ b/src/text_recognizer/networks/util.py
@@ -28,7 +28,7 @@ def sliding_window(
     c = images.shape[1]
     patches = unfold(images)
     patches = rearrange(
-        patches, "b (c h w) t -> b t c h w", c=c, h=patch_size[0], w=patch_size[1]
+        patches, "b (c h w) t -> b t c h w", c=c, h=patch_size[0], w=patch_size[1],
     )
     return patches
 
@@ -77,7 +77,7 @@ def configure_backbone(backbone: str, backbone_args: Dict) -> Type[nn.Module]:
 
     if "remove_layers" in backbone_args and backbone_args["remove_layers"] is not None:
         backbone = nn.Sequential(
-            *list(backbone.children())[0][: -backbone_args["remove_layers"]]
+            *list(backbone.children())[:][: -backbone_args["remove_layers"]]
         )
 
     return backbone
diff --git a/src/text_recognizer/networks/vision_transformer.py b/src/text_recognizer/networks/vision_transformer.py
index 4d204d3..f227954 100644
--- a/src/text_recognizer/networks/vision_transformer.py
+++ b/src/text_recognizer/networks/vision_transformer.py
@@ -29,9 +29,9 @@ class VisionTransformer(nn.Module):
         num_heads: int,
         max_len: int,
         expansion_dim: int,
-        mlp_dim: int,
         dropout_rate: float,
         trg_pad_index: int,
+        mlp_dim: Optional[int] = None,
         patch_size: Tuple[int, int] = (28, 28),
         stride: Tuple[int, int] = (1, 14),
         activation: str = "gelu",
@@ -46,6 +46,7 @@ class VisionTransformer(nn.Module):
         self.slidning_window = self._configure_sliding_window()
         self.character_embedding = nn.Embedding(vocab_size, hidden_dim)
         self.position_encoding = PositionalEncoding(hidden_dim, dropout_rate, max_len)
+        self.mlp_dim = mlp_dim
 
         self.use_backbone = False
         if backbone is None:
@@ -54,6 +55,8 @@ class VisionTransformer(nn.Module):
             )
         else:
             self.backbone = configure_backbone(backbone, backbone_args)
+            if mlp_dim:
+                self.mlp = nn.Linear(mlp_dim, hidden_dim)
             self.use_backbone = True
 
         self.transformer = Transformer(
@@ -66,13 +69,7 @@ class VisionTransformer(nn.Module):
             activation,
         )
 
-        self.head = nn.Sequential(
-            nn.LayerNorm(hidden_dim),
-            nn.Linear(hidden_dim, mlp_dim),
-            nn.GELU(),
-            nn.Dropout(p=dropout_rate),
-            nn.Linear(mlp_dim, vocab_size),
-        )
+        self.head = nn.Sequential(nn.Linear(hidden_dim, vocab_size),)
 
     def _configure_sliding_window(self) -> nn.Sequential:
         return nn.Sequential(
@@ -110,7 +107,11 @@ class VisionTransformer(nn.Module):
         if self.use_backbone:
             x = rearrange(x, "b t c h w -> (b t) c h w", b=b, t=t)
             x = self.backbone(x)
-            x = rearrange(x, "(b t) h -> b t h", b=b, t=t)
+            if self.mlp_dim:
+                x = rearrange(x, "(b t) c h w -> b t (c h w)", b=b, t=t)
+                x = self.mlp(x)
+            else:
+                x = rearrange(x, "(b t) h -> b t h", b=b, t=t)
         else:
             x = rearrange(x, "b t c h w -> b t (c h w)", b=b, t=t)
             x = self.linear_projection(x)
diff --git a/src/text_recognizer/tests/test_line_predictor.py b/src/text_recognizer/tests/test_line_predictor.py
new file mode 100644
index 0000000..eede4d4
--- /dev/null
+++ b/src/text_recognizer/tests/test_line_predictor.py
@@ -0,0 +1,35 @@
+"""Tests for LinePredictor."""
+import os
+from pathlib import Path
+import unittest
+
+
+import editdistance
+import numpy as np
+
+from text_recognizer.datasets import IamLinesDataset
+from text_recognizer.line_predictor import LinePredictor
+import text_recognizer.util as util
+
+SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "support"
+
+os.environ["CUDA_VISIBLE_DEVICES"] = ""
+
+
+class TestEmnistLinePredictor(unittest.TestCase):
+    """Test LinePredictor class on the EmnistLines dataset."""
+
+    def test_filename(self) -> None:
+        """Test that LinePredictor correctly predicts on single images, for several test images."""
+        predictor = LinePredictor(
+            dataset="EmnistLineDataset", network_fn="CNNTransformer"
+        )
+
+        for filename in (SUPPORT_DIRNAME / "emnist_lines").glob("*.png"):
+            pred, conf = predictor.predict(str(filename))
+            true = str(filename.stem)
+            edit_distance = editdistance.eval(pred, true) / len(pred)
+            print(
+                f'Pred: "{pred}" | Confidence: {conf} | True: {true} | Edit distance: {edit_distance}'
+            )
+            self.assertLess(edit_distance, 0.2)
diff --git a/src/text_recognizer/weights/CRNNModel_IamLinesDataset_ConvolutionalRecurrentNetwork_weights.pt b/src/text_recognizer/weights/CRNNModel_IamLinesDataset_ConvolutionalRecurrentNetwork_weights.pt
new file mode 100644
index 0000000..726c723
--- /dev/null
+++ b/src/text_recognizer/weights/CRNNModel_IamLinesDataset_ConvolutionalRecurrentNetwork_weights.pt
diff --git a/src/text_recognizer/weights/CharacterModel_EmnistDataset_WideResidualNetwork_weights.pt b/src/text_recognizer/weights/CharacterModel_EmnistDataset_WideResidualNetwork_weights.pt
new file mode 100644
index 0000000..2d5a89b
--- /dev/null
+++ b/src/text_recognizer/weights/CharacterModel_EmnistDataset_WideResidualNetwork_weights.pt
diff --git a/src/training/prepare_experiments.py b/src/training/prepare_experiments.py
index e00540c..6e20bcd 100644
--- a/src/training/prepare_experiments.py
+++ b/src/training/prepare_experiments.py
@@ -1,9 +1,7 @@
 """Run a experiment from a config file."""
 import json
-from subprocess import run
 
 import click
-from loguru import logger
 import yaml
 
 
diff --git a/src/training/run_experiment.py b/src/training/run_experiment.py
index c0f969d..0510d5c 100644
--- a/src/training/run_experiment.py
+++ b/src/training/run_experiment.py
@@ -73,7 +73,7 @@ def _create_experiment_dir(
     return experiment_dir, log_dir, model_dir
 
 
-def _load_modules_and_arguments(experiment_config: Dict) -> Tuple[Callable, Dict]:
+def _load_modules_and_arguments(experiment_config: Dict,) -> Tuple[Callable, Dict]:
     """Loads all modules and arguments."""
     # Load the dataset module.
     dataset_args = experiment_config.get("dataset", {})
@@ -104,7 +104,7 @@ def _load_modules_and_arguments(experiment_config: Dict) -> Tuple[Callable, Dict
         criterion_ = getattr(custom_loss_module, experiment_config["criterion"]["type"])
     else:
         criterion_ = getattr(torch.nn, experiment_config["criterion"]["type"])
-    criterion_args = experiment_config["criterion"].get("args", {})
+    criterion_args = experiment_config["criterion"].get("args", {}) or {}
 
     # Optimizers
     if experiment_config["optimizer"]["type"] == "AdaBelief":
@@ -187,18 +187,20 @@ def _save_config(experiment_dir: Path, experiment_config: Dict) -> None:
 
 
 def _load_from_checkpoint(
-    model: Type[Model], log_dir: Path, model_dir: Path, pretrained_weights: str = None
+    model: Type[Model], model_dir: Path, pretrained_weights: str = None,
 ) -> None:
     """If checkpoint exists, load model weights and optimizers from checkpoint."""
     # Get checkpoint path.
     if pretrained_weights is not None:
         logger.info(f"Loading weights from {pretrained_weights}.")
-        checkpoint_path = Path(pretrained_weights) / "model" / "best.pt"
+        checkpoint_path = (
+            EXPERIMENTS_DIRNAME / Path(pretrained_weights) / "model" / "best.pt"
+        )
     else:
         logger.info(f"Loading weights from {model_dir}.")
         checkpoint_path = model_dir / "last.pt"
     if checkpoint_path.exists():
-        logger.info("Loading and resuming training from last checkpoint.")
+        logger.info("Loading and resuming training from checkpoint.")
         model.load_from_checkpoint(checkpoint_path)
 
 
@@ -230,9 +232,9 @@ def run_experiment(
     experiment_config: Dict,
     save_weights: bool,
     device: str,
-    use_wandb: bool = False,
-    train: bool = True,
-    test: bool = False,
+    use_wandb: bool,
+    train: bool,
+    test: bool,
     verbose: int = 0,
     checkpoint: Optional[str] = None,
     pretrained_weights: Optional[str] = None,
@@ -264,7 +266,7 @@ def run_experiment(
     resume = False
     if checkpoint is not None or pretrained_weights is not None:
         resume = True
-        _load_from_checkpoint(model, log_dir, model_dir, pretrained_weights)
+        _load_from_checkpoint(model, model_dir, pretrained_weights)
 
     logger.info(f"The class mapping is {model.mapping}")
 
@@ -297,6 +299,7 @@ def run_experiment(
         max_epochs=experiment_config["train_args"]["max_epochs"],
         callbacks=callbacks,
         transformer_model=experiment_config["train_args"]["transformer_model"],
+        max_norm=experiment_config["train_args"]["max_norm"],
     )
 
     # Train the model.
@@ -309,7 +312,7 @@ def run_experiment(
         model.load_from_checkpoint(model_dir / "best.pt")
 
         logger.info("Running inference on test set.")
-        if experiment_config["criterion"]["type"] in custom_loss_module.__all__:
+        if experiment_config["criterion"]["type"] == "EmbeddingLoss":
             logger.info("Evaluating embedding.")
             score = evaluate_embedding(model)
         else:
@@ -341,13 +344,15 @@ def run_experiment(
 @click.option(
     "--nowandb", is_flag=False, help="If true, do not use wandb for this run."
 )
-@click.option("--notrain", is_flag=False, help="Do not train the model.")
 @click.option("--test", is_flag=True, help="If true, test the model.")
 @click.option("-v", "--verbose", count=True)
 @click.option("--checkpoint", type=str, help="Path to the experiment.")
 @click.option(
     "--pretrained_weights", type=str, help="Path to pretrained model weights."
 )
+@click.option(
+    "--notrain", is_flag=False, is_eager=True, help="Do not train the model.",
+)
 def run_cli(
     experiment_config: str,
     gpu: int,
@@ -367,6 +372,7 @@ def run_cli(
 
     experiment_config = json.loads(experiment_config)
     os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu}"
+
     run_experiment(
         experiment_config,
         save,
diff --git a/src/training/trainer/callbacks/lr_schedulers.py b/src/training/trainer/callbacks/lr_schedulers.py
index 907e292..630c434 100644
--- a/src/training/trainer/callbacks/lr_schedulers.py
+++ b/src/training/trainer/callbacks/lr_schedulers.py
@@ -22,7 +22,10 @@ class LRScheduler(Callback):
     def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None:
         """Takes a step at the end of every epoch."""
         if self.interval == "epoch":
-            self.lr_scheduler.step()
+            if "ReduceLROnPlateau" in self.lr_scheduler.__class__.__name__:
+                self.lr_scheduler.step(logs["val_loss"])
+            else:
+                self.lr_scheduler.step()
 
     def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None:
         """Takes a step at the end of every training batch."""
diff --git a/src/training/trainer/callbacks/wandb_callbacks.py b/src/training/trainer/callbacks/wandb_callbacks.py
index f24e5cc..1627f17 100644
--- a/src/training/trainer/callbacks/wandb_callbacks.py
+++ b/src/training/trainer/callbacks/wandb_callbacks.py
@@ -111,7 +111,7 @@ class WandbImageLogger(Callback):
                     ]
                 ).rstrip("_")
             else:
-                ground_truth = self.targets[i]
+                ground_truth = self.model.mapper(int(self.targets[i]))
             caption = f"Prediction: {pred} Confidence: {conf:.3f} Ground Truth: {ground_truth}"
             images.append(wandb.Image(image, caption=caption))
 
diff --git a/src/training/trainer/population_based_training/__init__.py b/src/training/trainer/population_based_training/__init__.py
deleted file mode 100644
index 868d739..0000000
--- a/src/training/trainer/population_based_training/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""TBC."""
diff --git a/src/training/trainer/population_based_training/population_based_training.py b/src/training/trainer/population_based_training/population_based_training.py
deleted file mode 100644
index 868d739..0000000
--- a/src/training/trainer/population_based_training/population_based_training.py
+++ /dev/null
@@ -1 +0,0 @@
-"""TBC."""
diff --git a/src/training/trainer/train.py b/src/training/trainer/train.py
index fb49103..223d9c6 100644
--- a/src/training/trainer/train.py
+++ b/src/training/trainer/train.py
@@ -33,6 +33,7 @@ class Trainer:
         max_epochs: int,
         callbacks: List[Type[Callback]],
         transformer_model: bool = False,
+        max_norm: float = 0.0,
     ) -> None:
         """Initialization of the Trainer.
 
@@ -40,6 +41,7 @@ class Trainer:
             max_epochs (int): The maximum number of epochs in the training loop.
             callbacks (CallbackList): List of callbacks to be called.
             transformer_model (bool): Transformer model flag, modifies the input to the model. Default is False.
+            max_norm (float): Max norm for gradient clipping. Defaults to 0.0.
 
         """
         # Training arguments.
@@ -52,6 +54,8 @@ class Trainer:
 
         self.transformer_model = transformer_model
 
+        self.max_norm = max_norm
+
         # Model placeholders
         self.model = None
 
@@ -124,6 +128,11 @@ class Trainer:
         # Compute the gradients.
         loss.backward()
 
+        if self.max_norm > 0:
+            torch.nn.utils.clip_grad_norm_(
+                self.model.network.parameters(), self.max_norm
+            )
+
         # Perform updates using calculated gradients.
         self.model.optimizer.step()
author	aktersnurra <gustaf.rydholm@gmail.com>	2020-11-08 12:41:04 +0100
committer	aktersnurra <gustaf.rydholm@gmail.com>	2020-11-08 12:41:04 +0100
commit	beeaef529e7c893a3475fe27edc880e283373725 (patch)
tree	59eb72562bf7a5a9470c2586e6280600ad94f1ae /src
parent	4d7713746eb936832e84852e90292936b933e87d (diff)