From 3b06ef615a8db67a03927576e0c12fbfb2501f5f Mon Sep 17 00:00:00 2001
From: aktersnurra <gustaf.rydholm@gmail.com>
Date: Mon, 14 Sep 2020 22:15:47 +0200
Subject: Fixed CTC loss.

---
 src/notebooks/00-testing-stuff-out.ipynb           | 357 ++++++++++--
 src/notebooks/02b-emnist-lines-dataset.ipynb       | 123 +++-
 src/notebooks/04a-look-at-iam-lines.ipynb          | 642 +++++++++++++++++++--
 src/text_recognizer/datasets/dataset.py            |   7 +-
 src/text_recognizer/datasets/emnist_dataset.py     |   9 +-
 .../datasets/emnist_lines_dataset.py               |   9 +-
 src/text_recognizer/datasets/transforms.py         |  13 +
 src/text_recognizer/datasets/util.py               |   2 +-
 src/text_recognizer/models/line_ctc_model.py       |  20 +-
 src/text_recognizer/networks/ctc.py                |   2 +-
 src/text_recognizer/networks/line_lstm_ctc.py      |   6 +-
 src/text_recognizer/networks/misc.py               |   1 +
 src/text_recognizer/networks/transformer.py        |   1 +
 src/text_recognizer/networks/wide_resnet.py        |   8 +-
 ...istLinesDataset_LineRecurrentNetwork_weights.pt | Bin 15375126 -> 61946486 bytes
 ...IamLinesDataset_LineRecurrentNetwork_weights.pt | Bin 0 -> 45257014 bytes
 src/training/experiments/default_config_emnist.yml |  69 +++
 .../experiments/iam_line_ctc_experiment.yml        |  94 +++
 src/training/experiments/line_ctc_experiment.yml   |  98 ++++
 src/training/run_experiment.py                     |   2 +-
 20 files changed, 1334 insertions(+), 129 deletions(-)
 create mode 100644 src/text_recognizer/datasets/transforms.py
 create mode 100644 src/text_recognizer/networks/transformer.py
 create mode 100644 src/text_recognizer/weights/LineCTCModel_IamLinesDataset_LineRecurrentNetwork_weights.pt
 create mode 100644 src/training/experiments/default_config_emnist.yml
 create mode 100644 src/training/experiments/iam_line_ctc_experiment.yml
 create mode 100644 src/training/experiments/line_ctc_experiment.yml

(limited to 'src')

diff --git a/src/notebooks/00-testing-stuff-out.ipynb b/src/notebooks/00-testing-stuff-out.ipynb
index ff9fb20..9d265ba 100644
--- a/src/notebooks/00-testing-stuff-out.ipynb
+++ b/src/notebooks/00-testing-stuff-out.ipynb
@@ -191,7 +191,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.networks import WideResidualNetwork"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -200,67 +227,299 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "----------------------------------------------------------------\n",
-      "        Layer (type)               Output Shape         Param #\n",
-      "================================================================\n",
-      "            Conv2d-1           [-1, 96, 16, 16]             864\n",
-      "       BatchNorm2d-2           [-1, 96, 16, 16]             192\n",
-      "              ReLU-3           [-1, 96, 16, 16]               0\n",
-      "         MaxPool2d-4             [-1, 96, 8, 8]               0\n",
-      "        Conv2dAuto-5             [-1, 96, 8, 8]          82,944\n",
-      "       BatchNorm2d-6             [-1, 96, 8, 8]             192\n",
-      "              ReLU-7             [-1, 96, 8, 8]               0\n",
-      "              ReLU-8             [-1, 96, 8, 8]               0\n",
-      "        Conv2dAuto-9             [-1, 96, 8, 8]          82,944\n",
-      "      BatchNorm2d-10             [-1, 96, 8, 8]             192\n",
-      "             ReLU-11             [-1, 96, 8, 8]               0\n",
-      "             ReLU-12             [-1, 96, 8, 8]               0\n",
-      "       BasicBlock-13             [-1, 96, 8, 8]               0\n",
-      "       Conv2dAuto-14             [-1, 96, 8, 8]          82,944\n",
-      "      BatchNorm2d-15             [-1, 96, 8, 8]             192\n",
-      "             ReLU-16             [-1, 96, 8, 8]               0\n",
-      "             ReLU-17             [-1, 96, 8, 8]               0\n",
-      "       Conv2dAuto-18             [-1, 96, 8, 8]          82,944\n",
-      "      BatchNorm2d-19             [-1, 96, 8, 8]             192\n",
-      "             ReLU-20             [-1, 96, 8, 8]               0\n",
-      "             ReLU-21             [-1, 96, 8, 8]               0\n",
-      "       BasicBlock-22             [-1, 96, 8, 8]               0\n",
-      "    ResidualLayer-23             [-1, 96, 8, 8]               0\n",
-      "           Conv2d-24            [-1, 128, 4, 4]          12,288\n",
-      "      BatchNorm2d-25            [-1, 128, 4, 4]             256\n",
-      "       Conv2dAuto-26            [-1, 128, 4, 4]         110,592\n",
-      "      BatchNorm2d-27            [-1, 128, 4, 4]             256\n",
-      "             ReLU-28            [-1, 128, 4, 4]               0\n",
-      "             ReLU-29            [-1, 128, 4, 4]               0\n",
-      "       Conv2dAuto-30            [-1, 128, 4, 4]         147,456\n",
-      "      BatchNorm2d-31            [-1, 128, 4, 4]             256\n",
-      "             ReLU-32            [-1, 128, 4, 4]               0\n",
-      "             ReLU-33            [-1, 128, 4, 4]               0\n",
-      "       BasicBlock-34            [-1, 128, 4, 4]               0\n",
-      "    ResidualLayer-35            [-1, 128, 4, 4]               0\n",
-      "================================================================\n",
-      "Total params: 604,704\n",
-      "Trainable params: 604,704\n",
+      "===============================================================================================\n",
+      "Layer (type:depth-idx)                        Output Shape              Param #\n",
+      "===============================================================================================\n",
+      "├─Sequential: 1-1                             [-1, 256, 4, 2]           --\n",
+      "|    └─Conv2d: 2-1                            [-1, 16, 28, 14]          144\n",
+      "|    └─Sequential: 2-2                        [-1, 32, 28, 14]          --\n",
+      "|    |    └─WideBlock: 3-1                    [-1, 32, 28, 14]          --\n",
+      "|    |    |    └─Sequential: 4-1              [-1, 32, 28, 14]          --\n",
+      "|    |    |    |    └─Conv2d: 5-1             [-1, 32, 28, 14]          512\n",
+      "|    |    |    └─Sequential: 4-2              [-1, 32, 28, 14]          --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-2        [-1, 16, 28, 14]          32\n",
+      "|    |    |    └─SELU: 4-3                    [-1, 16, 28, 14]          --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-3               [-1, 16, 28, 14]          --\n",
+      "|    |    |    |    └─Conv2d: 5-4             [-1, 32, 28, 14]          4,608\n",
+      "|    |    |    |    └─Dropout: 5-5            [-1, 32, 28, 14]          --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-6        [-1, 32, 28, 14]          64\n",
+      "|    |    |    └─SELU: 4-4                    [-1, 32, 28, 14]          --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-7               [-1, 32, 28, 14]          --\n",
+      "|    |    |    |    └─Conv2d: 5-8             [-1, 32, 28, 14]          9,216\n",
+      "|    |    └─WideBlock: 3-2                    [-1, 32, 28, 14]          --\n",
+      "|    |    |    └─Sequential: 4-5              [-1, 32, 28, 14]          --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-9        [-1, 32, 28, 14]          64\n",
+      "|    |    |    └─SELU: 4-6                    [-1, 32, 28, 14]          --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-10              [-1, 32, 28, 14]          --\n",
+      "|    |    |    |    └─Conv2d: 5-11            [-1, 32, 28, 14]          9,216\n",
+      "|    |    |    |    └─Dropout: 5-12           [-1, 32, 28, 14]          --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-13       [-1, 32, 28, 14]          64\n",
+      "|    |    |    └─SELU: 4-7                    [-1, 32, 28, 14]          --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-14              [-1, 32, 28, 14]          --\n",
+      "|    |    |    |    └─Conv2d: 5-15            [-1, 32, 28, 14]          9,216\n",
+      "|    └─Sequential: 2-3                        [-1, 64, 14, 7]           --\n",
+      "|    |    └─WideBlock: 3-3                    [-1, 64, 14, 7]           --\n",
+      "|    |    |    └─Sequential: 4-8              [-1, 64, 14, 7]           --\n",
+      "|    |    |    |    └─Conv2d: 5-16            [-1, 64, 14, 7]           2,048\n",
+      "|    |    |    └─Sequential: 4-9              [-1, 64, 14, 7]           --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-17       [-1, 32, 28, 14]          64\n",
+      "|    |    |    └─SELU: 4-10                   [-1, 32, 28, 14]          --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-18              [-1, 32, 28, 14]          --\n",
+      "|    |    |    |    └─Conv2d: 5-19            [-1, 64, 28, 14]          18,432\n",
+      "|    |    |    |    └─Dropout: 5-20           [-1, 64, 28, 14]          --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-21       [-1, 64, 28, 14]          128\n",
+      "|    |    |    └─SELU: 4-11                   [-1, 64, 28, 14]          --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-22              [-1, 64, 28, 14]          --\n",
+      "|    |    |    |    └─Conv2d: 5-23            [-1, 64, 14, 7]           36,864\n",
+      "|    |    └─WideBlock: 3-4                    [-1, 64, 14, 7]           --\n",
+      "|    |    |    └─Sequential: 4-12             [-1, 64, 14, 7]           --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-24       [-1, 64, 14, 7]           128\n",
+      "|    |    |    └─SELU: 4-13                   [-1, 64, 14, 7]           --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-25              [-1, 64, 14, 7]           --\n",
+      "|    |    |    |    └─Conv2d: 5-26            [-1, 64, 14, 7]           36,864\n",
+      "|    |    |    |    └─Dropout: 5-27           [-1, 64, 14, 7]           --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-28       [-1, 64, 14, 7]           128\n",
+      "|    |    |    └─SELU: 4-14                   [-1, 64, 14, 7]           --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-29              [-1, 64, 14, 7]           --\n",
+      "|    |    |    |    └─Conv2d: 5-30            [-1, 64, 14, 7]           36,864\n",
+      "|    └─Sequential: 2-4                        [-1, 128, 7, 4]           --\n",
+      "|    |    └─WideBlock: 3-5                    [-1, 128, 7, 4]           --\n",
+      "|    |    |    └─Sequential: 4-15             [-1, 128, 7, 4]           --\n",
+      "|    |    |    |    └─Conv2d: 5-31            [-1, 128, 7, 4]           8,192\n",
+      "|    |    |    └─Sequential: 4-16             [-1, 128, 7, 4]           --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-32       [-1, 64, 14, 7]           128\n",
+      "|    |    |    └─SELU: 4-17                   [-1, 64, 14, 7]           --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-33              [-1, 64, 14, 7]           --\n",
+      "|    |    |    |    └─Conv2d: 5-34            [-1, 128, 14, 7]          73,728\n",
+      "|    |    |    |    └─Dropout: 5-35           [-1, 128, 14, 7]          --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-36       [-1, 128, 14, 7]          256\n",
+      "|    |    |    └─SELU: 4-18                   [-1, 128, 14, 7]          --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-37              [-1, 128, 14, 7]          --\n",
+      "|    |    |    |    └─Conv2d: 5-38            [-1, 128, 7, 4]           147,456\n",
+      "|    |    └─WideBlock: 3-6                    [-1, 128, 7, 4]           --\n",
+      "|    |    |    └─Sequential: 4-19             [-1, 128, 7, 4]           --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-39       [-1, 128, 7, 4]           256\n",
+      "|    |    |    └─SELU: 4-20                   [-1, 128, 7, 4]           --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-40              [-1, 128, 7, 4]           --\n",
+      "|    |    |    |    └─Conv2d: 5-41            [-1, 128, 7, 4]           147,456\n",
+      "|    |    |    |    └─Dropout: 5-42           [-1, 128, 7, 4]           --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-43       [-1, 128, 7, 4]           256\n",
+      "|    |    |    └─SELU: 4-21                   [-1, 128, 7, 4]           --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-44              [-1, 128, 7, 4]           --\n",
+      "|    |    |    |    └─Conv2d: 5-45            [-1, 128, 7, 4]           147,456\n",
+      "|    └─Sequential: 2-5                        [-1, 256, 4, 2]           --\n",
+      "|    |    └─WideBlock: 3-7                    [-1, 256, 4, 2]           --\n",
+      "|    |    |    └─Sequential: 4-22             [-1, 256, 4, 2]           --\n",
+      "|    |    |    |    └─Conv2d: 5-46            [-1, 256, 4, 2]           32,768\n",
+      "|    |    |    └─Sequential: 4-23             [-1, 256, 4, 2]           --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-47       [-1, 128, 7, 4]           256\n",
+      "|    |    |    └─SELU: 4-24                   [-1, 128, 7, 4]           --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-48              [-1, 128, 7, 4]           --\n",
+      "|    |    |    |    └─Conv2d: 5-49            [-1, 256, 7, 4]           294,912\n",
+      "|    |    |    |    └─Dropout: 5-50           [-1, 256, 7, 4]           --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-51       [-1, 256, 7, 4]           512\n",
+      "|    |    |    └─SELU: 4-25                   [-1, 256, 7, 4]           --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-52              [-1, 256, 7, 4]           --\n",
+      "|    |    |    |    └─Conv2d: 5-53            [-1, 256, 4, 2]           589,824\n",
+      "|    |    └─WideBlock: 3-8                    [-1, 256, 4, 2]           --\n",
+      "|    |    |    └─Sequential: 4-26             [-1, 256, 4, 2]           --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-54       [-1, 256, 4, 2]           512\n",
+      "|    |    |    └─SELU: 4-27                   [-1, 256, 4, 2]           --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-55              [-1, 256, 4, 2]           --\n",
+      "|    |    |    |    └─Conv2d: 5-56            [-1, 256, 4, 2]           589,824\n",
+      "|    |    |    |    └─Dropout: 5-57           [-1, 256, 4, 2]           --\n",
+      "|    |    |    |    └─BatchNorm2d: 5-58       [-1, 256, 4, 2]           512\n",
+      "|    |    |    └─SELU: 4-28                   [-1, 256, 4, 2]           --\n",
+      "|    |    |    └─Sequential: 4                []                        --\n",
+      "|    |    |    |    └─SELU: 5-59              [-1, 256, 4, 2]           --\n",
+      "|    |    |    |    └─Conv2d: 5-60            [-1, 256, 4, 2]           589,824\n",
+      "===============================================================================================\n",
+      "Total params: 2,788,784\n",
+      "Trainable params: 2,788,784\n",
       "Non-trainable params: 0\n",
-      "----------------------------------------------------------------\n",
+      "Total mult-adds (M): 84.83\n",
+      "===============================================================================================\n",
       "Input size (MB): 0.00\n",
-      "Forward/backward pass size (MB): 1.69\n",
-      "Params size (MB): 2.31\n",
-      "Estimated Total Size (MB): 4.00\n",
-      "----------------------------------------------------------------\n"
+      "Forward/backward pass size (MB): 2.26\n",
+      "Params size (MB): 10.64\n",
+      "Estimated Total Size (MB): 12.90\n",
+      "===============================================================================================\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "===============================================================================================\n",
+       "Layer (type:depth-idx)                        Output Shape              Param #\n",
+       "===============================================================================================\n",
+       "├─Sequential: 1-1                             [-1, 256, 4, 2]           --\n",
+       "|    └─Conv2d: 2-1                            [-1, 16, 28, 14]          144\n",
+       "|    └─Sequential: 2-2                        [-1, 32, 28, 14]          --\n",
+       "|    |    └─WideBlock: 3-1                    [-1, 32, 28, 14]          --\n",
+       "|    |    |    └─Sequential: 4-1              [-1, 32, 28, 14]          --\n",
+       "|    |    |    |    └─Conv2d: 5-1             [-1, 32, 28, 14]          512\n",
+       "|    |    |    └─Sequential: 4-2              [-1, 32, 28, 14]          --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-2        [-1, 16, 28, 14]          32\n",
+       "|    |    |    └─SELU: 4-3                    [-1, 16, 28, 14]          --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-3               [-1, 16, 28, 14]          --\n",
+       "|    |    |    |    └─Conv2d: 5-4             [-1, 32, 28, 14]          4,608\n",
+       "|    |    |    |    └─Dropout: 5-5            [-1, 32, 28, 14]          --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-6        [-1, 32, 28, 14]          64\n",
+       "|    |    |    └─SELU: 4-4                    [-1, 32, 28, 14]          --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-7               [-1, 32, 28, 14]          --\n",
+       "|    |    |    |    └─Conv2d: 5-8             [-1, 32, 28, 14]          9,216\n",
+       "|    |    └─WideBlock: 3-2                    [-1, 32, 28, 14]          --\n",
+       "|    |    |    └─Sequential: 4-5              [-1, 32, 28, 14]          --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-9        [-1, 32, 28, 14]          64\n",
+       "|    |    |    └─SELU: 4-6                    [-1, 32, 28, 14]          --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-10              [-1, 32, 28, 14]          --\n",
+       "|    |    |    |    └─Conv2d: 5-11            [-1, 32, 28, 14]          9,216\n",
+       "|    |    |    |    └─Dropout: 5-12           [-1, 32, 28, 14]          --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-13       [-1, 32, 28, 14]          64\n",
+       "|    |    |    └─SELU: 4-7                    [-1, 32, 28, 14]          --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-14              [-1, 32, 28, 14]          --\n",
+       "|    |    |    |    └─Conv2d: 5-15            [-1, 32, 28, 14]          9,216\n",
+       "|    └─Sequential: 2-3                        [-1, 64, 14, 7]           --\n",
+       "|    |    └─WideBlock: 3-3                    [-1, 64, 14, 7]           --\n",
+       "|    |    |    └─Sequential: 4-8              [-1, 64, 14, 7]           --\n",
+       "|    |    |    |    └─Conv2d: 5-16            [-1, 64, 14, 7]           2,048\n",
+       "|    |    |    └─Sequential: 4-9              [-1, 64, 14, 7]           --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-17       [-1, 32, 28, 14]          64\n",
+       "|    |    |    └─SELU: 4-10                   [-1, 32, 28, 14]          --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-18              [-1, 32, 28, 14]          --\n",
+       "|    |    |    |    └─Conv2d: 5-19            [-1, 64, 28, 14]          18,432\n",
+       "|    |    |    |    └─Dropout: 5-20           [-1, 64, 28, 14]          --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-21       [-1, 64, 28, 14]          128\n",
+       "|    |    |    └─SELU: 4-11                   [-1, 64, 28, 14]          --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-22              [-1, 64, 28, 14]          --\n",
+       "|    |    |    |    └─Conv2d: 5-23            [-1, 64, 14, 7]           36,864\n",
+       "|    |    └─WideBlock: 3-4                    [-1, 64, 14, 7]           --\n",
+       "|    |    |    └─Sequential: 4-12             [-1, 64, 14, 7]           --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-24       [-1, 64, 14, 7]           128\n",
+       "|    |    |    └─SELU: 4-13                   [-1, 64, 14, 7]           --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-25              [-1, 64, 14, 7]           --\n",
+       "|    |    |    |    └─Conv2d: 5-26            [-1, 64, 14, 7]           36,864\n",
+       "|    |    |    |    └─Dropout: 5-27           [-1, 64, 14, 7]           --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-28       [-1, 64, 14, 7]           128\n",
+       "|    |    |    └─SELU: 4-14                   [-1, 64, 14, 7]           --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-29              [-1, 64, 14, 7]           --\n",
+       "|    |    |    |    └─Conv2d: 5-30            [-1, 64, 14, 7]           36,864\n",
+       "|    └─Sequential: 2-4                        [-1, 128, 7, 4]           --\n",
+       "|    |    └─WideBlock: 3-5                    [-1, 128, 7, 4]           --\n",
+       "|    |    |    └─Sequential: 4-15             [-1, 128, 7, 4]           --\n",
+       "|    |    |    |    └─Conv2d: 5-31            [-1, 128, 7, 4]           8,192\n",
+       "|    |    |    └─Sequential: 4-16             [-1, 128, 7, 4]           --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-32       [-1, 64, 14, 7]           128\n",
+       "|    |    |    └─SELU: 4-17                   [-1, 64, 14, 7]           --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-33              [-1, 64, 14, 7]           --\n",
+       "|    |    |    |    └─Conv2d: 5-34            [-1, 128, 14, 7]          73,728\n",
+       "|    |    |    |    └─Dropout: 5-35           [-1, 128, 14, 7]          --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-36       [-1, 128, 14, 7]          256\n",
+       "|    |    |    └─SELU: 4-18                   [-1, 128, 14, 7]          --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-37              [-1, 128, 14, 7]          --\n",
+       "|    |    |    |    └─Conv2d: 5-38            [-1, 128, 7, 4]           147,456\n",
+       "|    |    └─WideBlock: 3-6                    [-1, 128, 7, 4]           --\n",
+       "|    |    |    └─Sequential: 4-19             [-1, 128, 7, 4]           --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-39       [-1, 128, 7, 4]           256\n",
+       "|    |    |    └─SELU: 4-20                   [-1, 128, 7, 4]           --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-40              [-1, 128, 7, 4]           --\n",
+       "|    |    |    |    └─Conv2d: 5-41            [-1, 128, 7, 4]           147,456\n",
+       "|    |    |    |    └─Dropout: 5-42           [-1, 128, 7, 4]           --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-43       [-1, 128, 7, 4]           256\n",
+       "|    |    |    └─SELU: 4-21                   [-1, 128, 7, 4]           --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-44              [-1, 128, 7, 4]           --\n",
+       "|    |    |    |    └─Conv2d: 5-45            [-1, 128, 7, 4]           147,456\n",
+       "|    └─Sequential: 2-5                        [-1, 256, 4, 2]           --\n",
+       "|    |    └─WideBlock: 3-7                    [-1, 256, 4, 2]           --\n",
+       "|    |    |    └─Sequential: 4-22             [-1, 256, 4, 2]           --\n",
+       "|    |    |    |    └─Conv2d: 5-46            [-1, 256, 4, 2]           32,768\n",
+       "|    |    |    └─Sequential: 4-23             [-1, 256, 4, 2]           --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-47       [-1, 128, 7, 4]           256\n",
+       "|    |    |    └─SELU: 4-24                   [-1, 128, 7, 4]           --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-48              [-1, 128, 7, 4]           --\n",
+       "|    |    |    |    └─Conv2d: 5-49            [-1, 256, 7, 4]           294,912\n",
+       "|    |    |    |    └─Dropout: 5-50           [-1, 256, 7, 4]           --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-51       [-1, 256, 7, 4]           512\n",
+       "|    |    |    └─SELU: 4-25                   [-1, 256, 7, 4]           --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-52              [-1, 256, 7, 4]           --\n",
+       "|    |    |    |    └─Conv2d: 5-53            [-1, 256, 4, 2]           589,824\n",
+       "|    |    └─WideBlock: 3-8                    [-1, 256, 4, 2]           --\n",
+       "|    |    |    └─Sequential: 4-26             [-1, 256, 4, 2]           --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-54       [-1, 256, 4, 2]           512\n",
+       "|    |    |    └─SELU: 4-27                   [-1, 256, 4, 2]           --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-55              [-1, 256, 4, 2]           --\n",
+       "|    |    |    |    └─Conv2d: 5-56            [-1, 256, 4, 2]           589,824\n",
+       "|    |    |    |    └─Dropout: 5-57           [-1, 256, 4, 2]           --\n",
+       "|    |    |    |    └─BatchNorm2d: 5-58       [-1, 256, 4, 2]           512\n",
+       "|    |    |    └─SELU: 4-28                   [-1, 256, 4, 2]           --\n",
+       "|    |    |    └─Sequential: 4                []                        --\n",
+       "|    |    |    |    └─SELU: 5-59              [-1, 256, 4, 2]           --\n",
+       "|    |    |    |    └─Conv2d: 5-60            [-1, 256, 4, 2]           589,824\n",
+       "===============================================================================================\n",
+       "Total params: 2,788,784\n",
+       "Trainable params: 2,788,784\n",
+       "Non-trainable params: 0\n",
+       "Total mult-adds (M): 84.83\n",
+       "===============================================================================================\n",
+       "Input size (MB): 0.00\n",
+       "Forward/backward pass size (MB): 2.26\n",
+       "Params size (MB): 10.64\n",
+       "Estimated Total Size (MB): 12.90\n",
+       "==============================================================================================="
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "summary(e, (1, 28, 28), device=\"cpu\")"
+    "summary(wr, (1, 28, 14), device=\"cpu\", depth=10)"
    ]
+  },
+  {
+   "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 2ef7da7..84d853b 100644
--- a/src/notebooks/02b-emnist-lines-dataset.ipynb
+++ b/src/notebooks/02b-emnist-lines-dataset.ipynb
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,14 +40,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-09-09 23:07:57.716 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_load_data:134 - EmnistLinesDataset loading data from HDF5...\n"
+      "2020-09-10 20:11:30.358 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_load_data:134 - EmnistLinesDataset loading data from HDF5...\n"
      ]
     }
    ],
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 6,
    "metadata": {
     "scrolled": false
    },
@@ -212,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -235,6 +235,115 @@
     "print(emnist_lines)"
    ]
   },
+  {
+   "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,
diff --git a/src/notebooks/04a-look-at-iam-lines.ipynb b/src/notebooks/04a-look-at-iam-lines.ipynb
index 0f9fefb..093920a 100644
--- a/src/notebooks/04a-look-at-iam-lines.ipynb
+++ b/src/notebooks/04a-look-at-iam-lines.ipynb
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -40,8 +40,8 @@
      "output_type": "stream",
      "text": [
       "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",
+      "Number classes: 81\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: '_', 80: '<blank>'}\n",
       "Data: (7101, 28, 952)\n",
       "Targets: (7101, 97)\n",
       "\n"
@@ -56,16 +56,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(97, 80)"
+       "(97, 81)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -76,51 +76,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'A MOVE to stop Mr. Gaitskell from________________________________________________________________'"
+       "'A MOVE to stop Mr. Gaitskell from'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "def convert_y_label_to_string(y, dataset=dataset):\n",
-    "    return ''.join([dataset.mapper(int(i)) for i in y])\n",
+    "    return ''.join([dataset.mapper(int(i)) for i in y]).rstrip(\"_\")\n",
     "\n",
     "convert_y_label_to_string(dataset.targets[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 57,
    "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"
+      "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"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -132,7 +132,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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KsRJK6HBCFcZCCXTh0AWvUH0c6jzda0bvu1BCmZMxo3udhRtDTu3rVEb9v95PMi2ncodrQydRKFx7hjOsFhOGQhnPi5XRabvH45k3HSvUsXqfyfxSU1PJzs7G4/Hw+uuv09zcTEREBElJSaSkpJCamkpaWhqpqalkZWVRXFxMfn4+q1atoqmpiaqqKhobG+no6LC9gJbaHm/2uLfC1Qg5VyNAXG1aV5tuKCFQNfqXmv5ShIVw/Rgu/XDtm5+fT35+PsPDw1y5cgWfzxe2jE7XW6jfsKWMLaffoYiICLxeLxkZGYyNjTEyMrLAm1MIwapVq8jMzKSxsZHOzk5mZmYcPX0WG/9Ov0Pqd12Qe6vCo8FgMBgMBsONwJsWfizLmhFC/HfgBeaWc3/MsqzKq0lDf8DTPWacDONQD/ZOaTt5bizlLa+Tgej0AOpUB/lffwjXBQJ1n+6lIcvuVAZ5vC4uhMozVPnkZzUtJ2M+nIATypAKVVc1DkoowUXvl8WMlXBGhNr/TuNG7ne5XHi9XtxuNyMjI45pSrFHTlN0SnexceIkyrhcLqKiooiOjmZ2dpapqSmmp6dDjk81Tad89fMWM7j0sjm1lS5mhRN5nISTpRqeixlxToa9U/uq+zweD6tWrWJ4eJi+vj4mJiYWLY96bQghcLvd+Hw+Ll68yMmTJ/H7/QghaGpqIiIiAo/HQ2xsLOnp6ZSUlLBmzRoKCwtZsWIFK1asYO3atZw9e5bTp0/T3t7OyMiI3ce6V0c4IeV6NXDfjPF+Nb8DiwmxTuc47b8akStU+cLVVQjBxo0b2bNnD83NzYyNjdHU1GSntZj312LlC/fbF679vV4vOTk57Nixg7q6OqqqqpiYmJiXjtvtZvfu3ZSVlXHo0CFGR0cd76X675Q69cpJWA71u+RU7lB10MWs6/U6MhgMBoPBYAjHW4rxY1nWc8BzV3OOEGJefBLdgJb/5cOgU+wceZzTA7F8UJydncXlcuFyuRbEj3FKQ/8vp32pngLqlBCZnpPAIcuvGscyDbUMLpdrwZtTtT6h3lKrecoHZ1WMcBIZ1HPVNtb7Rhd49LqpaYQSydR6ONVBLY/T/nAP+4sJVGoaaiwKFZfLxczMjH1Mamoq+fn5pKSk8MorrzA1NRVSMJF1Diem6KKgPu1OltvlcpGQkMDKlSspKipieHiYxsZG2traGB8fdzR+ZJmdPJ7Ua0bGnlJFjFDtG074cWrbUP0R7jg1LXXcOBnZTuepeYQz3PR2Sk9P57Of/SyVlZU8++yzXLlyxfE60Me0apT29fVRXl7OyMgIExMT8+KtyCmHfr+f/v5+ampqOHz4MMXFxWzfvp0tW7ZQUlLC2rVr2b9/P08//TRnz56lvb19gTeHLoSGqptTuzgd+2YM3FB9uZhRvVjZl5rvmyWU6KNu1+NkhTs33HjU9zttD9U34cQft9tNfn4+ubm5xMTE0NbWRktLy7yxuZR20kUmp3vkYv2sTklNTk7m5ptv5u677+bo0aM0NDQwPj5uHwdz4lBpaSnFxcU0NjZy5coVxsbG5v12S/QpaxKXyzXPI9fpRZD8rE6TdUIXoMMdYzAYDAaDwXAj8BsP7uyEk9giH1DVBz/deJXbVeFFTUd+lg+t8Ib4oAtAaowU9XwZ66esrIy4uDg6Ojqoq6uzy6eKP3rZ1bTUPHXDX32onZ2dXVAPeZ5eb6cHYfmnPkyrBo4u7uhilSpi6GmH6z/58K0eqxvzenpqn8qy6AGn1TSdgizrop2su5MoNTs7u8CjQm1n6e2zd+9eDh48iNvtZmBggLNnzy4Q6tQ2k+2mli3UuFRFGrUOXq+X3NxcPv7xj7Nz504iIyMJBAK0trZy+PBhfvzjHzM6OjovhpGar1omvf2dvNyk2Kq2vyQQCOB2ux3FpHBijSquOYlgSxUydKNOP18fo3q7q+Nfzcfj8djTtLKzs7l8+TLt7e22149sE3mNxsXFER0dTX9/vz02APr7++nr62N6enreGNWFTVkmn89HfX09mZmZZGZmUltbS3Z2Njt27OAzn/kMra2tvPLKKzz//PM0NDTYfar362KEOy7c9bsU0fJqeTPCTTgh42qFpMWEsnDiZKj9ocRN/V6pprUUccZprEsx8cyZM5SWlrJmzRpmZmYoLy+np6dnQfw7p/Gi3x/1mGTqb0ooIQjeeOkhj/N4PCQkJJCQkEBMTAxut9tOW/6Pi4ujoKCAhIQEiouLqauro7Gxcd7vVqjfZsuai2uUkJBAWloaSUlJTE5O0tLSwsTExIJ7lrrogV53VZANJegZwcdgMBgMBsONyDsm/OjGXlRU1LwpLvpKUiqqMSrFGNUgdgqYqxrfMzMzYYWjZcuWcccdd5Cbm8vFixdpaGiwDUv1TaN8QJYChr4Kl9ObTn36mW5Qq0ak/vCqGxWWZREZGUlOTg533XUXLS0tHD582H4bK8soH5xVYUmvuzT8dUNabVsn40g3QPTzdeNJ95rS+0rmJ9tLN1j0/tdjOclyhBo7utFmWRaDg4P09/eTn59PUVERp0+fXmAkqf2vp6muEqeObV0Ikse6XC6ys7P54z/+Y9LT0/nKV75CXFwcO3bsYM2aNdx7771MT0/zox/9aN6qM07iR6g+0csYyttBlknvT7WN5bhWjUqJPubVcqn/nYxopz5xSsvJGNe/O4ldfr+f+vp6+vr6KCwsZNOmTbS3t1NRUTGvLEIINm/ezN69e8nIyOAb3/gGbW1tdp/KqV26Ya2WQb2uIyIi2LJlCw8++CDr16/npz/9KX/zN3/Dxo0b+ehHP8qKFSt46KGHKCkp4Wtf+xr19fXzrr2liAdLxWkshBJB3mzauvD1dghXahmdxMVweS12LYSqh573mxGcwomT6jb9mpPlvnz5Mr29vaxZs4aSkhJuvvlmfv7zn9u/X6qQK70WnYQ89XdFftevQf3+rCKFz5iYGLKysigqKiIlJQWv14vH48Htds8rd0FBAcnJybjdbnJycigsLCQmJoaJiQk7PVWcUfP2er3cfvvt3HzzzaxevZro6Ggsy2JkZIRLly5x7Ngxqqur6evrs8ul/p7rq3QCC17Q6G1uMBgMBoPBcKNxzYUfaQjLv4SEBPbv38/27dupq6vjtddeo7GxkcnJSeCNB0aPx7NAEFJRBQDdQNONCP1hXfc8kYZeYmIimZmZxMTEMDk5aactV10RQhAfH8/73vc+hoaGOHLkCENDQ3a55X/VGFCNQyeD2slgcDJcZF1SUlIoLS3ltttuY3x8nEuXLtHe3m6LaNKbQY/l4zT1x0mskeerYo1uGOlvXZ3EmqUaYPpxetuob4zV8qn106fi6OKLbH+5fXx8nNHRUbxeL8uWLVtwrNpuel2dPju1jVqGpKQk1q1bx8qVK/nBD37AiRMnAGhoaOCmm25i//793HPPPbz66qu0trbaY17vo3Dtq4s3ev+ox+pjQd8eyrh0wkns0dtS/b9YWk51cRon+nnqsa+99hrLly9ny5YtNDc3c/nyZfx+/7xzMjIyyMnJISkpifXr19Pe3j4vPb3Meh11QzQ+Pp7Y2FiioqJITEwkOjqa5uZmfvnLX/LAAw9QVFTEypUr2bVrl+31o497fbW1N8PViDtLuUbfap/qaTid47RyoS6I6eeFuoeqQotTjByn3wOncobKa7Hj1XKHWslQHhMREcH09DRXrlxh3bp15Obmsnv3bp599tl5+UkBWk9fzTPUyldqOk5CiHoPjYyMZM+ePTz00ENs3boVr9dLamoqUVFReDwe+/fL6/Wyfft2+3z5u5mamkpHRweRkZHs2rWLtLQ0qqqqqK2tnTeFOjk5mV27dpGTk8Ply5c5fvw4WVlZlJWVsWHDBtauXcuVK1c4fvw4L7300rzVHJ1+l/R2dRKbDAaDwWAwGG40rrnwIw13t9vN8uXLufXWW7n11ltJSUkhKyuLyMhIjhw5QkVFxTxDX3+wUw1pp4d7NWhkqDedToJFREQEk5OT9soliYmJpKSk0NHRMS8NKUbJh9bp6WlaW1upqakJuxJLOCNJfxusTyVRH9jld+mGn5WVRUREBLt27eKFF15gYGBgQZ6hjHF5jC4UhDOInN5i6+foxoiTIaLmpU+L0o9xElX0vtXrou9T05RtOD4+zvj4OBERESQmJjqKKuECq6r95mSAqFiWRXx8PDk5ObhcLi5dusTY2BjT09PU1NQQFRVFbm4uW7ZsYcuWLXR3dy+IOeSUdyjjR6KKYXrb6WnpbRwqP6c66+k4ldkpnaWIgvp14NT/al7q9Jn3ve99LF++nKKiIrKysuwl1lWDOxAIkJKSwvr16zl06FBIITmUwKbee2R8sbi4OLZv305CQgIA8fHxZGVlMTs7y/DwMJ2dnSHbOJzhrud5tYQbS+HQ752hzg81HsKdo38P5+njVKZQ5VLTdrqHXE2Z5G+X2+1mZmbG9h5VyxLqPPU3RghBTEyM/buXmJhIZ2cn/f39tLa20t3dTWlpKevXr6e4uJja2lpb8JHp6vHx9PwW+wzOS8TLa8Lj8bB8+XLWrl1LSkoKQghycnJITExkYGDArktGRgYbNmygsbGRuLg4kpOTyczMZMWKFfT09FBUVMR9991HZGSkHbB6cnJyXl94PB7i4uKIjIykt7eX2tpa6urqWL58OZs2bbKnSjY2NlJXV7dA+FLb3+n6CXffMxgMBoPBYLgRuKbCj3xIc7lc5OTksHPnTrZv305vby+tra2sWrWKDRs20N/fT2NjI8PDw/MeckMJCeEMTf0hT59G5FTG2dlZhoaGmJycJCEhgezsbDo6OhyNS4CYmBgyMzPZtGkTg4OD81ZikQ/RqveE9ODQyyjFpPT0dJKSkgAYHBxkcnKSQCDAyMjIgnaQD8DR0dHExMRw0003cfr0aYaHh+03svrDrpNxpeJkUMspSrLd5LLU+hQb3UCXb44TExMpKCggEAgwNDREb28vk5OTC6ahOZVFN77V7Xo8pKV4HejjZnp6Gr/fj8vlwuPxLMlQDWVsqca4kzEqhCA6OpqUlBRmZ2dtA8qyLMbGxmhoaODs2bPs2LGDDRs2cOjQoQXCj8xDrYc+NsMZe+qx6p/T+A5lcIcyttT90jNOjhN9rDilG45Q4yCUUAJz13tbWxs+n4/09HTy8vIoLS21l5uW5w0MDDAyMkJ8fDzFxcXExsbi8/nCXjtOApQkMjISt9uNy+UiMTGRnJwcIiMjGRkZob6+noGBAerq6rh8+fKCdJYqZLxThLpvOl2DTn0i6+X1eklJSWFqaorx8XGmpqYc01PHpdo+Eil+u91u/H6/vTKeU4w4vTzhhAB9jEZHR5OamkpeXh4JCQmMjY3R3d1NT08Po6OjYadTqnWKjo62p0MVFBSwbNkykpKSaG5upq2tDb/fz+TkJC6Xi+XLl7Nv3755QZ51ocapvUMJHfp9RD1W9/iU26RQFRkZSXZ2Ntu3b8fj8dDf309UVBRbtmwhPj6eQ4cOkZWVxZYtW8jMzKS0tJSOjg727dtHWVkZfr+fnJwckpOT6e7utvOempqio6ODgoICli9fzoYNG3jppZe4cOECNTU1jI2NcfPNN5OdnU1BQYHtIReqT5368Wp+HwwGg8FgMBiuR66p8CMfuuLj49m5cycHDhxgYGCAb3zjG7jdbj70oQ+xfv16SkpKOHfuHENDQ7Z4APO9C9R4OU6GATDPOHZCPVb3JBgYGMDn85Gbm0txcTGnT5+205THzczMMDw8TENDA4WFhezatYuWlha6u7vt5W6dAhir+cj9Ho+HmJgYMjIy2LRpE0VFRXi9XiorKxkcHGR8fJyLFy/aS+TKh/GZmRnGx8dxu93ExMSwbds2srOz6erqYnR0dF67O9VdnfKkT0uTolV0dDTJyckkJCTg8XjmCWOjo6NMTU0tCGCs1i0xMZGysjIefPBBpqamaGxs5Ny5c7S1tTEwMMDY2JgdiFk3dtV+deozPcC0PtYkepwdmRa8IZ7JAMuhDFh1u+4xEBkZidfrJTIy0u6X2dlZIiMjGRoamicyuN1uoqOjcblc86YNAgwNDVFZWcnk5CTFxcXExcXh8XjsuBY+n88eWzBn9EZHR9tTL6QBrbeT7EtdVFCNSJmH7kkAoVea0z1OZP1SU1NJS0sDwO/34/f7GRsbY2BgYFEvFad2dzJglyJQWZaFz+djeHiYqakpCgoK2Lt3L6dOnbK94qTwMzQ0RExMDDk5OeTm5nLlypV56aiim7pdHTNyzHu9XtxuNxMTE9TV1fHkk08SHx9Pa2srg4OD9PX1MTg4aHsHhrr+ltJGS0UXDJZynBOhjGhdiJTTWfWxI8fIihUrbLG8oaGB1tZWO/C2EHMB0OPj4wEYHx9nYmJiXlrynpmSkkJJSQnR0dGMjIzQ399Pf38/Q0NDdrwzl8tFVFSULRTL+83s7Ow8gTwU0dHRFBQUsGnTJrZt20ZaWho+n4/q6mrKy8upra1lcHBwwX1BJyoqiqKiIvbt28f69etJSUnB7/fj9XopLCxkZmaGyspKEhIS8Pv9xMTEcN9993H48GHa2trssjv1hS6WqeVQ7wEw31tI7Rd5rmVZzMzM2CJ9VlYWlmWRmZnJBz7wAXJycmhsbCQqKoqdO3dSX1/Pyy+/THFxMfn5+axevZodO3YwPT3Nnj17CAQCeL1eVqxYQXFxMUNDQ/aUrenpaSoqKsjMzGTz5s3ce++9WJbF8ePH6evro66ujpUrV5Kenk5sbCywcOqeU3vr95HFrimDwWAwGAyG65lr7vEDsGbNGjZs2ADAE088QWtrKwCvvPIKa9asYd26dZSVlVFTU2NPm1ANVfW77qquG5X62z63273A+0A/3rIsWltb6e3tpbS0lB07dvDzn//cXspZTW9ycpKXX36Z2267jaKiItavX09zc7NtNMoHao/Hg9frRQjB+Pi4vdw8YE9727JlCwcOHKCgoACYMxL27dtHIBCgsbGRf/mXf+H06dN23V0uFxMTE3R1dTExMWGvirJ27Vqam5sZHx+fF+tGFaHU/pDoK65I9/tdu3Zx2223UVBQQGRkJFNTUwwMDNDa2sqvf/1rqqqqbM8V1QCWZGRksHfvXjZv3szMzAw7duzg7rvvpqqqiiNHjvDKK68wODg4z6iT54cLLKz3rz7WnAQKdZsawFr2xdjY2AIhUR1rqnEuDdjY2FgyMzPZuXMnWVlZREVF2QGjs7KyePrpp2lqarLFFSGE7WFUWFhId3e3HcRZtm13d7cdb6akpIT09HSGh4c5c+YM5eXljI+P4/F4yM/PZ82aNeTk5DA0NMTJkydtjzPZ93IVHhk3S21D2edRUVGkpKQwMDDA1NSU43WkXyOhxlBKSgqf/exnueWWW+wYSj09PZw5c4Z/+qd/WjDNQ+9LXUBQ09ZFBN2rw6nPpJAbCATIyMhg48aN7N27l+eee84+JioqipiYGGJjY4mOjuauu+7im9/8pt0vuliml1/ul/07Ojpqr0jU09PDoUOH7GlBUlxTRWwnw1Wtqz629bZbCks1epfiQaEeK69X6eEkhc2UlBTq6+vnTYeS98KUlBQ+//nPs3HjRqampnjttdd46qmnOH36tH3smjVruOOOO4iMjOTMmTMcP37cXiI8MjKSoqIiNm/ezPbt21m/fr09/WpgYIDq6mpOnjzJsWPHGB4eJjMzk7179xITE8PY2BiDg4OMjY3R2dlJb2/vPA8hfWwLISgqKuLgwYPs3LmTc+fO0draSmlpKbfeeivr1q3j2LFjPP/88/T29i4YI5KIiAhWr17Npz/9aeLj4zl79iw/+MEPaGtrIzExkT179nDzzTezc+dOYmNjmZ2dJS4ujtWrV/Oxj32Mb37zm/YURdkPcqU5tV+cxpMuXKsrd8ny6nHS5HXT3NxMcXGx/ZuVm5vLsmXL8Pl8tLe3c+LECR5//HGGhoaYnZ2ls7OTdevWUVxcTGZmJqdOncLv97Np0ybb86ezs9O+JwohuHjxIn6/n/HxcW6//XYeeeQRDhw4wDPPPENSUhJr1qwhEAjQ3d09r3xOserUl0Xyu9qvensYDAaDwWAw3Ahc8xg/Xq+XdevWkZycTEtLCzU1NbYg09fXx9jYGAkJCXi9XqKjo+0gxTExMaSnp7Ns2TLS0tKYmZmhpaWF2tpae9oRzPdckKiBcXXDXf6Xb6fleZ2dnXR1deF2uykpKWH58uXU19fPmyomY9KcO3fOXob35ptvJhAI8Pjjj9Pf328/jO7YsYP9+/fjcrn4/ve/T01NjT11atmyZdx///3s27ePpqYmPv3pTxMIBLjzzjs5cOCA/SZ47dq1nDt3zq6XNHxHR0dpamoiMzOTyMhIO2aEulKXXIVFfdhVxQFVIJGGwfLly/n85z9PXl4efX19dHZ2Atgu/xs2bODmm2/m+9//PocOHbLjlajGt5zq5fV6GRgY4Mknn7QDdu7YsYPS0lLuvPNOnnrqKV5++WU7sLZqHMuyOnl4yL6Q/9X8QxnGal2FmJt6ER8fz+zsLFeuXLED/6pCouxr1dMsJSWFsrIyPvCBD7B+/XoCgYD9FjsqKgqv18vk5CRtbW20tbXZcWR6e3upq6vjzjvv5D3veQ9nz561g4fLcRoZGUlmZiaPPvqoHSw4OjqaD37wg5w/f57vfOc77Nixg127dtnTxgDuvPNOvv71r1NVVYXL5WL9+vVs3ryZoaEhfvjDH86rvxwLHo+HD37wgzzwwAM8/vjjHDlyhM7OTrv/ZNpqvC29T2Ra8fHxbN26lfe+9722iCTHud/vt8eh00o8Ms1Q0w9Vzxr9PD3YK7yxLLVsT+ktsWzZMj75yU/S1NREdXU1AHl5eeTk5GBZFklJSTz44IP88Ic/tL1MVENR9ZzSx5xcaWlkZITJyUm8Xi9paWnzvEz09tPr9mYFncXOW8zT52qR99qMjAxbtMjLyyMpKcle+vsTn/gEJ0+enHeP9nq9vP/972fFihUMDQ2RlpbGwYMHyczM5M/+7M/o6uoiNjaWz33uc5SWlhITE8MHP/hBTp06xV//9V/T1dXF3XffzZ133klycjJVVVV89atfxbIsioqKKCsrY+3atWRmZpKdnc0TTzzBo48+ypYtW+x7ovT0GR8fp76+nhMnTlBVVUVXVxdjY2N2f8kXD5s3b2bNmjV0d3fz3e9+l4GBAdLT07n33nvZvHkzu3btwu/38x//8R92X6i/N7KdvvzlL+P1evnOd75DeXm5PX23p6eHxsZG+vr6eM973kNxcTEzMzNMTEwQHR3NQw89RFtbG88884w97Uv2gX69OIneqigiy6d6waifVeGot7eXhoYGJicncbvd1NbW2osYtLa2Ul1dTVdXl31v7O/v56mnnuLSpUskJCRw7tw5GhsbiY+P5xOf+ARbt27llltuoaCggJdeeon6+nqGh4dxu90IIaipqSE6OpqbbrrJfgk0PT1Nf38/FRUVuFwu4uPjGRkZWfCiQtZTivgqsnwS/ffEYDAYDAaD4Xrnmgs/0rvF5XLR1dU1z83e7/czPT1NRkYGd911Fzt37qSjo4OzZ8+ye/duVq5cabv+z8zMMD09zfe+9z1ef/11ezUtuYyr+sZfNxbVt+y6l4o8b2pqir6+Prq6uli1ahW33HILjY2Nju7lfr+fb3/723z605+muLiYm2++GbfbzWOPPcbY2BgRERGUlJRQVlZGcnIyPp+Pv//7v7eNijvuuIM1a9ZQU1PDY489RldXF9PT0zz11FMkJCTYKwQVFBTYrvHSi6OgoIC77rqLbdu22WLDPffcQ3p6OsePH+fMmTPzgmHKdlANByeDOSkpiUceeYTc3Fy+9a1vUVlZabdxQkICjz76qC0wLV++nNTUVFv40b1qYmJiiIuLo6uri2eeeYZnnnmGVatWsX37drZu3crmzZvZtm0bL7/8Mv/2b/9Ga2tryADZTmKDGvBU9QQJZejqhnJqairp6elMTEzQ0NBgt5EUedQ0pHGxcuVKDh48yL59+xgdHeVv//Zvbc+1bdu2ceutt7J58+Z5wpEs29DQEBUVFXR0dLBp0ybWrVtHZWUl4+PjREZGkpCQQHp6OkePHuWxxx7D5/ORlJTE6tWrOXDgABs2bOBv/uZv8Hg8vPDCC5SXlzM6OsrGjRu57bbbePjhh/n6179OQkICt9xyC3v27KGnp4ef/exn+Hy+BW+6IyMj2bRpExkZGZSWllJXV2fH4JCob9Dlf9VYVL3wZLwTv9/PX/7lX9LY2MjY2Jg9XUdPS88nlEdNqOOlgKQLjvL4yMhIe2U+KcwlJibyuc99ji984QvMzMywYcMG8vLy6O/vJzExkfT0dDZt2sTrr79ue5no17465uR+KXKNj4/bYzgxMXGeCODkeRBq+WlVEJLTlZKSkigsLCQqKorJyUk6Ozvp6+ubF2fGqb2Waug6eauobQtz9/G0tDRuv/12fv/3f5+4uDjbQyUxMZGoqCgAPvWpT1FbW0tPTw+AHcPspptuor6+nscee4wHH3yQPXv2UFBQwL333su///u/U1paSk5ODsPDw3R1dRETE8PGjRv54he/yHe/+13uuOMOPB4Phw8f5umnn7andL3++utkZ2dzxx13UFZWxsqVK0lMTCQ5ORm/388vfvELWlpaiIyMZNmyZZSVlbF69WqKiooYGxujt7eXK1eucPLkSS5cuGALdaOjo0xPT5OSksKqVas4duwY/f39vPTSSyQnJ1NUVERmZuY8oVQdu7Gxsezfv5/4+Hj+4R/+gfPnzzM8PGwLTLJtjx8/zqZNm1i5ciVCCFv0yMnJ4ZFHHiEyMpJnn32WhoYGW1ACHMem3o+6aOskGKr3zUAgQFdXl+3RGRERQV1dHU8//TS9vb12cGvpFSfbSk7bk56NlmUxNDTEj3/8YwYHB9m5cyf5+fn83u/9HjD3+ymXiJd1bmpq4vXXXycQCJCfn09mZiZlZWWUlJTQ0dHBiRMnOHLkCH19fUxMTOD3+xesfKj+Bjl5AxkMBoPBYDDcSLwjq3pJl3yfz2e/iRNCsHnzZjIyMoiLiyM7O5u0tDSKi4vZuXMn09PTVFZW0tTUxPj4OLGxsdx///1s3bqVCxcuMDw8vEC80AUN+fAnH5Z1Y0aeJ2PudHZ2cv78edauXcvWrVv58Y9/vOCtqfzf3t7Os88+yz333ENJSQnbtm2js7OTZ599Fr/fz+joKH6/n7S0NHbu3MmOHTs4efKkbUjExcVRXV1tP1BblsXw8DCXL19m1apVrF+/nhUrVpCWlmYbUTDnYZGdnW0vsetyucjLyyMuLo61a9eyadMm/vzP/5zx8fEFgolqzKpxb6KiouzVXCoqKqioqLCnI1nW3PSCwcFBZmZm8Hg8dhper9d+0JftmpaWRmFhIampqZSXl9tGdFVVFZ2dnVy4cIFdu3bx4IMPcsstt5Camsp3vvMdzp8/v8DgVt9uh1qeV62j/iZbPUYaB9KATU9Pt6flqMfp04bcbjdJSUkcPHiQjRs30tTUxH/+539SV1dnewrFxsZSXFzMhg0bmJmZ4cqVK/OMMb/fT09PD08//TSPPPIIDz74INPT09TV1REXF0dpaSlut5vDhw/T2NjIzMwM3d3ddHR00Nraysc+9jHWrVvH2NgYNTU19jSJwcFBUlJS2LBhAytWrEAIYYtuQgji4+PteEy699TExARut5v09HTi4+PnCRW6t0tiYiIlJSWsXbuW48eP09jYaPf71NQUbW1tNDU1kZOTw+rVq2lsbKS/vz+s0KMLsHr5dDFnsX3q9pSUFOLi4hgYGODSpUv09/dz//33U1hYyGc+8xn8fj/Lly+nqamJ+vp67rvvPtauXcttt91GVVXVvCDPUtxRRRx1bEmjf3h42F4pLiYmZl6ga/U83dtHRx7j8XgoKytjy5YtFBUVkZiYOC+OUFVVFceOHaO8vHxB/14toUQD+T8hIYH169dz5513UlZWxpkzZzh79iyDg4Ps3r2bPXv2kJycTFZWFuvXryc/P9+OTRUXF0dhYSHJycm88sortLS08MorrxAfH8+GDRvssb9u3TqioqI4efIk5eXluFwuHnjgAVatWsX73vc+cnNz6ezstNtZGv4Ara2tNDc3s3z5cluU6OvrIykpiYaGBi5cuGDHzzl69CjFxcVs3ryZ3Nxc1q1bx+rVq1m7di1PPPEE586dw+PxMD4+zujoKCtXruSBBx4gOjrarktWVhY9PT00NzfPayv5WXrs7dy5k6qqKmpqahYEg5bjV8ajkr9xw8PD/Ou//isf//jHWbFiBQ888ABxcXE8/fTT9sqXel85vZxQ85H5OgWjVkVUmJv62tLSwunTp9m9ezeDg4OMjo7aXlHqsWq66hQ0uU96LF28eJEVK1aQl5dHVFQUY2NjjI6O2mLpwMCAHaMuEAgQFxdHSkoKy5Yto6SkhKKiIg4cOMD27duZmJhgcHCQ1tZWO1j60NDQvOnc6j08lChqMBgMBoPBcL1zzYUfYF5ATWnIp6WlcdNNN+HxeDh37hxXrlwhOjqau+++m9jYWNuYPHr0qD0d7KabbiI+Pt5+U6iiTs/QH/LUgMa6KCSEICMjgy1btpCTk0Nqaqrt4ZGZmWnHg5APuKrBe/HiRbKysoiNjSU7O5vbb7+d2dlZqquryczMxOv1EhERQVZWFu95z3uora21V7yKi4vD5XLZaUuGh4cZGxvD6/WSn5/PBz7wAQ4dOkR7e7tt7FdXV9PS0kJhYaG9KlVCQoIdsDbU210V1bCTwkV8fDw1NTUMDQ3Nm6ohxYzbb7/d9g5KSkrC6/ViWXNBQd1uN/Hx8ZSWlrJ69WqmpqZsozQyMpKMjAympqZsI0gIwUMPPcS2bdvsN8zV1dXzDG69P1VBRq+bfpy6Xf3u8XhITk4mNTUVn8/H2NjYgvaQ/6UoKOPqeDweqqurqaqqYnx83DbyJiYmmJqasgUVGfdDNT58Ph+nT5/mtttuY9WqVdx5552kpKTgdrvZtm0b3d3dVFRUMDExAcy9FZ+YmGBiYoLnn3+e4uJie/rU5OSkHTfqzJkz5OfnExcXR39/P2NjY7jdblJSUtizZw+HDx9mfHzcFhdlmeS0xvz8fEpKSmhsbKSjo2Neu8ppiWVlZezdu9d+E3/06FEaGhoYHBzE7/czPDzM0aNH+fCHP0xZWRnl5eW0trbO8ybQp56o49PJmFX3hRJJ9GPlcSkpKXg8Hrq6urh8+TJXrlwhNjaWBx54gE2bNtHU1ERtbS0VFRW0tLQQExNDUVGRfQ/o6+ubFyTYKQ91zFmWZV97gUCA2NhYvF6vPbZCjVOndCQZGRkUFxeTlZXFyMgIPT09xMTEkJuby+rVq8nJycHlctnX69th2Kr1k2WKjo5m8+bN7Nu3j7Vr13L58mV++ctfUl9fz/j4OMPDwzQ1NZGWlsb73/9+cnJyWLlyJS0tLfj9fuLj4ykpKcHlclFbW8v4+DjNzc309PTYgru8R0qv0KqqKsbGxoiJieHDH/4wGzZsIDo6momJCZKTk/F6vbbQMDs7y+TkJI2NjbhcLvv6kEHox8fHGRkZsacdJiUlkZqaaveRjPEUFxfH2NgYK1euJCYmhqysLLKysvB4PKxbt84uZ2trK+fPn6etrY3GxkZgoadWZGQkaWlp5OTk8Otf/9oOZq+/RJDePyMjI4yNjREVFUVjYyMnTpwgISGBD3zgA+Tm5nLbbbfZ97wLFy4suoiBjnqtOY01tf9nZmbo6enhpZdeYmJiwhbv5XQ59b6sCpmyHWQ6Mh5dW1sbg4ODNDc3k56ebq/E5vP5mJyctD9PTEzMe0bwer0kJiZy5coVCgoKyM3NJSUlheXLl1NQUEBpaak9Xa65uZmBgQE6Ojro7e1lbGzMDni/WGB5g8FgMBgMhuuVay78SI8RGXdDThHZuHEjq1ator6+nkOHDlFZWcmqVas4ePAg09PT9ltXOaVhZmaGsbGxeSsmqcipOqqhr3tw6A/L8vvq1au59957ycvLs4WKrKwsbrnlFjo6OmzDfnBwkNraWvuhube3l1OnThEZGclNN91ESUkJcXFxnDlzhpUrVzI9PU1raytJSUns3buXkydP0tjYaIskmZmZJCUl2QajXE0rKiqKmZkZYmNjee9734vL5eLChQt0d3czNTVFRUUF58+ft8srV1Fqa2vj+PHj9ltvJwNT/lc9JOLi4uxpZe3t7XZsGnnMzMwMly5dwu/3Exsby4oVK9i8eTNCCHu1L+m1tWXLFlJSUqisrKSmpgaAwsJC9u3bZxsCPp+PyspK/H4/qamplJWVceHCBS5fvjyvrOGMFKd9Tp4++jlyieakpCSGh4ftGEPSCI2MjCQqKorY2Fji4uKYmZkhPz+f5ORkJicnGR4etoOeyjf70ltNkpaWRm9v7zwDbWZmhvb2dl555RUefPBBdu/ebceuWrVqFWfOnLHPUQ2osbExXnvtNR5++GGSkpLIysoiJSWFyclJpqenqa6u5ty5c3aAaBl0Oz4+nrvvvpvBwUEaGxvn1TUqKsoWtpYvX8727dttYaq3t9cW5lJSUti4cSO7d++msLCQiIgIdu/eTXx8PJcuXaKlpYXe3l58Ph+nTp3i/vvvt5dvjoyMtINLq6h9ok/R0Mep/lkfF3qfy21SUBseHqa/v5+2tjZefPFFMjMzSU5OpqKigvLyctu76qWXXrK9SkpLS2lvb18gXKl5O41DKSLOzs7aUzBVA1k9NpwHgjSaY2JimJqaora2lsbGRoaGhoiKiqK4uJjU1FTbwyw1NdX2fnwrOAkJQgiWLVvGrl27WLt2Le3t7Tz55JNcunTJvgdXVlZSW1tLSkoKa9euJTc3l8LCQsrLyxkaGrLvLXIaUUxMDKmpqcTFxdmCjRQYZRDo5cuX09HRQU1NDdPT06Slpdn3ihUrVlBUVER9ff08QaWjo4PBwUEmJyeZmZmxp99Jr7ykpCS2bdvG3r17SU5Otl8gqHG/1qxZQ0ZGht0P4+Pj1NTU2EHXZ2ZmqKuro6qqiitXrtjXiT42VHFfF/YlahweKeT29fVx9uxZBgYGeOaZZ4iPj+fgwYMUFhZy4MAB2traqKiocAwCHmpsOU09lfWTx0mPNrl9ZGSE8vJyent7aWpqskUZXbhVRVs1L/Uli2XNTZsbHR2lpaVlQflUcVdeb3I6mc/ns71Ely1bRnZ2tj1NLzs7m/z8fFauXMnQ0BBDQ0O2CNTd3U1vby8TExP09fXh8/ns30SDwWAwGAyGG4V3RPiRYop82I6Pj+e9730vQ0NDvPjii5w4cYKoqCg7zoHP58Pr9doGeiAQYOXKlURHR8/zipDoAYt17w19KVu5Txr8q1atYuXKlRQWFtpT0bxeL3/0R39ku6R3dXVRXl5OU1OTvSKMEIL6+nr7jfyHP/xhMjMzeeCBBxgaGuLo0aMMDQ1x5513UlRUxKc+9Sl+8pOfEBsbS0JCgh335vz588zMzJCZmcn27dtJTk6mtraWiIgI8vLy+MhHPsK+ffuorq6mrq6O/v5+qqurueuuu/D7/bYhPz09ba/spcb4UftCnSYjP3u9XpKSkuxAvE7ntra20t/fT0xMDKtXr6agoIADBw7Q1NTE6OgoaWlpZGVl4fP5OHnyJD//+c+ZnJwkIiKC/fv386EPfYjk5GSGh4ftOA1er9degl56VugindObZVkXtY9h/htn3biQho8UT2JjY+nv78ftdpOYmEhMTAwJCQmkpqayfPlySkpKyMvLo7Oz016xLTk5mfz8fFuwkYb4/v37KS4uZnJykpSUFB544AH6+voYGBiwl7cOBALMzMzw/PPPU1hYyN69e9m7dy8ej4fR0VGqq6tJSkpiZGTE9pCTb9hHRkbo6+sjLS2Nbdu22at5jYyMMDQ0xPPPP2+LpfINelxcHFu3biUzM5Pnn3+euro6RkZGEEIQFxfHtm3bGBkZwev1smnTJtuoOnz4ME1NTQBs2bKFPXv2kJCQwKFDh/D7/ezbt4+NGzeyefNm+vr6uHLlClVVVXR0dDA0NGQHuvZ4PPOmmelGpzoe1T5X+9rJ40v2sz5tRR4bCARITk7G7XYzOTnJ5OSkHcvpK1/5CrGxsYyMjNjiJkBDQwOnT5/mnnvu4fbbb6epqYnOzk7H2Fi64SzH7ezsLNPT07bo4PF45nlOqfXWP6vI9Lu7uxkZGbEFCBmrqLm5mTVr1pCamkpkZCRxcXHzzluKt18onOomp5p1d3fzk5/8xPY4kfn4/X5blK+rq+OOO+6wvXJcLpcttMpgwZs3b+bAgQMUFRVRW1vLiy++iMvlYvXq1XaQ37y8PFpbW+1AyBMTEwwNDREdHc369euJiIjgF7/4BTU1NUxMTNgemNILSBWP5DWYm5vLBz7wATIyMqirq7OvRzlddnh4mPLycrq7u+nu7mZsbMz2HMnNzeV973sf6enpbNy40fYmki8FVO8/+V+OgZycHKKiohbEMJPCa3x8vB0Uvre3lyNHjjA9Pc3IyAg/+tGP7PunnP4Urt8kenByeYwq1KhjU/+9lF5I58+fDyvAy8Dm+nUo05Dpq1PEVI9NtbxOIrD8LmMAtbW1cfr0aWJjY8nIyGDVqlWUlpayZs0a1q9fz44dOwAYGRmhvr6ezs5OXnjhBS5cuGDf+wwGg8FgMBhuFK75cu7ywTI5OZm8vDyWLVvGfffdR0lJCf/8z//M6dOnGR4eJioqyjaiGhsbSU9Px+fz4Xa7KS0t5Xd+53c4d+4cr776KqOjo7ZRDAtX3VFX/JDGoBpzQ5ZN/r366qvs3LmT7Oxse6pOXl4eXq+Xzs5OWlpauHTpEidPnrQNIvkgOzs7S3d3Ny+++CIXL160Y1bU1dXR2dnJ7OwsFy5c4E/+5E/spX1lcMvi4mL+5E/+hP7+fmZnZ4mPj6ejo4Nf/epXHDlyhEAgwH333cf+/fvJy8sjPz+f97znPQghmJiY4JlnnmFkZIS7777bjq2zf/9+Tp8+bdfVaVlzVdiRgtHIyAgxMTHs37+fy5cv29OV5PSIffv2ER0dzdGjR1m2bBl5eXmsWLGC/Px8/H4/HR0dHDp0iCNHjtDQ0GCLcxEREZw+fZqNGzeyceNG0tPTSU1NZXZ2loqKCp599lmOHj1KS0vLvLHjNKVA7T9ZdvW/k+eIKnRFREQQGxtrj7X4+Hg7dsSWLVvIysoiJiaGyMhIPB4Pw8PDPPfcc3R0dDA6OsqKFSt43/vex80334zf7ychIQGAixcv8tOf/hS3283v/d7vce+99xIfH89PfvITGhoa7ADLQggSExNpa2uzp2rJmDB/+Id/yObNm3nhhReor6+nv7/fjhmTlJRkLzu/d+9eO6D4qVOnqKmpwefzUVBQwNatWyksLKS5uZnHH3+cgwcPsnbtWv7gD/7A9hCSK5FVVlbyk5/8hISEBLZv386GDRt4//vfz4EDB+jv72dkZIT4+HguX77MU089xZEjR/D7/bz66qts27aNXbt2UVhYaK/u5Pf7bY8xOQVMXpOyL/Vgx6pwol+b8rtueKoehE4GqcvlsqcCqSvFBQIB2zNAXdoa5qbVPf7443Yg7Y0bN1JbW0tLS4st3OllVMsjvRT6+/vp6+uzPYuam5sXHKeObTVNXWCKiopi3759LFu2jEuXLvH6668jhLCnesXExNDb22uvvKeXbanoopvcFggE8Hq9bN++HY/HY3sZquWV91k5BVEuPS49LOQKZ7m5ubS1tfHoo4+yfv16GhoaePLJJzl8+DBDQ0NERkbaopnL5aKgoIDVq1cTCARoaWnh6aefprKykj179tjjbevWrVRWVnLhwgU6OjrsaXVJSUns3r2byMhIfvWrX9HR0WGLcj6fj0AgwPLly+1+r6ys5NKlS5w/f57q6mrbk0m959TU1HD+/HluvfVWDh48yP3338/+/fvp7u7m4sWLPPPMM3ZMKzk+5fSyW2+9lVdffZVz587ZQp68r6alpfHwww+zceNGKisrefLJJ2loaLDFw6GhIb7//e/zq1/9ivj4eGpra+eNRf3ern9XPW/UgNLqNQYsmIamCzNS4FFFHTWmjpqePradXrqo+1RvIdVrV78mZBpSCB8eHubKlSv86le/IjIykoKCAkpKSlizZg1r1qyhuLiYjRs3cuXKFS5fvjzPq8lgMBgMBoPhRkC81SkBV4PH47GkB8T+/fvJz8+nu7ub/Px8/s//+T+cOHHCjqWRkJDA1q1b+cIXvoDb7aazs5Mf/ehHjI+Pc8sttzA6Osq3v/1tO/6MKvyoD+kyyLN8ANVX15Hb5BLwERERREdH8/DDD9tLDF+6dMlehv4f//Efee211+wpUPpqWTI9mbd8QJYP02qA4A9/+MPs2bOHtLQ0hBC2iz/MvaU8dOgQx48fp6Ojw37YjoiIIDk5mezsbLKysuwpEnV1dbS0tBAIBNi0aRNlZWVERERw7tw5e7qX/nZXtpXaZtIDZPXq1Xz961/H4/Fw+vRpKisr8fl8pKWlUVpaSlxcHF/72tfsAKMymGt8fDxdXV20t7fb06DkQ7z8i4yMJDY21l79x7LmpjcMDAzYXkHyDbzerxK1LrpxIAUEGXhaTksA5r2FDwQC7Nmzh09+8pPs37/fjlsxPj5OVVUVDQ0NdHR00NfXx9DQENXV1fT09DA7O8uGDRvYvXs3mzZtIiEhgZ6eHk6dOsWJEyfo7OzE5/MRHR3Nli1b+PznP09cXBzDw8N0d3czODjI7OwsKSkp5Obm0t/fz6uvvmovLV5WVmbHr4qIiGBqaoqhoSF7hbikpCSEEDz//PPk5eWxcuVKMjIybIFjaGgIn8/H5cuXOXv2LBUVFQwMDBAdHU1ZWRmFhYWkpaUxPT1Nc3OzHZh3amoKt9tNXFwcubm59pLY0sOiqqqK5ubmeTGfXC6XLa7ExMTYHlQrV66kq6vLbrOJiYl514r+tl8VUHV0gUg3WPWA3xLpZXHgwAE+8pGPUFlZyeHDhzl79uy8MjgFe/d4PHzpS1/innvuYWRkhKeeeoqvfe1r9hhRz9fLJANA33bbbXz0ox9l9+7dPProo7zwwgu2cKRfi06eOapH0/3338/HPvYxNm7cSHd3N2fOnLHj7UxMTPCLX/yCp556yo6L9WaNWiePKkl6ejp/93d/h9fr5cUXX+SJJ56YJ+Kp7Z6SksLHP/5xPvrRj/LlL3+ZY8eOERERwU033cTHPvYxXnnlFc6ePUtPTw9dXV12oH/LsvB6vfz4xz8mMTGRb3/725w6dQqPxwNgT/WV00xLSkrYs2cPW7duJTs7246TJmP9dHV1cejQIY4dO2avIinzWLZsGaWlpQC0t7fT09PD8PAwExMTC8acHjBbjq3c3Fy2b9/OTTfdxLZt24iLi2NoaIiXXnqJ48eP26J5bGwsv/u7v8tHPvIRBgcHOXr0KJcvX7ZX7IuNjWXfvn0MDw/z4osvUl5eTnt7u+3tp3uJyfuXHIuqoLKU/lVfjuhpyn3qfVf+jqr7dMHWyTtHpusUfB3emFamj1c5ppxW+dN/s3QvJdVbVN6fPB4PsbGxdow+GYB6amoqbHsZDAaDwWAw/JZxxrKsrU47rqnHj3wYvHz5MmvXrmXjxo2kpqby4osvcvr0aXvlKTllQC7rnZaWRmxsLFu3bqWpqckWM+Q0GJj/cKmuFBYVFUVKSgpCCNra2ha8rVf/pME2OTnJCy+8AMDBgwfJy8sjOjraPmdqaspeRcbpbb00EuSDq3ygBuwHzqGhIX70ox/x3HPP2eKHfCCVYsXAwMC8eASyfnLaUE1NjW0Q+P1+pqenmZ6epry8nIsXL9rb5cOtakjCwmXQJXIqzBNPPMFDDz3E+vXrKSoqwufz0d/fz4ULF3j11Vepqamx+2x4eJjW1lYiIiJs40p9c6y2VSAQsKfMye/SINbf5kpCeUSob6P1Y6RhpBsO0tiQHimVlZUkJibS0dHBqVOnqK2tpbu7255SI8WnyclJO9+LFy9y5coVnnrqKdxuN+Pj44yPj8/zpPH7/Zw6dYovfelL3HXXXaxevZrk5GTi4uLo7e3l/PnzvPDCC1RUVNDT02NP/7h06RIvvvgiy5YtY926dSQlJdlTpWTfX7582Q7InJSURHJyMikpKURGRtrLp8sgsbIOk5OTthAkjaPp6WlblJFjcHJykqGhIWpra3G73fY+tW5qn8p2ljGPOjo6uHjxItPT07YoqxqZav+pxqW8blWhVu5TPYRkP0p0I10dC7OzswwMDDAxMWEv564a8dI7R81H3n+eeOIJ8vPzbc8vj8djX4tO075UISsQCNDe3k51dTVr1qyhu7vb8d6j10VPT+4/fvw4BQUFxMbGUlBQwPr166moqOBf/uVfuHTpEleuXKGvr2+B+LUYutgU7lwZayUnJ4dt27bR29tLZWWlHVtHLtW+YsUKysrK2LBhAz/96U85e/Ysw8PDZGdnk5iYyOzsLK2trZw5c2bemFK9uWJiYmxvjo6ODnvaliq2j4yMcPHiRRoaGnj66adJTk62ve7klLC+vj47vosaoH5yctKOSWVZ1ryXB6rgINNS21Xez/1+P83NzfT19VFeXk5hYSGlpaXs2bOHO+64g71799LX10dXVxfd3d0AdHR0sGzZMm677TZ27drF1NQUMzMzXL58mZ/97GdUVFTQ2dlpewipYqL+X/fM0XHyqpF9rt9r5X9d1JHb1WtdXQFSpuX0W6iW3el3Uq2PLuyEul/IY9Uyq/cN+V32kxpTSU7pVOtpMBgMBoPBcKPwjqzq1d7ebos/K1eutFd+6e/vt4MYy6WA5ZQJj8fDmjVryMrKoqury156W7rtBwIBPB4P0dHRuFwuUlJSiI+PJz09nZSUFJqamvjud7/L8PCw41Qh+aApHyLlVKXOzk62bNnCli1beP3116moqGBoaGhJD93yIVR/yJYGTF9fH8PDw/Omo8EbD8q6Z4H8kw/b0jtGfUCWHivqdCKZhh47QTcu1bfbIyMjPPfcc/T29pKWlmZPNejo6KCtrY3m5mb7zbhsNykwqQawnrbqcSW9mPTyqW2gnq/HfdAFHfW/6umj95H6llwuq37s2DHGx8fp7u5e4EWmG30yUKwMwCrFA7Vv5LEjIyNUVVUxOjpKVlYWXq+XQCDA2NgYvb29jI+P09vbO88olasjdXZ20tjYaC8HLo1ZKcDJsTo4OEhbWxuRkZF2EGM1LhC8YVhJcUnvd9XoltfUxMTEghWK1OP1PpZ5ygC6TuJGOMFD9c5bqnjhlJZqcFqWZS/jXltbS29v74Kpjepn1ehsaGjgm9/8JnFxcXR2djI1NeVoqOrlkfs6Ojp49tlnqa+vp76+ft5Us1DlDnVP6e/v57nnnqOuro6MjAyGh4dpa2ujq6uLwcHBeasgqfe3pbZfqH1q+4yPj1NeXk5iYiKrV68mKSmJnp4ee2lvGQhdCqFS0JdtHhUVRXR0tC38+ny+eVMA1fEkY5VJgUWukgfzY7jJcTowMEB7e7sdZB2wx6Jsd10EloHw9b5Qr2G9P1RRUt7H5fLrfX19NDY2cv78eYqLi1m7di0ZGRnk5eVRWFiIEILU1FQqKipsL7ypqSkmJydpbW21V7xSy6znrQueej+GElDUfSpOoqnT53D5OolIElkGddpWqPKqeam/A3p6av/reTj1of676iQmGQwGg8FgMFzvXHPhJxAI4PP5qK6u5siRI7jdbpYvX86BAwfs1ayioqLsALuvv/46k5OT9nLq6enpJCcnk5aWxuDgIG632za63W430dHReDweEhISsCyL2NhYfD4fbW1t88rh9ECtPixPTU3R2NhIX18f3d3dtLe3U1FRYa8e4yQ66OmrHi7qKirqW0n1YVc1NvQ34Op+XUzSg9uqD7X6FJRQhoBadynKNDQ00N/fT1JSEi6XC5/Px9DQkP2WWhVDZPp6ek7fwxn/evmuxiDRjQm1vvpxsl/Gx8e5fPkyNTU1Ic8L1ba6keGUF8DY2BiXL1+mvr5+Xh7T09O4XC7b20mOkdnZWdtDRQac1seanP4hjU/pjRTKiFPfzqt9pYpqervrY9Kpv5z6QB9Loc5zwskgcxJ11H3qGHfa19vby7Fjx+xVzlTxRh8z8Ebb+Hw+jh8/TkREhC2GhctPZ2RkhJqaGhobG/H5fPPa82qQ12N9fT2tra14vV6mpqbmCVG/KdTr0e/3c/LkSWJiYti4cSMpKSmkpKTYnoY+n4++vj6am5tpbm7m4sWLtreOvEeo/1VDXJ1mJIVRNX/ZBk5lkyKvvAb0/tHvuXKbvj/UvcKpTdT8hRC2F+jIyAgtLS1cvnyZ2tpacnJy7N+s1NRUmpqaePXVV7l48aK9IqUMwC7rp1/H4YQVp/tsqH16XUPVy+keFk4cWiwNpzIulr/Td53F7jGhymwEH4PBYDAYDDci74jwEwgEaGxstA3fvXv3snnzZjsmy+TkJH19fZw7d45nn32W8fFxNm3axPr168nLy7MforOzs4mPjwfmP9zPzMwwNTVFW1sbbW1tVFVVceLECduY0I1Z+TkQeCPoszxucHCQ48ePc/bsWdvQUo103TgO96Cte7foy+bqD/xOD/uWZdnTHlQPGN2VXk1TpqWKT7pYoZ8Hb0yT6e/vD2lYO7Wnk2Ec7k2vaoirYpbsT1UA09MLVe9wXg+6oCY9Z5ziP+ntr9ddTVOvi0RdllgVWVRDT/+vexU4Gaqq+KPnpZdRN4h1IUvNU6bp5FGg1k3WZTGxZinGl2qYL9Z3ellCGapy+8DAAOXl5Y7T/kJ9lv+npqZCXtNO7ay3mxRFliL6hNsv20d6msm89LLr5dGP0wm3T90vj6mursbn81FbW8vKlStJSUmxp8e2tbVRU1NDc3MzPp/PjhmlevjJ8RkZGek49uCN+47M3+Px2FNKnTxA1PI7CRuh+lYer/anfs90Sk8VutX7qSx7IDC3VH1PT48dXyYmJsYOIt3Y2Gh7S6rnqOVV70G6t8xS+83JM0aWW+9bp2taRW8Xp/uBE4uNzXDb1HMXuxcvJlqp/5ci2hoMBoPBYDBcb7wjU72kuNPY2MgPf/hD6urqKCoqIjMzE5/PR319PRUVFbY4JMTcSlvHjh0jOjqapKQksrOzycvLIykpyfaYmJqaYnR0lO7ubtttXi7rK+NTqA+QoYx39WFfGizS8JLBItU0nIQUaUCrAo/qAaSeE8rgk0aSLKf0DpFpyzLIN94yHov+sC/LJfdL1LKqwpm6PZQRJP+kNwQsXApYF3SkgSONQVVoCCciOX0O9RDvZMDpfSyP0w0cPUiwmoba3mr/yn4KZ2zr9VTHgeoN4TSe9KCuevl1I049Ty+7HIt6H6p11senPEb2mdrO0hNJ9dyQ9VTHmVp+vY2WKvA4paOOXd0rQjc2nTzUdONSFwuc7gVOgpiahi68hUr7zRJq3Kv11+seru3DlcVJ8AoEAjQ3N9PS0sJLL720QCRU05T3Eznu5HRQGQ9I7w94Y/pVc3MzXq8Xr9dLdHQ0Q0NDC8qnTqlUy+c0JTRc/dRlyPU+U68xtU3VNNRz1etL3hdk/Kv+/v557STLqU7Rc5qKG64esl2d6uUk7OjTx5zGgZOYpp6vi1D6PVG/b8p7nvwu01HTVdOQON3f1P7Rp3bJc5wEXqf6GAHIYDAYDAbDjcSiwo8QIhf4PpAJWMC3LMv6RyHEXwD/FegNHvpFy7KeWyw9+aArjcbR0VEOHz7M4cOH7Yc29UFbejVERMwtEez3+xkcHKShoWHBG0yHss8zPvRlafVjZfnC7dcDwcr09Qd2vQwybfVBVp/uJdOUxoMMJCvP040aXbTQPXhk2XThRa2PPE4Vl2SbOok8uqGglkkVFqQ3l1pnWU51m/ysCyQyH5m+HqtGbS+nINW60e7k7aQLHFIc0I0StX3VPGW+qvGlrigj01UDJMuV3tTxODs7O09Ekf2uCox6X+lihy5GqN/1caELNWobqseobaq2sdoe8rNT7KbFPFx0g0wtoyqs6deNfr7a1up3te5O+TqVxynNUMa3KnTp+en94iQaObHU+5B6jNqPi4k5+rl62uEEKlWsgDfGrS5cwPx2Vz3c5L0/Nzd3gagij3e73Vy6dMn26IyJiZk3PtV7p1p//Zp3qm+o8aO2gVOd1fqoaeven1LI1a8nNW1VGJJpyDo5/Uap9wx1bMk+0Lc5CS96e6gijT7mnK41mZeTN5B6jlP5ZB3U8aNfC07jV48hptYt1G+s2u66MKSPj6VcJwaDwWAwGAzXC0vx+JkBHrUs66wQIh44I4Q4FNz3VcuyvnI1GarGnPrgK1eDUh86VUPZaQqLk5GuPzhKIzfUG0b1wVB1vVf3BwIBxwd51VDRy6LmoxvZss7SEyjUG0h1iVzVuFSFBvkAr7ebKhA4GZzyIVw9R81fig6qEaPWRW83fZvunSSPk8aDkwGsTgtRjWp9n95fervJbXo/6P+dxoycmiG9w1QxRm1bJyPUyagRQtgiGMwXDlWxTDX6dHFMPV6tAyw0sGV6+jhwCtIs+8nJSFbPU8e3LgzI8aEav7rhq6cfzjvHqT+dDFu1b2U99GtdL6fedvJc9Vi1zXVPtnCijZMXkFNdnK61cGKLvi2UMPR2GLJLFYzUNlO93dR7oBQDZX1lwPienh4mJyfJzs6et1+9nqanp3nttdfo7u5meHiYwcFB+zpzuo7VttavafldF2+c7hn6OfrvjoqT0LJY36nHhGtrPT1dXFT3yfZWRRUnQdjpWtTvL+q2UJ42uiijCytymy7eq15V8nj13uN0fettr4pV6nF6HfRy6f0XSrwyGAwGg8FguJ5ZVPixLKsT6Ax+HhVCVAPZbyVTp4dhfeqPfqzqQq96S+jGuDxHf8hV0w13vsxH9Y4J9WZUzUuuJqMLF7pRI8+Rxn4g8EZcIfUhXm0b/eFaN2xlGZ2WpZbpymlqqgGg5qe2g5qGWn8n7wl5rPpfPTbc6ix6H4cTp3SDU3/77SQwqNtDGcxO7RTKOFPHny48yPP1Ppft5+RFJNOXBo46/uX1ACyYnqWXSSLPV8uht63MS29Xr9c7b/yo9VU90/Q+DueF5iRWqUbsYmJFqLo6Xed6WksZG+HyUs/TvZ/0c/Trxum4cGJWqCktSy2nE075vZ2Eq7vTdiGEvfLV0NAQSUlJpKam2lO/JHJ89vX12auFySXcdSFAH2vgfA9x+i1wqot6bwyVj5qmWk/1vHCCdLg05VgLtV/fHqqc6vW1lPP130/1HrjUNtHrq6e51Po7XcdO+YRLWy2LWg7ZN6Ha2GAwGAwGg+F6JvTcJweEEAXAJuBkcNN/F0JcFEI8JoRIXkoaTlNSdANVPuTLB1C5TTXMQ03hUNNymtqkG+DqtBdp5OrTCHTjWjdi9byU9lrwWTeI1CDGqseC/iAsj1ENbvmn5q0+4Du5uqv565/VfOVnNX21HfQpCXr6annUsqpt6GT4q6KVanjr/asbX+q5Tm/HnfJSy6buU+sbqr/1dtL7O1SeTvt1Q8dpbDu9udbzVtvLqa/DXTP62FPRlyHX01LPD1d3XQgKZ4A5Gaqh2lGvi5Mop5dLvwaWaqiGaj+9nqHKFy5Np/vF1bBUI/utprnUsa2L4NPT0/T29lJTU0NERATLly+3PeH08/x+P2NjY/OWfFfrsti9P9S+UH9Xi9M40vc55R3qWHWb0z3Q6foNJ+6Fu/+p1+Fi9+JQ5XZKe6nXhlO59eeAUG20lH4O1Yah2spgMBgMBoPhRmDJwo8QIg54EvisZVkjwDeAlcBG5jyC/v8Q5z0ihDgthDgdTtDQxRfV8Jau4jIGi7pdf0B0ElD0B2j9u0RP08lDw+nB0+nBPNyDsDxWfQspBafFxB+9XeRnfUUk/SE61FSBUHVT83MStULlpe/X6yLbWc9Hb2vds8TJONBFHrXP9D52MgjUPtcFSblP3+bUZrrI5WS8LCYK6W2u5x3OUFHrs1RDU91uWZY9zdLpXP27ft05jVc9fb3+Tp4ZTu27mAeMelwowcfp+FDtEw6na9upH5eSp7491PelEkoE+E0ZuOGue32/7O+ZmRkGBgY4f/48vb29eL3eeWLlUsR8p/3qcfoxoYQGp89O5Xa6nvT89POXMmZD7V/q+NU/O/0P1VYy/VDn6vkv1teLldFJeHE6LtS+cGM7XB2vJi+DwWAwGAyG650lreolhPAwJ/r8yLKspwAsy+pW9n8b+KXTuZZlfQv4FoDb7bbkQ77ueh3Ke0ONfaLuk4ajPs1EndIij9XTDGco6l4dTm9Zdc8GJ2FJlkGmp4pQalwU3a1eradaHz0dpX0XCB9OhoHqXaQ+MDsZGE6ii75dnXahllFtX/VcKdA4rTqml0MXBvTz1H5Tp+GpYyDUeHJqZ6e20ttV1jOcB4RTOy5mMOnH6HmHEvL0ejgJc2oe4d7eL0W80D2dwh0rcWqvxYxL+Tlcf+l9ox+vjgGnfgzXz6HaxUlAcKpDqLTU/EKx1HZd7Nyl4HQN6GLAYuVV89aP1cVV2ScjIyNUVlaSkpJCd3f3PDFfv5863Xvk9nDt7FQvp3uOTiih10kkUfep28N9D9We+u9auEUI1LLq9VLT08e4XhY1zo3TPUhNS78XObFUgdbJMzXUdaWPBzUd9VyncofKZ7H7scFgMBgMBsP1iljsIUjMPU19DxiwLOuzyvZl1lz8H4QQfwTssCzrQ+HScrlcVmxsLPDGg5gMygrOQV8DgcC8IJDqg5sUTtTpAqqQpMemEULYKyzpIohqUKjnquKNU8BaWV41Zo/cLgOchhIj5HFqGdT4DDIgpjzHaZqRKlTJ+jst2a2u1qU+IDuJamr762XXcTIa1ODTat30cul5ud3ueSuZqefoba+mIeu3VMNRzVcvhzqu1Do6tddSRAEhhB07R98u0VcsU8ef2gf6taqvJiXPkyvhOQlXoQwteZ6+T+ave4yFE8HU/2qbqeW4WjHGSVhQ2yqcIKqujuY0JvT9evn1cerEYoKAzlIM5d8Ean86GcFLFXuuFnVVPTWfxbzqnPYtpYyLjR/92KXsCzXeQ40Jvex6UGH12pXfne4lS/GOcxKc9LLpx8jfXl0cCVXnxUQrNQ39GLU8ungXqj76eaHKqN+H9cUDQonAqqeswWAwGAwGw3XCGcuytjrtWIrwsxc4AlQA8mnqi8DvMDfNywKagI9LIShMWr2AD+hbetkNBsM7QBrmOjUY3u2Y69Rg+O3AXKsGw7sfc50argfyLctKd9qxqPDzdiOEOB1KhTIYDO8OzHVqMLz7MdepwfDbgblWDYZ3P+Y6NVzvXNWqXgaDwWAwGAwGg8FgMBgMht8ejPBjMBgMBoPBYDAYDAaDwXCd8k4IP996B/I0GAxXh7lODYZ3P+Y6NRh+OzDXqsHw7sdcp4brmmse48dgMBgMBoPBYDAYDAaDwXBtMFO9DAaDwWAwGAwGg8FgMBiuU66Z8COEeI8QokYIUS+E+NNrla/BYJiPECJXCPGyEKJKCFEphPhMcHuKEOKQEKIu+D85uF0IIf4peO1eFEJsfmdrYDDcWAghXEKIc0KIXwa/FwohTgavyZ8IISKD273B7/XB/QXvaMENhhsEIUSSEOKnQojLQohqIcQu85tqMLz7EEL8UfDZ95IQ4gkhRJT5TTXcKFwT4UcI4QK+DtwFrAV+Rwix9lrkbTAYFjADPGpZ1lpgJ/DfgtfjnwK/tiyrGPh18DvMXbfFwb9HgG9c+yIbDDc0nwGqle9/B3zVsqwiYBD4g+D2PwAGg9u/GjzOYDD85vlH4FeWZa0GNjB3vZrfVIPhXYQQIhv4H8BWy7LWAy7gQ5jfVMMNwrXy+NkO1FuW1WBZlh/4MXD/NcrbYDAoWJbVaVnW2eDnUeYeULOZuya/Fzzse8ADwc/3A9+35jgBJAkhll3bUhsMNyZCiBzgbuA7we8CuA34afAQ/VqV1/BPgduDxxsMht8QQohE4Gbg3wAsy/JbljWE+U01GN6NuIFoIYQbiAE6Mb+phhuEayX8ZAOtyve24DaDwfAOEnRb3QScBDIty+oM7uoCMoOfzfVrMLxz/APwOSAQ/J4KDFmWNRP8rl6P9rUa3D8cPN5gMPzmKAR6ge8Gp2R+RwgRi/lNNRjeVViW1Q58BWhhTvAZBs5gflMNNwgmuLPBcIMihIgDngQ+a1nWiLrPmlvuzyz5ZzC8gwgh7gF6LMs6806XxWAwhMQNbAa+YVnWJsDHG9O6APObajC8GwjG2bqfObF2ORALvOcdLZTBcA25VsJPO5CrfM8JbjMYDO8AQggPc6LPjyzLeiq4uVu6mwf/9wS3m+vXYHhn2APcJ4RoYm6K9G3MxRJJCrqpw/zr0b5Wg/sTgf5rWWCD4QakDWizLOtk8PtPmROCzG+qwfDu4g6g0bKsXsuypoGnmPudNb+phhuCayX8lAPFwajpkcwF0vrFNcrbYDAoBOcn/xtQbVnW3yu7fgE8HPz8MPC0sv2/BFci2QkMK+7rBoPhN4RlWV+wLCvHsqwC5n43X7Is6yPAy8CDwcP0a1Veww8GjzdeBgbDbxDLsrqAViHEquCm24EqzG+qwfBuowXYKYSICT4Ly2vV/KYabgjEtRq/QoiDzMUqcAGPWZb119ckY4PBMA8hxF7gCFDBG3FDvshcnJ//APKAZuAhy7IGgj+O/8ycO+w48PuWZZ2+5gU3GG5ghBD7gD+2LOseIcQK5jyAUoBzwO9aljUlhIgCfsBc3K4B4EOWZTW8Q0U2GG4YhBAbmQvAHgk0AL/P3MtV85tqMLyLEEL8T+CDzK1wew74Q+Zi+ZjfVMN1zzUTfgwGg8FgMBgMBoPBYDAYDNcWE9zZYDAYDAaDwWAwGAwGg+E6xQg/BoPBYDAYDAaDwWAwGAzXKUb4MRgMBoPBYDAYDAaDwWC4TjHCj8FgMBgMBoPBYDAYDAbDdYoRfgwGg8FgMBgMBoPBYDAYrlOM8GMwGAwGg8FgMBgMBoPBcJ1ihB+DwWAwGAwGg8FgMBgMhusUI/wYDAaDwWAwGAwGg8FgMFyn/F/8EDmzrmY55AAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -144,7 +144,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -156,7 +156,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -168,7 +168,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -180,7 +180,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -192,7 +192,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -204,7 +204,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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ONJu6qDBgNL79/f1xOp2zXlSZ7eUThNuFlJQUkpKS6Ovr49y5cxO6yAjCREwVHFi3MBkZGTHWdXR00NnZiaZpjI6OXmHd4stlyRe+xJPpxLeZqRvZjfgmzdRaSf/ta+p3XcBNS0tj/vz5jI6OUldXR2Njo1H/4wWd8VOzzyTAtHyjBUEQBEGYbZhudQFuNUopgoODKSgoYN26dQQHBxsjr4IgfHhRSpGdnc3KlSvJz8/Hbrff6iIJH1Cmcpka72o1MjKCy+XC5XJNO57PRMw06PDVuo3dSPQ6mIm1j/exOoODg9hsNjIyMsjMzCQgIMCnSDRTUWw65RMEQRAEQbiVfOgtfpRSxMXF8fjjjzN37ly6urq4ePEiDodjTDBNQRA+fCQmJpKTk0NwcDCRkZE4HI5bXSThNmI6YsFUQtF0mCz2zdUym0SO6VjnaJpGZWUlo6OjJCcnM3fuXBITEykrK5s0mPVEaYnLtSAIgiAIHyTEtAUICAggPj6e+Ph4vvGNb7By5UrCw8PF8meWobvkmUwmmTr3Q4L37Dg3+5prmkZLSwsDAwPY7XbCwsJuav63mpnU+USuTCaTCYvFgsVimfFze6uu+0zRz9NsNmM2m6f13bjaWbnGp+G93VddzXZLlInObzrX3Pu89N9T3WOVlZW4XC5iY2NZtmwZ27Ztw2q1+nSdG++KN74uZ2udCoIgCIIg+OKmKhuzsQGvaRpVVVX8+Mc/5q233iI2Npa/+7u/Y+vWrcTHx9/q4gleREZGsmbNGgoLCz90nfDZxM3qjCulsFqtREdHExcXR0BAwE0X/QYGBggLCyMrK4v09PSblu+txs/Pj6CgIMLCwvDz85t0X4vFQmhoKP7+/sa9YTabCQoKYsWKFTz99NPcc889ZGdnExQUhMViwWw2T3odzWYzycnJREVFYbVar/fpXTP6efr5+ZGYmEhhYSEbN25k9erVWK3WMffpdJ8XXyLNZPGBfM26NRMx4noIa1MdP1Xa03Urmyhuz/i/kZERn8fqAwb9/f0MDw9jtVrJyspi69atZGVlGaKddz56PC9f+ej3uAwOCYIgCILwQeGmunrd6BEyq9VKUFAQJpOJ1tbWaZfJ4XBw+vRpSkpKOHToEA6Hg7KyMtra2m5oeYWZUVBQwAMPPICfnx8HDhzg5ZdfvupZU5RSLFq0CKfTSXNzM52dnde5tLcPSilDfAkKCiIwMJCQkBCcTielpaXU1NQwNDQ0ZeDjmQaDjYmJYc2aNTz66KNYrVZaW1uprq6muLiYCxcuUFNTYwRjv1Hvlp6eHlwuF9HR0eTm5mI2myed3ctmsxEZGcnIyAi9vb309fV94CwD5syZwz333ENhYSFBQUEcO3aMn/zkJ8a52O12AgICAHA4HDzzzDM89NBDfO973+P48eNYLBYKCwv5/Oc/T15eHvpMjk1NTZw9e5aDBw9SWFhIbW0tr7/+Og0NDQwODhoiR0BAAF/5ylfYtm0bAEeOHOH111/n8OHDs2pmtblz5/LYY48xf/58w0K0t7eX119/nddeew2n0zkmMPN0XIN8CSW6JcvV3Oe6SKELUXoZhoeHJ5yKfKJyjl8/2exf49f7skSaqtyTrTebzYBbnJnOrFz6+evCz9DQEP7+/kRGRvLoo4/y3e9+l+HhYUZHR8e4eE0kvM12SypBEARBEITx3DYxfvz9/Zk7dy533303nZ2dvPDCC9PuJIyOjjI0NMTw8DBHjhxhZGQEp9N51Z0M3VIhIyOD0dFRSktLZ1WH5YNKdXU1TqeT9PR0lixZwunTp7lw4cKM01FKER8fz5YtWwgKCuLIkSMcPnz4Ayv0mc1mbDYbUVFRuFwuGhsbr5gJaDz6iHVAQAAhISFomsbAwAADAwNGB0hPe8WKFWzdupWIiAijw2UymQgICKC3t5fS0lKKioq4ePEiNTU1YzpiFouFpKQkEhISiI2NJSYmhqGhIY4dO0ZFRcWks+hFR0eTkZFBYmKiYYGSl5fHpk2baGlpoaKigkuXLlFSUkJNTQ29vb0MDw8zMjJidAgtFgsjIyNGR3cmKKVob2+nr68Pm81GQkICJpPpimdZKYXNZqOgoIBHHnmElJQURkdHKSoq4tSpU3R2dhpWM+Xl5bS2ts7a2QNzc3N58sknSU1NJSIigqCgINasWUNlZSVvvfUWQUFB3HPPPcyZM4eysjLeeecd7r77blJSUggICCAyMpK8vDyeeuopgoKC+OlPf4rdbueuu+4iJSWFDRs2sHjxYux2O/39/cTFxfHSSy9x4cIFhoaGsFqtbNy4kQcffJCoqChsNhvh4eHExcXh7+/Pnj17bnq9KaWw2+0EBQVhNptpamrCarXy2GOPsXz5ciorKzlx4gTBwcFs2rSJDRs2cPjwYUOYvNa8JxNQJpq1Sxd7IiMjyczMJC0tjcjISAC6urp4+eWXx8Sw87bSMplMdHd3X2Hx4i2CTCYMTTdmkS9xRncNDA8Px263MzAwQE9PD4ODg8Y7zWw2s3DhQqxWK6WlpbS2to6ZzctsNhtl93bLMpvNDA0N4XQ6GRoawmQyYbfbWblyJXl5eTQ1NRl56e+R8a503vU70WxpgiAIgiAIs5EPhPAzndFCk8lEREQE8+fPZ2BggDfeeIOurq5pT7+sN1i7urqm3Ffv9AYFBWGz2WhubjZGrMFteZSUlMS2bdvo7e2ltraW/v7+Wd8wtNvtREREYLfbaWlpobu7+4oy6yJDcHAw7e3tV9Whvlqampqorq4mPT2dtLQ0Vq9ezeXLlxkaGppROnqjPTExkZSUFPz8/HA6nezbt2/Gad1KAgICSEpKIjU1lYSEBOLi4hgeHub999+nuLjY6HRarVaioqJob2/H5XIRGRlJYmKiIcZERUUxMjJCX18fra2tlJaWUlVVRW9vL0FBQWzYsIGwsDBqa2vp6upiaGgIPz8/YmNjiY2NJT8/n4SEBOx2u5EGuF3zCgsLmTt3LpGRkVitVkJDQ7Hb7cTFxXHgwAFKS0vp6OjwWe8Oh8MQXkJCQqiqqqKzs5OUlBTi4uJISkpi4cKFNDc309zcTE9PD/39/QwODhqdt7CwMA4fPszZs2evqhPe19eHw+HA39+f7Oxs8vLyuHDhgtEJVUoRHh5OQUEBjz76KImJiQQFBRETE0NSUhK5ubn09PQY77CamhpOnTrFhQsXqKuru6XTw5tMJmw2GzabzejobtmyhezsbMrLyykuLiY1NZVFixaxYcMG3n33XcLDw1m0aBFLliwhIiKC48ePk5ubS29vLwMDA6Snp7N69WpSUlL4/e9/z44dO/D396etrY377ruPgoICXC4Xf/rTn1i+fDlLly6loqKC7u5uampqiIiIYOPGjQAcPnyYlJQUkpKSuOOOO2hsbOTQoUNj3rXXilKKmJgYgoODaW5uxuFw+LRgmTt3LnPmzMHpdLJ7927S09NZsWIF7e3tHD16lKKiIiIjI8nJySE5OZlFixbR2dlpWMIppYz/OlMJB1MJLROdjy7g5OXlsWDBAmJiYggKCiIiIoKoqCja2tr4/e9/T19fn5G2v78/cXFxbNiwgZaWFvbu3Wts19OMiYkhNzfXEFt08XI6lkwTWc7o6esiVVJSEmlpaSQlJWEymXC5XDQ0NNDe3k5bWxsVFRWEhYWxbds2BgYG6OrqoqOjA3B/mxITE5kzZw7FxcW0t7czODg4pv70NB0OB6Ojo4SGhpKTk8MzzzxDc3Mz9fX1FBcXc+nSJVpaWsZYAHmX19c1mY2u7IIgCIIgCDqzWvjRG5zR0dFYrVZ6e3uNUX0d74anPkIXHx9PUlISDofDpyvQRKOVk23XMZlMxMbGkpKSQnJyMoGBgVy4cIGysjKjMakLP2vXrqW9vZ3XXnuNgYEBn+nqQU9HR0d9xie4HkynYR4QEMDChQtZsGABISEhlJSUcPDgQUP8MZlMRoc2NTWV2NhYSkpKuHz58hWi1kT16+fnR3h4OAMDA/T19fm0ggoMDMRut+N0Ounr6xvTMe7r66OkpISsrCzy8/NZuXIlu3btMmZqmejcwR2zJCAggICAAMPCq7W1lTlz5rBw4ULDUqa8vJyBgYFp1eu14GvkXnfn8FUvZrMZPz8//Pz86Ovrw8/Pj7y8PJYuXUp6ejoRERGEhIQQGhqKzWajpaWF5uZmTCYTcXFx3HXXXRQXF+NyucjKyiIzM5OEhATDqsPlcuHv709fXx9Hjhxh7969FBcXEx8fz+LFizl69Ci7du2ipqbGiJERFxdHVlYWy5cvJyMjg66uLi5fvkxbWxsmk4mCggLuueceAgMD6e3tpaurC4fDQU5OjiEmHT58mDNnzlBdXX3FfdPc3MyFCxeYO3cu+fn5tLe3s2vXLqKiosjJySErK4uoqCgyMzOZM2cOo6OjhhWDy+UygjI3Nzdz8eLFGQs/3h1zu91OTk4O999/P8PDw1RUVBhWAwkJCWzZsoVly5bx5ptvMjg4SH5+Pjk5OSxbtozW1lYqKysJDAxk3rx5xntjcHCQlpaWa3rmva0xZppOQEAAc+bMITU11ejIr127loaGBvbv309jYyP5+fksWbLEmM5eF4dTUlLo7+8nNTWVyMhIzp8/T29vL/PmzSMvL4+hoSHeeecdqqqqGB0dpaenh4SEBNLS0qiurua///u/GR0d5c4772TBggWUlZXR3t5ORkYGCxYsoKSkhIaGBux2O9HR0YSHh7Nq1SpCQkKm7cbrCz3+jm4FZrFYWLp0KbGxsbz//vtG8F9vocJkMjF37lyWL19OR0cHx44do6CggLi4ON555x1OnjxpiFYnT57koYceIjc3lxMnTtDZ2TmhaDCVUDBRjKCJLHz072VwcDALFy5k3bp1xMfH09bWRmdnJ/7+/hQUFBAUFGS8Y3Vhw2azkZ6ezsMPP8zly5c5efLkmO1Wq5UlS5bwwAMP8MYbb3DixAlD1JrsvpvOPWkymYiJiWHevHksWLCA5ORk/Pz86O3tJTo6mkWLFtHb28vFixdpbW0lPj6eNWvWUF9fz4EDB4zYPAEBAaxcuZKVK1fS19dHX1/fFYKyv78/mqbR2dlJX18fAQEBxMXF8cgjj9DT00NDQwPHjh1j9+7d7N2716e45X1dLBYLdrudkZERw2rY+1mczrdXEARBEAThZjBrhR+9IxwWFsZ9991HXFwcZ8+e5fz58zQ1NTE0NDSm8TwyMkJ3dzd1dXWEhYWRl5dHeXm50RH0DvCoH+udl95otlgshlm5L+x2O2vWrGHdunWkpaUxPDzM0qVLefHFFykqKqKvr89IT29UhoaG0t3dfUUjVB9Ftdvt9PT00NbWdl0bifpMM1arlf7+/kmtC5KSkvjMZz7DihUrsFqt1NfX861vfYv9+/fjcrmw2WwsWLCAjRs3smTJEsLDwykqKuLf//3fqaioMIQDpZTPUXOLxWI02Kuqqjh37hw9PT1j9jOZTEaHvra21uhM6oyOjnL+/HnmzJnD/PnzSU9PZ8uWLbzwwgsMDAz4PD9/f39sNpthgREbG4vD4aC8vJx9+/aRnJzMvHnzWLVqFTabjV/+8peUlJRccY9cD/R70GKxYLVasVgshiWAv78/ZrOZwcFBOjo6rrg/g4ODiYuLIyIiglOnTpGUlMSnPvUpUlNTqa2t5cyZM4yOjrJ161Y2bdrEsWPHDIGooKCAZ555hpKSEkOo6OzspL6+nrNnzxp1nJOTw/z581m5ciX9/f3U1taSkZFBUFAQTqeTjo4OOjs7DYG1vb2diooK6uvr+fjHP47NZiM0NNQQCR977DEA3nrrLY4fP05XVxcBAQGsXbuWv/mbv+G+++4jMzOT4OBgGhsbjdF5HafTycWLF3nttdeIi4tj0aJFFBcXs2/fPnbt2kVAQAARERGkp6cTGBhIdHQ0YWFhREdHExwcjNPppKKi4qqswnQiIiKIiYkhOjoai8XC5z//eWJiYnjuueeoq6vDZDKRnp7OnXfeyYULF/iP//gPnE4nhYWFPProoyxevJjDhw/zox/9iKysLD75yU+yaNEi7HY7LpeLt956a4zLzUzvp+DgYJRSOJ3OK+pvOue2bt067rnnHoqLi2lrayMkJIQXX3yRs2fP0tfXR2BgIOC23vLz88PlchkuMvHx8RQWFmI2m439dXHV6XTS2dmJy+VidHSUlpYWKisrKS8vp7KyktLSUl588UUyMzMJDw8nKiqKyMhIli9fjsVi4ezZszz22GPEx8cb75Xw8HDCwsKu+j2plCIxMRGbzUZvby/19fUEBATw2GOPERoaSlNTk2Hp6H2M2WzGbrcTFRWF3W4nOTmZVatW4XA4OHHiBA0NDYYVSVFREY8//jg2m814vmGsOOPLsmei8k7mYjVeGNIFnIyMDD7xiU9gt9vZsWMHR48exeVycdddd7Fq1SojRpe3RafVaiUiIoLo6Gg0TSMqKgqHw2E8N4GBgWzdupUtW7bQ2NhIdXU17e3tYyzfpsLXeZtMJqxWKytWrODOO+8kNDSUc+fOsWfPHnp6elixYgWPP/44ixcvNizMkpKSCAsLo6urC6vVip+fH6Ojo9hsNjZv3kxqaqoRbFx3+/L+JusxAPv7+3G5XMZEAU6nk9DQUFavXo3ZbOb06dOMjIwwODhoiPLedW+xWEhLSyMrKwuHw0FjYyMtLS309vaKa7cgCIIgCLOOWSv8gNtKY9WqVTz77LP4+/vT3d3Nnj17+MUvfsH58+eNTog+wtbU1MTRo0fJz89nzZo17Nmzx+h42O12kpKSCA4O5vjx41eMkJvNZlJTU5k7dy779u0zrHe8UUoZaYeGhrJ//340TaOwsJBPfOITPPfcc1RVVeF0OmlsbMTpdBIdHc2KFSvo7e01YhHoDeW4uDi+853vEBISwhtvvMGrr746487bZAQFBZGdnc1dd93FCy+8MKHrm1KK++67j9zcXPr6+mhpaSEtLY2///u/58knn6SlpYX8/Hy2bdvG4sWLDauYlJQUHnzwQbZv346/vz+rV6/GZDLx3HPPjYlhonfannzySTZt2kRzczPf/va3uXjx4pjR9cDAQL761a+Sm5vL8ePHeeWVV9i7d++YOA1tbW28//77BAUF8fTTT/PFL36R/fv3c+nSpStcQEwmEytWrGDbtm3k5eURERHB6OgoDoeDjo4OfvCDH/DLX/6SRx55hPvuu4+nnnqKFStW8M1vfpMDBw5MKZbNFF3oW7hwIStWrCAxMRGr1UpPTw+RkZEMDg5y6NAhXnjhhTFChR4sd8uWLYSHh/PNb36Tr371qyxYsIC3336bt99+m+LiYsMq7nOf+xwZGRlUVlYyODhIQEAAYWFhrFq1CpfLxX/913+xe/duampqsFgsRkfdbrfz7LPPMn/+fObMmYPdbqe3txelFBs3buTy5cv09fXR09NjBFLXRbOwsDBqampob2/HbDYbrjEvvvgiZ86cobm5mdHRUZxOJ7t27eK+++5j2bJlhiXP8ePHKSkpGVNfmqYZnelf/epXfOELX+BjH/sYvb29HDp0iNbWVlpaWigpKRljnTE+iO21dMIsFgujo6P09/fj5+eHzWbjiSeeICEhgX/+53+mpaWF6OhozGYz77//vhEM+sSJE6SkpBAWFkZnZyd1dXXU1tbS3d3Npz71KbKzs1m/fj2HDx+mqanpqsv22c9+lubmZo4fP87ly5fH1N1k6PXl7+9PSkoKGRkZDAwMcODAARobG4068453oltKtLe3MzQ0RGpqKp/97GcZGhri0KFDtLe309XVRXt7OzExMWRnZxsi/dDQEEeOHKG6uprLly8zPDxMTU0NTU1NBAQE4OfnZwj23d3d7Nixg9HRUe6//35yc3NxuVyUlJT4dD+dLna7nYceeoisrCyKior4+c9/bsTGSkxMpKCgwBCcvetJn7kpNDSU9PR0li1bxoYNG9i+fbtxfjomkwl/f39aW1vHvI/MZrPxrvMlIvi6ZuMDDHv/6YyOjhoCv8lkIi0tjU2bNpGYmMj3v/99zp07x9DQkDHAYLfbAXeg/I6ODiOWnX6e0dHR2Gw2Vq1axdDQEC0tLcZ7KyMjA4ClS5eyf/9+KioqxrzjfT1n3kKXXo/ex+jWVOvWrWNkZIQ//vGP7N6927Cmq6mpoauri4SEBBITE3n88cex2+2GJXBUVBRBQUEMDAxgtVrx9/fHYrGM+dNjfgHGDF4NDQ2UlJRQUlJCZmYm/v7+vPnmmzQ3N2M2m2lpaTEEWe+YQXr9+/n5kZWVxfPPP09ycjIjIyNUVFTw7rvv8rOf/eyaLfkEQRAEQRCuN7NS+NEbtsPDw5w4cYKHH36Yj370o6xZs4aNGzcSFxfH1772NSOQrN4w7unpoby8nP7+fubMmUN2drYhLixevJi/+Iu/oKOjg3PnzjE4ODimsxgZGck3vvEN0tLSqKmp4fLly2PEC71hnZKSYljwnDx5krNnz9Lc3MzcuXMJDAzEarUyOjpKYGAgFouFiIgIvvCFL5Cdnc2ePXu4cOGCMaK8fPlyli1bZrgvlJaWcuzYsesmNkRERLBkyRJWrlzJgQMHOH36tOH6ZrFYsNls+Pn50d3dTUtLC//zP//DpUuXGB4e5nOf+xzLli3jySef5IUXXqCwsJD09HSKior44Q9/iMlk4utf/zoLFiygu7ubsLAw7r33XsxmMydPnjQshfRro49EBwcH4+/vT2JiotGo17Hb7SQkJBAdHU1OTg4LFy7k/fffH9OhGBkZoby8nD/96U/k5+ezceNG/uEf/oFnn32WiooK4/zMZjNZWVn89V//NU6nkzfeeINTp04xMjLCkiVLuPfee3nooYf49a9/zZkzZ5g7dy5xcXHMmzePX/ziF/z85z/nlVdeoby8fFILsOliNpvZsGED27ZtIysri76+PqPjtXLlSlJSUgB3R3z37t1UVlaOEbz0IK0JCQl8+ctfZvny5bz55pts376dqqoqQyTZv38/n/rUp1i6dCnnzp3jwoULlJeXG2Le5cuXef/996mpqSEhIYF169axdetWysvL6e3tJTc3l/DwcNLT01m3bh0Wi4Xh4WESExP5+te/Tn19PX19fURHR2O326moqODs2bPs2rXLyEcPtK6LWnoHWD8fp9PJ8ePHycnJITg4mPnz5/Pggw/yox/96Ao3O12o27dvHzExMXz84x/n/vvvx+VysXv3bmN/7/gp17PD1draaliu2O12hoaG6OrqYnBwkNDQUGN0XylFbm6uYX0wODjI/v37KSoqoqamxnDhO3/+PO+88w7Dw8P4+fkZFjUzRSlFamoqy5cvp7W1lebmZvr6+pgzZw69vb2cPn16ShccXdDRxQqAy5cv873vfY+Kigr2799Pb2+v4frZ39/PwMAAly9fpqKigvz8fCIiInj//fe5dOmSMRtiUVERmZmZfOUrX2FgYIBz587R399PaWkp5eXlxrXS3QwbGhoYGRkhKyuL9PR0SktLaWlp4fnnn+d3v/sdoaGhhivmtQg/usgZHBxMaGioIV7abDZCQkJYv349LS0tVFVV0dPTY9QzuGdr063KPvKRjxAeHk5hYSHNzc2Ul5cb4sqGDRsYHh7mzJkzOBwOo671evYWIcZfD7PZPEbsmei6eZfLe9p4m81GdnY2y5Yto7i4mPPnzxtxbFavXs26deuMIOOf/OQnGRoa4uzZs3R3dxMbG0tBQQEOhwO73c5nP/tZtm3bxtDQkDFTXWxsLBaLhXnz5rF+/Xo6Ozs5e/YsmqZdEb9ofFm9z0n/Juhlv/POO4mLi2P//v0cO3bMGNAxmUw0NjbS3NxMZGQkXV1dtLW1UVVVxcKFC4mIiGDTpk2YTCaKiopITU013MRWrlxJe3s7/f39hvVmWFgYDz74IEFBQVy+fJn9+/cTGhrK3XffzaJFi8jLy2P79u1cvHiRwcFBRkZGjIEj7zrXNA2LxcKdd97JuXPn+M///E8KCwu54447ePTRR0lJSeHLX/7yTXEZFgRBEARBmC6zQvgZ7z9vsViMkbv29nZ6enr46U9/Snt7O5s3byYrK4snn3yS73znO0Yaekemo6ODo0eP8sgjj/Cxj32MX//617S1tREXF0dycrLxv7q62hAJ9FE83Q1m3rx5hjWBLizpf/oIeHx8PMuWLaOmpoZ3333XmBUqPDycBQsWcNddd9HV1cXvfvc7Vq1aZcxm09jYaAR71oUSgBUrVhiWBSUlJdccNHl8IzUhIYFz584xPDxMQEAA8+bN4/7776enp4cf//jH/PGPfzRmPbFarbz00ksUFBSwYsUKdu7cSWhoKMHBwdhsNkwmEy0tLbz++ut8/OMfJz09HYvFgsPhID4+nnXr1nHmzBl6enqMDkF/fz8nTpwwpojOycmhoqICh8NhCDv9/f20tbWRmppKVFQUycnJBAUFjXEJ069zbW0tr732GitWrGDx4sU89dRTvPrqq1y8eJGRkRHDSiYoKIhf//rXHD9+nLa2NpRStLa2kpKSQlRUlGEl09DQYLigbNiwgU9/+tMUFBTw+9//nt27d1NbW3tNs7zFxsayefNmbDYb27dv57333sPhcGA2m3nggQf47Gc/S2JiIhEREaSmplJdXW3kp4+mu1wuw1VK0zROnTrFxo0bCQ4O5vLly+zcudMYwY+LiyM4ONiIr1JRUUFaWhoNDQ1GfCV9RDs2NpaEhAQCAgKw2+1omkZ2djaf+cxnGB4exmQy0dXVZQSHdblc9PX1sWvXLrZv305DQwO9vb1jhNKuri5sNhtLliyhoqLCCAqtP0dvvfUW3d3dPPHEE8ybN4+tW7cyMDDACy+8MMZV0Pu5e/vtt8nMzKSgoIDc3FxKSkq4dOnSmH2vN93d3ZSVlVFRUUFeXh4jIyO8/vrr/OpXv6Kurg6AhIQEmpqaWL58OX/5l3/JgQMHqKyspLOz0xCJ/P39DdfVqKgow93E261oJiilCAsLIyEhAZfLxR133MHKlSu599572bdvH2fPnp3yfu3v7zfKGBERQXV1NUePHmXz5s0sWbKEhQsXGm4uv/nNbwwLuNLSUsrKyliyZAlNTU388Ic/pKuri+HhYcrLyzl8+DBZWVksWrSIf/zHf+Tw4cOUlZXR3NxsuBXGxMSwceNG+vv7OXDgAA6HgyVLlmAymYyO9+DgIA0NDTQ1NaGUGtMJv9o608WekJAQgoODWbBgAVFRUfj7+5OVlcWDDz6IyWTi1VdfNYIbm0wmenp6DEu37OxslFJkZmbyzDPPMDQ0xODgIA6Hg9raWr7zne9w/vz5Me8272/ceNet8VY8+v7ejHen0i2H9N+AMfNdZGQkTqeTNWvWkJiYSE5ODqGhoXR1dfHGG2+wYsUK0tPT+dKXvkRtba0h1qanp/Pmm28yNDTEokWL8PPzM2KhHT58mMuXL7NkyRK2bdvGli1bDBdD3dpyqlhT4891dHTUsPSyWq3Y7XbCw8MxmUwEBgaSnZ1tBAo/d+4ce/fuNdxwg4ODefrpp8nPzycjI4Pe3l5DKEpJSeGuu+4iISGBM2fOUFJSgtls5p577iEhIYGdO3dy9OhRw93rxRdfJDw8nKamJiMu0MjIiGHlNv58lFIMDQ2xY8cOdu7cacRF27hxIxs2bMDPzw+r1SrCjyAIgiAIs4qbLvzoMRP02CZ6w1hvxAYGBpKTk8O8efMYHR3lt7/9rdH43LNnD0FBQTz88MMUFhbi7+9/hXtPX18fhw4dYtOmTWRnZ/ORj3yE3t5e4uLisFqtWK1W7r33Xl555RU6OjoYHR014uCEhITg7+/Pli1bjGCnunihB2C+dOkSCxcuJDo6mqVLlxIaGkpraysDAwOMjIwQERFBcHAwAwMDvPLKK1RVVVFaWkpOTo4xi5J+jO6usnr1anJzc1m9ejV2u52dO3eya9cuenp6ringsx7sNiQkhLS0NCPmhL+/vzHC293dTUBAgNHZ1oWVsrIympqaSE5OxmazGfEQUlJSuO+++wxLk87OTqMD0NjYSFpaGitWrKClpYWLFy8aM+VYrVY6OjoMt4QNGzYQFBTEmTNnqKyspL29nZCQECNuRFRUFIsWLWLbtm3s2LFjzEi/pmn09vZy5swZ3nzzTR599FHuvvtunE4nfn5+nDt3DpPJRFJSEgEBAcYUvrobXVdXF6dOnTIa6f7+/gwMDHDq1CleeuklKisreeyxx7jjjjuM6al37tzJ4cOH6evru6rrER8fT2JiIrW1tZSUlBhT05tMJnbt2kVhYaFxD+bl5XHgwAHj2dCtefRAsXa7naamJlpbW1m/fj133HEHhYWFZGZmUlFRQUxMDMXFxcazobtLrVu3zphdSylFV1cXx48fJzAw0JjZTL9vdPSYQ6Ojo2RmZpKbm8ucOXMICwujsLAQPz8/zp49a7jt6DOH1dfX43Q6Wbx4MS6Xi+zsbKqqqoyZwSwWC6GhoYaoExwczL333ovNZuP999+npKTEsKbRxVf9umVnZ2Oz2Qy3lRuJLmboLiH69WhoaMDhcGAymaioqGDHjh088sgjrF+/ntzcXFpaWowYKfp19Pf3N6xNGhoaOHLkyJgYVjNFr8Pc3FwjYHRcXByJiYmGe8pk9+rw8DAOh4P+/n6ioqJoaGigoaGBP/zhD2zatImYmBjq6up4+eWXOXLkiGHBV1lZaUyrXlRURFFRkSHq9fX1cfHiRV566SUaGhooKChg4cKFZGVlMTg4aATAHRgYoLW1lVOnTnHx4kVSUlKIiIhgZGSEuro6wxrJWzjR6/5qcblcuFwurFYrKSkprF+/nsWLF3Pp0iXa29u54447mDNnDlu3bsVsNlNZWUl/f78RwHdgYMCwmLl06RJHjx7FYrHgcrno6OigtraWpqYmiouLDZc/byaK7aMLIr5ci8db+Ey0rFsV6dcoPj6ebdu20d7ebtRzbW0tvb29nD9/nhUrVjBv3jySkpLw9/enpqaGP/7xj0bQ5iNHjhhujk6nk+7ubrq6uqiqqiIiIoKFCxeyfPlyhoeHefHFF8cIsNO5Vt4ilm79tnTpUuLj4xkYGDAGgBwOB++88w6XL1+mvLzc+A4cPHgQq9XKnDlzUErR1tZGW1sbDQ0NLFy4kCVLlpCUlERERAR5eXn09/djNpvZuXMnBw8eNN5PIyMjHD58mKCgIOrq6mhqamJ4eHjMgI/39fKOKdjc3GzsMzAwwK5duyguLmZoaMinq7ggCIIgCMKt5JZY/HjHTdDdJPT1ycnJLF26lOXLl+NwONi+fTv9/f2MjIxQWVlJSUkJHR0dxMTEYLVarwjaOjg4SFlZGXv37mXJkiWkp6fT09NDX18fFy5cYP78+axatYqysjKKi4vp7OzEarWSm5tLfHw8mqaxaNEiNm7ciMViobS0FJfLxfDwMM3NzTQ0NHD06FGUck/xq89g09jYSG9vL4ODg9TX11NSUsLp06fp7u6mra2N0tJSkpKSCA8PNyyZiouLaWlpoa2tjQ0bNlBYWMjGjRsJCwsjLCyM999/n+rq6queCn5wcNAQdvQguvpU3EFBQcTGxhIUFMSCBQs4d+6cMYvLyMgIDoeDlpYWwsLC0DSN8vJyUlJSjNliNE2jtbXVmC0L3B3RlpYWbDYbd999N5mZmbS2thpiSWhoKJcvX8ZutxMbG8uaNWtIT0+ntraWtrY2wsLCiIiIMOLEpKWlcf/99zMyMsLp06epqakxxAyXy0VLSwtvvvkmaWlp5Ofns379eqMzW1dXh8PhQCnFokWLcDqdVFZWGjO9dXZ20tbWRlpaGunp6QwNDXHmzBmOHj1KR0cHFouFlStXkpqaagQNDgsL48033zQ6VzNBd2ux2+0EBAQYIpymaTQ3NxujzfrU4DExMTQ2Nhr5dHd3097ebgSH1i0QSktLjQCjsbGxRh2dPHnSiCnV399PcXGxYamWkJBg3K/l5eWG5ZG3qKU/o3p9aprGxYsXuXjxIjk5OWRmZpKamsqSJUuIjY01LOT0qdUDAgJoaGggKSmJpUuXkpqaSkNDA9XV1bS2thIWFkZWVhYtLS00NTXhcrnIyMhg06ZNREZGcvLkSSorK2lqaqKzs9MQyfQR+eHh4asO2DwTNE2jtLSUw4cPk5ycTGpqKi0tLUbncHR0lObmZvbu3Yufnx9paWlER0cb8VD0zrg+64/D4aCiooJz584Z8VeuFl3ojIiIMAQks9lMUlISCQkJ1NXVTRo4emRkhL6+Prq6ukhPT6e/vx+Hw8GePXvo7e0lMjKSxsZGTpw4Mcb6sa2tjWPHjlFfX2/ELfIWKVtbWzly5MiYKdp1N1hwi/P19fVcvnyZoqIiBgYGSE1NxWazMTAwQG1trdFp9mUlozNTMWh4eJjW1laGhoZISUkx3vHvvPMOtbW19PT0GILBli1bjHuvubnZqC/dKvLgwYP84Q9/MNLVYxs5nU6Gh4cntE6ayCpmvGgyHca7Tw0NDVFZWcl7772H3W437rX6+npqamoMAbempsYItm2z2YzrdOnSJRoaGoz3vXe59Prv7OzkD3/4A42NjSQkJBhxdXwxneuji4eRkZFEREQYQr1uiaYHA9frVn/m6uvr2b17NxcuXMBisRj79/T0UF9fb7i22u12+vv7aW9vp7GxkaKiIkP00cWyhoYG3nnnHQYGBuju7jaeGe/69XXdhoeHjXMcHBykqqqK6urqMbMLCoIgCIIgzBZuicXPRI1bk8lEVlYWCxYsMKYYDggIMFwMBgcH6erqoqOjw3A5grGNU03T6Onp4ZVXXjHicLS0tFBXV4fT6SQyMpK0tDS2bt1KZGQktbW1xkxdSinKy8tJSkri7rvvJiYmhlOnTjEwMEB5eTltbW309fVx8OBBGhoaDCsLm81GbW0ttbW1VFZWUldXR1tbm2GtU19fT2NjozF1O2B0BjVNY8eOHbS1tdHV1cWaNWtYsmQJmZmZOJ1Ourq66O/vn3E966PvjY2NDA8Pk5WVRW5uLjU1Nca07HqMn23btjEyMkJLS4sx7XxkZCTDw8PU19fT29vLqVOnDDeJ1NRUHnroIWOGJ4vFfRs5HA7ee+89goKCSElJISsri+zsbMOqo6uri+3btxMQEEBBQQGJiYlkZmYyd+5cXC4Xfn5+hrCmd57nzp1LdHQ0b7/9Nu+++y5tbW309vYa1hTHjh3jN7/5DRaLhdzcXMPyR4/V0NHRwZo1a0hISODSpUvU19fT0dFBamoqw8PDLFq0iMDAQOrr6zl06JARk+RHP/oR1dXVbNy4kQULFnD//fcTFhbG9u3br6pRr1u5ZWRkUFhYSFdXF62trbhcLuLj4wkKCmJkZITAwEAKCgpYuXIle/fupbu7m9HRUTo6OmhpaTHcVXp6enA4HOzcuZOenh7uvvtuUlJSiI2N5eDBg+zatYuGhgZjxF53qwsLCyM/P9+4rn19fVRUVIy5byZ6PsvKyqiqquLo0aOGoFNQUEBISAjZ2dksWLDAsPbp6uoyrJqsVivh4eHExcWRl5dnWLK1tLRw4MABysrKcDqdrF69mvXr1xsWcHrw1crKSjo6OgBIT09neHjYeF5uNJqmUVdXx969ewFYtWoVR44cGSPY9Pf3U1ZWRn19PbGxsYYlRUhICGazmeHhYUN0KC8vp6Kigo6Ojmty59Q0jY6ODhoaGvD396esrIyRkRFWrlxpxKpqbm6e1GJQdwNsaWkB/hyAuKKigurqasPFcHwaTqeT6upqqqqqxpRHR59h8dixY5w9e5bAwEDsdjuBgYHGrH+tra3G+evWlC6Xi6amJqqrq32W+Vpd+fQA0RkZGdhsNsLDwzl8+DBvvfUWfX19tLe3U19fzx133EFoaCgpKSnY7XYaGxsNsRFgYGCAPXv2cPbsWcPSydu6ytt6R3+erqbs3sKR9+CIr3g5+jv2woULVFdXExAQQG9vr/Ht9C6f0+nk8OHDHD9+3LBY0i2sdNfc8aKHt8Cxa9cuzp49S3JyMlar1QjcPh3xyru8ej0dOHCAjo4OIiIicLlcNDc3U19fT39//5hnRBdq9Lqpq6sz3C2966e0tJTq6mpCQkKMwaHOzk6f10q/L2pra4109XP3ZWU1vq3hXVfjr9eNcD0VBEEQBEG4Wm6J8GO1WhkZGTEa0jr6rCKRkZFjLIL0bWaz2YiV0dbWNmYK7/EjcdXV1fznf/7nmAaYzWYjICCAL3zhC8yfP5+8vDyjwTs0NMTzzz/PxYsX+dznPsfixYtZv349y5Yto66ujl/84hdGw9PhcHDhwgWKi4vHzCCkW4L4GtHVR/71xq7e2dGFqj179nD8+HGysrL40pe+RE5OjhFQ+mobkQMDA9TX11NZWcnixYv53Oc+R3l5OUFBQSQnJ9Pb20tAQAD33nsvc+fO5dKlS9TU1OB0OklMTCQ5OZn33nvPmK1nx44dnD17lnXr1pGXl0dYWBhDQ0N0dHRw/vx5Dh06ZAhc8fHxJCcnExISAkBPTw9FRUW0tbVhMpl48803SU9PJzMzk7i4OAYHB2lra2P//v309PQQHh7OwoUL2bx5M2vXruXzn/88y5cvp6KigiNHjnDo0CHDUmTnzp3U1NTwhS98gdWrV/PJT36S4OBgfvKTn/Duu+8aMUuWLFmCy+Uy4pa4XC7OnTvHe++9x4kTJ2hubjZEndraWn72s59x5MgR7r//fj760Y9SVVV11XF+SktLaWpqYv78+Wzbto21a9caFgVz585leHiYlpYW+vv7SUhI4Otf/zqBgYHs3r3bcNeoqKigv7+fwMBAenp6GB4epr29nddff52dO3dit9uxWCx0dXWNcd3TNPdsTCUlJeTn57N27VrKy8upr68fEz9Jx3t5fCdOt5bq6uqiuLiYl156yRB2MjIyxrgdNTQ08Lvf/Y7u7m7Cw8MNFyQ90K9ulaXXaVFREcePH+fBBx9k3rx5rFy5klWrVjE8PGxY/Q0NDXH8+HHjet1odPGjoqKCmpoaXn31VcNFZLzYoXe0ddFEf3a93dWAKzqeV4OmaTQ2NvLjH/+Y6upqmpqaSEhIoKOjg4ULFxIaGmq8QydLo6enxxCIbDabUT7v+9yXC89knWN9WRe3ddcd7/S8z99isRgCpO5S5Kuz7av8M2F0dJRDhw5RXl5OVFQUQ0ND1NTUGML6qVOnuHDhAgEBAQQGBhrWMP39/WRlZZGWlsbAwACNjY2cOXPGEIMmCtbsyx1rukwmfI0XHLzdjxwOhxFUWner9j5OF3b0Z2n8NdXTHf+e885Pv8e9J1eYquwTbddFwiNHjhjiuLc4M16I1ss/XszyFqeUUoalovdxvsrgLUL5wpcLnq9yjU9zJpZbgiAIgiAINwM1VSNNKZUM/AqIBTTgp5qm/Ugp9U3gs0CrZ9dnNU3bOVlaJpNJi42N5aGHHqKpqYn33nuPgYEBo4Pi5+fHU089xT333EN0dDQlJSX87d/+LUNDQ1gsFpKSkti8eTNbtmxh3759/OAHP2BoaGhMA1AfQR7fENT/m81mkpOTWb16tWE9UFZWxpEjRwzXGrvdTm5uLna7nfb2dmpra+ns7PQpMk01yjlVA9F7u95Z1OPO6EF4r7aTqJTCbrezePFi/vVf/xW73c7g4CA1NTWcOHGCAwcOEB8fz+OPP05KSgr+/v5GR6Grq4udO3fy/PPP09HRMcbKRRfh9H31RvhUHVpfnThvYcu7Qa3XRUBAAIsXL+aJJ54gIyMDTdPYv38/r7/+OiUlJUYHxc/Pj/z8fB5++GEeeOABBgYG+MhHPkJrayshISHEx8cTGxtLWFgYISEhNDQ0cPLkSWMkePxsO+PLEBISQldXl+FuMFPMZjNz5szhrrvuorCwkOTkZMxmM62trRw7dow9e/bQ2tpKZmYmTzzxBJs3b2ZoaIhf/epX/OY3vzFEo7/6q7/irrvu4sKFC3zxi180ZkTyLjNc2dHx9/cnPz+f73//+4SHh/POO+/wu9/9jiNHjlx1LApfrjfef4Bx//ra11fHTY9BFRISwpw5c0hMTMRut2Mymbh8+TJNTU00NzcbMbVuNtMVYccLHTeyPPp/fSYkveM71XVVyj0z2L333sv/+l//i+rqah577DFDjLue5Z7M7Ucp9/TYuhvV9RDGJiuHL0FmontZ/x0QEMCKFStYvXo1u3fv5ujRo2OC/050Xt75XI+yezOZQOvrPCdylfN+Z3jf396DGuPFK18i4HTL72vf8d9s/btiMpmu+N7qy97bxr93xuerf5/G142O7nI+/ry8y+3N+Lr3VVcmk8mwMhIEQRAEQbhJnNQ0bYmvDdMRfuKBeE3TTimlgoGTwIPARwGHpmn/Mt1SmEwmLTIykk9/+tOYTCb279/PiRMnjO12u53HH3+czZs3k5GRweDgIPv27aOqqorY2FhycnKwWCycPXuWn//857S1tY2JtzK+s6Vb1ejbvDuW40cXdXN3vTGpx2DRG4zeHdiJRrvH1dsV26azv/57fF5Xi36+6enpJCUlGUEwHQ4Hg4ODmM1mAgMDSU5OJiUlxbCm0gM3664CerknGx31vg7jp9fW695X43uietEFJsCwZgG3u8HAwMCY6eL184yKiiIuLg6n02nEZ/JOSxcZR0dHDTeCyerYu4zXci30jrm/v/+YuBh6rBo9JogesFcPxl1UVERvby+aphETE8P69ev59re/TUtLC3/5l3/JpUuXDPe8yc5BKYXNZuNTn/oUmzdvpqSkhB07drB3795rninJm/H3x9XUl15eb4s3wBDnbqQwcL253p3/6eQ13fyUUiQkJLB+/Xq+9rWv4XQ6eeihhwzLt+tR5qnKNJmYcSOZ7nXR3xe6OKXPdOX97rjV6Ofi/W7zXj9+cGH8t9FXeuOFnvFu1ZP9nuyaj9/ma3DG2zLJl/Djq5x6Gb3Tn0hY9q4f77y81/kSuCZb9v6tp6cLsLPhHhEEQRAE4UPDhMLPlK5emqY1Ao2e371KqWIg8WpLonfa8/Ly8Pf3N2J1JCYmsnbtWrKzs+no6KCnp4esrCxWrVrFwoUL6e3tpbi4mDNnznD+/Hlj9qDJGqLeDVvvbfrsTjreDWe9sTg+6Or4hrD+ezIRZCphY6LjJsrvatFd33TrEN29TWdwcJC+vj4qKyuN/QcHB8cIXr7KON1z8P7tq2HtqwPlLbppmjZG5PEl1miaZsRR0a2zvAOsXm3Azet5HfR4O/rUx+PzALe4occxUkqNCezd2dnJ6dOnuXjxIgkJCcydO5eGhgZj2uCJrFH0dQMDA+zcuZPe3l6ampooLy+/rp2S61VX+vG+rtcHrRM12TviRuQ10/17e3tpbGykq6uLoKAgoqKiaGtru8IN90aW7VZc0+leF+97Wp9ifjKrr+lahF3vY2Fidybv3/r/iUQfX+/h6Q50eOcxXkjxxXQFoom+EbqA5eu8prKO8iXueO83kXXU+OXJBgRmizAoCIIgCIIAM4zxo5RKAwqAo8CdwBeVUk8BJ4CvaprWOVUaevDXvLw87rjjDiwWC/39/YSHhzMyMsK5c+doampiZGTEsFLRZwoqLy+nrq6O9vb2MYLERI0rX417X41TX43aiRpyE6U3VaN9Jg3A6yk26Gk4nU6cTucVpvx643RgYMCwHNGZbHTTO31f9ejr+MnKN9FIrneDfqLOh7cgpAfSHX+dp9P5uJGMF60my1+PjaIfpzM0NERzczPvvvsuH/3oR7Hb7WNieEyVv6b9OVCxPkXz9TivG8nt0HGazefgdDppbW2lurqaxMTE62bpozNdkfhWMBMh3td6X++6m8lU1ine+0xHZJnoXey9fbI68/XenmpgZLLvzUTnMj6dico+UZmmKttEZZ2ojL7Sms3PvCAIgiAIH06mLfwopYKA14Eva5rWo5T6CfD/AJrn/78Cn/Jx3DPAM/ry6OgoZWVlZGRkEB8fT35+PoODg7S3t3Pw4EFOnTpFU1MTAOfOnSMuLo6GhgZ6enqMWCxTNfY8+V6xbrIG8lTpTdVY9sVMBJwb2VD01ZD2JUJMVB/TLeNMxK/puKVMNmI92b4THTud46511H0yJruvpis8OhwO9u7dy5w5c6irqzOs16Zb5sHBQaqrq2d0zK1gNpftdsLlctHa2sqBAwdITEw0RHVhYmbynbiadK/H8b4Efl9uVtOxjJnImsfXQMhUAtNU72X9mzTVN9pXer7KMP7YmQh04wWfqa73RG5sgiAIgiAIs4EpY/wAKKX8gO3AO5qm/cDH9jRgu6Zp8ydLx2Qyaf7+/lgsFubOncvixYuJi4ujs7OTw4cPU1FRgdPp1PcdMwOJt2++dyN0po1l77gBUwlB3sxUtJhJeT7ondyrqY/rWYeT5XE198dsvh5KuWMZZWVlUVtbO2ZWLEG4Gvz8/Iypr/UA98IHF/0bp383wbdVK/w5do++fiprxKsRNGbynR3PVO5l3mUaL3D5EpHGz3Tnfc7j3cbG19N0rb+8111vCzpBEARBEIQpuKbgzgr4b6BD07Qve62P19zxf1BKfQVYpmnaY5OlpQs/40fRJhvdM5vNV0wt7N2gm6j807H48bV9uh3/q2nE+mK8CHUzuF5lv9b0rnc5xqepu0HNxPLqg8qNqEth9nK9BEq5b24/vIUf73U63t+56biHTWTt6us47/9T3VMTfY/Hf4cnKruvMk2V5kzKMd3jfJVR52riygmCIAiCIFwD1yT8rAL2A+cBPSLks8DjQD5uV68q4HO6EDRJWq1AH9A2/bILgnALiEKeU0GY7chzKggfDORZFYTZjzynwu1AqqZp0b42TMvV63qilDoxkQolCMLsQJ5TQZj9yHMqCB8M5FkVhNmPPKfC7Y5p6l0EQRAEQRAEQRAEQRCEDyIi/AiCIAiCIAiCIAiCINym3Arh56e3IE9BEGaGPKeCMPuR51QQPhjIsyoIsx95ToXbmpse40cQBEEQBEEQBEEQBEG4OYirlyAIgiAIgiAIgiAIwm3KTRN+lFKblVKXlFJlSqn/fbPyFQRhLEqpZKXUHqXURaVUkVLqrz3rI5RSu5VSpZ7/4Z71Sin1Y8+ze04ptfjWnoEgfLhQSpmVUqeVUts9y+lKqaOeZ/IVpZS/Z73Vs1zm2Z52SwsuCB8SlFJhSqnfKqVKlFLFSqkV8k0VhNmHUuornrbvBaXU/yilAuSbKnxYuCnCj1LKDPwHsAXIAx5XSuXdjLwFQbgCF/BVTdPygOXAX3mex/8NvKtpWhbwrmcZ3M9tlufvGeAnN7/IgvCh5q+BYq/l/xf4oaZpmUAn8GnP+k8DnZ71P/TsJwjCjedHwNuapuUCi3A/r/JNFYRZhFIqEfgSsETTtPmAGXgM+aYKHxJulsXPUqBM07QKTdOGgJeBB25S3oIgeKFpWqOmaac8v3txN1ATcT+T/+3Z7b+BBz2/HwB+pbk5AoQppeJvbqkF4cOJUioJuA943rOsgPXAbz27jH9W9Wf4t8AGz/6CINwglFKhwBrg5wCapg1pmtaFfFMFYTZiAWxKKQtgBxqRb6rwIeFmCT+JQK3Xcp1nnSAItxCP2WoBcBSI1TSt0bOpCYj1/JbnVxBuHf8GfA0Y9SxHAl2aprk8y97Po/GserZ3e/YXBOHGkQ60Ai94XDKfV0oFIt9UQZhVaJpWD/wLUINb8OkGTiLfVOFDggR3FoQPKUqpIOB14MuapvV4b9Pc0/3JlH+CcAtRSm0FWjRNO3mryyIIwoRYgMXATzRNKwD6+LNbFyDfVEGYDXjibD2AW6xNAAKBzbe0UIJwE7lZwk89kOy1nORZJwjCLUAp5Ydb9PmNpmlveFY36+bmnv8tnvXy/ArCreFO4H6lVBVuF+n1uGOJhHnM1GHs82g8q57toUD7zSywIHwIqQPqNE076ln+LW4hSL6pgjC72AhUaprWqmnaMPAG7u+sfFOFDwU3S/g5DmR5oqb74w6k9YeblLcgCF54/JN/DhRrmvYDr01/AD7p+f1J4Pde65/yzESyHOj2Ml8XBOEGoWna32malqRpWhru7+Z7mqZ9HNgDfMSz2/hnVX+GP+LZX6wMBOEGomlaE1CrlMrxrNoAXES+qYIw26gBliul7J62sP6syjdV+FCgbtb9q5S6F3esAjPwC03T/ummZCwIwhiUUquA/cB5/hw35FnccX5eBVKAauCjmqZ1eD6Oz+E2h+0HntY07cRNL7ggfIhRSq0F/kbTtK1KqQzcFkARwGngE5qmDSqlAoAXccft6gAe0zSt4hYVWRA+NCil8nEHYPcHKoCncQ+uyjdVEGYRSqlvAR/DPcPtaeAzuGP5yDdVuO25acKPIAiCIAiCIAiCIAiCcHOR4M6CIAiCIAiCIAiCIAi3KSL8CIIgCIIgCIIgCIIg3KaI8CMIgiAIgiAIgiAIgnCbIsKPIAiCIAiCIAiCIAjCbYoIP4IgCIIgCIIgCIIgCLcpIvwIgiAIgiAIgiAIgiDcpojwIwiCIAiCIAiCIAiCcJsiwo8gCIIgCIIgCIIgCMJtyv8PlEBKeECj0YMAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -216,7 +216,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -228,7 +228,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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NsNKgvtL9Vvp/Lde50WPMQtONXO9mzol2r1u53s2eczNlXEsZVnufa3nX1/v+Rt/zWs65ke9vZxlu5L2t9v2N9K21HvOg9IkbacM3U8Y73R5vx/VutQx3Y4y+3X1itbJejwd9nog8xmq1EhcXR3x8PDabjc7OTqanp2+6jDcz7tzqu7ZYLEpOiVxErvX930obu973Nzt3ruV+5vMsFgsej4eSkhJiYmLweDxK+WO1WlUdrVS2u9keV2Kt9ZyamsqWLVuoqqoiJiaG5uZmTp06RSAQWPUZb7QMt3uMXul4l8vFrl27KCsrw+FwYLFYaGpqWpPiZ7Xr36jsHvnsTqcTn89HUlISiYmJ9PX10dPTw9zcnOpvN1OuW20n0a63knJntfvLYyPLI/9fWlpa9v/11ksajeb+56YVP4ZhBIUQ/xp4HbAC/2AYxpW1nh85YQvxoZmn1WrF4XDg9XrxeDx4PB41ebvdbvx+P16vl4WFBWZmZjAMg/n5eebm5ggEAkxMTDA5OUkgEGBubo75+XkWFhai+nqvNkjqgU2j0Wg0N8r9thMaTXD/uGOxWPD5fGzevJn8/HwcDgevvvoqc3Nz973bjJSXYmNj8fl8BINBBgYGPtZuFxaLBbfbTWpqKlarFbvdrhatFsvqeUyECMUFcrlcLCwssLCwcN/GBbLZbJSVlbFlyxY2btyIzWbDarXi8/lUuT9KbUAIgdfrZdeuXcTHxxMMBklISMDpdN7QdW7kmdeiGPF4PKSnp1NSUkJJSQnZ2dnU1dXxy1/+kr6+Pubm5takZLnT3Mg6ZSUrJ+k1Ia9n/l9+Fs3y7V4/u0ajuXFuKeCOYRg/B35+k+cCocna6XTicDiIi4sjNjYWv99PcnIyhYWF5OXlkZCQgMViwWaz4XQ61U8gEFg2OS8uLhIIBBgbG6OtrY3BwUE6OzsZGhpiZGSE0dFRZmZmWFxcXGYRtJJQvNZB7aMwAK7mBnG3FFy6DCvzcasDXYb7vwx3i/u5Dh6EMph3bOX15Ofm/+9kGW6Eu1UGu91Oeno6L7zwAmlpaczPz3P16lVGRkaYmJi44/dfjevVgdVqJSUlhQMHDlBeXs7g4CAvvfQSg4ODt01p9VFrC1L5kZCQoNq8tBq/3gaf1WolNTWV4uJient76enpUW3gfqoHwzBwOp1s3LiRnJwcvF4vTqdTWaRMTEzcVYXV7agDIQQOh4OYmBgVlNvlcinF3b2QrW02G4WFhRw8eJADBw6QlJSEzWZj8+bNjIyMcOLECQYHB9Ua4l72icj6Mbv7rVR3kR4QkS6CkcrDSJdK+dn1FKoajeb+444Hd14JqVX2eDyUl5dz8OBBCgsLSU5OVpOZ2+3GarUuy8xgGIaazO12e1TTw4SEBNLT01WQuLm5OUZHR2ltbeXixYtUV1czMDDA/Px81EHSHDNoLURqwu9H1mKmrMtw7/i41YEuw/1fhnt9//uhDh6EMqw0l5njN1wvDseDUA8rIa2Il5aWyMjIoKGhIari536qg4SEBCorKzly5AherxeLxUJNTQ1nz55ldHT0rpThbrDWMkiLHZ/Ph9vtVrKh/JFxIqP1A5vNRnp6Ov/6X/9r8vLyuHTpEq+++iqXL19Wri43UrY7gXnR7fF4iI+Px+fzYbfbsdvteL1evF7vXStPZLlu5Tz57uTmrqxzqVS4m7K1LFdSUhKPPvoo+/fvV4pEwzBwu93s2LGDzs5ORkZG1PrhTrSFlZ77VjcB4Nr4P+b/o9V/ZGyjG10jaTSa+4e7qviRA4vFYiEmJobi4mKeeOIJduzYgcPhwOFwKEXPxMQEDQ0N9PX1MTIyQl1dnRLGPB4PCQkJeDwepqamlLmztBay2+0kJiaSlJSEx+MhJiaGhIQEcnNz2bVrF8PDw/zzP/8z1dXVDA8PEwgEWFpauuHgbWZW202K3FmVO0xCXD8ifuQgf7sH2vt19+5BLkM0Pm51oMtw/5RB7rhmZ2eTkpLC5cuXmZqauiv3hvujDj6uZYhm5fpxqYfFxUVmZmYYHx9XruQbNmygqamJ/v7+ayxn7oc6gA83zaSVRGxsLF6vl23bttHR0cHExMRNyzFrKcO9nitXKoP8X8Z/lJt+gUCAYDAYNfCxVKSsX7+eiooK/H4/09PTJCYmYrPZVAzJtZRBynTys4WFhdvxqNfcy2q1kpiYSGJiIjExMcvuabPZlEX73ViU32xbMMvCNpuNtLQ03G43FouFiYkJBgcHGR4eVkqHW7nXjeJ2uzl06BAbN27E6/UyMDBAbW0tu3btAiAnJ4eUlBScTucddQm9E+/PrOxf7X5mZalZAST/lu9EW/xoNB897qriRw46TqeTLVu28Pjjj7N+/Xp8Ph8tLS00NzczPj7OyMgIPT09DAwMKH97OXnDh2ksHQ4H8/PzWK1W5QbmcrmIjY1l79697Nmzh/n5eaamprBYLKSmpqpA0Z/73Oc4ePAg9fX1XLx4kStXrjA7O4vVal3RdHMtA7F5UI00x4y8XjRBRD7f0tKSsmySx0aaZ2o+mtwPgrNGA6ExKjY2lqqqKh577DGsVitzc3PU19ffVeWP5sFCiFAMmoSEBNavX48QgubmZgYGBpiZmbnXxQNCip+pqSkuXbrEjh07ACgtLSUnJ4fq6up7GudHyjhysWW325V7uvxZWFjA5XIRFxeHx+OhrKyMpKQk2tvbmZubu2dlvxtEU/rIz6TSwzAMpqammJ6eXtXix2q14vf7lXuRx+NRm5BrKYPdbic5OZmcnBylPF9aWqKxsZELFy4wNjZ2WxRx8p52u52YmBhVRqvVyvz8PGNjY0rBZVas3C558XbLLfKdWSwWkpOTCQaD2Gw2xsbGGBsbu2cxlnw+H1VVVSQmJjI2NkZ1dTU///nPiY2NpbS0lNjYWFJSUoiLi7vhsSxyE/heEq0MZkWOdOUyrzvkO5HvbaU1jEajuX+56xY/UohJTEwkKysLu91Od3c3P/zhD+nr62N2dpbp6Wmmp6dV4GbgGpNKea3FxUUlIFmtVrxeLykpKeTk5OB0Omlra+Py5cuMjIxQUFBAcXExqampZGRkkJycTEZGBjk5OSQlJfHuu+8yNzfH4uKiGgDN5owrTaLShxxQ/skulwubzaZ2n5xOJ3Nzc0xOTjI1NUUwGLxGwWTWxsfFxakdKCEEo6OjXL16lfHx8dsmRJjr825wP5lM329luNf3/ziUIdI8+XpliDRHj8atmjvfD+/CbreTlpZGUVERAOvXr6e/v5/p6em7IqDeD3Wgy3D7ymCxWIiPj6e0tJQ9e/aQn5+PxWKhra2No0ePcunSpVWz9aylDHLOd7lcWCwWlc3zRpU1s7OzXLlyhenpaVwuF6mpqUoe6OzsjOpKfj3M48eNLoxkUouMjAwyMjJITEzE5XLhdrtZWFhgbGyMpqYmJicnlYJDujZlZ2eTlJSkZI1b5X5ojzdCMBhkcnKSyclJEhMTcTgcBIPBqJY3kbKcDAwt5bXryXxS3vT7/VRUVLB582YyMjJISkpSwbZLSkqIjY3l3Xffva4i43oKAbO1/OLiIgsLC6qcCwsLdHV1qXuYy347FT+3W26R8q60+HG5XAghGBkZYXh4eJnL0d109UpJSSE1NRWXy8Xg4CAdHR309vbS1tZGYWGhsrhKSEigt7d3WR1f7z3eT5t+5vYRrUzmNZD578hraDSajxb3JMbP0tKSsuKZn59nfHycixcvMjU1peLuRC6oIgdWs1tWpPY5ISGBrKwsbDYbU1NTNDU1UV9fT3NzM52dnVRUVFBUVERcXBxZWVn4/X48Hg+Dg4PU1NSonR9p/SPNhVeauGW2sYSEBFJTU0lOTiYuLk4FoTYMA5vNxuzsLIODg3R1ddHW1sbIyMiy68iB2G63s2fPHg4dOkRcXBwAQ0NDpKen88477zA6OromU97rmXM+SJiVik6nE7vdzvz8PLOzs8t2ADXLkYJXYmIiACMjIw9MZhjZnzweD0IIld1vLf3G4XAoYdRms13Tl8bGxhgfH1/Wvla73v1cn3Lx4HA4ACgrK6O2tpbBwcEH3nLgQedmrFZv9X5JSUls2LCBffv2sXPnTrxeL3a7ndzcXMbGxujv76e9vf2GyyLnxsTERDIyMnC5XMTHx+NwOJidnaW3t5eOjg5GR0fXdG3Z7tvb2xkcHCQzMxOfz0deXh4FBQX09vau2V1HjhkxMTHExcXhcrkIBoOMjY0xMTGh0myvVC6peCgvLyc7O5uCggKysrKIj4/H6XQqy46xsTGuXr1KY2MjLpeLqakpXC4XEIpLEh8fj8vlYnJyUj3jxwHDMAgGg0xNTTEyMkJxcfGyZCBmFyiJnB9kbCB53MzMjErXHYk8R2YP27RpEzt27KCkpASbLSROSzc8v99PMBikpaWF+fl5AoEAdrtdWarPzs7e1PuRsrNUjAQCAerq6qKmcr/fkR4AWVlZynVqeHiYsbGxG7qGedPUvEaA5YGK5XErKTAsFoua961WK1NTU/T39zM3N0dXVxfBYFC9W7kpK5Vxa2Wt7+hOyQ2ruUmu1Oaj/X0/KbE0Gs3aueuuXktLS8zPz9Pb20tLSws+nw+Xy4Xf72dqakophSJ3Y+T5a/lcWuxIhQuEFmrDw8M0NjbS3NzMwYMHWb9+PUlJScrVQS7mZLp4l8uFYRiMjY0xMDDA2NiYKqO8r7TOyc3NZcOGDaxbt47c3FyVjlJORMFgkGAwyOjoKPX19Rw9epRTp04tm8DlLpLD4eCZZ55h69ateL1etWjdvHkzMzMznDhxYtmOXyRmgUa6tgkhVGr7e5Xu807FDJCCmN/vV7GdYmJiGBsbo7e3l5GREaamptSi/14vxFdyI7xbk2hkvICkpCR27dqFYRicOnXqtmaGWUsZ1vL5jWK1WlUQzNzcXCwWC11dXQwMDDA9PX1NRj95jsPhwOfzkZyczJ49e0hMTFQLAnns/Py8UiZ3dnYyOjp6zZi1Vu51HA3DMAgEAnR1dalFZH5+PpmZmbS0tNwVxc+9roMHoQxyzLdYLEoBLl2gJdI9SFq1Rmuvt1oPMuPQoUOHqKqqIhgMMjg4SEpKCvHx8ZSXl9PW1kZnZ+eaF0tyLvN4PCQnJ7N161a2bduG3+8nJSUFl8vFxMQEdXV1HD16lNOnT6uYfTLT50p9c3FxkeHhYVpaWsjOzsZut5Ofn8+GDRs4f/78MsXPajvjMTExpKenU1hYSHFxMYmJiWrTqampid7eXiYmJqIqFKTlUnFxMZ/97GdZt26dSs09MzOjElukpaWRmZlJXl4e9fX1dHd3MzY2ht1uJxgM4vP51Nw3MjJyy2P4/dAnViKyDNINZW5ujrGxMdxuN4uLiyQmJpKSkoLX612Wol1aMchNu/T0dOx2OxaLhampKWZnZ1eMCeR2u8nOzmbXrl0cOXIEv9/PyMgI7e3tjI2NYbVayc3NJSkpiby8PNavX8/U1BSBQIDExETi4+MZGxujubmZmZmZG7LglvKxdF2TCq329vZrFB93Q8a5lbYg5V2v10t2draKqTQ8PMz4+Li6/lo27eRY53a78Xg8WCwWvF4vi4uLSm6X8Zoi5T+zhZ7MlictCgOBgNpo7e/vV7K6tMKT64xom9V3mxt5F5HPv5JlY6S8bK6ryGM1Gs1Hg7tu8SN32RobG/F4PMpXNj8/n+7ubiWsyMEmWtYt+Z2c/OR3clIcGhpSZtZSgWOxWJQp+MWLF2loaGDXrl1KQPV4POzatYuEhASys7Pxer0qw8DU1BQDAwMcO3aM48ePq4xgQghiYmJ47LHHePLJJ4mNjV0Wk2dubg6LxcLs7KwqQ3x8PNu2bSMrK4uRkREaGhqU2btZkTU0NMTbb7/Npk2byMrKUsLG17/+df7Tf/pPnDlzhsnJyWWTjRRm5ASYmJhIdnY2+fn5uFwuLl68qHZEzcLwzShC7rXyxFwOj8dDaWkpjz32GJWVlSr1ZjAYZGBggFdffZXTp0/T0dHB3Nxc1LLfTWH2fposfT4fBw8e5KmnniIYDDIzM8PZs2cZHh6+Y/e83Sbo0a4fGxvL1q1befHFF9mxYwezs7O8++67vPbaa5w+fVrtiEssFgt+v5/i4mL27t1LZWUleXl5yyzJpJA9Pz+PYRi0tbXx6quv8vbbbzMwMLCqeff9zNzcHO3t7bS2tlJRUUFsbCzp6ekkJCRcY5V4LzArNYQQ1+zgftyR1iZOpxOfz0d8fDxZWVkkJCQQFxenFiczMzN0d3dz9epVBgYGrlFWmhVHq20ORC4MzGRmZrJnzx42b97M5OQkr776Kh0dHXzlK19RljrFxcW89957K8bHMI/FcpGVmZlJZWUlhw4dorS0FLvdrpQ7coNny5YtFBUVKbnA5/MxMDCwomuZbFfz8/NUV1eza9cuLBYLCQkJFBcXk5CQoObY1YiJieHw4cM89NBDlJWV4Xa7mZmZUYkqOjs7OX/+PO+//z5Xr169JmCwVLZ++ctfZsuWLSwuLtLc3MzFixepr68nEAiwbt069u3bR2pqKm63m4qKCrKzs+nr61Pv0eFwsG7dOq5evUpfXx9LS0vXVa7dL/P47ULKZE6nk5mZGdLS0igpKSE/P5+amhoVJ8ls4eH1epWFuLSsguUpr+XxNpuN/Px8Dh48yBNPPEFMTAy1tbV873vfo729nampKTweDxUVFXzxi1/EMAzKysqYm5sjOTmZHTt2qLTr3/ve9zhz5gzj4+MrBp+Wz2Quh1RYyR+Px0NlZSUnT55U8vX9PkZGKn3i4+MBGB4eZnh4mKmpqWva7kqWKtJiKzU1leLiYjZu3Ijf7ycnJ4fZ2Vneeecdzp49S2trq9poledGYrVaycvLW5ZRWG5+zM3NqQ0gmU1NegXIYyPLeyP1Icsj2+dq8sRqCr7VLJoix3v5Ey2Uhvl48+fy+vLZNRrNR4u7HuNH/oyPj6t0mZWVlXzwwQdq90NObrA82Fg0LbMctKQAKAM2yl17Gb/C5XKxsLCgFm/BYJBTp06p3c+HH36YnJwctVMnB3GbzaYyiMndm5/97GdcuXIFj8fD/v37efHFF3G5XNTX11NbW0tHRwfj4+PKvDcQCJCSkkJ5eTmbNm0iOzubuLg4fu3Xfo0//uM/pr+/f5k7UiAQ4Bvf+AYWi4WKigqefvppDh8+jNfrJS0tjd/7vd/jG9/4BhcuXFAxf2R54+LiOHDgANu2bVM7j3a7ncnJSR5++GG6u7t58803OX78uDKnvRHTU/njdruX7Ryv5RorKTyimZJe73pScHA6nXz+85/n8ccfx+/3K/93CAnUOTk5fPWrX2X37t28+eabvPnmm/c8VW807ub9I3e4vF4vTqeT+Ph4CgsLaW5uZmRk5LZO6rLdSFN3afkX2X5upB5Wait2u53CwkJ+4zd+g927dxMMBomNjeVTn/oUxcXF/OhHP+IHP/gBU1NTSojy+/3s2bOHF154gby8PIaGhvjpT3+q6iExMZGioiLcbjeGYZCTk0NOTg4HDx5kYWGBH//4xzcV6HAtfeJOI7MoNjU1sXHjRpxOJ3l5eWRlZdHc3HzL7cAcA03eL9ouYiRSoREfH09mZib5+fmkp6fT2tqqrCjMFpi3wv3wHm60DLI/5efns3v3bsrLy8nMzFzmaiTnOwgtKGZnZ+nv7+f73/8+x44dU0pMq9VKTEwMu3btQgjBu+++uyzGnnnMiI2NxW63EwgElgUAt1gsbN68maysLObm5mhsbOQXv/gFgUCADz74gF27duF0OlUWqpUUP+bnTUxM5PHHH2fv3r0UFhYCcPLkSc6ePcvk5CQul4uCggLKy8vJy8sjJSWFf/fv/p3asZfH1dbWRlX2y9/nzp1jamoKt9tNbGwsOTk5rFu3bpll0koLxUOHDvHcc89RVFREIBCgvr6e6elp1q9fj9frpbS0lOzsbMrLy/nWt77F2bNn1QaXzWajsLCQZ555hoqKChYWFvj+97/PW2+9RU9Pj1ISnTlzhrfeeosXX3yR7du3k5CQQHx8PB6Ph+npaZKTkwkEAlRVVdHd3U1DQwMdHR3XVfys1rfvhz6xEtHKYLPZ8Pv9pKamMjc3h8fjwW63s2XLFubm5uju7mZ0dFRt/thsNnJzczlw4AB79+5VykQpT8ofcx9IT0/nkUce4eGHH8Zut3P69Gn+9//+33R3dysFgQwYfuHCBSorK4mPj2fr1q1kZWUppaXFYuE3f/M3OXbsGMeOHaO+vp6RkZGoWcQirdwrKiooKCggOTlZxZN8+umnWVxc5PXXX6e1tVXJsFI2vJ1xIa/3Hm6ExMREDh8+TGpqKsFgkKtXr9LT06M2TeWGrfleZjk0OTmZvXv3snPnTkpKSkhKSsJutyNEKGOu1WqlpKSEsrIyXn31VS5duqTcs83XMv+dnJysFDzz8/NMTEwghFDu8HL9EKn8sFqtqv2sJBNHU+JFs2pazX3sZvql+X7yfzPm9i6vs7i4iN1uV89pbkNmSyeNRvPR4p7E+JGD3Pj4OKdPn+by5csqkGg0RY+ciM2DtPxcDk7m3eBAIIDX6yUmJobFxUV8Ph82mw3DMJYN2C6XC4/Hg8fjwev1EggEePvtt/nggw8AlHl5SUmJyj5WXl5OR0cH8/PzuN1uPvnJT+L1enn33Xd55ZVX6OrqWhb3Qw6MHR0dNDQ00NjYyJEjRygoKCA/P59169YRCARU5gdpsSP9vy9evMjIyAjNzc185StfITY2lqysLH7zN3+Tb33rWxw7doyJiQll5fPVr36V7du34/P5mJ+fZ3R0VMUIkMqrgwcPYrFYeOONN27IlUO6Be3evZs9e/ZQV1fH6dOnlZtI5K6VeWKLnCDkosXlcuF0OpUFlVyMr2VScblcPPvss3zmM59hbm6Of/mXf+Hs2bMEAgGysrJ44YUXKCwsxGKxUFZWBoT841977bVV07TeC+6VS8nS0pLaDQ8Ggyo45e3aCbbZbMTExJCTk8OGDRvYsGGDyobR19fHlStXuHr1qrL2M+8+rYQ0pV+/fj3Z2dmcOHGC6urqZSlGZcwtebwcWzZt2oTP52NiYoJXX31VCYdbtmzh4MGDpKenU1tby//8n/+T9vZ2NV7IeBEWi4XExER+9Vd/lfLycnJzc6moqODtt9++KSup+8GdQu4St7e343K58Hq95OfnK9cX2Tfljrgcd1dbVFqtVpXRqbi4GLfbvSyGw/T0NPX19XR1damsO5G7kT6fjyNHjrB//35ycnKUQA8wOjrKBx98wLFjx7hy5co1Flw3Uwf3w3tYaxlkYNlnnnmGgwcPqs0Ji8XCwsICk5OTyhpEWgJJ14S4uDgee+wxFcdpcXGRrKwsjhw5wiOPPKIswJqbm9XYIPvA5s2bVfa3M2fO8Oabb6qxNC4ujk2bNpGSksLQ0BCXL19mYmJi2cJIjvlut/u69eB2u/nMZz7D/v37SU5OZmhoiKNHj/LKK68wMTFBMBjEYrFw9uxZSktL+exnP0tmZiZpaWlqEZOTk0NBQQH19fVR26vcLOrv76e+vp6EhARcLhcJCQmUlZUpBdhK78HtdrN9+3ZSU1NZWFigtraWv/iLv2BmZobNmzdz6NAhcnJycLlcZGVlcfjwYS5cuKCUn7GxsWpDaWlpiZMnT/Lqq68yNDSkNlYgNDZ3dnby7W9/m8XFRXbu3ElaWpqSbWw2Gy6Xi4yMDA4cOMDMzAzf+c536Onpuelx/H7oE2vFYrHg8/koKCggJiZGZXK1WCykpKQoq8/XXnuNnp4eEhMTKS8vZ+vWrWzevHlZ25DxfuS8Ib9zuVwcOnSIrVu3EhsbS09PD//wD/9AR0eHcr+C0Jw6PDzM8ePHGR8fZ2BggJ07dxIfH6+sRqxWK9nZ2Xzyk5+krKyMEydO8O6779LW1rbMKgWWx5+Riom4uDi1YSNEyOr5S1/6Eo888ghXrlyhoaGBpqYmGhoaGBwcvCahyO3iZtuCxWJRlnyybmZmZmhra2N8fFyVU8oE5rWBEEJtTnz5y1+mvLwch8PB8PAwx44do7W1FZ/Px/79+0lJScHhcLBjxw4SEhLIzMzk1VdfVeOanHdk3Vqt1mVyrJTHDcNQ46vT6VTzn8/nIyMjg5SUFPx+P0NDQ3R2dtLX16eUb3LdIkMvyDkWYGZmRoVtkEoj8/NGWm2Z5bIbtVo3X0P+yDHZ7XaTlZVFWlqaCpIeCAQYHh5mcnKShYWFazZw5Pir0Wg+Wtx1xY95wSkX99JCwzwwRS7+zIOdeaCOPE+awRpGKCiz1WolNjZWLfaWlpaw2+3Exsby0EMPsWfPHsrLy5mdneW9997j5Zdfpq+vTw2iNpuN6upq2traeOaZZ5RAmpKSQmJiIsnJySwuLvLBBx8oS5+VMivIjBynTp0iNzeXpaUlJbjJsstnlxPAwsICPT09vPvuuwgh+PznP09SUhIFBQVs3bqV/v5+Ll++jMVi4ZFHHmH79u24XC7Onj3LpUuXGBwcVIFqt23bRnJyssrgc+rUKYaGhtYkCFgsFjIyMtixYwfPPPMMKSkpFBQUUFZWxqlTp6iurqazs1PtEptd3szvXird/H4/69atY8OGDWRnZ6v20NraytWrV+no6GBsbEwF+45sQzabjbi4OHbu3InNZlO7wA0NDSwuLjI4OMjo6Cjbtm1TO6SyDs6ePUt3d/eNNdwbZK2WS5HH3w3MZZM+8HKHXLpGrrU8UiiKtqPo9/vZvn07lZWVFBcXK2sE6QZZWlpKRUUFdXV1nDlzhs7OTtLT02loaGBoaEhZI0SSkJDAvn37OHz4MB6PB6fTSV1dnTp+aWlJKZZKSkpUxhMZyD0rK4vPf/7z1NTU0NraquJr5Ofnq2Cc7e3tatEq60JaNywsLDA8PEwwGFRplm9WmL5fdtWXlpbo6uoCUH1LBosVQpCZmalS3M7Pz9PZ2Ul1dTVNTU3L3HOdTidJSUmUlZWxZ88eioqKVHBfuYs5NzdHMBhUyp+6ujoaGxvp6OhQCu+SkhI+8YlPsG3bNpKSktTOqwyaL2MwZWRkcO7cOV555RUVF+JmMAvn0q1P7vTeLdbaFtxuNzk5ORw+fJg9e/bg8/loaGhgYGCA/v5++vr6mJycZHZ2Vln9eL1ekpKSyM7Oxu/309LSsszCVs6JSUlJBAIBldXK7IYcExPD008/rTYr2tvbVaBagJSUFNLT0/F6vXR1damYGD6fj/T0dJxOJxMTE/T19a0avFVaeh06dIjdu3cTFxdHXV0d77//PqdOnWJ4eFjNC3KOlNk7Z2dnsdvtxMfHq2xXkRtG0ZidneXy5ctUVFQQHx9PXFwcVVVVJCQkrBojSMaHiY2NVWPgxMQEo6OjyqX00UcfZcOGDdjtdvx+v4rJY7FYKCoqYtOmTSQlJbG4uMjJkycZGxtblrUJUP1mYGCAd955B5fLxYEDB1Tyh5mZGdxuN0II1TYA/vqv/1rJPTfK/TI2rQUhQi5ehYWFeDweFU4gKytLuQEdPnyY4uJiRkZG8Hq9+P1+rFYr/f39tLa2smvXLpaWllSMR6vVqmRTaVm3adMm0tPTmZiY4OLFi/T19al3ZXaXmZub4+rVqwwODpKXl6fa4dDQEBcuXMDtdlNZWYnf76ekpIS4uDjKy8s5ffo0P/vZz5TSIJrF3ZUrV+js7FQKIKlEla7KqampbNmyhZGREXp7e2lqalIK8omJiduavOFG24JZmRETE6OUJtLqtKenh9nZWeBDlyezpYp0zcvNzeVTn/oUFRUVTE1N8corr9DY2MjAwAATExNKJvjCF75AXl4e8fHxlJSUsLS0RG1tLXV1dddsKMsEMV1dXWzatAlAjV1jY2PExsYql66xsTH8fr9STEsF3OLiInV1dbz33nu88847SmEirb4KCgpITExU7Wt0dJSLFy9y4cIFBgYGVHuTsafM2Qqv14dv5F1IhZbf72fXrl3s3r2b4uJiYmNj1dppdnaWtrY26uvraWtro7e3VymCZDZleV/t8qXRfHS4JzF+on0mf+QgErn7a/7f/L0MXmk2QZTa+KWlJaxWKz6fj8TERGZmZkhPTyc7O5vk5GS2b99OYWEhMTExjI+Pc/z4cdra2pYJXUIIZmdnmZmZUdeUGXCkWalhGCoDjlmhJcsjryOzfLS3t7OwsIAQQgVvNNdBZF0EAgF6enp47733SEpK4tlnn1WWSNKKyG63s3//fpKSkqipqeHMmTNcvHiRyclJtcvr9/uJiYlRi4DExERGRkauaw4uzy8tLWX79u3KFDYnJ4e4uDi1k3Lq1CkuXLiwLCCmeTKSC4zs7GyqqqrYvHkzhYWFShheWlqioKBAuRvV1dVRW1t7TZwFuTCIi4tTSiM56cvFjFwoTk5OYrVa2bx5MwkJCUphdys7oddDChEyw5h8l9KyAe4fv/vFxUVlLSGFZ4/Hs8xFJBoWi0XFxRocHKSlpUW9J7kD+YlPfIJ9+/aRl5enMu80NzczNTVFTk4OsbGxqk0mJyczOjpKYmIiV69e5c0336Srq+sa5Y80uS4sLKSoqIilpSW2bNlCenq6yrqxuLhIX18fP/3pT+np6aGzs5Px8XH27t2rlBcyZkZ/f78KGhsXF8fc3BzT09PLdl3N95dWb9Lse3R0VCmBPsoCkNylHhkZUYryvLw89u7dS1paGjk5ORQVFRETE8Pc3Bz9/f1kZWXx6quv0tzczNLSEh6Ph7KyMiorKykrKyMnJ4fR0VEmJiZUf3C73dfEocnNzSUvL48LFy5w7tw5LBYL+/fvZ/fu3TgcDurr69WiRSoX1q9fj9/vVzFVhoaGePPNN28ou4oZi8WiUnmnpaWxuLjIG2+8cd9ZBsrsWAcPHmTv3r243W6OHz+uFg4jIyPLFAdmK1I5D/r9fgYHB1UAW+nSMD4+rhSZOTk5NDQ0qLHBZrORl5fHxo0bSUhIoKen55oMSfK32bLH4XCwfv160tPTMQyDlpYW6uvr1eIuGhaLhdjYWPbv309GRgb9/f1cvHiRDz74QN3XPE/KefXChQsMDQ2puIHmHfbVdqblNaRiV1rHyoW+DMwrxxY5xsn+Lq1mZZKKxMRExsfHlfJ5fn5exZyTiSdkf0hNTSUzM1NZvUq3b3PZJFJh3tzcTEtLCxUVFUrZA6GxXCY5kGNjc3MzR48eVXFkPqrj0/WQyuqioiIAxsfHee+998jPz6ekpERlW/V4PCrJRU9PD7W1tbS2tmK329m2bZuK8ZSYmKiOhVCbzM/PJykpCafTSW9vL42NjUopGC1ujBz3du3ahcfjYWpqio6ODt577z0VRHrnzp1qvE1JSSEmJoaenh7Onj27LAmBeeOzr6+PxsZGcnNz8fl8yyzDZGiCuLg4MjIyKCgooKioiOzsbGpqanjnnXdobW1d5sZ5NzG7HPn9ftLS0nA4HAQCAdra2hgbG1NzaWSflZuGaWlp7Nixg82bN+NyuXjrrbc4fvw4PT09KvuuxWJRc7kQoThhMotvcXGx2rCIjCFqGAbNzc3Mzs7i8XiUi3d3d7fKFDc5Ocng4CDz8/MkJiaqzeSkpCRcLhexsbEsLi7S09NDY2MjycnJHDhwgM2bN6vQC9ISenZ2loSEBBYWFrh48SLDw8PY7XbWr19Pbm4u09PT1NTULFMwmseeSMztL5qVkETW/4YNG3j66afZsGEDfr9fWbpBaLwpLi5m/fr19Pf309vby5kzZ5RC+3Zbj2k0mrvDPVP8SE17pNniSoqhaMofuNZHF0JZdyYnJ0lMTEQIQUpKChs2bCAzM5OysjJKSkrw+XwkJCQorfrMzIyyGjDf0+l0KgsZIUKxiaQvtsfjUdYtcrFsVvSYBQKpoJLBYaUSSZp0RwoPZsFWCnxdXV288sorbN26lfz8fDIyMsjJySEmJga3201JSQlWq5W2tjZaWloYHBxU509PT9PU1ER5eblKXy9dYVZDCqhS6M/NzVXpNrdu3aoWwampqXi9XsbGxmhpaVlm4iqRaTv37t3L7t27ycjIwGq1MjMzg8PhIDY2VpmcFhUVkZmZqSyupEJH1o/dbicuLo6YmBiWlpaU7758dzLAYWtrK5cvXyYrK4u4uDi1k3K7F+nyWaUZr9frJS4ujvT0dFWmmpoa9b7vF2TwcvjQvUZaaKxUTtkvNmzYwGc/+1nq6+t5+eWXlbum3W5n3bp1PPXUU2qHta6uTlklzMzMsG3bNtatW0dqaqr6cTgcWK1WCgsLGR4eJhAI0Nvbe43ix+l04na7cblcLC0tUVJSQnl5uXJbWVpaYnBwkNdee42LFy8yPj7OwsICgUAAj8ejUkx/4hOf4J133lGCG3woEMnnN48zNptNZUDLzMxkdnaWlpYWmpqa7qt3ejMYhsH09DQdHR1kZ2fjdrspKirC4/GQm5urTNNlPSUkJJCcnMzU1JRyJcjKymL37t3s2rULr9dLbW0tp06dWhZzy+/3K6vD1NRU5VKTnJyMw+GgoaGB2NhYdu/eTWpqKjU1Nbz77rucPHmSQCCg4q9MTU2xceNGUlNTycvL46GHHuLkyZNrCsYbDavVyrp169i5cycFBQVMT09z7tw5Zf1pRo4/MTExOBwOJicnl41Pd5L4+HgqKiqUG8OpU6d47bXXllnwRM4jZou1/v5+tbNtnnNnZmaUMtzhcFBSUsIHH3xAf3+/6tNFRUUkJSWpuDKTk5NqkQYhqxNZhri4OJUS/cCBA8THxzM8PExNTQ11dXXXdROUrjjSeqihoYGenh61WDPLD9LCr7m5mdHRUR5++GHl/iRdMdxu96qWDnIel9mBpFLm0KFDGIahlGpTU1PLFtoul0vF85OL9x07dqjxKT09nfT0dFwuF8PDw8vGM1k+n8+3bPc8csFrlgeWlpaYnJxkYGCAvr4+ZfEzOjrKyMgIubm5pKWl4ff7KS0t5TOf+QyBQIDz588vs5R6kJCWqnJTLxgM0t3dzYkTJ7h69Srt7e2sX79eBTqXi/LLly9TXV1Nd3c3mZmZSqaT87ZU4Mm5PT09XckOgUCAoaGhqOnhzRs/GRkZlJaW4nK5GBgY4MqVKypbnAwAXlZWplys161bxyOPPMLQ0BBtbW3L+rR8/7Ozs9TU1LBu3TrS0tKUMkDGt5Su/zExMWqjLzMzkw0bNjA4OMjAwMBNp5K/VeR4JK2+k5KSVDlaWlqUQjia0kfKVzk5OWzfvp3ExERGR0c5efIknZ2dy5TJ8r1IS1P5PmS4AymPm8cEWb8dHR1MT08rBWBpaSldXV2UlZVhtVrp6Oigq6uLkZER3G43ra2tJCYmsmvXLgoLC0lNTWXTpk10dnYyODhIVVUVn/jEJ4iNjVWuYIZhkJ2dTUpKCuvWrWNgYIDx8XEVpuKRRx6hpKREKZ0nJyeXzW3m2EDwYdtzOBw4HA6l3DQHMpfHyfpITExkx44dKvZaQ0ODckV0u92qH6SmpmIYBqOjoxiGsUwev9mNFo1Gc++46+ncgWUCmyTSTNGszFlJsywHO6/Xu8wUVAhBT08Pubm5CCEoLS0lIyND7ULOz8/T19fH4OCg2m0xp301Wy1kZWWxb98+tm/fTiAQoLq6mubmZiDk47qwsIDb7WbdunW0tbWpCVVOxOZnlIuFpKQkrFYrvb299Pf3Rw1eZ1b8AMqKpb29nYsXL6oUpfHx8cTHxy+LY2S2VJCCst1uV7uPcuJdS1R+u91OQkICW7Zsoby8HIDq6mref/99kpOTyc3Nxe12k5ubS0xMDBAyL5+amlomAHk8HtLT03nsscd46KGHsNvt9PT00NrayvDwMBkZGZSVleHz+XA4HGRkZBATE0N2djbf+c53uHr16rLFlQxGKjOvSX93+UzmCW92dpbp6WlmZmZUqtbbIfTI68sFgyyvTIedm5vL5s2blbXLN7/5Td5//316enpu+d63gnkxaI7xI9unfAcrIRdmX/jCF3jooYfIz8/nwoULalfK7/fzqU99ivLycubm5jh+/DhvvPGGcsMTQlBbW8vDDz/M3r17KSoqwu/3ExcXx+LiIjabjcOHDzM8PHyNoswwDJWe1Wq1KmXegQMHuHjxonIjmp+fV9YPcqF+8uRJkpOTKSwsJDc3l+3bt5OXl0dTUxNjY2NMT08TExPDhg0byMjIoL29XfUXp9NJYmIiu3fv5tlnnyUYDCoT+rNnz67olnYj72Itn98JzBaJjY2N7NixA7/frwTTmZkZ2tvbGRkZIT4+nuTkZGJiYkhJSeHZZ5/lwoULNDU1UVVVpbLqNTc3861vfUsFhzbv9DqdTjIyMigvL+fw4cPK+is9PZ24uDjWrVtHeno6CwsLygpoaGgIQMWu6e7uJhAIsG/fPmJiYtQY39jYeFPCqN1up7y8nC1btpCcnMzk5CRlZWUMDAxccz25CNy2bRvx8fGcP3+eq1evrmrFciPvYaXPhRAUFBSwadMm0tLSGBoa4qWXXqKhoUEJ+dE2RyI/i2adMDs7q+LT+Xw+ZQkrlUQ2m02lXJbKDnNcOoBAIKCsFNLS0ti7dy/JyckcOnSI0dFRLl26RHV1NQMDAysqH+TclJSUpCxqh4aGrnENi1zwGIahYuvJoP8Wi4WkpCSqqqr45S9/ucy6NhqBQEDNDU6nE5fLxXPPPcfWrVtpbGyktrZWLeamp6dZWFggLS1NufFYrVbS09P50pe+RH19vUqt7nK5mJqaor29ndOnTy+ziJWLJ6moSktLo7W19RqZwFw/0jqvs7OT7OxsDMPg7NmzvPXWW+zZs4eHH35YbW7t3buXpKQk/vZv/5Zz587R39+vrJeuF/D3bo5BN1oG8+dyc6+goIDY2FiCwSDV1dW0t7dz+fJlzpw5Q1JSEmlpacTHx7OwsEBdXR39/f3Mzc0pS8TBwUGVQTU/P5/8/HxlRSoV/y6XS2VzMisVzAtyi8WCw+EgPT2dw4cPK0WydGOfm5tjYWGBS5cuMTk5ybZt29i2bRulpaU4HA7279/P6Ogor732Gh0dHSrGlNmy69y5cxQXF6u4LDKYe1tbG8eOHUMIoaw1YmNjlZwsyy/79b1A1pVUskmk4jkya5lZzo+JiSEvL0/Fbuzt7WVwcBBAWd/Dh23C7/crK33DMFT8LrMyKNKCcGRkhOnpaWXpnp+fz4EDB8jIyGB0dJQTJ07Q0NDA8PAwDQ0N6t5jY2N8/vOfJyMjg+zsbA4ePMjVq1d59tlnSUlJobW1lffff58LFy6wtLTE5s2beeaZZ/D7/ZSXlzMwMMDw8DAxMTHs3btXuY9J1z4pU0vkO5Rjj8PhIDU1lezsbDo7O+nv71+2qSd/ZL0mJiayZcsWLBYLwWCQf/qnf6KxsRGA5ORkysvLef7550lLS1Ox/YLBoPr5KFs4azQfZ+5JjB9zusxIs2bzghRQ8W/kcXLAln9v2bKFnTt3Kqsdt9tNfHw85eXlauBMS0sjISGBwcFBlVb18uXLbN++nb1791JcXEwwGCQ+Pp6uri6EECQkJFBVVcX+/fspKipiaGiI1157jXPnzjE2Nobb7ebq1ascOHAAIQT79+/nypUrTE1NqcHWLBQIIZSCZM+ePQC8+uqr18QRMe/GmrX6UvAwDIPLly+ze/du3G63ykIi6ysYDFJWVkZ1dTWtra1KEJYuMvIa8/PzanKT9zCbeMpJwu12qyxh8fHxNDY28pOf/IT+/n7++I//mC984Qts2bJFLdyfeuopLl26xPHjx5UFiUyRvXv3brXzef78eU6cOEFdXR2BQEClq9+zZ49yJ5PX/P3f/33+9E//lHPnzi2LISQXq263m6qqKpqbmxkcHFR1BSG/9/3795Ofn6/cAVpaWtTOn6z3tU5gkS4NUiGQnJzMwYMHWb9+PS6XS2XLkjty8fHxfOpTn2J4eJjBwUEleN+LydMsPEh/9ZmZGeLi4tTPasFX7XY7KSkp7Ny5k9nZWbXTWldXx/j4OCkpKZSVlSGE4MyZM7z99tvU1dWpBc/S0hI9PT388Ic/pL29nSeeeIJHH310WbDAiooKTp8+rZQ5ZiYnJ5mYmGB+fh6v14vH4+GJJ57gl7/8JbOzsyrOlryXfM7u7m7ee+89EhIS+NrXvobT6WTXrl20trbS1dVFX18f8fHxZGdn84UvfIH/9b/+F+Pj42on9uDBg2zZsgUhhEoN397efkuKxJUWVndzwWVWxI+Njan+ZbfbGR4e5vvf/z4/+9nPCAQCSnl74MABcnNzVerioaEhNm3aRE5ODnNzc7S0tCxzy4EPx/fFxUUVM2BhYYHnnnuOrKwsZYW4fft2bDYbg4ODtLe3Mzg4uGyREgwGlTuE3Gl1Op2kpqbS3Ny8phTWZqSCvLm5mZKSEtLS0nC73Tz66KPLXC7kuT6fj6997Wt86lOfwul08q1vfUvFK7qVvny9tmCxWNRCT8YQaWlpiar0WQvmRdL8/DxDQ0OMjo6qzYmkpCTcbjezs7M4nU4KCgqUVZ50Y42JiVEBpOPi4lQ8iqSkJJKTk9mwYQNTU1McO3aM1157jba2NhUTaiVkJk65QJWbM5G79FKRKF2zcnJyeOKJJygrK1PHSkXul7/8Zf7kT/5k2dhrrq9gMMjp06cpLy/H4/FQWFiorCby8vJIS0tTGQJlW5idnVVzsJxjPR4PSUlJZGRkEAgEmJmZ4dKlSxw9elQp/c3z18TEBGNjYyp2yYsvvsjw8DD19fVMTExc4xosz5VlXlhYoL+/n5deeom2tjbq6upoa2vjc5/7HJWVlTidTsrLy/n617/O+fPnOXPmDJcuXaK9vZ3+/v5lirDIufBGx6BoKa1vlbWMjzL+YFFRkZJtZNKLhYUF5ubmGBkZUf3TrCyUfb+3t5fXX3+dL37xi0rBWlFRQU1NDYFAAIvFQnt7O4FAAKvVSkZGBvv371cZMGXGWBlPKycnh+eff55t27YxNzfH+++/r0IJWCwW5VocExOjkndIhavD4eCpp55ifn6eN998k6amJhW2QMrMXV1dvPzyy8zMzPDFL35R9cuSkhJOnDjB22+/zfe+9z1SUlLYs2cP8fHxnDhxgsuXLzM8PHxPLTXku4uNjSU5OVlZGc7PzytXKZlYRboCz8/Ps3v3bkZHR/H7/arPlZaW8txzz3HmzBnlfiXbdGpqqrIKlrJWMBhULnhmWU62B6vVyvT0NFeuXCEzM5P09HTy8vIoKioiGAzywQcfUFtbq2R8s+vniRMn2LZtm5LJCwsL+eQnP0lqaip2u51Lly5x6dIluru7MQyDoaEhpSD3+Xzk5ORQWFhIIBBQAcaDwSDZ2dmkpaUpS6FoMUSlIuff//t/T2pqKm+++Sa//OUvaWxsjBorSVobxsfHqz7Q3d2t4ro1NTVx8eJFuru7efTRRxFCcPz4cRWD7H5QCms0mpvjrqdzhw/T+ZqVGfChj200c2dzdih5jNPp5IUXXmDHjh14PB4AtRsj01zKIJQyW0ZLS4uycOjo6GB4eFhlnamsrKS5uZmcnBw+8YlPsG7dOpW95Ec/+hHd3d1q0F1cXKS9vZ0LFy6wd+9eMjIyeOqpp9QAL32LhQjFTSkvL2f79u1KKD1+/DiXL19WqUXNCoBIhZFZIFtaWqKtrY2BgQG1OzEzM8Ps7Cxzc3NK0VJeXk5nZycdHR1qV6usrAyHw8Ho6ChdXV1qIpHKJvMukIyNdOTIEZ588kkcDgeXL1/m6NGjDAwMANDe3s43v/lNenp62LVrl3IJ+cpXvkJOTg5lZWVkZWURHx+P3W5nYmJCLQA6OjpUtgA58TQ0NNDc3Extba1aZMvsFb/927/Nn//5n1NdXa0EYhkA2ul0sm7dOoqLixkaGmJ6ehqv10t2djaPPvooBQUFKg7QyZMnl+24rrZYMrsTyHqSk3pxcTFFRUUUFBSQk5OD1+tlcnKSy5cvKysmaW32b/7Nv1GuEtnZ2col7nr3v1OYJ225Uz41NYXf7ycQCJCdnU1ubq5aGEikKXFOTg4PP/wwiYmJzM7Oqp1uacm2fft2srOzEUJw+fJlhoaGlpmry8X/zMwMFy9eVELP1q1bVVuVu6tut/saQUO6p01PT6vdq9zcXKqqqmhtbVV9zxx0WC4Impubeffdd9m7d6+yMrFarZw+fVqlDk9JSeHgwYPYbDY6OjpU3CmXy0Vvby/f//73OXPmjMosdCvv8H6w+DEzPDys3CQnJiaora3l5z//uQoCPzc3x89//nMAvvCFL2C1WpX1od/vx+fzqQw78KFrGHyoLHU4HHg8HuLj46mqqsLr9TI4OEhra6sKNC4trWTwbrNFlXyf0vJHvgNpobIaaWlpSnkSExNDS0sLdXV1KnZKW1sbGzZsUK6MmZmZasEHKFeDxx57TFmobdy4kcbGRpXd8Ga5XluQrkOJiYkq2K95TrxZ5HwzNzfH+Pg4NptNLVxaW1vp7OxUceHkGLFt2zays7Pp7e1lcXFRBcpNS0sjLi4Oh8OhYm1I94O4uDji4+PVdzImXqTlr8ViUWO8xWLhoYceIjExkYKCAhobG1X8Nul2lpaWRllZGZs3byYuLo729naamprIzc0lJSUFt9vNI488gt/v54c//CFXr15lZGRkmUJZKr/eeOMNYmNjiYuLU4svmYVJLkYBFedP1r+MCeZwOIiJiVHtVSrW5QaPw+FQVj5yDmtpaWHr1q34fD5KSkr4+te/zvvvv8+ZM2eU+4uUZ6RCbevWrWzatInJyUleeuklmpqalNL75ZdfpqmpiV/7tV9TbpcyzkhlZSVDQ0MMDAzwJ3/yJ1y8eHHFuHPmDSdpASXdqqNxJ5QJaxkfpVVySkoKgAoSLGOiSMtrKUtFPq90n3rvvfd47rnnVHychx9+GIvFwne/+13GxsZUHEHp/nLo0CGSk5Opra2lvr5ezY0lJSXk5uYSHx9PR0cHr7/+OmfPnmVmZoasrCwqKyv53Oc+p+IFCREK+tzc3Mw777zDoUOHlDx69epVOjs7EUKo9irj77W3t3P8+HFycnJ45plngJAVTVVVFY2NjTQ2NjI6OkpdXR2GYVwTp+peIMMh2O12HA6H6l8Wi4UXX3yR5557DkDNEXFxccqibmlpiZdffpm2tjYVc87lcnHkyBEeeuihZfF9pGVpQkLCsk3RgYEB3nrrLWw227JMVbI+pPLuypUrbN++nfz8fNX/u7u7efXVV5WCSfZhKR/29fVx8uRJUlNTlRX+hg0b1Jwkn1kGDZcKwbKyMhU2wuVyMTMzo8pvt9vVmka+d/PaQD6bVMyXlpaytLSkrJ2cTuey+UHOweawFIuLi8zOziqlmYw/urS0xOuvv86bb76p5Cez4vtetyWNRnNz3BNXL/PfcgAzKznMA/FKFhly96OpqYni4mLl2yx3J10uF0VFRUrh0NHRQWNjoxI2hRAqZaEQIVeQvXv30tHRwSOPPEJGRgaTk5NUV1fzxhtvqPgCchBfXFxkeHiYd955h8rKSpKTk6msrFSBHC9evIjT6WT9+vXs2rWL/Px8FhcX6ezs5Pz585w/f14FzjQPzFIBY7ZGkYO13W7H5/Nx8OBBvF4vU1NTjI6Oqvg2V69eZffu3TidTrZt28bs7Cy/+MUvGBwcxO12k5ycDEBdXR3nzp1TO1mRu2c2m43ExEQOHz7M008/zdTUlAog2traqrIBJCQkqPgJcgchGAxSWlpKamoqNpuN6elpWlpaOHfuHFevXqWtrY2pqallwa/lBCxNSC9evMjCwgILCws8/PDDOJ1OcnJyePHFF/nmN79JdXW1WpieOHGCZ599lsTERJ588knKy8uZmZmhsLBQBd6Tga4bGxvp6+u7Ruloblvm9iYFE7kDnpmZSV5eHjk5OaSkpOB0OgkEAvT19dHS0sKFCxfo7e1ViwCZGaivr4+MjAz1fuV97tWEaX7fUuk2MDCg4h9lZWWRk5PDxYsXlWAgd7QzMzPZt2+fynAXGxvL/Pw8paWlXLlyhWAwqII5S2EzMkON2ax6bm6OxsZG3nrrLYqKilT8nuHhYRVLI9LyTQY77+7upqqqioWFBZxOp3LviPQ9N99Xxg06c+aMyohksVgYHx/n0qVLxMXF8fTTT+NwONi9ezcHDhxQ7bi2tla5HZrT437UMS+irFarsqKYnJxUFk3m9mq2xjTHW5FCYUxMDCUlJeTk5ACQmJiogqr7/X78fr9apMXFxdHX18f58+c5ffo0Q0ND9PX1UVVVRUpKClu3bmVsbIyLFy8uC7gtlYPm+64W30cIQXl5OS+++KLK1gSheHDvvvsuf/VXf8Xo6CgdHR309/dTVFREYmIilZWV9PX1MTc3h9PppLCwkN/4jd8gKSlJjVkyg1lNTQ2XL1++Yy4UMpbN3NycUlpeT9G11uvKmCOnTp1i48aNOBwOysvLaWtrY25ujm3btpGVlYVhGGoxkZmZSWpq6rI5cXZ2loGBARUjQi5gnnzySaqqqujt7VXZGmWMoLa2NqUokYoii8XC9PQ0ycnJJCQkqJhgMsmCnOdlu5TjyPvvv099fT02m40dO3bw8MMPs3nzZnw+H9u2baOgoICOjg5lSTY7O8vIyAh+vx8hhIo7V19fz5YtW5iammJoaIgf/vCHzMzMqIDVgUBAKXTm5+fVBk5qaioHDhxgx44dKn7PgQMHKCsro66ujuPHj6vMZNJy7fjx47hcLj71qU+pmERHjhxh+/btDA0NKQsAn8+nAlXLrFKnT5/m3LlzKoPb0tIS4+PjXLx4kW984xuUl5fz1FNPUVJSgtvtZn5+nqmpKXp6elbNrAYfWrcdOHCAQ4cOUVRURF1dHX/wB39w066ttxshhIqn5/F4CAQCKt7V4uLisjFajlWRsqdUBrW3t/PDH/6Qz33uc8THx5Oens4TTzxBfn4+J06coKuri8uXL+Pz+ZRb+tatW9m4caOSaeT8Mz4+zptvvslbb71Fd3c3S0tL7N69m+eee47y8nKVZfaNN96gvb2dzs5Ouru7iYmJYdu2bSo+j9PpVM8qLbLNi+/W1lZ+8pOfkJqayr59+7BYLOTl5VFcXMyVK1fo6OhQslRk9tx7gZQDZB+WQZRtNhvr169fZs0HKGuturo6ampqOHfuHCMjIzQ2NvL000+rjTSpUJFysuwDOTk5ZGVlqfeSmprK5z73OU6fPk1tba1qJ1KBkZmZSXFxMZWVlUrB7nA4CAaD+Hw+srOz6enpUXOk2d1KCEF7ezvDw8OqjmVmwcXFRSoqKlQcH+nuOjAwQF1dHdPT0wwODqp4eUNDQyqLWGJiIomJiUrxKtu0tAKT1uV5eXnqmPz8fKX0lgpDs5LK7DkhLdcj5UKpEJVtTb47c//RaDQfPe66xY8cbKItuKWW2WzOLBUgkaaNcvJ46623aGtrUwvCYDCIw+EgJSWFf/Wv/hXx8fFKAJBaenn9oaEhOjo66OvrU0EYP/WpT6l0t9XV1Vy+fHlZHB740Ppobm5O+e0fPnwYt9vNxo0biYuLY+PGjcr8PCEhgampKWpqajh//jzNzc0MDQ1dE2zSrOiRiykpOMfExJCWlsbOnTtVCvOrV6/S0NCgLA+OHj1KWVkZCQkJJCUlsW3bNqxWK2+//TaGYdDe3k5jYyPNzc20trZeEyRTKm+ys7PZsWMH+/fvJxgM8t5771FdXc3Y2Bh5eXnKokNmvpBB+szBMYeGhpQrghRshoeHmZ6eJicnh/LychwOB2NjY3R2dip3I/meWltblen4U089hdVqpaioiPLycqanpxkfHyc2NlbtPsp0qxkZGcr9a3Jykp///OecO3dO3UO+R6nIkG1RKoI8Hg+xsbGkpKSQlpamLFlkDCEZ5HZgYIDe3l7lIjQ4OKiUFVIQcDqdSnAQQqhUmJGWKJF9xLyTcycxW9B0dnaqepF+4jKriXyvSUlJbNmyRe0id3V1kZuby+LiooonIM3r5e6UjNU0PDy8bHdfCh5LS0uMjIzQ0NBAW1ubSqMqrfbMyhxZ5qWlJVpbW7l06RL79u1TKUjz8vKUmXjkTpR5gTw3N6cCc8odvoWFBbq6unj//fdxOBwcOXJEWZvJcxYWFpidnV1TOtyVFNYrHbfWz+8EUrEsFZxSoeJ2u0lLS6O8vJzR0VFldZKdnU1mZqYan0pKSnj88ceVu5bL5aKwsJCvfOUrylpA/kgLoImJCRoaGhgaGqKlpYXW1laV0ai2tpZDhw7hdDqprKxU776hoUHthpaWlrJnzx4qKytZXFykqamJtra2Va0OZMpsaSUosynt2rWLb3/72/T399PZ2Ul9fT0lJSXYbDa2bt3KxYsXSU1NpbCwkG3btrF9+3aVIltaeJSXl/P444/T3t6+okXEWt7Dap9LBe3o6CgZGRnqp6WlZVlmyBu5n/l46UIire5ycnLYvHkzsbGx7N27F7vdTk1NzTWZzqQiorOzk9bWVmU9WFhYSElJCQUFBSrWTUpKirKekmUeHx9ftpiQCw45b0tLw8TEROLj41WMPrmAam9vp6Wlhe7ubtrb2xkbG8Nmsyl35snJSbZs2aJi9vn9fnJycpiZmVFKG4vFouLwXLlyhebmZubn5wkEApw7d45jx44xOzurxsJgMKjkDelqZ7GEUt739fVRX1/P7t27VeYlmeY+PT2drVu3cvnyZerr6+no6KCnp4e3336bmZkZZWUg5+Hk5GQCgYBSNMsshbLPyIyFZvc1malRBiDu6ekhNTVVBbgeHx9ncHCQ/v7+684zcXFxbN26lUcffVTN80lJSddYg94p1jI+er1e3G43FotFvTOzpY9E1o38WyKPmZiY4M033yQ9PZ2NGzeSnJyM1+ulqKiIyclJ0tLSOHv2LKOjo3R2dlJaWorP51Pu/dKSqr+/n56eHrq6uujt7VXZn3Jycli3bp1yr3E6nSwsLKjzZHBpKZtIK26ztax5wS4tX5uamvjZz36mAlg7nU5KS0vZvHkz/f39qi/dLwt2qaxvamrijTfeYGJiQo0PQ0NDKhbj2NiY2tgcGBhgcHCQsbExAoEAo6OjTE5OkpCQQGxsrFJcSAsZGfsnKSmJRx55hHXr1inlYFVVFdnZ2WoMMWfrzczMJDY2dlkWNrlhAXDw4EFmZmaora1lbGzsmnqdmZlZZsXe1NREZmamGgMOHDhAXl4edXV11NfXEwwGqaurU9bk2dnZSi6Rlk55eXns3LmTQCCgXI+lsgpCVqipqalUVFSotpycnExRURHt7e20tbUtiwVk9iKQdWbOWmyWQaX1j3mzTrZDKTPfy01MjUZz49yTGD+RriZyIIlmEWQO0hvNUkGa2JuPlSnDZaBKKRTJyVP+npiYoLGxkaysLKVRLy8vX5aKVbo4mIPByftL8/gLFy6wfft2kpKSVKaa0tJSteiRCiSp9JFm7GYlVqRiSwZETkhIID09naysLNLT06moqMDhcFBbW8sHH3ygrJhkQMNz586xY8cOYmJiVAyOpaUluru76e7upqenh4GBARUoTqa8lYHh8vPzKSsro6KiArvdrkzOg8EgBQUFVFVVsXPnTmVKak4jan5Xly5d4p133qGjo4PR0VE1wXo8HrZv305VVRUej0cFax0aGlLxb0ZHR5W7h7Sgke4HW7duxe/3MzU1pXyU5a6HzBpk3qmQGXdkDAVpam+324mNjVVZuGQWFpmONzk5Wbk2WK1WRkdHlaJHTrzyPck6lcK/dFOKj4+nrKxM7Sa3tbUpV5rVuNuTqBRQAoGAqgOp8JqamsJms5Gbm0tFRYWK9SSDZ37605/GbrerLDwTExMqGKXNZmPjxo309fUBKCFU1pPL5VJtPC0tTZ0j0xIXFhaybt06pqenlcJF9kOZoWhyclIF9CwtLSU/P5/u7m6V0U5iVqJ6vV58Ph9LS6Fsd9JkeXJyktbWVt566y3i4uKorKxUmVxklp99+/bhcrloaWlRu/3RFhgfFeRuX0JCAps3b1ZxQQzDUK4njz32GNPT00BoBzMpKYmcnBwlSErrmNTUVDweDw6HA5vNxsGDB9XCfnh4mNHRUcbHx5WSoL6+noGBAYaGhtTC1mq10tzcTEdHh0rDXFVVhc1mIyMjg4mJCXw+H+Xl5axbt069i5MnT6qd1pUE0aGhIU6dOkVTU5PKBCbv4Xa7MYxQ3AUZ503GkJDvXJrSG4bB0aNHycnJobKyUlnJ7dq1i6NHj/LBBx/cEcXt4uKiUjDLwMUHDhxQMUrM2aZg5XYo+4JcXLjdbuVS19nZSXt7O3l5ecTHx7NhwwbS09MpLCxkfHycX/7yl4yPj6sFhLzH1NQUnZ2dqhxer5e6ujoaGxupqKggNzdXjc1y0St/S0si2c9lX3z//feJiYlRSnRpWTo+Pk5zczOTk5NKWdfd3a36MoRcsTo6OggEAkxMTDAwMEB2drZSaEurpfn5eZXKvr+/n5qaGtra2oCQcn9hYYHa2loVuD5SQSWtc6U8MDMzw8TEhFp4FhcXk5KSQnJyMqmpqRQUFJCXl0dmZqZSRNbX19PU1MTk5CRDQ0MUFxerNNcy6URPTw+Tk5NqDurv71cbGVJ+kG1O/h0MBpmdnWV0dBSHw6GC387PzysLkLWMVXKele7TycnJSnF+r8c6eX9ZT8FgUMXkinSBjFZWs5wpNxRef/11Ojo6yMzMxOv1qpgngUCA1tZWmpub6enpUXF55Jw3NDTEyMgIw8PDKu6PvL60XOnt7cXtdiur9NLSUpxOJ3l5eczNzZGRkYHX62VxcZGWlhZGRkbU/BKp2DWMUPbS0dFRzpw5wzvvvMOePXtUDKHi4mJOnTq1zFU6ck68F+9Pts/e3l6OHTumLJ2kpe/09DRTU1OMj48rqyDzpq1hhNzTx8bGlAukjAcm3bJGRkYAlDXR9PQ0JSUlKq5SXFwcOTk56ppLS0vKam9gYIDGxka6u7uVBdX27dux2+2UlpYyMjKCw+GgtbVVZfqT3gCJiYlKwSpjWY6MjCj34YKCAjIzM5VVtWyzMt28lNflxrKMIblhwwb17DU1NWqclJnjtm3bpjIPG4ah4pS1t7fT29u7TGlttnKTyu/R0VFlmS3leLMSyPzu5G/zOk6j0Xx0uOuKH/POvXniMQ8kZiEmUlEkj5XXMruDmDXV5oCXchdITqBy0b60tERzc7OyzMnLy1OZD+QOrxzAR0ZGlFZeCs4Oh0NlBJPuENJqJDk5mWAwyNTUFI2NjZw/f57Ozk61uxgthpEMmCkDWqanp1NcXExxcTE5OTm43W66u7s5c+YMx44do6mpScUOWFoKBWZ96623SEpKoqSkhNjYWHJzczly5IhyL5NBOOUEI2MvJCYmsn79epUiVPof/+QnP2FiYoKNGzeyY8cO9uzZQ2JionK1iOYnPTs7y/nz51UQPLMprcfjYdOmTWRnZ+Pz+dSuxvz8PMPDw8pCCFAZhKSpu91up7KyknXr1qlJaXp6WsUFkYE2zZm+tm7disViUUKy3OH1er1kZWUp6x4Zl0LeR7o+SFctaWo8PT2tFrX79u3D6XQqAUUGOZXxBvLz81Xq8IGBAS5duqRiYpjbsZm7IYhF9qdgMEhLSwv9/f1kZGSoQLkVFRVAKAjjgQMHqKqqwul0qswhLS0tbNiwgfXr12Oz2SgtLcVqtVJXVweE/PQrKytVHJgrV64wNDSkFvjx8fEkJSWRm5ur4kHNz8+rYK1VVVWqz/b29jI0NKQW5DJguxwnFhcXyc/PZ/v27QwODqp4JbKvSkVqcnKyckOSliJScFtcDKXDraur46WXXlIBi3Nzc1V6ahkj5tixY3R2djIyMsLExIRq59FilkRiVmTfyyCbEFLkZGZmUlVVxZNPPkleXp6yVJMLErObolywACrGjBRaZZ8BlDuoDBrc0dFBdXU1TU1NDA4OLhOYZZuXY4p0q5PZvnJzc0lPT6eyslLt8iYlJamAyufOneP8+fPXWDCa27kQgo6ODoaGhvD5fAwPD1NUVERZWdmyWC/S9UeOOenp6Tz99NNqQTEzM8PJkyd56aWXKC8vJy0tjdzcXGJjYykqKuLhhx+mpqbmptK7ryRIm+dFmcZ7fn4ev9/PkSNHCAaDKmPT1NTUMmVk5IaC7A8yfkZSUhIpKSnExMQwOTnJiRMnOHPmDNnZ2SqeUWpqKmNjY9TX1/OLX/xCBU83L67NO9AAY2Nj9Pb2cvXqVc6fP09FRQXZ2dmqLFLpHxMTo9qJTGkMofh79fX1qu/K2GoyOYB0QZRKDHNdSwtBGbB0cHCQmpoaSktL2bhxo2rPUiFkttqUFgWLi4s0NDSoPmreMIqUW8z1IDekpqen6e7uVpsIxcXFat6TsbCkNaRcyHZ1dTE8PMzp06dJTU1VmxTSQnFmZkZZFJjvZ16oRSLnVhkz60aZmpqiubmZhoYGioqKWFhYWFMsrTuNua9MTU2p9zg7O0tHR8c1brjXm1fNCppTp04ply6fz0cgEGBkZES9C9kPq6urVf2a+4P8WwYYl7LZ+fPn8fl8fOITn6C4uBifz0dVVRUbN25kbm5OLc4XFhZoa2vj1KlTKoZZtHdsVljIAN8pKSnK/VK643d0dKyqEL+ZOl/ts9WQzyGVpG1tbXR2dirFjXT3l89pztIqxxez4kIqL8zWLObNyMXFRY4fP87AwICK1yezoMGHlsCLi4tMT0/T0dFBbW0tTU1NDA0NYbVaKSwsxO12U1paitvtZtu2bfj9fmWl3N7ezsTEBA6Hg9LSUuLi4hgZGeHKlSucOXOGpqYmFYBcJs6QcaDm5uaUBeTi4qKST5ubm5menlaubCkpKcoF0G63Mzo6yuLiIrGxsZSWlrJ9+3b8fr9KPGO1WsnJyaGiokKVUSoi5Rwprf+ksjKaElnWv6zXSM+Mu2GZrtFobi/3jeLHvFtlRgpxq+1USMHMPPCbF3yAmpzNaVJlbI/q6mqWlpY4cuQIGzZswGq1UllZycaNGxkbG6OpqUmlbLbb7SpYojQLlbvg5uvLicpms7Fr1y58Ph+XLl1SpvBygoPQjpqcpGU6UanskQqaiYkJWltb+fa3v6182M2TqBzQr1y5wk9/+lMeffRRqqqqiI2NJSEhgV27dlFZWUlXVxcDAwMqo5d0cZMBmGdmZujt7eXy5cu8/PLLSuHlcrnw+XxqUpETtVysyd3EyclJzp07R01NDVNTU8q6RZZxZmaG6upqldJZ7vRICxkZTE9eVwq/kf7tcpdCBpuenZ0lPz+f8vJyCgsLVXDqLVu2sHHjRtXepFAud0AXFhZUsL2xsTFlRdbR0aHSaI6NjSnFjnQzOXToENu3b2dqaornn39e7fxmZGRQWVlJeXk5WVlZWK1W+vv7efnll1WsKLOpttmN6W4RqURdXFxkaGiIDz74gCeffBKv10t+fj4vvvgiHR0dStkxOTnJ8ePHeeutt1TAyH/8x3/kt3/7t0lLSyMxMZGYmBiKi4vVTpzFYmHHjh1UVlYyMTHB8PCwssBKSEjA6/Vit9uVANvZ2aniJ2VmZqr4Hk1NTbzzzjtMTEwoaxypiJILSLfbzeHDh5Xv/YULFwgGgyowbWlpKRs2bKCsrIyUlBT6+/t57bXXlFBvdqmRVifFxcU88cQT7Nmzh9TUVLxeL48//jg7duygq6uL+vp6ampqlFJrbm5OLbLMizIpUEnXMjkujYyMrKgAvBu7aSkpKTz00EO8+OKLeDwetZCRVnGy75uFadmHBgYG6OnpYWpqSrmjJCQkqMDnMltfSkqKErSlsC77pznGmMyII+P/SJdBGesiKytLBQVeXFxUbgAyk5y0cDDH7pJzgtVqJSsri9TUVDIyMigpKaGwsJBgMMilS5cYGxtjYWGBiYkJ2tvbqaurY+/evUrJODQ0RE1NDSdOnOD06dOMjo7S2tpKdnY2TzzxhFL+PP/88/z4xz+mtbVVuWncKmbr2L6+PhobG2lvb1djzFe+8hWqqqq4cOEC9fX19Pf3K4sDOf5KFwiHw4HL5SIzM5OysjI2bdpEXFwcHR0d/PSnPyUQCPDee++xZ88e0tPT1SL37bff5tVXX2VkZGSZW5G57ZpjcZkXC7W1tSpNsNx1NmeAkudFBguVn5nrINLlM1KxFWmFsrCwQDAYpLOzk56eHo4dO6bij5kt9cwu5bKNmhMvmGUOs4whyynLJs+X/Wh6epquri6uXLnCG2+8QVxcnAq62tvby/DwsNq9l+dLa8aVlE2Rzxj5HsyYz11pA20lDCNkFf2zn/2MwcFBjhw5wvT09D3PChXJ2NgYNTU1ym1UuoyvVi8rIdv23Nyccm+VmK0hZHDeaMF2ZZuS8oJ0mamvr6evr48rV67wta99jY0bNy4Lsjs9PU1bWxtdXV38+Mc/pr6+Xm0oRNsMNSMzUb3//vvs2bMHwzDo6elR1uCR7/9m6uZ2zkfmNmlW6pj/NoeFMNe9PF/ORRIpT0lkoGLprnn58mXcbjcJCQnKtQw+VLjIDHtStpb1NTY2xsDAAC+88AIbNmwgJiaGyspK1q9fz+TkJBcvXqSlpQWfz0dFRQVCCKqrq3n99deVhVhHRwdlZWVkZGSQlpZGRkYGFotFbYBMTEwwNDTE0NAQk5OTdHV1kZqayrPPPktFRYWKdbZlyxby8/OZm5tjZmYGh8Oh3Bz7+vp466232L17N5mZmcTHx7NlyxZsNhvf//73ldWi2TNCCKGSrERa+ERukke+P5niXaPRfLQQ1xv8hRDZwLeBVMAA/tYwjD8TQvwh8BVgMHzo7xuG8fPVrmWxWAxzbJVowtwK50WdqMxxcMwTm91uZ926dfzN3/wNiYmJnDhxgpdffpmjR4+qhZncwZVIAf+3fuu3WL9+PV6vVy0gpEWPFFrlBCMXb0tLSyr9YU9Pj1pMyTSOcgA1m1jLhYbcGfd4PCp6v/Td7u/vp6uri5qaGi5duqSCjErNfeSPHIxtNhtZWVns3r2bT37yk2RkZCxzTZKTo1mgHR0d5e233+b48ePU1dUpgUMSHx/PwYMHeeaZZygsLMTr9S6bZKempmhoaOBHP/oRR48eZWJiYpkgJBVTS0tLeDweSkpKeOihh9ixYwdZWVnqfZh/zJZRw8PDVFdXc+XKFZqamujp6VG7FfIdyHe4YcMGduzYQU5OjnLhk+/aMELm0SMjI/T19amg3w0NDSqAd2TAP3merL+srCz+8A//kF27dik3Odl25Tlzc3P09fXxzjvv8L3vfY+2tjbl5mReNEUKL7I9Ryo77zR2u53169fz9a9/XcVfkguxkZERampqePnll7l69apSkEmlwKZNm1TQ3Pj4eCWURdsdlxmjpPVEU1MTtbW11NbWqgC+u3bt4ld+5VfIzc1V7VW+D7krJa292tvbqa2t5Utf+pKqt5mZGZXJz+l0kpaWRkFBgQp8OD4+zpUrV/jud79LbW2tiiMiy2jGZrMRFxdHcXExu3fvZs+ePZSWli4TSgOBAMPDw4yPj1NXV0dtbS0LCwtqp91isSg3upSUFAoKCsjNzWV0dJQ//MM/ZHh4+K69ZzNCCAoLC3nkkUd49tlnlTWgjEslA2babDa1K9nZ2cm5c+c4deoUly5dYnJycpkyXQbVf/zxx3niiSdISUlRCuXJyUkmJiaURY1ULpkFSI/Ho8Zel8ulMolYrVbi4uKUsC6Vv4FAQF2ro6ODmZkZZfUnlfTyejJOl3yu+fl52tra+Ku/+iuOHj3K5OSkckctLS3l6aefJhgMKlec7u5utdNqGAZ2u52CggI+//nPc/jwYVJSUlhaWuLv/u7v+PM//3PGx8dvu2AshFApyqXCB1gmoEtXo76+Pnp7e5VlXUJCAn6/XynxBwcHee211zh58iQtLS1qAeD1ennyyScpLi5meHiYs2fPqrTW5r680uIxsi/J9yv7rXlxIXflzeNdpKWW+X7Rrr/WejOXxyx7RNtUirz2avdcyQrCbH0WqTySVqnmH/PnkfdcyaLnbswR8lnk80h55m7OT9dDyg9yzroZ6xbzuzJzveuYNxyjtZtIazur1aoyg7rdboLBoHLzGx8fV7HkIhVJ0dqg+d1ASJFfWVm5zB1SWrCtxN16j+bym+dPiXmcMJ8TGZBbfm5+X1JpId+FecPFnLAgMhizWblq3iwwy9TShcztdlNQUMBjjz1GeXk5cXFxqkw2m43JyUmmp6dpaGjg9OnTnD59epniWq4bZD+Sm6XS4tTc7qS1mAyyv3fvXrZs2YLD4WBubg6r1crc3JxKctHV1aUywGVmZvL888+zceNG4uPjCQQCVFdX8w//8A8q211ycjI7duzgmWeewev1cu7cOb75zW8yPj6u1imRMbzM5TP/bU7WotFo7hvOGYaxNdoXa1H8pAPphmGcF0LEAOeAZ4DPAFOGYfz3tZbCYrEYckBbbUd7pZ0ps6LBLDyaJwYhQoEgH3roIf7oj/6I2NhYXnnlFV566SWqq6uVYCDPM7uISd/oTZs2KbennJwcFctHBoyUE/T09DQ9PT3U1NTQ2dmpYvdIV7HKykoeeughsrOzMT+3VFIEAgG18JG76J2dnRw/fpz6+np6e3tVqna5C2+2jIrUzssJVU5UMohlZWUlW7ZsUVH/5QQrLSw++OADzp8/r5QpUsA316lcAOfl5Sn3F5/Px+TkJG1tbSrQZH9/v9oJkwtqqTyTk650afP7/WRkZLBhwwaVHthut+P3+7Hb7czPz9PV1cWxY8eoqamhtbVV+SRHuu3Jcsp00dKKKDMzU6XgXVxcVDvg4+Pj9PX1qdgx5pgH5p0oc1uT783tdvOVr3yFT37yk8uUAA0NDRw/fpz+/n56e3tpbW2lvb2d6enpZfE3IoWY6/WHO4lZcPR4PDzyyCM8/vjjrF+/nmAwyOnTp/nBD36g2kZkHBEZEDw5OVnFoYqJicHhcNDd3c369euVJZD86ejo4L333lMxp6QiQAoaHo+HdevWsXPnTqqqqsjPz1dxkgYHBzl//jwXL16kqalJxQR44okn+PVf/3VSU1MRIhToXcalkRl4rl69ypUrV6irq1PnStN9M+a+ZRbSfD4fCQkJbN26lV27dpGTk4Pf71fKBamUkJkCze1T3kO6US0sLFBdXc3v//7vK9fGe0FcXBxlZWU89dRTlJWVkZaWphZRUiiV7h5SKdzT06OCeJvjm8j2a7fbSUpKoqqqiueff16Zq5sFbykES6WRVAwtLi6q/tPQ0EBvby+Tk5MIEcqYVFpaSnZ2NkVFRco9U74rl8ullKtyzJKKHrvdTjAYZHR0lO7ubhobG1XcNWm1JJ9Fnuv1etVYIHePzSbuQoQCYB84cIBPf/rTPPHEEywuhlJ0f/WrX6WhoUEpzNbCSmNA5OdS4ZqTk8O+ffvYt28fBQUFqo6ltYl0mzQvDAOBAB0dHTQ1NSmXlqGhoWXzixBCxcEJBoPLssJEs/S5HpGL8EiFxr1QIETOnWs5Hm58kWyem8yWntHmgJXuJ8+921Y2ke3uZuvgdpZhNe5VWzLfH65VzMjPzG1AyinmPmDO1nQj/cysxJCK+mjXuRN1czNyy2oKLPm5ub6k/Bi5JjDPz2ZljlTCRVO6yXtFK3ekkjmyzqTs7PV6SUtLIzk5mdjYWOLi4hgbG6Ovr4/x8XGlADIH1TaXwyxfmhVVZnlbfi4VVjKJitwglpsiw8PDyrJeWqs5HA6ys7N59NFH2b59Oz6fj4WFBVpbW3nvvfdobm4mJyeHHTt2sG7dOgD+/u//nuPHjzM7O6vqIFLBG00JabFYVEwtjUZzX3Hzip9rThDiJ8BfAHu4QcWPEMIwB0c2Y1bqmAf+lYSNSGWP/O1wOEhISOB3f/d3efTRR+nv7+ef//mf+dnPfqayWETbfZQWIRaLRS1Qk5KSSExMVAGirVarCio3PT2tzEjlrrc5Y5SMF5ORkaEULomJiSQlJeF0OlUAOxkjRgY2lrEGJiYmrsnoIMsXbVcEWOYDbVaASFcXqYCSfuSjo6OMjIwwPj7O1NSUsiYymxbLiVW6dzmdTnw+n1KmyJ13GYRP+lubFSeR70/WtXQ78Hg8xMfHK3ezlJSUZQF8e3p61LUj358R3nk3T1JS+JEKMKl0M09i8t2ZBXDzJG1ug2bkhJybm0txcTFZWVnq/nV1dfT29jI9Pa18tc2xPu62Fc9qRD6XrKukpCQV7Bygvb1d1b/ZxUOeAx8GCJauJNI9zzAM5col26PD4VAZUGRqarNPv7ye1+slISFBBQ3Oy8vDMAwVEHhsbEy1WQjtdO7YsYNt27ap2DCy/JcvX6aurk5ZbMhYGZFBmVerJ9kHbDYb8fHxKk6WdNHMyMigsLCQmJgYUlNTcTqdqt3Nzc3h8/m4cuUKLpeLtrY2GhoaaGho4PLly0rYuhdIBYcMPpuVlbUs5ktbWxsjIyOMjY0xMjLC9PS0UtKZ+06kYkIqcFNTU1XAdK/Xi8fjUeOrtL6U15a7jVJwlv1I9ks5psqxND4+Hp/Pp5Q75rgNUviXfVwqAwcGBhgeHmZsbExZIJnHPIl57pFEW6zb7XYyMzM5dOgQn/3sZ/F4PFy+fJm//Mu/VAFh7wSyjmXby83NVcpHv9+P3+9X/UQuBGXcq/7+fmUtGRkzZqXnj1ycPAjcaUVG5KJfcjP3u9dKDc3tQS7ml5aWlrmkSszzkVkOWSvRLGmi/X8viex3kRu40WTbSDekleQz8yaduS4iFT/yd+TmW2T5Iud/QMkBDodDzfNmK5nIDHvynZoD4putacyfyzKYYx6ZFUBy3Jebt3Ij27whLDfx8vLyqKqqYt++fWRnZzM9Pc3Q0BBjY2NK5rbZbNTV1fHXf/3XDA4OXuM6J8tqritzOeW8cj+1L41GA9wuxY8QIg94D9gA/DbwRWACOAv8jmEYo9c5f0XFj1kbbibaLpn52Gg7oZmZmfzBH/wBfr+f48eP8+6773L16lWlSJHWJ+ZrmScYqSyQiwmZAUQO5NL9wOwOZN6Ri7QUkBYsbrcbn8+Hw+FQ5rw2m43Z2Vm10JEWLZHBCeV1ZRnNCqCVdjnMFk0yK5b8XLo0yYVzpFY/sq6j7Z5Es4gxZ6wy+2TLe5gnYPNOtHTNsFgs+Hw+Zco6OzurXDkid8HMvvWy/uV30l0scrFi3m2JVj6ziXC0sssfaQEid9mXlkJpyaVFTOQkeTNC3J0k8tnM7UQupIUQytrMXG+R9Q/L3SdkW5CpPuVn8j1JASlyV818Lflb7m4lJCQghGB4eHiZ4kGW3WKxKKWVjB1ktVoZHByku7ubvr4+lfUn2j3XWlfmfiXHCBl4Njk5WS28ZYwqIUJxpRwOB52dnVitVoaHh1Va7uuZ4d9pzO/F7XarwLMul0ulzZVjktnaxdz3o7UHKShbLBYVEFpm+5LKXjnOyT4u+45UxEgid4ABlRlHum2Zlb3m88yBf2WwfbOy0Sww30zdyXrLy8tTGRfb29upqalRLpF3CrNCPjY2VrU5malQKtBkf5LK7kAgsKxerjcu3Qmlw4OiyIj2HOb540F4Rs213KzS0Oz+F41Ixc/N3MN8/Tut3LwZoslV0VjtO/Mx8prmdYGUC8z3NNeF2VJIfm8eByPntch1RrRniVRKwbWxS82bovJvOW+tprQyK97NlrPmeTFyo1Zm4du0aRObNm0iISFBxVaTiRj6+vqUW5qcfyPrcaV4WfJ/8waqRqO5b7h1xY8Qwge8C/yRYRg/EkKkAkOE4v7834TcwX4tynm/Dvx6+N8tkYqf1QZ28+AdaWJouv6ywc/hcJCcnMynP/1pJicnOXnypIr9IJUjkVnAIgU1c8pDsxJHxjWRZZODotTGR2YdkIO1vIZ5AROplDArNVbyFTcrNyJ3R+S9zFH4I8+PLId8DvNkY17cmxdakZjfi3lSleVbqV1FKtrMzybLE02JYF50Ri405Tnm7+ViUNZn5KRsfldmIuvF/Flk+SMFGPP1V2Oti4KbEYqi9au13CtSQSP/j1RoRtZxtN0suDbQork8kUq3SMVYtDYn+4zsw5FCmVmYk+9eCKGUTOZ2uVZlz0oLush6ks8hBTLzmGE+Ryo0zH3dLNBF1pO5bqL9He34mz1H1qX8MT97NLeDaHUYbUySn0eeF21313w/8zHm55D1JZVq5nqU94xWhsi2u9Iz3AiyfHIX1uFwAChrsmiCcrRFWeT3q/1tJnKOiezzq8UGWenZVxtXVivvaudGI9q7WytrqZsbvV5km492r2hcb5xY6X63Wua1ciPzxGrnrqXOb/S5bkcZVmqDax0f11Ke1eSp1SxGo41B0WSGaOfcLJFljZRt7xdWakuryY2R30X2WfN8a5YJV6qPaNc0yyDm8SnaO1zp/crzzONyZDuJlF/Mm7SRc56cP6XsIGUcYNn/Uv43X0taxxYVFalkMRCao0ZHR+nq6qKxsXFZpkZ5z0irpWjPK691P7UtjUYD3KriRwhhB14BXjcM40+ifJ8HvGIYxobrXGeZxc9Kg6/8Psr5UY8zD0Ry0IuNjV2WeUEeE+lza1ZgRJpcRi7g5P3MA7NZ2SIXu+Y4FhLztc2aeXnvSLeT1Rbw0SakyEWz+Xw5CUZas5gXZOY6Mg/y0l3FXE+Ri3pzmc0TXrSJwryok9cT4sNAd9GEOPPkGU3xY/4+8hxzfZnrLdqiKPJaqy1OzZOrOYBfpCASTTCMthC7U6z1XpGTeaSQvZLwa25L5jo233Mtgt1ay76SIjLa95EKlhspQ7R3Ge3cSKEwmiBpPu6j4C6zWru/HawkDF9vMRUpkEZ+Z/47svx3euFzN/v0zWBupzoew7Xcbwtjzf3NWhQ/0c6B6HNItM9vJ/dz+15tXL/eOZGyhvzbrByJPGYl2VVeN5rsE7khYr5HtDIAy+TgSDczKe9LuVd+J783J54xz2VSTjdvbpm/NydGiYy/53a7cTgcKv6m2S3NHF/KPE+sZDEvN7sBHeNHo7k/WVHxc9107iI0Cvw9UGtW+ggh0g3D6A3/+yxQs5aSRBtE1jLxRS46DSMU2yVSYSIHrsnJyaiL0sgFoFSKwIdKIZnmUF7PrKwwx4sxKxrktcwuWmZLIKkgMgdzi1SQmJ81ctd7pR0Dc91ELrojFVuRE160hXnk9cyKHvl5NOXW9RRSKy28zZ9H7l6YJyE5kZmfyaxIM9d5pFLJ/Czm60VzdTCXLXInPbLtRirqoi36V1MGmOtgLZ/dDNGuEU2JY35vKwkyZiFC1vlKpuuRbWulZ4n8PlrZrndcZB+M9uzR7h9NOblSOa/3uXm8WKm9rHR+tOdd6fM7QeS9zOPa7Walulnp/UWOG2vZYV+pzd8Ma62H29VX71RbWKsi7260x5td6N7JPnG/LYrvx3HhXnA762G1OXW171Yrw1rvu9rxt9L27ofxaTWiPXs0GfxGr2f+bZ57o8kkkbJwZHmknG6WESMt+yOfPVIujFTsRMoB0c6XVqvmDWNz2cxzormezOsIuWYxu4sZhrHMXVp+Pj4+rsJWRMq+MstlNPk32tpBfn6vxweNRnPjrCWr117gfaAakFL37wMvAJWAAbQBXzU+VAStdK1BYJqQi5hGo7l/SUL3U43mfkf3U43mo4HuqxrN/Y/up5oHgVzDMJKjfXHDWb1uFSHEWWMF8yONRnN/oPupRnP/o/upRvPRQPdVjeb+R/dTzYPO6gEzNBqNRqPRaDQajUaj0Wg0H1m04kej0Wg0Go1Go9FoNBqN5gHlXih+/vYe3FOj0dwYup9qNPc/up9qNB8NdF/VaO5/dD/VPNDc9Rg/Go1Go9FoNBqNRqPRaDSau4N29dJoNBqNRqPRaDQajUajeUC5a4ofIcRjQoh6IUSTEOLrd+u+Go1mOUKIbCHEUSHEVSHEFSHE/xn+PEEI8UshRGP4d3z4cyGE+PNw370shKi6t0+g0Xy8EEJYhRAXhBCvhP/PF0KcDvfJ7wshHOHPneH/m8Lf593Tgms0HxOEEHFCiB8IIeqEELVCiF16TtVo7j+EEL8Vln1rhBD/JIRw6TlV83Hhrih+hBBW4C+Bx4H1wAtCiPV3494ajeYagsDvGIaxHtgJ/B/h/vh14C3DMIqBt8L/Q6jfFod/fh34X3e/yBrNx5r/E6g1/f/fgP9hGEYRMAp8Kfz5l4DR8Of/I3ycRqO58/wZ8AvDMMqATYT6q55TNZr7CCFEJvBvgK2GYWwArMDn0HOq5mPC3bL42Q40GYbRYhjGPPAS8PRdurdGozFhGEavYRjnw39PEhJQMwn1yX8MH/aPwDPhv58Gvm2EOAXECSHS726pNZqPJ0KILOBJ4O/C/wvgYeAH4UMi+6rswz8AHgkfr9Fo7hBCCD+wH/h7AMMw5g3DGEPPqRrN/YgNcAshbIAH6EXPqZqPCXdL8ZMJdJr+7wp/ptFo7iFhs9XNwGkg1TCM3vBXfUBq+G/dfzWae8efAv8eWAr/nwiMGYYRDP9v7o+qr4a/Hw8fr9Fo7hz5wCDwzbBL5t8JIbzoOVWjua8wDKMb+O9AByGFzzhwDj2naj4m6ODOGs3HFCGED/gh8G8Nw5gwf2eE0v3plH8azT1ECHEEGDAM49y9LotGo1kRG1AF/C/DMDYD03zo1gXoOVWjuR8Ix9l6mpCyNgPwAo/d00JpNHeRu6X46QayTf9nhT/TaDT3ACGEnZDS5/9nGMaPwh/3S3Pz8O+B8Oe6/2o094Y9wFNCiDZCLtIPE4olEhc2U4fl/VH11fD3fmD4bhZYo/kY0gV0GYZxOvz/DwgpgvScqtHcXxwCWg3DGDQMYwH4EaF5Vs+pmo8Fd0vxcwYoDkdNdxAKpPXTu3RvjUZjIuyf/PdArWEYf2L66qfAF8J/fwH4ienzz4czkewExk3m6xqN5g5hGMbvGYaRZRhGHqF5823DMP5fwFHg+fBhkX1V9uHnw8drKwON5g5iGEYf0CmEKA1/9AhwFT2najT3Gx3ATiGEJywLy76q51TNxwJxt9qvEOIJQrEKrMA/GIbxR3flxhqNZhlCiL3A+0A1H8YN+X1CcX7+GcgB2oHPGIYxEp4c/4KQOewM8KuGYZy96wXXaD7GCCEeAv4vwzCOCCEKCFkAJQAXgF8xDGNOCOECvkMobtcI8DnDMFruUZE1mo8NQohKQgHYHUAL8KuENlf1nKrR3EcIIf4j8FlCGW4vAF8mFMtHz6maB567pvjRaDQajUaj0Wg0Go1Go9HcXXRwZ41Go9FoNBqNRqPRaDSaBxSt+NFoNBqNRqPRaDQajUajeUDRih+NRqPRaDQajUaj0Wg0mgcUrfjRaDQajUaj0Wg0Go1Go3lA0YofjUaj0Wg0Go1Go9FoNJoHFK340Wg0Go1Go9FoNBqNRqN5QNGKH41Go9FoNBqNRqPRaDSaBxSt+NFoNBqNRqPRaDQajUajeUD5/wN/MDxLriQzGQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -251,70 +251,217 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data, target = dataset[0]"
+    "data, target = dataset[10]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 107,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = convert_y_label_to_string(dataset.targets[10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data1, target1 = dataset[110]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([1, 28, 952])"
+       "\"Griffiths resolution. Mr. Foot's line will\""
       ]
      },
-     "execution_count": 9,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data.shape"
+    "text"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "S = 30\n",
+    "S_min = 10\n",
+    "N = 16\n",
+    "C = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([97])"
+       "tensor([[ 6, 10,  2, 17,  8, 18, 16,  4, 14, 14,  6,  9,  4, 14, 10, 10, 12,  4,\n",
+       "         11, 18, 19,  4, 10,  8, 13,  2, 18,  4, 17,  9],\n",
+       "        [ 8, 13,  8,  5,  5,  6,  4, 10,  9, 14, 19,  6,  7,  6, 10,  6,  5,  3,\n",
+       "         14, 10,  1, 18,  3,  3, 13, 13, 16, 12,  5,  6],\n",
+       "        [ 1,  9, 18, 10, 10, 10, 10,  6,  7,  7, 14,  6, 12, 12,  3,  9, 14, 16,\n",
+       "         11, 14,  3, 10,  9, 15, 19,  8, 13,  5, 12, 15],\n",
+       "        [10,  2,  6, 11, 14,  1, 13,  1,  7,  2, 19,  1,  1, 17,  6, 16, 18, 12,\n",
+       "          3, 18, 19, 17,  9, 12, 14, 15,  3,  8,  1,  9],\n",
+       "        [12,  7, 14,  5,  2, 12,  1, 16,  9, 16, 18, 17,  6, 11,  2,  7,  5,  8,\n",
+       "         16,  6, 19, 13, 12, 17, 11, 13, 17, 12,  5,  1],\n",
+       "        [13, 11, 14, 18, 15,  8, 17, 13, 18,  5, 10,  6, 15,  3,  4, 11, 12,  5,\n",
+       "          4,  1, 17, 12,  7,  5,  5,  9, 19, 15,  4,  5],\n",
+       "        [12,  1,  4,  5,  6, 13, 19,  1,  1, 15,  3, 14,  8, 19,  7,  5, 19,  9,\n",
+       "          5, 11, 14, 10, 11,  1, 12, 19, 14, 13, 19, 15],\n",
+       "        [16,  3,  7, 10, 12, 15,  9, 18,  9, 10, 16,  2, 14,  4, 18,  3,  8, 12,\n",
+       "         16, 19, 11,  5,  9, 19, 11, 14,  7, 16,  4,  3],\n",
+       "        [ 1,  4,  2, 10, 10,  2,  3,  5,  9,  6, 16,  3, 11,  6, 14, 19,  3, 11,\n",
+       "          6,  3, 19, 17,  2, 12, 12,  4,  5, 15, 19,  1],\n",
+       "        [18,  9, 12,  1,  1,  1,  3,  8,  9,  7,  9, 19,  5, 12, 10, 17, 15, 14,\n",
+       "         18, 15,  8,  4,  1,  3, 14, 14,  2,  5,  9,  4],\n",
+       "        [15,  5, 15, 14,  8,  9,  8, 15, 12, 15, 18,  2,  7, 13, 19, 12,  1, 16,\n",
+       "         12, 11,  1, 12, 17, 16, 18,  3,  6, 11,  9, 11],\n",
+       "        [18, 17, 14, 11,  5, 15,  3, 10, 10, 16, 14,  9, 12,  7,  8, 13, 18, 11,\n",
+       "          6,  9, 16, 10, 14,  6,  5, 19, 11, 13,  7, 14],\n",
+       "        [18,  5, 11, 13, 15,  9,  7, 10,  3, 19,  8, 10, 13,  4, 11,  5, 14, 17,\n",
+       "          2, 16, 18,  8,  7, 16, 15, 19,  8, 13, 13,  9],\n",
+       "        [14, 14,  4, 10,  6, 14, 14,  1, 12,  1,  3,  6,  7,  6, 19,  9,  2, 19,\n",
+       "         13,  9,  1,  2, 11,  2,  2, 10,  3, 11,  1, 15],\n",
+       "        [ 6, 16, 19, 14, 14, 17, 18, 10, 18, 18,  2, 19, 15, 15,  1,  3, 12, 11,\n",
+       "         17,  7, 15,  6, 10,  2, 13, 17, 13,  6,  8, 14],\n",
+       "        [ 9, 19,  9, 13, 12,  9,  5, 18,  4,  7, 14, 14, 12,  1, 16, 19, 16,  2,\n",
+       "         15,  4, 14,  9, 15,  2,  9, 13, 12,  1, 12, 11]])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "target.shape\n"
+    "torch.randint(low=1, high=C, size=(N, S), dtype=torch.long)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "T = 50      # Input sequence length"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long)\n",
+    "target = torch.randint(low=1, high=C, size=(sum(target_lengths),), dtype=torch.long)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor([10, 62, 22, 24, 31, 14, 62, 55, 50, 62, 54, 55, 50, 51, 62, 22, 53, 74,\n",
-       "        62, 16, 36, 44, 55, 54, 46, 40, 47, 47, 62, 41, 53, 50, 48, 79, 79, 79,\n",
-       "        79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
-       "        79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
-       "        79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
-       "        79, 79, 79, 79, 79, 79, 79], dtype=torch.uint8)"
+       "tensor([50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_lengths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([20, 28, 20, 32, 30, 34, 14, 15, 21,  3, 20, 13, 28, 40, 15, 27])"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target_lengths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([17, 17,  5,  5, 15,  5, 16, 19, 12,  3,  6, 15,  6, 13, 10,  1, 19, 12,\n",
+       "         2, 13, 19, 13,  7,  4, 19,  9, 19,  1,  3, 16,  1, 12, 11,  8, 17,  6,\n",
+       "        10,  8, 15, 15, 18, 11,  2,  6, 17,  8,  1, 12,  3, 15, 10, 14,  3,  3,\n",
+       "        17,  4, 18, 15, 13, 18, 19, 12,  1, 17, 18,  9, 10,  8,  2,  3,  9,  4,\n",
+       "         7,  9, 12, 11,  9,  5, 12, 10,  4, 15,  8,  6, 17,  9,  7,  7, 18, 15,\n",
+       "        16, 16, 14, 17, 11, 14, 13,  9, 10, 19,  7, 13, 12,  5, 19,  3,  7, 18,\n",
+       "         7,  6,  5,  1,  6, 11,  1, 19, 18, 15,  6,  4, 13, 14, 12, 19, 18,  4,\n",
+       "        15, 14, 12,  1, 14, 18,  1,  4,  1,  7, 12,  6,  3,  9,  8, 19,  7, 13,\n",
+       "         1,  4, 14,  1, 14,  8, 19,  2,  6, 11, 19, 11,  3, 13, 14, 17,  3,  3,\n",
+       "        10, 10, 18,  2, 11, 10,  8,  2, 18,  9,  2,  1, 16,  2,  5,  9,  1,  4,\n",
+       "        16, 18, 12, 11, 12, 13, 13, 18,  2,  3,  2,  7, 18,  8,  2, 16, 12, 18,\n",
+       "        10, 15, 16, 12,  3,  5,  6,  2, 14,  3, 10,  2, 12, 14,  3, 14, 11, 14,\n",
+       "         6, 11,  5,  4,  6,  9, 17,  1,  7,  1,  6, 13,  7,  7,  2,  4,  4, 15,\n",
+       "         1, 11, 10, 12, 10,  4,  3,  3,  7, 19,  5, 19, 18, 17,  1, 11, 14, 12,\n",
+       "        18, 16, 17, 16, 12,  5,  5,  5,  5,  4, 12, 18,  1, 16,  3, 12,  9,  8,\n",
+       "        13, 18,  6,  7, 17,  1,  9,  2, 17, 10,  2,  3,  8, 11,  3,  3, 17, 11,\n",
+       "        17, 19,  6, 12,  4, 11, 18,  3, 18, 16,  7,  9,  6, 15, 10, 17,  1, 17,\n",
+       "         2,  7,  7,  3,  7,  5, 15,  8,  3, 15,  6,  4, 16, 12,  4, 11,  1, 15,\n",
+       "        14,  4, 14, 17, 16, 14, 18, 10, 17,  4,  2, 19,  8, 10, 11,  2, 18,  2,\n",
+       "        19, 10, 15, 15, 14, 10,  3, 16, 14,  5, 15,  4,  7, 13, 16, 14,  3, 14])"
+      ]
+     },
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -323,6 +470,403 @@
     "target"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([16, 53, 44, 41, 41, 44, 55, 43, 54, 62, 53, 40, 54, 50, 47, 56, 55, 44,\n",
+       "        50, 49, 74, 62, 22, 53, 74, 62, 15, 50, 50, 55, 67, 54, 62, 47, 44, 49,\n",
+       "        40, 62, 58, 44, 47, 47], dtype=torch.uint8)"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target[target < 79]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ts = torch.stack([target, target1])\n",
+    "\n",
+    "targets = torch.Tensor([])\n",
+    "target_lengths = []\n",
+    "for t in ts:\n",
+    "    t = t[t < 79]\n",
+    "    targets = torch.cat([targets, t])\n",
+    "    target_lengths.append(len(t))\n",
+    "\n",
+    "targets = targets.type(dtype=torch.long)\n",
+    "target_lengths = torch.Tensor(target_lengths).type(dtype=torch.long)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([42, 41])"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target_lengths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([16, 53, 44, 41, 41, 44, 55, 43, 54, 62, 53, 40, 54, 50, 47, 56, 55, 44,\n",
+       "        50, 49, 74, 62, 22, 53, 74, 62, 15, 50, 50, 55, 67, 54, 62, 47, 44, 49,\n",
+       "        40, 62, 58, 44, 47, 47, 47, 44, 54, 55, 40, 39, 62, 37, 60, 62, 55, 43,\n",
+       "        40, 62, 16, 50, 57, 40, 53, 49, 48, 40, 49, 55, 74, 62, 18, 48, 48, 40,\n",
+       "        39, 44, 36, 55, 40, 47, 60, 62, 22, 53, 74])"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "targets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([42., 41.])"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target_lengths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([430])"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([10, 22, 19, 13, 15, 23, 28, 14, 22, 21, 16, 14, 22, 28, 22, 26])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.randint(low=S_min, high=S, size=(N,), dtype=torch.long)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "targets = torch.stack([target, target])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "stack expects each tensor to be equal size, but got [0] at entry 0 and [42] at entry 1",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-83-692d40f2bb6a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m79\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m: stack expects each tensor to be equal size, but got [0] at entry 0 and [42] at entry 1"
+     ]
+    }
+   ],
+   "source": [
+    "ts = torch.Tensor()\n",
+    "for i, t in enumerate(targets):\n",
+    "    torch.stack([ts, t[t < 79]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([16, 53, 44, 41, 41, 44, 55, 43, 54, 62, 53, 40, 54, 50, 47, 56, 55, 44,\n",
+       "        50, 49, 74, 62, 22, 53, 74, 62, 15, 50, 50, 55, 67, 54, 62, 47, 44, 49,\n",
+       "        40, 62, 58, 44, 47, 47, 16, 53, 44, 41, 41, 44, 55, 43, 54, 62, 53, 40,\n",
+       "        54, 50, 47, 56, 55, 44, 50, 49, 74, 62, 22, 53, 74, 62, 15, 50, 50, 55,\n",
+       "        67, 54, 62, 47, 44, 49, 40, 62, 58, 44, 47, 47], dtype=torch.uint8)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "targets[targets<79]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.networks import LineRecurrentNetwork"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "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,},\n",
+    "            patch_size=[28, 14],                \n",
+    "            stride=[1, 6])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = crnn(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([157, 1, 80])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# output = output.unsqueeze(0)\n",
+    "targets = target.unsqueeze(0).type(torch.long)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "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": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ctc = nn.CTCLoss(blank=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 97])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "targets.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([157, 1, 80])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(6.9447, grad_fn=<MeanBackward0>)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ctc(output, targets, input_lengths, target_lengths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from einops.layers.torch import Rearrange\n",
+    "slide = nn.Sequential(nn.Unfold(kernel_size=(28, 14), stride=(1, 5)), Rearrange(\"b (c h w) t -> b t c h w\", h=28, w=14, c=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "patches = slide(data.unsqueeze(0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# remove batch size\n",
+    "patches = patches.squeeze(0)\n",
+    "fig = plt.figure(figsize=(20, 20))\n",
+    "for i in range(6):\n",
+    "    ax = fig.add_subplot(1, 6, i + 1)\n",
+    "    ax.imshow(patches[i].squeeze(0), cmap='gray')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/src/text_recognizer/datasets/dataset.py b/src/text_recognizer/datasets/dataset.py
index f328a0f..05520e5 100644
--- a/src/text_recognizer/datasets/dataset.py
+++ b/src/text_recognizer/datasets/dataset.py
@@ -23,7 +23,7 @@ class Dataset(data.Dataset):
 
         Args:
             train (bool): If True, loads the training set, otherwise the validation set is loaded. Defaults to False.
-            subsample_fraction (float): Description of parameter `subsample_fraction`. Defaults to None.
+            subsample_fraction (float): The fraction of the dataset to use for training. Defaults to None.
             transform (Optional[Callable]): Transform(s) for input data. Defaults to None.
             target_transform (Optional[Callable]): Transform(s) for output data. Defaults to None.
 
@@ -31,6 +31,7 @@ class Dataset(data.Dataset):
             ValueError: If subsample_fraction is not None and outside the range (0, 1).
 
         """
+
         self.train = train
         self.split = "train" if self.train else "test"
 
@@ -96,8 +97,8 @@ class Dataset(data.Dataset):
         if self.subsample_fraction is None:
             return
         num_subsample = int(self.data.shape[0] * self.subsample_fraction)
-        self.data = self.data[:num_subsample]
-        self.targets = self.targets[:num_subsample]
+        self._data = self.data[:num_subsample]
+        self._targets = self.targets[:num_subsample]
 
     def __len__(self) -> int:
         """Returns the length of the dataset."""
diff --git a/src/text_recognizer/datasets/emnist_dataset.py b/src/text_recognizer/datasets/emnist_dataset.py
index 81268fb..d01dcee 100644
--- a/src/text_recognizer/datasets/emnist_dataset.py
+++ b/src/text_recognizer/datasets/emnist_dataset.py
@@ -13,17 +13,10 @@ from torchvision.datasets import EMNIST
 from torchvision.transforms import Compose, ToTensor
 
 from text_recognizer.datasets.dataset import Dataset
+from text_recognizer.datasets.transforms import Transpose
 from text_recognizer.datasets.util import DATA_DIRNAME
 
 
-class Transpose:
-    """Transposes the EMNIST image to the correct orientation."""
-
-    def __call__(self, image: Image) -> np.ndarray:
-        """Swaps axis."""
-        return np.array(image).swapaxes(0, 1)
-
-
 class EmnistDataset(Dataset):
     """This is a class for resampling and subsampling the PyTorch EMNIST dataset."""
 
diff --git a/src/text_recognizer/datasets/emnist_lines_dataset.py b/src/text_recognizer/datasets/emnist_lines_dataset.py
index 8fa77cd..6268a01 100644
--- a/src/text_recognizer/datasets/emnist_lines_dataset.py
+++ b/src/text_recognizer/datasets/emnist_lines_dataset.py
@@ -19,7 +19,6 @@ from text_recognizer.datasets.util import (
     EmnistMapper,
     ESSENTIALS_FILENAME,
 )
-from text_recognizer.networks import sliding_window
 
 DATA_DIRNAME = DATA_DIRNAME / "processed" / "emnist_lines"
 
@@ -32,6 +31,7 @@ class EmnistLinesDataset(Dataset):
         train: bool = False,
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
+        subsample_fraction: float = None,
         max_length: int = 34,
         min_overlap: float = 0,
         max_overlap: float = 0.33,
@@ -44,6 +44,7 @@ class EmnistLinesDataset(Dataset):
             train (bool): Flag for the filename. Defaults to False. Defaults to None.
             transform (Optional[Callable]): The transform of the data. Defaults to None.
             target_transform (Optional[Callable]): The transform of the target. Defaults to None.
+            subsample_fraction (float): The fraction of the dataset to use for training. Defaults to None.
             max_length (int): The maximum number of characters. Defaults to 34.
             min_overlap (float): The minimum overlap between concatenated images. Defaults to 0.
             max_overlap (float): The maximum overlap between concatenated images. Defaults to 0.33.
@@ -52,7 +53,10 @@ class EmnistLinesDataset(Dataset):
 
         """
         super().__init__(
-            train=train, transform=transform, target_transform=target_transform,
+            train=train,
+            transform=transform,
+            target_transform=target_transform,
+            subsample_fraction=subsample_fraction,
         )
 
         # Extract dataset information.
@@ -128,6 +132,7 @@ class EmnistLinesDataset(Dataset):
         if not self.data_filename.exists():
             self._generate_data()
         self._load_data()
+        self._subsample()
 
     def _load_data(self) -> None:
         """Loads the dataset from the h5 file."""
diff --git a/src/text_recognizer/datasets/transforms.py b/src/text_recognizer/datasets/transforms.py
new file mode 100644
index 0000000..17231a8
--- /dev/null
+++ b/src/text_recognizer/datasets/transforms.py
@@ -0,0 +1,13 @@
+"""Transforms for PyTorch datasets."""
+import numpy as np
+from PIL import Image
+import torch
+from torch import Tensor
+
+
+class Transpose:
+    """Transposes the EMNIST image to the correct orientation."""
+
+    def __call__(self, image: Image) -> np.ndarray:
+        """Swaps axis."""
+        return np.array(image).swapaxes(0, 1)
diff --git a/src/text_recognizer/datasets/util.py b/src/text_recognizer/datasets/util.py
index 3acf5db..73968a1 100644
--- a/src/text_recognizer/datasets/util.py
+++ b/src/text_recognizer/datasets/util.py
@@ -126,7 +126,7 @@ class EmnistMapper:
             "?",
         ]
 
-        # padding symbol
+        # padding symbol, and acts as blank symbol as well.
         extra_symbols.append("_")
 
         max_key = max(mapping.keys())
diff --git a/src/text_recognizer/models/line_ctc_model.py b/src/text_recognizer/models/line_ctc_model.py
index 97308a7..af41f18 100644
--- a/src/text_recognizer/models/line_ctc_model.py
+++ b/src/text_recognizer/models/line_ctc_model.py
@@ -62,12 +62,26 @@ class LineCTCModel(Model):
             Tensor: The CTC loss.
 
         """
+
+        # Input lengths on the form [T, B]
         input_lengths = torch.full(
             size=(output.shape[1],), fill_value=output.shape[0], dtype=torch.long,
         )
-        target_lengths = torch.full(
-            size=(output.shape[1],), fill_value=targets.shape[1], dtype=torch.long,
+
+        # Configure target tensors for ctc loss.
+        targets_ = Tensor([]).to(self.device)
+        target_lengths = []
+        for t in targets:
+            # Remove padding symbol as it acts as the blank symbol.
+            t = t[t < 79]
+            targets_ = torch.cat([targets_, t])
+            target_lengths.append(len(t))
+
+        targets = targets_.type(dtype=torch.long)
+        target_lengths = (
+            torch.Tensor(target_lengths).type(dtype=torch.long).to(self.device)
         )
+
         return self.criterion(output, targets, input_lengths, target_lengths)
 
     @torch.no_grad()
@@ -93,7 +107,7 @@ class LineCTCModel(Model):
         raw_pred, _ = greedy_decoder(
             predictions=log_probs,
             character_mapper=self.mapper,
-            blank_label=79,
+            blank_label=80,
             collapse_repeated=True,
         )
 
diff --git a/src/text_recognizer/networks/ctc.py b/src/text_recognizer/networks/ctc.py
index 72f18b8..2493d5c 100644
--- a/src/text_recognizer/networks/ctc.py
+++ b/src/text_recognizer/networks/ctc.py
@@ -24,7 +24,7 @@ def greedy_decoder(
         target_lengths (Optional[Tensor]): Length of each target tensor. Defaults to None.
         character_mapper (Optional[Callable]): A emnist/character mapper for mapping integers to characters.  Defaults
             to None.
-        blank_label (int): The blank character to be ignored. Defaults to 79.
+        blank_label (int): The blank character to be ignored. Defaults to 80.
         collapse_repeated (bool): Collapase consecutive predictions of the same character. Defaults to True.
 
     Returns:
diff --git a/src/text_recognizer/networks/line_lstm_ctc.py b/src/text_recognizer/networks/line_lstm_ctc.py
index 988b615..5c57479 100644
--- a/src/text_recognizer/networks/line_lstm_ctc.py
+++ b/src/text_recognizer/networks/line_lstm_ctc.py
@@ -33,8 +33,9 @@ class LineRecurrentNetwork(nn.Module):
         self.hidden_size = hidden_size
         self.encoder = self._configure_encoder(encoder)
         self.flatten = flatten
+        self.fc = nn.Linear(in_features=self.input_size, out_features=self.hidden_size)
         self.rnn = nn.LSTM(
-            input_size=self.input_size,
+            input_size=self.hidden_size,
             hidden_size=self.hidden_size,
             num_layers=num_layers,
         )
@@ -73,6 +74,9 @@ class LineRecurrentNetwork(nn.Module):
         # Avgerage pooling.
         x = reduce(x, "(b t) c h w -> t b c", "mean", b=b, t=t) if self.flatten else x
 
+        # Linear layer between CNN and RNN
+        x = self.fc(x)
+
         # Sequence predictions.
         x, _ = self.rnn(x)
 
diff --git a/src/text_recognizer/networks/misc.py b/src/text_recognizer/networks/misc.py
index cac9e78..1f853e9 100644
--- a/src/text_recognizer/networks/misc.py
+++ b/src/text_recognizer/networks/misc.py
@@ -34,6 +34,7 @@ def activation_function(activation: str) -> Type[nn.Module]:
     """Returns the callable activation function."""
     activation_fns = nn.ModuleDict(
         [
+            ["elu", nn.ELU(inplace=True)],
             ["gelu", nn.GELU()],
             ["leaky_relu", nn.LeakyReLU(negative_slope=1.0e-2, inplace=True)],
             ["none", nn.Identity()],
diff --git a/src/text_recognizer/networks/transformer.py b/src/text_recognizer/networks/transformer.py
new file mode 100644
index 0000000..868d739
--- /dev/null
+++ b/src/text_recognizer/networks/transformer.py
@@ -0,0 +1 @@
+"""TBC."""
diff --git a/src/text_recognizer/networks/wide_resnet.py b/src/text_recognizer/networks/wide_resnet.py
index d1c8f9a..618f414 100644
--- a/src/text_recognizer/networks/wide_resnet.py
+++ b/src/text_recognizer/networks/wide_resnet.py
@@ -28,10 +28,10 @@ def conv_init(module: Type[nn.Module]) -> None:
     classname = module.__class__.__name__
     if classname.find("Conv") != -1:
         nn.init.xavier_uniform_(module.weight, gain=np.sqrt(2))
-        nn.init.constant(module.bias, 0)
+        nn.init.constant_(module.bias, 0)
     elif classname.find("BatchNorm") != -1:
-        nn.init.constant(module.weight, 1)
-        nn.init.constant(module.bias, 0)
+        nn.init.constant_(module.weight, 1)
+        nn.init.constant_(module.bias, 0)
 
 
 class WideBlock(nn.Module):
@@ -183,7 +183,7 @@ class WideResidualNetwork(nn.Module):
             else None
         )
 
-        self.apply(conv_init)
+        # self.apply(conv_init)
 
     def _configure_wide_layer(
         self, in_planes: int, out_planes: int, stride: int, activation: str
diff --git a/src/text_recognizer/weights/LineCTCModel_EmnistLinesDataset_LineRecurrentNetwork_weights.pt b/src/text_recognizer/weights/LineCTCModel_EmnistLinesDataset_LineRecurrentNetwork_weights.pt
index 9aec6ae..59c06c2 100644
Binary files a/src/text_recognizer/weights/LineCTCModel_EmnistLinesDataset_LineRecurrentNetwork_weights.pt and b/src/text_recognizer/weights/LineCTCModel_EmnistLinesDataset_LineRecurrentNetwork_weights.pt differ
diff --git a/src/text_recognizer/weights/LineCTCModel_IamLinesDataset_LineRecurrentNetwork_weights.pt b/src/text_recognizer/weights/LineCTCModel_IamLinesDataset_LineRecurrentNetwork_weights.pt
new file mode 100644
index 0000000..9bd8ca2
Binary files /dev/null and b/src/text_recognizer/weights/LineCTCModel_IamLinesDataset_LineRecurrentNetwork_weights.pt differ
diff --git a/src/training/experiments/default_config_emnist.yml b/src/training/experiments/default_config_emnist.yml
new file mode 100644
index 0000000..12a0a9d
--- /dev/null
+++ b/src/training/experiments/default_config_emnist.yml
@@ -0,0 +1,69 @@
+dataset: EmnistDataset
+dataset_args:
+  sample_to_balance: true
+  subsample_fraction: 0.33
+  transform: null
+  target_transform: null
+  seed: 4711
+
+data_loader_args:
+  splits: [train, val]
+  shuffle: true
+  num_workers: 8
+  cuda: true
+
+model: CharacterModel
+metrics: [accuracy]
+
+network_args:
+  in_channels: 1
+  num_classes: 80
+  depths: [2]
+  block_sizes: [256]
+
+train_args:
+  batch_size: 256
+  epochs: 5
+
+criterion: CrossEntropyLoss
+criterion_args:
+  weight: null
+  ignore_index: -100
+  reduction: mean
+
+optimizer: AdamW
+optimizer_args:
+  lr: 1.e-03
+  betas: [0.9, 0.999]
+  eps: 1.e-08
+  # weight_decay: 5.e-4
+  amsgrad: false
+
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  max_lr: 1.e-03
+  epochs: 5
+  anneal_strategy: linear
+
+
+callbacks: [Checkpoint, ProgressBar, EarlyStopping, WandbCallback, WandbImageLogger, OneCycleLR]
+callback_args:
+  Checkpoint:
+    monitor: val_accuracy
+  ProgressBar:
+    epochs: 5
+    log_batch_frequency: 100
+  EarlyStopping:
+    monitor: val_loss
+    min_delta: 0.0
+    patience: 3
+    mode: min
+  WandbCallback:
+    log_batch_frequency: 10
+  WandbImageLogger:
+    num_examples: 4
+  OneCycleLR:
+    null
+verbosity: 1 # 0, 1, 2
+resume_experiment: null
+validation_metric: val_accuracy
diff --git a/src/training/experiments/iam_line_ctc_experiment.yml b/src/training/experiments/iam_line_ctc_experiment.yml
new file mode 100644
index 0000000..141c74e
--- /dev/null
+++ b/src/training/experiments/iam_line_ctc_experiment.yml
@@ -0,0 +1,94 @@
+experiment_group: Sample Experiments
+experiments:
+    - train_args:
+        batch_size: 24
+        max_epochs: 128
+      dataset:
+        type: IamLinesDataset
+        args:
+          subsample_fraction: null
+          transform: null
+          target_transform: null
+        train_args:
+          num_workers: 6
+          train_fraction: 0.85
+      model: LineCTCModel
+      metrics: [cer, wer]
+      network:
+        type: LineRecurrentNetwork
+        args:
+          # encoder: ResidualNetworkEncoder
+          # encoder_args:
+          #   in_channels: 1
+          #   num_classes: 80
+          #   depths: [2, 2]
+          #   block_sizes: [128, 128]
+          #   activation: SELU
+          #   stn: false
+          encoder: WideResidualNetwork
+          encoder_args:
+            in_channels: 1
+            num_classes: 80
+            depth: 16
+            num_layers: 4
+            width_factor: 2
+            dropout_rate: 0.2
+            activation: selu
+            use_decoder: false
+          flatten: true
+          input_size: 256
+          hidden_size: 128
+          num_layers: 2
+          num_classes: 80
+          patch_size: [28, 14]
+          stride: [1, 5]
+      criterion:
+        type: CTCLoss
+        args:
+          blank: 79
+      optimizer:
+        type: AdamW
+        args:
+          lr: 1.e-03
+          betas: [0.9, 0.999]
+          eps: 1.e-08
+          weight_decay: false
+          amsgrad: false
+      # lr_scheduler:
+      #   type: OneCycleLR
+      #   args:
+      #     max_lr: 1.e-02
+      #     epochs: null
+      #     anneal_strategy: linear
+      lr_scheduler:
+        type: CosineAnnealingLR
+        args:
+          T_max: null
+      swa_args:
+        start: 75
+        lr: 5.e-2
+      callbacks: [Checkpoint, ProgressBar, WandbCallback, WandbImageLogger, SWA] # EarlyStopping, OneCycleLR]
+      callback_args:
+        Checkpoint:
+          monitor: val_loss
+          mode: min
+        ProgressBar:
+          epochs: null
+        #   log_batch_frequency: 100
+        # EarlyStopping:
+        #   monitor: val_loss
+        #   min_delta: 0.0
+        #   patience: 7
+        #   mode: min
+        WandbCallback:
+          log_batch_frequency: 10
+        WandbImageLogger:
+          num_examples: 6
+        # OneCycleLR:
+        #   null
+        SWA:
+          null
+      verbosity: 1 # 0, 1, 2
+      resume_experiment: null
+      test: true
+      test_metric: test_cer
diff --git a/src/training/experiments/line_ctc_experiment.yml b/src/training/experiments/line_ctc_experiment.yml
new file mode 100644
index 0000000..c21c6a2
--- /dev/null
+++ b/src/training/experiments/line_ctc_experiment.yml
@@ -0,0 +1,98 @@
+experiment_group: Sample Experiments
+experiments:
+    - train_args:
+        batch_size: 64
+        max_epochs: 32
+      dataset:
+        type: EmnistLinesDataset
+        args:
+          subsample_fraction: 0.33
+          max_length: 34
+          min_overlap: 0
+          max_overlap: 0.33
+          num_samples: 10000
+          seed: 4711
+          blank: true
+        train_args:
+          num_workers: 6
+          train_fraction: 0.85
+      model: LineCTCModel
+      metrics: [cer, wer]
+      network:
+        type: LineRecurrentNetwork
+        args:
+          # encoder: ResidualNetworkEncoder
+          # encoder_args:
+          #   in_channels: 1
+          #   num_classes: 81
+          #   depths: [2, 2]
+          #   block_sizes: [64, 128]
+          #   activation: SELU
+          #   stn: false
+          encoder: WideResidualNetwork
+          encoder_args:
+            in_channels: 1
+            num_classes: 81
+            depth: 16
+            num_layers: 4
+            width_factor: 2
+            dropout_rate: 0.2
+            activation: selu
+            use_decoder: false
+          flatten: true
+          input_size: 256
+          hidden_size: 128
+          num_layers: 2
+          num_classes: 81
+          patch_size: [28, 14]
+          stride: [1, 5]
+      criterion:
+        type: CTCLoss
+        args:
+          blank: 80
+      optimizer:
+        type: AdamW
+        args:
+          lr: 1.e-02
+          betas: [0.9, 0.999]
+          eps: 1.e-08
+          weight_decay: 5.e-4
+          amsgrad: false
+      # lr_scheduler:
+      #   type: OneCycleLR
+      #   args:
+      #     max_lr: 1.e-03
+      #     epochs: null
+      #     anneal_strategy: linear
+      lr_scheduler:
+        type: CosineAnnealingLR
+        args:
+          T_max: null
+      swa_args:
+        start: 4
+        lr: 5.e-2
+      callbacks: [Checkpoint, ProgressBar, WandbCallback, WandbImageLogger, SWA] # EarlyStopping, OneCycleLR]
+      callback_args:
+        Checkpoint:
+          monitor: val_loss
+          mode: min
+        ProgressBar:
+          epochs: null
+          log_batch_frequency: 100
+        # EarlyStopping:
+        #   monitor: val_loss
+        #   min_delta: 0.0
+        #   patience: 5
+        #   mode: min
+        WandbCallback:
+          log_batch_frequency: 10
+        WandbImageLogger:
+          num_examples: 6
+        # OneCycleLR:
+        #   null
+        SWA:
+          null
+      verbosity: 1 # 0, 1, 2
+      resume_experiment: null
+      test: true
+      test_metric: test_cer
diff --git a/src/training/run_experiment.py b/src/training/run_experiment.py
index 4317d66..286b0c6 100644
--- a/src/training/run_experiment.py
+++ b/src/training/run_experiment.py
@@ -211,7 +211,7 @@ def run_experiment(
     experiment_config: Dict, save_weights: bool, device: str, use_wandb: bool = False
 ) -> None:
     """Runs an experiment."""
-    logger.info(f"Experiment config: {json.dumps(experiment_config)}")
+    logger.info(f"Experiment config: {json.dumps(experiment_config, indent=2)}")
 
     # Create new experiment.
     experiment_dir, log_dir, model_dir = create_experiment_dir(experiment_config)
-- 
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