From ff9a21d333f11a42e67c1963ed67de9c0fda87c9 Mon Sep 17 00:00:00 2001 From: aktersnurra Date: Thu, 7 Jan 2021 20:10:54 +0100 Subject: Minor updates. --- src/notebooks/Untitled.ipynb | 89 ++++++++++++++++++++------------------------ 1 file changed, 41 insertions(+), 48 deletions(-) (limited to 'src/notebooks/Untitled.ipynb') diff --git a/src/notebooks/Untitled.ipynb b/src/notebooks/Untitled.ipynb index 7e812cd..d39e111 100644 --- a/src/notebooks/Untitled.ipynb +++ b/src/notebooks/Untitled.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -67,18 +67,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "config_path = \"../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1116_082932/config.yml\"\n", + "config_path = \"../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1213_175148/config.yml\"\n", "with open(config_path, \"r\") as f:\n", " experiment_config = yaml.safe_load(f)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -87,7 +87,7 @@ "'CNNTransformer'" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -98,14 +98,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2020-11-22 20:36:09.684 | DEBUG | text_recognizer.models.base:load_weights:432 - Loading network with pretrained weights.\n" + "2020-12-30 01:24:06.949 | DEBUG | text_recognizer.models.base:load_weights:432 - Loading network with pretrained weights.\n" ] } ], @@ -115,41 +115,25 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2020-11-22 22:45:47.919 | DEBUG | text_recognizer.models.base:load_from_checkpoint:379 - Loading checkpoint...\n", - "2020-11-22 22:45:47.920 | DEBUG | text_recognizer.models.base:load_from_checkpoint:381 - File does not exist {str(checkpoint_path)}\n" - ] - }, - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: '../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1110_073929/model/best.pt'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mckpt_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1110_073929/model/best.pt\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_from_checkpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mckpt_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/models/base.py\u001b[0m in \u001b[0;36mload_from_checkpoint\u001b[0;34m(self, checkpoint_path)\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"File does not exist {str(checkpoint_path)}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 383\u001b[0;31m \u001b[0mcheckpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheckpoint_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 384\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_network\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"model_state\"\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 385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[1;32m 579\u001b[0m \u001b[0mpickle_load_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'encoding'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 580\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 582\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_is_zipfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\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 583\u001b[0m \u001b[0;31m# The zipfile reader is going to advance the current file position.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_open_file_like\u001b[0;34m(name_or_buffer, mode)\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\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 229\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_is_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\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--> 230\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_open_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 231\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 232\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'w'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0m_open_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_opener\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 210\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\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--> 211\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_open_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\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[1;32m 212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1110_073929/model/best.pt'" + "2020-12-30 01:25:47.777 | DEBUG | text_recognizer.models.base:load_from_checkpoint:379 - Loading checkpoint...\n" ] } ], "source": [ - "ckpt_path = \"../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1110_073929/model/best.pt\"\n", + "ckpt_path = \"../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1213_175148/model/best.pt\"\n", "model.load_from_checkpoint(ckpt_path)" ] }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -158,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -168,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -177,7 +161,7 @@ "torch.Size([98])" ] }, - "execution_count": 112, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -188,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -197,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -206,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -215,21 +199,30 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/akternurra/.cache/pypoetry/virtualenvs/text-recognizer-N1c_zsdp-py3.8/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py:876: UserWarning: Argument fill/fillcolor is not supported for Tensor input. Fill value is zero\n", + " warnings.warn(\"Argument fill/fillcolor is not supported for Tensor input. Fill value is zero\")\n" + ] + } + ], "source": [ "data = ra(data)" ] }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -249,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 26, "metadata": { "scrolled": true }, @@ -257,10 +250,10 @@ { "data": { "text/plain": [ - "('because droch fis at Caully', 0.2531574070453644)" + "('becane gool big alls at boasty', 0.31098294258117676)" ] }, - "execution_count": 119, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -271,17 +264,17 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "data, target = dataset[110]\n", + "data, target = dataset[0]\n", "sentence = convert_y_label_to_string(target, dataset) " ] }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -290,13 +283,13 @@ "([], [])" ] }, - "execution_count": 111, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABG0AAABCCAYAAADt2ys3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAABIzklEQVR4nO29eZRcV3Xv/zk1dfU8qOeWWmrNrcHWiOVRsrFsYvNiDCHMYMgA/IITlvNLXiDv9wJJSCB5ITxeGJ6DDQuDbUwIGGyDIyPLkyQktSW1rLFbaqnnearq7qqu6r6/P6rO5dTtW9XdGoxs9metXl11hzPdc++q/b1776Msy0IQBEEQBEEQBEEQBEG4uvD8phsgCIIgCIIgCIIgCIIgzEREG0EQBEEQBEEQBEEQhKsQEW0EQRAEQRAEQRAEQRCuQkS0EQRBEARBEARBEARBuAoR0UYQBEEQBEEQBEEQBOEqREQbQRAEQRAEQRAEQRCEqxARbQRBEARBEARBEARBEK5CRLQRBEEwUErdqJRqUkqFlVLveJ3rXqKUspRSvjT7P6uU+tZFlHteKXX7pbdwTnV9Ryn192n2ZSulfqaUGlFK/fD1aM/lRim1QynVPsdj71NKvXyl2+Soc87tEwRBEARBEK5+RLQRBOGy4CYM/CaM1svA3wL/ZllWnmVZP/lNN8bEsqx/sCzrD3/T7bgEfg+oABZYlvXuiy1EKfU5pdT3Ll+zBEEQBEEQBOHqREQbQXgToJSqeCOWPR+UUt7XqarFwPE0bVBKqTf1czOdl89lYjFwxrKs+BWsQ7hIrvS9rpQqU0qpK1mHIAiCIAjCm403tfEhCG9mlFJFSqlPKqUOAN8xtv93pVSHUiqklDqtlHprcnuWUuorSqnO5N9XlFJZyX2lSqmnlFLDSqlBpdRLhjjxHaXUAaXUJ5RSRZfY5mql1I+UUn1KqRal1J9mOPY7SqlvKKWeUUqNAbcqpeqVUnuS7TyulPpd4/i7lFInkv3uUEr9v8a+tyuljiTP26uUuiZNnWeBpcDPkuFRWcn6vqCUegUYB5YqpW5QSh1MhvkcVErdYJSxRyn198l6wslwoAVKqe8rpUaTxy+ZZag+oJRqVUr1K6X+2ijb9jBRSgWVUt9TSg0k+3VwFqN7a3J8hpRS31ZKBZPl7FBKtSfnTTfwbaWURyn1V0qps8nyn1BKlRjt+KFSqjvZ/xeVUmvTjGe+Uup5pdRXlVKfB/4n8J7kuPyBUmqZUmp3so7+5BgVGefPmMtKqbcBnzXKOZqm7vuUUi8rpf5Xss8tSqnfMfZXK6V+mpzvzUqpPzL2pb1XXOr50+S4Lkwz7kop9W/JsTqlkvdjckehUuohpVRXsp9/r5Li5BzaX5K8jp3J/T9xVPrnSqneZNkfTdM2lFJ+pdS9SqmfAs2OMfhfyXnYo5T6plIq29j/R8lxG0yOY7XurFLqX5N1jyqljiml1iVP+xjQopT6vFKqLl2bBEEQBEEQhF8joo0gvIFIGtN3KKUeAy4AdwBfAH43uX8V8Clgq2VZ+cCdwPnk6X8NbAM2ANcCbwH+R3LfnwPtQBmJ8JXPAlZy3+8C/5As64JS6lGl1E41T4+T5PE/A44CNcBbgU8rpe7McNr7k/3LB36VPP+/gHLgfuD7yT4DPAR8PNnvdcDuZL0bgYeBjwMLgP8L/NTNCLcsaxnQCvy3ZHhUNLnrQ8AfJ9sRAp4Gvpos78vA00qpBUZR702eUwMsA/YB3wZKgJPA38wyXDcBq5Jj9D+VUvUux3wEKAQWJdvxCWAiQ5kfIHENlwEr+fW1B6hMtm1xsp/3A+8AtgPVwBDwNeP4nwMrSFyHV4HvOytLjscvgVcsy/pTy7L+hsQ8+kFybB8CFPCPyTrqk335XPJ817lsWdYvHOVcm6HP1wGngVLgn4CHlLI9PR4nMeerSYRt/YNS6rbkvkz3itnH/wncB2y3LCtdHpnrgLPJNvwN8J+GAPYdIA4sBzaSuJ//0HFuuvY/AuQAa0lch381zqskMTdqgD8AvqaUKna0fb1S6stAB/CXJOb0IuOQL5KYJxuS7ashIbqRHKd/BH4fqCLxLHo8ed4dwC3JcwuTxwwAWJb1JRL3RjlwKCnofUgplZNm7ARBEARBEATLsuRP/uTvDfBHwoBtJWEk/ylQ6nLMcqAXuB3wO/adBe4yvmsjGBJ5XJ4Els/ShtJk3a8m2/IpY995IAwMG3/jwMvJ/dcBrY7yPgN8O01d3wG+a3y/GegGPMa2x4DPJT+3khBmChzlfAP4O8e20yQMbbd6zwO3G9/3AH9rfP8QcMBxzj7gPuP4vzb2/Qvwc+P7fwOOpKl7CQmxbKGx7QDw3uTnzwHfS37+GLAXuGYOc+c88Anj+13A2eTnHcAkEDT2nwTeanyvAmKAz6XsomSbC43r9jDwGvAXjmPt9qdp5zuAw3OYyxnLSR5zH9BsfM9JtrOShDgxBeQb+/8R+M4c7pUdJISOLwMv635naEMnoBzX80MkxNEokG3sex/w/BzaXwVMA8Uude4gId75jG29wLbk59uAQ0AbCfFrpUsZChgDlhnbrgdakp8fAv7J2JeXnB9LkuWfISF6eTKMTRYJQecZYBD41mzzWP7kT/7kT/7kT/7k77fxTzxtBOGNQx1QDBwh4a0y4DzAsqxm4NMkjNpepdTjOmyBhEfBBePwC8ltAP9MIjTiv5RS55RSf5WmDQNAY7INxck2mbzDsqwi/Qf8P8a+xUC1SoTyDCulhkl49GQK6WkzPlcDbZZlTTv6UJP8/C4SYsQFpdQLSqnrjXr/3FHvIqPvc8HZjguO/WY7AHqMzxMu3/Nmqa/b+Dye5vhHgGeBx5MhMv+klPJnKNPsg3ntAfosy4oY3xcDPzbG6yQJkaNCKeVVSn1RJUKnRvm1J1epcf7dQDbwzQztQSlVkZyjHcmyvqfLmWUuO8u5WSVCpcJKKTMfkT2OlmWNJz/mJfs+aFlWyDEm+hpmulcgIVT9MfCPlmWNZOoj0GFZlmV812UtBvxAlzHO/5eEF8ps7V+UbP9QmjoHrNS8QeYcKichiL1G4jnS6nJ+GQmRqMFo2y+S28ExPpZlhUk8G2osy9oN/BsJz6xepdSDSqkCZwVWwotNP0smSXjHCYIgCIIgCA5EtBGENwiWZf05idCW14D/QyI3xN8ppVY4jnvUsqybSBiFFvCl5K7O5DZNbXIblmWFLMv6c8uylpIIh3rAkXtjhVLq74AW4H8Dx4ClyTbNlTYSb+qLjL98y7LuytRt43MnsMgRllVLwusBy7IOWpZ1Dwmj9CfAE0a9X3DUm2NZ1mPzaLuzHYsd++12vF5YlhWzLOvzlmWtAW4A3g58OMMpZuiLfe11cY5j24DfcYxZ0LKsDhIha/eQ8IApJOFdAQnvDM2/kzDyn1FK5WZo0z8k615vWVYB8EGznAxzOaW9lmW9ZCVCpfIsy3LNr+OgEyhRSuUb28xrmPZeSTJEYry/rZS6cZa6aoyQJrOsNhKeNqXGGBfMsf1tyfYXzeHYFCzLepyEt84jJEKnOpVS/66Uusk4rJ+EuLjWaFuhZVla+EkZn+Q1XsCv78WvWpa1GVhDIkzqL4xjFyilPqUSubh2A17gVsuyts23L4IgCIIgCL8NiGgjCG8gLMvqtSzry5ZlXUPCs6QI2KeUehgSeUCUUrcl87VESBhe2jPlMeB/qMQKLqUk8lPopLZvV0otTxqXIyS8KqaT+x4mEf5TBLzTsqxrLcv6V8uy+ubZ/ANASCWSy2YnPTbWKaW2zvH8X5HwGPhLlUieuoNEqNHjSqmAUuoDSqlCy7JiwKjR738HPqGUui6ZJDVXKXW3w2CfD88AK5VS71dK+ZRS7yFhnD51keVdFEqpW5N5Sbwk+hvj131240+UUguT+VT+GvhBhmO/CXxBKbU4WVeZUuqe5L58EmLDAAlvjH9IU8anSISh/UwZCWwd5JMIqRtRStWQatxnmss9wJL55lXSWJbVRiK07B9VIqHzNSQEDL2MeNp7xShjD4k8Qf+plHpLhurKgT9Nztl3k8jd84xlWV0k8jP9i1KqQCXyVS1TSm2fQ/u7SOQV+rpSqjhZ9i3z6H8kKYjdQSJnz3kSAtTZ5P5pEvfNvyqlygGUUjXq1/mnHgM+qpTakLw+/wD8yrKs80qprcl7zU8ixCrCr58lf5CsazvweWCRZVn/3bKsk3NtuyAIgiAIwm8bItoIwhsUy7IaLMu6n0Sogg5DySKRQLSfRGhFOYm8MQB/TyKXRSMJT5lXk9sgkVT2ORIG9D7g65ZlPZ/c902g2rKs+y3LevUS2jtFwjthAwmPnX7gWyS8NeZy/iQJkeZ3kud+HfiwZVmnkod8CDifDLP5BAmDGsuyDgF/RCJkY4hEGNh9l9CPgWQ//pyEcPGXwNsty+q/2DIvkkrgP0gINieBF0h4T6TjURIiwTkSOVv+PsOx/xv4KYlwuRCwn0ROIoDvkgiN6QBOJPfNIBkS9Mckkv0+qZKrVTn4PLCJhFD4NPCfxr5Mc/mHyf8DSqmLnZPvI+El1An8GPgby7KeS+7LdK/YWJa1i0RuoZ8ppTalqedXJO6vfhJJtX8vOYcg4RkVIDGOQySuZ9Uc2/8hEkLdKRI5az49x/NSsCyrzbKsL1iWtYJEcmvNfydxr+xP3lPPkUiOTXKc/j/gR0AXCQ/A9ybPKyAh+AyRmCcDJMIvIfFsWWxZ1rsty3o6+UwQBEEQBEEQMqBSQ+0FQRAEQRAEQRAEQRCEqwHxtBEEQRAEQRAEQRAEQbgK8f2mGyAIgiAIggCQXP3LmegboI9fr14l22W7bJftsv3Kbf+4ZVnfd9kuCMJvCAmPEgRBEARBEARBEARBuAqZl6eNUspKXbnUHa/Xi8fjIR6PczlFIcuymK3+uRwz3/MutszfJEqpyzr2lxOllN2+i2mj2/WY7RpdytzR2y5HW83vl6PNbxQu5prNpQzht4O5PM+u1uedIAiCIAiCIMyRfsuyZnjAzVe04dprryUQCNDb20tnZyeQ+mPZ4/Gwdu1a6urqeOaZZ4jH4/Mq31ne5WS+wszVYFTm5eVxww03MD09zYsvvsjk5OSMY5wGjRZFzHbp/Xrf9PS0/d0sw1mO27XweDwXLbgAXHvttWzcuJH29naee+652U+4jMx2jcw+e71eiouL+eAHP0hRUREPPfQQHR0d9tjNtR7nmM61LfNp9+tJunsFuKxtvBIC7MXgvGeuFOnuTXO/3mfeg3rO6m1u5znvVbf71+2a6jLdjtP7zXHJNC+cYqVlWUxPT6f023we6c8ejwe/308sFrPPcatD7xcEQRAEQRCENygX3DbOOxFxZWUl69evp6amBpgpsPj9fsrLy6mrq8Pj8czbeHIKDvNhth/ssxnr8zl+vuVfDDk5OdTW1rJ8+XLbe8mtDjfDyNxnjovbd/O/W7lu2y/WOPJ4PBQVFVFaWsro6OhFlXEpzMXbRhMIBKiurqa9vZ1wOExtbS1FRUWz1uEc33R1znW+KKXweC5/zvDZrqHH4yEnJwefL1XbTXevXO75f7HlXS3i1sXgdm9qnIKf83O6+1Jvd97r+pq5za9Mc8Msz+354dzn9kyanp627w3zL11f1q9fT3l5+Yy5mKkeQRAEQRAEQXgzMG9LUClFIBDA5/PNMNSUUni9XnJzc8nPz7e3mcfNZtxdbAiKLvtKcyWM03QsWLCA1atXMzw8TFtbm+ub/kyeQ5mMuHTnuOHmuXMp+P1+vF4vQ0NDl1TOlSYrK4tFixZx+vRpXnvtNRYuXMiCBQtmvf7O+e7cdrWQSUzyer1ce+21VFVVEQwGX+eWXV1cikh5Oeo2P5veKk7xJZ0A6zy2pKSEqqoqCgsLZ52fbn13evikO36ugo7zXnHi9XrZuHEjJSUleL1e1/G52u4tQRAEQRAEQbhczFu0GRsbY2xsjFgs5uqGb/6lc/NPRyahIRPpXPJnK/P1MsTSecDMdk5JSQlLly6lqamJc+fOzQg1c453pjoyiQZzHfPLNV5erxev18v4+PhlKc/kcrVRKWWLNp2dnRw/fpzS0lIKCgquqIGYyVNiPvh8PvLz82cYuXMlJyeHm2++mcLCwjl7zF2Osc/KysLv919yOZebK/GscBNMZruf0+13iqrpvGpKS0tZt24dW7ZsYdWqVQQCAYAUQXg2Lxu3Op0Cjlv7p6enXYVnt/rM8fD5fFxzzTVkZ2fbc3E2oUcQBEEQBEEQ3izMe8nvSCRCVlYWgUAg7VvYqakpOwlxuvwDVxI3Y+Zi63WGt7iFJThzNcylzHRlm98DgQB5eXmMjY0RiUTSluMW2uDxeJiampq1LXPB7Odc+5gOj8dDXl4eBQUFrn26WEyD9nIRDAZZtGgRExMTRKNRsrOzCQaDl60Ot7G8XGUvWLCA5cuXc+zYMdcwtEzX0ev1smjRIoqKimhtbSUUCs3p/rkcba+srCQSidDT0zPnczL1Jd39+5sy9s08LZkEEBNnm3U402w5dtzCoLZv3866desoKCigt7eXvr4+zp8/7/qsdmvLbH0zxRvzuZhOrEkn2Ohypqen8fl8rFmzhmg0aj/TLqfnnyAIgiAIgiBczczb08bn8xEMBgkEAq5vUn0+n/223CkmzMWodsuvMBecBsBcPGsuxnAzPXdM42IuOSDMet3GwmmIaNFmYGAgRaBxGm+mUaT3OQWb+XprZAqVulh0mbFYjPHx8csq2ujxvFy5XwKBALm5uQBEo1Gmp6cJBoMEg8HL5glyJYWD0tJStm3bxsKFC9OGzaTD7/dz3XXX8dprrxGJRC7JKJ7PuR6PhxtvvJG1a9fOq45M45jOG/A3gQ47y8vLY/Xq1SmCks7xoj/rv3TjNzU1ZYuzWsDRn01hSHtaWZaF1+tl+fLlHDhwgK997Wv86le/YufOnTPOMzGTA+s+mGQ6z9l3t3vT3K7bodsLCQGxsLCQcDjM4ODgjGTE5n8RbwRBEARBEIQ3I/O2cCcnJ/H5fPj9fvsHt/mj2xRs9H+3H+PpSPdWdjYyhQuY+zPlYHBrS6YQsHR1p2uXW9iC87Nbe2OxmGuYix4rbbA5c0Xoc7TxZhpNs4kbmbww0olOmdBzQOezmZqaIhaLzfn8uaL7PZeQnkx9yM/Pp6Kigmg0itfrZXp6mry8PHw+3xVfRehicF7PSCTC4OAg5eXl9r5M94fepq9RfX09p0+fdl2tLB2Xw2jWHnrOhLNXkktt91yFQsuyyMnJYenSpWzevNnersddXxPnve6cp6aQop9RWrgx7039fNDfly5dSktLC62trbS1tXHmzBmqqqrw+XwZ72stiDiflaawlEmINkOanAKRLjOdEJqTk8NNN93E3r17GRsbS+mPKWRfTNJ7QRAEQRAEQXgjMG/LSAs2pnu/+T8YDGJZFiMjIzO8PS41VOlyMJ9wFGd75yICzHX/bAa0x+OxjalMS9mab+ednjrT09O24JDuvPliGmnzQRtnWVlZtqeWZrYlxGerzymMmfXNBaeYp5SisLCQ6upqQqEQU1NTtlhztb7Nd7YrFosxOjrKxMREyna/309ZWRlr1qyhurqa06dPc/ToUaLRqF2ODmEbHR1NK1Dpa6YNZh0WeanebBc7vy6FS61rPvMsPz+fJUuWpAitzjL0uKarQ4uSzrmuxR7T+0+pXy+lvWbNGgYGBgiFQrbI4fV6ycnJIRQKpdRnCkfONpqefpDq5eL0qDE9AHW/3ISXqampGV6LOqn9tddeyyuvvGKLvOnqEgRBEARBEIQ3Ixcl2pjeMNow0D+28/PzycrKIhKJ2D/OTUFB/1i/3N4Kutzi4mKGh4ddw4Mu1jhzGin681z6MN+QJLMOj8djixuzhbg4jT6nEOHmYXQxzDfUxnme3+8nOzubrKysOQsszvG/XP3LNCeCwSD5+fm2YObz+WyD80rMXbc2mx4Mei5YlsXk5OSMMXOeOzk5STgcprCwEEgYwJWVldTW1lJRUUEwGGRsbMzOTxWLxZiamrLFwpKSEqLRqKsIEwwGWb58OdXV1eTn5+P3+5mammJ4eJhDhw4xNDR0UfNLKUVOTo69zLj2urmaSefBl+6a5uXlUVZWRktLywyh0OPxkJ2dTW1tLYFAgP7+fgYGBlLCCE2R1rlNf9btMdvm9XqpqqqycxRNTU3Z1zs7O5twOGy3I9Pc0l57boK927nmWJgeX6bwkuk54PP5KC0tpbm5+Yp45gmCIAiCIAjC1c68RRu/3088Hk9Zycj0dMjPzycQCNgGn/nG1+m6f7kNMr/fz44dO9i1a1fa5KlmqJZpgPv9fnw+H7FYbMYqTZrs7GyKi4spLy8nGAzS2tpKb2/vFTMunWETMPeVXZxluO1/vQ1iM/zGDIdwGq/p2mZ6H5keJJfSj0wimPYMUCqxkpQZcnKp6HtCz6n8/Hwsy6Kjo4Px8XG7XcFg0J5z+fn5eDweJiYmGBwcZHBwkJGRkZT2mN4PU1NTLFiwAJ/Px/Lly6mrqyM7O5toNMqFCxcYHBy0jzPRwpo5r/UYlJeXU1tbS3l5OdnZ2bZHhtfrpbS0lK1bt3Lw4EFGR0ddPW8y4fP5yM7OtvNluYkh80ELTEop4vF4Ro+1+ZTpts28x5weKebzLxgMUlRURHd3tz2/gsEgBQUFFBQU2KJOOBwmHA6neJLM9tx0u4/0Z6UU5eXldi4pfd3TJSt38y7Mzs6mrKyMuro6pqen6evr48KFCyl5j2bztEr3jDL7Z27XK8319fVlbKuERgmCIAiCIAhvVuYt2gSDQXt1KI3pzp+Xl4ff72doaCjFbV67umtRZ3R01E6wa/7ovhSjyufz8b73vY+DBw8yNjaWstKI1+u1jSId6jI5Ocng4CDRaJScnBxqamoYGBigv79/hoEQCASoqKigvr6edevWkZeXx9GjR9m7dy/9/f22QTgX48E04sw+m2/ITTKJBG7jpo3InJwcCgoK7DL0cu2QWFrZ5/OljNPrgdnXuRpaWkDQnhilpaWcP3/+inpiRKNRQqGQLZTk5eWlrIp2Kej7JSsri+rqapYvX87ChQuZmpri8OHDvPbaa0xPT5Odnc3ChQtZuXIltbW19r0Tj8dpb2/n7NmzNDc309fXN6MOpRR+v5/c3FwWLVrEjTfeiN/v5+jRoxw9ejRFGHJ6Pvh8vhSDXo97dXU1GzduZPny5Zw+fZoDBw7Q399PJBLB6/VSU1PDe97zHiKRCI2NjbZw46wjHT6fz/bkSyeczgUtjhQXF1NVVYXX6yUcDtPT08Pg4KCrp5Y+L13Yp95vhvn4fD4CgYAdMmpZFtFolGg0aucCco6t9uAKhUL4fD6KioooLS2lpqaG8vJyAC5cuEBTUxOTk5MzcgppDz83kcgt5Mp81ujyzWsSj8cJh8MpnpNu+P1+qqqq2Lx5M9dddx0TExN0dHTw05/+lL6+PtsLRj9nQ6HQDG+gTKF2fr/f9vjSnmR+v59gMEgkEkmZr26IaCMIgiAIgiC8WZm3aOPxeFK8UbQgoikuLsbr9TI0NGQnnAXIzc3l1ltv5aabbqKmpobGxka+/OUvzyvR6WzE43E7cazzTXNBQQHvec97qKurs708hoeHefLJJ2lsbMTn8/HBD36QxsZGdu/eTW9vb8r5S5YsYe3atQQCAX7yk58wPDzMfffdRzAYZPfu3XR1dV2W3BhmGfpteLpy3YxMU6C6+eabuemmm4hEIoTDYRoaGti7dy9TU1OsXbuW2tpannvuOUZHR6+I+OHsj2UlkipHIhFisZhrv9zasW7dOnp7e+np6SErK4sbbriBeDxOa2urPb8u99v28fFxhoaGWLt2LdPT0xQUFDAxMTEjREN74sw13415jerr61mzZg3j4+M0NjaSlZXFRz/6UT772c8SiUTYuHEjW7duxe/388gjj9Df34/H46GgoICtW7dy/fXXs3nzZh566CHb88gUCLRw9+lPf5qXX36ZF154gf7+/hltdXqEeDweQqGQ7UHh9/vZsGEDt912GwBf//rXU3JWaVGgubmZRx55hAceeIBIJMLx48cJh8MopWyxKdM4abFIh2pdbEJyn8/Hxo0b+fCHP0x5eTlFRUUMDQ3x/PPP861vfYtIJDLj+aBFtOzsbFuAjcfjTE5Ozsiz4/V6ycrKorS0lLVr11JeXk5eXh6WZdHV1UVTU5MdzmMKRPq87OxsAKqqqrjrrrvIzc2lpaWFhoYG2tvbmZiYcJ1Tzs9OUUYLI2biX1Ngmp6etq+B1+slOzub/Px82/PGvP7OeiorK7n55pupqanh4YcfpqOjg49//OPU19cTi8UYGBgAEgLre97zHh599FEmJibsUCotdjo9eLQgunTpUoqKihgdHeX06dNMTEyQl5dHRUUF4XDYfl6kC7+6mvNNCYIgCIIgCMKlMC/RRhubJSUlKX/XXXcd5eXlnDp1inXr1hEOh+nr67PDj/Ly8vjABz6Az+dj9+7dDA0N8dGPftQ1V8mlMDU1xcTEhJ1/BLDzc3z4wx/m+eef59FHH2V8fJy8vDzWrVvHJz/5ST7zmc8wPDzMrl27uPXWWyktLeWb3/ymLUxVVlZy9913c+rUKZ599lnb6HvyySf50Ic+xJEjR+jt7Z2zx0q6/rqFQZnhXG44ywgGg6xYsYKdO3dy9OhRPve5zxGPx9myZQtLlizhjjvu4KWXXuLd7343u3fvnncIy3xwiiher5fJyUk7se9s6Bwyg4ODQMLbaWBggJdeeon777+fL33pS7ZX1GyCjTZinSttmdt1nUopIpEIY2NjlJSUYFkWS5YsYWJiIiUUxOv1cvvtt5Obm8sLL7xgG65zGZdt27aRl5fH888/T09PD5ZlUVhYyDvf+U6ys7NZtGgRt912GwMDA3z3u9+1w6CmpqYYGBjgueeeY2hoiDvuuIN3vetdPProoymGt/aMeOc738mDDz7IU089xcTEhOu1NsURv99PXl6eLYh5PB7e9ra3sWXLFkZHR/nqV786Q2g1yzx//jzf+973WLt2LZOTkzQ2NpKdnc3tt9+OUopnnnmGaDQ6QzSZnp6msLAwJV/KXDGvZ0FBAW9961t5//vfz9e+9jUaGxuJRqPcdNNNfPjDH6avr48f/vCH9rm5ubksW7aM7du3s2HDBoaHh+nt7bWfIX19fezZs4fm5mZ7PLZt28Ytt9zCkiVLOHr0KHv27GFoaIj8/Hxuu+023vKWt9Da2sojjzxiJ/jVolBubi7FxcXccsstLF++nD179nD69GlGRkaIx+MpIqTpTaPzgpkirR47t/lrJgCGxL0TDAZTPCIrKytn3D/aG6ikpASfz0dnZyc+n4/bb7+d8fFxHn/8cTo7O5menubIkSPU1NRw4cIFhoeHycrKYsmSJZSVldltNUNQzaXJ9Tytr6/nrrvuorq6mi9+8Yt2+JbH4yE3N5fKykq6urrwer3289hcxtwpVIlwIwiCIAiCILzZuChPmy1btnDPPfcwNjbG+Pg4zc3NHDhwgJaWFq655ho8Hg/xeNzOR3DjjTfaLv/hcJi6ujr7jT/MTHRpkimHg0YbLvqHvWmwVFRU8Hu/93scPHiQEydO2Ll2RkdHuXDhAp2dnWzfvp2f//znHDp0iNraWqqrq9m8eTOHDh0C4O6776azs5OzZ8+meDSYxsNsIUxmrhGzn+n6pxPPZmdnzyjbNHi1IaQ/b9y4kVWrVvHcc89x5swZO9SgoaGBvLw8Nm3aRGtrK1lZWfT09NhhR87rcCmkK296etr20vJ6veTn59urFrmhzz9//nzK946ODl566SXuvPNO/uu//ove3t4Z4+gmzJhhJOlypuh9WrQpLCwkOzub+vp6zp07x8DAQIpXw69+9Sve9773kZubayfANg3pYDAIYBvkgUCA+vp6br/9dh566CFb3NReGIWFheTk5PD2t7+d1tZWDh8+7JqfaXp6mnPnzrFv3z7e/va38+ijj9r9VkpRWlpKfX09+/fv52c/+1lawcZ53bKysliwYAHnzp3DsizKy8t5y1vewtjYGD/4wQ9cw5bM621ZFk1NTdx+++20trbi9XqJRqMcOHCAL33pS5w+fZqWlhZb/NKeMcFgkNLSUqampuw5Yc7t2doNUFJSwtatW/nIRz7C5z//eZqammyvlYaGBuLxOJ/4xCf46U9/SjQaJRAIsGPHDq699lpOnjzJ008/zcjIiC1K5efns3jxYnbu3Mndd9/Ngw8+SH19PUuWLKGxsZHvf//7jIyMEIlEiMfjDA0N8fjjj1NbW8uGDRv47Gc/yxe+8AVbpCorK2PZsmUsW7aMI0eO8Nhjj9HV1WULmabAYQoveizM+Wlu0wKPMxxVf9bjG41G7fOrq6vZsGEDDQ0NMwSekpISbrnlFhYuXMg3vvENqqurKS8v58yZM3R3dxOPx/F4PCxatIiJiQmmpqbs+3nZsmX2inemN6b5jNbzffPmzWzatAmPx8O//Mu/EAqFiMVi9v5AIEBhYSHd3d14vV474bUpaJuCjSAIgiAIgiC8GZn3eqlZWVmMjIzwy1/+ki9/+ct86Utf4uGHH+all16iu7vbFhvMsKjVq1fT1NREZ2cneXl59htqp8s8pIoYcxFs9HHwa68awDaYS0pKKCgo4NixYzOSI4+OjnLo0CGqq6tRSjE+Ps7+/ftpaWnhhhtusJc2X7x4MV1dXfT19aUIAVu2bKG7u9sWcjLlacnUTyf6LXW6cAB9TEFBAe9617vIzc21jaiKigoikYhtGGuDLBqNcv78eVpbW7nhhhuoqqpKCaNwa7dSihUrVrBx40aqqqpmvQ76nHRYVmJZ6HA4zOjoqL2yUbpjdb91fhW9LRaL8corr1BcXMzSpUspLi7OeL55zdPVpduux3xycpLx8XHi8TgFBQWsWLGCzs5OhoeHU84dGxvD4/FQXFxsh72Yda1atYr6+noWLFgAJDyhtm/fzquvvsrQ0JBt2GZnZ7N06VI714nOr9Te3u4a+mFZFqFQiLa2Nvr6+lKuY0lJCatXr2bVqlW8+uqrcxJsNMFgkEWLFjE+Po7H42H9+vWMjY3R1NRkewRlQilFKBTi0KFDdHZ22td8cHCQvXv38ra3vY3y8nJ7jufl5bFz504+85nP8Ed/9EesW7fOziOU6f5weqUFAgFWr17N1q1beeSRR2hubmZiYsIWQkZHR2lpaSEcDlNaWmonAA4GgwwPD7N//346OjoYHBwkFAoRCoXo7e3l5MmTvPDCC5SXl3P77bezbt06urq6OHHiBP39/XbojvaCGhsb4/z58xw5coSJiQl27txp58Pavn0711xzDS0tLezZs4fOzk6i0egModmZfNjtOaBFQ7ck1Ga4kA5NMpPHZ2VlUVJSQmlpKSdPnkypUye6PnfuHNPT01RXV7Ny5UogETI4PT2Nz+cjNzeXzZs309XVlfL8Ky0ttQUjZ9tNcbugoICNGzdSVFTE008/zcDAgB0Wp/uhn999fX226OlWrgg2giAIgiAIwpuZeYs2Y2NjDA0NcebMGfbu3cuhQ4c4ffo0vb299o96vdywz+dj9erVhMNhhoaGgEQeh+rqao4ePZrWGJ3NyE7bmWQokfYeKSgooKSkhP7+foaHh1Pq08b/wMAA5eXl9lvb9vZ2uru7KSoqspOYFhYW2slFtdDh8/nYtGkTTU1NjI6OzmjLbPla3PI76M/6jbTz7bnTWPX7/axcuZLKykr8fj/XXnstwWCQ5uZmwuHwjHHs7++ns7OTpUuXMjo6SmVlpR2m5BaqtWDBArZv385b3/pWWwybK+mu3fT0NMPDwwwNDVFTU5P2/HTjpw2//v5+GhsbWblyJUuWLLGXRk9Xf7p55laHKdyEQiGWLVtmJ1d1rpQzNTXF0NBQSliIWZ/Oy5GXl2d7sqxcuZLjx4/bZfl8PsrLy9m0aROHDh0iEAiQlZXF+Pg4Y2NjMwxTM7dKIBCwl2vW9erkxjk5ObZwMhe091hRUZEtTi1btoyRkRHa2trSrr7kHMt4PM6xY8fsMBp9r+3du5fy8nIqKyvJycmxx6+3t5fjx4/boUk6x5LbnNT3uNO7YunSpdTW1jI4OMjLL79sJ9g2hYuxsTE6OzspKCiwBSO90t34+HhK/hp9zujoKOfOnePQoUPccssthMNh2tvbU5KP6+ujRZTx8XE6Ozs5dOgQN910E0VFRWzZssVedamtrY3Ozk7Gx8dTcvdkEn3NOeW8r908ytzGTwsz2nNGr0Bm1u3xeIhEInR1ddmhrVqIDwQCtti1adMmAFu01t4+OqGwz+dLEZHM0C4tsi1YsACPx0Nra2tKviPzPjfzG5l9NO8BQRAEQRAEQXgzMy/RxrIs+vr67LwD2hNBGyv6bbM2Nr1eL2vWrKGrq4t4PM7ChQtZuHAh8XjcDnkxy3aKGvPF5/OleJcUFRVRUlJCW1uba4iRXtFKh7AARCIR+vr66Ovro76+PuWNvN/vtwWbsrIyioqKaG5utsNXnH2YzxtgnSy0pKSE8vJySkpKUjw33DxilFLk5eXZx65YsQKApqamGSu3QOJN+ejoKHl5eZw5c4a6ujqysrJmtNvj8VBSUsLmzZvZsWMHGzZssFehMstzYzaxTc+h3t5elixZknGMMu2bnp7m0KFD5ObmsmTJEntlnEvB2e7JyUn6+/u54YYbGB0dJRwOp4ST6XOGhoZYuHAhJSUlroZ3MBi0RSWfz0dFRYW93LYe62XLllFaWsq+ffts4VEbrG4opSgqKqKurm6Gx1p5eTk1NTW2QDaXvuoyA4EAeXl5tnBSWFjI+Ph4SojWbMKCZSUS8prLkVtWImyqu7ubyspK2/MoEolw5MgRnnjiCY4ePWoLwM5+FxQUsHTpUtatW8fatWtZtWoVtbW19spNq1evprCwkEOHDtHd3T1DAICEmNTZ2Ulubi5er5fKykri8Ti9vb12cnJn3yzLYmJigpdeeom6ujq6uroYHBx0TVCsn4GWZTE2NsapU6eoq6tjxYoVrFq1CstKLOmuV1tKJ9hk8iBxCjTprqUTM+Ro2bJlFBcXc+LECdv7xhSA4vE44+PjtqDb09NDbm4upaWllJaWUldXx/XXX8/Ro0cZGBiwn7nxeJxQKERpaSnZ2dm2SGR6Dpp9MMUZ879uy+TkJKOjo/bz2SnWzXcMBEEQBEEQBOGNyLw9bXSyVf3WVb/ZNX9M67wU09PTLFiwAMuyKCoq4oYbbqCiooJ9+/bNSEY6FzL9MNdeDJFIxP6ek5NDfn6+65LIkAgTWLRokZ1fR/elq6uLw4cPs2nTJvsNellZGQUFBbbQs23bNhobGxkcHHTN8zGbV41pbOi219TU2ElON2zYYIckmXkszHJ0W+LxODk5Ofj9fiYmJlw9f8z2RKNRjh49yqpVq2wDVhtAehni66+/nne+851UVlYSiUTIyclJGetLoa+vj7a2NhYvXpzyRt7JbPWEQiGOHTtmJ5XOlLD5YohGo7S1tfGOd7yDw4cPp4gQZht1klndF7PdgUBghtCmDWCfz0dOTg7r1q1jzZo17Nu3j9bWVkZHR+28Km590klaly9fzubNm2d4wOhcSIFAwE6E68Q5tkr9OgFtaWmp3e6RkREAO1QwUzidiXPOaw+UV155hbKyMqqrq23PC53/JDc3l7GxsRTPIU1dXR333nsvf/AHf8B9993H+9//fnbu3ElOTg5FRUWUlZUxMTHB0aNH7fqd7YnFYvT39+Pz+cjKymL58uV0dnayZ88exsfH7ba6iQs6d41ektwUlUwPEi0+eDweioqKmJiYYMeOHYyMjHD+/Hk6Ojro7++f8fwzRQtn+93EYLOds10Ty7JsL8iKigpqa2uxLIvDhw+n5MoxRSct8GhPI4/Hw4oVK9ixYwd33HEHxcXF/OQnP7ETKOvre+7cOaqrq6msrLSXqNfjo0OfPB4P4+Pjdr6h7Oxs177o5cjLysrIzc0lKysLIEXsmmvyd0EQBEEQBEF4ozLvRMT6Dazf77cNDUj8eM7JySErKysl2eTY2Bj19fXs2LGDWCzGoUOHUpJfOsn05jSTce/1eqmoqGBsbCwlmWw4HCY/P3/GOXrp5GuuuYZdu3alCC/hcJju7m6WL18OwL59+9iwYQPj4+NEo1GWLFnCe9/7Xv7qr/4qxdhzC+dw9kUfZ+7z+/0sXbqURx55hJMnT3L27FkCgYAd9uM0IvVn7aXR39+P1+ult7fX9qxwvqm3LIvi4mKqqqpoa2tj//79/Mmf/AmLFi1ieHiYsbExO5fKAw88wIYNG/jCF75AeXk5tbW1LFq0KOO1mM+b7lAoxMjICDU1NSxcuHCG15Um3RwxxYCDBw+yaNEiVqxYwb59+1I8S9J5K8yGPk/nBlqzZg0nT560Vw1z9rW4uJjh4WHC4fCMkL+amhpGR0ftfEqRSITXXnuNP/7jP+Yb3/iGnS+opaWF5557DkgIo4FAgMrKSkpKSujt7U3pR3l5OXfccQcrV64kHA4zPj6eEhbT1tZGd3c3119//Zz77/f7uf7663n/+9/PHXfcQVZWFseOHaOpqYlbb70Vn89Ha2sroVAoJe+I6fWRLmG2uf306dPccsstVFRUkJ2dzfj4uD1eFRUVtLS02Pl0TIO8p6eHH/3oR0QiEdsLSHv2veUtb2FoaIgLFy7MWNnKbKNepru7u5vFixfj9/uZnJy0k+m6tV8/W3QoYV5enu2Vo9vtTC4eCASoqamxE0Tfe++9nDp1Ckh4+5w+fTplfMyyzPKcXjhO4df5PHDifP6Mj49z7733cvDgQfbt25cSQmbWo73XCgsLGRwcxOv1curUKd773vfy7ne/m//4j//gn//5nwmFQrb3i2VZRKNRzpw5w49//GM+8YlPcPjwYRoaGmhtbbXz3ujn7PT0NKFQiKqqKm699VaefPLJlFBNpRRDQ0M0NTXx6U9/mpycHA4ePMj58+cJhUIpeYAEQRAEQRAE4c3MvEWbqakpOxxDizN6KVyv18vw8LC9ig7AoUOH+PnPf86uXbt4+OGH2b9/P5AwbPRqT3r1kWAwSE5ODh6Px056OlcxwOv1snTpUjo7O20Dob+/n6amJjvprmlclZaWsnr1agoKCti7dy+QCKeanJy03/yPjo5SUVHBs88+y4oVK/jIRz7CH/7hH2JZFo899pgdhmEmTTUNOWeSUNOw1f+9Xi/V1dXcd999fPWrX+Wpp55ibGyM0tJStm/fzk033eRqGOvxCgQCDA4OkpOTY393Eyv8fj/XXHMNW7Zs4cc//jFjY2M8+OCDPPDAA3R1dREKhVi4cCFLly7liSee4O/+7u/svDNve9vbuPPOO/nP//xP29ugoKCA4uJicnJyiMVinD9/3tXDx4nuQ0dHB0899RQPPPAAf/mXf5mSK0aj+1FSUsLExERKwlO9X+ff8Pv91NTUMDw8PCN8ZL6YYoNe6UrPUT3ntTDo8/m48cYb+dGPfkR3d/eMsgKBAG1tbfT399seG1/5yld4+OGH+da3vkVraytPPfUUu3fvtoWKWCzGd7/7XX7nd36H6upqdu3axcjICFVVVezcuROfz8f+/fs5ceKE7S1lcurUKX7xi1+watUqvvjFL/LFL36RM2fOpKzU5QyHW758OX6/n/3799PX18c//dM/MTw8zL59+8jLy+Otb30rd955J7t27eKFF16gq6srxbNE/+n7zFy62qzLXDZde62Y+ZtGRkYIh8MzPCh6enpcRVCdgPvChQu0tLTMWHHJPFaHnPX29jIxMWE/b7QIo4VmXYf21qurq+OTn/wkP/rRj1iwYAErV660lwPX80I/AwsLC9m0aRPbtm2jr6+Pffv2sWPHDtavX08oFKK9vX2GWOM2T00Pq7k+B51LamuUUnYC8MLCQvr6+mYkuNZtCAaDbNiwgQ984AMcOHCALVu2UFNTg2VZfPvb36awsJDrr7+ez3/+8/ziF7/g6NGjDA4OEo1GmZqaIhQK8fjjj/Piiy9SX1/P9ddfz6233sr4+Ditra2cPXuWzs5OYrEYu3btwu/386lPfYrt27fz4osv0t7eTiwWs0M/Fy1aRHt7OxMTE/zZn/0ZWVlZNDc3c/DgQQ4ePEhvb6/dDzePR0EQBEEQBEF4ozNv0QYgJyeHnJwcfD6fbQjq3A8vvPACk5OT+Hw+qquruf/++2lsbKS2tpZ7772X+vp6+vr6mJiYoLCwkIqKCgoLC4nFYnR3d3Py5EkaGhpcDRXtWu806LThU1xcTE9PD1NTU9TU1BAMBhkdHbVX+NFhLMuXL2f79u2sXbuWH/zgB7bBoT0bQqEQw8PD9PT0kJ2dTV9fHz/84Q/52Mc+xl133UUsFuNjH/sYO3bsoK2tzV4FSOdg0G+FzeSt6d6Ea8Oxp6eHp59+2vZk0Ibk0NBQSkJV02ugqKjIzq+hl5PWRqj5Jtrr9bJ27VoqKyvp7u62jdsXX3yRiYkJ6uvrycrK4siRIxw/fpz29nZGR0eZnp6ms7OThoYGKioq+NznPkdJSQkej4doNGqvfBOLxWhububBBx90vWZOLwzLSqzcdfDgQW6//XbuueceXnjhBQYGBmaspBMMBvn93/99Ghoa7HmhjVJ9XDgcZmJiwnUVqfmi2+vxeCgvL+euu+7iscce47bbbrO9QPQ1CAQC3HjjjQQCAfr6+lJCkbSgU1JSYs8N3fdIJEJ7ezsVFRUsXLiQ973vfWzbto0jR45w7Ngx+vv7aWlp4dlnn2Xp0qXceuutdg6V3bt309HRwcjICBUVFUxMTNhJvjXxeJyGhgb+9m//lptvvpm/+Iu/oKenh4GBAcbHx+nr6+PYsWOcOHHCnls9PT22YFJfX297kEWjUfbs2UNTUxMrVqxg6dKl3H///YyMjNjedvF4nGg0SiQSsYWDn//855w7dy7FC0gnqvX5fExNTdnL0WsBTAs6Om+QOW+c4ULm58nJSYaGhhgdHU3xSHEeNz09zcDAAD09PUxMTNDd3c2WLVtYvXo1zz//vJ04WV+39evXs3nzZqqrq9m/fz979uwhLy+Pu+++my1bttDQ0EBHRwfxeJzS0lLWr19PRUUFoVCIl19+mVOnTjE5OcnPfvYzPv3pTzM2NmYL2n6/37Vveu5lEmn0/HPeB2a/zfP9fj9r166lrKyMiooK6urqaGpq4sKFC/bxOtfSjh07WLp0Kc8++yy7d+9OEYG0V+CePXvsufu7v/u7HDt2jJMnT9Lb2wskhLn29nYGBgbw+/3k5eWxYMECqqurueaaa+wkx729vTz55JO89tprbNq0iY0bN7J582b72dvX10dzczMvv/wyo6OjvPTSS5SVlbFo0SKWL1/O1q1b+fGPf5yyEqEgCIIgCIIgvNmYt2hjWZYdkuH8oRyLxTh37pz9lnT16tU0NDTw2GOPUVxcTGFhIfn5+axatco2zoeHh2lubmZoaIjBwUH7LTi4hxeZuWc02oApKCigsbHRzoVQVVXFtm3bbDFH57DxeDwMDw/zzDPP2MdDYiUUHSK0devWlGWaq6qq6Orq4gc/+AHnzp0DsMUmn89ne2WEw2HbgDfFBxPzezweZ2JigoGBAUZHR+0x1WEY+k2yOQ46hKG8vJz9+/fbXg6nTp3Csiw7BEqPy7Jly9i4cSOxWIyDBw/auSRCoRCvvvoqbW1t+Hw+RkZG6OvrSwkxmZyc5MyZM0QiEcrKysjPz7e9kczlis08JG7hG+b80XNlaGiIJ554go0bN/Lud7+bSCRCLBazr/PZs2d57bXXqKmp4cyZM8Cvw9q2bt3KsWPHGBkZITs7m9zc3LTJZOeDKdisXr0av9/P97//fXbu3MnmzZsJBAL09/eTk5PDxo0bueeee9i1a5edbNvsb3l5OWNjY4yNjRGLxfB4POTk5LB+/XoOHDhAR0eHLUxpD6l169YRi8W4cOEC/f39DA4OEgwGbe+Vnp4eO6wlFArR3d09437Q9+ipU6cYGRnh+PHj9rLpOlmsNpz18XplLD03zPxUOrxrYGCApqYmysrK7HmvhRW97LVeMW7lypWMjY3R0tKSck1WrFhhe7GZQpb2tnGKdm5zx0lhYaEtIJnhh6YXi5mvSa8E19jYCMCaNWv42Mc+Zof7+P1+YrEYY2NjtLa2cvDgQc6ePWuHwL3yyitUV1ezcOFC1qxZY68yNjIyQnt7O319fXR1dTE0NIRSiqamJvbs2cPmzZvZunWrfc/rhMtmn93udfN/ppAoZ+ik1+uluLiYTZs2sWHDBnbv3k1xcTELFy7kAx/4AOfOnaOrq4uxsTGWLFlCbW0tsViMY8eOcejQIYaHh1M8yyBx/4VCIQYGBujq6iIvL4/BwUFbGNdt1c9CSKw4GAqF6Onpwefz2QKkXr1vfHycgYEBCgsL8Xq99jzU944OOxwaGqK/v5+Ojg7OnTtHTU2N/Yy+FM86QRAEQRAEQbiambdoE41GOXDgAAMDA3ZuC9NYCIfD9kpIAwMDnD9/nrNnz5KdnU1VVRVVVVUUFRXZOUO6u7tpb29nZGTE9ngxDRa3N+zOxK4670RRUREXLlwgFosxOjrK4OAg4XCYvLw8CgsL7ZCsnp4eTpw4QWNjox1OAzA8PGwbbQsWLODcuXMMDw9TUlKCz+fj2LFjdHd309rait/vp6KigkAggN/vZ2pqirGxMdtbQYd4uIU/mP2Kx+MMDQ3ZOS/09tzcXAKBAENDQ6791slUjxw5YocHnDhxgqKiIrKysmxPkMWLF3PdddcBcO7cOVpaWlLGVHsowMwcMrqdw8PDhEIh20tCew+ZHkCZvAMyCTcNDQ1kZ2ezcOHClOWCIWGMQyLMbWxszD53enqaoqIi7rzzTsLhMIsXLwagoaHBtf754PP57HFbtWoVjY2NHD58mJKSEioqKti6dSvj4+O2kNfa2srevXvtJeVNAoEAx44ds/fp8ZyYmGDfvn2cO3eOqakpgsGgHW6ml0GORCKMjIykCHnOcQyHw5w/f95eAcxEJ4Y9e/YsLS0t9rUDbM8f8xxzOXtnQlztHRSNRunr6+PMmTMp3h66Pu3NFolE7ATkzntYh7t0d3fbHnO6HHNFJl23E7c8MIODg1hWYhlpZ04bfYx5X+t6u7q6gMQ8XL9+vb06XDQaZXR01A7n0R5z+u/48eP09fVRW1tLZWUlU1NT9opzfX199ipj+poPDw/zy1/+kvHxcerq6qiuruaGG25gYGCAsbEx21ttdHSUnp4e29suE+Yzwfk8tCyLYDBIWVkZdXV11NXV0dvby4EDB/D7/WzZsoVVq1axbNkyampqGBgYID8/n0gkwpkzZzhx4gTd3d0p3n3mNYnFYoyMjDAyMuKaa8fMrwTY4u7IyIidO8gUBcPhMM3NzSlhic6cSeaxY2NjDAwM0NHRQVlZmeS2EQRBEARBEN7UXFQiYp0w1W1ZadPQ379/f4qhOjw8zMmTJ+3jTfd+pzjjlvDVWZdGL8utw4z02+umpiba2tooLCy038hOTEzQ399vv70122xZFoODg4RCIc6fP09hYSEDAwNUVFTQ1NTE0NCQbVDppYLN880lgN3Gxg2dx+PIkSMpx+nws56eHnub2U69HPWFCxfsetva2giFQnY4QlFREZs2baKmpobnnnuOkydPMjk5OcMLwem15CayxONx2yPJNBT1sXPtr7NcHX7j9/vJyckhOzubYDBoCwyRSIRDhw6l5FAJhUIcP36cj3/842RnZxOPxzl27BgdHR1zqjcTeXl5FBcXU1tbS05ODk888QTj4+Ps2rWL2267jeXLl1NUVEQ8Hqe5uZnvfOc7jIyMuPY5FArR0NCQ4lEyMTGREv6nBRHtpQGpRqs5Vk7MFXgyoQ1lM6eNxrzeWoDRiXnTeUw5l6o2y1JKcfLkSXJzc+0QK12Gx+MhFovZooC5z7ISS6drMclNpDLrMcfk8OHDxONxO0eO23gppfD7/WRnZ6csLd3R0UFnZycvv/yyvax9NBq181qZzybd50gkQltbG+3t7bZQZebwcQpPsViMpqYmurq6WLRoERs2bGDlypXE43EGBweBhGjW2dlJJBKxV3rS5zv7m+67+TkYDFJaWkpVVRU9PT0899xz9sp6fX19vPbaa2zYsIHq6mri8ThHjhzhxIkTDA0N2TllnNfBrMdcCcr5/DDbbop6Pp/PFiCdq6KZ803Pf12+Hl9zrmovHZ3rSLxsBEEQBEEQhDcraq4GNoDH47F0GMKcCk/+kNY/7E2RxvyBnqauOb9BDQaD1NbW8o53vIOvfOUrKUsg6x/0pqFg7jMNhHSGaFZWlm2omh4yuj9Ow1f3ca5j5BR5PB4Pd955JzU1NTQ0NHD48GHX89zq0kuHX3fdddTV1dHY2MjLL79sh2s5PRXmc/3d2qDLuRyYxtds4+fxeMjOzmbBggXEYjFCoZDrUtFzRV/HtWvXsnHjRqampjh69ChnzpxJGTefz2fPI3MlHKfwOFcyiZMX0/75Hm/mK9HLiN9222189atfTevtYZ6Tbr+uw008cbYDEnmXtm3bxpkzZ+ykzekw7xlt1Ovv6dqclZVFZWUl99xzD1//+tdneHPoNujQHmcf3Tx89DlmCJEz+bgeZ6fAoZ8pOiwMUj2gnJ5O6cbXbUz1Pqe4qoVQs51ObylzXujxdatLt90p7Hm93pRnvXm+UorJyUk755auR6lEomQtujlXzTLH0qzLPFaHVckS4IIgCIIgCMIbmAbLsrY4N15UThtTCHEzrvR+/aNch9GkW1HGjdmWe9bGgfbWaG5uThFsnIaMM4GxaQyYxolbfXq5Zmd5ptGoj9XGoGn0uQkzZvluY6GXKdfJQtO1zVlmNBrl/Pnzdk4b0ytCt1OPg9Mgmw9z7d9cSechkY7p6Wk758XlQM+H06dP09zcbOfc0G0yjVV9vO6zXnFovmiDdT4i32ztn01QcR5v1qsFCL2kdqbyMgl2mfqi7xvzGaLFln379s2hp6n3jFMoyUQ8HufEiRMAdjJkc/6azye3Z4VeIcqc9+aqWfpcfY7zPtMinw4FsyxrRkiYOU66HlPY0H0222Xe325jYbbfTQxy1m3OR9MDxixP990pzJh91G3T11kLPZD6/Nb3kHk99PGmqGTOHZ3/SAtFFyuaCoIgCIIgCMLVzkWtHjXbG3bzODM5qymUpDPyZxMA3IxN02U+XdtmMyRNA8lNzHC+aTfLMw15wH6TbBr78zHKtVEXDodTViQy68hkmJsGjHm807jTxpL55toNNw8Y5/inO3euHiCXGt5wOYQjLew539ZrozCdyHWxxqJbUm0n8/VmytT/2a6FNpz1Kkpmeena4QxNyVS/NtjdQm/MNl4O49spYExOTtLd3W0v0+30Qkl3r+vv+rngtkqRvrdMAUP31xRGzHvIeT+a5ehjNG7hcqbgZwpYznaZbTFX5TL367nt9DA0hRnTK8Yp1prPTS28mHXpY3Xiai0kp3vOmuNi3h9Ogc4Usy71+SEIgiAIgiAIVysXJdqkw/njezYjOlMYRbry0/04zyQaZNpv1p/O60EbgE4hxGlU6P9uhp2b0evcpusJBAJAQoSai7t/ulAOZ7vcxuByjH0mLrcxlU48cOvDfAUPpxGcbhzdPl+s0PB6egfM5VrolY0y4ZwL8xWUMl3DyzUemYSV2epx9s8tQbfzu9v9oesxw6ecoonz+WE+N2fzaMz0bHW2xSkiK6VsEc05v511uj2n3YQf/d8UgvWx+llpel86PS/1fh12ZXrbaNHJ2V4tGEkyYkEQBEEQBOHNyrxFm3SeAW4GC6S6tOvtbgbfXAzs+QgA6QxsZ3mmwZDu+Ezf3Qx6MxFnuna7rbakv3u9Xjs/y1zEidk8XTJtm6uoNdsxrxfON/zOfZdadqbt6eq7UuMyX9HpUpmammJyctJVtMkkWM2VuRw7m+fRpdQ127zXAoDb/ZTuHjPno9vxmUTadNvSCb5u7Z6r4Krb6fRumW2cMl2PdN5K5vPUKdxAqieg2T+niGXW4SYciVAjCIIgCIIg/DZwUZ42s4kn5o9up9u9c7+bsHEpRttsRoabh4Rbf5xvnWdrvz7WbXsm3Mqampqit7d3zp4HlyJezPfYy+05czFlzvWc+c6jTOElvw3E43Gi0SiRSGRW8eRSxuY3Oa5Og3+2e9Y51zJ5vjjPM+tyChmZws2com8m5ioEuWGG52V6BprClFPodhO5zDxPZlt0SJTbmJr1OMPXnG1xa6eERwmCIAiCIAhvVi7K00Yz1zfubvvc8jRcCs433m51Otuuj8m0Uko648JZt8bNiHHzDsn0ljgajdLW1jaHXmfmchszcxHr5lvfxbbv9TT8L5f3x3y4HKLIfMc2Ho8zPj5Odnb2nPp8uefXb0LMcRMknOGQGuc9bD4f3EKfNHpfJqHIzVvHWY557sXMSS2cuInXZohROq9IZzvcPArN55t5rs514/f7U8bbeb5Zjr4GZpJis35nO0W4EQRBEARBEN6MXNTqUfPxIkmXkyWT90267ZneCps/9NPhXEo23Vtb0wgwz9FvjzOtbJVOjJrPuE1NTTE0NJTxmLkYzJdqxKTzIsrUjosVDObSnyv1Rn22ctN5W10JwcI0XDMdOxcDf77EYjGGh4cZHx9/04eeuIX16Ps93UpykPoMcRNnM3n56WPSMdszIl04kls5mTAFKdO7T9fhHBPzv3NeOFfIMsM+M3nFmM9ZXa4WjpwJk93EIeezWVaPEgRBEARBEN6sqPn80FVK9QEXrlxzBEEQBEEQBEEQBEEQfutYbFlWmXPjvEQbQRAEQRAEQRAEQRAE4fUhfRyAIAiCIAiCIAiCIAiC8BtDRBtBEARBEARBEARBEISrEBFtBEEQBEEQBEEQBEEQrkJEtBEEQRAEQRAEQRAEQbgKEdFGEARBEARBEARBEAThKkREG0EQBEEQBEEQBEEQhKsQEW0EQRAEQRAEQRAEQRCuQkS0EQRBEARBEARBEARBuAoR0UYQBEEQBEEQBEEQBOEq5P8HWVjnahOypUIAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] @@ -315,16 +308,16 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "('Boyis cheed iitrincy- tarisaing one', 0.3990435302257538)" + "('he rove fron his breakfait-nook bench', 0.6715805530548096)" ] }, - "execution_count": 112, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } -- cgit v1.2.3-70-g09d2